diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice Varicelle.ipynb similarity index 100% rename from module3/exo2/exercice.ipynb rename to module3/exo2/exercice Varicelle.ipynb diff --git a/module3/exo2/incidence-PAY-7.csv b/module3/exo2/incidence-PAY-7.csv index 59c205d071d73d5322faec2cf34ebba55b2abff4..c0923f5d7a995c0f5a34c5f93f9d1e8f3ef51dea 100644 --- a/module3/exo2/incidence-PAY-7.csv +++ b/module3/exo2/incidence-PAY-7.csv @@ -1,4 +1,8 @@ +<<<<<<< HEAD # @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202037],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2020-09-21T11:16:17+02:00 +======= +# @source="rseau Sentinelles, INSERM, Sorbonne Universit, http://www.sentiweb.fr", @meta={"period":[199049,202037],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2020-09-21T11:16:17+02:00 +>>>>>>> dc5ed36c59493b5364e24c583434584122c57877 week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name 202037,7,1225,36,2414,2,0,4,FR,France 202036,7,898,76,1720,1,0,2,FR,France diff --git a/module3/exo2/varicelle.ipynb b/module3/exo2/varicelle.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e5df920b6187a7a64c1f11beceba640a7ad8666d --- /dev/null +++ b/module3/exo2/varicelle.ipynb @@ -0,0 +1,2501 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 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" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": true + }, + "source": [ + "Les donnĂ©es de l'incidence de la varicelle sont disponibles du site Web du [RĂ©seau Sentinelles](http://www.sentiweb.fr/). Nous les rĂ©cupĂ©rons sous forme d'un fichier en format CSV dont chaque ligne correspond Ă  une semaine de la pĂ©riode demandĂ©e. Nous tĂ©lĂ©chargeons toujours le jeu de donnĂ©es complet, qui commence en 1984 et se termine avec une semaine rĂ©cente." + ] + }, + { + "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": [ + "Afin de palier Ă  une potentielle disparition du jeu de donnĂ©es de la source. Nous mettons Ă  disposition les donnĂ©es directement avec ce notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = 'incidence-PAY-7.csv'\n", + "\n", + "import os\n", + "import urllib.request\n", + "if not os.path.exists(data_file):\n", + " urllib.request.urlretrieve(data_url, data_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici l'explication des colonnes donnĂ©es [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Nom de colonne | LibellĂ© 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 infĂ©rieure de l'IC95% du nombre de cas de consultation |\n", + "| inc_up | Estimation de la borne supĂ©rieure 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 infĂ©rieure 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 supĂ©rieure 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 gĂ©ographique concernĂ©e (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", + "| geo_name | LibellĂ© de la zone gĂ©ographique (ce libellĂ© peut ĂŞtre modifiĂ© sans prĂ©avis) |\n", + "\n", + "La première ligne du fichier CSV est un commentaire, que nous ignorons en prĂ©cisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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.................................
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1554 rows Ă— 10 columns

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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": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En cas d'absence d'une semaine, nous pourrions la filtrer." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1554 rows Ă— 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202037 7 1225 36 2414 2 0 \n", + "1 202036 7 898 76 1720 1 0 \n", + "2 202035 7 828 0 1694 1 0 \n", + "3 202034 7 2272 371 4173 3 0 \n", + "4 202033 7 1284 177 2391 2 0 \n", + "5 202032 7 2650 689 4611 4 1 \n", + "6 202031 7 1303 100 2506 2 0 \n", + "7 202030 7 1385 75 2695 2 0 \n", + "8 202029 7 841 10 1672 1 0 \n", + "9 202028 7 728 0 1515 1 0 \n", + "10 202027 7 986 149 1823 1 0 \n", + "11 202026 7 694 0 1454 1 0 \n", + "12 202025 7 228 0 597 0 0 \n", + "13 202024 7 388 0 959 1 0 \n", + "14 202023 7 558 1 1115 1 0 \n", + "15 202022 7 277 0 633 0 0 \n", + "16 202021 7 602 36 1168 1 0 \n", + "17 202020 7 824 20 1628 1 0 \n", + "18 202019 7 310 0 753 0 0 \n", + "19 202018 7 849 98 1600 1 0 \n", + "20 202017 7 272 0 658 0 0 \n", + "21 202016 7 758 78 1438 1 0 \n", + "22 202015 7 1918 675 3161 3 1 \n", + "23 202014 7 3879 2227 5531 6 3 \n", + "24 202013 7 7326 5236 9416 11 8 \n", + "25 202012 7 8123 5790 10456 12 8 \n", + "26 202011 7 10198 7568 12828 15 11 \n", + "27 202010 7 9011 6691 11331 14 10 \n", + "28 202009 7 13631 10544 16718 21 16 \n", + "29 202008 7 10424 7708 13140 16 12 \n", + "... ... ... ... ... ... ... ... \n", + "1524 199126 7 17608 11304 23912 31 20 \n", + "1525 199125 7 16169 10700 21638 28 18 \n", + "1526 199124 7 16171 10071 22271 28 17 \n", + "1527 199123 7 11947 7671 16223 21 13 \n", + "1528 199122 7 15452 9953 20951 27 17 \n", + "1529 199121 7 14903 8975 20831 26 16 \n", + "1530 199120 7 19053 12742 25364 34 23 \n", + "1531 199119 7 16739 11246 22232 29 19 \n", + "1532 199118 7 21385 13882 28888 38 25 \n", + "1533 199117 7 13462 8877 18047 24 16 \n", + "1534 199116 7 14857 10068 19646 26 18 \n", + "1535 199115 7 13975 9781 18169 25 18 \n", + "1536 199114 7 12265 7684 16846 22 14 \n", + "1537 199113 7 9567 6041 13093 17 11 \n", + "1538 199112 7 10864 7331 14397 19 13 \n", + "1539 199111 7 15574 11184 19964 27 19 \n", + "1540 199110 7 16643 11372 21914 29 20 \n", + "1541 199109 7 13741 8780 18702 24 15 \n", + "1542 199108 7 13289 8813 17765 23 15 \n", + "1543 199107 7 12337 8077 16597 22 15 \n", + "1544 199106 7 10877 7013 14741 19 12 \n", + "1545 199105 7 10442 6544 14340 18 11 \n", + "1546 199104 7 7913 4563 11263 14 8 \n", + "1547 199103 7 15387 10484 20290 27 18 \n", + "1548 199102 7 16277 11046 21508 29 20 \n", + "1549 199101 7 15565 10271 20859 27 18 \n", + "1550 199052 7 19375 13295 25455 34 23 \n", + "1551 199051 7 19080 13807 24353 34 25 \n", + "1552 199050 7 11079 6660 15498 20 12 \n", + "1553 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 4 FR France \n", + "1 2 FR France \n", + "2 2 FR France \n", + "3 6 FR France \n", + "4 4 FR France \n", + "5 7 FR France \n", + "6 4 FR France \n", + "7 4 FR France \n", + "8 2 FR France \n", + "9 2 FR France \n", + "10 2 FR France \n", + "11 2 FR France \n", + "12 1 FR France \n", + "13 2 FR France \n", + "14 2 FR France \n", + "15 1 FR France \n", + "16 2 FR France \n", + "17 2 FR France \n", + "18 1 FR France \n", + "19 2 FR France \n", + "20 1 FR France \n", + "21 2 FR France \n", + "22 5 FR France \n", + "23 9 FR France \n", + "24 14 FR France \n", + "25 16 FR France \n", + "26 19 FR France \n", + "27 18 FR France \n", + "28 26 FR France \n", + "29 20 FR France \n", + "... ... ... ... \n", + "1524 42 FR France \n", + "1525 38 FR France \n", + "1526 39 FR France \n", + "1527 29 FR France \n", + "1528 37 FR France \n", + "1529 36 FR France \n", + "1530 45 FR France \n", + "1531 39 FR France \n", + "1532 51 FR France \n", + "1533 32 FR France \n", + "1534 34 FR France \n", + "1535 32 FR France \n", + "1536 30 FR France \n", + "1537 23 FR France \n", + "1538 25 FR France \n", + "1539 35 FR France \n", + "1540 38 FR France \n", + "1541 33 FR France \n", + "1542 31 FR France \n", + "1543 29 FR France \n", + "1544 26 FR France \n", + "1545 25 FR France \n", + "1546 20 FR France \n", + "1547 36 FR France \n", + "1548 38 FR France \n", + "1549 36 FR France \n", + "1550 45 FR France \n", + "1551 43 FR France \n", + "1552 28 FR France \n", + "1553 5 FR France \n", + "\n", + "[1554 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de\n", + "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", + "de nombre entier. C'est comme ça que Pandas les interprète.\n", + " \n", + "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", + "semaine. Il faut lui fournir les dates de début et de fin de\n", + "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous\n", + "écrivons une petite fonction Python pour cela. Ensuite, nous\n", + "l'appliquons à tous les points de nos donnés. Les résultats vont\n", + "dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il restent deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation\n", + "comme nouvel index de notre jeux de données. Ceci en fait\n", + "une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "Deuxièmement, nous trions les points par période, dans\n", + "le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", + "le début de la période qui suit, la différence temporelle doit être\n", + "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", + "d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", + "entre lesquelles il manque une semaine.\n", + "\n", + "Nous reconnaissons ces dates: c'est la semaine sans observations\n", + "que nous avions supprimées !" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "periods = sorted_data.index\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "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 une forte activité au cours de l'année, avec un creux au mois de septembre." + ] + }, + { + "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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UzAGwdsJMObC0QqlgtpIIzaD4u4eP4b998Sk8dXqxjmfdOiRcBW9dtisuZvdVAqzWIrLowUpSxY9PLgAovntOqgaWEqqzU25VWBXyk6cq/z/WdBZzKO5WYt5NZhx6AhLCXtEpeGPq4sxCAkDGlQdkjEM0rcFnKw/Aci+x2dKc9oQbhzqhGWZB5TAQttwh87F03s6rcLaS9YV8+vQSDJPid//9BcyspvLu124kVAOSQOAVBfQFZCwlFMTTOkK2cSCEoC8gYzmh4kcnFpzHFHIdMb+80uKLGatiPjQRqTjOVC4gHVd07BwMAYCTrUQIwVhfwFEOuddhQeOQ0rOKNGXR0/KfJ6c6uHGoE7pJIXryP97RbuuLd3IujtxpoYVjDtZiOL2Sws37hrGUUPHdFneP1IKEojtdaTf3+jEdSdlN9zKT9XoCEs4sJpydtmHSggtWxji0duxG0U3sGgpBNykeqjC4zt5vokRAetdQCD7JA6+Uub629Pkd48DSVAOygJBXRJc/8xl7iygH1nOJ075w41AnrJhD/sfLAqzHZmMAMp1XgSLZSt7M33/h0k0A0BG9luKK7hjGsV4/piIpy63klZz77B0J4/lzEawkNVy5rdd5XC6ablnhVt/pKpqJG/YM4pKxbnz2e8crmgCYyVYqrJritivuwtEuJ/0XsOIOk8tJUEodt9IfvHUvfvWqLVkDqhzjkNKyjIuXK4e2Ryx/F856KOZW6vKJCMoCjs5EAQCbenw4bft6C7mV2AIJABdt6rK+lBVM8droJBUDQS8r2gpgKaEi5BOxZzjs3Oeud1+KD75pJyYjSawmNTx3NoKkYgAhS0X86bcP47eu2+EoNBbwbUUopVB0A35ZwCffsR+/+IWn8C+Pn8If33pBycexgDRTTe7dPZBRYHe/70DW9bilLwBFN7EQUxy30h3XbM2Lk8lCZlStO1DtFYWW/jw51cOVQ53QCkyCAyx/76Yev6McNrn8u4Ua77GYQ1AWsK0/WLIvTjuRUDNupTG7v9LEcjLLrSQKHuzb1IVb9o84rhC2256NpnHvs5N48vTShnAr6SaFSa1r4PItvXjTnkE8dnS+7OPcNRG5cQpKKeKqjrBXxGDY6/REAoBxO2Pp3HISsbRudQwusDlhatakVusMhlfibqV2hxuHOmEVwRX+eDf1+J1MJbbwAYUb77EF8sLRLng8xGq33MZ1D69Mr+IT/3UYkaSKUI5xoBTo8kkFH8c+J+Z7Z43iVN3cEAFpZ96CHTQeCMpIauXdSqrLOOSms1quJjhBfDcjds+q+aiSF8tx496w+MTcmEPrfp6c6uHGoU5YRXD5ygGwXEnO7e78zBA3bNHbv6kLAEr24m8HHj06j3ufncAr01FXzCHjzmAGIxf2OTHlwFweG8Y4sK6n9u7cL1e2CdBc7yk3KM0+i2CBz8xpP5JU7RThwkbXfU1mxxyElv48OdXDjUOdKBaQBrINwqYCaYNuBkIywl4R1+4eBGBVuabbwNe7mtJwci5W8DiDBeMHQ14nHlNsh8uMBnOtMJeHZphQ7YB0qdGYzSajHGzjIAlFC9vcsJgDkO9WcoYjFTAOrP3IclzNqh/JRS6pHFr38+RUDzcOdYBSaqWyFjEOo7ZBIAQYtVUEISjYiynsk/Din78VN+8bBgD4y8z/3Sj8j28dxnu/9Eze8dWU5eLwSwIGQ1ZHUI+HOHMdCrlIAJdbSWFupQ2mHHLcSgE7tlSu5Ud2zCH7uoinixsHSfCgyydiOaE4c7kLUVQ5SB4ekG5zyhoHQsiXCSHzhJBXXMf+ghAyTQh50f73c66/fYIQcooQcpwQcovr+BWEkMP23z5P7Hw5QoiXEPIf9vFnCCHbavsWGw/bzUkFFnsg41YKeUXHhy7bbQkKIbiep1hA+rPfO4af2JXCrc5CTMEjr8xiMa7kVdlG0xo29/jx/d9/E37nxl3OcVaYVWwRC8nMrWR9Nm7lkBuQvuv7x3FoovD8g2bhHuMJAH5ZBKUoqxKzYw6VKwfAci0tJ7XSykEophy4W6ndqUQ5fAXArQWOf45Sepn97yEAIITsA3A7gP32Y75ACGFX1BcB3Algt/2PPecHAEQopbsAfA7Ap9f5XloG3bS+NMWUA1vouv2S86Us5FIqRKGA9PRKCv/y+Gl878jsek+5ofyf5yeh2/mluZ09V1MauvwSxvsCWYaABaWLLWLMBZWrHBS3ctBMmCbFPz12Ct86NF3Dd1Q9bBfOducsS61cfKmkcigRcwBs45BQEE3r6KokIJ1X57DxFSynOGVXJErpjwEsV/h8twG4j1KqUEpfA3AKwOsJIaMAuiilT1NLJ38VwDtdj7nHvv2fAG4ixbbQGwRHORQJSLNMkS6f5OzqCtU4FMInCXluJTYMXjdau+soAJgmxX3PTjrvl3WkZURTmtN/yo1jHIosdJLggSx6nKAsW7hUw4RqZIrg2GI7VWTYTbPIdSv5beNQbgpgyYC0q8V5ISzjoJV0KxFCnP8rb16FdOt3uuWsn2piDr9DCHnZdjv12sc2A5h03WfKPrbZvp17POsxlFIdwCqA/irOq+mwucbFAtJeUcBAyItuv+Ts6ipVDoWMw6NH5wBkBydblUMTEUwsJ/FLV4wByHSkZaymtILpqjvs/kADdhyiECFvZvYF24lruuksoIpuOMZheqW1+lPluZXshbhcxlJ2QDr7vsxYlFIOc9E0FN0sanSBzLXpy4o/CKB0Y1xznPWxXuPwRQA7AVwGYAbAXfbxQltlWuJ4qcfkQQi5kxBykBBycGGhdf3r7AtTqAiO8Yad/bhkvBte0QNJIGtzK7mMQ1LV8dTpJft1W98H/Mgrs5AFD95z5TgAK5XSTTHlcMv+EXzzQ9dg20Cw6HMHvYKzQLqVgzsgzRbbqUiqpXa9jlvJFZAGirfiZpSKOcRKBKQBoC/odYxzMXUBZIxDrnIAWruwkFMd62qfQSmdY7cJIV8C8KD96xSAcdddxwCct4+PFTjufswUIUQE0I0ibixK6d0A7gaAAwcOtM43G8DMagoDIS8kweMsRlKRIjgA+Px7L3duh7xixW6l3ID0k6eWnKAui3W0KpRSPPzKLK7dPeDMFFiOK87fNcNEQjUKGgfBQ3DF1r6Szx+UxUydg/2ZaIbpLKCKlnErJVUDkaSW1W+omThuJVedA1CBcbAfF5SFvF5MCUWH6CEFK+8BoC+Y+ZyLuZWAjCFwPw+7zdt2ty/rUg52DIHxiwBYJtMDAG63M5C2wwo8P0spnQEQI4RcbccT3g/gO67H3GHffheAx2grbekqIK0ZuOmuH+H/HLQ8ZyzYWko5uAn5xDW6lTK+3uOzVo+mHYPBlpf4r0xHMb2Swq0XjaDbL4GQbLdS1K5x6Pava8+S5VZyV0irLreSe7GdjrSOaynXrRSws6/KpS1rhgnRQxDyiVZfKRdxxWpxXiyE1xfMuOgqUQ7ZXVkF+7y5cWhXyn4LCSH3ArgBwAAhZArAnwO4gRByGSz3z1kAHwQASukRQsj9AF4FoAP4MKWUXbEfgpX55AfwsP0PAP4VwNcIIadgKYbba/HGGkk0rSGpGk6Qs1zMIZeQV1qTWwmA02Qtmtbhkzz2UKDW/qJ+78gsBA/BWy4chuAh6A3IWHIZB1YA11VAOVRC0Cs6Y0MV3Z2tZBlNk2bPLpiKJHHxWPe6XqvW5AWkpcqUg9Xg0YOgVywYkC7mUgIqVw5OQLpAzQM3Du1LWeNAKX1vgcP/WuL+nwLwqQLHDwK4qMDxNIB3lzuPVob5sdniVi5bKZehsDerlqEULJ0wpRrwSYKTaSIJnpbPVpqMJDHW63dcOVa2jEs52D7yQm6lSgh5RcdAsx23u84BQNbc46lWUg5arnKoMFvJHkcblMWCFdKljcP6lQMzGDzm0L7wlt01IJlnHOw6hxIxBzeffdclFb+Wk8WiGeiFNaEr7BMhekjLKwdVN7NiK33BwsphvcYhILsD0vkV0gAcZQG0VsZSsZhDuToH1TAhi4L13tUCbqUSxqHfFW8p1tAQyBisLLcSUw68Srpt4e0zagDb3bHFLVMEV6Fy6PJhqMtX/o7IXzSiaSv10x0Ib1U0w8xyn/XnKIdqjYPbtZJRDjQroyeSsF7DrTJaAWYcmPGsNFtJ003IAkHQK+apDPc0vUL0uoxDRdlKWQFpHnNod7hxqAH5ysFy71SagbQWfDn576z1gSQQJxDeqii6mRWHyXUrVWscWEDaGpzjUg565nNZSVmvt2so1FpuJd2A6CFOVT1rVVG+zsGEJFoxh6RiYDWlOZuEWBnlEJQFZ+Ev1rMKAGT7XHLHhLLz5rQn3DjUgFzjoDt1DrX/eDMB6WzlIAqels9WylUOfUEZK0kVhm3UojUISJt2PyKnZbeR61ayXmPPcKilah0UzczamXs8BD7JU0H7DKv7b1AWsJRQcdNdP8I//PAEAGAxpqA/VDxVlxCCvoAMvySUTJ4oGJBmyoG7ldoWbhxqQK5byYk5VOhWWgsZ5WDvDtM6uvyWcmh1t1KhmINJM59bNKVBFj15oy4rJeTNjLRM65lU1uyAtKUcdg6GEFd0JwjebBTdzCoyA6x01nIBadXOVgrIIlZTGhbjCo7PxpGw39uoqz18IfqCckmXElAm5sDdSm0LNw41gCmHaEqDadKKiuDWizsgzV4zk63U2l9UzaB5ygEAlhNWIdxqkeroSnG37XYrBzVHOXhFD4a6rEydRVcRXjNRdCOvWK2SmQ6awWIOmYX7/EoKM6uWy8w9WKoQ/aG1GIf8IjjuVmpfeLZSDWDFRya1/LzM9y+JtVcOftlOZdUMqLoJRTfR5ROxFG99t5KqZ0/H67dTKZfiKnYN1c44uJWDlcrqijkkVQRkwenRtBRXsXNw3S9ZMxTdzDcOFUyDY3UO7HO7ZKwb55aSOL+SBgCMlEl0eP812xDJ6W+VSyYg3b5FcKtJDT7Zk/UeOx1uHGqAe3cXdQUEK01lXQtM2qc1wynospRD67uVNDvtkpFRDtbiVK1xCBVSDq7GewCwktIQkDLGoWWUg2bmLUyBIrM73Kh2kP/dV4xj70gYr0xH8elHjuHUfBxA9qTBQrAhUqWQSymHNhg8BQC/+IUn8fOXjOJjb93b7FNpGbhbqQa4B8Fb2SJrK4JbC36XcYi6WjJLgmeDZCu5lIMdLD00EYGiG3Zwff37FcetpOqZxnt2zIEtZqspDX65BY2DbmRNWgOKu5VSqoHrPvMYHjo8A9WgkEQPugMSrts96EzMe34iAkKA4QpTpEvB4kTtHHOYiqQws5pu9mm0FFw51AB3T5vVlOb4/uuRreROZWXKwcpW2iDKwfWZ9Adl7BgM4ks/eQ0PHZ5FUtWxy27NvR5YQDqW1p1hP7pppbWGfSKUuApKLXdNX1AGIVZGTytQyK0UkAUsxvNdPi9OrmByOYVjszGnzoGx2Y4xPH82goGQt+K2LKXwSQIIyU7NzlRIt/Y1VwmKbkA1TKTb4L3UEq4caoB7d7eS1KCxmEOFLTHWgs8VkI6mMspB3oBFcKLgwQ9+/3p8+dcOIKnqiCSrcyuxKt9oWs9atBJqdr5/QBIheKw0zoUCi28zsIxDZdlKB89aTYsTiu7EHBjMjTQbTWO0u3rVAADvPjCG//muS+FxXc+i4IHoIW0RkGZV9e0wm72WcONQA5Kq7gxLcSuHShvvrQXBY81+SOXEHESBtHxvJTWnCA6w3s+NFwzjax+4Cj0BCdtLzGsoB6uPiKa0LF94UjGyirx8dvXxQMjbWm6lnF0+68Cby7O2cUiqep7BHQr7nD5dtTIOW/uDznAmN17R0xZ1DrmdfDkW3K1UA5KqgdEeH2JzcaymNIj2l7MedQ6A5YtWNNMZ5tLlFyF6rJgDpbRoi+Zmk5vK6uaizd145k9uqipbxCcJ8IoeyzjoJoJ2v6G4omM8mAnMBmz1NRCWW8c4aGZezCEgC3nKQTdMHDoXAQDEFcMpgmMIHoKRLh+mV1JlaxyqxSsJbeFWinPjUBCuHGpASjXQF5QhCx6spjQnr74eygGwp8GpVgAXsJQDW3RbNZ2VUuoUbBWjFmmE3X4JkaQK1TCdNtSWWynjrgq4lEPuDOtmUditlB/z6ZKNAAAgAElEQVSQPjYbcxrsJRS94GfKgtLlahyqxZojvfEX1Ixy2PiGrpZw41ADEqqOoCyiyy/ZbiW7fUYdYg4AnLYK0bQOQoCwV3Req1WnwTGjVWwqWa3o9kuYt4PMrLgrqRjwSR4nU2qjuJX8srUzN1xZaM/ZLqXxPj/idsxBzlGom+24Q92Vg+hpC+UQ48qhINw41ICUasAvC+j2i1hNqdBNE4Sg4hkNa8Vnz5GOpjSEZBEeV8M2d5O5ViKjpurr8ur2S5iPZhsH1c6ScuYzSxnjkFSNsi0qGkHBIricangAeHlqFcNdXuweClsB6QJxHKYYahVzKIZXFNor5tAGKqiW8JhDDUioOgKygG5bOWgGheTx1M3375cFuwhOdxZAtnvUWlU55LSkrhfdfglnl6xW3O7pZpLggVf0IK5k2p4P2HUWizEVW/qb81X42P0vYjDktWMO+W4lwNp8sGyr47MxXDDSZbUnV3TrWssxKruGQhA9BFv6A3U9d7nN3EqsXxnHghuHGpBUDQRkET0BGfOxtDXXt447ZL8kOBXSLEOHKYdWzVhylEMD3EpLiWzlYL0ucXbmjnEIW4VwC3Gl7gtpMV6eWoVASBG3knX+rIWGbpg4tRDHtbsHEEtriCtGwZjDOy7djEvHejAUrn/MQW3x9OlKiLMBUdytlAV3K1UJpRQp1chSDro99L1e+JlbKa05CyB7vVatdVAbpBy6/BJYF+4s4yB4nJ05c9cMBJtfJZ1SDZxZjMOk+fEYZ+CPXYF/dikJVTexdziMoGy5MAHkxRwED8GOKooJK8UrtUcqazzN3UqF4MahSlTDhG5SBL0iuv2SUwRXr0wlwI45qJZbiRV+ZbKVWvPLynaYtajYLYV7FoR79KVsu5UAV7ZS2HYrNdE4KLrhCtZnu5WYEWMZSyfmYgCAvSNhBL2iq01Lc77GXrE9UlnZ9EDNoC3f2biRcONQJUzy+yVLOVitG4y6G4e0XeeQUQ62W6lF+ytpdU7vZbgrrPOUQ85cAndX2GbhTp/M663kijkAVhqrh1gxBXfFd/OMQ3vEHFidAwDeQsMFjzlUCcs5D3oFJ86wEFPqG3OQPUhrBgxKnaArez21RS/uRrmV3MYhdwF1spVsX74seux25010K7n83MXcSsw4nJiNYVt/ED5JyJoNXe84TjHaJZU14TYOmlFytGonwT+FKknZktQvi7A3opiPKnXdzfklayQkAIzY6Yps0W155dCAgDQjK1tJJM7OnC267D6JMjMT6oVmZNcwFCqCAzJujxNzMewdCQNA1nCf3JhDo2iXVNZ4Ots4cCy4W6lKmD84IAnoC1g+7PlYuq4B6e0DIfgkDz54/Q78+hu3Acgoh1b1mSpNUA5ut5K7zsHderpQi4pGkbsQ5SsHu4hPNZDWDJxdSmDPsG0cZNd7a5ZykNrQrdQGxq5WcOVQJayjY8ArOMNrIkmtrtWpv3LVFrz39eNZdRQs5tCqqYUseCrXYTqeG7dxcAenrWylfOUQkAXn/7DRMJcSIQCl+TEH99jThZgCk2ZaYwRbJubQmtfbWkioXDkUgiuHKknZaYYBWXSMA1D/SuDcAju26LZsnYOjHOo7hrGSgLQ/SzkUbovdCJhLZofdiTbXrRRkbiXFcHa3rPtvawSk2yRbSTHQE7CuG24cMnDjUCVs1xmUBfQGMsahHoN+SpHJVmqNLyulFM+fWwa1iw4yMYfGKYew160ciCsgnVmEg97C09YaAVuILh3rAYC8QKhoG7SkqjtBU6YYsmMOzVMOhrnx0z9jaR399saOu5UycONQJU4qqyxkDXqvZ8yhEJlspdZQDg8dnsUvffFpPPjyDICMcaj3QuaTPM5rZMUcRFcqq5yrHJrrVrr1ohH8269diUvGuvPuE/KKiCu6oxyCraQc2mRUaELRnbGxXDlk4MahSphLggUP2Q6k0V/YTLZS87+olFL88+OnAADffek8gMwCUu/PhRDixBpCeRXS+W6loFfISmVsJGyXGpBFvPmCoYK9uAK2smHGIeQoB/d7a162ErCxjYNhUqQ0I2Mc2iDAXiu4cagSlgbJXBV9jnFotHJoTm+lV89HcfPf/wjLiUwh2RMnFnB0JorxPj+eOLGAWFpzlEO9W3YDQLffGpsqCR6nM64keHDl1j7cvG84y0A1UzmwXapfLv6ZBGVLOWTcShnXGLMlzapzYIaqVdqerwcWjGZNGLlbKQM3DlWSUg14SGbRY8ah0TEHZowana308tQKTs7HcXh61Tn2jZ+dw0iXD5/5pUuh6iYePTrvBKQboai6/ZKjEpiikgSCt+wbxpfefyDrvgFZQELVndhII2FupVJDjoJeK2DOmsOxOAohxElnbVbM4fXb+wAAT55abMrr1wJW49BvK4cUdys5cONQJVa7btFxCfSHmqMcpCYph9WUNY3utYW4c2wuqmDfpi5ctb0PI10+/P+HZzIxh4YoB8lZcNn/Q7EFNCCLoLQ5rhGmHHxSaeMQV4w85eC+3ayYw3hfADsGgvjRiYWmvH4tYJ8rcyvxzqwZuHGokvmYgkG79TPgUg6eRmcrNWcSHBtV+tpiwjkWV3QEvdYQotdt7cGZhXhDlcNwl89JTZSZkShilNgC24y4Q8atVMI4yAKStlvJK3qyFCmLOzQr5gAAb9oziJ+dWdqwgVwWy+l33Eob833UA24cqmQqknLGMgJAn91Do9G7Obb4Nbq3ElMOZ1zGIZbWHX902CshruhQnQ6i9V/I/vCWvY77iLWWKPb/4a5CbjTMv+0roabYUJ+YoueluoYc49C8r/H1eweR1kxnfOlGg6Wi9wZkeAiPObjhxqFKpnOMQ3+TAtJSk7qyrqasnZdbOSSUTLfYkE9EPK1D1a1RnfWajuemP+TFdruwjLmxiv1/BHP6FzWSStxKIa+IhGq5lYI5xoHFHBoR5C/G1dv7IYse/HiDupbiirW5CXoFu9sxVw4M3j6jCtKagcW44rQ0AIBeJyDdnDqHRhckMeUwvZJCWjMgeghSrs6WbHGz2pg33v3BdtXFYg5+VxVyo0lVYBys9h6WWylXOTQ75gBYn9/OwVDW5mAj4Q70s9nsHAuuHKrg/EoKADDWm68cmhVzUJsUkKYUmFxOOous41ayFcRKUm1Kg7iMcigWc8gexdlI0pqlpoQSBZNBrwjdpFhOqAWMg+1WaqJyAIDBsBcLsY2ZzuoO9PvtOSkci7JXFSHky4SQeULIK65jfYSQHxBCTto/e11/+wQh5BQh5Dgh5BbX8SsIIYftv32e2P4FQoiXEPIf9vFnCCHbavsW68dUxDIO2TGH5riVCCGQBNJw5RBLadhqz18+s5hAzJbprACNGYflpNaUHa5jHIosoLltsRtJWjPymu3lwtxe8zElK1MJaI2ANAAMhjaucVhKqCDEatLolTy8CM5FJd/WrwC4NefYxwE8SindDeBR+3cQQvYBuB3AfvsxXyCEsCv6iwDuBLDb/see8wMAIpTSXQA+B+DT630zjWbaVg5ut1Kz6hwAS600Puag4bJxqzfQa4uJAg3irKyh5YTSFOUgCeViDiwg3Rzj4C/hUgIyBmA+quTFHJyAdINVai6DYS8W4kpTakWq5fxKCkNhLyTBA58oQNEMPPjyeRyaiDT71JpO2auKUvpjALmpCLcBuMe+fQ+Ad7qO30cpVSilrwE4BeD1hJBRAF2U0qepdQV9Necx7Ln+E8BNpBFRyxowHUlB8BCMdPmcYz5JwB/cvAdvv3i04ecjCaSh2UqUUqymNGzq8WMw7MVrCwmnqMjpAcSUQ1xtSrEWC9YWrXPwNi/mkNaMkvEGIPM5qoaZ51Z6894h/MpVW+BpcB+vXAbDXmgGdVyMG4mZ1ZTTXt8neZDSDPzZd47gf//kTJPPrPmsNyA9TCmdAQBK6QwhZMg+vhnAz1z3m7KPafbt3OPsMZP2c+mEkFUA/QBavuxyeiWFkS5fnkr43Zt2N+V8JMHT0DqHpGpANym6/RLGev04v5pCjPUA8mUC0oAl31kGUSPJKIciMYemKgcTvnJuJZdByFUO1+zsxzU7++tybmuB1fksxBT0uDoTbwRmVtK4YNQaoOSXBSzEFCwn1KbOFW8Var2VK7SFoSWOl3pM/pMTcich5CAh5ODCQn1S5z5y7wv49CPHKrrvdCSV5VJqNqJAoDWwKyvbKXb7JfQHZSzFVSfAF84JSCu62ZyYQxnjwNw6zcpWKutWchXItepsY9aXaKGFeiy9tpiAWcbFSinFebdyEAWcWbCyriJJbhzW+22ds11FsH/O28enAIy77jcG4Lx9fKzA8azHEEJEAN3Id2MBACild1NKD1BKDwwODq7z1ItDKcWjR+fwQoX+xqlIEmM9rWMcJMEDrYHKgVVHd/kk9AVlLCdUx62UqxyA5oyzLFfn4PEQ+JuUwmgFpCtzKwGtaxyGXMqhFZhcTuKmu57AEyfmS95vJakhrZkYteew+yTBidm5G0l2Kuv9tj4A4A779h0AvuM6frudgbQdVuD5WdsFFSOEXG3HE96f8xj2XO8C8BhtUmTr/GoaCdXASrK871QzTMxG0y2lHCTB09DeSqvJjHLotY1DLJ3dWjq7bXZz6hzKFd81q213WjfLxhxCJdxKrcJgyFpcW8U4TEVSMCkwu1r6fM6vWgklm+wNnjtzLJLUyiqPdqfs1UYIuRfADQAGCCFTAP4cwN8BuJ8Q8gEAEwDeDQCU0iOEkPsBvApAB/BhSinbkn0IVuaTH8DD9j8A+FcAXyOEnIKlGG6vyTtbByfnYgAqk5QTy0mY1Go+1iqIHuI0uGsEuW4l1TaYQMaXH5KbO5RGFj1ljVKz2nanVQMjXd6S98mdWteKdNkt0lvFrbSUsM6jXBxpZsW6Vt3KgWGYFNG0tuFiKLWkrHGglL63yJ9uKnL/TwH4VIHjBwFcVOB4GrZxaTan5q3OopGkBkppyd3mCxMrAOCkcbYCkuCB1kjl4DIOrKfUuaUkQnbTPcBy27BpZs1o8zDc5cWQK5usEKwKudGk9cqzlYDWdSsRQlqqEG7RPo94mf/TmRzlkBv/WU6oHW0ceIW0ixO2clB1s6wP+vlzEYR9InYNhhpxahUhCaSh2Uq5ygEAJpYTeTvcZjaI+9ANO/Gt335DyftYMxMMfO3ps/jpycYlyaXU8gFpr5ipoG5V4wAAAy1kHJbseEE5g39+NQ3RQ5x23SxzjKWmd3rcgRsHFyfnMzMJIgXiDinVwF88cATnV1J4YSKCy7f0Nj3H3I0oeBrqVoqmNBBiZST1OcYhmd891I47NCMg7RWFsru/gCwgltbwNw8dw73PTjTozCqrc7CG+lj3adWYAwAMhuSWMQ5sMl28SAba/c9N4sa7nsDJuTiGu3yO8fXZ7d0v32J5A5a4ceAAVqbSqbk4hm0fcKTAhfHdl8/jK0+dxSe/ewTH52J43ZbWcSkBlnJopFsparfm9niIYxzSmomQT8q6Xyu0li5FQBZwdDaGlGY4GViNIK2bZdtnAK6CwlY2DmEvFlukNoCdRzHl8P1X53BmIYEfHp3Dpp7sAlYgYxwKrQGdRGt+W5vAbDSNmKLjym3W6EOWsTQfTeMd//xTvDy1gv88aNXxfe/IHCgFrtjaW/T5moGVrdRYt1K33zIEzDgAmRoH5/cmKodKCMqiU1keTTcm9mCYFKpulnUrARnj0NrKwYvlhAKjBTJ8mHIoZBwopXhxMpOqzmocAMAnM+Ngfa+5cuAAAE7OWS4lNheXZSwdmljBy1Or+NDXD+HZs8v4tTdss1MjWysYDTQnIM2MQ0AWnIBz7g7XMQ6tqhxcMZJYg5SDopdv183YKMrBpJlMoWbCqpsLBaQnl1NYjKv44Jt2QBY82Oaq2r96ex9+7uIRXLy5G35J6Hjl0LpXW4NhwWhmHFbsYOvEslUxOb2SgodYAc6QV8TxuRjCOe6TZtOoVFbTpJiLpbOMAyEE/UEZ51fTWbUNQGZRa2XlwIimGqMcKpkCxwjKAgQPKdtqo5kMhi33zNyqgqFw6eywerPElEOBVNYXbNXwjss24T1XjmPYlcm2eziML/zqFQDgFHV2Mtw42Jyaj6MvKGPHgJV9tGJfGBPLSXT7JXzg2u1IKDqGu3z4f2/Z28xTLYrVW6n+yuGRI7P47W8cAgC87aIR53hfyDYOed1DJfv8Wid47ybgMg61VA4fufcFvPPyTbjxguGs448dmwOz4aXmRzOCXhFBWWjIFL31Mt5nuWcmI0lcPNbdtPNIqQYSds1KoZYoL0ysICAL2DscLtk5uS8oY7nDW2hw42Bzcj6OXUMhyKIHIa/oZCudW0pia38AH2lSM721YAWk668cpu05FjfsHcTbXN1nWa1D0WwloTWLuFih2Z7hEE7MxSvKIiqHYVI88NJ5CB6SZxw+8V+HwXoAVPI63X6p5fPtWTHoxHKyqefB4g2y4CnoVnphIoJLxrrLttTnyoEbBwBWkOrEXAy3XbYJANATkLBi7xoml5PYv7l5O6G1IDaofUZM0UEI8OU7rsxK5WW1DrluJRaglsTW3Pn2BCxlc+2uQZyYiyOW1qs2DqxO5vhsLOu4bphYiClgAs8rln+dj96021n0WpUun4TegNQyxmGsz4/Z1XTW35KqjldnovjAtTvKPk9fUMbphXjZ+7Uz3DjAmrIVS+vYPWS17u0NyIgkVeiGialICj/XhNkM66FRyiGe1hGSxbwaD5axVFw5tKbP/Ocv2YTNvX4nTz+W1pw21OuFjR09tRCHbpjOTnUpocLt+avErTTeF2ipNi3FGO8LYLLJxoEFo7f2BXBmwerMyq7TJ44vQDMort9TvmknVw48WwlAJhi9e8iKN/QEJESSGmZW09BN6ozBbHWkBhXBxRUtTx0AGeMQzlUOLZ7K6pcFvGHngHOetUhnZcZB1U2cXcosmHPR7N1sJQHpjUJLGAc7W2prv5WFlHR1Onj4lVn0B2Un6aQUfUEZSdVAugndeluF9rkyq4Clse4etpRDT0DGSlJ1JPJG2LUB9pjQRriV7OK3XIoqhxYvgmN02dlntQhKu9uvuF1LzNXBNhzVuq9aiS19AUyvpJpa68AK4LbY31lW65DWDDx2dA437xt2KqJLwa7lTlYPrf1tbRAn5+PoCUjO0JJeWzmcs3d8bBfS6kgCacg8h7iiF1QO/UWMQ6vXOTC67LTcWqSzujuCHp/LGIc523X1vqu3AsguHtzojPcGoBnU6czbDBbjCsJeEf32d5kFpZ88tYiEauBWV3ZdKUbsTq3NVkLNpLW/rQ3i5FwMe4bCTqpgT0BGNK3h7FICkpA9I7qVaVQRXDHlcO3uAfzeW3Y7FaYMlsWU625qNTJupVorh6hzez6ahocAv/aGbXj0D67fMKq0EthufWKpeQvqYlxFf0h2aleYcvjpqUX4Jct9WAkX20koL0+t1udENwAdbxwopVYa63Cmu2pvQAKlVtrbeG+gIhnaCogCgWFS1HtWUlzRCy70AVnE771lT15sYftAEPfdeTVuvGAo7zGtRE3dSnbMYXOPHyfmMlkvc9E0BsNeiIIHO1uoo28tYMZhMtI84xBJqOgPeZ2qcqYcFmIKRrp9Fce9BkJebO7x46Wplbqda6vT8cZhIa5gNaU5wWjAylYCgOfORnB1CwxwrxTm06+3eogXUQ6luHpHf9nc8mYTsCuRa+FWYsrhsi09OLuUwPkVqzZkLqpkVeW2E6M9PnhIc10xSwkVvQHZaRvPCuGWE+qaXXiXjndz5dDJsGD0HjsYDcAZ/XnHNVvxyXfsb8p5rQdWgVzvjKW4ojtVz+0EIQRhn1gT5cAmy73v6q3wih588rtHAFjKodntJeqFJHiwqcdf91qHUgHvSEJFX1BylANzK63HOFwy1oOJ5WTHBqW5cchJYwWAA1t78dTHb8Qnb7uo5TNs3Ige61zrmbFkmrRoQLodCPvEmqSyshTI3UMhfPSmPfjekTk8enQO8zHFaQvfjvSHvAVnodSK589FsO/PHslLCQYsF/FyUkVvUHaULXMrLSVUJ2GiUi4ZY3GHznQtbZyVr06cmI+j2y9lFT0RQpzRgRsJphzUOioH1swsty13u9DlkxBN1U45+GUBv3nddmztD+CfHjuF5YTatm4lAAhIAlJlZjdXw7mlBBTdxNnFRN7fkqoBVTfRF5CzlAOl1FYUazMOF2/uBiGdG5TueONwai6O3UOhlm5qVims/fB9dZxmxnZi7aocunwSYjUsgvOJAiTBg18+MI4XJ60daDsrh4AslB2xWw2sm22hWQvM/dMblBGw60cSqoFoSodu0jUbh7BPwvaBII6c58ah46CU4sR8DLuH2yNr5Lrdg/iFSzfhHx89WTcpHLcXzlaeLVANllupeuVgNe/zOK0b/tvrNoMlvQ21sXLwyYKjmuoBc9cVMg5sBktfQIbHY41XTSi6UzXNah/WwkDQ27A27q1GRxuHxbiKlaTm9FRqB/76tosQ9Iq456lzdXn+WLsrB39tlENSNbKmvI12+3Htbqunz3CbBqQB5laqo3GwhyQtFxhJ6lYOgNXqPKHoznFWb7MWAl6h4FyITqBjjYNmmHjq9CIAtI1yAIDugISxXr/TVbYadMPM20Uz5dCuMYewT6xJzCGlGVlzIgDgt67bjl1DoQ3Tq2s91NutpDhupfwutY5ycBmHuKI7KmOtAWnAGgRVbBZ1u9OxxuEd//wkPnrfixA8BBeMdDX7dGpKyCs6O/xquOfpc7jxfz6RlTrYETEHRa+6P1BKNfImt123exA//Nj1LT0Lulrq7lbSi7uVlhOWUe8LMOMg5CiHdRgHb33fTyvTkcbBNCmOz0bx85eM4tGPXV91e+ZWI1yjoOrZxQQW42pWr5x2jzmw/kqFBsWshULKoRMISCJU3axb8z2mHAq5lSIJFYKHONX71q7fqMo4BGSx6mtho9KRxiGa1mBS4PItvVkDxtuFsE9EXKneNeLM0Xb1ymGKJNyGRXCAq79Sla6lpKpnxRw6BTZVr16uJcVRDgoiCRV/+u3DmLc3L8tJqzqaJQGEbLfSYlxBUBbW1QGXKYd6t6RpRTrSOLAind5A+y5wtVAOq/YC6W6HwJQDa0/QbnTZxqHazy+lmRUN8mk3fPZ7TtYpiMtSWZcTKn58cgFf/9kEfuurB5FSDac6mhH0iogpmlUdvY5MJfYchkmh6PXvdtxqdKhxsLMaWnwu73oJeUXE03rVu51V+3Nyt0OIKxr8ktDyfZLWC3MFVbu4pTpVOdjvuV4ZSyyVdTmh4tR83CpSm17FXz74KpbtvkqMvSNhTC6ncGwmtq5MJQB53V07ifb8hpchkpPy1m6EfRL0Gux2HLdSlnFo39YZAPK6ea4XK+bQgcahBm6lhw7PFG3ex65pkwKH7K7Jv/S6MTz40nksxJSsuALrAnx8LrauTCUg8346MSjdmcahzd1KoRrNJVgtYBxiab1t01iBTKC92sUgpRqOi6WT8FW5mOqGid/590P4ylNnC/7dPbbzhYkV7BwM4sYLhhBTdJxZTGRt+C4YCWPUHtqz3qFK7HroxFqHzjQOba4cmN88XoXf3DRp4ZhDmysHtlOsWjnkFMF1CtW6lVZTVrIIa3GeS1oznPkqSdXAjsEQ3rhzwKk+73O5lQgheLOtHtatHLzcrdRRRJIqRA9p2x0w2+1UE1SNpXVQag09WUqozmK5nlkOGwlHOVSxGFBKO9itVJ3yYvHAYsZB0c2syYw7BoPoDki4bLwHQP6G78a9tnFYb0Bazp4L0Ul0qHHQ0BOQ2qLZXiFy2xWvh5WU9SVlbYuZerBmObSvcQh4Mw3b1ouimzAp1pU6udHxy9aSst6YAytkO79aeA51WjOwqSdjHNg0vevs1iTubCXAGl17y/7hiseD5hL01iZBYSPSmcYhJ6uh3QjXYNwlcymxWbos7hBLt7dbySsKkARSlRuB+cU7UTn4beWw3rbdrGBtIaZA0Q08fXoJU66xo2nNxEh3pp3+jkGrTumt+4fhIcD2gexWOD5JwP963wFcZF/Ha4VlK8W5cugMIsl2Nw7Vu5VW7KA9Uw4TS0mYJkUkqaLb356BfEagyn46ziyHDlQOLOawXreSuyfY+ZU0fuMrz+GfHzvlHFN0EyGviG6/hLBPxGDISlHdv6kbL/zZWx33Uq1gSrITlUP7bgFLsJLUsG2gfZuf1cQ42MphS18Ag2Evjs5EcWYxgaRq4MLR9upFlYtVWbv+nSJzqXRiEZy/ylTWZZdxePLUIlKagddcg30UuxV6f1BG2J/tGq7HpsXJVupA5dCRxmE5qeLyQG13GK1ELXL1mVupOyDh8vEeHJqIODMiLh1r388OsNxB1ewUUx2sHLyiB4SsP1sp4mqo9+jROQDZ2XJp3YBXFPDrb9zm9MGqJ17RAw/h2UprhhBylhBymBDyIiHkoH2sjxDyA0LISftnr+v+nyCEnCKEHCeE3OI6foX9PKcIIZ8ndYwUU0qxklTR08ZuJUnwwC8J1cUc7B1ct1/CFVt7cXYpicePL8AvCdg11D4tzgvBWj2vl5QTc+i8vRchBAFp/Z1MlxOaU5Pw5OklAMBMNA1FN2CYFJpB4ZM8eN8123DbZZtrdt7FIIRYDfw60K1Ui5jDmymll1FKD9i/fxzAo5TS3QAetX8HIWQfgNsB7AdwK4AvEELY1uqLAO4EsNv+d2sNzqsgcUWHZtC8rIZ2I+SrboFbSVptMryigNdttez7w4dncNHmLifPvF2ptk1zZn50R4b04K9ipkMkqWK024e+oAzVroamFJiKpJyme43OAgt6RSQ70K1Uj6v3NgD32LfvAfBO1/H7KKUKpfQ1AKcAvJ4QMgqgi1L6NLWaAX3V9ZiawwKt7awcADbusjq3Uo9dQX7x5m6IHgLdpLikzV1KQPUDXjJupc5TDoBtHKqoc/Hkl7gAABgTSURBVOgLyk5l83a7a/LEctJpuucTG2t0A14Bca4c1gwF8H1CyPOEkDvtY8OU0hkAsH8O2cc3A5h0PXbKPrbZvp17vC6458y2M2G7+d56WUlpToDPJwnYv8kKQrPspXYm5K3OjZDSrMd2YkAasGY6rDdmE0lYLt9RO131lv0jAKxsOaYcvI1WDrJYVVHkRqVa4/BGSunrALwNwIcJIW8qcd9Cvgha4nj+ExByJyHkICHk4MLCwtrPFu45s+3tVrIG/lQec1B0A7/+b8/ilelVAMBqUsvK/rh8i+VaavdgNGDPDa4mW0m1dridGJAGrP5KKW19TR+XEyr6AhI224Vub9ozAL8kZCsHqbHKIegVqiqK3KhU9SlTSs/bP+cBfAvA6wHM2a4i2D/n7btPARh3PXwMwHn7+FiB44Ve725K6QFK6YHBwcF1nXOnuJVCawyqTi6n8PjxBfzsjBUEdLuVAOCON2zDH96yt63nHzNqFZDuXOUgrKsITjNMRNM6eoMydg2FIIse7B/txpa+AM4tJZ3iQp/YeOXAs5XWACEkSAgJs9sA3grgFQAPALjDvtsdAL5j334AwO2EEC8hZDuswPOztuspRgi52s5Ser/rMTXHGRnY5sZhrQN/2OfCUlhXUip6/JnPaPtAEB9+8662bTniJihboy41Y327X7YwdqpyCKxzjjTbuPUFZbznyi149GPXozsgYbwvgMnljHHwNlg5BLxiR7bsriZiNgzgW/ZiIQL