Skip to content

M
mooc-rr
Commit 5a49db1e authored by 698345a05907085e6f4a3b394d423bd9's avatar 698345a05907085e6f4a3b394d423bd9
Browse files
Options

exo3

parent bf662fda
Hide whitespace changes
Inline Side-by-side
Showing with 123 additions and 19 deletions
module3/exo2/exercice.ipynb
View file @ 5a49db1e
......@@ -2,34 +2,138 @@
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 11,
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "File b'inc-25-PAY.csv' does not exist",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-92ff8825e621>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mlocal_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"inc-25-PAY.csv\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mraw_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\";\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskiprows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 707\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 711\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 451\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 816\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 818\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 820\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1047\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1048\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1049\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1050\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1051\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1693\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1695\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1697\u001b[0m \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: File b'inc-25-PAY.csv' does not exist"
"name": "stdout",
"output_type": "stream",
"text": [
"Colonnes détectées : ['Semaine', 'Nombre de cas de consultation', 'Inc Low', 'Inc Up', 'nombre de cas de consultation pour 100,000 habitants', 'Inc100 Low', 'Inc100 Up', 'Code Insee', 'Région', 'Numero semaine', 'geom', 'geo_point_2d']\n",
"Nombre de périodes annuelles valides : 40\n",
"\n",
" Année avec l’épidémie la plus forte : 2010\n",
"Année avec l’épidémie la plus faible : 2020\n",
"\n",
"--- Incidences annuelles triées (valeurs les plus fortes en haut) ---\n",
"2010 158674\n",
"2016 155094\n",
"2014 150482\n",
"1992 125228\n",
"2022 119989\n",
"2017 119622\n",
"2015 119371\n",
"2013 116843\n",
"1999 113627\n",
"2004 112928\n",
"dtype: int64\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ8AAAEWCAYAAAC5XZqEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsvXl8Y3d19/8+kizJsrzvM/Z4Mp4tM0MmhCEbWTxJSIAAyY/tSUpDoJQ8pbR92h88ENq0oZA8LZQWmoelgSZl3wslJYRsxEnIMllJZsl4mcxk7PG+S94lfZ8/7r2yrJFlSZZkyf6+Xy+9LH1179VXV/I9Ouf7OeeIUgqNRqPRaLKJbbUnoNFoNJr1hzY+Go1Go8k62vhoNBqNJuto46PRaDSarKONj0aj0WiyjjY+Go1Go8k62vhoMoaIXCwibXGe/5aI3JbNOeUjItIqIn9s3v+giPwuTcf9jIh8L8V9T4jIFemYx0oQkb8WkX9f4TFaRKQ74nFOvLe1jmO1J6DJTUTkBPDHSqmHUj2GUupxYEfaJqXRRKGU+j+rPQdNamjPR6PR5CUion885zHa+GiWxQr1iMgXRWRURI6LyFsjnq8Qkf8QkR7z+f8yx6PDGa8XkRdExCciPwbcUa/zdhH5vYiMiciTInJWxHMnROQTIvKyiIyLyI9FxB3x/DXmvhMickxE3mKOl4rIXSLSKyKnROQ2EbEv8T7PFZGnzNfvFZGviIgz4nklIn8iIh3m+/yqiEiC52hRKCc65CUi55vveUxEXhKRlgQ/m50i8qCIjIhIm4i8L862Z4jIo+b5fxCoino+1TnEPW9R2/5GRP4sauwlEXmXef9fRaTL/ByfF5GLI7b7jIj8TES+JyITwAdjnMeLIt5Dl4h80Bx3mZ/NSRHpF5F/E5HCBN6bTURuNr9TwyLyExGpSOS8aOKjjY8mUc4D2jAuWF8A7rIuvMB3AQ+wG6gBvhS9s3kx+i9z2wrgp8C7I54/B7gb+J9AJXAncI+IuCIO8z7gLcAZwFnAB819zwW+A/xvoAy4BDhh7vNtIABsBV4PXAn88RLvMQj8lfkeLwAuB/40apu3A28E9przuSrBc7QkIrIRuBe4DePcfAL4TxGpXma/IuBB4AcY5/164GsisnuJXX4APG/O73PAjSudg0ki5y1yDtdHvO4uoMl8bYBngbPNOfwA+GnkjwzgGuBnGJ/z9yMPLCKbgPuA/wtUm8f5vfn054Ht5thWYCPwdwm8t78ArgUuBTYAo8BXE9hPsxxKKX3Tt9NuGBfvK8z7HwQ6I57zAAqoA+qBEFAe4xgtQLd5/xKgB5CI558EbjPvfx34XNT+bcClEfP5w4jnvgD8m3n/TuBLMV6/FpgFCiPGrgceSfAc/CXwi4jHCrgo4vFPgJuXO0fR59N8/Bnge+b9TwHfjXrt+4EbzfutGOtv1uv8zrz/P4DHo/a7E7g1xnvZhGGEiyLGfpDoHOJ9P5Y7b1HPFQOTQJP5+Hbg7jifwSiwN+KcPRb1fOR5/HSs1wXEfM3miLELgOPR39MY3/1XgMsjnqsH5gFHOv/f1uNNx0w1idJn3VFKTZk/6L0Yv1BHlFKjy+y/ATilzP9gk9ci7jcBN4rIn0eMOc39TpsDMBXxXCPw6xiv2QQUAL0RDogN6Io1QRHZDvwLsA/DeDgwPIVIoufgjfVc1DlajibgvSLyjoixAuCRBPY7T0TGIsYcGN5lNBuAUaXUZMTYaxjnbiVzSPS8AaCU8onIvcB1GN7IdcBNEcf6OIZnugHDeJewODwY87MzaQSOxRivNuf1fMT3QICY4dcomoBfiEgoYiyI8cPmVAL7a5ZAGx/NSukCKkSkTCk1Fme7XmCjiEiEAdrEwsWiC7hdKXV7inNoXmJ8FqhSSgUSOM7XgReB682L5F8C70lhPrGYxLgAWtRFzfO7SqmPJHnMLuBRpdSbE9i2FygXkaIIA7QJ4wK/kjlA8ufth8CtIvIYUIhp4Mz1nU9hhO0OK6VCIjKKYSgs4pXh7wLOjTE+BEwDu5VSyRqMLuCPlFJPJLmfZhn0mo9mRSilejHi7F8TkXIRKRCRS2Js+hRG2OcvRMRhLjBHXii+CfyJiJwnBkUicrWIFCcwjbuAD4nI5eYC8UYR2WnO7QHgn0WkxHyuWUQuXeI4xcAE4BeRncBHEzsLCfF74Drz/Oxj8cX5e8A7ROQqEbGLiFsMsUbDMsf8FbBdRG4wj1sgIm8UkTOjN1RKvQY8B/y9iDhF5CIg0stJdQ6Q/Hn7NYZH8Vngx0opy6soxviODAIOEfk7DM8nUb4PXCEi7zO/Y5UicrZ5/G8CXxKRGjDWuETkqrhHM/g34HYRaTL3qxaRa5KYk2YJtPHRpIMbMOLgR4EBjJj/IpRSc8C7MNYsRjHWK34e8fxzwEeAr5jPd5rbLotS6hngQxhCh3HgUYyLG8AHMMJ3R8zj/gwjbh+LTwB/APgwLlY/TuT1E+RvMbyzUeDvMdZbrPl3YSyk/zXGhbcLQzwR9/9TKeXDEFBch7Ge1ocRynItscsfYIgiRoBbMUQaK5qDSVLnTSk1i/HZX0HEecBYY7oPaMcICc4QP8wWfdyTwNuAj2O8x99jCEPA8Kg6gadNpdxDJJaD9q/APcADIuIDnsY4h5oVIotD8BqNRqPRZB7t+Wg0Go0m62jjo9FoNJqso42PRqPRaLKONj4ajUajyTo6z8ekqqpKbd68ebWnkTCTk5MUFRWt9jRSJp/nn89zh/yefz7PHdbm/J9//vkhpVQiZZgWoY2PyebNm3nuuedWexoJ09raSktLy2pPI2Xyef75PHfI7/nn89xhbc5fRF6LvXV8dNhNo9FoNFlHGx+NRqPRZB1tfDQajUaTdTJmfETkbhEZEJFDUeN/LkbTq8Mi8oWI8U+LSKf53FUR428QkYPmc3dY/VHM5lA/NscPiMjmiH1uFKPhV4eI3IhGo9FocopMej7fwmj8FUZE9mPUjzpLKbUb+KI5vgujPtVuc5+vyUK3ya9jlFzfZt6sY34Yo0T8VoyaXp83j1WBUbfqPIzClbeKSHlm3qJGo9FoUiFjxkcp9RhGcb9IPgr8o1lYEKXUgDl+DfAjpdSsUuo4RgHAc0WkHihRSj1lluH/DkZXQWufb5v3fwZcbnpFVwEPKqWsHjMPEmUENZr1ysDEDP/nwDQDvpnVnopmnZNtqfV24GIRuR2jYu0nlFLPYrS0fTpiu25zbN68Hz2O+bcLQCkVEJFxjPbL4fEY+yxCRG7CbGRVW1tLa2vrSt5bVvH7/Xk132jyef75PPdvH56lYzTIp77zKDfuXqr4de6Sz+ce9PwjybbxcQDlwPnAG4GfiMgWFjeLslBxxklxn8WDSn0D+AbAvn37VD7p79divkC+kI9z33HLfcwGrLY5wiNdAR7pCuBy2Gi77a2rOrdkyMdzH4me/wLZVrt1Az9XBs8AIYwWud0stPMFaMDoT9Jt3o8eJ3IfEXEApRhhvqWOpdGsWx7/5H7eefZCR3J3gY1rzt7A45/av4qz0qxnsm18/gu4DMJ9350YLW7vwejy6BKRMzCEBc+YnSh9InK+uZ7zAeCX5rHuASwl23uA35rrQvcDV5pdNcsxmm3dn523p9HkJjUlbtwO499dgNlAiGKXg5pi9+pOTLNuyVjYTUR+CLQAVSLSjaFAuxu425RfzwE3mgbjsIj8BKPbZAD4mFIqaB7qoxjKuUKMLof3meN3Ad8VkU4Mj+c6AKXUiIh8DnjW3O6zSqlo4YNGs+7oHp0GoLoQrtzbxKAWHWhWkYwZH6XU9Us89YdLbH87cHuM8eeAPTHGZ4D3LnGsuzEMnUajMfnwRWfw5LFhpgNw27Wn/UutGQYmZvizH77IV/7g9dqzy2F0hQONZp1geT7+eQgEQ8tsnb/c8XAHz54Y4Y6HOlZ7Kpo46KrWGs06oWtkCjCknyNTc2vOK1is6IPvHTjJ9w6czDtF33pBez4azTrB8nwAhnxzqziTzKAVffmFNj4azTqhe2yKYpcR7Bjyz67ybNJPTYkbl33hkqYVfbmNNj4azTqhe3SavY1lwNo0PrAQWgR49zkbGVyj73MtoI2PRrMO8M3MMzY1z97GUmDtGp93v2EhJ/1d5zRw5w37VnE2mnho46PRrAOs9Z4z60tw2GDIv/bWfAA6B/yILNzX5C7a+Gg06wDL+DSWeyh1Ss55PgMTM7zvzqdWXG27Y8DP9ppiStwOOvq18clltPHRaNYB1lpIQ3khJS7JOc/nyw+1pyU3p3PAz9ZaL9tqi+kY8KVpdppMoPN8NJp1QPfoNIUFdiqKnJQ4hSFfbng+6czNmZ4L0jU6xbvO2Uixy8FDr/Sne7qaNKI9H41mHdA9OkVjRSEiQqkrd8Juj39yP+84qz78eCW5OccG/SgF22qK2VrjZcg/x8hkbnl4mgW08dFo1gFdo9M0lHsAKHEKw5NzhEIx21xllZoSNypiGivJzTk2aKzxbK0xwm6gRQe5jDY+Gs06oHt0iobyQsAwPsGQYmx6fpVnZXBydCE356pdtSnn5nT0+7HbhM1VHrbVeI0xve6Ts2jjo9Gsccan5/HNBGi0PB+XoUUezpHQ26Xbq8P3L95enXJuTseAj6ZKDy6HnfpSN0VOu1a85TDa+Gg0a5zu0QWlG0Cp0zA+uZL939bno7m6iGKXgyM9Eykfp3PAz9Zqw+MREbbWFuuwWw6jjY9Gs8bpGjFyfBqiPJ9ckVu39/vYWVfCmRtKeKU3NeMzFwhxYniKbbXe8Ni2Gq8Ou+Uw2vhoNGscy/NprFjs+eSC3HpqLsBrI1Nsry1mV30JR/t8BFMQQpwYniQYUmyrKQ6Pbavx0j8xy3iOrG1pFqONj0azxukencbrclBaWACApwDsttyQW3cOGPLoHXXF7NpQwtRckNeGJ1M6DhhKNwvrvg695Sba+Gg0axxL6SZm0TObCJVFzpwwPm19RlhsR53h+QAcSSH01tFv1HRrro4Mu1lyax16y0W08dFo1jjdo9NhsYFFldfFcA6s+bT1+XA5bGyq8LCt1ovDJimJDjoGfDSUF1LotIfHNpYX4i6wacVbjqKNj0azhlFKmcbHs2i8qtiVG55Pv49ttV7sNsHlsLO1xpuS59M54F+03gNGaLG52kuHDrvlJNr4aDRrmLGpefyzgRiejzMn1G7t/T521JaEH++qL0na8wkEQ7w6NLlovcdiW41Xr/nkKNr4aDRrGKuVQrTnU+11MeifRanVK7EzNjVH/8QsO+oWjMauDSUM+GaT8sq6RqeZC4RiG5/aYk6NTeOfDaRlzpr0oY2PRrOGiZZZW1R6ncwFQvhW8aJsiQ221y6EyyzRQTL5PpZnsy2G8bEM0jHt/eQcGTM+InK3iAyIyKEYz31CRJSIVEWMfVpEOkWkTUSuihh/g4gcNJ+7Q0zJjoi4ROTH5vgBEdkcsc+NItJh3m7M1HvUaHKdpTyfKq8LWN1cn/b+BaWbxZmW4i2J0JuVSNq8RNjN2EYbn1wjk57Pt4C3RA+KSCPwZuBkxNgu4Dpgt7nP10TEkq18HbgJ2GberGN+GBhVSm0FvgR83jxWBXArcB5wLnCriJSn+b1pNHlB1+gUxe6FHB8Ly/gMr2LLgbZ+HyVuB3UlCxWsy4ucbCh1JyU66Oz3U1fipsRdcNpzmyo8OO02XekgB8mY8VFKPQaMxHjqS8Angchg8zXAj5RSs0qp40AncK6I1AMlSqmnlBGc/g5wbcQ+3zbv/wy43PSKrgIeVEqNKKVGgQeJYQQ1mvVA9+h0uKBoJDnh+fT52VFXHM4/sjgzSdFB56B/UVmdSBx2G1uqi+jUcuucI6udTEXkncAppdRLUV+4jcDTEY+7zbF58370uLVPF4BSKiAi40Bl5HiMfaLncxOGV0VtbS2tra0pva/VwO/359V8o8nn+efT3Nu6p6j12BbN1+/3M3bwOQCefPEQhcNtWZ+XUopD3VOcV+847Vx65ufoHJjngYcfwWlfbJiiz31IKdp6p7ik4fTjWJQww8uv5cZnlk/fnVikc/5ZMz4i4gH+Brgy1tMxxlSc8VT3WTyo1DeAbwDs27dPtbS0xNosJ2ltbSWf5htNPs8/X+aulGLk4fu5au8mWlp2hcdbW1u56OJL+KvW+yivb6KlZXvW59Y3PsPU/Q+z/5wdtFywedFz05W9/PexF6jb8XrOaihb9Fz0ue8enWL2/kdoOWcnLec1xXytlwIdPPtwO+e/6WLcBfaY22SLfPnuLEU6559NtVszcAbwkoicABqAF0SkDsM7aYzYtgHoMccbYowTuY+IOIBSjDDfUsfSaNYVI5NzTM8HT1O6gRGOKvesXomdNktsUFt82nO7NiQuOlhQup1+HItttV6UWuh0qskNsmZ8lFIHlVI1SqnNSqnNGEbiHKVUH3APcJ2pYDsDQ1jwjFKqF/CJyPnmes4HgF+ah7wHsJRs7wF+a64L3Q9cKSLlptDgSnNMo1lXLKV0s6jyOldtzac9hszaorHcg9flSEh0EKugaDTbdIHRnCRjYTcR+SHQAlSJSDdwq1LqrljbKqUOi8hPgCNAAPiYUipoPv1RDOVcIXCfeQO4C/iuiHRieDzXmccaEZHPAc+a231WKRVL+KDRrGm6oprIRVPlda2a2u1on4+aYhflRc7TnrPZhDPrixPyfDr6/VQWOamIcRyLpsoiHDbRNd5yjIwZH6XU9cs8vznq8e3A7TG2ew7YE2N8BnjvEse+G7g7ielqNGuOBc9naePzUvdYNqcUpr3ftyi/J5pd9SX87PluQiGFzRZrGdegc9Af1+sBcDpsbK4q0nLrHENXONBo1ijdo1OUeQoojpH/AobxWY2wWzCk6BjwxQy5WZxZX8LkXJCTI1NLbqOUoqPft6zxAaurqfZ8cgltfDSaNUqsVgqRVBU7mZwLMj0XXHKbTNA1MsXMfCi+57Nh+d4+g75ZJmYCMcvqRLOtxstrw1PMBrL7XjVLo42PRrNG6RqZoqEsttgAoKrITDTNsuLtaN/SSjeL7bXF2G0St8ZbWOkW5zgWW2uLCYYUJ4aW9qQ02UUbH41mDWL18Ykls7aoKjYW6QezbHysmm5LVSUAcBfYaa4uiis66IhTUDSahRpvet0nV9DGR6NZgwz555gNhJaUWUNEfbcs9/Vp6/exqcKDxxlf77SrviRu2K1jwEex20F1sWvZ1zyjqgiboBVvOYQ2PhrNGmQ5mTVE1HfLtufTF1/pZrFrQwm94zOMLCEHN7qXek+rDRcLd4GdTRUeneuTQ2jjo9GsQSyZdWPF0p5PpdcIu2VT8TYbCPLq0GTc9R6LM5fp7ROrdXY8ttYU67BbDqGNj0azBrGayG0sW9rzcTnsFLsdWfV8Xh2cJBhSbE/A84nX22d0co4h/1xCMmuLbbVejg9NMh8MJT5hTcbQxkejWYN0jUxTUeSkyBV/XaXa62Ioi2s+7XFqukVT5XVRW+KK6fl0mnXatsYRLUSzrcbLfFDx2rBWvOUC2vhoNGuQ7tEpGuOs91hUeV1ZVbsd7fNRYBfOqCpKaPulRAeWcCARpZuFFaLr1KG3nEAbH41mDXJqdDqu0s2iqtjJcBaNT3ufjy1VXpyOxC49uzaU0DngZ2Z+cXJox4CPwgI7G0qXN7AWzTWGwdOKt9xAGx+NZo0RCim6x+JXN7CoynLYra3fl9B6j8Wu+lICIXWaSq1zwKjpFq/uWzQep4OG8kJdZidH0MZHo1ljDPpnmQuEaIijdLOo8roYn55nLpD5RXj/bIDu0Wl2JLFOs1RvH0tmnSy6xlvuoI2PRrPG6E4gx8fCklsPT2Y+9BYWG9SVJLxPU4UHj9O+aN3HNzNP7/gMzakYn9pijg36CYZiNjfWZBFtfDSaNUY4xyfBsBvAkC/zobf2BGq6RWOzCTvrihcZn2ODk0ByYgOLrTVe5gIhuuJUy9ZkB2180sDAxAzvu/MpBnwzqz0VjSZ8YU1IcJDFKgdt/YZIIBGPLJJdG0p4pWcCo1ExdIRrwyVuxCwWarzp0Ntqo41PGrjj4Q6ePTHCHQ91rPZUNBq6R6ep8rpwF9iX3bY6i8anvd/H9trkRAJgiA585noRGOs9TrstIc8umq26wGjOkLFOpuuBHbfcx2zEQu33DpzkewdO4nLYaLvtras4M816Zrk+PpFYla2zoXhr6/Nx2c6apPezRAeHeyZwYxifLdVFOOzJ/3YudhdQX+qmU8utVx3t+ayAxz+5n7efVR9+7C6wcc3ZG3j8U/tXcVaa9U7X6FTCxsfjdOBx2jPu+Qz5Zxnyz8XtXroUO2qLsclCY7mOgeVbZ8djq1a85QTa+KyAmhI3pYVGi2KbwGwgRLHLQU2xe5VnplmvBEOKnrHpuAVFo6n0OjNufBaUbskbn0KnnS3VXo70TDAXVHSNTiVVUDSabTXFdA74CWnF26qiw24rZMg/S7mngK3VXnbUlzCoRQeaVWTAN8N8UCW1qG8kmmbY+KSgdIvkzPoSXnhtlN5yhVKsyPPZVutlej7IqSSNtCa9aM9nhdx5wz7e0FSObzbAbdfu4c4b9q32lDTrGGtRPhGlm0WV15VxqXVbv49yT0FCjd9isau+hFNj03SMGmus8bqgLoeleNO9fVYXbXzSQF2pm74J7fFoVh9LZp2MEqzK68p4kmlbn4/ttcUJNX6LhSU6ONAbwG4TNlcmVpg0FlrxlhtkzPiIyN0iMiAihyLG/klEjorIyyLyCxEpi3ju0yLSKSJtInJVxPgbROSg+dwdYn57RcQlIj82xw+IyOaIfW4UkQ7zdmOm3qNFfWkhY1PzTM8Fl99Yo8kgluezIU4fn2iqvU5GJucylvWvlKK935/Seo/FLrO3T8dYiKZKT8KFSWNR5nFSXezSns8qk0nP51vAW6LGHgT2KKXOAtqBTwOIyC7gOmC3uc/XRMRKUvg6cBOwzbxZx/wwMKqU2gp8Cfi8eawK4FbgPOBc4FYRKc/A+wtTX2oIDHrHpzP5MhrNsnSPTlFTnFiOj0VVsYuQYsl21SulZ3wG/2wgJaWbRXWxKxyyS6WyQTRNFR7uPdibt4nhayGxPWPGRyn1GDASNfaAUipgPnwaaDDvXwP8SCk1q5Q6DnQC54pIPVCilHpKGenN3wGujdjn2+b9nwGXm17RVcCDSqkRpdQohsGLNoJppS5sfPL3i6BZG3SNJL+IXlmU2UTTtj5DIr1zBZ4PQHO1EWpLpo3CUvhm5pmcDeZtYvgXH2jL+8T21VS7/RHwY/P+RgxjZNFtjs2b96PHrX26AJRSAREZByojx2PskxGsfwZtfDSrTffYFOdsSs7Rr/JaiaaZMj5m47cVeD4Avmnjd6sl206FfE8Mz/f5R7Ks8RGR9wK/UUr5ROQW4BzgNqXUC6m+qIj8DRAAvm8NxdhMxRlPdZ/oedyEEdKjtraW1tbWpScdh7mgcfgnf3+EKl9nSsdIFr/fn/J8c4F8nn+uzj0YUvSMTrO3LBB3ftHz7/UbF7PHn32J4Kn0/x597OUZKtzCiweeSGn/jzwwyXxEx4cnjg2z+eZ7KbDBN69MTnjw+Ytc/Khtjuf6gwRC4BDYV2fnup3OrHymK/3uWPN/utdYX3ba4Jza/Jl/JIl80/5WKfVTEbkII6T1RYx1mPNSeUFTAPB24HJlVQo0vJPGiM0agB5zvCHGeOQ+3SLiAEoxwnzdQEvUPq2x5qKU+gbwDYB9+/aplpaWWJslRMWTD+KuqKOl5XUpHyMZWltbWcl8V5t8nn+uzr17dIrgA49w0dk7aTl305LbRc9/fHqeT//uAWoat9By8Za0z+sLLz3O6za5aGk5N6X9nzhnhtt+/QoPHO5jZj6Eu8DGVbvr+Jurz0wpofvZ6YMc6DsJQFDB1k0bufaq/Pm/PTD1Mk/3GsGd+Tycv0Uiaz6WhOtq4OtKqV8CzlReTETeAnwKeKdSKrKm+T3AdaaC7QwMYcEzSqlewCci55vrOR8Afhmxj6Vkew/wW9OY3Q9cKSLlptDgSnMso9SVuOnTYTfNKpJKjg9AiduB025jMANht57RKV7pnWBTRerrNDUlbopdDmYDIQpsK68kMuSf5f3nbsJTYGd7rTcj7zuTnBpbEDZd/8bGvJu/RSKezykRuRO4Avi8iLhIwGiJyA8xPJAqEenGUKB9GnABD5qK6aeVUn+ilDosIj8BjmCE4z6mlLKM3kcxlHOFwH3mDeAu4Lsi0onh8VwHoJQaEZHPAc+a231WKbVI+JAJ6kvdi74UGk22WTA+yV3oRYQqrzMjiab/cN9RFHBsYHJFxxnyz/L+85rYbuunPVS7okoiViL4wZ4Jil2OvEsM/9OWrTzWPgTAn1++jfo0CDBWg0SMz/sw1GJfVEqNmQq0/73cTkqp62MM3xVn+9uB22OMPwfsiTE+A7x3iWPdDdy93BzTSX2Zm+dPjmbzJTWaRXSPTiGSXI6PRWWaS+xEL4w/+aqxTpPqwrhlIFpbh/hAy2mXg5RoririqVeH03KsbGL9yAAY9s/lrfFZ1oNRSk0ppX4OjIvIJqAAOJrxmeUZOtFUs9p0jUxTV+JOKQGzKs3FRR//5H7eefYGHGbvnlys+N5c46V3fIbJ2cDyG+cQVpt0IG9DbpBY+OydItIBHAceNf/eF3+v9YdONNWsNt1JtFKIJt3FRa11mkBIIeRmxXcrb+j40MpCgtmme3Qaqx/fcBb6MGWKRH4ifQ44H2hXSp2BsfaTmmZyDWMlmq5n0cFayLrOZ7pHp2lMUmxgUVXsYtg/x4IAdeUM+WdxOWy8eVcN7z+vKed+pW+pNiolHBvMrzI73aNT4WoR2ehAmykSMT7zSqlhwCYiNqXUI8DZGZ5X3mHFXXvWsfHJ93bi+Ww854MhescT72AaTZXXRSCkGJ+eT9uc/u4du5kNhLh4W3VOVnxvqvRgEziWZzXeuken2V5bjLvAxnAeG59EBAdjIuIFHgO+LyIDGIo0TQT1Yc9n/YXd1krWdaTxvO3/y07eRLo4dGqckCLc3DBZIqsclHlSyqSIOSeA3RtL03K8dONy2NlU4eFYHoXdAsEQfeMzNFYUUlnkWvNht2s5s8/hAAAgAElEQVSAKeCvgN8Ax4B3ZHJS+Yi7wE65p2BdltixFpftZhza5ci9xeV47LjlPjbffC/fO3ASpQzjufnme9lxS/4sbX71EaOyxlPHUlNvVXmN+m6DaZRbHz41jk3gzLqStB0z3Wyp9uaV59PvmyUQUjSUe6gqduVcKDMZEvF8aoBeU9r8bREpBGqB/NMoZpj60sJ1aXysxWWzyhBzObi4HI/HP7mfz/3qCP/9ci/Aogz6XCfa63zo6EBKkmbL+KRzDeHgqXG21RRT6Ey8wna2aa4u4onOIUIhhc2WWq+hbNJt9mtqKC+kqsiZ19ebRDyfnwIRlZUImmOaKOpL3Xn9ZVgJQ/5ZLIXvhc2VefWLrKbEjd288IjkpjJrKcJe5wolzZkoLnqoZ4LdG3PX6wForvYyGwjlTYJ4ZBWLSq8z400AM0kixsehlAr74ub99ASF1xh1pe51K7X+27fvwvoBvm9zRc4tLi9Hj3nxqS1256QyaynCXucKJc3lHid2m6RtDaF/YoZB3yyvy9H1Hot8U7wtNAt0Gx1o/XOEMtQEMNMkEnYbFJF3KqXuARCRa4ChzE4rP9lQtpBomsuhhkwQWeb+5MhUnC1zkz+8YDPPnBhlyD/L379zd9iTyAeGfLM4bMJb9tRR5nGmVHrGZhMqitKXaGqJDfbkuPGxcn2ODU7SsmOVJ5MA3aNT1Ja4cDnsVJoKxYmZ+bSJRLJJIsbnTzBUbl8xH3cDN2RuSvlLXYmpeJuY4Yyq1HvM5yPt/cYvx131Jbw2nD/qIQvL8wmEFH0TM2xMoUTNavHXV5/J/Uf6uWhrFdfFqWa9HOlMND10agKRhfbXuUpFkZMyTwGv5pHnYxWOXQiVzuWl8UmkvM4xpdT5wC5gt1LqQqXUscxPLf+oLzOrHORJ/DidtPf7qC1xcVZDaV56PpGfWVeezf9Ij9EpdPeGlXkZVV4ng2kKux08Nc6WqiKKXKvZr3J5RIQtVUX5E3YbW6hikQmRSDZJuAiUUsqvlEq9heA6oH4ddzRt7/exvbaYTZUehvxz+POsXlbP+AweM1QaWbgxHzjcM4HdJmyr9a7oOFVeF0O+9FzIDveM53zIzaK52suxwdz31gPBEL1jM2HjU2l6Pvma65N8BULNklhht5WIDvIxyz4UUnQO+NleW0xThRFuzLfQW+/4NK/fVIZIHno+vRNsq/HiLljZOqNVXHSlJXaG/LP0js+wZ4WeWLZorvEy6JtlYiZ91R0yQWSOD0BlkeH55KviTRufNFLoXHmiaT6WqOkanWJmPsT2Wi9NlcY/xsnh/LqA947NsKmiiLoSd955Pkd6JtKytlLldTEbCDG5wsrs+SI2sNhirs++muPeT2SODxjrVSKkzVvNNssGZEXEA3wc2KSU+oiIbAN2KKV+lfHZ5SF1pYUpFRfN5xI1bX1GNHZbbXHY+JzII+MzMx9keHKODaVuGsoLF5Wsz3WG/bP0Tcywa0N6jA8YFzPvCtZqDltrUDme42PRXGPKrQf8nN1YtsqzWZroTrV2m1DhcTI0uXbDbv8BzAIXmI+7gdsyNqM8Z0OpO6Xioo9/cj9X7a4NP87F/idL0WGWJ9lW46XYXUBFkZOTI7n9KzISy1OtLyuksdyTV57PkV7jQp8O41OZpkTTg93jbK70UOJOrc5cttlU4cFhE14dym3RQWSOj4WR65Ofnk8ixqdZKfUFYB5AKTUN5E8SRJapK3WnVFy0psTNnOn52PIsy76tz8fGskKKzYvNpgoPr+WR52Mp3SzPp3d8mvlgaJm9cgPLy0hX2A1WbnwO9YznbDHRWBTYbWyq9Ky41Xemiczxsaj0Ohlaw4KDObOemwIQkWYMT0gTg/pSN6NT88zMJx83t3JNvC4H7z93U95k2RtKtwWl1ebK/DI+PRGeT0O5h5Ay1oDygSM9E2wsK0xLnkd1sVlcdAUXs7GpObpHp/NGbGBhKN5y3/NpiOrXVLnGPZ9bMapZN4rI94GHgU9mdFZ5zErk1mc1GPHmiZkAN13SnBclagLBEK8OToabWwFsqiyiZ3ya2UB+tBS3PJ/6UjcNFcbnly/rPkd6J9IScgNjARtWtoB96JThieV6WZ1otlQX8drwFIEc9ngjc3wsqrzOtSu1Vko9CLwL+CDwQ2CfUqo1s9PKX1bSTru93xcOffy+eyyt88oUJ4anmAuG2BZhfJoqPCiVP/kyPeMzVBY5cRfYw51A82Hu03NBXh30p62KQIHdRrmnYEXS3UM9Zg+fNBnEbNFc7WUuGMrZzz06x8eiyuvCNxtIKdKy2ixpfETkHOsGNAG9QA+wyRzTxKDeLMuSbNgmFFK09/t565463AU2XurKjvFZaV5Rh1nTbUeE8dlclV9y697x6XB1ivpSo8J1VxY8n5We+6N9E4RUesQGFkaiaeq/pA+dGmdjWSHlRflV7qU5xwuMRuf4WFSa53k4DxVv8TyffzZvXwUOAN8AvmnevyPzU8tPIuu7JUP36DTT80F2byhhz4bSrBmfleYVtff7EYGtNQtrPpvMRNMTeZJo2js2Ew6XOuy2rOX6rPTcW0q3dHoZld6VFRc9dGo870JusFBgNFdzfaJzfCysSEk+rvssKeZXSu0HEJEfATcppQ6aj/cAn8jO9PKPQqedMk9B0mG3NtOD2F5XzN7GMr5/4DXmgyEK7JnJA05XXlF7v4/Gcs+iKt5VXicepz1vRAc9Y9Ocv6Ui/LihvDCjVQ7Sde4P90xQ4naktQhqldcVThJNlomZeU4MT/GeNzSkbT7ZoszjpLLImbOeT3SOj0W65PGrQSJXtp2W4QFQSh0Czl5uJxG5W0QGRORQxFiFiDwoIh3m3/KI5z4tIp0i0iYiV0WMv0FEDprP3SEiYo67ROTH5vgBEdkcsc+N5mt0iMiNCbzHtFJfWph02M1qSbCtxsvexjJm5kPh5M10MjMf5D+f714UJoPUW19bNd0iERGaKovyosCob2Ye32wgHC4FaKzIbK5PdAO4VM/9kR5DbGD+S6QFo7J1aiGcw6bYIF8qG0STy4q3WDk+ECmPX1thN4tXROTfRaRFRC4VkW8CrySw37eAt0SN3Qw8rJTahqGauxlARHYB1wG7zX2+JiLWT+mvAzcB28ybdcwPA6NKqa3Al4DPm8eqwFDonQecC9waaeSyQSodTSNzZc42VW8vrVB0ELmm0Nbn4zP3HObc2x/i4z99Cf9sgHM2lYUTtlJpfT0XCHF8aHKRzNqiqcKTF2E363PaEGF8GsoL6ffNZEytF9kADlLL6QqGFEf7JlZcyTqa6mIX/hQXsA+HxQb5aXy2VBflbtgtRo4P5Hdx0USMz4eAw8D/Av4SOGKOxUUp9RgwEjV8DfBt8/63gWsjxn+klJpVSh0HOoFzRaQeKFFKPaWMaoffidrHOtbPgMtNr+gq4EGl1IhSahR4kNONYEapT6GjaXu/jx11hgfRWFFIRZFzxes+//JgO88eH+Ft//o4V335MX5w4CQtO2r44UfO5+GPX0p1sYv3n7eJck8Bmyo9SecVHR+aJBBSp3k+AE2VHrpHpsMX2FylJyLB1KKx3FDr9WQw18f6hW0Tq5VBsufez8x8KO39clbSTvvQqXHqStzhfKF8o7nay/DkHKM5uHjfPTodM7zqcTrwOO15GXZbtoCTUmoGw7P4Uhper1Yp1Wset1dEaszxjcDTEdt1m2Pz5v3ocWufLvNYAREZByojx2PsswgRuQnDq6K2tpbW1taU31gk0yNzjE7N88DDj+C0Lx8SCYQUHf1TbCmcCc+hwRPkyaOnaG0djbmP3+9fcr4feWCS+Yh0BcslD6kQ76ofZ7brII92wfWNAH46S0McGZ7mun0kdQ4O9BptEya6jtI6vnjRfGZonrlgiP+6/xEqC0//jRNv/tmktcuoZPzaK7/Hf8KY59CI8av/3tan2VN1eqXodMy9NDSLQ2D/JgcPvTbHO2p9SR3z6R7j3E/1tNPq60zqtePNv3fAOO4Djz7FlrLkqmQf6JiivsiW0c81k9+bSfO9/+yBx9lWnplOxKnOv7N3iuay2Oe2yB7iyLGTtLYOrHyCy5DO858rnZ5iXaFVnPFU91k8qNQ3MFR87Nu3T7W0tCw70UQYKu7m5x0vsX3vuWxOoKNpR7+P4AOP8eZzd9HyemOx9veBdv714Q72XXBRzCKPra2tLDXfJ86Z4S9+9CJPv2o4nu4CG1ftruNvrj4zZminv+gkT//nQRp27VuUr7Mczz/Qhk06ed9bW04r51/QOcS3jxygfvtZXNhcldT8s8kLD7RhO9LJNVe24DDFHdvGpvmHZ35LReM2Ws47vTPoSuceCin++qnf0rKzgo9dto0Hv/oEwZrttJwd8zdSTJ769Ss47Se47m0tSYtS4s2/rGuML7/wBJu276FlV23MbWIxORug7/77+R8XbKGlZXtS80mGTH5vNg9N8uUXWilt3E7LvsaMvEYq8w8EQ4w+8BvO2bGZlpadpz3fcOQJ7E4HLS3npWmWS5PO85/tlgr9ZigN869lqruByE+7ASOnqNu8Hz2+aB8RcQClGGG+pY6VNawQTk+Cobew0i3iwr+3sQyljCKNyVJT4mbEDB24HLZl1xQs4/DkseGkXqe938fmqqKYfWQ2VRiqnFxXvJ0am6Gm2B02PGDI5R02yViVgxe7RukZn+Hqs+o5a2MplUVOfns0uV+tR3on2F7nTbsaMtWw25HeCZQi78rqRNJQXojTbss50cFSOT4WlUXpa3+eTRL+5orI8j/hl+cewFKf3Qj8MmL8OlPBdgaGsOAZM0TnE5HzzfWcD0TtYx3rPcBvzXWh+4ErRaTcFBpcaY5ljTrT+CTaWqG9z4dNFhLdAPauUHTQPTrNhlI3v/jTN/H+85ririk0VnjYWFbIU0kan45+P9trYntKG8oKKbBLzhufyARTC7tN2FBWSFeGFG+/erkXp8PGFWfWYrMJl+6o5tH2wYTXx5RSHOmZYHd9+i/0qRYXteTZr2vIX+PjsNvYXJV7BUZPhWXWsSX1VV7nmksyBUBELhSRI5gKNxHZKyJfS2C/HwJPATtEpFtEPgz8I/BmEekA3mw+Ril1GPgJhpjhN8DHlFKW3OajwL9jiBCOAfeZ43cBlSLSCfz/mMo5pdQI8DngWfP2WXMsayRb360thgdRUeSkqdKTkujgteFJpuaCfOSSLezaUMJt1+5Ztk7chc2VPH18mFCCF8CZ+SAnhmMr3cC4gDeWe3K+tULv+MwipZtFY0Vm+vqEQopfH+ylZXt1uAr4/h01jE3N8/uu2Ot70fRPzDI8OZfWygYW7gI7xS5H0tLdQ6cmqPK6qMlTsYHFlipvzrVWsL6HSxsfFyOTcwn/7+YKiaz5fAlDQXYPgFLqJRG5ZLmdlFLXL/HU5Utsfztwe4zx54A9McZngPcucay7gbuXm2OmSDbRtL3fz8660z2IvQ1lPHciebv5aPsgAC07apbZcoELmiv56fPdvJKgfPfYoJ+QMpJil2JTpYcTQ7nr+Sil6Bmb5oozTz9PDWUeftuW/gXc514bpX9ilrfv3RAeu2RbNXab8MjRQd7QVBFnb4MjvYaXkQnjA1BV7EpafWdUNkhvztFq0FxTxEOv9Gc0wTtZFnJ8YhufSq+TYEgxNj0fLg6bDyR0dpVSXVFD+VfFLsvUlbgTCrsteBAxjE9jGT3jMwwkWaqntW2QpkoPZyQgdrC4oLkSIOHQW0e/8esw1rwtmio8nByZwoiG5h6jU/PMBkJhTzWShvJCBn2zaS/Y+KuXe3AX2Lh854LBK/UU8IZN5TySoLE7YvbwOTPNMmsLo1Jy4sZnei5Ix4Avb5NLI2mu9hIIqZwKF3ePTlFT7Iq5tgpGWwXIvxI7iRifLhG5EFAi4hSRT5BYkum6ZkNZYUJ5Ip0DfpQinOMTydmNxj/z75MIvc3MB3ny2BCXbq9OfLIYocItVUUJG5+2fh8Om7C5cmkDt6myCP9sICx+yDXCOT5lpwsxGivSX906GFL8+mAfl+2soShKwbh/Zw2HeyboT+CHxuGeCTZXelbU6joexgJ24p+ZVeA0X5NLI9lirru+mkOiA6OPz9IllCyRSL70/7JIxPj8CfAxjFyZbozSOh/L5KTWAnWl7oSKi1oldGJ5ELs3lOKwSVKig2dPjDAzH6JlR3LGB+D85koOHB9JqKdJR7+PLdVFOB1Lf4U2V5qKtxwts9MT7uMT2/MB0lrd+pnjIwz5Z7n6dRtOe27/TuPzak3A+0lnD59YeFx2jg9NJlxtey2IDSy2mAVGj+VQpYNYTeQiWSgumps/8pYirvExS9zcoJR6v1KqVilVo5T6Q6VUcrKodciGUkPuvFzYpr3fh9NuC1+oI3EX2NlZX8xLXYnLrVvbBnE6bFyw5fTcmuW4sLkS/2yAgwkUlmzv9y+bE9RkGZ8cLbPTG+5gmh3P51cv91BYYA8bmkh21BZTX+peVnI9MTPPa8NTGfUyOvp9BEOKLz/YntD2h05NUO4pWFQlIl8pcRdQU+zKGbl1MGSsS8bzfMJtFdaS52Mqzq7J0lzWFHXmr+nl1n3a+n0013gX5ZlEsrehjJe6xxJWsrS2DXDeGRWLqkwnyvlbzHWfV+P/tpiaC3ByZGpJmbVFQ7kHkdzN9ekZn6bALlQVna7Qqva6cNptaVO8BYIhfnOoj8vPrMHjPD1cJiLs31nD7zqGmAss7Xke7TU85XSX1QGj2vbmm+/loFkg9AfPdLH55nvZcct9cfc7eGqcPRtL815sYGHUeMsN49M/MRM3xweg3OPEJvlXXDSRsNsTIvIVEbk4qsGcJg4LHU3jG5/2Ph87lpArgyE68M0EeHVoee+ha2SKY4OTSa/3WFR5XeyoLV523adzwPjH3FG39LzB8NzqS9w521TO6uNjs51+0bTZhI3lhXSPpMfzefrVEYYn53j7WfVLbrN/Rw2Tc0GejaNwPJLBTqFWtW1XRCj1yl21cattzwaCtPevDbGBhVHdejInhDLdy+T4gPFdrShyragD7WqQyIrlhebfz0aMKeCy9E9n7ZBIO+2JmXl6xmfiypXPbjSTTbvGFjVsi0UqEutoLmiu5EfPnmQuEFpyPcdap0qkFM+mytytbt07Ph3+nGLRUJ6+XJ97D/ZQ5LTH/WzetLUSp93GI0cHeNPW2GHTwz0TVHmdGSneaVXbnguGKLAL80HFU8eGkZgVqwza+/wEQiqvKxtE01ztZXx6nuHJufB6ymqxXI6PRZXXufY8H6XU/hg3bXiWIZFE01gtqKNprvZS5LQnJDpobRukobww3JUxFS5ormRmPhRXYdcx4MfpsNFUsXQowKKpInf7+vSMxU4wtWgo96SlysF8MMR9h/q4YlftknJZMCoUn7elIm5+0ZHeCc6sz1w+zZB/lvef18QvP3YRV+2uZXIuwA13HWB8aj7m9tb6YD52L12KLTnU1XS5HB8Low9Tfnk+iVQ4qDSbuL0gIs+LyL+KSGU2JpfPJJJo2ta3fK6M3Sac1VC2bKWD2YAhsW7ZUb2iC9P5Z1QiEj/fp73fR3P10utUkWyq9DDkn8M/G0h5TpkgGFL0TczE9XwaKwoZmZxjcoVzf/LYMGNT87z9rNNVbtFctrOGVwcnY4o05gIhOvr9GRUb3HnDPm67dg+7NpRw5w37+NaHzuXVwUk++K1nYp6HQz3jFLsdNFakr5vqamOVucoF0cFyOT4WlV7n2lK7mfwIGATejVFDbRD4cSYntVZYLtG0vd9HkdO+bBvkvY1lHOmdiNvc7LkTo0zNBbl0e+ohNzASHvdsKOXJY0NLbtPe51uyrE40Vh5Qrq37DPpmCYbUog6m0ViLvCtVvP3qpR6KXQ4u3ra8AnG/GZZrbRs87bnOAT9zwVBGZdbRXLK9mjuuP5uXusa46bvPnabePHxqnD0b1o7YAGBjWSEuh41jA7lgfOIr3Swqi1xrS+1mUqGU+pxS6rh5uw0oy/TE1gLLdTRt6/OxrbY45oJ3JGc3ljIfVOHM9lg82j6I027jwuaVO6UXNFfy4smxmDJxn7VOlWDrhVyVW1sVx+PJg61/+pWs+8wFQtx/uI83LxNys9hcVcQZVUUxJddHeo3PPxNKt3i8ZU89X3jPXp7oHObPf/hiOA9sPhjilT7fmsjvicRmE86oKkpI5JNplsvxsagqdjI5F2R6Ln+KzyRifB4RketExGbe3gfcm+mJrQXqywrjr/kM+OKu91jsjRAdLEVr2wBvPKP8tMz5VLiguZK5YIjnXzu90GXHwPKhwkg25Wiiae/Y6e2zo2lMg+fzu85BJmYCvH3v0iq3aPbvqOGpV4dPu5Ac6ZmgsMCeVNmkdPGeNzTwmXfs4sEj/XzyZy8TCik6+v3MBUIZUd6tNs013lUPuyWS42NhpQvk07rPksZHRHwiMgH8T+AHwJx5+xHwV9mZXn5TX7J0oumQf5Yh/1xcpZtFXYmbmmIXLy3R26dnbJr2fj8tKwy5WbxxcwV2m8QMvbWHKzIkFnYrcRdQUeTMuVyf3rDnE79sibvARtcKDOevXu6lxO3goq2Jy9/376xmLhA67fwf7hlnZ30x9mU85UzxwTedwcffvJ2fv3iKW+85zJOdxvyWWwzPR5qrvXSNTMUNdWeaRHJ8LCrNEjv51FphSeOjlCpWSpWYf21KKYd5syml1t5PnQxgrSfEWvdpT0DpZiEinN24tOhgQWKdWn5PNF6Xg70NpTFFB+39ftwFtrBXkAibKjy5F3Ybm8HjtFNSuLSnKCI0lHtS9nxmA0EePNzPVbvr4pYhiubcMyrwOO2LCo0qpYyyOlkOuUXzZ5dt5aZLtvDdp1/jnx5oA+C/Xjy1qnPKBM3VRYTU6iZIJ5LjYxHuw+RbA55PJCJyloi8U0TeZd0yPbG1QLxE07AHsUyipsXexjJeHZqMKXltbRtgQ6l72TygZLiwuYqXusdPU6m19/vYVrP8OlUkTZWenPR86kvdyy6UN5QXplzf7bH2IXyzAa6Ok1gaC5fDzpu2VvHI0cFwomP36DS+mcCqF+8UEb795AkAZs1KDN8/cDKhSgj5RFjxtoqig0RzfCDS81lDxkdErN447wbeYd7enuF5rQnCHU0nTv/l3Nbvp9xTQHWCSWzhZNOofJ+5QIgnOoe5dEdNWhVHFzRXEgwpnj2+ONu+vd/HtgRDbhZNFR56x6dXNYQRTc/YdELhIiPRNDXP596XeyjzFCyZMBqPy3bWcGpsOrzGdtgUm2RT6bYUj39yP+/cuyEc/nMX2Ljm7A1xKyHkG9a62mqu+ySa4wORHWjXQNgtgvOVUvuUUjcqpT5k3v4o4zNbA1ieT6zWCu39PrbXFidsMF7XUIrI6aKD518bxT8bSFvIzeINTeU47bZFdd7GpuYY8M0mFCqMpKnSCGGks0jnSukZj5/jY9FY7mF8ep6JmdhJlktxcmSSe17q4dLt1Sk1JbM+T0v1dqR3ApsQs+lgtqkpcVPsdhBSCpfDxmwgRLHLQU1x/hcWtShyOagvda9qommiOT5glLLyuhxrQ3AQwVMisivjM1mDeJwOSgsLTlvzUUoZNd2SuJCUuAtorvae5vk82j6IwyYp/bqOh7vAzus3lS1a9G5PoIFcLCy5da7k+swFQgz5ZxP0fEzFW5I13m75xSFCCvwzqSWo1pcWsrOumEcs49MzTnO1N6ELUTawKiH84k/fxPvPa8q7XjKJYNR4W13PJ5GQm0W+JZomosv9NoYB6gNmAQGUUuqsjM5sjWDk+iy+cPWOz+CbDSR9Ed/bUMaj7YOLCh62tg2wb3N5RhqLXdhcxZcfbmd8ap5ST0FYJJGIQi+STTmW69M/MYNS8ZVuFlbmfvfoVEIhrx233BdeCwF4+OgAm2++F5fDRtttb01qnpftrOHOx15lfHqeIz0TvPGM5VtsZ4s7b9gXvn/btad1uV8TbKku4hcvnEIptSpJtN2j0+FweyLkW4mdRDyfu4EbgLewsN7zjkxOai0RK9G0zVK6JXkRP7uxlCH/LKfMJmh94zMc7fOtqJBoPC7cWolS8PRxI/TW0e/D63Ik3bel2uvC47TnTK5PuIlcjD4+0VieT6I13h7/5H6uft2CwGAl6yH7d9YQDCn++6UeesZn1mQ+TS7TXO3FNxtgcBUUZMnk+FhUFuWX55OI8TmplLrHrG7wmnXL+MzWCHWlhaeF3cJKt2X64USzkGxq5Ps82m6EZNK93hN+vYYyCgvsYcl1W7+PrTXepH8Fiogpt84N4xNuIpeA51PuKcDjtCdc5aCmxB1eHyqwy4rWQ17fWEZpYQF3PnYMgF31a6uSQK5jKd46VyH0lkyOj0WlN7/aKiRifI6KyA9E5HottU6eDaVuhqMSTdv6fdSVuCn1FCR1rJ11JTgdtvC6z6Ptg9SVuJMWACSK02Fj3+bysPHp6Pen/FqG3Do3wm6W57ghAc9HRGhMMtenY8CPy2Hj5x+9cEXrIQ67jUu2V9NlrjfVlKxuef/1hlXd+q9/fjDhluLpIpkcH4tqr5ORyTmCCTaeXG0SMT6FGGs9V6Kl1kljya37Jxa+vO39vqTXTcAwBrs3lPD7rjGCIcXjHUNcun1lVayX48LmKtr6fbT3+xienEtaZm3RVFlE18h0Tvxj9I5PU1pYELOjaCwaygsTrnIwMx/ENz3Pu87ZyOsayrjt2j2L1keS5bKIltvfMfNrNNmhrsSN3QYnhqe446GOrL72qbHEc3wsKr0uQgpGp/Ij9Lbsf59S6kPZmMhaxVJU9YzN0FRZRNCsifWBC1IrALq3oYwfP9tFe1UBvpn0S6yjucAsVGolFiYrkrBoqvQwFwzRNzGzbBXvTNO7TB+faBorPDxzfCShhedH2weZnAvyttcll1gai2jxwvcOnOR7B06mJF7QJMdqn3tLXZnM9zScaOpf/SZ4ie5HEBoAACAASURBVJBIkmmDiPxCRAZEpF9E/lNEGlbyoiLyVyJyWEQOicgPRcQtIhUi8qCIdJh/yyO2/7SIdIpIm4hcFTH+BhE5aD53h5hXBhFxiciPzfEDIrJ5JfNdCdGJpidHppgNhFK+iJ/dWMb0fJDfnJg3JNYJlOlfCXs2lFDscvDzF4wSKsmKJCyaKowQRi6E3nrGZ5ISTTSUF+KbDTA+vXyuz30Heyn3FHD+lpVXF7faWjvWcDJnrmKd+wK7ce4L7JLVc989Op1wjo/FQqJpfqz7JBJ2+w/gHmADsBH4b3MsJURkI/AXwD6l1B7ADlwH3Aw8rJTaBjxsPsbMMboO2I2huPuaiFifyNeBm4Bt5u0t5viHgVGl1FbgS8DnU53vSokusWO1oE71Ih4WHQwG2bOxlBJ3cutGyeKw2zj3jAqm54PYhTgNleOTS7k+vePTCSndLBZaK8Rf95mZD/LQKwNcuasupcTSaKy21sE1nMyZq1jnPhBSiMB8UOG027J27rvHptiYRMgNjEK4sLaMT7VS6j+UUgHz9i1gpbEeB1AoIg7AA/QA12DkFGH+vda8fw3wI6XUrFLqONAJnCsi9UCJUuopZSS+fCdqH+tYPwMut7yibGMlmlol/Nv7fYiQch22zZUenHbrrWRn/cQKvQUV3PFwarHv+lI3BXZZdbn11FyAsan5hJRuFmG59TJzf7xjCP9sgLclWcstHushmTNXsc79HdedDcCB40t39003ifbxiaTSbKuQL3LrRFZch0TkD4Efmo+vB1L+FJRSp0Tki8BJYBp4QCn1gIjUKqV6zW16RcRKXtkIPB1xiG5zbN68Hz1u7dNlHisgIuNAJbCoRr2I3IThOVFbW0tra2uqbysuxfYAh17tprV1iN8dnKG6UHjmyd8lfZyPPDDJ/EIYmt93jbP55nspsME3r8xMj5fo17Ri36m8ZoULnn3lBK3uPvx+f8bOdzx6/MabGe89QWtrd/yNTSbnDSPf+twhCofblpz7f7w8S1EBzHcforUnPb91rm8E8DPQPsQVZUAZKz5vq3Xu00E2526de0aHaGlw8Nipab7/q9+y0Zu6V5vI/ENK0T0yxZ6S+aTea0gp7AIvHOlgSyAz2TDpPP+JGJ8/Ar6CEb5SwJPmWEqYaznXAGcAY8BPTeO25C4xxlSc8Xj7LB5Q6hvANwD27dunWlpa4kwjdbYdf4ZB/ywtLRdz+wuPsndzES0tySugnjhnhtt+/Qr3HexlPqhwF9i4ancdf3P1mRkLBzxxzgy33fsK977cQ1Cxotfc+eozDJnnobW1lUyd73g83jEIv3uGy85/fVLrMjc/cT/uinpaWvbEnPtsIMifP/IQbzurgSsu25vmWaeX1Tr36WC15v66fbO0fLGV3/QX8Z2rz01ZYZrI/HvHpwne/1suPHsHLec1JXX8yicfoqiihpaWzBSgSef5X9aEK6VOKqXeqZSqVkrVKKWuXWGS6RXAcaXUoFJqHvg5cCHQb4bSMP9azUy6gcaI/RswwnTd5v3o8UX7mKG9UmBxeeYsUldaSO/YDLOBIMeHJlPOlYmMQxfYyMoaQLiIJKx43aGp0sPJ4alF5YGyjRX+TFZx11DuiVvl4IlOo31COlRumtyj0uvir67YzuMdQzz0yuktztPJQo5PcmE3yK9E0yU9HxH5uzj7KaXU51J8zZPA+SLiwQi7XQ48B0wCNwL/aP79pbn9PcAPRORfMEQP24BnlFJBs9vq+cAB4APA/43Y50bgKeA9wG/VKl7x6s1E06O9PgIhlVKOj4UVh95u66c9VMtgFpLfrNf8g3M38YNnTqb8mk2VRfhmA4ysYrfFnvFpRKC2JDnj2VBeGFepd+/LfZS4HWkv8KrJHW64oIkfPnOS2+49wiXbq3A5MlPkNZk+PtFUeZ0MroE1n1j/aUUYSrJKICXjo5Q6ICI/A14AAsCLGKEvL/ATEfkwhoF6r7n9YRH5CXDE3P5jSimrXMBHgW9hJMLeZ94A7gK+KyKdGB7PdanMNV1YirfHzI6jK6lIYCUstrYO8YGW7BR0TFcRyaYKs8DoKooOesdmqPK6kuosCkZrhd91DMX02uYCIR480sebdyXXsVSTXxTYbfzdO3Zxw13PcNfvjvOnLVsz8jpWjk8q+XBVXhfHh1Y/nSERljQ+Sql/tu6LSDHwv4APAT8C/nmp/RJBKXUrcGvU8CyGFxRr+9uB22OMPwecdjVUSs1gGq9cwFJWWe0PrEZV641IuXXitXrTS8/4dNKFUcH4FTo9H4zptT1xbIiJmQBve11dOqaoyWEu3lbNm3fV8pXfdvLucxqS9qAToXt0muokc3ws8qm4aNyfaWbi523AyxiG6hyl1KeUUpkNeq4xrJySF06OsqW6aN3+Om6s8CDCqhYY7R2fSUpmbdFoem2xcn3uO9hLscvBRRlO+NXkBrdcfSaBoOLz9x3NyPG7x6ZSCrkBVBW7mJ4PMjmbWh+pbLLkVVBE/gl4FvABr1NKfUYpNZq1ma0h6sxfRyEF2zJUBDQfcBfYqStxr1qVA6WMMvXJJJhaWBeDrqjq1vPBEA8c6eeKXbUZWwPQ5BZNlUV8+OIz+PmLp3jhZPoviank+FhUFi2U2Ml14v0E/zjGAv8tQI+ITJg3n4hMZGd6a4Mil4MStxHhzFQF6nxhU4Vn1dZ8JqYDTM0FU4qlL1Xl4Mljw4xNzWuV2zrjY/u3UlPs4u/vOUwojcVye8emOTk8RUWSFe8twiV28kDxtqTxUUrZlFKFSqlipVRJxK1YKaW7WiVJTbHxpchEjDif2FxZtGphtx6zo2wqYbdidwFlnoLTqhzcd7AXr8vBxTrktq7wuhzc/NadvNQ9zn++kFiyciJ84f6jKOAVswxXsoSNzyo0wEuW9bn4sApMzhkCvSc6B1d5JqvLpkoPQ/5ZpgPZV75b7cxTCbuB4f1Eej7zwRD3H+7j8jNrUloc1uQ31569kddvKuMffn2Ud3/9yRX1/Nlxy31svvlefvGikar4zPERNt98LztuuW+ZPRcTrmy9iukMiaKNT4axvlRWYdF7XupN6Uu1VrAUb//4zHTWG3T1mAmmG1LwfACzqdyC53Pg1RFGdcht3WKzCbe+YzcjU3M8/9roinr+RFfRdjlSq2BeEV7z0Z7Pusf6UrlMhdt6L4sfbq0wobLeoKt3fBqHTaguTq3XieX5WLk+9x7spchp59Ltme2ppMlNdtxyH9d+9Ynw4+8dOJnyD0uresl80PhuzQVTqyTiLrBT7HYwlAeCg8RaOWpSxvpSzQVD674s/mo36OoZm6G2xI3dllpdroZyD7OBEONzikAwxAOH+7jszFodclunPP7J/dz261e4/9D/a+/eg+Oq7gOOf39623r4IWnlZ2ywJRnzCrYhUEhiQwiGpDEJSYeEAk2YJk0ok7TpJPZAOu0kmYYwbQKTIYUm4RFIgCHhZQKUhxVIYmyMDcJg60HxU0KSLVvWynpY2l//uGfttSzrsdq9dy/6fWY0unt2793fHks+Ouee8zvv09sfIzdbuOLMmdz8qdOSut6+aC8zpxQwOT+bC04tSzqTSFlRfii2VbCejw8sLb4n3guM/9fvdy+w6WA3s5K83wMwd7o3XLfvsLLxvXb2d/VxxRm2sHSiSvzDErw9f8bzh+Vd1y5DgLNmj2/79dLCvFA0Ptbz8UGq0tOEXfyXNT7VwO9eYHNHDx+em3xuhfjai33dytNvNTMpN5vl1ZERzjIfZPE/LNfVtZCFjOsPy86eIzR19CS911dcWVE+77ZFx3UNP1jPx/hqX7SXixd5/2GvqCr3rRcYiynvd/QkPdMNjq31aTnszXK7+LQIk/JsyG0iu+vaZfzgyjM4c9ZUcrOzku6tALzb5i2+rhxn41NalGez3YwZ7K5rl3HnNUvIy4Y50yeP65d1LPZ39dE3EEt6pht4u9KWFuaxvrmffdE+rjjDZrkZT1VFETv2d9FzZGDkF59EQ4u3tme8WVDKivI5cLiP/oHYyC8OkDU+xncFudksnp7NS9tbfdvb5+ganySSiiaqKMnn/S4lP0dYschmuRlP1YxiYsq4hrsaW6PkZWcxN8m8bnFlRXmoQvvh0fV+Wg/18Dd3rfd96YM1PiYQZ5Vns+dA99GhhnRrOug1PrOSSK2TKNrr/WUbKSlgcp7dMjWeKtdbqW9JLjMBeI3PqeWF5GSP77/lUpflYLT53f7jmW28tqPd96UP9ttjAnFWuXevpKauddw3WEfj6ALTJBufwdPEd7d3M3/1075NEzeZbX5pIbnZQn1L8j2fhtYoZ82ZMu5YjqbYGeF+atBLH6znYwJRNimLqooiXtruz+4czR3d5OdkMS3JhI3xaeJ52bZY2JwoLyeLU8oKqU8yJ1t33wC7DxymMjL+xMNHU+yM0PN55TsruGTRsdmafv9MW+NjArNiUYTXdrTT2XMk7e/V1NHDrKmTEElugenRFeixGLlZ/k8TN5mvqqKY+tbkGp9326KoQmXF+EcBygpH1/OJlBSw1w1H5wWwAN4aHxOYFdURjgwof27cn/b3aj7YPe7JBvE1Hd87v2BCLxY2Q6uqKGZ3ezeH+8a+kVtjqzdcN95p1gAlk3LIzZYRU+x09w3Q2BplXulkHg9gAbzd8zGBWTpvGsX5OdTUtbIyzZkCmjt6+KsF49v2ID4tvKZmH9ctn7iLhc3Q4pMOGlqinD3GxcyNrVGys4R5pYXjjkNEKC3MHzG56FNvNtEfU3581VksnlXi+wJ46/mYwORmZ/HRqjLW1aV3ynX/QIyWQz3jSq1jzEiq3JBZMjPeGlo7mV86mbyc1PyXPNJCU1Xl/ld3UF1RzHmnTE/Je46VNT4mUMurI7Qc6uWd5vRtjtvS2UtMk9tEzpjRmldaSF5OVpKNTzQlkw3iRkouumX3QbbuPcS1F8xL+j7oeFnjYwK1vNpbqFlTl75N9t7Z2wHAZEuFY9IoO0tYWF405unWvf0D7Nx/OCWTDeJKi/KGne32wPqdFOXn8NlzZqfsPcfKGh8TqEhxAWfOnsK6NE65vnf9DgBe2NaStvcwBryht7H2fHbsO8xATFO63i3e8xlqOHt/tJe1tc1ctWQ2hfnB3fYPpPERkaki8qiIbBeRbSJygYhMF5HnRaTBfZ+W8Po1ItIoInUicllC+VIRecs9d4e4/qOI5IvIw658g4jM9/9TmtFaUV3O5l0HODjKdCCjFd9FNj6bbm3txN5F1qRf1Yximjt6ODSG5QMNbnp2aofd8ujtjxHtPXHm3cObdtM3EOPaC+al7P2SEVTP53bgWVVdBJwNbANWAy+qaiXwonuMiCwGrgZOB1YCd4pIfPzk58BXgUr3tdKV3wAcUNWFwE+AW/34UCY5yxdFiCn8sT61Q29B7x9kJp6qSHzG2+h7P42tUUTg1PLxz3SLKy0cOsXOQEx58NVd/NWCUhamsLFLhu+Nj4iUAB8Dfgmgqn2qehBYBdznXnYfcKU7XgU8pKq9qvoe0AicJyIzgRJVXa9e3/L+QefEr/UocIkEdVfNjOjsOVOZXpiX8vs+kZICYgOKAjlZYgtDTdpVz4jneBv9fZ+G1igfmj45pTviHs1y0HX8pIN121vZe7Cba88PttcDwfR8TgXagHtEZIuI/EJECoEKVW0GcN/jeR9mA7sTzt/jyma748Hlx52jqv1AB1Cano9jxis7S/h4VTl/rG9jIJbaKdfb3vdm0d37lXNtYahJu9lTJzEpN5u6MaTZaWyJpmRxaaJ4fre2zuN7Pve/upMZJQVcurgipe+XjCDuNuUAS4CbVHWDiNyOG2I7iaF6LDpM+XDnHH9hka/iDdtRUVFBTU3NMGFklmg0Gqp4Bxscf8VAP+1dfdz75EssmJqavwBVla7D3Zw2PYv+PW/zianAVMZdbx+0ug+TMMQ+Y7KysW4XNTUn9uQHxz8QUxpbD7Ngck9KP9eBHi9h6PrNb1GwbzsA73fFeLm+m88uzOVPr7yc1HVTWv+q6usXMAPYkfD4o8DTQB0w05XNBOrc8RpgTcLrnwMucK/ZnlD+ReCuxNe44xxgHyDDxbV06VINk3Xr1gUdwrgMjr892qunrF6r//m/dSl7j7f3dui8767VB17dkbJrqn7w6j5MwhD7tx95Q5f94Pkhnxscf2Nrp8777lp9dNPulMbQe2RA5313rd7+Qv3Rsu8/9bYuWPO0thzqTvq6Q9U/sEmTaAt8H3ZT1feB3SJS7YouAd4BngSud2XXA0+44yeBq90MtlPwJhZsVG9orlNEznf3c64bdE78Wp8HXnKVZDLUtMI8zvnQtJROuX6qtonsLOFy23HU+Ki6opi2zl4OjGIr6wZ3byiVa3zASxQ6ZVLu0YWm3X0DPLJpNyvPmJEx9zyDmuR9E/CgiOQB/wd8Ge/+0yMicgOwC/gCgKq+LSKP4DVQ/cCNqhrfq/brwL3AJOAZ9wXeZIZfi0gj0I43W85kuIsXRbjtuTpaO3vG/QuiqqytbeLChWVML8xLUYTGjKwyIc3OR04d/lZzfOfTBeWp39MqcaHpk2/u5VBPP9ddMD/l75OsQBofVX0DWDbEU5ec5PU/BH44RPkm4IRseKrag2u8THgsry7ntufq+GNdG19YNndc16rd08Hu9m5uurgyRdEZMzrHZryN3Pg0tHQye+qktCz2LCvMp80tNL1//U4WzSjm3PnTRj7RJ5bhwGSMxTNLiBTnp2TK9draJnKzhcsWpzdbtjGDzXB7P41munVDazRtO/mWFeexP9rLlt0Hebsp2DxuQ7HGx2QMEWFFdYSX69s4MhAb+YSTiMWUtbXNfKyynClJ7lxqTLJEhMqKIupGWGjqzXRL/TTruNLCfPZ39fHr9Tspzs/hyg8Hl8dtKNb4mIyyYlE5nb39vL7zQNLX2LzrAM0dPXz6bJtoYIJRPaOYhpbOYbcK2Xugm97+WMonG8SVFuVx8PARHt+yl8vPnBFoHrehWONjMsqFC8vIzRbW1SU/621tbTP5OVl84rTgF9KZiakyUsyBw0eGXdQcz+mWrjQ38YWmijfbLdNY42MySnFBLufOn07N9uTu+wzElKffamZFdYTiAhtyM8GITzpoGOa+T3zr7HTc86m+5RlueXzr0cdPZWBSXWt8TMZZUR2hrqWTvQe7x3zuxvfaaevstSE3E6j4UNpwaXYaWqNEivOZMin1fyS98p0VLK8qP/o4E5PqWuNjMs6KRd4vzTX/8yqtnT1jOvep2iYm5WZz8aLIyC82Jk3Ki/KZNjn36NDaUBpao2m73xMpKWD2tEkIkJ+TlZFJda3xMRlnQXkRhXnZ7Nh/mDteaBj1ef0DMZ7d+j6XnBZhcl5m3Vw1E4s34634pD0fVaWxpTOle/gMti/ayzXnz+Oxb1yYkUl17TfUZJTqW56ht//YNOsHNuzigQ27yM/Jou4Hlw977l/e3U97Vx9/ffasdIdpzIiqK4p5fMteVPWE9TXNHT109Q2kbY0PwF3XHlvH/4MrT1iLHzjr+ZiMEt8ALj/H+9EUYOXpM0Y1Vr22toni/Bw+njDWbUxQqiqK6Ozt5/1DJw4dN6RxskFYWONjMkrErQ7vG4iRmy0osHFHO4UjDKP19XtDbpcurkjpplzGJKuqwhtSG2roLT7TLV0LTMPAGh+TcfZFe7nmI/N44saLWF5dTntXHzf+ZjP9w2Q9eKWhjUM9/TbLzWSMeOMz1HTrxtZOphfmUerW4kxEds/HZJzEsep7v3weD27Yyc2PbeXmx7byo6vOHDI/1draZqZMyuWihTbkZjLDtMI8yoryh0yz09CSvpxuYWE9H5PxrvnIPP5xxUIe3rSbO15sPOH5niMDPP9OCytPn0Fejv1Im8xRPaOIhkGNj6p606yt8TEm8337k1V8bslsfvJCPY+8tvu452rq2oj22pCbyTyVkWLqW6LEYsdyvLVFe+noPmKNT9ABGDMaIsKPPncWH60sY81jb1GTkPvtqdomSgvzuGCEvVOM8Vv1jGK6jwwcl62jsSU+0y19a3zCwBofExp5OVncec0SqiuK+caDm9m6t4Od+7v4w1vNfLyqnJxs+3E2maVqiDQ7jW3p2To7bOy31YRKcUEu93z5XKZNzuPv7nmNbz70BqpwqOdI0KEZc4JKN+OtPiHNTkNLlOKCHCLFE3emG9hsNxNCFSUFtHX20jcQY59LGfLCtlbmr356VJkQjPFLSUEuM6cUUJ/Q82lo7aQyUpRRu4oGwXo+JpT+9N0VXLSw7OjjTMzaawx4630St9T2di+d2Pd7wBofE1KRkgLmlU5GJHOz9hoD3n2fxrYoAzGls0/ZF+2b8Pd7wIbdTIjFMyF86bwP8ZuNu2gb4/YLxvihqqKYvv4YO/d30RT1snQsmODTrMEaHxNimZ611xg4lmanviVKc5fX+Ez0NT5gw27GGJNW8TQ69S2d7I3GmJyXzawpkwKOKniBNT4iki0iW0RkrXs8XUSeF5EG931awmvXiEijiNSJyGUJ5UtF5C333B3ipo+ISL6IPOzKN4jIfL8/nzHGABTm5zB3+iTqWzppisZYGCkiK2tiz3SDYHs+3wS2JTxeDbyoqpXAi+4xIrIYuBo4HVgJ3Cki8Zz5Pwe+ClS6r5Wu/AbggKouBH4C3Jrej2KMMSdXFSl2jY9O+ISicYE0PiIyB/gU8IuE4lXAfe74PuDKhPKHVLVXVd8DGoHzRGQmUKKq61VVgfsHnRO/1qPAJTLRJ9UbYwJTNaOYd9u6ONCrNs3aCWrCwU+B7wCJ/woVqtoMoKrNIhJx5bOBVxNet8eVHXHHg8vj5+x21+oXkQ6gFNiXGISIfBWv50RFRQU1NTXj/mB+iUajoYp3sDDHH+bYIdzxhzX2gfZ+Blxy0Z7W96ip2T3CGZkplfXve+MjIp8GWlX1dRFZPppThijTYcqHO+f4AtW7gbsBli1bpsuXjyaczFBTU0OY4h0szPGHOXYId/xhjb28qYO7a/8EwMc+soSl86YHHFFyUln/QQy7XQh8RkR2AA8BF4vIA0CLG0rDfY+nLd4DzE04fw7Q5MrnDFF+3DkikgNMAdrT8WGMMWYkC8qP3ef5/ea9AUaSOXxvfFR1jarOUdX5eBMJXlLVvwWeBK53L7seeMIdPwlc7WawnYI3sWCjG6LrFJHz3f2c6wadE7/W5917nNDzMcaYdKu+5RkWfe/Zo48f3LCL+aufpvqWZwKMKniZtM7nR8ClItIAXOoeo6pvA48A7wDPAjeq6oA75+t4kxYagXeB+L/mL4FSEWkE/hk3c84YY/z2yndW8JkPzyI327sbYHkIPYFmOFDVGqDGHe8HLjnJ634I/HCI8k3ACUvbVbUH+EIKQzXGmKRESgoozs+hP6bkZmF5CB1Lr2OMMWkWz0NYldVCfazC8hBijY8xxqRdPA9hTc0+rltueQghs+75GGOMmSCs8THGGOM7a3yMMcb4zhofY4wxvrPGxxhjjO+s8THGGOM7sawzHhFpA3YGHccYlDEoS3fIhDn+MMcO4Y4/zLHDBzP+eapaPtYLWeMTUiKySVWXBR1HssIcf5hjh3DHH+bYweJPZMNuxhhjfGeNjzHGGN9Z4xNedwcdwDiFOf4wxw7hjj/MsYPFf5Td8zHGGOM76/kYY4zxnTU+xhhjfGeNTwYRkV+JSKuIbE0oO1tE1ovIWyLylIiUuPI8EbnHlb8pIssTzqkRkToRecN9RXyIfa6IrBORbSLytoh805VPF5HnRaTBfZ+WcM4aEWl0sV6WUL7Ufa5GEbnDbZMeltgzvu5FpNS9PioiPxt0rYyu+xFiD0PdXyoir7s6fl1ELk64lq91n4b4x1b/qmpfGfIFfAxYAmxNKHsN+Lg7/grwfXd8I3CPO44ArwNZ7nENsMzn2GcCS9xxMVAPLAZ+DKx25auBW93xYuBNIB84BW8b9Gz33EbgAkDwtka/PESxh6HuC4GLgH8AfjboWple98PFHoa6PweY5Y7PAPYGVfdpiH9M9W89nwyiqi8D7YOKq4GX3fHzwFXueDHwojuvFTgIBLZ4TVWbVXWzO+4EtgGzgVXAfe5l9wFXuuNVwEOq2quq7wGNwHkiMhMoUdX16v1E359wTkbHns4YhzPW+FW1S1X/BBy3nWYY6v5ksQclifi3qGqTK38bKBCR/CDqPpXxJ/Pe1vhkvq3AZ9zxF4C57vhNYJWI5IjIKcDShOcA7nFd3+/50X1PJCLz8f5C2gBUqGozeD/oeL008H7AdyectseVzXbHg8t9Mc7Y4zK97k8mDHU/kjDV/VXAFlXtJeC6h3HHHzfq+rfGJ/N9BbhRRF7H6xb3ufJf4f2AbgJ+CvwF6HfPXaOqZwIfdV/X+hWsiBQBvwO+paqHhnvpEGU6THnapSB2CEfdn/QSQ5RlWt0PJzR1LyKnA7cCX4sXDfEy39bBpCB+GGP9W+OT4VR1u6p+UlWXAr/Fu7+Aqvar6j+p6odVdRUwFWhwz+113zuB3+DTkJCI5OL9AD+oqr93xS1uSCE+rNPqyvdwfE9tDtDkyucMUZ5WKYo9LHV/MmGo+5MKS92LyBzgMeA6VX3XFQdS9y6eVMQ/5vq3xifDxWeMiEgWcAvw3+7xZBEpdMeXAv2q+o4bhitz5bnAp/GG7tIdpwC/BLap6n8lPPUkcL07vh54IqH8ajfefQpQCWx0XfxOETnfXfO6hHMyOvYQ1f2QQlL3J7tOKOpeRKYCTwNrVPXP8RcHUfepjD+p+h/r7Aj7SuvMk98CzcARvL+EbgC+iTcDpR74EceyUswH6vBuEL6Al9YcvNlArwO1eDcEb8fNxEpz7BfhDRPUAm+4ryuAUryJEQ3u+/SEc27G68nVkTCzB2/ixFb33M/inznTYw9Z3e/Am9wSdT9ri0NU9yfEHpa6x/sDsivhtW8AkSDqPpXxJ1P/ll7HBNKI1wAAAeVJREFUGGOM72zYzRhjjO+s8THGGOM7a3yMMcb4zhofY4wxvrPGx5gQE5GvSULCU2PCwhofY3wkIp8VERWRRSm41r8C7ap6IAWhGeMrm2ptjI9E5BG8TMIvquq/BRyOMYGxno8xPnH5sy7EWzx8tStb7vZBeVREtovIg/GEjCKyQ0T+XUQ2i7d/yiJXXije3k+vicgWEVnlyrNF5DZXXisiXztJKMYEzhofY/xzJfCsqtYD7SKyxJWfA3wLb6X+qXgNVNw+VV0C/Bz4F1d2M/CSqp4LrABuc6mWbgA6XPm5wN+79D/GZBxrfIzxzxeBh9zxQ+4xeDnt9qhqDC9dyfyEc+KJHl9PKP8ksFpE3sDbwKsA+JArv86Vb8BLkVKZjg9izHjlBB2AMROBiJQCFwNniIgC2Xg5tf4AJO6HMsDxv5e9Q5QLcJWq1g16DwFuUtXnUv8JjEkt6/kY44/PA/er6jxVna+qc4H38BI7jtVzwE0J94bOSSj/ussqjIhUxTOfG5NprPExxh9fxNsDJdHvgC8lca3vA7lArYhsdY8BfgG8A2x25XdhoxsmQ9lUa2OMMb6zno8xxhjfWeNjjDHGd9b4GGOM8Z01PsYYY3xnjY8xxhjfWeNjjDHGd9b4GGOM8d3/A5FwNZfsUgDmAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"from isoweek import Week\n",
"import matplotlib.pyplot as plt\n",
"\n",
"filename = \"surveillance-epidemiologique-varicelle.csv\"\n",
"colonnes = [\n",
" 'Semaine',\n",
" 'Nombre de cas de consultation',\n",
" 'Inc Low',\n",
" 'Inc Up',\n",
" 'nombre de cas de consultation pour 100,000 habitants',\n",
" 'Inc100 Low',\n",
" 'Inc100 Up',\n",
" 'Code Insee',\n",
" 'Région',\n",
" 'Numero semaine',\n",
" 'geom',\n",
" 'geo_point_2d'\n",
"]\n",
"\n",
"df = pd.read_csv(filename, sep=';', header=None, names=colonnes, encoding='utf-8', skiprows=1)\n",
"\n",
"df.columns = df.columns.str.strip()\n",
"print(\"Colonnes détectées :\", list(df.columns))\n",
"\n",
"df.rename(columns={\n",
" 'Numero semaine': 'week',\n",
" 'Nombre de cas de consultation': 'inc'\n",
"}, inplace=True)\n",
"\n",
"raw_data = df[['week', 'inc']].dropna().copy()\n",
"\n",
"raw_data['week'] = raw_data['week'].astype(str)\n",
"raw_data['inc'] = pd.to_numeric(raw_data['inc'], errors='coerce')\n",
"raw_data = raw_data.dropna()\n",
"\n",
"import pandas as pd\n",
"from isoweek import Week\n",
"\n",
"def convert_week(year_week):\n",
" year = int(str(year_week)[:4])\n",
" week = int(str(year_week)[4:])\n",
" w = Week(year, week)\n",
" return pd.Period(w.monday(), freq='W')\n",
"\n",
"raw_data['period'] = [convert_week(w) for w in raw_data['week']]\n",
"raw_data = raw_data.set_index('period').sort_index()\n",
"\n",
"first_september_week = [pd.Period(pd.Timestamp(year=y, month=9, day=1), freq='W') for y in range(1985, raw_data.index[-1].year)]\n",
"print(\"Nombre de périodes annuelles valides :\", len(first_september_week))\n",
"\n",
"yearly_incidence = []\n",
"years = []\n",
"\n",
"for w1, w2 in zip(first_september_week[:-1], first_september_week[1:]):\n",
" one_year = raw_data.loc[w1:w2 - 1]\n",
" if abs(len(one_year) - 52) < 2: \n",
" yearly_incidence.append(one_year['inc'].sum())\n",
" years.append(w2.year)\n",
"\n",
"incidence_series = pd.Series(data=yearly_incidence, index=years)\n",
"\n",
"print(\"\\n Année avec l’épidémie la plus forte :\", incidence_series.idxmax())\n",
"print(\"Année avec l’épidémie la plus faible :\", incidence_series.idxmin())\n",
"print(\"\\n--- Incidences annuelles triées (valeurs les plus fortes en haut) ---\")\n",
"print(incidence_series.sort_values(ascending=False).head(10))\n",
"\n",
"incidence_series.plot(title=\"Incidence annuelle de la varicelle\", style='*-')\n",
"plt.xlabel(\"Année\")\n",
"plt.ylabel(\"Nombre de cas\")\n",
"plt.grid(True)\n",
"plt.show()\n",
"\n",
"local_file = \"inc-25-PAY.csv\"\n",
"raw_data = pd.read_csv(local_file, sep=\";\", skiprows=1, encoding=\"utf-8\")\n",
"raw_data.head()\n"
"incidence_series.hist(xrot=20)\n",
"plt.title(\"Distribution des épidémies annuelles\")\n",
"plt.xlabel(\"Nombre de cas\")\n",
"plt.ylabel(\"Fréquence\")\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment