{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse de l'incidence de la grippe grâce aux données du réseau sentinelle" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import urllib\n", "import pandas as pd\n", "from os import listdir\n", "import isoweek\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fichier déjà téléchargé\n" ] } ], "source": [ "data_url = 'https://www.sentiweb.fr/datasets/incidence-PAY-7.csv'\n", "filename = 'incidence-PAY-7.csv'\n", "\n", "curFiles = set(listdir())\n", "\n", "# téléchargement automatique du fichier\n", "# si non présent dans le répertoire\n", "if not(filename in curFiles):\n", " print('Téléchargement du fichier')\n", " urllib.request.urlretrieve(data_url, filename)\n", "else:\n", " print('Fichier déjà téléchargé')\n", " \n", "#print(listdir())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lecture et exploration du fichier\n", "le fichier est lu avec pandas\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" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020202272100567001FRFrance
12020217600281172102FRFrance
22020207832191645102FRFrance
320201973100753001FRFrance
42020187849981600102FRFrance
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11202011710198756812828151119FRFrance
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1920200375968410078369612FRFrance
20202002765344530853810713FRFrance
2120200179835701912651151119FRFrance
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25201949766214540870210713FRFrance
2620194875542338377018511FRFrance
272019477753650581001411715FRFrance
282019467263813163960426FRFrance
2920194574492261563697410FRFrance
.................................
15091991267176081130423912312042FRFrance
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15381990497114302610205FRFrance
\n", "

1539 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202022 7 210 0 567 0 0 \n", "1 202021 7 600 28 1172 1 0 \n", "2 202020 7 832 19 1645 1 0 \n", "3 202019 7 310 0 753 0 0 \n", "4 202018 7 849 98 1600 1 0 \n", "5 202017 7 272 0 658 0 0 \n", "6 202016 7 758 78 1438 1 0 \n", "7 202015 7 1918 675 3161 3 1 \n", "8 202014 7 3879 2227 5531 6 3 \n", "9 202013 7 7326 5236 9416 11 8 \n", "10 202012 7 8123 5790 10456 12 8 \n", "11 202011 7 10198 7568 12828 15 11 \n", "12 202010 7 9011 6691 11331 14 10 \n", "13 202009 7 13631 10544 16718 21 16 \n", "14 202008 7 10424 7708 13140 16 12 \n", "15 202007 7 8959 6574 11344 14 10 \n", "16 202006 7 9264 6925 11603 14 10 \n", "17 202005 7 8505 6314 10696 13 10 \n", "18 202004 7 7991 5831 10151 12 9 \n", "19 202003 7 5968 4100 7836 9 6 \n", "20 202002 7 6534 4530 8538 10 7 \n", "21 202001 7 9835 7019 12651 15 11 \n", "22 201952 7 7941 5246 10636 12 8 \n", "23 201951 7 5823 3675 7971 9 6 \n", "24 201950 7 6424 4276 8572 10 7 \n", "25 201949 7 6621 4540 8702 10 7 \n", "26 201948 7 5542 3383 7701 8 5 \n", "27 201947 7 7536 5058 10014 11 7 \n", "28 201946 7 2638 1316 3960 4 2 \n", "29 201945 7 4492 2615 6369 7 4 \n", "... ... ... ... ... ... ... ... \n", "1509 199126 7 17608 11304 23912 31 20 \n", "1510 199125 7 16169 10700 21638 28 18 \n", "1511 199124 7 16171 10071 22271 28 17 \n", "1512 199123 7 11947 7671 16223 21 13 \n", "1513 199122 7 15452 9953 20951 27 17 \n", "1514 199121 7 14903 8975 20831 26 16 \n", "1515 199120 7 19053 12742 25364 34 23 \n", "1516 199119 7 16739 11246 22232 29 19 \n", "1517 199118 7 21385 13882 28888 38 25 \n", "1518 199117 7 13462 8877 18047 24 16 \n", "1519 199116 7 14857 10068 19646 26 18 \n", "1520 199115 7 13975 9781 18169 25 18 \n", "1521 199114 7 12265 7684 16846 22 14 \n", "1522 199113 7 9567 6041 13093 17 11 \n", "1523 199112 7 10864 7331 14397 19 13 \n", "1524 199111 7 15574 11184 19964 27 19 \n", "1525 199110 7 16643 11372 21914 29 20 \n", "1526 199109 7 13741 8780 18702 24 15 \n", "1527 199108 7 13289 8813 17765 23 15 \n", "1528 199107 7 12337 8077 16597 22 15 \n", "1529 199106 7 10877 7013 14741 19 12 \n", "1530 199105 7 10442 6544 14340 18 11 \n", "1531 199104 7 7913 4563 11263 14 8 \n", "1532 199103 7 15387 10484 20290 27 18 \n", "1533 199102 7 16277 11046 21508 29 