X-Git-Url: https://git.njae.me.uk/?p=covid19.git;a=blobdiff_plain;f=uk_deaths.ipynb;fp=uk_deaths.ipynb;h=5f34a0e04ea8b04a3146507ac0d1e7cd2168ae59;hp=eed19c131198f2d91019eaa78712f60419ebf071;hb=a596078977bfdf8b1929a5e288cc70f745101f9d;hpb=7837a85ab27d56b7db6c668011f6737d5186f879
diff --git a/uk_deaths.ipynb b/uk_deaths.ipynb
index eed19c1..5f34a0e 100644
--- a/uk_deaths.ipynb
+++ b/uk_deaths.ipynb
@@ -1,8 +1,19 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Data from:\n",
+ "\n",
+ "* [Office of National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales) (Endland and Wales) Weeks start on a Saturday.\n",
+ "* [Northern Ireland Statistics and Research Agency](https://www.nisra.gov.uk/publications/weekly-deaths) (Northern Ireland). Weeks start on a Saturday. Note that the week numbers don't match the England and Wales data.\n",
+ "* [National Records of Scotland](https://www.nrscotland.gov.uk/statistics-and-data/statistics/statistics-by-theme/vital-events/general-publications/weekly-and-monthly-data-on-births-and-deaths/weekly-data-on-births-and-deaths) (Scotland). Note that Scotland uses ISO8601 week numbers, which start on a Monday.\n"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 127,
+ "execution_count": 137,
"metadata": {},
"outputs": [],
"source": [
@@ -19,18 +30,28 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 138,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- " publishedweek1620201.xlsx publishedweek522018withupdatedrespiratoryrow.xls\n",
- " publishedweek172020.csv publishedweek522019.xls\n",
- " publishedweek172020.ods 'Weekly_Deaths - Historical_NI.xls'\n",
- " publishedweek172020.xlsx Weekly_Deaths_NI_2020.xls\n",
- " publishedweek522016.xls weekly-march-20-scotland.zip\n",
+ " publishedweek1620201.xlsx publishedweek522018.csv\n",
+ " publishedweek172020.csv publishedweek522018.ods\n",
+ " publishedweek172020.ods publishedweek522018withupdatedrespiratoryrow.xls\n",
+ " publishedweek172020.xlsx publishedweek522019.csv\n",
+ " publishedweek182020.csv publishedweek522019.ods\n",
+ " publishedweek182020.ods publishedweek522019.xls\n",
+ " publishedweek182020.xlsx 'Weekly_Deaths - Historical_NI.xls'\n",
+ " publishedweek2015.csv\t Weekly_Deaths_NI_2015.csv\n",
+ " publishedweek2015.ods\t Weekly_Deaths_NI_2016.csv\n",
+ " publishedweek2015.xls\t Weekly_Deaths_NI_2017.csv\n",
+ " publishedweek522016.csv Weekly_Deaths_NI_2018.csv\n",
+ " publishedweek522016.ods Weekly_Deaths_NI_2019.csv\n",
+ " publishedweek522016.xls Weekly_Deaths_NI_2020.csv\n",
+ " publishedweek522017.csv Weekly_Deaths_NI_2020.xls\n",
+ " publishedweek522017.ods weekly-march-20-scotland.zip\n",
" publishedweek522017.xls\n"
]
}
@@ -41,19 +62,87 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 139,
"metadata": {},
"outputs": [],
"source": [
- "raw_data_2020 = pd.read_csv('uk-deaths-data/publishedweek172020.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
+ "raw_data_2015 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2015.csv', \n",
+ " parse_dates=[1, 2], dayfirst=True,\n",
" index_col=0,\n",
- " header=[0, 1])"
+ " header=[0, 1]\n",
+ " )\n",
+ "dh15i = raw_data_2015.iloc[:, [2]]\n",
+ "dh15i.columns = ['total_2015']\n",
+ "# dh15i.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "raw_data_2016 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2016.csv', \n",
+ " parse_dates=[1, 2], dayfirst=True,\n",
+ " index_col=0,\n",
+ " header=[0, 1]\n",
+ " )\n",
+ "dh16i = raw_data_2016.iloc[:, [2]]\n",
+ "dh16i.columns = ['total_2016']\n",
+ "# dh16i.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "raw_data_2017 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2017.csv', \n",
+ " parse_dates=[1, 2], dayfirst=True,\n",
+ " index_col=0,\n",
+ " header=[0, 1]\n",
+ " )\n",
+ "dh17i = raw_data_2017.iloc[:, [2]]\n",
+ "dh17i.columns = ['total_2017']\n",
+ "# dh17i.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "raw_data_2018 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2018.csv', \n",
+ " parse_dates=[1, 2], dayfirst=True,\n",
+ " index_col=0,\n",
+ " header=[0, 1]\n",
+ " )\n",
+ "dh18i = raw_data_2018.iloc[:, [2]]\n",
+ "dh18i.columns = ['total_2018']\n",
+ "# dh18i.head()"
]
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 143,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "raw_data_2019 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2019.csv', \n",
+ " parse_dates=[1, 2], dayfirst=True,\n",
+ " index_col=0,\n",
+ " header=[0, 1]\n",
+ " )\n",
+ "dh19i = raw_data_2019.iloc[:, [2]]\n",
+ "dh19i.columns = ['total_2019']\n",
+ "# dh19i.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
"metadata": {},
"outputs": [
{
@@ -69,284 +158,103 @@
" vertical-align: top;\n",
" }\n",
"\n",
- " .dataframe thead tr th {\n",
- " text-align: left;\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
" }\n",
"\n",
"
\n",
" \n",
- " \n",
- " Week number | \n",
- " Week ended | \n",
- " Total deaths, all ages | \n",
- " Total deaths: average of corresponding week over the previous 5 years 1 (England and Wales) | \n",
- " Total deaths: average of corresponding week over the previous 5 years 1 (England) | \n",
- " Total deaths: average of corresponding week over the previous 5 years (Wales) | \n",
- " Unnamed: 6_level_0 | \n",
- " Unnamed: 7_level_0 | \n",
- " Unnamed: 8_level_0 | \n",
- " Persons | \n",
- " Deaths by age group | \n",
- " ... | \n",
- " E12000001 | \n",
- " E12000002 | \n",
- " E12000003 | \n",
- " E12000004 | \n",
- " E12000005 | \n",
- " E12000006 | \n",
- " E12000007 | \n",
- " E12000008 | \n",
- " E12000009 | \n",
- " W92000004 | \n",
- "
\n",
- " \n",
+ "
\n",
" | \n",
- " Unnamed: 1_level_1 | \n",
- " Unnamed: 2_level_1 | \n",
- " Unnamed: 3_level_1 | \n",
- " Unnamed: 4_level_1 | \n",
- " Unnamed: 5_level_1 | \n",
- " Deaths by cause | \n",
- " Deaths where the underlying cause was respiratory disease (ICD-10 J00-J99) | \n",
- " Deaths where COVID-19 was mentioned on the death certificate (ICD-10 U07.1 and U07.2) | \n",
- " Unnamed: 9_level_1 | \n",
- " <1 | \n",
- " ... | \n",
- " North East | \n",
- " North West | \n",
- " Yorkshire and The Humber | \n",
- " East Midlands | \n",
- " West Midlands | \n",
- " East | \n",
- " London | \n",
- " South East | \n",
- " South West | \n",
- " Wales | \n",
+ " total_2020 | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
- " 2020-01-03 | \n",
- " 12254 | \n",
- " 12175 | \n",
- " 11412 | \n",
- " 756 | \n",
- " NaN | \n",
- " 2141 | \n",
- " 0 | \n",
- " NaN | \n",
- " 48 | \n",
- " ... | \n",
- " 673 | \n",
- " 1806 | \n",
- " 1240 | \n",
- " 1060 | \n",
- " 1349 | \n",
- " 1162 | \n",
- " 1113 | \n",
- " 1814 | \n",
- " 1225 | \n",
- " 787 | \n",
+ " 353 | \n",
"
\n",
" \n",
" 2 | \n",
- " 2020-01-10 | \n",
- " 14058 | \n",
- " 13822 | \n",
- " 12933 | \n",
- " 856 | \n",
- " NaN | \n",
- " 2477 | \n",
- " 0 | \n",
- " NaN | \n",
- " 50 | \n",
- " ... | \n",
- " 707 | \n",
- " 1932 | \n",
- " 1339 | \n",
- " 1195 | \n",
- " 1450 | \n",
- " 1573 | \n",
- " 1272 | \n",
- " 2132 | \n",
- " 1487 | \n",
- " 939 | \n",
+ " 395 | \n",
"
\n",
" \n",
" 3 | \n",
- " 2020-01-17 | \n",
- " 12990 | \n",
- " 13216 | \n",
- " 12370 | \n",
- " 812 | \n",
- " NaN | \n",
- " 2189 | \n",
- " 0 | \n",
- " NaN | \n",
- " 69 | \n",
- " ... | \n",
- " 647 | \n",
- " 1696 | \n",
- " 1278 | \n",
- " 1106 | \n",
- " 1407 | \n",
- " 1457 | \n",
- " 1073 | \n",
- " 2064 | \n",
- " 1466 | \n",
- " 767 | \n",
+ " 411 | \n",
"
\n",
" \n",
" 4 | \n",
- " 2020-01-24 | \n",
- " 11856 | \n",
- " 12760 | \n",
- " 11933 | \n",
- " 802 | \n",
- " NaN | \n",
- " 1893 | \n",
- " 0 | \n",
- " NaN | \n",
- " 53 | \n",
- " ... | \n",
- " 612 | \n",
- " 1529 | \n",
- " 1187 | \n",
- " 1024 | \n",
- " 1231 | \n",
- " 1410 | \n",
- " 1028 | \n",
- " 1833 | \n",
- " 1253 | \n",
- " 723 | \n",
+ " 347 | \n",
"
\n",
" \n",
" 5 | \n",
- " 2020-01-31 | \n",
- " 11612 | \n",
- " 12206 | \n",
- " 11419 | \n",
- " 760 | \n",
- " NaN | \n",
- " 1746 | \n",
- " 0 | \n",
- " NaN | \n",
- " 50 | \n",
- " ... | \n",
- " 561 | \n",
- " 1461 | \n",
- " 1136 | \n",
- " 1015 | \n",
- " 1262 | \n",
- " 1286 | \n",
- " 1092 | \n",
- " 1820 | \n",
- " 1233 | \n",
- " 727 | \n",
+ " 323 | \n",
"
\n",
" \n",
"
\n",
- "5 rows à 84 columns
\n",
""
],
"text/plain": [
- "Week number Week ended Total deaths, all ages \\\n",
- " Unnamed: 1_level_1 Unnamed: 2_level_1 \n",
- "1 2020-01-03 12254 \n",
- "2 2020-01-10 14058 \n",
- "3 2020-01-17 12990 \n",
- "4 2020-01-24 11856 \n",
- "5 2020-01-31 11612 \n",
- "\n",
- "Week number Total deaths: average of corresponding week over the previous 5 years 1 (England and Wales) \\\n",
- " Unnamed: 3_level_1 \n",
- "1 12175 \n",
- "2 13822 \n",
- "3 13216 \n",
- "4 12760 \n",
- "5 12206 \n",
- "\n",
- "Week number Total deaths: average of corresponding week over the previous 5 years 1 (England) \\\n",
- " Unnamed: 4_level_1 \n",
- "1 11412 \n",
- "2 12933 \n",
- "3 12370 \n",
- "4 11933 \n",
- "5 11419 \n",
- "\n",
- "Week number Total deaths: average of corresponding week over the previous 5 years (Wales) \\\n",
- " Unnamed: 5_level_1 \n",
- "1 756 \n",
- "2 856 \n",
- "3 812 \n",
- "4 802 \n",
- "5 760 \n",
- "\n",
- "Week number Unnamed: 6_level_0 \\\n",
- " Deaths by cause \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "5 NaN \n",
- "\n",
- "Week number Unnamed: 7_level_0 \\\n",
- " Deaths where the underlying cause was respiratory disease (ICD-10 J00-J99) \n",
- "1 2141 \n",
- "2 2477 \n",
- "3 2189 \n",
- "4 1893 \n",
- "5 1746 \n",
- "\n",
- "Week number Unnamed: 8_level_0 \\\n",
- " Deaths where COVID-19 was mentioned on the death certificate (ICD-10 U07.1 and U07.2) \n",
- "1 0 \n",
- "2 0 \n",
- "3 0 \n",
- "4 0 \n",
- "5 0 \n",
- "\n",
- "Week number Persons Deaths by age group ... E12000001 E12000002 \\\n",
- " Unnamed: 9_level_1 <1 ... North East North West \n",
- "1 NaN 48 ... 673 1806 \n",
- "2 NaN 50 ... 707 1932 \n",
- "3 NaN 69 ... 647 1696 \n",
- "4 NaN 53 ... 612 1529 \n",
- "5 NaN 50 ... 561 1461 \n",
- "\n",
- "Week number E12000003 E12000004 E12000005 E12000006 \\\n",
- " Yorkshire and The Humber East Midlands West Midlands East \n",
- "1 1240 1060 1349 1162 \n",
- "2 1339 1195 1450 1573 \n",
- "3 1278 1106 1407 1457 \n",
- "4 1187 1024 1231 1410 \n",
- "5 1136 1015 1262 1286 \n",
- "\n",
- "Week number E12000007 E12000008 E12000009 W92000004 \n",
- " London South East South West Wales \n",
- "1 1113 1814 1225 787 \n",
- "2 1272 2132 1487 939 \n",
- "3 1073 2064 1466 767 \n",
- "4 1028 1833 1253 723 \n",
- "5 1092 1820 1233 727 \n",
- "\n",
- "[5 rows x 84 columns]"
+ " total_2020\n",
+ "1 353\n",
+ "2 395\n",
+ "3 411\n",
+ "4 347\n",
+ "5 323"
]
},
- "execution_count": 50,
+ "execution_count": 144,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "raw_data_2020.head()"
+ "raw_data_2020_i = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2020.csv', \n",
+ " parse_dates=[1], dayfirst=True,\n",
+ " index_col=0,\n",
+ " header=[0, 1]\n",
+ " )\n",
+ "deaths_headlines_i = raw_data_2020_i.iloc[:, [1]]\n",
+ "deaths_headlines_i.columns = ['total_2020']\n",
+ "deaths_headlines_i.head()"
]
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 185,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "raw_data_s = pd.read_csv('uk-deaths-data/weekly-deaths-april-20-scotland.csv', \n",
+ " index_col=0,\n",
+ " header=0,\n",
+ " skiprows=2\n",
+ " )\n",
+ "# raw_data_s"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 184,
+ "metadata": {
+ "scrolled": true
+ },
"outputs": [
{
"data": {
@@ -361,535 +269,2262 @@
" vertical-align: top;\n",
" }\n",
"\n",
- " .dataframe thead tr th {\n",
- " text-align: left;\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
" }\n",
"\n",
"\n",
" \n",
- " \n",
- " Week number | \n",
- " Week ended | \n",
- " Total deaths, all ages | \n",
- " Total deaths: average of corresponding | \n",
- " Unnamed: 4_level_0 | \n",
- " Unnamed: 5_level_0 | \n",
- " Persons | \n",
- " Unnamed: 7_level_0 | \n",
- " Unnamed: 8_level_0 | \n",
- " Unnamed: 9_level_0 | \n",
- " Unnamed: 10_level_0 | \n",
- " ... | \n",
- " E12000001 | \n",
- " E12000002 | \n",
- " E12000003 | \n",
- " E12000004 | \n",
- " E12000005 | \n",
- " E12000006 | \n",
- " E12000007 | \n",
- " E12000008 | \n",
- " E12000009 | \n",
- " W92000004 | \n",
- "
\n",
- " \n",
+ "
\n",
" | \n",
- " Unnamed: 1_level_1 | \n",
- " Unnamed: 2_level_1 | \n",
- " Unnamed: 3_level_1 | \n",
- " Deaths by underlying cause | \n",
- " All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS) | \n",
- " Deaths by age group | \n",
- " Under 1 year | \n",
- " 01-14 | \n",
- " 15-44 | \n",
- " 45-64 | \n",
- " ... | \n",
- " North East | \n",
- " North West | \n",
- " Yorkshire and The Humber | \n",
- " East Midlands | \n",
- " West Midlands | \n",
- " East | \n",
- " London | \n",
- " South East | \n",
- " South West | \n",
- " Wales | \n",
+ " total_2020 | \n",
+ " total_2019 | \n",
+ " total_2018 | \n",
+ " total_2017 | \n",
+ " total_2016 | \n",
+ " total_2015 | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
- " 2019-01-04 | \n",
- " 10955 | \n",
- " 12273 | \n",
- " NaN | \n",
- " 1736 | \n",
- " NaN | \n",
- " 43 | \n",
- " 15 | \n",
- " 215 | \n",
- " 1199 | \n",
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- " 574 | \n",
- " 1456 | \n",
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- " 1180 | \n",
- " 1136 | \n",
- " 1021 | \n",
- " 1586 | \n",
- " 1136 | \n",
- " 718 | \n",
+ " 1161.0 | \n",
+ " 1104.0 | \n",
+ " 1531.0 | \n",
+ " 1205.0 | \n",
+ " 1394.0 | \n",
+ " 1146 | \n",
"
\n",
" \n",
" 2 | \n",
- " 2019-01-11 | \n",
- " 12609 | \n",
- " 13670 | \n",
- " NaN | \n",
- " 2214 | \n",
- " NaN | \n",
- " 50 | \n",
- " 20 | \n",
- " 280 | \n",
- " 1419 | \n",
- " ... | \n",
- " 643 | \n",
- " 1612 | \n",
- " 1279 | \n",
- " 1089 | \n",
- " 1299 | \n",
- " 1386 | \n",
- " 1137 | \n",
- " 1927 | \n",
- " 1394 | \n",
- " 809 | \n",
+ " 1567.0 | \n",
+ " 1507.0 | \n",
+ " 1899.0 | \n",
+ " 1379.0 | \n",
+ " 1305.0 | \n",
+ " 1708 | \n",
"
\n",
" \n",
" 3 | \n",
- " 2019-01-18 | \n",
- " 11860 | \n",
- " 13056 | \n",
- " NaN | \n",
- " 1972 | \n",
- " NaN | \n",
- " 59 | \n",
- " 29 | \n",
- " 319 | \n",
- " 1373 | \n",
- " ... | \n",
- " 627 | \n",
- " 1566 | \n",
- " 1197 | \n",
- " 979 | \n",
- " 1187 | \n",
- " 1344 | \n",
- " 1073 | \n",
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- " 1236 | \n",
- " 683 | \n",
+ " 1322.0 | \n",
+ " 1353.0 | \n",
+ " 1629.0 | \n",
+ " 1224.0 | \n",
+ " 1215.0 | \n",
+ " 1489 | \n",
"
\n",
" \n",
" 4 | \n",
- " 2019-01-25 | \n",
- " 11740 | \n",
- " 12486 | \n",
- " NaN | \n",
- " 1942 | \n",
- " NaN | \n",
- " 42 | \n",
- " 22 | \n",
- " 339 | \n",
- " 1438 | \n",
- " ... | \n",
- " 640 | \n",
- " 1593 | \n",
- " 1137 | \n",
- " 1037 | \n",
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- " 1291 | \n",
- " 1045 | \n",
- " 1875 | \n",
- " 1229 | \n",
- " 734 | \n",
+ " 1226.0 | \n",
+ " 1208.0 | \n",
+ " 1610.0 | \n",
+ " 1197.0 | \n",
+ " 1187.0 | \n",
+ " 1381 | \n",
"
\n",
" \n",
" 5 | \n",
- " 2019-02-01 | \n",
- " 11297 | \n",
- " 11998 | \n",
- " NaN | \n",
- " 1931 | \n",
- " NaN | \n",
- " 57 | \n",
+ " 1188.0 | \n",
+ " 1206.0 | \n",
+ " 1369.0 | \n",
+ " 1332.0 | \n",
+ " 1205.0 | \n",
+ " 1286 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " 1216.0 | \n",
+ " 1243.0 | \n",
+ " 1265.0 | \n",
+ " 1200.0 | \n",
+ " 1217.0 | \n",
+ " 1344 | \n",
+ "
\n",
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- "Week number Week ended Total deaths, all ages \\\n",
- " Unnamed: 1_level_1 Unnamed: 2_level_1 \n",
- "1 2019-01-04 10955 \n",
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- " 15-44 45-64 ... North East North West \n",
- "1 215 1199 ... 574 1456 \n",
- "2 280 1419 ... 643 1612 \n",
- "3 319 1373 ... 627 1566 \n",
- "4 339 1438 ... 640 1593 \n",
- "5 307 1367 ... 618 1485 \n",
- "\n",
- "Week number E12000003 E12000004 E12000005 E12000006 \\\n",
- " Yorkshire and The Humber East Midlands West Midlands East \n",
- "1 1148 964 1180 1136 \n",
- "2 1279 1089 1299 1386 \n",
- "3 1197 979 1187 1344 \n",
- "4 1137 1037 1131 1291 \n",
- "5 1131 963 1150 1202 \n",
- "\n",
- "Week number E12000007 E12000008 E12000009 W92000004 \n",
- " London South East South West Wales \n",
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- "\n",
- "[5 rows x 40 columns]"
- ]
- },
- "execution_count": 78,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "raw_data_2019 = pd.read_csv('uk-deaths-data/publishedweek522019.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1])\n",
- "raw_data_2019.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 79,
- "metadata": {},
- "outputs": [
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+ "53 NaN NaN NaN NaN NaN 1018"
+ ]
+ },
+ "execution_count": 184,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]\n",
+ "deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]\n",
+ "deaths_headlines_s.reset_index(drop=True, inplace=True)\n",
+ "deaths_headlines_s.index = deaths_headlines_s.index + 1\n",
+ "deaths_headlines_s"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "raw_data_2020 = pd.read_csv('uk-deaths-data/publishedweek182020.csv', \n",
+ " parse_dates=[1], dayfirst=True,\n",
+ " index_col=0,\n",
+ " header=[0, 1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# raw_data_2020.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1 787\n",
+ "2 939\n",
+ "3 767\n",
+ "4 723\n",
+ "5 727\n",
+ "6 690\n",
+ "7 728\n",
+ "8 679\n",
+ "9 651\n",
+ "10 652\n",
+ "11 675\n",
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+ "13 719\n",
+ "14 920\n",
+ "15 928\n",
+ "16 1169\n",
+ "17 1124\n",
+ "18 929\n",
+ "Name: (W92000004, Wales), dtype: int64"
+ ]
+ },
+ "execution_count": 147,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "raw_data_2020['W92000004', 'Wales']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "raw_data_2019 = pd.read_csv('uk-deaths-data/publishedweek522019.csv', \n",
+ " parse_dates=[1], dayfirst=True,\n",
+ " index_col=0,\n",
+ " header=[0, 1])\n",
+ "# raw_data_2019.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "raw_data_2018 = pd.read_csv('uk-deaths-data/publishedweek522018.csv', \n",
+ " parse_dates=[1], dayfirst=True,\n",
+ " index_col=0,\n",
+ " header=[0, 1])\n",
+ "# raw_data_2018.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "raw_data_2017 = pd.read_csv('uk-deaths-data/publishedweek522017.csv', \n",
+ " parse_dates=[1], dayfirst=True,\n",
+ " index_col=0,\n",
+ " header=[0, 1])\n",
+ "# raw_data_2017.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "raw_data_2016 = pd.read_csv('uk-deaths-data/publishedweek522016.csv', \n",
+ " parse_dates=[1], dayfirst=True,\n",
+ " index_col=0,\n",
+ " header=[0, 1])\n",
+ "# raw_data_2016.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 152,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "raw_data_2015 = pd.read_csv('uk-deaths-data/publishedweek2015.csv', \n",
+ " parse_dates=[1], dayfirst=True,\n",
+ " index_col=0,\n",
+ " header=[0, 1])\n",
+ "# raw_data_2015.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ ]
+ },
+ "execution_count": 153,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "deaths_headlines_e = raw_data_2020.iloc[:, [1]]\n",
+ "deaths_headlines_e.columns = ['total_2020']\n",
+ "deaths_headlines_w = raw_data_2020['W92000004']\n",
+ "deaths_headlines_e.columns = ['total_2020']\n",
+ "deaths_headlines_w.columns = ['total_2020']\n",
+ "deaths_headlines_e.total_2020 -= deaths_headlines_w.total_2020\n",
+ "deaths_headlines_e.head()\n",
+ "deaths_headlines_e"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "metadata": {},
+ "outputs": [
+ {
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+ ]
+ },
+ "execution_count": 154,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dh19e = raw_data_2019.iloc[:, [1]]\n",
+ "dh19w = raw_data_2019['W92000004']\n",
+ "dh19e.columns = ['total_2019']\n",
+ "dh19w.columns = ['total_2019']\n",
+ "dh19e.total_2019 -= dh19w.total_2019\n",
+ "dh19e.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "metadata": {},
+ "outputs": [
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+ "1 718\n",
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+ "5 745"
+ ]
+ },
+ "execution_count": 155,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dh19w.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dh18e = raw_data_2018.iloc[:, [1]]\n",
+ "dh18w = raw_data_2018['W92000004']\n",
+ "dh18e.columns = ['total_2018']\n",
+ "dh18w.columns = ['total_2018']\n",
+ "dh18e.total_2018 -= dh18w.total_2018\n",
+ "# dh18e.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 157,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dh17e = raw_data_2017.iloc[:, [1]]\n",
+ "dh17w = raw_data_2017['W92000004']\n",
+ "dh17e.columns = ['total_2017']\n",
+ "dh17w.columns = ['total_2017']\n",
+ "dh17e.total_2017 -= dh17w.total_2017\n",
+ "# dh17e.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dh16e = raw_data_2016.iloc[:, [1]]\n",
+ "dh16w = raw_data_2016['W92000004']\n",
+ "dh16e.columns = ['total_2016']\n",
+ "dh16w.columns = ['total_2016']\n",
+ "dh16e.total_2016 -= dh16w.total_2016\n",
+ "# dh16e.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 159,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dh15e = raw_data_2015.iloc[:, [1]]\n",
+ "dh15w = raw_data_2015['W92000004']\n",
+ "dh15e.columns = ['total_2015']\n",
+ "dh15w.columns = ['total_2015']\n",
+ "dh15e.total_2015 -= dh15w.total_2015\n",
+ "# dh15e.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# dh18 = raw_data_2018.iloc[:, [1, 2]]\n",
+ "# dh18.columns = ['total_2018', 'total_previous']\n",
+ "# # dh18.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 161,
+ "metadata": {},
+ "outputs": [
+ {
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+ "text/plain": [
+ " total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
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+ "43 NaN 9351 8970 9131 9100 9104\n",
+ "44 NaN 9522 8925 9389 9508 9025\n",
+ "45 NaN 10044 9509 9748 9844 9388\n",
+ "46 NaN 9976 9537 9609 10013 9325\n",
+ "47 NaN 10183 9300 9982 9942 9219\n",
+ "48 NaN 10269 9360 9902 9796 9233\n",
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+ "51 NaN 11159 10392 11755 10802 10043\n",
+ "52 NaN 7037 6670 7946 7445 8095"
+ ]
+ },
+ "execution_count": 161,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "deaths_headlines_e = deaths_headlines_e.merge(dh19e['total_2019'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_e = deaths_headlines_e.merge(dh18e['total_2018'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_e = deaths_headlines_e.merge(dh17e['total_2017'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_e = deaths_headlines_e.merge(dh16e['total_2016'], how='outer', left_index=True, right_index=True)\n",
+ "# deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_e = deaths_headlines_e.merge(dh15e['total_2015'], how='left', left_index=True, right_index=True)\n",
+ "deaths_headlines_e"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 182,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
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+ " 1133.0 | \n",
+ " 944 | \n",
+ "
\n",
+ " \n",
+ " 53 | \n",
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+ " 1018 | \n",
"
\n",
" \n",
"
\n",
- "
5 rows à 40 columns
\n",
"
"
],
"text/plain": [
- "Week number Week ended Total deaths, all ages \\\n",
- " Unnamed: 1_level_1 Unnamed: 2_level_1 \n",
- "1 2018-01-05 12723 \n",
- "2 2018-01-12 15050 \n",
- "3 2018-01-19 14256 \n",
- "4 2018-01-26 13935 \n",
- "5 2018-02-02 13285 \n",
- "\n",
- "Week number Total deaths: average of corresponding Unnamed: 4_level_0 \\\n",
- " Unnamed: 3_level_1 Deaths by underlying cause \n",
- "1 12079 NaN \n",
- "2 13167 NaN \n",
- "3 12418 NaN \n",
- "4 11954 NaN \n",
- "5 11605 NaN \n",
- "\n",
- "Week number Unnamed: 5_level_0 \\\n",
- " All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS) \n",
- "1 2361 \n",
- "2 3075 \n",
- "3 2829 \n",
- "4 2672 \n",
- "5 2479 \n",
- "\n",
- "Week number Persons Unnamed: 7_level_0 Unnamed: 8_level_0 \\\n",
- " Deaths by age group Under 1 year 01-14 \n",
- "1 NaN 52 18 \n",
- "2 NaN 73 17 \n",
- "3 NaN 59 22 \n",
- "4 NaN 50 25 \n",
- "5 NaN 41 14 \n",
- "\n",
- "Week number Unnamed: 9_level_0 Unnamed: 10_level_0 ... E12000001 E12000002 \\\n",
- " 15-44 45-64 ... North East North West \n",
- "1 208 1290 ... 640 1747 \n",
- "2 302 1561 ... 837 2113 \n",
- "3 286 1507 ... 783 1828 \n",
- "4 298 1459 ... 779 1855 \n",
- "5 339 1404 ... 740 1728 \n",
- "\n",
- "Week number E12000003 E12000004 E12000005 E12000006 \\\n",
- " Yorkshire and The Humber East Midlands West Midlands East \n",
- "1 1300 1085 1313 1404 \n",
- "2 1481 1266 1468 1677 \n",
- "3 1387 1175 1544 1562 \n",
- "4 1363 1165 1433 1553 \n",
- "5 1285 1154 1441 1468 \n",
- "\n",
- "Week number E12000007 E12000008 E12000009 W92000004 \n",
- " London South East South West Wales \n",
- "1 1130 1902 1393 783 \n",
- "2 1342 2371 1552 904 \n",
- "3 1239 2287 1535 885 \n",
- "4 1264 2184 1460 850 \n",
- "5 1208 2046 1382 815 \n",
- "\n",
- "[5 rows x 40 columns]"
+ " total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
+ "1 1161.0 1104.0 1531.0 1205.0 1394.0 1146\n",
+ "2 1567.0 1507.0 1899.0 1379.0 1305.0 1708\n",
+ "3 1322.0 1353.0 1629.0 1224.0 1215.0 1489\n",
+ "4 1226.0 1208.0 1610.0 1197.0 1187.0 1381\n",
+ "5 1188.0 1206.0 1369.0 1332.0 1205.0 1286\n",
+ "6 1216.0 1243.0 1265.0 1200.0 1217.0 1344\n",
+ "7 1162.0 1181.0 1315.0 1231.0 1209.0 1360\n",
+ "8 1162.0 1245.0 1245.0 1185.0 1239.0 1320\n",
+ "9 1171.0 1125.0 1022.0 1219.0 1150.0 1308\n",
+ "10 1208.0 1156.0 1475.0 1146.0 1174.0 1192\n",
+ "11 1156.0 1108.0 1220.0 1141.0 1175.0 1201\n",
+ "12 1196.0 1101.0 1158.0 1152.0 1042.0 1149\n",
+ "13 1079.0 1086.0 1050.0 1112.0 1172.0 1171\n",
+ "14 1744.0 1032.0 1192.0 1060.0 1166.0 1042\n",
+ "15 1978.0 1069.0 1192.0 998.0 1048.0 1192\n",
+ "16 1916.0 902.0 1136.0 1111.0 1092.0 1095\n",
+ "17 1836.0 1121.0 1008.0 1121.0 1076.0 1108\n",
+ "18 1679.0 1131.0 1093.0 1050.0 1006.0 1117\n",
+ "19 1434.0 1018.0 967.0 1119.0 1047.0 1020\n",
+ "20 NaN 1115.0 977.0 1115.0 1010.0 1103\n",
+ "21 NaN 1061.0 1070.0 1063.0 994.0 1039\n",
+ "22 NaN 1029.0 998.0 1015.0 999.0 1043\n",
+ "23 NaN 1042.0 1033.0 1076.0 1023.0 1106\n",
+ "24 NaN 1028.0 915.0 1031.0 988.0 1038\n",
+ "25 NaN 1053.0 993.0 1032.0 994.0 1025\n",
+ "26 NaN 1051.0 1046.0 994.0 1007.0 1032\n",
+ "27 NaN 981.0 1041.0 1040.0 988.0 1040\n",
+ "28 NaN 1077.0 1002.0 1014.0 1022.0 1011\n",
+ "29 NaN 964.0 928.0 1025.0 1041.0 1023\n",
+ "30 NaN 1041.0 933.0 978.0 979.0 956\n",
+ "31 NaN 1020.0 969.0 1011.0 987.0 985\n",
+ "32 NaN 1018.0 953.0 1002.0 997.0 1043\n",
+ "33 NaN 1028.0 978.0 1004.0 982.0 969\n",
+ "34 NaN 1011.0 941.0 1045.0 1017.0 982\n",
+ "35 NaN 1013.0 930.0 980.0 1039.0 954\n",
+ "36 NaN 980.0 970.0 1006.0 1007.0 977\n",
+ "37 NaN 1074.0 1020.0 972.0 983.0 991\n",
+ "38 NaN 1071.0 946.0 1049.0 966.0 1001\n",
+ "39 NaN 1142.0 1015.0 1056.0 1009.0 1010\n",
+ "40 NaN 1051.0 1042.0 1016.0 1072.0 1008\n",
+ "41 NaN 1143.0 1081.0 1133.0 1009.0 1028\n",
+ "42 NaN 1153.0 1031.0 1067.0 1070.0 989\n",
+ "43 NaN 1115.0 1019.0 1095.0 1052.0 981\n",
+ "44 NaN 1101.0 1085.0 1062.0 1032.0 1116\n",
+ "45 NaN 1184.0 1144.0 1126.0 1043.0 1028\n",
+ "46 NaN 1160.0 1084.0 1175.0 1174.0 1103\n",
+ "47 NaN 1229.0 1058.0 1178.0 1132.0 1054\n",
+ "48 NaN 1163.0 1062.0 1153.0 1159.0 1115\n",
+ "49 NaN 1108.0 1076.0 1237.0 1188.0 1089\n",
+ "50 NaN 1312.0 1212.0 1335.0 1219.0 1101\n",
+ "51 NaN 1277.0 1216.0 1437.0 1284.0 1146\n",
+ "52 NaN 1000.0 1058.0 1168.0 1133.0 944\n",
+ "53 NaN NaN NaN NaN NaN 1018"
]
},
- "execution_count": 79,
+ "execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "raw_data_2018 = pd.read_csv('uk-deaths-data/publishedweek522018.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1])\n",
- "raw_data_2018.head()"
+ "deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]\n",
+ "deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]\n",
+ "deaths_headlines_s.reset_index(drop=True, inplace=True)\n",
+ "deaths_headlines_s.index = deaths_headlines_s.index + 1\n",
+ "deaths_headlines_s"
]
},
{
"cell_type": "code",
- "execution_count": 89,
+ "execution_count": 162,
"metadata": {},
"outputs": [
{
@@ -905,263 +2540,569 @@
" vertical-align: top;\n",
" }\n",
"\n",
- " .dataframe thead tr th {\n",
- " text-align: left;\n",
+ " .dataframe thead th {\n",
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- " \n",
- " Week number | \n",
- " Week ended | \n",
- " Total deaths, all ages | \n",
- " Total deaths: average of corresponding | \n",
- " Unnamed: 4_level_0 | \n",
- " Unnamed: 5_level_0 | \n",
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\n",
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- " Unnamed: 1_level_1 | \n",
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- " Deaths by age group | \n",
- " Under 1 year | \n",
- " 01-14 | \n",
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+ " 699 | \n",
+ " 657 | \n",
+ " 639 | \n",
+ " 661 | \n",
+ " 611 | \n",
+ "
\n",
+ " \n",
+ " 48 | \n",
+ " NaN | \n",
+ " 689 | \n",
+ " 673 | \n",
+ " 636 | \n",
+ " 643 | \n",
+ " 589 | \n",
+ "
\n",
+ " \n",
+ " 49 | \n",
+ " NaN | \n",
+ " 737 | \n",
+ " 674 | \n",
+ " 630 | \n",
+ " 693 | \n",
+ " 659 | \n",
+ "
\n",
+ " \n",
+ " 50 | \n",
+ " NaN | \n",
+ " 699 | \n",
+ " 708 | \n",
+ " 708 | \n",
+ " 663 | \n",
+ " 688 | \n",
"
\n",
" \n",
- " 5 | \n",
- " 2017-02-03 | \n",
- " 12485 | \n",
- " 11128 | \n",
+ " 51 | \n",
" NaN | \n",
- " 2147 | \n",
+ " 767 | \n",
+ " 724 | \n",
+ " 762 | \n",
+ " 691 | \n",
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+ "
\n",
+ " \n",
+ " 52 | \n",
" NaN | \n",
- " 55 | \n",
- " 14 | \n",
- " 308 | \n",
- " 1239 | \n",
- " ... | \n",
- " 631 | \n",
- " 1625 | \n",
- " 1243 | \n",
- " 1045 | \n",
- " 1293 | \n",
- " 1384 | \n",
- " 1210 | \n",
- " 1920 | \n",
- " 1359 | \n",
- " 749 | \n",
+ " 496 | \n",
+ " 461 | \n",
+ " 541 | \n",
+ " 558 | \n",
+ " 535 | \n",
"
\n",
" \n",
"
\n",
- "5 rows à 40 columns
\n",
""
],
"text/plain": [
- "Week number Week ended Total deaths, all ages \\\n",
- " Unnamed: 1_level_1 Unnamed: 2_level_1 \n",
- "1 2017-01-06 11991 \n",
- "2 2017-01-13 13715 \n",
- "3 2017-01-20 13610 \n",
- "4 2017-01-27 12877 \n",
- "5 2017-02-03 12485 \n",
- "\n",
- "Week number Total deaths: average of corresponding Unnamed: 4_level_0 \\\n",
- " Unnamed: 3_level_1 Deaths by underlying cause \n",
- "1 11782 NaN \n",
- "2 12692 NaN \n",
- "3 11773 NaN \n",
- "4 11441 NaN \n",
- "5 11128 NaN \n",
- "\n",
- "Week number Unnamed: 5_level_0 \\\n",
- " All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS) \n",
- "1 2321 \n",
- "2 2690 \n",
- "3 2625 \n",
- "4 2373 \n",
- "5 2147 \n",
- "\n",
- "Week number Persons Unnamed: 7_level_0 Unnamed: 8_level_0 \\\n",
- " Deaths by age group Under 1 year 01-14 \n",
- "1 NaN 58 18 \n",
- "2 NaN 56 18 \n",
- "3 NaN 54 19 \n",
- "4 NaN 52 16 \n",
- "5 NaN 55 14 \n",
- "\n",
- "Week number Unnamed: 9_level_0 Unnamed: 10_level_0 ... E12000001 E12000002 \\\n",
