diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5b58a3ae26346460dd39e62a39c55243d7..b1dfbd3b84cbd0287f82f39dc5b165c559da6b48 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,10 +28,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" @@ -61,9 +59,976 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02021363130689214.016922.02014.026.0FRFrance
12021353126729277.016067.01914.024.0FRFrance
22021343130139481.016545.02015.025.0FRFrance
32021333103927042.013742.01611.021.0FRFrance
420213231558611009.020163.02417.031.0FRFrance
520213131885513664.024046.02921.037.0FRFrance
62021303139919695.018287.02114.028.0FRFrance
72021293136269618.017634.02115.027.0FRFrance
8202128386365430.011842.0138.018.0FRFrance
92021273106936838.014548.01610.022.0FRFrance
10202126370864109.010063.0116.016.0FRFrance
11202125379425540.010344.0128.016.0FRFrance
12202124348553011.06699.074.010.0FRFrance
13202123367104455.08965.0107.013.0FRFrance
14202122378795495.010263.0128.016.0FRFrance
15202121378275403.010251.0128.016.0FRFrance
162021203102787540.013016.01612.020.0FRFrance
17202119395396860.012218.01410.018.0FRFrance
182021183121359165.015105.01814.022.0FRFrance
192021173120588891.015225.01813.023.0FRFrance
2020211631650512735.020275.02519.031.0FRFrance
2120211531930615398.023214.02923.035.0FRFrance
2220211432107317099.025047.03226.038.0FRFrance
2320211332641322094.030732.04033.047.0FRFrance
2420211233065825919.035397.04639.053.0FRFrance
2520211132498820718.029258.03832.044.0FRFrance
2620211031953915951.023127.03025.035.0FRFrance
2720210931757213926.021218.02721.033.0FRFrance
2820210832088216907.024857.03226.038.0FRFrance
2920210732239318303.026483.03428.040.0FRFrance
.................................
189419852132609619621.032571.04735.059.0FRFrance
189519852032789620885.034907.05138.064.0FRFrance
189619851934315432821.053487.07859.097.0FRFrance
189719851834055529935.051175.07455.093.0FRFrance
189819851733405324366.043740.06244.080.0FRFrance
189919851635036236451.064273.09166.0116.0FRFrance
190019851536388145538.082224.011683.0149.0FRFrance
19011985143134545114400.0154690.0244207.0281.0FRFrance
19021985133197206176080.0218332.0357319.0395.0FRFrance
19031985123245240223304.0267176.0445405.0485.0FRFrance
19041985113276205252399.0300011.0501458.0544.0FRFrance
19051985103353231326279.0380183.0640591.0689.0FRFrance
19061985093369895341109.0398681.0670618.0722.0FRFrance
19071985083389886359529.0420243.0707652.0762.0FRFrance
19081985073471852432599.0511105.0855784.0926.0FRFrance
19091985063565825518011.0613639.01026939.01113.0FRFrance
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19111985043424937390794.0459080.0770708.0832.0FRFrance
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191419850138548965918.0105060.0155120.0190.0FRFrance
191519845238483060602.0109058.0154110.0198.0FRFrance
1916198451310172680242.0123210.0185146.0224.0FRFrance
19171984503123680101401.0145959.0225184.0266.0FRFrance
1918198449310107381684.0120462.0184149.0219.0FRFrance
191919844837862060634.096606.0143110.0176.0FRFrance
192019844737202954274.089784.013199.0163.0FRFrance
192119844638733067686.0106974.0159123.0195.0FRFrance
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192319844436842220056.0116788.012537.0213.0FRFrance
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1924 rows × 10 columns

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\n", + "1902 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1903 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1904 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1905 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1906 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1907 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1908 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1909 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1910 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1911 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1912 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1913 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1914 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1915 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1916 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1917 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1918 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1919 198448 3 78620 60634.0 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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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22021343130139481.016545.02015.025.0FRFrance
32021333103927042.013742.01611.021.0FRFrance
420213231558611009.020163.02417.031.0FRFrance
520213131885513664.024046.02921.037.0FRFrance
62021303139919695.018287.02114.028.0FRFrance
72021293136269618.017634.02115.027.0FRFrance
8202128386365430.011842.0138.018.0FRFrance
92021273106936838.014548.01610.022.0FRFrance
10202126370864109.010063.0116.016.0FRFrance
11202125379425540.010344.0128.016.0FRFrance
12202124348553011.06699.074.010.0FRFrance
13202123367104455.08965.0107.013.0FRFrance
14202122378795495.010263.0128.016.0FRFrance
15202121378275403.010251.0128.016.0FRFrance
162021203102787540.013016.01612.020.0FRFrance
17202119395396860.012218.01410.018.0FRFrance
182021183121359165.015105.01814.022.0FRFrance
192021173120588891.015225.01813.023.0FRFrance
2020211631650512735.020275.02519.031.0FRFrance
2120211531930615398.023214.02923.035.0FRFrance
2220211432107317099.025047.03226.038.0FRFrance
2320211332641322094.030732.04033.047.0FRFrance
2420211233065825919.035397.04639.053.0FRFrance
2520211132498820718.029258.03832.044.0FRFrance
2620211031953915951.023127.03025.035.0FRFrance
2720210931757213926.021218.02721.033.0FRFrance
2820210832088216907.024857.03226.038.0FRFrance
2920210732239318303.026483.03428.040.0FRFrance
.................................
189419852132609619621.032571.04735.059.0FRFrance
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189919851635036236451.064273.09166.0116.0FRFrance
190019851536388145538.082224.011683.0149.0FRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202136 3 13068 9214.0 16922.0 20 14.0 \n", + "1 202135 3 12672 9277.0 16067.0 19 14.0 \n", + "2 202134 3 13013 9481.0 16545.0 20 15.0 \n", + "3 202133 3 10392 7042.0 13742.0 16 11.0 \n", + "4 202132 3 15586 11009.0 20163.0 24 17.0 \n", + "5 202131 3 18855 13664.0 24046.0 29 21.0 \n", + "6 202130 3 13991 9695.0 18287.0 21 14.0 \n", + "7 202129 3 13626 9618.0 17634.0 21 15.0 \n", + "8 202128 3 8636 5430.0 11842.0 13 8.0 \n", + "9 202127 3 10693 6838.0 14548.0 16 10.0 \n", + "10 202126 3 7086 4109.0 10063.0 11 6.0 \n", + "11 202125 3 7942 5540.0 10344.0 12 8.0 \n", + "12 202124 3 4855 3011.0 6699.0 7 4.0 \n", + "13 202123 3 6710 4455.0 8965.0 10 7.0 \n", + "14 202122 3 7879 5495.0 10263.0 12 8.0 \n", + "15 202121 3 7827 5403.0 10251.0 12 8.0 \n", + "16 202120 3 10278 7540.0 13016.0 16 12.0 \n", + "17 202119 3 9539 6860.0 12218.0 14 10.0 \n", + "18 202118 3 12135 9165.0 15105.0 18 14.0 \n", + "19 202117 3 12058 8891.0 15225.0 18 13.0 \n", + "20 202116 3 16505 12735.0 20275.0 25 19.0 \n", + "21 202115 3 19306 15398.0 23214.0 29 23.0 \n", + "22 202114 3 21073 17099.0 25047.0 32 26.0 \n", + "23 202113 3 26413 22094.0 30732.0 40 33.0 \n", + "24 202112 3 30658 25919.0 35397.0 46 39.0 \n", + "25 202111 3 24988 20718.0 29258.0 38 32.0 \n", + "26 202110 3 19539 15951.0 23127.0 30 25.0 \n", + "27 202109 3 17572 13926.0 21218.0 27 21.0 \n", + "28 202108 3 20882 16907.0 24857.0 32 26.0 \n", + "29 202107 3 22393 18303.0 26483.0 34 28.0 \n", + "... ... ... ... ... ... ... ... \n", + "1894 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1895 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1896 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1897 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1898 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1899 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1900 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1901 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1902 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1903 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1904 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1905 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1906 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1907 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1908 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1909 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1910 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1911 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1912 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1913 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1914 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1915 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1916 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1917 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1918 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1919 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1920 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1921 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1922 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1923 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 26.0 FR France \n", + "1 24.0 FR France \n", + "2 25.0 FR France \n", + "3 21.0 FR France \n", + "4 31.0 FR France \n", + "5 37.0 FR France \n", + "6 28.0 FR France \n", + "7 27.0 FR France \n", + "8 18.0 FR France \n", + "9 22.0 FR France \n", + "10 16.0 FR France \n", + "11 16.0 FR France \n", + "12 10.0 FR France \n", + "13 13.0 FR France \n", + "14 16.0 FR France \n", + "15 16.0 FR France \n", + "16 20.0 FR France \n", + "17 18.0 FR France \n", + "18 22.0 FR France \n", + "19 23.0 FR France \n", + "20 31.0 FR France \n", + "21 35.0 FR France \n", + "22 38.0 FR France \n", + "23 47.0 FR France \n", + "24 53.0 FR France \n", + "25 44.0 FR France \n", + "26 35.0 FR France \n", + "27 33.0 FR France \n", + "28 38.0 FR France \n", + "29 40.0 FR France \n", + "... ... ... ... \n", + "1894 59.0 FR France \n", + "1895 64.0 FR France \n", + "1896 97.0 FR France \n", + "1897 93.0 FR France \n", + "1898 80.0 FR France \n", + "1899 116.0 FR France \n", + "1900 149.0 FR France \n", + "1901 281.0 FR France \n", + "1902 395.0 FR France \n", + "1903 485.0 FR France \n", + "1904 544.0 FR France \n", + "1905 689.0 FR France \n", + "1906 722.0 FR France \n", + "1907 762.0 FR France \n", + "1908 926.0 FR France \n", + "1909 1113.0 FR France \n", + "1910 1236.0 FR France \n", + "1911 832.0 FR France \n", + "1912 459.0 FR France \n", + "1913 207.0 FR France \n", + "1914 190.0 FR France \n", + "1915 198.0 FR France \n", + "1916 224.0 FR France \n", + "1917 266.0 FR France \n", + "1918 219.0 FR France \n", + "1919 176.0 FR France \n", + "1920 163.0 FR France \n", + "1921 195.0 FR France \n", + "1922 308.0 FR France \n", + "1923 213.0 FR France \n", + "\n", + "[1923 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2118,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2148,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,9 +2173,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", @@ -199,9 +2201,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.plot(style='*')" ] @@ -314,9 +2383,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2053781\n", + "2012 2175217\n", + "2003 2234584\n", + "2019 2254386\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\n", + "2018 2705325\n", + "1988 2765617\n", + "2007 2780164\n", + "1987 2855570\n", + "2016 2856393\n", + "2011 2857040\n", + "2008 2973918\n", + "1998 3034904\n", + "2002 3125418\n", + "2009 3444020\n", + "1994 3514763\n", + "1996 3539413\n", + "2004 3567744\n", + "1997 3620066\n", + "2015 3654892\n", + "2000 3826372\n", + "2005 3835025\n", + "1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,9 +2446,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.hist(xrot=20)" ] @@ -341,9 +2479,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -364,7 +2500,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo1/analyse-syndrome-grippal_fr.ipynb b/module3/exo1/analyse-syndrome-grippal_fr.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cecd261473be7274d1290451bd6f8ceb2c658f13 --- /dev/null +++ b/module3/exo1/analyse-syndrome-grippal_fr.ipynb @@ -0,0 +1,2515 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\" " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Nom de colonne | Libellé de colonne |\n", + "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", + "| week | Semaine calendaire (ISO 8601) |\n", + "| indicator | Code de l'indicateur de surveillance |\n", + "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", + "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", + "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", + "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", + "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", + "\n", + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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2020211631650512735.020275.02519.031.0FRFrance
2120211531930615398.023214.02923.035.0FRFrance
2220211432107317099.025047.03226.038.0FRFrance
2320211332641322094.030732.04033.047.0FRFrance
2420211233065825919.035397.04639.053.0FRFrance
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2920210732239318303.026483.03428.040.0FRFrance
.................................
189419852132609619621.032571.04735.059.0FRFrance
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1924 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202136 3 13068 9214.0 16922.0 20 14.0 \n", + "1 202135 3 12672 9277.0 16067.0 19 14.0 \n", + "2 202134 3 13013 9481.0 16545.0 20 15.0 \n", + "3 202133 3 10392 7042.0 13742.0 16 11.0 \n", + "4 202132 3 15586 11009.0 20163.0 24 17.0 \n", + "5 202131 3 18855 13664.0 24046.0 29 21.0 \n", + "6 202130 3 13991 9695.0 18287.0 21 14.0 \n", + "7 202129 3 13626 9618.0 17634.0 21 15.0 \n", + "8 202128 3 8636 5430.0 11842.0 13 8.0 \n", + "9 202127 3 10693 6838.0 14548.0 16 10.0 \n", + "10 202126 3 7086 4109.0 10063.0 11 6.0 \n", + "11 202125 3 7942 5540.0 10344.0 12 8.0 \n", + "12 202124 3 4855 3011.0 6699.0 7 4.0 \n", + "13 202123 3 6710 4455.0 8965.0 10 7.0 \n", + "14 202122 3 7879 5495.0 10263.0 12 8.0 \n", + "15 202121 3 7827 5403.0 10251.0 12 8.0 \n", + "16 202120 3 10278 7540.0 13016.0 16 12.0 \n", + "17 202119 3 9539 6860.0 12218.0 14 10.0 \n", + "18 202118 3 12135 9165.0 15105.0 18 14.0 \n", + "19 202117 3 12058 8891.0 15225.0 18 13.0 \n", + "20 202116 3 16505 12735.0 20275.0 25 19.0 \n", + "21 202115 3 19306 15398.0 23214.0 29 23.0 \n", + "22 202114 3 21073 17099.0 25047.0 32 26.0 \n", + "23 202113 3 26413 22094.0 30732.0 40 33.0 \n", + "24 202112 3 30658 25919.0 35397.0 46 39.0 \n", + "25 202111 3 24988 20718.0 29258.0 38 32.0 \n", + "26 202110 3 19539 15951.0 23127.0 30 25.0 \n", + "27 202109 3 17572 13926.0 21218.0 27 21.0 \n", + "28 202108 3 20882 16907.0 24857.0 32 26.0 \n", + "29 202107 3 22393 18303.0 26483.0 34 28.0 \n", + "... ... ... ... ... ... ... ... \n", + "1894 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1895 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1896 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1897 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1898 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1899 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1900 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1901 198514 3 134545 114400.0 154690.0 244 207.0 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96606.0 143 110.0 \n", + "1920 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1921 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1922 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1923 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 26.0 FR France \n", + "1 24.0 FR France \n", + "2 25.0 FR France \n", + "3 21.0 FR France \n", + "4 31.0 FR France \n", + "5 37.0 FR France \n", + "6 28.0 FR France \n", + "7 27.0 FR France \n", + "8 18.0 FR France \n", + "9 22.0 FR France \n", + "10 16.0 FR France \n", + "11 16.0 FR France \n", + "12 10.0 FR France \n", + "13 13.0 FR France \n", + "14 16.0 FR France \n", + "15 16.0 FR France \n", + "16 20.0 FR France \n", + "17 18.0 FR France \n", + "18 22.0 FR France \n", + "19 23.0 FR France \n", + "20 31.0 FR France \n", + "21 35.0 FR France \n", + "22 38.0 FR France \n", + "23 47.0 FR France \n", + "24 53.0 FR France \n", + "25 44.0 FR France \n", + "26 35.0 FR France \n", + "27 33.0 FR France \n", + "28 38.0 FR France \n", + "29 40.0 FR France \n", + "... ... ... ... \n", + "1894 59.0 FR France \n", + "1895 64.0 FR France \n", + "1896 97.0 FR France \n", + "1897 93.0 FR France \n", + "1898 80.0 FR France \n", + "1899 116.0 FR France \n", + "1900 149.0 FR France \n", + "1901 281.0 FR France \n", + "1902 395.0 FR France \n", + "1903 485.0 FR France \n", + "1904 544.0 FR France \n", + "1905 689.0 FR France \n", + "1906 722.0 FR France \n", + "1907 762.0 FR France \n", + "1908 926.0 FR France \n", + "1909 1113.0 FR France \n", + "1910 1236.0 FR France \n", + "1911 832.0 FR France \n", + "1912 459.0 FR France \n", + "1913 207.0 FR France \n", + "1914 190.0 FR France \n", + "1915 198.0 FR France \n", + "1916 224.0 FR France \n", + "1917 266.0 FR France \n", + "1918 219.0 FR France \n", + "1919 176.0 FR France \n", + "1920 163.0 FR France \n", + "1921 195.0 FR France \n", + "1922 308.0 FR France \n", + "1923 213.0 FR France \n", + "\n", + "[1924 rows x 10 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
168719891930NaNNaN0NaNNaNFRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1687 198919 3 0 NaN NaN 0 NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1687 FR France " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02021363130689214.016922.02014.026.0FRFrance
12021353126729277.016067.01914.024.0FRFrance
22021343130139481.016545.02015.025.0FRFrance
32021333103927042.013742.01611.021.0FRFrance
420213231558611009.020163.02417.031.0FRFrance
520213131885513664.024046.02921.037.0FRFrance
62021303139919695.018287.02114.028.0FRFrance
72021293136269618.017634.02115.027.0FRFrance
8202128386365430.011842.0138.018.0FRFrance
92021273106936838.014548.01610.022.0FRFrance
10202126370864109.010063.0116.016.0FRFrance
11202125379425540.010344.0128.016.0FRFrance
12202124348553011.06699.074.010.0FRFrance
13202123367104455.08965.0107.013.0FRFrance
14202122378795495.010263.0128.016.0FRFrance
15202121378275403.010251.0128.016.0FRFrance
162021203102787540.013016.01612.020.0FRFrance
17202119395396860.012218.01410.018.0FRFrance
182021183121359165.015105.01814.022.0FRFrance
192021173120588891.015225.01813.023.0FRFrance
2020211631650512735.020275.02519.031.0FRFrance
2120211531930615398.023214.02923.035.0FRFrance
2220211432107317099.025047.03226.038.0FRFrance
2320211332641322094.030732.04033.047.0FRFrance
2420211233065825919.035397.04639.053.0FRFrance
2520211132498820718.029258.03832.044.0FRFrance
2620211031953915951.023127.03025.035.0FRFrance
2720210931757213926.021218.02721.033.0FRFrance
2820210832088216907.024857.03226.038.0FRFrance
2920210732239318303.026483.03428.040.0FRFrance
.................................
