{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objective:\n",
    "\n",
    "3) Using Pandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add in the numpy,pandas, and matplotlib modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make printing of dataframes a bit prettier\n",
    "pd.set_option('display.colheader_justify', 'center')\n",
    "pd.set_option('display.precision', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cases based on CDC report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3310 1390.2 1191.6 0.5384615384615384 0.4615384615384615\n"
     ]
    }
   ],
   "source": [
    "#total_population = 331 million\n",
    "# summarize in terms of 100,000 individuals\n",
    "# young < 18\n",
    "# mid : 18-49\n",
    "# old : 50+\n",
    "total_pop = 3310\n",
    "# percentages in each age range\n",
    "mid_per = .42\n",
    "old_per = .36\n",
    "sum_per = mid_per + old_per\n",
    "# percentages in each age range relative to their sum\n",
    "mid_frac = mid_per/sum_per\n",
    "old_frac = old_per/sum_per\n",
    "\n",
    "total_mid =  mid_per * total_pop\n",
    "total_old = old_per * total_pop\n",
    "print(total_pop,total_mid,total_old, mid_frac,old_frac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[76237, 2666, 155], [13030, 146, 7]]\n",
      "[[25176, 3443, 851], [9779, 805, 181]]\n",
      "\n",
      " Totals \n",
      "          Pos   Hosp  Death\n",
      "UnVac  101413  6109  1006 \n",
      "Vac     22809   951   188  \n",
      "\n"
     ]
    },
    {
     "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: center;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Pos</th>\n",
       "      <th>Hosp</th>\n",
       "      <th>Death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">18-49</th>\n",
       "      <th>UnVac</th>\n",
       "      <td>76237</td>\n",
       "      <td>2666</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vac</th>\n",
       "      <td>13030</td>\n",
       "      <td>146</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">50+</th>\n",
       "      <th>UnVac</th>\n",
       "      <td>25176</td>\n",
       "      <td>3443</td>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vac</th>\n",
       "      <td>9779</td>\n",
       "      <td>805</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">All</th>\n",
       "      <th>UnVac</th>\n",
       "      <td>101413</td>\n",
       "      <td>6109</td>\n",
       "      <td>1006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vac</th>\n",
       "      <td>22809</td>\n",
       "      <td>951</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Pos   Hosp  Death\n",
       "18-49 UnVac   76237  2666   155 \n",
       "      Vac     13030   146     7 \n",
       "50+   UnVac   25176  3443   851 \n",
       "      Vac      9779   805   181 \n",
       "All   UnVac  101413  6109  1006 \n",
       "      Vac     22809   951   188 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pos', 'Hosp', 'Death'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# COVID cases June 20 - July 17\n",
    "# for each age range, create 2D arrays for weekly averages of total cases\n",
    "    # rows are UnVac, Vac\n",
    "    # columns are Positive Cases, Hospitiliations, Death\n",
    "#from CDC report Sept 10, 2021\n",
    "\n",
    "mid = [[76237,2666,155],[13030,146,7]]\n",
    "print(mid)\n",
    "old = [[17083+8093,1755+1688,290+561],[5027+4752,234+571,23+158]]\n",
    "print(old)\n",
    " \n",
    "# Create the pandas DataFrames: 3 columns, 2 rows\n",
    "df_m = pd.DataFrame(mid, columns = ['Pos', 'Hosp', 'Death'],index=['UnVac','Vac'])\n",
    "df_o = pd.DataFrame(old, columns = ['Pos', 'Hosp', 'Death'],index=['UnVac','Vac'])\n",
    "#sum all values into new data frame \n",
    "df_t = df_m+df_o\n",
    "print('\\n Totals \\n',df_t,'\\n')\n",
    "#merge all three into one dataframe, df_a, using keys based on age\n",
    "df_a = pd.concat([df_m, df_o,df_t], keys=('18-49','50+','All'))\n",
    "display(df_a)\n",
    "print(df_a.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18-49     76237\n",
       "50+       25176\n",
       "All      101413\n",
       "Name: Pos, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "18-49      7\n",
       "50+      181\n",
       "All      188\n",
       "Name: Death, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show how to get to specific values in MultIndex Pandas dataframe. \n",
    "#Can use combinations of keys and define all case in  dimension using :\n",
    "display(df_a[\"Pos\"][:,\"UnVac\"])\n",
    "display(df_a[\"Death\"][:,\"Vac\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FkuYAT6T1T4mIdWlzXwVmAv2Am9ID4CLgUklLyFrsJnTCRzMzMzOruE4P7gAi4vvA95skryZrxSu2/jRgWpH0emB4kfR3ScGhmZmZWU9SkeCuJ6qbekOlq9AuS6cfXukqmJmZWRu0es2dpGMk9U+vvyfpakn75V81MzMzM2urUgZU/HtErJL0UWAM2c2BZ+RbLTMzMzNrj1KCu8bBC4cDMyLiOmCz/KpkZmZmZu1VSnD3N0m/Bo4FbpTUp8R8ZmZmZtbJSgnSjgVuAcZGxGvAtsA386yUmZmZmbVPq8FdRLxNNlvER1PSWmBxnpUyMzMzs/YpZbTs94FvA2empN7A7/KslJmZmZm1TyndskcBRwBvAUTEi0D/PCtlZmZmZu1TSnD3XpqXNQAkbZFvlczMzMysvUoJ7uak0bJbSzoZ+BPwv/lWy8zMzMzao9XpxyLibEmfAt4AdgP+IyJuy71mZmZmZtZmrQZ3qRv29oi4TdJuwG6SekfEmvyrZ2ZmZmZtUUq37N1AH0mDybpkvwjMzLNSZmZmZtY+pQR3Sve6Oxo4PyKOAoblWy0zMzMza4+SgjtJBwPHATektFa7c83MzMys85US3J1OdgPjayJikaQPAHfkWy0zMzMza49SRsveTXbdXeP7Z4DT8qyUmZmZmbVPKaNla4BvAXsCfRvTI+LQHOtlZmZmZu1QSrfsZcBTwBDgLGAp8FCOdTIzMzOzdioluBsYERcBayLiroj4EnBQzvUyMzMzs3YoJbhrvFnxMkmHS9oXqO1IoZK2lnSlpKckPSnpYEnbSrpN0uL0vE3B+mdKWiLpaUljCtL3l/R4WnaeJKX0PpKuSOkPSKrrSH3NzMzMuotSgrv/lLQV8K/AvwG/Ac7oYLk/B26OiN2BfYAnganAvIgYCsxL75E0DJhAds3fWOACSb3SdmYAU4Ch6TE2pU8GXo2IXYFzgJ90sL5mZmZm3UKrwV1EXB8Rr0fEwog4JCL2j4i57S1Q0gDgY8BFafvvRcRrwDhgVlptFnBkej0OmB0RqyPiWWAJcKCkQcCAiLgvIgK4pEmexm1dCYxubNUzMzMzq2bNBneS/lvSV4qknyGpIy1hHwBWAL+V9Iik36T5a3eIiGUA6Xn7tP5g4IWC/A0pbXB63TR9gzwRsRZ4HRhY5LNMkVQvqX7FihUd+EhmZmZmXUNLLXefBS4skv5z4PAOlLkpsB8wIyL2Bd4idcE2o1iLW7SQ3lKeDRMiLoyIkRExsqampuVam5mZmXUDLQV3ERH/KJL4D4oHT6VqABoi4oH0/kqyYO+l1NVKel5esP5OBflrgRdTem2R9A3ySNoU2Ap4pQN1NjMzM+sWWgru3pY0tGliSnunvQVGxN+BFyTtlpJGA08Ac4FJKW0ScF16PReYkEbADiEbOPFg6rpdJemgdD3diU3yNG5rPHB7ui7PzMzMrKq1NEPFfwA3SfpPYH5KG0k2z+zXO1ju14DLJG0GPAN8kSzQnCNpMvA8cAxAms92DlkAuBY4JSLWpe18FZgJ9ANuSg/IBmtcKmkJWYvdhA7W18zMzKxbaDa4i4ibJB0JfJMsGANYCHwuIh7vSKERsYAsUGxqdDPrTwOmFUmvB4YXSX+XFByamZmZ9SQtzi0bEQtZ371pZmZmZl1cKTcxNjMzM7NuwsGdmZmZWRVxcGdmZmZWRVoN7iR9SNI8SQvT+70lfS//qpmZmZlZW5XScve/ZLc/WQMQEY/hW4uYmZmZdUmlBHebR8SDTdLW5lEZMzMzM+uYUoK7lyV9kDQ3q6TxwLJca2VmZmZm7dLife6SU4ALgd0l/Q14Fjgu11qZmZmZWbuUEtw9FxGflLQFsElErMq7UmZmZmbWPqV0yz4r6ULgIODNnOtjZmZmZh1QSnC3G/Ansu7ZZyX9QtJH862WmZmZmbVHq8FdRLwTEXMi4mhgX2AAcFfuNTMzMzOzNitphgpJH5d0AfAw0Bc4NtdamZmZmVm7tDqgQtKzwAJgDvDNiHgr70qZmZmZWfuUMlp2n4h4I/eamJmZmVmHNRvcSfpWRPw3ME1SNF0eEaflWjMzMzMza7OWWu6eTM/1nVERMzMzM+u4ZoO7iPhjevl2RPyhcJmkY3KtlZmZmZm1SymjZc8sMc3MzMzMKqyla+4OAz4DDJZ0XsGiAcDavCtmZmZmZm3XUsvdi2TX270LzC94zAXGdLRgSb0kPSLp+vR+W0m3SVqcnrcpWPdMSUskPS1pTEH6/pIeT8vOk6SU3kfSFSn9AUl1Ha2vmZmZWXfQbHAXEY9GxCzggxExq+BxdUS8WoayT2f9oA2AqcC8iBgKzEvvkTQMmADsCYwFLpDUK+WZAUwBhqbH2JQ+GXg1InYFzgF+Uob6mpmZmXV5zQZ3kuakl49Ieqzg8bikxzpSqKRa4HDgNwXJ44BZ6fUs4MiC9NkRsToingWWAAdKGgQMiIj7IiKAS5rkadzWlcDoxlY9MzMzs2rW0q1QTk/Pn82h3HOBbwH9C9J2iIhlABGxTNL2KX0wcH/Beg0pbU163TS9Mc8LaVtrJb0ODAReLqyEpClkLX/svPPOHf5QZmZmZpXWUrfssvTyZeCFiHgO6APsQ3Y9XrtI+iywPCLml5qlWPVaSG8pz4YJERdGxMiIGFlTU1NidczMzMy6rlJuhXI30FfSYLJr4b4IzOxAmaOAIyQtBWYDh0r6HfBS6molPS9P6zcAOxXkryULLhvS66bpG+SRtCmwFfBKB+psZmZm1i2UEtwpIt4GjgbOj4ijgGHtLTAizoyI2oioIxsocXtEHE82CndSWm0ScF16PReYkEbADiEbOPFgallcJemgdD3diU3yNG5rfCpjo5Y7MzMzs2rT0jV3jSTpYOA4slGopeZrq+nAHEmTgeeBYwAiYlEa3PEE2f31TomIdSnPV8laEfsBN6UHwEXApZKWkLXYTcihvmZmZmZdTilB2tfJZqS4JgVaHwDuKEfhEXEncGd6vRIY3cx604BpRdLrgeFF0t8lBYdmZmZmPUmrwV1E3AXcJam/pC0j4hngtPyrZmZmZmZt1eo1d5L2kvQIsBB4QtJ8SXvmXzUzMzMza6tSBlT8GvhGROwSETsD/wr8b77VMjMzM7P2KCW42yIi3r/GLl0nt0VuNTIzMzOzditlQMUzkv4duDS9Px54Nr8qmZmZmVl7ldJy9yWgBrg6PbYju5GxmZmZmXUxzbbcSeoLfAXYFXgc+NeIWNNZFTMzMzOztmup5W4WMJIssDsM+Gmn1MjMzMzM2q2la+6GRcReAJIuAh7snCqZmZmZWXu11HL3fhdsRKzthLqYmZmZWQe11HK3j6Q30msB/dJ7ARERA3KvnZmZmZm1SbPBXUT06syKmJmZmVnHlXIrFDMzMzPrJhzcmZmZmVURB3dmZmZmVcTBnZmZmVkVKWVuWTOzktRNvaHSVWiXpdMPr3QVzMzKxi13ZmZmZlXEwZ2ZmZlZFXFwZ2ZmZlZFfM2dmZmZdWm+nrdtOr3lTtJOku6Q9KSkRZJOT+nbSrpN0uL0vE1BnjMlLZH0tKQxBen7S3o8LTtPklJ6H0lXpPQHJNV19uc0MzMzq4RKdMuuBf41IvYADgJOkTQMmArMi4ihwLz0nrRsArAnMBa4QFLj1GgzgCnA0PQYm9InA69GxK7AOcBPOuODmZmZmVVapwd3EbEsIh5Or1cBTwKDgXHArLTaLODI9HocMDsiVkfEs8AS4EBJg4ABEXFfRARwSZM8jdu6Ehjd2KpnZmZmVs0qOqAidZfuCzwA7BARyyALAIHt02qDgRcKsjWktMHpddP0DfJExFrgdWBgkfKnSKqXVL9ixYoyfSozMzOzyqlYcCdpS+Aq4OsR8UZLqxZJixbSW8qzYULEhRExMiJG1tTUtFZlMzMzsy6vIsGdpN5kgd1lEXF1Sn4pdbWSnpen9AZgp4LstcCLKb22SPoGeSRtCmwFvFL+T2JmZmbWtVRitKyAi4AnI+JnBYvmApPS60nAdQXpE9II2CFkAyceTF23qyQdlLZ5YpM8jdsaD9yersszMzMzq2qVuM/dKOAE4HFJC1Lad4DpwBxJk4HngWMAImKRpDnAE2QjbU+JiHUp31eBmUA/4Kb0gCx4vFTSErIWuwk5fyYzMzOzLqHTg7uIuJfi18QBjG4mzzRgWpH0emB4kfR3ScGhmZmZWU/i6cfMzMzMqoiDOzMzM7Mq4uDOzMzMrIo4uDMzMzOrIg7uzMzMzKpIJW6FYtYp6qbeUOkqtMvS6YdXugpmZtaNueXOzMzMrIo4uDMzMzOrIg7uzMzMzKqIr7kzM+vGfG2pmTXlljszMzOzKuLgzszMzKyKOLgzMzMzqyIO7szMzMyqiIM7MzMzsyri4M7MzMysiji4MzMzM6siDu7MzMzMqoiDOzMzM7Mq4uDOzMzMrIo4uDMzMzOrIlUd3EkaK+lpSUskTa10fczMzMzyVrXBnaRewC+Bw4BhwERJwypbKzMzM7N8VW1wBxwILImIZyLiPWA2MK7CdTIzMzPLlSKi0nXIhaTxwNiI+HJ6fwLw4Yg4tWCdKcCU9HY34OlOr2h5bAe8XOlK9DDe553P+7zzeZ93Pu/zztdd9/kuEVFTbMGmnV2TTqQiaRtEshFxIXBh51QnP5LqI2JkpevRk3ifdz7v887nfd75vM87XzXu82rulm0Adip4Xwu8WKG6mJmZmXWKag7uHgKGShoiaTNgAjC3wnUyMzMzy1XVdstGxFpJpwK3AL2AiyNiUYWrlZdu37XcDXmfdz7v887nfd75vM87X9Xt86odUGFmZmbWE1Vzt6yZmZlZj+PgzszMzKyKOLgzMzMzqyIO7tpBUj9Jd6UpzpB0s6TXJF3fZL3Rkh6WtEDSvZJ2bWW750t6s+D9NpKukfSYpAclDS+hbkslbVfw/hNN61Ukz52SxjRJ+7qkC1orr0mevSTNbEuecuiqx0PSDyT9V5O0EZKebOPn20zS3ZIqOgCqyH5el/blAklzC9YbIukBSYslXZFGq3e07B53XremUsej2s7r1jTdzyntDEnvStqqIO39c1LSSZJ+0cp2N1onnbPN3m8tlXFfk7RNJb0kaVAbP9dsSUPbkqcryON4SNpC0srC/Cn9WknHtrF+p0r6Ylvy5MHBXft8Cbg6Ital9z8FTiiy3gzguIgYAfwe+F5zG0x/0Fs3Sf4OsCAi9gZOBH7esWo363KyW8UUmpDSSxYRjwO1knYuV8VK1FWPx+XA55ukTUhllyxNnzevyLY6W9P9/E5EjEiPIwrW+wlwTkQMBV4FJjfdkKSZkj6Rc327+3ndmkodj2o7r1vTdD8DTCS73dZRnVyXu8nOxbqCtE8CCyNiWRu3NQP4Vrkq1onKfjwi4i3gVuDIxrQU6H0UaPFHZBEXA6e1px7l5OCufY4Drmt8ExHzgFVF1gtgQHq9Fc3cRDn9AvkpG/+hDSP78iMingLqJO3Q3kqnX9wXp1+Hz0hqPAGvBD4rqU9arw7YEbhX0gxJ9ZIWSTqrYFsHSPo/SY+mVqz+adEf2fgfat665PGIiKeB1yR9uCD5WGC2pJMlPZT231WSNk9l76CsdfDR9PhIyndt+pyVtMF+LkaSgEPJzimAWRR8Yeahis/r1lTkeFThed2aDfazpA8CW5L9OJyYV6GS3pQ0Le2v+yXtEBH/AP7AhgHxBOBySQemc/eR9Lxb2k4vSWdLelxZr8PXUr57gE+qi7ecFpHX8Wj6Y/Ao4GZgE0nzlPX6PC7p/TnqJZ2Y9umjki4FiIi3gaWSDuxAXTrMwV0bKevS+EBELC1h9S8DN0pqIGtJmt7MeqcCc4v88noUODqVeyCwC9lMGx2xOzAGOBD4vqTeEbESeBAYm9aZAFwR2X1yvpumZdkb+LikvdM+uAI4PSL2Ifvl+E7KWw/8cwfrWLJucDze/8KQdBCwMiIWk/3yPCDtvydZ35pyHnBXSt8PaLw340LggBI+Yy6a2c99U4B0v6QjU9pA4LWIWJveNwCDO6GKVXVet6YLHI+qOK9b08x+nkj2+e8BdpO0fU7FbwHcn/bZ3cDJKb1w3/cBPgNcBTwFfCwi9gX+A/hxWn8KMATYN/U6XAaQAsUlwD451b/scj4eNwP7SxqY3je28r8LHBUR+wGHAP+jzJ7Ad4FD0zE6vWBbFf++cHDXdtsBr5W47hnAZyKiFvgt8LOmK0jaETgGOL9I/unANpIWAF8DHgHWFlmvULEbFxam3RARqyPiZWA50NjyVPirpbDr6lhJD6ey9yRrvdoNWBYRDwFExBsF/zyWk7WOdJaufjxmA+MlbcKG+3W4pHskPU72S3TPlH4oWXcJEbEuIl5vfA28V9CS1NmK7eedU4D0BeDc9Au62TmdJY1RuiYMOAL4TXr/QAnl97TzujWVPh7Vcl63pth+ngDMTsHR1WTfF+3R3E1mG9PfY32X4HygDiCdn1umlrnDyALAV8l6I/4gaSFwDuv3/SeBXzWeyxHxSkFZXe28bk1uxyNdJjCX7LzeDhhB1lUr4MeSHgP+RPbjaAdSi3j6zuly+7W7Ncd2Be8AfVtbSVINsE9ENH5RXkH2ywBJt5CdHPXANcCuwJKsB4XNJS2JiF0j4g3giymPgGfToyUrgW2Al9P7bQteA6wueL2O9efAtcDPJO0H9IuIhyUNAf4NOCAiXlV2UXlfspO9uS+mvqxv7egMXfp4RMQLkpYCHwc+BxycFs0EjoyIRyWdBHyihM/ah+xXZCVstJ8j4sX0/IykO4F9yVoQtpa0afpn8v6czhFxC9mMMaRzaWZE3Fli+T3tvG5NRY9HFZ3XrdlgP0vaGxgK3Ja+HzYDngF+2Y5tN57ThQrP6zWxfpaBwnMasuB6ArAH6wPrHwF3RMRRyi5BuLOx2nSf87o1eR4PyPbl98j22XURsSadxzXA/un9UrrB94Vb7too/ULqJam1gOJVYCtJH0rvP0XWTUFEjEkXPX85Im6IiH+KiLqIqAPejohdASRtrfUj274M3J0CDNI1AMW6V+4kDSZQdu3Y8cAdJXyuN1Pei1n/ZTEAeAt4Xdm1ZYel9KeAHSUdkMrpX3DdxofIulo6RTc4HpDtz3OAv0ZEQ0rrDyyT1JsNrzmaB3w1bbOXpAHp9UBgRUSsaX2vlF/T/axs5HDjtWzbAaOAJ9I/ozuA8SnrJFq5LqyQz+vSdIHjAVVwXremyPfLROAHjd8PEbEjMFjSLs1tQ9JRajK6OHkIGCXpn9J6I8kC3RdKqNrlZH8Dh7J+zvStgL+l1ycVrHsr8JXGc1nStgXLPsT6LvIuL+fjAdnfylDgFNZ/X2wFLE+B3SFkl+NAdk4fm87hYvu1ot8XDu7a51ayUTQASLqH7CLX0ZIaJI1Jv5JPBq6S9CjZP6ZvtrGcPYBFkp4i+wd0eipvE7LWpVeK5PkRsGsq8xGyayp+V2J5l5NdfzEbICIat7GI7J/jn1P6e2QX9J6fyrmN9b+mDgFuaNOn7LiufDxIddmTtF+TfwceINt3TxWknw4ckrq15rO+a+UQ4MY21rfcCvfzHkB92pd3ANMj4om07NvANyQtIbvm66JSNu7zus0qeTyges7r1hTu5wlkrfuFrqHlwTYfBN5omhgRL5HtlxuVdY2fC0xM3YstSsf2beD2yEZ6Avw38F+S/kw2n3qj3wDPA4+l8+MLkA1yIRth3dZRtpWWy/GA969DvIrs7+TulHwZMFJSPdkPlqfSuouAacBdab8WXuYziqwLt3Iiwo82Psi6Oy6tYPnDgZ9Vej8UqVcf4H5gUx+PspdxNbBbhY9vrvvZ53XPOx5d4bzOez+T/QipqfTnKFKvM4DJla5HtR2PSv8/anwoVcbaSNKXgFmx4b12ejRlN8QcHKVfR1XOsqv2eKSu4AkRcUkXqEvV7ufmVPK8bk13Ph5d6bxuTXfez81RdqPdS2P9oKFuoysfD0mfAhZHaXdwyK8eDu7MzMzMqoevuTMzMzOrIg7uzMzMzKqIgzsz67HSbRFC0u5l3OYPJP1N2Q2Bn5CU2xRVZmbFOLgzs55sInAv5Z839pyIGAGMA36d7vtmZtYpHNyZWY8kaUuy+1FNpiC4k7SJpAskLZJ0vaQbJY1Py/aXdJek+ZJukTSopTIim2/1bdJMBJJmKJv/dZGkswrKXCrpLK2fnHz3lF4j6baU/mtJz6WbFCPpeEkPphbCX6ebO5uZObgzsx7rSODmiPgL8IqyKcoAjiabx3MvsplIDgZIrW/nA+MjYn+yGyBPa6mAtM3FEbE8JX03svlf9wY+rmz6pEYvRzY5+Qyy6dEAvk92o9r9yG7OunPa7h5kN1welVoI17HhjBBm1oN5blkz66kmks0KANksCxOBh8nufv+HyO5W/3dJjdOc7UZ2Y9/GeSx7Ac3d3f8MSScDHwDGFqQfK2kK2XfvIGAY8FhadnV6nk8WYJLqchRARNws6dWUPhrYH3go1aUf2WTlZmYO7sys50nzQR4KDJcUZIFaSPoW2YTgRbMBiyLi4BKKOCcizpZ0NHCJpA+SBXP/BhwQEa9KmknBJOjA6vRcOEl8S3WZFRFnllAXM+th3C1rZj3ReOCSiNglsgnHdwKeJWspuxf4XLr2bgfgEynP00CNpPe7aSXtWWTb74uIq4F6YBIwAHgLeD1t97AS6nkvcGwq79Oka/fIJi0fL2n7tGzbliZLN7OexS13ZtYTTQSmN0m7imxS9VPIuj0XAn8BHgBej4j30sCK8yRtRfb9eS6wqJWyfgj8HtgDeCSt/wzw5xLqeRZwuaTPA3eRdQOvioiXJX0PuFXSJsCaVO/nStimmVU5Tz9mZtaEpC0j4s3Uffsg2cCFv1egHn2AdRGxNrUYzkgDKMzMmuWWOzOzjV0vaWtgM+BHlQjskp2BOal17j3g5ArVw8y6EbfcJdttt13U1dVVuhpmZmZmrZo/f/7LEVFTbJlb7pK6ujrq6+srXQ0zMzOzVklq9hpbj5Y1MzMzqyIO7szMzMyqiIM7MzMzsyria+7MzMys21izZg0NDQ28++67la5Kp+jbty+1tbX07t275DwO7szMzKzbaGhooH///tTV1ZHmVq5aEcHKlStpaGhgyJAhJefrct2ykraWdKWkpyQ9KengNLXObZIWp+dtCtY/U9ISSU9LGlOQvr+kx9Oy81TtZ4CZmVkP8O677zJw4MCqD+wAJDFw4MA2t1J2xZa7nwM3R8R4SZsBmwPfAeZFxHRJU4GpwLclDQMmAHsCOwJ/kvShiFgHzACmAPcDNwJjgZs6/+OYmVk1qZt6Q6Wr0C5Lpx9e6SqUTU8I7Bq157N2qZY7SQOAjwEXAUTEexHxGjAOmJVWmwUcmV6PA2ZHxOqIeBZYAhwoaRAwICLui+wuzZcU5DEzMzOrWl2t5e4DwArgt5L2AeYDpwM7RMQygIhYJmn7tP5gspa5Rg0pbU163TR9A5KmkLXusfPOO5f3k5iZmVnuyt2SWkoL55Zbbsmbb775/vuZM2dSX1/PL37xi7LWpb26VMsdWbC5H9nk2PsCb5F1wTanWFtltJC+YULEhRExMiJG1tQUncHDzMzMrFvpasFdA9AQEQ+k91eSBXsvpa5W0vPygvV3KshfC7yY0muLpJuZmZnl5rnnnmP06NHsvffejB49mueffx6AP/zhDwwfPpx99tmHj33sY0DW4jdu3DjGjh3LbrvtxllnnVWWOnSpbtmI+LukFyTtFhFPA6OBJ9JjEjA9PV+XsswFfi/pZ2QDKoYCD0bEOkmrJB0EPACcCJzfyR/HzMzMqtA777zDiBEj3n//yiuvcMQRRwBw6qmncuKJJzJp0iQuvvhiTjvtNK699lp++MMfcssttzB48GBee+219/M++OCDLFy4kM0335wDDjiAww8/nJEjR3aofl0quEu+BlyWRso+A3yRrIVxjqTJwPPAMQARsUjSHLLgby1wShopC/BVYCbQj2yUrEfKmpmZWYf169ePBQsWvP++8Zo7gPvuu4+rr74agBNOOIFvfetbAIwaNYqTTjqJY489lqOPPvr9vJ/61KcYOHAgAEcffTT33ntv9QV3EbEAKPapRjez/jRgWpH0emB4WStnZmZm1gaNtzL51a9+xQMPPMANN9zAiBEj3g8Om97qpBy3eelq19yZmZmZdVsf+chHmD17NgCXXXYZH/3oRwH461//yoc//GF++MMfst122/HCCy8AcNttt/HKK6/wzjvvcO211zJq1KgO16HLtdyZmZmZlaqr3Zz5vPPO40tf+hI//elPqamp4be//S0A3/zmN1m8eDERwejRo9lnn31YsGABH/3oRznhhBNYsmQJX/jCFzrcJQsO7szMzMzapPAedwAnnXQSJ510EgB1dXXcfvvtG+VpvA6vqe23377s98dzt6yZmZlZFXHLnZmZmVkFFLb4lZNb7szMzKxbyaaN7xna81kd3JmZmVm30bdvX1auXNkjAryIYOXKlfTt27dN+dwta2ZmZt1GbW0tDQ0NrFixotJV6RR9+/altra29RULOLgzMzOzbqN3794MGTKk0tXo0twta2ZmZlZFHNyZmZmZVREHd2ZmZmZVxMGdmZmZWRVxcGdmZmZWRRzcmZmZmVWRXII7Sf8taYCk3pLmSXpZ0vF5lGVmZmZm6+XVcvfpiHgD+CzQAHwI+GYpGSUtlfS4pAWS6lPatpJuk7Q4PW9TsP6ZkpZIelrSmIL0/dN2lkg6T5LK+xHNzMzMup68grve6fkzwOUR8Uob8x8SESMiYmR6PxWYFxFDgXnpPZKGAROAPYGxwAWSeqU8M4ApwND0GNveD2NmZmbWXeQV3P1R0lPASGCepBrg3Q5sbxwwK72eBRxZkD47IlZHxLPAEuBASYOAARFxX2STz11SkMfMzMysauUS3EXEVOBgYGRErAHeJgvESsoO3CppvqQpKW2HiFiWtr0M2D6lDwZeKMjbkNIGp9dN0zcgaYqkekn1PWWOOjMzM6tueQ2o2Bw4haxrFGBHsla8UoyKiP2Aw4BTJH2spaKKpEUL6RsmRFwYESMjYmRNTU2J1TMzMzPruvLqlv0t8B7wkfS+AfjPUjJGxIvpeTlwDXAg8FLqaiU9Ly/Y7k4F2WuBF1N6bZF0MzMzs6qWV3D3wYj4b2ANQES8Q/HWtA1I2kJS/8bXwKeBhcBcYFJabRJwXXo9F5ggqY+kIWQDJx5MXberJB2URsmeWJDHzMzMrGptmtN235PUj9QVKumDwOoS8u0AXJPuWrIp8PuIuFnSQ8AcSZOB54FjACJikaQ5wBPAWuCUiFiXtvVVYCbQD7gpPczMzMyqWl7B3feBm4GdJF0GjAJOai1TRDwD7FMkfSUwupk804BpRdLrgeFtqrWZmZlZN5dLcBcRt0l6GDiIrDv29Ih4OY+yzMzMzGy9vEbLjgLejYgbgK2B70jaJY+yzMzMzGy9vAZUzADelrQP2bRjz5HdSNjMzMzMcpRXcLc2zQwxDjgvIn4O9M+pLDMzMzNL8hpQsUrSmcDxwMfSfK+9W8ljZmZmZh2UV8vd58lufTI5Iv5ONvXXT3Mqy8zMzMySvEbL/h34WcH75/E1d2ZmZma5y2u07EGSHpL0pqT3JK2T9HoeZZmZmZnZenl1y/4CmAgsJpsh4svAL3Mqy8zMzMySvAZUEBFLJPVK04H9VtL/5VWWmZmZmWXyCu7elrQZsEDSfwPLgC1yKsvMzMzMkry6ZU9I2z4VeAvYCfhcTmWZmZmZWVLWljtJNUBNRDyRkt4FzpI0HPCACjMzM7Oclbvl7nygpkj6YODnZS7LzMzMzJood3C3V0Tc1TQxIm4B9i5zWWZmZmbWRLmDu5amGPP0Y2ZmZmY5K3dwt1jSZ5omSjoMeKaUDUjqJekRSden99tKuk3S4vS8TcG6Z0paIulpSWMK0veX9Hhadp4kleGzmZmZmXV55Q7uzgDOlTRT0tfSYxbZ9Xanl7iN04EnC95PBeZFxFBgXnqPpGHABGBPYCxwgaReKc8MYAowND3GduxjmZmZmXUPZQ3uIuIvwF7AXUBdetwF7J2WtUhSLXA48JuC5HHArPR6FnBkQfrsiFgdEc8CS4ADJQ0CBkTEfRERZHPaHomZmZlZD1D2mxhHxGrgt+3Mfi7wLaB/QdoOEbEsbXuZpO1T+mDg/oL1GlLamvS6afpGJE0ha+Fj5513bmeVzczMzLqOvG5i3GaSPgssj4j5pWYpkhYtpG+cGHFhRIyMiJE1NcXu4GJmZmbWveQ2t2w7jAKOSAMy+gIDJP0OeEnSoNRqNwhYntZvIJv5olEt8GJKry2SbmZmZlb1ukzLXUScGRG1EVFHNlDi9og4HpgLTEqrTQKuS6/nAhMk9ZE0hGzgxIOpC3eVpIPSKNkTC/KYmZmZVbVcWu4kjQJ+AOySyhAQEfGBdmxuOjBH0mTgeeAYso0tkjQHeAJYC5wSEetSnq8CM4F+wE3pYWZmZlb18uqWvYjstijzgXWtrLuRiLgTuDO9XgmMbma9acC0Iun1wPC2lmtmZmbW3eUV3L0eEW4tMzMzM+tkeQV3d0j6KXA1sLoxMSIezqk8MzMzMyO/4O7D6XlkQVoAh+ZUnpmZmZmRU3AXEYfksV0zMzMza1kut0KRtJWkn0mqT4//kbRVHmWZmZmZ2Xp53efuYmAVcGx6vEH7pyQzMzMzsxLldc3dByPicwXvz5K0IKeyzMzMzCzJq+XuHUkfbXyTbmr8Tk5lmZmZmVmSV8vdV4FZ6To7Aa8AJ+VUlpl1EXVTb6h0Fdpl6fTDK10FM7OyyWu07AJgH0kD0vs38ijHzMzMzDZU1uBO0vER8TtJ32iSDkBE/Kyc5ZmZmZnZhsrdcrdFeu5fZFmUuSwzMzMza6KswV1E/Dq9/FNE/LlwWRpUYWZmZmY5ymu07PklppmZmZlZGZX7mruDgY8ANU2uuxsA9CpnWWZmZma2sXJfc7cZsGXabuF1d28A48tclpmZmZk1Ue5r7u4C7pI0MyKea2t+SX2Bu4E+qW5XRsT3JW0LXAHUAUuBYyPi1ZTnTGAysA44LSJuSen7AzOBfsCNwOkR4UEdZmZmVtXyuububUk/lXSjpNsbHyXkWw0cGhH7ACOAsZIOAqYC8yJiKDAvvUfSMGACsCcwFrhAUmP37wxgCjA0PcaW7+OZmZmZdU15BXeXAU8BQ4CzyFrbHmotU2TeTG97p0cA44BZKX0WcGR6PQ6YHRGrI+JZYAlwoKRBwICIuC+11l1SkMfMzMysauUV3A2MiIuANRFxV0R8CTiolIySeklaACwHbouIB4AdImIZQHrePq0+GHihIHtDShucXjdNb1rWFEn1kupXrFjRpg9oZmZm1hXlFdytSc/LJB0uaV+gtpSMEbEuIkak9Q+UNLyF1VVsEy2kNy3rwogYGREja2pqSqmemZmZWZeWy9yywH9K2gr4V7L72w0AzmjLBiLiNUl3kl0r95KkQRGxLHW5Lk+rNQA7FWSrBV5M6bVF0s3MzMyqWl4td49GxOsRsTAiDomI/YEHW8skqUbS1ul1P+CTZNfuzQUmpdUmAdel13OBCZL6SBpCNnDiwdR1u0rSQcomtj2xII+ZmZlZ1cqr5e5ZSX8AJkfE2yntRmC/VvINAmalEa+bAHMi4npJ9wFzJE0GngeOAYiIRZLmAE8Aa4FTImJd2tZXWX8rlJvSw8zMzKyq5RXcPQ7cA9wj6diI+CvFr4PbQEQ8BuxbJH0lMLqZPNOAaUXS64GWrtczMzMzqzp5BXcRERdIehT4o6RvU2RAg5mZmZmVV17BnQAi4s+SRpPNLrF7TmWZmZmZWZJXcPeZxhdphOuhwEdyKqtbqJt6Q6Wr0C5Lpx9e6SqYmZlZG5Q1uJN0fET8DpiYDVLdyN3lLM/MzMzMNlTulrst0nP/Mm/XzMzMzEpQ1uAuIn6dns8q53bNzMzMrDTl7pY9r6XlEXFaOcszMzMzsw2Vu1t2fpm3Z2ZmZmZtUO5u2Vnl3J6ZmZmZtU25u2XPjYivS/ojRW5aHBFHlLM8MzMzM9tQubtlL03PZ5d5u2ZmZmZWgnJ3yzZeczciIn5euEzS6cBd5SzPzMzMzDa0SU7bnVQk7aScyjIzMzOzpNzX3E0EvgAMkTS3YFF/YGU5yzIzMzOzjZX7mrv/A5YB2wH/U5C+CniszGWZmZmZWRPlvubuOeA54OBybtfMzMzMSlPWa+4k3ZueV0l6o+CxStIbJeTfSdIdkp6UtCgNwkDStpJuk7Q4PW9TkOdMSUskPS1pTEH6/pIeT8vOk6RyflYzMzOzrqiswV1EfDQ994+IAQWP/hExoIRNrAX+NSL2AA4CTpE0DJgKzIuIocC89J60bAKwJzAWuEBSr7StGcAUYGh6jC3bBzUzMzProvIaLYuk/SSdJulrkvYtJU9ELIuIh9PrVcCTwGBgHNA4+8Us4Mj0ehwwOyJWR8SzwBLgQEmDgAERcV9EBHBJQR4zMzOzqpVLcCfpP8iCsIFkgytmSvpeG7dRB+wLPADsEBHLIAsAge3TaoOBFwqyNaS0wel10/SmZUyRVC+pfsWKFW2pnpmZmVmXVO7Rso0mAvtGxLsAkqYDDwP/WUpmSVsCVwFfj4g3WrhcrtiCaCF9w4SIC4ELAUaOHLnRcjMzM7PuJq9u2aVA34L3fYC/lpJRUm+ywO6yiLg6Jb+UulpJz8tTegOwU0H2WuDFlF5bJN3MzMysquUV3K0GFkmaKem3wELgzTRq9bzmMqURrRcBT0bEzwoWzWX9rBeTgOsK0idI6iNpCNnAiQdT1+0qSQelbZ5YkMfMzMysauXVLXtNejS6s8R8o4ATgMclLUhp3wGmA3MkTQaeB44BiIhFkuYAT5CNtD0lItalfF8FZgL9gJvSw8zMzKyq5RLcRUTjyFbSPel2iohWZ6iIiHspfr0cwOhm8kwDphVJrweGl1RhMzMzsyqR12jZOyUNkLQt8CjwW0k/ay2fmZmZmXVMXtfcbRURbwBHA7+NiP2BT+ZUlpmZmZkleQV3m6ZRrccC1+dUhpmZmZk1kVdw90PgFuCvEfGQpA8Ai3Mqy8zMzMySvAZU/AH4Q8H7Z4DP5VGWmZmZma2X14CKWknXSFou6SVJV0mqbT2nmZmZmXVEXt2yvyW7wfCOZHO6/jGlmZmZmVmO8gruaiLitxGxNj1mAjU5lWVmZmZmSV7B3cuSjpfUKz2OB1bmVJaZmZmZJXkFd18iuw3K34FlwPiUZmZmZmY5ymu07PPAEXls28zMzMyaV9bgTtL5QDS3PCJOK2d5ZmZmZrahcrfc1Re8Pgv4fpm3b2ZmZmYtKGtwFxGzGl9L+nrhezMzMzPLX14DKqCF7lkzMzMzy0eewZ2ZmZmZdbKyBneSVkl6Q9IbwN6NrxvTS8h/cZqybGFB2raSbpO0OD1vU7DsTElLJD0taUxB+v6SHk/LzpOkcn5OMzMzs66qrMFdRPSPiAHpsWnB6/4RMaCETcwExjZJmwrMi4ihwLz0HknDgAnAninPBZJ6pTwzgCnA0PRouk0zMzOzqtSlumUj4m7glSbJ44DGgRmzgCML0mdHxOqIeBZYAhwoaRAwICLui4gALinIY2ZmZlbVulRw14wdImIZQHrePqUPBl4oWK8hpQ1Or5umb0TSFEn1kupXrFhR9oqbmZmZdbZcZqjoJMWuo4sW0jdOjLgQuBBg5MiRHt1rZmbWBdVNvaHSVWiXpdMPr0i53aHl7qXU1Up6Xp7SG4CdCtarBV5M6bVF0s3MzMyqXncI7uYCk9LrScB1BekTJPWRNIRs4MSDqet2laSD0ijZEwvymJmZmVW1LtUtK+ly4BPAdpIayKYvmw7MkTQZeB44BiAiFkmaAzwBrAVOiYh1aVNfJRt52w+4KT3MzMzMql6XCu4iYmIzi0Y3s/40YFqR9HpgeBmrZmbWJflaJDNrqjt0y5qZmZlZiRzcmZmZmVURB3dmZmZmVcTBnZmZmVkVcXBnZmZmVkUc3JmZmZlVEQd3ZmZmZlXEwZ2ZmZlZFXFwZ2ZmZlZFHNyZmZmZVREHd2ZmZmZVxMGdmZmZWRVxcGdmZmZWRRzcmZmZmVURB3dmZmZmVWTTSlfALC91U2+odBXaZen0wytdBTMz68aquuVO0lhJT0taImlqpetjZmZmlreqDe4k9QJ+CRwGDAMmShpW2VqZmZmZ5atqgzvgQGBJRDwTEe8Bs4FxFa6TmZmZWa4UEZWuQy4kjQfGRsSX0/sTgA9HxKkF60wBpqS3uwFPd3pFy2M74OVKV6KH8T7vfN7nnc/7vPN5n3e+7rrPd4mImmILqnlAhYqkbRDJRsSFwIWdU538SKqPiJGVrkdP4n3e+bzPO5/3eefzPu981bjPq7lbtgHYqeB9LfBihepiZmZm1imqObh7CBgqaYikzYAJwNwK18nMzMwsV1XbLRsRayWdCtwC9AIujohFFa5WXrp913I35H3e+bzPO5/3eefzPu98VbfPq3ZAhZmZmVlPVM3dsmZmZmY9joM7MzMzsyri4K4dJPWTdFeaBQNJN0t6TdL1TdYbLelhSQsk3Stp11a2e76kNwvebyPpGkmPSXpQ0vAS6rZU0nYF7z/RtF5F8twpaUyTtK9LuqC18prk2UvSzLbkKYeuejwk/UDSfzVJGyHpyTZ+vs0k3S2potfIFtnP69K+XCBpbsF6QyQ9IGmxpCvSgKaOlt3jzuvWVOp4VNt53Zqm+zmlnSHpXUlbFaS9f05KOknSL1rZ7kbrpHO22VtypDLua5K2qaSXJA1q4+eaLWloW/J0BXkcD0lbSFpZmD+lXyvp2DbW71RJX2xLnjw4uGufLwFXR8S69P6nwAlF1psBHBcRI4DfA99rboPpD3rrJsnfARZExN7AicDPO1btZl1ONpq40ISUXrKIeByolbRzuSpWoq56PC4HPt8kbUIqu2RphpV5RbbV2Zru53ciYkR6HFGw3k+AcyJiKPAqMLnphiTNlPSJnOvb3c/r1lTqeFTbed2apvsZYCLZHRmO6uS63E12LtYVpH0SWBgRy9q4rRnAt8pVsU5U9uMREW8BtwJHNqalQO+jQIs/Iou4GDitPfUoJwd37XMccF3jm4iYB6wqsl4AA9LrrWjmPnvpF8hP2fgPbRjZlx8R8RRQJ2mH9lY6/eK+OP06fEZS4wl4JfBZSX3SenXAjsC9kmZIqpe0SNJZBds6QNL/SXo0tWL1T4v+yMb/UPPWJY9HRDwNvCbpwwXJxwKzJZ0s6aG0/66StHkqewdlrYOPpsdHUr5r0+espA32czGSBBxKdk4BzKLgCzMPVXxet6Yix6MKz+vWbLCfJX0Q2JLsx+HEvAqV9KakaWl/3S9ph4j4B/AHNgyIJwCXSzownbuPpOfd0nZ6STpb0uPKeh2+lvLdA3xSXbzltIi8jkfTH4NHATcDm0iap6zX53FJ709jKunEtE8flXQpQES8DSyVdGAH6tJhDu7aSFmXxgciYmkJq38ZuFFSA1lL0vRm1jsVmFvkl9ejwNGp3AOBXchuxtwRuwNjyObe/b6k3hGxEngQGJvWmQBcEdlQ6u+mO3fvDXxc0t5pH1wBnB4R+5D9cnwn5a0H/rmDdSxZNzge739hSDoIWBkRi8l+eR6Q9t+TrG9NOQ+4K6XvBzTevmchcEAJnzEXzeznvilAul/SkSltIPBaRKxN7xuAwZ1Qxao6r1vTBY5HVZzXrWlmP08k+/z3ALtJ2j6n4rcA7k/77G7g5JReuO/7AJ8BrgKeAj4WEfsC/wH8OK0/BRgC7Jt6HS4DSIHiEmCfnOpfdjkfj5uB/SUNTO8bW/nfBY6KiP2AQ4D/UWZP4LvAoekYnV6wrYp/Xzi4a7vtgNdKXPcM4DMRUQv8FvhZ0xUk7QgcA5xfJP90YBtJC4CvAY8Aa4usV6jYvW0K026IiNUR8TKwHGhseSr81VLYdXWspIdT2XuStV7tBiyLiIcAIuKNgn8ey8laRzpLVz8es4HxkjZhw/06XNI9kh4n+yW6Z0o/lKy7hIhYFxGvN74G3itoSepsxfbzzilA+gJwbvoF3ey0f5LGKF0TBhwB/Ca9f6CE8nvaed2aSh+PajmvW1NsP08AZqfg6Gqy74v2aO4+ZI3p77G+S3A+UAeQzs8tU8vcYWQB4KtkvRF/kLQQOIf1+/6TwK8az+WIeKWgrK52Xrcmt+ORLhOYS3ZebweMIOuqFfBjSY8BfyL7cbQDqUU8fed0uf3a3Zpju4J3gL6trSSpBtgnIhq/KK8g+2WApFvITo564BpgV2BJ1oPC5pKWRMSuEfEG8MWUR8Cz6dGSlcA2rJ8EeVs2nBB5dcHrdaw/B64FfiZpP6BfRDwsaQjwb8ABEfGqsovK+5Kd7M19MfVlfWtHZ+jSxyMiXpC0FPg48Dng4LRoJnBkRDwq6STgEyV81j5kvyIrYaP9HBEvpudnJN0J7EvWgrC1pE3TP5P3p/2LiFvIbipOOpdmRsSdJZbf087r1lT0eFTRed2aDfazpL2BocBt6fthM+AZ4Jft2HbjOV2o8LxeE+tvRFt4TkMWXE8A9mB9YP0j4I6IOErZJQh3Nlab7nNetybP4wHZvvwe2T67LiLWpPO4Btg/vV9KN/i+cMtdG6VfSL0ktRZQvApsJelD6f2nyLopiIgx6aLnL0fEDRHxTxFRFxF1wNsRsSuApK21fmTbl4G7U4BBugagWPfKnaTBBMquHTseuKOEz/Vmynsx678sBgBvAa8ru7bssJT+FLCjpANSOf0Lrtv4EFlXS6foBscDsv15DvDXiGhIaf2BZZJ6s+E1R/OAr6Zt9pI0IL0eCKyIiDWt75Xya7qflY0cbryWbTtgFPBE+md0BzA+ZZ1EK9eFFfJ5XZoucDygCs7r1hT5fpkI/KDx+yEidgQGS9qluW1IOkpNRhcnDwGjJP1TWm8kWaD7QglVu5zsb+BQ1k+ruRXwt/T6pIJ1bwW+0nguS9q2YNmHWN9F3uXlfDwg+1sZCpzC+u+LrYDlKbA7hOxyHMjO6WPTOVxsv1b0+8LBXfvcSjaKBgBJ95Bd5DpaUoOkMelX8snAVZIeJfvH9M02lrMHsEjSU2T/gE5P5W1C1rr0SpE8PwJ2TWU+QnZNxe9KLO9ysusvZgNEROM2FpH9c/xzSn+P7ILe81M5t7H+19QhwA1t+pQd15WPB6kue5L2a/LvwANk++6pgvTTgUNSt9Z81netHALc2Mb6llvhft4DqE/78g5gekQ8kZZ9G/iGpCVk13xdVMrGfV63WSWPB1TPed2awv08gax1v9A1tDzY5oPAG00TI+Ilsv1yo7Ku8XOBial7sUXp2L4N3B7ZSE+A/wb+S9KfyabcbPQb4HngsXR+fAGyQS5kI6zbOsq20nI5HvD+dYhXkf2d3J2SLwNGSqon+8HyVFp3ETANuCvt18LLfEaRdeFWTkT40cYHWXfHpRUsfzjws0rvhyL16gPcD2zq41H2Mq4Gdqvw8c11P/u87nnHoyuc13nvZ7IfITWV/hxF6nUGMLnS9ai241Hp/0eND88t206SvgTMig3vtdOjKbsh5uAo/TqqcpZdtccjdQVPiIhLukBdqnY/N6eS53VruvPx6ErndWu6835ujrIb7V4a6wcNdRtd+XhI+hSwOEq7g0N+9XBwZ2ZmZlY9fM2dmZmZWRVxcGdmZmZWRRzcmZmZmVURB3dm1mOle16FpN3LuM0fSPqbstkenpCU2/yjZmbFOLgzs55sInAvLd8Xqz3OiYgRwDjg1+mmvmZmncLBnZn1SJK2JLvZ6GQKgjtJm0i6QNIiSddLulHS+LRsf0l3SZov6RZJg1oqIyIWk91sdpuUf4ak+rTtswrKXCrpLEkPS3q8sSVRUo2k21L6ryU9l2agQNLxkh5MLYS/TjN3mJk5uDOzHutI4OaI+AvwirL5ZwGOJpukfS+yaeYOBkitb+cD4yNif7LZLaa1VEDa5uKIWJ6SvhsRI4G9gY8rmxuz0csRsR8wg2zuW4Dvk81CsB/Znfd3Ttvdg2w2jVGphXAdG073ZWY92Katr2JmVpUmkk35BNkUWhOBh8mmNvpDZFMR/V1S4xy2u5HN2tA4SXkvoLmpm86QdDLwAWBsQfqxkqaQffcOAoYBj6VlV6fn+WQBJqkuRwFExM2SXk3po4H9gYdSXfoBjQGkmfVwDu7MrMdJk30fCgyXFGSBWkj6FqDmsgGLIuLgEoo4JyLOlnQ0cImkD5IFc/8GHBARr0qayfq5awFWp+d1rP9ubqkusyLizBLqYmY9jLtlzawnGg9cEhG7RERdROwEPEvWUnYv8Ll07d0OwCdSnqeBGknvd9NK2rOlQiLiaqAemAQMAN4CXk/bPayEet4LHJvK+zTp2j1gHjBe0vZp2baSdinto5tZtXPLnZn1RBOB6U3SrgK+AJxC1u25EPgL8ADwekS8lwZWnCdpK7Lvz3OBRa2U9UPg98AewCNp/WeAP5dQz7OAyyV9HriLrBt4VUS8LOl7wK2SNgHWpHo/V8I2zazKeW5ZM7MmJG0ZEW+m7tsHyQYu/L0C9egDrIuItanFcEYaQGFm1iy33JmZbex6SVsDmwE/qkRgl+wMzEmtc+8BJ1eoHmbWjbjlLtluu+2irq6u0tUwMzMza9X8+fNfjoiaYsvccpfU1dVRX19f6WqYmZmZtUpSs9fYerSsmZmZWRVxcGdmZmZWRRzcmZmZmVWRLnXNnaSLgc8CyyNieErbFriCbK7HpcCxEfFqWnYm2aTf64DTIuKWlL4/MJNsSp4bgdPDI0fMzMy6lTVr1tDQ0MC7775b6apUTN++famtraV3794l5+lSwR1ZQPYL4JKCtKnAvIiYLmlqev9tScOACcCewI7AnyR9KCLWkU28PQW4nyy4Gwvc1GmfwszMzDqsoaGB/v37U1dXR5pHuUeJCFauXElDQwNDhgwpOV+X6paNiLuBV5okjwNmpdezgCML0mdHxOqIeBZYAhwoaRAwICLuS611lxTkMTMzs27i3XffZeDAgT0ysAOQxMCBA9vcctnVWu6K2SEilgFExLLGuRSBwWQtc40aUtqa9Lpp+kYkTSFr4WPnnXcuc7XNzKwa1U29odJVaJel0w+vdBXapacGdo3a8/m7VMtdGxX7tNFC+saJERdGxMiIGFlTU/Q+gGZmZmbdSndouXtJ0qDUajcIWJ7SG4CdCtarBV5M6bVF0s3MzKwbK3eraSmtmb169WKvvfZizZo1bLrppkyaNImvf/3rbLJJ29vHXnvtNX7/+9/z//7f/wPgzjvv5Oyzz+b6669v87Za0h1a7uYCk9LrScB1BekTJPWRNAQYCjyYunBXSTpIWVvmiQV5zMzMzErWr18/FixYwKJFi7jtttu48cYbOeuss9q1rddee40LLrigzDXcWJcK7iRdDtwH7CapQdJkYDrwKUmLgU+l90TEImAO8ARwM3BKGikL8FXgN2SDLP6KR8qamZlZB22//fZceOGF/OIXvyAiWLduHd/85jc54IAD2Hvvvfn1r38NwJtvvsno0aPZb7/92GuvvbjuuqyNaerUqfz1r39lxIgRfPOb33x/3fHjx7P77rtz3HHHUY47t3WpbtmImNjMotHNrD8NmFYkvR4YXsaqmZmZmfGBD3yAf/zjHyxfvpzrrruOrbbaioceeojVq1czatQoPv3pT7PTTjtxzTXXMGDAAF5++WUOOuggjjjiCKZPn87ChQtZsGABkHXLPvLIIyxatIgdd9yRUaNG8ec//5mPfvSjHapjlwruzMzMzLq6xta1W2+9lccee4wrr7wSgNdff53FixdTW1vLd77zHe6++2422WQT/va3v/HSSy8V3daBBx5IbW02VGDEiBEsXbrUwZ2ZmZlZZ3nmmWfo1asX22+/PRHB+eefz5gxYzZYZ+bMmaxYsYL58+fTu3dv6urqmr1XXZ8+fd5/3atXL9auXdvhOnapa+7MzMzMuqoVK1bwla98hVNPPRVJjBkzhhkzZrBmzRoA/vKXv/DWW2/x+uuvs/3229O7d2/uuOMOnnvuOQD69+/PqlWrcq+nW+7MzMysW6jEjZjfeecdRowY8f6tUE444QS+8Y1vAPDlL3+ZpUuXst9++xER1NTUcO2113LcccfxL//yL4wcOZIRI0aw++67AzBw4EBGjRrF8OHDOeywwzj88Hw+j8oxKqMajBw5Murr6ytdDTMz6+I8Q0XnefLJJ9ljjz0qXY2KK7YfJM2PiJHF1ne3rJmZmVkVcXBnZmZmVkUc3JmZmVmX1dMvH2vP53dwZ2ZmZl1S3759WblyZY8N8CKClStX0rdv3zbl82hZMzMz65Jqa2tpaGhgxYoVla5KxfTt2/f9mxyXysGdmZmZdUm9e/dmyJAhla5Gt+NuWTMzM7Mq4uDOzMzMrIo4uDMzMzOrIg7uzMzMzKqIgzszMzOzKuLgzszMzKyK+FYoZlY2nlDdzKzy3HJnZmZmVkUc3JmZmZlVEQd3ZmZmZlXEwZ2ZmZlZFek2wZ2kMyQtkrRQ0uWS+kraVtJtkhan520K1j9T0hJJT0saU8m6m5mZmXWWbhHcSRoMnAaMjIjhQC9gAjAVmBcRQ4F56T2ShqXlewJjgQsk9apE3c3MzMw6U7cI7pJNgX6SNgU2B14ExgGz0vJZwJHp9ThgdkSsjohngSXAgZ1bXTMzM7PO1y2Cu4j4G3A28DywDHg9Im4FdoiIZWmdZcD2Kctg4IWCTTSktA1ImiKpXlL9ihUr8vwIZmZmZp2iWwR36Vq6ccAQYEdgC0nHt5SlSFpslBBxYUSMjIiRNTU15amsmZmZWQV1i+AO+CTwbESsiIg1wNXAR4CXJA0CSM/L0/oNwE4F+WvJunHNzMzMqlp3Ce6eBw6StLkkAaOBJ4G5wKS0ziTguvR6LjBBUh9JQ4ChwIOdXGczMzOzTtct5paNiAckXQk8DKwFHgEuBLYE5kiaTBYAHpPWXyRpDvBEWv+UiFhXkcqbmZmZdaJuEdwBRMT3ge83SV5N1opXbP1pwLS862VmZmbWlXSXblkzMzMzK4GDOzMzM7Mq4uDOzMzMrIo4uDMzMzOrIg7uzMzMzKpILsGdpA9K6pNef0LSaZK2zqMsMzMzM1svr5a7q4B1knYFLiKbNuz3OZVlZmZmZklewd0/ImItcBRwbkScAQzKqSwzMzMzS/IK7tZImkg2Jdj1Ka13TmWZmZmZWZJXcPdF4GBgWkQ8m+Z3/V1OZZmZmZlZksv0YxHxhKRvAzun988C0/Moy8zMzMzWy2u07L8AC4Cb0/sRkubmUZaZmZmZrZdXt+wPgAOB1wAiYgHZiFkzMzMzy1Fewd3aiHi9SVrkVJaZmZmZJblccwcslPQFoJekocBpwP/lVJaZmZmZJXm13H0N2BNYDVwOvAF8PaeyzMzMzCzJa7Ts28B3ge9K6gVsERHv5lGWmZmZma2X12jZ30saIGkLYBHwtKRv5lGWmZmZma2XV7fssIh4AzgSuJHsfncn5FSWmZmZmSV5BXe9JfUmC+6ui4g1eLSsmZmZWe7yCu5+DSwFtgDulrQL2aAKMzMzM8tRLsFdRJwXEYMj4jOReQ44pCPblLS1pCslPSXpSUkHS9pW0m2SFqfnbQrWP1PSEklPSxrT4Q9lZmZm1g3kdZ87JB1OdjuUvgXJP+zAJn8O3BwR4yVtBmwOfAeYFxHTJU0FpgLfljQMmJDK3xH4k6QPRcS6DpRvZmZm1uXlNVr2V8Dnye53J+AYYJcObG8A8DHgIoCIeC8iXgPGAbPSarPIrvEjpc+OiNUR8SywhGw6NDMzM7Oqltc1dx+JiBOBVyPiLOBgYKcObO8DwArgt5IekfSbdJuVHSJiGUB63j6tPxh4oSB/Q0rbgKQpkuol1a9YsaID1TMzMzPrGvIK7t5Jz29L2hFYAwzpwPY2BfYDZkTEvsBbZF2wzVGRtI1G60bEhRExMiJG1tTUdKB6ZmZmZl1DXsHd9ZK2Bn4KPEw2cnZ2B7bXADRExAPp/ZVkwd5LkgYBpOflBesXthTWAi92oHwzMzOzbiGv0bI/iojXIuIqsmvtdo+If+/A9v4OvCBpt5Q0GngCmAtMSmmTgOvS67nABEl9JA0BhgIPtrd8MzMzs+6irKNlJR0PKCIubUyLiNWSTpT0VkT8vgOb/xpwWRop+wzwRbLgdI6kycDzZAM3iIhFkuaQBYBrgVM8UtbMzMx6gnLfCuVfyUa1NjUbuBNod3AXEQuAkUUWjW5m/WnAtPaWZ2ZmZtYdlbtbtldErGqamNJ6l7ksMzMzM2ui3MFd73SLkg1I6g9sVuayzMzMzKyJcgd3FwFXSqprTEivZ6dlZmZmZpajsl5zFxFnS3oTuEvSlmT3lnsLmB4RM8pZlpmZmZltrOxzy0bEr4BfpeBOxa7BMzMzM7N8lD24axQRb+a1bTMzMzMrLq8ZKszMzMysAhzcmZmZmVWR3LplJX0EqCssIyIuyas8MzMzM8spuJN0KfBBYAHQOO1XAA7uzMzMzHKUV8vdSGBYRERO2zczMzOzIvK65m4h8E85bdvMzMzMmlHWljtJfyTrfu0PPCHpQWB14/KIOKKc5ZmZmZnZhsrdLXt2mbdnZmZmZm1Q7unH7gKQ9JOI+HbhMkk/Ae4qZ3lmZmZmtqG8rrn7VJG0w3Iqy8zMzMyScl9z91Xg/wEfkPRYwaL+wP+VsywzMzMz21i5r7n7PXAT8F/A1IL0VRHxSpnLMjMzM7Mmyn3N3evA68BEAEnbA32BLSVtGRHPl7M8MzMzM9tQLtfcSfoXSYuBZ8kGUSwla9EzMzMzsxzlNaDiP4GDgL9ExBBgNPDnnMoyMzMzsySv4G5NRKwENpG0SUTcAYzo6EYl9ZL0iKTr0/ttJd0maXF63qZg3TMlLZH0tKQxHS3bzMzMrDvIK7h7TdKWwD3AZZJ+Dqwtw3ZPB54seD8VmBcRQ4F56T2ShgETgD2BscAFknqVoXwzMzOzLi2v4G4c8DbwdeBm4K/Av3Rkg5JqgcOB3zQpZ1Z6PQs4siB9dkSsjohngSXAgR0p38zMzKw7KPetUACIiLck7QIMjYhZkjYHOtpydi7wLbJ75jXaISKWpTKXpdG5AIOB+wvWa0hpG5A0BZgCsPPOO3ewemZmZmaVl9do2ZOBK4Ffp6TBwLUd2N5ngeURMb/ULEXSYqOEiAsjYmREjKypqWlv9czMzMy6jFxa7oBTyLpBHwCIiMUFrWrtMQo4QtJnyO6bN0DS74CXJA1KrXaDgOVp/QZgp4L8tcCLHSjfzMzMrFvI65q71RHxXuMbSZtSpOWsVBFxZkTURkQd2UCJ2yPieGAuMCmtNgm4Lr2eC0yQ1EfSEGAo8GB7yzczMzPrLvJqubtL0neAfpI+RTbf7B9zKGc6MEfSZOB54BiAiFgkaQ7wBNko3VMiYl0O5ZuZmZl1KXkFd1OBycDjwP8H3MiGo1zbLSLuBO5Mr1eS3SC52HrTgGnlKNPMzMysu8hrtOw/JF0LXBsRK/Iow8zMzMw2VtZr7pT5gaSXgaeApyWtkPQf5SzHzMzMzIor94CKr5ONbD0gIgZGxLbAh4FRks4oc1lmZmZm1kS5g7sTgYlpVggAIuIZ4Pi0zMzMzMxyVO7grndEvNw0MV1317vMZZmZmZlZE+UO7t5r5zIzMzMzK4Nyj5bdR9IbRdJFNrOEmZmZmeWorMFdRPQq5/bMzMzMrG3ymn7MzMzMzCrAwZ2ZmZlZFXFwZ2ZmZlZFHNyZmZmZVREHd2ZmZmZVxMGdmZmZWRVxcGdmZmZWRRzcmZmZmVURB3dmZmZmVcTBnZmZmVkVcXBnZmZmVkUc3JmZmZlVEQd3ZmZmZlXEwZ2ZmZlZFekWwZ2knSTdIelJSYsknZ7St5V0m6TF6XmbgjxnSloi6WlJYypXezMzM7PO0y2CO2At8K8RsQdwEHCKpGHAVGBeRAwF5qX3pGUTgD2BscAFknpVpOZmZmZmnahbBHcRsSwiHk6vVwFPAoOBccCstNos4Mj0ehwwOyJWR8SzwBLgwE6ttJmZmVkFdIvgrpCkOmBf4AFgh4hYBlkACGyfVhsMvFCQrSGlNd3WFEn1kupXrFiRa73NzMzMOsOmla5AW0jaErgK+HpEvCGp2VWLpMVGCREXAhcCjBw5cqPlZmZmVnl1U2+odBXaZen0wytSbrcJ7iT1JgvsLouIq1PyS5IGRcQySYOA5Sm9AdipIHst8GLn1dbMrHP4n56ZNdUtumWVNdFdBDwZET8rWDQXmJReTwKuK0ifIKmPpCHAUODBzqqvmZmZWaV0l5a7UcAJwOOSFqS07wDTgTmSJgPPA8cARMQiSXOAJ8hG2p4SEes6vdZmZmZmnaxbBHcRcS/Fr6MDGN1MnmnAtNwqZWZmZtYFdYvgrhr4uhgzMzPrDN3imjszMzMzK42DOzMzM7Mq4uDOzMzMrIo4uDMzMzOrIg7uzMzMzKqIgzszMzOzKuLgzszMzKyKOLgzMzMzqyIO7szMzMyqiIM7MzMzsyri4M7MzMysiji4MzMzM6siDu7MzMzMqoiDOzMzM7Mq4uDOzMzMrIo4uDMzMzOrIg7uzMzMzKqIgzszMzOzKuLgzszMzKyKOLgzMzMzqyJVHdxJGivpaUlLJE2tdH3MzMzM8la1wZ2kXsAvgcOAYcBEScMqWyszMzOzfG1a6Qrk6EBgSUQ8AyBpNjAOeKKitbJOUzf1hkpXoV2WTj+80lUwM7NuTBFR6TrkQtJ4YGxEfDm9PwH4cEScWrDOFGBKersb8HSnV7Q8tgNernQlehjv887nfd75vM87n/d55+uu+3yXiKgptqCaW+5UJG2DSDYiLgQu7Jzq5EdSfUSMrHQ9ehLv887nfd75vM87n/d556vGfV6119wBDcBOBe9rgRcrVBczMzOzTlHNwd1DwFBJQyRtBkwA5la4TmZmZma5qtpu2YhYK+lU4BagF3BxRCyqcLXy0u27lrsh7/PO533e+bzPO5/3eeerun1etQMqzMzMzHqiau6WNTMzM+txHNyZmZmZVREHd+0gqZ+ku9IsGEi6WdJrkq5vst5oSQ9LWiDpXkm7trLd8yW9WfB+G0nXSHpM0oOShpdQt6WStit4/4mm9SqS505JY5qkfV3SBa2V1yTPXpJmtiVPOXTV4yHpB5L+q0naCElPtvHzbSbpbkkVvUa2yH5el/blAklzC9YbIukBSYslXZEGNHW07B53XremUsej2s7r1jTdzyntDEnvStqqIO39c1LSSZJ+0cp2N1onnbPN3pIjlXFfk7RNJb0kaVAbP9dsSUPbkqcryON4SNpC0srC/Cn9WknHtrF+p0r6Ylvy5MHBXft8Cbg6Ital9z8FTiiy3gzguIgYAfwe+F5zG0x/0Fs3Sf4OsCAi9gZOBH7esWo363Ky0cSFJqT0kkXE40CtpJ3LVbESddXjcTnw+SZpE1LZJYuI94B5RbbV2Zru53ciYkR6HFGw3k+AcyJiKPAqMLnphiTNlPSJnOvb3c/r1lTqeFTbed2apvsZYCLZHRmO6uS63E12LtYVpH0SWBgRy9q4rRnAt8pVsU5U9uMREW8BtwJHNqalQO+jQIs/Iou4GDitPfUoJwd37XMccF3jm4iYB6wqsl4AA9LrrWjmPnvpF8hP2fgPbRjZlx8R8RRQJ2mH9lY6/eK+OP06fEZS4wl4JfBZSX3SenXAjsC9kmZIqpe0SNJZBds6QNL/SXo0tWL1T4v+yMb/UPPWJY9HRDwNvCbpwwXJxwKzJZ0s6aG0/66StHkqewdlrYOPpsdHUr5r0+espA32czGSBBxKdk4BzKLgCzMPVXxet6Yix6MKz+vWbLCfJX0Q2JLsx+HEvAqV9KakaWl/3S9ph4j4B/AHNgyIJwCXSzownbuPpOfd0nZ6STpb0uPKeh2+lvLdA3xSXbzltIi8jkfTH4NHATcDm0iap6zX53FJ4wrKPjHt00clXQoQEW8DSyUd2IG6dJiDuzZS1qXxgYhYWsLqXwZulNRA1pI0vZn1TgXmFvnl9ShwdCr3QGAXspsxd8TuwBiyuXe/L6l3RKwEHgTGpnUmAFdENpT6u+nO3XsDH5e0d9oHVwCnR8Q+ZL8c30l564F/7mAdS9YNjsf7XxiSDgJWRsRisl+eB6T99yTrW1POA+5K6fsBjbfvWQgcUMJnzEUz+7lvCpDul3RkShsIvBYRa9P7BmBwJ1Sxqs7r1nSB41EV53VrmtnPE8k+/z3AbpK2z6n4LYD70z67Gzg5pRfu+z7AZ4CrgKeAj0XEvsB/AD9O608BhgD7pl6HywBSoLgE2Cen+pddzsfjZmB/SQPT+8ZW/neBoyJiP+AQ4H+U2RP4LnBoOkanF2yr4t8XDu7abjvgtRLXPQP4TETUAr8FftZ0BUk7AscA5xfJPx3YRtIC4GvAI8DaIusVKnZvm8K0GyJidUS8DCwHGlueCn+1FHZdHSvp4VT2nmStV7sByyLiIYCIeKPgn8dystaRztLVj8dsYLykTdhwvw6XdI+kx8l+ie6Z0g8l6y4hItZFxOuNr4H3ClqSOlux/bxzCpC+AJybfkE3O+2fpDFK14QBRwC/Se8fKKH8nnZet6bSx6NazuvWFNvPE4DZKTi6muz7oj2auw9ZY/p7rO8SnA/UAaTzc8vUMncYWQD4KllvxB8kLQTOYf2+/yTwq8ZzOSJeKSirq53XrcnteKTLBOaSndfbASPIumoF/FjSY8CfyH4c7UBqEU/fOV1uv3a35tiu4B2gb2srSaoB9omIxi/KK8h+GSDpFrKTox64BtgVWJL1oLC5pCURsWtEvAF8MeUR8Gx6tGQlsA3rJ0Helg0nRF5d8Hod68+Ba4GfSdoP6BcRD0saAvwbcEBEvKrsovK+ZCd7c19MfVnf2tEZuvTxiIgXJC0FPg58Djg4LZoJHBkRj0o6CfhECZ+1D9mvyErYaD9HxIvp+RlJdwL7krUgbC1p0/TP5P1p/yLiFrKbipPOpZkRcWeJ5fe087o1FT0eVXRet2aD/Sxpb2AocFv6ftgMeAb4ZTu23XhOFyo8r9fE+hvRFp7TkAXXE4A9WB9Y/wi4IyKOUnYJwp2N1ab7nNetyfN4QLYvv0e2z66LiDXpPK4B9k/vl9INvi/cctdG6RdSL0mtBRSvAltJ+lB6/ymybgoiYky66PnLEXFDRPxTRNRFRB3wdkTsCiBpa60f2fZl4O4UYJCuASjWvXInaTCBsmvHjgfuKOFzvZnyXsz6L4sBwFvA68quLTsspT8F7CjpgFRO/4LrNj5E1tXSKbrB8YBsf54D/DUiGlJaf2CZpN5seM3RPOCraZu9JA1IrwcCKyJiTet7pfya7mdlI4cbr2XbDhgFPJH+Gd0BjE9ZJ9HKdWGFfF6XpgscD6iC87o1Rb5fJgI/aPx+iIgdgcGSdmluG5KOUpPRxclDwChJ/5TWG0kW6L5QQtUuJ/sbOJT102puBfwtvT6pYN1bga80nsuSti1Y9iHWd5F3eTkfD8j+VoYCp7D++2IrYHkK7A4huxwHsnP62HQOF9uvFf2+cHDXPreSjaIBQNI9ZBe5jpbUIGlM+pV8MnCVpEfJ/jF9s43l7AEskvQU2T+g01N5m5C1Lr1SJM+PgF1TmY+QXVPxuxLLu5zs+ovZABHRuI1FZP8c/5zS3yO7oPf8VM5trP81dQhwQ5s+Zcd15eNBqsuepP2a/DvwANm+e6og/XTgkNStNZ/1XSuHADe2sb7lVrif9wDq0768A5geEU+kZd8GviFpCdk1XxeVsnGf121WyeMB1XNet6ZwP08ga90vdA0tD7b5IPBG08SIeIlsv9yorGv8XGBi6l5sUTq2bwO3RzbSE+C/gf+S9GeyKTcb/QZ4HngsnR9fgGyQC9kI67aOsq20XI4HvH8d4lVkfyd3p+TLgJGS6sl+sDyV1l0ETAPuSvu18DKfUWRduJUTEX608UHW3XFpBcsfDvys0vuhSL36APcDm/p4lL2Mq4HdKnx8c93PPq973vHoCud13vuZ7EdITaU/R5F6nQFMrnQ9qu14VPr/UePDc8u2k6QvAbNiw3vt9GjKbog5OEq/jqqcZVft8UhdwRMi4pIuUJeq3c/NqeR53ZrufDy60nndmu68n5uj7Ea7l8b6QUPdRlc+HpI+BSyO0u7gkF89HNyZmZmZVQ9fc2dmZmZWRRzcmZmZmVURB3dmZmZmVcTBnZn1WOmeVyFp9zJu8weS/qZstocnJOU2/6iZWTEO7sysJ5sI3EvL98Vqj3MiYgQwDvh1uqmvmVmncHBnZj2SpC3JbjY6mYLgTtImki6QtEjS9ZJulDQ+Ldtf0l2S5ku6RdKglsqIiMVkN5vdJuWfIak+bfusgjKXSjpL0sOSHm9sSZRUI+m2lP5rSc+lGSiQdLykB1ML4a/TzB1mZg7uzKzHOhK4OSL+AryibP5ZgKPJJmnfi2yauYMBUuvb+cD4iNifbHaLaS0VkLa5OCKWp6TvRsRIYG/g48rmxmz0ckTsB8wgm/sW4PtksxDsR3bn/Z3Tdvcgm01jVGohXMeG032ZWQ+2aeurmJlVpYlkUz5BNoXWROBhsqmN/hDZVER/l9Q4h+1uZLM2NE5S3gtobuqmMySdDHwAGFuQfqykKWTfvYOAYcBjadnV6Xk+WYBJqstRABFxs6RXU/poYH/goVSXfkBjAGlmPZyDOzPrcdJk34cCwyUFWaAWkr4FqLlswKKIOLiEIs6JiLMlHQ1cIumDZMHcvwEHRMSrkmayfu5agNXpeR3rv5tbqsusiDizhLqYWQ/jblkz64nGA5dExC4RURcROwHPkrWU3Qt8Ll17twPwiZTnaaBG0vvdtJL2bKmQiLgaqAcmAQOAt4DX03YPK6Ge9wLHpvI+Tbp2D5gHjJe0fVq2raRdSvvoZlbt3HJnZj3RRGB6k7SrgC8Ap5B1ey4E/gI8ALweEe+lgRXnSdqK7PvzXGBRK2X9EPg9sAfwSFr/GeDPJdTzLOBySZ8H7iLrBl4VES9L+h5wq6RNgDWp3s+VsE0zq3KeW9bMrAlJW0bEm6n79kGygQt/r0A9+gDrImJtajGckQZQmJk1yy13ZmYbu17S1sBmwI8qEdglOwNzUuvce8DJFaqHmXUjbrkzMzMzqyIeUGFmZmZWRRzcmZmZmVURB3dmZmZmVcTBnZmZmVkVcXBnZmZmVkX+f4UCS1U+8vBeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pandas uses maplotlib to plot\n",
    "fig, axes = plt.subplots(nrows=3, ncols=1,figsize=(10,10))\n",
    "\n",
    "#plot all in the Positive column\n",
    "df_a['Pos'][:,:].plot(ax=axes[0],kind='bar',fontsize='10',rot=0)\n",
    "axes[0].set_title('Cases (CDC June 20-July 17, 2021)', fontsize=10)\n",
    "axes[0].set_xlabel('Age Range', fontsize=10)\n",
    "axes[0].set_ylabel('Positive Cases', fontsize=10)\n",
    "axes[0].legend(fontsize=10)\n",
    "\n",
    "#plot all in the Hospitilization column\n",
    "df_a['Hosp'][:,:].plot(ax=axes[1],kind='bar',fontsize='10',rot=0)\n",
    "axes[1].set_xlabel('Age Range', fontsize=10)\n",
    "axes[1].set_ylabel('Hospitilization Cases', fontsize=10)\n",
    "axes[1].legend(fontsize=10)\n",
    "\n",
    "#plot all in the Death column\n",
    "df_a['Death'][:,:].plot(ax=axes[2],kind='bar',fontsize='10',rot=0)\n",
    "axes[2].set_xlabel('Age Range', fontsize=10)\n",
    "axes[2].set_ylabel('Death Cases', fontsize=10)\n",
    "axes[2].legend(fontsize=10)\n",
    "plt.savefig('covid_cases.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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: center;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Pos</th>\n",
       "      <th>Hosp</th>\n",
       "      <th>Death</th>\n",
       "      <th>Hosp % given Pos</th>\n",
       "      <th>Death 10th % given Pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">18-49</th>\n",
       "      <th>UnVac</th>\n",
       "      <td>76237</td>\n",
       "      <td>2666</td>\n",
       "      <td>155</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vac</th>\n",
       "      <td>13030</td>\n",
       "      <td>146</td>\n",
       "      <td>7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">50+</th>\n",
       "      <th>UnVac</th>\n",
       "      <td>25176</td>\n",
       "      <td>3443</td>\n",
       "      <td>851</td>\n",
       "      <td>13.7</td>\n",
       "      <td>33.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vac</th>\n",
       "      <td>9779</td>\n",
       "      <td>805</td>\n",
       "      <td>181</td>\n",
       "      <td>8.2</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">All</th>\n",
       "      <th>UnVac</th>\n",
