diff --git a/activitysynth/notebooks/TOD_School_Category_Estimation.ipynb b/activitysynth/notebooks/TOD_School_Category_Estimation.ipynb new file mode 100644 index 0000000..2f2fab8 --- /dev/null +++ b/activitysynth/notebooks/TOD_School_Category_Estimation.ipynb @@ -0,0 +1,2158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "from collections import OrderedDict\n", + "from urbansim_templates import modelmanager as mm\n", + "from urbansim_templates.models import LargeMultinomialLogitStep\n", + "from urbansim_templates.models import SmallMultinomialLogitStep\n", + "import orca\n", + "import os; os.chdir('../')\n", + "import warnings; warnings.simplefilter('ignore')\n", + "\n", + "import pandas as pd\n", + "# import pandana as pdna\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "from functools import reduce\n", + "\n", + "import scipy.stats as st\n", + "from scipy.stats import skewnorm\n", + "\n", + "# import matplotlib\n", + "# matplotlib.style.use('ggplot')\n", + "\n", + "%matplotlib inline\n", + "\n", + "pd.options.display.max_columns = 80" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "trips = pd.read_csv('/home/emma/ual_model_workspace/spring-2019-models/notebooks-emma/HStrips_031219.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0HHPERHHPERTRIPoriginorigin_dwellorigin_STorigin_ETtrip_ETTTMODE
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" + ], + "text/plain": [ + " Unnamed: 0 HHPER HHPERTRIP origin origin_dwell origin_ST \\\n", + "0 0 10320533 1.032053e+09 home 13.750000 17.750000 \n", + "1 1 10320533 1.032053e+09 school 8.616667 7.550000 \n", + "2 2 10320534 1.032053e+09 home 13.750000 17.750000 \n", + "3 3 10320534 1.032053e+09 school 8.616667 7.550000 \n", + "4 4 10320535 1.032054e+09 home 14.833333 16.666667 \n", + "\n", + " origin_ET trip_ET TT MODE \n", + "0 7.500000 7.55 0.050000 shared \n", + "1 16.166667 17.75 1.583333 shared \n", + "2 7.500000 7.55 0.050000 shared \n", + "3 16.166667 17.75 1.583333 shared \n", + "4 7.500000 7.55 0.050000 shared " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trips.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare TOD and Dwell columns" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#select people who make both home-school and school-home trips:\n", + "tripsII = trips.groupby('HHPER').filter(lambda x: len(x) == 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "#make sure all home-school trip rows are listed first\n", + "tripsIII = tripsII.sort_values(['HHPER','origin']).reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "#move school-home trip info up into home-school trip rows\n", + "\n", + "tripsIII['school_dwell'] = tripsIII.groupby('HHPER', group_keys=False).origin_dwell.shift(-1)\n", + "tripsIII['school_ST'] = tripsIII.groupby('HHPER', group_keys=False).origin_ST.shift(-1)\n", + "tripsIII['SH_trip_ST'] = tripsIII.groupby('HHPER', group_keys=False).origin_ET.shift(-1)\n", + "tripsIII['SH_trip_ET'] = tripsIII.groupby('HHPER', group_keys=False).trip_ET.shift(-1)\n", + "tripsIII['SH_TT'] = tripsIII.groupby('HHPER', group_keys=False).TT.shift(-1)\n", + "tripsIII['SH_mode'] = tripsIII.groupby('HHPER', group_keys=False).MODE.shift(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "tripsIII = tripsIII.groupby('HHPER').first().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "tripsIII.rename(columns = {'origin_dwell':'home_dwell','origin_ST':'home_ST','origin_ET':'HS_trip_ST',\n", + " 'trip_ET':'HS_trip_ET','TT':'HS_TT','MODE':'HS_mode','TOD':'HS_TOD'},inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "tripsIII['HS_TOD1'] = (\n", + " ((tripsIII.HS_trip_ET.between(3,7.75,inclusive = False)) | (tripsIII.HS_trip_ET==3))*1 +\n", + " ((tripsIII.HS_trip_ET.between(7.75,8.5,inclusive = True)))*2 +\n", + " ((tripsIII.HS_trip_ET.between(8.5,9.5,inclusive = False)) | (tripsIII.HS_trip_ET==9.5))*3 +\n", + " ((tripsIII.HS_trip_ET.between(9.5,15.0,inclusive = False)) | (tripsIII.HS_trip_ET==15.0))*4 +\n", + " ((tripsIII.HS_trip_ET>15.0))*5 +\n", + " ((tripsIII.HS_trip_ET.between(0,3,inclusive = False)) | (tripsIII.HS_trip_ET==0))*5\n", + ")\n", + "\n", + "tripsIII['HS_TOD1'] = tripsIII['HS_TOD1'] - 1" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "tripsIII['Sdwell'] = (\n", + " ((tripsIII.school_dwell.between(0,3.5,inclusive = False)) | (tripsIII.school_dwell==0))*1 +\n", + " ((tripsIII.school_dwell.between(3.5,6,inclusive = False)) | (tripsIII.school_dwell==3.5))*2 +\n", + " ((tripsIII.school_dwell.between(6,8,inclusive = True)))*3 +\n", + " ((tripsIII.school_dwell.between(8,10,inclusive = False)) | (tripsIII.school_dwell==10))*4 +\n", + " ((tripsIII.school_dwell>10))*5\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "tripsIII['Sdwell'] = pd.to_numeric(tripsIII['Sdwell'])\n", + "tripsIII['HS_TOD1'] = pd.to_numeric(tripsIII['HS_TOD1'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add the demographic variables" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 148 columns

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" + ], + "text/plain": [ + " SAMPN PERNO RELAT GEND AGE AGEB HISP RACE1 RACE2 RACE3 RACE4 \\\n", + "0 1031985 1 1 1 74 NaN 2 1.0 NaN NaN NaN \n", + "1 1031985 2 2 2 73 NaN 2 1.0 NaN NaN NaN \n", + "2 1032036 1 1 1 46 NaN 2 1.0 NaN NaN NaN \n", + "3 1032036 2 2 2 47 NaN 2 1.0 97.0 NaN NaN \n", + "4 1032036 3 3 1 15 NaN 2 1.0 97.0 NaN NaN \n", + "\n", + " O_RACE NTVTY CNTRY LIC USER TRANS TPTYP1 TPTYP2 TPTYP3 \\\n", + "0 NaN 1 NaN 1.0 1.0 2.0 NaN NaN NaN \n", + "1 NaN 1 NaN 1.0 1.0 2.0 NaN NaN NaN \n", + "2 NaN 1 NaN 1.0 1.0 2.0 NaN NaN NaN \n", + "3 MULTI-RACIAL 1 NaN 1.0 1.0 2.0 NaN NaN NaN \n", + "4 MULTI-RACIAL 1 NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " TPTYP4 TPTYP5 TPTYP6 TPTYP7 O_TPTYP CLIP1 CLIP2 CLIP3 COMP MET \\\n", + "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " PASSTL FLEX EMPLY WKSTAT O_WKSTAT JOBS WLOC WNAME WCITY WSTAT \\\n", + "0 2.0 2.0 2.0 1.0 NaN NaN NaN NaN NaN NaN \n", + "1 2.0 2.0 2.0 1.0 NaN NaN NaN NaN NaN NaN \n", + "2 2.0 2.0 1.0 NaN NaN 1.0 1.0 HIDDEN SAN DIEGO CA \n", + "3 2.0 2.0 2.0 3.0 NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " ... HVLOG PTRIPS TOLLF TOLLR1 TOLLR2 TOLLR3 TOLLR4 TOLLR5 TOLLR6 \\\n", + "0 ... 1.0 2.0 3.0 NaN NaN NaN NaN NaN NaN \n", + "1 ... 1.0 2.0 3.0 NaN NaN NaN NaN NaN NaN \n", + "2 ... NaN 5.0 3.0 NaN NaN NaN NaN NaN NaN \n", + "3 ... NaN 18.0 3.0 NaN NaN NaN NaN NaN NaN \n", + "4 ... NaN 4.0 3.0 NaN NaN NaN NaN NaN NaN \n", + "\n", + " TOLLR7 TOLLR8 TOLLR9 TOLLR10 TOLLB1 TOLLB2 TOLLB3 TOLLB4 TOLLB5 \\\n", + "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " TOLLB6 TOLLB7 TOLLB8 TOLLB9 TOLLB10 HOVL NOGOWHY NOGOWHY_O InComplete \\\n", + "0 NaN NaN NaN NaN NaN 2.0 NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN 2.0 NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN 1.0 NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN 1.0 NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN 1.0 NaN NaN NaN \n", + "\n", + " Moto_trip WCTFIP WTRACT SCTFIP STRACT WPrimaryCity WSTFIP \\\n", + "0 1.0 NaN NaN NaN NaN NaN NaN \n", + "1 1.0 NaN NaN NaN NaN NaN NaN \n", + "2 1.0 73.0 17032.0 NaN NaN SAN DIEGO 6.0 \n", + "3 1.0 NaN NaN NaN NaN NaN NaN \n", + "4 1.0 NaN NaN 73.0 17030.0 NaN NaN \n", + "\n", + " W2PrimaryCity W2STFIP SPrimaryCity SSTFIP PERWGT EXPPERWGT \n", + "0 NaN NaN NaN NaN 0.052086 17.647568 \n", + "1 NaN NaN NaN NaN 0.052086 17.647568 \n", + "2 NaN NaN NaN NaN 1.223974 414.701494 \n", + "3 NaN NaN NaN NaN 0.863473 292.558373 \n", + "4 NaN NaN SAN DIEGO 6.0 0.941412 318.965100 \n", + "\n", + "[5 rows x 148 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "person = pd.read_csv('/home/data/CHTS_csv_format/data/Deliv_PER.csv')\n", + "\n", + "person.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "person = person[['SAMPN','PERNO','GEND','AGE','HISP','RACE1','RACE2','RACE3','RACE4',\n", + " 'HOURS','EDUCA']]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " SAMPN PERNO GEND AGE HISP RACE1 RACE2 RACE3 RACE4 O_RACE \\\n", + "0 1031985 1 1 74 0 1.0 NaN NaN NaN NaN \n", + "1 1031985 2 2 73 0 1.0 NaN NaN NaN NaN \n", + "2 1032036 1 1 46 0 1.0 NaN NaN NaN NaN \n", + "3 1032036 2 2 47 0 1.0 97.0 NaN NaN MULTI-RACIAL \n", + "4 1032036 3 1 15 0 1.0 97.0 NaN NaN MULTI-RACIAL \n", + "\n", + " NTVTY LIC JOBS HOURS EDUCA WSCHED DISAB INDUS OCCUP HHPER \\\n", + "0 1 1.0 NaN NaN 6 NaN 0 NaN NaN 10319851 \n", + "1 1 1.0 NaN NaN 6 NaN 0 NaN NaN 10319852 \n", + "2 1 1.0 1.0 40.0 6 2.0 0 54.0 15.0 10320361 \n", + "3 1 1.0 NaN NaN 6 NaN 0 NaN NaN 10320362 \n", + "4 1 NaN NaN NaN 1 NaN 0 NaN NaN 10320363 \n", + "\n", + " female white black native asian PI immigrant nolic HHVEH HHBIC \\\n", + "0 0 1 0 0 0 0 0 0.0 2 2 \n", + "1 1 1 0 0 0 0 0 0.0 2 2 \n", + "2 0 1 0 0 0 0 0 0.0 1 4 \n", + "3 1 1 0 0 0 0 0 0.0 1 4 \n", + "4 0 1 0 0 0 0 0 NaN 1 4 \n", + "\n", + " OWN INCOM HHSIZ rent \n", + "0 1 3 2 0 \n", + "1 1 3 2 0 \n", + "2 1 7 5 0 \n", + "3 1 7 5 0 \n", + "4 1 7 5 0 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "demo = person.merge(hh_df,on = 'SAMPN',how = 'left')\n", + "\n", + "demo.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get rid of null values" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "na_dict = {\n", + " 'GEND':[9],\n", + " 'AGE':[998,999],\n", + " 'HOURS':[998,999],\n", + " 'EDUCA':[8,9],\n", + " 'HHVEH':[98,99],\n", + " 'OWN':[7,8,9],\n", + " 'INCOM':[98,99],\n", + " 'HHSIZ':[98,99]\n", + "}\n", + "\n", + "for col in na_dict:\n", + " for vals in na_dict[col]:\n", + " demo[col] = demo[col].replace(vals,np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "demo = demo.dropna(subset = ['GEND', 'AGE', 'HOURS', 'EDUCA','HHVEH','OWN','INCOM','HHSIZ'])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9857\n", + "9857\n", + "8979\n" + ] + } + ], + "source": [ + "tripsIII['HHPER'] = tripsIII['HHPER'].map(str)\n", + "\n", + "trips1 = pd.merge(tripsIII, demo, on='HHPER')\n", + "\n", + "print (len(tripsIII.index))\n", + "print (len(trips1.index))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare data for use in MNL estimation (make dummy columns)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['minority'] = np.where((trips1['HISP'].isin([1.0]) |\n", + " trips1['RACE1'].isin([2.0]) | trips1['RACE2'].isin([2.0]) | trips1['RACE3'].isin([2.0]) | trips1['RACE4'].isin([2.0]) |\n", + " trips1['RACE1'].isin([3.0]) | trips1['RACE2'].isin([3.0]) | trips1['RACE3'].isin([3.0]) | trips1['RACE4'].isin([3.0]) |\n", + " trips1['RACE1'].isin([4.0]) | trips1['RACE2'].isin([4.0]) | trips1['RACE3'].isin([4.0]) | trips1['RACE4'].isin([4.0]) |\n", + " trips1['RACE1'].isin([5.0]) | trips1['RACE2'].isin([5.0]) | trips1['RACE3'].isin([5.0]) | trips1['RACE4'].isin([5.0]) |\n", + " trips1['RACE1'].isin([97.0]) | trips1['RACE2'].isin([97.0]) | trips1['RACE3'].isin([97.0]) | trips1['RACE4'].isin([97.0])),1,0)\n", + "\n", + "trips1['black'] = np.where((trips1['RACE1'].isin([2.0]) | trips1['RACE2'].isin([2.0]) | trips1['RACE3'].isin([2.0]) | trips1['RACE4'].isin([2.0])),1,0)\n", + "trips1['native'] = np.where((trips1['RACE1'].isin([3.0]) | trips1['RACE2'].isin([3.0]) | trips1['RACE3'].isin([3.0]) | trips1['RACE4'].isin([3.0])),1,0)\n", + "trips1['asian'] = np.where((trips1['RACE1'].isin([4.0]) | trips1['RACE2'].isin([4.0]) | trips1['RACE3'].isin([4.0]) | trips1['RACE4'].isin([4.0])),1,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['hh_inc_less35k'] = np.where(trips1['INCOM'].isin([1.0,2.0,3.0]),1,0)\n", + "trips1['hh_inc_less50k'] = np.where(trips1['INCOM'].isin([1.0,2.0,3.0,4.0]),1,0)\n", + "trips1['hh_inc_150kplus'] = np.where(trips1['INCOM'].isin([8.0,9.0,10.0]),1,0)\n", + "trips1['hh_inc_150kless250k'] = np.where(trips1['INCOM'].isin([8.0,9.0]),1,0)\n", + "trips1['hh_inc_250kplus'] = np.where(trips1['INCOM'].isin([10.0]),1,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['lessGED'] = np.where(trips1['EDUCA'].isin([1.0]),1,0)\n", + "trips1['GEDsomeBach'] = np.where(trips1['EDUCA'].isin([2.0,3.0]),1,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['age_less5'] = np.where(((trips1.AGE.between(0,5,inclusive = False)) | (trips1.AGE==0)),1,0)\n", + "trips1['age_12less16'] = np.where(((trips1.AGE.between(12,16,inclusive = False)) | (trips1.AGE==12)),1,0)\n", + "trips1['age_16less19'] = np.where(((trips1.AGE.between(16,19,inclusive = False)) | (trips1.AGE==16)),1,0)\n", + "trips1['age_19less27'] = np.where(((trips1.AGE.between(19,27,inclusive = False)) | (trips1.AGE==19)),1,0)\n", + "trips1['age_27plus'] = np.where(((trips1.AGE.between(27,100,inclusive = False)) | (trips1.AGE==27)),1,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['female'] = trips1['GEND'] - 1\n", + "\n", + "trips1['tenure_2'] = trips1['OWN'] - 1\n", + "\n", + "trips1['noveh'] = np.where(trips1.HHVEH.isin([0.0]),1,0)\n", + "\n", + "trips1['hh_size_4plusper'] = np.where(trips1.HHSIZ.between(4,8,inclusive = True),1,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['TOD_3to745'] = np.where(trips1['HS_TOD1'].isin([0]),1,0)\n", + "trips1['TOD_830to930'] = np.where(trips1['HS_TOD1'].isin([2]),1,0)\n", + "trips1['TOD_930to1500'] = np.where(trips1['HS_TOD1'].isin([3]),1,0)\n", + "trips1['TOD_1500up'] = np.where(trips1['HS_TOD1'].isin([4]),1,0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimate the model for Home-to-School Trip End Times" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [], + "source": [ + "@orca.table(cache=True)\n", + "def tripsA():\n", + " return trips1" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [], + "source": [ + "m = SmallMultinomialLogitStep()\n", + "m.name = 'STOD_choice'\n", + "m.tables = ['tripsA']\n", + "m.choice_column = 'HS_TOD1'\n", + "m.model_expression = OrderedDict([\n", + " ('intercept', [1,2,3,4]),\n", + " \n", + " ('less5',[0,2,3,4]),\n", + "# ('5less12'),\n", + " ('12less16',[0]),\n", + " ('16less19',[0,3,4]),\n", + " ('19less27',[0,2,3,4]),\n", + " ('27plus',[2,3,4]),\n", + " \n", + " ('female',[0]),\n", + " \n", + " ('black',[3]),\n", + " ('native',[2,3]),\n", + " ('asian',[0,2,3]),\n", + " \n", + " ('less35k',[2]),\n", + " ('150kless250k',[0]),\n", + " ('250kplus',[0]),\n", + "\n", + " ('lessGED',[0,2,3,4]),\n", + " \n", + " ('noveh',[3]),\n", + "\n", + " ('4plusper',[3])\n", + " \n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Log-likelihood at zero: -14,451.1430\n", + "Initial Log-likelihood: -14,451.1430\n", + "Estimation Time for Point Estimation: 0.81 seconds.\n", + "Final log-likelihood: -9,385.2482\n", + " Multinomial Logit Model Regression Results \n", + "===================================================================================\n", + "Dep. Variable: _chosen No. Observations: 8,979\n", + "Model: Multinomial Logit Model Df Residuals: 8,944\n", + "Method: MLE Df Model: 35\n", + "Date: Mon, 25 Mar 2019 Pseudo R-squ.: 0.351\n", + "Time: 16:32:43 Pseudo R-bar-squ.: 0.348\n", + "AIC: 18,840.496 Log-Likelihood: -9,385.248\n", + "BIC: 19,089.089 LL-Null: -14,451.143\n", + "==================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "----------------------------------------------------------------------------------\n", + "intercept_1 1.4351 0.174 8.231 0.000 1.093 1.777\n", + "intercept_2 0.6556 0.236 2.775 0.006 0.193 1.119\n", + "intercept_3 0.3501 0.268 1.306 0.191 -0.175 0.875\n", + "intercept_4 -2.4639 0.570 -4.323 0.000 -3.581 -1.347\n", + "less5_0 0.5197 0.125 4.163 0.000 0.275 0.764\n", + "less5_2 1.5944 0.115 13.857 0.000 1.369 1.820\n", + "less5_3 2.3554 0.177 13.275 0.000 2.008 2.703\n", + "less5_4 1.7310 0.820 2.112 0.035 0.125 3.337\n", + "12less16_0 0.8536 0.059 14.521 0.000 0.738 0.969\n", + "16less19_0 1.1646 0.069 16.760 0.000 1.028 1.301\n", + "16less19_3 1.2429 0.169 7.353 0.000 0.912 1.574\n", + "16less19_4 2.0394 0.522 3.910 0.000 1.017 3.062\n", + "19less27_0 0.9699 0.212 4.571 0.000 0.554 1.386\n", + "19less27_2 1.0356 0.240 4.311 0.000 0.565 1.506\n", + "19less27_3 2.0031 0.259 7.719 0.000 1.494 2.512\n", + "19less27_4 3.6530 0.575 6.354 0.000 2.526 4.780\n", + "27plus_2 1.1888 0.236 5.033 0.000 0.726 1.652\n", + "27plus_3 1.8772 0.264 7.101 0.000 1.359 2.395\n", + "27plus_4 4.7118 0.563 8.376 0.000 3.609 5.814\n", + "female_0 0.1071 0.049 2.182 0.029 0.011 0.203\n", + "black_3 0.3948 0.188 2.100 0.036 0.026 0.763\n", + "native_2 -0.2925 0.134 -2.177 0.029 -0.556 -0.029\n", + "native_3 -0.4258 0.182 -2.336 0.019 -0.783 -0.069\n", + "asian_0 -0.2358 0.089 -2.655 0.008 -0.410 -0.062\n", + "asian_2 0.2696 0.105 2.560 0.010 0.063 0.476\n", + "asian_3 0.2889 0.146 1.974 0.048 0.002 0.576\n", + "less35k_2 -0.2257 0.086 -2.633 0.008 -0.394 -0.058\n", + "150kless250k_0 -0.4313 0.078 -5.509 0.000 -0.585 -0.278\n", + "250kplus_0 -0.7532 0.131 -5.770 0.000 -1.009 -0.497\n", + "lessGED_0 0.3714 0.170 2.179 0.029 0.037 0.705\n", + "lessGED_2 -1.1101 0.216 -5.143 0.000 -1.533 -0.687\n", + "lessGED_3 -2.3321 0.217 -10.736 0.000 -2.758 -1.906\n", + "lessGED_4 -2.4942 0.389 -6.419 0.000 -3.256 -1.733\n", + "noveh_3 0.5608 0.216 2.600 0.009 0.138 0.984\n", + "4plusper_3 -0.2840 0.100 -2.835 0.005 -0.480 -0.088\n", + "==================================================================================\n" + ] + } + ], + "source": [ + "m.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [], + "source": [ + "m.name = 'school_TOD'" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Registering model step 'auto_ownership'\n", + "Registering model step 'dwell_work'\n", + "Registering model step 'TOD_choice'\n", + "Registering model step 'work_TOD_choice'\n", + "Registering model step 'primary_mode_choice'\n", + "Registering model step 'school_dwell'\n", + "Registering model step 'WLCM'\n" + ] + } + ], + "source": [ + "mm.initialize()" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving 'school_TOD.yaml': /home/emma/ual_model_workspace/spring-2019-models/configs\n", + "Model saved to configs/school_TOD-model-object.pkl\n", + "Registering model step 'school_TOD'\n" + ] + } + ], + "source": [ + "m.tags = ['school_TOD','emma','test']\n", + "mm.register(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimate the model for dwell time at school" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "@orca.table(cache=True)\n", + "def tripsB():\n", + " return trips1" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [], + "source": [ + "m = SmallMultinomialLogitStep()\n", + "m.name = 'Sdwell_choice'\n", + "m.tables = ['tripsB']\n", + "m.choice_column = 'Sdwell'\n", + "m.model_expression = OrderedDict([\n", + " ('intercept', [1,3,4,5]),\n", + " \n", + " ('TOD_3to745',[1,2,4,5]),\n", + " ('TOD_830to930',[1,2]),\n", + " ('TOD_930to1500',[1,2]),\n", + " ('TOD_1500up',[1,2]),\n", + "\n", + " ('less5',[1,2,4,5]),\n", + "# ('5less12',[1,3,4,5]),\n", + " ('12less16',[1,2]),\n", + " ('16less19',[1,4]),\n", + " ('19less27',[1,2]),\n", + " ('27plus',[1,2]),\n", + " \n", + " ('female',[4]),\n", + " \n", + " ('minority',[1]),\n", + "\n", + " ('less50k',[2,4]),\n", + " ('150kplus',[2,4,5]),\n", + " \n", + " ('lessGED',[4,5]),\n", + " ('GEDsomeBach',[[1,2]]),\n", + "\n", + " ('4plusper',[4])\n", + " \n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Log-likelihood at zero: -14,451.1430\n", + "Initial Log-likelihood: -14,451.1430\n", + "Estimation Time for Point Estimation: 0.82 seconds.\n", + "Final log-likelihood: -9,352.9999\n", + " Multinomial Logit Model Regression Results \n", + "===================================================================================\n", + "Dep. Variable: _chosen No. Observations: 8,979\n", + "Model: Multinomial Logit Model Df Residuals: 8,942\n", + "Method: MLE Df Model: 37\n", + "Date: Mon, 25 Mar 2019 Pseudo R-squ.: 0.353\n", + "Time: 16:23:45 Pseudo R-bar-squ.: 0.350\n", + "AIC: 18,780.000 Log-Likelihood: -9,353.000\n", + "BIC: 19,042.798 LL-Null: -14,451.143\n", + "======================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "--------------------------------------------------------------------------------------\n", + "intercept_1 -2.0897 0.138 -15.182 0.000 -2.359 -1.820\n", + "intercept_3 1.4607 0.059 24.936 0.000 1.346 1.575\n", + "intercept_4 0.9643 0.140 6.875 0.000 0.689 1.239\n", + "intercept_5 -0.4320 0.176 -2.456 0.014 -0.777 -0.087\n", + "TOD_3to745_1 -0.3557 0.181 -1.961 0.050 -0.711 -0.000\n", + "TOD_3to745_2 -0.2786 0.088 -3.160 0.002 -0.451 -0.106\n", + "TOD_3to745_4 0.6999 0.065 10.687 0.000 0.572 0.828\n", + "TOD_3to745_5 1.5685 0.114 13.724 0.000 1.345 1.793\n", + "TOD_830to930_1 1.2851 0.139 9.266 0.000 1.013 1.557\n", + "TOD_830to930_2 0.7291 0.091 7.970 0.000 0.550 0.908\n", + "TOD_930to1500_1 2.9451 0.153 19.189 0.000 2.644 3.246\n", + "TOD_930to1500_2 1.8094 0.128 14.182 0.000 1.559 2.059\n", + "TOD_1500up_1 5.7739 0.598 9.658 0.000 4.602 6.946\n", + "TOD_1500up_2 4.1758 0.597 6.996 0.000 3.006 5.346\n", + "less5_1 2.6541 0.188 14.086 0.000 2.285 3.023\n", + "less5_2 1.0986 0.148 7.441 0.000 0.809 1.388\n", + "less5_4 2.1206 0.125 16.977 0.000 1.876 2.365\n", + "less5_5 1.3584 0.240 5.649 0.000 0.887 1.830\n", + "12less16_1 -0.4348 0.209 -2.078 0.038 -0.845 -0.025\n", + "12less16_2 -0.5867 0.087 -6.724 0.000 -0.758 -0.416\n", + "16less19_1 0.8834 0.165 5.367 0.000 0.561 1.206\n", + "16less19_4 0.1739 0.080 2.168 0.030 0.017 0.331\n", + "19less27_1 1.7279 0.201 8.608 0.000 1.334 2.121\n", + "19less27_2 0.4914 0.158 3.101 0.002 0.181 0.802\n", + "27plus_1 2.6899 0.201 13.413 0.000 2.297 3.083\n", + "27plus_2 1.2468 0.164 7.607 0.000 0.926 1.568\n", + "female_4 0.1305 0.060 2.188 0.029 0.014 0.247\n", + "minority_1 -0.3062 0.099 -3.108 0.002 -0.499 -0.113\n", + "less50k_2 -0.2541 0.069 -3.696 0.000 -0.389 -0.119\n", + "less50k_4 -0.3018 0.070 -4.312 0.000 -0.439 -0.165\n", + "150kplus_2 -0.2113 0.090 -2.354 0.019 -0.387 -0.035\n", + "150kplus_4 0.2582 0.080 3.218 0.001 0.101 0.416\n", + "150kplus_5 0.3980 0.138 2.874 0.004 0.127 0.669\n", + "lessGED_4 -1.0338 0.119 -8.681 0.000 -1.267 -0.800\n", + "lessGED_5 -1.6882 0.170 -9.929 0.000 -2.021 -1.355\n", + "GEDsomeBach_[1, 2] 0.9046 0.139 6.507 0.000 0.632 1.177\n", + "4plusper_4 -0.2844 0.073 -3.911 0.000 -0.427 -0.142\n", + "======================================================================================\n" + ] + } + ], + "source": [ + "m.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "m.name = 'school_dwell'" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Registering model step 'auto_ownership'\n", + "Registering model step 'dwell_work'\n", + "Registering model step 'TOD_choice'\n", + "Registering model step 'work_TOD_choice'\n", + "Registering model step 'primary_mode_choice'\n", + "Registering model step 'WLCM'\n" + ] + } + ], + "source": [ + "mm.initialize()" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving 'school_dwell.yaml': /home/emma/ual_model_workspace/spring-2019-models/configs\n", + "Model saved to configs/school_dwell-model-object.pkl\n", + "Registering model step 'school_dwell'\n" + ] + } + ], + "source": [ + "m.tags = ['school_dwell','emma','test']\n", + "mm.register(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Validate models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Validation process\n", + "from scripts import validate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "validate.tp_rates(m)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predicted_choices = validate.get_predicted_choices(m)\n", + "pd.crosstab(m.choices.rename('observed'), predicted_choices, margins=True) # unnormalized" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "validate.model_crosstab(m)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns; sns.heatmap(validate.model_crosstab(m))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/activitysynth/notebooks/TOD_School_Distribution_Estimation.ipynb b/activitysynth/notebooks/TOD_School_Distribution_Estimation.ipynb new file mode 100644 index 0000000..0443ee8 --- /dev/null +++ b/activitysynth/notebooks/TOD_School_Distribution_Estimation.ipynb @@ -0,0 +1,3991 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import OrderedDict\n", + "from urbansim_templates import modelmanager as mm\n", + "from urbansim_templates.models import LargeMultinomialLogitStep\n", + "from urbansim_templates.models import SmallMultinomialLogitStep\n", + "import orca\n", + "import os; os.chdir('../')\n", + "import warnings; warnings.simplefilter('ignore')\n", + "\n", + "import pandas as pd\n", + "# import pandana as pdna\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "from functools import reduce\n", + "\n", + "import scipy.stats as st\n", + "from scipy.stats import skewnorm\n", + "\n", + "# import matplotlib\n", + "# matplotlib.style.use('ggplot')\n", + "\n", + "%matplotlib inline\n", + "\n", + "pd.options.display.max_columns = 80" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "trips1 = pd.read_csv('/home/emma/ual_model_workspace/spring-2019-models/notebooks-emma/school_pop_032519.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up distribution estimation functions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Create models from data\n", + "def best_fit_distribution1(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " st.norm, st.skewnorm,\n", + " st.alpha,st.anglit,st.argus,st.betaprime,st.burr,st.burr12,st.cauchy,\n", + " st.chi,st.chi2,\n", + " st.cosine,\n", + " st.erlang,\n", + " st.exponnorm,\n", + " st.exponweib,st.exponpow,st.f,st.fisk\n", + "\n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.norm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)\n", + "\n", + "\n", + "def make_pdf(dist, params, size=10000):\n", + " \"\"\"Generate distributions' Probability Distribution Functions \"\"\"\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Get sane start and end points of distribution\n", + " start = dist.ppf(0.001, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.01, loc=loc, scale=scale)\n", + " end = dist.ppf(0.999, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.99, loc=loc, scale=scale)\n", + "\n", + " # Build PDF and turn into pandas Series\n", + " x = np.linspace(start, end, size)\n", + " y = dist.pdf(x, loc=loc, scale=scale, *arg)\n", + " pdf = pd.Series(y, x)\n", + "\n", + " return pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def best_fit_distribution2(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " st.gausshyper,\n", + " st.foldnorm,st.weibull_min,st.weibull_max,st.genlogistic,\n", + " st.gennorm,\n", + " st.genextreme,st.gamma,st.gengamma,st.gilbrat,st.gumbel_r,\n", + " st.gumbel_l,st.hypsecant,st.invgamma,st.invgauss]\n", + "\n", + " # Best holders\n", + " best_distribution = st.foldnorm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Create models from data\n", + "def best_fit_distribution3(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + "\n", + " st.johnsonsb, st.johnsonsu,st.ksone,st.logistic,st.loggamma,st.lognorm,st.maxwell,st.mielke,st.nakagami,st.ncx2,st.ncf\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.johnsonsu\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Create models from data\n", + "def best_fit_distribution4(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " \n", + " st.nct,st.pearson3,st.powerlognorm,st.powernorm,\n", + " st.rayleigh,st.rice,st.recipinvgauss,st.t,\n", + " st.vonmises,st.vonmises_line,st.wald,st.weibull_min,st.weibull_max\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.t\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Create models from data\n", + "def best_fit_distribution5(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + "\n", + " st.gompertz,\n", + " st.arcsine,st.beta,st.bradford,st.dgamma,st.dweibull,st.expon,st.fatiguelife,st.foldcauchy,\n", + " st.genpareto,st.genexpon,st.genhalflogistic,st.halfcauchy,st.halflogistic\n", + "\n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.foldcauchy\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Create models from data\n", + "def best_fit_distribution6(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + "\n", + " st.semicircular,\n", + " st.halfnorm,st.halfgennorm,st.kappa3,st.laplace,st.levy,st.levy_l,st.loglaplace,\n", + " st.lomax,st.pareto,st.powerlaw,st.rdist,st.kappa4,st.invweibull,\n", + " \n", + " st.reciprocal,st.trapz,st.triang,\n", + " st.truncexpon,st.truncnorm,st.tukeylambda,st.wrapcauchy\n", + " \n", + "#st.levy_stable,\n", + "# st.crystalball,st.kstwobign \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.loglaplace\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def make_pdf(dist, params, size=10000):\n", + " \"\"\"Generate distributions' Probability Distribution Functions \"\"\"\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Get sane start and end points of distribution\n", + " start = dist.ppf(0.001, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.01, loc=loc, scale=scale)\n", + " end = dist.ppf(0.999, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.99, loc=loc, scale=scale)\n", + "\n", + " # Build PDF and turn into pandas Series\n", + " x = np.linspace(start, end, size)\n", + " y = dist.pdf(x, loc=loc, scale=scale, *arg)\n", + " pdf = pd.Series(y, x)\n", + "\n", + " return pdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Estimate distributions for actual home-school trip end times" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "#For HW\n", + "def best_fit_distribution7(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " \n", + "# st.t,\n", + "# st.tukeylambda,\n", + "# st.cauchy,\n", + "# st.foldcauchy,\n", + " st.johnsonsu, \n", + " st.gennorm, \n", + " st.dweibull,st.dgamma\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.norm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "HS = trips1.HS_trip_ET" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
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');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "johnsonsu(a=-0.53, b=0.71, loc=7.73, scale=0.29)\n" + ] + } + ], + "source": [ + "# # Load data\n", + "data = HS\n", + "\n", + "# Plot for comparison\n", + "plt.figure(figsize=(12,8))\n", + "ax = data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True\n", + " #, color=plt.rcParams['axes.color_cycle'][1]\n", + " )\n", + "# Save plot limits\n", + "dataYLim = ax.get_ylim()\n", + "\n", + "# Find best fit distribution\n", + "best_fit_name, best_fir_paramms = best_fit_distribution7(data, 200, ax)\n", + "best_dist = getattr(st, best_fit_name)\n", + "\n", + "# Update plots\n", + "ax.set_ylim(dataYLim)\n", + "ax.set_title(u'Trips to Work\\n All Best Fitted Distributions')\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "# Make PDF\n", + "pdf = make_pdf(best_dist, best_fir_paramms)\n", + "\n", + "# Display\n", + "plt.figure(figsize=(12,8))\n", + "ax = pdf.plot(lw=2, label='PDF', legend=True)\n", + "data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True, ax=ax)\n", + "\n", + "param_names = (best_dist.shapes + ', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']\n", + "param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, best_fir_paramms)])\n", + "dist_str = '{}({})'.format(best_fit_name, param_str)\n", + "\n", + "ax.set_title(u'Trips to Work with best-fit distribution \\n' + dist_str)\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "print (dist_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimate distributions for Actual work dwell times" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "dwell_exact = trips1.school_dwell" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "#For HW\n", + "def best_fit_distribution8(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + "\n", + " st.foldcauchy, \n", + "# st.cauchy,\n", + "# st.gennorm,\n", + " st.loglaplace,\n", + " st.johnsonsu, \n", + "# st.t, \n", + "# st.tukeylambda\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.norm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "foldcauchy(c=8.28, loc=0.02, scale=0.84)\n" + ] + } + ], + "source": [ + "# # Load data\n", + "data = dwell_exact\n", + "\n", + "# Plot for comparison\n", + "plt.figure(figsize=(12,8))\n", + "ax = data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True\n", + " #, color=plt.rcParams['axes.color_cycle'][1]\n", + " )\n", + "# Save plot limits\n", + "dataYLim = ax.get_ylim()\n", + "\n", + "# Find best fit distribution\n", + "best_fit_name, best_fir_paramms = best_fit_distribution8(data, 200, ax)\n", + "best_dist = getattr(st, best_fit_name)\n", + "\n", + "# Update plots\n", + "ax.set_ylim(dataYLim)\n", + "ax.set_title(u'Trips to Work\\n All Best Fitted Distributions')\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "# Make PDF\n", + "pdf = make_pdf(best_dist, best_fir_paramms)\n", + "\n", + "# Display\n", + "plt.figure(figsize=(12,8))\n", + "ax = pdf.plot(lw=2, label='PDF', legend=True)\n", + "data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True, ax=ax)\n", + "\n", + "param_names = (best_dist.shapes + ', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']\n", + "param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, best_fir_paramms)])\n", + "dist_str = '{}({})'.format(best_fit_name, param_str)\n", + "\n", + "ax.set_title(u'Trips to Work with best-fit distribution \\n' + dist_str)\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "print (dist_str)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/activitysynth/notebooks/TOD_Work_Category_Estimation.ipynb b/activitysynth/notebooks/TOD_Work_Category_Estimation.ipynb new file mode 100644 index 0000000..a3872f1 --- /dev/null +++ b/activitysynth/notebooks/TOD_Work_Category_Estimation.ipynb @@ -0,0 +1,1988 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import OrderedDict\n", + "from urbansim_templates import modelmanager as mm\n", + "from urbansim_templates.models import LargeMultinomialLogitStep\n", + "from urbansim_templates.models import SmallMultinomialLogitStep\n", + "import orca\n", + "import os; os.chdir('../')\n", + "import warnings; warnings.simplefilter('ignore')\n", + "\n", + "import pandas as pd\n", + "# import pandana as pdna\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "from functools import reduce\n", + "\n", + "import scipy.stats as st\n", + "from scipy.stats import skewnorm\n", + "\n", + "# import matplotlib\n", + "# matplotlib.style.use('ggplot')\n", + "\n", + "%matplotlib inline\n", + "\n", + "pd.options.display.max_columns = 80" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0HHPERHHPERTRIPoriginorigin_dwellorigin_STorigin_ETtrip_ETTTMODE
00103519811.035198e+09home14.00000017.3333337.3333337.7000000.366667drive_alone
11103519811.035198e+09work9.3833337.70000017.08333317.3333330.250000drive_alone
22103519821.035198e+09home10.41666719.5833336.0000006.2500000.250000drive_alone
33103519821.035198e+09work10.2500006.25000016.50000019.5833333.083333drive_alone
44103527421.035274e+09home13.58333319.1666678.7500009.1666670.416667drive_alone
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 HHPER HHPERTRIP origin origin_dwell origin_ST \\\n", + "0 0 10351981 1.035198e+09 home 14.000000 17.333333 \n", + "1 1 10351981 1.035198e+09 work 9.383333 7.700000 \n", + "2 2 10351982 1.035198e+09 home 10.416667 19.583333 \n", + "3 3 10351982 1.035198e+09 work 10.250000 6.250000 \n", + "4 4 10352742 1.035274e+09 home 13.583333 19.166667 \n", + "\n", + " origin_ET trip_ET TT MODE \n", + "0 7.333333 7.700000 0.366667 drive_alone \n", + "1 17.083333 17.333333 0.250000 drive_alone \n", + "2 6.000000 6.250000 0.250000 drive_alone \n", + "3 16.500000 19.583333 3.083333 drive_alone \n", + "4 8.750000 9.166667 0.416667 drive_alone " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trips = pd.read_csv('/home/emma/ual_model_workspace/fall-2018-models/notebooks-emma/HWtrips_031418.csv')\n", + "\n", + "trips.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare TOD and Dwell columns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#select people who make both home-work and work-home trips:\n", + "tripsII = trips.groupby('HHPER').filter(lambda x: len(x) == 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#make sure all home-work trip rows are listed first\n", + "tripsIII = tripsII.sort_values(['HHPER','origin']).reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#move work-home trip info up into home-work trip rows\n", + "\n", + "tripsIII['work_dwell'] = tripsIII.groupby('HHPER', group_keys=False).origin_dwell.shift(-1)\n", + "tripsIII['work_ST'] = tripsIII.groupby('HHPER', group_keys=False).origin_ST.shift(-1)\n", + "tripsIII['WH_trip_ST'] = tripsIII.groupby('HHPER', group_keys=False).origin_ET.shift(-1)\n", + "tripsIII['WH_trip_ET'] = tripsIII.groupby('HHPER', group_keys=False).trip_ET.shift(-1)\n", + "tripsIII['WH_TT'] = tripsIII.groupby('HHPER', group_keys=False).TT.shift(-1)\n", + "tripsIII['WH_mode'] = tripsIII.groupby('HHPER', group_keys=False).MODE.shift(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "tripsIII = tripsIII.groupby('HHPER').first().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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HHPERindexUnnamed: 0HHPERTRIPoriginhome_dwellhome_STHW_trip_STHW_trip_ETHW_TTHW_modework_dwellwork_STWH_trip_STWH_trip_ETWH_TTWH_mode
010351981001.035198e+09home14.00000017.3333337.3333337.7000000.366667drive_alone9.3833337.70000017.08333317.3333330.250000drive_alone
110351982221.035198e+09home10.41666719.5833336.0000006.2500000.250000drive_alone10.2500006.25000016.50000019.5833333.083333drive_alone
210352742441.035274e+09home13.58333319.1666678.7500009.1666670.416667drive_alone7.5833339.16666716.75000019.1666672.416667drive_alone
310353643661.035364e+09home11.33333319.5833336.9166677.4166670.500000drive_alone8.6333337.41666716.05000019.5833333.533333drive_alone
410372952881.037295e+09home17.16666721.83333315.00000015.4166670.416667drive_alone6.00000015.41666721.41666721.8333330.416667drive_alone
\n", + "
" + ], + "text/plain": [ + " HHPER index Unnamed: 0 HHPERTRIP origin home_dwell home_ST \\\n", + "0 10351981 0 0 1.035198e+09 home 14.000000 17.333333 \n", + "1 10351982 2 2 1.035198e+09 home 10.416667 19.583333 \n", + "2 10352742 4 4 1.035274e+09 home 13.583333 19.166667 \n", + "3 10353643 6 6 1.035364e+09 home 11.333333 19.583333 \n", + "4 10372952 8 8 1.037295e+09 home 17.166667 21.833333 \n", + "\n", + " HW_trip_ST HW_trip_ET HW_TT HW_mode work_dwell work_ST \\\n", + "0 7.333333 7.700000 0.366667 drive_alone 9.383333 7.700000 \n", + "1 6.000000 6.250000 0.250000 drive_alone 10.250000 6.250000 \n", + "2 8.750000 9.166667 0.416667 drive_alone 7.583333 9.166667 \n", + "3 6.916667 7.416667 0.500000 drive_alone 8.633333 7.416667 \n", + "4 15.000000 15.416667 0.416667 drive_alone 6.000000 15.416667 \n", + "\n", + " WH_trip_ST WH_trip_ET WH_TT WH_mode \n", + "0 17.083333 17.333333 0.250000 drive_alone \n", + "1 16.500000 19.583333 3.083333 drive_alone \n", + "2 16.750000 19.166667 2.416667 drive_alone \n", + "3 16.050000 19.583333 3.533333 drive_alone \n", + "4 21.416667 21.833333 0.416667 drive_alone " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tripsIII.rename(columns = {'origin_dwell':'home_dwell','origin_ST':'home_ST','origin_ET':'HW_trip_ST',\n", + " 'trip_ET':'HW_trip_ET','TT':'HW_TT','MODE':'HW_mode'},inplace = True)\n", + "\n", + "tripsIII.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "tripsIII['TOD'] = (\n", + " ((tripsIII.HW_trip_ET.between(3,6,inclusive = False)) | (tripsIII.HW_trip_ET==3))*1 +\n", + " ((tripsIII.HW_trip_ET.between(6,9,inclusive = False)) | (tripsIII.HW_trip_ET==6))*2 +\n", + " ((tripsIII.HW_trip_ET.between(9,15.5,inclusive = False)) | (tripsIII.HW_trip_ET==9))*3 +\n", + " ((tripsIII.HW_trip_ET.between(15.5,18.5,inclusive = False)) | (tripsIII.HW_trip_ET==15.5))*4 +\n", + " ((tripsIII.HW_trip_ET>=18.5))*5 +\n", + " ((tripsIII.HW_trip_ET.between(0,3,inclusive = False)) | (tripsIII.HW_trip_ET==0))*5\n", + ")\n", + "\n", + "tripsIII['TOD'] = tripsIII['TOD'] - 1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "tripsIII['TOD'] = pd.to_numeric(tripsIII['TOD'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "tripsIII['dwell_work'] = (\n", + " ((tripsIII.work_dwell.between(0,4.5,inclusive = False)) | (tripsIII.work_dwell==0))*1 +\n", + " ((tripsIII.work_dwell.between(4.5,7.75,inclusive = False)) | (tripsIII.work_dwell==4.5))*2 +\n", + " ((tripsIII.work_dwell.between(7.75,9.0,inclusive = False)) | (tripsIII.work_dwell==7.75))*3 +\n", + " ((tripsIII.work_dwell.between(9.0,10.5,inclusive = False)) | (tripsIII.work_dwell==9.0))*4 +\n", + " ((tripsIII.work_dwell>=10.5))*5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add the demographic variables" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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41032036311521.097.0NaNNaNNaN1NaN103203631175
\n", + "
" + ], + "text/plain": [ + " SAMPN PERNO GEND AGE HISP RACE1 RACE2 RACE3 RACE4 HOURS EDUCA \\\n", + "0 1031985 1 1 74 2 1.0 NaN NaN NaN NaN 6 \n", + "1 1031985 2 2 73 2 1.0 NaN NaN NaN NaN 6 \n", + "2 1032036 1 1 46 2 1.0 NaN NaN NaN 40.0 6 \n", + "3 1032036 2 2 47 2 1.0 97.0 NaN NaN NaN 6 \n", + "4 1032036 3 1 15 2 1.0 97.0 NaN NaN NaN 1 \n", + "\n", + " INDUS HHPER HHVEH OWN INCOM HHSIZ \n", + "0 NaN 10319851 2 1 3 2 \n", + "1 NaN 10319852 2 1 3 2 \n", + "2 54.0 10320361 1 1 7 5 \n", + "3 NaN 10320362 1 1 7 5 \n", + "4 NaN 10320363 1 1 7 5 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "demo = person.merge(hh_df,on = 'SAMPN',how = 'left')\n", + "\n", + "demo.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get rid of null values" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "na_dict = {\n", + " 'GEND':[9],\n", + " 'AGE':[998,999],\n", + " 'HOURS':[998,999],\n", + " 'EDUCA':[8,9],\n", + " 'HHVEH':[98,99],\n", + " 'OWN':[7,8,9],\n", + " 'INCOM':[98,99],\n", + " 'HHSIZ':[98,99],\n", + " 'INDUS':[98,99],\n", + "}\n", + "\n", + "for col in na_dict:\n", + " for vals in na_dict[col]:\n", + " demo[col] = demo[col].replace(vals,np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "demo = demo.dropna(subset = ['GEND', 'AGE', 'HOURS', 'EDUCA','HHVEH','OWN','INCOM','HHSIZ','INDUS'])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21285\n", + "17943\n" + ] + } + ], + "source": [ + "tripsIII['HHPER'] = tripsIII['HHPER'].map(str)\n", + "\n", + "trips1 = pd.merge(tripsIII, demo, on='HHPER')\n", + "\n", + "print (len(tripsIII.index))\n", + "print (len(trips1.index))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare data for use in MNL estimation (make dummy columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['minority'] = np.where((trips1['HISP'].isin([1.0]) |\n", + " trips1['RACE1'].isin([2.0]) | trips1['RACE2'].isin([2.0]) | trips1['RACE3'].isin([2.0]) | trips1['RACE4'].isin([2.0]) |\n", + " trips1['RACE1'].isin([3.0]) | trips1['RACE2'].isin([3.0]) | trips1['RACE3'].isin([3.0]) | trips1['RACE4'].isin([3.0]) |\n", + " trips1['RACE1'].isin([4.0]) | trips1['RACE2'].isin([4.0]) | trips1['RACE3'].isin([4.0]) | trips1['RACE4'].isin([4.0]) |\n", + " trips1['RACE1'].isin([5.0]) | trips1['RACE2'].isin([5.0]) | trips1['RACE3'].isin([5.0]) | trips1['RACE4'].isin([5.0]) |\n", + " trips1['RACE1'].isin([97.0]) | trips1['RACE2'].isin([97.0]) | trips1['RACE3'].isin([97.0]) | trips1['RACE4'].isin([97.0])),1,0)\n", + "\n", + "trips1['HISP'] = np.where(trips1['HISP'].isin([1.0]),1,0)\n", + "trips1['black'] = np.where((trips1['RACE1'].isin([2.0]) | trips1['RACE2'].isin([2.0]) | trips1['RACE3'].isin([2.0]) | trips1['RACE4'].isin([2.0])),1,0)\n", + "trips1['native'] = np.where((trips1['RACE1'].isin([3.0]) | trips1['RACE2'].isin([3.0]) | trips1['RACE3'].isin([3.0]) | trips1['RACE4'].isin([3.0])),1,0)\n", + "trips1['asian'] = np.where((trips1['RACE1'].isin([4.0]) | trips1['RACE2'].isin([4.0]) | trips1['RACE3'].isin([4.0]) | trips1['RACE4'].isin([4.0])),1,0)\n", + "trips1['PI'] = np.where((trips1['RACE1'].isin([5.0]) | trips1['RACE2'].isin([5.0]) | trips1['RACE3'].isin([5.0]) | trips1['RACE4'].isin([5.0])),1,0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['hh_inc_less75k'] = np.where(trips1['INCOM'].isin([1.0,2.0,3.0,4.0,5.0]),1,0)\n", + "trips1['hh_inc_75kless100k'] = np.where(trips1['INCOM'].isin([6.0]),1,0)\n", + "trips1['hh_inc_150kplus'] = np.where(trips1['INCOM'].isin([8.0,9.0,10.0]),1,0)\n", + "trips1['hh_inc_150kless250k'] = np.where(trips1['INCOM'].isin([8.0,9.0]),1,0)\n", + "trips1['hh_inc_250kplus'] = np.where(trips1['INCOM'].isin([10.0]),1,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['lessGED'] = np.where(trips1['EDUCA'].isin([1.0]),1,0)\n", + "trips1['GED'] = np.where(trips1['EDUCA'].isin([2.0]),1,0)\n", + "trips1['someBach'] = np.where(trips1['EDUCA'].isin([3.0]),1,0)\n", + "trips1['Assoc'] = np.where(trips1['EDUCA'].isin([4.0]),1,0)\n", + "trips1['Bach'] = np.where(trips1['EDUCA'].isin([5.0]),1,0)\n", + "\n", + "trips1['lessGED_GED'] = np.where(trips1['EDUCA'].isin([1.0,2.0]),1,0)\n", + "\n", + "trips1['no_higher_ed'] = (trips1['EDUCA'] < 5).astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['age_16less25'] = np.where(((trips1.AGE.between(16,25,inclusive = False)) | (trips1.AGE==16)),1,0)\n", + "trips1['age_25less40'] = np.where(((trips1.AGE.between(25,40,inclusive = False)) | (trips1.AGE==25)),1,0)\n", + "trips1['age_40less50'] = np.where(((trips1.AGE.between(40,50,inclusive = False)) | (trips1.AGE==40)),1,0)\n", + "trips1['age_50less60'] = np.where(((trips1.AGE.between(50,60,inclusive = False)) | (trips1.AGE==50)),1,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['female'] = trips1['GEND'] - 1\n", + "\n", + "trips1['tenure_2'] = trips1['OWN'] - 1\n", + "\n", + "trips1['noveh'] = np.where(trips1.HHVEH.isin([0.0]),1,0)\n", + "\n", + "trips1['hh_size_1per'] = np.where(trips1.HHSIZ.isin([1.0]),1,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['sector_constr'] = np.where(trips1['INDUS'].isin([23]),1,0)\n", + "trips1['sector_mfg'] = np.where(trips1['INDUS'].isin([31]),1,0)\n", + "trips1['sector_retail'] = np.where(trips1['INDUS'].isin([44,45]),1,0)\n", + "trips1['sector_transport'] = np.where(trips1['INDUS'].isin([48]),1,0)\n", + "trips1['info'] = np.where(trips1['INDUS'].isin([51]),1,0)\n", + "trips1['finance'] = np.where(trips1['INDUS'].isin([52]),1,0)\n", + "trips1['scitech'] = np.where(trips1['INDUS'].isin([54]),1,0)\n", + "trips1['sector_edu_serv'] = np.where(trips1['INDUS'].isin([61]),1,0)\n", + "trips1['sector_healthcare'] = np.where(trips1['INDUS'].isin([62]),1,0)\n", + "trips1['sector_oth_serv'] = np.where(trips1['INDUS'].isin([81]),1,0)\n", + "trips1['sector_gov'] = np.where(trips1['INDUS'].isin([92]),1,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "trips1['TOD_3to6'] = np.where(trips1['TOD'].isin([0]),1,0)\n", + "# trips1['TOD_6to9'] = np.where(trips1['TOD'].isin([1]),1,0)\n", + "trips1['TOD_9to1530'] = np.where(trips1['TOD'].isin([2]),1,0)\n", + "trips1['TOD_1530to1830'] = np.where(trips1['TOD'].isin([3]),1,0)\n", + "trips1['TOD_1830up'] = np.where(trips1['TOD'].isin([4]),1,0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimate the model for dwell time at work" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "@orca.table(cache=True)\n", + "def tripsA():\n", + " return trips1" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "m = SmallMultinomialLogitStep()\n", + "m.name = 'dwell_work'\n", + "m.tables = ['tripsA']\n", + "m.choice_column = 'dwell_work'\n", + "m.model_expression = OrderedDict([\n", + " ('intercept', [1,2,3,5]), \n", + " \n", + " ('TOD_3to6',[2,5]),\n", + "# ('TOD_6to9'),\n", + " ('TOD_9to1530',[1,2,4,5]),\n", + " ('TOD_1530to1830',[1,2,4]),\n", + " ('TOD_1830up',[1,4]),\n", + " \n", + " ('sector_mfg',[1,2]),\n", + " ('sector_retail',[1,5]),\n", + " ('sector_transport',[4,5]),\n", + " ('info',[1]),\n", + " ('finance',[1,4,5]),\n", + " ('scitech',[1,2]),\n", + " ('sector_edu_serv',[2,4,5]),\n", + " ('sector_healthcare',[1,2,4,5]),\n", + " ('sector_gov',[1,2]),\n", + " \n", + " ('age_16less25',[1,2]),\n", + " ('age_25less40',[1]),\n", + " ('age_40less50',[1]),\n", + " ('age_50less60',[1]),\n", + " \n", + " ('female',[[1,2],5]),\n", + " \n", + " ('minority',[1,2]),\n", + " \n", + " ('hh_inc_less75k',[1,4,5]), \n", + " ('hh_inc_75kless100k',[5]),\n", + "# ('100kless150k')\n", + " ('hh_inc_150kplus',[1,2,4]),\n", + " \n", + " ('lessGED_GED',[1]),\n", + " ('Assoc',[1,4]),\n", + " \n", + " ('HOURS',[1,2,4,5]),\n", + " \n", + " ('noveh',[4]),\n", + " \n", + " ('hh_size_1per',[4]),\n", + " \n", + " ('tenure_2',[4]),\n", + " \n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Log-likelihood at zero: -28,878.1445\n", + "Initial Log-likelihood: -28,878.1445\n", + "Estimation Time for Point Estimation: 9.52 seconds.\n", + "Final log-likelihood: -24,197.0957\n", + " Multinomial Logit Model Regression Results \n", + "===================================================================================\n", + "Dep. Variable: _chosen No. Observations: 17,943\n", + "Model: Multinomial Logit Model Df Residuals: 17,881\n", + "Method: MLE Df Model: 62\n", + "Date: Sat, 23 Mar 2019 Pseudo R-squ.: 0.162\n", + "Time: 15:41:41 Pseudo R-bar-squ.: 0.160\n", + "AIC: 48,518.191 Log-Likelihood: -24,197.096\n", + "BIC: 49,001.479 LL-Null: -28,878.144\n", + "========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "----------------------------------------------------------------------------------------\n", + "intercept_1 0.8927 0.125 7.132 0.000 0.647 1.138\n", + "intercept_2 0.7383 0.102 7.233 0.000 0.538 0.938\n", + "intercept_3 0.3676 0.086 4.260 0.000 0.198 0.537\n", + "intercept_5 -2.3202 0.129 -17.950 0.000 -2.574 -2.067\n", + "TOD_3to6_2 -0.4000 0.118 -3.380 0.001 -0.632 -0.168\n", + "TOD_3to6_5 1.0418 0.071 14.626 0.000 0.902 1.181\n", + "TOD_9to1530_1 1.6632 0.064 25.814 0.000 1.537 1.789\n", + "TOD_9to1530_2 0.9464 0.050 19.002 0.000 0.849 1.044\n", + "TOD_9to1530_4 -0.7953 0.051 -15.446 0.000 -0.896 -0.694\n", + "TOD_9to1530_5 -0.9901 0.083 -11.996 0.000 -1.152 -0.828\n", + "TOD_1530to1830_1 2.9298 0.140 20.955 0.000 2.656 3.204\n", + "TOD_1530to1830_2 1.7683 0.130 13.615 0.000 1.514 2.023\n", + "TOD_1530to1830_4 -1.1271 0.217 -5.182 0.000 -1.553 -0.701\n", + "TOD_1830up_1 1.7121 0.187 9.160 0.000 1.346 2.078\n", + "TOD_1830up_4 -1.0139 0.211 -4.804 0.000 -1.428 -0.600\n", + "sector_mfg_1 -0.7079 0.143 -4.952 0.000 -0.988 -0.428\n", + "sector_mfg_2 -0.6238 0.106 -5.875 0.000 -0.832 -0.416\n", + "sector_retail_1 -0.7501 0.101 -7.433 0.000 -0.948 -0.552\n", + "sector_retail_5 -0.2827 0.109 -2.595 0.009 -0.496 -0.069\n", + "sector_transport_4 0.2460 0.099 2.496 0.013 0.053 0.439\n", + "sector_transport_5 0.3908 0.123 3.169 0.002 0.149 0.633\n", + "info_1 -0.5215 0.136 -3.829 0.000 -0.788 -0.255\n", + "finance_1 -0.5278 0.157 -3.358 0.001 -0.836 -0.220\n", + "finance_4 0.1900 0.085 2.240 0.025 0.024 0.356\n", + "finance_5 -0.3907 0.153 -2.550 0.011 -0.691 -0.090\n", + "scitech_1 -0.5299 0.106 -4.978 0.000 -0.739 -0.321\n", + "scitech_2 -0.2690 0.079 -3.387 0.001 -0.425 -0.113\n", + "sector_edu_serv_2 0.3393 0.058 5.842 0.000 0.225 0.453\n", + "sector_edu_serv_4 -0.5565 0.058 -9.527 0.000 -0.671 -0.442\n", + "sector_edu_serv_5 -0.4902 0.092 -5.349 0.000 -0.670 -0.311\n", + "sector_healthcare_1 -0.4660 0.093 -5.024 0.000 -0.648 -0.284\n", + "sector_healthcare_2 -0.4117 0.077 -5.343 0.000 -0.563 -0.261\n", + "sector_healthcare_4 -0.2910 0.064 -4.542 0.000 -0.417 -0.165\n", + "sector_healthcare_5 0.3154 0.084 3.738 0.000 0.150 0.481\n", + "sector_gov_1 -0.6975 0.115 -6.070 0.000 -0.923 -0.472\n", + "sector_gov_2 -0.4604 0.082 -5.599 0.000 -0.622 -0.299\n", + "age_16less25_1 -0.4374 0.116 -3.762 0.000 -0.665 -0.210\n", + "age_16less25_2 0.3590 0.078 4.591 0.000 0.206 0.512\n", + "age_25less40_1 -0.6918 0.090 -7.710 0.000 -0.868 -0.516\n", + "age_40less50_1 -0.2679 0.081 -3.291 0.001 -0.427 -0.108\n", + "age_50less60_1 -0.2232 0.074 -3.032 0.002 -0.367 -0.079\n", + "female_[1, 2] 0.1611 0.040 4.075 0.000 0.084 0.239\n", + "female_5 -0.3918 0.056 -6.996 0.000 -0.502 -0.282\n", + "minority_1 -0.3457 0.064 -5.437 0.000 -0.470 -0.221\n", + "minority_2 -0.2746 0.045 -6.083 0.000 -0.363 -0.186\n", + "hh_inc_less75k_1 -0.1604 0.063 -2.541 0.011 -0.284 -0.037\n", + "hh_inc_less75k_4 -0.1140 0.043 -2.661 0.008 -0.198 -0.030\n", + "hh_inc_less75k_5 -0.1488 0.059 -2.513 0.012 -0.265 -0.033\n", + "hh_inc_75kless100k_5 -0.1505 0.073 -2.057 0.040 -0.294 -0.007\n", + "hh_inc_150kplus_1 0.2085 0.079 2.648 0.008 0.054 0.363\n", + "hh_inc_150kplus_2 0.1973 0.057 3.470 0.001 0.086 0.309\n", + "hh_inc_150kplus_4 0.1488 0.050 2.958 0.003 0.050 0.247\n", + "lessGED_GED_1 -0.1978 0.075 -2.627 0.009 -0.345 -0.050\n", + "Assoc_1 -0.2177 0.094 -2.312 0.021 -0.402 -0.033\n", + "Assoc_4 0.1568 0.054 2.916 0.004 0.051 0.262\n", + "HOURS_1 -0.0495 0.002 -20.905 0.000 -0.054 -0.045\n", + "HOURS_2 -0.0312 0.002 -15.814 0.000 -0.035 -0.027\n", + "HOURS_4 0.0151 0.002 7.985 0.000 0.011 0.019\n", + "HOURS_5 0.0479 0.003 18.447 0.000 0.043 0.053\n", + "noveh_4 -0.4456 0.138 -3.227 0.001 -0.716 -0.175\n", + "hh_size_1per_4 0.1820 0.059 3.106 0.002 0.067 0.297\n", + "tenure_2_4 -0.1070 0.047 -2.298 0.022 -0.198 -0.016\n", + "========================================================================================\n" + ] + } + ], + "source": [ + "m.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "m.name = 'dwell_work'" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Registering model step 'auto_ownership'\n", + "Registering model step 'dwell_work'\n", + "Registering model step 'TOD_choice'\n", + "Registering model step 'work_TOD_choice'\n", + "Registering model step 'primary_mode_choice'\n", + "Registering model step 'WLCM'\n" + ] + } + ], + "source": [ + "mm.initialize()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving 'dwell_work.yaml': /home/emma/activitysynth/activitysynth/configs\n", + "Model saved to configs/dwell_work-model-object.pkl\n", + "Registering model step 'dwell_work'\n" + ] + } + ], + "source": [ + "m.tags = ['dwell_work','emma']\n", + "mm.register(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimate the model for Home-to-Work Trip End Times" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "###model with p-values less than .01 (except hours4)\n", + "\n", + "m = SmallMultinomialLogitStep()\n", + "m.name = 'work_TOD_choice'\n", + "m.tables = ['tripsA']\n", + "m.choice_column = 'TOD'\n", + "m.model_expression = OrderedDict([\n", + " ('intercept', [0,1,3,4]), \n", + " \n", + " ('sector_constr',[2,3]),\n", + " ('sector_mfg',[0,2,3]),\n", + " ('sector_retail',[2]),\n", + " ('sector_transport',[0]),\n", + " ('info',[0,2,3]),\n", + " ('finance',[0,2,3]),\n", + " ('scitech',[0,3]),\n", + " ('sector_edu_serv',[0,2,3]),\n", + " ('sector_healthcare',[0,2,3,4]),\n", + " ('sector_oth_serv',[0,3]),\n", + " ('sector_gov',[2,3]),\n", + " \n", + " ('age_16less25',[2,3,4]),\n", + " ('age_25less40',[0]),\n", + " \n", + " ('female',[0,3,4]),\n", + " \n", + " ('minority',[0,4]),\n", + " ('asian',[2]),\n", + " \n", + " ('hh_inc_less75k',[4]), \n", + "# ('75kless150k')\n", + " ('hh_inc_150kless250k',[0]),\n", + " ('hh_inc_250kplus',[0,2]),\n", + "\n", + " ('lessGED',[0,2,3]),\n", + " ('GED',[0,2,3]),\n", + " ('someBach',[0,2]),\n", + " ('Assoc',[0,2]),\n", + " ('no_higher_ed',[4]),\n", + " ('Bach',[0,2,4]),\n", + "# ('Grad')\n", + " \n", + " ('HOURS',[0,2,3,4]),\n", + "\n", + " ('noveh',[2]),\n", + "\n", + " ('hh_size_1per',[2]),\n", + " \n", + " ('tenure_2',[2]),\n", + " \n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Log-likelihood at zero: -28,878.1445\n", + "Initial Log-likelihood: -28,878.1445\n", + "Estimation Time for Point Estimation: 15.89 seconds.\n", + "Final log-likelihood: -16,303.6766\n", + " Multinomial Logit Model Regression Results \n", + "===================================================================================\n", + "Dep. Variable: _chosen No. Observations: 17,943\n", + "Model: Multinomial Logit Model Df Residuals: 17,878\n", + "Method: MLE Df Model: 65\n", + "Date: Sat, 23 Mar 2019 Pseudo R-squ.: 0.435\n", + "Time: 15:43:55 Pseudo R-bar-squ.: 0.433\n", + "AIC: 32,737.353 Log-Likelihood: -16,303.677\n", + "BIC: 33,244.025 LL-Null: -28,878.144\n", + "=========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "-----------------------------------------------------------------------------------------\n", + "intercept_0 -3.5975 0.186 -19.354 0.000 -3.962 -3.233\n", + "intercept_1 -0.5666 0.077 -7.322 0.000 -0.718 -0.415\n", + "intercept_3 -1.3824 0.161 -8.574 0.000 -1.698 -1.066\n", + "intercept_4 -5.3857 0.381 -14.143 0.000 -6.132 -4.639\n", + "sector_constr_2 -0.8889 0.127 -7.007 0.000 -1.138 -0.640\n", + "sector_constr_3 -1.5972 0.365 -4.371 0.000 -2.313 -0.881\n", + "sector_mfg_0 0.3854 0.093 4.156 0.000 0.204 0.567\n", + "sector_mfg_2 -0.4664 0.083 -5.639 0.000 -0.628 -0.304\n", + "sector_mfg_3 -0.5908 0.190 -3.109 0.002 -0.963 -0.218\n", + "sector_retail_2 0.5408 0.061 8.850 0.000 0.421 0.661\n", + "sector_transport_0 0.6952 0.113 6.162 0.000 0.474 0.916\n", + "info_0 -0.9847 0.235 -4.193 0.000 -1.445 -0.524\n", + "info_2 0.3645 0.077 4.738 0.000 0.214 0.515\n", + "info_3 -1.7137 0.418 -4.099 0.000 -2.533 -0.894\n", + "finance_0 -0.8665 0.220 -3.943 0.000 -1.297 -0.436\n", + "finance_2 -0.3262 0.088 -3.701 0.000 -0.499 -0.153\n", + "finance_3 -2.2459 0.508 -4.421 0.000 -3.242 -1.250\n", + "scitech_0 -0.4545 0.150 -3.036 0.002 -0.748 -0.161\n", + "scitech_3 -1.6604 0.288 -5.758 0.000 -2.226 -1.095\n", + "sector_edu_serv_0 -1.0373 0.146 -7.129 0.000 -1.323 -0.752\n", + "sector_edu_serv_2 -0.8126 0.058 -14.037 0.000 -0.926 -0.699\n", + "sector_edu_serv_3 -1.2649 0.158 -8.029 0.000 -1.574 -0.956\n", + "sector_healthcare_0 -0.6587 0.133 -4.956 0.000 -0.919 -0.398\n", + "sector_healthcare_2 -0.3930 0.060 -6.573 0.000 -0.510 -0.276\n", + "sector_healthcare_3 -1.0317 0.172 -5.996 0.000 -1.369 -0.694\n", + "sector_healthcare_4 1.0408 0.176 5.904 0.000 0.695 1.386\n", + "sector_oth_serv_0 -0.7809 0.168 -4.649 0.000 -1.110 -0.452\n", + "sector_oth_serv_3 -0.8287 0.213 -3.893 0.000 -1.246 -0.411\n", + "sector_gov_2 -0.9998 0.073 -13.717 0.000 -1.143 -0.857\n", + "sector_gov_3 -1.2509 0.198 -6.312 0.000 -1.639 -0.863\n", + "age_16less25_2 0.9403 0.073 12.929 0.000 0.798 1.083\n", + "age_16less25_3 1.8572 0.111 16.767 0.000 1.640 2.074\n", + "age_16less25_4 1.0176 0.225 4.530 0.000 0.577 1.458\n", + "age_25less40_0 -0.4045 0.081 -4.998 0.000 -0.563 -0.246\n", + "female_0 -0.6178 0.069 -8.963 0.000 -0.753 -0.483\n", + "female_3 -0.2522 0.092 -2.728 0.006 -0.433 -0.071\n", + "female_4 -0.6180 0.154 -4.026 0.000 -0.919 -0.317\n", + "minority_0 0.1839 0.067 2.738 0.006 0.052 0.316\n", + "minority_4 0.4116 0.147 2.792 0.005 0.123 0.700\n", + "asian_2 0.2826 0.062 4.569 0.000 0.161 0.404\n", + "hh_inc_less75k_4 0.5729 0.157 3.644 0.000 0.265 0.881\n", + "hh_inc_150kless250k_0 -0.3779 0.113 -3.333 0.001 -0.600 -0.156\n", + "hh_inc_250kplus_0 -1.0206 0.289 -3.536 0.000 -1.586 -0.455\n", + "hh_inc_250kplus_2 0.3136 0.078 4.000 0.000 0.160 0.467\n", + "lessGED_0 1.9522 0.159 12.244 0.000 1.640 2.265\n", + "lessGED_2 -0.6981 0.101 -6.942 0.000 -0.895 -0.501\n", + "lessGED_3 0.6164 0.153 4.024 0.000 0.316 0.917\n", + "GED_0 1.5688 0.140 11.184 0.000 1.294 1.844\n", + "GED_2 -0.4103 0.064 -6.461 0.000 -0.535 -0.286\n", + "GED_3 0.4453 0.108 4.124 0.000 0.234 0.657\n", + "someBach_0 1.3898 0.140 9.945 0.000 1.116 1.664\n", + "someBach_2 -0.3665 0.060 -6.159 0.000 -0.483 -0.250\n", + "Assoc_0 1.1672 0.149 7.818 0.000 0.875 1.460\n", + "Assoc_2 -0.4979 0.067 -7.416 0.000 -0.630 -0.366\n", + "no_higher_ed_4 1.2515 0.294 4.256 0.000 0.675 1.828\n", + "Bach_0 0.6586 0.139 4.739 0.000 0.386 0.931\n", + "Bach_2 -0.2646 0.049 -5.435 0.000 -0.360 -0.169\n", + "Bach_4 1.0120 0.306 3.312 0.001 0.413 1.611\n", + "HOURS_0 0.0077 0.003 2.858 0.004 0.002 0.013\n", + "HOURS_2 -0.0267 0.002 -17.750 0.000 -0.030 -0.024\n", + "HOURS_3 -0.0482 0.004 -13.521 0.000 -0.055 -0.041\n", + "HOURS_4 -0.0160 0.006 -2.596 0.009 -0.028 -0.004\n", + "noveh_2 0.3511 0.114 3.084 0.002 0.128 0.574\n", + "hh_size_1per_2 0.2556 0.057 4.480 0.000 0.144 0.367\n", + "tenure_2_2 0.2035 0.045 4.566 0.000 0.116 0.291\n", + "=========================================================================================\n" + ] + } + ], + "source": [ + "m.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "m.name = 'work_TOD_choice'" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving 'work_TOD_choice.yaml': /home/emma/activitysynth/activitysynth/configs\n", + "Model saved to configs/work_TOD_choice-model-object.pkl\n", + "Registering model step 'work_TOD_choice'\n" + ] + } + ], + "source": [ + "m.tags = ['work_TOD_choice','emma']\n", + "mm.register(m)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/activitysynth/notebooks/TOD_Work_Distribution_Estimation.ipynb b/activitysynth/notebooks/TOD_Work_Distribution_Estimation.ipynb new file mode 100644 index 0000000..c96bcb6 --- /dev/null +++ b/activitysynth/notebooks/TOD_Work_Distribution_Estimation.ipynb @@ -0,0 +1,1859 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import OrderedDict\n", + "from urbansim_templates import modelmanager as mm\n", + "from urbansim_templates.models import LargeMultinomialLogitStep\n", + "from urbansim_templates.models import SmallMultinomialLogitStep\n", + "import orca\n", + "import os; os.chdir('../')\n", + "import warnings; warnings.simplefilter('ignore')\n", + "\n", + "import pandas as pd\n", + "# import pandana as pdna\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "from functools import reduce\n", + "\n", + "import scipy.stats as st\n", + "from scipy.stats import skewnorm\n", + "\n", + "# import matplotlib\n", + "# matplotlib.style.use('ggplot')\n", + "\n", + "%matplotlib inline\n", + "\n", + "pd.options.display.max_columns = 80" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "trips1 = pd.read_csv('/home/emma/ual_model_workspace/spring-2019-models/notebooks-emma/HWtrips_032319.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimate Distribution for Actual H-W trip end times" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "HW = trips1.HW_trip_ET" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "# Create models from data\n", + "def best_fit_distribution1(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " st.norm, st.skewnorm,\n", + " st.alpha,st.anglit,st.argus,st.betaprime,st.burr,st.burr12,st.cauchy,\n", + " st.chi,st.chi2,\n", + " st.cosine,\n", + " st.erlang,\n", + " st.exponnorm,\n", + " st.exponweib,st.exponpow,st.f,st.fisk\n", + "\n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.norm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)\n", + "\n", + "\n", + "def make_pdf(dist, params, size=10000):\n", + " \"\"\"Generate distributions' Probability Distribution Functions \"\"\"\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Get sane start and end points of distribution\n", + " start = dist.ppf(0.001, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.01, loc=loc, scale=scale)\n", + " end = dist.ppf(0.999, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.99, loc=loc, scale=scale)\n", + "\n", + " # Build PDF and turn into pandas Series\n", + " x = np.linspace(start, end, size)\n", + " y = dist.pdf(x, loc=loc, scale=scale, *arg)\n", + " pdf = pd.Series(y, x)\n", + "\n", + " return pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "def best_fit_distribution2(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " st.gausshyper,\n", + " st.foldnorm,st.weibull_min,st.weibull_max,st.genlogistic,\n", + " st.gennorm,\n", + " st.genextreme,st.gamma,st.gengamma,st.gilbrat,st.gumbel_r,\n", + " st.gumbel_l,st.hypsecant,st.invgamma,st.invgauss]\n", + "\n", + " # Best holders\n", + " best_distribution = st.foldnorm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "# Create models from data\n", + "def best_fit_distribution3(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + "\n", + " st.johnsonsb, st.johnsonsu,st.ksone,st.logistic,st.loggamma,st.lognorm,st.maxwell,st.mielke,st.nakagami,st.ncx2,st.ncf\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.johnsonsu\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "# Create models from data\n", + "def best_fit_distribution4(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " \n", + " st.nct,st.pearson3,st.powerlognorm,st.powernorm,\n", + " st.rayleigh,st.rice,st.recipinvgauss,st.t,\n", + " st.vonmises,st.vonmises_line,st.wald,st.weibull_min,st.weibull_max\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.t\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "# Create models from data\n", + "def best_fit_distribution5(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + "\n", + " st.gompertz,\n", + " st.arcsine,st.beta,st.bradford,st.dgamma,st.dweibull,st.expon,st.fatiguelife,st.foldcauchy,\n", + " st.genpareto,st.genexpon,st.genhalflogistic,st.halfcauchy,st.halflogistic\n", + "\n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.foldcauchy\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "# Create models from data\n", + "def best_fit_distribution6(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + "\n", + " st.semicircular,\n", + " st.halfnorm,st.halfgennorm,st.kappa3,st.laplace,st.levy,st.levy_l,st.loglaplace,\n", + " st.lomax,st.pareto,st.powerlaw,st.rdist,st.kappa4,st.invweibull,\n", + " \n", + " st.reciprocal,st.trapz,st.triang,\n", + " st.truncexpon,st.truncnorm,st.tukeylambda,st.wrapcauchy\n", + " \n", + "#st.levy_stable,\n", + "# st.crystalball,st.kstwobign \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.loglaplace\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "def make_pdf(dist, params, size=10000):\n", + " \"\"\"Generate distributions' Probability Distribution Functions \"\"\"\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Get sane start and end points of distribution\n", + " start = dist.ppf(0.001, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.01, loc=loc, scale=scale)\n", + " end = dist.ppf(0.999, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.99, loc=loc, scale=scale)\n", + "\n", + " # Build PDF and turn into pandas Series\n", + " x = np.linspace(start, end, size)\n", + " y = dist.pdf(x, loc=loc, scale=scale, *arg)\n", + " pdf = pd.Series(y, x)\n", + "\n", + " return pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "#For HW\n", + "def best_fit_distribution7(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " \n", + "# st.loglaplace,\n", + " st.burr12, st.fisk, st.skewnorm, st.johnsonsu, \n", + "# st.dweibull, st.gennorm,\n", + " st.laplace, st.t, st.nct\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.norm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "johnsonsu(a=-0.71, b=1.00, loc=7.12, scale=1.31)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# %matplotlib notebook\n", + "\n", + "# # Load data\n", + "data = HW\n", + "\n", + "# Plot for comparison\n", + "plt.figure(figsize=(12,8))\n", + "ax = data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True\n", + " #, color=plt.rcParams['axes.color_cycle'][1]\n", + " )\n", + "# Save plot limits\n", + "dataYLim = ax.get_ylim()\n", + "\n", + "# Find best fit distribution\n", + "best_fit_name, best_fir_paramms = best_fit_distribution7(data, 200, ax)\n", + "best_dist = getattr(st, best_fit_name)\n", + "\n", + "# Update plots\n", + "ax.set_ylim(dataYLim)\n", + "ax.set_title(u'Trips to Work\\n All Best Fitted Distributions')\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "# Make PDF\n", + "pdf = make_pdf(best_dist, best_fir_paramms)\n", + "\n", + "# Display\n", + "plt.figure(figsize=(12,8))\n", + "ax = pdf.plot(lw=2, label='PDF', legend=True)\n", + "data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True, ax=ax)\n", + "\n", + "param_names = (best_dist.shapes + ', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']\n", + "param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, best_fir_paramms)])\n", + "dist_str = '{}({})'.format(best_fit_name, param_str)\n", + "\n", + "ax.set_title(u'Trips to Work with best-fit distribution \\n' + dist_str)\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "print (dist_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimate distributions for Actual work dwell times" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "dwell_exact = trips1.work_dwell" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "dwell0 = trips1['work_dwell'].loc[trips1['TOD'].isin([0])]\n", + "dwell1 = trips1['work_dwell'].loc[trips1['TOD'].isin([1])]\n", + "dwell2 = trips1['work_dwell'].loc[trips1['TOD'].isin([2])]\n", + "dwell3 = trips1['work_dwell'].loc[trips1['TOD'].isin([3])]\n", + "dwell4 = trips1['work_dwell'].loc[trips1['TOD'].isin([4])]" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = dwell_exact\n", + "\n", + "bins = np.linspace(0, 27, 100)\n", + "\n", + "plt.hist(x, bins, alpha=0.5, label='dwell_exact')\n", + "plt.legend(loc='upper right')\n", + "plt.figure(figsize=(40,20))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "v = dwell0\n", + "w = dwell1\n", + "x = dwell2\n", + "y = dwell3\n", + "z = dwell4\n", + "\n", + "bins = np.linspace(0, 27, 100)\n", + "\n", + "plt.hist(v, bins, alpha=0.5, label='dwell0')\n", + "plt.hist(w, bins, alpha=0.5, label='dwell1')\n", + "plt.hist(x, bins, alpha=0.5, label='dwell2')\n", + "plt.hist(y, bins, alpha=0.5, label='dwell3')\n", + "plt.hist(z, bins, alpha=0.5, label='dwell4')\n", + "plt.legend(loc='upper right')\n", + "plt.figure(figsize=(40,20))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [], + "source": [ + "#For HW\n", + "def best_fit_distribution12(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " \n", + "# st.foldcauchy,\n", + "# st.cauchy,\n", + "# st.gennorm,\n", + " st.johnsonsu, \n", + "# st.burr,st.f,\n", + "# st.genlogistic,st.invgauss,\n", + "# st.t, \n", + "# st.tukeylambda, st.loglaplace\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.norm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "johnsonsu(a=0.49, b=0.94, loc=9.29, scale=1.26)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# # Load data\n", + "data = dwell_exact\n", + "\n", + "# Plot for comparison\n", + "plt.figure(figsize=(12,8))\n", + "ax = data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True\n", + " #, color=plt.rcParams['axes.color_cycle'][1]\n", + " )\n", + "# Save plot limits\n", + "dataYLim = ax.get_ylim()\n", + "\n", + "# Find best fit distribution\n", + "best_fit_name, best_fir_paramms = best_fit_distribution12(data, 200, ax)\n", + "best_dist = getattr(st, best_fit_name)\n", + "\n", + "# Update plots\n", + "ax.set_ylim(dataYLim)\n", + "ax.set_title(u'Trips to Work\\n All Best Fitted Distributions')\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "# Make PDF\n", + "pdf = make_pdf(best_dist, best_fir_paramms)\n", + "\n", + "# Display\n", + "plt.figure(figsize=(12,8))\n", + "ax = pdf.plot(lw=2, label='PDF', legend=True)\n", + "data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True, ax=ax)\n", + "\n", + "param_names = (best_dist.shapes + ', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']\n", + "param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, best_fir_paramms)])\n", + "dist_str = '{}({})'.format(best_fit_name, param_str)\n", + "\n", + "ax.set_title(u'Trips to Work with best-fit distribution \\n' + dist_str)\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "print (dist_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "#For HW\n", + "def best_fit_distribution8(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " \n", + " st.cauchy,st.fisk,\n", + " st.hypsecant,st.gennorm,\n", + " st.johnsonsu, \n", + " st.t, \n", + " st.foldcauchy,\n", + " st.tukeylambda\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.norm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "johnsonsu(a=-0.16, b=1.09, loc=9.37, scale=1.54)\n" + ] + }, + { + "data": { + "image/png": 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7nWjs0Jqbli4tLc2diIvsRUREpPXYsGEDQ4YMaeo0pIqafiZmtto5l3a0c7V0REREREQkDFRoi4iIiIiEgQptEREREZEw0M2QIiJhdCyPT67LI5JFRKT504y2iIiIiEgYaEZbRKQOjmVmWkRETmya0RYRERGRsJsxYwYvv/xyU6fRqFRoi4iIiIiEgQptEREREan0zDPPMGLECFJSUrj66qt54403GDduHCNHjuSss84iKysLgLvvvps5c+ZUnjd8+HAyMjJqjHHI+++/zymnnEL//v0rZ7evvvpqFixYUNln+vTpvP76641wpeGnNdoiIiIizdFbs2Dv2oaN2S0Zzn+g1sPr16/nvvvu48MPPyQhIYHc3FzMjBUrVmBm/O1vf+PBBx/k97//fb1iHJKZmcny5cvZuHEjU6ZM4fLLL+e6665j7ty5XHzxxeTn5/PRRx/x9NNPN+hlNxUV2iIiIiICwJIlS7j88stJSEgAoFOnTqxdu5b/+q//IjMzk/Lycvr161fvGIdccskleDwehg4dWjkzftppp/HTn/6Uffv28eqrrzJ16lS83tZRoraOqxARERFpbY4w8xwuzjnMrFrbzTffzK233sqUKVNYtmwZd999NwBer5dAIFDZr7S0tNYYh0RHR1cb65Crr76aZ599lueff56nnnqqoS6nyWmNtoiIiIgAMHnyZF588UVycnIAyM3NJT8/n549ewJUW9KRlJTEZ599BsBnn33Gtm3bao1xNDNmzOChhx4CYNiwYQ13QU1MM9oiIiIiAgSL3P/5n//htNNOIyIigpEjR3L33Xfzve99j549ezJ+/PjKgnrq1Kk888wzpKamMmbMGAYNGlRrjHnz5h1x3K5duzJkyBAuueSScF9io7Kq0/YtWVpamlu1alVTpyEirVRjPrBGj2AXOXFt2LCBIUOGNHUaja64uJjk5GQ+++wz2rdv39TpVFPTz8TMVjvn0o52rpaOiIiIiEiTWbx4MYMHD+bmm29udkX28dLSERERERFpMmeddRY7duxo6jTCQjPaIiIiIiJhoEJbRERERCQMVGiLiIiIiISBCm0RERERkTBQoS0iIiIilf74xz8yZMgQOnbsyAMP1P50ynnz5jFz5sxGzKzl0a4jIiItnKsIULYln5J12ZTvLqTzVYPxJrRp6rREpIV69NFHeeutt+jXr19Tp9LiaUZbRKQFK/58H5n3fUL2U+soTt+Pf18xeYu2NXVaItJC3XjjjWzdupUpU6Ywd+7cyhnrl156ieHDh5OSksKkSZMOO+/NN99kwoQJZGdnN3bKzZpmtEVEWijnD5D35lYi2kfT8fJBxAzsSMHy3Rx8J4PSLXnEDOjQ1CmKyHH43crfsTF3Y4PGHNxpMHeMvaPW44899hhvv/02S5cuZeHChZXts2fP5p133qFnz57k5eVVO+e1117jD3/4A4sWLaJjx44Nmm9LpxltEZEWqmR9NoFCH+3O7UubTpnYf2bTNuNmItpFkL9wKy7gmjpFEWklJk6cyIwZM3jiiSeoqKiobF+6dCm/+93vePPNN1Vk10Az2iIiLVThx5lEtHPELL4QsjeARWDmoX1iKrkZkyn+LIu4tG5NnaaIHKMjzTw3tscee4xPPvmEN998k9TUVNLT0wHo378/W7duZdOmTaSlpTVxls2PZrRFRFqg8swiyjMOEu9ew/zFcMEc+MUmOO0O2mTOJSrRkf9OBoGyiqMHExE5ii1btjBu3Dhmz55NQkICO3fuBKBv3768+uqrXHPNNaxfv76Js2x+VGiLiLRARSv2QIQjrvxFOOtuGHs9xCXAKTOxjn1o73mUQIGPwg93N3WqItIK3HbbbSQnJzN8+HAmTZpESkpK5bGTTz6ZZ599lu9973ts2bKlCbNsfsy51rGGLy0tza1ataqp0xCRVmruu5sabaxbzh50xOOBUj+Z939Cm9i1dPLOhVvWQUTkNx2+XAAvXsO+uBdxbRLpOnNkmDMWkYayYcMGhgwZ0tRpSBU1/UzMbLVz7qhrZTSjLSLSwhStzsKVB4gvfhzSrq1eZAMMmQJJpxJT9Ba+XYVUFPmaJlERkROcCm0RkRbEOUfRikyi2uUR5d0Oo2cc3skMznuA6MAKAMq25B3eR0REwk6FtohIC1KRW4p/fwmxvteCM9dta9lVpNtwogZ0w6yE0k0HGjdJEREBVGiLiLQoZdsOAhBd8WnwBsgjsJPOJNo+p2xTDq3lfhwRkZZEhbaISAtSlpGPeYrxdmsPfSYcufOAM4nxfE7FQT/+7JLGSVBERCqp0BYRaUHKt+QQzVps5LTgWuwj6TqMmLa7ACjbrHXaIiKNTYW2iEgLUVFQjv+An2jPeuh36tFPMMM7KJkITxalm3PDn6CItAoZGRkMHz68qdM4JhkZGTz33HNNnUYlFdoiIi1EWUZwfXZU9HZIHFq3k06aTIx9RtnXB3AVgTBmJyLS9FRoi4jIMSnPyMesnKikLuCJqNtJ/U8n2pOO80H5zoKw5icirUdFRQXXX389w4YN45xzzmH9+vWMGjWq8vjmzZsZPXo0AElJSdxxxx2MHTuWsWPH8vXXXwPw0ksvMXz4cFJSUpg0aVJl3Ntuu40xY8YwYsQI/vrXv1bGfPDBB0lOTiYlJYVZs2YB8MQTTzBmzBhSUlKYOnUqxcXFAMyYMYOf/exnnHLKKfTv35+XX34ZgFmzZvHBBx+QmprK3Llzw/9GHYW3qRMQEZG6KduSS5RtxPqNr/tJcQnEdK+AHQFKN+cRndQ+fAmKSIPae//9lG3Y2KAxo4cMptuvfnXUfps3b+Zf//oXTzzxBFdccQWff/457du3Jz09ndTUVP7+978zY8aMyv7t2rVj5cqVPPPMM/z3f/83CxcuZPbs2bzzzjv07NmTvLzgfSJPPvkk7du359NPP6WsrIyJEydyzjnnsHHjRubPn88nn3xCbGwsubnB5W6XXXYZ118f3GHpzjvv5Mknn+Tmm28GIDMzk+XLl7Nx40amTJnC5ZdfzgMPPMCcOXNYuHBhg75vx0oz2iIiLUCgzI8vq4QoWw99TqnXuZ6TJxJlmyn7KjtM2YlIa9OvXz9SU1MBGD16NBkZGVx33XX8/e9/p6KighdeeIFp06ZV9r/qqqsq//vxxx8DMHHiRGbMmMETTzxBRUUFAP/+97955plnSE1NZdy4ceTk5LB582YWL17MtddeS2xsLACdOnUCYN26dZx66qkkJyfz7LPPsn79+soxL7nkEjweD0OHDiUrKyv8b8ox0Iy2iEgLUL69AJwRHbkZeoys38kDJhO95HkKdg/C+QJYpOZYRFqCusw8h0t0dHTl9xEREZSUlDB16lTuuecezjzzTEaPHk3nzp0r+1iVXZAOff/YY4/xySef8Oabb5Kamkp6ejrOOf70pz9x7rnnVhvv7bffrhbjkBkzZjB//nxSUlKYN28ey5YtqzHH5vqsAP22FRFpAcoy8oEAUb3jwRtVv5N7jSEyahc4w5dVFJb8RKT1i4mJ4dxzz+UnP/kJ1157bbVjL7zwQuV/J0wI7vG/ZcsWxo0bx+zZs0lISGDnzp2ce+65/OUvf8Hn8wGwadMmioqKOOecc3jqqacq12AfWjpSUFBA9+7d8fl8PPvss0fNsW3bthQUNJ/7UTSjLSLSApRtzSXStuDpl1b/k71RRPVNhI1QvqeQqF5tGz5BETkhTJ8+nVdffZVzzjmnWntZWRnjxo0jEAjwr3/9C4DbbruNzZs345xj8uTJpKSkMGLECDIyMhg1ahTOObp06cL8+fM577zzSE9PJy0tjaioKC644ALuv/9+7r33XsaNG0ffvn1JTk4+ahE9YsQIvF4vKSkpzJgxg1tuuSVs70VdWHOdaq+vtLQ0t2rVqqZOQ0Raqbnvbmq0sW45e1C1184fYPdvlhPPAjr88EIYcGa9Y7oP/8SeNwYSm9aLjpe3zP1xRU4EGzZsYMiQIU2dRq3mzJlDfn4+9957b2VbUlISq1atIiEhoQkzC5+afiZmtto5d9SZj7AuHTGz88zsKzP72sxm1XD8RjNba2bpZrbczIZWOfbL0Hlfmdm53z5XROREUb67ECqM6IgN0GvsMcWwHilE2lZ8O3IaODsROVFceumlPPPMM/z85z9v6lRajLAtHTGzCOAR4GxgF/Cpmb3unPuySrfnnHOPhfpPAf4AnBcquK8EhgE9gMVmNsg5VxGufEVEmqtD+19HdfNAdPyxBemWTJTndYpyhuICDvMc5fHtIiLf8tprr9XYnpGR0biJtCDhnNEeC3ztnNvqnCsHngcurtrBOXewyss44NA6louB551zZc65bcDXoXgiIicc356DeMgnYkDKsQdp05HIuIO4igj82SUNl5yIiNQqnIV2T2Bnlde7Qm3VmNlPzWwL8CDws/qcKyJyIvDtyibSsw36TDiuOJHd2wTjZRY2RFoiInIU4Sy0a/q75GF3XjrnHnHODQDuAO6sz7lmdoOZrTKzVfv37z+uZEVEmiMXcPhzAkRaBvQ+vj/sRfbrDfgo35HbILmJiMiRhbPQ3gX0rvK6F7DnCP2fBy6pz7nOucedc2nOubQuXbocZ7oiIs2PP7cUV+Ehss0BiE88rljWM4VI245vuyYmREQaQzgL7U+BgWbWz8yiCN7c+HrVDmY2sMrLC4HNoe9fB640s2gz6wcMBFaGMVcRkWbJvzf4gJnIxHo+pKYm3UYQ6dmCb5+/2T5FTUSah1NOOaXWY8uWLeO73/1uI2bTcoVt1xHnnN/MZgLvABHAU8659WY2G1jlnHsdmGlmZwE+4ADwg9C5683sReBLwA/8VDuOiMiJqHxPARDA2/v4ZrMBaNuVqJhsiou8BA6WE9E++ujniMgJ6aOPPmrqFFqFsO6j7Zxb5Jwb5Jwb4Jy7L9R2V6jIxjn3c+fcMOdcqnPuDOfc+irn3hc672Tn3FvhzFNEpLny79yP1/bg6d4wD7CI7BqcGS/foxsiRaR28fHxOOe47bbbGD58OMnJyZWPWQcoLCzk8ssvZ/DgwUyfPr3yr2RJSUn85je/YdSoUSQnJ7Nx40YA/vOf/5CamkpqaiojR46koKCg1vjLli3j9NNPrzH+rFmzGDp0KCNGjOAXv/gFADNmzODll1+ulntzoUewi4g0Y769RUTaduh6QYPEi0zqBlsD+Hbm0WZI5waJKSLh8cGLm8je2bAfihN6x3PqFYOO3hF49dVXSU9PZ82aNWRnZzNmzBgmTZoEwOeff8769evp0aMHEydO5MMPP+Q73/lOcIyEBD777DMeffRR5syZw9/+9jfmzJnDI488wsSJEyksLCQmJqbe8YcOHcprr73Gxo0bMTPy8vIa9L0Jh7DOaIuIyLELlFfgL4gg0rMdupzcIDE9vZPx2h7KM7IaJJ6ItF7Lly/nqquuIiIigq5du3Laaafx6aefAjB27Fh69eqFx+MhNTW12kNrLrvsMgBGjx5d2T5x4kRuvfVW/vjHP5KXl4fX6613/Hbt2hETE8N1113Hq6++SmxsbKO+H8dCM9oiIs2UP6sYMCLb+8DbQOupu40g0v5JeVbXhoknImFT15nncDnSTdPR0d/8ToqIiMDv9x92rGr7rFmzuPDCC1m0aBHjx49n8eLF9Y7v9XpZuXIl7733Hs8//zx//vOfWbJkCV6vl0AgUJlzeXn5sV1wGGhGW0SkmfId2nGkewOuN+zQh8ioTCqKIgmU+I/eX0ROWJMmTeKFF16goqKC/fv38/777zN27LHt579lyxaSk5O54447SEtLY+PGjfWOX1hYSH5+PhdccAEPPfQQ6enpQHBd+OrVqwFYsGABPp/vmHIMB81oi4g0U75dBzBKiejTp+GCmhHVxWBn8IbImAEdGi62iLQaZsall17Kxx9/TEpKCmbGgw8+SLdu3SpvcKyPhx56iKVLlxIREcHQoUM5//zziYqKqlf8goICLr74YkpLS3HOMXfuXACuv/56Lr74YsaOHcvkyZOJi4s7rmtvSNZa9lJNS0tzq1atauo0RKSVmvvupkYb65azg38u3v+n5QT2bKTrtX1g0DkNFr/ijXvJ/PB0OlyURPzE3kc/QUQazYYNGxgypGF2GTpWOTlOJmeAAAAgAElEQVQ5jBo1iu3btzdpHs1FTT8TM1vtnEs72rlaOiIi0gw55/Dt9xHpyYCuwxo0tqfvEIxifDsyGzSuiLR8e/bsYcKECZVb58nx0dIREZFmKFDoI1DuJbJNFrTr0aCxrXsyXvsA/96IBo0rIi1fjx492LSp8f6C19ppRltEpBnyZYZuhEyIALOGDd6pP5Ge3fjzWsfSQRGR5kqFtohIM+TLDD6kIrJXGB4qExGJN66EirIYAmXaeUREJFxUaIuINEO+nfvwkENEr4FhiR/ZKbhsxL+/JCzxRUREhbaISLPkyywg0rMDug4PS3xvj47BcfYWhCW+iIio0BYRaXacc/jzHF7bBV0Gh2UMb+9egB//Tu08IiLfyMvL49FHH23qNFoNFdoiIs1MoMCHq/ASGV8K0Q34VMgqLHEQXsvEn5kXlvgi0jKp0G5YKrRFRJoZ3/5iALydo8M3SMIgvLYLX65uhhSRb8yaNYstW7aQmprKbbfd1tTptHjaR1tEpJnx7wtu7eft1jF8g0THExmTT2lRNK4igEVo3kWkuVk673H2bd/aoDET+/bnjBk31Hr8gQceYN26daSnpzfouCcq/WYVEWlm/Lv3YZQS0TO8j0f3dgRcBP7c0rCOIyJyotKMtohIM+PPysdrmViXQWEdJzIxHnaDP6uIyC6xYR1LROrvSDPP0jJoRltEpJnx5/qDO450Ds8e2od4+3QHwLdrb1jHEZGWo23bthQUaNvPhqJCW0SkGbGAw18UiTcqF+LC8FTIKjzdB+IhB//unLCOIyItR+fOnZk4cSLDhw/XzZANQEtHRESakfiSCsCDt72r8zm+sgqyMg6yd0se+dmlDD+1J137tTv6iV1OJtLzOf7sNseesIi0Os8991xTp9BqqNAWEWlG2hYHt9ury5pp5xzLX9rMumW7CQSChbk3OoKNH2Uy5JTujL9kALHtomoPENsZb+R+ig8OwTmHmTXINYiISJAKbRGRZqRDQWgP7Z5djtjPOceHr3zNF0t2MXh8NwaMTqRb//Z4IoxVb2awZslOtny2jzN/MIQBIxNrDmJGZDs/bn8UgUIfEW2PUJSLiEi9qdAWEWlGOhcU4KEET7f+R+y3alEGaxbvJPmMXpx6xcBqs9GnTD2JIRO7s/jvX7J43gY694inQ9eaZ8i9XdrAfvDtK1ahLSLSwHQzpIhIM9K2OLTjSELtW/uteW8nK9/YxuAJ3Tj1ewNrXPLRsVsc59+YTITXeOdv66jwBWqMdWjm3L9rf8NcgIiIVFKhLSLSjMSURhLpyYSOSTUez9ldyPKXN9N/ZBfO+P5gzFP7uur4jjFMvmYI2TsL+fi1LTX2iejdH6ME/66shkhfRESqUKEtItJMRPoCRASi8cYVg7fmZRwfz99CdBsvZ3x/MJ46PDa9X0oXkk/vxZolO8lYm33YcesyCK/txhd67LuIiDQcFdoiIs3EoR1HvJ1qvn1m96YDbF+bw6hz+xITF1nnuKdMHUDnXvEseWYDvrKK6gfb98YbkYU/v+7bCYpI6/bHP/6RIUOGMH369BqPz5s3j5kzZ9Z4LD4+PpypVTr99NNZtWpVo4x1PFRoi4g0E+2KfAB4u7U/7Jhzjo9f20Jch2hGnNGrXnG9kRGcdtXJlBT4WP/B7uoHPR68cWVUlLbB+Wtexy0iJ5ZHH32URYsW8eyzzzZ1Ki2eCm0RkWaic8FBwI+3Z8/Djm1N30/WtoOMvagf3qiIesfuPqA9PQd1IP3dHYfdGOntEAF48B8oPcbMRaS1uPHGG9m6dStTpkzh97//PZdccgkjRoxg/PjxfPHFF4f137ZtGxMmTGDMmDH8+te/rnbswQcfJDk5mZSUFGbNmgXAE088wZgxY0hJSWHq1KkUFwe3NJ0xYwYvv/xy5blVZ8ZrigPw0ksvMXbsWAYNGsQHH3wAwKmnnkp6enpln4kTJ9aYd2PR9n4iIs1Eh8JivHYASxxYrT1QEWDF/K107BbL4PHdjjn+6POTeP3hdDZ8nMnwSd8U897EtrAd/PsK6/SgHBFpHHlvbKF8T8PePxHVI44OFw2o9fhjjz3G22+/zdKlS7nnnnsYOXIk8+fPZ8mSJVxzzTXViliAn//85/zkJz/hmmuu4ZFHHqlsf+utt5g/fz6ffPIJsbGx5ObmAnDZZZdx/fXXA3DnnXfy5JNPcvPNN9eaT21xAPx+PytXrmTRokXcc889LF68mOuuu4558+bx0EMPsWnTJsrKyhgxYsQxvVcNQTPaIiLNRFwJeG0PdK5eaG/+NIu8rGLGXzKgTjdA1qbX4I4kJrXj839vJ1Dxzay2t2dXAPy7Mo85toi0PsuXL+fqq68G4MwzzyQnJ4f8/PxqfT788EOuuuoqgMq+AIsXL+baa68lNjb44b1Tp04ArFu3jlNPPZXk5GSeffZZ1q9ff8QcaosDwaIdYPTo0WRkZADwve99j4ULF+Lz+XjqqaeYMWPGMV59w9CMtohIc+AcUWUxWOQ+iEuodmj98j106BpLv5SEWk4+XGFhIWvWrMHr9ZKUlESXLl3weDyknd+XRX9Zy+ZPszh5fHcAPN2TMPLw7/U16CWJyPE50sxzY3Du8Juka9q3v6Y251yN7TNmzGD+/PmkpKQwb948li1bBoDX6yUQCFSeW15efsQ4ANHR0QBERETg9wdvJo+NjeXss89mwYIFvPjii01+w6RmtEVEmoHY0gCGl9JoH1T5R+XA3iIyv85nyCnda/3HpqqsrCwWLFjA3Llzeffdd3nrrbf4y1/+wv/93//x+uuv03Nwezr3jGP129txgeA/opZwEl7bgz9Ha7RF5BuTJk2qvCFy2bJlJCQk0K5du2p9Jk6cyPPPPw9Q7ebJc845h6eeeqpyDfahJR8FBQV0794dn89XrX9SUhKrV68GYMGCBfh8viPGOZLrrruOn/3sZ4wZM6baDHhT0Iy2iEgzEF8SnI0piK1+o+PGjzMxj3FyHdZmr1q1ioULF+L1ehk5ciTjxo0jMjKSjIwMtm3bxmeffUZ2djbjJp/Fsme+JmNdDv1GJEBsZ7zebMoP9gjLtYlIy3T33Xdz7bXXMmLECGJjY3n66acP6/Pwww8zbdo0Hn74YaZOnVrZft5555Genk5aWhpRUVFccMEF3H///dx7772MGzeOvn37kpycTEFBAQDXX389F198MWPHjmXy5MnExcUdMc6RjB49mnbt2nHttdc24LtxbKymPwu0RGlpaa6p/zwgIq3X3Hc3hTX+wB15pG4uY2f/JUy44TdA8CbIp3/5EYlJ7bjwpiPfzLNhwwZefPFFBgwYwGWXXVa5nrGqdevW8eqrr9KlSxe8WwfSa0BXzr8xGYD8B+6lIG8SPX97KubVHztFmsqGDRsYMmRIU6fRou3Zs4fTTz+djRs34vEc/++zmn4mZrbaOZd2tHP121REpBnoXJAPlJETn1jZtn1dDsUHyxk6sfsRz92+fTuvvPIKPXr04IorriA2NpYKv5+NH73PF++9TUb6anJ27eDkQQOZNm0aubm55HZcw5b1uyktDO3drS3+RKQVeOaZZxg3bhz33XdfgxTZx0tLR0REmoF2xWV4LY8Dsb0r2778MJPYdlH0Hd651vP27dvHv/71L9q3b8+0adOI8HhYt/RdVrz2AvlZe6v1jYlvyzk/vplrrrmGp+c9zcG4zWxeNZzk03sFt/jLAP++Am3xJyIt1jXXXMM111zT1GlUUqEtItIMtCn14LU95MUEl3IU5ZexfV0OqWf1rnVLv0AgwKuvvkpERATf//73Kc7Zz/Nz7iMvK5Ou/Qdyxu030KVvEgXZ2RzM3sfqNxfw+u/vJ+Xs85k06TssWbqMlcs/DxbaPYNrwP279sCwro123SIirZkKbRGRpuYckb4YnDcff0QbAL5asRcXcAw5pfZlI59//jl79+7l8ssvx+Mr45X778KAS26/i/6jxlTuUtIuIZGeDGXQ+Iksf/4frHrjVTptWE/bLiexu2gtmRkT6dYjCSMHf2Z5Y1yxiMgJoekXr4iInOBiygKYi6Qs+psid9PKvXTr356O3eJqPKe0tJQlS5bQu3dv+vXuxSv334W/vIyp/3MvA0aPrXErwAhvJKd9/4dM/dVsCnP20y5/DwFPOYveeBvrPEBb/ImINDAV2iIiTSy+pAKAwjbBX8n5+0vI2V3EgFFdaj3ngw8+oKioiLPOOJ3XHriHguxsLr39NyT07nvU8ZJSRnHuT/6bg1s3El/uYWfOJjL2FeD15uAv0D8LIiINRb9RRUSaWPuiEgDy4oOz19vW7AegX0rNhXZubi4rVqwgNTWVtW+8wr6MLXz3ljvoOXhoncccNG4io86fAttW4fF7ePP1RUTElVJRGovzB44eQERapby8PB599NGj9ktKSiI7O/u4x4uPjz/uGBDc83vOnDlH7TdjxgxefvnlBhmzLlRoi4g0sc4FeYCPnLbB3UW2rcmmU4842ndpU2P/f//730RERHBy92589dH7TJh6FQNGj6v3uJO+fy3d+59EZPYO9udmsTdWW/yJnOjqWmhL3ajQFhFpYu2KyvBaFgfa9IGyCjK/zqNfSkKNfffu3cvGjRs5ZcIEVjw/jw5duzNmytQa+x5NhDeSi26dRUxRAVZRwcrgltr49+Uf66WISAs3a9YstmzZQmpqKmPGjOG73/1u5bGZM2cyb968av1LSko477zzeOKJJwD45z//ydixY0lNTeXHP/4xFRUVPPnkk9xyyy2V5zzxxBPceuut1eIUFhYyefJkRo0aRXJyMgsWLAAgIyODwYMHc9111zF8+HCmT5/O4sWLmThxIgMHDmTlypWVMdasWcOZZ57JwIEDK/NxzjFz5kyGDh3KhRdeyL59+yr7z549mzFjxjB8+HBuuOEGwvEQR+06IiLSxGJLwWt7yY85Dc/uUpyrfdnIypUr8Xq9RB7YR+6eXVx6x2/wRkUd89jtEhIZccZUPln9LlsjepJqhbTfsQeGHfkhOSISfm+99RZ79+49esd66NatG+eff36txx944AHWrVtHeno6y5YtO+JyjMLCQq688srKvas3bNjACy+8wIcffkhkZCQ33XQTzz77LFdeeSUjRozgwQcfJDIykr///e/89a9/rRYrJiaG1157jXbt2pGdnc348eOZMmUKAF9//TUvvfQSjz/+OGPGjOG5555j+fLlvP7669x///3Mnz8fgC+++IIVK1ZQVFTEyJEjufDCC1mxYgVfffUVa9euJSsri6FDh/LDH/4QCH5wuOuuuwC4+uqrWbhwIRdddNFxvb/fpkJbRKQpOUdUeRucN5dybzzePTnEdYgmsU/bw7oWFxfzxRdfMPTkk1k9/wX6jx5L/1FjjjuFiVdezOdL3qS8c4C13i30zoo87pgi0vpdfPHF3H777UyfPh2A9957j9WrVzNmTPD3UklJCYmJicTFxXHmmWeycOFChgwZgs/nIzk5uVos5xy/+tWveP/99/F4POzevZusrCwA+vXrV9l/2LBhTJ48GTMjOTmZjIyMavm0adOGNm3acMYZZ7By5Uref/99rrrqKiIiIujRowdnnnlmZf+lS5fy4IMPUlxcTG5uLsOGDVOhLSLSmkT7HOaiKI8qgwqHZZXSb2IPzHP49nyrV6/G7/dTseNrXCDAmTNuaJAcYmKj6XnS2WzN/ZCvO3rI23+Qjg0SWUSOx5FmnhuD1+slEPjm5ujS0ur3b0ycOJG33nqLadOmYWY45/jBD37A//7v/x4W67rrruP+++9n8ODBXHvttYcdf/bZZ9m/fz+rV68mMjKSpKSkyvGio6Mr+3k8nsrXHo8Hv99feezb25oeel3TdqelpaXcdNNNrFq1it69e3P33Xcfdn0NQYW2iEgTii8O/iNR1MaD7SvFKlyN67MrKir49NNP6dm9GzuWLGT81Ctpn9itWp+5726q9/i3nD0IgJSzT2f3X9/H3xE+L9xHv2O4FhFp+dq2bUtBQQEAffv25csvv6SsrIzS0lLee+89vvOd71T2nT17Nvfeey833XQTf/nLX5g8eTIXX3wxt9xyC4mJieTm5lJQUEDfvn0ZN24cO3fu5LPPPuOLL744bNz8/HwSExOJjIxk6dKlbN++vd65L1iwgF/+8pcUFRWxbNkyHnjgASoqKvjrX//KNddcw759+1i6dCnTpk2rLKoTEhIoLCzk5Zdf5vLLLz/Gd612KrRFRJpQu6LgL/sD8bF49pTivEbPQYfPJ2/cuJGDBw/SzfkoiY5h9AWXNGge/UYkEh01gZjSXWyI2U9pUQkxcTXveiIirVfnzp2ZOHEiw4cP5/zzz+eKK65gxIgRDBw4kJEjRx7W/6GHHuKHP/wht99+Ow8++CC//e1vOeeccwgEAkRGRvLII4/Qt29wf/8rrriC9PR0OnY8/Hfc9OnTueiii0hLSyM1NZXBgwfXO/exY8dy4YUXsmPHDn7961/To0cPLr30UpYsWUJycjKDBg3itNNOA6BDhw5cf/31JCcnk5SUVLncpaFZOO6wbAppaWlu1apVTZ2GiLRSxzJbXBdpG3fQb7eXNcPWs23VMAJdovnZnRMO6/fUU0+Rn5eHW/UfRp5zIWfUsGzkeGa0AV6ds5qS7e+wuXM55086m3FnTqx3PBE5Phs2bGDIkCFNnUZYfPe73+WWW25h8uTJTZ1KvdT0MzGz1c65tKOdq+39RESaUPviUiJsPzkVvbGyAK57zGF9MjMz2bFjBwmRHnCO0Rc27Gz2If1SuoBvAO0CbVj16YqwjCEiJ568vDwGDRpEmzZtWlyRfby0dEREpAnFloDXMik4OBooI5B4eKGdnp5OREQEuZ9/zMkTTqVdl8Sw5NIvJYFVryQwsKKM1SVbyc3NpVOnTmEZS0ROHB06dGDTpvD8VbC5C+uMtpmdZ2ZfmdnXZjarhuO3mtmXZvaFmb1nZn2rHKsws/TQ1+vhzFNEpKlElUeDJ4dANgTaRUJMRLXjFRUVrFu3ji5t4/AXFZH23UvDlkuHxFjiE7z0qwju4b18yeKwjSUiciIIW6FtZhHAI8D5wFDgKjMb+q1unwNpzrkRwMvAg1WOlTjnUkNfU8KVp4hIU4n0BfAEYiiL9GHZZbjE6MP6bNu2jaKiIkozNtNn+Ai69j8prDn1G9kF87ehsy+G9V9uCMuT0kTkyPT/XfNxvD+LcM5ojwW+ds5tdc6VA88DF1ft4Jxb6pwrDr1cAfQKYz4iIs1KfEkFAPs8HbEABGootNeuXUukNwL/3l2kXXRsj1qvj74pPSgKGINcN8oCjo3r14Z9TBH5RkxMDDk5OSq2mwHnHDk5OcTEHL6kr67CuUa7J7CzyutdwLgj9P8R8FaV1zFmtgrwAw845+Z/+wQzuwG4AaBPnz7HnbCISGM6tLVfpi8RZ+C6VC+0y8vL2bBhA3EVPuITu5KUMirsOXXt147N+Dkp0JuPA1tYvngxQ4aPCPu4IhLUq1cvdu3axf79+5s6FSH4wadXr2OfBw5noX34Y3igxo9nZvZ9IA04rUpzH+fcHjPrDywxs7XOuS3Vgjn3OPA4BLf3a5i0RUQaR+eCPCCKrOLOuM5R4K3+R8ZNmzZRXl5OxI6vGXfhRTU+3ayheSI8RLXJJ7qiC529kezJPUBpSTExbWLDPraIQGRkJP366ZFRrUU4l47sAnpXed0L2PPtTmZ2FvA/wBTnXNmhdufcntB/twLLgMN3SRcRacHaF5XgIYeSwjY17jaydu1aor0RRBQdZNikxtsSq0PX4Ez78F7JOE8E7y9a2Ghji4i0JuEstD8FBppZPzOLAq4Equ0eYmYjgb8SLLL3VWnvaGbRoe8TgInAl2HMVUSk0cWVOPwUAnbYjZDFxcVs3ryZyII8+g4fEbYt/WrSbXCw6O/q64qnws/69fr1KyJyLMJWaDvn/MBM4B1gA/Cic269mc02s0O7iPwfEA+89K1t/IYAq8xsDbCU4Bpt/aYXkVYluiyKg4EInNdwHaOqHfvyyy8JBAIE9u5k2GlnNWpe7Qf3JuAcJXvK6N6pA/n+CvJzcho1BxGR1iCsD6xxzi0CFn2r7a4q39f4r4dz7iMgOZy5iYg0Ja8/gCcQy36/P3gTpKf6+ut169YRE+EhxhwDx57SqLlZwgD8rCBQFEvqWePY/d57fPjvt7jgqu83ah4iIi2dHsEuItIE4kJb+x3wxR62rV9xcTHbt2+H7L2cPP5UIo9ja6lj0qYjXs8+Ys1DYqfBeFyAjRs2NG4OIiKtgB7BLiLSBNoVlQNQFHCH3Qi5efNmnHN48nIYdnr9b4KMzs+hxydL6bHqP0SUl1Hetj3l8e0p6N6XjDMvxhff7sgBzIiLzyZwsD+7N+bRrVNH9uzPITdzD52696h3PiIiJyrNaIuINIHOBQcAKPX4oG31OY+vvvoKL45O7dvR8+RvP1C3dm32ZzLqsXs5/dfXcfIb/8DXJp4D/Yfgj4klJnc//d57lUn3/Jh+776Cp7zsiLGiOzi8Zuz9MpfR4yeA18vH775d/wsVETmBaUZbRKQJdCgqpjwQDx0DUGV/bBeoYPPmzdiBbIZMnFTnvbOLVq5kwpzbAMfWsy9j97jJFCdWn32Oy9zBya8/w8mvP0OfD97i8x/dAQyqMV5EQhzshNK9xZx00ljMvcXGjRu5wLlG2c9bRKQ10Iy2iEgTiC1yFAQMX2Lbau3+/Cx8Ph/egjwGjptYp1h5L7/Mjh9dR3nb9qz4xRw2X3T1YUU2QFH3Pnz24ztZefO9ODPGPPIbStauqzGmt3tnAOI9sPfrArp17kShecnatqXG/iIicjgV2iIiTSCqLIaiAJQntq/W7s/ZhQdH5/hYuvQ9+tPhsh97jMw7f03c2LGsuPV3FHfpftRzcgeNYOXP78PXJo4dP/oRJevWH9bH26s3UEG7aGPXVwcYNW48LjKKle/9u87XKCJyolOhLSLSyDwVjkgXQ0nAh+vwzf7Zzjl8ubvwFOQxaPzEoy7RKFiyhP0PPUy7iy6i918fwx8bX+ccSjslsvLn9xHRtm2w2F5fvdi2LgOIsP0kxPjZvfEAw0eMAGDTpuCNmiIicnQqtEVEGll8aGs/F3mg2vrsisJcnK8U78EDDDrKspHyjAz23H4HMcOG0f2392Le+t9yU9opkT5PP40nLpZdM2+m4uDBKkkm4vXsI95TQUFuKb4iSOzYgWJvFPu0fEREpE5UaIuINLK2+aUABOIOVmv35+4C5+gYE0XX/ifVen6guJhdN/8M83rp9ceH8URH19r3aKJ69aTXQw/h37ePvff+9psDZnjblOD1B/+Z2PXVAYanpBCIbsPaD98/5vFERE4kKrRFRBpZx/35AJR1rqjW7svZSURJISePnVDrshHnHJm/vouyLVvo8fs5RPbsedz5tBkxgoSf/ISDb7zBwUXfPMzX285BIIa2bSPZvekAQ4cHH9i7Yf3ha7pFRORwKrRFRBpZ/EE/5QFHUbcOlW2B0kICJQfxFuQdcdlI4dKlHHzzTRJm/pT4iXXblaQuEm78MTEpI8i8Zza+rCwAvJ3bANC3Tzy7Nx6gU6dOxEZHkeer4MDePQ02tohIa6VCW0SkkbUpi6DcFZMf26uyzZeXCYC3IkD3k2re2zpQWkrW/f9L1EkDSLj++gbNybxeev7ud7jycjJ/+Uucc3i7dwKgW/sKivLLObi/lIEDB1IR15avPvmoQccXEWmNVGiLiDQmf4A2zotZNsWRnb9pzt2N+cqxnidjnpp/Nec8+SS+XbvoduedWGRkg6cWlZRE19tvo+ijjyn497tE9OoNQEcrBILrtIcmjwBPBOtWr2rw8UVEWhsV2iIijcizv4xYD3ii9lfuOOICAfx5e/EW5uNJSq7xvPJdu8h5/Anann8ecePHhy2/DldcQfTAk9j3h99jnXrjIQdvYSFx7aPYvekA/fr1w2NGVl4+RXkHwpaHiEhroEJbRKQRtd1XGrzRMe6bIrWiIBtcgIiyYujWv8bzsh54ADweut5xR1jzs4gIEn/xC3zbd3Bg0Qd4PVn48yvoeXJHdn91gMjISHp2705FXDu2rP4krLmIiLR0KrRFRBpRfG45AIXxEZVt/5+9O4+O4zzvfP99q6r3FfvKDSRIkRRFUqI2a7FlS7ZlS6Js2TfezjgZx0k88UySmdw4yc1NxslxTrYb5yYzjp3czMkkzmprISXZWq3NoiSKlERRpESCJEiC2IEGGt2N7q6uqvf+0SBIClxACY0GiedzDo6Brvdt/lp/gI+LTz1vaawPtMZKtqEMc8ae3I4dZJ96mvqvfQ1fc3PFM0ZuvZXwDTcw8p3vYPqzOLkAbWtqyGdKpPpzrNuwAS8QYv8rL1c8ixBCXMqk0BZCiPniaaK58ki/VOzU0evOyHHMfBZj6boZW7TWDP/V/8BqaaH2Z788LzGVUjT++q/jjo9jj6bxShHaOuIA9B4Yp7OzE4BjJ3qw85PzkkkIIS5FUmgLIcQ8UekSEQO0LjIaaQLAswt4xSxmdgKWXDFjz+Qrr5B//XXqvvrzGH7/jOuVErpyPfG77yZ34BgAYa2J1QbpPThGXV0dsUgYOxTj2N435i2TEEJcaqTQFkKIeaJGbSKGwlQDjIfKEz2ck2P9/GFUMDpjz8h3/hqroYHkfffNa1aAhl/5FdzsEABO7wBtq5P0dY0DsOaKteU+7ddl+ogQQpyLFNpCCDFPVKpI1NT4jF5y/nqg3DainBJm68zZ2ZO7djG5c2f5bvb7OGb9vfK3txG9ZikA9uFjtHQmKWRLjA1M0rl6NRgGB/ftQ2s979mEEOJSIIW2EELME3PUJqwMHH8OlIHWGme8HzM3gVq6fsb6ke/8NWZdHcnPfrYKacvqf+4LaDtLbs9RWleVT7Ls6xpn+fLlKCDrwUjPsarlE0KIhUwKbSGEmA8Fl/Pqb6sAACAASURBVHDBxVCKfKD8QKSXG0NrD6vkQG3LGcvze/aQ27GDuv/4cxihUDUSAxDYeBPYQ5RGPGIxCMX99B8aJxAI0NrSghOJ0S3tI0IIcVZSaAshxDw42Z8NkImUH2ospXoBMOvay7O1TzPyvb/BTCap+dzn5jfou5kWgWgKFawn/eCDtK5K0neo3Ke9avVqvGCEQ1JoCyHEWUmhLYQQ88BIFYmY5WJ6LBoDwBk+ilGYxHxX24jd00P2mWeo+cLnMSKRec/6boFGFxWuI/X3/0BLR4xsqsjEaJ6Ojg5Qip7eXoqTuWrHFEKIBceqdgAhhFgM1KhNLOgBDiPRJrTn4hYy+PJZaF11xtqxf/4XME2SP/Pe72Z72qXffousM0xJT2J7eUxl0ei/ggb/Kiw1+4crrZoAasjASRVIDO8HAvQfSrNqSzs+y8IJxTj25uusvuHm95xXCCEuR1JoCyFEpXkaNWYTrylhecOMh5fiTgwDYAWiKOvUfGzDLjJ+//3E7rgdX1PjRf0xWmuGS10czj9Pd/5FCt7EWdcZWDT4O1kf+SRad85oW3k3qykBByDQsR7vgb/H3/7L9HWNs+b6ZpavWMGRfI7uN3ZLoS2EEO8ihbYQQlSYGi+hPIhqjakGyPi3UOp5tXzsemPHGWtbX30Ob2KC2i996aL+jKHJIZ4e+2OOF17FxM+S4DWsDN1CvX8VPhXCp4LYepJB+x0Gi/s5VniVn4z9GV/80eP86tW/ynUt153zva228oOa4auvJfW//4Smqw36p/q0Ozo66Orq4tCbb/BRrS9YtAshxGIihbYQQlSYGi0CEPACuIEsKAN39ARGPodad9ppkFqz9PlHCVxxBaGrr57Ve3va4/6u+/nzXX9OvmSzJfYlroh8DL8RnrE2oKIsDW5haXAL18S/yKH8c3RN/pCvPPEVPr7843zzA98k7Ju5z2hdjqIbX109KhQiPvI2PbmVTE7Y5T5tION4DB/rpnF5x4z9QgixWMnDkEIIUWEqZRMImSgs8kEX7di4pQKWXYSaU2P9ag7vJ953lJovfmFWd4YLToFfeeZX+P2Xfp91dev4VOOfc1XsU2ctst/NUCarwx/mkU89wi9v+mWeOPYEX37sy/Rn+2fmr1mGpQbwJlzin7iT4Ks/BqD/8DiNjY2Ew2GcSFzG/AkhxLvIHW0hhKgwY9QmnLCg4JENWZTGB0EpzFjdGQX10ucfxQ5HSdx11wXfM2Nn+M8/+c+8Nvga37j2G3xx7Rf5i6e6Ljpb0ArySxt/ifV16/mN53+Dzz36Of7itr9gc+PmU4ssP5Z/nFK2hprPfY6xBz4PeDz69FHckXFKkUbcaIYXnnuRHdFN5/yzfu2OmadfCiHE5UzuaAshRCXlXVTeJR5wABiLRnEHD4PnYrV0Ti8LpFM07XmJ3htuv+ABNaP5Ub7y+FfYM7SHP771j/nSui+9797oW9pv4Z8++U/E/DG+8vhX2Nm/84zrZrSEU4gSWLee8No1xCZPoEbKLTFWohltmnhjA+hS8X3lEEKIy4kU2kIIUUEqVS48a408UGI43oiTHsSczKLa106va935DIbn0XPTx877fuOFcX72sZ+lO93NX33kr7hzxZ1zlrUj0cH37/w+S2NL+S/P/BfeHn17+ppV4wMsvIxN8md+hrqhtzDGbSh5WMkmANxwFAaOzFkeIYS41EmhLYQQFWSkbLSChFPAUoOMWk142sXEQIWi5UVa0/by06Q61jHZ2HrO9yq5JX7t2V+jL9vHd+/4Lje3zf04vWQwyXfv+C4xf4yvPfU1eiZ6ALAa4gA4vUPEP/lJYpM9gEKN2hiBCEYwihuJo3sPznkmIYS4VEmhLYQQFaRSNjrpI1w0MNUAY5mpdotk8/SaZPc7RId66b3hI+d8H60133rlW+wa3MU3b/om1zRdU7HMzZFmvnfH93C1yy88+QuM5Eew2sp3rZ0T/ZjRCLnVS1CeizWQAcBMNOFE4ujeAxXLJYQQlxoptIUQolK0Ro2V0DU+rFIE15fBGTwCroPZvm56WdvLT+P4gwxs/sA53+r7b3+f+7vu56sbvspdHRd+WPL96kh08J2PfIfRwijfeP4b0LYEKOEMpADo+8BHiGV78B8v/2zFG0EpvMk0evLsB+UIIcRiI4W2EEJUiJpwUK7GH/ehtJ9CwMGZHMPK51DNKwAwiwVaXnuBgc0fwA2efSzfS30v8We7/ozbl97O1zd/fd7yb2jYwG9d91vsHNjJ/9f/LJYaxJ3qOZ9o7yDkDOHYQXA1VmKqTzsSB2kfEUIIQAptIYSoGJWyAQgHTQCGAgYaMH0hlFmertr0xg6sYoHeG24/63uki2l+56e/w/L4cr5187cw1Pz+2r531b18YsUn+Ou3/pasP4WTmfrzlaLUnkQrk9CRPoxAuNynHU1Kn7YQQkyRQlsIISpEpWy0T1HjZgE4rMvFtVXbNr2m7ZWnyTW0MLZy3Vnf41uvfItUIcUf3vKHZz21sdKUUvzujb9Le7Sd58KjlPIRtNYApLZcBUB0X3nSiJlowglH0X0Hp9cIIcRiJoW2EEJUiBqz0TV+6rMTgEtf3gXXwZga6xcaGaCu6y16r/8wnGUO9mPdj/Hj7h/zixt/kfV16+c5/SkRX4Q//eCfcsQ/CNqPmy63j9gN9QRLKRh3wfNO9Wm7JRgbqFpeIYRYKKTQFkKISnA8VLqErvUTmyxiqmFyJRezkEfVlUf4te18Bq0UfdfdNmP7YG6QP3j5D7iq/ip+fsPPz3f6GdbVrePahhoAdr759PTrusYkG2ojeWi/9GkLIcS7SKEthBAVoMZKKMCr9RMuGBRUL55hYPlDKGWA1rTsep5U5wYKNQ1n7NVa8wcv/wG2a/Otm7+FZVjV+RDv8vHVNwDw9Js/wfYmAZhc3Y5rhajf9dqpPu14nYz5E0IIpNAWQoiKOPkgpK7147PDHDPK86atmnJ/drznEJHhPvq23Dpj73MnnuO5E8/x9c1fZ3li+bxlvpDA8lVobGKTEV7L/AsAbnP50B2rbwLDLmLGm3BCYfTAEbTnVjOuEEJUnRTaQghRAcaYjQ6b+AyF0kGOKqfcn72k/NBj667n8SyLwY03nrHP0TZ/tPOP6Eh08IW1X6hG9HNStSvwqX6u81awP/djhu1DEDJRfpdMdDlNe17CSjQC4JkmDB+vcmIhhKguKbSFEKICVMrGq/UTnXQAGEBjFguommbwXJp3v8DwumtwwtEz9r2V3UZvtpffvv638Rm+akQ/NyuA5R9neaGOkJHkxfHv4mkXpznKWLKT5t0vTBfabjgGfYeqHFgIIapLCm0hhJhreReVd9G1fuoyE0xSpGAamP4QSilqD+0jODFG/zVnto1knCH2ZB7gY8s/xvUt11cp/PlZ0RJeIcaN8f9Iyunm4OTT6Pogji9C+GgvAccr92knG9D9UmgLIRY3KbSFEGKOnd6fXZ9JM2CMAWDVtgPQsut5nECQoSuvPWPfzom/RynFr2/59fkNfBGsWh9gsVZfS6P/Cl7P/Bt2XXlm9kS8g6Y9L5X7tIMh9GA32ilVN7AQQlSRFNpCCDHHjDEbrUAn/cRzRfpUP3gu5pJ1qFKJ5jd2MHjVDXj+wPSe/uI+jhVeYWP0PpojzVVMf36+5gQADdlxrot/mbw3zpv6UXTAYKRpA82v/fRUn7blg6Fj1YwrhBBVJYW2EELMMZWy0QkfmIpQ3qDPmCj3ZycaaXj7NXz5HP1bPji9XmvN7ol/ImzUsj56VxWTX5jV3gJAw0SKRv9qlgdv5K3JbZTqFOPJVdQd3EvICALledq6v6uacYUQoqqk0BZCiLmkdflEyFo/aI1b8jNhOpj+MEopWnY9RzGaYHTNVdNbeoq7GCodYFPss1gqcJ43rz6jvQNFnkQuD8CW+JfwtMvRyB5cQhT9cdr2vzbdpy0PRAohFjMptIUQYg6pjINyNLrWT9D2GFA5AKy6dsxigca3XmVw0wfQZvkQGk+77J74Z+JmC6vDH65m9FlRNcuwVD+RfLkvO241c0XkY+wNPg7AwLIbaN79U8x4I04giB4+ji4VqxlZCCGqRgptIYSYQycfhPRq/MRzNgPGGMrTmO3rqd+/G7NkM3D1TdPrj+R/yphznKvjn8dQC+MEyPMyTKzgBP5iaPqlTbHPko6mcMwSg8u2UHtkP0ErDIDn88Pg0SqFFUKI6pJCWwgh5pBK2WifgphF/cQY/cY4fruAkain+fUXKcaSpFaWD61xdYnXMv9KrbWCFcEbL/DOC4cVczHcOMor39UOGjHWRT9Of/QQE756ANq7DwNTfdp90qcthFicKlpoK6U+rpQ6oJQ6pJT6zbNc/69Kqf1KqTeVUk8rpZaddu3LSqmuqa8vVzKnEELMFZWy0TV+UIrYxBgplcVncqptZOONYJgAHJx8mqw7xJb4F1Hq0rnvYdX5AZNI/tTovvXRuxiK92BMWowuW8fS3S+W+7RrmkDmaQshFqmK/WZXSpnA/wTuBNYBn1dKrXvXsteBLVrrq4AfAn8ytbcW+D3geuA64PeUUjWVyiqEEHPC8VATpXKhDeQyY6DArGs/1Tay+QMAeNphb/YhGn1raAtsqmbqi2Y1lX8dN6ZHp18LGDGCjc0oFIfX3kjyeBf+QBzHH0CP9KDtfLXiCiFE1VSyIfA64JDW+giAUupfga3A/pMLtNbPnLb+ZeBLU99/DHhSa52a2vsk8HHgXyqYVwgh3hc1XkJp8GrLhXbKKWCY4C3bQvMP/45iLEFq1XoADuefJ+sOc2Piqyilznifbz95cN6zXwxrafngnfrMOEdomX59aevNuK+l6Ir5uA5oGE3RY2k8fxBj4AiwsTqBhRCiSir5b5VtQM9pP5+Yeu1cvgL8+D3uFUKIqjv9REjlaYaMSWIlhRWM0rBv13TbiKdd9mQepNZaQXvg6iqnvnhGWweKLPFc4YzXA/44k/FJKEQYb2ll2b43AHCjCbSM+RNCLEKVLLTVWV7TZ12o1JeALcCfXsxepdQvKKV2KaV2DQ8Pv+egQggxF9SYjQ6bEDQJZiZJqSxJ7dCwbxeWXWRgU3nayNHCy0y4fWyMfXrG3exLgYq3Yhr9RPIzs/sb62nMLuXVNUFa33kTwx/GTTZKn7YQYlGqZKF9Alhy2s/tQN+7Fymlbgf+L+AerXXxYvZqrf9Ga71Fa72loaFhzoILIcR7YaRsvKn+bKvnAFpBPKjL00aiCcZWrUdrzZuZB0hYrSwLXl/lxO+RUnjWBL5SaMYloz6GqS12tloorYkXHVx/AD3aSz4zUYWwQghRPZUstF8FOpVSK5RSfuBzwPbTFyilNgPfo1xkD5126XHgo0qpmqmHID869ZoQQixMBRc16ZZPhAS88T4MrfA3JKbaRm5AmyY9xd2knKNcFf00hjKrHPq9KwZKKDeO4Z75j426rvz5TXMVI3URWo8fQ6PxAiFO7H+rGlGFEKJqZlVoK6WuvNg31lo7wNcpF8hvA/+utd6nlPp9pdQ9U8v+FIgCP1BKvaGU2j61NwX8AeVi/VXg908+GCmEEAvR6f3ZAFkvT70XRY2UsOwig5vK00bezDxA1GxkZeiWqmWdC5mwARjEc+869TFg4sUtOia38OLqIsv3vg6AG01yfN+e+Q8qhBBVNNupI9+duiv998A/a63HZ7NJa/0j4Efveu13T/v+9vPs/V/A/5plPiGEqCpjzEYr0Ekfnp0na5RYUooQ7u7GDsdIdV7JkH2AodIBboh/5dI4BfI8xmIRagehKT3EeHzpGdd0XYDaniae7oStL2XwYeLWNNKzb2+V0gohRHXM6o621vpm4IuU+6Z3KaX+WSl1R0WTCSHEJUSlbHTcB5aBe+IdtIJmr0j9vtcZ2nAt2rR4K/swfhWhM3xbteO+bwOJRgDqztJ3resDGA4EGz7CaMygdmwM1+dn5MRxcuNj8x1VCCGqZtY92lrrLuB3gG8AHwT+Uin1jlLq05UKJ4QQlwStyxNHTvZnDx9DaUVDKYsvn2Nw441knEGOFV5hTeQOfMbMhwgvNSPR5ZgME8/ZM6559eX/DusLd/DKGk37oQPlPm1/kJ59b853VCGEqJrZ9mhfpZT6NuVe6w8Dd2ut1059/+0K5hNCiIUv46BK+lShXcjRoGOokVEcf5DRKzaxP/cjQLEu8onqZp0jthVFmYOECr6ZF8MWOmSSGK/lwLolNA8MAqCS9RyXQlsIsYjM9o72/wBeAzZqrX9Za/0agNa6j/JdbiGEWLSM0x6E9Ip5bBNavBrc7mMMr7+GglniwORTdIRuImLWVTnt3Cn5c1ilOOiZRyR49X6M0SKhtZ/D0Tl8JRursVnuaAshFpXZFtqfoPwQZB5AKWUopcIAWut/rFQ4IYS4FKiUjbYUOmbhntgPStHs1aBGTzC48UYOTD6Nowusj9xd7ahzKhfyQAcJFt0Z13R9AFXwWMYW3lgToqm/n7yyGBvoZ2JEDhgTQiwOsy20nwJObyoMT70mhBCLnhqz0TV+UApn+BhKQ2PRxcNjcN1m9ucepdm/nnp/R7WjzqnxaPmvhYaJmQ846roAAOaow/DGm2keGMJ2HOnTFkIsKrMttINa6+zJH6a+D1cmkhBCXEIcD5UuTfdnu4UMSS+ElR5m5IqNHOFNcu4IV0Yvr7vZAEPxWgAa06Mzrum4hfYp1EiRwIYvEk2XzyQzahuk0BZCLBqzLbRzSqmrT/6glLoGyFcmkhBCXDrUeAmlwav149l5XNOkzavFTQ8wuPEDvJ17jKjZSHvg6gu/2SVmKLEExSTJ7Fn+OlAKXRfAGLHx+2voa48TKOQx65s4vu9N9Fn6uoUQ4nIz20L7Vymf3viCUuoF4N8on/oohBCL2uknQrrH94Fh0Krr8bL9HLyihQF7H2sjH7ukj1s/l0ygEdPoJ3KO2y5efQCVdaDgktl8J02DQ2Rdl4mRYdJDg/MbVgghqmC2B9a8ClwBfA34T8BarfXuSgYTQohLgUrZ6LAJQRNn5BhoTZOXoBTMsUf9FBM/neGPVDtmZSgD1xrHX4yc9bKemqetRm0KGz5GbWoEFxPtC0j7iBBiUZj1gTXAtcBVwGbg80qp/1CZSEIIcekwxmy8k/3Z+Qxh1yCAj+H2CIfyz9ERvpmgEatyysopBG2Ul8B0Z7aC6Bo/2gBjpIjnD1DTUu7p1rW1UmgLIRaF2R5Y84/AnwE3Uy64rwW2VDCXEEIsfAUXNemia/x4hRyuZdHkhNB2jifX+nF0kbXhO6udsqImIuUDa5KZyZkXDYWu9aNGiwBsuPPTBAoFCpEQPdKnLYRYBKxZrtsCrNPyW1EIIaad0Z/dswcMgyV2DJ3v45n4OzQaay67kX7vNhqL09APzeODjCZnflZdH8A4kAHHI/GRj9Dw8MP0NtWRGTvCWH8fta1tVUgthBDzY7atI28BzZUMIoQQlxojZaNVuUXCGTkOWrNEt5I3B0i7A6yNXN53swEGki2AS20mc9brXl0Apct92mY0SkskjPaF0D6/tI8IIS57sy2064H9SqnHlVLbT35VMpgQQix0KmWjEz4wFW4hg1VyCAVqeaN2lKCRYHnohmpHrLhUZAmmGiQ+6Zz1uq7zo1W5Txtg9dXlrsNiLMLRt16ft5xCCFENs20d+e+VDCGEEJccrVFjNt7SMF4+i2tZNE6UIAhPNvTSGb4NU/mqnbLiXCMA5iiB4jn+0dNnoJM+1HC50F5+58fxf/vbuKEw3W+9jtYapdQ8JhZCiPkz2/F+zwFHAd/U968Cr1UwlxBCLGgq46AcPdWfvQ8Mk2VTPdvHggOsvlxH+p2FHZjELCXgHI/x6IYAKmVTsl18jY002zZGIImbzZPq7ZnntEIIMX9mO3Xkq8APge9NvdQGPFSpUEIIsdCdfBDSq/GX52cDy4oRStgEgzESVms1482rXAjATzh/9vYRr77cpz14JA3AsvZ2SoEQnuXn1Z1Pzl9QIYSYZ7Pt0f5l4CZgAkBr3QU0ViqUEEIsdCplo30KYhZuIYtVLBCOtNITGGRV5I5qx5tXqWgYgOb08Fmv6/oAGujtGgeg89ZbAShFw+zZ/ex8RBRCiKqYbaFd1FrbJ39QSlmAjPoTQixaKmWX52fnM7iWj+ToKF5NC/3+AVqjH652vHk1mCzfd6mfGDv7gqk+7b6D5UK7ffNm/I6DPxDDPT5Krpidr6hCCDGvZltoP6eU+m0gpJS6A/gB8HDlYgkhxALmeKh0CV3rxzu+D0yTtv5BAr4a/OYolvJXO+G8GootwSBNMlM85xrdEGCwewKn5GIYBq2BIE4wSsA2eGTnv81jWiGEmD+zLbR/ExgG9gK/CPwI+J1KhRJCiIVMjZVQgFfrxxk9DsCSjEZh0OBffBM0Cv4aDKOPSME85xqvPoDreAx2TwCwYu0VFMIRPMvHyzsfm6+oQggxr2Y7dcTTWv+t1vqzWuvPTH0vrSNCiEXp9BMhnUIWq5An3VZun1CRmmpGq5pSIINln2fySH0AFPRN9WmvuqE8Y9wIx3CPjXJk/Mi8ZRVCiPky26kj3UqpI+/+qnQ4IYRYiIwxGx028UoZXJ+PmtERUi0NaLzpfuXFJhfyQIcIFdyzL/Ab1LdH6Z3q025pbcUPmFaQ5tEgDxy8f/7CCiHEPJlt68gW4Nqpr1uAvwS+X6lQQgixkKmUjVfrx+vZB6ZF88AAVqwVUw0wGlle7XhVkYqVJ480pUfOuaats4aBI2nckodhGLTV1VGMxAk4Bs+9/iNKbmm+4gohxLyYbevI6GlfvVrrvwAW12P1QggBkHdRebfcNjJS7s92nQGWu21YRh9Z/+K8oz0wdSe/aXz0nGtaVydxSx6Dx8p92iuvuop8NIpnWoT7ijx34rl5ySqEEPNltq0jV5/2tUUp9UtArMLZhBBiwTm9P9stZPDlJ9m73KberqXkz8AiPU58ILEcgzFqzjN5pHVVEmB6zN/ylSsBsAIRlo8neKDrgcoHFUKIeWTNct3/c9r3DuXj2P+POU8jhBALnJGy0QpcM4sTCNIw0M87azehhiwmQ+foT14Eir4EhvkaoULdOdcEoz7q2iL0HhxjyyeW09LSgs8wMHwhmkZ8PH7iRQZzgzRFmuYxuRBCVM5sW0duO+3rDq31V7XWByodTgghFho1ZqOTPry+t8G0CKcHWRP9EACjU33Ki5XtP//kEYDW1VN92q6HaZosWbqUYjyBdjySaYtth7fNY2IhhKisWd3RVkr91/Nd11r/+dzEEUKIBUxr1JiNtzSMO9QNPpOB2DC3TDYALv01i/tObDYM8XyQcN5hMuw765q2ziR7nznB8LEMzR0Jlnd0cOToUSKmxXX2Kh7sepCf3/DzGGq2z+oLIcTCdTFTR74GtE19/RKwjnKftvRqCyEWBTXhoByNV+PDKebwFfL0r7+C+pyLpXoZia6sdsSqGo1HAGgZGzrnmtbOcp9278Hyce3Lly8HwGcFWTkY5UT2BK8OvFrZoEIIMU9mW2jXA1drrf+b1vq/AdcA7Vrrb2qtv1m5eEIIsXCcfBDS9WdwQmGiY8NEOj9NKO/HWMQTR046eUe/MZ0655pQzE9ta2T6gcjW1lYsy8IIRCj0jhA3Y/JQpBDisjHbQnspYJ/2sw0sn/M0QgixgKmUjfYpvOH9YFq4OkWLuRrTiWMHc4t24shJw7HlGIySzNnnXdfamaT/cBrP9bAsiyVLlmDX1eN4Lp8M38pTx54iXUzPU2ohhKic2Rba/wjsVEr9d6XU7wGvAP9QuVhCCLHwqFQRXRvA6z8IQGb5EuKT5UkjE+HFXWQD2FYUZQ0QyvvPu661M0mp6DJ8PAuU20eywRDaMNkwXIvt2fyo+0fzEVkIISpqtlNHvgX8HDAGjAM/p7X+w0oGE0KIBcX2MCYc3FofJaXxTebwXfXZ6QNaRhLxKgdcGOxADrOUPP/kkZN92l3lPu1ly5YB5T7t7N6DrK1dy4NdD1Y+rBBCVNjFPNYdBia01v8vcEIptaJCmYQQYsGZ7s/2jeCEY/jz44QCzTSm0ygK9Cfbq5xwYciGAfxEJ8/dPhJJBKhpDk/3abe1tWGaJmashsHhAe5dcQ9vp97m7dG35ye0EEJUyGxPhvw94BvAb0295AO+X6lQQgix0BipIhoonXgGDBOdSAAQz7lY6jijkY7qBlwgRuLlQVQtY4PnXdfamaT/0Diep/H5fLS3t1NqaMQFNmfa8Bt+eShSCHHJm+0d7U8B9wA5AK11HzLWTwixiKhRG53wwUQKtMbYeDcAgUIYrCGKlvxKBOhPnpw8Mn7eda2rk9gFl5GeDFDu004rA60MBn7yHLcvu50fdf+IonvuI92FEGKhm22hbWutNaABlFKRykUSQoiFxfM0KmXj1CicYAxfIYeZbCFgexhemHywUO2IC8ZwbAUmQyRypfOua+usAaB3qn3k5Dxtvz/CiXf2sXXVVibsCZ7tebaScYUQoqJmW2j/u1Lqe0BSKfVV4CngbysXSwghFo6x/hzK0WQnd+CEY/goTxhJZsp3W9PRs5+CuBg5ZhCsIUL5wHnXRZIBEo0h+rrKhXZ7ezumaeJramGkMMnm6HqaI808dOih+YgthBAVMdupI38G/BC4H1gD/K7W+q8qGUwIIRaK/sPlmc6+oT1gGFhLNwHQPHUC4lCipmrZFqJiMIfhJFHeuSePQPk49tP7tJcsWUKxpg7PUBx/ZDt3d9zNjr4dDE2e+6RJIYRYyC5YaCulTKXUU1rrJ7XW/6fW+te11k/ORzghhFgIBo+k8fxg4i+PrevYCEBdJodBmoHEsionXFjSEQPwkcxMnndd25oaipPOdJ/2ihUrGLdLaMPg6E+fZ+uqrXja45Ejj8xDaiGEmHsXLLS11i4wqZRKzEMeIYRYcPqPpMmGBijEavHZRZRVPpAllgPL61P1bwAAIABJREFUOM5YeHl1Ay4wg8nynOz2VP9517WtKf9LwIl3yvO0V6woT40Nxuvo6+thaXQJVzdezUOHHkKfZy63EEIsVLPt0S4Ae5VSf6eU+suTX5UMJoQQC0E+a5MeykP6RdxwBCtSW76gNT47iutL4RrnPwlxsemtXQ4UaUhnzrsukghQ2xrhxDspoDxP2+fzYbUsYcxnMrHrVbau2kp3upu9I3srH1wIIebYbAvtR4H/G3ge2H3alxBCXNYGj0wAEJ4cA2WgVpTbRsIFD6UDTIacasZbkMZDrVhGD7HchY+lb19TQ/+hNG7JwzRNli1bxmQkijYU3Q9v42PLP0bICslDkUKIS5J1votKqaVa6+Na6/89X4GEEGIh6T+SRisPJxQC7WHVLQGgLp0FIBULVjPewqQMHP84/mJLuaddlQvubz95cObSQh5fyeOvfvAWuiFI0YlRyE0SNX0c3L2bZ545we1Lb+ex7sf4jWt/g6Al/72FEJeOC93Rnr6FoJS6v8JZhBBiwek7nKLIUXKJGixPoczy/YmWsRHAYyBZX92AC1Q27ICOECqc/46/bgigAWOoPCrRSpQPvCHZxIThEhk8wdZVW8mUMjzT80yFUwshxNy6UKF9+r/7yfnCQohFxXM9BrsnCGT24oUiGDUt09dqM0Us1ctQfFUVEy5cI/EoAK2pC4zm8xnoWj9qqtA2IkkwfdiNbaTDAereeJFrm6+lNdIq7SNCiEvOhQptfY7vhRDisjdyIot2FKYeB6Ww2lZPXwtP+jHMHiYCLed5h8Wrt64VgObx0Quu1Q0B1JgNJQ+lDKxEEyXLAqUIHtiFoQzuWXUPL/W9xEBuoNLRhRBizlyo0N6olJpQSmWAq6a+n1BKZZRSE/MRUAghqmX/W0cByCaCoDVWslxU+0sephujEMxN9x+LMw3GVmCqfpLZ8x/FDuA1BlAa1Mip9hHtFNH+IPn8GKXBIe5ZeQ8azcOHH650dCGEmDPnLbS11qbWOq61jmmtranvT/4cn6+QQghRDfve6kaXesjHE5imH2WYANSmCwCMR8/7PPmi5ppBtDVIMB++4FpdF0Abp/VpJ8t92m7jMkaiIbLP/IQlsSVsadrCtsPbZKa2EOKSMdvxfkIIsai4nkvxhEEw+yZeMIxV0zp9rWVsGID+2rpqxbsk5EMFDDeJ6XjnX2gqdH3gVJ92KIHyBSnVNJAL+hl84gkAtq7ayrGJY+wZ3lPp6EIIMScqWmgrpT6ulDqglDqklPrNs1y/VSn1mlLKUUp95l3XXKXUG1Nf2yuZUwgh3m3H27sIlCIUAuVTC82Wzulr9ekcJkMMJORByPMZiwUAg8b0+AXXeo0BjIkSFFyUUliJJlztooGeg2/jZjJ8dNlHZaa2EOKSUrFCWyllAv8TuBNYB3xeKbXuXcuOAz8L/PNZ3iKvtd409XVPpXIKIcTZPLfrVbTWZBMBDMCKnRrjF520sMxjjIWWVi/gJaC/tvzfrC01eMG1uqE8H9sYPtk+0ox2S+hIgtGQn+zzzxP2hfnoso/y2NHHyDv5ygUXQog5Usk72tcBh7TWR7TWNvCvwNbTF2itj2qt3wQu8O+KQggxfyZLk4wcyWGW+rCjcRqSCZRR/nVpuhqrFKMYSKOVWeWkC1tvYgWKLLUTkxdcq2t8aL+BGiz3v1vJZgCclg5GEhEyTz0FlNtHcqUcTx17qnLBhRBijlSy0G4Dek77+cTUa7MVVErtUkq9rJS6d26jCSHEuT157ElaR5di5vfgBUJ0rrli+loiYwMGE1GZNnIh+UAdptlDZNJ34cVKldtHBgugNUYgghGK44SjFE2DgZd24Nk21zRdQ3u0nW2Ht1X+AwghxPtUyUL7bH8LXcyj4ku11luALwB/oZRaOeMPUOoXporxXcPDw+81pxBCnOHR/Y8RcZqYDJWnmK7ftGn6WmtqBIDBRLIq2S41xUAGy06Wj2K/AN0YRBU81ET5NEkr0YTr2GilGLYUuR07pmdq7+zfSV+2r9LxhRDifalkoX0CWHLaz+3ArH8raq37pv73CPAssPksa/5Ga71Fa72loaHh/aUVQgjgROYE/YfSaO0xmQhiGYqmpqbp643pNAYT9NXIYbmzUb7zHyCRK1xwrdcUADitfaQFtEuoZQmjyRiZx6emj6zcikaz/bA8Jy+EWNgqOQT2VaBTKbUC6AU+R/nu9AUppWqASa11USlVD9wE/EnFkgohxJSHDz/MmsGVaLuHUkOMJY2NGMapexKxrIFldDMa+VD1Ql5ChhJxaoagfaSfdPQC/+ckbKFjFsZQAW91DCvRCCjS8WbsSA9Djz3Bv9/6RbTlo8W/gX/Y+0MmB29DneXQoF+7Y/XM9xdCiHlWsTvaWmsH+DrwOPA28O9a631Kqd9XSt0DoJS6Vil1Avgs8D2l1L6p7WuBXUqpPcAzwB9prfdXKqsQQgB42mPb4W10TKxBufvRlo/1GzdOX1eexmfHcPyjuIa/ikkvHcfrVgBFmsbTs1rvNQVRw0VwNcryY8bqcP0BPDQ55VLXtReAzvCHyLiDDNryV4MQYuGq6BxtrfWPtNartdYrtdbfmnrtd7XW26e+f1Vr3a61jmit67TW66de36G13qC13jj1v39XyZxCCAGwe3A3g+lhtNdMIVoeH7d2/ZXT1+PZEgqLTEROJpyt8XA7lnGMeHZ2f914jQGUd/px7M24pTzatBiqidP0xksALAvegE+F6Jp8tlLRhRDifZOTIYUQYsq2Q9vYMLwajUs+7Cfss4jH49PXm8bLh9cMJ2LVinjpUQo7MIavWIPyZvFAZMPUcewzxvytYKChlqY3X0a5Lj4jyIrQB+gu7KDkyUxtIcTCJIW2EEJQnp39xLEn+ODQ1bil47jhKMuWLjljTfP4GIoCvbVLzvEu4mzSsfIDkcnsLApiy0DXBVCD5TvaZqwODAs32cikLkE+S82hcpfhqtBtOLrA0cLLFUwvhBDvnRTaQggBPHX8KfJOnuBYI9o8BIbBhquvOWNNMqPxqSMMRzrP8S7ibPpqawFYOtI7q/Ve09Rx7HkXZZhYiUZco/zA41AyTvMbOwBo8l9B3Gyma/KZygQXQoj3SQptIYSg3DayyW5nwmqmGCkCmpWrThXUytP4CzE8fz8lK1K9oJeg43WrUEzSOJ6Z1XrdNHUc+9Cp9hGvVMALxehtb6dpz0vguSilWBW+jQF7HxPOQMXyCyHEeyWFthBi0evN9rJzYCdbj1+NS4li0EdNJEIgEJhek8iWUPjIRJ0qJr00ZQONmOZRYrnZTZTVCR86YJw5TxtwWjsY94M/M07NkXcAWBX+EKA4NPlcJaILIcT7IoW2EGLR2354OwpF5GgUx+3GC0ZYtWrVGWtaUykA+mvkRMiLphTF4ASmXTurByJRCq8piDFYLB/HHoqjAhHcSALXzpOJhGl+40UAomY9rYGrOJR/Fq29Cn8QIYS4OFJoCyEWNU97bDu0jdv9VzFmJ3GD5T7iq7ZsOWNdc2ocg3FO1KysRsxLXipmAj7q0rNsH2kOomwPlbJRSuFLtuC4RTSKoys7y2P+vHJh3Rm6jaw7xIC97wLvKoQQ86uSJ0MKIcSCt3twN73ZXn5r8Cr2xZZTDB7EUkHa2trPWJfIGviMLkaid1QpaWV8+8mD8/Ln9NU10DAAy0b6GKmJX3C91xREA8ZAAbcugFXTgj14CLe+lWHb4ap0iuTRA4x3rGVZ6Dp86TBdk8/SEthQ+Q8jhBCzJHe0hRCL2kOHHiLqixJ5PY/DOKVQmJaGhjOOXfcKDlYphh1MyYmQ71FPbScGaerTudlt8BvoOj9qYKpPO9EESuE2tFPMpXB81vT0EUsF6AjdxNHCSzJTWwixoEihLYRYtHKlHE8ee5JPRW9meNSkpLrRlo8rN206Y519PA0YjMXkV+Z7lffXoqweIrnAhRdP8ZqDGONTY/4sP2asHsfnA7fE0TVX0vTGDtDlnu/O8G04ukh3YUelPoIQQlw0+VtDCLFoPX70cfJOnju7E4wlO3HC5Qce11+18Yx19jtHAOita5z3jJeTQiiL6dRiurM7wl43T435m54+0loe82f56WtqJDQ2QuJYFwANvtUkrFaZqS2EWFCk0BZCLFoPHXqI5fHlhH/6FunkEuwAxAJ+otHoGevsoyNY6gS9ySuqlPTykIr7AZPGsbFZrdcJHzpoYEy1j/hqymP+3NaV5JwsnmmV72pDeaZ26DYG7beZcPorkl8IIS6WFNpCiEXpaPoorw+9zs/EPsTwsQmKbi9uKMLKjo4z1mmtsYdNDPMo6WD7Od5NzEZPbRMAS0ZnWQgrhdccKs/T9jRGpAblC+Am6tDjg/Sv2VDu055qH1kV/iAKg67JZyv0CYQQ4uJIoS2EWJS2Hd6GoQxu6bIYS3Zi+06AUmy67voz1rlpG68UIh/OgVJVSnt56K1djckI9enCrPd4zUGUo1GjRZRSWMkWHO2igePLlhEeHSR+otzaEzHraA1s5FD+WTyZqS2EWACk0BZCLDqu57L98HZuar0J7+mfkm7bhBOexASWLF16xlq7ewiAkUSoCkkvL7YVRft6CE/GZr1HNwbQiun2ESvZgnZLeDVNpI0SnmFMt49A+aHInDvCK/2vzHl+IYS4WFJoCyEWnZf7X2Zocoj7orcwue8dRn0hSqEQzfW1mKZ5xlr7naNAiZ661qpkvdxkowWUmyBYKM1ug89A1wdOjfmb6tN2mpbhDXYz0nklza+/ON0+sjR4LX4VYdvhbRXJL4QQF0MKbSHEovPQoYdIBBJc+eYE6fhyCvoo2uefMW0EwO7J4FNH6I+vr0LSy09/Tflu9vLhvlnv8ZqDGBMO5BwMXxAzWocbCEKpyLEr1hMZ7ifecxgAS/npCN3MU8eeImPP7hRKIYSoFCm0hRCLSrqY5ifHf8InV3ySySeeIrPmVuzACAAbNl99xlrtakrjQQLhAQq+ZDXiXna6G1ehKNA2kpr1Hq+13LZj9JUPo7FqW3HtSTxfgFTQh2datLz2wvT6zvCHKbpFHj/6+NyGF0KIiySFthBiUflx94+xPZt7QzdQeOstRus6ccKaqN9HLHZm73BpIIf2fPib5CHIuTIWXoppdhPPmhdefFLUwotZGP0nx/y1AeUxf+7gYUbWbqZ59wvglR+ArPetZGViJdsOSfuIEKK6pNAWQiwqDx16iNU1q2l4+RCOGWQ4248birJy5coZa4v7jwHgX9083zEvX0pRCI1j2fWzPrgGQLeGUCNFsL3ymD9/CCdeC2MD9Fx5DaHxUZLd70z9EYqtq7byxvAbdKe7K/VJhBDigqTQFkIsGl1jXewb3ce9q+4l8/jj5DbdQdHqAaW45oYbZ6y3D/ZiqgGsNddWIe3lazgRAExaRkdnvcdrCaJ0efqIUgqrphXHK6FRjMTDuD4/LbtPtY/c1XEXpjLZfnh7BT6BEELMjhTaQohF46FDD2Epi4/5NlLYt4+JlTdih/JYCtqXLDljrdaa4oAiYHVBkzwIOZeON5ZbP5aOzP4ER13rRwcN1FSftq+mDTwXt64Fd+AwQ1deS/MbL6JcF4CGcAM3td3E9sPbcT137j+EEELMghTaQohFoeSVeOTII3xwyQdRT/4UlKI3X8KJRFjS3IxhnPnr0BnJ45WC+BtsMC6in1hcUG9yDZY6Tt24M/tNU6dEGoMFcDVWsgmUgdPYDr0H6d/0AQKZNLVde6e3bF25laHJIV7uf7kCn0IIIS5MCm0hxKLwwokXSBVS3LtyKxMPP4Jx7S2MTLwDpsWma2e2htgHy3dbA6sa5jvqZc81AjiBQYKFmun517PhtU6dEjlcRJk+rEQTruUDx2aoJo4TDJ3RPvKhJR8iEUjIQ5FCiKqRQlsIsSg80PUAdcE6rpmoxe7uZnLLJygFxkFrrlh/5Yz1xbePYpDGWre5Cmkvf+mYBzpEMpuf9R7dGESbCqP/1Jg/zynihaJ4vQcZ3HA9TXteQpXKh+H4TT+fWPEJnj7+NOliuiKfQwghzkcKbSHEZW8gN8ALvS9w76p7mXz0cfD5GDRqKUX81MejBAKBGXuKvSX85juo9muqkPjy11NfD8CKoROz32QqdFOgPOZP6+kxf07rKujZT9/VN+PL52h4+7XpLfeuuhfbs2WmthCiKqTQFkJc9rYd2oanPT7dcS8Tjz5K5OZbONz1GjoQZN1Z7ma7EzZuPkKgJgPWzCJcvH9H69ZgME5TKntR+7zWECrvosZLGMEoRiiBE4lBbpzRxkaK0QStrz47vX5t7Vo6azp56NBDc/wJhBDiwqTQFkJc1jzt8eChB7m++XrqDgziDA1h3HYXGbcXgM3X3zBjT/HQEACBFYl5zbqY5AN1KOsYkVz4ovZ5zUE0p06J9NW24ZYKaMNCn3iHgWtupuGtV3Ez5ePXlVJsXbmVvSN7OTx+eK4/hhBCnJcU2kKIy9rLfS/Tm+3lvtX3MfHoIxjhMEORDkohj7BpUlNTM2OPve8wigK+KzdUIfHikY3mMNwagoWLmD4SMNENAYwTedAaq64d0DitK9HH99G35UOYTonME09Mb7mr4y4sZbHtsDwUKYSYX1JoCyEuaz/s+iHJQJIPN9/CxONPELvjdt7etRs3HGHlyhVn3VM8PonfOIBadt08p11c+uriAHQMXkSfNuC1hVBZB5VxMKN1KF8IJ1kPIycYr28k19BKevvD0+vrQnXc3H4zDx9+mJJXmtPPIIQQ5yOFthDisjWaH+WZnme4e+Xd2C++jDcxQeTOT9I7tA+U4tqbbp6xxys4lDIR/LFhCErrSCV1Na1DkaV9eOyi9nltITSgTuRRSuGra8PxHLRS5Ycir/0gkzt3Uuo/dSDOZzo/w0h+hOd6npvjTyGEEOcmhbYQ4rK1/fB2HM/hM52fIb39YczaWsZrVmMH81hA+5KlM/YUj6QARWBpZN7zLjaZUAuGr4tY9uL6tAma6Ho/Ru8kAFZtO2gXt74VfXw/fVs+CFoz8eij01tuaruJpnATPzj4g7n8CEIIcV5SaAshLktaax7oeoDNjZtZRi3ZZ54hcfdd7N3xGk4kwrK21hmnQQIU9x4GSvjXd85/6EUoE81juEkik/ZF7fPaQhgTDmRKWIkmMH049e3Qd5DJZC2hjRtJP/zI9HrLsLiv8z529O3gRObiWlWEEOK9kkJbCHFZ2j24m6MTR7mv8z4mfvxjdKlEYutWDh3YBYbJdTffctZ9xcMT+NU7GCs/MM+JF6cT9eWHUTsGey5qn9caAsDozaMME19NK46h0K4DvQeI33M3xQMHKBw4ML3nU52fwlAG93fdP3cfQAghzkMKbSHEZen+rvuJ+qLcsewOxh96iMDq1RQblpNT4xhas2r16hl73FyJ0kSYYKwXYk1VSL34HGpaj8E4bSMTF7cxbOHV+jF6T54S2Y72HLx4HfroXuJ33gmWRXr79uktzZFmbm27lQe7HpSHIoUQ80IKbSHEZSddTPPksSf5ZMcnMXsGKex5k8TWrex/cS+laJjmmlpM05yxr3hgGIDAyth8R160coEGlO8I0WwUtL6ovV5bCGO8BFkHX00rKINSywo4vh8VjxH94AeZ2P4w2jk1PvCzaz7LaGGUZ3ueneNPIoQQM0mhLYS47Dxy5BGKbpH7Ou8jvX0bGAbxu+9iz86fgmlx7U03nnVf8c1DKHL4r9o0z4kXt3SsgPLixHIX36cNU+0jlg8r0YTr96PtPD379pL81L04w8PkduyY3nNT6000R5r5wQF5KFIIUXlSaAshLitaa+7vup91deu4omYN6e3bidx0E0ZdPal8P3iaKzdtPuvewrECAfMtVMdN/z979x2fZXU+fvxznvvZebIHZBJ2EvYGGeJCUesAUdzW1rbaYWtrWzu+dfy+2vZra2urdVtFBBEQFBUB2XtvMkhIAtl7Pvu+f39Ag4FQJYGwrvfrldcL7lzXec75w3B5cu7rdPKsL22HY2MA6FlaeHqJIWb0SEur7iN6wIfuDCVn83pcEyagRUZSO/+jlhTNpDGl9xQ2lGzgcP3pnQsXQojTJYW2EOKisqdyDzk1OUztPZXmzVsIFJcQfvPN5G3Pw++0EukIwWKxnJQXqHITdDuwR9WCTY6OdKaDcf0xUUV8deNp5+pJTkw1x46PRCcBEEjsxcEtGzHMGmE33kjjl18SrK1tyZnSawqa0uSlSCHEWSeFthDiojIvZx4Os4Pru19P3cKFmEJCCL3qSjYuW4ZhsTJk+LA28zwHigCw9Y3tzOkKwGONBOshQprCTv+cdtKx4yOHmzFZHWhhcQRsDprrainJziLi1lsw/H7qPvusJadLSBcmJE3go4Mf4Q/KS5FCiLNHCm0hxEWjyd/E54c+57rU63D4FPVffEHo5OswORwUleeBYTBiXNtt+7x78tGoxDxArl0/F2rDAijdRUSj5/QSnWb0GCva4WYwDCwxKehBHzhd5Gxejy09HVvfvtR9tKBV2m19bqPaU83yw8vP4CqEEKI1KbSFEBeNRbmLcAfcTO0zlYbFn2M0NxMxZSrFOUV4nWZCNRsOh+OkPEM38BaBzbIPlTT8HMxcFMQe/U1Cr5LTPKcN6MlOVEMAVefHEp0MgLNnGjmbNwAQfustePbswZuT05IzNmEs8SHxzM2eewZmL4QQbZNCWwhxUTAMg9lZs0mPSmdgzEBq587D2qMHjiGDWf3xZxgWK4OGDG0z11/ShB6wYevqA+3k89vi7MuLG4imjhBfeZo72hztPmKo1sdHmq0O6ivKqCg4RPi3vgVmM7ULju9qayaNqb2nsrFkI4X1p1/cCyHENyGFthDiorC1bCsHaw9yZ9qd+PLycO/cScTUqSilKCg5CLrOuGsmtpnr3XMIAHtGcifOWHyVz+wiYC/E7o5GC57eOW1sGkYXO6bD7mPHR5JpaHaj25xkb1yHOToa14QJ1C38GMN//Ez2rb1vRVMac3NkV1sIcXZIoS2EuCjMypxFuC2cyd0nUzt3HpjNhN9yM1XF5XhsGi6TDbvd3mau50AJZlWI1m9cJ89afFVptAWwkFxeftq5erIT5Q6iqnxYolMAcPbsS/bGNRiGQcRtUwlWVtK4alVLTpwzjsuTLmfhwYXyUqQQ4qyQQlsIccEraypjeeFybu11KzZDo27hQkKvmIg5OpoV8xdhWKz0799272zdG8BbbsVuPwCxaZ08c/FVWQl9UXjoWVp62rl6gh1DUy3HR1JSUvA4XNSUFFOen4drwgTMsbHUfth693pa32lUe6r58vCXZ2oZQgjRQgptIcQFb27OXHRD5/a+t9OwciXB6mrCp04FIK8wG3SdCddf0WauN6sKDA17qgmU6sxpixNUuHqhmTOJqLOfdps/zCb0eDumI27QDfr163f0+IjdSdaGNSizmfApU2hcswb/Vwr5MfFjSHQlMidrzhlejRBCSKEthLjA+YN+5mbPZVziOJJDk6mbOw9zXByuceOoq6zEbdUIwYrTeXK3EQD39hwUjdiGtv2ipOhESlEf1ogpGEHYaV7HDseOj/h0VJmH9PR0AEJ6ppO1/vjxEXSd2nnHL6rRTBrT+kxjS+kWDtYcPGNLEUIIkEJbCHGBW1a4jEp3JdPTpuMvK6NxzRrCb7kFZTazfP6nGGYL6WmD2sw1dANPnge7tgPV+8pOnrloy6EucQD0Kc4/7Vyjqx3DZkIraCYsLIzU1FSabQ7qKsoozc3GmpxMyGVjqJ03DyMYbMmb0nsKVpOV2Vmzz9QyhBACAPO5noAQQnTE7MzZJLmSGJc4jqp/vQK6fnTnEjiYmwk2CxNvvKrNXH9RI7rPhj2hHuxhnTltcQoH44YwMDOThEoL9DnNZJNCT3ZiymvE0+hnwIABfJKfT4gzlKz1a4jv1ZeIadMo+tljNK3fgGv80ZdfI+2RXNf9Oj7O/ZhHhz5KqDX0tD72haXZpzlR+Nk1p7s4IcSFSHa0hRAXrKzqLLaXb2d62nRUUKd2zoeEjB2LNSWF2opymi0mnEErrjBnm/nuHblAEPvA7p07cXFKR9v8Hcbmjjn9Nn+A3s2J0iFnaxkZGRlomoajR1+yNq7F0HVcV12FFhFB7Ycftsq7K+0u3AE3H+d+fKaWIoQQUmgLIS5cs7NmY9Ns3NLrFhpXryFQWkrE9DsA+HL+x8eOjZz67LVnfzlWlYk24OrOmrL4BkqjrYCZlHa0+TMirOjhFjI3lOBwOOjduzeNmoWGqkqKszMxWa2E33ILDcuXE6isbMnrF9OPgTEDmZ05G+N0X8QUQohTkEJbCHFBqvfV82nep1zf/XrCbeHUfDAbc1wcoRMnApBz6CAqEOSKm9ruNhKs8+KvdWAPPQTRPTtx5uLrZP6nzV/J6bf5g6O72uUFDVQVNzJgwAA8Pj9GWCRZG9YAEHH7NAgEqJ3/Uau86WnTya/PZ2PJxg6vQQgh4CwX2kqp65RSWUqpg0qpX7fx/QlKqe1KqYBS6rYTvne/Uirn2Nf9Z3OeQogLz8KDC3EH3ExPm47vSBFNq9cQcdttKIuFkvxDeKxWXEYIrvBTdBvZWwKAIy2yM6ctvoFKV080834i6kJOv80fR7uPmEyKrA2l9OnTB5vNhq1bL7I3rkUPBrH16IFz9GhqZ89u9VLkpNRJRNmjmJ0pL0UKIc6Ms1ZoK6U04CVgMpAB3KmUyjghrBB4AHj/hNwo4A/AKGAk8AellPxrKIQAQDd0Psj6gEGxg8iIzjh63lYpIqYd/f/15R8vApOJgYMuO+UYnp15aJRhHjK2s6YtvimlqIloQumhxNY2nX6+XaPbgGiyNpWimTTS09OpN0w01tVSuHcXAJF33om/uJjG1atb0myajam9p7LyyEqKGovO1GqEEJews7mjPRI4aBhGnmEYPmA2cPNXAwzDyDcMYzegn5B7LbDUMIwQO04UAAAgAElEQVRqwzBqgKXAdWdxrkKIC8jG4o0U1BcwPW06hs9H7bx5uCZOxBIfj2EY5BcXYfIFuOzaUW3mG34db5HCbt2FShndybMX30RmYioQIP1Ifrvy08bE01zvo3B/NQMHDiQQDKKiu7B/zQoAQq+8AnNcHDWzZrXKu73v7SgUsw7MamtYIYQ4LWez0E4EDn/l70eOPTtjuUqp7ymltiqltlZUVLR7okKIC8usrFlE2aOY1G0SDcuXE6ysJPLYS5AH9+zCb7cTpiIJCbe1me/JqsLQzTi6GaBZOnPq4hsqiB6MRdtPbLWlXcdHuvWPxhFq4cC6ElJTU3G5XJiTu5OzeT0+jxtlsRBx++00rVmLr7CwJa9rSFcmdZvE/Jz5NPnbsZsuhBBfcTb7aLd1l/E3/Wn5jXINw3gNeA1g+PDh8pq4EJeAw/WHWXV4Fd8d8F2smpWSme9jSUwkZOzRIyCrv1gKwNARE085xoHPdtAVPytssWS2oweyOPsMZaYurBZnzUAiGjzUhrV91v5UNLOJtNHx7PzyMO76oz21N27ciDMQ5ODmDWRMuJKIadOo/Ne/qPngA7o8/nhL7j0Z9/B5/ucsPLiQu9LvOtNLE0JcQs7mjvYRIPkrf08CijshVwhxEZuZORPNpDE9bTqerCyat2wh8q67UJpGMBiguKYazR1g6OX928w3/DoxNWZs2ibyosd38uzF6chOTAL0dh8fyRifgKEbHFhfwuDBgzEMA3NCt5bjI5YucYRefTV1c+ehezwteQNjBzIwdiDvZ76Pbpx4slEIIb65s1lobwF6K6W6K6WswHTgm94E8AUwSSkVeewlyEnHngkhLmH1vnrm58xncupk4pxx1Lz3HspuJ2LqFAB2rl1D0Goj0twVV+Spj42YDAt14TX4zK7OnL44TXmxQzGbsuha2b5fWEbEOUlKi2T/2mJiY+NISEggGN2Fgj27aKypBo6+FBmsq6P+88Wtcu9Jv4eC+gLWFq3t8DqEEJeus1ZoG4YRAH7E0QL5ADDHMIx9SqmnlVI3ASilRiiljgDTgFeVUvuO5VYDz3C0WN8CPH3smRDiEjY/ez7ugJt7M+4lUFND3cefEH7TTWgREQBsWLMGdJ3hY9q+ch3AvTkbE/VkJUjv7PNd0GSjKbQCsz8aV7OvXWNkjEugodrD4f3VDBkyhEavj6DNTubalQA4R43E2rMnNTNntrqo5upuVxPnjGPG/hlnYilCiEvUWe2jbRjGZ4Zh9DEMo6dhGP977Nn/GIbx8bE/bzEMI8kwjBDDMKINw+j3ldy3DMPodezr7bM5TyHE+S+gB5iZOZPhXYaTHp1O7dy5GF4vkXffDUBDbQ2VXj+WJuh3WdtXqht+HXeuD5u2mbzoCZ05fdFOuQlxAKQfzm9Xfo/BsThCLexbU0T//v0xm81YUnq2HB9RShF17z149u7FvWNHS57FZOHOtDvZWLKRnJqcDq9DCHFpkpshhRAXhGUFyyhtKuW+jPswAgFqZs3COWoU9r59AFj56SegaXQNTSM0yt7mGJ7sKoygmbqwajk2coHI7jICs8olocLbrnzNbCL9snjy91QR9JhIS0uj2eqkvDCfisJ8AMJvuglTeDjV77zbKve23rfhMDt4d/+7bYwshBBfTwptIcQFYcb+GaSEpnB58uU0LF9OoLiEyHvubvn+vgNZKJ+fkVec+gVH96ZsFA1kJ8qxkQuFX3PicRVh9cbhcAfaNUbGuP+8FFnMkCFD8AeDBMOi2bviaIcak9NJ5O3TaFi6FH/R8YtqIuwR3NLrFhblLaKiWVrICiFOnxTaQojz3s7yneyu3M3d6XdjUiZqZryHJSGB0CuuACBv/148Zgv2Zie9h3dtc4yjx0a8OMybyZVjIxeUvPijFwP3O7YDfbrCY50kpx99KbJbSjfCw8OxJHdn/5oVBPx+ACLvuguUonpmq4uKuTf9XnRDZ+aBmR1agxDi0iSFthDivPf23rcJs4ZxS69bcO/dd7Sl3913ocxHrwJYueQLMAx6dhuDPaTtC2iOHhux4EhqkmMjF5j98WOwmLJILvO3e4z+lyfRWOMlf3c1gwYNoj5o0Ox2k7t1IwCW+HhCJ11D7Ycfojcdv6gmOSyZq1KuYk72HLnARghx2qTQFkKc1/Lq8lhxeAV3pt2J0+Kk+u23MYWEEHH77QAE/H6OVNVgbvYzaHy/U47TvOFotxH7iKGdNXVxhvjNITSGlWD2RxFR7/n6hDakDowhLMbO7hWHGTJkCAAqoRt7li9piYm67z70hgZqFyxolftAvwdo8DUwP2d++xchhLgkSaEthDivvbvvXayalTvT7sRfXEz94sVETJuGFhoKwLqli9E1M05fPN0HxrQ5ht7sx53rx2Fei8qY3JnTF2fIgaQUIEj/gkPtyjeZFAMmJlFysI5Ag5levXrhD48mf89O6ivKAXAMHox94EBq3p2BoR+/qGZg7ECGxg1lxv4ZBPT2nRMXQlyapNAWQpy3Kpor+Dj3Y27pdQvRjmiq3z3a0zjqvntbYrZt24YKBOg/cAJmq9bmOM07S8HQCOnpBUdEp8xdnFm5sSOwaLvpUmUBo30X2KRfFo/ZprF7xWGGDx+ONxAk4Ipg78plwNFWf9EP3I+voIDG5ctb5T7Q7wFKmkpYkr+kraGFEKJNUmgLIc5b7x14j6AR5L6M+wjW11M7Zw5h11+PJSEBgCOH8qgPGFgbzKSPST7lOM0bDmJWBVjGTOqsqYszTDeZqYqsxxQMJb6qvl1j2JwW0kZ3JXtLGUldUwkLC0NL7sHelUvR9SAAoZMmYUlKour1N1pdYHN58uWkhqXy9r63Wz0XQoj/RgptIcR5qdHXyJysOVydcjUpYSlHX1Jrbib62w+0xCz79BMAIrWBJPZte6faX9GMr8JMiGMTqvepb4wU57893dJRNNOvsLDdYwy8Igk9YJC5voRhw4bRZCjq6uop3LMLAGU2E/XtB3Dv2oV727aWPJMy8WD/B8mszmRd8boOr0UIcWmQQlsIcV6amz2XRn8jD/Z/EMPno/rdGTjHjMaekQGAz+uloKwSc5OPgeMGYtLa/nHWvLEACOIcHAta2x1JxIWhOLw/mmUXEbVhmILt21WO7BpCSkYUe1YVMWjgYJRS6F0S2f3l4paYiClT0CIjqXrjzVa5N/a4kS7OLry++/UOrUMIcemQQlsIcd7xBr28s/8dRnUdRb+YftR9+hmBsjKiv/3tlpiVny3C0DQcTQmkXxbf5jiGbtC8vQSbaQfayCmdNX1xtihFSayOMux0L23/BTIDr0ymuc5HWVYzaWlpBMJjyNm6iYbqSgBMDgeR99xN48qVeLKzW/IsmoUH+j3A9vLtbC/b3uHlCCEuflJoCyHOOx/lfESlu5KHBj6EEQxS9dpr2NLSCBl//NbHHbt3Y/J56ZY6ksiuIW2O482rJei2EBKVA137d9b0xVm0s9swNCpIK2x/oZ3SL4qohBB2LC1k+PDh+HUdvyuC3cu+aImJvOsulMNB9Vtvt8qd0nsKkbZI3tjzRrs/Xwhx6ZBCWwhxXvHrft7a+xaDYgcxsutIGpYuxXfoEDHf/x5KKQCy9u7BbShsDQ4yxv2XlyDXZaNoxDF6QGdNX5xltc5uBJ17cDZH4Wxu3wU2SimGTEqhqqgJU1MY0dHRqMTu7P5yMcHA0THNkZFETLuNukWL8JeUtOQ6LU7uybiHNUVryKzOPCNrEkJcvKTQFkKcVxblLqKkqYTvDfweAJWvvIq1e3dCJx3vGLJyyRegB3ExkF7D4tocR2/205zlxmleixp8W6fMXXSOzOQYwGDwofb11AboPaILrkgbO5ceZtSoUbgNaPD6yNm8oSUm+v77Aag6YVd7etp0QiwhvLmn9RluIYQ4kRTaQojzRlAP8saeN0iPSmd84ngaV63Cm5lJ9EMPobSjPbLramspqavH2hggfURfrHZzm2M1bSkGXSOkRx24YjtzGeIs258wAYu2i/hyM0pv30uRmmZi8NUpFOfUEh/eHbvdjhHfjZ1ffNoSY0lMJPzmm6idMwd/eXnL8zBrGHf0vYMlBUs4VNf+Yl8IcfGTQlsIcd74Iv8LChsKW3azq155FXNCPOHfurElZsmC+aBMOJq6kz42oc1xDN2gaW0eVrUP6/ibO2XuovMETTYqYuox6SGklla1e5z0sfHYnGb2LC9h6NChuG1ODh/M4a8frOSFpdm8sDSbj/tNIuj3s/QPf2159sLSbNwV41CY+dkXz5/BlQkhLjZSaAshzgu6ofP6ntfpGd6TK1OupHnzFtw7dxL9ne+gLEfb8vn9fjIP5WNpdhPbZQBde4S1OZb3YC2BBjOu8C3QU3pnX4y2dx+JRgUZBWXtHsNqNzNgYhKHdleS1mMASil80V0x9q9viXHHxlMybALJ6xZjbahtee7Qwkl3Xkeeey11geIOrUUIcfFq+3euQgjRyZYVLONg7UH+OP6PmJSJyn/9Cy0mhoipU1tiVn3xOUFlwtEQS7+rE1tejjxR48osTNTguGwwmGQ/4WJUFdKdoHMWzuaJOJv9NDtb90h/YWn2KTJPoIJYFCz4sABzdDJ+3cCWux1jxPUomxOAvGunkbB1FanLF5J98/0tqQNcN3OgeTE7G+ZyeeRPztjahBAXD/kXSAhxzgX1IC/vfJke4T24LvU6mjZtpnnjRmIe+i4mux0AXdfZum07mteNUxtI+ti2e2cHajx48nyEWJajht/dmcsQnexMvBSJXUPv4cJU2Iw1ojco8LtCIfP4S5FNXZIoHTKOlNWfYWk8fv27Q4s4tqu9Rna1hRBtkkJbCHHOLSlYQm5dLg8PfhiTMlHxjxcxx8URcccdLTG7tm7BY4C9IYS+I5Oxh7R9y2PTugLAIKSfBs6oTlqBOBf2J0zAqu0kodzS7psiAYJ9QkGB7bAFLTQaX2wS+r41GMFAS0zudbdj9nlIXbGwVe4A182YlJldDfPa/flCiIuXFNpCiHPqP7vZvSJ6ManbJJo3bMC9dRvR3/9ey242wKrly1F+H3bfIAZOTGpzLCOg07SlGLtpE+bxd3XWEsQ5EjTZKI2rQ+lO+h5p/1ltHMd3tW1RfTA0jYCmQd7OlpDG+BRKhoyl26pFWBrqvpIaQZrzWnLdq2VXWwhxEim0hRDn1GeHPiO/Pp9HBj+CQlHx4j8wd+1KxLRpLTEFhw5R6/HibFbE90wkNiW0zbGad5Sje8244rIgcVhnLUGcQ5t7XIFZ5ZFWUA/GGdjVLgnDZA/F1yUZfc9KjK+MefD6O9F8Pnosbb17/Z9d7Z0NH7b784UQFycptIUQ50xAD/DKrlfoG9mXq1KuomnNGtw7dxLzgx9gslpb4hZ/vACCAayNaQw41W62btDwZTYWlYttwhWdtQRxjjXYu9IYkYPZH05Sed3XJ5zKsV1t7bAbW2wausVK0NsIxTktIU1dkykeMZGUNZ9hq6lsee7UIskIuZ5c9xqq/QUdWY4Q4iIjhbYQ4pz5JPcTChsKW+1mWxITiZhya0tMaUkJJdW1hDT7CY1IpdfQtm+C9GRWE6hVhLqWoQbITZCXki09h2OiikF5HTu68Z9dbXtZJMrqwBeXhLFnZauYg5OnowyDnl/MafV8oOsWLMrB9oZZHZqDEOLiIoW2EOKc8Aa9vLzrZQbEDOCK5CtoWLIUz969xDzyCOoru9mffTQPDB1rQ28yxiagWdr+sdWwNAtNleIYNxQs9jZjxMWpNDwDPWQbzuZoIho87R+oZVfbgy26L0G7k0DVEYya0pYQd0wXDl82iaQNy3BUlLQ8t5lCGeC6mULPFsp937C1oBDioieFthDinJidOZvSplJ+OvSnEAhQ8de/Yuvdi/Bbjt/kWFlZQWFZBU63B7PWg34TEtscy5tfh68kSKjtc9TIBztrCeI8srN7KgoPw3I6diV6sG8oaApnaQzKbMUXm4Cx68tWMXnXTsPQNHp9PrvV834hN2A3hbGt/v0OzUEIcfGQQlsI0ekafA28vud1xiaMZWT8SGrnzsVXUEDsY4+hNK0l7vMFC8AwsDf2oM+ILoRGtb1T3bA0GxN1OEclgSOis5YhziMH4y5Ds24gqiYMuyfw9QmnYtcI9glFK/Fhi+hNMCSM4JEDGPXHz2R7w6MouPxGErauwlWU3/LcYnIwyDWVEt8eir27O7AaIcTFQgptIUSne3vv29R563h06KPoTU1UvPQyjuHDcE2c2BJTW1tL7uEjONxNmPQ+DL4mpc2x/KVNeHI9uCyfYRr7g05agTjfGEojOyUEgGE5+R0aS+/twrCZcJTGgMmMLzbx5F3ta6bid4SQtuDtVs/7hkwiRItha/1MDEPv0DyEEBc+KbSFEJ2qormCGftnMLn7ZNKj06n6978JVlbS5Re/aHWl+uKFCwADl7sbyelRxCa33dKvYdlBFB5CBtogLKGTViHORzuTrsFqWUd8uQO7N9j+gcwmgulhmCsN7OG9CbjCCRbuw2isaQkJOF3kXncHMZk7iTmw/XiqsjI0dDqV/oMc8qzvyHKEEBcBKbSFEJ3qlV2vENAD/HjwjwlUVVH95luETpqEY/Dglpiamhoy8w5hb2rE8KczZNIpdrPLmmjeW4dL+wRtouxmX+qCmp3sFDMKxbDs/A6NpXcPwQgx4yiKRWkWvDHxGLuXt4opHD+Zppiu9P3o36AfL+x7OiYQZU5la/1Mgoa/Q/MQQlzYpNAWQnSa3Npc5uXMY1rfaSSHJVPx4j/QfT5if/bTVnGfLvgIDIPwQCoxyeEkp7d9lXr95zko3LgymiG2b2csQZzntidfj9WylvhyW8d2tU2KYP8wzPVgd/Ym6AonWLAHo7m+JcQwW8i+6T5CSwpI3LT8K6kaI8LvozFYzv6mzzuyHCHEBU4KbSFEp3l+6/M4zU4eHvQwngMHqJ0zh6i778LWvXtLTEVFBQfzC3A2NxB0pzHkmpRWR0r+w3ekAXdmAy5tAdo1P+/MZYjzWFCzk51sRmHq+K52ogM9yoqzMBqlWfFGd8XYvaJVTNngy6jp3pfei2aied0tzxNtg0i0DWFXw1y8ekOH5iGEuHBJoS2E6BTritaxtmgt3x/0fSJsEZQ9+xxaeDgxjzzSKm7RR/NB14nWehMaFUKv4W1fUFP/eQ4mGggdqCAuvTOWIC4QO1Kux2pe1/FdbaUIDo5AeRUOc0+CIWEE8ndhNNW2ism69UHs9TV0Xza/VfrIsPvwG252Nsxt/xyEEBc0KbSFEGddQA/w/NbnSQ5N5s60O2n4YgnNW7YQ+9NH0cLDW+KKi4spKC4hxN2Au7Y3Q6/thqad/GPKW1CPJ7cJl3k+pqtlN1u0FtDs5KRox3a1O3YluhFpRU914iiMRmk2vDFd0XcsbRVT2z2N4uET6L7sIxyVxy+3ibSk0Nt5BQeaFlMfKDlxaCHEJUAKbSHEWTc/Zz4Haw/y82E/x+zXKf/zn7H17UvEtGmt4hZ9NB8CAWLMGbginWSMPbmLiGEY1H+ahUnV4BoaAtE9O2sZ4gKyPeV6rJZVJJTbcTV17IXEYL9wlKbhDPZEd7gIFGe16qsNkHXzAxgmjbT5b7Z6PjT0TkzKzKa6f3doDkKIC5MU2kKIs6rB18BLO19ieJfhXJlyJdVvv42/uJguTzzR6nKa3IMHKa6oJNTTSFNND4Zd163N69Y9+6vxFnoIM3+I6UrZzRZtC2h29nV3ofAy+kB+xwazawTTw3CURmMyh+CNTUTftrhViDcimtzJd9Blz2Zi9h9v9+fUIhnsmsZh71aOeHZ0bB5CiAuOFNpCiLPqpZ0vUeOp4fERj+MvKqLy1dcInTSJkNGjWmJ0XefjBR+hfF6iLQNxRdhJHxt/0lhGQKf2kyzM6jAhI7tCRNtt/4QA2JU0Gc2+nMi6MGKr3V+f8F/ovVzgsuCq74lhteGvLsKoLm4Vkz/xWzTFJZA+73VU4Pguej/XDYRp8Wyqf0va/QlxiZFCWwhx1mRVZzErcxbT+kwjPSqd0meeQZlMdPnNE63itm7ZQl1jE9F+L43V3Rh6XSpmi3bSeI3rignWBolwzERd+avOWoa4QBnKzOa+aZioYlTmETCM9g9mUgSGRGCtD8dsisQbG4++tXXrPsNs4cDUhwgpLyZ1xcctzzVlYVT4t6kLFEu7PyEuMVJoCyHOCsMweHbTs4RZw/jJ0J/QsHQpTatWE/PjH2Pp2rUlzuPxsGzpErTmBiIcY3BF2MgYd/JudrDBR/2yPOymzdivngwhMZ25HHGByo2+jEDYGhzuMFJL6jo0lhFnR08JwVXRHTQzXk8dRkluq5jKjKGUDRhFz8Uf4Kgqa3mebB9Gkm0oOxvm0BysOXFoIcRFSgptIcRZ8emhT9levp1Hhz6Ky2+m7H+fxZaWRtS997SKW7n8S3yBIIlWB3Xl0afcza7/4hCGXyc8ZhmM/F5nLUNc6JRiddpEzOoQQ3Kq0IId2NUGggPDMaswrMEu+KPiCG5ZhGHorWIOTHsIlCJjzqutdtFHhX+boOFnS/2MDs1BCHHhkEJbCHHGNfoa+cvWv9A/uj9Tek+h8p//JFBeTvyTf0CZzS1xNTU1bNq8BUtdFRZjNGExdvqNP7nTiO9IA01by3Bpn2C54VEwWztzOeICVxHal7qYXZgDLgbnFH99wn9j0wgODMdVlQpKw2OzYmRvbRXiiYwl58Z7iN2/ja7b17Y8Dzcn0N91E7nuVWwu2dyxeQghLghSaAshzriXdr5ElbuK347+Ld79B6ieMYOI22/HMXhwq7hPFi7A0IOkRiZSX+Vk9C090cytfywZQYOauZmYVB1hvY5An2s7cyniIrEybSo28yq6FylCGzv2QqKe4kRFh+Fs7E7QFY5//yoMv7dVTMGE66lL6UX6vNcxNze2PB8cehuhWhee2fgMvqCvQ/MQQpz/pNAWQpxReyv38n7m+9ze93b6hfWh5De/xRwVRdxjP2sVl5mZSV5+AY7aCnwNQ4jrFkqvoSffAtm4rgh/qYcIy6uYbngS2riOXYiv02yNYX8PCyaaGbe3sGMvRipFYGgkDnciJt2GNyIGfdeXrWNMGnun/xBLUwN9F/y75bFZ2RgT/hD59fm8tfet9s9BCHFBkEJbCHHG+HU/T65/khh7DI8OfZTK11/Hm5VF16eebHUDpM/n45OFCzF53fROGUFzg4XLpvRCmVoX0YEqN/Vf5GE3bcQxfoRctS46ZEfSTSjXF7iaXPQorv36hP/GZUbvH0loTV8Mqw1fcSZGY+uXHBuSe1BwxU0kb1hKVNbuludJ9iFcm3otr+9+nYL6jt1cKYQ4v0mhLYQ4Y2bsn0FWTRa/GfUbLIdKqHzlVcJuvJHQK69sFbdyxQqa3G5i/W4qj/Sg24BoEvtGtooxDIOa+dmge4iI/Qw18ZeduRRxETKUxoqMK7GoTIZk12Px61+f9F/oPUMwR3TB6o7EFxlHcNPCk2Jyrr+LptgEBrz/DzRPc8vzX434FVbNyjMbn8HoyO66EOK8JoW2EOKMOFx/mH/t/BdXpVzFlYmXU/Kb36CFhtLlt79pFVdeXs6GDRsw11aS3O06Al6DMbecfI16845yvLn1hGtvY775SbA4Omkl4mJWHppOWXwOJt3MmH2HOzaYUgSGRRLi7guY8AQ96AX7WoXoVht77vkJ9poK0r5yhCTWGcujQx9lU8kmFhxc0LF5CCHOW1JoCyE6zDAMnt74NGaTmSdGPkHVG2/i2bePrv/ze8yRx3eqdV1n4UfzIRigd1xXCvfb6T8hkehEV6vxArUeahdkYzXtJ2RYFPS4vLOXJC5ia3pPx2ZbRJcqO8mlDR0bLMQM/eMIaehJMCQM3+5lGIHWLznW9kgn/8qbSV73BdEHjl/Dfnvf2xneZTj/t+X/KGsqO3FkIcRFQAptIUSHfZj9IRtLNvKzYT8j7FAlFf/8J6GTryP02tYdQjZv3kxRSSnO6jKUMRZbiIWRN/VoFWPoBtWzM8HvJSr0bdS1z3TmUsQlwGd2sabfYCwqh5EHqrH5OniEJDUEW3gPzF473vBIgtu/OCkm54a7aeyaRP/3/9HShcSkTDx12VP4db8cIRHiIiWFthCiQ440HOH5rc8zOn40U5NvpPjxxzFHRxP/hz+gvtIhpLq6mqVLlqA11tEvYxwVhw3G3NoTe4il1XgNq4/gy28gQnsZ8y2/A2dUZy9JXAIKIkdRHr8Pk25i7J6OdyEJDo8i1DsQlIa3+jB6dUmrEN1iZfc9P8VWX0P63NdbnqeEpfDjIT9m1ZFVLMpb1P45CCHOS1JoCyHaTTd0/mf9/2BSJp6+7Gkqnn8e36FDJPzxObSIiONxus5H8+ehB/wkWzVK8rsTlxpG+pjWV637ihqpX5KPw7QW54hESL+xs5ckLiGr+tyL1f4J0bUOehTXd2wwm4YxPAlnQzKB0Aj8Wz4+6cbI+m69yb32DhK3rKTuk09ant+dfjeDYgfxx81/pNJd2bF5CCHOK1JoCyHabXbmbLaUbuGXI35J6LZsat6fRdQDDxAyZkyruK1bt3L4SBH2iiISetyMpzHA5Xf2adXOT/cGqZ61HxO1RMZ+grruuc5ejrjE+DUnK/pfhlXtZWhWPa6mQIfGM2Lt2FIGoHk1vHY7+p6VJ8XkXTuNmh7plD75FL7DR1/G1EwaT499Gm/Qy+/X/V6OkAhxEZFCWwjRLgX1Bfxt+98YlziOG8PGUvyb32Lr04fYn/20VVxVVRVLvvgCrbGOIUPHc3Cbn37jEojrFtYSYxgGNfOyCVS6iTI/j+m2v4PNdeJHCnHGFYcPpqBbIZrhZuLOI5iCHSty9YwIXGoEhsmMuzQbvba81fcNTWPX/Y+ByUTRL36B4T96S2WP8B48Nuwx1hatZU7WnA7NQQhx/jirhbZS6jqlVJZS6qBS6tdtfM9IDosAACAASURBVN+mlPrg2Pc3KaVSjz1PVUq5lVI7j329cjbnKYQ4Pf6gn1+tPtoH+A8jfkfJLx5Hb24m8a9/wWSztcQFAgHmfvghQb+PBFOQyuK+OMNtjJnSq9V4jeuKce+uJMz8LvYrJ0PyiM5ekriEretxL3r4AhweB6P2F3dsMJOC0d0Iae52tAvJpgUYeusjJJ6oOOKffgrPrt1U/POllud3pt3J2ISxPL/1eQ7VHerYPIQQ54WzVmgrpTTgJWAykAHcqZTKOCHsO0CNYRi9gBeAP33le7mGYQw+9vWDszVPIcTp++fOf7Kvah9PjXkK07/n0rxlC13/8D/YerUuoFesWEFJaSmO0gJSM6ZQU+Jh4t19sTnMLTHe/DrqPsvDrm0mtHcVTHi8s5cjLnGGMvPZgAewWz4mqdxMakfPazs0zEOHY3Zb8DqdBE+8nh0ImzyZ8KlTqHrtNRrXrAFAKcXTY5/GbrbzxJon8Ov+js1DCHHOnc0d7ZHAQcMw8gzD8AGzgZtPiLkZeOfYn+cCV6mvtikQQpx3NpVs4u29b3Nbn9sYddhO1SuvEj5lChG33NIqLi8vj3Xr1mGpqWDE2GvI3mLQZ2QXUgfEtMQE631UvbcfsyonKvJ91G1vgknr7CUJQZMtjtX9+2NVexmeWU94QweL3Fg7zqTLUQa4qwvQq07eKe/6u99h69OH4l88jr+oCIA4Zxx/GPMH9lXt4187/9WxOQghzrmzWWgnAl+9duvIsWdtxhiGEQDqgOhj3+uulNqhlFqllBp/FucphPiGaj21/GbNb0gNT+VnyfdT/MtfYuvVk66//12ruKamJubNm4fm85IaHkJFUR9sTjPjbu/dEqP7glS+sw+jyU205X8xTX8NQqJP/EghOk1B1BjyuxViNmq5YnsxNm+wQ+OpvrE4tQx0uxPP1k8wAq2Ld5PDQdLf/4YRDHLk0Z+i+45edHN1t6uZ0nsKb+x5g/VF6zs0ByHEuXU2C+22dqZPfMvkVDElQIphGEOAx4D3lVJhJwYqpb6nlNqqlNpaUVHR4QkLIU5NN3R+t+53VHur+dPIZ6h67FcYHg+Jf/sbJsfx69F1Xeejjz6iubERV2URyRm3U1HYxPg7+uBwWYFjl9J8kIW/qIEo83NYbngEkoadq6UJ0WJ9j/twx3yKNaC4YscRTHrH+mubRw3G5gnD7wrFv27+SSHW1FTin3sWz969lD13vNPOr0f+mp4RPXli7ROUN5eflCeEuDCczUL7CJD8lb8nASf+7qwlRillBsKBasMwvIZhVAEYhrENyAX6nPgBhmG8ZhjGcMMwhsfGxp6FJQgh/uPtvW+z6sgqfjHs54T/4wM8u3cT/6c/YuvZs1Xc6tWrOXjwINbSQkZNuoO9q+rpO6orvYd3aYmp+/wQnn1VhJtfwzEiHYZ/p7OXI0SbDKXxWb8fYXa9T2iTnTF7Szp2mY3ZhG3MtWhecCsfgf2bTwoJu+Yaoh58kNpZs6mdd7QYd5gd/OXyv+AOuPn1ml8T1Du2uy6EODfOZqG9BeitlOqulLIC04GPT4j5GLj/2J9vA5YbhmEopWKPvUyJUqoH0BvIO4tzFUL8F1tKt/Dijhe5LvU6Jm+Huo8+IuaRRwi75ppWcdnZ2axcuRJzXSX909I5uCOC0Gg7E6Yf///kxg3FNK4pwmVeRGifBrjhLyCvZojziN8cwqJB9+KwziOhQqN/Xk2HxlNOC86Ma4+e1y7di15z8m9g4x77GSGXjaH0ySdp3r4dgB4RPfjtqN+ypXQL/9ol57WFuBCdtUL72JnrHwFfAAeAOYZh7FNKPa2UuulY2JtAtFLqIEePiPynBeAEYLdSahdHX5L8gWEY1WdrrkKIU6toruDxVY+TEprCry03UvbcH3FdeSUxP/phq7iamhrmz5uH2echwaRjdU2iscbHNQ/2w3qsy0jzznJqF+ZiN28jvOsqmPYOaJa2PlaIc6rensiXA0bj0FaQnu+n5+GOdSIxdY3GET0I3WrDvfljvB5Pq+8rs5nEv/4Vc0I8R378k5aXI2/udTM397yZ13a/xuojqzs0ByFE51MXyw1Uw4cPN7Zu3XqupyHERcWv+3loyUPsr9rPzH5/Rv/+rzHHxJD6wWw01/ELZbxeL2+99RYVpaWEHs5hwrTH2biwhhE3dmfkjd0BcO+vomrGfqzmbGJd/4f63mKITD0n63phafY5+Vxx4elTvoSR+3x49GFs6B9GUZeQDo3nWf85XqOGCJ+JR//f71vdjgrgzcsj/47pWBITSZ35HqaQENwBN/d9fh9FDUXMunEW3cK6dWgOQoiOU0ptMwxj+NfFyc2QQohT+tPmP7GtbBtPZjyGevxZlKaR/OorrYpsXdeZP38+ZWVl2A7nMH7KA2z9vI74XuEMn3y0IPAcrKFq5gEs5gJi7M+h7nn/nBXZQpyO7LhJ7OvtxqayGbO3lthqb4fGs425DrPfRK1V550/vXrSdeu2Hj1I/Otf8GZnU/TLX2EEgzjMDv52xd/QTBqPLn+UJn9Th+YghOg8sqMthGjTh9kf8vSGp/lO73u55cUdeA4coNu77+AYNKhV3JIlS1i/fj220kKGDxtB2eHB+D1Bbv/tCELCbXhya6n69z40iomz/hrTve9C6thztKqjZEdbnK6xeW+Qkj8QH4msGhJDZaTt65NOQfd7aFo7B92sCLX2wzRq6EkxKas+JWPuaxSMn8yBad8HpSj27mZJ9TNclXIVf7n8L8i1E0KcO7KjLYRot62lW3l247OMix/L1DnFuHfuJOHPfz6pyN62bRvr16/HWltBr6QEfL7R1Fd6uPah/keL7OwaKt/ei6bKiDX/GtMd/zrnRbYQ7bGu+3eoSFiPlRIu31FJTE37d7ZNFjvOoZNRukGjew/G7pPf9S+8/AbyrrqVbms+p/uyo51IEmwDeWzYYywtWMoru15p9+cLITqP+etDhBCXkqLGIh5b+RhJrkSeWBdD4+J5xD3+OGHXTmoVd/DgQT799FOsnia6ECB5wF1s+7yUsbf1IqF3BO59VVS9fwCLuYQY9XO0qS9An0mn+NT2kZ1p0WmUYlnfn3ANfyOmeAKX76BDO9taeCzOHiNoyt9KY+VqQvc7IaNrq5jsm+7DUVNJ34/fxRMRTcmIidyXcR/ZNdm8vOtlUsJSuKHHDWdidUKIs0R2tIUQLeq8dTy87GECRoC/5I6i6YN5RH3nQaK/82CruCNHjvDBBx+g+b24Ko4w/Fs/YtviUnoNi2PQVck07yynauZ+LObDxJp+jjbt79B/6jlalRBniDKxtO9PqUxY3bKz3aXS8/V5p2BOTscZ1xvdaqGx6AvIPqGNoMnE7nsepap3fwa89yIx+7ailOLJMU8yvMtwfr/u92wv297BRQkhziYptIUQAPiCPh5d8ShHGo7wcvUNBN98n/CpU4j7xS9axVVUVDBz5kzw+7DnZzLxrp+wcWEVcSmhXHFfGg2rjlA9OwurJY9Y0y+PHhfpd+s5WpUQZ9h/iu3E1dgoZPyuapJLmts9nKXvaBzOaIJOC82HPkNlt24jaFgs7HjoNzQkpjLkzT/RtGkzFs3CCxNfIMGVwKMrHqWwvrCjqxJCnCVSaAsh0A2d3679LdvKtvH35pux/eM9Qq+5mvinnmr1wlVtbS0zZszA73FjPbiXK+/+PtuXBLG7LEz+wQCaPs+nfnE+Dsc2YrUnMN35JqTJr7bFRUaZWNrnpxzpth2b2s/o/Q30Lmxo93DWoddhUxb8LkVz3ueY9tW2uo0y4Ahh6yNP0hzdhSMPP4x71y4i7BG8dNVLGBg8vOxhqtxVZ2JlQogzTAptIS5xhmHw161/ZXH+Yp5tuIaYv31AyGWXkfD88yjz8dc46uvreffdd2lqaMCau48Jt91N9rYIfN4gNzzUH8/CXJo2lhDq+Iwo6wuo+z4842eyhThvKBOre/6QrF5l2E0bGJzTzOCs6nZf124bPRVrIIgvNEjz4S8x7WpdbPtdYWz90VNo0dEUPvS9o12Awrrxzyv/SXlzOQ8ve5hGX+OZWp0Q4gyR9n5CXOJe3/06L+54kccrRzLijQ2EjBlN0ssvY7LbW2Lq6+v597//TV1tDbbc/Yy8+lpqKwdTnFPHDXf3xbaxmEBlMxG2t3CFbeHdns9T5ex5WvP42TV9vj7oBPIypDgfZJR+zIjMKpqD11MeZbBuQBwB8+nvY+kBP541M/FbrdjqnTijx6MPj4KvXGrzw/QQCu69F725mZQ338TRvx+rj6zmJ8t/wrAuw3j56pexae1vPSiE+GakvZ8Q4mvNypzFizte5Mflgxjx5kaco0aR9NJLpy6y8/YzaPRlNDUMoyirlkmTkjEvyUevayDW8ltcXXLgu0tPu8gW4kK2v+tNrByUhsv6JnHVOpM2F+N0B097HJPZgn3cnVg8brxhzbir1qGtrQCf3hJjTUqk24x30UJCKPz2t3Hv3s2EpAk8M/YZNpdu5ok1TxDQA2dyeUKIDpBCW4hL1Ce5n/Dspmf54aFejH9rO84RI0j+18uYHI6WmNra2mNFdi22vP0MGDEKk/0qCnZXMWloDPZNJZi1CuLUQ9gyUuDBxRCWcA5XJcS5URA5hoXD7sQe8g9cbh/XbiqlS9Xp99o2WWxHi+3mJjxhjTQ1b0BbVQbNx4tna1LS0WI7IoLCB79D844dfKvnt/jliF+ytGApv1v3O4L66Rf6QogzTwptIS5Biw8t5vfrfs+P9iZw+exMXJdfTvKrr7QqsisqKnjrrbeor6vFlreP/sNG4IiczOHN5VyXEoIjrw6nazuxwYcwT/wO3D4DbKHncFVCnFvVzu58OOxX6FFvYNePMH5nDf1z60773LbJ5jxWbDfiDW2ikU2YV5SivlK4WxKP7WxHR1H4ne/SuHYd92bcy0+G/IRP8z7lD+v/gG7o/+VThBCdQQptIS4xn+Z9yq9W/5Ifb45iwieFhF1/PUn/eLHVcZGioiLefvttPE1NWHP2kDFkGI7I66neUMpVUVZszV6iQl4iSv0Z0x1vwBVPgEl+nAjhsUQwf9DTHEndSohpOen5Hq7YXobNe3o7zCaHC/v4e7A2NeJzNlNv34y2uoz964pbYixdu5L63ntYU1I4/PDD1H/2GQ8NfIhHBj3CwtyFPL3haSm2hTjH5F9GIS4hi/IW8ftVT/DbZeFctqyEiNtvJ+H//oyyWFpicnNzeeeddwh6vViydjLosnGEhE3Gvr2CkSFm7I5aupi+hzP2CHxvFWTcdA5XJMT5RzeZWdPjh6wdkIDL+hoxtX4mbywlvuL0Lrcx2Z3YJ96LrakRv91DbeQmvnxvD6tnZxMMHi2gzbGxdJvxLs5Bgyj6+S+onjmTHwz6AQ8NeIh5OfN4cv2TcoxEiHNIrmAX4hKx4OAC/rjif/jfT5ykZlcS8+MfEfPII636ZG/bto1FixZhNylMmTsYce2N2OqGEptXg81qIjTiS8LcL6JGfRsm/S9Y7P/lE4W4tOXGTKRyeE8m738RS92tjNttJj++ie19Igl+w64kyurAdsX9qJXv4bHrNHTZzI7VASoKG5j03X6ERtnRQkNJfuN1ih77OWXP/D/8xcX86LHHMCkTr+5+FXfAzbPjn8Visnz9BwohzigptIW4BLyz7x3eWv5//PkjO7HlTcQ/+ywRU47f1qjrOl9++SXr1q3DpQzYv4Nx37qXkJwkIt2N+G0BumhPYDVVwB3/ll1sIb6hOkcyc4Y8x6jCN0nL30lqya10rSphU78YyqO+WRs+ZbFhvfJ+1JrZuA0vetxWiksH88H/NnH1AxmkDojBZLeT9OLfKXv2OarffAt/YSGP/OlPOC1OXtj2Au6Am79M/Iu0/hOik0kfbSEuYoZh8Pftf2fN4jf4zUKNkKCFpL//Hde4sS0xHo+HBQsWkJmZSaivGZWfzeSrfogjx44yDHyObHoZv0KlXQvf+hu44r72c6W/tRAnS6jfxdX738XffDcBI5GCrmZ29InEb/lmu9uGodO3cAVb8wrAaieOdPTSWAZfk8Lom3qgWUwYhkHNjBmUPfdH7BkZJL38MvNrVvD/Nv0/RnQdwd+u+Bth1rBv9Hnt+e+4Pf3whbgQSR9tIS5xft3PUxueIm/m6zw1yyA8vAvdP5jdqsiuqKjgjTfeICsrC1ddBfFVfqb2/wWhOXZqAjpB06v0tj+NuuVFmD7zGxXZQoi2FYcNYtaIZyhOWYNL+5BupV5uWF9CtxL3N+pMopSJG777MNeOGYWpqZ4yDqD1zGfH0nw+/NNWqooaUUoRdd99JL30Et5Dhzh021S+1dyb58Y/x46yHdz/+f2UNpV2wmqFECCFthAXpQZfAz9e/DChL8/h4c90QkeMovuHc7D16tUSc+DAAV5//XUa6mqJKipiojaaCZFT8NXp7HQ3E2f5FT2G2eCHW2DI3fCVs9xCiPbxa05W9XqUxUMnYAt9Hmcwl5H767l6SxnhDf5vNMbob93K3Xfdhb2+itKmQvRe+6ivr2POc1vYuawQXTcIvfIKUmfNwuRwUnD/A4zd1MDLV71ESVMJd392N9k18lsnITqDFNpCXGSKG4v5/+3deZhdR2Hn/W9VneVufXtXa2ntlmxL3i0bG4MXwMY4PIEwZNgyeF5mkiFvCAl580Ayed68eck4AxPIMgkDLwQmIZOwhC1kgm0MOAFsbHnHlmxZq6WWWq3e+25nrXr/OKdbLblbloxaslr1eZ6jOkvdvucenb79u3Xr1Pn1v38Xd3ziQe541NB1552s+txnUR0dACRJwt13381XvvIVPK25/FCRt3S9hz5vDc8FCY/VX+A1Kz7Oivf9Ibz9C9DWd5ZfkWUtPoPVy/jSlo+xc8Meqt7/oKs2xa1bR9myfYzCSQwFuO7KLfzKb3+E7sY4Y1MjDLc9RHV9zANf28U3P/E4Y4MNChduZO0/fJXy9ddx+P/9KKv/8n/z1zd/Fgzcefed/Pjgj8/AK7Ws85vto21Zi8iTR57kc//fr/LvvzFJWRbo/6OPUX3jbTPbR0ZG+NrXvsbhw4dZmZS5KboCX/pMeXUeGi6wxH+S295s8G/6tZ9pRBHbR9uyTl4lPMxNuz/DkiMrqKd3kErBc6va2LG6/KLRSY7vAx006vzjp/6UHWM1dKHEhas2U9/WSxIarrljLVe+cRUSw8inPsXIpz+Dt34dhT/6PX7zhT/h+fHn+a2rf4v3bnrvMaMPTbN9tC1rfifbR9sGbctaBIwxfO2Zv2f/H/9X7tiaIjasZd1ffhpv9eqZ7Y8//jj33HMPMoHXBBtZa5YSlod4ZiRgIFzOlWu2c91/eiuys/9n3h8btC3r1K2YfIzX7v5feJOvoaVfQ+ykbFvTzu7+ElplQXiuIGuMYeu3v8737v8X4o4eujo66HevZfDpgK7lZW5610aWb+ik/sADHPrwR9CNBp2/89t8vO9R7tv/PX5+/c/z+9f//otGJLFB27LmZ4O2ZZ0nojTiU1//MBf/xb2sHobSO97Oyt/9vZk7PU5OTvKPX/8We/bvZVnawY3RJpr6AAX3UR4cvwOhFK9/ey9rb77mtO2TDdqW9TIZzYUj93Hd7rtJW7cT6iuJnJRt6zrYu7zIB2+/cN6HDjy3ja9/5lNMVDoRrseVm69j7PEK9bGQja/q49VvuwA/muLghz9M8ycPUXnDG/juO9byZ3v+J5u6N/HJmz5Jf9vRD9o2aFvW/GzQtqzzwAvje/nGXe/jdXcfxpSLrP34J2i/5XVANjb24/c/zHcf+AE6TdkSr8ObGKbgfZ8Wl/JccCtLlsEbP3A91e7iad0vG7Qt62ejdMTmoX/k6n0PErV+jshcSqJSnl/Zxq6VZUJv7kusTNgiffDrBOEUaVsHqtjO9atvZOePplCuZMub1nDpTcuZ+ru/ZfjP/gzZ3s7IB3+R306/DMBdN9zFLatuAWzQtqwTsUHbsha5+/71fxJ89JNccDAluO5SLv3kp3G6u9FhysADz3PPg9/nUDJCn66yfDSk1niISztHeUr/X9Sjdq5642quefNa1Eneoe5U2KBtWaeHSgMuO/wNrtj/MEnwOgJ9HVpo9i0rsmN1hXpp7vvO6Z2PEj/zA4LupQjH5dqrriPZv4T9T4/T1lXg+retp78yweDv/A7hc8/hvPk27rp2gMdaz3Pnpjv54FUf5FM/2HfK+2uDtnW+sEHbshapenOSf7rrl9n0radJfEXP736EVW//JZKhJhMPHuAnT23lSfbiCMWKWsz4wZ9yaccB1LJ3s23wCqq9Rd7w7zexbH37gu2jDdqWdXpJHXPR8D1ceeC7uPVraKSvw+Aw2OOwY1WVkQ73RUNwmsYE6QNfJ0yaJO3ddLa386orb2HfjyNGB+r0rKyw5faVVH74FcY+/3lUe5UH3rGJP6n+hIu6L2aT+lU63FO7ZsMGbet8YYO2ZS1Cj9/7v5i867+x9EjM0DVruea/fBaOONQfOcyOgV1sdXdRFwE9UYvWvh30OlNcsukyto2/lfqU5pIbV3D9L6zHK8zdCna62KBtWQvEGNaMP8iW/d+kOrGKevpzGCo0ipqd/VVeWFokmtWtxBjDz7Ud4Tt/90Wmqt0Yr8DGDRu4cPk1PPuDUSaHW/SuauPKK13U5+8ifOYZglddwkevO8TeUsA17e/l4tLtc45KMhcbtK3zhQ3alrWINA4f5IH/+1dZ+aOdjHcV6HjfR1jiXUawY4wRPcnDxV0M6nHa0jpmYD9uUOfqKzZSL7+T3c806VxW5pZfumhBW7Fns0HbshZeX207Vw98heXDCa3kNiJzEVpoDvb67F5RYbgza+X+0K0badWm+NGXv8ijTz5F1L0M6Thc96rr6Cts5Kf3HWJqJKBvTRsX+btRf/vHkCbcc30Pf7tliJ7yZdzQ8X6qztKX3CcbtK3zhQ3alrUI6Cjiyb/8L4gvfpNC+0bCa2+jp3gJRJqpSsQT/rPsbIzg6wB/eAA9NsHGi9fRedG/Y/sDDQxw9e2rueqNq/nv9+865ed/uX80bdC2rDOnFI2y6cg/s+nQI6jmZXm3kgrNQsqeZVVe/7aLcHtLAAzt2cU9n/8MB5ohSbUL3/e58aabqaYrefye/dTHQvpWFlk19GPK9/01U50V/vrmiK0XSq6uvoeLy29CCjXvvtigbZ0vbNC2rHOYMYbBb32L0S9+h0r5IsSyS3BUCVFQxP0Nnpz6CU9NJSiT0jY5SDw0wtIVy1l3/bvZ9bhDbTRg/VVLePW/WT8zosiZHEHABm3LOguMoX/qcS4Z/Db9R2JayeuIzGZA4va5lK7qp3h5D6rq8ewD/8r9X/sSY26ZtNJOpVzmxhtvptBcwlPfP8jUSIBwU/oP/itrd/wz+5c7/NUtLSbWbOTV7b9Mj7d+zl2wQds6X9igbVnnoGQsYOKfH2Xy4d14pdUIqYhUQNuFZRIeZ+uBx3gqyi5OqgbjxAcG6OzuZuOr38LBXX2MHWzQs7LCDW/fQP+Fncf8bBu0Lev84ceTXDx8DxuHfkJ1qodmehOxyX6fvf4CxSuX422o8tRP7uXH9/wz9bZudKlCW6XCjTfdRIfo555v7UaORAiRsvTwQ6zZey/PrKnx5Rs0lTW3c1X13fiyfMzz2qBtnS9s0Lasc4BuxgS7Jwl3jdN6ZgjdyH4fo/ohdlV3s3ZTi+LgD3mosYxtbEQAfnMKefAFpFdErL8Fp7kRNZZiSor0knZ0f/FFow+8XDZoW9a570NbfNj2TZIn7qc51EsrvZHYrAXA7XVwLuxm1+HH+dGj99Js70EXyxQLBXTvBvziGpy9MXKgiTCGrrHtrDj4Y3b1buOfXltmyfpfYmPpDTPdSWzQts4XNmhb1iuQDlOi/VOEuycJdo0TH6yDAaMj0qFnqU0+y/0rn2PD6gOsqld5iKs5wDJcCaWwSbJvJ9IvIda8Fqd5IWrCYIqKdGMbel0Z5OkJ2NNs0Lasc98xv8cjO+GZbxA/9QDBcBet9NqZ7iWiaJj0J9l68EEO+iFxpQpC4C5Zj9+9Ee+gQO6rI0KDG02x7PBDDBUf4ns3KJZv+I/0+1fxW7fNf+dKy1pMTjZoL+wYX5Z1nkunIsIXJon2TRHumyIerIMGpEBVEqIjDxA/+yCj0Qvcc61mxeaAy6MLeSL8tzyMQ9FzqY4OoQ/to7pkKStvfhfPPd+LOmQwRUlyRRt6TRnU6Q3YlmUtUj0b4OaP4N4M7vg+2p6/l3Tbpwn2xQThVdDawhvKd2Aw7Kvv52m5l+HDO4mHduF0Lse/4UKcZjtmj8N+71bgNt78yG5aW+9j61Vf54krfo8rl1x5tl+lZb1i2BZtyzpNdJQSH6oTDdSJBmpEB2qkowEAwpV4K9twuwKip+9l/Hv/hB6tMdQBd18jaN+wnPXqOnaN+8RJSpur0Pt3IyZGWLFxMx3LX83hvV00p2J01UFvbEOvLJ32Fuzj2RZtyzr3ndTvcTAFu3+Aee67RDv2ETTWEOgriPRGWiJhm9rPdjVALDWOU8RZtgG3YzX+CyHuzjESU0LqGK+5jdqaIW755Xdy+WobuK3Fy7ZoW9YC0lFKfLhBPNggOlAjHqgTH2lkrdWAqnq4/RUqlzh4cjvsu4eJ7z7C4A4DseTZlXDvzRWWrbqOvuYqpsYaPC8lxaCGe3AfnjCsuPg6DJsZ2ucxNgKrLm7jstev5BsHR05bH2zLsiwAClXY/FbE5rfiG4N/5FmeuOerrBq7h6XjEcXgCi4KL2PIE+xIDzF44KcE+3+K4/fiXbEO4ZTwf7qHJF2HO3IFD9w1xAPup1l96zpuvf31C36TLMt6pbJnvmWdgNGGdCKcCdXTZTLagvzLIFF08PortG1YglccxEueQo0+hN63lfq/1BnaU6J+2EdLwY83+2y9op+V1S0sGxUwCMZpUTr8AnJilKVrN9BxNbx55QAAIABJREFU1S8wMriUwb2GYtXjyjcuY/NrllPtyYbp49Do2TsglmUtfkJA3yaeWP5unlj+bqSOWVrfxqqJrSw7soP2oU6OpBczWq4wwCTjgw9TMC7tnUvYctkKCo2YXU/vZbC1jH33uHzu7u9R7gu48nVXsv7yPiqdhbP9Ci3rjLFB27IAE2vikRbJcJPkSJN4uEVypEky0sLEeqae6i7gLi1TurQTtziCa55HjT+EOPgYPPIcYAjGHYYHlzO5s4RueQz3FLn/1qWML19DR7yEVTF44wI1eQQ5PIjveiy94FrSlZsYHfCYHIf+izp4zS+uYO3lPShHzr/jlmVZC0xLl0PVKzhUvQJWgdQJPfXnqL7wAGv37mE8WUWr2sXuwkF2HjhIyXis6VvC5VpSjWvUJ5uMDLex/R/28NCXd9PW57L+6hWsvqSbJWuqyAXuAmdZZ5Pto22dUWey7+7x/RJNrEkmApKxgHQsIBkNSEZaxEeapOPBTAs1AlRnAbe3iNNbwqmmuOoAbvwMcuQpOPw0jO4EkwfwYhdx++XUBjuYfOwQwa4DNMslHr5qJXuXL6UkliCQeFKgJkcRo0MUMPStuZzD9ZXI+jIkClNx0P1F0lUlaHPP2HE6EdtH27Ksl2LCFsWd/4LZ8yS1IEFXlxBVKhghKBnJmrSXVXoZS3UHDopWHDFhFJMaWkpSXltlySXd9F/cTfuSIsJ2jbPOAbaPtnXeEdpQiDSlIKXcSpn63gsk4yHJWIt0LCCdio6GabILFJ3uIl5/BefKJbidAscZwkn3IMefhcPPwHNPQ/3w0Qe1r4Sll8LmtxJ7q6g9M8rUDx+h8cQTjHZ3sXvDKvZecTHSyW4WUyXFnRhDjh/BN5re1Zch229gfKibkcMSVVLojUXilSVMu2v7XluWdc4RfpHgkjfBJW/iN65fzu7HHubZB3/E7n37iMrtbK8kbHeGUEbTp2N60yrr4tVc6K/IQvVAjeTAFPv/9x4CR+L0lmhbW6Vnczft69qR7vy3fLesVzobtK1zwnSILoYppeDFZSlMKYSa2TF1avsUquqhugr46ztwugqorgJOsYWj9yMbzyJGnoeRHfD0Tpg6ePTB0oHei2D9LVmwXnop9F1CPBFQ+/4PmPz8PQzv+BZHens5sG4lQ29/K0gXYwxu0kIdGcCpT1IplmjvuYjIexWNySWMD7u0dRXYfGMPG7b08eXdh224tixr0ShUKmy+6fVsvun1hM0Gux/byvNbH2TPrt20XJ/BSgeHyiFP8TwF8ySVyUkuGHHpN/147esol7vxh4HhJo2th6kZQ+yA6PAp9LfTtraK11fG6S4gKx7CdjuxXuFs1xHrjJrdpUBogx9rCqGmEGWTH03Pp8es95IXn6eJEjR9SaugstJXNAuSli9ATeJykGo0QEcwQHtwkPZggI7WAIW0NvMzIllirLSaseJaxoprGCutYby4holCP1q6iDShc8+zdG97jNLebTREyvCSXg4v7SPyswt6dBri1adwapO4rTqdS9bilS6gWV9GFLQjlWTZ+nZWX9LN6ku76VpWnvlq1HaxsCxrMZmvu1maJAzu2sHeJx5j59NPMjQ+SVJpJy1VQEgwBhVOsvzQYVYPTtLGEvTyC3Gqq5DuEgqqQEWCmtUwYYRBVSXukjJuTxuqu4jTWcDpLqA6C0h/7pbwl/O+eybvePly/y7Yu3KeWbbriHVGmUSjmwm6GedTQpqXuhmjG1l586Eafqzzae4PebESBJ4k9CRTZYcjnZLAlwReFqZbPgg5QTEZpC0ayqbwMCuCg3RMDlANDuGYeObnpUJR85cxUehnR++mPFBnwbruLTm2RdkYiqNH6Hvyfvx920hro0y0t/F0by/htdmYsCaNcBo1/LEhVLOG9Dro6bqAoLCFRPbRahVQBZ91V3Sy+tJuVl7cRaH8yuhzbVmWdTYox6H/os30X7SZ177rvTQmxnnhp0+w7+mn2LN7F1NhTFpq48DajRxYlwXvQmOYZUPP0jsyQimBqeXriNvXEcjlJHoJJakohYbK2ARlOYkSxwZrWTCoqoPqKuN0llEdPqrdp2c8ollQtHyJsS3i1gKzLdpWFpLDFBMk6CBFBwkmSNFhXgbJMdtnyjCv34wxkZ7/CRyJKjnIksPBICZysxAdzJ78fJ0r8fREHp6np8Mzy5VwiEo0giQ95ilCVWaysILJQj8Thf58fgUThX5qfh9GzPOZ0hgKI4OUdz6FPPICcVBjoq3MZHv70QAeBzjNBqpZQzVrGKeC669F6X4UyxGygClIdG8B0+uje30oK9slxLIs6ySZxgQc3oMe3E06dpBUJ6TFMmmxDCp7/1ZxTMf4GEuGR3CRrFp/MbSvYEpUGRn3mKpVKAqHkoSKTOl0WlRkQkFKlCghOHZYQYMhdlICX9AoutQL/kwADzyVNfj4ksgRfOgM3lretmifG2yL9iJgjAFtMInGJFlJoo8uxykm0pgoRcdZmU35uijFzKyfu54OU0hOEJKnOQJZcJC+QhQcZEEhy0XcgkKWXGTJmVVOz+fLnoI4gPoQP7j/UcrRKOVolPZ4hGXBKOXaKKVolHI0QjkeRZnkmKdOhEfN76Pm9zHQvmVmvublpb+UyKmc3DFt1fH370ANvYBujNMUCUfKZYyU0FtFpCVkq4E3MogKGqRJiHC78eQavHQFotQHwsVUXUyXR9rloXs8qDg2WFuWZb1MotwB669Crb8KRfZezfB+9JEX0GMDJI1xjOszXm1jtDf7JvKngKrvpTo5SU8cs75vOdVVm3B6NlBrCgYHJhkdimnUsiFSHWKqbki3M0q3nKCiYoqJoDP16WpWSU0PhtKL9k0LzeDDQ8iyQlULyPYKquqj2jxkxT1all1EwbH9xq1jLJqgbRJNfKQJxoDJiun5rML0+hevw5i8Ptk/+bzRBlKTlVpno7mlBqM1HLPNHFt39vp01vZEY1KThd9U56WBY5Y1JjaQZoGal/uFgxIIVyE9ifAUwpPZckEhqh5yep2vkH4WnKcDtMgDtSw4iEJeHj+WcxxAczSfDh2dHx2F/SNHl+tD2RRMAvCu2f9nCJpuJ023m4bXzVhpDU23m7q35Jgw3XI7TynEGmMwcYBuTKBGBhAjh0hbk4TSELt5Fw4FoqiQYYQ7NoQMmqRphKFMQazE42KksxThVjBFhelw0V0epsvDdHrg2rGtLcuyFoooVmDVJtSqTSjAMQZqYzC8n3BoB/HkQVQUgyoyWS4x7hfZKYGB7XBgG37QokMbejvbWXfZWkrLNiBlD1NHYp7e1oaYihHHdF80+H6DdneQHmecDjFBJ1OURYAjPEzQTtrqJB3uIDKdaDqBuf4OGGTBIIsSWXSQFR/VVkJWfGR5VqNUPm/D+eK3aLqOXLbsIvOdOz93tncDBCBF9kuj8nJ62ZEIRyIckZdyznXZ+nxZSYQrEUqAO3s5L2eCdB6q3TxAq5MIgsZA1IBgAloTWRlMHp2fva45Bs3pAD0GUX3+A1DshHIPlLqh0pdPS6DSxzd3JjS8HppeN023Y/4uHS+56wYTtdBBHR3U0PVx5NggulUjRqNnv/40QYYBMgpQYQsRhqAVynTiiX6k6kWoXlA+ps3BdPiYdhfT4WZD7s1zQY1lWZZ19vzmGzYwNT7CE9t+xLPPPsqRFwZgIsURJYxXRHtFtO9nF1vm3CTGTQyOV8K0L0W3rUAmRZzQRzRA1JNsOu4C/EpV0FZJaCu2aPMnqaoR2vQIpWScQjgOQYpOCmjThqY9L6ukpjqzbv62TYP0DbIgkEXFoSAhdB1C1yV0FbEjiR1B5GZl7GTdWWJHELsSnYd023XkzDrZriOLJmivX7vZfOwPvwTkjcDiuBKByT8wGgFvu6o/C8UiryTyRlMh8vWzQrLKyjkDtJq1fro8E7SGuAFhHcJaNkV5Ob1uejmYOjY4t/LwHEyATk7wJAIKVSh0QKkrC86lbij1HF2eDtTT64sdIOcPpifb92ymVTpsYMImadjANCbQ9XFo1UhNgjnuIkYRh8goRMYhMgxQkUakHtJ04sglCNWNVF0kyiNqi6HNw23vQLT50OZiKg4o26pgWZZ1LpgrWGqj2TO+m607f8T2XY8xtm8v5cMp7VEVJdtI/RLaL6I9f6bv9zSZpigE0i0iyl0Ivwdl2lCRy6bObprjCbXRgPpEmH1LPUup6lFudym3CcqlhEohpOQ1qLiTlOUYJTOEF41h6gG6EWUDBgQCHTtoU0UzHcjLaFPG5KWmApz4YnotUlKlKZZV/i21QhRcZNFDFAvIoput9xTCd47WmaM8qUY6CzgP+2hHrmCgr/DSFXOffmH4ZT3Py/rEqFOImxA1s3AcNSFuzZpvHre9AWGNbfsO4iUNvLSJlzbyqTlTipPoV5IKh1BVCJ0qgdNG6LQROusIqlXCrjYCVSV0Ktx61YVZSC505GU7+O0gT/8vnUljdBRg4uBoq3RzAt2qY6JmFrB1+uLuIjpFRhEyDnCiEBVFqFghdRGpO5GqDyHbEbKDVBVotNeICiFpWaCqJQrtPfjtvYiSwp09RNRpf4WWZVnW2SCF5IKuDVzwqg3wqvcBMBVNsX10O9tGtrH98NM899zj9A7WWDpcpD0s44kK2ikTF4qkXoEkjTBpC8TgzM/9SQ2UMRRKilJPmVKxQtGvUHCrOFQxoSJuwNSE4fALKUHDAKV8WgZsRrmScrtHqepRaPMoLXcpVhyKhZiiG1B0G5ScJg89v5OSPEwhrePHNfykiR8n+EmKF6c4CTipQKUKTDEL5qaMrmWTMUVSShhTxFBEU+SlwvrRA6iRrkG4ID0x84258BTCd5G+i/A9hO9m66a/RfcU0p3+pl0dLae7rXr5N/jn4bVMiyZonyphEhwd4egQpcNZ81l5/LyjI5QJ4UdtWf/k48Nx3Jo/SKfhKe6cAr+NflMkViUiVSZwqtT8ZYSqnK1zykT5tmOn6W3ZlErvpJ7y1s0v/ysnrTVBENBqtWg0GkxOTDAxNsrk+Di1Wo1GvU6z1aIVhLSiGOb6PTMaEcfIJELGEU4cIxNQiYswpSxMmw6krCJkG1oVafgtJjvGqfvjxMUUyiGq0qTYVqS9vISys57SefhLbVmWZR1V9apct+w6rlt2HVwKf8rzBBtrTMT72ZPsZyLez3hygHpzG31HAlYOK3oHXdrDNlzaQPlEfpGwUCD0fFq1GiOui1Fz3M3XGJw2gdep8BwXz/HwVAFXFlEUEbpEkBRpjLgc3icI6/q41nEX2JQ1AHkSfInxJfgK4+bLbRI8gXEl0knwVYAnG7znmk5cPYWI6ln3znAkK6M6ptVEt0JMEKGDGBOmmFCjY4OJQccSYzy0KWKSIiYuoptFDCWM8dH4GHy0yUpDgZcTIYXSCEcjHLIus+6x3Waz4K4QrgOug/DcbHLz8D5TL+9mO3vdrPWz6+KIsxrwF03QroaD3LHjP88TliMcHcyaD180PNxJ2wsgwC2BVwK3CG45ny9BZWk+X557+8zjTrDd8UEIvnAGb2aitSaOY8IwJAgCmrUaU5MT1KamaNRqNBoNWq0mrVaLIAgJo4gojom1JjWGOdOzMYg0QSTxTOklGpkKhHYR2kXqAsqUkLqEkBUEBbSShG5ArTTJeHmSWmGShjdA6mtksYBTLlPye2h3l9PhXkyP6kMK24/asizLOjkF2cZSfzNL/c0z64wxNFaMMhkf5GA6yPbkEFPJIFPxQWR9mL7RlCUThiWjkq6aQ2dQwNUlEunTKhYJ/QKR7xN7HrHnEzguxnGyQK7m+BtVAlE0KAGOkDhS4kiHRmCQuAjjIrWHSHxk6CMTHxEVUNpDGAeR/91NgASXz/1LHakUfrmHQmkpfsnFKzp4RYVXcPAKCq/bwSs4uIV83cw2B89NKcgAlyYiybumRrW8cbGVNzCO5WULEwaYMMSECSaMsyGB43y0s9hkUyIwqUAnYKZDelLAxFlYN7jZOuOi8fJlD2M8DB7ky3NfeHoKpEZIk00KkAahRDavRBb6VR7MlUA4Kp9kFvgdB+GqbN51XzxAxAksmqDtpQ16G8+TSI9EFkiFR8tpJ5E+qfTz9T5pXr5oXngkyicR89Xx8p/jk4o5PsWewIduPv0XKGRD/6UYneZdMAKI8+4YcQhxiIkjTBJi0hjSGJMmWX2dZCOnGI3BYICP/li89GtKkywwp2leGqQWKC2RWiG1k4dnH2kKWSs0PsqIrOuzTAllg6ZTY7I4yWSpxmj5EOPlOi2vjvEVjl+h4nRRVj2UVQ8VtYZVzlLa1FJcefJdgyzLsizrVAghqKgeKqqHFVx+zLYPvH4th+qHOFg/yGB9kMHGIM81snJw6iCt4d10TiT0Thl6J6HriKGzIelquZRbChlLQtenUSrSKhay1nG/QOR7JJ5H6rgEykErhVEORqm5w/lsWiPy0dOEAU8pJIKGkYhEISYVYlxBqiB1MInCJC4CH2k8hHEReGA8JEeDO4BxsgEYcDyM44PqAJW1Dm9a2Y7jKRxf4XoSp1PhegrHU7i+wvFktuzn632J60k+98PnUSLA1SGuDnDTFspkDaDTPQuUbhzTi+Dm9e1HM02UYKIYEyf5pDFxNkSxifNhj1MwCVmZgtESk8osrBsHk7qY1AHj5qHeweACTh728/npbcaFY+qd+s3nFjRoCyFuB/4cUMBfGWM+dtx2H/gicDUwCrzDGLMv3/a7wH8AUuCDxph7T/RcI6UL+JurvnbaX8PJMEZj0gSSCBNHWZlEkEaYJOaxHw8RRSFxFBNHIXEcE0cRSZKQxHFWJglxnJVJmpKkKfVmBGimL1idubhT5KH4VL4KSVOEzia0RmiD0OSTQBiJ0AphFMI4SOMitIdMXZxU4CYCR4OTGqTRQIJwAmLVInCa1JxJprwGtUqTiVKTsXKLkbYG9UJMW6FKd6Gb7mI3o5MeRdVBSXZRVhtZpXrYpHooqS6UsHdPtCzLsl55XOmyurqa1dXVc25PdcpIayQL3o1BhpvDjAQj7G6NMtIaYbQ5Qn1iGDM6SEdd01mHjga0Nwxt41BtQkegqLYEfkvjR4LAdWgVfIKCT+AXCHyP0PeJPZfIzQJ64jhoxyHNQ3oiJVpKjFRZWD+VoWi1zr6JNiYvswDPdFYIBRjJo5MStAAtswmZZQcUmGwe1Mw6YZx82UEbh0S5BNIB6YIqIZSThXiVDTaBBJTASEFq2lGuRLkSJy9VSeK4Kpt3JMoVOM70skA6EqUkUgmkI1BKIElQIkGSIk2EEjGSGGliSLPcRhplXX3T2esax6wzcQhJgolj+PjJHdYFG3VECKGA54FbgQHgEeBdxpjts+r8n8Blxpj3CyHeCfyCMeYdQohNwJeAa4HlwPeAjcaYeft79K9cbX79gx/Ox7hO83Gv81Zbk5WYfPzrvCUXMzPgdhZmBRw9Hnm4nT2wxawRShAiD7zy9NyoJN+37ATPgjDG5GV+khuBMAKmg7GR2UmsFcIIpBZ5C7NAJQInNbgJOInG0QYnTXG0RukYQ4gWAYkMiVRA4LQI3JCmG9D0Q2qFkHohoFZIaBQF9QI0CoKkWMZTbfiyjCcr+CIvZXZBSUFWKKpOirKdguqgKNttgLYsy7IsQJuUQE/R0pO00nECPUWo64S6RmjqM/NxUkM1JvEbdQrNFm0tQymAUgjlAEqhoRxydF1oqISCUgjFwCA1JFISK5kFdM8n8hxC3yNyPSLXIXEcEscldh0S1yV1FKlySB0HLSVaKoxUaCmy4C4FRkiQcqb8mU3nMHO0dR5jsrZ1w0zJrJb76VbHF8+LvFVeZPNm1jzTyzJfluTjMaOEQkhJqmU2epxQ2c8U2esXQmajqQkJIlsWSvGhP3r7WR915FpglzFmD4AQ4svAW4Dts+q8BfiDfP5rwF+KrMf6W4AvG2NCYK8QYlf+834y35PpNKbVeKmRRI7+Lwkz63/QkI3gYWZVyTfJmfn8P3L2f6gBjEYacTQIa5BGIDVHJwMyNajUoIxBpRqVGJTWuKnGiTXKpCitUTpFmgRNQioStEjQIiKVMYmMSGRMImNiJyKWMbGKCN2YyDG0PEHgQcuHlgeBB4EniXyXxC+S+AXSYhnpFHFEAVcUcGUZR3TjikK2ThZwRRFXFKjKMj2iMhOqPVHMTjjLsizLsk6ZFIqS6qSkOsFdc1KP0SYlNk1i3SIyLVq6xaRpEpsWsW4RmyaRbnHZqgKNuEE9qhE1pkimaphmC1otaAXIIEK06qgwwosMhQgKcVb6dShHUIzAS8BNTF5my9OTm4CbN3mmQOw6xE42JY4iUdmU5lPiHJ3PJpmV8uiyzrfpvDVeK4kWMrtjs5SYvGHTCJmHfYHh6Pqj3/TPahydGWp5Jo2fNQsZtFcAB2YtDwCvmq+OMSYRQkwC3fn6h4577IoTPVmhlbDpiWFUqnHylmyZ6qy7BCnCaBApGI0WGkOKESkmn9dCY0RWapFiyOppOb2ckkpNKlK01CRKkyhBoshPnHxypqfsRNKOzL7ScRRaqfwrnqzUjoNW2cUSqeuRui6p66LdEkgHKRQSB0d4KOGhhIsSXraMm6/L1k/XacOlY7qO8OxFgpZlWZZ1DpNC4Ys2fNl2wnofuubkrgczxpDohFba4i9+8Cx1EzJpIhITkZiQNJ9PTUhiIjQJeroB0CRcu6ZKGrTQYYgJwmy43iDERNmoJoRZt1kTZ5OIE0wS0WiEyCRBap01PCY6a2BMDSrKSlcbnCRvmNQGJwUnBaVBmqylXk03YM4qp9dNlzONn2RdW7LutgojJBoJSqGFIJXZDX+0lFlmkwIjJamQM/Naijz4Z+X0+pO1kEF7zkHcTrLOyTwWIcSvAL+SL9bf8e3/seOU9vD81AOMnO2dWOTsMV549hgvPHuMF5Y9vgvvvDrGv3WGnueLxy6eV8f4OHN32D/OQgbtAWDlrOV+4NA8dQaEEA7QDoyd5GMxxnwW+Oxp3OdFTwjx6Mn0KbJePnuMF549xgvPHuOFZY/vwrPHeOHZY/zSFrLD7SPABiHEWiGEB7wT+PZxdb4N3JnPvx34gcmuRvw28E4hhC+EWAtsALYu4L5almVZlmVZ1mm1YC3aeZ/rDwD3kg3v9wVjzDYhxEeBR40x3wY+D/xtfrHjGFkYJ6/3VbILJxPg10404ohlWZZlWZZlvdIs6DjaxpjvAN85bt3vz5oPgF+c57F3AXct5P6dp2xXm4Vnj/HCs8d44dljvLDs8V149hgvPHuMX8KCjaNtWZZlWZZlWeczOyiyZVmWZVmWZS0AG7QXISHESiHE/UKIZ4UQ24QQvzFHnZuFEJNCiCfz6ffn+lnW/IQQ+4QQT+fH79E5tgshxH8XQuwSQvxUCHHV2djPc5UQ4sJZ5+eTQogpIcRvHlfHnsenSAjxBSHEESHEM7PWdQkh7hNC7MzLznkee2deZ6cQ4s656pzv5jm+fyyEeC5/H/imEKJjnsee8D3FysxzjP9ACHFw1nvBHfM89nYhxI78ffl3ztxen1vmOcZfmXV89wkhnpznsfY8nsV2HVmEhBDLgGXGmMeFEG3AY8BbjTHbZ9W5GfhtY8ybz9JunvOEEPuALcaYOccQzd/ofx24g+xmTX9ujDn+pk3WSRDZPXEPAq8yxrwwa/3N2PP4lAghbgTqwBeNMZfk6/4bMGaM+VgePjqNMR857nFdwKPAFrL7GjwGXG2MGT+jL+AVbp7jexvZqFqJEOLjAMcf37zePk7wnmJl5jnGfwDUjTGfOMHjFPA8cCvZMMKPAO+a/bfRysx1jI/b/klg0hjz0Tm27cOexzNsi/YiZIwZNMY8ns/XgGd5iTtrWgviLWRvUsYY8xDQkX8Isk7d64Hds0O29fIYY35INsrTbG8B/iaf/xvgrXM89I3AfcaYsTxc3wfcvmA7eo6a6/gaY75rjEnyxYfI7g1hvUzznMMn41pglzFmjzEmAr5Mdu5bxznRMRZCCODfAl86ozt1jrJBe5ETQqwBrgQenmPz9UKIp4QQdwshNp/RHVscDPBdIcRj+V1Kj7cCODBreQD7gefleifzv6nb8/hn12eMGYTsgzqwZI469nw+Pd4H3D3Ptpd6T7FO7AN595wvzNP9yZ7Dp8drgSFjzM55ttvzeBYbtBcxIUQF+Drwm8aYqeM2Pw6sNsZcDvwF8K0zvX+LwA3GmKuANwG/ln/VNpuY4zG2r9YpEtkNr34e+Ic5Ntvz+Myx5/PPSAjxe2T3hvi7eaq81HuKNb9PA+uBK4BB4JNz1LHn8OnxLk7cmm3P41ls0F6khBAuWcj+O2PMN47fboyZMsbU8/nvAK4QoucM7+Y5zRhzKC+PAN8k+1pytgFg5azlfuDQmdm7ReVNwOPGmKHjN9jz+LQZmu7WlJdH5qhjz+efQX7x6JuB95h5Lo46ifcUax7GmCFjTGqM0cDnmPvY2XP4ZySEcIC3AV+Zr449j49lg/YilPef+jzwrDHmT+apszSvhxDiWrJzYfTM7eW5TQhRzi80RQhRBm4Dnjmu2reB92aDj4jryC4cGTzDu7oYzNt6Ys/j0+bbwPQoIncC/zhHnXuB24QQnfnX8rfl66yXIIS4HfgI8PPGmOY8dU7mPcWax3HXv/wCcx+7R4ANQoi1+Tdl7yQ7962T9wbgOWPMwFwb7Xn8Ygt6Z0jrrLkB+HfA07OG3/nPwCoAY8xngLcDvyqESIAW8M75WlmsOfUB38wzngP8vTHmHiHE+2HmGH+HbMSRXUAT+D/O0r6es4QQJbIRAv7TrHWzj7E9j0+REOJLwM1AjxBiAPh/gI8BXxVC/AdgP/kde4UQW4D3G2P+ozFmTAjxh2RhBeCjxpiXc0HaojbP8f1dwAfuy98zHjLGvF8IsRz4K2PMHczznnIWXsIr3jzH+GYhxBVkXUH2kb9nzD7G+agvHyD7gKiALxiXfWkkAAAB50lEQVRjtp2Fl/CKN9cxNsZ8njmul7Hn8YnZ4f0sy7Isy7IsawHYriOWZVmWZVmWtQBs0LYsy7Isy7KsBWCDtmVZlmVZlmUtABu0LcuyLMuyLGsB2KBtWZZlWZZlWQvADu9nWZa1iAghuoHv54tLgRQYzpebxphXn5UdsyzLOg/Z4f0sy7IWKSHEHwB1Y8wnzva+WJZlnY9s1xHLsqzzhBCinpc3CyH+VQjxVSHE80KIjwkh3iOE2CqEeFoIsT6v1yuE+LoQ4pF8uuHsvgLLsqxziw3almVZ56fLgd8ALiW7k+xGY8y1wF8Bv57X+XPgT40x1wD/Jt9mWZZlnSTbR9uyLOv89IgxZhBACLEb+G6+/mnglnz+DcCm/HbKAFUhRJsxpnZG99SyLOscZYO2ZVnW+SmcNa9nLWuO/m2QwPXGmNaZ3DHLsqzFwnYdsSzLsubzXeAD0wtCiCvO4r5YlmWdc2zQtizLsubzQWCLEOKnQojtwPvP9g5ZlmWdS+zwfpZlWZZlWZa1AGyLtmVZlmVZlmUtABu0LcuyLMuyLGsB2KBtWZZlWZZlWQvABm3LsizLsizLWgA2aFuWZVmWZVnWArBB27Isy7Isy7IWgA3almVZlmVZlrUAbNC2LMuyLMuyrAXw/wN6VP9uUpIvAgAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# # Load data\n", + "data = dwell0\n", + "\n", + "# Plot for comparison\n", + "plt.figure(figsize=(12,8))\n", + "ax = data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True\n", + " #, color=plt.rcParams['axes.color_cycle'][1]\n", + " )\n", + "# Save plot limits\n", + "dataYLim = ax.get_ylim()\n", + "\n", + "# Find best fit distribution\n", + "best_fit_name, best_fir_paramms = best_fit_distribution8(data, 200, ax)\n", + "best_dist = getattr(st, best_fit_name)\n", + "\n", + "# Update plots\n", + "ax.set_ylim(dataYLim)\n", + "ax.set_title(u'Trips to Work\\n All Best Fitted Distributions')\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "# Make PDF\n", + "pdf = make_pdf(best_dist, best_fir_paramms)\n", + "\n", + "# Display\n", + "plt.figure(figsize=(12,8))\n", + "ax = pdf.plot(lw=2, label='PDF', legend=True)\n", + "data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True, ax=ax)\n", + "\n", + "param_names = (best_dist.shapes + ', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']\n", + "param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, best_fir_paramms)])\n", + "dist_str = '{}({})'.format(best_fit_name, param_str)\n", + "\n", + "ax.set_title(u'Trips to Work with best-fit distribution \\n' + dist_str)\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "print (dist_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "foldcauchy(c=12.34, loc=-0.05, scale=0.73)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# # Load data\n", + "data = dwell1\n", + "\n", + "# Plot for comparison\n", + "plt.figure(figsize=(12,8))\n", + "ax = data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True\n", + " #, color=plt.rcParams['axes.color_cycle'][1]\n", + " )\n", + "# Save plot limits\n", + "dataYLim = ax.get_ylim()\n", + "\n", + "# Find best fit distribution\n", + "best_fit_name, best_fir_paramms = best_fit_distribution8(data, 200, ax)\n", + "best_dist = getattr(st, best_fit_name)\n", + "\n", + "# Update plots\n", + "ax.set_ylim(dataYLim)\n", + "ax.set_title(u'Trips to Work\\n All Best Fitted Distributions')\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "# Make PDF\n", + "pdf = make_pdf(best_dist, best_fir_paramms)\n", + "\n", + "# Display\n", + "plt.figure(figsize=(12,8))\n", + "ax = pdf.plot(lw=2, label='PDF', legend=True)\n", + "data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True, ax=ax)\n", + "\n", + "param_names = (best_dist.shapes + ', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']\n", + "param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, best_fir_paramms)])\n", + "dist_str = '{}({})'.format(best_fit_name, param_str)\n", + "\n", + "ax.set_title(u'Trips to Work with best-fit distribution \\n' + dist_str)\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "print (dist_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "#For HW\n", + "def best_fit_distribution9(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " \n", + " st.burr, st.skewnorm,\n", + " st.genlogistic,\n", + " st.mielke,\n", + " st.gompertz\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.norm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "genlogistic(c=0.31, loc=8.97, scale=0.79)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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l5P8E/EtE1OYf6s4iC/otnUW2tPndrWxrTSPZcvEzIqJf/m3Bi/m2DwKfSSk9l4+6P5ZSWp3XdGP+vmhIKX0lP0drwfR84LaU0m0ppaaU0h1kHzLenp/no+08V83fCA3J/9vyPdLm+6PERcAP0855ep8FVgD/GBH9IuI04E28/rncSPZel1Qwg7YkyEbUlpb8HzbAkpLfpwAfLx2RAyblxzV7peT3LewMEC3vZ0lKqanktkXABLLR5ZoW91v6e0tnkQWaRRFxd0Qc286+pSYBL7axbS2vDTjt7VuuTWQjps2af9/Yyr6vykPpt4AfRtu9zbtqH7LnvVTz6zAJWJO3ueySiDgF+HeylpD+ZGHvOxHR2ocvyD78TCN7vf+LrI+4tZlqWgbLdqWU5gMfI+uBXhERP4qI5vdsm69tRHw8sgs51+fv9eFkPfMtTQHObvHv4gRg73Lqy23K/9vyPdLu+yNvc3kT2QcaAFJK9cC7yT5IvQJ8HPgJr38uhwLrOlGjpF1k0JYE8DIwISJKv3qeVPL7ErIe2tIRuUH5SGhnLAMmRUTp//ZMBpaSfQXeQNbf21oNr5FSmptSehdZa8MvyAIFQEchbAmt9ArnHgcOKGffiDgvXj8bRulPc+vIU8DhJYceDixvHj3tQBXZaOSEMvbtjGVkIbFU8+uwBBgVEa8b8cxbR9p7zOflu84E7kkpzctHeucCDwGntFZMSmlRSumMlNLYlNIbydpZSr9RaQ6WJ1ESLMuRf/NwQv54E/Bv+aZWX9u8H/ufyVqhRubfLqyn9baMJcANLf5dDE4pXZWfq+UMIaU/T+X1rSX799fyPfJUBw/tQuD+1GIWnpTS4ymlN6WURqeU3kp28e2rz2X+QaM/JS1Skopj0JYE8ADZ1+yXRURNRLwLOLpk+7eBSyPijZEZHBHvyHuYO+Mhsh7sf8q/2j4JeCfwo7zd4L+Bz0fEoIg4iCxMvE5E9M+D7vB8FG9DXj/AcmB0RAxvo4abgFMim36uJrILMJtHWm8jGyVs9mtgr4j4WGRTsA2NiDcCpJRuKukDb+2nuXXkh8AlkU3dNxL4DFkvfGuP69SIOCLvax5G1ju9Fngm316V9wr3y/6M2sj665uPvysiPt/G4y51G3BAZH3mNRHxPmAG8OuU0stkPezfjIiR+et0Yv6YF3fwmG/Kzz8XmNP8vEbEEcAcWu/Rbr5Admj+up4PnJY/9lIXkAXLF1sce1JEtPrhKiIOjIg3R3YtwTZgKzvfJ98B/jUi9s/f04dF1qM+lOwD30qgJiI+y2tHm0vdSNZu89b8NavN65mYP1+XtvNclfZg/xD4TP58H0TWqvX9Nu6z2YWt7ZM/jtr839A/kI2ul+53Etn1DNs7OL+kLmDQlkRKaQfwV8AlZF8pn08WMrfn2+eR/Z//18mC33zKmBWhjfs5k6wHeBXwTeDClNKz+S6XkX1N/wrZBXm3NNfQiguAhRGxAbg0r5n8XLcAC/Kv80vbW8gD8NvJvlZfQ3Yh5OH5tkeA9SVheiNwKtmHgVeAF8imX+vMY/4dWRvFnWTtGYuAzzVvj2zmleaR4BF57evJ2hr2A05PKW3Lt59IFhZvIxuB3kp2YWmzSWT9zh3VtJqs//zjwGrgn4AzUkqr8l0uIOuBb+75/VgnH/PdZO0aP4tsjuefA19KKf1v/pjPax7Rzb2VbCaVtWSv5ekppZUtTnshrffsTyL7oNiaAWRzoq8ie/3GAc2zxlxN9i3I/5J9UPsuMBC4neyDxvNkr9U22mhhSiktAd6Vn3Nlvt8/0vn/b/0c2eu9iKz//Mv5+6b0W4Tmb0iIrE1qIvm0fi1cQDZCvoJsSslTW4Tq88hakiTtAVFmq5ukPiYiHgK+lVL6XgVr+Ddgr5RSe7OPdPV9ngZ8NKXU5gIr3VE+ivrTlFK5veq9QmQrfv40pXR7pWvp7iLiDcB1fe09IlWSQVsSABHxJrK+zVXsHPWanrcS7KkaDiLrH30CmE02cvvBlFKvWxJdktT71VS6AEndxoFkX6UPIfsa+717MmTnhpK1TuxD9tX3V8hWupMkqcdxRFuSJEkqgBdDSpIkSQUwaEuSJEkF6DU92mPGjElTp06tdBmSJEnq5R5++OFVKaWxHe3Xa4L21KlTmTdvXqXLkCRJUi8XEYvK2c/WEUmSJKkABm1JkiSpAAZtSZIkqQC9pkdbkiRJxauvr6euro5t27ZVupTC1dbWMnHiRPr167dLxxu0JUmSVLa6ujqGDh3K1KlTiYhKl1OYlBKrV6+mrq6OadOm7dI5bB2RJElS2bZt28bo0aN7dcgGiAhGjx69WyP3Bm1JkiR1Sm8P2c1293EatCVJktSjVFdXM3PmTA499FDOPvtstmzZ8prbDznkEA4//HCuvvpqmpqaALjrrrsYPnw4M2fOZObMmZxyyimF12nQliRJUo8ycOBAHn30UZ588kn69+/Pt771rdfc/tRTT3HHHXdw2223ccUVV7x63Jw5c3j00Ud59NFH+f3vf194nQZtSZIk9Vhz5sxh/vz5r7t93LhxXHfddXz9618npVSBypx1RJIkSbto6id+U8h5F171jrL2a2ho4Le//S2nn356q9unT59OU1MTK1asAODee+9l5syZAJx99tl8+tOf7pqC22DQliRJUo+ydevWVwPznDlzuOSSS9rct3Q0e86cOfz6178uvL5mBm1JkiTtknJHnrtacy92RxYsWEB1dTXjxo3jmWee2QOVvZY92pIkSep1Vq5cyaWXXspll11WsekIHdGWJElSr9DcUlJfX09NTQ0XXHABl19+ecXqMWhLkiSpR9m0aVOrtzc2NrZ5zEknncRJJ51UUEWts3VEkiRJKoBBW5IkSSqAQVuSJEkqgD3akiSp8u68snP7n/zJYuqQupAj2pIkSVIBDNqSJElSAQzakiRJ6lGqq6uZOXMmhxxyCIcffjhXX301TU1N7R6zcOFCbr755j1UYcYebUmSJO26zvbXd6SM/vvSJdhXrFjBueeey/r167niiivaPKY5aJ977rldVmpHHNGWJElSjzVu3Diuu+46vv71r5NSYuHChcyZM4cjjzySI488kvvvvx+AT3ziE9x7773MnDmTa665ps39upIj2pIkSerRpk+fTlNTEytWrGDcuHHccccd1NbW8sILL3DOOecwb948rrrqKv7jP/6DX//61wBs2bKl1f26kkFbkiRJPV5KCYD6+nouu+wyHn30Uaqrq3n++edb3b/c/XaHQVuSJEk92oIFC6iurmbcuHFcccUVjB8/nscee4ympiZqa2tbPeaaa64pa7/dYY+2JEmSeqyVK1dy6aWXctlllxERrF+/nr333puqqipuuOEGGhsbARg6dCgbN2589bi29utKjmhLkiSpR9m6dSszZ86kvr6empoaLrjgAi6//HIAPvrRj3LWWWfx05/+lJNPPpnBgwcDcNhhh1FTU8Phhx/OxRdf3OZ+XSma+1l6ulmzZqWubmCXJEl7iEuw9xjPPPMMBx98cKXL2GNae7wR8XBKaVZHx9o6IkmSJBXAoC1JkiQVwKAtSZIkFcCgLUmSpE7pLdf4dWR3H6dBW5IkSWWrra1l9erVvT5sp5RYvXr1bs2vXej0fhFxOvD/gGrgOymlq1psvxz4INAArAQ+kFJalG9rBJ7Id12cUjqzyFolSZLUsYkTJ1JXV8fKlSsrXUrhamtrmThx4i4fX1jQjohq4BvAqUAdMDcibk0pPV2y21+AWSmlLRHxt8C/A+/Lt21NKc0sqj5JkiR1Xr9+/Zg2bVqly+gRimwdORqYn1JakFLaAfwIeFfpDimlO1NKW/I/HwR2/SODJEmS1I0UGbQnAEtK/q7Lb2vLJcBvS/6ujYh5EfFgRLy7iAIlSZKkohTZox2t3NZq13xEnA/MAt5UcvPklNKyiJgO/DEinkgpvdjiuA8DHwaYPHly11QtSZIkdYEiR7TrgEklf08ElrXcKSJOAT4NnJlS2t58e0ppWf7fBcBdwBEtj00pXZdSmpVSmjV27NiurV6SJEnaDUUG7bnA/hExLSL6A+8Hbi3dISKOAK4lC9krSm4fGRED8t/HAMcDpRdRSpIkSd1aYa0jKaWGiLgMuJ1ser/rU0pPRcQXgHkppVuBLwNDgJ9GBOycxu9g4NqIaCL7MHBVi9lKJEmSpG6t0Hm0U0q3Abe1uO2zJb+f0sZx9wNvKLI2SZIkqUiuDClJkiQVwKAtSZIkFcCgLUmSJBXAoC1JkiQVwKAtSZIkFcCgLUmSJBXAoC1JkiQVwKAtSZIkFaDQBWskSZIq7s4rO7f/yZ8spg71OY5oS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQWoqXQBkiRJnXbnlZWuQOqQI9qSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEKDdoRcXpEPBcR8yPiE61svzwino6IxyPiDxExpWTbRRHxQv5zUZF1SpIkSV2tsKAdEdXAN4C3ATOAcyJiRovd/gLMSikdBvwM+Pf82FHA54A3AkcDn4uIkUXVKkmSJHW1Ike0jwbmp5QWpJR2AD8C3lW6Q0rpzpTSlvzPB4GJ+e9vBe5IKa1JKa0F7gBOL7BWSZIkqUsVGbQnAEtK/q7Lb2vLJcBvd/FYSZIkqVupKfDc0cptqdUdI84HZgFv6syxEfFh4MMAkydP3rUqJUmSpAIUOaJdB0wq+XsisKzlThFxCvBp4MyU0vbOHJtSui6lNCulNGvs2LFdVrgkSZK0u4oM2nOB/SNiWkT0B94P3Fq6Q0QcAVxLFrJXlGy6HTgtIkbmF0Gelt8mSZIk9QiFtY6klBoi4jKygFwNXJ9SeioivgDMSyndCnwZGAL8NCIAFqeUzkwprYmIfyUL6wBfSCmtKapWSZIkqasV2aNNSuk24LYWt3225PdT2jn2euD64qqTJEmSiuPKkJIkSVIBDNqSJElSAQzakiRJUgEK7dGWJEnaFSnB9qYgAQOrW12GQ+r2DNqSJKlb2NRQxW9eGc5vXhnBY+sHsr4hiymj+jUwe+RmzthrHW8bv54av49XD2HQliRJFdXQ2MQNi0bzny+OZ039zmjSP5oAWFNfw+0rhnP7iuFMHbSdLxy8lBPHbKpUuVLZDNqSJKlilqzZwt//6C88sngCADOHb+bciWuYM2YTew2oJwFLt/bjDyuH8b1FY1i4ZQAXPjydv5m8kk8f+LKj2+rWDNqSJKkiHlqwmg/9cB4btjWw14AdfGHGMk4du4FsDbtMAJMG1XPxlNWcP2k11y0cy1fnj+d7i8eyeOsAvnH4Imrt4VY35edASZK0x/3uyZe54Lt/ZsO2Bk45eBy/O+4FThv32pDdUk0VfHT6Sm6avYAR/Rr4w8phXPbYZOqb9lzdUmcYtCVJ0h71x2eXc9nNf2FHYxMXHjuFay+YxYj+jWUfP3vkFn48+0WG1zTw+5XD+ZenJxRYrbTrDNqSJGmPeWjBav72xkdoaEp85MTpXHHmIVRXtTOM3YYDh27n+0e9xICqJn60dDS3LBlVQLXS7jFoS5KkPWLJmi185MaH2d7QxDlHT+YTbzuIaK9XpANHjNjKl2bUAfC5Z/bhmY21XVWq1CUM2pIkqXBbdzR4WBajAAAgAElEQVTy4RseZt2Wek4+cCxffPehuxWym501YR3nTFzNjlTFPz450X5tdSsGbUmSVLhP/c8TPPPyBqaNGcxX33/ELrWLtOXTB77MhNodPLlhEN96aVyXnVfaXQZtSZJUqF8+upT/+ctSBvWv5roLjmL4wH5dev4hNU18+dAlAPzngnEs2dq155d2lUFbkiQVpm7tFj7ziycB+OwZM9h//NBC7ue40Zt5995r2dFUxVXP7V3IfUidZdCWJEmFaGpKfPwnj7FxWwOnzhjP+2ZPKvT+/mn/V6itauI3y0fw0JrBhd6XVA6DtiRJKsSP5i7hoZfWMGZIf676qzd0ycWP7dlnYD2XTlsBwL+9sBfJBSNVYQZtSZLU5VZs3MZVv30GgCvOPJTRQwbskfv90NRVjOrXwCPrBnPv6iF75D6lthi0JUlSl/vir59hw7YGTj5wLG9/w1577H4H1zTx4WkrAfjqi+Md1VZFGbQlSVKXuvv5ldz62DJq+1XxhXd1zXzZnXHBpNWOaqtbMGhLkqQuU9/YxBW/egqAj51yAJNGDdrjNQyuaeJDU7NR7WtfGrvH719qZtCWJEld5qYHF7Fg5WamjRnMB46fVrE6zp20mkHVjfxpzVCXZlfFGLQlSVKXWL+lnq/+4QUAPvm2g+hfU7mYMbxfE389YS0A3104pmJ1qG8zaEuSpC7xn398gXVb6jl2+mhOnTG+0uXwN1NWESRufXkEK7bXVLoc9UEGbUmStNsWrtrMDx5YSAR8+h0H7/ELIFszZdAOThm3gR2piluWjKp0OeqDDNqSJGm3feWO56lvTLz3yIkcOmF4pct51YWTVgPwk6WjaHSqP+1hBm1JkrRbnnl5A796bBn9a6q4/LQDKl3Oaxw/ehOTBm5n6bb+3LNqaKXLUR9j0JYkSbvlmjueB+C8N05m7+EDK1zNa1UFvH/iGgB+VGf7iPYsg7YkSdplj9et43+fXs7AftX87Un7VrqcVp09YS3VkfjDymFeFKk9yqAtSZJ22Vf+NxvNvui4qYwb2j3nqx43oIG3jN1AQwp+tnRkpctRH2LQliRJu2TuwjXc/fxKhgyo4SMnTq90Oe366wlZ+8gvXh5J8qJI7SEGbUmStEuuzkezLzlhGiMH969wNe07ccwmRvRr4PlNta4UqT3GoC1Jkjrt4UVreGDBaobW1nDJnMottV6u/lWJM/ZaB8AvXx5R4WrUVxi0JUlSp33zzhcBuOjYqQyr7Vfhasrz7r13Bm3n1NaeYNCWJEmd8vSyDfzh2RUM7FfNB07o/qPZzY4asYWJA3fwyvb+PLRmcKXLUR9g0JYkSZ3yzbvmA3DO0ZMZ1c17s0tFwLv3XgvAL1929hEVz6AtSZLKtmDlJn7zxMv0qw4+dGLPGc1u9q68feR3y4dR31ThYtTrGbQlSVLZvnX3i6QE7z1qYrdbBbIc+w/Zzn6Dt7G+oYYH1gypdDnq5QzakiSpLEvXbeW/H1lKVcBHTuyeq0CW423j1wPw2+XDK1yJejuDtiRJKst37l1AQ1PijMP2YeqYnnsx4el50L5jxTBnH1GhDNqSJKlD67fU8+O5SwC49E09dzQbYMbQbUweuJ1VO/oxd23P/cCg7s+gLUmSOnTL3MVs2dHICfuNYcY+wypdzm6J2Nk+8jvbR1Qgg7YkSWrXjoYmvv+nhQB8sAesAlmO018N2sNosn1EBTFoS5Kkdv3miWW8smEb+48bwpsOGFvpcrrEzOFb2ac2W7zm8fU9b/YU9QwGbUmS1KaUEt++5yUgG82OiApX1DUi4JSxGwD4w8qe3Qqj7sugLUmS2vTAi6t5+uUNjBnSn3fNnFDpcrrUmw3aKphBW5Iktenb9y4A4MJjp1Lbr7rC1XStY0ZtZlB1I09vHMjL2/pVuhz1QgZtSZLUqvkrNnLncyup7VfF+cdMqXQ5Xa62OnHC6E0A/HHl0ApXo97IoC1Jklr13fuy3uyzjpzIqMH9K1xNMd5i+4gKZNCWJEmvs2rTdn7+yFIAPnBC75jSrzUnj90IwJ9WD2FrY++40FPdh0FbkiS9zk0PLmZHQxOnHDyOfccOqXQ5hRk3oIHDh21he1MV96/uvY9TlWHQliRJr1Hf2MRNDy0C4APH997R7GbOPqKiGLQlSdJr3P7UK6zYuJ39xg3h2H1HV7qcwr1lXNY+8seVQ0muEqkuZNCWJEmv8YP7FwJw0bFTes0CNe2ZMXQrY/rX88r2/ryweUCly1EvYtCWJEmvemrZeuYuXMvQATX81ZETK13OHlEVcOKYbJq/e1Y5zZ+6jkFbkiS96oYHst7ss46ayOABNRWuZs+ZMzprH7nXCyLVhQzakiQJgHVbdvCLR7Mp/S48tvctUNOe5oVrHlozhG31jRWuRr2FQVuSJAHwk3lL2FbfxIkHjGV6L57SrzVjBzQwY+hWtjVVMW/h2kqXo17CoC1JkmhsStzwYNY2clEfG81uduKYrH3knhdWVrgS9RYGbUmSxJ3PrmDJmq1MHjWIkw4cV+lyKuLEvE/7nucN2uoaBm1JksQPHlgIwAXHTKG6qvdP6deao0ZuYWB1E8++spEVG7ZVuhz1AgZtSZL6uBdXbuLeF1ZR26+Ks2f1jSn9WjOgKnHMyOyiyHtfWFXhatQbGLQlSerjmqf0e88RExgxqH+Fq6ks+7TVlQzakiT1YZu2N/Czh+sAuOCYqZUtphuYk0/z96f5q0iux67dZNCWJKkP+59H6ti0vYGjp45ixj7DKl1Oxe07eDvjhw1g1aYdvLBiU6XLUQ9n0JYkqY9KKfGDvG3kouOmVraYbiICjtt3DAD3z7dPW7unrKAdEYcWXYgkSdqz7n9xNfNXbGL8sAGcdsj4SpfTbRy772gge36k3VHuiPa3IuLPEfHRiBhRaEWSJGmP+MH9CwE4741T6Fftl9zNjp2eBe0HF6ymsck+be26sv5VpZROAM4DJgHzIuLmiDi10MokSVJh6tZu4ffPLKdfdXDO0ZMrXU63MmnUICaNGsiGbQ08vWxDpctRD1b2x9eU0gvAZ4B/Bt4EfC0ino2Iv2rrmIg4PSKei4j5EfGJVrafGBGPRERDRLy3xbbGiHg0/7m1/IckSZI6cuODi2lK8I437M3YoQMqXU63c9z0vE/7Rfu0tevK7dE+LCKuAZ4B3gy8M6V0cP77NW0cUw18A3gbMAM4JyJmtNhtMXAxcHMrp9iaUpqZ/5xZTp2SJKlj2+ob+fHcxQBc6EWQrTpuP/u0tftqytzv68C3gU+llLY235hSWhYRn2njmKOB+SmlBQAR8SPgXcDTJccvzLc1db50SZK0K259bBlrt9Rz2MThHDHJS69a09ynPXfhGnY0NNG/xh52dV6575q3Azc3h+yIqIqIQQAppRvaOGYCsKTk77r8tnLVRsS8iHgwIt7d2g4R8eF8n3krV7qCkyRJHUkpvXoR5IXHTiUiKltQNzVuWC37jRvClh2NPF63rtLlqIcqN2j/HhhY8veg/Lb2tPYvtzOX7k5OKc0CzgW+GhH7vu5kKV2XUpqVUpo1duzYTpxakqS+6ZHF63hq2QZGDe7PGYftXelyurXjnOZPu6ncoF2bUnp1eaT890EdHFNHNktJs4nAsnILSykty/+7ALgLOKLcYyVJUuuaR7PfP3sStf2qK1tMN7czaHtBpHZNuUF7c0Qc2fxHRBwFbG1nf4C5wP4RMS0i+gPvB8qaPSQiRkbEgPz3McDxlPR2S5KkzluxYRu3PfEyVQHnHTOl0uV0e2+cNpoIeGTROrbVN1a6HPVA5QbtjwE/jYh7I+Je4MfAZe0dkFJqyPe5nWy2kp+klJ6KiC9ExJkAETE7IuqAs4FrI+Kp/PCDyebrfgy4E7gqpWTQliRpN9zy5yU0NCVOnTGeCSMGdnxAHzdycH8O3msYOxqbeHjR2kqXox6orFlHUkpzI+Ig4ECy3utnU0r1ZRx3G3Bbi9s+W3pespaSlsfdD7yhnNokSVLHdjQ0cdNDiwC4yCn9ynbM9NE8/fIG/vzSGo7fb0yly1EP05m5amYDh5H1Sp8TERcWU5IkSepqtz/1Cis2bueA8UNenbpOHTt62igAHnrJCyLVeWWNaEfEDcC+wKNAc5NSAn5YUF2SJKkLdcmUfnde2bn9T/7krt1PN9IctP+yeB3bGxoZUOMFpCpfuQvWzAJmpJQ6Mz2fJEnqBp5cup55i9YydEAN7zmiM0taaNTg/hwwfgjPL9/EE3XrmTV1VKVLUg9SbuvIk8BeRRYiSZKKccMDWW/2e2dNZPCAcsfY1Gxn+8iaCleinqbcoD0GeDoibo+IW5t/iixMkiTtvrWbd/CLR5cCWduIOu/oaVlPu0FbnVXux9rPF1mEJEkqxk/mLWF7QxMnHjCWaWMGV7qcHumN+Yj2wwvX0NDYRE11Z+aSUF9W1jslpXQ3sBDol/8+F3ikwLokSdJuamxK3PBg1jZy8XEuULOrxg+rZeroQWze0cjTL2+odDnqQcoK2hHxIeBnwLX5TROAXxRVlCRJ2n1/fHYFdWu3MmX0IE46YFyly+nRXu3TXmD7iMpX7ncff0e2DPoGgJTSC4D/YiVJ6saap/S74JgpVFXt4pR+AuzT1q4pN2hvTyntaP4jImrI5tGWJEnd0PwVG7lv/ioG9qvm7FmTKl1Oj9fcpz134RqamoxAKk+5QfvuiPgUMDAiTgV+CvyquLIkSdLu+GE+pd97jpzA8IH9KlxNzzdx5ED2GV7L+q31PL9iY6XLUQ9RbtD+BLASeAL4CHAb8JmiipIkSbtu47Z6fv5wHQAXHutFkF0hIuzTVqeVO+tIU0rp2ymls1NK781/93sTSZK6oZ8/XMfmHY0cM30UB+01rNLl9BpvnJ71af/ZPm2Vqax5tCPiJVrpyU4pTe/yiiRJ0i5rakqvto1c5AI1Xap0hciUEhFeYKr2lbtgzayS32uBs4FRXV+OJEnaHffNX8WCVZvZe3gtp84YX+lyepXpYwYzZkh/Vm3azkurNjN97JBKl6RurtzWkdUlP0tTSl8F3lxwbZIkqZOap/Q7/5gprmDYxV7Tp237iMpQ7oI1R5b8zIqIS4GhBdcmSZI6YfHqLfzxuRX0r67i/bOd0q8IR0/dOc2f1JFyW0e+UvJ7A9ly7H/d5dVIkqRddsODC0kJzjh8b0YPGVDpcnqlWXnQnrdwbYUrUU9QVtBOKZ1cdCGSJGnXbdnRwI/nLgHg4uOmVraYXuygvYYyZEANi9dsYfmGbYwfVlvpktSNlTvryOXtbU8pXd015UiSpF3xy0eXsWFbA0dMHsFhE0dUupxeq6a6iiMmj+DeF1Yxb+Fa3nHY3pUuSd1YuVdJzAL+FpiQ/1wKzCDr07ZXW5KkCkopvXoRpFP6FW+2fdoqU7k92mOAI1NKGwEi4vPAT1NKHyyqMEmSVJ4/v7SGZ1/ZyJghA3j7GxxhLdqsqSMBmLfIoK32lTuiPRnYUfL3DmBql1cjSZI67QcPLATg3KMn0b/GKf2KNnPSCGqqgqeXbWDT9oZKl6NurNx/jTcAf46Iz0fE54CHgB8WV5YkSSpH3dot/O7JV6ipCs5945RKl9MnDOpfwyEThtOU4C+LnX1EbSt3wZr/C/wNsBZYB/xNSulLRRYmSZI6dsMDi2hK8I7D9mav4c6AsafMnpK1j8x1mj+1o9webYBBwIaU0vciYmxETEspvVRUYZIkqX2btzdwy58XA/A3x0+rcDW9yJ1XdrjLrE3D+A5TmecFkWpHuStDfg74Z+CT+U39gBuLKkqSJHXsvx+pY8O2Bo6aMpKZk5zSb0+aNWIzAH9ZvI76xqYKV6Puqtwe7fcAZwKbAVJKy3BaP0mSKqapKXH9nxYCcMkJjmbvaWMGNDJ90Ha21jfy9LINlS5H3VS5QXtHSikBCSAiBhdXkiRJ6shdz6/gpVWbmTBiIKfNGF/pcvqkWSOzUW3n01Zbyg3aP4mIa4EREfEh4PfAt4srS5Iktef6+xYCcNFxU6ipdkq/SmgO2vO8IFJtKOtiyJTSf0TEqcAG4EDgsymlOwqtTJIkteq5VzZy3/xVDOpfzftmT650OX3W7LxPe96iNaSUiIgKV6TupsOgHRHVwO0ppVMAw7UkSRV2/X3ZpF9nHzWR4QP7VbiavmvqoB2MGdKfVZt2sHD1FqaNsbNWr9Xhd00ppUZgS0QM3wP1SJKkdqzetJ3/eXQpABc7pV9FRcCsKaMA+7TVunKburYBT0TEdyPia80/RRYmSZJe7+aHFrOjoYm3HDTOEdRuYNbUbOEa59NWa8pdsOY3+Y8kSaqQHQ1N/PDBRQB8wCn9uoXZU7MRbS+IVGvaDdoRMTmltDil9IM9VZAkSWrdb55YxsqN2zlor6Ect+/oSpcjYMY+wxjYr5oFqzazatN2xgwZUOmS1I101Dryi+ZfIuLnBdciSZLakFLiu/lFkB84fpozXHQT/aqrOGJytiqno9pqqaOgXfqveHqRhUiSpLY9uGANTy7dwKjB/Tlz5j6VLkclZuXtIw8vsk9br9VR0E5t/C5Jkvagb9+7AIALj51Cbb/qClejUrPzCyLnOqKtFjq6GPLwiNhANrI9MP+d/O+UUhpWaHWSJIkXlm/kj8+uoLZfFRceO7XS5aiFIyaPpCrgyaXr2bqjkYH9/SCkTLsj2iml6pTSsJTS0JRSTf5789+GbEmS9oDr7slGs88+ahKjBvevcDVqaciAGmbsM4yGpsSjS9ZVuhx1I+XOoy1Jkipg+YZt/OLRpUTAB+c4pV931bxwjfNpq1S582hLkqQK+N6fFlLfmHjboXsxZXQPW6DmzisrXcEeM3vqKL5//0LmLrJPWzs5oi1JUje1aXsDNz2ULVDz4ROd/Ks7a14h8pFFa2lscv4IZRzRliSpm/rRnxezcVsDR08dxRGTR5Z/YGdGkk/+ZOcL0+uMH1bL5FGDWLxmC8++soFD9hle6ZLUDTiiLUlSN1Tf2MT1+QI1jmb3DM2j2nNfsk9bGYO2JEnd0G8ef5ll67ex79jBvPmgcZUuR2WYnS9cY5+2mhm0JUnqZlJKXJtP6fehOdOpqnK59Z5gdsmIdkr2acugLUlSt3Pf/FU88/IGxgwZwLuPmFDpclSmfccOYdTg/qzYuJ0la7ZWuhx1AwZtSZK6mWvvzkaz/+b4qS633oNEBLOmZKPaf3Y+bWHQliSpW3l0yTrum7+Kwf2rOf+NUypdjjrp1T5tL4gUBm1JkrqVb945H4Dzj53C8EH9KlyNOmv2tOYLIg3aMmhLktRtPL98I//79HL611RxyQkut94THbLPMAb2q2bBys2s2rS90uWowgzakiR1E/9114sAvG/WJMYNra1wNdoV/aqrOGLyCADm2afd5xm0JUnqBpas2cKtjy2juipcoKaHe7VPe6Hzafd1Bm1JkrqBa+95kcamxLtm7sOkUYMqXY52w86g7Yh2X2fQliSpwlZs2MZP5tURAR89ad9Kl6PddMTkEVRXBU8t28Dm7Q2VLkcVZNCWJKnCvnvfS+xoaOKtM/Ziv3FDK12OdtPgATUcus8wGpsSf1m8rtLlqIIM2pIkVdC6LTu48cFFAHz0ZEeze4tZefuIC9f0bQZtSZIq6Af3L2Lzjkbm7D+GwyaOqHQ56iLNfdrOPNK3GbQlSaqQjdvq+d79LwHw0ZP2q3A16kqzp2ZLsf9l8TrqG5sqXI0qxaAtSVKF/PCBRazbUs/RU0dxzPRRlS5HXWj0kAFMHzuYrfWNPLl0faXLUYXUVLoASZL6oo3b6vn2vQsA+Ngp+xMRlSnkzisrc799wNFTR7Fg5WbmLVzLEZNHVrocVYAj2pIkVUDpaPax+46udDkqgBdEyqAtSdIetml7Q/cYzVahji65ILKpKVW4GlWCrSOSJO1hP7h/YTaaPXITxy7+Nizp4ICTP7lH6lLXmjRqIOOHDWD5hu0sWLXJOdL7IEe0JUnag0pHs/9+3xU4mN17RcTO9pGX1la4GlWCQVuSpD2oeTR79ojNHDdqU6XLUcGOdj7tPs2gLUnSHvKa3uz9ljua3QfM9oLIPq3QoB0Rp0fEcxExPyI+0cr2EyPikYhoiIj3tth2UUS8kP9cVGSdkiTtCT98IB/NnjrS0ew+4sC9hjJ0QA11a7fy8vqtlS5He1hhF0NGRDXwDeBUoA6YGxG3ppSeLtltMXAx8A8tjh0FfA6YBSTg4fxYG5wkSXtOZ+aY7uCCxY3b6rnunuaZRg4glty9O5Wph6iuCo6aOpK7nlvJ3IVrOfPwgZUuSXtQkSPaRwPzU0oLUko7gB8B7yrdIaW0MKX0ONBybdK3AneklNbk4foO4PQCa5UkqVDfvvelV+fNPs55s/uU5vaRuS/ZPtLXFBm0J/DaCYvq8tuKPlaSpG5l9abtfDfvzf6n0w903uw+5tWgbZ92n1Nk0G7tf0XKna29rGMj4sMRMS8i5q1cubJTxUmStKf8110vsnlHI28+aNyr072p7zhs4nD6V1fx3PKNrN9SX+lytAcVGbTrgEklf08ElnXlsSml61JKs1JKs8aOHbvLhUqSVJRl67bywwcXAfDx0w6ocDWqhNp+1Rw+aTgpwcOLHdXuS4oM2nOB/SNiWkT0B94P3FrmsbcDp0XEyIgYCZyW3yZJUo/yn398gR0NTZxx2N4css/wSpejCnHhmr6psKCdUmoALiMLyM8AP0kpPRURX4iIMwEiYnZE1AFnA9dGxFP5sWuAfyUL63OBL+S3SZLUY7y0ajM/mVdHdVVw+amOZvdlR09rDtqrK1yJ9qTCpvcDSCndBtzW4rbPlvw+l6wtpLVjrweuL7I+SZKKdPUdz9PYlHjfrElMHzuk0uWogmZNGUl1VfB43Xo2b29g8IBCI5i6CVeGlCSpAE8v28CvHltG/+oq/v6U/StdjipsaG0/Dp0wnIamxMOLbB/pKwzakiQV4Mu3PwvA+cdMYZ8RLlIiOGZ61j7y4ALbR/oKg7YkSV3s/hdXcedzKxncv5qPnrxvpctRN3HMtGyhIoN232HQliSpCzU1Jb502zMA/O1J+zJmyIAKV6TuYtbUkVQFr/Zpq/ezE1+SpC5062PLeHLpBvYaVsslJ0yvdDkq2p1Xlr3rUOANE+bwWN16Hl60lhMPcA2Q3s4RbUmSusi2+ka+fPtzAFx+2gEM7F9d4YrU3RwzPWsfechp/voEg7YkSV3k+/cvZOm6rRy011DOOrLV2WvVxzUH7QcXuDxIX2DQliSpC6zdvINv3DkfgE+9/WCqq6LCFak7au7TfmzJOrbssE+7tzNoS5LUBb72xxfYuK2BOfuPsfdWbXI+7b7FiyElSd1LJy4uA+DkTxZTRycs2tKfGx9cREQ2mi2155jpo3m8bj0PLljNnP39UNabOaItSdJuuvK5valvTJx15EQO3ntYpctRN7dz4Rr7tHs7g7YkSbvh/tWD+d2K4QzqX80/nHZgpctRDzBr6ij7tPsIg7YkSbuooQmueHYfAP7u5P3Ya3hthStSTzDMPu0+w6AtSdIuurluNM9tGsikgdu55IRplS5HPcgbp2XtIw/ZPtKrGbQlSdoFa3dU85UXxgPw6QNfprafi9OofDvn03bhmt7MoC1J0i64ev541jfUcPyojbx13IZKl6Me5tU+7Tr7tHszp/eTJKmTnt1Yy01LRlMdic8dvIwoem2azk55qG5v+MCsT/vxuvXMXbiWNzn3eq/kiLYkSZ2QUnYBZBPBBZNWc8CQ7ZUuST3U8fuNAeBP81dVuBIVxaAtSVIn/Hb5cB5YM4SR/Rr42L7LK12OerAT8qB93wsG7d7KoC1JUpk2NVTxhWf3BuDj+7/CiP6NFa5IPdlRU0YyoKaKp1/ewJrNOypdjgpg0JYkqUzXzB/PK9v7c/jwLZw70WnZtHtq+1Uza+pIAO5/0VHt3sigLUlSGZ7eUMv3F4+hisT/nVFHVdEXQKpPsE+7dzNoS5LUgaYEn3lmAo0puHDyag4dtq3SJamXeLVP26DdKxm0JUnqwI/rRvHIusGMG1DPx/d/pdLlqBc5ZJ/hDB/YjyVrtrJ49ZZKl6MuZtCWJKkdq3dUc9XzewHwLwcuY2hNU4UrUm9SXRUct2+2SuSf7NPudVywRpLUs3VmMZeTP9np01/53N6sb6hhzuiNnLHX+k4fL3XkuP3G8NsnX+G++as45+jJlS5HXcgRbUmS2nD/6sH8bNko+lc18YWDlxa/AqT6pOY+7QdeXE1TU6pwNepKBm1JklqxdUcjn3xqIgCXTV/BtMHOc6xiTB09iAkjBrJm8w6eeWVDpctRFzJoS5LUimt+/zyLtg7goCFbuXTaykqXo14sIjh+v7xP29lHehWDtiRJLTy2ZB3fuXcBVST+7dD/r707D5OrqvM//v5WVVfv+5ZOOp09kJCEQMIOsshmRHBBQR0EFR1UUFxmlHF0HNQfy7ihOAMOOjKobGIkjuyLIAhkI4vZm6yd7nSn932pqvP7oypJJ3STbujq29X1eT1PPXXr3lOVb+5zq+rTt849p4qgTz/nS3ydcXCYvwaPK5GRpKAtIiLST28owtcfWUfEwbVT6zk+t8vrkiQJnD4jGrSX72iguy/scTUyUjTqiIiISD93vfAGm/e1MaUwgy/PHMaY2cMZ/UTkCMXZqcwty2FjTSsrdzZx5qwir0uSEaAz2iIiIjHbatv42XPbALjlg/NJ96vLiIyes48pBuAvW+o8rkRGioK2iIgIEApH+Kffr6Mv7PjoyRUHf8oXGS1nz44G7Re26uLb8UJBW0REBLj7xe2s2dNMWW4aNy051utyJAktmpJPVmqAbXXt7G3WtQHjgYK2iIgkvQ3VLfzkma0A3H75AnLSUjyuSJJRit93cJi/F7borPZ4oKAtIiJJrScU5isPrqUv7PjEaVM4a1ax1yVJEjt7dgkAL2xVP0ZdQaMAACAASURBVO3xQEFbRESS2o+f3saW2jamFmbwjfeoy4h468AFkS9XNtAbinhcjbxTCtoiIpK0Vu5s5BcvvoHP4IcfWUhGUKPeircm5aUzqySL9p4Qq3c3eV2OvEMK2iIikpQ6ekJ89eG1RBxcd/YMFk3J97okEUCjj4wnCtoiIpKUvvfnjexq6OTYCdl86fxZXpcjctCh8bQVtBOdgraIiCSdx9bXcP/yPQQDPn5y5UJSA36vSxI56KSpBaSn+NlU00pta7fX5cg7oKAtIiJJZW9zF994ZB0A//reORw7IcfjikQOl5bi57QZsWH+1H0koSloi4hI0giFI9z4wOu0doc4f04pV506xeuSRAZ0Tqz7yPObNcxfItPl1SIikjR+9lwlK3Y2UZqTyu2XL8DMvC5Jks3ztwyp2XldKXybObywdT/dfWHSUtS9KREpaIuISPwNMVzE0/KmDH62Yhtm8OMrFlKQGfS6JJFBlaf3ceyEbDbva+PV7Q2cc0yJ1yXJ26CuIyIiMu419/q5cV0FEQefP2cGp88o8rokkaO6YG4pAM9uUveRRKWgLSIi41rEwZfXT6a6O8jCyXnceP5sr0sSGZLz5xwI2rU45zyuRt4OBW0RERnX/nN7Cc/X55CXEuLOj51Ail9ffZIY5k/KpTg7leqWbjbWtHpdjrwN+rQREZFx6+WGLH5UWYrh+Mn83ZTnZ3hdksiQ+XzG+XOifbOf2ajuI4lIQVtERMalfd0BvriuggjGDdPrOKe43euSRIbtQPeRZzbVelyJvB0K2iIiMu70ReALa6fQ0BvgzMI2vjRTIUUS0xkzi0hL8bF+bwv7WjRLZKJR0BYRkXHn1q1lrGrOZEJqL3cs2I1fw2VLgkpL8XPmzOjkNc9u1h+MiUZBW0RExpWl1Xn8clcxAXP8fOFuCoNhr0sSeUcumHugn7aCdqJR0BYRkXFjXUs6X99QDsC3j61mUV6nxxWJvHPnHVuKGbxc2UBbd5/X5cgwKGiLiMi4UNcT4LOvT6E34uOj5Q1cNbnB65JERkRxdionTSmgNxzhuc0afSSRaAp2EZFEMtypzM+9KT51jDE9EeO6NVPY1xPkpLwO/n1ONaZ+2TKOvGf+BJbvbOTx9fu4bOEkr8uRIdIZbRERSWjOwbc2TmJ1cyYT03r5z4W7CPo0i56MLxfPmwDAX7bW0dkb8rgaGSoFbRERSWi/3l3IQ3sLSPNF+MUJuyhOVQiR8acsN50TKvLo7ovwly37vS5HhkhBW0REEtYzddl8d/NEAG6ft4d5OV0eVyQSP0vmlQHw+N/3eVyJDJWCtoiIJKT1LencsG4KEYwbZ+zj0rIWr0sSiasD3Uee21RLd5+GrUwECtoiIpJw9nal8KnVU+kK+/jgxEa+NEMjMcj4N7kgg/mTcunoDfPXbfVelyNDoKAtIiIJpbXPxydXT2N/bwqnFbRz63F7NcKIJI0DZ7UfX1/jcSUyFAraIiKSMPoi8Pm1U9jansaMzG7u0ggjkmTeEwvaT2+qpSek7iNjnYK2iIgkhIiDb2wo56WGbIqCffz6xB3kpihoSHKZXpzF3LIc2rpDGn0kAShoi4jImOcc/L8tZTxSXUC6P8I9J+xkcoamopbkdNnC6Eg7y9ZUe1yJHI1mhhQRkajhzDo5yjNO/teOYu7ZVUyKRbhr4U4W5mkYP0lely6cyK1PbOaZTbW0dfeRnZbidUkyCJ3RFhGRMe3+PQXcvq0Mw/HD+Xs4u6jd65JEPFWWm87JUwvoCUV4ckOt1+XIW9AZbRERGbOeqM3hmxsnAXDznOp3Plb2cM7ai4xhly2cxGs7Gnl0zV4uX1TudTkyCJ3RFhGRMemlhiy+uLbi4IQ0V1U0eF2SyJixZP4EUvzGy5X11LV1e12ODCKuQdvMLjazLWZWaWbfGGB7qpk9GNv+mplNja2famZdZrYmdrsrnnWKiMjY8kpjJteunkqv83F1Rb0mpBE5Ql5GkLNnlxBx8Od1GlN7rIpb1xEz8wM/By4AqoAVZrbMObexX7NPA03OuZlmdiVwG3BFbNsbzrmF8apPRETegTh2wVjRlMGnV0+lO+LjI5Ma+bdjqzUhjcgALls4kWc21fLommo+ecY0r8uRAcTzjPbJQKVzbrtzrhd4ALjsiDaXAffGln8PvNtMH6ciIslqdXMG16yaRmfYzwcnNnHLcVX49K0gMqDz55SSGfSzZk8z2/frIuGxKJ5BexKwp9/jqti6Ads450JAC1AY2zbNzF43sxfM7KyB/gEz+6yZrTSzlfv3a9B2EZFEtq4lnatXTaMj7OfSCU38x7w9+BWyRQaVHvSzZH4ZAA+vqvK4GhlIPIP2QB+PR86TO1ibGqDCOXcC8BXgd2aW86aGzv3CObfYObe4uLj4HRcsIiLeWN+Szj+snEZbyM+S0mZ+NF8hW2QoPnLSZAAeWVVFKBzxuBo5UjyDdhUwud/jcuDIKYwOtjGzAJALNDrnepxzDQDOuVXAG8DsONYqIiIeWdWUwcdWTqc1FODCkhbuWLCbgMbEEhmSxVPymVaUSV1bDy9u06/7Y008P8pWALPMbJqZBYErgWVHtFkGXB1bvhx4zjnnzKw4djElZjYdmAVsj2OtIiLigb81ZHLVquiZ7PeWNnPn8btJUcgWGTIz48OLo+NoP7RC3UfGmrh9nMX6XF8PPAlsAh5yzm0ws5vN7NJYs18ChWZWSbSLyIEhAN8FrDOztUQvkrzOOdcYr1pFRGT0/WV/Fp9cfejCxzsW7CboO7KHoYgczYdOLMdn8MymWhrae7wuR/qJ68yQzrnHgMeOWPftfsvdwIcHeN4jwCPxrE1ERLzzRG0ON6ytoM/5+Fh5A9+bu1eji4i8TaU5aZxzTAnPba5j6et7ufas6V6XJDH6gU5EREbVH6rz+MLaKfQ5H5+asp/vK2SLvGMfOdB9ZOUenNMvQ2OFgraIiIwK5+CuHcV8ZX0FYWdcP72Wbx1To8loREbAeceWUpgZZGttO6t3N3tdjsQoaIuISNxFHNy8uYxbt5ZhOL597F6+NqtWIVtkhAQDPj68ODrY232v7PS0FjlEQVtEROKqJ2LcsK6C/9ldTIpF+OmC3XxqSoPXZYmMOx8/pQIzeGz9Pup1UeSYoKAtIiJx09rn45pV0/jzvjyy/GHuXbSD95W1eF2WyLg0uSCD844poTcc4cEVe47+BIk7BW0REYmLPZ0pXL58Jq80ZlEc7OPBk9/g9MIOr8sSGdeuOm0KAL97bTfhiC6K9JqCtoiIjLiVTRlc9uostranMTOzmz+cUslxOd1elyUy7r1rVjFTCjPY29zFc5vrvC4n6Sloi4jIiFpancfHVkynsS/AWYVt/OGUSiZn9HldlkhS8PmMfzglelb7vld3eVyNKGiLiMiIiDj4wbZSvry+gl7n4+qKev7nxB3kpES8Lk0kqXx4cTmpAR8vbt1PZV2b1+UkNQVtERF5x9pCPj63Zgp3bi/Fb46b5+zl3+dUE9C3jMioy8sI8qFF0Qls7vnrDo+rSW76CBQRkXeksj2Vy16dyZN1uWQHwvzqxB18okLD94l46dozp2EGf1i9l7o2XR/hlYDXBYiISBw9f0tcX/7xfTl87e+T6Qj7OSari7sX7mJqZm9c/00RObrpxVlcMKeUpzbWct8ru/jqhcd4XVJS0hltEREZtlAEbtkygc+tnUpH2M/7JjSz9JRKhWyRMeSz75oORC+K7OwNeVxNclLQFhGRYanrCfCJVdO4e2cJfnN865hqfrpgNxkBjdkrMpYsmpLPCRV5NHf28fDKKq/LSUoK2iIiMmQv1Gex5G+z+FtjNkXBPn67eDufnlqPmdeViciRzIx/jJ3Vvuel7fSFNQLQaFPQFhGRo+qLdRW5etV06ntTOL2gjcdO38apBZrpUWQsu2DuBKYXZbKnsYs/vr7X63KSji6GFBEZacO9APHcm+JTxwjZ05nCDesqWNOSid8cX5m5j+um7cevs9giY57fZ1x/3ky+8tBa7ny+kg+cMImAX+dZR4v2tIiIDMg5+GN1Hktemc2alkwmpvXy4Elv8IXpCtkiieTS4ycytTCDXQ2dPLqm2utykoqCtoiIvElDr5/Pr63gxvUVtIX8XFjSwmOnbWNxfqfXpYnIMAX8Pq4/bxYAdz5fSUh9tUeNgraIiBzm6bocLnp5No/X5pHpD3P7cXu4e+Eu8oJhr0sTkbfp/QsnMqUwgx31Hfxpnc5qjxYFbRERAaLTqP/T38v5zOtTqe9N4ZT8dp44YysfKW/SqCIiCS7g9/GFc2cC8JNntmkEklGioC0iIjxbl81FL8/m4b0FBH0RvnVMNfeftJ3J6X1elyYiI+SDJ0xienEmuxo6uX/5bq/LSQoadUREJInt7wnwnc0T+fO+PACOz+nkh/P3MDOrx+PKRGSkRzAK+H3880XHct1vVnHHM9v44InlZKUqCsaT9q6ISBJyDh7em8/3t5TREgqQ7o/w1Zn7+OSUeo0oIpKohhDML3JwYt4MVjfDL17czlcumD0KhSUvdR0REUkyb3Sk8rGV0/nnDZNpCQU4u6iVp87YwrVTFbJFxjszuGl2DQD3/HU7dW3dHlc0viloi4gkifaQj1u2TODil2fxSmMWBSkh7pi/m1+fuFN9sUWSyEn5nZw/p5TO3jA/emqr1+WMawraIiLj3IGJZ8576Rju3llCyBlXTGrkmTO3cNnEZo0oIpKEblpyLCl+48GVe1i7p9nrcsYtBW0RkXFsY2saV6yYzo3rK6jrSeH43E7+eGolt82rokDjYoskrRnFWXzqzGk4B99+9O9EIs7rksYlBW0RkXGotjvATRsmcckrs1jelEVhMMTt8/aw9JRKjs/t8ro8ERkDvnjeLEpzUllb1cJDK/d4Xc64pFFHRGT8GO5QWMNxlGGzxor2kI9f7Cjmv3cV0xX24TfHNZPr+fLMfeSmaIIKETkkMzXAN987ly/e/zq3PbGZi+dNIC8j6HVZ44rOaIuIjAN9EbhvdwHn/PUYfrq9lK6wj4tKWnjqjK18Z061QraIDOh9C8o4dXoBTZ19fP/Pm7wuZ9xR0BYRSWARB8tqcrno5WP41qZy6ntTODGvg9+fXMndJ+xiRqYmnhGRwZkZ3//AfIIBHw+vquKFrfu9LmlcUdAWEUlAEQd/3pfLxX+bzRfXTWF7ZyrTMnq4a+FOHjn5DRbnd3pdoogkiBnFWXz5/OjENf/yh/W094Q8rmj8UB9tEZEE4hw8WZfDTypL2dyeDsCktF6un17H5ZMaSdHpExF5Gz5z1jQeW1/D+r0t3Pb4Zr77/nlelzQu6CNZRCQBhCOOx9fXcMkrs7huzVQ2t6dTltbL9+ZW8dxZW/joZIVsEXn7An4ft1++gIDPuO/VXfx1m7qQjASd0RYRGcN6QmGWrt7L3S9uZ0d9B5BOSWofX5hWxxXljaT5NfatiIyMOWU5fOnds/jh01v5ykNreeJLZ1GYlep1WQlNQVtEZAxq6+7jd6/t5pcv7aCuLXpBY3l+Op8prVTAFpG4+fy5M/lrZT3LdzTyT79fxy+vXoxp+ti3TUFbRGQMqW7u4r5Xd/GbV3fR1h29IOnYCdl87pwZvHd+GYEXX/O4QhEZz/w+4ydXLOQ9d/yV5zbX8eu/7eSTZ0zzuqyEpaAtIuIx5xzLdzRy7ys7eXJDLeHYVMgnTyvgc+fM4JzZxTqjJCKjZmJeOrd9aD7X/WY1tzy2mYWT8zihIt/rshKSgraIiEe6w8aymjz+56cvsammFYCAz7j0+Ilcc8ZUTtQXm4h45OJ5ZVx92hTufWUX1/1mFX+64UxKstO8LivhKGiLiAzFCE7vXtmeykN7C3h4bz5NfQGglcLMIB8/pYKPnzqF0hx9mYmI9/71krlsqmlj+c5GvvDb1fz22lMJBjS80XAoaIuIjIKusPHYvlwe3FvA8qasg+vn5XTyyYtO470LykhL8XtYoYgklSGcPEgBfj41wPsaT2TFzib+/U8b+N7756kr2zAoaIuIxNHG1jQeqCpgaU0+baFokM7wh3nfhBauLG9gYW4XtujDHlcpIjKw4tQQd121iI/c/Qq/fW03kwsyuO7sGV6XlTAUtEVERlhNdwrLavJYWp13cPZGgONzOrmyvJH3lTWTFYgcesIIdksRERlpCyfn8eOPLOT6+1dz6+ObKctN47KFk7wuKyEoaIuIjIDWPh9P1OXyx+o8XmnMwhH9aTU3EOL9E5u5YlIjc3O6Pa5SROTtee+CMva1zuW7/7eRrz28lqKsVM6YWeR1WWOegraIyNvUFTZeqM/mT/vyeKYuh55I9CKhoC/C+cUtvH9iM2cXtZHq0+QyIpL4Pn3mNKqbu/jlSzu49t6V3Pupkzl5WoHXZY1pCtoiIsPQHvLx3P5snqjN5fn6HLrCh67APzW/nQ9MbOLi0hZyUyJv8SoiIgmkX/e2b2ZAy8Ryfl9dwDX3vMT/LtrB4vzOw9ufe9MoFzh2KWiLiBxFS5+PZ/fn8HhtLi/UZ9MbORSuj8/tZElpC5dMaGZSep+HVYqIxJ/P4LZ5VYSdsbQmn2tWTePeRTtYdGTYFkBBW0TkTZyD7Z2pPLc/m2frcljZnEnIRftcG46T8jq4uLSFi0tbFK5FJOn4DX4wfw9hB8v25fPxldP5r4W7OLe4zevSxhwFbRERoDdiLG/K5Nm6bJ7bn8OurtSD2/zmODW/nSUTWriopIXStJCHlYqIeM9v8KP5e0j1Ox7eW8BnXp/K7fP28MGJzV6XNqYoaItIUjpw1vrlhiz+2pDF3xqy6AgfmjAmLyXEuUVtnFfcyruK2slNCXtYrYjI2BPwwe3HVVEYDHHXjhK+sr6Cmu4UPn+O06Q2MQraIpI09vcEeLkhi5casni5MYua7uBh24/N6uK84mi4PiGvE7++J0RE3pIZfGP2PoqCIb63ZSL/sa2MzQ+s4fYPLSA9OITZboc7j0CCXWipoC0i41Z9j5+VzZksb8rkbw1Zh00eA1AYDHF6QTtnFrZxZmG7+luLiLxN106tpyKjly+vm8yf1lazfX87d1+1iPL8DK9L85SCtoiMC845qrpSWN6YyYqmTJY3Z7K9I+2wNmm+CCfnd3BWURtnFLRzbHY3Pp21FhEZEReWtLL01Eo+s3oqG6pbWfLDp7n1uL0smdDidWmeUdAWkYTUEwqzqaaNtXuaWbWriRU7G6lpmXNYmzRfhBPzOjgpv5NT8ttZlN+pyWNEROJodlYPj55ayVf/Ppln9+fw+bVTuLK+gW8fW01GIPk+fxW0RWTMc86xo76DtVXNrNndzJqqFjZVt9IbPnxSmNxAiJPyOzkpv4OT8juYl9NFUMFaRGRU5QXD3HPCTu7bU8j3tpTxwN5CXmnM4pbjqji9sMPr8kaVgraIjCmRiGN3YycbqlvZWNPCuqroraXr8P7TZjCzJIuFk/M4fnIeJ08tYNbGO9UVRERkDDCDT1Q0cHJ+Bzeum8zm9nQ+tnIGH57UyDdn15AXTI6RnBS0RcQz3X1httW2s6G6hY01rWysbmVTTSsdvW/+AC7OTmXh5LyDt/nlueSkpRzeaNMoFS4iIkNybHY3y06r5O4dxfzsjRIe3lvAc/tzuHHGPj5a3kjAd/TXSGQK2iISd33hCLsaOqmsa2NrbTvb6trZuq+Nyv3thCNv7tpRmpPK3LIcjpuYy3ETczh+ch5luWkal1VEJAEFfY4bZtSxZEIL/7JhEq81ZfGtTeXcu7uIfzmmhnOL2hivH+8K2iIyYnpDEXY1dMTCdBvb6trZVtvGjvoO+sJvDtS+WPeP4ybmMLcsh7kTc5hTlkNRVuoAry4iIolsRmYPD5y0nSdqc7h1axmVHWl8avU0FuV1cMP0Ws4uah93gVtBW0SGpS8cYU9jJ7saOtlR38HOhg521Hewq6GTqqZOBjhBDUB5fjqzSrKYXZrNzJIsZpVmc0xp9tAmNBARkXHBDN4zoZXzStq4b3chd24vYVVzJtesns6CnE4+P72OC0pax82EYQraIvImLV19VDV1srepi6qmLnY3HgrVVU1dA3b3gOgH6OSCdGaXZDOrNJtZJVnMKs1iZkkWGUF93IiISFSqz3Ht1HquLG/kN3sKuWdnEetaM7huzVQmpfVyVUUDV0xqJD/BL5rUN59IknHO0dzZR1VTVzRMN3fFlrsOhuu2ntCgzzeDSXnpTCvKZEphBtOKMplamMnUokwmF6STGtAZahERGZqsQITrpu3n6op67q8q4Ne7itjdlcqtW8v4cWUpS0pb+MDEJk4vaE/ICycVtEXGkb5whLq2Hva1dFPb2n3oPrZ8YFtX31ufIcgI+inPT2dSXjrl+RlUFGQwtSiTqYUZTC7IIC1FYVpEREZOut/xqSkNXFPRwAv12dy7u5C/1OewtCafpTX5FAf7uLSsmffNbGbBpFx8CTKWqzk3PiZzWLx4sVu5cqXXZYiMOOccLV191Lf3Ut/eQ8PB+x72t/dS19pNbVs3+1p6aOjoYShv6azUAOX56bFbxmGhujw/nbyMlMQc4eP5W7yuQERERsjuziBLq/NYWpPPzs5DF8mXZKfy7jmlXDi3lNNmFHpy8sfMVjnnFh+1nYK2yOgKRxytXX00dfbS3NVHc2cvTR19NHREQ/T+w8J0Lw0dPQOO2DEQn0FRVioTctMozUljQk7aYculOamU5qaRnRpIzCB9NAraIiLjjnOwpiWdR2vyeaqlnOqW7oPb0lJ8LJqSz+kzijh1eiELynNJ8cf6mAz3O+Hcm4bcdKhBW11HRN4G5xw9oQit3X20dYdo6w4dCs+dfTR3RpdbYuuaOvtoid23dvcN6axzf1mpAYqyghRlpVJ48D6V4qwgxdmp0SCdm0ZxVioBfwJ2YhMRERmEGZyQ18UJeV38m6tmQ1saz9Tl8HRdLhva0nm5soGXKxsAyPSHOSGvk+NzO1mQk8PC3E5K0wa/7ijeFLQlqRwIyJ29YTp7Q3T2hunoCR0My20Hg3MfrUeu6+k7rN1QzzIPJCctQF5GkPyMFHJj9wWZ0QBdfFiYjt6rT7SIiEg0dM/L6WZeTjc3zqyjvsfPq01ZvNKYxSuNmWzvSOOlhmxeasg++JwJqb0cl9PNrKxuZmd1Mzurh5mZ3aT549+rQ0Fbxpy+cITuvjDdfdH7ntCh5QP3XX2HgvKBsHzwvi9MZ0+IjgNhuifWJtZ+sKHphivo95GdFojdUshJPxSe89KD5GWkHHp8cDlITlpAZ51FRERGQFFqmEsmtHDJhBYAarsDvN6SwbqWDNa2pLOuNYN9PUH27Q/y7P6cg88zHBXpvUzJ6KUio4eK9F4qimuYXBC96D8nLWVE6otr0Dazi4E7AD9wj3Pu1iO2pwL/CywCGoArnHM7Y9tuAj4NhIEvOueejGetyc45Rzji6A1H6As5esJh+sKO3lCEvnCE3lCE3gP3A6yLto09p9+2A+G4py9M92GBObYcCtPTf10oMmJBeDApfiMjGCAz6CcjNUBG0E9War/AnJZyMEAfWk45LFRnpwV0lllERGSMKU0LcXFaKxeXtgIQcbCjM5XNbWlsbU9jW3sqW9rT2NmZyq6u6I0DZ7+3rj74OtmpAUpyUinJjl7fVJKTRkn2ofuhilvQNjM/8HPgAqAKWGFmy5xzG/s1+zTQ5JybaWZXArcBV5jZXOBK4DhgIvCMmc12zsVt1HLnHBEXvVAt4qK3cMQRiUA49jgScYRj612sbTi2vv9zD38NYu0PPTcUdoQijlAkGir7wo5wJBJdd2Bb+NDjcCRCX8TF2kaf8+Y2h7ZF20Zir3uo7YE2AwbocGTY/YbjxWeQluKP3gI+0lL8pKb4SUvxkRaI3qcH/WQGoyE5IzUWmgd4nJnqj64LBsgMBkgP+gkm4kCcIiIiMmw+i079PiOzh/fScnB9T8TY1Rlkd2eQ3V1BdnemUtV1YDlIW0+Itv0h3tjf8Y7+/Xie0T4ZqHTObQcwsweAy4D+Qfsy4Dux5d8Dd1p0KITLgAeccz3ADjOrjL3eK4P9Y1tr23j3D/9yeFg+GIw5LOgeDMb92oyVkOkln0Ew4CPF7yM1dh8M+Aj6D1+OtrGDbQ9f7zvsNVJjQTntsKAcW47dp/Zblxrwk+K38TkihoiIiIwJqT7H7KweZmf1vGmbc9DS56euN0Btdwp1PQFqe1Ko64ku1/WksGuI/048g/YkYE+/x1XAKYO1cc6FzKwFKIytf/WI5056q3+sJxR5x391mIHfDJ/P8PVb9vsMn0Vvfl90vVl0vT/W1meH2h1c57PDXy+2PcXvI+AzAn7D7/OREnudQL/1gf6Pfb5Y29j6w9oe8RyfD3+/5cAAyylHhOIDIdmfIIO/i4iIiMSLGeQFw+QFwwMGcYChJqZ4Bu2BajjyvPFgbYbyXMzss8BnYw/bd912yZZhVfjOFAH1o/jvJTrtr+HTPhse7a/h0z4bHu2v4dM+Gx7tr+Hzap9NGUqjeAbtKmByv8flQPUgbarMLADkAo1DfC7OuV8AvxjBmofMzFYOZaByidL+Gj7ts+HR/ho+7bPh0f4aPu2z4dH+Gr6xvs/ieVXYCmCWmU0zsyDRixuXHdFmGXB1bPly4DkXnapyGXClmaWa2TRgFrA8jrWKiIiIiIyouJ3RjvW5vh54kujwfr9yzm0ws5uBlc65ZcAvgftiFzs2Eg3jxNo9RPTCyRDwhXiOOCIiIiIiMtLiOo62c+4x4LEj1n2733I38OFBnvt94PvxrO8d8qTLSgLT/ho+7bPh0f4aPu2z4dH+Gj7ts+HR/hq+Mb3PzGlcOxERERGREaeZO0RERERE4kBBe4jM7EEzWxO77TSzNYO022lm62PtVo52nWOFmX3HzPb222dLBml3sZltMbNKM/vGaNc5lpjZf5jZZjNbZ2ZLzSxvkHZJfYwd7ZiJWeFsnQAABplJREFUXUT9YGz7a2Y2dfSrHDvMbLKZPW9mm8xsg5l9aYA255hZS7/367cHeq1kcbT3mEX9NHaMrTOzE72oc6wws2P6HTtrzKzVzG48ok1SH2Nm9iszqzOzv/dbV2BmT5vZtth9/iDPvTrWZpuZXT1Qm/FokH2WcN+T6jryNpjZD4EW59zNA2zbCSx2ziX1OJhm9h2g3Tn3g7do4we2AhcQHdJxBfBR59zGwZ4znpnZhURH3gmZ2W0AzrmvD9BuJ0l6jA3lmDGzzwMLnHPXmdmVwAecc1d4UvAYYGZlQJlzbrWZZQOrgPcfsc/OAb7mnLvEozLHlKO9x2InDm4AlhCdiO0O59yRE7Ilpdh7dC9winNuV7/155DEx5iZvQtoB/7XOTcvtu52oNE5d2vspEH+kZ/5ZlYArAQWE51PZBWwyDnXNKr/AQ8Mss8S7ntSZ7SHycwM+Ahwv9e1jAMnA5XOue3OuV7gAeAyj2vyjHPuKedcKPbwVaLjx8vhhnLMXAbcG1v+PfDu2Ps2KTnnapxzq2PLbcAmjjLTrhzVZUS//J1z7lUgL/YHjcC7gTf6h2wB59yLREdX66//Z9W9wPsHeOpFwNPOucZYuH4auDhuhY4hA+2zRPyeVNAevrOAWufctkG2O+ApM1tl0Zkrk9n1sZ93fjXIT2KTgD39HlehAHDAp4DHB9mWzMfYUI6Zg21iH8gtQOGoVDfGxbrRnAC8NsDm08xsrZk9bmbHjWphY8/R3mP67BrclQx+IkrH2OFKnXM1EP2DGCgZoI2OtcElxPdkXIf3SzRm9gwwYYBN33TOPRpb/ihvfTb7DOdctZmVAE+b2ebYX2XjzlvtL+C/gO8SPdi/C/yQ6JvisJcY4Lnjui/TUI4xM/sm0fHjfzvIyyTNMTaAoRwzSXdcDYWZZQGPADc651qP2LwamOKca491i/gj0YnCktXR3mM6xgZg0cnpLgVuGmCzjrG3R8faABLpe1JBux/n3Plvtd2i08R/EFj0Fq9RHbuvM7OlRH/qHpch6Gj76wAz+2/g/wbYVAVM7ve4HKgegdLGrCEcY1cDlwDvdoNcQJFMx9gAhnLMHGhTFXvP5vLmn2yTipmlEA3Zv3XO/eHI7f2Dt3PuMTP7TzMrGgv9G70whPdY0n12DdF7gNXOudojN+gYG1CtmZU552piXY/qBmhTBZzT73E58JdRqG3MSrTvSXUdGZ7zgc3OuaqBNppZZuxiI8wsE7gQ+PtAbce7I/orfoCB98MKYJaZTYudCbkSWDYa9Y1FZnYx8HXgUudc5yBtkv0YG8oxsww4cGX+5UQvnEnaM0Cx/um/BDY55340SJsJB/qxm9nJRL8bGkavyrFjiO+xZcAnLOpUohfH14xyqWPRoL/46hgbUP/PqquBRwdo8yRwoZnlx7pgXhhbl5QS8XtSZ7SH5019z8xsInCPc24JUAosjX2WBIDfOeeeGPUqx4bbzWwh0Z+4dgL/CIfvr9hVw9cT/dDwA79yzm3wquAx4E4glejPXACvxkbO0DEWM9gxY2Y3Ayudc8uIhsr7zKyS6JnsK72reEw4A7gKWG+HhiX9F6ACwDl3F9E/SD5nZiGgC7gyif84GfA9ZmbXwcH99RjREUcqgU7gkx7VOmaYWQbR0YD+sd+6/vssqY8xM7uf6JnpIjOrAv4NuBV4yMw+DewmNlO2mS0GrnPOXeucazSz7xI9yQBws3MuKX6hG2Sf3USCfU9qeD8RERERkThQ1xERERERkThQ0BYRERERiQMFbRERERGROFDQFhERERGJAwVtEREREZE40PB+IiLjiJkVAs/GHk4AwsD+2ONO59zpnhQmIpKENLyfiMg4ZWbfAdqdcz/wuhYRkWSkriMiIknCzNpj9+eY2Qtm9pCZbTWzW83s42a23MzWm9mMWLtiM3vEzFbEbmd4+z8QEUksCtoiIsnpeOBLwHyiM0fOds6dDNwD3BBrcwfwY+fcScCHYttERGSI1EdbRCQ5rXDO1QCY2RvAU7H164FzY8vnA3NjUxkD5JhZtnOubVQrFRFJUAraIiLJqaffcqTf4wiHvht8wGnOua7RLExEZLxQ1xERERnMU8D1Bx6Y2UIPaxERSTgK2iIiMpgvAovNbJ2ZbQSu87ogEZFEouH9RERERETiQGe0RURERETiQEFbRERERCQOFLRFREREROJAQVtEREREJA4UtEVERERE4kBBW0REREQkDhS0RURERETiQEFbRERERCQO/j8z/70lNx5LqQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# # Load data\n", + "data = dwell2\n", + "\n", + "# Plot for comparison\n", + "plt.figure(figsize=(12,8))\n", + "ax = data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True\n", + " #, color=plt.rcParams['axes.color_cycle'][1]\n", + " )\n", + "# Save plot limits\n", + "dataYLim = ax.get_ylim()\n", + "\n", + "# Find best fit distribution\n", + "best_fit_name, best_fir_paramms = best_fit_distribution9(data, 200, ax)\n", + "best_dist = getattr(st, best_fit_name)\n", + "\n", + "# Update plots\n", + "ax.set_ylim(dataYLim)\n", + "ax.set_title(u'Trips to Work\\n All Best Fitted Distributions')\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "# Make PDF\n", + "pdf = make_pdf(best_dist, best_fir_paramms)\n", + "\n", + "# Display\n", + "plt.figure(figsize=(12,8))\n", + "ax = pdf.plot(lw=2, label='PDF', legend=True)\n", + "data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True, ax=ax)\n", + "\n", + "param_names = (best_dist.shapes + ', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']\n", + "param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, best_fir_paramms)])\n", + "dist_str = '{}({})'.format(best_fit_name, param_str)\n", + "\n", + "ax.set_title(u'Trips to Work with best-fit distribution \\n' + dist_str)\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "print (dist_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "#For HW\n", + "def best_fit_distribution10(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " \n", + "# st.gennorm,\n", + " st.exponnorm,\n", + " st.foldnorm, st.gumbel_l,\n", + " st.logistic,\n", + " st.t,\n", + " st.foldcauchy,\n", + " st.tukeylambda,st.skewnorm\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.norm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "foldnorm(c=1.70, loc=0.05, scale=3.15)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# # Load data\n", + "data = dwell3\n", + "\n", + "# Plot for comparison\n", + "plt.figure(figsize=(12,8))\n", + "ax = data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True\n", + " #, color=plt.rcParams['axes.color_cycle'][1]\n", + " )\n", + "# Save plot limits\n", + "dataYLim = ax.get_ylim()\n", + "\n", + "# Find best fit distribution\n", + "best_fit_name, best_fir_paramms = best_fit_distribution10(data, 200, ax)\n", + "best_dist = getattr(st, best_fit_name)\n", + "\n", + "# Update plots\n", + "ax.set_ylim(dataYLim)\n", + "ax.set_title(u'Trips to Work\\n All Best Fitted Distributions')\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "# Make PDF\n", + "pdf = make_pdf(best_dist, best_fir_paramms)\n", + "\n", + "# Display\n", + "plt.figure(figsize=(12,8))\n", + "ax = pdf.plot(lw=2, label='PDF', legend=True)\n", + "data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True, ax=ax)\n", + "\n", + "param_names = (best_dist.shapes + ', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']\n", + "param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, best_fir_paramms)])\n", + "dist_str = '{}({})'.format(best_fit_name, param_str)\n", + "\n", + "ax.set_title(u'Trips to Work with best-fit distribution \\n' + dist_str)\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "print (dist_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "#For HW\n", + "def best_fit_distribution11(data, bins=200, ax=None):\n", + " \"\"\"Model data by finding best fit distribution to data\"\"\"\n", + " # Get histogram of original data\n", + " y, x = np.histogram(data, bins=bins, density=True)\n", + " x = (x + np.roll(x, -1))[:-1] / 2.0\n", + "\n", + " # Distributions to check\n", + " DISTRIBUTIONS = [ \n", + " \n", + " st.cauchy,\n", + " st.gennorm,st.genlogistic, st.invgauss,\n", + " st.johnsonsu,\n", + " st.powernorm,\n", + " st.foldcauchy,\n", + " st.loglaplace\n", + " \n", + " ]\n", + "\n", + " # Best holders\n", + " best_distribution = st.norm\n", + " best_params = (0.0, 1.0)\n", + " best_sse = np.inf\n", + "\n", + " # Estimate distribution parameters from data\n", + " for distribution in DISTRIBUTIONS:\n", + "\n", + " # Try to fit the distribution\n", + " try:\n", + " # Ignore warnings from data that can't be fit\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings('ignore')\n", + "\n", + " # fit dist to data\n", + " params = distribution.fit(data)\n", + "\n", + " # Separate parts of parameters\n", + " arg = params[:-2]\n", + " loc = params[-2]\n", + " scale = params[-1]\n", + "\n", + " # Calculate fitted PDF and error with fit in distribution\n", + " pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n", + " sse = np.sum(np.power(y - pdf, 2.0))\n", + "\n", + " # if axis pass in add to plot\n", + " try:\n", + " if ax:\n", + " pd.Series(pdf, x).plot(ax=ax, label=distribution.name,legend=True)\n", + " end\n", + " except Exception:\n", + " pass\n", + "\n", + " # identify if this distribution is better\n", + " if best_sse > sse > 0:\n", + " best_distribution = distribution\n", + " best_params = params\n", + " best_sse = sse\n", + "\n", + " except Exception:\n", + " pass\n", + "\n", + " return (best_distribution.name, best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gennorm(beta=0.71, loc=8.42, scale=1.40)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# # Load data\n", + "data = dwell4\n", + "\n", + "# Plot for comparison\n", + "plt.figure(figsize=(12,8))\n", + "ax = data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True\n", + " #, color=plt.rcParams['axes.color_cycle'][1]\n", + " )\n", + "# Save plot limits\n", + "dataYLim = ax.get_ylim()\n", + "\n", + "# Find best fit distribution\n", + "best_fit_name, best_fir_paramms = best_fit_distribution11(data, 200, ax)\n", + "best_dist = getattr(st, best_fit_name)\n", + "\n", + "# Update plots\n", + "ax.set_ylim(dataYLim)\n", + "ax.set_title(u'Trips to Work\\n All Best Fitted Distributions')\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "# Make PDF\n", + "pdf = make_pdf(best_dist, best_fir_paramms)\n", + "\n", + "# Display\n", + "plt.figure(figsize=(12,8))\n", + "ax = pdf.plot(lw=2, label='PDF', legend=True)\n", + "data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True, ax=ax)\n", + "\n", + "param_names = (best_dist.shapes + ', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']\n", + "param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, best_fir_paramms)])\n", + "dist_str = '{}({})'.format(best_fit_name, param_str)\n", + "\n", + "ax.set_title(u'Trips to Work with best-fit distribution \\n' + dist_str)\n", + "ax.set_xlabel(u'Time')\n", + "ax.set_ylabel('Frequency')\n", + "\n", + "print (dist_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use distribution to simulate synthetic home-work trip end times" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "obs = pd.read_csv('/home/emma/ual_model_workspace/spring-2019-models/notebooks-emma/synthetic_032319.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "obs['HW_ET'] = st.johnsonsu.rvs(size= len(obs), a=-0.71, b=1.00, loc=7.12, scale=1.31)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "while len(obs.loc[(obs['TOD'] == 0) & ((obs['HW_ET'] < 3) | (obs['HW_ET'] >= 6))]) > 0:\n", + " obs.loc[(obs['TOD'] == 0) & ((obs['HW_ET'] < 3) | (obs['HW_ET'] >= 6)),\n", + " 'HW_ET'] = st.johnsonsu.rvs(size= len(obs.loc[(obs['TOD'] == 0) & ((obs['HW_ET'] < 3) | (obs['HW_ET'] >= 6))]), a=-0.71, b=1.00, loc=7.12, scale=1.31)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "while len(obs.loc[(obs['TOD'] == 1) & ((obs['HW_ET'] < 6) | (obs['HW_ET'] >= 9))]) > 0:\n", + " obs.loc[ (obs['TOD'] == 1) & ((obs['HW_ET'] < 6) | (obs['HW_ET'] >= 9)),\n", + " 'HW_ET'] = st.johnsonsu.rvs(size= len(obs.loc[(obs['TOD'] == 1) & ((obs['HW_ET'] < 6) | (obs['HW_ET'] >= 9))]), a=-0.71, b=1.00, loc=7.12, scale=1.31)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "while len(obs.loc[(obs['TOD'] == 2) & ((obs['HW_ET'] < 9) | (obs['HW_ET'] >= 15.5))]) > 0:\n", + " obs.loc[(obs['TOD'] == 2) & ((obs['HW_ET'] < 9) | (obs['HW_ET'] >= 15.5)),\n", + " 'HW_ET'] = st.johnsonsu.rvs(size= len(obs.loc[(obs['TOD'] == 2) & ((obs['HW_ET'] < 9) | (obs['HW_ET'] >= 15.5))]), a=-0.71, b=1.00, loc=7.12, scale=1.31)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "while len(obs.loc[(obs['TOD'] == 3) & ((obs['HW_ET'] < 15.5) | (obs['HW_ET'] >= 18.5))]) > 0:\n", + " obs.loc[(obs['TOD'] == 3) & ((obs['HW_ET'] < 15.5) | (obs['HW_ET'] >= 18.5)),\n", + " 'HW_ET'] = st.johnsonsu.rvs(size= len(obs.loc[(obs['TOD'] == 3) & ((obs['HW_ET'] < 15.5) | (obs['HW_ET'] >= 18.5))]), a=-0.71, b=1.00, loc=7.12, scale=1.31)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "while len(obs.loc[(obs['TOD'] == 4) & ((obs['HW_ET'] < 18.5) | (obs['HW_ET'] >= 27))]) > 0:\n", + " obs.loc[(obs['TOD'] == 4) & ((obs['HW_ET'] < 18.5) | (obs['HW_ET'] >= 27)),\n", + " 'HW_ET'] = st.johnsonsu.rvs(size= len(obs.loc[(obs['TOD'] == 4) & ((obs['HW_ET'] < 18.5) | (obs['HW_ET'] >= 27))]), a=-0.71, b=1.00, loc=7.12, scale=1.31)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "obs.loc[ (obs['HW_ET'] > 24),\n", + " 'HW_ET'] = obs['HW_ET'] - 24" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assign synthetic work dwell times from distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [], + "source": [ + "# need to create a dwell_exact column first\n", + "\n", + "# obs.loc[obs['TOD'].isin([0]),'dwell_work'] = st.johnsonsu.rvs(size= len(obs['TOD'].isin([0])), a=-0.16, b=1.09, loc=9.37, scale=1.54)\n", + "# obs.loc[obs['TOD'].isin([1]),'dwell_work'] = st.foldcauchy.rvs(size= len(obs['TOD'].isin([1])), c=12.34, loc=-0.05, scale=0.73)\n", + "# obs.loc[obs['TOD'].isin([2]),'dwell_work'] = st.genlogistic.rvs(size= len(obs['TOD'].isin([2])), c=0.31, loc=8.97, scale=0.79)\n", + "# obs.loc[obs['TOD'].isin([3]),'dwell_work'] = st.foldnorm.rvs(size= len(obs['TOD'].isin([3])), c=1.70, loc=0.05, scale=3.15)\n", + "# obs.loc[obs['TOD'].isin([4]),'dwell_work'] = st.gennorm.rvs(size= len(obs['TOD'].isin([4])), beta=0.71, loc=8.42, scale=1.40)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tripsIII['dwell_work'] = (\n", + " ((tripsIII.work_dwell.between(0,4.5,inclusive = False)) | (tripsIII.work_dwell==0))*1 +\n", + " ((tripsIII.work_dwell.between(4.5,7.75,inclusive = False)) | (tripsIII.work_dwell==4.5))*2 +\n", + " ((tripsIII.work_dwell.between(7.75,9.0,inclusive = False)) | (tripsIII.work_dwell==7.75))*3 +\n", + " ((tripsIII.work_dwell.between(9.0,10.5,inclusive = False)) | (tripsIII.work_dwell==9.0))*4 +\n", + " ((tripsIII.work_dwell>=10.5))*5)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [], + "source": [ + "obs['dwell_exact'] = st.johnsonsu.rvs(size= len(obs), a=0.49, b=0.94, loc=9.29, scale=1.26)" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [], + "source": [ + "while len(obs.loc[(obs['dwell_work'] == 1) & (obs['dwell_exact'] >= 4.5)]) > 0:\n", + " obs.loc[(obs['dwell_work'] == 1) & (obs['dwell_exact'] >= 4.5),\n", + " 'dwell_exact'] = st.johnsonsu.rvs(size= len(obs.loc[(obs['dwell_work'] == 1) & (obs['dwell_exact'] >= 4.5)]), a=0.49, b=0.94, loc=9.29, scale=1.26)" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [], + "source": [ + "while len(obs.loc[(obs['dwell_work'] == 2) & ((obs['dwell_exact'] < 4.5) | (obs['dwell_exact'] >= 7.75))]) > 0:\n", + " obs.loc[(obs['dwell_work'] == 2) & ((obs['dwell_exact'] < 4.5) | (obs['dwell_exact'] >= 7.75)),\n", + " 'dwell_exact'] = st.johnsonsu.rvs(size= len(obs.loc[(obs['dwell_work'] == 2) & ((obs['dwell_exact'] < 4.5) | (obs['dwell_exact'] >= 7.75))]), a=0.49, b=0.94, loc=9.29, scale=1.26)" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [], + "source": [ + "while len(obs.loc[(obs['dwell_work'] == 3) & ((obs['dwell_exact'] < 7.75) | (obs['dwell_exact'] >= 9.0))]) > 0:\n", + " obs.loc[(obs['dwell_work'] == 3) & ((obs['dwell_exact'] < 7.75) | (obs['dwell_exact'] >= 9.0)),\n", + " 'dwell_exact'] = st.johnsonsu.rvs(size= len(obs.loc[(obs['dwell_work'] == 3) & ((obs['dwell_exact'] < 7.75) | (obs['dwell_exact'] >= 9.0))]), a=0.49, b=0.94, loc=9.29, scale=1.26)" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [], + "source": [ + "while len(obs.loc[(obs['dwell_work'] == 4) & ((obs['dwell_exact'] < 9.0) | (obs['dwell_exact'] >= 10.5))]) > 0:\n", + " obs.loc[(obs['dwell_work'] == 4) & ((obs['dwell_exact'] < 9.0) | (obs['dwell_exact'] >= 10.5)),\n", + " 'dwell_exact'] = st.johnsonsu.rvs(size= len(obs.loc[(obs['dwell_work'] == 4) & ((obs['dwell_exact'] < 9.0) | (obs['dwell_exact'] >= 10.5))]), a=0.49, b=0.94, loc=9.29, scale=1.26)" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [], + "source": [ + "while len(obs.loc[(obs['dwell_work'] == 5) & ((obs['dwell_exact'] < 10.5) | (obs['dwell_exact'] >= 24))]) > 0:\n", + " obs.loc[(obs['dwell_work'] == 5) & ((obs['dwell_exact'] < 10.5) | (obs['dwell_exact'] >= 24)),\n", + " 'dwell_exact'] = st.johnsonsu.rvs(size= len(obs.loc[(obs['dwell_work'] == 5) & ((obs['dwell_exact'] < 10.5) | (obs['dwell_exact'] >= 24))]), a=0.49, b=0.94, loc=9.29, scale=1.26)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add work dwell time to home-to-work trip end times to get work-to-home trip start times" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [], + "source": [ + "obs['WH_ST'] = obs['HW_ET'] + obs['dwell_exact']" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [], + "source": [ + "obs.loc[ (obs['WH_ST'] > 24),\n", + " 'WH_ST'] = obs['WH_ST'] - 24" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}