diff --git a/docs/nodes/AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/examples/EX1/app.json b/docs/nodes/AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/examples/EX1/app.json
index 0b29d550c2..c1a7d0662b 100644
--- a/docs/nodes/AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/examples/EX1/app.json
+++ b/docs/nodes/AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/examples/EX1/app.json
@@ -18,7 +18,7 @@
"desc": "path to the file to be loaded.",
"functionName": "LOCAL_FILE",
"param": "file_path",
- "value": "PYTHON/nodes/AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/assets/President_Barack_Obama.jpg"
+ "value": "AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/assets/President_Barack_Obama.jpg"
},
"file_type": {
"type": "select",
@@ -73,8 +73,8 @@
"dragging": true
},
{
- "width": 380,
- "height": 293,
+ "width": 225,
+ "height": 226,
"id": "IMAGE-b931ca30-e0fa-40c0-b7fd-bf3fd9d52ac0",
"type": "VISUALIZERS",
"data": {
@@ -115,8 +115,8 @@
"dragging": true
},
{
- "width": 380,
- "height": 293,
+ "width": 225,
+ "height": 226,
"id": "TABLE-f1d5b18c-5c89-4577-b460-53c7827703d4",
"type": "VISUALIZERS",
"data": {
@@ -173,7 +173,7 @@
"desc": "path to the file to be loaded.",
"functionName": "LOCAL_FILE",
"param": "file_path",
- "value": "PYTHON/nodes/AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/assets/class_names.csv"
+ "value": "AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/assets/class_names.csv"
},
"file_type": {
"type": "select",
@@ -228,7 +228,7 @@
"dragging": true
},
{
- "width": 208,
+ "width": 231,
"height": 96,
"id": "TORCHSCRIPT_CLASSIFIER-51ef4ad1-ce39-4cf8-84ed-0474f8a25283",
"type": "AI_ML",
@@ -286,8 +286,8 @@
"dragging": true
},
{
- "width": 380,
- "height": 293,
+ "width": 225,
+ "height": 226,
"id": "TABLE-a791e475-0516-453c-af76-33fa8864ff67",
"type": "VISUALIZERS",
"data": {
@@ -389,5 +389,6 @@
"i": "INPUT_PLACEHOLDER"
}
}
- ]
+ ],
+ "textNodes": []
}
\ No newline at end of file
diff --git a/docs/nodes/AI_ML/IMAGE_CAPTIONING/NLP_CONNECT_VIT_GPT2/examples/EX1/app.json b/docs/nodes/AI_ML/IMAGE_CAPTIONING/NLP_CONNECT_VIT_GPT2/examples/EX1/app.json
index 97f4dc2a97..25a8e00c4b 100644
--- a/docs/nodes/AI_ML/IMAGE_CAPTIONING/NLP_CONNECT_VIT_GPT2/examples/EX1/app.json
+++ b/docs/nodes/AI_ML/IMAGE_CAPTIONING/NLP_CONNECT_VIT_GPT2/examples/EX1/app.json
@@ -2,8 +2,8 @@
"rfInstance": {
"nodes": [
{
- "width": 150,
- "height": 150,
+ "width": 160,
+ "height": 160,
"id": "LOCAL_FILE-37e74f08-bfb2-49d1-bec2-d962fda0f0a6",
"type": "LOADERS",
"data": {
@@ -17,7 +17,7 @@
"default": null,
"functionName": "LOCAL_FILE",
"param": "file_path",
- "value": "./PYTHON/nodes/AI_ML/IMAGE_CAPTIONING/NLP_CONNECT_VIT_GPT2/assets/President_Barack_Obama.jpg"
+ "value": "AI_ML/IMAGE_CAPTIONING/NLP_CONNECT_VIT_GPT2/assets/President_Barack_Obama.jpg"
},
"file_type": {
"type": "select",
@@ -66,8 +66,8 @@
"dragging": true
},
{
- "width": 150,
- "height": 150,
+ "width": 208,
+ "height": 96,
"id": "NLP_CONNECT_VIT_GPT2-f4c9d884-68df-45a4-a06a-9c56750ff1c1",
"type": "AI_ML",
"data": {
@@ -106,8 +106,8 @@
"dragging": true
},
{
- "width": 380,
- "height": 293,
+ "width": 225,
+ "height": 226,
"id": "TABLE-15ab5a91-a962-4f8e-ba9c-8a159d576dbb",
"type": "VISUALIZERS",
"data": {
@@ -146,8 +146,8 @@
"dragging": true
},
{
- "width": 380,
- "height": 293,
+ "width": 225,
+ "height": 226,
"id": "IMAGE-ec76f3cf-6397-45cf-a1e0-ce43c89a9327",
"type": "VISUALIZERS",
"data": {
@@ -233,5 +233,6 @@
"i": "INPUT_PLACEHOLDER"
}
}
- ]
+ ],
+ "textNodes": []
}
\ No newline at end of file
diff --git a/docs/nodes/AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/examples/EX1/app.json b/docs/nodes/AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/examples/EX1/app.json
index 8303943d7a..f6d3963bc2 100644
--- a/docs/nodes/AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/examples/EX1/app.json
+++ b/docs/nodes/AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/examples/EX1/app.json
@@ -3,7 +3,7 @@
"nodes": [
{
"width": 208,
- "height": 116,
+ "height": 96,
"id": "HUGGING_FACE_PIPELINE-160a47cc-72e8-4088-a4af-43efdc9904d6",
"type": "AI_ML",
"data": {
@@ -78,7 +78,7 @@
"desc": "path to the file to be loaded",
"functionName": "LOCAL_FILE",
"param": "file_path",
- "value": "./PYTHON/nodes/AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/assets/ada_lovelace.png"
+ "value": "AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/assets/ada_lovelace.png"
},
"file_type": {
"type": "select",
@@ -125,13 +125,13 @@
}
],
"path": "PYTHON/nodes/LOADERS/LOCAL_FILE_SYSTEM/LOCAL_FILE/LOCAL_FILE.py",
- "selected": true
+ "selected": false
},
"position": {
"x": -405.26756052777466,
"y": -122.64869035489248
},
- "selected": true,
+ "selected": false,
"positionAbsolute": {
"x": -405.26756052777466,
"y": -122.64869035489248
@@ -139,8 +139,8 @@
"dragging": true
},
{
- "width": 380,
- "height": 293,
+ "width": 225,
+ "height": 226,
"id": "TABLE-b1ca06f2-ecee-4858-a075-cdfb097be848",
"type": "VISUALIZERS",
"data": {
@@ -182,8 +182,8 @@
"dragging": true
},
{
- "width": 380,
- "height": 293,
+ "width": 225,
+ "height": 226,
"id": "IMAGE-b9af5a25-af44-43cd-bc00-315e08b0088f",
"type": "VISUALIZERS",
"data": {
@@ -253,5 +253,6 @@
"y": 575.2550328315427,
"zoom": 1.2199420644594634
}
- }
+ },
+ "textNodes": []
}
\ No newline at end of file
diff --git a/docs/nodes/AI_ML/REGRESSION/LEAST_SQUARES/a1-[autogen]/python_code.txt b/docs/nodes/AI_ML/REGRESSION/LEAST_SQUARES/a1-[autogen]/python_code.txt
index facb4c1f1e..0c1c6372cc 100644
--- a/docs/nodes/AI_ML/REGRESSION/LEAST_SQUARES/a1-[autogen]/python_code.txt
+++ b/docs/nodes/AI_ML/REGRESSION/LEAST_SQUARES/a1-[autogen]/python_code.txt
@@ -9,48 +9,53 @@ def LEAST_SQUARES(
) -> Matrix | OrderedPair:
- if b is None and isinstance(a, OrderedPair):
- x = a.x
- y = a.y
- try:
- a = np.vstack([x, np.ones(len(x))]).T
- p = np.linalg.lstsq(a, y, rcond=None)[0]
- except np.linalg.LinAlgError:
- raise ValueError("Least Square Computation failed.")
-
- slope, intercept = p[0:-1], p[-1]
- res = slope * x + intercept
-
- return OrderedPair(x=x, y=res)
-
- if isinstance(a, OrderedPair) and isinstance(b, OrderedPair):
- x = a.y
- y = b.y
-
- try:
- a = np.vstack([x, np.ones(len(x))]).T
- p = np.linalg.lstsq(a, y, rcond=None)[0]
- except np.linalg.LinAlgError:
- raise ValueError("Least Square Computation failed.")
-
- slope, intercept = p[0:-1], p[-1]
- print("=============== This is slope: ", slope)
- print("=============== This is intercept: ", intercept)
- res = slope * x + intercept
-
- return OrderedPair(x=x, y=res)
-
- elif isinstance(a, Matrix) and isinstance(b, Matrix):
- x = a.m
- y = b.m
-
- try:
- a = np.vstack([x, np.ones(len(x))]).T
- p = np.linalg.lstsq(a, y, rcond=None)[0]
- except np.linalg.LinAlgError:
- raise ValueError("Least Square Computation failed.")
-
- slope, intercept = p[0:-1], p[-1]
- res = slope * x + intercept
-
- return Matrix(m=res)
+ if b is None:
+ if isinstance(a, OrderedPair):
+ x = a.x
+ y = a.y
+ try:
+ a = np.vstack([x, np.ones(len(x))]).T
+ p = np.linalg.lstsq(a, y, rcond=None)[0]
+ except np.linalg.LinAlgError:
+ raise ValueError("Least Square Computation failed.")
+
+ slope, intercept = p[0:-1], p[-1]
+ res = slope * x + intercept
+
+ return OrderedPair(x=x, y=res)
+ else:
+ raise ValueError("For matrix type b must be specified!")
+ else:
+ if isinstance(a, OrderedPair) and isinstance(b, OrderedPair):
+ x = a.y
+ y = b.y
+
+ try:
+ a = np.vstack([x, np.ones(len(x))]).T
+ p = np.linalg.lstsq(a, y, rcond=None)[0]
+ except np.linalg.LinAlgError:
+ raise ValueError("Least Square Computation failed.")
+
+ slope, intercept = p[0:-1], p[-1]
+ print("=============== This is slope: ", slope)
+ print("=============== This is intercept: ", intercept)
+ res = slope * x + intercept
+
+ return OrderedPair(x=x, y=res)
+
+ elif isinstance(a, Matrix) and isinstance(b, Matrix):
+ x = a.m
+ y = b.m
+
+ try:
+ a = np.vstack([x, np.ones(len(x))]).T
+ p = np.linalg.lstsq(a, y, rcond=None)[0]
+ except np.linalg.LinAlgError:
+ raise ValueError("Least Square Computation failed.")
+
+ slope, intercept = p[0:-1], p[-1]
+ res = slope * x + intercept
+
+ return Matrix(m=res)
+ else:
+ raise ValueError("a and b must be of same type!")
diff --git a/docs/nodes/LOADERS/REMOTE_FILE_SYSTEM/REMOTE_FILE/examples/EX1/app.json b/docs/nodes/LOADERS/REMOTE_FILE_SYSTEM/REMOTE_FILE/examples/EX1/app.json
index 09c4ac7e72..5917a61263 100644
--- a/docs/nodes/LOADERS/REMOTE_FILE_SYSTEM/REMOTE_FILE/examples/EX1/app.json
+++ b/docs/nodes/LOADERS/REMOTE_FILE_SYSTEM/REMOTE_FILE/examples/EX1/app.json
@@ -1,899 +1,152 @@
-[
- {
- "cmd": "REMOTE_FILE",
- "id": "REMOTE_FILE-84b2a824-ef44-48be-8306-b4b5a1bb2461",
- "result": {
- "plotly_fig": {
- "data": [
- {
- "name": "0",
- "source": 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yJFY2w/PrsyW2zlIwAAAAwKMQ4YOmIc/VQpy/igVm+9V5saL4qimu8A9FwrIprje9EU5QAAABhEYhwwdMQ4BoEoBzBwRDkAAAAGkhgHDB0xjkFzdrLW+XB6dq9SlvV8A6AvbnXLaFyrFkv5AAAAAL1W5gcAAy/5b3cGy0p7p/r5ynL9/tZG56DbbeU7AD13qezGg4V2WpzvpPP5CAAAAL0kxgFDqPuH/AQGwWGUu7e+Fu39/bV8B6DnLpXdeHCjnT6b304X8xEAAAB6QYwDgHds68VufPXk8ZwoBzAYUsQ/lmXcXthJd0Q5AAAAek2MA4Ajchjlvlx9FOu7z0U5gH4r4sJhlLuxky7nMwAAABwFMQ4Ajtjuy4P4euOpKAcwKIq4kIq4udBOS6IcAAAAR02MA4AeOYxyn68sx/L2s1ZKqZPfAaCnzn03yt3cTDP5BQAAAHhbYhwA9NhBtxvfPNusf7H6bVWUAxgI51IRN/fGY2mhnRqiHAAAAO+SGAcAfXIY5f7vx/9Vvb+10Tnodlv5HQB6qh4Rn4pyAAAAvEtiHAAMgJX2TvXzleW6KAcwEA6j3OZCOy3Od9L5/AIAAAC8KjEOAAbIYZS7t74W7f39tXwHoOculd14IMoBAADwpsQ4ABhAWy9246snj+dEOYCBcansxoMb7fTZ/Ha6mI8AAADwY8Q4ABhgh1Huy9VHsb77XJQD6LMU8Y9lGbcXdtIdUQ4AAIBXIcYBwBDYfXkQX288/VuUSyl18jsA9FARFw6j3I2ddDmfAQAA4JAYBwBD5DDKfbH6bXV5+1lLlAPosyIupCJuLrTTkigHAADADynyA4BB9//+04PrKRX/Iz+H46hSlvFBrd46O1nrVspyNt8B6LmHRYrG2H58dmW22MpHAAAAjh8xDhg6v/vTg4vdVNzOz+G4OztZ63w4PbtXKct6vgHQc62IaI7vRVOUAwAAON7EOGDoiHHw085O1jrnpurtkycqZ/INgJ5rRUSzW8bitWqxlI8AAACMPjEOGDpiHLyamZMT8XH99Nrk2NhcvgHQF7e6ZTREOQAAgOOlzA8AgNGw9WI3vnryeO7e+lo823uxnO8A9NylshsPFtppcb6TzucjAAAAo0mMA4ARt/ViN37/dPX9L1cfxfru87V8B6Dn/hLldtKd+e10MR8BAAAYLWIcABwTuy8P4uuNp3OHUS6l1MnvANBDRVwoy7gtygEAAIw2vxkHDB2/GQfvRqUs44NavfVebWq8KIpqvgPQc/eKFM2rtWIxHwAAABheXsYBwDF10O3GN88261+sfltd3n7WOuh2N/M7APTUr1MRNxfaaenGTrqcjwAAAAwnL+OAoeNlHByds5O1zofTs3uVsqznGwA99zCKaI6/iMUrs8VWPgIAADAcxDhg6IhxcPTOTtY656bq7ZMnKmfyDYCea0VEc3wvmqIcAADA8BHjgKEjxkHvzJyciI/rp9cmx8bm8g2AnmtFRLNbxuK1arGUjwAAAAwmvxkHAPyorRe78dWTx3P31tfi2d6L5XwHoKfqEfFp2Y0HC+20ON9J5/MLAAAADB4xDgD4WVsvduP3T1ff/3L1UazvPl/LdwB67pIoBwAAMBx8phIYOp/cf/CbiOL3+TnQOxMnKvHRzOzaL05Wp4uiqOY7AD2W4m43RePaVHEnnwAAAOgvMQ4YSp/cX0r5GdB7lbKMD2r11nu1qXFRDmAAiHIAAAADR4wDhpIYB4PlMMqdnax1K2U5m+8A9Ny9IkXzaq1YzAcAAAB6S4wDhpIYB4Pr7GSt8+H07F6lLOv5BkDPPSxSNEQ5AACA/hHjgKEkxsHgOztZ65ybqrdPnqicyTcAeu5hFNEcfxGLV2aLrXwEAADg6IhxwFAS42B4zJyciI/rp9cmx8bm8g2AnmtFRHN8L5qiHAAAQG+IccBQEuNg+MycnIj/a3pmeXr85Pv5BkDPtSKi2S1j8Vq1WMpHAAAA3h0xDhhKYhwMr4kTlfhoZnbtzMQpL+UABsOtbhkNUQ4AAOBoiHHAUBLjYPgdRrlfnKxOF0VRzXcAeu5WkaJ5tVb8IR8AAAB4c2IcMJTEOBgdlbKMD2r11nu1qXFRDmAApLjbTdG4NlXcyScAAABenxgHDCUxDkZPpSzjV5NTm+/XpstKWdbzHYAeE+UAAADeCTEOGEpiHIy2s5O1zofTs3uiHMBAeFikaFytFYv5AAAAwM8T44ChJMbB8XB2stb5h8npZ5NjY3P5BkDPiXIAAABvQIwDhpIYB8fLzMmJ+Lh+ek2UAxgIDyNicXwvmldmi618BAAA4PvEOGAofXJ/aSsifL4OjpmZkxPxfm16+fRE9f18A6DnWhHRFOUAAAB+mhgHDKVP7i/diYgL+TlwPEycqMRHM7NrZyZOeSkH0H+tlGIxnYjmtWqxlI8AAADHnRgHDCUxDoj4S5Q7N11fn6tOThZFUc13AHruVreMhigHAADwd2IcMJTEOOC7KmUZH9TqrfdqU+OiHMBAuFWkaF6tFX/IBwAAgOOmzA8AAIbNQbcb3zzbrH+x+m31wbOtzYNut5XfAaCnLqUifr+wk+7Mb6eL+QgAAHCciHEAwMg46Hbjv7Zbs5+vLNfvb210RDmAPiviQlnGbVEOAAA4zsQ4AGAkrbR3qodRrr2/v5bvAPTQYZRrp6UbO+lyPgMAAIwyMQ4AGGkr7Z3qV08ez91bXwtRDqDvzqUibopyAADAcVLkBwDD4JP7S3ci4kJ+DvBzZk5OxPu16eXTE9X38w2AnnsYEYvje9G8Mlts5SMAAMAoEOOAoSTGAW9r4kQlPpqZXTszcWou3wDouVZENEU5AABgFIlxwFAS44B3ZeJEJc5N19fnqpOTRVFU8x2AnmqlFIvpRDSvVYulfAQAABhGYhwwlMQ44F2rlGV8UKu33qtNjYtyAAPhVreMhigHAAAMOzEOGEpiHHBUKmUZv5qc2ny/Nl1WyrKe7wD03K0iRfNqrfhDPgAAAAwDMQ4YSmIc0AtnJ2udD6dn90Q5gAGQ4m43RePaVHEnnwAAAAZZmR8AAPAXK+2d6ucry/X7Wxud9v7+Wr4D0ENFXCjLuL2wk+7Mb6eL+QwAADCoxDhgOKV0Jz8COCor7Z3qV08ez91bXwtRDqDPDqNcOy3d2EmX8xkAAGDQiHEAAK9o68VuHEa5P+92lvMdgJ46l4q4KcoBAACDTowDAHhNWy924z82nrz/5eqjWN997qUcQH8dRrmthXZq3NxMM/kFAACAfhLjAADe0O7Lg/h64+ncl6uPYvX5znpKqZPfAaBn6hHx6d54LIlyAADAIBHjAADe0u7Lg/jj5saZL1a/rS5vP2uJcgB99d0otzjfSefzCwAAAL0kxgEAvCMH3W5882yz/sXqt9UHz7Y2D7rdVn4HgJ6pR8SlshsPRDkAAKCfxDgAgHfsoNuN/9puzX6+sly/v7XREeUA+u7vUW47XcxHAACAoyTGAQAcoZX2TvUwyrX399fyHYCeulSWcXthJ90R5QAAgF4R4wAAemClvVP96snjuXvrayHKAfRZERcOo9xCO/02nwEAAN4lMQ4AoIe2XuzGV08ez/37k5VY330uygH0UxEXIuJfFtpp6cZOupzPAAAA74IYBwDQBzv7e/H1xtO5L1cfiXIA/XcuFXFTlAMAAI6CGAcA0Ee7Lw/+FuWWt5+1Ukqd/A4APXMY5bYW2qlxczPN5BcAAABelxgHADAAdl8exDfPNutfrH5bFeUA+q4eEZ/ujceSKAcAALwtMQ4AYIAcdLt/i3L3tzY6B91uK78DQM98N8otznfS+fwCAADAzynyA4Bh8Ls/PbjYTcXt/BxgFJ2drHU+nJ7dq5RlPd8A6Llb3TIa16rFUj4AAAD8EC/jAAAG3Ep7p/r5ynL9642n0d7fX8t3AHrqUtmNBwvttDi/nS7mIwAAQE6MAwAYEuu7z+OrJ4/n7q2viXIA/XepLOP2wk66I8oBAAA/RYwDABgyWy9246snj+f+/clKrO8+F+UA+qmIC4dRbqGdfpvPAAAAYhwAwJDa2d+Lrzeezn25+kiUA+i3Ii5ExL8stNPSjZ10OZ8BAIDjS4wDABhyuy8P/hbllreftVJKnfwOAD1zLhVxU5QDAAAOFfkBwDD43Z8eXOym4nZ+DkBEpSzjg1q99V5tarwoimq+A9BTrYhoju9F88pssZWPAADA6BPjgKEkxgH8vEpZxi+rpzofTs/uVcqynu8A9JQoBwAAx5QYBwwlMQ7g9ZydrIlyAIOhFRGfdctoXKsWS/kIAACMHr8ZBwBwDKy0d6qfryzXv954Gu39/bV8B6Bn6hFxqezGg4V2WpzvpPP5BQAAYLSIcQAAx8j67vP46snjuXvra6IcQP9dKrvx4EY7fTa/nS7mIwAAMBrEOACAY2jrxW589eTx3L8/WYn13eeiHEAfpYh/LMu4vbCT7ohyAAAwesQ4AIBjbGd/L77eeDr35eojUQ6g34q4cBjlbuyky/kMAAAMpyI/ABgGv/vTg4vdVNzOzwF4OxMnKvGryanWe7Wp8aIoqvkOQE89LFI0rtaKxXwAAACGhxgHDK1P7i+l/AyAd6NSlvFBrS7KAQyGh0WKxth+fHZlttjKRwAAYLCJccDQEuMAjl6lLOOX1VOdD6dn9yplWc93AHqqFRHN8b1oinIAADA8xDhgaIlxAL11drImygEMBlEOAACGiBgHDC0xDqA/zkycivPTM2uTY2Nz+QZAz93qltG4Vi2W8gEAABgMYhwwtMQ4gP6aOTkRH9dPi3IAg0GUAwCAASXGAUNLjAMYDLWx8Tg3XV87M3FKlAPosyLiX192o3ltqriTbwAAQH+IccDQEuMABsvEiUp8NDMrygEMghR3uykaohwAAPSfGAcMLTEOYDBVyjI+qNVb79WmxouiqOY7AD2U4m4RsXi1VizmEwAA0BtiHDC0xDiAwSbKAQyUh0WKhigHAAC9J8YBQ0uMAxgeZydrnQ+nZ/cqZVnPNwB66mGRojG2H59dmS228hEAAHj3xDhgaIlxAMNHlAMYGK2IaI7vRVOUAwCAoyXGAUNLjAMYXjMnJ+Lj+um1ybGxuXwDoKdEOQAAOGJiHDC0xDiA4SfKAQyUW90yGteqxVI+AAAAb06MA4aWGAcwOiZOVOKjmdm1MxOnRDmA/hPlAADgHRLjgKH1yf2lOxFxIT8HYHiJcgCDo4j415fdaF6bKu7kGwAA8OrEOGBoiXEAo6tSlvFBrd56rzY1XhRFNd8B6KEUd7spGqIcAAC8mTI/AACAfjvoduObZ5v1L1a/rS5vP2ullDr5HQB6pIgLZRm3F9rpDzd20uV8BgAAfpqXccDQ8jIO4Hg5O1nrfDg9u1cpy3q+AdBTD4sUjau1YjEfAACA/5MYBwwtMQ7geBLlAAbGwyiiOf4iFq/MFlv5CAAA/IUYBwwtMQ7geJs5OREf10+vTY6NzeUbAD3Viojm+F40RTkAAPg/iXHA0BLjAIgQ5QAGSCsimt0yFq9Vi6V8BACA40qMA4aWGAfAd02cqMRHM7NrZyZOiXIA/XerW0ZDlAMAgIgyPwAAgGG0+/Igvt54Ovfl6qNY332+lu8A9NSlshsPFtppcb6TzucjAAAcJ2IcAAAj5TDKfb6yHMvbz1oppU5+B4Ce+UuU20l35rfTxXwEAIDjQIwDAGAkHXS78c2zzfoXq99WRTmAPiviQlnGbVEOAIDjyG/GAUPLb8YB8LrOTtY6H07P7lXKsp5vAPTUvSJF82qtWMwHAAAYNV7GAQBwbKy0d6qfryzX729tdA663Va+A9Azv05F3Fxop6UbO+lyPgIAwCjxMg4YWl7GAfC2Zk5OxMf102uTY2Nz+QZATz2MIprjL2LxymyxlY8AADDMvIwDhldKd/IjAHgdWy9246snj+fura9Fe39/Ld8B6JlzkeJ/7I3H0kI7NW5uppn8AgAADCsxDgCAY+8wyn25+ijWd5+LcgD9U4+ITw+j3Hwnnc8vAADAsBHjAADgr3ZfHsTXG09FOYD+q0fEp2U3Hiy006IoBwDAMBPjAAAgcxjlPl9ZjuXtZ62UUie/A0DPXBLlAAAYZmIcAAD8iINuN755tln/YvXbqigH0Hd/iXI76c78drqYjwAAMKjEOAAA+BmHUe7/fvxf1ftbG52DbreV3wGgR4q4UJZxW5QDAGBYiHEAAPAaVto71c9Xluv3tzY6L14erOc7AD1yGOXa6Q83dtLlfAYAgEEhxgEAwBtYae9U/2310Zl762vR3t9fy3cAeubXqYibC+20JMoBADCIxDgAAHgLWy9246snj+fura/Fs70Xy/kOQM+cO4xyC8/T9ZubaSa/AAAA/SDGAQDAO7D1Yjd+/3T1/S9XH8X67nMv5QD651yk+B9747G00E4NUQ4AgH4T4wAA4B3afXkQX288nTuMcimlTn4HgJ6oR8Sne+Ox9D93UnO+k87nFwAAoBfEOAAAOAKHUe6L1W+ry9vPWqIcQN/UiyL+uezGg4V2WhTlAADoNTEOAACO0EG3G98826wfRrmDbnczvwNAz1w6jHI3dtJv8hEAAI6CGAcAAD1wGOU+X1mevb+10Tnodlv5HQB65lIq4vcLO+nO/Ha6mI8AAPAuiXHA0EoRf8jPAGAYrLR3qp+vLNfvb210Xrw8WM93AHqkiAtlGbdFOQAAjpIYBwytE2Vs5WcAMExW2jvVf1t9dObe+lq09/fX8h2AHjmMcu20dGMnXc5nAAB4G2IcAAD02daL3fjqyeO5e+tr8WzvxXK+A9Az51IRN0U5AADepSI/ABgWv/vTg4vdVNzOzwFg2E2cqMRHM7NrZyZOzeUbAD31MCIWx/eieWW22MpHAAB4FWIcMLTEOABG3WGU+8XJ6nRRFNV8B6BnWhHRFOUAAHgTYhwwtMQ4AI6LSlnGB7V6673a1LgoB9BXrZRiMZ2I5rVqsZSPAADwQ8Q4YGiJcQAcN4dR7uxkrVspy9l8B6CnbnXLaIhyAAD8HDEOGFpiHADH2dnJWufD6dm9SlnW8w2AnrpVpGherRV/yAcAAIiIKPMDAABg8K20d6qfryzX729tdF68PFjPdwB65lIq4vcLO+nO/Ha6mI8AACDGAQDAEFtp71T/bfXRmXvra9He31/LdwB6pIgLZRm3RTkAAHJiHAAAjICtF7vx1ZPHc/fW1+LZ3ovlfAegRw6jXDst3dhJl/MZAIDjR4wDAIARsvViN37/dPX9L1cfxfrucy/lAPrnXCripigHAECRHwAMi9/96cHFbipu5+cAwN9NnKjERzOza784WZ0uiqKa7wD0zMOIWBzfi+aV2WIrHwEAGF1iHDC0xDgAeHWVsowPavXWe7WpcVEOoK9aEdEU5QAAjg8xDhhalx88mNnbLzbzcwDgxx1GubOTtW6lLGfzHYCeaaUUi+lENK9Vi6V8BABgdIhxwFD75P5Sys8AgFdzdrLW+XB6dq9SlvV8A6CnbnXLaIhyAACjSYwDhpoYBwBv7+xkrXNuqt4+eaJyJt8A6KlbRYrm1Vrxh3wAAGB4iXHAUBPjAODdmTk5ER/XT69Njo3N5RsAPZTibjdF49pUcSefAAAYPmIcMNTEOAB492ZOTsT7tenl0xPV9/MNgB5KcTeKaP7TZPFZPgEAMDzEOGCoiXEAcHQmTlTio5nZtTMTp7yUA+ivh0WKxtVasZgPAAAMPjEOGGpiHAAcvYkTlTg3XV+fq05OFkVRzXcAekaUAwAYQmIcMNTEOADonUpZxge1euu92tS4KAfQV62IaI7vRfPKbLGVjwAADBYxDhhqYhwA9F6lLONXk1Ob79emy0pZ1vMdgJ4R5QAAhoAYBww1MQ4A+uvsZK3z4fTsnigH0FetiPisW0bjWrVYykcAAPpLjAOGmhgHAIPh7GSt8w+T088mx8bm8g2AnrolygEADBYxDhhqYhwADJaZkxPxcf30migH0He3ut1YvDZV3MkHAAB6S4wDhpoYBwCDaebkRLxfm14+PVF9P98A6KEUd7spGqIcAED/iHHAUBPjAGCwTZyoxEczs2tnJk55KQfQTynuRhHNf5osPssnAACOlhgHDLX/fn/pD0XEr/NzAGCwTJyoxLnp+vpcdXKyKIpqvgPQMw+LFI2rtWIxHwAAOBpiHDDUPrm/dCciLuTnAMBgqpRlfFCrt96rTY2LcgB9JcoBAPSIGAcMNTEOAIZTpSzjV5NTm+/XpstKWdbzHYCeaUVEc3wvmldmi618BADg7YlxwFAT4wBg+J2drHU+nJ7dE+UA+kqUAwA4ImIcMNTEOAAYHWcna51/mJx+Njk2NpdvAPRMKyI+65bRuFYtlvIRAIDXV+YHAAAA/bDS3ql+9eTx3L31tWjv76/lOwA9UY+IS2U3Hiy00+J8J53PLwAA8HrEOAAAYKBsvdiNwyj3593Ocr4D0DN/j3Lb6WI+AgDwanymEhhqPlMJAKNv4kQlPpqZXTszccrnKwH6KcXdborGtaniTj4BAPDjvIwDAAAG2u7Lg/h64+ncl6uPYvX5znpKqZPfAaAHirhQlnF7YSfdWWin3+YzAAA/zMs4YKh5GQcAx0+lLOODWr31Xm1qvCiKar4D0DMPixSNq7ViMR8AAPg7L+MAAIChctDtxjfPNutfrH5bffBsa/Og223ldwDoiXOpiJsL7bR0YyddzkcAAP7CyzhgqHkZBwBERJydrHU+nJ7dq5RlPd8A6JlWRDTH96J5ZbbYykcAgONKjAOGmhgHAHzX2cla5x8mp59Njo3N5RsAPSPKAQB8hxgHDDUxDgD4ITMnJ+Lj+uk1UQ6g7251y2hcqxZL+QAAcFz4zThgyKU/5CcAAFsvduOrJ4/n7q2vxZ93O8v5DkDPXCq78WChnRbnO+l8PgIAHAdiHDDcUmzlRwAAh7Ze7MZ/bDx5/8vVR7G++3wt3wHomUtlNx7caKfP5rfTxXwEABhlYhwAADDydl8exNcbT+e+XH0Uq8931lNKnfwOAEcvRfxjWcbthZ10R5QDAI4LMQ4AADg2dl8exB83N858sfptdXn7WUuUA+iTIi4cRrkbO+lyPgMAjBIxDgAAOHYOut345tlm/YvVb6v3tzY6B91uK78DQA8UcSEVcXOhnZZEOQBgVIlxAADAsXXQ7cZKe6f6+cpyXZQD6Ktz341yNzfTTH4BAGBYiXEAAAARf4tyX288jfb+/lq+A9AT51IRN/fGY2mhnRqiHAAwCsQ4AACA71jffR5fPXk8d299TZQD6J96RHwqygEAo0CMAwAA+AFbL3bjqyeP5/79yUqs7z4X5QD64zDKbS600+J8J53PLwAADDoxDgAA4Cfs7O/F1xtP575cfSTKAfTXpbIbD0Q5AGDYiHEAAACvYPflwd+i3PL2s1ZKqZPfAaAnLpXdeHCjnT6b304X8xEAYNCIcQAAAK9h9+VBfPNss/7F6rdVUQ6gf1LEP5Zl3F7YSXdEOQBgkIlxAAAAb+Cg2/1blLu/tdE56HZb+R0AeqCIC4dR7sZOupzPAAD9JsYBAAC8hYNuN1baO9XPV5brohxAHxVxIRVxc6GdlkQ5AGCQFPkBwDD573988NuiKP4lPwcA6KczE6fi/PTM2uTY2Fy+AdAzD4sUjbH9+OzKbLGVjwAAvSLGAUPtd396cLGbitv5OQDAIJg5OREf10+LcgD91YqI5vheNEU5AKAfxDhgqIlxAMAwqI2Nx7np+tqZiVOiHED/iHIAQF/4zTgAAIAjtrO/F19vPJ37cvVRrO8+X8t3AHqiHhGf7o3H5kI7Lc530vn8AgDAURDjAAAAemT35cHfotzy9rNWSqmT3wGgJy6V3XggygEAvSDGAQAA9Njuy4P45tlm/YvVb6uiHEBf/SXK7aQ789vpYj4CALwLYhwAAECfHHS7f4ty97c2Ogfdbiu/A0APFHGhLOO2KAcAHAUxDgAAoM8Out1Yae9UP19ZrotyAH10GOXa6Q83dtLlfAYAeBNiHAAAwAA5jHJfbzyN9v7+Wr4D0BO/TkXcXGinJVEOAHhbRX4AMEx+96cHF7upuJ2fAwCMipmTE/Fx/fTa5NjYXL4B0DMPo4jm+ItYvDJbbOUjAMBPEeOAoSbGAQDHRW1sPM5N19fOTJwS5QD6pxURzfG9aIpyAMCrEuOAoSbGAQDHzcSJSnw0MyvKAfRXKyKa3TIWr1WLpXwEAPguMQ4YamIcAHBcTZyoxK8mp1rv1abGi6Ko5jsAPXOrW0ZDlAMAfkyZHwAAADD4dl8exDfPNutfrH5bXd5+1kopdfI7APTEpbIbDxbaaXG+k87nIwCAl3HAUPtv/+vB+fJE8SA/BwA4biplGb+snup8OD27VynLer4D0CMp7nZTNK5NFXfyCQA4nsQ4YOh9cn8p5WcAAMfZ2cmaKAfQb6IcAPBXYhww9MQ4AIAfNnNyIj6un16bHBubyzcAeuZekaJ5tVYs5gMAcDyIccDQE+MAAH6aKAcwEB4WKRqiHAAcP2IcMPTEOACAVzNxohIfzcyunZk4JcoB9M/DKKI5/iIWr8wWW/kIAIweMQ4YemIcAMDrEeUABkIrIprje9EU5QBgtIlxwNAT4wAA3kylLOODWr31Xm1qvCiKar4D0BOtiGh2y1i8Vi2W8hEAGH5iHDD0xDgAgLcjygEMjFvdMhqiHACMFjEOGHpiHADAu3N2stb5cHp2r1KW9XwDoGdEOQAYIWIcMPTEOACAd0+UAxgAKe52UzSuTRV38gkAGB5iHDD0xDgAgKMzc3IiPq6fXpscG5vLNwB6RJQDgKEmxgFDT4wDADh6ohzAQLhXpGherRWL+QAADC4xDhh6YhwAQO9MnKjERzOza2cmTolyAP3zsEjREOUAYDiIccDQE+MAAHpPlAMYCA8jYnF8L5pXZoutfAQABoMYBwy9T/64tBRFnMvPAQA4epWyjA9q9dZ7tanxoiiq+Q5AT7QioinKAcBgEuOAoffJ/aU7EXEhPwcAoHdEOYCB0EopFtOJaF6rFkv5CAD0hxgHDD0xDgBgsJydrHU+nJ7dq5RlPd8A6Jlb3TIaohwA9F+ZHwAAAMDbWGnvVD9fWa7f39roHHS7rXwHoCculd14sNBOizd20m/yEQDoHTEOAACAI3EY5e6tr0V7f38t3wHoiUupiN8v7KQ789vpYj4CAEdPjAMAAOBIbb3Yja+ePJ4T5QD6qIgLZRm3RTkA6D0xDgAAgJ44jHJfrj6K9d3nohxAPxxGuXZaurGTLuczAPDuiXEAAAD01O7Lg/h646koB9Bf51IRN0U5ADh6RX4AMGw+ub90JyIu5OcAAAyHSlnGB7V6673a1HhRFNV8B6AnHkbE4vheNK/MFlv5CAC8OTEOGHpiHADAaBDlAAZCKyKaohwAvDtiHDD0xDgAgNFzdrLW+XB6dq9SlvV8A6AnWinFYjoRzWvVYikfAYBXJ8YBQ0+MAwAYXaIcwEC41S2jIcoBwJsp8wMAAAAYFCvtnernK8v1e+tr0d7fX8t3AHriUtmNBwvttHhjJ/0mHwGAnybGAQAAMPC2XuzGV08ez91bX4tney+W8x2AnriUivj9wk66M7+dLuYjAPDDxDhg6KXwmQwAgONi68Vu/P7p6vtfrj6K9d3nXsoB9EMRF8oybotyAPBqxDhg6BWpu5SfAQAw2nZfHsTXG0/nDqNcSqmT3wHgiB1GuXZaurGTLuczAPAXYhwAAABD6zDKfbH6bXV5+1lLlAPoi3OpiJuiHAD8MDEOAACAoXfQ7cY3zzbrh1HuoNvdzO8AcOT+FuUW2qlxczPN5BcA4DgS4wAAABgZh1Hu85Xl2ftbG52DbreV3wHgyJ2LiE/3xkOUA4AQ4wAAABhRK+2d6ucry/X7WxudFy8P1vMdgCNXj79Guf+5k5rznXQ+vwAAx4EYBwAAwEhbae9U/2310Zl762vR3t9fy3cAjly9KOKfy248WGinRVEOgONGjAMAAOBY2HqxG189eTx3b30tnu29WM53AHri0t+i3Ha6mI8AMIrEOAAAAI6VrRe78funq+9/ufoo1nefeykH0B+XyjJuL+ykO6IcAKNOjAMAAOBY2n15EF9vPJ07jHIppU5+B4AjVsSFwyi30E6/zWcAGAViHAAAAMfaYZT7YvXb6vL2s5YoB9AHRVyIiH9ZaKelGzvpcj4DwDAT4wAAACAiDrrd+ObZZv0wyh10u5v5HQCO3LlUxE1RDoBRUuQHAMPmkz8+aERRfJqfAwDA2zo7Wet8OD27VynLer4B0BOtiGiO70XzymyxlY8AMAy8jAMAAIAfsdLeqX6+sly/v7XRefHyYD3fAThy9Yj4dG88lhbaqXFzM83kFwBg0IlxwPArYik/AgCAd2mlvVP9t9VHZ+6tr0V7f38t3wE4ct+NcovznXQ+vwAAg0qMA4ZeKcYBANAjWy9246snj+fura/Fs70Xy/kOwJGrR8SlshsPRDkAhoXfjAOG3u/+9OBiNxW383MAADhqEycq8dHM7NqZiVNz+QZAz9zqdmPx2lRxJx8AYBB4GQcAAABvaPflQXy98XTuy9VHsb77fC2l1MnvAHDkLpVl3F7YSXfmt9PFfASAfvMyDhh6XsYBADAoKmUZH9TqrfdqU+NFUVTzHYAeSHE3imj+02TxWT4BQD94GQcAAADvyEG3G98826x/sfptdXn7Weug293M7wBwxIq4EBH/stBOSzd20uV8BoBeE+MAAADgHTuMcp+vLM/e39roHHS7rfwOAEfuXCripigHQL/5TCUw9HymEgCAYXB2stY5N1VvnzxROZNvAPREKyKa43vRvDJbbOUjABwVMQ4YemIcAADDZObkRHxcP702OTY2l28A9IQoB0BP+UwlAAAA9NDWi9346snjuXvra/Fs78VyvgNw5OoR8eneeCwttNPifCedzy8AwLskxgEAAEAfbL3Yjd8/XX3/y9VHsb77fC3fAThy9Yi4VHbjgSgHwFES4wAAAKCPdl8exNcbT+cOo1xKqZPfAeDI/T3KbaeL+QgAb8NvxgFDz2/GAQAwSiplGR/U6q33alPjRVFU8x2AHkhxt5uicW2quJNPAPC6xDhg6IlxAACMokpZxq8mpzbfr02XlbKs5zsAPZDibhGxeLVWLOYTALwqMQ4Yev/tfz04X54oHuTnAAAwKs5O1jofTs/uiXIAffOwSNEQ5QB4E2IcMBI+ub+U8jMAABg1ZydrnX+YnH42OTY2l28A9MTDIkVjbD8+uzJbbOUjAPwQMQ4YCWIcAADHyczJifi4fnpNlAPom1ZENMf3oinKAfBzxDhgJIhxAAAcRzMnJ+L92vTy6Ynq+/kGQE+IcgD8LDEOGAliHAAAx9nEiUp8NDO7dmbilJdyAP1zq1tG41q1WMoHAI43MQ4YCWIcAAD8Jcqdm66vz1UnJ4uiqOY7AD0hygHwPWIcMBLEOAAA+LtKWcYHtXrrvdrUuCgH0B9FxL++7Ebz2lRxJ98AOF7EOGAkiHEAAPB/qpRl/GpyavP92nRZKct6vgPQAynudlM0RDmA40uMA0aCGAcAAD/t7GSt8+H07J4oB9AnKe4WEYtXa8ViPgEw2sQ4YCSIcQAA8GrOTtY6/zA5/WxybGwu3wDoiYdFioYoB3B8iHHASBDjAADg9cycnIiP66fXRDmAvnlYpGiM7cdnV2aLrXwEYHSIccBIEOMAAODNzJyciPdr08unJ6rv5xsAPdGKiOb4XjRFOYDRJMYBI0GMAwCAtzNxohIfzcyunZk45aUcQH+IcgAjSowDRoIYBwAA78bEiUqcm66vz1UnJ4uiqOY7AD1xq1tG41q1WMoHAIaPGAeMhE/uL21FRD0/BwAA3kylLOODWr31Xm1qXJQD6BtRDmAEiHHASPjk/tKdiLiQnwMAAG+nUpbxq8mpzfdr02WlLP0DHEAfFBH/+rIbzWtTxZ18A2DwiXHASBDjAADg6J2drHU+nJ7dE+UA+iTF3W6KhigHMFzEOGAkiHEAANA7ZydrnX+YnH42OTY2l28A9ECKu0XE4tVasZhPAAyeMj8AAI6fbrcb+/v7+TEAwA9aae9Uv3ryeO7e+lq09/fX8h2AI1bEhVTEzYV2Wrqxky7nMwCDxcs4YCR4GQdvp/P8eTxa/jYma7W1mdmZueqpU/kVAIAfNXNyIt6vTS+fnqi+n28A9MTDIkVjbD8+uzJbbOUjAP0lxgEjQYyDt3MY4w6Nnzy59sv/1y9FOQDgtUycqMRHM7NrZyZO+XwlQH+0IqI5vhdNUQ5gcIhxwEgQ4+Dt5DHukCgHALyJiROVODddX5+rTk4WRVHNdwCOXCsimt0yFq9Vi6V8BKC3xDhgJIhx8HZ+LMYdqoyN//n0L06fnq5P5xMAwI+qlGV8UKu33qtNjYtyAH1zq1tGQ5QD6B8xDhgJYhy8nZ+LcYdOnDix84tfnjkxXa/7YxoA8MoqZRm/mpzafL82XVbKsp7vAPSEKAfQJ2IcMBLEOHg7rxrjDpUnTrTO/PLMeG1qqlqWZT4DAPyos5O1zofTs3uiHECfpLjbTdG4NlXcyScAjoYYB4wEMQ7ezuvGuL8pihezp2dj9vTpk6IcAPA6zkycivPTM2uTY2Nz+QZAD4hyAD3jr2YAwJtL6eTmxp9PfvO///PFxvr6i263m98AAPhB67vP46snj+fura9Fe39/Ld8BOGJFXCjLuL3QTn+4sZMu5zMA746XccBI8DIO3s4bv4zLFEXRmazVXv7il2dqY2Nj+QwA8KNqY+Nxbrq+dmbilJdyAP3xsEjRuForFvMBgLcjxgEj4ZM/PvwsivSP+Tnwat5VjPuu2tTUjigHALyuiROV+GhmVpQD6J+HUURz/EUsXpkttvIRgNfnM5XAiOj+IT8B+mtne7v28JsHsfp4ZWd/fz+fAQB+0O7Lg/h64+ncl6uPYnn7WSul1MnvAHCkzkWK/7E3HksL7dS4uZlm8gsAvB4xDgA4UodR7tuH/7XTef48nwEAftDuy4P45tlm/YvVb6uiHEBf1CPi08MoN99J5/MLALwaMQ4A6Ind3d3ao+VvY/nhwy1RDgB4VQfd7t+i3P2tjc5Bt9vK7wBwpOoR8WnZjQcL7bQoygG8PjEOAOipF7svZg6j3LPWs3wGAPhBB91urLR3qp+vLNdFOYC+uSTKAbw+MQ4A6IsXuy9mnqyuxoP//GZXlAMAXsdhlPt642m09/fX8h2AI/eXKLeT7sxvp4v5CMD3iXEAQF+9PDiYeLK6Gg/+93/uiHIAwOtY330eXz15PHdvfU2UA+iHIi6UZdwW5QB+mhgHAAyEly9f1p6srsZ/3v/T3p/XN6Lb7eZXAAB+0NaL3fjqyeO5f3+yEuu7z0U5gF47jHLt9IcbO+lyPgMcd2IcADBQUkrjf97YiAf/+z9FOQDgtezs78XXG0/nvlx9JMoB9MevUxE3F9ppSZQD+LsiPwAYRp/88UEjiuLT/Bx4NZ3nz+PR8rf58WAoihf1ej1mTs+eHBsby1cAgB81caISv5qcar1XmxoviqKa7wAcuYdRRHP8RSxemS228hHguPAyDgAYbCmdbG1tnXz4zYNYW1nd29/fz28AAPyg3ZcH8c2zzfoXq99Wl7eftVJKnfwOAEfqXKT4H3vjsbTQTo2bm2kmvwBwHIhxAMDQ2H72bFyUAwBe10G3+7cod39ro3PQ7bbyOwAcqXpEfHoY5eY76Xx+AWCUiXEAwNA5jHIr3z7a7zx/ns8AAD/ooNuNlfZO9fOV5booB9AX9Yj4tOzGg4V2WhTlgONCjAMAhla73R57tPxtfPtf/7UrygEAr+Mwyn298TTa+/tr+Q7AkbskygHHhRgHjISijK38DDg+dju7E4dRrr2zk88AAD9qffd5fPXk8dy99TVRDqA//hLldtKd+e10MR8BRkGRHwAMo9/96cHFbipu5+fAq+k8fx6Plr/Nj4dWpVI5OH3mTGW6Pp1PAAA/qTY2Huem62tnJk7N5RsAPZDibjdF49pUcSefAIaVl3EAwMg5ODioPFldjQf/+c3us9azfAYA+FE7+3vx9cbTuS9XH8X67nMv5QB6rYgLZRm3F9pp6cZOupzPAMNIjAMARtbLg4OJwyj35/WN6Ha7+RUAgB+0+/Lgb1FueftZK6XUye8AcKTOpSJuinLAKPCZSmAk+EwlvJ1R+0zljynL8mBmdrYyc3o2ytL/JAEAr65SlvFBrd56rzY1XhRFNd8BOHIPI2JxfC+aV2aLrXwEGGRiHDASxDh4O8clxh0S5QCAN1Upy/hl9VTnw+nZvUpZ1vMdgCPXioimKAcMEzEOGAliHLyd4xbjvmuqPh2nf/GLGBsbyycAgJ90drImygH0TyulWEwnonmtWizlI8Ag8a/gAMCxtt16Fg+/eRBrq6uxv7+fzwAAP2qlvVP9fGW5/vXG02jv76/lOwBHql4U8c9lNx4stNPifCedzy8ADAoxDgAgvh/lOs+f5zMAwI9a330eXz15PHdvfU2UA+iPS4dR7sZO+k0+AvSbGAcA8B3brWfxaPnb+Pa/lg9EOQDgdWy92I2vnjye+/cnK7G++1yUA+i9S6mI3y/spDvz2+liPgL0ixgHAPADdjudiigHALyJnf29+Hrj6dyXq49EOYB+KOJCWcZtUQ4YFEV+ADCMfvenBxe7qbidnwOvpvP8eTxa/jY/5jsqlUr39Jkz5XR9Op8AAH5SpSzjg1q99V5tarwoimq+A3DkHhYpGldrxWI+APSCl3EAAK/g4OCgfLK6Gkv/+U33WetZPgMA/KiDbje+ebZZ/2L12+ry9rNWSqmT3wHgSJ1LRdxcaKelGzvpcj4CHDUv44CR4GUcvB0v415fpVI5mDk9W5mu16Ms/X8TAPB6zk7WOh9Oz+5VyrKebwAcuYcRsTi+F80rs8VWPgK8a2IcMBI+uf/gNxHF7/Nz4NWIcW+uLMvuzOxsOXN6VpQDAF6bKAfQV62IaIpywFET44CR8cn9pZSfAa9GjHt7ohwA8DZmTk7Ex/XTa5NjY3P5BsCRa6UUi+lENK9Vi6V8BHhbYhwwMsQ4eHNi3LtTluXB5FStcvoXv4ixsbF8BgD4SaIcQN/d6pbREOWAd0mMA0aGGAdvTow7GlP16RDlAIA3MXGiEh/NzK6dmTglygH0x60iRfNqrfhDPgC8LjEOGBliHLw5Me5oTdWnY2Z2Nk6ePJlPAAA/SZQD6LMUd7spGtemijv5BPCqxDhgZIhx8ObEuN6onqrG6V/8IqqnTuUTAMBPqpRlfFCrt96rTY0XRVHNdwCOmCgHvAUxDhgZYhy8OTGut0Q5AOBNiXIAffewSNG4WisW8wHgx4hxwMgQ4+DNiXH9cfLkyajPzsZ0fTqfAAB+1tnJWufD6dm9SlnW8w2AIyfKAa9MjANGhhgHb06M66/K2Fic/sUvRDkA4I2IcgB99TAiFsf3onllttjKR4AIMQ4YIWIcvDkxbjBUKpXu6TNnSlEOAHgTMycn4uP66bXJsbG5fAPgyLUioinKAT9EjANGhhgHb06MGyxlWaaZ2dli5vRslGWZzwAAP0mUA+irVkR81i2jca1aLOUjcDyJccDIEOPgzYlxg0mUAwDexsSJSnw0M7t2ZuKUKAfQH7dEOSBCjANGiBgHb06MG2xlWabper2oz87E2NhYPgMA/CRRDqDvbnW7sXhtqriTD8DxIMYBI0OMgzcnxg2Pqfp0nP7FL0Q5AOC1VcoyPqjVW+/VpsaLoqjmOwBHLMXdboqGKAfHjxgHjIxP7i9tRUQ9Pwd+nhg3fEQ5AOBNiXIAfZbibhTR/KfJ4rN8AkaTGAeMjE/uL92JiAv5OfDzxLjhVf1/2Pub5yjPBO/3vO4bBJjCJNiao+roesam66nq6NXTi4mYiZiFXQtEEEacZtdR0RGlszkTZzU5f8BEp/8DeVXRtWmI6KjoWY29mY3DERLhMwjkiZBAIBDYUuIWKkFCkqUUSmXeynsWVXTbV/mFFylfP5/l9cu9MV/u6zr6Rnjr7bfDG0ePxhMAwI/6q58c2/6b4yebB9PUP2wE6LxykofS/3osuRgPwGBJ4wMAAPrH9rPtsPb1f4S1r78O28+exTMAwA9a36q/8b+vf11Yfvp4O2u3a/EOwL56J0/Cv/7LVr76u3o+GY/A4PBlHDAwfBkHr86XcYPjjaNvhDePF8LxwvF4AgD4UScOHwn/vfDWxk9GRsbiDYB9VwshTB1qhqn/5WTyNB6B/iXGAQNDjINXJ8YNnoMjI+Gtt98W5QCAVyLKAXSVKAcDRowDBoYYB69OjBtcohwA8DqOHDgYfn7i5MbokaOiHEDn1UIIH7fTUPrf3khW4xHoH96MAwAYYFmrFR7+4Q9h9auV8KTyOLTb7fgnAADfq7GbhZuPH41d/cNaqDSebcQ7APuqEEL4TdoOK/+ylV/87Xb+bvwDoD/4Mg4YGL6Mg1fny7jhkaZpOHHyZDjx1smQpv5dFgDwcg6mafg/HivUfnbszUNJkrwR7wDsu0vtdrj4v72ZTMcD0LvEOGBgiHHw6sS44fM8yr1ZOB5GRkbiGQDgBz2Pcn/1k2Ptg2l6Mt4B2Gd5mGnnoSTKQX8Q44CBIcbBqxPjhtubhePhrbffFuUAgFfyVz85tv03x082D6ZpId4A2Gd5mAlJmPq//ST5OJ6A3uFuIgCAIbdZ+2Mof7USNv7wh9BqteIZAOAHrW/V3/jf178u/LG104g3APZZEt4LIfy//2UrX/1dPZ+MZ6A3iHEAAIQQ/ivKra89CNvPnsUzAMB3+qufHNv+v/7Vf6sdHzl8JN4A6Jh38iT8679s5av/z/uP/jEege46GB8AADDctur1sFWvhzeOvhHeevvt8MbRo/FPAADC6JGj4Zcn394cSdM3QwhvxDsAnXe9vHpi6cH9vw8h/Hu8Ad0jxgEA8J22n22HtWf/IcoBAN9y4vCR8N8Lb238ZGRkLITwZrwD0HlP6puVT68vjNYb24UQctcGQ48R4wAA+EHPo9zBkZHw1ttvh+OF4/FPAIAhEEW4sXgHoPN2slbtsxsLhfVqdTTegN4hxgGDI8+nQ5K8Fx8DsDeyVis8/MMfwpPHj0U5ABgiJw4fCT8vnHx6bOTQiSDCAfSEdp4/W7xfbs19ebcQb0mSPo3PgO4S4wAAeCnfjHInTp4IxwuFkKZp/DMAoM8dGzkU/vbk288j3IloBqBLypVHG7PLd8bqje14CiGEkORhPj4DukuMAwDglWStVqg8fBSeVB6HEydPhhNvnRTlAGAAHDlwMPz8xMmN0SNHx4IIB9AzthqNyszS4uh6teorZegzYhwAAK+l3W6HJ48fh6fVqigHAH3syIGD4W8KJ+v/hzeOHguuowToGe12e/uLr+4lN+6XvQsHfUqMAwBgTzyPck8ePw5vFo6Ht95+O4yMjMQ/AwB6TBThjsU7AN2z/GCtcvXe8mgzy+IJ6CNiHAAAe26z9sewWfujKAcAPexgmoa/OX5i869+8uabQYQD6CnVrfrGzM3Fscf1zZf+Gm524vR0fAZ0lxgHAMC++WaUO378eHjj6NH4JwBAhx1M0/Dfjh3f+dmx43maJG/GOwDds5O1ap8v3RpZffTQdcEwQMQ4AAD23fMo98bRN8Jbb78tygFAF3wjwoU0SQ7HOwDddb28WlsorxRcSQmDR4wDAKBjtp9th7Vn/yHKAUAHiXAAvW29Wq1cXro5Wm9sF+INGAxiHAAAHfc8yh0+fDgUTp4MxwvH458AAHvgZ8eOh1PHT+yIcAC9Zydr1T67sVBYr1Zf+l24HzATHwDdJ8YBANA1Ozs74eEf/hCePH4c3nr7bVEOAPbIT48eC39TOFkfSdNjIQQhDqCHtNvt7cWv7zfnvrzrSzgYEmIcAABdl7Va34pyx948FtI0jX8GAPyIKMIdi3cAuqtcebRx+dbiWDPL3og3YHCJcQAA9IznUa7yMA0nTp4MJ946KcoBwAv4U4Q78WwkPXA0iHAAPWer0ajMLC2OrlerY/EGDD4xDgCAntNut8OTx4/D02pVlAOAH3Di8JHwyxNv1984ePBYCOFovAPQXbvtduPK8u38zoO1vXwX7nvlIczHZ0D3iXHAwEjTMN3Owz/H5wD0r29GuZ+8eSy89fbbYWRkJP4ZAAydE4ePhJ8XTj49NnLoRPAlHEBPWn6wVrl6b3m0mWXxtG+SkD+Nz4DuE+MAAOh57XY7bNb+GDZrfwxvFo6LcgAMrROHj4S/OX6i/uahw8dCCCfiHYDuq27VN2ZuLo49rm925Gs4oPeJcQAA9JVvRrkTJ0+Gw4cPxz8BgIFz4vCRcKpwonF85PCR4Es4gJ60k7Vqny/dGll99NC7cMC3iHEAAPSl51HujaNvhLfefju8cdQzOQAMniMHDoZfnHjr2VtH3jgaQjgS7wD0huvl1dpCeaXQySspv0ueJ6vxGdB9YhwAAH1t+9l2WHv2H6IcAAPlyIGD4dTxE83/6ehPDoUQ/McNoEetV6uVy0s3R+uN7UK8dcOBRIyDXiTGAQAwEL4Z5d48XgjHC8fjnwBAz4si3KF4B6A37GSt2mc3Fgrr1ap34YAfJcYBADBQtp9th+1n2+HJ48fhrbffFuUA6AsiHEB/aLfb24tf32/OfXm3J76EA/qDGAcAwEDKWq3w8A9/EOUA6GkH0zT87Njx8N+OHd9Jk+RwvAPQO8qVRxuXby2ONbPsjXjrFbtp/jQ+A7pPjAMAYKA9j3KVhw/DiZMnw4m3ToY0TeOfAUBHfSPCNdMkORRCEOIAelR1q75xZfn22Hq1OhZvvWbug/H5+AzoPjEOAICh0G63w5PHj8PTalWUA6BrviPCuZISoEftttuNK8u38zsP1no+wgG9TYwDAGCofDPKHS8UQuHkiTAyMhL/DAD21DciXCtNkpEgwgH0tOUHa5Wr95ZHm1kWTwAvTYwDAGAotdvt8LRaDU+r1fBm4Xh46+23RTkA9sVPjx4Lp46faBw6cOBICMF/bAB62JP6ZuXT6wuj9cb2aLwBvCoxDgCAobdZ+2PYrP1RlANgT0UR7ki8A9A7drJW7fOlWyOrjx72c4SbiQ+A3iDGAQDAnz2Pcj85diycOHkivHH0aPwTAPhRf45wzUMHDhwKIhxAT2vn+bPF++XWQnml4EpKYL+IccDA+LdfnJr+9fJqfAwAL22rXg9b9Xp44+gb4a233xblAHgho0eOhv9+4q3s8IEDB4M34QB63nq1Wrm8dHO03tiOJ4A9JcYBAMD32H62Hdae/Ud44+gb4cTJk+Enx47FPwGAcOLwkXCqcKJxfOTwkeDvWgB63k7Wqn12Y6GwXq3285WUQB/xB0QAAPgR28+2w/az7XBwZCS89fbb4XjhePwTAIbQicNHwrvHT2SFQ4cPBtdRAvS8dru9/cVX95Ib98uFeBsM+XR8AvQGMQ4AAF5Q1mqFh3/4Q3jy+LEoBzDEogjn71YA+kC58mjj8q3FMe/CAd3gD4wAAPCSvhnljh8/Hk68dTKkaRr/DIABc2zkUPjvJ94S4QD6SHWrvnFl+fbYerU6Fm8AneIPjgAA8IqyVis8efw4PK1Ww4mTJ0U5gAF15MDBcOr4ieb/dPQnh4K/SwHoC7vtduPK8u38zoM1EQ7oOn+ABACA19Rut78V5d4sHA8jIyPxzwDoM0cOHAzvHi+EsaPHQgjhULwD0JuWH6xVrt5bHh3CKynn4wOgN4hxAACwR55HuSePH4c3C8fDW2+/LcoB9KEowgHQJ57UNyufXl8YrTe2R+NtGKQhfRqfAb1BjAMAgH2wWftj2Kz9UZQD6CMH0zS8++aJ8NfH3ownAHrYTtaqfb50a2T10cOhjHBA7xPjAABgH30zyp04eTIcPnw4/gkAXXYwTcPPjh0PPzt2PDuQJP6uBKBPtPP82eL9cmuhvFIYwispgT7iD5gAANABm7U/hpGDI2IcQA/5jgjn70kA+sR6tVq5vHRztN7YjqfhlWWr8RHQG/whEwAAOqTZas6EPLwbkvBOvAHQOSIcQP/ayVq1z24sFNarVVdSRmYvnF2Nz4DekMYHAADA/qj/8Y/Tv//bd98NIf9fQh7K8Q7A/vvZsePh//LTv26/82YhuJISoH+02+3ta/eWG/92ebqwXq3GM0BP84dOAADosN//8tTFEMLFXy+vTIY8KflSDmD//fTosfDu8RPZ4QMHDgb/OBmgr5QrjzYu31oc8y4c0K/EOGDQzIQQ3osPAaAXiXIA+y+KcP4eBKCPVLfqGzM3F8ce1zfH4o2/UIsPgN7hD6EAANBlz6PcP91deb+dJ6XgH5YAvLY/RbhC+/CBg2nw9x8AfWW33W5cWb6d33mwJsK9uPn4AOgd/jAKAAA94t9+cWo6hPC+KAfw6k4cPhL+e+Gt3Z+MjBwIrqME6DvXy6u1hfJKwZWUwCAR4wAAoMeIcgAv78ThI+Hd4yeywqHDB0MIB+IdgN72pL5Z+fT6wmi9sV2IN4B+J8YBAECP+maU283TySTkv4l/AzDsThw+Et55sxBOHD4Sgr/nAOg7O1mr9vnSrZHVRw9H4w1gUPhDKgAA9Lg/R7npf1xaKSUH0pIoB/AXEQ6APtPO82eL98utuS/v+hJuT+TT8QnQO8Q4AADoE//+d6dWQwiTohwwzI4cOBjePV4IY0ePxRMAfWK9Wq1cXro5Wm9sxxPAQBLjAACgz4hywDAS4QD631ajUZlZWhxdr1ZdSQkMFTEOAAD61PMoN7myUmw2QzEkSTGE4JofYKCIcAD9r91ub3/x1b3kxv2yCAcMJTEOAAD63MVTp56GEEqTKytTohwwKEQ4gMFQrjzauHxrcayZZfHEHkpDOh2fAb1DjAMAgAEhygGD4GCahp8dOx5+duzN9oEkTeMdgP5Q3apvzNxcHHtc3xyLN4BhI8YBAMCA+WaUa2VhMm8nxZCEd+LfAfSS74hwQhxAH9pttxtXlm/ndx6siXAAfybGAQDAgPpzlJsKIUz9enllMuRJSZQDeo0IBzA4rpdXawvllYIrKQG+TYwDAIAh8PtfnroYQrgoygG95N3jJ8LPjr2ZH0jSJIhwAH3rSX2z8un1hdF6Y9sV6V3SyA7Ox2dA7xDjgAGTz4eQvBefAgB/IsoBveCnR4+Fd48X2ocPHExDCEm8A9AfdrJW7fOlWyOrjx6OxhudNX/hV0/jM6B3iHHAYMnDU/8rDwA/7nmU+6e7K++386QUQvCPWYB9F0U4X8IB9Kl2nj9bvF9uzX1515dwAC9AjAMAgCH2b784NR1CeF+UA/bTT48eC+8cL4QjBw6GIMIB9LX1arVyeenmaL2xHU8AfA8xDgAAEOWAfTF65Gj4+YmTzyMcAH1sq9GozCwtjq5Xq66k7D3l+ADoLf40DAAA/KdvRrndPJ1MQv6b+DcAP+bE4SPhnTcL4cThI/EEQJ9pt9vbX3x1L7lxvyzC9a7V+ADoLWIcAADwF/4c5ab/cWmllBxIS6Ic8CJEOIDBsvxgrXL13vJoM8viCYCXIMYBAADf69//7tRqCGFSlAN+iAgHMFiqW/WNmZuLY4/rm76GA9gDYhwAAPCjRDnguxw5cDD87cm3RTiAAbGTtWpz9+4euvNgbSze6GFJ/jQ+AnqLGAcAALyw51FucmWl2GyGYkiSYgih8O1fAYPuyIGD4d3jhTB29Fg8AdCnrpdXawvllYIrKftQHubjI6C3iHEAAMBLu3jq1NMQQmlyZWVKlIPhIcIBDJ4n9c3Kp9cXRuuNbX+WA9gnYhwAAPDKRDkYDiIcwODZyVq1z24sFNarVe/CAewzMQ4AAHht34xyrSxM5u2kGJLwTvw7oL8cTNPw7psnwl8fezOeAOhT7Tx/tni/3Jr78q5/QAXQIWIcAACwZ/4c5aZCCFO/Xl6ZDHlSEuWg/xxM0/CzY8fDz469mR9I0iTeAehP5cqjjdnlO2P1xnY80cfSkE7HZ0BvEeMAAIB98ftfnroYQrgoykH/+I4IJ8QBDICtRqMys7Q4ul6tjsUbAPtPjAMGSh7CvL8tAIDeIspB73se4f76J2+Gg2kagggHMBDa7fb2F1/dS27cL3sXDqCLxDhgoBxIw9N2Hp8CAL3geZT7p7sr77fzpBRCeO/bvwC64adHj4WfF04+j3AADIjlB2uVq/eWR5tZFk8AdJgYBwAAdNS//eLUdAjhfVEOuuunR4+Fd44XwpED/moAYJBUt+obMzcXxx7XN30NNyRmJ05Px2dAb/EnbgAAoCtEOegOEQ5gMO1krdrcvbuH7jxY8y4cQI/xJ28AAKCrvhnldvN0Mgn5b+LfAK9PhAMYXNfLq7WF8krBlZQAvcmfwAEAgJ7w5yg3/Y9LK6XkQFoS5WBvnDh8JPy8cDIcGzkUTwD0uSf1zcqn1xdG643tQrwB0DvEOAAAoKf8+9+dWg0hTIpy8HpOHD4S3nmzEE4cPhJPAPS5naxV++zGQmG9WvUuHAvxAdB7xDgAAKAniXLwakQ4gMHVbre3F7++35z78q4v4XjuaXwA9B4xDgAA6GnPo9zkykqx2QzFkCTFEIK/gIKICAcw2MqVRxuXby2ONbPsjXgDoLeJcQAAQF+4eOrU0xBCaXJlZUqUg/9y5MDB8O7xQhg7eiyeABgAW41GZWZpcXS9Wh2LNwD6gxgHAAD0FVEO/kSEAxhsu+1248ry7fzOgzXvwvFDVuMDoPeIcQAAQF/6ZpRrZWEybyfFkIR34t/BoBHhAAbf8oO1ytV7y6PNLIsniOSr8QnQe8Q4AACgr/05yk2FEKZ+vbwyGfKkJMoxiEQ4gMFX3apvzNxcHHtc3/Q1HMAAEeMAAICB8ftfnroYQrgoyjFIDqZp+Nmx4+Gvf/JmOJim8QzAANjJWrXPl26NrD566F04gAEkxgED5eDBMN9sxacAwLAR5RgEIhzAcLheXq0tlFcKrqTkVSRJ+jQ+A3qPP80DA+XP11QBAIQQ/hTlfv+3776bJvmvQggz8Q696GCahnePnwj/57G/Du+8WRDiAAbUerVa+X/9fz8Pc1/eFeJ4ZUke5uMzoPf4Mg4AABh4//aLU9MhhPf/6e7K++08KYUQ3vv2L6A3vHv8hC/hAAbcTtaqfXZjobBerXoXDmBIiHEAAMDQEOXoVT89eiy8c7wQjhzwv+kAg6rdbm8vfn2/Offl3UK8ATDY/CkfAAAYOt+Mcrt5OpmE/Dfxb6ATRDiA4VCuPNq4fGtxrJllb8QbAIPPn/YBAICh9ecoN/2PSyul5EBaEuXoFBEOYDhsNRqVmaXF0fVqdSzeYC/MTpyejs+A3uNP/QAAwND79787tRpCmBTl2G+jR46Gn584KcIBDLjddrtxZfl2fufBmnfhABDjAAAAnvuOKPcPIQTvuvDaThw+Et55sxBOHD4STwAMmOUHa5Wr95ZHm1kWTwAMKTEOAAAg8jzKTa6snGg2QzEkSTGIcrwCEQ5geFS36hszNxfHHtc3fQ0HwLeIcQAAAN/j4qlTT0MIpcmVlSlRjpchwgEMj52sVft86dbI6qOH3oWj02biA6A3iXEAAAA/QpTjRR05cDD87cm3RTiAIXG9vFpbKK8UXEkJwA8R4wAAAF7Qt6JcK/xDyJNSSMI78e8YPkcOHAzvHi+EsaPH4gmAAbRerVYuL90crTe2/eMcAH6UGAcAAPCS/hzlLoYQLv56eWVSlBteIhzAcNnJWrXPbiwU1qtV78IB8MLEOAAAgNfw+1+euhhEuaEjwgEMl3a7vf3FV/eSG/fLvoSjZ+QhzMdnQG8S44CBk4ewkITwP+JzAID99M0ol4ek6M8jg+lgmoZ33zwR/vrYm/EEwIAqVx5tXL61OOZdOHpNEvKn8RnQm8Q4YOAkITyNzwAAOuV5lPunuyvvt/OkFEJ479u/oB8dTNPws2PHw1//5M1wME3jGYABVN2qb1xZvj22Xq2OxRsAvAwxDgAAYB/82y9OTYcQ3hfl+psIBzB8dtvtxpXl2/mdB2siHAB7QowDAADYR6JcfxLhAIbT8oO1ytV7y6OupKQf5HmyGp8BvUmMAwAA6IDnUe7Xyyt/n4e0mIT8N/Fv6A0/PXos/LxwUoQDGCJP6puVT68vjNYb26PxBr3qQCLGQb8Q4wAAADro9788NR9CmPzHpZVSciAtiXK946dHj4V3jhfCkQP+VxlgWOxkrdrnS7dGVh89FOEA2Df+DwMAAKAL/v3vTq0GUa4niHAAw6ed588W75dbC+WVgispAdhv/k8DAACgi74jyv1DCKHw7V+xH0Q4gOG0Xq1WLi/dHK03tuMJ+spumj+Nz4De5P84AAAAesDzKDe5snKi2QzFkCTFIMrtixOHj4SfF06GYyOH4gmAAbaTtWqf3VgorFerrqRkIMx9MD4fnwG9SYwDAADoIRdPnXoaQihNrqxMiXJ768