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Commit 9a37942f authored by cded6f222a164a22601711b16e547edb's avatar cded6f222a164a22601711b16e547edb 💬
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Analyse_de_l'incidence_de_la_varicelle

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module3/exo2/exercice.ipynb
View file @ 9a37942f
{ {
"cells": [], "cells": [
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
]
},
{
"ename": "TypeError",
"evalue": "index type not supported",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-a619383472b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0myearly_incidence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myearly_incidence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'*'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;31m#<matplotlib.axes._subplots.AxesSubplot at 0x11c090f60>\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/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\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[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 253\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_legend\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/plotting/_core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 975\u001b[0m \u001b[0mstacking_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacking_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 976\u001b[0m \u001b[0mis_errorbar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_errorbar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 977\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 978\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_legend_handle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewlines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 979\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_ts_plot\u001b[0;34m(cls, ax, x, data, style, **kwds)\u001b[0m\n\u001b[1;32m 1016\u001b[0m \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstyle\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[1;32m 1017\u001b[0m \u001b[0;31m# set date formatter, locators and rescale limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1018\u001b[0;31m \u001b[0mformat_dateaxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1019\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1020\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_timeseries.py\u001b[0m in \u001b[0;36mformat_dateaxis\u001b[0;34m(subplot, freq, index)\u001b[0m\n\u001b[1;32m 340\u001b[0m TimeSeries_TimedeltaFormatter())\n\u001b[1;32m 341\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'index type not supported'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0mpylab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: index type not supported"
]
},
{
"data": {
"image/png": 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K4sTCE7n7jLu7uWSmp7AaijEmJdF3yPtys3Jp0iYawg0cPvjwDJXO9ATWKW+MSdkJj5xAi7YkXc9GeR1crFPeGJN2nxr9KcYPHE9utje5tyCMzh9N3+y+gN0tf6izgGKMSdm9Z9/LyaNOpinc1PoslJw8GsON5Gbn2jT2hzgLKMaYdikPlTNp8CQUZdLgSVQ3VnP50Zfz+AWPc/nRl7Ovfl+mi2gyxPpQjDEpizfSy/pMDm7Wh2KMSbtYI73GDxxvfSYGsIBijGmHwv6FzN86v83UK9urt3NG0Rn2HBRjAcUY0z7RI72yJMtGdhnAAooxpp2CI71ys3NRVRvZZQCbesUY0wHloXIuP/pyLjvqMuZumEtZfVmmi2R6ABvlZYwxJiEb5WWMMaZbWUAxxnRaaV0pX5//dWv6OsRZQDHGdJgfSO5eeXfkOfPm0GV9KMaYDos3+7DdOX9wSbUPxUZ5GWPaLdHDtmZOnMmPTvpRN5fI9ATW5GWMaTd/CpZYXtjyAuc9fV43l8j0BFZDMca02/l/Oz9uDWVk/5HM+dycbi6R6QmshmKMabf5l8xnZP+RZJPdJl0QTh93ut01f4iygGKMabfC/oWU1pcSJtwmXVHmrp+boVKZTLOAYozpkHiTRC66fFGGS2YyxQKKMaZDbJJIE8065Y0xHWaTRJqgpDc2isg44BHgMKAFuF9V/0dEhgFPAhOArcDlqlrhtrkJuBoIA/+uqgtc+jTgYaAf8CJwvaqqiPR1+5gG7AOuUNWtbptZwM9ccX6lqrNd+kRgDjAMWAl8TVVjDztx7MZGY4xpv3RODtkM/FBVjwVOAa4TkSnAjcArqnok8Ir7G7fsSuA44HzgTyLiDwW5F7gGONL9nO/SrwYqVPUI4C7gDpfXMOBm4GRgBnCziAx129wB3OX2X+HyMMYYkyFJA4qq7lbVle51NfABMAa4GJjtVpsNfN69vhiYo6oNqroFKAZmiMgoYJCqLlavWvRI1DZ+Xk8BZ4mIAOcBC1W13NV+FgLnu2VnunWj92+MMSYD2tUpLyITgI8D7wAjVXU3eEEHGOFWGwPsCGy206WNca+j09tso6rNQBVQkCCvAqDSrRudlzHGmAxIOaCIyADgaeA/VHV/olVjpGmC9I5skyivtoURuUZElovI8tLS0lirGGOMSYOUAoqI9MELJo+p6t9c8l7XjIX7XeLSdwLjApuPBXa59LEx0ttsIyI5wGCgPEFeZcAQt250Xm2o6v2qOl1VpxcWFqbydo0xxnRA0oDi+iseAD5Q1d8HFs0DZrnXs4BnA+lXikhfNxLrSGCpaxarFpFTXJ5XRW3j5/VFYJHrZ1kAnCsiQ11n/LnAArfsVbdu9P6NMcZkQCo1lFOBrwFnisi77ucC4HbgHBHZCJzj/kZV1wJFwDpgPnCdqvrzM1wL/AWvo34T8JJLfwAoEJFi4Ae4EWOqWg7cCixzP7e4NIAbgB+4bQpcHsaYbmBPaDSx2AO2jDHtduuSW5m7fi6XHX0ZPz/l55kujuli9oAtY0zaRT9Yq2h9EUXri+wJjQawubyMMe3gP1grLzsPgLzsPGZOnMmCSxdkuGSmJ7CAYoxJWWH/QvL75NMQbiA3O5eGcINNCGkirMnLGNMuNiGkiccCijGmXe4+425K60r58Rs/5s7T77TaiYmwJi9jTLvdt+Y+Vu5dyb2r7810UUwPYjUUY0zK4o3y6pPVh+MLj7cayyHOaijGmJTFG+X12YmftRqLsRqKMSZ10aO8QuEQL2x5IbLc7ks5tFkNxRjTLv4or8cveJyLJl/EyP4j7b4UA1gNxRjTTnefcXfk9W2n3cYti2/hqQ1P2X0pxgKKMaZz7L4U47PJIY0xxiSU6uSQ1odijDEmLSygGGOMSQsLKMYYY9LCAooxxpi0sIBijDEmLSygGGOMSQsLKMYYY9LCAooxxpi0sIBijDEmLSygGGOMSQsLKMYYY9LCAooxxpi0sIBijDEmLSygGGOMSYukAUVEHhSREhF5P5A2TEQWishG93toYNlNIlIsIutF5LxA+jQRec8tu0dExKX3FZEnXfo7IjIhsM0st4+NIjIrkD7RrbvRbZvb+UNhjDGmM1KpoTwMnB+VdiPwiqoeCbzi/kZEpgBXAse5bf4kItlum3uBa4Aj3Y+f59VAhaoeAdwF3OHyGgbcDJwMzABuDgSuO4C73P4rXB7GGGMyKGlAUdU3gPKo5IuB2e71bODzgfQ5qtqgqluAYmCGiIwCBqnqYvWe6PVI1DZ+Xk8BZ7nay3nAQlUtV9UKYCFwvlt2pls3ev8HvZaWQ+eBaMaY3qWjfSgjVXU3gPs9wqWPAXYE1tvp0sa419HpbbZR1WagCihIkFcBUOnWjc7roDfpJy/y47mrM10MY4w5QLo75SVGmiZI78g2ifI6sEAi14jIchFZXlpaGm+1XiHUFAZg7oqdSdY0xpju19GAstc1Y+F+l7j0ncC4wHpjgV0ufWyM9DbbiEgOMBiviS1eXmXAELdudF4HUNX7VXW6qk4vLCxs59vsWapDzclXMsaYDOloQJkH+KOuZgHPBtKvdCO3JuJ1vi91zWLVInKK6wO5KmobP68vAotcP8sC4FwRGeo6488FFrhlr7p1o/d/UGvR3tN/sn1fHWU1DZkuhjGmG+UkW0FEngD+BRguIjvxRl7dDhSJyNXAduAyAFVdKyJFwDqgGbhOVcMuq2vxRoz1A15yPwAPAH8VkWK8msmVLq9yEbkVWObWu0VV/cEBNwBzRORXwCqXx0Ev3Is65D/zu1fJzhI2/fqCTBfFGNNNkgYUVf1SnEVnxVn/NuC2GOnLgY/FSA/hAlKMZQ8CD8ZI34w3lPiQ0psCCvS+8hpjOsfulO9FUmnyqmlo5qG3t6C9qHnMGHNwSFpDMT1HcwpX/L96fh1zlu1gUuEATj+qdw9CMMb0LlZD6UVSuamxsq4JgPpGGxFmjOleFlB6kXAKzVhZ7hM9GLovrp+zisvu+2emi2GMSZE1efUiqXRyuzk3e9UQ43iefTfu7UXGmB7Iaii9SEtL8nWyIgGliwtjjDFRLKD0Is0pRBR/Xhob5WWM6W4WUNrhwbe2cNWDSzO2/1SasbJcRDkU4smKbeVMuPEFiktqMl0UYwzWh9Iutzy/LqP7D7eryevgjyh/X+X1sbxdXMYRIwZkuDTGGKuhdECm7gBPab+uhpLKPStdpbua2/z3mJ0VawLqQ1NFbSMrtkU/vsiY7mEBpQP21zdlZL+pNXl5J9emVKozXaS7Am6ze499srs2oOypCvHO5n1duo9kHntnGzPveTPpel/5yztceu/ijPahNYdb+Mubm6ltODjuhapvDCdfyQAWUDqkKkMBJZUTtX+x3ticuYDSXbWjcKSG0rmvsapyw1Nr4gaNrz+0lCvuXxJ5Hk1HdeZpmz995n3W7tpPdSjxd2/d7v0AhJoy9/k/vXInv3rhA+5/Y3PGypAuG/dWc+x/zee51TaEPRUWUDqgMkFAeem93azeUdkl+00toHgRJZMBpbv7bzpbPymraeTJ5Tu45q8rYi7/qKIegO3ldR3ex+odlUz6yYss3tS5mk5ZTWNK69V1YqaEW59fx7/OXt7h7ffu9x5bkOnJQRubW/jt/A+pquv4BeCOCu8zL1q+I8maBiygtEv/3GwgcQ3l2sdWcvEf3+6S/ad2Y6P3O5MBpbtOJOnay/u7qgAYP6x/zOUFA3IBr+mro5Zt9fo1Fqzd0+E8gKQ1FF9dJ5ppHnhrCy9/sLfD2/t9WrEuLFpalHPvep0H39rS4fxT9VZxKX96bRNff7jjIzP9G4UPgTEuaWEBpR3653qD4jLW5NWOb3XDIRBQfKncn5NIZZ131T80Pzfm8pxs79+kMyfpAX29705n+xWSPbXTb/LsbPNcZ/gBJdb3oKKukQ17a7plxGS/Pt4x31Fe3+E8GlzT4aEwajIdLKC0Q7IaSlf/E6fSBt8c9tZZtaMiYx2zwRNJOsoQ7337eTeGO7eP+kbvpBGvbz/HnSDrmzoeDLKy0jODQWOSwRb+ybwzwa+zst1Vfay+tFA3XuiEmr1j0Jlg0JCGPNJBW1oIN/f8QQ4WUNrBDyjxRnlVdqKtNhWp1FD8f+K3i/cxZ1nH2n13VtTx0nu7O7QttA0oHa2ttMkjzvv2UzvbvOf3N8Tr3Pf7pdJxktZONtQlu6iQNJY13r6awy38beXOuMuzEtRQunPEVDpqF/5FYqanMlr61H+T/asCyvb07L4cCyjt4J9Y4rWlV9Sl1mHaUamcnIPDhdfvqe7Qfv519nKufWxlh5tnggGgoyO+gu+jOUkNpLNDpP2TRnac/wa/X6ozJ8NIE2QnT0zJvgN+7aAztSlffZwa95/f3MIPilbz7OqPYi73A02ssnZnU5xfu+hME6w/Wi7TUxkN2/AkAOW7NmW0HMlYQGkH/8S1qTT2VB/prKEUlxwYDFJpSgqefKWDw5/8wLirsmNtz8EyJGuiiSfYB5Ssj6QphRpKfWOY5Vtj3/BX25j4KtQ/1J0KKE3paTpJdnL0+1DSUUOpjTNSbEtZTcJ9NLnPK2aTVzsCSmeGWUNrDaUzh7y+h9RQRN176WR/YVezgNIO/j9IeW3smkhVfWt6vGaY9z+q4tl3Y1/Z+d7aWMbZv3+Dp1fsbJMePJnE+4IHT77SwQG1eX28pr2aJDWUqvqmmLWY4EkzWe0inmCtI95J1E9PJWh9+9EVfPG+xeyPMUrKDxTxPjO/LHWduLr2T6SdPS8la/bMSmMfSl1D7DxqXXo/9z2J5n/msQJCqvfH3PLcOmb8+pWkNYNdlfVx1wmlpYaS2oXA7S99yCOLt3ZoHyvnP8y7r8xJuI64b05TqGfPW2cBpR38E8u+OAElWEOJdzX7rUeWc/2cdxPeJ7CryqsZvF1c1iY9+KWO90/SlIYaSq5r+0kWUE745T84/XevHpAevDJt7mANJRhQmuIEJT8AJOtD+aiynjc2lAKxR1n5n0W8fPyTc6IayrPvfsTZv3897snNr3F1tnkueQ3F+9AT1QRSrSX4J+RoflCOFxwaE7zXVPf94NtbKKtp4Pk18fvytu2r5VO3L+K+12PfQOnvqz2jIw/Mw3sPDUkC4X2vb+K/nl0bc9nazR+x5r3Vcbf9xJLrOfHNbycpiQsodfuTrJdZFlDawf8HKa9tjHn1FbzhsSZOwNjt+l/2JbhBrW+O97FEX3kH/4x3YunsEFqAXLf/miRDVCH2jXbBY9OUwtXho0u28bsFH7ZJC57c4wdPb539Scp549NrIq9rY1x1+wGjIc4J1A84iS4Cfli0muKSGvbXx17HP7klukJvaA7z6oclCa/Kk10p+6O8Yr1P8GqVx/x8Pn98tThhPonK6jeJ1jTEbuL1R0HGGroe7JeJd7wBRg3OA+CD3fFPoB+5JtlX15fEXB7plE9DDSVef1JwnZhlaA6z5MEfMvGp8zpcBoAs97k311tAOWj4Vflwi8ZsOgnWUJLN95WoAz/e6JQ2zUBxTizBq/lEV+7FJTX88dXimCcvP6BVJ6ihJPonDZYhlf6Nn/39ff74atvOxrY1lDhXwi69sh2DIWIFhUiTV5z9RGooCYJBjhtzXFbbEHO5f3JOVMt5+O2tfOPhZSxcF/+mwmQVHL8PZe/+2ANHSqu98t3zysbEGdHa7xOvDDVxgla5+zxiBYzgyTde0APId/ftxGtehtbvd7zvol/Dam7RuOtUh5oSTqbpv4dEFxOJHp+wo7yech3IQKmnIdTxmRb8GkpLKP5Am+0b3qW2umtm6UiVBZR2aGxuYZi7+S3WFz14YkvWQZ/oH8VvaorufwgGiHhNScH0RFeAX/nLEn63YH3Mq/tUaijBgBgdXIMn5ngdu8kEr47jXQH6xyPZ6LrCAX1by5OghhLrZB9u0ciVdn2C9+L3V8W7kEjlStcfBJFoipdkV9v+xciOOHn4d9rHu/E1+B2Ld8+I/17ijQL0/w9i1XCCzaiJvl/+/0+i/xN/nXi1trLq1m3jHfefPPM+l967mJLq2AHYfw+J+qT+/YlVcZft3R+igoEAVO1LPPtAXU1V3GV+H0pLQ+zgFW5uZvzjp7PtnpkJ99HVLKC0Q30YMsa5AAAYWklEQVRTmDFD+gHxAkrrySTZ3fSJAo5/NRTd1BM8Uce7YzrYf5Hobnm/qSrWCbCP60NJdDdzsKkrejRY8KTUns7h4Ak9+P7i1ZT895csePtXuxB7OK3f2R6rbyx4ZZrovfj3l8RrfvNPzqn0IUTnEQwiyfoD/GOyO87Q9mR32geDRLwain9M4gWU8lo/aB24/S+fa/1OxarlgzeC0b9ISBRQ/HXijfvYUlYbeR3vwub9j7yT+KaS2pjLIxcCCT77RH2GpdUNlKsXUKrLDwwoofrW/a595bG4+8hWV/6G2E1eFWXe5JVTmt6Pm0d3sICSguVby/nPp1bT3KKMHuK17cY6+VTWNzLatf3GOlEHm5cS11BiX80G/8HjBaymsEam+Th+7JC4+/CDVayTcbADMl4tx286gdbJE33BgJKsYz9oX6C5KHiyiXfi8v/ZkwWU4Paxaihl7r1U1jUd0LzWZqBFnBPsP4vLIley8ebaakihhlLu9rWnKup4pjDiDbyTmb9uvO9HMKDEau4Mfl7xaih+P1G8QO/XUKLf65yl2+Puq03+oebI+yxPUPv0P5tYtXVV5YM9+8l3NyPHG7GWrInQH1Le3KJxm5DbXEBFveeymgYqXECprzwwoNRUtTa3nbTqprjDgvPU+05kNcauoVTs2RYzvbtZQEnBF+9bTNFybwjvqMFeDSV6BtO9+0Ms2VweuXKPFXCC/0CJ2v39ZdFXcA2Bf5x4J4zmcAvHjR4EtE4ZkkisfIIngpL9sfsEymoCASW6hhJu3T7eP7IvGLCCQTYYkOM1jfjl3BPnZOCrbWxmUJ4XZKOv0Osbw3xUWR/pBI4eLOFfBWdJ/KvUe19v7f+J2ynvTjqJ+g1KXbNLdO0ieKxfTDCDgf9ZZkn8q/9gwFu2tSJuHhC7hhJqCkeOe7zmPf9zjL7QuPFv77X5O97nWuG2z8/NTtLk5S0LXtz4ymoaqQ41c+J476IqXvDyQ2q8Gt2+wLGP9fmrKvtqGxjavw9w4MVPaXUD+1yTV8P+0gO2r69p2+exd2fsGxfz1WvCzGqKXZOqLW29g37Fiw/EXKc79OqAIiLni8h6ESkWkRu7Y5/j3Iy0wX/Y51bv4uRfvwJ4V5OD8nLYXXXgTYGPLG69iiiNM8qrOtQUmTIl+uQUCnyh4/0z1zY0M7if9+VO5S71WFeAtQ3NDHQn4HhXbsF/wGDTArS9YkvWh7K7sjWf4Mk8lSavYBv9kgQPwKptCHN4QT7Q9uQMsNDNqnvShGExl/tXwaOH9Ivb5BU8ocU7kVfV+RcaDXGvdEtcPsu3VrQ5kZUE8v9ngunvK1xZxw/rT01Dc8z+lmBZV2w7MKA8GZiuJ1YNZdu+upivffWNYRqaWyjIz6WirikSHKC1NvC9M48A4p/k/SA+ecQAquqb4vYXlrpjVFLdcMDJ3r/5+ARXS4/XYuBfMEXXCsGrDW7bVxep8ZfWHBi49oeaaQpr5PsV/T+7Z3+IUB+vDM3VBwaUULX3GSwe9y0APnrv9QPL0dxMf/H2nR0noDRUtN6zlr/qLzHX6Q69NqCISDbwR+CzwBTgSyIypSv2NaHACyJTxwzmSzPGAUQeuLNtXy3fC3TKvfyDzzBmaP8Dpj0pqQ7xuwXrI38/sXR7m1rOC2t2M/OeN5n6i39E0kqrGyInhZXbK5i9eFtkBFask8HSLeXsqgoxtL83cCDe6KjH32lteojVeVtW0xCp5cS6+m9pUf6xbg+ThudzeEF/Xnqv7ZTswWCQbPqW4LDQ4Mk8OAS7rKaB/3r2fbYGApeqUhNq5isnjyc3J4tnVsa+WTTUFGbP/hCHDc5jaP8+B3S+/sNNJ3/WsSOAAwOof3IbPbhfzJE+b2wo5UP3WedkCbe/9GGbkyh4n+PqnVWu3LE73Vdtr2BzaS0njhtCfVOYh97eGlkW3UcVr2P+yWXe53rMYYNQPbA2pqosWl/CiIF9GTmoLxtjzMbw1yWtFz2xaig//7vXRn/Jx8ewvbzugGG9/gXKtMOHAvDBntbl+X1z+Oop45n1qQlA/Pu5/AuLyYUDUG0NlEGNzS28GPjebd3X9kTr//+deYz3ua7fe+B73VlRHwlqsb7na3ZWUtPQzJdPHg+0PrwsaJvb74njvKDxUWXbz3bv/hAjRowCoKX2wIuB0H7vXrOBR3+GOu1L09YlB6xTuntr5HWf5jgjyko+oE77smzweQxvavswsHVL5lO8umseqRGt1wYUYAZQrKqbVbURmANc3BU7+uvVJ/OP73+G5753Gv1zcxjSvw+rd1bxtQfe4fTfvRZZ74lvncIRIwby6SOH886Wcv7tsRVU1Tfxy+fWMuM2rwbz6NUnc+VJXlA64ZZ/8IMn3+WtjWVc9/hK1u5q/cLefslU6pvCPLViJ7sq6/m2e/jTzKmjOGfKSP7y1hZ+MW8tr60vYX+oiaVbyrn8/xYDMGZoP7LEu3u3uKSG+sYwqsqbG0s57Y5F/OSZ1qaH3y1Yz4a91YSawuysqOPs37/O/lAzp0wqALwmllBTmFCTl8efXitm0k9eZNX2Sq44aRyfnFTAnv0hVm2voDncQnltY+Rk2CdbeH1DKaGmMC0tiqo3fPP5NbsoWraDk257mWsfWxkpy4vv7aY53ML7H1Vx3+ubGDmoL2OH9uO389fzyOJt/PzZ91H18nmruIz6pjATh+fzuamjeHL5Dp5ZtTPSL7Crsp7bX/qQY34+n+KSGkYPzmPEwDyeXbWL/3l5I+v3VPOTZ97j+TW7OXHcEM46diR9soW3i/cRblEqahu54ak1XD/nXQA+cfhQymoaKVq2gxXbvPdaXFLNVQ96z9qYODyfr55yOOA94XFXZT1/XbKNbz2ynJNuexmASz8xFoDbXljHa+tLqG1oZu2uKm59fh1f+NM/ATj3uJFMHTOYF97bzf5QEyXVIb77+CqGD8jljkunAjDpJy/y7o5K9tU0EGoK8/DbW5hw4wv8+U3vGSOnH10ItM44vacqxHWPrWTiTd7nVjCgL0eNHMhbG8si/VAVtY38oMh7r189ZTz9c7NZub0i8rmHmsLc8NQalrrpa7552kQAfjR3NS0tGpk14e+rvMB+0Ymjyc3O4lfPf0BxSQ0n3fYy1aFmJhTkU5Cfy8C+Odz6/Dre3VFJc7glUrN7/6MqvvuE9504+9iRXtquqsjnurOijrP++zWO+tlLAFwx3ftfWu3yCTWF+c2LH3DzvLWMHdqPT4wfyvhh/Zm3ehdvbCgl3KLsq2ngj68Wc+5dbwBejW7plnKq6ptocX0lP//7+3z38VX0zcniqk8eTm52Fsu2lEfKUVxSw72vbeKiP3gn6gtPGB1J97+jr64vYcnmcsYWDKSKfPpWtF5QAmx+/x108Z8AOOyIE9mUdxxjyt6mxTUZt4TD7NryIXue+C4tKmzKnsjohk3U13rBUVtaqCov5Z25/83JZX9jW+5kwgVHM5xKdmxcTVVFGYv/7zqmzL+CCX/7HI0NHX+eT6ok05OedZSIfBE4X1X/1f39NeBkVf1uvG2mT5+uy5d3/El0vuKSGq55ZDmbA1fMq35+TuR5Gs3hFmY9tJS3i1uvSHJzsvjyjPH84qLjAPjGQ0t5df2BVWCAWz//Mc4+dgQX/u/bba7aP/uxw7jrihP5cE81n4/zEK+jRg7gD1/+BA++taXNbMP9+mRH2r4PG5THmceOQNWrKYF38vfvH5k0PJ+nrv0Uv5i3lnlxHn36w3OO4rtnHsHqnVUxy3LKpGGcdsRw7vzHBsC7t6W5RWN2Kv/PlSey6MMSnn13F31zsiIjlS48YTSfPnI4tz63LtLsNTAvB6F1JNTC73+G/aFmLr33n5H8BuXlHDBS6pUfns4rH+zl1y+2vYES4DeXTOVLM8Yz68GlvL4h9mfy+LdO5st/fifuPpbcdBaHDc7jjvkf8uc3Nsdsblz7y/OYvXgrv52//oBlACeMG8Lsb5zEX97cwh+ibjy887ITuOiE0Zx028sJRxDefslUzjvuMD5+68K467z5n2fwz01l3PC0d2GRm5PVphnu6Ws/xWvrS/jfRV4ZhvbvQ01Dc+T78di/nsypRwxn5j1vsnbXfrKzpM3nOmXUIJ773mnMWbadnz7TdtTRip+dTcGAvry+oZRZD7Z98FWWtE4pNHPqKH5z6VROvX0R1aFmssSbBSB4XI8fO5i//9upfPq3rx7Qjwfw56umc86Ukfzx1eI2rQNB1591JGOG9uM/n/JufhVpO/fXTZ89hm+fPpnvPr6S59fsZmDfHLKypM1nMPP4UfzhSx/nnLveiNyT4n+Ps7OE+746jRHPfY0T6t+hSbOpkf5kE2YQXm3mwz5TOOani1n27J84adVNANRrLopEmroWj76KgceezZSXZ1FHHjWSzwCtZYC0vu8PPltEv0EFFM6ZSb4cGDyKv/AiR5xwaszjkIyIrFDV6UnX68UB5TLgvKiAMkNVvxe13jXANQDjx4+ftm1b+kZDVNY18tzqXXz55MMjdyj7Qk1hXnxvNxv21jBpeD6XTR8bmVocvOaoJZv38VZxGYUD+nLakcM55rBB7KyoY8yQfogI+0NNvLhmNyu3VzBjYgGXfHxMZK6mjXureeXDEvbuD6EK/XKzOfvYEUw73OsLUFWKS2p4YukOsrO8kWOHDcpj5vGjOGLEgEg51uysZPWOSj6qDLE/1MT5xx3GZ44qjJTxjQ2lrN5ZRd+cLKpDzUwc3p/Lpo2LlAPg9Q2lrNhaTkO4haH9c5l++FBOHDeEnOwsXl63l5XbK7w2fVVys7MpGJDLp48czlEjB0bmDQs1hZmzdDubSmvJyRbOOXYkHx8/lH5ulE64RXly2Q4+3LOfhqYWcnOyuOKkcXxszGAAtu+r469LtgLee+2fm80pkwo4bFAeU8cOjuRRtHwHbxeXUVnXxL+dMZnRg/sxYbjX/l1SHaJo2Q5CTS00NIfJ75vDhSeMJkuEicPzeWLpdhau20ttQzNjhvYjW4QzjhnBeccd1ubz37i3mr+t+oh7X9vEr78wlRkThzG5MB8RQVWZt3oX89/fw7hh/Vm5rYIpowdx7b9Mjgz4CDWFeeCtLSz6sIRxQ/tx8YljOMM13TSHWyhavpOPKuvIy8mmtjFMQX4ul08fR062RIZIr9xewbx3d1Hb0Ez/3Gy+8ImxHDFiQKQ/INyi/G7BekJNYfr2yaKpWZlUmM9Zx45g1OB+tLQoTy7fwbvbK2lqaaFvThbD8nP59umTGZTX2gH9wprdbC6rpX9uNs3hFo4ZNYhTjxge6cdbuqWczaU15GRncdEJoyP3OPnHe+G6vZTsb2BHeR2D+vVhQkF/jhw5kBPGDWFA3xx2lNfxzKqP2F/fRGO4hX652RQO6MuXZoyPvNcPdu/niaXb2VMVYuLwfKaOHcyxowYxuXBA5Hj++Y3NLFpfwpRRg6hvCjNl1CBmHj8qcsyfX7OLjXtraHG1sYnDB3DOlJEUDuwb+V9/+J9bqaxroqG5hX59srnoxNFtjunWslqeX7OLsppGwi3KhOH5nHbEcI4+bCDVVeWsffYupGobSDZIFprdl6zCo5h06qUMP2wc2tLCO4/8DNm/A83xypU14hgGTziRo6efCcDqV+cSWjefrMYawrkDYcg4codPYspnLiGvn/c93rFxNTtffxhByDnsGI4/5yqamxroP2AwHXUoBJRPAr9Q1fPc3zcBqOpv4m2TrhqKMcYcSlINKL25D2UZcKSITBSRXOBKYF6Gy2SMMYesnOSr9Eyq2iwi3wUWANnAg6oae7pPY4wxXa7XBhQAVX0ReDHT5TDGGNO7m7yMMcb0IBZQjDHGpIUFFGOMMWlhAcUYY0xaWEAxxhiTFr32xsaOEJFqIPYcDJ7BQPzHpsF4YHuC5ankkWx5uvJIVtbuKEdvKWcq63THZ99bypmOPHpLOeHg+Y52Zh9Hq7oHuyTiT2R2KPwAy5Msvz/J8tIU9pEsj4TL05hHwrJ2Rzl6Szl7ymffW8qZpvfaK8qZSll7y3e0M/tIdu70f6zJq63nkiyvTLI8lTySLU9XHsnK2h3l6C3lTGWd7vjse0s505FHbyknHDzf0XTsI6FDrclruaYwH01Xbd+dektZrZzpZeVMv95S1q4sZ6p5H2o1lPszvH136i1ltXKml5Uz/XpLWbuynCnlfUjVUIwxxnSdQ62GYowxposc8gFFRB4UkRIReT+QdoKILBaR90TkOREZ5NL7iMhsl/6B/wwWt+w1EVkvIu+6nxEZLGeuiDzk0leLyL8Etpnm0otF5B4JPvWrZ5Wzq4/nOBF51X2Oa0Xkepc+TEQWishG93toYJub3HFbLyLnBdK77JimuZxddkzbW04RKXDr14jIH6Ly6urvaDrL2pOO6TkissIduxUicmYgry49phGpDAU7mH+AzwCfAN4PpC0DTnevvwnc6l5/GZjjXvcHtgIT3N+vAdN7SDmvAx5yr0cAK4As9/dS4JOAAC8Bn+2h5ezq4zkK+IR7PRDYAEwBfgvc6NJvBO5wr6cAq4G+wERgE5Dd1cc0zeXssmPagXLmA6cB3wH+EJVXV39H01nWnnRMPw6Mdq8/BnzUXcfU/znkayiq+gZQHpV8NPCGe70QuNRfHcgXkRygH9AI7O+B5ZwCvOK2K8EbTjhdREYBg1R1sXrfskeAz/e0cqazPPGo6m5VXeleVwMfAGOAi4HZbrXZtB6fi/EuJhpUdQtQDMzo6mOarnKmqzzpKqeq1qrqW0Cbh59303c0LWXtah0o5ypV3eXS1wJ5ItK3O46p75APKHG8D1zkXl8GjHOvnwJqgd14d6TeqarBk+dDrtr78y6rUqZWztXAxSKSIyITgWlu2RhgZ2D7nS6tp5XT1y3HU0Qm4F3dvQOMVNXd4P1D49WcwDtOOwKb+ceu245pJ8vp6/JjmmI54+nW72gny+rricf0UmCVqjbQjcfUAkps3wSuE5EVeFXNRpc+AwgDo/GaE34oIpPcsq+o6lTg0+7naxks54N4X5rlwN3AP4FmvOputO4Y5tfeckI3HU8RGQA8DfyHqiaqbcY7dt1yTNNQTuiGY9qOcsbNIkZal3xH01BW6IHHVESOA+4Avu0nxVitS46pBZQYVPVDVT1XVacBT+C1Q4PXhzJfVZtcE83buCYaVf3I/a4GHqd7mhlillNVm1X1+6p6oqpeDAwBNuKdvMcGshgL7IrOtweUs1uOp4j0wftHfUxV/+aS97omAr/5pcSl76Rt7ck/dl1+TNNUzi4/pu0sZzzd8h1NU1l73DEVkbHAM8BVquqft7rt/94CSgz+SA0RyQJ+BtznFm0HzhRPPnAK8KFrshnutukDfA6vmScj5RSR/q58iMg5QLOqrnPV42oROcVVza8Cnu1p5eyO4+ne/wPAB6r6+8CiecAs93oWrcdnHnCla5OeCBwJLO3qY5qucnb1Me1AOWPqju9ousra046piAwBXgBuUtW3/ZW79f8+3b38ve0H74p5N9CEF8mvBq7HG1GxAbid1htABwBz8Tq81gE/1tZRICuANW7Z/+BG1mSonBPwZlX+AHgZODyQz3S8L/0m4A/+Nj2pnN10PE/Dq/avAd51PxcABXgDBTa638MC2/zUHbf1BEbJdOUxTVc5u/qYdrCcW/EGcNS478qUbvqOpqWsPe2Y4l2s1QbWfRcY0R3H1P+xO+WNMcakhTV5GWOMSQsLKMYYY9LCAooxxpi0sIBijDEmLSygGGOMSQsLKMb0ECLyHRG5qh3rT5DArM7GZFpOpgtgjPFuklPV+5KvaUzPZQHFmDRxE/jNx5vA7+N4N3JeBRwL/B7vxtgy4OuqultEXsObv+xUYJ6IDARqVPVOETkRb0aB/ng3o31TVStEZBreHGh1wFvd9+6MSc6avIxJr6OB+1X1eLxHG1wH/C/wRfXmMnsQuC2w/hBVPV1V/zsqn0eAG1w+7wE3u/SHgH9X1U925ZswpiOshmJMeu3Q1nmUHgV+gvewo4VuZvNsvKlpfE9GZyAig/ECzesuaTYwN0b6X4HPpv8tGNMxFlCMSa/ouYyqgbUJahS17chbYuRvTI9hTV7GpNd4EfGDx5eAJUChnyYifdzzKuJS1SqgQkQ+7ZK+BryuqpVAlYic5tK/kv7iG9NxVkMxJr0+AGaJyP/hzQb7v8AC4B7XZJWD9zCxtUnymQXcJyL9gc3AN1z6N4AHRaTO5WtMj2GzDRuTJm6U1/Oq+rEMF8WYjLAmL2OMMWlhNRRjjDFpYTUUY4wxaWEBxRhjTFpYQDHGGJMWFlCMMcakhQUUY4wxaWEBxRhjTFr8P9YfSc/wt3/2AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import isoweek\n",
"\n",
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n",
"\n",
"raw_data = pd.read_csv(data_url, skiprows=1)\n",
"raw_data\n",
"\n",
"raw_data[raw_data.isnull().any(axis=1)]\n",
"\n",
"data = raw_data.dropna().copy()\n",
"data\n",
"\n",
"def convert_week(year_and_week_int):\n",
" year_and_week_str = str(year_and_week_int)\n",
" year = int(year_and_week_str[:4])\n",
" week = int(year_and_week_str[4:])\n",
" w = isoweek.Week(year, week)\n",
" return pd.Period(w.day(0), 'W')\n",
"\n",
"data['period'] = [convert_week(yw) for yw in data['week']]\n",
"\n",
"sorted_data = data.set_index('period').sort_index()\n",
"\n",
"periods = sorted_data.index\n",
"for p1, p2 in zip(periods[:-1], periods[1:]):\n",
" delta = p2.to_timestamp() - p1.end_time\n",
" if delta > pd.Timedelta('1s'):\n",
" print(p1, p2)\n",
" \n",
"sorted_data['inc'].plot()\n",
"\n",
"#<matplotlib.axes._subplots.AxesSubplot at 0x1198af710>\n",
"sorted_data['inc'][-200:].plot()\n",
"\n",
"#<matplotlib.axes._subplots.AxesSubplot at 0x11bc57c18>\n",
"\n",
"first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
" for y in range(1985,\n",
" sorted_data.index[-1].year)]\n",
"\n",
"year = []\n",
"yearly_incidence = []\n",
"for week1, week2 in zip(first_september_week[:-1],\n",
" first_september_week[1:]):\n",
" one_year = sorted_data['inc'][week1:week2-1]\n",
" assert abs(len(one_year)-52) < 2\n",
" yearly_incidence.append(one_year.sum())\n",
" year.append(week2.year)\n",
"yearly_incidence = pd.Series(data=yearly_incidence, index=year)\n",
"\n",
"yearly_incidence.plot(style='*')\n",
"\n",
"#<matplotlib.axes._subplots.AxesSubplot at 0x11c090f60>\n",
"\n",
"yearly_incidence.sort_values()\n",
"\n",
"yearly_incidence.hist(xrot=20)\n",
"\n",
"#<matplotlib.axes._subplots.AxesSubplot at 0x11c2cfa90>\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
...@@ -16,10 +147,9 @@ ...@@ -16,10 +147,9 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 2
} }
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