{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence de la varicelle sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1991 (fin 1990) 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-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"varicelle.csv\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'explication des colonnes 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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202039712081762240204FRFrance
1202038722867873785315FRFrance
2202037715844052763204FRFrance
320203679191001738102FRFrance
4202035782801694102FRFrance
5202034722723714173306FRFrance
6202033712841772391204FRFrance
7202032726506894611417FRFrance
8202031713031002506204FRFrance
920203071385752695204FRFrance
102020297841101672102FRFrance
11202028772801515102FRFrance
1220202779861491823102FRFrance
13202026769401454102FRFrance
1420202572280597001FRFrance
1520202473880959102FRFrance
16202023755811115102FRFrance
1720202272770633001FRFrance
182020217602361168102FRFrance
192020207824201628102FRFrance
2020201973100753001FRFrance
212020187849981600102FRFrance
2220201772720658001FRFrance
232020167758781438102FRFrance
24202015719186753161315FRFrance
252020147387922275531639FRFrance
26202013773265236941611814FRFrance
272020127812357901045612816FRFrance
28202011710198756812828151119FRFrance
2920201079011669111331141018FRFrance
.................................
15261991267176081130423912312042FRFrance
15271991257161691070021638281838FRFrance
15281991247161711007122271281739FRFrance
1529199123711947767116223211329FRFrance
1530199122715452995320951271737FRFrance
1531199121714903897520831261636FRFrance
15321991207190531274225364342345FRFrance
15331991197167391124622232291939FRFrance
15341991187213851388228888382551FRFrance
1535199117713462887718047241632FRFrance
15361991167148571006819646261834FRFrance
1537199115713975978118169251832FRFrance
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153919911379567604113093171123FRFrance
1540199112710864733114397191325FRFrance
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15531990517190801380724353342543FRFrance
1554199050711079666015498201228FRFrance
15551990497114302610205FRFrance
\n", "

1556 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202039 7 1208 176 2240 2 0 \n", "1 202038 7 2286 787 3785 3 1 \n", "2 202037 7 1584 405 2763 2 0 \n", "3 202036 7 919 100 1738 1 0 \n", "4 202035 7 828 0 1694 1 0 \n", "5 202034 7 2272 371 4173 3 0 \n", "6 202033 7 1284 177 2391 2 0 \n", "7 202032 7 2650 689 4611 4 1 \n", "8 202031 7 1303 100 2506 2 0 \n", "9 202030 7 1385 75 2695 2 0 \n", "10 202029 7 841 10 1672 1 0 \n", "11 202028 7 728 0 1515 1 0 \n", "12 202027 7 986 149 1823 1 0 \n", "13 202026 7 694 0 1454 1 0 \n", "14 202025 7 228 0 597 0 0 \n", "15 202024 7 388 0 959 1 0 \n", "16 202023 7 558 1 1115 1 0 \n", "17 202022 7 277 0 633 0 0 \n", "18 202021 7 602 36 1168 1 0 \n", "19 202020 7 824 20 1628 1 0 \n", "20 202019 7 310 0 753 0 0 \n", "21 202018 7 849 98 1600 1 0 \n", "22 202017 7 272 0 658 0 0 \n", "23 202016 7 758 78 1438 1 0 \n", "24 202015 7 1918 675 3161 3 1 \n", "25 202014 7 3879 2227 5531 6 3 \n", "26 202013 7 7326 5236 9416 11 8 \n", "27 202012 7 8123 5790 10456 12 8 \n", "28 202011 7 10198 7568 12828 15 11 \n", "29 202010 7 9011 6691 11331 14 10 \n", "... ... ... ... ... ... ... ... \n", "1526 199126 7 17608 11304 23912 31 20 \n", "1527 199125 7 16169 10700 21638 28 18 \n", "1528 199124 7 16171 10071 22271 28 17 \n", "1529 199123 7 11947 7671 16223 21 13 \n", "1530 199122 7 15452 9953 20951 27 17 \n", "1531 199121 7 14903 8975 20831 26 16 \n", "1532 199120 7 19053 12742 25364 34 23 \n", "1533 199119 7 16739 11246 22232 29 19 \n", "1534 199118 7 21385 13882 28888 38 25 \n", "1535 199117 7 13462 8877 18047 24 16 \n", "1536 199116 7 14857 10068 19646 26 18 \n", "1537 199115 7 13975 9781 18169 25 18 \n", "1538 199114 7 12265 7684 16846 22 14 \n", "1539 199113 7 9567 6041 13093 17 11 \n", "1540 199112 7 10864 7331 14397 19 13 \n", "1541 199111 7 15574 11184 19964 27 19 \n", "1542 199110 7 16643 11372 21914 29 20 \n", "1543 199109 7 13741 8780 18702 24 15 \n", "1544 199108 7 13289 8813 17765 23 15 \n", "1545 199107 7 12337 8077 16597 22 15 \n", "1546 199106 7 10877 7013 14741 19 12 \n", "1547 199105 7 10442 6544 14340 18 11 \n", "1548 199104 7 7913 4563 11263 14 8 \n", "1549 199103 7 15387 10484 20290 27 18 \n", "1550 199102 7 16277 11046 21508 29 20 \n", "1551 199101 7 15565 10271 20859 27 18 \n", "1552 199052 7 19375 13295 25455 34 23 \n", "1553 199051 7 19080 13807 24353 34 25 \n", "1554 199050 7 11079 6660 15498 20 12 \n", "1555 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 4 FR France \n", "1 5 FR France \n", "2 4 FR France \n", "3 2 FR France \n", "4 2 FR France \n", "5 6 FR France \n", "6 4 FR France \n", "7 7 FR France \n", "8 4 FR France \n", "9 4 FR France \n", "10 2 FR France \n", "11 2 FR France \n", "12 2 FR France \n", "13 2 FR France \n", "14 1 FR France \n", "15 2 FR France \n", "16 2 FR France \n", "17 1 FR France \n", "18 2 FR France \n", "19 2 FR France \n", "20 1 FR France \n", "21 2 FR France \n", "22 1 FR France \n", "23 2 FR France \n", "24 5 FR France \n", "25 9 FR France \n", "26 14 FR France \n", "27 16 FR France \n", "28 19 FR France \n", "29 18 FR France \n", "... ... ... ... \n", "1526 42 FR France \n", "1527 38 FR France \n", "1528 39 FR France \n", "1529 29 FR France \n", "1530 37 FR France \n", "1531 36 FR France \n", "1532 45 FR France \n", "1533 39 FR France \n", "1534 51 FR France \n", "1535 32 FR France \n", "1536 34 FR France \n", "1537 32 FR France \n", "1538 30 FR France \n", "1539 23 FR France \n", "1540 25 FR France \n", "1541 35 FR France \n", "1542 38 FR France \n", "1543 33 FR France \n", "1544 31 FR France \n", "1545 29 FR France \n", "1546 26 FR France \n", "1547 25 FR France \n", "1548 20 FR France \n", "1549 36 FR France \n", "1550 38 FR France \n", "1551 36 FR France \n", "1552 45 FR France \n", "1553 43 FR France \n", "1554 28 FR France \n", "1555 5 FR France \n", "\n", "[1556 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ?" ] }, { "cell_type": "code", "execution_count": 6, "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": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apparament, il n'y a pas de valeurs manquantes. Nous procédons toutefois à la mise en place d'une élimination de données manquantes dans l'éventualité d'une mise à jour future de cette analyse." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202039712081762240204FRFrance
1202038722867873785315FRFrance
2202037715844052763204FRFrance
320203679191001738102FRFrance
4202035782801694102FRFrance
5202034722723714173306FRFrance
6202033712841772391204FRFrance
7202032726506894611417FRFrance
8202031713031002506204FRFrance
920203071385752695204FRFrance
102020297841101672102FRFrance
11202028772801515102FRFrance
1220202779861491823102FRFrance
13202026769401454102FRFrance
1420202572280597001FRFrance
1520202473880959102FRFrance
16202023755811115102FRFrance
1720202272770633001FRFrance
182020217602361168102FRFrance
192020207824201628102FRFrance
2020201973100753001FRFrance
212020187849981600102FRFrance
2220201772720658001FRFrance
232020167758781438102FRFrance
24202015719186753161315FRFrance
252020147387922275531639FRFrance
26202013773265236941611814FRFrance
272020127812357901045612816FRFrance
28202011710198756812828151119FRFrance
2920201079011669111331141018FRFrance
.................................
15261991267176081130423912312042FRFrance
15271991257161691070021638281838FRFrance
15281991247161711007122271281739FRFrance
1529199123711947767116223211329FRFrance
1530199122715452995320951271737FRFrance
1531199121714903897520831261636FRFrance
15321991207190531274225364342345FRFrance
15331991197167391124622232291939FRFrance
15341991187213851388228888382551FRFrance
1535199117713462887718047241632FRFrance
15361991167148571006819646261834FRFrance
1537199115713975978118169251832FRFrance
1538199114712265768416846221430FRFrance
153919911379567604113093171123FRFrance
1540199112710864733114397191325FRFrance
15411991117155741118419964271935FRFrance
15421991107166431137221914292038FRFrance
1543199109713741878018702241533FRFrance
1544199108713289881317765231531FRFrance
1545199107712337807716597221529FRFrance
1546199106710877701314741191226FRFrance
1547199105710442654414340181125FRFrance
15481991047791345631126314820FRFrance
15491991037153871048420290271836FRFrance
15501991027162771104621508292038FRFrance
15511991017155651027120859271836FRFrance
15521990527193751329525455342345FRFrance
15531990517190801380724353342543FRFrance
1554199050711079666015498201228FRFrance
15551990497114302610205FRFrance
\n", "

1556 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202039 7 1208 176 2240 2 0 \n", "1 202038 7 2286 787 3785 3 1 \n", "2 202037 7 1584 405 2763 2 0 \n", "3 202036 7 919 100 1738 1 0 \n", "4 202035 7 828 0 1694 1 0 \n", "5 202034 7 2272 371 4173 3 0 \n", "6 202033 7 1284 177 2391 2 0 \n", "7 202032 7 2650 689 4611 4 1 \n", "8 202031 7 1303 100 2506 2 0 \n", "9 202030 7 1385 75 2695 2 0 \n", "10 202029 7 841 10 1672 1 0 \n", "11 202028 7 728 0 1515 1 0 \n", "12 202027 7 986 149 1823 1 0 \n", "13 202026 7 694 0 1454 1 0 \n", "14 202025 7 228 0 597 0 0 \n", "15 202024 7 388 0 959 1 0 \n", "16 202023 7 558 1 1115 1 0 \n", "17 202022 7 277 0 633 0 0 \n", "18 202021 7 602 36 1168 1 0 \n", "19 202020 7 824 20 1628 1 0 \n", "20 202019 7 310 0 753 0 0 \n", "21 202018 7 849 98 1600 1 0 \n", "22 202017 7 272 0 658 0 0 \n", "23 202016 7 758 78 1438 1 0 \n", "24 202015 7 1918 675 3161 3 1 \n", "25 202014 7 3879 2227 5531 6 3 \n", "26 202013 7 7326 5236 9416 11 8 \n", "27 202012 7 8123 5790 10456 12 8 \n", "28 202011 7 10198 7568 12828 15 11 \n", "29 202010 7 9011 6691 11331 14 10 \n", "... ... ... ... ... ... ... ... \n", "1526 199126 7 17608 11304 23912 31 20 \n", "1527 199125 7 16169 10700 21638 28 18 \n", "1528 199124 7 16171 10071 22271 28 17 \n", "1529 199123 7 11947 7671 16223 21 13 \n", "1530 199122 7 15452 9953 20951 27 17 \n", "1531 199121 7 14903 8975 20831 26 16 \n", "1532 199120 7 19053 12742 25364 34 23 \n", "1533 199119 7 16739 11246 22232 29 19 \n", "1534 199118 7 21385 13882 28888 38 25 \n", "1535 199117 7 13462 8877 18047 24 16 \n", "1536 199116 7 14857 10068 19646 26 18 \n", "1537 199115 7 13975 9781 18169 25 18 \n", "1538 199114 7 12265 7684 16846 22 14 \n", "1539 199113 7 9567 6041 13093 17 11 \n", "1540 199112 7 10864 7331 14397 19 13 \n", "1541 199111 7 15574 11184 19964 27 19 \n", "1542 199110 7 16643 11372 21914 29 20 \n", "1543 199109 7 13741 8780 18702 24 15 \n", "1544 199108 7 13289 8813 17765 23 15 \n", "1545 199107 7 12337 8077 16597 22 15 \n", "1546 199106 7 10877 7013 14741 19 12 \n", "1547 199105 7 10442 6544 14340 18 11 \n", "1548 199104 7 7913 4563 11263 14 8 \n", "1549 199103 7 15387 10484 20290 27 18 \n", "1550 199102 7 16277 11046 21508 29 20 \n", "1551 199101 7 15565 10271 20859 27 18 \n", "1552 199052 7 19375 13295 25455 34 23 \n", "1553 199051 7 19080 13807 24353 34 25 \n", "1554 199050 7 11079 6660 15498 20 12 \n", "1555 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 4 FR France \n", "1 5 FR France \n", "2 4 FR France \n", "3 2 FR France \n", "4 2 FR France \n", "5 6 FR France \n", "6 4 FR France \n", "7 7 FR France \n", "8 4 FR France \n", "9 4 FR France \n", "10 2 FR France \n", "11 2 FR France \n", "12 2 FR France \n", "13 2 FR France \n", "14 1 FR France \n", "15 2 FR France \n", "16 2 FR France \n", "17 1 FR France \n", "18 2 FR France \n", "19 2 FR France \n", "20 1 FR France \n", "21 2 FR France \n", "22 1 FR France \n", "23 2 FR France \n", "24 5 FR France \n", "25 9 FR France \n", "26 14 FR France \n", "27 16 FR France \n", "28 19 FR France \n", "29 18 FR France \n", "... ... ... ... \n", "1526 42 FR France \n", "1527 38 FR France \n", "1528 39 FR France \n", "1529 29 FR France \n", "1530 37 FR France \n", "1531 36 FR France \n", "1532 45 FR France \n", "1533 39 FR France \n", "1534 51 FR France \n", "1535 32 FR France \n", "1536 34 FR France \n", "1537 32 FR France \n", "1538 30 FR France \n", "1539 23 FR France \n", "1540 25 FR France \n", "1541 35 FR France \n", "1542 38 FR France \n", "1543 33 FR France \n", "1544 31 FR France \n", "1545 29 FR France \n", "1546 26 FR France \n", "1547 25 FR France \n", "1548 20 FR France \n", "1549 36 FR France \n", "1550 38 FR France \n", "1551 36 FR France \n", "1552 45 FR France \n", "1553 43 FR France \n", "1554 28 FR France \n", "1555 5 FR France \n", "\n", "[1556 rows x 10 columns]" ] }, "execution_count": 7, "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 semaine est collé à l'année, donnant l'impression qu'il s'agit de nombre entier. C'est comme ça que Pandas les interprète.\n", "\n", "Un deuxième problème est que Pandas ne comprend pas les numéros de semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque isoweek.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 8, "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 comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans le sens chronologique." ] }, { "cell_type": "code", "execution_count": 9, "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 le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives entre lesquelles il manque une semaine." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Toutes les périodes sont contigües. Cela est cohérent avec le fait qu'il n'y a pas de données manquantes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "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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uuSX8fjeAGVKq2NHhy9nVA1fDLVIrwfDYtLO4skqDdHunplbqgUefhdn42+vrvOvGdpxsN5FXnMOctzYAAFrbzSqbr971Ev5XcR3uLdRDWvwsyGRzCNU9hwF4Liz6MhG9QkS/JqIRYdl4ACprszwsGx9+18sT1wghOgG0ABiVZWw9jZ6Oc6gD2hCBDXir8v59F/yD81dhzeZWAAFxUMfQ1x59lvGQp6u0jrzUSpHNyFLnjy+swA8efDWX/qpBkd+MhzdxIKLBAP4A4GIhxCYEKqK9AEwBsArAf8uqzOXCUm67Rh/DLCKaS0Rz163z5566A3l5K8Uple31+uLEve3Zt7GqZXtu7XGeW7YtyueR+D63C29/IdL3d3RVEmqlnrQ5eCHDeOQef/0TS7FgRUvm62pFf0hJnzWnV73AizgQUSMCwnC7EOKPACCEWCOE6BJCVAD8CsC0sPpyABOVyycAWBmWT2DKE9cQUQOAYQDe18chhLheCDFVCDF19OjedTnLax5FRlAHdehrE3dVy3Zcds8CzLp1Xm5txlxufvdaTVO3/H1ZIuiur7myZpkLqu3gjJ8+VdV1tSCWHNxjvuHJpZlsRHmjF7vuk/DxViIANwJYLIT4H6V8rFLtYwAWhN/vA3BO6IG0BwLD8xwhxCoAm4loetjmuQDuVa45L/x+FoDHRF34bsbEQfWr59CbxOGvr63FZ258LopWBmL3vy1tnabLMoONc6jRlbWa65a+txXvrN8at1Fd192GnpgLeamVfDMAAMAVsxfjnpdWuivWiLfXb8XUKx6J/o/USoXNIQGfILhjAHwGwHwieiks+08AnySiKQjWzjIAXwAAIcRCIroLwCIEnk4XCSGkNepCADcDGAjgwfAPCIjPbUS0BIHEcE5tt5UdW9s68ezS9Zix/xiv+nmtz6aGYGK6iUM+/VWDy+5dgHff344VG7Zj8pghAOLxSiNuHuDEexsD6/MOqlX/qS6wfU1qyzKcaoeelzdI1jM6euJZ/3bOO3hvS5xUMesY6wVO4iCEeAr8XHnAcs2VAK5kyucCOIgpbwVwtmss3YnL71+EO+e+i9n/eiwOHJfOka8jr2nU5Cs5JLxnRI+e1CU1XqoeviOUwRsb8hyH2f5S7Safy3rvYzaHLIxCtc8tr+nlui9dQTC42b4lbW/vwsqW7dhr9OCqx5RSmcmzLArikEARIR1ibeipsrql1at+XlqvSK3kUHiqG0JPSxHcgUSSmDU3lKMyIQTufWkFWquMNOX00/EJZen63ZkQT90+euJxZ5tP/nVfX1NdNtu890lTe20aUzSoqcxXDHHh7fMw47//VpNtQidXkeRQqJUSKIhDiKEDGwHAO1gnN8mhwU9ySLhW9jCHw6l72hi10t/fXI+v3PESrqrSLZHzbKmVg632UVUSz7u2MeSNvjYeG1w2I93V2GUI/3voUZYlfsU0JtdY6h0FcQgRZ+SMJ8gPHlyMSZfM5jfjnL2VstgcenoKS+5d3TDbI7VSPIU2hZlMfaUvE/JMn1G1zUGZBz2RsjubWqkHvKdyasflrZS1H/0402pQT0kFa0FBHELIuaZOm//921IA3avSaG7wVSv1PiebWI/h96xOLfe+tML4G2uQTnaXGXk8q77GqfcEg5uXdCo3YtNmrvfj6lXGBXVmSMWuI6VW8sxvVm8oiEMIOUk5roKbM7WuHSEE/u+V2G1P173qSARl9aDscP0Tb+Kd97cFY8hh9XzljpeMv3ESCiyxDz6j6Sf26EybcV/Zw15fsxnPLrUnCnSlpNeLXc9Brs8shzilB5Vc47LPwlspieI8hxByWpQUckkUEIGN29rRsr2DrV8tHlywGl/+7YvR/9lsDjV2ngHffyC2H5gWrhAC775fe6S0T6qFrMiDA66TkJvMOOWaJwAAy6463VjHdYBT5rQe4RypyeZQ9ZX1hUJyCCEnLzFT54grHsG37lmQKKt1v1i/JXl4vduVtbb+8kDyjIP4+2/nvIPjf/g4XnhnQ+q3LIgN0n5qJZ9Nu79IDllsDkfsFqQxO2SC2+W6WuSWOyz8NHLlwvpvAi3bO7CpNQi6tBGHnz++BJf8wXzMqq4d2NrWZR9jnaIgDiEim4PnGq1FtfPyuxtxmXZMZnuX3f2zz9kcQhCAeW8HREGeweyLt9fz9TlvpWrvefYrq6q7MDGg2ptwdpHhBuUzGT98YDeNJkcwbtAA8N6WNrR1dqXXkeUxnPijv0bfba6sP/zza7jjeXNac32Nr94UOFAUNockCuIQQi5OXw6ulg362/cuSJVl8VbqLQ7HtIHFR3km/3fhjGuTuX6yGgZd1X71xFLc+NRbfo2FOGj80FRZX+Mo/+fh1wHkF6jGIS+7linj6dQrHsGFv3khtY70esdc9RjO/uXfAQDrt7ZH5d2hVupjr7nXURCHEPHGFsOaEbSGvrYxue0z2Rxq6LsWcBGkAip3n21km7W8TPbsmNnv+soHFme+hjvHoB43DdM9t3Z0YeO2dv5HBja10mOvrk29VX3PX7FxO55ftiF1rY+30n6XPYi1m9Ju1aa8UX2NCehtFMQhhNzYGG9N0wVV98URhyzHhPaWgTSZSpuU7wHyGlVCrcSUcfXyArdx9NUtozemwSeufxZTLn/Yu/4ba4MIbaO3knYTvhu0j7dSa0cFf81wMFKBJAriEELONd+Nt5Z1yU1s90lw+fRdC0zpBXRmu9Z8PgmDtDXzXlXdWMFLDt3/xKvJ69OdwzK1/fK7G6tqzxjn4NmvDt84hywpMQqvtCQK4hBCbs4c18rh25pBOQvYDchxTW+5sqpIGMWZ/Ee1GvRs7XTnLavPtrckh2qenYBIeb3lhbzv2ewG7VcvdZ1nv1mIrukdPP7qWjw4PwfHhn6GgjiEENFn928F5XJ27jQhWfQSceC4P0IcG1Ir58Un3pNtp+t3x7vqLZuD77NTz8/488I1OOKKR7DsvWxeYnnAFunOwRwEp6uVfNurXnIwPWtTm5+7+XlcePsLfgPzxPIN27Ddcq52X0DdE4e5y97HPt96EO+HnhA9sRFwG5DzmFD1ZLJeog4J1VZiCDmdGsb0U62xOwvUpsussbL7n7dvHMtRP3g0VbZ8Q35HtUq4njcX6b69vQv3vLiCvTYRI2NhdHzntu904BgaY8hFDy6rY69+HJ+9aU7PdVgF6p44XPfXN9HeWYl89ZM+9t3jK8irLvwlh95SK5lUW7qX0brNtak6kkFw5nfQYwbpHnjevpzw5tb8Tt6zoZpb/t7sRbj4zpfw3FupE36Nc8flrWQcn+fz4kwTpitdbV5+/6IotX8e4J5TX0LdE4e0MbX7wUoOjo7VedtTLnfPaXlzkvmdYuiqnxfeqc5oaTtSsqfoYUMv2ByEEPj9vOXOevr7kOgrSUZlNt4tDAEzzZ3utjlkMkg7fv/102/hv2qwNfY31D1x0FUiPeGxwKkuXP2aFhfXzg1PLsWkS2bj3+40J7kDAr2n7fyKT1z/bOL/t9dvY8crN6dXlrdY+3M+Wy7OwRIhndebUtthEy9285RY5ZniXH8f3Yqc7/l9JYBNnQc6o+PL+HirlbjYHMO1Pn3XEnzX31D3xCElOfSEzaEK1YWvWmnpe1txxewg+OtPL9qNhsde/XgqStmGvyxcHX1frQQXSdXPlja7ysP32fJxDj2zKLkjsbvTxrNy43a8vmazs96GrebAs+4QHKq55zjLQPo31UZhc8v2fc2+RGTlxrQ9xnRvPofL9REhrUfgJA5ENJGIHieixUS0kIi+EpaPJKKHieiN8HOEcs2lRLSEiF4jolOV8iOIaH7427UUKvWJqJmI7gzLnyOiSfnfquH+tP85T5nc+2QazhbnYK6bdROV6bh98PLyFjyyaA2A2JWXiDLko7LDFk3LXZsXwVDbaSill4SNWaxVB/0vNz+Pz970vLNef+JXbfOhvbOCjdt5KQLIYnPwq3frM297X9tT6tr+Ek/hIzl0AviaEGJ/ANMBXEREBwC4BMCjQojJAB4N/0f42zkADgQwE8AviEgeDHsdgFkAJod/M8Py8wFsEELsDeAaAFfncG9e6A3JgYOrW5uHh4ruPuXqjy+mdeO+PboWBZdbqZbEe1MmDs98TXNDeknYxv2F2+Zl7kPFq6vdUgPgiAjuhlfeXevgC7fNxbQrY48rV26luJ6w/m+D77U+55XkQUD6i2bKSRyEEKuEEC+E3zcDWAxgPIAzAdwSVrsFwEfD72cCuEMI0SaEeAvAEgDTiGgsgKFCiGdE8HZu1a6Rbd0NYIaUKnoaPfHeuA3cNWFUw5qtKu+KmR847yHvZIXOttM12QOAPNvbf2w6iZ5rXM2N2TStLdvsZ47nxSWq9h4dvokOs6C71sHjrznSWRg6Xq3lSMoyPv2URVNgnA9xaK/hBDqJ/pLDKdNKCNU9hwF4DsAYIcQqICAgAHYJq40HoObLXR6WjQ+/6+WJa4QQnQBaAIxi+p9FRHOJaO66dfnkTNEXVk+8N9aTPotayVK1r3Al7MltLnt0ZJBm2qtiDNWwF02M0cE27iED7OdlvbluS/ZBMPj0r54z/tZXvJWqeUe+ksMvHn/Tqx5n9+rQNnTTGvEySPsYJhzoJ7TBnzgQ0WAAfwBwsRBik60qUyYs5bZrkgVCXC+EmCqEmDp69GjXkL2gL6xkioieQxaDtG0S53GUpxXMm+I2Jzb4qIYAp6qIjVdvyXaaG8vp323jdu7M1e/c6j27zhjPG9VsYPKaLJKMb4T0bc8mbQem8bV1uDMem9xbvSQHR/ZkH+xQkgMRNSIgDLcLIf4YFq8JVUUIP9eG5csBTFQunwBgZVg+gSlPXENEDQCGAeiRCBFbnEMtG+2tzyzDpEtm8x48rFrJJTn4Ea1uJw4aCPxmkMWFUILL/V+LzaEajpq3OVj66IYx+PSbZQxZ8I1T982xNTd8JQcdpnrc9Nc3dNMa8Vk6HYXkECPU/d8IYLEQ4n+Un+4DcF74/TwA9yrl54QeSHsgMDzPCVVPm4loetjmudo1sq2zADwmutGk/0+/fAY/e+wNAMzGllO3Nzz5FgDgPSZamFvMmbyVullyyProWzuZFOQ1rCGWODAk0SWJeLvOKu1kVSu5Nv9aHAR6k8PMw33XZx6t11x0fXs11ePGnSIOyri+fMLe0XefgLlhg5r8BmiB+l4XrbQpYXoXPpLDMQA+A+BEInop/DsNwFUATiaiNwCcHP4PIcRCAHcBWATgIQAXCSHk7nEhgBsQGKnfBPBgWH4jgFFEtATAVxF6PnUHhBCYs+x9/Ogvr/O/59VP2JLv5uBaR8ncNOZ6PhvKO+u34aEFfJbJF97ZgD0ufcAcjav9LwD89rl3UvWqSUtuA3upo7lqehvYxKmVzPBRKk26ZDbOv9ntrpql30Qf3WB0qEqtJL+Ew/FhVGbdOlfr15ui88Wc5KAdwavWaVCSYPpkcD3A08nBBrWX0659Ess3+LuT9yTs1jQAQoinYF4DMwzXXAngSqZ8LoCDmPJWAGe7xpIH9NPH0mOxX//bCz6AT1mMg0AQeLN2UyAxcJwM51Dk6tc3f71PBOfxP3zc+NvjrwbawWeX1qbV4yQHt0GaUSvVkA5c7W/W8Xvi+ieWOq9pyujK6sKcMH/Oo6+uddRMwz9auG/qKXw2W11y8E13YXZ5TZfpa0IlWo2KpOjTdx4GaX3sm7Z3AiMMlXsRdRch3aVvshaDNIeJIwax5e2dlWiRHn3VY2gLRVndUwLgOT1Xv13KbmurWqtaSdpIdmpOc9BAeuyPGTY905GiNnDpuW1ZWdWSSZfMxinX/M1Y44xDxuL4fXgnBtceZpUcHFz7v//hFXvjtn6rYKJdhKJlu931lmuzWtQkfVTZNreO9DWhzk01l5YPMbMtr+/evxD/+af5zjaERl/O/bWd2ewt1B1x0N9tSk3imB8NZcJJ+49JiJcrN27HPt96EHc8/26qvq8ByzUtOxNxDubateqpt4bEYXAzL1T6KjD4VMn2sdXqyvr6mi3GFB7k6UPj42b8+KtrMfWKR9Da0dWt6RSqSTfiuuZzjjTRkc2lhnkkn8lGRwwIh1rPc+BKdSk2ERGvSA61BsHd9PQyVsWaHmOyjfd68KaeAAAgAElEQVS2+J/J3ZOoP+KgvVyd83NNjxIRSpScJDI3zgPMaVGcOqgag7SvzaFWyaG1I1hJAxiXzizgXVntkK/itmeW4fFX1yby4vi6snZ08hIWkafnkEfivStmL8J7W9q6XVfcHWqllx3JERuYg6h8oY9jxcbsz6fWrKycakiXCJJqpfh+ueett+fzTra121XXfSUWyYX6Iw7a//rLd03OgDhQYsOQKqTmhvSG6uuf7rQ5KOO0TdCbn17m1Z8Lpo3U1/ZZjSurxMvLW/C5m5/Hadc+mflaW4LCqiUHW33lgrWb7HmWlqzdjJk/fsJbtePv1hl/d13hmt+Sk65l/5IMV1sVMQG1EkSuOKVWUoal5tLi7GRrNW9DH+br87fMtf6+Q8U57EjQ30tWTrtEwbGY6gtuDQNvOE8XzoDFbbDcZP/aXS/je/+3KNWOacStHV14SMmc2h3g5vVuI9N2GNa453jUumfXxm0dbL6luDm7dJJIomihDJyNw/S73oeqrHrG4OElcc0jb+DV1ZvxxOt+0f2+MzNJEO1XudpsKpvP1PDFb559G5+7aQ5+5eEAoKMaVVqinLlDfY2rz6jR4a2kl/lsF39/0z4P+gtxcHor7WjQJ0/65dtfXLlEICKWOAxgPF1YgzTDn3KT7g8vBNlGLjvjgKTNwTBE1/kNeYBbQL5Ro0+/+Z71d+6gnchIXUV0dUpyqNLlU+37nhdXYOk6eWYzJUSNgTWq4lL9ejLeWTYbp02NyUqr4uDxwzB/hV019XCYubca+Nsc/MttBmmXt1Iqa6zHAHdimMRko84m+gTqTnLQX0yaq7BfToxaSSbjamSIA+sBwexR81e0oI0JJuPHyQ8yz2MHjZwZ84OeFA3gN6zbmPTJKriNyRYhzZfxA9cN0gtXxhtcMk27mXBvau3AxRoBVmsPasqX11Kf4W4jB0VutkfsnvR7TBBEpfybp+2fuc+GSHLgn2OjxSaRB0PsrVYyrAFu3Hqb6lpyxTmkNA0e43MlvyxsDn0U+ntJEQfH9eVS2iAtZxA3J7jJbpo6f3rBfDiPj+TQE/CNfOYWgI9UpsN2xgMHEwnVhYZHF/MuuETAV0/eJ1HWFar0Lv1D2k1RJcgDm7IvJ9s+oqvI5PGy+jGzJjsLEXDNJw5l216ylk8I2OiwOfSVja0WyWF7e8yEqQwJ72Gnt+9+AK4q/UWtVH/EwfGyXe+tHEoO6jyS31muk5lwJu2GLV10wlvJUMe0z9z89Ft4LTw3wOVJEbVlaMxfvWO3B3DgPGVsKZ659mwbpXpPak+Jekhv2JIwr2pJniqmPyOn2ooTIi3XqO9KiJh46kTUtmF/7LAJGDdsQKp8/or0Od8f3Gc0pu0x0tjWA/NXYdn6rcbf84BvEJzZtuK2G6hH47q8lVKJAT3G56qRR/K+nkD9EQft1emupq7NT24yHJfBn/DmP7bhA815W9R4iayMx3fuX4SZP3kCr63ejK/d9XK2izXUpBN26rtrV1kkVSy6uqi6VCaSOOj3lDJUO8bIpRSxjejYqx9PtC2Jgk5E1Y2Sm78cAeLGevXHD4neAff7l25/oarYhSzQs69mBSuxaoWbtsdE1xXnkGYm3WOwOQU8u3Q9PvSjv7ob6QOoP4O0Q4folBxKhMZSKbHQ5WS4a+67OGj8sGT7HBExbAmDDIas97e2a5JDdrFUCODiO1/C4lW1Jfry96l3c2E6bFw0y9W5pBOL5GAeQ7pMeorpY1ihnU/sejYyhsTVHwchhJfkkJSWgnqcjZlT4ZUonpvVKD7ySNanu46akCV9hr4G1USRjSVVcmDa09vyUSsZylu2d+DXT73lvL6voA4lhyR0ruKnYbZWE0pEaG4sJXy4ZQutHRX8+93JdAnchmFW2fA4/HsPY52yaKrNeDpsYO28QEUgUlG56vmUqbBtrr4GaZPYb9uDE7YJUOo9SI8zXco879fJaGPXttHKnDWgEsRPTJ2Y+l1tWz4fXcLyic3xKSMiL+HqtIN3dVfqJtxw7lQAZiaODWSzMYDK/eblrbStPf2e39vShkO/+xf8pQZPrp5G/REH5WUfeeUjmPv2hsTvHHenokRB9LC60G1rM4taydaOmqDMxKG54icGNzey121u9VcVdHRVcOqPn3DW8/UkUtVltmdVHScbQ9/3jBw7JcdeLlHEebpSobg2jlZG16wOY8b+u+DE/XZJ1QHsLrqmbmXbHCEwSQ5xH+Z7GdBQxlP/cYLxdx0n7DsaOw+uPdU1AOwzZgiAbKe56a9NXT9qdZ98YL7G5KsfejXx/+ZWP1tfX0IdEof4+zqHCHv4bukD6okIAxoCyUEuINt08RFDo7FZWkrolaskOKYN8eDv/EUZgx2+uaJ8z3y+/P5F1mt8fjPV+9OLSe8vSmx+6nfVNpFEuUToCEU1V7S7a4TcKWXqxl0ic/4nAYGhAwPirm80tqjwoF2ujJcmfNRcDWVi06uYXlG5VMrNw86WiNE0Bt3WIwTw0SnjcON5U3H0XvFpxLzkkPz/zwv9OP/r/vpm4qwGLttvX0f/G3EPYlfGywMIjpIUwi81RqbjLS0LSJVo9E3PBtfGkRW+Ki1fNdAry9NeM3yD1fcL8MGI5q7iuo0litRJbQ6p0kXAOLWS6qEW2EX43VmIOBJ9/7FDvPqVTfEqpHR9K3FS+mgolwwJCvlrG8tpVV21cJ0MyKoalcL5y1uwfMN2tHdVMGP/MSAizP7XY7Hr0AEGRi5dtn6Ln11ke0dMxH29sPoS6o44VMt1q5BHScoNO+vJbFkySkqogWY3PZ02aq3YuJ3N7pjsvvYJynncqJBcqq/koMK2ubKpMjwMiBIdXRWjI0BS/ZQMcGwol6J36Dwrw/EzR6DUqGohzHEPFQFMGDEID37lOPzn6fsn1DRmD63w0yMlCABQyfy7euuNJcp0wl1DuZQpOaANsl9jEBxTrk7Zj/zsKQDAA/PjNDMHjhuGg8YPS6mfAF59Zcr8u6Oh/ohDhg3SLCaH+X7CmZPV5lDrMuES/B1z1WOpst1GDso94MaVi8qafsExFhvd8WW87mTSpgMhcVDjHAx7m1r8yWm7oaFEkSrNtcG5xsi9C109Y950BYiA/ccORXNDGfdcdAzb7l8YtQffZnosgVqJ33x1ySELcWgs5S85ZAmC8+m7XPJTK9n6tl3bXwLfVNQfccjwjkwvNOZePNrIcK6BWvzWe+ZgowGWYDkVuhEwy71/5Q4+T5OLe44IJ+s1kq6vppC2Sg7sok8X/vJvb7LXd3QJs5eYZpcZuVPw3PbbdUjCIO2yH7kYD+7+mhVdtIDFk00kCdfOg5tT41+/pS2R3kNu9Km09EJg8aq0x1nJ4qykvruGsp9Xk0S5RJnmng3xyYCmNRSXTxgx0FpXRblEBoO0n+S/drM9I28/1Cq5iQMR/ZqI1hLRAqXsO0S0QjtTWv52KREtIaLXiOhUpfwIIpof/nYthTOWiJqJ6M6w/DkimpTvLSZhe0e7DGlOLjpDPdJUJ1kP3zHqS5V2XnxnA18JwOABfi6pFZHs3+eoShd3PGnUTtbfY+LAjce1udp+q211qe/VhX+evjt+eNYh+OfpuyfyaLmkJtPPcpNasymtq1YPVRJCGDlynXCo9eRzNRGvsrbK//jCCvzk0bTLtqp2S6uV4oLmcok/6tbwjvJUK+lSu0TL9g6s3Lg9ege//OfD8dvPTw/G5dF3SUumKeHrdffjR+wu8Duq5HAzgJlM+TVCiCnh3wMAQEQHADgHwIHhNb8gIik3XwdgFoDJ4Z9s83wAG4QQewO4BsDVVd6LF2wTJfAfD37/1RNLjdklI9E7bIrjwiSeX5bc5LsqAq8Yslra3BVV2PzhE+0hP45lapjsbcxQ3kgvUY4ibP2JYvy7jchy9e3tSTz2tQ9i710GJza/BDetSQ7lEuHsqRNTebSqHf+wgbwLMZDMCipgPmxHJxxJt9PgU1fpmQzSpkBINVBQvxP11gY1N2RTK+VokJbpLnT7zcn/8zccfdVjkCMf1NSAxgYzo6KjROStVvrJo0tSZdwJcKpU3A9pg5s4CCGeAOCb7vNMAHcIIdqEEG8BWAJgGhGNBTBUCPGMCFbQrQA+qlxzS/j9bgAzSJeDc4RtopQU8ffHj7wOwJFGOqxs8x6Sabcllq3fasytIgzfdTTorKABQojMHIvp0cvNoMthkLZJDq6RWBdxDatrz9GDgy8+EdJaJVLyaDkln/Bn/QwPW5ZOldvu6KrgsjMOYKWciqZWIkZy0Hshpi5gljACbyV+rOq9D2oqZyIO5RLl4QsBIJ77umOEjKyO8pxRPGfzVivd//LKxP+mbMryLBbAT3rpa6jF5vBlInolVDvJHMLjAagWweVh2fjwu16euEYI0QmgBcAodBvML6msGM7kgjpm750x/zunJOplsTnokG6Rl51xQCohmmsCyU3G94CiihB4+718jrKUe4FL7x4T03Q9VyCU3VvJr6xW6HteqRS/F7daLPj92/ct1Nq0EAelydaOCnYe3Iwf/OPBTD2RaEelN3I6GNWg2v8mt0pOGon7UIlDg7cHVNC/XXL4+in7WH5NwiQ5SMh7U2M2fPblQHJIl/tc65Nvaoe0ORhwHYC9AEwBsArAf4flJrcIm7uEnysFACKaRURziWjuunV+p2mlGra8pDJRtBHIQQ0f1IghA5JqAdXmkNV/WS6y8cMHYKRuMHZcW47UWX59CgF8/fe1JdqTiCUHe70Gi+Sw75gh6UIFVq+vHFaXLcDMBFUXfcj4dFCkCrm56JylLjgYNFpRHAQnreoj5CQHfV48EqYl15szPUpbEJx6zcDGMk8c+EtBZJ+zWRQFjaHqzBSMGTN3ChPnJTnwTNcba92pYt5Xshd8cJ/RhnH1P+pQFXEQQqwRQnQJISoAfgVgWvjTcgCqQnwCgJVh+QSmPHENETUAGAaDGksIcb0QYqoQYuro0fxLcI7d8ltJdbkL52sjo8IpKRxJq+WAHg6RCoAYD46EzSE9UqlSzpIZdTsTeGWDaSHJvp1qJcsxk05vn6ySQ04qMxvU9Ox77Gw3xsvR6MFy+vkL7EVQiANndxA2TyahNwUAeDtMr60fDuQKmgvaStZJurJmi3OILXmG3zO8llJoB9LzXEncGCa2I5CiVkrX0w8tMqmV/u1ON3O1QSEOB40fytapNh9ab6Iq4hDaECQ+BkB6Mt0H4JzQA2kPBIbnOUKIVQA2E9H00J5wLoB7lWvOC7+fBeAx0Y0KOrfkEOiMZYoClTgMHxRIEKo7HZdky6d//TQ5wM1dlCPu3VdyEGxUbrUg8nBlteh5Xdy/7ec8xHKj5KC0rRMQUg3Svq6qKdWUn81hVChJchuvMJQH/Wr9h5BBmv8xcz+cOWVcqr4O9d7Vpja1duBaxQjbUDJHUvPt2tddFkIDBOP/2eNLWOZAOpGobrncXNSN9yaDtA+2KnsAx0yaxtDX4fSJJKLfAfgQgJ2JaDmA/wLwISKagmDOLgPwBQAQQiwkorsALALQCeAiIYR8chci8HwaCODB8A8AbgRwGxEtQSAxnJPHjZlgVSGUArXSd5V8P/LA9ds//wHsOTrgHFVdpm+uIQk5STgbnWv+lErkFNH1vvIiDpITcy2g2Fsp/ZuNsFQqwno2cdbDg6rlL/RtSiXibm8lUxv2a47acxQuOH4PnLBvkHTPlFnUTNx4o4OUahvKJey361DcGwrrtnfIbdSX378Id8+LTYaljBHSRORQ3Xk3lUBbZ4XN8ST7jNVK6d916czkyirxk3OmAODjf9TrTDmU+iFtcBMHIcQnmeIbLfWvBHAlUz4XwEFMeSuAs13jyAt2DiZYXw8uiEPr5cs+Zu+dlXrxpMvKbMj6JcW+wY6TKaPwOt9kfoHaKx95VmY1dUkOkiPzOctXxeseul0dtsfA/WZUy1jqJFxZneORun+9DZvkEKjsTtxvjNIO17Z5/CaD9AXH7Rl9V6+ds8zsfBjZ05T3vHFbMi1LQ8lgmzBJJMhXcpDY3NppIQ5xOhBu3ukcfmO5ZM2/NXRAo9HrTG2+ySA51I3NoT/Dua9qi5ATE7MYpA8cl9RBSpUQUVrqcI2NiFAmymBzyG4wt8HFXQF2V9bfzeFTW/ggq1huIq5Zod6zawjynnXibXVlFSLlPmpyqTTZTEzj+4dDY1WSeqUt+p7zxNM3zbKSZkOF8ZhbiycrUXqNmZim+798bOJ/W9LG5oaSVXLQ38nQgQ3Y0taZckOOB2omYup4mw2SQz15K/Vb2Cg4Udq1ijdIxwsoq7goJ1K5RHhzXXKRqk3phwbJ8ak6cHdf5rozTOcGGNqSB8GYDIHT9wzOHpbiuv6cXWdXz11mjggHgDs0wtLZVbEmAcxCTJIpu3WbAymcub1N+bvet9WVFWmJwBRA6LKZ6ONTVSe+zDmnp9eZGJMNxRSFThbqUCJKqXik5K4zNgdPSJ6yuGy92U17QGPZanPQnQRkoOImw7kLgdTO96W2blIrVWNzeHTxmtRpgz2J+iMOTr1xsgL3shOSg6NB3XisqpVcfaf6RTbDmYCZY9EXmgsU/rnu1+Qh8oXb5lmv+9Y9C6y/L9W43Q98/1HM/PGTxvrV6ng5tZJJp68jIiIptZL5Gl79xRuks0oO1iSIBnCxATpxMJ31bSoPmC7+4RFi91SJLeEG7UqJb8sxNrCxzEpB6phUyDQmWw0ZV8kgLQHJZ2UySHNr+/IzD2TrSpx/y1ycfq15jnc36o442CCDdZJqpfSEUNNnuDZLfWHFrqxx2XGTA3uGaz8jIpRL2dRKJhw3OZsr8OABQcoE3eYgjfSyq/iA+mS9J994L1N/Lqgn43HgNiPTZpOwOWi/BZJaup6K6P0ZnrfNlZXb9I8L7VtDByTzLunN/PFLRwMweyupm7Wvf1E8t+O29Ih+k+TAzcs/XHi01ebQWREpyUH+7zw/w7IQmhtLqRxoan19PHpKHB02yUF97mbVU7psYng+hw0+AXbdhbojDj4eJ+pC+uS03VJ1KKorEgEwHPTNNPZWivvYqanBa2yEbGqlPN3nxg8fGLiyamqllGeOxVupJ8H1397pHpROdJI2BwP369pYtA0jUY/xQmool3DGIWNTmVf1etKeZZIcyp5qpQ8ftCue/+ZJ0f+6J117l5noBP2an8+AcJO2PXk9HYy0B7R12T3t5Nri7AQJycFgC1IRrWnDSEtERqKoXqEmUlTB0bET9t0Fx4dBc30xvUb9EQeHS11F4dAGNJZS0dFBvVhc/dQNz1n729qWnOByDqjEIeZoHWolyqZWyhJ4ExnKDb83NwSnfz21JCkBkCa6R5KDdi921Ur+C4Nr0sft+HnNk0cNVjSNMgqKNG4s2ti079zGXSJy1pNMTDUSi4qGcgmjh8SEiDTHg5TNwUDsuFHIfE22d9yoPSCpDnNJDnLOcoGeA5QobrlebPFBnDpN/91lkL7pc0div7F8FgATo3bEbiPYfvsCsag/4mB75uGClFPANBlUcdWURE/ivS1tiQhKNc7hDxcGaoGj9hzlHhuAw3cbkUmtZIN+a/I+zBsg79selUi1UsgF6oSp1gA3qb7yBbcYTQZsterhuyWjiX2yskaGT8NU0D1j9PPAuVmmS4icV1NJ29BSkoNnAIHOeQd2lvh/nTik7if85J65dKKoRnJw2RzkZt/KEJFG5UAi2bdtfUXEwfQ7LAbp8KLdRw4y2nlsRAdIPzvfQNfuRP0RB48Kkhs2EQd9Ubrw1bviwJk4ayThiN1H4NXvzcRx