ThI+GdNwvhxOEj8QTAAGu329tffHUvuXG/7L+nAHSFGAcAANCDRLm9I8IBDK9y5dHG5VuLY96FA6CbxDgAAIAe9q0o1wr/EPKkFJLwTvw7/pIIBzC8qlv1jSvLt8fWq9WxeAOAThPjAAAA+sCfo9zFEMLFXy+vTIpy3+/IgYPh3eOFMHb0WDwBMOB22+3GleXb+Z0HayIcg24mPgB6lxgHAADQZ37/y1MXgyj3F0Q4gOG2/GCtcvXe8qgrKQHoNWIcMIDy+RCS9+JTAIBB880ol4ekmITwP+LfDAMRDmC4PalvVj69vjBab2yPxhsA9AIxDhg8eXgakvgQAGBwPY9y/3R35f12npRCCEPxD5NEOIDhtpO1ap8v3RpZffRQhAOgp4lxAAAAA+LffnFqOoTw/qBHuYNpGn527Hj465+8GQ6maTwDMODaef5s8X65Nffl3UK8wfDIp+MToHeJcQAAAANmUKOcCAfAerVaubx0c7Te2I4nAOhZYhwAAMCAeh7lfr288vd5SItJyH8T/6YfiHAAbDUalZmlxdH1atWVlAD0HTEOAABgwP3+l6fmQwiT/7i0UkoOpKV+inLvHj8hwgEMsXa7vf3FV/eSG/fLIhwAfUuMAwAAGBL//nenVkOfRLmfHj0W3jleCEcO+N9WgGFVrjzauHxrcayZZfEEhDAfHwC9y//VAAAADJnviHL/EEIofPtX3SHCAVDdqm/M3Fwce1zfHIs34E/SkD6Nz4De5f9uAAAAhtTzKDe5snKi2QzFkCTF0KUoJ8IBsNtuN64s387vPFgT4QAYKP4vBwAAYMhdPHXqaQihNLmyMtXpKDd65Gj4+YmTIhzAkLteXq0tlFcKrqQEYBD5vx0AAABCCJ2NcicOHwnvvFkIJw4fiScAhsiT+mbl0+sLo/XG9r789wYGVpatxkdA7xLjAAAA+JZvRblW+IeQJ6WQhHfi370KEQ6AEELYyVq1z5dujaw+ejgab8CPm71wdjU+A3qXGAcAAMB3+nOUuxhCuPjr5ZXJ14lyIhwAIYTQzvNni/fLrbkv7/oSDoChIcYBAADwo37/y1MXwytEuSMHDoa/Pfm2CAdAWK9WK5eXbo7WG9vxBAADTYwDBk8SVuMjAAD2xjejXB6SYhLC/4h/E8KfIty7xwth7OixeAJgyGw1GpWZpcXR9WrVlZSwN2rxAdDbxDhg4KRJWG3n8SkAAHvpeZT7p7sr77fzpBRCeC8EEQ6A/9Jut7e/+OpecuN+WYSDvTUfHwC9TYwDAADglf3bL05NhxDe/3+sPfrH0SNHp0aPHB2LfwPA8ClXHm1cvrU41syyeAKAoSPGAQAA8Mr+tZqf2BkJpSQJ//d4A2D4VLfqGzM3F8ce1zf94wwA+DMxDgAAgJf2r9X8RPNQKDZDKCYhFOIdgOGy2243rizfzu88WBPhACAixgEAAPDCvhnhgggHQAjhenm1tlBeKbiSEjoln45PgN4mxgEAAPCjRDgAYk/qm5VPry+M1hvb/rsAAD9AjAMAAOAH/a6eTzaTMBVEOABCCDtZq/bZjYXCerU6Gm8AwF8S4wAAAPhOv6vnk3kSSnkI78QbAMOnnefPFu+XW3Nf3vWPMwDgJYhxAAAAfIsIB0CsXHm0Mbt8Z6ze2I4noMPSkE7HZ0BvE+MAAAAIIYhwAPylrUajMrO0OLperY7FGwDwYsQ4AACAIffbzfz9NAmlPAnvxRsAw6ndbm9/8dW95Mb9snfhAOA1iXEAAABD6nmECyIcAN+w/GCtcvXe8mgzy+IJAHgFYhwAAMCQEeEA+C7VrfrGzM3Fscf1TV/DQQ9rZAfn4zOgt4lxwMDJsrCaHohPAQAQ4QD4LjtZqzZ37+6hOw/WvAsHfWD+wq+exmdAbxPjgIHz7393avXXy6vxMQDA0Prtdv5u2g6lEMJv4g2A4Xa9vFpbKK8UXEkJAPtHjAMAABhQ/xnh2iIcAN/2pL5Z+fT6wmi9sV2INwBgb4lxAAAAA0aEA+D77GSt2mc3Fgrr1ap34aA/leMDoPeJcQAAAAPiX6v5ieahMCXCARBr5/mzxfvl1tyXd30JB/1tNT4Aep8YBwAA0Of+HOGKzRCKIQR/yQrAt5QrjzZml++M1Rvb8QQAdIAYBwAA0KdEOAB+yFajUZlZWhxdr1bH4g0A6BwxDgAAoM+IcAD8kHa7vf3FV/eSG/fL3oWDQZPkT+MjoPeJcQAAAH3kX7bykggHwPdZfrBWuXpvebSZZfEEDII8zMdHQO8T4wAAAPrA7+r5ZJ6EUgjhnXgDgOpWfWPm5uLY4/qmr+EAoMeIcQAAAD3seYTLRTgAvsNO1qrN3bt76M6DNe/CAUCPEuMAAAB6kAgHwI+5Xl6tLZRXCq6kBIDeJsYBAAD0kN9u5u+nabgowgHwfdar1crlpZuj9ca290NhyKQhnY7PgN4nxgEAAPSA327m76dJKIUkvBdvABDCn66k/OzGQmG9WvUuHAD0ETEOGEx5KIfEvyYHAHqfCAfAj2m329uLX99vzn1515dwANCHxDhgMCVhNbjaCQDoYSIcAC+iXHm0cfnW4lgzy96INwCgP4hxAAAAHfTb7fzddDdcFOEA+CFbjUZlZmlxdL1aHYs3YHjNTpyejs+A3ifGAQAAdMBvt/N303YohXb4TUjiFQD+ZLfdblxZvp3febDmXTgAGBBiHAAAwD76VoQDgB+w/GCtcvXe8mgzy+IJAOhjYhwAAMA+EOEAeFHVrfrGzM3Fscf1TV/DAcAAEuMAAAD20L9W8xPNQ6EY2uGf4w0Avmkna9U+X7o1svrooXfhgBexEB8A/UGMAwAA2APPI1wzhGIIoRDvAPBN18urtYXySsGVlMBLeBofAP1BjAMAAHgNIhwAL2O9Wq1cXro5Wm9s+28GAAwJMQ4AAOAViHAAvIydrFX77MZCYb1a9S4cAAwZMQ4AAOAl/a6eTzaTMBVEOAB+RLvd3l78+n5z7su7/psBvK7V+ADoD2IcAADAC/pdPZ/Mk1DKQ3gn3gAgVq482rh8a3GsmWVvxBvAy8tX4xOgP4hxAAAAP0KEA+BlbDUalZmlxdH1anUs3gCA4SPGAQAAfA8RDoCXsdtuN64s387vPFjzLhwA8J/EOGAg5SFZTUL+XnwOAPAifruZv58moZQnwZ8nAHghyw/WKlfvLY82syyeAPZEkqRP4zOgP4hxwEBK8vZqSJL4GADgBz2PcEGEA+AFPalvVj69vjBab2z7Gg7YV0ke5uMzoD+IcQAAwNAT4QB4WTtZq/b50q2R1UcPRTgA4AeJcQAAwNAS4QB4We08f7Z4v9xaKK8UXEkJALwIMQ4AABg6v93O303boRRC+E28AcD3Wa9WK5eXbo7WG9vxBADwvcQ4AABgaPxnhGuLcAC8uJ2sVfvsxkJhvVp1JSXQNbMTp6fjM6A/iHEAAMDAE+EAeBXtdnv7i6/uJTfulwvxBgDwosQ4AABgYP1rNT/RPBSmRDgAXla58mjj8q3FMe/CAQCvS4wDAAAGzp8jXLEZQjGE4GsGAF5Ydau+cWX59th6tToWbwAAr0KMAwAABoYIB8Cr2m23G1eWb+d3HqyJcEAvmokPgP4hxgEAAH1PhAPgdSw/WKtcvbc86kpKAGA/iHEAAEBf+5etvCTCAfAqntQ3K59eXxitN7ZH4w0AYK+IcQAAQF/6XT2fzJNQCiG8E28A8EN2slbt86VbI6uPHopwAMC+E+MAAIC+8jzC5SIcAC+pnefPFu+XWwvllYIrKYF+kocwH58B/UOMAwZTElbjIwCgv4lwALyO9Wq1cnnp5mi9sR1PAD0vCfnT+AzoH2IcMJDSJKy28/gUAOhHv93M30/TcFGEA+BV7GSt2mc3Fgrr1aorKQGArhDjAACAnvTbzfz9NAmlkIT34g0Afky73d7+4qt7yY375UK8AQB0khgHAAD0FBEOgNdVrjzauHxrccy7cMCgyPNkNT4D+ocYBwAA9AQRDoDXVd2qb1xZvj22Xq2OxRtAPzuQiHHQz8Q4AACgq367nb+b7oaLIhwAr2q33W5cWb6d33mwJsIBAD1HjAMAALrit9v5u2k7lEI7/CYk8QoAL+Z6ebW2UF4puJISAOhVYhwAANBR34pwAPCKntQ3K59eXxitN7YL8QYwaHbT/Gl8BvQPMQ4AAOgIEQ6AvbCTtWqfL90aWX30cDTeAAbV3Afj8/EZ0D/EOAAAYF/9azU/0TwUiqEd/jneAOBFtfP82eL9cmvuy7u+hAMA+ooYBwAA7IvnEa4ZQjGE4C9OAXhl69Vq5fLSzdF6YzueAAB6nhgHAADsKREOgL2y1WhUZpYWR9erVVdSAgB9S4wDAAD2hAgHwF5pt9vbX3x1L7lxvyzCAYQwEx8A/UWMAwAAXtvv6vlkMwlTQYQD4DWVK482Lt9aHGtmWTwBAPQlMQ4YSO08PI3PAIC997t6PpknoZSH8E68AcDLqG7VN2ZuLo49rm+OxRsAQD8T44CB9Ptfnpr/9fJqfAwA7BERDoC9sttuN64s387vPFgT4QCAgSTGAQAAL0yEA2AvXS+v1hbKKwVXUgL8kHw6PgH6ixgHAAD8qN9u5u+nSSjlSXgv3gDgZT2pb1Y+vb4wWm9se2sUABh4YhwAAPC9nke4IMIBsAd2slbt86VbI6uPHo7GGwDAoBLjAACAvyDCAbCX2nn+bPF+uTX35V1fwgEAQ0eMAwAA/pMIB8BeW69WK5eXbo7WG9vxBMCLmY8PgP4ixgEAAOG32/m7aTuUQgi/iTcAeBVbjUZlZmlxdL1adSUlwGtIQ/o0PgP6ixgHAABD7D8jXFuEA2BvtNvt7S++upfcuF8W4QAAghgHAABDSYQDYD8sP1irXL23PNrMsngCABhaYhwAAAyRf63mJ5qHwpQIB8Beqm7VN2ZuLo49rm/6Gg5gr2XZanwE9BcxDgAAhsCfI1yxGUIxhFCIdwB4FTtZqzZ37+6hOw/WxuINgL0xe+HsanwG9BcxDgAABpgIB8B+uV5erS2UVwqupAQA+GFiHAAADCARDoD98qS+Wfn0+sJovbHtvy8AAC9AjAMGWS34y0cAhtC/bOUlEQ6AvbaTtWqf3VgorFer3oUD6JxafAD0HzEOGGTzIYT34kMAGFS/q+eTeRJKIYR34g0AXlU7z58t3i+35r686x95AHTefHwA9B8xDgAA+tzzCJeLcADssXLl0cbs8p2xemM7ngDohCR/Gh8B/UeMAwCAPiXCAbBfthqNyszS4uh6tToWbwB0RC2EfKrZOjQVD0D/EeMAAKDP/HYzfz9Nw0URDoC91m63t7/46l5y437Zu3AA3ZLkn6StdnH2wtnVeAL6kxgHAAB94reb+ftpEkoh8SYqAHtv+cFa5eq95dFmlsUTAJ2xkIakOHtufDoegP4mxgEAQI8T4QDYT9Wt+sbMzcWxx/VNX8MBdEctSZLS1XOnp+IBGAxiHAAA9CgRDoD9tJO1anP37h6682DNu3AA3XOpmY0U5y/86mk8AINDjAMAgB7z2+383XQ3XBThANgv18urtYXySsGVlABdM5OnoTj3wfh8PACDR4wDAIAe8dvt/N20HUqhHX4TkngFgNf3pL5Z+fT6wmi9sV2INwA6opznoTR3fvxiPACDS4wDAIAu+1aEA4B9sJO1ap/dWCisV6vehQPomvzDZnZoypWUMHzEOAAA6BIRDoD91s7zZ4v3y625L+/6Eg6gW5L8k7TVLs5eOLsaT8BwEOMAAKDD/rWan2geCsXQDv8cbwCwV8qVRxuzy3fG6o3teAKgM8ppSCZnz41PxwMwXMQ4AADokGNvHDnyL1t5qRlCMYTgCwUA9sVWo1GZWVocXa9Wx+INgI6oJUlSunru9FQ8AMNJjAMGV548DUkenwJA15z9+//TP4YQ3onPAWAv7LbbjSvLt/M7D9a8CwfQPZea2UjRu3DAN4lxwABrz4eQ/M/xKQAAwKBZfrBWuXpvebSZZfEEQGfM5Gkozn0wPh8PAGIcAAAAQJ+qbtU3Zm4ujj2ub/oaDqA7ankeinPnxy/GA8BzYhwAAABAn9nJWrXPl26NrD566F04gK7JP2xmh6ZcSQn8GDEOAAAAoI9cL6/WFsorBVdSAnTNTJrtTs5eOLsaDwDfRYwDAAAA6APr1Wrl8tLN0XpjuxBvAHREOQ3J5OzE6el4APghYhwAAABAD9vJWrXPbiwU1qtV78IBdEcthHzq2sSZUjwAvAgxDgAAAKAHtdvt7cWv7zfnvrzrSziA7rnUzEaK3oUDXocYBwAAANBjypVHG5dvLY41s+yNeAOgI2bSkJRcSQnsBTEOAAAAoEdsNRqVmaXF0fVqdSzeAOiIWp6H4tz58YvxAPCqxDgAAACALttttxtXlm/ndx6seRcOoGvyD5vZoSlXUgJ7TYwDAAAA6KLlB2uVq/eWR5tZFk8AdMZMmu1Ozl44uxoPAHtBjAMAAADogupWfWPm5uLY4/qmr+EAuqOchmTSu3DAfhPjAAAAADpoJ2vVPl+6NbL66KF34QC6oxZCPnVt4kwpHgD2gxgHDKwkDU/zPD4FAADonuvl1dpCeaXgSkqArrmUZrslV1ICnSTGAQMrCWFeiwMAAHrBerVaubx0c7Te2C7EGwAdsZCGpOhKSqAbxDgAAACAfbKTtWqf3VgorFer3oUD6I5anofi3Pnxi/EA0CliHAAAAMAea7fb24tf32/OfXnXl3AAXZKH8FErGynNX/jV03gD6CQxDgAAAGAPlSuPNi7fWhxrZtkb8QZAR8yk2e6kd+GAXiHGAQAAAOyB6lZ948ry7bH1anUs3gDoiHIIefHaxJmP4wGgm8Q4AAAAgNew2243rizfzu88WBPhALqjFkI+dW3iTCkeAHqBGAcAAADwipYfrFWu3lsebWZZPAHQGZfSbLfkSkqgl4lxAAAAAC/pSX2z8un1hdF6Y3s03gDoiIU0JMXZidPT8QDQa8Q4AAAAgBe0k7Vqny/dGll99FCEA+iOWpIkpavnTk/FA0CvEuMAAAAAfkQ7z58t3i+3FsorBVdSAnRHHsJHrWykNH/hV0/jDaCXiXEAAAAAP2C9Wq1cXro5Wm9sxxMAnTGTp6E498H4fDwA9AMxDgAAAOA77GSt2mc3Fgrr1aorKQG6oxxCXrw2cebjeADoJ2IcAAAAwDe02+3tL766l9y4Xy7EGwAdUQshn2pmh6ZcSQkMAjEOGFjtPDyNzwAAAH5IufJo4/KtxTHvwgF0SZJ/krbaxdkLZ1fjCaBfiXHAwPr9L0/N/3p5NT4GAAD4C9Wt+saV5dtj69XqWLwB0BELaUiKs+fGp+MBoN+JcQAAAMDQ2m23G1eWb+d3HqyJcADdUUuSpHT13OmpeAAYFGIcAAAAMJSWH6xVrt5bHnUlJUB35CF81MpGSt6FAwadGAcAAAAMlSf1zcqn1xdG643t0XgDoCNm8jQU5z4Yn48HgEEkxgEAAABDYSdr1T5fujWy+uihCAfQHeUQ8uK1iTMfxwPAIBPjAAAAgIHWzvNni/fLrYXySsGVlADdkn/YzA5NuZISGEZiHAAAADCw1qvVyuWlm6P1xnY8AdAJSf5J2moXZy+cXY0ngGEhxgEAAAADZ6vRqMwsLY6uV6uupATojnIaksnZc+PT8QAwbMQ4AAAAYGC02+3tL766l9y4XxbhALqjliRJ6eq501PxADCsxDgAAABgIJQrjzYu31oc8y4cQNdcamYjRe/CAXybGAcAAAD0tepWfWPm5uLY4/rmWLwB0BEzeRqKcx+Mz8cDAGIcAAAA0Kd22+3GleXb+Z0HayIcQHeU8zyU5s6PX4wHAP6LGAcAAAD0nevl1dpCeaXgSkqAbsk/bGaHplxJCfDjxDgAAACgbzypb1Y+vb4wWm9sF+INgA5I8k/SVrs4e+HsajwB8N3EOGDQzYQQ3osPAQCA/rKTtWqfL90aWX30cDTeAOiIchqSydlz49PxAMAPE+MAAACAntXO82eL98utuS/v+hIOoDtqIeRT1ybOlOIBgBcjxgEAAAA9ab1arVxeujlab2zHEwCdcamZjRS9CwfwesQ4AAAAoKdsNRqVmaXF0fVq1ZWUAN0xk4akNDtxejoeAHh5YhwAAADQE9rt9vYXX91Lbtwvi3AA3VHL81CcOz9+MR4AeHViHAAAANB15cqjjcu3FseaWRZPAHRE/mEzOzTlSkqAvSfGAQAAAF1T3apvzNxcHHtc3xyLNwA6YibNdidnL5xdjQcA9oYYBwAAAHTcbrvduLJ8O7/zYE2EA+iOchqSSe/CAew/MQ4AAADoqOvl1dpCeaXgSkqArqiFkE9dmzhTigcA9ocYBwAAAHTEk/pm5dPrC6P1xnYh3gDoiEvNbKToXTiAzhLjAAAAgH21k7Vqny/dGll99HA03gDoiJk0JCVXUgJ0hxgHAAAA7It2nj9bvF9uzX1515dwAN1Ry/NQnDs/fjEeAOgcMQ4AAADYc+XKo43Z5Ttj9cZ2PAHQAXkIH7WykZIrKQG6T4wDAAAA9sxWo1GZWVocXa9Wx+INgI6YSbPdydkLZ1fjAYDuEOOAwZbn0yFJ3ouPAQCAvdVut7e/+OpecuN+2btwAN1RDiEvXps483E8ANBdYhwAAADwWpYfrFWu3lsebWZZPAGw/2oh5FPXJs6U4gGA3iDGAQAAAK+kulXfmLm5OPa4vulrOIDuuJRmuyVXUgL0NjEOAAAAeCk7Was2d+/uoTsP1rwLB9AdC2lIirMTp6fjAYDeI8YBAAAAL+x6ebW2UF4puJISoCtqeR6Kc+fHL8YDAL1LjAMAAAB+1JP6ZuXT6wuj9cZ2Id4A2H95CB+1spHS/IVfPY03AHqbGAcAAAB8r52sVfvsxkJhvVr1LhxAd8yk2e6kd+EA+pcYBwAAAPyFdp4/W7xfbs19edeXcADdUQ4hL16bOPNxPADQX8Q4AAAA4FvKlUcbs8t3xuqN7XgCYP/VQsinmtmhKVdSAgwGMQ4AAAAIIYSw1WhUZpYWR9er1bF4A6ADkvyTtNUuupISYLCIcQAAADDk2u329hdf3Utu3C97Fw6gOxbSkBRnz41PxwMA/U+MAwAAgCG2/GCtcvXe8mgzy+IJgP1XS5KkdPXc6al4AGBwiHEAAAAwhKpb9Y2Zm4tjj+ubvoYD6II8hI9a2UjJu3AAg0+MAwAAgCGyk7Vqc/fuHrrzYM27cADdMZOnoTj3wfh8PAAwmMQ4YKClaZhu5+Gf43MAABhG18urtYXySsGVlABdUQ4hL16bOPNxPAAw2MQ4AAAAGHDr1Wrl8tLN0XpjuxBvAOy7Wgj5VDM7NOVKSoDhJMYBAADAgNrJWrXPbiwU1qtV78IBdEOSf5K22sXZC2dX4wmA4SHGAQAAwIBpt9vbi1/fb859edeXcADdsZCGpDh7bnw6HgAYPmIcAAAADJBy5dHG5VuLY80seyPeANh3tSRJSlfPnZ6KBwCGlxgHAAAAA2Cr0ajMLC2OrlerY/EGQEdcamYjRe/CARAT4wAAAKCP7bbbjSvLt/M7D9a8CwfQHTN5GopzH4zPxwMAhCDGAQAAQN9afrBWuXpvebSZZfEEwP4r53kozZ0fvxgPAPBNYhwAAAD0mepWfWPm5uLY4/qmr+EAuiL/sJkdmnIlJQAvQowDAACAPrGTtWqfL90aWX300LtwAN2Q5J+krXZx9sLZ1XgCgO8jxgEAAEAfuF5erS2UVwqupAToinIaksnZc+PT8QAAP0aMAwAAgB62Xq1WLi/dHK03tgvxBsC+qyVJUrp67vRUPADAixLjAAAAoAftZK3aZzcWCuvVqnfhALrjUjMbKXoXDoDXJcYBAABAD2m329uLX99vzn1515dwAN0xk6ehOPfB+Hw8AMCrEOOAgfZvvzg1/evl1fgYAAB6UrnyaOPyrcWxZpa9EW8A7Ltanofi3Pnxi/EAAK9DjAMAAIAu22o0KjNLi6Pr1epYvAHQCfmHzezQlCspAdgPYhwAAAB0yW673biyfDu/82DNu3AA3TGTZruTsxfOrsYDAOwVMQ4AAAC6YPnBWuXqveXRZpbFEwD7r5yGZHJ24vR0PADAXhPjAAAAoIOqW/WNmZuLY4/rm76GA+i8Wgj51LWJM6V4AID9IsYBAABAB+xkrdrnS7dGVh899C4cQHdcamYjRe/CAdBpYhwAAADso3aeP1u8X24tlFcKrqQE6IqZNCQlV1IC0C1iHAAAAOyT9Wq1cnnp5mi9sR1PAOy/Wp6H4tz58YvxAACdJMYBAADAHtvJWrXPbiwU1qtV78IBdEX+YTM7NOVKSgB6gRgHAAAAe6Tdbm9/8dW95Mb9ciHeAOiImTTbnZy9cHY1HgCgW8Q4AAAA2APlyqONy7cWx7wLB9AV5TQkk96FA6AXiXEAAADwGqpb9Y0ry7fH1qvVsXgDYN/VQsinrk2cKcUDAPQKMQ4AAABewW673biyfDu/82BNhAPojktptltyJSUAvU6MAwAAgJe0/GCtcvXe8qgrKQG6YiENSdGVlAD0CzEOGAYzIYT34kMAAHhZT+qblU+vL4zWG9uj8QbAvqvleSjOnR+/GA8A0MvEOAAAAPgRO1mr9vnSrZHVRw9FOIAuyEP4qJWNlOYv/OppvAFArxPjAAAA4Hu08/zZ4v1ya6G8UnAlJUBXzKTZ7qR34QDoZ2IcAAAAfIf1arVyeenmaL2xHU8A7L9yCHnx2sSZj+MBAPqNGAcAAADfsJO1ap/dWCisV6uupATovFoI+dS1iTOleACAfiXGAQAAQAih3W5vf/HVveTG/XIh3gDoiEtptltyJSUAg0aMAwAAYOiVK482Lt9aHPMuHEBXLKQhKc5OnJ6OBwAYBGIcAAAAQ6u6Vd+4snx7bL1aHYs3APZdLUmS0tVzp6fiAQAGiRgHAADA0NlttxtXlm/ndx6siXAAXZCH8FErGynNX/jV03gDgEEjxgEAADBUlh+sVa7eWx51JSVAV8zkaSjOfTA+Hw8AMKjEOAAAAIbCk/pm5dPrC6P1xvZovAGw78oh5MVrE2c+jgcAGHRiHAAAAANtJ2vVPl+6NbL66KEIB9B5tRDyqWZ2aMqVlAAMKzEOAACAgdTO82eL98utuS/vFuINgA5I8k/SVrs4e+HsajwBwDAR4wAAABg469Vq5fLSzdF6YzueANh/C2lIirPnxqfjAQCGkRgHDIF8PoTkvfgUAIDBs9VoVGaWFkfXq1VXUgJ0Xi1JktLVc6en4gEAhpkYBwy+PDwNSXwIAMAgabfb2198dS+5cb8swgF0QR7CR61spORdOAD4S2IcAAAAfa1cebRx+dbiWDPL4gmA/TeTp6E498H4fDwAAH8ixgEAANCXqlv1jZmbi2OP65tj8QbAviuHkBevTZz5OB4AgG8T4wAAAOgru+1248ry7fzOgzURDqAr8g+b2aEpV1ICwIsR4wAAAOgb18urtYXySsGVlABdkOSfpK12cfbC2dV4AgC+nxgHAABAz3tS36x8en1htN7YLsQbAPuunIZkcvbc+HQ8AAA/TowDAACgZ+1krdrnS7dGVh89HI03APZdLUmS0tVzp6fiAQB4cWIcAAAAPaed588W75dbc1/e9SUcQHdcamYjRe/CAcDrE+MAAADoKevVauXy0s3RemM7ngDYfzN5GopzH4zPxwMA8GrEOAAAAHrCVqNRmVlaHF2vVl1JCdB55TwPpbnz4xfjAQB4PWIcAAAAXdVut7e/+OpecuN+WYQD6Ir8w2Z2aMqVlACwP8Q4AAAAuqZcebRx+dbiWDPL4gmA/Zbkn6StdnH2wtnVeAIA9o4YBwAAQMdVt+obMzcXxx7XN8fiDYB9V05DMjl7bnw6HgCAvSfGAQMvD2E+iQ8BAOiKnaxVm7t399CdB2siHEDn1ULIp65NnCnFAwCwf8Q4YOAdSMPTdh6fAgDQadfLq7WF8krBlZQAXXGpmY0UvQsHAJ0nxgEAALCvntQ3K59eXxitN7YL8QbAvptJQ1KanTg9HQ8AQGeIcQAAAOyLnaxV++zGQmG9Wh2NNwD2XS3PQ3Hu/PjFeAAAOkuMAwAAYE+18/zZ4v1ya+7Lu76EA+iK/MNmdmjKlZQA0BvEOAAAAPZMufJoY3b5zli9sR1PAOy/mTTbnZy9cHY1HgCA7hHjAAAAeG1bjUZlZmlxdL1aHYs3APZdOQ3JpHfhAKA3iXEAAAC8sna7vf3FV/eSG/fL3oUD6LxaCPnUtYkzpXgAAHqHGAcAAMArWX6wVrl6b3m0mWXxBMD+u9TMRorehQOA3ifGAQAA8FKqW/WNmZuLY4/rm76GA+i8mTQkJVdSAkD/EOMAAAB4ITtZqzZ37+6hOw/WvAsH0Hm1PA/FufPjF+MBAOhtYhwAAAA/6np5tbZQXim4khKg8/IQPmplIyVXUgJAfxLjAAAA+F5P6puVT68vjNYb24V4A2DfzaTZ7uTshbOr8QAA9A8xDgAAgL+wk7Vqn91YKKxXq96FA+i8cgh58drEmY/jAQDoP2IcMPAOHgzzzVZ8CgDAd2nn+bPF++XW3Jd3fQkH0Hm1EPKpaxNnSvEAAPQvMQ4YeBdPnXr66+XV+BgAgEi58mhjdvnOWL2xHU8A7L9LabZbciUlAAweMQ4AAGDIbTUalZmlxdH1anUs3gDYdwtpSIqzE6en4wEAGAxiHAAAwJBqt9vbX3x1L7lxv+xdOIDOq+V5KM6dH78YDwDAYBHjAAAAhtDyg7XK1XvLo80siycA9lkewketbKQ0f+FXT+MNABg8YhwAAMAQqW7VN2ZuLo49rm/6Gg6g82bSbHfSu3AAMFzEOAAAgCGwk7Vqny/dGll99NC7cACdVw4hL16bOPNxPAAAg0+MAwAAGHDXy6u1hfJKwZWUAB1XCyGfamaHplxJCQDDS4wDAAAYUOvVauXy0s3RemO7EG8A7LMk/yRttYuupAQAxDgAAIABs5O1ap/dWCisV6vehQPovIU0JMXZc+PT8QAADCcxDgAAYEC02+3txa/vN+e+vOtLOIDOqyVJUrp67vRUPAAAw02MAwAAGADlyqONy7cWx5pZ9ka8AbC/8hA+amUjJe/CAQDfRYwDAADoY1uNRmVmaXF0vVodizcA9t1Mnobi3Afj8/EAAPCcGAcAANCHdtvtxpXl2/mdB2vehQPovHIIefHaxJmP4wEAICbGAUMhD2EhCeF/xOcAAP1o+cFa5eq95dFmlsUTAPurFkI+1cwOTbmSEgB4UWIcMBSSEJ7GZwAA/aa6Vd+Yubk49ri+6Ws4gE5L8k/SVrs4e+HsajwBAPwQMQ4AAKDH7WSt2udLt0ZWHz30LhxA5y2kISnOnhufjgcAgBchxgEAAPSw6+XV2kJ5peBKSoCOqyVJUrp67vRUPAAAvAwxDgAAoAetV6uVy0s3R+uN7UK8AbDvLjWzkaJ34QCAvSDGAQAA9JCdrFX77MZCYb1a9S4cQOfN5Gkozn0wPh8PAACvSowDAADoAe12e3vx6/vNuS/v+hIOoPPKeR5Kc+fHL8YDAMDrEuMAAAC6rFx5tHH51uJYM8veiDcA9lv+YTM7NOVKSgBgv4hxAAAAXVLdqm9cWb49tl6tjsUbAPssyT9JW+3i7IWzq/EEALCXxDgAAIAO2223G1eWb+d3HqyJcACdV05DMjl7bnw6HgAA9oMYBwAA0EHLD9YqV+8tjzazLJ4A2F+1JElKV8+dnooHAID9JMYBAAB0wJP6ZuXT6wuj9cb2aLwBsO8uNbORonfhAIBuEOMAAAD20U7Wqn2+dGtk9dFDEQ6g82byNBTnPhifjwcAgE4R4wAAAPZBO8+fLd4vtxbKKwVXUgJ0XC3PQ3Hu/PjFeAAA6DQxDhgS+XwIyXvxKQDAflivViuXl26O1hvb8QTAvss/bGaHplxJCQD0CjEOGA55eBqS+BAAYG/tZK3aZzcWCuvVqispATpvJs12J2cvnF2NBwCAbhLjAAAAXlO73d7+4qt7yY375UK8AbDvymlIJmcnTk/HAwBALxDjAAAAXkO58mjj8q3FMe/CAXRcLYR86trEmVI8AAD0EjEOAADgFVS36htXlm+PrVerY/EGwL671MxGit6FAwD6gRgHAADwEnbb7caV5dv5nQdrIhxA582kISm5khIA6CdiHAAAwAtafrBWuXpvedSVlAAdV8vzUJw7P34xHgAAep0YBwAA8COe1Dcrn15fGK03tkfjDYD9ln/YzA5NuZISAOhXYhwAAMD32Mlatc+Xbo2sPnoowgF03kya7U7OXji7Gg8AAP1EjAMAAIi08/zZ4v1ya6G8UnAlJUDHldOQTHoXDgAYFGIcAADAN6xXq5XLSzdH643teAJgf9VCyKeuTZwpxQMAQD8T4wAAAMKfrqT87MZCYb1adSUlQOddSrPdkispAYBBJMYBAABDrd1ub3/x1b3kxv1yId4A2HcLaUiKrqQEAAaZGAcAAAytcuXRxuVbi2PehQPouFqeh+Lc+fGL8QAAMGjEOGA4JGE1PgIAhld1q74xc3Nx7HF9cyzeANhfeQgftbKR0vyFXz2NNwCAQSTGAUMhTcJqO49PAYBhs9tuN64s387vPFgT4QA6bybNdie9CwcADBsxDgAAGArXy6u1hfJKwZWUAB1XDiEvXps483E8AAAMAzEOAAAYaE/qm5VPry+M1hvbhXgDYF/VQsinrk2cKcUDAMAwEeMAAICBtJO1ap8v3RpZffRwNN4A2HeX0my35EpKAAAxDgAAGDDtPH+2eL/cmvvyri/hADpvIQ1JcXbi9HQ8AAAMKzEOAAAYGOvVauXy0s3RemM7ngDYX7UkSUpXz52eigcAgGEnxgEAAH1vq9GozCwtjq5Xq66kBOiwPISPWtlIaf7Cr57GGwAAYhwAANDH2u329hdf3Utu3C+LcACdN5OnoTj3wfh8PAAA8F/EOAAAoC+VK482Lt9aHGtmWTwBsL/KIeTFaxNnPo4HAAD+khgHAAD0lepWfWPm5uLY4/rmWLwBsK9qIeRTzezQlCspAQBenBgHAAD0hd12u3Fl+XZ+58GaCAfQaUn+SdpqF2cvnF2NJwAAfpgYBwAA9Lzr5dXaQnml4EpKgI5bSENSnD03Ph0PAAC8GDEOAADoWU/qm5VPry+M1hvbhXgDYF/VkiQpXT13eioeAAB4OWIcMBSyLKymB+JTAKBX7WSt2udLt0ZWHz0cjTcA9lcewketbKTkXTgAgL0hxgFD4d//7tTqr5dX42MAoMe08/zZ4v1ya+7Lu76EA+i8mTwNxbkPxufjAQCAVyfGAQAAPWG9Wq1cXro5Wm9sxxMA+6scQl68NnHm43gAAOD1iXEAAEBXbTUalZmlxdH1atWVlAAdl3/YzA5NuZISAGD/iHEAAEBXtNvt7S++upfcuF8W4QA6Lck/SVvt4uyFs6vxBADA3hLjAACAjlt+sFa5em95tJll8QTA/iqnIZmcPTc+HQ8AAOwPMQ4AAOiY6lZ9Y+bm4tjj+qav4QA6q5YkSenqudNT8QAAwP4S4wAAgH23k7Vqc/fuHrrzYG0s3gDYd5ea2UjRu3AAAN0hxgEAAPvqenm1tlBeKbiSEqDjZvI0FOc+GJ+PBwAAOkeMAwAA9sWT+mbl0+sLo/XGdiHeANhX5TwPpbnz4xfjAQCAzhPjAACAPbWTtWqf3VgorFer3oUD6Lj8w2Z2aMqVlAAAvUOMAwAA9kQ7z58t3i+35r6860s4gE5L8k/SVrs4e+HsajwBANBdYhwAAPDaypVHG7PLd8bqje14AmB/ldOQTM6eG5+OBwAAeoMYBwAAvLKtRqMys7Q4ul6tjsUbAPuqFkI+dW3iTCkeAADoLWIcMDzyUA5JeCc+BgBeXrvd3v7iq3vJjftl78IBdN6lZjZS9C4cAEB/EOOA4ZGE1RDEOAB4XcsP1ipX7y2PNrMsngDYXzNpSEqzE6en4wEAgN4lxgEAAC+kulXfmLm5OPa4vulrOIDOquV5KM6dH78YDwAA9D4xDgAA+EE7Was2d+/uoTsP1rwLB9Bx+YfN7NCUKykBAPqXGAcAAHyv6+XV2kJ5peBKSoCOm0mz3cnZC2dX4wEAgP4ixgEAAH/hSX2z8un1hdF6Y7sQbwDsq3IakknvwgEADA4xDgAA+E87Wav22Y2Fwnq16l04gM6qhZBPXZs4U4oHAAD6mxgHAACEdru9vfj1/ebcl3d9CQfQeZea2UjRu3AAAINJjAMAgCFXrjzauHxrcayZZW/EGwD7aiYNScmVlAAAg02MAwCAIbXVaFRmlhZH16vVsXgDYF/V8jwU586PX4wHAAAGjxgHAABDZrfdblxZvp3febDmXTiADstD+KiVjZRcSQkAMDzEOAAAGCLLD9YqV+8tjzazLJ4A2F8zabY7OXvh7Go8AAAw2MQ4AAAYAtWt+sbMzcWxx/VNX8MBdFY5hLx4beLMx/EAAMBwEOMAAGCA7WSt2udLt0ZWHz30LhxAZ9VCyKeuTZwpxQMAAMNFjAOGRh6S1STk78XnADCorpdXawvllYIrKQE67lKa7ZZcSQkAQAhiHDBEkry9GpIkPgaAgbNerVYuL90crTe2C/EGwL5aSENSnJ04PR0PAAAMLzEOAAAGxE7Wqn12Y6GwXq16Fw6gs2p5Hopz58cvxgMAAIhxAADQ59rt9vbi1/ebc1/e9SUcQIflIXzUykZK8xd+9TTeAAAgBDEOAAD6WrnyaOPyrcWxZpa9EW8A7KuZNNud9C4cAAA/RowDAIA+tNVoVGaWFkfXq9WxeANgX5VDyIvXJs58HA8AAPBdxDgAAOgju+1248ry7fzOgzXvwgF0Vi2EfKqZHZpyJSUAAC9DjAMAgD6x/GCtcvXe8mgzy+IJgP2U5J+krXbRlZQAALwKMQ4AAHpcdau+MXNzcexxfdPXcACdtZCGpDh7bnw6HgAA4EWJcQAA0KN2slbt86VbI6uPHnoXDqCzakmSlK6eOz0VDwAA8LLEOAAA6EHXy6u1hfJKwZWUAJ2Vh/BRKxspeRcOAIC9IsYBAEAPWa9WK5eXbo7WG9uFeANgX83kaSjOfTA+Hw8AAPA6xDgAAOgBO1mr9tmNhcJ6tepdOIDOKoeQF69NnPk4HgAAYC+IcQAA0EXtdnv7i6/uJTful30JB9BZtRDyqWZ2aMqVlAAA7CcxDhgeSViNjwCgm8qVRxuXby2OeRcOoMOS/JO01S7OXji7Gk8AALDXxDhgaKRJWG3n8SkAdF51q75xZfn22Hq1OhZvAOyrhTQkxdlz49PxAAAA+0WMAwCADtnabpy4Xl7dvvNgTYQD6KxakiSlq+dOT8UDAADsNzEOAAA65P8z///zLhxA511qZiNF78IBANAtYhwAAAAwiGbyNBTnPhifjwcAAOgkMQ4AAAAYJOU8D6W58+MX4wEAALpBjAMAAAAGRP5hMzs05UpKAAB6iRgHAAAA9Lck/yRttYuzF86uxhMAAHSbGAcAAAD0q3IaksnZc+PT8QAAAL1CjAMAAAD6TS1JktLVc6en4gEAAHqNGAcAAAD0k0vNbKToXTgAAPqFGAcAAAD0g5k8DcW5D8bn4wEAAHqZGAcAAAD0slqeh+Lc+fGL8QAAAP1AjAMAAAB6VP5hMzs05UpKAAD6mRgHDI12Hp7GZwAAQE+aSbPdydkLZ1fjAQAA+k0SHwAMsl8vr+bxGRDC9rNnYe3r/4iPAQA6rZyGZHJ24vR0PAAAQL/yZRwAAADQbbUQ8qlrE2dK8QAAAP1OjAMAAAC66VIzGyl6Fw4AgEElxgEAAADdMJOGpORKSgAABp0YBwAAAHRSLc9Dce78+MV4AACAQSTGAQAAAB2Sf9jMDk25khIAgGEixgEAAAD7bSbNdidnL5xdjQcAABh0YhwAAACwX8ppSCa9CwcAwDAT4wAAAIC9Vgshn7o2caYUDwAAMGzEOAAAAGAvXUqz3ZIrKQEA4E/EOAAAAGAvLKQhKbqSEgAAvk2MAwAAAF5HLc9Dce78+MV4AAAAQkjjAwAAAIAXkYfwUTMbeVeIAwCA7+fLOGDY1EIIhfgQAAB4KTNptjvpXTgAAPhxYhwwbOZDCO/FhwAAwAsph5AXr02c+TgeAACA7ybGAQAAAD+mFkI+dW3iTCkeAACAHybGAQAAAD/kUprtllxJCQAAr0aMAwAAAL7LQhqS4uzE6el4AAAAXpwYBwAAAHxTLUmS0tVzp6fiAQAAeHliHAAAABBCCCEP4aNWNlKav/Crp/EGAAC8GjEOAAAAmMnTUJz7YHw+HgAAgNcjxgEAAMDwKoeQF69NnPk4HgAAgL0hxgEAAMDwqYWQTzWzQ1OupAQAgP0lxgEAAMAwSfJP0la7OHvh7Go8AQAAe0+MAwAAgOGwkIakOHtufDoeAACA/SPGAQAAwGCrJUlSunru9FQ8AAAA+0+MAwAAgAGVh/BRKxspeRcOAAC6R4wDhkuePA1JHp8CAMCgmcnTUJz7YHw+HgAAgM4S44Ah054PIfmf41MAABgQ5RDy4rWJMx/HAwAA0B1iHAAAAAyE/MNmdmjKlZQAANBbxDgAAADoZ0n+SdpqF2cvnF2NJwAAoPvEOAAAAOhP5TQkk7PnxqfjAQAA6B1iHAAAA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y: %{y}
color: %{z}"
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],
- "layout": {
- "autosize": true,
- "margin": {
- "b": 0,
- "l": 0,
- "r": 0,
- "t": 30
- },
- "template": {
- "data": {
- "scatter": [
- {
- "type": "scatter"
- }
- ]
+ "edges": [
+ {
+ "source": "REMOTE_FILE-8c60012f-ed3f-44bb-9bb8-2133c612719d",
+ "sourceHandle": "default",
+ "target": "IMAGE-73c5c588-7da2-4368-b5b8-b879345f6d27",
+ "targetHandle": "default",
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}
- },
- "title": {
- "text": "IMAGE"
- },
- "xaxis": {
- "type": "-"
- }
+ ],
+ "viewport": {
+ "x": 690.2867594438088,
+ "y": 316.4751139323974,
+ "zoom": 0.6711480679105897
}
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- "text_blob": null
- }
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+ "textNodes": []
+}
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diff --git a/docs/nodes/LOGIC_GATES/CONDITIONALS/CONDITIONAL/examples/EX1/app.json b/docs/nodes/LOGIC_GATES/CONDITIONALS/CONDITIONAL/examples/EX1/app.json
index ab90846aa2..bf5b216aec 100644
--- a/docs/nodes/LOGIC_GATES/CONDITIONALS/CONDITIONAL/examples/EX1/app.json
+++ b/docs/nodes/LOGIC_GATES/CONDITIONALS/CONDITIONAL/examples/EX1/app.json
@@ -2,8 +2,8 @@
"rfInstance": {
"nodes": [
{
- "width": 210,
- "height": 208,
+ "width": 96,
+ "height": 96,
"id": "CONDITIONAL-c44f8006-f1fb-41be-ab39-de40becaf62c",
"type": "CONDITIONALS",
"data": {
@@ -69,29 +69,21 @@
"dragging": true
},
{
- "width": 130,
- "height": 130,
- "id": "CONSTANT-3ab4671f-65c7-48d6-a2c2-d03c4cd4bd8f",
- "type": "GENERATORS",
+ "width": 380,
+ "height": 293,
+ "id": "LINE-3c753e5a-5d90-4d92-92f9-b955d32bd24e",
+ "type": "VISUALIZERS",
"data": {
- "id": "CONSTANT-3ab4671f-65c7-48d6-a2c2-d03c4cd4bd8f",
- "label": "8",
- "func": "CONSTANT",
- "type": "GENERATORS",
- "ctrls": {
- "constant": {
- "type": "float",
- "default": 3,
- "functionName": "CONSTANT",
- "param": "constant",
- "value": "8"
- }
- },
+ "id": "LINE-3c753e5a-5d90-4d92-92f9-b955d32bd24e",
+ "label": "LINE",
+ "func": "LINE",
+ "type": "VISUALIZERS",
+ "ctrls": {},
"inputs": [
{
"name": "default",
"id": "default",
- "type": "OrderedPair",
+ "type": "OrderedPair|DataFrame|Matrix",
"multiple": false
}
],
@@ -99,47 +91,39 @@
{
"name": "default",
"id": "default",
- "type": "OrderedPair"
+ "type": "Plotly"
}
],
- "path": "PYTHON/nodes\\GENERATORS\\SIMULATIONS\\CONSTANT\\CONSTANT.py",
+ "path": "PYTHON/nodes\\VISUALIZERS\\PLOTLY\\LINE\\LINE.py",
"selected": false
},
"position": {
- "x": -17.83614094036632,
- "y": 74.67650991195404
+ "x": 582.9519442261868,
+ "y": -94.35986044465847
},
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- "x": -17.83614094036632,
- "y": 74.67650991195404
+ "x": 582.9519442261868,
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},
"dragging": true
},
{
- "width": 130,
- "height": 130,
- "id": "CONSTANT-8ac72ae0-8f52-47e4-a1c9-76167f0b0706",
- "type": "GENERATORS",
+ "width": 225,
+ "height": 226,
+ "id": "LINE-6a05bf10-f19b-401d-b8e4-0a4d56d8f27c",
+ "type": "VISUALIZERS",
"data": {
- "id": "CONSTANT-8ac72ae0-8f52-47e4-a1c9-76167f0b0706",
- "label": "4",
- "func": "CONSTANT",
- "type": "GENERATORS",
- "ctrls": {
- "constant": {
- "type": "float",
- "default": 3,
- "functionName": "CONSTANT",
- "param": "constant",
- "value": "4"
- }
- },
+ "id": "LINE-6a05bf10-f19b-401d-b8e4-0a4d56d8f27c",
+ "label": "LINE 1",
+ "func": "LINE",
+ "type": "VISUALIZERS",
+ "ctrls": {},
"inputs": [
{
"name": "default",
"id": "default",
- "type": "OrderedPair",
+ "type": "OrderedPair|DataFrame|Matrix",
"multiple": false
}
],
@@ -147,119 +131,177 @@
{
"name": "default",
"id": "default",
- "type": "OrderedPair"
+ "type": "Plotly"
}
],
- "path": "PYTHON/nodes\\GENERATORS\\SIMULATIONS\\CONSTANT\\CONSTANT.py",
+ "path": "PYTHON/nodes\\VISUALIZERS\\PLOTLY\\LINE\\LINE.py",
"selected": false
},
"position": {
- "x": -9.281435332839152,
- "y": 314.1934465450072
+ "x": 662.6259690852644,
+ "y": 291.83974468546364
},
"selected": false,
"positionAbsolute": {
- "x": -9.281435332839152,
- "y": 314.1934465450072
+ "x": 662.6259690852644,
+ "y": 291.83974468546364
},
"dragging": true
},
{
- "width": 380,
- "height": 293,
- "id": "LINE-3c753e5a-5d90-4d92-92f9-b955d32bd24e",
- "type": "VISUALIZERS",
+ "width": 208,
+ "height": 96,
+ "id": "CONSTANT-f7e4da4a-6c7f-4d91-b4f6-b1fa0113ae52",
+ "type": "GENERATORS",
"data": {
- "id": "LINE-3c753e5a-5d90-4d92-92f9-b955d32bd24e",
- "label": "LINE",
- "func": "LINE",
- "type": "VISUALIZERS",
- "ctrls": {},
+ "id": "CONSTANT-f7e4da4a-6c7f-4d91-b4f6-b1fa0113ae52",
+ "label": "8",
+ "func": "CONSTANT",
+ "type": "GENERATORS",
+ "ctrls": {
+ "dc_type": {
+ "type": "select",
+ "options": [
+ "Scalar",
+ "Vector",
+ "OrderedPair"
+ ],
+ "default": "OrderedPair",
+ "desc": "The type of DataContainer to return.",
+ "overload": null,
+ "functionName": "CONSTANT",
+ "param": "dc_type",
+ "value": "Scalar"
+ },
+ "constant": {
+ "type": "float",
+ "default": 3,
+ "desc": "The value of the y axis output.",
+ "overload": null,
+ "functionName": "CONSTANT",
+ "param": "constant",
+ "value": 8
+ },
+ "step": {
+ "type": "float",
+ "default": 1000,
+ "desc": "The size of the y and x axes.",
+ "overload": null,
+ "functionName": "CONSTANT",
+ "param": "step",
+ "value": 1000
+ }
+ },
+ "initCtrls": {},
"inputs": [
{
"name": "default",
"id": "default",
- "type": "OrderedPair|DataFrame|Matrix",
- "multiple": false
+ "type": "Vector|OrderedPair",
+ "multiple": false,
+ "desc": "Optional input that defines the size of the output."
}
],
"outputs": [
{
"name": "default",
"id": "default",
- "type": "Plotly"
+ "type": "OrderedPair|Vector|Scalar",
+ "desc": "OrderedPair if selected\nx: the x axis generated with size 'step'\ny: the resulting constant with size 'step'\nVector if selected\nv: the resulting constant with size 'step'\nScalar if selected\nc: the resulting constant"
}
],
- "path": "PYTHON/nodes\\VISUALIZERS\\PLOTLY\\LINE\\LINE.py",
- "selected": false
+ "path": "GENERATORS/SIMULATIONS/CONSTANT/CONSTANT.py",
+ "selected": true
},
"position": {
- "x": 652.9812004323767,
- "y": -19.860651714669302
+ "x": -129.7356522942301,
+ "y": 302.9519163096686
},
- "selected": false,
+ "selected": true,
"positionAbsolute": {
- "x": 652.9812004323767,
- "y": -19.860651714669302
+ "x": -129.7356522942301,
+ "y": 302.9519163096686
},
"dragging": true
},
{
- "width": 225,
- "height": 226,
- "id": "LINE-6a05bf10-f19b-401d-b8e4-0a4d56d8f27c",
- "type": "VISUALIZERS",
+ "width": 208,
+ "height": 96,
+ "id": "CONSTANT-5ff1d064-b39f-4340-808a-a34b8ab85898",
+ "type": "GENERATORS",
"data": {
- "id": "LINE-6a05bf10-f19b-401d-b8e4-0a4d56d8f27c",
- "label": "LINE 1",
- "func": "LINE",
- "type": "VISUALIZERS",
- "ctrls": {},
+ "id": "CONSTANT-5ff1d064-b39f-4340-808a-a34b8ab85898",
+ "label": "4",
+ "func": "CONSTANT",
+ "type": "GENERATORS",
+ "ctrls": {
+ "dc_type": {
+ "type": "select",
+ "options": [
+ "Scalar",
+ "Vector",
+ "OrderedPair"
+ ],
+ "default": "OrderedPair",
+ "desc": "The type of DataContainer to return.",
+ "overload": null,
+ "functionName": "CONSTANT",
+ "param": "dc_type",
+ "value": "Scalar"
+ },
+ "constant": {
+ "type": "float",
+ "default": 3,
+ "desc": "The value of the y axis output.",
+ "overload": null,
+ "functionName": "CONSTANT",
+ "param": "constant",
+ "value": 4
+ },
+ "step": {
+ "type": "float",
+ "default": 1000,
+ "desc": "The size of the y and x axes.",
+ "overload": null,
+ "functionName": "CONSTANT",
+ "param": "step",
+ "value": 1000
+ }
+ },
+ "initCtrls": {},
"inputs": [
{
"name": "default",
"id": "default",
- "type": "OrderedPair|DataFrame|Matrix",
- "multiple": false
+ "type": "Vector|OrderedPair",
+ "multiple": false,
+ "desc": "Optional input that defines the size of the output."
}
],
"outputs": [
{
"name": "default",
"id": "default",
- "type": "Plotly"
+ "type": "OrderedPair|Vector|Scalar",
+ "desc": "OrderedPair if selected\nx: the x axis generated with size 'step'\ny: the resulting constant with size 'step'\nVector if selected\nv: the resulting constant with size 'step'\nScalar if selected\nc: the resulting constant"
}
],
- "path": "PYTHON/nodes\\VISUALIZERS\\PLOTLY\\LINE\\LINE.py",
+ "path": "GENERATORS/SIMULATIONS/CONSTANT/CONSTANT.py",
"selected": false
},
"position": {
- "x": 673.0558583074628,
- "y": 333.5593015742577
+ "x": -113.23746586941505,
+ "y": 31.426340142776155
},
"selected": false,
"positionAbsolute": {
- "x": 673.0558583074628,
- "y": 333.5593015742577
+ "x": -113.23746586941505,
+ "y": 31.426340142776155
},
"dragging": true
}
],
"edges": [
- {
- "source": "CONSTANT-3ab4671f-65c7-48d6-a2c2-d03c4cd4bd8f",
- "sourceHandle": "default",
- "target": "CONDITIONAL-c44f8006-f1fb-41be-ab39-de40becaf62c",
- "targetHandle": "x",
- "id": "reactflow__edge-CONSTANT-3ab4671f-65c7-48d6-a2c2-d03c4cd4bd8fdefault-CONDITIONAL-c44f8006-f1fb-41be-ab39-de40becaf62cx"
- },
- {
- "source": "CONSTANT-8ac72ae0-8f52-47e4-a1c9-76167f0b0706",
- "sourceHandle": "default",
- "target": "CONDITIONAL-c44f8006-f1fb-41be-ab39-de40becaf62c",
- "targetHandle": "y",
- "id": "reactflow__edge-CONSTANT-8ac72ae0-8f52-47e4-a1c9-76167f0b0706default-CONDITIONAL-c44f8006-f1fb-41be-ab39-de40becaf62cy"
- },
{
"source": "CONDITIONAL-c44f8006-f1fb-41be-ab39-de40becaf62c",
"sourceHandle": "true",
@@ -273,6 +315,20 @@
"target": "LINE-6a05bf10-f19b-401d-b8e4-0a4d56d8f27c",
"targetHandle": "default",
"id": "reactflow__edge-CONDITIONAL-c44f8006-f1fb-41be-ab39-de40becaf62cfalse-LINE-6a05bf10-f19b-401d-b8e4-0a4d56d8f27cdefault"
+ },
+ {
+ "source": "CONSTANT-5ff1d064-b39f-4340-808a-a34b8ab85898",
+ "sourceHandle": "default",
+ "target": "CONDITIONAL-c44f8006-f1fb-41be-ab39-de40becaf62c",
+ "targetHandle": "y",
+ "id": "reactflow__edge-CONSTANT-5ff1d064-b39f-4340-808a-a34b8ab85898default-CONDITIONAL-c44f8006-f1fb-41be-ab39-de40becaf62cy"
+ },
+ {
+ "source": "CONSTANT-f7e4da4a-6c7f-4d91-b4f6-b1fa0113ae52",
+ "sourceHandle": "default",
+ "target": "CONDITIONAL-c44f8006-f1fb-41be-ab39-de40becaf62c",
+ "targetHandle": "x",
+ "id": "reactflow__edge-CONSTANT-f7e4da4a-6c7f-4d91-b4f6-b1fa0113ae52default-CONDITIONAL-c44f8006-f1fb-41be-ab39-de40becaf62cx"
}
],
"viewport": {
@@ -299,5 +355,6 @@
"i": "INPUT_PLACEHOLDER"
}
}
- ]
+ ],
+ "textNodes": []
}
\ No newline at end of file
diff --git a/docs/nodes/LOGIC_GATES/TIMERS/TIMER/a1-[autogen]/python_code.txt b/docs/nodes/LOGIC_GATES/TIMERS/TIMER/a1-[autogen]/python_code.txt
index eed957bcde..354d6ba75b 100644
--- a/docs/nodes/LOGIC_GATES/TIMERS/TIMER/a1-[autogen]/python_code.txt
+++ b/docs/nodes/LOGIC_GATES/TIMERS/TIMER/a1-[autogen]/python_code.txt
@@ -1,4 +1,4 @@
-from flojoy import flojoy, DataContainer, DefaultParams, send_to_socket
+from flojoy import flojoy, DataContainer, DefaultParams
from flojoy.utils import PlotlyJSONEncoder
from flojoy.job_result_builder import JobResultBuilder
import plotly.graph_objects as go
@@ -7,50 +7,17 @@ import json
from typing import Optional, cast
-@flojoy(inject_node_metadata=True)
+@flojoy
def TIMER(
- default_params: DefaultParams,
default: Optional[DataContainer] = None,
sleep_time: float = 0,
) -> DataContainer:
- node_id = default_params.node_id
- jobset_id = default_params.jobset_id
- remaining_time = sleep_time
- start_time = time.time()
- current_time = start_time
+ time.sleep(sleep_time)
result = cast(
DataContainer,
JobResultBuilder().from_inputs([default] if default else []).build(),
)
- while current_time - start_time < sleep_time:
- fig = go.Figure(
- data=go.Indicator(
- mode="number",
- value=int(remaining_time),
- domain={"y": [0, 1], "x": [0, 1]},
- delta=None,
- )
- )
- send_to_socket(
- json.dumps(
- {
- "NODE_RESULTS": {
- "cmd": "TIMER",
- "id": node_id,
- "result": {"plotly_fig": fig},
- },
- "proceed_to_next": False,
- "jobsetId": jobset_id,
- },
- cls=PlotlyJSONEncoder,
- ),
- )
- sleep_interval = min(1, remaining_time)
- time.sleep(sleep_interval)
- remaining_time = sleep_time - (current_time - start_time)
- current_time = time.time()
-
return result
diff --git a/docs/nodes/TRANSFORMERS/IMAGE_PROCESSING/EXTREMA_DETERMINATION/examples/EX1/app.json b/docs/nodes/TRANSFORMERS/IMAGE_PROCESSING/EXTREMA_DETERMINATION/examples/EX1/app.json
index 2adc21917e..90e96252b3 100644
--- a/docs/nodes/TRANSFORMERS/IMAGE_PROCESSING/EXTREMA_DETERMINATION/examples/EX1/app.json
+++ b/docs/nodes/TRANSFORMERS/IMAGE_PROCESSING/EXTREMA_DETERMINATION/examples/EX1/app.json
@@ -18,7 +18,7 @@
"desc": "path to the file to be loaded",
"functionName": "LOCAL_FILE",
"param": "file_path",
- "value": "/home/trbritt/Desktop/Data/projects/flojoy/studio/PYTHON/nodes/TRANSFORMERS/IMAGE_PROCESSING/EXTREMA_DETERMINATION/assets/highrange.tif"
+ "value": "TRANSFORMERS/IMAGE_PROCESSING/EXTREMA_DETERMINATION/assets/highrange.tif"
},
"file_type": {
"type": "select",
@@ -95,7 +95,7 @@
"desc": "path to the file to be loaded",
"functionName": "LOCAL_FILE",
"param": "file_path",
- "value": "/home/trbritt/Desktop/Data/projects/flojoy/studio/PYTHON/nodes/TRANSFORMERS/IMAGE_PROCESSING/EXTREMA_DETERMINATION/assets/lowrange.png"
+ "value": "TRANSFORMERS/IMAGE_PROCESSING/EXTREMA_DETERMINATION/assets/lowrange.png"
},
"file_type": {
"type": "select",
@@ -172,7 +172,7 @@
"desc": "path to the file to be loaded",
"functionName": "LOCAL_FILE",
"param": "file_path",
- "value": "/home/trbritt/Desktop/Data/projects/flojoy/studio/PYTHON/nodes/TRANSFORMERS/IMAGE_PROCESSING/EXTREMA_DETERMINATION/assets/hubble_deep_field.tif"
+ "value": "TRANSFORMERS/IMAGE_PROCESSING/EXTREMA_DETERMINATION/assets/hubble_deep_field.tif"
},
"file_type": {
"type": "select",
@@ -970,5 +970,6 @@
"i": "INPUT_PLACEHOLDER"
}
}
- ]
+ ],
+ "textNodes": []
}
\ No newline at end of file
diff --git a/docs/nodes/VISUALIZERS/PLOTLY/PROPHET_PLOT/examples/EX1/app.json b/docs/nodes/VISUALIZERS/PLOTLY/PROPHET_PLOT/examples/EX1/app.json
index dcbd48e1f3..8f8a664851 100644
--- a/docs/nodes/VISUALIZERS/PLOTLY/PROPHET_PLOT/examples/EX1/app.json
+++ b/docs/nodes/VISUALIZERS/PLOTLY/PROPHET_PLOT/examples/EX1/app.json
@@ -1,296 +1,249 @@
{
"rfInstance": {
- "nodes": [
- {
- "width": 208,
- "height": 96,
- "id": "PROPHET_PREDICT-bd418ba3-7564-4c30-8123-8b7fb4b0adcf",
- "type": "AI_ML",
- "data": {
- "id": "PROPHET_PREDICT-bd418ba3-7564-4c30-8123-8b7fb4b0adcf",
- "label": "PROPHET PREDICT",
- "func": "PROPHET_PREDICT",
- "type": "AI_ML",
- "ctrls": {
- "run_forecast": {
- "type": "bool",
- "default": true,
- "desc": "If True (default case), the dataframe of the returning DataContainer\n(\"m\" parameter of the DataContainer) will be the forecasted dataframe.\nIt will also have an \"extra\" field with the key \"original\", which is\nthe original dataframe passed in.\n\nIf False, the returning dataframe will be the original data.\n\nThis node will also always have an \"extra\" field, run_forecast, which\nmatches that of the parameters passed in. This is for future nodes\nto know if a forecast has already been run.\n\nDefault = True",
- "functionName": "PROPHET_PREDICT",
- "param": "run_forecast",
- "value": true
- },
- "periods": {
- "type": "int",
- "default": 365,
- "desc": "The number of periods to predict out. Only used if run_forecast is True.\nDefault = 365",
- "functionName": "PROPHET_PREDICT",
- "param": "periods",
- "value": 365
- }
+ "nodes": [
+ {
+ "width": 210,
+ "height": 96,
+ "id": "TIMESERIES-1a5e52b5-927f-4db4-97e4-dbec355de32d",
+ "type": "GENERATORS",
+ "data": {
+ "id": "TIMESERIES-1a5e52b5-927f-4db4-97e4-dbec355de32d",
+ "label": "TIMESERIES",
+ "func": "TIMESERIES",
+ "type": "GENERATORS",
+ "ctrls": {
+ "start_date": {
+ "type": "str",
+ "default": "2023-01-01",
+ "desc": "The start date of the timeseries in the format YYYY:MM:DD.",
+ "functionName": "TIMESERIES",
+ "param": "start_date",
+ "value": "2023-01-01"
+ },
+ "end_date": {
+ "type": "str",
+ "default": "2023-07-20",
+ "desc": "The end date of the timeseries in the format YYYY:MM:DD.",
+ "functionName": "TIMESERIES",
+ "param": "end_date",
+ "value": "2023-07-20"
+ }
+ },
+ "initCtrls": {},
+ "outputs": [
+ {
+ "name": "default",
+ "id": "default",
+ "type": "DataFrame",
+ "desc": "m: the resulting timeseries"
+ }
+ ],
+ "path": "PYTHON/nodes\\GENERATORS\\SIMULATIONS\\TIMESERIES\\TIMESERIES.py",
+ "selected": false
+ },
+ "position": {
+ "x": -410.3431553006602,
+ "y": -52.85412608012075
+ },
+ "selected": false,
+ "positionAbsolute": {
+ "x": -410.3431553006602,
+ "y": -52.85412608012075
+ },
+ "dragging": true
},
- "initCtrls": {},
- "inputs": [
- {
- "name": "default",
- "id": "default",
- "type": "DataFrame",
- "multiple": false,
- "desc": null
- }
- ],
- "outputs": [
- {
- "name": "default",
- "id": "default",
- "type": "DataFrame",
- "desc": "With parameter as df.\nIndicates either the original df passed in, or the forecasted df\n(depending on if run_forecast is True)."
- }
- ],
- "pip_dependencies": [
- {
- "name": "prophet",
- "v": "1.1.4"
- },
- {
- "name": "holidays",
- "v": "0.26"
- },
- {
- "name": "pystan",
- "v": "2.19.1.1"
- }
- ],
- "path": "PYTHON/nodes/AI_ML/PREDICT_TIME_SERIES/PROPHET_PREDICT/PROPHET_PREDICT.py",
- "selected": false
- },
- "position": {
- "x": 170.22990354212553,
- "y": 112.44847772859924
- },
- "selected": false,
- "positionAbsolute": {
- "x": 170.22990354212553,
- "y": 112.44847772859924
- },
- "dragging": true
- },
- {
- "width": 380,
- "height": 293,
- "id": "PROPHET_COMPONENTS-58b33495-28de-4caa-924c-aa98d7b679ca",
- "type": "VISUALIZERS",
- "data": {
- "id": "PROPHET_COMPONENTS-58b33495-28de-4caa-924c-aa98d7b679ca",
- "label": "PROPHET COMPONENTS",
- "func": "PROPHET_COMPONENTS",
- "type": "VISUALIZERS",
- "ctrls": {
- "periods": {
- "type": "int",
- "default": 365,
- "desc": "The number of periods out to predict.\nOnly used if the node passed into this node (i.e. PROPHET_PREDICT) did NOT return the forecast.\nIf the forecast was included in the DataContainer, this parameter will be ignored. default is 365",
- "functionName": "PROPHET_COMPONENTS",
- "param": "periods",
- "value": 365
- }
+ {
+ "width": 208,
+ "height": 96,
+ "id": "PROPHET_PREDICT-bf17e862-67c6-4a3a-b49f-9c78a7885d21",
+ "type": "AI_ML",
+ "data": {
+ "id": "PROPHET_PREDICT-bf17e862-67c6-4a3a-b49f-9c78a7885d21",
+ "label": "PROPHET PREDICT",
+ "func": "PROPHET_PREDICT",
+ "type": "AI_ML",
+ "ctrls": {
+ "run_forecast": {
+ "type": "bool",
+ "default": true,
+ "desc": "If True (default case), the dataframe of the returning DataContainer\n(\"m\" parameter of the DataContainer) will be the forecasted dataframe.\nIt will also have an \"extra\" field with the key \"original\", which is\nthe original dataframe passed in.\n\nIf False, the returning dataframe will be the original data.\n\nThis node will also always have an \"extra\" field, run_forecast, which\nmatches that of the parameters passed in. This is for future nodes\nto know if a forecast has already been run.\n\nDefault = True",
+ "functionName": "PROPHET_PREDICT",
+ "param": "run_forecast",
+ "value": true
+ },
+ "periods": {
+ "type": "int",
+ "default": 365,
+ "desc": "The number of periods to predict out. Only used if run_forecast is True.\nDefault = 365",
+ "functionName": "PROPHET_PREDICT",
+ "param": "periods",
+ "value": 365
+ }
+ },
+ "initCtrls": {},
+ "inputs": [
+ {
+ "name": "default",
+ "id": "default",
+ "type": "DataFrame",
+ "multiple": false,
+ "desc": null
+ }
+ ],
+ "outputs": [
+ {
+ "name": "default",
+ "id": "default",
+ "type": "DataFrame",
+ "desc": "With parameter as df.\nIndicates either the original df passed in, or the forecasted df\n(depending on if run_forecast is True)."
+ }
+ ],
+ "path": "PYTHON/nodes\\AI_ML\\PREDICT_TIME_SERIES\\PROPHET_PREDICT\\PROPHET_PREDICT.py",
+ "selected": false
+ },
+ "position": {
+ "x": -49.95156682663276,
+ "y": -38.779822683898544
+ },
+ "selected": false,
+ "positionAbsolute": {
+ "x": -49.95156682663276,
+ "y": -38.779822683898544
+ },
+ "dragging": true
},
- "initCtrls": {},
- "inputs": [
- {
- "name": "default",
- "id": "default",
- "type": "DataFrame",
- "multiple": false,
- "desc": "the DataContainer to be visualized"
- }
- ],
- "outputs": [
- {
- "name": "default",
- "id": "default",
- "type": "Plotly",
- "desc": "the DataContainer containing Plotly visualization of the prophet model"
- }
- ],
- "path": "PYTHON/nodes/VISUALIZERS/PLOTLY/PROPHET_COMPONENTS/PROPHET_COMPONENTS.py",
- "selected": false
- },
- "position": {
- "x": 515.4222395732635,
- "y": 258.7002797805195
- },
- "selected": false,
- "positionAbsolute": {
- "x": 515.4222395732635,
- "y": 258.7002797805195
- },
- "dragging": true
- },
- {
- "width": 380,
- "height": 293,
- "id": "PROPHET_PLOT-b4f3da83-86a3-429c-8543-82f844c04794",
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