{ "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 = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"varicelle.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02021447868053431201713818FRFrance
12021437816451791114912717FRFrance
22021427944360371284914919FRFrance
32021417402122395803639FRFrance
420214074441245464287410FRFrance
52021397229110563526315FRFrance
620213874325226763837410FRFrance
7202137719647543174315FRFrance
82021367344117305152528FRFrance
92021357256211074017426FRFrance
10202134714293782480204FRFrance
112021337382918305828639FRFrance
122021327410818956321639FRFrance
1320213174793230172857311FRFrance
142021307719041911018911616FRFrance
15202129768004109949110614FRFrance
162021287973402173115033FRFrance
172021277902643161373614721FRFrance
182021267728441081046011616FRFrance
1920212579351654012162141018FRFrance
20202124712034893715131181323FRFrance
2120212379116642011812141018FRFrance
2220212274817275268827410FRFrance
2320212176092345887269513FRFrance
242021207748546011036911715FRFrance
25202119766544370893810713FRFrance
262021187391221105714639FRFrance
2720211774686287864947410FRFrance
2820211674780289166697410FRFrance
29202115711215762714803171222FRFrance
.................................
15841991267176081130423912312042FRFrance
15851991257161691070021638281838FRFrance
15861991247161711007122271281739FRFrance
1587199123711947767116223211329FRFrance
1588199122715452995320951271737FRFrance
1589199121714903897520831261636FRFrance
15901991207190531274225364342345FRFrance
15911991197167391124622232291939FRFrance
15921991187213851388228888382551FRFrance
1593199117713462887718047241632FRFrance
15941991167148571006819646261834FRFrance
1595199115713975978118169251832FRFrance
1596199114712265768416846221430FRFrance
159719911379567604113093171123FRFrance
1598199112710864733114397191325FRFrance
15991991117155741118419964271935FRFrance
16001991107166431137221914292038FRFrance
1601199109713741878018702241533FRFrance
1602199108713289881317765231531FRFrance
1603199107712337807716597221529FRFrance
1604199106710877701314741191226FRFrance
1605199105710442654414340181125FRFrance
16061991047791345631126314820FRFrance
16071991037153871048420290271836FRFrance
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16091991017155651027120859271836FRFrance
16101990527193751329525455342345FRFrance
16111990517190801380724353342543FRFrance
1612199050711079666015498201228FRFrance
16131990497114302610205FRFrance
\n", "

1614 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202144 7 8680 5343 12017 13 8 \n", "1 202143 7 8164 5179 11149 12 7 \n", "2 202142 7 9443 6037 12849 14 9 \n", "3 202141 7 4021 2239 5803 6 3 \n", "4 202140 7 4441 2454 6428 7 4 \n", "5 202139 7 2291 1056 3526 3 1 \n", "6 202138 7 4325 2267 6383 7 4 \n", "7 202137 7 1964 754 3174 3 1 \n", "8 202136 7 3441 1730 5152 5 2 \n", "9 202135 7 2562 1107 4017 4 2 \n", "10 202134 7 1429 378 2480 2 0 \n", "11 202133 7 3829 1830 5828 6 3 \n", "12 202132 7 4108 1895 6321 6 3 \n", "13 202131 7 4793 2301 7285 7 3 \n", "14 202130 7 7190 4191 10189 11 6 \n", "15 202129 7 6800 4109 9491 10 6 \n", "16 202128 7 9734 0 21731 15 0 \n", "17 202127 7 9026 4316 13736 14 7 \n", "18 202126 7 7284 4108 10460 11 6 \n", "19 202125 7 9351 6540 12162 14 10 \n", "20 202124 7 12034 8937 15131 18 13 \n", "21 202123 7 9116 6420 11812 14 10 \n", "22 202122 7 4817 2752 6882 7 4 \n", "23 202121 7 6092 3458 8726 9 5 \n", "24 202120 7 7485 4601 10369 11 7 \n", "25 202119 7 6654 4370 8938 10 7 \n", "26 202118 7 3912 2110 5714 6 3 \n", "27 202117 7 4686 2878 6494 7 4 \n", "28 202116 7 4780 2891 6669 7 4 \n", "29 202115 7 11215 7627 14803 17 12 \n", "... ... ... ... ... ... ... ... \n", "1584 199126 7 17608 11304 23912 31 20 \n", "1585 199125 7 16169 10700 21638 28 18 \n", "1586 199124 7 16171 10071 22271 28 17 \n", "1587 199123 7 11947 7671 16223 21 13 \n", "1588 199122 7 15452 9953 20951 27 17 \n", "1589 199121 7 14903 8975 20831 26 16 \n", "1590 199120 7 19053 12742 25364 34 23 \n", "1591 199119 7 16739 11246 22232 29 19 \n", "1592 199118 7 21385 13882 28888 38 25 \n", "1593 199117 7 13462 8877 18047 24 16 \n", "1594 199116 7 14857 10068 19646 26 18 \n", "1595 199115 7 13975 9781 18169 25 18 \n", "1596 199114 7 12265 7684 16846 22 14 \n", "1597 199113 7 9567 6041 13093 17 11 \n", "1598 199112 7 10864 7331 14397 19 13 \n", "1599 199111 7 15574 11184 19964 27 19 \n", "1600 199110 7 16643 11372 21914 29 20 \n", "1601 199109 7 13741 8780 18702 24 15 \n", "1602 199108 7 13289 8813 17765 23 15 \n", "1603 199107 7 12337 8077 16597 22 15 \n", "1604 199106 7 10877 7013 14741 19 12 \n", "1605 199105 7 10442 6544 14340 18 11 \n", "1606 199104 7 7913 4563 11263 14 8 \n", "1607 199103 7 15387 10484 20290 27 18 \n", "1608 199102 7 16277 11046 21508 29 20 \n", "1609 199101 7 15565 10271 20859 27 18 \n", "1610 199052 7 19375 13295 25455 34 23 \n", "1611 199051 7 19080 13807 24353 34 25 \n", "1612 199050 7 11079 6660 15498 20 12 \n", "1613 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 18 FR France \n", "1 17 FR France \n", "2 19 FR France \n", "3 9 FR France \n", "4 10 FR France \n", "5 5 FR France \n", "6 10 FR France \n", "7 5 FR France \n", "8 8 FR France \n", "9 6 FR France \n", "10 4 FR France \n", "11 9 FR France \n", "12 9 FR France \n", "13 11 FR France \n", "14 16 FR France \n", "15 14 FR France \n", "16 33 FR France \n", "17 21 FR France \n", "18 16 FR France \n", "19 18 FR France \n", "20 23 FR France \n", "21 18 FR France \n", "22 10 FR France \n", "23 13 FR France \n", "24 15 FR France \n", "25 13 FR France \n", "26 9 FR France \n", "27 10 FR France \n", "28 10 FR France \n", "29 22 FR France \n", "... ... ... ... \n", "1584 42 FR France \n", "1585 38 FR France \n", "1586 39 FR France \n", "1587 29 FR France \n", "1588 37 FR France \n", "1589 36 FR France \n", "1590 45 FR France \n", "1591 39 FR France \n", "1592 51 FR France \n", "1593 32 FR France \n", "1594 34 FR France \n", "1595 32 FR France \n", "1596 30 FR France \n", "1597 23 FR France \n", "1598 25 FR France \n", "1599 35 FR France \n", "1600 38 FR France \n", "1601 33 FR France \n", "1602 31 FR France \n", "1603 29 FR France \n", "1604 26 FR France \n", "1605 25 FR France \n", "1606 20 FR France \n", "1607 36 FR France \n", "1608 38 FR France \n", "1609 36 FR France \n", "1610 45 FR France \n", "1611 43 FR France \n", "1612 28 FR France \n", "1613 5 FR France \n", "\n", "[1614 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(data_file, skiprows=1)\n", "data" ] }, { "cell_type": "code", "execution_count": 8, "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": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 9, "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": 10, "metadata": {}, "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
.................................
2021-04-12/2021-04-18202115711215762714803171222FRFrance
2021-04-19/2021-04-2520211674780289166697410FRFrance
2021-04-26/2021-05-0220211774686287864947410FRFrance
2021-05-03/2021-05-092021187391221105714639FRFrance
2021-05-10/2021-05-16202119766544370893810713FRFrance
2021-05-17/2021-05-232021207748546011036911715FRFrance
2021-05-24/2021-05-3020212176092345887269513FRFrance
2021-05-31/2021-06-0620212274817275268827410FRFrance
2021-06-07/2021-06-1320212379116642011812141018FRFrance
2021-06-14/2021-06-20202124712034893715131181323FRFrance
2021-06-21/2021-06-2720212579351654012162141018FRFrance
2021-06-28/2021-07-042021267728441081046011616FRFrance
2021-07-05/2021-07-112021277902643161373614721FRFrance
2021-07-12/2021-07-182021287973402173115033FRFrance
2021-07-19/2021-07-25202129768004109949110614FRFrance
2021-07-26/2021-08-012021307719041911018911616FRFrance
2021-08-02/2021-08-0820213174793230172857311FRFrance
2021-08-09/2021-08-152021327410818956321639FRFrance
2021-08-16/2021-08-222021337382918305828639FRFrance
2021-08-23/2021-08-29202134714293782480204FRFrance
2021-08-30/2021-09-052021357256211074017426FRFrance
2021-09-06/2021-09-122021367344117305152528FRFrance
2021-09-13/2021-09-19202137719647543174315FRFrance
2021-09-20/2021-09-2620213874325226763837410FRFrance
2021-09-27/2021-10-032021397229110563526315FRFrance
2021-10-04/2021-10-1020214074441245464287410FRFrance
2021-10-11/2021-10-172021417402122395803639FRFrance
2021-10-18/2021-10-242021427944360371284914919FRFrance
2021-10-25/2021-10-312021437816451791114912717FRFrance
2021-11-01/2021-11-072021447868053431201713818FRFrance
\n", "

1614 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", "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 6654 4370 8938 10 \n", "2021-05-17/2021-05-23 202120 7 7485 4601 10369 11 \n", "2021-05-24/2021-05-30 202121 7 6092 3458 8726 9 \n", "2021-05-31/2021-06-06 202122 7 4817 2752 6882 7 \n", "2021-06-07/2021-06-13 202123 7 9116 6420 11812 14 \n", "2021-06-14/2021-06-20 202124 7 12034 8937 15131 18 \n", "2021-06-21/2021-06-27 202125 7 9351 6540 12162 14 \n", "2021-06-28/2021-07-04 202126 7 7284 4108 10460 11 \n", "2021-07-05/2021-07-11 202127 7 9026 4316 13736 14 \n", "2021-07-12/2021-07-18 202128 7 9734 0 21731 15 \n", "2021-07-19/2021-07-25 202129 7 6800 4109 9491 10 \n", "2021-07-26/2021-08-01 202130 7 7190 4191 10189 11 \n", "2021-08-02/2021-08-08 202131 7 4793 2301 7285 7 \n", "2021-08-09/2021-08-15 202132 7 4108 1895 6321 6 \n", "2021-08-16/2021-08-22 202133 7 3829 1830 5828 6 \n", "2021-08-23/2021-08-29 202134 7 1429 378 2480 2 \n", "2021-08-30/2021-09-05 202135 7 2562 1107 4017 4 \n", "2021-09-06/2021-09-12 202136 7 3441 1730 5152 5 \n", "2021-09-13/2021-09-19 202137 7 1964 754 3174 3 \n", "2021-09-20/2021-09-26 202138 7 4325 2267 6383 7 \n", "2021-09-27/2021-10-03 202139 7 2291 1056 3526 3 \n", "2021-10-04/2021-10-10 202140 7 4441 2454 6428 7 \n", "2021-10-11/2021-10-17 202141 7 4021 2239 5803 6 \n", "2021-10-18/2021-10-24 202142 7 9443 6037 12849 14 \n", "2021-10-25/2021-10-31 202143 7 8164 5179 11149 12 \n", "2021-11-01/2021-11-07 202144 7 8680 5343 12017 13 \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", "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 7 13 FR France \n", "2021-05-17/2021-05-23 7 15 FR France \n", "2021-05-24/2021-05-30 5 13 FR France \n", "2021-05-31/2021-06-06 4 10 FR France \n", "2021-06-07/2021-06-13 10 18 FR France \n", "2021-06-14/2021-06-20 13 23 FR France \n", "2021-06-21/2021-06-27 10 18 FR France \n", "2021-06-28/2021-07-04 6 16 FR France \n", "2021-07-05/2021-07-11 7 21 FR France \n", "2021-07-12/2021-07-18 0 33 FR France \n", "2021-07-19/2021-07-25 6 14 FR France \n", "2021-07-26/2021-08-01 6 16 FR France \n", "2021-08-02/2021-08-08 3 11 FR France \n", "2021-08-09/2021-08-15 3 9 FR France \n", "2021-08-16/2021-08-22 3 9 FR France \n", "2021-08-23/2021-08-29 0 4 FR France \n", "2021-08-30/2021-09-05 2 6 FR France \n", "2021-09-06/2021-09-12 2 8 FR France \n", "2021-09-13/2021-09-19 1 5 FR France \n", "2021-09-20/2021-09-26 4 10 FR France \n", "2021-09-27/2021-10-03 1 5 FR France \n", "2021-10-04/2021-10-10 4 10 FR France \n", "2021-10-11/2021-10-17 3 9 FR France \n", "2021-10-18/2021-10-24 9 19 FR France \n", "2021-10-25/2021-10-31 7 17 FR France \n", "2021-11-01/2021-11-07 8 18 FR France \n", "\n", "[1614 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = data.set_index('period').sort_index()\n", "sorted_data" ] }, { "cell_type": "code", "execution_count": 11, "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": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OdA4dlUjSG5cFU0TlSjXDUz534TrfuoNz+NETb+H8n76Axe/s7PrODejhe6h9BonEQQixQQgxzz/eBeBNAOMtl1wA4G4hRKsQYiWAZQBOJKKxAAYLIWYJb4vzOwAXKtfc6R/fB+BMyVXkUNBDJ71pRxtmX1PFSp0Lne0qVqpTOIeof4Dlem5GdsE74b6ELAzmsvBzWOhzDRt3trLn2zoqOO+WmXihl4ad781IpXPwxT3HApjtF32RiBYQ0W+IaJhfNh7AGuWytX7ZeP9YL49cI4ToANAEIG1A/l6PLDOm7QvgCEFntwQupqwvr9qGeW9vB+AtXiqzUe0jruVWhms6i71TV0yn9TuasWj9Tlz7wMLad+ajtzr3dTWciQMRDQRwP4AvCyF2whMRHQRgGoANAG6SVZnLhaXcdo0+hiuIaC4Rzd28ebPr0DNHd/E0+wJtuHvOaqzZtjeTtmxhsm3yc9tj4Aio/r4u/uUs3P7CSgAecRCpdQ7xWrV8NxwhMHEOT7yx0bndLIbMGRWokJ7upS70DdoXvpPeACfiQER18AjDH4QQfwYAIcRGIURZCFEB8GsAJ/rV1wJQXVsnAFjvl09gyiPXEFEJwBAA2/RxCCFuE0JMF0JMHzVqlNsd9iJ095xvbivj6j8vxKW/fqmq6/VFLoiEGhErdQ48oTHXn7ViayRUh60uK/vvgjWP24yY9BD//Lu5zu1mYeCQFM1Weqyv2d6Mvy1Yz1fKGN39nfQWuFgrEYA7ALwphPiRUq76/H8IwOv+8UMALvEtkA6Ap3ieI4TYAGAXEZ3kt3kZgAeVay73jy8C8LTo6aY5KeDq1duVj2TNtr249LaXIorGlnbPemZXS4fpslSwcWBVWw1Vcc1LK8J9iJ1j6Z4pyZqyZqJz6Dw4vZEKaT3W1lHBF+96NYMe7Xjg1XU4/5aZNe+nL8CFczgFwCcBnKGZrd7om6UuAHA6gK8AgBBiEYB7AbwB4DEAVwohyn5bnwdwOzwl9XIAj/rldwAYQUTLAFwF4OpM7i4F1m7fi3mrt3d1twDgHGSuK5emZ5dswqwVW3Hvy6GaSI4zbT6LJJoW1TmYVz0XcR63uKcRAzo5wWVoXeUCbviZKKQzGLQr59BV+PI987F9b/YRcvsiEj2khRAvgJ+fj1iuuQ7AdUz5XABHMuUtAC5OGkstccZNz6Gto+LkSZm1JKG1o4J+dcXEel3KS/kramtHOSiSHs4Npax8J+OB9ySqj47aOVj77SZelrdWymIWdv6GCoHOgT/P5aKwYfE7O3HwqIF55ON9APkb8NHmL3zdIc1qM4SViKPrxhbqA0LIMBP1CnFoL1fwwKvrqnpuVoV06tY6e6G8PFkRXiMpjxEcIdhX7LxDsRKPNJzDyi17MOPHM3GDksgoR/chJw4a1AidXQVnsVIX0i1uAZRETM2E99Onl+HL98zHY6+/k74Px35jqOVzSOsE535Z9agR55DlfDJtDtKYX2/d7flKzFud7D2eo/bIiYMGNTjc5KsfxhWM9UfW/nmunEN3SDXUbzvQOZTC+9+0swUAsKOKTGjhc+RMQ6u7284qja1SJeZkV+zg2T72EYW0TTQIpNP3yPnQ2zPV9RTkxEGDvgA8nsJuvFrYcheo6FLOgREsyY82za51x942/ObFlYY+/B5UhbTLqmezcuqsWMlyfXdZK3GbkUw0DhncTsEiGkyLYsHuM5Gja5ETBw1dsQA0Nbfj6cUh0XHlHLrKQ/qtjbuCRDQqqun9G/ctwP++uAqAeReZul3bAs7u7t2XUpf3zyvQa/duWGulDMyVku61XBG4e85qq1I51Btlodz2/uaMw76BPJ+DDmZiZuURLPHFu+ZhphJrxplzyHQUZjy6MNQfqN+8elypCKzb0ZzYVpNF3CSbi4Ts7uROtNNipZSEJ8v2TaiVAjxpLHe/vBrX/uV17GxpxxWnHsSPLUGspPchhDCKZQsZiJWIcg/prJBzDhq4efXeG5+J/O7sh7li857Ib2edQzfMelOXtzy9FO+98Rms2rqHryCvd+lDOa6Fh3Qa2LgzW9NdHSeyK2Ir7fD9BWx+A0nEXC+29fn2Vm8TZnsHv31xJf71j2ZnOpPIMxdVpUdOHDTUeg7d98ra2I7b3ZS1a6DuvqM78fB41vKtAIANTS3O7a7dHr1v+cHy1j+MM1ti+3sxa8VW5/FwcHOCq16BXs2aXitFuCuXZQ0pYvCQ3rizBeWKiD0X08L/xBsbceVd8xL7+95f38BfXzOH4TA9l1xUlR45cdBQa53D1/70WqxsXzNlNYmSbNFTTWPTP9b2hJScndkQv+eGZ1KF9pg0vH+80CpW4ryv0w3Y9R1u2hUS3ayfU5bgIutu2d2Kd/33U7jhscVxzkE5nrtqGyZf/TAWrW/Cqi0hB9oZ3ZrpueQWUOmREwcNTvMy4w/T3ZS1eyd4VPxTXd4FVx+BriCERUapa3WC68LH//tZbyv91qZjU7Obd7VGFlMrIWKc4LbtaQMAPL14U+zlqgu/tAR8YekWDOwXqj/LDvd76LcexTsM12oi1r0t3H1XICcOGrpjErluarpjfkeESv4PQnLANXN7ishKyL/qQtR1W2KWODjcTle/Br6/2jynpuZ2nHDdk/j+w2+E/VtueMk7u/w6yjtUzuvzw9TWgIZSYh0VrR0VPLMkOQ+3RE4c0iMnDhq6Yw5ZlaAivpjWGhxB0MfialVkFz9Vp/zNCkWGEPWlNYS7152+ddnji9z8e5Zt2m1pXzDWStHzEuq7cBUBsfUMl+ZipfTIiUMVWLF5j9VEMy1s0zbyMWXWYwIiyW/U4xCZipVUXYalHjemzoBjUlzfRbWopok9rR1obisnV0w9luyU6EZrJYtYSW1f7cOVeUwjbqswkttNO1tw0+NLIjnEc4TIiYMG1/m2eRefM7e6Ps2dViKcQ/fKlWwKaVdUGE4oskjVQFpiGisvVqrtM3YVb6jz64t3vYp3X/9U5Lzt+btGQrUNZd2O5kB34CZq4yvZFNIqqnntHDdgGgf33L/6p9fw06eXZRaqv1wRWLHZzEn1NPR54nDv3DU46b/DDy/L3ZQrbAuGOv+7e3/DPZukxVT3To4Qh6ANt76yBq+QNiOTMTk0UakI3P3ymkiZ7mtge+wtGcXqusNPpari4QUb0MT4PZg2Dq6mrCpcPy0u4KupeU7JLZNXZSVyuvmJt3DGTc9h5Ra7709PQZ8nDt+4bwHe2amYDTrOkyw3uLY+u4NzEIZjNWZ1kDvYL9rR3Ja+beZ2bOKqrJXVnMNUWg/ptCNyWYhcrHVsqJVT5eqte3HlXfPwpXviTmim24pxDoahRcVKbk+VEweZ7oirK+faH2avxj+WbYmdT4s5K72Mgpt2uvv+7Mvo88QhpjB1vi67hcq2Xpj8DGqFrbtbceuzy5U+TTqH6JhufGyJU/vc4pCFuKoacJyDywzozHu4ndmN6/jFM8sT69iek6voKu1tNPs77XXb42FTWMdAxJ+Vmfsi5sgOjoiaCJ6N4D702np8/PbZjr32HeTEQfvt7Oma4RicdQ4JbfzqueWYfPXDuE4xQ+TqqXmhdfz7/QvQoVCrVzh5rBDB4vROwi5JX8Si9yMif7saaa2VOjtKF2fHpuZ23PzkW53qpzPEq1riLPUTfivqaCL1nCQ4jmNIY57K9lujjUh3i3+zQk4cSJeJu16X3RjcxUrmegvXNeEHj3oZtH4907w7/b+X3saMH880stE7NQ/jNdvCXeKW3aGStNrb58RKWVg8VYMCM/uTCHC12LanDa+8naz4vOWppVX3IeE6ziTLMRO4uS/nXrQxhnMwdhCWu86tDTviG5N0YqWMsY94rWeFROJARBOJ6BkiepOIFhHRl/zy4UT0BBEt9f8OU665hoiWEdESIjpHKT+eiBb6524hf2UmogYiuscvn01Ek7O/VcP9xUpcOYcsxUquCmlzPVel2qL1HtfwdopIszJt45funh+UVStW4+6VF1fVfv9VYqiDrVuZoayasX37gddxyW0vJdZzzc9te/ruo+PEMrbayS23dpSxqyVUWOtXROZzJ0Wmv3/p7ViZUSGdm6umhstM7ADwVSHE4QBOAnAlER0B4GoATwkhpgB4yv8N/9wlAKYCmAHgF0RU9Nu6FcAVAKb4/2b45Z8BsF0IcTCAmwHckMG9VQXXSZqlKMTWkrrjsY2Nl5+nB9eKqoPwKrmTRrtYST/oWp0DtxDb3isXFyu4LmE6LFrf5DSmxrpicqUEOOscUlqJhR7y5pd0yW0v4UO/+IfflhvnQCDM9pW5QG285Dur5E+D3uJImUgchBAbhBDz/ONdAN4EMB7ABQDu9KvdCeBC//gCAHcLIVqFECsBLANwIhGNBTBYCDFLeDPkd9o1sq37AJxJtZghDKpVSGe5EbFzDm46hyxyCqdBtd2ZPGRt9Trbpwn1HHFI+V7lmF5fZ1/8XadzZ6PKAinCsXTiWhNe1fI/x8JnGK777T9WVdWfuwiNIUoZz6deJlVKp3PwxT3HApgNYIwQYgPgERAAo/1q4wGoRtpr/bLx/rFeHrlGCNEBoAnACKb/K4hoLhHN3bx5c5qhm+9Je6Wui0OWsVrsOge3il1NHKr9FJJ2q0nJY7IEzzkkg3slSVZIrq/nH8s7Txw6o3PIOgaRi4f0H+esjvw2PSruvto5ZwcGnC1AlqLh3ghn4kBEAwHcD+DLQgizuQv/boWl3HZNtECI24QQ04UQ00eNGpU05KrgHuM+S+Jg20G7cQ5dDdfFzuYEJ+Fqymr3QUj/dHjOoTZPuSuXIWfRaApTUBVp9iE2nYPECkenMe5a1yyKXalz6O7oyVnBiTgQUR08wvAHIcSf/eKNvqgI/l8ZInEtgInK5RMArPfLJzDlkWuIqARgCIBt6Ao45iXQYZtrf1uwHpOvfpgNKZy2LZMCL14vmwnpvui7Qf9Q+PAZ6cDVt92+aaxFzlwpJZyfVxdydklz4d9nHAYgvVgpvVVZPNmPC/Ex6c+4+2rvRP71zMVKvYwRcbFWIgB3AHhTCPEj5dRDAC73jy8H8KBSfolvgXQAPMXzHF/0tIuITvLbvEy7RrZ1EYCnRQ3NVS78+YuBkjXu5+DWhu0DvHuOJ1V7a+Mup7Zc/RxsfXZ2Z8R9yCYQQoeo9P0ox9LPQSm0Bd7LekpwH3OtQjtnvW5Uy2ElXctbk1X/TGRI76Ath6bqivyyxF1r4xyuPffw4LgrQ3av3daM7XvcIgbsy3DZOp0C4JMAziCi+f6/cwFcD+BsIloK4Gz/N4QQiwDcC+ANAI8BuFIIIVeSzwO4HZ6SejmAR/3yOwCMIKJlAK6Cb/lUC1QqAvPX7AjMM23WNPZ2LOf8Nlx3ErZ1vexoreRijTF7xdZYzB6JA655BB/91azENiRmLnULN+Ci04kUBWE5zPfD+QtU8+mzssxOrCE3PrYYk69+mCViXakTSprDcmPO6hwyzFgrAPxCs3RzMbBIwznYQoWoYkNu81SrV/KN+xfgVC3vfE9EKamCEOIFmDc+ZxquuQ7AdUz5XABHMuUtAC5OGksW0ENtt7S7fw1jBjdg407PEcz0AW7Z3RrkiHZdEGyfcoQ4WOolhR2uVAQ+lmBn//Kq7TjpwOHWOknjSAIrVuqEdU3YVnh24vBGrN/RkshNsSG7U96c2oVcCNvKFTSUoiap2VvGmBtMuoWChQCz4hckE2xXuHEO7g+rw/KOVQ7Edl9ZQW1vV6t7utp9FX3OQzpph207/b5DRmH0oIZYvY5yJVicp3//Sby91XMwc5bLWzrtiHAObvU4uCruXPDamh3JlQxw9fhOWkQmX/0wbp+5IqyvnCsVCvjNp06oanxOy59SiSPKrY5y8M7AtlBznsMqgk0LK7pL7stVf1KtNRTnnGi61rYBiHIOid0a8a0HFuK7D76eWE9/LMt7ePjuPkcckuYmJweXKBDhBx8+CkB0oh587aP49J0vxxvLIEaMqy4hiXOwEYeozD/j3ZSDH4laRrZ6WuH3H36T7XNvW4fTXXD3qr/3m594C5+8wxyUjXs/LYw+xmVBdYm95IJPWMYLhIsm/y7M80h//q55I6LtJ6Nk4BzSEhuVOHRG5/B/L63GnUr/6xChAAAgAElEQVRObxP0V7xwrZvj476KRLFSb0MSa2wTdRQKpLDkUTy7JO534W5SaD7XoQiBO6NzsMlmk7iOLMGZ5r62ZgdmLd+KgQ2lTJzrmprbnTKLuWSC+0lCrCNu0WllRJUuDux7U2R76wwRl/4d/GLr3tfm3ekTXrkYFZgU0qk5B1WsxNRLa1TR0l5GvxQe7P3rO+/t3p3oc5xD0tZFaH9VFChcUFx2Iq47Qdva3FFWF1NzxdstwfaSxtKVlhwbmtTcGWG/l/76JXzwZy9Yr7XuapVzrnokdslzeBSqPJl7d3vaOnDxL/+BFxSlvQvRc83D0FmEnIObzsH03KsZr8s+xKSQ5i61i5XCdrjN09rt7vHFAOCqe+cnV1IwoKFn7737HHFImptyweJ2OEVSOAf/vG1yunpv6h/fovVNmP79J7B1d6umc+Cvb2kv47m37B7j+occCarcdbQhEp+I6zbYpaYclF692p11WqUrt5iu296Ml1dtx1eUxcRlPF3lPCWV5dWaC7+5YSdO/+Gz+L2DqCXefnIdo4c0Q4tsGxuVA8nCCS7Je11/xz3d7aHvEYdOcA6kEAc512ysabW5fG97fgW27G7DzKVbnKyVrvnzwsQ+XLmYrK1qdJNXVwsitlaK79vlPjg9gPoufvVcctIdbnHiOBcXsZLe1NRxg411O/OebJFfXdfQlVv2JIYMWc1E/nXhUk01OOJpm08qB8KKiVM+xCGNdanqd2Wwv1qg7xGHTugcigUKPnIpw2y1EAfXyXH/K2vZcgHhpHN4eVWyM7nNgiaLOVxvkBNb63XRt/PDx92T58i1Zu32vXyOAkN9FdUqpPX38LETJvIVNXzkuAnJlRTYFNI2w4YsxI8u1mom7oUbmm1MajInjoikJQ5pIx93pS6vFuh7xCGJc5BiJebT8XQOUc7BRgBc54YttkyUc6h+sm3fE08Kn0W7adtQLUi4a6we0rb+Y2KlZBABP7lkWqRMvs8P/jSq/zAtmlx5SwdHHMzjMM05k2JWxxdOP4gtf3srP68C4sA8ZNdQLtXChb6kIRo2hlhdzLnvNDX3lTB2vb2yo1h5X0XfIw6O583WSvK8MNYL2qpyp6UukC46Bw7lisAP/74kSN+4xWJZkgXn4LpwjPL9REx4/I2NAHjCYX3Wev0qYx5JUeD2vVFi+vdF77DXu4qVbMORXJ3elCs3dtCogfja+w+Jlb++Lh4f81PvnhwsmvrIm/a24w+zzXqELMKXJDUxcmCDkRvg5lg0goCIiO8Kyg+OiKflHJLuXvo3SeScQw9D0gS3nS4QBRNO7kRsMs9q2XB1wVJ3H6axc3P8+aWb8bNnluFbDyzEg/PX4ZcW+XkW4gLXhePco8Yq18TPL0zIi+AKFwUwgWLj7jDs9ky6pSz8HM73uRS9pTSmkAVG5MER2GvPOzx8Mtrpr9/3Gv62YIOxjyzWuvlrklOlptE5qHN3T1s5MkaK1Iu3l1ZMZJvjV907P6Zj6enZ5/ogcUg6b+YIikQo+RNK7grUyfmjJ6Ky7c76NO3Y2x7RFaRZw+Ui19pewZfuno/F75iDAKrNVksnqvkO0l6SxkHLBdx6bdrt6btCW7+cHsq2Di3btNtvK9pY/xSmkNwuuMiUFYgCQqU/Txt3Cf+KzuLbDy6ynq8vkvldMuXq+9KJsvoqsxArccMSQmBnSzv+PG8dM7auMU2uFfoccUjC80s9k1CTzkGaAcoFQH3/enL4qjkH/+9//u0NfPVPoTlkNa25fADqME1jXuEQCsCFe3ANQZ720enVnayVmH5MFmYmZzhWrMQo/1VO5ozDRsfOgxnLwAYz56DfHkd8OG6FYNfrSHzn/CNiZbXeCB83aShGD+6XSqxkT6Mr2HoSacVK3Abh1zNX4OjvPc7WzzmHHgZ1Ak2++uHY+Z8/szxWT4KI0K/Oe2RyR28jAFnIaFX5dZrmkvqORMt0IA5n3PScQ5/J43pzQygHtz67lO3r91utpWdaOTG3I232PZ0jY1B+nH7oKJx8YCzRYez++td3jnMoMRSDFEdO2/N8z5SRsTKTUv64SUPZxElpce5RY60cVpIpaywlqeDrSXCcVRLu1aIac5ERJEwiyp6CvkccHPff3MJVLBAafPd5ycLarJVcxUojBtQ71TON3S5f58+t3rY38OBV2+3MbsflyvsUs90s8zM8vXhTciUNRNF7LxYotSjgJGaR5/QT6qInYArdEX0eaaKTclwCF7+OiJz0MRxhMb2tUrGQiVlyseCJvFIppNW6/uGXz5qCv3zh3RHi6pLsR889weEb9y+IcJc234eujDxQC/Q94uD4vrhqnlgpyjm4JupJ1Zfh2/3xk/Y4PxIFcvtWZe7eqFjJqQsWaT8GW+20pqz3zo3u6Fz8Coii8u1SgVJzDv1KcdFPMxMjqV6pZzbV9P4O6uctanrYbxX6/XF36+rwyYEzozW931KBqjKH5nb6nKgvPM8opJX3db8v9x81qAHHThqGI8YNxrfOO9w4dp3bemShWRlvwtD+ZuLQs0lDXyQOrvWYioUCBYG3As7BstF0T/Re/TTatKslZiWhWmFY7esRV6pXs9sJTCNTXmpzuOqs70U1OodSgVKLArhxchFwG+uSPzXZ0nfOPwJ/+9f3YOLw/s7j4MQxpvcRipXM98pa8hiql4qFjMyhBQpkVkhz5XIKtbSXgwReas6W848eB4D/TvXmqtnMDWk0c/0559DD4LwQc8SBKJBTysmWRbjtzkyhGT+eGSs7duKwdPoJ5bgasZIUQaT9GNJ2xb27NT5h7Ox3ePKBI1AqFlLfP7tgMW2o0TxNc1CW1xULOHL8EADA9P2Hxeot37w7SCglwZuy2qGfV39zYbNNj6auQJnskjsqHutgmkc8cRDhtT5UkZgUrXHiX/09uEZcVcdnEyv1cNrQB4lDwvlzj9rPr8crsMLYP955u5+D45i0epxZnAnbmFy1UiwBuClm1f4XrY87TiWBk0/b8OwSTz+QxbcTWJdpjTmNiIDB/sd99IQhVekcOHCiKVVMY7pvWa5yPcdMHBqrdyZjHMCJ0RZv4N+li0K6jlFYmLi5YiHuL1INyhVhFSupi/Ig38xXlqn9q+IiuZlzGR8Xc2rjznjiJJ3bNCHrnOddjUTiQES/IaJNRPS6UvY9Ilqn5ZSW564homVEtISIzlHKjyeihf65W8ifzUTUQET3+OWziWhytrcYhe199a8vhjbgTL26IsU+LFt7zmxqxpNIKP9Lj2O+X1nf3n+SiCatWOlPvlLaet8pdQ7qPTTWFR3FSoSzDh+NH158DK56/yGeriYtN6P9Hj6gHq+uZnJcC/44Wsc7oS70ruPR16hnl2zCTU/wMaVChbS5cc76yMg5FAuZEPpyRaBUpNguv7WjjJVb9gR9/ODDR+EvV77bH5MUjfJtFgJOn+Mcor85PQun53N9Jz3cktWJc/gtgBlM+c1CiGn+v0cAgIiOAHAJgKn+Nb8gIsmr3QrgCgBT/H+yzc8A2C6EOBjAzQBuqPJeHGGRs1KoyT3lhqdj5w/db3AsZPf6puZYPYmXVkRD/O5sMcc3csFwR6smd6IkD8x1Dhw5AOceOdZcAeFH5SxW8qulNWXlIBc6+ffAkQPw0jfZ1Obxa/1YWRcdPwENpaJvKePYcTDQ6AXb9rRhZ0s8f7BKvAR4mb5sSj3jqnvRlavSsY6DiXNQfzeqIpaE9+VxDk7DtKKjIlAqFGK+Jl/70wKc/sNnsdt/rv3ri6gveuOTjJ5JfxVENOCIg/Zsv/vQolj0Ymm0oeLtbWHMKrtjZs+mDonEQQjxPIDksJ8eLgBwtxCiVQixEsAyACcS0VgAg4UQs4T3xH4H4ELlmjv94/sAnEkcj5wRbO+roFhdcFm5GuuLwYcr59rnfv+Ksb0n34yaV84yxIN3mULH7z8MB48a6FDTG5vLvBQRHoNH0cESxRSrJ7Hf1AtxvEgX840c1ODLgdNPIY9zSDcoLiw1C6XZjnIFN150tDGFqlpeLedgu84aFl22l0ohbX7WoxNiaakoVyqoK1IsD8pMX3QoAxqSIt4NOQcz4TKd5y555e2Q6zOJjL/0R8Ux1So5MJ/rCeiMzuGLRLTAFztJrdl4AKpN4Vq/bLx/rJdHrhFCdABoAhA3Hs8IiQuhVuF/P3UCDhg5AIAnX7SJnZIgLWF++Ynj3Aflo65otv/WIYTA7JWu9Ny+gyeKeoHzY0vHOQS7PZtUKa1ZrLbrdrVWUlGw2Nib8MD89U711FZb2isYO6QR39W8kMN7SE/Y9P2Uzf9Gtu98q9pCrKMUiBXj54+bNAzvO2SUUzcB56BNOE6fVNAWfdP9Sp2Da7jvgUrIkp3NPKevchf2KLY9mzpUSxxuBXAQgGkANgC4yS9nza0t5bZrYiCiK4hoLhHN3bzZnvnMBCvnwJjRjRrUEEz+umIhjMpahZRVTpaDRw+Kjsnh2lKhkEpc9Nt/rHIel3WXiRScg7NUyb7bM7VlDfFtKLdBJyCFlGKl3a1x8ZEJ6sKp7oAjdSB1Du5jkNDFSpmGbpC6KSPnYM5JDZjjSun1KxWBulIhZk6sxztTrQblbZo2MPKx6M+jtaOM5ZvjIc0blWCH2/eGxh7HTgoNA9orKnHo2QTAhqqIgxBioxCiLISoAPg1gBP9U2sBqBlKJgBY75dPYMoj1xBRCcAQGMRYQojbhBDThRDTR41y243E2rDpHArx8431xWCn4CmkzTuRJMiJFBcBJDdWKBBcTfBdCVeokDaDHJS0Uqzg7tch/2b/YaVZWGNpHRUzypMOHJ54/d42O3GIiIeU8tZ2PvQKq3NwfEaTNJ8I23W6KM4VpoWwLkGs6Col7qgI1BUo5ici2/3h40v89sJvKJFzkByG9sH+yJAASm1GDdt+xNgwK5+rL0xPJxxVEQdfhyDxIQDSkukhAJf4FkgHwFM8zxFCbACwi4hO8vUJlwF4ULnmcv/4IgBPixpqcmwtlwqeM4+6I2ysKwbOSDJhONns7Rz61nd5Li0VU8jDXa0xZWv2hSRZ1FJKyTlUAuKQPLZIWYpHXo3aSiWETn2lGI/a3n5DPDm8vsEIiINqreTY/okHDMeNHzk6+G3bvATcllJn1vKtmL9mh/UCU5PFgjl5UBpUKgKbd7di7fZmvLhMSS/rNzvHF5VySbdMCunAL0kb24amuIlqpDNEib9qvaXqROyZ84ynegQSI3sR0R8BnAZgJBGtBfBdAKcR0TR4T3IVgM8BgBBiERHdC+ANAB0ArhRCSM3u5+FZPjUCeNT/BwB3APg9ES2DxzFcksWNmWBXSHs3dOR3/x6UNdYV8bNLj8OcVdswZnA/AN630jnOQSMODm2lkYenDmORsJAkiShKBV7nYF4svPK/zDf7c7jeQvAoNXl9tWKlMCtbMpLqqDtMAeCAkQPwnfOPwKm+DF5/PvL5qeNKE85DOs6pbXHgFNKX/volc8MJnJ5NIW27TkdHReAN38/md7NW4ZSDR8bG6SGertd0v6FuQitXhvzs107DaT981h9rWK5eoxKHjoqbzqGHMw7JxEEIcSlTfIel/nUArmPK5wI4kilvAXBx0jiyglWsxOgcGuuL6FdXxNlHjAnKChTK4Q8cOcCa5lOFnEjVyJSJyDmQX5ZzslBAojgrECtp5aYPRz7jFYzM1wY7p5H+rnmFdNBgIpKIsMqBCuE5eJ2uhOs2Xa6Oi0scZILqt2bkApQe0u70Te/TZq0mIJy5uHFDGzGksQ5b97RFrAX150wUt0KKZoSLtutZQEU/HnWDNm5oozJeZexKQ2pWvijRt3EOPZs69D0PaSvnQNCnOOc16cmmvWPbBJBWThIB56ApHVwWtmIhjUw/XT3rLhOEcoKcqkDRD1WC8y4F3IiX64fF6Q3Uv/aLzToH2zupT1DASugLkk6NjDoHZVxJxOHI8aEsXH0WtlDS1RqKm+63LkGs6OJAf9PFx+Bzpx6IQb7H+sylW4x1G0qFuFjJ8jIG9avDLt3HSBlTJGKu0ozaokocXK2Ver2fQ18CxznwCVPCerbJYVI822zSX17Fm6CmEys5VQtkp1axEpkVcNIprxSacEXwhT/MY68TQiQuerq1VXu5wotYNIeugDhU5ecQvlfro04w7ZSoGBYa7rxXR6jNA+DzUatQcxK4RjHhdA4uMPsS+MTSQFBNnIPa3vunjkGpWEAD46Wsd9uvrhjL5W7jqoc01qGpOWo8UIg8N1XHo3Ig4XFdRKzE10kaNwAsWt+EV95ONjNvaS93O3Hpc8TB9ryLjgHE1DwAtgVCX8zkT5tC+klDuIs0ZpbuMt5KrH8dNoV0QOwMcl2TaKMiEOSScMWUax/Fv/yf2eGwms8oLlZSOQcLXAgIcw3XX6QKI3Y8ePRA5TwjfVcqu3IEpjShSTDVDq3V+PMmoqXuwAshVWf6jTbcr64YiJXKCToHABjQUMSeVp04hMcmp0P1WOUcbCKs9ypJkrjv9bxbXsBHbp1lHCvgRVo+7NuP4c4U5ui1QN8jDpYPQlVI2uBq1aLvuDmF46UnTnJa2TxntFpxDjaxUpzIjRvSL/I7sFZyXGxqKYsNFNIOCyXv52B/JudMHVPV+DnZ+2UnTwYAHDNhiF8nPq6vvf/QYMHh3qsa+M1Vtl+lVIl9Jjd85Cjrs97Q1GJMx6nmR7el7NS5p8a6oiLK9MpsRhMepy9iZcGx0rf6btVLTMOzh4Cpbp6/utrbVD33VnW+XFmh7xGHFJyDKfWhJCJCiFjoZBW6pyfHOQyoL0ZZWcvYsg7k19bhwjnEiZK+CBUNnIMrJo9wz1uQJXQRnhpbyfYe5Lk0REIwnENjfREnTB4WZCwLNw9hzfpSASdOHm7sT9Vf2RbYC6eNw7xvnx0bUxpw9RvrS1aP6wVrm/hQHAjnH6CKA5PRr65gDZ+hL8pKyLQAXJY8QLdWCn+ontMqOHPkOdeeyZ5zhXwu/Q19dhW6t/dugO196VE5rzztYLaeNGV99PV3rH3tbY3K1UOdQ/gJ6AHYjAHE0oiV3KoFcn+rkp4Ir661Wb6oHtJuPR+lmFwCnbOuCuXnca4sCS8ui8a6IqUds4hE5S7c+xKCH5vqgR6IlfQ+LQpfVedgu/X6UiHQEZkC78XGRubFMhgbJTvVmYgWK1ZyQD+Fcwh1Dnbu10WXqENe8stPHIfxQ/kNDHfPowf1859ddTNb3kvaUPhZow9yDpYXRlHOgdGNAQgVl1t2t1r72tXaEakjF/4CAZedvL937BjRksg9JILrjrY5IA72DysJYcz8sMzW5tD+dZGFkrsvZ+VqID/Xy5OvnTI6GsiwUFDEhYZrSsr7SqdyEKySPLoAxzkHWQfg32vRkXNQRYPBTj9hzHp7vD0AJSq4zToHEavj8t4a64upkm7p3zVgnteczmHS8AFOnIYKW0a7JMh3xWbj60L0PeJgOafuHAHL7sJXXLq8us8rStTQz4HwvQ9OxVvf/0As37M8LhYIT171Psz+5pl47uun+ZZUjsTB0R9C2pIniZVMZepYgeiHYktYpC8yHHE4bL/BsbI0KJm+ZgXfPPfwyO+IRZjhWRcULimNWGnB2iZs3BU37eX0V3HFtZlzUB3fbAur+oxDzsEr27E3njDK6zf6m5t/Uc6BR9EwsNaOkLNOwzmo+TqS8jkA8e/a1h9nraT6Vei47fkVkd/HTBzit1+9bk2ajpueW1eh7xEH+wYjAtMECkotL086zamTVo2tVCgQ6kuFmLJMHjaUCjh49ECMGdwP+48YUBOxkozZb3omRAZTXk0sIa1V1I9h4bomY7/6R6PKnk11kqBXN3F9KnRZuK5zGDM4Hm5aWq1saGrBz59e5jy+DU0t2LE3HuVTFStJ3wT9kcuf3DP52vsPceqfMwOWJZf/78vsNTqnw70SIoUTMU4kbSzlCl55e1uEc0izDtYVC0HYbiexEtO2SzBAeUjkvlB/5SzvfRCqyA3io81/Lkme57VGnyMOtqVTl02a5kPBT4uoTjDdgueTJ3liozMUj1gufIauLJN1dHmj56lsn22nHToK50wdY/xI99cUv4eM8cQqi98J00lOGBZ6ixaI9xbQS4OdrVLWyiz4Evrw9EBrXB1zW15FmS5VErOiA+cQ36Ejok9Qg61JyA/2y3fPx59ftadzvXDauOQxKJzDz55Z5o8r+fmG4wnv08o5cAuxX/SmITWs3h4bFZcokXPQ8eMnl+Ijt87Cis1hQiITlx5zJFSgbphUXV1Mv8CVGTkH5ViE/bgSL/k+VHP3tGj1xb1puKlaoM8RBzvnEA1PbXo1UiGtfsSPX/W+SB3uxXLWSvrElRZOJW3rSw5ipY6y8HcsfD25q5GQC/iX7g6Tl6jDLiZ8FLKfeiafQ6vFya1SEZF2uQXAlXOQz1Im3ZHNuijz9HcUcYIzhH2QuStaHWKZuNwBa0mjcw4WnUO0nqPOQfNz4IizN44oR2jxQfTaY85/7n0HxsoWv7MLALBld1ycpRNGG0dQpDClqDV/BSWHnZdQv7HAekz53xW6Mt8Vc1dtw/cffhOAp3gHgJ88uRSPLNyQvrFOou8RB6ZMmqzqC7WJcsvYSupHrH/QMua7Otl+P+ttrx+1rtaH9I3QF7ciI1bSP5ym5vZAqapm4FLDOU9TEta3Mt63Ec/RQvwZTN9/WLhY+f3XMeEkWjosxEG7Dz3zl1fH/mXJ+zAxCCbzSRVxMWKUc+BakISQC6uiw2VxYH1rDOJN4ahL4qCGQEl6Mr/79Il+v95vmxrG4xzMLR40cmCw4A/rX4f+Sr4EwONmb/vk8cbrk8TAXGylWD2mHdOQOf0fkbm+CQUiZ78kFf/x1zfC/v3Lb37yLWO0gVqi7xEH5n3dcfl0vPbd98eovVEfTd4CZ3LBB9SY/WGZ9InQOQcVcqHUk51zCq7ZWo7q4QPqA49mdThD/Hg1APDt80MlLLeAf/X9hwbHHOfw/06aFIxZ7kbrSpqcAvzH+sRXTgUQv4+k5O+cp3VSfCMT5/DX18LMbXoN1RvcZHoq30u/umLs3Hc/GM3sVm24k7hYyfubJFY0rV91RcJXzo7rJkzNyaixuiWYyZQ1bJAZU2QDFedqJw3vj/dP3Y+t7zVpvmfXcCfcLl7/Xr8x41C/HVU85XMORKlFPAXGQsoFqog3KaZZrdEHiUP8lTXWFTGksS6W9cy0IyI5KUkti9aRlhjcpGXT8waLkvd3UL+oC4oXlTXamL6o/Phj04JdkiopUPsbOTDkKDjO4Z+OGYcHrjzFu65AsYVKjsXrX4qVipHxvL6uKZIoRWLKmEHO9t/qInLhz19kxiDr8eUm65J//eOrsfsIfivtSYFCvUak6yycw9ET0vtvEGdmqQ296PepO1XG2+LLn/366Th6wtBYvaTxyXpy3vHhO8LPgCNe6nuQmyoVSRyYbfNdLIS780hIC32MiIuV9Olx5DjfS12p9sc5q/3r0wqV5FxKTx5UInTnrLe71Uu67xEHpiyYwDrnYGjDW4CFlXNoKPmWQEyPukIaCPud4IuAfn3Z9NgYZZ2mve1YuWUP7pm7JlJn2ID6YMeiTkxVxLL/iAF48qpTcd7RY42in/39MRQLcc5BJRZljXMQAli2aRfO/+kLQWIW7t4rAvj0b+ey5yWSOHI5rm/c95o2Pg8u1iWcdZpKpIniC55USHOtjx3SGPntFIrFqxgvUyCjnmaV+lP1aN5kiJwL2GOABW0p3OWyTbtj56NEWipYwpb0KAQxzsGqS4CTzkH/ruW4uX5ltV0t7Xhpxbagrmk6qeJbvb1qdA76M//2A68batYefc9Dmnlh0iaeEP0AbGIlXSatvtQfXnwMzpkaNWXl7My9PqOse7lSQalAQfa5sP1wwT/vpzOxdjsftkPKYTss3qcHjx6EAfVFlnMAwg+taJAnyxJ5S6pC+vyfvMC2yd2HDUl19Ng6OooOZoB6jUIBEOXo+f0G94uESJHiKm5ujBvaiBMnD8ccPyyHfgtqTpCgD2J2ulrjcoGV+qjjJg3FvNVxUZspEm1cfOb9FRD4osJJcWMDwgWak6ETwnfBcTb7De4XtMO9e50z02GPeqxER444+cXHmMSd6SFAVG61QNFnO17J/zC4sQ6bdsWdYeuKBaOi3wad45WGFt2BPsg5mFlf0mZRokK6oJaFxxcdPyH8wP3Zpoao5iJpyg+woyxYkUhBESuZCEMwNhH9qLhddLFQMO625IdGFF9uiDzFN4BAuagqpDnlcvR6N/vvJPqh62R0uFgr6YuwHhqDKP6xppE9qwthQ6mAA7X8HrI913ttL1eweVcrlvjWPq6IL4QehAB2NsfFf+rYgHCzwA2zQGFWNu7dv+vAEZFxVDR5rOSw9T4DWImDopBWdQVaPf27BuKEVP8OlyuJqAhRzkEV+Zo2MQP7lbC7xZ5jnENaxXct0eeIAzfZQlGBrnPgm5Cy6RsfW6LU9SqrbLK6K2w2mHYGH6r/t70s2IUvTZiNihCRXRxn0VOwyP4DzqHA60fe40cJHTHQi9Uj79mFI+D6ffKqU/HnL7w7UpbU1owjQyXmq6u3B8cfOX6CP/bkryxuMholXF6iI03ko4kBY1DaVOsI8DosTjat9ynnZ7ki8O7rn8Ie37N95jdOZ8cW74OnDgJ2Ihyk4hTRv3qfUmzZbvBtUf0F9CZ0sdINfh5sGQfKNg/UYJRq5rjYGBmdQzxKq1/OXU/Afoofkylyq4rBXIIhB3S3b4OKROJARL8hok1E9LpSNpyIniCipf7fYcq5a4hoGREtIaJzlPLjiWihf+4W8r8UImogonv88tlENDnbW4zCpnOIWSsZ2pAKaT1J+XNfPy3ywaq7Qsk5/ODDR2lt+eMKxE8V1jMySRxzzxUn+WP2OQflO+UWSk45+E/HeE5bYwb1w4eOHY/bPjmdXdBuvOhovHTNmfj5x4/DOSREk3YAACAASURBVFPHpIqqylmsHDx6EI6bNCxSlkQcLvaJAAB8SvHwPf9o7x5MOodoiGt9bIphQBAMTycO7h+veqXuNKmOQb/Vw8cOYsfcXhaR3bkuejTBqDcS9hAgenA7PnwGWcVKQOhsOX5oo3+vZp3DuKGN+Pi7JimEyaZz8FLn/mnuGnzjvgVBOWe2qpfFRD4Wok8U5VRV4m3kHBpKkTSxrujueEoqXDiH3wKYoZVdDeApIcQUAE/5v0FERwC4BMBU/5pfEJHkG28FcAWAKf4/2eZnAGwXQhwM4GYAN1R7My7gdQ4KcVDKzdZK/Iey/4gBGDM43GGou0IZk16389b7aK8IViSSlAlOsu9ygVPZbG43wtnXf3T6RO9cgXDzx6bhmIlDWQLZUCpivyH9cPSEofjVJ6cHOhs3zsFNrJRUR3US5L4n00cW9X/gxEresRQr2RLTc1BPRx2qTLvCcDc9rH8dLjt5fwztXx+pIZ9vkkLaJEoz6RwAu8WSSpQAw8KJkBCbRIr//N4Dcd+/nBxwnGo7XFh8NTqyTVcg5/rfF/EJstRr9HZ0x8tQ98foVXTFvFLFNOfrS4UgDEYa7EO0IZk4CCGeB6CbnlwA4E7/+E4AFyrldwshWoUQKwEsA3AiEY0FMFgIMUt4X8zvtGtkW/cBOJPSbM9Swqpz0OIcmUbhasOsTsqmZs8bVJex6uPqKFfYoHFS5JFkAbN8826sb2qJLCQm4hBb+JjZ4PIq5HVpxF5JCO3X3QhOvB/CHz77rlh5WyTBjN5ONBMcEXD9R0JO76jxQ4zcpDQR/s4Hj8Cxk4bioFEDYouImXMQfh3+XqRy/frH3jT07mHEQN5yxjRoT6xkEdso4ixvfDx1kENWF9wnvnIq/vrF9wDwCPL0ycNjHtcAr5BWvfz1PqNpUd1ynHDJfto7eHEh66uh/VY3Xibr4rpiIWIUosOUJrcaC6daoVqdwxghxAYA8P/KAELjAaj2lWv9svH+sV4euUYI0QGgCcAI1AhWayVth2EMvOe4wAX+EECQGrBfHf/IZb2OimDFSlxYbA6cFYtpUaoIEbHy4EQxMZEEKzePf/Qm6J6jauypGy86OjiWHzMXo+mFf9dl7fx7Omy/QWy56bpI4D3hhSI57dDR+IuvDykUKOA81Eu/f+GReOmaMwAAU8cNwV++cAr615cU5bbwOZH4OMsVgRW+8lN3XpSQ81DPP8GBs4jSEayDwj6fxvmmudIY4gePLgYAXP/hozB1nBd3qkgUbK5U4jBlzCAcpfl9yH7VUB5GzkH+iHFuUeJQriQrcfXvesfetkAH+PC/vSfSLvc44qHLVZ1DeHyXshmpL5E1LtTfF9lzwewLyFohzb0mYSm3XRNvnOgKIppLRHM3b67OOcSkcArOO+gc9F33rwzu/4T4zkz3rNUndkfZJFby/iYtwN85/4hYGatz8HUT7crWh6+XjNA0kse7DhgehEgoaKKarypRRaUjEhDe5/X+gqRiwrCorF32r5oY2sYTXMf8FkLgzQ07PWuV4JkjGDv3PApEsVhY6gKnKmR1PL14E9rKFXSUKxAGzmFjk9kXIemevLI4EfTGZY84dOsnvHc2bmi/yE73khMnBfdULIQ6hyQxSoEhIhua4pZ3pGwg9M2BqivwQsXEQ+ereZwl5HjbOiqY9p9P4KHX1uOgUQMw1Z9zZPm+9FeicgvqXH73wWG/pUIhImbb29aBr94b+uRUo3iuJhxHZ1Atcdjoi4rg/93kl68FMFGpNwHAer98AlMeuYaISgCGIC7GAgAIIW4TQkwXQkwfNWpUVQPn2Gg1PWNU58C3oXpmAtFIpiqIgLU7mvHFu8K4KCMHRuXJun11R6USW2iA8MNKCqHQWB8XWwWxoxh5rZrnmotH5DKJ1cWGwx8++64gRIIuCpiqEASVOMnHu8bBzjvckSbblavP3xR47wM/mRlpV83gJ+816pHLazFDKx95fbza+UeP9ccujKKnc/xnZ0pVqXWbWKZKUPTFUFX0Dx9Qj1LB4xr0evJXQXGUtIlR1HGoc25nc1xpKxXIf563Fu+98Rlje5xYac61Z0ZyXHjthd+1ajWoKplVbio2Hu232qdps1ZXLEREmH+auxb3zwuFJ9Vkebtt5orkShmiWuLwEIDL/ePLATyolF/iWyAdAE/xPMcXPe0iopN8fcJl2jWyrYsAPC2qza/nAK7hAf5HR0BkdphGUSSKmoqaxE8gPLxgA/62IIyoOGpQNLR3uOuWOgezQto0JtUCiptz3z7/CHzq3ZPxgSPHhvUK3uKl7uIOHRMXw7hscPQAbaaxe+2ZFdIqTZQf3VOLN/GVmTF2aDtXbjyHK2G4Y9ZKhejHHoYJ8c9TGB/ryTfDcXH9qJZPwfXMy5mqhG3wiEO8zpD+dbhg2rjYxoIDG+5E/628L33s/3PxMdH7KHiRT3VluLw3VaykP3/T2NoNDqFBn/5izkUiHaz4GMhglGobrGjUGzCAqM6pQeHibdyvLg6MWisxFyAuVtKJiCkwpO0JPv/WZqzZthe/f+ltS63s4GLK+kcAswAcSkRriegzAK4HcDYRLQVwtv8bQohFAO4F8AaAxwBcKUTgc/p5ALfDU1IvB/CoX34HgBFEtAzAVfAtn2oG5ekfMXYwXvnWWRjczwtMp8smTS+qWKBI7CKz4jpepseSUav86rnleHXNDqMpK+BNspMPjKpkBig7Sm5xGTmwAd/7p6kxH4yKiJpGDmB2pqZdZ7Qsuoja2rD5V6hjT7M9kEmLtu6JhoBOG0+ftJ2oHI3KWXJZ09iYQwjvIbw+3qckiGUh/IXOtNFItuAy9RGPIRXK102K0WB8vohHTxbEiZWS7PrlHLZ573vj854ZNwf+9q/vDetRnJM2mW0LAEs37sJv/7Ey0g+0X6b4UQDwyL95fW/a1YpNflY/01yuKxYiz0x/d9VkeetXV8Qn75iNbz/wOnZW4UORFol8qhDiUsOpMw31rwNwHVM+F8CRTHkLgIuTxpEV1AWjrkgRCw/1gwbMC1SpSFoIZMMH7aDgVfuSCj9ddg5Ew0WM1OK5zFCiWrLWLgYlshAuwdySJ3HIOfAPTG3DZmESFSvxdb7KRBedOLw/Vm1NH2bAls9BhazVUCqwi49JiRn6SvD9qWXeYsgrpGW9ihA4dtJQvMoYHQT1XPRGyvtSQz9c/YHDYtfKjVA86KMIzksHtFsSMuPJe1V31CznYHH4nKT41EhzbPX7M92/EMDZNz8fKVej/Vo5B//vEeNCrnPZpt0YPaifcZ6WCoWIU6D+bZjCu9iEJo11Rez0va657IlZo895SKvPnksTqRIP04svEGnJU/i+uGJ9oeYmZR0zcWS9ckVEJtDFx0+IcAS8OWq8zFOYiphJX+xah7Z0xa0NukI6ei6ZOHAfv1FJrxTbLEeiYwsvkrvqEyYPx+dOPRA/vPgY1syYjTlEoeLSpnMIiENF+Kas/Nik5VuS6Ia/1v7bBvlMYpyD/7dYsFvlcP1G9Fw2ziFxbBQzJeUt7pKT/QRXsSLCeJvSJN00l+tKFFGeZyEobygVgrWhmnmQFn2bOMTYbY1zMLRRKjjaV7NiBAOLr7TH7U5lmTSLDMaiERKTzb8OaXHVngnnIMUUceXg8v8+N1rX8uw4hbSpLxWmD0Ut5cIrxMM4R/Uhj7+xMRjzNecejjGD+zlzDmrIBi49rIRsTxJ9czwv790nLcQPL8g2Y1jRV0jLNKPXfchj/iP50B2JjZxL7QmJh7gw5hzkXIqKLc2cg46DRoWxrvTseNHxxK9tKBXw2OsbgjhjOuqLBavOoeywuH/pzCmR31JPCNhztGeFvkcclGNuFx8VK5kXMRfKzYuVXDgHzvY7FCupE5gTjbgg2BEmmR/GdA5msYX+uEYPii+mtkBzUZ2D6dnHy1x2rjJtKUX6iNaRehgbOGMBVhSlzCX5QbNEuhC+V5vOQRKuasJ2x0xZDeNmOV1fIS3Fj4HFlHJPrsFHgwB9Crdq5GqFsIpYZL2KEJEwFSbOmWvqia+8L6zj/3Xd4ReI8C//Z87OVlcsRN6X/tpM4ly1WvzbDgMlvryKD4mfJfoecRC2iRndsRitlQrRgGymemnY94gzmsXPoaW9jEcWvqOUV0ccpJgiaWF1aS00ZU2umxQjSiIN59BiCD2udiM5B2t+ADgEN2QeCGdAoC5IQtll65Blcj6ZxUq8eKcaBNyKw3uQTouyXz1UCjHt6CFiJOStJVn6EclNkMPYBDBz6ZagjFf08pyIKqJULbh0qGUyPa0+h6WyWkLOCfl96fVd3mPMmo4o8PtoZDIRZo2+RxyU45hYiaKLh038ob5ckzwzzcKqEptnl8Qd/ORE1k07dUKSEMla6df7mxRznntG8TreX5dFv6Mi8OD89Yn1bPoeHUkWN4A9amfYtp14AIitWKcdOgofO2FirJqqkA5NYRmxkl8mc0CYvfK9Bc5Vvm+DDFmh5/M4TDHzDcbnb4QkhxmIN/3zusd7XZHwyrfOZvuVcziqr+NFntx7uPbcw7V6THgNk7VSIhcSvS8Vatm/nnEwgDgHpyqrgfAZm96XCwcY49qVZ8X5M2WNvkccVJ2Ddvf6tDJaK2lycxcxiQmyRtIuLoitr022+OLtKlby6iVaPTg0Z7P00NHssEgD0gY/3iL38ZvCoY8ZHFp1SSJoez4uQQEjGfYI+O3/dyIbL0vuftVreMLqFf6bn3THxDnoTosfmx4nSK4oFQsoFShIZSvxvkPijqUFivo5lAIxWHhP6kI3oKFkXLjkrb13StgP90wIcc7xZx8/Fv986oHRsRXi78uWnMoFekgZWab2qZdxCHNw+BsErc2k744ofi8FCp0mL5w2nrssU/S5THDqEqb7C+iySTPnUIjI6rMRK4XHHz42/uID8YPWl84ppNE5AHzsIhUmeXWkLBArOeyGLNpLPe4U1xx3vTFXhoP1U7Tt5Hrq6SRCI5+HXDxZcSFjMWdqryI8kczH3zUJ//2ho9h6rmgoFRLfPRBGA5CiIGmCKZ+DGiRPjtMEeW9qDT7ul1eoctCcrkvnWsz9Jos8VbGSulEb2FDCkMa6SJ9AdJ7crqX0BaIJmrz60fOmOSvvp45RnhSIMGl4f9QVKZJfolbos5zDbZ88Hl847eDIOT0piEkuWCy4KgYdOAeSfYUfqh6wzKvns+Q651C1WMmNc3hw/rrktvy/LjoHVSZ81uGjI+eG9q/HA1eegn9530EA+IW6heE8XLgRbhGJK6STOQf1dNLblW3JBYL74PX3ZQqrIEUo7WUR5JR2BSf27FdXjHEOHDyFNGKcQ0AcNM7BNjQ9sxxgXvTjZVx7jgEw4WLKGszi4H6+fs6heP0/zokYiMj5q36GgxXiISF1Dm0dFQghYlZNJjGn3EDc9NFjYs+hWJAe4enef7Xoe8TB/ztpRH9m1xZdMEwEoFQoRMzxTBPP5RuWVVTOgYutJCelTrDiFlduE0dWS5Jhq/1ddPwEnMVE/eRMWaVsNl43PJbKPRXTJg7FwAazDTl3ey47YO5dxk1Zk7mfKOdgrqeGdZdcJu/5Hl8AOMhgdB1lPvaW65glGkoFYw7x6PgQUUgXNbFKoUA4+SA1FaiNc/D+lst2YsKKmgy6CVeTcr3anZ8+ke1TiKiDX6xP6dGuzCfuvQb+CBWBe15eg9+8uDJy3kQcBIDLT94fHzxmHPscuECDtULfIw6WuUQU3RmaFs5igSIfVmfESgFHoFAHbmcoJ6W+A9a7Vhebaz5wGH7+8ePYfp11Dgq+f+GRrJmt7HK14qU8rD8fByiaP9u8EAI80a3GlBMAfvaM3XsXADbsaImF4NARSSNr+UxVJah8t2xARe0ZmHJjy7nZbgjpnhZ1KcRK5Uq4m5bj++4Hj8DIgfUYNbABR08YGoTwto0sFI0mKaS9v4OUcC4mfY3LfNC/awA4VYvcKhfztnIlFANaDAiSdI6q+Ek3MGmsK6K5jc8SVy6LMCy8dm7bnjYI8JxVLdDniIME92EXC4XIZDNNvGKBnD4s2+IhwVlwcLuKIPSANiZ996fSlcPHDsZ5R48Fh1Dn4PV1/+ffzdbbXwlXYBZ5eOXf++sbQVkdE6dfh2mOh17D8XPVWnLOXLoFe7UPUv+oXYL8Ra6xvN5nl2zGgrVNKFfC+FUc0Y9bm5mfsechXWHFUzZwCuKitus+6cDhxn7LivOdHN+MI8di7rfODrzz5dxw0TlEzcDjL5SYBZgVNRWSw4QD0kQ5Wk8nSuP8kDVrtzcH847Tb6lOixJ7mHSgYaBMEXundUUyiqzLQhif5UOvrUelwkfurQX6HHGQOz9uDtdpoQDMOoeopYfZWil5PLKOGoHy7a17mHpyUkZXTN2ZRt3t2PLR6pyDTOiu4+oZYbydNPlt6x12tzZvYAB4/I14QhTVq/ULpx3kPB4AOOI7f8fzb4W7uKRl5fMJ7bs8jZb2siJW4hPbqLDpHDoqFVQEL8awQc8hAiDmqT55xIBYHSBUSOs6B66eHKcJ8lSSjb+cFmo1rtkCUSR+ka3jCE1nGutfX0KpQNjT2oGnl3ie8dxj5kLnT/FzZOtjA6RjY/Sc7ieloqOicA5M/7nOoYYIrSziKBWjns9mnQNFxDGHjeUzjrm8RNnOj59cGpRxH4+cbPq5WC5cR+IgIcUopo8+EvPecD+cr4RJPKLmJDCNTt7r3XPWRMp/+Ynjg7wQADDekEfDFUn6hen7D7Ne4/KNNreXA/2ULRS7hE3nIOeK6dmmQZGic9h0L9JD+lfPrbCOL0i1m2DBBcQ3ODq4zIIcs1QgwsadyYmQCIhQB5nhLtaef69fuee14LeOQKzkf4f/duYUjGXaU31/OO6QW1ua28po6wg5Q+5ZCpiDM2aNvkcc/L/cAy4VC5GduCm0hCpW+q8LpnbqY+XWJ27iBPLask4c4kpVdZwmyA9VEiUuXSPgJh7i2GrTMxmt+B7Y0rACwA7NwmOq5mhUS9nr/iP648zD48p39Wm79N/cVg533Q4KaZvOQU6LahLFxPqNiUbN4qxyRQROelzgQUAlDrY+vb/qd/WZ9xwYr+e3EXGWY8Y3e+XWRB2RN6aoh7Q6B1XI8OThOMzvK9mjPRSN6roLkyL9ukc8sezslV46WL3p8UMbIUSuc6gZwp1f/AGXChRZbI9mTEqB6KJbtMh/Xd4hR364RSlUXEev0E1bo2OzEYfobzPnkHwTHDF7/1Q+l3GUEzGNLbo7C8p167LEkdkxYgC/SACIZROTiFgrOfTR0l4OuDtuYXUV1akLgs1a6TefitvccygW3Ky8ipr4yTReeW9WnQPi8no1BLeE9AGImMgy/ZrCpsT7jXJ8pjHqeVps1kodAXGwi0YrQmDppt2Rc6UCH5tt3XYvkZS8L73pQf1KsUCDtUSfIw4SLOdQKAT2yJ95zwG4kHFGA6KTxraLc6HwnGiD81SVfeqhInTOISJWsrH42rhNC46enIjDqb7Hq8zS9T8XHc16DbtCDk2PGKsTqs7uoPSF6ZZLjw2OTc+OyxRnQ7Oic+AIrf4eTPJ4tZqNYI8a6OYcpYuVTNO4SNGFrFM6B8n9Jojztu6OcwNJ3NKU0QNj8Y3UfqMcH9+G7jfBWiv5F0uCb3xu/om7X14di6AqxVc65LuX71ef3x0VkXMOtYRN56B+dJwNvkTJcXeuv8MfffQYvqKlfQlZtE1jo3c0t7H1kscWPVdvIA4uIrNCgXDygSOCuiYRVew6066LsQgBgAH1UYf+rL+RUQNVkRdf50tnhWGUbd3LOEAVEe7QOYKp92OSx0c4h05yq169qFjJdJ2AwOyVYQRQM+eQRudgJw57GDPPpAXxilMPjMU3kvA4B+W3lXOwcyuBv1FZGraYxXEAsGj9TrYfzikz4DCL8ZzvAIJc3rm1Uo0QWisxYqWi+gG67bptliP6hP7wcRMMNc3t623JHAMSx0yIEjFXsZJ+xnQfrvqUQiHkaly4DcC8IJH2AUrokSizttpQn4EpzMfoQf3COPuW7g/2LVgqQgSWbQ11jFhJu4ctzK5Z78o251wfSUt7GW9uiC9cOl5etT3y22bBl9S/PJcUJp7TYSWJ3xosUUr1ZD+23X45IkKL1ynEOAe+MdNz+MpZh6BIFAuDAyDGYXK5q73H3zXUoc/FVrJaKyk7MlsMIFfOIY3pZxK48bx49RkYo6UMje4w3QmXi7WSdXzKTrTTnINcRLRdtP4Mst5BRQirww7YZgsb2rmHvigc0dTv6YJp49j21IXCJlZyFTks3xyVg7v45ADAyIG8yXPJIArhxpbEOXBipaRpaNuQ6JyDed6R5r1t5hykv5FpXKY+vnTWFPxtwXqWQ5RtynVIb8F7bj2EcyCiVUS0kIjmE9Fcv2w4ET1BREv9v8OU+tcQ0TIiWkJE5yjlx/vtLCOiW6iGhrxqTBgd6gJpW1hVJbStnnruho/wgdLck4vEy8YPbYzpCtT7shE4/ZTpkZvETTqIQsee+qJ5F+diCuq6iOjXuxIlE9T3ZVvk5COxhW6QVwshAqLJ+hso/Vx64iRMGBZX0Or1shAr6c/W5bpvnXc4BvWLxxECwm/C1ow8l5S3/KNMxNkkomflVsmNOOicA/dNyA1ERwLnwJVfefpBYT/M3JZthpyDdr5SQaXSs3QOpwshpgkhpJnE1QCeEkJMAfCU/xtEdASASwBMBTADwC+ISH4ttwK4AsAU/9+MDMbFIrRVYkQ36uLguOjbXpTcdR83aSg+dsIkw3jcqIPrhEi9+02AOxeQ/hrTCDhTxpU/ODdWT+Vq/uuCqXj+66c79WtC0fH9h+E9zFDzA3zzLwsB8AuYSnttO8KF63YEx2k4BxP3Wo2nuY0TTuMhvXxz3MlTxUdPmOjsOS7BEd6gX0TzOdt8OiJJtzhTVk0fZtQ5+O9VJUrSbNczD45fo+sx9GdZrqDHWytdAOBO//hOABcq5XcLIVqFECsBLANwIhGNBTBYCDFLeNvK3ynXZA65c+UesKtIpuhIHCSrnYV4yXVCqOOx9uvYnospq96v6uhmg8mUUtc5TB03mP0ID9svVEBOmzis02GM1R25LWJpmM/b3JYcrrrY8Hkf3OaSat5s4xz0V+469VyquYhQrX4OKT4DzjfABqtYiaKRe105B268gSVd2e7nEIS7UaiAvCePc+DESl6ZSfRdrlR6VGwlAeBxInqFiK7wy8YIITYAgP9XxmUeD0B1eV3rl433j/XyGIjoCiKaS0RzN2+OZ0tzHbAJEUsfq1jB7eXUOdh+u0L3BDXJptM6wSXBxQlO73fySF404joGWS6J65jB/KLveq+uUPNJ/HmeOVS57MrG9XFqCY6jKkaIg3lsh+4XeuHbw2ek23EHVznMh6w4B4mfKqbD8brufQPAqEFmnxUCInG1bGa7W3a3hvWs1kpuYqVI3ga/qgyDruPQMd47vuj4CWzbHb61Uk/hHE4RQhwH4AMAriSiUy11uVsSlvJ4oRC3CSGmCyGmjxoV9wVwgkXn4LrrdvVQdVHSnX5oNKfBquvPY+vt2Bt6C3/42PH48cemsfXcFdLGUxGk0TlI2HwcXKQZsqUJfniMmz+afK+m98UlxRnUUMJjX47bxLvm5VXj5pjAhYDgxqiW2RZodWw2IwG9C9smR4XJDNS1rWo4hw8ew29w1PYkkr65kQMtxIGi78qYn7xAeHHZ1uC3/m3KOkCoPLb5TADAyAFx82iTKevIgQ0YPqAeM47cL6gncciYgegoe34OPYI4CCHW+383AfgLgBMBbPRFRfD/ylCXawGomqYJANb75ROY8prAZsqqFrnuum2LnVQW29qaMmYQzvG9iT98nDn134ePDR/RgIZSon01kKSQDs99/8IjjfXcrZW8v0Tuu1XTs5NSk7aOCg7bbxCG9OeVoC7E4RzGU/vLZx8SEUlJ9HPMy+tmrYTEOoBmQOBIHNJYoX3xjCmGmiG+c/4RuPj4ZDPrznIOaThovaZtLgP2eaqP25jhURufjdMLTFkN45LfZ/+G8L3J+y8Sr5BuL1ciYlzJLfavL2LGkWPR3F7Glt2tzql2O4uqiQMRDSCiQfIYwPsBvA7gIQCX+9UuB/Cgf/wQgEuIqIGIDoCneJ7ji552EdFJvpXSZco1mcNmyurMOTjK4WWI5qSJvWmXx8py9t0S6gJpiqAKuHMO6ncw1iKrTxveIU3cnzGGGDdqxFhXm/6kgHAqBhl0Iu6cg/fXLlbyKt0zd42xjj4+26NTFa6uvjVPXnVqYmRZwAsc2VmxUjEQoVoaSLHj1Z9sEgdk61cnHGYlcvK7kHWkebIpfLp8VlEvdLkexK3FVm/di7tfXoONO0Ox1mDfMqx/fSlIgDVz6RajL0zW6IyfwxgAf/EfdAnAXUKIx4joZQD3EtFnAKwGcDEACCEWEdG9AN4A0AHgSiGEJIGfB/BbAI0AHvX/1QS2wHuunIPrghmKlez1Xl3tWaL8fdFGe0UfIwy25kC46/aO3eS/WehEQuLgvt847yhTrgmFONiUrw6mx9wz4JzRAHcuyS0Cqff3wfl2Jtg1xLpKEGxOZNEhOW5iUt43h2B8GXEO+uY+6ZuzvQtdNGrUOSjVTOFk5DikwYKJUMs+/rE8FFPJIZYKBTSXo7v/55bGdagyD0drRxkDHI08skTVPQohVgCIxYMQQmwFcKbhmusAXMeUzwVglm1kCDUxuo6C44K51g+QlYRArJSxkNAWMK4anUMSZ+OCYOKnyDWQJBprKwurtVREmWviHJg+XD24TZDjtm6SHR+D+gwWrG0y1lPfpR5Py9Sv62t13ey46J9sLandmJzpJHTRT2fmqC4eMrWk+i+Z8nTL+SSD45mICLd+yKIC4+eweVdrrP6ABkkcKgEX0ZXow+Ez4ufU+WBb5PYq4h9bToA6hx2mCteP1FWs5Cr/zYA2pBYr/dcFGADRLgAAEq9JREFUUy1teX/bOsrOZptJAeFUdCYooNpmGsucw/bjc36o41MtZWz1bHm/1THZQkr84bPvCo5dM8uZOC4AuO15L9/D/DU7jHXUDdlTV51m7Uv/qjg5uzNR04iDiVN6TRm7MSOff+n98zwDS9MIbAYvRYoTv2WbdsXq9/Pn6SUnTAwy1XUl8vAZClwjmtrCdKsIFdJuY3Od7DY/AnVotp1ehHPIgLORTWzf226tJ5+/bbIHct1EnUMyIeTKO+tJrSrfjWPTfu/PhKZW2wKSFL7hmKdPjichCvpVmuhnuc+TDxwRtu3I7fXrJFHdrWyqkvrUN12ThsefX13RLYe0+r73G9wP/2UxwAjbduMITITalguiWIiH7JbcoMrVFgqExf81A/XFAlZsiYY76Qr0Qc7Bh4WyA3aPS1XUMX0yn3tXree66LuKn1wVknZWPFudw85mO1HQ4cLVtJUrzqKxNJyD7d26wCW8h6uncjRPg21DEp4zhbAAonPIHowuPHbl9mycg82UVEINN5+k59AfLUfQXTkedZP03x8+0ug3o8L0LvRv1DQHuA2cfMxcsp+OcgWNdUXM+eZZkfJ+dUUUCtRljm8q+hxxkFtXXucQHpvMJ3XYRDyucZMkXD9Su527Wxtp0l1+5j0HWM1dq4Klz8AaSNgT27gQQq74WEs49qH+e7dZLs3ylYy2ZDn6MzVxcVEjCPO9uvvWhG3YvYbD9hodTXht4rhbLp2W2Kd0COtXV0jk3pJSuALuHI96f67fh0mcqc8zE3Hgss3JZ15iOJ6OisBhY81m22kMPbJCnyMOrtZKA+vNopukBOkSd7/smTE+svAdp/pFx8luExe5ch9qmICkXf+3zz8Cnzhpf2sd12cSwCGiKeAeR8i0eOqy/1MPGWXlqJ792mkA7KInlwxq+iJkWljVXaxJCQq4K2RVDsPVCsnmXXyh4olvW/hdFKaSc/gYE1hPh8tschUPqiJM10XWlfC0G+Z9Q6lo/E5l6lUVHWVhD9mTwtAjK/Q94mDROSx5J5Tr2T5GWzTOzsB1Ya8rdX6iqHNTz9VcDZJi9If9mg0CJNRnb03DqpxyFd195/zDrecHNpQwelAD/tOiMP/K2cmOZTpczGezCAHiGgtLhY0j+I9/CjlGmzmly0ItnbpOPmhEQk03i7LPMvmnOQxtDAmXjTbc+ekTg2PbQv3pUw4IjveziKhM0Wf1GE6yro1wZW3x6II+SBzs0RRdcNwkTyFoyjFdLVzktoB9R5iUglFCdd8/5eCRTtfYkBSGWUIOz65zCI+tu2lHHwHTNRxKxQLmXHsWLphm9la3yfz1fqR46stnHZJ4jetO34Ysc4gA0Y3IoH5m4uAy9ncfNBJzrj0TM47k/VtUjB6UrBf47HsPSKyjj822yMpwLQBQbyGY6q2edXg8xIaEabFXPaRb2st44o2NaC8LZ51TV6HvWSv5f7lH7fptfvCYcThh8vBORwGV6F9fxN62Mn7+/8yByFTYFHFyMTrEz0RmghQDffjY8RifgZmcq1RJcg6u4Unsyvfw2HVnlYViz0X+L7upKxIGNzZYdVMS15x7WGeH5qykdYX6vPQ0rdF63l8bAQHcFn0gyolM/P/bO9NgKaorAH/nLSzvsYk8EIUHqIgiGhEKNYEgwY1oBROXoJYQTZVapdFYZlGzVohVaBLjlipDGYiaRBMrSQWXxELLJUaNSlwR3InivuITIwic/OjbvH4z08u81zM9PXO+qqnpd9+dO/fM7e7T99xzzxle+vxM+oAXVA5Rp2nQDBQ1xP7MdreO9uiNkE1AiUgXwdhKF926husf/C8AcyaFx4srJ/JAWjSecogIvJfURRUoSzHE7u507x0JL5wos9LAfs08/sPDGBRzkfonZxob4CDZAiIkMyv18KTp44J0VNu9JapPPt0hm5XBCc+riSNL74Uoh7TG06dH7pKItkcPHcicSR2cPbd8k1sp/G9aevI0DtmrOD5WOQRNbVFrI0El8suQwJbQ/SAyfsf2yO/16/306CmcMKM7n0tzU3dirHXvdue2iDzXM1AOjWdWcu9x3kppcOnx3gbytjJj9oTx7cMnsd/YYbGbuIa2tcYqJN/8lJYtM/nMwXuPzLQWXJBOGB8qKWnMHJLsFN6+ka8gmFoUcWM2ZZchnHdovHkqTcrZaLb8lBlM7Qzfg1EO22deLU19vjEGb/pR0WeDs9Q9RoUrav83GRXzgOj3u7VZihJJlVq3XPl0ePicoJJeeFC0c0haNODMIWJFOmX8CyXK3xy8hb6Nm7fGTpPPnLM7Z87ZPZW+ba3QzCFuIXH7jCVyOp5sQToL329I5snid23rNi07sm0Yt3yjOMx4palgxt7o73UXaBpmsqS/f9Lv8s+7OA8tXyEUnsPBNYeo3e6l2gI4OcZzMC0abubgU+qc703qxCj8hCBDYkw8N552IBfM2zNxBrU06Lb9p9We937TGQcl+t5Im25wQTphbKUoLpjXbctPQxkmW3MIrpuUnxOj0fF/imp6cPkeZWfMjo5k66ccHdQ/+qGvJTBz6Nmfpu07ov0YTXEEf4cJI6LNWWnRgDMH773U6ZI0n3NSxg5vo3N4Gz+ZH72BbNeOQZw+O3oBOW38J5f0zErxMwKvnvcedSPseWPtu1lpbCD0QhrS+v0Li5dU+D29cS/tK0k932qduFna2OEDeeW96ECYSbMZDmhtZu3iI2Jnv/61E6f0g+Eyggzs18T/Pt2KqvbYNR5Fcy8eNvpK4ymHiGQ/aW9fGNDazL3fmZNuoymRtllpW8RCfxAt11spap9D4t3gpdvuC7eePZMxO4SnQw1+z0efhOfpAFjylX244aGXU+kXeHkcoiL35gEJubEWsvLc2bHxldpamxnUvyWR62uS0Cr+np64GaT//8J6bf1a2LpN2bx1G2vfKA64V4osFqQbTzlEzBx8e/ixCbJi1QtpuchpmTOHqK/dsrU4QUpf2LWjexqedB9IHHvvHL3HJdjtF9/ZGF4RWDCjkwUBb5a+snsKXk9Zs8k9UceZWpPczFuam3jyx4elZrZL4o4N8NoGL+/75oINon6fCyPNxsl6ztyJzNkzfF9F2jSecnDvUWsOUeaCeuHEAzp59s0uzpqTjuvhTkMHsPaNrthsaknMT8HwFM+/3fdolHuNHrJ9L8mmhNP4vpLVYnml+PLU8E2BlWCD27W/Q1v8/pAkpLme42/4TLoe8vK7PR8O2lysp40FyiHO/HhulT3VGk45zJuyE3uMGlTSHTSp3bweaOvXwiXHFuVq6jWXfXU/7nn2bcbHLJb5JoCon3hzYObQHhMUbt2SIxP176h9R/OnR9bTFrGRq9H42bH7JooTlfQ3TpOLj9mXy+98LtHmwWqzNYHHXZBCt1hfORx5xT97lF+zaHoKvUuPhrtSxu3YzriQzSuzJo7gt/evY/9x6fhqNxLD2vpFhpzw6Rjcn7VvdEWaA4J5d9N6Wlp89BQWfXZ8arva4wjaiO88b3ZVvrNcjksQAC8rDpk8ikMm923zW6XwHyzjFpPv+tbBPPzSexy29049yv0ZxweB3CezJo5g2rjw8P9ZUDPKQUSOAC4HmoFrVHVJtfswd69RrF18RJ/j/RvhXL5gKneseZPdOsK9s/YPbKRKEns/Cf1bmmPXCdIk+Ew5IWYnrZEvdh/pnbtxwQYnjGgv6XZauID+0IVzGZnSeZ4mNaEcRKQZ+BVwKLAeeFhEVqjq09XuiymGyjK8vR/Hxzyxdgzuz+D+LXRtivbyqWWC51EWniZG5ThxRicDW5uZHwhnXg6FEYxrUTFAjSgHYAbwvKq+CCAiNwLzgaorB6M2uOO82SWTrueFHRImizLyR1OTcEwfPBrzYrauFeWwC/BK4O/1wAEhdY0GYNSQAamZlLJARFh89BT2jojlYzQmE0a0s27Jkby3cXOP9bVao1aUQ+kNy4WVRE4DTgPo7EzPL9wwKkG1YuAY+aQWPbGC1EpspfVA0BA9BnitsJKqLlXV6ao6vaMjPPa5YRiG0TdqRTk8DEwUkQki0g9YAKzIuE+GYRgNS02YlVR1i4icBdyO58q6TFVXZ9wtwzCMhqUmlAOAqt4G3JZ1PwzDMIzaMSsZhmEYNYQpB8MwDKMIUw6GYRhGEaYcDMMwjCJE005/ViVEpAt4psS/OoEkabWGAhuqXC9pW7UsQzn10pSjHmRIWq8eZEharx5kKKdeEjkq3bdJqhqftEZVc/kCHgkpfzvh55dWu14ZbdWsDFnJUQ8yZChrzZ5P9SBD2nJUum9h987CVz2alT5IWO/mDOolbauWZSinXppy1IMMSevVgwxJ69WDDOXUSyJHVn3rQZ7NSo+oalHqpLDyPFEPMkB9yGEy1Ab1IAPUhhxJ+5DnmcPSMsvzRD3IAPUhh8lQG9SDDFAbciTqQ25nDoZhGEblyPPMwTAMw6gQuVAOIrJMRN4SkacCZZ8RkQdE5EkRuVlEhrjyfiKy3JU/LiIHBz4zzZU/LyJXiEjV8jemKMPdIvKMiDzmXiOrKMNYEblLRNaIyGoROceVDxeRlSLynHvfIfCZC9zv/YyIHB4oz2QsUpYhk7EoVwYR2dHV/0hEripoKxfjECNDbq4JETlURFa533yViHwh0FZm96eSJHFpyvoFfB7YH3gqUPYwMNsdnwosdsdnAsvd8UhgFdDk/n4IOAgvudDfgXk5lOFuYHpG4zAa2N8dDwaeBSYDlwDnu/LzgYvd8WTgcaA/MAF4AWjOcixSliGTseiFDO3ATOAM4KqCtvIyDlEy5OmamArs7I6nAK9mPRZhr1zMHFT1XuC9guJJwL3ueCVwjDueDNzpPvcWnuvYdBEZDQxR1QfUG4nrgKMr3XefNGSoQjcjUdXXVfU/7rgLWIOX4nU+cK2rdi3dv+t84EZV3aSqLwHPAzOyHIu0ZKhGX8MoVwZV3aiq9wGfBNvJ0ziEyZA1vZDjUVX1E5mtBgaISP+s70+lyIVyCOEp4Evu+Di6M8k9DswXkRYRmQBMc//bBS/jnM96V5Yl5crgs9xNn3+Q1dRTRMbjPQX9Gxilqq+Dd7HgzXagdG7wXaiRseijDD6ZjkVCGcLI0zjEkZdrIsgxwKOquokaGYsgeVYOpwJnisgqvOncZle+DO+HfQS4DLgf2ELCPNVVplwZAE5S1X2AWe51clV7DIjIIODPwDdV9cOoqiXKNKK8aqQgA2Q8FmXIENpEibJaHYco8nRN+PX3Bi4GTveLSlTL9P6UW+WgqmtV9TBVnQbcgGcLRlW3qOq5qrqfqs4HhgHP4d1sxwSaKJmnupr0QgZU9VX33gX8gSqbOESkFe8i+L2q/sUVv+mmxb6p4i1XHpYbPNOxSEmGTMeiTBnCyNM4hJKzawIRGQP8FVioqi+44pq7P+VWOfgeCSLSBHwfuNr93SYi7e74UGCLqj7tpnZdInKgm3YuBP6WTe89ypXBmZlGuPJW4Cg801S1+ivAb4A1qnpp4F8rgEXueBHdv+sKYIGzqU4AJgIPZTkWacmQ5Vj0QoaS5GwcwtrJ1TUhIsOAW4ELVPVffuVavD9lthJezgvvqfp14FM8Dft14Bw8z4BngSV0b+gbjxetdQ1wBzAu0M50vBPnBeAq/zN5kQHPY2MV8ATeYtblOM+ZKskwE2+q+wTwmHt9EdgRbwH9Ofc+PPCZ77nf+xkC3hdZjUVaMmQ5Fr2UYR2eQ8RH7vybnMNxKJIhb9cE3kPgxkDdx4CRWY5F2Mt2SBuGYRhF5NasZBiGYVQOUw6GYRhGEaYcDMMwjCJMORiGYRhFmHIwDMMwijDlYBgVQETOEJGFZdQfL4GIvYaRNS1Zd8Aw6g0RaVHVq7Puh2H0BVMOhlECF0TtH3hB1KbibVRcCOwFXAoMAt4Bvqaqr4vI3XgxsD4HrBCRwcBHqvpzEdkPb/d7G94Gp1NV9X0RmYYXR+tj4L7qSWcY8ZhZyTDCmQQsVdV9gQ/x8mxcCRyrXjysZcBFgfrDVHW2qv6ioJ3rgO+6dp4EfuTKlwNnq+pBlRTCMHqDzRwMI5xXtDv+ze+AC/EStKx0UaGb8UKi+PyxsAERGYqnNO5xRdcCN5Uovx6Yl74IhtE7TDkYRjiFsWW6gNURT/oby2hbSrRvGDWDmZUMI5xOEfEVwQnAg0CHXyYirS4ufyiqugF4X0RmuaKTgXtU9QNgg4jMdOUnpd99w+g9NnMwjHDWAItE5Nd40TWvBG4HrnBmoRa8ZEyrY9pZBFwtIm3Ai8AprvwUYJmIfOzaNYyawaKyGkYJnLfSLao6JeOuGEYmmFnJMAzDKMJmDoZhGEYRNnMwDMMwijDlYBiGYRRhysEwDMMowpSDYRiGUYQpB8MwDKMIUw6GYRhGEf8HpX0qwXR6YncAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "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", " 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": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "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": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 229363\n", "2002 502271\n", "2018 543281\n", "1996 553859\n", "2017 557449\n", "2019 584926\n", "2000 605096\n", "2015 613286\n", "2012 620315\n", "2011 645042\n", "1995 648598\n", "2001 650660\n", "1993 653058\n", "2005 654308\n", "2006 657482\n", "1998 660316\n", "2014 673458\n", "1997 679308\n", "1994 682920\n", "2007 701566\n", "2013 708874\n", "2004 736266\n", "2008 745701\n", "2003 770211\n", "2016 780645\n", "1999 784963\n", "1992 821558\n", "2009 822819\n", "2010 848236\n", "dtype: int64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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