{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# l'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": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = 'http://www.sentiweb.fr/datasets/incidence-PAY-7.csv'" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "import os\n", "import requests\n", "\n", "if not os.path.exists('incidence-PAY-7.csv'):\n", " r = requests.get(data_url, allow_redirects=True)\n", " open('incidence-PAY-7.csv', 'wb').write(r.content)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202119767314409905310614FRFrance
12021187391221105714639FRFrance
220211774686287864947410FRFrance
320211674780289166697410FRFrance
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5202114711197799414400171222FRFrance
620211379714628913139151020FRFrance
7202112711520841514625171222FRFrance
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920211079056645211660141018FRFrance
10202109710988793814038171222FRFrance
11202108711281836114201171321FRFrance
122021077135611031516807211626FRFrance
13202106713401981016992201525FRFrance
14202105712210898815432181323FRFrance
15202104712026882615226181323FRFrance
162021037891363751145113917FRFrance
172021027779554301016012816FRFrance
18202101710525775013300161220FRFrance
19202053711978840615550181323FRFrance
20202052712012828515739181224FRFrance
21202051710564757413554161121FRFrance
22202050770634744938211715FRFrance
2320204975026314569078511FRFrance
24202048766834312905410614FRFrance
2520204774999296370358511FRFrance
262020467375219635541639FRFrance
272020457369620165376639FRFrance
2820204474391237564077410FRFrance
2920204374376250562477410FRFrance
.................................
15591991267176081130423912312042FRFrance
15601991257161691070021638281838FRFrance
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1564199121714903897520831261636FRFrance
15651991207190531274225364342345FRFrance
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15671991187213851388228888382551FRFrance
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1587199050711079666015498201228FRFrance
15881990497114302610205FRFrance
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1589 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202119 7 6731 4409 9053 10 6 \n", "1 202118 7 3912 2110 5714 6 3 \n", "2 202117 7 4686 2878 6494 7 4 \n", "3 202116 7 4780 2891 6669 7 4 \n", "4 202115 7 11215 7627 14803 17 12 \n", "5 202114 7 11197 7994 14400 17 12 \n", "6 202113 7 9714 6289 13139 15 10 \n", "7 202112 7 11520 8415 14625 17 12 \n", "8 202111 7 9386 6678 12094 14 10 \n", "9 202110 7 9056 6452 11660 14 10 \n", "10 202109 7 10988 7938 14038 17 12 \n", "11 202108 7 11281 8361 14201 17 13 \n", "12 202107 7 13561 10315 16807 21 16 \n", "13 202106 7 13401 9810 16992 20 15 \n", "14 202105 7 12210 8988 15432 18 13 \n", "15 202104 7 12026 8826 15226 18 13 \n", "16 202103 7 8913 6375 11451 13 9 \n", "17 202102 7 7795 5430 10160 12 8 \n", "18 202101 7 10525 7750 13300 16 12 \n", "19 202053 7 11978 8406 15550 18 13 \n", "20 202052 7 12012 8285 15739 18 12 \n", "21 202051 7 10564 7574 13554 16 11 \n", "22 202050 7 7063 4744 9382 11 7 \n", "23 202049 7 5026 3145 6907 8 5 \n", "24 202048 7 6683 4312 9054 10 6 \n", "25 202047 7 4999 2963 7035 8 5 \n", "26 202046 7 3752 1963 5541 6 3 \n", "27 202045 7 3696 2016 5376 6 3 \n", "28 202044 7 4391 2375 6407 7 4 \n", "29 202043 7 4376 2505 6247 7 4 \n", "... ... ... ... ... ... ... ... \n", "1559 199126 7 17608 11304 23912 31 20 \n", "1560 199125 7 16169 10700 21638 28 18 \n", "1561 199124 7 16171 10071 22271 28 17 \n", "1562 199123 7 11947 7671 16223 21 13 \n", "1563 199122 7 15452 9953 20951 27 17 \n", "1564 199121 7 14903 8975 20831 26 16 \n", "1565 199120 7 19053 12742 25364 34 23 \n", "1566 199119 7 16739 11246 22232 29 19 \n", "1567 199118 7 21385 13882 28888 38 25 \n", "1568 199117 7 13462 8877 18047 24 16 \n", "1569 199116 7 14857 10068 19646 26 18 \n", "1570 199115 7 13975 9781 18169 25 18 \n", "1571 199114 7 12265 7684 16846 22 14 \n", "1572 199113 7 9567 6041 13093 17 11 \n", "1573 199112 7 10864 7331 14397 19 13 \n", "1574 199111 7 15574 11184 19964 27 19 \n", "1575 199110 7 16643 11372 21914 29 20 \n", "1576 199109 7 13741 8780 18702 24 15 \n", "1577 199108 7 13289 8813 17765 23 15 \n", "1578 199107 7 12337 8077 16597 22 15 \n", "1579 199106 7 10877 7013 14741 19 12 \n", "1580 199105 7 10442 6544 14340 18 11 \n", "1581 199104 7 7913 4563 11263 14 8 \n", "1582 199103 7 15387 10484 20290 27 18 \n", "1583 199102 7 16277 11046 21508 29 20 \n", "1584 199101 7 15565 10271 20859 27 18 \n", "1585 199052 7 19375 13295 25455 34 23 \n", "1586 199051 7 19080 13807 24353 34 25 \n", "1587 199050 7 11079 6660 15498 20 12 \n", "1588 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 14 FR France \n", "1 9 FR France \n", "2 10 FR France \n", "3 10 FR France \n", "4 22 FR France \n", "5 22 FR France \n", "6 20 FR France \n", "7 22 FR France \n", "8 18 FR France \n", "9 18 FR France \n", "10 22 FR France \n", "11 21 FR France \n", "12 26 FR France \n", "13 25 FR France \n", "14 23 FR France \n", "15 23 FR France \n", "16 17 FR France \n", "17 16 FR France \n", "18 20 FR France \n", "19 23 FR France \n", "20 24 FR France \n", "21 21 FR France \n", "22 15 FR France \n", "23 11 FR France \n", "24 14 FR France \n", "25 11 FR France \n", "26 9 FR France \n", "27 9 FR France \n", "28 10 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1559 42 FR France \n", "1560 38 FR France \n", "1561 39 FR France \n", "1562 29 FR France \n", "1563 37 FR France \n", "1564 36 FR France \n", "1565 45 FR France \n", "1566 39 FR France \n", "1567 51 FR France \n", "1568 32 FR France \n", "1569 34 FR France \n", "1570 32 FR France \n", "1571 30 FR France \n", "1572 23 FR France \n", "1573 25 FR France \n", "1574 35 FR France \n", "1575 38 FR France \n", "1576 33 FR France \n", "1577 31 FR France \n", "1578 29 FR France \n", "1579 26 FR France \n", "1580 25 FR France \n", "1581 20 FR France \n", "1582 36 FR France \n", "1583 38 FR France \n", "1584 36 FR France \n", "1585 45 FR France \n", "1586 43 FR France \n", "1587 28 FR France \n", "1588 5 FR France \n", "\n", "[1589 rows x 10 columns]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv('incidence-PAY-7.csv', skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202119767314409905310614FRFrance
12021187391221105714639FRFrance
220211774686287864947410FRFrance
320211674780289166697410FRFrance
4202115711215762714803171222FRFrance
5202114711197799414400171222FRFrance
620211379714628913139151020FRFrance
7202112711520841514625171222FRFrance
820211179386667812094141018FRFrance
920211079056645211660141018FRFrance
10202109710988793814038171222FRFrance
11202108711281836114201171321FRFrance
122021077135611031516807211626FRFrance
13202106713401981016992201525FRFrance
14202105712210898815432181323FRFrance
15202104712026882615226181323FRFrance
162021037891363751145113917FRFrance
172021027779554301016012816FRFrance
18202101710525775013300161220FRFrance
19202053711978840615550181323FRFrance
20202052712012828515739181224FRFrance
21202051710564757413554161121FRFrance
22202050770634744938211715FRFrance
2320204975026314569078511FRFrance
24202048766834312905410614FRFrance
2520204774999296370358511FRFrance
262020467375219635541639FRFrance
272020457369620165376639FRFrance
2820204474391237564077410FRFrance
2920204374376250562477410FRFrance
.................................
15591991267176081130423912312042FRFrance
15601991257161691070021638281838FRFrance
15611991247161711007122271281739FRFrance
1562199123711947767116223211329FRFrance
1563199122715452995320951271737FRFrance
1564199121714903897520831261636FRFrance
15651991207190531274225364342345FRFrance
15661991197167391124622232291939FRFrance
15671991187213851388228888382551FRFrance
1568199117713462887718047241632FRFrance
15691991167148571006819646261834FRFrance
1570199115713975978118169251832FRFrance
1571199114712265768416846221430FRFrance
157219911379567604113093171123FRFrance
1573199112710864733114397191325FRFrance
15741991117155741118419964271935FRFrance
15751991107166431137221914292038FRFrance
1576199109713741878018702241533FRFrance
1577199108713289881317765231531FRFrance
1578199107712337807716597221529FRFrance
1579199106710877701314741191226FRFrance
1580199105710442654414340181125FRFrance
15811991047791345631126314820FRFrance
15821991037153871048420290271836FRFrance
15831991027162771104621508292038FRFrance
15841991017155651027120859271836FRFrance
15851990527193751329525455342345FRFrance
15861990517190801380724353342543FRFrance
1587199050711079666015498201228FRFrance
15881990497114302610205FRFrance
\n", "

1589 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202119 7 6731 4409 9053 10 6 \n", "1 202118 7 3912 2110 5714 6 3 \n", "2 202117 7 4686 2878 6494 7 4 \n", "3 202116 7 4780 2891 6669 7 4 \n", "4 202115 7 11215 7627 14803 17 12 \n", "5 202114 7 11197 7994 14400 17 12 \n", "6 202113 7 9714 6289 13139 15 10 \n", "7 202112 7 11520 8415 14625 17 12 \n", "8 202111 7 9386 6678 12094 14 10 \n", "9 202110 7 9056 6452 11660 14 10 \n", "10 202109 7 10988 7938 14038 17 12 \n", "11 202108 7 11281 8361 14201 17 13 \n", "12 202107 7 13561 10315 16807 21 16 \n", "13 202106 7 13401 9810 16992 20 15 \n", "14 202105 7 12210 8988 15432 18 13 \n", "15 202104 7 12026 8826 15226 18 13 \n", "16 202103 7 8913 6375 11451 13 9 \n", "17 202102 7 7795 5430 10160 12 8 \n", "18 202101 7 10525 7750 13300 16 12 \n", "19 202053 7 11978 8406 15550 18 13 \n", "20 202052 7 12012 8285 15739 18 12 \n", "21 202051 7 10564 7574 13554 16 11 \n", "22 202050 7 7063 4744 9382 11 7 \n", "23 202049 7 5026 3145 6907 8 5 \n", "24 202048 7 6683 4312 9054 10 6 \n", "25 202047 7 4999 2963 7035 8 5 \n", "26 202046 7 3752 1963 5541 6 3 \n", "27 202045 7 3696 2016 5376 6 3 \n", "28 202044 7 4391 2375 6407 7 4 \n", "29 202043 7 4376 2505 6247 7 4 \n", "... ... ... ... ... ... ... ... \n", "1559 199126 7 17608 11304 23912 31 20 \n", "1560 199125 7 16169 10700 21638 28 18 \n", "1561 199124 7 16171 10071 22271 28 17 \n", "1562 199123 7 11947 7671 16223 21 13 \n", "1563 199122 7 15452 9953 20951 27 17 \n", "1564 199121 7 14903 8975 20831 26 16 \n", "1565 199120 7 19053 12742 25364 34 23 \n", "1566 199119 7 16739 11246 22232 29 19 \n", "1567 199118 7 21385 13882 28888 38 25 \n", "1568 199117 7 13462 8877 18047 24 16 \n", "1569 199116 7 14857 10068 19646 26 18 \n", "1570 199115 7 13975 9781 18169 25 18 \n", "1571 199114 7 12265 7684 16846 22 14 \n", "1572 199113 7 9567 6041 13093 17 11 \n", "1573 199112 7 10864 7331 14397 19 13 \n", "1574 199111 7 15574 11184 19964 27 19 \n", "1575 199110 7 16643 11372 21914 29 20 \n", "1576 199109 7 13741 8780 18702 24 15 \n", "1577 199108 7 13289 8813 17765 23 15 \n", "1578 199107 7 12337 8077 16597 22 15 \n", "1579 199106 7 10877 7013 14741 19 12 \n", "1580 199105 7 10442 6544 14340 18 11 \n", "1581 199104 7 7913 4563 11263 14 8 \n", "1582 199103 7 15387 10484 20290 27 18 \n", "1583 199102 7 16277 11046 21508 29 20 \n", "1584 199101 7 15565 10271 20859 27 18 \n", "1585 199052 7 19375 13295 25455 34 23 \n", "1586 199051 7 19080 13807 24353 34 25 \n", "1587 199050 7 11079 6660 15498 20 12 \n", "1588 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 14 FR France \n", "1 9 FR France \n", "2 10 FR France \n", "3 10 FR France \n", "4 22 FR France \n", "5 22 FR France \n", "6 20 FR France \n", "7 22 FR France \n", "8 18 FR France \n", "9 18 FR France \n", "10 22 FR France \n", "11 21 FR France \n", "12 26 FR France \n", "13 25 FR France \n", "14 23 FR France \n", "15 23 FR France \n", "16 17 FR France \n", "17 16 FR France \n", "18 20 FR France \n", "19 23 FR France \n", "20 24 FR France \n", "21 21 FR France \n", "22 15 FR France \n", "23 11 FR France \n", "24 14 FR France \n", "25 11 FR France \n", "26 9 FR France \n", "27 9 FR France \n", "28 10 FR France \n", "29 10 FR France \n", "... ... ... ... \n", "1559 42 FR France \n", "1560 38 FR France \n", "1561 39 FR France \n", "1562 29 FR France \n", "1563 37 FR France \n", "1564 36 FR France \n", "1565 45 FR France \n", "1566 39 FR France \n", "1567 51 FR France \n", "1568 32 FR France \n", "1569 34 FR France \n", "1570 32 FR France \n", "1571 30 FR France \n", "1572 23 FR France \n", "1573 25 FR France \n", "1574 35 FR France \n", "1575 38 FR France \n", "1576 33 FR France \n", "1577 31 FR France \n", "1578 29 FR France \n", "1579 26 FR France \n", "1580 25 FR France \n", "1581 20 FR France \n", "1582 36 FR France \n", "1583 38 FR France \n", "1584 36 FR France \n", "1585 45 FR France \n", "1586 43 FR France \n", "1587 28 FR France \n", "1588 5 FR France \n", "\n", "[1589 rows x 10 columns]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 45, "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": "code", "execution_count": 46, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1990-12-03/1990-12-091990497114302610205FRFrance
1990-12-10/1990-12-16199050711079666015498201228FRFrance
1990-12-17/1990-12-231990517190801380724353342543FRFrance
1990-12-24/1990-12-301990527193751329525455342345FRFrance
1990-12-31/1991-01-061991017155651027120859271836FRFrance
1991-01-07/1991-01-131991027162771104621508292038FRFrance
1991-01-14/1991-01-201991037153871048420290271836FRFrance
1991-01-21/1991-01-271991047791345631126314820FRFrance
1991-01-28/1991-02-03199105710442654414340181125FRFrance
1991-02-04/1991-02-10199106710877701314741191226FRFrance
1991-02-11/1991-02-17199107712337807716597221529FRFrance
1991-02-18/1991-02-24199108713289881317765231531FRFrance
1991-02-25/1991-03-03199109713741878018702241533FRFrance
1991-03-04/1991-03-101991107166431137221914292038FRFrance
1991-03-11/1991-03-171991117155741118419964271935FRFrance
1991-03-18/1991-03-24199112710864733114397191325FRFrance
1991-03-25/1991-03-3119911379567604113093171123FRFrance
1991-04-01/1991-04-07199114712265768416846221430FRFrance
1991-04-08/1991-04-14199115713975978118169251832FRFrance
1991-04-15/1991-04-211991167148571006819646261834FRFrance
1991-04-22/1991-04-28199117713462887718047241632FRFrance
1991-04-29/1991-05-051991187213851388228888382551FRFrance
1991-05-06/1991-05-121991197167391124622232291939FRFrance
1991-05-13/1991-05-191991207190531274225364342345FRFrance
1991-05-20/1991-05-26199121714903897520831261636FRFrance
1991-05-27/1991-06-02199122715452995320951271737FRFrance
1991-06-03/1991-06-09199123711947767116223211329FRFrance
1991-06-10/1991-06-161991247161711007122271281739FRFrance
1991-06-17/1991-06-231991257161691070021638281838FRFrance
1991-06-24/1991-06-301991267176081130423912312042FRFrance
.................................
2020-10-19/2020-10-2520204374376250562477410FRFrance
2020-10-26/2020-11-0120204474391237564077410FRFrance
2020-11-02/2020-11-082020457369620165376639FRFrance
2020-11-09/2020-11-152020467375219635541639FRFrance
2020-11-16/2020-11-2220204774999296370358511FRFrance
2020-11-23/2020-11-29202048766834312905410614FRFrance
2020-11-30/2020-12-0620204975026314569078511FRFrance
2020-12-07/2020-12-13202050770634744938211715FRFrance
2020-12-14/2020-12-20202051710564757413554161121FRFrance
2020-12-21/2020-12-27202052712012828515739181224FRFrance
2020-12-28/2021-01-03202053711978840615550181323FRFrance
2021-01-04/2021-01-10202101710525775013300161220FRFrance
2021-01-11/2021-01-172021027779554301016012816FRFrance
2021-01-18/2021-01-242021037891363751145113917FRFrance
2021-01-25/2021-01-31202104712026882615226181323FRFrance
2021-02-01/2021-02-07202105712210898815432181323FRFrance
2021-02-08/2021-02-14202106713401981016992201525FRFrance
2021-02-15/2021-02-212021077135611031516807211626FRFrance
2021-02-22/2021-02-28202108711281836114201171321FRFrance
2021-03-01/2021-03-07202109710988793814038171222FRFrance
2021-03-08/2021-03-1420211079056645211660141018FRFrance
2021-03-15/2021-03-2120211179386667812094141018FRFrance
2021-03-22/2021-03-28202112711520841514625171222FRFrance
2021-03-29/2021-04-0420211379714628913139151020FRFrance
2021-04-05/2021-04-11202114711197799414400171222FRFrance
2021-04-12/2021-04-18202115711215762714803171222FRFrance
2021-04-19/2021-04-2520211674780289166697410FRFrance
2021-04-26/2021-05-0220211774686287864947410FRFrance
2021-05-03/2021-05-092021187391221105714639FRFrance
2021-05-10/2021-05-16202119767314409905310614FRFrance
\n", "

1589 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 \\\n", "period \n", "1990-12-03/1990-12-09 199049 7 1143 0 2610 2 \n", "1990-12-10/1990-12-16 199050 7 11079 6660 15498 20 \n", "1990-12-17/1990-12-23 199051 7 19080 13807 24353 34 \n", "1990-12-24/1990-12-30 199052 7 19375 13295 25455 34 \n", "1990-12-31/1991-01-06 199101 7 15565 10271 20859 27 \n", "1991-01-07/1991-01-13 199102 7 16277 11046 21508 29 \n", "1991-01-14/1991-01-20 199103 7 15387 10484 20290 27 \n", "1991-01-21/1991-01-27 199104 7 7913 4563 11263 14 \n", "1991-01-28/1991-02-03 199105 7 10442 6544 14340 18 \n", "1991-02-04/1991-02-10 199106 7 10877 7013 14741 19 \n", "1991-02-11/1991-02-17 199107 7 12337 8077 16597 22 \n", "1991-02-18/1991-02-24 199108 7 13289 8813 17765 23 \n", "1991-02-25/1991-03-03 199109 7 13741 8780 18702 24 \n", "1991-03-04/1991-03-10 199110 7 16643 11372 21914 29 \n", "1991-03-11/1991-03-17 199111 7 15574 11184 19964 27 \n", "1991-03-18/1991-03-24 199112 7 10864 7331 14397 19 \n", "1991-03-25/1991-03-31 199113 7 9567 6041 13093 17 \n", "1991-04-01/1991-04-07 199114 7 12265 7684 16846 22 \n", "1991-04-08/1991-04-14 199115 7 13975 9781 18169 25 \n", "1991-04-15/1991-04-21 199116 7 14857 10068 19646 26 \n", "1991-04-22/1991-04-28 199117 7 13462 8877 18047 24 \n", "1991-04-29/1991-05-05 199118 7 21385 13882 28888 38 \n", "1991-05-06/1991-05-12 199119 7 16739 11246 22232 29 \n", "1991-05-13/1991-05-19 199120 7 19053 12742 25364 34 \n", "1991-05-20/1991-05-26 199121 7 14903 8975 20831 26 \n", "1991-05-27/1991-06-02 199122 7 15452 9953 20951 27 \n", "1991-06-03/1991-06-09 199123 7 11947 7671 16223 21 \n", "1991-06-10/1991-06-16 199124 7 16171 10071 22271 28 \n", "1991-06-17/1991-06-23 199125 7 16169 10700 21638 28 \n", "1991-06-24/1991-06-30 199126 7 17608 11304 23912 31 \n", "... ... ... ... ... ... ... \n", "2020-10-19/2020-10-25 202043 7 4376 2505 6247 7 \n", "2020-10-26/2020-11-01 202044 7 4391 2375 6407 7 \n", "2020-11-02/2020-11-08 202045 7 3696 2016 5376 6 \n", "2020-11-09/2020-11-15 202046 7 3752 1963 5541 6 \n", "2020-11-16/2020-11-22 202047 7 4999 2963 7035 8 \n", "2020-11-23/2020-11-29 202048 7 6683 4312 9054 10 \n", "2020-11-30/2020-12-06 202049 7 5026 3145 6907 8 \n", "2020-12-07/2020-12-13 202050 7 7063 4744 9382 11 \n", "2020-12-14/2020-12-20 202051 7 10564 7574 13554 16 \n", "2020-12-21/2020-12-27 202052 7 12012 8285 15739 18 \n", "2020-12-28/2021-01-03 202053 7 11978 8406 15550 18 \n", "2021-01-04/2021-01-10 202101 7 10525 7750 13300 16 \n", "2021-01-11/2021-01-17 202102 7 7795 5430 10160 12 \n", "2021-01-18/2021-01-24 202103 7 8913 6375 11451 13 \n", "2021-01-25/2021-01-31 202104 7 12026 8826 15226 18 \n", "2021-02-01/2021-02-07 202105 7 12210 8988 15432 18 \n", "2021-02-08/2021-02-14 202106 7 13401 9810 16992 20 \n", "2021-02-15/2021-02-21 202107 7 13561 10315 16807 21 \n", "2021-02-22/2021-02-28 202108 7 11281 8361 14201 17 \n", "2021-03-01/2021-03-07 202109 7 10988 7938 14038 17 \n", "2021-03-08/2021-03-14 202110 7 9056 6452 11660 14 \n", "2021-03-15/2021-03-21 202111 7 9386 6678 12094 14 \n", "2021-03-22/2021-03-28 202112 7 11520 8415 14625 17 \n", "2021-03-29/2021-04-04 202113 7 9714 6289 13139 15 \n", "2021-04-05/2021-04-11 202114 7 11197 7994 14400 17 \n", "2021-04-12/2021-04-18 202115 7 11215 7627 14803 17 \n", "2021-04-19/2021-04-25 202116 7 4780 2891 6669 7 \n", "2021-04-26/2021-05-02 202117 7 4686 2878 6494 7 \n", "2021-05-03/2021-05-09 202118 7 3912 2110 5714 6 \n", "2021-05-10/2021-05-16 202119 7 6731 4409 9053 10 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "period \n", "1990-12-03/1990-12-09 0 5 FR France \n", "1990-12-10/1990-12-16 12 28 FR France \n", "1990-12-17/1990-12-23 25 43 FR France \n", "1990-12-24/1990-12-30 23 45 FR France \n", "1990-12-31/1991-01-06 18 36 FR France \n", "1991-01-07/1991-01-13 20 38 FR France \n", "1991-01-14/1991-01-20 18 36 FR France \n", "1991-01-21/1991-01-27 8 20 FR France \n", "1991-01-28/1991-02-03 11 25 FR France \n", "1991-02-04/1991-02-10 12 26 FR France \n", "1991-02-11/1991-02-17 15 29 FR France \n", "1991-02-18/1991-02-24 15 31 FR France \n", "1991-02-25/1991-03-03 15 33 FR France \n", "1991-03-04/1991-03-10 20 38 FR France \n", "1991-03-11/1991-03-17 19 35 FR France \n", "1991-03-18/1991-03-24 13 25 FR France \n", "1991-03-25/1991-03-31 11 23 FR France \n", "1991-04-01/1991-04-07 14 30 FR France \n", "1991-04-08/1991-04-14 18 32 FR France \n", "1991-04-15/1991-04-21 18 34 FR France \n", "1991-04-22/1991-04-28 16 32 FR France \n", "1991-04-29/1991-05-05 25 51 FR France \n", "1991-05-06/1991-05-12 19 39 FR France \n", "1991-05-13/1991-05-19 23 45 FR France \n", "1991-05-20/1991-05-26 16 36 FR France \n", "1991-05-27/1991-06-02 17 37 FR France \n", "1991-06-03/1991-06-09 13 29 FR France \n", "1991-06-10/1991-06-16 17 39 FR France \n", "1991-06-17/1991-06-23 18 38 FR France \n", "1991-06-24/1991-06-30 20 42 FR France \n", "... ... ... ... ... \n", "2020-10-19/2020-10-25 4 10 FR France \n", "2020-10-26/2020-11-01 4 10 FR France \n", "2020-11-02/2020-11-08 3 9 FR France \n", "2020-11-09/2020-11-15 3 9 FR France \n", "2020-11-16/2020-11-22 5 11 FR France \n", "2020-11-23/2020-11-29 6 14 FR France \n", "2020-11-30/2020-12-06 5 11 FR France \n", "2020-12-07/2020-12-13 7 15 FR France \n", "2020-12-14/2020-12-20 11 21 FR France \n", "2020-12-21/2020-12-27 12 24 FR France \n", "2020-12-28/2021-01-03 13 23 FR France \n", "2021-01-04/2021-01-10 12 20 FR France \n", "2021-01-11/2021-01-17 8 16 FR France \n", "2021-01-18/2021-01-24 9 17 FR France \n", "2021-01-25/2021-01-31 13 23 FR France \n", "2021-02-01/2021-02-07 13 23 FR France \n", "2021-02-08/2021-02-14 15 25 FR France \n", "2021-02-15/2021-02-21 16 26 FR France \n", "2021-02-22/2021-02-28 13 21 FR France \n", "2021-03-01/2021-03-07 12 22 FR France \n", "2021-03-08/2021-03-14 10 18 FR France \n", "2021-03-15/2021-03-21 10 18 FR France \n", "2021-03-22/2021-03-28 12 22 FR France \n", "2021-03-29/2021-04-04 10 20 FR France \n", "2021-04-05/2021-04-11 12 22 FR France \n", "2021-04-12/2021-04-18 12 22 FR France \n", "2021-04-19/2021-04-25 4 10 FR France \n", "2021-04-26/2021-05-02 4 10 FR France \n", "2021-05-03/2021-05-09 3 9 FR France \n", "2021-05-10/2021-05-16 6 14 FR France \n", "\n", "[1589 rows x 10 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = data.set_index('period').sort_index()\n", "sorted_data" ] }, { "cell_type": "code", "execution_count": 47, "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": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "sorted_data_pure = sorted_data[periods.year>1990]" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+6MaDbMvOFvzinwsLy7RNCwe7C4//VzuZfcw4H3p1Fb7219nJ72rEShx8GYgiz2bLaqrnq3+djS07W/wbqQOINz3rdKSB97pWv7o6vIgDETUiIgy3CSH+AgBCiFVCiDYhRAXArwBMjrMvAzBaKT4KwPI4fRSTnilDRA0ABgBYDw1CiJuFEJOEEJOGDh3q94TtgM4aZF1BzvbNv72Kax+Yi3++Xow4m9YLLihardyEz9qkewg/9GrxiKrcOPBfF/2frshpdjLrG2u34efTUq/rjhizadA/d95fP/lGh5kcy3fdxWhWl4ePtRIBuAXAa0KIHyvpI5Rs7wcgt2H3ApgaWyCNRaR4niGEWAFgCxEdF9d5IYB7lDIXxdfnAXhMdGHBftftWX3xu2cW44o7s05r67Y2A6j++E3Tzrq+r7R4bepC6gKnXyiscyjCOVhZB70f6XXmG3WEWClpyt3Yd+57DXd0gIf21ffOwVcVDjHAHz5OcCcC+BiAWUQkV4ovA7iAiCYgGnaLAVwKAEKIOUR0J4BXEVk6XS6EkDz7ZQB+C6AXgAfiPyAiPr8nogWIOIaptT1WMbRVBP4xZyXOOmxvr0neXYjD1++JYhv9+IMTkjRpCdRYLuYiY6L1qoOSbxl3W1UV67KwEWLbQtzRnGapVMx5sSPO9/7t04uT6z1sWLQ7nMRBCPEU+HF2v6XMNQCuYdJnAjiMSd8J4HxXX9oLf5yxBF/762xc94HD8aFj9nXm76xB1hUG967YH6BHQ3X+k3oU1sTMUM1Tq1jJpx/ttHL69rkjvqUwXLc3fNVRvXvYl58N23Zhe0sbRg7sVYde7XmbhvZG8JAGEsXdG2u3eeXvwhKvdoH6vM1teeKwctPOwjoIppHkkgvjXWVVHYaiPa6XWEm/t92gWO8q1koq+vQoW+8f971HceK1j9XQIx3da97WikAcAPTr2QgA2LzD86CW9uyMrd1OaljdCSacgyJWet//PIWLfjPDWoep6zYb9GqftzOId3u2aBMd2drtaLFSGgrF7224RLjNdfZa72Z7upoRiAOApnihU60nfvrIfIy58j40t+Z3YvUeZL5xfTrLLVB1qJLvSO3L6i3NVdfN6hw6QKxUFzAN+X4ijoAZvZ3rsEZ2pLVSV12Eu2i3uiwCcUCqGCspM/uWp94AAOzcxc3M+g6zXKTSLgZVcZiGIvAvv3zjDtw+Ywl7j1u0OoIG1iK6spWsRedgktVbOQfPD9EhYqWkLZPxgZ7esct1dxMH14pAHJAO0ZLn1rzWMbZ0/XbMXJy6cezyZJ87cmxfoZy7UGu7F/1mBv760nJrHt5nYPexVqqHzsFl0cXeK9hutWhpq+CO55ZYnR9LTCgUFXp6WKu7NsJ5Dkh3xpwMdNOOFmzakfU0rXVMn/T9aZnf3sShA3daf3nxreS6jRWBAFubW71iEq3fppwFrb9iRqxUa1itjnpP9W6l7uGQlNdYa9W/eHwhfvTw6yiXSjhv4ig2j8sJrrNpQWe3v7shEAekg1ldk+QCdfIPpuXy13vH4ytW6jyFtCJWUtKn/OhxrNqc6huEEOzC7uPDlSEO1XXTr8E6wEa7vKOyMp002f0fu/8QTF+0HpPHDq6+vRoHz7qYwG/eUX1IjiJ9WLvVrccSQuCDv3wGnzhxLM46fIQzf5HgigFBrBRBcg6+2WtYfbizgv05h84BJ0oQEBnCAPgtVDMW5aKixPXVB0II3PuyXYTVJVDggeW4HDGgJ1NNx+ocrNZRxFsrbdi2Czt2teXK2uqa9J1HnH1pqwg8t3gDLrvtBWdeANjZ0rV1e10NgTigY3UONz2eD9PgrZDuJNahwuzqua6Ydr5WBW4iVuJFV0Vx/cOvW88/4DB+WN/iDYHvs3fgPSbN9P5++qglimkXkpWYxsZR334YZ/30Cef3PODL9+PyP/ot9AAv7gyoHwJxQLozzoqVzPnrPSR9OYfOOMAlapfjHLh8xetmrZXIfM+FGx5bULhMuVRMkNVeJsWu91d0LazH2rlq885sUERLXlO4cABYvG577nvq2doqAvcxBwWZlOCqifWFDj+bgOIIxAHppPTnHOq7Svsuqp1liseKlViCUbx/3CPV6iHtA/VT+353H/jrALg0e2Hurq1Eq3IiXjVD57UVm3Hsdx/FH5RzL2zVvLluuzVPzlrJc7yYzudQicMTtXroB+QQiAPSwdxZTma+Acg6TefALWRMPvNpb+42+MXSXa4eKMo5SKjdKzp2eIV0FX2wlNnJOHAWwaI4nMzTC9d55ZdB7uruJGrwAqyHc2CAGYE4IN2x+e4gP3/7S+5Mhdqvb756w2StpKOa/skiahsd7WlbqpI41IJqOIdKRWTNgh1oVhSw9TLv9XlTpraq9XMw5au3zmH5xh24/uHXg7NcjEAckA6+zjpMx9vLtZ37YQJ3HnERhXS+rGCvJTr6O5SrbJA9vrCGhYUrqh5KdN+sFTj62w9jjRKuxLboq6FfalnvHpi9Ett3tcbtuWF0gtNK+44XUz4TR1Et/uOPL+Cnj87H3JVb6lbnknXb2RA8uwO6NXFYtXknxn/lfsx6axOAzhQr+eXrrB0N36yfkjqCHqY7X4aXp3fM83JiJZ93XUvvfK2VdIdJIOtUaOtmPQPX3TlzWS7twdkrsYHhZLx1Dr7j3pBeb7HSjpjTqtc5E9t3teLkH0zDf/35lbrU19Ho1sTh8Xmr0dKW2sXXUzFZBF2djTV5SOuQu0sXeH1F3l62w8RKnOOepW2bwtzXu5v75twmoYgYSYdqBVfvV7lmSzM+/Yfncenvn8/dM4YByf325TT59PYyZf3j9CX414K1Ndcj/Sp2V2V5tyYOuUlO7GW7w59zaN9+SPxjTvY8ZV9T1gtv8TMn5I6vzCh3O1iwxHIO7dzmH6fnAxHqi+ULSzawZVX649vPajYg3FeQadI3Z+mG7Uxbfn3w3fmb+t7W1j5f6bbpS/CRX09vl7p3J3Rr4qCP/k7jHDyjbrp2Wn97eTnGXHkfTmZEESqWbdieixelQt8Nbtyez8vNV5OsVn+tFe6ZPC2i2gNVi5WqUCpLPPTqKmd95974tEcfOpbr9Glt1eadXmX9dVR8er05h/aa/V1bLmBGtyYO+mDoBKMVAHaOQOUqXBzGZ//0IgBgyfr8bk7F26+bhrN+8oRv9zIhL95Y43dangofWbPIXHfsdGLFSpb83B7Cl9vZvqsVTxtEFup7sSkx/cO88Ne5+moY91zRafPWZM5GSfqgjwPPNkxERDeU2GE4Bc8X9d4bdpaBS73gJA5ENJqIphHRa0Q0h4g+H6cPJqKHiWh+/H+QUuYqIlpARPOI6AwlfSIRzYrv3UCxgJaImojojjh9OhGNqf+jss+W/d1Jn9O2g8qepVC/Npdv4nd3HK57cC5Wa7vBelnl2MJntCeNUL99I2OuZHu8aXNXV93uLU8uwocNIgv1W/uLXGrPV+RTus5skGirCFQqAuvUAHq5TUJtVno6cfjW31/1qs+EehOH3ZVjkPDhHFoBfEkIcTCA4wBcTkSHALgSwKNCiPEAHo1/I743FcChAM4EcCMRycNibwJwCYDx8d+ZcfrFADYIIcYBuB7AdXV4tsLgorJ2BOycg79YqT0xf/XWzO8iPbGKlXgzpfhf8efdf68+hcs0NeTPMra17Tqbwoa3Nu4w3lPfi81M0zt+U427Ca4dvUbTPCECrn/kdUxUAujlTVn5dnWuw5dz+JPhQCkOHSGOq+ZgrK4EJ3EQQqwQQrwQX28B8BqAkQDOBnBrnO1WAOfE12cDuF0I0SyEWARgAYDJRDQCQH8hxDMiemu/08rIuv4MYAp1wOrc0XyC8RhIy+jhdtmdgdwxDDX0hXOq8xWBuHDYyAGFy/RoyE8Dn+fjsuhEVIdvuO8VVs6umpFrfyB+bFY/Q4QAHtb0Kr6mrH9/RSO+nkSkCDoiTllnxUKrFwrpHGJxz1EApgMYLoRYAUQEBMCwONtIAGpYzGVx2sj4Wk/PlBFCtALYBGAI0/4lRDSTiGauWVN/87D2XnzNzkFmcA5oXQG1cDG8KatfmgvV6I3q8Y7lTv+rf51tzWfb86jPy5mIusrUkq/ou67FG17CtCn67b8WW8tJLFxjJ8Q27oBru95iZTlHurqpugnexIGI+gK4C8AXhBCbbVmZNGFJt5XJJghxsxBikhBi0tChQ11ddkKfp+0ttjHV7juIO3WQ1VFpKZQNX8J6Q33O+H8VbVVjcfb6qryVVdFX7UtfbL1Tv7XqBZ1HcVGjK1e9R5YQeUKoj1/TeH552abMbxMRcTn52b5JR2y6dlOakMCLOBBRIyLCcJsQ4i9x8qpYVIT4v9TSLQMwWik+CsDyOH0Uk54pQ0QNAAYA4E+FqSNyxEH5mLXsIT52y3Qc+c2HcummQW5TPqpjuEsNtnqLldTnrKVyzw+nZuN14e4+COY5nO16hoK3nVpWz3XtitMPrF9lCtgzwR2/jXUZMrpMYX0NPSTqLciuJBuf3RM+1koE4BYArwkhfqzcuhfARfH1RQDuUdKnxhZIYxEpnmfEoqctRHRcXOeFWhlZ13kAHhMdsE3W2Ui1wVpc6J+cv5b1I6hGrFTNAlQtirzyWvriL1Yq3ko1nIOvB7gNpjMHdNhEF1mFtC83aW6riL6qloOLXG1L6EeM+r4z01x0PZPOHXx+ynjjPaD+OkjZv+272rBgdf3iNXUUfDiHEwF8DMA7iOil+O9dAK4FcDoRzQdwevwbQog5AO4E8CqABwFcLoSQ26DLAPwakZJ6IYAH4vRbAAwhogUArkBs+dTesFnR2CZntTDtRm2ESB3EtZLLtzbuwIOz84epAMADs1Zg7FX3J2GadeiLmvfpdShureQrVuIUktVM8HMm7JPvg0c5VWyiPtP3HngNY668j11wbToRb9PODJdlyechfkpMU71aZsobnoer78q7ZmV+1xoZwBnFVrvfoLx8lluvM+sg22+rCJz2Y3+/oq6CBlcGIcRTMM+5KYYy1wC4hkmfCeAwJn0ngPNdfWl3KGOplXHN/8mHJuALd9jDddt2COZBbq4vQxwsU9hnF3bitY8Z7931QmQrMJ+RvwP5ecO9H19kJ21eaedb867WChrL2f2N2s+L3z4Wtzy1yFnP8QfsBeD1TJoxPpBBB6R+p1/+8w0AQEubQI8GzZfGUyFtQ62exdn+FGu7SN06lmmhNmrdfrnK61OiXpIBX+Sts0SHmsjXiu7tIa0rzJRrju08cvRAth65g93VWrHuEKrZAbUo/bAq2Goc7Fvj0NB9m5z7BQDAz6f5H8eZE985xB2+O+hDv/EPjPvy/dqCnd6fesxoHDKiv7OeHuX8NDC96yIEvuhhO9XspG3v6umF65y29nIOFNXzyMXVyDkwDercpu93Nuvq7OX1OazWU8t8+fWTb+Aij2NJ9SZ0zqmro1sTBx3qYG1h+M7+PRswab9BOHFcamU7bd5qjP/KA5i1bJPTAsIsVjKXyQQX8xQ/VYNtzdFC1seTOLy0dGPVbfEKYL98OlorIuPtrRYh8pMUlLhZoLX98f+dge8/ONfsrct0llMq2/pTzfkGrhIy4nA1pqy2V+dql7uvWxfVygE5OYcccTDfA/xFkt+57zX80yPSqv58d8xcasjZNdGtiYPNsYsbkCUilIgy8sqH5kSOPi8v2+jcfRl3o5ZyqqesrfZaiYNcyGxHZtbLRoBb3FZs2okZi9bjucXrWcc4GzKhqTOFbKG11VxcbKVs69PmrcGNjy80ir+4xUY9iU3CpjD3Fyv55QOA5RvtYVJ8rafyfcjebc1xBfkyOnGoVYzmeg+5cg7OwWYhxsEWvJJtfzdD9yYOOT8HO0olAlH2ozfHA6pnY7kqixDA3x7bVv8rmm14vUGO9otg3Vb+sJoP/vIZnP+LZwoLo00hRnw5BzZMhKEP6re675UVSjq/2Hz4V8/iEcVT2NfPwYZq4m2ZFdJkrMe2GZD3ZPmcAYfIP+uuHOdg6XC2KmsfTNAJgEtsvGyDObQJh3f88HHr/d2bNHR34qANX9fkLFG081OzSblyz8ZSrnzO6cdQr55v1rJNmPSdR7Bh267MpLNxGP8zbb61776wyabrtRP65t+570vpAAAgAElEQVTm1NYZz2wEP69XnxhCabo/gd/S3IqnF67Dp/+QejvXQx+Zfd4a35FUSDP12BZvWd+S9dvx9usey51P4aPDqOZYWa4PEkP7NWXr1xi3agIb2rDOcRDT7uoZLdG9iYPFCY5DuUQolbKDTJ721LOhnJtM+m5KGAak3u7Ppy3A2q3NePaNdV6cw4OzV+JfC9bZO+8Jq0WU4/1MHjuYTV+pRXRtM+z09TT/qSXYS5tliEo0WLFS3Mfm1jZcfe8cJZ2vz8Q55Nq19Emto4mJ96T3rR5Io6za+5O/l14v27AjFxG1nuui7zvXDQv0+9nw94zOoe5OcPWtr6PRrYlDUSQ6B1VxHctaGxtKObmzzrqaFl49QFmaHxrnwMPkm1ANbDtM507PczKok9jm5+CLCk8bIs5BmfCmkBTcoiDrvPreOfjt04udfeN0DsUV0un1AUP7JtcHaxZXJq/5//34Mcb2TDop3/7ocHEGPp/Q1wnOzMVloT9jbv4pPzmdQ70P+9rNGYfuTRzyCmmXWIli8YpaRt7LL54WfVgG983KOqapY1RV9LXnYJNtFg05oMLXHFKNgmor4S1Pz3wPTeeg5ONiKJkgF5Y/zchamGzZySshuXVuO3P4jE3MpSu7pW2AbmlrslYiAJeesj9bt+uQe+5VW2N+OcQykU2/PY/vztqskNaIgfZ7h0ac1fHJEaZ6H/YVFNK7MQorpCn6UydNYu8Nyg32PFtbbLAIoXMOxcrf/eIyTH9jXVyXY2EXsg0z3NYhfv0a0qeH9f5zizfEffHdWZoWS80BzVCeSzedp3C1QV/C7US5k8lsC6b0NQGi7yUtx8olu7gkrZtw1VkHs/fmMYTx+g8dqSiks3UuXb8d9zDnVsh87o2CG97f15BNT9cXfJ2QuzgHm6VeNdi+q9WdqQujWxMHHV46ByI+Fg9EboLZrCVsUJWEPjoH04LzxTtexodufhbLNmzH52+3e3ZLmFh9gseC4En8jj8g9RPhyvzinwu96pEw7WJJlysZwGUxeYBv2JYuOCMG9FT6wBAHTqxk6cfHbsk6VkkxR4O2aLlMrn3x/qNGKWNNu3fjv/BYDSfe+fTrD8++6VWXObaSfb5t3pFdnNVvxMZWqqNYadrc1fjATc/Urb7OQDcnDsWslYgIDSXKLByyyA8feh1PaWcD6wpo34ksd3Mbt7dk5Namxdc1pL/3wNzEGcoFm7+ESaEuUWusnKrzwUxA/aZ7PpcpttbyTby5Izd2OM7BV65dESIhCuVc6GvlWjXd9aqZh979tVt5Sxy5gNZDxLg2Y9JsGXeenEObRtB1nY96l6tTf8/VYtOOFvxPgQgCXRXdmjjoY+E2zRyPQ1NjOePMIyfBy0s34oo7X87kdZm2uvDVv87GF5VYTtXuEgf0avTOa1oUKwJ4YckGa1nf7rkmqe0e9w6zi2UW1c53k1jpzXVpfCC1XW+dg9Kf4f2bMHpwL7YdISK/GiAv7qinLJtMrIOCb7z3EKVfIumfFaLYu+fqu+b9h1nb0t+5i1PPhM+ok85BH4+rN+/Ekd98CM+/aZ8ruwO6LXH45K0zvU/bUtGzoZQ4vgH2SVKtWEnF5p2KHLqK8gDQzxASg1Oumo5evPvFt/Dx3z5nbceX+M1bmcq/67HQ2RTSPigiVsq061BwusRKB4/oj8+cOs5Qd8pl5M9e5jmlaghhGpXV/LznHjUql2b6bifEIsOiX5XLP7xfT2vfcgppi3WS/psTDVcjVrrof5/LjDmX1/TuhG5LHB55jTcflThp/F5selNjCTtb/UJauKyXJA7dRwsOZwxmZmnMBkN9h1+dP5CoxbAouo5kJPLvn2oaajeXZNI4bkLJ+XfFa5nIbBuUDZ+RT/cJ2a725d1HjMjd5xXS2R6ZdqtCCPTvFRH1LTtbtXv8dTXHXPpEZW0oKz4hCcEy5S056+PAhjcv2ftm4wyi+9HvGz9yNKb956kZJzmOmOsc2pzl7qgDT7y+BvfPWpn8bmooO8vsLuh2xGHO8k0Yc+V9znxD+zax6U0N5QznYKMOOWuKGnfJv/nXIjbdueHxaFZm0WPk+KKxlPcQ94HdXNKvPlO2SB/to5BO8zTGK1LR98AFLOTESqrOQQhz/wSAfQZEIid981BXsZLSngkqcUjFSnwJqScpalnH5ZbEzlshrUYwrgh862+RY96Bw/th7F59cOnJ++P8iaNyeSV04qBuNGxQuYX2Pmq4I9HtiANnnsfB9Il7apyDDXknOENbVSlRI+xsacOT89da8xQZri2+WmUN5RJVxdnYynByf56b4CvJLQAeL/bgeCE2cVCmvnBd4A5E0hcPo4JaAMP698SDXzgJ3zr7sAyHkXX6Ky5GU+FDPBuZsLWmYZIQh8KcQz7NpQ7Jb77S698/+yZWx06Psp6GcgnvP3pkLq/eXlJfFXNhN3dtyKDbEQdnWG2HHXeZyP8AHk+FdC3j6ev3zHYShyJo8SR8OhrK1cVespXxre5XT77Bpre0Vbx8G9Tr/z7zbQD8otyarIYkXItLpFfg71WEAAE4aO/+6NlYxj++cLLSbrp7993suGDj4EpMJ03Z5eFLxXUOZh2AOWBlPl3mVQ0H1N6XbdZWWpIthEm2mMKx7EHUodsRB7etfva/Dn3A2ua/vvE1m+Rlb/iyswCwYLVdF8DVb8OX/u9lNt21v9Rt8V14emFE0Gw94892zqepMl8VLW1uL10g2jHuFYsRpczYZK2k9cZ6l+V8MvoCc/8EsjvZYf0Un4q4jmnzVuPmJ1LCyFVFMHt1A+7duQnGzVPJvqCbwHIOSVuGMsr1xP0GAUiJepajSt+M7B9H/HOxmjyJQ7aOwkW6LJxPT0S/IaLVRDRbSbuaiN7SzpSW964iogVENI+IzlDSJxLRrPjeDRR/MSJqIqI74vTpRDSmvo+YhWk3N7B3ZO4p75q+cZFjFX0V0rXAp8pfPcnrKuqJhjKvczC973tejHa8NmJdy1GkANC/V4MzwJ5M/9tnT8T//vsxyU7ep2WbM9qQPj3YA5H0ek1iJSE07qak3otq0RXVJgp+zs//xd9Qipg3Q3y6KX/RTYINDYaFfPnGHdi8swUVIdDUUMIrV78T7zhoGAB+cVa7JLkg3pE1C065/Denv9CeQx18SONvAZzJpF8vhJgQ/90PAER0CICpAA6Ny9xIRPIN3wTgEgDj4z9Z58UANgghxgG4HsB1VT6LF047ZDibvnF7tLsSQuD2GUuMg6CkWWvY7JlXadFI31zPB8irhWi0Nxc7JZ50LjQYdA6m4xjTWE7mOlmxgVdvgMf/81SMGMD7EHAYMaAX/u2gYen39Xixthzrtu3Cqs1ZmXdccebSFLJBIHvesEpE5DvzCfdABCxcYwnM6DgmtKfB+sb0fsqKzqGI9RRXnbR80jcJJ1z7GM64/glIX4r+PRtzscFMVlyJWIkZeHofrrn/NTRrR71+9k8v5so9vTCNiNytOAchxBMA1nvWdzaA24UQzUKIRQAWAJhMRCMA9BdCPCOiUfU7AOcoZW6Nr/8MYAr5aMmqxGH7DLDeF7CHb0hZXeG0af7c7dmBNGMR/xp9LBxOOGAI3ja8H1O2fZFMdkc+k87BHA3UzYJxZX2J4Zi9+sQNufOqo62ULB5+7Uj4RsZVu7+rrYJ3HrJ3IhLJ5NM5B+VavucGTVFclSkr1zEFvXrwxMGokGbO4/YBNweklRR3ZO+KTTtRESL5XiWLLoH9vqzKIZ/4xOtufZ7r0KfdFbXoHP6DiF6JxU5ydI8EoIaxXBanjYyv9fRMGSFEK4BNAIagnWAiO5kdT5xp/6F98PLX35nJV1LycXbsKvTYLi1tAj0aSrnQyj7jqbFc4nfhhsL1csbxDUbWUCqx64trsvieglctOB8Ga35tB2qDzNNWEV5nCgPZz9XcWkGPhhK++/7D2XxmzkESh9r3UC6dQ69GA+dgKOGyVvrqu/nAgFz+1KzYpJBOv2/ZsuirkPTUd+PR1/NMdVsduyuqJQ43ATgAwAQAKwD8KE7nRquwpNvK5EBElxDRTCKauWaN32Rk6uDTk4ZFcj24d4/EEUlHRQjWAzbbVva3EAIlAg4fmeVefMZTjwZepm8qe6viaFYLJDH0UUizYiXjbJUKfQvnwD5vfWZfxgnOc2epQy5a+mFGNqj9l/4yqh9Bkk9TVnPDVi9XzYbAdkwoYLbYMb2fVGfDZzDqWJi0xoYor8nnRIi0PhtRV62t5GZHz7djVxt7TGi/nun8V0VMpoOtuj3nIIRYJYRoE0JUAPwKwOT41jIAo5WsowAsj9NHMemZMkTUAGAADGIsIcTNQohJQohJQ4cOrabrzt2jUEhZz8ZyjpioP12cgw7JBlez3+vRUDLEGuLz67LSatHgKVaK/Bz8xUoya7tzDl7cgrJDT7xy3W1LP4btzf6hmdVqZWA4zo9A31GpzyEXoEZNhCOtkvbqaw+JrsJ2TChg0YkYdUkqB25uz6c+KTbj/EWA2OItri8VBzJiJeVachj62PrGvbPBQSVmUi8J5Dd4EnsQbaiOOMQ6BIn3A5Bv9l4AU2MLpLGIFM8zhBArAGwhouNifcKFAO5RylwUX58H4DHRjoevutYKIYB1cbTIngpLPSx2vVdlmzta7IuCvkuqiPQ0uWyb7sdtKBHPChsmtX6Ye7WQk8nlMdxYLrELvYs4FPWQtr0qrq6ipLgI5yDfsStr1h8ihTztrcxyDjxHA6T6EH3hlgEhH7niFEePUnDWSvcrh0/ZrKnY+hxiqkKcQ/xeTOHm567cktSXcgSyf2mNmfdo4Bze2shH21Xn1zZlE6AT5iT/HkQcnAI1IvoTgFMB7EVEywB8A8CpRDQB0TddDOBSABBCzCGiOwG8CqAVwOVCCLmFvQyR5VMvAA/EfwBwC4DfE9ECRBzD1Ho8mOV5DOnR/589Nj9hz5saowHwh4uPxfjh0URWJ9OuVvtIyHlcxqKCoocMAdEizTv98Pmb60QcyLDT0lEuEUuoTNZKMl0/Bc+nrAmuBZ0MvzjFbxHxgCtv5sAmER2T+ruLJydhMRo5JzMIzQw3356uc5CcyMDexTkHFZ+57QXrfbUPOlzWXuZYUvk0H+W27J+sl1VIK2/PpLiW6Xv17YG7P3MiTvr+tFy/1PFl8oHYk8RKTuIghLiASb7Fkv8aANcw6TMBHMak7wRwvqsf9YJrH6kecCLPOn67EoRPNWV17fgP2jtrXSRlpDkC5TGeSgXDUzS3FCMOZtPE6H/vHvah0lgm1sLHZPUjJ9GLS/K+AK6yJrCcQ0FrJSrwfdN2vbLF9UZa1OP2T20uONGNjXMwtXfKgXlRq2/fTNmMsZ+cOgfDfSN1yCdxRFOHzJGcM5E4weX7BKTcSIu2sZPlS0QYOdBkAp2WMeti9hzi0O08pE1sLSd+aGTYfVXx5dqpThqTVVq1VURy1KgKn+FUomKmnUV33SbIhctk0qjn02E8t9mje/XgHHyiq2Z3ltF/IYRzYT0w5iYLdVPkNyjcDlkguzBzOge92fGMqfN0g/l0Uq/hmFAJW3gPtj5F58BB168koUC0p3nkilMy72WbQa8jHdV0sVK2U+llv56Rs+tmzWvcpN/JiASVa25tMLa/m6LbEYciHhScXFEd/K5dgr6YV0R0LrC+G1Mn5pPzeSuscskgVjK0bfJMPmrfgZYe5yGJqWuRbTR4SF/4mxlM7qjfLqW5/v7aKsKonIzqzLff7LAo06GKHVzzXHavyG4xWvSzaZxJat5aSdE5JHJ1d3u20BlRxfa6bOE97PkFy6XrFlYPzI5Cn6jj68DhfTFuWN/MyWymkPE9Y9Gvr1ipX1MDiPKWXSXjuxbKdZqnh1HkVYw6CCFwx3NLvHSE+sl27Y1uRxyMYEYySxzi/0IIt6y5TScO0cCzcQ73G2TwRMQ77hj6YOrZ5DG8CZ4JyY5MaZw768JkymqCEAIvL3XHy1fxyVufwxHMGRRpnfk0l7kxYHaCs4mVejWWC8cPivooclyqUaxkqiP+uj7tu/xJ9bt6nWaFNN/2iP7xAT2CH4M6l7Q1DgGiLo6JY5uS1aTzkkYjNt2YrpDu26MBWzVOxBY6Pb1OfzUWMPE9b2L+sCSJv7+yAv991yz87LH5xjwAcOPjC3DQ1x7Epu0dd5hQtyMOprnCJXNyxZKy03LNTT3wmvRzUBeH9xwxwhqjR223HkrSsyeMZNNN6B+z4eqzfOTY/XL5uNhKVkukKtjvafPsvi1cczs9dC+cSMHFOUw5eJhijluAcxD5MdiTcTTjOIzfxs6TCefg0Z6JNBw5OuIgdTGQziGaFk3u+/2/M96GvWKrPlPfdD2CJIyqAYVMU4mm6nCoQr67svYc6ifJETjKjxWTrkQdw+p0XruFP2Ob49h13aMKycGs28bXJ/HTRyLisXWXv9l0reh2xMGoc2CSP33KAUy+VOywdMP23H0V+pkAiZ9DRkFW8nLsKmqtVA8fAQAYNShSzmXOzWZt0vOcTZFT3t7hGcPJBu49qseemmT40HaWgJv4l4jSII1FOCbwC/anTzkgI6rgOAxpW+8KK6+CG9d3XXY87rjkuOh+0q+oLl28UcS6qG9TA1xOdTrnIMVMuxjioM5VyYX/cUb2nPdErBRXK9+JupnRH4F7pBLx/TZxDiMG9gQH7rE/ceJYHDi8L/buny/jO3Tk/KvmjIlq0e2IQwGVAwb1yZsEqjuMr98zx1p+u0blpZ+DKmPOs/V8XUTE2/0bhpc5ppHenn2wSUW0alXEKYqjSZ8nhiaMH9a3UD98wD2y6UzsbNvptSq7thFtUji5Ij0Xgt+NE2W/pUB+Yc45enk0zBlaDOvXUxHHpP0C8ibQJmsyUwwj1anOR+cgCYFKHOT7UTkHOebmawYO8jnku5H51I1ZjnGgvMOm2Tkvf/219xyCcwwcuMlD++h9B/HjKc7vuy75GFjUC92POBTRSPMVAPDbtd3z0vIsWxorGRvKJRytKIYzA9ASdoAbF6ax4juIbApegNe7cISH4xxsC3PfWDFYT3AExudENzXyqOoE5+QcqhErGRZNgmZTXxG5sao76FW7TKiLru605utZz7Wtntnta60kvaDVdiX9UBXScszp47p3j6zOQSTEQeUcsu8xIsRZpFxKTrCUXsWX+w7ubYxrZd3ccbRB6ZMP2orad9eAYlGl9gCYvoGvJ23JNIYMmPnmBhwTK4GlnwMA/N+nT0BFCPz3Xa+wjjbD+jXhj586Dv17NWBbcxv+OP1NdhEyTWZfsVJza8V6KDo3CUzEQV+cv3v/a8Z69WfhiJkUGfiCJ57u9zAgPssDsMfoUUFKniJMz/xVW7GFMctUxVSAYXhpffNpl1t0MsRBM2X1OTxKza93T+dEdOicQwPDOUioPhEJcdA2HLrOQY4B1RiEtGGkE2LAk3NAuss3+WtI3YAOIv6dySTf9acjOYfuRxxqZRyQHYQuqLvSSqyQBqIJWgblBoWcBE2NJYyTopd+knPIN2oygfMdRDtb2tC/Z6NZgejLOTDhM9Q49zr0R+Geo/A8MHPtADwd4pTF0rb4Usw5LFi9Fb99erF3F59awIeAVsVUb6zZii07W3P9zVu5pR382QVHsfVy34DjHCQ+dgtveqyDFZ8QQVIwEwes+weUy4Q5yzdhrUMhm+oSsvX2SsRK2XxSVwbkN4REeW9+k39GhmDHP5hQWAlmLM76lcz86mlJ/+zjyXxPRa0HYBVBECsl6b7lo//q4Hrvkfvkdtg/nToBQHYgSZ2DXp+6o5DssM5+RzqHfH/0RfUb7z0EgJn91J+zuaUCIQTeUA6EGTOkd3LNRQz15RxsgQn1xYUVQRWcB5wuxOisZahD1SnJb/ypk8bmy8ff7fxfPO08Hex0wwFTen9kV797/1wAwOr4sKC0b1mRpvpo+w/t42xDoswM9sJ0mCmQ0TkIsJNKP4OiTIR33/AUPqccosP1RS6Kut6tl8GUVd3U5IJnMv3XD/GSUNuT771IvC55/Kxpc1dU11YvQxMfdDviYILv51ZNWSU+P2U8vvG+QzP5JLubFRnlzwwmZAeknAR5xR2/0OkKRDnI9UloQnNrW87BSF1kuXp4hXTez2G7lThkf3O6gaKhCG556o1cWtHJpCp9k50is8iVYrm1j6msF5Q2XlgSnS6oGzToOoeK4zuZwIlETK/aJNozHcfpmke6Twf3bjmrHvmsA3o1ZtKlwYR6HguQHaN5ziFPBFJdSfYOK+qrQvoQiSHz6UK5b8LNT6SHjwWFdAdicGyRpIe6MCEVK6UfqUT5HU0uImtF4L5XVmDFpmzsf9LYTWmCp092085DX4Dl3FMPafnk29Od7/B+2Ym3s6WSG3Aq08GFCZhyUH4nrMvMAbsDWkVkFbMc5+BDHAb1bsQ74535K8vyTnUZsZKzNk0hLdOYxZQQfY/ejrAieh9MUBcnudDlfQ4Q9y3mHJR7vocy6QXTXTXfyfs/dxJfBatzIGue4f2bcmJK3RfoitMPxHXnHZGrW76LobEfhURPg1hJnY95ApTfyKTls+mctZLJHN4GzkJKbc9mKCM5SSBwDh2KEw4Ygue/eprxbGkdnMKtRJSfyPF/OVhfXbEZQH4xJ2Tln3IHrS/K0trByYaSXFgUj1Nl4dDNc5tb23KT2hUUbu8B+Z2dqqCV4MJ8P3LFyQDyz+HSOZiO4RwxoFe6iDMTzERgLvn982w6KfbywrBbBSJxoRC8A9vPP3x05reP6EC1s5ciSn0hsJmy+q5Xh4zoj77KATYpUeLz7z+0L5tuFiulz6F/04ZSKccR69/nQ8eMThwvVch3obfbSzdljfOp7y5vygroxFAP3Pf190Ti2Yx5cUGzUxWqdZuKpE5fnUMHWit1e+JQIsKQvk3eH5xTXJWIcjJ+nYiYTdyyv+Vk0Rcd3QOUwx8/dWyyA1LFNKYgYUDEOajr/4TRA/Hdc9Pgub7iCj1q7IxF63O7sP2H9sHYvaLFRr9nEkHJyfNvP3zc0G6ah9s8FxVNqYu0LMmbLUbEmhO7yFDcEj49ULmCJJ6VJmpr0DiK7MLl0QiA+z9/ktWU1RecwjljygqBuSuzPgnlEuV0abo40RSzKOEIdOIQc27pOQ1Rui1oI6dzkO9Blku+oZLv/lkrM3nZug33VIMDvk98QX1j8eFfTe+wGEuBOMTfxJdyc8cgEuVFALqCy+ZQpX7/g0dEg/KnU7PWJyrbvGNXG+au3Izn38xaRozdq0/Srs3O+4WvnY5fXTgJQGStpD57iaJIlwNj805OIc1B5Rxun7EEH/zlM2w+9Tk+dPOzSbrpqE0XF711ZyseeW01VmzaYeAclD56PErGCc7SdinefXKc1SDtPAUvT+b4v0D6zlu0DUepRJnovNlqqxM3qB7Ns9/yj3Vl1DkwnLVEQ4ly40nnjkznJEhCqc8j3Vop8R53cA454oAs50Ha/N3Z0obf/GtR3BYzzuJyHz9hbO6e7F81X4jTxfmaG9eKQBySM5I9F0FGNlkqUTJ433X43rj9kuNyNukmhzBdOdbaJtC3qSEnupH9bBMCX/q/l3DmT57E9xRZpCxLynVSVnu0wX16YPTgNCyGKu8sayIN30PsSdE5XPmXWdZ8QPT+fOSnroV18boohMmN0xY6HRx9dtcZpa9FHhztBPmFYkDvxkxwQrXdgb0bcQBjWaQuqvo3UNFQKiULBicPLwrV+u49P3vKu5zJQ1r3VFZxxKgBTuLgOidBfyWSmOhipcz8zFkr5U1ZdZ2FTuQ+q1hTcaPMNU4byiXsaq3kFd7JGOPLcevG1ffaIzPUC4E4xF/Fn3OQO63s4it/jhnSB8ftPyQnyzVbtWTFMW2VCrtbV8Udzy2OrFk2amGHhbJYqbtObnFT5drqXV326nMaV1TOQ7Yu5LPkFfgucYJP+0V0srZ6ZLuq05OOkkHBmNaTllKfYXDvHjhkn/z5w+lOVSQiRG7X2FCmRJfz7BupDwOn+/CBS+dgAqtzQBo3jLPH/+bZh+XESipxKJF5vKXi2Wy9ehwmTqykfz+Wc5BipUr2m8sxkAmlzwyItoR48S+yb1MDWisiZ11oG2MATxxmvrnBkLu+6PbEQaLoJFEHtRDpOQmqXXN8F0Aai10309OVYy0Vwe7W1Z2NvK/7ETSU+cnJ7W7VAIIqJJsuB7sv5+By8snmzVtePfqlU3DnpcfnlL++dbZWBB56dZU1j09VKvG37eoiMZrdyoSD6giZqU/Zqaa74PzC0FCKjB9mLduEXz8ViTmuOP1AjB7cO5fXB0UNb0yLtKxLLtbcotbUUMpZfqniWE6kJH1MTGdYSH2aKlZqq4iMXJ71czA8V0X75vK3qnvj5pPtvQBA/178IUMuzsEV3qY94SQORPQbIlpNRLOVtMFE9DARzY//D1LuXUVEC4hoHhGdoaRPJKJZ8b0bKP5iRNRERHfE6dOJaEx9H9EOnY10QQ4M9SMLACcfOBR3XXY8Ljx+v0x9cnBJzuHWT0zO1EfQTFnbKqwSWGWb5bV6KPovPno09hnYK5kI2Wik+edQiaHK1fzw/CMBALd98licN3GU8QS4F792Ol7+xjvxvXMPx+feMS4x7bQibrTExGEaPbg3Jo8djL5NWad9W51fftdBybUakG3cMH8LG0MXM6as3NCQpokm2qkm6wfGsMRakf3LiJ9fPO3AXL6GcgmtlQrWbUsd5KYcXIeItp5EODlLwnCf03lJEKUcojRJVTlILozLp07aP8pn2JnLuaKKXafe/Az+8sJbabu5fuQth/JisKyEIBtyJH2epDwjzlLRLx7X8vwKHaZNhk9ssPaCD+fwWwBnamlXAnhUCDEewKPxbxDRIQCmAjg0LnMjEckvfhOASwCMj/9knRcD2CCEGAfgegDXVfsw1aCSsJEpW2+D/IablY8sB9DE/QYnbLEeuljuZHTrFt1cm9wAACAASURBVE7nwIqVFGsM7v6Zh42I8knOQVXIMc+RKswFPnd7Kk+Vk3bifoPxw/OPNNp0D+rTAwN6NeKCyfviine+zU/hpoiVjMdSaiPSppYoG3Zz6gJy5KhUhFPUpDQ1M8y+g48cu28imvAJAa82yzlCqvkFBAb2asR+Q3rjrMNH5PI1lAhtFcHGR1IxmIkozMNv3EvIccVGCFbFmsqiJs8zIBB69Sjj7599O37x0cjcVxV/cpyDrhjWW21IOId0fkixa1pH/jn05837KWXbUy3+uIXcRLwk5LPpnIDrrXPm4B0FJ3EQQjwBQD+I9mwAt8bXtwI4R0m/XQjRLIRYBGABgMlENAJAfyHEMyKacb/Tysi6/gxgCpnIaDtAjmHfFuVE3KzI+7nxoFtPrN0a7fR6abJhQlZ23VIRbDwjtb5tzWZTNul4tnS9/awJ9cQzm/WD94cw2HGb2jZNoiKWPiqHoC6W4xTb/N9/8thcObuuIG1XKGn/debbkjz7D+2bOP3pnIMMY3HlWQfhyNEDcdjI/plnMBEUlZMzcRcAsHpLM/40YynWbU1jEXFZf6dxqCbYrIs4JGEsmPwCKSelcg63ffJY3PqJyckCedjIAegT76RV8SyndyJtHunjQc4V6zGhuliJkSu1JGE3KFsmzqeOL9aZrSLbz93K9LOlNZtBPv8jBpFoRzq96ahW5zBcCLECAOL/kq8dCWCpkm9ZnDYyvtbTM2WEEK0ANgEYgg5Cwjl4Ugc5RlS7fNY7VRErbdnZgu89EFkWNenEQdtxt1UqbH2qWGnDdnOQMnmyVHOrQ6xkmUxcuy7oxNBVpzrm1d39tedmvWOFYeP0m3+fhFMOHMr288vvOji5Vh2qkt2npYtElCjXE3kwgM+cOg4XxSJDGSaiIkTm5d72yWPx8BdPAQActHd/3HP5iejToyHHOdh0DhUhjNyFijcUp0DuGw3r35RL4+BL/PeJrecqQqBSEfjW318FAHzr7EPx3iP3iesiVucwpG9T5lupfVYXP3MwP7O/kNSJ5RwELdDn3IZtu9Aci1bviz3CE2Id51RFvbbov+r4V8PyNxrMk+Xzv2Fw8uxE2lB3hTQ31oQl3VYmXznRJUQ0k4hmrlljPzLSF7p1ggucTH+fgb3y+RR2faNy7mtOrKSVa2kzKKQVsZKtr2ccurexz1x9zrW8IEdlGswT9xuE7517eNyf7ELw9femcalGDsq+y4oQrP39+GH9cvkkTLbyyQJjeAYJSbwemxvv5jTRRokocfpTP1WJKEfYdcsY1clNxWsrIp3JrGWbrOKqtK1sGzr8TbP9xsGf4pPjejSUMqKRC48fk+lHOj8c4tn4v0oc9NAyQJYjaG2r5Kyg9CNFfRZTlVtfvXknjvr2w3jktVU4YtQAHDi8X/IsAK8w5jylOWulS07eP7nukXAO6bu7+8VluF072U5HUSfOeqLakN2riGiEEGJFLDJaHacvAzBayTcKwPI4fRSTrpZZRkQNAAYgL8YCAAghbgZwMwBMmjSpLm9NV0i7JqUt/rwKOagfe2011ivhiDmlm66QdomVbMG3vP0S4v/Ocws8iYOLc7jrshOUvFkR1MT9EnuG3OJaESIjwjNBfSeub+jmlqI8/31X5K+hL2SSc9jR0pZZILhnLxGhDelYMVk4nXbwcNz94lsQMHMXAHDq24bi8Xlrss59TD5/MWncd4Zkfun0VCE+YkBEtCsVkRN1pHOIWLES227cQVcgOXXTMe4rD+TuS2MKjhMeP6wvHr7iFKbtlNCrRh2qWMt23Cmn/+D9MNKP0BhvWFSi+cU7Xs5XrqEziUO1nMO9AC6Kry8CcI+SPjW2QBqLSPE8IxY9bSGi42J9woVaGVnXeQAeE/U4M9ITJ46LnJV8iQN3IDoHOfh//dQi/OXFt6z51MdtrRgU0pINZ17NMWMG5fLp+K8z34Z7/+PEXD61Ns45y1espFtn2SAXXw56OOmKAO6btcJZp80jVkIugG6CmBV76c9GRHhsbrQfekE5RpOrNeKSlD4YFv7hsRhIipVM7/3yfxsHIKuoZNtlS/P9i/qVv/fZKeOT69Qxj1nQFfGbzZRVhXwHLpm6jHXlWhI4sZIpGCEhfV71WZoUrl5TOeS4PyA6ES5Jq2Tv6Uh0DgUVzNz7Oe1gvzhwtcLHlPVPAJ4B8DYiWkZEFwO4FsDpRDQfwOnxbwgh5gC4E8CrAB4EcLkQQgrnLwPwa0RK6oUA5DbgFgBDiGgBgCsQWz51BGZ8ZUpqeoose2pCQhwc8U2KqNTl5//pI/Px2oot7O5fPenqYiXKKpD6VgAwnlD1mVPH4YhRqQyUU+BdevIBuXJFxW0+Vi82hbRurSSEwG3T86y39CDnIpga371Bbp3vX3Yx0k9LK5eIDQRokplnj4q1+520VYTVf4LbmXMLiK8OTV8EJd4+bq/M72QxF3nOQX7zElHybKrCnG83/m6e4ifTN5MbI06sZLYki4wJXliyAXcrGzfOhJzTJcg27rz0+CTczcOvrrT2M5E4FCQOncg4uMVKQogLDLemGPJfA+AaJn0mgMOY9J0Aznf1oz0wTAlfnXAODuIgP3Kz587IBWk5sWVnC65/5HUAETvM5kO0M9JDDHzr7PS1+rebn0zcwl6Uc/DyJSD+PF2A5xx0HDl6YMZKpQ1aFE4DSZMWKW7ikCVeskuqfLtcykfiNdWrZjOJjNLFTdj9JxiZPkccuAN92Po0wifx7XOyU1WKjCoVkYsMKn+WKB0vP32UPy5TQiU29nzmTceE0QOT98FtdkwxIyPOQeDcG5/OpP/z9VSPqSvCde4PAIb374kRA3ritRWbcefMZfjY8WOcpqz14Bw8jMbrguAhrcEVZy7lHFwf2X9yCmR3ejaxUiR2SNOP3ndgJsY9tyhw6wSnI+DGob/OwSyj1WPw2/wcOJ1Dvi21f+mOO03j+/jFO14y1pmtXxMrxf//64yD8PETx+CcCSNZ7s7IOah5KoLd1SecYcVuyso5mXGPU/YMmGgaplxyuUQs56Dq7XzPHLJ5gWf6YRFXEjMOdLNhvlL38prWnRdFqvXK7yDnLCeOBGoQK3Fmsx3ETXS7M6RNkIPLJVaSg8Ctc/BtV5pNpl+cU0irbLO6i9JZYTZIHDPV9RhKQG0srGxBXyCfvWpKIk+XKBF/5KnaLwnWbj1TV/Q/G6OHf/nSMMC5MGjtypPyBvRuxDdiyypunHDvT35fNQ8vVor+tyU6B3PfgOwiw70jX87BVoeO6LuJRBT07bOjd6HybEXbdSmkbZsO9T2WCxAHAthBcKZi6adzwqu3pB7pahvJsb7lEma/tSlzZKz6JhJTVoMYjdP3Re2n+a/7wOG4bfoSL3PxeiAQhxjyQ7qIgxyQza12nYO3OAaILVTSNK4PqjWGOjb0nZqvWCllw9O0WsRKpjN4uYOBbAppHfyCm18U1O/h6rGPhZaaZeGavH6BCxBnen8+fg4J8XfqHBixEsexeO7gU2KTrYNrvhx7Z8sFvV/sQyKbj0J2e44XqeB26RwYcVFSB6nXKeclYTw/nPJRWQHgpo+mhzSlpuh5ZDmH6EdjmXJRbVUv9R4OzsG07qjZG0qlJKZXRyCIlWLocmUT5E7dZcrqu3+SC5G6g+cO51Gdhm5+Ij0rWe+vjbBk0/KD37Tz9YFtEnNt+w5wN+cQ/drarAZas9fp1DmU7BFXAdN75rg2JrYSZ3CgxAay6xyi/xsVR0jO8ct3B+9rfSfzRmKlSqZsetgSFd6c+Jqyct+DO7To+SVp6Awb5+DadNh0aOr3vOiEMQCicDMqbv3E5MzRw42Mn4MKk2I+c054mRJlekcgEIcYchDWz5TVs12GHX72jbybh2xXtwLJxao3LFA6OJ0DN+j8iZyZ/dfRWqngrheWuTPCLWuWC+225lblvr3XPmaRrkWrUVsF33/USJw8fmgunx7kzeT9nART3LADT85f69Q5rFXGAauQ9lylpd/N9uZsQDjuqE6dc5B6F1XnoHflEcbPAEjnm8uUtWRYpIf2a8KPPjghzRdnnLN8c5JmspzTOUO+f+Y61JQTDoiCOezVNxv2RfcI5/wcVJjGW3bTWMqJKdsTgTjEkHPRtbB46xz8oxJBQGREA6rTnITJLlxfRPx3bnmiZMvnQjKZPAbudktsKB2uWDnyeXe0mDkHPbqpi2spkW79xORRXvTe/Xvi+g9NMHJtuhycGxuyqAyzYnrtUlykRgV+2979cvl8xTvS8m2rRhz0s8aBiBtpU3QOCeeQtJn/Xtz524C/tRJnVbd3/5547iunYaQSmSA9RtdH58CLlbLtmutQ25DtuoiclAZwpqwlMoub1GobSmTketoD3ZY4jNLCNPgubonOoU5+DvJjq83+e8yqZuuLKtTNCH3EShz0w1EAsKPO31oJcf88FJu+FAz891jkiCukp0xSnARNdWbKE7nDPxB/7epLa6XC+7HkrLT4+tTzpXv3KGPxte/GwN6+EVjzkI5f25r5UNKZtkuRiW/COZSz3CKBcouk6d0kVmaO9yxfixpPzG59l6aZdQ4enIO2eZLH5gLIiIsS3Yny3OcePRI65CFHHBEY3KeH8Qx1lXg2lCm2fgsK6XbDc185Db175APgAe5B01BnsZI0q1MH8iHaAfVAukPRZZOms6tdfeF0BKxYyZdziPOpcaRMNEBdCE8+MC+KefALJ+G5xRvwtb/OZhfJNYrlCBs3ymHx5MM56ERYR9+mRgBR6AXbG1J9JqSy2WaqLGEK1Sxf3a62SmHLIA49Y7HSNsPipEJyDqnOIVrwVLGSi7OVkMlOnUOc8ZVlqSe6zYlQXThtNbuWV72FtorAv58wBle/79BMelkTrQHInUkCRESkXKKEOGzflRLj/r0asWz9jlwZvd6J+w0G0UKjpV+90S05h6H9mpKQwSn8xCzeOocCfg4Q2UnFKqTjL6XvPHRZMTdxevfID1aOGLoIY2OZkuB5uf7F9akncP1Ziadk6uPEfQfl7h+0d3/sFYs13N8jn6a/Ar2KeugcMh7UlkVa3aXKiJxs7CyNkprbT3eqRTgwE4pwDpHOId2gSL1Laq1EGVFPlMbX5evnAETvkBvDet/UvkQ/TPXlw8v/9fITc3nU+ioVwXLlqVgpTVu9uTmXD4hP8Yvf3SFf/0eS3q9nI3a1VViuQuoc7rn8RAzo1Wi0tGoPdEviwCGxM3csCqnOwWHK6mtKSNFuRx2snBu/HKy6zFKXFavj94LJ++Kqsw7C1GNGQwd3FraMSGnCaQcPxwWT9zU/CLK7epO82Uckk/phWLtkECtl09QvOm/lFufUWrFpJ+57xR3PyQeqE5xcGGzhUSRM41ANn+EbZNEGaWJpEmtk2i5FxFr2TS6Wn33HOPRtasCRowZiUJ8eePcR6SFFrjAgXmJIoszCyc0tTqxkCm2f1ZREYp0Jowdm8ugBNtsETxxkXypCJO9y4w6+XVPYmP49I8LHfQP9XROCKWuHo4GRHXLw9ZD25RxSVj1t1+bUpHMsOzXdh7pY9u5RxqWnHMDa5Ks6h/2G9Mak/Qbh+APsx2jY9BnSRO/iW59L0kyH3qu1mHeWiPuXfxdq+JAjlLMgkvq1OtU63n/jvzy827PgFL5qt2zSnftmrcCC1VuxY1dbShxYziH727Roqh7S9eAcUk44HUf/8+Gj+LykWSvFG6Vj9x+C2d88AwNiubxKtIzE39NaKcqb5ZjtYqUUG7bz0Xx1nUNPJsT73gN6olwiLIkPzapU+HZVhXSfpmi8b97Bc2HlEm/CnZzbztyUSbLtElFHRc8IxEFCTlhXYCzTIq3DVxzc0lZBRQDPLU7NVzew1krpopAtr8l4lYlp21mqi29rm8CYvXgPTQCYclB0lpONOCzdEE8ipTt6DCgOLmcvlRORUPUU3z47F64r/+6VPm3f1YaTfzDN2S8VV5yeP8tZZe19vvWaLc2KWMmtczBzDqnuqajO4Zr3c+8qP55NR4yWtPAZZQN7rI4To84hLupDHHQxH1ej7nNhg76+cpuYxnIJvRvL2LarFTMWrY90PMzjlpVN5b5Dojl00Qn7GdtlNztx+5yeS+aXr9pUR3sgEIcYjRobaULqBBfttFwyeBdkPPn/9+dXkjRu15goIlt14sCbxgF2q6BETNVaweYdLeyCJSFl5LbFaBNz5oKJOPTtmcqPXSabLyuKSAD43DvG4WcXpDtb7nwMnWurdTJxO/1s7CX3x5ZEGODFhvq7NSnEVUWur2WaRE/mXZWZcW96njJF4TNu+udCAObNR0OGOPB94U6CM4Ky/eMIjmyHG4f56rJOjqOU0NuZOkvR837wl88AMMUto2TB7tOjjGPGDMKHjuFFrzL8iA4pjuI+uRz/csxs2tGCmW9uwIOzV1qesD4IxCFGg2dgrLLi51AukVkG7ylW4iYYN2FKBuLFEwcy1p3pIQE3Pr4QW5pb2QVLQkaUtBEbTqFpOpFtmBKIz7QQSeKlT/b9h/bN7PTIIn+WqIU2qKd5ZetMK/VZo3e2tuWCtKnQ363JxFPNV5Q42BSqrmNlZfnWisCMReut7aschZFziP+rGyHOygeIlLKq0x83ZGQ73GlyubY1zmFvw5Gq0iM8acP0vIpo2GacYBIrSaMAzufjl/+MoiGksZmi77RuG6/0ricCcYghJ6w8WUo3dU3ylbLEwQRfjp9bAo7dPy/7L5GJOHBcBmX+m6De5yxo9Hs2zkEvP2m/QeyB8Xpe185Sny/6BOWK+wTv88X+BnGbWqOPue/2XW3JQshxafpYMusczGV07K8Fc+MWN7mOZ4iDob5yiZxhXgC/0wg5zuHjJ45h87ZWBNZuTRdDbhxyY/1z7xjH1kfIjiuTeKxElLFCMo1/KW4Twj5HSjGHsWJT1mxVzhObz4dujVUPM2YXAnGIIZ1U3tq4AyMG9MT0L7PHVWQmpF2m7/fxuHVLt5yI6ov+b92V3aFznEN6KLqLOKTXNrFSj4aY2Fjqk7L/sfFi+vX3HuK1aBrFSopOJNMXrZ82EYOEjTT8+sJJubSTxqcH3Ri/Y0as5MbOXW2J7wLHpel9NhEHldNyEYfjtE0Gt6CUk02Hh59DiTI6uZp0Dol4zB563FV/2k7299kT9sGnT80fXiUbV5syzZNyKRu+wsY5RD4swmqlSLG10vHfeyyTLgmkzVu8QfMpKco1VoNAHGKorP64YX2TiJM61Alm5RyYtDsvPT6X5runlQNTj63EKcYTu3OnWMmPc5A7G0sWDOrTA/2aGhLPcZNISW/XFUNIF7Hp9u42yxUJ26IzqE/+O8vTvQDzO/z5R9IInjbq8IPzjgAQ6Sjkt+J0MUWd4AD37lG/yz0K57djIuptFYEn569Nfteic+BMx33t93nikE37xnsPNfpGRJxD2pZRPEbkDO8hy7dVZMRdB+fAfFZpKWbTv+je6IE4dCDUnbOVIyhRMrCLcg6Txw5mcvpB1vfn57MB645nRFByiDl1Dso1JweX8BErAdGE39lqdvRK+udpUQLkOSOd6PgwaLb2uFvqxDM9xsEj+uP8iaOiPljalpxURYhkEWhirGP0yb55J28OmSGsTs4we98WSfZF5Sxs0ztVg9oBZss+9aChIsTf136f9zfIptks5Yj8DoeSCmkJkz5SiotsodaBmMNgBtzRsSOolThontgdQRy6ZfgMDiqLbGKXJRpKUewdkx01YF9sVfhGWOTGwoyvTEG/pvzOV3gOIG+dg4dCWt6XfhcmfYOtD1y6rmPRw4f4EAfbosPdyu583RsAu4c0Je1I/wrOrl5vR7XIyubj++nKC/gvKL7Ljn7Cn4SPnwNPHPzmgk82K3FANlCj6RvrR8HazmJoq9gPaQKkWCmb9sxV78DLSyPCbOccpFjJ3ud6oibOgYgWE9EsInqJiGbGaYOJ6GEimh//H6Tkv4qIFhDRPCI6Q0mfGNezgIhuIN+APnVEg8duUcLnw6jEYVi/JvzxU8ey+bzFSowF0rB+PdGLUZzLOt3EQemvJa9c6H0U3JI4+Pg4AO7FQ1e4j9WUrD5mpDZxBbfQNHhY2wApsbT1QBavCIGdFs5Bff37DemN9x65D99mAc5Bn0a+TnM+s+/r7zmEDesNZDdXpqksUzO6Fc/J4CN+4syP1T6pkYFNr0UXKxlNfGOFdEXYfU+kh7mKEQN6KQ6pXYtzqIdY6d+EEBOEEFKzdyWAR4UQ4wE8Gv8GER0CYCqAQwGcCeBGIpKz5CYAlwAYH/+dWYd+FUJW0Vz7a2lU6vjQMaNxwgF78Rk9J0Ti/OTBe/vKJXVvahOkKMcd3jvd2dh0DipMPZRdV2Xhb3z3XbnYPSpRe9+R++DPn87rdaycA/NMKmG3vUN5y7aYJu9YAJ+57QUAPOEkSg/K8SFIQP5MiXydhr7UATbO2MdaSXZFFVWZxtd/vjPrhOjiHFxcKwHYphh22DiHjELaqD+J/CYqlWJipXcfHoUZke/SNrdz1kq7CXHQcTaAW+PrWwGco6TfLoRoFkIsArAAwGQiGgGgvxDiGRHN1N8pZToMRXZkPuxvg4fctQiqoVeuAaR2i9vNSkh9DOfAk60vylcid6A0CVM0UNm3XZmYOrys+cDhfQFEYg41nLKEVefApGWtbYxFk3y27yvvVIRIzKRNYUWS+DmWNtV4VS7RZU7n4D0O3flsz+yzcEnnMRWmMVM0TIjN8i5qG9ixSz3/w8IReFor+YiVSppYSW7IfBwCGxLiIJI22xu1EgcB4CEiep6ILonThgshVgBA/H9YnD4SwFKl7LI4bWR8rafnQESXENFMIpq5Zs2aGrueRRFZro/iTJXh14PK6+sbF1NIh2sAqUrPXhbikNhhe3AOALDPwF7enIPpXcsJazpWMVuHtKYq/p65RU7VCdge2Yfoc/4aJpFbSlzt9R4+Mvr2Nj0RkF/ivXUOHtlsc8Q3IKCa69yjRuIyg+mpPo5de7MhfXldSNouZYLcmV5jiQhblDliNnmNrJWEcGwUKLvBkpwCF/Y7X1bmSdtsb9RKHE4UQhwN4CwAlxPRyZa83NMIS3o+UYibhRCThBCThg7NnwNQC3zMKyW8OAfP3efUyWnE1AG9GrH42nez+dQIkyeN3wt3f+ZENp+KIgNon4E9jfek/NZ9pGPUnmlnXASJQjrmHP7xBfPQklyV6XnPPGxvNv2CyfvimDH5kOGqHsf2xH4K6ei/OmZMxKGcEAdLo0gJuVNsqN33HQ9DHYsrV3c17ajv7TP/Ns44bopy3qbYUGnDWf8K07AulwjPvLEu+f3vJ47l+yej1Tr8HCIPaTNx4M6R7t2jjE+dlLabiB67OnEQQiyP/68GcDeAyQBWxaIixP9Xx9mXAVBjR48CsDxOH8WkdyiKcA4+1hIZ0YSlvpPGD8VR+0ZObx+cNMqYb+J+6SLWq7HsNQGLEAf9gHQVkk33JQ5FQkmbapRVSGslKTriIBdVE6fU1FDGqW/Lbya+ffah7MKuLlK2jUCiczDmUIkDX78KHzEVkB4R+vg8O/esV3PAUHNwRYm7Ljseow2xhlTUg3PwtVTS549r+rmaL1P2pD9T23q7pvAeiVip4vJzyIqpkkOTLArplrZKhkM8YFg0D3w46lpRNXEgoj5E1E9eA3gngNkA7gVwUZztIgD3xNf3AphKRE1ENBaR4nlGLHraQkTHxVZKFyplOgzqR523akvN9amLjku8s2D1VgDZA0N0NDWUMSTeEQ3p63cspC9xsImUgDxLa84X/XeJO9Q5sHd/nmNRTVnLJbJ7W0viUHA3ZbJoUWXftmf20RHI5/jn6+lCbhYryf/259DP8HC1PWH0QCy+9t1OcQsAjHec6SFhe9dlTzNmFbaAl7oKweeYVxt0kadRIe05nGT4jJZKxTr2o+Nn0+fUz2rQN183P7EQLW3ZAIuSQLmiQtcDtfg5DAdwd/whGgD8UQjxIBE9B+BOIroYwBIA5wOAEGIOEd0J4FUArQAuF0JIwd9lAH4LoBeAB+K/DoU6QJZv5I/sqxauRUvKNWe/tcmaTw76IX3ck9ynXd98Uk7qqq5UxSJ99gS7yWYUKtlRn3CHCylCNlRiaVuI5M7SbjYZ/b/7xVStZsovn9MV/NFFzJO2tT74wHfXb9shV3MIkW2x03fwc1fWtnnTF3CjKavv/KHIqqm1TTjD5L+5bnvyO0cctLH23fvnAsi+G7mx0M9xaQ9UTRyEEG8AOJJJXweADUwkhLgGwDVM+kwA+WDzHQguumfd6vacna7BuLU5Eic4Zaox/Ce6/b6vEkzedlmLqDCe5xB/j12tFafJZuIYZCMOBVbIXj18FdLRfx9PeR9RpDx/W3KSJtiIEdd2EZm9rxm3zeCgGmXpfkPMoqyiOgdXbp1zM9Xv4oAlpFVTS5tIHEZN+d5Yuy35resc1MB76qZE5aqkSLIjOIcQPiNGe3gcyip910rXpJKmkL5ipXo9U3qAvIs4SJ2D37D69jnm/YCsq7m14lwMfQINFnkT6qJgk4WXPTglecfX2dEHRb3qXXb/5x6VGgf6bihsTo4vLd1ovMfh22cfir0sIq+ixGbmmxus93XCZhqvTy9cx6brmLtyCx56dRXWbm1OvJ056POnLeHI85zDFkV0qJpzTzl4OADgsJFp/K/2QiAOMdTxV6+JXFTM4pvPpBjT4bvzca01vrbVcjezw8HyysV8lObQpiIlDm1OTsTHMaiYaEUlDuZ8crLb3kuyIMT19LE4GxbF6YcMt2eI23aZFX/0+P2Sa18rGJtF2iqPMxVUuM6v1rvkG5rFBLX8kaMG4BNvH1NTfSr0+FMq9OdIj42NdXqqmavCRRw4LDXGOP2Q4Xj1W2fgiFH5yM31RiAOMdRdtu/gGzXIvLgB6aLhK9Lw3bUVYXd94DLNTeO52OuRp9p57xytitzof0tbxcmJ1NtrVIYo+DBgWAAAEBJJREFUj+q2WSu5FdIpbah/2IOPHref9X454eT8OL4isHEOBzJnbldbF5Dv34ePNR2w5QeVWF533hHGCMz1hr6pS3QOTPQDGZX3A0ePwkUnjMmU83UwrRWBODDw3Zl/xyIWAVJ2sN6cQ72Jg8lLWeJ9R+6DifsNwiWnGOLjVwmfoHa7WitOT2C/eDPZe7d+YrIxp2rKaaObtz6zGIBd/JDGzYl+9/EcWzbIPrl0MfK9uaO3Fu+DjXO41HB6ngnnTjSbcAP1d/jq3ZT2vSM8jSWGa5Z50tdCfh91k9YSD5jJYwcV0pfVE4E4xFAXqtGD7RyBhK/4yXcA+kZy9VX41kvnMKhPD9x12Qm5uEa1wtY7dVH1NY0tIlY68YB8qHOJpoYyPjRpdNy++Ss3e1iMyGblLtF342GD7JNLFyM5Bl8rMxdOOTD1FbHt9os6QZoC+Eno37XWca2O43o4k6khTWzo15PnHOR3amM4h3rEeasWgTjEUMeITVFaDXzHsitUuIQv5+BLbDoa0jnVGtROeUTXzrGoQrqhRM6F9cIT9kNTQwmnvm2YMc9PpkZhtW16BH0hs3EO5zt20BJyCXF9X/mMRQIw2vCD849Irm1Eznd8AkgcQG3IH/DE5/MVB2fiU1nezV8vd0chAIDvvv/w5Fo/mlWFrvtp1RTS2fDgWX1EZyCc5xBDnSCunYzEYfu44xvpddsw0HMH4jv5+njKJi+YPNqdqY5IrZ/MedR35pKZr9rcnCujwyeAmopD9xmAed85y5pnWHyegY2DlF0a3r8JqzY344ap/DkNal4XpOLStRhKDtNpZea5lqvt9e1pHlu+YqCXv/5ONDW6G9dDu5i+34yvTMGEbz3srC97hrm5ryMGmEPKqFA/w80fm2jM19SQ3UTofg7yu762YjMWxSavnck5BOIQQx0jrsE9+5tnQAjhrcjyPcTkyrMO8sqnKkxtOGQfP3O37517hDtTHeFzSLrvQUQAsGlH5B+wVjtCVYVqDmjzxi0C/ehGDmr4jIYSYV+LPb88L4A73U+F9JB2iTPkOG5yvD/fzYs6L3wd8WwY0Ntv/uibHFW8pcLX/0MdTz4nIAJ2Qqy+v0G9zWbmUhTXr6kBW5pb8YGjI05Rd4I766dPevWvvRGIQ4wigfeKyo1dMYkkfJWVrsXy1W+d4XUITmdBTgLbDj4T68pzgtj1A/V3GpLfwXb4jKpYd303uUk0HfQjIU0/+zuIg2zb2a7nUGnwOMRH4oLJljNMCkL9/guuOctIBPyt/dJ8vRtt4rE03+2XHmfMpxJNm4NqQhx6NuDFr5+elDOFz9D70NEIOgcG9bKOMLnFV4vvnXs4Dhze13g8o0TvHg3sCXFdBXIRt73m7KE2tVtnNTtCUlQDH/GeXEN9rK6kZsTFaX7zfZHTWD/HZkKG4Wh0cJq+1jBFJBzfO/cIJ5HzRY/MTr92z2xZX+8eZSv3on5fec4zB3Ws2t6lJA5tQqChXMr5yXDEISikuxjqZTn3vnhy6LLGanHB5H3x0BdP6TA752rhMmWUc8A2kTI6B8/dky3XUaPr7zQkd6o2EYsaI6qIotaGD0wchZlfPc2pO/n/7Z17jBXlFcB/Z3dxCS9RYC2FIhswyFYUZVFErIoVhTbFRGy0REDbWlu1tjGmPmpqUpuqbU19JZa0UGltbYw2xVZrrK0SK62K8hBxUXzUBxYbXwtEED39Y75hh3vnzsxd5s6DPb/k5s5++92537nn3jnfd+Z85/jus/iVQ8JoupwuVEndRUmjAv0UF7WSPu7ulzTwI6lR8o1DxTylKWLl0Js8VWlhxiGEtOKKu11q5coQtkruu2hG7J6JMnHZqRMi/59kx/WebqVkX9OoFdqVcyYmOkdviArdDIayxm9G857T2qHv34uJC7BIev3J6zqVdHKQNCy13n1CcQEb/vc4bve7P0n8+JM9rUO/iDKhURUaG02xp6Al58KTxvPcmx8wrT36BuOk0fszKUFlt7IQdzGvzCkThtQRreQT5Y1JWpmuHnw3QVhpUh+p48b67t3UKbkh29zMOK5qYNKVQ16bsZK6FX1m1yju5FPPbLzr2tNi3983SlFJ9yDgVqowAr6bKyxQon+CaK5GYcahgRw55gAevyI0QW2fpsetVLtP0H/cm+IxWdA2pD/3fms6HSNrR4UFZXxnW+1oKoBF08dy/7q3mNURfXFLyvyjx9AxcnBkISeg0PenoL6InbXXzIqNpPqUC1G9+OTxsedL4hL2v6tx31N/glL5NW1pbqKlSdix6+OqOu1puaR7gxkHI3NU41cOwd/ZhwlDT+OMQ/vwgbvjx9Mi6kYl7CljXELC8W2DefrqU1IZF3gz2jjDAPFuz7ypZ6afZI/S8EGtvPzjOamthJIm2PSNQ1ixpv1amtjx0Sc8t3nPxH12z8HIhRvmHc4tZ9felNUokuRCCl5UV2yMLofp14E+PZB6OowHLjk+6RBTI8ffdmLqnZ2eNTXbTZONcGelec6elUP05TQq+2xrSxM7P/6EjRVVKBtRSiApxZ4yGA3ly53p/sgfvvSERFX0eu451O5Tz2/i7gumJ+rn3zgekuFMucj7TYJcPHM8x8TcGwN45bovZDCaao4bP4zTJ0cb/7zoyb6crF8Y727/iGUrX2XZylf3aB8Vk/m5kZhxqODzE2vn0jGiGTdiEONGDIrtN2JwK5ve3hY5Yw3OmNLMdfXoZSdmlqIZ9qwweM83kxmxPLh0VnSEWd7c+bXam9DyxnfLfRiz0fLkiW1cNWci8xLm0Pr1uVNTz0hbD4UxDiJyGnAT0Az8UlWvy3oMXdeeluumk77CbV85ir8/vyUylUTQ13pMe7zfPCkHD6udGK0RBH/ah9ZZ58AoB/4N7oOGRG9OFRG+niCd+ZD+Lay95tRUxrY3FOJKKCLNwG3AbKADOFtEOrIeR2tLc66Wuq8wbFArZ8a4tESEOZO8qJ2kiRCLSHB1lEYtB6N49O/XzNJFU1m6aGoq55t5aDG8F4UwDsDRwIuq+pKq7gTuAubmPCYjZ34y7wjuOn/a7plZGWnE/gqjeJx0aNvufSW9YWIgHPqQg4qxwizKVGYU8Frg79eBY3Iai1EQBra2MC0mQ2kZuGHe4bElZY2+jR9J1/VWN+MiakJkSVGMQ5gvpypoXUTOB84HGDNm7+rIGkZWpB0VZuy7TCjQfamirHlfB4K/oNHAm5WdVHWxqnaqaueIEeE53Q3DMIy9pyjG4UngEBFpF5H9gLOA5TmPyTAMo89SCLeSqu4SkYuAB/FCWZeo6vqch2UYhtFnKYRxAFDV+4H78x6HYRiGURy3kmEYhlEgzDgYhmEYVZhxMAzDMKow42AYhmFUIWmVJMwaEekGuiqaxwD/SfDy/YH3C9yvr8nRiPc2nYRjchSrXx5yTFDV+N12qlrKB/BUSNvbCV+7uOD9+pQc+5IsJofJUXQ5wq6dYY99za30XsJ+9xW8X1+ToxHvbToJx+QoVr+85IilzG6lp1S1M66tjJgcxWNfkcXkKBZ5yJH0Pcu8clicsK2MmBzFY1+RxeQoFnnIkeg9S7tyMAzDMBpHmVcOhmEYRoMovHEQkSUiskVEng20HSEiK0VknYjcJyJDXPt+IrLUta8RkRMDr5ni2l8UkZtFJNN6oCnK8YiIdInIavfIrKagiHxGRP4hIhtEZL2IXOLaDxSRh0TkBfd8QOA1V7jPvEtETg20562PNGUpjU5EZJjrv1VEbq04V246SVmOMunjFBFZ5T73VSIyM3CuXH8jseFMeT+AzwFHAc8G2p4ETnDH5wE/dMcXAkvdcRuwCmhyfz8BHItXWOgBYHZJ5XgE6MxJFyOBo9zxYGAjXs3vG4DLXfvlwPXuuANYA7QC7cAmoLkg+khTljLpZCAwA7gAuLXiXLnpJGU5yqSPI4FPu+PDgDeKoA/VEoSyquoK4J2K5gnACnf8EHCGO+4AHnav24IXJtYpIiOBIaq6Ur1PfRlweqPHHiQNOTIYZiSqullVn3bH3cAGvBKvc4E7XLc76Pls5wJ3qeoOVX0ZeBE4uiD6SEWWLMccRr1yqOo2VX0M+DB4nrx1kpYcedMLOZ5RVb+w2Xqgv4i05q0PKIFbqQbPAl9yx2fSU0VuDTBXRFpEpB2Y4v43Cq/anM/rri1v6pXDZ6lbLl+d+VLTISJj8WY9/wYOUtXN4P048FY7EF4bfBQF08deyuJTFp3UojA62Us5fMqojzOAZ1R1BwXQR1mNw3nAhSKyCm/pttO1L8H7EJ8Cfg48DuwiYY3qHKhXDoD5qjoJON49zsl0xICIDALuAb6jqh9EdQ1p04j2zElBFiiXTmqeIqQtc52kIAeUUB8i8lngeuAbflNIt0z1UUrjoKrPq+osVZ0C/B7P/4uq7lLV76rqZFWdCwwFXsC70I4OnCK0RnXW9EIOVPUN99wN/I6MXRsi0g/vS3+nqt7rmv/rlsG+e2KLa69VG7wQ+khJlrLppBa56yQlOUqnDxEZDfwRWKCqm1xz7voopXHwow9EpAn4PnC7+3uAiAx0x6cAu1T1ObeM6xaRaW6JuQD4Uz6j76FeOZybabhr7wd8Ec81ldV4BfgVsEFVbwz8azmw0B0vpOezXQ6c5Xyo7cAhwBNF0EdaspRQJ6HkrZO05CibPkRkKPAX4ApV/affOW99+IMo9ANvRr0Z+AjPmn4VuAQvCmAjcB09m/nG4mVq3QD8DTg4cJ5OvC/JJuBW/zVlkgMvQmMVsBbv5tVNuIiZjGSYgbe0XQusdo85wDC8G+gvuOcDA6+5yn3mXQSiLQqgj1RkKalOXsELjtjqvosdeeskLTnKpg+8SeG2QN/VQFve+lBV2yFtGIZhVFNKt5JhGIbRWMw4GIZhGFWYcTAMwzCqMONgGIZhVGHGwTAMw6jCjINhNAARuUBEFtTRf6wEMvYaRt605D0Aw9jXEJEWVb0973EYxt5gxsEwQnBJ0/6KlzTtSLyNiguAicCNwCDgf8AiVd0sIo/g5cA6DlguIoOBrar6UxGZjLf7fQDehqbzVPVdEZmCl0drO/BYdtIZRjzmVjKM2kwAFqvq4cAHeHU2bgHmqZcPawnwo0D/oap6gqr+rOI8y4DvufOsA37g2pcC31bVYxsphGH0Bls5GEZtXtOefDe/Ba7EK8jykMsC3YyXEsXnD5UnEJH98YzGo67pDuDukPbfALPTF8EweocZB8OoTWVumW5gfcRMf1sd55aQ8xtGYTC3kmHUZoyI+IbgbOBfwAi/TUT6uTz8NVHV94F3ReR413QO8Kiqvge8LyIzXPv89IdvGL3HVg6GUZsNwEIR+QVeNs1bgAeBm51bqAWvGNP6mPMsBG4XkQHAS8C5rv1cYImIbHfnNYzCYFlZDSMEF630Z1U9LOehGEYumFvJMAzDqMJWDoZhGEYVtnIwDMMwqjDjYBiGYVRhxsEwDMOowoyDYRiGUYUZB8MwDKMKMw6GYRhGFf8HK+A2MLkTMbsAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data_pure['inc'][-200:].plot()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "first_sep_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data_pure.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Period('1991-08-26/1991-09-01', 'W-SUN'),\n", " Period('1992-08-31/1992-09-06', 'W-SUN'),\n", " Period('1993-08-30/1993-09-05', 'W-SUN'),\n", " Period('1994-08-29/1994-09-04', 'W-SUN'),\n", " Period('1995-08-28/1995-09-03', 'W-SUN'),\n", " Period('1996-08-26/1996-09-01', 'W-SUN'),\n", " Period('1997-09-01/1997-09-07', 'W-SUN'),\n", " Period('1998-08-31/1998-09-06', 'W-SUN'),\n", " Period('1999-08-30/1999-09-05', 'W-SUN'),\n", " Period('2000-08-28/2000-09-03', 'W-SUN'),\n", " Period('2001-08-27/2001-09-02', 'W-SUN'),\n", " Period('2002-08-26/2002-09-01', 'W-SUN'),\n", " Period('2003-09-01/2003-09-07', 'W-SUN'),\n", " Period('2004-08-30/2004-09-05', 'W-SUN'),\n", " Period('2005-08-29/2005-09-04', 'W-SUN'),\n", " Period('2006-08-28/2006-09-03', 'W-SUN'),\n", " Period('2007-08-27/2007-09-02', 'W-SUN'),\n", " Period('2008-09-01/2008-09-07', 'W-SUN'),\n", " Period('2009-08-31/2009-09-06', 'W-SUN'),\n", " Period('2010-08-30/2010-09-05', 'W-SUN'),\n", " Period('2011-08-29/2011-09-04', 'W-SUN'),\n", " Period('2012-08-27/2012-09-02', 'W-SUN'),\n", " Period('2013-08-26/2013-09-01', 'W-SUN'),\n", " Period('2014-09-01/2014-09-07', 'W-SUN'),\n", " Period('2015-08-31/2015-09-06', 'W-SUN'),\n", " Period('2016-08-29/2016-09-04', 'W-SUN'),\n", " Period('2017-08-28/2017-09-03', 'W-SUN'),\n", " Period('2018-08-27/2018-09-02', 'W-SUN'),\n", " Period('2019-08-26/2019-09-01', 'W-SUN'),\n", " Period('2020-08-31/2020-09-06', 'W-SUN')]" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_sep_week" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_sep_week[:-1],first_sep_week[1:]):\n", " \n", " one_year = sorted_data_pure['inc'][week1:week2-1]\n", " \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": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "code", "execution_count": 73, "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": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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