+5htDk/++wl48bKTcf+Xj8WPz5mCErnTUMR9+U8wV7Bc2XYSjIJYcnDjkNAo7jPOCSPc+lkVXIs+C+7UA3dN/F/ykBx06UmHro5Qb3f+ipYoGj/RplZPIL056cb/lLeSL3HQngshyYC8rXkFGeNFmAcgz6W2vWLd5tDI2Bw4QifXgWnu6kfWqutGf1YuKczqyopYVWzyAfjJo69bx6j36rvGuxP1RxwcD90vWEUuSnvdf5oapJNSF58qOQDS5c6ss9512ACM2KkJB08YhkFNDc7gORWZiIMjR1TZEBWrF9lOgtMh9bM+dbOK2b4n0QHJd24NghMCu49KEyn1nn/z7NvOscn+pLQ29+20G68aVfzq6k3B3NAGlzK4Kvf36Q/s5p3aXSeaqnfRq6vTZz90aLYbW9ZaH/KkB46VS4RXV2/Cui1tibLUuLvsxIE04umlVjL9DnNUuJQYicyqvGeXJtWVT/77CdoYkz33hVxM9UccLL+5OBwJTnKYsd8uCe8SADj3qEkAgGOV6GoREQezsTCR5ExbFKbUwipOPmAMPjltYiajsBTNZ7+yKio7/eA4hZYpY2dE2MIny2WONerEo1O93OPLHATHpl92jyml11cN0uC58QaF/eNccvW+5P3a7okU1c5//GE+AGCb5mZJlEynovaz765DUnVNSEkOSt+cVKOfX2BT25DUK1mgP9NyiTDzx0/ivF/PSXeiIJYcki9b7U51Rfax1ZleCectpg+NQN6qPHlolsnWUUgOvQCX3pFh0Jh6aU7/9EPG4tbzP5CoFweExZAbA59cLc0B6ovaR63U2VWJgre4iM2ROzWlOLq2zq7Uolc5rXLJrBtX6zYwWVk5nTCgpvd2LwRfTkoGM9349Fup34yco/q8Gb1+dJngN1mXtJQyNhrKVQRG3OD7y+8GkcCc4VXNGqs2l3LVNHfF2BxIITjp+rsbjorlpG5StJEcQR43bEBKwuHWxoSRA1NlprO91TmvEnert1LUJ1+nS1iOcA3bJbInWUz2F3yaDiQqbA69gvRD32dMcFLYxBGDEhyCMX11xCiqnGhaJ8xtfg/MDzhzdZ7Fiyf4tEX+lkrpFBa6WN2yvSOUggSOnDQyKr/ohL2i7+NHJBdba0cltRHo/tv64vj8sXtE36W3owwaVOf2VkN09E4Z1Eo+dUoUSE0AMJ9JreCz3tJqJWWjBJ/8zmVnGdSUlChjNZBLcnBvGCaDuX6tjeHRJQeV4HBQPZvUfrlp6zp05/DdR6RiifTn8qUP7YXfXTA9da18Hnr95oY435JqXLdJDvoa5PpqajARh7ANi1pJh1xLJvtl4a3UC+Ce+azj98ILl52M3UcNCr2V7C841vUq7SLN8XC5Xf4YHinKqZVkNRvXwKX6vvP5ZGqJkTs1Ra6QKqEZqrjl7jIkeQodp7c9aq/4QL4yk2ztjEPHxTrTcMySa1MJJ5dy40dnH4rDQs8gn007YcRdzmdvHTd8IBtHIpH0/onLv//A4ug79w7VyGciYIJGWMslSRD5G/ncMZO0m0mPRwchTWy4pIdEaiLG+HdfxnNwcwP+Y+a+qb5NY7vps0emykxpQ2RbseoxjXKJUh4++tj/Yco49uxyuU70btOSQ1jfQYxNY5R97L3LEEwLma2EihJScvBXK8laheTQh8A98sYyRaoWuQGY6gJ8FCmnk7RlME2uh2R71usoPXH0+j86+9CIo1S5OdvEbe3oSqYeLpfw2aMnYWp4UExwrWYQVb7LMUnjohzSqpbtrFHzrCMmKFxT3O+/axuVhDq2j/zsKbaOSjg58d604d3zUnxym36V6lIqRLDRffmEvRN14qhwtvlUSgXOgKyDYwK4YK+GEkWGWVt7pjf/2Nc+iCN2H5msq3DbOgfLPdeoLtO+Kjlwz79ElJIcUilsDIb1mDho9RXioNocrAZpi1OIeu3Re6fdzm96elnUl2mN6feo7xX60AqbQy+Ae+bRCw3fmEvElL+7JYewHjMpy4zkICEX+967DE5dp6qVVrVsx5pNrfjpY28k6gwf1BRwTBWRkBx0w9+8b52Emz4XcIJtnZXE/ZRKARGcHB7tGaiVtLEQKc8iuFhuhEIItHV24agfPIbP3vR86j7k9QDw5rotUZk5wtS9WN7f2o4/vbgC67e0sT7pKqE0Sf+cWkl1FSVKL+T49Dt+jFJtKRHx+Q5OVr9njmloKMUpTYQ2H5PtmW6Y6ztQpQkhcP0TS1P9mcDdj2pzePf9dKK8IPGenpU1WafZkJZb3rf+WBKSA+L1or7/lE2GURVzfelS0MZt7Xh19eboN5Na6bzQOSXuT6qVeE60L3grOc9z2NHATWA54fXXagullzWUylF5U0MJ//NPh2JSaLjjWlEXa9rmEHw576jd2b7lZD/qB4+x4wvaD9pTNTplbRGOGtyMiWH8gC45RCe6hWPh1EqqI0okOUi1kog5KhPkPnPX88ut9QA/NcmW0Jvnd3PeyaRWUsE5AOjGWZ2rs9kcPjltYqpNXXL44gf30i9jA8c49VxjuRQxAEkbWPWcp0yf8cbaLXj01bXabzbikC5TjbTceQmTxwxOERydwzdJDqa0JqqkptocvNJnGKroOdGCPglTLn840ZdPio3kWIP6QaqQWO1bSA69AF7vGTwG+eLduXKCz6TkECcQ26mpjDMOGWfNJ6/OoTiIKin+lpmImhL5cRXS5qCe+cxxNarRXHX/lGOXk7TMGKSJ4sRgg5oC7i6SHOA+QlW/b8DuSugLYsYK+EkfvOQQbkKIuWoVsUtuun3VOCoRGUjDL+OHp/XpULyV5Jg4R4VyiSKi8eI7sRF+5E5N1vuKe0n/ID18uOh/Tm2i34/eluybS18ySzmQSCLlfWTIvhr3myxX370q+bkcAGx19PQybC3t0QxUJB5Tu0ND77otmstwnuewVIv6Iw7MM48lBz9jErfpC4GICz9wXBD5a5tw6uJNSw6VxLj0vn09dypCoKuSLDPdS6WSHOeg5nJYHpSVSumnUyLC0aHRWnqwRN5KFZh3+hCmI0V/d8F0PPa1DybKXGtFVcF1dgnMnr8qVcfHyJe2OcRxJYHNIb1wYwLrbD5sR0oOkhvl3jMgtyBdolQhTzDb2taJS/8YxEOccchYfOyw8V5j4YiG5LatKlgFnAu22uFtSAcAACAASURBVJZkSjjiwHHauoSkSw4//9ThYX+SaCfrqzyVqp7b1mZmVlwMYVpyYNrQZo+PpDokjI3Sz5HoC2dOO4kDEf2aiNYS0QKlbCQRPUxEb4SfI5TfLiWiJUT0GhGdqpQfQUTzw9+upfBtEFEzEd0Zlj9HRJPyvcUkTL7Y6mdcl4euZ5doaijhoYuPw88/fXjYXlLdIIRAuUS48EN7YfgghThoE05uRtxCdBGHu794VNR3QBziBcktRJWAqYv3jllBO18/dV+ctP8YfPigXdlN7IqPHYRnLj0Rl51xAI7fZzRm7L9LdM+u6W1KHXDUXqOw5+iknt61WL7/sYOj74tXxQbwPXaOffJ91htnN4r7DmwOunZEpl72z5abHI8p8lzW2ze0+3z+2DSX3VAmdFYqiQ31jEPGse+KA18r4La5tcLNSVtKczWy2HQMp3Rp3jN8V7qEpBOH08OMtabzM5L2vEDSW7CiJeHIoI/E9bTiExzT0q6E/mjU+WCaGzKxpy459BdvpZsBzNTKLgHwqBBiMoBHw/9BRAcAOAfAgeE1vyAiKVtdB2AWgMnhn2zzfAAbhBB7A7gGwNXV3owXmGcecQWeTejum2qz++06NArECuoimr0dXQJdFYGdmpKqBt3IFUkOzFkSrsR7U0NXO2lzULlNTq2kEqbvzY5dOuWmOm74QNxw3lTs1NzAcpnNDWWMHTYQu4/aCbf+y7RMKTE4bs2kPnKtFXX/UDcwdRyHeBxwxKmVhLIJEaXHeOaU8am+TO0F7SQlB06iUzOZjh0+AIdOGIZjJ++cqlcODdLqZsUdWGMy9NukFu6Zc/do8hqSbcU2B945YuROTbjvy8fgR/90aKqPpnKJZWpUycp28p60n7xsOVJUvcblrWSVHLRnqW7wpvUgCV+7lp68X0gOQognAOgnkp8J4Jbw+y0APqqU3yGEaBNCvAVgCYBpRDQWwFAhxDMimEG3atfItu4GMIN82Z4qwD1yOWeTxiZDZaXeFmUR2jyb5BzZ1Bro502HosfjCS7gJQe/ibO6pRVtnRW0Knp/kyQCBJzQU2+ss7bJGaTTdeQiE05OvWThwnS4bA6jB8d6e3Uz2VuRQK76+CHOfji7SsLmAMJHDh2H8cMHKtcEn9xmKvP1X/Lh/aJcW7EOnO9T9iubqwizt9HSdVsx+5VViZPyOGOmJGCpfrgySqsZJTijuGSS2HlJ8fNRicNfv/Eh3DkrDmw7ZMLw6FwTtQ/TsZtqGpmU5FCiZD0fRiX8NBn1ZZs2l9e05MB/VyFtdPpZIyYpqydRrc1hjBBiFQCEn/JA4vEA1Iis5WHZ+PC7Xp64RgjRCaAFwCh0E7iXqouMUV2Ht5KazsB0ZKGaQG3qFY+EdTXJQSNKktvnbA5E5CVyLloZqFZWtrSm+uHupSLM50Prde11gk9hUEtwddVqMw+K8zmdtH98zrVc4Jze+tGvfRC7KQnx1Mf2zdP3j767iDI/xvhcBRnLMmboADx9yYmJOkBaFfDaFTNxwr7BPXzxg3vhm6cfELQj70njRlUIERv7hRCsdKHiBSV5H0dIyyXCZMY12jQnBHjizj3/zorAvLc34Okl6wEAv1HSyJQpDgxTN7wJIwbhA3sml7m8R1XaNaXEV4m2vvnrzh4V4bYn6hJBV0XgvS1xqv2PHDouWY9TTzN9VBipak9F1SmjrvXn6nP2SHcjb4M09waEpdx2TbpxollENJeI5q5bZ+dyTeBeqm9OHAk5+dqUiWvizFRXOgnTJiWr2byVyqV0cBSHi0/ah72WGx8QPANb8B3AGGs5TxfExMY0Tpnigjvjd9dhsQRw4n5jou9yaD9/fEmqvb00+4Tab1aCwElHFSHw9vqtePy1ddb8UrqhWvdU0pkAm83hiVCKW7u5FRVhzusTtx3/btpXWEmPfYfy/aVfoLppnXZwkN68IgRue2ZZVK6qvwIXaH4DNI0vQRwM10iiXamIFJetEhSpBtTv/Xwl9Yvat2zp8vsX4sgrA2bu0g/vF3H4ET/DzW3m+XIxFl9Sgihlu+qzmff2+/jBA68yHfQsqiUOa0JVEcJP6Qy9HMBEpd4EACvD8glMeeIaImoAMAxpNRYAQAhxvRBiqhBi6ujR2c5AjttIl+meCKaQdonoMBJlEpoiIwmEtZvb8KXb50VlOw82uBmG3dkkB5taSRouAV4cl/eVOFtYlRwcPrI+2j417xQ3yqP3GoVfnTs1bC+s60HtosC/ja2Omkm1imnIph659BlCAKdf+5SxQXnN6k32selebpHNgVmF0ttoa1sXKhWz1PbJacFyUzcX0/xguWeDajCQ/NI4dMLw6Pvx4TnkXRURqfLGDUu65ZZKZPVW0vsF/N04K0Lgwtvn4YJb5ybKVRdqzvX7tvOnpWJL9PMc/vjCiui3pkQ6juCTGyG3Bci5aHonHHH4+HXP4Jml69n6PYlqicN9AM4Lv58H4F6l/JzQA2kPBIbnOaHqaTMRTQ/tCedq18i2zgLwmOjGrFNcw9L4KieInOimORodY+g4BS5sFHfPW44H5q+OivQ8Mek4h6BdjuDY1Er/HRr0gmvTv48Y1IT/d+Le+O0FsdivprBwSg4eNge52ZgkBz2ZH+D2agrGF3zeOfdde0XEEeZqH77Qa0tiLO1Ltql5+3PveLWtq0O4Mco52VURqDCcr8S/HBNwwB2dHsTB81FIbltv5ti9d8YIxQVbrpOuSly3rDlRNJTiyHrXqYnyObjmYdA3AAH8eeGa1G+JMYYqMnVUbBSztul3KBRFlQBthwJxzFPMCPD3Edkc+oCNQYePK+vvADwDYF8iWk5E5wO4CsDJRPQGgJPD/yGEWAjgLgCLADwE4CIhhCTjFwK4AYGR+k0AD4blNwIYRURLAHwVoedTd0F9qaOHNGPON2ek4hJcJ2hJTk9Pcc3WZZrSuXpVVF2wogU/f/xN4zjKZFYr6QnHdBABXztlX+y9y5BUvYqywLmIXXWcNsTpAHjZQeXibEGCqesy8AsJycFQx1jOSA5q31xgny8B0omhPc4h3oSEMPfBqWyyuEGabQ5pSfLS0/ZL/B9F0SuMhZ5ELzgdLajHeVElxhJ+dnlEedoMzTIOIq6nXcsxXeGnEMDNT7+VSDO/U7NCHDwkh79+/UPYJYz7mfPW+2G7/Filt1KHhXD2VoZWZ/oMIcQnDT/NMNS/EsCVTPlcAAcx5a0AznaNIy+oj7lMlMhOKieIa7H7HoAetMlv8Ik6yoQ746exLzZrIyjFXMY+Ywbj9TVxXiI1EMx63rPaHjPZTYZkH7VSzB2DXUFJySHdtwmmjUCqVVQkEtQZhmyS+vRHLoPgZA4j/cCdoA7fh6menj6DYyDku+8SgeTQaGBYIq5c4TxNxIGVRA3jrAiBy+9flCiXTFRqjBURPXO9j3KJ8F54ott3tPZ0ZJEcpF2Eg5pSXDVc6/0k2gvLtrR1Wsdpc3mVa32SYnB+YP4qHL/P6MQY1N4bDQZpFZ0VkUrc1xOouwhpdSdKbwRhueOpyAXgo1ayeQhFdQzucVycg8oxqYvjX2dMTmzevnnl9aMUuXHEfbvbS3C8zO9XfTwOVpMcnJfNwWhkTQ9K3VxMhP5njGGba0/mVpLS3lZFcrj64wfj9s9/wFty0D1i5Hvk3pXqAWUzSMcBZvEDMj1OLi27SWoRAlFCORPUQ47GDgtce2coHmZAcG96gJcJcihdHioW1YvM1aaMT4nHzdQLPzkOXnVZjyUMTq0Uf5fvQ65hl1rJRhx6KyCu/hLvKVOKUyEA7o21ISIOPmolnvtXYXKP43MrqecLxPXTx4k6h5ao53WEpiVltzo+IO3t8vC/HY8xwwYkzpRwGf5VGI8aZZ6vuphq5bfk85ZjbdneEf32iSN3A+BvQNX11fHZw4x0GeVrssc5yGIfg/Sb67amymySgwtyjJ0VgXFhfqgvfTCZzrxUSmddNYHzVjJX9hujPKxJnbu2s9C5vj+07y6pekKk37varHQLbyyX8PK7G3HfyyvZenLd2mwOHV2Vqtywa0XdSQ7qfDJ5GLmO+sskOViu1+ukJAeTtxKTollvk0+VYeZQq0lKx1dC1J7a5JABjQnCEPQdfLriK4L2+HLuVakbZa3xlKVScC+2Vny7UGNAAHuEtNxPg+dojnPgJIcsGT1NtlmfJlTpRj8mNlnPbyxZ5qJ6Qp+rXkUkTyPk1WuS0CXX9PBBjYmAx2itIskoqG0AsQtuU7mEM3/+dKLezoNVtRehqVzqk5JDQRwUyI3EKTnINLseNgduV0lxLo6FryJInyGwcuP2RFSs6RS6RDeWsfiolfSNlm9PaUctZ2aaKXiMg2nD4IigGpzouzGZoJ4kZqvj2xbgm1spfDZCJCSXdL3gUwbMAf6SDGCwiZXI6dYs6wX9Ke7XDHHwHY4rB1Oyrh8Bk1LQdxU7gm1t6Jv0YROHa/Vi6U8nwmqz/+/EQILSz9u++XNHRlHzEo1lshqkeytauv6Ig/LdtEe7jvqTxKNqtZJhoetTgFtocrP61j0LEuW65O4pyaeMpNw4orpe7fHcn21B6mcG6L8H7fH9ce1uV+wCvpl2TZA2h6y5bm75l2lsWwDjysq8KznH3ly7BfNXtDglB2n0BcxBcBy4qdjcUE65nernRgPxHOsSiuTA3EwlYQMCFl1+aqqOOhYfbyX1EB+JxjLhfz9zhFbPnl5DrQcAHVoaC121qzqP6ERYnYunHRxE+g/S8qipKqpo3A2F5NAnoIqiXB4dwIM41GiQTquVNH1DCOOB8pX0ZpW+F/MC4ManNmc0SKdmi1mFIESSQHB9Z4lBMKkQONVbgjhoY+Y2bRukA0AWVc23zzgAH9wnHaSpZ+mNEz7y3DsAfOPuVxLXcuMDkiqOD4eRy9ViQGMJbZ0VHDB2aFT2py8dbexb9VayBYIBQQbWQU2mJIBhfR+BnJEcHrr4eJx6YPLeZZyDCnZ9h0V6RLbOZKkq4JTkwPThs7E3lktOm0NvoP6Ig/JdRnhK+EoOWYLgWNWQgSjp02O/XYdAh1Qr6XMuZXPIqFbiDkxPXe/BhavpM9ZujrlZm71DYnclPxIAjFWibU2c+3bGA2dbhyo5JKFGkftA6qxrjR1Qf9PPNLYlRIz/N40v+JSqh/nfOQX7ZLhHk+TQ1lnBQeNj4jBEsxcBSW8l6W7Jveek5GB+ODGx8Y1z4K9P1au468l5q0tMJtWzUKQlILAjqM3qh2UBwNlHqEkiYjSVS2yAoDwsyHVoVneh/ryVwnd1/WeOwIn7JUW81LmuBpQjm4P7pbHcuoUb4cajl3WFRkoV+ph9XVmz2Bzmr2jRxpKuo3pevacQh4GMt4W+4f3i04cn/v/QvrvgjlnT8cD8Vbj3pZXgoJ7dIKEe6uJjJ7FB6qwzEQfLb2oKcLmh+xhxOXVNMD6ppzdH1WdFc0MJG7a1JxwFOD/7crSZB8/H1LfqAWQlnOFnh0+cA6WlSVYgoDRjwWceCPvWuHTVGK3WC9RKwfcfnX0oztI2fu50QI7AAqHNIexXVVXv1NyA7R1dCWN6T6LuJAfJn08YMSjlYidfvGsDaahVrWRQAXEJwnTIzSWlVkpxOOlrm8rmDTqrTv2C4/ZIZJfU+xUiuSkMbEr3rW/cIwY1pepM33NUJC1xUM/OkFBVA6lYFrYVM+LnneEaywZNiJ+1NPo2MgYiH+8zQHHB7DJLIVkxoDGQHNRN2jbGrjD5nYmAzVCYMC/Joco4B/ORp/q16fYiwqQRh6+evC/bh6pW4ux7ahCjxBpD7q0G5RzwY66Kz4UfHEZmb+8lyaHuiIN8VyYXPsDnYJksxMGsT9b7FXBvXmVKu4kG5ZrkoPRx8UmT8d1/ODChJtDHp97zFM1DQ8foIc345ukHWFVFC1e2OHWl+iK1BXqZaJdrM7Spwp5e8p71WgBYt7ktEQTlA6fkEH6PAqXYSPhkmckDKY4qDiWHrKIRg6aGEto6uxLqHTaVi0IcuiqVhAT09VP2weG7BfPo6L13jqL3ba+LU8WYQJSOSTB7z/k4R6TVShcct0eKqZESVHtnJZImbU4n6nvbsK09VU+OUT5qNU24PCGvt9RK9Uccwk/eUBw8jk7HphZ5K1WrVtIKpZqqq+JOzVwiiqJmE22U0vUkdh7cjPOOnmQxagZSy8Hjh2GfMYPxUcf5w7bcU7LfB+avjtQmvvEkpluXye84qK9q/7Ec8dML4q+fvuE5NmpYhRq85A3LO2zvquC6vwa5szqUQCkd+iZvci3VbQ5ZJQdTdHalknShZBkBhTvurIjEvPjyiZPxxy8dE/0fndNueTZS3ep11jdRap3yJ8alJQebWkk1DHOBZxPCc+LfeX9bHOHuIJzSY2mzIVLclCdK5korDNI9hEhyYLZtyfk4D70pEYjyUyvJ/6+YvdhNHEq8msNmwHRy1+Hk7OiqYNKotKpI4qsnB2dE2Bg7tSe5eE15YUzeYly9ihB4ZFE6A+eUiXG+n59+8rD0eFJqpWTBfpc9xHdqwMHjPY4a9Wyr00Ic9Gdj2jBVm0OJ/GMuJLjo5RIlPZBMiBLvVQQ6u8w2ByCeg7apKH/yyspK6XXKNc0xFjZXVlVy4IjD0IEBN7+lrTNK680R2MgeI+JYh88ctbvhXvhYGvluivQZPQRbVKrcxNo8KHVDiSLi8Imp6eRvEqZANtP/Lt2/nOy6Mc6mo3YRhxIFXGLL9g52o5KQ+YVsYr96u2qUKIehA5LTz6QCknrjO55Ppuv+4VmH4OOHx4ZANXumqc1atS4zD3K7ifr2YTsrXFffmwKh1CjzrFKDqbq08bg2adnfhm0duHPuu2wshEQkOVhIZxZvJULa7ZRlrIiwfktSncN5BnEG6QkjBqbqqR5av/xbIAGyEouMcK8IDG4u4+i9RuGfDPuEjMLX0ViO++oN1B1xsKmVpEHNFq0oUS5R5Fnw1VPSp65J8GqlZGlDJuIg1UrJcpsk4dJDExGuf2IpADOXH/zm5mTUe9saeg2ZzgHeVT8YxqJWEkJEZ3BL7D92aGJh8rpf41AzY6emMi40pDNX4Rt4F6mVDCf+qTA98yiquFJJpGz3gU3dJyVJG2Tf1zz8OoDAPuPqy8fm4JNOhYiwtmW7Vpaut3TtFmzWbEY8EYslMAmZTFCF6qGllyXqKSo3W4S7vJ5b9w2RmrsIgusRxBx3+mXJjbE13PQnjkxPDomGUimSHGwcm4+Yn5Qc7HWDhZsmIroKQJ2wHGeaaFP52ZYkLZIcPMVcKRn85vMfYH9Pn6VtVj9xx1ams9umkXJltQ3YgQPHD3Pm3Qr69GtPvjPu/fjbHIJ6QriZgG+cmvS8MW1Y5ZABcb1nOW997ldudFabQ/iT2q8qGaqY9/aGxPno6nhU6IQB4NVFkc3BcbqjesCRtZ5CRGyHNQV9B/N7w9akhCPXeJYAzDxRd8RBwiY5vPv+Nuw7Zgge+srxxuvLJYomks1A67NwuAVznHIOrwrJRet5nXQuT23SrVaKf7eqlcrphcFhSEgU2rsq+MfDxmO/XdOGYiC9UdskBy7WQB7OHrXHeaHo/1teyJGTRqTKfv3Zqc7x6fCtJ11FTWlSVJi4R7Wai3Dtobkem+ZFuUSRe6oN8nouhkWHvEdbOvyIOHhkCGbH41hsu48ahFsNEfKRzUFZR9y6bmCIA/fc1TiHisPRRGY9OOx7DyfKJfNZpM/oIcQG6TTkBO7oEthn1yGRKxkHdWHZJYfk//+pnaaVFSUKguDWaiL8hm1JlUtifB4eUBI2tZKv5HDOkRMxqCnIz2NSKen9AuaNW55prO9VehoGg8rZG9ITRcWonWIVhO09f0zx8LKplVR3Yuntph5DaerLpP9X7895gqH2s1GtFHKyruR78vpVLe5zvWO1ktvmoBLCLPp2F3H8+OETUknvJLgT9UznqQBJAmY3SAdqJdvcMXkrFcShhyFTQ3Abkco1uxaa+rJNwT9AejGcf+yeXuO0tVepiESiNSCdeiJhc3DGAsSw3YuPzSEaoxBocxCHlCeRUXKQ3Fpys9pJIw42/3UJGyfK/aY+O9vGljhC0/K4j9l75+iZyMU/oNHtymrS52d5z/rAzJJD4I2zYEU6+pwbo08cSGyQdo9OJUpZtkXX/dtsMpHk0KlKDmZbUCIlCJdxWJUcLCnXZV2OOHxlRpDZtTBI9xCskkMGDx+1rk2nrxOZWiNYdbe3iSMH4vbzp6fsI9lcWdW65nrS68g1WaUOtb2zYvRUkvVUmIPggk9dtaIHKPk8WdvIeXdCP+Kgbua2cZSIokG0dXSBiPfm0jec7//jwak6+ph8vNJM1yb79puj+hjHag4GKmQMkY/koPIAWZhmF0NnOzBHDkvN1WWNX3BIDrKulBxs6sxAbZosu2PW9Ci3Wr80SBPRMiKaT0QvEdHcsGwkET1MRG+EnyOU+pcS0RIieo2ITlXKjwjbWUJE11KtJ7RYYIuQVo2xLlWMr9pGlUb+6yMHeI7S1m+ScxcC2G3UoPRGm8WV1XMzaPRwZQViHWp7l11y0GEahRyfrlrR2/bJ8mojbNwvKvfoUg1IWI2uyhjaOgMPI1dSwsN3G85meQ3qxd+zqA+5/33bieppz+NPStCbjjgIztye/C0hOWRRKznGbZccgmvVaGT+sK205MClhgGkYR+hzcE+bj0C/gN7jEy4zfYG8lArnSCEmCKEkJa7SwA8KoSYDODR8H8Q0QEAzgFwIICZAH5BRPKpXgdgFoDJ4d/MHMbFIvZVMnMFQBy1bII6cWybq+Q8Dxg7FJ87Zo8MI+VRIkoYzYzptTNxlPHvnP47+i0kdK65GhwWExiQMxEHwzA5L5a3fnBaqp66SM86YgLu/uJRqTq2sXMbUeI927g/pZ5LcpBxKv/7xFK0Gg6M8lVnqYTFdRynzumbqvtKt7aULam60iDtcS8vvLMxKjO9Li71ui3yGQCaGfWdXm+roiJjvcgYg7QpBbmMX6gId4CgTgCIKHEMa2+gO2wOZwK4Jfx+C4CPKuV3CCHahBBvAVgCYBoRjQUwVAjxjAhW563KNblDbgDcHE24dGawOdggOc88EqIBwKbWTk1y4CdOORNxiL9z+m+JRs+NXt2wTJkoOZhUUPpRmPuPHcpy200NpUi9tvPgZkydNDJVx0ocmDL12W3SjoVMjlH5bnlMpVCr5Frw6ibqK9m5XJZ1hsgkIfies6GPy7ZmfCSHoO/k/4MNGy+f9iNdT51TAyyMj0RScjDbglRNj11ykK6sdqLITYcsJyV2B2olDgLAX4hoHhHNCsvGCCFWAUD4KVMyjgeghrguD8vGh9/18hSIaBYRzSWiuevWrat6wCYkFmQGtZINjZELXzbiYPLv/t2cdxL/X3jC3mw9NS24zcgMJJN92fSyNvuBCvVWucytJpg4X9me3GDGDDVH4sqAMtNGZTqrAuA3D/V5zFn2vvHackJysOtOhHDrkZOSg7Vq5PbMBdNpXSdgmpP+jI9ZlWlq0yd3mMQZh4zFt87Yn6/H3CrLMChzahfLvJGXqsSBj3MIPhco6esbTV5foUuwcATBSVdtHfL5Zjn2NU/UapA+Rgixkoh2AfAwEb1qqcs9HWEpTxcKcT2A6wFg6tSp1T0xi80hi1ugfnygCZHkkIE2/O0bH0qdPcvhex89CJ+Zbs7XIuF7ZCgATByZdueUsMVAmPq2ifK+kO2NGz4Qy9Zvw48/McVYV3Zt2qh2HcobTQ8aPxTfPiNtEzJxhaYxqmPg6wWfeuoHUz29bQ4yzsDFgKTO/DDU1zlVLo0Ed72f5OAzxqD/C47b0yh5+ko3TQ0lIHT0Gj3YbDCXBL3TGecQzOc/vbgiKjOlDZHqoi6Ht5IpQrpfq5WEECvDz7UA/gRgGoA1oaoI4ac8IHg5ADW5yAQAK8PyCUx5t8Dmylqt+6cNDR66ViA4fEhCTyuh4l9nTI6+D7HFYSTuxf81n3LAGONvvvYD9dH5EhQb5Ltq66zgwHFDMZw590FCLiPTRkVE+NbpaW70qn88BMMGpTcinwAvwH+zkpuQMzWFp80BAJrDMXIHHyXa1Nr5/HG8W7U6ttMPHosHv3IcW09/t3a9uvRWsg4xsbBsBNRXulHnrE3tJttT++TUqPpSOveo3Y0ET400txn5idKn1clyk1TRE6h65RLRTkQ0RH4HcAqABQDuA3BeWO08APeG3+8DcA4RNRPRHggMz3NC1dNmIpoeeimdq1yTO2yurOri0fP46PB9YXIBubg6deLa1APT94j16KMGmzfJhHtqBucvG2fnq1ZKGElzsLXIR9PeWXEaXSV1yGrjGWrgUL2Ns6payaFCAICv3fWyvT1PSSSo6x6f3s7L/3WKUepU3TknjBxoOcHMnzg0ejJJqg1NzwKgwpcYq+vKNr6mKD224oXEMAb6WrLGOIWSQ2eXsBImSQBMrrO9JTnUolYaA+BP4UJoAPBbIcRDRPQ8gLuI6HwA7wA4GwCEEAuJ6C4AiwB0ArhICCFn4YUAbgYwEMCD4V+3IPJWYt6V+m62tNnz/Jtys+uQm2MWN0MbIVF/U6N3dfhGcGeBv+SgELocJAd5z+2dlSg1hwm2HPsS3OZdq/orIS15vL+/vW63meX1zlSot22bjqpaycaopLIL2+JAPA3Sat/tXeY16Pt4VIbGRlC4uW2Lc5CwZRSQkkNnxc7UlIjQ2tHFGp45N9eeQtXEQQixFMChTPl6ADMM11wJ4EqmfC6Ag6odSxbYznNQNw2Xf/WKjdutv0vIzdEdVOdrw4jb2dkiOagLweXF4gvbQkj2HX/37dtkgAfi99LeVXG251IrAbzU6OPJYoM6d/LY2NV97Mk37CfW+W4dvg4XatoY3/fXUCLPpIT2Ouo+aMrJBWRRK8Xv1So5VCEVBwXmukGOKncwaLmUTisu0dCLkkMdp89I/9YNzFq0oTpVA56dToOniAAAEVpJREFUqwtwxE424qB+z4k4VCE5+KqVrvyYmTeIjLidFack4ic5pMvyMJxL2FUIyd9MgZF5HPdp69u2WZ1/7B7Rd9/3Z/NyA4Cbnl4GAHj53Y3WehLfPG1/jBtuzorsbZBW1UoZJYdasWLjdvzhheXY1NqJRRZ7EFFSladCejz1BuqPOHjaHPJCg6/kkIFDk7BtlCqHkzXPvwn+3FX83Vcism0u0bGsnRXnZiU8bA7cL7735gObA4A+xcYYvKeySB/ynieNMnua6X3bpMCdBzdHJ945bTwe7VUDV74mf5uDolay3Iq+Rr6TQzYDFXPeMrtBl8gsHTQwAXI9hfojDvILKzn4cVZZIBdNlnQcedRT0dPEIW+VlmyvvbPLuVl5EQfOUy1HsdFqc/A2HmcgDuHn/ztxsrWeb4oPINb9+0oOpkhvCelZ59uey4W4mvXiq1Y6bLfh+GwO2Qx8YbOj9aZBuu6Ig9w9eJtD/N2WrlvF1y2nwGVBtflsfNDs6Y7pQqlEOO3gXXHTZ4+010twqHm4sgaf7V0VJ4cqbUVZztgw5S2SMJ2tYYJdakn+lgfhju7Z8WyycPfb2gPO3XcdmNQiEtf9c+Cq7cssfPLI3ay/+06rLOlPZHaAPDzsssAkPQJhrERBHHoGNm8ltcwWhZtoz/HefjcnCAp/9NW11nrVRqX6IC/JAQB+8ekjcMJ+u1jrZEn65wPZRqBWctkcwjFk4Lz/fea+1t9vO58/yc4E2waoD8uWy8oXcgq67tlXxQfEUfO+68CFYQP906gAYGNOVPi+3yxee+PCY0GzcOq+R8LaYJUcLCqn7kb9EQdPm8Os491nBQPZ8s3ngWrsInkSBx+UcrZ3qEdhur2V3Fy0/suB44Y5x7DfrkNwGRNBzcFmc9Dfn80Q7rIhRPCchFlUfFLn79rUP/UBO4cvkcXge+gE9/sY7CnRqIcwuaTzpe9tBQC8+I7daH7RCX57w/931iHR98N2G26sZ2MQSr0oOdTheQ5+EdK+k/mUA80Rxd2BLNyfhK/++vRDxmZum4P6HF1eLD5IGFId979mU5ArwbZBV8OJPXSx+chYCXm8po9KKwh8Ag6bWN2moULaB7KcQeIL13zzdQGOvPYcnPbL3z7Fy3Ns58F+Es0/Hj4BXw0DDvOyKx0w1k28gCQxuvacw4z1bAxUQ4miFPkbt7Vj/ooWHDfZrgbNC/VHHMJPXnLwb2fx5TPR3llxir95w5VKvBb8/FOH59JOIto7B5tD4tQ9z/tvtejA2zrtxtNa4XPuw7jhAzF5l8FWA7u812+cald7bQ3tAy5uupp3kVcwnmRQXDYM3/Wkb/SmaO/ugK/tRn12NrdznRE9af+Y4SwpBukLbp2L55dtwILvnuotOdWC+iMO1sR7/gthYFPZOylbFuw/1hz4AwAjMhCjV75zSs3BXdUgb4NeNXETNrR3E3EoEdAF++YRGdc9UoHIui6DuIzWd2281XiO+RKUfccMsf4+fvhAfGjf0bj4pHwcOFS8eNnJGJ4DkyYlPxfU5zjreD4/FZAkYLbNXJUc3vz+aamjA6Ra6bXVmwEAd8x5B+cfu0em/aoa1J/NIfzkxNvucFL46ScDcdKXA3PVGtTUgBn77YLPHj3J2dbQAY3dEtzT00hGXHvej2WNt3XaPWuqhWQ8bKoYNdrbxYHKOepyevjKSZMxuLkB++5q36CrkQJ8rzl7qjnCHQi445s/Nw1TLGq0atHcyJ+mlxVS5XzXF9KHRKlQ03qPtEgEkpFxrUHVm7BcosS9qDEQm0Im4IrZi/HggtXWNvNAHUoOZot0dwTBHb57cEqqyzArF6GPNHKjw5W0t5G3/ayauAnbUaZ77jy45jFxkIvY5n2SJdpb3rbrcZ6w7y5Y8N1THbXc9hr2Gk8HgN5EHqpLFS4pZKsjQE9Cztuhjnxgtr3B5Mq6cZs9MWge6P9sZZXg02fkTxwkMTJl/ZTYb9ch+LeT9sHP/v/2zjXGquoKwN+aGWbaQQQdB4QRFCgVcXzOaKUFMVIRrJbGB7E1go9GSWzVmiaibdMftCna1lSlCSUVorWt2rRN0foofaix2ipUfCCgaE1ESWlTRUBlpK7+OPswh7n3nnPucJ4z60tu5syefffstde9Z5299t5rfan2wlVZ8E90nt8V/jQZl+CTVNwbXJh74NwTq+aSSoyw7Hf+bKAnxrZc2btLK5kbcH/cSmkEAEyapNyY/ihHHfb0d7fdsaA7tJ4/dlHuvrCZRVNjAx9W+SwnFS8tjEFnHMK2skoKo9E+rIUJhwzle+cdE1pPRLjms5MYPbx2PJmysHdHWELtBW/0cb8UYTdUEUnloJPvdw/7svv/ds9HGsOt5JHUTMzvVz2H+qKeynvX8PIzIlH/e9on4snryxIVQ6yzYzgbF89m5lHhOxX9oYv6rIXNHFqaGuip4gZNOlxJNQafWylmsp+kaGlq5M9fPy3xdotMfw6ihRFcI4jrQohaWEzDGXLvlafw1jsfhNbZN2x2+PhcP3sy1923jskRawlxaWlq5IGvTmNCex2pW0swc4hixSUnxVpnWjRnMj/8wyZG1sjsFiTOFu3GvSlrI9YcQjaNtDQ1VI0zlca9quJ/pP4fCkb4IbhMuzJg8d1K/XBxVyW4uyiumyPqafuchM50BBnR2syUMeG7zYIGoW8+8L5MndjGUzfMjB3CIg6dHcNpbY7fXtxzNUX+6jQ3NYS6+nwWzpjIy9+Zk9gahn/OIeozGzVzqJb0KCwRUlIMPuPgfoatOSQZobMM3DLvuNC8zPXifxHbYx5UiiJ4LuHtXdXj3vtMGuktNkcdTvz+BRWpSDKhyDfRIK3+xoiIDvvjfMqEtpR7lA1Jusd8uxrlCg03Do30/O8j3u9JZ4ddGIPOrTSn81A+OeqA0Klcju7TXDg3JNFOfzj7mNG8t3tPYu0GZw5RQQRXXzcjVptJ73CJS/Dmc2bGp+vr4b4rp3Lfmjcid9p8euIhvL7kcxn1al+uOHVCamdWksCfOUQZnLA1qsYGYfO2nZyz9Il9yuedNHb/OxjBoDMOh7cN5fC26j5X378aN16MUZ2GBuHCk5Mbw2DS9wVTkzsJ+/SNM6vuBEmTDwOyLHORSotIZ8dwOjvihYnIixvPOirvLoTiuwN3R0SsHdHazM3nH0u32/Ye5OH13nmGzdt27i0794R0d9v5FMY4iMhs4FagEfipqi7Jug9NjQ1sXDx70LmV8uTuyz8Vua981pRRLHloIwBtCbmqAEaGhEpOi+CTbp47fIz08UNxt4Wk8/WZ1119JjCkQQg6Ujcunp1IvLI4FMI4iEgj8GPgDGAL8IyIrFLVl7LuS1YDb3hMi7GtckJ7OofW8iTtsxZG/rQPa2H5xV10VZkRxOWgoc3s6unNV5/l/akoj8gnA5tV9TVV7QHuAebm3CejQDy56HQevnZ63t3Yb/xoov05rWyUj1lHH7pfs92rZ/Zm9ws7eZ8GhZg5AB3AG4HftwD1ZVgxBjRjRnycMZT/gOCso0excMZEvjx9fN5dMUrAvO6xzOsey9bt72ceRLMoxqGa87VipVBErgCuABg3zhaNjfIxpLGBRXMm590No2TkETmhKHPbLUBwReYw4K2+lVR1uap2q2p3e3s2CS8MwzAGI0UxDs8Ak0RkvIg0AxcCq3Luk2EYxqClEG4lVd0jIl8BHsHbyrpCVdfn3C3DMIxBSyGMA4CqPgg8mHc/DMMwjOK4lQzDMIwCYcbBMAzDqMCMg2EYhlGBGQfDMAyjAkkqP23WiMgOYFOVP40DwrOoeAwHtudQr566ecmSRpsmSzJ1iy7LQNLLQJIlWPdIVY1OL6iqpXwBa2qU/zvm+5fnUa/ONnORJaXxMVmy7aPpxWSpWrfWvbPvayC6ld6JWe/+nOrVUzcvWdJo02RJpm7RZRlIehlIstRbt9RupTWq2h23vIyYLMXEZCkmJkuybZd55rC8zvIyYrIUE5OlmJgsCbZd2pmDYRiGkR5lnjkYhmEYKVF44yAiK0Rkm4i8GCg7TkSeEpEXROR+ETnQlTeLyEpX/pyInBZ4T5cr3ywit0kOCXwTlOVREdkkIuvca2QOsowVkb+IyAYRWS8i17jyg0VktYi84n4eFHjPDW78N4nImYHyXHWTsCy56qZeWUSkzdXfKSJL+7RVKr1EyFI2vZwhImvd+K8VkdMDbWWjl7jboPJ6AacCJwIvBsqeAWa468uAxe76KmClux4JrAUa3O9PA1PxEgs9BMwpsSyPAt0562U0cKK7Hga8DEwBbgYWufJFwE3uegrwHNACjAdeBRqLoJuEZclVN/2QZSgwDVgILO3TVtn0EiZL2fRyAjDGXXcCb2atl8LPHFT1ceC/fYqPBB5316uB89z1FOBP7n3b8LaDdYvIaOBAVX1KvdG9C/hC2n3vSxKyZNDNWKjqVlX9h7veAWzAS/c6F7jTVbuT3nGeC9yjqrtV9Z/AZuDkIugmKVmy7HMt6pVFVXep6hPAB8F2yqiXWrIUgX7I8qyq+gnP1gMfE5GWLPVSeONQgxeBz7vrC+jNIvccMFdEmkRkPNDl/taBl23OZ4srKwL1yuKz0k2Pv5X1dL8vInIE3pPO34FRqroVvC8E3qwHqucJ76BgutlPWXwKoZuYstSijHqJoqx6OQ94VlV3k6FeymocLgOuEpG1eFO0Hle+Am+w1gA/Ap4E9hAzR3VO1CsLwEWqegww3b0uzrTHAUTkAODXwLWq+m5Y1SplGlKeOQnIAgXRTR2y1GyiSlnR9RJGKfUiIkcDNwFX+kVVqqWil1IaB1XdqKqzVLUL+CWezxdV3aOqX1PV41V1LjACeAXvJntYoImqOarzoB+yoKpvup87gF+Qk0tDRIbgfdB/rqq/ccX/clNf3zWxzZXXyhNeCN0kJEshdFOnLLUoo15qUka9iMhhwG+B+ar6qivOTC+lNA7+TgMRaQC+CSxzv7eKyFB3fQawR1VfctO1HSJyiptOzgd+l0/v96VeWZyb6RBXPgQ4G881lXW/BbgD2KCqtwT+tApY4K4X0DvOq4ALnd90PDAJeLoIuklKliLoph+yVKWkeqnVTun0IiIjgN8DN6jqX/3KmeoljVXuJF94T9NbgQ/xrOblwDV4q/0vA0voPcx3BF6k1g3AH4HDA+10430gXgWW+u8pmyx4OzLWAs/jLVTditspk7Es0/Cms88D69zrLKANbyH9Fffz4MB7vuHGfxOBHRZ56yYpWYqgm37K8jreRomd7nM5pcR6qZCljHrBe1DcFai7DhiZpV7shLRhGIZRQSndSoZhGEa6mHEwDMMwKjDjYBiGYVRgxsEwDMOowIyDYRiGUYEZB8NIARFZKCLz66h/hASi9RpG3jTl3QHDGGiISJOqLsu7H4axP5hxMIwquOBoD+MFRzsB75DifOAo4BbgAOA/wCWqulVEHsWLf/UZYJWIDAN2quoPROR4vJPvrXgHly5T1bdFpAsvhtZ7wBPZSWcY0ZhbyTBqcySwXFWPBd7Fy7FxO3C+erGwVgDfDdQfoaozVPWHfdq5C7jetfMC8G1XvhK4WlWnpimEYfQHmzkYRm3e0N64NncDN+IlXlntIj434oVD8bm3bwMiMhzPaDzmiu4EflWl/GfAnORFMIz+YcbBMGrTN7bMDmB9yJP+rjralirtG0ZhMLeSYdRmnIj4huCLwN+Adr9MRIa4ePs1UdXtwNsiMt0VXQw8pqrvANtFZJorvyj57htG/7GZg2HUZgOwQER+ghc183bgEeA25xZqwkvEtD6inQXAMhFpBV4DLnXllwIrROQ9165hFAaLymoYVXC7lR5Q1c6cu2IYuWBuJcMwDKMCmzkYhmEYFdjMwTAMw6jAjINhGIZRgRkHwzAMowIzDoZhGEYFZhwMwzCMCsw4GIZhGBX8Hx/csIEogwU7AAAAAElFTkSuQmCC\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 croissant de l'automne au printemps. Le creux des incidences se trouve vers la fin de l'été (août/ septembre)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "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 sur la majeure partie de l'année, 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 très faible en fin d'été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent an décembre 1990, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1991." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "first_septembre_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er 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": 16, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_septembre_week[:-1],\n", " first_septembre_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": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { 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pcjOrd04mw8ALLJpZvfPU4CHkp8nNbKRwy2QIeelrMxspnEyGkJ8mN7ORwslkiA106WsP1ptZLSoqmUj6qKSNkn4laY+kz0i6R9Lbkl5L2xdyx98pqVXSXkmLcvG5kl5P362RpBQfI+mZFG+RND1XZpmkfWlbVr6qD49Hbp7H6qWXM2vyOFYvvbzbUti98WC9mdWiopZTkbQOeDkivi/pI8A5wNeB9yLi2z2OnQWsBxqBycCLwJ9ExElJ24GvAf8EPAesiYgtkr4C/OuI+FtJTcB1EfGXkiYAO4F5QACvAHMj4uiZrrVWl1PpOVhf4MF6MxsOQ76ciqRxwGeBRwEi4g8R8bs+iiwBno6Izoh4E2gFGiVdBIyLiG2RZbDHgaW5MuvS/kZgQWq1LAKaI6IjJZBmYPGAa1kDPFhvZrWsmG6uPwbagR9KelXS9yWdm767XdIvJf1A0vgUmwK8lSt/MMWmpP2e8W5lIqILeBe4oI9z1R0P1ptZLSsmmTQAnwYejogrgN8DdwAPA58A5gCHge+k49XLOaKP+GDLfEDSCkk7Je1sb2/voyrVze+pNrNaVcxDiweBgxHRkn7eCNwREUcKB0j6HvA/c8dfnCs/FTiU4lN7iefLHJTUAJwPdKT453qU+VnPC4yItcBayMZMiqhTydqOneD29a/y4I1XlK314PdUm1mt6rdlEhHvAG9JuiyFFgBvpDGQguuAXWl/M9CUZmjNAGYC2yPiMHBc0pVpPOQW4NlcmcJMreuBl9K4yvPAQknjUzfawhSrOM+6MiuOp7uPDMUup/JV4EdpJte/AF8G1kiaQ9bttB+4FSAidkvaALwBdAErI+JkOs9twGPA2cCWtEE2uP+EpFayFklTOleHpPuAHem4eyOiY3BVLQ8vkWI2MPk/vFZf96lKX44NEb9pcYDajp1g9XN7eGH3O5x4/xRjR49i0eyPc9cXP+nBcrMcT3evLX7T4jDzrCuz4ni6+8jiVYMHoTDr6sbGaTy1/QDt7gs2+xD/4TWyOJkMgmddWbkMxazAauI/vEYOj5mYVdDdm17nR9sPcFPjNA9OW0WVOmbilolZBXhWoNUbD8CblVkxz1V4cNrqjZOJWZkV80CrB6et3riby6xMBtp15cHpD6v3CQn1zAPwZmXiB1pL5wkJleMBeLMq4a6rwfOEhNrnMROzMvJrBAbHExJqn1smVtVqrQ/dD7QOjlt1tc8tExt2A1mS3Ev9jxxu1dU2D8DbsCtmkNUrzpoNr1IH4J1MbNgMJEF4ZpTZ8PIS9FYzBjLI6j50s9riAXgbNgNNEH6oz6x2OJnYsBpIgvDMKLPa4TETMzPzmImZmVWek4mZWQ8DeRbKMk4mZmY9+GHZgSsqmUj6qKSNkn4laY+kz0iaIKlZ0r70OT53/J2SWiXtlbQoF58r6fX03RpJSvExkp5J8RZJ03NllqV/Y5+kZeWruplZd5fdvYXpd/yEJ1sOEJEtODn9jp9w2d1bKn1pVa/Ylsl3gZ9GxL8C/hTYA9wBbI2ImcDW9DOSZgFNwGxgMfCQpLPSeR4GVgAz07Y4xZcDRyPiUuAB4P50rgnAKmA+0AisyictM7Ny8oKTg9dvMpE0Dvgs8ChARPwhIn4HLAHWpcPWAUvT/hLg6YjojIg3gVagUdJFwLiI2BbZFLLHe5QpnGsjsCC1WhYBzRHRERFHgWZOJyAzs7Lyw7KDV0zL5I+BduCHkl6V9H1J5wIXRsRhgPQ5KR0/BXgrV/5gik1J+z3j3cpERBfwLnBBH+fqRtIKSTsl7Wxvby+iSmZmvfOCk4NTzEOLDcCnga9GRIuk75K6tM5AvcSij/hgy5wORKwF1kL2nEkf12Zm1ic/LDs4xbRMDgIHI6Il/byRLLkcSV1XpM+23PEX58pPBQ6l+NRe4t3KSGoAzgc6+jiXmZlVkX6TSUS8A7wl6bIUWgC8AWwGCrOrlgHPpv3NQFOaoTWDbKB9e+oKOy7pyjQeckuPMoVzXQ+8lMZVngcWShqfBt4XppiZmVWRYtfm+irwI0kfAf4F+DJZItogaTlwALgBICJ2S9pAlnC6gJURcTKd5zbgMeBsYEvaIBvcf0JSK1mLpCmdq0PSfcCOdNy9EdExyLqamdkQ8dpcZmbmtbnMzKzynEzMzOpApdcTczIxM6sDlV5PzC/HMjOrYZfdvYXOrlMf/PxkywGebDnAmIZR7F19zbBdh1smZmY1rFrWE3MyMTOrYdWynpi7uczMalxhPbEbG6fx1PYDtFdgEN7PmZiZmZ8zMTOzynMyMTOzkjmZmJlZyZxMzMysZE4mZmZWMieTGlfp9XjMzMDJpOZVej0eMzPwQ4s1q1rW4zEzA7dMala1rMdjZgZOJjWrWtbjMTMDd3PVtGpYj8fMDLw2l5mZ4bW5zMysChSVTCTtl/S6pNck7UyxeyS9nWKvSfpC7vg7JbVK2itpUS4+N52nVdIaSUrxMZKeSfEWSdNzZZZJ2pe2ZeWquJnZcKvn58IG0jK5OiLm9GgGPZBicyLiOQBJs4AmYDawGHhI0lnp+IeBFcDMtC1O8eXA0Yi4FHgAuD+dawKwCpgPNAKrJI0fRD3NzCqunp8LG4oB+CXA0xHRCbwpqRVolLQfGBcR2wAkPQ4sBbakMvek8huBB1OrZRHQHBEdqUwzWQJaPwTXbWY2JEbCc2HFtkwCeEHSK5JW5OK3S/qlpB/kWgxTgLdyxxxMsSlpv2e8W5mI6ALeBS7o41xmZjVjJDwXVmwyuSoiPg1cA6yU9FmyLqtPAHOAw8B30rHqpXz0ER9smQ9IWiFpp6Sd7e3tfVbEzGy4jYTnwopKJhFxKH22AZuAxog4EhEnI+IU8D2yMQ3IWg8X54pPBQ6l+NRe4t3KSGoAzgc6+jhXz+tbGxHzImLexIkTi6mSmdmwKjwXtukrV3HT/Etof6+z3zK1NGDfbzKRdK6k8wr7wEJgl6SLcoddB+xK+5uBpjRDawbZQPv2iDgMHJd0ZRoPuQV4NlemMFPreuClyB6AeR5YKGl86kZbmGJmZjXlkZvnsXrp5cyaPI7VSy/nkZv7f6SjlgbsixmAvxDYlGbxNgBPRcRPJT0haQ5Zt9N+4FaAiNgtaQPwBtAFrIyIk+lctwGPAWeTDbxvSfFHgSfSYH0H2WwwIqJD0n3AjnTcvYXBeDOzelWLA/Z+At7MrMq0HTvB6uf28MLudzjx/inGjh7Fotkf564vfnLIxln8BLyZWZ2pxQF7L/RoZlaFam0hV3dzmZmZu7nMzKzynEzMzKxkTiZmZlYyJxMzMyuZk4mZmZXMycTMzErmZGJmZiVzMjEzs5I5mZiZWcmcTKxu1NK7H8zqjZOJ1Y1aeveDWb3xQo9W82rx3Q9m9cYtE6t5L3/zaq6dM5mxo7P/nceOHsWSOZN5+VtXV/jKzEYOJxOrebX47gezeuNuLqsLtfbuB7N64/eZmJmZ32diZmaV52RiZmYlKyqZSNov6XVJr0namWITJDVL2pc+x+eOv1NSq6S9khbl4nPTeVolrZGkFB8j6ZkUb5E0PVdmWfo39klaVq6Km5lZ+QykZXJ1RMzJ9andAWyNiJnA1vQzkmYBTcBsYDHwkKSzUpmHgRXAzLQtTvHlwNGIuBR4ALg/nWsCsAqYDzQCq/JJy8zMqkMp3VxLgHVpfx2wNBd/OiI6I+JNoBVolHQRMC4itkU26v94jzKFc20EFqRWyyKgOSI6IuIo0MzpBGRmZlWi2GQSwAuSXpG0IsUujIjDAOlzUopPAd7KlT2YYlPSfs94tzIR0QW8C1zQx7nMzKyKFPucyVURcUjSJKBZ0q/6OFa9xKKP+GDLnP4HswRXSHLvSdrbx/XVgo8Bv630RQyxeq9jvdcP6r+OI61+l5RysqKSSUQcSp9tkjaRjV8ckXRRRBxOXVht6fCDwMW54lOBQyk+tZd4vsxBSQ3A+UBHin+uR5mf9XJ9a4G1xdSlFkjaWcp871pQ73Ws9/pB/dfR9RuYfru5JJ0r6bzCPrAQ2AVsBgqzq5YBz6b9zUBTmqE1g2ygfXvqCjsu6co0HnJLjzKFc10PvJTGVZ4HFkoanwbeF6aYmZlVkWJaJhcCm9Is3gbgqYj4qaQdwAZJy4EDwA0AEbFb0gbgDaALWBkRJ9O5bgMeA84GtqQN4FHgCUmtZC2SpnSuDkn3ATvScfdGREcJ9TUzsyFQd8up1ANJK1LXXd2q9zrWe/2g/uvo+g3wfE4mZmZWKi+nYmZmJXMyGSaSfiCpTdKuXOxPJW1LS8z8D0njUvwjkn6Y4r+Q9LlcmZ+lZWpeS9ukXv65YSfpYkn/S9IeSbslfS3Fy7bsTiWVuX51cQ8lXZCOf0/Sgz3OVfP3sJ/6Vd09HET9Pq/s2cHX0+e/z51r4PcvIrwNwwZ8Fvg0sCsX2wH8u7T/N8B9aX8l8MO0Pwl4BRiVfv4ZMK/S9emlfhcBn0775wH/DMwC/gG4I8XvAO5P+7OAXwBjgBnAr4Gz0nfbgc+QPWe0BbimzupXL/fwXODfAH8LPNjjXPVwD/uqX9Xdw0HU7wpgctq/HHi7lPvnlskwiYh/JJuplncZ8I9pvxn4i7Q/i2y9MyKiDfgdUNXz3SPicET8PO0fB/aQrVZQzmV3KqZc9Rveqx6YgdYxIn4fEf8b6PYmsnq5h2eqX7UaRP1ejfQMIbAbGKvskY5B3T8nk8raBVyb9m/g9MOevwCWSGpQ9qzOXLo/CPrD1LT+T9XQfdCTslWfrwBaKO+yO1WhxPoV1MM9PJN6uYf9qdp7OIj6/QXwakR0Msj752RSWX8DrJT0Clmz9A8p/gOyG7gT+C/A/yV7Zgfgpoj4FPBv03bzsF5xPyT9EfDfga9HxLG+Du0lVvQSOpVShvpB/dzDM56il1gt3sO+VO09HGj9JM0mW6n91kKol8P6vX9OJhUUEb+KiIURMRdYT9avTkR0RcQ3IlvyfwnwUWBf+u7t9HkceIoq6jqRNJrsf+IfRcSPU/hIajYXuj9KWXanospUv3q6h2dSL/fwjKr1Hg60fpKmApuAWyLi1yk8qPvnZFJBhRkgkkYBdwP/Nf18jrKla5D0eaArIt5I3V4fS/HRwH8g6yqruNTMfxTYExH/OfdVOZfdqZhy1a/O7mGv6ugenuk8VXkPB1o/SR8FfgLcGRH/p3DwoO9fpWYejLSNrOVxGHifLPMvB75GNuPin4G/5/RDpNOBvWQDaC8Cl6T4uWQzu35JNmD2XdIMoUpvZLNeIl3ba2n7AtmrBLaStay2AhNyZe4ia43tJTdbhGyywa703YOF/y71UL86vIf7ySaWvJf+v55VZ/fwQ/Wr1ns40PqR/QH7+9yxrwGTBnv//AS8mZmVzN1cZmZWMicTMzMrmZOJmZmVzMnEzMxK5mRiZmYlczIxM7OSOZmYmVnJnEzMzKxk/x+qftJYVEr3+gAAAABJRU5ErkJggg==\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": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2002 516689\n", "2018 542312\n", "2017 551041\n", "1996 564901\n", "2019 584066\n", "2015 604382\n", "2000 617597\n", "2001 619041\n", "2012 624573\n", "2005 628464\n", "2006 632833\n", "2011 642368\n", "1993 643387\n", "1995 652478\n", "1994 661409\n", "1998 677775\n", "1997 683434\n", "2014 685769\n", "2013 698332\n", "2007 717352\n", "2008 749478\n", "1999 756456\n", "2003 758363\n", "2004 777388\n", "2016 782114\n", "2010 829911\n", "1992 832939\n", "2009 842373\n", "dtype: int64" ] }, "execution_count": 18, "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 750000 à 850000 personnes, sont assez régulières: il y en eu huit au cours des 28 dernières années (soit environ un quart des épidémies)." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "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.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 }