{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse de l'incidence de la varicelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "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", "from os import path\n", "from datetime import date" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence de la varicelle sont disponibles du site [Web du Réseau Sentinelles](https://www.sentiweb.fr/france/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_varicelle_url = 'https://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": 19, "metadata": {}, "outputs": [], "source": [ "## On importe les données depuis Sentinelles\n", "\n", "raw_data_varicelle = pd.read_csv(data_varicelle_url, skiprows=1)\n", "raw_data_varicelle\n", "\n", "## On choisit le nom du fichier local dans lequel on stocke les données\n", "local_file_name = \"incidence-PAY-7_local_copy.csv\"\n", "\n", "## On teste si ce fichier existe déjà\n", "if path.exists(local_file_name):\n", " ## Si oui, on compare les données avec les données actualisées du jour\n", " local_data_varicelle = pd.read_csv(local_file_name, skiprows=1)\n", " diff = (local_data_varicelle != raw_data_varicelle)\n", " \n", " ## Si les dataframes sont différents, on réécrit le fichier local.\n", " if True in diff :\n", " raw_data_varicelle.to_csv(\"incidence-PAY-7_local_copy.csv\", sep = ';')\n", " \n", "else:\n", " raw_data_varicelle.to_csv(\"incidence-PAY-7_local_copy.csv\", sep = ';')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202105712379910715651191424FRFrance
1202104712026882615226181323FRFrance
22021037891363751145113917FRFrance
32021027779554301016012816FRFrance
4202101710525775013300161220FRFrance
5202053711978840615550181323FRFrance
6202052712012828515739181224FRFrance
7202051710564757413554161121FRFrance
8202050770634744938211715FRFrance
920204975026314569078511FRFrance
10202048766834312905410614FRFrance
1120204774999296370358511FRFrance
122020467375219635541639FRFrance
132020457369620165376639FRFrance
1420204474391237564077410FRFrance
1520204374376250562477410FRFrance
162020427400019796021639FRFrance
172020417396120995823639FRFrance
18202040720786753481315FRFrance
19202039710492371861213FRFrance
20202038722537823724315FRFrance
21202037715844052763204FRFrance
2220203679191001738102FRFrance
23202035782801694102FRFrance
24202034722723714173306FRFrance
25202033712841772391204FRFrance
26202032726506894611417FRFrance
27202031713031002506204FRFrance
2820203071385752695204FRFrance
292020297841101672102FRFrance
.................................
15451991267176081130423912312042FRFrance
15461991257161691070021638281838FRFrance
15471991247161711007122271281739FRFrance
1548199123711947767116223211329FRFrance
1549199122715452995320951271737FRFrance
1550199121714903897520831261636FRFrance
15511991207190531274225364342345FRFrance
15521991197167391124622232291939FRFrance
15531991187213851388228888382551FRFrance
1554199117713462887718047241632FRFrance
15551991167148571006819646261834FRFrance
1556199115713975978118169251832FRFrance
1557199114712265768416846221430FRFrance
155819911379567604113093171123FRFrance
1559199112710864733114397191325FRFrance
15601991117155741118419964271935FRFrance
15611991107166431137221914292038FRFrance
1562199109713741878018702241533FRFrance
1563199108713289881317765231531FRFrance
1564199107712337807716597221529FRFrance
1565199106710877701314741191226FRFrance
1566199105710442654414340181125FRFrance
15671991047791345631126314820FRFrance
15681991037153871048420290271836FRFrance
15691991027162771104621508292038FRFrance
15701991017155651027120859271836FRFrance
15711990527193751329525455342345FRFrance
15721990517190801380724353342543FRFrance
1573199050711079666015498201228FRFrance
15741990497114302610205FRFrance
\n", "

1575 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202105 7 12379 9107 15651 19 14 \n", "1 202104 7 12026 8826 15226 18 13 \n", "2 202103 7 8913 6375 11451 13 9 \n", "3 202102 7 7795 5430 10160 12 8 \n", "4 202101 7 10525 7750 13300 16 12 \n", "5 202053 7 11978 8406 15550 18 13 \n", "6 202052 7 12012 8285 15739 18 12 \n", "7 202051 7 10564 7574 13554 16 11 \n", "8 202050 7 7063 4744 9382 11 7 \n", "9 202049 7 5026 3145 6907 8 5 \n", "10 202048 7 6683 4312 9054 10 6 \n", "11 202047 7 4999 2963 7035 8 5 \n", "12 202046 7 3752 1963 5541 6 3 \n", "13 202045 7 3696 2016 5376 6 3 \n", "14 202044 7 4391 2375 6407 7 4 \n", "15 202043 7 4376 2505 6247 7 4 \n", "16 202042 7 4000 1979 6021 6 3 \n", "17 202041 7 3961 2099 5823 6 3 \n", "18 202040 7 2078 675 3481 3 1 \n", "19 202039 7 1049 237 1861 2 1 \n", "20 202038 7 2253 782 3724 3 1 \n", "21 202037 7 1584 405 2763 2 0 \n", "22 202036 7 919 100 1738 1 0 \n", "23 202035 7 828 0 1694 1 0 \n", "24 202034 7 2272 371 4173 3 0 \n", "25 202033 7 1284 177 2391 2 0 \n", "26 202032 7 2650 689 4611 4 1 \n", "27 202031 7 1303 100 2506 2 0 \n", "28 202030 7 1385 75 2695 2 0 \n", "29 202029 7 841 10 1672 1 0 \n", "... ... ... ... ... ... ... ... \n", "1545 199126 7 17608 11304 23912 31 20 \n", "1546 199125 7 16169 10700 21638 28 18 \n", "1547 199124 7 16171 10071 22271 28 17 \n", "1548 199123 7 11947 7671 16223 21 13 \n", "1549 199122 7 15452 9953 20951 27 17 \n", "1550 199121 7 14903 8975 20831 26 16 \n", "1551 199120 7 19053 12742 25364 34 23 \n", "1552 199119 7 16739 11246 22232 29 19 \n", "1553 199118 7 21385 13882 28888 38 25 \n", "1554 199117 7 13462 8877 18047 24 16 \n", "1555 199116 7 14857 10068 19646 26 18 \n", "1556 199115 7 13975 9781 18169 25 18 \n", "1557 199114 7 12265 7684 16846 22 14 \n", "1558 199113 7 9567 6041 13093 17 11 \n", "1559 199112 7 10864 7331 14397 19 13 \n", "1560 199111 7 15574 11184 19964 27 19 \n", "1561 199110 7 16643 11372 21914 29 20 \n", "1562 199109 7 13741 8780 18702 24 15 \n", "1563 199108 7 13289 8813 17765 23 15 \n", "1564 199107 7 12337 8077 16597 22 15 \n", "1565 199106 7 10877 7013 14741 19 12 \n", "1566 199105 7 10442 6544 14340 18 11 \n", "1567 199104 7 7913 4563 11263 14 8 \n", "1568 199103 7 15387 10484 20290 27 18 \n", "1569 199102 7 16277 11046 21508 29 20 \n", "1570 199101 7 15565 10271 20859 27 18 \n", "1571 199052 7 19375 13295 25455 34 23 \n", "1572 199051 7 19080 13807 24353 34 25 \n", "1573 199050 7 11079 6660 15498 20 12 \n", "1574 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 24 FR France \n", "1 23 FR France \n", "2 17 FR France \n", "3 16 FR France \n", "4 20 FR France \n", "5 23 FR France \n", "6 24 FR France \n", "7 21 FR France \n", "8 15 FR France \n", "9 11 FR France \n", "10 14 FR France \n", "11 11 FR France \n", "12 9 FR France \n", "13 9 FR France \n", "14 10 FR France \n", "15 10 FR France \n", "16 9 FR France \n", "17 9 FR France \n", "18 5 FR France \n", "19 3 FR France \n", "20 5 FR France \n", "21 4 FR France \n", "22 2 FR France \n", "23 2 FR France \n", "24 6 FR France \n", "25 4 FR France \n", "26 7 FR France \n", "27 4 FR France \n", "28 4 FR France \n", "29 2 FR France \n", "... ... ... ... \n", "1545 42 FR France \n", "1546 38 FR France \n", "1547 39 FR France \n", "1548 29 FR France \n", "1549 37 FR France \n", "1550 36 FR France \n", "1551 45 FR France \n", "1552 39 FR France \n", "1553 51 FR France \n", "1554 32 FR France \n", "1555 34 FR France \n", "1556 32 FR France \n", "1557 30 FR France \n", "1558 23 FR France \n", "1559 25 FR France \n", "1560 35 FR France \n", "1561 38 FR France \n", "1562 33 FR France \n", "1563 31 FR France \n", "1564 29 FR France \n", "1565 26 FR France \n", "1566 25 FR France \n", "1567 20 FR France \n", "1568 36 FR France \n", "1569 38 FR France \n", "1570 36 FR France \n", "1571 45 FR France \n", "1572 43 FR France \n", "1573 28 FR France \n", "1574 5 FR France \n", "\n", "[1575 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data_varicelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Non." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data_varicelle[raw_data_varicelle.isnull().any(axis=1)]" ] }, { "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": 6, "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", "raw_data_varicelle['period'] = [convert_week(yw) for yw in raw_data_varicelle['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il reste 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": 7, "metadata": {}, "outputs": [], "source": [ "sorted_v_data = raw_data_varicelle.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": 8, "metadata": {}, "outputs": [], "source": [ "periods = sorted_v_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": [ "Il n'y a pas de manquements dans nos périodes : nous n'avons pas supprimé de semaines dans notre analyse car nous n'avons pas rencontré de données manquantes." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEKCAYAAAD5MJl4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsfXeYHMWZ/vvNbNAqZyEUECARhAAJhJBNMCCiE9gGn3AAbGwwxj77fOc78O9wRHdggznjMxzYZDjCYWMwOUcLxBKVUAAJJCSUs7Rx6vdHd3VXV1fqmd7ZXbbe55lnZqqrq6q7q+urLxNjDB4eHh4eHiIKnT0ADw8PD4+uB08cPDw8PDxS8MTBw8PDwyMFTxw8PDw8PFLwxMHDw8PDIwVPHDw8PDw8UvDEwcPDw8MjBU8cPDw8PDxS8MTBw8PDwyOFms4eQLkYOnQoGzduXGcPw8PDw6Nb4bXXXlvPGBtmq9dticO4cePQ2NjY2cPw8PDw6FYgovdd6nmxkoeHh4dHCp44eHh4eHik4ImDh4eHh0cKnjh4eHh4eKTgiYOHh4eHRwqeOHh4eHh4pOCJg4eHh4dHCp44eHxs0dpewj2NK1Aq+VS4Hh5Z0W2d4Dw8bLjuuXdxxeOLUSTClw4d3dnD8fDoVvCcg8fHFuu3twAAtuxq7eSReHh0P3ji4PGxhxcqeXhkhycOHh9bEHX2CDw8ui+sxIGIehHRHCJ6i4jmE9EvwvKfE9GHRPRm+Pm0cM7FRLSUiBYR0UlC+aFENDc8djVR8PoSUT0R3R2Wv0JE4/K/VA8PDw8PV7hwDs0AjmOMHQxgMoCTiWh6eOwqxtjk8PMwABDRRAAzARwA4GQA1xBRMax/LYDzAEwIPyeH5ecC2MQYGw/gKgCXV35pHh4eHh7lwkocWIDt4d/a8GMS454K4C7GWDNjbBmApQCmEdFIAP0ZY7MZYwzArQBOE865Jfx9L4AZnKvw8PDw8Kg+nHQORFQkojcBrAXwBGPslfDQ94jobSK6kYgGhWWjAKwQTl8Zlo0Kf8vliXMYY20AtgAYohjHeUTUSESN69atc7pADw8PD4/scCIOjLF2xthkAKMRcAGTEIiI9kYgaloN4MqwumrHzwzlpnPkcVzPGJvKGJs6bJg1kZGHR9l4ZO5qNLe1d/YwPDw6DZmslRhjmwE8C+BkxtiakGiUAPwRwLSw2koAY4TTRgNYFZaPVpQnziGiGgADAGzMdCUeHhoEUkx3vLR0PS6443X85tFFHTQiD4+uDxdrpWFENDD83QDgeADvhDoEji8AmBf+fgDAzNACaU8Eiuc5jLHVALYR0fRQn3AWgPuFc84Of58O4GmW9Y328JBASobUjs07A6e5VVt25TkcD49uBZfwGSMB3BJaHBUA3MMYe5CIbiOiyQjEP8sBnA8AjLH5RHQPgAUA2gBcyBjj/PkFAG4G0ADgkfADADcAuI2IliLgGGbmcG0eHhXBb088ejKsxIEx9jaAKYryrxvOmQVglqK8EcAkRXkTgDNsY/HwqAa8nZyHh/eQ9ugh2LSjBcf/9jksXbvdWpfTBs85ePRkeOLg0SPwxMI1WLp2O/7nuXetdTnnwHxUJo8eDE8cPDw8PDxS8MTBw0MDL1by6MnwxMHDI4XqaaTXb2/GhXe8ju3NbVXr08PDBZ44eHxskbA6KoMLqAbj8PunluChuatxb+MKe2UPjyrCEwcPDwnelNXDwxMHDw8PDw8FPHHw6FHIwhR4hbRHT4YnDh4eErxUycPDEwcPDwM86+DRc+GJQzcBYwwvLFmXOfy0RyAeyuLtXM0khP5penRVeOLQTXDfGx/i6zfMwT3e5NEZUYwkvwR7eGSGJw7dBCs37Up8e9hRLgNQzcB7Xr/h0VXhiYOHh4Q48J6HR8+FJw4ePQLlZIWrhn7HE6DqYs6yjfj7u+s7exjdAi6Z4Dw8uj2yKaQ7cCAenYovXzcbALD8ss908ki6Pjzn4OHRifB0yKOrwkociKgXEc0horeIaD4R/SIsH0xETxDRkvB7kHDOxUS0lIgWEdFJQvmhRDQ3PHY1hTaDRFRPRHeH5a8Q0bj8L9XDIxuqIfLpLLHSrx99B+MueqiTevfoDnDhHJoBHMcYOxjAZAAnE9F0ABcBeIoxNgHAU+F/ENFEADMBHADgZADXEFExbOtaAOcBmBB+Tg7LzwWwiTE2HsBVAC7P4dpyxY7mNtz28vvez6Cbw0VkVI5+olJU07cCAK551p4Rz6Nnw0ocWACeeLc2/DAApwK4JSy/BcBp4e9TAdzFGGtmjC0DsBTANCIaCaA/Y2w2C1bYW6VzeFv3AphB1X5bLLj0oQW45K/z8PwSr8zqbiiXnldzH+A3HR5dDU46ByIqEtGbANYCeIIx9gqAEYyx1QAQfg8Pq48CIHpqrQzLRoW/5fLEOYyxNgBbAAwp54I6Chu2twAAdrW0d/JIPFxR9v6iS21LOhaeKHno4EQcGGPtjLHJAEYj4AImGaqrXi1mKDedk2yY6DwiaiSixnXr1tmG7eERoauvgZ3FKHf1++LRechkrcQY2wzgWQS6gjWhqAjh99qw2koAY4TTRgNYFZaPVpQnziGiGgADAGxU9H89Y2wqY2zqsGHDsgz9Y4OP68u8ZVcrtuxs7exhJNCdb/Xy9TvQuDz1CqXQna/Ro2PhYq00jIgGhr8bABwP4B0ADwA4O6x2NoD7w98PAJgZWiDtiUDxPCcUPW0joumhPuEs6Rze1ukAnmZdlN99+b0NuOmlZZ09jI8dDv7F4zj4l48711+4eitOv/bvHSLm64w9fN7T/ZgrnsXp/zO76v16fHzg4gQ3EsAtocVRAcA9jLEHiWg2gHuI6FwAHwA4AwAYY/OJ6B4ACwC0AbiQMcbf4AsA3AygAcAj4QcAbgBwGxEtRcAxzMzj4joCN/99OQDgG0fsmUt7y9bvQP9eNRjSt96pftdS03cefvXgAjS+vwmvf7AJR4wf6nxeFkuk7rhwzn53A6aOG2SvGKL7XaFHtWAlDoyxtwFMUZRvADBDc84sALMU5Y0AUvoKxlgTQuLS03DsFc+id10RC355sr2yR1l4dtE6nDp5dwBuntKdIf/Po8+5K7fgzD++jG8d6b5x6Yb0z6NK8B7SXQA7vQVUh2L2exs6ewhVwYYdzQCARWu2OZ/jw5l76OCJQ5nojiIHDzdUM2R3niiE3EeWcXe3a/SoHjxxcIT8DrW0lzplHB5JmBa3cgl4d9XrxKHGs1/3EwvW4I/Pv5fziDy6M3xU1jLR0lZCfU3RXtGj07CrNS2uy6SQ7mYiFxXnwBhL6TM+2LAzde63b20Mvo/eq+MG6NGt4DkHR8hLSnOb5xy6OhpqyyPe1YytlKdYh4+6JDTaXkp2sGVXK47+zTMd0r/HxwueOJSJUqm6b9WDb6+yV+pBcBH9DA3Ng4/ff7il5scEXKwkTM02aZ5ub25L/O9u3JFH9eCJQ5mo9iu1eM12eyWPBPgzqikUynpeVckhnSOTEomVhDKbbsxzDh46eOLgCPkd8i9V10e5z6jbKqTDb1ERL3O48qX5aeyhgycOZcKz410DpufAj5X7rKqxAcizj0IhWPpFeiBLP9ObHD+PPdTwxMERqR2Xf6e6PPgzamuPH1aeXMH1z7+LcRc9lFhgl63fgTdXbM6vkwwIaYNRIS2jp05jTxTt8MTBo1vDxbLoqXfWYnEWr2HHdeM/Hn4nVf/YK57FaX94ybmvLMSKMYZrnl2KDzfvSh3buKMFX7o2CLS3WYhuKy+CfpMToMr2JN0SnjiUCT+3ugZMIiNx13zTS8tzbRsQnc7KR5bF+YONO/HrRxfh27c0po4t37Aj+r1sffy7Xeog1V0PncilnkoVM8AThzLh2dKOx/OL1+Hx+R+VfX5bBi/2Ocs2YmtTsON+YWm2RFJ5zAUXDqI1FI81KZ371LDtkHuq7swTBzs8cXBEV7FW6klz+qwb5+C8214r+3zZxl+HXS3t+PJ1s/GtcEd+3XNBGAnXe53HI2EMuOfVFbj/zQ/tPSkoQUFDXWzWSjt6aNDHnvQelQsfPsOjqnh8/kcoMYaTJ42sqB0XXYMrceC7yDdXbE4kD6rG+iGu6f/657cBAKdOHqWsy/S0QU8cLGKlIy57Gpd/6UCXoX6s4ImDHZ5zcERXUeR1Vxt8jvNuew3fuf31qvTV6ihW4gtoS1sJm3a2ZO6nkrmQKYJq+K0iBLp5YbNWAoB/+/Nc90F8TLB07XbcNnt5Zw+jS8NzDmWip8pquxNcFkYgKZdPxMxyFitVR+fAiViWDYJ8C7r53iI3nHbNS2gvMXxt+h6dktypO8BzDmXCs6VdA6bn0NruSByEFbS95K7EzjPvw5ML11rrxGKl9GKmEyvJynI/bQPwjYPrBqInwkociGgMET1DRAuJaD4R/SAs/zkRfUhEb4afTwvnXExES4loERGdJJQfSkRzw2NXU0iyiaieiO4Oy18honH5X2plSCmkO2UUHlngaq2UdBqLy6vJHT6/2G4hxRcyFR3QipU6aRczf9UWZ7FeZ8JVL9UT4cI5tAH4Z8bY/gCmA7iQiCaGx65ijE0OPw8DQHhsJoADAJwM4Boi4rGTrwVwHoAJ4YcnTj4XwCbG2HgAVwG4vPJL61h4U9auD9cXX1xA2zJwDhzVmgq//NsCAOpw8TriUMblVIzl63fgM1e/iFkPLax+5xnhOQc9rMSBMbaaMfZ6+HsbgIUA1OYUAU4FcBdjrJkxtgzAUgDTiGgkgP6MsdksWFlvBXCacM4t4e97AcygLi4I9FOq60NFHFSzSlzcsywWFEVBrc5smLN8IwAkLKo4XK2VqoGNoVL/DUMYkVtnL8cFt5dvppwXPOegRyadQyjumQLglbDoe0T0NhHdSESDwrJRAFYIp60My0aFv+XyxDmMsTYAWwAMyTK2jkZXsVbyDIs7XPQH763bnnC0E4mDs59DF3gmBR3n0AmDi4Zi6Pun98/HI/PKd3DMC55z0MOZOBBRXwB/BvBDxthWBCKivQFMBrAawJW8quJ0Zig3nSOP4TwiaiSixnXrsnmx5o/OmVRdxUrq9pffxypFjJ9qwcm6x0GkctyVz+GS++dH/8tZLLrGE1HfkH//6zwlp9GhI1HkleiqKEeM2FPgRByIqBYBYbiDMfYXAGCMrWGMtTPGSgD+CGBaWH0lgDHC6aMBrArLRyvKE+cQUQ2AAQA2yuNgjF3PGJvKGJs6bNgwtyvMCV3FQ7orbHTWbm3Cv/91Hs5VxPjpSiiHkH60tUk437GfLsA66DiHt1duwR2vvF/VsWSx4rr71Q+wVrjn1YbnHPRwsVYiADcAWMgY+61QLrq4fgHAvPD3AwBmhhZIeyJQPM9hjK0GsI2IpodtngXgfuGcs8PfpwN4mnWFN86AzhpctcUEjcs3Yub1s7FpR+wctiuM7bO9uVV3WpdAObfqe//7hnPdaBHM3k1FyDoHqv0mFTLoYv7tz3NxwR0d7xT52vsb8cn/fCpV3uZo7twT4eIEdwSArwOYS0RvhmU/AXAmEU1G8G4sB3A+ADDG5hPRPQAWILB0upAxxvnaCwDcDKABwCPhBwiIz21EtBQBxzCzssvKjs07W/D2yi04eh83jqTTOIcq73T+68klePm9jZi3aguOmhDcm5bQWqau2LXdZKp1p6o9F1TdmYZQbdMO3p+rxGZ7U5u9UoX4zWOLsGpLmkPxnIMeVuLAGHsRaoHmw4ZzZgGYpShvBDBJUd4E4AzbWDoS37/zDbywZD3euOQEDOpTlzqeTq/YOZOqs+ayuADyvMS1XZ04VHivnJnXKj+Tam8QOhq964vG463tJby7bjv2261/2X3orLm8tZIeXfvtriJWbgqUqxsdY+t0ns6huh2rchZwzqG+Nn6pGWO4742VmcJkdzwqu1e2s+N7U91nopoD7pZVHT/WrHku+tSZ96iXP/IOTv6vF7BcyFNR7phkeM5BD08cQvTrFUzQrbvUcvSuopDuCpoY7oRVL3AO97+5Cv9091u47vn3qjoW0+2onHPIt15eUC9oXWBihODhPVwJkU3sxf0l1m1vLntMOs6hO3hxdxY8cQjB5ediPJ5zbpqDcRc9pKyf927RdQczWCHyqgbEF52/ULU18Qu3MVRYr9tW/gucN6qmc6hSP1F/VYoCWy4iziGnvqLc2B2wy+8Km62uCk8cQqhY9WcX6X0p8p5ULYqQCCJGDWwAAPSqre4jU9ms82vX7cZUYIzhlr8vz29gXQBZd8h5oRKxUjWQxVopS3uVxInSBVzorNhT3QGeOITgU6RaL/qKjTvx2vuxK4eNOPBxVVNE+pfXV0YB4Sq9L0vWbsfPHphvr5gj1GPOQNCsxyt/GOW0oVrQqmeZxXD3qx9Y52tQ17VN8/GI2FRwkTo/EK9z0MPncwjB54hqqqzf3pzK21spDTnq188k/je3twOotY+vinP5qicXx/1r0hwwxiJlvgmdIdv1YqUA4q650vnz4Nur8W9/nosVG3fhX07aV1mHE7y87ksxXNkrWcj1ObY9cdDBcw4h+C5TNVemXvokXliyPlk/5yXBlnsgfuE6ZzLr2O8bXlyGo379DBau3mo8vzPewY7uMxYrVd6GC06ZtBsA4GvT90gdM41B5KAqnT9bmwKDjQ079FZ9LNrIVP4AdrW0Y03oQW1ayP/wzFJcFKZZVUErVvKcgxaeOISIJrTjy1PJvL/0wQWpMrtYqfJ+s0LsS/eiz1kWiMY+2LgzU9vbm/NxfNKNa/n6HWhcnorAkrVxt2pVIti9Q5PPhtq0X0A15q3co60Pua/tzW3YorAGNI39mze/iiVrtwMwE4ffPLYId726Qnu8KwUm7C7wxCFEtDOvgt/Tn15cliqzEocK+ssD4gZLfM9cbdrljdsv/9Zx+gfGGI654lnsyBBwbs+hfSrosJJT3U4ulRj+/PrKTOdwVDv6vU6sNG3Wkzj4F49bzz/v1kbsf8mjAIDZ722IyiuLkacJae4tWbXwxCEEnySd5UzkzjlUj0y45DmIxCIZh6XaQeYFVTKccmC9pPIuPdmH48mbBOdM1TnuYqXysHZbk2RKqic4urm6U0Os5bE/vmBNFL+rRtjyu+zy97/kUaxX+EN0tUx53QGeOITgE8+ZPe+g/m09dgXP7MQSkbOXcB7Er5yNciV764r8DqpwTlsiR0X2HtdsbcK0WU8lDBRMo+C+Lq49mYbUpz62mXFRD+xqbcfsdzekyrViJa9z0MIThxBZZfp5L9I24hDrRDoHuvG5Ojy5Kl4rscQpt42goTLO4f1V8FRcFyfr1sFQQba0ywq+2D/9zlqn5/iNm18FkI88v5iRc9DV053qFdJ6eOIQIqv53RWPLcq5f8d6ncU5iKaswiAii52M7emuI3s46nxuiDLblJXgudUzwXnBExax1rYStuxMiuVMBEoUs1V6t7IQQld5vqlN0vw2QbXg63rQ3f+bX1qG+au2OPb48YQnDiFiPwK3yS8qyvKArV9+tJrWFeKY2jVipUjunnVR15R39EYui8ipGiJG1+sVTUf/9OIyHPxLu2KXo7lVIA4VDHb+qq2JvB425JFlrRwRYRZuQPc+/fxvC/CZq1/M3rkGzW3t+GBDNou+zkaPJw7PLV6HST97DDtC08rOk+nbjmfjbPJGYvFXWC5ltVbS9pOH13EZTVQinqqEe3El9qf87gXLGPTHmtvySxN6xeOLU2XPL16HlZvSC5+r32M1RLR6sVK+fevw4/97G0f/5pmqp2ytBD2eOFz5+CJsb27D6jARSGc5mdlekFInuEiLPZUStEEQK0mhDVSWIpn6dLw8k3lmOc+wHJVDHgHmqvE4k5Zw+Xd41o1zcNJVz6fK8+FyBe9uxzPUC776bJu10mWPvJOL6PK5MAxNnoS6o9HjiUMqiU8Hv6y6idbVFNLzPtwSEUwgyaqLQ5U5hwffXq1sz5lzyOECE+Nz7Lfa2dIAhDkwPrTWe3iu+p4695Po0z4mNyRvmMqnxDW3h6lH8bm4Ehtl7CmdjkulnxAq/89z70ZOeD0NPZ44yKtCxxMHdblt4kdiJcv4nl20FlN++bg21DjHhu3NxsTun/19Ut66VHhB+BiIKLp98z/MprzLSyGtbLviFsJ2Othybc1WO5fVXmL4ribHcsJ/wTCGLONzr2uvuNU1/aehKfHtrETMl0Uh3ZF6r+7kVtHjiUM6/WfHQtu+VefAq5krnnPTq9i00+5gduilT2Laf6QTrusgcgQfCUSF37+80i1mtlayHHdlCFQmmraxxJZa2a/9oy1NeOcjczwqAHhlmd7wQSfqk5GHE1y5WLDKfo0mlMM5fLg5HQhSxxGplNdyWR5MZWdwppXCShyIaAwRPUNEC4loPhH9ICwfTERPENGS8HuQcM7FRLSUiBYR0UlC+aFENDc8djWFgmMiqieiu8PyV4hoXP6Xqru+5P+O9kDWi5XM57lyDh2F9dub8efXgvAN//7XeVF53qEZ8rg88R675pxQVTNFkm1tL0VevOU8k/Nvfw3n3PSqtV6/elOkXkczWEcOA8ifeHywMUjtaUoCZTZlzT6/rnvOPRuhmnOQiEOOU7wbMQ5OnEMbgH9mjO0PYDqAC4loIoCLADzFGJsA4KnwP8JjMwEcAOBkANcQEY8Udi2A8wBMCD8nh+XnAtjEGBsP4CoAl+dwbWWho00pde3bdp+d7QQHALe/8n7iP2OsghdHfSUsB+sRseVKXmxTpNy75nyg7M8Vb4WpLytBwoDMJFbK1Ga+M4yI8NzidThs1pOGPvXni1xqJSJH3Zkq+i9zDnnekmonhqoEVuLAGFvNGHs9/L0NwEIAowCcCuCWsNotAE4Lf58K4C7GWDNjbBmApQCmEdFIAP0ZY7NZcIdulc7hbd0LYAblvSV1RgdzDpr2XTmHzowiqXog5ezsAP0L5yqiMfWalymrSaFq66JSr2QOkyOWLqRJul7827oJUZRV8iYS8iGEQFbdSfnKa7ksL5EpUN1kXZUik84hFPdMAfAKgBGMsdVAQEAADA+rjQIgxs5dGZaNCn/L5YlzGGNtALYAGKLo/zwiaiSixnXr9Ck8M12T9L+rKqTbuwLrIEFUSOeFXF6enO5Rq2Ew/XrFMX9UC9GdAmdRCS76y1ztMde5mkmspDheyTtRyR5PjkKgG4cq/Lu8oGs3IyqxknRuHiE2+F3oTiHCnYkDEfUF8GcAP2SMmbRMykgEhnLTOckCxq5njE1ljE0dNmyYbchOkCdvhyukdR0YrU1YlxArqV5011e/nIUsDzj7TSjKTDoHstjfV2OHmFz03eRKdp2DwtLHMjdNKJB9juha+O9nlib+6+aGKqKxXKYXK9kV0nlkMeTvzseOOBBRLQLCcAdj7C9h8ZpQVITwe21YvhLAGOH00QBWheWjFeWJc4ioBsAAABVmanFDR3EOp/zuBcy48tlUuV6sZLI2EX93rclVrv+ClkZmvTzVTlcolA/rhqu6jjaDzkGsrxpzNWSi7mIl+039cZjyU1XVdW6qUA1lrmp8LjmuATVxkIvyDM73sRIrhbL/GwAsZIz9Vjj0AICzw99nA7hfKJ8ZWiDtiUDxPCcUPW0joulhm2dJ5/C2TgfwNOvAVfD0a/+OP4S7kpS1Uk5784Wrt+LddTtS5Xqxkr6tLGKBSpH1tu9qdXV0cms3j8deDjFVLWL7j+zn2mOqRBciOk+4LjRJIqI+ybSIm7qxDUEdlkQW+VQ2N1TFLdJuXzz3P794YPTbxVrJlsLXBfwuLFu3IxU0savChXM4AsDXARxHRG+Gn08DuAzACUS0BMAJ4X8wxuYDuAfAAgCPAriQMca1cxcA+BMCJfW7AB4Jy28AMISIlgL4EULLp44AYwyN72/Cb0J5pqxQ7XhrJd0E13esDXpXBrY2teKZRWuVx9Zta8aeFz+MOySrpKhvxRj/9tYqRU3VuW7jy3p9//HwQmMblTzPgb3rnOopOQciHP3rZ5TcY3C8/HHFHZvHoDqmq2fKh22KVWT3BUnj0XkfJdsytpDuM12ejXMQEwi5WCvlyTl87YZX8Pk/5BfQryNRY6vAGHsRei55huacWQBmKcobAUxSlDcBOMM2ljywdVdSeSVPbtsu5qJT9sNlj7xjrLNwtV4lo2eN9e3pQleUg+/c9hr+rkiGAgBL1m4DADzwpnrBl8eomxSBiWt5q19WmawttMHkMQOxeM02bRYyDgLhsweNTDj7mYYiXp+q2nOL1xnzapPmvCxI3it9ay5rmylpk+mdsD2ugsJoYeNO98iuib4ylJssjGqL8Z5YNd9SOoecc4m+302is/Y4D+lKzNT61BVx/P4jAAD7johFDoyxSGm1ctNOYwTNLLsfDpGtrVTsNc8Q5mJHc7CA9q1X7xnkMfJgYjKU8fRlnUOZzoDR+cLvcRc9lMj+JbZdUyDccPZhTm3KDnOmZ2IL6/D0O2ruTNdXOXAlpC8utVv2mXJTqJ4JH75tDKrrbJWVxWWIx2zlJl+FmqKQQEipc3DbMO5qacdRv34af393vXbMHPJtUEWx7WroccTBJu80TdRCgTB+eF+M6F+PKWMHRuW3v/w+Jvy/R7B2axM2W+SJWrmp4Zw8OQcTi8zDlvfREAfXhVtFcGWipt0FKkUY9rIz//iysu0dLe3OIpyipCiQe12/vRmTfvYY3l65OamQLicKbA5ipYT/gmEITa0lPBuKErViJcN41MHp7P0Cat1LuTJ8/cYqXZZa4IVnVFOIlz2ln4N0vTrG4d1127Fi4y7Meigt2rRhyZquH8yv5xEH6b88OUwvOt8FFYgSk++exsB9Y9WWprLsyAHzDkxMmlKpwtZku88dt+pr1NPCVfaaJZ6+jEfnfYT/a1yRcEBTdetqQbN1V6uz5ZCNc3hp6Xpsb27DH19YljRlLeORmJwHy1HQ2s5YawhfkWhTUWZ67FbCqLhMWVnsiiwRjU2cQ62Fc+AcdNSWpl8+XcpZ6HUbsK6EnkccpOcsz1PTe8l3lgFxiMv5otqrtmB9WfQKaf05Cc7B0PbabfooqxwuYZR1u0jXNdAlTeOzi9SijksfWogf3/s2bnppeXyu0lFJ379oAIrTAAAgAElEQVT4DLbuanXWf8i7XNfrfWmpWazw0ZYmnHjVc1i9RQgIZ9qpO4tZ3OqJ0ForRQrpbDoH2xhURLDZ0QchNQ5dueKAWaxk5hyWbUhaGeryfPPNREt7ySiuDZC8D3WaDVhXQtcfYc6QXw75wZsmO188iJKLfFMb33EXyw5sJhONRR9tw6G/egJrtzYl7O1N7ctWIOp+rFW0u1rVIjGkT9qiJ+tOXwVRaanezbq11ywRQx2dIFJxDvp2xXn0u6eWGMdw16sfYPGa7bhzThw4wGTq6sw5wG1eJNtWl8cK6TRMOgdxrEdc9nQi5hSgEytl2JGJ49BM3qxipVqLtZLrmiDOlw2W9KnyvMvTAqqj0OOIgzz75Ty3phdTFCuJ1ZrEHL227h3lpje9tAwbdrTgqXfWSpyDuoGla7fjp/fPt/ReGVQLslKhryi6WeAEXCAuKi626Lr+29pLVoe1qE+DzmHeh1tw7bPvAggtjYSDDbVFZIVJrFQO55CXW1B2U9a47MPNu1LhPlRcG7+PmcemKXcRK4kQOQd17odkmU6sJE4XMZyKCzxx6IJI6Rxk9tNwbkwckhOS74RKjFlfUt3xJxas0dRPLsC65lXpOfP2I1Q1t2VXWgGvelkfcPSH4BB3Za4WNFF9qZ64PN3xij7m0XH7DU/8F+/fZ3//It75aJvQbnysoc5MHFSe1kYFsNi2QHjOP3qvZD3NTfjrhUek+wu/daEgyMA6mPU75jnm4gzoLFbSbawUZfKYxVslWitVYlnnMjYO+TbIm9KuiJ5HHGSdQ2om6M/V6Rxiyw273Yru+ENSKkhx8UgopDXnq97BvDcntny7HHl0azP1rCSkgw4nTByR+M8XdZXfytsrYxnzYIVoTYQqQqurKas4o1KcjTgHhXIC8LXpY5Xt/dM9byrLI1PWzLGV9MeAYB7bdD4dY8qa/L9L8HOptVgruYbPEIuzcgKec+iCSOsczMdF8Dku6xzERDzlWiuZ4KpzSPfF8NzidXjEMQdxuSIxGXmn+lSKlQwvl/wMyzUb5S/wDinqJxFww4vLov+nHry7sZ0mRVJ505DEhSyhSJWIgymsyqWnHQgVVmxMZ0n77ZcPFnQIyWNrtzbh3tdWps5RjUEFl6lQqe+Oi0J6a1PM4dr8HGROQTfVxD5snIBsLZZnGPCOQs8jDpZdgWkyc5a8QKRxngFsS2z2NJhMGqP7+SUGnH3jHFxwx+vY1dKOH9z1Rqa+0+05cg45zHsxJ3VWsVIa5VEH7hkr9yWPx7YzblLFnzKccuTlT8d9CeWyH4bueWQlhl88ZLTAOSRx9k2vprhaEeVyyiLmfbjVSXSTJfSMXHerIP4UTVlVnINcotuIiH2YOIEvXzc7VdaeQ7ymjkbPIw7Sf3lymBYdnhC+plhIOvKEP3/+wHzMXWk2aXOfEsEE3rKrNUpHCehNOFULlDh5n1i4BvdrwmIke9TDnTjY6+m8sDlEEYqqNdPLmF68rcNJoVigqA+5r42SZYrtvqjESqYh7UhwDnHbMueQ3DLE/8pJwMTnj/zs1lnMo/PgHIDkNWdtS1Us78xFAi06wSmnkVTmIs5ScQJt7SXsaG7DnGXpANOec+iCSLGM0kO67WV10DkR9TUFNAuiAt7C7Pc24Od/W2BsP6uS+NePLsK3b2kU+ipvUg3qrc9F7IpSCVi8Zpu1Xh7Tviis6FmtlfLo/9h9h0cbAJk4vCj5NdgeqYpzEInf6YeOTh1XtV0sFKRjGqVDGdD6tgjtitFMVcdFcDt+56i4hmO/Of0gAPqNm4vYMeEhbRMrOVorifPiXUWMr+/f+QYO+Nlj1nO7KnogcYh/H/Tzx1L2yaageRy9agtobnXzWk7HaXEbp/iybhNk3lloi9i3ziNzicNiL+LEq57P1K8OquxdIorCC+xqXhnXl3QO1tGk69QWKXIYtF2P7XizRedwwsQROGzcIOW5Ou4g6FfdXyWhOUxXcuT4oen6mhOm7jHI2p4rpozlbWl28BnzQIsOaC7WSi5ipUsfWphyhHvE4HfkrZW6OLY2mRcoHXrVFpWcgwqp8Byaygfs3j/xXxvx1GF8HC6xd05wWOw53MNnuLX3vCZwH5DkHNTmlfp2/68xqUAtJ0JsTbEQi5UqVKLYrJUI+jGKhG5bkz6icKWLsCnwHoe441aNQURswefWv6kaF6fpxUrpA+mwOMA5nxyHW785DcP79dLWU43lEo3/kDwHP/v7ZChukxlvd8gI1+OIQ5ZncuCoAcry+ppC0vHN0GbaUtb8MtmgsxxRrS3JHbS9A15Dt5baJnSxoJZb6/D+hnQyJA7RLyKrtdKNgiVRuagpUKSQtikPbfdll4I4yPGrdAsJA3DQ6GAejhnUO9mvsPmseK3hOgfDPKkppJcLm1LcVQxquoe2eaXkLIX58eKS9WhpK6G+poCj9wnSC9993vRUPVN7Ktg2S6Z8IN2ANvRA4pBhj6Wr26u2qDRPVMHkjJPsK4ksm932EsOTC9NOdGJfeYg4bS9D0bLDKxeq5rLsvMqRstQUyJgmVIRtKK1tLDWOXoJzG4Pe74Ex4Jh9h+OB7x2BM6eNSRzLy1opMTbDtdQqOAfdtRdi6uA0HmaQsnCORcs5WDjLr93wCgAkklwdvtcQ7Dm0j/K9cCZoVuKg1/N1A5VDDyQOWWT2mglbJHIKaQGoZJ+63U/5s+Wml5bhuufeM7Zpm8gusFlY2Nh/Ga4jUt0blTiA6wgqIbQcNcVCdL22cdpurWp+9JJCbqiIA79uAnDQ6IEgIlz71UNS9draS/jLG3pfBBeYYitxiGEn4jGW354IF84hi0Jadc9lK64CacRKjoO2vQ/9e5mIQ9enDj2POFiO7z4glkfq6pIUW8k0R+Rjrmv07S/rwzzIWLkp7dgk95WFNtz16gpluZVzIP4Su3XmGgtKyTkoCPfOUHyTVkhnpw7FQny91pAoVt+WdFmvWjG+j04sGHyLx/YY0kdoN6hw89+X4y+vfxiV665XdASTYUoTylFToJSznD4ndX4bBdu8Ekt3698rrJuuJ+tMigW1v5LrRo2nGtZBNj1OduLURafCShyI6EYiWktE84SynxPRh1JOaX7sYiJaSkSLiOgkofxQIpobHruawtlDRPVEdHdY/goRjcv3EpMwPfjBferQT6D2urpE7hMoPfkq0zmoz7VzI2IyHNfzZeh0MBzcwijvea9aFMYMbtDWF2sP61efqa87vnU4nvvxMWFwRTfOwXb7lirMHHvXidZj6rSqkQ5IWOzFanxqyX4XOpzyX/oMhTodgfivrlhIkR2txZTTiMR2zJyDHJUAALY1tWLlpp1R+bVfPQS3njstGLeiPZnzKUgSAA6X+bu9uQ1zLWG6XeNndVW4cA43AzhZUX4VY2xy+HkYAIhoIoCZAA4Iz7mGiDj/fC2A8wBMCD+8zXMBbGKMjQdwFYDLy7wWJxh3KAWKXo6f3Dc3EWgNAEb0DxaaAiXbMSUz3ywFplu2Xp0esJIQAroz88oLMLRvHeqKBYwf3tdYLyvnwGElTorDk8ekTT/ld/HzB++Op/75U5nESkeMH4o9hvRJxM+yDk9TQZc0CZCc/BhQp5TnB+2KG1BR/MTvs7xDVV0vEeHDzWoOE3CzVioUKOrfliZUJDYunJtpzhQKhNpCIZVB7vP//RKOvPyZaMy962ui/NCq9moLCs7BUaz0lKTT+++nlyrHmrdFX2fCShwYY88DSLv4qXEqgLsYY82MsWUAlgKYRkQjAfRnjM1mwYy/FcBpwjm3hL/vBTCDVNuonGCa/EVBXPS/YfRO0Saay4kJ8aR6a8VmY3+/ejDpFPfqcvWtdFlPj5owNGKbXc515Qhi8YmuBqFQsJt0lquQLoM2GOvx9kb0rzfKfU1I7lSzi42AZCJ7GeKzaWkvYdYXDkQ/yReFtyu+Dcnw4yFxkPopSyHtqCMoSOIirc4B5uNyshvbe1kj+J1wLFu/Izw31s3w9V/Vnhx+RA6gGY8lXXiu4IgKAJs03NqbwnpgDsve9alDJTqH7xHR26HYiW/jRgEQBdYrw7JR4W+5PHEOY6wNwBYAQyoYlwVm9lU+eu6Re+Iv3/0kgHiHVijEk0/mDGRsl2zTW9tL6NerBjecPTXbsBEsNmq7bPU1mVKCirBNVJ4Ix2bSWZPRlNW1f1eFI7d44ccy7TEovXC42urz4/dJSmGXcNVAkBltRP9euPQLk5LtKq4jmeci+JYXvXJgygSXqCd1pasfERFNO3/4SlKxbuQciFBbLGjDjfP7UKCYs1FNfZkgFQrli5W2Navf+8R1dG+VQ9nE4VoAewOYDGA1gCvDctXtYIZy0zkpENF5RNRIRI3r1ukdqEwws83pyT6kT10kHoh3gvGOQ2ZVXfovEGGSJL93mSx1xYLyZdRdkz11YQAbK0zhx8o5GHQOM6RcCSIqlelziNFxgXhilbOTFnN22Lrn9V5YkgyrIYfYFiFeU3OoSJcXedV1i4SCH5dNTMtRwNtO4Qr0FHHQNaeJ1cQhM1XGDIyF4Bp1mx1R/CaLu0yxqYoKPQYAzH53g34wIWSHRA7R/Nl0S/POtdIRKIs4MMbWMMbaGWMlAH8EMC08tBKAaIw9GsCqsHy0ojxxDhHVABgAjRiLMXY9Y2wqY2zqsGHDyhm61SpCPt5QV4weOCcOwQQMyuR8uClIM6TEWGISR+NymCx1NQWNXbYaKs9cFZw5hwqslUyExRq8zbEwVUT8K/tiGYgcghZdxyfPBVPOBpHz4Q6VNrNQIDmd+LjkmEtx9GDDoHXj0oyBP1tXnUMk3tH0k07JauYcagoFbf7z658PzbgpTZTEKSsT36KGc3hck3hLhBhdobeQ7Em8H0aFdDdQOpRFHEIdAscXAHBLpgcAzAwtkPZEoHiewxhbDWAbEU0P9QlnAbhfOOfs8PfpAJ5mHUhWbQo3sGQsnIbaYqRrGDc0MCMMdpXB8Z2WaJJp6w6GAlFqwXK54FohpIMLnImDJI5Rgux23SadQ5YoqunxVSYeK08Gn10hLXdjzhMd/x7SN/Ck1cXhKiTESnaFNM8/8er/O948cAGqoT65YE1kCVWQiAMfmz5KcPhDR2wcuKSoLhE+2tqEexpXKmNyPfXO2mhsss4hOe/SosNyA+A1C++W+EzE9kybkq5PGgBr4lMiuhPAMQCGEtFKAD8DcAwRTUZwjcsBnA8AjLH5RHQPgAUA2gBcyBjjd/ECBJZPDQAeCT8AcAOA24hoKQKOYWYeF6aDOTwAobWd4ZwbX43KGmqL2He3frj2q4dErveE2MxRJwflkHdIJRYsPKmFw2G21NW4W1cAmjwCCvBdvV65GExz2yLNwytkjaKqSnFqg6o1WQxUlnglREEwV3YVe8nP2qTzYCwITvfdY/fGsfsOT7TDUVIQHVX6VNl+f0dLsIAO6etuxqvyS/jWrY3C8eR3NAadnwPXYWiOu+amkPtcvn5HSiQb94mUzsGmyzARhyd/9Clc8dgiPDo/HUBPHL84vo+TtZKVODDGzlQU32CoPwvALEV5I4BJivImAGfYxpEXjJwDERgrYfZ7scyRi5JOOXCkUC9eMGwih7GDk/FwWChWSrHVDmMPdA4OFUO4cg6RtZLmOJFeeSeiXM5BtuiSoWrPqHvhOgfNguaCpCmr+br5HJAJftFEHBBwkMftF6cmTXEO4bfOWknHORywe3rxtCqahXEpj3NxUqpddXtc0qU7LltymaaWSGTlrHxyPVncZUqUVVtTwC6DQcmI/vWo1Zgji8RB/J3gHIycY9enDj3PQ9rEviqslVSTgyj2rLTtALgoiqO9FIqVytA56MVK6nPd/Rw456DbBQafcsVKjDG8/J7eGtoWw8jV7C/mHJI77vKS37j3Gy/iaZm29hyG1EqbzjjHryMtShLry/2OUJg7i3GFVJA9n2XwSzHlsU60Z/G4rpOIw+adatNQHiCPwzSP6msKKasrk66rX68ao9e4qHdSHeMQNwFtzsRBf6yroOcRB5spq3RYFWyMMnAO7ZJQtsS4x6d+5qgyRwGBNVAWsVLmRVX3ohOFMnh1hYkjg3DjEXGQ7vHC1eacEVxmrIPsYdzU2q4cq6uOwAViiBSrToQv4tIjNS4OUOuj5DpyO6KoMOZY7MRvi8Xk2ubnwOdrWkyq4zTi9lTDkzmHH93zlrKdw/dKWrV/tFWfma5XbTGlCDcF9BvQUIstOw0hRQjaGyISfpFgJhTSGf0cPtiwE89aiHg10fOIg02sJM0GeYcDcJ0Db8+8csi77RJjofWPNC7h9xML1ElCRNv7xLk64qDZ6R8ydmDiv0sMoUIGhbRc7dNX68M2uGDWwwuj32u3NmG/Sx7FTX9fnqrHrzfLIh3Vkf7Hik1m3VDww/LCmdZBCH9YelxpCx5+XlzeIAbsi/rVDi/u28I92fwcIs4hxe2o6x/Kk/1ojss+BybvbRGD++idGnvVFmKdg0Mujn71NcakU4T0esAhcgviPTGJT7937Pjot6raMVc8g3NuejV9oJPQ44iDCSrOQZ7EQFJZaRPdyCITxhCKleIZ9dmDRib6NbH2WZzgdIv5r05Lqn5iayU1+vWqAUApLojHN+LnZc3n4AoxDMWKMMigKYOX3H05amlRsWm6nLpiQa9zMImVFGElPndwoNfieiqVFdTYIb3x889NDMfmzjnoqkwJNwqunIN8N1X1Zx42BoeNG2xsTw4tYtLPJPozPIuG2mJKIW3Uk5F+fEAoWtRwHn3qYyItjt2kcxDfU7XRhmEwnYAeRxxsJnPycS4yERHIo4PftpzKrSmxEkstIrXFglNspaJGtKO7Jp0llczS8zHq2tl9YEPAOUiETt5tFqUXMy801BXtlZC2VlKJZXR4UwqDwp9RiTELtyns8KWF0yZzlo/X1xRx3H7D0b+hJqqjamdquPCqwmvooBJj3vfdT+LObydl+jqOmJ+d4ngVN6dvfY1VhyFbWLn6ZJimVn1tMRqoykclvWlIUwdXs2kxZIkoVrIZbbzwr8c6td8V0POIg2F6ye/PUROGKmPYi+Knm15abuxvZ3PSYqgUcg6iuIqQnLim3Rtj6RdSV98mBuLg+bBNuzwi4BWNLoQjfuHzpQ79HOMj2RTrWUARoTOTbdEfQk6UZnaCUy/q4lxgQlmyT0RjE8dqgqrG8P694nhhkmOC7NC3dltgbhyHxeD3WtEXiYSSKfuWs8q5cD9if6qNTyBWSpaZFutAd5gW+5qOi+MYO7g3Xr/kBL2fg+KaRoYpAT62TnDdGbZ1Q1xYtBOW3HfHd85J5mXgOoe6mkKU/hEER7GSemeelXOQr4pntdPKmwtppz0gvbjZkrLI2G+3fk71Tpg4Qvinb7wkLarx5WQXLInOXiZiIy4g8mKQks+LCaKYOlopCXOBaRb/eGzuxFA1lU35BnSe/zFHlfyWx2fjHGRDj4KjSJIv3ioz7bqioHNQmrImIW/KgKTYlqAOzMfH2a9XDQb3qUuID1uEd051d0nzDndFWP0cPm6wyRhdnlnBtSKC3fsHG3Zi7JBYjswn8H3fPQIlxnDRn+dKYwwary0SHvnBURjcpx4bdzTj0XmBorrEGIrC1NMG3rM46HE0W8I3FFVOe0hPflUmuJ0taoXfsfsOc1ZCui7tKY4q/G9MuhLiK4ePTfxPiJUsY4vFSknImwsxNtDrH2zGpFEqkWVsTs2/5eGTtEC7QHUHxPFF+/ywzeXr1fm9ZWc5pUKa7DqMdG6F4NvG7fLFXuXgGTiXJhdfm3OdfFSsLhJqGQyi13hcLr5zzy1Oxn8bP7yvYE3V9alDD+QczA/FRSJByBZyV+SgS6V4UhULQbRJIvW4aosFjB/eD4P71GH88H4JUYcLXHMgR5yDwZvVRXSh8pCWo9Jy9K6rcV7cXMVEaY4hgEvUUtkqLbHImHQOhcBb/rH5H6UD70n3TA7SOO/Dran2AmITdPja+5vCQh3ngHCM8QDvOf8TynE+sygdqFIkmvJifuofXlK2Q9Lipno0W3a2Wv0cUkHwCoQVG3dqiRJHTBzUDp6yyM2sj6bU3EqGvwB0D59LAIDkcza9c6dNHlUx57CrpR3fuqUxRXg6Aj2POBiOyaZretl/+tgXDxmVqsetSsT5J06quF9pjGF9Vfx5QGFBIQ3mT2cF4cDl5Cgcuw9MZlHjcWIemReb0PJQIWK/MuRdZCxWivvVhfDI4mQmvkimU3R+AuWEtBYXGT4nRg5IO5dROL7zb3sNH2xMJnKS++3fEOhOrGKq8PD5t70W9SGiIC2AYnOyR74JoiLVJU1o0Defg2aRlkhEXPwcRg1swFG/fgYnXPW8sX++eIvxz5T9smR9XV35aFLnoDYd5+3zy9KJlUQMaKiNvcw1m0EXrNnahCcXrsH6bdlDzmRFzyMOji+mCWIKSY5fnjoJZ39ij0RZX4UilSukU+MSfreFq7/8AvG/8iIom7dyTqVNY4fXR0oqw+XLb3wQW+yI8vFiIa1sBeKXg/cfKaSF4TRpXmKdz4YKzkREI0VzESvJiHbnpXhOqJ6byjcmPpb8H+/09f2abOujOhIHKd6eLIRQzTmY+5adzFTXQmT3SZfHeeIBu1nOCMDnmrwG8/ZkfYxRrIT0+y7OITFsuwwGCIt9fC06UW7CaU5jdWjD1qZWfO/O1wEAw/tnS39bDnogcUiXjQp30gN71yZeDVM4CfmlUDmJ8ekgKseeXLgmFWhOJkqcNZUXNVnZxnH/m6sS//lkFSfqpw/Uv3wqFr09QRzSCumvTR8bmw2GdeuiFI1xPV3UWtOLJ8PGgp//qb3Cemqdg8uCmXZI433Hy6VsfsnP041PDnznEgJctUGRd7+pbGzCsSyEULxmWecg4/4LjwjPkftWnSBw32WKDk8/dDSe/NHRqXo84ZR8D/ncM/k5pIaiIPYy5zBhRGA0IWdgZIIEQLznJgU2R0HxjHV1Rby6bGMkinS14KsEPY84KMouOmU/vHHJCRjQUOemc5B2J4A6wqPM4vKwGGslllDeLbZK+SPkfnU75Li9ZDsA8Pzi9erKUFumnDE1Tr8hBjTjOHXyqBTnwB0GxWtRxeD/01lTlbsn7lUrI9oFlpgyRlDfuqRfAAd/HLLZJIcpGVLClDVs50cn7KOsp5szv/3ywYn/8W5bP8kKlI7vJW86+Fqk8mw3JRgSMWlUf/Sqif1HbNZFB49JOsu1R4ROXd+mkJYh9/upfYZh/PC0NZsugnA9T0YU/ne1VgraiuvIXPg/zpgAANhHsqwTxUqJ8Bm6hERiv6S3guJtqyBu9lydBitBzyMOihtfV1PAoD514YS2T2fVi0SUlvHLqRJ1E1XeLfLJGngmx+AbV9PicuGxe0f96pKjAMD0vQZHv1WcwxcPGY0zp40J+6WUSIUQL6D8ujhx4Je5fP2OKM+viOMnjgiDFybLfxC+iDL49d7+yvv4wzPvpo7LSkgZGtqAq55YLFyPzKXxNmNid/ie6ey1ogJZxsDedYn/thhWvEH5OuTnyDkhVe5vV87hwe8fJRESPldtYqXkxkgvpo3rPbnQHi9IfjVkD+qoXhTwUs05yO+mSyhwWScoom99DaaMHZi4zh3NbXhx6foo9EYydau6P5FomOaMqQ2xWDen80TPM2VV3Hj+QskySN3zU+XHDTiH5EtsW7TEemKNUYMCMdcfz0rmmeYvc4kxtLaXsOijbSn9wcSRA2LiIExI2a78xnMOw+otTZhx5XNapfGEcOcWiJX0kMVKjDFs2dmKY654VnsOD0Hy1zc+jMpUYhsgXjhWblKbvnIi9V9PLsZN35gWlfPbruMcCgqZu9wmQ8w5yOsut6t3F50E3zY5uLw+y5wD5yh5ubigl5tPWlwoF32k9/qXxU+6SxHvpy6QpIjUYq8hDm0KgggInEPI5UZRWY2cQ/o9Vl2PvC785yNBrK/Fa4KAkInwGRqiKa8VpimjG7I4D/LIG25DzyMOirIaYdeRmCiaRyizrkDwwPnDu+CYvXHixBGR9Yp98UguMK3tDHXFAsZIlifRTp0xXPHYIlz3/Hs4ZVJSl9BWKkUvpsiGyi9b77oa7D2sL4oF0lp+iLF70otnWiEdiZUYcMn982ACz5fwr/e+HZXt0ugnbMSVE0PZXJM/P92LJL7UchcJJzheKDVTWySt1ZUsow7aSopizvnkuFQdUiwcsnkkv542RdiTsomD8Puk/9JbDJnMaDkY0+vHdJBr6YhDtPhKZ9QKGwAxF4c5TwQfLwO/AypiQoLRAWMMt7+cdGxVJWBKXbbI3RUJLYb0wrp75sVKHQzVfY85h6QVkpZzUDh7iQmA9tutH6aMHSRMmuBIS7vZ/I6jvVRS7qKLwos5N5SXr9qSDGHMWNyeuKjI+ouozQKlLD/icfDFNe2lCwCbwzDQvUOZP++DgWGbIU4+ELDF7YxpTf9E2NYXVVh18TydqEWXzQuQzEXDhmT+qVgg7S6wd306HpS8oI4e1JCqoxI5yFZnfCFsbWdgjOGld+PkVOUuGi5+LEG94NsmIovun+LxXnzKfqm68jXX12jiaWkW/aRZrqPyH+m2lMQBZtNYkTFt14i9xP/9e9Vim8b/B9DfU5E4uOqWKkHPIw6KV5kvEinOwbIo8fy6wbmET4Sx5/liGb9IwTcX3+w7QhU2Iu6stZ0pd4DigsWPyzuQvvU1EVEycQ5imzr5J9+lFZRiJcKR44cCAIaGOZAjnUPJrrkhhTnwp/YZht+fOQW3nTstUW6LQ7PHkDihkqiwPvGAEdH4VRDLdZxDSeAc5PVT5dfBoeoxCu9RSvaR7Dd972SxEt84tJdKeOCtVfjbW4G12hVnHFzxomGb8zJHYEuGozo+qE9SF6PKFSLrHG795rREe/LcES9bJNimzHHRJk80oFByDvF9UR3/t5NjYqdzvhP/9utVY9w86e5pi7DZ62nRVtsAACAASURBVBKcAxHdSERriWieUDaYiJ4goiXh9yDh2MVEtJSIFhHRSUL5oUQ0Nzx2NYVbFSKqJ6K7w/JXiGhcvpcowcg5uJne8Yn/5opNifKvHj4W95z/CRy///CwvSSHwcUm1591aOI8ud+2Ukm50+f9tpdi4rBwdexle9U/HIwZ+w9XWiup8lLwMcqT8b7vfjK4nml7YMZ+w/Hto/ZS5ia47EsH4uWLZ+A/vnAgjt13GA4bF8bwV/aURFFhsVEsED538O4YMygpTrOZsoo78G8I8fA/ufdQyxj0xyLCXop1DnL1A0cPDHaNbpKTaBHShfgO+o2fB3/Gnz1oZKIOL29tZ4kQJEdPMF+vCTGPaxHhRb42vL6uXpo4DGhQm18GZs3JMnkzc/Q+w7DviH7axTcRCiQU9T23eF0iP4JuEyQWq8Q9ojWhinM4aHScH0WnMBf/9utlziOhFSsJY6uGzsGFc7gZwMlS2UUAnmKMTQDwVPgfRDQRwEwAB4TnXENEnD+8FsB5ACaEH97muQA2McbGA7gKwOXlXowLVLdddGZJiJV0OofwucisIRFh2p6Do/bkOCrcIaxXbVE6LzmutnamFIWI4izV8S9MGR3El5Fk0oBerMRfzPvfjBXDU8YGi/yA3rW44ZzDMLRvvdLLtb6miN0G9MKEEf1w0zemRaIAW5jruN9kJX7fZJGMTW4t309XmHbZqsB7sujlmq8eoiSuqrpAOt6Pqn9xozBxZH8ct9/wVE5o/izbSyy5g6xgvbCZssZd8Lml3sHzNlTh208OHd0GhkTi2X85BnN+MkNpDqyyVhJ38GnOgRK/GQNeWqo33+btyVA5sYn9qvKpJK2V+PiSdcQ5UldTsOgc1OXi+9wlxEqMsecByOYGpwK4Jfx9C4DThPK7GGPNjLFlAJYCmEZEIwH0Z4zNZsFTvVU6h7d1L4AZpHqzcoJq8scvvxvnwAdnkhvy9oBY7rp+WyCGShEHSdfR2s40nEPYHlOLnTh4U9yaAtDvNPiLtGBVOs6PCarWlEpc3flEWnHRIMkE1BZqoFetfY/z8D8elSpLKKSlEcc75PgIAZgq+GIEIjz1tfJwKr/+0kH40iGjg7kg7XqV01yYg6rcH0A8D377xOLEIqMS/v3vtw5XjE7RbbSRcavHzWvFR8hNrxnicYs77X88fgJ+f+aUKMruuKF9MLx/L6VoU51ki7Qci6w/cgmJrQoZovL5SRAHRUga8TnG/h964lVbLBiDYq7ThMYQT+kSYiUNRjDGVgNA+D08LB8FYIVQb2VYNir8LZcnzmGMtQHYAiBtUJ4TVNwAn8AB+yjU1cwv/qB1oSFixDLNtduacNWTgV29vJjJnEN7qaTROcRiJZOJXm9FchzdXOIsuKtFkAmi9YdtQTeZgMqEkV/q+xvSPhOvX3KCUmQ2bkhSNKUKDy7Kj/U6B0GsRMC9F3wyUU/l0LTo0pNxwaf2BgB8+bAxuPLLBye86vm9US38jMUpM0tMTUDEMtGhUvWIRg9yi7VkSxPKMbxfYIXF5+d37wjCOXzv2PH4lxP3jdpSheCuLQZiQ/maVNyXanMkmqjKi79K52CbsTFBDDn71nZs3hls4D5zYCzKS4iVLPdHF7bjL9+N501tsZDQH8i47JGFyvKEdWQVtMV5d6F6HrrnJG7IdMeSjROdR0SNRNS4bl15UQlVz5Y/cHHyaQeBpDzahEisxICPBKsieTGTdQ6tJaa0VorluPp4+4B7ngTeJmNMa7EUjdFho6Ly/xAxoKEW5x65Z1jX5ikc/+b1Hpu/JlVPjqfPofKulbF1lym5fNBmS3sJt7/yflCmzb+QvI76mmJqARRjMPFpoyK4PCz7tqZWMA3nIPcf/bYcNzcUfNn221eeEXh97yMZVfzziftEC3qhkNzIxF3ouNf0e6naGfNFv1RKW7mJ70NkXiw18aVDRyf+R3qWsO8ZVz6Hc29pBAB888hxifZM1koiYmulZPn+QkbJumIh4djY1l7CT+6Lw/brXotEvLMuzDmsCUVFCL+5ichKAGOEeqMBrArLRyvKE+cQUQ2AAUiLsQAAjLHrGWNTGWNThw0bpqpiheq+iwpH8bhu8efyfl3UUw6+QDw8dzWuETx7UzsnSdfR1l5K2G1zxMSGRTkYVFAtOrqpxFl1F0c9G2QzRxnfPWZvXPLZIFJtoZDccYsWHzJsEgKV/FUXdFAEDwmhbDNs8uaXluO9dSHHorgHTS3teH/DzvQBCUQKnYOivX84LHh9drW2h2Il9Y0f3i+I2yTeatdsasrxhd+qR3f+0XtFvwf0rkW/XjWKUDHi7p+UOgfd8ERxUVSm0seEi/5P7puLr98wJ3Fsl+Dlr+JKH/z+kfjsQbun2gPid15U7tcVk6FFeB0bcZC5QxVqi5QQK72xYjP+95XYd0J3n0SupasopFV4AMDZ4e+zAdwvlM8MLZD2RKB4nhOKnrYR0fRQn3CWdA5v63QAT7O8M9QLUDXNA+/JOgcdC1kscjtz8wLEH981z76LR+d/ZKwroq1dwznw3ZhiVyTa+uuUVVeccXAUzlscYzliJZW4Q+SUVM3JC4XYZx+FXwAHY8xo3aHaRaXyXStuiRieRKfgFPtVtbFqS1Pkc2KCqHQ1pffkO3LGgvulW/A5kRWJoFKF4biGyGlCRVz86f0T/4sFTS7z8LtAACkiCGuHouAiVYsfv4d3vboidWywYB4rzy1tewZRWr0g+hV1glbioOEcRAQ6h7hCehOqvlNitS6hkCaiOwHMBrAvEa0konMBXAbgBCJaAuCE8D8YY/MB3ANgAYBHAVzIGOMk/QIAf0KgpH4XwCNh+Q0AhhDRUgA/Qmj51FEQH8PoQQ2Y8/9mYNzQwE5e9IQE9BOBL0a2rFVZ5IK8pf9+egkWrN6qtlbiu7EScML+IxLH5BwNqrlz+qGjcfzE5HncI5df62DJBj2q53ANvJaYA0GE7FHuugUoMYZbZy9PlZ8VhkhXvfjLFfqJdLv6Y7xF0RmrktcxEB1yeXlQpvNzAILnocr9EdcLDojGDWqxV5YnlybqewxJ6yyKlA4yGZwbi2f5O7JmayxO1d1u032Qy3SbmOu+fqhQL5hb4v1QE5vgu7WdJeJsAcmw9i6cAw9185cwHIxps1VTLCTEYnKTukdWbbGSNXwGY+xMzaEZmvqzAMxSlDcCmKQobwJwhm0cuUF4EMUCRQo2IJb91xSCUBi6rE6xWMnGOTi+mKE8q629hCseDyapvNgDSWsleW7cdM5hUt3k7k63SHBLEV5T502cViKq2+JQSXXEyZ01ZLeq6mlTRoVtxR0P71ePtduasXmn2UMbMOuX+PUmdpAVvJCiY5ZJrMR3hO2hf4U+0VL4LT4JRVXXRURnrcR1DPIYdWEzgGDMfNx/fGFZdFzvMGe27onGCPU8AJB4j13b47jr1Q/wu6eWJMr61IlipXgjo9sQniBtukxzu04SK5kc+nRtViJCdEUPjK1kuMHhDqEYEgct5+BIHFy3mtxKSuzOyDlIk2nUwAbsNaxvsq7kWWTUOZTi+6InDpaLQLx46l6Mr03fI9mvM3HQLSpBf+KukNeUXzhxYV+3rRnD+tUnFpq0tVLwLab25L/u/PZ0ZbRZE0TzStnJTYQYIkVnyiqOzyZWcuVedRsZVZucc5DFIWIsLlW/uset8pDOmlgp3V5y7DoxFaD2ouZRDgCJ63Ocs6ZqtcVCwqFNXmZGDkhvDIGkmNtHZe0AJBV4yWMUUoeaIqEZeqUm1we0tmWT0+vALV7Eiacy5YvDNUiyXA0L7gK+y+KEUE78HvWhGLOuT8aSRPitn56I2hpKvnAKJaQOuheNL6TitUahpA3trd6yC8P61VuspfRiiE/sPQSf2DubtXWSc+DtKRbA8Pa3h/PBlqJVlF2r6uoi0urgsvbxeFx8F/2d0Gw3IRNXjEW32VJaK+kU0nY7A2V8KhUHxUtU3IDoZyGKlUwRazkYY7jxpWXa47U1SZ2DTPBUMbeAJOetMljJGz2cOEiiEgoeVLEQe6Cq4Mo5uDJ+oVQpsViZZKSyB7LqhZZfBh2d4os0f0F0nIOTn0Okc0iOaUDvdNiENGFWo7ao5zAi0YowNv7SmRY5HuNKrKJzghNLXcWEKhDScYHUcvVYrFQqmcSBQXnSCS6NzGIlB+pQKCQ3FAN7J3NjizoHEfrnqLBWUgxbxWEoxxeKgUSOQJnmNuxDdmz78Un7Jushnk9L1tiJw6I123Ddc+9pj9eGOocgm1z62nWiqy6nkP64Qbzt8gLMJ4EcL18Gn/itlq2vu6VI0K9IjFSRRsXk7lc+vjh1XFU36sMgNmCMRVySzkQuLYHTEy9Rh+E6Ph1qCgWtYlY1Vh5+3DQCntxIXGjkNUdOhxmUOQ1ZCdESzuTnIAbzM/k58PItu+Lgj6ordt1gxsEaU9qXVN2aQgHtpXjOqNLZqq3D1LGVVDt9nTWcC7PJueFbZ7+fGJOqXyBtlbjX0D7JeoI4a7giFLsMsbnBferw3I+PSRyvC9/tNknMyKFL0mWzlMobPY84GJS0nH2MnM1snIPBEQ3IIlYKJp/Y3QtL0nFhRLGSLdR1qmvtIhOIO7jyXTf9XHbNiVDmlnmcWvQ096qmqBc/qe4vd4YybTC5TbypTuzQl+2FvEKhwAW4UjO5GKgWfr7xWLBqK1ZtadLOIV6+bntMHFSLh6s9PFe872hJyt+5P0Wy76AvPmfk6LQFSr9bf/jKIYbAe26Wa3KYGY7//sqUZD3Fbtykc5ANT+rlCAYQCbt+oOceuSf61BUTIqnRgxoSUYOBePPJJQ/pEO3qPjrQwl+JnidWEn6rdA6MxUHvdA+J6xx0SXLi9rJBJEa6GC+Am1LMdVHguyxTIDWxb91/IL5eF7+J1M5QU7+2WNCOSXWNvKqp94gQGqyVYpPSuMxG6/9h6hicLnnhiu3FOgf+S79g/fDuN4PzdNu38NRtgpe3KmyK6zzg+aRl5ayccIq3GXAOSVGkSZeiM5EG3C3XCgWASa/cg98/EpNGJQMTcm442Yee05Xfc9EBLm4v+G3avRcLhHaWDIZ4yNh0XvSIOLQxoC6tB9WJq22hO/JGD+Qc4t9HSiGOOecQx8vXLUrBbTOFsAjac+QcEFuncJw5bWyqnihWEqFaPF25Fu6dGu1idGN0aE4Mn7Fuuzp4mG58A6Rge4eGAe6KhXSuaQ5jwD2H90i8jWlrJT4HzIHtEucYhiPKy9ulRVVEStRp4RxaSyWMG9Ibyy/7jNqIwXEexJyDLV5Y0He7woiB30NVjyYa5WqcoLJw01s1JaF2ggsgp/eVRbqqZwcgCgWTGJ+Uy+R7x41P9VsbchYtiuCFfepqsFPzDBzyYuWKHsc58Ed3/dcPxQzJkYwv0tzCQ8s5FDjnkFG0o60YjErcGUwa1T9VjU/wZmmWqERMfFHhuzzdUIiSykXdovrye/Y8wCJns14TWZJDflc/J+UsuPGcw7B8/U6cd1ujdlepS2AEuImDjFnC+K5SEDnYn6e+gmiRw+X6ymx/0iXpDQTi8dXW6e+Ds1gpvJem5Dhim6VSWk9l8vw2bZS4IYgL0mE20nVUnIiSSGrESkP6JkVp4pm83Xm/OAl9pfztxUJsZcahmqPcPJpvyDbtjEWDDXVFbbpcvgn89IG7KY/njR7LOYwZ3Dv94ggLKmAyvctXrMRNaMX5bIqtJOs6djSnx8EXmd6hB+1AhcUQEMvCbToHEd88Yk9MHJkmXrFCOlbWD+2bllkDyR3fbv17pRaPfr1qceDoARFno9q1a1NJwqxPiPMBJ0tV4xONDmzP00w84t0xVziqI48mGzGFWgfCrIHGhddRrBTOk+2WMPR8TAnOIdI16XUpJhrlqnNQcQSqeaGO1ZRuj58pbgInjuyPvYfJCmlRrBR8q+65yv9DFTGYc1pt7QxPLliTyKPep97EOTCMHNAL13z1UOXxvNHziEP4bZKZx+ExzH4OpuB3gLu5Gd85icRItasUI4WKEIOORX2HdaeOG4SffW4ifv0ltaK0QIHteN8wztCn9jEHNOxdV8RPPzdRk6gmKFu8ZltkYjmiv5o4iPfftH6p5MccqoQwHKa15vJH34EcVjxlrRR+v7Y85phsC63paHC7gk5MZsPyoqNL0sSrtbarw7tnBe9HVkirUAgXwVZJIf35g0ehrqYQRT/9viBSsXEOLjoHtS5BVdE1VlPwLb53Fxyzd2qsYriLOFFTuts4yZbwHiudWRG19cqyDYljDbVF7NQ8A1OsrY5AzyMOkVw0fZOjdKGKSSMiEu/YxEqOY6oJRT/ihFYppfjCYSNKgOg9XMA3jthT6WvA6zEw9K2vQa/aAv79M/sr63G4hNkWTQh1oh/xZTVNeC5nVvVrIg4mrNi4C8s37DQq+PjCt0oItV4J57B2WzPunLMibDs0ATU4OnLYrJXaciIOqpzkP/vcRG1dURTJCcvYIb2x+NJTsHforZ98xvq+C0TWOGW8ns7kWK4n7w7UpqxJ8Q6gzio4emADVm7aiZIQNUHHOQBJMZXSC14Qw8mbrNqagsHPgVXFM5qj5xEHcLlo+pisWNMRh1jnYBErOb6zLW0llBjwqrBLXbM1LbOPRQkOxCF8spqNpzDGWCE9ZlBvrYc0jwJq8lBVvaj69KRuN4dHAfnNY4uM/ckcj83s79grnsX/CI5KcnXVs7Vxgq5OcnzxUPmypI24NNZj4XdryZwVUIXfzZycKuMLm0gcRmhs+rn4xOYbU+O4ASBSZ1iTodIlqJp1jtWk2ASqjBz6N9SitZ2hua2E34YB+pTJuLguIRHSRC8BUHECRdKvO6m0sB2MnkccDBYV/GWVk7LIkDkHMSKkCFd57+pwd/pPd78VlakcYXhzNh8HIJ50tvAJ3GZ9444W7UIOiI47pjwSqvPUbYrhsk1D1MVgev7Hxyb+TxiejC3lYv1iMkuUuULVQi7D9b2Nncfs1kW6XWQkwiiDc1BxXHwozRaPa163VALuCHMQ6JTmReH6jKJD2CMc85ppT2o157BespYzWSuJfe8txSgT+3j6nbVRmerdLkbPJGjvj1J4/Li94LvE0os9NyBR4cG3V2GzIUFV3uhx1komnUOsWDO3ERGH1hJGD2rASQeorQdcX1nVxFW9LCrWX9tmeIG23W6BKJr0qvDMHFw8ZA5zreIc1P3v1l+MhmveVaq6HCuNNe9wAk2SHueZfznGeo4rN9Rq4BzkuWCKRwQEzyPrblLpme1oZAEERG1nW1uUoKZSzsE1CGOBgLVCCHBA/Y69o4h/pA7HEXyL91jlj8H3N7YEUrFYqaTtU6ynEivxoJ8ydjS3ocTgFG04L/RAziEiD6ljXKTCd08z9hueqgMkxUq6XRPgzjmoRAcqxXBsJWUnDrxr0/jEeoA5baaJq+AQZfgNoez2P754oLJuvSDbtVuyOCgrrTXMmLh70vpqkLRImJy4skAMVaIS4cmLhW5BEueWjTDKXs4mOXiLlG5ThUKBIEqBdNyp2I/pVZB1Djpu85lFa7FhR0uizJVrUop3kOaGTbnbbe8zfw6cs9c9l0isVAKWrk0SMm4mLMONs8oXPY44cKieM9/JfbBxJ44YPwR/+OohynM5u1xi5slZyWb28L3SUT95e6626ME5NuIQHzddiwtx4AtoTaiwPP/ovbThh5PWSpZdpUskzgplsTxxEMcxEnF2CjzoMITW9tjKxxSWnUOXU0SsZ9sAHDk+6eypTsEZxENKbjw04iKSEs/oOAcxO6FN5yA8ZJ3Pgyotr4047D2sD+79zic0HQdfIueg4sJqonfJ2BX45fJnpjcmCL4fmbcaD8/9SDpGSkMJFz1j3uhxxMGkcxB3QBNH9ldaLgT1xBdTfwtV4pL7vvvJ9Ji0LSTBX+qN0u5JWTfSOdjESvFv0wJrcjgTcfqhozG8Xz1a2kvGc8QXx2bK6mrmWAlUeb2HCTtu3Yv+nwJnZBKPHRdyoSXGogVYjuEDuOscxFrW3bN0WCeGKhI5caUFokRq1CaNKMrVIo2IEkQwS5QI27V/bfoemDpusLrf8Fu8xyrCyfuwGRxEOoeSWazE74Uq/De3XJThiUMVEFsrqcRK4i7azevUNDlV78MURawV15eBTyo5h+4oRdY4fn0uOgcOk+xax+qn2wNa2oOQ4qZzSPNbNT630ApOw8uEWofnfOa0sRjaN+CYTATqsHCBYgxoDvUZKic+edrJitWoHrktvKrjumtpKzG89v6m6L+u2acExSygF0eKGxOzWCm5e3dNqBOca752k6OkLvBeqg9p0bfVa7VwDrr7/9XDx2oV0rbcMR2BnkccjNZK8e0w7bjFYwtWb9XWcxHFZIFqSK9fcgIe/eFRqXLedRbOoVKxUtAeRYufK+dgVFYWHHMMdICJXzEhFrH3bSZywTdDzDmozCbl6/jh8fso2xOrZXnGqj50cL2jU8akNzxAcoNlGiJRcveeRbxu4xxM8bf4mTYlfKysd1RIh0REd5t15bO+cGDkYCjDxUIxb1S0ehHRciKaS0RvElFjWDaYiJ4goiXh9yCh/sVEtJSIFhHRSUL5oWE7S4noaqpUgGxARBwUPSSsK0y6BMdtqsiJjB7UoJV9OouVwkGLi+7gPnXKOPmqFJoqJBWb+nquYiUiisQMpnOSOgd9eypLFjlfL5BeyPYd0c86Vhu4yJDIrhfh9XTgx0oC56DirMTnNWO/4Zg8ZqCxTyC7/0UlSlwZ3/nU3inLMY4k52C+fy7cwlhVhNiKOIfgWxVhIDE+RyvBiHNo0zvKAfGzE6/4xHBO1xQ/XmKlYxljkxlj3Kj3IgBPMcYmAHgq/A8imghgJoADAJwM4Boi4k/uWgDnAZgQfk7OYVxKxLZKetki4M45mCDGRzLJPl3jtLtOUiADcRB+9zK8SC52/kF/MVvtShxWb24y1EuKlX512iRcr/ArEXd11371ENx53nSn8ZrAh+hq2uxCQBhjuPrppdr6SV2Mvj3xudqejUz08xTBmd6FLDqH+av0HDjHH76SNhCxeQybvOgj4tDixhFYiUPEOfAQG+5ipYtO2S9qQ6WQdnnn80ZHiJVOBXBL+PsWAKcJ5XcxxpoZY8sALAUwjYhGAujPGJvNglXyVuGc3CGmMpSRkL87TnoTEjqMHJihLC81r2sbq3jNqnwAHFnEShy6zF9AkjhvM1hfyd6ux+wzTLloHiYQ3sP3GpKL6el763c41ePDcXnENpGJa8iJ8YLTn83RUb5fzjG/HOqY2hKJlqktQlLnoAsSqbpMu1jJwDmEo2q2cA6Rma9l9y77Q+iGpraUDE6u0ZiydkfOgQF4nIheI6LzwrIRjLHVABB+c2eBUQBETerKsGxU+FsuT4GIziOiRiJqXLduXdkD1kF8aKZJ55q0PWHKZ2jPVawkE5jdDCkLXTmHlZt3Rr/rDS+SaxwjkTjsKWXAStTLkL5SJA6qgIRye9UMMQC4WbOInIMJ4uMy7baLhdiaSndPOOSjrpyvy200cw6izsF0b5L/77/wCE09M6elwnBN4Ecgvj6dtVXURzjA219+31wvbJBzzrZcHGIUBF61oHGC44TJmMMkZ1Ta0xGMsUMAnALgQiI62lBXdaeYoTxdyNj1jLGpjLGpw4aZo4dqYdA5uFruuHIOoljJ9P6eeVic2Kd3XRHLL/uMsp4oOpk0qj9e/LdjtW3yCW1bKFds3BX9PlDKqCXClXMQuzNN5CyxlbgMd98R/Qx+E8Kz09zs286dpiy/9Zvqcle46Bz4lLHtPpO6BHO/3NHQZkkm32vXe7+7wgpOhnkTJYrI9G2I4/nsQSNTaTVNfdkInS5kfBbwPlZuCt6VHx4/QVkvDp/BOQezWEkUu0abOdJxDkHZHd+qXFzqioqIA2NsVfi9FsB9AKYBWBOKihB+c7u3lQDGCKePBrAqLB+tKO8QmExZO1KsZOIcjpwwNMp8NlMgFDLEl7V3bY02SB4AzFkWBPHL4lnJ5Z4q1JbBOZjG57q3J6LImuS0KUqGEkCSCOoWjKMmpDcUZ04bi6MtYcpt4N0ZRSfSrlKHpFex+S59sDHg+h5fsMZpfBxjBunDpHDcfu7h2F+Rs0OG63timv/iZZot3LL1D5i5qigXvM2UVXoOX9DMw2L0jEuJ/+n2kv2LfdQUNU5w4cbQ1aQ8D5TdExH1IaJ+/DeAEwHMA/AAgLPDamcDuD/8/QCAmURUT0R7IlA8zwlFT9uIaHpopXSWcE7ucE1lmIfns7jbtu3W3lu3HUA6ZaGIYoGwV5iIxFWmvrXJPRaLOfCeu58DRx6hRUTHLJPiNYvdv4g82PRYIa2vw2/Fzx+Yb2yrnOuwOUXyez19r8FYftlntOHbRey7m5u1l4kzTcZWso8PSKfAFaGOrGq+R3n47shrgTYBU1jO56uOMEV5WRShSlSmrIwxfOvWRgBAbU31RKaVBN4bAeC+8EJrAPwvY+xRInoVwD1EdC6ADwCcAQCMsflEdA+ABQDaAFzIWJQu/AIANwNoAPBI+OkQmALviRTDvNNx5BwcneUAYFMYUOvNFZud2hzS14045CV/dycOIufgtlv85hF76tsrxKIYE7FxJUodAacIuGGdJ2y7fKGJvC4jUphniEBlJuzxJsv0niS4IEsKVQ6T2M1ZnFukVCIiZT1HbjidulUTSyq80Txoo24zw+fLkwvFKK+8rzRx2Cpk58vbd8qEsokDY+w9AKn0YoyxDQBmaM6ZBWCWorwRwKRyx5IFpmQ/4sLmal5qQsKT2nWn7PgCDHHkHPJIBAO471hEwmlaLMV7PX0vtYkvr2dKjqPqN0uE1iwLpg58qrgSLxPEefLAW6vwu5lTKhla2HfQZpZEMSbCXluIM6OZxEA1jk6EHwkJlb44ZbS2nisnVVcsoLW93XpOveNCW1dMGmro3ik5lL9uIVetBaIBlqBo0wAAEi9JREFUiUwc3t8QW811C7FSd4Up2Y84kUyx/l0hLliuG3jbYr4t3EXkFSXUFeWIlUxiIPHIiZqQ50BwD1ucxEpOw1O0X955Irg1lU4RDqSJ0A9mqJWayQ1K5WML2gy+bc/wq4fH+i7TDlV8Dr0cd9+m9pZvCHQnfetrcLzCwZHD3flUMAQxnOPq2CnHwNJtAvij45kQdZsZ09rDEymJWChEYXAdcx7oecTBUeeQB3EQ4bqDt9Xju5K+Bh8CEXlZdbqO31Uh7boLLFJ8zaZ4V+XmczCd5UoQ+UzJwjkM1YgFy7mOf9QQGg6+SbEtLGdOi4mD0QpJuC8m82dRHOKyqJn8bAB37jup69PXk8ekS84jm3HrNgHbJX8dm1gpWRa2XUgrpFsEhXl/x/c+D/Q84sB/KJ5bQjThOBHPnDbGXglZ5KXmR8J3qa4ey3nJ3131LO4Kabd+C86cQ3nXOaBB/7K53uMNYXA8Y4ReqSnd1qMcMeApk/ScFxDfmzqDB7xYDzA/O3GOmjiHQb1jAugiK9/HEvLE9daIi7lrpOGvHD5WGZZFrgfo740cSr9WMx/ELIjyOIN8Dslj3DT23u98Ag0WAponehxx4KyDWucQ/+5T7/YQ/kkTGC3ddvadtwrcmsNZzJOjcvYLU0bhWk2OCw5yXGBc13JRrGTWYbi1J+ObR+qV4dd8TZ3+VQY3JjAr4JPHdE6F5RgQ2IgYP271hxAOmxZVsT8T5yDGhXIhtJeeZlY7us5lV9FLn7p4kTbNVVm8p5uH25uTznQ6hbfKMU9USMvRX7kUYx9HC7K80OOIg8laSVyYB/dxc55xnYjOYhTLC8ClXa5WC3l6C1/1D5NxyoEjjXVcfUXc70fsq2FafMvlHPrU620yVNn4TDCGb5f+6wLClWNaa/PY58dtC7TrXPny1JhbdvWcd+E8xw3Ve9QD7uNz3TiNECIMmOaPSEQAPSGROThdvfqaYmrdiIwGKIglJhrERGlls1gU5ICeRxyMOoe4VJciVIa7LsGpmlUMFImVHF/K6pt1xr9NC0IWsRJH3mKlG89Ry5hFfOdTe+OoCUONdbizmFnn4MY5lBOQ2BY+gx+3Rm917Fv0EO5rIK6uGOTgdwEAvR25eVdRr7hAm0zIxw7pnfC01t3H3Qc2JKIMmDZwchP8P59Dosqzrd3sN9FR6IHEQe8hLRa5srANBrZahOviJcppVeAspqs83NWqKS8i4qrHL0fMlkWm7wKXGFkXnbIfbjv3cGOd/UcG7L4tL0XQZ1DnuP3dNh8usHGvrmkuXaeA+O6o5OdZ8dJFx2Huz0+01jOF3xZx9ifHZR6DKX86ABy9j3mDwMHXjXFDepu9x6W5IvuNcNHSkwvWRIruam/0Kn+y3QxxyO40suw+3/rpiairKRgtckS4chiXfG6i8TifRDbWea9hffDeuh342vQ9jPU4Xv/pCU71bFC5/qvgeqd3tMRKvrzFSnknCDIGdQyveGjfeowd3Nu40PWuK2JnSzt+N3OyU78mpToQzz2bKLKc++G6OTKhd115y9DnD95dWV4O9yXOMxWc/ZTCarrw/BycCNTVFNDSVoqeDX9WpRKwYNXWyDO6pkAV50nPip5HHEyB9zLwUS4hCBJtOz5YG5vev6EG67e3WF/0h//xKLSVmDPxykue6eo86DrRn10UR981XXM5Vj55vWvRszVcOq/S0l6yigeC62xPhCE3wbajjvJrdABxGGIJbPcvJ+7jPAez4KWLjsPuA/RRibPi2H3NnNy7YXgbG5pagx3/OE0CJA7OBfzklP3w9U+Mi+YvL29nLBH6JkuMtLzQ88RK4bfKWikPb1l9v/k83F98fhLGDemN0YPMETN71RYzyYPzkme65v8tTwxk4hyyt/f/2zvTYCmqKwB/Z97jQQABeYCyyCIikS1spYIoaERBEjExi8YAkZTGiEvMgpqlkio1QSuxXLBKqQRKkxhTKWOCS7Q0CVomxgVXEBFFoxJKohHFDX1w8qNv8+bN0t3z6Jnunjlf1dT03Llz+54+M3P63OWcuIyDBrmje87lvZl/lxjWr7BhhKVzRkcKjvdxhN3M+eethDCjfM4xozhr5sjKGw6hpSkX6530T04cG/j+4694cxKfDFkxtP19L87V/mWiB/vsMQZNuZIB+Ha5POxJ0oCeQ/kZ6Wp4bdMObOWhzW9GHi8NY96EgcybELxiqDOkdc4hn1rGlakEPx9AUGIZ//J6xiHifEuITs6edRBnzzootJ094UdCvMM4lz1Xm7jH36Ouurrsc+MD3/cTV/UJGeprj8fVUY6mPM8hadL5a6sBpf6bot71VoLvGoZN3N1x7gwuCVnnXU3iugvzr+HSOaODz+eeK/mNB92lRv0x3frN9jzecaVe/PAjzzgEjb/7XulHu3aH/0k7XcT197fd7cPo9Yng72CGbEPsK3fCvv8Lp3lzd5OHls7p7eNvhOsVYhya8zyHDuVOrrZdu6tys1oJDWccgpayBkTL7jQXzz2EoX27h+7+HDe4NwsiTh6nmT2rLkK+2VEz1eUTdMfdFhKT3yff3Y/LOPgpOwcGjIF3iDkVNrzjnuMaZvbzgIwdVD6ZE9Q+g97eEGZgx0QYbquEn3x2LBsvnRNqRHydhXkiuVxpz8GfF9rZtjv2ED6V0njDSgHJfqrhOcwY1Y8Hlh4de7tpxd/BHfafL+63U1kY6fI/uKi6y58w3xmTcfjOcaOZPWY/xgVk0sv/uoX19WefH89P79pQNpdypZxx5AgOO7Avk4fuG1ivWw1DM+wtYTcVty2ZHprEB2D+xEFsej18sjmXE7rmol+fsPkdv/+Fcvg7zne27Y6cw7xaNJ5xCPAcUjDMl3l2R/Qc/HcruVkNGkqIupoj392Py3Noac6FLl3Mvxm58+mtXPeV8nWPG7t/YKTaSmluyoUaBoCenVxSmgRh8zZdm5uIsh7j6lMmxRKev5Cw+bF/u0i0hd9B3+PY2baL5X/bFHu/KqHxhpXcc9Ccw4Qhwe53PXHzGYdx4Zzy6UErpW8P7883bO29dGJYKehubITLOxzWXq9uXTjWbUCLy3OIQtx7KqpBJRPSLc250J3j1STOlUrV2D8QdYJ78xsdvZZ247A7dENstcnOrUJMzB23Pwfv17Pk6qHdAbun65XpI/sxfWR8P/IzjxpJa8+unDy5fNIWaL/WlfxptgbEu9q3RwsvL5sXqZ2ZB/fnvg3bGNgnvnXyYeRLOXZQvOPhcXLBsQcz4YDwm6PnL51bg94U873jR0fec5AkUWOujRnY8Vr7nzv35ifYsv2DPeX5uTZqRcMZh2GtPRjWWjrA12A3cVdu56URTktzrkNegHL4RiFoEreQuLLaffXwYYwf0qdD1NBqkz9d8qclR9TsvJVy/rHBuSGSZsnR4Ut300DYhsM1353Fwy+9yQnjOw4f+jet+YahpTkXug+jGqTGOIjIHOBqoAn4paouq3UfBvTqxnOXzInsEhqdp2fXZq768kSmjWwNrLd0zmiuuHtjrOcWkZoahkLSul/D2HvGDe7Fui3vhK5IG96vR8kotL0LlhtfetK4yCFw4iYV31IRaQKuA+YCY4BTRSQ4yFCV6NalqaGGlZLkpEmDO4RNLoW/yctfLppV/FVZU4aFTwwb2eWmxYdxw4IpnY5W26dgnqHSsPFxkhbP4VDgBVXdDCAitwDzgWcT7ZWRCm47ezrDywwFZoVWlxY0jkB1Rnrp26OF4/dipVnhzur++0TLK1MN0mIcBgOv5r1+DQiOk2w0DJMiLMNMO+MG9ea8T4/iS1ODJ+qNxqa5KcfLy+bx4ce7eO2tDwJDslS9L4mduSMltx0UVRI5EzgTYOjQ2s/eG0ZnyeWEb8+OllLWMLp1aUp8KDUVcw54nkJ++qYhwH8KK6nqClWdqqpT+/dPbizOMAyj3kmLcXgUGCUiI0SkBTgFWJ1wnwzDMBqWVAwrqWqbiJwD3IO3lHWlqq5PuFuGYRgNSyqMA4Cq3gXclXQ/DMMwjPQMKxmGYRgpwoyDYRiGUYQZB8MwDKMIMw6GYRhGEVKNRBe1QER2AKUisg0FXonQRG/g7RTXiypHNc6dlCyNJkclddMuS6PJUY02ayXLaFUNzlsMXtrELD6Ax8qU/zfi51ekvF4kOepJlkaTo55kaTQ5sixLuf/Owkc9Dittj1jv9pTXiypHNc6dlCyNJkclddMuS6PJUY02k5SliCwPKz2mqlOjlmeNepED6keWepED6keWepEDaidL1PNk2XNYUWF51qgXOaB+ZKkXOaB+ZKkXOaB2skQ6T2Y9B8MwDKN6ZNlzMAzDMKpE6o2DiKwUkW0isi6v7FMi8pCIPCMit4tIL1feIiKrXPlTIjIr7zNTXPkLInKNJJALNEZZ1ojIRhF50j0G1FiOA0Tk7yKyQUTWi8j5rryviNwrIpvc8755n7nYXfuNInJ8XnlieolZjkzpRERaXf13RWR5QVuZ0UmIHFnTyWwRWeuu/VoROSavrdrrJOqyraQewFHAZGBdXtmjwEx3vBi4xB0vAVa54wHAWiDnXj8CTMNLLPQXYG6GZVkDTE1QJwOBye54H+B5vNzfVwAXufKLgMvd8RjgKaArMAJ4EWhKWi8xy5E1nfQAZgBnAcsL2sqSToLkyJpOJgGD3PE4YEuSOkm956CqDwD/KygeDTzgju8FTnbHY4C/us9tw1saNlVEBgK9VPUh9a70TcBJ1e57IXHIUoNuhqKqW1X1cXe8A9iAl+p1PnCjq3Yj7dd4PnCLqu5U1ZeAF4BDk9ZLXHLUqr9BVCqLqr6nqg8CH+a3kzWdlJMjDXRClidU1U9yth7oJiJdk9JJ6o1DGdYBJ7rjL9KeRe4pYL6INIvICGCKe28wXrY5n9dcWRqoVBafVc5V/lEt3f5CRGQ43h3Pw8B+qroVvB8GnscDpXOEDyZFetlLOXyypJNyZE0nYWRVJycDT6jqThLSSVaNw2JgiYisxXPXPnLlK/Eu3GPAVcA/gTYi5qhOiEplAThNVccDR7rHgpr22CEiPYFbgW+p6jtBVUuUaUB5TYlBDsieTso2UaIszToJIpM6EZGxwOXAN/yiEtWqrpNMGgdVfU5Vj1PVKcDv8MZ+UdU2Vb1AVSeq6nygD7AJ7092SF4TJXNUJ0EnZEFVt7jnHcDNJDC0ISJd8L7wv1XVP7ri150L7A9PbHPl5XKEJ66XmOTIok7KkTWdlCWLOhGRIcBtwEJVfdEVJ6KTTBoHf9WBiOSAHwLXu9fdRaSHO54NtKnqs8512yEihzvXciHw52R635FKZXHDTP1ceRfgM3hDU7XsswC/Ajao6pV5b60GFrnjRbRf49XAKW78dAQwCngkab3EJUdGdVKSDOqkXDuZ04mI9AHuBC5W1X/4lRPTSbVnvPf2gXc3vRX4GM+Cfh04H2/m/3lgGe2b+YbjRWrdANwHDMtrZyrel+NFYLn/mazJgrc6Yy3wNN6k1dW4FTM1lGMGnlv7NPCke5wAtOJNom9yz33zPvMDd+03krfSIkm9xCVHhnXyMt4CiXfd93FMRnVSJEcWdYJ3c/heXt0ngQFJ6cR2SBuGYRhFZHJYyTAMw6guZhwMwzCMIsw4GIZhGEWYcTAMwzCKMONgGIZhFGHGwTCqgIicJSILK6g/XPKi9RpG0jQn3QHDqDdEpFlVr0+6H4axN5hxMIwSuEBpd+MFSpuEt0lxIXAIcCXQE3gD+JqqbhWRNXjxr44AVovIPsC7qvpzEZmIt/O9O94mpsWq+paITMGLofU+8GDtpDOMcGxYyTDKMxpYoaoTgHfwcmxcC3xBvVhYK4HL8ur3UdWZqvqLgnZuAi507TwD/NiVrwLOU9Vp1RTCMDqDeQ6GUZ5XtT3GzW+A7+MlYbnXRX9uwguH4vP7wgZEpDee0bjfFd0I/KFE+a+BufGLYBidw4yDYZSnMLbMDmB9wJ3+exW0LSXaN4zUYMNKhlGeoSLiG4JTgX8B/f0yEeniYu+XRVXfBt4SkSNd0QLgflXdDrwtIjNc+Wnxd98wOo95DoZRng3AIhG5AS+C5rXAPcA1blioGS8R0/qQdhYB14tId2AzcLorPx1YKSLvu3YNIzVYVFbDKIFbrXSHqo5LuCuGkQg2rGQYhmEUYZ6DYRiGUYR5DoZhGEYRZhwMwzCMIsw4GIZhGEWYcTAMwzCKMONgGIZhFGHGwTAMwyji/xUw1KMgrUmdAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_v_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Zoomons sur les dernières années pour apercevoir la périodicité." ] }, { "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_v_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 septembre de l'année $N$ au\n", "1er septembre de l'année $N+1$.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", "de référence: à la place du 1er septembre de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er septembre.\n", "\n", "Comme l'incidence de la varicelle est relativement 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": 12, "metadata": {}, "outputs": [], "source": [ "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_v_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er septembre, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", "\n", "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_sept_week[:-1],\n", " first_sept_week[1:]):\n", " one_year = sorted_v_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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAD8CAYAAACLrvgBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAG0VJREFUeJzt3X+M3PV95/HnyyyxgWKyhjXxD8BUcVAMSSEeGfeS65W4tZ1LhY0KyV5oWBVLboCmpDoJ7JATOnAlqE6X1orCYYWCgQTw+YpwW3xkMReVax2bdSAFQ1xvAjGOHXa5dcBE8pa13/fHfDae3ax3PzM7u/NjXw9pNN95z/fzme+HL+v3fH58v6OIwMzMLMe0Wh+AmZk1DicNMzPL5qRhZmbZnDTMzCybk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZtpZaH0C1nXfeebFgwYJaH4aZWUPZs2fP2xHRNtZ+TZc0FixYQFdXV60Pw8ysoUj6ac5+Hp4yM7NsThpmZpbNScPMzLI5aZiZWTYnDTMzy+akYTZF9Lx7jM/dv5Oeo8dqfSjWwJw0zKaIjTv288IbfWx8dn+tD8UaWNNdp2FmQ13yte30D5z41etHdx3g0V0HmN4yjX0bPlPDI7NG5J5GCXffrRk9f9tVXH35XGacXvxzn3H6NFZdPpfnb7+qxkdmjchJo4S779aMZs+cwdnTW+gfOMH0lmn0D5zg7OktzD57xinL+AuUnYqHp3D33Zrf2+/1c/2VF/GFJRfynd0H6B0jGZR+gdpwzccm6SitESgian0MVVUoFKLce0/1vHuMDU+/xnf3/pxj759gxunTWHHph7jjsx8d9duYWbMZ/gVqkL9ANT9JeyKiMNZ+Hp6isu67WTPy/IeNxcNTSbndd7Nm5C9QNhYnjeT+L57slW1YfVkNj8SstvwFykbjOQ0zM/OchpmZVZ+ThpmZZctKGpL+XNJeSa9IekzSDEmzJHVK2p+eW0v2Xy+pW9I+SStK4oslvZze2yhJKT5d0hMpvkvSgpIyHekz9kvqqF7TzcysXGMmDUnzgD8DChFxGXAa0A6sA3ZExEJgR3qNpEXp/UuBlcA3JZ2WqrsPWAssTI+VKb4GOBIRHwa+Dtyb6poF3AlcCSwB7ixNTmZmNrlyh6dagDMktQBnAoeAVcDm9P5mYHXaXgU8HhH9EfE60A0skTQHmBkRO6M4+/7wsDKDdW0FlqVeyAqgMyL6IuII0MnJRGNmZpNszKQRET8D/htwADgMvBMR3wXOj4jDaZ/DwOxUZB7wZkkVB1NsXtoeHh9SJiIGgHeAc0epy8zMaiBneKqVYk/gYmAucJakPxqtyAixGCVeaZnSY1wrqUtSV29v7yiHZmZm45EzPPV7wOsR0RsR7wN/C/w74K005ER67kn7HwQuKCk/n+Jw1sG0PTw+pEwaAjsH6BulriEiYlNEFCKi0NbWltEkMzOrRE7SOAAslXRmmmdYBrwGbAMGVzN1AE+l7W1Ae1oRdTHFCe/daQjrqKSlqZ4bhpUZrOta4Lk07/EMsFxSa+rxLE8xM7MJ51vE/7oxbyMSEbskbQV+AAwALwKbgN8AtkhaQzGxXJf23ytpC/Bq2v+WiDieqrsJeAg4A9ieHgAPAI9I6qbYw2hPdfVJuht4Ie13V0T0javFZmaZfIv4X+fbiJgN0/PuMf70sRf5xheu8I36pqipeIt430bErEL+BUfzLeJPzXe5NUv8C442yLeIPzX3NMwSf7u0UoO3iH/y5k9y/ZUX0ftef60PqS64p2GW+NullfJv7IzMScOshH+AyGx0Xj1lU4JXRJmNzqunbMI10oVPXhFlVh0enrKKNcKFT14RZVZdHp6ysjXShU897x5jw9Ov8d29P+fY+yeYcfo0Vlz6Ie747EebZpjKQ29WDR6esgnTSEtTp8KKKA+92WTy8JSVrdH+IW7WFVEeerNacNKwijTSP8TNut7++duuOuXQm9lEcdKwijTrP8SNpNF6fNYcnDTMGlgj9fisOXj1lJmZefWUmZlVn5OGmZllc9IwM7NsThpmdaiR7utlU4uThlkd8lXeVq+85Nasjvgqb6t37mmY1ZFGuq+XTU1OGmZ1xFd5W70bM2lIukTSSyWPdyV9RdIsSZ2S9qfn1pIy6yV1S9onaUVJfLGkl9N7GyUpxadLeiLFd0laUFKmI33Gfkkd1W2+Wf0ZvMr7yZs/yfVXXkTve/21PiSzXynrinBJpwE/A64EbgH6IuIeSeuA1oi4XdIi4DFgCTAXeBb4SEQcl7QbuBX4PvA0sDEitku6Gfh4RHxJUjtwTUR8XtIsoAsoAAHsARZHxJFTHaOvCDdrDv6dkMk1UVeELwN+HBE/BVYBm1N8M7A6ba8CHo+I/oh4HegGlkiaA8yMiJ1RzFQPDyszWNdWYFnqhawAOiOiLyWKTmBlmcdsZg3IK8jqU7mrp9op9iIAzo+IwwARcVjS7BSfR7EnMehgir2ftofHB8u8meoakPQOcG5pfIQyZtaEvIKsvmX3NCR9ALga+J9j7TpCLEaJV1qm9NjWSuqS1NXb2zvG4ZlZPfMKsvpWzvDUZ4AfRMRb6fVbaciJ9NyT4geBC0rKzQcOpfj8EeJDykhqAc4B+kapa4iI2BQRhYgotLW1ldEkM6s3lawg8xX0k6ecpPGfODk0BbANGFzN1AE8VRJvTyuiLgYWArvTUNZRSUvTfMUNw8oM1nUt8Fya93gGWC6pNa3OWp5iVgb/QVmjKXcFmec/Jk/W6ilJZ1KcW/jNiHgnxc4FtgAXAgeA6yKiL713B3AjMAB8JSK2p3gBeAg4A9gOfDkiQtIM4BHgCoo9jPaI+EkqcyPw1XQofxERD452rF499eu+9uTLfHv3Aa5fciEbrvlYrQ/HrGqGz38M8vxH+XJXT/lHmJpYJX9QXuZojaTn3WOn/J10//9bHv8Ik1U0oehuvjUSX0E/+XzDwiZWzh+Ulzlao/LvpE8uJ40ml/sH9fxtV52ym29Wz+7/4skRlQ2rL6vhkUwNThpNLvcPyt18M8vhOY0J1kjLXX2jPDMbi1dPTTAvdzWzRpC7esrDUxPEE8tm1ow8PDVBKlnu2khDWWY2NTlpTJBKJpZ9jYSZ1TsPT02g3OWuHsoys0bhifAKVfN2G74VgjUy33qmOfg2IhOsmkNJvkbCGpmHVacWD0+VaaKGknwrBGs0Hladmjw8VSYPJVmlmm0Yx38LzcXDUxPEQ0lWqpxl0s02jOO/hanJw1MV8FCSDSpNBKe64r+Zh3H8tzD1eHjKrALl/MCVh3FOarYhumbi4SlrSI1yVXw5V/x7GOekZhuim4o8PGWTIvcbZs5wTz0oNxFM9WGcZh6im2o8PGWTYqy7/Vbye+a19iePdNF29owhiaD090vsJA/R1T/f5dbqQu43zEb85UD/Ylw+D9E1D89p2ITKHfv3PyrNzz/y1Rzc07AJVU4ymOrj/s2uXnpmXsE1Plk9DUkflLRV0o8kvSbptyXNktQpaX96bi3Zf72kbkn7JK0oiS+W9HJ6b6Mkpfh0SU+k+C5JC0rKdKTP2C+po3pNt8mS+w3z/i8W2LD6MhbNncmG1Zd5fsAmhFdwjU/WRLikzcDzEfEtSR8AzgS+CvRFxD2S1gGtEXG7pEXAY8ASYC7wLPCRiDguaTdwK/B94GlgY0Rsl3Qz8PGI+JKkduCaiPi8pFlAF1AAAtgDLI6II6c6Vk+Em9lIGnGxxWSq2nUakmYCvwM8ABAR/xYRvwBWAZvTbpuB1Wl7FfB4RPRHxOtAN7BE0hxgZkTsjGKmenhYmcG6tgLLUi9kBdAZEX0pUXQCK8c6ZjOz4Sr5NU37dTnDU78J9AIPSnpR0rcknQWcHxGHAdLz7LT/PODNkvIHU2xe2h4eH1ImIgaAd4BzR6lrCElrJXVJ6urt7c1okplNNV5sUR05SaMF+ARwX0RcAfwSWDfK/hohFqPEKy1zMhCxKSIKEVFoa2sb5dDMbCrzCq7xy1k9dRA4GBG70uutFJPGW5LmRMThNPTUU7L/BSXl5wOHUnz+CPHSMgcltQDnAH0p/rvDynwvq2VmZsPUywquXPW40mvMnkZE/Bx4U9IlKbQMeBXYBgyuZuoAnkrb24D2tCLqYmAhsDsNYR2VtDTNV9wwrMxgXdcCz6V5j2eA5ZJa0+qs5SlmZtb06nGlV+51Gl8Gvp1WTv0E+GOKCWeLpDXAAeA6gIjYK2kLxcQyANwSEcdTPTcBDwFnANvTA4qT7I9I6qbYw2hPdfVJuht4Ie13V0T0VdhWM7OGUM/36vK9p8zM6kwt7tXlW6ObmTWoel7p5duImJnVoXq9rY6Hp8zMzMNTza5RfuHOzJqLk0aDqseleGbW/Dyn0WDqeSmemTU/9zQajG+6Zma15KTRYOp5KZ7ZVDWV5hidNBqQb7pmVl+m0hyjl9yamVWomX7YyUtuzcwmWCVzjI0+lOWkYWZWoUrmGBt9KMtLbs3MxiH3dh/NslzecxpmZpOgFneuLYfnNMzM6kizLJf38JSZ2SSp1zvXlsPDU2Zm5uEpMzOrPicNa1iNvt7drBE5aVjDavT17maNyBPh1nCaZb27WSNyT8Majm8Pb1Y7ThrWcJplvbtZI8pKGpLekPSypJckdaXYLEmdkvan59aS/ddL6pa0T9KKkvjiVE+3pI2SlOLTJT2R4rskLSgp05E+Y7+kjmo13Bqbbw9vVhtZ12lIegMoRMTbJbG/BPoi4h5J64DWiLhd0iLgMWAJMBd4FvhIRByXtBu4Ffg+8DSwMSK2S7oZ+HhEfElSO3BNRHxe0iygCygAAewBFkfEkVMdq6/TMDMr32Rcp7EK2Jy2NwOrS+KPR0R/RLwOdANLJM0BZkbEzihmqoeHlRmsayuwLPVCVgCdEdGXEkUnsHIcx2xmZuOQmzQC+K6kPZLWptj5EXEYID3PTvF5wJslZQ+m2Ly0PTw+pExEDADvAOeOUtcQktZK6pLU1dvbm9kkMzMrV+6S209GxCFJs4FOST8aZV+NEItR4pWWORmI2ARsguLw1CjHZmZm45DV04iIQ+m5B3iS4nzFW2nIifTck3Y/CFxQUnw+cCjF548QH1JGUgtwDtA3Sl1mZlYDYyYNSWdJOntwG1gOvAJsAwZXM3UAT6XtbUB7WhF1MbAQ2J2GsI5KWprmK24YVmawrmuB59K8xzPAckmtaXXW8hQzM7MayBmeOh94Mq2ObQG+ExH/W9ILwBZJa4ADwHUAEbFX0hbgVWAAuCUijqe6bgIeAs4AtqcHwAPAI5K6KfYw2lNdfZLuBl5I+90VEX3jaK+ZmY2Db41uZma+NbqZmVWfk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZNicNMzPL5qRhZmbZnDTMzCybk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZNicNMzPL5qRhZmbZnDTMzCybk4aZmWVz0jAzs2xOGmZmls1Jw8zMsjlpmJlZtuykIek0SS9K+vv0epakTkn703Nryb7rJXVL2idpRUl8saSX03sbJSnFp0t6IsV3SVpQUqYjfcZ+SR3VaLSZmVWmnJ7GrcBrJa/XATsiYiGwI71G0iKgHbgUWAl8U9Jpqcx9wFpgYXqsTPE1wJGI+DDwdeDeVNcs4E7gSmAJcGdpcjIzs8mVlTQkzQc+C3yrJLwK2Jy2NwOrS+KPR0R/RLwOdANLJM0BZkbEzogI4OFhZQbr2gosS72QFUBnRPRFxBGgk5OJxszMJlluT+OvgNuAEyWx8yPiMEB6np3i84A3S/Y7mGLz0vbw+JAyETEAvAOcO0pdZmZWA2MmDUl/APRExJ7MOjVCLEaJV1qm9BjXSuqS1NXb25t5mGZmVq6cnsYngaslvQE8Dnxa0qPAW2nIifTck/Y/CFxQUn4+cCjF548QH1JGUgtwDtA3Sl1DRMSmiChERKGtrS2jSWZmVokxk0ZErI+I+RGxgOIE93MR8UfANmBwNVMH8FTa3ga0pxVRF1Oc8N6dhrCOSlqa5ituGFZmsK5r02cE8AywXFJrmgBfnmJmZlYDLeMoew+wRdIa4ABwHUBE7JW0BXgVGABuiYjjqcxNwEPAGcD29AB4AHhEUjfFHkZ7qqtP0t3AC2m/uyKibxzHbGZm46DiF/rmUSgUoqurq9aHYWbWUCTtiYjCWPv5inAzM8vmpGFmZtmcNMzMLJuThpmZZXPSMDOzbE4aZmaWzUnDzMyyOWmYmVk2Jw0zM8vmpGFmZtmcNMzMLJuThpmZZXPSMDOzbE4aZmaWzUnDzMyyOWmYmVk2Jw0zM8vmpGFmZtmcNMzMLJuThpmZZXPSMDOzbE4aZmaWzUnDzMyyjZk0JM2QtFvSDyXtlfRfU3yWpE5J+9Nza0mZ9ZK6Je2TtKIkvljSy+m9jZKU4tMlPZHiuyQtKCnTkT5jv6SOajbezMzKk9PT6Ac+HRG/BVwOrJS0FFgH7IiIhcCO9BpJi4B24FJgJfBNSaeluu4D1gIL02Nliq8BjkTEh4GvA/emumYBdwJXAkuAO0uTk5mZTa4xk0YUvZdenp4eAawCNqf4ZmB12l4FPB4R/RHxOtANLJE0B5gZETsjIoCHh5UZrGsrsCz1QlYAnRHRFxFHgE5OJhozM5tkWXMakk6T9BLQQ/Ef8V3A+RFxGCA9z067zwPeLCl+MMXmpe3h8SFlImIAeAc4d5S6zMysBrKSRkQcj4jLgfkUew2XjbK7RqpilHilZU5+oLRWUpekrt7e3lEOzczMxqOs1VMR8QvgexSHiN5KQ06k556020HggpJi84FDKT5/hPiQMpJagHOAvlHqGn5cmyKiEBGFtra2cppkZmZlyFk91Sbpg2n7DOD3gB8B24DB1UwdwFNpexvQnlZEXUxxwnt3GsI6Kmlpmq+4YViZwbquBZ5L8x7PAMsltaYJ8OUpZmZmNdCSsc8cYHNaATUN2BIRfy9pJ7BF0hrgAHAdQETslbQFeBUYAG6JiOOprpuAh4AzgO3pAfAA8Iikboo9jPZUV5+ku4EX0n53RUTfeBpsZmaVU/ELffMoFArR1dVV68MwM2sokvZERGGs/XxFuJmZZXPSMDOzbE4aZmaWzUnDzMyyOWmYmVk2Jw0zM8vmpGFmZtmcNMzMLJuThpmZZXPSMDOzbE4aZmaWzUnDzMyyOWmYmVk2Jw0zM8vmpGFmZtmcNMzMLJuThpmZZXPSMDNrAj3vHuNz9++k5+ixCf0cJw0zsyawccd+Xnijj43P7p/Qz2mZ0NrNzGxCXfK17fQPnPjV60d3HeDRXQeY3jKNfRs+U/XPc0/DzKyBPX/bVVx9+VxmnF7853zG6dNYdflcnr/9qgn5PCcNM7MGNnvmDM6e3kL/wAmmt0yjf+AEZ09vYfbZMybk8zw8ZWbW4N5+r5/rr7yILyy5kO/sPkDvBE6GKyJG30G6AHgY+BBwAtgUEX8taRbwBLAAeAP4XEQcSWXWA2uA48CfRcQzKb4YeAg4A3gauDUiQtL09BmLgf8HfD4i3khlOoCvpcPZEBGbRzveQqEQXV1d+f8FzMwMSXsiojDWfjnDUwPAf46IjwJLgVskLQLWATsiYiGwI70mvdcOXAqsBL4p6bRU133AWmBheqxM8TXAkYj4MPB14N5U1yzgTuBKYAlwp6TWjGM2M7MJMGbSiIjDEfGDtH0UeA2YB6wCBr/1bwZWp+1VwOMR0R8RrwPdwBJJc4CZEbEzit2bh4eVGaxrK7BMkoAVQGdE9KVeTCcnE42ZmU2ysibCJS0ArgB2AedHxGEoJhZgdtptHvBmSbGDKTYvbQ+PDykTEQPAO8C5o9RlZmY1kJ00JP0G8L+Ar0TEu6PtOkIsRolXWqb02NZK6pLU1dvbO8qhmZnZeGQlDUmnU0wY346Iv03ht9KQE+m5J8UPAheUFJ8PHErx+SPEh5SR1AKcA/SNUtcQEbEpIgoRUWhra8tpkpmZVWDMpJHmFh4AXouI/17y1jagI213AE+VxNslTZd0McUJ791pCOuopKWpzhuGlRms61rguTTv8QywXFJrmgBfnmJmZlYDOUtuPwU8D7xMccktwFcpzmtsAS4EDgDXRURfKnMHcCPFlVdfiYjtKV7g5JLb7cCX05LbGcAjFOdL+oD2iPhJKnNj+jyAv4iIB8c43l7gp5ntr0fnAW/X+iAmSLO2ze1qPM3atvG066KIGHOoZsykYZNLUlfOWulG1Kxtc7saT7O2bTLa5duImJlZNicNMzPL5qRRfzbV+gAmULO2ze1qPM3atglvl+c0zMwsm3saZmaWzUljEkj6G0k9kl4pif2WpJ2SXpb0d5JmpvgHJD2Y4j+U9LslZb4naZ+kl9Jj9ggfN2kkXSDp/0h6TdJeSbem+CxJnZL2p+fWkjLrJXWndqwoiS9Obe6WtDFdy1MTVW5X3Zyzctsl6dy0/3uSvjGsrro5X+l4qtm2Rj5nvy9pTzo3eyR9uqSu6pyziPBjgh/A7wCfAF4pib0A/Ie0fSNwd9q+BXgwbc8G9gDT0uvvAYVat6ekDXOAT6Tts4F/BRYBfwmsS/F1wL1pexHwQ2A6cDHwY+C09N5u4Lcp3jpmO/CZJmlX3ZyzCtp1FvAp4EvAN4bVVTfnawLa1sjn7Apgbtq+DPhZtc+ZexqTICL+keJFi6UuAf4xbXcCf5i2F1G81TwR0QP8AqjL9eQxOXdAnnTVatfkHvXYym1XRPwyIv4vMOQXfertfEH12lZvKmjXixExeKulvcAMFe/OUbVz5qRRO68AV6ft6zh5j60fAqsktah4G5bFDL3/1oOpy/xfaj0kUEoTdwfkmhpnuwbV3TnLbNep1O35gnG3bVAznLM/BF6MiH6qeM6cNGrnRoo/aLWHYrfz31L8byie0C7gr4B/png7FoDrI+JjwL9Pjy9O6hGfgib2Dsg1U4V2QR2eszLadcoqRojV/HxBVdoGTXDOJF1K8cfs/mQwNMJuFZ0zJ40aiYgfRcTyiFgMPEZxHJyIGIiIP4+IyyNiFfBBYH9672fp+SjwHepgCEQTfwfkmqhSu+runJXZrlOpu/MFVWtbw58zSfOBJ4EbIuLHKVy1c+akUSODKzIkTaP4G+j/I70+U9JZafv3gYGIeDUNV52X4qcDf0BxiKtmUrd9ou+APOmq1a56O2cVtGtE9Xa+oHpta/RzJumDwD8A6yPinwZ3ruo5m4wVAFP9QbEncRh4n2LGXwPcSnElxL8C93DyQssFwD6KE17PUrzzJBRXe+wB/oXiBNdfk1bo1LBdn6LYxf0X4KX0+I8Uf3VxB8Ue0g5gVkmZOyj2qvZRsnqD4mT/K+m9bwz+92jkdtXbOauwXW9QXMTxXvp/d1G9na9qtq3RzxnFL6C/LNn3JWB2Nc+Zrwg3M7NsHp4yM7NsThpmZpbNScPMzLI5aZiZWTYnDTMzy+akYWZm2Zw0zMwsm5OGmZll+/+mYFOfctSjewAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une liste triée permet de repérer les valeurs les plus élevées plus aisément, elles seront situées à la fin." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "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": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre bien que la varicelle ne déclenche pas d'épidémies fortes, qui touchent environ 10% de la population française. Les épidémies les plus fortes touchent un à deux % de la population française." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "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", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }