confi_testing_parallel-IQR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from seirsplus.models import *\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import json\n",
    "import  pickle\n",
    "from shapely.geometry import Polygon, Point, MultiPoint\n",
    "from scipy.spatial import ConvexHull\n",
    "from dateutil import parser\n",
    "\n",
    "from multiprocessing import Pool, freeze_support\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Below section for creating graphs and monte carlo simul\n",
    "\n",
    "def get_three_tuple(df):\n",
    "    arr = []\n",
    "    for row in df.iterrows():\n",
    "        v1 = np.percentile(row[1], 25)\n",
    "        v2 = np.percentile(row[1], 50)\n",
    "        v3 = np.percentile(row[1], 75)\n",
    "        arr.append(np.array([v1,v2,v3]))\n",
    "    return np.array(arr)\n",
    "\n",
    "def single_run(model_init, model_run, i):\n",
    "    model = SEIRSNetworkModel(**model_init)\n",
    "    model.run(**model_run)\n",
    "    time=pd.to_datetime(pd.Series(model.tseries),unit='d',origin='2020-04-01')   \n",
    "    \n",
    "    df_tempI=pd.DataFrame({'t':time, i:model.numI})\n",
    "    df_tempI.set_index('t',inplace=True)\n",
    "    df_tempI['t']=df_tempI.index.date\n",
    "    df_tempI.reset_index(drop=True,inplace=True)\n",
    "    df_tempI.set_index('t',inplace=True)\n",
    "    df_tempI.reset_index(inplace=True)\n",
    "    df_tempI = pd.pivot_table(df_tempI, index='t', aggfunc='mean')\n",
    "\n",
    "    df_tempDI=pd.DataFrame({'t':time, i:model.numD_I})\n",
    "    df_tempDI.set_index('t',inplace=True)\n",
    "    df_tempDI['t']=df_tempDI.index.date\n",
    "    df_tempDI.reset_index(drop=True,inplace=True)\n",
    "    df_tempDI.set_index('t',inplace=True)\n",
    "    df_tempDI.reset_index(inplace=True)\n",
    "    df_tempDI= pd.pivot_table(df_tempDI, index='t', aggfunc='mean')\n",
    "\n",
    "    df_tempR=pd.DataFrame({'t':time, i:model.numR})\n",
    "    df_tempR.set_index('t',inplace=True)\n",
    "    df_tempR['t']=df_tempR.index.date\n",
    "    df_tempR.reset_index(drop=True,inplace=True)\n",
    "    df_tempR.set_index('t',inplace=True)\n",
    "    df_tempR.reset_index(inplace=True)\n",
    "    df_tempR= pd.pivot_table(df_tempR, index='t', aggfunc='mean')\n",
    "\n",
    "    df_tempF=pd.DataFrame({'t':time, i:model.numF})\n",
    "    df_tempF.set_index('t',inplace=True)\n",
    "    df_tempF['t']=df_tempF.index.date\n",
    "    df_tempF.reset_index(drop=True,inplace=True)\n",
    "    df_tempF.set_index('t',inplace=True)\n",
    "    df_tempF.reset_index(inplace=True)\n",
    "    df_tempF= pd.pivot_table(df_tempF, index='t', aggfunc='mean')\n",
    "\n",
    "    return df_tempI, df_tempDI, df_tempR, df_tempF\n",
    "\n",
    "\n",
    "def monte(N, initI,theta_E, theta_I ,mf, checkpoints, G, sim=2, initDI=0):\n",
    "    beta = 0.187\n",
    "    sigma = 1.0/5.2\n",
    "    gamma = 1.0/15\n",
    "    mu_I = 0.004\n",
    "    p = 0.6\n",
    "    Q = None\n",
    "    beta_D = 0.155\n",
    "    sigma_D = 1.0/5.2\n",
    "    mu_D = 0#0.0004\n",
    "    theta_E = theta_E\n",
    "    theta_I = theta_I\n",
    "    phi_E = 0.4\n",
    "    phi_I = 0.8\n",
    "    initN = N\n",
    "    initI = initI\n",
    "    initE = initI*4\n",
    "    initD_E = 0.0\n",
    "    initD_I = initDI\n",
    "    initR = 0\n",
    "    initF = 0\n",
    "    x_i = 0.001\n",
    "    psi_E = 0.04\n",
    "    psi_I = 1\n",
    "    q = 0.3\n",
    "    gamma_D = 0#gamma\n",
    "\n",
    "    # Creating dataframe\n",
    "    time_index=pd.date_range(start='2020-04-01', periods=90)\n",
    "    df_I=pd.DataFrame(index=time_index)\n",
    "    df_DI=pd.DataFrame(index=time_index)\n",
    "    df_R = pd.DataFrame(index=time_index)\n",
    "    df_F = pd.DataFrame(index=time_index)\n",
    "    \n",
    "    model_init = {'G' : G, 'Q' : G_Quarantine, 'beta' : beta, 'sigma' : sigma,\n",
    "                 'gamma' : gamma, 'mu_I' : mu_I, 'beta_D' : beta_D, 'sigma_D' : sigma_D,\n",
    "                 'gamma_D' : gamma_D, 'mu_D' : mu_D, 'theta_E' : theta_E, 'theta_I' : theta_I,\n",
    "                 'phi_E' : phi_E, 'phi_I' : phi_I, 'psi_E' : psi_E, 'psi_I' : psi_I, 'q' : 0.3,\n",
    "                 'initI' : initI, 'initE' : initE, 'initD_E' : initD_E, 'initD_I' : initD_I}\n",
    "    \n",
    "    model_run = {'T':120, 'checkpoints':checkpoints, 'verbose':False}\n",
    "    \n",
    "    pool = Pool(processes = 32)\n",
    "    \n",
    "    results = []\n",
    "    for i in range(1, sim+1):\n",
    "        results.append(pool.apply_async(single_run, args=(model_init, model_run, i)))\n",
    "    \n",
    "    pool.close()\n",
    "    pool.join()\n",
    "\n",
    "    for res in results:\n",
    "        df_l = res.get()\n",
    "        df_I=df_I.merge(df_l[0], how='outer', right_index=True, left_index=True).ffill(axis=0).fillna(np.NaN)        \n",
    "\n",
    "        df_DI=df_DI.merge(df_l[1], how='outer', right_index=True, left_index=True).ffill(axis=0).fillna(np.NaN)        \n",
    "\n",
    "        df_R=df_R.merge(df_l[2], how='outer', right_index=True, left_index=True).ffill(axis=0).fillna(np.NaN)\n",
    "\n",
    "        df_F=df_F.merge(df_l[3], how='outer', right_index=True, left_index=True).ffill(axis=0).fillna(np.NaN)\n",
    "        \n",
    "        df_I =df_I.interpolate(method='linear', limit_direction='forward', axis=0)\n",
    "        df_DI =df_DI.interpolate(method='linear', limit_direction='forward', axis=0)\n",
    "        df_R = df_R.interpolate(method='linear',limit_direction='forward',axis = 0)\n",
    "        df_F = df_F.interpolate(method='linear',limit_direction='forward',axis = 0)\n",
    "\n",
    "    df_I = df_I*100.0/N\n",
    "    df_DI = df_DI*100.0/N\n",
    "    df_R = df_R*100.0/N\n",
    "    df_F = df_F*100.0/N\n",
    "    \n",
    "    return df_I, df_DI, df_R, df_F\n",
    "\n",
    "#     # Changes for looking at actual numbers\n",
    "#     df_Iavg=pd.DataFrame.mean(df_I, axis=1)*100.0/N\n",
    "#     df_DIavg=pd.DataFrame.mean(df_DI, axis=1)*100.0/N\n",
    "#     df_Ravg = pd.DataFrame.mean(df_R, axis = 1)*100.0/N\n",
    "#     df_Favg = pd.DataFrame.mean(df_F, axis = 1)*100.0/N\n",
    "        \n",
    "#     df_Icstd=pd.DataFrame.std(df_I.cumsum(axis=0), axis=1)*100.0/N\n",
    "#     df_DIcstd=pd.DataFrame.std(df_DI.cumsum(axis=0), axis=1)*100.0/N\n",
    "#     df_Rcstd=pd.DataFrame.std(df_R.cumsum(axis=0), axis=1)*100.0/N\n",
    "#     df_Fcstd=pd.DataFrame.std(df_F.cumsum(axis=0), axis=1)*100.0/N\n",
    "\n",
    "#     df_Istd=pd.DataFrame.std(df_I, axis=1)*100.0/N\n",
    "#     df_DIstd=pd.DataFrame.std(df_DI, axis=1)*100.0/N\n",
    "#     df_Rstd=pd.DataFrame.std(df_R, axis=1)*100.0/N\n",
    "#     df_Fstd=pd.DataFrame.std(df_F, axis=1)*100.0/N\n",
    "\n",
    "#     return {\n",
    "#         'df_DI':df_DIavg, 'df_I': df_Iavg,'df_R': df_Ravg, 'df_F':df_Favg,\n",
    "#         'df_Icstd':df_Icstd, 'df_Rcstd':df_Rcstd, 'df_Fcstd': df_Fcstd, 'df_DIcstd':df_DIcstd,\n",
    "#         'df_Istd':df_Istd, 'df_Rstd':df_Rstd, 'df_Fstd': df_Fstd, 'df_DIstd':df_DIstd,\n",
    "#            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_graphs(numNodes, coeff):\n",
    "    G_normal = nx.erdos_renyi_graph(n=numNodes, p = coeff*1.1/numNodes, seed=42, directed=False)\n",
    "    G_relaxed = nx.erdos_renyi_graph(n=numNodes, p = coeff*0.301/numNodes , seed=42, directed=False)\n",
    "    G_Quarantine = nx.erdos_renyi_graph(n=numNodes, p = coeff*0.006/numNodes , seed=42, directed=False)\n",
    "    \n",
    "    return G_normal, G_relaxed, G_Quarantine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "freeze_support()\n",
    "plt.rcParams['figure.figsize'] = (15, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('region_sim1.p','rb') as fp:\n",
    "    S5,S10,S20 = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "I = 21\n",
    "Et = [0.000, 0.0001,0.00016,0.0002]\n",
    "It = [0.0001, 0.002, 0.006, 0.007]\n",
    "sim = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S5['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, np.log(S5['total_pop']))\n",
    "\n",
    "final = []\n",
    "initI = I\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim);\n",
    "with open('data/region1_S5c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Region 1 - 5000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1])*100/numNodes for j in S5['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff = (S10['total_pop']*np.log(S10['total_pop'])*S5['area']*S5['F(S)']**2)/(S5['total_pop']*np.log(S5['total_pop'])*S10['area']*S10['F(S)']**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S10['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, coeff*np.log(S5['total_pop']))\n",
    "\n",
    "final = []\n",
    "initI = int(I*coeff)\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim);\n",
    "with open('data/region1_S10c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Region 1 - 10000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1])*100/numNodes for j in S10['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff = (S20['total_pop']*np.log(S20['total_pop'])*S5['area']*S5['F(S)']**2)/(S5['total_pop']*np.log(S5['total_pop'])*S20['area']*S20['F(S)']**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S20['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, coeff*np.log(S5['total_pop']))\n",
    "\n",
    "\n",
    "final = []\n",
    "initI = int(I*coeff)\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim);\n",
    "with open('data/region1_S20c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Region 1 - 20000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1])*100/numNodes for j in S20['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('region_sim2.p','rb') as fp:\n",
    "    S5,S10,S20 = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "I = 5\n",
    "Et = [0.000, 0.0000,0.0002,0.0002]\n",
    "It = [0.0001, 0.001, 0.001, 0.004]\n",
    "sim = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S5['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, np.log(S5['total_pop']))\n",
    "\n",
    "final = []\n",
    "initI = I\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim);\n",
    "with open('data/region2_S5c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Region 2 - 5000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1])*100/numNodes for j in S5['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff = (S10['total_pop']*np.log(S10['total_pop'])*S5['area']*S5['F(S)']**2)/(S5['total_pop']*np.log(S5['total_pop'])*S10['area']*S10['F(S)']**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S10['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, coeff*np.log(S5['total_pop']))\n",
    "\n",
    "final = []\n",
    "initI = int(I*coeff)\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim);\n",
    "with open('data/region2_S10c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Region 2 - 10000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1])*100/numNodes for j in S10['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff = (S20['total_pop']*np.log(S20['total_pop'])*S5['area']*S5['F(S)']**2)/(S5['total_pop']*np.log(S5['total_pop'])*S20['area']*S20['F(S)']**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S20['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, coeff*np.log(S5['total_pop']))\n",
    "\n",
    "\n",
    "final = []\n",
    "initI = int(I*coeff)\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim, initDI = S20['timeseries'][15][1]-1);\n",
    "with open('data/region2_S20c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Region 2 - 20000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1]-1)*100/numNodes for j in S20['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('region_sim3.p','rb') as fp:\n",
    "    S5,S10,S20 = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "I = 4\n",
    "Et = [0.000, 0.0000,0.0002,0.0002]\n",
    "It = [0.0001, 0.0007, 0.001, 0.004]\n",
    "sim = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S5['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, np.log(S5['total_pop']))\n",
    "\n",
    "final = []\n",
    "initI = I\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim);\n",
    "with open('data/region3_S5c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Region 3 - 5000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1])*100/numNodes for j in S5['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff = (S10['total_pop']*np.log(S10['total_pop'])*S5['area']*S5['F(S)']**2)/(S5['total_pop']*np.log(S5['total_pop'])*S10['area']*S10['F(S)']**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S10['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, coeff*np.log(S5['total_pop']))\n",
    "\n",
    "final = []\n",
    "initI = int(I*coeff)\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim);\n",
    "with open('data/region3_S10c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ZOnQo7777LuD1fI4bN44BAwZwzTXX4Jw7IuYNGzawbNkyZs2aRZs2XnqQmZnJhAkTAJg0aRJDhgxhwIABzJs3D4BgMMi0adMYOHAggwYNYvbs2VXnGj9+PEOGDOHss8/m888/B+C5555j4MCBZGdnM2rUqAa9ptI0VNVTRERERKLunpc/Y+3OA016zv4nHM2MiwZE1La0tJRp06bx+uuv07dvX7773e/yyCOPcMstt3Dffffx5ptvcswxxwDwi1/8gvT0dILBIGPGjGHNmjVkZWVFHFdSUhLZ2dl8/vnnpKSk8Mwzz/Duu++SnJzMDTfcwFNPPcWvf/1rHn74YVatWgXAunXramx3/vnnc+2117J06VIyMzPJz88nPT2d6dOn06lTJ2677TYArrzySn74wx9y1llnsXXrVs477zzWrVvHPffcw1lnncXdd9/NK6+8wvz584+I97PPPiMnJ4ekpKQan8+f/vQn0tPTKSkpYejQoVxyySVs3ryZHTt28OmnnwJUDW297rrrmDt3Ln369GHZsmXccMMNvPHGG8ycOZPFixfTo0ePiIbBStNT4iciIiIiLV4wGCQzM5O+ffsCMHXqVObMmcMtt9xyRNtnn32WefPmEQgE2LVrF2vXrm1Q4gdU9ay9/vrrrFy5kqFDhwJQUlJC9+7dj2hfW7sPPviAUaNGVZXtT09Pr/F6r732GmvXrq16fODAAYqLi1m6dCkLFy4EYMKECaSlpTXoeQA8+OCDvPDCCwBs27aNL774glNPPZWNGzfygx/8gAkTJjBu3DiKi4t57733uPTSS6uOLSsrA2DkyJFMmzaNyy67jIsvvrjBMcjXp8RPRERERKIu0p65WNu0aRO//e1vWb58OWlpaUybNq3BZfODwSCffPIJ/fr1Y+/evUydOpVf/epXdR7jnKux3csvvxzRNUOhEB988AGpqakNihVgwIABrF69mmAweESv35IlS3jttdd4//336dChA6NHj6a0tJS0tDRWr17N4sWLmTt3Ls8++yz3338/Xbp0qerFDDd37lyWLVvGK6+8wpAhQ1i5ciVdu3ZtcKzSeFGd42dm483s32a23szuqGF/OzN7xt+/zMwy/O3JZvaYmX1iZuvM7M5oxikiIiIiLVtSUhKbN29m/fr1ADzxxBN84xvfAOCoo46iqKgI8HrKOnbsSOfOndmzZw+vvvpqg65TUVHBnXfeSc+ePcnKymLMmDEsWLCAvXv3ApCfn8+WLVsASE5OpqKiAqDWdsOHD2fp0qVs2rSpanv1mAHGjRvHQw89VPW4MvkaNWoUTz/9NACvvvoqBQUFR8Tcu3dvcnNzmTFjRlVP5ebNm3nllVfYv38/aWlpdOjQgc8//5wPPvgAgC+//JJQKMQll1zCrFmz+Oijjzj66KPJzMzkueeeA7xktnKe44YNGxg2bBgzZ86kW7dubNu2rUGvq3x9UUv8zCwJmAOcD/QHrjCz/tWafQ8ocM6dAswG7vW3Xwq0c84NAoYA11cmhSIiIiIiDZWamsqjjz7KpZdeyqBBg2jTpg3Tp08HvHlp48eP55xzziE7O5vBgwdz2mmnceWVVzJy5MiIzj9lyhSysrIYOHAgBw8e5MUXXwSgf//+zJo1i3HjxpGVlcXYsWPZtWtX1XWzsrKYMmVKre26devGvHnzuPjii8nOzubyyy8H4KKLLuKFF16oKu7y4IMPsmLFCrKysujfvz9z584FYMaMGSxdupQBAwawcOFCevXqVWP8f/zjH9mzZw+nnHIKAwcOZNq0aXTv3p3x48cTCATo168fd9xxB8OHDwdgx44djB49mpycHK666qqqnsqnnnqK+fPnk52dzYABA6pehx//+McMGjSIgQMHMmLECLKzsxvzY5SvwWqq7NMkJzY7E/iZc+48//GdAM65X4W1Wey3ed/M2gK7gW7Ad4ArgW8DnYH3geHOufzarpebm+tqWldFRERERGJj3bp19OvXL9ZhiLRINf1+mdlK51xuTe2jOdSzBxDeh7vd31ZjG+dcANgPdAUWAAeBXcBW4Lc1JX1mdp2ZrTCzFfv27Wv6ZyAiIiIiItICxGtxlzOAIHACkAa8bWavOec2hjdyzs0D5oHX49fsUYpIwgiFYN8+718REWkewSCUl8c6CpGmZwZt23r/JopoJn47gJ5hj0/0t9XUZrs/1LMzkIc3zPMfzrkKYK+ZvQvkAhsREWmE8nIoKIB27WIdiYhI6+Gc9yXS0gSDkJSkxK/ScqCPmWXiJXiV8/bCvQRMxZvDNxl4wznnzGwrcC7whJl1BIYD90cxVhFp4SoqoE0bJX4iIs3p4MHEujEWiVQivq+jNsfPn7N3I7AYWAc865z7zMxmmtm3/Gbzga5mth64Fahc8mEO0MnMPsNLIB91zq2JVqwi0vKVlnqfzImIiIi0RlGd4+ecWwQsqrbt7rDvS/GWbqh+XHFN20VEGqu01BuLLyIiItIaRXUBdxGReFFWpsRPREREWi8lfiLS4gUC3iTsRByPLyIijZOXl8fQoTkMHZpDr17HkZnZo+pxebVSow8+eD+HDh2q95xjx45m5coj140eO3Y0AweeypAhWQwadBo333wjhYWF9Z7v3nt/GfkTquatt5bw/vvvNfi4vn0z+PLLL4/YXlxczA03XM9pp/Vm+PAhjB07mg8/XNbo+Bpj165dTJp0YdXj5cs/ZOzY0fTv34dhw05n4sQJfPrpJ80a0+OP/5mbb77xsG2PPfYow4blcPrpOaSkpDBo0CBycnK44447ajnL4ZYsWcJ77331s5s2bRoLFiw4ot2+ffsYP37813sCYZT4iUiLFwgo6RMRaW26du3K8uWrWL58FddeO50f/OCHVY9TUlIOa/vww5ElfnV57LGnWLlyDStXrqFdu3ZMnjyx3mO+TuK3dGnjEr/aTJ9+Denp6axd+wUffLCSefMerTFBjKYHHriPq6++FoA9e/YwZcplzJz5S9au/YJlyz7i9tvvZMOGDUccFwgEmjXOqVP/m2XLVvHRR6s44YQTePPNN1m1ahW//vWvq9oEg8Faj6+e+NWmW7duHH/88bz77rtNErcSPxFp8SoqYh2BiIgk/egW2o4d3aRfST+6pUExvPHG65xxxmBOP30Q1113NWVlZTz88IPs3LmTcePOYdy4cwC48cbvc+aZueTkDGDmzBkNukZKSgq/+tVv2Lp1K2vWrAbg6aefZOTIMxg6NIcbbrieYDDIT35yByUlJQwdmsPUqVNqbQewePE/GDbsdHJzsznvvDFs3ryZP/xhLg89NJuhQ3N455232bdvH5dffgkjRgxlxIihvPeelyzk5eVxwQXjyMkZwPTp1+BqWF9jw4YNfPjhMu65ZxZt2njpQWZmJhdcMAGAyZMnMXz4EHJyBvDHP84DvMTmmmumMXjwQE4/fRAPPDC76lwXXjie4cOHcO65Z/P5558D8PzzzzF48EByc7MZM2ZUja/dCy88z3nneT1cjzzyMFddNZUzzxxRtX/kyLOYOHESANdcM43/+Z/pnHXWMO6883ZWr17F2WcPZ8iQLC699NsUFBQAh/fSfvnll/TtmwF4PXmXXXYxF144nv79+3DnnbdXXeexxx5lwIC+jBx5Bu+/H3nS1alTJ370ox+RnZ3N+++/T0bGV72rK1asYPTo0WzevJm5c+cye/ZscnJyePvttwFYunQpI0aM4OSTTz6s92/SpEk89dRTEcdQF814EZEWTxU9RUSkrKyUa6+dxquvvk7fvn25+urv8vvfP8JNN93Cgw/exz//+SbHHHMMADNn/oL09HSCwSDjx4/hk0/WMGhQVsTXSkpKIisrm3//+3OSk1N47rlnWLLkXZKTk/nBD27gL395il/84tc88sjDLF++CoB169bV2O68887nhhuu5bXXlpKZmUl+fj7p6elce+10OnbsxK233gbAd797JTfd9ENGjjyLrVu3cuGF57FmzTpmzbqHkSPP4ic/uZtFi17h0UfnHxHv2rWfkZ2dQ1ItfyznzfsT6enplJSUMGLEUL797UvYsmUzO3bs4OOPPwWoGtp6ww3X8fDDc+nTpw8ffriMm2++gcWL3+AXv5jJ3/++mB49etQ4DHbTpk2kpaXRzl93ae3az7jqqql1vs47dmznrbfeIykpiSFDspg9+yFGjfoG99xzN7Nm3cPvflf3anCrV6/iww8/pl27dgwadCo33PAD2rZty89/PoP3319J586dGTfuHLKzB9d5nkoHDx5k2LBh/O53v6u1TUZGBtOnT6dTp07cdpv3s5s/fz67du3inXfe4fPPP+db3/oWkydPBiA3N5e77rorouvXR4mfiLR4qugpIhJ7wXpuwqN+/WCQjIxM+vbtC8BVV01l7tw53HTTkb2GCxY8y/z58wgEAuzevYt169Y2KPEDqnrW3nzzdT7+eCUjRgwFoKSkhO7dux/RvrZ2y5Z9wFlnjSIzMxOA9PT0Gq/3xhuvsW7d2qrHRUUHKC4u5p13lvLMMwsBuOCCCaSlpTXoeQDMmfMgL774AgDbt29j/fov6Nv3VDZt2sgtt/yA88+fwNix4yguLuaDD97jyiu/Ks5fVlYGwJlnjuSaa6YxefJlTJp08RHX2L17F8cc063WGM46axgHDhzgm98cx333PQDAJZdcSlJSEvv372f//kJGjfoG4P1sw2OozbnnjqFz584AnHZaf7Zu3cKXX37JqFGj6dbNi2Xy5Mv54ov/RPIykZSUxCWXXBJR2+omTZpEmzZt6N+/P3v27Kna3r17d3bu3Nmoc1anWyERafFKS6FDh1hHISIiiWDTpk3Mnv1b3ntvOWlpaVxzzTRKS0sbdI5gMMinn37Caaf1Y+/evVx11VRmzfpVncc452ps9/e/vxzRNUOhEG+//QGpqakNihWgf/8BrFmzmmAweESv31tvLeGNN15j6dL36dChA2PHjqa0tJS0tDRWrFjNv/61mD/8YS7PP/8sv/3t/XTp0qWqFzPcnDlz+fDDZbz66iuceeYQ3n9/JV27dq3a3759e8rKvnqd+/cfwKpVH/Gtb3lzJd95ZxkLFy5g0aK/V7Xp2LFjvc8tKaktoVAI4IifY0pKu7B2SV97rmBqauphr1/btrVfu7rKnk7gsOG4paWltG/f/mvFVUlz/ESkRQsEwDkVdxERae2SkpLYsmUz69evB+Dpp5/g7LO9HqJOnY6iqKgI8HrKOnbsSOfOndmzZw+LF7/aoOtUVFRw11130rNnTwYNyuLcc8ewcOEC9u7dC0B+fj5btmwBIDk5mQp/Inpt7YYNG8477yxl06ZNVdsrYy4uLqq67je/OY45cx6qerx6tZd8nXXWKJ555mkA/vGPV6vmvoXr3bs3Q4bkMnPmjKqkY/PmzSxa9Ar79++nS5c0OnTowOeff86yZR8A3ny5UCjEt799CffcM4uPP/6Io48+moyMTJ5//jnAS2Aq5zlu2LCBM84YxowZMznmmG5s377tsBj69OnLli2bqx5Pn/4/PPHEnw8rYFNbAZ7OnTvTpUsa77zjzZcL/9lmZGTw0UcrAVi48MjKmdWdccYw3n77LfLy8qioqGDhwufqPaY2GRkZrFzpXfv555+v2n7UUV+93+rzn//8h4EDBzY6hnBK/ESkRWvmQl8iIhKn2rVLZd68R7nyyks5/fRBtGnThuuumw7A9753HRddNJ5x484hKyubnJzBDBp0Gt/97pWceebIiM4/deoUhgzJYvDggRw8eJAFC14EoF+//txzzywmTBjHkCFZXHDBWHbv3lV13SFDspg6dUqt7bp168acOfO4/PKLyc3NZsqUywGYMOEiXnzxhariLvfd9yAffbSCIUOyyM7uz7x5cwG4664ZvP32UnJyBvDiiwvp1atXjfHPnftH9u7dQ79+pzB48ECuvXYa3bt357zzxhMIBMjK6sddd93BsGHDAdi5cwdjx45m6NAcpk27ip//3Oup/POfn+LRR+eTm5tNTs4AXn7Zex3uvPPHnH76IAYPHsjw4SPIyso+7PodO3YkM7N3VWJ+3HHH8eSTz/DTn95Jv36n8I1vjGDhwgV8//uHL61Qaf78x7jzzh8zZEgWq1ev4ic/uRuAW/Wxg7UAACAASURBVG65jXnzHuGMMwaTl1d/ldLjjz+eu+76GaNGncno0SM59dR+9R5TmxkzZnDzzTeTm5t7WE/gRRddxAsvvHBYcZfavPnmm0yYMKHRMYSzmir7JKLc3Fy3YsWR66qISOtWVAS7dkGnTrGORESk9cnPX0ffvo2/cZbW5cUXX+Cjj1Zyzz2zYh1KvUIhSEmBNlHuRhs1ahQvvvhijXMz161bR79+h/9+mdlK51xuTefSHD8RadFU0VNERCQxTJz4bfLy8mIdRtzYt28ft956a6MK8tREQz1FpEUrKVFFTxERkURx9dXXxDqEuNGtWzcmTZrUZOdT4iciLZZzUFamxE9EREREiZ+ItFjBoCp6ioiIiIASPxFpwfwK2SIiIiKtnhI/EWmxtJSDiIikp39V1nnt2s8477xzGTjwVE47rTczZ86oWmD78cf/TI8e3Rg6NIdBg07jgQdmxypkkajQzBcRabFU0VNEJL5s2+YV3Woq7dtDz56RtS0pKeHii7/FQw89wtix4zh06BCXX34JDz30ADff/EMAJk++nAceeJi8vDwGDTqViy+eTM9ILyAS55T4iUiLpYqeIiLxpaSkaddVLS6OvO1f//o0I0aMZOzYcQB06NCB++9/mDFjzq5K/Cp17dqV3r1PYffuXUr8pMXQUE8RaZFU0VNERMKtXfsZgwcPOWxb7969KSkpobCw8LDtW7dupbS0lEGDspozRJGoUuInIi2SKnqKiEhDLVjwDEOGZNG//ylcf/0NpKamxjokkSajxE9EWqSKCi/xExERAejXrz8ff7zysG0bN24kPb0rXbp0Abw5fitXruGtt97jrrvuYPfu3bEIVSQqlPiJSIsUCKi3T0REvnLFFVN49913eP311wCv2Mutt97E3Xffc0TbIUNyufLK/+Lhhx9o7jBFokaJn4i0SKroKSIi4dq3b8/ChS9x772/YMCAvpxwwjGMGDGSK66YUmP72277Xx5//FGKioqaOVKR6DDXQsZC5ebmuhUrVsQ6DBGJE1u3ekM9k5NjHYmISOuVn7+Ovn37VT2O5XIO1b344t+4/fZb+ec/3+Skk05quqCkVQiFICUF2sSwG23dunX069fvsG1mttI5l1tTe9W7E5EWp7KiZ4cOsY5ERETCxdPKCBMnTmLixEmxDkOk2Wiop4i0OKroKSIiInI4JX4i0uKooqeIiIjI4ZT4iUiLEwjEdsy9iIh8paXUkxCJJ435vdKtkYi0OCUlSvxEROJBUlIqBQV5Sv5EmpBzjry8PFJTUxt0nIq7iEiLU1ICbfW/m4hIzHXqdCIFBdv58st9sQ5FpEmFQt69RqzqCaSmpnLiiSc26BjdGolIi+IclJeroqeISDxISkqmc+fMWIch0uSKi+Gkk6Bdu1hHEjkNhhKRFiUQUEVPERERkeqU+IlIi1KZ+ImIiIjIV5T4iUiLUlGhwi4iIiIi1en2SERalNJSJX4iIiIi1en2SERaFFX0FBERETmSEj8RaTEqK3oq8RMRERE5nBI/EWkxVNFTREREpGZK/ESkxVBFTxEREZGaKfETkRZDFT1FREREahbVWyQzG29m/zaz9WZ2Rw3725nZM/7+ZWaW4W+fYmarwr5CZpYTzVhFJPGpoqeIiIhIzaJ2i2RmScAc4HygP3CFmfWv1ux7QIFz7hRgNnAvgHPuKedcjnMuB/gvYJNzblW0YhWRlqGkBJKTYx2FiIiISPyJ5mfjZwDrnXMbnXPlwF+BidXaTAQe879fAIwxO6IswxX+sSIitaqs6JmUFOtIREREROJPNBO/HsC2sMfb/W01tnHOBYD9QNdqbS4H/hKlGEWkhQgEIBRSRU8RERGRmsT1bBgzGwYccs59Wsv+68xshZmt2LdvXzNHJyLxJBBQ0iciIiJSm2gmfjuAnmGPT/S31djGzNoCnYG8sP3foY7ePufcPOdcrnMut1u3bk0StIgkpvLyWEcgIiIiEr+imfgtB/qYWaaZpeAlcS9Va/MSMNX/fjLwhnPeKlxm1ga4DM3vE5EIFBVBSkqsoxARERGJT22jdWLnXMDMbgQWA0nAn5xzn5nZTGCFc+4lYD7whJmtB/LxksNKo4BtzrmN0YpRRFqGUAgOHYKOHWMdiYiIiEh8ilriB+CcWwQsqrbt7rDvS4FLazl2CTA8mvGJSMtQOcxTc/xEREREahbXxV1ERCJRVqakT0RERKQuSvxEJOEdOKCF20VERETqosRPRBJaKAQlJUr8REREROqixE9EElp5OTinoZ4iIiIidVHiJyIJrbQU2uh/MhEREZE66XZJRBJaUZGGeYqIiIjUR4mfiCQsze8TERERiYwSPxFJWJrfJyIiIhIZJX4ikrBKSjS/T0RERCQSumUSkYRVXKxhniIiIiKRUOInIglJ8/tEREREIqfET0QSkub3iYiIiEROiZ+IJKSyMs3vExEREYmUbptEJCGVlkJSUqyjEBEREUkMSvxEJCGVlSnxExEREYmUEj8RSUjl5Ur8RERERCKlxE9EEk4o5H1pjp+IiIhIZHTbJCIJJxiMdQQiIiIiiUWJn4gknEDAW8pBRERERCKjxE9EEk4wqPX7RERERBpCiZ+IJJyKCs3vExEREWkI3TqJSMIpL1fiJyIiItIQunUSkYSjxdtFREREGkaJn4gknIoKaNs21lGIiIiIJA4lfiKSUCrX8FNxFxEREZHIKfETkYSiNfxEREREGk6Jn4gkFK3hJyIiItJwSvxEJKFoDT8RERGRhlPiJyIJRWv4iYiIiDScbp9EJKGUlSnxExEREWko3T6JSEIpK9NSDiIiIiINpcRPRBJKRYUWbxcRERFpKCV+IpIwtIafiIiISOMo8RORhBEIxDoCERERkcSkxE9EEkYwqDX8RERERBpDiZ+IJAyt4SciIiLSOEr8RCRhaA0/ERERkcbRLZSIJIzSUiV+IiIiIo2hWygRSRjl5VrDT0RERKQxlPiJSMLQGn4iIiIijaPET0QSgtbwExEREWm8qCZ+ZjbezP5tZuvN7I4a9rczs2f8/cvMLCNsX5aZvW9mn5nZJ2aWGs1YRSS+BQJaykFERESksaKW+JlZEjAHOB/oD1xhZv2rNfseUOCcOwWYDdzrH9sWeBKY7pwbAIwGKqIVq4jEPy3lICIiItJ40ezxOwNY75zb6JwrB/4KTKzWZiLwmP/9AmCMmRkwDljjnFsN4JzLc84FoxiriMS5oP4HEBEREWm0aNbH6wFsC3u8HRhWWxvnXMDM9gNdgb6AM7PFQDfgr86531S/gJldB1wH0KtXryZ/AiISPxJxDb/Hlq9nU35RrMMQERGRJhYIwANXZtOuXeLcnMRrYfS2wFnAUOAQ8LqZrXTOvR7eyDk3D5gHkJubq9k/IhEIBGDnzsTrQQsEEmsph/JAkN+//2+OTk3mqHbJsQ5HREREmpALASRW+hHN26gdQM+wxyf622pqs92f19cZyMPrHVzqnPsSwMwWAacDryMiX0sgACUl0KFDrCNpmLZtE2sph8KScgCmjziNSQM1IkFERKQlKS6GlAT6QBqim/gtB/qYWSZegvcd4MpqbV4CpgLvA5OBN5xzlUM8bzezDkA58A284i8i8jVVLomQSElUIirwE7+09ikxjkREREQkiomfP2fvRmAxkAT8yTn3mZnNBFY4514C5gNPmNl6IB8vOcQ5V2Bm9+Eljw5Y5Jx7JVqxirQmoVCsI2gdlPiJiIhIPIlqB6VzbhGwqNq2u8O+LwUureXYJ/GWdBCRJqRlEZpHYUkZAF2U+ImIiEgcSJwyNCLSJAKBxKuOmYgKDvk9fh3axTgSERERESV+Iq1ORYV6/JpDYUk5bdsYnRJt5reIiIi0SEr8RFqZRFwPLxEVlpTTpX0KpixbRERE4oBu/0RamUBAFT2bQ0FJGWntNcxTRERE4oMSP5FWJhhUj19zKPB7/ERERETigW7/RFoR575ax0+iq+BQOWkdlPiJiIhIfFDiJ9KKaA2/5lOoHj8RERGJI0r8RFqRYNDr9ZPoKgsEOVQR0Bw/ERERiRtK/ERaEQ3zbB6FJd4afurxExERkXihxE+kFdFQz+ahxE9ERETijRI/kVZEiV/zKDhUBkC6iruIiIhInFDiJ9KKBIMa6tkcCqp6/DTHT0REROKDEj+RVqSiQmv4NYfKxC9NQz1FREQkTugWUKQVqahQj19zKCwpp20bo2NK21iHIiIiIgIo8RNpVQIBSEqKdRQtX0FJGWnt22HKskVERCROKPETaUUCAQ31bA6Fh7R4u4iIiMQX3QKKtCLBoBK/5lBYosRPRERE4otuAUVaCee0gHtzKSgpI01LOYiIiEgcUeIn0kqEQl7yJ9FXWFJOmpZyEBERkTiixE+klQgGYx1B61AWCHKoIqilHERERCSuKPETaSU0zLN5fLV4uxI/ERERiR9K/ERaiVAo1hG0DgWHygBI66ChniIiIhI/lPiJtBJK/JpHoXr8REREJA4p8RNpJYJBDfVsDpWJn+b4iYiISDxR4ifSSlRUaA2/5lBQ4g/1VOInIiIicUS3gSKthBK/5lFQUk5ymzZ0SGkb61BEREREqug2UKSVCASU+DWHgkPlpHVIwTSuVkREROKIbgNFWgklfs2jsKRchV1EREQk7ug2UKSVCAaV+DWHwpJy0tprKQcRERGJL7oNFGkFnNMC7s2loKRMPX4iIiISd5T4ibQCwaCX/En0aainiIiIxCMlfiKtgHr7mkdpRZCSiiBpHZT4iYiISHxR4ifSCoRCsY6gddAafiIiIhKvlPiJtAJK/JpHQUk5gIq7iIiISNxR4ifSCijxax6FfuKnOX4iIiISb5T4ibQCwaDm+DUHJX4iIiISr5T4ibQC5eVaw685FBzy5/h10FBPERERiS+6FRRpBQIBJX7NobCknJSkNnRITop1KCIiIiKH0a2gSCugxK95FJSUk9Y+BdO4WhEREYkzuhUUaQWU+DWPgpIyze8TERGRuBTVW0EzG29m/zaz9WZ2Rw3725nZM/7+ZWaW4W/PMLMSM1vlf82NZpwiLV0wqMSvORQcKtf8PhEREYlLbaN1YjNLAuYAY4HtwHIze8k5tzas2feAAufcKWb2HeBe4HJ/3wbnXE604hNpLUIhVfVsLoUl5WSkd4p1GCIiIiJHiGYfwBnAeufcRudcOfBXYGK1NhOBx/zvFwBjTJNjRJqU1vBrPoUl5RrqKSIiInEpaj1+QA9gW9jj7cCw2to45wJmth/o6u/LNLOPgQPAXc65t6MYq0iLFQqpt68mhSXlbCs82GTnqwiGKA0ESWuvoZ4iIiISf6KZ+H0du4Bezrk8MxsC/M3MBjjnDoQ3MrPrgOsAevXqFYMwRWKvvNz7qk0g0HyxJJLbX17Op7sLm/y8xx6V2uTnbIlKStQbLSIiiSsYjHUEDRfNxG8H0DPs8Yn+tprabDeztkBnIM8554AyAOfcSjPbAPQFVoQf7JybB8wDyM3NddF4EiLxbv9+2LcP2tbx25yc3HzxJIJgyPGffQcY0+d4JvTvWf8BEUpJakPWCWlNdr6WLBiEY4+NdRQiIiKNl2j3V9FM/JYDfcwsEy/B+w5wZbU2LwFTgfeBycAbzjlnZt2AfOdc0MxOBvoAG6MYq0jCCgSgfXtopxGGEdt54BDlwRDDenVj+EndYh1OqxMKeVVmO3eOdSQiIiKtR9QSP3/O3o3AYiAJ+JNz7jMzmwmscM69BMwHnjCz9UA+XnIIMAqYaWYVQAiY7pzLj1asIomsvFxLNTTUxrwiADK7qgJnLIRCifcpqYiISKKL6hw/59wiYFG1bXeHfV8KXFrDcc8Dz0czNpGWIhiEFBWSbJDN+cUAZKQfFeNIWiclfiIiIs0vosTPX5Pv2PD2zrmt0QpKRCIXCECq6ok0yKb8Io49qj0dU+K1vlXLFgrVPSdVREREml69f3rN7AfADGAP3rBLAAdkRTEuEYmAqiI2zqa8Yk7WQusx45wSPxERkeYWyZ/em4FTnXN50Q5GRBpGiV/DBUOOLQXFDO11TKxDabWCQSV+IiIizS2SkhDbgP3RDkREGi4R15CJtR37D1IeDJGpHr+YSkqKdQQiIiKtSySfuW4ElpjZK/hr6wE45+6LWlQiEpFQyBs2J5Hb5Bd2yVRhl5hS4iciItK8Ikn8tvpfKf6XiMSJUAjMYh1FYtmU7y3lkKEev5jSEiQiIiLNq97Ezzl3D4CZdfIfF0c7KBGJjOb4NdymvGKOO6o9HVTRM6aU+ImIiDSvev/0mtlAM/sY+Az4zMxWmtmA6IcmIvUJBHQD3VCb8ou0cHuMOaf3rYiISHOL5E/vPOBW59xJzrmTgB8Bf4huWCISiUBAQz0bIhAKsSX/ICdrfl/MKfETERFpXpH86e3onHuz8oFzbgnQMWoRiUjEyst1A90QO/YfoiIUIrOrEr9YCYUgOVkfWIiIiDS3iKp6mtlPgSf8x1fhVfoUkRgLBFQdsSE2V1X01FDPWAmF9J4VERGJhUj6Cq4GugEL/a9u/jYRibFgUD1+DbExz6voeVKaEr9YCYW0eLuIiEgsRFLVswC4qRliEZEGCgQgNTXWUSSOTfnFHH+0KnrGkhI/ERGR2Kj1z6+Z3e+cu8XMXgaOWCLaOfetqEYmInXSUg4NtymvSAu3x5gSPxERkdio689v5Zy+3zZHICLSMEr8GiYQCrG14CDDM7rFOpRWTYmfiIhIbNT659c5t9L/Nsc590D4PjO7GXgrmoGJSN2CQW89NInM9kKvoqeWcog9JX4iIiLNL5KyEFNr2DatieMQkQYKhVQSvyE253uFXbSUQ+ypIJGIiEjzq2uO3xXAlUCmmb0UtusoID/agYlI3TTUs2E25RdjQIYqesacEj8REZHmV9eAm/eAXcAxwO/CthcBa6IZlIjUT4lfw2zMK+L4o9uTmqxF5GJNiZ+IiEjzq2uO3xZgC3Bm84UjIpEKBHQD3RCb8otV0TMOOKf3rYiISCzU++fXzIab2XIzKzazcjMLmtmB5ghORGpXUaE5fpEKBENsLSjW/L44YKbET0REJBYi+fP7MHAF8AXQHrgGmBPNoESkfhUVuoGO1Pb9BwmEHJnpmt8XS5VLOegDCxERkeYX0W2jc249kOScCzrnHgXGRzcsEalPIABJmq4WkU35xQCcrB6/mAqF9J4VERGJlUhWUzpkZinAKjP7DV7BF/UziMRYMAgpKbGOIjFsyvMqep6kip4xFQpBcnKsoxAREWmdIkng/gtIAm4EDgI9gUuiGZSI1E/FXSK3Kb+I44/uoIqeMVY51FNERESaX71/gv3qngAlwD3RDUdEIqGlHBpmY14RJ3dVb1+sKfETERGJnboWcP8EcLXtd85lRSUiEalXMOiVxZf6BYIhthUe5KzMY2MdSqsXCml4soiISKzU9dnrhc0WhYg0SCikyoiR2lboV/RUj19c0PBkERGR2KhvAXcRiUMa6hm5yoqeWrw9PijxExERiY16Z1uYWRFfDflMAZKBg865o6MZmIjUTolf5DblF2FAhtbwiwtK/ERERGIjkuIuVR+Tm5kBE4Hh0QxKROqmxC9ym/KKOaFzB9q1VUXPWHNOiZ+IiEisNOhPsPP8DTgvSvGISAS0lEPkNuUXaZhnnDDT+1ZERCRWIhnqeXHYwzZALlAatYhEpF7l5SruEomKYIithQc5+2RV9IwXSvxERERiI5IVlS4K+z4AbMYb7ikiMRIIQJJGLtZrW+FBgiGnHr84ULmGnz6wEBERiY1I5vj9d3MEIiKR01DPyGzKKwLQUg5xIBTShxUiIiKxVO+to5mdbGYvm9k+M9trZi+a2cnNEZyI1CwYVOIXiU35xbQxOClNiV+sVfb4iYiISGxEcuv4NPAscDxwAvAc8JdoBiUitXNOPX6R2pRfxAlHq6JnPAgGlfiJiIjEUiS3jh2cc0845wL+15NAarQDE5GaOVd/G/Fsyisms6vm98UD5yA5OdZRiIiItF6RJH6vmtkdZpZhZieZ2e3AIjNLN7P0aAcoIocLBpX8RaIiGGLb/oNkauH2uBAKKfETERGJpUgG3lzm/3t9te3fARyg+X4izSgUUmXESGz1K3qerB6/uKHhySIiIrETSVXPzOYIREQiEwrFOoLEUFXRU0s5xA0lfiIiIrETSVXPZDO7ycwW+F83mllEA3bMbLyZ/dvM1pvZHTXsb2dmz/j7l5lZRrX9vcys2Mxui/QJibR0Svwis9mv6NkrrWOsQxGfEj8REZHYieTP8CPAEOD//K8h/rY6mVkSMAc4H+gPXGFm/as1+x5Q4Jw7BZgN3Ftt/33AqxHEKNJqKPGLzMa8Inp07qiKnnHCOSV+IiIisRTJHL+hzrnssMdvmNnqCI47A1jvnNsIYGZ/BSYCa8PaTAR+5n+/AHjYzMw558xsErAJOBjBtURajfJy3UBHYlN+kQq7xBEzLeAuIiISS5EkfkEz6+2c2wDegu5AMILjegDbwh5vB4bV1sY5FzCz/UBXMysF/hcYC9Q6zNPMrgOuA+jVq1cEIYlEX7QrbibaGn7BkOOnr37EzgOHmvW62woPMvqU45v1mpFojRVZK5+zihKJiIjETiSJ34+BN81sI2DAScB/RzUqrxdwtnOu2Oq4U3DOzQPmAeTm5rbC2ymJNwUFsG9fdK8RCkH79tG9RlNaszOfJRt2k3V8GkenNl89/+OPbs/Yvic02/UiEQpBUVHrXNagUyclfiIiIrFUZ+JnZt2A/XjDNrv7m//tnCuL4Nw7gJ5hj0/0t9XUZruZtQU6A3l4PYOTzew3QBcgZGalzrmHI7iuSMwUFUG7dq3zxr42b6zfRbu2bZg96QzaJ0fyWVPLFQx6CVDPnvW3FREREWlKtQ4YM7NrgM+Ah4BVQIZzbk2ESR/AcqCPmWWaWQreun8vVWvzEjDV/34y8IbznO2cy3DOZQD3A79U0ifxLhSCkhIlfeGCIceb63czIqN7q0/6wHuPtNXLICIiIjFQ1y3ILcAA59w+f17fUxyZuNXKn7N3I7AYSAL+5Jz7zMxmAiuccy8B84EnzGw9kI+XHIokpPLyWEcQf1bvzCf/UBlj+sTXkMtYUeInIiIisVLXLUi5c24fgHNuo5m1a+jJnXOLgEXVtt0d9n0pcGk95/hZQ68rEgtlZYlVdKU5vP7FTtq1bcOZGd1iHUpcCIXUIywiIiKxUVfid6KZPVjbY+fcTdELSyTxtNaiHbUJhhxL1u9mZMaxGubpc049fiIiIhIbdd2C/Lja45XRDEQkkTnnze/r0CHWkcSPVTvyKCgp59w+8bekQiypV1hERERiodbEzzn3WHMGIpLIysu9YXwqV/+VN9bvIrVtEiMyutffuBVR4iciIiKxoFsQkSZQVqakL1wgFOLN9bsZmdmd1OSkWIcTV5T4iYiISCzoFkSkCRQXa35fuFU78inUMM8jOKfET0RERGKj3lsQMxsZyTaR1so5OHQIUlJiHUn8eOMLb5jnmSdpmGc4MyV+IiIiEhuR3II8FOE2kVapogKCQQ31rBQIhbxqnhrmeZjK3j4lfiIiIhILtRZ3MbMzgRFANzO7NWzX0XgLsosIXmEXJX1f2ZxfTGFpOWdlHhvrUOJKKARJ+p9TREREYqSu5RxSgE5+m6PCth8AJkczKJFEcvCg1mYLt6eoBIAenbW2RbhQSO8TERERiZ26lnN4C3jLzP7snNvSjDGJJJSDBzW/L9ze4lIAundKjXEk8SUU0vtEREREYieSz5/bmdk8ICO8vXPu3GgFJZIoKiogEIBU5ThV9hSVkGRG1456UcKpx09ERERiKZLbkOeAucAfgWB0wxFJLOXlXtEO+cq+4lKO6diOpDaa+BhOPX4iIiISS5EkfgHn3CNRj0QkAR06pF6c6vYUl9JNwzyP4JyKu4iIiEjsRFJY/GUzu8HMjjez9MqvqEcmkgBKS5X4VbevuJRjj2of6zDikpZyEBERkViJ5JZ1qv/vj8O2OeDkpg9HJLFUVGj4XjjnHHuKShiZqYXba6LET0RERGKl3sTPOZfZHIGIJBrnVNilugOlFZQHQxzbST1+NVHiJyIiIrFS722ImXUws7v8yp6YWR8zuzD6oYnEt6BKHR1hT7G3hl/3o5QNV+ecEj8RERGJnUhuQx4FyoER/uMdwKyoRSSSIJT4HWmf1vCrlZmKu4iIiEjsRJL49XbO/QaoAHDOHQJUp11aPSV+R9pTVJn4aahnuMrePtP/nCIiIhIjkSR+5WbWHq+gC2bWGyiLalQiCUCJ35H2FpeQ1MZI79Au1qHElVBIvX0iIiISW5FU9ZwB/APoaWZPASOBadEMSiQRlJfrZr66vcWlHNMxVYu3VxMKadkPERERia1Iqnr+y8w+Aob///buPcyuu77v/fs7o/vdkm8gWRiDIdiBhFQxLbk8BMr9Yq4NtwAJrZM2BtKWHAhpCA8hKQkpHFLgnOMUDoQmNZQAxxAHSAq50ZTYCSZgGxeHJFgy1lykGWkkjWb2Xt/zx9pjjUYjaUbae9bSWu/X88zj2Xuv2fOTv3tm1mf/fuv3pVzi+YbMHBv4yKSam5lxs46FRg4f8/q+RRSFbT8kSVK1lrKr5wuATmb+QWZ+FuhExPMHPzSp3pzxO9XI1LTBbxFFAatXVz0KSZLUZkuZr/jlzJycu5GZE5TLP6VWm501+M2XmYxMTXPZZjd2WcjgJ0mSqraU4LfYMV6tolbrdsuTeXdpPGGy17z9Emf8TpHpmwSSJKlaSwl+t0fEuyPiEb2PdwN/PeiBSXXW7ZYn8zph/+GyeftlBr9FeT2oJEmq0lJORV5H2cD9Y8DNwDTws4MclFR3tnI41Ynm7S71XIzBT5IkVemMSzYjYhj4bGb+2AqNR7ogdLsu81xo/1Q543fpZmf8FmPwkyRJVTrjqUhmdoEiIrau0Hikr7AN6wAAIABJREFUC8LsrCfyC41OTTM8FFy03ubtC3mNnyRJqtpSNmmZAr4eEX8EHJm7MzNfP7BRSTVnD79T7T88zSU2b19UhK8XSZJUraUEv0/2PiT12MPvVKNTx7jMZZ6nyCxDn0uDJUlSlc4a/DLzIxGxHtidmfeswJik2pudhTVrqh5FveyfmuYxl22rehi1UxS+SSBJkqp31sVHEfFc4A7gc73b3x8Rtwx6YFJdZUKn49K9+TKT0alpWzksoihglZ1PJUlSxZZy6vo24DpgAiAz7wCuGuCYpFqzlcOpJo7N2Lz9NLpdg58kSareUoLfbGZOLrivGMRgpAuBwe9UI70efpdttoffQpmwenXVo5AkSW23lPeh74yIlwPDEXE18Hrgfw52WFJ9GfxONfJg83Zn/BYqCoOfJEmq3lJm/F4HXAscB34PmAR+bpCDkuqs06l6BPUzcrjXvN3gdwp7+EmSpDo47YxfRKwDfgZ4JPB14J9lpqe8aj1bOZxqZGqaVUPBRRts3r4YNwKSJElVO9PpyEeAPZSh75nAb67IiKSas3n7qfZPHeOSTesYslndony9SJKkqp3pGr9rMvOxABHxQeCvVmZIUr0543eq0alpLt3kxi6LmWvgLkmSVKUznY7Mzn3iEk/phNlZg99C+w9Pe33faUT4epEkSdU7U/D7vog41Ps4DDxu7vOIOLSUJ4+IZ0TEPRFxb0S8eZHH10bEx3qPfyUiruzdf11E3NH7+FpEvOBc/nFSv3W75S6Nrmg8Ya55+6WbDX6n44yfJEmq2mmXembmeb1HHRHDwPuBpwJ7gdsi4pbMvGveYa8FDmbmIyPipcCvAz8OfAPYk5mdiHgI8LWI+Iwzj6pat2voW+jgsRlmi4LLXOp5irllnr5mJElS1ZbSx+9cXQfcm5nfBoiIm4HrgfnB73rgbb3PPwG8LyIiM4/OO2YdkAMcp7Rk83v4xaFJ1n3u00S3+vcjjj33X5CbNi/62Jf/fj8Hjh4f2Pee6+F3iUs9T1EULvOUJEn1MMjgtxO4b97tvcATTndMb3ZvEtgBjEXEE4APAQ8DfmKx2b6IuAG4AWD37t19/wdIC80Pfht+77+w9VfeWN1g5jn+Q0+mu0jwu2/iCD//mdsH/v2HAq7asXjwHJTp6fJ6yzrLhE2bqh6FJEnSYIPfecnMrwDXRsRjgI9ExB9m5vSCY24CbgLYs2ePs4IauNnZE9drDe+/n2L9Bkb+9JvVDgooLnvIovff+cBBAH7rBU/gim0bB/b9168eZsu6NQN7/sV0OnD55bBmZb/tsjnjJ0mS6mCQwW8fcMW827t69y12zN6IWAVsBcbnH5CZd0fEFPC9wOCnLqQzmN/Db2hshOKSyyh2XnHmL6rQ3fsnWb96mMfv3MHwUPMuNFu3DtbaM16SJOmsBrnX3G3A1RHx8IhYA7wUuGXBMbcAr+59/mLgi5mZva9ZBRARDwO+B/iHAY5VWpL5PfyGDoxS7Lik2gGdxV37J3jUJVsbGfrA3TIlSZKWamCnTb1r8m4EPg/cDXw8M++MiLdHxPN6h30Q2BER9wL/Dphr+fDDlDt53gF8Cvg3mTk2qLFKSzV/qefQ2AjFjkurHdAZdLoF3xo9xDWXba16KANhY3RJkqSlG+g1fpl5K3DrgvveOu/zaeAli3zdR4GPDnJs0rnodE4sLRweH6Vz7fdXO6Az+Lvxw8x0Cx5z2baqhzIwBj9JkqSl8bRJWqKiKP8bAWQyND5Ct8ZLPe/ePwHANQ0MfkUBq1bZH0+SJGmpDH7SEnW75fJCgJg6TMzM1Hqp5137J9i6bjUP2dK8xupzwU+SJElLY/CTlqgoTswwDY2PlvfVesZvkmsu20Y0cFrM4CdJkrQ8Bj9pieaWekK5sQtAcXE9Z/yOzXb4+wOHG3t9n8FPkiRpeQx+0hJ1uyc+HzpQ7xm/e0YOUSQ8pqE7ehr8JEmSlsfgJy1Rt3tiqedwb8avW9Nr/O7qbezijJ8kSZLA4CctWaczr4dfza/xu3v/BJdvXs/2DWurHsrAGPwkSZKWzuAnLdHs7LzNXcZGKDZugnXrqh3Uady9f6Kxyzzn2MNPkiRp6Tx1kpZodnbejN+B0dpu7DJxbIb7Dx1rZP+++Qx+kiRJS+epk7REnQ4MD5efD42PUmyv7zJPaGbj9vkMfpIkSUvnqZO0RN3uibAxPDZS2xm/u/dPEsCjLm3uUs9Mg58kSdJyeOokLUFRnLyr59D4KN2abuxy1/4Jrty+iY1rmrv7SYTBT5IkaTk8dZKWoChOhD4yGRofqeWOnpnZ29ilucs851o5PFgPSZIknZXBT1qCojjxeRw+RMzOUtSwh98Dh49x8NhMo6/vK4oT11pKkiRpaQx+0hJ0u+V1ZVC2coB69vD79vhhAK6+ZEvFIxkcm7dLkiQtn8FPWoL5Sz0fbN5ew81d9k4eBeCKbRsrHsngGPwkSZKWz+AnLcH8pZ7D4/Wd8ds3cYSNa1axdd3qqocyMAY/SZKk5TP4SUtwUvP23oxft4Z9/PZOHmXX1g1Eg3c+KQpYs6bqUUiSJF1YDH7SEiwW/Go54zd5lF0NXuY5x1YOkiRJy+Ppk7QEnc684Dc2QrFpM6xbV+2gFugUBfcfOsrOrRuqHsrAGfwkSZKWx9MnaQlOmvE7MFrLjV32H56mWyS7tjrjJ0mSpJN5+iQtQadzonfc8NgIRR2v75s4AsDObc2e8cs0+EmSJC2Xp0/SWWSe2s6hrtf3AY2f8Ysw+EmSJC2Xp0/SWcxv5QAwND5Ct4ZLPfdOHmHtqiEu3ri26qEMnMFPkiRpeTx9ks6i2513I5OhA2O1nPHbO3GUnVs3Nr6Vw6pVJ2ZfJUmStDQGP+ks5s/4xaFJYnaWYkf9Zvz2TR5hV8N39CyKE9daSpIkaekMftJZzA9+Q2Mj5X01m/ErMlvRw29uxk+SJEnLY/CTzmL+Us/hA/Vs3j42Nc1Mt2h8Dz+DnyRJ0rkx+ElnsbB5O1C7zV32tmRHz6KA1aurHoUkSdKFx+AnnUWnc3IrB6B2ffz2TZY9/HY1vIefwU+SJOncGPyks5iZOXXGr25LPfdOHGXVUHDppvVVD2XgbOUgSZK0fJ5CSWfR6ZzYSXLowCjF5i2wtl698vZOHuGhWzYwPNT8PgcGP0mSpOXzFEo6i2533ozf+GjtZvsA9k0eZWfDl3nOMfhJkiQtn6dQ0hlknry5y/DYSO16+GUmeyeONn5jFyjrYfCTJElaPk+hpDOY38MPyhm/bs1m/A4em+HobKfxrRyg3GTHBu6SJEnLZ/CTzuDU4DdCUbNWDid29Gz+jB+c2GFVkiRJS2fwk85gfvN2ioKhA2O1a+Wwd2Kuh1+zZ/zmmrcb/CRJkpbP4CedwfwZv5icIDqd2m3usm/yKEMBD9nS/ODnMk9JkqRzY/CTzmB+8HuweXvNlnrunTzCZZvXs3q42T/OczN+kiRJWr5mnylK56nbPbG0cPhAL/jVbMavLTt6druwenXVo5AkSbowDTT4RcQzIuKeiLg3It68yONrI+Jjvce/EhFX9u5/akT8dUR8vfffJw9ynNLpzMzM6+E3NgJAt2btHPZNHmFXC3r4ZRr8JEmSztXAgl9EDAPvB54JXAO8LCKuWXDYa4GDmflI4D3Ar/fuHwOem5mPBV4NfHRQ45TOZH4PvweXetZoxu/Q9CyT07OtaOXgUk9JkqRzN8gZv+uAezPz25k5A9wMXL/gmOuBj/Q+/wTwlIiIzPxqZt7fu/9OYH1ErB3gWKVFzc6eOuNXbL+4whGdbK6Vw84WLPUEm7dLkiSdq0G+f74TuG/e7b3AE053TGZ2ImIS2EE54zfnRcDfZObxhd8gIm4AbgDYvXt3/0auRiiKcsbufHQ6J5YXDh0Ypdi8BdYO7j2IX/3jr/FH99x/9gN7uplA/3v4dTqn9jCsWlEY/CRJks5VrRdORcS1lMs/n7bY45l5E3ATwJ49e3IFh6YLwOHDcP/959cCoChg3bry8+GxEYoBXt/XLZIvfesBrr54C4/ftWPJX7dt/Rqu2r6pr2M5cgS2bOnrU563NWu8xk+SJOlcDTL47QOumHd7V+++xY7ZGxGrgK3AOEBE7AI+BbwqM/9ugONUQ83MlGFh/fr+PN/Q+OhAWzn848Epjs52eOHjHsYzH7NrYN/nbDqdMuzu3FnZECRJktRng1w4dRtwdUQ8PCLWAC8FbllwzC2Um7cAvBj4YmZmRGwD/gB4c2Z+eYBjVIPN35GzH4YOjNId4MYudz5wEIBrL982sO+xFN3uiVlOSZIkNcPAgl9mdoAbgc8DdwMfz8w7I+LtEfG83mEfBHZExL3AvwPmWj7cCDwSeGtE3NH7qNce+qq9Tuf8lnkuNDQ2MtAZv288MMHmtau5os/X6y2XwU+SJKl5BnqNX2beCty64L63zvt8GnjJIl/3DuAdgxybmm92tn/LPCkKhg6MUWwf5IzfBNdevo2Y6xhfkaIol8hKkiSpOdwjT41UFOVHvzJUTBwkut2B9fA7MtPh78cPV77Mc4798iRJkprF4KdG6nb7+3zDB3rN2we01PPu/RMkcO3lFw3k+ZfL4CdJktQsBj81Ur970A2N94LfgGb87nxgAoBrLqt2xi+z3BCnn9dGSpIkqXoGPzVSv2f8hsZGyucdUB+/Ox84yO6LNrJlXbWN6jodr++TJElqIoOfGqnvwW+AM36ZyZ0PTPC9NVjmOdfDT5IkSc1i8FMjdTp97uE3Xs74Fdsv7t+T9nz30DEOHpupxcYu3S6sXVv1KCRJktRvBj81Ut+bt4+PUmzdNpB1kN/oNW6v+vo+KK/xW13talNJkiQNgMFPjTQz098NSobHRgbWw++u/ROsXTXEIy7ePJDnXy539JQkSWoeg58aaXa2/zN+3QG1crjzuxM85tJtrOrngM+DwU+SJKl56nGmKfVRZnmtWj9n/IYOjA5kY5eZTpf/PXqoFtf3FUW5zLMm+VOSJEl95CmeGqcoyvDXT0NjIxQDaOXwv0cPMVsUtQh+nY4bu0iSJDWVwU+N0+9WDhQFQwfGBjLjd+f+snH7tTVo5dDt2spBkiSpqQx+apxut78zfkMTB4iioBjANX53PjDBpZvWccmm6hOXrRwkSZKay+CnxikKiOjf8w2yeftdDxysRRuHOW7sIkmS1EwGPzVOp9Pn4DdWNm/v9rmdw0yny3cPHeOqHfVo4wAGP0mSpKYy+KlxBtG8Hej7Us/7Dx0jgZ1bN/T1ec9FZhmW+7kTqiRJkurD4KfGGUQPP+j/Us99k0cA2LWt+uDX7ZatHPo5UypJkqT6MPipcWZn+9zDb7xc6llsv7h/TwrsmzwKwM6tG/v6vOei03FHT0mSpCYz+Klx+h38hsdHKbZdVE6J9dH9h46yfvUwF61f09fnPRe2cpAkSWo2g58apSjKENPvzV26A2jevm/iKA/dsoGowfrKouh7rpUkSVKNGPzUKP1u5QDlNX6DaOWwb/JoLTZ2meOOnpIkSc1l8FOjdLv9f86h8ZG+B78ik/sP1Sf4RRj8JEmSmszgp0YZTPAbpejzUs+xqWlmukUtNnbJLHdBtZWDJElScxn81Ch9D37dLkMHxwfQymFuR8/qZ/xmZ2H9+qpHIUmSpEEy+KlROp0+9/CbOEAURd+bt+87VJ/g1+kY/CRJkprO4KdGmZkZTPP27gBm/IYjuHxz9YmrKGDt2qpHIUmSpEEy+KlRZmb63Lx9rNe8vc/X+O2bPMplm9exarj6H8FMWzlIkiQ1XfVnnVIfzc4OZsav/9f4HanNxi4RBj9JkqSmM/ipMTLLzV36OuM3Pjfj1/+lnnW5vm/t2v73PpQkSVK9GPzUGEVRhr9+Gp6b8dt+cd+e8/DxWQ5Nz9Ym+G2ofhiSJEkaMIOfGmNgPfy2be9rd/P7a9TKodt1R09JkqQ2MPipMbrd/s/4DY2N0O13K4de8HtoDYIfeH2fJElSGxj81BhF0f9r1YYOjPb9+r69k0cAarG5Cxj8JEmS2sDgp8bodAYQ/MZGBtLKYdv6NWxc07/lo+ei2y1XsPZzF1RJkiTVk6d8aozjx/sfYobG+z/jd39NdvScnXVjF0mSpLYw+KkRigIOHSpbE/RNt8vQwfFGt3JwYxdJkqR2MPipEY4eLcNfX5u3HxwnMvu6uctst2Bk6lgtgl8mrFlT9SgkSZK0Egx+aoSxsf6HmKEHe/j1b8bvu4eOUqQbu0iSJGllGfx0wTt+vPzoe/AbGwGg6OOM376a9PArChge7mt7QkmSJNWYwU8XvMnJwQSYB2f8+niNX12at3t9nyRJUrsY/HRB63ZhYgLWrev/cw+N93/Gb+/kUdatGmbHhn7uQrN8nY47ekqSJLXJQINfRDwjIu6JiHsj4s2LPL42Ij7We/wrEXFl7/4dEfGliJiKiPcNcoy6sB0pe6H3vX8fwPD4KBlBcdGOvj3nvsmjPHTrBmIQA16GoujzDqiSJEmqtYEFv4gYBt4PPBO4BnhZRFyz4LDXAgcz85HAe4Bf790/DfwS8MZBjU8XvsxyU5dBzPZBr4fftu3lxXB9sm/yCA/dUo+pNjd2kSRJao9Bbu1wHXBvZn4bICJuBq4H7pp3zPXA23qffwJ4X0REZh4B/iIiHjnA8ekCNz1dNiE/1+D36W98h+8cnHrw9rV/9Udcec9XH7z96K/+GVMbtvJbf37XYl9+TvZNHuW63f3tC7hcmeUMqRu7SJIktccgT/12AvfNu70XeMLpjsnMTkRMAjuAsaV8g4i4AbgBYPfu3ec7Xq2gmRkYGSlDyLmanT338DI92+U3vvh1Vg8NsXq4XHb5ht9+BxcfOcjMqhPbg/5/j30Kt3zjO+c+yAXWrhpizxXLWzp6+HB/l7JmwsaNg1keK0mSpHq6oN/zz8ybgJsA9uzZcx4RQittZgamps5vZ8lVq849+I0emQbgTU95LM96zC7I5PLfOMTUv34jh9/yzgePe1Lvo0oRcOWV/Q1q/Wx0L0mSpPobZPDbB1wx7/au3n2LHbM3IlYBW4HxAY5JNTE7W146V9V1ZqNTZfC7dFO5TjSmDhMzM31t1t4PRVGG2373KJQkSVK7DPJ9/9uAqyPi4RGxBngpcMuCY24BXt37/MXAFzPPZ/GfLhQzM33dM2XZ5oLfxRvL4DeIZu390O16LZ4kSZLO38BOKXvX7N0IfB4YBj6UmXdGxNuB2zPzFuCDwEcj4l7gAGU4BCAi/gHYAqyJiOcDT8vM/u2yoUodP15t8BvpBb9LejN+g2jW3g9FMbhdSyVJktQeA51LyMxbgVsX3PfWeZ9PAy85zddeOcixqVozM9UGmtEj02xcs4qNa8ofgeEBNGvvh6JwmackSZLOn1s8aMUVRflR5QYjY1PTXLLxRPKcm/Hr1uwaP5d6SpIkqR8MflpxnU7VIyiXes4t84R51/jVbKknGPwkSZJ0/gx+WnHdbtUjKDd3OSn4jY9SbNpcywvqqrwWUpIkSc1g8NOKqzr4dYqCA0ePnxz8DozWcrYv0557kiRJOn+eUmrFHT9ebZg5eHSGbubJ1/iNjVDsqNfGLlA2bXfGT5IkSefL4KcVV5cefpfOm/EbHq/njB844ydJkqTz5ymlVlzVPfxGj5zcww9gaHyEbs2C39yOnhFVj0SSJEkXOoOfVtzsbNXN248B84JfZrm5Sw17+K1eXfUoJEmS1AQGP62obrfcsKTKWazRqeOsGgq2rS87o8ehSWJ2lqKGPfxs3i5JkqR+MPhpRdWhh9/o1DQXb1zHUC99zjVvr+OMnz38JEmS1A8GP62ouRm/Ko1OHTulhx/Ur3l7UTjjJ0mSpP4w+GlFdbvVb1YyeuT4gh09R4D6BT+wlYMkSZL6w+CnFVX1jp6Z+eBSzzlzM37dGvbxs5WDJEmS+sHTSq2oqoPf4eMdpjvdk2b8hsac8ZMkSVKzGfy0oipv3r5oD79Ris1bYO3aqoZ1WgY/SZIk9YPBTysms/oefqNTiwS/A6O1m+2ba3nhUk9JkiT1g6eVWjHdbtUjKHf0BE7e3GVspHY9/GzlIEmSpH4y+GnF1CP4HQc4ZXOXbs16+HW7Bj9JkiT1j8FPK6bTqUEPvyPTbFu/htXDJ176Q+MjtVvqaQ8/SZIk9ZPBTyum06n+mrWRqWMnLfMks9zcpWatHAx+kiRJ6ieDn1bM8ePVB7+xqeNcMm+ZZ0xOEJ1OLWf8XOopSZKkfjH4acUcP159mBmdmj6llQPYw0+SJEnNZvDTiqm6lcPxTpeJ6ZmTgt/wgV7wq9nmLmDwkyRJUv8Y/LQiiqL6a/zGjpQ7ep404zc2AkC3ZjN+mdUvi5UkSVJzeGqpFVGPVg695u0bF1vqWa8Zvwhn/CRJktQ/Bj+tiDoEv5FFmrfPzfgV2y+uZEyLmZvtc8ZPkiRJ/eKppVZEHXr4jR3pzfjND34HRik2b4G1a6sa1imKwtk+SZIk9ZfBTyvi6NE67Oh5nPWrh9m45sRAhsZGarfMs9u1h58kSZL6y+CnFTE1VX2YGZk6xiWb1hERD943PD5au1YO3S6sXl31KCRJktQkBj8NXKdTfSsH6PXwm7exC5Sbu3Rr1soh0+AnSZKk/jL4aeBmZqoeQWn0yPGTru8DGBofqd2MX1EY/CRJktRfBj8N3LFj1c/2FZmMHZk+aUdPioKhA2O1u8bPHn6SJEnqN08vNXCHD1d/fd/4keN0izxpqWdMThCdTu1m/OzhJ0mSpH4z+Gmgut1yqWfVO3r++bf3A3DN5dsevO9E8/Z6BT8w+EmSJKm/DH4aqLpc3/fZu+7jkRdv5nsu3frgfcPjvebtNdzcxaWekiRJ6idPLzVQ09Pl0sUqfWv0EN8cmeQ511xxUiuHuRm/7vb6zPh1OuXGLgY/SZIk9ZOnlxqoqanqd6j8zF33sXpoiKc/eudJ9z+41LNGM37HjsHFF1c9CkmSJDWNwU8DUxRlkKky+B3vdPn8N/fxo4+4jK3rT95hZmist9Rzez2SVrdbXgu5aVPVI5EkSVLTGPw0MLOz5X+rXOr5Z9/ez+Hjszz32itOeWzowCjFlq3VbznaMz1dzva5zFOSJEn95immBqYO1/d99s77uHzzevZcceqs3vDYSG16+BVF+V9n+yRJkjQIAw1+EfGMiLgnIu6NiDcv8vjaiPhY7/GvRMSV8x77hd7990TE0wc5Tg3GkSPVtnG4f/Iot903xrOv2cXQIgl0aHy0Nq0cjh2DHTts4yBJkqTBGFjwi4hh4P3AM4FrgJdFxDULDnstcDAzHwm8B/j13tdeA7wUuBZ4BvCB3vPpApEJR49Wu4ryD+6+jwCefc2pyzyhXOrZrcHGLpnlx5YtVY9EkiRJTTXI+ZjrgHsz89sAEXEzcD1w17xjrgfe1vv8E8D7otxv/3rg5sw8Dvx9RNzbe76/HOB4+25q7CCT/3h/1cOoxGwHxr4LGzZU8/0zkzv+4qs8Z9smdh74Lhw49ZihkQcofuCfrvzgFjh2DLZvr77JvSRJkpprkKeaO4H75t3eCzzhdMdkZiciJoEdvfv/14Kv3ckF5q7f+hDX/cobqx5GZa6s+Pt/au6TXz79Mce3P5Rjx1ZiNKdXFLB169mPkyRJks7VBT3HEBE3ADcA7N69u+LRnOqhz3kqXy7eQ2e26pFUIwKiwu2D1gwP8ZjLt57UtP0kQ8Pw1GexbdvKjmuhNWuq73UoSZKkZhtk8NsHzL+4alfvvsWO2RsRq4CtwPgSv5bMvAm4CWDPnj3Zt5H3ya7rHseu6x5X9TAkSZIktdwg52NuA66OiIdHxBrKzVpuWXDMLcCre5+/GPhiZmbv/pf2dv18OHA18FcDHKskSZIkNdbAZvx61+zdCHweGAY+lJl3RsTbgdsz8xbgg8BHe5u3HKAMh/SO+zjlRjAd4GczszuosUqSJElSk0U5wXbh27NnT95+++1VD0OSJEmSKhERf52ZexZ7rMKtNyRJkiRJK8HgJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HCRmVWPoS8iYhT4x6rHsYiLgbGqB6EVY73la6BdrHe7Wf92s/7tcqHU+2GZecliDzQm+NVVRNyemXuqHodWhvWWr4F2sd7tZv3bzfq3SxPq7VJPSZIkSWo4g58kSZIkNZzBb/BuqnoAWlHWW74G2sV6t5v1bzfr3y4XfL29xk+SJEmSGs4ZP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gVzMRMVz1GDR4EeHPXotFxCURsabqcWhlRMSjI+Kiqseh6kVEVD0GSSujjj/vnnzWQET8WES8AyAzu4aCZouI5wHvtM7tFBHPB94P7K7jHwX1V0Q8DbgF+NHebWveIhHxpIh4S0S8IiJ2pDvqtUpEPD4inhcRD4uIzb37/NvfUBHxwxHx0xHxIxFxaWZm3epdq8G0TZTWAG8A/m1EvAsgM4uIWF3t6DQIEfF04G3AFzKzqHg4WmERcR3wTuD9mXnv/JPAuv1x0Pnrhb7/CPwj8OMAnvi3R0Q8F3gvsAV4JvCD8x7zDYCG673J+1HgWcDrgV+NiIf0zvH8fd8wEfEM4P8Fvhd4GfCRiHhU3epdm4G0UZZmgN8B3kQ5A/D/9B6brXRw6ruIeBxlD5h3ZOYfR8T2iPjBiLgqIrZUPT6tiCuBWzPzTyNid0S8KiJeEhEPq9sfB52fiHgS5c/7azPzacCVEfFT1Y5KKyUi1gMvB27IzDcDfwc8LiKuretMgPonItYB/wJ4TWb+DPAp4AnAb0bELt/4baQnAv9nZr4O+A/AF4Gb54W/WrzZ4y+dCs17EawGHg+8Ebg8Ij7MiNVJAAALlklEQVQdEZ+IiGGvA2qUB4B7gIdGxA8AnwF+EXgX8IteA9QKU8DcjM/vUv6heBLwyYh4hCcDjbIP+PHMvKN3+/eAR4CzPS1RABcDz4iIXcArKE/83wh8ICJ2+vPeaAk8BNgDkJl/AdwBHABucD+H5pj3+3wSeBhAZh7IzHdR/t5/V0RcXJfVHga/CkTE1ojYMu9F8ClgPDPvo5z9eyqwPjO7vRlBXcB69d6emSPAq4GnA58EPpyZzwfeDeyk9wtDzTL38967eRvwnIj4LHBzZv5MZv4s8AXKn3td4Hr1vigzv5WZX5n30J8Ar4iIZ9TlBED916v/tsw8Dvwc8DTgt4H/npkvAt4K7KcXCNQsC+r/a8CLI+LtEfGfKN8I+CTwKE68AagL3Lzf5x8DXhoRr5/38Ecpf96vWPGBnYbBb4VFxIuB/w58ISJ+KiIeC3SB7RHxdspfFK8HNkTEb1Y4VPXBvHrfGhE39O5+JfB/ZOZvA2Tml4E1lO8OqkEW/LzP1f+ZwKOBJ887tKC8DkgXsHn1/sN5v98ByMxvUP5+f0VEbKtqjBqcefX/XET8NHAgM38IuJVyBpjeG7yrKd/sU4MsqP9PARPAvwdWUb65/6LM/BKwiRoFAZ2biHhyRLxp7nZm7gWuB34yIt7Qu28/sJ7yur9aCN94XDkRsRP4Q8pZn63A84FpyneArgJ+Bfj5zPx079jhzPxOVePV+Vmk3i8AjlO+83vbvONeBPwS8Dzr3RwL6r8FeCFwDPg45c/9F4H3Ub7x82LgFZn5zWpGq/O1SL1fABwFPpeZf9Y75vsoN/v4l5l5b1VjVf+d5vf9Ucqf9yngP1MGwBHg54GXZua3qhmt+u0053fHKP/e/828436Ccrnvj2XmgSrGqvMXEU8Bfp9yFc/tmfkL8x77PsqdnD9Bec73AuC5dfmd74zfyloHHAH+NjP/BPgA5R+EZwGHgef0Ql9k5j5DwAVvYb3fDxwCnh8R3wMQEa+hNwtgvRtnfv3/lLL+U5QX/O8F/glwPzALvNLQd8FbWO8PUJ74Pz0iHgOQmV8D/opyhlfNstjv+yPAS4ANwHsoZ/ufB/ykoa9xFju/O0K51HPu7/0LgBuBnzD0XfC2Ue7QfSOwMyLeOfdA7/f8E4G7KM/tX1yX0AfO+K24iPi/gPuAd2fmdO8XwhuAL2fmf+2FPovSEIvU+9GU1338ZWb+TkRcBQzV6ZeC+ucM9b8tMz9U7ejUb2f7ea92dBq0Jfy+X0N53nW80oFqIJZQ/+3Aht6SQF3gImJDZh6NiD3A64AHMvNNvceG6rp5kzN+K2Terj+fprzA9+URsb73Lv8ngdf0XkSGvgY4Q73voaz3qyJic2Z+29DXPEuo/8sjYmNlA1RfLfHnfUNlA9RAnaX+n6Ks/6bMnDH0Nc8y/t4fMPQ1R2Ye7X16B+VlG5dHxJt6K7lujIhVlQ3uDAx+A7bItt1fAr4KXAP8QpSN2i+iXBJUy3cHtHTLrLe9GhtmmfXvrvDw1Gf+fm+3JdZ/G2X9Oys8PA2Yf+/bZZF6A5CZHcrw9xbK3p3vAf6kd3/t1DKNNkFE7ACmM/MIlNu9RsSazJyJiD8CxoDnAP8D2Aj8q8ycrm7EOh/Wu92sf7tY73az/u1m/dtlYb17963KzE5v+e7R3tLepwPbgR/KzLuqGu/ZeI3fAETEC4Gfodyi/3cpL/b9Su+xfw68DPiFzByJsrHrVGZOVDZgnRfr3W7Wv12sd7tZ/3az/u1ylno/GfhJyt34H4iIl/ce/0ZlA14Cg1+fRcRDKaf7X0a51nsPsJuyt8tf9h57V2Z+orJBqm+sd7tZ/3ax3u1m/dvN+rfLEuv9G5n5+5UN8hy41LP/VgHfmevbEhH3Ak8HXgQk8KLM3OvunY1hvdvN+reL9W43699u1r9dllxvKJf8VjbSZXBzlz7LshfboYj4zd7tbwNfAPYDV/ReJEMXygtEZ2a92836t4v1bjfr327Wv12WU+8LqeYGvz6IiB+KiBdGxCt7d/0SsDki3giQmX8H3A68LCLWZU17e2hprHe7Wf92sd7tZv3bzfq3SxvqbfA7TxHxLOAm4HuBt0TEr/V28/kU8IiIeG/v0E2U2/kOVzNS9YP1bjfr3y7Wu92sf7tZ/3ZpS73d3OU8RMTVwH8Dfi4z/yIirgT+M/AKyhfFVcBbgc3AFcCrMvOr1YxW58t6t5v1bxfr3W7Wv92sf7u0qd4Gv/PQe6E8PjM/HhHDlI1a/wD4l/O3c42Iy4FjmTlZ0VDVB9a73ax/u1jvdrP+7Wb926VN9Xap5zmIiN0RsRr4h8z8eO/uIjPHgXuBI73j/glAZj5wIb9I2s56t5v1bxfr3W7Wv92sf7u0sd4Gv2WKiGcDtwIfAP5rRHxP76G51hjbgY29C0NvjohLKhim+sR6t5v1bxfr3W7Wv92sf7u0td728VuiiAhgF/BO4EbgbuCVwJci4p9n5p29Qx8A3gZcDjw/M0crGK7Ok/VuN+vfLta73ax/u1n/dml7vQ1+S5SZGRH3A38JfAsYycz/FBEd4AsR8eTMvAc4CPwo8JzM/GaFQ9Z5sN7tZv3bxXq3m/VvN+vfLm2vt0s9lyAiHhkRP0h5sedW4BXZ2xUnM98LvJdy69dh4I+BpzXpRdI21rvdrH+7WO92s/7tZv3bxXo743dWEfEc4Ncok//Xgd8FfisihjPzP/YO+zjwi5nZBf6wmpGqH6x3u1n/drHe7Wb92836t4v1Lhn8ziAingi8C3h5Zn41Im4CrgOeCPyv3jsCNwM/DDw+IrZn5oHqRqzzYb3bzfq3i/VuN+vfbta/Xaz3CfbxO4PeC+VRmfnh3u1LgA9n5rMj4irgPwDTwBOA12Tm1ysbrM6b9W43698u1rvdrH+7Wf92sd4nGPzOoPcOwMbMPNT7/CHAZ4BnZeZ3I+JhwL7eMRd0Xw9Z77az/u1ivdvN+reb9W8X632Cm7ucQWZ2M/NQ72YAE8CB3ovklcBbgNVNf5G0hfVuN+vfLta73ax/u1n/drHeJzjjt0wR8WHgu8DTaPh0sKx321n/drHe7Wb92836t0tb623wW6KICGA1ZaPH1cBTMvNb1Y5Kg2K92836t4v1bjfr327Wv13aXm+D3zJFxGuA2zLzzqrHosGz3u1m/dvFereb9W83698uba23wW+ZIiLS/2mtYb3bzfq3i/VuN+vfbta/Xdpab4OfJEmSJDWcu3pKkiRJUsMZ/CRJkiSp4Qx+kiRJktRwBj9JkhaIiG5E3BERd0bE1yLi30fEGf9mRsSVEfHylRqjJEnLYfCTJOlUxzLz+zPzWuCpwDOBXz7L11wJGPwkSbVk8JMk6QwycwS4AbgxSldGxJ9HxN/0Pp7YO/SdwI/0Zgr/bUQMR8S7IuK2iPjbiPhpgIh4SET8We+4b0TEj1T1b5MktYftHCRJWiAipjJz04L7JoBHA4eBIjOnI+Jq4L9l5p6IeBLwxsx8Tu/4G4BLM/MdEbEW+DLwEuCFwLrM/NWIGAY2ZObhlfvXSZLaaFXVA5Ak6QKzGnhfRHw/0AUedZrjngY8LiJe3Lu9FbgauA34UESsBj6dmXcMesCSJBn8JEk6i4i4ijLkjVBe67cf+D7KSyamT/dlwOsy8/OLPN+PAs8GPhwR787M3xnIwCVJ6vEaP0mSziAiLgH+b+B9WV4fsRX4bmYWwE8Aw71DDwOb533p54F/3ZvZIyIeFREbI+JhwP7M/G3gvwA/sEL/FElSiznjJ0nSqdZHxB2Uyzo7wEeBd/ce+wDw+xHxKuBzwJHe/X8LdCPia8CHgfdS7vT5NxERwCjwfOBJwM9HxCwwBbxqBf49kqSWc3MXSZIkSWo4l3pKkiRJUsMZ/CRJkiSp4Qx+kiRJktRwBj9JkiRJajiDnyRJkiQ1nMFPkiRJkhrO4CdJkiRJDWfwkyRJkqSG+/8B4uCKdwJSZ+sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Region 3 - 10000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1])*100/numNodes for j in S10['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff = (S20['total_pop']*np.log(S20['total_pop'])*S5['area']*S5['F(S)']**2)/(S5['total_pop']*np.log(S5['total_pop'])*S20['area']*S20['F(S)']**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-bab704613f49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# With current testing and social distancing throughout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mnumNodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mS20\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total_pop'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mG_normal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG_relaxed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG_Quarantine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen_graphs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumNodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoeff\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS5\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total_pop'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-3-18423b33c4bb>\u001b[0m in \u001b[0;36mgen_graphs\u001b[0;34m(numNodes, coeff)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgen_graphs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumNodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoeff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mG_normal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merdos_renyi_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumNodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoeff\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1.1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnumNodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mG_relaxed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merdos_renyi_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumNodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoeff\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.301\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnumNodes\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mG_Quarantine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merdos_renyi_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumNodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoeff\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.006\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnumNodes\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-697>\u001b[0m in \u001b[0;36mgnp_random_graph\u001b[0;34m(n, p, seed, directed)\u001b[0m\n",
      "\u001b[0;32m~/Networks/networks/lib/python3.6/site-packages/networkx/utils/decorators.py\u001b[0m in \u001b[0;36m_random_state\u001b[0;34m(func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    462\u001b[0m         \u001b[0mnew_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m         \u001b[0mnew_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrandom_state_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    465\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_random_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Networks/networks/lib/python3.6/site-packages/networkx/generators/random_graphs.py\u001b[0m in \u001b[0;36mgnp_random_graph\u001b[0;34m(n, p, seed, directed)\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0medges\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m             \u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_edge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S20['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, coeff*np.log(S5['total_pop']))\n",
    "\n",
    "\n",
    "final = []\n",
    "initI = int(I*coeff)\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim);\n",
    "with open('data/region3_S20c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = 'Region 3 - 20000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1])*100/numNodes for j in S20['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('region_sim4.p','rb') as fp:\n",
    "    S5,S10,S20 = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "I = 3\n",
    "Et = [0.000, 0.0000,0.0002,0.0002]\n",
    "It = [0.0001, 0.0015, 0.0017, 0.004]\n",
    "sim = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S5['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, np.log(S5['total_pop']))\n",
    "\n",
    "final = []\n",
    "initI = I\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim);\n",
    "with open('data/region4_S5c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = 'Region 4 - 5000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1])*100/numNodes for j in S5['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff = (S10['total_pop']*np.log(S10['total_pop'])*S5['area']*S5['F(S)']**2)/(S5['total_pop']*np.log(S5['total_pop'])*S10['area']*S10['F(S)']**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S10['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, coeff*np.log(S5['total_pop']))\n",
    "\n",
    "final = []\n",
    "initI = int(I*coeff)\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim);\n",
    "with open('data/region4_S10c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = 'Region 4 - 10000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1])*100/numNodes for j in S10['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff = (S20['total_pop']*np.log(S20['total_pop'])*S5['area']*S5['F(S)']**2)/(S5['total_pop']*np.log(S5['total_pop'])*S20['area']*S20['F(S)']**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# With current testing and social distancing throughout\n",
    "numNodes = S20['total_pop']\n",
    "G_normal, G_relaxed, G_Quarantine = gen_graphs(numNodes, coeff*np.log(S5['total_pop']))\n",
    "\n",
    "\n",
    "final = []\n",
    "initI = int(I*coeff)\n",
    "theta_E = Et[0]\n",
    "theta_I = It[0]\n",
    "mf = 1\n",
    "checkpoints  =  {'t': [15,25,50],\n",
    "                   'theta_E': Et[1:],\n",
    "                   'theta_I': It[1:],\n",
    "                 'G': [G_relaxed,G_Quarantine,G_Quarantine]\n",
    "                      }\n",
    "\n",
    "temp = monte(numNodes,initI=initI,theta_E = theta_E, theta_I = theta_I ,mf=mf, checkpoints=checkpoints, G = G_normal, sim=sim);\n",
    "with open('data/region4_S20c.p', 'wb') as fp:\n",
    "    pickle.dump(temp, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = 'Region 4 - 20000 Population Cumulative Cases'\n",
    "\n",
    "plt.title(title)\n",
    "\n",
    "dfa_DI = temp[1]\n",
    "cumu = get_three_tuple(dfa_DI)\n",
    "\n",
    "plt.xlabel('Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Percent Population')\n",
    "plt.plot(dfa_DI.index, cumu[:,1], label='Total Detected Cases')\n",
    "plt.fill_between(\n",
    "    dfa_DI.index,\n",
    "    cumu[:,0],\n",
    "    cumu[:,2],\n",
    "     color='b', alpha=.1, label='IQR'\n",
    "    )\n",
    "plt.plot(\n",
    "    list(dfa_DI.index)[:len(S10['timeseries'][15:])],\n",
    "    [float(j[1])*100/numNodes for j in S20['timeseries'][15:]],\n",
    "    'r', label='Total Detected Cases (Ground Truth)'\n",
    ")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('IQR_cumu/'+title+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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