       "      <td>101413</td>\n",
       "      <td>6109</td>\n",
       "      <td>1006</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vac</th>\n",
       "      <td>22809</td>\n",
       "      <td>951</td>\n",
       "      <td>188</td>\n",
       "      <td>4.2</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Pos   Hosp  Death  Hosp % given Pos  Death 10th % given Pos\n",
       "18-49 UnVac   76237  2666   155          3.5                  2.0         \n",
       "      Vac     13030   146     7          1.1                  0.5         \n",
       "50+   UnVac   25176  3443   851         13.7                 33.8         \n",
       "      Vac      9779   805   181          8.2                 18.5         \n",
       "All   UnVac  101413  6109  1006          6.0                  9.9         \n",
       "      Vac     22809   951   188          4.2                  8.2         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#compute conditional probabilities given Positive test  and add to the dataframe\n",
    "df_a['Hosp % given Pos']= 100.*df_a[\"Hosp\"][:,:]/df_a[\"Pos\"][:,:]\n",
    "df_a['Death 10th % given Pos'] = 1000.*df_a[\"Death\"][:,:]/df_a[\"Pos\"][:,:]\n",
    "display(df_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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: center;\">\n",
       "      <th></th>\n",
       "      <th>UnVac Pos</th>\n",
       "      <th>Vac Pos</th>\n",
       "      <th>UnVac Hosp</th>\n",
       "      <th>Vac Hosp</th>\n",
       "      <th>UnVac Death</th>\n",
       "      <th>Vac Death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18-49</th>\n",
       "      <td>85.4</td>\n",
       "      <td>14.6</td>\n",
       "      <td>94.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>95.7</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50+</th>\n",
       "      <td>72.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>81.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>86.5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>84.3</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       UnVac Pos  Vac Pos  UnVac Hosp  Vac Hosp  UnVac Death  Vac Death\n",
       "18-49    85.4      14.6       94.8        5.2       95.7         4.3   \n",
       "50+      72.0      28.0       81.0       19.0       82.5        17.5   \n",
       "All      81.6      18.4       86.5       13.5       84.3        15.7   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#compute conditional probabilities given All Pos Hosp and Deaths, what % of those are Unvac vs Vac  \n",
    "df_c = pd.DataFrame(index = ['18-49', '50+', 'All'])\n",
    "df_c['UnVac Pos']= 100.*df_a[\"Pos\"][:,\"UnVac\"]/(df_a[\"Pos\"][:,\"UnVac\"]+df_a[\"Pos\"][:,\"Vac\"])\n",
    "df_c['Vac Pos']= 100.*df_a[\"Pos\"][:,\"Vac\"]/(df_a[\"Pos\"][:,\"UnVac\"]+df_a[\"Pos\"][:,\"Vac\"])\n",
    "df_c['UnVac Hosp']= 100.*df_a[\"Hosp\"][:,\"UnVac\"]/(df_a[\"Hosp\"][:,\"UnVac\"]+df_a[\"Hosp\"][:,\"Vac\"])\n",
    "df_c['Vac Hosp']= 100.*df_a[\"Hosp\"][:,\"Vac\"]/(df_a[\"Hosp\"][:,\"UnVac\"]+df_a[\"Hosp\"][:,\"Vac\"])\n",
    "df_c['UnVac Death']= 100.*df_a[\"Death\"][:,\"UnVac\"]/(df_a[\"Death\"][:,\"UnVac\"]+df_a[\"Death\"][:,\"Vac\"])\n",
    "df_c['Vac Death']= 100.*df_a[\"Death\"][:,\"Vac\"]/(df_a[\"Death\"][:,\"UnVac\"]+df_a[\"Death\"][:,\"Vac\"])\n",
    "\n",
    "display(df_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pandas uses maplotlib to plot\n",
    "ax1 = df_c.plot.bar(rot=0, figsize=(5, 5),fontsize='20')\n",
    "ax1.set_title('Conditional Probabilities (CDC June 20-July 17, 2021)', fontsize=20)\n",
    "ax1.set_xlabel('Age Range', fontsize=20)\n",
    "ax1.set_ylabel('Probabilities (%)', fontsize=20)\n",
    "ax1.legend(fontsize=10,loc=1)\n",
    "plt.savefig('covid_cond_prob.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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: center;\">\n",
       "      <th></th>\n",
       "      <th>Pos</th>\n",
       "      <th>Hosp</th>\n",
       "      <th>Death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18-49</th>\n",
       "      <td>5.9</td>\n",
       "      <td>18.3</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50+</th>\n",
       "      <td>2.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>4.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pos  Hosp  Death\n",
       "18-49  5.9  18.3  22.1 \n",
       "50+    2.6   4.3   4.7 \n",
       "All    4.4   6.4   5.4 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute incidence ratios. \n",
    "#Relative risk for unVac compared to Vac for Pos, Hosp, Death categories\n",
    "ir = pd.DataFrame(index = ['18-49', '50+', 'All'])\n",
    "ir['Pos']= df_a[\"Pos\"][:,\"UnVac\"]/df_a[\"Pos\"][:,\"Vac\"]\n",
    "ir['Hosp']= df_a[\"Hosp\"][:,\"UnVac\"]/df_a[\"Hosp\"][:,\"Vac\"]\n",
    "ir['Death']=df_a[\"Death\"][:,\"UnVac\"]/df_a[\"Death\"][:,\"Vac\"]\n",
    "display(ir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pandas uses maplotlib to plot\n",
    "ax = ir.plot.bar(rot=0, figsize=(7, 10),fontsize='20')\n",
    "ax.set_title('Incidence Ratios (CDC June 20-July 17, 2021)', fontsize=20)\n",
    "ax.set_xlabel('Age Range', fontsize=20)\n",
    "ax.set_ylabel('Risk Ratio (UnVaccinated/Vacinnated)', fontsize=20)\n",
    "ax.legend(fontsize=20)\n",
    "plt.savefig('covid_ir.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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