4d0rpI4SQ5wDcTwj5AIAJAO8GAErpEULI/QBeBaAD+DCllH3iHwLwFQB+AA/b/+rCeF8Ab7topCE50s0k5FtbzGEprgDIfEFXkhq627SleTmcfjqKge7A2heimKJDFjyQGxw4bRV868xWYtXRPQEZsujBeJ+lUrf0BfDkqUVXQLqxRjfkFTpSOazbOFBKzwC4tMDxJQA3FXnMpwB8qsDxgwAuWu+5rIWb9w3j5n3DjXipphL2SYirOkyTOjN1S7FkK4eVlIa0ZkDRzbZvk1EMpxOnqq/LQC5EFQyG1zd5rB2w3EprNw7FVP2WPj9SmoFpu1NrowPS1bZT2ah05tamAwh7RVBa+ZAS9sVcSaqOoVhvD/yNTrDKHv5zsTSGujrYOKzTreSM781JFhm2W3SzSmlvgxVZUBaQ1AyYZnVjdzca3Di0KWvtr8TcStGUhuW4vYPrWONQXdvu+aiC4bCv/B3blPW4lR46PIOfnVkGkH/dDdgqbCpiKYdmFMFRarXu6CQ6s0qnA2AuoeWEik09/jL3znYrLSYsQ9Ef6szdb7VD5edjCq7Z2V/LU9pQBCQroG+YtKJq+sW4gt/+xiHn99yOyQP2dTi9YimHRqeyBly9yjqpJUrnvNMOY7vd5/61xURFvewzbqWMcuh0t9J60lnTmoHVlIahTo45yJk212y2SClOz8cBAO+4dBNGu315ymDAnuLGlIO34ams9vtRDCDc0JduKtw4tCnb+oMgBDi9EIeiG/jm89N494ExSELhXRczDtG0hoU4Uw6dbRzWUuWbUg3E0hoUux/QUIe7lQCr3qMS43DGbsn9R7fuxVhvfh1NyCvCK3owY0+Ha3wRnK0kO6yFBo85tCk+ScB4bwCnFxJ4+PAs/uRbh/HgywVrCwEAi7ZaoBQ4u5iALHjaehxoKVjM4exiEr/z74dw37MTZQ3FPz56Em//p59iLmotYB0dkLZ3/pVmLJ2ej8MnebCpu7D7kxCCgZAXhh0QbnTMgc2aj6a4ceC0CTsHgzg9H8ehiQgA4NsvFDYObMIba3Z2ZiGB/pDcEQVvhWAxh3ufncCDL8/g4/91GL9334slH3N8NoqFmIIXJqyZF52sHDJupcqMw5nFBLYPhEqmXLOgtIcAYoO7AvfZCnrZNWuiE+DGoY3ZORjCmcU4nj9nGYefnlrEou0ychNNazBM6szjPbMY71iXEmBVNhNiBZb3DIdw3e4Bx99dDDZj+4kTVreYTlYObMb4rO0GKsfphbhz7RVj0L4efZLQ8E1Lf9D6v1xO5H932hluHNqYnUMhpDUTR85HcfO+YRgmxYMv5asH5lLaORhyfu8Ldu7i5vEQxzXy5guG0BeUSwanTZM6xuO51yIQPaTt27OU4sptfRgIefHlJ18re19FNzC5nHSuvWKwjKVGu5QAoNcuhGTfk06BG4c2xv2Fe8+BcVwwEsYP7NGLbphc3jGQ2b0NdGimEoMFIW/YM2T3qSre4XYhrjiBaNUwMRj2VlSV3q74JAG/ed12/OTkojNathjnlpIwqeUCLQUzDo0ugAMAUfCgNyBhiSsHTrvg/sJdvqUHe4bDBd0jTC5vdxmTTi2AYwS9IkJeEQe29drtz3VYs6jyYZW7rLakk9NYGb961RZ0+UR86Sel1QNLYy2vHDJupWbQH/LymAOnfegPedEbkLC1P4D+kBej3T7MrKbzFrmlAsqhUwvgGLuGQvj5S0YhCR6EfSJ0kzqN33KZjFjG4aYLrLlWgx0cjGaEfRKu3T2AI/Z8kEIsxRX84FVLyW4fKKMcws1TDoC1Weo0t1Jn5ip2EO+4dJPTBG6k2wdVNxFJalnKYMm+6Ie6vAjK1mCTTi2AY9z9vivAbCjL1Y+ltYIzGiaWLDX2ln3D+K8Xpjs6GO1mvDeAH746X7D5Yyyt4U2feRwJ1cCt+0ccN14xHLdSk5TDQEjG8dlYU167WXDj0OZ88rZMs1uWqjqzmsoyDssJFWGfCK8ooCcgI6GmOjpbCbBy61lSTJedfRNN6xjqyr/vZCSJ4S4vLt9iTcnr5L5Kbsb7AlANE3OxtDP2kzG7mkZCNfBXt+3H+67ZVva5nIB0k5RDf9CLpcRSU167WXC3UgcxYn9Bc1MMpyJJp/Ml85t3ulvJDWtiWCxjaWI5iS19AYx2+/GZd12C91w5XvB+nQabxzC5XCjOZbsyy8QaGINNzFYCLLfSSlKDvs4BUBsRbhw6iIxyyBgHSilenFxxZkM7xqHD3UpuQt6MW6kQU8tJjNttH375wDhGurlyAIDxXmszwgL2bpxZ7hWm/Hb5RciCp2kxBxYQX07WP+6gG+ufQlhLuHHoIAZCXggekqUcpiIpLMZVXGa7RNjc6E53K7kp1f5c0Q3MRNPOLpmTYXOvH4RkAvZu2CJb6XVGCMFg2Nu0li5MSS81ICj9+/e/hI/c+0LdX6ccPObQQQgeguGwN0s5vDBp5aFfPs6Mgwyf5Omo1sTlyBiHbOVAKcWfffsIKIUTb+Bk8IoChsO+gm6lSIKNBK180t4/3H5Z0xQti9E1Ip318NQK9BYYLMRXgA5jpNuH2WgKZxbiiKV1vDARgU/y4IIRqxfx+6/Ziiu39Tb5LFuLTLZStnL43z95Df9xcBK/e+Mu3LB3qBmn1vKM9/kLK4eEZndbrTyGcOW2vlqe2ppgbqVC7WdqiWlSTK+kQCkqnodRL7hx6DBGu/04OhvFb3/jEM4sJjAY8uKSzT0Q7VbeF4524cLRAik5HQxzZURdxoFSiq8/cw5X7+jDx27e06xTa3nG+wJ4+nR+lk8kqeaNA21lWH+leruV5mMKNMNSDXPRNE7NxxFJqrjtss11fd1C8JhDhzHS7cNriwkcm41BN0xMr6S4S6QMgocg5M1uofHqTBTnlpK47bLNHdu9thLGewOYjaah5IzYXE6oG6r/VLdfguAhdXcrsWl31u0UPv/oSXz2e8fr+prF4Mahwxjt9oFSq+3xF371dfBJHly3e7DZp9XyhH0i4i7l8Mgrs/AQ4K37hpt4Vq3PeF8AlALnV7LTpy3lsHGMg8dD0BuQ695fyd3eZjqSwom5GOaiaZhNiEFwt1KHwdIsb9g7hFsvGsXhC4eLTofjZLCUQ8Y4PHR4Bldt7+f1IGXYYmdxTSwns1pkLMVV7BqqrMahVRgI1b+Fhts4HJqIOK7MxbiCoa7GpkjzVaHD2DFgfSF/+cAYAHDDUCFhn4iYYrmVJpaSOL2QwK0XjTT5rFqfbQOWcXhtIZ51PJLcWG4lABgMezEfrWxGxXqZiqTQH5TRH5Tx+PF55/j0Sul5IvWArwwdxr5NXfjRH96At+7nC9taYJ1ZAeC1JWvm8b5NPHBfjsGQF2GfiNMLCedYWjOQVI0N5VYCLBV0dik/86qWTEWS2Nzrx+Zef1YK8EyFg5NqCTcOHcjW/tIdMDn5WDMdLOMwZadmjvUWnnnMyUAIcSYSMiJ2AdxGawu/fSCI1ZSGlTpWSU+vpDDW68fmHuva8knWEn2eKwcOpzWxlIPlVpqKpCB6SEfPiV4LOwaDOD2fUQ5rbZ3RKrBN1WuLiTL3XB+UUkxHUhjrDTjG4ZKxHgRkIS+g3wi4ceBwKqDLJzrBwalICpt6/E0tUNpI7BwMYTaadhoXRhKWkd1oLVq22/GTs0u1Nw7Pn1vGt1+chqKb2Nzjd1TpnuEQRrt9TVEOPFuJw6mAsE+EqptQdANTkSR3Ka0BNpHw1Hwcx2ejTmuIjaYcxvsC8BDg7GJt4w6mSfHfv34ICzErTXas1w+Wubp3OIxzS0nMrGaMw3ws3RDVyo0Dh1MBrIVGPK1jOpLCDXt5bUilsBGgn3/0JB47Nu/0U9poMQevKGBTj7/myuHl6VUsxBS864oxpDQDB7b1QdEM7BkO4Y27BvDKdBRHZ6xBQ3PRNK7/7OP4k5+7EO+vYA5GNXDjwOFUAGuhsRhXMR9TMNbLu7BWypb+AAQPwWPHrNTMlaQGQjLt4TcS2/qDOFvjmMMPXp2F4CH407dfiB6mpvwSvv/71wMANvX4sRhXoOgGvvD4KegGxQ176t/Li8ccOJwKYJ1Zj81GAfBMpbXgFQVntsMf3boXfklAj92OYqOxbaD6dNaXJlfw4W8ccrKefvjqPK7c1psxDDmM9lgupEPnVnDvs5N494ExbOmv/+aEKwcOpwKYW+nIeWYcuHJYC/s2dUEzKH7z2h3o8cs4nVMUt1HY1m+ls0YSKrr8Eu556ix+6YoxdPslvDS5ggtGwyU7zRomxR9/82Ucm40h6BXwwet34vhcDP/fz+8r+phN9gTH3733BVBQfPjNu2r+vgrBjQOHUwF7R8LwSwL+/ZkJAFw5rJW/+cWLoegmZNGDX7lqS7NPZ91ss9NZzywmkFB0/OWDryKlGbhl/whu+5cn8Y5LN+Efb7+saDPG+w9O4thsDK/b0oP7D07hkVdm4ZM8Javtdw2FIAse9AUl/NVt+xu2MeHGgcOpgL6gjA9cux3//PgpiB7izNzmVEYxl8lG4+KxbgDAk6cWsZK0UnJ/8OocKLXSix546Tyu2dmP974+3wDqhonP/eAEDmztxdd/8yq881+ehFcScNe7L3HqGgox0u3DoT+7GUFZaGgHYG4cOJwKufP6Hfj6M+cQ9okb0l/OqZ7hLh9ev60PD758HrpBQQjw4uQKlhIKLhvvQdgn4i+/+ypu3jeMZ84s477nJnDXL1+KobAPPz21iPmYgr+87SL4JAEPfeQ6eCq8jpoxHpUHpDmcCunySfiH91yGj996YbNPhdNEfuHSUZyYi+PMYgK3XzkOAJhcTuHtF4/iL96xH4pu4O8ePob/8e3D+MnJRbz37p9hPprGd148jy6fiDdfYKVBV2oYmkXLGAdCyK2EkOOEkFOEkI83+3w4nELcsHcIb79ktNmnwWkit140Crauf/BNOzHe57ePj2DnYAi/ePkY/vP5KcTSOv76nRdhZjWN27/0M3zvyCzefsnomkajNpOWMA6EEAHAvwB4G4B9AN5LCCkevudwOJwmMRj24rrdg9gzHMK2gSB+443bcdtlmzBuz6746E274ZM8+PU3bMP/c/VW3PMbr8fcahpJ1WjKuM/1QlggpaknQcg1AP6CUnqL/fsnAIBS+rfFHnPgwAF68ODBBp0hh8PhZFhNalANE4PhwsOelhMqegOSE0B+cXIFPzq+gN+9cVfT3UmEkOcppQfK3a9VAtKbAUy6fp8CcFWTzoXD4XBK0h0oXd2d2xrksvEeXDa+sWa1t4RbCUAhU5onaQghdxJCDhJCDi4sLDTgtDgcDqczaRXjMAVg3PX7GIDzuXeilN5NKT1AKT0wOMgbn3E4HE69aBXj8ByA3YSQ7YQQGcDtAB5o8jlxOBxOx9ISMQdKqU4I+R0A3wMgAPgypfRIk0+Lw+FwOpaWMA4AQCl9CMBDzT4PDofD4bSOW4nD4XA4LQQ3DhwOh8PJgxsHDofD4eTREhXS64H83/buLVauKY7j+PcXR4hqq3oRojQSQQktEncS0gdeSEhoRIsXtwRvVCSePGgQoQ/VaKVFmpISShAaGnWN0qg6cakIbRrSqN6EkPw9rDXp6JxzeubMnrP3PvP7JDszZ80+/671zz6z9lqdWUvaA3x7QPFEYFdB/8QUYEdBsaDYutUhXkNReaxDe4uO6WuwM0Xmr+ptHU68Rj5OjIiDfxcgImp5AJ8PULakm/E7jFdY3eoQr+g81qG9Xaijr8GK5K8GbT1ovHbzMdamldaUXYEhFF23qscrWh3a6xxWK16Rqt7WwnNX52mlz2MYi0dVNX6vcB5HzrnrjPP3f+3mo84jhyU1j98rnMeRc+464/z9X1v5qO3IwczMuqfOIwczM+uSnukcJE2X9J6kfkmbJd2Ty4+W9I6k7/PjpFw+OZ+/V9KipjjjJW1sOnZIeqKsdo22ovKYX5sraZOkryS9JWlKGW0aLQXn7vqct82SFpbRntE2gvzNkbQhX2MbJF3eFOucXP6DpCfV2JXH9ivy41RVPoBjgbPz8/HAd6QtSRcC9+fy+4FH8vNxwMXA7cCiIeJuAC4tu311yyNpXa/fgCn554Wk3QBLb2MNcjcZ+BmYmn9eDlxRdvsqmL/ZwHH5+RnAtqZYnwEXkPaSeRO4suz2Ve3omZFDRGyPiC/y8z1AP2kHuqtJf1zkx2vyOfsiYj3w12AxJZ0MTAM+6GLVK6XAPCof4/Jd2wQG2MNjLCkwdycB30VEY8erd4Fru1z90o0gf19GROOa2gwcLukwSccCEyLi40g9xYrG79h+PdM5NJM0g3RX8SlwTERsh3Txkd7sh2susCpfYD2nkzxGxD/AHcAmUqcwE1jaxepWSofX4A/AqZJmSOojvbFNP8jvjCkjyN+1wJcR8TepQ9na9NrWXGZNeq5zkHQksBq4NyJ2dxjuBmBl57Wqn07zKOlQUucwGzgO+ApYUGglK6rT3EXETlLuVpFGrT8B/xZZxyprN3+STgceAW5rFA1wWk/e4A2lpzqH/Ia0GnghIl7Oxb/mYSb58bdhxjoL6IuIDV2pbIUVlMdZABGxJY+8XgQu7FKVK6OoazAi1kTEeRFxAWmNse+7VecqaTd/ko4HXgHmRcSWXLyVtBVxw4DbEve6nukc8rz2UqA/Ih5veuk1YH5+Ph94dZgh59KDo4YC87gNmCmpsQDYHNIc8phV5DUoaVp+nATcCTxTbG2rp938SToKeANYEBEfNk7OU097JJ2fY85j+H/3vaPs/xEfrYP0qY8gTV9szMdVpE9+rCXdea0Fjm76nZ+A34G9pLuNmU2v/QicWna76pxH0qdw+nOsNcDksttXo9ytBL7Jxw1lt62K+QMeBPY1nbsRmJZfOxf4GtgCLCJ/IdjH/sPfkDYzsxY9M61kZmbD587BzMxauHMwM7MW7hzMzKyFOwczM2vhzsGsCyTdLmleG+fPkPR1N+tk1o6+sitgNtZI6ouIxWXXw6wT7hzMBpAXdnuLtLDbbNLy0POA04DHgSOBHcDNEbFd0vvAR8BFwGuSxgN7I+JRSbOAxcARpC9d3RoROyWdAywD/gTWj17rzA7O00pmgzsFWBIRZwK7gbuAp4DrIqLxxv5w0/lHRcRlEfHYAXFWAPflOJuAh3L5s8DdkdZHMqsUjxzMBvdL7F+T53ngAdKmMe/kjcMOAbY3nb/qwACSJpI6jXW5aDnw0gDlzwFXFt8Es5Fx52A2uAPXltkDbB7iTn9fG7E1QHyzyvC0ktngTpDU6AjmAp8AUxtlkg7NewUMKiJ2ATslXZKLbgLWRcQfwC5JF+fyG4uvvtnIeeRgNrh+YL6kp0krfj4FvA08maeF+oAnSFtQDmU+sFjSEaTVfG/J5bcAyyT9meOaVYZXZTUbQP600usRcUbJVTErhaeVzMyshUcOZmbWwiMHMzNr4c7BzMxauHMwM7MW7hzMzKyFOwczM2vhzsHMzFr8BzGc73nkIqbbAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", + "entre deux années civiles, nous définissons la période de référence\n", + "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", + "1er août de l'année $N+1$.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", + "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", + "de référence: à la place du 1er août de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er août.\n", + "\n", + "Comme l'incidence de syndrome grippal est très faible en été, cette\n", + "modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent en décembre 1990, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_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 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": 24, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_week[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1]\n", + " assert abs(len(one_year)-52) < 2\n", + " yearly_incidence.append(one_year.sum())\n", + " year.append(week2.year)\n", + "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici les incidences annuelles." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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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": 26, + "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": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", + " française, sont assez rares: il y en eu trois au cours des 28 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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