{ "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 1984 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "data_file = \"incidence-PAY-7.csv\"" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202035788101895103FRFrance
1202034723033814225306FRFrance
2202033712841772391204FRFrance
3202032726506894611417FRFrance
4202031713031002506204FRFrance
520203071385752695204FRFrance
62020297841101672102FRFrance
7202028772801515102FRFrance
820202779861491823102FRFrance
9202026769401454102FRFrance
1020202572280597001FRFrance
1120202473880959102FRFrance
12202023755811115102FRFrance
1320202272770633001FRFrance
142020217602361168102FRFrance
152020207824201628102FRFrance
1620201973100753001FRFrance
172020187849981600102FRFrance
1820201772720658001FRFrance
192020167758781438102FRFrance
20202015719186753161315FRFrance
212020147387922275531639FRFrance
22202013773265236941611814FRFrance
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24202011710198756812828151119FRFrance
2520201079011669111331141018FRFrance
262020097136311054416718211626FRFrance
27202008710424770813140161220FRFrance
2820200778959657411344141018FRFrance
2920200679264692511603141018FRFrance
.................................
15221991267176081130423912312042FRFrance
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1527199121714903897520831261636FRFrance
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15291991197167391124622232291939FRFrance
15301991187213851388228888382551FRFrance
1531199117713462887718047241632FRFrance
15321991167148571006819646261834FRFrance
1533199115713975978118169251832FRFrance
1534199114712265768416846221430FRFrance
153519911379567604113093171123FRFrance
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15491990517190801380724353342543FRFrance
1550199050711079666015498201228FRFrance
15511990497114302610205FRFrance
\n", "

1552 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202035 7 881 0 1895 1 0 \n", "1 202034 7 2303 381 4225 3 0 \n", "2 202033 7 1284 177 2391 2 0 \n", "3 202032 7 2650 689 4611 4 1 \n", "4 202031 7 1303 100 2506 2 0 \n", "5 202030 7 1385 75 2695 2 0 \n", "6 202029 7 841 10 1672 1 0 \n", "7 202028 7 728 0 1515 1 0 \n", "8 202027 7 986 149 1823 1 0 \n", "9 202026 7 694 0 1454 1 0 \n", "10 202025 7 228 0 597 0 0 \n", "11 202024 7 388 0 959 1 0 \n", "12 202023 7 558 1 1115 1 0 \n", "13 202022 7 277 0 633 0 0 \n", "14 202021 7 602 36 1168 1 0 \n", "15 202020 7 824 20 1628 1 0 \n", "16 202019 7 310 0 753 0 0 \n", "17 202018 7 849 98 1600 1 0 \n", "18 202017 7 272 0 658 0 0 \n", "19 202016 7 758 78 1438 1 0 \n", "20 202015 7 1918 675 3161 3 1 \n", "21 202014 7 3879 2227 5531 6 3 \n", "22 202013 7 7326 5236 9416 11 8 \n", "23 202012 7 8123 5790 10456 12 8 \n", "24 202011 7 10198 7568 12828 15 11 \n", "25 202010 7 9011 6691 11331 14 10 \n", "26 202009 7 13631 10544 16718 21 16 \n", "27 202008 7 10424 7708 13140 16 12 \n", "28 202007 7 8959 6574 11344 14 10 \n", "29 202006 7 9264 6925 11603 14 10 \n", "... ... ... ... ... ... ... ... \n", "1522 199126 7 17608 11304 23912 31 20 \n", "1523 199125 7 16169 10700 21638 28 18 \n", "1524 199124 7 16171 10071 22271 28 17 \n", "1525 199123 7 11947 7671 16223 21 13 \n", "1526 199122 7 15452 9953 20951 27 17 \n", "1527 199121 7 14903 8975 20831 26 16 \n", "1528 199120 7 19053 12742 25364 34 23 \n", "1529 199119 7 16739 11246 22232 29 19 \n", "1530 199118 7 21385 13882 28888 38 25 \n", "1531 199117 7 13462 8877 18047 24 16 \n", "1532 199116 7 14857 10068 19646 26 18 \n", "1533 199115 7 13975 9781 18169 25 18 \n", "1534 199114 7 12265 7684 16846 22 14 \n", "1535 199113 7 9567 6041 13093 17 11 \n", "1536 199112 7 10864 7331 14397 19 13 \n", "1537 199111 7 15574 11184 19964 27 19 \n", "1538 199110 7 16643 11372 21914 29 20 \n", "1539 199109 7 13741 8780 18702 24 15 \n", "1540 199108 7 13289 8813 17765 23 15 \n", "1541 199107 7 12337 8077 16597 22 15 \n", "1542 199106 7 10877 7013 14741 19 12 \n", "1543 199105 7 10442 6544 14340 18 11 \n", "1544 199104 7 7913 4563 11263 14 8 \n", "1545 199103 7 15387 10484 20290 27 18 \n", "1546 199102 7 16277 11046 21508 29 20 \n", "1547 199101 7 15565 10271 20859 27 18 \n", "1548 199052 7 19375 13295 25455 34 23 \n", "1549 199051 7 19080 13807 24353 34 25 \n", "1550 199050 7 11079 6660 15498 20 12 \n", "1551 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 3 FR France \n", "1 6 FR France \n", "2 4 FR France \n", "3 7 FR France \n", "4 4 FR France \n", "5 4 FR France \n", "6 2 FR France \n", "7 2 FR France \n", "8 2 FR France \n", "9 2 FR France \n", "10 1 FR France \n", "11 2 FR France \n", "12 2 FR France \n", "13 1 FR France \n", "14 2 FR France \n", "15 2 FR France \n", "16 1 FR France \n", "17 2 FR France \n", "18 1 FR France \n", "19 2 FR France \n", "20 5 FR France \n", "21 9 FR France \n", "22 14 FR France \n", "23 16 FR France \n", "24 19 FR France \n", "25 18 FR France \n", "26 26 FR France \n", "27 20 FR France \n", "28 18 FR France \n", "29 18 FR France \n", "... ... ... ... \n", "1522 42 FR France \n", "1523 38 FR France \n", "1524 39 FR France \n", "1525 29 FR France \n", "1526 37 FR France \n", "1527 36 FR France \n", "1528 45 FR France \n", "1529 39 FR France \n", "1530 51 FR France \n", "1531 32 FR France \n", "1532 34 FR France \n", "1533 32 FR France \n", "1534 30 FR France \n", "1535 23 FR France \n", "1536 25 FR France \n", "1537 35 FR France \n", "1538 38 FR France \n", "1539 33 FR France \n", "1540 31 FR France \n", "1541 29 FR France \n", "1542 26 FR France \n", "1543 25 FR France \n", "1544 20 FR France \n", "1545 36 FR France \n", "1546 38 FR France \n", "1547 36 FR France \n", "1548 45 FR France \n", "1549 43 FR France \n", "1550 28 FR France \n", "1551 5 FR France \n", "\n", "[1552 rows x 10 columns]" ] }, "execution_count": 7, "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 ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202035788101895103FRFrance
1202034723033814225306FRFrance
2202033712841772391204FRFrance
3202032726506894611417FRFrance
4202031713031002506204FRFrance
520203071385752695204FRFrance
62020297841101672102FRFrance
7202028772801515102FRFrance
820202779861491823102FRFrance
9202026769401454102FRFrance
1020202572280597001FRFrance
1120202473880959102FRFrance
12202023755811115102FRFrance
1320202272770633001FRFrance
142020217602361168102FRFrance
152020207824201628102FRFrance
1620201973100753001FRFrance
172020187849981600102FRFrance
1820201772720658001FRFrance
192020167758781438102FRFrance
20202015719186753161315FRFrance
212020147387922275531639FRFrance
22202013773265236941611814FRFrance
232020127812357901045612816FRFrance
24202011710198756812828151119FRFrance
2520201079011669111331141018FRFrance
262020097136311054416718211626FRFrance
27202008710424770813140161220FRFrance
2820200778959657411344141018FRFrance
2920200679264692511603141018FRFrance
.................................
15221991267176081130423912312042FRFrance
15231991257161691070021638281838FRFrance
15241991247161711007122271281739FRFrance
1525199123711947767116223211329FRFrance
1526199122715452995320951271737FRFrance
1527199121714903897520831261636FRFrance
15281991207190531274225364342345FRFrance
15291991197167391124622232291939FRFrance
15301991187213851388228888382551FRFrance
1531199117713462887718047241632FRFrance
15321991167148571006819646261834FRFrance
1533199115713975978118169251832FRFrance
1534199114712265768416846221430FRFrance
153519911379567604113093171123FRFrance
1536199112710864733114397191325FRFrance
15371991117155741118419964271935FRFrance
15381991107166431137221914292038FRFrance
1539199109713741878018702241533FRFrance
1540199108713289881317765231531FRFrance
1541199107712337807716597221529FRFrance
1542199106710877701314741191226FRFrance
1543199105710442654414340181125FRFrance
15441991047791345631126314820FRFrance
15451991037153871048420290271836FRFrance
15461991027162771104621508292038FRFrance
15471991017155651027120859271836FRFrance
15481990527193751329525455342345FRFrance
15491990517190801380724353342543FRFrance
1550199050711079666015498201228FRFrance
15511990497114302610205FRFrance
\n", "

1552 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202035 7 881 0 1895 1 0 \n", "1 202034 7 2303 381 4225 3 0 \n", "2 202033 7 1284 177 2391 2 0 \n", "3 202032 7 2650 689 4611 4 1 \n", "4 202031 7 1303 100 2506 2 0 \n", "5 202030 7 1385 75 2695 2 0 \n", "6 202029 7 841 10 1672 1 0 \n", "7 202028 7 728 0 1515 1 0 \n", "8 202027 7 986 149 1823 1 0 \n", "9 202026 7 694 0 1454 1 0 \n", "10 202025 7 228 0 597 0 0 \n", "11 202024 7 388 0 959 1 0 \n", "12 202023 7 558 1 1115 1 0 \n", "13 202022 7 277 0 633 0 0 \n", "14 202021 7 602 36 1168 1 0 \n", "15 202020 7 824 20 1628 1 0 \n", "16 202019 7 310 0 753 0 0 \n", "17 202018 7 849 98 1600 1 0 \n", "18 202017 7 272 0 658 0 0 \n", "19 202016 7 758 78 1438 1 0 \n", "20 202015 7 1918 675 3161 3 1 \n", "21 202014 7 3879 2227 5531 6 3 \n", "22 202013 7 7326 5236 9416 11 8 \n", "23 202012 7 8123 5790 10456 12 8 \n", "24 202011 7 10198 7568 12828 15 11 \n", "25 202010 7 9011 6691 11331 14 10 \n", "26 202009 7 13631 10544 16718 21 16 \n", "27 202008 7 10424 7708 13140 16 12 \n", "28 202007 7 8959 6574 11344 14 10 \n", "29 202006 7 9264 6925 11603 14 10 \n", "... ... ... ... ... ... ... ... \n", "1522 199126 7 17608 11304 23912 31 20 \n", "1523 199125 7 16169 10700 21638 28 18 \n", "1524 199124 7 16171 10071 22271 28 17 \n", "1525 199123 7 11947 7671 16223 21 13 \n", "1526 199122 7 15452 9953 20951 27 17 \n", "1527 199121 7 14903 8975 20831 26 16 \n", "1528 199120 7 19053 12742 25364 34 23 \n", "1529 199119 7 16739 11246 22232 29 19 \n", "1530 199118 7 21385 13882 28888 38 25 \n", "1531 199117 7 13462 8877 18047 24 16 \n", "1532 199116 7 14857 10068 19646 26 18 \n", "1533 199115 7 13975 9781 18169 25 18 \n", "1534 199114 7 12265 7684 16846 22 14 \n", "1535 199113 7 9567 6041 13093 17 11 \n", "1536 199112 7 10864 7331 14397 19 13 \n", "1537 199111 7 15574 11184 19964 27 19 \n", "1538 199110 7 16643 11372 21914 29 20 \n", "1539 199109 7 13741 8780 18702 24 15 \n", "1540 199108 7 13289 8813 17765 23 15 \n", "1541 199107 7 12337 8077 16597 22 15 \n", "1542 199106 7 10877 7013 14741 19 12 \n", "1543 199105 7 10442 6544 14340 18 11 \n", "1544 199104 7 7913 4563 11263 14 8 \n", "1545 199103 7 15387 10484 20290 27 18 \n", "1546 199102 7 16277 11046 21508 29 20 \n", "1547 199101 7 15565 10271 20859 27 18 \n", "1548 199052 7 19375 13295 25455 34 23 \n", "1549 199051 7 19080 13807 24353 34 25 \n", "1550 199050 7 11079 6660 15498 20 12 \n", "1551 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 3 FR France \n", "1 6 FR France \n", "2 4 FR France \n", "3 7 FR France \n", "4 4 FR France \n", "5 4 FR France \n", "6 2 FR France \n", "7 2 FR France \n", "8 2 FR France \n", "9 2 FR France \n", "10 1 FR France \n", "11 2 FR France \n", "12 2 FR France \n", "13 1 FR France \n", "14 2 FR France \n", "15 2 FR France \n", "16 1 FR France \n", "17 2 FR France \n", "18 1 FR France \n", "19 2 FR France \n", "20 5 FR France \n", "21 9 FR France \n", "22 14 FR France \n", "23 16 FR France \n", "24 19 FR France \n", "25 18 FR France \n", "26 26 FR France \n", "27 20 FR France \n", "28 18 FR France \n", "29 18 FR France \n", "... ... ... ... \n", "1522 42 FR France \n", "1523 38 FR France \n", "1524 39 FR France \n", "1525 29 FR France \n", "1526 37 FR France \n", "1527 36 FR France \n", "1528 45 FR France \n", "1529 39 FR France \n", "1530 51 FR France \n", "1531 32 FR France \n", "1532 34 FR France \n", "1533 32 FR France \n", "1534 30 FR France \n", "1535 23 FR France \n", "1536 25 FR France \n", "1537 35 FR France \n", "1538 38 FR France \n", "1539 33 FR France \n", "1540 31 FR France \n", "1541 29 FR France \n", "1542 26 FR France \n", "1543 25 FR France \n", "1544 20 FR France \n", "1545 36 FR France \n", "1546 38 FR France \n", "1547 36 FR France \n", "1548 45 FR France \n", "1549 43 FR France \n", "1550 28 FR France \n", "1551 5 FR France \n", "\n", "[1552 rows x 10 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de\n", "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", "de nombre entier. C'est comme ça que Pandas les interprète.\n", " \n", "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", "semaine. Il faut lui fournir les dates de début et de fin de\n", "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous\n", "écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il restent deux petites modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation\n", "comme nouvel index de notre jeux de données. Ceci en fait\n", "une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans\n", "le sens chronologique." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", "le début de la période qui suit, la différence temporelle doit être\n", "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", "d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", "entre lesquelles il manque une semaine.\n", "\n", "Nous reconnaissons ces dates: c'est la semaine sans observations\n", "que nous avions supprimées !" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 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": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "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()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", "entre deux années civiles, nous définissons la période de référence\n", "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", "1er août de l'année $N+1$.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", "de référence: à la place du 1er août de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er août.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1985." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_september_week[:-1],\n", " first_september_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " assert abs(len(one_year)-52) < 2\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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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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\n", 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