From ff2e597ac009af327a468f17c943d54fc993e824 Mon Sep 17 00:00:00 2001 From: 8765daa77f27c001b133a69561ec2403 <8765daa77f27c001b133a69561ec2403@app-learninglab.inria.fr> Date: Tue, 23 May 2023 16:06:01 +0000 Subject: [PATCH] Exercice 03-2 --- module3/exo2/exercice.ipynb | 1542 ++++++++++++++++++++++++++++++++++- 1 file changed, 1539 insertions(+), 3 deletions(-) diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe37..6960347 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,1542 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence de la varicelle\n", + "1. Quelle est l'année avec l'épidémie la plus forte ?\n", + "2. Quelle est l'année avec l'épidémie la plus faible ?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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 1991 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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": 6, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'raw_data' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# tester que le fichier local n'existe pas avant de le télécharger\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mraw_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'raw_data' is not defined" + ] + } + ], + "source": [ + "# tester que le fichier local n'existe pas avant de le télécharger\n", + "raw_data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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172023027657630601009210515FRFrance
182023017815354701083612816FRFrance
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2020225176226382286309513FRFrance
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262022457382717205934639FRFrance
272022447427122316311639FRFrance
2820224375863330284249513FRFrance
292022427377019505590639FRFrance
.................................
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1693 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202319 7 9377 6090 12664 14 9 \n", + "1 202318 7 10671 7291 14051 16 11 \n", + "2 202317 7 9184 6162 12206 14 9 \n", + "3 202316 7 11387 8014 14760 17 12 \n", + "4 202315 7 14040 7613 20467 21 11 \n", + "5 202314 7 15247 11032 19462 23 17 \n", + "6 202313 7 13322 9700 16944 20 15 \n", + "7 202312 7 10374 7218 13530 16 11 \n", + "8 202311 7 4919 2880 6958 7 4 \n", + "9 202310 7 4854 2731 6977 7 4 \n", + "10 202309 7 7004 4548 9460 11 7 \n", + "11 202308 7 8175 5316 11034 12 8 \n", + "12 202307 7 6595 3782 9408 10 6 \n", + "13 202306 7 9595 6017 13173 14 9 \n", + "14 202305 7 6237 3907 8567 9 5 \n", + "15 202304 7 6299 3973 8625 9 6 \n", + "16 202303 7 6063 3798 8328 9 6 \n", + "17 202302 7 6576 3060 10092 10 5 \n", + "18 202301 7 8153 5470 10836 12 8 \n", + "19 202252 7 5171 2717 7625 8 4 \n", + "20 202251 7 6226 3822 8630 9 5 \n", + "21 202250 7 6590 3100 10080 10 5 \n", + "22 202249 7 5095 3212 6978 8 5 \n", + "23 202248 7 4985 3043 6927 8 5 \n", + "24 202247 7 6087 3733 8441 9 5 \n", + "25 202246 7 3033 1392 4674 5 3 \n", + "26 202245 7 3827 1720 5934 6 3 \n", + "27 202244 7 4271 2231 6311 6 3 \n", + "28 202243 7 5863 3302 8424 9 5 \n", + "29 202242 7 3770 1950 5590 6 3 \n", + "... ... ... ... ... ... ... ... \n", + "1663 199126 7 17608 11304 23912 31 20 \n", + "1664 199125 7 16169 10700 21638 28 18 \n", + "1665 199124 7 16171 10071 22271 28 17 \n", + "1666 199123 7 11947 7671 16223 21 13 \n", + "1667 199122 7 15452 9953 20951 27 17 \n", + "1668 199121 7 14903 8975 20831 26 16 \n", + "1669 199120 7 19053 12742 25364 34 23 \n", + "1670 199119 7 16739 11246 22232 29 19 \n", + "1671 199118 7 21385 13882 28888 38 25 \n", + "1672 199117 7 13462 8877 18047 24 16 \n", + "1673 199116 7 14857 10068 19646 26 18 \n", + "1674 199115 7 13975 9781 18169 25 18 \n", + "1675 199114 7 12265 7684 16846 22 14 \n", + "1676 199113 7 9567 6041 13093 17 11 \n", + "1677 199112 7 10864 7331 14397 19 13 \n", + "1678 199111 7 15574 11184 19964 27 19 \n", + "1679 199110 7 16643 11372 21914 29 20 \n", + "1680 199109 7 13741 8780 18702 24 15 \n", + "1681 199108 7 13289 8813 17765 23 15 \n", + "1682 199107 7 12337 8077 16597 22 15 \n", + "1683 199106 7 10877 7013 14741 19 12 \n", + "1684 199105 7 10442 6544 14340 18 11 \n", + "1685 199104 7 7913 4563 11263 14 8 \n", + "1686 199103 7 15387 10484 20290 27 18 \n", + "1687 199102 7 16277 11046 21508 29 20 \n", + "1688 199101 7 15565 10271 20859 27 18 \n", + "1689 199052 7 19375 13295 25455 34 23 \n", + "1690 199051 7 19080 13807 24353 34 25 \n", + "1691 199050 7 11079 6660 15498 20 12 \n", + "1692 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 19 FR France \n", + "1 21 FR France \n", + "2 19 FR France \n", + "3 22 FR France \n", + "4 31 FR France \n", + "5 29 FR France \n", + "6 25 FR France \n", + "7 21 FR France \n", + "8 10 FR France \n", + "9 10 FR France \n", + "10 15 FR France \n", + "11 16 FR France \n", + "12 14 FR France \n", + "13 19 FR France \n", + "14 13 FR France \n", + "15 12 FR France \n", + "16 12 FR France \n", + "17 15 FR France \n", + "18 16 FR France \n", + "19 12 FR France \n", + "20 13 FR France \n", + "21 15 FR France \n", + "22 11 FR France \n", + "23 11 FR France \n", + "24 13 FR France \n", + "25 7 FR France \n", + "26 9 FR France \n", + "27 9 FR France \n", + "28 13 FR France \n", + "29 9 FR France \n", + "... ... ... ... \n", + "1663 42 FR France \n", + "1664 38 FR France \n", + "1665 39 FR France \n", + "1666 29 FR France \n", + "1667 37 FR France \n", + "1668 36 FR France \n", + "1669 45 FR France \n", + "1670 39 FR France \n", + "1671 51 FR France \n", + "1672 32 FR France \n", + "1673 34 FR France \n", + "1674 32 FR France \n", + "1675 30 FR France \n", + "1676 23 FR France \n", + "1677 25 FR France \n", + "1678 35 FR France \n", + "1679 38 FR France \n", + "1680 33 FR France \n", + "1681 31 FR France \n", + "1682 29 FR France \n", + "1683 26 FR France \n", + "1684 25 FR France \n", + "1685 20 FR France \n", + "1686 36 FR France \n", + "1687 38 FR France \n", + "1688 36 FR France \n", + "1689 45 FR France \n", + "1690 43 FR France \n", + "1691 28 FR France \n", + "1692 5 FR France \n", + "\n", + "[1693 rows x 10 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# télécharger les données dans un fichier local\n", + "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 jeu de données ?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "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": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aucune donnée manquante à gérer.\n", + "Je garde en mémoire le jeu de données originelles." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "data = raw_data.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit 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 semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque isoweek.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos données. Les résultats vont dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# fonction de conversion des semaines de la colonne \"week\"\n", + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int) # conversion en string\n", + " year = int(year_and_week_str[:4]) # recuperer les 4 premiers caracteres = annee\n", + " week = int(year_and_week_str[4:]) # recuperer les autres caracteres = semaine\n", + " w = isoweek.Week(year, week) # conversion au format iso\n", + " return pd.Period(w.day(0), 'W') # retour valeurs au format iso\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il reste deux modifications à faire:\n", + "\n", + "1. Définir les périodes d'observation\n", + "comme nouvel index de notre jeu de données. Ceci en fait\n", + "une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "2. Trier les points par période, dans le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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": [ + "Ceci s'avère tout à fait juste puisqu'il n'y a pas de retour." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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I+gH4I4CvCCE2IBAR7QVgAoBlAH4qqzK3m8x7bDq51NsnoouJaAYRzVi5cqXr0LsdpJGF68felZzD1Pmr8ezcfHPtLFbKGbpchcve8E+/mhqJbqohDtVu7ln3uzrBfff+OWwcpnogK+eI9OruKoshTxzqD6c3S0RNCAjDnUKIPwGAEGK5EKJTCFEGcDOASWH1JQBGKbePBLA0LB/JlCfuIaISgIEA1kCDEOImIcREIcTEoUOHuj1hHVDtAa4UypVclapdqXM4/+ZpuOCW6RXdm7XnVbPn5pWp3z6t8qB5tdIxGa87TsTs99fj0TkfONWt3lHTfr0jXLuPv7kcc5dvrK6zCqDn0/aoPVyslQjALQDeFEL8TCkfrlQ7G4CMgfAAgCmhBdIeCBTP04UQywBsJKIjwjY/DeB+5Z4Lw9/PBfCk6MYxI6o9yUslaaNOga74v3/Mx3/dnz+UhY5GmLLm7qPbrqZ8fhSuJ+Zq16gck2ne2kNZ563PL8TJ1z1TVV82bG3vxLE/eipV7jmH+sPFCe4oABcAmE1Er4Vl3wJwPhFNQPCdLgTweQAQQswhonsBvIHA0ulSIYTUBl4C4LcAegN4OPwHBMTnDiKah4BjmFLdY+XHK4vWYlj/Fowc3CezbrUbTVMx+PDaHUUdjdrYrnn4LQDA9848sCH9uZyYu8OmXm/TiDztNyp7YFZCqo4qnArz4N1Vm7FoTWuq3BOH+iOTOAghngMvAfib5Z6rAVzNlM8AkNp5hBBbAZyXNZZ64pwbXgAALLzm9My61S5LKad15Rx2dGulSpjE7kA0XJH1/vLke26Ub0zsp8m3lIfrXbS6FYP6NmFAr6b84zDMjRcr1R/eQ7oCVCvxirx2twOdg45yWeDPry5xDDPtJmuvTKzU+Emp9D1k3+dOHdw5h9qwDqZm8mzOx/74KXzs/z1X2TAMc+M9pOsPTxwqQLXLstmBc1A/7u70Gdz38hJ89Z6Z+O0LC6tuq9KYRQC616RkIGsjq4dYqVpkRXjJmzL1vdVp0ZDTOAxz0930dTsiPHEw4J6XFmHMFQ9hxcatNW+7JHUOVuKg/t59dsLVYcKclUymsggNGG5XzEiluoes15enWXfz5+oQx/8ytNQgF1WTyK0bfRI1w1sfbMCYKx7CgpVu0YzrDU8cDLh3RuCvt4g58dRqYdpMWcvdlHOIYBjUq4vWYsEq3qs2BUtspSxHOvYet14rRr3ESnmcBtWmrKHOq1w0USpbw/XGhS/ge9oRFdJ/eTWw7H/E0Vy53vDEoRJUsS6fn7cqykls4xzKCc6h8v5c8fU/zKxJO2eHin0X5AxOm0C1Ooc83Fi1G2HWWHO17zxse8XX31+PVxaZvZvlmEycSuOi2/D974g6B8kl5RXZ1Qs+n0MG1rW2Y8OWZPyYajamf/l1nKrSThwSZ8SK+3PFfS8vyazDcVE21HP/qHZvEKL+JqoSWd96vmircV3b+LOaPCNUEJus8+rpwJgHprnbMYlDvoCc9YYnDhn43O0zUmW1Eyu56hxq0181mDp/Nc6/eRrGDutX87YrMmWtts8q78+DrI2spalova7CdV3U6vmqXXtL1mYfKK780yyMGNQbl50wLnXNNHfdZQOtJQoWMWtXwIuVDLCdjCp5d+2dZZxzw/OJsraO7qlz4DbreSs2hj/dlWVvLrPnbK5GNPHw7OpCSTdSyV/Lrhq1KZJB57B03ZbwenYbQggcfW3au1nHXdMX4yd/f4e9ZtIt7Ig6hyi3fDehDp44VIBKNpb3127BK4uSyVNcxUqNXitsfxVs5Jf9/tW6jOXu6Ytw90uL0xcM6N+SZpAbOaXcelnf2m69boKztVKNHlBt59m5K/Hha57MJMzj/vNvuPTOV2oyBlMb3UUuX0tIsdIvnpjbxSMJ4IlDBahkWW5pT+cTsIqV1N8bTB24vAH1kDFnWUuacMWfZueqX2DsISuZ0kp1TdxdD8xaar1uQjWxlTrLIjKGyEJkraRM1OvvB5zga4vXWX1U2jsFHpq9LPcJ+NRfPJsqMz3vDkgbEma7G7ooT4YKTxwqQCUbC0ccrDJjhW40+jtoFFvbKIVwkSMOOWa12nFmnnJzTLdzyBWmzWsfeQuHX/04Vtt8VEIsWrM5NbS885B3FXFiSNNazJP4aHuBeohZG/oTdSU8cUD+k3klJ0guyJ6tne4mVuq+efmywTlS5Uu5WV3/WbfnWU+uYSu4MT/+xnIAwNrW7FPpXdMXh+2YTFmzx1CLQ4ZRIb0Dsg7qnHYH2ueJA3gW1bb4H3k9v5MK965tCyCpkG7sSuE+yKpCXRgQhYWu0fOZFNwc59BIZG2SG7d2YCvDWXJwi2llmNMKpoEbuuvbqsUGl8eUdf7KTfi/f8yvvtMugpr0qTsopT1xQH7Lhwdn5reUyfuRJYbU4HWSl1hWCrKY7lXSnemUW2QGn+fbq/rZmb5U0c6X737NOTBdLaKR/v7FRc51E2KlnP3UYn/LY630yV9NwzUPv4XN2zqq7rejs4yFrp7+NYJ6hul60uCJA656YA4u/E3OTGeVnMC4123TOXShKSv34XXx4bsqcBxFI7kxbn/7+eNJi5S5jibCrnkUbBvzb55/N1U2a8k6vP7+eqd2XMWwlcyxLi7K4+fQ2tYR9ls9fvDQmzjuJ0/jg/W1j61mgso5dId4aj2eOPz2hYWYumB13fvhOQebzsF+bz3BLUxdrLS2tXYKs1o9Xx6xUkXWShWOs5aESGZgqwQ2+v7xXz4feU2rMI3dxUelEiZH54xMj8uLPgP89O9vR345lWJauCfUcp1nQZ3TSkTXtUaPJw6NAn8CM9dvpM7h2bkrtb6z75GBCatBVkKZ4Fr14K2V6gPOSVCfT5dQJUY4Drwefg4Srs6LlZx+da7VaK3ELFI5rlufX5gIU7O9QF2mJqfARsITBwNsCtiK5OHMV23XObhZK72yaC0OuupRjLniIXPfQuCtD8zeyhfckhSrcWa39XR0qDdnxFsr5e/UZU/8zK1pEaXaV2dZVBXk0FkZXCPyx4VmF0I4LYdKOAedMzKZrNo4B8Ae8bi7otDNTAI9cWgQ8spuEzF0LO2ec8ML2LjVroC788VFmPzzZ/H8vFUZowwwa/G61Pi617LNB+6jq2TryKIns5esx/INaRm1et8zGpeWF64mnNxY12/Jr6h9aFZsfJF776pgklNBLvMQB2V8a7qBn0BedDPakE0ciGgUET1FRG8S0Rwi+nJYvhMRPUZEc8Ofg5V7riSieUT0NhGdopQfRkSzw2vXU8gHElELEd0Tlr9IRGNq/6hdC24xu3IO1Zq1zVkacA0LV