- " 15-44 45-64 ... North East North West \n",
- "1 215 1232 ... 602 1665 \n",
- "2 287 1372 ... 738 1840 \n",
- "3 301 1374 ... 676 1786 \n",
- "4 286 1273 ... 627 1666 \n",
- "5 308 1239 ... 631 1625 \n",
- "\n",
- "Week number E12000003 E12000004 E12000005 E12000006 \\\n",
- " Yorkshire and The Humber East Midlands West Midlands East \n",
- "1 1208 1016 1243 1187 \n",
- "2 1351 1170 1439 1450 \n",
- "3 1383 1190 1414 1484 \n",
- "4 1242 1116 1362 1488 \n",
- "5 1243 1045 1293 1384 \n",
- "\n",
- "Week number E12000007 E12000008 E12000009 W92000004 \n",
- " London South East South West Wales \n",
- "1 1173 1899 1228 744 \n",
- "2 1296 2161 1419 825 \n",
- "3 1276 2195 1346 835 \n",
- "4 1198 1999 1278 881 \n",
- "5 1210 1920 1359 749 \n",
- "\n",
- "[5 rows x 40 columns]"
+ " total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
+ "1 787.0 718 783 744 809 725\n",
+ "2 939.0 809 904 825 711 1031\n",
+ "3 767.0 683 885 835 720 936\n",
+ "4 723.0 734 850 881 717 828\n",
+ "5 727.0 745 815 749 690 801\n",
+ "6 690.0 701 801 723 700 720\n",
+ "7 728.0 748 803 690 657 710\n",
+ "8 679.0 695 789 701 696 739\n",
+ "9 651.0 684 634 715 721 736\n",
+ "10 652.0 622 896 634 734 712\n",
+ "11 675.0 666 918 653 738 661\n",
+ "12 719.0 628 774 635 668 680\n",
+ "13 719.0 654 633 658 712 666\n",
+ "14 920.0 642 730 642 742 580\n",
+ "15 928.0 637 743 577 738 660\n",
+ "16 1169.0 580 688 654 714 671\n",
+ "17 1124.0 678 614 727 629 662\n",
+ "18 929.0 688 633 600 569 628\n",
+ "19 NaN 600 530 687 652 589\n",
+ "20 NaN 658 667 615 611 622\n",
+ "21 NaN 627 603 602 624 614\n",
+ "22 NaN 518 538 586 500 588\n",
+ "23 NaN 626 607 584 584 648\n",
+ "24 NaN 598 561 599 578 606\n",
+ "25 NaN 542 562 608 558 595\n",
+ "26 NaN 564 599 546 549 597\n",
+ "27 NaN 534 625 556 524 535\n",
+ "28 NaN 588 583 564 599 554\n",
+ "29 NaN 553 548 551 560 574\n",
+ "30 NaN 543 560 586 610 529\n",
+ "31 NaN 570 574 590 580 546\n",
+ "32 NaN 542 537 572 641 564\n",
+ "33 NaN 589 518 592 574 548\n",
+ "34 NaN 553 565 570 619 557\n",
+ "35 NaN 558 511 555 470 603\n",
+ "36 NaN 582 594 542 552 489\n",
+ "37 NaN 567 579 609 576 554\n",
+ "38 NaN 572 598 585 563 555\n",
+ "39 NaN 611 560 593 592 664\n",
+ "40 NaN 597 595 631 562 552\n",
+ "41 NaN 590 606 640 587 652\n",
+ "42 NaN 622 640 650 624 612\n",
+ "43 NaN 670 633 608 624 607\n",
+ "44 NaN 642 604 595 644 593\n",
+ "45 NaN 653 642 598 626 606\n",
+ "46 NaN 674 656 666 681 613\n",
+ "47 NaN 699 657 639 661 611\n",
+ "48 NaN 689 673 636 643 589\n",
+ "49 NaN 737 674 630 693 659\n",
+ "50 NaN 699 708 708 663 688\n",
+ "51 NaN 767 724 762 691 646\n",
+ "52 NaN 496 461 541 558 535"
]
},
- "execution_count": 89,
+ "execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "raw_data_2017 = pd.read_csv('uk-deaths-data/publishedweek522017.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1])\n",
- "raw_data_2017.head()"
+ "deaths_headlines_w = deaths_headlines_w.merge(dh19w['total_2019'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_w = deaths_headlines_w.merge(dh18w['total_2018'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_w = deaths_headlines_w.merge(dh17w['total_2017'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_w = deaths_headlines_w.merge(dh16w['total_2016'], how='outer', left_index=True, right_index=True)\n",
+ "# deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_w = deaths_headlines_w.merge(dh15w['total_2015'], how='left', left_index=True, right_index=True)\n",
+ "deaths_headlines_w"
]
},
{
"cell_type": "code",
- "execution_count": 98,
+ "execution_count": 163,
"metadata": {},
"outputs": [
{
@@ -1177,535 +3118,568 @@
" vertical-align: top;\n",
" }\n",
"\n",
- " .dataframe thead tr th {\n",
- " text-align: left;\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
" }\n",
"\n",
"\n",
" \n",
- " \n",
- " Week number | \n",
- " Week ended | \n",
- " Total deaths, all ages | \n",
- " Total deaths: average of corresponding | \n",
- " Unnamed: 4_level_0 | \n",
- " Unnamed: 5_level_0 | \n",
- " Persons | \n",
- " Unnamed: 7_level_0 | \n",
- " Unnamed: 8_level_0 | \n",
- " Unnamed: 9_level_0 | \n",
- " Unnamed: 10_level_0 | \n",
- " ... | \n",
- " E12000001 | \n",
- " E12000002 | \n",
- " E12000003 | \n",
- " E12000004 | \n",
- " E12000005 | \n",
- " E12000006 | \n",
- " E12000007 | \n",
- " E12000008 | \n",
- " E12000009 | \n",
- " W92000004 | \n",
- "
\n",
- " \n",
+ "
\n",
" | \n",
- " Unnamed: 1_level_1 | \n",
- " Unnamed: 2_level_1 | \n",
- " Unnamed: 3_level_1 | \n",
- " Deaths by underlying cause | \n",
- " All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS) | \n",
- " Deaths by age group | \n",
- " Under 1 year | \n",
- " 01-14 | \n",
- " 15-44 | \n",
- " 45-64 | \n",
- " ... | \n",
- " North East | \n",
- " North West | \n",
- " Yorkshire and The Humber | \n",
- " East Midlands | \n",
- " West Midlands | \n",
- " East | \n",
- " London | \n",
- " South East | \n",
- " South West | \n",
- " Wales | \n",
+ " total_2020 | \n",
+ " total_2019 | \n",
+ " total_2018 | \n",
+ " total_2017 | \n",
+ " total_2016 | \n",
+ " total_2015 | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
- " 2016-01-08 | \n",
- " 13045 | \n",
- " 11701.4 | \n",
+ " 353.0 | \n",
+ " 365 | \n",
+ " 447 | \n",
+ " 416 | \n",
+ " 424 | \n",
+ " 319 | \n",
+ "
\n",
+ " \n",
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+ "
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+ " \n",
+ " 3 | \n",
+ " 411.0 | \n",
+ " 332 | \n",
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+ " 397 | \n",
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+ "
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+ " \n",
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+ " 335 | \n",
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+ "
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+ " \n",
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+ " \n",
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+ " \n",
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+ "
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"
\n",
" \n",
- " 2 | \n",
- " 2016-01-15 | \n",
- " 11501 | \n",
- " 13016.4 | \n",
+ " 21 | \n",
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+ " 308 | \n",
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+ " \n",
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+ " NaN | \n",
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"
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" \n",
- " 3 | \n",
- " 2016-01-22 | \n",
- " 11473 | \n",
- " 11765.4 | \n",
+ " 25 | \n",
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"
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- " 11289.0 | \n",
+ " 27 | \n",
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- " 1810 | \n",
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"
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" \n",
- " 5 | \n",
- " 2016-02-05 | \n",
- " 11052 | \n",
- " 10965.6 | \n",
+ " 29 | \n",
" NaN | \n",
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+ " 275 | \n",
+ " 287 | \n",
"
\n",
- " \n",
- "
\n",
- "5 rows à 40 columns
\n",
- ""
- ],
- "text/plain": [
- "Week number Week ended Total deaths, all ages \\\n",
- " Unnamed: 1_level_1 Unnamed: 2_level_1 \n",
- "1 2016-01-08 13045 \n",
- "2 2016-01-15 11501 \n",
- "3 2016-01-22 11473 \n",
- "4 2016-01-29 11317 \n",
- "5 2016-02-05 11052 \n",
- "\n",
- "Week number Total deaths: average of corresponding Unnamed: 4_level_0 \\\n",
- " Unnamed: 3_level_1 Deaths by underlying cause \n",
- "1 11701.4 NaN \n",
- "2 13016.4 NaN \n",
- "3 11765.4 NaN \n",
- "4 11289.0 NaN \n",
- "5 10965.6 NaN \n",
- "\n",
- "Week number Unnamed: 5_level_0 \\\n",
- " All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS) \n",
- "1 2046 \n",
- "2 1835 \n",
- "3 1775 \n",
- "4 1810 \n",
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- "Week number Persons Unnamed: 7_level_0 Unnamed: 8_level_0 \\\n",
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- "\n",
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- ]
- },
- "execution_count": 98,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "raw_data_2016 = pd.read_csv('uk-deaths-data/publishedweek522016.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1])\n",
- "raw_data_2016.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 114,
- "metadata": {},
- "outputs": [
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},
- "execution_count": 114,
+ "execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "raw_data_2015 = pd.read_csv('uk-deaths-data/publishedweek2015.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1])\n",
- "raw_data_2015.head()"
+ "deaths_headlines_i = deaths_headlines_i.merge(dh19i['total_2019'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_i = deaths_headlines_i.merge(dh18i['total_2018'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_i = deaths_headlines_i.merge(dh17i['total_2017'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_i = deaths_headlines_i.merge(dh16i['total_2016'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines_i = deaths_headlines_i.merge(dh15i['total_2015'], how='left', left_index=True, right_index=True)\n",
+ "deaths_headlines_i"
]
},
{
"cell_type": "code",
- "execution_count": 115,
+ "execution_count": 186,
"metadata": {},
"outputs": [
{
@@ -1730,534 +3704,565 @@
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- "text/plain": [
- " total_2020 total_previous\n",
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- "execution_count": 115,
- "metadata": {},
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- "source": [
- "deaths_headlines = raw_data_2020.iloc[:, [1, 2]]\n",
- "deaths_headlines.columns = ['total_2020', 'total_previous']\n",
- "deaths_headlines"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 116,
- "metadata": {},
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- "dh15.columns = ['total_2015', 'total_previous']\n",
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{
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+ "execution_count": 187,
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+ " 12649.0 | \n",
+ " 14308.0 | \n",
+ " 13137.0 | \n",
+ " 12136.0 | \n",
+ " 13159.2 | \n",
"
\n",
" \n",
" 52 | \n",
" NaN | \n",
- " NaN | \n",
- " 7533.0 | \n",
- " 7131.0 | \n",
- " 8487.0 | \n",
- " 8003.0 | \n",
- " 8630 | \n",
+ " 8727.0 | \n",
+ " 8384.0 | \n",
+ " 9904.0 | \n",
+ " 9335.0 | \n",
+ " 9806.0 | \n",
+ " 9231.2 | \n",
"
\n",
" \n",
" 53 | \n",
@@ -2819,156 +4824,152 @@
" NaN | \n",
" NaN | \n",
" NaN | \n",
- " 7524 | \n",
+ " NaN | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " total_2020 total_previous total_2019 total_2018 total_2017 \\\n",
- "1 12254.0 12175.0 10955.0 12723.0 11991.0 \n",
- "2 14058.0 13822.0 12609.0 15050.0 13715.0 \n",
- "3 12990.0 13216.0 11860.0 14256.0 13610.0 \n",
- "4 11856.0 12760.0 11740.0 13935.0 12877.0 \n",
- "5 11612.0 12206.0 11297.0 13285.0 12485.0 \n",
- "6 10986.0 11925.0 11660.0 12495.0 12269.0 \n",
- "7 10944.0 11627.0 11824.0 12246.0 11644.0 \n",
- "8 10841.0 11548.0 11295.0 12142.0 11794.0 \n",
- "9 10816.0 11183.0 11044.0 10854.0 11248.0 \n",
- "10 10895.0 11498.0 10898.0 12997.0 11077.0 \n",
- "11 11019.0 11205.0 10567.0 12788.0 10697.0 \n",
- "12 10645.0 10573.0 10402.0 11913.0 10325.0 \n",
- "13 11141.0 10130.0 9867.0 9941.0 10027.0 \n",
- "14 16387.0 10305.0 10126.0 10794.0 9939.0 \n",
- "15 18516.0 10520.0 10291.0 12301.0 8493.0 \n",
- "16 22351.0 10497.0 9025.0 11223.0 9644.0 \n",
- "17 21997.0 10458.0 10059.0 10306.0 10908.0 \n",
- "18 NaN NaN 11207.0 10153.0 9064.0 \n",
- "19 NaN NaN 9055.0 8624.0 10693.0 \n",
- "20 NaN NaN 10272.0 10141.0 10288.0 \n",
- "21 NaN NaN 10284.0 9636.0 10040.0 \n",
- "22 NaN NaN 8260.0 8147.0 8332.0 \n",
- "23 NaN NaN 10140.0 9950.0 9766.0 \n",
- "24 NaN NaN 9445.0 9343.0 9367.0 \n",
- "25 NaN NaN 9458.0 9256.0 9627.0 \n",
- "26 NaN NaN 9511.0 9212.0 9334.0 \n",
- "27 NaN NaN 9062.0 9258.0 9263.0 \n",
- "28 NaN NaN 9179.0 9293.0 9376.0 \n",
- "29 NaN NaN 9080.0 9127.0 9113.0 \n",
- "30 NaN NaN 9112.0 9141.0 8882.0 \n",
- "31 NaN NaN 9271.0 9161.0 8941.0 \n",
- "32 NaN NaN 9122.0 9319.0 9038.0 \n",
- "33 NaN NaN 9093.0 8830.0 9299.0 \n",
- "34 NaN NaN 8994.0 8978.0 9382.0 \n",
- "35 NaN NaN 8242.0 7865.0 8149.0 \n",
- "36 NaN NaN 9695.0 9445.0 9497.0 \n",
- "37 NaN NaN 9513.0 9191.0 9454.0 \n",
- "38 NaN NaN 9440.0 9305.0 9534.0 \n",
- "39 NaN NaN 9517.0 9150.0 9689.0 \n",
- "40 NaN NaN 9799.0 9503.0 9778.0 \n",
- "41 NaN NaN 9973.0 9649.0 9940.0 \n",
- "42 NaN NaN 10156.0 9864.0 10031.0 \n",
- "43 NaN NaN 10021.0 9603.0 9739.0 \n",
- "44 NaN NaN 10164.0 9529.0 9984.0 \n",
- "45 NaN NaN 10697.0 10151.0 10346.0 \n",
- "46 NaN NaN 10650.0 10193.0 10275.0 \n",
- "47 NaN NaN 10882.0 9957.0 10621.0 \n",
- "48 NaN NaN 10958.0 10033.0 10538.0 \n",
- "49 NaN NaN 10816.0 10287.0 10781.0 \n",
- "50 NaN NaN 11188.0 10550.0 11217.0 \n",
- "51 NaN NaN 11926.0 11116.0 12517.0 \n",
- "52 NaN NaN 7533.0 7131.0 8487.0 \n",
- "53 NaN NaN NaN NaN NaN \n",
+ " total_2020 total_2019 total_2018 total_2017 total_2016 total_2015 \\\n",
+ "1 13768.0 12424.0 14701.0 13612.0 14863.0 13751.0 \n",
+ "2 16020.0 14487.0 17430.0 15528.0 13154.0 18318.0 \n",
+ "3 14723.0 13545.0 16355.0 15231.0 13060.0 16738.0 \n",
+ "4 13429.0 13283.0 15971.0 14461.0 12859.0 15712.0 \n",
+ "5 13123.0 12799.0 15087.0 14188.0 12571.0 14560.0 \n",
+ "6 12534.0 13222.0 14111.0 13805.0 12697.0 13730.0 \n",
+ "7 12412.0 13347.0 13925.0 13212.0 12016.0 13510.0 \n",
+ "8 12300.0 12877.0 13753.0 13330.0 12718.0 13071.0 \n",
+ "9 12334.0 12479.0 12190.0 12819.0 12733.0 13181.0 \n",
+ "10 12415.0 12396.0 14859.0 12580.0 12493.0 13007.0 \n",
+ "11 12499.0 12018.0 14367.0 12089.0 12489.0 12475.0 \n",
+ "12 12112.0 11797.0 13397.0 11833.0 10983.0 12027.0 \n",
+ "13 12507.0 11260.0 11310.0 11453.0 11738.0 11987.0 \n",
+ "14 18565.0 11445.0 12272.0 11305.0 13060.0 10325.0 \n",
+ "15 20929.0 11661.0 13843.0 9761.0 12757.0 11575.0 \n",
+ "16 24691.0 10243.0 12639.0 11000.0 12310.0 13061.0 \n",
+ "17 24303.0 11452.0 11596.0 12356.0 11795.0 12023.0 \n",
+ "18 20059.0 12695.0 11538.0 10372.0 10401.0 11586.0 \n",
+ "19 NaN 10361.0 9821.0 12114.0 12002.0 10138.0 \n",
+ "20 NaN 11717.0 11386.0 11718.0 11222.0 11692.0 \n",
+ "21 NaN 11653.0 10974.0 11431.0 11013.0 11334.0 \n",
+ "22 NaN 9534.0 9397.0 9603.0 9192.0 9514.0 \n",
+ "23 NaN 11461.0 11259.0 11134.0 11171.0 11603.0 \n",
+ "24 NaN 10754.0 10535.0 10698.0 10673.0 10858.0 \n",
+ "25 NaN 10807.0 10514.0 10930.0 10611.0 10629.0 \n",
+ "26 NaN 10824.0 10529.0 10624.0 10526.0 10525.0 \n",
+ "27 NaN 10328.0 10565.0 10565.0 10412.0 10545.0 \n",
+ "28 NaN 10512.0 10467.0 10643.0 10647.0 10278.0 \n",
+ "29 NaN 10324.0 10353.0 10426.0 10672.0 10028.0 \n",
+ "30 NaN 10422.0 10356.0 10147.0 10612.0 10021.0 \n",
+ "31 NaN 10564.0 10408.0 10239.0 10433.0 9893.0 \n",
+ "32 NaN 10406.0 10542.0 10278.0 10439.0 10153.0 \n",
+ "33 NaN 10405.0 10091.0 10569.0 10312.0 10352.0 \n",
+ "34 NaN 10279.0 10199.0 10698.0 10637.0 10354.0 \n",
+ "35 NaN 9478.0 9046.0 9372.0 9226.0 10239.0 \n",
+ "36 NaN 10918.0 10680.0 10781.0 10681.0 9092.0 \n",
+ "37 NaN 10892.0 10496.0 10692.0 10401.0 10573.0 \n",
+ "38 NaN 10792.0 10498.0 10875.0 10183.0 10381.0 \n",
+ "39 NaN 10954.0 10463.0 11027.0 10278.0 10826.0 \n",
+ "40 NaN 11113.0 10869.0 11101.0 10671.0 10700.0 \n",
+ "41 NaN 11403.0 11048.0 11357.0 11016.0 11108.0 \n",
+ "42 NaN 11625.0 11177.0 11389.0 11134.0 10799.0 \n",
+ "43 NaN 11415.0 10885.0 11152.0 11048.0 10966.0 \n",
+ "44 NaN 11567.0 10866.0 11366.0 11463.0 11026.0 \n",
