drcjar/ipfjes-geo.ipynb

## ipfjes-geo.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns; sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('for_cosetta.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.gp_coords.notna()] # get rid of rows which lack gp coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumrisk = df.groupby('participant_id').risk.sum().reset_index() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxcat = df.groupby('participant_id').jobcat.min().reset_index() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "ev_exposed = df.groupby('participant_id').exposed.any().reset_index() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates(subset='participant_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['participant_id', 'pt', 'years', 'agegroup', 'ethnicity', 'es', 'cs', 'distfromcentre']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, cumrisk, on='participant_id')\n",
    "df = pd.merge(df, maxcat, on='participant_id')\n",
    "df = pd.merge(df, ev_exposed, on='participant_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.cs = df.cs.fillna('No')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant_id</th>\n",
       "      <th>pt</th>\n",
       "      <th>years</th>\n",
       "      <th>agegroup</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>es</th>\n",
       "      <th>cs</th>\n",
       "      <th>distfromcentre</th>\n",
       "      <th>risk</th>\n",
       "      <th>jobcat</th>\n",
       "      <th>exposed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100009</td>\n",
       "      <td>case</td>\n",
       "      <td>81</td>\n",
       "      <td>80 to 84</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>11.851470</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70003</td>\n",
       "      <td>case</td>\n",
       "      <td>55</td>\n",
       "      <td>55 to 59</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30.403741</td>\n",
       "      <td>47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80001</td>\n",
       "      <td>case</td>\n",
       "      <td>87</td>\n",
       "      <td>85 to 90</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>5.549752</td>\n",
       "      <td>43</td>\n",
       "      <td>2.2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10002</td>\n",
       "      <td>case</td>\n",
       "      <td>75</td>\n",
       "      <td>75 to 79</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>6.779659</td>\n",
       "      <td>44</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80010</td>\n",
       "      <td>case</td>\n",
       "      <td>77</td>\n",
       "      <td>75 to 79</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>33.762778</td>\n",
       "      <td>23</td>\n",
       "      <td>2.1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   participant_id    pt  years  agegroup ethnicity   es   cs  distfromcentre  \\\n",
       "0          100009  case     81  80 to 84     White   No   No       11.851470   \n",
       "1           70003  case     55  55 to 59     White  Yes  Yes       30.403741   \n",
       "2           80001  case     87  85 to 90     White  Yes   No        5.549752   \n",
       "3           10002  case     75  75 to 79     White  Yes   No        6.779659   \n",
       "4           80010  case     77  75 to 79     White  Yes   No       33.762778   \n",
       "\n",
       "   risk  jobcat  exposed  \n",
       "0     0     1.0    False  \n",
       "1    47     1.0     True  \n",
       "2    43     2.2     True  \n",
       "3    44     3.0     True  \n",
       "4    23     2.1     True  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f36ab4f4320>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36ab580f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.pt,df.jobcat).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f36ab4d1860>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kuOOGkkqlWLHifaZNm8LPf/54DqtWZ+GQi5SBdJbPLSgoIJWqe7b6X//6Rf1rqa3ZQ5e2U0vL577wwn9z3313U11dzW23/SQH1akzMtCl7dDa8rlDh57A0KEnsGzZazzwwGzKyu7Jeg0+7zbLEvC8W4dcpAyls3zu3wwaNIRVq8pZvXp1O1WnzsxAlzKQzvK55eUrqa2tBSDGd/nqq6/o3r17e5apTsohF3VY6UwzzLZ0ls99/vnnePrpBeTn57Prrrty/fW3+MGo2oWBLmUgneVzzz13LOeeO7Z9CpIacMhFkhLCQJekhDDQJSkhDHRJSggDXZISwkCXpIRw2qI6rI/+ND2r59tv8LRW23z55ZdMnHgRGzd+RU1NDSecMJzx4y/eqs3GjRu58cZrifEdCgu7M336LfTsuU9Wa5WaYg9dysAuu+xCWdlsHn74EebO/TlLlrzCW2+9uVWbp556gm7duvGf//lrRo0aw733/jRH1aqzMdClDKRSKQoKCoC6NV1qajZtcxfoyy+/wKmnfgeAYcOG88c//qF+KQCpLaU15BJCGAGUAXnAnBjjjEbHrwImAJuASmBcjPHDLNcq7RRqamoYP/48Pv54JWed9Y/077/18rmVlRWUlu4FQH5+Prvv3pU1a9bQo0ePXJSrTqTVHnoIIY+6pzOdCvQDRocQ+jVq9ifg8BjjocAvgJnZLlTaWeTl5TF37s/55S8X8M47y/nggxVbHW+qM+5SLmoP6Qy5HAmsiDF+EGPcCMwHzmzYIMb43zHGDVs2lwC9s1umtPPp1q0bgwcfxpIli7faX1paSkXFp0DdsMz69esoLHS1RbW9dAK9F7CywXb5ln3NGQ/8bkeKknZW1dXVrF27FoAvv/wrr776B/r02X+rNsceezy/+91TADz//HMMGXKEqy2qXaQzht7UldjkJzwhhHOBw4GhrZ20qKiA/Py8NN5eSVJS0i175zr5tqydK12fffYxV101hZqaGmpraxkxYgQjR55GWVkZAwYMYPjw4Vx44blMnjyZMWP+L927d+fOO+/M6s+ttpGEf6N0Ar0c2LfBdm9gVeNGIYQTgauBoTHGL1s7aXX1htaaKIEqK9fmuoQdsueevXjggXlb7ausXMuYMePqXwNcc81N27TRzq2j/Bu19IsnnUBfCvQNIRwAfAycA4xp2CCEMBi4DxgRY6zY9hSSpLbW6hh6jHETMBF4BngHeDTGuDyEMD2EcMaWZrcBXYHHQgjLQghPtlnFkqQmpTUPPca4AFjQaN+0Bq9PzHJdkqQMeaeoJCWEgS5JCWGgS1JCuHyuOqypS9/P6vluPqJv2m1ramqYMOE8SkpKmTnzJ1sdc/lc5Yo9dGk7PPbYI/Tpc0CTx1w+V7lioEsZqqj4lMWLF3H66SObPO7yucoVA13K0F133cGll17R7PoszS2fK7U1A13KwKJFL9Gjxx4cdNDBzbZx+Vzlih+KShl4883XWbToRZYsWcTGjRtZv34d06dfw7RpN9S3+dvyuaWle7l8rtqVgS5l4JJLJnLJJRMBeO21V5k//2dbhTn8ffncAQMOdflctSsDXR1WJtMM29qcObM56KCDOe64oXznO2dyww3TGDVqJIWFhVx33c25Lk+dhIEubachQw5nyJDDAZgw4ZL6/bvuuis33nhrrspSJ+aHopKUEAa6JCWEgS5JCWGgS1JCGOiSlBAGuiQlhNMW1WGNm7Ewq+d7aMq302p39tmnU1BQQJcueeTl5fHgg/O2Ol5bW0tZ2e0sXryI3XbbjalTryOEg7Jaq9QUA13aDnfddR89evRo8tiSJYtYuXIl8+f/iuXL3+L222/hgQcebucK1Rk55CJl2UsvvcCIEaeRSqUYMOAQ1q1bS1VVVa7LUidgoEsZSqVSXHXV5Ywbdy5PPPHLbY5XVVVSWrp3/XZp6V5UVVW0Z4nqpBxykTJ0770PUlxcQnX151x55eX06bM/gwYNqT/e9MMsXJxLbc8eupSh4uISAIqK9uD444fx9tvLtzpeUlJKRcUn9dsVFZ/Wf4/Ulgx0KQNffPEFGzasr3+9dOnv+frXD9yqzXHHDeXppxdQW1vLW2+9SdeuXSkuLs5FuepkHHJRh5XuNMNs+vzzz5g6dTIANTU1nHTSKRx99Lf49a9/AcDIkWdzzDHHsnjxIkaNGrll2uK17V6nOicDXcpAr169efjhR7bZP3Lk2fWvU6kUkyb9sD3LkgCHXCQpMQx0SUoIA12SEsJAl6SEMNAlKSEMdElKiLSmLYYQRgBlQB4wJ8Y4o9HxXYF/Bw4DPgNGxRj/nN1SJUktabWHHkLIA2YBpwL9gNEhhH6Nmo0HqmOM3wDuBG7NdqGSpJalM+RyJLAixvhBjHEjMB84s1GbM4G/Lfj8C2B4CMHViCSpHaUz5NILWNlguxw4qrk2McZNIYQ1wJ5As4tAl5R06xCB/5s7Gv/uknYOXptqLJ0eelPB23h90HTaSJLaUDqBXg7s22C7N7CquTYhhHygO/B5NgqUJKUnnSGXpUDfEMIBwMfAOcCYRm2eBC4AFgNnAwtjjPbQJakdtdpDjzFuAiYCzwDvAI/GGJeHEKaHEM7Y0uxBYM8QwgrgKmBKWxUsSWpaqunHZUmSOhrvFJWkhDDQJSkhDHRJSggfQdcBhRAOou7u3F7UzfdfBTwZY3wnp4VJyil76B1MCOGH1C2/kAL+QN200hTwSAjB2UXaaYUQLsx1DUlnD73jGQ/0jzF+1XBnCOHfgOXAjCa/S8q964H/l+siksxA73g2A/sAHzba33PLMSlnQghvNHMoBezVnrV0RgZ6x3Ml8FwI4X3+vmjafsA3qLsBTMqlvYBTgOpG+1PAK+1fTudioHcwMcanQwjfpG5Z417U/UcpB5bGGGtyWpwETwFdY4zLGh8IITzf/uV0Lt4pKkkJ4SwXSUoIA12SEsJAl1oQQrguhLBLruuQ0mGgSy27FjDQ1SH4oag6vRBCLXU3vZxM3bNwp8YYHw8hzAIuA96kbo7/sBjj6txVKrXMHrpUZ3OM8VvAGcD9IYTSGOPlW459K8Y4yDDXzs5Al+o8CBBjjMBrwNG5LUfKnIEubStF3SqWUodioEt1LgQIIfQFBgG/37J/LdA9V0VJmfDWf6nOlyGERUAxcHGMsWLL/juAhSGEL/BDUe3knOWiTm/LLJduMcZ1ua5F2hEOuUhSQthDl6SEsIcuSQlhoEtSQhjokpQQBrokJYSBLkkJYaBLUkL8fyf5lkCwbq4ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36ab54ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(df.pt,df.jobcat)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f36ae4ca3c8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f36ae4f9080>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f36ae506e80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f36ae49af60>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36aedd2f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='distfromcentre', by=['pt', 'exposed'], sharex=True, sharey=True, bins=100, figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f36aedd8cc0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5BiIc3NbqDiOGIXvMCYKwLiLCbQTlKNvxPNy1YeB2rxd695EFQRAkHdFGJwH2jqc6PmbFzDV+b3C+IAjCRogIt9HNq33trUc6PpZLx9f4vetdRxAEIYzYEW0EXq2Xdujs4bY/ttH5giAI3RAR7kCQ+d3MYyK6giBsBRHhDdgoF7wTtSD24jUEQehPRITX4fSZhQ1zwdvdU67Ta/zRI89gQGPBh+xbJwiDi0zMrUOnbHChVFtTB6LbuVt9jWKp1rJf3XZfQxCE/kVGwh0I7IFvPnu5ZcEGdM4Fw9ZrQcwvldYc2+nXEAShfxERbiNsDwR1H8LLkDtlgmHrueD2WsTB6+7kawiC0L+IHdFG+Ct/OP8b2AM7nQvu9LxsOk5OsseCMBTISLiNsD0QTL4VSzXqdYdD4+3Jhe3ngrvlknfyNQRB6F9EhH0CH3husYSLNxpNJ63Gf4fG07z73lON8zsJ4lajZpI9FoThRUQYeELPNXzgrF96MihnGYyGN7ICJGomCMJWEE8Y+OzXXmj8nEpajOeSWJbJSqnGofE0b73r+g1FU6JmgiBsBRkJ4215HyaVtEj55SjDFsR6SNRMEIStICNhvC3vO7GZSNh6ZS5LFZv5pRIzCyvML5VIWMaW2yoIwmAhIkxzy/t2NhMJ6xY1S1gmS4UKtu2Au3a7JEEQhhsRYZpb3h8aT2MaRs8+cJjwtvfBNd55z80cGh/BskwwwLJMxnPJdbdLEgRhuBBP2Ge98pXbucaDX3x+ne2SBEEYdkSEt0CnPHBYfMOPX12pEo+ZjZhaQCJu8tGHTnNutkDNdohbJtcdym16UcZGbYmafm+fIESNiPAm6ZQHDmd/2x+3YmZL7QmAcsWmVLFb8sgAuDTqSPQiVBu1JWr6vX2C0A+IJ7xJunm5wfH2x9NJi7FcklrdaXjFY9kE6aS1piRmkCnu1S/eqC1R0+/tE4R+QER4k3TKA3vHy10fTyctRjNJfvG+O3j3vaeo2l5+uD1HHNzv1S/eqC1R0+/tE4R+IDI7QimVAr4IJP12/InW+peUUieBjwETwBPAj2qtq1G1s5320pOlik2xVMMAPvrQaRKWSbm2dpFGOHMcXCMolRkQlLDsNZ/cqQzmZp6/2/R7+wShH4hyJFwBXq+1fhnwcuD7lFKvBD4E/KbW+iZgEXhXhG1cQzgPXKrYjQxwNh1ndrHEUrFKqWKv+7zg5/aSmEH5yl7zyd3O65eSl/3ePkHoByIbCWutXaDo3437/7nA64F3+MfvB34Z+K29bl83wqUnv3XmCpbVuvNGKmmRSsQYzSS6lqEMX+OcYVCz6ySsGNceym4qPdCtDGa/THr1e/sEoR8wXNeN7MWVUjHgceBG4L8Cvw78ndb6Rv/x48Bfaq3XLeBg23XXsmK73dw1/MyHv0Cn9880Df7TT9+15+0RBCFyNl2TINKImta6DrxcKTUGPAi8qMNpG/6VWFxc3VY78vkc8/OFjo+tl3MdzyY6ep6HxtNdr7eX5PM5/vZrZ/smp7tTmeH1Pq/9zqD2bVj6lc/nNn2NvkhHaK2XgM8DrwTGlFLBH4djwMWo2hXkXGcXSzhuM+ca1H3od88zqJPcrf17yUbvpSAMK5GJsFIq74+AUUqlgTcCTwN/C/ygf9p9wEPRtHDjnGunehGbrTmxm4TrJIeJIqcrmWFB6EyUdsQR4H7fFzaBT2itP6mUegr4mFLqV4BvAL8bVQN7ybnuRM2J3aK9TnJAFDldyQwLQmeiTEc8CdzW4fjzwJ1736K17Pec6+HJDM+cu0KhVMOuO1gxL8lx3eHN+1bbZb+/l4KwW/SFJ9yv9LvnuxEnrjnAYlst48VChWNT2T1vy35/LwVht5ACPuuw33OuZy8uM5ZLUgyNhLPpONNzxY2fvMPs9/dSEHYLEeEN6GfPdyMuLayQTlqNHaMDovJh9/N7KQi7hYhwB06fWeBTXznL9Lw3sTWeS5JJWVRtJ/Ks7WY4PJnhhUvLjfvtdS72Sz8EYZART7iN02cW+KNHnuHMjFdsvVKrc2F+hecuLrNStvdVvjW8d16nOhf7pR+CMMiICLfx6JMzLXV+Hcdt3IaP74d8a3jvvJVSrWWPu4D90A9BGGREhNuYXyq11Pl1Q7fh4/sl33rq5CTvvvcUU+Np8mPpNdss7Zd+CMKgIiLcRn4s3ajrC81qHAa0HN9v+dZOm416x/dXPwRh0BARbuO1tx5pqfNrmkbjNnx8v+VbJacrCP2JpCPaOHVyknfec3NrOmIiSEe4+zbfKjldQehPRIQ7MKh51kHtlyDsZ4ZehE+fWeCxz2imZ5dJWCZgULXr+yoP3Ilwv/Z7XwRhkBlqEQ5q3MYtk5WyzYVCBfAWZwQ5WmDfiVe4X+HavbD/+iIIg05PE3NKqf+tw7H7dr45e0s4IxvOABf2WR64HandKwj7h17TEf9VKaWCO0qptwPv2Z0m7R3hGrfhDPB+zAOHkdq9grB/6FWEfwT4hFLqkFLqfwf+I/B9u9esvSGcnQ1ngPdzHhgkEywI+4meRFhr/Y/AvwX+GvggcI/WenY3G7YXhDOy4Qxwbh/ngUEywYKwn1h3Yk4p9Wtth1zgKeA9Sim01j+3ay3bA4JJqq/ry9TrBcazCTAMqjVnX+dow/06P1vY130RhEFno3RE+yZlf7ZbDYmKUycnufvOEwO3Hfeg9ksQBo11RVhr/f/sVUP2E6fPLPgrz0o7msHdresKgtC/9BpR+xml1Kj/8x8qpb6tlLpnd5vWnwQZ3NnFUksGd7t1eXfruoIg9De9piN+TGt9VSl1NzAF/Djwq7vXrP5ltzK4ku0VhOGkVxGu+7d3Aw9orb+yiecOFLuVwZVsryAMJ70uWy4ppX4BLy/8GqWUASR2r1n9R+DXzi2WcGlG2oKdjLPpOKfPLHDq5OSWvN38WJrZxbVCLNleQRhserYjgDzws1rrS8D1wAO71ah+I+zXZtNxbNvhynKZK1fL2LYDLsRjJn/6hef55FfPbsnblWyvIAwnPY2EtdbPEFqmrLV+Dvh/d6tR/UbYl00lLcaBy1fLuK5LwoqRS8cb2wZ9/hsXyI2s/ZLw6JMz646Gpd6vIAwnGy3W+JDW+r1Kqf9Fc7u1Blrrt+9ay/aYsIWQsGKA29ji/txsgVSi+ValkhaxmAEYa5YIF0s14jGTpWKFmu3VoIhbMcrVOhsR1PsN2vLgF5/n0SdnRIwFYYDZaCT8qH/7yd1uSJQ8oecapR7LFZsLBW+NylguibNYorBaA5eWTTLD9SXCJOMxFq6WqTvNv1nVWp2lYqXhGa9HYH0ESBlKQRhsNlqs8RdKqRhwvdb6l/aoTXvOZ7/2QuPncBnLYqlGOmmRTccplGotIpxLx9d+NQBGM3FWQtdo4G5sScD6UTURYUEYPDacmNNa14Hv2YO2RMalhebq7E4lLdNJi9xIgkPjaUzD4NB4mh+552beec/NLcfeetf1xK0YpmlgeG4FhgGxmIGL21PcTKJqgjBc9BpR+5RS6t8DfwAUg4Na69WtvrBS6rh/vcOAA/y21vojSqkJ4OPACeAs8Hat9eJWX6cXDk9meOHSMuWKTb3u4rguBhCLmVxaWKVm1zEMg7FMgn/2PSdbRqTto9NHn5xhdrGEbTs4rovjuNQdF9eFRHzjMIpE1QRhuOg1ovZr/n+XgAKeEG+3MowN/Dut9YuAVwI/qZR6MfA+4HNa65uAz/n3d5U33nkt5YrNYqHijWBdcFyo2Q6VWh3XBQM4M7PMA488s27c7LW3HiGbjuO4LvW6J7643oh4sVCRqJogCC30GlHb8dVxWusZYMb/uaCUeho4CtwLvM4/7X7g88B7d/r1w9yuphjLJij4Cy9ipknNrhPMrcVMA9M0AM8zXs+fPXVyknfeczP/4y+eanjDcSvGaDZBOmlJVE0QhBb6YqNPpdQJ4Dbg74FDvkCjtZ5RSk1t9Pzx8REsK7atNjgYXHMw07h/frZI1fZiZbFQEqLuuCytVMnnc12vdXc+x1985Ryuu3bqbqPnBs+/+84Tm+xBZzZ6rf3KoPYLBrdv0q/ORC7CSqks8KfAe7TWy6Gt7HpmcXHL1jTgvYnj2USLFxuLGRg2uC7UbM+SAM9WuDBX5P2/89V1R6jt1ws4NJ7esxq/+XxuIOsJD2q/YHD7Niz92oogR1qERykVxxPgB7TWQcH4WaXUEf/xI8DcXrSl3XPNpeNgeCtUXNe/xfOK45a54XJk8XYFQeiFyETYLwL0u8DTWuvfCD30MHCf//N9wEN70Z5TJyd5613XNyJn1x3Occ1khmQ85k3W4Y2CrZhB1W7G2LrletuvF0TYxNsVBCFMlHbEa4AfBf5RKfVN/9h/wNtI9BNKqXcBLwBv26sGBcuGA95//2McnhwBYGZhpbFwO5wlXi+/2349QRCEdiITYa31o3jJr068YS/b0o1wZteKmV7FNFqXLEt+d2vIVk6C4DGUhdl7Jezf5vz6wdCsJdx+jtAbspWTIDSJPB3Rz7RndsdySXBdqrYr+d1tIPUxBKGJiPAGiK+780h9DEFoIiLsIx7l3iH1MQShiXjCNOsJi0e5N0iGWhCayEiY1nrCYcSj3B2kPoYgNBERprWecBjxKHcP8doFwUNEmGY94XbCHqV4xv3DE3qOT33pOfkshIFAPGG8esKdCDxKybX2D6fPLPCHn35KPgthYBARxqsnvF6dh/VyrcLeIp+FMGiIHeGznkcpudb+YX6p1FLfuXlcPgthfyIivA6BDzy3WMLFW7oc3nE5ylzrsHrU+bE0VwqVDsclYyzsT8SO6ELYB86k49i2w2KhQrliN86JKtc6zB61ZIyFQUNGwl0Ie4xpf/RbLNUolmpcdzgX6chzmGsvnDo5yejoiJ+OkIyxsP8REe5Cuw+cTlqkkxamYfDue09F1CqPYfeob1dTHJ9IR90MQdgRRIS70K2+QcIy+OhDp9f1Ynfbr5XaC4IwOIgn3IVOHmOpYrNUrK7rxe6FXyu+qCAMDiLCXei0R9x4NtmSjggIe7R7kWOV/esEYXAYCDtitWyTiJst2w7tBJ32nOtE2IvdK79Wai8IwmAwGCJcqbG86mLFDFIJi2Q8RtzaniB38nU7ebHlik2t7vD++x8jP5YmYZmUa86a64lfKwhCJwZChAPsutuIkZmmQTIeIxk3ScRjmEa3PUXXEvi6AYGv+10q3yLC5YrNYqHCWC7Z8H/LFRuXZqwtQPxaQRA6MVAiHMZxXEoVm1LF29I5bpkkEzGS8diGtkU3/3Z6rshb77q+UQe3VncYyyVbBDeVtEglYoxmEpJjFQRhQwZWhMO4QNV2qNoOBWpYpkEiESPVxbZYz9cNe7Hvv/8xHHftedWaE3mWWBCE/cFQiHA7tuNil21WyzaGAbFknNWyTSoR46lzV7i6UqVYqmHFTOKWSc12sOsO2XSc02cWGiIseV1BELbL0EfUXBfK1TrLq1W+fHqGj//Ns8QMA9dxqVbrLBerVGt1cCEeM1syv5LXFQRhuwy9CIf5+rfncF2Ix2McyCRwXBcXz18eyyYaGeHAM5a8riAI22Ug7IgvfPMiB0dTHM1nyabjW77OYqhEYjJhEYtVibmAAZYVw647mIbBXMiCkLyuIAjbYSBE+FNfPdf4eSyb4Gg+y7F8hmP5LEfzGVKJ3ro5nkty8fIKK2Wbml2n7sd9TQMqVZtkwqLueqPiK8tlEvEYCcvzjY1NROAEQRACBkKEwywVqywVr/CtM1caxw6OpjiWz3JsyhPmI5OZjqmIw5MjfPvcIo7rNgQ4YHmlygG8EfIdt0w10hYAhgEJq5lJ3umVe4IgDC4DIcLvedutnJstMj1X5MJ8kUtXSjhuMzt2+WqZy1fLfPPZywCefzuRboyUX3JjnlQMLi2sksskWPJtiZaxrQF23eEH7jzOTcfGWl7fdaFSq1Op1YEapgFxK0YibpKwvOXUMlIWBKETkYqwUur3gDcDc1rrU/6xCeDjwAngLPB2rfXiete55mCGqfERXnHLFAA122FmYYXp+RUuzBeZni9yealMIMuO6zKzsMrMwiqPfRv+/EtnsGIGpmH41gJYvmYahgEG5MdGMA3WCHAnnBZRbi4WiVvDyNHiAAAgAElEQVQmCStGPG5uagWfIAiDS9Qj4d8H/gvwB6Fj7wM+p7X+oFLqff79927monHL5NpDOa49lGscK1dtLlxe4cLcCtO+MC8Vq43H7boLuA2LoYkn3csrFY4ezOC6btdR7Xeml/j6t+dYLFQYzyW545Ypbjo21rJYZAVveyTLNIjHY8RjZtfiQ8O6j5wgDBORirDW+otKqRNth+8FXuf/fD/weTYpwp1IJSxuuGaUG64ZbRwrlmo89vQsX3t6jnK1TtWu43ZYAeeda6PPX+VX//Dxho1xbCrL0YMZciMJvjO9xF997Xzj/IXlSuN+p9Gz7bjYFZsgZxG2MOIxE31+kT/74pnG+UH9CkCEWBAGiKhHwp04pLWeAdBazyilpnbrhbLpOJeurDKaTZCpu7iuV2/iarFKFy1mpWyjzy+hzy81jo1mErh+pjhheUuhTdMbLX/923NbsjA+9/g0dt3BwLNEgsH3MOwjJwjDRD+K8KYZHU3jsjWPdXm1BoAVMwCDuJVgpexZBkcmR3BdF7vuUq3VqdoO+fERzs8WqIVsi6sr1dAVm9dLxGOUqzaLqzWOH8qRiMd6btfVlRoxc22f5q6WMBNWY0n1RmmMfD7X9bH9zKD2Cwa3b9KvzvSjCM8qpY74o+AjwNxGT7h6teR7upvnwEicqyvVlucH4hccMwyDZMLimoNJfviNN1N3XOYWV7kwH/jLK1y8vNJyXbvuYte9+hS//kePYxpwaGKEowczXo55KsvhiTQxs7OAHhiJs7BcWXN8MpPg4qXllmOG4S2pthr/GVgxk0OHDjA/X9jS+9LP5PO5gewXDG7fhqVfWxHkfhThh4H7gA/6tw/t5ovdccsUn3t8uuVYJmV1tCPu8NMXMdPgyGSGI5OZxrGnz13hL//uBWr+BFzNrrcIu+PSSGR8Xc8D3mj5yGTG85fzWY7lsxwcS2EaBnfcMtXiMbe3IYzr0pJbbrxmLMbycrlFnGMxo6vwC4Kw90QdUfufeJNwB5VS08Av4YnvJ5RS7wJeAN620XXGc0nsuku97mA7Lo7/X/Dzetx0bIxsNsXnv/5CKNVwHKBj0qEbL7puAitmtjzn1hsmSSctpv0R84X5lZal0Xbd5fxckfNzRWAWgGQ8xjUHMxzLZ7j5+Cgzl1colmpMHEht2IZ2HNftKM4GNMTY8m+9+4YsNBGEPcZwu8UB9hHz84WunXBdl7rjUq+71B1PpOt1T5zrrovruIxPZLhyZaXbJXaUlXKtaWPMeVZGsVRb9zkjKYtjed/G8Jdk50YSG77WxBb6ZUBo1Gw2hDlmGo3JxqgZ1K+2MLh9G5Z+5fO5Tf+S9KMdsaMYhuF7pACdJ8bOLazwqS89xwuzBaq2gxUzOXrQsxpuPDraNba2GTpliF9/+zFc12V5pdoyWp6eL1Ku1hvPXS3bPHP+Ks+cv9o4NppJtNgYR/OZNVsqbQUXqNUdvJBGveUxw4CY4YmzaXoj58Z/fWBzSK5a2I8MvAhvxOkzCzz85bMUVqosFZopB9Mw+OzXpxnNJHjxiYnGSNpx/JF1aHS9geOxYYZ4NJtkNJvkJScnAM9GuLJc9lb8zRWZvuxN/LUnMq6uVHnqbHMx4eRoqlG46Fg+Sza3s8XlXRds18V26h0fN6AhzlbMszgs3+rYbZuj276AILlqob8ZehEOagMX2iyBYqlGOmk1crmmZRDvUn7ZcX0/uu6Jc812Gv40eN5yJ7pliE3D4OBomoOjaV5+40GAjomMSwurLTUyFq6WWbha5h+eXWhcZ2o83bQyNkhkbBfXb2fdWbvyMBBow/DaZZpG4zawOoLbrSzp7rYvoOSqhX5n6EV4fqlEpVb3dkn29SxYZnx+tsClhZWWLY3CdPr6CzSOHRxN8cqXHObqShXT8K4btjbCk3Qb0SmRUbMdLl1Z9W2MIufnVlr2x3Ncl0tXVrl0pUMi46C/4i+fIT+W3vVaFoFAN+91JxBs01+k4om3gWl49tJIqUa5areI+Xr7AgpCPzP0IpywTC5eXgXWSoPrekL3R488wzvvublFiDt9/f2jR57BgMYOHHNLZR7+8lnSiRjlmhO6rre6Lj+aIpWIYdsOdcfdQJrWErdMjk9lOT6VBTzb4y//7gXP0615++JVavWQ+LUlMp7yEhmJuOeBe96yN/E3nktGVvmtMaLu8o4kipWWuh8AuZE4l69W8OsteRgwMZpmpVzDoCnosdAoXBCiZuhFOPiVNU0Dp8OCD9M0KJZqa77Wdvr6G6QcUu0TZG1iZhgGBvC6244ylk0CHVIcdbexwWiv4vz1b89hmgZJM0YyHsOKGdh1l9FMnO9+8eHmxN9cscV+qdYczswUODPTnOVtT2QczWc40EMiIyq+S3m5atcN/TF14eU3HaSw2jl9Eh5xe7essUcMY+sWiSD0wtCLcNWuMzmaYqlYwa7bLY/F/PKWdt1Z87W209dfu70SfPAaNYe33nW9b1OUyY+l1szcr5fisOsONduhVnew/dtOiY1u9kZhtcbNx8e4+bjnP+9EIiO43YlExk4QeOubyXa3jLg7zzU2CJIhnTzsmNlfET5hf9Efv0ERkh9LMz3vZWkNo9WzDcpbGgZcvlriQw88QdWukx9Lk7DMFosBWJMAKFdslopV7LrDf3/oW4znkoDLt854O38cy2d506uv23DiKFjxlg4dCwtzYD2M55Idlzp7r9vEMIw1iQzXdbmyXGmU+QyWYm+YyDiQakblpjJcM5nZVI2MneSmY2ObWsyyGYJkyHpRmHA6xAwLtf8Htulzi1gLTQZ+scZGfPKrZ/nUV841LIBumCZYpsl4LkkqaXkTedAyEixV7IYnXK7YLCyXqdddrxaF0axFYYVGTeO5JD/S5jdvBcd1+Ydn53nwS2e8r+QuDTviezvsBtILdcdlfqnkLyrxRsyXrqy2eMztGAYcGh8JZZgzHJoY2dGI2lYWofQTBmAE9ofRtDwMA6byOa4srGCa4cfY9+ItizW6M/Qj4em5IpOjSWavlDDoPG9v0HygUKqRSlref4kYo5lEi8UAnl/8rTNXcF0ao6KwVeG4LqbvRRc6+M1bwTQMbrtpirgV49EnZ5hbLHE0n+Wl149z/ZHRRlxuM8RMg8MTIxyeGGkkMuy6w6WF1cZoeXq+yPxiqfG+uS6NRMbjfiLDS3aMtGzAmh9LD+3XdxdwHRenca/J8kqV5dVqh2etL96mEXjbayOAQn8z9CI8v1RiJBUnFisTMw1qdQf8yR0D/3+hyZ6wmFZrDu++99Saa546Ocn773+MC5dXGk8M/6q1Wh5r/ebtcOrkZEPQw3+l645DteY0SnKuN5pdDytmcmzKyxwHVKp1Li4ES7G9LaWuhPzpuuP6gr3C3/vHEnHTr5HRFOYoExn7gfXEuxthi8Rb2Wg2fg5PPgrRMfQiHHjCtr125Zvb+J93W7MdTNNgqVihZnsF1z/60OmOy2PzY2kuXl6lVq+vyQeH/81bMZP82M6ubOvE0+cWWzLNrz51mJuPj3kFfqr1LY2UA5KJGCePHMCuO0zPFRlJWRyaSHN8KovjwgV/r7/l1dZExtmZAmfDiYyk1WJjHJ3K9nUiYz8QXkDjsXYGMhhZN/LYDS+blnTIINgi/cjQi/CxqSzfeObyhkuPA1xclotVYjGDyQOprstjj01leeKZ+Y4phnDcKZeON2yM3aJTpvnBL53hrXdd77V5xBuRl6t1qrU6Nbv3WFxA+9Lsqys1rp5Z5HvvPM7rbz8G4Ccy/F2xL3sj51IllMio2Hxn+irfmW4mMg5kEo2R8tF8hqMHs0xs7W0QuuC4nkXW6y9BOCnSFOnWuJ9ksXtn6EV4eq5ILGZQd9b/N2gaBvG4Sa1W9/4RxsyWPHC7rzs9V2TiQIqrxSo1u+6VjzRNLKuZOT02leVNr9o4HbFdelnSa8VMsmkT0nFc16VacyhXbSq1ek+/m70szT6QSfDizAQvPhFKZBQqnij7/vLFyystS56XV6o81ZbIyI+lOTI5EhLm6BIZw0gvSZGAwA5xrRhLhUqLYBt4o2vDaF0dOWyZ7KEX4fmlkp8kMNfkb1tsA8ubpJpZ8Hze9lRJpxxxOmmtydGahsEv3nfHjvdjPTa7pNfbSSRGMhHzBNl2GiPkbqPkbhnl9ZZmG4b3bWLyQIqX+TUyHMdlbqnEhdDE36WF1kTG/FKJ+aUSTz634F8HpsbSnihPeaPmwzucyBC2RmCH1GynsX/iRrRPQMZMw78/mJOPQy/C+bE080tlVsu1NdZB+KMNfqGtmIntl7tsvY7n6wb1JOb8xEA2HW8R4kTc5KMPne5YbnGjWhRbLc+YH0szu7hWiHvxog3DIBn3VuABLaJcqTV3D+k1o7wRZiiR8V3KO2bXQzUy5laYWVxl5vJKs9aH61kss4slHn+mmcg47I+Wg5V/U0OcyNhPbGUCMuxrt6+C7Hdfe+hF+LW3HuHpc4tdv1kFv7TZdLxxu1SokPPvh68T9l6z6TiLhQpL/kgw7WeHSxW7sTIt7CcDa3zbBx55piWLvNXyjK+99UjLtcPHN0tYlHN4k5Wlis0rXjTFZ/6+t+2YNosVMxvlOXmxlxOemV3m4uWVUFW5IleWWxMZ3oRgKJFhNRMZR/NeAaMJSWQMBDvua7fZI4Yv5rvB0IvwqZOTjTe5fSTsunDD0VFecnKC6bki80tlThzOcezWI4374SXIH33odOO5qaTFOF4OeKVU48ThHFeLlTWr7KC7ZxvUd2i3NDabKw7OXW/Z9FaJWyZxK8FrX3oNmVS88RrjucSmt2PaDMm4l8g4eeRA49hq2ebCZT8md9mzM5ZDO2FXbYezlwqcvdRMZKST4RoZnkAfyEgiY9DZjK8dEOSxw+VYrVScwmq14XFvhaEXYYByrU68g39oGPDed9ze83XavddgUYdpGLz73lO8//7HujyvTKevXd1qUWwlVxzOD+8Wt92U57ab8gAND7BcsbcVf9sMIylrzdLl5dWqVxg/VIe5VGnWCCl1SGTkRuIt9TGO5TOMpFq/+QjDh+tCvTFS825XyzYrZbv7k3pARBjIjSRYLja/yjqO6321MQz+/X/7MnHL5LpDua6jx3YfOJeOtyQnAh+4m08ceLPtvm2771yq2BRLtXXzyf2CN0I2yabj1Gw/adGWR+605dNOj5wPjCQ4cGKCF4USGYuFSsuKv4vzrYmMwmqNp88t8vS5ZiJjIpf0C+N7wnzNwUzDJxeE7SAiDPzTV5/g449ogMb2RS4QM6DoLzAIJn+ArnWFM75fvFioME6zhkTgA3fyiaHpzbb7trl0vDE+LlXsxvPGc8l9tX1PYFnkQnnkJ5+7vO6WT7uFYRhMHEgxcSDFrTc0ExnzS6WGMF+YLzLTlsi4UqhwpVDhH59vJjLyfiIjsDEOT0oiQ9g8IsLA299wMysrFT7/jQvNLKPZ6vC0b3cUEPZzA1EtlmoUSzWua/OBO/nE7aPZdt82OPatM1ewLHPNKHu/bd8T5JGffG4BK2bg+HG/4Ftety2fdhPTNDg0McKhdRIZ0/NF5vw4I3h/lOcWS8wtlnhCEhnCNhAR9nnzq07w5led4P33P4bj0sgDBwT+7EZ1hYNscDcfuN0nDtPNtw1qUXSyVvfr9j3zSyUMwyDmF+gIhHipWO04SbrXtCcyAKo1v0bGXLMO88Jy8/3vlsg4cjDT3IBVEhlCGyLCbdRsh4sLqzgdFO+F2QKpRIwPPfA4VdshP5amZjtcKVSw614tCcfxiuUYhsGHHnii5XEr1hzJJiyja164E+tlfbez1XtU28S398fwZ50PjaeZ8t/XSq2+xkeOkkQ8xonDBzhxuJnIKFXslsL4nRIZ5y4VONeSyIhx9KBvY0x5W0qNSiJjaBERDvHJr57l4uWVrqkV14VSpc5zF5eZOJBi5VKBK8FIyKVFLCwTnrt4FddtLt20bYfFQoWRWp1yvLnvXC/+bres77GpbNet3u/O59btb5TbxK+XXTYMg0Q8RiIea/jIXtKi7lW56yPSyXUSGZc9f3l6boXVlkRGnWcvXOXZC2sTGTdeO85kNiGJjCFCRDjE579xoafzHMdt7CcXrL4Jx8mCHGFwzLJixGJGYzRcr7uksmvf+vX83W5Z3/XqQtx954l1+xHlNvGbyS4HO4tkUnHqjkM2k6C4XGpJNPQT6yUyghHzhcsrVGu9JDKaG7AePZghmZBExqAhIhyiWFq7dLkTLq0ZXheXWMxYY2G4ocfzYyON4zMLnXeF2Mjf7eQZP/jFtaPJXq7lnRPtNvFbyS7HTJPsSIKJAynqjpe0qFTrfSvIsE4i42qpsfHq9LqJjCvedYD8eLplA9YjksjY9wy9CJ8+s8Ajn/gHnp1eahmZrIfrQr3uYsWMRvF3u97cst7wfwimXtp/SbLpOOWKTaFUa/GKrzu81j5o92yPTWX91XqlrnvdQW91IbZTU6IfiJkmmVRzhFyp1ilXt1aKc68xTYND4yMcGh/h9pu9BS523aFUd3nq2cuNUfPs4mozkUE4kXEZaO5+cjQ08ZcfS3tbagn7gqEW4dNnFnjgkWe4Uqi0bGjZC4bhzYY7jtvYYibAhcZxaNadCHjRdeM89nSz9GPgFb+mrZZDu2d77lKBb37nMmO5JOmkxexiqeNed9BbXYidrCkRNTHTZCRlMpKK4zgu5apNqQ895PWwYibX5TPkEjG+m0OAtxv4zOVVwhuwLlxtS2RcXuHC5RW+5v+bilsm10xmGoXxj+UzTB5ISSKjTxlqEX70yRkKpRr1dTb4bMcwIGHFcHGxYiau6zY84aRp4rgu9bo3Eruxre5E2McdyyUphkbC2XSc6bnimvaFCWpJBJlloOted718zd/NmhJRYpoGI6k4I6l4Y3FIqWJveUunKElYMa47nGv5llSq2C2Fiy7Mr3A1lMio2Q7nZgucm20mMlKJ2Jql2AcyCRHmPmCoRXh+qYRdd3C7fHk1gHjc5MhkppkbNuDwZKu/eyR0P8A0jK51Jx784vMdaw1vlEEOfOj2mhLd9rrrhb2oKRElweKQbDpOtVanVK1TrtqR55C3QzppceOxUW48Nto4trxabWwjFSzHXg3VNChXOyQy0nFvwi+facTlMpLI2HP6dst7pdT3AR8BYsDvaK0/2O3crW55/9GHTje+wvUbQWk9123aG2G67Qzd8Tp4hUfCH3UibvKKW6a4vFRiet6bKDyWz/KmV/e200c3r/rcbIGa7XSst9FLJvmTXz3rrVwsVvwONndfSFgm47kkVdth4WoZ13XJpOL8kzuP8+ZXnejh3fBwXbeRQe5155DNstW6GBMTGa5c6Txxuxlc1+Ub35nna0/NcaVQadSB3sh2G88lW6rKHT2Y3ZFERi/92otaIjv92u39eqk6tOmvFn0pwkqpGPAM8E+AaeAx4Ie11k91On+rIvxTv/kFViu9VfsfVEyjdeJwPJfkR+65eV0hbveqyxXbyz+nrJbRV+Bdv/Wu64G1tTGA5j53eAL8yS+fxXYcnA5aYRqtlQeDf+2xmMEPvPbkpoQ4TFCgPlykfju077cX8L13Ht/wF3unRLhTG1zX5ZUvOUTMNBs2xszCyrp9NoCDY+nQir8MhycyxK3NJTI26td23rPtspOf11ZEuF/tiDuBZ7XWzwMopT4G3At0FOGtMuwCDGvLqRZKtQ1zwt286sJqrWVWPlxvY71rBa8V5LQ7CXCntjaOOy6f/8aFLYtwY2EIntXjibJ3uxVJ7mW/vd2mUxsMw+DZ6av88BtvbklkzC7620n5JT/nFlcb77VLczupb3zHS2SYhsHhiTRH81mOT3l2xtT4yLYSGVG+Z1F/Xv0qwkeB8J+maeC7u508Pj6CZUmIfauEJ2fqjsvSSpX8OqvtFovVlpFQve5NTjqug2W0Ho9bJksrVVyXjqOn8GutlO3NTRT5p7p4dV3Xa/NWcF2Xsu8hlyt1r7xpDyyv1rBia/tRKNWYmMhs+PxeztnJNkzlc7z05uYOKNVanfOzBc7OLHtLrmeWmb2y2njccV0uLqxycWGVx77dTGQcP5TjxJEDXHfkACeOHCA/nm7ZtHO9fm33PdsOUX9e/SrCnX4Tu/4GLC6udntI6IGwJWXFTMYyCebnC13PH88mWvLFsZi3JNs0jNZrWSY12+HQeBpYWy8ZvFoRwWtlUlajdGhvDfduDMMr6L5em7eLBd7S6R4m9g6MxDvutzeZSWxoNeyUHbGdNgCMj8QZv2GS227wvqWUKrYXhfOXYU/PF9ckMp6/cJXnQxN/qUSskca45eQko2mL0S6JjO22dzvs5Od1NJ/d9Ov3qwhPA8dD948BF3f6RUYzca6ubOKXfgBp/waZS8c3zAm354tzfp3k3Ei8xRMO8tHd6iWHHwN43W1H+eSXz2KanS2Jdk+4cdw0eN1tR9dt804Q7K13YCTui3G9o2Vxxy1THT3Gndhvr1d2ug3ppMWNR0e58WgzkVHwExnT6yQynruwzHMXlvnCN71f32w63jrxl8+STccjfc+i/rz6dWLOwpuYewNwAW9i7h1a6291On+rE3MA//Y/fykSIfY2Duzuf5qmQSZlEYuZ1OveZprBBIpheF//7HrnCaz26/ScjpjK8qZXbTYd4eWLg3TEC7NFqnaduBXjukPZLumI7pnkZjqi6tez7JCOqDssLPnpiHScf/KKzaUjdhLHdRspi0qt3niPo05HbKcNW8V1XZaKlUZh/Gm/rOdGW92PZRMcy2dJJWIsLJepVOtMjqYkHRE1SqnvBz6MF1H7Pa31B7qdux0RBsjnc7v6VTYqpF97i+u6VGt+xbdavWM51I3YSRHuBxzX5fJSmaVSDX3mil8jo5dERqpRuOhYPsORyc0nMvaCQU5HoLX+NPDpqNshCL1iGAbJRIxkIsYBQpudVu0dib7tR0zDYGo8zS03HOTma7w6zHXHYfZK63ZSs1faExll5pfKHRMZx/yl2NtNZPQLfSvCgrDfCW92GiyfHmZBDoiZJtcczHDNwQx3vsg7Fq6REfjMl0M1MjomMmImRw6ONHZAOZrPMDmaaklk7AdEhAVhDwgvn67ZDqWqt/nrViyLQaRTjYxy1W6Z+LswX/TmCnxqdYcXZr15iIBUIsY1B5v1MY5NZbsmMvoFEWFB2GOC3acPjDRjb5WqvSvLp/czqYTFDUdHuaE9kXHZq8EcCPRKWyLj+YvLPH9xuXEs4ycyjrUlMvoFEWFBiJAg9kYmQbVWJ5uOs2Su3SBA8MiNJLjl2gS3XDsOBImMakvhovZExkqphn5hCf3CUuPYWDbRmPQLrIxUIho5FBEWhD4hEY8xmk1SHUtTs/2KbxUZIa+HYRiM55KM55Kcut6LOzquy+Wr5VBMrsjFy62JjKVilaXiFb51prlrycGxlLcB61TG37VkbxIZIsKC0IfErRhxK8aBkYS/Y4hNOZRDFrpjGgZTY96u3bfd5NXICBIZYWG+dKXUWIoeTmR889lmIuPQRLrFxjg0kSZm7qwwiwgLQp/TiL35OWQR5M0TTmS8wk9k1GyHmYUVz8aYK3LhcpHLS+XGCkjHdZlZWGVmYZXHvu0ds2IG1xxsrvh7yY1g4W4rkSEiLAj7hJYcsl8TuVxtXakn9E7cMrn2UI5rD7UlMi6vcGGuuXNJOJFh192WRMb/+tvnSMabNTJeqg5tuh0iwoKwDzEMg1TCIpWwQtXeOteyEHonlbC44ZpRbrimmcgolmotE3/T8yuslJqlDiq1ZiLjZ7fwmiLCgrDPMQyjsV1WUMtCBHnnyKbjqGvHUaFExtWVqrfpaqHCs+cXuTC/Qrm6tfrkIsKCMECYYUH2d50uV+tUN7mbuNAdwzAYyyYZyyYbtSMc1+XKcnnjJ3dARFgQBpROu06XKza2ZN52HNMwODia3tJzRYQFYQiQZdP9i4iwIAwZnZZNb7RbiLB7iAgLwhDT624hwu4hIiwIQmvCwmlucCoTeruPiLAgCC14E3oWIymLuuNQqkgd5N1ERFgQhK7EzNYJvXLVpiQTejuKiLAgCD0RTOjlRqBa86q8SR3k7SMiLAjCpknEYyTiMdyReKgwvUzobQURYUEQtky4hoXjulRrnhhXanUZIfeIiLAgCDuCGRJk8CyLii/KQndEhAVB2BUCyyI3AmPjaaqlqiyb7oCIsCAIu07cipFNx/2UhWzdFEZEWBCEPUW2bmpFRFgQhMgIdgrJheogh3dKHgZEhAVBiJxOdZBLlTq1+uAvmxYRFgShr+hUB7lUsakPqIEsIiwIQt/SWgc5WKVXHyhBFhEWBGFfEEzoMQI1u7lKb79H3iIRYaXU24BfBl4E3Km1/nrosZ8H3gXUgX+jtf6rKNooCEL/EghybgRqtuMvnd6fld7MiF73NPDPgS+GDyqlXgz8EPAS4PuA/6aUiu198wRB2C/ELc+uODia5uBoimw6TjwWlbRtnkhGwlrrpwGUUu0P3Qt8TGtdAc4opZ4F7gS+urctFARhPxL2kOuO07As+rk4fb95wkeBvwvdn/aPrcv4+AiWtb0Bcz6f29bz+xXp1/5jUPsWZb/qdaexSm+nc8gTE5ltPX/XRFgp9VngcIeHfkFr/VCXpxkdjm1o8iwurm6maWvI53PMzxe2dY1+RPq1/xjUvvVTv2KO01gYst0R8sREhitXVhr3j+azm77Gromw1vqNW3jaNHA8dP8YcHFnWiQIguDtFjKSMhlJNS2LUiW6Sb1+syMeBv5YKfUbwDXATcDXom2SIAiDSsw0yaRMMqGFIXtd6S2SKUSl1D9TSk0DrwI+pZT6KwCt9beATwBPAZ8BflJrPVwLyQVBiARvUi/OwbE0kwdSZFIWMbOTQ7rDr7vrr9ABrfWDwINdHvsA8IG9bZEgCEKT8H56jdKbu7TBab/ZEYIgCH1Fs/Smt1tI2S+/uVN6LCIsCILQI41dyVwAAAfTSURBVMFuIQcyCSq1OiMpiyWDbQmyiLAgCMIWSMZjjOdS1EpVqjWHctXe0nVEhAVBELaBYRiN4vRbYf8ssBYEQRhARIQFQRAiRERYEAQhQkSEBUEQIkREWBAEIUJEhAVBECJERFgQBCFCRIQFQRAiRERYEAQhQkSEBUEQIkREWBAEIUJEhAVBECJERFgQBCFCDNeNZnM7QRAEQUbCgiAIkSIiLAiCECEiwoIgCBEiIiwIghAhIsKCIAgRIiIsCIIQISLCgiAIETLUuy0rpb4P+AgQA35Ha/3BiJu0LZRSZ4ECUAdsrfUdSqkJ4OPACeAs8Hat9WJETewJpdTvAW8G5rTWp/xjHfuhlDLwPsPvB1aBH9NaPxFFuzeiS79+GfhXwLx/2n/QWn/af+zngXfhfZ7/Rmv9V3ve6B5QSh0H/gA4DDjAb2utPzIgn1m3vv0yO/S5De1IWCkVA/4r8E+BFwM/rJR6cbSt2hHu1lq/XGt9h3//fcDntNY3AZ/z7/c7vw98X9uxbv34p8BN/n8/AfzWHrVxK/w+a/sF8Jv+Z/by0C/yi4EfAl7iP+e/+f9m+xEb+Hda6xcBrwR+0m//IHxm3foGO/S5Da0IA3cCz2qtn9daV4GPAfdG3Kbd4F7gfv/n+4G3RNiWntBafxG40na4Wz/uBf5Aa+1qrf8OGFNKHdmblm6OLv3qxr3Ax7TWFa31GeBZvH+zfYfWeiYYyWqtC8DTwFEG4zPr1rdubPpzG2YRPgqcD92fZv03dz/gAo8opR5XSv2Ef+yQ1noGvH9QwFRkrdse3foxCJ/jTymlnlRK/Z5Satw/ti/7pZQ6AdwG/D0D9pm19Q126HMbZhE2Ohzb74U0XqO1vh3v695PKqW+J+oG7QH7/XP8LeAG4OXADPCf/OP7rl9KqSzwp8B7tNbL65w6CH3bsc9tmEV4Gjgeun8MuBhRW3YErfVF/3YOeBDva9Bs8FXPv52LroXbols/9vXnqLWe1VrXtdYO8D9ofnXdV/1SSsXxROoBrfWf+YcH4jPr1Led/NyGWYQfA25SSp1USiXwzPSHI27TllFKZZRSueBn4B7gNF6f7vNPuw94KJoWbptu/XgY+BdKKUMp9UrgavAVeD/Q5oX+M7zPDLx+/ZBSKqmUOok3ifW1vW5fL/hph98FntZa/0booX3/mXXr205+bkNdylIp9f3Ah/Eiar+ntf5AxE3aMkqp6/FGv+BFD/9Ya/0BpdQk8AngWuAF4G1a614nhyJBKfU/gdcBB4FZ4JeAP6dDP/xfkv+CNxO9CvxLrfXXo2j3RnTp1+vwvtK6eDGu/zMQJKXULwA/jjdD/x6t9V/ueaN7QCn1WuBLwD/ixbgA/gOed7rfP7NuffthduhzG2oRFgRBiJphtiMEQRAiR0RYEAQhQkSEBUEQIkREWBAEIUJEhAVBECJkqKuoCdGilHKBHPAo8CqtdanLeWPAT2itfy107Ca8+BPA/6e1fmC327sTKKXeAlzUWvdl5lfYe2QkLESOX4WqowD7jAE/13bsnwNf0Vrf1i7ASql+Hly8hXUKuvRxpTRhl5CcsLBnKKX+OfCreJXEPg28H28kXPBvV/FC/K8HKkBRa/0apdSngO/FW5W0ileC9D/hDSIuAm/FW9X0FeC7gbLW+k1KqX8B/CxeoP45vED9nFLqx4B3AEvArcAF4F8Dv463wukx4J1aa1cpNQr8JvAKvLD+l7TWP+WvsvwAcBeQwAvz/19a66JS6veBMnAz3hLWr+KtGLsH+J9+Hy4Dv4G3iOHDeAsCXgH8CvBF/7FbgRTwt8DPaK3r2/oAhL5ERsLCnqCUmsJbY3+v1vrVQLXDaS8D3gi8WGv9MrwC6AA/CSz5I+ZX+yPfj+KVQ3y51vo5/7xTwPf6AnwK+CBwj9b6VjwB/8+h13oFnrDdApSAP8YT5hcDLwXe4J/3YWAFeJnfpl/2j/8c3nLbO7XWL8f7Y/Dzoeufwita/hLgu4A3+sW9HwY+6Lf7D/xzX4q3wvGVWutP4gnwF7TWd+KtyprCW4ElDCD9/LVNGCxeCTyhtdb+/d8GPtR2zvN4S8h/Vyn1N8AnN/kaf6y1tv2f7wY+HapJ8N+Bfwid+2Wt9bT/8zeAs1rrqwBKqX8AbgQ+i/eH4Lv8Qi1orS/7z/kB4IBS6gf9+8m26/+51rrsX+8JvIpbf92l3d/RWn81dP8HgDuVUv/Ovz+CVxhGGEBEhIW9olOJvxa01leVUi/Bq6fwBuBDSqnbN/EaxbbXa/fawvfLoZ/rHe5v9LthAP+31vpvujy+mesV2+4bwFu01s9v0AZhABA7Qtgrvgrc5qcaAP6P9hOUUnkgrbX+DN5WOFeB64FlYGSTE26fA75fKXXYv/+v8Ea2m+WTwM/6RWdQSh30jz8M/IxSKu0fzymlXtTD9ZaB0Q3OeRh4XzBJp5Q66FfkEgYQEWFhT/BrHP8E8BdKqa/gVZhq5zjwWd8OeBL4S+Dv/KpvDwD/6D+3l9f7Fp5H+9dKqSfx/Oaf3kLT/y3epOFpv13/0T/+QTz74TH/+o8CvYjwHwLvUEp905847MR78EbP/6CU+kfgM+yDnSeErSHpCEEQhAiRkbAgCEKEiAgLgiBEiIiwIAhChIgIC4IgRIiIsCAIQoSICAuCIESIiLAgCEKE/P8WZHFnn5vwWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36aedd8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"distfromcentre\", y=\"risk\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f36ac1b5358>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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URphIuWRdj5Ch0xA1qQkbHD2T4GBXEz84M1hyzkQqG/x0qAmbaLV1qDgc0+/iwMt90hhPEARhAUSIykg7Hh0NkVnjuSrcA+PpkvHilVMOrbaORP0eQr/xeMmxIjyCIAizkdBcGR2Ns0UIoKMhWvH9kKGV/Cw/XhAEQZgfEaIyHtjTXnE8Z9cuf78hGgp+mhWPFwRBEOZHQnNl5GzZR88kGJhI0dEQLbFrl79/a7yOI5tquHQtWfF4QRAEYX5EiCpwsKupREjK7doP7GnnyJ44PzgzSP94CoXigT3t1yU+3onjs0oAgZQFEgRh/SNCtAA5u3aO/rEU//PZ84CiJmzmx3LHLEeMvBPH8yV/wC8BJGWBBEHYKEiOaAHK7drgW7YnUs6s8aNnKldQWAjvySdmD0pZIEEQNgiyIqpAcZis/+CnUM3NaLWFNuHFVu1ichbvpaL6+mYPZrJzHCtlgQRBWF+IEJVRHiZrnxxiIJtFxcmLUblVO8dyLdtaZyfqcm/poJQFEgRhgyChuTLKw2RHBl/1n+Qa5+Fbtsvt2rB8y3a+cncxUhZIEIQNgqyIAnLhOO/HP4JQoXL2gfFeuPQcx9r3k9i2LW/PhsoW73L3G7ssOGvP63zLvS4uASRlgQRB2ChoSlXOd9xMDA1NXtdN1L9+ipEvfBEIcjCZjP9GvD3vWtO6tmN+6ffmvc4s91uuSV7RdQCMzz18wwSlra2eoaHJG/JZN5r1fG+wvu9vvd8bUDl+L1REQnNA8ptFNeGKw2FFrrXFhMRmud9y55e538T5JgiCUEBCc4DTWzAKaHV1KPDFI5tF69q+6JDYLPdbzvlW5oAT55sgCEIBESLA7OrCOXc+/1qrq4O6ukWF44qZ5X4Lh/wwXzjkh+nGRn1RavJzSZLvEQRBkNAcADWfeKji+FIdarPcb7kwXyTq54oyGUBBOIz7R1/GO3F8GbMVBEFYX4gQAdH778f43MNoXdtB19G6ti/LUKDfe6jkOvrefej//F+AYQAahMMlxgXJFQmCIIhrDlieg+dU7xg/ODNYUgi1uM5csY1bXe6FpqYS5xyASibRrdvw3jgD6TREIui37VlycdNKBVNz568Fd9J887se1sK9rSbr+f7W+70hrrklITmiZVCpEGpx0dNyGzcoSAyiKCpiGuSMvDOv+2G7AE+pfJ5pMV/WlQqmrqXiqGt9foIgVB8JzS2DSoVQoVD0dJaNO5crKrZxj4364+WFTYPXiw3bVSyYuoTzV5u1Pj9BEKqPrIiWwcB4Ov88mXGYSDlkXcXgRJpTvWO8pczGnbeEj4/5OajObajpabTaWlSirGJ3YPVerMW7YsHUJZy/2qz1+QmCUH1kRbQMOhojgC9C16az+WrcCnjk5CVe6j446xytrg79nkOEvvE45pd+D/22Pf4b5cVNg9eLLW6qdXbOMb42iqOu9fkJglB9RIiWwQN72gFm9STKFUI9Zt1X8bxiO3je6l1e2DR4vVjreMWCqUs4f7VZ6/MTBKH6SGhuGeTccb/39JuAImToNERNasIGAIPheozPPTxvwdLiQqeepvmuuWjUd80tobhppYKpa6k46lqfnyAI1Ufs28xvJZ3Ppv27T9v0j81uhrelKcavv3/39UxpxWhrq2fwye+tin16uayUnXs9W4Bhfd/fer83xL69JCQ0Nw85m3b/WAqlVN6mfarX702UC9GVs9y+RKtB6tlncf/oy74lXHl5+3S1qjrk7NxrZT6CIFQfEaJ5WMimfbCriU/ds50tTTF0XWNLU4xP3bO9ZGNrtSmpLF5EtezTYucWBKEcyRHNQ7FNu2R8ohCOO9jVtKaEpxynt7e04GrYb/pHlezTYucWBKEcWRHNQ86mPWu8IXqDZ7J8tGistOBqJuO/jlbnHsTOLQhCOSJE83Az5IAWZm2ZUcTOLQhCORKam4dcyO3omQQDEyk6GqIc2RNf06G4clQqBfH22aG51Gy3341A7NyCIJQjQjQHzte+ivftx9k/Psb+xiZe/uhnOdZwJ994vpcfnBmcVW17rWJ2deFkHCgrtqrGx3A+/2+rYuXW7z0kwiMIQh4JzVXA+dpX8b72P/xVhFKcjrXzjQGdq+f6Ktq41zLFTf/U1FQhX9TYJNZpQRDWBCJEFfC+XWp5PtZ9j/9kdKRkPGfjXsuUNP0bH5vVnA/EOi0IQnWR0FwlxktXOoN1m/wnrlsyXmzjXsvkQmHZTz4Eypv1vlinBUGoJrIiqkRjae6nfeqa/8QwSsZvJhs3iHVaEIS1iQhRBfSPPVTy+nDPSf9Jc0vJ+M1l4xbrtCAIa5MNH5rzThxn5HtPke25yEvdBzlq3cdgw920/0In7znxVxzo+SkHZgbROzx+uGPbTWXjLr63XHHRhaqCC4Ig3Gg2dPXtXAFO09R5obaTR7cHfYTicbRaP5m/1mrHLZbie3OcQl7I+NzD60Z41nMFZ1jf97fe7w2pvr0kNnRorrgA59H2/YU3xgpmhZvBGVcJKS4qCMLNwoYWouICnIORxsIbmUz+6c3ijCtHiosKgnCzsKGFqNhF1p4eL7wRDuef3mzOuBzikBME4WZhQwtRsYvsyOCrhTeaCjmhm80Zl0MccoIg3CxsaNdcLmlvPv1dDvRcQJs5y7HdhxgM1980zri5KL43p+eCOOQEQVizbGjXXI7lOHi8E8fxnnwC1deXt0avxJf8Sl93vbuT1uu9wfq+v/V+b4hrbkls6BXRcslZo3PkiocC1yUaq3VdQRCEtcyGzhEtl9WyRovlWhCEjYgI0TJYLWu0WK4FQdiISGhukRTnbtS14YLFu6jzqbbbmnXsUvI8Wmcn6nJvhXGxXAuCsH4RIVoE5bkbwmEY6AelChW5MxlUIoHzta+ijj2TP3QpeR79Iw+Wfk5uXCzXgiCsYyQ0twjKczdaXR2Ypi9EaCXN5sqb6uWvsYg8j37voUITO11H69q+rmrDCYIgVEJWRAHF4bR8Fe5QPR2NEd4zqXNg1gkKzBDazp2l4+Nj0NLCX255G09vvp1JM0q9k+L9g6/wiUXMI9fELjcf9yt/iPfkEytmDxcEQVhriBABqWefzYfETjd28WjNbricgDj0K8Wjt74bzh7jwHhR/iYcqnyxxib+csvb+Pa2u/NDk2aMb3e+A/2FPj7+tsqld4oRG7cgCBsJCc0ByW8WwmkVq3A3NXEsvq/0pKZm/1GG/rGHeHrz7bM/xDT43qsDi5qP2LgFQdhIVG1FZFnWNuDPgQ7AA75q2/YfWJbVAjwO7AAuAh+3bXt0Nefi9PorHTU1xaAWg3QaNA0cB3XpAqTSDGpRVH8/NDai37YHIzAQVGoyN/n/HgfHBaXwNB1X11GexrXpDKd6xxYsGyQ2bkEQNhLVDM05wL+1bfuUZVn1wE8ty3oG+Axw1LbtL1mW9ZvAbwKfX82JmF1dZE+/DIlB2ieHGKhvA88DzwXHAU0jPjUMM0mory+p2VYpVFZfE2Ey5eApheMWqg/pmsYjJy8BzCtGYuMWBGEjUbXQnG3b/bZtnwqeTwJngK3AR4CvB4d9Hfj51Z5LzSce8vcDAYd7TgajClTwEzjc87w/PDa6YIjsZ/Z3AOB6pSXwGqK+7i/UbE8qZwuCsJFYE0VPLcvaAfwY2A/02rbdVPTeqG3bs5MxRTiOq0zTuK45DLzzfrxr1yCT4fS2/RzddpBE7Sbik8McufgPHBh4I3+seZtF/K+/O+/1vv7j8/x/PzqH6ykMXaO5Jsym+gjgr4z+5Jfunvf81LPPknz8Wzi9vZhdXdQ89HGi999/XfcoCMINQ4qeLoFFheYsy3qnbdt/Vzb2adu2vz7XOYvFsqw64C+Bh23bnrAsa8nXGB1NXtcc2trqUbssiPrhsAMkuf0f/hdMTfl7hTQNpRSnt+7j2J53Mrj5FjZ/4wUe2NM+Z4jtg3vaeLV3hP6xQodXJ+sCsKUptnDl4b0H4bcPogEuMAlMLqNa8Xqvcrxe7w3W9/2t93sTlsZiQ3NfsYoUwrKsjwMPX++HW5YVwhehR23bzlnFBi3L2hy8vxmYP461QswKhzU1gx788yjF6a17eezOn2egthUVidE/luKRk5c41Ts25zUf2NNecfxmbbYnCIKwGizWrPBJ4FuWZb0PuAv4v4Aj1/PBlmVpwB8DZ2zb/m9Fbz0FfBr4UvDzyev5nMWSMx3kXHD63n14kQhc6YN0mmO77/OFyTQhXVjlHD2TmHNVlBs/eibBwETqpm+2JwiCsBosSohs237Fsqx/AzwDGMB7bdsevM7PPgR8CnjFsqzTwdi/xxegb1mW9VmgF/jH1/k5iyZX1SBH9pMPwY5bABhs2+ZbugEymfwxAxMp5uNgV5MIzzysVoNBQRBuHuYVIsuyfrdsSAGvAw9bloVt27+x3A+2bfs55k7oXddqa6UotlG3p8cZiAaCkqu8DXQ0RKsxtXWBVJAQhBuLZVlNwIO2bf9JtedSzEI5oumyxxPAq0Wv1zXFeaMjg68W3mgqrHAk37N8pIKEINxwmoBfrPYkypl3RWTb9m/fqIlUm/lCRN5T3+FA32W0mbMc232IwXC95HtWAKkgIQirR7At5jvAacACXgMywFssy/oR8Me2bT9StQkWsVj79q/iT3rcsqxHgLcDv2Lb9vdXdXY3iOKipzA7RJQTpLcFD2FlkAoSgrDq7MTP6Q9blvUY8DLwim3b767utEpZrH37M4EIvQeI4y/t/vPqTevGUlz0tBgJEa0uUkFCEFads7ZtDwfPTwCxak5mLhYrRG7w8z34e35OLOHcNU+u6Gk5EiJaXaQRoCCsOrssy9oUPL8bGGQNtv9Z7IRmLMv6D/j7iQ4Fe4DCC5xz02B2deGcOz9rvDhEJDbj1aHcMr9U5L+LIMzLBeD3Lcu6FT9H9A3gIcuy/hJ4zLbtv6zq7AIWK0SfAf4l8Ou2bQ9YltUNPLpqs7rB1HziIVJf+OKs8VyISGzGa5OFcnuCIDBl2/YvlI19sCozmYfFbmh9k6KSPrZt9wCzv7lvUqL3348xNlOxtxDMbzOWL7zqMV9uT/67CMLNw0IbWv+LbduftyzrL8j1QyjCtu2Pr9rMbjCVQkS5sI/34x9BKARNzWh1dfn3q51D2uhhKcntCcLc2LZ9Ebiv2vNYDAutiJ4Lfv71ak/kelAj1/wacLEatFBoRa5ZEo4LhfyyPolBFOTFqJo2YwkXLi63JwjC2mehDa3ftSzLAHbatv1bN2hOSyeVgmwWRkdRZghiMf8RjaJpy2sLUhKOa2qGRFBab2wUAiGqps1YwoUL5/YEQbg5WDBHZNu2a1nWu27EZFYEJwuTWZic8PsIxWp8UaqpQdMX7zgv3vWv1dX5ccmxUchm0bq2l+SQqoFUJVg4tycIws3BYl1zf2NZ1q8Bfw5M5QZt276+jnSrjVKQnPYf10DV1kJtHVps4T1d5bv+tbo6fyVUUwNK4X7lD/GefGLOvMxq52+kKoHP9dq/BUGoPotdIvxu8BjAbxY6FfxcEySzHu5iWp5PT/t5nr7LqOEhVHIa5XkVD620619NTaESCV8AlJfPy3gnjpccl8vfLHTc9SBVCQRBWC8s1r69pqsoTGU9xtIuMVOjxtSImAtM13V9UZr2C4i7xFFpIBrL55TKG+VpndtgaAhmZi8Cy/MyNyJ/U2l+EpYShJuXK1u3vQ+/fNpO4DzwJ1uvXF4X9TwXYs2VelguCkg6iqSj0DWPqKERMTUihoaxgGHBm5qG0WnQdVRNDdTU8uJQmh9MtjJw8BfoeE+EB/a085Z/98uzP3dqCnXyONlPPpQPwd2o/I2EpQRhfRCIUHH9zluB/3xl6zauV4wsy/oF4NfwvyZfBr4F/Ef86jjXgE/atj1oWdb9wB8EpyngXbZtT1qW9evAx4EI8J3VMK6t6ZXOcvGUL0qjKY+BaZdE0mEi7ZJ2PNR8ITzPg6kpXnz1Eo88e5b+4SmU69A/NsMjJy/xUvfBksPV1JTvplOqJARHNFLx8hstfyMIwqKZq0fQP7uei1qWtQ/4D8Bh27ZvB/41/racu23bvgP4JpBrcPprwP9p2/YB4J34pd3eB+wC7gIOAHeuhnltXQpROVkPJrOK4ZRH/7TLtRmjdbjJAAAgAElEQVSXqayH41UWpR9cDsJvngeOC5ksZLMc674bpYpySmOj/s+m5tILzLECk/yNIAhzsHOJ44vlMPDtXAVu27ZHgE7gacuyXgF+HdgXHHsc+G+WZf0K0GTbtgO8L3i8CJwCbsMXphVlXYTm9P6r0NgChrHgsQpIuYqUqxgHwjqEal08pdA1De/UT+nvCaEcx98kG4lAOg2Ow4BpoB28Cy5fQiUG/YvF20uqLQCQSmF87mHJ3wiCsFjO44fjKo1fDxqzq+L8P8B/s237Kcuy3g38JwDbtr9kWdbf4Neie96yrAeC879o2/b/vM55zMu6EKLGf/VZVDhM9pZbcXbuItu9m2z3Lry29jlXJzkyHoykXCaTLqHXX8H4i8dp3f1+ErEmtHTaNzSYJug68Ykh1OvHMf7pp9EP3onzX7+EGuj3RUvX8/uUtM5tkr8RBGEp/AmVe7z96XVe9yjwHcuyft+27WuWZbUAjcCV4P1P5w60LKvbtu1XgFcsy7oHf/XzNPB/W5b1qG3bU5ZlbQWytm0nrnNeJawLIQLQMhnC9uuE7dfzY15dPdnu3TjdgTjt3IVqaKx4vlIwc/wEKhTj4Mh5vrvt7eiuh6bpaK6Hpusc7n/Zv+6xZ9AP3ol+5L243/i678JzXZSmga6jv+9nUEotu6qDIAgbi61XLn//ytZt4OeEcq65P71eo4Jt269ZlvU7wLOWZbn4Ibb/BPyFZVlXgOeBW4LDHw6an7rA68D/tm07bVnWHuCkZVngb935p8CKCpE2b/L+JuHcw59X+ps2oZ430afm397ktsXJ7vRXTE73brI7umlob2ZifAbnS78DQQ7oTGMnz9d1ca22iU3TY9w91cv+sV7CnkNYudR+8b8Q0v1QnnfsGdTAAFpHB/rh96IfvNP/sGhQaigWRQtVp31TW1s9Q0NrZsvXirKe7w3W9/2t93vDD2kJi2RdrIhSD32KbDoLSqEPDRLqeZNQz1nM82cJXTiHlsnkjzWGEhhDCaI/8eu5Kk1Hbd9O/Y5bSWeS2NFWjm1/G4loI63jCX72lWfYM3wRNm3CjUaZMcKk4nGmky6aBuE9BwjtO4Cha+gaGJqG6SkMXYPUjP8YBWUY+VJDxfuVBEEQNjrrQojyaBpevIN0vIP0PYHD0HUx+3p9Uep5E7PnLGbfJbSgooKmPLSLF4hdvEAMeAdwx6t/R1/jZnpbttLbvJWYl2XHyFVoafELqd7t536UgrSrSLtQng/UNDADYTJ0MDQPI50lND6JqZMvyko0BuGwCJMgCBuW9SVElTAMnO234Gy/hdR73uePpVKELp3H7HmT0PmzhC+cQx/oz58Sdh12jlxm50hhA6qn6WSSYzh33oWTniE7PopqbC7/tDxKQVZBFuVHXMuEykhOYerTmHogVrEoRiSCGw7jhSOgaeiahqEXHrqIlSBUBaUUimDLYNFzL0ht+DtBFJ6C2rRDTWT9f7WuJOviX+vajEMNClNf5Bd1NErW2kvW2ssM0NAY46cv93LiuZfZMXiBXQM9dA9fpD49nT9FVx7R1DQc/6H/ANzWONmdt/q5pu7dZFJpvJ++gBoeRmttRbvnEPq+/RWn4Cpwi1dTmSRQVD7IMCEawTtzBvV3P4LBQfSOdswH3k/o7W/LixSArpF/HTL0/LggbAQWIxKFceUfV+l5cCxK4QEUXWMpqXQRoqWzLswKd//W00rXYFPUIB4ziNcYtNcYxGMm8RqD5og+72riUgoee2WE4ZRD1sPvbeR6dI9cYnfiAlbiHHf0vUbIc9DnKJIK/v/QTiRGNlZLpqaWbKwW92P/B/pbb1/WfXmvvYr31Hf8F7ruKw46+qc+g5EzRFTAN+/5pY3a2+oZHZ3GLFpZmbq2LkKB6znhDevj/iqJhKcUrZvqSQxNlojAjRCJG8GWjgbiDdGb/xfsBrJuZNtTMDTjMjTj8tpI6XshHdpiBWHKiVU8ZtAQ1vlRr7/yqQvpjKYDodE0els6mQ7X0jmZ4FpjHHbsIPKxjxM6XzBDmL0XC/kmIJSeIZSeoWZsGAD1X3+rZG+T070bt33zgvubANTJomrdnof/G+ii/uYp1PYuCIWDh4lmFjrTqmC15aKYTjtMpZxZ19Zz5oog5JdbVeUEzNc9CQneTOT+qFzMHxmLDTX57/vjxaLgCwGo/DHB+8XXnK+aVijFyHRm7gOEDcW6EKIPddczMJlhMOkLUcot/Q3IenB12uXqtDvr3Kih4eEbC0wNYoZGOuPhahoo+Lkzx9gzegmamyGbxe3agdu1g9S7g3xTOo156TzGV79CKDlFeGYaM5POX1/zPELnbELn7PyYV1uHc8utJeLklZcJAtTwcMX7VcNDkM74j9wY+OE80wAz5Lc3D4XmrK3nef5flo678J+TWiBSWlEI0JhDsHRtcV+Eq42nFK6n8JTyNVz5z5Xy70dDA81fZGr495a/T/x7yL1e7Od5XtGXO4Uv4uLXuS/xkteUfsHnv/DDIYYn08F44Ys+d1LuuNxn5NCC/wYaENxmkXiszVWEsHaxLGsHcK9t248t47y/tm27cn6iiHUhRD+/q8G3b+P/Yk5mFYNJh0TSJTHjBj8dEjMuTllkLSdaJX+bmUHRUs/gf7/1vbyQGic+M048otg8laUtZhIxgi+oSARn9x7St70FNeTv8dIch/DMNKGZacJelnAmjT4xnr+8Pj1F+NXThF89nR9zW1oLe5t27sK55Va01tb8NYvRWlsr/0O4jv9IF4TQzUyhpjO+MIVDeZHS9IXLIeVQiqJ+T4sUrpxQzSFWuefz5bPyX84U/ir3BcZ/Hk5mGEtm8FRBCHLPV/KLVltAXBf663+5pLIumfL/YRdBbkUi3Fzc/VtPz2oD8fxvv38ttIHYAfwTYJYQWZZlBjXprot1kSN69Sevq2wqveB3pKf8ityJGdcXqkCkriZdRlOzV0vz0RTRC/momEnrcB+tzz5Na3oCs6gwqv7hj6Lv3Yc+PETo/NnAqXcO88I59NTMnNdXmobb0krG9cjE6sjW1JKNxPzKDR/+6JwmiHIaGmNMjFf4HF3zRckMVlFGyK/VZ+hgGGg3cD+eHohR7hMXKyQtLbWMjEwvfOBNynq+v/V8b8vJEQUiVKnEz7+/XjGq0AbiP+KXFGoDhoB/Ztt2r2VZfwZMAG8DOoDfsG3725ZlPQ/sAS4AXwdGgZ8FokAtcAS/cerPBJ/xBdu2H99wKyKttQ0tWNkolF9yR+WC2B5+TAJ0z2MTHps8xZ6gLA+eR2N9hOfsYZ69OsPQjEvY0GjKJMmMj5PQaxiL1M36zLG0x1ja482xbDDSBAceQlOKTelJ2rwZ4pvq6WiMEx/N0F63iea72tDfEdSf81yMK30Fceo5i3n5IprrC6KmFOa1IUygZuyaf2+aTnbzFpwXjuOMDJHt3oXbscU3MiwVT0Em4z8qoDT8UF8oECgzFIT9zCWtphY1lTmqoAvCBmK+NhDLFqKiNhCHbNseDmrNfR34c9u2v25Z1i8Cfwj8fHDKZuA+/DpzTwHfBn4T+DXbtj8UXPMzwD3AW23bHrEs6x/ht4i4HWgF/sGyrB8vZZ7rQoiK0dD8L9Al4Pa8xt5nvsue/n7e6NrH8zvuZLi2kdZ4K+/f1sCtzWGGJlIMTqZJTGVJJLMMTvvhvqls6Zeo0jSGow0M08CZGeDsVP49XYOw7uceGsIa+5pbuP2t76L97vfQENbRslnMS+cJ9Zz1Ber8Wcz+K/nzNeURvtpH+Gof8DcAeDW1Qb5pV76unte8adn/foUbARzHf1R4C0MHTfd/6oYvhoYOmgGmDroJhr7iorUYvBdP4R39PgwMQEcH+pH3od9xcOETBaF63LA2EEFB0weD9x/BX83k+Cvbtj3gdcuy2ue57jNBSwnwhet/2bbtAoOWZT0LvB1/9bUo1p0QLRXvxVNMffPPwfF4o34LT8VugcER2KQxhMaTb4zwkTu2sG97B51F5ykUOA7JmQyJiVTwSDM4lWFoOstgcrZpwlOFnNSMoxhMznDsih82ixhaEOrbTHx3J+0HHqAtZtJBisbec4TOnyV07k3M82cxcn2QAD05Tfi1lwi/9lJ+zG1uyeea9LfuR2vvQtXUruw/nOsBHiwQHS6YKHKCZYChFQRLW9lwoPfiKbxv/FlhoP9q/rWIkbCGuZFtIMopfj9d9Hy+X8jiuOp1/+KKEB39Prm/2U9uKur3NDEBsRp/vGeEfZsbSs7T8HMstfUhbqmv5ZatZdd1skxOpxkan2FwIsUz58YYT7s4nsLxZv+fkXYVl6ccLk/N/mavNTuJd24nvusDxGM6Hd4MW4YusfXSG9T3vIHZc7Yk32SMjmC88DyRF56Hb/mBYGdLp2+CyK2cum7xw243AtcJqkvMj9IIhCp46EYQDjT8FRYQWNrm7D3lHa0cxfCOPSNCJKxlbmQbiBPAJ/BXQ5/E79g6H5NA/Tzv/xj4Zcuyvg60AO/Cb7gXXewkN7wQMTAABpyp38Ib9VvI6CYK0FCEE2PUkmVophbfOFLKa/0TnOi5xvBkhtb6MPd2b2Lf5oZ8aKh2YIDajg52HnkfP47U0hbOWWc9XM/DcT0cV3FHW4TBpG+cGE65lKdMph3FhQmHCxPFItUF8S4aOz9A/AM67SrNlskEWwYu0vnGT9l6/jXCbjZ/tHm1D/NqHzznV4VQhomzfQfZnYU2Ge7mrcvLN60UinxLjcXgTNegptKBMPkrLtXX5+cFi/8O1IC+PtT4eGE1ZuiBHS7YKBy08NC0DdG0WFhjPP/b7//+3b/1NJS1gbheo8IcbSB+BfgTy7J+ncCssMBlXgYcy7JeAv4M36xQzHfwc0Yv4f/W/YZt2wOBWWFRrAvX3Gs9Qyq7iP0wlXB+70vYSZ2/6riDgUgD2aL8kqk8NKXYPj3Er969ueQv6tf6J3jyxauzrvfh+iS3/dWfzRr/03d/hiGjZtZ4vCHKP79vh99cz83ipjNcm0gxOJEmMZUmkfQdfkNJl9G0twjztI+mPFpSk3RMDbNlfICtU9foHOhh8/ggbVPXMCr8d/eiMZydtxZ6OO3cjdeyaVGbb6tBJUeg+7X/WdnyHo9jfPaXF3dhvUis9ECwKoUU9UC8VikPtp6dZev53qSywtLZ8Csi/cj7OHH8AjA70OmiY+L6Vb3LQjsneq5VvN7JNwa4rcL4PRd/ylPd75w93t3if7ZpgmliRmK0NzSSyxIq1wEnC+kMmWSKoYkUQ2X288SMy0SmdL+J0nSuxRq5FmvktbbukvcM5dGenmDzyFW2Dl9m8/ggmycG2TI+SMvrrxB+/ZXCv0FTSyBKu/x9Tjt3oWpnuwjXCto9h1C5skjF43cvoVuup4DFhRMhWHiVi1fukTNw5ERslcVLEG5GRIjuOMjoJQ1Gx1Gahum5uJrh5yuApmySrGZAUXVugOHJyrbnYa/yP+ltva+hfewhTvaMMDyVprUuwj3dLbNyT+Vohukn+yMxIg2NbO1QbM1kIJuBTBYyaXA9Zhx/f1T/N/+SoUg9iWgTiVgjQ9FGZnIbdANcTedqtImrW5r46Za9Je9Fsmk2TwwG4pRgy/ggmy8PsuXV12hKT6EBzuatftminDht3wnh6jT+Kye3v0o9f7xQfPbuuYvPrhjLFi+dfImH3OsgTOgaDmp6xn8/P07wXM+HJCWcKNzsrAshaqoJo1D5Ui25kipQKNmSK7aY25WvlMrnYjbHNPpGFSHPxdF0wMXF/+We0kM0zoygRkdx/uJxuHQBBgZove19JJo70GKl4bZWvdRsoEZGfOOD8tj9+V9mdzQC036VbW3HLXgfe2hJSXQNDcIR/xEY4ZTjEMtk2J5J02lMoK6czR+vmzoThBnavIPhIx8KNvMWqk1kyzbup0MRLm7q4uKmrlmfXZue9oUpt4I6fprNf/t9OqaHCW/enF8xZbt3427Z6oezqoC+bz+stvCsBJ4ir1wVBMwzPai0GbmMElHT53rkVmOGCJiw5lgXOaKhocll34R34jinH/sr/rzzXpKayXC0AUfzv0ANz0VXHi3JMX7hje+z99KrePE4Xm09rzZ08kTn21GbWkvEqDhHpEZGYDTI6xm6b3lWyn+eK1K6aRPGL/3LFXN0eS+ewn3kT4MNvZ7/sY5XsRqDpxRjaX8lNZR0GZwplEUannFZStqtKTnO5vFBtgSrqY6ZUeL1UVq2tKF330p25y68Ta0rmm+as2rEOuGG3F/5frBgNbbaIUXJEQnFrIsV0fXgPfkEd0z04V7yOFp/C6Mde1GGhqY8Ik6G+vQUNU6G45v38Y6zP4HEFbTObbzrmk2DM8Mx7S4Gd9xGvD7K/VYrb9nSSKYR0t//HpmLF4IKBUah2gP4gpT7l5+YWFFrce463rFnYKAfc9tW1F33oe/ZA6lUiSNN1zRaogYtUYPbymquup5iJO0GZonSmn0jqdmmibGaRsZqGjmzeXfJuKY8WgdG2GK/SntqjPaIRltzLa2d7TTeugO9fv7QpLDKLHI/WI6K+bByM0d5nkzXb2jJKOHmY8MLkerrw0tOcfvF46iGXl6K78bVdZSmkzbDoNVDaopErNkvJprJooaGIJ3i9vPnuf31E5hf/D30e4t6Dt1/CO4/ROauA3iOi6tpuJpO1giRNUyyeqgQiclmZ+Wfrhf9joN5QWoq/suzEdyf/j3qme+jBgbQWlrmbN5n6BptMZO2mMm+skINWVcxnMoJVLCKSrokprOMl32hKU1nqL6VofqyQq1jYPz9FPFkHx0qRTym09raSOvWOPGGKE0L9JASqsgS82FQtEcsECZXzaDGU0Gui6L82By5sMBiL+HE9cmGFyKiUbzz5zjdYfHY7R8iq5s4+c2TfiI5W9NEczIIsSkPhof8zaC6Dkrh/tGXAdDvLTizvBPHwfPQlYeuIKQcok7B4OA5MRzdIBurwYm34hoa7jIt6IvFe/EU6rFv+Ldm6KixUdTf/jXEYui7LH/j6SIIGRqba00215pAwQjhvfYqyb/+a4aiDSRiTQxFGhgK1zG8aQsJvYYpo9TQ4Oom/XWt5GV4DBibBqYJew5xPUu8NkxbSy3x2hDtMYO2GpP60Ppo7LehKNsj5iV1SC497KggEC4d0AvPcyFFil8vIG7BuSJu1UeEKAgyHeu+B/A3shbj6jq66xX+Z825IRzHd4oFfYS8p75TKkRPPgHNLb5oFZ8HYBjoKMKeQ7g2hvmhD6I3RHE9RdpxSWc9Uo634sVAy6sOaMEvo/rJSbT73uVbxTNZSKf81d8iN5bmUCePE/WybEteY1uyYG/Xkv4enqmsR2IyzfCVBEOJMRLTWRIqRH+shXSo1NmX0U36MOmbBqZTQCr/XkyHeK3J1sYwzSbEa0zag0roMVO+VNY9ikJIcYUuB8xyLc4rbgTv6YgtfwUQIUql0TvaGaxvBU+hNN23cOs6KohrtyTHyIQi0NoG14JmdZoG8Xa0On9Pjeq7nL+kd+I43skTfmXrcNgXLc8rlKXRdf8Lv7HRNxEEAqY9f4Lwk08Q6uujrrOT9IceZObA20j9wwsrU8RzYGCOcX9NohkmxEyIxfx7coLeRpk0pGYWrFg1dyM/f7wupFPXEmNny3Zge+GA5DTT5y8x3NvP0PAEiZTLQKieq00dDNa34ZQVsZ3x4NKkw6XJ2Su4+pBGvMYkHgvaxQct49tiBmFDVlHCPBS7GJeJAjwjDQ3bVmRKG4UNL0RaZye8eYb2iSEG6loJuQ5Zw0R3/b+2TM+hxs3Q7s2gtbWh0qm8wOREyL+O/z+ed+J4EKoL+mlqmh/GiwdbVMdG88cCqGPP4O3dB5AP8QFwuZfIf/8y0cPvJfPDoziaQUY3yV7pxXnkT/FYRhHPjg7on10Ngo7Nlf9tgk221Nb6dvhUCmZm/BVThdXakhv55aippXb/Xmr3783LkzY+SqjnLMb5HzJ2uZ+h0SQDkXr6Gzu42tBOf2OcobpWvLKSRJNZxeR4lp7x7KyPaYnotAX9o3Kt4ttrDDZFjXkb9AnCkpC2JktmwwuR/pEHcf/dr3E4+xyP3fnz1KcmGaktWMga0tNgGBzuf80faGqGxGA+JJe/zoc/CgQhueLjcuQqZldoCe499Z05ezd7336cUEsLIeUR8wpfrt73/wr3vneQzPihvEXd65H3lVamzo0ffu+C52poEI1BNOaLUjoNqTSkkkGYZIWqGgSoxmYyB++Cg3cRATqVYvtgP+b5s4R63sR88XtwuZdEtJH+hjj9je1caWynv6Gd/sb2kv+GOUbSHiNpD3u0VKR0DVqjRr7RYW4VFa8xaBbThCCsOiJE9x5CbdrEgYuvA4pjuw+hARkjRNjNckvqGu+9by+3T2qoSR197z748Efh3JuovstondtKwmuqrw8Ara7Oj2SNjfphOE2Dpma02tntGFTf5bnDXuNj0NIye959vUTCJjVhk6zrMZNx/W6ujjdnd9Nyazcdm9EPv3fJKysNDSJR/9HYiMpkYGYG/a1v9e9nNaoaaBpuxxbcji2k770fgIbaEDWvvYF1/iz7z71J6PyPMfouoymPlBlmoCHO1UCcrjb6AtXf2M5EtLSQsKfw7ekzLoyUfmxIh7ZYQZjyXXnFNCEIK8aG39AKYPxfn2fmR8/mu5We3nwbx7rv4ULLNjI1dYQ3tdAdr+WBPe0c7Gqadb534jjek0+g+vpQ14Znhe3U1JR/7WQSUL4gFYf1uraDUqjLvbOurUZG0IqESE1N+eKmaeh334v+kQdLTBJKKVJZj8m0Q9bxbvjGQZVJ++G7meSiQxTea6+iThaJ1xyW8nIqbfjUUjOYF3qClZO/ejKGS8OFk5HaQJzi/s9NW7nauo2BmmZS2uL/NosaWok4xWMm7TUGbTGD2tD1mybW84bd9Xxvm7duomNXl/yFsgQ2/IoIoOYTDzFz+mVIDHJ68208duDDJENRRmqaIByC6QwkFP1jvnOrWIwKOaGAcBgSg34ribo6XzgSg36OKHiv+H0ohPVKrhOgf+wh1LFnAArXAoi3oy73zrKOa5pGLGwQCxuksi6xiMmoNmfkb8XRgvJDqrHRzyUlZ+Y1OnivveqHJgPUUCIf3lvOSkpFY2T37Ce7Zz+5rzltfMxvLNjjd72t7XmT3UPn2T1U2nNMAWOxRq42xrmydRd9nbvob97CQLSRhGvilN1DylX0Tjr0VjBN1IW0Cqsog7aYSURME4JQgggREL3/fszf/I+4f/JVjrXcBZrGRE2DL0KBHXMi5VATNjl6JlEqRLmcUEA+JJfJBO64TKm7DvwVzfgY2t59JWE98PNF5SE/b+8+f/zk8bxlvHhFVW4dz99XyKC1PoI3EyXteKQdv/9R1vNWfc+SH76LQSSGUp4vSDPJ/Kozhzp5vOL56vnjK1YvTjU2kbnj7WTueHswoDASA/lVk9nzJqGLPWiZDM0z4zTPjLNv4Cz8tHANRzdIdO+j79a3cmXLTvobOxgwakmkPK5V6CE1lVVMZbOcn5htmmiK6AVXX06sagxaowammCaEDYiE5oC2tnqGhiYB+MyfvsB4Mo1TlP/XlYehPLpGr6LrGl965X+hhaNonZ14L57y/+LPZDm9bR/Hdr+TwZom2scTHJm6yO2vPgeRiL86yGR9cWtq5qVt+/nhh/85A+NpOhojc4b9isl+8iF/Q205uo7xL38lHx7UOjvzIbvieyvGU8oXJdcjefInJJ9ZAXv4IlCu4wtScgYcB+dLvzPnPZmf/w/zXmtFwzuui9nXi9ljE+o5h3n+LOblS2iV5hagQmGyO3aS6ra4esternbsYCDcQGLGIxG06RhNL36vi67Bpqgf2muvMehqidGgubTHTJqj68s0IaE5oRhZERXxrRf6GJ1OzwpjecFm1mQoys7hS3DuHKq1DTU54W9YNU1Ob93LY/s/6MfAsg4Dda082rIFNdDPgQsv+jZow4BMhtNGM49tvw8tCPX1j6V45OQlgHnFSOvsrJhHIhotCesVh+z4yAcqXkvXNMKmhvn3Jwl/9cvUaTpJI0zy6pW8s241xEgzTKhrgLoGVDaLtnkzqv/qrNjhgpbvlcYwcLbfgrP9FlKHg7FUitCl8/6KqecsofNnMRKFvVhaNkP47BuEz75BA09yG+DV1ZPdeStO926yO3czvaebwVBDXphyRWUHkw5T2dJ79hQMzbgMzbi8PgL0Fb6oTS0wTdSUGifaawwawrqYJoSbGhGiIv73qwMYnke2QskPw3OZiNRy+M2gvfvoCKRrfIFxnHxlhnwyJNi8emznO3whct3CWP7YUsrDfuXoH3mwYh5pTuv3U9+ZU4jyxwShRVN5NDgp6p0UM0aYmaPfw12lVVEOLRRC/8DP4n3jz1Ce8ldGnutvv1qG5XvFiUbJWnvJWnsL+abJCd8+HghTqOdN9MmJ/Cn61CSRl18k8vKLADQBHa3xgjh178bZ242Kxkhmvbw4FVc+TyRdUmWhU0dBf9KlP+kCpeHNiKEVGSYKuaj2mpUxTQjCaiNCVMRkykFXXtCqoPQvzIiToSE1yYErr/vvu64fatN1ME0G69uKjtbyopOo2+RvaHUcfzwcYrC5w6+0UMbARGrWWDG5PFB5Hsn9yh9WPL642sNc5OzmRTOnxs1Qc+lNaIgwlXaYybirZnaYZSlv34Z23/3ou3b5e5XWGKq+gcyBt5E58LZgQKEPJ/wV0znbzztd7EErmrsxnPCde39/wj9F03G3bqO++1Zadu7m1u7dONu2+3/U4DsfJ7OKad3gfCKZt5Ynkg5DM+7sHlKu4vKUw+Wp2aaJWjNw9hVXm4j5r8U0IawVNrwQneod40ffep7LFwZwo824uTpSJfht95qT48FLVag75SkwTdqnrzFQu8k/tyhMEk+O+GJVV5evqNCenWIgFkdNT8HYWL5SQ3t75dVQsT1c6+zkpeypG40AACAASURBVPc8yFGt1c8vTUY43H2Q28+9MOu84goOczFXuE/r3IZp6DTXhGmMKZIZl2TaIbsKJofiauHFKM+FmZS/aTZduSNu1dE0vLZ20m3tpO++zx9zXYwrvQWn3rk3MfsuoQV/fGjKw+y7hNl3idizRwE/3+Ts2Jnvemt276Zz9042m+XhO7+HVK7y+VBRm47hCqaJaUdxYcLhwsRskWoM6yXClFtRtcYMQmKaEG4gG1qITvWO8ec/eA0G+kliotBQmo4fXiv8Ihqei2OYjNQ0cXrrXn9VZBj+KicIuR0+e5zHDnzYP0GpwvjQGX+sqKLCkcFXebTtg5Ao2t+SyfCe557Au2V2Fe/icNyLE/Do6QTEQauto38sxTd23IcaSnBgvFRQcrbw+Zgr3Fd8rq5p1EVM6iImacdlOu2Syq7eKimHphtQW+uXGPLcYH/SzCzn3ZrDMHC7bsHtuoXUu9/nj6XTmJfOEwryTeb5s5iDhfYfWjZD6OwbhM6+kR9TdfU03nIrTrfflt3p3g1NzfkeUnsoq2buKa6l3FldeBNJ3zRR/p9rPOMxnvF4c6zU2acBLVGd9mAVFS8Sq03rzDQhrA02tBD94MygvyJxXCZqGzGVC55vTlC53zWliDkZ6lOT1GRTHLPu48Bwj1+BNxzyV0S6xoF+G8y/5dju+0jUNBOfGubIRA93tMfg0L8oqcRw54c/iva95ziqtTEYbaQ9Nc7hxGscGO+tXMW7iKPtgaV5bAxqfQu3VlvHD+97kDtemW39Xoi5wn1znRsxDSKmgespkhk/bLcaq6RyfFGqg9q6vPNOC2nATeK8ikRwdu/B2b2nkG+amiR0/lxghvAf+sR4/hRtapLIKy8SeeXF/Ji7qZXszt0FcbrlVlSN3yHY0IOCrzUm+8t6SGVcxdBMUf+oQKyGZlwmMqWxPgVcS3lcS2V4vew2TA1ay8QpF/JrFNOEsEzWrH3bsqwPAH8AGMDXbNv+0lzHLte+/avfepm+K8PMmJGFDy5H+bW5Dc8l5GZIm2E83QA0DM+lcWaCjokEU5FaEvWtOIaJ6TrsHL7EP3rpb/1V1UIYZqm1WSk+/3P/HhX8sicjNUxGasnqJiE3y7/54VdLrnt6616OWe9ksKmD9ulrbE9c5FLzVgbrWmmfHOLw+ec5sLkOLZtBnT0LgLZrN8Znf2lRIpYLGWauXCW9bTupnRaZixdR53sKhWF3ds+yg3svnlqwmrjzF4+jnv5b3xTieYUuoJrm76VqbUVLp1CJIf/92lq0w+/F+JkPLvzveoNZdOUIpdCvDftmiPNniV7qQTv7JnpqbrFVmoa7pdM3QeTEqWtHoRV9wOvX0pwYmGF4xuP/b+/OgyS56gOPf7My6+zuqjm75z56jrcajaQZIQkQBFiHJSFYJORjQUEYjG1t7OL1snbYC8axy8auNvB6DTjWC6wPAthAlr2xrNFiwBYI2+gCBCshdLw5ekaj0cz0nJqZ7jozK/ePl3V0dfVR092VldW/T4RCXVlZ1fk6e+rX773f+7016Rg3r0uzZ7X5vS+41SnBqTlYFVpX8c4iaVtBOaSpgWokbTPQUg5pPunbV1pxYzHM9vOai6Rvd64nA5FSygYOAD8LHAd+CLxPa9320/tKA9H7P/fEtB1FF1OsWqVqWWbKKLhCp+qxZvIcv/rkQ/MLRi0+dcsDnMoOm8oPTYU9Ha/Custnuf+Zr7LvtRdNOvkN99WfzydSnM+sZNXkBTKVRlLE/c98lX0nXzYf7jXDIzgf/b1Zg1HrkGGt6oObXUGhWGYykTFp76tXY2UyxN7/QWL7rzdBqF3h1eB5CILQ/3rYbNTntinLX5ubq6l9wDk21r0/h/2zd9QLsYattXJETezd75nzQzWbS3PpwgT2ideCTL0Dpgd17AjWLHtF+Y6Du3WUyo5duKO7eWn1Vv5yIhsMOzfcMzow64drLWniTJB6XutB1YLVPGvtApBxrKasPoetq9MM4TGctkm12UNqIT+3hXrxXImvjU0vizXXz6tGAlHnenVo7ibgkNZ6DEAp9TBwD0wbKViQS+7UuaDFVtuiwHwX88HpxWJcSg7y2O63XFEguvXA4zx0w31cbincmS1NANTf97Hdb53y/KWkOf9yanBKIGp7Ha9fmLFaQ71tLUOGterizvmzDDkOA+UCk4k0E5ccyGSoPvaoCUQtm/PV3y94HjA9IWgfhGDmGnaeh//3j2G9/wOm5l0+bxYbh1iWf8GVI2I23qYteJu2wNtvN8fK5WC+6SDxsQM4Y4dwTr5Wf4nluvWhPvgb3ghcm0jz6sh2Xlk3yrGR7RxbN8qTp+xZP1gtyyKbsMgmEuxoyaOp+j4XS1XGC1Pnok4XPM4WPFpHa/Ouz9FLLkcvuUAJjjQ+6HOJ2LRe1JpnfsJqyybuT/0dWMyKGzN58lT7ntqTp4rz7hWJzvRqINoINOceHwfeONPJK1dmcJzOd0b0lzAIzfY9XTvO6Snp3vNXCxqfvuUBwPSEsqUJMmUTXGrvO97y/rXN5Sr21CGb2vlTxvYrFezxE6xdOzXYTXnd+Alo+kvWrZgK477nYcXj2EC2UmDg9SKldauZPHOKlasGOHf29JTX1Z09zapVpjL56YlLZufYuX4YrXwzr2LeZwBYhe/7+KUifj6Pny/Q7RGAixfOt2/vhfNkc+k5X9/+nDSs3Q837AfABdyJy8QOaKyDmtjBA8T0y1gXGqXE0+UCu199kd2vNv7ouDi4gsG9V1PdrfB3K6q7lEkOmacVTNnesM6r+pwtuJyccDk1UeHUhMvJiQrjkxXO5r0ZkyYONu8hteV2rM0+K8sTDBcvMlK6yNriRUZKlxm1HdZmnCXbQ+pC+QK2M/29L1Sq87pnonO9Goja/YbN+Aly4UL+ir5JDJ9ql4ORhY/jVRi+fOaK32Pfay9y3Wsvcio7PO252vuOXD4z5XnHc3FtM5fU7vwpH9CJBN7IhralgWq8kQ1T0r79eNzMC9n2lPeKxeNkinnSw2spF8qwei3eqZPT33DD+nqVcH8wC5cuTT9nLpbJNGtfbTyFn0madPBC3mzy1wXVlavabxY4vGbOOZLOyuA4sONq899dmPmm8+eIjx3ghe8/z5rjY2wZP0Kq3Gh3buJ1ePoJ7KcbvTZ3w6YgQ2+XmXfast2sg+tQGhhNwWgqDmviwRGoeD5nih4T2Bw9lzfDfUFP6mJr0oRlcT45xPnkEC+zqfHEt0/U95AayUyvNrFigXtIrUxYnMlPH3dcm7HndT8GBjNX/L2Xq14NRMeB5kUwm4A2W4suzDVZi+eu4PNuvqbMEQXsapVsaYJbD7Qfspmv2hDd9ONPtH0+W7rM+cxKhooT089v2eWUFSvnTP2elvZd2whw5SpoqjRQS1t33v0eEimH9DvvZOLzn2XCSeE2zVk0b85n3Xm3mSNy7PnNEdXYDtadMycrWFhmG/R0UIi1FpSWcOHsYm4W2Nk3tqiuXkNp9RrKo2/gc2OTWH6V4fMn2TJ+hC2nxrjuwlEGTxzD8hoTpc6J4zgnjsPj3wXAtx3crdsamXo7duOt3zj9d2ae4rbFhgGHbC7N7oGpwaLoVutzUeOvnOD00ROcSeUYT6+YllA0ZQ+pcy3fI8aUjL7hjM1I8HhwHntI3bwu3XaO6OZ1qStqs5hbryYrOJhkhduA1zDJCvdrrV9od/5Cip7+3peeuLJgNGfW3EXWXTrDZHKA8aasuR1nX+G+577BvhMvmQSB2lqkdmwHMunG836wM2qwoPbZ0et5bNuNJl388hluPfBEm6y5t3J6xTqGJ88HWXMbOD24xpw/9gP2rR/Aciv4Bw8AQdbchzrImmtK+2bnbjh0gOrLL5keRzJJ7Ko90yuMB6/LnzjFxOZRvNvu7CxrLpmA1UHW3Nmz5vmhIax33YvzC/+so9sIS79wtvrCT69os8DFLAxqssCKnCt4rE7b3LwuZeY7ymWcV48SP3Sgsftt03xT2/akM7ijOxs9p9HdVFetnrKQey7zzpp7+gmqZ8+SH97Amf1v5szI1ik1+07nXcodJE2kHatt5fPhtE26aQh1xp/XPEiyQud6MhABKKXuBj6DSd/+gtb6wZnOXczq2/0mCm3Ll10mip1XbViKTf/qQanNlhXdFlaFamtyAufIYeKHtcnSO3QA+/Xzs77GW7Gqnj5usvV24Q8Mznj+YrXN930ultunn59pkzQxm2wiVq983rzZ4dq0TaKDckgSiDrXs4GoExKIZhalthUrpmpDyZ1f1Yal3n3W91yTdTeZD2oFdlcvbZUQO38uSB9vbDAYK8w+N+uu34g7aobzKqM7cbeO1pcJdKNtVd/nfNFsyTHeUvn8fHF6pYmZWMDKZIy1GZuRlsrnq1P2tKQJCUSdk0BEtD6sOxXFtlWD7c4L5dmDUje3QffdiikvlC+Y9U1d0EuBaJpqFfvUiWBjwaDn9MoY1iwB27cd3C3bqOzYRXzv1Vxavw1vw8b65pPdVKn6nCt4jBdMssR4wa2XRWpNmphNLWmi3oPK2Fy1eRV3vu0qCUQdkEBEND+s5yvqbfOqPoWyx2TZxW0ZZ+lmIGrmVyrBGqX8ki6c7elA1E6lgnPsaFAZ4gDxw4ewTx7HmuUzpppK427faYbzduymsmMX1VVrOppvWmxFtxqUQwq26KgN9xU8Jivz+6h5+j/cKYGoA72aNScEYOqnDaYcBlMO+bLHRLHSldp2s7HiccjlIJczC2cLwTboIS6c7QnxOO4Ok8AA7wTAyk/iHDlUH86LHz6Iff5s/SWxYoHES8+TeOn5+jEvtyLYWDAITqM78QdnXtO22FJOjM1DMTYPTU9bn6xU6wt4x/PelMSJUsi/l1EmPSKi32uYTT+2rRaQhnKZUHpE7fj4Zjv4fMHMKy3CP6vI9YjmKXbhHNlTx6g8/9Og93SIWH72++iOrK/3mCo7duNu3Q6J3qly4Ps+l8om/ZxMhl+84xrpEXVAekQicjIJm0zCZmAoyeWL+dB7SBCsUUqmIZk2QalYbGxbIaaorlxNddsmJq/aHxyoYo+fnLIlu/PKGFalsfjaGT+JM36S1JP/AIBv27ibt+KONoKTt3FTKPNNYCqT5JI2uaTN+o25UK4hyiQQicjKJB2GsykKZY/LJZeK2xuFTi0sSKUhlcZfUYViCfKTPbnjbE+IxfDWb8Rbv5HSW28xx9wKzquvmC3Zg2w9+7VX6/NNlucRPzpG/OgY6ce+BUA1mcLdvsP0mII08uqa4VDnm8T8SCASkZdO2KQTdj39u1iZuTJ1t1lWrFHNoYfWKPU8J467fSfu9p0Ub38HAFYhb9Y3jZldb+NjB7HPNUplxUpFEi+/QOLlxrr3ajZn1jaNNpIh/KFs15sjZieBSPSNVNwmFTeb9k2UXCZL7pLvItuJKTvOuq7JupvsXjp41PnpDJU911DZc039mHXxQmM4LwhOsclGGavYpYskn32G5LPP1I95w+vMcF4tOG3bAcnemW9ajiQQib5jxyxy6TiDSacnAxKA5TgwmIXBbCMdvDApmXcd8nMrKV9/E+XrbwoO+Ga+qVau6PBB4kfHsCqNHqh9+hT26VOknvqeeUkshrtp65Qt2d1NW8AOZ75pOZJAJPpWFAISNNLB/VzWDN3lJ5ak5t2yYFl46zbgrdtA6ea3m2Oui3P8lXr6ePzQgWC+ycwpWtUq8WNHiB87Qvq7Zr8sP5Ggsn1nfa6psmM31bUjMt+0RCQQib4XmYDUXB3cdYklgYnyzEVxxfw4Du62HbjbdlC89S4ArGIB58jhRnA6fAD7bGO7DqtcJqFfJNG0KXR1cKixJXuwzsnPSobcYpBAJJaNqAQkMEN3dm4Ay3PwS8Vg6E5SwReLn0pTuWovlav2UvupWhdfb9TSCzL1YhONNXixicskn/sRyed+VD/mrR2mEqSQ1+ebRMckEIllpzkgTZZcJno4IAFYyRQkUyYVPF+QrLsl4udWUN5/I+X9NwYHfGJnxoNt14PKEEcOYTX97O0zp7HPnCb1/cfNS6wY3vbt8L2/D6EF0SWBSCxbdswim46b8kGl9vXseollxaZm3eUnTVBawnp3y5plUR1eR2l4HaU3v80c8zyc48dModfDh3DGDuK8+kpjvsmvwtjhEC86miQQiWUvZjXq2RUrHvmyWYvU070kx4FsDrI5M3RXq3fXw9fcF2wbd+t23K3bKd4aHCsWib8yVq8MkTp7KtRLjCIJREI0aV6LVKh45Eudb9jXbY2huxWN0kKloqSCd0sqRUXtoaL2UAAGNq4O+4oiRwKREG3YMYvBpMNg0qHiVZksuRQqVao9/OE+pbQQwbbytaAkw3eih0kgEmIOcTvGikyCnO9TqFTJl11Kld7+YDdFWE1PCQi2qyiayuCSDi56jAQiIebJsqx65W/Xq5Ive+QrHl6PD90BWImk2TYhl8Mvl818kuyhJHqEBCIhroBjx8imY2TT8cgkONRYiQQkEpBbgV8Ktj+XNUoiRBKIhFigWoJD1fdNL6ns9cyWFHOxanso1dYo5SehaR8gIbpBApEQiyRmTU1wqAWlXk5wqJm6RqlWhFXWKInukEAkxBKI2zFy6RjZlEMxSHAo9niCQ43lxJvWKMnQnVh6EoiEWEKWZdU37vOqPvmyS77s9XQFh2YydCe6QQKREF1ixyyGUnGGUnFKrke+5FGISoKDDN2JJSSBSIgQJB2bpGObtUnBXFI5KgkOMnQnFpkEIiFCFLMsBpIOA00JDoWyhxeBBAeQoTuxOCQQCdEjagkOuWBtUiphY1nI0J3oexKIhOhBqbjN2mwKr1AOhu56v/hqjQzdiU5JIBKih9mxxhYVZbdaz7qLQi8JZOhOzI8EIiEiIuHESDgJcunoFF+taR26iyUtmCjJ0J0AJBAJETlti69GKcHBiWPnBrA8R4buBCCBSIhIay2+OlkyxVejQobuBEggEqJv1Iqvul6VyQjVuQPJulvuJBAJ0Wecpjp3hYrHRDE6GXfQmnVXDIKSDN31MwlEQvQpM5fkkEmYjLuJkhuZPZNqrGCXWRm6628SiIRYBhJOjFVOAq/qM1lyI5XcADJ01+8kEAmxjNgxi2w6zlDKoVCpMllyI1PjrkaG7vqPBCIhlqHmFPCKZ4btChFaKFszbeiukIdyOezLEh2SQCTEMhe3Y6zMJMimTCXwibKLF6HkBpChu6iTQCSEAKaWEzJrkqKzq2wzGbqLHglEQohpamuSaltTTJbcyA3bgQzdRYUEIiHEjGpbUwylHApBQIrSmqSaqUN3rkkDl6G7niGBSAgxp+YN/KJYSqiZ5TgydNdjJBAJITpSG7bzqj4TwZqkqJQSaiVDd71BApEQ4orYMYtcOt4oJVTyqERsTVLNtKG7Qt70lLxo9vqiRgKREGJBWksJTZZcChErJdTMchwYysJQtjF0VyxARNsTBRKIhBCLprZ5X7bqky+7TJaiVUqoVX3ozq9CoWiSHGTobtFJIBJCLDo7ZjGUijOUiptsuwjtJtuOZcUgk4FMRobuloAEIiHEkkonbNItpYSibMrQXblsglIhDxHu+YVNApEQoitqpYRyaZ/MQIJLF/O4EVyT1MxKJCCRwM/loFQ0mXeWFfZlRY4EIiFEV8Usi6F0nJFsimLF7CQbtX2SWllYEGx7HhvOhn05kSOBSAgRmuY1Sf2Q3CCujAQiIUToaskNg8lobm8uFkYCkRCiZzSvSSq5jVJCUR62E3OTQCSE6ElJxybp9EcpITE7CURCiJ7WT6WERHsSiIQQkdBvpYREQyiBSCn1B8A/BcrAYeCXtdavB899DPgVwAN+Q2v9t2FcoxCid/VbKaHlLhbS930U2Ku1vhY4AHwMQCm1B3gvcDVwF/BZpZQd0jUKIXpcLdtuJJtk5UCChBPWR5pYiFB6RFrrv2t6+DTw88HX9wAPa61LwBGl1CHgJuCpLl+iECJCzLCdTaallJAM20VDL/z58CHgm8HXG4FXm547HhwTQoh5qZUSGsmmyKXj2LaU3Ol1S9YjUkp9G1jX5qmPa62/FpzzccAFvhI81+43Zs6/aVauzOA4CxvBW7t2aEGv72XStujq5/Z1s22FssvlghvZ7c373ZIFIq317bM9r5T6APAu4DatdS3YHAc2N522CTgx1/e6cCF/pZcJmH8QZ85cXtB79CppW3T1c/vCaJsFOJ7Jtssv4bDdhnVSa65TYWXN3QX8W+DtWuvmKPII8JBS6lPABmAX8IMQLlEI0YfidowVmQTZtE++7DFZciNfAbwfhDVH9MfAEPCoUupZpdTnAbTWLwB/BbwIfAv4sNZa+tJCiEUVsywGkw4j2RSrBxOk4r0wXb58hZU1t3OW5x4EHuzi5QghlrFaBXDXqzJZ9qSUUAiksoIQQgCOHSOXjpFNOfVhO6kA3h0SiIQQoollWQwkHQaSUgG8WyQQCSHEDJorgE+WXCZl2G5JSCASQog52DGLbDrOUMqhUDGVG6QC+OKRQCSEEPPUXEpIKoAvHglEQghxBaZVAC97eJLccEUkEAkhxALUKoAPpeIUyh5JqQDeMfmJCSHEIkknbHKZRNiXETkSiIQQQoRKApEQQohQSSASQggRKglEQgghQiWBSAghRKgkEAkhhAiVBCIhhBChkkAkhBAiVBKIhBBChEoCkRBCiFBJIBJCCBEqCURCCCFCJYFICCFEqCxfdnQSQggRIukRCSGECJUEIiGEEKGSQCSEECJUEoiEEEKESgKREEKIUEkgEkIIESoJREIIIULlhH0BYVJK3QX8EWADf6a1/mTIl7RgSqmjwGXAA1yt9Q1KqVXAXwLbgKPAL2qtL4R0ifOmlPoC8C7gtNZ6b3CsbVuUUhbmXt4N5IEPaq1/HMZ1z8cMbfsE8GvAmeC039VafyN47mPAr2Du629orf+26xc9T0qpzcCXgXVAFfgTrfUf9dG9m6l9n6AP7l8Ylm2PSCllA/8deAewB3ifUmpPuFe1aG7RWu/TWt8QPP4o8B2t9S7gO8HjKPgicFfLsZna8g5gV/DfA8DnunSNV+qLTG8bwKeDe7ev6UNsD/Be4OrgNZ8Nfn97lQv8ltb6KuBNwIeDNvTLvZupfdAf96/rlm0gAm4CDmmtx7TWZeBh4J6Qr2mp3AN8Kfj6S8C9IV7LvGmt/xE433J4prbcA3xZa+1rrZ8GViil1nfnSjs3Q9tmcg/wsNa6pLU+AhzC/P72JK31yVqPRmt9GXgJ2Ej/3LuZ2jeTSN2/MCznQLQReLXp8XFm/2WKCh/4O6XUj5RSDwTHRrTWJ8H8IwKGQ7u6hZupLf1yP39dKfUTpdQXlFIrg2ORbZtSahuwH/g+fXjvWtoHfXb/umU5ByKrzbF+KLz3Fq319Zjhjg8rpd4W9gV1ST/cz88BO4B9wEngD4PjkWybUmoQ+N/AR7TWl2Y5tV/a11f3r5uWcyA6DmxuerwJOBHStSwarfWJ4P+ngf+DGQIYrw11BP8/Hd4VLthMbYn8/dRaj2utPa11FfhTGsM3kWubUiqO+ZD+itb6q8Hhvrl37drXT/ev25ZzIPohsEsptV0plcBMJj4S8jUtiFJqQCk1VPsauAP4KaZdHwhO+wDwtXCucFHM1JZHgF9SSllKqTcBF2vDQFHRMi/yHsy9A9O29yqlkkqp7ZhJ/R90+/rmK8iC+3PgJa31p5qe6ot7N1P7+uX+hWFZbwOhlLob+AwmffsLWusHQ76kBVFKjWJ6QWBS8x/SWj+olFoN/BWwBTgG/ILWer4T5aFRSv0F8DPAGmAc+PfAX9OmLcGHwx9jspLywC9rrZ8J47rnY4a2/QxmWMfHpDf/89oHslLq48CHMBlbH9Faf7PrFz1PSqm3At8DnsekNwP8LmYepR/u3Uztex99cP/CsKwDkRBCiPAt56E5IYQQPUACkRBCiFBJIBJCCBEqCURCCCFCJYFICCFEqJZ19W0RPqWUDwwBjwNv1loXZjhvBfCA1vq/NB3bhUkHBvivWuuvLPX1Lgal1L3ACa21rCURAukRiR4RVCtuG4QCK4DfaTl2H/Ck1np/axBSSvXyH1n3MkvRS6nMLJYbWUckukopdR/wnzGVp78B/EdMj+hy8P88ZnHjrUAJmNBav0Up9TfAnZjV6nnMFh5/iPlj6gTwc5jV7k8CbwSKWut3KqV+CfhtzCLDw5hFhqeVUh8E7gdeB64FXgP+FfAHmJXvPwTer7X2lVI54NPAjZgFjN/TWv96UJHjQeDtQAKzwPFfaK0nlFJfBIrAbkx5l6cw1QTuAP4iaMNZ4FOYxZ2fwSySvBH4T8A/Bs9dC6SA7wK/qbX2FnQDhOhB0iMSXaOUGsbU4LpHa30zUG5z2nXA7cAerfV1mM3jAD4MvB70nG4OekCfx2wfsE9rfTg4by9wZxCE9gKfBO7QWl+LCWL/rel73Yj5cP8nQAF4CBOc9gDXALcF530GmASuC67pE8Hx38GUo7lJa70PExA/1vT+ezGbvV0NvAG4PdgQ7RHgk8F1fzk49xpMJYw3aa2/jglC/6C1vgmzWn8YszJfiL7Ty8MXov+8Cfix1loHj/8E+P2Wc8YwJZf+XCn1GPD1Dr/HQ1prN/j6FuAbTXXL/gfwXNO5T2itjwdf/z/gqNb6IoBS6jlgJ/BtTDB8Q1DMEq312eA17waySqmfDx4nW97/r7XWxeD9foypzPzoDNd9UGv9VNPjdwM3KaV+K3icwRTPFKLvSCAS3dSuHP4UWuuLSqmrMXXXbgN+Xyl1fQffY6Ll+7WOPTc/LjZ97bV5PNe/Dwv4l1rrx2Z4vpP3m2h5bAH3aq3H5rgGISJPhuZENz0F7A+y3QB+tfUEpdRaIK21/hZmK+mLwChwCch0mITwHeBupdS64PGvYXo4nfo68NtBcU6UUmuC448Av6mUSgfHh5RSV83j/S4BuTnO0+MdTwAAALdJREFUeQT4aC1xQSm1JqjcLETfkUAkuibYI+kB4P8qpZ7EVCJutRn4djA09hPgm8DTQbXwrwDPB6+dz/d7ATNn86hS6ieY+ad/fQWX/m8wiRQ/Da7r3wXHP4kZivth8P6PA/MJRP8TuF8p9WyQTNHORzC9qOeUUs8D30J29RR9SrLmhBBChEp6REIIIUIlgUgIIUSoJBAJIYQIlQQiIYQQoZJAJIQQIlQSiIQQQoRKApEQQohQ/X9G6rvABcgerwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36ac1bc390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " sns.lmplot(x=\"distfromcentre\", y=\"risk\", hue=\"pt\", data=df, palette=\"Set1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pandas has a get dummies function for this which would be more elegant\n",
    "df['pt'] = df['pt'].str.replace('case', '1')\n",
    "df['pt'] = df['pt'].str.replace('control', '0')\n",
    "df['pt'] = df['pt'].astype(int)\n",
    "\n",
    "df['es'] = df['es'].str.replace('Yes', '1')\n",
    "df['es'] = df['es'].str.replace('No', '0')\n",
    "df['es'] = df['es'].astype(int)\n",
    "\n",
    "df['exposed'] = df['exposed'].astype(str)\n",
    "df['exposed'] = df['exposed'].str.replace('True', '1')\n",
    "df['exposed'] = df['exposed'].str.replace('False', '0')\n",
    "df['exposed'] = df['exposed'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f36abff9cf8>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36abff9b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"risk\", y=\"pt\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f36abef2710>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36abe958d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"risk\", y=\"pt\", data=df,\n",
    "           logistic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36aab9fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "X = df[['distfromcentre']].copy()\n",
    "X['constant'] = 1\n",
    "\n",
    "y = df[['risk']].copy()\n",
    "model = sm.OLS(y, X)\n",
    "result = model.fit()\n",
    "\n",
    "# Create a plot just for the variable 'Poverty':\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig = sm.graphics.plot_fit(results, 0, ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   risk   R-squared:                       0.011\n",
      "Model:                            OLS   Adj. R-squared:                  0.008\n",
      "Method:                 Least Squares   F-statistic:                     4.197\n",
      "Date:                Thu, 12 Jul 2018   Prob (F-statistic):             0.0412\n",
      "Time:                        12:46:04   Log-Likelihood:                -1553.8\n",
      "No. Observations:                 375   AIC:                             3112.\n",
      "Df Residuals:                     373   BIC:                             3119.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "distfromcentre    -0.0572      0.028     -2.049      0.041      -0.112      -0.002\n",
      "constant          10.6770      1.016     10.512      0.000       8.680      12.674\n",
      "==============================================================================\n",
      "Omnibus:                       90.881   Durbin-Watson:                   2.027\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              154.232\n",
      "Skew:                           1.501   Prob(JB):                     3.23e-34\n",
      "Kurtosis:                       3.928   Cond. No.                         46.8\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.567830\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                     pt   No. Observations:                  375\n",
      "Model:                          Logit   Df Residuals:                      370\n",
      "Method:                           MLE   Df Model:                            4\n",
      "Date:                Thu, 12 Jul 2018   Pseudo R-squ.:                 0.06872\n",
      "Time:                        12:50:28   Log-Likelihood:                -212.94\n",
      "converged:                       True   LL-Null:                       -228.65\n",
      "                                        LLR p-value:                 2.507e-06\n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "risk               0.0017      0.010      0.167      0.867      -0.019       0.022\n",
      "distfromcentre     0.0321      0.008      4.114      0.000       0.017       0.047\n",
      "years              0.0014      0.004      0.390      0.697      -0.006       0.008\n",
      "es                 0.1937      0.259      0.747      0.455      -0.314       0.702\n",
      "exposed            0.0995      0.321      0.310      0.757      -0.530       0.729\n",
      "==================================================================================\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)\n",
    "y=df['pt']\n",
    "X=df[['risk', 'distfromcentre', 'years', 'es', 'exposed']]\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    2.5%     97.5%        OR\n",
      "risk            0.981636  1.022232  1.001728\n",
      "distfromcentre  1.016953  1.048552  1.032632\n",
      "years           0.994443  1.008377  1.001385\n",
      "es              0.730222  2.017199  1.213674\n",
      "exposed         0.588420  2.073539  1.104587\n"
     ]
    }
   ],
   "source": [
    "params = result.params\n",
    "conf = result.conf_int()\n",
    "conf['OR'] = params\n",
    "conf.columns = ['2.5%', '97.5%', 'OR']\n",
    "print(np.exp(conf))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}