7tZX1xy5ysAAqIiUY8QzfUwj+VQKqaXuWlKVzEnZddn/9gvn2NPrOopngvlkQfunEMa1VjxbGnrxCblEOIUW6kC6vDNP85K/G2y3OXK67FGG6nva1wYdDe4cA4dAL4mhNgPwBEALiWi/QFcAeAJIcQ4AE+EfyO8NgXAAQAmA7iBiGTYyRsBXAxgXPhvclh+EYC1QoixAK4DcG0Nnq1bIb+fQ8bNDcDiNXFEzXou27yPd9J+w3LVby4xy9zQ6WW/fyXnaLKhSko4QpWrrSp2q2oszo77yVO4/sl5ANw3TNdQHSqen5c0DjFbK21/YqMsfOcvr3f1EBLIXKlCiGVCiFfC3zcCeBPACABnArgtrHYbgLPC388EcLcQYpsQ4l0A8wBMIqLhAAYIIaaKgFe8XbtHtnUfgBOpu5HRapHbz6HrnOCAgJ1P2LjX4W1EPXCPZ+lvz6H5woa3cJyDYU7XWbyHZ7xnTo5jg9rTG0vtkWoz23JWSNd2zSzfkNzoXdaDSwysLKc+kxiNK6+HuXVXfHvdBbmOMaG45xAALwLYRQixDAgICAB5nBsBQA2ZuSQsGxH+rpcn7hFCdABYD2AI0//FRDSDiGasXFmd7DYTNV5ovJ+DTefgppCuF4TQdA71INUVPpdpKKZyjnMwzWk/i9hn+rtr7AMzQCX03/pzvqCBOtx9DNKo1XlLwE0k+LIDMc0SdZo9pNNltTxP7mhn00rgTByIqB+APwL4ihDCdvzhZlVYym33JAuEuEkIMVEIMXHo0KFZQ64bKlk3nL22nXNQ6nUBcSgLkei3nvqB3KeznENp4YiDqel6nD5r+P6S0kY3gwaJ7iiK2dpu5xxM1kqNihzck+FEHIioCQFhuFMI8aeweHkoKkL4c0VYvgTAKOX2kQCWhuUjmfLEPURUAjAQQGXHtC7G9x98gzUrza9zUMVKjYfeZ33ESo1BE6uQNjh31WGLqaWIJ2uD/+xRY2SvqWu1TJBTq/WQNTVGayXWz6EWI9L7r32bfD/dj3C7WCsRgFsAvCmE+Jly6QEAF4a/XwjgfqV8SmiBtAcCxfP0UPS0kYiOCNv8tHaPbOtcAE+KOs7W6dc/i1uee7cubZva5R7H6iGthuyueir4+7NMadWrbUxU2Wohu8/9eDnrc6dMYxN12GBqGUBUbYsjZFEwQ6bPWhEHIYBZS9KhNipqK+NlqsTwU0fszpbHqKFYKfy5YNVmbKpBrKYs1CJmVq3hwjkcBeACACcQ0Wvhv9MAXAPgZCKaC+Dk8G8IIeYAuBfAGwAeAXCpEEJ6VV0C4NcIlNTzATwclt8CYAgRzQNwOULLp3qgo7OMOUs34PsPvlFxGy6nS33j5d59IziHVxetjcIvq+Ne19qGPa78G35jImZIipW+cd8stl4tYHu+qx6Yk6s+h2H9W9JtVDip//nn2VaHQw61VGom/V/ynZ67o1gpa0iqvrpUKCjl9mc/ab9dqh0aAOBLd73aEG9r1zwdjUSm0bUQ4jmYSfKJhnuuBnA1Uz4DwIFM+VYA52WNpRZY4xgrhXvgg0cOtJ6Ylq3fEv3e3inQXFJbYU6vlg9DPUlU802ffcMLbPmi0Ez1T6/yoRwCzqG+m4lL+1mntnMOGYE/vfq+tQ6nZDbGDMoYz505IppK1PJQWI1CulbjqOW6yCJY6vWmohqYLl038bVVSQhVQjNz8TpzxRqh3ZJPvqvQ4zykXeOW6WurVCBcdPQeAICDRg6Myjs6y5H888gfPhmV6yeBvCG7OxMDrf3C2bwtYOb6NPPng0YcMl37GHPFQ3hneRxITf3wLz1hbIWd88Wucus8IppaSkinL3RTxdVUCV7HxZDVsqpbKKqcQ4aHtKndd5ZvxOFXP44VG/NFW61FGHAb2roh59DjiIPrqUdffAUinBiyqkP7xWKKsf/5MP7pV1NT9+uhlXmFtHks6v31sG2X4Y37NhfZ6/XkGvRxuoz7S3e9qtSPy532ci62kvL7ms1tOOi7j2bmRlaxrcMtOQ9Q2416wcrNWMGE6JAoFdO5n6uF3lQjCY9Kg1XOgRUrQeUs+HZvff5drNy4DY+FWfFM0A8JWSFqqsV2KVba0eC6sHUFUaEQO9norDDnHKUHEMsr71UXv+udQuipBs13SgUz6z2M4KOs14Hx0TkfoKlYiLyF2W60QtP8udijcxu+2tzU+auxcVsHbnrGPYvYtvYy+jS71a01oZWBEbnnaipY5rRC6G3V8pSbqXNQKqg6B25tqwTb1KxcL0vXbTHU4FGo8zG6OxKHHsg5uKFT29wLRFEANxeJQopzyKmQVj8KUz3dnE+vZrOAkFdMJ2UhRI1Pn3FbX/jdK7jothl2aynt78SpMFGu/G6gE1x5tfqO99dtwSnXPYNFq1uNdSSqSMFgBfcMMedQebtZCXe2Zfgm5OorY5zqGiklOId0XTU3tun55QHvf5/Kl060lmbAHDxx6AZw3fD0zT1vfteUzoEbi1a6eE0rDv3+Y3hv9eakQtqwkS3Q0hjq40qPwd26RRjGXClsOhcXhy1T8vVKbdtNr1Bt792VZu/d+15egreXb8Qtzy3I7ivv4KoA59MhMbB3U+b9x/7oKfzvU/MSZfpcbc0hUsuC/j32akqOX92US0p8DJNzXNSuodw1LLZ+aHLNwFcp2rxCuuvheqLSTwoFiheWXNC28Mn6qZ31c9CK/vzq+1izuQ33vbwEnQ46h5N+9g9re9VYQAiRTQT1Z8p7+rERan1qVfGRSuRcdAQ29/tFq1vxo0ffUvqJ6/RuNn8eeXQOpnmPv34WAAAgAElEQVS0heqoFDKxEXcQ+Nj44Zn3L1rTip8+lkw0o7elhvGuFvoo9TlR14EatDDrkGe6XmnOBM85eERIEYcCRSypXHc22au+IXBrde6KTaxzmRCaKavjmFPsf2flJzwhhAPLn/y7dZu5P64pKW7hr2mcg30oAMwJXrj9QLZ/7I+fwnuhaEgnNM1FXlkPZId9SEAZ1k59Y0XFZz48JvPW75yxf64xRKfrnGJMG2ohXeRCmADpNZvKBGfiHCrcrF1pg14vi1OpFp44dANUqpAmIKVzsJ2s0/oAvu5fXott9Empm1BIu9q2a9XWbjZHGHVpKzO0gVM7IvFTRTR/tmshVIVgXrESgXDI7oOs7UvoIaMBfiPK4zGezOoX/24TAUkQgIuP3dO5ryaLkr/Sw28t9sXBJu291rY+12xWQrjoKvjySsOj1JtzkAfNcw8bmVGzceh5xMHVlFUnDkTRRiQ/dtt60a+ZFqt6olI3uo4KtJgCAve9vAQzQlv41UziGve24v+NdXTuiKlvm6NXLc5FKZ1DlaERmjRzk/bObIW7fB5uY9hq2LQ4bNiqKEqVclXBasO3Ttsv8TeB2DEduecQ7LNr/6Af7fLsJetTucJdUU+zZv0x9L83KnOXJwGWacyuYb0Xavq8euscJNfrcmBoFLrPSLoZ9I9v/Zb2iEDIhWk7TeiL11TVpCRMcg4uIw76+PofZuLc/5uKFxesxveqCBEihMi0ssn6sGU7AE9mbnzabDGit6V+1HlDiRMhJZfqLIvM06C8zJtNuhPv//nbWwoHFZebRC0qTM/HbY4/OPtAo87hY798DkvW5jPflHBZf1l5GUybtV6uP5fqX6C+Lu7dFZVFYrRWUuqYdIbf+vNsbND8Guquc5Cm5Y4HhkagxxGHSsVKcnE0FQrRNXXTuP6JuYn6aZ0D33FfRim5rrU9sfm4ntzUPj550zS89cHGxPU8p++yyH8647ideIO1tcP0n9ZIs/e6+TmkdRYd5XJKR6FnLhMWDjEP5xC0lWwTAHo1mXUaru2pKFD8hmvqqOZQJysRkmteBn0TTnwHGZxDkbJ1EknuPF1n/ZZ2/J4JkdIonYPnHLoQWa94fZjgXPdzkGgpFSI7b3UB/ky38NDFSjnGeOeLi3ClkkUrD+cgoZsE5sXGre14Z8Umax19XHzOCpH4ycElv3aCc1DKXcmdbqXS0SlSBgV6aArZDze+LHPOC44YzbalttTbgTjkOUcWKJuT2nPnvjlaDOBium3r9hOHjjSu4XSASvOhSv3eskJ2m/0c4kryW5d4/I3lGP/ff2fvM+0HtYJci6MreD/1Qs8jDsqq4aJrvjBvFQCzA1lzqRCZMdo4zdTJxfhxxL+bTsHOhxalXl8mZlIeD+BP/frFzIBj+ri405W0qLE9A/fdpXQ+hn6drE8UfZFER1lkWojYOIcsR7D//vgBbFvqO2qpgoBzxLaQMPfl0VQs4PHLP+LUx74G/QUHGwcXHFRMYqUk9Peu/tVH4bK5d6KKlUwETT1kHH7141EYGQB4dbGZ+zFZwtUKsv3j9g6SmJ19yAhb9Yag5xGHjOtygzvj4N3Y6y2lQsTqWq2VMk5E0XgcvjzXZZmlsFu4uhWvLlob9mtva+n67MBkqWdkvtiv/2FmZn/cWNNOcPFXfce09+JyRz+HlGliWWQqGeUQRgzqlbqWxTkUNM0nxzm4mMPmPzDYYyuViuTMVUYiDifiIMfFn+hNBykutL2eInfMkD649TOHY8rhcQ4x7iCiipVmvLeWFS3pHOT3/hrr5Qb1NsdDqXe4c1WstPtOferalyt6HnHIeMdyPfXvxTsotTQVHYmD1q9pPPbhAACufshNseySA+KBmUsTf1eTPSvr1AcAry5alxqbDo5L06tXkzyeiBMrlTM5BzksboPOG0JCPr8QItqcuTwTOvK8n0Ih5pBMs10qkHN+ZGlNlUesxFUlkJFYccVy3te1tuGBmUuxcHUrjt93GJqKBfzxkiODOg6Z4JasTYc20Z/9AyWIodWDvL60QSEOSaOXrkSPC7yX9ZblojMt5kDnkC1WSkceNdWzDgdAMmaMxEtM6Ga1KZv3dt7+zfcmb2YzrkWimXw6h7RYid/QKlUUdpRFZvYtm55EfsyuG60cZlkE+ogzDt4N40cNst/k0J6KAiniN8PQS8WCM6EtRZZPOcbFlBXI3Ab3CspCoAjCef+XjnZ82OidMHxgL3bN6NwaFxvL9uz9DAdC0zhrCek301QqoEBUt6CXeeA5Bw1ygZnqFQsU+zlYVkxadmqSucbleU6J3IeTdLbi70vpCapY9SlTVo4D0H6y7TD36R+/KY9BlgklEBAWuYl/LszJ0Vl28HNIqwnia1GdfPMnIEBEEWHgFMRT58eOeKYlYdI5yOc0rbdSgZxDSERmsS6cg1WsRMZvxeYcOddgEFEgYgPvFbXnyrvB2ghHvRNfSZ1Dc7EAQvfgHHoecci6brHLB4KF6eQhrW+cDu/6R4+8nV0Jlo9VKXZdXH/PiGtvH0jyT+4Uv2ZzkHlPWPZwnuNwG4KLopAI2HVAIMIZMbg3gIBzyLrT6sGdk6iqTal70ASGezj/5mmZ7b3P+CxwJrs6SkV3g+YolphTbXtd0/tUp3FQnyZr3WhcBX5965wDV+fnjydNztUqdr2YfUzVQtU5kIXTaiQyiQMR/YaIVhDR60rZVUT0vpZTWl67kojmEdHbRHSKUn4YEc0Or11P4RGHiFqI6J6w/EUiGlPbR0zCddMxm8K5OcGlNnCHDd0Vpn7V4s1ttYucaUJWXBwVVlNWhnDsOjCtBOYwwCIKUHHVxw/Azz85AYeP2SkYjxCZa8F2XXeSykJs0ovEDp75+plTPhFw8nXPMOXZTmDB5uNGHuJwMXk4h/Q1lduWaG3rCMOdB+V/+MKR+MJH9nLqLzigZY9JX45qGt+4jlB+N7dVz2x4QEAcChTMFZFZR9NIuHAOvwUwmSm/TggxIfz3NwAgov0BTAFwQHjPDUQkjblvBHAxgHHhP9nmRQDWCiHGArgOwLUVPosTss6LQgAvv7cGv3n+XfY6KZzDqk3mfNTzNff7N5ZtMIwnGx8JzdskjJYfOSiNqe5+wwfkaCMJmyl4LrNfIAoDYcM/vnEchg3IJiKEIB3qWYeM0OJjVS5WyovoWxduGcuc2tKg+jmY6vRpLuYPPpdjiKYcE+3aO/7MrS/h2B8/FY2zf69SJBbKEnUWiQ8foj/zL59Mcglf/P2r0PHs3FXWsZvarjW2dZQj67BSgYwhvN9ctiERUqSeyCQOQohnALglrgXOBHC3EGKbEOJdAPMATCKi4QAGCCGmiuBruB3AWco9t4W/3wfgRHI92lSAzNMiBB6a9UGqXI6oQPEHzaUHlfjOX15P/P3Ocl5+mjWeEYN6Y6hm1WI6NbmwvlwYBxWlHGZBLgppU10VWYphE0YPcXMYUleTKht3WQsA8IHFrDeS8TONfeOUfZS24jZdch1H7VvGpSPwkLaLd3o3ldxzGljESvqatFkrNRUKKd3Q9HeDLSWyCANFYqGs5VAomBS2ycLH31yR+Hv1ZvNhLqvfeusAWts60CdM2du/V4nNWS2EwKm/eBafufWluo5Fohqdw2VENCsUOw0Oy0YAWKzUWRKWjQh/18sT9wghOgCsBzCkinFZ4SJKUPfHf3zjOADxpunK0uro6Cxj0pidcMbByZj6Wad9zqzNdLJaz1g1mWDUqeQgDlmhD2x1E2Op84ennjVUziGrVzmsGywxoPS6Kv79uL2i38sKUa725GPmHMiqGAZCzsGxH1NaXCBW7EvYznOlYsBt8wYLIuqrkDF2dVwunIOOrDzQ1syEdeYcWts60Sd0XO3XUmItreQzv5wRqqRWqJQ43AhgLwATACwD8NOwnD/o2POt2K4lQEQXE9EMIpqxcmX9IkyqG+SAXoGSTLJ8BaKK0j6WRWAqt9fQfrnuKzCWHibidNsLCzPbi06whjby+BNkBU1L1LVcs5nA1hpxuoNszqHa0yKnAxBwC/MgwRkMGG9x4Eh6NxedOYc4uVX6WtGwUFjOIfx29LzqgOpLkg6JbxuXvmZa2zoyOQPuNK7CrpCuL3XY0tYZcQ59WkrY3JYea6UcdqWoiDgIIZYLITqFEGUANwOYFF5aAmCUUnUkgKVh+UimPHEPEZUADIRBjCWEuEkIMVEIMXHo0KFcFYexZ1/nxBCSc6jUQaUsRCKbnOt4AmWe1paBOLmkb8zsL4dEL49ZrK3beke8VEEJziFD55Cj3cy6kjgIYcyHzWHR6nSqUhthz3p9xQKBHL96m8ohvY5DzojTOYTfDueR/utnZZpVsnIqKoqFtML225oYl4PMDdG3mY9pZT3cZLZeHba2d0aBGEsF3vS3kd8JUCFxCHUIEmcDkG/mAQBTQgukPRAonqcLIZYB2EhER4T6hE8DuF+558Lw93MBPCm6UFUvAKxWFM1yM9l7l0BBWqmDSvBe0zF+MmXOnFjJMIA8kUKNsWdy6Ry0NnOEMM+6r5YrQJ1zVXSRfVBwH4RrbgiBJHeWdZ8MsTHj2ycpbfEoMJwKEOS7liiSuymrTZ+icw626Luy7q+eSefbnrVkPQBJ2NysowqMQnrpOvdw5KbWbd3We0vqKItongI/B6ZOneM76XAxZb0LwFQA+xDREiK6CMCPQrPUWQCOB/BVABBCzAFwL4A3ADwC4FIhhNyxLgHwawRK6vkAHg7LbwEwhIjmAbgcwBW1erhKMHvJusTHNLB3E265cCJu/vREAEkb67zKW656tnw1TYxMJwiXHAPRJmXoNg/n4Eq0APvHW+9wyCrUDagaU9ZU3fDn+ZN2x0NfOtrYltDkSlmnQRnkced+sQLYqnNgzvsyvhUg0926ipXM/aXXcra8Xg9rr4IoHleW2LZQIOj7pHzukYN744Z/OdR6v2n+7GJR+5gqxfvhd1EWCnEgYjmwShKAVYNMI3EhxPlM8S2W+lcDuJopnwHgQKZ8K4DzssZRK2S9ZM6q6MT9dol+VxXSE0YNyoxjr/bLbfRZa67AcA4mguLCOSQ2Ka6/HLyk3oRto/vqPTON1/hQ37WDelZWN7yaipXCyiMG9cIBuw1k25LvTd1Xs97ZRZriF4gdC3WQgylrkYlQa4JN56AroCPOQavXVCQnbpRgV4An2ixQyvpJDqd3U9EpZhUHW6/1kOg8O3clLrhlOo4euzOen7cak0IfHCK3SMX1Rg/0kK5eyWj6EFR84tBkLtiyECgUmP4dlG+uJ/Q8B4ss0UR/JgmRhCmswnOKzbgOW5iLeiv7kmIlReeQ0W0eL2j5Xs1RVGNORa2Sxe2ddtDwVNlFv+VNGRPEISzTn6FYMOsLdBSioKzp6/pjrtiwLdXWwN5NeOY/jncycmguFRRTVvu89+uVtuZRvbmzLOGNoWxsos86rNHXwqCUz4VpAuR8mwR/uq9IvdHziEPG/GadqlQ/h7whu4kIf5u9jK3PeW8G40nHkTGdIHI5wRnGHqeZTEMq8oqGE+Wvn3vX2J8tBLhuhdHWUa7pKUl9peR4OgWSp/3MulmERqjmefGIsogDZxW00WB1k/BzCDvTExoVGdbQNHZVec/1peLS378CILk2D9l9EIYP7O2k42gJA87ZxiMxoFcTNmiJelQT3ixiZBYrWe5JtSHwx5eXZFpAbW3vNK4h/d3Ld63uMSo6u5vOYUdD1vSqa76ZSdmXjK1kbkff8KRYSRdbyQ39rWXJlJ4SxUJ6oZgWt8uGmsX1FCl9epNB4iTHEnMO8X16QvZqsPe3H8YvLPLpahBvQA7EwYG70GE6XASmsyJVRxo6mOCqH5B1Y84h6EsPLc5xDiZCOTY0u2aD6RnG0KZseHnG3qup6CxW6ttSwqZtSXEc58uiI6t9vfiosUOUa8mLryxah6/9YWbK2VVFZ1lg3+88gv/+Kx9yXz/MyXEHRijp+pwpcD3R84hDjq+dSwCv6gCstvvaiywbTjSx/buBtefESgYi4EIconEZqsqwxWqfHwsd9+StRYb9z7Ix70pwpskuYiUh3IPzRSIjbds8cEQYjiTBOcT47sf2x/iRA40xovJYjxHSm/a2zuQmWiBKrUPuGT995GjsObSv8bpJxLGNIQ4uAQ9UziFrGQfVkpXUHkzEYVj/INSKKVhjHoW05BhWaHnHVUgjjD++vIS93lxMmtS+HeZ8LxgU0l7nUGdkcg7KMvt//3xI+rrCOUgzPA76AiwLftFmi7nSfg4mnYOL1Y882Zm2vd0GBVFLuSxlsv3YkSxGNcl46g31PamiCzNBDn66iJUkhxjrHJLXz5+0u9IWUnV6NRVx2Oidqg5nMv1bJyYISSRWYkQXaWVyuvN+LSXFlDXd3+OGaL6qGIthvI0ohdFIgexNkJgxqe/MRItkMMdxw3hHVK7XOz/3IQCMUYgci+X1tIbBL/u08H4VfTR/C0loTJyDJw51Rh6dw1hmEUl54NzlvBhIYosWFTXQOQDnHpZUVMvhmM0T3a2VHFIbZOZ0tiW9l4uzFH716jjyiBAajQdnxXoeVedg3pALUZ2s71Eq2mPOIQlVB2BSWnPvOL7mNq8yAKGukNaJwwcbtqY5B4NOITKKZSqYxqtm13P3qIj7NPWngii9kReUBzfNmRACB40YiD9fepTxevJvYNTgPtHvtroc5NybzMNNhzky+FI1OsdDjyMOeQwUOXZYinlMSkGJ5+atSliKSJ3Dt0/fDwBwumaFYrNdT4uV+Lq6KIuD9BI16hyYk2p6M0uz/7riU0VXhx8+cs9YdqxuQKZRRVY6Fu5CQjommU6Sqg7ArPg1i1Lyc2TJDVZXer68cG3qffKmqmlCo8J0ilWJkYmbMiEWV9rrcWlHExZphl1NABjSrxn9DJZ4pnkIxmTiHMwP1xaK9EyiQdMcBpxR+lqjP6MelyY0zwRz71QqpF1OdH9/4wNMPjCU14c6h0F9mvHW9ydj7vJNeGj2MiXsQIzHLz8WfZpL6CwLXH7vaykT1Wp0DluiFKd83aYiQxy0v2NRR9wGFw5ZImvOd+prTuxeC3zxhLHR73Lkgc7BflpXRUEmSNm7+eQf/FQv62vHJGMGEIVUcIW+LN/ROFxu2bKmqkDK8kmF6Xl1ToWDaWN3VUhznIO6Sk0cixC8Ir0z9E7+7gNzEuXjRw0y+40YOEUVkjCbRIMmj2f++TznUHfkmV6OAFAoAnAyz1M+7IA4BHf1aipaT2Vjh/XHboN6Y9ROfVjOoZqQ3XLzzxKp2MCd8NRE7elx2Qfmuugr5UDUk5sL5yCfb1t7J37yqD07nzyF3vtSEIz43hlJ5aPcqMpC4M0wp0eau0jrlQBgzJA+6GvxN+EQi4KCn1+++7XMe7i+17S2JYIUpu5haMCClZsij19uTBI6lykD8+nhM0y+Nlk6BxO3JSDYb7rdwPV+7eS9lYOCeSM3QRJKM+fA92uK/NxglUMPJA6ZOgflBGLgHKSISEK3NPnhOQcBAPo2x+VlwVvNZI2H86rWF86tnz0cgJt7/S6hbPrpt/motiznoBWpYhcXZFVzdTar9ODEWbLYrJXkSe/eGYutvhsAsPtOgUx6eUgcU+k7lff8H/fNApDWRxHxhO+gkekUolnIsgzirpuIbiRSYZbVwDClp4oTfvqPBDEyza8u6mpWIh6r9+2/G594isuUphJ90xyUy/w3bSIOqpJcX6IuPkUV6xyQj1urF3ogcXCfYO6UIZWH6qXHv/aRRB1pAqv2JYRGeLQYODYv1SxTVtlSE3PqP3PCbom/5Yf5+Ju8tQnnJKXPguzH1ekua1G7vpGyEHh2bv5Q7Yl5L6hj4nt2lX3H7cRKetO7AYC5KwIfF31zDIwc0m3nid2lw/Ru/uVDu6fKuOcMNjTzqfmqjx+Q+PvfjtkjVUdPCBT1p3VYCg8kulgpT5RUlRM3cw4AJwiyBbQzOeaZDBAkFq9pxRfvCkStg/sEYtOfPPp2wsrLFILblCa00bq7nkccMq5zp0xoZfqi1evpXsZrN7dh1aZtCQ9HvWmzQjLdn25CK/tXRRD7K+k+D909PoHqTlE6XDak5pD4ZTEqUpeQ7U+g/p6ufOqBuwb9CWDh6tbM8elQHylhyirS19U6nJ+LCXLedEclzhxUFyeYxAimnAk26GIlHR8fv1u60Lj2zG0N7B1zDpyF2+eP3RPfPn3/cFDJ59DXepPGOXRGehx+XETpMauK42bDexPaoS4ej3mBkqFObJbMv6O7pi+KDgE79w++g18+NQ+fu31GVMfk8UyGw4IXK9UZ3KQ//fXjMPXKEwDocXiYBijYFAsJLiAJ3YP4moffAgA8MiedflTWMVouUDoCpa44k0NRTyL9FVHXlaftF/2+jcn5cJ5iXltixUrJMvnx2TiHiaMHR0Hj1A9rr6Hp1J7qdZk+UoXciMpCOEeNfeT1tPkqkPzYZa+XHR8rrIF4o+eUwZcev1fib3kKlroafX3F/GF8QfeBkTqHIP5SfM2Vc7gmFGMGbYEdBwDsu2t/djPjNkcBnrBxY+M4n3/+0O7obciboB8AmmTYCI1jM1vzEMM5hPeWgZGD++Cw0YPTN4L/pm2brilVqvzb9IZWbYqd40zPYeIcAgMFZpzez6G+4Da0/r1K7OnHZMoqhEiYy+mcQ2zlEfQlFXDqR6grpE36gqJBHq2PCUgGt1PHNFQJ98zF8vmhsrnwpqzJv5uKyY3wpYXpDf38SbtHsmR1TZ99yIhUXTkvW9s78cmbpln7d3WsuuTOV9QWot84S6TDwmiYUR8hgeTCp0wYldx05KNxRBXgN2t9sygpm2KSi2KbTOCsCbthyqRYVGTLIW0MCmhoW9bmwjYUNSW/iRvgoNctamKlrNhlnI5G5zqO3HNIghCsb23HWx9sZJ07rZyDNibTdR3q9/fonOX4y6vvp+q0atne/vWo4DBFhjF5zqHe4OSrhThgWVKslK4b6ByS5nJGpxvtb0r8njyVSbnnjVoselXksGh1aypc84RRg6J21ZOISrzG7NwXj19+LI4euzNLHErFQnQ656yVUjoHxeLpwVlLcd7/TU3dA8QfzkOzlkZlXLgBOWxzGsSgob/OXOrsFKZyGOp7VMUPXAhtIJ4Drqs9Nc5Hvr8B4fzp1wvMyVN/TrnRtneWE5uCi05H3/DVzeytDzZk3g/YN2EAmMEQ/wQHR+k2VNGOOsJyWaSeS8637j+TRwGri390c9Cv3xeEjP/HO2mdlbxHjaUkEeeY0MVKGQc2bfP4yj1pq7F3tXhkQ/o1R/dyzXudQxegWKBodSWtldx0DmraxRv+5dBIEZcytyuobQc/5YciTzwHj0paqBDFOauP/fFTOPraJxPXZbRXwO6dOnZYf/RuLmKbIX+AXPwucu6miCMQuMzi3yA/rCv/NDsqW86YvLoklAeAb9w3y5k4JOea4RyULk06Iw57De2X4DLlWtglfOc/+sTBifqcE5UevlwS285yctt02QtsszH5589mN2DpxxbriDMPVmGS+3cKkTo1xUluEPYnvwl+XIzKIXUvIX4uIQQeM4T7CPoJKraU0tIDyRGmAmkmRpOGi0TQFOPJcw5dBG5+S4UCyz5yL5izSVethE47aLjRPryFOU3J7iTrrsuZ9dAKrZoZZGc5DuiXlUawVOAVn4BidcM89IatSfY3EitZe1OtT+IyVZEZ921vRxVRuCYjUk+2HDdYVjZi/ZGz9BrqZdmGfIY+zdnet7pYSVqIdZQFFqyMT5PDw1hA9sHwxaxYydCE0RuXIWzsEDjOQX1nyoQJkX7fcs3FxCgUKxn1cGbdTjS3Sp9ZzqGyLe45pTn6Ru0bEOluEnDRjaX8l8qS6zHoHDznUF9w8xuIlcLrSrnJlBUQuPPF96IyqXiTH7NuGy39IP7tmD2je/SmOw0n96yc1bsN6h2djFW9Bbc2icwbcRSOm5GdjxkSiErk8zUzsZU4cM4/3zljf9z86Yk4euzOUVlWO+omKZX7WSgyXFrwOyPf14lDxrFPvRp5uIc/deIVcy0K52DQOXR0lnHKz58BECiPv3jiOOs4grHwYiVudzGGqja2HV7P2JO4NaoSh3MOjfVMgSGA9vxFjTiUAx8BU0gWLo2mrsSOxy4s4sp4TGobKooFQt/mYiq5kNkwNh5jFtLOrfJe/pvodsSBiH5DRCuI6HWlbCcieoyI5oY/ByvXriSieUT0NhGdopQfFuadnkdE11M4e0TUQkT3hOUvEtGY2j5iEpwct6REqlQXCPd+pfLt9qnvJcqf+NpH8NcvytzBUnQRNNaruYimIuFzCnGIxxNAnvp1X4VCwewsc/6kUfjJeeOjk6+JTY2fJ20/fc/FRwAAfnfRh3DeYSNZxfxpB+2KZ//jeNx3yYdxzLidccZ4GRLE3JcA/4H079WEk/ffBYOVkBlRjgnDcw4bECvUl28wh0hWwZmvquUC8SaVFsHJPAbZ/cSnTrBtqRvs3rsE7X7qiNGJOiVFrCQxbpf+VqWuCbF1TXrwphN0VtA/E0cqCYAuBikVkqlBezUV8c3J+yr9JduRnJN83LIQ2Oc7D6dk8hKqyEhCPpt+og84lfT4zzg4jm0W38s/Z9+WkjGpj4tC2gSd9nEiMRVqmU1MViu4rL7fApislV0B4AkhxDgAT4R/g4j2BzAFwAHhPTcQkdxtbgRwMYBx4T/Z5kUA1gohxgK4DsC1lT6MC7hJLxT4CJQ2Jzgdew3tFyWB1y2RtrWXmU03SUDkqV8/uZts4AHglAN2xcDeTVF/WeyzPOHNVvwkJu0RWOpMHLMTfnzeeGPgvVE79cGIQb1xx0UfwoBeUjSU1Z/7NSHMEVB7MbLgLJhO/4lDgEE0cGlo2mpW1CpiEohEXb1bdS2MGtwHB44YEM25RMQ5KBOwrrWy/Bi2074xgyC3EUEo64o/wT9/xQl48msfSU6jhRIAACAASURBVIlBbBZvqiGAhHx+NXyGlTBTeuXJ7yfeYCWRzP4ushTgzaVCiovJ5qbs14M2ko0cGprfGk1Zlfrvrkrnuq81MomDEOIZALq5wpkAbgt/vw3AWUr53UKIbUKIdwHMAzCJiIYDGCCEmCqCGbldu0e2dR+AE4k7ctYIRhaaka/ysZUo0964oFGHlRu3JeIsqf1JyI0hrXMwi5VksbyeZn21sSN4vkVrYkeylLWLtQV5T/AzS1dgOz3FBEZpy0QcGG5GVXhKHxUVqp5EH4YMu65KlVRHQXmST59wg/Ib/uVQHDNuZxQL6Xzi6fmUh4Bg7rk5kSfnXz/7rjLmdD0Z0TfRvoUY6TBlEjMSQaSJ1h8vORL/+MZxAAIP6D2H9ovmU4LTW6kOdXp3us4ha+MlhjrIIUoRr6pDzCYOIYE3KMCbioUUVy65+UfnVH6C18f1kb2HAuB1OME4498bIWGqVOewixBiGQCEP4eF5SMALFbqLQnLRoS/6+WJe4QQHQDWA0jblNUIWfJtdYGYorJmL96wLSHw8ntr8NDsZVipmXDqJzzJumcppBMIi7nNk7e0Ck9SlgdwIcu2aJ16fyZ8SZOnl4XAprY0cXvlOyez4pVffeqw6PfhA3unrqsfXsoPJeTGYhEE4U//Hsf457jI0w8ajre/HzC7x+49FHdc9CEUFZPDSOfAECJAmqny72Xj1iAf8m+ej4kDt0732TWdUjTFqVjeDSceEkLgk78KfEuOGbczvnLSuKgdjiM9bPROGD0kaa5LlPxuWhmLODUAoT42NXeyrGNDYKYa11nfGueTvufzR4b9hc8HnnNQ3wMXruOWCydGvzcVKWVhlmX84bJ3V5PPoQG0oeYKaW47EJZy2z3pxokuJqIZRDRj5cr8MXaMDSO5eJX+UvX0zfoLH9krVUeVd85czGeL09uWpzNOIZ1lYcRtGhwK0aaYwbNnthP8NLWz7679cdJ+w6xKuZ37JcN0l4XAf/55dqreTn2bWQul/oa0mhx4zgG4/7X3DddDHUBiLcTxk6IypB22dEIkPWWfemsFTNF8F69JRzLl3jkXitqokGagnn4nhCbTZYEokuolx+2FQaE1GVHcVpYuSw85zi0LlaPRvfTl2jeZzh4+Jul4qMrkhRAY/72/47E3luOA3QZgrzDvtfoNZmVIjDLnKtWO32dY9HupUEiYib8wfxWue/wda5t59FU6uMOJ/nd35hyWh6IihD9XhOVLAIxS6o0EsDQsH8mUJ+4hohKAgUiLsQAAQoibhBAThRAThw4dWtnITS9EWUw2FApJU1YuW5z8YB+ctRQ3PD0/YzhBYx2d5YRiPB4XGdldeWJmRRUkf8bXZFs2NtspYkOGWOmRrxyLQX2aU+O6/OS9E2NRIQSw0KCA5MwCsySPo4f0UYabntMNW9vxhzC3r96SbFp1OOQelRTphnxH+jMfHuoXhvZvgRD8/F4UBq0br/i48KddfgwcOIX0dz+2f/T7SfsFm59KhEYM6q2Y98aOoVnRfgNxpbVK9L4enLkMJ1/3TOKa9L3hOJUph4/CH77wYa2teN5VXYDKYaqKeW4u1Y02ygOuzJmqUG8qJcVK/3zzi3gvI8aXjfuROaVN3yEnXtu4tR3/eGeVU/u1QqXE4QEAF4a/XwjgfqV8SmiBtAcCxfP0UPS0kYiOCPUJn9bukW2dC+BJUUdXwCyv07w23dxQ5SL/y2tLEzFWEnWi+4OfnWXBhmAoMlFZJWRIY27DOfXA4fjMh8fg22fEG4I0kbMRBxd1T1aM+7he8m9bClIhzEH1XEJ66Nhnl5ib4jgHm1Wa3BSfnRt/jNx7lqFUgKRnrgqZ70GerjlCPmJQb0wcPRh9m1XnuvQz5aDb7CHnY0rQPfme1bXQUiqy85JlCmoSg6iQr5CLLzYkNOSIY5LZuXc1E5zq8c8FShSC34TViLE2U1YgiP1kCuttgm0fmbZgNV5+b21C9/f5Y9Nm7mobX7n7Ndw1fVH0dyPySbuYst4FYCqAfYhoCRFdBOAaACcT0VwAJ4d/QwgxB8C9AN4A8AiAS4UQkoe8BMCvESip5wN4OCy/BcAQIpoH4HKElk/1gvrOTtpvGGb+10cB8C+Eg5POwUVur3AqD85aigdnLWNDV9jESnFb6Q6bSwVc9fEDElnWpM7B1ly1G5CKPHmluWecFMY84jcId3BRV5My5GzRDCsqgWqGG7atdRaH/xapgI36mNQ+2PNRjpej333BEaON45JoKRXikCIUj3X9lnbYIA8dDsNi6/30vPEAeLESZ80rOYel67bgf5+clyjnfucYn29O3hfHhgrgO6ctCvvlnyFQSOcjDjaCWioSPnHjC4myrypcdYF5hzLcu0v7tUKm4FYIcb7h0omG+lcDuJopnwHgQKZ8K4DzssZRK6jvv6WpGCUtiXUO9vt1nQMrbnD4ilVTOxmCgvMeVj2yieLxn7jvsES9YoESpwluD5KExkZsXDZ0W7TOZL3MpiJwY/rsUWMA8JxDHsKT2vyR/LhMFj8quGdVN3TVRp0bZ2c476Zh6xypOceCfazR2tMG/PWP7sO2leAcmpJezXIjf2jWMthgMr1M1GGIkYT0eeEU0uwzI3i8f/3tS3jrgzgN6rQFa5Q68RrlxGK9moo4bu+heOadlbhnxmJce+7Bxm+/VCRsac+3GdssGrn1nHTaNM+VxMfHDzdeqxV6Xg5p5fekPD687sA5JF4ad8DLwTmo4LKwqWaCpHT3icNGpurxUZOSfZaFnQC6jN1mVcKJLlzaZsUo4Q3cbaa2Xpi/Ch/ea2dr3QJphDT8edMFh2FbR5klPOyHqmzoqqxe7yu4P9ioTP4XBaLEJsaJDVziXqmK30S5dgKPNmtl32wuFhJEzvV0WqBsMYccuYtIM8soRD7kulYzR6OuUdMmqxMN07ffXGvOwSAhkOD0n+o03HLhRIwd5maEUg16HnFQZjwRrTP86aJYS9gbM9Qhz6FWHY/phCz7UwmTXjf4iNxkw1nWG1kwbUDTrjwRuyqhLvLkquFl+sn+EmMwcGcvL1ybJg76vcR/vB89IEgqtGx92nqI5xBjmJzg1FhOZSGi3AWptrQxsfPhQhwM5angguGfnUJg/MiBaOsUKBULCQ9jV+JAGrE11QHs3CaniGX1TYjHruLAEXGCK3WNzl/JGzqoz/fSwjWpJFoSpSIZTVd3HcDHv1I5hytP3Rc/VMK+cH4gaom8bprTcQ0gDEBPjK2k/J6k1tmsXHCP1p5B3FDJePgTRbxI1HY5f4gsRM5flmd043rkR5xsZ1ctUFw+nUO6zHZSNjXNhSRPneYLSZ1DKsous8Wy77mgKqTluLRNWNU5CF4hLcfYpoydI+CciMX0KvVy/c6iugERYZiMJKyMx+QZrYNcOAde2pWAfN1qWzZjBL3PBy49Oq6jRCCYt4L3JlY3fFPYecCuczCt0c7EIVRbExl52tVAjBxySVSrQM8jDglWTSEO4c9qvH71tqx1ZCWlP85aqRBGUp2/clPCbI/zh0i2z51OQp2D1ZTV/flcLVRcwBFlORbWvt9IHLKEa8H41Y1FPxVy41bl8Wo7elyodPiMeBM2OcEF9ZIh17l92UWsVMw4dUqoOoDOcllJOCSpXDr2zxjFPDgxdmRzGXrEVVsd1SrHxjXqjml8SHxzLutTQk7x9IOS8vv7Lz0q8XdTsWD0LnfJ8qY/A8s5MAc/o6lrng+rCvQ44qDuxgmxUnSyyck5MHVynb6VFjjbaan0vDSR2SzNZbiECI49pG3jymzG2ZQ1TxQU08ncNCaTWInL9JVqlyjx8aY+Qq3ppiLh+2embCmgRgctM9wdEG/W76/bgtcWr7NG8VRt6W3E0gYZVmSrRiT1g4eqkO7oFNFcx7QhyTn0bS7ika8cy/bpZFEX/tTr/WLKhLidcAwz3lubGicHF6mXEGblsAyEqPsqjddyqjQVCe0dbnqLaGw2hbT2TAuvOT15PYq1FbTd2taR2BsaRBt6ns5BRSViJc55i6mV2Xee0zenVEuHhs4egYwLZRUruYw9kqPb6+USrzmKUfQx6HDRp+gycv3Uq4/7r188OmESHNdTg7bx45If8q3PL7SOu0BIiJUuO2Fsqg7HOeiPKyOlbtHyfuiJbJKcg2DFlMmc5E1smBbATT8RW23FZTd/eiJO3n+XRJ86bGIlm3Ne9J1adGxElLIS41AqFox9mZ47yTnoB4b44334y8ek+9M4hx/+LRmmPp+lXuXocZyDug4OHT0odT2vWIk7PbtQdu79cmx7HPIiWa5zDk7KSkIUAC7PuFJ1FDNcG/QhDeqT3mAluHmXp2CLwUoEU/Y9ID3OgibC0TkHvTszwYxNWW35OFSY1leBKNKX/PCcg3DGwbul6rhE8C4VCygVCJu32cVrau6ETiFi3Ug4QF2PYFteBcoORimncObidcY2TYEuDU1ZDyex1DYZ7fc/JqdNetXv4RunJK8DAcFtY3RZANBpYMPLlrkrFQnH7j0U40cNwn7DB0CH/JaXrQ+yJq7VIvR6nUOdIF/Zry44DP80cVTimsspwkUhnUucovx+1NidU9dlyAt9XNk6h3RfktDYrPLyEDaXEOEqzjlkROLvx756bPQxcvPOebxGY4D+/MFPPqtZmjOwcQ6uJrhBn8G9UiatBwnU58Ck5FUJls3c1QXNpUIqeb0OOcxOjXOQM0FIWuhkremsEBv8xm//G7D7dtg5h+CnKlZ6+dsn4d+PS3JkwVqI/+Y4xKZiLIbUiYRRFxGuua+etHfqaFEKDRlM39o7oe/GF0P/J5N5dL3R84hDuN7HDOmb3gTgIubROYc0XF4dFz2Tiz4qw2ekOQf7guFyIEQ6B8tHpcq9B/Qq4a+XHZ2qI7tSFeS//9yHjPWAwMFP527G7dI/OvFzhCbiHDjrIW3m9cCJat96y7oYRJ+PdKRTHkTx6VWeIPWNXf/bTFDjEA2cvwvXFsBzri2lAja3uXEOUucgRR2RpzclZe+2/UgnthxMYk5uTCp4D2n5ruOyX/7zIWx/wXrn3w0QiGfVg8kGxhu8WChEhPL065N5uU3P3VkWOGT3QfjySePY5xTCvK5kgMcPwnzrWQfBeqHnEQekNw8JF3ttU+L0ZDvZ44hFonF/fBx83qs5vWCCn6cfPBzfPn0/nKB5UKttSQLA1VFDJXz5pL1x0MiBbDsAMF8xEezfK+3d7bKIbVYseuJ5FSlTzbDOvTOWoLNsTxazbP1WvLooFm+kOAfHSKeEWCHdHraRZWJsDrYWn0o5k+Y8aCoWsCWTc+B1DmdO2A3NpQLOPnQkxg7rF0Vvtb3LQsHFOipdprfoYlRhghpFFYg32LaOcuyDwnkma9/8OoY4BIeqoI4axqI5zPPAcavqnOrdrmltM8bZ0tsI7revqXqh5xGHyBojjWIhHbddR4o4VGhVEst84/vnLN2Qqied7vRedOsT2d6AXiV87pg9eR0EBactyY7f+KlDU1XUgHWGA2y0Wf7iiblRWVOJJ2wSJiW4rMEp9uRHzYUV0ZtT+3pmbr5w7jKDX2pQ5gIAwcnu3hlBhE1bVF21yKTAVC2oTJwDh2PGpaMTFwuU8PdQLYKiOirnUBaR7f2YnfvinR+cij12DnI2lCwEWiKwbMriHOzrg/sbAO6avjhVxo1FF0GOGBRELH5/3ZaYc2BulKbiEpu2pomqiTNqMiSFAoL3HD2P1u+NT89HuWye05SJbkr81hjq0OOslSKZKjO/TQVCW0bsen0ROljTsZAf3VV/fSMqm7pgdaoe5xik3q/Xs57wKJArdXQKtJQKKQsWIIhz01wKFHDGVJvMh86JxNTbTfMkT5R/ZzJqSaKw26DeCcugoD3zzH/21pcSf2eJCo/YM5lbKv0x2u8Hgvdjmq8iETrCQZg2UbWPooFz4J5DDVcioSq3AWAwYwggDw+d5aSfg6mefV1lW4m5GBXoIT6AONdEoh6z/vRcG/3CfB+tbR24PjzE8Gl/A2V6n+YiWts6ceqBuzJ1eALQVCoAbZ1o7yyjWEh+S+WyUMSiaQRpWPk51R3uPOfQIMQnWE6+SZmKNX1DPXPCiFQdl81E/Tit9RgOA0hvIHKhmT7yoK3gNL5mc5s1eb08YZksoHjCmm4vIWqyWOkAwL0zkifEv152dCLD2wTN9twkVqoFKkmd2t4pjHOqftymMAwJ73cD5+B6ECkWyGhdE9UJ+3t18TqsbW03EraIc7A1RubwEnGVyjgHQ3eZkG0tWLk50r9wNLdYIHQKgdFD+uLk/XfBhxmjkNgHJPmM8n1z3/CM99Zia5ingo/VZd7kv3BckEDsjIOHJ/rXn63e6HGcgwS7wRUL0Qs1QRUr/etRexgjqWbBxdsViBeQztHoRCBKtWgLOYFAdCGT3GSNzSQD1m3oAV6sNGxALK4xqmHDPvSw0ON2STomZX0Q9uvmjevqsxnnNsMYbegol42beqGAKCqiC+fAEVoA6MV4abP9UdKqhrUCCt/xd/7yOgDzocKm91H7y2vlB6TnmavDmZbq1bisgFxIclasFFor2ayHTBnqpE+JThhffz+I0fRKqNfSu504ejCEECCOVUKQxGvnfi0YEO4tqse4Op56owdyDsFPbnpLRWJj86hQxUrGzcDh3XEfo8zOlWgrrKezmiaFtE2p58qORiIqww06Ad1tYC82AFmzcpI26RxkF1msdDXfwwBGWS4hZdMqUtZNDn10lIVRkaw+i80hS8K0rrhc2Wx/BUpYkrmEHzGp2mKlqu3QkdSlqI5ttjG4nIhPOygdmlqvxmdCTHM8fKjsYG3a414FP2Wubwm5pnVpw6ZtSb2F3mqvpmKQFdCy+xJZ9HRerFQfxNZKjNyyUIgW+fNXnMDery4w02LiPoTdHILSfenEcakyWW+LtiGbTFntwercVlUW5zApTH0p8f2zDnQSG3BQTSpV6M+nz6n+3dhOrsM0wnXEnvH4uflKBeLLeIxyWUQKaQ7qPNqslSRsosEDdks7TXH9uXIOEovX2rPw5TFldQ36m2WiefnJe7OOoSYfl0SdsMzmqSz7lHGvjN9zWD7lpmmJcmmhlp0pL/l3Zxih1xaNoEBhLgqGanvOoU7I4hwAoE9zkT1RAppsOIdM/vf/dkTib9cENpFYSeNo9FOXvLWSSKY6IuJgfD7CMeNi2azRvFe53aiQZj5iwPx8cXvJ+nmikKteqdyc928pJYi56WOUmcQEgvfDBecDkmM3RfdM6hzMn6XLxpBH5yCxYCUfubTowDmkiUP6ZWxmTGttitaWUgFfOjHtIwA4cg5hY20Z1ocFosxETLJ9NbEQEL9LlzSqKjrLwerNIrhlIfDjR99mrlm7qxl6LnGwnKZsG6x6ybx5psvGhKaBXDs2mD5KfUN2G3s+zsGmv1DbMiti49/NmzfPOaRqaUNxTDXAQiXqppAN1557cGY7h48eHI5FYFtHOSFGU6G+k9Wb2tg66jBsnIPLK1y1qS1yoAL4g5D+bjcwJpzBWGQIE0vHlHx/3KvZyLRv4xxc/Imi7m3EIYtIFmS8MfP3YXodn/rQaADpEBpZ4dIDYmSfUwKwcuM2/OqZBcx4GkMdepxCOrZVSk+wVARaN1jlmh75UsLFiclVxKPXO3bvofifsw9MydHl82SZHLpAnipt+gt1jkwfsjp2k+mpyVQ3Xc8++CyFqArV0ss0J1yWQB2yXIggj4SJc1DH/nPG5wBIrkebJZnLulm1aZveeAr6Gr/s+HSgP7WerdcCZScqWrlxW6pMX1/qn5yZdVwvW6wk59yJOAiEOge+Drf2bv3s4ZE3tR5CI+W9r93fURaApT95j86pxNfM99USVXEORLSQiGYT0WtENCMs24mIHiOiueHPwUr9K4loHhG9TUSnKOWHhe3MI6LrqY5eHmoCdR1ZsnYguVDeYJzWgKRCcae+zXjwi+kQFK7QdZNFAkYOTsth5YK0nzrzESRboDe1G9OJ2YVz4D48zglMr9avJXmuySNWUodrE51xv3N1ykIEYiXDhiYPFKN26h3lEEi3lT0mwE057gJ13scM6YMLPzyGrRcppG3KU80JjnsVx+2TdtZLRxaOx2SPq6Xfl67jyjkQxfm9zTqHdNmRew6JDoH6wSbtoJn8u7NcDjgHy7gKBfOabpQTXC3ESscLISYIISaGf18B4AkhxDgAT4R/g4j2BzAFwAEAJgO4gYjk13QjgIsBjAv/Ta7BuFjY9pC8YiXTRqyaIl509B44cEQ6BIUrdPbfdMCW5VZTVlfOwVHOLGHkHJTlb7IC4zadd35waqpMimP22aU/7rhoUioGf1ZuCRUq52D60NRpNM2CvHXN5jY8N28VZi1Zx9aLrL8s8/nonA+i320e0pXImzkuWV3jLpxyVviMNZtjcRm3Ro/YcwiGa0YZtn5txCHVv8VaKUvnUKRArBR4LJvWQrq8V1MxGr+uR9L/5izDssJnuITOrzfqoXM4E8Bt4e+3AThLKb9bCLFNCPEugHkAJhHRcAADhBBTRXCsv125p/aw6Bwic9AqZe0uFk2u0E8+WVtgLXUOtrbUa31beOmkS3f6Bzlh1CD2I5XK3z4tRTZkRB4dRMlhY1TLsxSVMjlNu8ERLHIqtEzImRNiT+dqFdI62JM1Zc8B4OYEp29knN8BkB67jUO36RxcFNKS/ruIlaT1kFHEaDoEhkRc5xz0daDPT2e5bA2fATRO6WxDtcRBAPg7Eb1MRBeHZbsIIZYBQPhTGu+PAKC6wS4Jy0aEv+vlKRDRxUQ0g4hmrFyZL34O0xZXCMD+AaoLpcmwgNWTX56XzFlI3f1S0nP4/MNHpeoAsbisEX4O6rWmImG4Icm6yz6mz/UwQ0pHWc8Y5iHHPKunUtN9yRSyBnGDY3+yLdt8TBwdm9c21YD7y4LKsbmsd6vyVLk0sHcT/uesgzL7DP42t2lK7QkwznMGz2cAeEThyNgxkapzyLe2Ys5BJw4BQZIm32nOQVpH2Q9yqlHBvx2zh/U56oFqicNRQohDAZwK4FIi4vMIBuBmQljK04VC3CSEmCiEmDh0aPr06AKb+EEuApMTEpB80SZZOzmeylTMvfpUDGZiyavs+vfOPACnMk5BQDxhdnl1fM2mm8ije2kuFsxhNhy2T3mrJIw/Pm+8tZ7pA77jIj5k+CNfSWfa6t0c6wZcNoQsziHrKV3EdGqWtVr4qqjYhSHeqtGEbb2bIouqUJ/rMx8eg4F9eKfDPJwDl1dBQp8DLuKN7Gtda6A0/sShI/kxFWSmRTOxMs25KXyGJA4/PCcgkupcD+vfEhET69agXfvm5H3x+WP3xCG7pxOU1QtVEQchxNLw5woAfwYwCcDyUFSE8OeKsPoSAOqxdySApWH5SKa8LrD5OcgFZVNuuvg5qLB9zP9zdrB4mopkFFH94Kw4vIOuhFWxYOXmcHy2scS/3/uFI4315DNaT5ROIjjzWKIxhW9iW0cZ44b1Y8ORqGMx9be3Ek1W4isn7o19d007jam5LkzPmBArmcZOyZ8mxITNXEcNjWETK6lNjNP0LhxuuuCwKMKqCpUgWA8BOTkH+5rRiEOFIlB9E+eso/T7z5/Ec9wyZLdw8HMYrTnkRbmeUzqHYDzy8KiGgjnlgF2xaVsHlirRYm19qn1dedp++PO/H2W8p9aomDgQUV8i6i9/B/BRAK8DeADAhWG1CwHcH/7+AIApRNRCRHsgUDxPD0VPG4noiNBK6dPKPTWHLSrr6tAEcNEa3lsUcOcEovqW6nIPMMmqgWTyc46z0OEqereFlHBSzEdiperMLmUX7Z3lDFl7cmwu6NvCWw8lNkajmEplHfj25fNdFmbsMsFFqatyDnaFdNjn8WPx2OUfsfYLAPvsmiaagObr4aBzcOUc2jrNscn0x7ctD9tbbtbD1TODS/lQmN5zIc5xYhLnyWJTwi3VjLdcFvj6H2YCiL8NqYPp21zEwN5NWLO5DRu2duDxN1fABH0ojbJQUlGNn8MuAP4cDroE4PdCiEeI6CUA9xLRRQAWATgPAIQQc4joXgBvAOgAcKkQQq6kSwD8FkBvAA+H/+qCmHNIT7YeE4VDwjzToT/bh5cVHhxILvIhLsTB0qTqC2D72OVaN9ntA4ovhCPncKQWFjuqo5gcupjh5iEOLU08cVAJmottu8lvxXUksi1bUEeVINj8ZOSlvJZnOlRCbPdnMWfjk1Cv2BTAWVnyXKErq7MU7oAlsCAFxKHDcjiR708/NMr6amwlNVlQFHGhKdhmt3WUMcggctPRHayVKiYOQogFAFICYiHEagAnGu65GsDVTPkMAOnwmHWALRNcLZ3XJPKw2Vl1uLj8OmzkRuV+beOSbDqXalS/35Vz+OgB6WBsajttneaopmq9PLNvNDVW+jGKEhx8IfRi014nI84uXG3mSGsprlRhWtPqKdnJG9nS3QrFwc3kxwHk0znY0FxMrkvWV0aLEmzjEDvLAu1lYVx/pmFGnINyyFMJiPw2pI6royyiSKtZ6AJGIYWeGz6DuWbbnCTymhJWejri7rcp6SRMkRwBLXyxZVyynlW84SB6UnHhkWP4dsLb1bSKtnp55t9UU/VzcHHOM8fQSpb/jsmjDZjjKalQN3Ebtyk3IpsfwI8+EYf+MK1p9ZRsa+vmZ4PwDdPfXWOsMzsMUd1UJEwcs5Oxno52i8x9psFnRPajgjMM0ctMRPLlRWsxbcEatHWU8Y7BI9m05kqMKevbH8SOsXIMkvh+cuKohE6N0wVl9dlI9NjwGdzO4RTUzPGlFcie0MMV6ubbp9l8kpewnSjVRWx7DFnNbmoX/OQydbH1HTZYuzgl5BxyzKeprrq5mAMCZusl9PZNynQXjtSVyEqnLtuGfoIS+j0rT0PQlnldZYU1UWHTYwHpECcms2UAmB8aWHBQOZ1igfCrCw5L1dFFRKb5VZ/v+Xnp8/QrnwAAEZFJREFUTIwAbyoLxHOrEjlVf6iO863vT0ZzsYBn562KymzOsd2ANvQ8zkEeFTmZnouXrfrSPmMIOQC4Wfy49Jd0xnIhXuZr6odgG1eUkN3S1pK1W1JtVgK1DxcT4jxivT7N/NlHFYWZYjIlxuXIOZhEbKlYRwxcuFZA4RwM+hTAjbCpBNLkrwPEG7jLtO+fEU5cn2qbSNIGlSv4rzP2x55Ds622XER1JiLQr8VO9DsVnYP8Hv707x9OzH2vpiIKBUo6YFqG1B04hx5HHGzWSnni8wB2yi8tGKp9ybUUYyUV0uZ6PzlvPI4ZtzNG7ZSO4SRRHUmI4Sprr0TnMJnJBwwkU466vHNzLu0kTJ7iLnDlHOS6snEOalO9DERE5WYeeX2Zsa2ffzIIFNjPQGhVqGbXHPIER7Qh4afiOG8u82v6JkwOeZwTnBQh7mUgWGof9qjH9rE2Aj2POFh0DvI0bENW7HYd1eocXE48yf7MrzRBHCxv/tDdB+OOiz5kPdnVavEmT7nZ/bnSyuZSwTj+vi2lKKeDacNSy11j7pg2UBeFr+t7LjsQB3W8pjlQ+7OZUkcKVIfhjR5ilqED+Q4UNn2XmhHPWcxrmN89h8ZjNhEHk38R5wQnfzeNv5SwSrOM3XMOjUccldU8+VMMISoAPjOTDdW+Y9eTkYSNU1eHnpUQPgu1Wrrq/LjY97tyUjcxcmgV35y8Dwb0KhmVgmOH8f4BKvShmPwqLj5mz8y23DkHqXOwiZWy23FdVy6iHy5bGwfbmHVwXt0SA3q7cWinH8xHE1DxrVP3i34fP4qXBJgIMecEJw+PLv4zrmbg+zAOno1AzyMO4U/bp6FH/FQxyMGcVEW1YqW8nIdNKaiehl0sn2zIy0GZ4BodVMLmK6Aia1M7bp9hmHXVKUa9hAv04Zrs5F2WgIvSGnDzQcm75mzTLgm2rcWHv3wsZl310cx+bApoiae+fhwAu7hPfbetTIY5iQkjs0NNqGvuR+fyoVtMxEF3gnth3iqsCOMhNRneZ1aiKQl55cR9h+HRr9qiEtUPPc9ayRKVVcLGVQzt34Lp3zoRQ/plL3TATc66/3CzIs9V3DC0fwtWbtxmlLMDMcv7n6ftZ5RFu6JaRbSES5RbIA5p/dTbbgEXG8GVq0YN5x7Gx+4JxhLUG7UTn3oWiDep3hnvJeYcakccbCd6+ZZt+pTezUX0RvZ6UsVreigKCZf1rtZZrzid6bBxohIqBzXAEE1WFwuOHxlwGFJE1NEpUC4L/POvXwQQrD0TZ+YS7RdQOOUuVD70POIQ/sxK7m2DnrDeBhfiMH6U+YTjGtf+2f84Hp1le6RHuaHXYsHVinNQh2LjHLY4cgwSjfAwdRWJySvnHGImIHKdZCm1OyM/B1umNGsTEfo2F7G5rRPXfIKPogoAowb3wXH7DMVXT9rbrVEL5LB+ct54nHMIG3g5Wps2fx11jZsUv0Ayl4MpcrCqszDpFlTi8LcvHYN9w5AkktvrKItENjjbJ6+ucZcYbpU6CtYCPY842DTSdUBOFUUKRISzDxmRKXJx4QTkBuRoNWlvq0bEIak8zdY5uLdb8ZBy9KEqF7OJuG3GBvVpwuA+TfjemQdY2+gULtZKbg/fXCpgc1snDt19sLXObz87yak9VzQVyRLJN4Dr8vr4+N2M115bHDvSmWMrKX0b5k01nW0uxVGIY86hbFXq///2zj1IivII4L++OyDyljc5OY9SAiJGHodPUFBBiEZMmZRES0CsEA0pzaMSRaOxKpUqTCUmKqkylEI0JmqipoLRxNIkavkGVEREFIUoSiQmCojxAXT+mG+44XZndvZ2dmfmrn9VUzf37bff9vbOTE/3fF93kOB5fGaIgQz2q3RCSyV0OuPgE3X+JBEyOe2Iody3dmvkTBU/jFLKO/j52cXrDpfLPuOQwAHnL/yJE0eOYv/1BJUl8QuS0KzJSOKu0WjN9hsuVLeGep67qnTcfk+ZU1njEHeNRaX4P2H07+z9jVvZL8oL9i/qUQYkzp158FlSMKQVfOZQqqiQT9BwT4xYTe57kGkah073QNonSuVJzMfe6ZL49YoIE3xpXCMXTTmE706v3GWPg+/FJBFW8hf+3DS3pUTPaNozeyMM/2EmxEtZUSn7h5XiVy6rhB3/846rqIkRtU7xEhc/1Bf1cXHS5sdl6ihvpfjFJ48I7VN2luU2i1L9SnLxj7d4X8zPhlDpzVcldDrPIc5Bl0TE5JKTD2XjOzuZ0Bzusnepr+PSGaMq/7CY+Bf0JOKY/rlQ6YWlLsGw0uDerSdSWM3qJIlbPMl/JYkL3pHD+rBy83uRGXrj/rz7CkTVKK7d6hWE48f940xDLcWssY2ceviQyJBruTdKbb2s+jrh0717Y3sOfvip1PTUf7oEfqMiJqtUm85nHPZlZQ0/KErNGInDhIP78cSioslpU8M3ekkszfcNTdwpmGEERYmzCC6K4PdKwnN49HtTS8xqa92PMg5njmtk+RObI2c0xeWmuRP51/aPIi9q5Ybgal0rIMpI9ujWwJqrptMzZOaQz/imvgyIMWOw1LO4cg1j25uhLnXCnj26X8XGKPaFBSOmIkPrM72wWV21oPMZhxjPo885uqkmsqRFkrOVksw6G0Wc2UfBseLeyUXRVOLEDF5Uf79qC9+ZPrJov2H9uvPsldMqlge85H5hCf6yju/NlVoxHlZmNMjdFx2XiEz+MRNW8rctbW+G6uuE3XuVu1ZvifX+w4b2Zt5xzcw/fnhkv9bnM/bMoWZE5VbyaW9CsKxz2cxRzJ44jNMTcNmHuKmBB8TIFBtF8Hd4elPxrJgA8yd5J9OimeFhuC71dZzk4sxJGIdSBM/bYDH4vPA1t3I7bGV30vhrEpIwbiKSiMfTWpck3lhtPY0u9XXs3rt33yyyku+vE64+4/CSNx5+9CKNCnA+nc5zmDlmCJ8b3LOspfwdhQE9u7E4kOu/EpacM57HNr5LY9/whV1xCKbxiEpJPmPMEDYvPq3keJNHDODvL29jSJ/4a1HaS9CbWXBC6RQZteTK00eXfJi5cOqhLJx6aI0k8m5OFt2zljGN6cXR2+I7AlElaoO09Xr+s+sTbnvqjf3apowcWLFc1549ll8/vokjIpJ7VptOZxwO7t8jNEHYUc39eGZzeFETo5V+PbpGThGMS3Ch0tVfjJ7jH4d5xzUzdlhfxkXM3U+KYHnIKI8mDS6YFB22SIOJzf14KEbd61riPwAvlTPtzgXH8OGne2J5ytfNHlexXI19D+CK00ZXPE4lZMY4iMgM4DqgHrhJVRfXWobbFxyTWFoIw2PZvBaaIlJ/Dw3c4Uetdo2LiNTEMMD+oas03X+j/Qzq5R1/jQdGe8BHh9RAD3LyqEHcPG9iInJlgUwYBxGpB34JTAO2ACtFZIWqvlRLOerrJNVFJx2Rk0YVrx3t072rlxl107u7Us0j0x58gzAjom6ykW26NtSxfN7EkoWK4nDK6OhjPW9kwjgARwEbVfV1ABG5A5gF1NQ4GOlw14XH8maMWhpZw19rEFVJzcg+/mK59jC4dzfe2eFV+htSRs61PJAV49AIvBn4fwtQvFq70eHo37Nb7Cy3WWLyiAF8Y8ohzDu+OW1RjJR4+vJTAHjp7R2JeB9ZIivGoVg8oSD4LyILgAUATU0dey2CkX0a6uv4fg1XuBvZpaMZBsjOOoctQLD82kHA2207qepSVW1R1ZaBAyufLmYYhmEUJyvGYSUwQkSGi0hXYDawImWZDMMwOi2ZCCup6m4R+SbwAN5U1mWqui5lsQzDMDotmTAOAKp6P3B/2nIYhmEY2QkrGYZhGBnCjINhGIZRgBkHwzAMowAzDoZhGEYBElX0PMuIyE5gQ8jLTcAbIa/59AG2J9AnybHiyJ3k5yU5lskev0+SY5nsyfaJ2y/Pso9U1eg6pQCqmssNWBXx2r9jvH9pEn2SHCuO3Ca7yW6yZ+L75Vn20GtncOuoYaX3Y/S5N6E+SY4VR+4kPy/JsUz2+H2SHMtkT7ZP3H55lj0WeQ4rrVLVlnJfyzJ5lRtM9rQw2dOhM8ieZ89haTtfyzJ5lRtM9rQw2dOhw8ueW8/BMAzDqB559hwMwzCMKpEL4yAiy0Rkm4i8GGg7UkSeFJG1InKviPR27V1FZLlrXyMiUwLvmeDaN4rI9VKDwr8Jyv6wiGwQkefd1v7yVfHkHiYi/xCR9SKyTkQuce39RORBEXnV/T0w8J5FTrcbROTUQHtN9Z6w7JnWu4j0d/0/EJElbcbKtN5LyJ51vU8TkdVOv6tF5KTAWDW/zlSFOFOa0t6AE4DxwIuBtpXAiW5/PvAjt78QWO72BwGrgTr3/zPAsXjFhf4CzMyR7A8DLTXU+VBgvNvvBbwCjAZ+Alzm2i8DrnH7o4E1QDdgOPAaUJ+G3hOWPet67wFMAi4ElrQZK+t6j5I963ofB3zW7Y8B3kpL79XacuE5qOqjwH/bNI8EHnX7DwJnuf3RwN/c+7bhTTlrEZGhQG9VfVK9X/BW4Mw8yF5tGYuhqltV9Vm3vxNYj1fOdRZwi+t2C606nAXcoaofq+omYCNwVBp6T0r2asoYRrmyq+ouVX0M+Cg4Th70HiZ7GrRD9udU1S9Itg74jIh0S+s6Uw1yYRxCeBE4w+1/hdZKcmuAWSLSICLDgQnutUa8inM+W1xbGpQru89y52JfWUtXVUSa8e6UngYGq+pW8E4oPA8HitcBbyRlvVcou0+W9R5GHvReirzo/SzgOVX9mGxdZyoiz8ZhPrBQRFbjuYGfuPZleD/IKuAXwBPAbmLWqa4R5coOcK6qHgFMdtt5tRBURHoCdwPfUtUdUV2LtGlEe9VJQHbIvt5DhyjSljW9R5ELvYvI4cA1wNf9piLdcjklNLfGQVVfVtXpqjoBuB0vToyq7lbVb6vqWFWdBfQFXsW76B4UGKJonepa0A7ZUdW33N+dwO+oQdhDRLrgnSi/VdV7XPM7znX2QxfbXHtYHfBU9J6Q7HnQexh50HsoedC7iBwE/BGYo6qvuebMXGcqJbfGwZ+9ICJ1wA+AG93/3UWkh9ufBuxW1ZecS7hTRI5xLuoc4E95kN2FmQa49i7A6XihqWrKKMDNwHpVvTbw0gpgrtufS6sOVwCzXdx1ODACeCYNvScle070XpSc6D1snMzrXUT6AvcBi1T1cb9zlq4zFZPWk/ByNry7663Ap3iW+QLgErwZBa8Ai2ld0NeMl611PfAQcHBgnBa8g+w1YIn/nqzLjjerYzXwAt7Dr+tws2mqKPckPHf4BeB5t30B6I/30PxV97df4D1XON1uIDBDo9Z6T0r2HOl9M96khw/cMTY6R3ovkD0Pese7qdsV6Ps8MCgNvVdrsxXShmEYRgG5DSsZhmEY1cOMg2EYhlGAGQfDMAyjADMOhmEYRgFmHAzDMIwCzDgYRhUQkQtFZE4Z/ZslkLnXMNKmIW0BDKOjISINqnpj2nIYRiWYcTCMIrjka3/FS742Dm/B4hzgMOBaoCfwLjBPVbeKyMN4ubCOB1aISC/gA1X9qYiMxVsF3x1vYdR8VX1PRCbg5dP6EHisdt/OMEpjYSXDCGcksFRVPw/swKu3cQPwZfXyYi0Dfhzo31dVT1TVn7UZ51bgUjfOWuCHrn05cLGqHlvNL2EY7cE8B8MI501tzZtzG3A5XmGXB10G6Xq81Cg+d7YdQET64BmNR1zTLcAfirT/BpiZ/FcwjPZhxsEwwmmbW2YnsC7iTn9XGWNLkfENIzNYWMkwwmkSEd8QfBV4Chjot4lIF5fPPxRV3Q68JyKTXdN5wCOq+j6wXUQmufZzkxffMNqPeQ6GEc56YK6I/AovK+cNwAPA9S4s1IBXlGldiXHmAjeKSHfgdeB8134+sExEPnTjGkZmsKyshlEEN1vpz6o6JmVRDCMVLKxkGIZhFGCeg2EYhlGAeQ6GYRhGAWYcDMMwjALMOBiGYRgFmHEwDMMwCjDjYBiGYRRgxsEwDMMo4P9ENVkoM4LO3gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# graphique sur les donnees\n", + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visuellement, l'année avec l'épidémie le plus fortpic se situe entre 2000 et 2005.\n", + "Un zoom sur ces années montrerait mieux la situation des pics annuels." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PeriodIndex(['1990-12-03/1990-12-09', '1990-12-10/1990-12-16',\n", + " '1990-12-17/1990-12-23', '1990-12-24/1990-12-30',\n", + " '1990-12-31/1991-01-06', '1991-01-07/1991-01-13',\n", + " '1991-01-14/1991-01-20', '1991-01-21/1991-01-27',\n", + " '1991-01-28/1991-02-03', '1991-02-04/1991-02-10',\n", + " ...\n", + " '2023-03-06/2023-03-12', '2023-03-13/2023-03-19',\n", + " '2023-03-20/2023-03-26', '2023-03-27/2023-04-02',\n", + " '2023-04-03/2023-04-09', '2023-04-10/2023-04-16',\n", + " '2023-04-17/2023-04-23', '2023-04-24/2023-04-30',\n", + " '2023-05-01/2023-05-07', '2023-05-08/2023-05-14'],\n", + " dtype='period[W-SUN]', name='period', length=1693, freq='W-SUN')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "periods" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "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']['1999-12-27/2000-01-02':'2004-12-27/2005-01-02'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visuellement, l'année avec l'épidémie le plus fort pic se situe en 2003." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": true + }, + "source": [ + "Etant donné que le pic de l'épidémie se situe parfois 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", + "Comme 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": 16, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 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 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": 17, + "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": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot de l'incidence annuelle\n", + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une liste triée permet de plus facilement répérer les valeurs les plus faibles (au debut) et les plus élevées (à la fin)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 229363\n", + "2021 363278\n", + "2002 502271\n", + "2018 543281\n", + "1996 553859\n", + "2017 557449\n", + "2019 584926\n", + "2000 605096\n", + "2015 613286\n", + "2012 620315\n", + "2022 638443\n", + "2011 645042\n", + "1995 648598\n", + "2001 650660\n", + "1993 653058\n", + "2005 654308\n", + "2006 657482\n", + "1998 660316\n", + "2014 673458\n", + "1997 679308\n", + "1994 682920\n", + "2007 701566\n", + "2013 708874\n", + "2004 736266\n", + "2008 745701\n", + "2003 770211\n", + "2016 780645\n", + "1999 784963\n", + "1992 821558\n", + "2009 822819\n", + "2010 848236\n", + "dtype: int64" + ] + }, + "execution_count": 19, + "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 française, sont assez rares: il y en eu trois au cours des 35 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "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.hist(xrot=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +1553,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - -- 2.18.1