20 \n", "1534 199101 7 15565 10271 20859 27 18 \n", "1535 199052 7 19375 13295 25455 34 23 \n", "1536 199051 7 19080 13807 24353 34 25 \n", "1537 199050 7 11079 6660 15498 20 12 \n", "1538 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 1 FR France \n", "1 2 FR France \n", "2 2 FR France \n", "3 1 FR France \n", "4 2 FR France \n", "5 1 FR France \n", "6 2 FR France \n", "7 5 FR France \n", "8 9 FR France \n", "9 14 FR France \n", "10 16 FR France \n", "11 19 FR France \n", "12 18 FR France \n", "13 26 FR France \n", "14 20 FR France \n", "15 18 FR France \n", "16 18 FR France \n", "17 16 FR France \n", "18 15 FR France \n", "19 12 FR France \n", "20 13 FR France \n", "21 19 FR France \n", "22 16 FR France \n", "23 12 FR France \n", "24 13 FR France \n", "25 13 FR France \n", "26 11 FR France \n", "27 15 FR France \n", "28 6 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1509 42 FR France \n", "1510 38 FR France \n", "1511 39 FR France \n", "1512 29 FR France \n", "1513 37 FR France \n", "1514 36 FR France \n", "1515 45 FR France \n", "1516 39 FR France \n", "1517 51 FR France \n", "1518 32 FR France \n", "1519 34 FR France \n", "1520 32 FR France \n", "1521 30 FR France \n", "1522 23 FR France \n", "1523 25 FR France \n", "1524 35 FR France \n", "1525 38 FR France \n", "1526 33 FR France \n", "1527 31 FR France \n", "1528 29 FR France \n", "1529 26 FR France \n", "1530 25 FR France \n", "1531 20 FR France \n", "1532 36 FR France \n", "1533 38 FR France \n", "1534 36 FR France \n", "1535 45 FR France \n", "1536 43 FR France \n", "1537 28 FR France \n", "1538 5 FR France \n", "\n", "[1539 rows x 10 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rawdata = pd.read_csv(filename, skiprows=1)\n", "rawdata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "nettoyage du fichier\n", "\n", "a priori il n'y a pas de lignes vides" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rawdata[rawdata.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Conversion de la colonne week en isoweek standard" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# conversion de la date en format isoweek bizarre, en format datetime\n", "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "rawdata['period'] = [convert_week(yw) for yw in rawdata['week']]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
020202272100567001FRFrance2020-05-25/2020-05-31
12020217600281172102FRFrance2020-05-18/2020-05-24
22020207832191645102FRFrance2020-05-11/2020-05-17
320201973100753001FRFrance2020-05-04/2020-05-10
42020187849981600102FRFrance2020-04-27/2020-05-03
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "0 202022 7 210 0 567 0 0 1 \n", "1 202021 7 600 28 1172 1 0 2 \n", "2 202020 7 832 19 1645 1 0 2 \n", "3 202019 7 310 0 753 0 0 1 \n", "4 202018 7 849 98 1600 1 0 2 \n", "\n", " geo_insee geo_name period \n", "0 FR France 2020-05-25/2020-05-31 \n", "1 FR France 2020-05-18/2020-05-24 \n", "2 FR France 2020-05-11/2020-05-17 \n", "3 FR France 2020-05-04/2020-05-10 \n", "4 FR France 2020-04-27/2020-05-03 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rawdata.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# classification par dates croissantes\n", "sorted_data = rawdata.set_index('period').sort_index()\n", "\n", "# Vérification qu'il n'y a pas de rupture de date\n", "periods = sorted_data.index\n", "# construction de la liste des n-1 premiers, et n-1 derniers\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": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1990-12-03/1990-12-091990497114302610205FRFrance
1990-12-10/1990-12-16199050711079666015498201228FRFrance
1990-12-17/1990-12-231990517190801380724353342543FRFrance
1990-12-24/1990-12-301990527193751329525455342345FRFrance
1990-12-31/1991-01-061991017155651027120859271836FRFrance
1991-01-07/1991-01-131991027162771104621508292038FRFrance
1991-01-14/1991-01-201991037153871048420290271836FRFrance
1991-01-21/1991-01-271991047791345631126314820FRFrance
1991-01-28/1991-02-03199105710442654414340181125FRFrance
1991-02-04/1991-02-10199106710877701314741191226FRFrance
