{ "cells": [ { "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 = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02022127154331105319813231630FRFrance
1202211711678828615070181323FRFrance
22022107133141003616592201525FRFrance
3202209710485760013370161220FRFrance
4202208712088874115435181323FRFrance
52022077140031078917217211626FRFrance
620220679798704812548151119FRFrance
7202205710851779713905161121FRFrance
820220479547672112373141018FRFrance
92022037139721068017264211626FRFrance
102022027849560261096413917FRFrance
112022017137931059716989211626FRFrance
12202152713239961116867201525FRFrance
13202151713326962917023201426FRFrance
142021507141281031217944211527FRFrance
152021497136741036916979211626FRFrance
16202148711549850314595171222FRFrance
17202147711419837614462171222FRFrance
182021467821657241070812816FRFrance
1920214578965646811462141018FRFrance
202021447873656361183613818FRFrance
212021437814551641112612717FRFrance
222021427944360371284914919FRFrance
232021417402122395803639FRFrance
2420214074441245464287410FRFrance
252021397229110563526315FRFrance
2620213874325226763837410FRFrance
27202137719647543174315FRFrance
282021367344117305152528FRFrance
292021357256211074017426FRFrance
.................................
16041991267176081130423912312042FRFrance
16051991257161691070021638281838FRFrance
16061991247161711007122271281739FRFrance
1607199123711947767116223211329FRFrance
1608199122715452995320951271737FRFrance
1609199121714903897520831261636FRFrance
16101991207190531274225364342345FRFrance
16111991197167391124622232291939FRFrance
16121991187213851388228888382551FRFrance
1613199117713462887718047241632FRFrance
16141991167148571006819646261834FRFrance
1615199115713975978118169251832FRFrance
1616199114712265768416846221430FRFrance
161719911379567604113093171123FRFrance
1618199112710864733114397191325FRFrance
16191991117155741118419964271935FRFrance
16201991107166431137221914292038FRFrance
1621199109713741878018702241533FRFrance
1622199108713289881317765231531FRFrance
1623199107712337807716597221529FRFrance
1624199106710877701314741191226FRFrance
1625199105710442654414340181125FRFrance
16261991047791345631126314820FRFrance
16271991037153871048420290271836FRFrance
16281991027162771104621508292038FRFrance
16291991017155651027120859271836FRFrance
16301990527193751329525455342345FRFrance
16311990517190801380724353342543FRFrance
1632199050711079666015498201228FRFrance
16331990497114302610205FRFrance
\n", "

1634 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202212 7 15433 11053 19813 23 16 \n", "1 202211 7 11678 8286 15070 18 13 \n", "2 202210 7 13314 10036 16592 20 15 \n", "3 202209 7 10485 7600 13370 16 12 \n", "4 202208 7 12088 8741 15435 18 13 \n", "5 202207 7 14003 10789 17217 21 16 \n", "6 202206 7 9798 7048 12548 15 11 \n", "7 202205 7 10851 7797 13905 16 11 \n", "8 202204 7 9547 6721 12373 14 10 \n", "9 202203 7 13972 10680 17264 21 16 \n", "10 202202 7 8495 6026 10964 13 9 \n", "11 202201 7 13793 10597 16989 21 16 \n", "12 202152 7 13239 9611 16867 20 15 \n", "13 202151 7 13326 9629 17023 20 14 \n", "14 202150 7 14128 10312 17944 21 15 \n", "15 202149 7 13674 10369 16979 21 16 \n", "16 202148 7 11549 8503 14595 17 12 \n", "17 202147 7 11419 8376 14462 17 12 \n", "18 202146 7 8216 5724 10708 12 8 \n", "19 202145 7 8965 6468 11462 14 10 \n", "20 202144 7 8736 5636 11836 13 8 \n", "21 202143 7 8145 5164 11126 12 7 \n", "22 202142 7 9443 6037 12849 14 9 \n", "23 202141 7 4021 2239 5803 6 3 \n", "24 202140 7 4441 2454 6428 7 4 \n", "25 202139 7 2291 1056 3526 3 1 \n", "26 202138 7 4325 2267 6383 7 4 \n", "27 202137 7 1964 754 3174 3 1 \n", "28 202136 7 3441 1730 5152 5 2 \n", "29 202135 7 2562 1107 4017 4 2 \n", "... ... ... ... ... ... ... ... \n", "1604 199126 7 17608 11304 23912 31 20 \n", "1605 199125 7 16169 10700 21638 28 18 \n", "1606 199124 7 16171 10071 22271 28 17 \n", "1607 199123 7 11947 7671 16223 21 13 \n", "1608 199122 7 15452 9953 20951 27 17 \n", "1609 199121 7 14903 8975 20831 26 16 \n", "1610 199120 7 19053 12742 25364 34 23 \n", "1611 199119 7 16739 11246 22232 29 19 \n", "1612 199118 7 21385 13882 28888 38 25 \n", "1613 199117 7 13462 8877 18047 24 16 \n", "1614 199116 7 14857 10068 19646 26 18 \n", "1615 199115 7 13975 9781 18169 25 18 \n", "1616 199114 7 12265 7684 16846 22 14 \n", "1617 199113 7 9567 6041 13093 17 11 \n", "1618 199112 7 10864 7331 14397 19 13 \n", "1619 199111 7 15574 11184 19964 27 19 \n", "1620 199110 7 16643 11372 21914 29 20 \n", "1621 199109 7 13741 8780 18702 24 15 \n", "1622 199108 7 13289 8813 17765 23 15 \n", "1623 199107 7 12337 8077 16597 22 15 \n", "1624 199106 7 10877 7013 14741 19 12 \n", "1625 199105 7 10442 6544 14340 18 11 \n", "1626 199104 7 7913 4563 11263 14 8 \n", "1627 199103 7 15387 10484 20290 27 18 \n", "1628 199102 7 16277 11046 21508 29 20 \n", "1629 199101 7 15565 10271 20859 27 18 \n", "1630 199052 7 19375 13295 25455 34 23 \n", "1631 199051 7 19080 13807 24353 34 25 \n", "1632 199050 7 11079 6660 15498 20 12 \n", "1633 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 30 FR France \n", "1 23 FR France \n", "2 25 FR France \n", "3 20 FR France \n", "4 23 FR France \n", "5 26 FR France \n", "6 19 FR France \n", "7 21 FR France \n", "8 18 FR France \n", "9 26 FR France \n", "10 17 FR France \n", "11 26 FR France \n", "12 25 FR France \n", "13 26 FR France \n", "14 27 FR France \n", "15 26 FR France \n", "16 22 FR France \n", "17 22 FR France \n", "18 16 FR France \n", "19 18 FR France \n", "20 18 FR France \n", "21 17 FR France \n", "22 19 FR France \n", "23 9 FR France \n", "24 10 FR France \n", "25 5 FR France \n", "26 10 FR France \n", "27 5 FR France \n", "28 8 FR France \n", "29 6 FR France \n", "... ... ... ... \n", "1604 42 FR France \n", "1605 38 FR France \n", "1606 39 FR France \n", "1607 29 FR France \n", "1608 37 FR France \n", "1609 36 FR France \n", "1610 45 FR France \n", "1611 39 FR France \n", "1612 51 FR France \n", "1613 32 FR France \n", "1614 34 FR France \n", "1615 32 FR France \n", "1616 30 FR France \n", "1617 23 FR France \n", "1618 25 FR France \n", "1619 35 FR France \n", "1620 38 FR France \n", "1621 33 FR France \n", "1622 31 FR France \n", "1623 29 FR France \n", "1624 26 FR France \n", "1625 25 FR France \n", "1626 20 FR France \n", "1627 36 FR France \n", "1628 38 FR France \n", "1629 36 FR France \n", "1630 45 FR France \n", "1631 43 FR France \n", "1632 28 FR France \n", "1633 5 FR France \n", "\n", "[1634 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ " raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02022127154331105319813231630FRFrance
1202211711678828615070181323FRFrance
22022107133141003616592201525FRFrance
3202209710485760013370161220FRFrance
4202208712088874115435181323FRFrance
52022077140031078917217211626FRFrance
620220679798704812548151119FRFrance
7202205710851779713905161121FRFrance
820220479547672112373141018FRFrance
92022037139721068017264211626FRFrance
102022027849560261096413917FRFrance
112022017137931059716989211626FRFrance
12202152713239961116867201525FRFrance
13202151713326962917023201426FRFrance
142021507141281031217944211527FRFrance
152021497136741036916979211626FRFrance
16202148711549850314595171222FRFrance
17202147711419837614462171222FRFrance
182021467821657241070812816FRFrance
1920214578965646811462141018FRFrance
202021447873656361183613818FRFrance
212021437814551641112612717FRFrance
222021427944360371284914919FRFrance
232021417402122395803639FRFrance
2420214074441245464287410FRFrance
252021397229110563526315FRFrance
2620213874325226763837410FRFrance
27202137719647543174315FRFrance
282021367344117305152528FRFrance
292021357256211074017426FRFrance
.................................
16041991267176081130423912312042FRFrance
16051991257161691070021638281838FRFrance
16061991247161711007122271281739FRFrance
1607199123711947767116223211329FRFrance
1608199122715452995320951271737FRFrance
1609199121714903897520831261636FRFrance
16101991207190531274225364342345FRFrance
16111991197167391124622232291939FRFrance
16121991187213851388228888382551FRFrance
1613199117713462887718047241632FRFrance
16141991167148571006819646261834FRFrance
1615199115713975978118169251832FRFrance
1616199114712265768416846221430FRFrance
161719911379567604113093171123FRFrance
1618199112710864733114397191325FRFrance
16191991117155741118419964271935FRFrance
16201991107166431137221914292038FRFrance
1621199109713741878018702241533FRFrance
1622199108713289881317765231531FRFrance
1623199107712337807716597221529FRFrance
1624199106710877701314741191226FRFrance
1625199105710442654414340181125FRFrance
16261991047791345631126314820FRFrance
16271991037153871048420290271836FRFrance
16281991027162771104621508292038FRFrance
16291991017155651027120859271836FRFrance
16301990527193751329525455342345FRFrance
16311990517190801380724353342543FRFrance
1632199050711079666015498201228FRFrance
16331990497114302610205FRFrance
\n", "

1634 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202212 7 15433 11053 19813 23 16 \n", "1 202211 7 11678 8286 15070 18 13 \n", "2 202210 7 13314 10036 16592 20 15 \n", "3 202209 7 10485 7600 13370 16 12 \n", "4 202208 7 12088 8741 15435 18 13 \n", "5 202207 7 14003 10789 17217 21 16 \n", "6 202206 7 9798 7048 12548 15 11 \n", "7 202205 7 10851 7797 13905 16 11 \n", "8 202204 7 9547 6721 12373 14 10 \n", "9 202203 7 13972 10680 17264 21 16 \n", "10 202202 7 8495 6026 10964 13 9 \n", "11 202201 7 13793 10597 16989 21 16 \n", "12 202152 7 13239 9611 16867 20 15 \n", "13 202151 7 13326 9629 17023 20 14 \n", "14 202150 7 14128 10312 17944 21 15 \n", "15 202149 7 13674 10369 16979 21 16 \n", "16 202148 7 11549 8503 14595 17 12 \n", "17 202147 7 11419 8376 14462 17 12 \n", "18 202146 7 8216 5724 10708 12 8 \n", "19 202145 7 8965 6468 11462 14 10 \n", "20 202144 7 8736 5636 11836 13 8 \n", "21 202143 7 8145 5164 11126 12 7 \n", "22 202142 7 9443 6037 12849 14 9 \n", "23 202141 7 4021 2239 5803 6 3 \n", "24 202140 7 4441 2454 6428 7 4 \n", "25 202139 7 2291 1056 3526 3 1 \n", "26 202138 7 4325 2267 6383 7 4 \n", "27 202137 7 1964 754 3174 3 1 \n", "28 202136 7 3441 1730 5152 5 2 \n", "29 202135 7 2562 1107 4017 4 2 \n", "... ... ... ... ... ... ... ... \n", "1604 199126 7 17608 11304 23912 31 20 \n", "1605 199125 7 16169 10700 21638 28 18 \n", "1606 199124 7 16171 10071 22271 28 17 \n", "1607 199123 7 11947 7671 16223 21 13 \n", "1608 199122 7 15452 9953 20951 27 17 \n", "1609 199121 7 14903 8975 20831 26 16 \n", "1610 199120 7 19053 12742 25364 34 23 \n", "1611 199119 7 16739 11246 22232 29 19 \n", "1612 199118 7 21385 13882 28888 38 25 \n", "1613 199117 7 13462 8877 18047 24 16 \n", "1614 199116 7 14857 10068 19646 26 18 \n", "1615 199115 7 13975 9781 18169 25 18 \n", "1616 199114 7 12265 7684 16846 22 14 \n", "1617 199113 7 9567 6041 13093 17 11 \n", "1618 199112 7 10864 7331 14397 19 13 \n", "1619 199111 7 15574 11184 19964 27 19 \n", "1620 199110 7 16643 11372 21914 29 20 \n", "1621 199109 7 13741 8780 18702 24 15 \n", "1622 199108 7 13289 8813 17765 23 15 \n", "1623 199107 7 12337 8077 16597 22 15 \n", "1624 199106 7 10877 7013 14741 19 12 \n", "1625 199105 7 10442 6544 14340 18 11 \n", "1626 199104 7 7913 4563 11263 14 8 \n", "1627 199103 7 15387 10484 20290 27 18 \n", "1628 199102 7 16277 11046 21508 29 20 \n", "1629 199101 7 15565 10271 20859 27 18 \n", "1630 199052 7 19375 13295 25455 34 23 \n", "1631 199051 7 19080 13807 24353 34 25 \n", "1632 199050 7 11079 6660 15498 20 12 \n", "1633 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 30 FR France \n", "1 23 FR France \n", "2 25 FR France \n", "3 20 FR France \n", "4 23 FR France \n", "5 26 FR France \n", "6 19 FR France \n", "7 21 FR France \n", "8 18 FR France \n", "9 26 FR France \n", "10 17 FR France \n", "11 26 FR France \n", "12 25 FR France \n", "13 26 FR France \n", "14 27 FR France \n", "15 26 FR France \n", "16 22 FR France \n", "17 22 FR France \n", "18 16 FR France \n", "19 18 FR France \n", "20 18 FR France \n", "21 17 FR France \n", "22 19 FR France \n", "23 9 FR France \n", "24 10 FR France \n", "25 5 FR France \n", "26 10 FR France \n", "27 5 FR France \n", "28 8 FR France \n", "29 6 FR France \n", "... ... ... ... \n", "1604 42 FR France \n", "1605 38 FR France \n", "1606 39 FR France \n", "1607 29 FR France \n", "1608 37 FR France \n", "1609 36 FR France \n", "1610 45 FR France \n", "1611 39 FR France \n", "1612 51 FR France \n", "1613 32 FR France \n", "1614 34 FR France \n", "1615 32 FR France \n", "1616 30 FR France \n", "1617 23 FR France \n", "1618 25 FR France \n", "1619 35 FR France \n", "1620 38 FR France \n", "1621 33 FR France \n", "1622 31 FR France \n", "1623 29 FR France \n", "1624 26 FR France \n", "1625 25 FR France \n", "1626 20 FR France \n", "1627 36 FR France \n", "1628 38 FR France \n", "1629 36 FR France \n", "1630 45 FR France \n", "1631 43 FR France \n", "1632 28 FR France \n", "1633 5 FR France \n", "\n", "[1634 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ " def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 8, "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": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "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": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEKCAYAAAD5MJl4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsvXeYJFd97/09lTrP9MTd2ZnZvCtplaVVQBJCIEASYINtsAWvQbblV1wsG2NsY2T7AhdfgX19bfM6oAu6cAGZIF0yRsIKKCCQVhoFtKtd7Wq1YfJO6Akdq7qqzvtH1amuzt3TcabP53n2mZ7qrp7q3u7zO99fJJRScDgcDofjRmj1BXA4HA6n/eDGgcPhcDh5cOPA4XA4nDy4ceBwOBxOHtw4cDgcDicPbhw4HA6Hkwc3DhwOh8PJgxsHDofD4eTBjQOHw+Fw8pBafQFrpb+/n27fvr3Vl8HhcDjriueee26BUjpQ7nHr1jhs374dY2Njrb4MDofDWVcQQk5X8jjuVuJwOBxOHtw4cDgcDicPbhw4HA6Hkwc3DhwOh8PJgxsHDofD4eTBjQOHw+Fw8uDGgcPhcDh5cOPAwfG5GJ56bbHVl8HhcNoIbhw4+Pxjx/Gx7/yy1ZfB4XDaCG4cOEhqBhKq0erL4HA4bQQ3DhyouolkmhsHDoeTgRsHDjTbOFBKW30pHA6nTeDGgQNVN0ApoBlmqy+Fw+G0Cdw4cKDqllFIadw4cDgcC24cOFDTllHgcQcOh8PgxoEDVbeMAjcOHA6HwY0DJ+NW4saBw+HYcOPAcYwDVw4cDodR1jgQQkYJIY8SQo4QQl4mhPyxfbyXEPIQIeRV+2eP65w7CCHHCSFHCSE3uI5fSgg5aN/3z4QQYh/3EELutY8fIIRsr/9L5RRDtY1CSuPGgcPhWFSiHHQAf0opPQfAlQBuJ4TsA/BxAI9QSvcAeMT+HfZ9NwM4F8CNAD5PCBHt57oLwG0A9tj/brSP3wpgiVK6G8A/Afi7Orw2ToVw5cDhcHIpaxwopTOU0uft21EARwAMA3gngK/aD/sqgHfZt98J4FuUUpVSehLAcQCXE0KGAHRRSp+iVrXV13LOYc/1bQDXM1XBaSy6YUI3reK3VJqnsnI4HIuqYg62u+diAAcAbKKUzgCWAQEwaD9sGMCE67RJ+9iwfTv3eNY5lFIdwAqAvmqujbM23IVvXDlwOBxGxcaBEBIE8B0AH6GUrpZ6aIFjtMTxUufkXsNthJAxQsjY/Px8uUvmVICmc+PA4XDyqcg4EEJkWIbh65TS79qHz9iuItg/5+zjkwBGXaePAJi2j48UOJ51DiFEAtANIJJ7HZTSL1JK91NK9w8MDFRy6ZwyqC7jwAPSHA6HUUm2EgHwJQBHKKX/6LrrhwBusW/fAuAHruM32xlIO2AFnp+xXU9RQsiV9nN+IOcc9lzvBvBTyrvANQXVFWfgdQ4cDochVfCYqwG8H8BBQsiL9rG/BPC3AO4jhNwKYBzAewCAUvoyIeQ+AIdhZTrdTillq86HAHwFgA/AA/Y/wDI+9xBCjsNSDDfX+Lo4FcKqowHuVuJwOBnKGgdK6ZMoHBMAgOuLnHMngDsLHB8DcF6B4ynYxoXTXFQec+BwOAXgFdIdjls5VOpWopQiruqNuiQOh9MGcOPQ4WTHHCqrc3jo8BlcdufDWE2lG3VZHA6nxXDj0OFkuZUqzFY6sRBHQjOwHOfGgcPZqHDj0OEwt5IiCRXHHJYTllHQDB6j4HA2Ktw4dDhMOYR9csXGYSWZzjoXAKaXk1iIqfW/QA6H0xK4cehwWMyh2yc73VnLsZLUAGSqqyml+O0vHcAnf/hyYy6Sw+E0nUrqHDgbGNXurRT2y44iKAd7HDMOx+diODEfR9gnN+YiORxO0+HKocNhaqHbp6wh5mAZh4ePWJ1Tlis0LhwOp/3hxqHDcWIOfhlJrbJU1lzl8PCRMwAyRoPD4ax/uHHocJhxCHmlymMOiYxxWIipeH58CV5ZwHJCg2nyllgczkaAG4cOR9UNeCQBPlmsyK2kGyaidnW0qpsYOxUBpcD152yCSeHcx+Fw1jfcOHQ4atp0jINuUqSN0q6l1VRm8dd0E1H7990DQQDAckJr3MVyOJymwY1Dh6PqJjyyCK9sjfku11/JndGkGqbjltrc7QUALPG4A4ezIeDGocNhbiWvYhmHcq4lt3HQdNMxJpu7LOPAlQOHszHgdQ4djqpn3EoAkCqTseRe/DXdhGnPZGLKgWcscTgbA24cOhwr5iA6xqFa5WCYljEZDHkAAEtcOXA4GwJuHDoczTDhkQV4ZcvDWCzmEE2l8cpsNNs4GAZ0g8IjCei2q6N5zIHD2RjwmEOHo6aNLLdSMeXw9QPj+M0vPIVXz8QAAIooODEHryxCEgV0eSWscOXA4WwIuHHocFTdhCKJZQPSpxcToBR45MgZBBQRPkWEqlvZSkx19AQUrhw4nA0Cdyt1KIemVhBT9QIB6cLGYXo5af1cSWFLtxdpkzrKwSNZ54b9Co85cDgbBK4cNihTy0nc/o3ni8YQPvfwMdzx3YOZVFZmHPTSxgEAuv0KPJLlVnIrh7Cv8s6uHA6nveHGYYMydiqCH780g9fmYwXvX03qGI8kEFd1eCQRAdutFEvlt7+glGJqOQmBWL93+yQokgDVyFYOPX6ZKwcOZ4PAjcMGhXVMTRRxE0VVHYZJcWZVhUcW0B/0QBEFTLoUAmMlmUZCM3DVrn4AQNinuALSLuXgV/hcaQ5ng8CNQ5ujGyb+4tsv4fhcYQVQjLRhFafFizTCcx/3SAIEgWCkx4eJSCLvsVO2wXjHBUMArKlxGbeSO+YgI6rqZfszcTic9ocbhzZndjWFe8cm8M1nxqs6jy3QxZRDLMs4WIv7SK8fE5F85TC9nAIAnDPUhY++ZS/eefEWKFK+cujxKwDA4w4czgaAG4c2hzW2+/nxharOY8ahmHKI5SgHABjt8WFiKaMcnj0VwSd+cAhT9rEtYR8+fP0eXLWr3zIORr5yAHh/JQ5nI9DRxuGXE8v4rS88VbYTaStR09Yi/8psFAsxteLztBLKQdNNJyYBAIptHLb2+rGcSGM1Ze38H3x5Fl976jTuPzQLRRLQF1Ay54gCVN1AKm1VWANwqqS5cuBw1j+dbRwml3HgZKSqRbfZaC7//S9eW6z4vLRuxRxiBZRDrppwlEOvHwCcuAOb1fDMyQiGwz4ILF0JliuKpbIy5cDSYZlB43A465eONg5s96zq7buYuUd3/qIK11Im5pBvHJjBGA77AAAee1Hf6hgHK+4QdaW1bgl7s56DxRzUtOHEHJiRUXlAmsNZ93S0cdDtecftvNNlhqs/6METx+YrzgTKxBzy3UrMOJw33AXAHXPIUQ6q7tQ2bOn2ZT2H4iqCY8qB/Wzn95PD4VRGRxuHtKMc2jjmYF/j+6/chumVFL721OmKztNKKAfmVrpgJAwATuuMbr+MkFdygtLRVBr7t/ViOOzDBaPhrOdQJAEp3YRmZLKVWOyind9PDodTGR1nHJ47HcHnHj4G3TCd3XVbu5Xshfam8zfj2r0D+NzDx7BYQYzEUQ4FAtJR2zhcubMPn/3183H9OYPOfVt7/Rh3xRz6Qwp+9rE34v1Xbst6DkUUELUD1yzW4LiV2vj95HA4ldGBxmEJn3v4Vai6Cc0uFGvnxYy5aLySiL9++zmIpnR8/8XpsuexgHSiREA65JXw3su3wq9k+i+O9vhdAek0Qh45KxDN8EiCU2jHjALLWmrn95PD4VRGxxkHWbResqa7lEM7p7LaC61HFrB7IAgAWK0gVbSUcmD9k4Ke/Ka8wz0+zKxYRW/RlI6gt3DjXuZCAtzKgcUc2vf95HA4ldFxxoEtaul15lZiLS68slB2lCeQiTkUKoJjAelAAePQG1CQ0AzEVR0JzUCoiHHwuIyDoxy4W4nD2TB0nHFgykHV14txsK6NGTWfLBYMMudSqn0GMw6FlEOvXejGgtIhr1zw+Qsrh4wq43A465uOMw4el3LQdBZzaF83CIs5KLZR8ysSklr5xbdU4724qsMnixALxBJYf6TTi8w4FHErifnKgRBitfLmxoHDaQiptIF/e/Q4Dk2tNPxvdZxxcGIObrdS2rr9i9eq61/UDFTdgCQQSPZ1+xQRyXR55VCqZXdMLR5L6Asy4xAHAHQVjTmIzm2mHADAY7fV4HA49Wc5kcbf/+dRHGwH40AI+TIhZI4Qcsh17FOEkClCyIv2v7e57ruDEHKcEHKUEHKD6/ilhJCD9n3/TAgh9nEPIeRe+/gBQsj2+r7EbNiON63TLLfSI0fO4H13H8CrZ6KN/PNVw8Z4MnyyiGSRTqtunJiDpoNSmnVfTDUKupSAjHJg6axBT3m3Ulb8QebKgcNpFMyl7FfEMo+snUqUw1cA3Fjg+D9RSi+y/90PAISQfQBuBnCufc7nCSHsVdwF4DYAe+x/7DlvBbBEKd0N4J8A/N0aX0tFyMwvbhgu42BgOWFlAJ1azJ9n0EpU3XDaWwCWcijWhtsNe22UAqmciuVYKo2Ap/CHi8UcyrqVCsQcACtjiVdIcziNgX33fXIbGAdK6RMAIhU+3zsBfItSqlJKTwI4DuByQsgQgC5K6VPU2sZ+DcC7XOd81b79bQDXM1XRCBQnlZVm1TmwDKDJpTYzDuls5eBXxIqyldxtNuI5Aex4CeXQ7ZMhkLXFHNht7lbicBoDMw7u2qRGUUvM4Q8JIS/Zbqce+9gwgAnXYybtY8P27dzjWedQSnUAKwD6Cv1BQshthJAxQsjY/Pz8mi5akSy7oxlmpn1G2nTe9Kml/GE3rWStbiVWBAfkB6Vjql7UOIgCQdivONPfimUrsYI3IFs58IA0h9M4mFvJ1yZupULcBWAXgIsAzAD4B/t4oR0/LXG81Dn5Byn9IqV0P6V0/8DAQHVXbKOI1puadqWyaobhzHSYKjBDuZW4h+kA1bmV2O4+t/leKeMAAD1+GYbdlLBonUMx5SCLPJWVw2kQbGNYzC1cT9ZkHCilZyilBqXUBHA3gMvtuyYBjLoeOgJg2j4+UuB41jmEEAlANyp3Y1WN7FYOrmwl9qZPtply0HQza5deqVtJM0x025PZcusi4qpesACO0RfwAABkkWQt/G6UHIPg3OZuJQ6nYThuJblN3Up2DIHxawBYJtMPAdxsZyDtgBV4foZSOgMgSgi50o4nfADAD1zn3GLffjeAn9Lc9Jo64mQrGdm9lRJtqxzW6FYyTITtyWy5LTSiJVJZAaAnYJ0X8sooFv7JDkjnxhy4cuBwGkEz3UplzQ8h5JsArgPQTwiZBPBJANcRQi6C5f45BeCDAEApfZkQch+AwwB0ALdTStnK9CFYmU8+AA/Y/wDgSwDuIYQch6UYbq7HCytG4QppA5JmLYKRuIaEpjcl4FMJqm5mZSb4FAnJtAHTpAUb4jHSBnXSUt3N99iI0GCJ18cyloq5lICMcSAkPzi9GOPGgcNpBJmAdBsYB0rpewsc/lKJx98J4M4Cx8cAnFfgeArAe8pdR73wFOmtRJDZXU8tJbFnU6hZl1QSVTccBQBkPhQp3ShpwNJ6xq3kVg4sOF1KOTDjUCouwQyCRxKy1IVHErlbicNpEG2VyrrRyOrK6spWSqYNZ+rZZBu5ltR0dsyBfSjKuZY0l1vJHXMo1XSPwRRHJcrBHSy3fuduJQ6nUSTTBnyyWNJrUC86zji4u7JmYg4GkprhzFBup3RW9xhOIONrLJexlDZMhG3lEFPzjUMpVZBxKxVOYwUyRsEdbwB4hTSHw0hoet07Llgu78arBqADjUPBeQ52EdzWvgBkkbRVxpKVypqdrQTASb0thGFSmNRqfSEQ4PRCAn/1vYOIq7ozC6KrxMJfTcwhXznwVFYOBwD+x0+O4h3/8mRFCSRuWFywEAnVaEowGuhI48BSWWmecQh6RAx1+9oqY0nVzazMIOZWKqUc2OtSJAEBRcL/fW4CXz8wjrHTS5hdtQb5bO72FD3fMQ4l1AUzWHnKgaeycjjQdBM/eHEKqm7i8EzhJnmffeAIvv3cZN7xP/u/v8Qff+uFguckNIMrh0ZBCIEiCjnKwXIr+WQJm7u8mLMX0HYgt31GJW4l1nRPFgn8HhF2PRuml5POlLfN3b6i52diDsXVRSYgXTjm0MBsZA6n7Xni2DyW7H5tL01mjMNKMu2ogh+9OI1HX5nLO3diKYFjRdxRibQBX5MyKTvOOADWjtoyDnbMwQ5I+xQBfUEFCzG1xVdoQSnNq5BmGUql3Eos0K5IAgIeCaJAIBDLOMyupBDySiVjDgMhD4IeCSM9xQ2IIBBI9mQ6N4okgNLMPAkOpxP53gtT6A0o6A96HONgmBQ3fe4J/OtPXwVgbeIKfY+TmoHFuFbweZOaDn8TMpWADjUOskiyMnhU3XRqG/qDnqL/Mc1Gt2MHuUVwQDm3krUwy6KAq3b14fdfvwObu7yYWk5iejmJoW5vyb/rlUU8+mfX4d2XjpR8nCIJBWMOQHsPUKqUaCqNT//ocElDzClMJytHTTfx0JEzeMcFQ7hoNIyXJpcBAC+ML2F6JYUzq9bmk21Kc0mlrS7RupEfd4irRlNaZwAdahwUSXBy/wmx0sNSaRNeWUR/0IPlRDqrq2mrYFk/ue0zgPyWGACwmkrj+y9MOdcuiwL++7vOxx03nYMtYZ+lHFZTGCrhUmIMhDzOgKFiKJJQMFvJfe3rmQMnIvjyz09muQU45bnv2Qlc9OmHMN5m7e+bxVJCg6abOHtzFy4Y6caJhTiiqTQesV1IbOOkFlMO9rFIIn+TmuRupcYii0KmGEyRHB+gTxadSWiRNlAPqv0hKZTKWuhDdc9Tp/GRe1/EhD2ohwXfAdjGIYWZlVRZ5VApnoLKYeMYB9bqnCuHyrlvbAIf+85LWEmm8dpCrNWX0xLY2tHjl3H+SDcoBQ5NreKnR5hxsGJymm4iWWD2CctuWozlr0EJ7lZqLIokFKwU9iuWcgCA+Wjr4w6OcqjQrTR2yupXyNxi7rYWW8I+zKwksRBTK1IOlTDa48eoXRvCYMZiI6SzspoQbhwq567HXsNw2Pp8rdgB2U5jiRmHgIILhrsBAHc9/hqO2kFmVTed77Za0K1k3ceMg6ab+MvvHcTMShIJjaeyNhRFFJxdoTsw65NF9NvKoR3iDoXcSsWMg2lSjJ1eAgAs23JUdhmH4bAXaYOCUtRNOXzztivx5zeclXUsoxzW/4LKNhCpDWDomoFumJiIJPD6Pf0ALPdKJ8LcQb0BBX1BDz547U48+ao1f6Y/6IGqG05GYW7MQTdM577FuLVBfXUuim8cGMdPX5lDkqeyNhZFEpCwZxy4lYPPpRwW2kI55LuVBDtDKHc3e2wuimjKWsxYCp3sUhxutbC5TsZBFgWIOWX8TsxhA4wKjdnvZ6rKIqZOZWYlBd2kOH/E2i0vd7pysFPC73jbOXjwT67FF95/KfZuCkJNm873I/d77N6ILNjKgX0Op5aS0E1asvVNPelI4yCLQsE2Ej5ZRH/INg5tkM6qFXArAdZ15iqHsVNLzm32pcyNOWRu18c4FCKTrbQBjIO9gUhtABXUDNho2Z39QXR5JUfBdhpsc8ba1wDA7sEQbjh3s1MHxDZ+ucrBXU29aK9BbNN3ajEOoDlN94AONQ6KKDiLayhHOQQU0Wo73U5upZygr99u2+1m7FTE2cWzL6WS5VZyK4f6xBwKoWxEtxKPOVQEW7y29fkR9itYTnamcojENYS8UpZbl8G6FrONXyqdXTDq/qyxmENUtd7HUwuW8eVupQYiS0ViDooIQgj6g572cCul82MOgHWduf1axk4v4dJt1ijvpQIxhy6fhIAili2AqxUn5rAR3EpOttL6fy3NYDySgCIJ2NzlRY9fdnbQncZSQnNa0OTCGlO6lbX7tnvTx2IOecqBG4fGoYhWFS9gNadjMLnWH1Sw0GLl8NRri44BK+xWytQ5rKbSmFxK4sqdfQBcMQeXcSCEYEvYV7dgdDE2kluJK4fqOL0Yx9ZePwSBoNuvYKVD3UqRuObEG3LxSIIVc3B9P9yfL/emj8UcmHHIDPppTsyhPcadNRn3YpubygpYGQWsB1ErOL0Yx3vvfho3nrsZQL5byZczR5q1Bb5wpBsCsfq3AIAiZQeLbzp/CI2Gvbeasf4X1IxxWP+GrhmcXkxgm53a3OOXcdre6XYaSwkNA8HCjS1z3UqApRbCrtsAsKnL4yiH1VS2Ags0STl0pHFwB2pDOQFpwDIOB6daVxXLAsrsGnKVg18RnYwIADg6axUbnbU5hIAiFXQrAcBH37K3YdfM2FDZSjwgXTGUUoxHEnjdLku9hn1y1me0k1iKp7G3yCTJ3IA0kL35YMZhpMePIzOrADLZSgzuVmogShHl4LXf9L6ggkhcg2m2pj8Mk5msdbhSJlvp2JkoAoqI4bAPAY/kKIdCAbFGs5HcSjE7EMjdSuWZj6lIaAa29wUAAN1+BaspHUaLvkOtZCmhobeYW4nFHNwGwfVdZmnTIz0+JDQDCU133EqMZrmVOtI4uBfN3FRWwFIOukmdRbbZ5BZd5cUcctxKR2ej2LMpBEIIAh7Riae0xjhspGwluwfOBlBBjYb1Udral3ErAWjZd6hVpNIGEpqBnmIBaUmEYVIkXN9ftzLNKAcro3AxpiGaSmfVE/FspQZSSDnIInEWU1brwHx+zSZ3p+qRc1NZs7OVjp2J4ixbxrqNndJK47ABFlTePqMyVN3APzx4DJJAcM7mLgCZHP9Oq3VgLt1SAWkAzkRGILvI0u1WAqzgdjSlY1tfpk0Ndys1EPeiyWIO7sIStuuJxFukHHKNQwm30kJMxWJcw97NlnFwS05ZavwQ8lwkUYBA1r9bKW1kRjUWaqvcSRyfi+KzDxzB916YdIL0bj71w5fx1IlF/I93X+BU34ftxbGRtQ4LMRW3fW2srXo4saZ7vYHCg7LYd9ntKspSDlq2cpiPqoimdOzoC4DYX+cAdys1jkLKwW2N2QJbqC12M3AbB4EAUk6LClYEZ5oUx2atTCWmHNyl9a1wKwGZjIz1jHsR7HTl8M1nJvCFx0/gT+79Jb7wxIm8+x99ZR5vv2AIv35JZv5H2Nd45fDC+DIePHwGh6bbp6X6kr2hLKoc7E1o1JWBlNTy01pZ7GYuqiKm6gj7FfQFPCAkfzRvo+jQbKX8mINbObBhGtUOBq8X7uwFj2QV5rlxri9t4NU5K1Npz6YgACDoGgSSa1SahUcW1n1X1liWcVjfr6VWppeT2NEfgKabGC+QnhpXdQyGslM32eLYyP5KbIFdbaO4xpKr6V4hHLeSyzhk1TmkDUgCwVC3F4QAs6sprKbSCHklbOryIKHpeetBo+hI45ClHJhxcEk1v2zdjrfMOLCGe0JedTSQUTZxVXc+jKxhoN9+PYooNO1DlAtL11vPsGC0KJCOT2WdXklhOOyDppuYzqn/oZQipulZKeFAJubQyCppZsBzs3laCfs+hovGHJhyyFxzMqsIzoRPFiGJAvqDHsyuJBFTdXR5JWzq8uJME+fbd6Rbya0cPJIIWSTwuaeteYpPW2sG7MNy1uZQXrwByBi0uGYgrurwyaKTzcDuc9dyNBvLrbS+jQNbeHoDyoYIrtfCjD1adijsxcxKMuu+hGaAUuR1Cu3yyiAEDa2SZgtsbpFYK2ExB3fTPTcFA9I5yoGl1G/q8uDkQhyUAiGvjMt39OLirT2NuvQ8Ol45yCKBRxKzArkBJ+bQOreSIgq4ZGsPXpxYzrufpbLFVR0xVc/6YrJrlwsYlWZhKYf1vdtmxqEvoLTF4KdWoekm5mMqhmzlMLsyA9OkEOzNSKzA0CzAai3f7ZMLBqQ//9hxPHdqCV/6nctqujbHOLSJW+ng5Arue3YCW7q9ReN9zBOwmtIhCgSGSbOMQyptOC7uTSEvnh+3ui2HvBJuvnxrg19BNp1pHOxdNSGW28AjCfC6Yg5eWQAhQKJAZkYzSKUNeGQBf/m2cwoWETFjYBkHIyvOwOIRrQpGA3ahzzrfbbOAdH/Q44xd7UTOrKZAKbCl2wvNMJE2KBbiKgZDVlZSodb3jLCvcPO9508v48DJSM3XxooUV9vArRRXdbz37qfR5ZXwv95/adHHZdxKafgVK+swlVMQ5xiHbq/z/oW8hZVII+lItxJTDrLtl/dIQla2EiEEfllsaczBK4tQcq6LwYxDwnYrZSkHV8yhVSji+o85OMohqHT0JDjWY2wo7HMGRs0sZ/zerLVDQePgVwp2N15KaIipupPwEVN1jC8mqu5I0E5upXk7q+hP33oWLhgJF31cxq2kwyOJ8EpCdszB7VYKZZpk5iqzZtCRxoHtqtkC+gdv3I2bLxvNeozfI7XQrWSUHOjBGm/FCrmV2iDm0Bf0YHJpfe+2445byQPDpEgbnWkgWIxhS7fX6ejrjjuw96nQdLIrdvTi6ZOLODy9mnWcBW0XYiq+/ORJnPfJ/8S1f/8o7h2bqOraYo5bqfXKoZh7LRfFla3ENqW5MQcW/9zUlckAC3Hj0ByYUWAL6G9fuQ1X7+7Peowl+VrlVjJL5jJnlIOOuKpn7dqCbeBWumpXH04tJta1O8ZxK4WsrJNOrXWYXs4oBzZNcNqlHKIl3Ep/cN1udPtkfOb+I1kDbVhDvvmYiufGl5y57dXOUGHKIdoGyqGUe80NUw4JzXIde6TsVjhZMYeujHLo4sahOcgut1Ix/IrkpDM2m5RuZMVAcmFB55hawK3EAtItNA7X7h0AADx+bL5l11ArMdWAIgqOr7dTq6RnVpLOgKgevwyPJBRUDoUWxW6/jA+/aQ+ePL6AF+zECsOkTpB6IapidiWF3YNBEIKq1RkzTO0ScwAKKyg37lY4HkmETxHzmvAxV/JglnLgMYem4BHLG4eAIiKZblEqq2bAKxU3Dk6qbcGAdOuzlXb2BzAc9uGJdWwcLKNr+YSBjdErai1ML6ewxY41sIFR7lqHcu6UN5xlbRTYbIfVZNppDDkfs4zDlm6fFaeq1ji0URFcRjmU7nvkTk1XJAFe2Yo53P3ECdw3NmHFHGwDstmlHFrhVurIbCW2cOZozs6gAAAgAElEQVS2wnbjU8SWFdekdBPdvuI7BVkUoEiCU+fg7rWSCUi3LuZACMG1e/vxo1/OIG2YLVUxa4XFctgXtVPdSjMrSQyFM4vUULcXs4WMQ5Edc3/AbmJpTzWLuOoe5lZVzK6mMBT2QhGrr6rPFMG13jgwL4N7smQh3MbBIwmglCCVNvCVX5yykh9cbqUevwJZJDApSsYgG8X6+9bWgdyYQyECitSymIOaNpwdazECiohoKo1k2sgJSLc+5gAA1+4ZQEzV8dJkfp3GeiBmx3IyxqEzlcPMSsrJUgKAoW4fZpYzbqVYSodkp4MXossnQRIIFu04g3sA0JGZVRgmxeZuHxRJqMqtRCl1AtJRVW/Z7BVGxq1UTjm43UpWCn1M1TG7msKJ+TgSrlRWQSAYDHkR8kot6XbQkcZBrsCt5PeILc1WKhVzAKyYyJwdwAsWiDmUUkXNYI/dCHByKVnmke1J3FEO1vvYiS00VN1AJK5lzR3fEvbiTFSFbi/k7H0qtngRQtAXVLAYsz6r7rqHQ/akw6Euq2isGuWQSpvQTYr+oAJKgViLNnIMpmLKdUyVReJ0V2XG4eRCHIZJEVN1yzi40tcHuzxlg9yNoiONg1JRQLp1xiFZJpUVsAwCMw5u5eBXRBDSeuXAZuiu1+riOHcrOTtzt4uzP2il9q66du3lFq/egMdxKzHlMNqbiV0Mhb22cqh89x+1C+BYBlWr+yvFVR1+RXQqx4vB6qoAS0V45Xz3tXtjeO6WLuwcCNb/giug7ApCCPkyIWSOEHLIdayXEPIQIeRV+2eP6747CCHHCSFHCSE3uI5fSgg5aN/3z8TeahBCPISQe+3jBwgh2+v7EvNRcuocChFQpIK965tBuVRWwFI2c3YTLreUJYQgoEgtLYIDLHeCIgqYj61P47AQ09AXUJzEgE50K7HNkXvyGAuMsiBwbip1IfqDiuNWYjGHvYOZGctDtlupGuXAFlQWLG91UDqu6WUzlRjMteSRhKyebgz3xvC//ep5+PIt++tzkVVSyQryFQA35hz7OIBHKKV7ADxi/w5CyD4ANwM41z7n84QQ9krvAnAbgD32P/actwJYopTuBvBPAP5urS+mUhzlUGIYjl+RoOpmS2bgVuJWCnokZ1ee++UMeERILQxIA5aR6g+uz75EhklxZjWFoW6vY6SX4hr+6/cPtdVgmUYT1/LTM7vslEq2OMdUvWzhV19AcaYqLiU0KKLgjBP1SAJ6/LLlVqoi5sBUTbsoBytrsFLjkEmIYd9zQjJG2O1WEgUCqUUbvbJ/lVL6BIDcRijvBPBV+/ZXAbzLdfxblFKVUnoSwHEAlxNChgB0UUqfolY1zNdyzmHP9W0A15NiDsw6wQLR5dxKQPM7s5omhaqbeaNBc/ErInTbcOXuWD70hl1Zg1daxUDIg4XY+hsTuRBToZsUQ2Gf8+V9/Ng87nn6NJ46sdDiq2seLAOnoHKwM4RiqlF2x9wXzHYr9QRkp8W8NbeAVK0cmI9/i51J1XLlYKc+VwJrvmcpB+ucoS4v9gxa7qNWZCYVYq0maROldAYA7J+D9vFhAO4a+En72LB9O/d41jmUUh3ACoC+NV5XRVQUc3DadjfX18x6EpX7gLgDX7k7lt+5egfeYBeitZKBkGddKodpOxtnqMvrGIdXZq0WEK0aHdsKEgWUQ8hRDrZxSKXzZjnk0hdUkNAMJDUDkXgaPX4FA/ZwIDZWVBFJVdlK7O8P28qh1f2VYim94vGdjltJFp1N4EiP34ktlPMaNIt665VCO35a4nipc/KfnJDbCCFjhJCx+fm1F1jl9lYqRKvadrPAZ7mYQ6F+Su1Gf9CDhTaKOcRVHcftyXmlmHUFStn/w6lFqxVIJN4+r6fRFFIOXT4Wc9Cdx5TbMTu1DnEVywkNvQHFSVhgMYM1xxzaxq2kV1yo5riVxIxyGOn1YdeANRq0ULPNVrBW43DGdhXB/jlnH58E4O5gNwJg2j4+UuB41jmEEAlAN/LdWAAASukXKaX7KaX7BwbWvjOupM7B55qZ0ExYymTZVNYCbbrbjYGQB4sxtSVxm0J8+cmTeOe/PpnV56cQLItmS3fGrcReQ0cqByVfOWTcSnrZwi82MnMxpiGS0NDjVxy3ElMOslhdnQMzBkNF3EpPvbbY1GaJ1QWkM24ltvkY6fFjt+1WCqxz4/BDALfYt28B8APX8ZvtDKQdsALPz9iupygh5Eo7nvCBnHPYc70bwE9puW9vjQgCgSSQMu0zWqMcWBvjcsoh6P7Clvlytor+oAcmzUzHajWTS0nENaNsn6SZ5SQ8koCwHSgVXemJSw2cbNZusJb17o1IyCOBEKufkWlSxDW9bMuIPrux3mJcdWIOQ2EvRIFge7+1W662zTuLOfT4FfhkMcutdGhqBe+9+2k8fPhMxc9XK7k9zkqRcSu5lEOPD286exPu/LXzmjrtrRRlXw0h5JsArgPQTwiZBPBJAH8L4D5CyK0AxgG8BwAopS8TQu4DcBiADuB2Sin7Jn4IVuaTD8AD9j8A+BKAewghx2Ephpvr8srKoEhCyf5DrRoVylImy8Uc2KxogZQ3JK2C+ZUXYqpzu5WwjJmYqmdN/stlZjWFLWGfU9jltVuVAO1j6JpBokBhlyAQBBUJ0VQaibQ1IrRctlK/q+ZlJZlGr60cHvjj12OHbRzkKiuko6k0vLIAWRQQ8kpZbiU2SGixif9XsQpSehksIK2ImWyl0R4/FEnA/3PFtoZdY7WUfTWU0vcWuev6Io+/E8CdBY6PATivwPEUbOPSTDZ3e7OGaeSSyVZqcszBdiuVy1Zi0rNUdWqrYQZhPqrinKEWXwzgZE4lVAMIFX8cm5nM8LoGP3WScWCvOXejwhbjSjuRMuVwYiEOk1pDgABg76bMf4Kn2lRWVXdcXF0+OUs5jJ2yjEO94xD/+fIsDJPibednf5h1w0QqbVYRkLbdSrKIi7eGcc3ufpw33FXXa60H7bnlbALfv/1qfOi6XUXvZ//R81EVn/zBIaw0KVXOCUiX6MoKZL6QrSqtr4T+NquSZsHxWJk40mxOPyF3l8xOMg6JIlW/XT4Zq8m0s/iW+wz6FQk+WcTYKWseMjMWbqptnxFN6U6WVJdXcgLklFKMnbb+DhsjWi++9LOT+OdHXs07zoxopbE/xVUEt60vgH///Sta0pK7HO27sjSYrjL/GSwg/cChGTx9IoKrd/fjredubvh1VZ6tlFEO7YrbrdQOsFz7YkkGt3z5GewaCOJMVM1SDswNcPHW8LqeUVEtcc0o6H7LVQ6VbFB6AwqeO72ELq+EN549mHd/1e0zUpnsoLBfwVzUSiKYiCSdzUiszspB1Q1MLiVBKc1S69W8D0B2EVw7095X10KYcnhh3Ooq2qxUOSfmUCZjgX1p29k4BBQRPllsC+WQ0HQnEB0vEEdaTaXxxKvz+PLPT8IwaVabaq8kQhEF7BvqsgfCd0afpYRWuLCry2u5cSqdfgbAmfZ26zU7C27MqlUO7srsHr+CiG34n7VdSqJA6v6dVXUTMVXP8yJU6l5juHsrtTPcOBTBKwsgJFOU1qye8ZW6lYKOW6l9P2CEEPSHlLZQDouuSu1CE/5emliBO0duS5ZbScBIrw/9thLqFNdSXC2tHGJVLIqDXVbr6d+5envB+xWpuphD3JVU0BuQnZ5NY6eXEPJK2D0QdCbF1QtmvNydhqeWk04TwsqVQ8at1M6077azxRBC4HcFIps1ijCZrrDOgQWkKwyCtYqBoKctmu+5DVQht9KLE5af+rZrd+KLT5zAaK/fue/G8zbDMDP5+pG45hRfbWQSml4w5z7ElIP9naik+OuOm85GTNWLDrFSRAJNN/NcNsVIpQ3nO9Ab8CCVNpHUDJxejGPvphBEQhrgVmLGIYHzhrvx4MuzuO2e5/C+K7YCKJ+1xXCylbhxWL/4PZJjHJqnHCpLZQ2ug4A0AAyGvHh1Ltrqy8jq8VQoIP3ixDJ2DQTwFzeejRvO3ewUJAHAbddaiQvMZdEptQ5xzUC4wGLe5bOUw6zdFbgvWD5NuVzbabZQ6iYtWZzKSKYzo3R7A9Y1LsZVzEdV7B4MQtVNJw5RL1Q7k3AiksTsSgof+85LAIBHjlj1FFVnK7W5cWjvq2sx7l1TvfyXE5EEbv/683m7V9Ok+P4LU85ux1O2ZXf7xxwAq7hnajlZtiq50Sy6lENuejKlFC+ML+Oi0R6IAsGl2woXIfX4M8qhE0gUaSYX8sowTIrD06sYDNVnGA0rSK007pB0DcVh/y9L8TTm7ZqaoEeqv3JIZ5TD3z5wBGraxK6BAM6sFu6OXIyMW6l9XcIANw4l8SnWiMORHl/dGns9eyqCHx+ccQLdjLHTS/jIvS/ivrEJEFJ+V+GXRQikNYPHq2G0149U2my5a4kVREkCyTPMk0tJLMY1XLw1XPI53G6lTiBRIlsJAF6aWnaK2GqFKYdKC+GsmSfW4spSY2dWklhOpDEQ9CDklcqmLFeLal/bqcUEHnllDr9y4RBuPC+TwVhxV9Z1kq3U3itLiwl5JezdFIJHFuqmHFhM4eiZKK7Z0+8cn1mxglxTy0k7GF5aWgsCwb+97xJcMFp6QWs1o72Wb34iksBgiaLDRrMQUxHySFAkIW/ReH7cijdcVOa97PbJEEj2HOSNTLxIzIFlG01Ekrh6V3/e/WuBLZSVKAfDpNAM03G9MuXwqt1UcSDkQUzT6xonpJQ61/bUa4vQDBPX7h3IShypOFtJXh9uJW4cSvBf374PAPD3Dx7FSp38zKx30rHZbD/83GpmZ11pP/ebzm+DsuMybLUDuxORJC5tYWeAxZiGvqACg9I85fDwkTn0BRScM1S6SlUUCMJ+xcmM2egkVMNxX7pxq9V6KQfHrVSBcsitBeqzu76+Yn+nrDkiKjTdhKobdXHfsGA0IdY1CgS4Zne/M1NFEkjFi/2bz9mEM6uq0268XWlv09Vizh/pxvkj3ejK6d1SC8w4HD2TbRzOrGaCZ+3Sz70ejPQw45Bo6XUsxFT0BT3W+FdXzEHVDTz6yhzeeu6mrAZ7xejxyx3hVtJ0E5phFlYOriB1vYyDpwrlwNQ3izmEvBJEgTgbroGQx6k4LpS2vBaYcWAL+gUjYYTtHlFbe/1VtbHZ1OXFR9+yt+y86VbDjUMFsNS9esA+2K+eicJ0tbKei6roDyp2I72NYxy8soiBkAcTS601Dov2TOigJ3s2+C+OLyKm6hVXv/cGlI4wDklnfnS+cuhqoHKopEo607nY+p4IAkGPX8Zr8xm3EgsO1yvLkBktlnV1rWuY1pU7e7G5q3Uu00bB3UoV0OWV6ua/zFTpGphaTjr59GdWU9jZH8TO/kzK3EZha68f4y1WDotxFZds64Gqm1h2uYV+cmgWIY+Eq3ZVNnxwMOTFwamVRl1m25CZH104WwmwXCxsFnStKFVkKzG3ktv92htQnHTlvoDHqTmol+Jn38krdvRiMabiVy/c4tz3iV85t+ndm5sBNw4V0OWToekmUmmj5l190uXSOHYm6hiHuaiKc7d04S9uPLvsvIH1xmiPD8/aTddagWFSROIa+oMKVpIappYz7+9jx+bwhrMGKvZL79vShR8fnMFKIo1uf/s1S6sXbLErrBys1z3S46tbOiZrn19JzCFZwDiwoHSPX4YiCU5cpF4ZS8ytNNLjw48//Pqs+4Ieqe3rjdYCdytVQKiOu5Bk2kCfnRLpjjucWU1hU5cXo73+rFbGG4HRXj9mVpJNnczlZiWZhkmt3WVAybiVDJNiLqqWLdByc8FINwBsePXAfPWFlINXFiAJBNv76uNSAqpVDqZ9HdnKAYCTEccGYNVNOdh/s90zjOpJ57zSGujKGapeCwnNwEDIgy3dXvxywqp1iKk6EpqBTV2tH4jTCEZ7/DApMLNc34rVSmExgt6AgoAnk/++lNBAKdBbhQK4YNhKd/3l5HKZR65v4iWUAyEEW/v8uHCkfmnUimQFZ6tSDkpm+WLGgXUCDjrKoU4xB/u62r02oZ5sPC3UAJhyqEfcIZW2Kjuv3TuAu392As+PLzn9ZlpZB9BImOtsPJKom4+6Gli7ix5/JiBNKXXqFXoraP/A6PbL2N7nx0sb3DgkmHIo0hLih394TV130YpoqYB0JdlKWn7/sTzjYLt56lUlrdoGqd2rmutJ55jBGgjVUTkkNath2Iev34PBkAd//b1DmF62CuAGN6hyYIpoPtY45fC/f3YCd/74cMH73MrB7xFhUsuHzKqmmZuvUi4YCeOlyQ3uVmLKoUjVb9AjlZzBXi1yCeWQ1Az89JXMPOhCAWkWc2DGoZ4bOiATc+BuJU4WXT77g5as/YOW0Az4ZBFBj4S/evs+HJ5ZxTcOjAOw8p83Isw1kdQaF3P42asL+MnLswXvYwqhx05lBSxXnttoVMMFI92YWUnVvbFbO8H6TzWr668iFm+f8c1nxvF7XxlzamVSOXUOQKaFxoCtAj2SAFkkdQ9Ic+XAyaKeysGd8fS28zZjIORxFrWNahzYDq+RWViqbjgDX3JhFc29fsVZ7OKq7iiH6o2D5Wt/eWp1rZfb9rCgfTHlUG+YClELuJWYC4/VyiQLzDzJVQ6EkLo232OB8k6KOXTOK62BrjpnK7E+9JIo4NcvHgalVgfYjZgOB2R2eMkG5oKn0ibiRaa0LSfS8MoCfIro9L+JqXpGUfirMw5shGg7DDFqFEw5+JtUkOkp0XiPZYZN2wkNuRXSAHD2UAhnbw5l9ccKea0hQJ+5/wjGF2urs2F1DtytxMkioEggBHWpkmZuJcZvXDoCwJqUtVGRRQJRIA1WDtaiUqh6ORLX0GsbAJaamdAMROIaQl6p6t0gMzC5rb83EnFNh0cSINUxrlCKYi2746qOEwtxAHBicyktf6EeDHnxk49ci+2uiu2gR8IjR87gi0+cwENHzqAWHLdSmVb6G4nOeaU1IAiWRK2XcvC6djx7N4Vw6bYe7KxTG4J2hE3Va+RiyrJJChmHpbiGngAzDhnlsBjXqg5GA5kpfIVmUW8Uoim9qUq2WMvuwzOrzvhWZhySaWuDVa6XUdArOTURuXOfq6UTs5U2ph+jAXR5ZazW+AEzTKvtr1/Oftv/z+9eVtPzrgd8iljQ5VMv2HMXcvVEEpoTV2ALXlzVEYmrjtGoBo8kQBSIk+65EYnEtKpjMbVQTDkctLPCtnR7McWUQ9rMcikVw90DqtbvbifWOXTOK62RUB36KxUq3gEsw8MK7TYqPqXByqGEW2kpriHsz1YOCdVwmvFVCyEEAUXc0MrBbVCbARsNquU03js0vYKBkAcXbQ07xsEaEVp+6er2KfDKAvoCSlY/rbXAK6Q5Reny1d6ZlRXvVDqvYSPha7BbKeVyK82sJJ0dJwAsJdJOFTRrQR1TdSzVsAAGcrq7bgS+/ORJ/NE3XwBgx2maaBwIIVAkIU85HJpawXlburCl24dpe9xsrmu2GH98/R78+61XYEvYV7tbSbdmOEht3ma7nnDjUCH1mOmQyc/uPG9eo91KTDksxjV89v5X8Ot3/RzPnopAN0ysJNN5MYe4XefQG1hb4aFfEbPmQmwEnjy+gJ/agdulJhsHwKp1yI05TC0lsaM/iC1hH1JpE0uJNFI5SR3F2Nrnx/7tvQj7ZSzXbBysoUGVzmzYCHDjUCEBj1RzW95EBysHfwPdSrphOhO5IjENx85EkTYoPnjPczg8Y9UisIVOFgX0BxW8OLGMtEHRG1ibOy/gkZDYYMphLppCXDMQTaVrUlVrJVc56IaVntzlk7DFHrIzvZx0AtKV0uWTsZKoMeagmx0VbwC4cagYvyLVPFWqWMyhE/DJYla78mqhlBatK3AXTi3EVJxajOPN5wxiOaHh84++BiC7luHaPQN47Ng8AHDl4IKNqj12JuZ0sW0mskiyjAOrbg55ZWcC29RysurW+WGfXBe3UifFGwBuHComoIg1+5iZ8vDJnehWkmqqc/j58UVc8ZlHMFlgopzbXfXy9CpSaRNvPHsQF42G8dOjcwCyF7o3nj0Iw1YaawlIA8ibKLfeMcyM8T1qj9tshXJwu5WYG7fLK2FL2KoDspSDWZVx6PZZbiVKy0+ZK4aqmx1V4wBw41AxAY+1uBnm2j9ghXrCdAo+WahJOZxcjMMwKV6bj+fdl3LtNmftWdw7+4N4/Z4BZyeaqxxYXHEtqayApSQ3UhHcYlwF+2gfyXHFNQtZFKC6jAPb7Ye8MnoDVubRtK0cqvkOhf0yDJPWpPRYzKGT4MahQjKVtWvfLbLGc50Zc6gtZsNaXczY6YxuWIFSj2suw66BAK7d2+/87l7ouv0yLt3WA2DtyiHgqV1JthPMpQQAr8y2xjgoopDVstutHAgh2BL2YXIpiaRmwFfFLp61xK8lnVXTTac5YKfQWa+2BurRMiEzerHzjINXFp1q1bXAZjJMr+R3QmXPO9Rt+aWDHgkDIQ8uHAkjZP+/hXMG+tx03hACioj+KmY5uNloymE+6jYOrXMraVluJUs5dNmL+2iPHxNLiaoD0t0+63XUEnfgbiVOUVg3z1paADO3Uq1zqNcjfkWEZpjQ1zgqtKRysJuisYyWnQMBEEIgiQKu2t2HgCLmvee/c9V2PP6xN67ZxceK4GrxY7cTrP24IgrOjr3ahoS1kpvKyopO2WyG0V4fJiJrCEjbG4NaMpbUNA9Ic4rgrqxdK4W6SXYKTC2tNSgdsb/YMyspvDy9gn2f+AlO2Q3ZmHIYtoOW7j5Vf37DWfif77kw7/kEgaxZNQDW54HSxrYhbybMrbR3szVPu5BBbTSymJ3KypQDa5k/2uPHSjINVa8+IA2gploHHnPgFCVQh2ZrnVznwL7Maw1KLztupSSeem0RCc1wWjmnbOUw5CiHoHPe7sEQbjp/aM3XXQy/U0y3QYxDVEW3T8ZojzXGtTfYXNUAMLdSRolF85RDZsRstQFpoHa3Eq9z4BTEXVm7VpJpA4rdtK3TYMphrX76iONWSjmFbeP2ZDDW92bvpiBkkWT19G8UAaX2BIV2Yi6awmDIg0F7WM5a6z9qIVc5rCbT8Mmi05SPGS6gug1WJiC9duOgdWCdQ+cl3K8Rlq1USzpcpWX/G5Fap8EtJ9KQRWsmxIETEQBwxkaymMO2vgDG/votzmLQSPzKxlMOg10eZ65Ir7/5jSA9BeocQq7OqqO9Pud2Nd8jnyxCEYXaA9LcrVQ5hJBThJCDhJAXCSFj9rFeQshDhJBX7Z89rsffQQg5Tgg5Sgi5wXX8Uvt5jhNC/pm0YQMTv1K7ckhoRkdmKgEZN8BalIOmm4ipOvZuCgGA050zVzl4JKEphgGoT2pzOzG3qmIw5G2xcsiukI6qaSdTCbAUAMs+qyZziBBitdBIrj2VVdUNnq20Bt5IKb2IUrrf/v3jAB6hlO4B8Ij9Owgh+wDcDOBcADcC+DwhhK2UdwG4DcAe+9+NdbiuulIvt1KnK4e1NN9j8YZ9Q13OMa8sOMaBxRyaGUD11yF7rV2glGI+qmIw5HHmmK+151Qt5FZIryazlQMhBCN23KHa71HYX1sLDZXXOdSFdwL4qn37qwDe5Tr+LUqpSik9CeA4gMsJIUMAuiilT1ErL/BrrnPaBicgXYMbodoUvI0EW0zXohwitnE4d0vGOLxh7wCml5NIG2ZLeu0HN9Co0JVkGpphYiDkwWBX+8Qcoqm0k6nEGO2xXEvVZvyFfXJNMQde51A9FMCDhJDnCCG32cc2UUpnAMD+OWgfHwYw4Tp30j42bN/OPZ4HIeQ2QsgYIWRsfn6+xkuvDkkU4JGEmtwIne1Wsj5qa4k5sGD0nk0hyKLV9/+6swZhUjjtFIBmKwe2WVj/yuGMncY62OXFtt4Azt4cwiVbGx/UzyW/CE7PmuYGZDKWqlUO3TU036OU2gHpzvru1hqQvppSOk0IGQTwECHklRKPLRRHoCWO5x+k9IsAvggA+/fvb3r1UcAj1ZTKmkwbTZ3L206wGRbJNbx/bMfXF1SwqcuLsF/GDruWYTySQEo3IArEyWppBvWomG8XWGB/tMcHnyLiJx+5tiXXoeRmK5VQDtVuBLr9slP5XS2s62+nZSvV9GoppdP2zzkA3wNwOYAztqsI9s85++GTAEZdp48AmLaPjxQ43nZY/XRqKILTOtet5KuhzoEphx6/gt+7egduvWYHtto7yPFIoiXVq37XRLn1DovdbHXVEbSC/qAHqm7io/e+iOWEhtUCyuHq3f24cKQbW/uqu9aBoAfzMTVv0lwlMDXTacZhzdtYQkgAgEApjdq33wrg0wB+COAWAH9r//yBfcoPAXyDEPKPALbACjw/Qyk1CCFRQsiVAA4A+ACAf1nrdTWSgFJbm+ZEB6eyOnUONQSkw34Zv3fNDgBWi2lFFBzl0Gyj67HrVTZCttLEUgIBRWx6L6Vc3v+6bVhKaPjCEycQ9ErQdDMrWwmwXIs/+MNrqn7ui0bD0HQTB6dWnKaLldKJ86OB2txKmwB8z846lQB8g1L6E0LIswDuI4TcCmAcwHsAgFL6MiHkPgCHAegAbqeUspXiQwC+AsAH4AH7X9vhr3GofCtGL7YLHkkAIVatR7VE4mkEFDHL5ysKBCM9PkxEEggoUtO/uIQQe8bHxnArjfb6Wz4C0yuL+NiNZ+PZUxE8bg9jCnnr44bdv70XAPDsqUhVxoFS6sS0eMyhQiilJwDkNa2hlC4CuL7IOXcCuLPA8TEA5631WppFwLP2OdJJzUBU1TEQan4WSDtACIFPXtuo0OWEVnDuwnCPD1PLKWzt9bfEXVeP0bHNRNNNfOPAaZxaTOB1u/pww7mbAVhupW19gTJnN4+LRsO4+2cnAdTPOAyEPNg5EMCzJyP4L2/YVfF5t93znNOxlmcrcYoSqGEmAet6OdihxgGwlNeaspWKzDPuD3qwGFOhpo2WSN1Uu+MAABaBSURBVP71Nir0a0+dwqd+dBj3PH0an7n/CABrZzwRSbY83uDmQlf7ky5v/eotLt/ei2dPRWCWGdh1+9efx12PWeNlX5ldxYsTywDA6xw4xQl41j5Hei6aSRfsVLxVzpEeOxXBm//xcbw0uYJwgfbRfQEFCzEVKd2Ep0XKYb2kshomxVd+cQqXb+/FHTedjdOLCcytprAQ05BMG04WUDtw4UjGOORmK9XCZdt7sZrScfRM6aylp04s4tlTVouWpXgm/ZUrB05RAp61xxxYS+ROVw7VuJUePzaP43MxROIatnTnG9X+kAeptIlIXIW3RcqhlhbuzeShw2cwuZTE712z3eV/X8pkKlWZ/dNIRnp8zoS+ermVAODyHdbrfuxo8RopSilWkmlE4prTtoXRaTEHbhyqoJadIncrWems1biVTszHsb3Pjwf/5Fr8xY1n593P5jFMLSVbohyCNda9NIOFmIqPfOsF3PHdlzAc9uEt+zbj3C1d8Mkinj0VweRSe6SxuiGEOK6l3GylWhjt9eOa3f2467HjWIypBR8TU3UYJkUkrjlZcpdttwLYzerb1S5w41AFAUVE2qBIakbF7pF7nj6NFyeWMRdVIQmk6dO12gmfUp1b6bX5GHYOBLF3U6hgQLrPnjmwlEi3SDm0v1vp8aPz+P6L07hsey/+5X0XO8WCF28N49lTEYwvWsZhpKd9jAMAXLqtB5JAEK7zgvzJX9mHhGbgfz54tOD9rOAyEtewZN++5art+M6Hrspq39IJcONQBaw/0J99+5e48NMP4qP3voiFIjsQwPLzfvpHL+Pun53A3KqKgZAHQgfOcmBUoxxMk+LkQjxrqlsuA65Jbq3IVur2yTVNF2sGi3Hr8/mPv3URLtmaSeG8bHsvjsys4rFj8xgMedquOPPWa3bgu39wlVOJXi/2bArhPftHcd/YJIwCgWnWYiOm6phdtdR+r1/Bpdt6Wp7q22y4cagC1vriwZdn0R9Q8N0XpvCTQ7NFHz+7mkLaoHhlZtUZptLJ+KvI9ppaTkLVzaypbrm4x3y2IlupN6BgOZFe81zsZrAQ0+CRBKdxJOOKnb0wKfDC+BLeccGWFl1dcbyyiAtGGtPfac9gEIZJnTGkbtzN+U7MxwCgoGrtBLhxqAK/3cM/bVB8+Po9AKz+L8VgPWtOLsQxuZTEQKhzM5UAYKjbi4lIEnP2jqwUJ+z50LsGiisHd3prK3a+/S63VruyEFPRH/Tk7Xpft7MP99x6OQ785ZvxiV/Z16Kraw2lJsMtu2Y+HJ+zjUOHuoK5cagCJnEJAd6ybxNkkZQsimPGwaSWgWDtkDuV979uG3TTxJeePFn2sWzXVko5KK7hPq1RDtb/J3PdtCOLMc0xYm4IIXj9noGOLMosNFP6Hx48iseOzmUde83+DIZbMBWvHeDGoQoCdszh/OFu9AU9CHokxEoZh6Vk1u+d7lba1hfA2y/Ygn9/+jRWyuy2T8zHEfJKBRc2N+z+VigHFhCPxNY+YazRLMRU9AU7+3OXi6McbEOQ0HT866PH8b0XprLUxGvzcfgVse3iMc2CG4cqYKMhr9s7AMAq0Cnkt2RMRhLY3OV1ms4NdrhbCQA+eO1OxDUDDxyaKfm4EwtWplK5ICBb+LwtKFBiufgL8fY1DsWUQyeTqxyOzkZBKTCzkspSDvNRtWNdSgA3DlWxayCI37hkBL95mdV5POQt3WtpYimBrX1+nLXZmn3c6coBsEZ9KqKAk4vxgvevJNP4p4eO4YXxZewqkanEYBlLrShQYjGPSImMtVZCKcVinCuHXFjtxIpdx3B4ZhUAMLuSwnJCw0DIA7Yn6dRGmQA3DlXhlUX8w29e6OSEBz0SoiXy3CciSYz2+HH2Zis/utNjDgAgCATDPT5MRpIF77/32XH8f4+8iou3hvEHb9xd9vkybqXmf5TDfgUCARbbVDmsJnWkDeooHI4FcysxlXB4OmMclhJp9PoVRzF0arwB4MahJiy3UmHjoOoGzkRTGO314ZKtYcgiwXC4ffrXtJKRHp9TmZvLQkyDVxbw9d+/ErsHiwejGX0tVA6iXdTYrsZhwQ6Ud2LQuRQeSYRPFp34AlMOmmHi5EIc3X7ZUQxcOXDWRJdXKhpzmFpKglJgtMeP37hkBI989Dou721Gevx5wXpGJK6htwo/L6t1aFVTtL6gUrQVQ6tZtAPlfQH+ucsl7LdmShsmxSszUWfjdmI+hrBPdj6DPObAWRNBr1R0TCRb/EZ6fBAE0laNzVrNaK8PkbhWsPXEUrzw7IZitDJbCbB2lpEmKYenXlt0Bs9UAqve7+MB6TxYdfvpxTiSaQPXnzMIwEo7D7uUAzcOnDXBAtKU5pfhH521pOpoGzU0axdG7ZjNRAHXUrHZDcXYYQetN7WoFXpfwNMUt9LMShLvvftpfPu5yYrPYYqmnyvWPLp9lnJgLqU3nT2YdV9vkLmVeMyBswZCXhmGSfP6BY2diuAfHjyGS7aGsbmD5zcUgxnMiQJB6aW4VtVubc+mEA785fW4aLQxrRbKYbmVGm8cTtsN8k4XyfIqxEJMAyFATwcHVYvR7ZOxkkjj2GwUAgGu3NnnDPMJ+xXHrVRojkinwI1DDbBeS+5COFU38MF7nsOWsA93f2B/RzfaKwYbLFMoKB1Zw5ztVqkGwHIrrSTTSDe4v9Kk7aacXi7feoSxELPy9KUOm2BWCSzmcHIxgeEeH7yyiE3dlsLq9vGANMCNQ02wQSSrLuPw8+MLWIxr+MQ79vEAdBF6Awp8spinHNKGidWUvq78vOz/eKnBrqUp2zhMLiehGyZ+/6vP4qnXFvMe979/dsKZYsYL4IpjxRw0nFqIY7s9P3uo29q0hP2yk3beyS45bhxqgM23dWcs/eTQLEIeCVft7mvVZbU9hBCM9vryYg4stXA9+XlZDUGj4w5Ty9Z7NbWUxKnFBB4+MofHjs5lPWYlmcad9x/Bvz992r4mlWcqFSHsV5BKmzg+F3PiVkP2tMGwT8Fb923G//rtS7B3U/l06o0KNw41ELSVA8tY0g0TDx0+gzedM9hxIwWrZbTH7zQmZCzZFavrqUWyYxwaHHeYWraUw0JMxcvTKwCA6ZVsF9PYqQgozcQn5qMqz1QqAquSTqYNRzlsto1Dt0+GIgm48byhjpvh4IYbhxpgbiVWCPfMyQiWEmncdN7mVl7WumC01zIO7kwvlhJaTZ1Dq2GLb6M7s04tJSHZ8avH7RnIM8vZbrkDJy130ngkAU03MbGUdBY+TjbuCXNMOYzYtQ4960i5NpL6jlnqMEI5bqUnjy9AFgmutRvzcYqzoz+AuGZgLqo6AWXmt19PyoHN6JhdqTxQXC2mSTG9nMJ5w914cWIZjx+zjUPO33z6hBWDiMQ1HJpegWHSiqrMOxH3POjttnF418XD6PLJbTcytVVw5VADLFuJKYfxSAJbwj5nnCinOLvsOQ2sZz5g1TgA6ytDpNsno8cv49Ri4XYg9WAhpkIzTFyxoxdAJr4xu5pyRl1GU2kcmlrBHtsYPPqKFY/YVWIeRifDeiaJAsGInT0X8sp450XDrbystoIbhxrINQ5Ty0nng8YpzU57wtuJ+UzePlMO663Z2fb+QFX1B9XCqu0v2dYDlhnd7bNqbOajljtr7PQSTAr85n6rY/AjR2zjMMjdSoVgymG0xweZp/oWhL8rNSAKxOrMahuHyaUkb65XIZu7vPDJYpZxiMTTCHqkdRfM394XcILAjYAFo7f3BRwX3DW7+wEA0yvWfQdORCCLBO+82JoHfXhmFcNcxRYl7LPU6fYK2sJ3Ktw41EjQIyGmppFKG5iPqtxfWSGCQLCjP4ATCxm30lJCW3eqAQC29fkxvZKsqu9RJWi6ic89fAwPHz4DABju8TmbjzfYca0ZuyjuwMlFXDASxmDI67jldvF4Q1FCXgmySLCzn79HxeDbihph/ZWmlzON9jiVsXMggJcmV/DK7CqefNUqHlxP8QbG9r4AKLVmhu/ZFKrb8z5+bB6fe/hVAJYbJOiRMNzjw9jpJbx+r6UcZlaSSGg6Dk6u4LZrdwIAtvb6EYlr2M3jDUURBIK7P7Af5wx1tfpS2hZuHGokaBsH1t6Au5UqZ+dAEPcfnMEd3z2IF8aX4ZNFXG4HXdcT2+yOu6cW62scHjlyBkGPhD9761747fjW288fQsgrOeNnp5dTeO70EnST4oqdfc71vDixzDOVynDdWYPlH9TBcONQIyGv1aOF+YVHeBfWitk1EIBJgRfGlyEQqyBpPSoHlidfz6C0aVI88soc3rB3AL9z9Q7n+FvP3Yy3nmvV0Qx1ezG9nMSBExGIAsGl23oAANvs2gZuHDi1wGMONRKyB/5MLiUgCgSb+NStimH+3m6fjL9513kA1mf//LBfQbdPxqkajcNnHziCv/mPwwCAg1MrmI+qePO+4rvbLWEfZlaSePrEIs4b7nay567c0YvhsA9nD9VPxXA6D64caiTkkbCcSGMiksRQt5d3wKyCnQMB+BURv3v1drz3sq04OhvFjeu0unx7n9/JWDo6G8WTxxdw6zU7sJpK49DkCq6ys4uK8dzpJXzh8RMArPbRz5xchECA6/YWNw5D3V78/PgCTAp8+E2ZedtX7e7Hzz/+pjq8Kk4nw41DjVyxsxffenYCDx6ebdlMgfVKwCPhsT+/Dv0BDwSB4NPvPK/Vl7RmtvUF8NzpJQDAvz16HD/85TTefM4gvvXsBO567DX82/suwdsvGCp4rmlS/M1/HMZgyIMev4I/+ubzSKVN3Hju5pLV4hdv7cH9B2fxwWt34v+1g9EcTr3g29wa+dULh7FnMIhU2sRwmMcbqmUw5N0QMy8uHA1jajmJ43NRPPGq1d7iocNn8MDBGQDAx7/zEsaL1EI8fmweL04s489uOAt/9+4LEPLK+PD1e/Av77u45N987+Vbcei/3YA/un5Py8akcjYu3DjUiCgQ/OlbzwLA01g7mbedb7nDPnP/K1hOpCEQ4P/8/BROLSbwoet2AQT41I9ehmlS3P6N5/Hf/+MwTLv1xfdfnEKPX8avXTyMi0bDePav3oyPvmUvr9zltBTuVqoDN5y7CZ94xz68Zd+mVl8Kp0UMdftw2fYe/PSVOQgEuPnyrfjGgXEQAvzu1dsR9Ej4+/88ik//x2H8+CVLTcQ1A3/19nPw4Mtn8OuXDHNjwGkr2ubTSAi5kRBylBBynBDy8VZfTzUQQvB71+xwZiNzOpO3n2/FFC4cDeM9l44AAPZv68FgyItbrtqOHr+Mr/ziFC4aDeND1+3CN58Zx2994Skk0wZv+MZpO9rCOBBCRAD/BuAmAPsAvJcQsq+1V8XhVMfbzh+CIgp4y75NuHAkjDfsHcAtV20HYLVZuf2NuyEJBJ/61XPxsRvOwn95wy68PL2KLd1e7LdrFDicdoG4h6207CIIeR2AT1FKb7B/vwMAKKWfLXbO/v376djYWJOukMOpjPHFBDZ3e6FIhfddEVeLEEop/v3AOLZ0e3H9OdwlyWkOhJDnKKX7yz2uXWIOwwAmXL9PAriiRdfC4ayZrX2lXYvuCnBCCN5/5bZGXxKHsybawq0EoFAuY56kIYTcRggZI4SMzc/PN+GyOBwOpzNpF+MwCWDU9fsIgOncB1FKv0gp3U8p3T8wwEdxcjgcTqNoF+PwLIA9hJAdhBAFwM0Aftjia+JwOJyOpS1iDpRSnRDyhwD+E4AI4MuU0pdbfFkcDofTsbSFcQAASun9AO5v9XVwOBwOp33cShwOh8NpI7hx4HA4HE4e3DhwOBwOJ4+2qJBeC4SQKICjALoBrNTpaev5XI183n4AC3V8vnZ+DzvpvVsPz9dJ799Ge+/Y399GKS1fC0ApXZf/AIzZP79Yx+es23M18nnZa2/H62vAa+2Y926dPF/HvH8b7b2r9u9vBLfSj9r0uZrxvPWind/DTnrv1sPz1Zt2fr2d9t5lsZ7dSv9/e3cbYkUVx3H8+yMtMCVNNDYqpDc9SWQWKRlRYWAQBAUl4a5GgRWU77II6kVCSomphEoZ9oBIVGRJhUlJD1QUiQ+t5QORhhSVqavQG/+9mLN123u33Xt3rjN37+8Dw8z+Z+655/z3smfO7NwzX8cgJo8ajtq57UPl3A2N89e4onNX7/u38shhTdEVKFA7t32onLuhcf4aV3Tu6nr/lh05mJlZ87TyyMHMzJrEnUMJSDpf0keSuiXtkvRwip8tabOkPWk9LsXHp+N7JK3sU9adkrancpYU0Z5TqYHczZT0jaQdaX1jRVlTU3yvpOWSak0lP6zknL9Fkg5I6imqPadSXrmTNErSJkm7UzlPF9muf+R5a5WXhm9J6wCuTNtjgB/IHpe6BFiY4guBxWn7TGAGMB9YWVHOeOAnYEL6eR1wU9HtK1nupgDnpu3JwM8VZX0FTCd7vsh7wKyi29di+ZuWyuspul2tlDtgFHBD2j4d+KQMn73CE+ylxi8F3gZmkn3JryPFOoDv+xw3t0/ncDXwYcXPc4Dni25PGXOX4gJ+B85Ix+yu2DcbWF10e1olf33ibdE5NCN3ad9zwH1Ft8eXlUpG0iSyM4wvgXMi4hBAWk8c4OV7gYslTZI0AriN/z5EaVhrIHe3A99GxF9kj6o9WLHvYIq1jSHmr63llTtJY4FbgS3NrO9glGbKbgNJo4E3gAURcbTeS94RcVjS/cAG4CTwOXBh7hUtoXpzJ+kyYDFwc2+oxmFtcytfDvlrW3nlLp3QrQeWR8T+JlV30DxyKAlJI8k+YK9FxJsp/IukjrS/A/h1oHIi4p2IuCYippMNb/c0q85lUW/uJJ0HvAV0RsS+FD5I9njaXjUfVTsc5ZS/tpRz7tYAeyJiWfNrPjB3DiWQ7op5EeiOiKUVuzYCXWm7i+ya5kBlTUzrccADwAv51rZc6s1dGrZvAh6NiM96D07D/2OSpqUyOxlEvltdXvlrR3nmTtJTZBPpLWh2vQet6H96eAnI7jwKYDuwLS23kN19tIXs7H8LcHbFa34E/gB6yM56L03x9cB3abmr6LaVLXfA48DximO3ARPTvquAncA+YCXpS6LDeck5f0vSZ/FkWj9ZdPtaIXdko9QAuivi9xbdPn9D2szMqviykpmZVXHnYGZmVdw5mJlZFXcOZmZWxZ2DmZlVcedg1gSS5kvqrOP4SZJ2NrNOZvXw9BlmOZM0IiJWFV0Ps6Fw52BWQ5pI7X2yidSmkE3H3AlcAiwFRgO/AXMj4pCkj8nmsroW2ChpDNnspM9IugJYRTY18z7gnsjmwZoKrAVOAJ+eutaZDcyXlcz6dxGwJiIuB44CDwIrgDsiovcP+6KK48dGxPUR8Wyfcl4GHknl7ACeSPGXgIcimwfLrFQ8cjDr34H4dw6cV4HHyB7SsjnNvHkacKji+A19C5B0FlmnsTWF1gGv14i/AszKvwlmjXHnYNa/vnPLHAN2/c+Z/vE6ylaN8s1Kw5eVzPp3gaTejmA28AUwoTcmaWSam79fEXEEOCzpuhSaA2yNiD+BI5JmpPjd+VffrHEeOZj1rxvokrSabIbNFcAHwPJ0WWgEsAzYNUA5XcAqSaOA/cC8FJ8HrJV0IpVrVhqeldWshnS30rsRMbngqpgVwpeVzMysikcOZmZWxSMHMzOr4s7BzMyquHMwM7Mq7hzMzKyKOwczM6vizsHMzKr8DcZhQTtm6y54AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 1, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1986 0\n", "1987 0\n", "1988 0\n", "1989 0\n", "1990 0\n", "1991 50677\n", "1992 656000\n", "1993 825671\n", "1994 601390\n", "1995 657596\n", "1996 667294\n", "1997 632212\n", "1998 624302\n", "1999 760258\n", "2000 660461\n", "2001 656975\n", "2002 563415\n", "2003 589547\n", "2004 678928\n", "2005 832896\n", "2006 655727\n", "2007 574493\n", "2008 778119\n", "2009 738993\n", "2010 847724\n", "2011 781579\n", "2012 633840\n", "2013 698277\n", "2014 658318\n", "2015 648607\n", "2016 635356\n", "2017 736724\n", "2018 564245\n", "2019 561400\n", "2020 540874\n", "2021 218007\n", "dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_august_week[:-1],\n", " first_august_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)\n", "yearly_incidence" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1986 0\n", "1987 0\n", "1988 0\n", "1989 0\n", "1990 0\n", "1991 50677\n", "2021 218007\n", "2020 540874\n", "2019 561400\n", "2002 563415\n", "2018 564245\n", "2007 574493\n", "2003 589547\n", "1994 601390\n", "1998 624302\n", "1997 632212\n", "2012 633840\n", "2016 635356\n", "2015 648607\n", "2006 655727\n", "1992 656000\n", "2001 656975\n", "1995 657596\n", "2014 658318\n", "2000 660461\n", "1996 667294\n", "2004 678928\n", "2013 698277\n", "2017 736724\n", "2009 738993\n", "1999 760258\n", "2008 778119\n", "2011 781579\n", "1993 825671\n", "2005 832896\n", "2010 847724\n", "dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ " yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ " yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }