{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse de l'incidence de la varicelle " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os.path\n", "import tqdm.notebook as tqdm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence de la varicelle 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 1990 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.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": 3, "metadata": {}, "outputs": [], "source": [ "filename = \"data.csv\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Données sauvegardées trouvées.\n" ] } ], "source": [ "if not os.path.isfile(filename):\n", " data = pd.read_csv(data_url, skiprows=1)\n", " data.to_csv(filename, index=False)\n", " print(\"Données téléchargées et sauvegardées.\")\n", "else:\n", " print(\"Données sauvegardées trouvées.\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02020297709261392102FRFrance
1202028772801515102FRFrance
220202779861481824102FRFrance
3202026769401454102FRFrance
420202572280597001FRFrance
520202473880959102FRFrance
6202023755811115102FRFrance
720202272770633001FRFrance
82020217602361168102FRFrance
92020207824201628102FRFrance
1020201973100753001FRFrance
112020187849981600102FRFrance
1220201772720658001FRFrance
132020167758781438102FRFrance
14202015719186753161315FRFrance
152020147387922275531639FRFrance
16202013773265236941611814FRFrance
172020127812357901045612816FRFrance
18202011710198756812828151119FRFrance
1920201079011669111331141018FRFrance
202020097136311054416718211626FRFrance
21202008710424770813140161220FRFrance
2220200778959657411344141018FRFrance
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2420200578505631410696131016FRFrance
252020047799158311015112915FRFrance
2620200375968410078369612FRFrance
27202002765344530853810713FRFrance
2820200179835701912651151119FRFrance
292019527794152461063612816FRFrance
.................................
15161991267176081130423912312042FRFrance
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1520199122715452995320951271737FRFrance
1521199121714903897520831261636FRFrance
15221991207190531274225364342345FRFrance
15231991197167391124622232291939FRFrance
15241991187213851388228888382551FRFrance
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\n", "

1546 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202029 7 709 26 1392 1 0 \n", "1 202028 7 728 0 1515 1 0 \n", "2 202027 7 986 148 1824 1 0 \n", "3 202026 7 694 0 1454 1 0 \n", "4 202025 7 228 0 597 0 0 \n", "5 202024 7 388 0 959 1 0 \n", "6 202023 7 558 1 1115 1 0 \n", "7 202022 7 277 0 633 0 0 \n", "8 202021 7 602 36 1168 1 0 \n", "9 202020 7 824 20 1628 1 0 \n", "10 202019 7 310 0 753 0 0 \n", "11 202018 7 849 98 1600 1 0 \n", "12 202017 7 272 0 658 0 0 \n", "13 202016 7 758 78 1438 1 0 \n", "14 202015 7 1918 675 3161 3 1 \n", "15 202014 7 3879 2227 5531 6 3 \n", "16 202013 7 7326 5236 9416 11 8 \n", "17 202012 7 8123 5790 10456 12 8 \n", "18 202011 7 10198 7568 12828 15 11 \n", "19 202010 7 9011 6691 11331 14 10 \n", "20 202009 7 13631 10544 16718 21 16 \n", "21 202008 7 10424 7708 13140 16 12 \n", "22 202007 7 8959 6574 11344 14 10 \n", "23 202006 7 9264 6925 11603 14 10 \n", "24 202005 7 8505 6314 10696 13 10 \n", "25 202004 7 7991 5831 10151 12 9 \n", "26 202003 7 5968 4100 7836 9 6 \n", "27 202002 7 6534 4530 8538 10 7 \n", "28 202001 7 9835 7019 12651 15 11 \n", "29 201952 7 7941 5246 10636 12 8 \n", "... ... ... ... ... ... ... ... \n", "1516 199126 7 17608 11304 23912 31 20 \n", "1517 199125 7 16169 10700 21638 28 18 \n", "1518 199124 7 16171 10071 22271 28 17 \n", "1519 199123 7 11947 7671 16223 21 13 \n", "1520 199122 7 15452 9953 20951 27 17 \n", "1521 199121 7 14903 8975 20831 26 16 \n", "1522 199120 7 19053 12742 25364 34 23 \n", "1523 199119 7 16739 11246 22232 29 19 \n", "1524 199118 7 21385 13882 28888 38 25 \n", "1525 199117 7 13462 8877 18047 24 16 \n", "1526 199116 7 14857 10068 19646 26 18 \n", "1527 199115 7 13975 9781 18169 25 18 \n", "1528 199114 7 12265 7684 16846 22 14 \n", "1529 199113 7 9567 6041 13093 17 11 \n", "1530 199112 7 10864 7331 14397 19 13 \n", "1531 199111 7 15574 11184 19964 27 19 \n", "1532 199110 7 16643 11372 21914 29 20 \n", "1533 199109 7 13741 8780 18702 24 15 \n", "1534 199108 7 13289 8813 17765 23 15 \n", "1535 199107 7 12337 8077 16597 22 15 \n", "1536 199106 7 10877 7013 14741 19 12 \n", "1537 199105 7 10442 6544 14340 18 11 \n", "1538 199104 7 7913 4563 11263 14 8 \n", "1539 199103 7 15387 10484 20290 27 18 \n", "1540 199102 7 16277 11046 21508 29 20 \n", "1541 199101 7 15565 10271 20859 27 18 \n", "1542 199052 7 19375 13295 25455 34 23 \n", "1543 199051 7 19080 13807 24353 34 25 \n", "1544 199050 7 11079 6660 15498 20 12 \n", "1545 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 2 FR France \n", "1 2 FR France \n", "2 2 FR France \n", "3 2 FR France \n", "4 1 FR France \n", "5 2 FR France \n", "6 2 FR France \n", "7 1 FR France \n", "8 2 FR France \n", "9 2 FR France \n", "10 1 FR France \n", "11 2 FR France \n", "12 1 FR France \n", "13 2 FR France \n", "14 5 FR France \n", "15 9 FR France \n", "16 14 FR France \n", "17 16 FR France \n", "18 19 FR France \n", "19 18 FR France \n", "20 26 FR France \n", "21 20 FR France \n", "22 18 FR France \n", "23 18 FR France \n", "24 16 FR France \n", "25 15 FR France \n", "26 12 FR France \n", "27 13 FR France \n", "28 19 FR France \n", "29 16 FR France \n", "... ... ... ... \n", "1516 42 FR France \n", "1517 38 FR France \n", "1518 39 FR France \n", "1519 29 FR France \n", "1520 37 FR France \n", "1521 36 FR France \n", "1522 45 FR France \n", "1523 39 FR France \n", "1524 51 FR France \n", "1525 32 FR France \n", "1526 34 FR France \n", "1527 32 FR France \n", "1528 30 FR France \n", "1529 23 FR France \n", "1530 25 FR France \n", "1531 35 FR France \n", "1532 38 FR France \n", "1533 33 FR France \n", "1534 31 FR France \n", "1535 29 FR France \n", "1536 26 FR France \n", "1537 25 FR France \n", "1538 20 FR France \n", "1539 36 FR France \n", "1540 38 FR France \n", "1541 36 FR France \n", "1542 45 FR France \n", "1543 43 FR France \n", "1544 28 FR France \n", "1545 5 FR France \n", "\n", "[1546 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(\"data.csv\")\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Non." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data = raw_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": 8, "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": 9, "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." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "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": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "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 creux en septembre." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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wuWn0ezK2HIbDUQZC0Vm7lWBMHNqDIeIGFs7ScnAqn0/1DQNQmyYgDWMtShSllEgrDiLyPRHpEJE9SWufFpHTIvK8/e9VSd/7hIgcFpEDIvKKpPVLRWS3/b2viN0ER0S8IvITe32biKzI7o947pIYszmV5VDlpX8kQiQWz8rrOamrlZ4ymgO+jGMObf3jaxxmQqPfQ01FOYftqu/kGofZ4IjD6V4rHXZ6y2H85DhFKSUysRz+C3hlivUvGWNeYv/7LYCIbARuBTbZ53xNRJy2mF8HbgfW2v+ca74b6DXGrAG+BHxhlj9LyeHk4E8Zc/BbqZ5TNa+bKUN26wy/101TwJux5TCxAG4miAhrmv0Jy+FMojp6tm4l63d1KgNxcFxQGpRWSpG04mCMeRzINFn+tcCPjTGjxpijwGHgMhFZCFQbY7YaK7r3A+B1SefcaX/+U+BGma61ppJgzHKY2q0EZM21NGY5WDOWMxUHZzzobNxKYMUdWm1xcFpnLJx1tpJ73HWmTWVVt5JSwswl5vBBEXnBdjs57TAXAyeTjjllry22P5+4Pu4cY0wU6AdSls2KyO0isl1Etnd2Zq/yt1hx2kBM9QaXEIcsBaWHwjHKywSP20VTwEvX4GhG40md8aCzsRzAijt0D4XpHQpzpm+EmopyqlI0y8sEdSspSmbMVhy+DqwGXgKcBb5or6d64jfTrE93zuRFY75ljNlsjNnc1NQ0sx2fg6RzKzX4HcshO7UOw6NRKj3Wm2tTwEvckFGqbHswRMDrnvUbuhOUbu0c5GxfaNbxBhizHJyAdM00vZUci0xbaCilyKzEwRjTboyJGWPiwLeBy+xvnQKWJh26BDhjry9JsT7uHBFxAzVk7sYqadK7lay6gmzVOgyFY1TZk9Wa7WE4maSztvXPrgDOITlj6XTfyKzjDTA2KvRsXwiXgN8ztWA58QntzKqUIrMSBzuG4PB6wMlkuhe41c5AWokVeH7WGHMWGBCRK+x4wjuAXyWdc5v9+Z8BvzdadZQRwZEILoGqKd7gairKKXNJ1txKw+Eold4xywEyK4SbbY2Dw+LaCnzlLg53DM6pAA7A6y7D43Ylahyma8HhcbvwlbsIakBaKUHS2vkicjdwPdAoIqeATwHXi8hLsNw/x4C/BjDG7BWRe4B9QBT4gDHGqSB6H1bmUwVwv/0P4LvAD0XkMJbFcGs2frBSIBiK4ve6p3yDc7kkq/2VhkaTLQfrzT6TdNaOYIjVqzPrmpoKl0tY1ehn16k+gqHorIPRDgGvm+5oeNp4g0O1XSWtKKVGWnEwxrw1xfJ3pzn+DuCOFOvbgfNTrIeAN6XbhzKZ6VpnODRUZa+/0lBSzMHprprOckiMB62ZeeuMZNY0+/nt7rMALJqDWwks11L3UIbioM33lBJFK6SLmOma7jlks7/SUDhGldeyHCo8ZQS87rTikBgPOge3EsDqJj9ROzNq8RzcSjAWlE4nrGDFczQgrZQiKg5FzFTtupNp8GfPrTQcjo7LOGqqHqt1iMTiKWdXn+2zaxzmKA5OUBpmXx3t4KSzZmI5BHzlGpBWShIVhyImE8shu26lWMKtBNDkt8QhHI3z1z/cwcv//Q8c7Road86eM/0ArF+Qfr7zdDji4JKxTKnZ4vdav7PM3UpqOSilh4pDEZNRzMHvJRiKEo7Ovb/ScDiaCEgDNFf7ONM/wgf/eye/399B3MC2I93jztl5vI+GKg/L6ivn9NorGitxiVVI5y6b223rpP5ONz86+VgNSCuliIpDERMMRTOKOQD0Ds/NtRSPG4bDsUQqK1iWw6neER7c186nb9lIfZWHPx7rHXfezhO9XLysjrl2RPG6y1jeUDXrbqzJOLUOMwlIa3a1UmrMrmRVyTuxuGFwNH3ModE/1kJjLn7/kYiVkZxsOThZQ5989QbeuWUlT7d2s/34WP1iz1CYo11DvHnzUrLBP75qA77ysvQHpmEmMYdqXzmRmCEUiVPhmftrK9ljcDRK/0hkzgkKSmpUHIoUZ9RlesvB8s/PtIWG0zPJmfk85DTdS7Ic3nb5Mi5ZXscly6zWWi9dUc+D+9rpCIZorvbx3AnLirhkWe2MXnsqXr6xJSvXcSqfMwtIO51ZIyoOBcaXHz7Ib3e38dTHX5bvrZyTqFupSAmmabrn4LiVZprO+nf3PM87vrct8fXw6GTLodLjTggDwOYV1ufbj1uisPNEL26XcOGS7IhDtvDPJJVVm+8VLEe7hjjbP0I8g+aPysxRcShS+kem76vkkOxWypTDHQP86vkz7D87kFhzpsBVTtOLaNOiGnzlLv54zHIt7Tzex4aF1QX3xO1YDHWVnrTHOr/ffq11KDjag6PEjc7byBUqDkVKppZDtc/urzQDt9I3/3AEsOZAOFlOzvxopwguFR63i5csrWX7sV6isTjPn+zLmkspm7x8QzN3vP58zssgvVYth8LFGSLVN5K9UbjKGCoORUqiXXeamIPLJTOqkj7TN8Ivnz+dsDg67RoJJ+aQru32S1fUs/dMPztP9DESiXHJ8rppj88HlR43b798eUYZVGNtu1UcColoLE6XfW/2Dev/TS5QcShSxiyH9DkFDVUeujJ0K333yaPEDfzNjWsBq2keJMcc0otD3MB3n7Ssj+SYRDFSnWjbra6LQqJrMIyTXTzXNG0lNSoORYrzJJtJUDVTy6F3KMzdz57gtRct4mL7Tb09ON5yqEwTP7h4WS0ugQf3tdMU8LKkrrjTDNWtVJg4LiUYi78p2UXFoUgJhqJImmE1Dg1+b0bi8IOtxxkOx/jr61bTXO10XXUsh8zcSgFfORsWVmOMlcJa7OPAvW4XnjKXNt8rMNqTxEHdSrlBxaFICY5Epp3lkIzlVkrfWvvHfzzBdeuaWL8gQEOVlzKXJFkOllspneUAlmsJit+lBCAiVFe41XIoMDqSxEHdSrlBxaFI2H2qn5f922OJsZyZNN1zaKjyMJCmv9KOE72c7Q/xhksWA1bxW6Pfk3hCGw5HKXMJXnf6W+aKVQ0AXLayPqP9FToBHfhTcLQFQ5S5hCpPmVoOOULFoUjYfbqfI11D/G5PG+C0685MHOr96Qvh7n3+DL5yFy/fMFaF3FLtS0x6szqylmXkJnrFphZ+/aGrE3GLYqfa59aAdIHRHhylye+l3u/RmEOOUHEoEhzT+aEXOwDHcsis+0lDmhYa0Vic3+4+y43ntYyLKTQHvOMsh3SZSg4iwvmLazI6thjQaXCFR3swREu1l9oKD33qVsoJKg5FgvMHsLW1i4FQhIFQ5pZDQ5oq6a1HuukeCnPLRQvHrTdX+xLDfIZGY1ROUwB3LqNzpAsPSxx81FaW06tupZyg4lAkOH7VSMzw+MEua5ZDhjGHdP2V7n3+DH6vm+vXN49bbwn4ElXSQ+FooptpqRHwuXXgT4HRHhy1xUHdSrmiNP/ai5De4QjrWvx0Dozy0L42y62UQQEcQGPCrTRZHEajMR7Y28bNm1omtcNOpLMOjjJsxxxKkeoKHRVaSIQiMfpHIiyosVrGq1spN6g4FAn9I2HqqzxcsLiWh/a1WbMcMrQcqivcuF2SMp318YNdDISi3HLRoknfa7HFoSMYYigcZcEc50AXK9U+N6FInNFoDK+7NAWykHDiYM0Bb0Io4nGTUVq3kjnqVioSeocj1FV6uGljC8FQFGMyq44GK0DcaM97nsh9u85QW1nO1WsaJ32vOWCJQXtwdNIUuFLC+T1rxlJh4NTeOG4l7cyaG1QcioS+4TC1lR6uWduIx641yDRbCSwXUUcKcXj+ZB9b1jRSnmIuc3KV9NDo+PnRpYRjoWlQujBwWmcsqPFRawu3dmbNPioORYAxhr7hCLWV5VR53WxZbRWZZWo5gGWCJ1eVOtdtD4amHLOYXCU9HI5NO8vhXMaZBqdB6cLAuY9bAla2EmgLjVyg4lAEDI5GicYNdfYfgjMuM9OYA0BTwDfJrdQ/EmE0Gqc54E15TplLaPJ7abNjDtPNcjiXGXMr6RtQIdAeDOF1u6iucCfEQVtoZJ/SfBQsMpynotoKKyX1DRcvYXg0lhjLmQnNAS/dQ2EisXjChZTsu53yvGovJ3qGMWb6KXDnMmNuJbUcCoG24CgLanyICLX2ND9NZ80+ajkUAQlxsJ+SKjxl/NW1q1LGCabCiR8kZyy1J/lupzwv4ONo1xAw/RS4cxknZVirpAuD9mCIFjtZIhFzULdS1lFxKAIck7muKv3M46lwMo+SXUvtSb7bKc+rHstyKlXLwZk57Yikkl86giFa7Aca5/9G3UrZR8WhCOgbcdxKmccYJuLEFTqCY+LgZC85VkUqkoXDX6KWQ6XHzasuWMAPtx6fFNRX5hdjDG3BEC32/ewucxHwudVyyAEqDkWAUwHq+FdngyMAHRMsh5qK8kmV0cm0JAlHqVoOAP/7lecRjcf54oMH872VkiYYihKKxMfFyWoryzXmkANUHIqAiTGH2dDo9yJCYh4EQFt/aNybfyqSrYpSjTkALG+o4h1XruCeHSd58Www39spWRJprElxMu3MmhtUHIqA3uEwfq97RgHoiZSXuaiv9Iy3HAZGp81UgrFYBZS25QDwoZetodpXzj//9sV8b6VkaUvEycYeWrQza25QcSgCnAK4udIU8I6POdhtj6cj+fuZznM4V6mt9PChl63hiUNd/OFgZ763U5KkSr/Wzqy5QcWhCLBaZ8xdHKz5DNaTVzxu6BgYTetWaqjyUGY3NCvVeQ7JvOPKFdRVlvPrXWfyvZWSJJFhlywOFeXqVsoBKg5FgNN0b640+cf6K3UPhYnFTVrLwWVXSYNaDgAet4vzF9fwYpvGHfJBezBEtc9NRVKfLycgHY+bPO7s3EPFoQjoH4nMKVPJwalZiMdNUtvj9G24m6utYLavXG8XgA0LqznYPkgkFs/3VkqOY93DLJrQC0w7s+aGtH/tIvI9EekQkT1Ja/Ui8pCIHLI/1iV97xMiclhEDojIK5LWLxWR3fb3viL2pHoR8YrIT+z1bSKyIrs/YvHTOxyeU42DQ3PASzRu6B0OZ1QdPXaejyqPG/u/rOTZsDBAOBrnSKcWxc0n8bjhuRO9XLysdtz6xM6s4WicA20D876/c41MHgX/C3jlhLWPA48YY9YCj9hfIyIbgVuBTfY5XxMRx/77OnA7sNb+51zz3UCvMWYN8CXgC7P9Yc5FYnFD/0gk0XRvLjhWQsfAaFJgb/qYA8CmRdWsaKyc8+ufK2xcWAOgKa3zTGvnIAOhKBcvG99TbGJn1u89dZRXf+WJKcfiKpmRVhyMMY8DPROWXwvcaX9+J/C6pPUfG2NGjTFHgcPAZSKyEKg2xmw1xhjgBxPOca71U+BG0UfUBAOhCMbMrQDOIbkQrj0YQsSqf0jH39y4ll++f8ucX/9cYVVTFZ4yl4rDPLPjeC8Aly5PLQ5OC43fv9hBNG442K7Ww1yYrRO5xRhzFsD+6EymXwycTDrulL222P584vq4c4wxUaAfaEj1oiJyu4hsF5HtnZ2lkUrYm4UCOIexFhohOgZCNFR5M6qdcLkE9xxqLM41ystcrFvgZ5+Kw7yy80QvtZXlrGqsGree3Jl1IBRh5wlLRFo7B+d9j+cS2f6LT/XEb6ZZn+6cyYvGfMsYs9kYs7mpqWmWWywunBS9bGQrTXQrZeJSUlKzYUE1+84EsQxhZSI9Q2Fu+c8nOZTFp/cdx3u5ZFndpNhXcmfWp1u7idpZS4c7VBzmwmzFod12FWF/7LDXTwFLk45bApyx15ekWB93joi4gRomu7FKFsePWpMFy6HCU0bA66ZzYJS2/hAL0qSxKlOzYWE13UPhlHO5Fdh+rIfdp/vZbruC5krfcJjWzqFJLiUY35n18YOdVHnKOG9BQMVhjsxWHO4FbrM/vw34VdL6rXYG0kqswPOztutpQESusOMJ75hwjnOtPwN+b/RxLEFvFi0HgKZqLx0DllupWcVh1mxcVA2grqUpcPz9yRX5c+G5E30AXLJssjgkd2b9w8FOrlzdyHkLArSqOMyJTFJZ7wa2AutF5JSIvBv4PHCTiBwCbrK/xhizF7gH2Ac8AHzAGBOzL/U+4DtYQepW4H57/btAg4gcBv4OO/ODdTuiAAAgAElEQVRJsXAsh2xkK4EVdzjdF6JrMKxupTmwYYElDi+e1aBnKvbbqaTtA9lpcb7jeC9lLuGipTUpv19bWc5zJ/s41TvCdeubWN3k50x/iKFRrX2YLWlLXo0xb53iWzdOcfwdwB0p1rcD56dYDwFvSrePUqVvOIwIBGYwL3o6mgM+HtzXBkw/HlSZnprKchbXVqjlMAVjlkP2xGHDwsCUzR9rKzzsOmlZF9etbWLvmX4AjnQOccGS1IKiTI+moBQ4fSMRairKE/2N5kpzwEsoYlX2quUwNzYsrNZ01hQkFwh2zDIm88Ceszy0rx2AaCzOrlN9XJrCpeTgZPOtaKhkWUMla5r9ABzuVMtutmiznAInW32VHJLnM6jlMDc2Lgzw+/3thCKxaQcmlRpHugaJxg1+rztRiT8T+obDfOQnzxOKxPnkqzdwxaoGhsMxLkkRjHZw0lmvXWdlMS5vqKLMJRqUngNqORQ4fcPhRDZGNkjupaTiMDc2LqombtBWDRNwfh9Xrm6ga9Bq8DgT7n72JKFInCtXNfBPv3mRj//8BSB1MNrBSWe9dq0lDh63i+X1lbR2aIuT2aLiUOD0DWendYZDkzN71yXUZ9EiKUU2LHSC0upaSmZ/2wDlZcIVqxqIxQ3dQ5m7lqKxOD/ceoyrVjfwo/dczq0vXcqe00GaA16W1FVMed7iugqqPGVcuXqsfnZ1s5/DWgg3a9StVOD0DocT/tNs4FRJNwe8uLIUxyhVltZV4ve62WMHPxWLg20DrG7ys9juntoRHM2o+y/A7/a2c6Y/xGdeez5lLuFzb7iANc1+Ar7pGz++86oVvOaiRVR5x97S1jT7eXR/B5FYfE5TFEsVFYccc7hjgPIyF8sbqtIfbBOLm0QAOltT4BycP1KtcZg7Lpdw8bJath3Rms1k9rcNcOnyukTCgzW3PLOMoe89dZTlDZW87DyrI4+I8J5rVqU9z1deNqmV95omP9G44Xj3cFYfsEoFldMc87H/eYH337Uz4+P/e9sJrvjcI3QPjhKJxRkcjWY1IF1d4cbjdml1dJbYsqaRQx2D9hugMhCKcLpvhPULAokHkPYMC+FeONXHjuO93Hbliqxk5622BUF7LM0OFYcc0x4MsfdMMOMb9PGDnXQOjPLFhw4mCuCyaTmICK86f0Eiq0OZG1tWNwKwtbU7zzspDA62W/f5+pZAYoJgplXS33vyKH6vmzdtXpL+4AxY3WRZ65qxNDtUHHKIMYZuu6f8fRnOHN59up8yl/DjZ0/wzBHrDScb7bqT+Y9bL+Ztly/L6jVLlY2Lqqn2uXnqcFe+t1IQOJlK6xcE8Lhd1Fd5MqqSfu5EL7/adYa3XrY0awWfAV85C6p92kZjlqg45JChcIxw1Co4u2/XmbQdPLsHRzndN8JfX7uK6opyPnPfPiB7rTOU7FPmEq5c3cBTh7u1QytWZXSVpyyRWdQc8Katkg5H43z8Z7tZUO3jwzeuzep+1mjG0qxRccghvbbVcPGyWlo7h9K2Wth92sp6uWZtE3/78nV0DVrmeG2FppwWMlvWNHK6b4QTPcP53kre2d8WZN2CQCKzqKXal7ZK+ht/aOVA+wD/9Lrzs2Y1OKxuqqK1Y1CFexaoOOQQx6X0tsuW4XYJ9+06O+3xu09Z4nD+4mredvmyRIZFNmMOSva5yo47PHW4tOMOxhgOtA1w3oJAYq054J22SvpwxwBf/f1hbrloETduaMn6ntY0+xkKxzjZM5L1a5/rqDjkkB67+Gd1s5+r1zamdS29cLqfVU1VBHzllJe5+MIbL+BVFyxgYY1mFhUyq5uqaA54ebq1tOMOnYOj9A5HWNcyJg4t1b5pq6T/8Rd7qPSW8albNuZkT9evb0YEfrrzVPqDlXGoOOSQ7kHLcmio8nDLhYs43TfCTrsvfSp2n+rngsVj+eCXLq/na2+/VEd0FjgiwpY1jWxt7SY+w1YR5xIH28YylRyaq71TVkkPjkbZdrSHd161IqNZ5rNhaX0l169r4u5nTxCJxXPyGucq+q6TQ5xBPfVVHm7e1ILH7eIXz6V+gukYCNEWDI0TB6V4uGp1A91DYQ6U8FB7J117TctYwVliNG2KdFYni8hpQ5Ir/uLK5XQOjPLg3vacvs65hopDDukeCuMpc+H3ugn4yrnlwkX86JkT/NvvDkx6wtxjB6MvXFKbj60qc2TLGifuULqupSOdgwS87kR9AzChSno8h2xxWJvj6uXr1jWzpK6CHz5zLKevc66h4pBDegbD1Fd5Epkb//yG83nL5qV89dHD3P7DHQyEIoljXzjVjwhsWpTbpyglNyyqrWBZfSU7sjQzOV8c7hjkyUOzE7jWziFWNfvH9UCarkr6UMcAnjIXy+orZ7fZDClzCW+/fDnPHOnhUAlbdjNFxSGH9AyFqasaS0P1usv4/Bsv4NO3bOTRAx287dvbCEWsKaq7T/Wzusk/rnGYUlwsqauY9XCbQuFrjx7mg3fvnFXqZ2vnYKIq2WG6KunWjkFWNVXNS0ztzZuX4Clz8aNnjuf8tc4VVBxySPdQmIaq8TUKIsI7t6zk62+/hN2n+/n/fm0Vuu0+3c+FGm8oauqrPPTY6cvFSs9wmL7hCJ2DMxO5wdEoZ/tDrG4a7yLyuF00TFElfahjMNH/KNc0+L28+sKF/GznaZ0rnSEqDjmkd9hyK6Xi5k0LeO91q7lr2wm+88QROgZGddZtkdNQ5aF7hm+qhYbTz+tQ+8yqio/aY0EnigNYM0QmVkmHIjFO9AznPN6QzGtesojB0SgvnNIW65mg4pBDnJjDVHz05nVsXl7HP/3mRQAuVHEoauqrvARD0aJOmQyOOOIwM998IlOpeXJr+lRV0q2dgxgDa5sDk47PFUvslt4ztYpKFRWHHDEajTEwGp3kVkqmvMzFV956MXWV5bgENi5UcShmGvzW/3VvEbuW+h1xmGGzutbOQcpcwrL6yeKQqkra6ZQ6n3MWnCmI6Xo9KRYa/cwRvUPWH1ndNOIAVpbLd27bzJ7TQSo8OqS+mHEeBLoGw0U5TMkYMyYOM3QrtXYOsqy+Eo978vNmcpW0M6fhcIclJisac5uplExNRTmeMpdaDhmi4pAjnMDkdJaDw6XL67l0eX2ut6TkGMeFWKxB6aFwjGjcIAIHOwYwxkw7mjOZ1o6hSZlKDi1JVdJOUdyh9kGWN1Tidc/fA5GI0BTw0pnhfIlSR91KOcJ5g5gu5qCcWzhupVStIooBx2pY1xygbzhC12BmIheLG452DaUMRgM0paiSPtQxMK/B6LG9eNVyyBAVhxzhvEE4bxjKuU99leXTLlbLod/OVNq8og6w3sAd7tt1hmv/5VHu2X5yUnX/qd5hwrH4lOIwsUo6HI1zLE9zna3MKRWHTFBxyBHOG0Q25z8rhU1thZVY0J3hE3eh4VgOL11huTiT4w4//uMJTvYO879++gJv/MbTiXYvMJaptDpFphJYcTWAXSetc453DxGLm3nNVHJoVsshY1QcckTPUBiR7I/4VAoXl0uor/Ik5ngUG/0j1r7XtvgJ+NwJy6F/JMK2Iz3cfu0qvvimizjZM8zrv/YUB+101yN2jcOqxqksBx83b2zh208coa0/lMiEypfl0DMUTkxoVKZGxSFH9AyFqav0JLIzlNLAqpLO35OpMYZP/PyFWc2WcCyHmopy1jb7E5bDYwc6iMYNN29cwBsvXcIDH7kWX3kZX7h/P2BZDvVVnmkz8z756o1E44YvPLCfQ+2DiKQumMs1TkC8WONC84mKQ47oGZq+AE45N8l3C42uwTB3P3uSu589OeNzHXGorfSwriWQeMJ/+MUOGv0eXrLU6hjc6Pfy/uvX8Mj+Dra2dk+bqeSwrKGSv7pmJb947jT37jrNkrqKvKRuj9U6qDikQ8UhR3SrOJQkDX5vXt1KR2z//85ZdIftH4lQ5hKqPGWsafbTMxSmrT/EYwc6uPG8lnFW8Lu2rGBRjY9//u2LHO4czMgKeP/1a2ip9tLaOZSXeANYMQeAziJvkDgfqDjkiJ6hMPUabyg5rP5K+ROHVtv/f7pvhLP9M5ub3DccoaaiHBFhrT3N7a5txxkIRXn5xvHznX3lZXzsFevZfbqfnqFwRuJQ5XXzD6/aAOQn3gBJloOKQ1pUHHJE71CYek1jLTnqqzz0j0Ty1l/JsRyAGc+W6B+xxAFgnT3N7c6nj+Erd3G1Pcwomde9ZDEb7SluU2UqTeQ1Fy3ik6/ewNsuWzajvWULZxypWg7pUXHIAfG4oXd4crtu5dzH+T93RsTON0e6hljT7KeivGySONy17TiPHeiY8txkcVhQ7cPvdRMMRbl6TVPK+IDLJXz6NZtY0+zPeIKhiPCea1axojEzMck2HreLusrylJPplPGoOOSAvpEIcaPV0aVIvgvhjnQOsr4lwEVLa8aJQ89QmE/fu5cvPXRwynODSeIgIgnXz00bm6c857KV9Tz8d9clnsiLgeaATy2HDFBxyAFOKqOKQ+mRaKGRh7jDaNSakbCqqYpLl9ex90yQ4bA12Oa+XWeIxAwvnO6nbwqrJtlyAMu1JAIvO68l5fHFSlPAqzGHDFBxyAHOG4OKQ+nhuJXykbF0onuYuLHqBy5dXkcsbhJVyT/feYqA140xsLW1O+X5fRPE4fZrV/Pvb74oEcQ9V2gOeNVyyAAVhxzg+JtVHEqPRGfWPLRocDKVVjVVcckyqz/SzhO9HO4YYNepfj7wsjX4vW6ePDy5QC4eNwRHItRWjonDmmY/r794yfxsfh5pssVhNnOyS4k5iYOIHBOR3SLyvIhst9fqReQhETlkf6xLOv4TInJYRA6IyCuS1i+1r3NYRL4imfYJLlC6E+26z60nLiU9tZUeRPITc3B6HK1q8lNb6WFNs5/tx3r42c7TlLmEN16yhCtW1acUh8FwlLhhnOVwrtIU8BKOxQmO6Czp6ciG5XCDMeYlxpjN9tcfBx4xxqwFHrG/RkQ2ArcCm4BXAl8TEScF4uvA7cBa+98rs7CvvNFju5Xqqs79PzRlPGUuoa4yP/2VjnQO0VLtxe+1xrRsXl7HzhN9/PK501y3rommgJctaxo53j3MyZ7hcec6HVmrS0QcAM1YSkMu3EqvBe60P78TeF3S+o+NMaPGmKPAYeAyEVkIVBtjthrLzvtB0jlFSfdQmIDXPa+DTJTCIV+FcEe6Bsc1v7tkeR39IxHO9od4wyWLAbhmrVWv8NQE6yG5r9K5jtNfSeMO0zNXcTDAgyKyQ0Rut9dajDFnAeyPTh7cYiC54cspe22x/fnE9UmIyO0isl1Etnd2ds5x67mjZyicdjyocu6Sj/5KxhhaOwbHFaNdutzy6AZ8bl6+wco4Wt3kp6XayxNTiENtCYiDVklnxlzHhG4xxpwRkWbgIRHZP82xqeIIZpr1yYvGfAv4FsDmzZsLNprUO6x9lUqZBr+HA20D6Q/MIt1DYYKh6DjLYVVjFUvqKnj5hhZ85ZYVKyJsWdPIo/s7iMcNLrtfUsJyqDz3xaG5WqukM2FOloMx5oz9sQP4BXAZ0G67irA/OiWZp4ClSacvAc7Y60tSrBct3YNaHV3K5MNyOJKUqeQgIjzwkWv5x1dvGHfsNWsb6R2OsO9sMLFWSm4ly+Xr0phDGmYtDiJSJSIB53PgZmAPcC9wm33YbcCv7M/vBW4VEa+IrMQKPD9ru54GROQKO0vpHUnnFB3GGE71DtNS48v3VpQ80VDlpXc4QnQe+ys5PZUmNsDze92Ul43/M9+y2oo7JGct9Q2XjjiICM3VWuuQjrlYDi3AkyKyC3gW+I0x5gHg88BNInIIuMn+GmPMXuAeYB/wAPABY0zMvtb7gO9gBalbgfvnsK+8cqp3hGAoyqZF1fneipInnCrpXvsNdz5o7RzE63ax2B7JOR3N1T7WtfjHFcP1j0TwlLmoKC+NJIomv1ZJp2PWMQdjzBHgohTr3cCNU5xzB3BHivXtwPmz3Ush4czWvWBxTZ53ouSLRCHcUHjeqouPdA6xsrEqEUNIxwWLa3ni0FhSR/9IhGq7XXcp0BzwJepClNRohXSW2XOmH7dLWNeSn2EmSv6pT7TQyN2TaSxu+N6TR7lv1xk6BkIc6Rqa0djNDQsDdAyM0m1XcltN9+aan1I8aH+l9JTO3TBP7D4dZG1LIJEdopQeDfPQmfXp1i4+++t949b+9MKFGZ+/foH18HKgbYCr1njpGwmXRLzBoTngpX8kwmg0pvVIU6CWQxYxxrD3dD/na7yhpJmPzqwP72vHV+7ip++9kn941Xm85qJF/OmFizI+/7wF1j2630657R+JUFtCkws1nTU9ajnMgVO9wzQHfHjclsa2BUN0D4U5X+MNJU2d3V8pVy00jDE8tK+dq9c0sXlFPZtX1M/4Gk0BLw1VHva3Wems/SORvM11zgdNSbOkl9RV5nk3hUnJWQ6hSIwT3cPpD0zDQCjCTf/+OF9+ZGx4yp7T1h/a+YvVcihlylxCbUV5Yq5Httl7JsiZ/hA3b5zbnIXzFgYSxXr9w5GScistqLayuk73zWzOdilRcuLwnSeOcO2/PkooEkt/8DQ8e7SHkUiMn+44RSxuFWvvOd2PS2DDQhWHUieXhXAPv9huDeHZMPWEtkw4b0E1B9oHiMTiDIxGS6LpnsOqpipcAofaNWNpKkpOHBbWWE8MZ+b4xPC0nSPeHhzl6VarmGjP6X5WN/mp9Ki3rtRpDvjYdbKf3hwIxEP72rlkWd2cR3OuXxAgFImz53Q/xpRGXyUHX3kZy+orOdg+v21OiomSE4dFdpHQ2f65lc4/dbiLzcvrCPjc/GLnacBKY9V4gwLw4RvX0jk4ym3ff5aBUPaK4U73jbD3TJCb5uhSAthgB6W3He0BSqM6Opm1LQEVh2koQXGw2lrMxdfYPTjK/rYBbjivmT+9cBH372njePcQ7cFRrYxWALhydQNff/sl7DsT5N3/tZ2R8NzcmA4P72sHyIo4rG3x4xLYdsSygktNHNa1+DnWPcxoNDv/N+caJScOC+yeR2f7Zm85PHPEetK6cnUDb7hkMSORGF980ApMq+WgONy4oYUvveUlbD/ew9//dFdWrvnwi+2saqyaUcHbVPjKy1jRWMX2Y71AaXRkTWZdS4BY3HC0ayjfWylISs457nWX0ej3zinm8FRrF36vmwsX11DmEpbWV3DvLquR7Ea1HJQkbrloETtP9PKjZ44zEo5R4Zl5wVU4Gqe1c5AXzwZ55kg3f7llZdb2t2FBNUc6zwKlFXMAEl0MDrYPJuo+lDFKznIAWFzr40z/7MVha2s3l6+sx13mQkR4gz2EfWVjFdW+0voDU9Jz/fpmIjHD9uM9Mz5335kgF37md/zJl5/g7+7ZRXmZi1suyrzYLR1OpTSUnltpVVMVZS7hkMYdUlKS4rCwpmLWlsOZvhGOdg1x5eqGxNrrL7YG12m8QUnFS1fU4XZJIsNtJmw/3kMoEucLb7yAh/72WnZ96uasui7PSxKHUkplBcuLsLxBM5amouTcSmBlLD1+qBNjzIy7UDptjq+ye+IDrGis4pOv3pAYy6goyVR63Fy8rHZW4tDaMYjf6+bNm5fmpGOq407xul0l2Q9sXbNmLE1FSVoOi2p9DIdjielXM+Gp1i7qqzzjnrgA3nPNKi5epuKgpObK1Y3sPtVHcIZpra2dQ6xuqspZK+0ldRVUecqoLbFgtIOVsTQ056LYc5ESFQenEG5mGUvRWJytrd1cuaoh4775igJw1eoG4gaePTKzuENr52BWMpOmwuUS1i8IlFy8wWFtS4C4GRuzqoxRkuKw0E5nzTTuYIzh0f0d/MmXn+BsfygrOeZKaXHxslq8bteMXEuDo1HO9odY3Zw7cQD42M3r+ejN63P6GoWKk7F0qENdSxMpyZjD4kSVdHpxGByN8r4f7eCJQ12saKjkG39+Ka/YpOKgzAyvu4zNK+oSrVYy4aj9NLu6qSpX2wLgqjWN6Q86R1nZWIXbJRp3SEFJikOj30t5mXA6A7fSF+7fz5OHu/g/f7qRv7hieaI9t6LMlKtWN/KvvztA9+AoDRn0RXLGWObSrVTqeNwuVjRWcaBNG/BNpCTf6VwuYUGNL63l8OzRHn74zHHeddVK3n31ShUGZU446c/PZBh3aO0cpMwlLG/IreVQ6qxr8atbKQUl+26XrtYhFInx8Z+9wJK6Cj72inXzuDPlXOXCxTX4ve6MXUutnYMsr6/Uh5Ics7Y5wIme4az1vzpXKNm7bnFtxbTZSl955BBHuob43Bsu0BbcSlZwl7m4bGV9xkHp1o4hVqlLKeesawlgzJgbT7EoWXFYWOOjLRhKDOpJ5ljXEN98/Ah/dukSrlnblIfdKecq165t5GjXEEfSvBE5DeFWN6tLKdc4LUT2nQnmeSeFRcmKw6LaCmJxk3LA+GMHOojFDX9z49o87Ew5l3m5nQb98Ivt0x53qneYcCyuweh5YHVTFU0BL48f6sz3VgqKEhaHqec6bDvaw+LaCpbW6+BxJbssqatkw8JqHto3vThoptL8ISJct66JJw51EY3F872dgqGExSF1rYMxhm1He7hiVUOq0xRlzty0sYUdx3vpHpxstTq0dsxPjYNicf36JvpHIuw61ZfvrRQMJSsOU82SPtQxSM9QmMtX1edjW0oJcPPGFuIGfr+/Y8pjDncM0uj3UFvpmcedlS7XrGnCJfDYAXUtOZSsOFT73Pi97kkZS8/YIxOvWKmWg5IbNi2qZmGNb5xrKR43dATH7sXWzkHNVJpHairLuWRZnYpDEiUrDiLCwhrfJMth25EeFtX4WFpfkaedKec6IsLLN7TwxKEuQpEYsbjhQ3c/xxWfe4R7tp8Ect9wT5nM9eub2H26P2WSSilSsuIAVtzhbP/Y05oVb+jm8lUNOWuRrChgxR1GIjGePNTFJ3+5m9/sPsuKhir+109f4N9+d4De4YjGG+aZ69Y1A/D4QbUeoOTFYbzl0No5SNdgmCs03qDkmCtWNeD3uvn4z3dz97Mn+eANa7j/I9fwqgsW8NVHDwOwJsfdWJXxbFpUTaPfwx8KQBwGR6P86Jnjs5o5ky1KWxxqKugeCicGfWy1e95crvEGJcd43C6uW99E1+Aof37FMj568zq87jL+862X8NbLluJ2CRsW6tjZ+cTlEq5d18TjhzpTFsf2DYfnJdW1Ixjizd/Yyid/uYfXfPVJXjybn+K8khaHjfbM50/8fDfRWJxtR7ppqfayvEHrG5Tc87Gb1/OpWzby2decn3BjlrmEf379Bez45E20VPvyvMPS4/r1zfQNT05p7RkKc8O/Pcb77to5bt0Yw3t/uIM3f2Mrh7PQvO9wxwCv/9rTHOse4pOv3kAoEuP1X3uKn+04xXA4ijGTRStXlLQ4vOy8Zj528zp+8dxpPvDfOxP1DRpvUOaDlY1VvGvLyklTBUWEmhId25lvrl3biMft4pt/aB33Rvylhw7SOxzhoX3tPLi3LbH+PztO8cDeNnad6uNVX3mSbz9+JKXVkQn724K88etbGY3G+cntV/Kea1Zx34eu5qIltXz0f3ax8f/+jjX/eD8XfebBROJCLinpjnIiwgdftpYqr5vP3LcPUJeSopQytZUePnrTOj53/35+9fwZXnfxYg60DXDXtuO87fJl7Dzey2fu28fVaxsZHI1yx29e5LIV9Xz1bRfzj7/cwx2/fZFHD3Tw7Xdspso79vZ6oG2A7cd7eMPFS6jwlE163VAkxofvfg6P28XP33dVojtDc8DHXe+5nF+/cJa2YIiBUISBUJQV89DGvaTFweFdW1ZS5XXzzT+0csN52mhPUUqZ91yzigf3tfN/f7WHK1Y18E+/2UfAV87f37yew52DvOkbW/nyI4c41TvCSCTG5954Ac3VPr71F5dyz/aT/MMv9vDO7z/L9991GX6vm4f3tfPhHz/HcDjGfzx8iA/esIZbL1uK1z0mEp+/fz8H2we58y8vm9S2x13m4nUXL57vXwMynz6sbLJ582azffv2fG9DUZRzkGNdQ/zJl59gQY2Po11DfOqWjbxry0oA/v5/dvGznaeIG/j7V6znAzesGXfub144y4d//BwXLanhpo0L+Jff7eeCxTV86GVr+fYTR3j2qFVL9d7rV/PmzUvZeqSbd33/j7zzqhV8+jWbcv6zicgOY8zmtMcVijiIyCuBLwNlwHeMMZ+f7ngVB0VRcsmdTx/jU/fuZXVTFQ985FrKy6wQbc9QmBu/+Bgt1T7u+9DVifVk7t99lg/d/RzRuOGVmxbwpbe8hApPGcYYnjrczX88fJDtx3tpCniJxQ1Nfi+/+uAWfOWTXU7ZpqjEQUTKgIPATcAp4I/AW40x+6Y6R8VBUZRcEo8bvv6HVq5b18T5i2vGfe9s/whVXjfVvqkTB5481MXB9gHeedWKSUkHxhieOdLDVx89xPMn+vjp+66at9TlYhOHK4FPG2NeYX/9CQBjzOemOkfFQVGUc4FY3FDmmr8MyUzFoVBSWRcDyblZp+y1cYjI7SKyXUS2d3bmv4pRURRlrsynMMyEQhGHVL+dSSaNMeZbxpjNxpjNTU2aVaQoipIrCkUcTgFLk75eApzJ014URVFKnkIRhz8Ca0VkpYh4gFuBe/O8J0VRlJKlIIrgjDFREfkg8DusVNbvGWP25nlbiqIoJUtBiAOAMea3wG/zvQ9FURSlcNxKiqIoSgGh4qAoiqJMoiCK4GaDiAwAB1J8qwbon8GlZnr8RJYBJ3L4erq/ub2e7m9ur6f7y97rzebcbO2vEeiyP19ujElfC2CMKcp/wPYp1r81w+vM6PgU53fm8vV0f7o/3V/x7m+u52Zrf1O9X07371x0K92X4+Mn0pf+kDm9nu5vbq+n+5vb6+n+svd6szl3Pvc3jmJ2K203GfQHKZV9TIXub27o/uaG7m9uZGt/s7lOMYlWzOQAAAemSURBVFsO38r3BmwKZR9TofubG7q/uaH7mxvZ2t+Mr1O0loOiKIqSO4rZclAURVFyhIqDoiiKMgkVhwwQkSUi8isROSQirSLyZbtB4FTHf0REKqf6fo72ODifrzcTROT1ImJE5Lx872U60v0OReQxEZn34GWh33+FfO+B3n+zRcUhDSIiwM+BXxpj1gLrAD9wxzSnfQSYV3EocN4KPInVbTdj7PGxJY3ef1lB779ZoOKQnpcBIWPM9wGMMTHgb4G/FJEqEfk3EdktIi+IyIdE5MPAIuBREXl0PjcqIn4ReUREdtp7eq29vkJEXhSRb4vIXhF5UEQq5mtPwBbg3dh/nCJyvYg8LiK/EJF9IvINEXHZ3xsUkc+KyDbgyvnY44T9Xi8iv076+qsi8s753kcSRXH/FeK95+wLvf9mhYpDejYBO5IXjDFBrJL29wArgYuNMRcCdxljvoI1qOgGY8wN87zXEPB6Y8wlwA3AF+0nT4C1wP8zxmzCKqx54zzt6XXAA8aYg0CPiFxir18GfBS4AFgNvMFerwL2GGMuN8Y8OU97LGSK5f4rxHsP9P6bNSoO6RFSjCy1168FvmGMiQIYY3rmc2MpEOCfReQF4GGsOdwt9veOGmOetz/fAayYpz29Ffix/fmP7a8BnjXGHLGfhO8GrrbXY8DP5mlvxUCx3H+FeO+B3n+zpmDmORQwe5nwpCMi1VhjTY+Q+g83X7wdaAIuNcZEROQY4LO/N5p0XAzIuWkvIg1YbpHzRcRgDXIyWHM7Jv7enK9D9h9svogy/qHJN9WB80Sx3H8Fde+B3n9zRS2H9DwCVIrIOyARpPoi8F/Ag8B7RcRtf6/ePmcACMz/VqkBOuw/zhuA5XnYQzJ/BvzAGLPcGLPCGLMUOIr1lHaZWGNhXcBbsAKGhcBxYKOIeEWkBrgxz/splvuv0O490PtvTqg4pMFYJeSvB94kIoeAg1j+1X8AvoPl+31BRHYBb7NP+xZw/3wFBO03h1HgLmCziGzHepLbPx+vPw1vBX4xYe1nWL+nrcDngT1Yf7ATj5tXnN+hMeYkcA/wAtbv87l87qvQ778CvvdA77+57UnbZxQ/InIR8G1jzGX53ksmiMj1wMeMMX+a7704FNvvsFAoxt+b3n+ZoZZDkSMi78UKqH0y33spVvR3ODv095YdCvX3qJaDoiiKMgm1HBRFUZRJqDgUGSKyVEQetatO94rI39jr9SLykFj9dx4SkTp7vcE+flBEvjrhWm+xK2v3isi/5OPnUYqLWdx/N4nIDrtqeoeIvCzpWpfa64dF5CtJRXNKAaDiUHxEgY8aYzYAVwAfEJGNwMeBR+z+O4/YX4OV2fJ/gI8lX8TOAf9X4Ea7crVFRPKdtqkUPjO9/7qAW4wxFwC3AT9MutbXgduxKqjXAq+cnx9ByQQVhyLDGHPWGLPT/nwAeBGrGvW1wJ32YXditQ3AGDNktwEITbjUKuCgMabT/vph5retgVKEzOL+e84Yc8Ze3wv47Bz+hUC1MWarna77A+ccpTBQcShiRGQFcDGwDWgxxpwF6w8YaE5z+mHgPLsxmhvrD3Np7narnGvM4v57I/CcMWYUS1BOJX3vlL2mFAjaPqNIsbtN/gz4iDEmOFN3rTGmV0TeB/wEiANPY1kTipKWmd5/IrIJ+AJws7OU4jBNnSwg1HIoQkSkHOsP8y5jzM/t5XbbVMf+2JHuOsaY++zuk1cCB4BDudqzcu4w0/tPRJZgVSC/wxjTai+fApYkXXYJVjdZpUBQcSgy7IyO7wIvGmP+Pelb92IF/LA//iqDazXbH+uA92O1Y1CUKZnp/ScitcBvgE8YY55yDrZdTwMicoV9zXeQwT2rzB9aBFdkiMjVwBPAbix3EFh9drZh9WRZhtVv501OC2e7Q2Y14MHqp3+zMWafiNwNXGRf47PGGKe1saKkZKb3n4h8EvgE463Sm40xHWKNvPwvrC6t9wMfMvqGVDCoOCiKoiiTULeSoiiKMgkVB0VRFGUSKg6KoijKJFQcFEVRlEmoOCiKoiiTUHFQlBwgIu915j5nePwKEdmTyz0pykzQ9hmKkmVExG2M+Ua+96Eoc0HFQVFSYDeVewCruOti4CBWFe8G4N8BP1Y76ncaY86KyGNY/am2APeKSAAYNMb8m4i8BPgGUAm0An9p97a6FPgeMAw8OX8/naKkR91KijI164FvGWMuBILAB4D/BP7MGOO8sd+RdHytMeY6Y8wXJ1znB8D/tq+zG/iUvf594MN2bytFKSjUclCUqTmZ1A/oR1htIs4HHrK7kJYBZ5OO/8nEC4hIDZZo/MFeuhP4nxTrPwT+JPs/gqLMDhUHRZmaib1lBoC90zzpD83g2pLi+opSMKhbSVGmZpmIOELwVuAZoMlZE5Fye07BlBhj+oFeEbnGXvoL4A/GmD6g325kB/D27G9fUWaPWg6KMjUvAreJyDexuor+J/A74Cu2W8gN/AfW+MvpuA34hohUAkeAd9nr7wK+JyLD9nUVpWDQrqyKkgI7W+nXxpjz87wVRckL6lZSFEVRJqGWg6IoijIJtRwURVGUSag4KIqiKJNQcVAURVEmoeKgKIqiTELFQVEURZnE/w93mxHyrMCp+wAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-100:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etant donné que le creux de l'épidémie se situe début septembre, à cheval\n", "entre deux années civiles, nous définissons la période de référence\n", "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n", "1er septembre 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 septembre de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en septembre, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent en decembre 1990, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er septembre, 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": 22, "metadata": {}, "outputs": [], "source": [ "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)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "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": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2002 516689\n", "2018 542312\n", "2017 551041\n", "1996 564901\n", "2019 584066\n", "2015 604382\n", "2000 617597\n", "2001 619041\n", "2012 624573\n", "2005 628464\n", "2006 632833\n", "2011 642368\n", "1993 643387\n", "1995 652478\n", "1994 661409\n", "1998 677775\n", "1997 683434\n", "2014 685769\n", "2013 698332\n", "2007 717352\n", "2008 749478\n", "1999 756456\n", "2003 758363\n", "2004 777388\n", "2016 782114\n", "2010 829911\n", "1992 832939\n", "2009 842373\n", "dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }