hospital_data.ipynb

   1 {
   2  "cells": [
   3   {
   4    "cell_type": "code",
   5    "execution_count": 2,
   6    "metadata": {},
   7    "outputs": [],
   8    "source": [
   9     "import itertools\n",
  10     "import collections\n",
  11     "import json\n",
  12     "import pandas as pd\n",
  13     "import numpy as np\n",
  14     "from scipy.stats import gmean\n",
  15     "import datetime\n",
  16     "\n",
  17     "import matplotlib as mpl\n",
  18     "import matplotlib.pyplot as plt\n",
  19     "%matplotlib inline"
  20    ]
  21   },
  22   {
  23    "cell_type": "code",
  24    "execution_count": 3,
  25    "metadata": {},
  26    "outputs": [
  27     {
  28      "name": "stdout",
  29      "output_type": "stream",
  30      "text": [
  31       "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
  32       "                                 Dload  Upload   Total   Spent    Left  Speed\n",
  33       "100  1292    0  1292    0     0   1196      0 --:--:--  0:00:01 --:--:--  1196\n"
  34      ]
  35     }
  36    ],
  37    "source": [
  38     "!curl \"https://api.coronavirus-staging.data.gov.uk/v1/data?filters=areaName=United%2520Kingdom;areaType=overview&structure=%7B%22date%22:%22date%22,%22areaName%22:%22areaName%22,%22areaType%22:%22areaType%22,%22newAdmissions%22:%22newAdmissions%22,%22cumAdmissions%22:%22cumAdmissions%22%7D&format=csv\" | gunzip > hospital_admissions.csv"
  39    ]
  40   },
  41   {
  42    "cell_type": "code",
  43    "execution_count": 4,
  44    "metadata": {},
  45    "outputs": [],
  46    "source": [
  47     "raw_data = pd.read_csv('hospital_admissions.csv', \n",
  48     "                       parse_dates=[0], dayfirst=True,\n",
  49     "                      )"
  50    ]
  51   },
  52   {
  53    "cell_type": "code",
  54    "execution_count": 5,
  55    "metadata": {},
  56    "outputs": [
  57     {
  58      "data": {
  59       "text/plain": [
  60        "date             datetime64[ns]\n",
  61        "areaName                 object\n",
  62        "areaType                 object\n",
  63        "newAdmissions             int64\n",
  64        "cumAdmissions             int64\n",
  65        "dtype: object"
  66       ]
  67      },
  68      "execution_count": 5,
  69      "metadata": {},
  70      "output_type": "execute_result"
  71     }
  72    ],
  73    "source": [
  74     "raw_data.dtypes"
  75    ]
  76   },
  77   {
  78    "cell_type": "code",
  79    "execution_count": 6,
  80    "metadata": {},
  81    "outputs": [
  82     {
  83      "data": {
  84       "text/html": [
  85        "<div>\n",
  86        "<style scoped>\n",
  87        "    .dataframe tbody tr th:only-of-type {\n",
  88        "        vertical-align: middle;\n",
  89        "    }\n",
  90        "\n",
  91        "    .dataframe tbody tr th {\n",
  92        "        vertical-align: top;\n",
  93        "    }\n",
  94        "\n",
  95        "    .dataframe thead th {\n",
  96        "        text-align: right;\n",
  97        "    }\n",
  98        "</style>\n",
  99        "<table border=\"1\" class=\"dataframe\">\n",
 100        "  <thead>\n",
 101        "    <tr style=\"text-align: right;\">\n",
 102        "      <th></th>\n",
 103        "      <th>areaName</th>\n",
 104        "      <th>areaType</th>\n",
 105        "      <th>newAdmissions</th>\n",
 106        "      <th>cumAdmissions</th>\n",
 107        "    </tr>\n",
 108        "    <tr>\n",
 109        "      <th>date</th>\n",
 110        "      <th></th>\n",
 111        "      <th></th>\n",
 112        "      <th></th>\n",
 113        "      <th></th>\n",
 114        "    </tr>\n",
 115        "  </thead>\n",
 116        "  <tbody>\n",
 117        "    <tr>\n",
 118        "      <td>2020-03-23</td>\n",
 119        "      <td>United Kingdom</td>\n",
 120        "      <td>overview</td>\n",
 121        "      <td>1687</td>\n",
 122        "      <td>6794</td>\n",
 123        "    </tr>\n",
 124        "    <tr>\n",
 125        "      <td>2020-03-24</td>\n",
 126        "      <td>United Kingdom</td>\n",
 127        "      <td>overview</td>\n",
 128        "      <td>1931</td>\n",
 129        "      <td>8725</td>\n",
 130        "    </tr>\n",
 131        "    <tr>\n",
 132        "      <td>2020-03-25</td>\n",
 133        "      <td>United Kingdom</td>\n",
 134        "      <td>overview</td>\n",
 135        "      <td>1968</td>\n",
 136        "      <td>10693</td>\n",
 137        "    </tr>\n",
 138        "    <tr>\n",
 139        "      <td>2020-03-26</td>\n",
 140        "      <td>United Kingdom</td>\n",
 141        "      <td>overview</td>\n",
 142        "      <td>2222</td>\n",
 143        "      <td>12915</td>\n",
 144        "    </tr>\n",
 145        "    <tr>\n",
 146        "      <td>2020-03-27</td>\n",
 147        "      <td>United Kingdom</td>\n",
 148        "      <td>overview</td>\n",
 149        "      <td>2165</td>\n",
 150        "      <td>15080</td>\n",
 151        "    </tr>\n",
 152        "    <tr>\n",
 153        "      <td>...</td>\n",
 154        "      <td>...</td>\n",
 155        "      <td>...</td>\n",
 156        "      <td>...</td>\n",
 157        "      <td>...</td>\n",
 158        "    </tr>\n",
 159        "    <tr>\n",
 160        "      <td>2020-08-01</td>\n",
 161        "      <td>United Kingdom</td>\n",
 162        "      <td>overview</td>\n",
 163        "      <td>133</td>\n",
 164        "      <td>132672</td>\n",
 165        "    </tr>\n",
 166        "    <tr>\n",
 167        "      <td>2020-08-02</td>\n",
 168        "      <td>United Kingdom</td>\n",
 169        "      <td>overview</td>\n",
 170        "      <td>109</td>\n",
 171        "      <td>132781</td>\n",
 172        "    </tr>\n",
 173        "    <tr>\n",
 174        "      <td>2020-08-03</td>\n",
 175        "      <td>United Kingdom</td>\n",
 176        "      <td>overview</td>\n",
 177        "      <td>89</td>\n",
 178        "      <td>132870</td>\n",
 179        "    </tr>\n",
 180        "    <tr>\n",
 181        "      <td>2020-08-04</td>\n",
 182        "      <td>United Kingdom</td>\n",
 183        "      <td>overview</td>\n",
 184        "      <td>126</td>\n",
 185        "      <td>132996</td>\n",
 186        "    </tr>\n",
 187        "    <tr>\n",
 188        "      <td>2020-08-05</td>\n",
 189        "      <td>United Kingdom</td>\n",
 190        "      <td>overview</td>\n",
 191        "      <td>128</td>\n",
 192        "      <td>133124</td>\n",
 193        "    </tr>\n",
 194        "  </tbody>\n",
 195        "</table>\n",
 196        "<p>136 rows × 4 columns</p>\n",
 197        "</div>"
 198       ],
 199       "text/plain": [
 200        "                  areaName  areaType  newAdmissions  cumAdmissions\n",
 201        "date                                                              \n",
 202        "2020-03-23  United Kingdom  overview           1687           6794\n",
 203        "2020-03-24  United Kingdom  overview           1931           8725\n",
 204        "2020-03-25  United Kingdom  overview           1968          10693\n",
 205        "2020-03-26  United Kingdom  overview           2222          12915\n",
 206        "2020-03-27  United Kingdom  overview           2165          15080\n",
 207        "...                    ...       ...            ...            ...\n",
 208        "2020-08-01  United Kingdom  overview            133         132672\n",
 209        "2020-08-02  United Kingdom  overview            109         132781\n",
 210        "2020-08-03  United Kingdom  overview             89         132870\n",
 211        "2020-08-04  United Kingdom  overview            126         132996\n",
 212        "2020-08-05  United Kingdom  overview            128         133124\n",
 213        "\n",
 214        "[136 rows x 4 columns]"
 215       ]
 216      },
 217      "execution_count": 6,
 218      "metadata": {},
 219      "output_type": "execute_result"
 220     }
 221    ],
 222    "source": [
 223     "hospital_data = raw_data.dropna().set_index('date').astype({'newAdmissions': np.int64, 'cumAdmissions': np.int64}).sort_index()\n",
 224     "hospital_data"
 225    ]
 226   },
 227   {
 228    "cell_type": "code",
 229    "execution_count": 7,
 230    "metadata": {},
 231    "outputs": [
 232     {
 233      "data": {
 234       "text/plain": [
 235        "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c8fcb5d0>"
 236       ]
 237      },
 238      "execution_count": 7,
 239      "metadata": {},
 240      "output_type": "execute_result"
 241     },
 242     {
 243      "data": {
 244       "image/png": 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Znwz6zgVLbr8st32KpCfn6DuNT48FYNex6vay363cyw9f3saRk/WeqpZSSrXzyaC/v9TRb754dg53zh1NaLAwPCnKw7VytPQB8kuqAKhvbmXP8Rpa2gy/e2evJ6umlFKAjwb9faU1JEU78t18/7Jc8n56OUnRYZ6uFrGRoWTGR7CrxNHS31ZURZvdMDU7jn9vK2GbrqOrlPIwnwz6+8tqyU2JaX8fGxHqwdp0Nj59SHvQ33LkFAB/XjSVxCgbD7y1W9M0KKU8yueCvjGGgrJaRqdGe7oqLo1Li+XgyTrqmlr59MgpRiRHkRkfyfcvy2V9YUX7D4FSSnmCzwX9kqpGaptayU2N6X1nDxifPgRjYPexaj49UsnUrHgA5k0YCkB+SXVPhyul1KDyuaDvnLkz2luDfoZjMPftncc5WdfMtGFxAKTEhBEdFqILrSilPKrXoC8iWSKySkR2i0i+iNxulSeIyLsist96jrfKRUQeFpECEdkuItM6nGuxtf9+EVl8JhXe3x70vbN7Z+iQcOIjQ3l5czEA07IdLX3HHbtRHCiva9+3tc3Oa1uKaW2ze6SuSqnA05eWfitwlzFmLHAecKuIjAPuAd43xuQC71vvAeYDudZjCfAYOH4kgHuBWcBM4F7nD0V/7CutJTkmjLhIW38PdQsRYXx6LFUNLUTZgjv9RXJ6mob3dpfyg+XbeGvncU9UVSkVgHoN+saYY8aYLdbrGmA3kAEsAJZauy0FFlqvFwDPGYf1QJyIpAFXAu8aYyqMMaeAd4F5/a3w/tIar23lO41Ld3TxTM6K65T5c2RKNMesMQmAbcWO+fzv7y51fyWVUgGpX336IpIDTAU2AKnGmGPg+GEAUqzdMoCiDocVW2XdlfeZ3W66TNf0RuOtoO/s2nEamey4geyg1cWzwwr6q/aWaxePUsot+hz0RSQaeBW4wxjT0xQUV0ntTQ/lp3/OEhHJE5G88vLyTttKqhqob24j18tb+jNyEogJD+FzY1I6lTtz7h+wllXccbSK1CFhVDW0kHdYp3IqpQZfn4K+iITiCPgvGGNes4pLrW4brOcyq7wYyOpweCZQ0kN5J8aYJ4wx040x05OTkzttW7XH8RHeOnPHKT0ugh0/v5Jzh3Vu6WcnRBEcJBwor6WoooGqhhb++8IR2IKDtItHKeUWfZm9I8DTwG5jzB86bFoBOGfgLAZe71B+ozWL5zygyur+WQlcISLx1gDuFVZZr4wx/HX1Af739Xxm5MQzOTOuTxfnbWwhQQxLiKSgrJbtRx0pGc4fmch5IxN5f3dZL0crpdTZ60tL/wLg68ClIrLVelwF/Bq4XET2A5db7wHeBAqBAuBJ4LsAxpgK4JfAJutxn1XWqwff2ccDb+3h85PSeP7mWdhCfO72gnYjrBk8O4qrsAUHMTo1hrljUyg8UacLsCilBl2vi6gYY9biuj8e4DIX+xvg1m7O9QzwTH8qCPDixiPMHZvKwzdMJciD6+AOhJEpUazZV86nEZWMTYvBFhLEpWNS+Nnr+by/u5SRyd49XqGU8m1e32Qur2niZF0zs0cm+nzAB8dc/eY2O3mHK5iY6ci/nxkfyZihMazeV97L0UopdXa8PujvPe64A3fMUO8evO0rZ0vebmBSxmdjE+cOi2d7URV2u2bhVEoNHu8P+s60C34T9D9b7GVCRmz768mZcdQ0tXLwZJ2rw5RSakB4f9A/Xk1StM0rFkkZCHGRNpKibYSFBHW632BSluMHYHuxLrSilBo8PhD0azjHT1r5TuPTY5maHUdo8Gdf/6jkaCJCg9lWVOXBmiml/F2vs3c8bV9pLTfMzOp9Rx/yxy9P6XIrckhwEBMzYrWlr5QaVF7d0m9utdPQ0uY3g7hO8VGO9X1PNykzlvySalo0D49SapB4ddBvbGkD4JyhQzxcE/eYlBVHU6u9fcaSUkoNNO8O+q2OFm9uSmDcsDQ50zmYq/36SqnB4d1Bv6WN7IRIosK8fuhhQGQnRBIXGar9+kqpQeP1Qd/fZu70RESYmBHL1iIN+kqpweHVTeimVjvneHka5YE2JSuORz88wIHyWoYnRtHcZmdrUSU7iqs4WtlASWUDX5iWybwJQz1dVaWUD/LqoA8EVEsfHAuwtNkLuOzB1UTZgmlus9PS5pjgGR0WQpvdcKK2SYO+UuqMeH3Q97fpmr25KDeJ/3z/QnYerWJXSTURthBm5MQzNTuehCgbD7y1m2fWHqSxpY3w0GBPV1cp5WO8OuhPSI8NuFTDIsL49FjGp8e63D5jWAJ/XV3ItqJKZo1IdHPtlFK+zqsHckXwi3TKA8m5BKOuqauUOhNeHfRVV/FRNnJTotl0qE+LjimlVCca9H3QjOEJbD58ijbNva+U6icN+j5oRk48NY2tmq5BKdVvGvR90PRhCQDkHdYuHqVU/2jQ90GZ8REMHRLOpkM6mKuU6p9eg76IPCMiZSKys0PZz0XkqIhstR5Xddj2YxEpEJG9InJlh/J5VlmBiNwz8JcSOESEGcMT2HSwAmO0X18p1Xd9aek/C8xzUf6QMWaK9XgTQETGATcA461jHhWRYBEJBh4B5gPjgEXWvuoMnTcigePVjdz24qcUVdSf0TkaW9pY+skhik+d2fFKKd/T681Zxpg1IpLTx/MtAJYZY5qAgyJSAMy0thUYYwoBRGSZte+uftdYAXD99CzKqpv465oDvJtfyrRhccRF2BiZEsVdl5/j8v4G5/oE4aHBbCuq5K6Xt1FQVsv6wpM89rVz3X0JSikPOJs7cm8TkRuBPOAuY8wpIANY32GfYqsMoOi08lmuTioiS4AlANnZ2WdRPf8WGhzEnZeP5oaZWfzlgwL2l9aSf6yKt/OPM39CGhMyOt/R29jSxkW/XUV5TRNDwkOoa24jOTqMuWNTWJl/nKOVDWTERXjoapRS7nKmA7mPASOBKcAx4EGr3NXts6aH8q6FxjxhjJlujJmenJx8htULHGmxEdx/7USW33I+y799PgDrC0922e/tnccpr2li8fnDuHZqBt+6aAQr75zDLxZMQER4ft1hd1ddKeUBZ9TSN8aUOl+LyJPAG9bbYqDjKuaZQIn1urtyNUDSYiPISYxkfWEF/33RiE7bXtpURHZCJPdeM75T109sRChXjEtl2aYj3H5ZLhE2TeKmlD87o5a+iKR1eHst4JzZswK4QUTCRGQ4kAtsBDYBuSIyXERsOAZ7V5x5tVV3Zg1PZNOhCuwd7tY9crKedYUnue7cTJd9/TfNzqGyvoXXtx51Z1WVUh7QlymbLwLrgHNEpFhEbgZ+KyI7RGQ78DngTgBjTD6wHMcA7dvArcaYNmNMK3AbsBLYDSy39lUD7LyRCVQ1tLD7eHV72SubixCBL03PdHnMzOEJjE0bwrOfHNIpoEr5ub7M3lnkovjpHva/H7jfRfmbwJv9qp3qt1nDHemW1xdWMD49lja74eXNxczJTSYt1vVArYjwtfOy+Z9/7iS/pLrLILBSyn/oHbl+Jj0ugmGJkWywBnNX7yvjWFUj10/P6vG4qyemERos/OtT7eJRyp9p0PdDs4YnsOFgBUcrG7jn1R1kJUQwd1xKj8fERdq4eHQK/95eotk7lfJjGvT90HkjEqlqaOH6x9dR19TKUzfOICyk91k5C6emU1rd1P5XglLK/2jQ90POZRRLqhp4eNHUPi8uP3dsKtFhIfxLZ/Eo5bc06PuhjLgIFkxJ55cLJnDZ2NQ+HxceGsyV44fy1o7j7SkblFL+RYO+n/rTDVP52nnD+n3cginp1DS1smpP2SDUSinlaRr0VSezRyaSEGXjrZ3HPV0VpdQg0KCvOgkJDmLu2BRW7SmjudXu6eoopQaYBn3VxZXjh1LT1Mo6axZPa5udJ9cUcqC81sM1U0qdLQ36qosLRiURaQtmZb6ji+e1T49y/5u7ufaRj11m8PxofznLNh5xdzWVUmdAg77qIjw0mEvOSebdXaU0NLfxx3f3MTZtCClDwvn60xs6JWarbWrl9mVbuee1Hazdf8KDtVZK9YUGfeXSleOHUl7TxA9f3kZJVSP/e/VYXr1lNlOz47lr+TZ2Hq0C4Jm1B6moayZ1SBh3v7qd2qZWD9dcKdUTDfrKpUvOSSEkSPjPjmNclJvE7FFJxEaG8sTXzyUx2sYdL22ltLqRJ9cUcsW4VB796jRKqhp44M3dnq66UqoHGvSVS7ERoZw/0nFn793zxrSXx0Xa+P11kykoq2XBXz6mtrmVH1wxmnOHJXDzBcN5YcMRthdXeqraSqleaNBX3bp73hh+f93kLqmWL8pN5qbZORyvbuSaSemMGToEgO9cMhKAjQcr3F5XpVTfnM3C6MrPTciI7Ta3/j3zx5AUbeO6DimbE6PDSIyysa+0xl1VVEr1kwZ9dUbCQ4O57dLcLuW5qdHsK9X5/Ep5K+3eUQNqdGoMBWW1uuyiUl5Kg74aULmpMdQ2tVJS1ejpqiilXNCgrwbU6JRoAO3XV8pL9Rr0ReQZESkTkZ0dyhJE5F0R2W89x1vlIiIPi0iBiGwXkWkdjlls7b9fRBYPzuUoTxud6liwZb8GfaW8Ul9a+s8C804ruwd43xiTC7xvvQeYD+RajyXAY+D4kQDuBWYBM4F7nT8Uyr/ER9lIjgnrcTD38Mk6Pv/nj/j1W3soPlXvxtoppXoN+saYNcDpE68XAEut10uBhR3KnzMO64E4EUkDrgTeNcZUGGNOAe/S9YdE+YnRqdE9tvRf2lREfkk1T6w5wJzfruJ3K/e4sXZKBbYz7dNPNcYcA7CeU6zyDKCow37FVll35V2IyBIRyRORvPLy8jOsnvKk3JQY9pfVYrd3ncFjjOGN7ce4cFQSH919KddMTueRVQdYvU//WyvlDgM9kCsuykwP5V0LjXnCGDPdGDM9OTl5QCun3GN0agz1zW0crWygtc3OR/vLaWlzLMiy42gVRyrquWZSOhlxEfzmi5PITYnm7le2U9XQ4uGaK+X/zjTol1rdNljPzgVVi4GsDvtlAiU9lCs/NDrVMYNnf1kNv3tnL19/eiN/fG8fAG9sP0ZosHDl+KGA4yavB6+fTHltE7/4d77O71dqkJ1p0F8BOGfgLAZe71B+ozWL5zygyur+WQlcISLx1gDuFVaZ8kO51gyeJ9YU8tfVhSRF23h8dSE7iqt4Y1sJc3KTiY0Mbd9/UmYc371kJK9tOcqCRz7m5bwiGlvaPFV9pfxaX6ZsvgisA84RkWIRuRn4NXC5iOwHLrfeA7wJFAIFwJPAdwGMMRXAL4FN1uM+q0z5odiIUFKHhLG+sIJJmbG8dfscEqNsfHPpJkqqGvn85LQux9wxdzS/XDiB+uY2/t8r27nu8XVU1Wt3j1IDTbz5z+np06ebvLw8T1dDnYFvPruJLUdO8cb3LiQzPpJ38o+z5PnN2EKC2PzTucSEh7o8zhjDmzuOc+dLWxmTFsPzN88iNsL1vkop10RkszFmuqttmnBNDYpff3EiTS12MuMjAbhi/FBump1DWGhQtwEfQES4elIa4aFB3PL3zdz4zEZeWnIe4aHB7qq6Un5NW/rKa7214xjfeWELP7lqDEvmjHS5T1FFPW/tPEZ+STXDEiIZlRrDxaeNGSgVaLSlr3zS/IlpXJSbxKMfHmDRzOxOfyGU1zRx+7JP+eTASQDSYsP597YS7AYSo2z87Jpx/NfkdERczRZWKnBpwjXl1X505Rgq61t48qOD7WV7jlez8JGP+fRIJXfPG8NHP/oc6358Gbvum8crt5xPZnwEty/byree29x+f4BSykFb+sqrTcyM5aqJQ3n6o0IuHJXEmn3lPPvJIaLCgnn5lvM7rewVHhrM9JwEXvvuBTyxppDfvL2Hp9ce5JaLXXcNKRWItKWvvN4PLh9NQ0sb1/91HY9+WMCMnHhev/XCbpdyDA4SvnPJSK4Yl8of39tHUYUmdVPKSQdylU94dXMxNY0tXD0pneSYsD4dU1LZwOV/WM30nASe/cYM7d9XAaOngVxt6Suf8MVzM7npguF9DvgA6XER3HXFOazeV87bO48PYu2U8h0a9JVfWzw7h/TYcP619ainq6KUV9Cgr/xacJBwUW4ynxw4SavO5FFKg77yfxfmJlHT2F6EFC8AABWvSURBVMqOo1V92n9faQ0r84/T0KxJ35T/0Smbyu/NHpkIwMcFJ5ia3f0qnWU1jTz07j5e2lSE3UB0WAifn5TGD688h6Tovo8lKOXNtKWv/F5idBjj04fw0f4T3e5TUdfMlQ+t4eW8YhbPzuG5b85k3oShvPbpUb7/4qcuVwFTyhdpS18FhAtzk3hm7UHqm1uJtHX9Z//39Yc5Vd/Cv269gClZcQDMGZ3MjJx47n51B0+tLew2/49SvkRb+iogXDgqiZY2w4aDjmUciirq21M0NLa0sfSTQ3zunOT2gO90/fQsrhyfyu9W7iW/pPcxgcr6Zsprmgb+ApQaIBr0VUCYkZOALSSIVXvKeODN3cz53SpuXppHc6udf356lJN1zXxrzogux4kIv/7CJBKibHzvxU+pbux+YZdVe8q49MHVXPK7VSzPK9KlH5VX0u4dFRDCQ4OZmZPAc+sOA3BRriOPzw+Wb2XXsWomZAzh/BGJLo+Nj7Lx8A1T+epTG7jtH5/yzOLphAR/1l5qsxv+783dPL32IGOGxhAXGcqPXtnOe7tKmT9xKMMSoxifPoSwEF0TQHmeBn0VMK6dmsGhk3X8/JrxzB2Xyl9XH+CBt/YA8KcbpvSYpmHWiETuv3YCd7+6g1/9Zzc//6/x7dv++N4+nl57kBvPH8ZPrhqLLTiIp9YW8vt39vHOrlIALh6dzNJvzhzcC1SqDzToq4DxxXMz+eK5me3vv33xSJpa7awvPMlVE7uu23u6L8/IZn9pLU+tPUhwkHD3vDF8cuAEf/6ggOvOzeS+BRPa910yZySLZ+dQVNHACxsO87ePD1FYXsuI5OhBuTal+koTrinVD212w33/zmfpusNMzorjyMk6UoeE88/vXkCEzXX3TVl1I+c98D63XDySH80b4+Yaq0A0aAnXROSQiOwQka0ikmeVJYjIuyKy33qOt8pFRB4WkQIR2S4i087ms5XyhOAg4RcLJvDIV6ZxoKyW5lY7j3x1WrcBHyBlSDgXj07mtS1HadP5/srDBmL2zueMMVM6/KrcA7xvjMkF3rfeA8wHcq3HEuCxAfhspTzi6klpvPeDi1nxvQsZ2Ycum+umZ3G8upG1Bd3fIKaUOwzGlM0FwFLr9VJgYYfy54zDeiBORHrvSFXKSw2NDe9TwAe4bGwKcZGhvLK5uF+fUVXfQmOL5gBSA+dsg74B3hGRzSKyxCpLNcYcA7CeU6zyDKCow7HFVlknIrJERPJEJK+8vPwsq6eUdwgLCWbB5HRW5h+nqr77uf4drd5XzoW/+YCvPbVB1/pVA+Zsg/4FxphpOLpubhWROT3s62o+XJcOTmPME8aY6caY6cnJyWdZPaW8x/UzsmhutfP3DYd73ff5dYf45rObGBIRSt7hUzz4zr7Br6AKCGcV9I0xJdZzGfBPYCZQ6uy2sZ7LrN2LgawOh2cCJWfz+Ur5kvHpsXzunGSe/KiQ2qZWl/vUNLbwg5e28r+v53PJ6GRW3jmHRTOzeXz1AVbtKXN5jFL9ccZBX0SiRCTG+Rq4AtgJrAAWW7stBl63Xq8AbrRm8ZwHVDm7gZQKFLfPHU1lfQvPrTvUXmaMoay6kbd3HmP+nz7iX1uP8v3LcnnixulEh4Vw7zXjGDM0hjuXb+U962av07XZTacuoJrGFnaVVLMy/zhPrz3I0k8O6foACjiLefoiMgJH6x4cN3n9wxhzv4gkAsuBbOAIcJ0xpkIctzv+BZgH1APfMMb0OAlf5+krf7T4mY3sOFrFO3fOYdnGIyxdd7g9SVt2QiQPfXkK5w7rnPf/0Ik6ljyfx77SWq6emMbP/2t8+3rB1Y0tLHzkYwrL64gOCyE4SKhq6DpukBEXwf9cPZb5E4bqIvF+rqd5+npzllJutuXIKb7w6CeEhQTR1Grn0jEpXJSbxLi0IUzOiiM81PWc/+ZWO0+sOcDDHxSQmxLNq9+ZTXhoMD/91w7+seGI4w7jFjstbXYy4iPIio8kK8HxvK+0hntX5LPneA13zh3N7XNz3XzVyp006CvlZX6wfCsllQ388IpzmJ6T0K9jP9hTyjefzeP66Zl8eUY2X3r8E74xezg/u2Zcj8e1ttm546WtrMw/zn++fxGjU2Nc7ldR18zXntrADTOzuPH8nH7VTXkHDfpK+ZkH39nLnz8oID4ylPDQYN79wcVEh/WeSutkbRNz/7CaEcnRvPzt8wkK6trN89iHB/jN245EdPctGN8l8J+sbcKALiHpxXoK+ppwTSkfdMfc0WwtquSj/Sd48sbJfQr44Fg68qdXj+Oul7fxwobDfP20gN5mN/x9/WFm5MQTF2njZ6/nc/CEI79QQ3MbawtOsOXIKWIjQnlpyfmcM9T1XwvKe+kiKkr5oOAg4bGvncuL3zqPy8el9uvYL0zL4KLcJH779l5O1HZe5WvVnjKOVjZw0+zhPPKVacyfMJS/fXyIX7+1hz+9v5+m1jZu+9wobMFBfO3pDRw6UTeQl6XcQLt3lApABWW1XPnHNSyamcWvFk5sL7/xmY3sPV7N2rsvJdRaKMY51VOE9kHm/aU1XP/XdUTaQrj/2glclJtMsIuuotM5F5h31a2kBs6gZdlUSvmmUSnRfHVWNi9uLKKgrAaAgyfqWLOvnEUzs9sDPkCELZgIW3CnWUW5qTE8f/Msmtvs3PS3Tcz57Spe7SWvUJvd8NWnNnD7S1sH56JUn2jQVypA3X5ZLpGhwfzfm3v4uOAE33txCyFBwldmZvfp+AkZsXx896U88pVpJMWE8aNXt7OrpLrb/f++/jDrCk/y720l7CjufZF5NTg06CsVoBKjw7jt0lF8sKeMrz61gZO1zfzhy1NIGRLe53PYQoK4elIaS78xg/jIUH782naXawaUVjfyu5V7mTk8gZjwEB79sGAgL0X1gwZ9pQLY4tk5fHFaJj/7/DhW/fAS/mty+hmdJy7Sxs+uGc+24iqe/eRQl+2/fGMXzW12fvPFSSw+P4e384+3dysp99Ipm0oFsPDQYB68fvKAnOuaSWn8c0sxD76zl6OnGshNjeZETRPv7S5lW3EVd84dzfCkKL554XCeXnuQRz88wB+unzIgn636ToO+UmpAiAi/unYidyz7lH9sPExjiyMB3JSsOO6ZP4ZvXjAcgIQoG4tmZrN03SHKqpsYlRLNZWNTuHBUEiJCY0sbz607xJ7jNdQ3tSECF4xKYu7YVCLDgjl8op6K+mYyrVQTthDtsOgPnbKplBpwdruh+FQDEbbg9sRwHVXWN/Obt/eSX1LF/tJaGlramD0ykasmpvHYhwc4WtlARlwEUWHB1DW1cbSyweXnBAlcOiaVH1w+mnHpQwb7snyGpmFQSnmtptY2/rHhCH/+oICKumbGDI3h3mvGc/7IRMCRerqgrJYPrPUEhiVGER8ZSvGpBvaW1rBs4xGqG1u5Ylwq8yYM5aLc5E4/NCdqm/jLBwXYjSEnMYrosBCOVNRztLKByZmxLJiSQXyU7ayuYcuRU9z6whZa7Ya4iFCumpjGnZePPqtzng0N+kopr1fT2MLOo9XMyIknJLjvXTZVDS08uaaQZZuOcKK2GYDzRySyaFY2wSL87+s7qWlsITwkmBpr8ZrgICExykZZTROhwcLVE9P4ydVjSYnp+8wlp4q6Zq5++COCRJgzOonC8jo2HKzggS9MZFEfp78ONA36Sim/Z7cbdh2r5v3dZbyypYiiCkeX0ISMITx43RRGp0ZTUddMbVMr6XERhAYHsaukmpc3F/HChiNE2oK595pxLJyS0WW9gdY2O5sOnaK5zc4FIxPbf5TsdsNNz25i/YGTvPqd2UzMjKXNbrjpbxvZUFjB8lvOZ0pWXJ/rv2JbCRnxEczoZ+bV02nQV0oFFLvdsLbgBGU1TSyYkt7pDmNXCspq+dEr29hypJJLx6Rw/7UTSIuNYO/xGp5bd4i3dx7nZJ3jr4i02HAWTs3AGNh1rJo1+8r51cIJfO28Ye3nO1XXzDV/WUtrm+GOubnMGZ1Mm93w4b5ydh+rZkpWHBePTibVuifiaGUDd7+ynbUFJwgSuOuKc/jOxSPb01XUNbVy/5u7eSf/ON+eM5IbZw8jLOSzO6Rb2uw89dFBCspqqW9u5fGvT9egr5RSPWmzG5795BC/W7mH0KAgxmcMYX1hBeGhQcwdm8pVE9MIEnhxYxFr9pcTEiSkxUYwf+JQ7pk3pstfB/klVXz7+c0Un+o8CB1pC6beymcUHxlKpC2EU/WOH5R75o9h06FT/HtbCReOSuL8kYnERYby+OoDFJ9qYEJ6LDuOVjEsMZI7547m85PSaGy1890XtrBmXzkZcRFE2oJ5765LNOgrpVRfHD5Zx//8cycHT9TxlVnZfGVmdpeB3vrmVsJDgntNHOcchF69r5zgIOHi0ckMT4pib2kNq/eWc7SygbqmNkKDhe9eMorsxEiMcfz4PLKqoH2MIjshkt9fN5mZwxNYva+c//vPbvaW1pAZH0FEaDCFJ+q4f+EEbrDGELR7RymlfFB9cyvHqxpJj4volPDObje8v6eMRz8soKC0lr98dRoXj05u366LqCillA+KtIUwIjm6S3lQkHD5uFQuH5dKa5u9X7Od3H4rm4jME5G9IlIgIve4+/OVUsqf9Cfgg5uDvogEA48A84FxwCIR6Xk1Z6WUUgPG3S39mUCBMabQGNMMLAMWuLkOSikVsNwd9DOAog7vi62ydiKyRETyRCSvvLzcrZVTSil/5+6g72p+U6fpQ8aYJ4wx040x05OTk13srpRS6ky5O+gXA1kd3mcCJW6ug1JKBSx3B/1NQK6IDBcRG3ADsMLNdVBKqYDl1nn6xphWEbkNWAkEA88YY/LdWQellApkXn1HrojUAHsH4FSxQNVpZUnAibM8x0DVxZvP0dv35GvX447z9Pff1mDVY6DOM1jnOJPvyVuux1N1cfWduTrHOcaYGJdnMMZ47QPIG6DzPHG253Z1joGqizefo7fvydeux011Oet/t152PYNyjjP5nrzlejxVF1ffWX+/20BZXPLfXnKOgTqPt5xjoM7jLecYyPOcLW+6Hm85x0Cdx9/q0q9zeHv3Tp7pJmmQN5/bn+j31H/6nfWNfk/919fvrKf9vL2l/4SPntuf6PfUf/qd9Y1+T/3X1++s2/28uqWvlFJqYHl7S18ppdQA0qCvlFIBxK+DvohcKyJGRMZ4ui7eyPpunu/wPkREykXkDU/Wy1eISK2n6+BLevu+RORDEQn4gd3Bjlt+HfSBRcBaHOke+szK+x8I6oAJIhJhvb8cOOrB+iilzjBu9ZXfBn0RiQYuAG7G+vJE5BIRWSMi/xSRXSLyuIgEWdtqReQ+EdkAnO+5mrvdW8DV1utFwIvODSIyU0Q+EZFPredzrPKPRGRKh/0+FpFJbq21l7D+Tb3R4f1fROQm6/UhEfmFiGwRkR36F2fP35fqMW5192/sKhHZIyJrReThvvyV7rdBH1gIvG2M2QdUiMg0q3wmcBcwERgJfMEqjwJ2GmNmGWPWur22nrMMuEFEwoFJwIYO2/YAc4wxU4GfAf9nlT8F3AQgIqOBMGPMdrfV2LecMMZMAx4Dfujpyiiv113c6sL6f/avwHxjzIVAn3LR+3PQX4QjoGE9L7JebzSOlbvacLRqL7TK24BX3VtFz7OCdQ6O7+fN0zbHAi+LyE7gIWC8Vf4y8HkRCQW+CTzrlsr6ptes5804vmeletJd3HJlDFBojDlovX+xh33buTXLpruISCJwKY7+aoMjo6fBEdROvzHB+b7R+iEIRCuA3wOXAIkdyn8JrDLGXCsiOcCHAMaYehF5F8dSl9cDgTz41krnxlP4adubrOc2/PT/t37q7fsKWD3ErRW4/s5cLUrVK39t6X8JeM4YM8wYk2OMyQIO4mjVz7Ty+QcBX8YxYBLongHuM8bsOK08ls8Gdm86bdtTwMPAJmNMxeBWz6sdBsaJSJiIxAKXebpCXk6/r+51F7fA9Xe2BxhhNcjAEc965a9BfxHwz9PKXgW+AqwDfg3sxPGFnr5fwDHGFBtj/uRi02+BB0TkYxytjo7HbAaqgb+5oYpeR0RCgCZjTBGwHNgOvAB86tGKeSn9vvqkp7jV5TszxjQA3wXeFpG1QCl9SNMcUGkYROQS4IfGmM97ui6+TkTScXT3jDHG2D1cHbcTkcnAk8aYmZ6uiy/Q72twiEi0MaZWRAR4BNhvjHmop2P8taWvBpGI3Ihjls//BGjAvwXHoNlPPV0XX6Df16D6lohsBfJxdMf+tbcDAqqlr5RSgc5vWvoikiUiq0Rkt4jki8jtVnmCiLwrIvut53ir/Ksist16fGL9+ek81zwR2SsiBSJyj6euSSmlBprftPRFJA1IM8ZsEZEYHPOiF+KYdVJhjPm1FcDjjTF3i8hsYLcx5pSIzAd+boyZZaVg2IcjJUExsAlYZIzZ5YnrUkqpgeQ3LX1jzDFjzBbrdQ2wG8jAMZd8qbXbUhw/BBhjPjHGnLLK1wOZ1uuZQIF1A1czjhskFrjnKpRSanD5TdDvyJq3OhXHYGOqMeYYOH4YgBQXh9yMIwcNOH4oijpsK7bKlFLK5/ndHYJWwqJXgTuMMdWOmUw97v85HEHfmY7B1QH+0QemlAp4ftXSt3LBvAq8YIxx5jwptfr7nf3+ZR32n4TjztIFxpiTVnExkNXhtJlAyWDXXSml3MFvgr51c8LTOAZn/9Bh0wpgsfV6MfC6tX82jmRYX7cy2jltAnKtVA02HOlNVwx2/ZVSyh38afbOhcBHwA7AecPQT3D06y8HsoEjwHXGmAoReQr4Io5cIACtxpjp1rmuAv6II/XAM8aY+912IUopNYj8JugrpZTqnd907yillOqdBn2llAogGvSVUiqAaNBXSqkAokFfKaUCiAZ9pXogIj8XkR/2sH2hiIxzZ52UOhsa9JU6OwsBDfrKZ+g8faVOIyL/A9yII/FeOY403VXAEsAGFABfB6YAb1jbqnDc7AeOZeuSgXrgW8aYPe6sv1I90aCvVAcici7wLDALR0LCLcDjwN+c+ZlE5FdAqTHmzyLyLPCGMeYVa9v7wC3GmP0iMgt4wBhzqfuvRCnX/C7LplJn6SLgn8aYegARceZdmmAF+zggGlh5+oFWhtfZwMsdsruGDXqNleoHDfpKdeXqz99ngYXGmG0ichNwiYt9goBKY8yUwauaUmdHB3KV6mwNcK2IRFjLbl5jlccAx6z03V/tsH+NtQ1jTDVwUESuA0fm145rLyvlDbRPX6nTdBjIPYxjfYVdQB3wI6tsBxBjjLlJRC4AngSagC/hyPD6GJAGhALLjDH3uf0ilOqGBn2llAog2r2jlFIBRIO+UkoFEA36SikVQDToK6VUANGgr5RSAUSDvlJKBRAN+kopFUA06CulVAD5//gkZ5TeVWreAAAAAElFTkSuQmCC\n",
 245       "text/plain": [
 246        "<Figure size 432x288 with 1 Axes>"
 247       ]
 248      },
 249      "metadata": {
 250       "needs_background": "light"
 251      },
 252      "output_type": "display_data"
 253     }
 254    ],
 255    "source": [
 256     "hospital_data.newAdmissions.plot()"
 257    ]
 258   },
 259   {
 260    "cell_type": "code",
 261    "execution_count": 8,
 262    "metadata": {},
 263    "outputs": [],
 264    "source": [
 265     "# data_by_day.newAdmissions.dropna()"
 266    ]
 267   },
 268   {
 269    "cell_type": "code",
 270    "execution_count": 9,
 271    "metadata": {},
 272    "outputs": [
 273     {
 274      "data": {
 275       "text/html": [
 276        "<div>\n",
 277        "<style scoped>\n",
 278        "    .dataframe tbody tr th:only-of-type {\n",
 279        "        vertical-align: middle;\n",
 280        "    }\n",
 281        "\n",
 282        "    .dataframe tbody tr th {\n",
 283        "        vertical-align: top;\n",
 284        "    }\n",
 285        "\n",
 286        "    .dataframe thead th {\n",
 287        "        text-align: right;\n",
 288        "    }\n",
 289        "</style>\n",
 290        "<table border=\"1\" class=\"dataframe\">\n",
 291        "  <thead>\n",
 292        "    <tr style=\"text-align: right;\">\n",
 293        "      <th></th>\n",
 294        "      <th>cases</th>\n",
 295        "      <th>deaths</th>\n",
 296        "      <th>cases_culm</th>\n",
 297        "      <th>deaths_culm</th>\n",
 298        "      <th>cases_diff</th>\n",
 299        "      <th>deaths_diff</th>\n",
 300        "    </tr>\n",
 301        "    <tr>\n",
 302        "      <th>dateRep</th>\n",
 303        "      <th></th>\n",
 304        "      <th></th>\n",
 305        "      <th></th>\n",
 306        "      <th></th>\n",
 307        "      <th></th>\n",
 308        "      <th></th>\n",
 309        "    </tr>\n",
 310        "  </thead>\n",
 311        "  <tbody>\n",
 312        "    <tr>\n",
 313        "      <td>2019-12-31</td>\n",
 314        "      <td>0</td>\n",
 315        "      <td>0</td>\n",
 316        "      <td>0</td>\n",
 317        "      <td>0</td>\n",
 318        "      <td>NaN</td>\n",
 319        "      <td>NaN</td>\n",
 320        "    </tr>\n",
 321        "    <tr>\n",
 322        "      <td>2020-01-01</td>\n",
 323        "      <td>0</td>\n",
 324        "      <td>0</td>\n",
 325        "      <td>0</td>\n",
 326        "      <td>0</td>\n",
 327        "      <td>0.0</td>\n",
 328        "      <td>0.0</td>\n",
 329        "    </tr>\n",
 330        "    <tr>\n",
 331        "      <td>2020-01-02</td>\n",
 332        "      <td>0</td>\n",
 333        "      <td>0</td>\n",
 334        "      <td>0</td>\n",
 335        "      <td>0</td>\n",
 336        "      <td>0.0</td>\n",
 337        "      <td>0.0</td>\n",
 338        "    </tr>\n",
 339        "    <tr>\n",
 340        "      <td>2020-01-03</td>\n",
 341        "      <td>0</td>\n",
 342        "      <td>0</td>\n",
 343        "      <td>0</td>\n",
 344        "      <td>0</td>\n",
 345        "      <td>0.0</td>\n",
 346        "      <td>0.0</td>\n",
 347        "    </tr>\n",
 348        "    <tr>\n",
 349        "      <td>2020-01-04</td>\n",
 350        "      <td>0</td>\n",
 351        "      <td>0</td>\n",
 352        "      <td>0</td>\n",
 353        "      <td>0</td>\n",
 354        "      <td>0.0</td>\n",
 355        "      <td>0.0</td>\n",
 356        "    </tr>\n",
 357        "    <tr>\n",
 358        "      <td>...</td>\n",
 359        "      <td>...</td>\n",
 360        "      <td>...</td>\n",
 361        "      <td>...</td>\n",
 362        "      <td>...</td>\n",
 363        "      <td>...</td>\n",
 364        "      <td>...</td>\n",
 365        "    </tr>\n",
 366        "    <tr>\n",
 367        "      <td>2020-08-14</td>\n",
 368        "      <td>1129</td>\n",
 369        "      <td>-5359</td>\n",
 370        "      <td>314927</td>\n",
 371        "      <td>41347</td>\n",
 372        "      <td>120.0</td>\n",
 373        "      <td>-5539.0</td>\n",
 374        "    </tr>\n",
 375        "    <tr>\n",
 376        "      <td>2020-08-15</td>\n",
 377        "      <td>1440</td>\n",
 378        "      <td>10</td>\n",
 379        "      <td>316367</td>\n",
 380        "      <td>41357</td>\n",
 381        "      <td>311.0</td>\n",
 382        "      <td>5369.0</td>\n",
 383        "    </tr>\n",
 384        "    <tr>\n",
 385        "      <td>2020-08-16</td>\n",
 386        "      <td>1077</td>\n",
 387        "      <td>4</td>\n",
 388        "      <td>317444</td>\n",
 389        "      <td>41361</td>\n",
 390        "      <td>-363.0</td>\n",
 391        "      <td>-6.0</td>\n",
 392        "    </tr>\n",
 393        "    <tr>\n",
 394        "      <td>2020-08-17</td>\n",
 395        "      <td>1040</td>\n",
 396        "      <td>5</td>\n",
 397        "      <td>318484</td>\n",
 398        "      <td>41366</td>\n",
 399        "      <td>-37.0</td>\n",
 400        "      <td>1.0</td>\n",
 401        "    </tr>\n",
 402        "    <tr>\n",
 403        "      <td>2020-08-18</td>\n",
 404        "      <td>713</td>\n",
 405        "      <td>3</td>\n",
 406        "      <td>319197</td>\n",
 407        "      <td>41369</td>\n",
 408        "      <td>-327.0</td>\n",
 409        "      <td>-2.0</td>\n",
 410        "    </tr>\n",
 411        "  </tbody>\n",
 412        "</table>\n",
 413        "<p>232 rows × 6 columns</p>\n",
 414        "</div>"
 415       ],
 416       "text/plain": [
 417        "            cases  deaths  cases_culm  deaths_culm  cases_diff  deaths_diff\n",
 418        "dateRep                                                                    \n",
 419        "2019-12-31      0       0           0            0         NaN          NaN\n",
 420        "2020-01-01      0       0           0            0         0.0          0.0\n",
 421        "2020-01-02      0       0           0            0         0.0          0.0\n",
 422        "2020-01-03      0       0           0            0         0.0          0.0\n",
 423        "2020-01-04      0       0           0            0         0.0          0.0\n",
 424        "...           ...     ...         ...          ...         ...          ...\n",
 425        "2020-08-14   1129   -5359      314927        41347       120.0      -5539.0\n",
 426        "2020-08-15   1440      10      316367        41357       311.0       5369.0\n",
 427        "2020-08-16   1077       4      317444        41361      -363.0         -6.0\n",
 428        "2020-08-17   1040       5      318484        41366       -37.0          1.0\n",
 429        "2020-08-18    713       3      319197        41369      -327.0         -2.0\n",
 430        "\n",
 431        "[232 rows x 6 columns]"
 432       ]
 433      },
 434      "execution_count": 9,
 435      "metadata": {},
 436      "output_type": "execute_result"
 437     }
 438    ],
 439    "source": [
 440     "data_by_day = pd.read_csv('data_by_day_uk.csv', index_col='dateRep', parse_dates=True)\n",
 441     "data_by_day"
 442    ]
 443   },
 444   {
 445    "cell_type": "code",
 446    "execution_count": 10,
 447    "metadata": {},
 448    "outputs": [
 449     {
 450      "data": {
 451       "text/html": [
 452        "<div>\n",
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 456        "    }\n",
 457        "\n",
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 459        "        vertical-align: top;\n",
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 461        "\n",
 462        "    .dataframe thead th {\n",
 463        "        text-align: right;\n",
 464        "    }\n",
 465        "</style>\n",
 466        "<table border=\"1\" class=\"dataframe\">\n",
 467        "  <thead>\n",
 468        "    <tr style=\"text-align: right;\">\n",
 469        "      <th></th>\n",
 470        "      <th>cases</th>\n",
 471        "      <th>deaths</th>\n",
 472        "      <th>cases_culm</th>\n",
 473        "      <th>deaths_culm</th>\n",
 474        "      <th>cases_diff</th>\n",
 475        "      <th>deaths_diff</th>\n",
 476        "    </tr>\n",
 477        "    <tr>\n",
 478        "      <th>dateRep</th>\n",
 479        "      <th></th>\n",
 480        "      <th></th>\n",
 481        "      <th></th>\n",
 482        "      <th></th>\n",
 483        "      <th></th>\n",
 484        "      <th></th>\n",
 485        "    </tr>\n",
 486        "  </thead>\n",
 487        "  <tbody>\n",
 488        "    <tr>\n",
 489        "      <td>2019-12-31</td>\n",
 490        "      <td>0</td>\n",
 491        "      <td>0</td>\n",
 492        "      <td>0</td>\n",
 493        "      <td>0</td>\n",
 494        "      <td>NaN</td>\n",
 495        "      <td>NaN</td>\n",
 496        "    </tr>\n",
 497        "    <tr>\n",
 498        "      <td>2020-01-01</td>\n",
 499        "      <td>0</td>\n",
 500        "      <td>0</td>\n",
 501        "      <td>0</td>\n",
 502        "      <td>0</td>\n",
 503        "      <td>0.0</td>\n",
 504        "      <td>0.0</td>\n",
 505        "    </tr>\n",
 506        "    <tr>\n",
 507        "      <td>2020-01-02</td>\n",
 508        "      <td>0</td>\n",
 509        "      <td>0</td>\n",
 510        "      <td>0</td>\n",
 511        "      <td>0</td>\n",
 512        "      <td>0.0</td>\n",
 513        "      <td>0.0</td>\n",
 514        "    </tr>\n",
 515        "    <tr>\n",
 516        "      <td>2020-01-03</td>\n",
 517        "      <td>0</td>\n",
 518        "      <td>0</td>\n",
 519        "      <td>0</td>\n",
 520        "      <td>0</td>\n",
 521        "      <td>0.0</td>\n",
 522        "      <td>0.0</td>\n",
 523        "    </tr>\n",
 524        "    <tr>\n",
 525        "      <td>2020-01-04</td>\n",
 526        "      <td>0</td>\n",
 527        "      <td>0</td>\n",
 528        "      <td>0</td>\n",
 529        "      <td>0</td>\n",
 530        "      <td>0.0</td>\n",
 531        "      <td>0.0</td>\n",
 532        "    </tr>\n",
 533        "    <tr>\n",
 534        "      <td>...</td>\n",
 535        "      <td>...</td>\n",
 536        "      <td>...</td>\n",
 537        "      <td>...</td>\n",
 538        "      <td>...</td>\n",
 539        "      <td>...</td>\n",
 540        "      <td>...</td>\n",
 541        "    </tr>\n",
 542        "    <tr>\n",
 543        "      <td>2020-08-14</td>\n",
 544        "      <td>1129</td>\n",
 545        "      <td>-5359</td>\n",
 546        "      <td>314927</td>\n",
 547        "      <td>41347</td>\n",
 548        "      <td>120.0</td>\n",
 549        "      <td>-5539.0</td>\n",
 550        "    </tr>\n",
 551        "    <tr>\n",
 552        "      <td>2020-08-15</td>\n",
 553        "      <td>1440</td>\n",
 554        "      <td>10</td>\n",
 555        "      <td>316367</td>\n",
 556        "      <td>41357</td>\n",
 557        "      <td>311.0</td>\n",
 558        "      <td>5369.0</td>\n",
 559        "    </tr>\n",
 560        "    <tr>\n",
 561        "      <td>2020-08-16</td>\n",
 562        "      <td>1077</td>\n",
 563        "      <td>4</td>\n",
 564        "      <td>317444</td>\n",
 565        "      <td>41361</td>\n",
 566        "      <td>-363.0</td>\n",
 567        "      <td>-6.0</td>\n",
 568        "    </tr>\n",
 569        "    <tr>\n",
 570        "      <td>2020-08-17</td>\n",
 571        "      <td>1040</td>\n",
 572        "      <td>5</td>\n",
 573        "      <td>318484</td>\n",
 574        "      <td>41366</td>\n",
 575        "      <td>-37.0</td>\n",
 576        "      <td>1.0</td>\n",
 577        "    </tr>\n",
 578        "    <tr>\n",
 579        "      <td>2020-08-18</td>\n",
 580        "      <td>713</td>\n",
 581        "      <td>3</td>\n",
 582        "      <td>319197</td>\n",
 583        "      <td>41369</td>\n",
 584        "      <td>-327.0</td>\n",
 585        "      <td>-2.0</td>\n",
 586        "    </tr>\n",
 587        "  </tbody>\n",
 588        "</table>\n",
 589        "<p>232 rows × 6 columns</p>\n",
 590        "</div>"
 591       ],
 592       "text/plain": [
 593        "            cases  deaths  cases_culm  deaths_culm  cases_diff  deaths_diff\n",
 594        "dateRep                                                                    \n",
 595        "2019-12-31      0       0           0            0         NaN          NaN\n",
 596        "2020-01-01      0       0           0            0         0.0          0.0\n",
 597        "2020-01-02      0       0           0            0         0.0          0.0\n",
 598        "2020-01-03      0       0           0            0         0.0          0.0\n",
 599        "2020-01-04      0       0           0            0         0.0          0.0\n",
 600        "...           ...     ...         ...          ...         ...          ...\n",
 601        "2020-08-14   1129   -5359      314927        41347       120.0      -5539.0\n",
 602        "2020-08-15   1440      10      316367        41357       311.0       5369.0\n",
 603        "2020-08-16   1077       4      317444        41361      -363.0         -6.0\n",
 604        "2020-08-17   1040       5      318484        41366       -37.0          1.0\n",
 605        "2020-08-18    713       3      319197        41369      -327.0         -2.0\n",
 606        "\n",
 607        "[232 rows x 6 columns]"
 608       ]
 609      },
 610      "execution_count": 10,
 611      "metadata": {},
 612      "output_type": "execute_result"
 613     }
 614    ],
 615    "source": [
 616     "data_by_day.loc['2020-07-03', 'cases'] = 576\n",
 617     "data_by_day"
 618    ]
 619   },
 620   {
 621    "cell_type": "code",
 622    "execution_count": 11,
 623    "metadata": {},
 624    "outputs": [],
 625    "source": [
 626     "data_by_day = data_by_day.merge(hospital_data[['newAdmissions', 'cumAdmissions']], how='outer',\n",
 627     "    left_index=True, right_index=True)"
 628    ]
 629   },
 630   {
 631    "cell_type": "code",
 632    "execution_count": 12,
 633    "metadata": {},
 634    "outputs": [],
 635    "source": [
 636     "data_by_day['deaths_m7'] = data_by_day.deaths.transform(lambda x: x.rolling(7, 1).mean())\n",
 637     "data_by_day['cases_m7'] = data_by_day.cases.transform(lambda x: x.rolling(7, 1).mean())\n",
 638     "data_by_day['admissions_m7'] = data_by_day.newAdmissions.transform(lambda x: x.rolling(7, 1).mean())"
 639    ]
 640   },
 641   {
 642    "cell_type": "code",
 643    "execution_count": 13,
 644    "metadata": {},
 645    "outputs": [
 646     {
 647      "data": {
 648       "text/html": [
 649        "<div>\n",
 650        "<style scoped>\n",
 651        "    .dataframe tbody tr th:only-of-type {\n",
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 653        "    }\n",
 654        "\n",
 655        "    .dataframe tbody tr th {\n",
 656        "        vertical-align: top;\n",
 657        "    }\n",
 658        "\n",
 659        "    .dataframe thead th {\n",
 660        "        text-align: right;\n",
 661        "    }\n",
 662        "</style>\n",
 663        "<table border=\"1\" class=\"dataframe\">\n",
 664        "  <thead>\n",
 665        "    <tr style=\"text-align: right;\">\n",
 666        "      <th></th>\n",
 667        "      <th>cases</th>\n",
 668        "      <th>deaths</th>\n",
 669        "      <th>cases_culm</th>\n",
 670        "      <th>deaths_culm</th>\n",
 671        "      <th>cases_diff</th>\n",
 672        "      <th>deaths_diff</th>\n",
 673        "      <th>newAdmissions</th>\n",
 674        "      <th>cumAdmissions</th>\n",
 675        "      <th>deaths_m7</th>\n",
 676        "      <th>cases_m7</th>\n",
 677        "      <th>admissions_m7</th>\n",
 678        "    </tr>\n",
 679        "  </thead>\n",
 680        "  <tbody>\n",
 681        "    <tr>\n",
 682        "      <td>2019-12-31</td>\n",
 683        "      <td>0</td>\n",
 684        "      <td>0</td>\n",
 685        "      <td>0</td>\n",
 686        "      <td>0</td>\n",
 687        "      <td>NaN</td>\n",
 688        "      <td>NaN</td>\n",
 689        "      <td>NaN</td>\n",
 690        "      <td>NaN</td>\n",
 691        "      <td>0.000000</td>\n",
 692        "      <td>0.000000</td>\n",
 693        "      <td>NaN</td>\n",
 694        "    </tr>\n",
 695        "    <tr>\n",
 696        "      <td>2020-01-01</td>\n",
 697        "      <td>0</td>\n",
 698        "      <td>0</td>\n",
 699        "      <td>0</td>\n",
 700        "      <td>0</td>\n",
 701        "      <td>0.0</td>\n",
 702        "      <td>0.0</td>\n",
 703        "      <td>NaN</td>\n",
 704        "      <td>NaN</td>\n",
 705        "      <td>0.000000</td>\n",
 706        "      <td>0.000000</td>\n",
 707        "      <td>NaN</td>\n",
 708        "    </tr>\n",
 709        "    <tr>\n",
 710        "      <td>2020-01-02</td>\n",
 711        "      <td>0</td>\n",
 712        "      <td>0</td>\n",
 713        "      <td>0</td>\n",
 714        "      <td>0</td>\n",
 715        "      <td>0.0</td>\n",
 716        "      <td>0.0</td>\n",
 717        "      <td>NaN</td>\n",
 718        "      <td>NaN</td>\n",
 719        "      <td>0.000000</td>\n",
 720        "      <td>0.000000</td>\n",
 721        "      <td>NaN</td>\n",
 722        "    </tr>\n",
 723        "    <tr>\n",
 724        "      <td>2020-01-03</td>\n",
 725        "      <td>0</td>\n",
 726        "      <td>0</td>\n",
 727        "      <td>0</td>\n",
 728        "      <td>0</td>\n",
 729        "      <td>0.0</td>\n",
 730        "      <td>0.0</td>\n",
 731        "      <td>NaN</td>\n",
 732        "      <td>NaN</td>\n",
 733        "      <td>0.000000</td>\n",
 734        "      <td>0.000000</td>\n",
 735        "      <td>NaN</td>\n",
 736        "    </tr>\n",
 737        "    <tr>\n",
 738        "      <td>2020-01-04</td>\n",
 739        "      <td>0</td>\n",
 740        "      <td>0</td>\n",
 741        "      <td>0</td>\n",
 742        "      <td>0</td>\n",
 743        "      <td>0.0</td>\n",
 744        "      <td>0.0</td>\n",
 745        "      <td>NaN</td>\n",
 746        "      <td>NaN</td>\n",
 747        "      <td>0.000000</td>\n",
 748        "      <td>0.000000</td>\n",
 749        "      <td>NaN</td>\n",
 750        "    </tr>\n",
 751        "    <tr>\n",
 752        "      <td>...</td>\n",
 753        "      <td>...</td>\n",
 754        "      <td>...</td>\n",
 755        "      <td>...</td>\n",
 756        "      <td>...</td>\n",
 757        "      <td>...</td>\n",
 758        "      <td>...</td>\n",
 759        "      <td>...</td>\n",
 760        "      <td>...</td>\n",
 761        "      <td>...</td>\n",
 762        "      <td>...</td>\n",
 763        "      <td>...</td>\n",
 764        "    </tr>\n",
 765        "    <tr>\n",
 766        "      <td>2020-08-14</td>\n",
 767        "      <td>1129</td>\n",
 768        "      <td>-5359</td>\n",
 769        "      <td>314927</td>\n",
 770        "      <td>41347</td>\n",
 771        "      <td>120.0</td>\n",
 772        "      <td>-5539.0</td>\n",
 773        "      <td>NaN</td>\n",
 774        "      <td>NaN</td>\n",
 775        "      <td>-723.714286</td>\n",
 776        "      <td>970.428571</td>\n",
 777        "      <td>NaN</td>\n",
 778        "    </tr>\n",
 779        "    <tr>\n",
 780        "      <td>2020-08-15</td>\n",
 781        "      <td>1440</td>\n",
 782        "      <td>10</td>\n",
 783        "      <td>316367</td>\n",
 784        "      <td>41357</td>\n",
 785        "      <td>311.0</td>\n",
 786        "      <td>5369.0</td>\n",
 787        "      <td>NaN</td>\n",
 788        "      <td>NaN</td>\n",
 789        "      <td>-736.285714</td>\n",
 790        "      <td>1051.714286</td>\n",
 791        "      <td>NaN</td>\n",
 792        "    </tr>\n",
 793        "    <tr>\n",
 794        "      <td>2020-08-16</td>\n",
 795        "      <td>1077</td>\n",
 796        "      <td>4</td>\n",
 797        "      <td>317444</td>\n",
 798        "      <td>41361</td>\n",
 799        "      <td>-363.0</td>\n",
 800        "      <td>-6.0</td>\n",
 801        "      <td>NaN</td>\n",
 802        "      <td>NaN</td>\n",
 803        "      <td>-743.571429</td>\n",
 804        "      <td>1097.285714</td>\n",
 805        "      <td>NaN</td>\n",
 806        "    </tr>\n",
 807        "    <tr>\n",
 808        "      <td>2020-08-17</td>\n",
 809        "      <td>1040</td>\n",
 810        "      <td>5</td>\n",
 811        "      <td>318484</td>\n",
 812        "      <td>41366</td>\n",
 813        "      <td>-37.0</td>\n",
 814        "      <td>1.0</td>\n",
 815        "      <td>NaN</td>\n",
 816        "      <td>NaN</td>\n",
 817        "      <td>-744.000000</td>\n",
 818        "      <td>1094.142857</td>\n",
 819        "      <td>NaN</td>\n",
 820        "    </tr>\n",
 821        "    <tr>\n",
 822        "      <td>2020-08-18</td>\n",
 823        "      <td>713</td>\n",
 824        "      <td>3</td>\n",
 825        "      <td>319197</td>\n",
 826        "      <td>41369</td>\n",
 827        "      <td>-327.0</td>\n",
 828        "      <td>-2.0</td>\n",
 829        "      <td>NaN</td>\n",
 830        "      <td>NaN</td>\n",
 831        "      <td>-736.714286</td>\n",
 832        "      <td>1079.428571</td>\n",
 833        "      <td>NaN</td>\n",
 834        "    </tr>\n",
 835        "  </tbody>\n",
 836        "</table>\n",
 837        "<p>232 rows × 11 columns</p>\n",
 838        "</div>"
 839       ],
 840       "text/plain": [
 841        "            cases  deaths  cases_culm  deaths_culm  cases_diff  deaths_diff  \\\n",
 842        "2019-12-31      0       0           0            0         NaN          NaN   \n",
 843        "2020-01-01      0       0           0            0         0.0          0.0   \n",
 844        "2020-01-02      0       0           0            0         0.0          0.0   \n",
 845        "2020-01-03      0       0           0            0         0.0          0.0   \n",
 846        "2020-01-04      0       0           0            0         0.0          0.0   \n",
 847        "...           ...     ...         ...          ...         ...          ...   \n",
 848        "2020-08-14   1129   -5359      314927        41347       120.0      -5539.0   \n",
 849        "2020-08-15   1440      10      316367        41357       311.0       5369.0   \n",
 850        "2020-08-16   1077       4      317444        41361      -363.0         -6.0   \n",
 851        "2020-08-17   1040       5      318484        41366       -37.0          1.0   \n",
 852        "2020-08-18    713       3      319197        41369      -327.0         -2.0   \n",
 853        "\n",
 854        "            newAdmissions  cumAdmissions   deaths_m7     cases_m7  \\\n",
 855        "2019-12-31            NaN            NaN    0.000000     0.000000   \n",
 856        "2020-01-01            NaN            NaN    0.000000     0.000000   \n",
 857        "2020-01-02            NaN            NaN    0.000000     0.000000   \n",
 858        "2020-01-03            NaN            NaN    0.000000     0.000000   \n",
 859        "2020-01-04            NaN            NaN    0.000000     0.000000   \n",
 860        "...                   ...            ...         ...          ...   \n",
 861        "2020-08-14            NaN            NaN -723.714286   970.428571   \n",
 862        "2020-08-15            NaN            NaN -736.285714  1051.714286   \n",
 863        "2020-08-16            NaN            NaN -743.571429  1097.285714   \n",
 864        "2020-08-17            NaN            NaN -744.000000  1094.142857   \n",
 865        "2020-08-18            NaN            NaN -736.714286  1079.428571   \n",
 866        "\n",
 867        "            admissions_m7  \n",
 868        "2019-12-31            NaN  \n",
 869        "2020-01-01            NaN  \n",
 870        "2020-01-02            NaN  \n",
 871        "2020-01-03            NaN  \n",
 872        "2020-01-04            NaN  \n",
 873        "...                   ...  \n",
 874        "2020-08-14            NaN  \n",
 875        "2020-08-15            NaN  \n",
 876        "2020-08-16            NaN  \n",
 877        "2020-08-17            NaN  \n",
 878        "2020-08-18            NaN  \n",
 879        "\n",
 880        "[232 rows x 11 columns]"
 881       ]
 882      },
 883      "execution_count": 13,
 884      "metadata": {},
 885      "output_type": "execute_result"
 886     }
 887    ],
 888    "source": [
 889     "data_by_day"
 890    ]
 891   },
 892   {
 893    "cell_type": "code",
 894    "execution_count": 14,
 895    "metadata": {},
 896    "outputs": [
 897     {
 898      "data": {
 899       "text/plain": [
 900        "cases               687.000000\n",
 901        "deaths               43.000000\n",
 902        "cases_culm       277855.000000\n",
 903        "deaths_culm       42741.000000\n",
 904        "cases_diff         -299.000000\n",
 905        "deaths_diff         -87.000000\n",
 906        "newAdmissions       347.000000\n",
 907        "cumAdmissions    125307.000000\n",
 908        "deaths_m7           134.428571\n",
 909        "cases_m7            954.285714\n",
 910        "admissions_m7       353.714286\n",
 911        "Name: 2020-06-22 00:00:00, dtype: float64"
 912       ]
 913      },
 914      "execution_count": 14,
 915      "metadata": {},
 916      "output_type": "execute_result"
 917     }
 918    ],
 919    "source": [
 920     "data_by_day.loc['2020-06-22']"
 921    ]
 922   },
 923   {
 924    "cell_type": "code",
 925    "execution_count": 15,
 926    "metadata": {},
 927    "outputs": [
 928     {
 929      "data": {
 930       "text/plain": [
 931        "<matplotlib.axes._subplots.AxesSubplot at 0x7f01f6713410>"
 932       ]
 933      },
 934      "execution_count": 15,
 935      "metadata": {},
 936      "output_type": "execute_result"
 937     },
 938     {
 939      "data": {
 940       "image/png": 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\n",
 941       "text/plain": [
 942        "<Figure size 432x288 with 1 Axes>"
 943       ]
 944      },
 945      "metadata": {
 946       "needs_background": "light"
 947      },
 948      "output_type": "display_data"
 949     }
 950    ],
 951    "source": [
 952     "data_by_day[['cases_culm', 'cumAdmissions', 'deaths_culm']].plot()"
 953    ]
 954   },
 955   {
 956    "cell_type": "code",
 957    "execution_count": 16,
 958    "metadata": {},
 959    "outputs": [
 960     {
 961      "data": {
 962       "text/plain": [
 963        "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c41f2850>"
 964       ]
 965      },
 966      "execution_count": 16,
 967      "metadata": {},
 968      "output_type": "execute_result"
 969     },
 970     {
 971      "data": {
 972       "image/png": 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\n",
 973       "text/plain": [
 974        "<Figure size 432x288 with 1 Axes>"
 975       ]
 976      },
 977      "metadata": {
 978       "needs_background": "light"
 979      },
 980      "output_type": "display_data"
 981     }
 982    ],
 983    "source": [
 984     "data_by_day.loc['2020-04-01':, ['cases_culm', 'cumAdmissions', 'deaths_culm']].plot()"
 985    ]
 986   },
 987   {
 988    "cell_type": "code",
 989    "execution_count": 17,
 990    "metadata": {},
 991    "outputs": [
 992     {
 993      "data": {
 994       "text/plain": [
 995        "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c40cbdd0>"
 996       ]
 997      },
 998      "execution_count": 17,
 999      "metadata": {},
1000      "output_type": "execute_result"
1001     },
1002     {
1003      "data": {
1004       "image/png": 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CoFYPFX0MLBFCJOnJ3yX6NkUv8sBHeVzx/Df88NkN7C2xs3pvGWc9vo4fPruBVbtKgh7T4PK0kIFozeJxaVTVN7GzKLIpTNUNLpo8XtLjWz65CyFYOiWDrw9UhlR+WWpzkBpnxhgV/M88wRKNLcRB8cW6AQjsKTh9Yjomo4EPdxSHdA5F6CQkJDBq1CjefPNNQLtxbt++HYD58+fz1VdfYTAYsFgszJgxg7///e8sWrQI0MI/K1eupKCggIKCArZs2eLPAyxcuND/88svvxzx+rxeL0ePHuXUU0/lwQcfpKamhrq6uqCS1YEsXrzYf919+/Zx5MgRxo8f3+51Kioq8Hq9XHjhhdx///3dLmsdSggoHVgvhNgObAQ+kFKuBP4MnCmEyAfO1F8DfAgcBPYDzwI3AUgpq4D7gU361336NkUvsuNYLTkpseSV2Dnr8bVc/Y9N/kauotrgidwGpzto/N/HSblaTD3SaiB/7D6xbejm7CmZuL2Sz/KCzyAuqKj3G4fWTWCtSYgxYneE6AHYHJiMBr8uEWjdzyePS2PlzhIVBuoBXn75ZZ5//nmmT5/O5MmTeecd7RnTbDYzfPhwFixYAGgegd1u94dqjhw54n8PtPBSQkIC33zzDY8//jhPPvkkc+fO7dKYSI/HwxVXXOFP5v7yl79kyJAhPPbYY0yZMoXp06cTExPD2Wef3eK4m266CY/Hw9SpU7n00kv55z//2eLJvzXHjh3jlFNOYcaMGVx99dX86U9/injNQZFS9tmv2bNnS0XPUe90yZw735d/WbVXVtU55UMr8+QL6w9Kh8stx9z1gXzgoz1Bj/vJik1y6aNrOjz3kr+skctf+CaidX2RVypH/uZ9ubmgss17Ho9XLvjjp/K6FZuCHrv4wc/lkr+skXUOl1z66Jp295NSyh8+87W88G9fhrSmn7+6VZ70wGdttv9n61F9rVUhnaevsXv37t5egqKLBPsdAptlCPdY1Qk8iMkrsSOlNrg7yWri9qXj+fHCUZiNUSRbTe02SjU0eTr0AABGp1k5UtkQ0bp8M3yHxrd9ejcYBEsnZ7B2X7lflM6HlJLiWgd7S+3c/uZ2SmxtS0kDCScEVFLrCFpNdPrEdExRBj5SYSBFP0QZgEHM7iIbAJODlFSmxJmpqGvPALg7zAEAjEyxcrS6IeSa/UB8IaDWSWAfZ03JwOn2sqZViKmhyUOT28voNCsf7SyhpsEV9KbtI95iDD0JHKQqCTQjsig3VReuU2EgRf9CGYBBzK4iG4kx0WQFmWqVYjVRWe8MelwoHsDIlFhcHhnR8JVSu4Ok2GjMQcpMQZs9kGI18dHOlknq6gbNYF2/aDTLZgwDYGgHBiAhJjQPQOqD5TPbySecMzWTYzWNbC+MPKasUPQGSg56ELO72MakzISgZXEpcSaOHAkewgnVAAAcqWpgeHJsWOsqqW3bBBZIlEFw5qR03ttehMPlwRKtraVGT/4mWU38+QfTGJcez5kT09s9T4IlmoYmD26PN2ilkJSSPcV2Pt5VgsPlbXdNZ0xKJ8og+GxPKTOGDwnnoyoUvYryAAYpbo+XvGJbux21KVZzxzkAc+chIICCyvAlkzUZiPYNAGilpvVNnhaduL71JsWaiDFF8bNTx5JkbdsD4CMhRvsM7VUC3fTyVs55Yh1PfJ7PrBFDOHXC0KD7JcZEkxRroqIuuMekUPRVlAcwSDlUUY/T7Q0a/wfNA6hzuls8YftoaHITG92xB5CZYMFkNESUCC61OZiQEd/hPj4DEXjT9YWAkq3RQY9pTYJF28/mcAU1FN8V1nLS2FQevXQGafHtJ5MB4sxR1Dm7Zw6CQnG8UB7AIGWXngBu3wPQboiVrbwAr1fS6OrcAzAYBMOTYsL2ADxeSbm94xAQNHf3VgYkqn0hoCGx7T/1B5IQoxuAdhLBNoeLsUPjOr35g6ZW2roqSRE+gWJr4bB69Wq++uor/+urr766zypw9iWUARik7C62YTIa/Lr2rUmJ0256Va0qgRxuD1IGF4JrTU6KlcNhegCVdU68suPkLUCqvr5AD8AXAhoSE5oHEG/RjFiwRLDXK6lzuv1GojOsZiN1ygD0Gq0NgCI0lAEYpOwqqmV8ejzR7cgkpOhP2BWtKoGax0F2bgBGpMRypKohrPLIUr0HIL2Tp+5YUxSWaEMLD6WmoYkEi7Fd6YfW+ENAjW0NgN3pRkpIsIQWJbWaovwy2Yrw+MMf/sD48eM544wz2Lt3L0BQKWiA9957j/nz5zNz5kzOOOMMSktLKSgo4Omnn+bRRx9lxowZrFu3DoC1a9dy4oknMnr0aL83UFxczOLFi5kxYwZTpkzx7ztYUTmAQYiUkt1FNpZOzmh3H38IqJUH0OD0DYTv/E8nJ8VKQ5OH8jpn0KauYJR0IAMRiBCCFKu5VQ4geCy/PTpKAtt1ryAcD6Agwsa3vsIDGx8gryqv8x3DYELyBH4z7zftvu/T6fn2229xu93MmjWL2bNnc/311weVgj7ppJPYsGEDQgiee+45HnzwQR555BFuvPFG4uLiuP322wF4/vnnKS4uZv369eTl5XH++edz0UUX8corr7B06VLuvvtuPB4PDQ39+3fWVZQBGISU2BxUN7g61NT3h4BaewAu7WYZqgcAcLiyIWQDUNqBhHNrUuNbNqtVNzSFHP+HgBxAkBCQLy8QqgcQp0JAEbFu3Tq+//3vExur/a2cf/75OBwOvxS0D6dT+zssLCzk0ksvpbi4mKamJr/iZzAuuOACDAYDkyZNorRU046aO3cu11xzDS6XiwsuuMAvQT1YUQZgEPKd3rDUXgUQaDd4s9HQxgOo93sAoeUAQDMAocoll+kSzikhPMmnWk1+jwE0A5AW13nC1kecyYgQwUNAPqPgCxN1xkBIAnf0pN6TtO5D8Xq97UpB33LLLdx2222cf/75rF69mnvvvbfd8waKrPnCkIsXL2bt2rV88MEHXHnlldxxxx1hj3gcSKgcwCDksz2lxJuNTM1qv2lJC7GY2shBNPqngXX+7JA1JAaDgMNhVAKV2pwdSjgHkhJnamGgqutdJIXhARgMgnizEVvQEJDuAYQRAmpo8ihV0DBZvHgxb7/9No2Njdjtdt577z1iY2PblYKura0lK0sbJLhixQr/eeLj47Hb7Z1e7/DhwwwdOpSf/OQnXHvttd0ur9zfUAZgkOHxSj7dU8apE4ZiMnb860+JM7eRg/DPAw7BAzAZDWQlxYRVCVQaQhNY6/X5nu5qwgwBAcRbooN7APq2+JBDQNq/R4NL9QKEw6xZs7j00kuZMWMGF154oV/Tvz0p6HvvvZeLL76YRYsWkZraPMrzvPPO4+23326RBA7G6tWr/fN633rrLW699dae/YB9HBUCGmRsOVxNVX0TSya3L5HgIyWurSJoR/OAgzEy2Rq2B5A1JDQDkBpnxuWR2BrdWEwG6ps8ITeB+dD0gNp6AOGGgHweUb3TTVwnPRKKltx9993cfffdbbavXLmyzbZly5axbNmyNtvHjRvHd99953/tMyQ+6uq0jvHly5ezfPlyFBrKAxhkfLyrBFOUgVPGB5c1CCTZampbBeQrAw3xJjcyJZbDVWF4AO2obgYjNaBUNdwmMB8JFmOHSeDQPQBtP5UIVvQnlAEYREgpWbW7hIVjU0J6Sk2N08osA+v4fSGgUJLAoCWCaxpcIY1wdLo9VNU3hR4CsmpJvsq6Jr8MRDg5ANA9gGB9AA4XsaaokHsKfAaxvyeCFYMLZQAGEXkldo5WNbKkg/r/QFKsJpxur/+pHwJCQJ1oAfnwl4JWdR4GKrfrTWAdDHEJJDVe9wDqnM1CcOGGgCzRQfsAbA5XyOEfAKueA1AegKI/oQzAIOLjXSUIAWd0IJEciK8XIDAMVN/kxmQ0hPxknONXBe08DLTtaA0A2UmhyUc3ewDNIaDwPQBjO0lgt79RLBTi/B5A/0sCq0E2/Zeu/u6UARhErNpVyuwRSSGJm0FzLX6gHERjkyekJjAfWUnasJniTgbDeLySJz7LZ0yalQWjU0I6d1JsNEJARRdCQPGWaOxOd5vJZXZnuB6AZgD6mxyExWKhsrJSGYF+iJSSyspKLJbQQqbBUOUKg4R6p5vdxTZ+dea4kI9JCaK4We/0hNQD4CPObMRqivJr/LTH+98Vsa+0jr/+cCZRhrYDaoJhjDKQFKtNLouO0o4ZEhtuCKg5eZsYUPNva3T7k8yhYDX1zyRwdnY2hYWFlJeXd76zos9hsVjIzs6O+HhlAAYJvkqX1BCf/iG4HESjyx1yCaiP9ASLX+IhGG6Pl8c/zWdCRjznTs0M69ypcSYq7E2YjVG6QFx4a2uWhHa1NAAOF6PTrCGfx5cD6G9J4Ojo6A7lFBQDGxUCGiT4bkyhlm9CQAgowAMIZRxkazozAO9sK+JgRT2/OGMchhCf/pvXqDWDVTc0hR3+gZZDYQKxO9zhhYD8HkD/ywEoBi/KAAwSfJUu8WEYAEt0FFZTVIsQUIPTE3IJqI/0BDOl9vYNwN9W72fysASWhtCc1hqfHER1fVPYFUDQrAgaOBRGSomt0RVyDwBoshKxpqh+5wEoBjfKAAwSfLHpuDBuaqCFgQJDQA0ut/9pN1Q0D8AZNNHodHs4UF7PkkkZQYfTd0ZqnJnyOqcmBd1NHkCjy4PbK0PWAfKh6QEpA6DoPygDMEjwh4DCvHknW00thq5E5gFYaHJ7/aWagZTWasYlM0T5h9akxpmwO9yU2RxhdwFDswEI7AXwC8GFEQICnyS0CgEp+g/KAAwS/CGgMD2A1DhTmxxAJB4AEDQMVFyrlYdmdjIApj18ieqiWgfJYVYAQWAIqNk4hSsE50OFgBT9jZANgBAiSgjxrRDiff31KCHEN0KIfCHE60IIk77drL/er7+fE3COu/Tte4UQS7v7wyjaxx8CClOoLMVqpjJg6lZ9kzuiHABASW1bA+DT84/YAATMDYjEA/D9ewSGgGxhTgPzoeYCK/ob4XgAtwJ7Al4/ADwqpcwFqoFr9e3XAtVSyrHAo/p+CCEmAZcBk4GzgL8JIcK7kygiJpIqIIBkXRFUSomUUmsEM4cfAgIoC9ILUFzrGwEZE9Y5fQSWtSZF4AEYowzEmY0tksA2R3jTwHzEDYChMIrBRUgGQAiRDZwLPKe/FsBpwL/1XVYAF+g/L9Nfo79/ur7/MuA1KaVTSnkI2A/M644Poegcu1OTcOhsBkBr0uLMuL2Syvommjxe3F4ZViMYwFDdAwhWClpS6yDebIxYQjnVGmAAwpgHHEhrRdDmEFAkSWCVA1D0H0K9GzwG/Brw6q9TgBoppe9xpxDI0n/OAo4C6O/X6vv7twc5xo8Q4nohxGYhxGbVndh91DncYZWA+shNjwNgX6k9YBpYeB6A2RhFUmx0i/GNPoprGzsdAN8RKQHdupFUAUHboTB+DyAMLSDQhsKoEJCiP9GpARBCfA8ok1JuCdwcZFfZyXsdHdO8QcpnpJRzpJRz0tLSOlueIkTqne6wwz8A4zPiAdhbYg97GEwgvlLQ1pTUOrpkAKxmIzF6929ypB5AjLFVFVB4w2D8azGpEJCifxGKB7AQOF8IUQC8hhb6eQwYIoTw3VGygSL950JgOID+fiJQFbg9yDGKHqYuwklVaXFmkq0m3QD4xkGGf570BAtlQauAHAyLMP7vw+cFhKsD5CPBEt0qBKSFy8KVlYhVc4EV/YxODYCU8i4pZbaUMgctifu5lPJy4AvgIn235cA7+s/v6q/R3/9cah1A7wKX6VVCo4BcYGO3fRJFh9gd7rCbwEAbDj8+PZ49XfYAzG2qgFweL+V1zi55ANBcChppCEgbC9myCijcBDA0zwWuV81gin5CV/oAfgPcJoTYjxbjf17f/jyQom+/DbgTQEq5C3gD2A2sBH4mpVQZs+NEfVPks2rHZ8STX2qnzhG5B5CRYKGizonb4/VvK7M7kTLyElAfqVYTJqMhIsMEehK4sWUjWLjhHwicCqb+rBX9g7D+J0spVwOr9Z8PEqSKR0rpAC5u5/g/AH8Id5GKrlPncDM6NTIDMCEjnoYmD3tL7UBkHsDQBAteCZUBIx9L9CawrnoAI1OsHKqsj0hKAjQPwEh2jioAACAASURBVO5wIaVECKHpAIXZAwABQ2FC9AC8XsnuYhuThyVEvHaFoiuoTuBBQp0zshAQNCeCtx7RJnZFmgSGls1gvh6AzC7mAH61ZByvXb8g4uPjLUa8Eur1EFekISBfh3SoieDn1h/ke39dz89f26Y0hBS9gjIAg4RIk8AA49LjEQK2Hq4GtGRnuGT45CACSkFL/E1gXfMArGYjQ+MjP4dfEE4vBbU1hjcNLHAdENpQGLvDxd9WH2B4cgzvf1fED/72FYcrO5+brFB0J8oADAJcHi8OlzdiA2A1GxmRHMsxfaxjqAPhA/HJQZTam0tBi2ocxJqiInra7k78Q2H0RLDdEd48YB/NQ2E6zwE8t+4QNQ0u/vaj2fzzx/MornVw/YtbOj1OoehOlAEYBNRHqAMUyPj0eP/PsWFKQYBWqRNlEJQGhIBKbFoTWG/Hv4fqchKH9cH1Nocr7C5gCEwCd+wBVNU38fz6Q5w9JYOp2YmcPC6Ny+eP4EB5XZvZxApFT6IMwCAgUiG4QCboeYAog8AUFf6fTZRBkBZnbhECKq51dLkCqDuYlj0EqymKdfnlNLk1bymyMtDQksBPrzlAfZOb2wLmM2ckWjTJjbqOZycrFN2JMgCDgEiHwQQyPiMB0BLAkT6xpyeYW8hBlNQ6yEjoWgK4OzAZDZwwJoV1+RXNXcARVAGF4gHYHS5WfFXA92dkkRvgVflyJMHkMhSKnkIZgEGAr36/SyEg3QOItNYetFJQnyKo2+OlzO7sEx4AwKLcNA5XNrDjWC0Q/iwAaM6NdDQUZuOhKpxuLxfNzm6x3ZcILw4ima1Q9BTKAAwC6iKUgg4kJyUWk9EQ9jCYQDISLP6hMBV1TXi8sssVQN3FotxUAD7cUQyErwME2lxgaydDYb46UInJaGDWyKQW233/DsEUUxWKnkIZgEGAzwBE8lTrwxhlYFx6XNjDYAJJTzBT0+DC4fJ0eRJYdzMq1Up2Ugwf7yoFIgsBgVYi25EB+PpAJbNHJLXRGUq1mjEaREgewJp95XykGyqFoisoAzAI6I4QEMAvTh/HTaeMjfj40WmatPS/vj7s7wHoahNYdyGEYFFuGrWNkSmB+ojrYCpYdX0Tu4ttLByb0uY9g0FoiqkhGIC/fbGfO/79nVIeVXQZZQAGAd0RAgI4Y1I6507LjPj4syZncM7UDP740R5e36yNhugrHgDAYj0MBJF7S1ZzVLtDYTYcrATghDGpQd9PTzCH5AHYHG7qnG7+u+1YRGtUKHwoAzAI6I4y0O7AYBA8cvEMpgxLZPXecsxGQ8QSzj3BiWNTMegFTpGGgKym9j2Arw5UYjVFMS07Mej7mYkxIeUAfJVKL204gia0q1BEhjIAA51V/8spe+4lziSIMvS+4FiMKYpnr5pDeoKZ7KSYXm8CCyQxJpoZw4dgEGCNMNfR0Vzgrw5UMG9UMtHt9FGkJ1gornV0elO3O9wkxkSzp9jm12dSKCKhdx8JFT1P3ofMqDrAXUYDcE5vrwbQKl7e+umJfXJ84mVzR5AYEx2xYbK2YwBKbQ4OlNdz2dwR7R6bmWih0eXBpt/ggyGlxO5wsfzEHN7cXMjLGw4zu1VFkUIRKsoDGMhICbZjNBjiuNz7HnzzTG+vyE92UiwT9OayvsQlc4fzjx+3UTkPGas5KmgfwNcHfPH/tglgH+mJbRVTW1Pf5MGrz1D4/sws3t9RTHV9U8TrVQxulAEYyDRUgdvBO4mX841pPqz8DRSs7+1VDWisJmNQaeevDlSQGBPNpMz2jZ4vId5RN7Av/h9vieaKBSNpcnv5+9qDXVy1YrCiDMBAxlYIQKFM46nk/4EoE+xb2cuLGthY25kLvOVwNXNzkjF0kIfJaDUoJxi+yWUJlmjGZ8RzyZxsnl5zgE92l3bD6hWDDWUABjK1WpngUXcS0bFxkDIWyvf18qIGNsEE4Rqa3BysqGfysI5DXkN1yeyS2vYF4Zo9AO069y2bwrTsRH75+jb2l9V1ae2KwYcyAAMZm2YACtxJxJuNkJoLFcoA9CTB5gLvLbEjJUzqxACYjVGkWE2U2DrwAFoZAEt0FE9fMRuz0cAN/9pMYzs9CApFMJQBGMjUFoIhmkKnVbsxpY6DmsPgUnozPYVvKExghdOeYm2Wckfxfx8ZiZYOk8B2vas7sE9h2JAY7r9gCgfK6/n2SHVE61YMTpQBGMjYjiEThmFv8mpS0KnjQHqhSiUNewqfWF5gInh3cS3xZiPZSZ3LXmTovQDtYXME13XyGZcipSaqCANlAAYytceQCVm4PFKLTafmattVGKjH8M8FdrT0ACZmJoTUW5CRaOmwG9jWjlZRRmLnCWSFojXKAAwQ7A4Xf1m11x8jBsBWiMuqaffEmY1aEhigIr8XVjg4GJ1mBWBbodah6/VK9hTbOo3/+8hIsFCtK6YGw+5wY4oyYDa2/K9riY4i2WpSHoAiLJQBGCDc995unvh8Px/vLNE2eL1gK8YRG2AATFZIHAEVe3txpQOb9AQL07IT+VQvyzxS1UBDkyek+D90PhfA7nARbzEG9SYyO8kfKBStUQZgALBqVwlvbtFq/nfqE62oLwOvi3pLBhAwDlJVAvU4Z0xM59ujNZTbnewutgEwMUwD0N6N3OZwtytUl5looahGhYAUoaMMQD+nos7JXf/ZwYJ0yQfxf6T0SJ72ht4DYDOlAwFKoKnjtBCQ1xvaBeylsO2V0PdXcMbEdKSEz/NK2VNsI8ogyE2PC+nYzmYD+zyAYGQmxqiZwoqwUAagn3PPO7uwO9w8fKKbya6djC37FLfH6+8Cro1OAwINQC64GsBe1PnJvR54czn896fw6W976iMMOCZmxpM1JIZPdpexu8jGmDRrmwlg7dGpB9DYvgHISLRQ0+BSvQCKkOnUAAghLEKIjUKI7UKIXUKI3+nbRwkhvhFC5AshXhdCmPTtZv31fv39nIBz3aVv3yuEWNpTH2qw0Njk4aOdxVy9MIdsUQ7AbHaTX1bn9wAqjUOBgGEwqeO076GEgTb8DY58Ddlz4au/9ikxub6MEIIzJ6Wzfn852wtrQ47/g6bxYzVFtVsKane4251WNmyIb7B8ZGGg7wprqFLCcoOKUDwAJ3CalHI6MAM4SwixAHgAeFRKmQtUA9fq+18LVEspxwKP6vshhJgEXAZMBs4C/iaEiHzArIK8EhteiSYHXHMEgLmGvew8Uql1ARtjqPJoVSn+p0afAehMEqJsD3x2P0z4HlzzMYw/Fz76NeR90FMfZ0BxxsR0HC4vFXXOkOP/PoYmWCivCy4HYXe42/cAErQ+g1CmirXG7fFy2TMbeHiVKhAYTHRqAKSGT2QkWv+SwGnAv/XtK4AL9J+X6a/R3z9daCULy4DXpJROKeUhYD8Que6ugl1FWoJx8rAEvwGIEw4q9m+E2qOQmEW9Hg7wh4DihoIlsWMPwN0Eb98I5jj43mNgiIILn4P0KfDp73r0Mw0U5o9O9t+oQy0B9ZFsNVFVF/xJ3OZwEd+JBxBJIvhodSMNTR42F1SFfayi/xJSDkAIESWE2AaUAZ8AB4AaKaWv26UQyNJ/zgKOAujv1wIpgduDHBN4reuFEJuFEJvLy8vD/0SDiF1FNhJjoskaEqPd8NOnABBTtEELASVkUed0IwTE+iZcCaEngtsxAF4PvH09FG+D8x6HOC2HgCkWJl+glZA2qJtEZ0RHGThlvBZ+C9cDSLGagoZi3B4vDU2edkNA6QmdzxNoj/xSTa5iX2kdtY2uTvZWDBRCMgBSSo+UcgaQjfbUPjHYbvr3YO2OsoPtra/1jJRyjpRyTlpaWijLG7TsLqpl8jC9w7TmKGTNosIykhz7t0jbMUjMxu5wE2dqVTfuqwQq3Q3/+j78dQ5s/Rd43PDBr2DX27Dk9zDxvJYXHK47bMe2HL8P2Y+5+dSx/M85E0iNM4d1XEqcicr6tiEgn75QeyEgS7QmJhdJM1h+gJKo0hMaPIRVBSSlrAFWAwuAIUII319iNuArKykEhgPo7ycCVYHbgxyjCBO3x0teiV0L/7gatbr/ISOoy1zAbJEHdaWQkEW9093cA+AjNRfqSuDpk+DYVu3p/t2b4S8TYcs/4KRfwom3tL3osFkgDFC46fh8yH7O+Ix4rl88Juzjkq0mqhtcbWYK+GcBdDCwXhOTCz8EdKCsjmSrCYOArYeVARgshFIFlCaEGKL/HAOcAewBvgAu0ndbDryj//yu/hr9/c+lNuX6XeAyvUpoFJALbOyuDzLYOFBej9PtZfKwRE31EyBxBLHjTiFeNCKkFxK1EJC/AsjHiBPBYIQ518DPv4Xr18ClL0F8Bsy7AU6/J/hFzXEwdDIcVb+2niTFasbjlW1CMa2loIORmRgTURI4v6yOycMSmJCRoAbNDyJCGQqfCazQK3YMwBtSyveFELuB14QQvwe+BZ7X938e+JcQYj/ak/9lAFLKXUKIN4DdgBv4mZRSFSxHyK4ireNXSwDv0TYOGUHa6FHwsfZSJmRR0+BqTgD7GDEf/q9Cywf4mHhe25BPMIbPhR3/1hrDDKqNpCdIiTMBUFnvJMlq8m+3t6MEGkhmooVNYSZyvV7J/rI6fjhvBDkpXv6ztRCPVxLVwfQyxcAglCqg76SUM6WU06SUU6SU9+nbD0op50kpx0opL5ZSOvXtDv31WP39gwHn+oOUcoyUcryU8qOe+1gDn11FNizRBkanxWnxf4AhwxHxGRQZswH45coKvj5YSe7QIF2oIShTBiV7HjhtUJ4X4coVnZFi1XIGla0qgXweQHtJYIDMIRZqG11B5xK3x7GaRhpdHsYOjWP2yCTqmzzsLbFHsHJFf0M9wvVTdhXVMiEjQXtKqzmihXTiNeG30uS52j518dy/bDL3XzCl+y6crZ1b5QF6jmT9qb91JZB/GExHBiDR1wwWehjIN0oyN10zAABbVCJ4UKAMQD9ESsnuIlvzjNnao5CQpdXrA2MuuJu8Offz4a/P4coTckKWIQiJlDEQkwyFKg/QU/hCQBWtDIBvFkBnOQCA4prwDcDYtDiyk2JIizerRPAgIZQcgKKPUVjdiM3h1hLAoHkAQ0b4308YlkvCsNyeubgQmhdwVHkAPUVSrO4B1AX3ADrLAUB4chD5ZXZS48z+fMPsEUlsUQZgUKA8gH6ILwHs7zCtOdrCAPQ4w+dqDWGN6ibRE5iMBhIsRqpa9QLYHS5iTVEYo9r/b+trBgsnBJRfVtciTzR7ZBJHqhoosytl0YGOMgD9kF1FmsTwhIx4TbbBXgyJwzs/sLvw5QFUQ1iPkRJnbhsCcrg6jP9DczNYqAZASsn+0jrGBhiAeaOSAfjgu+IwV63obygD0A/ZXljL2LQ4LbZvKwTk8fUAsmZrDWGH1h2/aw4yUoLoAXUkBBdI5hBLyCGgMrsTu9PdYl7BtOxEFoxO5skvDoRVTaTofygD0M/weCVbD1czd5RWreETgWPIcfQAzPEw5jTY8aamHaTodpKD6AGFagAyEmJC1gPKL9UTwAEegBCCO5aOp6LOyT++LAh90Yp+hzIA/Yw9xTbqnG7m5mhuenMPwHH0AABmXqFJTh9cfXyvO0gIpgdkc7g6lIHwkZ0UQ0FlfUheQH6ZVu+fOzS+xfbZI5M5fcJQ/r7mALUNShxuoKIMQD9j4yGty9MXp6X2qBaOSWgjrNqzjD8HYpLg25eO73UHCSlWM1X1TS30gDQPoHMDcOUJIzEIwa2vbdOmw3VAflkdiTHRpMaZ2rx3+9Lx2Bxu/r72QPgfQNEvUAagn7GpoIrspBh/vTc1RyB+GER1fmPoVoxmmHqxNiBGVQN1O8lWE14JNQF6QB2NgwxkTFocv79gChsPVfHE5/s73PdoVQM5KbEt1WJ1JmYmcN70Yfzzq4JODYmif6IMQD9CSsmmgirm+cI/oJeAHsf4fyAzLgePU9MGUnQrvmawwFLQjsZBtuYHs7K5cFY2f/08n68OVLS7X5nNyVC9dDQYi3JTaWjycCyCITOKvo8yAP2IQxX1VNQ1MXdUoAE4cnxLQAPJnA7pU2Hby71z/QFMaz0gh8tDk8cbkgfg475lk8lMsPDcukPt7lNmd5Ce0P68gtGp2kjRgxX1IV9X0X9QBqAf4VN59CeAG2ug9kjznN/jjRAw83Io+labIazoNnx6QJV6JZBfCC6EJLAPq9nInJzkdoXdnG4P1Q0u0uPb9wBG6QbgULkyAAMRZQD6ERsPVZNiNTEmTftPybHN2vfhc3tvUZP0UdD7Pu69NQxAUuNaGoBmIbjw1FvGZ8RzrKbRP00skDKbFl5K7yAElGw1EW8xckh5AAMSZQD6EZsKqpiTk9ScsDu6CRBaY1ZvkZCpzSLe/2nvrWEA4tPl8TWDhaIDFAyfxINv5m8gZXbNAKR1EAISQjA61UpBpTIAAxFlAPoJpTYHR6oamsM/oEkyD52kNWb1JmPPgCNfg8PWu+sYQERHaXpAvl4AnxJoqElgH+PStb8NX8NXIGU2rVmsoxAQaGGggyoENCBRBqAfsLfEzt1v7wAC6v+9Xijc3LvhHx+5Z4LXDYfW9vZKBhSpceY2IaBQ+gACGZ4ci9loYF8QD6DUZwA68AAARqXGUVTbiMOlur4HGsoA9GG8Xsltb2xj6WNr+fpAJb84I5epWboEdMU+cNZqE7p6m+HzwRQP+z/p7ZUMKJID9IBCmQccjCiDIDc9jr3BDIDdSXSU8MtPt8eoNCtSwuHKhrCurej7qHkAfZij1Q38Z+sxLpyVzf+eO7HFfFj/QJbhfcAAREXD6JMh/1OQMvJxk4oWJFtN/tj7vlI7RkPnN+tgjBsaz1cHKttsL7M5SYszY+hk9u+oFL0SqKKO8Rm9HG5UdCvKA+jD+MTAzp2W0fLmD1r83zIEksf0wsqCkHumpkxavre3VzJgSInT5CDsDhdvbi7k3GmZxJjCn+6Wmx5Pic1BbWNLTZ8yu6PDJjAfOamxAByqUB7AQEMZgD5MdYNmAII+9R3dpOnyG/rIr3DsGdp3FQbqNlJ0RdDXNx2lzunmmoWjIjrPOF3qeX9ZyzBQqa3jJjAf8ZZo0uLNHKpom0hW9G/6yN1DEYyqeu2JLdlqAmcdbHsV3E5w1EJ5Xt8I//hIzIa0iaoctBvx6QE9veYgc0YmMX34kIjO46sE2lvS8gZeanN22AMQyKhUq+oFGIAoA9CH8enAJFtN8M1T8N8b4fklsPMtQDZP5uor5J4JBV9qHcqB2Eu1qiVFWPiHw9c5ueakyJ7+AbKGxBBrimpRCeRweahtdDE0vnMPALQ8gDIAAw9lAPowVfUuoqMEcWaj1mmbOByqD8H7v6TXG8CCMekC8Lpg74fN2yry4dHJ8Na1anhMmPj0gLKGxLBkUnrE5zEYBLlD4/za/wDlehNYKDkA0CqBKuqa/NVIioGBMgB9mOr6JpJiTYj6Cq3mf+aVcMM6rfRz1GKwJPT2EluSNUsbTLPzP83btq7QegR2/QfeuVl5AmGQkajdnK8+MafDQfChkJsez76AZrDmHoDQQ0AABcoLGFAoA9CHqWpo0sI/+z8BJIw/C5JGwnWfwFXv9Pby2iIETP4+HPwCGqq0gfXbXoWJ34NT74btr8AHt2mloopOGTs0jtevX8CPF+Z0+Vzj0+Mptzup1ivLSnUdoFBDQD5VUBUGGlgoA9CH8XkA7FsJ8ZmQMa35zb5aaz/5+9oTf977sPcDaKiAWVfD4jvghJthyz+gZEdvr7LfMH90Spef/gH/0HdfHqDMHp4HMCIlFiFQkhADjE7/soQQw4UQXwgh9gghdgkhbtW3JwshPhFC5Ovfk/TtQgjxhBBivxDiOyHErIBzLdf3zxdCLO+5jzUwqGpoIi1WwP7PYdzSvnvTDyRzBiSNgl1vw5YVWt5izKna2k/8ubZP60qhhirwqNhyTzIxUwsXbi/UEvSlNl8XcGjSEmZjFFlDYpQHMMAI5dHCDfxKSjkRWAD8TAgxCbgT+ExKmQt8pr8GOBvI1b+uB54CzWAA9wDzgXnAPT6joQhOdX0TM+UeaLLDuLN6ezmh4Q8DrdEGxs+8Agx681J8ujZA5sDnzfu7HPDkPD2xregp0hMsTMxM4JPdpYAmBDc03hJ0FGR7jEq1clipgg4oOjUAUspiKeVW/Wc7sAfIApYBK/TdVgC6MDzLgBelxgZgiBAiE1gKfCKlrJJSVgOfAP3krnb88XglNY0upjd8DUYLjDq5t5cUOpO/D9KjD4y5ouV7Y0+DIxu0vgaA/FVQX64Nly/dffzXOohYMimdzYerqahzUmZ3MjSEJrBAcvRSUKlyOAOGsIKLQogcYCbwDZAupSwGzUgAQ/XdsoCjAYcV6tva2976GtcLITYLITaXl5eHs7wBRU1DE0J6GVvzpVbxY4rt7SWFTsZUGDoZxp+jNYgFMuZ0rVS0YJ32+rvXwZoG5gT47L7jv9ZBxNLJGUgJn+0p1bqAO5GBbs3IlFhsDjc1DSpcN1AI2QAIIeKAt4BfSCk7En4P5lPKDra33CDlM1LKOVLKOWlpaaEub2DhcePe+gqfmO4gofFo89St/oIQcM1H8INn2743YgFEx2p5gIYqzQOYegmcdCvs+wgOf3381ztImJgZT3ZSDB/vKg1ZBiKQHF0UTg2HGTiEZACEENFoN/+XpZS+Iu9SPbSD/r1M314IBE4pzwaKOtiuaM3rl5P++S9wYmL3ov8HM37U2ysKH0ticK/FaIacRbD/M9j9DniaYNolMP+nEJcBn96rykR7CCEESyZlsD6/ApvDHXITmA+fKJyShR44hFIFJIDngT1Syr8EvPUu4KvkWQ68E7D9Kr0aaAFQq4eIPgaWCCGS9OTvEn2bIhB7CexbyaHcH3NO0x/xTji/f1T/hMPY07WO5i8fh9TxkDldMxan/AaObtD6CBQ9wtLJ6TR5tGa8UHsAfGQnaaWgygMYOITiASwErgROE0Js07/OAf4MnCmEyAfO1F8DfAgcBPYDzwI3AUgpq4D7gU361336NkUgez8CYE/GeYDw68EMKMacrn2vPqQ9/fsM3IzLNYnrba/03toGOLNHJmnNhYTeA+DDEh3FsMQY5QEMIDodCCOlXE/w+D3A6UH2l8DP2jnXC8AL4Sxw0JH3ASTlcEiMAPZFNACkz5MyRpOMqDkCUy9u3m40w5QLNQPgsPU9qYsBgDHKwOkThvLmlsKwDQBoYaBwPACXx8vrm46ydHIGaWF6HIqeR3UC9yWcdji0BsafS1WDi1hTFJbo8AeA9HmEgPk3wuwfa9IWgUz/IbgbtfyAoke46oQcTh6XxsiU8CvLRqZYw9ID+s/WQv73vzu57Jmv/UPoFX0HZQD6Evs/05KiE85ploEYqJzwMzjvsbbbs+dAyljY/urxX9MgYWp2IiuumRfRw0VOSizVDS5qQygF9Xolf197kBHJsRTXOrjsmQ1+ETpF30AZgL7E3g8hJhmGL2gWghtsCAHTL4PDX0J1QW+vRtGKkXop6OGqzr2AT/aUcrC8ntuXjmfFNfMotTn40bMbaHIrRdi+gjIAfQWPSxN9G3cWRBmprh+kBgBg2mXa9+/e6N11KNrQ3AvQcSJYSsnTaw4wPDmGc6ZkMDcnmQcvms6B8nq+OlBxPJaqCIFOk8CK48Thr7RRjxPOATQhuNFpcb28qF5iyHCtV2DjM2AvhvhhWvdw9WGoOQz1FeCoAREF132q7a84LvjyBoc7yQNsKqjm2yM13Ldssl/N9PSJQ7Gaoli5s4RTxg/t8HjF8UF5AH2FvR9qmj9jTgOgut41sHMAnbHoV1pj2K7/whe/hzUPamEhEQXpusxEfTlseq63VzqosERHkZlo6dADsDlcPLxqL8lWExfPHt7i2NMmprNqdylujwoD9QWUB9BXOPHnMPpUMFlxuj3UOd0kW0OT6h2QjDkVfrpe+9nl0HIDxlZlhI1VsPVFOOUuiA6/pFERGSNT2i8F/XhXCb99Zyfldie/v2AqMaaWieazp2Tw3vYiNhZUceKY1OOxXEUHKA+gr5CYpU38Ar/YVtJgzQG0JtrS9uYPMPcnmhHY9fbxX9MgJicluCz002sOcMO/tpAUa+Ltmxbyo/kj2uxzyvg0LNEGVu4sOR5LVXSCMgB9kMo6bWxf8mAOAYXCqMWQOg42BRGdU/QYI1O0AfH2VgPiX990lHmjknnvlpOYPnxI0GNjTUZOHpfGyp0leL1K86m3UQagD1LdoBsA5QF0jBAw9zo4tgWObe3t1QwaclLaisIdqqjnUEU950zJILqTEZZnT8mkzO7k26M1PbpORecoA9AHqapXBiBkpl8G0VZY94gmL91VpASvSlB2RI4+ID7QAKzeq4kBnzYhvdPjT5s4lOgowcqdxT2zQEXIqCRwH8TnAagcQAhYEmHedZqy6N6PIOckrUooOkYbMjPnmvY1hRw2cNrAHA+uRq3v4NuXwNUAP/kc4lSpYjByUqyYjQbW7ivn3GmZAHyeV8boNCsjQpCXSLBEs2B0CuvyVT9Ab6MMQB/E5wEMiRnEVUDhcMbvtKE5ee9D3odQ9C001WtjKV0NcOr/tD2m6iA8c4rWexFI9jxNpO6ta+HK/zbPM1b4iTFFcfGcbN7YVMivlowjzmLkm4NVXHnCyM4P1pmWncjf1xzE4fJ0KEnx7ZFqHlm1j3mjkvn56bndsXxFAMoA9EGq65tIjIn2N9AoOkEIyJqlfZ3+W22blPDqD2HT83DSbS3LRL0e+O9N2j7nPqKVmXrdWm9B2jj49mV45yZY/Sc47X975zP1ca5fNIZXvjnCC18WMHtkEk0eL6dNCN1jmpSZiNsr2V9Wx5SsxDbvV9Y5uf/93fx3WxFGg+Drg5UsmZzOhAylENudqDtMH6SqwaXi/11FCDjhJmiogB2tJCU2/A2OfA1nP6AlkU+8GU76hXbzB5h5Ocy8EtY+BPmfS4Uq9gAAIABJREFUHP+19wNGpMRyztRMXt5wmHe3F2E1RTE3Jznk4ycN027ku4uCT5d99NN9fLCjmJtPHcuaX59KgsXIPe/sUgPpuxllAPogVfVOZQC6g5xFkD4Vvv5b85jJsjz47H4Yf64mPd0e5zykDbZ/5+a2YSIFADeePAa7081724s4KTcVkzH028nI5Fispih2Fwc3AFsO17BgdAq3Lx1P1pAYbl86nm8OVfH+dypx3J0oA9DHkFJyrLpRGYDuwOcFlO+BA59rg+j/9X0wWTUp6o5GbUbHwLL/B/Vl8Nl9x2/N/YgpWYmcNFbr5j01TG0fg0EwMTMhqAfQ2ORhX6md6dnNvQSXzR3BlKwE/vjhHhqa3F1buMKPMgB9jM2HqymobAgrnqrogCkXQlw6vH0DvHShVvFz5duhVfhkzYJ5N2h5hKMbe36t/ZBfnjmOCRnxnDGp8/LP1kwalsDuYlubhrDdxbV4vLJFM1mUQfC78ydTXOvgkVX7urxuhYYyAH2MF78+TLzFyLIZw3p7KQMDoxkW3KQpiJ74c7hhLQybEfrxp90NCcPgvVs1yW5FC2aPTGLlLxaTGhf+uMdJmQnUOd0UVje22L7tqBZym57dMjk8e2QyVy4YyQtfHmJTgRon3h0oA9CHKLM5+GhHMZfMGU6sSRVodRsLb4Xb82HJ/eGLxpnj4ZyHoWw3fPN0z6xvkOJPBBe3zLF8V1hDZqKFoUFmFt959gSyk2K4483tNDZ5jss6BzLKAPQitY0u/vTRHr+w1qsbj+L2Sq5YEHo9tSIEhIC4tMiPn3AOjD1TqwoK7DZ2NUJjddfXN0gZlx5PlEGwq1UeYPvRGqZlty0NBbCajTx44XQKKht48OO847HMAY0yAL3Iy98c5u9rDvK9J9bz3vYiXtl4mJPHpTFKb7VX9CGW3A9OO6x5QHvdWA3PnQEPj4cP74DaY22PKd4O6/4CbufxXWs/wRIdxZg0a4tEcG2Di4LKBqZlBxeTAzhhTArLTxjJP78qYOsRZYC7gjIAvYSUkn9vKWTysARGp1m55dVvKbU5uSqMbkrFcWToRJh9tTaApmgbvHwxVOzTvIPNL8Dj02H1n5t1hMr2wIrz4bPfaZVH3aFTNACZlJnQohT0u2OaQNyMdtREfdxx1gTS4y3c/fbOiIbLFFY38MDKvKCy1oMJZQB6ia1HajhYXs/yE3N488YTue6kUZw2YagalddFXt7zMl8Xfd0zJz/lf8AYA8+fqamPXvQCXPxPuGUrTFqmdQ6/fgWU7tJu+kYLLP0jFG6GZ0+Div09s65+zORhiRTXOvzyJ9t1hdBg3cGBxJmN3HPeJPYU21jx9eGQr1fT0MQfPtjNaQ+v4anVB3h4kFcUKQPQS/x7y1FioqM4Z2omJqOB//3eJF64ei5Rhg5q0/sJG4s3svyj5Tz73fHV6ZdS8sTWJ3h+5/M9c4G4NDj5Dq0a6IKnYOJ52vakkXDhc3DWA7BvJTx1oqZBdOV/4ISfwdXva6Jz797cM+vqx/gSwXt0L2B7YS2j06wkhqCDddaUDE4dn8ZfVu2luLax0/03FVSx5NG1PL/+EBfMHMYPZmWxcmcxZXZH1z5EP0YZgF6gscnDe9uLOWdqJnHmgVPtc6zuGDd/djPXrrqWrWVbeXt/907qklKyYtcKCu2FQd+3NdlocDewo3wHHm8PVYic+HOtomj6pS23CwELboSr/gsjToTL/62pkgIMn6dNLzuyAeore2Zd/ZRJmZoBeH79IcrtTrYfrWnRANYRQgh+d/4U3F7Jnz5sPyEspeSF9Yf44TMbsJqNvHfLSTx40XRuPnUsLo/k9Y1HI1q7y+PF6e7flUjKAPQCH+8qoc7p5uI52b29lG7jiO0IV310FZtLN/OLWb/g1lm3ctR+lNL60m67xobiDTy8+WHePfBu0PeP1WmJ2AZ3A/treijc0llF0ajFcM1H2k0/kPFnARLyV/XMuvopSVYTvz5rPOvyyzn14dWU2Z1t6v87YkRKLJfMGc4nu0txtZMLeGXjEe57fzenThjKOzcvZPIw7fyj0+JYlJvKKxuPhJ1H2HmsltMeWc25T6yntqH/9od0agCEEC8IIcqEEDsDtiULIT4RQuTr35P07UII8YQQYr8Q4jshxKyAY5br++cLIZb3zMfp+0gpeX3TUYYnxzAvDPGsvsxR21Gu+fgamjxNrDhrBddOvZYTh50IwNayzid1NbgacHs7b+9/Ne9VAIrqioK+X1zXrBOzvXx7KEs/fmRMh7gMLUSkaMFNp4xl5S8WM314IkLA/NEpYR2/YHQKjS5Pu8Jya/eVMzIllr9fMZsES8vQ0pULRlJc6+CzPG2gzdGqBg6U13V4vX9vKeTCp76iye3lcGU9P315S7vGp68TigfwT+CsVtvuBD6TUuYCn+mvAc4GcvWv64GnQDMYwD3AfGAecI/PaAwmahqauP5fW/j6YCVXzB+JoZ/G+6WU/Gr1r7js/cu47uPruGrlVTg9Tp5b8hzjk8cDMD5pPNZoK1tKt3R4Lq/0suydZTyx9YkW2ysaK1hZsNKv/nis7hhrCtcAUFQf3AD4PIBYYyzbyrZ16TN2OwYDjFuqaRK5mzretyxPm2ngGTyaN2PS4njp2vls/J8zmJgZnuTz3P/f3nnH13j9D/x9sqfIlCmREGKT2LRBqV1aWlp+qsNXdavuatH17fhWN1WbtmrXqBo1aleESIyExEpkyd7rnt8fz82VK+smSILn/Xp53XvPPc+553k8OZ/nfKaPspRUFh0cEZdJe8/GFf699WvlgrudBT/uieaFX0O5/4vdjPzhAJn5FT/VrwuNZfrqMAK97dnyUh8+fbg9B6NTmLEhgmvZBew7l8zaY7Hl6iU3VKoVAFLKf4Abr+xDwFLt+6XAyDLty6TCYaCxEMINeBDYIaVMlVKmATsoL1Tuak7GpjPkm33siUxixrDWTL7Pt76nVGuu5V1j+6XtFJQUUKgpxKeRj97iD2BsZExHl47VCoCotCgSchJYf349RWVSLXz272e8vvd1fj37KwCrIpWUzp1dOle+A8iJx8rEiu5u3TmRXLEA2H5xO+vP3VrbhMH4D1KMwZcPVt4nI07xGJofDJ/5KPmLEsLraob1ihACZ9uap5RwaWSBt6MV/14oLwBSsguIS8+jfSVeRSbGRjzerSlhV9LZG5nMmEAvsvKL+fXI5Qr7r/z3Cs1dbFj2VFecbMwZHejJ1GA/Vh69QtBHO5mw8F9eWx3G/V/sYfGBCxQWN+ydQW0tkE2klPEAUsp4IUSp76IHUNaiEqttq6y9HEKIySi7B5o2bVrL6TU83lobjkbCmik99ZJc3YnEZMQA8EaXN+jh3qPSfkFNgvgm9BvS8tOwt6h4w3c04SgA6QXp/BP3D/2b9iclL4Wdl3diZWLFF0e/wMvWi3Xn1tHPqx/ejbxZemopJZoSjG+o1hWXHYe7jTsdXTqy68ouUvJScLTUVyfMOTaHpNwk+nj2wcnS6WYuQ83xvR+MzSFqG/gGKymqc1PBuswct70NUgPDv4H4k0qVs4UPwsPzIWBY3c73DiLI24HdkUlIKRFlsryGxylpJqpyK32mjy9NHa0JbulMIwtT4tLzWLj/Ak/29NGrVhafkce/F1OZNsBfr1jT9IEtcbOzoKBYQ2u3RpgYG/H1zihmbTrNxrCrrHuup96cGhK32ghc0VnKKtrLN0o5X0oZJKUMcna+ifD9BsTFazmcjs/kmT7N7vjFH64LAF+7qncxgU0CgartACEJIXjYeOBk6cSm6E0AbDi/gWJNMQsGLsCnkQ8v7nqR9IJ0xrUah7uNO8WymOS85HJjxefE42btRgfnDkB5O8CVrCvEZsdSqCnk1zO/Gn7Ctwoza0UIRG5VFv5VE+ALX6WgvZRw/m84/Qfc95oSdDbsKyV5nUsr+P0J2PUxpERfr22goqNrM3tScwqJTtYP7IrQCoA2HpWrlSxMjRnRwV1nH5hyvx/JWQWsP64f3b1FW4tgmLYOcilGRoIJPXx4po8vPZs70bWZA788041pA/w5fjmdK6nVu6jWF7UVAIla1Q7a1yRteyzgVaafJ3C1ivZ7gq0RCYDit3w3EJMeg7WpNS5WVQettXFsg7mxeaVqII3UEJIYQlfXrgxtNpS9sXtJzU9lTdQagpoE0c65Hd/1+w4bUxuaN25OF9cuuNsoWVIrUgNdzb6Ku407rR1bY2JkUk4AlAaIBTgEsDJyJTlFVUeBXsu7RlZhVpV9aoz/g5B2AX7opgiCpj2VegMbX1BSSjj4Ka6mpdi6wpNboO1o+Odz+K4zfOkPm1+F7KTKf+ceo7Qa2Y12gPC4DJo5WZcz/lZFr+aOtPOw46e90ZSUSVW96WS8NnLfptoxhBAM1v69H4q5ZvBv1zW1FQAbgVJPnonAH2Xa/0/rDdQdyNCqirYBA4UQ9lrj70Bt2z3B1oh4Ong1xtPeqr6ncku4kHEBXzvfare1ZsZmtHduX6kAOJd2jszCTLq4dmG433CKNcXMPDiT2OxYxviPAcCrkRdrhq/hpwE/IYTA3VorAG4wBGcXZpNZmIm7jTsWJhYEOASUMwQfunoIV2tXZnSfQVZhFmui1lQ5/6k7p/L0tqcN8lAyGP9BIIyV3cDT22HSn3D/m3B8BaRGK5XITG7Qg5taKoFmz/8Lw+ZAsz4Qugy+7Qz756i5hoBmTtY42ZiVEwARcZnVRhXfiBCCKff7cTElly3hylP/5ZRcwq6kM7yD4Wnam7vY4GRjzqHoimM/zidlMfWXY8zceIqF+y9wPukWP2wYQLU2ACHEb0Aw4CSEiEXx5vkvsEoI8TRwGRij7f4nMAQ4D+QCkwCklKlCiA+Bo9p+s6WU90RylCupuZyMzeDtwa3qeyq3jJiMGJ2bZ3UENglk/sn5ZBdmY2Om/+RUqv8PahKEm40bLe1bsvvKbuzN7XnA+wFdPzcbt3Lvb9wBlAqE0h1CB+cOrI5aTVFJEabGppRoSjiScIQB3gNo59yOLq5dWH56OY+3ehxT4/JPhxqpITo9mkJNIUtOLeGZds8YdL7VYucJzx0AOy8w116Pvu+AcyvIiIXm/Ss+Tghwbqn8C3pKSSux/T3YORMu7oexv4FJBVXkCrKU3EUJ4ZB8VhE8Ni7QuCk0fwAsarY4NlSEEAR5O+gJgNScQuLS83iyp0+NxxvU1pVWrra8sSYMG3NjziYoi/PQdm7VHKk/p+6+DhyOSS1nmwBYfugS204lYmlqTHZBMV+bm7D1lT51+qBoiBfQOCmlm5TSVErpKaVcKKVMkVL2l1K20L6mavtKKeXzUko/KWU7KWVImXEWSSmba/8tvp0n1ZD4S6v+GdzW8BunIZNZmElyXjK+jQ3zYgpsEohGaiq0AxxNOIqnjaduUR/up6RWGNl8JGbGFZfEtDSxxMHCobwA0H4u3SF0dOlIQUkBZ1LPAHAq5RRZhVn0cFOM1pPaTCIxN5G/Llbsl5+cm0yhphAbUxvmnpjLhYwLBp2vQbgEXF/8S2n7MPR6qeL+FeHUHB5fCcO+Vkpdrp8MZaOfsxJhx/vwv1awdJhiXD6zSdk57JwJa55SVEmrJsLFA7fktOqbLs0cuJKaR0KGktrBEANwZRgbCZY/3Y3mLjY8u+wYC/ddoFPTxng51Gxx7u7rSEJmPhdTcvXapZTsikwi2N+Z8JkD2fHqfWik5LVVYXpqp9uNGgl8m/kzIp62Ho1o6nh3qH9i0g0zAJfS0bkjduZ2OjfOUkr1/0GuQbq2kc1HMsJvBE8EPFHlmO7W7sTn6BcH1wkA7Q4gqEkQliaWfHf8OzRSw8GrBxEIurl1A6C3R28cLBx0u5AbKY0peKvrW1iYWDDz4Ew0sgG69AVNggEfwqn1StnLXR/BitHwdTs4+J1ic3hiLbwWBW9egHfi4O04eHoHdP4/uLhPERDHllb/Ww2cG+MBwmOVxHJVGYCrwtnWnJWTe9DD15GUnEKGta95lb4efoqH141qoOjkbK6k5tG3lQtCCFo0seWDEW04ciGVBftiajXf2qAKgNvI1fQ8jl9Ov2ue/gHdk7ChAsDCxIIJARPYG7uX0ymnde1l9f+l2Jnb8XHvj2liXXV9WTcbtwp3AObG5jhaKH9wjpaOTA+azuH4w/we+TuHrh4iwDFA544qhKCFfQui0irOBhmbreQb6uDcgTe6vEFoUmg5IdZg6PUS9J4G4auV+gOZV5XF/YUQJWNpiwfAtsw1NbdRUlUM+QJePgl+/WDTS7D38zvaw6i1WyPsLE35cU80GXlFtTIA34iNuQmLnuzCD493ZkItCjX5OlnjYmvO4Rh9AfD3GcWA37dM7e8xgZ4MauPKl9sjOakVXrcbVQDcRjacUJ4iB99h3j/74/YzZccUvQW7lJiMGMyMzPCwqTCMo0IeD3gcW1Nbfgr7SddWVv9fU0p3ALLMYnU15ypu1m56etYx/mPo5d6Lr0K+4mTySZ36pxR/e3+i06MrTBwXmxWLQOBu484IvxF0d+vON6HfkJR73fNGSkmRpoFEfD7wAbwSDm/HwtSDMPRLcPSr/jhzGxi3EjqMg90fw8Fvqz+mgWJibMQ3YztyPimLSYv/JexKRq3UPzdiZmLE0PZK1t6aIoSgh58jh2JS9O7XXWeTaOVqi0djS72+nzzcDicbcx776TDrQitOengrUQXAbSK/qIRF+y/Sp4WTQW5jDYGEnASm7ZnGczuf48DVA7yz7x0KS/TTFsRkxOBt510uCKsqbM1sGd96PLuu7CIyNZLQxFAWRSzCu5G3TmVTE9xt3CkoKSAl//pTVakLaFmEEMzqOQszYzOKZXG5oDV/e3/yS/K5klU+G2RsVixNrJtgZmyGEIIZ3WdQWFLIZ/8qFcGu5V1j/NbxPLb5MQpKGogXTuOmYFYLVaOxqZLeusVAxauooOpcOA2Z4JYufDu2EyeupJOQmV9pBHBd0t3XkeSsAl2MQkZeESGX0ujXqrwbtYO1GRue70V7TzumrQrjjTVh5BZW74V2OSWX11aF1TgxnSoAbhOrQq5wLbuA5/s2r++pGMTBqwd5eOPD/BP7Dy91eomv+35NdEY080/O1+sXkx6Dn50BT5Y38ETAE1ibWvP6P6/z1LansDSx5H/3/69Wc60oFiA+J75CYdLEugkf9vqQbq7d6OTSSe+7FvYtACpUA8Vlx+Fpcz1ba9NGTZncfjLbL21nxekVjP9zPGdTznIu7ZxBdQ+i0qIaXn6isggB972hlLoMNcAeUJAFEesUY/K3neC3cUrN5Pj6T8I3uJ0bn4/ugKmxoHsNE8vdDnpo51CqBtp3LpkSjaxQAAA0aWTBL89044W+zVl9LJZBX++r1JW0lDWhsawNjWXW5lM1mpsqAG4DRSUaftobQ6C3Pd2aNbyMnxkFGfxw4gf+uvAXSblJrDi9gud2PoertSvrR6zn2fbP0r9pf4b7Dmdh+EIiUyMByC/OJy47zmD9f1nszO14vNXjXMi4QF+vvqwctlIvd1BN0AkAretnblEuqfmpOg+gG+nXtB8LHlxQzrPIz84PI2FUoQCIzYotp+aa1HYSvna+fHb0M/KK81g6eKlyjSIWcj6t6vTTb+97mwlbJ/Di3y/qIqkbHF5dwKePYjyuKrYgOVIxMq+ZBDF7wTkArp1TDNDzg+HAt/VuSxgd6MmpWYNoV4PU0rcLb0crPO0t+fbvc2w7lcCuM0k0tjKlU9PK82GaGBsx/cGW/D65B0YCxv18mOmrw/gnKpn8ovIqywPnr2FsJFgXGsfO04anYFcFwG1gw/E44tLzeKFv8waZA2RT9Cbmhc3j9X9ep//q/nx29DP6evVlxeAVeDW6HrD9Rpc3aGTeiBkHZpBfnM/FzItIJM0aN6vV7z7X8TkWP7iYr4K/wtbMttbz1wWDaXcACTmKq21N1UkWJhZ4N/LmXNo5vfb84nyS8pLwtNWv12BmbMYnvT9hgPcAVgxZQVuntkzvMh0bUxtmHqrcS6igpIDo9GjaOrYlJDGEh/94mC0xW8r1kwYumkWaonKquVtGn2mQFQ9hSuptshIhIeL693npytO+kQlM3AzTo2Dcr/BiCLxxAVoNgx0zlJ1BoQH1djUapWTmbYhqro3O/nYghGDe+EAcrM34z/JjbDgRx/3+zgZV/+vazIGtL9/H072bsSnsKv+36F86zd7B4gPX3ZKz8os4cSWdp3s3o5WrLe+sNzx5YMO4QncRJRrJ3L3RBLg1Irhlw8xldCT+CJ42nqwcupLpQdP5oMcHfBX8FVam+vrjxhaN+aDHB5xNPctLu17iTIriU1+bHQCAqZEpQa5BNy0UbcxssDWz1QmAUpfN2tgT/O39y+0ASse9UQAAtHFqw1fBX+FlqwhKBwsH3ujyBmHJYWw4v6HC34hOj6ZEljCp7SS2PLyF9s7t+ejwRzrBpZEaZh6cybM7njVozm/ve5uJWyfenqpnvn3BvZOSn+j38TCnNczrpbxPuwTrnoX0S/DociUiuawtyMoBHl0G/T+A0xtgXh+4fKTi38lNVXYK33WGBf3hqwD4fQJE/qXsJnJTFeFwl9DWw45NL/bmjUEtsTY3YVQnw50oLM2MmTGsNSfeH8iiJ4No7d6Ir3ZE6XYCR2JSKdFIgls68+WYDqTkGP5woAqAW8zOM4nEJOcwNdivQT79F2uKOZp4lB7uPWjj1IaJbSYy2n80RqLiW6Ff037M7jWbw/GH+fTfTzESRvg08qnbSVeAh42HLhag9LUyFVBVtGjcgtjsWL28QKUuoGVtAFUxzHcYzeyasf1SxdW+SlVorRxa4WDhwMe9P6ZElvDBwQ90dYzXnlvLkfgjOmFWFccTjxORElFpZbSbQgjoMx3SL8Olg9B9KvR9F87tVHT957bD4M/Au5IssEIou4j/+0Opnbx4EGyfAfkZ1/tE71ZyIe2YoeQ6eugH6DZFiWj+7TH4Pgg+b6bUVs6vuMjLnYipsRFTg5sTPvNBgltWnUerIizNjOnXqgnTBviTlV/MDq2qZ//5a1iYGhHobU9bDzueDzbcRnf3FKRtICzYF4OnvWWDdf2MuBZBTlGOLiDKEEY2H4mUkvcPvk9T26aVRunWJW7WbjrvnYuZFzExMsHZquY7Ln97fwDOp5/XZREtrTlc0Q6gIoQQdHXtyqboTRRrijEx0v+zOpt6FisTK914XrZeTA+azoeHP+SlXS+xJ3YPvT16sz9uP4evHuYR/0cq/a20/DSS8pIwEkZ8e/xbBvoMxNrUusbnXUpBSQHFmmL9MQKGwZT94OR/PS9R+8fg71lKCougp6sfuNl9ijvq9vcU19KQRdBhrJKK4sC3ythPrFJ2G6X0fx8uH1LUQemXFbfU7e/BiDvXNfV20MPXEXc7C9aGxjK8gzsHzl+jazNHzE2U3dhL/VvwmoFjqTuAW8iJK+kcvZjGpF7N9PKFNySOxCtb8q6uXavpqc+oFqOYEzyHVwNfvR3TqjEeNh5czb7KwvCFrDi9gqAmQZXuYqrC30ERAGXVQLHZsVgYW+iCygwhyDWI3OJcnZqsLJFpkfjb++vNb4z/GLq7ddct/t/2+xYXSxcOxR+q8ndK5zm1w1Su5V1jUcQig+dYEbMPzeaJLU+Ut1+4ttNPSmfvrQSVDZilPOUbgrmtUtfg2d0QMEJJQ3HgG+j0BEzerb/4g/J7vsHQ/lG4bzr0eEHxSDr/982c4l2HkZFgVGcP/olK5mRsOueSsund/Pq9WpO1p2GuUncoC/bFYGtuwmNdvKrvXE8cSThCK4dWlRZoqYoHvB/QS9JWn7hZu5FbnMvXoV8z0Gcg3/T9plbjuFu7Y21qTVTqdQEQlxWHp61njVR4pQFtIYkheu1SSiJTI8t5PAkh+KT3Jzzf8Xm+vP9LTI1M6e7enSPxR6pMOVEqAEb7j2Zws8EsPbVUrxZyTTmWeIzojGgOxN3GfEAenWHUXJh2Bv6zT1H5mBmwa+n7rrJT2PiSvgrJEIrylfKbJQ0kUO8W83BnTzRSKTQF0Kt57YobqQLAABIy8hk7/xCDv9nHEwsO896G8HI1P2PTctkakcC4bk2xMW+YmrW84jxOJJ2gu1v3+p7KTdPOuR3GwpiXO7/MF/d9Uc6AbShCCFo0blFuB2Co/r8UJ0snfBr5lBMAcdlxZBdlV+jy6mzlzJQOU3Tql+5u3UkvSNclsKuIqLQoHC0ccbR05JXOryAQTP17Kok55V3/pJTMDZvLtosVZ17PKMjQ2Rx+O/ubwedaa6ydwK294f1NLZQAtayrsHmaYa6l+Zmw/2vFTXX5KFg8RMmyepfh52xD56aNOR2fiYO1GQGutct3pAqAakjKyufxnw8TEZeJR2ML8os0/HrkMp/9dVav35IDFwGYWIvUs3XF8aTjFGmKaqT/b6h0cunE0fFHeabdMzdtbPe39+dc+jmklEgplRgAW8O9NEoJcg0iNDFUzzsnMk1rALavPh14aaRyaeGaiohKi9LZLdxt3Pm+//fE58Qzfut4XaK+UlZGruTHEz+yMHxhhWOdTVXu4Y7OHdkft5/LmRXXwa3s2Pzi/Eq/P5VyiohrEQa7tlaKZ5CyE4hYo8QZVEb8SUVIzGkDOz+AJm2UJHlJZxRvpDOba+9VpNEY5tJaxzwSqDyk9PRzrLDgvSE0zEfVBkJKdgFP/HyEhMx8lj3VlSBt1aEPN59m4f4LjOjgQddmDuyJTGLpoYsMb++ml9ujoXEk/ggmRiZ0dulc31O5JZga1T7JV1n87f1ZFbWKqzlXsTSxJLc4t8Y7AFDUQGui1nA27SxtHNsAigeQkTCiuX31EeFOlk60sG/B4auHK6w/UKwpJjo9mrEtx+raurl1Y/GDi3lu53NM2DqBN7q8wXC/4ZxMPsnn/36OpYklkWmRZBVmlYu9KBUA73V/j7Gbx/J75O+83uWJ5zt0AAAaYUlEQVT1aucZmRrJo5se5ZXAV3iq7VPlvi/RlPDs9mfJKszCu5E3Q5sNZWyrsbVSOwLQ5zXF9XTfl0o9hWb3QeSfipdSZhxkxkNOEphYQJtR0PVZ8FDKkdJyCKyeqJTUtPOCdqOVcpv2PtfHv/CPkggPFNWUlJCfrsQ85KUqLqmyBFzbQ5uR0OZhcKhdLMytZFh7d+b/E8OIGhSpuRF1B1AJUkqe/zWUy6m5LJgYpFv8AV4b6I+XgyVvrT3JwfPXmLLiGC1cbJn1UNt6nHHlSClJz0/n4NWDtHdqX2t1yd1KF9cumAgT3t3/LtHp0YDhHkBl0dkBEq6rgc6mnsW7kTeWJoY9GPRw60FoUih5xeXryF7OukxBSYHOcF1KgGMAK4aswKeRD+8deI/xf45n2p5puNm48UnvT9BIDceTjpcb73TKaVytXWnp0JIHvB9g/fn15Bbllut3IwvCFyCR/Jvwb4Xfn007S1ZhFqOaj6KJVRPmhs1l6PqhLDu1jKLa6OSFgKFzoPkA2PyKEjuw/T0lXsDaBVoOgiFfwmtnYdS864s/KHUTnvkbHv5ZKbpz4Fv4sScc/0VZ6CPWwYpHIO0iaIqVILiseMUg7dwSWg2F3q9C8NtK29+zFTfVsN+rn3dKNJxcBSW3sKJcGewsTdn7el8Gtqm9x6G6A6iEfeeucTgmlVkj2tDTT9/AYmVmwqej2jN+4REeX3CEZk7WLH2qK3aWt+aJ9Fby+dHPWRW5Spew7IWOL9TzjBoevo19+bj3x7y17y3e+uctwPAYgLI0sW5CU9umhCSGMLGNUjE1Ki2K9k6G6717uPdg2ellHE88Tk8P/aprpXaKlvbl7Qmetp4sH7KcLTFbmHNsDtlF2cwbMA8vWy9MjEwISQzhPs/79I45k3qGVg6Kampcq3H8dfEvPj/6OVM6TMHV2hUpJRcyLlAsi3Vqp4sZF9l2cRsWxhaEJYVRoikplxiwVAC+0OkFXKxciE6P5oujX/BFyBesP7+eX4b8YvBDSG5RLuvPryfAIYDOY5bArg/BwZei5v05o8mlvbMB19bUQvEsav8opF+B9VPgj6lw4hdlF9G0O4z7DSyr2aEEv6Ucv+E5pQBPZpwiHCpSQeZnKDaI9EtKUN3Aj5W03KAInuxESDqtJN5rOVhJyFcPqAKgAqSU/G97JB6NLRnbtWKPnt4tnPi/Ht7siUxm+dNdcbY1r7BffXI+7TzLTy/nfs/76ebWDVdrV3p79K7vaTVIhvgOIbMwk4+PfAzULqoYFDvAzks70UgN2UXZxGXHMdp/tMHHd3bpjKmRKRtjNtLDvYeefSMqNQoTYUIzu4rVD0bCiOF+w+nftD9ZhVm6ugptHduWq8ucW5TLxYyLDPYZDCg2lYf8HmLduXVsOL+BwCaBXMy4SFKekqLh/R7vM8Z/DIsiFmFmbMYLnV7gy5AvOZd+TidESglJDKGpbVNcrJRgJ7/GfswbMI9tF7cxfe90fjnzC8+2rzrqubCkkFWRq/g5/GdS81PxaeTDxpEbEYOVbKwLw+bxw4kfWDpoKZ2b1ECl2dgLJm5U6ibs+VR5wn9kgVJ32dDjx69VhMDfs5SFfNB/9YWAlLDxRUVADJgNIYvhl0fA3A4Eyo6gTOAhLq1h8OdKZHUdowqACvj7TBJhsRl89kg7XXBFRcwa0QYpqbUB5lZzY93RRRGLsDSx5KNeH9HYonE9zuzOYGyrseQX53M86Xit1WRBTYJYd24d3x3/ThcxfeMCWRVWplZMaD2BRRGLcLFy4dXOr+r+T6PSovCx86k2EM/K1Epv/kGuQSyJWEJuUa6uPTItEokkwDEAULyhPur9EVM6TGFN1Br2xu6lc5POdHfrzq4ru5h9aDZJuUlsit7EmJZjGOA9gC9DvuRY4jG989NIDaGJoRW6Cz/o8yCbozezOGIxj7Z8FDvzyhO1fR36NctPL6ebazdaO7VmccRijiYcpatbV4o0RbriPHPD5vLzwOqzsephZAz3v67UVrZyMDyuoRQTc3h4Adi4wuEfQGqUBbx0nJBFcPoPeGAW9HpZiXIOXQYp2oSBwkixQbgEKPaFHTOUqmy+weDdWynW492z+l2BRgOn10Oz+xUPq1qgCoAb0Ggk/9sRhY+jFQ93rloNIISo8b1zK5FSMi9sHofiDxGbFUt2UTYzus9guN9w4rLj+PPCn4xrNU5d/GvAk22f5EmerPXxfb360sOtBwvCF+jaaiIAAF7p/Ao5RTksjliMhbEFUztOBRQBcGNKa0MIbBLIgvAFhCWH6TyNSov9BDgE6PX1tPXklcBXeCXwFV3bcL/hTNszjXlh8zARJkxqMwk3GzdcrV05nnRcr4RnaaW3ygr9vNj5RUZvHM3CiIVMC5xWYZ8iTRGbozczwHsAXwV/RX5xPmuj1rI6ajVd3bqy6/IukvOS6enek4NXDxKaGFrlLuCf2H+UFN6tx9PHo8/1hyTrm0gVbWQED36sLPqHvgeEYouI2q4IgOYPQE9tjWcTc8UwXRn+Dyq2iVPrYLfW08lLq5ayqiSbcHGhosYKXw1uHWDSVsNiK248jRofcZezOTyeM/GZvPxAC0wbaDRvKb+e/ZUfw36kRFNCL49e+Nv7896B99h5aSdLIpYghNDpolXqBhszG+YPnM+2R7bxcueXmdx+Mk6WNXs6E0LwTrd3GNl8JHPD5vLhoQ+5lneN+Jx4nS6+JnRy6YSRMNJTA51NPYuDhYNOTVMVZsZmfBX8FY+0eIT/dPgPbjZKidPOLp0JTQzVc/UsjYOoTAD42/sz1Hcov575Va+6WlkOXz1MWkEaw32HA0rW1hF+I9h5eScpeSmsPLsSDxsP5gTPwcHCgR/Dfqxy/ktPLeVQ/CGe//t5JmydQMS1iCr7G4wQMPAj6P48/PuTovM/thia94dRPylCwhBMLSH4TXj+CLx5EUZ8B1dDYdGDSgK+GynMgZXjlMW//VhICId1k2vl5qruAMqQkl3ArI2naOvRiBEdau4HXpdEXIvgy5AvCfYM5tt+3yKEILcol8k7JvP6P69jhBHDfYfjat0wcxLd7bjbuFfoymkoRsKImT1mYm9uz+JTi9kTuwegVgLA2tSaAIcAvSC1MylnCHAMMDiGwszYjJk9Z+q1dXbpzJ8X/iQ2O1aXHTUkIQQPGw+dkKiIqR2n8teFv5gbNpcPenxQ7vs/L/xJI7NGevaqMf5jWHFmBV8d+4qQxBBeDXwVK1Mrnmr7lE4VFdgksNxYqfmphCSGMKntJLxsvZgXNo/JOyazatiqWnl67b68m+9PfM+0wGn08uilCIEHP4am3RQ3VJ8+tavKVoqlvVLP2cFPWeR/7gc+vcCmiaI6Sj6rLPh5aTD8WwicqKTU+OtN2Pa2kqfJxvBEcw37EbcOkVLy7voIsvKL+d+Yjgbl6q5LNFLDpcxLJOQkkJCTwPS903G2dOaj3h/p/oitTK34of8P+Nn5USyLmdR2Uj3PWuVmMDYyZlrQNL7r950u6Ko2AgAUNVB4cjgFJQW6+gStHVrf1PxK1S6hiaGA8jdU2UJcFi9bL8a2GsuaqDXljNN5xXn8fflvBngPwLSMDty3sS+dXTqzMXojZkZmjGo+CoBHWz6Kg4UDP5z4ocKgsz1X9qCRGgb7DGaM/xiWDFoCwLQ903SecTsv7WTqzql8d/w7QhNDKdZU7ra5MGIhUWlRTNk5hY8Of6S4zQoBrR9SVDk3s/iXxacXPLWdErcOSjDbyVVwfIVSia3lYMUQHajd3Xf7D3R5Bo7Mgx+6wGeGF69XdwBa/jhxlb9OJfDW4Fa0dK19sZJbjUZq2HV5F3PD5uqlKzARJiwZvKScIc3O3I7FgxYTlx1XqbeIyp1FsFcwa0esJSotSufZU1OCmgSx7PQyvj/+PU6WThTLYp0BuLb4NfajkVkjQpNCeaj5Q0SnR5NWkFap+qcsL3Z6kd1XdvPBwQ9YM3wNFiYWAOy9spe84jyG+g4td8yYlmMITQplULNBuqAySxNL/tP+P3z676fsuryL/t799Y7ZcWkHHjYeOjuMl60Xn/T+hBd3vcjsQ7ORUrIpZhPOls4cuHqA+Sfn09qxNb8M+aVcVteYjBjCksN4vuPzZBZmsvz0cvbH7ee97u/R26M3Ukr2xe3jQNwBRjQfoQsGrIz47Hh+i/wNf3t/Ojh3wNPmev6piGsRLDq9iD1cZNbgWQz3G175QELA4C9I9R/IwbgDHEyNANZX+dulqAIAJY/P+39EEOhtz7N9alfs5HaQkJOgFGJJPYNPIx/e6fYOZkZm5BTlEOAYoEtffCO2ZrY1NjyqNGxcrV1vSp0X5BqEh40HS04tAUAgql2gqsNIGNHJpROhiaHkFOWw7dI23W9Vh5WpFTN7zuTZ7c/yw4kfeC1ISWC85cIWXKxcKoxWH+g9kPDkcD2jMyi7gNVRq/ki5At6e/bG3Fhxyc4qzOJw/GGeaPWEnqor2CuYZ9o9w4LwBRgLY57r8BzPtn9WiTk4t57/HfsfW2K28FDzh/R+Z8P5DRgLY0b7j8bJ0om+Xn2ZfWg2z+18jv5N+xObFUtkWiQCwW9nf2OE3whe6vxSpXaWOcfmsPXiVt1nC2MLGpk1wtzEnCtZV7A1s8W7kTfvH3gfBwsHReVUCbtj9/LqoTcpkSU0Njfc6UPcdK6O20hQUJAMCQmpvuNNkFNQzCNzDxKXnsemF3rj41T73Oq3koScBJ7a9hRp+Wm83e1thjQbUu6JREWlpmQWZhKbFYtGamjrdPOR64siFjHn2BzdZy9bL7aM2mKwbWHWoVmsO7eOx1s9TiPzRsw/OZ8nWj3B9C7TazSPw/GHeXb7s7zY6UUmt58MwOaYzby9722WD15OR5eOev2LNcUsO72Mbq7daON0XRBKKXls82NkFmayadQmXbqRIk0RA1YPoJ1zO77r952uf2FJIQvDF/Jz+M942HjwTLtnuM/zPhZHLGb5meXYmtqyfMhyvBvpq2UuZFzgoQ0PMbHNRIb5DuNE0gmuZF0hszCT7KJsOjh3YLT/aDRSw6S/JnE56zKLH1ysN9dS8orzeGjDQ9iY2TC752wCHAIwMTY5JqWsXhKXJsBqiP8CAwPlrSA5K1/uOpMoU7IL9NpLSjTymaVHZbO3Nsu9kUm35LduBVezrspBawbJ7r90l2FJYfU9HRWVSknITpAz9s+Q807Mk3su75FpeWk1Oj6zIFM+vuVxGbg8ULZd0lZ2XNpRRqZG1mouL+96WXZZ0UXGZcXpPvf7vZ8s0ZTUaJy9V/bKtkvaylWRq3Rtuy/vlm2XtJV/X/q7wmMKigvK/c75tPOyz2995JC1Q2RqXqred+/ue1cGLQ+S13KvVTufpJwk+eCaB2Xv33rL0MTQct//eOJH2XZJW3k0/qiuDQiRBqyxdb4DEEIMAr4BjIEFUsr/VtbXkB1AUlY+EXEZxKblUVwiKdFIzEyMsLM0xcRYsDU8ge2nEygqkQgB7TzsaOdhh7W5CXFpeWwJj+eD4a2Z1Ktu9eXFmmJOpZwiMjWSNk5tCHAIIL84n5WRK1kUsQiNRsNPA36inXO7Op2Xikp9UaQpokRTorMH1JTYrFhG/jGSEk0JnZp0Ijw5nJHNR/Ju93drNI6UkvFbx5OYk8iWh7dgbmzOK7tf4XjScXaO2VmjJIQnkk7w9Lanae3Ymp8H/oyFiQVx2XEMXTeUca3G8WbXNw0a50rmFZ77+zmuZl9ldq/ZDPMdBih2hBEbRnC/1/18ef+Xuv5CCIN2AHUqAIQQxkAUMACIBY4C46SUpyvqf6MAKC7REJmYxdELqRy9mEbIpVQSMwuq/E17K1Me6ezJ/S2dOX45nX+ikolOzia/SENBcQlP9mzGjGGGu8NJKckqysJEmGBhYkGJLCE1L5WU/BSMhTH2FvZYmVhxJvUMxxKPcSblDPE58VzNuUqxphgnSyfszO2ISY8huyhbN66DhRLwkZqfSi+PXrwW+Bot7FsYNCcVFRWFyNRItl7Yyr64fZxPP8+ywcsqtZVVRalKqaV9S8xNzDl17RQTWk/Q2SpqwvaL23lt72v42fkxqsUozqaeZdvFbWx9eGuNjPoZBRm8svsVQhJDCPYKpkXjFoRfC+d40nE2jtyol76koQqAHsBMKeWD2s9vA0gpP62ov3VTd9l9xnPYWJiQV1hCXHoehcVKsIOdpSleDla4N7bEvbEljtZmGBsJBJLCkhJyi4rILyrGycYcE2NlcTcWxhgJIwQCjdRQrCmmUFNIfnE+BSUFWJhYYGNqg5mxGUWaIopKipBIjIQREsm5tHOEJYXp8qNUe74IfOx88LTxxN3GHVMjU1LyUkjJT8HL1ose7j1o5dCKk8knOXj1IDlFOTzZ5sma5TZRUVGpkCJNUa1Thksp+e+//yUyLRIzIzOsTa15o8sbVcY3VMW2i9tYemop4deUCl6P+j/KjB4zajxOUUkRc0LnsPfKXuKy4yiRJTzf8XmmdJii16+hCoDRwCAp5TPazxOAblLKF8r0mQxMBrDwsQhsPrP6POo3i4WxBabGpuQX51OkqTxdrYeNBx2cOxDgEIBE6nyzHS2VCk0aqSEtP42swiyaN25OR5eOVeY7UVFRubeISY9hX9w+RviNqH19BC1FmiKSc5NxtXYtVw/bUAFQ124lFelZ9CSQlHI+MB+gU2AnuWfsnhr/iJEwuv60r1XtSCnRSA0lsgSJxESYYCSMMDM207t4hSWFFJQUYGpkiqmRKUbCSDlGSr3AFBUVFZWa4tvYF9/Gt8bV3NTItNZZa0upawEQC5TNr+wJXK2ss7EwrvMnaDNjs3LZFk2E6n6poqJy91HXqSCOAi2EEM2EEGbAWGBjHc9BRUVFRYU63gFIKYuFEC8A21DcQBdJKU/V5RxUVFRUVBTqXLchpfwT+LOuf1dFRUVFRR81G6iKiorKPYoqAFRUVFTuUVQBoKKionKPogoAFRUVlXuUBp0OWgiRBURqP9oBGbdo6Ht5LCfg2i0aqzbcDWMZcg3vhvOsy7Fqc19WRkM9x9s9Vtlr2FJKWX1lK0NShtbXP8qkNAXm38Jx79mxMDBN7J18jrd7LEOu4d1wnnU8Vo3vyzvwHG/rWDeslwZdzztJBbRJHUsd6w4Z61aPdy+MdStpqOfY4MZq6CqgEGlIVRsVg1Gv6c2jXsNbj3pNb56y19DQ69nQdwDz63sCdyHqNb151Gt461Gv6c0zv5L3ldKgdwAqKioqKrePhr4DUFFRUVG5TagCQEVFReUepV4FgBBCCiGWl/lsIoRIFkJsrs953ekIIUZpr22r+p7LnYZ6T94+hBDZ1fdSMZTqrqcQYo8QokpDcH3vAHKAtkIIS+3nAUBcTQYQQq3WUgHjgP0o9RYMRghhfHumc0dx0/ekisqdQn0LAICtwFDt+3HAb6VfCCG6CiEOCiGOa19batufFEKsFkJsArbX/ZQbLkIIG6AX8DRaASCECBZC/COEWC+EOC2EmCeEUgdTCJEthJgthDgC9Ki/mTcoanNP7hNCdCzT74AQon2dzvoOQHsvbi7z+XshxJPa9xeFELOEEKFCiHB1B1s9VV1PQ2gIAmAlMFYIYQG0B46U+e4scJ+UshPwPvBJme96ABOllP3qbKZ3BiOBv6SUUUCqEKKztr0r8BrQDvADHta2WwMRUspuUsr9dT7bhklt7skFwJMAQgh/wFxKebLOZnz3cE1K2RmYC0yv78nc7dS7AND+kfigPGndWCjGDlgthIgA5gBtyny3Q0qZWieTvLMYh7KAoX0dp33/r5QyRkpZgvJE21vbXgKsrdspNmxqeU+uBoYJIUyBp4AldTLZu4912tdjKP8HKreRhqI/3wh8CQQDjmXaPwR2SylHCSF8gD1lvsupo7ndMQghHIF+KDpsiVJ2U6IsYjcGfJR+ztcKBRV9anRPSilzhRA7gIeARwE1qrViitF/8LS44fsC7WsJDWd9ashUdz2rpN53AFoWAbOllOE3tNtx3QD3ZJ3O6M5kNLBMSuktpfSRUnoBF1Ce9rsKIZppdf+PoRiJVSqnNvfkAuBb4Ki6O62US0BrIYS5EMIO6F/fE7rDuanr2SAEgJQyVkr5TQVffQ58KoQ4gPI0q1I144D1N7StBR4HDgH/BSJQhMKN/VTKUJt7Ukp5DMgEFtfBFO8otN56BVLKK8Aq4CTwC3C8Xid2h3KrrqeaCuIeQAgRDEyXUg6r77nczQgh3FFUQq2klJp6nk6DQgjRAfhZStm1vudyN3CrrmeD2AGoqNzpCCH+D8Vb6F118ddHCDEFxfHgvfqey93Arbye6g5ARUVF5R6lTncAQggvIcRuIcQZIcQpIcTL2nYHIcQOIcQ57au9tv0JIcRJ7b+D2m1P6ViDhBCRQojzQoi36vI8VFRUVO4G6nQHIIRwA9yklKFCCFsUX9+RKN4UqVLK/2oXc3sp5ZtCiJ7AGSllmhBiMDBTStlNm7IgCiVMPxY4CoyTUp6us5NRUVFRucOp0x2AlDJeShmqfZ8FnAE8UHynl2q7LUURCkgpD0op07TthwFP7fuuwHltYFMhSsDTQ3VzFioqKip3B/VmBNYG0XRCMZw1kVLGgyIkAJcKDnkaJUcLKELjSpnvYrVtKioqKioGUi+RdtqEZWuBV6SUmUKI6vr3RREApekLKjpAtWarqKio1IA63wFoc6WsBX6RUpbm/UjU2gdK7QRJZfq3R4mwfEhKmaJtjgW8ygzrCVy93XNXUVFRuZuoay8gASxEMex+VearjcBE7fuJwB/a/k1RkkNN0Ga3LOUo0EKb2sAMJe3xxts9fxUVFZW7ibr2AuoN7APCgdJgmXdQ7ACrgKbAZWCMlDJVCLEAeAQl3wVAsZQySDvWEOBrlHD8RVLKj+vsRFRUVFTuAtRAMBUVFZV7FDUVhIqKiso9iioAVFRUVO5RVAGgoqKico+iCgAVFRWVexRVAKioqKjco6gCQEVFReUeRRUAKioqKvcoqgBQUVFRuUf5f9CTrL0Vn0qUAAAAAElFTkSuQmCC\n",
1005       "text/plain": [
1006        "<Figure size 432x288 with 1 Axes>"
1007       ]
1008      },
1009      "metadata": {
1010       "needs_background": "light"
1011      },
1012      "output_type": "display_data"
1013     }
1014    ],
1015    "source": [
1016     "data_by_day.loc['2020-03-01':'2020-07-02', ['cases', 'newAdmissions', 'deaths']].plot()"
1017    ]
1018   },
1019   {
1020    "cell_type": "code",
1021    "execution_count": 18,
1022    "metadata": {},
1023    "outputs": [
1024     {
1025      "data": {
1026       "text/plain": [
1027        "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c4070190>"
1028       ]
1029      },
1030      "execution_count": 18,
1031      "metadata": {},
1032      "output_type": "execute_result"
1033     },
1034     {
1035      "data": {
1036       "image/png": 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\n",
1037       "text/plain": [
1038        "<Figure size 432x288 with 1 Axes>"
1039       ]
1040      },
1041      "metadata": {
1042       "needs_background": "light"
1043      },
1044      "output_type": "display_data"
1045     }
1046    ],
1047    "source": [
1048     "data_by_day.loc['2020-03-01':'2020-07-02', ['cases_m7', 'admissions_m7', 'deaths_m7']].plot()"
1049    ]
1050   },
1051   {
1052    "cell_type": "code",
1053    "execution_count": 19,
1054    "metadata": {},
1055    "outputs": [],
1056    "source": [
1057     "data_by_day['cases_m7n'] = data_by_day.cases_m7 / data_by_day.cases_m7.max()\n",
1058     "data_by_day['deaths_m7n'] = data_by_day.deaths_m7 / data_by_day.deaths_m7.max()\n",
1059     "data_by_day['admissions_m7n'] = data_by_day.admissions_m7 / data_by_day.admissions_m7.max()"
1060    ]
1061   },
1062   {
1063    "cell_type": "code",
1064    "execution_count": 20,
1065    "metadata": {},
1066    "outputs": [
1067     {
1068      "data": {
1069       "text/plain": [
1070        "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c3fa8790>"
1071       ]
1072      },
1073      "execution_count": 20,
1074      "metadata": {},
1075      "output_type": "execute_result"
1076     },
1077     {
1078      "data": {
1079       "image/png": 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92jHbbzbpxensjN9JUn4SC88tpLt9d9aNWMfn/T6nsrqS4ynHVX6dDcHbwYzlMwJIvlXCC2suUlyu/RdcJFo/kri3FvJSFDdSu824t9SAEtzZtTuYGTDW3+l/TwTMArk+BK8CIDwrHAAf6/qvuHdqY0ZPVyvWnUukqrrudFsjXSP2jt/LWI+xAGw4l4ShrpynujsT6BiIn60fK8JX8PaJt9GR6fBl/y/xt/NnlPsonEycOJZ8TKXX2RgC3a35Yao/ocm5vLw2mNIKqUGIRMtGEvfWwoXlgAg9X1Z56t8RNzl3LYd/DXRHT+euHwkjK+j8FIRugrJCIrIjcDVzrblgVB/P9XElKaeYY7H1h0/ySirYHXqDsf5tMDPQRRAEZvvNJq0ojcjsSD7p80lN6EYQBAa1HcT5tPOUVDZdHHx4Z0cWTfTjVHwWc/64REVV7TIKEnWzevVq5syZo9KcJ598ktzcXJXmLFu2jLVr16o0R1N8/fXX+Pv74+/vT+fOnZHL5eTkPKypnXqRxL01UF4MwasVBcAsVGuCkldcwce7I+nsZFZ3Y4vuz0N5AURsJTIrUqmQzB2G+thjZ6rPunOJ9Y5dtD+GkooqnrnLh75t+jK03VBe8n2p5sD1DoPaDqKsqoxzN84p7Y86mNjdmc/G+nA4OoNP90Q1qe1HjX379tUU+1KW2bNnM2OGkiU3NMxbb71VUy/niy++YODAgVhZ1b47oimkVMjWQPxhxeFnj5dUnvrl/mhyispZNbNH3bncbXuCnQ/pF5eTaZivkrjrymVM6+nCD0fiSMwuop113eGi/RFpbDifxL8GuNPZybzmcUEQ+GbQN3XO6W7XHRNdE46nHNdIC7+H8WxvVyJS89kSnMw7I7wx0W9Bv0Z/vws3w9W7poMvjPjyoUPGjRtHcnIypaWlzJ07l5dffplVq1bxxRdf4OjoiKenZ01hrpkzZ2JoaEhMTAyJiYmsWrWKNWvWcPbsWXr16sXq1asBRY31oKAgDA0N66zr/u6777J79250dHQYOnQoixcvZsGCBZiYmPDmm28SEhLC7NmzKS4upn379qxcuRJLS0sGDRpEr169OHr0KLm5ufz+++/079+fyMhInn/+ecrLy6murmbbtm106NCh1mtNSEhg+PDh9OvXj3PnzuHn58fzzz/Pxx9/TEZGBhs2bKBnz573zLm7nk1CQgIjRoygX79+nDlzBicnJ3bt2oWhoXpvRks799ZAahDI9cAlUKVp565l8+eFZGb1c7tHVO9BECDgeSLyrwLKHabezfReLsgEgQ3nk+p2PbeEt7eG0cXZnP8M9VJ6XV25Ln2d+nI85XhNlcn0onQqqpqm+uOkAGdKK6o5EKG5lExtYuXKlQQHBxMUFMQPP/xAamoqH3/8MadPn+bQoUNERd37LufWrVscOXKE7777jtGjRzNv3jwiIyMJDw+v6ZZ0h/3799OmTRtCQ0OJiIhg+PDh5OTksGPHDiIjIwkLC+PDDz+s5dOMGTP46quvCAsLw9fXl08++aTmucrKSi5cuMCSJUtqHl+2bBlz584lJCSEoKAgnJ0f3OQlPj6euXPnEhYWRkxMDH/88QenTp1i8eLFfP755/eMLS4uZv/+/UyYMKHmsbi4OF577TUiIyOxsLBg27Ztyn+xlaQFbTkkGkzqJbDvDDqqFcP6+kAsThaG/Pvx2ruTe+gyhYjzX6KDgLeVat1p7M0MGOZjz+agZOY/4XnPbdPSiirm/nmZqmqRH6Z2vTferwQDnQdyIOEAEVkRBKcH8/2l73mm4zO82eNNldZpCN3bWeJsacjOkFQmtKROT/XssDXFDz/8UHPBKDk5mXXr1jFo0CBsbW0BmDJlCleuXKkZP3r0aARBwNfXF3t7e3x9fQFFEbKEhAT8/f/X1MXX17dWXffKykoMDAx48cUXGTlyJKNGjbrHn7y8PHJzc2sKhD333HNMmjSp5vm7a7cnJCQA0Lt3bxYuXEhKSgpPPfVUnbv2O7i5ud3j85AhQ2pez5317rBnzx769u17T0jGzc2t5jXe7YM6kXbu2k51Fdy4rGiXpwJx6QUEJ95iZh9XjPTq+RtvYEaEpSMdKirRb8CPzLOBruQWV7A37H/dF/NLK5jx+wWCk27xxYQuD6xD8zD6O/VHJsh448gbfBv8LfpyffZd31dnvXh1IwgCY/3bcDo+i8yCMo3ba8kcO3aMw4cPc/bsWUJDQ+natSve3t4PrRKqbO11oM667jo6Oly4cIEJEyawc+dOhg8frpLPddVunz59Ort378bQ0JBhw4Zx5MiReuff/xrq8n/jxo21SgzfPV9T9eMlcdd2sq5AeaHK4r7pYjK6coHx3ZzqHSuKIlGU06mkBK6fUNnFQHcrOtiZ8Ovxq+wKSeX8tWym/nqOy8m3+GFqV8b4tVF5TQALAwsC7APIK8vj7R5vs6DPAjJLMrmccblB66nKOH8nqkXYG1ZnP/hHhry8PCwtLTEyMiImJoZz585RUlLCsWPHyM7OrulJ2lDqquteWFhIXl4eTz75JEuWLKkVyjE3N8fS0pKTJ08CsG7dunrL/F67dg13d3feeOMNxowZQ1hYWIN9vkNeXh7Hjx9n7NixjV5LVaSwjLaTeruBgQriXlZZxfbLqTzRyb5WI446TRSmkl9VQqcqIHo3eAxRyUVBEJj7eAfmbgxh7kbFL6GBrozlMwIY5GWn0lr380X/LyiuKMbV3JXiimL05focSDhAd3vV/tg1hA72pnRyNGNnyA2e7+umcXstleHDh7Ns2TK6dOmCl5cXgYGBODo6smDBAnr37o2joyPdunWjqqphdwPCw8Nr1XUvKChg7NixlJaWIooi3333Xa15a9asqTlQdXd3Z9WqVQ+1s2nTJtavX4+uri4ODg589NFHDfL3bnbs2MHQoUMxNlb9nWljkeq5azt750P4FngnEWTKvRH7KyyN1/64xJoXejLQ07be8QcTDvKf4/9ho743PsmX4T+xIFO9oFdpRRXJOcUkZBfT3tYYd1sTldeoj/nH5hOSEcKhiYeQN8BHVfntxFU+3xfD0TcH4daA0JI6kOq5t14aU89dCstoO6nB0MZfaWEH2HgxCScLQ/p52NQ/GEUNdx2ZDh18JkNRJiSdbZCrBrpyOtib8kQne40IO8DQdkObNDQz2q8NggBbg5ObxJ6EhLJI4q7NVJRCeoRKIZmUW8Wcis9iYndnpRtRRGVH0cGiA3qeI0DHAKJ2N9RjjTPAeQAGcgMOJBxoEnuO5oY80dGeDeeTKCmXShK0JrKzs2tumN79706v2JaOJO7aTHoEVFeqJO57w9IQRUWetjKIokhUThSdrDuBvgl4PK6oYVPdMq/eG+ka0d+5P4eTDlNV3TRi+2J/d3KLK9h26cFF0iS0D2tr65obpnf/s7ZWvZdvcyCJuzaTGqz4qIK4n4zLxNvBFGdL5bo03Si6QV5ZnkLcATqOgYIbiotTLZRhrsPIKsnil9BfaIozpR6ulnRxNmflqetUP6BImoREUyOJuzaTGgymjmCmXCphSXkVF6/fon8H5WLtoAjJAHS0un2o4zVcUSkyQv036tTF4y6PM7b9WH4L+433T71fUxNeUwiCwKx+blzLKuKoEkXSJCSaAknctZnUYGjTTenhFxJyKK+qpn+H+jNk7hCVHYWOoIOn1e2eowbm4DVCkaFT2TIbV8hlcj7r+xlvdH2Dvdf28uo/r2p8B/+kryNtzA1YcfJ6/YMlJJoASdy1ldI8yI4Hp671j73NySuZ6OnI6OmmfGW6qOwo2lu0R19+Vz68/3QozlYULGuhCILAS11e4u0eb3M+7TwXb17UqD1duYyZfV05ey2bK+kFGrUlIaEMkrhrK3cq/zmqIO5xWfR0tbqnvsvDEEWR6Ozo/8Xb79D+MTC2hdA/lLbdXEzynIS5vjmbYjdp3NadRif/RD/aoZkFCxawePFilecdO3aMM2fO1Hw+c+ZMtm7dqk7X6mTDhg33ZMPIZLJaN161EUnctZW021ejHbsoNTw9v5TY9AKV4u03i25yq+xWbXGX64LvJIjdD8VN13ygIRjoGDCu/TiOJB0hszhTo7bszQzo5Ggmxd0byP3i3lQ8/fTTNZkw69atw9XV9Z7CZdqKVH5AW0kLVRymmih3ff9UXBYA/RpwmFpL3EHRp/XcL4qD1Z6q15FvSiZ7TWZN1Bq2xW1jtt9sjdoa7G3LsuPXyCupwNxQV6O26uKrC18RkxOj1jW9rbx5p+c7Dx2zcOFC1q5dS9u2bbG1taV79+5cvXqV1157jczMTIyMjFi+fDne3t7s2bOH//73v5SXl2Ntbc2GDRsoKSlh2bJlyOVy1q9fz48//gjAiRMn+Pbbb7l58yaLFi1i4sSJpKWlMWXKFPLz86msrGTp0qX079+/Tr9MTEx47bXXOHz4MJaWlnz++ee8/fbbJCUlsWTJEsaMGXPP+Lvrrt+ZP3fuXPbu3YuhoSG7du3C3t6+kV/RpkHauWsrN8PAQbldOyhSIG1M9OjoYKb0nIjsCOSCHE9Lz9pPOviCnQ+EblR6vebCxcyFPm36sOXKFiqrNdvcerCXHVXVYs0f00eB4OBgNm7cyOXLl9m+fTsXLyrON15++WV+/PFHgoODWbx4Ma+++ipATZOLy5cvM3XqVBYtWoSrqyuzZ89m3rx5hISE1Ih1Wloap06dYu/evbz77rsA/PHHHwwbNoyQkBBCQ0MfussuKipi0KBBBAcHY2pqyocffsihQ4fYsWNHnbVjNm3adI+4FxUVERgYSGhoKAMGDGD58uVq+7ppGqV27oIgDAe+B+TAClEUv7zveRdgDWBxe8y7oijuU7OvEneoKIHMWPAeVf9YoLpa5FR8Fv08bJApeSsVIOhmED42PhjoGNR+UhDAfxoc/BCyr4J1e6XXbQ6meE1h7tG5HE85zhAX1QqfqYJ/WwvMDXU5GpvByC6OGrPzIOrbYWuCkydPMn78eIyMFHcnxowZQ2lpKWfOnLmnhnpZmaI0ckpKClOmTCEtLY3y8nLc3B5cdG3cuHHIZDI6depEeno6AD169OCFF16goqKCcePGPVTc9fT0asoB+/r6oq+vj66ubp1118+fP4+RkRGdO3e+Z/6dWvHdu3fn0KFDKnxlmpd6d+6CIMiBn4ERQCdgmiAI979P/xDYLIpiV2Aq8Iu6HZW4i/QoEKuUjrfH3Cwgq7CcfiqkQBZXFBORFUEP+x4PHtRxtOJj7N9Kr9tcDHAegL2RPVuvaPaATkcuY4CnLcdiMx+pC033126vrq7GwsLinpud0dHRALz++uvMmTOH8PBwfv31V0pLSx+47t11z++ksw4YMIATJ07g5OTEs88++9CG2Lq6ujW+NaTu+t3zNVV3XVMoE5bpCcSLonhNFMVyYCNwf3FiEbjzft8ceLQLXGuatNsn+Y5+Sg0/c1URIujrofy16ZDMECrFSno4PETcLV3BtiNc2a/0us2FjkyHke4jOXvjLDmlmj0EHuxlS1ZhGZE38jVqp6UwYMAAduzYQUlJCQUFBezZswcjIyPc3Nxq6riLokhoaCigqHHu5KTILFqzZk3NOqamphQU1J9GmpiYiJ2dHS+99BKzZs3i0qVLjX4N1dXVbNmyhalTpzZ6rZaCMuLuBNxd8i7l9mN3swB4RhCEFGAf8HpdCwmC8LIgCEGCIARlZmo2c6FVczMMDCzAvK1Sw0/FZ+Fua4yjufINeINuBiEX5HS1qyfV0mu4okpkSa7SazcXT7o9SZVYxaEEzb61HuBpiyDwyGTNdOvWjSlTpuDv78+ECRNq4uUbNmzg999/x8/PDx8fH3bt2gUoUiUnTZpE//79sbH53wH/6NGj2bFjB/7+/jVNNuri2LFj+Pv707VrV7Zt28bcuXMb/RpOnDiBs7Mz7u7ujV6rpVBvPXdBECYBw0RRfPH2588CPUVRfP2uMfNvr/WNIAi9gd+BzqL44H5nUj33RvDbINA3g+fqr85YXlmN/6cHmdDNmc/GKd/c+tl9z1JNNRue3PDwgUnnYOUwmPA7+E5Uev3mQBRFxu0ah4W+BWtGrKl/QiMY+/NpBGDna301agekeu6tGU3Xc08B7t4iOlM77OyZTAAAACAASURBVDIL2AwgiuJZwABQPudOQnmqKhQxdyXj7aEpuRSXV6kUklEq3n4H5x5gZA1XmqbEbmMQBIERbiO4lHGJm0U3NWpraCd7QpJzScgq0qgdCYkHoYy4XwQ6CILgJgiCHooD0/u3jEnAEABBEDqiEHcp7qIJMmOhqgwclbtkcTo+C0GAQHc1x9vvIJNDh6EQdxCqWv5h05NuTwKw/7pmzwkmdndGJsDmIKmJR1PQq1evWnXXw8PDm9utZqXeVEhRFCsFQZgDHECR5rhSFMVIQRA+BYJEUdwN/AdYLgjCPBSHqzPF5urf19q5eftmqpI57mfis/F1MsfCSE9pE0rH2+/gOQxC/4Tk8+Cq+TBEY3Axc8HH2od91/cxs/NMjdmxNzNgsJcdW4JTmP+EJzpyzV4pEUWxVsbKo8T58+eb2wW101gJVeonThTFfaIoeoqi2F4UxYW3H/votrAjimKUKIp9RVH0E0XRXxTFg43ySuLBpIWCrrFSeeVFZZVcSrpFn/aqRcgu3ryIj40PRrrK1Xyn/RCQ6WhF1gzACLcRROdEE38rXqN2pvRoS2ZBGUdjNfsm1sDAgOzs7CapXS/RNIiiSHZ2NgYGddwxURKp/IC2kRqsuB2qRPPnCwk5VFaLDYq3P+fznPI+GZhBu76KuPvQz5Sf10yMcBvBT5d/Ytpf0xjnMY4ZPjNoa6pc5pEqDPa2w9ZUn00Xk3iik+aurDs7O5OSkoKUgda6MDAwwNlZuY5pdSGJuzZRmg+pl6D/fKWGn4nPQk8uI6Cd8iV+I7MjqRQr6W6vfHcnQFEp8vDHUHATTB1Um9vE2BnZsWn0JlZHrGZr3Fa2XNnCNO9pvOL/CmZ6ypdnqA9duYwJ3Zz57cRV0vNLsTdr+C7soXZ0dR96y1Pi0USqLaNNJJ5W3Ex1H6TU8DNXs+nWzgJDPeVK/AIk5icC4G6hYr6v2+3CTQmnVJvXTLibu/Np30/Z/9R+nurwFBuiNzBq+yj2J6g3tDSlR1uqRdgaLPVXlWhaJHHXJq4dAx1DRfphPRSXVxJzs4Cersrv2gGSC5LRkengYKTi7tvBD/RMIeHBl09aIvbG9nzU+yM2j96Mo4kjH53+iOKKYrWt72ZjTFcXCw5Gajb1UkLifiRx1yauHYN2fUBHv96hkTfyqaoW6eJsoZKJ5IJknE2ckSsR078HuY7CNy3Zud+Pt5U3bwa8SUllCSdST6h17UGedoSl5pFT1DLbEkq0TiRx1xby0yAzRumQTGiyohxAl7bmKplJLkhu+OGiW39F67/8tIbNb2a62XXDxtCGA9fVeyFrgKcNoqgouywh0VRI4q4tXD+u+Og+SKnhIcm5OFkYYmeq/CGeKIok5Sc1XNxd+yk+allo5g5ymZxhrsM4kXKCwvJCta3bxdkCCyNdTlx5dGq8SzQ/krhrC9eOKa752ytXHyYsJQ8/FXftOaU5FFcW42Lm0gAHUVysMjDXWnEHGO46nPLqco4mH1XbmnKZQD8PG07EZUq56BJNhiTu2oAoKsTdbSDI6v+W5RSVk5RTjF8D4u1Aw3fuMrki3/269op7F9suOBo7qj1rZqCnLZkFZUSn1V/SVkJCHUjirg1kXYGCNOXj7SmKeLtf2yYWd1CEZm5dhzztTP2TCTKGuw7nTOoZ8sry1LbuAE9Fo5TjV6S4u0TTIIm7NnD1dojAfaBSw0OTcxEE6OykWlgmqSAJAQEnk/vL9auAq3blu9fFMLdhVIqVHE48rLY17c0M8HYw5YQk7hJNhCTu2kD8IbD2UHQ+UoKwlDw62Jlgoq/aBeTkgmQcjR3RkytfZKwW9p0VZwPRexq+RjPTyaoTrmau7IzfqdZ1B3raEpSYQ1FZy6+eKaH9SOLe0ikvVsSwOwxVargoioQm56ocbwdIzm9EGuQdZDLo/jzE/AVZcY1bq5kQBIGJnhMJyQwhNidWbesO9LSlokrkZJyUNSOheSRxb+kknFLUb/d4XKnhKbdKyC4qVzneDoqwTFszNRTQ6jVbcdHq9JLGr9VMjPMYh75cn02xm9S2Zg83K+zN9NlwPlFta0pIPAhJ3Fs6cQdB10iRhaIEdw5T/VUU9/zyfHLLctVTHdHEFrrNgNBNkJfa+PWaAXN9c4a5DmPvtb1qy3nXlcuY0duVk3FZxKVLWTMSmkUS95aMKELcAUUKpK5yl5FCk3PR05Hh5WCqkqk7mTIupg3Mcb+f3nNArIazP6tnvWZgitcUSipL2Httr9rWnNbTBX0dGavOJKhtTQmJupDEvSWTFQe5SdDhCaWnhCTn4utkjq6KnX/UkgZ5N5btwHcSBK+G4hz1rNnE+Nr40tGqI5tiN6nt8pGVsR7juzqx/VIKucVSrRkJzSGJe0sm/pDio5LiXlFVTVhKHl0bEG9PzlezuAP0+zdUFMPZn9S3ZhMiCAKTvSYTnxvPseRjalt3Zl9XSiuq+fOC1F9VQnNI4t6SiTsItt5goVyoJCatgLLKarq6WKpsKrkgGRtDG+Vb6ymDXUfo/BScWwaF2pnfPcp9FN5W3rx36j21Zc54O5jR18OatWcTKK2oUsuaEhL3I4l7S6WsEBLPqBSSuZx8CwB/lwZmymig1RyD3ofKEjj1rfrXbgIMdAz46bGfMNY15tV/XiW9KF0t6742yIO0vFK+2h+jlvUkJO5HEveWyuV1UFUO3qOVn5KUi52pPm3MVWvnJoqienLc68LGA/ymw8XftTZzxt7Ynl+G/EJRRRH/OvQvQjNDG71mHw8bZvZxZdXpBI7GZqjBSwmJe5HEvSVSUQKnvlNc5XfppfS0y0m36OpigSAIKpm7lHGJjJIM/Gz9VPVUOQa+rcicOfG1ZtZvArysvFgyeAm5Zbk8s+8Z5h+bT2ph4/5YvTvCG28HU97aEkpw4i3+uzeKgP8eYuaqC1zPKlKT5xKPKpK4t0SCVkJhOgx6T+kpOUXlJGQXNyje/nv471jqWzK6vfLvElTCsh10f07xbkRLb60CBDoGsu+pfbzi9wqnUk8x5585jcqiMdCV8/3UruSXVjJh6RlWn0nAv60FwQm3GPbdCRYfiKWiqlqNr0DiUUK14iMSmqe8GE4tAbcB4KrcxSWAkNvxdlUzZWJzYjmZepI5/nMw1DFUaa5KDHwXwjbDgffh6S2as6NhjHSNeNX/VRyNHfnozEcEpQfRw6H+nrYPwsvBlJ+mdSUuo5BJ3Z2xMzMgo6CUL/fF8NPReG7klvDNZD+V341JSEg795ZG0EooylBp1w6KeLtcJuDrrFolyJURKzHSMWKq91SV5qmMiS0MfEeRAXTloGZtNQHD3YZjpmfGnzF/NnqtoT4OvDbYAzszxVmJnakB307x5z9PeLL9cipf7VdffRuJRwdJ3FsSN0LgxCLFjdR2fVSaejkpF28HU4z0lH8zllyQzP6E/UzynIS5vmp/FBpEz5fBugMceA8qtfsCj6GOIU91eIojSUe4WXRTIzbmPObBM4EuLDt+lVWnr2vEhkTrRRL3lsKNy7B2DOibw5gfVZpaVS0Skpyrcj2ZDdEbkAtyZvjMUGleg9HRg+FfKJpon1/aNDY1yGSvyVSL1Wy9slUj6wuCwCdjOvNEJ3sW/hVNRn6pRuxItE4kcW8J3AiBtWMV/Udn7lUcQKrA1cxCCssqVT5MPXfjHIGOgdgZ2ak0r1F0eAK8RsLhBRCuGVFsKtqatqW/c3+2XtlKRVWFRmzIZQLvP9mRymqRjRelG60SyiOJe3MjirD7ddAzgZl/qSzsAEEJisPUbipcXiosL+Ra3jW62HZR2V6jmbAcXHrD9pcgYlvT21cjU72mkl2arfaeq3fjZmNM/w42/HkhiUope0ZCSSRxb27i/4GbYYoDVCXLDNzPhevZ2Jjo42ZjrPScyOxIRER8bXwbZLNR6BnD9M3QNhC2vaTVB6x9nfrS0aoj3wZ/S355vsbsPN2rHWl5pRyJkS48SSiHUuIuCMJwQRBiBUGIFwTh3QeMmSwIQpQgCJGCIPyhXjdbMSe/ATMn6DKlwUtcTLhFLzcrldLlwrPCAehs07nBdhuFvokiJdLWG/bMhTLtrG8uE2R83Odjckpz+C74O43ZebyjHQ5mBqw/n6QxGxKti3rFXRAEOfAzMALoBEwTBKHTfWM6AO8BfUVR9AH+rQFfWx+JZyHpDPR5Q3HY2ABSbhWTmltCD1fV4u0RWRG4mLo0TZbMg9A3gdHfQ0EaHP28+fxoJD7WPjzb8Vm2XtlK0M0gjdjQkcuY2rMtJ65kkpgt3V6VqB9ldu49gXhRFK+JolgObATG3jfmJeBnURRvAYiiKL13VIZT3yqaSXdreLbKheuKWuk93axVmheeFd58u/a7adsDAp6H88sgrfE1W5qLV/1fxcnEiU/OfkJZVZlGbEzt4YJcJrDqdIJG1pdoXSgj7k7A3cf0KbcfuxtPwFMQhNOCIJwTBGG4uhxstaQEKS70BL4Keg0vs3vheg5mBjoqdV5KL0onozijeQ5T62LIx4o/cnv+DdXaWQLXSNeIjwI/IiE/gZ9DNNN9ysHcgCk92rL6TAI7L2tnETaJpkMZca8rkHt/QQ0doAMwCJgGrBAEoVbqhiAILwuCECQIQlBmpnbW91YLRVmwZaYi1t7jxUYtdSEhhx6uVshlysfbI7IigGaMt9+PoQUM/xJuXFKcQWgpfZz6MNFzIqsjVhOSEaIRGwtG+xDobsVbW0M5ezVbIzYkWgfKiHsKcHctWGfgRh1jdomiWCGK4nUgFoXY34Moir+JohggimKAra1tQ33WbqoqFMJelAlT1iuErYFkFpRxLbOInm5WKs0LzwpHR9DB28q7wbbVTucJ4DsZjn0B1082tzcN5s2AN2lj0oYPTn1AcUWx2tfX05Hx6zMBtLM25l/rgvj5aDyHotJJyytRuy0J7UYZcb8IdBAEwU0QBD1gKrD7vjE7gcEAgiDYoAjTXFOno62Ggx9CwkkY/QM4dWvUUhcT7sTbVRP3iKwIPK080ZfrN8q+WhEEGPUtWLnDthe1tnOTsa4xn/X9jKSCJI1lz5gb6bJqZg8czA34+kAsL60Not9XR9kbdv+eS+JRpl5xF0WxEpgDHACigc2iKEYKgvCpIAhjbg87AGQLghAFHAXeEkVRes94P5lXFAeHvWaDX8NTH+9w4XoOhrpyOjspn/FSLVYTkR3RPPnt9aFvCpPWQGkubJultfVnejj04NlOz7IxdiP7ru3TiI22VkYcnDeQsAVD2f5qH7q5WPDvjSEciVFPpygJ7UepPHdRFPeJougpimJ7URQX3n7sI1EUd9/+vyiK4nxRFDuJougriuJGTTqttYRvAUEG/eapZbkL13Po1s4CXbnyd9ES8hIoqihqmeIO4NAZRn0H14/D1ucVYSwtZF63eXSz68bHZz4mOjtaY3bMDHTp5mLJ7zN70NHRjNnrL3EmPktj9iS0B+mGalMhigpxdxsApg6NXi67sIzom/n0dFU9BRJa0GFqXfhPh+FfQcxe2PEvrcyg0ZXr8s2gbzDXN2fu0bnklOZo1J6ZgS5rX+iJq7URr/95mZJy7fuaSagXSdybitRLcOs6+E5Sy3KHo9MRRXi8k2pFv6KyozDSMcLN3E0tfmiMwNnw+CeK2jOHPmpubxqEjaEN3w/+nuySbL4J0nwWkKWxHv8d50t2UTlbgqUiY486krg3FeFbQK4PHdXTyu5AZDpOFoZ0cjRTaV50TjTeVt7IBC341vf7t+KC1/llkBXf3N40CB8bH0a6j+SfpH80drnpbnq4WtK9nSW/Hr8mteh7xNGC3/BWQHWVYgfqOVRR1reRFJZVcioui2E+DirVk6mqriImJ4aO1h0b7UOT8dj/gY4B/LOguT1pMENdh1JUUcSZ1DMatyUIAq8MbE9qbgl/haVp3J5Ey0US96bg+glF67zOE9Wy3PHYTMqrqhnmY6/SvMSCREoqS+hopUXibmIHfedC9B5FLR4tpJdjL8z0zDiY2DTVLx/ztsPT3oSlx642qoG3hHYjiXtTEL4V9EzBc5haljsQeRMrYz0CXFXLb7+TtaFVO3eA3q+BqSMc+j/FwbSWoSvT5TGXxziWfIzyKs2nd8pkArMHtic2vYADkVJq5KOKJO5NQWE6dBoDuoaNXqq8spqjMRk83tFOpZIDoBB3fbk+7ubujfajSdEzhsEfQMpFCG18Q+rmYGi7oRRWFHL2RtO8+xjt1wZ3G2Pe+PMy684lSjv4RxBJ3JuCZ7YqbqSqgTNXsygoq2SYj+rplNE50XhaeqIjU76JdovBfzq49IG/3tTKw9VAx0BM9UybLDSjK5ex9ZU+9PGw5v92RjDnz8sUl1c2iW2JloEk7k2FXD2CeiAyHWM9OX09bFSaJ4oi0dnR2hVvvxuZHCasAB192DoTKrSrWbSuXJfBbQdzNOlok4RmAKyM9Vj5XA/eGe7N3+FpvL01TNrBP0JI4q5FiKLIkZh0BnjaYqArV2luSmEKBRUF2hdvvxtzJxi3FG6GK+LvWsYw12EUVBTwxYUvyC5pmuocMpnAK4Pa8+YwL/aGpbHi5PUmsSvR/EjirkVEpxWQnl/GYC/VLi6BFh+m3o/XcOg9By78BmFbmtsblejTRlESeHvcdkZsH8GPl3+korppyiu8MrA9Izo78MXf0VJ5gkcESdy1iGNXFA2uBnqpXi45OicaHUGHDha1KjFrH48vgHZ9YfccuKGZuumaQEemw8e9P2bn2J0Mch7Eb2G/8cqhV8gry9O4bUEQ+HqSH+1tTXh25QX6LzrClF/PsuZMgsZtSzQPkrhrEcdiMunkaIa9mYHKc6Ozo/Gw9EBP3rBerS0Kua6ieqSRDWx8WuvKA7uZu7Fo4CI+7/c5lzIu8cy+Z0jMT9S4XRN9Hda80JOXB7jTta0lBaWVfLw7km8PXZFi8a0QSdy1hLySCoKTbjHYW/VduyiKROdo8WFqXZjYwtT1UJwFm56Bcu1rGj26/WhWDF1BblkuU/ZOYc/VPRoX2TYWhrwz3JsfpnVlz+v9mNTdmR/+ieM7SeBbHZK4awmn4rKoqhYbFG9PL04npzSnZXVeUgdtusL4ZZByQbGD17IMGoBu9t3YNGoTXpZevH/qfd48/ib55flNYlsuE/hqQhemBLTlhyPxLD1+tUnsSjQNkrhrCUdjMzAz0MG/rept+e6U+W2xNdwbg894GPMTXDuqaF+ohfXf25i0YeWwlcztNpcjSUf4+PTHTWZbJhP44ilfxvq3YdH+WPaFS/VoWguSuGsB1dUix2IzGeBpi44KjTnuEJ4Vjq5MFy8rLw141wLo+jQ8uRiu/A1rx0FeSnN7pDJymZwXfV9ktt9sDicd5nza+SazLbu9g+/mYsH8zSGEJuc2mW0JzSGJuxYQlZZPVmHDUiBB0TPV28q7dRymPoieL8G4ZZAWAkv7KKpwaiEzO8/EycSJLy98SWV1090oNdCV89uMAGxM9HlxbRBJ2epv7i3RtEjirgUcv6LIBhngqfphalV1FZFZkS2785K68J8Gs0+CjSdsfQF2vqZ1B636cn3eCniL+Nx4NsdublLbNib6rJzZg4qqaqYtP0dyjiTw2owk7lrA+es5eNqbYGuqr/Lc63nXKa4sbp3x9rqwcofn98OAtyBkA/w2CBLPQG4SFGVpRcu+x1weI9AxkJ9CfiKrpGkvHHnam7J+Vi8KSiuYtvwcqbklTWpfQn1I4t7Cqayq5lLiLXqoWN73DlrRM1XdyHXgsQ9hxk4ozYNVI2CJL3zdXvFv6ywI3QgVLVO4BEHgvZ7vUVFVwX+O/YeKJj4k7uxkzoYXA8krqeC5lReolDo6aSWSuLdwotMKKCyrpKdbw8XdVNeUdmbt1OyZFuA+CF45CxN+V2TUjFgEniPg+nFF4+1l/SElqLm9rBN3C3c+7fsplzIu8cWFL5rcvq+zOV9P9CM+o5C9UkcnrUQLa78+WlxIyAFosLhHZEXQ2aazdvRM1QTG1uB7Xwes6mq4+g/s+Tf8/oSi09OAtxR141sQI9xGEJsTy+8Rv+Nl6cUU7ylNan9oJ3u8HUz56Wg8Y/zaIFOxf4BE8/KI/sZrDxeuZ9PWyhBHc9UbfZRWlnLl1pVHKySjDDIZdHgCXj2jqBN/6jv4sTtc3tDiYvKvd32dfk79WHRxUZPUoLkbmUzgtcEexGcUciDyZpPalmg8kri3YERR5GLCLXq6WjdofnRONFVi1aNzmKoqBuYw9md44QCYOcGuV2FZP0U8voVchpLL5LzR9Q3Kq8s5kHCgye0/6euIu40xPx6Jl8oTaBmSuLdgrmYWklNUTk83ywbND8+8fTPVVhL3h+ISCC8eVsTmRVERj//eH6J2N7dnAHhbeeNp6cmu+F33PN4UYiu/XQ8+Ki2fo7EZGrcnoT4kcW/BnL9+J97esJ17RFYEjsaO2Biq1rXpkUQQFLH5V8/C9C1gbANbnoPL65vbMwRBYGz7sYRlhXEt9xoAyfnJPLH1CVaEr9C4/XFdnWhjbsDqM5qvXCmhPiRxb8FcvJ6Drak+rtZGDZofkR0hxdtVRRDAcyg8/7ci22bXa3BheXN7xUj3kegIOuy6uouq6io+OP0B6cXpfH/pezbGbNSobV25jDH+TpyOzyKnqGlaBEo0HkncWzAXrufQ09UKQVA9SyG/PJ/kgmQ6WXfSgGePAHpGMG0jeI2EfW9C0Mpmdcfa0Jp+zv3Ye3UvqyNXcznjMp/2+ZRBbQfx+fnP+evaXxq1P6qLI1XVonSwqkVI4t5CSblVzI280ganQMbmxAK0rhruTY2OPkxeAx2Gwl//gSsHm9Wdce3HkVGSwZJLS3is7WOM8xjH4oGL6W7fnfdPvc9nZz/TWG9WnzZmuNkYszfshkbWl1A/kri3UIISbgEQ4Nqww9So7CiA1lfDvamR68LEVeDgqygpfONys7kywHkAFvoWWOpb8lHvjxAEAX25Pj8N+YkpXlPYFreNkTtGsj5qvdoPWwVBYKSvI2evZpNZUKbWtSU0g1LiLgjCcEEQYgVBiBcE4d2HjJsoCIIoCEKA+lx8NAlKzMFEXwdvB7MGzY/JicHOyA5rw4Ydxkrchb4JTN8MRlawYTJkxDSLG7pyXZYMXsKyJ5bd83011jXm/V7vs33sdrradeWri1+x8PxCqtScsz/Kz5FqEfZLoRmtoF5xFwRBDvwMjAA6AdMEQagVyBUEwRR4A2i6QtStmKCEW3R1sUDewFuB0dnRdLKS4u1qw9QBntmuOHBd/STcDG8WN7rbd3/gOYq7uTs/D/mZ532eZ1PsJt48/iZlVerbZXvZm+JhZ8LeUCk0ow0os3PvCcSLonhNFMVyYCMwto5xnwGLAO3rddbCKCitIDa9gG4uDQvJlFSWcD3/Oh2tpXi7WrH1hJn7QMcAVo+C1ODm9qgWMkHG/ID5vBXwFoeTDvP+yffVFqK5E5q5kJBDer70a97SUUbcnYDkuz5Puf1YDYIgdAXaiqK492ELCYLwsiAIQYIgBGVmalfH+qbkclIuotjwePuVW1eoFquleLsmsPGA5/eBgZlC4GP2NbdHdTLDZwZzu83lYOJBdsbvVNu6o/3aIIqw4XyS2taU0AzKiHtdcYGarYAgCDLgO+A/9S0kiuJvoigGiKIYYGureuOJR4WgxFvIBOjawJ17dHY0gJQGqSksXWHWIbD1go3T4ezPiputLYznfZ6np0NPvrjwBYn56rmA5GFnwpO+Dvx+8hrZhdLBaktGGXFPAdre9bkzcHfQzRToDBwTBCEBCAR2S4eqDSc4MQdvBzNM9BtWtDM6JxpLfUvsjezV7JlEDaYOihBNx1Fw4H04ubi5PaqFXCZnYb+F6Mn1eOfEO2qrCz//CS9KKqr4+ehVtawnoRmUEfeLQAdBENwEQdADpgI1RTdEUcwTRdFGFEVXURRdgXPAGFEUW2ah7BZOZVU1l5NyGxySAcXO3dvKu0GXnyRUQM8IJq0F38lwZCHE/9PcHtXCwdiBT3p/QmR2JG+deEspga8WH96cw8POhEnd27L+XKLUqakFU6+4i6JYCcwBDgDRwGZRFCMFQfhUEIQxmnbwUSPmZgHF5VV0b9cwca+oqiAuN046TG0qZDIY/T3YdYJtL0Jucv1zmpgh7Ybwbs93+SfpH/5z/OGdnS7evEj3dd15/Z/XOZV66oFCP/fxDiDAkkNXNOW2RCNRKs9dFMV9oih6iqLYXhTFhbcf+0gUxVpl80RRHCTt2htO0O3mHA0V9/jceCqrKyVxb0r0jGDyWkWZ4M0zoCS3uT2qxdMdn+b9Xu9zNPkor/zzCocTD1NYXlhr3M74nejKdQnLCuOVw6/wyuFX6lyvjYUhMwLbse1SCjE38zXtvkQDkG6otjCCk3JxMDPAyUL15hygiLeDVHagybHxgPFLIS0Efund7KUK6mKa9zQ+7v0xUVlRzDs2j/4b+/PDpR9qni+vKudI0hGGuQ7j0MRDTPeezpkbZx5Y0mDOYx6YGujy2d4oqdZ7C0QS9xaEKIoEJeTQ3dWywfHy6OxojHWNaWvatv7BEuql42hFXXgDc/hjEux+Q9HSrwUx0XMix6ceZ9WwVfRz6sfKiJWkFqYCcObGGQorChnmOgw9uR7DXIcB/2uyfj8WRnrMe7wDp+OzORSV3mSvQUI5JHFvQVzNLCQtr5Q+7RteMiD2Vixell6Pbs/U5sapO/zrOPR5Ay6tgYMfNrdHtdCV6RLgEMAHgR8gILAuah0ABxIOYKZnRi/HXoAilVZH0CE0M/SBaz0d2A4POxMW7oumrLJltSh81JEUoAVx4koWAAM6NOwOQLVYTWxOrHR5qbnR0YcnPoVes+Hcz3BuaXN7VCcOxg486f4k2+O2k16UztHkozze7nF0ZboAGOgY4GXl9VBx15XL+L9RnUjMLmbV6YQm8lxCGSRxb0GciMvEzcaYhXuybAAAIABJREFUtlYNa86RXJBMcWWxJO4tAUGAYZ+D9yjY/x5cWttiLzqVVJYw//h8iiqKGNZu2D3P+9n6EZEVQWV15QPXGOhpy+Md7fn24BXOXdNMyWEJ1ZHEvYVQVlnFuWvZDOjQ8JZ4MTmKaoVeVl7qckuiMcjk8NRyaNcHdr8Oa0ZDZmxze3UPHpYeDHQeSFhmGBb6FvRw7HHP811su1BSWUJ8bvxD1/lmkh8u1ka8vDaI+IwCTbosoSSSuLcQghJuUVpRzQDPhpdliMmJQUfQwcPCQ42eSTQKPSN4bg+M/AZuhsHSPorWfS1I5J/v/DwAQ1yG1IRk7uBn6wdAWGZYrXnVYjWR2ZHsit/F79E/MnZAPLo61Ty38iIZUmGxZqdh99sl1M6JK5noygUC3Rt+mBqTE4O7hTt6cj01eibRaGRy6PEidBoHx76Ey+sUjbc7joYxP4GhRbO6182uG5/2+ZTebXrXes7JxAkrAytCM0OZ7DW55vHiimLePfkuR5OPAqAj06GyuhI7zzakXRvKhGWwamZPPOxMmux1SNyLtHNvIZyIy6J7O0uMG1hPBpAOU1s6xjYwcjHMi4SB70DsflgzCgqbt0KqIAiM7zAeB2OHOp/zs/W751A1oziDmftncjzlOP/u9m/2jNvDxacv8usTv2KqZ4jccTWFesd46pfTnL0qxeCbC0ncWwAZBaVEp+U3KiSTVZJFZkmmJO7agLENDH4fpm+ErHhYNQLyUpvbqwfSxbYLifmJ5JbmEpUdxfS/ppOQn8CPj/3ILN9ZuJq7oiPToU+bPmwZs4WOVh3p4H4VOzMDZqw8z7cHYymtkNIkmxpJ3FsAp+IalwIJ/2uILYm7FuHxODy7HQrTYcXjkNIyq3bcibsvufT/7d13eFRV3sDx75mZNBJSIQGSEFpCDQECofcixRUQBFHWhuIri93Vtazr6lpe14auZXnFFbFLk7iAoNIDSOihhBQI6SGEFBJSZua8f9wJBoQkDDeZlPN5nnkyc+/Nub+5DL85OfeUd7hz3Z0IIfhs0meMCBrxu2OdDE4MDxpOfH4c/5nXi8nhbXn3l0QmvL2VDUeysFobXo+hpkol9wZgy4kz+Lk706Otfeulwm89ZcJ8wvQKS6kPIUO0xT+MTloNfu+njo7od3r69cQojKxIWEEPvx58PeXraisRwwKHYZVWjp3by6Jb+/LlvQMxGQXzl+1l+OubWPRTAhlqNsk6p26oOpjFKtl64gyju/ljsHO9VNBq7u3c2+Hl4qVjdEq9aBMO8zdrs0pGPwy//APc/cErEAbcB6HjtX7zDtLCqQWzus7CZDDxaL9HcTI6VXt8eKtwWjq1JCYjhgkdJjCkSyvWPzyCH49k8c2eVN779Rv+9auRkYHjuG1ge0aFXd9nX7kyldwd7EBqPudKKhjd1f+6yjmWd0w1yTRmLXzh9u8g9hPIjtNusmYd0uao6TAcRj8LwQO1KYYd4JmBz9T6WJPBxKB2g9ievh0pJUIInE0G/hDRjgFdDExasQKzNBNbmMVPn45jZmR7Xp/RWyV4nank7mCbjudgNIjram8vqSghpTCFyR0n6xiZUu8MRoi677fX5nKtmWbLa/CfidCiFXQeDb1vhdBxDguzNoa0G8LGlI0kFyTT2bvzxe1LjyxFIpnSaQr/Tf4vvfyLWb7vRpyMgpenhasEryPV5u5gm+JziGzvg1eL6v/Urc6JcyeQSDUytakxOcPA+fDQAZi+GLqMhaRN8MUM+HwG5Bx3dIRXNbTdUAC2p2+/uC2vNI/lJ5YzpdMUXh32Kn/u/2dSSnczov9Rvvo1lReij6ipg3WkkrsDZReWciSjkNHdrq9JZkPKBkwGE339++oUmdKguHpCxGy4eTE8dkybsyZ1jzbadfWCBjXatVJbj7Z08upETEbMxW2fH/2cMksZ88LnIYTgjp53ENUmilyxnfuGd+SznSm8rVZ20o1K7g60OT4HgNHd7G+SKbeUE50UzZjgMfi42r/uqtJImJxh8J/goX0QNR/iVsL7UfDlbG3Ua2GmoyO8aGjgUGKzYonPiyerOIuvjn/FuJBxdPLqdPGYaV2mkVqUyoTIYmb3D+bdXxJZtivFgVE3HSq5O9Avx3No6+VK14CW9peR+gv5ZfnMCJ2hY2RKg+feCia99tto14wD2pw1b3WDD4fBple0bQ5s5hgZNJJyazkzo2cyfvl4zlec577w+y45ZlzIONyd3FmduJqXp/diXHd/nv8+jrWHG86XVGMlHNXG1b9/fxkb2zAHbdSHcrOVvi9uYGrfQF6ZHm53OfM3zCelMIV1M9apBTqaMykh+wgk/gQnfoTUXSCt4BcK/e6AiDngYf9fiPaFJDmad5TThafJOJ+Bj6sPN4fe/LvjXoh5gbUn17Jp1iYM0pW5S3ZzIDWff0zrxZyo9vUac2MghNgrpexf03EqGzhI7Kk8isst19UFMq0ojZ2ZO5kWOk0l9uZOCGjTC4Y9AvesgycS4Kb3oIUfbPwrvNUdfn4JzGX1GJKgp19PJnWcxLzweVdM7KA1zVwwX+DHUz/i5mxk6T1RDOvSiqdXHub19cfVqFY7qYzgIFsStFkgr2dJvdWJqxEIpneZrmNkSpPg3kqrsc/7ERbshl43w7Y34N8jtJuxDUhE6wg6enVkZcJKzFYzHi4mltzZnzlRwXywOYn5y2LJPV9/X0pNhUruDrI9IZd+7e2fBdJitbA6cTVDA4decTY/RbnIv5vW0+b25VBWBEvGQ/QjUJLn6MgArYY/I3QGB88cJOqLKG5eczPPxzxH/14JPHSDL1tPnOGGt7eqRbivkRrE5AC558s4klHIExPsnwfmcO5hskuyeTTyUR0jU5q00PGwYBdsfhV2/xuOfg+T/wnhMx0dGXO7z6W1W2viz8WTlJ/EjowdRCdHA9AtMoz87AHct+w8dw4K5dkpPXA2qXppTVRyd4AdidoskMOuY1TqlrQtmISJYYHD9ApLaQ5cPWHiq9DndvjhUVgxD84mwcgnHTp/jdFgZHKnyUxGG2UtpSS5IJmdGTtZmbiScy2+wLe7O18eH82BtEl8cPsAAr3dHBZvY6C+/hxge0IuXm5OhAfaP8nX5tTN9AvopyYKU+zTphfc9V+tF83mV2DNQrBUODqqi4QQdPbuzNwec1nxhxUsnbiUqLZ9cG3zA0lOLzP5o2X8pJppqqWSez2TUrI9MZehXfww2jmPRmpRKon5iYwKHqVvcErzYnKGaR/CiCe1AVCLR8Hp3Y6O6neEEPQL6MeH4z7kzZFv4udZgbXtezy05R7u/u4D8i8UOzrEBkkl93qWdKaYzIJShnW5jiaZ1C0AjAoapVNUSrMlBIx5FmZ/ARfOwScT4PuFDWqkayUhBBM6TGDtjGgei3wCbw8zsSUfMubLWRxKz3V0eA2OSu71bFuCtl7m8NBWdpexOXUznb06E+wZrFdYSnPX/Ub4068w5CE4+BUsioB1f4GiLEdH9jvuTu7c3etOdsxdzx87P0OF6TS3rnyC/9uapPrEV6GSez3bnpBLB78WBPu2sOv3C8sL2Zu9VzXJKPpz8YAJL8GDe6H3LfDrYninN/zwGJw75ejofkcIwZPD5nBX9/kYPffy+s7/MPi1n3lhzRH2puQ1+xkma9VbRggxEVgEGIGPpZSvXbb/MeBewAycAe6RUqrZfy5TWmFhV/JZpvcLtLuMHek7MEuzSu5K3fHpAFPfh2GPwY5FsH+ZNq98z2kw6E8QFOnoCC/x6IA/kVx0nO38gJ+Arw6259OYtvQJ9uWhsV0Y3dUfcVlPoNIKC3HpBRxIzSc1rwR/T1eCfNxo5+1GoLcbAZ6udt8TayhqTO5CCCPwPjAeSAP2CCHWSCmPVjlsP9BfSlkihHgAeB2YXRcBN2bfxqZSXG5hcnhbu8v4KeUnfF19CW9l/3w0ilIrfp3hpndh1NOw633YuxTiVkBQFLSN0EbBegZCp1Hg7bgmQoMw8OrwV3lk0yPsyfoe5/bga/IjO/MB7vk0n0BvN8ICPAjxc6fwQgVHMwtJyDmPxdaE4+Fi4nyZ+ZIyTQbBkC6tuC0qmLHdA3AyNr5GjhonDhNCDAZekFLeYHv9NICU8tWrHN8X+JeUcmh15Ta3icNKKyyM+udm2vu24Jv7B/2uJlEbKYUp3LT6Jm7vfjtPDniyDqJUlGqUFWm9avZ/DgVpUJr/2z7/HtC6K5QXaw/31tpr/+7QaTS4eddLiHmleezM2MlLu16ir38/xvo8w6bjOZzMLebU2WI8XEz0bOdJz3ZeRAR7ExHshX9LV4rLzGTkXyDd9jiVW8wPhzLJLCjF09WEm7MRs0XiZDTQ3q8FHfxaMKiTH1N6t8XFZKyX91apthOH1aZZJhBIrfI6DRhYzfHzgHW1KLdZ+TY2lazCUt6aFWFXYgf46OBHOBucuafXPTpHpyi14NISBj2gPUDrF382CRI3QsIGyDoMzh7g7A6ZB+HYGm1mSqMzdB4LPadD2ARwq7t1B3xdfZnSaQq5F3J5I/YNbu2axPuRI2v8PXcXE6EBLQmtMv32XyZ1Z8uJHDYezcZqBSeT4EK5ldN5xfx8LIdvY9N4Ze1x5g5qz5yo9gR4utbZ+7JHbWrutwA3SCnvtb3+IxAlpXzwCsfOBRYCI6WUv5vpRwgxH5gP0L59+8iUlObRLF9Zaw/2dePb+wfbldyT85OZ9v007up5F4/1f6wOolQUnVWUaot8H1kNR1ZBUQYII4QMgYhbIXyW1te+Lk5tqWBG9AzMVjOrpq7Cxeiia/lSSrYl5PLJjpNsjj+D0SAY3dWfWf2DGBHWGlenuqvN17bmrluzjBBiHPAeWmLPqenEzalZ5rOdp3j++yN8ce9Ahnaxrwvkn7f8ma1pW1k/Y71acUlpfKxWyNgH8Wvh2A+QGw+eQdoUxf3uAJO+yRcgJiOG+zfez7xe83igzwO6J/hKJ3OL+TY2leV70zhTVIa7s5FRXf35Q0Rbxvdoo/uNWT2Tuwk4AYwF0oE9wG1SyiNVjukLLAcmSikTahNgc0nuxWVmRr+xmRC/FnbX2hPOJTBjzQzuDb+Xh/o9VAdRKko9khISf4atr0PqbvDtDJNeh9Bxup/q8c2PsyFlAy5GF/r69+WGDjdwY6cbcTXp34RSYbGyIzGXH49ks/FoNrnnywjxa8F9wzsxMzJIt9q8bsndVthk4B20rpCfSClfFkK8CMRKKdcIIX4CwoHKYW2npZQ3VVdmc0nub288waKfE1jxwGAiQ3ztKqNypZqNMzequWSUpqMyya97EvKSoOsUrSYfNEC3ScwqLBXEZMSwK3MXMRkxJBck4+vqy6yusxjabihhPmG0cLJvzEl1LFbJhiNZfLQliYNpBQR4urBgVBdmDwi+7iSva3KvC80huWcXljLqn5sZ082f92/vZ1cZJRUljPluDONDxvPS0Jd0jlBRGgBzGcS8B9vfgfIiaNNbWwQ8fBYY9OuCKKUkNjuWpUeWsiVNm8JDIAj1CWVhn4WMCh5ld2eH6s65M+ks7/yUwK+n8mjj6crzf+hxXd2hVXJvAP783UFWH0jn58dG0d7PvtrBmqQ1PLv9WZZOXEq/APu+IBSlUSg7D4e+gT0fQ85RaNcXJv4vtK+uc559souzOZZ3jON5x1l3ch3JBckMaTeEp6KeopNXJ93PV5nkX1l3jLj0Qib1asPfp/bEv+W1Nw+p5O5gRzMKmfLeNuYN7chzN/awu5y719/NmQtniJ4WrXutQlEaJKsVDn8HP/0NijKh10wY/yJ42T+yuzoV1gq+Of4NHxz4gHJrOU8OeJJbwm6pk/9vZouVxduSeeenBFo4G/n7TT25KaLdNZ1LLZDtQFJKXog+gpebEw+OCbW7nNOFp4nNjmVal2kqsSvNh8EAEbNhYSwMfwKORcO/+sOW1+tkaUAngxNze8xlzfQ19A/oz0u7XuKRTY8QlxtHSUWJrucyGQ0sGNWFtQ8Np2Mrdx7++gALvthHfFYR54rLdZ34TNXc68Dq/ek88s0BXpkezm0D29tdzrv73mVJ3BI2zNhAgHuAjhEqSiNy7hRs+Ks2KMrgBGE3QJ/bIGySrm3yAFZpZdnRZbyz7x3MVm1KgiCPIEYGj2Rih4n0bt0bg9DnnGaLlf/bdpK3N56g3GIFtGkPRoS15o+DQhgR1vqK3ShVs4yDFJZWMPbNLbTzcmXlgqF293G1WC1MWDGBrj5d+WDcBzpHqSiNUOYhOPi11mRTnKPNbzP2eW30q85/2WYVZxGXG0dSfhJxZ+OISY+h3FpOiGcIj0U+xujg0br9NZ1ytpj9p/PJKy4nPf8Caw5mcKaojCAfN6b3DWRqn0C6+HtcPF4ldwd5Mfoo/4k5yeoFQ4kItn8+jZj0GO7/6X7eHPkmEzpM0DFCRWnkLGaIWw6bXob809q8Nh2GayNfA3qCd4juI1+LyovYlLqJJYeXkFyQzOC2gxkXMo6CsgKKyovo5N2JqDZRtPNod93nqrBY2XAkm6/3nGZHYi5WCZEhPjwzuTuRIT4quTvC/tPnmPnRTmYPCOaV6dc3a+Nftv2FbWnb2DRrE87GuhmirSiNmrkM9n0Gx3+A1F+hsn1cGMCjjfZTWgABRidtFKybr3Zj1jMQ2oRDYCT4dqp1zb/qzdeiiiIATAbTxSacDp4d+OugvxLVNkqXt5hTWMqagxks3ppMTlEZ0/q0Y9Gcfiq516fswlL+8N52XJwMRC8chncL+xPy+fLzjP52NFO7TOW5Qc/pGKWiNFGWCq3Z5mwC5CVDQbq23WDQJi8zl4OlTLshW5iu7bfYpr9y9gDPdtCyLfh1gfaDIWQweAVd9XQlFSUUlRfh7eqNk8GJxPxE9mTt4evjX5NSmMLdve5mYZ+FOBmddHl7xWVmPtycxOJtySS8PFkl9/pSWmFh9uJdJGQXsXLBELq18byu8lYlrOL5mOf5fPLnRLSO0ClKRVEuspi1+W3S90JWnNblsigTco5rA6kAWoVB18nQbUqtR82WVJTw+p7XWZGwguCWwUzqOInxIeNxd3InrSiN9PPpZJzPILM4E4vVwojgEYwMGklL55Y1lg1wIieXrgGtVXKvD1JKHv/2ICv3p/PR3Egm9mpz3WXe8+M95JTkqL7tilLfLGbIOQKndkDCj3BqO1jN2qjZoQ9Dj2lgrHmm9E2nN/H5sc+JzY7FKq2X7DMKIwEtAqiwVnDmwhmcDE6E+YTh4+qDp7MnReVFZJVkUVhWSIhnCKE+oZiEiX05+zh69igH7zyoknt9+HTHSV6IPsqj48J4eJz9fdorZZzP4IYVN7Cwz0Luj7hfhwgVRbFbaYE2ZXHMe1qTj6s3eLfX2uwDekLn0drKVFe5gXv2wlm2pm0FIKhlEIEegfi38MdkMGGVVg6dOcTGlI0kFSSRX5pPQVkBLZ1bEuAeQEunlpwqPEVifiJmq5nwVuFEBkTycOTDKrnXtT2n8pizeBejuvqz+I+RGHSY2nPxocW8t/891s9YT6BH3YzIUxTlGlmtcGIdJGyEwgxtJaozx7Ubtk7u0HGEthBJ57Fa8tfxL26rtGKRFpwMWvu9nisxKVeQXVjKgi/2EezbgrdmR+iS2Mst5axKWEVkQKRK7IrSkBgMWtt7tym/bSst0Jptkn7RVqI6YVuAzsVTa6/37QQe/tpasxUXtBu5JefAt6PWUyc4SjumplMLg10Dp1Ryt8P5MjPzP4uluMzMF/cOxNNVnzviiw8tJu18Gs8OelaX8hRFqUOuXr8lfCkh9wSc3Apn4rWbtam7ofiMrYumAI8AbYnBpJ/BXKpti5gDY56rk3lzVHK/RmVmC/+zbC9xGYX8e24kYQG1u8tdk/i8eJYcXsJNnW9iWOAwXcpUFKWeCKEtCN666+/3lZ3X+thXdou0mLX2+wNfwu6PtCUII+/SplRoE65bk45qc78GFqvk4a/388OhTN64JYKZkVfvB3stzFYzt6+9naziLL6f+j3ervWzUryiKA52LkUbaRu3EqwV4N8TggeAT0fti6LLuN++FGxUm7vOrFbJ0ysP8cOhTJ6Z3E23xA6w7Ogyjp49ypsj31SJXVGaE58QuHkxTHwNjqzUkvyxaCg5q+33Cta6YPadC05u11S0Su61YLFKnlx+iBX70nhobCjzR3TWreys4iw+PPgho4JHMT5kvG7lKorSiLTwhQH3ag/QbtamxMD2t2HtE/DLP6DnNAi/pdZFquRegwqLlaeWH2Ll/nQeGRfKI+PCdC3/zdg3sUorTw14Sg1YUhRF4+oFXSdB2EQtye/9FA59p/2sJZXcq3GuuJw/fbmPmKSzPD4+jAfHXv8gpap+zfyV9afWsyBiAUEt9WvmURSliRACOgzVHuXFcGI9/H1mrX5VJferSMgu4t7PYsnML9X15mmlCmsFr+x+hUCPQO7udbeuZSuK0gQ5u0OvGbU+XCX3y5gtVj7ZcZK3Np7Aw8WJr+YPIjLER/fzfBr3KUkFSSwavQhX07UvkqsoilIdldyrSMgu4rFvD3I4vYDxPQL4x7ReBHjqn3jj8+L54OAHTAiZwOjg0bqXryiKopK7zeb4HBZ+uR9XJwPv39aPyeFt6uQGZ7mlnKe3P423izfPDXpO3URVFKVOqOQOLI05xd+jj9CtjSdL7upPW69r6096LT448AEJ5xJ4f+z7+Ljq39yjKIoCzTy5Syl5bf1x/r0lmXHdA1h0ax/cXermkqQVpfH23rfZkLKBm0NvZkTQiDo5j6IoCjTj5G6xSp5ddZiv96Ry+8D2vDi1F0YdZnasJKUkpTCFQ7mH2Je9j+ikaIwGIwv6LGBer3m6nUdRFOVKmmVyLy4z88R3B1kXl8XC0V14fEKYbm3fxRXFRCdF8038NyTmJwLg7uTOxI4TebDvg7Rxv/6VmhRFUWrS7JJ7fFYRC77Yy8ncYp6b0p17h9c8n3JNpJQczj3MqsRVrDu5juKKYrr7due5gc8RGRBJR6+OGA1GHaJXFEWpnWaT3M+Xmflq92ne3BhPS1cnPr93IEM6t7quMsst5fw3+b8sO7aMhHMJuJncGB8yntldZxPeKlz1hFEUxWGadHIvrbBwOL2A9XFZfLsnlaIyMyPCWvPmLRG0bulid7mphamsSV7D8hPLyb2QS5hPGH8b/DcmdpiIh7OHju9AURTFPrVK7kKIicAiwAh8LKV87bL9LsBnQCRwFpgtpTylb6i/V262cjqvhNN5xaTnl5KRf4HcojLOlVRwpqiUY5lFlFusmAyCKb3bcvfQjvQJvrYpdYvKizice5jUwlTSzqdx6Mwh9uXsQyAYGjiUO3rcwaC2g1QtXVGUBqXG5C6EMALvA+OBNGCPEGKNlPJolcPmAeeklF2EELcC/wvMvp7ASiss5BSWkVlwgazCUjILSskqKCWnqJTconKyCktJO1eCtcpaIyaDwM/DGZ8Wzvi6O3P30A5EhvjQv4Mvvu5XXp28kpSS3Au5JJxLIDE/kaSCJA7nHibxXCIS7STOBmc6eHXg4X4Pc2OnG9XNUUVRGqwaV2ISQgwGXpBS3mB7/TSAlPLVKsf8aDtmpxDCBGQBrWU1hft2CpJjXnoQq5RUWK1UmCVlZgsl5dqjtMLyu99xNRnwcDXi7mrA3cWAt5sJb3cTXm5OeLqZcHMygpBYrBYs0kKpuZQL5guUWcoulmGVViqsFZit5otJu9RcSmJ+InmleReP83Hxobtfd/r496FP6z509u5MK7dWdi1UqyiKohc9V2IKBFKrvE4DBl7tGCmlWQhRAPgBuZcFNR+YD+DawZV48+e/7TTaHram8KvN6HLe9sBS9cWlKlcLdzO54WZyw8XogkBc3GcymHAyOF1sSjEZTIwMGklX366E+YTR2bszvq6+V78iiqIoDVxtkvuVGpMvr5HX5hiklIuBxQB9I/vKzbdursXpL2UQBozCeMnPqu3dAqHavxVFafZqk9zTgOAqr4OAjKsck2ZrlvEC8qiGURjxcvG6hlAVRVGU2qpNA/IeIFQI0VEI4QzcCqy57Jg1wJ225zOBX6prb1cURVHqVo01d1sb+kLgR7RW8U+klEeEEC8CsVLKNcASYJkQIhGtxn5rXQatKIqiVK9W/dyllGuBtZdte77K81Kg9styK4qiKHVK9etTFEVpglRyVxRFaYJUclcURWmCVHJXFEVpgmqcfqDOTixEERBve+kFFOhUdHMtqxWXjQi+jrLs1RTKquk6NoX3WJ9l2fu5vJqG+j7r8/93VyllyxpLkVI65IHWjbLy+WIdy22WZVW9ng0prsZWVk3XsSm8x3ouy67PZSN8n/X2/7u217ShNMtEq7JUWaqsJlmW3hrq+2xwZTmyWSZW1mJmM6V21PXUh7qO+lLXUx9Vr2Ntr6kja+6LHXjupkhdT32o66gvdT31sfgqz6/KYTV3RVEUpe40lDZ3RVEURUcquSuKojRBdZbchRBSCLGsymuTEOKMEOKHujpncyCEmG67tt0cHUtjoz6TdUcIcYU10RR71XQ9hRCbhRDV3lSty5p7MdBLCOFmez0eSL+WAmwLfyiXmgNs5xqnVbYtdN7cXfdnUlEai7pullkHTLE9nwN8VblDCBElhIgRQuy3/exq236XEOI7IUQ0sKGO42tUhBAewFBgHrbkLoQYJYTYKoRYJYQ4KoT4SAhtFW8hxHkhxItCiN3AYMdF3qDY85ncJoToU+W4HUKI3vUadSNg+yz+UOX1v4QQd9menxJC/F0IsU8IcVj95Vmz6q5nbdR1cv8auFUI4Qr0BnZX2XccGCGl7As8D7xSZd9g4E4p5Zg6jq+xmQasl1KeAPKEEP1s26OAx4FwoDNws227OxAnpRwopdxe79E2TPZ8Jj8G7gIQQoQBLlLKQ/UWcdORK6XsB3wIPOHoYJq6Ok3utv8AHdBqSGsv2+0FfCeEiANwVFgBAAADj0lEQVTeBnpW2bdRSlntGqzN1By05ITt5xzb81+llMlSSgtaTXSYbbsFWFG/ITZsdn4mvwNuFEI4AfcAn9ZLsE3PStvPvWj/Bkodqo827TXAG8AowK/K9peATVLK6UKIDsDmKvuK6yGuRkUI4QeMQWszlmhLHkq0BHX5YIXK16W2hK9c6po+k1LKEiHERmAqMAtQIy6vzMylFUbXy/aX2X5aqJ/c09jVdD2rVR9dIT8BXpRSHr5suxe/3cy6qx7iaOxmAp9JKUOklB2klMHASbRaepRtAXMDMBvthqtydfZ8Jj8G3gX2qL8qryoF6CGEcBFCeAFjHR1QI3dd17POk7uUMk1KuegKu14HXhVC7ECrhSrVmwOsumzbCuA2YCfwGhCHlvAvP06pwp7PpJRyL1AI/KceQmxUbL3ayqSUqcC3wCHgC2C/QwNrpPS6nmr6gUZOCDEKeEJKeaOjY2nKhBDt0JppukkprQ4Op0ERQkQA/yeljHJ0LE2BXtdTjVBVlBoIIe5A61XzrErslxJC/A/aTfznHB1LU6Dn9VQ1d0VRlCZIt5q7ECJYCLFJCHFMCHFECPGwbbuvEGKjECLB9tPHtv12IcQh2yPG9qdIZVkThRDxQohEIcRf9IpRURSludCt5i6EaAu0lVLuE0K0ROvLOg2t10GelPI1W6L2kVI+JYQYAhyTUp4TQkwCXpBSDrQNkz+BNjQ8DdgDzJFSHtUlUEVRlGZAt5q7lDJTSrnP9rwIOAYEovUNXmo7bClawkdKGSOlPGfbvgsIsj2PAhJtg3LK0QbrTNUrTkVRlOagTm6o2gaA9EW7CRUgpcwE7QsA8L/Cr8xDm/MDtC+E1Cr70mzbFEVRlFrSfZSYbXKrFcAjUspCIURNx49GS+6VQ+av9Avqrq+iKMo10LXmbpt7YwXwhZSych6JbFt7fGW7fE6V43ujjfybKqU8a9ucBgRXKTYIyNAzTkVRlKZOz94yAliCdpP0rSq71gB32p7fCXxvO7492kRCf7TNclhpDxBqG07vjDa17Rq94lQURWkO9OwtMwzYBhwGKgd6PIPW7v4t0B44DdwipcwTQnwMzECbPwHALKXsbytrMvAO2hDwT6SUL+sSpKIoSjOhBjEpiqI0QWr6AUVRlCZIJXdFUZQmSCV3RVGUJkgld0VRlCZIJXdFUZQmSCV3RVGUJkgld0VRlCZIJXdFUZQm6P8BE+5Sj+n3YHwAAAAASUVORK5CYII=\n",
1080       "text/plain": [
1081        "<Figure size 432x288 with 1 Axes>"
1082       ]
1083      },
1084      "metadata": {
1085       "needs_background": "light"
1086      },
1087      "output_type": "display_data"
1088     }
1089    ],
1090    "source": [
1091     "data_by_day.loc['2020-03-01':'2020-07-02', ['cases_m7n', 'admissions_m7n', 'deaths_m7n']].plot()"
1092    ]
1093   },
1094   {
1095    "cell_type": "code",
1096    "execution_count": 21,
1097    "metadata": {},
1098    "outputs": [
1099     {
1100      "data": {
1101       "text/plain": [
1102        "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c8407f90>"
1103       ]
1104      },
1105      "execution_count": 21,
1106      "metadata": {},
1107      "output_type": "execute_result"
1108     },
1109     {
1110      "data": {
1111       "image/png": 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\n",
1112       "text/plain": [
1113        "<Figure size 432x288 with 1 Axes>"
1114       ]
1115      },
1116      "metadata": {
1117       "needs_background": "light"
1118      },
1119      "output_type": "display_data"
1120     }
1121    ],
1122    "source": [
1123     "data_by_day.loc['2020-03-01':, ['cases_m7n', 'admissions_m7n', 'deaths_m7n']].plot()"
1124    ]
1125   },
1126   {
1127    "cell_type": "code",
1128    "execution_count": 22,
1129    "metadata": {},
1130    "outputs": [
1131     {
1132      "data": {
1133       "text/html": [
1134        "<div>\n",
1135        "<style scoped>\n",
1136        "    .dataframe tbody tr th:only-of-type {\n",
1137        "        vertical-align: middle;\n",
1138        "    }\n",
1139        "\n",
1140        "    .dataframe tbody tr th {\n",
1141        "        vertical-align: top;\n",
1142        "    }\n",
1143        "\n",
1144        "    .dataframe thead th {\n",
1145        "        text-align: right;\n",
1146        "    }\n",
1147        "</style>\n",
1148        "<table border=\"1\" class=\"dataframe\">\n",
1149        "  <thead>\n",
1150        "    <tr style=\"text-align: right;\">\n",
1151        "      <th></th>\n",
1152        "      <th>cases</th>\n",
1153        "      <th>deaths</th>\n",
1154        "      <th>cases_culm</th>\n",
1155        "      <th>deaths_culm</th>\n",
1156        "      <th>cases_diff</th>\n",
1157        "      <th>deaths_diff</th>\n",
1158        "      <th>newAdmissions</th>\n",
1159        "      <th>cumAdmissions</th>\n",
1160        "      <th>deaths_m7</th>\n",
1161        "      <th>cases_m7</th>\n",
1162        "      <th>admissions_m7</th>\n",
1163        "      <th>cases_m7n</th>\n",
1164        "      <th>deaths_m7n</th>\n",
1165        "      <th>admissions_m7n</th>\n",
1166        "    </tr>\n",
1167        "  </thead>\n",
1168        "  <tbody>\n",
1169        "    <tr>\n",
1170        "      <td>2019-12-31</td>\n",
1171        "      <td>0</td>\n",
1172        "      <td>0</td>\n",
1173        "      <td>0</td>\n",
1174        "      <td>0</td>\n",
1175        "      <td>NaN</td>\n",
1176        "      <td>NaN</td>\n",
1177        "      <td>NaN</td>\n",
1178        "      <td>NaN</td>\n",
1179        "      <td>0.000000</td>\n",
1180        "      <td>0.000000</td>\n",
1181        "      <td>NaN</td>\n",
1182        "      <td>0.000000</td>\n",
1183        "      <td>0.000000</td>\n",
1184        "      <td>NaN</td>\n",
1185        "    </tr>\n",
1186        "    <tr>\n",
1187        "      <td>2020-01-01</td>\n",
1188        "      <td>0</td>\n",
1189        "      <td>0</td>\n",
1190        "      <td>0</td>\n",
1191        "      <td>0</td>\n",
1192        "      <td>0.0</td>\n",
1193        "      <td>0.0</td>\n",
1194        "      <td>NaN</td>\n",
1195        "      <td>NaN</td>\n",
1196        "      <td>0.000000</td>\n",
1197        "      <td>0.000000</td>\n",
1198        "      <td>NaN</td>\n",
1199        "      <td>0.000000</td>\n",
1200        "      <td>0.000000</td>\n",
1201        "      <td>NaN</td>\n",
1202        "    </tr>\n",
1203        "    <tr>\n",
1204        "      <td>2020-01-02</td>\n",
1205        "      <td>0</td>\n",
1206        "      <td>0</td>\n",
1207        "      <td>0</td>\n",
1208        "      <td>0</td>\n",
1209        "      <td>0.0</td>\n",
1210        "      <td>0.0</td>\n",
1211        "      <td>NaN</td>\n",
1212        "      <td>NaN</td>\n",
1213        "      <td>0.000000</td>\n",
1214        "      <td>0.000000</td>\n",
1215        "      <td>NaN</td>\n",
1216        "      <td>0.000000</td>\n",
1217        "      <td>0.000000</td>\n",
1218        "      <td>NaN</td>\n",
1219        "    </tr>\n",
1220        "    <tr>\n",
1221        "      <td>2020-01-03</td>\n",
1222        "      <td>0</td>\n",
1223        "      <td>0</td>\n",
1224        "      <td>0</td>\n",
1225        "      <td>0</td>\n",
1226        "      <td>0.0</td>\n",
1227        "      <td>0.0</td>\n",
1228        "      <td>NaN</td>\n",
1229        "      <td>NaN</td>\n",
1230        "      <td>0.000000</td>\n",
1231        "      <td>0.000000</td>\n",
1232        "      <td>NaN</td>\n",
1233        "      <td>0.000000</td>\n",
1234        "      <td>0.000000</td>\n",
1235        "      <td>NaN</td>\n",
1236        "    </tr>\n",
1237        "    <tr>\n",
1238        "      <td>2020-01-04</td>\n",
1239        "      <td>0</td>\n",
1240        "      <td>0</td>\n",
1241        "      <td>0</td>\n",
1242        "      <td>0</td>\n",
1243        "      <td>0.0</td>\n",
1244        "      <td>0.0</td>\n",
1245        "      <td>NaN</td>\n",
1246        "      <td>NaN</td>\n",
1247        "      <td>0.000000</td>\n",
1248        "      <td>0.000000</td>\n",
1249        "      <td>NaN</td>\n",
1250        "      <td>0.000000</td>\n",
1251        "      <td>0.000000</td>\n",
1252        "      <td>NaN</td>\n",
1253        "    </tr>\n",
1254        "    <tr>\n",
1255        "      <td>...</td>\n",
1256        "      <td>...</td>\n",
1257        "      <td>...</td>\n",
1258        "      <td>...</td>\n",
1259        "      <td>...</td>\n",
1260        "      <td>...</td>\n",
1261        "      <td>...</td>\n",
1262        "      <td>...</td>\n",
1263        "      <td>...</td>\n",
1264        "      <td>...</td>\n",
1265        "      <td>...</td>\n",
1266        "      <td>...</td>\n",
1267        "      <td>...</td>\n",
1268        "      <td>...</td>\n",
1269        "      <td>...</td>\n",
1270        "    </tr>\n",
1271        "    <tr>\n",
1272        "      <td>2020-08-14</td>\n",
1273        "      <td>1129</td>\n",
1274        "      <td>-5359</td>\n",
1275        "      <td>314927</td>\n",
1276        "      <td>41347</td>\n",
1277        "      <td>120.0</td>\n",
1278        "      <td>-5539.0</td>\n",
1279        "      <td>NaN</td>\n",
1280        "      <td>NaN</td>\n",
1281        "      <td>-723.714286</td>\n",
1282        "      <td>970.428571</td>\n",
1283        "      <td>NaN</td>\n",
1284        "      <td>0.200248</td>\n",
1285        "      <td>-0.766067</td>\n",
1286        "      <td>NaN</td>\n",
1287        "    </tr>\n",
1288        "    <tr>\n",
1289        "      <td>2020-08-15</td>\n",
1290        "      <td>1440</td>\n",
1291        "      <td>10</td>\n",
1292        "      <td>316367</td>\n",
1293        "      <td>41357</td>\n",
1294        "      <td>311.0</td>\n",
1295        "      <td>5369.0</td>\n",
1296        "      <td>NaN</td>\n",
1297        "      <td>NaN</td>\n",
1298        "      <td>-736.285714</td>\n",
1299        "      <td>1051.714286</td>\n",
1300        "      <td>NaN</td>\n",
1301        "      <td>0.217021</td>\n",
1302        "      <td>-0.779374</td>\n",
1303        "      <td>NaN</td>\n",
1304        "    </tr>\n",
1305        "    <tr>\n",
1306        "      <td>2020-08-16</td>\n",
1307        "      <td>1077</td>\n",
1308        "      <td>4</td>\n",
1309        "      <td>317444</td>\n",
1310        "      <td>41361</td>\n",
1311        "      <td>-363.0</td>\n",
1312        "      <td>-6.0</td>\n",
1313        "      <td>NaN</td>\n",
1314        "      <td>NaN</td>\n",
1315        "      <td>-743.571429</td>\n",
1316        "      <td>1097.285714</td>\n",
1317        "      <td>NaN</td>\n",
1318        "      <td>0.226425</td>\n",
1319        "      <td>-0.787086</td>\n",
1320        "      <td>NaN</td>\n",
1321        "    </tr>\n",
1322        "    <tr>\n",
1323        "      <td>2020-08-17</td>\n",
1324        "      <td>1040</td>\n",
1325        "      <td>5</td>\n",
1326        "      <td>318484</td>\n",
1327        "      <td>41366</td>\n",
1328        "      <td>-37.0</td>\n",
1329        "      <td>1.0</td>\n",
1330        "      <td>NaN</td>\n",
1331        "      <td>NaN</td>\n",
1332        "      <td>-744.000000</td>\n",
1333        "      <td>1094.142857</td>\n",
1334        "      <td>NaN</td>\n",
1335        "      <td>0.225776</td>\n",
1336        "      <td>-0.787540</td>\n",
1337        "      <td>NaN</td>\n",
1338        "    </tr>\n",
1339        "    <tr>\n",
1340        "      <td>2020-08-18</td>\n",
1341        "      <td>713</td>\n",
1342        "      <td>3</td>\n",
1343        "      <td>319197</td>\n",
1344        "      <td>41369</td>\n",
1345        "      <td>-327.0</td>\n",
1346        "      <td>-2.0</td>\n",
1347        "      <td>NaN</td>\n",
1348        "      <td>NaN</td>\n",
1349        "      <td>-736.714286</td>\n",
1350        "      <td>1079.428571</td>\n",
1351        "      <td>NaN</td>\n",
1352        "      <td>0.222740</td>\n",
1353        "      <td>-0.779828</td>\n",
1354        "      <td>NaN</td>\n",
1355        "    </tr>\n",
1356        "  </tbody>\n",
1357        "</table>\n",
1358        "<p>232 rows × 14 columns</p>\n",
1359        "</div>"
1360       ],
1361       "text/plain": [
1362        "            cases  deaths  cases_culm  deaths_culm  cases_diff  deaths_diff  \\\n",
1363        "2019-12-31      0       0           0            0         NaN          NaN   \n",
1364        "2020-01-01      0       0           0            0         0.0          0.0   \n",
1365        "2020-01-02      0       0           0            0         0.0          0.0   \n",
1366        "2020-01-03      0       0           0            0         0.0          0.0   \n",
1367        "2020-01-04      0       0           0            0         0.0          0.0   \n",
1368        "...           ...     ...         ...          ...         ...          ...   \n",
1369        "2020-08-14   1129   -5359      314927        41347       120.0      -5539.0   \n",
1370        "2020-08-15   1440      10      316367        41357       311.0       5369.0   \n",
1371        "2020-08-16   1077       4      317444        41361      -363.0         -6.0   \n",
1372        "2020-08-17   1040       5      318484        41366       -37.0          1.0   \n",
1373        "2020-08-18    713       3      319197        41369      -327.0         -2.0   \n",
1374        "\n",
1375        "            newAdmissions  cumAdmissions   deaths_m7     cases_m7  \\\n",
1376        "2019-12-31            NaN            NaN    0.000000     0.000000   \n",
1377        "2020-01-01            NaN            NaN    0.000000     0.000000   \n",
1378        "2020-01-02            NaN            NaN    0.000000     0.000000   \n",
1379        "2020-01-03            NaN            NaN    0.000000     0.000000   \n",
1380        "2020-01-04            NaN            NaN    0.000000     0.000000   \n",
1381        "...                   ...            ...         ...          ...   \n",
1382        "2020-08-14            NaN            NaN -723.714286   970.428571   \n",
1383        "2020-08-15            NaN            NaN -736.285714  1051.714286   \n",
1384        "2020-08-16            NaN            NaN -743.571429  1097.285714   \n",
1385        "2020-08-17            NaN            NaN -744.000000  1094.142857   \n",
1386        "2020-08-18            NaN            NaN -736.714286  1079.428571   \n",
1387        "\n",
1388        "            admissions_m7  cases_m7n  deaths_m7n  admissions_m7n  \n",
1389        "2019-12-31            NaN   0.000000    0.000000             NaN  \n",
1390        "2020-01-01            NaN   0.000000    0.000000             NaN  \n",
1391        "2020-01-02            NaN   0.000000    0.000000             NaN  \n",
1392        "2020-01-03            NaN   0.000000    0.000000             NaN  \n",
1393        "2020-01-04            NaN   0.000000    0.000000             NaN  \n",
1394        "...                   ...        ...         ...             ...  \n",
1395        "2020-08-14            NaN   0.200248   -0.766067             NaN  \n",
1396        "2020-08-15            NaN   0.217021   -0.779374             NaN  \n",
1397        "2020-08-16            NaN   0.226425   -0.787086             NaN  \n",
1398        "2020-08-17            NaN   0.225776   -0.787540             NaN  \n",
1399        "2020-08-18            NaN   0.222740   -0.779828             NaN  \n",
1400        "\n",
1401        "[232 rows x 14 columns]"
1402       ]
1403      },
1404      "execution_count": 22,
1405      "metadata": {},
1406      "output_type": "execute_result"
1407     }
1408    ],
1409    "source": [
1410     "data_by_day"
1411    ]
1412   },
1413   {
1414    "cell_type": "code",
1415    "execution_count": 23,
1416    "metadata": {},
1417    "outputs": [
1418     {
1419      "data": {
1420       "text/html": [
1421        "<div>\n",
1422        "<style scoped>\n",
1423        "    .dataframe tbody tr th:only-of-type {\n",
1424        "        vertical-align: middle;\n",
1425        "    }\n",
1426        "\n",
1427        "    .dataframe tbody tr th {\n",
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1434        "</style>\n",
1435        "<table border=\"1\" class=\"dataframe\">\n",
1436        "  <thead>\n",
1437        "    <tr style=\"text-align: right;\">\n",
1438        "      <th></th>\n",
1439        "      <th>cases</th>\n",
1440        "      <th>cases_culm</th>\n",
1441        "      <th>deaths</th>\n",
1442        "      <th>deaths_culm</th>\n",
1443        "      <th>newAdmissions</th>\n",
1444        "      <th>cumAdmissions</th>\n",
1445        "      <th>deaths_m7</th>\n",
1446        "      <th>cases_m7</th>\n",
1447        "      <th>admissions_m7</th>\n",
1448        "      <th>cases_m7n</th>\n",
1449        "      <th>deaths_m7n</th>\n",
1450        "      <th>admissions_m7n</th>\n",
1451        "    </tr>\n",
1452        "  </thead>\n",
1453        "  <tbody>\n",
1454        "    <tr>\n",
1455        "      <td>2019-12-31</td>\n",
1456        "      <td>0.0</td>\n",
1457        "      <td>0.0</td>\n",
1458        "      <td>0</td>\n",
1459        "      <td>0</td>\n",
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1465        "      <td>0.0</td>\n",
1466        "      <td>0.000000</td>\n",
1467        "      <td>NaN</td>\n",
1468        "    </tr>\n",
1469        "    <tr>\n",
1470        "      <td>2020-01-01</td>\n",
1471        "      <td>0.0</td>\n",
1472        "      <td>0.0</td>\n",
1473        "      <td>0</td>\n",
1474        "      <td>0</td>\n",
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1476        "      <td>NaN</td>\n",
1477        "      <td>0.000000</td>\n",
1478        "      <td>0.0</td>\n",
1479        "      <td>NaN</td>\n",
1480        "      <td>0.0</td>\n",
1481        "      <td>0.000000</td>\n",
1482        "      <td>NaN</td>\n",
1483        "    </tr>\n",
1484        "    <tr>\n",
1485        "      <td>2020-01-02</td>\n",
1486        "      <td>0.0</td>\n",
1487        "      <td>0.0</td>\n",
1488        "      <td>0</td>\n",
1489        "      <td>0</td>\n",
1490        "      <td>NaN</td>\n",
1491        "      <td>NaN</td>\n",
1492        "      <td>0.000000</td>\n",
1493        "      <td>0.0</td>\n",
1494        "      <td>NaN</td>\n",
1495        "      <td>0.0</td>\n",
1496        "      <td>0.000000</td>\n",
1497        "      <td>NaN</td>\n",
1498        "    </tr>\n",
1499        "    <tr>\n",
1500        "      <td>2020-01-03</td>\n",
1501        "      <td>0.0</td>\n",
1502        "      <td>0.0</td>\n",
1503        "      <td>0</td>\n",
1504        "      <td>0</td>\n",
1505        "      <td>NaN</td>\n",
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1507        "      <td>0.000000</td>\n",
1508        "      <td>0.0</td>\n",
1509        "      <td>NaN</td>\n",
1510        "      <td>0.0</td>\n",
1511        "      <td>0.000000</td>\n",
1512        "      <td>NaN</td>\n",
1513        "    </tr>\n",
1514        "    <tr>\n",
1515        "      <td>2020-01-04</td>\n",
1516        "      <td>0.0</td>\n",
1517        "      <td>0.0</td>\n",
1518        "      <td>0</td>\n",
1519        "      <td>0</td>\n",
1520        "      <td>NaN</td>\n",
1521        "      <td>NaN</td>\n",
1522        "      <td>0.000000</td>\n",
1523        "      <td>0.0</td>\n",
1524        "      <td>NaN</td>\n",
1525        "      <td>0.0</td>\n",
1526        "      <td>0.000000</td>\n",
1527        "      <td>NaN</td>\n",
1528        "    </tr>\n",
1529        "    <tr>\n",
1530        "      <td>...</td>\n",
1531        "      <td>...</td>\n",
1532        "      <td>...</td>\n",
1533        "      <td>...</td>\n",
1534        "      <td>...</td>\n",
1535        "      <td>...</td>\n",
1536        "      <td>...</td>\n",
1537        "      <td>...</td>\n",
1538        "      <td>...</td>\n",
1539        "      <td>...</td>\n",
1540        "      <td>...</td>\n",
1541        "      <td>...</td>\n",
1542        "      <td>...</td>\n",
1543        "    </tr>\n",
1544        "    <tr>\n",
1545        "      <td>2020-08-14</td>\n",
1546        "      <td>NaN</td>\n",
1547        "      <td>NaN</td>\n",
1548        "      <td>-5359</td>\n",
1549        "      <td>41347</td>\n",
1550        "      <td>NaN</td>\n",
1551        "      <td>NaN</td>\n",
1552        "      <td>-723.714286</td>\n",
1553        "      <td>NaN</td>\n",
1554        "      <td>NaN</td>\n",
1555        "      <td>NaN</td>\n",
1556        "      <td>-1.685296</td>\n",
1557        "      <td>NaN</td>\n",
1558        "    </tr>\n",
1559        "    <tr>\n",
1560        "      <td>2020-08-15</td>\n",
1561        "      <td>NaN</td>\n",
1562        "      <td>NaN</td>\n",
1563        "      <td>10</td>\n",
1564        "      <td>41357</td>\n",
1565        "      <td>NaN</td>\n",
1566        "      <td>NaN</td>\n",
1567        "      <td>-736.285714</td>\n",
1568        "      <td>NaN</td>\n",
1569        "      <td>NaN</td>\n",
1570        "      <td>NaN</td>\n",
1571        "      <td>-1.714571</td>\n",
1572        "      <td>NaN</td>\n",
1573        "    </tr>\n",
1574        "    <tr>\n",
1575        "      <td>2020-08-16</td>\n",
1576        "      <td>NaN</td>\n",
1577        "      <td>NaN</td>\n",
1578        "      <td>4</td>\n",
1579        "      <td>41361</td>\n",
1580        "      <td>NaN</td>\n",
1581        "      <td>NaN</td>\n",
1582        "      <td>-743.571429</td>\n",
1583        "      <td>NaN</td>\n",
1584        "      <td>NaN</td>\n",
1585        "      <td>NaN</td>\n",
1586        "      <td>-1.731537</td>\n",
1587        "      <td>NaN</td>\n",
1588        "    </tr>\n",
1589        "    <tr>\n",
1590        "      <td>2020-08-17</td>\n",
1591        "      <td>NaN</td>\n",
1592        "      <td>NaN</td>\n",
1593        "      <td>5</td>\n",
1594        "      <td>41366</td>\n",
1595        "      <td>NaN</td>\n",
1596        "      <td>NaN</td>\n",
1597        "      <td>-744.000000</td>\n",
1598        "      <td>NaN</td>\n",
1599        "      <td>NaN</td>\n",
1600        "      <td>NaN</td>\n",
1601        "      <td>-1.732535</td>\n",
1602        "      <td>NaN</td>\n",
1603        "    </tr>\n",
1604        "    <tr>\n",
1605        "      <td>2020-08-18</td>\n",
1606        "      <td>NaN</td>\n",
1607        "      <td>NaN</td>\n",
1608        "      <td>3</td>\n",
1609        "      <td>41369</td>\n",
1610        "      <td>NaN</td>\n",
1611        "      <td>NaN</td>\n",
1612        "      <td>-736.714286</td>\n",
1613        "      <td>NaN</td>\n",
1614        "      <td>NaN</td>\n",
1615        "      <td>NaN</td>\n",
1616        "      <td>-1.715569</td>\n",
1617        "      <td>NaN</td>\n",
1618        "    </tr>\n",
1619        "  </tbody>\n",
1620        "</table>\n",
1621        "<p>232 rows × 12 columns</p>\n",
1622        "</div>"
1623       ],
1624       "text/plain": [
1625        "            cases  cases_culm  deaths  deaths_culm  newAdmissions  \\\n",
1626        "2019-12-31    0.0         0.0       0            0            NaN   \n",
1627        "2020-01-01    0.0         0.0       0            0            NaN   \n",
1628        "2020-01-02    0.0         0.0       0            0            NaN   \n",
1629        "2020-01-03    0.0         0.0       0            0            NaN   \n",
1630        "2020-01-04    0.0         0.0       0            0            NaN   \n",
1631        "...           ...         ...     ...          ...            ...   \n",
1632        "2020-08-14    NaN         NaN   -5359        41347            NaN   \n",
1633        "2020-08-15    NaN         NaN      10        41357            NaN   \n",
1634        "2020-08-16    NaN         NaN       4        41361            NaN   \n",
1635        "2020-08-17    NaN         NaN       5        41366            NaN   \n",
1636        "2020-08-18    NaN         NaN       3        41369            NaN   \n",
1637        "\n",
1638        "            cumAdmissions   deaths_m7  cases_m7  admissions_m7  cases_m7n  \\\n",
1639        "2019-12-31            NaN    0.000000       0.0            NaN        0.0   \n",
1640        "2020-01-01            NaN    0.000000       0.0            NaN        0.0   \n",
1641        "2020-01-02            NaN    0.000000       0.0            NaN        0.0   \n",
1642        "2020-01-03            NaN    0.000000       0.0            NaN        0.0   \n",
1643        "2020-01-04            NaN    0.000000       0.0            NaN        0.0   \n",
1644        "...                   ...         ...       ...            ...        ...   \n",
1645        "2020-08-14            NaN -723.714286       NaN            NaN        NaN   \n",
1646        "2020-08-15            NaN -736.285714       NaN            NaN        NaN   \n",
1647        "2020-08-16            NaN -743.571429       NaN            NaN        NaN   \n",
1648        "2020-08-17            NaN -744.000000       NaN            NaN        NaN   \n",
1649        "2020-08-18            NaN -736.714286       NaN            NaN        NaN   \n",
1650        "\n",
1651        "            deaths_m7n  admissions_m7n  \n",
1652        "2019-12-31    0.000000             NaN  \n",
1653        "2020-01-01    0.000000             NaN  \n",
1654        "2020-01-02    0.000000             NaN  \n",
1655        "2020-01-03    0.000000             NaN  \n",
1656        "2020-01-04    0.000000             NaN  \n",
1657        "...                ...             ...  \n",
1658        "2020-08-14   -1.685296             NaN  \n",
1659        "2020-08-15   -1.714571             NaN  \n",
1660        "2020-08-16   -1.731537             NaN  \n",
1661        "2020-08-17   -1.732535             NaN  \n",
1662        "2020-08-18   -1.715569             NaN  \n",
1663        "\n",
1664        "[232 rows x 12 columns]"
1665       ]
1666      },
1667      "execution_count": 23,
1668      "metadata": {},
1669      "output_type": "execute_result"
1670     }
1671    ],
1672    "source": [
1673     "dbds = pd.read_csv('data_by_day_uk.csv', index_col='dateRep', parse_dates=True)\n",
1674     "dbds.loc['2020-07-03', 'cases'] = 576\n",
1675     "\n",
1676     "dbds = dbds[['cases', 'cases_culm']].shift(-25).merge(dbds[['deaths', 'deaths_culm']], left_index=True, right_index=True, how='outer')\n",
1677     "dbds = dbds.merge(hospital_data[['newAdmissions', 'cumAdmissions']].shift(-14), how='outer',\n",
1678     "    left_index=True, right_index=True)\n",
1679     "\n",
1680     "dbds['deaths_m7'] = dbds.deaths.transform(lambda x: x.rolling(7, 1).mean())\n",
1681     "dbds['cases_m7'] = dbds.cases.transform(lambda x: x.rolling(7, 1).mean())\n",
1682     "dbds['admissions_m7'] = dbds.newAdmissions.transform(lambda x: x.rolling(7, 1).mean())\n",
1683     "\n",
1684     "dbds['cases_m7n'] = dbds.cases_m7 / dbds.cases_m7.loc['2020-05-15']\n",
1685     "dbds['deaths_m7n'] = dbds.deaths_m7 / dbds.deaths_m7.loc['2020-05-15']\n",
1686     "dbds['admissions_m7n'] = dbds.admissions_m7 / dbds.admissions_m7.loc['2020-05-15']\n",
1687     "\n",
1688     "dbds"
1689    ]
1690   },
1691   {
1692    "cell_type": "code",
1693    "execution_count": 24,
1694    "metadata": {},
1695    "outputs": [
1696     {
1697      "data": {
1698       "text/plain": [
1699        "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c3e41e90>"
1700       ]
1701      },
1702      "execution_count": 24,
1703      "metadata": {},
1704      "output_type": "execute_result"
1705     },
1706     {
1707      "data": {
1708       "image/png": 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\n",
1709       "text/plain": [
1710        "<Figure size 720x576 with 1 Axes>"
1711       ]
1712      },
1713      "metadata": {
1714       "needs_background": "light"
1715      },
1716      "output_type": "display_data"
1717     }
1718    ],
1719    "source": [
1720     "dbds.loc['2020-03-01':, ['cases_m7n', 'admissions_m7n', 'deaths_m7n']].plot(ylim=(0, 1.5), figsize=(10, 8))"
1721    ]
1722   },
1723   {
1724    "cell_type": "code",
1725    "execution_count": 25,
1726    "metadata": {},
1727    "outputs": [
1728     {
1729      "data": {
1730       "text/plain": [
1731        "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c3b7bb90>"
1732       ]
1733      },
1734      "execution_count": 25,
1735      "metadata": {},
1736      "output_type": "execute_result"
1737     },
1738     {
1739      "data": {
1740       "image/png": 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btgytVsvnn3/Oe++9B8DOnTt5++23yczM5K233mLmzJlkZGQwe/ZsiouLqamp4cMPP2TEiBENxuXk5MSjjz7Kli1bcHd357XXXuO5557j/PnzLF26lKlTp15Uv7XW7W8tajXQZsovq+KRNYcY9uZW3tsaT0aRjtSCcqKTC3jo80P8kZDb+ElaSK1e8siaQyzbkcjcQR34bP6giz78/8zOWstfx3XlWFoRPx/LuG5xKsqVHDp0iK+++oojR47w/fffc/CgYaDgggULeO+99zh06BBLlizhkUceAajfSOXIkSPMmTOHt956i5CQEB566CGeeuopYmJi6j/QMzIy2L17Nz/99BOLFi0C4IsvvmD8+PHExMRw9OjRK35bLysrY/To0Rw6dAhnZ2deeOEFNm/ezLp16xpc6+frr7+2qASgrgCa6fWNp9h8Mov7R4Re1KFaVFHNrGV/8OBnh/jm4SGE+7m0eixvbz7NphNZvHBzd+4f0alJx0yLDGT5ziQWbzrN6G7eONtZt3KUiqVoyjf11rBr1y5mzJiBg4Ph/9LUqVPR6XT88ccfF63BX1lZCUBqaiqzZ88mIyODqqoqQkNDL3vu6dOno9Fo6NGjB1lZWQAMGDCA++67j+rqaqZPn37FBGBjY1O/VHRERAS2trZYW1s3uG7//v37cXBwoFevXlf1ezAFdQXQDMfTivj2cCr3Dgvl+YndLxpN42pvzaf3DsTBVss9Kw+Skl/eqrFsPpnF+9sSmTMguMkf/gBajeDvk3uQWlDOze/u5sh5tRirYnp/Hq6s1+txc3MjJiam/nbq1CkAHn/8cR577DGOHTvGRx99hE6nu+x5L1x3v27hy5EjR7Jz504CAwO56667rrgJvLW1dX1sply3v7WoBNBEUkpe/fkUbvbWPHpDWIN1Atzs+eSegZRX1TD9/T1EJ7fOPLfk3DKeXhtDRKArL03t2ezjh4V5sfbBIdTqJbOW7eWNX+I4llqEXq9W3lCuv5EjR7Ju3ToqKiooKSlhw4YNODg4EBoaWr8PgJSSo0ePAoY18gMDDetIrlq1qv48zs7OlJSUNPp6586dw8fHhwceeID58+dz+PDha34Per2eb775hjlz5lzzua4n1QTURL+fymZvUh4vT+2Jq/3lm016BLiw7tFh3L8qmjv+u5+XpvZkUoQfbg6Xb5tvjs0ns3j++1g0QvDB3H7YWWuv6jxRIR5sfHIEf//hOMt2JLJsRyKejjaE+TjhbGeNi50VHo42eDrZEuRuzw3hPjjZqj8XpeX169eP2bNnExkZSceOHevb79esWcPDDz/MK6+8QnV1NXPmzKFPnz689NJLzJo1i8DAQAYPHszZs2cBwzaRM2fO5Mcff6zvBG7I9u3bWbx4MdbW1jg5OV3xCqCpdu7cSVBQEJ06Nf1q3Bw0aT8AUzGX/QCqa/WMf2cnCNj0l5FYaxu/cCosr+Lhzw+zNykPgCB3e+YNCeGBkVf3B6KrruUfPx5nbXQqPfxdWDonkq6+zld1rj/LLa1kV3wOO8/kklZYQYmuhuKKavLKKtFV6wGws9Ywoacft0UFM6Szp5ph3Eao/QAs37XsB6C+0jXBd4dSScotY/ld/Zv04Q/g5mDDZ/MHsi8pn2NpRWw+mckbv8YxoZcfwR7Nn4n7wfZE1kan8sjozvzlxq7YWLVc652Xky0z+gYxo2/QJc+VV9VwIr2YdUfS+OloOj/EpBPu58x9w0OZHhnYonEoinJ9qf+9jdBV1/Kf3+OJDHbjph7NG+NvpdUwvIsXD4/uzAdz+6MRsHxnUrNjKK+qYfXeZG7q4ctzE8Kv64eug40VA0I8eG1GBAf+diNvzeyNlPDct7Hc9+lBKqqubps+RTEngwYNIjIy8qLbsWPHTB1Wq1NXAI34Yv95Mop0LJnV55qaPfxc7ZjZP4ivo1N4fGwYPs52TT527cEUCsureWiUadsX7ay13BYVzKz+QXx9MIXn1x1j/qqDrJgXhYON+lOyVFLKdt+kt3//flOHcFWutQlfXQFcQVllDe9vS2BoZ0+GhV370gkPjuxMTa2ej3efbfIxNbV6Vuw+S1RHd/p39LjmGFqCEII5Azvw9m192JeUx32fHqSssqbxAxWzY2dnR15e3jV/kCjXn5SSvLw87Oya/mXyz9TXtiv4ZM9Z8sqqeGZ8txY5X4iXI5N7B/D53nM8MioMV4fGJ2FtPJ5JakEFL05p/nDP1jajbxAaIXh67VHmrtjPp/cOaLHRTsr1ERQURGpqKjk5OaYORbkKdnZ2BAVd2nfXVCoBXEZ8VgnvbU1gXA9f+nVwb7HzPjy6M+uPprNqbzJPjO1yxbpSSj7akUhnb0fGhvu0WAwtaVpkIHbWWh7/4gizP9rHZ/MH4uNy9d9IlOvL2tr6ijNplbZNNQE1QFddyxNfxeBka8UrM1p2Wnd3fxfGhPvw6R/JjXagvr8tgRPpxTw8OsysF24b39OPT+8dQEpBOXes2E+tmlCmKBZBJYAGvPXraU5lFLN4Vu9mddY21cOjO5NfVsXa6JTL1vntRCZLfjvD9MgAbu0X2OIxtLShYV68eWtvErJL2XEm29ThKIrSBCoB/MmehFxW7jnLvCEdGRPeOks7DwjxIKqjO8t3JlFdq7/k+dOZJTz1dQy9g1x549beFjNCY0IvP7ycbPli/+UTm6Io5kMlgD9ZuuUMgW72PD+pdWdHPjSqM2mFFfwUm35ReWxqIXev3I+DrRUf3dX/qpd6MAVrrYbbooLYGpdFZtHlF+hSFMU8qARwgdjUQg4mF3DvsJBW/+AdE+5DV18nlm1PIrtEh5SSDUfTmbVsL1YaDZ/NH4i/q32rxtAa5gzogF5yxeYtRVHMgxoFdIFP9iTjaKPltgHBrf5aGo3gkdFh/OXrGAa++ju2Vhoqa/QMDPHggzv74eVk2/hJzFAHTwdGdPHiqwPnefSGMLRm3HmtKO2dSgBGWcU6fopNZ+6gjrhcp01SpvcNxNfFjjNZJaQWlONiZ82Dozpb/Po6dwzswMNrDrPzTA43mOnwVUVRVAKo9/m+c9ToJfcOC7murzuksydDOnte19dsbTf28MXLyZZP/0hWCUBRzJhlf9VsIbrqWtbsP8+N3X3p6Olo6nAsnrVWw33DQ9hxJkftOKYoZkwlAGD76Wzyy6qYNyTE1KG0GfOGhODuYM3SLfGmDkVRlMtQCQD47WQWrvbWDO5kHouttQWOtlYsGNmZHWdyOKyuAhTFLLX7BFBTq2dbXDZjwn2wauJmL0rT3D2kIx6ONuoqQFHMVJM/8YQQWiHEESHET8bHoUKI/UKIeCHE10IIG2O5rfFxgvH5kAvO8byx/LQQYnxLv5mrcehcAQXl1c3e7EVpnOEqoBM71VWAopil5nzlfRI4dcHjN4F3pJRdgAJgvrF8PlAgpQwD3jHWQwjRA5gD9AQmAB8IIUw+zXXLqSxstBpGdvU2dSht0l2DO2JvreX7w6mmDkVRlD9pUgIQQgQBNwMrjI8FMAb41lhlFTDdeH+a8THG58ca608DvpJSVkopzwIJwMCWeBNXS0rJ5pNZDOnsiZOtGhHbGhxtrRgT7sOvx7PUKqGKYmaaegWwFHgOqFu5zBMolFLWbQOVCtQtWRkIpAAYny8y1q8vb+CYekKIBUKIaCFEdGtvUpGYU0pyXrlq/mllE3r5kVtayaFzqhlIUcxJowlACDEZyJZSHrqwuIGqspHnrnTM/wqkXC6ljJJSRnl7t26zzG8nswC4sbtKAK3phnAfbKw0/HI8w9ShKIpygaZcAQwDpgohkoGvMDT9LAXchBB17SZBQN2ylqlAMIDxeVcg/8LyBo4xiS0ns+gd5Iqfq9rBqjU52Voxqqs3vx7PRK+agRTFbDSaAKSUz0spg6SUIRg6cbdKKecC24CZxmrzgB+N99cbH2N8fqs07Di9HphjHCUUCnQBDrTYO2mm3NJKjqQUqm//18nEXn5kFOk4mlpo6lAURTG6loHvC4GnhRAJGNr4PzaWfwx4GsufBhYBSClPAGuBk8CvwKNSyivvidiKtp/OQUrDssxK6xvb3RdrreDX45mmDkVRFKNmDX2RUm4HthvvJ9HAKB4ppQ6YdZnjXwVebW6QrWFbXDa+Lrb0DHAxdSjtgqu9NcPCvNh4PINFE8MtZpczRWnL2uXU16oaPTvP5DAm3Ed9EF1HkyL8Scmv4GCyGg2kKOagXSaA6OR8SipruKGbav65nqb0DsDV3ppP9pw1dSiKotBOE8DWuGxsrDQMC/MydSjtir2NljsGdWDTiUxS8stNHY6itHvtNgEM6eSJo5r9e93dPaQjQghW/ZFs6lAUpd1rdwngbG4ZSbllavSPifi72jMpwp+vD6ZQWlnT+AGKorSadpcAtsZlA2r4pynNHx5KSWUN30anNF5ZUZRW0+4SwM4zOXT2diTYw8HUobRbkcFu9O3gxmf7zpk6FEVp19pVAqjVSw6fK2BQp7a1CbsluqVvIIk5ZSRkl5o6FEVpt9pVAojLLKaksoaBIWrrR1O70bgC66YTamawophKu0oA0cYJSFEh7iaORPF3tadPkGv9iqyKolx/7SoBHEjOJ8DVjiB31f5vDsb19ONoSiGZRTpTh6Io7VK7SQBSSg6ezSdKNf+YjfE9Dc1Am0+qZiBFMYV2kwBS8ivILqlkQKhKAOYizMeZTt6ObDqhmoEUxRTaTQI4kJwPwADV/m9WxvXwY19SHkXl1aYORVHanXaTAKKT83Gxs6Krj7OpQ1EuML6nLzV6ydbT6ipAUa63dpMADiQb2v81GrX8sznpE+RGoJs9a/adx7BxnKIo10u7SAC5pZUk5ZQxQHUAmx2NRvDgqE5Enytgb1KeqcNRlHalXSSAuvH/qv3fPN0WFYyPsy3v/Z5g6lAUpV1pFwlg/9k8bK00RAS5mjoUpQF21loeHNWZvUl5HDibb+pwFKXdaB8JICmffh3csbXSmjoU5TLuGNgBLycb3tsab+pQFKXdaPMJoKi8mlOZxQzqpNr/zZm9jZYHRnRiV3wuO8/kmDocRWkX2nwCOJicj5QwKFStAGru7hrSkS4+Tjz51RG1ZaSiXAdtPgHsP5uHjVZD3w5upg5FaYSDjRX/vTuKWr1kwWeHKK+qIbtEx8rdZ/kmOoXqWr2pQ1SUNqXNb4q7/2w+kcFu2Fmr9n9LEOLlyLu39+W+Tw8yYeku0gorqNUb5gd8sD2Rv47ryqRe/mo+h6K0gDZ9BVCiq+Z4WpFq/7cwo7v58MLNPRACFozsxJanR7Li7iistYLHvjjCM98crU8KiqJcvTZ9BXDoXAF6S2n/zz8LxekQMszUkZiF+4aHct/w0PrHYT7O3BDuw3tb41m6JR69lPz7tki06kpAUa5am04A+8/mY6UR9OtoAe3/u9+Gw6uhxzS46Z/gHmLqiMyOViP4y41dsdZqWLzpNHoJS2dHquYgRblKjTYBCSHshBAHhBBHhRAnhBAvG8tDhRD7hRDxQoivhRA2xnJb4+ME4/MhF5zreWP5aSHE+NZ6U3X2J+XRO8gVBxsLyHMT3oQb/gbxm+H/DYRDn5o6IrP16A1hPDOuK+uPpvPLcbWXgKJcrab0AVQCY6SUfYBIYIIQYjDwJvCOlLILUADMN9afDxRIKcOAd4z1EEL0AOYAPYEJwAdCiFbrmS2vqiE2tchyNoC3cYBRz8Fj0dBhEPyyEArPmzoqs/Xw6DBCvRxZvjNRLSKnKFep0QQgDUqND62NNwmMAb41lq8CphvvTzM+xvj8WCGEMJZ/JaWslFKeBRKAgS3yLhqw/2w+NXrJEEtJAHVcA2H6h4CA314wdTRmS6sRPDCiE0dTi9QicopylZo0CkgIoRVCxADZwGYgESiUUtYYq6QCgcb7gUAKgPH5IsDzwvIGjrnwtRYIIaKFENE5OVc/I3TXmVxsrDQMtMQdwFyDYMRf4eSPkLTd1NGYrVv6BeLlZMtHO5JMHYqiWKQmJQApZa2UMhIIwvCtvXtD1Yw/G+qRk1co//NrLZdSRkkpo7y9vZsSXoN2J+QwKNTDcsf/D30c3DrCL4ugVu2W1RA7ay33Dgthx5kcTmUUmzocRbE4zZoHIKUsBLYDgwE3IbjzxOIAACAASURBVERd72oQkG68nwoEAxifdwXyLyxv4JgWlVmk40xWKcPDvFrj9NeHtR1MeB1yTsG+D00djdm6c1BHHG20fLA90dShKIrFacooIG8hhJvxvj1wI3AK2AbMNFabB/xovL/e+Bjj81uloZduPTDHOEooFOgCHGipN3Kh3Qm5AIzocvVXEGah2yTDbdurkKvWym+Iq4M19w4LZcPRdN7fpn5HitIcTbkC8Ae2CSFigYPAZinlT8BC4GkhRAKGNv6PjfU/BjyN5U8DiwCklCeAtcBJ4FfgUSllbUu+mTq74nPwcrIh3M/C9/8VAia/A1a2sP4x0Ku1cBry1E1dmdE3kMWbTrN8p7oSUJSmanSAvJQyFujbQHkSDYzikVLqgFmXOderwKvND7Pp9HrJnoRchod5tY0JQs5+MOEN+OFhOLAcBj9k6ojMjlYjWDyzN9W1el7bGMeBs/l09XUmzMeJSRH+ltsPpCitzAJmSDXPqcxickurGG7pzT8X6nM7HP8efn8Zuk0E946mjsjsWGk1vDM7Ei8nW3bG57D9dA41esnXB1NYMS8KZztrU4eoKGanzS0Gtzu+rv3fgjuA/0wImLIUELDxWVATnxpkrdXw0tSebP3raOL+NYF/z+pD9LkC5q7YT35ZlanDUxSz0+YSwM74HLr6OuHrYmfqUFqWaxDc8H8QvwlObTB1NGbPSqvh1v5BLL+rP6czS5izfC+66lbpclIUi9WmEkBuaSX7kvIZ293X1KG0jkEPgW+EYZmIyhJTR2MRxnb3Zdmd/TmTVcrqvcmmDkdRzEqbSgA/HU2nVi+ZHnnJBOO2QWtlaAoqyYBtr5k6GotxQ7gPI7t688H2RIp1alKdotRpUwngh5h0wv2c6Wbpwz+vJCgKou6F/csg7bCpo7EYz43vRmF5NSt2qmUjFKVOm0kAybllxKQUMr1vG/32f6EbXwJHH9jwBNTWNFZbAXoFunJzhD8rdp8lt7TS1OEoilloMwlg/dF0hICpfQJMHUrrs3OFSYsh8xjse9/U0ViMp8d1pbJGz2s/n1IbzCsKbSQBSCn5ISaNgSEeBLjZmzqc66P7FOh2M2x7HfJVs0ZTdPZ2YsHITnx/JI3p7+9RC8gp7V6bSADH04pJyilrH80/dYQwXAVoreHTyZDSKssqtTkLJ4Sz7M7+ZBXrmPr/drM2OqXxgxSljWoTCWD90TSstYJJvfxNHcr15RoI89aDxgo+mQh7P1CTxJpgQi8/Nj81ikGhniz6LpbfT2WZOiRFMQmLTwBSSjYey2REF29cHdrhdP+AvvDgDugyHjY9D7Ffmzoii+DuaMNHd/WnV6Arj35xmMPnC0wdkqJcdxafAI6nFZNWWMGEXn6mDsV07N1h9ufgH2lYOrpGLXvQFI62Vqy8ZwC+Lnbcs/IAD6yO5oUfjvFNdIraZ1hpFyw+Afx6IgOtRnBTW53921QaDYz9u2Ej+cOrGq+vAODlZMvq+wYyuJMnKfnlbDiawbPfxrLou2NqpJDS5ln0aqBSSn45nsngTh64O9qYOhzT6zwWOg6DHW9B5B1g42jqiCxCR09Hlt8dBRj+pt7ZfIZ3tyaQWazjg7n9cLS16P8minJZFn0FEJ9dSlJOGRPaW+fv5QgBY/8BZdmw/yNTR2ORhBA8Pa4br98Swe6EXO78eD9llWqyndI2WXQC+OVYJkLA+J7tvPnnQh0GGzqE9yxVC8Zdg9sHduD9O/oSm1rE/aui1UqiSptk2QngeAZRHd3xcW5jSz9fq5HPgq4IYr4wdSQWbUIvf5bM6s2+s3k8suYwVTWqT0BpWyw2ASTnlhGXWaKafxoSPACCBsK+D0Gvvrleixl9g3hlei+2xmXzwOpo1RyktCkWmwC2n84GUKN/LmfII1BwFk7/YupILN7cQR1545YIdsXncMd/95GnFpNT2giLTQC7E3Lp6OlAB08HU4dinsKngGsH2PeBqSNpE+YM7MBHd0URl1nCrGV7KVH7CihtgEUmgOpaPXsT8xge1ob2/W1pWisYtADO7YH0I6aOpk24qYcvn9w7gKTcMj7aoRbgUyyfRSaAI+cLKauqZUQXb1OHYt763Q02ToZ5AWpma4sY2tmLKX0CWLE7iaxinanDUZRrYpEJYHd8DhoBQzp7mjoU82bnCqOeg9MbYde/TR1Nm/HsuG7U6g0TxhTFkllkAtgZn0ufYDdc7dvh4m/NNfQJiJgFW/8FcT+bOpo2oYOnA3cO7sja6BTis9RcC8VyWVwCKCqvJja1UDX/NJUQMPU9COgH3z0AmcdNHVGb8PiYLjjaWLF402lTh6IoV83iEsDepFz0EkZ0UR3ATWZtD3O+MDQJfX4rFJwzdUQWz8PRhnlDQ9hyKouMogpTh6MoV6XRBCCECBZCbBNCnBJCnBBCPGks9xBCbBZCxBt/uhvLhRDiXSFEghAiVgjR74JzzTPWjxdCzLuagHfG5+Jka0VksNvVHN5+ufjDnd9BTQV8fguU5Zo6Ios3KyoIvYTvD6eZOhRFuSpNuQKoAf4qpewODAYeFUL0ABYBv0spuwC/Gx8DTAS6GG8LgA/BkDCAF4FBwEDgxbqk0Ry743MZ3MkTa63FXbyYnm8PuP1rKEqFNbNAp/bEvRYdPR0ZFOqh9g9QLFajn6JSygwp5WHj/RLgFBAITAPqFp5fBUw33p8GrJYG+wA3IYQ/MB7YLKXMl1IWAJuBCc0JNiW/nPP55QwPU6N/rlrHITDrU8iMNVwJ6IpMHZFFuy0qmOS8cg4mqx3FFMvTrK/RQogQoC+wH/CVUmaAIUkAPsZqgcCFO22nGssuV95kfyQami2GqQlg16bbREMSSD8Cq6dDRaGpI7JYEyP8cLK14hu1ubxigZqcAIQQTsB3wF+klFdqOxANlMkrlP/5dRYIIaKFENE5OTkXPfdHYh5eTraE+Tg1NWzlcrpPgds+g8xjsHICJO8xdUQWycHGism9/fn5WIZaKE6xOE1KAEIIawwf/muklN8bi7OMTTsYf2Yby1OB4AsODwLSr1B+ESnlcilllJQyytvb+8Jy/kjMY2hnT4RoKJcozRY+CeauhapS+HQSrL3b0D+gNMusqCDKq2r5KfaSP2dFMWtNGQUkgI+BU1LKty94aj1QN5JnHvDjBeV3G0cDDQaKjE1Em4BxQgh3Y+fvOGNZkyTmlJJTUslQNfu3ZXUeA48dhBv+BvGb4b9jIOuEqaOyKP06uNMzwIX/bImnokotv61YjqZcAQwD7gLGCCFijLdJwBvATUKIeOAm42OAjUASkAD8F3gEQEqZD/wLOGi8/dNY1iR/JOYZglHt/y3P2t6wZMQD20Bo4ZOJcH6/qaOyGEIIXprak/QiHe9vSzB1OIrSZI3udi2l3E3D7fcAYxuoL4FHL3OulcDK5gRYZ09CLkHu9gR7qOWfW41POMzfBJ/NgNXTYPoH0OsWU0dlEQaEeDCjbyDLdyYxs38QIV6Opg5JURplEYPpa/WSfUn5qvnnenDrAPf+Cn4R8O29sPE5qKkydVQW4fmJ4dhYaXh5wwk1L0CxCBaRAE5lFFNUUc3Qzqr557pw8oZ7fobBj8KBj2DlOMg4auqozJ6Pix1/ubEL207nsOlElqnDUZRGWUQCqBv/r5Z/vo6sbGDCa4ahooUp8NEo+OkpKG9yt027NG9oCN39XfjHj8cpqlC7hinmzSISwK74XDp7O+LrYmfqUNqfHlPh8UMw6CE4tMqQCArVpKfLsdZqeOvW3uSWVvL6xlOmDkdRrsjsE0Cxrpp9SXmMVZu/m469G0x8A+ZvNiwdsXoqFGeYOiqzFRHkygMjOvHVwRT+SFCL7inmy+wTwPbTOVTXSsb1UAnA5IL6w53fQkmWYZRQaXbjx7RTf7mxKyGeDiz8PpackkpTh6MoDTL7BLD5ZBaejjb07dDshUOV1hA80DB7uPA8LL8BUqNNHZFZsrfR8vbsSHJLqrjjv/vIK1VJQDE/Zp0ApITtcdnc2N0XrUYt/2A2QobDfb+CRmNYR+jAf9Wm8w3o18GdlfcMIKWgnLkr9pNfpobTKubFrBNAWWUNJZU13KSaf8xPQCQs2GFYSmLjM/D1nVCWZ+qozM6Qzp6suHsAZ3PLmLnsD86oPYQVM2LWCaBYV429tZbhavtH8+TgAbd/BTf9C85sgg+HQsIWU0dldoZ38WL1fQMprqhh2v/bw/eH1YJ7inkw7wRQUcPIrl7YWWtNHYpyORoNDHsCHtj6vz2Hv7kHitQ2iRca1MmTjU8Mp3eQK0+vPcoDq6NJyFZXA4ppCXOesm7r30V+8dM2bu0fZOpQlKaoroA978Lut0FoYPDD0P8ew/ISCgA1tXqW70rig22JlFfVMLN/EHcM6kifIFe1zLnSYoQQh6SUUY3WM/cEkJlwAndHG1OHojRHwTnY/Hc4tcHQORx2Iwy4H7rcBBp1NQeQX1bFe1vjWbPvPFW1ejp6OjCldwDT+waqDY+Ua9YmEkDvyH4yNuawqcNQrlZhChxebbiVZoJbR+hzu6HvQGsN9u7g1Q08O4OVramjNYmiimo2nchkw9F09iTkopfQK9CFp27sqiY/KletTSSAqKgoGR2txplbvNpqiPvJMFz0XANbTwothI6AvndB+GSwbp9LfmQX69gQm8GXB86TlFPKqzMiuH2gaj5Tmk8lAMU8VZZCTSXoq6E0C3LOQNYxOLHOMLnMzhW63WzYs7jzDYbNatqZ8qoaHllzmO2nc/jrTV15bEyY6h9QmkUlAMWy6PWQvBNivoQzvxjWHLKygw6DIXQU9JhmaCpqJ6pr9Sz8Npbvj6SxYGQnnp8YrpKA0mRNTQCN7gimKNeFRgOdRhtutdWQvMswtyBpB/z+Mmx9BfrPg1GLwLntt41bazUsmdUHJzsrlu9MoqZW8vfJ3VUSUFqUSgCK+dFaG2YYdx5jeFycDrvfgeiVcPRrGL3QsFmNtm3/+Wo0gpen9kSrEazcc5YavZ4Xp/RUy6IoLcasJ4IpCgAuATBpMTx6ADqNgs3/gI9vhKwTpo6s1Qkh+MfkHiwY2YnVe88xb+UBtbCc0mJUAlAsh2dnmPMFzPzEuEvZSMOexW18DSIhBP83qTtv3hrBgeR8Jr+3m0Pn1M5syrVTCUCxLEJAr1sMVwN974KDK+DdSNi5BHTFpo6uVc0e0IHvHx6KlVYwc9le/v6D2nZSuTZqFJBi2XJOw+YXDSOH7Fxh0MOGJSjs3UwdWasp0VXz9uYzrPojGQ9HW/41rScTI/xNHZZiRtQwUKV9STsMu/5tmHDmEgQzPzYMIW3DjqcV8fz3xziWVsS0yAD+ObUXrg7Wpg5LMQNNTQCqCUhpGwL7wZw1cP/vhtFBn0wyNAvVtN1NWHoFuvL9I0N5+qau/BybwU3v7GDzySxTh6VYEHUFoLQ9uiLY8Bc48T04eEHv2wz9Bb49TB1ZqzmeVsQz3xwlLrOEKX0CeGlKDzyd2uf6SopqAlLaOykNm9Mc+QziNoK+BgYugLH/ANu2udpmVY2eZTsSeW9rPE62Vjw/qTuz+gepyWPtUIs1AQkhVgohsoUQxy8o8xBCbBZCxBt/uhvLhRDiXSFEghAiVgjR74Jj5hnrxwsh5l3tG1OUJhHCsPz0bavhmTOGD/8Dy+GDIYZ1h2ra3lh6GysNT4ztws9PjCDMx4nnvo1l9vJ9HDibT63efL/oKabT6BWAEGIkUAqsllL2Mpa9BeRLKd8QQiwC3KWUC4UQk4DHgUnAIOA/UspBQggPIBqIAiRwCOgvpSy40murKwClRZ3fBz8+BnnxYOsK3SdD1/EQMsKwRHUbotdLvjmUwmsb4yiqqMbdwZobuvkwMyqIIZ08G70qkFKqKwcL1qJNQEKIEOCnCxLAaWC0lDJDCOEPbJdSdhNCfGS8/+WF9epuUsoHjeUX1bsclQCUFldbDWd3wPHvDRvWVBYDwtCJPOxJ6D7VcPXQRhTrqtlxOoetcdlsjcumqKKabr7O3D4wmG5+LgR7GFZbjc8q5UxWCSczijmRXszZ3DJc7Kzwc7XHz8UWP1c7fF3siAh0ZUQXb2ys1PgRc9bai8H5SikzAIxJwMdYHgikXFAv1Vh2uXJFub601oYdysJuhMlLIe3Q/xLC2rshsD+MfRFCR7aJROBiZ82UPgFM6ROArrqW9THpfPJHMi9tONlg/QBXO3oEuDC2uw8luhqyinRkFus4llZEbqlhRJW7gzUTevnj52JHjV6PAPxc7Qlytyfczxkfl/a5n4MlaunVtBr6HyOvUH7pCYRYACwA6NBBbYahtCIrG+g4xHAb+Swc/RK2vQarp4J/JAx93NCPYOPUJraytLPWctuAYGZFBXE+v5zz+eWk5Fegl5Jufs509XG+4jwCXXUtexJy+SEmnXVHUtFV6+tzZF1DgkbAyK7e3BYVzI3dfdWVgplTTUDKdVWrr+Vw9mE2JW8isTCRWllLrb6WcI9wpnSeQh/vPqZte67WGRLB3vcNfQV1bJyhwyDoNsnQb+AS2CauEK5WXaeyViOoqdWTVVJJan45uxNy+fZQKhlFOlzsrJjYy5+pkQEMDPXAWquSwfXS2n0Ai4G8CzqBPaSUzwkhbgYe43+dwO9KKQcaO4EPAXWjgg5j6AS+4opWKgFYrgJdAbE5sSQXJ3Ou+Bx5FXkUVBZwrvgc+bp87K3s6e7RHWutNUg4mnMUXa2OIKcgenn1ItQ1lHCPcEYEjjDUARILE/kq7is6uHRgYuhEvOy9Wu8N6PWQ+DvkxBl2MSvPhYTfoeCs4Xk7N/DqCgF9ocdU6DCkTVwltIRavWRXfA7rY9LZdCKTsqpanG2tGBrmyZhwH27uHYCTbdteytvUWiwBCCG+xPAN3gvIAl4EfgDWAh2A88AsKWW+MHx1+3/ABKAcuFdKGW08z33A/xlP+6qU8pPGglMJwDLtz9jP09ufprjKsDibm60b3g7euNu64+vgy6jgUYwIHIGDtUP9MaVVpWw5v4Ut57aQWJhIWmkaEomnnSe3dLmFrPIsfkr6Ca3QUq2vRiM0DPIbxM2dbmZsh7E42TiRU57DkewjFFcVoxEabLQ2jAoahbONc8u8MSkNCSFpO+SeMWxnmRYNNTpw9IYu4wwb2oSOBGe/lnlNC1dRVcuOM9nsOJPDzjO5pBVW4GRrxfS+Adw9JISuvi30b6NcRE0EU0zi67ivef3A64S6hvJ/g/6Pru5dcbV1bfZ5Kmsr2Z+xn7Wn17IzdSc2WhtuD7+d+3rdR2FlIT8n/czPST+TWpqKrdYWL3sv0krTLjmPh50HT/R9gulh0xFCkF2ejZO1E042LTQZrLIUEjbDyfWQtA0qjCObHbzAu5vh5tMDfHuBXy+wbb8feFJKjqQU8vm+c/wUm0FVjZ6RXb25f3goI7p4qWGnLUglAOW6qtXXsjh6MWtOrWFk0EjeHPFmi33IZpdnY6WxwsPu4rH6Ukpic2PZmLSR7PJsIn0i6efTDx8HH/RST0ZZBksPL+VI9hE87DwoqSqhWl+Ng5UDd/a4k3k95+Fi49IiMQKgr4XMWEjeY7hSyD0D2XFQWWR4XmgMySB4IITfDJ1uaLfNRvllVXx54Dyf/pFMTkklUR3deWlqT3oFNv/LgnIplQCU66aipoKFOxeyLWUbd3a/k2einkFrJh9sUko2JW9iW8o2fB19CXQM5EDmAX479xvONs4M8R9CR5eOdHbrzOjg0ThaO7Z0AFCcZti9LP0IpOyHlINQVQLO/oZ1iiJmGa4Q2uE34MqaWr47lMbbm0+TV1bF7Khgnh3fTa1jdI1UAlCui8yyTJ7e/jTHc4+zcOBC5nafa+qQmiQuP46Pj33MqfxTpJakUitrcbR2ZGrnqcwJn0Mn106t9+I1lXDmV4j5AuI3g6w1dCj3uR0G3A92LXhVYiGKddW8uyWeT/9IxsFGy7Pju3HHoI5q/+OrpBKA0qqklGw8u5FX971KjazhjRFvMKbDGFOHdVWq9dWcyD3B2tNr+TX5V2r0NYwLGcdDvR8izD2sdV+8LBdOrYdj38G53YbRRUMeg4H3g7176762GYrPKuHF9Sf4IzEPXxdb/FztcXewJiLQlRl9A+nk3TYX8mtpKgEoraZGX8MLe17g56SfifSO5LXhrxHsEmzqsFpEXkUea06tYc2pNVTUVNDZrTMedh6427njbe+Nt4M3HZw7MCp4FNaaFt58Jf0I7HgLTm8ErY1htnKvWw2ji9rRVYGUko3HMtl0IpOC8iryy6o4lVGMXkJksBuTe/szvqcfwR4OjZ+snVIJQGk1/47+N5+e+JRH+jzCgt4LzKa9vyUV6gr5Iu4L4vLjKNAVUFBZQG5FLmXVZQAEOgXyYO8HmdJ5ClaaFh7TnnkMYr407GdQkgEaK8M8gy43GTqOfXuBpn1Nqsoq1vFjTBrrjqRzKsMwvLhXoAuPjA5jQk8/NKqp6CIqASit4uekn1m0axFzus3hb4P/Zupwrruy6jKiM6P54OgHnMw7SYBjALPDZ3NL2C242bXwPsT6WkOncfxvcOY3yD5hKHfwgk6jIGQ4hIwEt2DDFUM76UQ+n1fObycz+epgCgnZpXT3d+GxG8K4qUf7W3pCSklKfgUnM4pILajA39Wejp4ORAS5qQSgtKzYnFjmb5pPD88erBi/ouWbQCyIlJLtKdv57NRnHMw8iK3Wlv6+/enh2YNwj3D8Hf3xtvfGy8Gr5X5PxemQtMMw3+DsTsPVQR2NFdi5gmeYYe6BSyDUVhk6nK3twckXnHzAO9xQpw1ctdXqJeuPpvGfLfEk55Xj6WjDjL6B3NTDl95BbtjbWN57rK7Vk1NSSXphBSkF5aTmG38WVJBfVoVeSqSEqlo9ZZU1lOhqqKzRX3Kec29OVglAuXaVtZVsSNzAhsQNHM4+jK+DL19N/qp1l2GwMPEF8Xxz5hsOZx0msTCRGllT/5yDlQOTO01mdvhsurp3bbkXlRLyEuHcHijLgaoywyS03HjIOQXleSC0YGUL1RVctPaitYOhGcm3B/j0NCyFHdDXYpNCTa2eXfG5fH0whS2nsqjRS6w0gh4BLnT3c6GbnzN9O7gRGezW7Mlm2cU6jqQU1vdB2BqvMArLq8gvq8bXxZbpfQMvmdGs10vSCitIyCklKaeMs7mllFfVYqPVYKU1xKCXUFmtJ6e0kpySSnJKdOSVVfHnj2RvZ1uC3O3xdrJFIwRCGDb/cbS1wtnWilAvR7r7u9DBw4GMIh3n88uYGBGgEoBybTLLMnly25OczDtJqGsoUztPZXrYdPXhfwWVtZUkFiaSU55DbkVu/cJ3lbWVhHuEMzRgKEMChtDXpy+22lYc666v/d8Hur7WkBBKMgzzETKOGvoZsk6ArtBQx87VsIxFt5uh20SL7XQuLK/i0LkCDp8v4Mj5QuIyS8gvMyxj3beDG4+MDqOrrxNHU4s4mW7oS3C00eJoa4WdtRZ7Gw35ZdUcMR6fVljR4OvYW2txd7Amu6SSGr2kV6ALfi52VFTXUlRRTVJOGeVVtfX1ne2scLGzprpWT41eIgAhBNZagbezLT7Otng72+HjbIuPiy0BbvYEuzsQ5G6PnXXzE7PqA1CuSUx2DH/Z9hd0tTpeHfYqYzqMUVP1r1KhrpAfE39kW8o2jmYfpUbWYKOxIdInkgF+A+jt3ZsIr4iWW7OoqaQ0JIXzeyFxq2Gxu5IM0NoaO5xHQ8eh4N3dYjudpZTklFay6XgmH+1MIrXgfx/o1lqBQFBVe2kTSoCrHX07utM32I2+HdzoGeCKjVZTX7fuQzm3tJL1Men8FJtORbUeBxstTsZv5V19nQnzcaKTtyOejjbX9f+PSgDKVfvl7C/8bfff8HP0470x79HZrbOpQ2oz6jqR92fu52DmQU7nn0Yam2d6e/XmoT4PMTxwuGmSrV4PqQfh+HeG3dJK0g3lDp6GpSt6TIPQUYZNdSxQTa2eTSeyKKqopneQK938nLHWaqiq0VNeVYOuWk9FdS0ONlp8LXxTG5UAlGaTUrL65GqWRC+hn08/3h3z7lUt5KY0XXFVMcdzjxObE8sPCT+QVppGP59+jAsZh6e9J152XgQ7B+Pj4HN9k4KUUHgOzu2FhC2GmctVpYaJauGToed0w9WBTQsvnaG0CJUAlGap0dewJHoJa06t4aaON/H6iNdbt41auUR1bTXfx3/PR7EfkVORc9Fz9lb2BDsHE+AYgL+TP93cuzEiaAQ+Dj6XOVtLB6cz7I9w8kc4/YthL2WhMYwqCuwHYTdB2Nh2vdqpOVEJQGmyosoint3xLHsz9nJn9zt5dsCzaIRltvm2BXqpp0BXQJ4uj9zyXFJKUkguTia1JJX0snTSStPqJ6R19+jOAL8BRHhF0Nu7NwFOAa0fYE2lYRhq6kFIOwypB0BXZJiLENgfPDqDRyi4BBiaj+w9DH0I9ftGag0jlKwdDJ3Pdq6G7TmVFqMSgNIkSUVJPP7746SXpfPCoBe4teutpg5JaYSUkoTCBHak7mBX6i5O5J2gsrYSgGGBw7iv53308+3HybyTRGdFo0FDV4+udHPvhoedR8s3JdXWGCasnd4IaYcg/yyUZjbvHPYehgltLoGGNZBsXQzJwz3EkEw8w8C+hSfatWEqASiNOpJ9hMd+fwwrjRVLb1hKX5++pg5JuQrV+mrOFJxhd+puvoj7gnxdPjYaG6r0VZfUrds8x9fBlxDXEEJdQunq3pU+Pn1adinsqjIozYLyfMMNCdTtIF8LtdWGndR0RVBRaOhwLkwxLJ2tKwJdsWHJ7Au5BIJvT+gwGEJHQ0Ckxc5daG0qAShX9Pv531m4cyH+jv58eOOHBDkHmTokpQXoanSsT1xPUlGSYZip7wC0QsvpgtOcKThDVlkWORU5ZJZlklycTL7OsC23VmgJ9winp2dPwtzD6OTaCTdbNxysHPCw97goOZRX/yyhcAAADeJJREFUl/NT0k9U66txs3UjwCmASO/Ilr+yqCqHwvOGfZhzTkP2/2/v3IOjvK4D/jur1UqrJ3qhSgiDQysTDNg4hEcTwHEmJKZx7LRNG9IJNs1MxpPxpJ2xx3XaplOStPU0nrap6dRDbLd2k9iJxs34kZjWfyQTbMAlBiIjXsYYDJaQEJJWK+1qn6d/3E9iJRBIIK0ee34zO/t99969e76rq+/s+c655x6Gtma30A3AX+gsh8JyKK11VkJ1o3vVLHbbcuZo6LIpAGNUmo438Z2932Fp9VK237GdisLcSztsOEKxEC0XWtjfvp+32t/iWPcxwiN+eft9fjY0bOBziz7HmfAZnnr7Kbpj3cPa3PWhu/jm2m8S9AdH/a7Be811K4q+83DqV87/MNDjWRBt0Hni4u5r4B4jldW7/ZqLq52DOlAKmnZO7Fivi2oqmwcVC2DRHbNmL2dTAMYlqCo7mnew/eB21jes57ENj13xH9bIPVSVjkgH7/W+R1+8j0gywtGuo/zs5M+GrIW1dWv52q1f44ayGwjFQrz63qs88ZsnaKxo5KGPPkRXtIvT4dO83/s+p3tPczZ8lkgyQjwVR0SoKqyipqiGlbUruf+W+yduAZwq9HVA5zG3FeeFdyB8zpVFOt3+zfE+QJzVUFDi0mf0dTD0iKrho9C40Tmz61fM2D0ZTAEYwwjFQjx+4HF+fOzH3PWhu9j2sW05nczNGB+JdIK9rXspDZRy69xbL6nfdXYXj+x6hN5471BZXXEdC8oWML90PiX5JQTyAqQ1zYWBC7T1tbG3bS/VwWoeXvUwGxrc/grheJidp3byyslXOBU6xfzS+SwoW8Da+rVsunETgbxJiBZKJdwjpmM/dwvgzjVfrAtWOuuhuMYph0WfgPlrIH96LxQzBWAAcCZ8hmdbnuXFd18kmoyyZckWHlz5oIV5GhNOR6SDIxeO0FDawLySeRT6r3yTbOlsYduebRzpOnJJXWNFI8trltPa18q7Pe/SHmmnOljN5sWbWVS+iEBegIK8AoL+IEF/kJJACeUF5RNj0Ua7ofUgtO6H0Acuj1JvqztPJ124a+3NzkKoX+GshZrF08ohbQogxwnHw+xo3sEPjvwAgE03bmLLki3cVHnTFEtmGBdJpVPsPLWTc/3nSKQT+H1+1s1bN2yeqip72vbwTMsz7G7dfcX+CvIKqCqsora4lppgDRWFFZQFyphbNJdl1ctorGgk/1pTWcT6XPbVU69D20Fo/c1Fn0N+kbMU/EFnHfjyXcoMf4F7jFQ4x/kZfmu5y8QarHD1k+SkNgWQo5zoPsHOUztpOt5E90A39/z2PTyw4oHsrRg1jEmkra+NUDxELBUjnooTTUaJJCP0xfsIxUKEYiHOR8/TEemgI9LhyuIh0uqSuAV8AepL6inKL6Ikv4Sbq29mQ8MGbqm5Zfw7u6XT0HXSrX1oPeAsh2TUrZpOJy6GukZ7INrlLIlhiNuroaDU+SQCJe48PwjFc6G8wb2KKp0CKSx3bQvKnP/CP/pKfVMAOcRAcoCXT77Mc0ef453ud/CJj7V1a/n6bV9nSdWSqRbPMKaUtKZp72+nubOZ5vPNtEfaiSQihGIhDl84TFKTBP1BSgOlFPmdYphTOIc5BXMozi+mIK/AWRbBKuYWzWVu0Vzqi+upClaN71FqpAvaD0H7YbfGIRmHRMRFIw30Ogd1YgAS/c4xHW5zEUujkRfwlMccZ1EUVQ5FPMnGb49JAUzwZqZGNgnFQvzo6I94/ujzdA108eHKD/ONVd9g48KNlrPfMDx84qOupI66kjo+vfDTw+rC8TB7WvdwoOMA/Yl+osko4XiY7oFuTvacJJqMEkvFGEgNDFkRgwR8AeYUzhlSEEX+IkoDpZQGSqkOVlNTVENZoIy0pklpCr/PT9AfpKi+kdqiWupK6qgoqBg9LDaVcFFM0W4X7joQco+hYmGnNOJ9TnEM9HjRTO1OufR3jHlsTAHMMFSV072naTreRNPxJqLJKOvmrWPr0q2srF1pOfsNYxyUBkrZuHAjGxduvGK7tKbpifVwPnKe9kg7rX0uJ1NPrId4Kk48Fac/0U9vvJcz4TN0RjuJJCNX/f6ALzCkNPw+P8l0kkQ6QSKdIJlOktKUs0oCJRT5i8iTPPJ8eaiqq5cU/mI/gdIq/L5aoskoA6kB4Cdjun5TADOAcDzM7tbdvPHBG+xt20tbfxt5ksedN97J1qVbJ3arQcMwLsEnPioLK6ksrBxzIEV/op9wPIzf58cnPhKpBNFklP5kP+39Tom0R9oJx8P0JfpIpBIE8gLk+/LJz8vHL35EhGgyOrQmI61pkukkPvFR4C/AL36SmiSeihNJRij0F1IdGLv1n3UfgIh8BvgekAc8qaqPjtY2V30A0WSUls4W9p3bx5vn3uRgx0FSmqIsUMbqutWsqVvDunnrqCupm2pRDcOYhozVCZxVC0BE8oB/Az4FnAX2ichLqno4m3Jkg7Smh0zC7oFuuge66Yn1EI6H6Y33DkUs9MZ7h8y2cDxMZ6STcMItxReExZWL2bp0K+sb1rOsetn4IxUMwzBGIdt3k1XACVU9CSAizwN3A5dVAOf6z/Hdfd8d1xcMbq8HF3OPjNZOVVH0EufOYPnIvhLpBPFUfCgELZ52x9FklIHkAPFU3D2/SyXoS/QN+/xIivOLKQ+UU1ZQNhR5UFtUy5q6NdQEa1g0ZxEfqf2I7chlGMakkW0FMA84k3F+Flid2UBEvgp81TuNPbzq4UPecTkQYmLI5b6qgc4J6utamA19jWUMZ8N1ZrOva5mXozFdr3Gy+8ocw7E5KlQ1ay/gC7jn/oPnXwYev0L7X2cc75hAOXK2r8wxnU5yzaS+xjKGs+E6s9zXuOflDLzGSe1rxP1yTOOZ7YQwZ4H5GecNQOsYP/vyBMphfVlfk9nXRPeXC31NJNP1GqddX1mNAhIRP3Ac+CTwAbAP+JKqtozS/tc6Bk+2MXZsTK8fG8OJx8b0+skcw7GOZ1Z9AKqaFJEHgP/BhYE+PdrN32NHdiTLKWxMrx8bw4nHxvT62THK8ahM61xAhmEYxuRhSeENwzByFFMAhmEYOcqUKgARURH5r4xzv4icF5FXplKumY6IfN4b28VTLctMw+bk5CEifVMtw2ziauMpIr8UkSs6gqfaAugHlorI4D5un8JFB40ZL7LIGM5m4HXgi+P5kJeqI9e57jlpGDOFqVYAAK8Cv+cdbwaeG6wQkVUisltEDnjvN3nl94lIk4i8DPxv9kWevohICfAx4Ct4CkBEbheRX4nIT0XksIg8IeJ2shCRPhH5loi8CaydOsmnFdcyJ3eJyK0Z7d4QkeVZlXoG4M3FVzLOt4vIfd7xKRHZJiL7ReRts2CvzpXGcyxMBwXwPPBFESkElgNvZtQdBdar6grgb4C/z6hbC9yrqndkTdKZwT3ATlU9DnSJyG1e+SrgQWAZsAj4fa+8GDikqqtV9fWsSzs9uZY5+SRwH4CINAIFqtqcNYlnD52qehvw78BDUy3MbGfKFYD3T7IQ90vr5yOqy4EmETkE/DNwc0bda6ralRUhZxabcTcwvPfN3vH/qepJVU3hftF+3CtPAS9kV8TpzTXOySbgsyKSD/wp8J9ZEXb28d/e+1u4v4ExiUyX5+cvAY8BtwNVGeXfBn6hqp8XkYXALzPq+rMk24xBRKqAO3DPsBW32E5xN7GRCz4Gzwc8pWAMZ1xzUlUjIvIaLrvtHwG2qvXyJBn+w7NwRH3Me08xfe5P05mrjecVmXILwONp4Fuq+vaI8nIuOuDuy6pEM5M/BJ5V1QWqulBV5wPv4X7trxKRG71n/3+McxIbo3Mtc/JJ4F+BfWadjsppYImIFIhIOS4tjHHtXNd4TgsFoKpnVfV7l6n6R+AfROQN3K9Z48psBn46ouwF4EvAHuBR4BBOKYxsZ2RwLXNSVd8CeoH/yIKIMwovWi+mqmdwG9Y2Az8EDkypYDOUiRpPSwWRA4jI7cBDqvrZqZZlNiMi9bhHQotVR+wylOOIyC3A91V11VTLMhuYqPGcFhaAYcx0RGQLLlror+zmPxwRuR8XePDXUy3LbGAix9MsAMMwjBwlqxaAiMwXkV+IyBERaRGRP/PKK0XkNRF5x3uv8Mr/RESavdduz+wZ7OszInJMRE6IyCPZvA7DMIzZQLY3hKkD6lR1v4iU4mJ978FFU3Sp6qPezbxCVf9CRH4XOKKq3SJyJ/C3qrraS1lwHLdM/yxuY5nNqnrZzeUNwzCMS8mqBaCqbaq63zsOA0dwG8XfDTzjNXsGpxRQ1d2q2u2V78VtIQluVesJb2FTHLfg6e7sXIVhGMbsYMqcwN4imhU4x1mtqraBUxLA3Mt85Cu4HC3glMaZjLqzXplhGIYxRqZkpZ2XsOwF4M9VtVdErtb+EzgFMJi+4HIfMG+2YRjGOMi6BeDlSnkB+KGqDub9aPf8A4N+go6M9stxKyzvVtULXvFZYH5Gtw1A62TLbhiGMZvIdhSQAE/hHLv/lFH1EnCvd3wv8KLX/gZccqgve9ktB9kH/I6X2iCAS3v80mTLbxiGMZvIdhTQx4FdwNvA4GKZv8T5AX4C3AC8D3xBVbtE5EngD3D5LgCSqrrS62sT8C+45fhPq+rfZe1CDMMwZgG2EMwwDCNHsVQQhmEYOYopAMMwjBzFFIBhGEaOYgrAMAwjRzEFYBiGkaOYAjAMw8hRTAEYhmHkKKYADMMwcpT/BycCD2/auv7rAAAAAElFTkSuQmCC\n",
1741       "text/plain": [
1742        "<Figure size 432x288 with 1 Axes>"
1743       ]
1744      },
1745      "metadata": {
1746       "needs_background": "light"
1747      },
1748      "output_type": "display_data"
1749     }
1750    ],
1751    "source": [
1752     "dbds.loc['2020-03-01':'2020-07-02', ['cases_m7', 'admissions_m7', 'deaths_m7']].plot(ylim=(0, 6000))"
1753    ]
1754   },
1755   {
1756    "cell_type": "code",
1757    "execution_count": 50,
1758    "metadata": {},
1759    "outputs": [
1760     {
1761      "data": {
1762       "text/html": [
1763        "<div>\n",
1764        "<style scoped>\n",
1765        "    .dataframe tbody tr th:only-of-type {\n",
1766        "        vertical-align: middle;\n",
1767        "    }\n",
1768        "\n",
1769        "    .dataframe tbody tr th {\n",
1770        "        vertical-align: top;\n",
1771        "    }\n",
1772        "\n",
1773        "    .dataframe thead th {\n",
1774        "        text-align: right;\n",
1775        "    }\n",
1776        "</style>\n",
1777        "<table border=\"1\" class=\"dataframe\">\n",
1778        "  <thead>\n",
1779        "    <tr style=\"text-align: right;\">\n",
1780        "      <th></th>\n",
1781        "      <th>areaType</th>\n",
1782        "      <th>areaName</th>\n",
1783        "      <th>newCasesByPublishDate</th>\n",
1784        "      <th>cumCasesByPublishDate</th>\n",
1785        "    </tr>\n",
1786        "    <tr>\n",
1787        "      <th>date</th>\n",
1788        "      <th></th>\n",
1789        "      <th></th>\n",
1790        "      <th></th>\n",
1791        "      <th></th>\n",
1792        "    </tr>\n",
1793        "  </thead>\n",
1794        "  <tbody>\n",
1795        "    <tr>\n",
1796        "      <td>2020-03-10</td>\n",
1797        "      <td>overview</td>\n",
1798        "      <td>United Kingdom</td>\n",
1799        "      <td>NaN</td>\n",
1800        "      <td>373</td>\n",
1801        "    </tr>\n",
1802        "    <tr>\n",
1803        "      <td>2020-03-11</td>\n",
1804        "      <td>overview</td>\n",
1805        "      <td>United Kingdom</td>\n",
1806        "      <td>83.0</td>\n",
1807        "      <td>456</td>\n",
1808        "    </tr>\n",
1809        "    <tr>\n",
1810        "      <td>2020-03-12</td>\n",
1811        "      <td>overview</td>\n",
1812        "      <td>United Kingdom</td>\n",
1813        "      <td>134.0</td>\n",
1814        "      <td>590</td>\n",
1815        "    </tr>\n",
1816        "    <tr>\n",
1817        "      <td>2020-03-13</td>\n",
1818        "      <td>overview</td>\n",
1819        "      <td>United Kingdom</td>\n",
1820        "      <td>207.0</td>\n",
1821        "      <td>797</td>\n",
1822        "    </tr>\n",
1823        "    <tr>\n",
1824        "      <td>2020-03-14</td>\n",
1825        "      <td>overview</td>\n",
1826        "      <td>United Kingdom</td>\n",
1827        "      <td>264.0</td>\n",
1828        "      <td>1061</td>\n",
1829        "    </tr>\n",
1830        "    <tr>\n",
1831        "      <td>...</td>\n",
1832        "      <td>...</td>\n",
1833        "      <td>...</td>\n",
1834        "      <td>...</td>\n",
1835        "      <td>...</td>\n",
1836        "    </tr>\n",
1837        "    <tr>\n",
1838        "      <td>2020-06-29</td>\n",
1839        "      <td>overview</td>\n",
1840        "      <td>United Kingdom</td>\n",
1841        "      <td>815.0</td>\n",
1842        "      <td>311965</td>\n",
1843        "    </tr>\n",
1844        "    <tr>\n",
1845        "      <td>2020-06-30</td>\n",
1846        "      <td>overview</td>\n",
1847        "      <td>United Kingdom</td>\n",
1848        "      <td>689.0</td>\n",
1849        "      <td>312654</td>\n",
1850        "    </tr>\n",
1851        "    <tr>\n",
1852        "      <td>2020-07-01</td>\n",
1853        "      <td>overview</td>\n",
1854        "      <td>United Kingdom</td>\n",
1855        "      <td>829.0</td>\n",
1856        "      <td>313483</td>\n",
1857        "    </tr>\n",
1858        "    <tr>\n",
1859        "      <td>2020-07-02</td>\n",
1860        "      <td>overview</td>\n",
1861        "      <td>United Kingdom</td>\n",
1862        "      <td>576.0</td>\n",
1863        "      <td>283757</td>\n",
1864        "    </tr>\n",
1865        "    <tr>\n",
1866        "      <td>2020-07-03</td>\n",
1867        "      <td>overview</td>\n",
1868        "      <td>United Kingdom</td>\n",
1869        "      <td>544.0</td>\n",
1870        "      <td>284276</td>\n",
1871        "    </tr>\n",
1872        "  </tbody>\n",
1873        "</table>\n",
1874        "<p>116 rows × 4 columns</p>\n",
1875        "</div>"
1876       ],
1877       "text/plain": [
1878        "            areaType        areaName  newCasesByPublishDate  \\\n",
1879        "date                                                          \n",
1880        "2020-03-10  overview  United Kingdom                    NaN   \n",
1881        "2020-03-11  overview  United Kingdom                   83.0   \n",
1882        "2020-03-12  overview  United Kingdom                  134.0   \n",
1883        "2020-03-13  overview  United Kingdom                  207.0   \n",
1884        "2020-03-14  overview  United Kingdom                  264.0   \n",
1885        "...              ...             ...                    ...   \n",
1886        "2020-06-29  overview  United Kingdom                  815.0   \n",
1887        "2020-06-30  overview  United Kingdom                  689.0   \n",
1888        "2020-07-01  overview  United Kingdom                  829.0   \n",
1889        "2020-07-02  overview  United Kingdom                  576.0   \n",
1890        "2020-07-03  overview  United Kingdom                  544.0   \n",
1891        "\n",
1892        "            cumCasesByPublishDate  \n",
1893        "date                               \n",
1894        "2020-03-10                    373  \n",
1895        "2020-03-11                    456  \n",
1896        "2020-03-12                    590  \n",
1897        "2020-03-13                    797  \n",
1898        "2020-03-14                   1061  \n",
1899        "...                           ...  \n",
1900        "2020-06-29                 311965  \n",
1901        "2020-06-30                 312654  \n",
1902        "2020-07-01                 313483  \n",
1903        "2020-07-02                 283757  \n",
1904        "2020-07-03                 284276  \n",
1905        "\n",
1906        "[116 rows x 4 columns]"
1907       ]
1908      },
1909      "execution_count": 50,
1910      "metadata": {},
1911      "output_type": "execute_result"
1912     }
1913    ],
1914    "source": [
1915     "dbd = pd.read_csv('data_cases_2020-Jul-05.csv', index_col='date', parse_dates=True).sort_index()\n",
1916     "dbd"
1917    ]
1918   },
1919   {
1920    "cell_type": "code",
1921    "execution_count": 51,
1922    "metadata": {},
1923    "outputs": [
1924     {
1925      "data": {
1926       "text/plain": [
1927        "date\n",
1928        "2020-03-10         NaN\n",
1929        "2020-03-11       456.0\n",
1930        "2020-03-12       590.0\n",
1931        "2020-03-13       797.0\n",
1932        "2020-03-14      1061.0\n",
1933        "                ...   \n",
1934        "2020-06-29    313091.0\n",
1935        "2020-06-30    313780.0\n",
1936        "2020-07-01    314609.0\n",
1937        "2020-07-02    315185.0\n",
1938        "2020-07-03    315729.0\n",
1939        "Name: newCasesByPublishDate, Length: 116, dtype: float64"
1940       ]
1941      },
1942      "execution_count": 51,
1943      "metadata": {},
1944      "output_type": "execute_result"
1945     }
1946    ],
1947    "source": [
1948     "dbd.newCasesByPublishDate.cumsum() + dbd.loc['2020-03-10', 'cumCasesByPublishDate']"
1949    ]
1950   },
1951   {
1952    "cell_type": "code",
1953    "execution_count": null,
1954    "metadata": {},
1955    "outputs": [],
1956    "source": []
1957   }
1958  ],
1959  "metadata": {
1960   "kernelspec": {
1961    "display_name": "Python 3",
1962    "language": "python",
1963    "name": "python3"
1964   },
1965   "language_info": {
1966    "codemirror_mode": {
1967     "name": "ipython",
1968     "version": 3
1969    },
1970    "file_extension": ".py",
1971    "mimetype": "text/x-python",
1972    "name": "python",
1973    "nbconvert_exporter": "python",
1974    "pygments_lexer": "ipython3",
1975    "version": "3.7.4"
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1977  },
1978  "nbformat": 4,
1979  "nbformat_minor": 4
1980 }