1991-02-11/1991-02-17199107712337807716597221529FRFrance
1991-02-18/1991-02-24199108713289881317765231531FRFrance
1991-02-25/1991-03-03199109713741878018702241533FRFrance
1991-03-04/1991-03-101991107166431137221914292038FRFrance
1991-03-11/1991-03-171991117155741118419964271935FRFrance
1991-03-18/1991-03-24199112710864733114397191325FRFrance
1991-03-25/1991-03-3119911379567604113093171123FRFrance
1991-04-01/1991-04-07199114712265768416846221430FRFrance
1991-04-08/1991-04-14199115713975978118169251832FRFrance
1991-04-15/1991-04-211991167148571006819646261834FRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 \\\n", "period \n", "1990-12-03/1990-12-09 199049 7 1143 0 2610 2 \n", "1990-12-10/1990-12-16 199050 7 11079 6660 15498 20 \n", "1990-12-17/1990-12-23 199051 7 19080 13807 24353 34 \n", "1990-12-24/1990-12-30 199052 7 19375 13295 25455 34 \n", "1990-12-31/1991-01-06 199101 7 15565 10271 20859 27 \n", "1991-01-07/1991-01-13 199102 7 16277 11046 21508 29 \n", "1991-01-14/1991-01-20 199103 7 15387 10484 20290 27 \n", "1991-01-21/1991-01-27 199104 7 7913 4563 11263 14 \n", "1991-01-28/1991-02-03 199105 7 10442 6544 14340 18 \n", "1991-02-04/1991-02-10 199106 7 10877 7013 14741 19 \n", "1991-02-11/1991-02-17 199107 7 12337 8077 16597 22 \n", "1991-02-18/1991-02-24 199108 7 13289 8813 17765 23 \n", "1991-02-25/1991-03-03 199109 7 13741 8780 18702 24 \n", "1991-03-04/1991-03-10 199110 7 16643 11372 21914 29 \n", "1991-03-11/1991-03-17 199111 7 15574 11184 19964 27 \n", "1991-03-18/1991-03-24 199112 7 10864 7331 14397 19 \n", "1991-03-25/1991-03-31 199113 7 9567 6041 13093 17 \n", "1991-04-01/1991-04-07 199114 7 12265 7684 16846 22 \n", "1991-04-08/1991-04-14 199115 7 13975 9781 18169 25 \n", "1991-04-15/1991-04-21 199116 7 14857 10068 19646 26 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "period \n", "1990-12-03/1990-12-09 0 5 FR France \n", "1990-12-10/1990-12-16 12 28 FR France \n", "1990-12-17/1990-12-23 25 43 FR France \n", "1990-12-24/1990-12-30 23 45 FR France \n", "1990-12-31/1991-01-06 18 36 FR France \n", "1991-01-07/1991-01-13 20 38 FR France \n", "1991-01-14/1991-01-20 18 36 FR France \n", "1991-01-21/1991-01-27 8 20 FR France \n", "1991-01-28/1991-02-03 11 25 FR France \n", "1991-02-04/1991-02-10 12 26 FR France \n", "1991-02-11/1991-02-17 15 29 FR France \n", "1991-02-18/1991-02-24 15 31 FR France \n", "1991-02-25/1991-03-03 15 33 FR France \n", "1991-03-04/1991-03-10 20 38 FR France \n", "1991-03-11/1991-03-17 19 35 FR France \n", "1991-03-18/1991-03-24 13 25 FR France \n", "1991-03-25/1991-03-31 11 23 FR France \n", "1991-04-01/1991-04-07 14 30 FR France \n", "1991-04-08/1991-04-14 18 32 FR France \n", "1991-04-15/1991-04-21 18 34 FR France " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data[0:20]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Hw3LIVWgsrM6swbEoDlMsMmGzKQIep6SyCkWPiIOQRmjMCBA77Om3h2U59I/kz600EonhddlpDBguq2yth3OhME67os7nmv3gKWxo8NPWPZKcDNcZHKPe78IzTX3EbPjdjqRbKVO77lQqKxxSBCcUPbOKg1LqW0qpbqXU/pS1/6OUOquUetn898aU731cKdWqlDqilHp9yvoOpdQ+83tfUuZfj1LKrZT6obn+nFKqJb8/opArQ+HMXUXBeBN0OWx5dSuNjsfxmTEHgJ4s4xldwTCNAc+sFcyZ2NDoZywaT8Yazg6G5+RSsvB7nAyZdQ5V02QqWVR6nOJWEoqebCyHbwM3ZVj/gtb6IvPfQwBKqa3ArcA285yvKKWsR7GvAncAG81/1jU/AAxorTcAXwA+M8efRcgTmcZsWihlPKnn161kWA6WOHSHsrccrOypXNnQaPRLsuIOHYNjrKiauzhUmm27p5vlMPlYpwSkhaJnVnHQWj8B9Gd5vZuBH2itI1rrE0ArcJlSajlQqbXepY3Ko+8Ab005517z8x8DN6iZbHJhwQmNxabtTQRQ53fltbPocCSGz+1IcStll7F0LhSec4xgfcNExpLWOtk6Y65Yo0KzEYeAxyGWg1D0zCfm8MdKqb2m28mahtIMnEk5pt1cazY/n7o+6RytdQwIAnWZXlApdYdSardSandPT888ti7MxEyWA0Ctz523VNZ4QhOOJvC67NR4XThsKie30lyC0WCk5NZ4nbT1DBMaizE6HmdF9dyuBYY4DGUpDpUVEpAWip+5isNXgfXARUAn8DlzPdMTv55hfaZz0he1vkdrvVNrvbOhoSHTIUIeCM0QcwAMt1KeLAdrloPf7cBmU9T73Vm5lYbCUUbG4znXOKSyodFPa/cwZ81MpflYDgGPc1JAeiaMmIO4lYTiZk7ioLXu0lrHtdYJ4OvAZea32oHUbmYrgQ5zfWWG9UnnKKUcQBXZu7GEBSA0ZrTrno58xhysATlel/F6DQF3VpaDlcY6V7cSTIhDRx7EwW9OgwuFs7EcjGNznXonCIvJnMTBjCFYvA2wMpkeBG41M5DWYgSen9dadwJDSqkrzHjC7cADKee8x/z8FuDXeqEGBwizkkjoGbOVAGr9LsaiccbG599fyapp8LmNvIWGQHaWw7mgccxc3UoA6xv8DIxG2d8RBJiXW8kSUz1Du+6JY43vD4v1IBQxs7acVEp9H3gNUK+Uagc+CbxGKXURhvvnJPBBAK31AaXUj4CDQAy4S2ttvYN8CCPzqQJ42PwH8E3gPqVUK4bFcGs+fjBhboyY8whmijnU+6z+ShFWurzzer2plkNjwM2+s8FZz5tr64xUrIylJ4724LSr5M81F1ILBrMJSIPhvpst7VUQCsWs4qC1vi3D8jdnOP5TwKcyrO8GtmdYDwPvmG0fwuKQ7Ks0Q7ZSsoXG8Dgra+YnDknLwTVhOfQNR4gnNPYZ6hfy5VYCePnMICtrvHOql7Dwe7IXB8sqC45FWTXjkYJQOKRCWpjE1EE/majzW1XS8487JC0H94TlkNCGVTIT54Jhqiqcc65oBlhRVUGF005Cz8+lBBOdWSF7t5KkswrFjIiDMImZ2nVb1Jnul948pLOOjKdbDjDRQuN4z3DG1+kMjs3LpQRGnyOrQ+t8gtEwxa00W4V0xcRwIEEoVspOHHa19fF/HjyQNgVMMJipXbdFPi0Hy61kWQ4NAeMNv3sowm8Od/P6Lz7BJ362f9I5Wmv2tgfZsjww79ff0GC4luZTHQ0TcQTIwXKQWgehiCk7cTjaNcS3nzmZDGgKk5mpXbeF12XHnaf+SiMRw61kWQ5WlfQDL53lg//5ItG45tnjk0d6nh0co3sowiVratIvmCNW3GG+lkOqOFRXzNwIcMKtJJaDULyUnThsXmY8bR4+NzTLkeWJ5QcPzGA55LO/klUEl1rnAPCzlztY3+Dnb964mYHRKG0p7bX3nB4E4JLV+RSH+bmoLLeSy27D45z5z8qfHPgjloNQvJShOFQCcLhTxCETlh88MEMRHBjtJ/LRtntkPI7LbsPlMG5Fj9NOvd/NpqYA3/3Dy3ntliYAXjg5kDxnz6kBKpz2pNDPh+s2N/J3b9rCVevr53Udn8uBUkasZrbWYHabIuCW/krFyv6zwbzOSF+qlJ04VHmdLK/ycORcqNBbKUpCY1G8LjvODLMcUqmdYwuN4Gh0kotoNGJMgUvlx3deyf0fvopan4u19T7qfC5eODlRNL/n9AAXrKzKOG8iV9wOO3947bqkOM0Vm03hdzmomsEdl0plhVMC0kVIcDTKW7/8NP+1+8zsB5c4ZScOAJuWBcStNA2zNd2zqPPn7lbafzbIzk/9iieO9SbXRswpcKm01PuSbhqlFDtbathtWg7haJyDHSF25CHekG/8HseswWiLgMchbqUipCM4RiyhJSZJmYrD5mWVtPUMMx5LFHorRcds7botjOZ7ubmVvvrbNqJxzbGuCWG2ZjnMxKUttZzuH6UrFGZve5BYQucl3pBvqr0uarOssq6skIE/xYhVXDk4Kv832dnAJcbmZQGicc3x3uFkDEIwyN5ycBOOJsw399lvoxO9Izy8rxMw0lQtRiLxZBrrdFzaUgvA7pMDnBkYBeDi1dWzvuZi889vPx+/O7uivEqPg45BeTotNqy+XoNi1ZWp5WDmxx8R11Ias7XrtkhtoZEN9zxxHIfdRo3XSXeKyT4SiSXTWKdj64pKKpx2XjjZz55TA7TUeanzz70P0kJx0apqNjRmFySXUaHFybmk5ZC/YVZLlbIUh3X1fhw2JXGHDMzWrtui3iyEyyYo3R0Kc/+L7dyyYyXrG/x0pXRdHRmPz2p5OO02Ll5dbYjD6YG81DcUGglIFyfiVpqgLMXB5bCxodHP4U7JWJpK9paD8eSeTTrrN58+QSyR4I5r19FU6aErZQzo6HgsK1fMzpZaDnSE6B0eL8p4Q65YM6cTMtOhqLAeXEQcylQcwMhYErfSZLTWhMayjDmYbqXeWdxKoXCU7z57mjeev5yWep8xzCeUW8wB4NKWCUEoCXGocJLQE72lhOLAshyCEnMoX3HYvKySjmCYoDwhJBkZjxuzHLLJVsqyv9JDezsZjsT4o2vXAcZwnqFILNlTaXR89pgDwMWra7DbFD6XnU15KH4rNBMzHUQciglLHIYjsbLPZixjcTCD0l3lbT188L7d/O8HjMZ22bTrtvC6HHicNvpm6cz64CsdrKv3ccHKKgCaKg13VPdQhERCM5pFzAGM9hQXr6rm8nV1M855WCpI873iIxZP0DscocY7MW+jnClfcVhu9Vgq77jD3vYgP33pLOOxRDJ7JpuYAxitu2cKSHeHwuw63sebL1yRbCnRaHVdDYUZM1sU+LJM//zGe3byhXdelNWxxY71O5agdPHQOzxOQpO0TINj5Z2xVLbisKzSQ6XHUfYZS4OjUYbCMZ4/0U9obPZ23anMViX90L5OtIbfuXBi5LhlOXQNRZL+9mwsBzCKzLKtQC52xHIoPiyXklX7NFDmLueyFQelFJuXV5Z1xlI4Gk8+vT96qCv5RjVb0z2LOp9rxpjDg690sGV55aTc/8bKCcthNJKb5VBKWHEdqXUoHqwaB8tyKPeMpbIVBzDiDke7hss2ndC6+ZWCXx3sytmtVOtzTxtzONM/yp7Tg7wlxWoAI4XT7bDRPRRhOJKb5VBKBMRyKDqs4szzmixxELdS2XJeU4DhSKxsm2wNmj7Vazc2cHZwjOdPGJ1PsymCA6MQrm9kPONUvf8xW2W85YIVk9aVUkatQyicnB89tfFeOSDZSsVHVyiC3aaS0wHFcihjlldNjKQsRwZGjJv/dy9pRin4n73GG/pMg35SqfW5iMQSjIyn975/8OUOLl5dzapab9r3GgNuukLhifnRZehWctpteF12hsStVDScC4VpDLiprHBgt6nkw1O5UtbiUO+fPMy+3LCyMTY0+rloVTVDkRgVTnvWsw2sqW3dUyyvtp5hDnaG0qwGi6ZKD91DkZSYQ/lZDmD2VxoTy6FY6AqFaaz0oJSiusIplkOhN1BIrDe33lly9UsVKxujxuvixq3GxLVsCuAskmmpU8R1/9kgANduzDxdrbHSTXcoNVup/CwHMH7XEpAuHrpCYZrM94Qqr4hDWYuDVeVbrpbDgBlwq/G6uNEcx5ltGisYb/KQLg5WSuCyqsxzmRsDHoYjseTvvRxjDiCdWYuNrlAkec/WeF3iVir0BgqJ22GnqsJZtpZDcDSKy2HD4zQaEbbUean25iAO07iVzgUjeF325DS3qVi1Did7RwDSxoSWC8Y0OHErFQPhaJzgWJQmM9Va3EplOuwnlXq/q2zFYWB0nBqvM1m9/KXbLkaRfWuKqgonLoctzfLqGgrTZPpuM2G5o070juCwKVx5mAW9FKmscHLcFEihsFjWbmOKW6ncC2TLXhwaAu6ydSsNjkap8bqSX1+wMrfpakopGvzuNLdSdyictA4yYX3vRO8IXpd9WhEpdSo9MtOhWLBadVtupeoKl9Q5FHoDhabe75617XSpMjganXc7isbKdHHtCkWS5nnmc4zv9Y2Ml22mEhipwIOj45wLlmedTTFhWQ7WfVvjdTIyHi/rzqxlLw7lbDkYbiXX7AfOQGPATXfK8B6ttZH1MYM4WFXSUL5prAC37FiJw2bjc788UuitlD1JcTBdntXSmVXEod7vZjgSYyxDIVepMzgWzSkAnYnGgGeSWyk4FiUSSyR9t5mwqqSBrGY5lCqrar289+oWfrynnQMdwUJvp6zpCoXxOG3JVO4q86GpnF1LZS8O5VrroLVmcHSc6jxYDoOjUSIxQ1yn+m6nw4o7lGNfpVTuum4DVRVO/umhQxnbkAiLwznTFWrFv6yZDoNiOZQvDVaVdJmJw8h4nGhcJ/8I5opV62C55qb6bqc9zzTfy7F1RipVFU7+7IaNPN3ax+NHegq9nbLFKICbuGerKyzLQcShbLEsh3KLO1jmcj7cSjBRCDfVdzvteWI5JPn9y9ewtt7HPz10qGw7BBea7lCYphRr1/q7GBC3Uvli9VcqN7eS9UQ0X7fSRH8l4/dniUTjDKmsIJZDKi6HjT+6dh3Huoc52Sd1D4uN1ppzKa0zICUgLZZD+WK10OgdKq8nhMGUvkrzYcKtZFgM54JhqiqceJwzv+lLzGEy1oztg2U8fKpQhMIxwtHEJFeo3y2dWcteHJx2GzVeJz3D5ZVrPpAnt1Kdz41NTXYrLZsl3gBIttIUNjb5cdgUh0QcFp0TZpX6qtqK5Jp0Zs1CHJRS31JKdSul9qes1SqlfqWUOmZ+rEn53seVUq1KqSNKqdenrO9QSu0zv/clZaYFKKXcSqkfmuvPKaVa8vsjzk69312GlkN+xMFuU9T73Um3UtdQZFaXEky0KSjnOodU3A47Gxr9HOwQcVhsXjw1AMDFq2smrVdP6cy6rz04qaan1MnGcvg2cNOUtY8Bj2mtNwKPmV+jlNoK3ApsM8/5ilLKejT8KnAHsNH8Z13zA8CA1noD8AXgM3P9YeZKQ8BddtlKyZhDxfzcSmD8/qw/mu5ZCuAsVtZ4WVVbwXnLArMeWy5sWV7Joc7y7udTCPacHqC5uiLtvq1O6cwaicW59Z5dfP6XRwuxxYIwqzhorZ8A+qcs3wzca35+L/DWlPUfaK0jWusTQCtwmVJqOVCptd6ljWTu70w5x7rWj4Eb1CI32zFaaJSXOAyMRvG5sh/sMxNGlXSERELTPRSZsa+SRYXLzpMfvZ7rNjXO+/VLhS3LA5wLhekfKS8rttDsOTXAJWtq0tZT3Uq7Tw4wMh7nSFf5iPdc3xmatNadAOZH6y+8GTiTcly7udZsfj51fdI5WusYEATqMr2oUuoOpdRupdTunp785YSXYwuNfBTAWVhV0r0jEeIJnVXMQUhn63IjKC1xh8WjY3CMzmCYHavTm06mDvx54qjxftPaPVw2xYr5DkhneuLXM6zPdE76otb3aK13aq13NjQ0zHGL6dT73YyOxxmJlE+HzMGxKDW++cUbLBor3fQNR+gcNNseizjMiS3LDRebiMP0fP6XR7jzvhfzdj0r3rBjTW3a92q8E51+s3PPAAAgAElEQVRZf2uKw1A4VjYPknMVhy7TVYT5sdtcbwdWpRy3Eugw11dmWJ90jlLKAVSR7sZaUMqxhcbA6Hhe4g1guJUSeiINM5uYg5BOnd9NU6VbgtIz8Njhbp470Ze36+05PYDHaWPz8vTYV3WF0Zm1fWCUw+eGkmNvW7uH8/b6xcxcxeFB4D3m5+8BHkhZv9XMQFqLEXh+3nQ9DSmlrjDjCbdPOce61i3Ar/Ui2231Vq1DGYlDcHT+TfcsGsyCtn3m7OhsYg5CZrYsr5Rah2mIJzTHuocZSOnlNV/2nBrgwpXVODMMnLL+Pn7+SicA7796LQBtPSIOACilvg/sAjYppdqVUh8APg3cqJQ6Btxofo3W+gDwI+Ag8Ahwl9ba+l/8EPANjCB1G/Cwuf5NoE4p1Qr8L8zMp8VkooVG+QQCB0bH8yYOVurq/rNBlJqoOhdyZ+vySlq7h/P25ldKnOwbSc5XyIdrZ2w8zoGOEDsyBKNhonvAAy+fpd7v5tXnNeB3O8rGcpg1yVxrfds037phmuM/BXwqw/puYHuG9TDwjtn2sZBMbb6XSGiebO3l2g312GylN6UskdAEx6Lzro62sGoWDncOUe93Z3wKE7Jjy/JKYglNa/cw21ZUFXo7RcWRlLGdXaEIK2u887re3vZBYgnNJaunEwfj4enwuSHefkkzNptifaOfVrEcyodanwuloNd8Gvn53g7e863neaq1t8A7WxiGwjESev59lSwsy2s8nhCX0jzZuqISQOIOGUgVh+5Q7sVoPUMRPvGz/ZzpHwXgxdNGMDpTGitMrgF69XlGAsyGBn/ZWA4iDoDDbqPW60paDt977jQAp8ybqNRIts6Y54hQC7fDnnzKmq0bqzAzLXU+PE6bFMNl4Mi5oeR9NnVueTZ87bdt3PfsKW65+xmOdg2x59Qg6+p91PoyPyRZr6UUXLPBCEavb/TRFYoQCpd+Ww0RB5OGgJveoQit3cM8d8JIljo7MFbgXS0M1gCTfKWywoRrSdJY54fdpti0rJKDnTIZbipHu4a4rKUWh00lW8Nny3Akxg9fOMPla2vRGt5x9y6eO943rdUAE+KwfUUVdabreUODH4DjPaXfPVfEwaTeb7TQ+P7zp3HYFLU+F2cHS1McJpru5cetBBMtuKUAbv5sNdtolEuxVTaEo3FO9o2weXkljQF3cuJgttz/YjtDkRh//YbN3P+hq6j2OhmKxKaNN4DRmbUh4OYN5y9Lrm1oNMShHFxL0vXMpCHg5ljbECd6R3jdtiYGRqKcHShNt9Jgnt1KMGE5SMxh/mxdUcn3nz9N+8AYq2rnF3QtFY51DZPQsHlZgIZKT04N8BIJzbefOclFq6qTYvBfd17JfbtO8eYLl097nlKKx//yNZPaz6+u9eK0q7IQB7EcTOr9LrpCEQZHo7zrsjU011QUreUwHkvwwxdOE40nsj4nOBolbk4Zy9csh1QaKi1xEMthvuw0XR3PHs9fsddSx+pptGlZgKbARBfgbHj8aDcnekd439UtybXGgIe/eN0mKj0zPyD5zLkOFg67jZY6n4hDOWHl5q+u9XLV+jqaqyvoHook86qLiafbevnr+/fx81c6Zj8YiMUTXP+5x/nkg0bX9YHRKEpBZV4tB0MUsmnXLczMpqYAdT4Xz7SJOFgcORfC5bCxptZLU46Ww388fZKmSjdvPH96KyEXNjT6OV4G6awiDiZWOuZtl63GZlM011SgNXQGi896SKbcZikObT0j9I2M893nTnOgI8jg6DiVHuekJ6L58qqN9dy4tYn1ZsBOmDs2m+LK9XU83dorcQeTw+eG2Njox2G30RhwZ10lfeTcEE8e6+X2K1vyVn+zodHPqf7RonxwzCciDiZXrKvjpm3LuPVSozXUympjKlQxZixZLZ2fPNbLQBbtnfe2DwLgdtj4x/8+yMBolJo8VUdbbGwK8PXbd846HlTIjqs31NM9FCmbVg2zcbRriE3m7A/LdTmba0lrzScf3E/A4+C2y1bnbS/rG/zEE7rk532LOJisqK7g7nfvoMbMeW6uMcShvQjjDpY4xBKah/efm/X4fWeD+Fx2Pv6GLTx7vJ/Hj3TnNVNJyD9Xrzfy6p9uFdfS4Og4XaEIm5oMcbDiW7PVOvzwhTM8e7yfv33jlmlrGeZCuWQsiThMw/KqCpQqTsuhb2ScZZUe1jX4snIt7W0Psr25it+/fDXnNfkZCsfy1ldJWBhW13lZWVPB0yVapZ8LVmV00nIIWJbD9HGHrlCYTz10iCvW1fLOS1dNe9xcWNfgA0QcyhaXw/BtFmPG0sDIOLU+F2+5YAXPnuib8Y8kGk9wqDPEBSurcNhtfOLNW4H8ZioJC8PV6+t59nhfMsusXLEylTYvM1qLWOnSMxXC/e8H9jMeS/Dpt19AvgdLel0Omqsr2H+2tAsVRRxmoLm6omgthzq/i7dcuAKt4b/3dk577LGuYSKxBOevNCZdXbuxgb96/SZu2bFy2nOE4uCqDXWEwjEOdJT2m9BsHD43RKXHkRSFGq8Lh01N61Z6/Eg3vzjQxZ/feB4t9b4F2dPrty3jN0e6c8qaWmqIOMxAc423KC2HftNy2NDoZ+vySn6+d3rX0r6zRjD6/OaJDp93XbeBq81eMULxcuV6Y1puuccdjpnBaMsCsNnUjFXSz7T14XLY+MA1axdsT79/xWqicc2PXjgz+8FLFBGHGWiurqAzOEaiyMz6/pHxpFvoLReu4KXTgxydZvD53vYgAY+DNVJpu+RoDHg4r8nPM23lHXdo6xlJBoEtZqqSPtY1xPoG/4K2jl/f4OeaDfV877nTxHIoRl1KiDjMQHNNBdG4nlMHyIUiEoszHIlRZ2ZfvO3iZqoqnLzza7syvonsOxvk/OaqkpxLUQ5ctb6eF072l+3wn/6RcfpHxtPqZ2aqkm7tGWZj48LX2/zBFWvoCIb59eHu2Q9egog4zECy1mGweHosDYwYrS9qzdGmy6o8/Oyuq6nzu3n3N5/nvl0nk4VT47EEhzuHOH+lDI1ZqlyxrpZwNLGkW3hrrfnK461z8s9blchp4lDpoSvD9UbHY7QPjKVZGgvBa7c0srzKw33Pnlrw1yoEIg4zkKx1KKKgdN+I8bRUl5K3vbbex08/fBWvPq+BTzxwgG8+dQIwCofG4wkuaK4uyF6F+bOsyrgHe4vIes2V9oExPvvIEX78YnvO57ZNIw6NATeDGaqkj/eMoDWLYjk47DbeddlqnjzWW5LtNEQcZqA5aTkUjzhYBXC1vsk9jAIeJ1+/fSev39bEpx8+zJ7TA+xtN7JcLhDLYcliPQT0Z1EJX6xYjR5bu3J/A23rGcHlsCUf1Cymq5I+1m1YWBubFqeNyzsvW4XDpviuOSCslBBxmAGf20G111lU6awT4pBexGa3KT57y4Usq/LwJ997iSeP9VDtdbJyyh+WsHSoM92HfUtZHMaMvR+bQ9FYW/cw6+p9aX3ApquSPtY1jMOmWFO3MCmsU2kMeLi0pZYXTw0syustJiIOs9BcXVytu6ezHCyqKpx8+V2X0D0U5uH95zi/uSrvRUDC4uF1OfA4bfSPLF23UtCcPNjaPZxz5t/x3pGMzRynq5Ju7R6mpd63oJlKU1lW5aFnCbv9pkPEYRaKrRCuf2Qcm5p5UM+Fq6r5mzduASbXNwhLkzqfm77hpWs5WOIwFo3n9KAVicU53T/K+oZ0K2C6KunW7sXJVEqlMeCmZyhSch10ZRLcLDTXVPCU2Tq5GJ7A+8wah9lSU997VQsBj5NrN0qx21Kn1uda0m4lSxzASJLIdrrd6b5R4gnN+gxv9pmqpCMxY5Tomy/Iz9yGbGkIuBmPJwiNxagqoZ5lYjnMQnN1BaPj8WRQrdD0D49n1WFSKcUtO1bKZLYSoNbnWtIB6eBoFOtZJpe4g5WptK4+XRwyVUmf6B0hoWGD2b11sbBmwZRaKw0Rh1mwgrnFEnfoHxlPthUXyoM6/xIXh7EodX43TZXutEr+aDwxrTumrceYl7Aug1sJ0qukj5nZUIvvVjIewEot7iDiMAvN1YYJXCy1Dv2j45NqHITSp87nond46fq0g2NRqiucbGwMTGpzHYsnuO5fHud3/v1pXjqdnu3T1j3M8ioPPndm7/fUKunW7mFsyqj7WUwmLAcRh7Kiqaq4TEar6Z5QPtT63ERiCUbHl2YLjeBYlKoKJxub/BzrmshYeuHkAO0DY7T1DPO2rzzDR3/8yqTJhm09wzOOnV1V6+VE30hylG9r9zCra72LPo3QmpsulkOZUedzY1OzjyRcDOIJzYBYDmXHUi+EGxw1xaExMClj6dFDXbgcNh7/q9fwwVet4yd7zvLH39+D1hqtNW09IxkzlSzee1ULAJ9++DBgFMBtaFzceANAwO3A7bAVzQNkvhBxmAW7TVHvdxfFf/zg6DhaI5ZDmVEMhXC/OdzNbfc8O6cOpJblcF7TxHhNrTWPHuri6vV1NAY8fPyNW/jbN23h6dY+Hj/aQ/dQhOFILGOmksWqWi8ffNU6Hni5g11tfZzoHVm0yuhUlFI0VrrFcihHGivdReFPtJ4cJSBdXtQmLYfC3YOPHupi1/G+OTUADI1FqfIalgMY6azHuoc51TfKa7c2JY/7/cvX0FLn5dMPHU4GrmdyKwF86DXrWV7l4c9/+DLRuGbDLMcvFA3+4niPyCciDlnQGPAUhVvJEoe6aaqjhdLE+v/uLWAh3HEzc+jFU/05nReLJxiKxKiqcFLlddIYcHOse5hfHewC4LVbJsTB5bDx0Zs2c6RriM/98igwuzh4XQ4+/sYtnDOL4QphOYDxHiGWQxnSGCiOp4KJ1hliOZQTVnv2QsYcjvcaWUa7c+whFArHAKOtC2AGpYf41cEuLlxZlVaH84bty7h4dTUvnxnE57InK6Fn4i0XLOeyllqUml1MFoqGInmPyCciDlnQGHDTNxIp+MQny+ds+aCF8sDnsuN22AomDkPhaLLYbE+O4mBVRyfFoTHAoXNDvHxmcJLVYKGU4m/N1i/rG/1ZdSVQSvGl2y7ma3+wY9q014WmMeAmOJbeQnwpI+KQBQ2VHrQufGfMZMzBK+JQTiilqPO5CtZf6USv4VK6cl0dHcEwHSkFoSd7R/jgfbsJhTN3ELDEodo7YTmMx4yHrBu3pYsDwM6WWj746nW8Y8fKrPe4rMrD67Yty/r4fGPVOpSSa0nEIQsarSKXAscd+kfGCbgduBzy31Zu1PpdBQtIW/GG37vUeLNObU/9radP8IsDXTx+pCfjuVMth/PM1hYrayrYNEObi4+/YQvvvrJl3ntfLEqx1kHeZbKgsUh6p/SPjCf9z0J5UetzF8xybesxKo9v2racCqc9KQ7jsQQPvtIBwFPHMovD4Kix5wm3kh+ljEB0MTSyzBcNfrOFeAmJg3RlzYLGyuL4j5fq6PKl3ueibQ7DcvLB8Z4RVtd6qXDZuWhVdVIcfn24m8HRKI0BN08dy9y5OGRaDpWmOFR7XXz7fZdxYYlNJxTLoUxp8BeHW6lvRKqjy5VCdmZt6xlmnZkFtLOlhoOdIUYiMe7f005DwM1d122gIxjmZN9o2rlT3UoArz6vgeoSi5vV+VwoVfgHyHwyL3FQSp1USu1TSr2slNptrtUqpX6llDpmfqxJOf7jSqlWpdQRpdTrU9Z3mNdpVUp9SRWZvely2KjxOovArRQRy6FMqfW7GIvGGR2PLerrJhKaE70jrDOb2V2ypoZ4QvP4kR5+c7ibt13czKvPawAyu5aCY1EqnHbcjsXtd7TYOOw26nwueoqgk0K+yIflcJ3W+iKt9U7z648Bj2mtNwKPmV+jlNoK3ApsA24CvqKUsu6YrwJ3ABvNfzflYV95pTHgKehTgdZa2nWXMZbFuNgZS2cHx4jEEsk2FpesNp71/vnhQ8QSmrdf0syaOi/N1cZQrKlYfZXKgYYSK4RbCLfSzcC95uf3Am9NWf+B1jqitT4BtAKXKaWWA5Va613a6En8nZRzioZCt9AYjsSIxrW4lcoUa2b4YruWjptprJblYPVIah8YY9uKSjYvq0QpxbUb63mmrS+tFsjqq1QOlFoh3HzFQQO/VEq9qJS6w1xr0lp3ApgfG831ZuBMyrnt5lqz+fnU9TSUUncopXYrpXb39GTOjlgoGgJuekKFMxknqqOldUY5UlegKmkrCL4upfJ4x5paAN5+yUQdwtUb6hkKx9h3Njjp/HISB2uWdKkwX3G4Wmt9CfAG4C6l1KtmODZTHEHPsJ6+qPU9WuudWuudDQ0Nue92HjQE3PQUcOBKsjpaLIeyJOlWWnTLYZhKj4P6lBTq121toqnSzc0XrUiuXbW+DoCnjk12LQXNpnvlQIMpDta8iqXOvMRBa91hfuwGfgpcBnSZriLMj93m4e3AqpTTVwId5vrKDOtFRWPAQzSuGSjQLOn+YemrVM4UqjPr8Z4R1jVMbmNx3eZGnvub11Lvn7Bi6/xutq2oTIs7lJvlEEtoBseKY978fJmzOCilfEqpgPU58DpgP/Ag8B7zsPcAD5ifPwjcqpRyK6XWYgSenzddT0NKqSvMLKXbU84pGgpdCHeq30gTXFFdUZDXFwqL3+3AZbctekDaSGPNbuzmNRvq2XN6gJHIREZVOYlDQ5EUy+aL+VgOTcBTSqlXgOeB/9FaPwJ8GrhRKXUMuNH8Gq31AeBHwEHgEeAurbXVpepDwDcwgtRtwMPz2NeCUOgWGvvPBmmqdCdvQKG8UEpR53ctqltpOBKjKxTJutPpVRvqicY1L50eBCAaN0ablos4NAaMYtlSiTvMuUJaa30cuDDDeh9wwzTnfAr4VIb13cD2ue5lMSh0lfT+s0G2ryitqlIhNxa7EO6E2VNpplGdqWxfUQnA4XMhrtlYn9Z0r9RpKJIebPlC2mdkSSHdSqPjMdp6hnnj+csX/bWF4qHWt/CWwxNHe/jlwXPsWFND75DxWuuytBzq/IZla02LGxxNr44uZaz3iJ5hEYeywud24HPZC/JUcKhziISG7c1iOZQzdT4XJ/tGFvQ1PvfLI7zSHuQ/nz0NgE3Bmjpv1udvXhbg8LkQMNE6o7JMxMHnduAt0HvEQiDikAONlYWpgDzQYeSOb2+uXPTXFoqHOr97QQPSXaEwr7QH+Ysbz+P6LY08d7wfv9uRU+uLzcsC3LvrFLF4Itl0r1wsBzBrHcRyKD+MCsiFdSsFR6O09gwlC43AiDfU+VwsmzJSUSgvan0uRsfjhKNxPM789yqy5jq/fvsyzmsKsG0OMa7NyyoZjyU42TcyEXMoI3FoCLjpLmCxbD6Rrqw5kDpLOhyN866vP8u7vv4s//zwIf5nb2demqJ9+fFWbrl7F2dTpm3tOxtiW3NVSfW/F3JnoQvhHj3UxZo6Lxsb5z6HedMyY4DP4XNDabMcyoEV1RW0D4zNfuASQMQhBxoDHrpDRpX0N548zjNtfQyORvnWUye463t7+NdHj837NZ481ovW8LOXzgKGCB3rGuJ8cSmVPclCuAVwLQ1HYjzT2seN8xzCs6HRj92mONw5RHDMeFgql5gDGMOMzg6OMRxZ3O65C4GIQw40VroZi8Zp6xnhK4+38Ybty3joz65l/9+/nvUNvuSs3bnSPzLOoU4jmPeTPe1orTnaNUQsoSWNVUgWQN5v3hv55ImjPYzHE7x2a+a5ztnicdpZV+/j8LkhgmNRfC47Tnv5vM1YY1CPdQ0VeCfzp3z+1/KAlar2sfv3EotrPv6GLQC4HXZW1XrpDM7P17irrQ+Ad+5cRVvPCHvbg+w/a4iFZCoJ21ZU8t6rWvj2Myf5Qh6s1FQePdhFtdfJzjU1sx88C5vMjKXgWLTkhvrMxoQ4FGZqXz4RccgBqwJy96kB3n/NWlanpPgtr6qgY3B+vsZn2nrxux389Rs243LY+MmedvZ3BKmqcLKyRtpmlDtKKf73m7fyjh0r+dJjx7jniba8XDcWT/DrI91cv7kRRx6e8rcsr6R9YIz2gdGycikBrKr14nbYOFoCloNkK+WANSe23u/mruvWT/pec7WHvpHxeWWS7Grr47K1tdT6XNy4tYkHX+lgWVUF25srJRgtAGCzKT79uxcwGo3zTw8dZtuKKq7eUD+va75wcoDB0Sivm6dLyWKzGZR++cwgF6+uzss1lwp2m2JDo5+jBZr3nU/EcsiBVTVeVtVW8Ik3byHgmfxEtLzKeLKfq2upMzjG8d6RZOvj372kmYHRKIc6QxJvECZhtyk+/3sXUuG0J9NPcyUcjbP7ZD/3PXuKL/zqKC6HjWs35qcNvpWxFIklyipTyeK8pkBJxBzEcsiBCpedJz96fcbvWcHCzsEx1tZn14smlWdajXjDVeuNp8BrNzZQZ7ZL2CbxBmEKboedS9fW8kxb+mjObLjzP1/k8SPGwKxKj4P3Xd2Cz52ft4Pm6goCbgdDkRjVFeUVcwDY2OTnpy+dJRSOUulZuuIolkOeWFFtxCPOzjHu8HRbL7U+V9Ikd9pt/I45TMVqaCYIqVy1vo6jXcNzqtrf2x7kdVubeOZj1/PKJ1+XTK7IB0qppPVQLoN+UtlUIkFpEYc8sazKEIe5uJW01uxq6+PKdXXYbBOxhT+5fiP/8o4Ls258JpQXlgty1/G+nM7rHxmnf2Scy9bWsqK6YkHiWZuXm+JQpm4lYMkHpUUc8oTbYafe755TxtLJvlE6g2GuNP/YLWp9Lm7ZsXKas4RyZ9uKKgIeB7tydC219RhPtOvnUQk9G5uXGdZuuWUrgeFWq3DaRRyECZqrPXNyKz2y/xzAvLNOhPLCblNcsa6OZ9pysxzazEyaDQtokW5ZbohDOc48t9kUG5v84lYSJlhRXZGTW+lo1xDv+4/n+cwjh7lgZRUtObRGFgQwXEun+kZpHxjN+py2nmHcDtuCjpy9ZHU1X7rtYm7Y0rhgr1HMbGwMiOUgTGAVwmXT2uAbTx7npi8+we5TA3z8DZv50QevlFoGIWes7LZdOVgPbT0jrK33Ybct3P2mlOJ3LlyRU7vvUuK8Jj/dQxGC5sCjpYiIQx5ZUe1hdDxOaGzmplsHO0J8+uHDXLepkd/+1XV88NXrF6QFs1D6nNfkp87nylEchhc03iDAeWa21tHupWs9iDjkEctMnynuEIsn+Ov791JV4eRf3nFhstOmIMwFpRRXrjfiDtlYrOFonDP9owsabxBKI2NJxCGPJAvhgtOLw7eePsG+s0H+/uZt1IgwCHngqvX1nAuFs+oKfKpvlIRe2EwlAVZUefC7HUs6KC3ikEdWmLUO06Wznuwd4XO/PMqNW5t40/nLF3NrQglj1Ts83Tp7SmsyjbUh9yp+IXuUMnssieUggNGQz2lXdEyTsfTZXxzGZbfxf9+6XYLPQt5YU+elpc7Lo4e6Zz3WSmNdVy+Ww0KzZXmA/WeDxBP5nb2xWIg45BGbTbGsypPRcognNE8e6+XNFy6nSWZBC3lEKcWNW5vY1dY36wSy1p5ho0jLJQkQC81V6+sJhWO8fGaw0FuZEyIOeWZFVQWdg+mWw6HOEEPhGJevrctwliDMjxu3LmM8nuCJoz0zHieZSovHtRvrsSn47ZHZLbpiRMQhz6yorsiYrfSs2f/m8nW1i70loQy4ZHU1NV7njC28EwlNW/eIxBsWiWqvi4tX1/D4LIJdrIg45JkV1R66QuE0P+NzJ/pZU+dNzn0QhHzisNu4fnMTvz7cTTSeyHjMuVCYsWic9ZLGumi85rwG9rYH6R3OvXNuoRFxyDPLqyqIJfSkNsqJhOb5E/1cvlasBmHhuHFrI8GxKLtPDiTX9rUH6R4y3JxWptIGcSstGq/eZAxQms3dV4yIOOSZZrPWoSOl1uHwuSGCY1GuWCfxBmHhuHZjAy6HLelaemR/Jzd/+Sne8m9PcaxrKJmpJJbD4rF9RRX1fhe/FXEQllen1zpMxBtEHISFw+d2cM2Gen516BxPHevlT7//Mtubq0hoeMfXdvHIgXNUehzU+6X4crGw2RSv2tjAE0d7llxKq4hDnpkYFzqRsfTciT5W1VYkrQpBWCheu6WJM/1jvP/eF1jX4OO+91/Oj++8koDHwbPH+1nf6Jcam0Xm1ZsaGBiNsrd9aaW0ijjkmUqPE7/bwal+o5VBIqF57kS/pLAKi8JrtzSiFCyv8vCd919GldfJmjofP77zKi5tqeG1W5oKvcWy41UbG7ApkjO7U4nE4uw+2Z+xL1b7wGhW/bIWChGHBeCKdbV8//kz/PSldo52DzE4KvEGYXForPTw/T+6gv+680oaU4otmyo9/NedV3HXdRsKuLvypMbn4sJV1Tyeod7hH//7ILfcvYtfHDg3af2nL7VzzWd+wx9950X6CpTpJOKwAPzrrRdz+dpa/tePXuHvHzwIIJlKwqJxxbo6GgNShV9MvOn85bzSHpwkAkfODfG9505jU/D3Pz/IiFnd3j8yzj/8/CCra708cayH13/xSX5zePEL6UQcFgCf28G33nsp129qZNfxPpqrK1hVK1PeBKFcuf3KFratqORvf7qPvuEIWmv+8b8P4nc7+PrtO+kMhvnio0cB+L//c5DhSIyv376Tn//xNdT7Xbzv2y/wn8+eWtQ9izgsEB6nnbvfvYP3X72WO1+zvtDbEQShgLgcNj73excSGovxdz/bz6OHunmqtZePvPY8btjSxK2XruJbT5/kG08e5yd7znLnq9ezaVmATcsCPPDHV3P95kY++eCBRa2XUIUMeMyHnTt36t27dxd6G4IgCFnz1cfb+Mwjhwl4HDQE3PziI6/CabcxMDLODZ//Lf0j46yt9/Hwn107aTrkcCTGLV99hrMDY/zkw1ex0RwmNBeUUi9qrXfOdlzRWA5KqZuUUkeUUq1KqY8Vej+CIAj55o5XrePi1dUMhWN84k1bcdqNt+Aan4tPvHkLboeNf3rb+Wljg/1uBxn3kB0AAAmTSURBVN9876W4nXbef+8LixKkLgrLQSllB44CNwLtwAvAbVrrg9OdI5aDIAhLke6hMHtODXDT9vSBX+FofMZ58i+fGeSdX9vFR2/azAeuWTun18/WcnDM6er55zKgVWt9HEAp9QPgZmBacRAEQViKNAY8GYUBmFEYAC5aVc0jH3kVLXULn+BSLG6lZuBMytft5toklFJ3KKV2K6V29/QsvV4lgiAI82VtvW9RqtyLRRwy/aRp/i6t9T1a651a650NDQ2LsC1BEITypFjEoR1YlfL1SqCjQHsRBEEoe4pFHF4ANiql1iqlXMCtwIMF3pMgCELZUhQBaa11TCn1x8AvADvwLa31gQJvSxAEoWwpCnEA0Fo/BDxU6H0IgiAIxeNWEgRBEIoIEQdBEAQhjaKokJ4LSqkxIJe4xGrg9DxesgoILuDxqcxlr7K/CXLd31xeS/Y3v9eS/eXntbI9vx7oNT9fo7WevRZAa70k/wE9C3l8hvPvWcjj57tX2d+87o2cX0v2J/sr5P5yPR/Ynet1l7JbKdeBrPMd4PrzBT4+lbnsVfY3Qa77m8tryf7m91qyv/y8Vj7Oz8hSdivt1lk0j5rr8YWk2Pcq+5sfsr/5IfvLnbnsaSlbDvcs8PGFpNj3KvubH7K/+SH7y52c97RkLQdBEARh4VjKloMgCIKwQIg4CIIgCGmUrDgopYZn+f7jSqmCBY2UUiuVUg8opY4ppdqUUv9qNh2c7viPKKUWfsLH5Nec8XdYaJRSb1NKaaXU5kLvZSaK8V6U+2/+LJX7b66UrDgUM8qY1PET4Gda643AeYAf+NQMp30EWNQ/ziXAbcBTGF18s8YcS1u2yP2XN0r6/itpcVBKvUYp9d8pX/+7Uuq9BdySxfVAWGv9HwBa6zjw58D7lVI+pdS/KKX2KaX2KqX+RCn1p8AK4DdKqd8s5kaVUn6l1GNKqT3mnm4211uUUoeUUl9XSh1QSv1SKVWxmPsCrgY+gPnHaf5/P6GU+qlS6qBS6m6llM383rBS6h+UUs8BVy7WPlP2W0z3otx/edgXS+j+mwslLQ5FzDbgxdQFrXUIo+T+D4G1wMVa6wuA72qtv4Qx/Og6rfV1i7zXMPA2rfUlwHXA59TEjMKNwJe11tswCn9+dxH39VbgEa31UaBfKXWJuX4Z8BfA+cB64O3mug/Yr7W+XGv91CLusxiR+2/+lPz9J+JQGBQZxqCa668C7tZaxwC01v2LubEMKOCflFJ7gUcxZns3md87obV+2fz8RaBlEfd1G/AD8/MfmF8DPK+1Pm4+DX8fuMZcjwP3L+L+ihm5/+ZPyd9/RTPPYYGIMVkAPYXayBQOMOUpRylViTEq9TiZ/3ALxe8DDcAOrXVUKXWSid9jJOW4OLAoZr1Sqg7DNbJdKaUxBkRpjHkgU3931tdh8w+2UBTTvSj33zxYovdfzpS65XAK2KqUciulqoAbCr0hk8cAr1LqdkgGqD4HfBv4JXCnUsphfq/WPGcICCz+VqkCus0/zOuANQXYw1RuAb6jtV6jtW7RWq8CTmA8pV2mjHGzNuCdGAHDYqCY7kW5/+bHUrz/cqYkxcG8sSNa6zPAj4C9wHeBlwq6MRNtlKW/DXiHUuoYcBTDt/o3wDcwfL97lVKvAO8yT7sHeHixAoLW7xDj97ZTKbUb4ynu8GK8/izcBvx0ytr9GL+rXcCngf0Yf7BTj1tUivFelPtv3iyZ+28+lGT7DKXUhcDXtdaXFXovS5Wl+DtUSr0G+Eut9ZsLvReLpfh7LAaW4u+tGO+/+VByloNS6k6MQNDfFXovSxX5HeYH+T3ODfm9FQclaTkIgiAI86PkLAdBEARh/og4CCilVimlfmNWnB5QSv2ZuV6rlPqVMvrv/EopVWOu15nHDyul/n3Ktd5pVtYeUEp9thA/j7C0mMP9d6NS6kWzYvpFpdT1KdfaYa63KqW+lFIwJ+SIiIMARg7+X2ittwBXAHcppbYCHwMeM/vvPGZ+DUZmyyeAv0y9iJn//f+AG8yq1SalVLGkDwvFS673Xy/wFq31+cB7gPtSrvVV4A6M6umNwE2L8yOUHiIOAlrrTq31HvPzIeAQRiXqzcC95mH3YrQMQGs9YrYACE+51DrgqNa6x/z6URa3pYGwBJnD/feS1rrDXD8AeMz6keVApdZ6l5mu+x3rHCF3RByESSilWoCLgeeAJq11Jxh/wEDjLKe3ApvNpmgOjD/MVQu3W6HUmMP997vAS1rrCIagtKd8r91cE+ZAqbfPEHLA7DR5P/ARrXUoV3et1npAKfUh4IdAAngGw5oQhFnJ9f5TSm0DPgO8zlrKcJikY84RsRwEAJRSTow/zO9qrX9iLneZpjrmx+7ZrqO1/rnZefJK4AhwbKH2LJQOud5/SqmVGNXHt2ut28zldmBlymVXYnSTFeaAiINgDX/5JnBIa/35lG89iBHww/z4QBbXajQ/1gAfxmjHIAjTkuv9p5SqBv4H+LjW+mnrYNP1NKSUusK85u1kcc8KmZEiOAGl1DXAk8A+DHcQGH12nsPoB7Qao9/OO6wWzmZ3zErAhdFL/3Va64NKqe8DF5rX+AettdXWWBAykuv9p5T6O+DjTLZKX6e17lbGuNVvY3RofRj4Ey1vcnNCxEEQBEFIQ9xKgiAIQhoiDoIgCEIaIg6CIAhCGiIOgiAIQhoiDoIgCEIaIg6CsAAope60ZjRneXyLUmr/Qu5JEHJB2mcIQp5RSjm01ncXeh+CMB9EHAQhA2YDuEcwCrEuBo5iVNxuAT4P+DFaR79Xa92plHoco5fU1cCDSqkAMKy1/hel1EXA3YAXaAPeb/ah2gF8CxgFnlq8n04QZkfcSoIwPZuAe7TWFwAh4C7g34BbtNbWG/unUo6v1lq/Wmv9uSnX+Q7w1+Z19gGfNNf/A/hTsw+VIBQVYjkIwvScSend858YLR22A78yO4bagc6U43849QJKqSoM0fituXQv8F8Z1u8D3pD/H0EQ5oaIgyBMz9TeMkPAgRme9EdyuLbKcH1BKBrErSQI07NaKWUJwW3As0CDtaaUcpozBaZFax0EBpRS15pL7wZ+q7UeBIJm0zmA38//9gVh7ojlIAjTcwh4j1LqaxgdQP8N+AXwJdMt5AC+iDGqcibeA9ytlPICx4H3mevvA76llBo1rysIRYN0ZRWEDJjZSv+ttd5e4K0IQkEQt5IgCIKQhlgOgiAIQhpiOQiCIAhpiDgIgiAIaYg4CIIgCGmIOAiCIAhpiDgIgiAIafx/PeAeWQQtmV4AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-100:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les cas de varicelle semlent suivre une périodicité annuelle, avec un creux en septembre." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "# retrait de l'année 1990 qui en commence que le 3 Décembre\n", "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W') for y in range(1991, sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_sept_week[:-1],\n", " first_sept_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": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "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": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Le maximum est de 842373 pour l'année 2009\n" ] } ], "source": [ "max_vari = yearly_incidence.max()\n", "annee_max_vari = yearly_incidence.idxmax()\n", "\n", "print(\"Le maximum est de {} pour l'année {}\".format(max_vari, annee_max_vari))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "hideOutput": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Le minimum est de 516689 pour l'année 2002\n" ] } ], "source": [ "min_vari = yearly_incidence.min()\n", "annee_min_vari = yearly_incidence.idxmin()\n", "\n", "print(\"Le minimum est de {} pour l'année {}\".format(min_vari, annee_min_vari))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hideCode": true }, "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 }