+ "45 NaN 12177.0 11588.0 11767.0 11803.0 11312.0 \n",
+ "46 NaN 12146.0 11552.0 11773.0 12209.0 11338.0 \n",
+ "47 NaN 12472.0 11289.0 12102.0 12064.0 11178.0 \n",
+ "48 NaN 12455.0 11392.0 12046.0 11901.0 11216.0 \n",
+ "49 NaN 12275.0 11687.0 12342.0 12733.0 11748.0 \n",
+ "50 NaN 12853.0 12078.0 12924.0 12076.0 11713.0 \n",
+ "51 NaN 13566.0 12649.0 14308.0 13137.0 12136.0 \n",
+ "52 NaN 8727.0 8384.0 9904.0 9335.0 9806.0 \n",
+ "53 NaN NaN NaN NaN NaN NaN \n",
"\n",
- " total_2016 total_2015 \n",
- "1 13045.0 12286 \n",
- "2 11501.0 16237 \n",
- "3 11473.0 14866 \n",
- "4 11317.0 13934 \n",
- "5 11052.0 12900 \n",
- "6 11170.0 12039 \n",
- "7 10590.0 11822 \n",
- "8 11056.0 11434 \n",
- "9 11285.0 11472 \n",
- "10 11010.0 11469 \n",
- "11 11022.0 10951 \n",
- "12 9635.0 10568 \n",
- "13 10286.0 10493 \n",
- "14 11599.0 9062 \n",
- "15 11417.0 10089 \n",
- "16 10925.0 11639 \n",
- "17 10413.0 10599 \n",
- "18 9137.0 10134 \n",
- "19 10637.0 8862 \n",
- "20 9953.0 10290 \n",
- "21 9739.0 10005 \n",
- "22 7909.0 8213 \n",
- "23 9873.0 10157 \n",
- "24 9386.0 9548 \n",
- "25 9365.0 9312 \n",
- "26 9228.0 9190 \n",
- "27 9138.0 9205 \n",
- "28 9388.0 9015 \n",
- "29 9350.0 8802 \n",
- "30 9335.0 8791 \n",
- "31 9182.0 8617 \n",
- "32 9172.0 8862 \n",
- "33 9070.0 9148 \n",
- "34 9319.0 9121 \n",
- "35 7923.0 9026 \n",
- "36 9399.0 7878 \n",
- "37 9124.0 9258 \n",
- "38 8945.0 9097 \n",
- "39 8994.0 9529 \n",
- "40 9291.0 9410 \n",
- "41 9719.0 9776 \n",
- "42 9768.0 9511 \n",
- "43 9724.0 9711 \n",
- "44 10152.0 9618 \n",
- "45 10470.0 9994 \n",
- "46 10694.0 9938 \n",
- "47 10603.0 9830 \n",
- "48 10439.0 9822 \n",
- "49 11223.0 10365 \n",
- "50 10533.0 10269 \n",
- "51 11493.0 10689 \n",
- "52 8003.0 8630 \n",
- "53 NaN 7524 "
+ " previous_mean \n",
+ "1 13870.2 \n",
+ "2 15783.4 \n",
+ "3 14985.8 \n",
+ "4 14457.2 \n",
+ "5 13841.0 \n",
+ "6 13513.0 \n",
+ "7 13202.0 \n",
+ "8 13149.8 \n",
+ "9 12680.4 \n",
+ "10 13067.0 \n",
+ "11 12687.6 \n",
+ "12 12007.4 \n",
+ "13 11549.6 \n",
+ "14 11681.4 \n",
+ "15 11919.4 \n",
+ "16 11850.6 \n",
+ "17 11844.4 \n",
+ "18 11318.4 \n",
+ "19 10887.2 \n",
+ "20 11547.0 \n",
+ "21 11281.0 \n",
+ "22 9448.0 \n",
+ "23 11325.6 \n",
+ "24 10703.6 \n",
+ "25 10698.2 \n",
+ "26 10605.6 \n",
+ "27 10483.0 \n",
+ "28 10509.4 \n",
+ "29 10360.6 \n",
+ "30 10311.6 \n",
+ "31 10307.4 \n",
+ "32 10363.6 \n",
+ "33 10345.8 \n",
+ "34 10433.4 \n",
+ "35 9472.2 \n",
+ "36 10430.4 \n",
+ "37 10610.8 \n",
+ "38 10545.8 \n",
+ "39 10709.6 \n",
+ "40 10890.8 \n",
+ "41 11186.4 \n",
+ "42 11224.8 \n",
+ "43 11093.2 \n",
+ "44 11257.6 \n",
+ "45 11729.4 \n",
+ "46 11803.6 \n",
+ "47 11821.0 \n",
+ "48 11802.0 \n",
+ "49 12157.0 \n",
+ "50 12328.8 \n",
+ "51 13159.2 \n",
+ "52 9231.2 \n",
+ "53 NaN "
]
},
- "execution_count": 121,
+ "execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "deaths_headlines = deaths_headlines.merge(dh19['total_2019'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines = deaths_headlines.merge(dh18['total_2018'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines = deaths_headlines.merge(dh17['total_2017'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines = deaths_headlines.merge(dh16['total_2016'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)\n",
+ "deaths_headlines['previous_mean'] = deaths_headlines['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)\n",
"deaths_headlines"
]
},
{
"cell_type": "code",
- "execution_count": 162,
+ "execution_count": 188,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 162,
+ "execution_count": 188,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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9+wCIjo7mjTfeICnp+sLdO3fupKamhlOnTpGWlsbLL79Mbm7ubY9Rf7UX8VBBmUsL9UVkQPnhBz/ko6KPuvWa9468l9UzV7d7fM2aNZw7d474+HgeffRRAPbu3Ysxhueee47FixezZs0asrKyiI+PZ/ny5SxatIilS5e27PW4adMm5syZ0+lYEhISWl5HRUXhcrmoqamhqKiIsrIyZs+eDcCyZcvYs2cPycnJ7e46YIyhsrKS+vp6qqurGTJkCIGBgR5/L+1REBPxUEGpi/snjuztYYiI9Gvr16/n9OnTpKens2vXLrZs2UJGRgZXr15lxowZJCUlsX79ep5//nneeecdoGnD7dTUVHx9fcnJyWHJkiV0dZvDXbt2kZCQgI+PD/n5+YSHh7ccCw8PJz8/v8Pzv/SlL/Hmm28SFhZGVVUVGzduZOTI2/+doCAm4oHGRstnZS5CtVBfRAaQjmauesLhw4dZsmQJTqeT0NBQ5s2bx7Fjx26aaaqrq2PlypWkp6fjdDrJzs7u0vtkZmayevVqDhw4AEBb+2x3tuzkgw8+wOl0cunSJYqLi3nggQd45JFHmDRpUpfGciMFMREPXK2sob7RqpiriEg3aisQtWXjxo2EhoaSkZFBY2Mjvr6e/784Ly+PRYsWkZKSQkREBNA0A5aXl3ddn7Fjx3Z4nVdffZXHH38cb29vRo8ezdy5czl+/PhtBzEt1hfxwGelNQCM0RoxEZHbEhAQQHl5OQBJSUns2LGDhoYGCgsLOXToEDNnzryuD0BpaSlhYWE4HA62b99OQ0ODR+9VUlLCggULWLduHXPnzm1pDwsLIyAggKNHj2KtJSUlhYULF3Z4rbvuuouDBw9iraWyspKjR49y77333sI3cD0FMREPXC6tBlRDTETkdgUHBzN37lyio6M5cuQIsbGxxMXF8dBDD7FhwwbGjBlDbGwsXl5exMXFsXHjRp555hm2bdvGrFmzyM7Oxt/f36P32rRpE2fPnmXt2rXEx8cTHx/PlStXANi8eTNPP/00kZGRREREkJycDMDu3bsJDw/nyJEjLFiwoGUPy2effZaKigqio6OZMWMGTz31FLGxsbf9fRhPpwX7munTp9uuLtQTuVUpR3L57puZfPAvDzM6QGFMRPqvrKysdp8MlO7R1ndsjEmz1k6/sa9mxEQ8UFDqwsthCPH36e2hiIjIAKLF+iIeKChtqiHmcKiYq4hIX7N//35Wr77+CdCJEyeye/fuXhqR5xTERDxQUObS+jARkT5q/vz5LWu5+hvdmhTxQEGpS09MiohIt1MQE+mEtVYzYiIickcoiIl0osxVT1Vtg2bERESk2ymIiXSioNQFqIaYiIh0PwUxkU4UlCmIiYh0l5KSEl566aUO++Tm5vLqq692eq3c3Fyio6PbPZ6amkpiYiIxMTEkJiZy8ODBlmNpaWnExMQQGRnJqlWrWrZb2rlzJ1FRUTgcjus2Fv/5z3/eUhQ2Pj4eh8NBenp6p2PsjIKYSCcKmqvq69akiMht684g1pmQkBDefvttTp06xbZt21i6dGnLsRUrVrB161ZycnLIyclh3759AERHR/PGG2+QlJR03bW++tWvkp6eTnp6Otu3b2fChAnEx8ff9hhVvkKkEwXufSZDFcREZIAp+L//l5qsj7r1mj5T72XMP/9zu8fXrFnDuXPniI+P59FHHwVg7969GGN47rnnWLx4MWvWrCErK4v4+HiWL1/OokWLWLp0KZWVlUDT1kVz5szpdCwJCQktr6OionC5XNTU1FBUVERZWRmzZ88GYNmyZezZs4fk5GSPdh34xS9+wZIlSzrt5wkFMZFOFJRVEzJsCEO8NIEsInK71q9fz+nTp0lPT2fXrl1s2bKFjIwMrl69yowZM0hKSmL9+vU8//zzvPPOOwBUVVWRmpqKr68vOTk5LFmyhK5uc7hr1y4SEhLw8fEhPz+f8PDwlmPh4eHk5+d7fK0dO3bw5ptvdun926MgJtKJglKVrhCRgamjmauecPjwYZYsWYLT6SQ0NJR58+Zx7NgxAgMDr+tXV1fHypUrSU9Px+l0kp2d3aX3yczMZPXq1Rw4cACAtvbZNsaznVPef/99/Pz8Olyb1hUKYiKduFzqInzE0N4ehojIgNNWIGrLxo0bCQ0NJSMjg8bGRnx9Pf/LcV5eHosWLSIlJYWIiAigaQYsLy/vuj5jx4716HqvvfZat92WBC3WF+mUirmKiHSfgIAAysvLAUhKSmLHjh00NDRQWFjIoUOHmDlz5nV9AEpLSwkLC8PhcLB9+3YaGho8eq+SkhIWLFjAunXrmDt3bkt7WFgYAQEBHD16FGstKSkpLFy4sNPrNTY2snPnTr7yla908VO3T0FMpAOuugZKqur0xKSISDcJDg5m7ty5REdHc+TIEWJjY4mLi+Ohhx5iw4YNjBkzhtjYWLy8vIiLi2Pjxo0888wzbNu2jVmzZpGdnY2/v79H77Vp0ybOnj3L2rVrW8pOXLlyBYDNmzfz9NNPExkZSUREBMnJyQDs3r2b8PBwjhw5woIFC67bw/LQoUOEh4czadKkbvs+jKfTgn3N9OnTbVcX6ol0Ve7VSv74+fd4/s/j+FJieOcniIj0cVlZWR49GSi3rq3v2BiTZq2dfmNfzYiJdKC5mGuYbk2KiMgdoMX6Ih1o3t5INcRERPqu/fv3s3r16uvaJk6cyO7du3tpRJ5TEBPpwGXtMyki0ufNnz//urVc/YluTYp04LMyFwE+Xgzz0d9ZRESk+ymIiXTgcmm1ZsNEROSOURAT6UBBWY2CmIiI3DEKYiIdKCitVg0xERG5YxTERNpR39BIYXmNSleIiHSjkpISXnrppQ775Obm8uqrr3Z6rdzc3A73fExNTSUxMZGYmBgSExM5ePBgy7G0tDRiYmKIjIxk1apVLdst7dy5k6ioKBwOx00bi588eZLZs2cTFRVFTEwMLper0zF2RkFMpB2FFTU0WghVEBMR6TbdGcQ6ExISwttvv82pU6fYtm0bS5cubTm2YsUKtm7dSk5ODjk5Oezbtw+A6Oho3njjDZKSkq67Vn19PX/5l3/Jli1byMzM5L333sPb2/u2x6hHwUTa0Vy6QjNiIjJQ/e/r2Vy9WNGt1wwZP4wHvjyl3eNr1qzh3LlzxMfH8+ijjwKwd+9ejDE899xzLF68mDVr1pCVlUV8fDzLly9n0aJFLF26lMrKSqBp66I5c+Z0OpaEhISW11FRUbhcLmpqaigqKqKsrIzZs2cDsGzZMvbs2UNycnK7uw4cOHCgZTsmaNqqqTt0OiNmjBlvjPmtMSbLGJNpjPlbd/tIY0yqMSbH/c8R7nZjjHnBGHPWGHPSGHNfq2std/fPMcYsb9WeaIw55T7nBWOM6ZZPJ3IbPlMxVxGRbrd+/XoiIiJIT09n1qxZpKenk5GRwa9//Wu+853vcPnyZdavX88DDzxAeno63/rWtxg9ejSpqamcOHGCHTt2sGrVqi6/765du0hISMDHx4f8/HzCwz/fti48PJz8/PwOz8/OzsYYw/z587nvvvvYsGFDl8fQFk9mxOqBv7fWnjDGBABpxphU4EngN9ba9caYNcAaYDWQDEx2/9wPbAbuN8aMBL4HTAes+zpvWWuL3X2+ARwF3gUeB/Z2yycUuUWfz4gN7eWRiIjcGR3NXPWEw4cPs2TJEpxOJ6GhocybN49jx44RGBh4Xb+6ujpWrlxJeno6TqeT7OzsLr1PZmYmq1ev5sCBAwC0tc92Z3NA9fX1HD58mGPHjuHn58fDDz9MYmIiDz/8cJfGcqNOZ8SstZettSfcr8uBLGAcsBDY5u62DXjC/XohkGKbHAWCjDFhwHwg1Vpb5A5fqcDj7mOB1tojtumbSWl1LZFe81mZiyFeDkb43f4aABERuVlbgagtGzduJDQ0lIyMDI4fP05tba3H75GXl8eiRYtISUkhIiICaJoBy8vLu67P2LFjO7xOeHg48+bNIyQkBD8/P77whS9w4sQJj8fRni4t1jfGTAASgPeBUGvtZWgKa8Bod7dxwMVWp+W52zpqz2ujXaRXXS51MSbQt9O/JYmIiOcCAgIoLy8HICkpiR07dtDQ0EBhYSGHDh1i5syZ1/UBKC0tJSwsDIfDwfbt22loaPDovUpKSliwYAHr1q1j7ty5Le1hYWEEBARw9OhRrLWkpKSwcOHCDq81f/58Tp48SVVVFfX19fzud79j2rRpt/ANXM/jIGaMGQbsAv7OWlvWUdc22uwttLc1hm8YY44bY44XFhZ2NmSR21JQ5lIxVxGRbhYcHMzcuXOJjo7myJEjLQvgH3roITZs2MCYMWOIjY3Fy8uLuLg4Nm7cyDPPPMO2bduYNWsW2dnZ+Pv7e/RemzZt4uzZs6xdu5b4+Hji4+O5cuUKAJs3b+bpp58mMjKSiIgIkpOTAdi9ezfh4eEcOXKEBQsWtOxhOWLECL797W8zY8YM4uPjue+++1iwYMFtfx/Gk2lBY4w38A6w31r7n+62j4E/ttZedt9efM9ae48x5mX361+07tf8Y639a3f7y8B77p/fWmvvdbcvad2vPdOnT7c31vcQ6U5JG35L/PggXliS0HlnEZF+Iisrq90nA6V7tPUdG2PSrLXTb+zryVOTBvgJkNUcwtzeApqffFwOvNmqfZn76clZQKn71uV+4DFjzAj3E5aP0RTsLgPlxphZ7vda1upaIr3CWktBmUulK0RE5I7y5KnJucBS4JQxJt3d9s/AeuB1Y8zXgQvAn7uPvQt8ATgLVAFPAVhri4wxa4Fj7n7ft9YWuV+vAH4KDKXpaUk9MSm9qriqjtr6RpWuEBHpB/bv38/q1auva5s4cSK7d+/upRF5rtMgZq09TNvruABuembT/eTjs+1c6xXglTbajwPt71Eg0sMul1YDKuYqItIfzJ8/v2UtV3+jLY5E2vBZWVMNMS3WFxGRO0lBTKQNzcVcFcREROROUhATacNnpS4cBkYN8+ntoYiIyACmICbShsulLkYF+ODl1H8iIiJy5+i3jEgbmoq5ao9JEZHuVlJSwksvvdRhn9zcXF599dVOr5Wbm0t0dPvP+qWmppKYmEhMTAyJiYkcPHiw5VhaWhoxMTFERkayatWqlu2Wdu7cSVRUFA6Hg9b1SnNzcxk6dGhLYdhvfvObnY7PEwpiIm0oKHUxJlC3JUVEult3BrHOhISE8Pbbb3Pq1Cm2bdvG0qVLW46tWLGCrVu3kpOTQ05ODvv27QMgOjqaN954g6SkpJuuFxERQXp6Ounp6WzZsuW2xwee1RETGXQKSl3MjQzp7WGIiNxRv/3pVq58er5brzn67kk8+OQ32j2+Zs0azp07R3x8PI8++igAe/fuxRjDc889x+LFi1mzZg1ZWVnEx8ezfPlyFi1axNKlS6msrASati6aM2dOp2NJSPh8Z5SoqChcLhc1NTUUFRVRVlbG7NmzAVi2bBl79uwhOTm5x3cdUBATuUFFTT3lNfV6YlJE5A5Yv349p0+fJj09nV27drFlyxYyMjK4evUqM2bMICkpifXr1/P888/zzjvvAFBVVUVqaiq+vr7k5OSwZMkSurrN4a5du0hISMDHx4f8/HzCw8NbjoWHh5Ofn9/pNT755BMSEhIIDAzk3//933nggQe69uHboCAmcoOC5tIVqqovIgNcRzNXPeHw4cMsWbIEp9NJaGgo8+bN49ixYwQGBl7Xr66ujpUrV5Keno7T6SQ7O7tL75OZmcnq1as5cOAAAG3ts920y2L7wsLCuHDhAsHBwaSlpfHEE0+QmZl501i7SkFM5AYq5ioi0jPaCkRt2bhxI6GhoWRkZNDY2Iivr+f/f87Ly2PRokWkpKQQEREBNM2A5eXlXddn7NixHV7Hx8cHH5+mtcOJiYlERESQnZ3N9Ok37ePdJVqsL3KDy5oRExG5YwICAigvLwcgKSmJHTt20NDQQGFhIYcOHWLmzJnX9QEoLS0lLCwMh8PB9u3baWho8Oi9SkpKWLBgAevWrWPu3Lkt7WFhYQQEBHD06FGstaSkpLBw4cIOr1VYWNjyvufPnycnJ4dJkyZ19ePfREFM5AYF7n0mNSMmItL9goODmTt3LtHR0Rw5coTY2BSnyq8AACAASURBVFji4uJ46KGH2LBhA2PGjCE2NhYvLy/i4uLYuHEjzzzzDNu2bWPWrFlkZ2fj7+/v0Xtt2rSJs2fPsnbt2payE1euXAFg8+bNPP3000RGRhIREUFycjIAu3fvJjw8nCNHjrBgwYKWPSwPHTrUMtYvfelLbNmyhZEjR97292E8nRbsa6ZPn267ulBPxBPP7TnFr05e5sPvPtbbQxER6XZZWVk9/mTgYNPWd2yMSbPW3nQfUzNiIjcoKHURqtuSIiLSA7RYX+QGBWUuwnRbUkSk39i/fz+rV6++rm3ixIns3r27l0bkOQUxkRsUlLqIGTe8t4chInLHWGs7LdfQn8yfP79lLVdv6+qSL92aFGmltr6RqxW1jAnUPpMiMjD5+vpy7dq1LgcG6Zy1lmvXrnWpvIZmxERa+byGmPaZFJGBqbmGVmFhYW8PZUDy9fW9rmp/ZxTERFopaAlimhETkYHJ29ubiRMn9vYwxE23JkVaad7eSIv1RUSkJyiIibTSHMRUvkJERHqCgphIKwVlLvyGOAn01V17ERG58xTERFopKHUxJtB3QD3WLSIifZeCmEgrBWUu7TEpIiI9RkFMpJXmGTEREZGeoCAm4tbYaPlMM2IiItKDFMRE3K5W1lDfaFW6QkREeoyCmIibSleIiEhPUxATcfu8mKuq6ouISM9QEBNxa97eKFT7TIqISA9REBNxKyh14eUwhPgriImISM9QEBNxKyh1ERroi8OhYq4iItIzFMRE3C6XqnSFiIj0LAUxEbcLRVWMC9JCfRER6TkKYiJAcWUt+SXVTBsb2NtDERGRQURBTAQ4c7kMgOixw3t5JCIiMpgoiIkAp/NLAYjSjJiIiPQgBTER4PSlMsYFDWWE/5DeHoqIiAwiCmIiQOalUs2GiYhIj1MQk0GvoqaeT65WEj1O68NERKRnKYjJoJd1uQxrtT5MRER6noKYDHqZ7oX6mhETEZGepiAmg97pS2WEDPNhdID2mBQRkZ7VaRAzxrxijLlijDndqi3eGHPUGJNujDlujJnpbjfGmBeMMWeNMSeNMfe1Ome5MSbH/bO8VXuiMeaU+5wXjDHa6E961On8poX6+ldPRER6miczYj8FHr+hbQPwb9baeOC77j8DJAOT3T/fADYDGGNGAt8D7gdmAt8zxoxwn7PZ3bf5vBvfS+SOcdU1cPZKBdHjtD5MRER6XqdBzFp7CCi6sRlo/s01HLjkfr0QSLFNjgJBxpgwYD6Qaq0tstYWA6nA4+5jgdbaI9ZaC6QAT9z2pxLxUPZn5dQ3WlXUFxGRXuF1i+f9HbDfGPM8TWFujrt9HHCxVb88d1tH7XlttIv0iNP5TVsbRSmIiYhIL7jVxforgG9Za8cD3wJ+4m5va5GNvYX2NhljvuFek3a8sLCwi0MWuVnmpVICfL0YP3Jobw9FREQGoVsNYsuBN9yvd9K07guaZrTGt+oXTtNty47aw9tob5O1dqu1drq1dvqoUaNuceginzt9qYzoscO1UF9ERHrFrQaxS8A89+uHgBz367eAZe6nJ2cBpdbay8B+4DFjzAj3Iv3HgP3uY+XGmFnupyWXAW/e6ocR6Yr6hkY+ulymQq4iItJrOl0jZoz5BfDHQIgxJo+mpx//CvgvY4wX4KLpqUeAd4EvAGeBKuApAGttkTFmLXDM3e/71trmBwBW0PRk5lBgr/tH5I47V1hJTX2jCrmKiEiv6TSIWWuXtHMosY2+Fni2neu8ArzSRvtxILqzcYh0t9MtFfU1IyYiIr1DlfVl0Dp9qZSh3k4mhgzr7aGIiMggpSAmg1bmpTKmhgXgdGihvoiI9A4FMRmUGhstZy6VaX2YiIj0KgUxGZQ+LaqioqZeT0yKiEivUhCTQSnzUtNCfVXUFxGR3qQgJoPS6fwyvJ2GKaEBvT0UEREZxBTEZFDKvFTKlNAAhnjpPwEREek9+i0kg461lkz31kYiIiK9SUFMBp3LpS6KKmuJUiFXERHpZQpiMug0V9TXQn0REeltCmIy6GReKsNhYGqYFuqLiEjvUhCTQSfzUimTRg3Db0inW62KiIjcUQpiMuiczi8jWoVcRUSkD1AQk0HlakUNBWUubW0kIiJ9goKYDCqZl8oAmKYZMRER6QMUxGRQ0ROTIiLSlyiIyaBy5lIZd430Y/hQ794eioiIiIKYDC6nL5USpduSIiLSRyiIyaBR5qrj02tVWqgvIiJ9hoKYDBpn3Av1NSMmIiJ9hYKYDBpaqC8iIn2NgpgMGpmXyggN9GFUgE9vD0VERARQEJNBJPNSKdGaDRMRkT5EQUwGheraBs5eqdD6MBER6VMUxGRQyCooo9FClJ6YFBGRPkRBTAaF5q2NVLpCRET6EgUxGRQy80sJ8vNm7HDf3h6KiIhICwUxGRROuxfqG2N6eygiIiItFMRkwKutbyS7oIKocVqoLyIifYuCmAx4OVfKqW1oVCFXERHpcxTEZMDLzHcv1FfpChER6WMUxGTAy7xUiv8QJxOC/Xt7KCIiItdREJMB7/SlMqaNDcTh0EJ9ERHpWxTEZEBraLScuVSm9WEiItInKYjJgPbJ1Uqq6xpUyFVERPokBTEZ0DIvlQJoj0kREemTFMRkQMu6XI630xA5elhvD0VEROQmCmIyoBWW1zBqmA/eTv2rLiIifY9+O8mAVlxVy8hhQ3p7GCIiIm0a0EHMWkvJZwW9PQzpRUWVtYzwUxATEZG+aUAHsdyME/xk1dN8dv5sbw9FeklxVS0j/RXERESkbxrQQaz4Uh4AOR8c6eWRSG/RjJiIiPRlAzqIlRddA+B82vu9PBLpDbX1jZS76jUjJiIifVanQcwY84ox5oox5vQN7X9jjPnYGJNpjNnQqv2fjDFn3cfmt2p/3N121hizplX7RGPM+8aYHGPMDmNMt/3WrCwuAqDwQi5lhVe667LST5RU1QIwQkFMRET6KE9mxH4KPN66wRjzILAQiLXWRgHPu9unAV8BotznvGSMcRpjnMCLQDIwDVji7gvwQ2CjtXYyUAx8/XY/VLOKomv4jxgJwLkTH3TXZaWfKHIHsWAFMRER6aM6DWLW2kNA0Q3NK4D11toad5/m6aaFwGvW2hpr7SfAWWCm++estfa8tbYWeA1YaIwxwEPAL93nbwOeuM3P1KKi+Brj7o1iRNg4zqcpiA02RZXuGTGtERMRkT7qVteITQEecN9S/J0xZoa7fRxwsVW/PHdbe+3BQIm1tv6G9ttmraW86BrDRoxkUuJMLmaepLa6qjsuLf1EcWUdgNaIiYhIn3WrQcwLGAHMAr4DvO6e3TJt9LW30N4mY8w3jDHHjTHHCwsLOxxgbXUV9TU1DBsZTETiTBrq68k9+WGH58jAUtSyRsy7l0ciIiLStlsNYnnAG7bJB0AjEOJuH9+qXzhwqYP2q0CQMcbrhvY2WWu3WmunW2unjxo1qsMBVrifmBw2Mphx90zD13+Ybk8OMkUVujUpIiJ9260GsT00re3CGDMFGEJTqHoL+IoxxscYMxGYDHwAHAMmu5+QHELTgv63rLUW+C3wJfd1lwNv3uqHaa25dEXAiGAcTicT4hM5f+IYjY0N3XF56QeKq2oJ8PXSPpMiItJneVK+4hfAEeAeY0yeMebrwCvAJHdJi9eA5e7ZsUzgdeAMsA941lrb4F4DthLYD2QBr7v7AqwGvm2MOUvTmrGfdMcHa54R8x/Z9NRkROJMqsvLuJyT3R2Xl36gqFJV9UVEpG/z6qyDtXZJO4f+sp3+PwB+0Eb7u8C7bbSfp+mpym7VXENs2MhgACbEJ+JwOjmf9j7j7pna3W8nfZC2NxIRkb5uwN6zKS+6hq//MLyH+ADg6z+M8KlRnNM6sUGjqLKWkVofJiIifdiADWIVRddaZsOaTbrvfq7lXaDks4JeGpX0pOLKWlXVFxGRPm1AB7HmqvrNIhKb7oBq78nBoUi3JkVEpI8bsEGssvjmGbGgMWGMHDdetycHgaraelx1jSpdISIifdqADGKNDQ1UlpQQcEMQg6ZZsbys09RUVfbCyKSnNG9vNFLFXEVEpA8bkEGssrQYaxtvmhEDmJQ4k8aGBnIzTvTCyKSnfL69kU8vj0RERKR9AzKIta6qf6OxU+7FNyBQtycHuObtjTQjJiIifdnADGLNNcRG3BzEHA4nkxKm88mHx2lsUJX9gaq4UtsbiYhI3zcwg1gHM2LQtE7MVVHOpY+zenJY0oM+XyOmICYiIn3XgA1iDqcTv8DhbR6/O/Y+HE4vzp3Q7cmBqqiyFoeBQF/dmhQRkb5rwAYx/6CRGEfbH8/Hz4/xUTFaJzaAFVXVMsJvCA6H6e2hiIiItGvABrFhNxRzvdGk+2ZSfCmP4sv5PTQq6UnF2vBbRET6gYEZxIqL2l0f1qy5yr5mxQamIm1vJCIi/cDADGJt7DN5o+GjQwm5awLnFcQGpOIqbfgtIiJ934ALYrWuamqrqzoNYuCusv9RJq6Kih4YmfSkoso6zYiJiEifN+CCWEvpik7WiEHTOjHb2Mgn6cfv9LCkBzU22qYZMRVzFRGRPm4ABjF3MVcPZsTCIqfgNzxI68QGmHJXPQ2NVsVcRUSkzxt4Qay442KurRmHg4kJ08lNT6Ohvv5OD016yOfbGymIiYhI3zbwglirqvp1DXUcvHCQRtvYbv+IxJnUVFWS/9GZnhqi3GGqqi8iIv3FgAxiQ4b6McR3KPs/3c/f/vZv+dX5X7Xb/+7YBJze3pw/8X4PjlLupGIFMRER6ScGXhAr/ryY68dFHwPwUvpL1DXWtdl/iO9Q7oqK5VzaB1hre2yccuc035rUGjEREenrBl4Qa1VDLKc4h6FeQ8mryGN3zu52z5mUeD8lBZcpupTXU8OUO0gzYiIi0l8MwCD2eVX97OJsHrnrEeJHxfNyxsu46l1tnjPpvhkAnDuu25MDQVFlLUO8HPgNcfb2UERERDo0oIKYbWyksqQpiJW4SiisLmTKiCmsum8VV6qvsOPjHW2eFxgyilETJqmMxQBRVNlUVd8YbfgtIiJ924AKYlVlpTQ2NDBsxEhySnIAmDxiMjPGzGBW2Cx+cuonVNZVtnnulPvncunjM1zLu9iTQ5Y7oKmYq25LiohI3zeggljr0hXZxdkATBkxBYBVCasorinmZ2d+1ua5sY88jtPbmxPvvtkzg5U7pqhSQUxERPqHgRXEWhVzzSnOIcgniJChIQDEjIrhwfEPsi1zG6U1pTed6xc4nGkPPMiZQwepKrv5uPQfxVXaZ1JERPqHgRXEbpgRmzJiynXrhFYmrKSiroKfZv60zfPv+8JC6utqOZm6tyeGK3dI0xox7TMpIiJ934ALYsY4GBo4nLMlZ5k8YvJ1x6eMmMLjEx/n51k/52r11ZvODxl/NxPi7iP9wK+or2u77pj0bXUNjZRWa0ZMRET6h4EVxIqL8AsK4lLVZarrq1vWh7X2bPyz1DbU8uNTP27zGokLnqCypJiP/3DoTg9X7oCSqqYArTViIiLSHwysIFZ0jWEjmtaHAUwOmnxTn7sD72Zh5EJe//h1Lldcvvl4bALB4XeR9qs9qrTfDxVrw28REelHBl4Qc68PMxgigiLa7PfN2G8C8PLJl286ZowhccETFH76CRczT93R8Ur3a9nwW9sbiYhIPzAgg1hOSQ7jA8bj5+3XZr+wYWF8+Z4vs+fsHj4t+/Sm41P/6I8ZGjictF+1vy2S9E3N2xtpjZiIiPQHAyaI1dXW4KqsaCrmWpxz00L9Gz0d8zRDnEN4Mf3Fm455DRlC/GNf4PyJYxRdyr9TQ5Y7oEi3JkVEpB8ZMEGssqgIAJ/hAXxa9mmbC/VbCxkawl/c+xfs+2RfS/HX1uIe/QJOLy8VeO1niiqagliQyleIiEg/MGCCWHMNsbIhNVhspzNiAE9FP4W/tz+bPtx00zH/oBFMfeBBMn/3G6oryrt9vHJnFFXVMszHCx8vbfgtIiJ934AJYuXuqvoFjmKATmfEAIb7DGd51HJ+e/G3nCq8eWH+fV9YSH1tjQq89iPFlbWM8NdsmIiI9A8DJohVumfEchsu4+v0JXxYuEfnLZ22lBE+I/jvD//7pmOj7prAXTHxpO9/h4Z6FXjtD4qq6hjp79PbwxAREfHIgAliFcXX8PLxIafqPJFBkTgdnt2a8vf25+sxX+fI5SMcKzh20/HpC56goriIj48c7u4hyx1QrO2NRESkHxkwQay8qIhhI4LJLun8ickbLb5nMaOHjuZHx35Esav4umMT4u5j5NhwFXjtJ4oqa1W6QkRE+o0BE8Qqiq7hGxRIcU2xR+vDWvP18uWfZ/0zZ0vOsvidxZy+errlmHE4SFzwBFc+OUd+VmZ3D1u6WXFVrYq5iohIvzFgglhl8TXq/ZpuR3Z1Rgzg4bseZnvydgyGZXuXsTN7Z8sM2NSkB/ENCOT4r/Z065ile7nqGqiqbdCMmIiI9BsDIohZa6koLqLSpx64tSAGEBUSxY4/2cHMMTP5/pHv89zvn8NV78J7iA/xjyZzLu19igsudefQpRu1bG+kICYiIv1Ep0HMGPOKMeaKMeZ0G8f+wRhjjTEh7j8bY8wLxpizxpiTxpj7WvVdbozJcf8sb9WeaIw55T7nBWOM6eqHqC4vo6Gujqte5YQMDWGk78iuXqJFkG8QLz78It+M+yZvnXuLv3z3L7lYdpG4xxbgcDg58e5bt3xtubMUxEREpL/xZEbsp8DjNzYaY8YDjwIXWjUnA5PdP98ANrv7jgS+B9wPzAS+Z4wZ4T5ns7tv83k3vVdnmou5XuIqk4M+nw27VlHDur1ZVNc2dOl6ToeTZ+Of5cWHX+Ry5WUWv7OYtIpTTP2jeWS+92tcFRVdHaL0gGJtbyQiIv1Mp0HMWnsIKGrj0EbgH4HWjxIuBFJsk6NAkDEmDJgPpFpri6y1xUAq8Lj7WKC19ohtWpCVAjzR1Q9R4S7m+knjpesW6r+ZfomXf3eetzJubb/IpPAkdvzJDsIDwll5cCUfT6qirsbFyd/su6XryZ3VPCM2Qov1RUSkn7ilNWLGmD8F8q21GTccGgdcbPXnPHdbR+15bbR3SYV7n8nSIa7r1oelfdpUimLHsYttnueJ8IBwUpJTWBS5iB9/9hqVYUM4se8tGurrb/macmcU69akiIj0M10OYsYYP+BfgO+2dbiNNnsL7e299zeMMceNMccLCwtb2ptvTVb7NLTMiFlrOf5pET5eDk5cKOHslVvfL9LXy5fvz/0+/zr7XzkWXkBlURHv/fr1W76e3BlFlbUYA8OHqqCriIj0D7cyIxYBTAQyjDG5QDhwwhgzhqYZrfGt+oYDlzppD2+jvU3W2q3W2unW2umjRo1qaa8ovgZ+QzBOJ5OCJgGQX1LNZ2U1/PW8CLwc5rZmxZp9ccoXWbf8ZSqHNfLeWz+nqq7qtq8p3aeoqpagod44HV1+3kNERKRXdDmIWWtPWWtHW2snWGsn0BSm7rPWFgBvAcvcT0/OAkqttZeB/cBjxpgR7kX6jwH73cfKjTGz3E9LLgPe7OqYKoquUeMHdwfejY+zaZ/B5tuSj00L5ZGpobxxIp/a+sauXvom0aNiiHn4cYKuGTb/+ke3fT3pPsWVdbotKSIi/Yon5St+ARwB7jHG5Bljvt5B93eB88BZ4H+AZwCstUXAWuCY++f77jaAFcCP3eecA/Z29UNUFBdR5n3z+jD/IU7uHRPA4hnjuVZZy8GPPuvqpds0/0+WY52GrPcOcubamW65pty+ospaBTEREelXvDrrYK1d0snxCa1eW+DZdvq9ArzSRvtxILqzcXSkvOgqRcMrSWxVuuJ4bjEJd43Ay+kgacooxgT6suPYRR6PDrudtwLAL3A4kTNnU3f89/zboe/x84W/wMvR6Vcpd1hxVS13jfTr7WGIiIh4rN9X1q+vq8NVVkaV7+cL9Stq6vmooIz77m4qVeZ0GL6UGM7vsgspKHV1y/smPvonDKkz1Jy5yM+zft4t15TboxkxERHpb/p9EKssbrrDWeXb0HJrMv1CCY0WZvrVkrfqbync9CJf9LqKs76eX6bd/qJ9gPBpMYwIG8eMK+N4Mf1F8srzOj9J7hhrLcVVtdpnUkRE+pV+fz+twh3EGv29GDtsLADHPy3CGJh48veUHThAeWoqWMsuryHkpE2i8OP5+M+6n6HR0RjvWyt1YIwh9uH5FP/sFYaXebH26Fq2PLKFW9ihSbpBeU09dQ2WkSrmKiIi/Ui/nxFrriEWMioch2n6OGmfFnNPaACNGekMmTCBKUf+QPim/6bikQUMrSjl6n/9F58u+Quy75/Fhb/6Btd+/GOqT53CdrFI67R5D+P08uLPqmfxh0t/4J3z73T75xPPNBdz1YyYiIj0J/1/RqzoKgB3jY0EoKHR8uGFEp6IG0PVqycInP8YzqAgAh55hPvmPciMH/yaL4z35V/Guaj64H0q3/+AK8//BwA+997LhNd34Bji2S/zpkX7c8jNSCPuiRh+dOxH/NG4P2KE74jOT5Zu1by9UbCCmIiI9CP9fkbssysXaXBYIsOmApD9WTkVNfXMcZTSWFbG0MTElr6+3k6eiB/H7k+qsEkPMea73yXiV+8w+X8PEfrP/0TNRx9RvP1nXXr/uEcep6aykqe8/4Ty2nKeP/58t34+8Uzzht+aERMRkf6k/wexzy5Q6VvPPSPvAeC4u5DrPYXnAfCbPv26/otnjKe2vpE3W20E7jVqFCOXLcM/6QGubt5MfVFbe5y3rWnR/lgKj6bzVPRTvHXuLf5w6Q+3+7Gki4oq6wC0RkxERPqVfh/ESq8VUu3TQGRQ063JtNwiRgf44HPmJF5jxuA97vo9xKPHDWdaWGCbWx6F/uM/0lhdzdVNL3r8/sYYYh5+nPyPzvCl4GQmBE5g7ZG1VNdX394Hky75fI2Y9pkUEZH+o98HsZrSMhqHeTPcZzgAaReKSbwriOrjx/FLTGzzKcbFM8aTeamM0/ml17X7REYyYvGXKd6xg5pz5zweQ5R70f7Hv/0t3539XfIq8ticsfn2Pph0ybXKWrydhmE+/X7Zo4iIDCL9OohZazEVtQwNCgLgSpmLi0XVzPWrob6wEL8Z09s874n4cQzxcvD68ZtnxUJWrsQxdCifbdjg8TiaF+2fOXSQ+JGx/NnkPyMlM4Wsa1m39sGky4oraxnhN0TlQ0REpF/p10GssrwUZwOMCBkDfL4+LLb4EwD8Wi3Ub224nzePR41hz4f5uOoarjvmNXIkISu+SeXvDlFx+PcejyX24cdxVVaQc/T3fDvx2wT5BPGvR/6V+saulcSQW1NUpar6IiLS//TrIPbxxZMAhIXeDTTVD/PxcjDy7Bmcw4czJCKi3XO/MmM8Za569mcW3HRsxNKleIeHc+WHP8Q2NLRx9s3GRzUt2j/5m30M9xnOmvvXcObaGW1/1EOKtb2RiIj0Q/06iJ27eAaACePuBZpmxOLGB+H6MI2h06djHO1/vFmTghk/cmibi/YdQ4Yw+h/+gZqcHEp+ucujsbRetH8t7wLz757PvPB5vJj+IvkV+Z1fQG5LkbY3EhGRfqhfB7G8gqYF9ZPDo3DVNZCZX8qcIEvdpxfavS3ZzOEwfDlxPH84d40L16puOh4w/zGGJiZS+MILNFRUeDSeqHkP43B6cfI3+zHG8C/3/wvWWjZ9uKnrH066pLiyVqUrRESk3+nXQezqlUsABIWEknGxhPpGy4zyCwD4Te84iAF8aXo4DgM729gI3BhD6JrVNFy7xrWt/+PRePwChzN55mzO/O431NXWEDYsjCVTl/Cr878ipzinC59MuqKh0VJSXacZMRER6Xf6dRCrKL5Go68TryFDWhbqh1/8GOPnh+/UqZ2eHzZ8KElTRvHLtDwaGu1Nx4fGxBD4p/+Hop/+lLp8z24vxj6S3LRo//2moq5fi/oa/t7+vJjueW0y6ZqSqlqshZF+qiEmIiL9S78NYg22AVNRi1egPwAnPi0mYpQ/Dekf4hcfh/H27Jfy4unjuVzq4lBOYZvHR3/rW2AMV/7jPz263vioGILGhHHy1/sACPINYnnUcn5z4Tecvnrao2tI1zRvbzRymE8vj0RERKRr+m0Qq6mvwc/lJGBkMI2NlrQLxcwZ7UNNdvZ1+0t25uGpoQT7D+H1NhbtA3iHhTHya09R9u67VH34YafXM8YQ+/Dj5H+UybW8pmsunbaUET4jeOHECx6PSzyn7Y1ERKS/6rdBzFVbg1+Nk5BR4zh/tYKSqjrmuPLBWvymz/D4OkO8HCxKGMevsz7jWkVNm31Cnn4a56gQrqz/IdbefAvzRlF//Ih70X7TrJi/tz9fj/k6Ry4f4YPLH3g8NvFMkbY3EhGRfqrfBrHaygaG1jgZNTqcNPf6sEmXc8Dbm6FxsV261uIZ46lrsOz+sO11YA5/f0b/3d9RnZFB2bvvdnq91ov262ubQsLiexYz2m80L3z4gkdhri+z1tJoG3t7GC1abk1qsb6IiPQz/TaIUQsGw7ARwRzPLWaEnzc+Z04yNDoah69vly41OTSAhLuC2HHsYrshafgTT+AzdSpX/uM/aHS5Or1m7CPuSvvvN1Xn9/Xy5Ztx3ySjMINDeYe6NL6+Zs/ZPcz42Qx++MEPuVp9tbeH8/mMmG5NiohIP9Nvg5ijoWlPwWEjg0m7UMz9Y/2ozsz0qGxFW/48cTw5Vyo4nV/W5nHjdBK6+h+pv3SZopTtnV5vfFQsQWPCyHAv2gd4IvIJxgeM54UPX+hTM0pddeTSEYwx/OKjX5C8K5kfHftRrwayospa/IY48fV29toYREREbkW/DWK4g0zj0EDOF1Yyr6EQ6uq6tFC/tQUxYQxxOnjjw7x2+/jPmsWwhx7i2ssvU3+14+DR1qJ9b4c3z8b/P/bOMzyqMm3A95mW3nsvJKSQQpWOQEAQpSpNFBSxI8SLRAAAIABJREFUrbqW3dW1t3Vtn64FOwiKoiAdVEroEDqkFxLSe+9lMjPn+zFJIKQngGT33Nc115D3vOU5kwnzzFMf52LZRfam7+2VnDcDCaUJjHUey445O7jN8zZ+TPiRGVtm8NHZjyitL73h8jQ3/JaQkJCQkOhv9FtFTETfAzKlRm8FCSi+BIKA8dChvdrPwlhJWIA9O6NyadR2bK2y/8ff0TU0kPfqa4hN8V8dMWjiFBRKFed+29oydrvX7fha+bIycmW/bAhe21hLRmUG/jb+uJu78/a4t9k+ezth7mGsjVvL9M3T+fjcx5TXl98wmUpr1diYSoqYhISEhET/o98qYjqhEZARVdSIUi5gkRyHgZ8fcnPzXu85b6grxdVqjnZQUwzAwMsLh+efp/rAAbKeeKLTeDFjcwuCJk8l7vABKov1e8oEGU8OfpKMygx2XNrRa1n/LJLKkhARCbQObBnztPDknfHvsG32Nia6TuS72O+Ytnkan57/lIqGiusuk2QRk5CQkJDor/RbRUwUNCAzISaljBBHExqiorrsL9kVtw60w8pYyZbznVfRt77vXhzffIOao8fIeuhhtNU1Hc4dMesuQOTsri0tYxPdJhJsG8yXUV/SoG2/ZMbNSnyJvtG6v7V/m2velt68f+v7bJm1hXEu4/g25lumb57O1uStbeZeS0pr1VLGpISEhIREv6TfKmICIoJgSnVmNZPl5Yh1dRiPGN6nPVUKGTNDndkXX0BlfWOnc60WLMD5gw+oPX+ezOXL0Za374ozt7UnYPwkYvbvpbZCP0cQBP469K/k1+Tza9KvfZL5RpNYmoi1oTX2xvYdzvGx8uHDiR+yaeYmfCx9eOf0O5TVl103mcpqGiWLmISEhIREv6TfKmIyQK40x6VBYHB5OkCfLWIAc4e40KDRsTsmv8u5Fnfegeunn9CQkEDGsvs7DOC/ZfZ8NI1qzv22rWVslNMoRjqO5NuYb6ltrO2z3DeKxNJEAqwDEAShy7l+1n68Nvo16jR1rE9cf13kadBoqW7QYC0Vc5WQkJCQ6If0W0UMnQimFnho5NinJ6D0cEdhZ9fnbQe7WeJta8Lm8x1nT16JWVgYrl99iTozk4x776MxL6/NHGtnFwaOGkfk3t+or6luGX9y6JOU1pfyY8KPfZb7RqDWqkkpS2nXLdkRPlY+THSbyPqE9dQ0duzC7S1lTe2NrCTXpISEhIREP6RfK2IVhqaYigJVSXkYD++bW7IZQRCYO8SFU2mlZJd1z1JlOnYs7qtXoSkuJmPJvagzMtrMGTlnPuq6OiJ372oZC7ULZaLbRNbGrr0hQe19JaU8BY2owd+m+4oYwIrgFVSqK9l0cdM1l6m5mKuNpIhJSEhISPRD+q8iBiTq9B++RUoXjIe1o4hlnYH/GwiFiT3ad84QFwC2R+Z2e43x0KG4f78WXW0t6ffeS0Nycqvr9p7eeA8dwbk/dqCur2sZf2LwE1Q3VrM2bm2PZPwzSCzVv45XZkx2h1C7UEY4juCHuB9Qazsv+dFTmtsbSTFiEhISEhL9kX6tiGXoVCgVDZRZ+bdfUT9xJ1QXwL5Xe7Svm7Uxt3has+V8do/6QhoNGoTHuh8QEMi4byl1sXGtro+cu5D6qkqir6i272ftx+1et/NTwk83RbugzogvicdEaYKrmWuP164IWkFhXSE7L+28pjI1W8SkrEkJCQkJif5Iv1bEqhUm2GtzKLcaiMzJpe2E9GMgU0DyHkg93KO95w114VJRDdHZPXMZGvj64vHTj8iMjcm8/35qz51rueY80B+3QSGc3bW1pRk4wOODH0etVfNt9Lc9OutGk1iaiJ+VHzKh52+b0c6jCbAOYE3cGrQ67TWTqcUiJiliEhISEhJ9pCE5mdJ1NzZuu18rYjIjc+wyT6OVqShMv6pHZH0l5EbCqMfAwg32vgy67vd3vD3YCZVCxtYLndcUaw+Vuzse639CYWdH5oMrKP1hHbomxWvk3AXUlJUSdzi8Zb67uTtzfOawMWkjX0d9fVPWFtPqtFwsu0igTc/cks0IgsCK4BVkVGawL3PfNZOrpFr/uloaSVmTEhISEhJ9o2jl5xS8/Ta6hhv3OdxvFTFREBhnLcM8/QwCIlkJV9WpyjoFohZ8pkDYq5AfDTHdr9llYaRkaoBDly2POkLp6IjHj+swGjKYgn//m0vTp1O+eTNu/oNw8vHj9PbNaDWXWxw9M+wZJrlPYmXkSuZtn8exnGM9PvN6klGZQZ2mrkcZk1cT5h6Gp7knq2NW98jl2xlltWosjJQo5P32rSwhISEhcROgq6+n+sgRADSFhTfs3H776aVFxoT6bBTaeuycDMhKuKrZdPoxkCnB9RYIuhucQuHAW9DYcUuiq5k7xIWSGjVHLnbc8qgzFDY2uH/3He7frUZha0feSy+TNnMWwR4DqCwqICniSMtcCwMLPpr4EV9P+RqZIOOx8Md45uAz5FW3LYfxZ5BQmgC0X1G/u8hlcpYHLSexNJHjuceviVylNWopY1JCQkJCos/UREQg1umT6SRFrBvoBBm++cnILCxwH+xEYXolDbVXVMNPPwauw0FlDDIZ3PYvqMiCU191+4xb/eywNlF12fKoMwRBwGTMGDw3/ILr5ysRlErkH32GuQ5OrF+LTts6XmqMyxg2z9rMU0Of4ljOMWZvn82qmFU0ajuv9H+9SSxNRCVT4W3p3ad97vS+EwdjB1bFrLomcpXVqqX4MAkJCQmJPlO1dx80FSuXFLFuoBPkmCTFYTx0KG6BNogi5FxsajPUUAW5F8Bz3OUFXhPAdxoc/QhqSrp1hlIuY1aoM/sSCqio65siJAgCZmFheG3bissH7+Nbq6G8rJQTSxZRc/p0q7kquYoVwSvYPmc7Y5zH8Mn5T5i3Yx4nck/0SYa+kFCSgK+VL0pZ32KxlHIlywYt41zBOSILI/ssV6nU3khCQkJCoo+IGg3VBw9iMmE8ICli3UIuk6HJSMd4+DAcvMxRGMgvuyczm+LDrlTEAKa+CeoqOPJ+t8+ZO8QFtUbHHzHXxkUoyOVYzJzJ+C3bMDcxI0FdTcbSZWQuf5C6mNhWc51Nnfl40sd8EfYFWlHLw/se5m+H/kZ+Tdftl64loiiSUJrQJ7fkldzlexcWBhasjlnd573KatTXrL3RUwee4t3T716TvSQkJCQk+g+1Z8+irajA8q67EFQqGgskRaxLVOiDvY2HDUOukOEy0JLsxKaA/fSjl+PDrsTeH4YuhTOroORSt84JcbXA286ELb3InuwMuYEho+57gAqlnMblS6mPjyd90SLqIttaica7jmfr7K08PvhxDmcfZta2WXx87mPO5J+5IRmWuTW5VKore50xeTXGSmOW+C/hUPYhksuSu17QAaIoUlpzbVyTFQ0VHMw6yKaLm6hUV3a9QEJCQkLiv4aqvfsQDA0xHT8ehb29ZBHrDiYCCEZGGAbqlQM3f2vKC2qpKq3Xx4e5DNPHh13NxBdBbgD73+jWOYIgcNdQV06nlZJVem2bcweOn4SZjR3x1WV47/4DhYM9uS++1G7arIHcgEdDH2Xb7G2MdhrNmrg1LN+znDHrx7B8z3K+jPySs/lnr3nleoDEEn1F/WtlEQO4J+AejBRGrI7tvVWsRq1FrdVhfQ1ckyfzTiIi0qBtYHfa7q4XSEhISEj8VyDqdFTt34/JuLHIjIxQODhIilh3UDTUYxQaiqDSfwi7BlgBkBWT2zY+7ErMHGDsXyF+O2Sdbn/OVcwe7AzAtmttFVMoGT5zHrlJ8eTnZuP0xpuoU1MpXvl5h2tczVz5ZPInHFt0jJWTV7LYfzHV6mq+jPqSB/Y8wJifx7Bizwq+ivqK8wXnr0mQf0JpAjJBhq+Vb5/3asbCwIL5A+ezO2032VXda7B+NWU1166Y64ncE5gpzfCx9GFL8pY+7ychISEh0T+oj41FU1CA+dSpACjs7SRFrDuI9fUYD7vc1sjayQRjCxXZ59Pajw+7ktFPgKkD7HkJulHPytXKmJFe1my9kHPN6l81Exx2G8YWlpzcugHT8eOwuPsuSlavpi4mptN1ZiozbnW7lb+P+DsbZ27k2OJjfDrpUxb4LaBCXcEXkV+wbPcyxvw8hj/S/uiTjAmlCXhbeGOkMOrTPlezNHApgiD0us/mtWr4LYoiEbkRjHQayV2+dxFXEkdSaVKf9pSQkJCQ6B9U7dsHCgWmEycCoLS3p7Gw8Jp/3ndEl4qYIAjfCYJQKAhC7BVjHwiCkCgIQrQgCFsFQbC84toLgiCkCIKQJAjCtCvGpzeNpQiC8M8rxr0EQTglCEKyIAgbBEHo3qeqCMYjLjf6FgQBN39rstMbEQUVuN3S8VoDU5j0EmSfhoQd3Tpu3lAXUotriOphy6OuUKoMGHbHHDKiL5CfchGH559HYWdH3osvtlTj7w7mKnMmuU/iuRHP8evMXzm66CgfT/oYJ1Mn1sSu6ZOMiSWJ19Qt2YyDiQOzB8xma/LWXvXZLL1G7Y3SK9PJq8ljtPNo7vC+A4VMwbaUbX3aU0JCQkLi5kcURar2hWNyywjkFhYAKOztEWtr0dXU3BAZumMRWwtMv2psHxAkimIIcBF4AUAQhEBgETCoac0XgiDIBUGQA58DtwOBwOKmuQDvAf8RRdEXKAMe7JbkAhiFhrYacg2woq5BRbH1dFCZdL5+yL1gFwDhr4Oma4Xn9mAnDBQytpzvnRutM0KnzsDAxIRT2zYiNzPD6c03aEhOofjLL3u9p4WBBWHuYczzmUdCaUKv3X/FdcUU1hUSYB3Qa1k644GgB9CIGtbFr+vx2mbXZF9jxCJyIwAY4zwGK0MrJrtNZlfqrj+9dpuEhISExPVFfekS6vR0zJrckgAKewfgxpWw6FIRE0XxCFB61dheURSb+/OcBFyb/j0b+EUUxQZRFNOAFOCWpkeKKIqpoiiqgV+A2YIgCMBkYFPT+u+BOd0RXDAyQmbU2lXm5q3/QM6WT+x6A5lcX86iNBXOdW0xMjdUMjVQ3/JIrel5y6POMDA2Zujts0g5c5Kt771BrYcbFrNnU/LNt9THx/dp7zD3MAD2Z+7v1frEUn2gfoDN9VHEPMw9mOoxlY1JG6lSV/Vobek1ihGLyI3A3cwdVzP923iu71zKG8o5lH2oT/tKSEhISNzcVO3T9z42nRzWMqawtwduIkWsGywHmoOQXICsK65lN411NG4DlF+h1DWPd4nMuG1GpEnFBawUmWRVenVPct+p+kKvh96F+q5djvOGulBW28jhXrY86oyRcxcwbtFScpLiWff8XzlnZ0qdnQ25L76E2AMX5dW4mbvhZ+XXZ0XMz9qv1zJ0xYNBD1LdWM2GpA09Wldao0YuEzA3VPT6bLVWzZn8M4x2Ht0yNtppNPbG9mxN3trrfSUkJCQkbn6q9oVjFBqK0sG+ZUxhbweApqDghsjQJ0VMEISXAA3wU/NQO9PEXox3dN7DgiCcFQThbJWuHatU+lHcDGLIy5GhadS2vd52Q5j6FtSV6ivud8F4XztsTFRsvXDt3ZNyhZKRcxew4tPVjJy7gNToCxxyNOdsdQkZKz/t095hHmFEFkZSVNtzBTKhJAFXU1fMVeZ9kqEzAmwCGOsylnXx66jXdL8XaFmtGitjFYLQ3tuoe0QVRVGnqWOs89iWMblMzuwBszmee5yCmhvzhyghISEhcWNpzMmhPj4es9suuyXRNKBU6ktVNd7sFjFBEJYBdwJLxMupBdmA2xXTXIHcTsaLAUtBEBRXjbeLKIrfiKI4XBTF4dbOzm0npB/DzbkOTaOO/EvdDKp3HgwhC+Hkl1Ce1elUpVzGrMHOhMcXUlF7feKHDE1NGbdoKSs+XcXgaXeSY2PO1hMHCf/kA2ore5coMMV9CiIiB7MO9nhtQmnCdXNLXsmKoBWU1peyK3VXt9dci4bfEbkRKAQFIxxHtBqf4zMHnahjZ+rOPu0vISEhIXFzUhUeDoDZlCmXB3c+heyHachMTdEUXnvvV3v0ShETBGE68DwwSxTFK6uc7gAWCYJgIAiCF+ALnAbOAL5NGZIq9AH9O5oUuIPA3U3rlwHbe3UnTf0lnQe5IpMJZDVX2e8Ok1/RP+96Rl+DrD1rWxPzhrii1urYch2sYldiYmnF5AceYdm/PsKlTk3U8cOsfnIFEb+up6G2Z4VlfSx98DT3ZF/Gvh6tq1JXkVWVdV0yJq9mmMMwHIwdOJ3fvdpuAGU1jVj1sb1RRG4EIXYhmKpMW427m7sz3GE4W5O33rAUZgkJCQmJG0fVvnAMfH1ReXjoB3LOQ9TPoK5CYWt988SICYLwM3AC8BMEIVsQhAeBlYAZsE8QhEhBEL4CEEUxDtgIxAO7gcdFUdQ2xYA9AewBEoCNTXNBr9A9KwhCCvqYsd6VWm/qL6nyHY2DtznZCaVdr2nG0g0mvQgp4fDNRPjIH7Y9DnHb2sSOBbmYE+pmyRs74/nbxigKq7rvSusN1r4Dmf7kP5iQlImTmSUnNq1n1WNLiF79Wrf3EASBMPcwzuSfoaKh+1a15lpa1ytj8koEQSDYNpi44riuJzdRWqvGug8WsdL6UhJKEhjjPKbd63N955JZlcn5wvO9PkNCQkJC4uZDU1JC7fnzl7MlRRH2vtxyXWFtfvMoYqIoLhZF0UkURaUoiq6iKK4WRdFHFEU3URQHNz0evWL+26IoDhBF0U8UxT+uGP9dFMWBTdfevmI8VRTFW5r2nC+KYu+aJzb3l3S7BbcAawozq6iv6YH7cNzT8PdkmPs1eIyFxJ3w6zJ43xvW3gnHP4XCRARg/YqRPHrrAHZE5RD2f4dZdTSVRu21zaS8EvOpU3AaEUzQwePMt4/HXlbEvr3nOPbZSx1aazRlZZT+9BMZS5dRtmEjUzymoBW1HMo61O1zE0oTgOuXMXk1g2wHkVmV2W1lsaxGHyPWW07m6tsadaSITXGfgonSRKq0LyEhIfFfRtWBA6DTYTa1yS2Z+BtkHIfgBQAoLIz6R7D+TUVLf0kTXP2tQeRyE/DuYmoHoYtg/hr4Ryo8sBvGPAl1ZbDvFfhiJHwSgkn48/xzGOx5egLDPK34128J3P7JUY6n9LwoaadUF8Kx/8DKYTja/o5MoUOb5MC8Nz4m2EXDqWNRHPjiXcQmV6quoYHK3XvI+svjJI+fQMFb/6I+MZH8t97CO7MRB2MHwjPDu318Ymkitka22BrZXtv76oBg22CAblnFdDqRsj5axCJyI7AwsOiwmbmx0pjpntPZl7GPanV1r8+RkJCQkLi5qAoPR+nqioG/v76W6L5XwNYPbn0OAKWZksaiohsSmvLfoYg1VLfqL+ngaYbSUE52Yg/ck1cjV4DHaJjyOjx2HJ6Jgzs/BodgiFwPa6bjrctkzf0jWLV0OGqNjiWrTvHYj+fILutDc3CdDlL2w8al8FFTwVkzJxSLv8LhrXepzyynfF8UU19dxXCHEiKPHGfXmy+Q8/IrJI+fQM7TT1MXE431vffitXULPuH7UDo6kvPss8ywHkdETgS1jd2TL6E04Ya4JZtpVohiijtv7wRQUdeITqTXFjFRFDmRe4JRTqOQy+QdzpvrO5c6TR170vf06hwJCQkJiZsLbXU1tREnMJsyRZ91f3a1vqbobf8CMycAFMZAYyPa8vLrLk/vCzDdTGSdbNVfUiaX4TLQiqyexIl1hYUrDH9A/yhNg++mw7q5CMt3MyXQi3G+tqw6msrKgykcTCrkLxN9eHiCN4bKjj/kW9FQBae+hvM/QHkGGFnDyEdh6DKwGwiAeYhI5d5wij75FANfX/ytR1Mdf5zEhDgqquuYcOt4rGfPxWT0KAT55XNdPv6YjMWLmfpDAmsnNXA05yjTPKd1JAkA9Zp6UstTmeg6sbevWI8xU5nhZeFFbHFsl3Ob2xvZmPZOEbtUfonCusIO3ZLNhNiG4G3hzdaUrdw18K5enSUhISEhcfNQffgwYmOj3i1ZVwaH3wPvifraooIAKlMUMn15U01hIQorq+sqz3+HRSz9GMgUrfpLugVYU1lcT0VR3bU/z9oL7tsKmnpYNweq8jFUynlisi/7/zaRMH8HPtp3kan/OczeuPyuTZuVefDd7XDgLbDygLtWw98SYdrbLUoY6APanV57DcHIiKyHH6F04x6Czc0ZrcigwNSIU+YGqIYPbaWEARgFDcLhxRdQnIpm0RlD9md0Xdw1pTwFrai9YfFhzQTbBhNTHNPla9bc3qi3FrHmtkajnUZ3Ok8QBOb6zCWqKIrU8lQANGo12//vbfJSpMbgEhISEv2NqvBw5La2GA0eDEf+D+rK9daw5pqUpvYoVHrP0Y2IE/vvUcSa4sOacQvQa7B9ck92hkMg3LsZqotg3Vyo1Z/jYmnE50uGsn7FSIyUch5ed47vI9I73qcwAVZNgbI0/X7LdkLw3aAwaHe6ws4Ot5Wf4fDiC/gcPIj75r2MmRXKdOdksuKi2fSvl6mvbhvPZLloEeYzbmf2gVryj++nQdt5TkRLoP4NdE0CDLIZREl9CQW1nb/5m9sbdRUjlhFbwp5vY8mMK2ml3EXkRuBl4YWTqVOXMt054E4UwuVG4DmJ8aScOcGprRu7XCshISEhcfOga2ig5vARzCZPRihP13uihtwLjsGXJ5k6oJRXAjemzVH/V8QaqvW1P5rcks1YOhhjamVAVkIPA/Z7gutwWLweSlLgp/l6WZoY42PLb38dz0Q/O97dnUhGSTtd3NOOwOppoNPAA7+Dz5S2c9rBeMQIrJcu1bdkEASY/QWDvM2Y6Z1FYdolNr7xT2rKW9+3IAg4vvkWoos9j2yp5XR85zXFEkoSMFOZ4WLarY5T14zmgP2u4sTKarvXZ/L8ngxSzhWy87Mofn7zNHFHc6ipq+VswdlW1fQ7w9bIlgmuE9hxaQeNukYyYi4AkHruDFWl1zhBQ0JCQkLiulETEYGutlbvlgx/HeQqmPxy60mm9sibWmzfiOr6/V8Ruyo+rBlBEHD1tyI7qZTSvBpqKhrQNl6HEhPeE+Hu7yD3PPxyD2guW5qUchnvzAtGKZPx3KZodLor3G3Rv8K6eWDuBCvCwSm09zIYmsPCH/E1KWROaB1lBXlseP15Kotav4HkpiZ4fvo5pvWgefUDRG3HbaASSxPxt/bvU/ug3uBn7YdCpugyTqyk2SLWiWuyrkpNXko5Q6e5E3Z/AHKFwKGfkvjxpZOEpIUx3GxUt+Wa6zuXkvoSjmUfIyMmEktHJ0RRR9zB7mehSkhISEj8uVSFhyMzNcXECUjYAWOfAjPH1pNMHZDVFSK3srohFrH+H6zfEh82ss0l90E2JJ7I5+c3TrWMKVQyDIyVGBgrMDTRPxsYKzAwUWJpZ8SAYfYY9TQAPGAmzP4ctj0Gmx+Eu9fqsy4BJwsjXrojgH9uieGnUxncN8pDX5Ji/xvgMQ4W/QhG1yAQ0N4fZq/Ec9MD3D35PrYeKeKX157n7pffwtrZtWWaaeAgziwZzOgfIkn7z0cYz51DbUU5NeVl1FVVIup0iALIIrMJsB/Mud+26ZUxQUAQBARBhiATsHBwwjNkSN/lvgqVXIWflV+XilhZjRpDpQwjVcfJEGnRxYgi+AxzwM7dDL+RjuReLGfzlkMMzZjKxZU6xOHxhIa5Yedu1ul541zGYWtky/aYTbilZzJ2/hKy4mOIObiXW+bOR9ZJ5qWEhISExJ+PqNFQvf8AprfeinDgVTBzhjFPtJoTcyibmMMTWWz4DQq7UDQFkiLWNe3EhzUzYKg9s59RUVeppr6mkYZaDQ21jdTXamho+rmyuI6GWg31tRo0DVqObkzGK8QW/9FOuA+yRibvptFw8D36Kvy7/wk7n4JZn4FMv3bhCDd+i8nj/T/imJv7IaYxP0DQ3TDniw5jwXpF0DzIOYfLiZUsWPwem389xi+vPU/AuIktylZtRTnVpY3sDhkAZw7rH+0wBHNISOUQqR0ed/sTfyNw/KRrJ3/zbdgGsSt1FzpRh0xo//UvrWns1BoGkBZZhJm1IbZu+vZFgiDg4mfFkUE/YT/QhUWax0mIyCPpVD7OvpaEhrnhGWKLTNbWCqiQKZg5YCaHwn/FTbTBPXgwVs4u7Pr4PTKiI/EaPKzvNy4hISEhcd2oPXcebXk5Zn6mei/WnC9b6Q5arY5zf6RTU2FEncoChY2lZBHrkub4sHFPt3tZJhNw9eu+tak4u5rEE3lcPJ3PpQtFGJur8BvpiP9oJ6yd2yp6bRj12OVUWEMLfdZjkyXp3ZkDSP78n5jGnEcc+wxC2Kstito1ZcrrkHMe+1Ovs/CJn9n23S/EHNiLiYUlxpZWWLu44ujvz6/pm5l1AazqBbzeeQcL7wEYmZsjyOT8dmkXrx9/jfUz1uNt4YUoivqHTgeiiE6r5bfPPmDf159h4+qOg9eAa3oLQbZBbEjaQHpFOt6W3u3OKatVY92J5VJdryEroYxBE5xbuVeL64pJKkti+tDpTAgeyMiZXsQfzyP6YBZ/fBWD+yBr7vhLSLsK+ByfOST+vB3BQInjAF8cvAdgZG5BzP49kiImISEhcZNTFR6OYGCAafmv4BgCIYtaXU89X0RNhT7spVpni8LSmIaU9OsuV/9WxDqID+sttq6mjJvvy+h5A8iIKSHxRB5R+7O4sC8Te09zAsY44TvcHgPjThpNT3xBnwp78nMwstRX6a0uxGXbApyEKF5ufIAgi+Usuh5KGIBcCfPXwtcTsD70LA/8+wCCcVtldPfBVDY4n+fVVVXoPvsCs7VrEBT6t0Ni5UUUBgb4OvijkLX/Fpn59D9Z98LT7PjwbZb8+z8Ym1tcs1u4MmC/I0WstIv2RplxpWg1OrwH27UaP5F7AqAlUN/AWMmQqe6ETnYl5lAOx35N5tjGZCYs9muzp7eFN55lFhTZNSLIZMgEOYNuDeP879upLivF1Mq6V/dus1TyAAAgAElEQVQrISEhIXF9EUWRqvBwTPwdkNWehPlftjGGRB/MQqGSoVHrqNLaYW6mRFNSgqjRtHw+Xg/6d7B+J/FhfUEul+E92I4Zj4Ww7N2xjL3bB22jlsPrk1jz3HH2roqlIL2y/cWCANPf1WvaB9+G/W/qy1MUJcHCn0jxWMjbvyWQV3Ed6ps1Y+agV8bKMxG2P66v1n8VYe5hxJiW0fi35dSePUvRp5+1XEssTWSg1cAOlTAAYwtLZj/7IjXlZfz2yfvoOgn87yme5p6YKE06jRPrqr1RWlQRhiZKnAa0VhAjciOwNrTGz7q1oiWTywgNc2PwVHdiDucQfTC7zZ7lBfkYVOtIMS8hqigKgODJ09BptcQdkoL2JSQkJG5W6mPj0OTlYWaSBH4zwGtCq+sF6ZXkp1YyeKo7AFVaOxQmAuh0aEpKrqts/V8R6yA+7FphbK5i8BR3Fr58CwteHEHgOGcy40vZ9N5Zjm68iLpe03aRTAazV+p/2Uc/BHUN3L8LWcAM3r8rFI1O5IUtXRct7RMeo+G2tyHpN9jxREuds2ZudbsVhUzBPn81lgsWkPnjDg59fJDi3CoSSxO7VcjV0WcgU1Y8TmZsFEd//v6aiS6XyQm0CexUEevMIqbV6EiPKcEz1LaVi1En6lraGnUUezZ67gA8Q2w5tvEimXGt//gyYyL1ZzvSUlPM2tkFt8BgYg7uben5KSEhISFxc1EVHg4yAVOnapj6ZpvrMQezURrIGTzFHYVKRrXWDoWR3sBwvePE+q8iJurarR92vRAEATt3MyYsGsjSt8cQPMGF6APZ/PLmaTLi2tGW5Uq4e43+F74iXK8wAu42xjw33Y9DSUVsPp9zfYUe+QiMexaifoGVw+HCTy3WMXOVOSOdRnIkOYLEAQs4NeIl4hJFfn3rNANTR+Jn6d+tI4ImTmHwtDs4u3MLicfbD/zvDUG2QSSWJaLWqttcU2t0VNVrOrSI5V4sR12nwTu0dbPy5LJkSupLOm1rJJMJTF0eiLWzKXu+jaU093L9t4yYSExtbBkdOJk/0v5o6dkZPGU6FQX5ZMZG9+ZWJSQkJCR6QU+MGVV/7MLYrh7F2OVg69vqWk1FA8lnC/Af44SBkQIza0OqcEah0nuuJEWsI9TV1zQ+rCeojBRMWOzHvL8PRaGSseuzKPatiaOu+iqlQWmor1Fi7dVqeNloT4Z7WPHmzjgKKuuvn6CCAFNeg0cOg40PbP8LrLkd8mNpVGsZlXsntx5dTsKpAgYNt2Rs5rfYFFzgluxZ1H2hoCAmo1vHTFy6Ahf/QPZ89SmF6R1nWfaEYNtgNDoNF8sutrlW3kUx19TIIhQGctwCWsdstbQ1cu68rZHKUMEdj4cgV8n57Yso6qrV6HRaMmOj8AgazNyB86jV1LI7fTcAviNGY2hqRvT+3T2+z1ZknYaYTX3bQ0JCQuJ/gMYGLRv/fYbI8Mwu59aeO4c6MwczD+DW59tcjz+Wi04rEjJRX+rJzMaQatEBhVxfpF1SxDqiofq6xIf1BCcfSxa+dAvD7/Ak5Wwh618/RdKprntLymQC798dQoNGx0tbYykvrCX6YDYntqZcn6KzjsHwwG6Y/Tm64hQS/vMqPz23h5oIY7ItExEXXGLiQ8MJ3fkjmhm5mBavpr5aYPNnSRx4fCVVESc6vSe5QsnMZ17A0NSUHR++TV11VZ9FDrIJAtqvsN/S8LsdRUzUiaRFFeERaI3iqhpjEbkR+Fr5Ym9s3+X5ZtaGzHg0mJpyNX98FUN+yiXqq6vwCBnMUPuh+Fr5si5+HaIoolCpGHTrZFLOnKS2orw3t6vn97/rS59ILk4JCQmJTok6kEVxVjUnt6VSUVTb4bz6pCSyHnoQpYkG8yWPgolNq+tajY7Ywzm4D7LB0sEYAFNrQ6oarVGIJSCX03id+032X0VMXX3d48O6g1wpY+RMbxa8OAILOyPC18Sza2UUlSUdB+Nr1FrkhQ08bW2Lx6lyfnr1JEc3XOT8nkxObr90fQSVycgymMHG+nUcqHgCU20m81z/j5JhB9hf8QcAglzOEc9adiytZ+E/gnExqyRBG8jWlYlEzVlG6Q/r0Fa2n6RgYmnFrGdfpLq0RB+8r+tb8L6jiSM2hjbtxomlFendhZbtZK8WZFRSU6HG66psyTpNHecLzjPGqWO3ZBsZvC2YvMyfvJQKDv2obwnlHhSKIAgsC1xGSnlKi5UtePJ0dFoNcYe7bqjeLoWJkBelf1+XpfVuDwkJCYn/AeprGrmwNxMnHwtkcoGjG5LbNRaoU9PIvHchMrEW90eGo5jyTJs5KecKqa1UEzJZbw1r1DUimDZSpzFGW12GwtYWTWHRdb2f/quINdb+KW7JjrBxMWXeP4YxboEvuSkV/PzmaaIOZLW0NdJbvbLY+VkUq/92lF2fRSGk1aAzlhNhruX254cSNMGFyPAsshKubaPykpxqdn4WyY5PI2lshNtWDOKu54bjZFfNlIxIksuTycjQx3clluhbG1kEeDP7w7u57f6BqG3cOOF4H6d+PEfSrZPJe+VV6pOS2pzj5OtH2IN/ISP6Asd+WdcnmQVBIMg2qI0ill1Wy8vbYvG0MWawm2WbdWmRRchkAh5Brb/1nCs4h1qn7jQ+rD0GjnBk+B2eFFyKw8TKGRNLfSmQGV4zsDOy4/s4fZKCjasbLv6BxBzY07skjJgrGojnRfV8vYSEhMT/CBf2ZqKu1zBhkR+3zPQiI7aE9OjWfX8bszLIWDwH1LW4PzUV1Yqf9LHbVxF9MBtLB2PcA6wRRZHnDj/HN6lfAFBdoUVhby+5JjtEFMGje02bbxQymUDoZDcWv3oLzj4WHNuYzMZ/n+HHV080Wb2SqSisJWCcM3c+EcqKD8dzzz+Gc0ap4YPjlxhztw9WjsaEr42nvrqxz/KIOpEjGy6y4V+nKUirZOzdPtzz2ih8hzsguA2Hhw4SNvJZAPZvXUrR3hcoqS8h0CawZQ/fUa4s+fcEvIc7keo9iwtjXyYn/Axp8+6ifNu2NmcGT76N0Km3c2b7JpJOHO2T/EG2QaRVpFGl1rs6qxs0rPj+LGqtjlXLRmCsalteIzWyGOeBlhiatP6Di8iNQCVTMdRhaI/lGDLVGVGXS32tI6mR+m9GSrmSewLu4UTeCZJK9UppSNh0yvJyyY7vvGF5G3Q6fe9Rz/F6d3u+FPQvISEh0R41FQ1EH8jCd7gDtq6mBE9yxdrZhKMbk9Gom7Ics9PIWDATXW097s/Px2DZynYLqOenVVCYXknIJFcEmcDGpI2EZ4ZTrMgDoEptgcLm+veb7L+KmCD8qfFhnWFuY8SdT4Qy5YFAdBodFnZGjF/oy5I3R3HvW6OZsHAgHkE2KFRyfB3MeGqKL3/E5rM3sZCpDw6ivrqRA+sS+lTeQhRFjm1KJuZgNoMmuHDvW6MZPMUdufKKX7lMjtPYZxlk5cd+G2cSLnwHgP/ZH/WZlg16BcjITMW0h4KY9lAQDUY2nBn6HKmj/8Kxr48T/sY2jvxykYPrEghfE8+eb2Oprx+NoZk7uz75iI+e2cxXX1/o1b0E2wYjIhJfEo9OJ/L0L5EkF1bz+T1D8bE3bTO/NK+G8oLaNkVcQV/IdZjDMAwVhj2WI+9iIqJOg7WLP/u+i6MoS/+6zB84HyOFET/E/wCA76ixGJiYEL1/T88OyDoJFZkwdCnYB0CepIhJSEhItMfZ39PRaUVGztInwcnlMiYsHEhVST3n92SgzUkhc9FMNFUNuL28HMN79B1u2iP6QDYqQzl+oxy5WHaR98+8j7WhNdWqMgCqtLYoLE3QSDFiHaA0BoO2H8Y3C4Ig4DfSkXteH8XMJwcTMskNS3vjduc+MsGbYBcLntsUxV//iKPA05C0qGK+WRvNruhczqSXkllSS31j9+OuIsOziD6QTehkN25d7NfGQnQlU7ymE62r5vCIJQD4FWfD1kfgA1/49QFI+gM0anyG2bP41ZF4D7EjXeFPhsd0krMNSTySTnpMCVkXy0hNKSMxtZIC5TS0ggpd/hZqzufy3MYoNNqeBaEPshkEQGxxLO/vSSI8oYBX7ghgwsC2ihboi7gCeIW2vl5QU0BKeQpjXXpnQc2IjUQmVzDr6RkYmij5/YtoaioasDCwYK7PXH5P+53C2kKUKgMCx08m+dRxaisrun9A9AZQmoD/HeAYqndNXs8acxISEhI3Kw3VoG4/+L6iqI74o7kEjHPGwu7y56mLnxW+w+05vzudxKUPoC7V4Pb6kxgvbJsh2UxNeQOXzhUSMMYZraKR5w4/h5nKjDfHvEmNqhwEkSqtHUoLA7QVFegaGq75rTbTf1scqW5eJaynKOQyPls8hI/2XSS3vI79unrGKHU4nirmlfgcyuSXP5QtjJQ4mBvgZWvCSC8bRnnb4O9o1qpRdfKZAiI2pzBgqD1j7/bp8vwp7lP45PwnbMk5iIe5B6ZLd0LWKYj5FWK3QNwWMLKCwDkYBc9n2vLRTF0+CHRaEp5/Bdlv2zg1aAJv+tyJTpDhYm/ElIABBJk8TeLqdxGqd3Lm1O08UtfIynuGYnRVNmNHWBpa4mbmxp6UM5w+5ciSke4sG+PZ4fzUC0XYe5pjatW6kfqJPH1bo67KVnREZkwkzgP9sXSwZMZjIWz5v3P8/mUMc58dwr2B9/JL0i+sT1jP08OeJjhsGhd27yTh6EGG3TGn6801DRC3FQLu1CeeOIVA5I9QlQfmzr2SV0JCQuKmRxShIgvyY6EgFvJj9I+yNFAYgu9tEHQXDJwGSiMATu9KRZALjJjh2Wa7UcMrST1VT4z13dzxqDEmdz/U6fGxR3LQiSLBk1x4/8z7XKq4xNdTv2aAxQB0Mh0yY42+qKuJ3oCgKSpC5ep6zV8G6M+KmFH3m3n3BzxtTfh08ZCWn6vL6vn5rdM8Y2qC2wIvimrUFFbWU1DZQH5lPQl5VeyJ05tLLYyUjPSyZqS3DX4yJTHrk3H2tWTKAwEIsvZNsq3OtvDEx9KHlPIU/K399WZc91H6x/R34dJBiNmIGL0B4dwaao0cOWF+O6+XTidLMZZlA6tYFLefNSoN9u+9h7+bTUujbTf5X9n71cfMrvidb+JvY8kqNauXjeiwBtjVuBgN5ETOOcYMuJ/XZw1q1cD7SqrL6inMqGLUnLa9KSNyIrA1ssXX0redlZ1TV1VJQdolxsy/BwA7dzOmLh/EH1/HcPDHRKY8EEiYexgbL27k4ZCHsXP3xGmgP9Hhuxk6YzYNyckUfvghrh99hMyknQzf5L1QXwEhC/Q/O4bon/OiJUVMQkLivwOdTh/7mh9zWekqiNX/39eMtbe+1NLge6CmSP8FNWGH3ujiN4MSx7u5eFrJkCnumFi2/rItJuyh/NnH8ZJPJ2XAPEr8QjDrRBxto464ozl4BttysuYomy5u4oGgBxjjPAaNToNMkKE1aaCq3A6FYS6gryUmKWJX06Qh/7diamVI2NIA/vgqBsPEKhbMbWvZyimv41RqCSdTSziZWsr5mEIWVxlQq4AoKy0lJzMY5W2Dp60JJip5h0oM6HtPtihiQH2jlsT8KuJyK4jLdSIu/wEy6u5ggu4Mc7THCatbg6fBKeJnrmT0Kx8ibPoZ3n0P49f+ge6zlchN9UpH8KQwYg4Vkpf0C48b7mZVxm3M//oEPyy/BWfLzn+H2WW1nEkyRbAq58073VDKO/akp0XpM2aujg/TiTpO5J1gguuETu+/IzJjo0EU8Qge3DLmPdiOkTO9OLUjDVs3M5YNXsa+jH1sTdnKkoAlhIRNZ8+XH5OTGId842ZqDh+h9uxZTG+9te0BUb+AqQN4TdT/7BgECPr/tPym91heCQkJiZuK9GOw58XL2eBKE3AYpLd2OQTplS/7wLahRtPegYxjELsZ4ndw8sgAVAQxVPwSUmc2JTfJES/8TM5zL1KTa8jwF4ZQkmbM0Y0XcQ2wQqFs3/uSfK6AuqpGnEcZ8njEGwTbBvPkkCcBUMgU2Bja0GBcQ1WJHQoDfZHy6xkn1n8Vsf8BvAfbETjemfN7M3ELtMHVr7UV0MXSiHlDXZk31JXqsno2vHMWtbGWkqHmJOSVs/vi5UwPmQBmhkrMjRSYGTQ9GyoxN1RiZqhApwhEIaiIiLViY/hhLhXVoG0qvWFmqGCQszl3jfJjkPMIXJxfQFsUzoAdjzPg5GLw+BHuvx+5pSV5L71M5v334/btNyis9PIGTRxHcbYaXdUuVmh+Y73sdu76MoLvl9/CQIf2v7fUNGVIautcwQqyai7iY+PS4WuVGlmElaMxVo6trU4JpQmUN5T3yS2pMjLGccDAVuPDbvekOKuaE1tSmOkymMF2g1kXv46FfgvxGzWOg2u/ITp8Nz779M3Aay9caKuI1ZbqLWIjHgJ505+igZn+m6FUwkJCQqI/U3IJ9r0KibvA3BVmfqovOWXl1W4GYxvkCvCeCN4Tyfd/jfSPohnpE43hxV8gdhWY2CO63kLe98eoyjLG/m9PYbPsQSYklrL940gu7M1kxB1ebbYVRZHoA9lYOhrxYd6biIi8N+E9lLLLcdT2xvZUGpRirPNFIdfXrbyemZOSInaTM+5uX3IvlhO+Jp5Fr9zSbtB9Q20jOz+LQqvWMv/vQ7F11Ss3eRV1nE4rJb+insr6RqrqNVTVa6is0/87q7T28s8NGuB14syNGORszLRBjgxyNmeQswWuVkZtrUmOc8HeD35ZDGtmwIwPsJzzAHJzC3KeeYaMe5bgvnoVSmdnPIJskSvdCJn6FDHhX7C4cDs75Hcy/6sTfHf/cIZ5tG5FpNOJPL1BnyH59X138PczXxFbEssk90ntvkb1NY3kXixn8FT3NteOZutLaIx26p0ilhEbidugEGTy1t+sBEFg8rIAygpq2bMqlnuW3s9zUU+zP3M/0zynETB+ErH7d+NSVopKLqcush3FKn47aNWX3ZLNOIVAzrleySshISHxp1JbCkc+gNPf6GO9wl6FUX/ptRdLFEVO7srEyExJyBNPgPwRSN6L7sKvFKyPoCLdGNvHH8PmoUcBcPW3xmeYPed2Z+A30hFz29bn5qdWUpRZhW5cDlHFUbw/4X3czNxazbEztqNUUYC9GEB9fR2CSkWjpIj976I0kDN1eSCb3z/HoR8TmfZwUCulSNuo44+vYigvqOXOJ0NblDAAJwsjZg/u2Ip0JVqdSING225trg5xCISHDsLmFbDraciLwuz293FfvYqsx/5CepMyZjFgAFZOJpTmq1j4xntsfvtVZmZv47DHLO759hSf3zOUKYEOLdt+sDeJffEFvD4zkCkBbvgk+7RbYb+ZjNgSdDqxjVuyQdvAhqQNjHQaiY2RTQerO6a8IJ+KgnyGzZjd7nWVoYIZjwXz6ztnKd9hiqe/Nz/E/cBtHrcREjaNqL2/kWtnReio8VTt3Yuo0SAornh9ozeCrR84hbbe2ClUHx9RV/ZfFwspISHxX4pGDWdWweH3oKFSX45n0ktg2nVLuc7ISigl52I54xb4ojJUIIpyqnNNKPgun8YcA2xWPIjtE0+2WjP2bh/SY0s4ujGZO/4S0upa9MEs5IawWv0Jc/3mcrvX7W3OdDB2IEZIwx+oLm8u6nr9quv33/IV/0PYe5gzcpY3ly4UkXgir2Vc1Ins/z6enIvlTF4agJu/dSe7dI5cJvRMCWvG2BqW/ArjnoFza+D7OzH2c8Vj3Q+IGg0ZS+6lLjoarxAbci+WY2rtxKI338PUyprxl7YwWlXAIz+eY+OZLAC2nM/my0OXuOeKDMnmCvsd1SJLjSzCxEKFvUdrN+fW5K0U1xXzSMgjPb8v9G5JAPcr4sOuxsLOmGkrgijLq2Fm5iNEF0UTWRSJnbsnlmotOc52GI8Zg662lobk5MsLyzIgM0JvDWtjbWz6jyO/48KwmpKS65pOLSEh8T9K0UVIDofCBKhvv6Ud6C1VRZlV1FY0QMIu+GIk7HkBnIfAo8dg5id9VsJEUeTktlTMrA0JGu9CQ2oqWSseIvuJJ5EZG+G+dg32f/97G4+NqZUhI2Z4kh5dTHrM5Yr71WX1XDpfSIL9CVytnfnnLf9s90xbmRX5Mv1nUnWVqFfEpBgxiSFT3cmML+HIhmScBlhi6WBMxNZLJJ8tZPTcAfiNdPzzhJPJYcrregVi++Pw9a0YLvwRz/U/kfngCjKW3Y/9y/9BpxPJii/FZ5g9i954jy3vvEZo4lbMAmfy3GaRyOxyNp3NZrS3DW9ckSEZZBvE5uTNZFVl4W7e2v2oUWvJjCvBf7RTqwzRRm0j38V+xxD7IQx3GN6r28qIicTU2gZr584zZdwCrRk9z4eIzSmM1s5kbexa3rG6H7eCEmLc7KmwtgCgLjISw4AA/aKYX/XPwfPbbthsIcuLAq8JbS6LOh1p8+cjNzHBfc0aFLa2vbo/CQkJiVbknIfvpulDJpoxsAALFzB3aXp2JafGk4gzNhQ22QXM5ZU4mN6H49AQHEeOwMbGjO4VKeqc1AtFFGVWMXGhF8Uf/R+l69YhMzbG4aWXsFq8qLWH4SpCw9xIPJHH0Y3JuPrrA/djDueg04lE2h3kmwmfY6xsXdtTq2lk18fvURcXSc2YpqKutYbY2VvRkNi2rd+1QrKI9RMEmcCU+wORywX2fRfHhX2ZRO7LJPhWF4bc1jY26k8haB48uA8UBrB2Bqqig3iu/wmVpyf1r/0VlVJs+XZibG7B/Ff+jYtfIJ6x21linsX6U5k4WxryxZKhrTIkg22DAYgpbmshykooRaPW4X1VEdddqbvIq8nj4ZCHe5UtKep0ZMZG4RE8pFvrB09xY+AtDoSkhXEpuoCcXZtxqm5AaWBIQvQ55La21EVGNm0u6ou4uo8BK4+2m5nYgplzhxX2G5JTSDAaRUaFFRlLl13X2AUJCYn/EWpLYeMyfRb30u1w12qY8gaELtQH2FcXUBJ1jl1bRLbtsKG2oJgJ5l8zxnojdm5m5HILRw/K+PXdc3z7zBG2fHCO45tTuHS+kIqsYqoOHKQhNQ1R173C3jqtjpPbUzE30aJ4ZRmla9diOXcOA3b/gfV993aqhAHIFTLGLxxIZVEdkfsy0ai1XDiUSrpVDI+MW46ftV+r+VqNht8++YCUMyfR1dZjXK9FrtRQpbVBaWUmBetL6DG1MmTSvf7s/iaWwowqvAfbMW7hwF4pGtcNxyB4+BBsfhB2/hXF8Eg81qwi++m/YZV9hnRxGNr7/JHLZRgYGzPvhTfY9cl7cHYXL4+byW333NumxtgAywEYyg2JLY7lDu87Wl1LjSpGZaTA2e9yA3CNTsOqmFUE2gQy1rl31fQL01Opr67CIzi068nog/cn3etPUW4lYclLyU/4DM/RowkYFkT80YN4hwRRe6FJEcuLguKLcOdfOt7QKaTDnpOlR8+Q5TYFhVzE+uy/yLxvKe7fr0Xp+CdaRSUkJPovOi1seQiq82H5bnAZ1upydVk9p3amkZSTh9JQzuhpZoQEylBoFsGAyWCot/pXldZTkFZJfmoFBWkVRB/MInKfPqTEsK4Ip/yduFREYenrgmFQEIaDBmE4KBCVhwfCVZmUMZvPUV5QS1Dsdxi4OOPw5RcYBQf16LbcAqwZMNSOs39kUFBViFgvRz6qgsX+T111+1p+X/khyacjGDztTiL37MKmQoXcRE11nR0Kcy262lq01TUtpZmuJZIi1s8YMNSeIVPdKSuoZerywFYV9W8ajK1hySbY/wYc/wR52lHcHlxG/oYaCjRyEt/8nMDXHkeQyVCoVMx69kX2fv0pcYd3sifpFHYeXvqHuyd2Hl5YODgSYBPQJmBfp9WRHlWMZ7AN8issaHvS95BZlcnHkz7utZKa0Y34sKtRqOTM/MsQvn/tACmuS3Efr2PY5IkkRhzmaEMFI7Kz0ZSUoIjeAHIVDOqk8r5jiL60hboWVK3N5xnnswAnNDqB7LvexGfDM2TctxSP79eidJaKwEpISPSQIx9ASjjc+Z9WSlh9TSPn92QQfTAbURQJDXNj2HRPDE3bb5lnZm2ImbUhPsP0sWGVR4+T+Mp/qDTzoGLI7aQZ3Ukad2CvzsTx931Yr/0emahDZmqKYWCgXjkLDKTq5CnOZIdgrqsj5NmFWM6Z1UZR6y5j7/YlPbaY9IM1VJgU8s/Zf231uaDTafnj84+4eOIot967nGF3zCHucDg2FQZozeupqrZFYar35GgKC5Cbti0a3lckRawfMuaurtsW/enI5DD1TX1j9qMfItv/MqF21pzLHU7a6Wws//4PnN59B5lKhUwuZ9qjT+Hk60dWfCxFGWmknjuDKOpN2AoDA0KsVVxSFnBWth1HjwHYeXhRnKWmvqaxVbakTtTxbfS3+Fj6MMmt/XIX3SEjJhJbNw9MLHuWtWhmbchwy3jON4Tye1oNjzg6M+tvL7Hl369xzssR9zOnsYzZpG/b0VlGpFMIiDoojAfXyzFuok5HXpEcla2akBkDOfvb/7N31vFRXen/f9/xmUw8mbgbSUiCBNfiUqxQ3ULd5bv1Ld2t6y7tbm0rW6HUKG1pobi0uCdESUIIcXedSUbu748bAiEBEkr767Lzfr3mNeHKuedOyJnPfc5zPk8BUc+/j/XZeylcvITAzz5D5d+3lbJ27NixQ+422PEqJFwPQ28BwGK2kr6jlKSNBbQbLUQN92b43BCc3PtuQVG/ciUVL7yIR2gog957GJW/H43VRrL2lpG1T02aKghdooxQQyv+LenYspOp/+ILxI4OigMn0x7qxpQ7o3AdcvHjmSiKbKlZzxG/wwzOn8agSYG4njHu2mxWNv/7X2Tv3cm4G24mcc5VAHiFhONZ3kS7j5FmqwGFVpqWtFRVoQ61CzE7/20MmC29ytPQJH+Gz5YcGvyiadrwGpaCdPzf/xi5IQBBJiNh6iwSps4CwNzRTl1JMVWFJ6kpLCA757X3OjUAACAASURBVAgBxQ3s/PQ/AMjkCmInPYxcKSMw9rQ1xc9FP5PXmMffx/8dmXBxT1DmjnZKszO7+tIfRFHE49BW3N1KqCmdy/61uYyZP4hpd9zHpg/eYtu3y1noX4UQf+35GzozYf8MIWbKyqJWH4aPt8DQGUEcP1TJwUMW5n30CaV33k7h4sUEfbYcVeAfJG/Qjh07f1zqC2H17ZLT/ew3QBCkWsU/nKClrp3AGDdGLgjDM+B8BYO6I1osVL76GvVffIF+wgR8X3+9azrP2VPLyPlhDJsTQmFaLZl7ysg4ZiOD4QSOn0HME154yOvY93kNfgGOBA2++Ah/QWMBz+1/jiOVRxgWP5yhY70YPjL6dD9tNra8/zbHdv/CmGsXM3zeoq59XqHhuB5Pp1nViEL0Q5RbgN/O1NUuxOz8PvjEw+zXCZafYN+PRThOD6Z5Sz6F8yYRePcYFBPvhMBRXVYOSpUar9BwvEKl6F9400xmrZ7F0thHSBQjWfv6y+QlHSAwbgZKtbQ+RxRFPkz7kCCnIKYFTbvorpblZGE1mwmK7/u05Cnac3IwFxYRdtVEdpfug02j8QpwIXbSNEref4+MllZ21g1gYsQF+uccABqXHnliZTtS6FD7EDTMB4VSzvhrI1n3Tio55d7ELv+UoltupfDGxQR+thx1SE9XaTt27NgBwGyCVUukOpDXrMCCml2fZ5G1txzPQMeLskSyNjdT+tDDtO7Zg9stt2B49BEEec/1k3K5jNDBnoQO9qSp1kjWvnKy9paz6aMsFEoZFrONkfPCLiq1xGw182nmp3yQ+gFqhZrnRj/HgvAF3doSbTa2/ucdMnduY9Si6xl5VfcHY6+wCORWgWZzMa7EYLJ0Fv7+jYSYfdWknd+VkM4nnPoF/yHg1afoaFVT8PZ+Ot66Et4dAUUHej3P39EfZ40z2eZ8IoaPxt0/FGPjCUIHnbZu2F26m6y6LG6Pux257OIXTxelpyCTy/GP7l9iKEDzli0gk5Gw8A4qB6fR4FLO9s+O0drYTvzABIJqG0iq8iRp86bzNyQIkng9q9RRUbo0EISMkkRW0EB3QhI8OLKhALNXCIGffSb5ty1ZQnteXr/7b8eOnf8RNj0B5Smw4D0abb58/48ksvaWM3RGEIv+kthvEdZRVETBddfTeuAAPi++gNcTj/cqws7GyV3LiDmhLHlpFLPujScgxo2ESQF4hzr3+5ZSq1O5Zt01vH30ba4IvIK189dyVcRV3UWYKLL9k/dI/3kLIxZcy6hFN/RoxytECgAYm0sAaG01I3NwwFxpF2J2LgNcvHQ4G7QUpNegn7eYoC++wqZ0o2B3CMayNlj5J2gq63GeIAgMdB/YlbCvc41EtJbjHaIGpD+uD9I+wNfBt8fKyv5SmJ6CT8QAVJr+l+Ro2rwF3bBhKD08WBK/mM1Bn2HpsHHyaDUOBhsxJbWEBgew4/OPyNm/5/yNecdD5TGwmgEp5F/RqMVR3oreVdN12NhrIkCEPaty0URFErTiMxChcMlNmI4f7/c92LFj5zLn6JeQtBzGPkR++3BWvXyY5loTs++NJ3GsM5bSkj7bTAC0HjpEwdXXYK2pIfDjj3FZtOjCJ52FTC4jJN6DWffES2PaBbBZrWz/5H22f/Ieqbu28PK2Z1i8YTHNHc28Peltlk1Yhoe2u8eiKIr8/OkHpG7dyLB5ixhz7Y29Rt1cvX0QVXLEpjoAWhptne76diFm5zIhOM6Dkpx6OkwWtPHxBH31FTJHVwo3Kmk6aZG8bCwdPc4b6DGQEw0naDO3YWr1BUQqT0rC7FDFIdKq07gt7rZuxVv7i7G5icr8PIL6sVryFO0nTtCRl4fjtKkAzAiegdLNiknfSN7RKrTWFARgzIDh+EZGs/GdZRQfO7d7Pj4JYG2XrC6AlrRjNDiG4Beo7naYk7uWxNnBnEyppjCjFnV4OEErViDI5RTddDOm7Ox+34sdO3YuU8rTYP3D2IInsL/xT2x4Lx1nTy3XLB2Ge00aJ6ZMJW/qNHISh5F/9TWULX2K2k+X07JnL+bKqh4VThq+/56i225H7u5O8KpvcBgx/He5jb3ffE7K5nWk/byZbe++hfo/SSzZHcEDpZNxOdZKVcFJbDZr1/GiKLJjxUekbF7H0CsXMO76m8459SnIZKh83HBosiAINpqbZSi8vOw5YnYuH4Lj3EndXkxJdj2hgzxRh4QQvPJriu+5l9IdGbSW5ODluRTZvGXdzhvoMRCbaCM5N4OmOieUagfyjx5hwJgJfJj2IQatgXnhvdeFPC82K5zYDilfUmwOB1G8qPywpi1bQBBwnCIJMaVcyY2xN7Ln+EFUxx1ZEZrLGGcN5vQM5j//LCv/9hhrlr3Idc/9HY+AXoxdT5U6Kk8Dr1gKd2Rgk3kTMrbnsYMmB5K9v4Jd3xzn+qjhqENDCPp8BXk338yPD99H7IRBxM0YBhaTJHItJknkdf3c+a73golP9iy7ZMeOnf9+jA2wajGtymC2Vj9F6YFiYsb5Mu6aCJpWraTypZfRxMbismgR7XknaM/NpWXXLhpXr+5qQubsjDoiHHVEBGJHB43fr8ZhzBj8/vkGcien3+U28pIOcWjNd7TGOPNdYBrxhHGtfjqUNFKafYwT+/cCoNLq8I0cgG9UNK319aRu3cCQmXOZcOOtF8w/cwz0w624CpVXC81GLQEGT4xJyb/J/diFmJ3fHZ8IF1QaOQXpNV3WEwpPT4K/+pKqf71J3Sef0PbSj/jJQ9BceV/XeQM9BoIIGT9VoVTp8Y4cSn5KEskVSRyqOMTjwx5HLVef67I9aSqD5M8heQU0SbkAhTXxqLTeeIdF9vu+mjdvQTtkCEqv0/XVlsQsQV2/hvovZWxkDILPfmIPbCelahKzH1/K6uf+yvevPMMNLyzD0f2sUkUeEaDQdibsX09xTgMyPAgYFtzj2nKljPHXRbL2zRSObili2OwQVEFBWG6eS+mG7ZQm55C79wDTwo+jdzoz2iiAQgMKFYhAeyMMXASe/b9/O3bs/IGx2eCHuymr0rO5/UU62o1MvimaqBFeVL3+OnUff4J+0iT8lv0Dma67d6Glro72XEmYnXo1rVuPrbkZ1xtuwGvpkxd0ur9U1FWWsuatV6l3trA5KJv7Bt/PrQNvRSmXZkJEUaSpupLSnCxKszMpy8li36ovARg0fTYTb7qjT4sAvELDqdudAopKWtpdUPoraa6SIoKX2kTdLsTs/O7I5ZLlRGF6LaJN7KoRKahUeD3+GA4jhlH20H0UPP42hmITrnc/jCAIeGg9GNI2AWu+jtFXhaLRwYlDu1jx87u4adxYGLHwwhe3WSXjwqTlcHyT5NUVegVMfwk6Wih6/VMCIgKQ9SHJ9Ew6Cgpoz8nB68nuRWTlMjnXj13A59+u5prqRNTDwOnzvTy24Qk63PTMnDkO5+9PsvrVZ7nuuddQ685wbZbJpWXl5WmIZjOVRhc8XJpRqnrvW0C0G+FDDSRtKiRqhDdOHlqKs4+glZuJ8AoirVRkRf5Qxg8aSsyf/4xM7wIyxenoV+UxeG8UlB21CzE7di4zxD3/JOWoiv0tz+PkqWHOn+Nw81RS+sgjNG/cJAmqp5b2mmCvcHNDMWJ417Rjm7mNytZKmhur8AgYiiD7faREatlRfnzxGVQWCw1z/fh+6oc96g8LgoCzwRtngzcx4yQvSVNLC811NXgEBPVZRAVHxJHFd5jFCpptkSicrYhmM9aGBhSu/fOXvBB2IWbn/wvBce6cSKqiurgZQ1D3cLZ+wkRCV6+i/I6rqHzzI1qTs/F57TWsGkeGHJ9Fo2MlCZMn0t7mAoJAbeZxFl93c48Crt1oLIWjX5yOfjl4wpj/gyFLwE0y6GsszqXBvJLBTi39vp+mLVsBcJw6tcc+oeY4YYqdpLXM44aFwyn7fC//8riH7/3LWF+4FZcEkamHTfz72Xu5dulL+LqcUWTcJwHSv6X2QBqtOm+iIizn7ceYReEUZNSy+5vjzLwrmvyiOkJ9dUxd9iFR+/ey8e1lbM5M4cTixUx44BFcz+yvRyQodZIQS7iAz1k/qC4qwNnTgEp7nt+PHTt2pKhV2VFwDQYH9wse3ic62mhP/p6fv2/nZPvNhA7yZNJN0SjaWyi69W6MSUkYHnsMt1tvQRAEmjqaOFF/gipjFdVt1VS3VXf9XNVWRbWxmlZza1fzYc5hPD7scUb7jb40/e2Fpo4m3kp+i4LVW4mudSRg8Wz+MvvePosqjV6PRq/v1zVDgqLpUNhQmKvBOgqZXkrct1RV2YWYncuDwIHuCAIUpNX0EGIAiuBY/N//gPqn/0TV/r2cnDuPwqteQNGuZmvECu4xz8DV0ZV2g5rAGj3XRV3X+4WKD8Pu1yF3sxT9CpsEM16GyJnSdNwZFB6X7B6CjIelwtz9CD83b96MJiG+9xJDaasI0x4kpW0+5RYDgkqFX2ErL133Ek+NeIrtRdv5RbsSv521vP7qXSx7Zd3pAcYnHo58zMntR4FQQifFnLcfelcNw2eHsG/1CY5+sxqTRc4aXyPOpfsYPWoMtwwewvbXX+FYWjKVb/+DUau+YcDTz6AKCAC5QspLK7t0eRCW9na++sv9+AX6sPCVD/9YdVHt2Pmj0NEKqV/Dgfeg9gQIcggZB9FzIXoO6A0XbuNMLB1w8hdI/472rB38WPE4tZZhjJkfQML0cMylpRTccSfmkhL83ngdp1mSeXW9qZ6FaxdSbazuakolU+Gp88SgMxDpGslYv7F46jzx1HpiE218kPYBd227i/H+43k08VFCnC+df6EoiqzPX8+yw8twKjAxodCDuBmzmHa+Or2XCK1KR6OLFaWxDoVcgbmzjJ6lqgqioi5wdv+4oBATBOET4EqgShTFgZ3b3IBvgGCgALhGFMV6QRpl3wRmAW3AzaIoJneecxPw185mXxRF8bPO7UOB5YAW2AD8n3j2sgw7lx1avQrvUGcK0msZPqf3khFC2ETc7n4Y3fcvk3E8gePHbfjqTlKvLSajJgNvB28ynSsYctwFoc0MZ+oqYwNsfx6OfAIOHjDmz53Rr3MPEoXpqegdtbi1n5SeSv2G9OleOkpKMGVmYnjs0Z47bTZIX4VXVAQOmSry0+uJjo3FmCLVstQpdcwJm8Oce+fwQfujeB/MIrskjeiATmf9zoT90iIzankrhpgLl/uIn+xP1v5yDv2cBYgcdiwnbeejfDn7S0KcQ5j51PNEHT7ApreXsa2xnKIl1zPsqmvxvOMOZH5D4MinYLVIwuxXUpW+F4sVCvPLSVv/LQlXXvOr27Rj57KhqQwO/Ucap0wN4DsY5r4D9QVw7EdY/zCsfwSCRkPMPEmUOZ3Dbd5mhcK9kP4dZK0FYz0dKm9+anmFOpsbs+4dSHC8AWN6BsV3341osRD46SfoEk9X7njl4CvUt9ezbMIyQp1DMegMOKmczvsANTNkJl9lfcUHaR9w1ZqruG7AddydcDfO6v77gJ1JfmM+Lx14iYMVB0lUxZBwzIIhMoTJi+/8Ve32h3ZPNeQ0IzpZMVmlz8BSWXnJr9MX+4rlwIyztv0F2C6KYgSwvfPfADOBiM7XncB70CXcngFGAMOBZwRBOBXbe6/z2FPnnX0tO5cpQXHuVBc101Lffu6DxjyEMnE6x0NmohNaidj0Di+usJGXvpv/pP+HGm9JsxekdkZxRBEyf4B3h0PSpzDyHnjwKEx55rwirL2tjfzkw4QMHo4gV8CxNX2+j+ZT05LTenHLLz4ADUUICdcSOthAUWYtyvghmDIysHV0t+gYPe0qZKLAnp1nXNsQg9WqpErwx0vf2qeIklwuY8I1YRiNJdh0enSOTijlSh78+UGaOpoACB02klve/YTwIcPIMbiwbvMa0ubNpaVKDxYj1OT0+f7PR0XKbgC8Nc3s/PpzGirKL0m7duz8V1OWAqvvhH/Fwd5/SdGvWzbBHb8gDr4RJv8N7j8C9+yHiX8BYz1sfBzeiIaPpsK+d6ChSBrvig/Dxr/AGzHw2RxJiIVPxXz1KtYpv6SqyZ3pd8YRHG+g+ZdfKFyyBJlGQ/DXX3UTYdsKt7GxYCP3JNzD9ODpRLhG4Kx2vuCYo5KruHngzaxbsI75EfP5KvsrZv8wm6+zv8ZiO38qRW8YLUbeOfoOC9cu5FjtMZ4a8iQTkzxQKtVc+X9PIP+dFgUAyL1dEGwiorWWtnbpu8b8G1hYXFCIiaK4C6g7a/M84LPOnz8D5p+xfYUocQBwEQTBB5gObBVFsU4UxXpgKzCjc5+TKIr7O6NgK85oy85lTnCctEqwMKPm3AfJZCTr/0q91Z9JPssJfO1pfBoFhjz2BcNfXMcD+f5otTryDuyR6qZ9dQ18ezM4esMdP8OMV0B94Tpp2Xt3YG43ETdtLgSPk54o+xiYbd68GU1MjDS9dzbJn4PKEQbMJnSwJxazjTrvBESzmfZjx7odOjBmFEYHkYqUM7zFlBpKOsZjUToQOLDvOSMu7QcQrZUoxHgmOk7jjYlvUNJcwuO7Hsfa6a2jdXRizhPPMOuBR2lzc2GHs5J9H/6ExSiTIoKXgPITOeiVHcyZOxzBZmbTv17ol1GkHTuXDTYrZK+HT2fDhxMgez1i4u20z1tLg2oR5Z9u5uRVC8mJT6DsyaVYW1vBK0YSYvfuh/sOw6S/SjYzW56SRNw/wuDjKVJEzT8Rrl4Oj53AMvd9Nmw1UHGyiam3xhA6yJP6lSspue9+1GFhBH+zslvx6npTPS8ceIFot2huil6C1WLu9+25a915ZtQzrLpyFVGuUbx88GUWrV3EvtJ95zynxljD3tK9fJLxCU/seoIFaxYw6qtRfJD2AdODp7N2wVpc91ZTU1LErPsfwcnD82I++YvGIcAbAJu1ktZmC3JX19/ES+xipaWXKIrlAKIolguCcGoC2w8oPuO4ks5t59te0st2O/8DuPk64OimoSC9lthxvf/a68pbObKtkog4FUF1u6HOzKZnp6FdtZkBZQIhmzJp9XGjoLmZnG++wsHdimbEIrRxt6FxiqQvax9FUSR12yY8g0LwDo+Exnmw7s9QmQHecec911xRgTE1Fc8//7nnTmM9ZK6GQTeAWo9vuA2NXklpmzNBQFtKCtpBp/3KBEFAMcAXWXIZLU0N6J1cACholqYpQ6cl9OFuJPJ/+Q4Am9qf4HQfhswawtKRS3l+//O8mfwmDyc+3HXN6LET8Y8eyLrXniNdEBhYV4tvaTIMvrHP1zsXFRUN+LipcLryaSYdnsSmfAXJG35k6JVX/eq27dj5r8DUJOV/HXwfa0U+xnY/jMr5GIvlmNbsxNq4FgCZXo82Ph7N7Nk0rllD2+HD+P797+iGDJba8YwEz8dg/GNQdxKOrYWKdAifDANmg0aaCrRabGz6IJ2S7Hom3xRNaLia8qefoWHVKvQTJ+L3xus97CleOfQKTR1NLIt+luUP3kVrfT2OHp64eHnj7OWNi5eP9LPBGxdvn+6ru88iyi2Kj6Z9xM/FP/P6kde78sdujr2ZyrZKcupypFd9DnWm0zEebwdvolyjuCLgCsb6jWWI1xAydmwj45etjLzqWkIGDb3Ev5gL4+7jT6siA5WtlOamAFwMBixV1Rc+sZ9c6hhfbzFM8SK29964INyJNI1JYGDguQ6z81+CIAgEx3uQtbcMS4cVxVm2DKJNZMeX2ShVcsYuHg4nXoe19xM22JWXpsm5MfpGZniOx/LW45SUyTGGGpA3qGj+bh98tw8EAXV4OI7TpuH5wP3n7EdlXi7VBSeZfFvnKpwBV0q5GcfWXlCIdU1LTu9lWjJtlfT0OvRmQCrhEZrgQe6RKkL8AjAeTYGbu58SM3ICuUkr2bnze2bPuQ2AMlMQTpZCHF1iz/+BnsJqJj87D0HjxJHgQ4wpWMjWjzOZOmc2OVE5fJr5KRGuEcwJm9N1iqO7BxNvu5evnn6MomYffC9BRMxYV0WDUUZWSAfNJ9dyww2Pc/zNN9j91XKCBw3D3b+XCKIdO5cLVdlw+D+YdqyiIQfaal1or/OVFg0Jh1GHh6GfOgXdoEFoExJQhYUhyKRJKpdrrqbssccpvPFGPO6+G4977kZQnlExxC0UxvZ8+LNZbWz5OJPC9FomXBeBV/Fu8h76J9amJqkI9yMP9/D72l64nY35G7nb9ToOvPEeKq2W4fMX0VhVSWNlBScO7cfY3NTtHI3eERcvb1y8ffGNiiZwYAJuvv5d05iCIDA5cDLj/MZ15Y/tKtkFSMn/YS5hjPcfT5RrFFFuUUS6RvbIKasuKmD7x+8REBvPqKt71oP8PfDSeXHIuR2HpkpaWkOlMke/QY7YxQqxSkEQfDqjYT7AqVhdCXDm6OoPlHVun3jW9h2d2/17Ob5XRFH8EPgQIDEx0Z7QfxkQHOdO+o4SSnLqu6YqT3FsbxnlJxqZtGQAOicVDFkMxQe4Iu1r9sVN4tbKUmQbZhLp7cvWchdaFt7OoOtvwlJfjyk9HWNqGi07d1Lz7ru4XHNNN6PVM0ndtgmlWkP02InSBr0nBI2R8sQmPXXe/jdt2Yw6MhJ1yFn5Z6IoeZX5DpEsKDoJHWLg2N5yWgZOQZayoUd7E4ZfScrHX5BzcC+z59yGqbGVeoUfke0bocIfHHvaY5yNNXc7hU0O1AfLaI+uYGh0EKnbizmRXM3IkQsodqrg2X3PEuwUTJznaaFpCItADpS3KaEyTVp5ddbK0v5QcUQSqamOdWw/sozEWV8zbZgry3d1sOmdf3D9S//st1+bHTt/aKwWOL4R6+73adqdTMNJB0y1egSlAt3IUTgOlkSXNj4eueO5UyZ0Q4YQsuZHKl94kZp//5uWvXvw+/vfUQX1UoGjE5tNZNvyLE4erWbEGB0O7zxMRUYGusREvP72VzS9rPRrMDXwwoEXGNUSgXnrEZw9vVj41PM4eXQfK9vb2misqqCxsoKGynIaKstprKqkJDuT7L07AdC7uhEwMIHAzpeTh2dX/ticsDkcrTpKkFMQwc7BFyxD197Wxk9vvIJap2P2g48hk/3/GScMOgO1zh341tXT1KZBYfCk/TcoGXexQmwtcBPwauf7mjO23y8IwkqkxPzGTrG2GXj5jAT9acCToijWCYLQLAjCSOAgsAR4+yL7ZOe/EN9IFxRqOYXptd2EWGtjO/tW5+EX5cKAUT6nT5i1DK/yVN5K2QoIMPwO1JP+ht+rL3MyJYmx19+EwtUV/fjx6MePx3HqFPLnL6B1z25cFvY0fG1vayV7306ix0xAfWa4PmYebHhUeqo1DOi175bqaoxJyXjcd1/PnSWHoeoYzHmr22b/KFdUWgWVjjHoKj/FXF6O0uf0/TlrXGgN1qHNraLDZCR/SyqiTE6QLhnKfSDiwkKsfOc3tNsUpLnWMtZvASOHhRF3hT/JmwrJ2F3KIHEhet8gHt+0lM8WfoxBJw26coUCTxd3aoxlWNvMyKuOgW//Sz119SPjECAyovRWysz5LN37FCvnvcKU44tYl6/g8NrvGbHAvorSzmVASzVi0nJMGz6lPr2FpiIHRIsL6rAQvO66Dqc5c/rkPZVRk8EPuT8w2nc0k4Mm4/vaq+gnTqD8mWc5ueAqvJc+ifPChT0S6EWbyC9fZJN7uJIY7QkcXvonFoMB32XLcJo965wJ968efhXPXDNRGWYMYZEs+MszaB172gmpdToMwaEYgnuucG+orKAoI4Wi9FQKUpPJ2v0LAK4+vl2iLCA2nilBU/rySSKKIls/fJuGinKufvolHFwurWdXfzDoDNQ6dYBoo9Ekogz3wlJbi2ixXNJKAn2xr/gaKZrlIQhCCdLqx1eBVYIg3AYUAVd3Hr4BybriBJJ9xS0AnYLrBeBw53HPi6J4anL4Hk7bV2zsfNn5H0GhlBMwwJWC9BrGi5FdA8bub45jNduYeMOA7oOIUgvXfgE7/w6Jt4G/lDcQMjiR3V8tp7muBke304JOHRWFwmCgZVfvQixr9w4s7e3ET5nZfceAKyUhlrX2nEKseds2EEWcepuWPPIpqPQwsPs15QoZIfEe5KdYCRJkGI8e7SbEAPyHDqYj+yCZh3ZRmdyM3OpAUHiTVHPyQljaOZmRiSAYKHZrYbSvZLLo4Kxm3LWRDJoayJENBYj7hhJYGsc/K77kL3fegbOzZHboNyCWpPoaGqo1uJcl/yohVlFQiFVnw6d1AD75A9gmX8F7/jt5cOpCjn//C/u+/ZKQwYm9Du527PxXUJKEZce7NG3cSsMJNe2NSgSNK07zrsT1mmvQxMf3aaVzbn0u7xx9h5+Lf0YmyFh1fBVXBFzB0hFL8Z45E+2gQZQ98RfK//o3Wnbuwvv557qEnSiK7Pw6m+x95YSUbcM7fx3ut9+G+933INefO5dre+F2irfsYeRxV4IHDWHuQ0+i1Gj6/RG4eHnj4jWD+MkzEG02aooLKcpIpSgjlWO7d5C6dSMIAq7evijUauRyOTK5AplCepfL5cgUCmSd280mIyeTDzP2+psIiDl/ashvzamIGEC7pRGboxPYbFhq6845w3IxXFCIiaJ4/Tl2Te7lWBHoJTwAoih+AnzSy/YjwMAL9cPO5UtwvAf5qTXUlrbg4e9Ifmo1ecnVjJwfiotXL27srsEw/9/dNp0SYgUpycRNOi2MBEHAYdxYmrds7fEUI4oiads2YggJwzssovs1nHwgYKQ0PTnh8V773bR5C6rQUFTh4d13nErST7ge1D3dnEMHe5JzsIIGw0DcU1K6zBRPMXrYTDZ+u4+k3ZuQ107A3VKOKmQglKf22o9unNhOfqMDNm8NglrJEK/uXmiObhquuHEAg6cF8tM3+5FnxrHiqX0Mnx7OoCmBBI4dT9KBXRQ3GHD/FXlioihSXttOi4seByu4GLRMPnkjazVvM3H+40w++gMlWR5sevcN/vTKP5Erzj9VYcfOHwVRFDHniVa7rQAAIABJREFUpGBc8RdajmTTXKJFtOnRREfg/ehinGbO6iGASrOPoVCr8QoJ67a9qKmIf6f+mw0nN+CgcODeyAeZYbiS7RVb+SjvPeZXzOfBwQ9ybdS1BC7/lLpPP6XqX29inDsPn1dfwWH0aHa+s4fMTDOBRVuJ9anG+18/dlsR2RsNxnp++ODvDMlzIWrMeGbe+/AlsYUQZDI8g0LwDAph6Oz5WC0WKvJyKcpIobogH6vFjM1qxWa1YLVYMZuNtFut2CwWbDZb1/a4SdMYPrcPJet+Y9w0brQ5iKCQI1qqaJdL4stSVfn7CjE7dn5rgjptGQrSanFy17Jr5XHc/RwYNLXvCzI8AoLQu3uQf/RINyEGoB8/gcbvV2NMSenmm1Oem0N1UQFT7zhHIn/MXNi8FGrzwL37AGqpq6Pt0CHc7+ylgGzat1KSfuItvTYbGOOGQi2nLnwCvik7e+yP90rgY98ONMfy0OhnEOkrk4xdj60BU2PX6qjeaDr8LTXtDhR4dzDEawhahbbX41wMOhY/MJn3fvmE3K0NyNYrSN9RwuCpUnSuzOTCoF8hxBrzUjFZ5JjkHsjMHQwv+Y79vjcwI+d2Xtz5b1ZMf5Kptc+xpkjgwPcrGXPt4ou+lh07vyXW5ubOnNNUjKlpGJMPY22SSvzIdC64XDsfl+tu6DUHCyBlywZ+/uR9REQGTpxJ5OgFVNY2sit7HyXllTh2hHCn+CqKNi3W3SIbyAL8WcxLWFTt5B6p5hX9FwwMisLPexKqpxNoXLmchgf/Rm3cleRpBhFYf5gJD03GccrkC0bgLGYz/37pfkLylIRMHs/s2x/tWiRwqZErFPhFReMXFf2btP9bI5fJcde5Y3XRINRXYhKlGYJLbWFhF2J2/r/j4KzGEORIQXoNbc0dtDS0M/3OgcjlfR8cBEEgdFAi2ft2YrWYu0VYHEaPAoWCll27uwmxtG2bUGq0DBgzvvdGozuF2LE1MO7hbruat28Hmw2n6dO7n9OVpD+4W5L+mShUcoJi3SlJDSPs4PvYTCZkZ0wJKGVKHGJCEAoqsJkLCR4RAT6d9h4V6RA8tvf+mo0UpCQBgaQ4FnOr74Ut+e6eeAuPCI+w+th2Fjc+yoEfi3HWOlLd3IatNA2Z2ShNB/eTiqTtALh1hOHYXIzl6C8MHSaw230hcUdm8WZgOU9EhxFrbOTgj98SOnQ4PuGXtmyInf9COtqk3ErfIfAbiYPzIYoi7Tk5GFNSJeGVlkbHyZNdnoIqDw16tzq0w73R3vAc6mGTu69mPKutPV9/xqE13+HgGoWpTUPGLxvI2LkHpW4KDspgIrS+eHg64+rhiKObGkd3DQ7OajpMFlob2mmpb+dkmYKyyipOpFRSam6T2vaYh9UpAps5HzexjpiHZqGKu/A0aIexjeUvPYY6txHFFVEsuOMxe9mxC2DQGmh2F3GpqaG1w4YGuxCzc5kSHO/BoXX5VBY0ETfRH++Q/pfHCBmcSNr2TZRmZxE4ML5ru9zREd2gQbTs2oXh4YcAMLW2kLN/NzHjrzh3MWqXAOkLIWttTyG2eQvKwEDUA87KHys5DFWZMOfN8/Y1bIgneclVNDgEYcrMRDe0u0dOwtAJVG78HkwZeF1xPVLKJVKe2LmEWO5WTjboUDipaXSwdOWHnQ9BEHhxzIssaVrCKsXbzC68H0dDGBUtjbRUyXCqyICAYRds52zKc9JBZsPBFo1T60F8XnqJ8qefJnGQigNOsylZ18ChP93KxMIHKDSOZ9O7/+TG195EqVL3+1p2LiP2vAG7/gHuETDybml6X3XuPKdLhbm8nMY1a2n84Qc6CgsBkLu5oY2Px/nK2WjczGhPvIPcWgwTn4AxD523BJjVYmbz+2+RtfsXnAyJdFjGIh9VS2ZlOQEF1Ti0rMZ35Ahm3fYgOqfzj3UTGUCDqYHXk17nq6yfGFURSlSBBktbExq9K7WNJ/nxn1LJNHf/QHwjB+AbGY1P5IBulhJtTY18+/LfaCwoIG+kgrfveNUuwvqAQWegwv0kLohU19YRIJNdcnd9uxCz84cgOM6DQz/lo3dVM3LexSVvB8YlIJMryE850k2IATiMH0/1G29grqxC6WXg2K5fsHT0kqR/NjHzYNszkmu/q7R03NrQQOuBA7jffFPPgSxpea9J+mcTNNAduUKg2nOQNGV6lhAb6TuadYoULOY8BK0GFI6g94aKcyfsW9K+p6jNjaYYHW5aNyJdI89/b53olDremvQWN6y7AYu8A7lLJBZ5CmXVzjiVHb0oIVZRWoVRL0Mj0+LprcJl4VXInBwpffgR4gZqEcQpfLv2KDGJ1zPdtJ7vi2LZ+80XTFx8W5/ab0s+iikjHdfrrkNQXbzFhp0/GDmbwD1cqoax/hHY/oI0xT/8znPXWLxIbCYTzVu30fjDD7Tu3w+iiG7YMNzvuB3diBEo/f0R2pulqPjRz8EQCwu+A5/487bb3tbG2jdepig9Ba3PaEzG4eyOWkmW7iAzrpjBDbF/ofLnQxz68VuWZ97DFTffyYAxE84rinSomVs3GM8DxzE3t1Li2oRubjS3XPUcejRU5OVSlpNFWW42uQf3kf7zFkDy+/KJiMInIoqs3Tuoqypj59Ba/r7kE5Rye15mX/DUebLLIZkBOFNXXUuIhweWSrsQs3MZ4hGgZ+AEP8KHGlBpLu6/pUqjxT9mIPlHjzDhxlu77dOPH0f1G2/Qumc3zlddRdq2jXiHRfRInu1BzFxJiGX9BKOlXLKmTZvBYumRZI+xATJWQ8J1FyyrpNIoCIhxp8I0lNbkbbifpT80pTpk6mis5hyK0lMJGZwofQGca+VkRyulKfsx2yJJcyxlpM9IZELfp3Z89b48MeIJdh3Nx6FF+sIrNbox4CLyxKymNiobweTqACL4JUrC2mnqVGTvvI3wwIM0DtRDyUje9dLxhMc6EkRIWv8j4Ykj8I8+99oda2MjVa+/QcOqVQA0/Pgjfv/4B+qwC/we7fzxaSqHynSY/AyMfQiKD8L+d2Hvm7DvbYiZD6PuBb+Ld1gXRRFjSgqNP/xI04YN2FpaUPr54XHvvTjPn9e9TFn+bvjxXmgqkfoz8UlQnD9i21RbzVcv/oWWiipaDHFoTCM5Er2GwaPCeCHiMaLcpOn38GsiiRw5lq0fvM2Gt5eRtWcHU26/t4d/l6m1hZRN60jasAZTSzOBcYMYMm8B6zr2sDxzOZt+mEWseywRrhFExkcSOWERM5xCMFbVUZabRVlONmXHs8g/egS5Vs3GYeXMn3gTMe4xF/0Z/q/hpfOiXNGATPCgqalOMnW1R8TsXI4IgsCE6399jlDo4ER2rPiIpuoqnDxPD2pn2li0xkZTW1LEtLsevHCDbqGSu/6xNV1CrPGnn1CFh6GOPisBNf1bqWB2p5P+hQgb4klBWg2Vx2sIEMVuT8T5O3OQyf2xykRyDu6RhJh3PJzYDr3lbR3fxMlGB2RyGblONdzYh2nJsxnrN5ZvHXfgWxKBVqGiyqbFVpx84YK0Z1GTuh2rKMOx3Qel0ILXtNN90U+YQMAH7yPeez8tMXrEpBg2jHuIKa3PUuA8nU3v/Yvrnn0NvVv3upqiKNK8cSMVL7+Cta4Ot5tvRpsQT8XzL5C/cBFeTzyOy3XX2ada/pvJk/IKiZgKggCBI6VXfSEc+hCSV0DGd9Jq5lH3ShYzfTD6FEURc2kZTevXS1OPBQUIWi1O06fjvGABumGJ3ZPVzUbY9hwcfE/6+79lEwSOOG/72XXZrD+0CtM3B1F0QLN3Ir7GifjPVHDXnNdRyHp+1XoGBnPdC38nZdM69qz8nOUP38vY629i0PRZmFpaSN6whqOb1tFhbCN0yDBGLLgW30gpFeL/GMqs0Fl8nf01OfU5rM5djdFiBEBAIMgpiAjXCCJGRRA5axGJghuP7H4UJ4cA7oq/qx+/FDsGnQEEUGv0GI31KLy8MBcXX/jEfmAXYnYuK0I6hVh+yhESpp6OWAmCgMP4cTRv3kJeuD8qrY4Bo8+RpH82MfPg5xehqYyOJhvGpCQ8H3qo+5e+KEreYT6D+uy9FRzngSCIVCiDMZeWovI/XWSiJLcZ55ZKcgytaA7tY9odDyDziQfRKiUznx0VyFhNfpsnsmAPrPJ8RvmO6tu9nYGjyhG9vwxZiQwXQxB1ba2Yjh9D197Sqw3HuahIkYr86m1RONkqUQcHd9vvMGoUQf/5ANs9D3Aw+i5y90YREzGJGbIcvssN4pM/38WIBdcwZPY8lCo1HSWlVDz/HK27dqOJjSXgg/fRxkrlnrRDh1K+9Ckqnnuelp278HnpRclfqaUSGgqlL/GGIggade7cOjt/DHK3Ijr4YG53Qt7SgszBQfobcw2C6S9Jxa+PfikJpFVLwCUQBi4C74HStKF7OKIgw1xcjCkrC1PmMen92DGsdZJtpW7YMNzvvBPHadNOW0zYrFB9HCrSyNq7k2278vBSNxESvZDQOU/gFhDZay2+OlMdq3JWsSF/A235ZUxKMqBUKPGIuwbHQh/GLJIsYc6HTCZnyKx5hCWOZNtH7/LL8g9I376JhqoKLB0dRA4fzfAF1/QauY9wjeDpUU9LtyDaKG0u5Xj98a5XTl0O2wq3IXZWDZQLcr4c8xYquX0qvz946qRC42q9DmN1OaJ7EJakpEt6DbsQs3NZ4erjh7PBi5NHuwsxAP248VT/8APH9+9m4OQZfTcvjO4UYlk/0XRUGtScr5zd/ZiSI1KS/pX/6nNfNQ5KfAM1VLcNpi35aJcQM7WYqTPpCFPWctK3jeDkNoqPpRPk15mbUp7WXYiZmqjP2EW9KYFKTxPhLuFdbvn9JSYmFOsBUHiEYyrLpbpSR1BFGgT1PcJWfjIXq9KKqArH4FLe6zG6xERCP3qfjnse4vCA+/gp/yZudX6QmxZMY1eBI3tWriBt20YG+4bg8N2PCIDXYw/humgeAhbJUsTUiLKhkIAlMdS711C1bicnp4zFd0QTeu+W7heUKeGGlRDeN3dvO78zVguc/IW6yniqps8AQNDpUBoMKLy8UBgMKL0MKAxeKCJfRWHKQ1n0E9b179BeJ8dUr8TUoMbUoMLW0Vn9Ti5DHRqCfuIENNEx6CeMR+XlBpWZkPW1tAK5Ih0qj4HFSG6TOxtLo/HQixj14exKqmJX0iM4engSOjiRkMGJBMYmoNRoOF5/nPu230dFawWT2uIIPOyLs8Eb/+ibyDvazoi5oRcUYWfibPDiqiefI3vPDg6s/oaI4aMZMf9q3P371oZMkBHgFECAUwCTg05bfLaZ28hryCOnPgdPrSexHn2sV2unCy+dFwAyVzVU26hT61E3NGDr6EB2ifJT7ULMzmWFIAiEDE4kY8c2LB0dKM74Q3EYPYpSd2esVisJU2b0vVHPSPCMRsxcQ+NaAV1iIko/v+7HnErSj1vUr/5GjAliR2E7FYeP4jJX2laYUgaCQNAAVxwjg7ClGck9uI+g2+4BtXPPhP2cjeQ3SRGrg9oTzPbtXx/OZGzoKH7QpqLskFZylTQ5E1R2tF9CrKKyCbNCjSCT4zsk+JzHaRMSiP7oHYx3P0Zq1P2slL/MkoxHmOfmzQkf2FHYzs6aajxCXJgSkItb8WPwz8d6tCMAbm6u6G7wo2yzkeId4Dp1PIa7bkTmFQFaF/jiKvhmMSxZe1GLD+z8xpQeAVMj5dlNNPg44DJ/Ph6tMixV1VgqKzEmJ9NcVYVoNp91olRFQ1ApUPs44RQgotHVo9FWo3Y2I5OXgCYdOgbAj29A3UnojBChcZHSDhJvJb/VjXXf78Y7PIxFf30RlVZHU001BalJ5B890uUQL1cocAj1Z5cqA62vgled7iN743p8IwfgFX4DWfvqSZwVTOKs4H5/BIIgED3uCqLHXfGrPsoz0Sl1xHnGdaspa6d/nIqImQ1KOA7VHWb8kSwszpzF+DXYhZidy46QwYmkbF5PSVYGwQmnneVlej0lPh642kQ8g0LO00IvxMzF9MMbdJz0xO3mm7rvMzZAxveQcO0Fk/R79HWQgR1fZlNYYOGUEUbBvpMozG34jx/IcE0LBR7r0R/ax+Rb70bwjuuZsJ+5mnyTDxpPN+q0hX2yrTgX0W7RfOKyAeeagcgFGRVWR8TiZIQ+znS2VxdSa1SiUziDBvynDD7v8ZqYGOL/8w9a7nue3LDbWad4ifDiXRjTyhiqsdE6zIW0WgUrKxKIdfVl7LiB6F2cQaGRpktdAsElCDROaIDgR9upev116ld8Tmt+M37LlqEZYIAbV8Mn0+HLRXDrJjD8OoNJc4eVkqw6VBoFWicVOicVap3ifyZHTRRFLGVlKDw9L82q1dytmI1K2isE9oy4gu8d1jF3+GweHPIszmrnrmta6+uxVFVhqazEXFmJTKNBEx2NKiSke+2/tjqoypKiX1WZUJ0DXjHSQhrvOPAaCM7+IAgUH0tn7cvP4B4QxFVLn++ys3Hy8CR+slS6x2oxU5p9jI3bv6QoLZVBLXrIgGzWET58FG7+C0j7pYJBUwIYPqefY4udPzSOSke0Ci1NHu0Igpra5ga7ELNj50IExMShUKrIP3qkmxArzcqkGRvxJVVdNhZ9JmYeTf98D0Eh72ni2s8k/TPROanwdGijvM0fW1sbglZLaWE7ro3HcRh6L6PrZezw/oaglAZKj2fh75MARz6R8lpkcjDWYz7+C8UtI7AM0qGUKRnqdfGryuQyOc6BauTlGlzcfahrNWJKS0Lbx9rclYc3AQKOlgAEsRm9x4WFqT4qhpBld9P61LeUBSykTBkMp26hDURNOwoOkpl1lMzsahw9xuDsMxond0dCEjwISdByai2bTK3Ge+lS9OPGU7b0SQquvgbPhx/G7aYlCIt/gI+nw+cL4NbNXXYkfcXW0YExOZnczekkl3hilHcvjixTCOgcJVF26iWJNDUavQKFUo5CJevxLlfKUKrkyFWyfpkY/16IoijlXWVmYszIkHKvMjOxNTfjumQx3kuX/vqLnNhKc1skhYFTcGQMSzIns6VlBXOL5vJo4qNcGXolgiCgcHND4eYGZ/v3nY3ODYLHSK/zUHY8mx9eex4ngxeLnnoBjcM5ciFlMlYaN/O5y0Ym3DCB+6IfpSI9EwQBkzGSpI1FxE3wY/TC8P8ZMf6/giAIeGo9qZI14SP3oqGxGri0pq52IWbnskOp1hAQG0d+yhGu4M6u7anbNqLSaPBpaKF1T+9FwM+F6BZJY7EefZgWufMZBozdkvTPH/05FyHRjhxq01O+Ow1tXBxGq4pwfSsyjYbBhsFUedsQ5QK5B/fhPyheEn01uVIx8uz1FDfrsFpFMp0rGGI4d1mjvhITG0LVQZB5BtJcXUJjbg1aY4M0xXcByo8lAyCqYzAY+j68RAyayJGHU+A/L5AW5YEqMIJgbSi+an+8lL7oxMU0106jMGUtjZU7MDal0OA8lryjQcgVGgKi3QgbYiAkwQONgxL9uLGErllD+d+epuq116j7fAWaAdGofRehLvoK9b/mof7zBgTXc3tTiaJIR14erXv30rJ3L7WpeeQEzKXWIw5HoZ7h7KUj5Qi2gHA0sxdhcfakrbGjqzpEVVEzxmYzok3s8+cgyAQ8A/REjfQmYpgXWv2vizZZbVYOlB8gtz6XP8X8CaXs/N5RoihiLinBlJmJKSMDY2Ympsxj2JqapP4plaijonCYOZsT+SLGbYfwesKKIL/w6sVz0lwJ5alUlA6m2j0GpY8FJ5kbM7Jvp6r5BM81v8iPJ37kqZFPEercf49BURSpaK3AXeveLVG9quAkq199BgdnF67+64vnNFZtM7fxxO4n2FG8gz9F/4nHEh9DLpPj7RtM0qYCkjaeJHqMD+OujbSLsMsUg85AZXsTgQp/2lpLsAp2IWbHzgUJGZzIz59+QH1FGa7evrQ1NZJ7cC9xU2agzq+mZVf/hFjrgYNYjeDkWSJNe+jcpB2lSf1O0j+byGmxHErKIO9AMY4WybYhME6K1qnlahL8h1DnXUbuoX1MnP6otIKrPFUSYhmrOWkOQqFSkazM5UHfORfdj1OMjx3BF4pDiDgjCgIljc54l6dC6IQLnltRVIoos9Kh88M33rNf171u0v/xc0QcbZXJZNRksLJ2K6YWEwBOKifiAuKIHRxLZEMM1ev3U1+8Dplcjto9jJJjgeSnBqBQueE/wI3woZ6EJHji/87bNK1dS8vOnbTn5tKyaxdYNEA7fDsZVXAI6qgo1BER0is4mKasDBp376TjwGGE6jqsMgV5sXMpHbwIUSZSFXWUXcGHWS2auHZ4BINXHEZ8bSOu11+H58MPI9efjqqINhFTqxlTqxlLhw2L2YbFbJV+7rBiNUvvFrMNS4cNc7uFomN17P4ml73fnSBooDsDRvl0GgCfES2zmqX/e55RoHXt8VkWNxXzw4kfWJu3lsq2SmlbczF/HfnXc4oFU1YW5c88iymtc+pbqUQTGYnTzJloYmPQxMaiiYig6Hgzv3xznCYHIx5uzkQdPoLDyHPbO1yQvO1YzQLNJQosBlcGj/VmxIQBJG0sIGmjwG31L/Gz8SsWVi3klthbuDP+TjSK8y+0aWxv5FDFIfaW7mV/2X7KWstwUjkxK2QW88Pn42Vy5LsX/4pKo+Pqv73Uwy7lFJWtlTzw8wPk1Ofw5PAnuSH6BgBsVhuH1xdwZEMBEcO8mPinAQgyuwi7XPHUeZLeXIJepaLWZKPF0QFzZeUla98uxOxcloQMHgaffkD+0SRcZ/pybOd2rBYLCVNm0pFXQvPmLYgWS/e8kvPQtO4nZHoH9D5lkL0ehnQWqT7yKSgd+p2kfybOQQac28soKteiP1yMrq0Cz7Gno2ujfEbxo/v7uKeJVDbJ8FZopIT9iKmIeTvIb52IOswHmzz3V+WHncLLwYtmtypokoRURYcTYkkywoWEmNVCWW0HGrMUkfMd1L/8CUEQmBw4mcmB0qovi81CXkMeGTUZpNekk1GTwUfpH2ETbQgDISLEQEitMx5lBSjrj0td0LlQkBZGfkoIcpU/gTEehA0ZRtAzM9A5qWhqq6MiK5mGvWsxHdiA0FKC5kgV+k2bEM4IXDVrID1EIHdILC7iIhw7DOR7pJI7YC86FyUeWnfare280rgFzY3wUJIfCV+vpHn7dryffhrHydI9CDIBraMKrWPfI1ujFkBNSQs5B8rJOVRJfmoNGgclEcO8GPD/2rvv+CivM9HjvzO9q86od4SEaEIUIzAYU2IbY+zELY6dZJPYSXadjbNJborv3Thl96bs3jgbO7sp9iZxyiaObWJsAzYYML2JJkAUIQRIQr3NqE557x8zFBkJiWJGhOf7+eijmfc9884ZHWn0zCnPKezD3fBn1P4/QVcT6E2Q/yGY/FG6c+awpnYDy44tY1fDLnRKx6zUWXxt+tcoby7nNwd/Q05MDo8VPTbg+UI9PTQ9/zytv/kt+thYkp5+GmtJCeax+QNWhXW29LDuxcOc2NdMbJKNtHwX9cFC2lauurpArHINXe0e2l3h4caJU3LRG3TMuCeXnGI37/62gnkHH2NG9gJe6vspK0+s5Olbnqb/nQPUVhxk/LyFjJ09l+P9p9hat5XNdZspby4npIWwG+3cknwLjxU9RnlzOcsql/HW7ldYsiMNi97Cff/r6QH5Bi90uPUwT777JL5+H8/Nf4656eF0N53NPaz+70PUV3VQOCuF2x8tQCdB2N+0JFsSa/vaiDWFaAF87gQCjU3X7PoSiIm/SbFJycSlpnNi7y6m3LmE/e++TWpBEYkZWXTOmUvHK6+Gtxa6YBPwoYS6u+lcvYaYu+9Gl7AsnNy15OPQ2xGepD/pocuepP9+GbFdHOhJpbM+RFrHUazF53vrSlNLeS7pWdApju7cRrKnKNwjVrGc1j4Tnb5+fEl+4i3x5zJ3X63YTDPGXW7MrlhavN307duMZe4/XfIx3mPb6A6YcPsT8KKRmHF1PxODzkBBfAEF8QXcPzb88+j2d3O49TDlzeVUtlfS2N1IZVcDvqZ24upCpDf1kNLchl4ro1en4/CuDE7sLkTp4+g2tNFuqaXD3IDP1ERPYTL6kIYrpCfOkEF8jxFXt8IyNh3HjFmYNseRcQTsbiOzH8ojf+J8YODPoNZXy6tHX+WXrmXEZOr4wqpWAk9+AeOCuWQ98z2MnitLI5KY7iDxgXxKP5zHqX21HFmzj0Pv9VC+3kCcIZ/CzMcpuDMfW9tO9h9+jWVNW1jlsNOlU2RaPTxV/EXuGbOUJHt46f3CrIWc9p7m33b9G5muzHNBhW/jJuq/8x38NTXEPvggnq9+ZeDQOxD0h9iz5hRlK6pBQemH85i8IIPTFa28dayTU1urSLuMDzUDhIJwfC2drdk0uosIxHThjD/f2+XOcPLgN6ZRtuokZSsUTzT9gB1j/8o/vvMkj76bhcFoZP1LL7D2d7/iZHI3leldxBfm8cTEJ5iVOouJ7okDhmPr6qp4+TvfpC/UzbJpVby08TFmV83m3rx7mZcx79zQ5YaaDXz1va/iMrl46a6Xzv1dHdvZwPo/HAZg0aeLGDsj+fJfs7jhuK1u+oJ9OGx9oCx0OI0yNCnESOROmcred1ZwYs8u2s7UMvMjDwPhNBYYDPg2bBxRIOZ9dy1adzcxS++BthBs+3l4peTZSfrTPnX1dS1O4MBW0NCREu9HZz6/lUp+XD4OZyx9aTaObd/CnIUTUYf+CkpxIpQPwDbLMW5JueWytjW6lKKibI7t6ke5U2hva8W3t4Lhsq7Vl60FQDPlEBOrrnirqkuxGW2UJJVQklRy0blufzeN3Y2caauhunwPTQcP03/0DP6u8CbOBsLJDhLPPeLCIT0/BpMOv1J0bzxC25YmTLbpzLjvTqbekYfeGPm5tp+GytXhHQ7qy0lLnsgXM0v5+9If8t70Nl6b8iqJyzZz//oNHNwtlhlgAAAgAElEQVSygP7PPUzJ41/HYLi8ff2CwQDd1e/Rve+PaMfeJj3Yh6uogDPGB2ltKGBrVQZbqyCk5hNQc3HqgnxMhbBqfVi1fvSbFOtt29E74tFZrJitBh7OehKfFuLr677Bb2b9BMd//YXON9/ElJND1u9ewjZ9OqFQkO6Odsx2O3qDkVOHWtjwp6N0NPaQN8XN7AfzzwVK6QVx6PUaTaYsunfuxF56+UmEqS1D62qjrdJDZ0keiWMv/v3VG3TMWJJDzuRE3v1tBSV77qXYPYb+wNusmnIaS2wMtzRlMuZoBzln7LiqrUyYl8CYjIwBQVhXexurfvQD9H0af/ftZ3koQc/y48tZfnw5G2o2nBu6TLAm8F/7/ouCuAKeX/A8HpuH/t4AG/98lMNb60nKcfGhz4zHlXh1czHFjcNjD3+g0hz96PRJtNEggZgQI5FTPJ2yt15n9Qs/w2J3kD8zvIJK73RiKy7Gt3Ejni9fupcHoOON5RhSU7BOnQq1hPe9O7oqnDssZfIVT9K/kGfWZBzvbKbLnkp6ycAhPZ3SUZpayuHEXZj3dNGkn4+ntwNObOBE/9040+zU6LbxuWswLHnW7CklHHlpM+1GF+h11NcFSexqAfvgc2kAzhyrADR6nOMZW5h0zeoyUjajjeyYbLJjsinNvhXuCU/UbqyuorujHYPJhNFkxt+naGvop/VML801vTSf9BIIGlFKodNrYDiMjr30ed9mz5s7oWk8kxPbMJ9cC81Hwk8WkxHeQaG+HA6/iRFYaHKwMH06NQ/ez8rbm3D/djMFP/kDK5f9haOfXUBHsoPeYC/9wX56g730BfrO3e4P9tHr76Ev2EuPv4teLXj+hSWfDRg7gBcgG2KS3OS0TiLDlM3YmLFk2HLRaXpC/f0EW2sItdUQ9NUS7NYTMsXRoUumutzCFO5jMovZsWIXCe0hsj/5FTK/+BhGu4WGqkpWPP//aK0Nb9+idEY0zYzeaCUuKY6ejlg2/Y8Ds92Oxe7AYncQl9RHk6+QjpVvX1kgdmw13S1m2o2ZoIxMmTb0nqHuDCcPfnMaZStPsvXlNaCMPF38C2bPm4zBoCfg91O5cysH1q1my1/+wJZX/kjO5BImzr+DlLGFvPIv/wdfayv3/+/vkZQ7BoCnSp7iC8VfYPuZ7fz1+F9ZVrmMvmAft2fczg/m/ACb0UZDdSerXzxIZ3MP0xZnM/3ubHSjcHWr+OB4rOFArN/RhzIk0dl7ir5GmSMmxLDSxo3HaLbga2mmZHF4u5yz7HPn0vTjHw+bxiLQ3EzX5i0kfOYz4f3o0qaCKw3e+2E4OeSSZ69JXU15eeSf+TZdykns55646HxpSin/Er+SySqTY2eCeIC+oJ6ahi5Mt6ScK3Ot2K02+uI66OmOxwbU9TgZX7cblb9oyMfU1TZj7dcTjIslKW/4FZbXg1Jq0O1hMi5IMB4KBGn58/do2HeIluQHyZo5jyy9kZNb32ZneTUbV7WyXRdkUnYSJfMfxlm8BBLHhvdDhPBm1ae2wMktcHIr6Zv+gyeA/lITOwuySV/fRc4/r2TDNBNr55gJWXVYQiFMoSDOYIDEQD/mQD9mLYRZ07CGNGyuNGxp07Gnz8BmicNmtGE32rEZbNiMNmwGGw6TA7vRPsirjuzA4K0PD53v+zXU76fDH0vF0ek0qHE0uPOpyrqLEyd1vPfldZiDa2j3HsFi0pOXOZbTzYloWh8JaUbssdDf3YW3uYnmU9X0+nz093QPeMblB2DMr54nrXA8qQVFuNyeka0grFyNryOL5oQigjo/Y4suPa9Qr9cx/e5sypbXYDTnceCVdk6s2caE29IYPyeVwllzKZw1l47Geg6sX8OBdatZ/uP/i1I6dAY9H/nGt0krGJhDTq/TMyttFrPSZtHZ30lVexUTEyei0FG2qpody09gizFx35enkJp/8cII8bfv7E4lPocfnT6JIBodoSBBX9f5rbKuggRi4m+WwWgkc+Jkju/azqQFAzPpO+bOoenHPx42jUXnipUQDIaHJQF0Ohh3D2z/eXiS/oQrn6R/IaXTkZ5jpXv3DqwTf3rR+ZkpM+k1hzBkJnLs0HFmx+g5ZRhPKBjkaGwzeTF55+YDXSsxmSb0+3MwmUy0mGz4927ANEQgFvI1U+81EO+34gU82a5By41GOoMe9yP/jNv0OBx8HN4JH8+Oyyb7I4tosE9m557TlG3fxu5fb2TcHD3T77Ge337GlQIT7g9/QXhV7entmE5uYfbJLQSsJ2jab+P2nRoL9vSSWAJxU+PQuTzgjAdbwvkva3w42E8cc/UvzJkMpU8SKPwY7b/7Nc2v/YY44wkKPjWR3RNO8I3Kn3F3ewnJh3y0dbWhNxYQsi6g1mshz7WdW51/wBVvgszS8M4KWbMgsQB0OkLBIL1dPk6VH2bVL1eDfy+H3lvLvjWrAHDExZNaUERawThSC4pwZ+Wgf/8cMl8TWu0eOk8WUD92HKR2YzAOnwYj3MPZyoc+93GciZPYt/Y021+vYteKasbOSGLS7Rkkpicz+6HHKH3gEar37ebI5g2Mm3M7mRMmX/LaLpOLYk8xvrY+1vzmILVH2skrcTPv0UIs9ssbXhZ/O85m129z9KL04ffZDpuZQGMjesfVJ/CVQEz8TSu9/xEyJxSTkJ4x4Li5oACDxzNsGouON97AXDQO85gL/jEW3RsOxCY+AJZrF3C4v/Ql/LW1g2YqT7InkReTR21qP4GtTbTc8ilOnNAw2WrYzAEeTB1hxtXLMK4om337mlHuRNp8XXTv3IHpwcHLtu5dTVDTo9MnoTNAfOrVf0q8rnR6+PAvID4X7G7IXwQJ4Z60JGDJPOhorKfsrdcpX/sOB9evIbdkOqUPfIzkvPyB17LFQ8Fd4S/A0N9Fiq+RuLp2Gn/ynzRu2kRbnQvPV/4R5x0fuqa5p87mAeveVUbP7jK6y3bTX1UFgGvxXSR985sY3G7maxqfrvbTsXs3XUYzdz/1NXKmzOJMZTsmk47UGA+cSoSTm6F6Exx4JfwE1jjInIUuqxRb5iwKS6ezby0EKpKZl30S4+OfovbIIeqOVFB75BBHt20CwGJ38NC3f4A7M/t8ZY+vpb/TgNdrImRMIqNoZMN9VWU7QCnypk7HFhNL9qREWup8lK+r4ci2eio2nyFtbCyT5meQPSmR3CnTyZ0ysm2tgsEQ1fubWff7wwT9IW7/eCHjZqVIfrCbnFlvJtYcS7OplwSdhaDBQqc1HIiZcyUQE+KSknLHnJsPciGlFPa5cy6ZxqKv6gS95eV4vv71gScyZsKi753vAblGrBMnYJ04YcjzpamlvNn0Kvfh4ZhhOieqV+DMz6SPimuStuL9Jk3IZx/NtNkcWExGmg9XM9SA45nybQD0OMfiyXSNygzxwzKYYME/D3k6xpPM/E99jpn3f5R976xg98rl/OHpf2LM9FJmPfTowCDjQiY7xOdgiYfMF36Fb+MmGn/0I2q/9CWsJSUkff1rWCdfuqdmKFogQO+RI/SU7aZ79256ysoINIWX1etiYrBNmULMh+/DPnMm1onh/QZ9rS28/Yuf0r13L2TG8PKYCgqTvRRaDWRPPLuUIR6SJ8CMJ8JJi9tOnBt6DZ7azPaT7/Ku3codmQvJnvw0u2pzaF/3R4q+/Qye7Fym3LEEAG9LM7VHDvH2z/+Dfe+8xcLHnzxf+crVeJsSaIkvAmDmjJHth1i1ewcpY8Ziizn/25iQ6mDeo4XMvC+PQ5vqKF9fw8qfl+NKtDBxXjrjZqdithro7wngbe3F29qLr7UXb2vfBbd76WrvQ9PAnelk0aeLiEu+wT5QiA+Mx+ahIRgk29CCsiXRYesgcI3miUkgJm5ajmHSWHS++QYohWvx4oEndDqY/cXrVMvzSlNL+X3F73Fmp1G2cjm93k58Mz1Xva3RUJzxFoK2Pnx94X0caztMjPHWh4e83ufMseMYgkH6YseRlD14hvK/FTZXDKUPPELJ4nvZvfJ1dr2xjMpd2yicNZfSBz5GfGraJR/vmHMr9tKZtL/2Gk0/fY7qhz+K6+678Xz5ny7eTD5C6++nv6aW/pPV+E+dov/kyfAHhf37CXWH52sZU1Oxlc7EVjIV29QSTHl54XmNFziydRNrXvgZgf5+Fnz67ylasIgjaz7HtzZ/izRHGsWe4oufXCm0uBwqtF7eDNSxsv8QzT3hxQSHGzby/6Z1sQtFoz6NrO3bccw+v62QMyGRwllzOb5rO0e2bOT2v/sseoMxnLai8l18DW7qUifQa/OSnBY/7M/e19ZK/fFjzH7444Oet9iNlNyRRfHCDKr2NrN/3Wk2v1LJ9uVV6A06+roDA8rr9ApHnBlnvIW0gjic8RZiPFbypyUNTKArbnpum5umjlM49U30GJPwWqrpPXOGa/FuJ4GYuGldKo2Fpml0vPEm9tKZl7cn5QdoWtI0DDoD3mwrVNcCsNNaxZTYKdiMtmv+fEopYjJNdFWNQ6cro1lvw79/HcbZjwwsGApxur4HR5+ZXmXCk3N1+cNuFGabjdL7H6H4jiWUvbmM3SuWc2TLRopum0/p/Y8Q4xl6zp4yGIh76CFci++m5cUXaP31b/CuXk38Jz+BtaTkXLDVfzL83V9XB6HQucfrXC5MWVnE3Hcf1qkl2EpKMKakDPl8vV0+1v73z6nYtJ7kMWO568kvE58anhj/7LxneXTFozy17in+ePcfSXOcDwZrvDWsOLGCN6ve5ETHCYw6I3PT57IkdwlVTQd47uCLdFc8g8XxJVo9k/GuWjUgEDuraM7tHN78Hif2lDFm+kyo20ugtZ2uWjuduWOwZPeMaPjvxJ5dAORNnXHJcjq9jjFTPYyZ6qHplJeKzXWgFI74cNB19svqMkkyVjEiSbYkjjYfxKlvot6fgaYUzaeruRYzcyUQEzetS6Wx6Nm7F//p0yT+wz9EqXYXsxltFLuL2WuooRhIyM7mYO97PJX61Af2nAXjMvAdVgTi7LT6eujevJaY9wVi/roDtAcsJKjw1j5JN9BE/WvB6nBy60c/QcldS9nx+ivsfectKjauZ+L8D3HLRx7CGZ845GP1Djuep54i7uGHaXr2J7T86oVz53ROJ6asLKyTJxOzdCmmrExMWVkYs7LQx8aOeN7S8bIdrHnxP+lqa2XWg49yy4cfQnfB3pCxllieX/A8j654lC+8+wWem/8cW+q28GbVm+xp3APA1KSpfKLoEyzKWkSMOdwH0OSezM8O/jdvNO9iQnaI474JdLzzXZK/9S2UceDE9qxJU7DFxFKxcV04EKtcjbfOQocrF52ykD9xZKsRj5ftwJnoJnGoYeBBuDOduDOvTaJjcfNyW9209HVi0zfhD4TnHDY11F+Ta0sgJm5qQ6Wx6HzjDZTFgnPRwijW7mKlqaU81/Acjyx6nLb4EHjf+0Dmh52VPTaJMmpodlqJs5rp2Hvooq74hrLVoBRdzjTMdsNNm+jSFhPLvE88ztQl97F92V8of/dtDqxfzeRFiyn+0GJMVht6ozH8ZTCg050PhozJyaT+8AckfO6zBDs6MGVnX1awNZi2M7Ws++2vOLFnFwnpmdz7lf998cKCiJyYHJ6d9yyfX/157notvMggLyaPp0qeYnHOYlIdF2+O7ra5mZUykzeCW7ir6c8c5kHaiKdr23Ycc24dUFan11M4ay771qykt8uH5dhqfC1J1KdNIqSCzJ4+yJDo+wT6+zlZvofxty2UyfPiuvPYPIQIoYxtoHNhREdLZ/s1ubYEYuKm5rht7kVpLLT+fjpXrMQ5f/6ADZxHg9KUUp7b8xxqwVgO1mwkrj+OwvjCD+z53BlO0IfwBZ3Eqibq6rrJ1LTzObSAmu07AAjGTCIpy3XT/5N0xiey8DN/z/R7PsK21/7EnlVvsHvF6xeVUzodeqMRgyEcnOkMBgxGI0m5+UyxLyEl9spysfl7e9n+15fZ9cZr6I1G5n3icYrvWHJx+oj3uSXlFn5024840HyAu3LuoiCuYNi2vDf/I/yvM1tpDK5Apx6gJXkKnW+vuigQAxg353Z2r1zOsQ3vMP7kbrpOp1FfWkh3Qgt2x/DB++mD+wn09Q07LCnEB+FsLrE+hx/VpnCaXbR1tV6Ta0sgJm5q5rFjMSQlDUhj4du0mWB7O66zucNGkaKEIlwmF5trN7OlbgszU2Zes22NBqM36HClm3A2jQOqaAxZCVQfwJBzfoXb6eomLP0mCGXcUPnDPmgxniTu+PxTzLj3AWoqDhL0+wkG/AT8foJ+P6Fg4NztYCD83d/XS9XunRze/B6e7DyK77ybwtm3DUhGPBRN0zi6bRPrf/civpZmiubOZ+6jn8IeO/IkpIuyFrEoa+ikve93e+btOI0O3koyckvzMVpTp+Fd/X20Z565aHgyKXcMcanpVKxbQXariV5lR9OlEjembUTPdbxsB0azhYyika2uFOJaOhuIdTn9ANjtSdT0tuHv68NoHv7v81IkEBM3NaUU9jm3Dkhj0fHGcvRxcYNOOo42vU7PLSm38Hb12/QGeylNvXbZ9IeSk59M26le/HYdbXYL3evewHU2EOvtpLHPgE4ZCKFuuvlhIxGXkkZcyqVXUl6ov7eHio3r2Pv2W7zz85+y4fe/ZsLtiyj+0GJiPINvMt18+iRrf/0LTh/cjzs7lyVf/BpphUXX6iUMyaw3c2fOXbxZ+Tof1m+iVvs0XX0GurZtwzFnzoCySimKbp3H5pd/T31PAs3J4R0AJk8dPnmtpmlU7d5J1qRiDIPk2RPig3Y2EGtzhVfempzpaK1HaDhYTnrJ8HsWX4qszxU3PcecuYS8Xnr27iXo9eJbuw7X4sUXfaIfLUpTS+kN9p67/UFLHROHXjPQ6DLQbjfTtWPbuXPe3W/TazDhc4R7XaRH7OqZLFYmL1rMJ/7teR565vtkjp9E2Vt/5YUvPsGyH36H6r1laJEVlH3dXax/6Ve89LV/pKm6igWf+Qce+/6z1yUIO2tp3lJ6Qv2czOsBoDWlhM6VqwYtWzhrLgBHu+KpyZxEj9HLtKKhc+ed1XTyBN6WJnJlWFJESbwlHr3S02wxYNV3oreHd9aoP1h+1deWHjFx07swjUX/yVNofX3ntzQahc7uKZkbk0uyffAekmspOTc8Pb/bGENA309D5RnOJkqoXvkWAKGYfJxOCzaX9FZcK0opMoomklE0EW9LM/vXrGT/u2/z6vefIS4llTEzZnFw/Rq6OzuYtOAOZj/8cWyu65/DbbJ7MlmuLFbE6VlYXktb2iS8a36F9u1nLtolIjZUT7LBS43DRq8hi0BaO3r98NsaVZWF5yGONEO+ENeaTulItCbSgI4UXQN+VYgpEKT++NGrv/Y1qJ8QN7QL01h0vPEGxsxMLJMmRbtaQ0p3pjMjeQb35F2fYNHmMmFPMGIlvOKuvkcR7OwEoPbIKdA0LMZJ0hv2AXImJDL74Y/zxM9+zeJ//CoWp4udr79CjCeJx/7vsyx64gtRCcIgHDAuzVvKrrYKEtLaadZn0N/VR9e2bRcXrlxDVk8HPosJXaCblMKR/c4c372D5DFjL2u+mxDXWpItiSZCOHVNdPdAnK+HUycqCQWDV3VdCcSEIJzGoq+igu7t24m5555Rv/LvxTte5PGJj1+350sfk0BS3wRCBo02u5WejavQgkEaujV0BDB0u/Bk3xyJXKPJYDQy7tZ5fOx7/87nf/E7Hvnuvw26hdf1dk/uPSgUVeP0hJSRdve4wYcnK9eQ0BACFMH+Q8ycPvzE+672Nuorj5JXIsOSIrrcNjeNoV4c+ia6vEHS2rz09PZQvW/3VV1XAjEhCKexAEDTiLlnSXQrMwol57qw9DtoiA2GJ+xvepeeravpMFvw2S3AzZfINdrssXEXbWEULSmOFGYkz+Ct4ApMhgAdaWPxvvM2Wn//+ULdrfQd2g0tfnSmVAKBQ4xJzhr22lV7dgLI/DARdR6bh8ZAF059E4EAJBhjMesNHHzv3au67uj4KxYiysxjx2JITsYyeRKm7OxoV2fUSc4LD3v1WGPoMRlp2X+IM2++it+gpzcmFaXCGczFzWvpmKWc6jqJM99Io3Mywa5ufJu3nC9QtQ5frQm/wYrOWIwK9lJz8MCw160q24EjIRF3Vs4HWHshhuexefAGujEbmgEIJueSabRyfNc2enzeK76uBGJCEJkY/Yufk/bv/x7tqoxK8akOjGYdZkM2AGdaeji97wgAMZaJxKXYMVlk7c/NbGHmQqwGKyfiK+ghlq7YNLx/+fX5AsfW4D3joG3cHPTGPJTJxKGNay95zUB/Pyf37yWvZPqony4g/vadTWHRbwwnctXNXkhR6VyCgQBHNm+44utKICZEhKWgAFNGRrSrMSrpdIqknBhStWmEVIg2m4XmvhAaIWw9eTIsKbAZbSzKWsTbwVdBgTdvMt5NOwj19kAoRKB8DT2Nek6njaff0E/uLbdwbMdW/L29Q17z9KFy/H29MiwpRoWzgZjP1hU+MHkWeZ/7PO7MbA5uuPLhSQnEhBAjkpwXg7UznhaXnza7hQ6bBa8thOozyopJAcC9effSohoxp4RoTLmNUD90/f770FCOr9KLpkFrKJm2xFqmzl+Mv7eHyrLtQ16vavcODGYzmeMnX8dXIcTgPNZwINbqNGLQBfC29aKUYvy8hdRXHqWl5vQVXVcCMSHEiCTnxoCm8DqtdFrNdFjN+FzheWHSIyYApiVPI9Weysm4g7T4HPgdLrx/fRkOLcdXZ6Evowh9vwNnniK9cDzOBDcVG9cNei1N0zhetoOsiZJNX4wOZ3vEmq0OnOYOfC3h3tzC2behdLor7hWTQEwIMSLJOeFgy2rJRVMKTadw2cagN+qIT7NHuXZiNNApHUvylrDJEE5d0TXzXrwnNYJrf4Kv3kr9+PDq5InFuSidjnG33kb1vt10d7RfdK3mU9V4m5vIlbQVYpSwG+1YDVYaTGYc+ha8reFAzB4bR86UaVRsWEsodPk5xSQQE0KMiNlmJD7VTopxOhoaAHFqIu4MB3q9vJWIsKV5S2m21qAcAZpTSgn5dTTutaMF4LQ5hVbrGWYVhDPkj5tzO1ooxOEtGy+6TtXuSNqKEsmmL0YHpVQ4qatej1OdwdvWd+7chNsW4mtr5eT+vZd9XXn3FEKMWHJuDLb2ZNodfrrNQfQdSTI/TAyQ5cqi2FPM6bgKzjTqwBVD+3E7msNFnzeW7uQmYszhdCiJGVm4s3Op2HTx8OTxsu0k5ebjiIu/3i9BiCG5bW4aVQinVkNPZz8Bf7gHLKdkOhaH84pyikkgJoQYseTcGEK9imPZiiPZGgSVzA8TF1k6Zin77Vvx9wXpu+1BALpm3oMuZMBTOHAYu+jWedRXHqW1rubcse6Ods5UHiVPVkuKUcZj89AY6sehD+cS87WGe8UMRiOFs2+jcudWert8l3VNCcSEECOWnBsOutLdt+JylwBIj5i4yB3Zd9AUW42mD9GaNg2AavcY/Lp+phcXDShbOPs2lNJRsWn9uWNVe3aBpsmwpBh1PFYPTYEuHPomALxt59OvTJi3kKDfz5FBhtov5aoCMaXUPymlDiqlDiil/kcpZVFK5Siltiuljiml/qyUMkXKmiP3KyPnsy+4zjcjx48ope64mjoJIT44sUk2zHYD80x3caf9Psw2AzFua7SrJUYZl8nFbTlzqIs5Rk2zkcw//J76bhf1riqmppUMKOuITyBz4mQqNq5D08JzD6vKduCIT8CTkxeN6gsxJI/NQ78WAEMLAL7W84GYJyePhPTMy149ecWBmFIqDfgiME3TtAmAHvgo8EPgWU3T8oE24DORh3wGaNM0bQzwbKQcSqmiyOPGA3cC/6mU0l9pvYQQHxylFCm5MXhPBeiuC/eGScZzMZileUs5HrMPb3MvzeZsdB0WtAwvVsPFgfu4W+fR0dhA3dHDBPx+qvfvIVey6YtRyG1zA+A1tQMa3pbzgdjZnGJnjh4eMNQ+nKsdmjQAVqWUAbABZ4D5wCuR878F7ovcvjdyn8j5BSr8V3Yv8CdN0/o0TTsBVAIyMUCIUSo5L4a2+m5aa30yP0wMqTS1FG/KGQA2vHwYgKzxCYOWzZ9RisFkpmLjOmoOlePv7ZG0FWJUSrIlAdBiBLvVP2DlJIQ/VCid7rIm7V9xIKZpWi3w78ApwgFYB1AGtGuaFogUqwHSIrfTgNORxwYi5RMuPD7IYwZQSn1WKbVLKbWrqanpSqsuhLgKybnhFW+aJvPDxNAMOgMLiubSYqujs7EPr7mFmeNKBi1rstoYM30mR7Zu5Nj2LRhMZjInSjZ9Mfqc7RFrtLpwWroGDE0COOLiyZ5cwqGN60acU+xqhibjCPdm5QCpgB24a5Ci2tmHDHFuqOMXH9S0X2qaNk3TtGlut/vyKy2EuGqeLBdKpyK3nVGujRjNluYt5WTcAQDq4iqZ6J44ZNlxc+bR6/NSvu4dMidOxmgyX69qCjFiZ7c5arQ4WVSwmjuemHBRmfG3LcTX0sypA/tHdM2rGZpcCJzQNK1J0zQ/8BowC4iNDFUCpAN1kds1QAZA5HwM0Hrh8UEeI4QYZYxmPYnpDhxxZuwx8s9SDC0/Lh8t2wuAJc+PUWccsmzWxClYXTFooRB5MiwpRimj3ki8JZ5GswVXsAqL/eLf6bypM7DYHRwa4fDk1QRip4CZSilbZK7XAuAQsA54IFLmk8DrkdvLI/eJnF+rhZfILAc+GllVmQPkAzuuol5CiA/YrQ/mM+/RwmhXQ9wAFkybxe9KvsX4kqxLltMbDBTOngtKkVMy7TrVTojL57a6adTrwdcw6HmDyUTBrLkc27GVvu6uYa9nGLbEEDRN266UegXYDQSAPcAvgbeAPyml/iVy7MXIQ14EfqeUqiTcE/bRyHUOKqVeJhzEBYAnNU27/M2ahBDXTWp+bLSrIG4QS/Y9MZcAAAaaSURBVPOWcrTtKHfn3j1s2dkPfZyCmXNwxideh5oJcWU8Ng+N3nrwNoQnyw6yunf8vAXsW72CI1s3MWnBpbNyXXEgBqBp2jPAM+87XMUgqx41TesFHhziOv8K/OvV1EUIIcTo4zQ5+c6s74yorNlmI62waPiCQkSRx+bhkNYPgR7o84Ll4kVLyXljiU9N59CGd4cNxCSzvhBCCCHECHlsHlqDPfgBfI2DljmbU6z28CHa6i897V0CMSGEEEKIEXLb3GhAyyXmiUF4JbBSumEn7UsgJoQQQggxQmeTujYaLh2IOeMTyZpUzMENa9FCoSHLSSAmhBBCCDFCbmskqateP+TQ5Fnjb1uAt7mJ04fKhywjgZgQQgghxAh5bJGkrkbTJXvEAPKmz8Rss3Nw/Zohy0ggJoQQQggxQnGWOAw6A41W57A9YkaTmYLSORzdsWXIMhKICSGEEEKMkE7pcFvdNJmsw/aIQTinWKCvb8jzEogJIYQQQlwGt81NQ3wWzPnKsGVT8guZtODOIc9LICaEEEIIcRmSbEk04oes0mHLKqVY9NkvDHleAjEhhBBCiMvgsXlo6m66JteSQEwIIYQQ4jK4rW58fh/d/u6rvpYEYkIIIYQQl+FcCovuS6+aHAkJxIQQQgghLoMEYkIIIYQQUXIuEOuRQEwIIYQQ4rqSHjEhhBBCiCixG+3YjfZrsnJSAjEhhBBCiMvktrpp6B4+s/5wDNegLkIIIYQQN5UpninYjfarvo4EYkIIIYQQl+m7s797Ta4jQ5NCCCGEEFEigZgQQgghRJRIICaEEEIIESUSiAkhhBBCRIkEYkIIIYQQUSKBmBBCCCFElEggJoQQQggRJRKICSGEEEJEiQRiQgghhBBRIoGYEEIIIUSUSCAmhBBCCBElEogJIYQQQkSJBGJCCCGEEFEigZgQQgghRJRIICaEEEIIESUSiAkhhBBCRIkEYkIIIYQQUSKBmBBCCCFElChN06JdhyuilGoCTka7HjexRKA52pUQIybtdeOQtrpxSFvdWKLdXlmaprnff/CGDcREdCmldmmaNi3a9RAjI+1145C2unFIW91YRmt7ydCkEEIIIUSUSCAmhBBCCBElEoiJK/XLaFdAXBZprxuHtNWNQ9rqxjIq20vmiAkhhBBCRIn0iAkhhBBCRIkEYmJYSqn/Vko1KqUOXHAsXim1Wil1LPI9Lpp1FGFKqQyl1DqlVIVS6qBS6qnIcWmvUUYpZVFK7VBK7Yu01Xcix3OUUtsjbfVnpZQp2nUVYUopvVJqj1Lqzch9aatRSilVrZQqV0rtVUrtihwble+DEoiJkfgNcOf7jn0DeFfTtHzg3ch9EX0B4Cuapo0DZgJPKqWKkPYajfqA+ZqmTQaKgTuVUjOBHwLPRtqqDfhMFOsoBnoKqLjgvrTV6Ha7pmnFF6SsGJXvgxKIiWFpmrYBaH3f4XuB30Zu/xa477pWSgxK07Qzmqbtjtz2Ev6nkYa016ijhfkid42RLw2YD7wSOS5tNUoopdKBu4EXIvcV0lY3mlH5PiiBmLhSSZqmnYHwP3/AE+X6iPdRSmUDU4DtSHuNSpGhrr1AI7AaOA60a5oWiBSpIRxIi+j7CfA1IBS5n4C01WimAe8opcqUUp+NHBuV74OGaFdACHHtKaUcwKvAlzRN6wx/eBejjaZpQaBYKRULLAPGDVbs+tZKvJ9SagnQqGlamVJq3tnDgxSVtho9ZmuaVqeU8gCrlVKHo12hoUiPmLhSDUqpFIDI98Yo10dEKKWMhIOwP2ia9lrksLTXKKZpWjuwnvC8vlil1NkPyelAXbTqJc6ZDSxVSlUDfyI8JPkTpK1GLU3T6iLfGwl/yJnBKH0flEBMXKnlwCcjtz8JvB7FuoiIyLyVF4EKTdN+fMEpaa9RRinljvSEoZSyAgsJz+lbBzwQKSZtNQpomvZNTdPSNU3LBj4KrNU07VGkrUYlpZRdKeU8exv4EHCAUfo+KAldxbCUUv8DzCO8c30D8AzwV+BlIBM4BTyoadr7J/SL60wpdSuwESjn/FyWpwnPE5P2GkWUUpMITxjWE/5Q/LKmad9VSuUS7nWJB/YAj2ma1he9mooLRYYmv6pp2hJpq9Ep0i7LIncNwB81TftXpVQCo/B9UAIxIYQQQogokaFJIYQQQogokUBMCCGEECJKJBATQgghhIgSCcSEEEIIIaJEAjEhhBBCiCiRQEwIIYQQIkokEBNCCCGEiBIJxIQQQgghouT/Ay8+LlQY/03lAAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
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