189419852132609619621.032571.04735.059.0FRFrance
189519852032789620885.034907.05138.064.0FRFrance
189619851934315432821.053487.07859.097.0FRFrance
189719851834055529935.051175.07455.093.0FRFrance
189819851733405324366.043740.06244.080.0FRFrance
189919851635036236451.064273.09166.0116.0FRFrance
190019851536388145538.082224.011683.0149.0FRFrance
19011985143134545114400.0154690.0244207.0281.0FRFrance
19021985133197206176080.0218332.0357319.0395.0FRFrance
19031985123245240223304.0267176.0445405.0485.0FRFrance
19041985113276205252399.0300011.0501458.0544.0FRFrance
19051985103353231326279.0380183.0640591.0689.0FRFrance
19061985093369895341109.0398681.0670618.0722.0FRFrance
19071985083389886359529.0420243.0707652.0762.0FRFrance
19081985073471852432599.0511105.0855784.0926.0FRFrance
19091985063565825518011.0613639.01026939.01113.0FRFrance
19101985053637302592795.0681809.011551074.01236.0FRFrance
19111985043424937390794.0459080.0770708.0832.0FRFrance
19121985033213901174689.0253113.0388317.0459.0FRFrance
191319850239758680949.0114223.0177147.0207.0FRFrance
191419850138548965918.0105060.0155120.0190.0FRFrance
191519845238483060602.0109058.0154110.0198.0FRFrance
1916198451310172680242.0123210.0185146.0224.0FRFrance
19171984503123680101401.0145959.0225184.0266.0FRFrance
1918198449310107381684.0120462.0184149.0219.0FRFrance
191919844837862060634.096606.0143110.0176.0FRFrance
192019844737202954274.089784.013199.0163.0FRFrance
192119844638733067686.0106974.0159123.0195.0FRFrance
19221984453135223101414.0169032.0246184.0308.0FRFrance
192319844436842220056.0116788.012537.0213.0FRFrance
\n", + "

1923 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202136 3 13068 9214.0 16922.0 20 14.0 \n", + "1 202135 3 12672 9277.0 16067.0 19 14.0 \n", + "2 202134 3 13013 9481.0 16545.0 20 15.0 \n", + "3 202133 3 10392 7042.0 13742.0 16 11.0 \n", + "4 202132 3 15586 11009.0 20163.0 24 17.0 \n", + "5 202131 3 18855 13664.0 24046.0 29 21.0 \n", + "6 202130 3 13991 9695.0 18287.0 21 14.0 \n", + "7 202129 3 13626 9618.0 17634.0 21 15.0 \n", + "8 202128 3 8636 5430.0 11842.0 13 8.0 \n", + "9 202127 3 10693 6838.0 14548.0 16 10.0 \n", + "10 202126 3 7086 4109.0 10063.0 11 6.0 \n", + "11 202125 3 7942 5540.0 10344.0 12 8.0 \n", + "12 202124 3 4855 3011.0 6699.0 7 4.0 \n", + "13 202123 3 6710 4455.0 8965.0 10 7.0 \n", + "14 202122 3 7879 5495.0 10263.0 12 8.0 \n", + "15 202121 3 7827 5403.0 10251.0 12 8.0 \n", + "16 202120 3 10278 7540.0 13016.0 16 12.0 \n", + "17 202119 3 9539 6860.0 12218.0 14 10.0 \n", + "18 202118 3 12135 9165.0 15105.0 18 14.0 \n", + "19 202117 3 12058 8891.0 15225.0 18 13.0 \n", + "20 202116 3 16505 12735.0 20275.0 25 19.0 \n", + "21 202115 3 19306 15398.0 23214.0 29 23.0 \n", + "22 202114 3 21073 17099.0 25047.0 32 26.0 \n", + "23 202113 3 26413 22094.0 30732.0 40 33.0 \n", + "24 202112 3 30658 25919.0 35397.0 46 39.0 \n", + "25 202111 3 24988 20718.0 29258.0 38 32.0 \n", + "26 202110 3 19539 15951.0 23127.0 30 25.0 \n", + "27 202109 3 17572 13926.0 21218.0 27 21.0 \n", + "28 202108 3 20882 16907.0 24857.0 32 26.0 \n", + "29 202107 3 22393 18303.0 26483.0 34 28.0 \n", + "... ... ... ... ... ... ... ... \n", + "1894 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1895 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1896 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1897 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1898 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1899 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1900 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1901 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1902 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1903 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1904 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1905 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1906 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1907 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1908 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1909 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1910 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1911 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1912 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1913 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1914 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1915 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1916 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1917 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1918 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1919 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1920 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1921 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1922 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1923 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 26.0 FR France \n", + "1 24.0 FR France \n", + "2 25.0 FR France \n", + "3 21.0 FR France \n", + "4 31.0 FR France \n", + "5 37.0 FR France \n", + "6 28.0 FR France \n", + "7 27.0 FR France \n", + "8 18.0 FR France \n", + "9 22.0 FR France \n", + "10 16.0 FR France \n", + "11 16.0 FR France \n", + "12 10.0 FR France \n", + "13 13.0 FR France \n", + "14 16.0 FR France \n", + "15 16.0 FR France \n", + "16 20.0 FR France \n", + "17 18.0 FR France \n", + "18 22.0 FR France \n", + "19 23.0 FR France \n", + "20 31.0 FR France \n", + "21 35.0 FR France \n", + "22 38.0 FR France \n", + "23 47.0 FR France \n", + "24 53.0 FR France \n", + "25 44.0 FR France \n", + "26 35.0 FR France \n", + "27 33.0 FR France \n", + "28 38.0 FR France \n", + "29 40.0 FR France \n", + "... ... ... ... \n", + "1894 59.0 FR France \n", + "1895 64.0 FR France \n", + "1896 97.0 FR France \n", + "1897 93.0 FR France \n", + "1898 80.0 FR France \n", + "1899 116.0 FR France \n", + "1900 149.0 FR France \n", + "1901 281.0 FR France \n", + "1902 395.0 FR France \n", + "1903 485.0 FR France \n", + "1904 544.0 FR France \n", + "1905 689.0 FR France \n", + "1906 722.0 FR France \n", + "1907 762.0 FR France \n", + "1908 926.0 FR France \n", + "1909 1113.0 FR France \n", + "1910 1236.0 FR France \n", + "1911 832.0 FR France \n", + "1912 459.0 FR France \n", + "1913 207.0 FR France \n", + "1914 190.0 FR France \n", + "1915 198.0 FR France \n", + "1916 224.0 FR France \n", + "1917 266.0 FR France \n", + "1918 219.0 FR France \n", + "1919 176.0 FR France \n", + "1920 163.0 FR France \n", + "1921 195.0 FR France \n", + "1922 308.0 FR France \n", + "1923 213.0 FR France \n", + "\n", + "[1923 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de\n", + "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", + "de nombre entier. C'est comme ça que Pandas les interprète.\n", + " \n", + "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", + "semaine. Il faut lui fournir les dates de début et de fin de\n", + "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous\n", + "écrivons une petite fonction Python pour cela. Ensuite, nous\n", + "l'appliquons à tous les points de nos donnés. Les résultats vont\n", + "dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "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']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il restent deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation\n", + "comme nouvel index de notre jeux de données. Ceci en fait\n", + "une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "Deuxièmement, nous trions les points par période, dans\n", + "le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", + "le début de la période qui suit, la différence temporelle doit être\n", + "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", + "d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", + "entre lesquelles il manque une semaine.\n", + "\n", + "Nous reconnaissons ces dates: c'est la semaine sans observations\n", + "que nous avions supprimées !" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], + "source": [ + "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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SkU1NTU0Z38dQRWIJonF1mqdCAcs08igSd+5l+lVuLdMwppB4ChoiEsQJGD9T1V8AqOohVY2ragL4EbDCPbwemJ1SvQ444JbXpSnvV0dEAsAkoDnDufpR1dtUdbmqLq+trfXylnKW3IDJyTT81qeRR+G+jvCj3xFEhIBPbPSUMQXGy+gpAW4Htqnqd1LKZ6Qc9j7gZffxOmCVOyLqRGAB8JyqNgAdInK2e86rgQdT6iRHRl0JPOr2ezwMXCQiVW7z10Vu2ajrjjpBojQUsEwjz5JNUKmZBjhNVBY0jCksXkZPnQt8GNgqIlvcsn8CPigiS3Gai/YCnwRQ1VdE5F7gVZyRV9e7I6cArgPuAEpwRk39zi2/HbhLRHbhZBir3HM1i8jXgI3ucV9V1eahvdXhSc7LKA35KS/yW59GHh0dcuvvVx70i/VpGFNgsgYNVX2S9H0LD2WocxNwU5ryTcCSNOW9wFWDnGsNsCbbdY60npSgURpyJvclEorPl+7WmFyk6whP/m2ZhjGFxWaEe3Q00whQVuR8I07d/tUMXTjm3MfiYP9/jgGfBQ1jCo0FDY+SzVElbqYB2KzwPOmNJpcRGdA8FbDmKWMKjQUNj1L7NCpLggC090TH8pImjGSmUWQd4cYUPAsaHqUGjZoyZ3n0I12RsbykCSM55LY42D/TCFnQMKbgWNDwqCelearaDRotFjTyojeaKdOw5iljCokFDY+S7e4lwaNBwzKN/EhmGkUDO8L9NrnPmELjZZ6GIbXd3d/XYdtsQSMvwoN1hPt9toyIMQXGMg2PwrEEIs6Es1DAR0VxwIJGnvTG4gT9gn/AnJeQ30csYc1TxhQSCxoehWMJigI+3H2jqCkLWfNUnoSjiWOyDEjOCLdMw5hCYkHDo0is/wdbdVmI5q7wGF7RxBGOxY/pBAdrnjKmEFnQ8GjgB1t1WRHNXTZPIx/CscQxw23B5mkYU4gsaHgUjib6rY1UY5lG3vRGB8s0bEa4MYXGgoZHyT6NpKqyEM1dEZwV3M1whGOJYxYrBCfTiFmmYUxBsaDhkdM8dbQJpaYsRDSudNj6U8M2aPNUwEfEMg1jCooFDY/CsUS/yWfJCX7NnTaCargGbZ6ynfuMKTgWNDwa2DxVXW6zwvPFCcjWEZ4PneGYLW9jRpSX7V5ni8ifRGSbiLwiIp9zy6tFZL2I7HR/V6XUuUFEdonIdhFZmVK+TES2us993932FXdr2Hvc8g0iMjelzmr3NXaKyGrGiNPu3r95CmxWeD6Eo3GK02UatglTzv7lwZdZ/ZPnxvoyzATmJdOIAX+vqouAs4HrRWQx8CXgEVVdADzi/o373CrgVOBi4Acikvy0vRW4Fmff8AXu8wDXAC2qOh/4LnCze65q4EbgLGAFcGNqcBpN4ejAIbe2aGG+ZM401AYb5OCl+ja2H+wgYTPpzQjJGjRUtUFVn3cfdwDbgFnA5cBa97C1wBXu48uBu1U1rKp7gF3AChGZAVSq6jPqfArcOaBO8lz3Axe6WchKYL2qNqtqC7Ceo4FmVEUGNE/VlBUB1jyVDwMDclLI78y+t6VEvInGE+w93EU4lqCp04aDm5GRU5+G22x0OrABmKaqDeAEFmCqe9gsYF9KtXq3bJb7eGB5vzqqGgPagJoM5xp14QEzwktCfoqDPpurkQcD+4uSAn6nzJqovHnjSFdfgH2zuXuMr8ZMVJ6DhoiUAw8Af6eq7ZkOTVOmGcqHWif12q4VkU0isqmpqSnDpQ3dwNFT4GQbR2z01LBlmhEOEI1ZpuHFzkOdfY/fPGJBw4wMT0FDRII4AeNnqvoLt/iQ2+SE+7vRLa8HZqdUrwMOuOV1acr71RGRADAJaM5wrn5U9TZVXa6qy2tra728pZyFY3FC/v63a1ZViX2jy4PBhtwmm6eiCcs0vNjZ6AQNEcs0zMjxMnpKgNuBbar6nZSn1gHJ0UyrgQdTyle5I6JOxOnwfs5twuoQkbPdc149oE7yXFcCj7r9Hg8DF4lIldsBfpFbNurSZRrzast5/XDXWFzOhBGLJ4gldJBVbq15Khe7GjupqyphRmUx+yxomBHiZROmc4EPA1tFZItb9k/AN4B7ReQa4E3gKgBVfUVE7gVexRl5db2qxt161wF3ACXA79wfcILSXSKyCyfDWOWeq1lEvgZsdI/7qqo2D/G9DpmqHrPKLcC82jKauyK0dEWockdTmdxE4sn9wdMvIwLWPOXVzsZOFkwtpzsSZ1+LBQ0zMrIGDVV9kvR9CwAXDlLnJuCmNOWbgCVpyntxg06a59YAa7Jd50jq2450QBPKSbVlALx+uJNlZdWjfl0TQW80/b0FZ7tXOBpYzODiCWV3Uyfnza+htTvK4ztHpm/PGJsR7kHyQ2vgB9u82nIAdjdaE9VQ9W2jm6YjPGTNU57Vt3QTiSWYP7WcOdWlHGoP0xuNZ69oTI4saHjQt4f1gA+2uqpSQn4fuw93pqtmPAhnyDSSzVMxW7Qwq8PuKL5plcXMqSkFnEBiTL5Z0PCg79vwgNFTfp8wd0qpZRrD0Ove28FWuQVrnvKiO+KstlxWFGB2tRM0bASVGQleOsKPe319Gmk6a0+aUs6Oxo7RvqQJI3Om4Q65taCRVVfYCb6lIX/fagUNbb1jeUlmgrJMw4NMH2wn1Zbx5pFu+2AboqODDGzI7XD0ZRqhAJNLgwC0dtt2xCb/LGh4cLQj/NgPtvlTy4kllPWvHhrty5oQwn3NUxmG3FrQyKo74mYaRX6Kg35KQ35bTNOMCAsaHoTdUSjpMo2LTp3OaXWT+OzPX+D3Lx8c7Usb944OuU2XaSSbp6wjPJvUTAOgqjREc7cFDZN/FjQ8SDahpNvHurwowM8+fhZzp5Txwz/vHu1LG/eODrlNt4yIZRpeJfs0StwBBVVlQWueMiPCgoYHmdrdASqKg5w7r4adhzps74ccJfuLijP0aURiFjSy6Y7EKA358fmc7KyqNGQbhJkRYUHDg0zfhpNOnl5BVyTO/tae0bqsCaE3w71NloUtaGTVFYlTGjoaeKtKQ7Ra85QZARY0PIgMsoxIqoXTKgDYcciG3+Yi08i0ZPZhM5uz6w7HKA0dHUFfVRq0TMOMCAsaHmRrngIn0wDYftBmh+ci071NTvjrsaCR1TGZRlmI9t4YMesPMnlmQcOD5OipdB3hSZXFQWZOKmb7wUz7U5mBejOMTEuWJUdYmcF1R2KUFaVmGs6qy6091hlu8suChgeDrXI70MnTK9h+yDKNXIRjCUJ+X18HbiqfTygK+PqCthlcV/jYTAOwfg2TdxY0PPAaNBZOq2B3Y6c1CeQgHEu/a19ScdBvfRoe9ETifXM0AKrdTKO5yzINk18WNDyIxBL4fULAnyVoTK8gEk+w94gtYOiVsyPi4H1FJUG/9Wl40BWJUVp09D4mlxJpsUzD5JmX7V7XiEijiLycUvYVEdkvIlvcn0tTnrtBRHaJyHYRWZlSvkxEtrrPfd/d8hV3W9h73PINIjI3pc5qEdnp/iS3gx112b4NJy2Y6nSG77QmKs8G2x88qTjosz4ND7oHZhpu85QtJWLyzUumcQdwcZry76rqUvfnIQARWYyzVeupbp0fiEjy68+twLU4e4YvSDnnNUCLqs4Hvgvc7J6rGrgROAtYAdzo7hM+6sKxRMZO8KR5U52d/HY3WdDwKt3e66msecqbrnDsmHkaAC02K9zkWdZPQlV9HGffbi8uB+5W1bCq7gF2AStEZAZQqarPqDNl+k7gipQ6a93H9wMXulnISmC9qjaraguwnvTBa8SFowlPmUZpKMCsySXsarSg4VU4mkg7GzypKOin1yb3ZRSLJwjHEv3maZSE/BQHfdY8ZfJuOH0anxGRl9zmq2QGMAvYl3JMvVs2y308sLxfHVWNAW1ATYZzjTqneWrwD7ZU86aWs8syDc/CsXjGTKMk6KM3YplGJt1uJlZW1P/faFVpyJqnTN4NNWjcCswDlgINwLfd8mPHTYJmKB9qnX5E5FoR2SQim5qamjJd95CEY94yDYD5teXsbuwikbA1qLzIlsUVB/19S42Y9Lr7NmDqv6daVWnIMg2Td0MKGqp6SFXjqpoAfoTT5wBONjA75dA64IBbXpemvF8dEQkAk3CawwY7V7rruU1Vl6vq8tra2qG8pYwiWdrdU82fWk5PNM6BNluDyoveLFlcccD6NLLp6tvqdUCmURa0Pg2Td0MKGm4fRdL7gOTIqnXAKndE1Ik4Hd7PqWoD0CEiZ7v9FVcDD6bUSY6MuhJ41O33eBi4SESq3Oavi9yyUZecgObF/KnlANav4VH3gOUvBrLRU9llzDSsecrkWdY9wkXk58A7gSkiUo8zoumdIrIUp7loL/BJAFV9RUTuBV4FYsD1qpr8mngdzkisEuB37g/A7cBdIrILJ8NY5Z6rWUS+Bmx0j/uqqnrtkM+rXPo0UoNGTVkRS2ZV4o4uNmn0ROKUZAgaJSHLNLLpyzQG3MeashCHO8NjcUlmAssaNFT1g2mKb89w/E3ATWnKNwFL0pT3AlcNcq41wJps1zjSwrEEZWVZbxXgjI+vLgvx7T/soCca53urlnL50jHpvx8XeqKZM42igE3uy6bHHSgwMPhOKS+ivTeW05ceY7KxGeEeeB1ym7RoRgWxRIKKogC/ealhBK9s/HM2Dxo8IBcH/X3Lp5v0jvZp9L+PtRVFABzptCYqkz/evj4f5yLxRE7f1P7jyrcSjiW485m9/GzDm3SGY5QX2a0eKJFQeqOJvi1K0ykO+ojEE8QTij/NooYmtU/j2EwDoKkjzMzJJaN+XWZiskzDg3CWpS4Gmjm5hBOnlHHpW2YQiSV49LXGEby68SvZ7JSpeSoZUMI27HZQR/s00mca1q9h8smChgdelxEZaNmcKqZWFPGQNVGl1T1IW3yqvo2YbILfoJL3sXTAkNtk0GjqsKBh8seChgfO5L7cOxJ9PuG8+VPYsq91BK5q/OvrwM3SPAXYUiIZdIVjBHxyzLDwmnJn/SkLGiafLGh40BvNvNRFJnNqSjnU0WvNK2l0R51mlWwd4WD7hGeSnOsycGh3UcDPpJKgNU+ZvLKgkUUkliCW0CF3ZM+pLkUV9rfYDPGB+ppVPDRPWdAYXFc4dszIqaQp5SGaLGiYPLKgkUW328mYqQklk9nVpQC82dydt2uaKHpz6NOwoDG47gwTJGsrijjcYUNuTf5Y0MiiK5J+BVGv5rhBY59lGsfwlGm4AxBsKZHBOXNdBgsaxZZpmLyyoJFFdzh7u3smteVFhAI+9lmmcYxuD0NuLdPIrr03RmVxMO1zU8pDHLaOcJNHFjSyGG6m4fMJdVUlFjTS6Ek2/WUIyMlmF8s0BtfaHenbE3yg2ooiOsIxG7Js8saCRhbDzTTAaaKyPo1j9TVPZRpy6w51tvWnBtfWE2VSSSjtc8lZ4TaCyuSLBY0s+jKNYQSN2VWllmmk4W1yX7JPw4JGOqpKW080Y6YB9PVrdPRGcXYeMGZoLGhkkRw9NXC2bS7mVJfS3hujzTbE6acnEkeEjEu0FFmfRkbdkTjRuDKpZJCgkbL+VFt3lLd941G+u37HaF6imWAsaGThZYRPNrOrncXi9rVYtpGqOxKnNHjspLRUR9eesj6NdFp7nC8ikwcJGlPdTGPP4S5+tWU/Hb0xbnvidQ629Y7aNZqJxYJGFl156NOwuRrp9URjGTvBAYJ+wSe29tRgWt09wDM1Ty0/oYofP7GHnz77BnNrSoknlC//aiv//add7D3cNZqXayaArEFDRNaISKOIvJxSVi0i60Vkp/u7KuW5G0Rkl4hsF5GVKeXLRGSr+9z33W1fcbeGvcct3yAic1PqrHZfY6eIJLeEHVX5yDTm1Zbj9wnbGtrzdVkTQk+WrV4BRITioO3eN5g2N9MYrCNcRLjh0kUc7gyzs7GTj59/ElefM5c/bmvkPx7eztpn9o7exZoJwUumcQdw8YCyLwGPqOoC4BH3b0RkMc52rae6dX4gIslPhVuBa3H2DV+Qcs5rgBZVnQ98F7jZPVc1ztayZwErgBtTg9No6YrECAV8BD3uEZ5OcdDPgqnlvFTflscrG/+y7Q+eVBz002trd6WV7CcbrE8DYNk+EfW9AAAgAElEQVQJVVz2lhmUhfy8d+lMvnTJKTzy9+9gTnUph22DJpOjrJ+Eqvo4zt7dqS4H1rqP1wJXpJTfraphVd0D7AJWiMgMoFJVn1Fn6MadA+okz3U/cKGbhawE1qtqs6q2AOs5NniNuO5w/Ji9l4fitLpJbN3fZiNXUvREM+8PnlQS9Ns8jUH09WkM0jyV9B9XncZDnzufyuIgQb+PebXl1FYU0dxlQ3FNbob69XmaqjYAuL+nuuWzgH0px9W7ZbPcxwPL+9VR1RjQBtRkONeocr4ND3/XvbfUTaa5K8L+VltOJMlrplEU9Nk8jUG0dnsLGqWhACfUlPUrqy4L2VawJmf57ghPNwxGM5QPtU7/FxW5VkQ2icimpqYmTxfqVXckNuTZ4KlOmzUJgK3WRNWnOxKnJJg9IBcH/IQtaKTV1hMl5PcNaUHNKeUha54yORtq0DjkNjnh/k7uZ1oPzE45rg444JbXpSnvV0dEAsAknOawwc51DFW9TVWXq+ry2traIb6l9Loi8awjfLw4ZUYFQb+wdb8FjaSeDAvtpSoO+qx5ahBtPREqS4IZhy0PpqasiJbuCImENZka74YaNNYBydFMq4EHU8pXuSOiTsTp8H7ObcLqEJGz3f6KqwfUSZ7rSuBRt9/jYeAiEalyO8AvcstGVXc4lpc+jaKAn5OnVVjQSOG1eaokZKOnBtPaPfhs8Gyqy0LEE9o3AssYL7J+hRaRnwPvBKaISD3OiKZvAPeKyDXAm8BVAKr6iojcC7wKxIDrVTX5f/t1OCOxSoDfuT8AtwN3icgunAxjlXuuZhH5GrDRPe6rqjqwQ37EdUXiTC5NP5wxV6fOrOTR1/LbfDae9UTifavYZlIc8Pe13Zv+Wrujg07syya5HeyRrjBVZfn5N24mvqxBQ1U/OMhTFw5y/E3ATWnKNwFL0pT34gadNM+tAdZku8aRlK8+DYCZk0s43BkmEksQyrB0xvFAVemO5jDk1jKNtNp6osycXDykujVlzmzxI50R5k/NcrAxruP7k8uDrnB+Rk8BzJjk/M/d2GFLOETjSjyhnkdPWZ9Gem09USqHnWlYZ7jxzoJGFj2R/PRpAEyrdIKGrftzdFkQL4MMKouDdPRa81Q6rd0RJg8yGzwbCxpmKCxoZJBIuE0oRfnJNKa7mcbBdgsa3dHkml7ZA3J1WYj23hjRuGUbqaLxhNvnNrRMo8rtqztie22YHFjQyKA3Fkd1eOtOpZpR6ax2a5lGbmt6JTtpW7rtG3GqNo+zwQcT9PuYXBq0CX4mJxY0MugKJzdgyk/QqCwJUBz0WdAgpXnKw+ipavcbcUuXNVGlavWw7lQ21WUhmq15yuTAgkYGfRsw5akjXESYMamEBmueSsk0st/bqjLnQ9E+3Ppr63Hux3CCxpSyItsK1uTEgkYGfZlGnobcAkyrLOKQZRp9AdnLgoXV1jyV1qF258M+uaXrUFSXhawj3OTEgkYG+c40ACfTsKBBR69zb8s9DDJINk/Zh1t/+1ucxS/rqkqHfI6acmueMrmxoJFBsgklv5lGMY0dvcf9ej8H3NV+Z3iYmNbXEW4fbv3Ut3RTURQYVvNUTbmz/lTMRqYZjyxoZDAymUYx0bge99+a97f2UFEcoLI4+wde0O+jojhg34gH2N/aw6yqkmGdY0p5CFVotqY/45EFjQySfRr5GnILRyf4HTrOO8P3t/Qwa7L3D7zqspD1aQxQ39JD3TCDxlS3P6Sx3TrDjTcWNDIYiUwjOcHveO/X2N+a2wdeVam1vQ+Ua+BNp7bC+ffY1GFBw3hjQSODrhHo05jpBo36lu68nXM82t+ae6ZhQeOotp4oHeHYsDrBwRnNB5b5Gu8saGTQ1hMl4BOKA/kLGrUVRUytKGLLvta8nXO8ae+N0tEbY2YOQaOqNGQd4SmSXzqG26eRHK7baJmG8ciCRgYNrT1Mn1SMz5f7rmiDERGWz61i096WvJ1zvEkOFc3lA6+6LGidtSmODrcdXtAoCvipKg3aysvGMwsaGRxo7c3p27BXy06oZn9rDw1tPXk/93jQFzRyap4qojea6Ft+5HhXP4R7OJipFcV9EwWNycaCRgYH2nr6+iDy6cy5VQDHbbaxv3VomQbY0NCk/a09FAd9fbPlh2NqZZE1TxnPhhU0RGSviGwVkS0issktqxaR9SKy0/1dlXL8DSKyS0S2i8jKlPJl7nl2icj33X3Ecfcav8ct3yAic4dzvbmIJ5SDbSOTaSyaUUlJ0M/mN47PoHGgtYdQwMeUMu/LXySX8W62FVkBp0+jrqoU93+VYZlaUUyjdYQbj/KRaVygqktVdbn795eAR1R1AfCI+zcishhn/+9TgYuBH4hIsof5VuBaYIH7c7Fbfg3Qoqrzge8CN+fhej1p6ggTS+iIBI2g38fpcyaz6Y1R3/K8INS3OhlcLn1FyW/Ulmk43jjSPez+jKRplUU0dYSP+1UKjDcj0Tx1ObDWfbwWuCKl/G5VDavqHmAXsEJEZgCVqvqMqipw54A6yXPdD1wo+fhq5cEBt79hqPsvZ7N09mS2NXQQiR1/yzfsb8l9JrMtJXJUNJ5gd1MnC6dX5OV8UyuKiCXUArLxZLhBQ4E/iMhmEbnWLZumqg0A7u/klvWzgH0pdevdslnu44Hl/eqoagxoA2oGXoSIXCsim0RkU1NT0zDfkiO5NtJIZBoAC6aVE08obzYfX/M1ovEEOw51MK+2PKd6U8qdpizb9RB2N3USjSuLZ1Tm5XzJVQpsVrjxYrhB41xVPQO4BLheRN6e4dh0GYJmKM9Up3+B6m2qulxVl9fW1ma7Zk9GOmgkPzR3NXaOyPkL1cv72+iOxDnrxGNif0aTSoLUVhSx89Dxdb/Sea2hA4BTpucnaEytTM7VsIBsshvW+hiqesD93SgivwRWAIdEZIaqNrhNT43u4fXA7JTqdcABt7wuTXlqnXoRCQCTgFHpCDjQ2ktFkbcF9YbiJDdo7G46vj4EN+xx/vOtOLE657onTytnV2NHvi9p3NnW0E7I7+Ok2rK8nG9qxfjKNNa/eoiHtjYQTyhfWLmQ2dXDmxVvcjPkTENEykSkIvkYuAh4GVgHrHYPWw086D5eB6xyR0SdiNPh/ZzbhNUhIme7/RVXD6iTPNeVwKNuv8eIO9Da42nZ7qEqLwowY1Lx8Rc0Xj/CvNqyIW0ctGBqBTsbO4/7DtttBzuYP7WcoD8/XZJHZ4UXfqbx2sF2PnHnJh7f0cQj2w5x8S2Ps2nv8TmgZKwM51/dNOBJEXkReA74rar+HvgG8B4R2Qm8x/0bVX0FuBd4Ffg9cL2qJmdqXQf8GKdzfDfwO7f8dqBGRHYB/xd3JNZoONDWM2JNU0nzasvZfZw0T4VjcSKxBJv2tnDWSbk1TSUtmFZOdyTeN8/jeLWtoZ1FeerPACgO+plcGhwXi2j+6oUD+H3CHz7/dh7+/NsREda9eCB7RZM3Q26eUtXXgbemKT8CXDhInZuAm9KUbwKWpCnvBa4a6jUOR0NrL6fVTR7R15hXW8YDz+9HVfMy3r5QqSqXfO8JWrudRfbOGkLTFMDJ05zRQjsbO47bJonDnWGaOsIsmpGfkVNJJ00pK/j+tURC+fWLBzh/wRRq3IERC6dX8NpBa7IcTTYjPI22nihHuiJ5WaIhk/lTy+kMxyb8bNxdjZ283tRFachPRXGAt82bMqTznDzV+aDccRx3hr96oB0gr5kGwCkzKnntYAej1Po7JJvfbGF/aw9XLJ3VV3bytAp2HCrs655oLGiksdmddHfGnKosRw5PcgTVRG+ienLXYQB+/omz2fqVlUPqzwCYVBpkakUROw4dP98s79u0r9/KAQ+/cpDioI+ls/ObBS+aXkFbT7SghzT/4vn9FAd9vGfxtL6yhdPKae2O2n4go8iCRhob9jQT9Aunzxnh5qmpTtDYOcGDxlO7jjCnujQvTUonT6s4bobdHu4M86VfbOWTd23iSGeYaDzBQ1sbePeiaZQV5W9jMDiauWxraM/refOlvTfKg1v285enzez33k92JzhuP46+SIw1CxppPLenmdPqJlMczN8+GulMrShi5qRint59eERfZyzF4gmeff0I584fWpPUQAunO80Rx8Nqt7958QDxhNLaHeWGX2zlse1NtHRHuTyleSZfkh++2xoK88P3l8/vpzsS58PnnNCvfKHbz7V9lPs1nn+zhc/+/AXae6Oj+rqFwILGAN2RGFvr24Y0jyBXIsKFi6bx+I7D9EYn5ofgi/VtdIZjnJenoHHBwqmEYwn+vCM/M/8L2a+2HGDRjEr+8eKF/OHVQ1z3081MKgnyjpPzM4E1VWVxkLqqkoLsVFZVfvrsG5xWN+mYwSk15UVMKQ+NapNlPKHc8MBW1r14gH/51cuj9rqFwoLGAC+82UosoaMSNADevXgaPdE4z+w+MiqvN9r+uO0QPoFz5g1tmO1AZ51UzeTSIL9/uSEv5ytUew93sWVfK1csncknzj+J2z68jIXTK1h9zgmEAiPzv+0p0yt5rQCap9q6o/3WGNuwp5mdjZ186OwT0h5/8rQKtg+zyfJgWy87PQaeB56vZ/uhDs45qYZfbTnAg1v2D+u1xxsLGgNs2NOMT2DZCSPbCZ509knVlIX8rN92aFRebzTFE8ovnq/nnQun5mXfB3BWCH7Pomk8sq2RcGxiZmeRWIJ/WfcKfp/wl2+diYhw0anT+e1nz+f/XrRwxF530YwKXj/cNeZZ78fWbuT9tz5NNO4s5nnXs28wqSTIX542M+3xJ0+rYMfBjiFfd2t3hPff+jR/8Z9PsnFvM6o66ATSnkic7/xhB0tnT+aua1awdPZk/u232+gMx4b02uORBY0Bnt51mCWzJo3Y8iEDFQX8vP3kWh7Zdoj4BJvp/MTOJg61h7lqWV32g3NwyVum0xGO8fSuiZedJRLKP9z3Io/vaOLr71sy4hNMUy07oYp4Qnlo69hlcS+82cLmN1p4/XAX922qp7Gjl4dfPshVy+ooCaXvY1x56nR6onHWPr037fNHOsN9fWDxhLL9YAd/2t5IPOEEh7+/90UaO3qprSjioz/ZyJk3PcKpNz7M1Wue46GtDf0CyJqn9nCwvZd/unQRAb+Pr7z3VJo6wtz62K6834tCZUEjRVc4xpZ9rXnrtPXq8qWzONQe5rdj+D9rPsUTyov7Wrnj6b1UlQa5cNG07JVycO78KdSUhfjRE69PuPH533tkJ+tePMAXLz6FD5w5Z1Rf++0Lalk0o5L/fHQXsfjYLNl/x9N7qSgKcFrdJL73yA5ueGArsYTyN4M0TYHT9HnBwlr+60+7jlk6v6UrwspbnuBvfvwssXiCv/7Rs6y85XE++pON/PDPu/nphjd45LVG/vnSRfz8E2ez7IQqzptfw5XL6thzuJNP/+x53veDp2jtjnCkM8ytj+3mPYun9TVfL509mfedPosfPb6H2x7ffVxsdWBBI8Vze5qJJZRzhzj5bKguWjyNebVl/OBPu8b9ukqJhPKZ/32ey//7KR7b3sT7z6jLext8UcDP9RfM5+ndR3hi58QZeXbvpn1875GdXLmsjk+946RRf32fT/jchQvYc7iLnz/35qi/fmN7L799qYGrls/my5ctprEjzFO7D3PNeSdy4pTMizPecOkiusIxvv/ozn7lNz20jcOdYZ5/s5WP/GQjG/Y08/fvOZmLFk/jlj/u4OsPbeMdJ9ey+m1zmV1dytqPreCWVafztSuW8Ng/XMC3rnor2xo6WL3mOT5w27P0RON88eJT+r3Gly9bxHkLpvD1h17j//zPMyO258vGvc2suu0Z9hzuGpHze2VBI8VTuw4TCvhYPnd0+jOSfD7h0++cz2sHO3jktcbsFQrYzQ+/xu9ePshnL1zAr64/l38c8D9YvvzN2XOoqyrh33/3Wl/b93j2X4/u5B/vf4lz59dw0/uWjNmyMhctnsYZcybz/x58hY/dsXFU2+ofeH4/sYTy4XNOYMWJ1Wz853fz8ldW8v/+YnHWuidPq+ADZ87hrmfeYK/7ofrY9kbu31zPde+cx+lzJvPkrsO8c2Etn3nXfL7x/tOYVBIi5Pdx8/tPS3u//T7hymV1fG/VUrbub6MnEucnHzmT+VP77wVTU17Emo+cyX//9Rm82tDO//mfZ2jN84ZWvdE4X7jvRZ59vZkP/XgDDW1jt/6aBY0UT+0+wvITqkZ8fkY67106kynlReN68bXHdzTxP39+nQ+dPYfPv3sBS2dPHrGRPkUBP/986SK2NbRz02+3jchrjJbn32zhW3/YweVLZ/KTj6ygKDD6//6SfD7hfz9xNl+65BT+tL2Rb/7+tVF5XVXlvs37OHNuVV9WMaW8iEAOK/l+/j0LCAV8/Ntvt7H9YAd/d88WTplewecuXMDX3/cW3rmwln+7wgnI1WUhHrjuHB647m1Mn5R5NetL3jKDP3z+Hfzh82/n7RmGO1922gzu+MiZ7Dncxb88+Irn684kkVCe3n2Yf3nwZfYe6ebLly2ivSfK3/7vC2mbZkejXzS/00rHscOdYbY1tPOFlSM3OiWToN/H+Qum8PiOJhIJzWn/7ELQ0etMQJtXW8aXL1s8Kt+UL3nLDD527omseWoPZ86t5rLTZoz4a46Eb/9hOzVlIb7+vreMWJDNRXHQz6feMY+Dbb3c8fRe/vKtMzlz7sgOQX9hXyuvN3XxqbfPG/I5plYUc/0F8/mPh7fzx22HqCgKcOuHllEc9LNoRiV3fHRFv+NPqPG+H8nA7GIwb5s/hc9euIDvrN9BTXmIWZNL+MCZs6kY4sCab/z+NW57/HUAPrhiDh8//yQqigN88YGt3PnMGzyx8zDbGtqpKgvS2B7mpNoy7r72nCG9llcWNFwlQT+3fGApb83zmj65eNu8Gn75wn62H+rI+4J0I+VgWy9//aNnef1wFyJw/6fOGdVM7YZLT+GZ14/wnfXbuWTJ9HERbKPxBLG4UhLy8/iOJp7adYQvX7Yo70uDDNcXVi7kj9sO8fG1m7hl1VIuWDg1e6UchWNx1m05wH2b6ykJ+rl0mIH/0++cx7ITqnhy52HOnT8la1/ISPj0O+fx7OtH+MlTewG4b1M9n75gHvUtPfzVGbOYMSnziLg/bW/kl8/v5y9Om8HtT+7hr06fxeffczJ1VU69q5bN5mcb3uTGda8Q9AsrT51OR2+MRdMreUvdpJF+e8hEG32yfPly3bRp01hfxpAcaO3hbd94lC9ftoiPnz/6HaGp4gmlMxyjNOSnvSfK64e72H6wg9KQn1OmV7J4ZiWqykd+spHn9jTzyXecxBlzqjKm7yPlwS37+dzdW1jzkeW865T8jtQaDlXlmd1HeOjlBs6bX8vyuVU8sbOJbz28g8OdYf7itJn85qUDTKss5g+ff/uYNItms/dwF5/66Wa2H+rgm+8/jauWz85aR1X58RN72PxGC93ROPNqy/jw2Sf07VaZetw/3PcSDzxfT1HAx9++az6fedeCkXoro0pVCccSbNzbzKd/9jwdvU7f0JTyEF9YuZCyogBnnVjTt3jnkc4wL9W3URz087E7NtLjzjmpLgvxyP99B1UD5jltrW/jxnUv88WLTxny/jQDichmVV2e9TgLGoXlXd9+jDnVpcek0qPptYPtfOquzew90j3oMafOrKSiOMCzrzfz1ctP5epz5o7eBQ4QjSd4+zf/xOzqUu659uyC2JvkQGsP/3Dfizy9+wh+n/Rra140o5J5tWX85qUGzps/hVtWLWVK+dBW/h0NPZE4n7hzE0/vPswtq07nvW9NP8kuae3Te7lx3SucOKWMsiI/Ow91MqW8iF//7XlsP9jBG0e6iMQTvN7UxR1P7+Vv3zWfz7/75HGRJQ5FY3svB9p6Cfl9XP+/z/eNfgr4hIuXTOej587l7+7Zwr5mp3N71uQSbv3QGdzx9F4ue8uMvA9ZH8yEChoicjHwPcAP/FhVvzHYseM9aPzLgy9z36Z61n3mXE6cUkZXOM6k0sztoY0dvfzs2Tdp7AhzzrwalsysZE51aU6diOA0Fax9ei/fXb+TiuIAHz33RKLxBJXFAeqqSlk8s5LeaJwndh5m3YsH6I3GWXZCFV/5y1PH/H/4nzy1h3/99aucv2AKX1i5kEUzKvu2Qx3NTa5i8QR3b9zHfzy8nWg8wRcvPoUrl9Xx5x1N7G/pYcmsSaw4sRq/T2hs72VKedGY3zsvuiMxVq95jk1vtPDZdy1gxqRiAn4fF506jcriIG09Uf746iEOd4b59vodnDuvhjUfORMR4aX6Vq784TMUB3y09/YfjXXJkun891+fMS7uQT70RuPsa+6mJxrnNy81cOcze+mNJqgoDvBvVyyhvqWHi5dM79s2YTRNmKAhIn5gB87WsfXARuCDqvpquuPHe9DYfrCDv/nxs3SGY5QE/bT2RPnCyoXUVZXywOZ6gn6hoa2Xfc3dXHPeSQQDwi1/3Ek0nqA8FKDDHSJZW1HE9e+cx4zJJbT1pF+JsyjgY9GMSsqKAjz/RgvffPg19jX3cMHCWm5+/2lMrRy5PdLzTVX56YY3+fpvt9ETjVNRHOAzF8znQGsPP9+4j4qiAFMri5laUcS0yiKmVhQzY3IxFyycyvTKYvYe6eJQe5i9R7rY1tDOshOqWDp7Ms++foRIXEGV/a29zJhUzJJZk/AJJFQJ+n2cPK2CVxvauX9zPX96rZGGtl5WnFjNze8/bUza1EdKbzTOF+5/iV+njPAL+X3UVhRxpCtMb9QZ+lxXVcIvP31uv31TfvXCfn7w2C6uPmcuF5wylZDfRyjgo7I4UBCZ4Vipb+nmx0/s4a/OmDXiO4VmM5GCxjnAV1R1pfv3DQCq+u/pjh/vQQOczOHfH3oNVaUnGufhV5x1qU6oKaUsFKCqLEhRwM+j7pyOi0+dzhcvOYU51aW8eqCd1w62c9/mep7b05zT654yvYJ/vmwR5y8Y/X6JfGnqCPPs60d44Pl6HtvehE/gitNnURTw09TRy6H2MI0dvTR1hEkoiEBZKNBvPkIo4Es7s3dgM1OSE0CgLOTn/AW1vO+MWVy0eNqE/DBUVZ5/s5WashCtPVF+//JBGjt6qSwO8r7TZzG3pozSIn9flmfGj4kUNK4ELlbVj7t/fxg4S1U/k3LMtcC1AHPmzFn2xhtvjMm1jgRV5Wcb3qSsyM973zoLf0oa/9j2RmJx5d2Lj23zVFVe3t+OCEwqCZLu86szHGNbQzuRWILaiiLecfLUfucf7za/0UxFcbBvb/FU8YTyxpEu1r14gMOdYU6rm0zd5BJmTi5hdnUpT+46zN7DXZw7v4bKkiCoM29gf2sPOw514BPB5xN6IjFe3t/O9EnFXHH6LMoLbASUMV5NpKBxFbByQNBYoap/m+74iZBpGGPMaPMaNMZDDlkPpI7zqwPG77RpY4wZx8ZD0NgILBCRE0UkBKwC1o3xNRljzHGp4BtgVTUmIp8BHsYZcrtGVfOzsIsxxpicFHzQAFDVh4CHxvo6jDHmeDcemqeMMcYUCAsaxhhjPLOgYYwxxjMLGsYYYzwr+Ml9uRKRDmD7gOJJQFueX2oKkK8NqvN9fSPxfvN9znzePyj8e5jP89m9Gx67f8eaApSpavY1hFR1Qv0Am9KU3TYarzOMc+X1+kbo/eb7GvN2/8bDPczn+eze2f3L9/lyuSfHS/PUr8f6ArLI9/WNxPu1e1hY58unQn+vhXzvoPDfb17PNxGbpzaph/VTxsvrTFR2/4bO7t3w2P07Vi73ZCJmGrdNsNeZqOz+DZ3du+Gx+3csz/dkwmUaxhhjRs5EzDSMMcaMEAsaLhGZLSJ/EpFtIvKKiHzOLa8WkfUistP9XeWW17jHd4rIfw041wdFZKuIvCQivxeRKWPxnkZTnu/fB9x794qIfHMs3s9oGsK9e4+IbHb/jW0WkXelnGuZW75LRL4vE3H7wAHyfP9uEpF9ItI5Vu+n4OVzaNd4/gFmAGe4jytw9iVfDHwT+JJb/iXgZvdxGXAe8Cngv1LOEwAagSnu39/E2a52zN/jOLl/NcCbQK3791rgwrF+fwV2704HZrqPlwD7U871HHAOIMDvgEvG+v2Ns/t3tnu+zrF+X4X6Y5mGS1UbVPV593EHsA2YBVyO88GF+/sK95guVX0S6B1wKnF/ytxveZUcB5tG5fH+nQTsUNUm9+8/Au8f4csfU0O4dy+oavLf1CtAsYgUicgMoFJVn1HnE/DOZJ2JLF/3z33uWVVtGM3rH28saKQhInNxvo1sAKYl/xG5v6dmqquqUeA6YCtOsFgM3D6Cl1twhnP/gF3AKSIyV0QCOP+jz85SZ8IYwr17P/CCqoZxPijrU56rd8uOG8O8f8YDCxoDiEg58ADwd6raPoT6QZygcTowE3gJuCGvF1nAhnv/VLUF5/7dAzwB7AVi+bzGQpXrvRORU4GbgU8mi9IcdtwMj8zD/TMeWNBI4X7gPwD8TFV/4RYfctN+3N+NWU6zFEBVd7tNBPcCbxuhSy4oebp/qOqvVfUsVT0HZx2xnSN1zYUi13snInXAL4GrVXW3W1wP1KWcto7joGkU8nb/jAcWNFxu/8PtwDZV/U7KU+uA1e7j1cCDWU61H1gsIsmFv96D08Y6oeXx/iEiU93fVcCngR/n92oLS673TkQmA78FblDVp5IHu00wHSJytnvOq/Fwv8e7fN0/49FY98QXyg/OSB7FaU7a4v5cijOa5xGcb7uPANUpdfYCzUAnzre8xW75p3ACxUs4677UjPX7G2f37+fAq+7PqrF+b4V274AvA10px24BprrPLQdeBnYD/4U7gXci/+T5/n3T/beYcH9/ZazfX6H92IxwY4wxnlnzlDHGGM8saBhjjPHMgoYxxhjPLGgYY4zxzIKGMcYYzyxoGDPKRORTInJ1DsfPFZGXR/KajPEqMNYXYMzxRGTdjmUAAAGOSURBVEQCqvrDsb4OY4bKgoYxOXIXxfs9zqJ4p+MsxX01sAj4DlAOHAY+oqoNIvIY8DRwLrBORCpwlt7+logsBX4IlOJMyPuYqraIyDJgDdANPDl6786YzKx5ypihWQjcpqqnAe3A9cB/AleqavID/6aU4yer6jtU9dsDznMn8EX3PFuBG93ynwCfVWf9LWMKhmUaxgzNPj26btFPgX/C2dBnvbtZnh9I3ZfhnoEnEJFJOMHkz27RWuC+NOV3AZfk/y0YkzsLGsYMzcD1dzqAVzJkBl05nFvSnN+YgmDNU8YMzRwRSQaIDwLPArXJMhEJuvs1DEpV24AWETnfLfow8GdVbQXaROQ8t/xv8n/5xgyNZRrGDM02YLWI/A/OKqr/CTwMfN9tXgoAt+BsJ5rJauCHIlIKvA581C3/KLBGRLrd8xpTEGyVW2Ny5I6e+o2qLhnjSzFm1FnzlDHGGM8s0zDGGOOZZRrGGGM8s6BhjDHGMwsaxhhjPLOgYYwxxjMLGsYYYzyzoGGMMcaz/w9h9J+xz8VaswAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", + "entre deux années civiles, nous définissons la période de référence\n", + "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", + "1er août de l'année $N+1$.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", + "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", + "de référence: à la place du 1er août de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er août.\n", + "\n", + "Comme l'incidence de syndrome grippal est très faible en été, cette\n", + "modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1985." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", + "\n", + "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici les incidences annuelles." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2053781\n", + "2012 2175217\n", + "2003 2234584\n", + "2019 2254386\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\n", + "2018 2705325\n", + "1988 2765617\n", + "2007 2780164\n", + "1987 2855570\n", + "2016 2856393\n", + "2011 2857040\n", + "2008 2973918\n", + "1998 3034904\n", + "2002 3125418\n", + "2009 3444020\n", + "1994 3514763\n", + "1996 3539413\n", + "2004 3567744\n", + "1997 3620066\n", + "2015 3654892\n", + "2000 3826372\n", + "2005 3835025\n", + "1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", + " française, sont assez rares: il y en eu trois au cours des 35 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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