From 73183cf666cd0c075dc81f3a15707570a159f096 Mon Sep 17 00:00:00 2001 From: zixuanzh Date: Tue, 1 Jan 2019 14:48:52 +0800 Subject: [PATCH] sir model first cut --- demos/susceptible_infected_recovered.ipynb | 453 +++++++++++++++++++++ 1 file changed, 453 insertions(+) create mode 100644 demos/susceptible_infected_recovered.ipynb diff --git a/demos/susceptible_infected_recovered.ipynb b/demos/susceptible_infected_recovered.ipynb new file mode 100644 index 0000000..a2c9d95 --- /dev/null +++ b/demos/susceptible_infected_recovered.ipynb @@ -0,0 +1,453 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SIR Model for Viral Marketing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from decimal import Decimal\n", + "import pandas as pd\n", + "import numpy as np\n", + "from datetime import timedelta\n", + "\n", + "%matplotlib inline\n", + "import pandas as pd\n", + "from tabulate import tabulate\n", + "\n", + "from SimCAD.configuration import Configuration\n", + "from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \\\n", + " ep_time_step" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sim_config = {\n", + " 'N': 1,\n", + " 'T': range(50)\n", + "}\n", + "\n", + "seed = {}\n", + "env_processes = {}\n", + "initial_condition = {\n", + " 'Budget': float(50),\n", + " 'S': float(1000),\n", + " 'I': float(10),\n", + " 'R': float(0),\n", + " 'beta': float(0.05), # contact rate between S and I\n", + " 'gamma': float(0.2), # recover rate from I to R\n", + " 'timestamp': '2019-01-01 00:00:00'\n", + "}\n", + "\n", + "# Parameters\n", + "epsilon = 0.03\n", + "subscription_fee = 1\n", + "incentive_cost = 10\n", + "stickiness_cost = 5\n", + "delta_beta = 0.1\n", + "delta_gamma = 0.1\n", + "\n", + "ts_format = '%Y-%m-%d %H:%M:%S'\n", + "t_delta = timedelta(days=0, minutes=0, seconds=1)\n", + "def time_model(step, sL, s, _input):\n", + " y = 'timestamp'\n", + " x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)\n", + " return (y, x)\n", + "\n", + "exogenous_states = exo_update_per_ts(\n", + " {\n", + " 'timestamp': time_model\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Behaviors choose actions depending on states\n", + "# 1) increase contact rate, beta (create incentive to spread)\n", + "# 2) decrease recover rate, gamma (improve stickiness)\n", + "\n", + "def add_incentive(step, sL, s):\n", + " delta = 0.0\n", + " potential_delta = s['beta'] * delta_beta\n", + " if (s['S'] > 3 * s['I']) and s['Budget'] > \\\n", + " abs(potential_delta * incentive_cost * s['S']):\n", + " delta = potential_delta\n", + " return {'delta': delta}\n", + "\n", + "def add_stickiness(step, sL, s):\n", + " delta = 0.0\n", + " potential_delta = s['gamma'] * delta_gamma\n", + " if (s['R'] > 2 * s['I']) and s['Budget'] > \\\n", + " abs(potential_delta * stickiness_cost * s['I']):\n", + " delta = potential_delta\n", + " return {'delta': delta}\n", + "\n", + "def dummy_behavior(step, sL, s):\n", + " return {'delta': 0.0}\n", + "\n", + "# Mechanisms incur cost to modify beta or gamma\n", + "# 1) incur cost to create incentive to spread\n", + "# 2) incur cost to improve stickiness\n", + "\n", + "def incur_incentive_cost(step, sL, s, _input):\n", + " y = 'Budget'\n", + " x = s['Budget'] - abs(_input['delta'] * s['S'] * incentive_cost)\n", + " return (y, x)\n", + "\n", + "def incur_stickiness_cost(step, sL, s, _input):\n", + " y = 'Budget'\n", + " x = s['Budget'] - abs(_input['delta'] * s['I'] * stickiness_cost)\n", + " return (y, x)\n", + "\n", + "def update_beta(step, sL, s, _input):\n", + " y = 'beta'\n", + " x = s['beta'] + _input['delta']\n", + " return (y, x)\n", + "\n", + "def update_gamma(step, sL, s, _input):\n", + " y = 'gamma'\n", + " x = s['gamma'] - _input['delta']\n", + " return (y, x)\n", + "\n", + "def S_model(step, sL, s, _input):\n", + " y = 'S'\n", + " x = s['S'] - s['beta'] * s['S']\n", + " return (y, x)\n", + "\n", + "def I_model(step, sL, s, _input):\n", + " y = 'I'\n", + " x = s['I'] + s['beta'] * s['S'] - s['gamma'] * s['I']\n", + " return (y, x)\n", + " \n", + "def R_model(step, sL, s, _input):\n", + " y = 'R'\n", + " x = s['R'] + s['gamma'] * s['I']\n", + " return (y, x)\n", + "\n", + "def collect_subscription(step, sL, s, _input):\n", + " y = 'Budget'\n", + " x = s['Budget'] + s['I'] * epsilon * subscription_fee\n", + " return (y, x)\n", + "\n", + "mechanisms = {\n", + " 'spread': {\n", + " 'behaviors': {\n", + " 'dummy': dummy_behavior\n", + " },\n", + " 'states': {\n", + " 'S': S_model,\n", + " 'I': I_model,\n", + " 'R': R_model,\n", + " 'Budget': collect_subscription\n", + " } \n", + " },\n", + " 'create_incentive': {\n", + " 'behaviors': {\n", + " 'action': add_incentive,\n", + " },\n", + " 'states': {\n", + " 'beta': update_beta,\n", + " 'Budget': incur_incentive_cost,\n", + " }\n", + " },\n", + " 'improve_stickiness': {\n", + " 'behaviors': {\n", + " 'action': add_stickiness\n", + " },\n", + " 'states': {\n", + " 'gamma': update_gamma,\n", + " 'Budget': incur_stickiness_cost,\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "single_proc: []\n" + ] + } + ], + "source": [ + "config = Configuration(\n", + " sim_config=sim_config,\n", + " state_dict=initial_condition,\n", + " seed=seed,\n", + " exogenous_states=exogenous_states,\n", + " env_processes=env_processes,\n", + " mechanisms=mechanisms)\n", + "\n", + "from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n", + "exec_mode = ExecutionMode()\n", + "exec_context = ExecutionContext(exec_mode.single_proc)\n", + "executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n", + "raw_result, tensor = executor.main()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(raw_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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vMKbLGIsiE8LzSBLzEv52G+P7xLNgRyG1Do3dJn0rdkYlFSVM/XQqeWV5bsOeGPWEJDAh\nGpEk5kUu7ZfIJxsOs+HQUYZ3k57vO5u6RhpHTh3hyTFPEuoXWj8sPiRebkoWogmSxLzIRX3i8bMp\nvtlWKEnMy2mtySnNQWtdX/bF/i/4Lvc7Hh/1ONf1vs7C6ITwHpLEvEhEkD+jusfw9dZ8LhuQWF+u\ngH7JEQT5S8/i3kBrzWPfPcZn+z5zG3Zp2qVM7TPVgqiE8E6SxLzMpAFJPPnJVn748jKX8ptGpfH0\nDwZZFJVoiw93f8hn+z5jSp8pDI4fXF8eYA/gotSL5FlyQrSBJDEvc0NmGj3iw6iuddSX/XfpAb7Y\nks/vJw+UBh8e5njlcU7VNDz0NK8sj2dWPcPo5NE8PupxbEp6ZBHibEgS8zL+dhvn93R9RMapqlq+\n3VXE6gMljJbnkHmMbw99y4OLHnR5OjJATFAMf7rgT5LAhGgHksQ6gYv6xBPoZ+PLLfmSxDzEiaoT\n/G7570iPSOfmfje7DMtMypRndQnRTiSJdQIhAX5c1DueL7fk8+T3+2OTU4qW++vqv1JSUcI/L/mn\nS3+GwndVV1eTk5NDRUXF6Ue2UFBQEKmpqfj7e0en0R6XxJRSUcDrwEBAA7cDO4FZQDpwALhea33U\nHP8x4A6gFrhfa/1Vx0dtvcsHJvH1tgI25R5nSNcoq8PxGVprXtn4CttLtteX1TpqWZK7hNsH3i4J\nTNTLyckhPDyc9PR0j228o7WmuLiYnJwcMjIyrA6nVTwuiQHPA19qra9VSgUAIcDjQJbW+hml1KPA\no8AjSqn+wFRgANAFmK+U6q2100OVfMQlfRPxsyneWn6A0oqGnu6D/G0M7xbtsT8ab/fJ3k94ZeMr\nZERmuPQWPyl9EvcMvsfCyISnqaio8OgEBsajoGJjYykqKjr9yB7Co5KYUioSuBCYBqC1rgKqlFKT\ngfHmaDOARcAjwGRgpta6EtivlNoDZALLOzRwDxAZ4s8FveL4aF0uH63LdRn27x+PYGL/xGZqijN1\n5NQR/rz6zwxLGMZ/L/+vNNQQp+XJCayON8TozKOSGJABFAH/VUoNBtYCDwCJWuu6zuTygbotcgqw\nwql+jlnmQil1N3A3QFpa531a8j+mDmVXwcn6z1rDXW+t4cst+ZLEzlJlbSWbija5lL217S0qayr5\n7djfSgITXuPpp5/m3XffxW63Y7PZePXVVxk1apTVYZ0xT0tifsAw4D6t9Uql1PMYpw7raa21Uko3\nWbsZWuvXgNcARowY0aa63iQy2J+R6a7dUV3cJ54FOwqoqXXgZ5cN7ZnQWjN9/nRW5a9yG/bAsAfI\niPSOawdCLF++nE8//ZR169YRGBjIkSNHqKqqsjqss+JpSSwHyNFarzQ/f4CRxAqUUsla6zylVDJQ\naA7PBbo61U81y4RpYv8k5mw4zNrso4yS5vdn5Jvsb1iVv4p7Bt/DiMQR9eUh/iHScEN4lby8POLi\n4ggMNK7fxsV5/60eHrVrrrXOBw4ppfqYRZcA24C5wK1m2a3AJ+b7ucBUpVSgUioD6AW47y77sIv6\nxBNgt/HNtoLTjyzcVNZW8tza5+gd3ZufnPcTMpMz6/8Gxg30uusHwrdddtllHDp0iN69ezN9+nS+\n/fZbq0M6a552JAZwH/A/s2XiPuA2jGQ7Wyl1B5ANXA+gtd6qlJqNkehqgJ/5YsvEloQF+jG2Zyxf\nbyvgie/1k41uC2odtXyb8y3lNeX1ZWsL1pJbmssbl72B3SYdLIv28bt5W9l2+ES7TrN/lwh+c1XL\nZwbCwsJYu3YtS5YsYeHChUyZMoVnnnmGadOmtWssHcnjkpjWegMwoolBlzQz/tPA0+c0KC83sX8i\nT3y8hc25x+mZEFZfHmC3yXUyJ69sfIVXN73qVn5F+hVkJmdaEJEQ7c9utzN+/HjGjx/PoEGDmDFj\nhiQx4dkm9jOS2NUvLnUpT40OZuH/jcdfEhl5pXm8ufVNJnabyAPDHqgvVyhSw1NbqClE253uiOlc\n2blzJzabjV69egGwYcMGunXrZkks7UWSmA9IiAjiXzcPI7u44TTZoaPlvLPiICv2FXNBr3gLo/MM\n/1j3DwB+MeIXJIclWxyNEOdGaWkp9913H8eOHcPPz4+ePXvy2muvWR3WWZEk5iMuH+i6Ya6oruXj\ndbl8vjnf55JY1sEs8svy6z+XVpXy+f7PuWvQXZLARKc2fPhwli1bdvoRvYgkMR8V5G9nQr9Evt6a\nz1OTB/jMtbFNRZt4cOGDbuXpEencOehOCyISQpwNSWI+7HuDkpi38TCrDpQwtof33y/SGi9vfJmo\nwCjev+p9guxB9eWhAaH427yj124hRAPf2P0WTbqodwLB/nY+35x3+pE7gY1FG1mau5RpA6aRFJpE\nVFBU/Z8kMCG8kxyJ+bDgADsT+ibw5ZYCrhzkei1oQHIkkSGda8P+ysZXiA6M5oa+N1gdihCinUgS\n83FXDU7ms8153PjvlS7ll/RN4I1pIy2K6uz9eumv+XTfpy5lNY4afj7854T4h1gUlRCivUkS83GT\nBiTx8fSxVNY46ss+WpfDh+tyKS6tJDYssIXanmlnyU7m7JnDhakX0ju6d315kD2IG/veaGFkQoj2\nJknMxymlGJoW7VIWGezP7DU5fL4ln1tGe9+NkK9uepUw/zD+OO6PRAZGWh2OEB4jLCyM0tJSq8No\nV9KwQ7jpmxRO78Qw5m7wvgcC7Dq6i2+yv+GmfjdJAhPCB8iRmHCjlOLqwV3469e7yDlaTmq0Z15D\nKqko4ZM9n1Dr1OfzkpwlhPqHckv/WyyMTAjRUSSJiSZdPTiFv369i3kb87hnfA+rw2nSS+tfYvau\n2W7l9w29T47ChPARksREk9JiQxjSNYrns3bxzors+nJ/u+LvU4a4XUfraMcrjzNv3zwm95jMr8f8\nur5coQiwB1gYmRCt8MWjkL+5faeZNAiueKZ9p+kFJImJZj1+ZT9mrT7kUvb11nzeXHbA8iT24e4P\nOVVzilv630Kg3ftaUAoh2ockMdGszIwYMjNiXMp+PcfO7DWHOFFRTUSQNTdD1zhqeG/He2QmZdIn\nps/pKwjhaXzwiOlckSQm2uRHw1N5e0U2n2/KY2pmWofMc/nh5RyrPFb/ec+xPeSX5fN45uMdMn8h\nhOeSJCbaZHBqJD3iQ/lgbU6HJLENhRu4+5u73crTI9K5MPXCcz5/ITqTznaPGEgSE22klOLa4V15\n9ssdHDhSRnpc6Dmd3/+2/49w/3DevOJN/GwNq2tCcAJ2m/2czlsI4fkkiYk2+8HQFP7y1Q6ueXkp\nIf4NiSQxMoh37xxNcED7JJeCsgLmZ8/nxn43unQfJYQQdSSJiTZLigzit1cPYHPO8fqysqoaPt+c\nz+eb8/jR8NR2mc/sXbOp1bVM7Tu1XaYnhOh8JImJM/LjMekun7XWTPjbt7y36uAZJbGKmgqKK4rr\nPzscDj7Y9QEXpV5E1/CuZxuuEKKTkiQm2oVSiqkju/KnL3awu+AkvRLDW11Xa80tX9zCjpIdbsNu\n6CfP/hJCNE+SmGg3Pxqeyl+/3snM1Yf49ff7t7re6vzV7CjZwc39bna57ysyIJIxyWPORahCiE5C\nkphoN3FhgVzWP4kP1+VwSd8EUA3DeiWEEx/edM8a7+54l6jAKB4Y9gBBfkEdFK0QvsdutzNo0CBq\namrIyMjg7bffJioqyuqwzookMdGubhqVZjwp+nXXJ0X3S47g8/vHoZRyKc8rzWPhoYXcNuA2SWBC\nnGPBwcFs2LABgFtvvZWXXnqJJ554wuKozo4kMdGuxvaM49P7xlFaWVNftnxvMc9n7WbV/hJGdY91\nGX/WzlkAXN/n+g6NUwhfN2bMGDZt2mR1GGdNkphodwNTXB+DMjg1ijeXHeBvSz+gbMNcHNpRP6yw\nvJCLu15Ml7AuHR2mED6rtraWrKws7rjjDqtDOWuSxMQ5FxxgZ8rIrrxz8O8kBlQxuktm/TCbsnHb\ngNssjE6IjvfsqmebbI17NvrG9OWRzEdaHOfUqVMMGTKE3Nxc+vXrx8SJE9s1BitIEhMdYly/Gt4r\nyqF7wG386YKHrA5HCJ9Ud02svLycSZMm8dJLL3H//fdbHdZZ8cgkppSyA2uAXK3195VSMcAsIB04\nAFyvtT5qjvsYcAdQC9yvtf7KkqBFi1YXf43SdhasTaHP2i/qy+02xZ9+OIjJQ1IsjE6IjnW6I6Zz\nLSQkhBdeeIFrrrmG6dOn4+fnkamgVWxWB9CMB4DtTp8fBbK01r2ALPMzSqn+wFRgAHA58LKZAIUH\nqXHUMG/vPDKTzuen4wYz7fz0+r/EiCBeyNqNw6GtDlMInzJ06FDOO+883nvvPatDOSsel36VUqnA\n94CngbrzTpOB8eb7GcAi4BGzfKbWuhLYr5TaA2QCyzswZHEayw4vo7iimF+P/iGXdOvrMqx/cgQP\nzNzAol2FTOibaFGEQviGxo9imTdvnkWRtB+PS2LAP4BfAs79FiVqrfPM9/lA3dYuBVjhNF6OWSYs\nkl+Wz93f3E1ZdVl9WVl1GVGBUU0+/+vKQck888UO/r14vyQxIUSbeVQSU0p9HyjUWq9VSo1vahyt\ntVZKtenck1LqbuBugLS0jnkasa/6ePfHHDh+gMk9J2NTDWerL0i5AH+7v9v4/nYbt52fzh8/38GW\n3ONuzfOFEKIlHpXEgPOBq5VSVwJBQIRS6h2gQCmVrLXOU0olA4Xm+LmAcxfnqWaZC631a8BrACNG\njJCLL+eI1pq5e+eSmZTJU+c/1ep6U0am8fz83Xz/n9+5lEcG+/PpfePoGhPS3qEKIToJj2rYobV+\nTGudqrVOx2iwsUBrfTMwF7jVHO1W4BPz/VxgqlIqUCmVAfQCVnVw2MK0vnA9OaU5XN3z6jbViwz2\n5+Wbh3P/Jb3q/+6b0JPyqhpeXbz3HEUrRMfT2vP3ob0hRmeediTWnGeA2UqpO4Bs4HoArfVWpdRs\nYBtQA/xMa11rXZi+be7euQT7BXNp2qVtrntR73gu6h3vUnaktJLZa3K4f0IvEiKkX0Xh3YKCgigu\nLiY2NtatD1FPobWmuLiYoCDv+b15bBLTWi/CaIWI1roYuKSZ8Z7GaMkoLFRRU8FXB75iYreJhPi3\nz+m/n17Ug1mrD/HGd/t57Mp+7TJNIaySmppKTk4ORUVFVofSoqCgIFJT2+fp7B3BY5OY8GyPL3mc\nzUc213+urK2ktLqUq3pc1W7z6BYbylWDu/DOimwGpkRitzXsvfZODKNnQusfvCmE1fz9/cnIyLA6\njE5Hkphos4MnDjJv3zyGxA8hOTS5vvyybpeRmZTZQs22mz6+J59uyuO+99a7lEeH+LP4lxcTHuTe\n4lEI4TskiYk2m39wPgDPXvjsOe99vk9SOEt+eTEnKxoe7ZJdXMbdb69lxrID3Duh1zmdvxDCs0kS\nE202P3s+A2IHdNjjU7pEBbt87pMUzqX9Enht8T5uGZNOZLAcjQnhqzyqib3wfPll+Ww+splLu7W9\nBWJ7evDS3pyoqOE/3+23NA4hhLXkSEy0yfxs41TimTSjb08DUyK5fEAS/1yw2yWR2WyKx6/sy5SR\n0jOLEL5Akphok/kH59MzqifpkelWh8Kvvt+PrjHB1DY8KJrVB0r4w6fbmdg/iZjQAOuCE0J0CEli\nolkzts7g5Q0vu5SV15Rzz+B7LIrIVWp0CE98r79L2e6Ck0z6x2JeXLCHJ6/q30xNIURnIUlMNElr\nzayds0gKTWJcyrj6cn+bP1P6TLEwspb1SgznuuFdeXvFAW47P136XRSik5MkJpq0+9huDp08xJNj\nnuS63tdZHU6b/Hxibz7ZmMuPXlnmckoxKsSfv1w7WBKbEJ2ItE4UTcrKzkKhuLjrxVaH0mZJkUE8\nd/0QhqZF0S02pP5vU85x/vDZNqvDE0K0IzkSE03KOpjF0IShxAXHWR3KGblyUDJXDkp2KXtp4R7+\n8tVOlu05wtie3vm9hBCu5EhMuDl08hA7j+5kQtoEq0NpV3eMyyA1Opjff7qNGucmjUIIryVHYsLN\ngoMLALgkrckHB3itIH87v/peP376zjoG/fZrnPoTZlBqJG/elkmQv926AIUQbSZJzMftOrqL6fOn\nU1VbVV/jW5ukAAAgAElEQVRWVl1G35i+pIZ7z+MYWmvSgCT++INB7CsqrS8rq6rlvVUHefXbfTxw\nqfTFKIQ3kSTm4+bumUtxRTE/6vUjl/JJ6ZMsiujcUkpx4yj33jxOVFTz0qI9TB7ShfS4UAsiE0Kc\nCUliPkxrzYJDCxiVPIpfjf6V1eFY6snv9+fbnUU89tFmpp2f7jJsTI9YIuSRL0J4JEliPqzuXrDb\nBt5mdSiWS4wI4pHL+/DrT7ayfF+xy7CBKRHMmX4+fnZpByWEp5Ek5sOyDnrvvWDnwi1j0hnXK57y\nqoZnl204dIwnPt7Ca0v2MX18TwujE0I0RZKYD1t4cCGD4wd77b1g50JGo+thA7pE8t3uI/xj/m4m\nDUiiR3yYRZEJIZoiScxH5Zbmsr1kOw8Pf9jqUDze7yYPYNneYia/uJTQwIYm+CEBfvz52vMYmR5j\nYXRC+DZJYj7AoR08tOghsk9k15eVVhtNzDvbDc3nQkJ4EK/fOoIP1+a4lC/ZfYQH3lvPlz+/UBp+\nCGERSWI+YMuRLWQdzGJYwjBig2Pry6/ucTVpEfLwyNYYmR7jdsS1/uBRrv3Xcn47dyvPXT/EosiE\n8G2SxHxA1sEs/JQfL0x4gcjASKvD6TSGpkVz78U9eT5rN8WlVQT6NbRe7BYbwi8v74u/tGgU4pyS\nJNbJaa1ZcHABI5JGSAI7B+6d0JNDR8vZdvhEfZlDa77eVoCf3cYjl/e1MDohOj9JYp3cvuP7OHDi\nADf1u8nqUDolf7utyVOJj364iVcW7WVM91gu7B1vQWRC+AZJYp1c1sEsALkXrIP95qoBrDt4lAdm\nrmdMj1iXYZMGJDF5SIpFkQnRuUgS6+SyDmZxXtx5JIYmWh2KTwkOsPPyTcN45MPN7C5o6Gz4ZEUN\nX27JJzY0kHG95P48Ic6WJLFO5INdHzBnz5z6zxrNtuJtPDjsQQuj8l09E8L58J6xLmVllTVc89JS\n7ntvHfPuG0dqdIhF0QnROSittdUxdKgRI0boNWvWWB1Gu9Nac+kHl2JTNjIiMurLA/0CeXL0k8SH\nyHUZT7GvqJTJLy7F389GTGhAfbm/3cYvL+/DxX0SLIxOiKYppdZqrUdYHUdjciTWSWwt3kpheSFP\nj3uaq3tcbXU4ogXd48N4/dYRvL0iG+d9yG15J/jZ/9bxwU/H0r9LhHUBCuFFPCqJKaW6Am8BiYAG\nXtNaP6+UigFmAenAAeB6rfVRs85jwB1ALXC/1vorC0K3XNbBLOzKzkWpF1kdimiFUd1jGdXdtcFH\nwYkKJr+4lDtnrOblm4cT7PSU6egQfxIigjo6TCE8nkclMaAGeFhrvU4pFQ6sVUp9A0wDsrTWzyil\nHgUeBR5RSvUHpgIDgC7AfKVUb611rUXxW2bBwQWMSJR7wbxZYoTRvdV1/1rONS8tdRlmtyleuWkY\nlw1Isig6ITyTRyUxrXUekGe+P6mU2g6kAJOB8eZoM4BFwCNm+UytdSWwXym1B8gElnds5Nbaf3w/\n+47vY0qfKVaHIs7SwJRIPrt/HDvyT7qUv7p4H/e9t5537xrF8G7S4bAQdTwqiTlTSqUDQ4GVQKKZ\n4ADyMU43gpHgVjhVyzHLGk/rbuBugLS0ztdXYN29YNKZb+fQPT6M7o0e+TIqI4YfvbKM299cw9hG\n951lZsQwbWw6SqmODFMIj+CRSUwpFQZ8CDyotT7h/OPUWmulVJuaVGqtXwNeA6N1YnvG2tEOHD/A\n8+uep8bR8ODGLcVbGBA7gKRQOdXUWcWGBfLW7aP4xQcb2VvUcN9ZRbWDL7bkc7Kihvsv6WVhhEJY\nw+OSmFLKHyOB/U9r/ZFZXKCUStZa5ymlkoFCszwX6OpUPdUs67Rm7pzJokOL6BXdsMGKD45n2sBp\n1gUlOkRabAizfjLGpczh0Pzig008980uKmtqGdil4ZqozaYY2yOWcHlMjOjEPCqJKeOQ6w1gu9b6\nOadBc4FbgWfM10+cyt9VSj2H0bCjF7Cq4yLuWLq2lgUHvmZczAD+OWi660C/IDiyB/yDAOVcCU4d\ng1MlUFvjWsc/CKK6QUQXsNkR3sdmUzz7o0Gcqq7hpYV73Yb3S47gf3eOcrkfTYjOxKNudlZKjQOW\nAJsBh1n8OMZ1sdlAGpCN0cS+xKzzBHA7RsvGB7XWX7Q0D4+/2bm8BNa/DRvehYrjLoO21pYyNTGa\np4qKuaa0rB1nqtyTWHQG9J4EKcNAOQ2z2aHb+RAijQs8idaavUWl1Dgafs97C8t4aPYGMuJCefHG\nYYQEOD+V2k5UiCQ20XqeerOzRyWxjuAxSay0EOZMh5zVruVVZeCoNhJFbA+XQS9U5fCfsr0synyK\nKOem9FpDTQVUnDBenSkbBEdBcAzYG220qkrh6AE4kQva0VCuHZC3CQ4sgdoq99j9gmDAD6HLUNdy\n/yBI6A8J/cC/ie6UpOFBh1u65wh3zFhNRbXDpdym4HdXD+CWMenWBCa8jqcmMY86ndgpaQ0l+8Cp\nIQYncuGTe6G8GAbf4Jpc/INh0HWQNNBtUllzJjMiKZOofpM7IHCMhHo027Ws8gRsmgWbZsPGd1s/\nrYgU6H05dBsLdudrNArCEiE63Ui2zpQd7LKKno3ze8Yx995xrD941KX8yy35/PqTrRwrr+aOCzJc\nhgX62bHbZIejU6ithlOu/3sctXDysLEDW1XuOqymAo4dhOM5rtssDyZHYueS1jDvAVg3w31YVBpM\neQeSB7dqUvuO72PynMk8Pupxbuh7QzsHegZqKqGy1LWs8gQUbIWiHa4/AK2hYAvsXQDVjX40LVF2\n6DIE0sa4J7jwZOhzpZzWPEPVtQ5++cEmPl7v3g4qLSaE128dQe/EcAsi82FaQ9kR4/q1M0eNkVhK\n9rv/fqrLjfLjOcb1b+dplRe7l7eGPQAiU8Ee6FKs7l3pkUdiksTOpTX/gU9/DiNuh/RxDeXKBt3H\nQ3B0k9VqHDU8s+oZjpw6Ul+WV5bHtuJtzL92vvc+VqW6wjgqxWmd0w44mW/8EKtcb/Cl4gQcWgm5\na5s+ranskDLcbMziJLyLcUozPNn1FKbND1JHGDsQAodDM2dDLkUnK+vLarXmv0sPUFFdyws3DGWA\nUx+OCkVcWIDcj1bHUWskl4pjruW11cZRTsl+99P7lSehZC8cb3QKH21cYqg8QZsou7E+R3V1v1wQ\nFAUxGcaZDpf/mYLwJKNRV1CjHn7sARAaDzab+6w89HSiJLH2cuA7Y2Ncp+IYfPGokaxunNWm1n8r\n8lZw19d3kRaeRqBfw97Q0Pih/HrMr9svZm/hqG10zU5D4TbYNgcOrXK/nnfskHG6pDnR6cYP1Zl/\nsLExiEg1kp2zyBQjWcZ0N3ZA6qkmf+zeLudoObf9dzW7C0vdho3uHsNLNw4jNiywiZoervKkubPU\n6HvVVBhJ52i2kYCcVRyD4r3G+uS8qdQOKC0wrl+3xNbo9gb/ECOxRHV1HxYaZ6xjofGuSUfZILKr\n0dgqsNHRsc3eYS2LJYl5iHOSxLbNhdm3uJfH9IC7spo94mrOU8ufYt6+eXw75VuC/YLbKUgfc+qo\n0dLTWeVJOLgCspe6b8gqS+FYtrFhaovgGOMWhcYNWfyDjaPB+D5GQ5jGw0ITzFOkzhsrZUyr8d6x\nBU5UVPPl5nyqaht2EI6WVfHiwj3EhQXy5FX9CQtsSPZB/jaGdo3GdibX0moqjf+NM0ctlOYbp8Oq\nTzUa37xucyLXGK+O1sb//egBKCt0reNwQKVra1839gC3U2gEhhm/48gU11a6AGEJRuOrkLhGSccO\n0d2MnSU/L0z2zZAk5iHaPYnV1sDLo4y99+vfwmWjFNXV2GC1ZXKOWia8P4ERiSP42/i/tV+conUc\ntbg8HwVt7InnroETh93HLSuEE3lNnDY6AYU7oPoMboUIS3K/1qfsEBJttjL1B5S54WzqFfPVZuy5\nh8aBf6jxXeq/2+nem58bvc87foqP1+dSWlFdv6YrNHYc9I/RjE/zI9DWaJtSUwGlRca1nsZH1BXH\njGs3bWaeErM3OpoJjDQSSHiS+1FzeJJxFNR4p9Lmb9bp0imPrNuLpyYxafp1tta/DcV7YOq7xl73\nWVpXuI6SihImpk9sh+BEmzV1aiahr/HXVg6HcRqqcSOXqjIoK3K7DxBtXmM5stv92khtjXGUUbjN\nmF5dcql/xXx1OJU5zNsuGh3JnIVkYDpAE52AlJ4I5ujWMAICGzUI8AsgMj4FW0J/9+UbGG4kj+Bo\n91swwhKNI6CARqfQ/AKMOn5yn5uQJNY2ZUeM0xt1dC0sega6jjJayrWD+dnzCbQHcmHKhe0yPWEh\nm81o5WW1qjKjUQ00Ou1lHrk19141HGud9r2ysTf3JA/N3sDhYw1HpRpNRbWDISFRvHDVUNJim7h/\nUIizIEmstRy18O+LjT3lxq57s11u5HVoB/Oz5zMuZRwhTd0sLMSZCAg1/s6xwV2jyHp4vFv555vz\nePTDTVzy3CJCA103OaMzYvn9NQNICJcHfoozI0mstfYtNBLY+MchaVBDeWQqJJ93RpN8bs1zfJP9\nTf1nh3ZQeKqQS7tderbRCuExrhyUzHmpkby9PJuK6oaGGBXVDj7ekMvKvxfzk4t6uDQU8bcrLuuf\nRLT0+ShOQxp2tNb702Dft/DwjnZpcXSi6gTjZ42nZ1RPekQ1dC8V6h/KwyMellaJwifsKSzl4fc3\nsvHQMbdhsaEBPHlVf64e3EXuTfMA0rDDm5WXwI7PjJuW26nJbFZ2FtWOap4c8yQD49y7mBLCF/RM\nCGPO9LEcKa1CO92IlXesgifnbuWBmRt4aPZG5za/RIUE8NDE3kwd2fXMmvSLTkWSWGts/sDoMWLI\nTe02yS8PfElqWCoDYge02zSF8EZKKeLDXXcOE8KD+OiesXy4LofsYtfbFFbvP8rjH2/mnRXZ9E50\nfQJ23+QIpo1NJ8hfHi3kKySJNVZeAvu/db1XaM1/IOm8M7721VjxqWJW5q3k9oG3y2kSIZphtymu\nH9HVrVxrzdyNh/nXt/tY73QastahmbPhMG8tO8Dt4zKIcHoYqFJwQa94kiKlAUlnI0mssfm/gXVv\nuZd/7zn3sjP0TfY31OpaLs+4vN2mKYSvUEoxeUgKk4ekuA1bsa+YP3y2jT98tt1tWKCfjWnnp3Pl\nwGRsTjuPIYF2esSHuY0vvIM07HCmNTzX32h9OPH3DeU2P6NPszO4m/945XGyDmZR69ST9MwdM3Fo\nBx9P/rjN0xNCtMzh0BScrMDp+aCcOFXNvxfv4+MNuTS1yRvdPYb7J/SiV6Oe+yOC/Qj0k1OTIA07\nvEPhdqOHhfGPnlkPDU14cf2LzNw506384eEPt8v0hRCubDZFcqRr696UqGCemzKEn03oyf4i12ts\n+4+U8e8l+7jx9ZVu04oI8uPHY9K5eXQ3woMaNpdKQbC/XS4HeABJYs72ZhmvPS9pl8lV1lby2f7P\nuKzbZTyS+Uh9uU3ZiA2KbZd5CCFar0d8WJOnDm8Z042vtuZzosK1i7Ble4t5adEeXly4x63OgC4R\n3H5+BhP6Jri0kvS3K0ICZNPaUWRJO9uTBfF9262roIUHF3Ky6iTX9r6WhJCEdpmmEKL9Bfnbm7zG\ndsuYdPYWlbJwRyEOp/OQldUO5m06zMPvb3SroxRc2Cuem0d3o2eCa8KMCvaXG7jbmSSxOlXlkL0M\nRt7ZbpOcs2cOSaFJZCZltts0hRAdq7mjt3sn9GTpnmJ2Frg+RuZIaSUfrcvhrrfcr73bbYqL+yQw\neUgXIoJde1HunRjmdhpUnJ4ksTrZy6C2EnpOaJfJ5Zfls+zwMu467y7sHfTQOiFEx1FKMa5XHON6\nxbkNe2hib77bc4Tj5a4PzdyRf5IP1+Uwf7v7c+uUMvqSHNcrzqX1pJ9NMaZHLAO6RMg1uCb4ZhLT\nGvYvdn3Y3qaZxsMLu53f5snVOGqYuWMmJ6sa9si2l2xHo7mmxzXtEbEQwov4221c3KfpSwgPX9ab\nbYdPUOPUfNKhNcv2FPPR+hz+8tXOJutlxIXSPc61I+fwID8u7pvAhL4JhAc18XwcH+CbSWzvAnjn\nh+7lvS9v80MsAb4+8DXPrn7WrfzirhfTNcL9Zk0hhO/yt9sY3DXKrXxkegz3X9KTyhqHS3lpZQ3z\ntxXw+ZZ8Ck66Pnx1Y04lczYcRimwNzpKS40OZkLfREamR2N3anhiU4qBKZGd5sZv30xiB74z7v26\n7QvjtU5crzZPSmvN29veJj0inTmT52BT8mRYIcSZUUq5dZkV5G9namYaUzPT3MZ3ODTrDx3lu93F\nVNU23IuqNWzLO8E7K7P5z9L9Tc6rb1I46bGhLk+RCvK3MzI9hjE9YokM9o4jO99MYgdXQPIQ6Hr2\nDS42FG1gS/EWfjXqV3LtSwjRoWw2xfBuMQzvFtPk8LLKGvYfcb0vrqrWwer9JSzeXcS+I6Uuw46V\nV/Px+txzFu+54HtJTGvIXQuZd7XL5N7e9jYRARFc1eOqdpmeEEK0l9BAPwamRLqVD0uL5icX9XAr\n11qz70gZaw6UUFHtelpzmvsVE4/ge0msutxohZg2ps1Vtx7ZysaihvtCqh3VZB3MYtqAafIkZiGE\n11NKNXtLwbSOD6dVfC+JVZmHz2mj21QtrzSPW7+8lcraSpfyYL9gbuh7Q3tFJ4QQog18MImVQVxv\nCHW/t6Mlf1/7dwA+vvpjYoMbuowK8guSpzALIYRFfDOJtfEobH3her448AU/HfxTekb3PEeBCSGE\naKtOkcSUUpcDzwN24HWt9TPNjuyoafF62K6ju/hy/5cuj0pfeHAhCcEJ3DbgtvYLWgghxFnz+iSm\nlLIDLwETgRxgtVJqrtZ6W7OVmkli24q3cefXd1JWXeZyv1egPZCnz39aGm8IIYSH8fokBmQCe7TW\n+wCUUjOByUDTSSwokuMh0WQXbXJ5UOXxyuP8aumvCPMP44OrPqBLWJcOCF0IIcTZ6AxJLAU45PQ5\nBxjV3Mi7VA3jZl3Q5LCEkATemPSGJDAhhPASnSGJnZZS6m7gboCoblE8NPwhukd2x9/u2q1K/5j+\nRAW592kmhBDCM3WGJJYLOPeym2qW1dNavwa8BjBixAh920BpoCGEEJ1BZ+itdjXQSymVoZQKAKYC\ncy2OSQghRAfw+iMxrXWNUupe4CuMJvb/0VpvtTgsIYQQHcDrkxiA1vpz4HOr4xBCCNGxOsPpRCGE\nED5KkpgQQgivJUlMCCGE15IkJoQQwmtJEhNCCOG1lNb69GN1Ikqpk8BOq+NoozjgiNVBtJG3xext\n8YL3xext8YLE7Kyb1jr+HEz3rHSKJvZttFNrPcLqINpCKbVGYj63vC1e8L6YvS1ekJi9gZxOFEII\n4bUkiQkhhPBavpjEXrM6gDMgMZ973hYveF/M3hYvSMwez+cadgghhOg8fPFITAghRGehtW72D+M5\nXQuBbcBW4AGnYTHAN8Bu8zXaLI8165QCLzaa3hRgkzmtZ1uY73BgM7AHeIGGI8YLgXVADXBtCzEv\nAo4DlcABIN0p5iNALVDUXMyNvltdvFuBZ4HHzLh2ApOaiflVp/qrgY11MTeu77SMt5sxFwFrgCXA\nXqAYKAeqMXrqfwzYZ5adqlvGTjHnASfN6T1rDnOLuYllXFf/EHDCeRk3E3PjZTzYrL8XqHJaxncB\nG8x5nQQ0MLMjl3Ez60URsAtwABNotF4AaRjrwx48Z704bC7frUAh3rFevArsMGOuxPPXi2PmMtoA\nLABK8KD1wql+IDDLHLYSSAceNpdjlvk9Pm2hfpPb7+bWiya2s2dV32ncupjjzM83mcu+7s8BDGlx\nGqeZQTIwzHwfjvGj729+/jPwqPn+URp+GKHAOOCnOCUxjERxEIg3P88ALmlmvquA0YACvgCuMMvT\ngfOAt2g+iSUDfwL+ZcacB3zhFPO/gatw/TG7xFz33cyYjwEvmOPNwdiIBAIZ5nt7EzHvw3gkDBgr\n8n/MmB/EWEGd66cAw4DpwBvmMl5gjvdn8/vPBN40l99GjB/TdRg/rJecvtvvzXGeMuc7A7i9iXna\nm1jG75vfOR34B7AF40fUv5mYGy/jvWb9P5vT/qCJZfxjM+aOXsZ23NeLQmAxxgZsBo3WCzP+j8xY\nPWW9eBWYbcaxCO9YL1Zh3MrzZ3N8T18v8mnYXsw3Y/Wk9aKu/nTgX+b7umcofgVkAz8wY17UQv3m\ntt9N/V/tTWxnz6q+0w5EXcxxTQwfBOxtKUdprVs+nai1ztNarzPf1+3FpZiDJ2P8GDBfrzHHK9Na\nfwdUNJpcd2C31rrI/Dwf+FHjeSqlkoEIrfUKbXyTt5ymfUBrvQkjOzcbM8aPf4YZ82pgrFJKmTE/\nibHnl9NCzHXfrTvG3tIks7wKKNJaV2qt92PsbWQ2EXMQ4G/W+QdwvhnzSGBmo/pp5jKeDLxuLuNe\nGE+onoyxggzFWEkTzfpHtdbvY/zgEp1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BNn/OGx7HdzuyKCir5qZTkzwdXpvogknMJC5HQIj0ThRCdGqPfLadhVsza3+P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\nESevSyexYJc5xVqTxP6zdB/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count151.000000151.000000151.000000
mean315.519268150.861455543.619277
std262.34312623.127729258.137254
min46.76772410.0000000.000000
25%98.897388142.907707369.363864
50%222.600751151.473138644.034432
75%470.722775163.532271763.921377
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