{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os.path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Version en ligne (originale)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#raw_data = pd.read_csv(data_url, skiprows=1)" ] }, { "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": "markdown", "metadata": {}, "source": [ "### Version hors ligne avec fichier local (modifiée)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "local_file = 'incidence-PAY-3.csv'\n", "\n", "if not os.path.exists(local_file):\n", " download_data = pd.read_csv(data_url, skiprows=1)\n", " download_data.to_csv(local_file)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true }, "source": [ "La première ligne a déjà été supprimée lors de l'import dans le fichier local. \n", "Le fichier local a été crée si il n'existe pas, on lit le fichier local." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "raw_data = pd.read_csv(local_file)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1957 rows × 11 columns

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267176.0 445 \n", "1937 1937 198511 3 276205 252399.0 300011.0 501 \n", "1938 1938 198510 3 353231 326279.0 380183.0 640 \n", "1939 1939 198509 3 369895 341109.0 398681.0 670 \n", "1940 1940 198508 3 389886 359529.0 420243.0 707 \n", "1941 1941 198507 3 471852 432599.0 511105.0 855 \n", "1942 1942 198506 3 565825 518011.0 613639.0 1026 \n", "1943 1943 198505 3 637302 592795.0 681809.0 1155 \n", "1944 1944 198504 3 424937 390794.0 459080.0 770 \n", "1945 1945 198503 3 213901 174689.0 253113.0 388 \n", "1946 1946 198502 3 97586 80949.0 114223.0 177 \n", "1947 1947 198501 3 85489 65918.0 105060.0 155 \n", "1948 1948 198452 3 84830 60602.0 109058.0 154 \n", "1949 1949 198451 3 101726 80242.0 123210.0 185 \n", "1950 1950 198450 3 123680 101401.0 145959.0 225 \n", "1951 1951 198449 3 101073 81684.0 120462.0 184 \n", "1952 1952 198448 3 78620 60634.0 96606.0 143 \n", "1953 1953 198447 3 72029 54274.0 89784.0 131 \n", "1954 1954 198446 3 87330 67686.0 106974.0 159 \n", "1955 1955 198445 3 135223 101414.0 169032.0 246 \n", "1956 1956 198444 3 68422 20056.0 116788.0 125 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 49.0 71.0 FR France \n", "1 64.0 86.0 FR France \n", "2 137.0 167.0 FR France \n", "3 217.0 251.0 FR France \n", "4 270.0 308.0 FR France \n", "5 234.0 268.0 FR France \n", "6 171.0 199.0 FR France \n", "7 121.0 145.0 FR France \n", "8 67.0 85.0 FR France \n", "9 39.0 55.0 FR France \n", "10 45.0 61.0 FR France \n", "11 61.0 79.0 FR France \n", "12 85.0 105.0 FR France \n", "13 98.0 120.0 FR France \n", "14 102.0 124.0 FR France \n", "15 74.0 94.0 FR France \n", "16 77.0 97.0 FR France \n", "17 71.0 93.0 FR France \n", "18 53.0 73.0 FR France \n", "19 49.0 67.0 FR France \n", "20 53.0 69.0 FR France \n", "21 55.0 71.0 FR France \n", "22 47.0 63.0 FR France \n", "23 39.0 53.0 FR France \n", "24 25.0 37.0 FR France \n", "25 23.0 35.0 FR France \n", "26 33.0 49.0 FR France \n", "27 35.0 51.0 FR France \n", "28 31.0 45.0 FR France \n", "29 33.0 47.0 FR France \n", "... ... ... ... ... \n", "1927 35.0 59.0 FR France \n", "1928 38.0 64.0 FR France \n", "1929 59.0 97.0 FR France \n", "1930 55.0 93.0 FR France \n", "1931 44.0 80.0 FR France \n", "1932 66.0 116.0 FR France \n", "1933 83.0 149.0 FR France \n", "1934 207.0 281.0 FR France \n", "1935 319.0 395.0 FR France \n", "1936 405.0 485.0 FR France \n", "1937 458.0 544.0 FR France \n", "1938 591.0 689.0 FR France \n", "1939 618.0 722.0 FR France \n", "1940 652.0 762.0 FR France \n", "1941 784.0 926.0 FR France \n", "1942 939.0 1113.0 FR France \n", "1943 1074.0 1236.0 FR France \n", "1944 708.0 832.0 FR France \n", "1945 317.0 459.0 FR France \n", "1946 147.0 207.0 FR France \n", "1947 120.0 190.0 FR France \n", "1948 110.0 198.0 FR France \n", "1949 146.0 224.0 FR France \n", "1950 184.0 266.0 FR France \n", "1951 149.0 219.0 FR France \n", "1952 110.0 176.0 FR France \n", "1953 99.0 163.0 FR France \n", "1954 123.0 195.0 FR France \n", "1955 184.0 308.0 FR France \n", "1956 37.0 213.0 FR France \n", "\n", "[1957 rows x 11 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Unnamed: 0weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
1720172019891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " Unnamed: 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n", "1720 1720 198919 3 0 NaN NaN 0 NaN \n", "\n", " inc100_up geo_insee geo_name \n", "1720 NaN FR France " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1956 rows × 11 columns

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\n", "20 20 202149 3 40168 34716.0 45620.0 61 \n", "21 21 202148 3 41842 36364.0 47320.0 63 \n", "22 22 202147 3 36598 31338.0 41858.0 55 \n", "23 23 202146 3 30059 25302.0 34816.0 46 \n", "24 24 202145 3 20364 16564.0 24164.0 31 \n", "25 25 202144 3 18999 15042.0 22956.0 29 \n", "26 26 202143 3 27040 21935.0 32145.0 41 \n", "27 27 202142 3 28343 23382.0 33304.0 43 \n", "28 28 202141 3 25043 20586.0 29500.0 38 \n", "29 29 202140 3 26286 21842.0 30730.0 40 \n", "... ... ... ... ... ... ... ... \n", "1927 1927 198521 3 26096 19621.0 32571.0 47 \n", "1928 1928 198520 3 27896 20885.0 34907.0 51 \n", "1929 1929 198519 3 43154 32821.0 53487.0 78 \n", "1930 1930 198518 3 40555 29935.0 51175.0 74 \n", "1931 1931 198517 3 34053 24366.0 43740.0 62 \n", "1932 1932 198516 3 50362 36451.0 64273.0 91 \n", "1933 1933 198515 3 63881 45538.0 82224.0 116 \n", "1934 1934 198514 3 134545 114400.0 154690.0 244 \n", "1935 1935 198513 3 197206 176080.0 218332.0 357 \n", "1936 1936 198512 3 245240 223304.0 267176.0 445 \n", "1937 1937 198511 3 276205 252399.0 300011.0 501 \n", "1938 1938 198510 3 353231 326279.0 380183.0 640 \n", "1939 1939 198509 3 369895 341109.0 398681.0 670 \n", "1940 1940 198508 3 389886 359529.0 420243.0 707 \n", "1941 1941 198507 3 471852 432599.0 511105.0 855 \n", "1942 1942 198506 3 565825 518011.0 613639.0 1026 \n", "1943 1943 198505 3 637302 592795.0 681809.0 1155 \n", "1944 1944 198504 3 424937 390794.0 459080.0 770 \n", "1945 1945 198503 3 213901 174689.0 253113.0 388 \n", "1946 1946 198502 3 97586 80949.0 114223.0 177 \n", "1947 1947 198501 3 85489 65918.0 105060.0 155 \n", "1948 1948 198452 3 84830 60602.0 109058.0 154 \n", "1949 1949 198451 3 101726 80242.0 123210.0 185 \n", "1950 1950 198450 3 123680 101401.0 145959.0 225 \n", "1951 1951 198449 3 101073 81684.0 120462.0 184 \n", "1952 1952 198448 3 78620 60634.0 96606.0 143 \n", "1953 1953 198447 3 72029 54274.0 89784.0 131 \n", "1954 1954 198446 3 87330 67686.0 106974.0 159 \n", "1955 1955 198445 3 135223 101414.0 169032.0 246 \n", "1956 1956 198444 3 68422 20056.0 116788.0 125 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "0 49.0 71.0 FR France \n", "1 64.0 86.0 FR France \n", "2 137.0 167.0 FR France \n", "3 217.0 251.0 FR France \n", "4 270.0 308.0 FR France \n", "5 234.0 268.0 FR France \n", "6 171.0 199.0 FR France \n", "7 121.0 145.0 FR France \n", "8 67.0 85.0 FR France \n", "9 39.0 55.0 FR France \n", "10 45.0 61.0 FR France \n", "11 61.0 79.0 FR France \n", "12 85.0 105.0 FR France \n", "13 98.0 120.0 FR France \n", "14 102.0 124.0 FR France \n", "15 74.0 94.0 FR France \n", "16 77.0 97.0 FR France \n", "17 71.0 93.0 FR France \n", "18 53.0 73.0 FR France \n", "19 49.0 67.0 FR France \n", "20 53.0 69.0 FR France \n", "21 55.0 71.0 FR France \n", "22 47.0 63.0 FR France \n", "23 39.0 53.0 FR France \n", "24 25.0 37.0 FR France \n", "25 23.0 35.0 FR France \n", "26 33.0 49.0 FR France \n", "27 35.0 51.0 FR France \n", "28 31.0 45.0 FR France \n", "29 33.0 47.0 FR France \n", "... ... ... ... ... \n", "1927 35.0 59.0 FR France \n", "1928 38.0 64.0 FR France \n", "1929 59.0 97.0 FR France \n", "1930 55.0 93.0 FR France \n", "1931 44.0 80.0 FR France \n", "1932 66.0 116.0 FR France \n", "1933 83.0 149.0 FR France \n", "1934 207.0 281.0 FR France \n", "1935 319.0 395.0 FR France \n", "1936 405.0 485.0 FR France \n", "1937 458.0 544.0 FR France \n", "1938 591.0 689.0 FR France \n", "1939 618.0 722.0 FR France \n", "1940 652.0 762.0 FR France \n", "1941 784.0 926.0 FR France \n", "1942 939.0 1113.0 FR France \n", "1943 1074.0 1236.0 FR France \n", "1944 708.0 832.0 FR France \n", "1945 317.0 459.0 FR France \n", "1946 147.0 207.0 FR France \n", "1947 120.0 190.0 FR France \n", "1948 110.0 198.0 FR France \n", "1949 146.0 224.0 FR France \n", "1950 184.0 266.0 FR France \n", "1951 149.0 219.0 FR France \n", "1952 110.0 176.0 FR France \n", "1953 99.0 163.0 FR France \n", "1954 123.0 195.0 FR France \n", "1955 184.0 308.0 FR France \n", "1956 37.0 213.0 FR France \n", "\n", "[1956 rows x 11 columns]" ] }, "execution_count": 8, "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": 9, "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": 10, "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": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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KVMcpP/bDht3NHyts0zn3x09g4bce0p6rJw2lWn6ekaxTN/WsoVTByDAuqN8WUUWqYfJSjOVPANsol5zZtm/QeE4NkmoWXouohZ/VeufK5JWshXCzEhkTGn0NwgKlBKrhlB8LGkhUqvmsyYTTJTI8hTUyFtriWHiGWoQFSgnUplN+7PSQaj5JSi5YzWRLL8XaHfuxtWegXEUqwP02TpUqaizM7sdQd6kpWKCUQHVNXianfP1TC508KQVKOlu6yev8nzyJt3zv0XIVqYByVNPTr+zGlj39peVv2ai0Hqhm6P9YhrdeKYGqmLzGgVNeUU2h2ZCML1AqTTm+3vnh/3E+tBppi5cxCAuUysAaSgmYNJQDwxl85/41FY0QMneD8dNBKukLVj6UdAyTV6Wp9liY31euegV5fus+HP9fD6Knf6Sk+2v37dY3LFBKwDS7+fkjG3D94xtxx7KtZc/TujnkGOoh1XyWvA+ldjWUnKUtjBbVzP76x19B72AaS17ZU9L9onZfb13DAqUETGONCjWtRsjpWJAnUQfISg6krg+lylFeuZD8q+0Ud/OvYjHUAtRSrQHVrsOxCguUEjCZvNSO9pUY8PKbQ47dD2wpqtnZlTWt2lFzYYtn1U4NVYvysuS7sfuAu99XpVALUEvd0YCjwisDC5QSMJm8lG2/Eg6/ehIYpQ7GSpDUwrNW22kbln+1Z9dhO1/3D2dw1g8fxxV/WFnRMigNZf9QaZt4VnvCMFZhgVICZg2l8iuYzT6U2ukgcYtiu380FmhXc782W/7VftVhbe01uQvByq7eipZhbmcrAODVEtf7sIZSGViglIBpFbXaEqMiGoqavRvPVxfvIFNqWao9UAJ5YVXtooSZvKpdT66GoinIiHQwVnqfr6aY6dfSBGwswQKlBGwmr2q01VrqH3E7ay08Cpu8zIRVzWhXW6n51UIbG4uwQCkBu1O+cl75Wv2mvLdccUtSzdmj2hq/2jPYsCivfNhwtcoYri0DlRcscc2e1Z4wjFVYoJSAqbOrwagq9tkaMYNUMq3RGAPygRWVzyuMsPyrLewUumKM/kBdYgBIbVThmIMFSgmY7Nvu9zwqkKfNQ1FL/SN2Z7U55WMmH5p2BSP1iiEsf1dDGaWyBMnv5VVYglzIuVqi2u93rMICpQRMJq/R+OZ5rfYDv1O+1LDh8l4Xh2prKOHvuTbChrXnRmkn5Li7BdRqP6p3WKCUgGl2o45SBSSKzcxRSx0kdthwBQdMWz3Wig8ljGpvvRK2DdCoaU9xAz9q9/XWNSxQSsC09YoSNJXQUPKhmqbztTtrjZyGZaAsR71aNQ9l8qqyihL2Pqs9GIYJ27yGUtlCxhWqbPKqDCxQSsD4PRRl8qqold+QddUHGf3vktKKkEepRB1Iqm3yCkM9Q7UmEWGTGxH4W7EyjIHQ9LFIbIFCREkieo6I/iz/30lEi4lovfw72XPtFUS0gYjWEdF5nuMnEdGL8tw1JG1GRNRERHfI40uJaJ7nnkUyj/VEtCjucxSDbVCqiIZicXbWUgcpfaCLdl+c6o28AWWMPMpCDa31KCb/0dLs4jr/WUOpDOXQUD4HYI3n/5cDeFgIcQSAh+X/QUQLAFwE4FgA5wO4loiS8p7rAFwK4Aj573x5/BIAe4UQhwP4MYCrZVqdAK4EcCqAUwBc6RVclcbolJd/K6mf1OrWK96OXemtV+JgnQy4ZajdAafanwAOIzdKKkpcgVDL77eeiSVQiGgOgHcCuNFz+EIAt8rftwJ4j+f47UKIYSHEJgAbAJxCRLMAdAghlgjnLd8WuEeldReAs6X2ch6AxUKIHiHEXgCLkRdCFcfolB8FH0qp5ytNWRc2VtQpH+26as9gQyOpRq0UpvzNfhLbFkG1AsuTyhBXQ/kJgC8B8LqpZwghtgOA/DtdHp8NwPvlqS55bLb8HTzuu0cIkQHQC2BKSFqjgklDGQ1t35iF58RT63dXviAhxLZvV1NDoSouTo1IfA2wPE4uXSr5wIpKO+XjaWm1/H7rmZIFChFdAGCXEGJF1Fs0x0TI8VLv8WdKdCkRLSei5d3d3ZEKakJtrWJbh1KRnVeK6EAfvWlp+QtQBCV7UCw3lkNziWryqrqGEuancJ3y5U870v0h5+KWLXIZYj9D9SXK5t39uPXpzdUuRlmJo6G8CcC7iWgzgNsBnEVEvwawU5qxIP/uktd3AZjruX8OgNfk8Tma4757iCgFYCKAnpC0ChBC3CCEWCiEWDht2rTSnlSSsOwmrBppJWY/dpNX9TtIuahslJc3vdoNzQ0jvkkx5v0hE6dKTqq8xF3vUu3PEwDA+3/xNK68Z3XJX52sRUoWKEKIK4QQc4QQ8+A42x8RQnwUwD0AVNTVIgB3y9/3ALhIRm7Nh+N8f0aaxfqI6DTpH7k4cI9K6/0yDwHgAQDnEtFk6Yw/Vx6rKEqgmNahqE5UydltrX6xsZxhw6YEyuHv9a3o1yREdbAOJb65J6ZJMkLZKk3cCVS1NVAA6B10Pg5WA0UpG6kKpPldAHcS0SUAXgXwAQAQQqwmojsBvAQgA+AyIYQSzZ8BcAuAFgD3y38AcBOAXxHRBjiayUUyrR4i+iaAZfK6bwgheirwLD7UYGPcy6uSi7ps5qAyZLl8cw/amxtw1Mz2ou/1dfASy2IVGGWIbvLKiZwQSBhi8qptYw99xlGIousdSCORANqbG4z36wZ1W3h7uRgtTYgpjrIIFCHEYwAek7/3ADjbcN1VAK7SHF8O4DjN8SFIgaQ5dzOAm0stcym4Ji9L2HBFByND2uXI8v2/WAIA2Pzdd8ZKJ+5gYt4NIH76UT8EVgszWBP5spVWxij1d/w3HkRzQwJrv/kOzf32tCtdfXEnbbX0fo0LpesQXilfBFGd8lX5YmMNOZFL37Av/MZyzEpzlnKOVpSSjqjCbrTW+Qylwx0N2r28Rsk3EXdhYw3Jk5oSbnFhgVIEDfKzo8MZfa9RDaMaE45qN0lh+F1SWoYOVo4IIm/auo48WlFKOopdI1O64C7tvvz95jqKW7boZYh3fy0M4vkJaHXLUU5YoBSB+o71wEhGez7fmSqgoVRx5lwsldpnyTX0lElD0eYxCoEVxrx95TDnH1tgxzVJqr86DS9WytFx309Mf10tUO0AkHLCAqUE+of1YX6ZrNJQqmEuGfUsA/lHM9dESyv8eJwB0ftutCabamqZEV9imIYQhdjPFnL/aO02HM+LVFsTs1rQlsoFC5QiUO99MK0XKCr6yxRWXI68KzdvLR8lm2Ks5+PNSoGAQNEklBcopdrmYwQM+NIJyyNeXvE1SPNwPlrfQxEx31MtKAWqCLVQlnLBAqUI1HvPGjyPSnWtiMnLEj1T7UmO34dSGZNXGeSJr550HbmaXwKMem/cVx13AIsm7OLlEbUMpT5LtfuLF9ZQxinqvZs0kGyN2N+rgfBLlJhp6RNwZ3QxRkS/yaswHXfmW2Iecd599G+1xK3gmLeHCI3RavtxNclaGsRrqSxxYYFSFMqkFa6hVGTrFcvsfCy0SdszuBpgjDx8YcMh50t9h1G3dolDfHlSnnLp60+9o1HyoZRs9itfWUolb7arckHKCAuUIlCNMGNoARkpaKqjodSOzatiTnnL+Sj4NBTNvCC2D8Xz9MUmEXUtT9zQ5tgmL/U3LBKtws3RfU8l+itryik/hiQKC5QiyPtQ9A1AmcIq0VZtnbiG+kflnPKulhbD8W11yhdeV1z63rSKS8MnjCo4QYj/eQHz/aO2jiemebmWxnA2eY1TVEcyfw8l3uxW5bFz/5D5fMkpV5ZyDobmzxzLwSpC8jc88Qp+u/TVwjQsWkDckFxf+jHuDSP2wsbSbiu4X2vyquCkypdPTHNR1TV6cJTXuMeuocQXKHcu34pTv/0wXuzq9edtSbLabTKquabYtKIc1/Ht+9biy398seC43YdSPmdv8RqK53fIrXHrN/7nc0POaX5Vgrih07U0iPNeXuMUmw+lHIvilm50Nk1et7MvmLvvT2HZaqdRlmsGXHC8DIvmbAO+mmGXPvP1/C7ahxLthrhmufhjvVlDiiKsegfTOO3bD+OeF7SfMIpEPniiVNNkDfWXGipLXFigFIHN5KVWysdpIMmE+uZKDXwBqAj8A2llOnmYqSUqUVfKlz7zLZOGEnZdzAHIJixtTuJw7clujuvuG8KO/UP44YPrwgsSVgbENHlFvO+sHz6G258pNJ2Wg7hraWoRFihFYDV5WSJP0tkclm7cE5pHKum8kqAWZHNIV3uSY/twVXFphR+Pk77fx1GYkNvJS44eyv8udqCI+ly2EHLr/ZY7Td/7yd+vymGuv7AUVL3EMfXkB+P4gt+ch8DG7n5c/odC06mNB1bvwMU3PxPpWjZ5jXOMTnl53NQhr330FfzDDX8LFSpSnli3yC84XnUvSvmwOeXLtngwREMphymlaE3C54cy3xt3CxpbsaIOcHoflMrDnIZKP55AsWtC4ffbrzGZtqPwj79agSde7o7UBjjKa7zi+lD009esZTB6bd8gAGBD9wFjFqmE80qCnc3W5EarTfb0j+CvG3YX5l/Gslg1lBhp+7/YqDsfz5RiSz/83ogDecz6teVjG+jd28NMhhHyt+Wz+KWd2Cb7TGEa/rSKJcp96TJsyhelDYwhecICpRhsJq/8Xl76+9uanA9kDo7oN5cE8j4U5Y8pKEOVG9/Hf/kMPnLjUowYvgkDlK4t2Z4t7j5bThoeDUJn8nLLMvoaSlQfStwNGG3Fss3MwwbjfP3Z87fl8+nbluOCa5405BNTk4xwTdrQB4vBNPn0YjMx1hMsUIrAdcobGoDqIKZGTvrPl/tQX4UMphH1a4aVZu0OJ/os2FFGJ/+QqXHUFKxOefXXnMe1j23AdY+9oj0XR0OJvH19hc2bNqe8qhtde47yDGpC1tM/Yr1270DaUAb/32KJIvjLoaFEMeuNJZNXWb4pP15Qr92kPeTXoZSeB8leakrC7F8YHdQYEpy9xdlyJIhtNwBbJ+3aO2A85x/wNRpKBJPX9/7iRCd95q2HFd4P+0BlIqrZMK4P1zaA2TQH1f4TiUKJEmXH7agO8fDz0a6z3a9+64SjqZ8XQxQ/DIcNj1NsA1o5Vsob8w6UoeD8KDVK1fEyIbO3uCWx+VBsE8c3X/2o8Zx39h0aNhyehZHyRXlFsBmVSFynvNLQE5pROIoOGaV/2P048SZvtokFEK6h5HICNz+1Cb0GDUqRjSCUKvH9pGrBAqUI1OzTtlLeNLir7hfWn0zX2PrgaM1x1CBSYF/2zfhiDnjG46r+S++B9jUY6rpyzHyL1VCiXR93YaPVKW85r4SyRkGJNMD7BnNLCL6JcoYNm8ocJlCefmUPvvHnl3DVfS9pz7sTr3Fm8mKBUgTuDNnQAFy7bpwZh2yIRZu2ytgmwwaqvMmr/BqKbUB11y/EeFa7D8VusgkjykBlJGCGMV4WoWyPrN2JHb36PeFsd9tm1WqQJJ2GEiEUzzshM/Ulm4YSfy+vwrSChDnld/U5dTtgCLBRE6/x5kNhgVIE6rWbbKvKUV2OqI0CDcV2fRklSlgfUB2lYOGl93fMothMXvE+sOX9XZhOPo9S0xfa31GIenWUKK9P3rIcF/73U/p8LBnZIpPUc+k0lCih3d56sWn75jRUfqVqkuETCyB80qSCBTpaGrTnVdVEifKqs00xQmGBUgyuDd+goShzSQVXvo7GZCZ0IJQ9JdjZItv/AYxkcnjz1Y/goZd2as/bFjbGWhBn2b4w/sJGb16VuVedM5VRDZY79w+Hnjdhq1/XKa/RUKJoeN4B1NaXTMQOG7ZMLIBwgTKUdjSTxqR+CGUNhbGiGrHJLuquALaEDYdpEwR9bHG+g5oGEWOSRRPWCfI+lBCTl6Usu/qG0LV3EF+7e5X2PuP98nicFcy2LyrGNqVE8A8Y740oguyfUbDlU5iWF6sPJcQpH0V78g6gto/VmRgNTVKZvJIaVUytw9KdA+BOvNiHwhixOQKzlo4ehShrVXSUs0mGKiiuhhI0eYXP/P1p6EOjowYelGvrFd1rih2OGiF8eiidxU8eehnDGb/93S/sQvKQ50w2/qj+B1M+tnDZvA9FUzYoDcV8v1ecx7BiAAAgAElEQVRg2fbFM5GP8irtPUVZL6QiGZOaB1UTKuOaM/mXNRTGiHrtppDZ/DflTRqKXVrko7z8adg+qlRWDSVCYsE6CMb1R6HQTySf0XB91C07wvP0zc+NeZRnoNKnccvTm/GTh9bjlr9uDilbWB7RBYYOm2nNGjbsRnnpnPKht8pr7ALFpnnE3TUhysLGEdnGE5pRUmkopvK7vsYIESTsQxmn2GLf1czNvLGj3Zxikjn22Xv5JErYgKQ6ykhAoNi2NIlC3qoXbtaLI1C8nVcf5eX/WyzewcmoycrEg6vA/QO9uQC2ohUjUHTX2iYUeYGiyVuFzoeUMhvBh2I1ean8yuJD0V+jhEFKI1FU+zeVX/Vj1lAYI66GYmjsroZimXFEaWQFs3c1I4t4fRzCbP9u9ErQ5OXtoDaHqklgBP6azscSKD7BZz5fDg3FlEJzQxJA3rFbLN6i6eoirskrqoaiDRt287DfD4SE4Fs1lHjvKcqOBsqspROcaYtAyUdDRojyYoEyPrHNkF0fisXkVcqAaIueiZria/sG8ZdVOyx5mc9RhI5iNcmo744HS20xY9j2UotC1LDh0rEPVA1JfWBDVLOht9y64IhimpeuDmxmmrD3W+yM3LTmxfaOlcm11PcVxYeSlid0jndVR0YNRf6NVh/WS+qGkgUKEc0lokeJaA0RrSaiz8njnUS0mIjWy7+TPfdcQUQbiGgdEZ3nOX4SEb0oz11DctQioiYiukMeX0pE8zz3LJJ5rCeiRaU+RynYYudNkR1qgAmL/FBRXsErrG0uYs/6+2v/in/69YrQa8I6gZqUjmTMGoqtKFlXOPqPu2HBFqFZLh+K3uRVPg3FVEyTJ60UU6FWoFi3LQlPM+peX2F7oYU9irf9Gz8FYZnZqwG9HCvljRpKSCSX7ZsuvFK+eDIAviiEOAbAaQAuI6IFAC4H8LAQ4ggAD8v/Q567CMCxAM4HcC0RJWVa1wG4FMAR8t/58vglAPYKIQ4H8GMAV8u0OgFcCeBUAKcAuNIruCqB3zau77TuYGQUOAg9D+QbYkEjs/fTSKi1CaEfcAr1oTh/gwOBb9YZ0QZfIDRFYVq6csUzeXl/h73DUtMX2t86TM8f/F2Qh+chdJFexTjttRqKb78zTfruFkOFadu0dO/9YWW17W+VdoWa+ZrDv3wfvnPfGu25KD6Uh9c666R0AiVtmTwWY40wXdPdN4zrH39l1PbpKwclCxQhxHYhxLPydx+ANQBmA7gQwK3yslsBvEf+vhDA7UKIYSHEJgAbAJxCRLMAdAghlgin5m4L3KPSugvA2VJ7OQ/AYiFEjxBiL4DFyAuhiqDeuTJX6DqMzSmvOk/YrMUdVAPXWKO8jCnqCWvoYYOB0qAKzDWe36VGIdkEsk3gRMHmP4ibRyRfkiHyQvh+h7QRz2+dhhJ1HQlg8qHkQs+HaSi5CPXn11BMAsWiJVnCdlXa1z+xUXsuSvDEfS86pmFd2HDWsitGoggNxfQIX/zfF/Cd+9di1bb91jRqhbL4UKQp6gQASwHMEEJsBxyhA2C6vGw2gK2e27rksdnyd/C47x4hRAZAL4ApIWnpynYpES0nouXd3d2lPSDyja4hafiiohD2dSqWsGLvuWBDFYG/QYod/0JnkCFpGdehRDAhKIzb/7v1p79PJWuz8asZ5ZS2xoJztkV1ccNRo0S7GU1eETP15qH70FlUh3bwt8IXhaU9b57ceLUX0/N4Jwylbr2i2kCp76mYCZAu+MD1oRjaoutrjLCVsCn/A0NOFGBwvVItE1ugENEEAL8H8G9CiDBRqutHIuR4qff4DwpxgxBioRBi4bRp00KKF45656mEXpXNRukkSkMJGRDVrcF26IYcW/wzUQkbdKJEeYVrKJa8TT4Ui/8i6nYbB3e2OmUN2WsK0Nvp4y6YK8aXZLBqWu/1ntM75W0aij5Phbde9AJHvT/zuWA+XjJF9BUT6Vy4hmLrD7bPGHhpShUOk7ZdMdQ4MRzyZdNgWoVpOPnG2RkCAK577BWs7NoXK42oxBIoRNQAR5j8RgjxB3l4pzRjQf7dJY93AZjruX0OgNfk8Tma4757iCgFYCKAnpC0KoZquI2ycQUbkm/1r0lDieBINEVz5QWNabAtjlKjtMiwYMtv6gkvTf4Z9ELZdL+tDvLXqXo2nwP0gr2ce3nZNNXg80f+YqPnOp0PxVo/3sFU0wwylsHWDarQpe0T2HZhYTZ5xXPK2yc13t/6ixfM6gCQ7/O+/C3m7aYG556wz30rTK89aZi8FsvVf1mLd//8r7HSiEqcKC8CcBOANUKIH3lO3QNgkfy9CMDdnuMXycit+XCc789Is1gfEZ0m07w4cI9K6/0AHpF+lgcAnEtEk6Uz/lx5rGLkNRQpUAId2dv+bd94CB3MDQ1V/c8YAVVkmwsWQUTo5IDX5BUMebUL1OD54GW2L16q66MLlMLr/BqKTqD4/xaL18wVZYZuKlt4Hnm0Jq+I9e+kFS6QtOtcQgbzKEEJWU/bsQWwmMhvfRJ+3oT3NlMaAyMZ51qdULUJlJQTbzQYYa2RqZ5ShvDyYhhth36cTwC/CcDHALxIRM/LY18G8F0AdxLRJQBeBfABABBCrCaiOwG8BCdC7DIhhKrtzwC4BUALgPvlP8ARWL8iog1wNJOLZFo9RPRNAMvkdd8QQvTEeBYrquOplxwcFLxCwrxYSzVCcz7uLLzQHuRLo7B8xVHgo4moYZj38tKnpcOsgYTPOqOuQ1GvQi8wwgVnlE/YessTtK9Hmfmq2XeYySsMW0SdTRj6tShd+cKFgmlCELzevOdd/nepTnllSjK9p6hffARCfBjDGeN591MVhnyapYayfyj8i46Afd1aHJEQ9k2XSlCyQBFCPAWzf/Fswz1XAbhKc3w5gOM0x4cgBZLm3M0Abo5a3riodqO2qw42Mr+Gok8jaxEK3nQLo7z8acQl2BGi7AAL5KO8wvbyss6QLYOILQos6mChS8amoYSZcwrLAyQpeMwz+zfOnvXli+p/sT5DZJOjfkDO+ARK4f35PesKz63d3hd6r5O/Z/JVokBRpiSjFmgZSP11rb9WvSdtRKc8Z7I2tMjdEDZ294eWAzA/g7s4MoZQiLJSv5zwSvmIqIHCpKFE2kE1pxqhXaAURnlZNJRinfIGHw0QrRMUOuXDByEvpnUorrnJ0AfU9VYfTYimYxOcYSGxgH1mG2WgMvvBor1D71W2Z9Th1yI0570CJaSOdGL3mc15Q4F5n64IfcXyDEMZJVBMeUT7SJjzW39NWDSb204M2ag0ldkstCyW9hzH5JXORGtT5YIFSkSU89LkQ4m0P1EEh29+Fh7IfxQ1lMt++6z1vnTwfq+GFtmHoi+D7RPLtqiXvB9EN9h6yqEZDWz7sdn8C8EFsDpMQssnjEKEi8/kZTHr2e7X5ePXUMxanN20ZniPEQSKbZBVGkoUoaUtmzcvi1AKi3Qzr/QXvnKGYcrfNS/HcMoHN3GtNCxQIqI6XoMhyks1igSZO0NeTQ4bLGR+hsHG1D68l8+f2mZMX1G4jsZ6C4B8ByoweUXYwyqYd/Aqm8krakhv2K7OXiGiew9h2o33vOmaSAOVrLuCzyhHHTc814UNdk6aGoFj2XHZFvobJVrRdC8QTUMJX/wrXB+Kqa/ZZvU+DcUyeQjbTcBkjVL3mL45779Wf9wN0Y8QemyCTV41inrpDW4on/9FqQbWmErYNRRLZ3HS1wsss1PeOT5vSisOm2YXKIUmr2ijmauhhIQN2yZFRh9JiInBezzO98a96wIK6jiXX5xqKoPt87XRthVRdRgmlPX5B9PVjRfeY7atWewCxSx0bU0mivZRilPe+0ylOvWj+PzCzFruSnnTzuMyzUhRXoayRtmI1QabvGqUwpXygfO5/Hm7D8XcQGwLpmxhwwmiSDPdgtmx/RbffWEzQLtTXuYZuMz2xcsoW9d4r9Nd5jUBBB23Nu0jeL8ufWH8Tx5V/rC1PGFPaFvJ7vcThYcV2zScsPNRNcUgUTSUsLTVO2gM6Wu26Ca/FqfJPxe+84XpHebTd/5G01AMAkX+HYnhlGeTV41S6JT3vyjVQBuTiZB1KOrasHzkX4M5xBo2HPETwqa9wmwoc0f41uvRO7P/eHhZbHUQTF+XzkiIhmIbSAHg83c8n7/G4r8wRyApk5d5g80w/BqKWYMATBpK/rcuR1uUV5iz2lQO03HTNWGTBvUOWxqTyOSEdcW+Nn1P+y3JjxTRPLth1wFrezWavNR+YHGc8ixQao+tPQN4dK2z4N+0l5fqGKEmL6V9RFilHhwHbA5rn4ZiTL2wvO79EdudaWYWZSB183YHpKDQDB+obHWQTyf/N5iHV6AEB60oIb+PrN3l/taWw3PIZkoJDva2XX7dfC0zfJ9JSbd5pHewtAlFncAyaNrvu+5pAMCEppRMx1D+rLf8BpNRBIHS2pg05mMbSKMKTdN5mz/Ue/+2fYOhZTFPJOIvbIzyCeJyEmdh47jh/J88gX6putr28opi8gp7x/koo8BgG0jDREQFJTTK6/RDp1jvC6rS3tQiR3kZjhvDal0Nzzbj8wsG79pDb+c0+cGCaRjz0Q7G+nJ4MQ3IUbfYsM3wvcnoN8C0zd6jzc6DAnHFlr0AnL2vDgybhYI/xF5fhrC6cDUUudYjk8shmUj6rrGZRaP6iQDTWp3woABvmnsHRjBX7i+nS9PsQ3H+xlmcyCavGqTfYwdtMCxsdE1eqRCB4g6Y5pdsDinVCxr3vByeiaKtSQkWwRZKmi+fivLSlw+IMOBbBK7RjKA+wFWEQAmmFaqhWGalhfmE5216DWnXbCgCx71mGDPeGX6YBhFMU1cuvcAI9xN5Bb/uXajNFO17mZn7Qlik2kjW6Y8tSkMJcZqbsGmDfi0qTEszCRRgensTAODAUOFaFP87CC1qvHUoLFBqG+VDCb4nr4aSE4ZGmhO+vzpM17iz8wqZvHyzWsOMKJcTnrUgZg0lcvRP4LqwFdje46b6DV4HFD7nSDbnzmzDfChRFhnq3oWvHgxpZA2h16VoKDahtr13qOC81dxjmRx4j+n2Emsy1K/ufqPJSJivGdZoKEGKccprTVoyzQSZzodP8HJCuKY/XaRXFD+SSrueTF4sUIrEtAPozv1Ox1WzM+0aB7cRmtM3aihuGkUXWUuhwPLMag2dxPtMwciTKHsj5dORDxGwz9nWgNgW9HnLYnpPw5mca3sv2O3A51sIeQD3msIy+Muov8/0tT/bLr+6fG1RXrvkFzp9+Xg1oZAIJqA0gZLfnqjglHu/ej/GNVshWpbXKW8qo20g9QqhMC3PmSCa0w/zobSFCBS/JqtPQ7WTwZH4Gorhm25lhwVKkTQk9Or8JbcuB5DfFC7sOxVhYcOqs5sGfJsZIZWMFjZc6EPxnrObIcL38grPW80eE4aNFW1RXkC4Yz6bE+6gFuzwI5mccSCy2c2D6Lde8Q7Ghno0mLy8g6BuoNaV02byOjBcuDmhd8Kg1VAs5fCmP5wtHCxt3yoJez9uubwCJVP4DgEYJwZA3iwGhFsLnHIW5u+uK0smtP0pbein3vK3NckdhzWhw1F2llDt5+a/btKej4LyoYySPGGBUix5k5e+ESg1POw7FWGTJ9MKXJWdLW5f97lSHWFOedPszisI43wC2DRrMm3d7+bhG0jN6edE/hsWwWcZ8Woohu1zTGaOILaZscnsko+UC5q88v//2t2rQvM1BYcA/vrv09jvs758Ne3Uc/+IRmDYNJThdASBktJHS2rzCGooWSVQUsY0VBlM573vKUzgNBh8orb1YjmRj3YbsmgoRk3WU8btveGRYibUc+q+OlkJWKAUiduRDQ1JOe213/p2B0z7wsbCKC/9cYV7PKIPJWylvMlm63f2BjUo7+9oAiWoodhWYPv8PIY6VHk3uDPgwsGouSEJosL3kNfy9GaOwvKED+amejRtfe6t0/W7DoTm64avawfD/G/dFwNtPhTvYKu7P6wdAPlt38OCU2wCxfveCgRKBJOXbwGr5rz33dg0FF2AjDvxM37OWrgCT7e4MYrJyzvh0PnCopDvayXdXjQsUIokJTuyqRGYZsZAvpGH+UFsGweGzYiSCWdz+SjmGpPTX3cuWDbA8oEti8k3b/Ly36+SsEV5AUDvoP47E6qITYYBaySTQ2MygVSCjD6UhgRF1FAKj3nTNK+ido4HB5rITvmcQEPS7IPwvhudQEj7ZueF6Xtn1FqTlxCudqk7f+EbDjKmDTh1ZDN5ecsY3MvKNXmFOP+9z60T7F6/hm5yoiYbDalCE7LPx2Q0z3pMXjoNJZLJKy94w0ygYeRNXqyh1CQdzQ0AzINmY4iGohpFFA3FGOUVMutLUHTnm2lzSyKzqSYbMlh6ixXV5OW17NoW6wFOJ1Qd7NU9A/prXA1F/6nikUwODckEkhqBkt8NIWEUyrMntbiC0LZtiWkNgCrT3oERXz5Rvx2ezSFUQ/ELFJ3Jyjs7L7x/0CZQcsI17XrPz5rYjPedOAdnHjnNvU5HLifcT+TaNlINPg/gNXmF+FBCdkQAgMF0zm0jYVpcQ7JwoXKkKLWcQCqRQGMqYXXKh5m82uQzRvk2vQ5Vj4lRGulZoBRJR0uh3fYt33vE/d3apHwoYQLFPHDkP23qvyZsK211PGhCCiOoqquO0ZxKGs1JXrt0uiBs2K7Cu3m5H5jSz/RMtwvkY/tNX8LzrgcCCjt8OptDYyqBVCJRIGy8g0jYXl6dbY2+vHT5A+YtM1T9DmdyPi0l6hYb2VzOsx5KU0bP4KMTCDYNZXAk606MdELRJ1Cy3pm+QGOKkFARXCZTTgQNJczk5YYNuz4Uc18D9BOkoZEsJrc671Hn41BtozmVRDYn/EECai+xlHmbJWUxaGlIYkhj8oqyb1wml3MjxUrVUNKsodQ2x82eCMDfCLb25B1mp853VpmH2W3DBMregRHtNaqBh836lMkrCkGBp9JtbigcaBUD6Yx7TYHA9Gko0fI2CZGw/ZEmtToa4v5B/YeLXMGo1igEyrlxdz+InM5u8qE0JM0mr5FMzk3bpqGYB8v88Z7+Eev1QdJZ4UYT6ga0EYvJS4W4A/pnGBjJYqKsZ5OGouoguHtzKpFwJzZhu0qnkiSDHyKYvAwLQFsazELJ70MpfIaBdMYVKN6JkltGWS7lp/FOoPLCJmHcSyyXc8yCrY3JksOGM1nhamFxBQr7UGqQ9544G5NalMmrsBGcfugUo81zZdc+7B1wZtXGTfNywhVOwTF9xNVcDN+4EHknd5Sw4eCsTzW8plTSOLD1DzsdY2JLQ+FKeV9ZbCYvKRwNq5HDtv+f1OIMAkYNxTOzBAJrZzI59A1l8Ni6bq0PRY0ZyQQZO/lwJhtqaoli8sqaBEpEDUUFFgTTUiifQ0tDUjsQ3b5sa2j6g+ksJraECxTdQJfO5pBMkBtpaJKPmZxAkkhrdnSvyfrT9ZIPG07Ja8NNXrrzgyNZd3IypDELqnLpojbVuSZ3YlFY/qxwnrGlIYlBncDK+a/Vkc7l3GfURdvZ2LKnH/+7vAtAYQBMpWCBUgQLZnWEqvN9w+m87T7Qyj5923L3tyky5LYlm93fwZmnf3+lwnuzOeVDMTcc7yAZnLmqgamlMWkc2NTnTCe1NBZ08ihbkivUvSbnpul2IZxQzASZnfJqJqls9N56884UHQ1Fb1Y0rT0AnIFKCTWbKcOk6aWzAlOk2cwbvRNdQwnXktTg19aUss5stT6Ukaw7cdKavIRwZ+5BX0WD1DzU/015JhOkfQf5Z/AIFINT3t16RfMMXt+Rzvw8lM6FmrzSgTy8ZVDtxBT4ocqUSBCaG5LadSi2KDNVRiXYdVqUjc/f8TzW7exz/sMaSu2gnIyfeNN8d/ala0Srtu03hg0fNbMDAHDkjAlGB9vL8uU3pgpNSl61XZe3kA0YMG/5kQ5xdLqzrlTCuFLeq6EEO4nXfGTTkHSmP9t3zFUZG1IJtDc3aNdXAPlBXLctx7AcOD539hF6DcV1ypN2kNo3MIKcADpkJ+8b1qzxCImEy1+Tw9Gz2gHk33nw3rccMVV7r0pXmbzCQmbbm1Nap7wX3bsaSGesGop25p4VSCYS+UlXiMM9mSCkEiHfM/EcHzZqKNGc8rrzAyMZTG6TGopmsFbPpdaSeN+lamOhAiXnaAUtjUmtwPKOASZteGjEoymWsEXGTu8uCdHmKrFhgRKBb7/3dXj839/qdIKkeUvpL//d0e4354PnG5MJHD2zHemswJ7+EbywdZ/v/Egmh98945gizjt2pq9DCCGQzgq0N6u4ds1AJlVsJ2xY/xxe1T44UKhO0dRg11CmdzShfySLfQN5c03ap0FFM3mZhKTZrp5DQ4LQ0ZLCfoOGotLUmYTUwHFwZytSml2h1WOnEgntrPE7960FADy1oRsA0G8VKGbBOLm1EY2phC8NNfCdePCkUM0incn7MMKivNqa9Cavtsak+1VPvYaSc30opnUoroaS9YffphKUn1SFRCQmE44mYxIo3glL8BlGsjkQef1khWl4hYQpbLi9uQHJBGkHfPVcrmlPs66lKWV+B84OyJAmL51AyR8z1oHF9GgjrSlzpWGBEoHZk1pwyBSnA6oZi5qte2cXHz71EDSm9OGqAyMZtDYmsWl3PwBg5bZe3/l/v+sF93dTKuFr5KrBTZvgRDgpX4yXnAASUuCZGk/Y+gKvozEn9LNLNfi9TgYm7POUw2aS85KPZMvnY1tsp/JIJQntTQ1GH4r7HO4WOIUmr+aGJFJJKpj1ZTzmMl0nV3mq2blOoKRDbP++50gQWhuTgSivvKkqLEw0nc3ld/Q1BH+kEoTmVLIgHSEEBtJZN3pIV9WDIxlMamlEYzKhrWedD0VtHJpKklu2YcPnb9VeXjqhrhgYybiLiIPPoNYSqXesG7APeAV1MMQ9JzCUdsyGzamEtq7Vc+U1FK8mpiYtUkMp2EInh5xwBI7J5OXNU1cF6WwOmVw+CKWUsOFdfXkNJeoap7iwQCkS1RFVg1W2/HMXzMCEppRRQ9k3kMak1kacffR0APlv0yvufv41AMC7jj8ITYFGPiR/HzPLMZutCggjwOkkCXJCXk0D2dCIebDLR3mZzQjdB0ZABMye3ALAvDgs8uaQyM/uvAJuKJ3FW7//KD516/KC+1LJhNRQTFFeYRqKEigJZzAPCIRhzyCSzuUKTBHquZTJ66t3ry7I/8BQBkRAR3NKq+mNZHLYsX8IyUQCrQ1J9Hu0zf6RDJpSCTQ3FAoCLwMjeYFgChtuTDlrIIITh+FMDkLkhWLwGTPZHPpHnMCDjpYGrSbomLzUtiJ+f1gqQe5Aa3qGoXQWTakkGpJknHkPjGTd8Oygr2pYPl+7XBPWpxF6XpNosC0rTb210Rnw9RqKkNcUmrzSrslLr6Go525KJYxRXl6fiK6/qDIpq0SxGkqw7cX5Ln0xsEApEqXOL9vcAwC4a4UTRbH6tf0AgAmyAQRndnsHRtDZ1oiff/hE+f/8+R89uM79PTiSLRhQ3vHTJwAApx7aCQCulqN4bd8gntqwG0miUIFyxvcfdX8HG2g655916Rrgrv1DmDqhSbstdyZgSw/DOytXA77SFjqaU9g3MILNewbw0Jqd/jJmhWPyajZrKOmsXzB6wz2HPBpKW2PK1TIVqpNPaEpBiMJZnfqvGmSc/Pz19Pj63UglCI2ppPZb4Dc9tQkjmRz6hzOYNakFXXvzIefdfcOY3tEkJxT62f2KLT3YsX/IuABW1UFDMlEwMfHWgRqMg2a578u2uGl3P3YfGHbNsG4d5ASGMllM73C0ZTWYq/aSTCTQmAxfjNc3lEFHc8pZo6F5zlxO4Mn1u90Q+uA1AyMZNDck3cFW970R76aYwcFVtb+WBiVQzBqKWu2uWyjZZOgrIx6B0taU8mlLCq92q6sn1bdaGlNoTPrf4+rXenHjkxtD13vtD9RJTug3qSw3LFBK5Mn1u9E/nMG37l0DALjq748DABw0sQXJBGHN9ryztW8oje29Q+hsa0RLYxJNqYTbWQDgmkc2uL+J/CYvIfKhxGrGE5w1fuAXS9C1dxBZ4UTZmMIkvQQdnQPD/oFGp6Gs2b4fMzqa3MF6SCMYAP0Ot17Weuom/zlcpzzT2puMEVyZrNJQ9DNnID9gKpOM1xyhNL3mhgQmaDq6GsSVwPTWQXffMBa/5Ai4D50y143m230gb1bYN+D4xtJZgQlNSew5ULh1vIrk6x1MY3Jro28m3d03jGlSYJuCDr7/gDPgb97dL8Nug6bLHG55ejN6B9NaDUUNVJ3SIR2cPT/58m4A0A6CgDO4CwFMamlAc0MCPf3Oe1Dtq7Ux6Q60upk/4Ey22psbjOagnX1O5JsSdsFrNu8ZwCGdre570tXVgeGMK3CCbXmznJDNntTi9DWNUHt1j1O/sya2yLJ4NBRLlJca/BtTSUxoSmoF3jNyQjqxRR9goqwJLQ3Jgvf4L799Dt+6dw02BiaWXr5w5/MFx7xjTqVggRKDe1dud38vOMgxR7U0JnHMrHas3eEMmkIIvO7rDwIANnY7DaCzrRF7+/UvN0nkrgXJZHO+mclJh3RqB0L1zeqe/hGkkgltRMjWvf6tSoID8r5Bpzwz5MwzGKa4+8AwXujqxVlHz3DNJd7BSHUyIr9vJcgfn+vCDs/COuXoV1uUT29vNvpQ0nJBXIchymtgJIPP3e50JBX2mtFoKE2pJNqaUj5zE5AfCNzVyZ56XNmVD6I4/7iZ+OlFJwDwL7D0vpdjZ0/Mh2x6n0EOkjnhrCr3DlTdfcOY1t6Eae1N6Okf0Wofew4476mzrRFtjckCLcu7qWRjKlmg6ajBeYr0xwUH6yNnTAAAfPjUgz1lzpdR5dfalMJh0yZg7Y79vmef0JTClLZGtDYmsV7z/EII9A05g32LwRy0ebfTVj/xpiiKlM8AABp2SURBVHkACqOwuvuGMXNiM9oaUyAym7yU/yGoRar+Mm9qK5oaklpfz96BNCa2NLhpBE2yANBm2O04384SmNDUgMF0tkBL+sOz2wAA86e24YCm/H/buAeAI1CaUglf8IMSJLv7CicsisfWdfv+f3Bnq1HAlxMWKDHwDjhqbQLgvLyuHqdTvLwz38FPkyarSa2N7mxhxZYeX5pJjw16JJtzB86vXrAAh0+fgPZm8+w1nXW2tNCZvLYE9r7ymjKG0ll85Y/OdumHTnUGlD39+cY6nMninB89DgB4/eyJboSPT6DIDjelzaxhbNnTj8/f8YLvmBI+qi6VKSWIEI6AbZQ+lL7hTEFHfuLl3XhV1vtkaX/3mp2U8GptTGJCc8pndti0ux9LXnE6sZrZetceeOs8lUi4GpBXiHgH9/lT2tC1d9AY/j17UovPPNk/nMG6nX3YP5jB1AlNyOaEdkapBMZZx8xAe8D05yyMzb/nphANRa2DCQYWHDTJ0bDPXTADX71ggay3/HOpyL6JLQ2YNbHFNd1+T2pOE5pTSCUTmDelDX/yTLi8+WdzAh0tDU4ElEZD+dD//A0AcP6xM9GYLNwLq7tvGNPbm5FIECY0pgrCt4fSWWzZM4A5k5zvuAc19m45EE9rb0ZzQ0Jr8vrV37agp3/EswzAE9wx4vdvBNuheicdLQ2uCbw/8JwnHDwJ09qbMGtis7Y/f+n3K908dJom4Gi5uw8M473X/tXVuoD8TghvPWqae+yJL70Nh06bUJBGuWGBUgLXf+wkAH5fhlohDwBzO1vRtXcQuZzA5j35ay5583wAjrmha+8gFr+0Ey9J34tCmbwAZ2amZl9qAJjQ3KBd/6BoSBL2D2YKBjI10B4xvbBR/fejeZObcrjv7ssPZis273UHjtmTW1wN5eE1u9xrVIc5aFKzUaB84pfL3N8/+MDxAOBqamqWOKOjWXtv33AGOeEMZMosFzQleDv2YbLz9MoBcNW2XvzPE5sAOEIvqOm97QeP4Y/PObNGXWSPd3BPJcm9Rn38aOnGPTjvJ46v60vnH4V5U9uQzQmfjwQADul0Brkr/u4YNCTz29wo7XXe1DbMnOjUwTOb8pONkUwO7/75U+7/P3rqwWhvTvnq4OKbl+LSX60AADz4+TPQ0pAsGMjULHWOfM/dAbOcMhURkbsxoTdMXa1tmN7ehOkdTdi8ux8jmZyrrc+U7++l7fvR0z9SoD0oja5d+lB0GopiyoRGOeDnrxkYyeDAcAbT5J5uugnWfS86ZTl53mQAfi21dyCN57fuQ1MqgY7mFCa2NBS0V6+pUpk2vRqCErDuosPAYK/Sm9jSgAlNhRMPwLEmHGGZIAKOBtPo8YV5J6B3Lt+Khd96CM++ug8/eehl9/jnbn/Ozf+OS0/DHZeeZky/3LBAKYEzjnAk/01PbdKenzu5FSPZHHb2Dbkzxoe/eKa7in1yayPW7ujDp29bjj1yQD11vqO9JBPkOn137h/ymRIAx2ntbfDeQfSDC+dgclsjegfTeN3XH3CP/+HZLnzzzy+hozmFBz9/hivYlFNvj8f8NlWaQry+Ae+gc9i0Ce7MTA3AAPCLx14B4PiQdhlUcaWqT25twAIZsaZ23P3V37Y4dScHXIV6/n3SVj+ptREdMv9XPbPx7r5hXPbbZ93/HzWzHURO0MT+oTQu+NlTeGn7fiTlOpa2xhSG0jltJJZaQe0dBLxBFA2JBOZNdcLI7125Hcs29+Afbvibe/7cBTNdwf2iJyJv8+5+vNDViw8unINp7U1o8Jgnd0m/wQcXzsEb5k4CAHz2N/nnWbejDyu7nLSOntkOIvL5WvYNjOCvG/a41x85ox2HTGlF72DabS9rd+zH+65bAsAxeU1ubfAJvO29g7htyZaCVeJezUuVc3p7E06YOwmD6axrQlL17sWbvhACn7jFmVS0NzdoTV4nfnOx+3velDafn2VgJIMFX3Pa9UGTmt10gkLrC3c6WvCZcobu9TX8x+9X4s8rt2PqhCYQETrbGn39qad/BF//00vu/1VEn1foqMnFTOlfCX6GQAnNiS0N7u7k3jVbQgg8v3UfJrc1oqO5AfsGA7tOZ3Noa0ziQ6fMxdzOVuca2f68Gv5Dngnd3oE0hjNZ3PjkRvxtoyN00tkcTj10Ck49dApGi7oWKER0PhGtI6INRHT5aOXb0pjEmw7Pv6R7/vlNvvPz5JqV/350A7r2DmJCUwqHygEIcMwdiu37nA6qXnqSyFVV739xuztgKNV5275BrH5tP+Zdfi+29w66neGL5xyJb//969wG7FXjVQcbzuRARK6f5Glp4lH+lK9dsMCd+XkHa6WJ3f+5t6AxlcAkOeBe8PpZAIAVW/a6WtPr5kzEpt39vu3ll23uwbzL7wXgLNp79qvnYOoEJ43fLH0VK7t6cf+qHQCc/dBmTWzGoXLh3Rap4Skf0OTWBnfQetfPn3I74gU/e9LNb8V/vh0NcvuUF7p6ccktec0omxPO7FvOHNUM/nCP5jan03k/Nz65EY+udTptr2dAaGlMYuqEJnzstEPQ2pjEB36xBF4Onz4Bxx7UgeaGBP71d8/hxic34u7nt+GtP3gMQF5oNybzPhQlDKZ3NLvrjQDgsXVO/tv25etTaV/tzSn0yQCI3ZoAgMPkM6n392sptAFgRnszZk9uwTbPgH/6dx7x1UlbY+FCWhXNOL2j2V2b9eR6x17/juNmugEbv77kVAD+dvTc1n1Ys32/W/agyetvG/e4e5stPGQyUsmEM+BL86sSqEB+TdbElgY8sHonjrvSETRev+Yb5k7GUTPasXzzXvfYX1Y77UwJwakTmrCnPz+gf/+BtfjTC04I/3++85j8BEtq7Lv2D+Frd69Ge3PKbaNes+ETL3e7E5uOlhRmyb6u+jngmJ+zOYF5U1oxt7MVQ+mcb9L2Snc/+keyOEVOMg+fPgEvdO1DNifwujkToWP55h5c//hGN0gIAD771sO111aSuhUoRJQE8N8A3gFgAYAPEdGC0cr/V5881f2tOpbiDQc7M8xf/+1V3PL0ZhwYzvj22JrjmYXfsXwrUgnCabLxvG7OREzvaMbUCY245pEN+MiNSwHkTV6neWYbp3/nEdwhN/o7cmY7UskELj3jUPf8ii097owSAH75iZMBAG89ylkL85Ebl2Lz7n7s6hvGyfMm45Nvnu+Gc/5o8cuuqWHFlr2YP7XNXQcDOLPkJ17uxp3Lt+Kp9U5k0EdPOxhvP2YGAODBl3ZgOJPFcCbrM9385wULQERuR31y/W7f502ntTdhyRVn4xrp9F61rRf7h9JuPbQ0JPH6OZPc69WA5d1mQmkYimWeAUUJQaVl7RsYwaNrd2GDx5mtIntuX7YVn7hlGbbtG3Q1lIe+cKZ73cnzO7Vf4wOcb6pMaXOe8Vv3rnGDBQDg7GOc+m9uTGLfQBp/XvmaKzBndjS7W5cAwMd/uQyf+fUKbNqdH5iVs3rWpBas2rYfO/cPueU7aGIz7vqn0wHkAxMWr9kJIQRWbMkHFhw8pRWzJ7Xgle4DWL7Z78f73vteDyA/O9+yZwD9wxk8tX63q5VPaEph3lSnHX9Nrsd5zbMv2QkHT0IyQXhw9U7sPjCMpRv3+My7Hc0pHDZ9Arb3DmFjt1P3yzzt5NefcvrX7Ekt2LbPCZP+P49GrCYAavuUA8MZn5b6ruMPQjJBOGRKKzbsOoB9AyM+LeNnH3La19QJjRjO5LB/MINMNr9bxRlHTsOn3nIoOpqdsF0lsK+VmngmKzBLmiZ/8fgrbrretVOdbY1uv+2RE5JMNod/u8NpC+84bpar6W6SJs90NueaThfMcoTHW46Yin0DaVz76Abcu3I7jp7ZjmNlENDTl5+FfzzzUPSPZPGjxXmz1yNfPNPdGX00SdkvqVlOAbBBCLERAIjodgAXAngp9K4ykUgQTpnfiWc29bi2ZsWEphSOmD7BdaAqW6vigwvn4IWt+9w1LIdOa8MbD5+K+/71LThG7vH07uNnu/Z5AO5M5xvvPhYbdh1wB+kfykakfA8NyQSu/ciJ+OxvnsX7rlviNvrL3nYY3niYsz/UkTPa3Y351Kz5vSfOBuBsLvmFc47EVfetwdFf/Yub/zkLZvieYWJLA9bu6MOX7lrpHvvWe17nzri/de8a32wJcHxPJx482a2/i06ei9uXbcXVf3Ecus9+9Ry3ro49qAMzOprwH79/0T0P5DW5tx8zHQ+t2YUzv/+YL4/zjp3hDshB+/RX/u4YfOotjrlvvgw+CN5/cGer619QvOm7j7i/vZrM/MBEYtbEZtzyiVPc/yc1e4b/4qMn4aRDnMnDCXOduvjn3zo275PnTXbv+f1nTnfNU/ev2oH7V+3AxJYGPP+1c9zJidJ0T/32w276v/30ae4gpXxN1z++EULA1Q6+8nfHAHCE1wOrd+L9Hg3rvSfOxgdPngsAOH6uMyD9y++e8z3Du493vsjo1aQA4FY5YQGcSLmTDp6M3z/bhd8/2+W77g1zJ2HBrImY29mK796/Fmf98HHf+bOOnu5qOp1tjXh47S4c9uX73PNrvnG+a447ZlYHHljthHOffNVD7jVXvmuBW45Xewbwhm/kTWmLTj8E75LPoNrB8d94EItOPwSAI4y+/35HqBI5Oypc/8RGXP/ERjeNmz6+0KOh7caPHlyHR9btck2Yp8zvRFMq6W5h86W7VmL55h7sOTCC57fuQ0OScOxBHa5J8Cv/twqXve0w15cGwN0e5+R5TntRff20Q6fgP995DASc/v7ZMw/H9Y/ny/bO18/C/Kn+tjla1K2GAmA2AO+qqy55bNT47adOxQtfO9f9LLCXP/3Lm93fN3/8ZN+5plQSP/jA8a7z/dqPOIsdFxzU4Q4WX73gGFel/vzbj3R9KKlkAnf+4+n4w2ff6KbX2pjE0R7b9ULpjATyu9kulI1SseTys1y/DQAcNSN//6I3znM/ZKX49FsO9f3fqwkBTiMGnAb+EU/IqWJGRxPOO3am79g/nXkYAMckc9IhkzG5NS94icgduJQZ5NmvnuMOuD+96ATXPKf43adPwy8+epL7/6vf93pfOS884SC3fpXg9vLAv52BJ770NjQkE3jmK2cXnPeaOYFCf8EXzjnSd+zr7/YrzMfPnYTzj8vXwdulpqKY3p4PSDjpkE78+3lH+c6fPK/Tp+kqX56XQ6bktd+5nXnBeIMcDK94x9H4tKyTs47xTxIAv5BsSiXdmbCXa+TsnojwwpXn4vRDp+B3nz7NNYUqrvvoiQX3fuJN8/B/l70JLY1JTG9vdv1Fio+/cZ7vHZ4caLfnHzvTFSaAX2NXPPb/3upqwN76BhwT19fedaz7/1M8feDWJY5J8HNnH+EKNAD4+rv87/FrFyxwJ2f/79wjAThryVZtcwT2ZW87DLd90plYtDfl5+x3Lu/Cw2t34fg5E3HPP78ZRIQ5k1vQ3pTChl0H8Pk7XsDPHtmA2ZNa8MKV+XFlbmcrTpBWjyltjfjsWw9DKplwI9AmtjbgoS+cgVPnd+KFK8/Ff3/4xNBdxysJRfn+eC1CRB8AcJ4Q4lPy/x8DcIoQ4l8C110K4FIAOPjgg0/asmVLQVqV4qXX9mPdzv14zxtma1/wUDrrfo62FHb0DmHznn5tp3ropZ3oPjCM25dtxVlHTce/nn24tgz3rtyOZAI4Z8FMXzmEcPZmIkAumNTPPVZt68URMya421Ao9vaP4Nv3rcHASBYff9M8nDB3klbw7uobwl0ruvDBhXPdQcBbhlXb9uP2Za/i9MOm4ILXH+Q73903jG37BvHImp1429HTccLBk6Hj6Vd2Y2ZHc0HY5HDG+TLhM5t6sOCgDndGH3yOs3/0OBadPg+fPmO+b5U84Phk7l+1HY+v68a33/s6bT09vWE3Xt7Zh3cdf5C7/kPRN5TGpt39ePqVPbjwDQe55rYgyzf34Pi5kwrSH0pnsbG7H4+s3Yn3nTRHe/+a7fvxjp8+iTOOnIYbL17oi0jcfWAYT67vxo8Wv4yT53Xiv959rK8ehBD443PbkEwQprU34agZ7QXPEMaeA8PY3juEdTv6sLNvCJ858zBfOxzJ5LB3YAR3LNuKc4+dgaNn+gVYNiewflcfEnLj0yNm+IW4EAJ/WbUDHS0NeLVnACcePLlA0GdzAhu7D6CzrRGZnNBGEr62bxBPru9G31AGl7x5fkFfEULgTyu3o6M55ZqMAcdkev0TG5HO5HDW0dMxe3ILDu5s9d2/YdcB3LZkM846ejr6hjJ4+zEzfEJxe+8g/rJqB9bt6MPh0yfg/ONmYs5kf3CKqivvuxtNiGiFEGKh9bo6FiinA/i6EOI8+f8rAEAI8R3TPQsXLhTLly83nWYYhmE0RBUo9WzyWgbgCCKaT0SNAC4CcE+Vy8QwDDNuqVunvBAiQ0T/DOABAEkANwshCrd/ZRiGYUaFuhUoACCEuA/AfdYLGYZhmIpTzyYvhmEYpoZggcIwDMOUBRYoDMMwTFlggcIwDMOUBRYoDMMwTFmo24WNpUBEgwDCQosnAugNOX8wgFct2djSqPR5WxmrXT6g9stY6fKVI42xXoflKEO91yFQO2U8SghRuF9RECHEuPkHoNty/oY490dMo9Ln4z5jRc/XQxkrXb56KGO1y1cPZRxPfQXAcltZhRDjzuS1z3L+TzHvj5JGpc/HfcZKnwdqv4yVLl850hjrdViOMtR7HQL1UUaX8WbyWi4i7EdTqftHAy5jfGq9fEDtl7HWywdwGStRjvGmodxQ5ftHAy5jfGq9fEDtl7HWywdwGYshUjnGlYbCMAzDVI7xpqEwDMMwFWLcCxQiupmIdhHRKs+x44loCRG9SER/IqIOebyBiG6Vx9eob7DIc48R0Toiel7+m67Lr8LlaySiX8rjLxDRWz33nCSPbyCia6iMn3QrYxkrVYdziehR+c5WE9Hn5PFOIlpMROvl38mee66QdbWOiM7zHK9IPZa5jGWvx2LLR0RT5PUHiOjngbRqog4tZayJtkhE5xDRCllfK4joLE9aFevTJRMlFGws/wNwBoATAazyHFsG4Ez5+5MAvil/fxjA7fJ3K4DNAObJ/z8GYGGVy3cZgF/K39MBrACQkP9/BsDpcD7CeD+Ad9RgGStVh7MAnCh/twN4GcACAN8DcLk8fjmAq+XvBQBeANAEYD6AVwAkK1mPZS5j2euxhPK1AXgzgH8C8PNAWrVSh2FlrJW2eAKAg+Tv4wBsq3Q9xvk37jUUIcQTAHoCh48C8IT8vRjA+9TlANqIKAWgBcAIgP01VL4FAB6W9+2CE3K4kIhmAegQQiwRTku8DcB7aqmM5SqLoXzbhRDPyt99ANYAmA3gQgC3ystuRb5OLoQzcRgWQmwCsAHAKZWsx3KVsRxlKUf5hBD9QoinAAx506mlOjSVsZKUUMbnhBCvyeOrATQTUVOl+3SpjHuBYmAVgHfL3x8AMFf+vgtAP4DtcFav/kAI4R1IfynV469WWP00le8FABcSUYqI5gM4SZ6bDaDLc3+XPFZJii2joqJ1SETz4Mz6lgKYIYTYDjgdHY7GBDh1s9Vzm6qvUanHmGVUVKweI5bPRC3VoY1aaIte3gfgOSHEMKrTp62wQNHzSQCXEdEKOGrpiDx+CoAsgIPgmBm+SESHynMfEUK8DsBb5L+PVaF8N8NpWMsB/ATA0wAycFTiIJUO7yu2jECF65CIJgD4PYB/E0KEaZam+qp4PZahjEAF67GI8hmT0ByrVh2GUSttUV1/LICrAfyjOqS5rOohuyxQNAgh1gohzhVCnATgd3Ds04DjQ/mLECItzTV/hTTXCCG2yb99AH6LypoftOUTQmSEEJ8XQrxBCHEhgEkA1sMZwOd4kpgD4LVgulUuY0XrkIga4HTg3wgh/iAP75SmA2WK2SWPd8GvNan6qmg9lqmMFavHIstnopbq0EgNtUUQ0RwAfwRwsRBCjUWj3qejwAJFg4roIKIEgP8E8At56lUAZ5FDG4DTAKyV5pup8p4GABfAMfmMavmIqFWWC0R0DoCMEOIlqUL3EdFpUnW/GMDdlSpfKWWsZB3KZ74JwBohxI88p+4BsEj+XoR8ndwD4CJpq54P4AgAz1SyHstVxkrVYwnl01JjdWhKp2baIhFNAnAvgCuEEH9VF1ejT0ei3F7+evsHZ/a8HUAajtS/BMDn4ERfvAzgu8gvAJ0A4H/hOMdeAvDv8ngbnGillfLcTyEjbka5fPMArIPj6HsIwCGedBbC6RSvAPi5uqdWyljhOnwzHHPASgDPy39/B2AKnACB9fJvp+eer8i6WgdP9Eyl6rFcZaxUPZZYvs1wgjUOyHaxoAbrsKCMtdQW4UzG+j3XPg9geqX7dKn/eKU8wzAMUxbY5MUwDMOUBRYoDMMwTFlggcIwDMOUBRYoDMMwTFlggcIwDMOUBRYoDFMjENE/EdHFRVw/jzw7PDNMtUlVuwAMwziL6YQQv7BfyTC1CwsUhikTcrO/v8DZ7O8EOIs6LwZwDIAfwVkYuxvAx4UQ24noMTh7mb0JwD1E1A7ggBDiB0T0Bji7C7TCWbj2SSHEXiI6Cc5+aAMAnhq9p2MYO2zyYpjychSAG4QQr4fzaYPLAPwMwPuFs6/ZzQCu8lw/SQhxphDih4F0bgPwHzKdFwFcKY//EsC/CiFOr+RDMEwpsIbCMOVlq8jvufRrAF+G82GkxXIH9CScbWoUdwQTIKKJcATN4/LQrQD+V3P8VwDeUf5HYJjSYIHCMOUluJdRH4DVIRpFfxFpkyZ9hqkZ2OTFMOXlYCJSwuNDAP4GYJo6RkQN8tsWRoQQvQD2EtFb5KGPAXhcCLEPQC8RvVke/0j5i88wpcMaCsOUlzUAFhHR9XB2jv0ZgAcAXCNNVik4HxZbbUlnEYBfEFErgI0APiGPfwLAzUQ0INNlmJqBdxtmmDIho7z+LIQ4rspFYZiqwCYvhmEYpiywhsIwDMOUBdZQGIZhmLLAAoVhGIYpCyxQGIZhmLLAAoVhGIYpCyxQGIZhmLLAAoVhGIYpC/8fZ04o0Do6DDMAAAAASUVORK5CYII=\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 mieux la situation des pics en hiver. Le creux des incidences se trouve en été." ] }, { "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", "entre deux années civiles, nous définissons la période de référence\n", "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", "1er août de l'année $N+1$.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", "de référence: à la place du 1er août de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er août.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1985." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", "\n", "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." ] }, { "cell_type": "code", "execution_count": 15, "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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "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": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2021 938731\n", "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2053781\n", "2012 2175217\n", "2003 2234584\n", "2019 2254386\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\n", "2018 2705325\n", "1988 2765617\n", "2007 2780164\n", "1987 2855570\n", "2016 2856393\n", "2011 2857040\n", "2008 2973918\n", "1998 3034904\n", "2002 3125418\n", "2009 3444020\n", "1994 3514763\n", "1996 3539413\n", "2004 3567744\n", "1997 3620066\n", "2015 3654892\n", "2000 3826372\n", "2005 3835025\n", "1999 3908112\n", "2010 4111392\n", "2013 4182691\n", "1986 5115251\n", "1990 5235827\n", "1989 5466192\n", "dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", " française, sont assez rares: il y en eu trois au cours des 35 dernières années." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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