{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# COVID-19 exploratory data analysis\n",
"\n",
"ver. A.L. 20200512, 20210519 \n",
"\n",
"**Slightly modified from Greg Rafferty's** https://github.com/raffg/covid-19; <br>see also his\n",
"dashboard to monitor the COVID-19 pandemic https://covid-19-raffg.herokuapp.com and his [portfolio](https://github.com/raffg/portfolio/blob/master/README.md)\n",
"\n",
"### Uses data provided by the [Johns Hopkins Center for Systems Science and Engineering](https://github.com/CSSEGISandData/COVID-19)\n",
"\n",
"Requires: \n",
" - plotly: https://plotly.com/python (`conda install plotly`)\n",
"\n",
" - cufflinks: https://plotly.com/python/v3/ipython-notebooks/cufflinks (`pip install cufflinks --upgrade`)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#import sys\n",
"#!{sys.executable} -m pip install plotly==4.14.3\n",
"#!{sys.executable} -m pip install cufflinks --upgrade"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning objectives\n",
"\n",
"- How to read (updated) data from the web\n",
"- How to organize and analyse data using `pandas`\n",
"- How to make interactive graphs using `plotly`"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"plotly: 4.14.3\n",
"cufflinks: 0.17.3\n"
]
},
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import glob\n",
"import re\n",
"from datetime import date, timedelta\n",
"import io\n",
"import requests\n",
"import plotly\n",
"print('plotly:', plotly.__version__)\n",
"# Standard plotly imports\n",
"import plotly.graph_objects as go\n",
"from plotly.offline import iplot, init_notebook_mode\n",
"# Using plotly + cufflinks in offline mode\n",
"import cufflinks\n",
"print('cufflinks:', cufflinks.__version__)\n",
"cufflinks.go_offline(connected=True)\n",
"init_notebook_mode(connected=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# # Load files from folder\n",
"# path = 'COVID-19/csse_covid_19_data/csse_covid_19_daily_reports'\n",
"# all_files = glob.glob(path + \"/*.csv\")\n",
"\n",
"# files = []\n",
"\n",
"# for filename in all_files:\n",
"# file = re.search(r'([0-9]{2}\\-[0-9]{2}\\-[0-9]{4})', filename)[0]\n",
"# df = pd.read_csv(filename, index_col=None, header=0)\n",
"# df['date'] = pd.to_datetime(file)\n",
"# files.append(df)\n",
"\n",
"# df = pd.concat(files, axis=0, ignore_index=True, sort=False)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"04-01-2021\n",
"04-02-2021\n",
"04-03-2021\n",
"04-04-2021\n",
"04-05-2021\n",
"04-06-2021\n",
"04-07-2021\n",
"04-08-2021\n",
"04-09-2021\n",
"04-10-2021\n",
"04-11-2021\n",
"04-12-2021\n",
"04-13-2021\n",
"04-14-2021\n",
"04-15-2021\n",
"04-16-2021\n",
"04-17-2021\n",
"04-18-2021\n",
"04-19-2021\n",
"04-20-2021\n",
"04-21-2021\n",
"04-22-2021\n",
"04-23-2021\n",
"04-24-2021\n",
"04-25-2021\n",
"04-26-2021\n",
"04-27-2021\n",
"04-28-2021\n",
"04-29-2021\n",
"04-30-2021\n",
"05-01-2021\n",
"05-02-2021\n",
"05-03-2021\n",
"05-04-2021\n",
"05-05-2021\n",
"05-06-2021\n",
"05-07-2021\n",
"05-08-2021\n",
"05-09-2021\n",
"05-10-2021\n",
"05-11-2021\n",
"05-12-2021\n",
"05-13-2021\n",
"05-14-2021\n",
"05-15-2021\n",
"05-16-2021\n",
"05-17-2021\n",
"05-18-2021\n",
"05-19-2021\n"
]
}
],
"source": [
"#```\n",
"# Load files from web\n",
"file_date = date(2021, 4, 1)\n",
"dates = []\n",
"\n",
"while file_date <= date.today():\n",
" dates.append(file_date)\n",
" file_date += timedelta(days=1)\n",
" \n",
"files = []\n",
"for file in dates:\n",
" file = file.strftime(\"%m-%d-%Y\")\n",
" print(file)\n",
" url = r'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/{}.csv'.format(file)\n",
" raw_string = requests.get(url).content\n",
" dff = pd.read_csv(io.StringIO(raw_string.decode('utf-8')))\n",
" dff['date'] = pd.to_datetime(file)\n",
" dff.rename(columns={'Country_Region': 'Country/Region'}, inplace=True)\n",
" files.append(dff)\n",
"\n",
"dff = pd.concat(files, axis=0, ignore_index=True, sort=False)\n",
"#```"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 191184 entries, 0 to 191183\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 FIPS 156768 non-null float64 \n",
" 1 Admin2 157008 non-null object \n",
" 2 Province_State 182976 non-null object \n",
" 3 Country/Region 191184 non-null object \n",
" 4 Last_Update 191184 non-null object \n",
" 5 Lat 186960 non-null float64 \n",
" 6 Long_ 186960 non-null float64 \n",
" 7 Confirmed 191184 non-null float64 \n",
" 8 Deaths 191184 non-null float64 \n",
" 9 Recovered 33890 non-null float64 \n",
" 10 Active 33826 non-null float64 \n",
" 11 Combined_Key 191184 non-null object \n",
" 12 Incident_Rate 186958 non-null float64 \n",
" 13 Case_Fatality_Ratio 189206 non-null float64 \n",
" 14 date 191184 non-null datetime64[ns]\n",
" 15 404: Not Found 0 non-null object \n",
"dtypes: datetime64[ns](1), float64(9), object(6)\n",
"memory usage: 23.3+ MB\n"
]
}
],
"source": [
"dff.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2020051\n",
"\n",
"```\n",
"dff.info()\n",
"\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 165400 entries, 0 to 165399\n",
"Data columns (total 18 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Province/State 4358 non-null object \n",
" 1 Country/Region 165400 non-null object \n",
" 2 Last Update 7617 non-null object \n",
" 3 Confirmed 165381 non-null float64 \n",
" 4 Deaths 164959 non-null float64 \n",
" 5 Recovered 165012 non-null float64 \n",
" 6 date 165400 non-null datetime64[ns]\n",
" 7 Latitude 4799 non-null float64 \n",
" 8 Longitude 4799 non-null float64 \n",
" 9 FIPS 143674 non-null float64 \n",
" 10 Admin2 144198 non-null object \n",
" 11 Province_State 148664 non-null object \n",
" 12 Last_Update 157783 non-null object \n",
" 13 Lat 155291 non-null float64 \n",
" 14 Long_ 155291 non-null float64 \n",
" 15 Active 157783 non-null float64 \n",
" 16 Combined_Key 157783 non-null object \n",
" 17 404: Not Found 0 non-null object \n",
"dtypes: datetime64[ns](1), float64(9), object(8)\n",
"memory usage: 22.7+ MB\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Save to disk (overwrite previous version)\n",
"dff.to_csv('./data/csse_covid_19_daily_reports.csv', encoding='utf-8', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"tmp = pd.read_csv('./data/csse_covid_19_daily_reports.csv')\n",
"df = tmp"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Rename countries with duplicate naming conventions\n",
"df['Country/Region'].replace('Mainland China', 'China', inplace=True)\n",
"df['Country/Region'].replace('Hong Kong SAR', 'Hong Kong', inplace=True)\n",
"df['Country/Region'].replace(' Azerbaijan', 'Azerbaijan', inplace=True)\n",
"df['Country/Region'].replace('Holy See', 'Vatican City', inplace=True)\n",
"df['Country/Region'].replace('Iran (Islamic Republic of)', 'Iran', inplace=True)\n",
"df['Country/Region'].replace('Taiwan*', 'Taiwan', inplace=True)\n",
"df['Country/Region'].replace('Korea, South', 'South Korea', inplace=True)\n",
"df['Country/Region'].replace('Viet Nam', 'Vietnam', inplace=True)\n",
"df['Country/Region'].replace('Macao SAR', 'Macau', inplace=True)\n",
"df['Country/Region'].replace('Russian Federation', 'Russia', inplace=True)\n",
"df['Country/Region'].replace('Republic of Moldova', 'Moldova', inplace=True)\n",
"df['Country/Region'].replace('Czechia', 'Czech Republic', inplace=True)\n",
"df['Country/Region'].replace('Congo (Kinshasa)', 'Congo', inplace=True)\n",
"df['Country/Region'].replace('Northern Ireland', 'United Kingdom', inplace=True)\n",
"df['Country/Region'].replace('Republic of Korea', 'North Korea', inplace=True)\n",
"df['Country/Region'].replace('Congo (Brazzaville)', 'Congo', inplace=True)\n",
"df['Country/Region'].replace('Taipei and environs', 'Taiwan', inplace=True)\n",
"df['Country/Region'].replace('Others', 'Cruise Ship', inplace=True)\n",
"df['Province_State'].replace('Cruise Ship', 'Diamond Princess cruise ship', inplace=True)\n",
"df['Province_State'].replace('From Diamond Princess', 'Diamond Princess cruise ship', inplace=True)\n",
"\n",
"# Replace old reporting standards\n",
"df['Province_State'].replace('Chicago', 'Illinois', inplace=True)\n",
"df['Province_State'].replace('Chicago, IL', 'Illinois', inplace=True)\n",
"df['Province_State'].replace('Cook County, IL', 'Illinois', inplace=True)\n",
"df['Province_State'].replace('Boston, MA', 'Massachusetts', inplace=True)\n",
"df['Province_State'].replace(' Norfolk County, MA', 'Massachusetts', inplace=True)\n",
"df['Province_State'].replace('Suffolk County, MA', 'Massachusetts', inplace=True)\n",
"df['Province_State'].replace('Middlesex County, MA', 'Massachusetts', inplace=True)\n",
"df['Province_State'].replace('Norwell County, MA', 'Massachusetts', inplace=True)\n",
"df['Province_State'].replace('Plymouth County, MA', 'Massachusetts', inplace=True)\n",
"df['Province_State'].replace('Norfolk County, MA', 'Massachusetts', inplace=True)\n",
"df['Province_State'].replace('Berkshire County, MA', 'Massachusetts', inplace=True)\n",
"df['Province_State'].replace('Unknown Location, MA', 'Massachusetts', inplace=True)\n",
"df['Province_State'].replace('Los Angeles, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Orange, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Santa Clara, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('San Benito, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Humboldt County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Sacramento County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Travis, CA (From Diamond Princess)', 'California', inplace=True)\n",
"df['Province_State'].replace('Placer County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('San Mateo, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Sonoma County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Berkeley, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Orange County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Contra Costa County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('San Francisco County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Yolo County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Santa Clara County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('San Diego County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Travis, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Alameda County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Madera County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Santa Cruz County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Fresno County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Riverside County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Shasta County, CA', 'California', inplace=True)\n",
"df['Province_State'].replace('Seattle, WA', 'Washington', inplace=True)\n",
"df['Province_State'].replace('Snohomish County, WA', 'Washington', inplace=True)\n",
"df['Province_State'].replace('King County, WA', 'Washington', inplace=True)\n",
"df['Province_State'].replace('Unassigned Location, WA', 'Washington', inplace=True)\n",
"df['Province_State'].replace('Clark County, WA', 'Washington', inplace=True)\n",
"df['Province_State'].replace('Jefferson County, WA', 'Washington', inplace=True)\n",
"df['Province_State'].replace('Pierce County, WA', 'Washington', inplace=True)\n",
"df['Province_State'].replace('Kittitas County, WA', 'Washington', inplace=True)\n",
"df['Province_State'].replace('Grant County, WA', 'Washington', inplace=True)\n",
"df['Province_State'].replace('Spokane County, WA', 'Washington', inplace=True)\n",
"df['Province_State'].replace('Tempe, AZ', 'Arizona', inplace=True)\n",
"df['Province_State'].replace('Maricopa County, AZ', 'Arizona', inplace=True)\n",
"df['Province_State'].replace('Pinal County, AZ', 'Arizona', inplace=True)\n",
"df['Province_State'].replace('Madison, WI', 'Wisconsin', inplace=True)\n",
"df['Province_State'].replace('San Antonio, TX', 'Texas', inplace=True)\n",
"df['Province_State'].replace('Lackland, TX', 'Texas', inplace=True)\n",
"df['Province_State'].replace('Lackland, TX (From Diamond Princess)', 'Texas', inplace=True)\n",
"df['Province_State'].replace('Harris County, TX', 'Texas', inplace=True)\n",
"df['Province_State'].replace('Fort Bend County, TX', 'Texas', inplace=True)\n",
"df['Province_State'].replace('Montgomery County, TX', 'Texas', inplace=True)\n",
"df['Province_State'].replace('Collin County, TX', 'Texas', inplace=True)\n",
"df['Province_State'].replace('Ashland, NE', 'Nebraska', inplace=True)\n",
"df['Province_State'].replace('Omaha, NE (From Diamond Princess)', 'Nebraska', inplace=True)\n",
"df['Province_State'].replace('Douglas County, NE', 'Nebraska', inplace=True)\n",
"df['Province_State'].replace('Portland, OR', 'Oregon', inplace=True)\n",
"df['Province_State'].replace('Umatilla, OR', 'Oregon', inplace=True)\n",
"df['Province_State'].replace('Klamath County, OR', 'Oregon', inplace=True)\n",
"df['Province_State'].replace('Douglas County, OR', 'Oregon', inplace=True)\n",
"df['Province_State'].replace('Marion County, OR', 'Oregon', inplace=True)\n",
"df['Province_State'].replace('Jackson County, OR ', 'Oregon', inplace=True)\n",
"df['Province_State'].replace('Washington County, OR', 'Oregon', inplace=True)\n",
"df['Province_State'].replace('Providence, RI', 'Rhode Island', inplace=True)\n",
"df['Province_State'].replace('Providence County, RI', 'Rhode Island', inplace=True)\n",
"df['Province_State'].replace('Grafton County, NH', 'New Hampshire', inplace=True)\n",
"df['Province_State'].replace('Rockingham County, NH', 'New Hampshire', inplace=True)\n",
"df['Province_State'].replace('Hillsborough, FL', 'Florida', inplace=True)\n",
"df['Province_State'].replace('Sarasota, FL', 'Florida', inplace=True)\n",
"df['Province_State'].replace('Santa Rosa County, FL', 'Florida', inplace=True)\n",
"df['Province_State'].replace('Broward County, FL', 'Florida', inplace=True)\n",
"df['Province_State'].replace('Lee County, FL', 'Florida', inplace=True)\n",
"df['Province_State'].replace('Volusia County, FL', 'Florida', inplace=True)\n",
"df['Province_State'].replace('Manatee County, FL', 'Florida', inplace=True)\n",
"df['Province_State'].replace('Okaloosa County, FL', 'Florida', inplace=True)\n",
"df['Province_State'].replace('Charlotte County, FL', 'Florida', inplace=True)\n",
"df['Province_State'].replace('New York City, NY', 'New York', inplace=True)\n",
"df['Province_State'].replace('Westchester County, NY', 'New York', inplace=True)\n",
"df['Province_State'].replace('Queens County, NY', 'New York', inplace=True)\n",
"df['Province_State'].replace('New York County, NY', 'New York', inplace=True)\n",
"df['Province_State'].replace('Nassau, NY', 'New York', inplace=True)\n",
"df['Province_State'].replace('Nassau County, NY', 'New York', inplace=True)\n",
"df['Province_State'].replace('Rockland County, NY', 'New York', inplace=True)\n",
"df['Province_State'].replace('Saratoga County, NY', 'New York', inplace=True)\n",
"df['Province_State'].replace('Suffolk County, NY', 'New York', inplace=True)\n",
"df['Province_State'].replace('Ulster County, NY', 'New York', inplace=True)\n",
"df['Province_State'].replace('Fulton County, GA', 'Georgia', inplace=True)\n",
"df['Province_State'].replace('Floyd County, GA', 'Georgia', inplace=True)\n",
"df['Province_State'].replace('Polk County, GA', 'Georgia', inplace=True)\n",
"df['Province_State'].replace('Cherokee County, GA', 'Georgia', inplace=True)\n",
"df['Province_State'].replace('Cobb County, GA', 'Georgia', inplace=True)\n",
"df['Province_State'].replace('Wake County, NC', 'North Carolina', inplace=True)\n",
"df['Province_State'].replace('Chatham County, NC', 'North Carolina', inplace=True)\n",
"df['Province_State'].replace('Bergen County, NJ', 'New Jersey', inplace=True)\n",
"df['Province_State'].replace('Hudson County, NJ', 'New Jersey', inplace=True)\n",
"df['Province_State'].replace('Clark County, NV', 'Nevada', inplace=True)\n",
"df['Province_State'].replace('Washoe County, NV', 'Nevada', inplace=True)\n",
"df['Province_State'].replace('Williamson County, TN', 'Tennessee', inplace=True)\n",
"df['Province_State'].replace('Davidson County, TN', 'Tennessee', inplace=True)\n",
"df['Province_State'].replace('Shelby County, TN', 'Tennessee', inplace=True)\n",
"df['Province_State'].replace('Montgomery County, MD', 'Maryland', inplace=True)\n",
"df['Province_State'].replace('Harford County, MD', 'Maryland', inplace=True)\n",
"df['Province_State'].replace('Denver County, CO', 'Colorado', inplace=True)\n",
"df['Province_State'].replace('Summit County, CO', 'Colorado', inplace=True)\n",
"df['Province_State'].replace('Douglas County, CO', 'Colorado', inplace=True)\n",
"df['Province_State'].replace('El Paso County, CO', 'Colorado', inplace=True)\n",
"df['Province_State'].replace('Delaware County, PA', 'Pennsylvania', inplace=True)\n",
"df['Province_State'].replace('Wayne County, PA', 'Pennsylvania', inplace=True)\n",
"df['Province_State'].replace('Montgomery County, PA', 'Pennsylvania', inplace=True)\n",
"df['Province_State'].replace('Fayette County, KY', 'Kentucky', inplace=True)\n",
"df['Province_State'].replace('Jefferson County, KY', 'Kentucky', inplace=True)\n",
"df['Province_State'].replace('Harrison County, KY', 'Kentucky', inplace=True)\n",
"df['Province_State'].replace('Marion County, IN', 'Indiana', inplace=True)\n",
"df['Province_State'].replace('Hendricks County, IN', 'Indiana', inplace=True)\n",
"df['Province_State'].replace('Ramsey County, MN', 'Minnesota', inplace=True)\n",
"df['Province_State'].replace('Carver County, MN', 'Minnesota', inplace=True)\n",
"df['Province_State'].replace('Fairfield County, CT', 'Connecticut', inplace=True)\n",
"df['Province_State'].replace('Charleston County, SC', 'South Carolina', inplace=True)\n",
"df['Province_State'].replace('Spartanburg County, SC', 'South Carolina', inplace=True)\n",
"df['Province_State'].replace('Kershaw County, SC', 'South Carolina', inplace=True)\n",
"df['Province_State'].replace('Davis County, UT', 'Utah', inplace=True)\n",
"df['Province_State'].replace('Honolulu County, HI', 'Hawaii', inplace=True)\n",
"df['Province_State'].replace('Tulsa County, OK', 'Oklahoma', inplace=True)\n",
"df['Province_State'].replace('Fairfax County, VA', 'Virginia', inplace=True)\n",
"df['Province_State'].replace('St. Louis County, MO', 'Missouri', inplace=True)\n",
"df['Province_State'].replace('Unassigned Location, VT', 'Vermont', inplace=True)\n",
"df['Province_State'].replace('Bennington County, VT', 'Vermont', inplace=True)\n",
"df['Province_State'].replace('Johnson County, IA', 'Iowa', inplace=True)\n",
"df['Province_State'].replace('Jefferson Parish, LA', 'Louisiana', inplace=True)\n",
"df['Province_State'].replace('Johnson County, KS', 'Kansas', inplace=True)\n",
"df['Province_State'].replace('Washington, D.C.', 'District of Columbia', inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Interpolate values for missing South Korea data on March 11 \n",
"# (we skip this, but see the original https://github.com/raffg/covid-19/blob/master/eda.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# South Korea data on March 10 seems to be mislabled as North Korea\n",
"df.loc[(df['Country/Region'] == 'North Korea') & (df['date'] == '03-10-2020'), 'Country/Region'] = 'South Korea'"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 191184 entries, 0 to 191183\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 FIPS 156768 non-null float64\n",
" 1 Admin2 157008 non-null object \n",
" 2 Province_State 182976 non-null object \n",
" 3 Country/Region 191184 non-null object \n",
" 4 Last_Update 191184 non-null object \n",
" 5 Lat 186960 non-null float64\n",
" 6 Long_ 186960 non-null float64\n",
" 7 Confirmed 191184 non-null float64\n",
" 8 Deaths 191184 non-null float64\n",
" 9 Recovered 33890 non-null float64\n",
" 10 Active 33826 non-null float64\n",
" 11 Combined_Key 191184 non-null object \n",
" 12 Incident_Rate 186958 non-null float64\n",
" 13 Case_Fatality_Ratio 189206 non-null float64\n",
" 14 date 191184 non-null object \n",
" 15 404: Not Found 0 non-null float64\n",
"dtypes: float64(10), object(6)\n",
"memory usage: 23.3+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
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" <th></th>\n",
" <th>FIPS</th>\n",
" <th>Admin2</th>\n",
" <th>Province_State</th>\n",
" <th>Country/Region</th>\n",
" <th>Last_Update</th>\n",
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" <th>Long_</th>\n",
" <th>Confirmed</th>\n",
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" <th>date</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Afghanistan</td>\n",
" <td>2021-04-02 04:20:36</td>\n",
" <td>33.939110</td>\n",
" <td>67.709953</td>\n",
" <td>56517.0</td>\n",
" <td>2489.0</td>\n",
" <td>51788.0</td>\n",
" <td>2240.0</td>\n",
" <td>Afghanistan</td>\n",
" <td>145.182144</td>\n",
" <td>4.403985</td>\n",
" <td>2021-04-01</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Albania</td>\n",
" <td>2021-04-02 04:20:36</td>\n",
" <td>41.153300</td>\n",
" <td>20.168300</td>\n",
" <td>125506.0</td>\n",
" <td>2241.0</td>\n",
" <td>91875.0</td>\n",
" <td>31390.0</td>\n",
" <td>Albania</td>\n",
" <td>4361.178678</td>\n",
" <td>1.785572</td>\n",
" <td>2021-04-01</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Algeria</td>\n",
" <td>2021-04-02 04:20:36</td>\n",
" <td>28.033900</td>\n",
" <td>1.659600</td>\n",
" <td>117304.0</td>\n",
" <td>3096.0</td>\n",
" <td>81632.0</td>\n",
" <td>32576.0</td>\n",
" <td>Algeria</td>\n",
" <td>267.505610</td>\n",
" <td>2.639296</td>\n",
" <td>2021-04-01</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Andorra</td>\n",
" <td>2021-04-02 04:20:36</td>\n",
" <td>42.506300</td>\n",
" <td>1.521800</td>\n",
" <td>12053.0</td>\n",
" <td>115.0</td>\n",
" <td>11365.0</td>\n",
" <td>573.0</td>\n",
" <td>Andorra</td>\n",
" <td>15599.559956</td>\n",
" <td>0.954119</td>\n",
" <td>2021-04-01</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Angola</td>\n",
" <td>2021-04-02 04:20:36</td>\n",
" <td>-11.202700</td>\n",
" <td>17.873900</td>\n",
" <td>22399.0</td>\n",
" <td>538.0</td>\n",
" <td>20508.0</td>\n",
" <td>1353.0</td>\n",
" <td>Angola</td>\n",
" <td>68.151942</td>\n",
" <td>2.401893</td>\n",
" <td>2021-04-01</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191179</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Vietnam</td>\n",
" <td>2021-05-19 04:20:42</td>\n",
" <td>14.058324</td>\n",
" <td>108.277199</td>\n",
" <td>4512.0</td>\n",
" <td>37.0</td>\n",
" <td>2687.0</td>\n",
" <td>1788.0</td>\n",
" <td>Vietnam</td>\n",
" <td>4.635366</td>\n",
" <td>0.820035</td>\n",
" <td>2021-05-18</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191180</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>West Bank and Gaza</td>\n",
" <td>2021-05-19 04:20:42</td>\n",
" <td>31.952200</td>\n",
" <td>35.233200</td>\n",
" <td>304074.0</td>\n",
" <td>3437.0</td>\n",
" <td>293808.0</td>\n",
" <td>6829.0</td>\n",
" <td>West Bank and Gaza</td>\n",
" <td>5960.580357</td>\n",
" <td>1.130317</td>\n",
" <td>2021-05-18</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191181</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Yemen</td>\n",
" <td>2021-05-19 04:20:42</td>\n",
" <td>15.552727</td>\n",
" <td>48.516388</td>\n",
" <td>6586.0</td>\n",
" <td>1297.0</td>\n",
" <td>3072.0</td>\n",
" <td>2217.0</td>\n",
" <td>Yemen</td>\n",
" <td>22.081429</td>\n",
" <td>19.693289</td>\n",
" <td>2021-05-18</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191182</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Zambia</td>\n",
" <td>2021-05-19 04:20:42</td>\n",
" <td>-13.133897</td>\n",
" <td>27.849332</td>\n",
" <td>92520.0</td>\n",
" <td>1263.0</td>\n",
" <td>90892.0</td>\n",
" <td>365.0</td>\n",
" <td>Zambia</td>\n",
" <td>503.264912</td>\n",
" <td>1.365110</td>\n",
" <td>2021-05-18</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191183</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2021-05-19 04:20:42</td>\n",
" <td>-19.015438</td>\n",
" <td>29.154857</td>\n",
" <td>38595.0</td>\n",
" <td>1583.0</td>\n",
" <td>36371.0</td>\n",
" <td>641.0</td>\n",
" <td>Zimbabwe</td>\n",
" <td>259.672943</td>\n",
" <td>4.101568</td>\n",
" <td>2021-05-18</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>191184 rows × 16 columns</p>\n",
"</div>"
],
"text/plain": [
" FIPS Admin2 Province_State Country/Region Last_Update \\\n",
"0 NaN NaN NaN Afghanistan 2021-04-02 04:20:36 \n",
"1 NaN NaN NaN Albania 2021-04-02 04:20:36 \n",
"2 NaN NaN NaN Algeria 2021-04-02 04:20:36 \n",
"3 NaN NaN NaN Andorra 2021-04-02 04:20:36 \n",
"4 NaN NaN NaN Angola 2021-04-02 04:20:36 \n",
"... ... ... ... ... ... \n",
"191179 NaN NaN NaN Vietnam 2021-05-19 04:20:42 \n",
"191180 NaN NaN NaN West Bank and Gaza 2021-05-19 04:20:42 \n",
"191181 NaN NaN NaN Yemen 2021-05-19 04:20:42 \n",
"191182 NaN NaN NaN Zambia 2021-05-19 04:20:42 \n",
"191183 NaN NaN NaN Zimbabwe 2021-05-19 04:20:42 \n",
"\n",
" Lat Long_ Confirmed Deaths Recovered Active \\\n",
"0 33.939110 67.709953 56517.0 2489.0 51788.0 2240.0 \n",
"1 41.153300 20.168300 125506.0 2241.0 91875.0 31390.0 \n",
"2 28.033900 1.659600 117304.0 3096.0 81632.0 32576.0 \n",
"3 42.506300 1.521800 12053.0 115.0 11365.0 573.0 \n",
"4 -11.202700 17.873900 22399.0 538.0 20508.0 1353.0 \n",
"... ... ... ... ... ... ... \n",
"191179 14.058324 108.277199 4512.0 37.0 2687.0 1788.0 \n",
"191180 31.952200 35.233200 304074.0 3437.0 293808.0 6829.0 \n",
"191181 15.552727 48.516388 6586.0 1297.0 3072.0 2217.0 \n",
"191182 -13.133897 27.849332 92520.0 1263.0 90892.0 365.0 \n",
"191183 -19.015438 29.154857 38595.0 1583.0 36371.0 641.0 \n",
"\n",
" Combined_Key Incident_Rate Case_Fatality_Ratio date \\\n",
"0 Afghanistan 145.182144 4.403985 2021-04-01 \n",
"1 Albania 4361.178678 1.785572 2021-04-01 \n",
"2 Algeria 267.505610 2.639296 2021-04-01 \n",
"3 Andorra 15599.559956 0.954119 2021-04-01 \n",
"4 Angola 68.151942 2.401893 2021-04-01 \n",
"... ... ... ... ... \n",
"191179 Vietnam 4.635366 0.820035 2021-05-18 \n",
"191180 West Bank and Gaza 5960.580357 1.130317 2021-05-18 \n",
"191181 Yemen 22.081429 19.693289 2021-05-18 \n",
"191182 Zambia 503.264912 1.365110 2021-05-18 \n",
"191183 Zimbabwe 259.672943 4.101568 2021-05-18 \n",
"\n",
" 404: Not Found \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"... ... \n",
"191179 NaN \n",
"191180 NaN \n",
"191181 NaN \n",
"191182 NaN \n",
"191183 NaN \n",
"\n",
"[191184 rows x 16 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# Re-order the columns for readability\n",
"df = df[['date',\n",
" 'Country/Region',\n",
" 'Province_State',\n",
" 'Confirmed',\n",
" 'Deaths',\n",
" 'Recovered',\n",
" 'Lat',\n",
" 'Long_']]\n",
"\n",
"# Fill missing values as 0; create Active cases column\n",
"df['Confirmed'] = df['Confirmed'].fillna(0).astype(int)\n",
"df['Deaths'] = df['Deaths'].fillna(0).astype(int)\n",
"df['Recovered'] = df['Recovered'].fillna(0).astype(int)\n",
"df['Active'] = df['Confirmed'] - (df['Deaths'] + df['Recovered'])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# Replace missing values for latitude and longitude\n",
"df['Latitude'] = df['Lat'].fillna(df.groupby('Province_State')['Lat'].transform('mean'))\n",
"df['Longitude'] = df['Long_'].fillna(df.groupby('Province_State')['Long_'].transform('mean'))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 191184 entries, 0 to 191183\n",
"Data columns (total 11 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 date 191184 non-null object \n",
" 1 Country/Region 191184 non-null object \n",
" 2 Province_State 182976 non-null object \n",
" 3 Confirmed 191184 non-null int64 \n",
" 4 Deaths 191184 non-null int64 \n",
" 5 Recovered 191184 non-null int64 \n",
" 6 Lat 186960 non-null float64\n",
" 7 Long_ 186960 non-null float64\n",
" 8 Active 191184 non-null int64 \n",
" 9 Latitude 190176 non-null float64\n",
" 10 Longitude 190176 non-null float64\n",
"dtypes: float64(4), int64(4), object(3)\n",
"memory usage: 16.0+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of unique Country/Region: 191\n"
]
}
],
"source": [
"n_reg = len(df['Country/Region'].unique())\n",
"print('Number of unique Country/Region:', n_reg)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Confirmed</th>\n",
" <th>Deaths</th>\n",
" <th>Recovered</th>\n",
" <th>Active</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th>Province_State</th>\n",
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">2021-04-01</th>\n",
" <th>Alabama</th>\n",
" <td>515866</td>\n",
" <td>10553</td>\n",
" <td>0</td>\n",
" <td>505313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alaska</th>\n",
" <td>63298</td>\n",
" <td>313</td>\n",
" <td>0</td>\n",
" <td>62985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arizona</th>\n",
" <td>842192</td>\n",
" <td>16977</td>\n",
" <td>0</td>\n",
" <td>825215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arkansas</th>\n",
" <td>330609</td>\n",
" <td>5636</td>\n",
" <td>0</td>\n",
" <td>324973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>California</th>\n",
" <td>3671349</td>\n",
" <td>59411</td>\n",
" <td>0</td>\n",
" <td>3611938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">2021-05-18</th>\n",
" <th>Virginia</th>\n",
" <td>670834</td>\n",
" <td>11042</td>\n",
" <td>0</td>\n",
" <td>659792</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Washington</th>\n",
" <td>424050</td>\n",
" <td>5653</td>\n",
" <td>0</td>\n",
" <td>418397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>West Virginia</th>\n",
" <td>158888</td>\n",
" <td>2763</td>\n",
" <td>0</td>\n",
" <td>156125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wisconsin</th>\n",
" <td>670362</td>\n",
" <td>7726</td>\n",
" <td>0</td>\n",
" <td>662636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wyoming</th>\n",
" <td>59321</td>\n",
" <td>713</td>\n",
" <td>0</td>\n",
" <td>58608</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2784 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" Confirmed Deaths Recovered Active\n",
"date Province_State \n",
"2021-04-01 Alabama 515866 10553 0 505313\n",
" Alaska 63298 313 0 62985\n",
" Arizona 842192 16977 0 825215\n",
" Arkansas 330609 5636 0 324973\n",
" California 3671349 59411 0 3611938\n",
"... ... ... ... ...\n",
"2021-05-18 Virginia 670834 11042 0 659792\n",
" Washington 424050 5653 0 418397\n",
" West Virginia 158888 2763 0 156125\n",
" Wisconsin 670362 7726 0 662636\n",
" Wyoming 59321 713 0 58608\n",
"\n",
"[2784 rows x 4 columns]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['Country/Region'] == 'US'].groupby(['date', 'Province_State'])[['Confirmed', 'Deaths', 'Recovered', 'Active']].sum()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>Confirmed</th>\n",
" <th>Deaths</th>\n",
" <th>Recovered</th>\n",
" <th>Active</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
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" <td>30611086</td>\n",
" <td>554686</td>\n",
" <td>0</td>\n",
" <td>30056400</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>0</td>\n",
" <td>30153408</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>556236</td>\n",
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" <td>30230568</td>\n",
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" <tr>\n",
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" <td>557097</td>\n",
" <td>0</td>\n",
" <td>30290251</td>\n",
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" <tr>\n",
" <th>2021-04-07</th>\n",
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" <td>559690</td>\n",
" <td>0</td>\n",
" <td>30362696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-08</th>\n",
" <td>31002264</td>\n",
" <td>560711</td>\n",
" <td>0</td>\n",
" <td>30441553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-09</th>\n",
" <td>31084962</td>\n",
" <td>561600</td>\n",
" <td>0</td>\n",
" <td>30523362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-10</th>\n",
" <td>31151497</td>\n",
" <td>562316</td>\n",
" <td>0</td>\n",
" <td>30589181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-11</th>\n",
" <td>31197877</td>\n",
" <td>562616</td>\n",
" <td>0</td>\n",
" <td>30635261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-12</th>\n",
" <td>31268107</td>\n",
" <td>563092</td>\n",
" <td>0</td>\n",
" <td>30705015</td>\n",
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" <tr>\n",
" <th>2021-04-13</th>\n",
" <td>31345985</td>\n",
" <td>563924</td>\n",
" <td>0</td>\n",
" <td>30782061</td>\n",
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" <tr>\n",
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" <td>31421360</td>\n",
" <td>564896</td>\n",
" <td>0</td>\n",
" <td>30856464</td>\n",
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" <tr>\n",
" <th>2021-04-15</th>\n",
" <td>31495649</td>\n",
" <td>565795</td>\n",
" <td>0</td>\n",
" <td>30929854</td>\n",
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" <tr>\n",
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" <td>31575640</td>\n",
" <td>566667</td>\n",
" <td>0</td>\n",
" <td>31008973</td>\n",
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" <tr>\n",
" <th>2021-04-17</th>\n",
" <td>31628013</td>\n",
" <td>567359</td>\n",
" <td>0</td>\n",
" <td>31060654</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-18</th>\n",
" <td>31670031</td>\n",
" <td>567692</td>\n",
" <td>0</td>\n",
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" <td>31799237</td>\n",
" <td>569027</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2021-04-21</th>\n",
" <td>31862094</td>\n",
" <td>569886</td>\n",
" <td>0</td>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" Confirmed Deaths Recovered Active\n",
"date \n",
"2021-04-01 30541255 553733 0 29987522\n",
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"2021-04-03 30674153 555423 0 30118730\n",
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"2021-04-08 31002264 560711 0 30441553\n",
"2021-04-09 31084962 561600 0 30523362\n",
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"2021-04-14 31421360 564896 0 30856464\n",
"2021-04-15 31495649 565795 0 30929854\n",
"2021-04-16 31575640 566667 0 31008973\n",
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"2021-04-20 31799237 569027 0 31230210\n",
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"2021-04-26 32124869 573132 0 31551737\n",
"2021-04-27 32175725 573782 0 31601943\n",
"2021-04-28 32230850 574751 0 31656099\n",
"2021-04-29 32289049 575616 0 31713433\n",
"2021-04-30 32346971 576354 0 31770617\n",
"2021-05-01 32392274 577069 0 31815205\n",
"2021-05-02 32421641 577401 0 31844240\n",
"2021-05-03 32472201 577887 0 31894314\n",
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"2021-05-05 32557669 579541 0 31978128\n",
"2021-05-06 32605183 580334 0 32024849\n",
"2021-05-07 32652472 581088 0 32071384\n",
"2021-05-08 32686965 581705 0 32105260\n",
"2021-05-09 32708357 581947 0 32126410\n",
"2021-05-10 32745255 582347 0 32162908\n",
"2021-05-11 32778906 583023 0 32195883\n",
"2021-05-12 32814784 583872 0 32230912\n",
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]
},
"execution_count": 25,
"metadata": {},
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}
],
"source": [
"\n",
"df[df['Country/Region'] == 'US'].groupby('date')[['Confirmed', 'Deaths', 'Recovered', 'Active']].sum()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2.07%'"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# fatality rate\n",
"'{:.2f}%'.format(100 *\n",
" df[df['date'] == df['date'].iloc[-1]]['Deaths'].sum() /\n",
" df[df['date'] == df['date'].iloc[-1]]['Confirmed'].sum())"
]
},
{
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"}}\n",
"\n",
" }) }; }); </script> </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"fig = go.Figure([go.Scatter(x=df[df['Country/Region'] == 'US'].groupby('date')['date'].first(),\n",
" y=df[df['Country/Region'] == 'US'].groupby('date')['Active'].sum())])\n",
"fig.update_layout(\n",
" title=\"US: Active COVID-19\",\n",
" xaxis_title=\"Date\",\n",
" yaxis_title=\"Active infected\",\n",
" font=dict(\n",
" family=\"Courier New, monospace\",\n",
" size=16,\n",
" color=\"#7f7f7f\"\n",
" )\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"geo_us = df[(df['date'] == '2021-05-17') &\n",
" (df['Country/Region'] == 'US')].groupby('Province_State',\n",
" as_index=False).agg({'Longitude': 'mean',\n",
" 'Latitude': 'mean'})\n",
"\n",
"temp2 = pd.read_csv('./data/csse_covid_19_daily_reports.csv')\n",
"df4 = temp2[temp2['Country/Region'] == 'US'].groupby('Province_State', as_index=False).agg({'Confirmed': 'sum'})\n",
"\n",
"df4 = df4.merge(geo_us, left_on='Province_State', right_on='Province_State')"
]
},
{
"cell_type": "code",
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" lon = df4['Longitude'],\n",
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" text = df4['Province_State'] + ', ' + ': ' + df4['Confirmed'].astype(str),\n",
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" landcolor = \"rgb(100, 125, 100)\",\n",
" showocean = True,\n",
" oceancolor = \"rgb(150, 150, 250)\",\n",
" showcountries=True,\n",
" showsubunits=True,\n",
" showlakes=True,))\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
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"eu = ['Albania', 'Andorra', 'Armenia', 'Austria', 'Azerbaijan', 'Belarus', 'Belgium', 'Bosnia and Herzegovina',\n",
"'Bulgaria', 'Croatia', 'Cyprus', 'Czech Republic', 'Denmark', 'Estonia', 'Finland', 'France', 'Georgia',\n",
"'Germany', 'Greece', 'Hungary', 'Iceland', 'Ireland', 'Italy', 'Kazakhstan', 'Kosovo', 'Latvia', 'Liechtenstein',\n",
"'Lithuania', 'Luxembourg', 'Malta', 'Moldova', 'Monaco', 'Montenegro', 'Netherlands', 'North Macedonia', 'Norway',\n",
"'Poland', 'Portugal', 'Romania', 'Russia', 'San Marino', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden',\n",
"'Switzerland', 'Turkey', 'Ukraine', 'United Kingdom', 'Vatican City']"
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"title": {
"text": "Number of active cases by European country "
}
}
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"source": [
"df3 = df[df['Country/Region'].isin(eu)]\n",
" \n",
"data = df3[df3['date'] == df3['date'].iloc[-1]].groupby('Country/Region').agg({'Active': 'sum',\n",
" 'Longitude': 'mean',\n",
" 'Latitude': 'mean',\n",
" 'Country/Region': 'first',\n",
" 'Province_State': 'first'})\n",
"\n",
"data.loc[data['Country/Region'] == 'France', 'Latitude'] = 46.2276\n",
"data.loc[data['Country/Region'] == 'France', 'Longitude'] = -3.4360\n",
"\n",
"data.loc[data['Country/Region'] == 'United Kingdom', 'Latitude'] = 55.3781\n",
"data.loc[data['Country/Region'] == 'United Kingdom', 'Longitude'] = 3.4360\n",
"\n",
"data.loc[data['Country/Region'] == 'Denmark', 'Latitude'] = 56.2639\n",
"data.loc[data['Country/Region'] == 'Denmark', 'Longitude'] = 9.5018\n",
"\n",
"data.loc[data['Country/Region'] == 'Netherlands', 'Latitude'] = 52.1326\n",
"data.loc[data['Country/Region'] == 'Netherlands', 'Longitude'] = 5.2913\n",
"\n",
"\n",
"fig = go.Figure(data=go.Scattergeo(\n",
" lon = data['Longitude'],\n",
" lat = data['Latitude'],\n",
" text = data['Country/Region'] + ', ' + data['Country/Region'] + ': ' + data['Active'].astype(str),\n",
" mode = 'markers',\n",
" marker_size = (100 * data['Active'] / data['Active'].max()),\n",
" marker = dict(reversescale = False,\n",
" autocolorscale = False,\n",
" symbol = 'circle',\n",
" line = dict(width=1, color='rgba(102, 102, 102)'),\n",
" colorscale = 'Reds',\n",
" cmin = 0,\n",
" color = data['Active'],\n",
" cmax = data['Active'].max(),\n",
" colorbar_title=\"Active Cases\")))\n",
"\n",
"fig.update_layout(title = 'Number of active cases by European country ',\n",
" geo=dict(scope='europe',\n",
" projection_type=\"natural earth\",\n",
" showland = True,\n",
" landcolor = \"rgb(100, 125, 100)\",\n",
" showocean = True,\n",
" oceancolor = \"rgb(150, 150, 250)\",\n",
" showcountries=True,\n",
" showsubunits=True,\n",
" showlakes=False,))\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
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0pDBqbCXs7/Az0L6Ojrvd+d4WEREREfFnvby00zDmL7iSwVTxyqJX2NZNJwonGsLYdk7iQ40RQAgjE09sshxkbB5JUeWoPeG6CMtwxrQzxcpgTuB8azAVRTvZd+TEoaygkAOEkJgYwyGbndJWPzZHGHHmIGpsJRxoc/JiMJ3PNaltT+Q6Vl/yNcVOGJY8+YSpaM21nUtqYjAVRV+xu4snSp1lMkUAHjwtlxSzqaCeCMsEpnXzKnP9TOdyfUoUlUV5bGwVEjQ6v+bdvGrMSTO48Mh0sPqSQgodTQxJtBLc5jUotdUQbY4A7BSWnGy/uNmcXwoMYcaRMPF0nLiPhpGenkBdyYfce+8/WJV/CHcHj5+ftweII9FS3+Y52Ck3RhMDHCgpoapL2z2VISQntRdMWph87gC8rr183bLfErgpI4EGx5esyj/W0PfodL7Wr8mpGEwWrkhP5doTfqYxvJvCxWnpl7e7utmZfJYiLcPbra87jw3tCTWGEwI0uE58leOs1nb64IDJZATc1J6i93J9SSHfOGFY8jRGGU+2749+3seS3O7rbOHC5EF4Xbv4sqh7jkFjkqztXBpOmAm8LjfVXfyuoePjbve9t0VERERE/Fmv9BRqrX9SBvPTN/Hf2Z+xePnlLJk/pgfKCCHqJPlKuKlrExkMpjjC2r0mhlhzEN6iyuaTETPsL6kAPLy96He83eEWm6fU0eoENMIyhNiTnoAdz+OopBJIMMd0cItohsQa8RZUUe6Ads8SO8letJZ/rPyU9QV7KLHVtgo4APq3/KnRWYnDCWHWONqfrHEmDFyYfgFROWvIyS3l2iNTbGx56/naGcK30qe1jAzzOMqpBA7kPMe1OR1tL4T6k4aSVZQ7XAQbo4ntxJPp7D4CmDrnfp40vsAfl6/hgXvXEGUZy7WzZvL9jCmtRls0Pz7s5ZG7ftXh48a43NQC0Z3e7qmE07+DECZmcAxQQlWrEVbj0y5n7PIXWJO1idtTLiICsOWtJb/Na3IqYebzueeBnlySPoQhlrh2rzmTz1KwMaaDo9exY0Ptkc/66Rwb3I5CXlv5Ph/mFbHTVtkm0Ongkc2n6hh0ckc/PwmWmJNO+Tr6eY9MHNLhaKKB1jjAziFbDRB1RnU1H9fP7Lm1dbLjbne9t0VERERE/Fmvh0IQTvr8ObyXt5jPMl/gzfSFXGcM6nK/CW8735B3Tvcuud10pIzjl60P56p5dzCzg2+Sg43RjGlvdEG3VtY9duf8nTsW5uG2jOU7s+ZwT+JABpgiMOAmZ+kfeT7vxPv0M/bMMxmQPINp5o9Zn53LvjmzGUYdn+ZswWU8h2+lDjjh9uekzuau9I5WugthZNLJvul30+SCzrwqXd9H4Vw4az5vZpTzWfYqXsn8lFeXPMWbK8dy/6O/4IYkU6vHH86dD93A6A5OXENMllYnp53ZbvcKtTQ3Af9bXi6bnRdyoameT7I3H3lNTn+EVfczENz5Bee6gbvl2HC8zh8b6ks+5qd3/T++dsVyecY13JoygkGmCEKNQezPeYlfrdzVc+V3UlA379OTH9d75jXs6BPed97bIiIiIiKnzwehEPQzJfOr+cnMWfglzz/+PskPRLVTiOGkJxyNzlqcp5gG0V3qHXaqaW+wTTkl5V4MphgijtQ60BID2Ik0j+Xy1DP9ZrxjIeZYYgCHo7KDW1RRVt75kS7tK+aFpRuoMI5l8dO/PWHFuIITeujEEGWCYlsZVdDtjcSDjWO4Nm0wH2Z+ydqi7zLL8jUf5tUzJPkyJreqLcQc09wXxjiE1NTk0wzbYog2G/EWnWqkVdf20fHPJ5bUjFtIzZhFQc5r/PbxD3niwf8j6bUfMdYYQ6y5+U01Mmkyl59ihbjOb/dU26miwtkE7TQiPmSrAGIwH3ddNFdmXMCzeev5KPcwKam7+DCvnmEpx78mvaMW52m0KjuTz1K9o6yDY0MVO8/o2ODmo5Wv85Uzmh8+upB72kxxMhb1XMLVzxRNBCfbH0dvF4vZFMQuWxkO2v+8HyqxAwYGtvRF8p/j+qn503tbRERERKRn+Kwpwsi0W/lRciiOon/xVNY+TjxPMGA0QqOrFlc7PTaqinZS1huFAg2OXRS20+y2wVbIZlsTUdZzGHbkZNuadA5mPHyZ+xUndifqPmHWCZxnDmJf/hdtGqE2czu+JrfIS//E8xjZhWlprXld5ZQ5moiyTiDxhJOgfWwtamhz2TDOTwyl1lZIfieaA5/OKIMp6RcwGBvr8/Zgy1vLFlc4l6Sfd1xz4DDruYwxwb789Xxz2v2UwpmYHA+UsDbvcIe36vo+ao+BpLRbWXD1YBocu9jjaH785JR4oIyc3IOn+Rza2+6p2MkvqGjn8n18nn+YYONwxlqPf56WlMtINTeyPucrCvPWs8UVzsXpE0+57PnpiDYb6Cj8aXTu4+vTaEp9Jp+lBsfedo8N9SUb+LLkTI4NdezfWwNYSElu2+PJTVFBaaefX1eFJ4478vlpf38cM5zpSc2f963t7ncb6/IPEmwcxfmJR/db7x/X2+u71Fm9+d4WEREREfEFH3bKjOXWBTcyyujhs6xcik+4Ppqxic1NSj/Lr2lzXTlvZ26kuncKBfby4tLP26w8VMW7S//NLlcIl2Uc6y8RmXQpNySFUpz7Kn/O2ndCs1+vq5I9trbPp+uCjWOYMysBl20tS5bvaHOSWc4bi96k0BXOVSeswtaVx4jGbAqi1raTMufxK/BsznyV905o1BzNNRmTCGMvzy16n5I23/h7Xe6W/dHPFMMAI9iLtrOrC8FNROJFXGQN4sucXN7KKSDINIGZbUZRBBvP5YcZw2lwfMYfHl/XzuprbvaV2E/ZiHl82uWMNXpYtXwFn9jaDl9w0+A6nX1UR2m7r38Ve8oqCTZG0//I00lMu4YLTB5WL3+WtwpOTEEanTZsrVZ+6+x2T85D9vLX+apNv6XtWZm8V9JEYmoqE9qMfgo2juHGtMGU5a9laXbza5Ke0rlV9LpqqNVKGFVsytvT5ho3X2T9hy2nMcrkzD5Le3lx6fGrknldNl5Z8iH7OZNjQzhxZiNQTkHR8U/qYME7PJfdqYTvtAQbk7ghbTC1trU8ubTghADrWMgSzsxZF2M+8nkvPa5MN5szX+L1Ii9j02Zyfst7pveO60dHgO3ILzntgL4339siIiIiIr7gk+ljR4VZr+DXs3K5e+XeNo15m6VkzGRU1gqWL/wLzrlppFgjcTvKWJW1ii+dZqwmJ70x02BocgqxRUuZfdsapiWPIM5Yx478L1hXVIM15VbuSG89p8TCHQ/9hO33LuXtRb9jfdZYJiUOJIRaDtnsbCsowZp+PysWnHvGdU2c9VN+mv8If1/5v9ycdz6pyQPp56pia94mttqaSE6/k5+mndhrp/MSuC59GGtW5vPLu/7MVanjMVNOQf4XbLAN48b0kazMPv4eQ9Nu5f68PTyW/Rrfn72WyckjiDO6OWTby9aiQTz57/s4D4DRzEw182n2x/z63jquSh2E2xHF1fOuYuxJRxAN47r0BN5auobXSjwkpqWS1M5IqKlzf8rPi/7C33Oe4zv577bUUe6ws6uohGrzlby6bPZJ+2+HWi7hfxZ8wbzHv2TBbQ9wfkoiVhNUOexsK6jghkf/zO3JXd1HNp659zE+NyYwMTGeeEsc0djIz/uKdUUeps76dssJtME8lYce+hY/ffADHrnrV/wzeRzjrJF4nJWU2crYVlDBTY8u5Zep/bq03ZMJNU9gmmUzd8x+gItSJ5JghrKirXycV0qwJYV7509pd5REcsblDMl8lU/yYHx6KmO6ODqt3vElf1roILyd60alzmJeWnMPl9iUb5Fu3cDbKxexwPUd0pMH0s9Vzhe5q3knz0uiNYhvbF16aOD0P0tR1glEFf2D2betbTk2bM1bzxclrjM8Nhi46LrpmD9dw9IHH2FH2kTGWjzsKdjOmjwnl2RMpyJzU9efaKcYSJt3B9cV/Il3Mp/g+ryxTE0aSLCrlv0lO7GbMnhl8ZVEA+bk7/HwnCJ+s/I1brktj4tTEokzutlTsJW1BYeITrySB+dPOm4KZ28d1yMSU7jImsVbuS9w98JCUhNDqWIMP5ozqUtTW8/0vS0iIiIi4s98GgqBgalzbyc99/f8u53lwcOsV/Ds0yH8bWk27y1/kVedTQywDGFaagbPz53Ef+7/Nf/p8RpDmJx2Cw+mXsnypW/xTu5q9jmaGGCxctO8bzNv1hQGtrlHqGUqi5dZeGPl27ydu4OcnEJcrhBizHGcm3oNN2WM7JbKgo0Wbn/iD4zOfJ0XsjbzVuaXeI3hWBMnMG/ud7gtfeQZTnMwcPG8+3nM9CIvZBXwxsqthJhiGZN8EU89dAPnlLzC2zklbe4TzXce+D0jk99mWdYmNuXmUusKYYBlCOenTWu1rwxcvWABlbzIi7lf8sLSJgZbZ3DFXGhnLuFxxqelkrB0BcWuaNLSx7fbMyjYOJzbn1jIhKy3WZG1+UgdEGaKJTEphe9nXNypEVRj0+/lVesanl2ew/r8L9jkdBNmimVM8mSSrUGnsY8sXDvnYg5nb+HLvF2sdroJNoZjTRzHvAe+zW3po497PsNTfsA/l53LiuXv835+Ie/m1+I1GhhoGc5ls67hupZm2V3bbkcGJ83gsYd+TE7mSlZk5bLWVkuIKZYL0m/hznlXcW4HvVTCrFNJT/onLxQM4MqMiV3u4+R22vg0p/00Z7L5ypZQKNiYwG8X/4ohS9/inZxXeSCz+fVISp7MY0/fQEjWQuZndfHBOf3PksE0gccezeCdHjg2WFJ+wFOPhrNo+Tpysv7Dh8ZwrInncd8T93Ft4k5+lNVToRD0M43h4acfYeryt1iZu42cnEI8GBhoGcVlaYmtwjsDF8/7HSuSPuCZzBzWZK+inub35/XzZvOjWSeufNd7x/Vh3PfEz2l6/E1W5X7A5txwxqUO4kdd3MqZvrdFRERERPxZUFNT0+k2XRERAcDr+obf3vYYnxuv4JUVt/bg0vL+4hvuv/ExNphvJHNZhpYnP4sF3ntbRERERAKJD3sKicjZwpa3hk9tTVyQfrlOmuWsove2iIiIiJzNFAqJyBnxuor5f8s34DKO5cb0ob4uR6Tb6L0tIiIiImc7H/cUEpG+qNG5i9dWbuSQq4otuZ+Tb2ti5vzbSO2g55BIX6H3toiIiIgEEoVCInIaHHyes5r1NhdRFivfX/BDfpahyTVyNtB7W0REREQChxpNi4iIiIiIiIgEIPUUEhEREREREREJQAqFREREREREREQCkEIhEREREREREZEApFBIRERERERERCQAKRQSEREREREREQlACoVERERERERERAKQQiERERERERERkQCkUEhEREREREREJAApFBIRERERERERCUAKhUREREREREREApBCIRERERERERGRAKRQSEREREREREQkACkUEhEREREREREJQAqFREREREREREQCkEIhEREREREREZEApFBIRERERERERCQAKRQSEREREREREQlACoVERERERERERAKQQiERERERERERkQCkUEhEREREREREJAApFBIRERERERERCUAKhUREREREREREApBCIRERERERERGRAKRQSEREREREREQkACkUEhEREREREREJQAqFREREREREREQCkEIhEREREREREZEApFBIRERERERERCQAKRQSEREREREREQlACoVERERERERERAKQQiERERERERERkQCkUEhEREREREREJAApFBIRERERERERCUAKhUREREREREREApBCIRERERERERGRAKRQSEREREREREQkACkUEhEREREREREJQAqFREREREREREQCkEIhEREREREREZEApFBIRERERERERCQAKRQSEREREREREQlACoVERERERERERAKQQiERERERERERkQCkUEhEREREREREJAApFBIRERERERERCUAKhUREREREREREApBCIRERERERERGRAKRQSEREREREREQkACkUEhEREREREREJQAqFREREREREVSWwowAAIABJREFUREQCkEIhEREREREREZEApFBIRERERERERCQAKRQSEREREREREQlACoVERERERERERAKQQiERERERERERkQCkUEhEREREREREJAApFBIRERERERERCUAKhUREREREREREApBCIRERERERERGRAKRQSEREREREREQkACkUEhEREREREREJQAqFREREREREREQCkEIhEREREREREZEAFOLrAkREAo3d7qW83Mv+/V5KS71UVzf5uiQREekhRiMMHRpMfHw/4uODiYvTd7IiIuI/gpqamnQ2IiLSw+x2L4WFjWzf7sHl8nU1IiLiK1FRQSQk9GPixBCiooJ8XY6IiAQ4hUIiIj2koQEKCz1s395IebnX1+WIiIifiY8PZty4fowcGUJoqK+rERGRQKRQSESkmzU0wFdfedi82a1RQSIickpGI0yaZOC88xQOiYhI71IoJCLSjQoLPeTmKgwSEZGui4oKYurUEMaOVdtPERHpHQqFRES6gd3uJSfHrWliIiJyxuLjg5k61UB8vJpSi4hIz1IoJCJyhjZu9LBhg9vXZYiIyFlm6lQDU6Zo1JCIiPQchUIiIqdJo4NERKSnxccHk5Zm1EplIiLSIxQKiYichuJiLzk5DeodJCIiPc5ohLS0UBISNJ1MRES6l0IhEZEu2r7dw+rVmi4mIiK96/LLDYwbp+lkIiLSfRQKiYh0QU6Oi8LCRl+XISIiAWrixBBmzDD4ugwRETlLaAyqiEgnKRASERFf27LFQ06O5i6LSN9XUfAKV162gBfyG7p0v0bnRu6765/sa3WZ1/U19974yy5vq2vcbMt9l9/c+1vSrr2DyZfdzsU3LuCOB99hm7PvjrXR+FMRkU5QICQiIv6i+feRi7Q0o69LERE5TXWsztxIqKWRdzM3cUvyRUR08p4H8r9guzP6uMuCjRaumnU58daeOi7W8dGSP/K7rCqunnszf12QSLzRwyFbGVuLvAwy9d3FABQKiYicggIhERHxNwqGRKQvczs2kJkXyW0PXMSrj6/hc9uFpFmOD1Z25b3BX5Z+wuaSCrzGGM5NvZU7U/J4eFEeB5whzLn2Y8DC/U//N9daD5C9chUTk64hvuB5bnrcxVNv/pxJxmPb3Jb5e+7OHsvLy2ZjoY5PMl9g8cp8djuaGGwdxy3z7+C2lAHt1uvI/yd/yKzih08s5GcpppbLB1qGkJR87HZ7897gj8s3UFBip8oVwsikaSx4YC4XWYIANwU5r/HH5Z+xraSWEFMM1sQUfv3Q97nAHHTcc95Q5CTUPIRr5szhnllJRwKzct5f+iLPZRew29FEjDmOCSnX8LsHLsVyBq+FQiERkZNQICQiIv5KwZCI9FVfZ6/Cbp3BlakpVCW+TWbWbtLmJbRc78h/mZ8++AVXLZjPQ6lDMTpt7HaaOT9xKs+43Ny20sJLK2YzrJ1tx6ZcxmTjX1mV62RSWtSRS21kZ+9jSvqdWIDNmX/lsewh/M/iJ5luDaYg51XuX/gXYhYv5PrEtl123HyWtRFj8nf4fqtAqD3R5lHcOO9CHk2KJxobryx8nCcWrWPlExfRz/YJDz1eyHVPPMay5AG4HWVsKahg5JFAqKLgn9z7+HZmPfA7FqcMoLIojwcffIo/Gv+H32cMZn/Oy/w518RjTz/JVIuRAyUlbLeFE3var0Iz9RQSEenAhg1uBUIiIuLXCgsb2bLF4+syREQ6zev6hlezDnBxxjRiiSU9PYlt2asodB3ty1PHqsxPGZR2G/ekj2awKYIBllGcn9i/U9sPNo7hxrRoPsn+gqojl9UWfcIHJQlcmzYI+IZ/rLRx8/w5TLdGAuEkpc3i5tEHeSd7TztbLGf33gbiE61Et3NtazGJ55OWHE9/Y/OUtvT0MVTaduIAgnDjBoxGAwYgwjyE6anjj4Q6dWSv/JRh6bdxW8ogDBiIS5zBnRkWPs/+gnLA63LjcjVhNEYABgZbz+HSlHjOdOkBjRQSEWnH9u0eNm7UP7JFRMT/rV3rxmhEy9WLSJ9QlvsB65yTeDK1eapWfOpljF/yHFm5Tn6dFgXY+LrIy5i555x24JGSMZ3GO9byheMSLjMHsTF7E+Ep1zPZHESDbS97HZV8cu88/t7mfmMGV7azNQNBhs70DHKzK+8Dns/cyE5bLR7A67Lj5lI8gNFyBQ/PL+Kh+3/NO0nnc1PGTK5OHdkyNezrIief5/4Pk1cev9Uo6yjqgKFpP+Dn+c9w922/ZFLqNG7KuIrLkjoXlJ2MfnOIiLRht3tZvdrt6zJEREQ6be1aN3FxwcTFaSKAiPizct7O3IzT2cDPbvxJy6Uul5udmbn8OO3qlulQ/c7gUcKsl/DtxPfIzjnIJRmV/DunjksWTCYCaACCjBYWLvsD11o7c8yMZkhsENklZdQypsOG2OV5L/PjB3fyw0d/wcKUQRiAgzlPcvPSo7cwMDljPlnp5azLWcXLS5/g+eVT+dPiuZxncgMGrlvwR36fYW7/AYwWvvvA77nWsYsPs1ex+P5fsSzlxzz50EVnNIVMvzVERFppaIB//asnl7IUERHpfi4XZGe7aNCvMBHxY7VFH/GfogHcs/gPvLni8ZafV5/4Fo0FH/BhkRewMNoazLb8Ek7/a9pY0tNHsyVnPQX5a8k3nkt6SigABrOFYaZyNuRXdHJbBqZfNYnD+e/yr4KOD7Jf520nIukybjoSCAGU2g6dcLtgYywz0m/hmRX/RSqfkZlzGLBwbmIwW/N2UnuKaiLMo8iYcyf/XHYbjbn/OrLPTp9CIRGRVrKzG3C5fF2FiIhI11VXN5GdrVRIRPyVm9WZ6zAmX8MNyfHEW+JafsamXM33kp1kZW7BTTgzZ03nYM4KnsnZjcPpptJRxhcFpbiBAZY4mmyFbCqqgSN9etozMu1yhpbk8eTKfBJSL2fskZXIgo3n8qM5o/hg6VMsz93fsv1NublsKmlqd1vxqd/jpykNLL7/Ef6StYkdJRWUOyopKdrJZ3l7qAWGJ8ZRVfIleSU1QB278t7gb1nlx569o5BVuUUccDZX7CgpocQRgtkcAYSTPieNurwV/DFzM/udbqqddr7JX8+q/MMAHCzIIye/lAoXQB17i3ZSbYxhqLkzU9s6puljIiJHbNjgprT0zJJ2ERERXyot9ZKb6yY19Uxbj4qIdC+3YyP/lxvMdY9Ob6dhcyw3zrmQVxd+wHrHJFJTfsAzD73B40sXkb6wEq8xminpt7EoKR5T8tXcl/4Mf73rLh4zDue/Fi/k+sQTH6+faRLXpCznkZxIFi44/gaTZt3HX40reXrpozxdUovXGE5i0mTuXHAR0F7IEsutjy4kIettXsxawVtLKqh1QaQ5lgkps5iQMoIRabdyV/4zPHrHXTxANONTUpn/UAZPPm4/so0qPl75TxYuPEStK4QBliFcOedufpQaBkD/pO+x9Ilo/rL0Jb63tPk2g63ncNO8MQB4nbt4ccnLPFBSiddoYLB1LN976A5SzzAUCmpqamo/ChMRCSDFxV59uyoiImeN668PJT5ekwJEROTkFAqJiNDcXLqr08bsdi8NDc2H0NJSLw0NUF6ukUYiIuJ7UVFB3HxzGKGhvq5ERET8mUIhEZFuVl3dRHV1E1VVXqqrm1oCJ01NExGR3pSQEEx6ulIhERHpmEIhEZFednRUkd3eiN3upbpaI4xERKRnpKeHkpCgaWQiItI+hUIiIn7i6Aij/fsbW/6s0UUiInImjEb4wQ/CNY1MRETapVBIRMTPHQ2INm92U17e/GcREZHOOu+8EK1GJiIi7dKS9CIifqi5F1ETpaXNU8w0YkhERE7XV195GDeuH3FxmkYmIiLH00ghEZEe1pmVzY6uXLZ/f+NprYQmIiJyMvHxwVx/veaQiYjI8TRSSEQCzpYtHoqLGzt9e4U0IiLS15WWetm+3cO4cfrnv4iIHKORQiIScNqbihUVFURUVFC7tz8aCtntXhoamlpWDzudFcPi44NbHmvo0H40NMCGDW6tPiYiIj1OTadFRKQthUIiImfgaBPoo4FR61FFrcOfk4VO0Dx6acMGd4+PSIqPb+4nYTTS0lsiNDTohD4T+/cfG0nV+jmdbhgmIiL+QU2nRcRX6qqr8Xg8vi7jOCEGA+Emk6/L8CmFQiIifqC01EtubtdHDLUOd1oHT3Fx/Vq+CY6NDe6xb4WPjrravt1DYWHnp+SJiIjv3HxzqJpOi0ivu3/KJezN3+LrMo4z/srLeSj7LV+X4VOaVCwi4kOlpV42bHC3O6XNZAoiOjqoJdQZOrQfcHwQ5GtHRx7Fxxux2xs0ikhEpA9Yu9atptMi4hMdj5v3DX+rxxcUComI+EBhoYcNGzxUVzcRGxvMyJHBxMUFt4zwORq29CVXX23k5ZfrfV2GiIicQmmpl+JiLwkJfe93jYj0XUH4Xwjjb/X4gkIhEZFetn27h+jo5qWBT9ZnyJ8d7S1UWurFbm/Ebm/urSQiIn3D2rUuEhLCfF2GiASQoKDmH3/ib/X4gkIhEZFe1teWA25upN1EebmX/fsbj2s8LSISKNzuGiori7Hbt+JyVRMRYaF//wRiYhIwGCJ9XV6XVVc3aYl6EelVQQRpZI4f0m8BERFpYbd7KS/3Yrc3r6SmAEhE+rKamoPU1R1s+b/LVU1l5W6C+9V1eJ+mpmCGD7uaESOuwO2uYdu2ldjLv6Ti8D4AogaYaGz0EBxkwLmluuV+gwaPISx0GCaTheHD04iMHNTjz+9MbdyoUEhEek8Q4G+TVhVSKRQSEQlYzVO/vEdGAnnbbXYt0pdUVhbjdtccFwDU1BxkwIBziIubcNIRHWVl69m/fx1lZWtxuxuINJmJjBxEWGg8JpOFkJBI+vdvvn9MTEIvPzPprD17PmLvvvc47CjB7T51j7O4YQNOuMzj8bBp05NUVu5iz54PCA5pImawCWv8ECKi2063iqOh1kVNZT1ORwl2+w68jU14vV6SkuZ007PqORotJCK9KSgoiCB/m6/lZ+X4gn4DiIgEkOJiL4WFboqLFQBJ32K3bwWgoqIYj6cGp9NGXb0Nj6eSw479x902LDyM0IhQGpvcBAcHUbVvKwUFKwGINJmJiLAwMC6ZmJgEDh3aQlnZWmpqHMTERRFnjcYQFkJ9jQuv5wAVtXs5XB2Eu95Dfd3xIcNgSyLexnAGDjyP8PBBLVOJxHciIwdjHjAZ84DJLSHhCYIam69z1RMVG0FohPGEm4RHhlFU9A5xwwYQFRtBo6f5mGnfVwGAu95NU2MQrno39bXH3hcREWas1pl9IhA6SqOFRKS3qNG0f9JvABGRs0R19fHNnqurvVQfmdnQ0NBEcXGjmkFLn7Nt+4tsK3ij5e+mmCiCgptoCvISFhFKPyNYhwwBIDTCSL+QjgemN9S6qHe6qKvZy96yEgoKnIRHhdPfYiLePOK4+544IqRZo8dLQ62r+f815RACu/ftwOumJTQaNHgc0VGjiYlpHqHUF6YRnS3i4iYQFzfhpLfZvHkp9kPbiBs2oN1ACCBmkAmTOYJ+IcHs2FBCo6cRgEGDR+H1hhITbiEiYjCRkc0/4eGD+uzrrNFCItJb/DEU8rfpbL6go7+ISB/RNvTZtauR8vITR/xUVTXhdCr8Ef93dPRPR8LDBzEw7gLsg7Zy6GAhMQNNxA3rjyHs9P75EhphJDTCSAymI5cM7PI2+oUEtwRGUeaII5f2B44FRrWVB7DZS9i95z08bg8GQxj9B4xiYFxyn+k1czYbYB6OYW8Y9n2HcTrqCI0MITQylMiYMBo9XuprXLgb3DQ2NFFTVUejp5Hrrnu1TzaT7iyNFhKR3uCPq4+JQiERkT4jKirouCXs4+NP/G5j40YPdru7N8uSAGS3b23p23Po0Fd4Gh0cduwnMjKW6dMf6tQUqk1f/IU9uz8+5e36DxiOdXg61uHplOzNZueXzeFQzKAoDMaQ0w6IesLRwKj1KCN3vYfysgoO2Qo4dLCA6OiRCoV8zDr8aqzDr6ayspiKil0cOrQF+6F8Du5unoYYETmAmOjxxAxKoP+YUURGDj6rAyHQaCER6T3+Fgr5WTk+oSO/iMhZoLTUS06OS9PDpNscXX67oqKY2loblVU7qTi8q6V5b1h4GCGhwYSE9cMYacQ6ZAjV9ho++eR+Jk68kxEjruhw2xs3LmZ/6VoSJg3tcPoONE/3OnyggsIdK2ioa6D/gOEMGTKNysoiSr4uO2n9Q84ZSMwg00lv09Nqq+px7K/EWVFL3MDxJI2/9ZRTm6T3xMQ094A6+l6tqTkY0IGdRguJSE8Lxv+maykUUigkItKnVVc3sXatS42jpdusW/e/lJWtb/m7KSYKb5ObiOhw4qxRGMIGdNhvJyI6DEN4FZs2PcmmTU92+BghBgPWcwefNBCC5uleloQ44FhA5Kg4SENdA5GRsURExBMTM5K4uPMwGo8FQPv3f8b+sg9bRhH1Cwk+5WN1p8pDTioPOKmtrsM64hJmTLstoMOGviLQXyONFhKRntbcU8jfYhh/q6f36agvItJHbdzoYfNmNy6XryuRs8no0ddTVraeuGEDiBvev8v3Nw+JJmpABG6Xp8PbnM60r9YBkbveQ/XhWhqcu9m9ZztFRe/Qf8Bw4odcTFzcBJKSvk9Z2boORxNFxUQRYjTQ0FALQERM+LHaQkMwhB6rrb7Ghbfx+NC1zllPU2MTwcHBGAzNAVnV4SoaPZ4jzy+UIZaLGH/R9wM+aJC+RaOFRKRH+WFPIX+rxxd01BcR6WPsdi/Z2ZoqJj0jLm4C48bNpmhnZofLdZ+KIaxne/0YwkIwD4lu+XttVT21lU6K975FQcFKDIYwBg6cQGLijQwZMg2jMZLKyuKW21dUFONyNS/N53Y7qazcDUGNBAc3UOk4TG3NoZbb9h8Qj9EYRlNTP5q8oQBYzCMxGI6fmjbK2rwKFTRPSzrbe9DI2UmjhUSkJ/lj/uKPNfU2HfFFRPqQjRs9bNigRtLSs5KS5lBVXcTB4m3Ej4076TLv/uBoc+e44c0rgNVW1VNdvg3Hjq1s2fI8kZGxxMZOYujQCzu1ZLlIINuypVGhkIj0iCAg2M9iGP+qxjd0xBcR6QMaGiA7u4HSUvUOkt5xweRf8tFHP6d8bwWDEsy+LqfT+oUEE2WOaFku/uhUs0rnBkrW5QAwaPA4hg+76qTNsEUCVXm5l9JSb7srXIqInIkg/G+6VpC/FeQDOtqLiPi50lIvL79cp0BIepXBEMmFFz5MtaOevdttuOs77hHkrxo9XtwuD95GL55GD2ERzf1/Dh7YTrljo4+rE/FfmzdrRKqI9IwgP/wJdBopJCLix7Zv97B6tf5xLr4RE5PAhdMfZvOWp9mz1UZ/i4kBlmi/nE7mrvdQX+uiodaFq9aNq85DfW09AAMHjcZiHktk5BD690/Q9DGRU9i920t1dRNRUTpdEpHuE0yQpo/5IYVCIiJ+bMuWRl+XIAEuLm4CV6Q9zZ49H7Ft+woOl+0ndljMcY2efcld72H3V/tp9DSPpBs4aCwD+4+m/8hz6N9/FDExCV3ept2+VcGRBLzNmz2kphp8XYaInEWC/HL1MT8ryAcUComI+Knq6ibKyzVlTPzDiBFXMGLEFRQUrGTnrrepOuBkwNBoYgaaTn3nHuQorcRkimfKBb/uVABUU3OQurqDuN01VFY2r0JWWbmb4H51HLAVtdxuxMhLuWDyL3uydBG/VljoYepUA6Ghvq5ERM4Wil/8k0IhERE/VVysUULif5KS5jB69PXs2PEvdu56i8oDTuKs/YmIDuv1Wmqr6jl8oIpLLnnglIFQZWUxH310T8vfQwwhREZH4nY3EBEdTiNgPXcI0NysuuTrtQAKhiRguVzNwdDEiTpdEJHuEQQE+1ky5Gfl+ISO8iIifmr7doVC4p8MhkiSkuYwYsSVbNv2CiVfr8bUP4KYwVFERIf1Ws+hijIn1hGXdGqqV0hIJHEDx1NRsQPz0FNPf7OeO5i9BZ9RWbGb+PgZxMQkEBEx6LSmo4n0VVu2KBQSke7jl9PHfF2AH9BRXkTEDzU0oKlj4vciIwcxZcp9jB//fb7a+iz7C5tX9IqMjsQY2Y+I6PAeC4kqDzqpc7q4cOptna71kov/yObNS9m58994PV7ihvfv8PahEUaGJw2iuryKktK3qd/hwuNuXoFt0OBxREeNJibmHEaMSOuW5yPij6qrm9i+3cO4cTplEJHuoRDG/+gILyLih4qL+97y3xK4IiMHMX3aQ0Bzk+aDB7dgL9/M/sJtzdcfCYkGj4w9o8epdtRSW1WHq6aRmqoaxo2bTWTkoE7dt6bmINu2raSkJAdjmIGImFNPdwuNMBIaYQSaG1pXH66l8mA1Bw9s5+CB7QAKheSsV1jYqFBIRLpFkB+uPuZv9fiCjvAiIn6otFSjhKRvioubcGQ61xzc7ho2blxMWdl6GupCTjsUqnbUsr/wANC8utjwkZcwcOCELk3l2rPnQ0pKcgAIMfajtqoed4MHQ2hIu/2Q3PUeaqvrqa2so6GmeXl7gzGMgQMnEm1NZNCgiVqhTAJCaakXu91LXFzvTAsVkbOXX04f87N6fEGhkIiIH5oxw4jd3qApZNInud017Nz5Djt2vEm/EBhyzkBM5ojT3t7h0iqs1suZMuW+097G6NHXM2jQRCoqiqmpKaOyaif2A8W4XfUAhEWEEWIMJrhfMDWV9TR6PEREDiAm+lxGJE7scgglcjbZssVDWprR12WISB+n/MU/KRQSEfFDoaFw/fWhrF3rorBQDaelb6msLKagYCWG0BBirbFEnUEgVFtVT211HeMv+v4Z1WQwRLYaxXTM0aXpKyqal6evrT3A6JETiYs7r9NT00TOdsXFjTQ0oOXpReSMBOF/wZC/1eMLCoVERPxUaChHvplVMCR9S1zcBKZP/y9K9q5if+FGQgwhRMVF0H9QVEuPns6qPOAkfuiUHgtoOgqLROQYl6u51516C4nImQgK8sMl6f2sHl/QkV1ExM+lpRmJj/ewerXb16WIdFp8/HTi46fjdtdQWvo5RTvfoHjzPsLCQzHFRRATZ8IQdvJ/hrjrPVTaq5l0yY29VLWIdGTLFjWcFpEz448jhUShkIiIT1VXN2G3N1Fa2ojd3nH/oOrqpl6sSqT7GAyRjBhxBSNGXEFNzUHKytZTVPQG9r17iYgKJ2awCdOACLweL27X8avuVR6sJqb/MI3iEfED5eVqOC0iZyboyH/+xL+q8Q2FQiIivchu97as5FJa6lXYIwElMnIQiYnXkZh4HZWVxeze/QFlZWspK9rT4X2mT/9FL1YoIiejhtMiciaCAH+LlRUKKRQSEelx1dVN5Oa6KC314nL5uhoR/xATk8CkSfOYNGkedvtWDIZIre4l4ufUcFpEzpifpTB+Vo5P+FtQJyJy1omKCiItLZQZMwzExuqwK9JWXJyWexfpC442nBYROR1BQcf6CvnTT6DTSCERkV4QGgrjxoUwblwIpaVetm/3aEUxERHpc9RwWkROVzAQ7GcxjH9V4xs6oouI9LL4+GDi443MmAGFhR42b/bgdKq3kIiI+D81nBaR0xUU5H9LwPtbPb6go7mIiI+EhsLEiSHcemsYRvXtFBERP1ZTc5Cionf4/POFzJ37HdauXevrkkSkzwnyu/80VkgjhURE/IIaUIuIiD9xu2uw27eyf/86yh351DgdhIaHEhoZQlCIl48/+ZgZM2b4ukwR6UMUwfgnhUIiIiIiIoLdvpXS0nXYy/OpOLyXEIOByJgwIuOMWEYPxxDWfOpQ7ahldc5qfnH3LzCZTD6uWkT6Cn+cPhbsZ/X4gkIhEREREZEAVVa2nv3711FW9hludz3hUeFExoRhjR9CRHRYu/eJMkcQYghh7dq1XHXVVb1csYj0Vf44Usjf6vEFhUIiIj5mt3t9XYKIiASQtkFQTFwUcdYoTObB9AvpXMtRU2w4r2e+rlBIRLpEIYz/USgkIuJj6ickIiI9rTuCoNYGDI6mePMebDYbFoulByoWkbNNcJD/Tdfyt3p8QaGQiEgvaWiAr77y0NDQ1DI6yG73KhQSEZEeUVlZzI4d/+q2IKi10AgjEVHhvPnmm9x1113dVLGInM2ap4/5Wwrjb/X0Pi1JLyLSS0JDwWSCLVs8lJZ6KS1VICQiIj2rzPYZoVHBjJ46giGj44gZZDrjQOgoU2wEH3/ycbdsS0TOfkF++hPoFAqJiPSiceNCGDu2n6/LEBGRABATk8AFkxfgdNRSW1Xf/dsfaMJR7mDt2rXdvm0ROfscXX3Mr358vVP8gEIhEZFelpZmVDAkIiK9Ij5+OuPGzebArnIaartveGrlISclX5UB4HQ6u227InL28vWIII0Sap96ComI+EBamhG7vYHycq08JiIiPaem5iB19WV43B6qy2sJjTCe9rYaPV4O26pwltfR6IFzRt3IiBFXMmbM8G6sWETOZv4WxPhbPb6gUEhExEeuvz6Uf/1LwZCIiHQ/t7uGnTvfoaBgJVEDTFjPHUJEdNjpbaveQ6XdSYWtGqMxipHW7zB69PUYDJEAbN/uJiEhtDvLF5GzUBBBBAf5VwzjZ+X4hEIhEZFeUlp6YvgzdaqB7OwGH1QjIiJnq+3bX2PHjjcJ6tfI0LGDiTJH/H/27j26jfu6F/0Xb2DwGAAESIIUCVEkLYmiRTuWY8lS1EZqI+euuE7cNPVS07Sn58Rp4pyV2MlxfdP0lbpebW/j2D1J4yS+bV5V3aR24qjr1Eks+UYKbcsRJYOiKMokBT5EgMSTA2CGAGYA3D9g0KRI8QlwBuT+eDFeEcHBJk2BwJf7t/ear5URshi7PAmD3o6uvQ/B6z264DYjI3lkMsWFCoQQQgBh6Ae471MTePJnD+NWuYtZBoVChBBSZr/6lYjeXok2ixFCCNlQo6OnMTDwPWTu0YOEAAAgAElEQVSyHJyNLJwe27qul5PyCPmnUVd3O/bf9edL3tbvl7BrF720IITcnBLn+CxXTz47iWce/Wt8Z2gvvv2fn8Du2fdE8ZOvfgfPnu5HIKVFc9ut+MQjf4hjbebKFlwB9MhNCCFlduedOuzdq0NvL4VDhBBCKi8S6cPA1e8iFhuGvc6CBk9jWdbOj1+ZhEHrxh3v+tyyt/X7cxQKEUKWVH2h0Az+68l/xOv6bXDeMI7t3De/gqfe8OCLf/8PuLNeQt9L38OffvafwH7389jvVNpnuTTaPkYIIRVgMBTDoY9+1IR9++hJMiGEkPLj+RDO/vIxnDnzBaRz4/B21sPVZC9LIBQcCiOXUePwe/6f2dlBSykdISOEkJtRYlRy85pE+P7jq/iXyffgseMt896Tz17Gv70UxX0P/gGOtNlhtbhw4MMfwwPeQfzgpYnFrxa7jMc+9il89ptvQSjrZ7B+FAoRQkgFGQxAS4sG+rUveyGEEEIWNTb+XwiH+qHRapGXCohNcogFE8hJ61tgEAsmIHAiDh/+2xUFQiVXr0rrul9CyOYm9/r51aykn3zj+/jLn7D4i8fvgVefm/e+7OQ4/KkmdHXMfXysQVeXA4NvDi8IfXKpt/D4o19D4t0P4YkHb8Hap7xVBv36mhBCKigSyePFFzN0hIwQQkjZ7d71B3C77gDPT4HnpzAw8BwAwGjWr3nTGBdKITQSxR13fAYs27L8B8wxMJDD3r308oIQchMqlfLWfS1ST3rsFB59MoiH/v4x3GpRgb/h/blUFAJMcFjm/7nNyWImxiEBoDTRTUpdw/9+9GsYb3sQT316j+ICIYBCIUIIKbtAoNhCH43m4fOJFAgRQgipGJerEyZTLcbGfw6tTgdno23NgVBGyCI8No29ez++6Jax5USjeSSTBVitCnvRRwhRDKU9OtxYTy51GX/5xZ/j0CNfxG80r+9glVqfwHe+9DTOTXbg2ae6sL7R/5VDoRAhhCwhEskjEMgvGexEIjlkMouvnCeEEEIqaWjoJK5c+R4MZjW8nfXQGdf29L60er6x4TDa2u5dcz1+P3ULEUIWp8RB0zcaOf0T/GwsDP0XP4t/nv3T4i95/9v7LuLOD/8JvvxbNWAwhHgKgPOdj03EOJic7Gz4k0uNI13/m7h78mX84zcP4SvUKUQIIdXJ55OQShXkLoMQQgiZxfMhXLj4JOLxQTgbbetaP5+T8sVAqPEg9t3x8LrqoiNkhJClKC0UurGelns+hf98tzjvz/j+F/CJJ3k8/uzvY5elBnqLiJ2WF+Hr5/Hrh0pnyKLw+eJo6moGA0AAoNLvwUOP/C6ah2z4H5/9Br5121/hM4ccG/BZrQ4NmiaEkCW4XGp85CNG1NTQwyUhhBBlGBo6idOnP42Z7Ai8nfXrDoQCVyOwmBvRtfeT664tGs0jEqHOWUJIdVLrWTTUu254M8OgZ+Cod8FpUQG4Bcc/XI///OYJnBnikclyOPeT7+K50e348G9tX3BNtu39ePzTO/D8334dpyeV94tmepVDCCHLMBiA++4zYOdOjdylEEII2cJKK+j7r3wbjkYztu2uW/NxMeCdQEirqsHhw3+3qk1jSxkYyC1/I0LIlqNSqWZnTSvlba2tS7cdfxifP5TCU49+Bgfe9wi+9BMVHnr8kzhSv/gFd97zR3j43WE88cV/x5jC5o2qCoWC8qIqQghRqNOns7h6lZ7sEkII2XivvfY3iMR60NzhWVcYBBQDofErkzBqXXjPe/6hbIEQAFitKnz0o2sbdk0I2bz+7egxTPddlruMeeoOvwf3//Df5C5DVtQpRAghKA6UzmSWv92RI3rceivNSiCEELLxGhr2Q6PRKjoQAoBkskBHyAghi1Ip8G2ro1c2hJAtLRLJo7tbnN0c1tCgRkuLBg0Narhci+fm0Sg90SWEELLxGhr2o6fnaWSELAyMfs3XmboWhTpvrkggVDIwkMOhQ/T7Z0LIOyiAUSYKhQghW5bfn8dLL81vDwoE8rMBkdWqejsk0qKlRT3vNoQQQshG0+nMYO3bEJ/iUN/iWtM1MkIWiWgK+/d/oWKBEACMjORw6JCuYtcnhBBSHhQKEUK2rJYWNT75SRMCgTwmJnLzAiGg2P5+9WpudoZQS4saej39joMQQsjGikT6EAi8hmDwVfB8FIxt7fN6DIweVocFgcBraGjYX8Yq50smC/D78/N+qUII2dpKg6aVRGHlyIJCIULIltfQoEZDwztPWv3+PHp7xQUdQX4/dQgRQgjZOD7fNzE29jJEMQ2rwwKz24DatsZ1HR0DAEeDFWOXX8Hu3b8Hs7m2TNUu5PdLaGlZX62EkM2jOMNHWTGMsqqRB0X3hBByg5YWNe67z4B9+yg3J4QQIh+dzoJ8QUL7nV407nLD6bGtOxACAMZmhNVhwZUr/1qGKm/O76dtnYSQd6jejoSU9UaxEIVChBByE3feqcPv/I4BFgv9sCCEELLx2tvvg1qtQSomlP3ajgYrxsZeAc+Hyn7tkmyWumwJIfPJHwLR5rEbUShECCFLcLnU+MhHjNi+nR4uCSGEbCydzozWHR9CbIIr+7U3rltIquj1CSGErA+9yiGEkGUYDMD732/Ae9+rg55GIxBCCNlA7e33IZPOYuRSAJHr00jGBGSEbFmuXeoW+mX3owgGz5Xlmje6ejWHTGb52xFCNj8VAJVKWW+EBk0TQraA3l4Je/eu/+Fu1y4trl7N0Up6QgghG0anM2P//i8gHO5FIjmI0NQoxGwaAMDWsIA6B4PZAKNZv+qtZIzNiNbbmxAeH8drr/0NGLMLu3d9FF7vkbJ+DoEAbSEjhABQKW+GDwVDFAoRQraA7m4RgUAO732vAQaD3NUQQshCosiD4/zg+RBmZkKIx4dhNtfC7d4Lj+cuucsjMmto2D9vfXzp+yQU6gXH+THDjSM0EgAAGIx6NOx0r3ggtc6oRUN7LdxNErhwCr7er2Fg4LvYtetjZQuHaAsZIQSgOT5KRaEQIWTT0+uLgy4jkTTuuUcPl2vtv61saFBTpxAhZB5R5DE8fPKm73e5Ouf9/7nBTy4fR2jqrdn3GU1GaA1qaI0aCNP9GBo6CZ3OCLe7Ex7PIbhcnRVdIU6qg9lcC7O5dsH3ViTSh6HhFzB17TIad9VCo135zzudUQtXkx0Ojw3xYAK+3q+it/cZtLffj9bWe6HTmddcL20hI4QAbwdCCkuFFFaOLCgUIoRsei5XMchJJgv44Q8zuPNOHa2bJ4SUBcf58frrX4KYS0Cr10CvNy24TX//iXn/32gyQmfUQmNQQavTonmPBxqtetHOjtoWJ4REGsnoFfh634QkSrA7mtDgeQ88nrvAsi0V+9xI9XG5OsGyLTh16iFEx6dR2+Jc9TU0WvVsOMSFU7jmfwGDgy+sKxwqbSGjI2SEEKI89KqIELLl/OpXIiYmcrjnntUfJ7Na6fcJhJCigYHn0N9/AjaXGQ0tDTftyqhvt6/5PjRaNaxOBlYnAwDICFkkoyn4x3+E/v4T0OmM8HjuRmPjAbhcnevq5iCbg05nxoEDf4FTpz6DgqoAjVYz+z61Rg2jeX74qNNroTMufEmg0arh9Njg9NjAhVIYuvZDDA4+D4/nILq6Pr7q7zU6QkYIARQ4U0juAhSAQiFCyKaVyQCXLkmIRBYe9woE8vjhD1d3nCyTKf62kxCytYkij9fP/TXi8UF4Wt1gay0bdt8GRg8Do4erCRDTEoREGhz3KwTOn4UkinDXdsBTfxAez110zGwLY9kW7N//BQwN/QQQAZVahEolIptNz84eWoqVtUKr1yGfz0EUi6vDdHo9+ASPsbHT2L37+BpCITpCRshWp8SZQjRoGlAVCoWC3EUQQkg5lcIgn09cNsTR64GDB3XYtauYkSeTBSSTBUQieWSzwMREcZVuNEpzhAghxZktr732JWj0BTTeUrtoh4VckjEBPCdghssiM5OB2VwDj+dutLbeRwERuanSjKu5pqf9yGaTC25bW7sXLNuy5o60e+4x0BEyQrawH//m+5Ho75e7jHlchw7i//r3E8vfcBNTzjMZQghZp9WEQSXZLPDKKyK6u1f+MYSQral0XMxeb0V9i0vuchawOhkwNiMENo14kAOfiGJo6CS0WjM6Oo7LXR5RqNLQ6rluHGBdLnSEjJCtTZGdQnIXoAAUChFCqt5awqAbUSBEyNYSifQhEHgNieQgVCoJAJDP64FCcf4Ky26HXm+dvX0g0I0UP4HGnXWz830qISflkZfyq+5AilyfhjCdhpCcgU5f3FbWtv12uN2dNIyaKAZt7yRkq6NYSIkoFCKEVD2DAdi3T7voRrHScbDlTEzMn3WQyRSfvNKxMUI2h0ikr/gWvYDQ1AAAwOqwwGDWLXr7ydgw8lLx779OZwC0eXg7PRU5LpYRsuC5NFIxHkIiDQAwO0yw1VjAupefV5QRsoiMx6HVmrF//xfQ0LC/7DUSUg6l49krneVHCNlcKBJSJgqFCCGbmtWqWtHGsIaGd56gBgJ5DAxISCYpECKkWnGcH8HgOYQjbyIcKs4vMFlNMJiLK+AZm1G22nJSHkLinRBIzEjQMzowNiNcTQ4AwPRUAlP+KKb8EbC1VthrrYuurAeKw6eb93gwdS2Ggavfhdlct2x3UCTSV7EjQoQsZWAgh0OHKBQiZEtS0WBnJaJQiBBCUPzt5dWrubfDIJq/T0g1ikT6MDT0AsLhPohiGmabGTpGjcaddWBsxpuujN8oOSmPiatTEBJpqDVqMKwRrm0OMDbjgg4kxmZETsojFRMQDU4jHkzAwOjhbGBhcTALPhfGZkRzZz1C/hhOnfoMOjqOY9euB+bdRhR5DA+fxODgC8Wvj9kJr/cetLbeS6vsyYYZGcnh0KHFO/QIIZsfZULKQ6EQIWTLymSAkREJAwM5mnNASBXj+RAGrn4PoyO/gKPOhtodLBhbnewh0I24cArZtLjiTiWNVg221gK21gIxLSEanEZwKAxPqxts7cJjZRqtGp52Fyw1DN4a/AECwbO4691/AQAYHz+NwcHnodGq4Gg0w+pwIxkXcM3/AgYHn4fX+z7aUkY2BB0hI2TrUmIgRJ1LFAoRQrYgvz8Pv1/C1au55W9MCFG0gYHnMDj4AowW+Y+FLSce5GCvta2pRp1RCzPLYHoyCcsyg65LW8iGL4zj9OlPQxTT0Bt1cDXb54VJTo8NTo8NXCiFiamXMTR0Et7tv4a21vtpODWpKDpCRgghykGhECFkS0gmC+jtleD35+h4GCGbQCDwOi5d+gYyWQ7uZseinTPLyUl5ZITshgRJXCiFnJSHw2Nb+zXCSdhc5pt2QOWkPFJxAYloCnx8BmqNGgarGh7P0mFZqRtJSKQRnfgVRk/9Au7aDuze9VGaO0Qqgo6QEbI1qd7+R0mUVo8cKBQihGxapeNhPl+OtogRsklwnB+9l76BcKgfrm0ONHga13RMLBkTEBqJQsxIYGxG1LXU3HSQczlw4SQsDtOaj7SV5gs17qyb9+diWoKQTM8LgiwOExp31sG6TEfRjRibEYzNCDEtITw+ijNnvgCzuQYM0wC3+1YAAMu2QKczw2SqpaNmZM3oCBkhW5NKiYOmlVaPDCgUIoRsOlevFjuC/H4KggjZLESRx5UrJzA0dBIWO4PW25tWtR4+I2SRk/LISXnEgxyERBqubQ5YaxhM+aPw+ybg8Njg2uYo+ywiIZGGkEij9famNV8jFRMAFOcGRa5PIyflIHBpZIQstHoNLE5mTUHQYnRGLRraa+FukpCMC8hL1zFyfRB6vQlvDSYhidK829fVtyGfM4Flt0Ovt84GRjqdmY6hkZsKBCgUImQrUloGo7R65EChECFkUyjNCfL7c8hm5a6GEFJOpaNiUj616rlBGSELv29iwZ8bGD0Y1ji7zj0ZEzDlj4ALJVHX4gLrXv1xtMVw4RSm/JFFN4ytRizIAQDGLgehZ3TQG3WwOs1oaHdXrMNJZ9TCOXvczT7v32JagpiVikfw+CigBSZjw9CodMgIGaRn0rPXaWjYB4/nEBoa7qItZ2TWwEAOe/fSSxFCthIVlBfCKK0eOdAjMSGkalEQRMjmxvMhXLr0DAKB83Btc8DhqV91F09OKnYMzu0sKh6PimHscnB2zTvrtoCxbUM8mEBwKAwulATDmm64Vg4Z/p0HG4Y1wepkFg1lxLSE4HAYaT4Lp8cOV5N9wW1Wo4ACXNsc675OueiM2tmv5826k0ozm6anrsDX60NPz1NoaNgHr/cYPJ67NrJcAiAYPAeO80OrNcNuL3ZwVfIYoCjyCATOIRB4DR0dxxd0jUWjeSSTBVit9JKMEELkRKEQIaSqUBBEyNYwNHQSV658D0aLFi1djWvuhhG4NPSMbl6XztzjUeHxGKb8UUTG43A1FUMX1m3B1GgUiWgKWq1m3vUY2ztBUSKaQmQ8DrVGBavTDIY1weJgEJ9MIDIeB2MzomVv47o6hEqyggimRbmb1Raj0apn5xQBxTlOyegV/Oq8Dypo4PHcjcbGAxQQVVAweA4TE68hGHwVBeRgYS2QRAmp3iQAwGyuwbFj/1LW++T5EK5cOTF7n1q9GqdOnUNz8xF0dX18XreY30/dQoRsJdQppEz0KEwIUTwKggjZOiKRPvRe+jpSqSCcjeyc40vvKM0H0mjVKwqLbgx2SkrhUHHOUGI2HHJ4WGy7YaDzYlxN9tkB0Mk4jyl/FMGhMNQaddnm+2wmVicDq5OZ/Zpx3K8w9tpp6HRGeL2/Ca/3N2SZQcRxfgDYNPOP5gZBopgG67LC1Wydt6GPCxkRHA5j166Plu1+R0dPY/z6SwhNDcDqsMy7TyGRxuTwWQRfehXt7fdj164HANARMkK2GiVuHyMUChFCFCoSyePq1RytkCdkixBFHj7ftzA2dhqsy4qW226+VWy0L4B8bv7jQqkbxWDWQ6PVQGfQQmfQIhnjYXUuPcdGo1XD1WSHw2NDPFjs8okHObiaHMvOFtJo1bMr3YHii18Doy/7sOrNZO7XrLbFiVRMwMTUyxgaOgmzuQZe7zE0NR3ZsO1mZ878CUSxOAPJbHHCbK6FzdoOvd46u23N5erckFrWiuP8GB19GcFgN3g+NhsEWZx1C74XuVAKweEw7rjjM/B6jy56vUikD5FI34ruO5tNYnT05yggB4bVL9rZx9iM2HH7NnChFN4a/AFGx36KWzs/AWA/HSEjZAtR4vYxpdUjB1WhUKBXW4QQWWQypZkCeSSTQCSSQyZT3EhCCNk6Bgaew+DgC9AaVXA325ccJJ2MCZi4OoX2O70Ail1DQPGYGACkhQzyUh7ZtAgpmwOAVc/iKXUORa4Xj4C5mhyrGm5dbmJawvDFcdRur1m0c2qzENPFbWfcVAqZmczsgGqv90hF73d09BR6ep4uzpfysBAzEtJCBhqVDlyUm72dTmeEw7kdRkMDLJZ62QOjd4KgV8HzUVgdFlicJliczE1DyZUEQgMDz6G//wQsrBVa3fzfH0tSBvn8wp/RrNu65P3ONffvV11dB1wuGx577CHU19ev4LMmhFSzn97zAaT6++UuYx7nobvx3hPfl7sMWVGnECGkoij4IYTcTGmrWCbLwd3smHe85WaS0dS8F5+lsOZmoU1p0PRqlDqHWLdldiA1YzPC0+ouy3yg1VJr1bDXWxEaiSIV42UPqSqltO3M6bFBSKRnB1T39j4Dj+dutLffV5EjXl7vUZjNdXjt9b9Chs+itsU5+z4PnLMDs8W0BDEzhVhqDByvQ3//O4GRd/uv4Y53fa7std2I50MYHz+N0dGfguejMFlNsLoZ1N/StOz3ppBIIzgcBsu2wGyuQyTSN2/QtCjyeP3cXyMeH6zo8ce5f78i4+O4fDkJn+8QhUKEECIT6hQihFRMJgP88z/PyF0GIURheD6ECxefRDjUD3u9Fe4m54IOg9LK8xtdH5hE3XbXigKkciltEhMSabBuC1zbHLKEQ6WtaYkID8ZmRF1LTcXW0StFaf5QKjaDZDwFu6MJzU33wOs9Uvb19hznx5kzj8Fk06BuR82Ku16K83Ki2LbtYEWCIZ4PIRg8h7HxlzAdHy8GQTUMrA5mVd+HOSmP4GAYWq0B05H4gvfrdEZojSo0tLk29Ps7MBjC7tZOPP744xt2n4QQefz0ng+AV1inkIM6hSgUIoRUzsCAhFdeEeUugxCiEKLI48qVExgaOgmLnUFdS82iLz5zUh7DF8YWzA0qab/TK8vMHiGRRmQ8jjSfgavJKdtRLqWEVBtNTEvgwikko8Ls8bLdu3+/rN1DHOdHT8/fIy1F0LS7fsXfZxkhi7HLU2ULhjjOj3C4bzYIMpgMMLF61HjsZf1vPTd8TfNZWb6nS0dCf/7zn2/4fRNCNtbPFBgK2SkUouNjhJDK8ftzcpdACFGI0dFT6O39BlSaHJr3eJY8/hQYCkFr0GJH17YNrHB5jM2I5j0eTPoj4EJJ2UIhnVGL5j2e2ZBq+OI4WLcFtdtX1t1SrXRGLVxNdria7EjGBKSiV3Hq1Gewd+/H0dZ2b1nug2VbUF//HvhHf7Sqr6WB0aN5Tx3GLncDwJqCoRtnBJltZugY9aKDm8tFZ9TOhkxyHUks3W93dzcOHjwoSw2EkI2hUqmgUthkZ6XVIwcKhQghFUNzgwghwDuDfFcy8DkWTGAmkYG307NB1a2ercaC6cmk3GXMhlRCIo3AYAjDF8Zk7WDaSKX19gxrRP+VbyM42Y133f5IWTaWDQ//GI7GlR9PLHXbiBkJRqsOoyO/gMOxHTtafnvZjw0GzyEc7p0NglYzI2iz0GjVYF1WCoUI2QJUUN62L4WVI4ut8dOGELLh/P48slm5qyCEyK0UCHla3cvOAcoIWUTGY3A1OTf9rJxyYmxG2GttiAU5mNnNN4B6KWytBYzNiCn/CE6f/jTuuOMRNDTsX/P1RkdPoYAcWPfS36uT/giSkRnkpHfmXjFmB8zmZtQ0N6DG+a6bfizH+TE4+CKCwVchimmwLivMbv2WCoJuZLDo0XOhR+4yCCFkS9qaP3kIIRU3MUFHxwjZ6lYTCAFAYDAMhjVtiU6XcuJCKUSux9G4s25Lhmk6oxbbdtchMj6N119/Yl3HyYavvQBLjXHZo2NarRYWSz269n5q3gavlZiYeA2TobNwNdthcdZt6iN/K2V1MBgeGcfw8DBaW1vlLocQUikqBR7XUlo9MqCfQoSQihgZoVCIkK1stYFQZHwaYkaCp9W9AdWtTyl4yQjyt0NmhCyCw2F4Wt0VWyFeLVxNdtjrrQhOnlnTx/N8CNPxcRRuMuB8LoY1gpu+Dperc01H1rR6DdhaCwVCb9MZtWCsJnR3d8tdCiGkglSqt4+QKemNQiEKhQgh5ReJ5JFM0mJDQraq1QZCQiKNyPU4PG3uqniRXKoxJ8k7Ny0jZDHaF4DNZV7R13krMLMMwqGra/tYcy0OH34CAidh5FJgyf++pWAwEulb9f1wnB96RremGjczvVmLM2fXFugRQqqDSqXAN7m/KAqg/GdehJCqQwOmCdm6VhsI5aQ8gkNh2OutW77TZTVyUn72uF1D+/qHK28WpU1WawlrAMDl6sSR934VBk0drl28DiGRXvR2Gq0aZpsZgcBrq74PUeSh1dIEhxs56mwYHRnF5OSk3KUQQipG9r6gRd4IhUKEkLKjVfSEbE2lQKh5V8OKO1fC4zEUCgW4m5wVrq681Bq1rJ1CU/4IMkIWdd4a2WpQIo1WDZPVhFCod83XMJtrcfToV9HYcBhjl4OIBROL385pxMjoS3jpp3+E0dHTy143EunD0NBJzMwE1lzbZmZg9NDqtPD5fHKXQgipENm7ghZ9o2CIfk1BCCmrTIY6hQjZiuYGQozDsKKPScYETE8m0bzHUxXHxuYymvXI8FnZupvqWlxIvz1PqHmPp6L3lRGySPNZCNwMdEYdDIweRkavyE1ZOSmPQiGPSNQH4Pi6rrVv38Nwu/ei99IzSCczaLhl/rwrp8cG1m1BPJiAr/druDLwfeze9VE0NNwFjvMjEulDPD6MdGYC8dh1AIDRZITWpAbDsuuqbbNiWAPOnj2LY8eOyV0KIaQCVLP/oyBKq0cGyvtpTgipan6/tPyNCCGbSiDw+qoDoeKxsRBc2xyzR37Iymm0ajTtrIe/dwLBoTA8beUf0C3EMwgMhyCJEhizA66aOyHMTCI06YcopqHRamGy6GG0GhQRFAmJNELXYlCrzOja+8dluabXexThyEXEuPOLvl+jVcPVZIfDY5sNh3p6ngIAmG1maPSA0WZEs8cDA6OvuvBzo1lrLDh37pzcZRBCKkhpGYzS6pEDhUKEkLKiLqG1E0Ueo6OnMTz8AvQGI/R6E/J5PVDQwGRyw2Kpn3d7l6sTLNsCnc4sU8WEFIfmXrjwlWK4s8JACAACQyFoDVq4muwVrG5z0xm18HZ6MNoXhMofQX2Lq6zXF1IzkEQJdkcT9t76SbhcnbPv4/kQOO4apqevIRzpQWhyDKKYgUargYHRgWFNGxoUTY1EEQ8m0Nr6AXR0/N6ij4sc50cwWAwc5q6RX+pxlOdDGBs9s2w31txwSMpKs4OoyeqUAuLu7m4cPHhQ5moIIWVXOrOlJAorRw4UChFCykrJ84RKg0dL/w6HL0GlziAeGwOgwt69n4DXe2TD6woGz2F09KcIBM7DYDLAWsMAEJCDMHubaeEaItM5qNVqaLUGpPkZDA7+GPfc8/9ueL2ElHCcH2fPPgazQ7eqcIcLpcDHZ9DS1VjB6ipLz+ggJGYAyBtqGRg9tu2qw9jlIExmY1m3kLma7GDdFsQCHM6c+QLctR141+2PwGyunX1raNiP0jGtuUFRJPoGQpMTbwdFahgYPRjWBMZmLGtnWEbIYupaDGIaOHz4iXnBVYko8rhy5QSGhk7CaDbAaGKQ4pKQxCEXyL0AACAASURBVHc6WxmzC7t3fXTBz4ArV/4VVodlxTVrtGpotBQIrZVGqwbrslIoRMgmRZmQMlEoRAgpm0Agj2xW7irmO3/+KYyNzR8Aanc5kM3OQM/ooNVqUbuDRYbPoqfnKYyN/2z2Rc9qlIKdcLgPHs/daGw8AJerc8nfPg8Pv4hgsBs8H4OjzobmPZ4Vv/DwX+TR2vpB6hIisuE4P86ceQwWpw51O1Y+7FhMS5gaiaB2e01Vd1M46mzw+ybAhVKyr4NnbEZ4Wt0IDoeh1qrLOudIZ9SibkcN7PVWhEdH8NOf/g80Nx/B7t3HFzxOLhUUhcIXEI32Ymy8OGTZaDHAZDXAaDaAsRpX3E0kpiWkhSwyQhZZXkIilkRD4z7c8eufW/TxcHT0NHp7n4FaW7jhMfadMC8jZBGfKh796u19Bu3t96O19V4AQDD4KlzNtlV+1ch6GCx6XLh4Qe4yCCEVoMRQiFIhQFUoFApyF0EI2Rx++UsRly4pZ6ZQz4UvY2LiVdTtcEKjVS8buIhpCVP+KDJ8Hrt3/z7a2u5d+vZzjnvxfBSOOht0Jh0yqSx4Lg1JFNHQsA8ezyG4XJ3Q680IBM5h/PpLCE0NwGQ1wVrDgHVbVjVnggulEBlP4J5j36ZQiMhCFHm89NIfrToQykl5jF0OQqNVV3w48kaIBROIjMfg7WxQRMAVGZ9GLDhd0XqERBrxQBLJeAodHcfR2nrvqh+HIpE+hEK94BJXEQn3v91NpIXRqoPJYpzXTSQk0hASacyk0shlCkgLxRXx7tqdqHHejtravYt2B3GcH72XvoF4fBD2OsuKOtlyUh5cOAVuKoXMTAYs24J0NoCW2xpW9fmR9RHTEoYvjuOZZ55Ba2ur3OUQQsro7Ac/COHKFbnLmMd299048C//IncZsqJOIUJI2YyMKOfoWM+FL+P69W4076lb8YsjnVGLbbvrEAsm0H/l2whOdi/aNcRxfgwOvoixsdOzx70aOrwLgp1kTEAyegWX+/uQnim+kNHqdGBYPVq6Gtf8oi02waG56X0UCBFZiCKPs7/8X9Do83A1O1b1sYGhEAoooHFnXYWq2xilbhUxIyKfKyBwNQznNhYWByPrIGFXkx3ZdBajfYGKBUOlwIYLmTB07YcYHHxhtrNmpY9JLlfngvlEkcglTE8PIxq9iLHxiXm3t9sbUWO7De4de2G37wDLttz02nOPirEuK7yd9SvuQtJo1XB6bHB6bEjGBCRCYTgbqEtoo+mMWlhsZnR3d1MoRMhmowJ15igQdQoRQsoimSzg+99Py10GRJFHT8+XMRV6c1WB0ILrvN01lJoW0NFxHLt2PVDsCrr2PKbj47DYGbB11hUf08gIWaRT2XUfM0nGBExcncKxY8+u+ogbIeslijzOnn0M2VwIjbtqVxWABIfCSMYEeDs9iuiqWamclEdGyEJIpJEVRAiJNCRRgk5vBMu2wFXTBVFMYWz8ZRQKEqwuBs56VtYtXNevTkFMS2je46l4SMWFUgiPxeFtvgddXQ+W7bql2W+LdQHdzNyjYnU7nLTVropN+iNgjU48+61n5S6FEFJGv/zQByEMKKxT6MDd2P/P1ClECCHrpoQB06UXrDOZyXUFQsA7XUPJmIC3Bn+A/v4T0Op0sNQY0Xp706pf8BkYfVleCGf4LHQ6AzjuGoVCZEOVOoTSYhhNHfWrChtiwUTVBUJcKIV4IIH0TAZA8biS294O+/ZWuFy3Lvj719X1IEZHT2Fo+HkMX1x9cFxO7iYH/L4JpGJCxecdsbUW5HJ5RGO+sl53NWHQWo6KEWUrzuwaxeTkJOrr65f/AEJIdVCpoFLYUCGl1SMHCoUIIWWhhFCov/9fMT09CrPDhGRMQDYtrWiW0FKsTgaMzYhUTIDFKe/REACzL3Zef/0J3HHHZ+D1HpW1HrI1zAZC2dUHQlwohdBIFJ5Wd1UFQsHhMHbteuCmM2sW4/Uehdd79O0jpj/G2NVXYDQZYKuzrHp22HpEJ6bB2Mq7iWwpRrMeoZFxiCK/ocda13NUjCibgdFDp9fB5/NRKETIJkKnx5SJfnISQtYtkyluHpPb9u2/CZ3OAkGYwkz6OkJT4xCzxSNtGq0WZpsJKk0BOqMOBka/4sBIo1XLvl1oLleTHTqDFj09T0MUhWUHYhOyHjwfwrk3/nJNgVBGyM5uGlPS36GllAKh9YSuLNuCffseRlfXgxgcfBHD134EjWZjHkfEtIREhN/QQd6lx9Fw+NLbm8c2xsun/iey2elVbW4k1cNk0+Ps2bM4duyY3KUQQsqFEiFFolCIELJufr8yNo6xbMuiA0gjkT5ksylMT19bEBi5tjmq8qhB6cVl/5VvY3p6GPv2fVbmishmVFo7r9HnVx0IiWkJo30BWBwMnJ7qGNZbjkBoLp3OjI6O4xCEKUxzb2xIKBQej83b3rVR7C4HpqevbWgodOe+R3DmzBcgpiWgOr7FyCpYayzw+cp7LJEQIq/iSnplJUNKq0cOFAoRQtZNCV1CSykd/bjxxcrJkx+BwVwdx1kWw9ZaYLToMd5/Fj0XcrjjXZ+TuySyiZQCodWunQeKw5mvX52C0WxAQ3t1zL4qdyA0l9u9FxO+s2W95mLk6BIq0RqL3zOrxfMh6PXmNR07c7k6cccdn0FPz9PQGbXULbTJMDYjJtJT6O7uxsGDB+UuhxBSBsVQSO4q5lNYObKgUIgQsm5KmCe0HI7zQxT52Y024cibEMU0NNrVrdRezPZtO1DjcOHSwJvIitl1X281DIweZrsBoyO/gFZjLev2H7J1rScQAoCJq1NVtXp+aiSKeDBRkUAoEulDMNiNnCQhI2QrOldJri4hAFBr1ZjhA6v6mNHRU+jpeRoAUFu3C/V174HHc9eqhuh7vUcxPT2M0bdeQlPH+hYMEGXRaNVgXVb4fD4KhQghpIIoFCKErEsgkEd2Y3OQJfF8COPjp5HNJsElhiEIk+BTsdn3W1gr8gURjM2E2u01ZXnx9L7D7wcAZMQM+gY2rtU9I2QxdS0GMVPA/v1f2NBjG2TzKgZCfwKLU7+mQCg4FEaaz6Jlb6Psg9lXKh5MYNeuB8oaCA0NncTQ8I8g8BGwLiua91R281qpS0iuIK40bHqlBgefw6VLJ+BpdUNn1CIRncDVwe+it/dbsDua0Nx0D9zuzkWPBN+oq+tBiFICY5e7seP2bVXzfUeWZ7DocebsGXzqU5+SuxRCSFkob/sYtQpRKEQIWadr15TVJTQ2/l+40v88AECtUcHmtqDZ64FOr63YVpqpSBA1Dhei8UhFrr+YyPVpRMbjaGjchzve9bkN3fhDNq9iIPQoTKx2TYFQZHx6dvV8NW2BsjrKN+uH4/zoufAPSKWCsNdZ4bmlaUO+FmqtGnpGh8h4HIzNuOHBSCnwikT6lt3W5vM9jbHxM/C0umfnLJUC+oyQRXxqGsMjJ9Dby8NsroHHcze83t9YMiDq2vtJcNMjmBgIoXFXLQVDm4TVwWB4ZBzDw8NobW2VuxxCyHopcP2Y0jIqOVTPMzZCiCKNjCgrFNq96w/Q4DmMkZGfIxh8FdOTMYhCHnqzBvZaa0V+U//iz14o+zVvhrqDSKXMDYTWOgcom84CKECrr66nFwazDtHYRQDH13yNG9ejt9y2sZ1SGq0a3j0NGL0cwEjvBLbt2tijVBqtGibGBJ6fWjIUikT6MDbeDZPVAIuTWfB+A6NHfYsLQLH7KRkXMDH1MoaGTsJscaLGeRsaGw/A47lr9mNKHaKSlILAzyAVE6pm2x1Zms6ohcVmhs/no1CIkE1AgZmQ4uqRg6pQKBTkLoIQUp2SyQK+//203GUsieP8CIf7MDr2X+Cmr8NoMsBkN4CxmWBd5AWJklF3EKmUQOB19PR8eV2BUMk133WooELzHk/VdGsIiTTGLgdx//0/WdPHRyJ96On5MqR8CnU7nLIOPM5JeYxeDkDKSPB2NmxoMDQxEEZj3QfQ0bF0uMZxfrz22l8BmpkVd/WIaQlCIg2BS4OLJKHTGeHx3A1JSiAQOA+9UQd7vQ2s21I133dkZSb9EbBGJ5791rNyl0IIWadzv/vbSA8MyF3GPNb9B/Cub2ztx5fq+lUeIURRqmHAdGlNfVvbveD5EILBc4hEL2Li6nlodVqY7UZ42txyl7kkIZFGZGyauoNI2Ykij+Hhk+jvPwGby1yWTWHePQ3w+64jPB6b7fhQOt3bnU0rOfo0lyjyuHDhKUxMnIO93oqGpnrZAwmNVo0dXdsQGAxhtC+wocGQwazD2NjLcDha53Xy3IhlW3D06Ndw5syf4NrF62jeU79sjTqjFqzRArbWgtoWJ1IxARz3KxSQR/MeD20e28QcdTb4faNIpVKwWKgDjJBqpsTtY4RCIULIOkxMKD8UmstsrkVb273weo+AMRWPecwkM3KXtUBOykNIpJGK8cjwEtJCmrqDSNmVulsyWQ6NO+vK1jmXEbKQsjnYaqrjxZuQSCN0LQaGca7q79fo6Gn09j4DrVGFlq5GxW29amiv3fBgiGGNyPBJvPba3xRraNgHl+v2RTeK6XRmHD36VZw//xX4fa9g14HlB0qXaLRqsLUWOiK2RRgYPXR6Hbq7u3Hs2DG5yyGErAedH1MkCoUIIWuSyQAjI3m5y1iVUlfE4OALUGsL84acyi0jZMFzaaQTWSRiSej0RrjdnWhrPgiX69ZVrWgmZClzZ98Uu1vKN/smJ+VxfWAS9nqr4js3clIeketxxIMJtLZ+AB0dv7eiUIjnQ7hw8UnE44Ow11ngarJvQLVr09Bei+tXpzDaF8At795e8ftjbEYwNiNyUg2ERBrJ6BXEBvve3ii2Da6a2xcMjN6+/TcxNvZKxWsj1c1k0+Ps2bMUChFS5SgTUiYKhQghaxIIVE8gNDcM0mgBV7NVEWFQMiYUu4FSItIzGTCMEx7Pr6Hjlr10RIxUxNzZN+s5cjM+MAk+PjPvzxibEWJGgtaghbvJWY5yK6bUHaRWmXH48BMrPjI2MPAc+vtPwGJn4O2sr5oNaxbHxs5P02jVsDqZ2e6zYug9g6noKxgaOgnG7EJb64fg9R7Z0LpI9TKzDHw+n9xlEELWTXkr6ZVWjxyq49kMIURx/H5J7hJWjOP86O8/AQAwWa1ICxmI16XZtc0bdeyjtEknncwgEU0BABoa98HbvvjxCkLKZWF30Npn3wiJNPj4DBp31s27hsAVh85baxjZ5+rczFq7gyKRPvRe+jpSqaCiOgyXk5PySMUENO6sk7UOA6MvPs56ADHtABdO4a3B76G391twu2+VtTZSHSxOBsHhMHw+H7q6uuQuhxCyRjRTSJkoFCKErEk1DJkucbk6cfjwE+D5KfD8FDjODykTw9jlwdnbWFkrcgURDGuCzqCFzqCFgdGv+8VtMiZASMxgZjozpxvo19HRTt1AZGMEAq/j0qVvrLs7qGTKH4XNZV4wg0jpx8XW2h00OnoKPT1Pw1Fn2/A18+vFhVPQ6jWK2rSoM2rharLD1WQvdktGR2CxK6c+okwarRqM1UShECHVjs6PKRKFQoSQVYtE8shm5a5idVyuzkVfBPJ8CDMzIUxP+8HzQXCJYXCxSQh8ePY2bA0LqHPQGXUwMHrojdqbdhfNzga6oRuoibqByAYTRR49PV9GIHAerm0OODzr34zFhVIQMxK2ydx5shpr7Q4CgKGhk+jt/VZVdQfNFQ9ysNfa5C7jpkpHzHJS9RxHJvJhWCPOnD2Dj33sY3KXQghZI9Xb/xBloVCIELJq1TRPaDlmcy3M5tpFAyOO84PnpzA9fQ2CMIWZ9HVMjL/TXWRkjNDq1TBaDRDTIlLxNHKSBLu9Ee6aA+ho3wu3+1baGEY2XCDwOnp6nizrZqxSuOL0sFUzSycWTCB6nYNBz66qOwgAzp9/CmNjp6s2EBISaYgZqSp+A1pN3VdEPgxrxOhlWk1PSDVT4vExpdUjh+p4VkcIUZRqOjq2HizbApZtWXDMSxR5cJwf09N+ZLNJRKI+sKZ6tDXvpU1hRDaiyGN09DSGh18Az0fh2uYo62aseDCBQqEAh0e5nSclQiKNyNg0xEwB7W0fRkfH8VV9/PnzTyEQPKvIVfMrxdiMqN1eg8h4HFwoCU+bW/FH/AhZCmMzQqfXwefz4eDBg3KXQwhZA5VKpbgQRmn1yIFCIULIqm2mTqG10OnMNxxHW90LTkLKKRLpw8jIyxgbOw2jyQhLjQn1tzSVtZtHTEuIBadRt92l6K4OMS0hHkwgNsmhufm96Op6cNWdej0XvoxAsBtNHXVVGwiVOD02sG4LwuMxjF0OgrEZUddSU/WfF9m6GJsR3d3dFAoRUs0Ul8JUph5h6Ae471MTePJnD0PpKxUoFCKErIrfv7UDIUKUQBR5BALnMHzteUzHx8G6rGjcWVexgcLh8Ri0Bq2ij1FFrk9jejIJs7kehw//yaqOipX4fN/E9evdaN5T/YFQiUarRn2LCzUeO4LDYfh9E2DdFtRur1F0wEfIYhjWiJ4LPXKXQQjZ5AL93fiXEz/FmTcDCKcKcNR7cODIh/Hog7fhnX7pKH7y1e/g2dP9CKS0aG67FZ945A9xrK36xkZQKEQIWZWJia1xdIwQJeI4PwYHX0Qw2I0CcrDUmNB6e3m7ghYjZXOwOZUZCJW2iuVyKnTtfQhe79E1XYfj/Bge/k807/FsmkBoLp1Ri+Y9HgiJNCb9EQxfGIOzwQ7XtvIdMSSk0hibEcHhcQwPD6O1tVXucgghq1ScKaSsTqGF5STQ/+YQth96AA98ugF1Tj3C/b/Al770VTzh/Bv87YeLyzbOffMreOoND7749/+AO+sl9L30PfzpZ/8J7Hc/j/1OZX2Oy6FQiBCyKlv96BghG+3GriCLnYGr2aborp2NIKYlTPmjSE0Lq94qthhf79fBuqybfu4OYzNiR9c2cKEUpkaiNG+IVBWdUQujyQCfz0ehECFVSJkzhW4syIbfOP4H8/7Ecttv4FjHj/F/xiIA6pDPXsa/vRTFfY88hiNtxedjBz78MTzwyv+NH7w0gf3Hty24HzF2GX/22a8hfeizeOLBW1CZ3u61oVCIELJiyWQB0SiFQoRsBJ4PYXj4RYyO/vztriDjhnQFVYNYMIHQSBQu927cdfSPwbIt67re6OgpTE8Pw9tZX6YKlY+ttYCxGTF8cRxiWgKUPz+cEACAkdXjwoULuP/+++UuhRCyWioobyvmsvXM4Mov/wM/6LfjQ4+3AQCyk+Pwp5rwwY65v4yqQVeXAyffHIZwQyiUS72Fxx/9GhLvfgj/oLBACKBQiBCyCnR0jJDK4zg/LvV9HaGpAZisJriarVu+K2iunJRHaCSKvXs/jra2e9d9PVHk0dv7DdjrLFsucOPCKWj1Gvr+IlXFzDI4d+6c3GUQQtagmAkpKxW6WTX+l57Cf//qVaRSAgzOHfiDRz6H3+8wAAByqSgEmOC44cenzcliJsYhgXd+1yKlruF/P/o1jLc9iKc+vUdxgRBAoRAhZBW2yip6QuQyOnoKPT1Pw+JkqnodeiVx4RQYs6MsgRAAZLM8RDENg5kty/WqRU7Kz26UI6SalAbq0xYyQqqPEo+P3SwW8h55ED98t4CZFIfh/m7805N/gfHYn+Kvfmvh0bCbUesT+M6Xnsa5yQ48+1SXYptyae0EIWTFaJ4QIZUzMPAcenqehqfVjW07N8/2q3JLRQV46sv3QtBsrkVz83vBTSXLds1qwIVTAFSwVGhjHSGVZHNa4PP55C6DELIGxWBISW+L16nWM6hxurCtuRW/ds/H8LcP7sD/d+JnGAGgc9aAQQLx1PyPScQ4mJzsbPiTS40jXX837racxz9+sx9CBb+u60GhECFkRSKRPLJZuasgZHPqufBlvDX4QzTurKOjPEvICFkIyRm0tX2wrNfdvfv3kJoWkBG2xoNcaSZTHa2lJ1XKaDPgfM95ucsghGwh+TlPEfT1O7DTMglfPz/nFlH4fHE0tTXPHhFT6ffgoUd+F3/+5/cj9tI38K1fxjey5BWjZwKEkBWho2OElJ8o8jh9+n/i+vVuNHXUzh6LIIuLTkzDXbsTZnNtWa9rNtfC5d6N6cnN3y0UHAojMh6Hp9VNASSpWmbWiPGxcUxOTspdCiFkFUqdOUp7myuf9eNH/3EWvrEwprMiMlkOV9/4P3j821ex58ghNAIAbsHxD9fjP795AmeGeGSyHM795Lt4bnQ7Pvxb2xd83mzb+/H4p3fg+b/9Ok5PFir/hV4lmilECFkROjpGSHlxnB+vv/4l5NUCdtzeqOiODbVWDUmSZK0hJ+UhcBm0772nItfv2P37OHPmC3A2sBUfOH3k4PvgbfSi+/xZvHVtoKL3VZKT8pi4OoU0n4W300PHE0lVMzB6aHVa+Hw+1Ndvna2BhFQ7lUp5g6YX0oIb6safnfgOJmMi8noT6pqbceQPH8HHf+sW6N6+1W3HH8bnU8/iqUc/g5FYAZ62W/HQ45/EkfrFP7+d9/wRHn7jL/DEF/8dbf/0AJoV9GOYQiFCyLIyGQqFCCm36elr4Pko2u/0KjoQAgAjY4CQmJG1hlRMgEqlhdd7tCLXd7k6wdq3IRqcRn1LZYcvt3nbAeSxc8fuDQmFxLSE61enUEABLXsbt9yWNbI5MawBr776Ko4dOyZ3KYSQFVqsM0duN9aj1jfhDx97DH+47EfacOzBR3DswcXfy7R9BD//2fzbf+jPv4IPrbXQClL2s1BCiCJQIERI+ZnNdQCg+ECoRMxIyEnyPRbw8TQ89Qcqeh9trb+NVDRd8c+zf6gPwswMLg1UflBuRsjC33sdGq0a3j0NFAiRTcPMMrhw4YLcZRBCSNWrjmeihBBZTUzQPCFCtjKHxwaVRoWxy0FZgiExLSERS6K9vbwDpm/k9R6FJIoVHzj9yzd+ge//6NsYuX6tovfDhVLw+yZgcTBo3uOpmgCSkJWwOBmk02kMDw/LXQohZIXk3zS2+NtWR88OCCHLGhmhUIiQrazUZVJAQZZgKBqcBmvfBpZtqej98HwIAKDTV383zdRIFMHhMDytbjS0l3cwNyFKoNGqwVhN6O7ulrsUQshqqJT2RqEQhUKEkCUlkwUkk8qbkk8I2VhyBkPphAhv8/srfj8zM2+HQlV8xCon5TE+MAkulELzHg9tGCObmt6spdX0hFSTUgijpDdCoRAhZGl0dIyQytDpzAAg65ye1SoFQzkph3gwsSH3mZPyKBQK4BJvVfy+pqf9sLDVG6KIaQljl4MQMxK8nR4wNqPcJRFSUbYaC670X0EqlZK7FELIShTXjynvbYujUIgQsiS/n0IhQiqhdBSq0vNryk2jVUPK5mAwb8wuVY1WjYZbXJiYeBVDQycrel/ZbBL5glTR+6gUIZGeN1CaVs6TrYCxGaHT6+DzVX5oOyGkDBQwP4hmCi1EoRAhZEm0eYwQMpeQSAPAhnahGBg93M129PZ+C5FIX8XuJxLpA2MzVez6lcKFUhi7HKSB0mRLYmxGmitESLWQuyOIuoQWVb2H5gkhFRcI5JGtriYGQraszl1d2NHUCpVKjeGxQfRVaN15ms9Cz+g2PHhgay2Y4dN47bUv4ciRr8JsLv/w5Fw+Dl2VBSrBoTC4cAqeVjfNDyJbksGiR8+FHrnLIISsiALn+CitHhlU1zMfQsiGonlChFSH3/3AcRy4/SDq7A64bSwO3H4QD9x7vCL3JSRmYJTpaFJ9iwsGsxrn3vgriCJf9uvHYxPQaKrjqVFOymPschDJmICWrkYKhMiWZXUwiEVjmJyclLsUQshy5O4KWuSNMiEKhQghS/D76egYIUrXuasLrJVFnp9GLi0gnxGQ56dhMVtxx953l/3+MnwWZpYp+3VXytPuxkw6hNfP/XXZg6GGxn0IDofhvzgBLpxS7BDwjJDF2OUgJCmHlr2NND+IbGk6oxZGkwGvvvqq3KUQQpYh9+ygRecJUSpEx8cIIYvLZIBoVJkviAjZLGrrdkDg4uuaz7OtrgkFKYtCoTD7Z4VCASpJQnODFz29b5SjVADF7VZiRpJ1q5VGq8a2XbUIDPnx0kt/hAMH/gxNTbfCZFJBpwNmZoBEYm2PXfvv+nPwfAijoy9j+NqPER6Nw+pi4KxnK7qmPifl5w0cL81tKplJpVHIFaDV6qDR6JCIc1BrVNi+t5HmBxECwMjqceHCBdx///1yl0IIWYoKFMIoEIVChJBF0YBpQsiNhEQaao26ogHJSuiMWng76zHpjyAc/h7u/cDDsDuai2GYSoWhoRwGB9d2/NVsrkVHx3F0dBzH6OgpDA0/j+GL42Wf2ZMRskjGBGR5CYlYct77aut2AADyeQNQ0KDeuR063Tv37bSlMDb+c0wNx2D3WGj1PNnyzCyDc+fOyV0GIWQ5ShzurLR6ZEChECFkUX5/da5lJqSaFAqadV/j+tQ4mhqaoVLNzHYLqVQqqHQ6jAVG1339uWb4NBhWOQFEfYsLv/P+38aMcB1i6jrs9nZoDFa0tRnh9+cgrfNhzOs9Cq/3KEZHT6Gn52motWpYnWs7OiemJQjJNARuBvx0GpIowW5vhLvmdtzWeTdcrs5VXa+t7YO4MvBdjF0+A1uNBe5mh+xhHSFyKQWjPp8PXV1dMldDCLkZFZS3Al5p9ciBnj0QQhZFnUKEVF4hb1j3NfoGfOhs74TVYkchWzx2pNLrkUwlcenKm+u+fklOyoOPz4B1W8t2zXKwO+yQhBRmuCTCoTfhcO6ESd8Mm02NWKw8j2Ne71Hw/BSGhp+H3qhd0QyfnJSHkEhDSMwgy+fAJ3jo9EZ46g+grXkvGhr2Q6czr7kms7kW++74PNrbfhu+3mcwfPEKXE0OOOptdKSMbDkarRo2pwXd3d0UChGiZEqc4aO0emRAoRAhZIFksoBksrD8DQkhivDcyX9F564utDW3AwCGKrCSikSAuQAAIABJREFUfuLqFAqFAhweW1mvWw5qjQYmixH8tIBMlocJWHeX0I06Oo5DmAlg7HI3dty+bTZ4EdMSxKwEIZGGmBahKmiQ4nhIYrEAd+1ONG0/DLe7EyzbUt6iALBsCw6/5+9mu5liAQ4texupa4hsOXpGhwsXL8hdBiFkKUo8PkYoFCKELOT30yp6QjYCw7gRS10ry7X6BnxlD4JKgkNhpPksvJ0exXWhvOW/ivbt7ZAyIrRGK2yuNqRn8mseNr2UfXd8Htz0CMYuBWE0m8BFudn3ORzbwBgbwbItsLftgNlcV5EQaDGjo6fR2/sNmKwm1O9wUiBEtiSGNWL08ihSqRQslvLN/yKElBF1CikSPWsghCwwMUGhECEbgWHqEJlW9lHN4FAYyZgAb6dHkavPXz1/BnyKRw3jRlPTfkSjalzur9xj2OHDfwef75tgmDrsbt/Y8OdGPB/ChYtPIhzqh2ubA64muyx1EKIEc+cKHTx4UOZqCCGLmV0DryAKK0cWFAoRQhYYGVH2i1RCNhONRrk/ipUeCAFAVszixf84CYtpJ/bfdRRAZUNtnc6MffseXvPHc5wfghACx/mRSk1CFHk4HK1wuYrHy1Y6Z2ho6CSuXPkeDGY1Wm9vou4gQgBY7AyFQoQoHYUwikPPIAgh89CAaUI2jt2+A4NDaVnuOyflEbkeh2ubY9EjYdUQCAFALJjATErEgTv/WO5S5hFFHpFIH8LhXnCJYQjCJPhUbPb9bA2LXEGEVq+BMDWA/v4TAACzxQmbtR0ORytYtgUc519w7XDkTcTjg3A22uBU4IwnQuRitBhwvue83GUQQm5CBSivNUdp9ciAQiFCyDx0dIyQjcOyO5CTJIhpaUM7PYREGqFrMaRnMshl82i4xT3v/bFgoioCoZyUR/Q6hz0d/w1mc63c5YDj/AiH+zA5dRahqQEAxc6F/5+9e49u6zzvfP8FsDcAblxJAiTBqyhSd5l0LCWW7VpT20mUzkma1E3bnHQ1J5019TROzuTaTGbWrJ5Mm2ZNb2nS05mTOp1pO20z7iVxHKeduI7tiRxHUizJpixKlEmKN5EgiRtx2wSwN4DzB0RKFEkREglik3w/a3FpidjY+wVJiMBPz/O8dqcNt0uivqP09VwphAtQR1bNkUnlmE9fQp0Z4K3BNE5Pabe3fF4jny8Nr85lNGxOqwiEBOEWisfOeP+EmCskCEZlxEHTRltPFYhQSBCEJUSlkCBsHoejAdlqJ6PmNi0UCl+bIzwRo6vrvbS0PMip079FeGJucR5NXi8QnohSF/AaOhACCI/HcDqb6O5+X9XWEAyeYXLyFJFIH+l0BFuNjRqPlZZ9jbjqlDs6l02xYlOseFh4M1u34nHhiTnC12KbHiYKgtGJuUKCYGwmkwmTwVIYo804qgbxSkIQhCVEKCQIm8vr7SCbnr3jAOFOaRmdmZEI2XSBY8f+A83NxwA4ct9nOH36y8g2CU+Dk5mRMJJNMvzQYjWRITaT4PjxL1R1HadO/Q52hw2330nT3srP9lETGcLXYgS6/CIQEoQVeHwuEQoJglEZcfcxAWPtKysIQlWJQEgQNl993duYT2Yreo1kVGX0zSCWoo9HH/2TxUAIoLn5GD09v0ZofI5oMEEinKap01fR9axXVs0xMxyhvf0RfL7DVV2Lv2EPzlqFuoC74iFNVs1xbWAat8+Bp0G0xgjCSmxOq5grJAgGtpALGeXDYIVLVSH+i0kQhEVinpAgbL7SsOlcxc4/MxohFkywf/+HOHjwwyse0939PubiVxgfPYnb51hswTCSZFQlFU2TTWlk5rN4vK309j5R7WVRY28lmpqs+HXyeoGxi1M4axWa91R/fpIgGJXDY2ekT8wVEgRDMmClkGgfE6GQIAg3EZVCgrD5HI7Gigybzqo5Zq5G0TJFjh//8poVNb09HyMcfhO70zhFxGoiw9x0kkQkBUBzy1E69ryNQOB+QwyWBlCURiJzlX1BmdcLjPcHkWySCIQEYQ02xYpslUULmSAYkclkwBDGaOvZfCIUEgRhkQiFhM107vwfoqohPO4urFbXstBibm4EXU8DEIsNo+eTtLS8nd2dP1+N5VaMx9MJlAIQj31j/lc7PpsiNB6joeFejvz0Z5Flx5r3kWUH7W0nGBn9tmF2tYpci1Mj7+bYsZ9d0vJmJA0NPQwMPL1mqLd3937e0Xs/L/74BwRn7qyyqKAXyKo52g8F1rtcQdgRFLddhEKCYERGbNcy2nqqQIRCgiAAIhASNtfZs19lcupVaptcTEdHkcw2Ll365pJj7DV2bIoNTctilsykoioeT3uVVlxZ7R3HGR8+STKSpq7Fc9ftW3m9QGRijuh0nJ6eX7vjXbk6Ot7JwMDTqImMIVrI8lqBju7HDBsIQSnU8/kPMHZxkMbd9asODK+v9WO12rDJtju+hmyXsCoyiUjKEN8XQTA6m9PKyVdO8uSTT1Z7KYIg3MyE4drHDLeeKhChkCAIgJgnJGyeoaHnmAq+QvuhxiVbnjftWXm3q7xeYOpKGJ//AD33bM8X+EePfI5dHe9hdOz7jPefxF3nwhtw3lEAoCYyzF6NYjY5eOyxry1WIN0Jh6OB5pajzM1cNkT4kFEzOByN1V7Gbcmyg+MP/y6XLn2TgYGnqWvyUN/mJavmiM8madhVj0Uyc+rcK5y7cIacdnfzo7wNbmLBONz5t1UQdhyHx87I6CTT09M0NTVVezmCINzMaBmM0dZTBSIUEgQBEJVCwuYYG3uRCxe+QaDLvyQQup2Jy9PYJR8PHPvNCq+uuny+w/h8hzmw/yNcufJNRvtfwl3nwuVXllWf5PUCaiJDVs2RS+skokmgVHHU2/OxstrFVtPe9m5Onz5LY2cBi1S9+UJqIgNQ9d3FynXw4IdpaXmAU6f+EyNvTKJrOgCKu2Zxp7C7DYQAXLUKs6MRsmqu7OeOIOxUN88VEqGQIBiHyYAzhYy1muoQoZAgCIAIhYTKi8dHuHDh6zTsqi97O+3ZkSj5rJmHH/mDdQUdW4nD0cB9932Kffs+zOXLf8P4lZex2a24/A60jEYmpZGdL21h72/YR3PD2zi4dzd+/z0b8jVqbj6G4qglHkpVdbZQJp1DcdRW7fp3w+Pp5LHH/gtjYy8RCNzP88//6w0bHr7QQpaOZ0QoJAhlWJgrdOLEiWovRRCEBQbcfcxw66kCEQoJgiACIaHi0ulZTp78AjUeqeygYXokTDqa4/jx39sxgdDNHI4Gjh79NAcO/DJDQ98hEj2Px3UvHYFu/P7Dd9UeVi4jDJzOZXIoSmvVrn+3ZNlBd/f70LT0stu0jE5GzWFXrHcVFrnrnMRnk4YZBC4IRmZzWjn/+vlqL0MQhJuJQdOGJEIhQRDEPCGhojQtzZmffBGbw1T2dtrx2RRz00mOH/9yRcOPrcDhaKC394lNvaYRBk5r83maAr1VufZGiMdHANCyOsGhEGoig5bVMVvMFPIFbIoVT4MLj99Zdpueq14hfC225k5ngiCIuUKCYEQmUSlkSOIVhSAIolJIqKhLl/6G+UyI9sPlvShPRlWCwyGOHPnkknky8fjIitUXC7bK7BkjSadnGR5+lo6Ody4J34wwcDqTzhlqyLSmpRkefo6aGj8dHY+Vfb+ZkQiKx05twIPDY8emWMmqOWIzCaJTc4Qnovja6sqq/pGspZdtyZgqqoUEYQ02xYokS2KukCAYipgpZEQiFBIEQYRCQkWNj/8AX7u7rGqIrJpj5moUv/8e5udDnDr1O2Sy14hFJ297vxrFx8+8579v1JK3vXh8hLcGv83E+A8B6Op6/7JjqjlwOq8XyOu6IUKhhTBocPDbWCTIzJcGYK8VDM3OXsDpcdJ60L/sNptipanTB50QnpgjPBEjFozja6vF41953lZeLzDeH8SqyKseIwjCUnaXVcwVEgQDMYn2MUMSoZAg7HAiEBIqaWzsRYrkcd6ye9ZqYjMJdE0jNneZjD6CxWbC6rbSHghgU6wrhhPjF6cJNBzb6KVvS+HwRS4P/DWh2Us4vQq+1lrC12I4HMvb+qo5cDqrlnbpqmb1Vzo9y8TESwwOfguzBL52N54GJ9FgggsXvo7Xu3vN1sZCUV/zOr42L7UBN7FggpmRCOGJGJ4G17LjkpE0RYp0HGqu6q5wgrCVKO4azp0/V+1lCIKwwITx2rWMtp4qEKGQIOxwYp6QUEnB6VdRPCuHOSvxt9Xhb6sr+3gto6Mm59n1jnetZ5k7wtmzX2V8/CVqG9109rZgU6ylbd+vrX6f9rYTjI49s+mhULV3HpuaOs3p01/GapfxtXuX7JZXF3CTSWU4efLfcfz4764aDIUjfSjumrKuZ5HMi+HQzEgYNT6/7Bgtq2N3lFrPqtXSJwhbjZgrJAgGY8SZQqJUSIRCgrDTiUohoVLS6VmmJs/SfihQ9n3utAIiGVNRlLodP4x6LWfPfpWp4CuLYVC59ux5P8NXv0N4Yg5fm7eCK1wql8nhdu/btOvdTNPSvPnm1/E2uUotXito3tNAcDDMufO/z8M/9fsr7o6XzyeQ7/Dn2SKZVx3GrmV0QhNRxvuDePxOAt3L29IEQVhKzBUSBGMRmZAxiVBIEHY4EQoJlTI29gNsNbaKVjWkIirt7cvn4exUQ0PPoetpUqlp5udDmC3zRCPXKJKn7WDDskBobjqJv2H18EWWHRzY/yv0X/pzagPlzYXaCKWdx7o35Vq3Ghx8Fi2fpLmt+bbHNXTWMXFpmlde+QIPP/yflwVDsegk7YHyA9G1yHaJ5j0NeBszpWCowSUqhgShDIrHJuYKCYJhGC8VMtrg62oQoZAg7GAiEBIqaWLiBTyNlRuIu9A61tHxzopdYyvp63uKsfHnsTtkzJIJq91KHmjY7cGuWJdtYR6fTZGIpHjssSdve97u7vcxNPRtQhPRVStnNlpWzeH17t6Ua90snZ5lYOBpWvY1rhmAWSQzbQebGL84zblzf4jXu5dQ6M0lx8jWjX+ZpbjtSFZLqcVOhEKCsCaHRxFzhQTBKMSgaUMSkwoFYQcT84SESgmHL5JOR3DVljdg+m7EQyk83tYVhyTvNGNjLzI8/D2a9/poPdBI854GfG1efG1eXHXKskBIy+iExmP09PxaWa13PT3/hrnpJFpm7cHJ6xWfTWEyWfD776n4tW715ptfx+lVcJU5GN0imWne62MudZHRa9+maLu2+OFrrUW2S1hlK0d63oHTsXx49N1S3HYyqcyGnU8QtjPFbScaiTI9PV3tpQiCsNA/ZqQPQVQKCcJOJiqFhEoZHX0Bp3d5GLFRsmqOuZkUvT0fqcj5t5J0epYLF/6Uhl31ZVeOFAoFdE2no+PRso5vbj6Gz3+A0PgEzXsrO8smOhmnve3dK87pqaRw+CJTU2fp7G25o/vZFOtt52ZZrTby+Tw2q41UOrneZQJgd9qJBeMbci5B2O5ku0SNYhdzhQTBAEwmk+HatYy2nmoQlUKCsIOJUEioFK+3m0w6V5HKkqyaY7x/hubAT9HR8diGn3+ruXz5bzBZ8ne0Q1hp+KrM1NTpsu9z8MCvkIikSjuWVYiayJDN5Oju/kDFrrGa2dkLeH21dzSIuxypdJI3+s8RiYU37JyuWgUtq1f0eyEI24msWPjxj39c7WUIgmCAwqBlhUIiFBKhkCDsVCIQEiqpu/t9eL17mBmJbOh583qB2ZE5Ghvv5ejRT2/oubeidHqW8fGX8XfUrXrMrtbdHLnnHdTXLp0H5Ky3c+7c13jp5Y8zNPQc6fTsba/l8x2mveM4c8HUhqx9JdHJOO3tj4iWwDXIdgm3z0F4IlbtpQjCluDwKLz++uvVXoYgCFD9FEi0jy0jQiFB2KHEPCGh0o7c91lScyrJqLoh58vrBSYuTyOZ6jly32c35Jxb3fx8KchZbQbOu37qBO8+/jP0Hujl53/ml9i7e//ibU2dPrre1ga2Oa4M/g+ef/5frxkMHdj/EeZTOeKzGx8MqYkMqTmVAwd+ecPPXW1aplTVEw9t3NfN31aHmsiIaiFBKIPitjM/P8/w8HC1lyIIO1u1AyARDK1IzBQShB1KVAoJleZwNLB//4cYHPoHFLd93duZz1yNYJd8PPzw7276vJnbqakxXf+AZBISCWM8t+prfXS2d1PIpDAV8hSLBd7e8w7eujqweIxsl2jq9DHy+mRZFToORwNdu3+O0bFn8DRs7M5yyXCa5pajhqoS0jI6yZhKfDZJVs0tu32lGU6KpwYto6Fl9cWPm5kt5rIHWd+ObJdQ3HbCE7HbzjQSBGHpXKGurq5qL0cQdi4DZjBippAIhQRhxxKhkLAZDh78MAMDT5NV17d9dnAoxHxS5/jDnzdMILRrl4W9e8xYpNKLibyexyJZABgfy3PlrTx65TfrWpXVagOgWCwu/mm13vgeXLs8g9lixmI1UyiY6O19Ys1zxuMjdHS8c/F7Wu78HTWRYbw/iMvjwmSBfFGnxllay8I5YjMJ7jn++B09xkrI6wVS14MgNZFBslpw1ik0dtYvO1aNL6/SUePzmCUzirsG2SYh2yVka+nP8MQcs6MRsmqudJtNWtfzwlnnYHY0QjSYwOGxb/g8JEHYTmTFwvnz53n88er/OyMIO5XJiJU5BltONYhQSBB2IBEICZttPVVC0WACNa5x/OHfK2v79EqrqTHR22PB6zWTn4+RS6hQKLVj5jFhstppba6lsVHmjb480Wh1nm/BmUniiRguhxNTsUjRbGZg8BIA0yNhsmoBj6eDcPAy+/d/aMWwTdPSTE2dIRS6QDD4YzQtw5Ejn0Rx1JKOZ8oOIWaGI7S3P4LX200ul0TTUsTjo5gtKpMTVwHwN+zD5zu8cV+Au5CcSzL3WgyzxYzb76Cxs/62j3HlQMe76vG1ATe6rqPG58llNPRcHtkmEej231E4pCYyhCdiqIkMVkVmbjbB7GhkMWRy1jk2pDpPELYTh0ehr6+v2ssQhJ3NhAhhDEiEQoKwA4l5QsJmCYcvAqyrgiEdzaDlsrz44ieRZTt19a0UClYki4uOjsdobj62Ucsty9GjFhz2AtpccDEMuqFIMTePnptHUjzcf7+HV1/Vq9ZS9szz/8CDR4/jdrqZnLnGuQs/IT6bYm46yfHjX8bnO0w6PYvVeiMQisdHCIUuMj7xfeZiE0iyjN0l42t3MZ+WGRj4a3z19xJNnClrx7P4bIpCwUxv7xOGqfJaTV7XadnXuCHtXSuxSGaaOm8M/M7rBUITUcb7g3j8Thp21d82yElGVWLBOGoig9vnoLO3ZfG5tTC3KBlLM3llBgBHbQ3N3Q0iHBIESiFucDjE8PCwaCEThKoxYqWQwdZTBSIUEoQdSFQKCdWyMKD4TubRNHXVoeVK4UNeL5BNl3Y0S2fGOHeuD7//zzctbNizx4JDMaFFg0Dxtsfm1TiYrfT22HjlR9V5zuW0HP/71A8W/55Vc4TG5+jp+bXFqhyHo4F4fITBwWeJRN8gnYricDuQFTPthwJLKlicusLIG1Ooaoj5xPIZO7fK6wVmx2Ls6f6g4QOhPXvez8DA01jtm/fSaCEkctc7mR4JM3x+nMZOHx7/jefHQjtbeCKGltVx+xx0va0N+ZZ1ynYJj925+NxKRlVmRsLMjIRp3tNAVs2Rvt7u5qpVlt1fELa7hblCQ0NDIhQShCoxmUyGm+FjtPVUg3hFIAg7kAiFhGqIz6YIDodKlSdOa9nVQ7JdWvIG9uYqjtE3ggwOPsvBgx/e8PXeqqbGRHe3BS0RYq1AaEE+FcZR18quXRZGR6tboZfXCwTfCtPc/ADd3e9bctvc3FXGx18i0OWnac/ywGGBRTLjbXQSvlaqAFtrrlAsmMBmc7Fnz/s37oFUiCw7UJS6O2qL2yiK287u3lbCE3PMjESIzyapDXhIRdPEQynMFjN1AQ8ev7PsMKf0PPExeWWG+eQEWlbHqsgU9AKzoxFsihXFY8fb4BKziIQdw2I309fXx4kTJ6q9FEHYmUwYrjLHYMupChEKCcIOIwIhYTOl0zPYa+yLgdCRI58kFH6d8f5X2f221nW3tdQ2uxge/g579ry/4pUobreJYqHUHla+IsWcittdU7F1lSs4GMJm89Pb87Flt/l89wBgd1rXDB08fifhazEAMqnVQyEtozM3k6K350nDVwkt8Pl6y26Lq8j127x4/E6CwyEmr8zgrFPW1c7mqlPwNrmw2q1LqoMWqobmZhPEggnMFhOuOgeBbv9GPhxBMBzFXcO58+eqvQxBEARDEU3mgrDDiHlCwmZKp2fQNH0xEOroeIzeno+R1/PEgol1n9/T4MQiweDgsxuw2ttzu03ktbVbpm5VyOeoXX328LoshC1a5vbbnEWDCVJzKsfu/+KKAY3D0bBYJbOW0EQUj7eV9vZHSMdVsmoONZFZ8hENJggOh3A4GunoeOzuHlwV+P096JnqBueyXSLQVQpnGjvq1z3fqKnTR13AvSTssylW6gJudve2suftHTTu8hEPpdb8ORKErc7hsRONREmlUtVeiiDsTAu7jxnpQxChkCDsNKJSSNhseV1fDIQATp/5bewOG762jUlKFqqFNC29IedbjctlwlS481CoqGsojsr8uvV4OvH5DxCdit/2OIdn7Z2tfL5eEjMpsurqj1FNZEiE0/T2PInf30MinGakb5Lx/uCSj1zchsPeRm/Pk3f8mKrJ691NRs1UPRyR7RJmixk1sXZIt14WyYynwYlktWzK9QShmhYqG8UuZIJQJSbTjR3IDPMhgiHRPiYIO4wIhYT1GBt7CYejAYB0epb5+VkAurret2IFyp4976el5YHFreQvXfomsdggHYebNmxNngYnsalkxWcLJZNF6r13PnvFJMmkU5Wr0Dt44Fc4efI/4G1afTaMTbFS46phaOg79PY+seIxvb1P0NcHI30v42urxde6PLQLj8/R3nEcn+8wHk8nDkcjHk/nlmkPW4uqln6ew9di69rWfe/u/UzNTJJKJ+96LXaHlfl0Bg/lD2VfD8VtJx1X72gIvCBsRU5vaWv6hx56qNpLEYQdyIDVOUZbTxWIUEgQdpBwWARCwt1Jp2c5c+a3mZsbW/ycvcaO3VHDfFplbOx5jh37zcXwZ4EsOxY/Fw5fZGDgaVr2NW74zkebMVsoEinS3W0jd4fv880WK3ORyr3g8PkO4/MfIDQ2RuuBxiW3OR0u6mt9+Gr9HGifR0vD/e8wYy7mgQJFk0ROl0gk8oCLXbs+y9jYOzl1+o8ZfXOKlj0Ni9+raDCBli0uziSSZcfiDmZblaalmZo6QzD4I0Khi2haBo/PhZbVFrd1l20SituOzWHD4bGvOZTZKlvxumvJ5XJ3FQppmVK7pZrIoGziLCqHR1mcFSUI25ndaWPgykC1lyEIO5MBdx8TRCgkCDuKqBIS7sbU1GnOnfsKmEstNfsfWBr85HU34fEYJ09+np6eX191hkw6PVPaeawCOx1tRrXQ/HyRQh7MNS4K82W+2TdbMFkVIpHKzvLq7fl1Xnzxk8wF03R3d7G7aze7Wtrw1voAiI9eJW/VKXh1sheeQVFKnzcBNTY7Ln8rRbOMtdlNT88R/uW/fIqhwde4fOV1JkKjzBdSRK7Ft8TW8mtJp2cJBs8wPfMKszMDSLKM4rHSsNuD4m5cUhm0MJA5k8oQnZpjdjSPbJPwtdXirFVWrCLKaTl+8sapO16XltEJX4sRD6VQ3HbaDwVQ3Gu3/W2UeCiJbBMvC4XtT/HYudx/udrLEISdyYjtWkZbTxWI3/6CsIOIIdPCndC0NH1932B8/CV8rbXUBtwMvja2bBtyi2SmcXc9ck2Cc+e+Rjx+lZ6eX1t2vo6Oxxgb/2dC4xP422u3XLXQ/HyRK2/l2b/PSyGrQmHt55Pk8jEXLzI5WdlAdu/eLnbv/gTtHY1QhMTQAKlXTjI3PU4uGATAIlsoFoooNY0UXC23PZ/F30RDSyf19z6CZlXRtRxXdo/icn2IaLR64XI8PsLY2A9IJAex25rx+3vwejuXVaitpq/vKYaHv4etxkaNx7pm8GJTrNd/1ku7kWkZnXgoxcxIhJmRMJ09rRvycxwNJpgdjVQlDAIWB4R39t7+50IQtoOb5wr19vZWeTWCsLOYjDjc2WjrqQIRCgnCDhKJFKu9BGGLiMdHOHf+95nPhOjsbVl8ES1ZLatuQ14XcGN3WBl963+hqkGOHPnssnBmoaIlEUlhkSQc7hosNhN2h22xTedubUa10OhonqYmEx5PgHxilmJ+taHMJiwuH5ht9PVpFVkLQEuLmb17zNhrzIy+GmfuhR/B1BCFQpG8XqBYKGBzWrFIFkwmyKXLG6CcD02jhqbhDdDNBbI+K/fd+39Qt18iGskzOFTctHAonZ5lYuIlxsaeJ52OUOOqKe0glBpnNvRjMvMZZNmOt3Y3ft+9BAL3rxoSjY//gECX/67n5sh2CV+bF5vDSnAotGHBZiwYx9dau2HD1+9UXi99L3MZfc32OEHY6iySGafbIUIhQRCE60QoJAg7RDJZJJkUoZCwtoGBp7l06Zt4fC7aDzctaZGx2uXbDr9V3HbaDjYyO3KZV370Gxy57zeWvEH3eDp5/PHvEo+PkE7PMDd3lXCkj3hwGjUdAsBd76R5r/+u1l6qFnoGVZ2lpeUBfL7DG141dPaszp49FnbtaiI/n6SQUylqOaCISbZjkmQsNV7SapG+szrz8xv/vGtsNHPwgBnJUiD1xo+Ivv4qqeA5CmYNq13GbDZhtloAy7L75nJJcrkkVqurrGtJBTPm6Szz//wswdM/xH38vdx//x4mr+kMDhUr8vgWWrzGJ77PXGyCGlcNLr9C0962ZUGMltHJqDnS8QmGrg6SSgU5evTTy845NvYiRfI417nFO0A6rmJ3bEx4omV0tKyOq37967pbrjoFX2stwaFZrIebRTAkbHubwXN5AAAgAElEQVSSYuatt96q9jIEYecxLWz5ZSAGW041iFBIEHaIcFgEQsLtlaqD/oBUKkjLvkZcK7x5zqSzePy3DxNsipWmrjqGX59gdPSFFXe78nhKLT/NzceAUlWPpqUJhd7k3Lk/JBpMUBdw3/FjWKgAicdfY+rsK+iaRnPzUe6559cXd01bL12Hy5fzzMwU2b9PweNd+vVQ03mmrhYYHNz4dk1Jgt4eCw2NFhIDF4n+8DmK2fK3ETfJJgq5eaKRK5jNZmy2WmSrC7u9FrN5eYC0YOG2THiC2F//Nu69R6k7/jg//dOtjI7oDA4V0DdgF/exsZcIBn/E1NTZxRavmyvVViLbJWS7hKtOIRpMEAldYGDg6WVDsMcnXsBas/pjrBY1kUGyWqoexPjavOQyOa4NzLCrp+Wudl0ThK2ixmEX29ILQlUYsH1MpEIiFBKEnULMExJuZ2joOS5c+AYen4vOe1d+QxifTQGmsiotZkYieLytq25/vhJZdtDcfAxN+3XOnftaWTs9rcTT4FwMh5JRldBY3223Yr9b0WiBH58CyON2m5EkKtpSVVdnprfHjEmNMf0//458aPqWI8wUC7cPf2WrBawWrEXQtTw5bY5MMkoiPoos26lfZTexQiFPOjVJOj2LWTIz0/c84YsvUbv/IZrf/zHq62s4d75wV1VD8fgIg4PPEgz+mCJ5lDKCoNU4PHayyRTjk89x6dI3Vzxm8LXSDno2xYpFMmNzlNrrZJuEbJPI6wWy6mptgSVaVl+cw7Pe+T/JWHrTZwitprHTx1j/FOP9QTFfSNjWFLed4HCI4eFhurq6qr0cQdg5DDhTSOyGJkIhQdgxxM5jwkrS6VnOv/4VYrFBGnbV37Y6Jx5K4qytWbOCIBpMkFULPPrIF+9qTR0djxEKXWC8/yS739a6rooFV52CltWZnj614aHQzRKJyj6/du2ycOCAhbm+s6inX1ixOshm95FMjlMsrv16y2QqBUSytVQ5k8to5LPLg5BCIU8mEyOVHAcT2Fw2JMlMoVBEy2hELv+QuZGzBH7xMzz44H289pq5rK+FpqUZG3uJoeFnUNNhnF4FX7sLZ93KO3qVy6ZYCeytu/4334rHZNUceb1AJp2joBfIqFmy6Vzp7/nS2ssJaayKzHh/aYi3o7aGhva6OwqyFnYbS0VVAl131y650SySmY5DzQyfnyA4FCLQbYx1CcJGk+0SNYqdoaEhEQoJwmYSu48ZkgiFBGGHiEREKCQsNTT0HJcv/xU2h5mOw023HZqbVXOoiQyBbj9qIoOW1TFbzMtazLJqjtnRCEeOfHJd7VpHj36auRcHCQ6GaD3QeNfnAXDVKsyOThCPj5S9S5WR9PRINDVC5AfPkrl0ftXjFMVPKjmBltOx3sXW4rJsQ9ez5HJJtFySXC5BPq9hMpuQbBZkm7z4uslsNmFTrBSLoGUzTP7Vl4g/9gHuOfJOhofrmJ62rXiNYPAMY2PPL7aHueoVAivMCaqkheBmI6pz8noBNZEhHkqW3XalJjKEJ2KLVUbV2G3sdiySmY7DAUb6JtGyOoEu/6Z+fwRhs1jsZvr6+jhx4kS1lyIIO4cBK4UEEQoJwo4gqoSEm91cHVTX4i5rdk9kcg6A4FBpGLRktaDn8pgtJjwNLrwNLiSrxOzIHM0tR+noeGzd63zg2Bd56aVPEJ6YW9euTLJdosZVs+p8IyPrucdCk79A6O+/sUK72HI2Wy3ZTBTuIhTKZufJht7EZDZhtpgx28zIkg2LZfWQw2QqDR/HDplXv8e1yau0vedDKEobV6/eCAzD4YucPfcV1HQYt89huCDkblmkUjCquO2M9F0jNBGlqXPlCqV4KEV0Kk5WzeH2Oe66RW4z2JRSC9/MSITh1yfw+J2iakjYdhR3DefOn6v2MgRBEKpOhEKCsAOIeULCgqmp05w795WyqoNu1ryngeY9Sz+X1wukoiqR4ByxYAKzxYTdVsuR+z67IWt1OBrwNxxG1a+s+1yueqXiLWQbrfceE00NBWa/9WdlBULxuRHm5yNY72JXLLNkweowY5HMmM139z94stVCcfIKM99+Cu97/k86d+3mjb44F978U0Kzl/A2uWg52LEtBxhbJDPNexoY7w8yN51c8RizxYzb76B1X+OWqLyxKVbaDwWIz6YIDotWMmH7cXjsjIxOkkqlcDpX3lFTEIQNZsRKIaOtpwqM/6pEEIR1C4dFpZBQYrU60bQMu/as/825RTIvDnXWMjpTQ2HaW969YVvAa1qaqcmztOxbX/sYgMfvZHZ0jHD44rJdqYxozx4LTQ1FZr/1DbSZSXRdBUDXsxQKWQDyeg6Xux2z2UI8fpVMNobdffvKntVIGxTUmExgmQsy99xf4PvgE0Sj3yJXmKDrbZvbIlYNC5VPDbvqV9yufmGw9UZzOlzkclly2u2HY9+tUquoieBQCGedY8VdCQVhK1qo1Ovr6+Ohhx6q8moEYacwGW+ws9HWUwXb+xWaIAiAaB8TbvD5DiNb7aiJzIa+uZPtEibTne88dTuh0JsAG7JOi2TG43MxOvqC4UOhlhYz3d0Wwv/4P8mHpknEh8lkEgCl+T6SRKFYoJgvUIxrFIp5tJyKzWW9q0CoEizpKLEXn+UXfumD/OPJ7xCJhau9pE1hVWRgY+YVlau5sYXRiasVO7+vzYvNYSUZSREcChGkiKvOQcOu+m1Z9SXsLE6vIkIhQdhEJlEpZEgiFBKEbS4cLpCrzH8gC1uU33+YZOTyhv+Pv1WRCYcvrnlcOHxx8bibA5pbw5qpqVN4fK4NW5/isRMcPwV8esPOuVHGxl5iauoUgUArx+5/FxP/+CJq3ynyeo5sLrliBZCuF8gkE1gki6ECoQX6yEVib7Txvkc/wD98/29JpVduq9pOJMlCQd/cEP6tqwMVv4arTln89yIZVQlNRBnvD9J+KCCCIWFLszttDFyp/HNIEITrjLj7mCBCIUHY7kTrmHArX/3biA6tHd7cKUmSIL/2/KrLA39JIjmKbLVy6dI3Vzymtq6FVDKMr33tIdjlctYpBIdDTE2dprn52Iad927F4yMMDj5LMPhjiuSpbXDxs+8/QfTiSyROfwezyUShUMRsMa8Y+EiSmYIiI1slQ76+kmQLqRefRQk08p7j/5Lv/uCZirU4GYXiriEeSq5rMLrRLQzWHuufEsGQsOUpHjuX+y9XexmCsHOISiFDEr/FBWGbE61jwq0CgfvJqBm0jL7msYf39/Kz73qcw/t7yzq32Zy97e2aliY0e4XmvT5aD/rY/0Dn4kf7ocDih+zO4GlUNrQNZ6GFbCr4ow07553StDRjYy/x0ssf58UXP0k0cRpfu4vOe5v56L/6KHIxg3b6n6hx2bA5rUh2iby+etBmtRkzEFpgli1Envlr7MUCDx49Xu3lVFxtwE1eLxCemKv2UirKIplxeGrIqjlSUbXayxGEu7bwO6avr6/KKxEEYbtRh/6Od737j3iz2gspg6gUEoRtLhze2DkvwtbncDSgKHUkY+ptt6Ovr/Xx4H0/RVHP0eQPMDpx9bYtQLJNIjwzddtrT02dRpLlFbfivjkAqtRMFk+jk/H+kxzY/xEcjoaKXGMl4fBFxsafZ2z0h1gkCZevZsnw5UBjC4GGZoJ/8dUl95NkCxqg5fLIVsumrXejFPMFivPzpP73P7H38Y9y9sKZbd1GZpHMNO6qZ2Y0jMfv3NLDtfN6gfC1GNn0jeoum8OKRbKgxufJpHO07GsUg6eFLc/pdtDX10dvb3n/+SEIwjoYsFJopcHXWrSfv3jqOZ77yRDXokVqm7p470c/zCfeswt58agI3/2Tv+TPXrrEVEqivfse/s1nPsqJ7o3ZcGUziUohQdjGslmIRESlkLCcz9dLNrVxrTx5vUAipOJvuP0Q5+D0qyieO98yfaMobjuuWieXL//Npl1zbOxFTp78D0QSr9Gyr5E9b2+jqdO3JDA4es87iA/0UUgsrTAxXe+9Lxa3XribTecoForYXXa0a6OkpsZ58MjD1V5WxXkanEg2iXgoVe2lrMvklRnisykUd83ih5bVUePzmCUznT0tIhAStgVJMfPWW29VexmCsEOYbgRDhvm4dY0aJ7/5Pcbq/gW//dU/4Iff+wp/8ok2/vdX/pA/+8mN3+1nnvojvvoTG5/6vT/g5W9/id94NMfvfuq/cjq69V6ziVBIELYx0TomrKa5+QHi4dtXbERiYd68coFwIs65i6/dtsIjMjFHUbdx5L7PrnrMwhbzrnrnXa97I9Q2uxgff5lTp36HoaHnyhqOvR4DA3+Fr7WW1lWqKvbu3o/PW496+uVltxUKRYqF4pab2VIoFNFzeST7jfa21A//iV2tnQQaW6q7uE0gSVuvqutWvrZaCvkCiseOr82Lr81L675G2g8FaN3XuKWroAThZg6PItrHBGGzVD0AWuFjWSok89gn/h1feuIBetu9uJweDvzUB/n5e3Oc+0mpIr6Q6+d/fj/C+5/4v3i024vL6eOBD36ED3UM8nffn1zxoWvRfr7wkSf51FNvYbTGa/EbXRC2sXB47aG/ws6WVXMrtnItOHXulTXPEQ0mSEYzHH/495Dl1UtmF7aY38ztuleiuO007KonnbpIYvQi6UQaAG9tK27XXvz+HrzeTjyeznVfa2zsRbK5BM2BFqyylbpaHy2NrSTTCSKxMJFYmKOH307q9VPLqoSgVIFlMmG4ncXWYjabkBUZbV7DIluwWMzkQ9MkBvp4++F38N2ZZ6q9xIpS3DWoiXlg6w6cVtx23D4HMyMROnu3f5An7Fx2xUomk2F4eJiurq5qL0cQtjfTyu1aVVXWeuJEogVafsoHQG56gpFUGx84ePPr3np6e2t57o1h1A+3Lrl3PvUWX/r8fyHxjo/zB0/sxWh1tiIUEoRtTFQKCSvRtDTnzn0Fb5PrtoFQObJqjtnRCEeOfHLNEKW5+RgebyszVyM07/Wv67rrVZqldGOekprIoMbTRFOnmQ39mMx8hscf/+66rqFpaQYG/gpvo4vew2/jHb33I1lkspk0kmTFIsnktCw2SWbyjTMrniOv5TFv0aoTq02ioBfIpnLUuO2YTJA+/TKBj36KXa27Gb12tdpLrAgto18PhLa+xk4fw+cniM+m8DRUt8JPECpFtkvUKHb6+vpEKCQIFWYy4EyhctYz+tK3+F+po/zho7UA5FMRVGqoveVXo7vOw3w0ToIbrzL11FX+38//Fya6n+CrnzhkuEAIRPuYIGxrIhQSVnL6zG8j2U00dfrWdZ68XiD4Vpj29kfo6HisrPscPfIbzCd1osHEuq690RS3fbE9xllfg9e7/sqIwcFnyeYS/MIvfJBj9z4AmRRabBrzfJJCMoI2N0NbQ4Amhxuby7XiOQp6AbO8dX9Vl0LHIrn50vyqQmKOxOU+9nftr+7CKkBNZBjvDzL8+gRQar/a6iySGWdtDfHQ9h0OLggAsmJhaGio2ssQhO2v2q1iK7aP3d7ET/6az/xJmI9/6aPc47yzQMtsTfCXv/U1/nb6IJ/9TC+rb+9SXVv3laYgCLcVDotASFhuaOg5YrFBmrvXFwgBBAdD2Gx+enufKPs+Hk8nB/b/CtHJOFpGX/caKkHL6NTXv21959DSDA9/h6MPvI3du7rJp2IUc9klx1jMFixZFS06y65H37/sHAvzhCR5a1YKQem1lmSXyWs3/j3KXr1MoKG5iqvaeOGJOcb7g0hWC529LbQfClS9TXKj1Ld4URMZwz5fBWEj2J12zp0/V+1lCIJgMIMvPcXH//M4v/Klz/L+btvi5+W6ehQSxG7ZUyIRjVNT51kMf/KpCTJND/Kg8yx//NQlw80SWiBCIUHYpkSVkHCreHyECxe+gb/du+4hseGJObJqgWP3f/G2c4RW0t39PhxKMzMjkXWtoVJSc/N4vetrIRgcfBaLBO9973shq0J++Rtqu81OXtfR42GsLjfeffcsuT2vFzCZS/N5tjJJtlAsFNH10r9J2rUxZNnGrtbdVV7ZxqkNuDFbzNid9nW3ZBqNTbEiWS3LdlOLz6ZQExnyeuF6y1yGZFQlfG2O8LVSSDbeHySvi99FgvE5PHaikSjT09PVXoogbH/Vrgwqq1pI483vfpVPPaXyma9+jp87uLRPzNq0m33OafoupW/6bIS+vhht3e2LLWIm6yE+/plf4jd/83Gi3/9TvvGjWKW+qusiQiFB2KYmJ8WQaeEGTUtz+vRvUdvovuvZIHm9QDyUYvziNOFrMY7c9xkcjoay759OzzIw8DTPP/+rxOPjWCQLWTV3V2upFC2jk9d1fL571j54tXNcrxJq3OVDqXFQ0LIrHqfUKBQyKlCEYhGlvnHJ7fmcvmXnCd3MbDZhkS3o2VIwVszOkxy5sq12IbNIZnxttYQnotsyBKlr9i5pIUtGVYLDIcb7gwy+Nsbw6xOM9weZvDKDGp9Hjc+juGvQ9bwIhoQtwaZYka0yw8PD1V6KIGxv1Q5/ygqF5nnlqd/j03+h8cQXfpG91gRT0+HrH3E0APby4Q828b2nvsnJoTTZXJwz3/0fPD22iw/+7K5lD9vT/TN86RO7+dZ//v94adp4W9aLQdOCsE1FIsb7B0eongtvfh29kKK5vemO76tldKLTcZJhFZNJor3tnXQ/+IGyAiFNSzM1dYaJa99ndmYAq13G2+SmaW+bIbe0jodTyHLNHYVdtxocfBaTJc/ufbtKn1ihSghAsStkZ65vW1rQUPyBxdt0vUBeK2Bz2Va871ZjsVrI3RQA6hMjdB55sKzd7baKuoCb6NQcMyNhmvfc/c9PtWkZnWRMpZAvBTnzqQx6No+W1RnvD2JzWInPJvG11uJr8y4GuytVSNUG3Iz1TzHeH6T9UACLJP4vUjAumyLT19fHQw89VO2lCML2ZcBB07cup5Dr5y//4S2iOfhPn/r3S24zWw/xp9/7DY5YTdz74U/zudSf8dXPf5LRaJFA9z18/Esf49GmlR/fvvf8Kz79k/+HL//Hv6X7v36IdgMVFhvvFbkgCOuWTBZJJkUoJJSMjb3I2OgP6extuaM3ZfFQimQ4TWpOxeNtpbfnV8seKA0wNXWa06e/jCTLOOvtdPa2GLa1RsvozIxEyKoFenr+zd2fR0szPPwMvnYvkWio9EmLtCwYqrGXCotLlUKAWUYNBW+cZ17DYrUgbZM30SazCW76JylzdYDa4++hvtZHJBauyDX37t7PTx97DHU+xV8/85cVucatGjt9TF6ZoXnP7Y9TE5klf5etkiFC0qyaY+ziFGaLGatdBkCyWqips+OqK7WJZtQsbr8TX5sXWDkMWmCRzHQcahbBkLAlWGQzb7zxRrWXIQjbmwFDIVi6HrP1KH/2z+W8bnBz4onPcGKV0ZpK9y/ywj8vPf7nfvOP+Lm7XWYFVf8ViCAIGy4cFoGQUJJOz3Lhwp/i8ZfXMqZldOLhFKnIPHm9SKDpQe4/8oE1t5tfid9/D7LVhq/t7lvWNkMyqjJzNYLXu4eHHvnsBlQJFfA0OMlpOTQ9h1m2UbglFLJareiZeYqFAsUC5PNF5q5dYz6ZhWKRQr5IjWd7DCoGKOh5zDeFAYXEHHktW9FQqLmxFSig1DhxOlyk0pXfQct6PdjJ64Ul4UdeLzA1NEs6tvZW9TbFunhfxVOD4rZvytDqhUDIWavQ2OnbsPBGBEPCViHbZEZGRqq9DEHY3kzXt6U3EqOtpwpEKCQI25CYJyQsGBr6Dg5HPXOhycVBsXbFjmQ1I9dIWCQLittOXi+QDKdJRFIoSj17u3+Fjo5H73iI9M1k2UGg6UHCs6cNHQrNjkZwKK0cf/h313WehZlJgS7/4udeu/CT0nb0uQwUVn5eWhQX84k4ybFBLNe3n5ct5i0/YPpmBb1Yqha6yXxoBpezcpuzvjnwBjarjXAstCmBENyomsmqucUgJxlVCQ7NItkkWvY1YpHMS4IfKIWxWq4UHGbSOQrXZ/AkIinCEzHMFhM1bjsOj4KrVqlIVVEyoiLZpIq0vi0EQ8PnxwkOh2jd17j2nYSqy6o5ktHy98px1SmGrQYtR23ATfhajL6+Pnp7e6u9HEEQhE0jQiFB2IYiETHUUyi5ebv4dHqW+flZ5uZGyOWShMMX0TNRxiemAGjvOM69h96Dz3d4w65/4MAv8/zzLzMzGkFx16z4hrjaAt1+xvtHSadn77pKSNPSnD//Nax2eUkAdnGgj4a6Rna376Ywn1jclt5utVOcV7EoXopmC4Pf+wcspjyW6y07283CTKFi8cZ/yFX6JyASC/P8D/+xwldZamGgcnw2SWRqDj2bJ6vmFufvrEa232gfu7kqyNfmJa8XSjt7RVJEp+aYHY0g2yQUtx15hZ8Xj8+5eC4toxO+FqOu2VPWm3WpgoPNQxNRwIS/rbZi1xA2TjSYYHY0QktrC4pSs/h5SZKRpeU/d3PxOUb6xlFcNTjrFTx+p6H+nS+HRTLjdDsYHh4WoZAgVIwB28eMtp4qEKGQIGxDYjt6YSUORwMOR8OGhj7lXHP//g8RiZ5jdmQCLXdjloq7zkW+qFPjtJe283ZYqzJbRXHbcdU6uXz5bzh69NN3fP+pqdOcO/cVJLuJln3LQ6UXTn6fw9293H/fA1jcbop5DcnjR7JnSA4PMPzPz6DOzuCorVnh7NuDbLWgzYOW1RZn1WizQXz+7VUxsvAmOBlVcdbWUFNnp3Vf47p+pi2SGVedgquuNIdqYRC0mphHiy9tR9P1POGJGB6/E5vDRvh6EJOMpum6r72qb9LnppMEuvxbupJkJ8jrBWauRsipOp/73Oc4ceJE2fednp7m29/+Nj88+UMGR8dw1ztx+RyLP7tbgdkKfX19PP7449VeiiBsSyaT6dYRPtVntPVUgQiFBGGbEYGQYDQHD34Y+PDi38PhiwDMzl5AVWcYH3958TabYqWzd/O3Kq9tdjHe/zIHDvxy2dVCweAZhoafITR7CcVtp2Vfw+Kb7oW2i2QkTVbNMXx+gldefJXm5mYOHz2A1Wonc/EsoZe+R6FQmgFWKBS3VcvYrSSbhJbRkW0yJlNpa3qbvP0CgkCXn5nR8IbO5bmZbJeoC7ipC6zceqcmMoQnYiSjKr62Ojx+J2P9U4xemES23Zh5lL1pN7jNmFnkrFNIx1VDt5LudFk1R3AwjK/ez3/8nf9IV1fXHd2/qamJJ598kieffJJXX32VV199lRdeeAFJlnD5FLwNLsOHgnannYErA9VehiBsXyYMWJljtPVsPhEKCcI2I+YJCUZXU9NAMHiG6ZlXmYtNIMkyiseKq965KW9OV7LQ0na7aiFNSzM1dYZQ6ALB4I/RtAxOr4LZYkZNZBh8bQwAs8VEIV/EqsgobjuNnfWLc5sG+ge48tYVjvS8nbm3SuHYQhC03UMh2SajZ/Ql1ULm4vYbiu9pcBKaiBILJm7bMlYpittO+6HAks91HGomFkws/t0slSrzFiQiKdREpqLtY65aBzOjYcb7gyieUlXcQoWg0VpKd6L4bIrgcIhHHn2Ef/t//1uczvWFdw899BAPPfTQYkD0d3//d1uivczhsTMyOkkqlVr310AQhBUYcfcxo62nCkQoJAjbTDgsKoUE4wqHL3Lq1G9hrbEgK2ZDbFOfjKpMD0dwOlo5cOCXl9yWTs8SDJ4hHH6dqamzSLKMw2PH1+7CWde45E2NmsiQ1wsU9ALOOmXZG56bd2GaT2YpbsNA5FZaLo/JBJJswWQqzRZaqBbKTY7ScP9PV3uJFeFvq2NmNExtwG2IN74WyXzbgGozwljFbacu4EXXddTrbW+6nienanj8TgLd/jXOIFRCXi8QmZgjOh2/43axcjidTk6cOMGJEye2RHvZwu+jvr4+HnrooSqvRhC2IQOGQobbDa0KRCgkCNuMaB8TjGps7EXOnfsa3iYXTZ2+ai+HvF4gfC1GLJhg//4PXW9zg3h8hLGxHxCOvM5c7Bq2GhsW2YRsk+i6r3XV8938xjrQ2EJ9rY9wNEQ0FianlVp1FoIhgEwiC1q+FJiYoVjYXiGRlsuTS5cet2YxUSzeeIyZZAalvpG5Cm1HX23VrhYyItkurfi1UBMZxvuDOOuMFQ7sBFk1R2h0Dqtk4+tf//odt4vdqbXay+qaPHc0fyseShGbSpBRs2UdL8kS3iYXtU23D2vddU6Gh4dFKCQIwo4hQiFB2EaSySK53NrHCcJmO3v2q4yPv0Sgy2+ImSJZNUfwrTBmk4Pjx7+8OHy7r+8phoe/R42rBle9Qmd7qZIpq+YY6Zskrxdu+2aivtbHieM/g9PhRstlsEhWCsUC59/8CW9ceh0oBUNFcw5bfT2J8RE0yQyYKBYKQOXadzZDsQi6lief08lrBayOUltQTtWwyObrFUMmMskM8xkwqeVvd73VGK1ayKgUtx1fay3BoVnsPa2bPmh+p4rPpgiNxzhy3xG+8IUvbHqr1ErtZcOvj+P0Krh8Djz+1dejJjKEx+fIawV+8Rd+seydwoaHh3n6b59mtG8KZ33NqiGUVZE5e+4sH/nIR+768QmCsBrjVQoJIhQShG0lHN5elQbC1qdpaU6f+W1isbcq2iqWVXNk0jky6SzZdA6bw4pllfkoeT1PLJigveM4vT0fQ5Ydi7dNT5+iYVf9siG+NsWK2WJmvD+4+Abf43ctCbicDhcfePfPQy6DlgiVrgWYLFbuO3QUMPHGpfMApNUk9kY/msdOTtUoFLZ2hV8pCMqja3lMgFm2YHPZkK5/rezOpd93u8uORbYQCacIDoWQ7fLiXJlqzZXaaKJaqHy+Ni9qYp5rV2aqMmh+pwkOhYiHUnzsYx+r+i5bN7eXDQ8P861vfYuTr5wkNBZbVj2kZXRC4zESkRTvete7+MhHPkJTU1PZ1+rt7eXxxx/n+eef58//4s8Zfn0Cj9+Jr7V2STiUin7lJZYAACAASURBVKqEr13e8McqCAJgMhmvXcto66kCEQoJwjYSDosh04JxxOMjnDv/+2j5GB2HA2VXAKiJDLNXozh9yopl/jcHQPlskUQ0CYBstePxdNLe3Es8PoI2n17x/Bbg2LGfpbn52LL1ptMRmva2rXi/QLef7PV2qFwmx8xoGLvTuhh0/fT9j2LK6+S1pduEF/M5TDk4cs/bF0OhwvWdLsxmE3anlflEpqyvjRFl0zn0XB6LbMHmsCLJa1c7mUzg2bePt66Oks8X0OLzhCdii7e37GvcFq1Edc1ewhNREQqVwddWy3h/sNrL2Na0jE5wKExRZ1Paxe5UV1cXn//855dVD7nrnVisZmLBBIcOH+JX//2vll0dtJKFEKqvr4//9t//G5dfv4y73om3yYUaz2Aqmvn617++gY9MEIRFRtx9zGjrqQIRCgnCNiLmCQlGEY+PcPLkv8PmMNO8z192+8zCDjjt7Y8QDvcRC07i9pfCgZUCoMaGXg7u3Y3Hs7vsreRXMzr6Aq5a56rhlatOWRJUTA3OMnZxis7rLS8N/kYKmZXboYr5HJLFQ6CxheDMJKFYmN0NN3aIKuSLyBXc+akSisXSbKBioYjdbcNiubMWqYLJhKNWoXVf4+Ln8nqBwdfGsN70PdAy+pZtKcplctgdtmovY0sJDoXEfKF1uF2L68xIhIb6Rv7oj/7I0Dtr3Vw91NfXx/PPP8/518/zxS9+cUPn/PT29vLHX/tj+vr6+Pu//3vOnDkDwOc+9znDBWaCsF2YDDhoWuxIL0IhQdhWxM5jghEsDJR2+xw07yk/qFloaThy5JN0dDwGwKVL32R8/J9RHI0bGgCtZHziB/jaXGUf37yngfH+INeuzNB+KIBkkdHLvG9Oy4KrVD2Sz5eet3caqlTLYqtYLo/ZYqLGU3NXr+9MNjvZ3NIBsVm1VIllNt/4Wly7MkNWzaG47dgcVhR3DXbFauigKK8XUBMZ0rF5agOeai9nS7ApVgJdfpKxNJNXZjBbTLjqHGsGRAs/M9XexdAIsmqO8f4gDbvqV5zJUygUePjhhw0dCN2qt7d3XVVBd3KNhd3RNnoHNkEQbmLEUEikQiIUEoTtQgyZFozgwoVvMDT03LKB0moiw8xwBKvNRr6ooXhqkG0Ssk3CplgJDobIqgUee+xreDydi/c7ePDDi7uCVdLU1Gm0XAZnXePaB9+kZV8jY/1TjPcHiSdiOFat9imFHMlUYvFP2957gFKVkCFfI92kUCiiZXXyuTzFQrHUKuYsr1VsJSZbDUp9A5E3frzk8wszhRZmfSjuGrJqjpZ9jWTTOdTEPPHZFIV8gT1v7zDcAOe8XmBqaJZ0bB6zxYziseOqFRUv5bBIZjwNTjwNTvJ6gVRUXQyIZJuEs07B2+DCbDajJjOo8XmS0TSFfBGzxUTjLp8hhthXU2RyjrraeqLXEiTDaRo76w0dnhrNwu5ogiAIO434TSEI24QYMi1Uk6alOXfuD5kNvUH7ocDisOC8XmDmaoREJEVX13txOAKk00ESyUHCM2NoudIsHa+3hYce+U8VqQAqx9TUKTw+1x2HDAtbzI/1T/Gjl1/l3T/zLtCXb49ssdUQiYZIpUvtb1Mzk0hWOxZ/E7mJCUwGrxLK6wX0jI6syMhWad0BltzagablCM5MLvm8TbHSfihQ2l1oIkZwOITzetteqVqkVF311k/GUBMZw7UYjfcHKVLcNjORqmWlgCgeSjLSV/p5MVvMOGtraPz/2bvz8Lju+77377POihksg5UESRDcV0mkLMm2aFuS5SVWkqu0Tprc5LZ5WsftvbdxbrO296ZpkqZJatdO49TN0thuFttJI1vxIm+iYq3UQkmkuJMgCGIZApgFs52Zs98/hhgSAkCCWGfI3+t55qGEOXPOb0ACmPng+/t+NyWItobJTVa3nQJ3bDDkOh7lvMXH/vW/ZP/+/fzu7/0uZ0+cpXVdfFbjfEEQhLVTh78Fq7f1rAERCgnCbUI0mRbWUi43yNTURXy/WhUUCOsY+QpXBtJEo108/PB/nFEBNM22S+Ryg8TjfTOmgK2WVOoEFy48wdjYa3T3ty/qHNPB0EuvHGHzpn627OhD9h1810GSFVB1fFnhO8/9r9pjiqUC41eu4LZtwj43iFxnFS9vp2oKtlTtBbAcr50Cm3cyNjE27/3hWLAWDs0V1IXjQbLJXO0+TVfXvCIieWES23Tov6e37iqYGtn1AZFdcfA8b9ZWsdbuGIoikxyYxMiX6d6yuK/lRpabLNZ68QB85tOf4YknnuDzX/g8xlSZzr62NV6hIAjC9OuI+gph6m09a0GEQoJwmxBNpoW1lEjs4YMf/AJDQ09z+sz/JDU8BMCOHT9xw+1fmhYhkdizWssEqkHU0NBhBi5+lVIxRUtnbEZ102IoqsyG3d185Wtf5q7d93Dv/QdoT3SSmphkbCzJc6/+gEjbzIbDJ0+dYv/WnWjJC4R7NmOMXcTOpZb69FaEJIGiK9hlG01fekPscP92vEqZf/bRf8H45DjPvvJMrYpqxnHz/J00tURIJ6dmTauKtITo3bHwEdXLJZPMU8gYbNzTLQKhFXSj4C/eESUY1Rk6kcQ5c4VQ9Nq/HVmRicSDt3XfofxEkX/8Yx+d8bHHH3+cd77znbWqIc8TvzwSBGGN1eP0MUGEQoJwuxBNpoV6sHHjw2zc+DBDQ0/T3Lx5zuqgtZLLDXL+/JMkky+gqBLRthBdW3uXtcKkZ2sHb558neeff4Hg1abIjuOQnywRvFJtpDt9vRNvneTQu99Dz0//n9jFAo55L2e+9EfLtpblpugqjjl7a9wtn6e9i1i8FVUv4Zll1nX2sHfHXbx09LkFn2O6cmSaXXGoGBbJC5NkkvlV3S5j5CtMXErT3d9+W4cOjSAQ1tm4p5vR8xP4brn2ccdxmbiUrjZGjwWJtUUJNwXXvLpsuRj5ChXD5NFHH511X1dX14yqoUZqMi0Iwm2oDiuFREglQiFBuC2IJtNCvZmeHrbcnn/hl/F9mX17f27BgdPQ0GGGR77NxPgZmlqiJDbEVrTvyFwT19q6m0kOTDJ4fIREbyut3TEuXRjCsssoTQkco4gaWHyl0mpQVRkTcBwPdQnVMKGdd1MsFfB8D8lz8T2XgL60se1asLp9zNvURnJgctWqQkzDYuTMFZq7mu7YXjb1JhDW2bx//ayP2xWnOhEuZzA+mMZzvVoD63AsRDgWbNgqr9x4kfc99D66uuavknv88cd59NFHRSgkCMLaqsfJGvW2njUgQiFBuA2MjoqScOHO4Psumcw5nn7659m16yfp739szl5EpdIEw8OHOX/hCXzfJRzX6b97eauCboUWVNmwu5tMMk9qOEtuorpVajg5QmswhHtljPSZY2uytlshyeC5HiwlFNqyg6NvvcaurbuJhJuwHZuzF08vy/riHVHKpQpDJ8bov2cDiipjVxxsy1nS9sC5uI7H2PlJwvEQXX2JZT23sPy0oEo8eK3CzDQsSrkKxUyJbLI6FTAcC86o5msEdsUhlyrwQx/+oZseKwIhQRDWXj2GQmu9gLXXOD/1BEGYl5g8JtwpVKWVSPMYTW1RBgb/jqHL32Hvnp+jp+d+YGbjaD2okehtJtoarpsKgNbuGE0tYYbPXgHg+9//Pr3/ey/pN1/HvXRqWfr1rCRZqfYVkiRpUWuN3PdepECIk+eO88bJ12hrSZDOLl8fpULGwDJsPNfn8skkwYhObrIIQLw9OmcDYtOw5q0qKmQMTMMinojOCgpGz47j4y+6QbmwtgJhnUBYr201NPIVRs6MUzGshgqFcpNF2hJt7N+/f62XIgiCsAAS9ZfC1Nt6Vl/j/NQTBGFe6bToJyTcGeLxPorjJ2lqDROOBckm8xw58ju0d+yibExQXKbG0StJC6ps3r+eTDLP0Plh/uHws9x/33tJnXoTSdJRtfoNhoJRHatiYxsWjimjhzUUZWGBmxQIEb37AZ4/+hyWXd3vupyBkOt4JC9MEI6HSKxvIZPM1Sq0FFVm6EQSLkzSsakNRZVrY++NfAWoNqmOxMN4roeRK9c+roc1UsNZ4u1REutbZgQGwbBeN4GjsDThWJBgRMcsWTS1htd6OQs2NV7gEz//ibVehiAIwoJIddlout7Ws/pEKCQItwExeUy4U0QindimA1QnfiV6m2lqCzM5fIlgc4DOZW4cvZJau2O0dseYMEdo6noY6x3vIv/yC7hBFS2gIsv1+SJFD2qouopl2FTyJnpEX1DVUOiu+6jYFucunln2NV0+mcTIV9DDGuu3dwLQ0h2bEdhs3NPN8OkrtYllpmERS0Tov7uXimFRyhlkxqaQVZlwLEiit6UWLE4HSANvDNfCoXh7E8mBSTr7PBEM3SZcp7F+ltoVh2AowOe/8Hm2bNlCf3//Wi9JEAThxkRPobrUGK+cBUGYl5g6JtxJIpFOKkZlxscCYb0WBDQiy7Y4fvYY+x96FGvgBGY6h1OpBl+yKiPLEpIiISsykiwtuDJnsQItbajhKIqmI6kasqohayqKqoGsgefgOhae7WCXK1QKBori4VsGVi475znlWDMt972Xfzjy9LKvd7qB8Nurw94e1ATCOn371zN6dhxVV1i/vbMWIGpBtVodMk/v8nAsyIbd3bPCIVmRyCbzJHqbl/15CavLyFcwDauhvpdoQZXu7QlSl7N8/OMf55Of/KTYRiYIQn2rw1CovlazNkQoJAgNToRCwp1keuLYjfrANKKjx19he992YgffSenlf8Dz/OrNcfFcH8/2sMtO7XhZkZBkiUAksOjXVsG2juqtuY1QS4JAS4Jgc1vtfses4Ng2rmlhWSaOaeFYFqquEw4E0AIB1EAARdNAUlCuhlZWMY9VmMLMT2HmspQmxwjc806uTIxx8tQJClmDUs4AoK2neUnb/AoZg2wyh6zICzqPosps2N296OtNh0O5iSLJgUkApiZEKHQ7SA1nibaGG6bScJqiyoSbQ2TH83R2Nk6gJQjCHUqMpK9LjfWTTxCEWUSTaaGR5XKDTE0NMjl5HIDm5n6am/tIJPbMefz0pLFG2+axEC8efYFHD30IN5+jcvqN6vaxt1W7TIdFVskC/9ZexwQTnUR7NhDt6qVp3SaUQJByborM6BiD5y4zPvg86eERpkbHMKZyt7R23/OJd3UR7+mkdV03bVdvHTvuZuP7fwS1JcGpc6fYu+kAY/ooY/Io+XyOyyeTs7ZqLZSRrzBxKY0WUFm/Y3XfDOczRfSwRldfQmwduw0Y+Uqt2qwRFSYN3v/+999wJL0gCEJ9qL9KIVErJEIhQWh4osm00ChKpQlSqRPkcgPkC+eZGK/2lgmGgqih6hvrickXqZSr28NaWjfQFN0yKyhq79iKkUvVbSPpxbo0cpEXX3+e+97zQSZTSdzJK7OOkWUJ1/HwPZ9gU+Cm54z1baN5807iG7eg6AFyyTEuHX+Li1/5OiMnTmOWjGVZuyRLZEfHyI6Ocem1N6uVTJKEFIrQsmcfSkuMcHc3/ffs5dBD7wYglU1xfuAcr7z4KkMnhwnHgrR0x7FNB8/1KBcrtUoiu+Iw8MZw7XpaQMU2HaKtYdp7W1a1aiyTzFPOm2zc031bVavdyVLDWcKxYEN+TzHyFfKZAj/zMz+z1ksRBEG4uXpsNF1v61kDIhQShAYnmkwL9W5o6GmOH/8TbLuMqqkEIzrBaIB12zsJx4KzKi1cx8M0LIxcgUzxyIygqKNzG9nMGNHE7VmdceLMMdqbE/T/2D/lyuf/AN8sz7jf98EyLLTQ/I2oI719tG7eSXP/TpAkBl5+mWef+CyX3zyBVS7P+Zil8tzq9yFZlfFdH8/xkIIBtEQnQ2fPMT42jhbUeOmvNQLRCJsP3MWWBw5yz91388DBd5LOpHn9tdf5h+8/i+1aV5tZK7VKIgBVV+jZ2gFApWQRb4/ecpXOXbsPoGs6r7z50qz77IqDbTk3DQZyEwVi7RERCN1GVF3B8xqz6jY3XuR9D71PVAkJgtAQpDrsKSQKhUQoJAgNTfQTEhpBqTROIKywYfPCJoMpV6c/Xf/m/FpQNEkwVg0dblfPHHmaxId+nLbH/ynpJ74wIxiyyhaSVJ0Adj1JUWnduY/2PfcSbG5j4uJFvvsnn+fscy/imNaKrtf3fXzPR1LkanWQKuFLMnJ7D7mpHOPJcfSwjhqo/t2bxRKnf/ACp3/wAgDdO7ay85FDHDr0bt7/6Ps5ef4Ex069TrFUoL3XYXwoTTFj0N3fXvs3sdiKDsd1cFxnxscKGYOJS+naVDtZkWhqjdDaE58V/NgVB9Ow6NnavqjrC/WpuTPG5ZNJ7IrTUD2F7IpDLlXghz78Q2u9FEEQhAWSqL8Upt7Ws/oa5yefIAiziFBIaBSqpi3pzdZcQdHt7Mnvf5WPfvjHiR36ALnvfQ0A1/VwTJfAddvGlHCUjn3vILHzLhRd59yRV3nuL3+b7GhyVdbp+z6+W21uVKtckmXUrvU4ksRUITcjEJpL8sx5kmfOc/iz/4O9H3iIB37sh9n9Iz/DwOULvHnyKIWsQaVoEu+ILnm9J84cm/H/dsUheWGCaEuY9Ts6UXWVYsYgN1lg8NgoWkAl3tFEPBFFC6rkJovIirSmVUJGvkKlZGGWzOqfRrVqqmNT24r0N4pGmgAolgrLfu56EY4F0cMa6eQUXX2JtV7Ogk0OZ9i9Z7eYOCYIQuOox0ohEQqJUEgQGploMi00oraWBJpWfVMd0AOYlgnAlYmxtVxWXbFsi6eefYoffuRHaYm1MPWNL2OlplA0BVWVkfUAXfceomPPAWzT5I3vPM1rX/smxUxmVdY3HQb5fvV7kDwdRmg6SnsXjiRxJXUFWZORWXhQ8dZ3DvPWdw7Tu28XD/zE4zz+oY9yetNp/u7LX12WddsVp7bWYtYgM5YjGAnUtqUBxDuixDui2BWHQtZgaiJPajhb62MkKzKDx0bp2dq+quGQ63hcPpnENCz0sIYe1GhqjZDobWF8MMXA65dp7WmmpSu27OFQUzR2W4dCAG3dzYxfSjVUKGSVHfL5PMVikWh06aGpIAjCiqvHUKjOlrMWJH/6FZ0gCA3nySdN0VNIqHvp9HMgvcxd9+1j4/pNM+7zXBtZubYVqmQUuTg8wNj4KEMjg6u80voTjTTxoUMfJui6jPzZHxEISHTcdZDuA4eQVJXXvv4UR77yBHalsqrr8j0fz/WQr46hB5ACIeTOHizHYiI9gb8MPVpaN6zng//3x2hu7ebcyGneOvcGjuPc/IFvk5sskpsoYOQrM8KdaEuIzgVMEDMNi0LaIN4eRVZlkgOTFDMGLd0xEutbVmwC2fCZK9VKLKBSqoanG/f0zBlG5SaKTA5n8FyP1qsNugNhXUxHWyDX8Tj/6hDrtnfS1Bpe6+UsiOt4DJ+6guTJfPrTn6a/v3+tlyQIgnBDk9/4H7iZ8bVexgxadx9t7/8na72MNSVCIUFoYJ/73Mo0jRWE5RAKSWzdqtDeXiQ5cpSwbOAW0jj5LL5jzzpejbWiRGJITS0EWzopGQVePf4K5y6eWYPV1w9d0/mh9/4wTUqYLl1C0zROPfscP/jCX1PKTq3Jmny/2tdJUZXqdNlIDKW9k5JRJJ1NzzpekqUlhURd23by6Md/luauVl478QrnBk7jM//5UsNTGPnq90cjX6kFQM2dMYxchUBEX/IbfyNfYeTMOK3dcRK9zaRGpiikS4TjQcKx0JxN1G9F8sIkhYxBa3e89rGpiTyO5dLd3z7vdrrcRJHxS+lrzb8Vie4tHQ0TdKyV6Ql3ffvXNVwT8fGLaco5i8985jMiGBIEoa5NfvPP6zMUeuQn1noZa0qEQoLQoMbGPJ580lzrZQjCLKoKu3aprFsnY+WnKA2+xvjgi4RiQWRlYW+SJVVD79xIoHsjuUKe548+R3J8dIVXXp80VeOB/Q9yYO87MCYn+eZ/+n0mj79Jx7ZtZC9fXvUqoWmu7SLrGkpbO3I0RjaXpVDM1+5XFZWW5hZCwWoY4fs+5YpBeip9ywGRkTVQNIX7fvzHeOinP0q+nON7zz0175amc69cItoSRg/qhOMrVzFzZTBFKVtGVmRs06G1O46RL2Pkq38nc103HA+RWN8842PTk88qJQvbtDFLFka+MmdAkZsokhyYvGEwNG26wik1kl3RvkO3g+kwbds7Nq71UhZl7PwE+VSJX/zFX+QDH/jAWi9HEARhTpPf+nz9hUJdfbQ98uNrvYw1JUIhQWhQx487vPDC7GoLQVhLoZDEwQMKId2hcvFNnHwaxzEpFC5RqeRRFAVZk1B1dUEBkaRqBDbsINi+jn848vQdVzWUaO3g0Qc/gG9JvP7336alJU7ftu3EEwk82wTXxS5XSA8Pc+6FFxh8afao9ZXih5pQ2trxJZ90Nk3FvBZO6ZpOZ3sXeC7SdRU9viSDJJEcT86aAjbvdXyf8lSZYCyI67g0d/Xw2C/+azo2reOlN57n/ODZ6mS6fAW7YteaL2+9d+OKBiCmYTE+mMbIV4i2hunub59xPdOwKOUqsyblZZI5tIBKz9Z2ZLm6FW06RJruFRQMB25YzZRJ5kkNZ+bdSjbXWodPX0GSJDbs6m6oCVvLLZPM47ke5WIF3/WxTac2eS7aGmb99s41XuHiTQeGIhgSBKFepb71BZxsvYVCm2h7WIRCIhQShAZ0+LDF2bPuWi9DEGpiMZn771PwjCmMc0fBnRlaep5DpZLFNLNUKjkkWUJRFVRdQdGUG55bb19HePNezl48ww+OPL2ST6MuSJLEgb3v4O7dBxg7P0D67BBb79qPbxbxbRMNH8mxQFbxfap9mYIR8uMTvP7kk4weO3bziyxQbeT81d5BcjSG3NqG60nkclOUzNKsx3R39qBKIM3xCsOXJCzXYXxy/heFru3Wtj/5ro9jOYRbqgGJ53m4ts+BH3mMBz/6OGfOnOGrX/0qvuIRDOsEo0EiV6uDVoKRr5AazmLkK4RjQVq647e0Nct1vFpPIqhOvursa7vl9Y6dn6BcMNm0b92Cwq/pnjkbdnffMVP8rmdXHEbOjmObDsGIjh7WUFUVLaDWQrLb4fMyHQy9853vZMuWLVy5coXx8erXWmdnJ7/0S7+0xisUBOFOlvrWF+s0FProWi9jTYlQSBAa1N/8jUk6LZpMC/VhOhBys6OUL7510+PnDYgCSrVPzRyUSIzIzns5PzRwewdDrsxHHv1hutq7eP2b36GvfzuaouLlJ/Edq3aYLMsEdB1FUfA9F1wXKdKKGonxg//x5wy+9BKSLCFJUm1KmHTdxI/rf/xLb5sE4nvVIKh2jKIg6UGItaCEQlTMMql0CrNsEmya+UY6GAjSkeiEOfpG1agaY1dG560WKufK4IOkXFvX268D0LZxAz/5m/8OT4FvPvP3M7avrYTpN9yxRIS2dc1LCp4KV0Ohxfb6cR2PwWMjNHfESPQ23/R4I1/h8snkildQ1aNCxiB5YYJgJMC67Z23/fOfrmIDUHUFPVj9dzo1XuDQoUP82q/+2louTxCEO1jqqS/iZCfWehkzaF2baHvoH6/1MtaU8hu/8Ru/sdaLEATh1j37rNg6JtSHWw2EACRJRtMihEJtRCKdKEoA33OpGCVkRZpza5lvmzi5NF1b9iApym3ZYygSjvLQPY8S8DW+9Qd/zP2PvB/Fc3AzSSQ8JEmq3QAc18XHQ5JlZE0H18L3PTa/60HyqQyZwUvXAp6rfXymmz57rjfjvhk330fSNJRYM0p7B1p7J0QiVFyLickJMhMZbNNG1dVZVV6hUJiAriPd4HdOvqxgWibOPMGRrMo4poMe0dFDOmpg7u1O5VyOk88+z9Z7D3DP3fcxNjGGUZ5dubRcRs9WG0t39rWh3qS67WYCIY1ASLv5gfOQZQktqDE+mKKpLXLT9dimQ26ySLw9uuS1NxLTsLh8Mknbuha6t7Qjy7f/7GFVU4h3NBHvaKKpLVJtfh4PEmkOceLoaXK5HPfee+9aL1MQhDuQceE4nmlcG01fBzcl2kK4b9daf2rWlAiFBKEBjY15YuuYUDfuvVdBsXOUz7++qMdfHxApik6pkEaSmLNiyLdN3GKOTbvfwdjE2LyNhhtRe1snP/zIjzI1eoW/+rf/nsf/1c8huxZefhJFq45+n+vm+z6u5+I4drV7j2sjyRL9h97HhbMXsB0XJRJBDgTxfUBR8CUZyfNQoxFkXUcOBJGb4sjhCEqsGbWjA7U1gRyJYDo2U/kpMlOZat8gGRRNwbVdtLA2q8ooFAzdNBSSZIVSqThvpZAkSTgVZ0G9p+xyhePf/wGJjb28+z2PkM1lmcpnb/Gzf3O5iSKFjEHPto66CRYCIY2KYZGbKBJLRGesa9vmHeh6oPY1IisylaLJ5OUstukQjoXq5nmsJKtsk5sssmF391ovZc2pmkIwqvPys6/S2dnJli1b1npJgiDcYYyB4/imMeOXXGt9k6PNIhQSoZAgNJ7BQZfhYbF1TFh7mzYpdHdJlM++gr/AxsE3omkRFEWnYkzhuR6qPjsY8swyUiDExs27ODtwGtdr/IB04/o+PvTej/Dm957hf/3G77Pzvnew78F34eXGkeZqzDMP3/dwPRe7XMTXw6AFuHT2PHIoiByJoDW3IoVjSNE4aqK9GgQ1xZGjMRxZxtd1bEWiWC6TncqQzWUxysasih7papXK2wMhAN/zaIrGwLvB9yhZIZPLMN9Uecdy8F0ffYHbs3zP4+wLL1PITvGBH6v2BUhOjC3osQs1enacls4Y0ZbQsp53qSLNYTLJHHbFpqk1MuO+qVy29vUhyxLxjibC8RBTEwVs0ybacvuPqZ+ukEr0ttQ+ZhoW+VSJ1GiWeOLGE9xuN1pARQtofPeb3xfBkCAIq84YeAu/7iqFmglturNDoTt3/IQgNLBUSgRCwtpTVdi2VaYy9BaeSZ3jfAAAIABJREFUWV6284bD7WhahEzmNJZho4dnb7GpXD5DZM872bvzLo4ef2XZrr0WdmzZzaF3vJfv/OkXePGvv4YPvOcf/ShuIYPvLn6bqJW9wrZ77+GFp77BlfGZTR092yMcDWPa5k3Hw7uOi+/6827hmnVd26JQzBMJR5DnCIY8WaZQyN3wutNVQrfq+HcPU5rK8Y9+7f8hEo7y3CvP1O7btXUPRtng0sjFWz5vJpnHdTxaumO3/NiVpqgyvTu7GDqRJJDM03p1jelsas7jw7Egbd3NpEay0LeaK10b0/2Dxi+lscsOxalqL6fm5nWUptKYhrViTcnrVbyjGoR98pOfZMuWLfT396/xigRBuGNMhzH1pN7WswZu7057gnCbSqVEf3hh7fX1KeBa2JPL39tH08K0tu7EtV0sY3Yw4js29uQo+3bsX/Zrr6aD++7jwXe8h2/9tz+rBUKxjmZira3VPfdL4JYNNF1H10N4bwtnZE2mYlZmBTPVyV4ulmFRyVcwsgZmwcQqW9zKXIqpwhSmZeHJCr4sgyzjKwq+olIySuTyU/Ov23HxPR8lsLi+NwOvHOVL//532LppK48++CFkufpSJ5vPUijdeiNq1/FIDWdo7Y7XbYPiQFinc1MbE5fStQbWNxJtDWObDqZh3fTYRmLkK4xfSjNyepxLx5KceWmQwWPV709eOUZn20Pcf/+/5bHHvkR/f7WpaKV4e30OFireESWWiPCJT3xiyecyzWVYkCAIdwRJYs0rg2bdBFEpJAiNSEwdE+rB+nUS5ujZFTv/dDCUSp1E0eRZDY2t8SEC3RvZtnkH5y6eWbF1rJSD++7j7t0H+Nqn/pA3v3EYHwg2Bejs7QXAsypLOr+sSBSn0qzftpVLx08hqzKyItcaTSv6tUlvnudhFqpVQ5IkISkSiqaghTUUVaGcK+NUHLQFNkb2PZ/J9AShUJigHkDXdSqmiWlWqn2JbsAxHRRNqYU5i3H5+En+8tf+A//kt/4d73/3B/nuc08tujH55HAGkOqySuh68Y4otumQvDCBvqfnhtUviioTbQ0zcTlD746uVVzlynAdj9RIlmwyT3f3fXR29aHrTTQ39xEKdRCJdACQyw1y/vyTHD36X/BxiSUiBKN3VpXQ9Zo7Y1xOJRf9+LExj1dftRkb8+jrk+nrU9i0SSUQWMZFCoJwm6nHEKYe17S6RCgkCA1mbEwEQsLaa22VCYZkcpmVHSuqaWGi0R5KpSSheHBGDxvfsbEyE/T19jdcKLRz6x7u2XOQJ/7zH3DsW8/UAiFJkmhqa8Uxy+Av/WtdV2WaWuMEmgJ4tofneriOh+96OKYDEqiaWqskCsaCczZ21oIaVtlC1uQ5G4DPp1w2KJcXXvHkuR6uVe2B45jOgreszSV57gJ//f/+Nj/9u7/Be+9/hGde+t4tn8PIV5i6UmDD7u66rRK6XqK3mYphMnZ+kr796254bHtvC0MnkgyfuULPlo6GeH5zMfIVJi5mkKUIhw79DonEnhn3l0oTXLjwdQYGnqBUShNtDpPY0ES0Ndywz3mtFQo+hw9bM16PDA56DA56gD0jICoUPKzrirFMs/qLre3bFZqaxBsxQbjz1GF1Tr2tZw2IUEgQGozoJyTUg7Y2CbtUgiX0vFmopqZ1mGYGs2QRjM78FbRXyNCzYfuKr2E59fX28+C97+G7f/YFjn/7H2YEQgCFdAY1EGI52me7rodpGCjqtaog13ExCyah5hCu5eLaLp7joYf1eSd9qQEVx3IwC9V9IrJardySFRlZledsOD0f3/dxLGdGk2nf8/Hcamil6AqSJGFX7CWFQgBXzg/wt7/9n/nxX/8VSuUSr7z54oIf6zoeyQuTNHc1EY4Fl7SO1dTe28LgsVFcx7th6BEI62zc083o+Qkun0zeMPgyDQurUt1uVi5WcMouG/auXlB2/fWnGbkysixTnDLo7/8Iu3b9FJpWbbRt2yXGxl4mmXyesbHXCIQCxDujdG3rRQuKl76LVSj4vPqqfdPpp9cHRPPRddi3T/xdCMId5+rEr7pSb+tZA+K7sSA0mHxe9BMS1l5bG7j5yVW7XnPzNtLpE7OqR+zsBOHNe2lrSczbWLeerOvu5ZF3P8qLTzzJ0a9/G9d2CVwXCAFMjIwAIGkBfHtpzTrkQIiJ4ZEZH/NcD0mpBjlqQF1w8BJsCuL7Pp7j4TnV3kN2ufqmbzocqt3m2fplV2zsil3donbdOHRJkqrBla6gBbRqcGQ6S64WArj0xnG++sn/yuO//AnKFYO3zry5oMdNDmfwfZ/23tYlXX+1TW8bMw1rVpjlOtVfKkyHOYGwTldfgssnZ28hMvIVclcMcukcAJoepLl5I12tWxkY+Mac519upmExOZSlOGWg6UFaWnrwvAD4Cht6qhVBHfv2zaoOOnXqrxgY+AZ6WKNv/7o7rpH0rRoYGLhhs+lUyuP4ceemYdCtOH7cEaGQINyJ6rCPT50tZ02I78aC0GBEPyGhHoRD4GZvvWnvYqlqgGh0PYXiMIqm1AIF/+qodF2v/yYWidYOPnjow5x8/iV+8MUvzbrfsRxkVcYqlynlcuiKtqhQyPd8fB9kTUNRVSZHr/XScR0X13SR5cW9ApKkaq8hRVPQ0Grn9FwPz/awDftaQ2qJWuWRoinV7Wo+aCENLTB/b6LpQAiJWoNrNaAu6TeL5154mW//8ef50M/9LBWzzPnBG/fCarRtY28nK3ItAIJqGJS9kiczNkUwEmDD7u7afZVStfomN1msTS4DyIzmCGobOXToV2b05QHIF85j5CZWLBRyHY/08BSZKzkS7Tt51wf+zYzr38z+/R8jEunm+PE/ZWqiQOemthVZZ6PTdJVYa5SPf/zjbNy0kQ9/6MM8+uijaFqUsTGPwUGHsTGPQmH5fxlVKPiMjXn09DTe15cgCEsw3Wi6rtTbelafCIUEocGInkJCPQgEFUo3aRi83CKRTiqVDGapTLDpWghklvK0tSQW3Uh4NYSCIT78vo9w8c23+MZ/+aMZ9zkVB9Mxwa+GJ4FogCuXL7Nx8waoFG/5Wp7rARIEopQLBpODo8iKPGN71kIbRi9EbWtaAGzTxqk46BEdfPCuBhOu7aLqKmpw/nDHcz2cioNjOUiyhB6qVndMVxcFIoFZzcZvxZvf+i5NbS2858d+hKn8FJPp8TmPM/KVhtw2dr1gRMcsWTS1hoFqZVBmLEc4HsLIVTjz0iDAjOdn5Mu1UMiuVEe333Pop2dV4QDEmrYyMjqIrMoEI/qyfp5SI1NMXSmga/E5ewQt1JYtj9Hc3MdLR34T15qkc3NbQwZ8K0kLqvRsb6e90kI6meFzn/schw+fZffupU8kW4gzZxx6ekQVlyDcUeqwUqju1rMGxE9HQWggK/HbOkFoJC0tW/E9H6t8rVeF59gE6rhSSELi/Q9+mHKuwFd/97/gX23q7NX+9NFDOoGmAK7t4jouh7/yN6AGUMLxec/r+8waEz89Yl4JhQi0dvDUF7+IElDwfR9JkQjGgwQigSVN9roR3/WRlWozakWrhk9aSCPYFEQLabT3rue+D3+YxPqZTZArhQqVfAXP8whEA4TiodrWtlA8hBbQMIsmlmHNes634gdf+BLnX3uTR971ATR1utLJIzdZJHlhknOvXOLyySSBiN5w28auVymZaG/bdhdtCeE5Hhv3dLNhd/UWjoUIx0Js2N3N+u2dtWMnhzMk2nfOG8hs2vR+WlvupjjpcflkdfR78lyG8UtpcpPFOUfdT/doyk3OH3SmhqfIjZfZtfOf8cEPfn7RgdC0RGIPhx78PWQ3zvDpK0s61+1MC6rE2qIAbNr0k6t23bNnXTHOXhDuOHUwgn7Wba0/J2tPVAoJQgNJpUQoJNzZZFklHt9MNnsBVVfmbYxcT+7Zdy/tLW382c//Kq41s/Hq26d9qQEVq2RRSKd57u+/znv+tx/FMw38tzX0nu7tAxKyKtV+yeVdHSmvxNsZeOstLp08Ud2qtUqZmed6N5xOduChh9j9wANIEqRGrlV2eU41DJqvEkgLaciajFWyqk2xI/M3xZ6LY1X7E3mOx9/++u/zs5/9FHvWH+BLX6pu45MVmWhLiM5NidtiKpXn+jMaKtsVh2LWoHNTYkZ/nbkqfOyKQz5V4tChn573/PF4Hw888O+qx9slcrlBJiaOk8sNkkueI1mq9huLtTahBCSaO5rIjOUoZAwKGYPxwRRNrRFae+Iz1iOrMroWZsuWx5b8Oag9x3AHSDKBoKhIuZFCqkSifectbdNbDum02EImCHcWifpLYeptPatPhEKC0EBSqeVr8igIjSoYbCEcbqNSzBKKB9EjMdKZ+mwy3dnezT27D/Kt//YnZEZmbm+bq1pHC2m4lotVsnjj8DNsu/tuujb04uUn8czqaPfpSV2SJOH74DnXfV9QNNR4Kz4yT33+Cyv51ObkOR5acP6taSdfOkIgHObCsWO1j7lX13+zrWGKqhCMBbFKFpVCBS2koerzb0fzfb/WDNv3fBRdIRgPIsvw1Gc/y//x+79Fzszw7Se/R1NrmI5Nq7+9aNP6zWzbvIMXjz5HsVRYlnMa+eq2zuvDluTAJMFIgHhH9KaPv3IpRTjctuAqHU2LkEjsmXH89UFRMvkiw6eSOLZN3/51qLpKMWOQmywweGwULaCihzVC0SCarlIqLW8D+1On/opyZYINe7qW9by3E7vikB3Pc+jQr671UgRBuM1J9Th9TBChkCA0EjGOXqgXuaxJoKkFJ59ek+uragRJqU5FUlQNc4lTulZCQA/y6IMf5Nxrr3P8u88s6DGSJCEpEo7l4Lkef/eHf8S7fuQxDjz0EG4hgzWVwr86PUyWJXzfx/eqlUJKtAU52kI+nebrf/g5rHJ5hZ/hbLIq41jOvAHPyPnzjJw/P+Nj09PQFkKSJALRALZpYxvVm6IpyJpc/VOWa42q7YqNxNUJa2/rZTRxcZBnv/y3PPRTH2UyO87RZ49TKVmr3lh6/667SLS2k7jYvmyh0NsVMgZGvkL/3b03PTY3UaSULfPww7+7pGteHxTt2vWTvPbapxlLPo9VcQiEdeIdUeIdUeyKQyFrUClWmBrPo18NFHO5QeLxviWtASCVOsHAwDcatmH4apkczhBvXr/k7XqCIAg3VY+NputtPWtAhEKC0EDSabF9TKgPrq+DsnzNim+V5zng+8iBEACWVX+h0Lvf8R6sUpmvf+oPb+lxvuejBTU81yM/nuWpP/2fvPXcEX70X36Mpq5+XKuMgovvOkiyjCcpaJEYjlnh2a99jZMvHVm1QMixHCRJQlarI+61YLX3jx/2b/qbQNu0cU231vz6VmiB6gQz13ZxbRen4mAbNtLVoKw68l5GVqR5m2q//HdP0nf3Pj70yA+RK01x6cQol08mVzVAePqF79HTuY6x8ZFlO2f46pbEYsYg3hGlkC4SbQ3P2E42F9OwmLw8xb59/2JZAplpQ0OHyRcu4Ng2hXSx1vwaqr1sqs2tY5iGxeCxajVdqTS+5DXYdomjRz/V0A3DV4ppWFRKFkaujGP6lPIlDhz452u9LEEQ7gSi0XRdEqGQIDQQ0WhaqBfj4x7bNndjXj69Jte3rDyyKqO1dlIyCqSz9bV9rLtzHf0btvDFX/l17PItTmnzARkCoWrjaYDJkSH+4j/9Dp29vbT3rqdj/Xra1/VgWzZDZ86QS6cZOHZ81cIg13Fxyg6e59caZ8uKXOvz41ScGWGMZVgoujKj35Bt2NUtXZHgontDKZpSq0ryPA/XdmvVQa7jYhZMvJA3d2NtH77+qT/kX/3Jf2Xf7rvwfZ/BYyNkk3kSvc2LWs+tKpYKnLt4ZtnPG2uPkEnmiHdEa72EbsR1PJLnUvT0PLAs/Xxsu8TQ0GEGBv4O0yoQbQvSf3fvDYOpQFinu7+diaEMQ0NP096+F02LLHoNr732GVAqtPfe+LnfKUzDIj2cJ5+pVqSFIy3EY7tp795Hc3OfqBISBGF11GEoJLaziVBIEBqGGEUv1JPxcY+dOwPIgRCeufrblHzfRpYlpKYWRsfHVv36NyLLMu+9732cePElxs6cu+XHqyEVu2yj6uqMbVh2pTLn9qvV5LkedtnGtV2CoTai0XWoagDbNrCsAsVCteLFNm1krRrEeI6Ha7k4plN9ToFrDcLVgLpszcJlWUYOXDuXoirIqoxt2ASic3faLmWmePYrT/Dgjz/OhcGzNHfEKGRKqxYKrZSWzhhTV0YZeH0YkIheV50D1RDo+mqo8YtpgoEE+/f9yyVf+8yZL3P+/BMoKkTbQvR09yy48ireEUVWZdKXj3H48P/F3r0/R0/P/XMeWypNUC5PEI/3zQqPxsaOkEy+TN/+dWLb2FXJcylamu9i1/0PLzlwEwRBWLz6C4UEEQoJQsMQ/YSEelIu+1TKLlpiHebohVW/vm2bhCJRgi2dXHrr9VW//o3s3XEXoWCYp//ki4t6vBbQqtuhyjZ6uD4mJnmeh1N2cCwHXY/S0r4ZVb0WtGhaNXTwfa+6hcvzMQvXtvTpYR1JkbAMG6fgrNq69bCOWaiOsp/vc/nqV7/OgQ89wgMHHuTb2W+SGsmSmywSb795U+Z6FQjr6GENy7Bp6Y4B1SAoeyVPbqKA67i09jTT0hUjN1mkXLA59OAvLzkoGBs7wqlTf013f/uCmlrPpak1TDgWJJvMc+TI79DcvBFNjyDLFpn0CLZ9rfJO08I89tiXa/9v2yXGxl7m+PH/TmJ9y4xm23ey1PAUritx8OAnRBgkCMKakiQJqd6mfYmQSoRCgtAo8nmxdUyoL+fO++zZ3Y95ZQjeNjJ9JTlONWwIbdhGyShwaeTiql37ZkLBMAf33sszf/UVjOzUos+jBTUsw0ILaXVR1ux7Po7l0Nq2HV2PzX2MXw17QvHQdR/z8X2/tn0rFKtWPk1PHLu+Sshzq4HScj5fWZHRIzpm0URWZNTA7Jc9nuPyjc98jp/6rf+PUxtPkB6dYnwwTWo4S6K3pWHDoc3715ObKDI5nCE3UcBzfVRdobWnGUWRmRzOkBmbwnN9Dhz4+WXq4fPparNoaXY10q1QVJlEbzNNbWEK6Tw+eVwgsaEJLdgCwMSlDB1t95HLDTI09H1S6TeZyg6jahqReLDhq72Wi2lYpEay3H//v13VQCgalVi3TqanR2bdOgVdl0inPUZHXcbGPFIpD8tateUIglAvRKPpuiRCIUFoEOm0qBQS6svoqMe2rRKBro2rWi3keSZyIEioZxPfffapVbvuQty7/36KU1McffJbiz6H53pY5WoPnnoIhK43XyA0l+mePoqqIOty9c+rIVAoFKpWH7nXqobsio3v+ugRHVmRsUoWjjWzqkhWq32LFE2pNbe+GUVTUHQFy7CQNXnO/kKXj5/k3Ouv884DDzI+maxW1STztXBo/Y7Ohqw6mZ7ylZso1v7/+vtSw1OkRrJs3Pjwkq+laREOHPgFxsZeInn5JZIXJom1RYm0hIi2hBcVEAXC+pyfd9fxqJRMevY+wPG3PsvkxHmau5ro27+uIf+eVtLkUJZE+855t+Ett+3bFe69V6OpafbXZk9PNSSaVij46Hp9fY8TBGGlSVdvQj0RoZAgNAjRU0ioR+fO++zbtxUnO4Fr5FflmrZdJrx5D1cmk3VVJRQOhdm2eTtf+9R/xbtaCXOrPNejUqigaAqByNx9cOqVJFVfUniuh6zI2CWbYKgNWVKx7SkqRgVZkdFCGomudmzbZjI9AVQrilzbRZLl6vNXFTzHq/YCuu61o2d71SbXZjUskhV5zteWiqagXR1v7rnVBtRaWJu74fRVh//0L/j45z5NT+d6xsZHSPQ209IdY+jkGIW00dBhw3xbucLxICzf4DN6eu6/Gj78AmNjR2YFRE2JyIzpYwtlVxwqhlWdmlUwKRctNC1ET8/9RCKdvPTSf8AqlW/493snyiTzmIbHu973b1b1unMFQks5ThCE20gdNpoWIZUIhQShIYipY0K9Gh31SLQ5dG47SPGt51ZlG5neuxUzIvPCy4uvxlkJ+3beQ3FqirPPv7zoc7hWNRipt0BousLHto1a/6C307QwwWAc2ygiqzK+7xOLbUCWVWADjmMyNXUWz/EoFAszqoRcqzo1LBQLYpdt7Eq1OfT1jbah2jxaoxr2uE51nD1z5OVOxcExHfSwjl22q48LzD2aflp2LMmFN9/knj0HayPiFVUmHAti5MtAY29HMvIVXMerhTKFjEH68hSaFiKXG1zWMfQwOyC6cOHvmRw6u+BQKDdZpJAqUS5auE7130p7x3Y627bS3N9Pc/NmAOLxPh5++I949tlf4dJbo2zY3dXQAd5ycR2P9EiO3bv+GZFIx6pdV7xeEQThhuoxFKqz5awFEQoJQgNIpcSLLKF+nTzl0XS/RmTX/ZTeem5Fr6W1r8OJ6zz5tT+nLBVW9Fq3Qtd0dm3dzXf++M/BX/zXq6zJ+JXV6890q6b7Bs0nFt9MOvUWdsWmqRYIValqAFnS8DEpFGdWlTmmg6JXAyAtpFUnlN2k6kNRZ464v54arE5wM4tXm127YGQNgrHgDaedPfsXf8PPfup3SLR2kMpUq5hCkSCl7OpP2FtuRq5CaiRLMBxEViSMQpkdO36CrVt/ZMV7zfT03M/Q5acINwdvemwhY5AayuJ5Mht6H6kFQDcKrTQtwsMPf5bXXvs0g8eeWVKj69vF+MU00WgXW7Y8ttZLEQRBuE79hUL1tlV/LYhQSBAaQCq1uK0ogrAaHAeOHHF46H0RInsfpHTqyIpUDGnt6whv3sf/+otPMTwyXFeNZPfuuAvbNDnx9LNLOs90yOE67ryBx1pwLAdFUW/aU0iWVeLN/eSmLhKJdM5xhIQ/R2jmU93mde08S9sGJEkSelivBk1+tV8RcMNACGD8wkWGz5/nnj0H+e6z1Uo0Lahimw7jl9Ik1rfM6I2TSeaJt0cbYuy5VbHo7r6P9vZ9TE1dYOc7f2pVK0iSY2+wbvtc/yaqjHyFzGiO4pRBf/9H2LXrp245rDp48Bdob9/H0aN/gJEv072lfanLbkhGvkI+XeThh//jql9bbHUXBOGGpDoMYeptPWug/l/FCIIgxtELdc9x4PAzNhU3RHTvgyjhhTckXojQ5r2EN+/j+HGH5579XrUXSp2QkNi9bQ8v/O1X8Zylj1uXFBnPrq+vec/yCATaFnSsrsdoS+yZ875wpBPHdGo9gaYFojq+62OVlncckaJWG1J7jlfrMXQzL3zp79i0vo9IuFppEo4FWbe9k0K6xKXjoxQyBq7jcflkkolLaQZev0xqZPGT5laL70rE431s2fIYBw/+wqoGQmNjRwDm3DpmVxwmBjNcPpkkFtnFBz7wZ+zf/7FFVy9t3PgwDz/8BxhTLpeOJUmNTGFXlv512Shcx2PiYob+/o8s+5ZAQRCEpZKmt4/V003sHxOVQoLQCAr1s0tGEOblOPDSEZfduxQ6d96PPX4RM7m0cfVqrA1t3TaUcJwXXnBIJq8A1FVlxrruXnQtwPHvPrMs51NUGddxa71z6oIEnr/wv8frt41dLxhsIRbvI58brE0Sqx4vE2gKUMlXkNW5R8cvllNxkGRpVn+i+QwefZPCVJYtm7Zx7NTrQDXMCMfWk03mGT07jqxIqAGVvv3rGDoxRj5dpKUrVlf/Lt/ONp15qrdWnmFMAjByepx4ZxNNreHqhLcreaauFGlp2cyhQ79CYp4w8VbF43188IN/ztDQYS5ceILU8PCSGl03EkWVQYZ05k1su7RiWwMPHlS5916tVhmUSnmYpk+hUL2JJtKCIMxNhDD1SIRCgtAAxDh6oVE4Dhw77rFuncz2bZuJJjZgjp7FzkzcUjikhGMENu5Ci7UwOupx/nWHctmnXK72eamnRrLbNu/gwpvHMIulZTmfoimzRrHfjOd6OKaDGlBvukVqMdSgSqWQwYmuR1WX1gQ7HE5gWXnMQoZAU+BaMKTI6GG9Ojr+usBoqRzbveVzHT/8A3Y/fKgWCkH1zXait5mmtjDp0Sk6+xJXAyKZjbt76joQAqgYlTULhbZseYzu7vs4ffqvuHz2GYKhALbtEgg0sX/fv2LjxoeX/ZqaFmHLlsfYsuUxUqkTXLr0veq1w0GibSHiiSha8PZ8Gdy7q4uxsymee+5XeeihP1yRa0yHQdMj5q8fNS8IgjCvumw0XWfrWQO3509DQbiNiP35QiMaHfUYH4ctW1S2bNmHrIBdKlBODuMZ1dI3zyzjmWXUWGv1QYqK1tKJ2tqNrChksy6nXnDI5699DUxNDRIM1c/WMVVV6Vvfx9e+snxvvCRFqvXBUfT5Gy77vo9jOrhWdQqXqgWp5CuoAQ01ePNGzbdCURUUTaFYHKG5uX9J5/I8B1UNUvF97HJ1ytg0NaDiOi5m0SQUDy112QAEIjpmwcQqWeiRhYWJJw8/x7se/1HaWhKks6mZ5wvr9Gytbr2KtzeRHJjEyFfqugJlevtUKLR6W8beLhLp4ODBX2D//o9x/vyTAKvS5BogkdhDIrGH/fs/NqN6qH19K83dTXUf6N0qRZXp2Z7g4hujHH39Uxy4Z/lH0o+NeZgmBOprUKIgCPVOhEJ1SYRCglDnrn9DLAj1qrNTJtHiEY864LvEO5pq97keyApEW2O0du1CVWV818UyykiyguOrmLZCpeyTSvuMn/QYH7eYqz2PZRVQA/XzBm7zhi14rsvAq6/f/OAFkuXq9inHdLDLNrIiI6vVKpppvu9jFk0kXyYSWU8g0IyqBrCsPMXCKJVccdnDoelqISvcftOG0zdTKaerzaDnCGn0sE4lV8E27ZuOkV8IWaluTbuVYCg9PMrEyDBb+3aQzj4/73HR1jDyJYmJS2nCseCqhQumYVHKVSjlDCzDJt7RRGL9/I3X7auVZ6vZR2g+mhZh166fXLNrX1/rTZQtAAAgAElEQVQ99Nprn6SUm6Bzc2tdVR8uB0WV2bC7k+FTL+J7CgcPfmLZr/GXf1lm/36NvXtVEQ4JgrBw9RbC1Nly1oIIhQShzolx9EI9UtVqENTV6dPaKqNqCsbFs9gXxwCYeP4SAJ5ZwZ28Unuctn4TAHIgSKCjG0UBqXMToXWbwPdxbZcb/XROpU6gButnKte2vh2cPvIqrrW809amAyDP9bDL9tXPS9V0IKRIGi1tu2b079H1GK1tsWo4VByhkiuh6mq14kiVlzTxY7paKJs5R7y5n2CwZVHnkWWV1radZDOnMIsmwaaZlV+SJKGGqiPlVV1dliklM4Ihw5oRsM3lI//in5O9NMrWg/t4+Y0X5pyYNt1sWg2oeI5HNplfsYl414dA5XwFz/VRdYVwLEi8vYnMWI7cRIHuLe2EY7Mr6Soli3BkcX9ft6tEYg8PP/xHHH39UwyfepPOzW11Xe21GIGwTu+uDgaPHaa9fe+yb9OzLHj1VZszZxzuvVdl+3bxtkIQhJuox+ljIhUSoZAg1DvRT0ioJ62tMlu3QGubimfZGAMnmHrtDNbIJXyzctPH2yOXav9tDpyp/bcUCBLs30ls3Ua6duxh3z6diXGHU6d9yuXqG/JU6gSp1IkbjrVeTZqq0d3RwzP/7Qsrdg1ZkUEC+WoFyoxAqHXXvA2ddT1Ga+suDCOFZWYwjQK+56FqCnp0cb/S33foQQKhEC9+7ZtMZS8QDLURi22Ydw03fF6ySkvrLiYnjmEZNnp4ZkWQFtBwTBezYBJoCsz5AjIcjuD7PuWysbBrXq24mivguZ4eCtHU3MzgmdNsf/f9JFo7mEyPzzrOczxMw6Jv/zqsisPo2XHi7VFsy8HIV9ACKuGm4JL61hQyBskLEzNCoM5NCcKxmedt6Y4xOVyd4BVvj9KxqW1G1ZLneITDXYtex+1K0yLcf9+vc+rUX3PmzJdp6Y7RuWlhU/YaRSCs093fztGjfwCwIv2bCgWfw4dtTBP27RNvLQRBuJF6bDRdb+tZfeI7tyDUOTGOXlgpudzggkcWh0ISW7fAuvUqhTPHufLdF2ZUAC2Vb1Yon3qD8qk3yH3vawT6d9B097t473s3MDricOp0kZde+k2au5rq5rf5XR09eK7D8FsnV/Q6mq7jeS6u42KX7ZsGQtcLhxOEwwkALCtPJn0WxXFR1FuvtspcnfymhTQUXcEqZUmn8sSbNy9qO9l0xVAmcxqr5M/a1hVsClApmPMGQzcLd+biOi566MZVQla5zJf+8ycB2PnoIXo6180ZCmnBalPvStEi3hElHAsy8MYwAHq4Gmp5rlcNh2JBwvEQWkBF09WbBkWu4zF2YYJStkxifQvx9hs3RVZUma6+BLG2KFcGUwy8fpmm1giBSIBIPEjFMGmNilBoPrt2/SQdHft46chvYpfH6d7aflv1GYp3RHFdj+PH/zvNzZtXbFT9q6/abN8utpIJgjA/qQ57CtVf5dLqE6GQINSxQsHHstZ6FcLtJpcb5Phbf8zkxCkAOjq3EQysp7m5n+bmvhljoVUVdu2UWbdepTx2mYknnp5R7bNSzIEzmANn0NZvouX+93PgQIF88T2MFS9h2fXxRdHd0cPIwEV8d+WC20hzM2ahQj6VAUDXozS3bF1UdY6ux1C1IJ69uFBo5Pz52n9Pb8eyyzaZ9FnCkU6i0Z5bXpemhWltrQZDdtlGC12rGJIk6YbB0EIrhKZ5ngc+yNrC3+xfPn6K7nt2zZhCdr1wPEgpZxDviNLd307FsGb0Fpre9mXky4wPpvHm+bciKxLByLV30pWSWRt5fyu9bsKxIJv3ryc3UaSUM8iMTTFxqbr1cH1HfVTY1atEYg8Pve+zHDnym1x8Y4QNu7tuqz5Drd0xKsUKzz77Kxw69HsrEgxZFhw/bvP/s/fu4XGc5332Pec9YnFYEiDAEwhShCiRlEzSOlCWLVI2pcQJU9lxbMdxnXytGyfpFztVFMepUzdJfaWt29pJm6SJYyetm6Z2bUdyclmxZcqfZNqSSEkmRYmkCBDgASdiF8Ce5zzfH0uABHEGFtgF+d7XNZeEnZn3fWc5OzPvb57n9+zbt3QvMIFAcJNSg6LQcgUKFbu+yuFf6eM/f+cT7FyeLiqGEIUEghomlxN+QoLK4TgFuru/xRtv/A2x+gjtu9vwXJ9iZpiR/GWuDP8Qs1ROAWtobGPPWx7kwEP34BUznP3iN7BnEIM0LYKqRlC1CJpa2Sge53Ivl/7qM3gbW3j7ux5BDj/Isz86Qu/l8xXtZzGsW9vG2SMzmxAvFcdyGO7pJ97QQn3DVnQ9vigx6Hp0LYFlp+becB5IkoQe0VF0BbM4jGNnabpOUJwvmhYhkdjC2GgXkiyhGteO8XphqJQpYUQNFG3hgpbv+bimiyRLCzLevvT6ae4+dBAJiYCp1+NIXZjUpRGsoo0R0adE8xgRHSOi07juWiSVY7oTxs/jeK6PVbgmdsYao5P2WSiJtTESa2MAXD49hEoDbW33Lbq9W4VodC0HD/5Xjh//L/SceJa1m5uIJkLkRorlaK9p/JpWE63b1nL5jWH6+n60bNFCJ0+67NqliWghgUAwPRK1JwrNogp1/eBv+e0/fIYNH/wM//mD629Ym+ap//rXfPHIG/TnVTZu3cm/+I2PcGjr8lfVrDRCFBIIapi+Pm/ujQSCedDf/wKvvfZnuH6Btu3Nk1KwbpzoFLMm9999H7vv3MnI81/HPPlSecUMc2nLKVEqpcqRGIBhRFHVKKoaQVHKMwPXLeD75fPZdYr4gYcsKaja7CKSLCvk8wMYF9Ok/voM0be+g3c9+Cg/fOUHnDpzYjFfRUVQFIU1TWt45tTpKeu8RaZnjRMEAU7RwXN96hLtRELJpQx1EroRp1S6UrH2oGxALcdlzKxFsZiaSFdbCKFQA3WJdrKZHnzHQ9YVVL38iCJJEuG6EE7JwcpbqLo679LyQRBgF+wJo24ttLAIhguvvYGm6TQ2JEmPDk9ZPx59ceFUP5vubJ1XZIkWmj59bDnSIq/0jGAVAu558IllEwFuRvbu/QRr1uzi5Gt/RvryGLqmk7o0CkBdYxw9qq5akUjVljeKZ9x8+oEHRLSQQCCYjtXhKeTbg3zjv/4pf/njBra0TD/eF//8v/D5l9bxr//D59jX4nLq6f/J73z8T0j8j8e5t7HWjnF2hCgkENQwwk9IsFQKhSu88up/ZvjKGyTXN9CwrmVWrwxd0zn0kz/BhpZWUn//N/iXe9HD83u49/0A3/PxXQvTNvGLwxO+L4qqIEngBwGyIiHJEm4QYJu5if0lSUKWJo/Ncz2QmBBZCi99Hz+X4Z4HH2FNfZJnX/jeQr+SitCyphUJib7TZ/F9H9/18R0fz/EmjnncJFrRFGRFLkepKLNHqfiej12wUWSNxsbtaHOIZgtF1+MEQfnfaa6xLARJklB1hUL+8qJEISj7H+l6nHy+D6s0ilN0UHQFNaQiy/KEl5GVs+ZVrt73fMyciSTL6BEdxyxXcVNDs1c0C4JgYr2VyzM6PMy65tZpRSEoR1/0n7vChVP9tO9avyRj6UpypWeEXNrmwQf/UAhCi2DTpoPU12/hjTf+gs9//vfRdThx4gQnTpzg2PFjXHz9MgDhaAhFk9HCKoqqYER0FFVelYJRpXjtNZfdu1Xi8dU1KRIIBCuBVHsePtOMx8sPcp4H+dM/eZAX/vDXeOmG9b79Ov/76TSHf+OTHNhajsy9770f5v3P/jZffbqPe6dEFYEz8jqf/vh/w3zg43z2o7dRGw6ZZWrjyUUgEExLLjf3NgLBdNyYKtZx94Y5J6uxaJxHHvwJwp7Pla9/ecFG0rIsIcsKLCK9ZyaCQMXMmVgFi9DVqlml069ipwboeM8v0tTwczz1zDdX3GcoqsUZ7L1MIZXD93wURUXTE0TCdYRC9bhuEccp4bkWrpfHtkoEVyOpFFVBkiUkRSqXib+a0uRaLk7JwQg1UFe3acmpYtMhyyqKouK5XkVFIQA1pGJa5qKjhQBU1aC+fgu+72KaYxSLA5gZE0VT0MIaslIWh+yiXY5QmuEYxgUhRVPQIzqSJJXT3K76E+kxfUoa2XiElmu76BF9Io1t8HwPa5rXzjru1m1rOX/iMpfPDrHxjnVVNynOjRQZGcxw8OAXhCC0BBKJdu6777M884zM4cMG+/fvZ//+/QDk83m6u7sZHBxkaGiI7u5u0iNpzp45O7G/qqnEElECySOxNn5LCUXHjjkcOHDzeDIJBIIKsUo8hbTGu3jiNwBKPDfNI6Y9eIme/AZ+Zsf1qWJN7N7dwLd+3E3xBlHIy7/JHzzx38i+9Vf5XI0JQiBEIYGgphHl6AWLoZwq9t/xpkkVm4mmhiQ//fDP4A4PMvr3f0tglVZgpHMjSWBEdcychW266FeFLW94kMEvf56Gx36R9/7Ez/HUM39HvrB8Kqrn+mSG85hZm+xIjtvW7GSkL0M0uh5dj0+J6NH1uikVuXzfxXWLWFYOz7Pw/RJWbrJZcrxuI9FoZQyBfd+dVljS9ASOMwYV9vwYF16WEi00jiyrE5XTHKdIPteHmR2bEGs8x8Mu2DOWq3dKTjlq4zrz5nF/IitvYWZMZLUcQTQuLHl2ucKbaqjYJZsgCFANlfSly2zbsnHOMYejBrmRIq7toqjVnQzn0nla2/YKQahCpNM+Tz5pceCARjJZPl9isRi7d+9m9+7dU7YfF4zG/3u57zLPHnkWI6zT2JYg1hCpmnDoec6K9HP2rMeuXf7E9yUQCARATYpCi4lc8vJpioRpiE3+vK4xQWkkQxYYfwp08+f54yf+G5e2fpTP/9odNScIgRCFBIKapb9fCEKChXP8+H/h4sVnr6aKNc9r4qFrOocefJRS9xly3/3mCoxyYZSjQ3Tsoo3veqi6iqzKyFaJ0W98mYZ3v59HHvyJZY0Yyo8UGe3Ps3HDO9lx2y7uv/8Bzv3g2wsScGRZnVYscpwinmehKEZF0sV832VstItYvHXaUvG6Xodtjy65n+lQQypmxsS2s4sqUz8dmhahoXEbxWKq7Dnk+mgRDStnYeWtScLOOL7no04TGVcWhkL4no9TcjCzJlpYQwtpeI43SSgaTzcb6RukLp6YdYwDXcPkRopsunNd1StWlc3jbXbseVdVx3GzkU77fO1rFvG4RGurTFubzObN05dfHxeMgInIol/6xV/iO9/5Dv/3//5fhi+MEk9GaGxJrHi6oee5c29UIb72NYumJpnOToX2dkWkkwkEgrIeVGOi0HJ6HMl6lr/+vS/w4uAOvvj53VTmyajyCPleIKhRslkhCgkWzubN7wTKpbLn+yb6p9/5GEqpRP65p5dzaEtCMxSMmA6ShF2yKWVMSlkTK5Mj/dTfEPF9Hrrv4WXrv1QwSSS2sHv3R2ltvZdI2Gekr78ibWtahFCooSKCkG1nGb7yY2w7h2VNHzmlaZFyNS6r8pNDWZYnooUqTSSSpCl5B4EHTsGZMJs2syZ2wZ4wOgcI/GDW9DhZkTFiBkbMwDVdzKxZ9hu6am6tGipG3CDwAvrP9qKrOro2vdiTujRWM4IQlAVMSVJobb232kO5KcnlAs6e9ThyxOFrXzPn7f3X0tLChz/8YZ566ik+/usfJ67X0/3qJS6fHiI3Upy7gQqhRzS6u/+OTKZnRfpLp32OHnX4yldMnnzS4uxZF8taka4FAkFNItXgsnC0xiYiZBnNT/48O5Ih3JiYEH+8/CXMlvu5P3acP/rzN1i5q/3CEKKQQFCjpFKiHL1g4SSTd9LR8W6unB/Bc+eerLzj3oMkIjFGv/FXNZMyNhOqphCK6kQSYcJ1BrIq41oOxStjDH79b1gTT7Jv59Sy28WsyZsv9TLQNTyv72Q6PCsg2VR+8y/LEI6ojA4MLOl4Kk02e5GR9FkUXUFWZQJ/+uqFmhahLtGOXbTLRt4VRgtrWFYB285Wvm0tQlNyJ4ocxspZyErZM8j3/XKEUvFapbEbK8DFm5qmtKdoZX+nwA9QdGVSuXtZltFjOmMDA5h5m5A6vWiXGc7RvLmpYoLQbVs6uW1L56L3L2ZM1rWI8vMrQS4X8LWvWZw8uTCB9dChQ/zlF/+SP/uzP+O+ffvpOztE74l+Rgam/mYcs7LibUt7klijzgsv/B6OU6ho23PR3+9z5IjDl75U4umnLXp6xMsvgeCWYzx9rNaWBaK3bGF7bJATb1x/HU1z4sQoG7ZunEgRk/Q7+NXf+Dl+93cfY+Tp/85f/GB5IrWXihCFBIIaRfgJCRbLjh0/jyxFGb40Mut2d3bupmN9O8Pf+HLNC0I3IisyRuSaQBRkUgz/3f9i2/pO4l4TQ71pciNFhnrTXHx9gFhDhEKmRPcrF0ldHluwOJQbzVNfvwWAaLR86xztX5gR93Lh+y7p1ClKxSsYMQM9oqOoCq4784QvEkkSCjdh5218r7LXGlmWUQ2V0ZE3KRZTFW273L5KY9PtNDZtRw4M7KKNrCgTXkNW3kKSpz7gWcXy+znXcrEL5TRDp+QQ+AGhRGiS/9A4iqoQ+A6ua2OmPc78qGfSuZMbKeJYLrEKlpPf1LqZTa2bF7Wv5/pkUjlaW4UotJIcPerw9NPWgiNgOjo6eOKJJ/jmN7/Jz77nfVzpTTMykMUxXUYGslw8NUj3q5c486Me+t8cZmQgi1Vceops85YmPPI8/4MnVlwYGqenx+fppy1RZVUguOUoVx+rpWVx6Wy38cH3tvD3f/43PNdVwLIzvPjU/+BvL2zmvT+9ecrWia2P8ge/toWv/+GfcmSw9l78C1FIIKhRxIOSYLFoWpRdu36ZscHcjG+ZN6/fwv1veYDMc08vuMpYrSErMnpIRc0OUnrpH3nv+95LY6KJvrNDZK7k2XjHOlq3rWXrno00b04yNpSl+5WLONmA5qZ1c7Y//h0mEmVRyDDKnjX5VHpZj2u+ZLMX8QKLUCJ0LdJFBs8zZ92vvn4LRqgRK2dVXBgaN4TOZnqWRRiCsjfS9eKQa7nllDGJaVPH7FIJ3/exSzauXU4Zc0wH1Zi9RD1AIZOmviFBrHGyQXAunZ8oQV4pvvuDp/nuDxaXylnMlv/NRerYytPT43P06OIEm1gsxoc//GEef/xxrvSm6X71Em4u4NDDj/C5z32Oz3zmMxx88J24uYCeE31cODnAQNcwmeH83I3PwIYdLTjeCC+//J8W3UYlOHp0ZYyvBQJBjVDtiKBpo4QWl0J21wc/weMP5Pn8E7/Ofe/6DX7vKYlf/YOPcaBl+va2P/JLfOKtw3z2X/8fLq5s0dw5kYIgqD2pSiC4xbEs+NKXVlfkhqC2SKVO8dxzn6LzvumrD33w8C/gnHqF4kvfX9mBrQCJn/wA+Wicb/7j1wGmnbBnruRpCK2huXktZy6+TmJNDMd0cWwXs2DjWA5WwcaxXJyr3juPPfYUAM3NMls3F/iTX/zoyh3UDLiuRWr4JEbMmJT65LkeVs6iZd2+OdsYG+vGtsdmrOS1pPFZLnbRpi7RvuSKZHNh21mKhUFMM4Oqq2gRbcrxmLmyaBL4AbIqI0kSru0SToRnPfb3fPo3Odc3xBs9r5FYe63USDFrcvH1AdZ1rJn0ebXwXJ+eH/eze9evsGnTwWoP55bkl34pPK359Hw5evQoLS0tdHR0TLt+cHCQEydO8MMf/pAf/vCHSzr3rKLNpTeGaF33IHv3fnzxg14iDz2k0dkpat8IBLcC2VM/wC8uX7XYxaDUNRHvfGu1h1FVxBVYIKhBROqYYKmMjfUQS0yeKDimSzFrsu/evYQ1g+yJF6s0uuUl9/y3WfdPP07H5q30Xj4/7TaJtTEct8Brb55gZCDDQNfwxDo9oqGqCpG6MPJVQelK77WoIEWRcKzaeMVTKqWQZHmSIARMpE+5roWqzj5DravbxEi6iJ230aIasly5qBfVKD9mZDM9uG6Rurq5S7svlvHKbradJZu9gJkxUcMqmqEB4FgOgRdg1BmTjtFz/XIVs6g+47Hn0nlUVcWxXQa6hieEQsdyUXWlouljS0FRZWJNIU6f+V9CFKoSZ8+67Nq1+Mfr8WplM9HS0kJLSwuHDh3iG9/4Bn/xxb8gUhdaVBUzI6KzYUczPSeOsGbNzqqdM4YhEhcEgluGGixJX3PjqQJCFBIIahCROiZYKradww/KE9di1iTdP0ZhtEQoFOL+ux8g9+qPVp2P0Hzxs2OMvvh97t+zf0ZRCMoT6OSGehrW1WEVbTRdnXZiNZ6SM7GfEuA5tSEK2VYaVVemfD4ubvi+BcwuCo179IykT2NlLYy4MWvlroWiGiqSLFEqXMH33QlvpuVC1+tIJndSKAyRy15ElmUkRcIpOWjhqaKXEdWxizZmxpwUceX7Pp7j4dkeZr6IFgszNpQlUhciUhcGwDZtsqkCmeE8jeuqU2jWKtqoujoREde0rp7uwUtcuPA9IQxVgZMnlyYKLYTHHnuM555/jgs9Pay/vXlRbRgRnXUda3j55S+gadEVTz3UdWhvF6KQQHCrsHgPn+Wj0lHSqxFxFRYIahDLElmdgqWRSp9Aj2gMdA1z8fUBFEVm4x3r2L9/P26hQPEmjRIap3TiRcK6Ma8qTooqL+hNu6pKOFZt+GAoamxSKfYb8WeoQHYjsqySXLMTI9SwLB5DiqZgxA0sc4RMpruibc9ENNpMNNaKXbAJvAACJkrOX4+syITiISRFxnd9HMvBzJqYGRPXLPsUqZpEW0cLW/dspHXbWpIb6kluqKd121rWdawhdWl0SdXtForn+mSG81w8NUjPiT7OHbvAhZMDXDozSCaVJ1IX4tLlf1yRsQgmk8sF9Pev3IudT/7WJ/GsgEtnBhddqSyxNkZyfQMvv/yfVqxU/Tjt7VNF7etJpXxRwl4guJmoAWPpqUbT1f5Sqo+IFBIIapCVfKAU3JyUikMUCjlUXaF9dxtGREfXdB58+37Gnvv2lCgh3w8I/ABJlpCnqdy02gisErlXfsT+PQ/Qe+k8dgUjexQlwDFrI8pK1+uw81PLm7qWiyTL6Hp8Qe3V13cwNnYeKzeCFtYm0r8qgazIGHEDMzeK551G1xOT1ttWlki0mVCooWJ9xuNtOHYWxyxXHvM9f0qp+nEUVcYxy2KfaqjldLKrEVNB4KLr05ecT6yNEYrpXDo9yED3MOu3Ly5iYz5YRZuR/gzFjIWqqDzyyCM89thjDA0N0d3dTVdXF6fPnCaVHcWzLuA4BTQtumzjWSjlqBCFeHz6a4xhSCSTk99XZrM+x4655POr52XJmTMura3Tny+VpqWlhd///d/nL7/0l5x+9TSx+giJ5jjxBaY0JjfU4zkezz33SQ4c+GOi0bXLNOIb+k1O/3767FmXEyc80mmfvXtV9u3TVmQ8AoFguam9SCGhCglRSCCoSXK51fPwK6hNCoURInUh2rY3T6SV7Lz9LpTAIX/yFUKxaylFQRBgFWy44bRTdIVQdGUmNstB8cSLRO54C7d13M6pMycW3Y5ZmCwo1ZKnkK7H8T0f3/cnpUX5ro9hNCDLC7/N19dvIZczKOT7AZZFGHJKJYql4qR1kiSRGetGadqBplXOo6cusYWxsTfxMfGdmUWhcQFIkiT0yOTz3rFsNGXmiAYjorNmQyNDvctTaS0znCczlKeYK3HHnXfw6D99lEOHDk2sb2lpYffu3ZP2uXhxkH/4h+oLQrGYRHu7Qlubsqg0odZWmc5OlTNnXM6e9VbFS5OVvofv3r2bP/rCH9Hd3c3Xv/51vvvd7xKKGBhRDS2k0dBSN68Kec1bmnCsIV586TO87YH/uCKCYl+fN5FuZ1nw2msuJ0442NddYsspedqSDLwFAkFtsPhaX8uHVHMjWnmEKCQQ1CBCFBJUguSGhkkTgfa2dsyTL0EAZm5yPP64ADQeMeQ53oSZ7molsEqUuk+zZf2WRYtCQ71pRgey7Nnz69faDUA1akMsU1UDRVHxHR/ZGI9qCXBtl1jD4iNu4vE2FMUgm+khIJgwa64EsiJjxKaf3dlFm5H0aZqSd85pkD1fVNUgmdxJsZgim+lBkqVpha7xSLkbTbsBFE1lLilCC6n43vJcu8eN0B977DE+9rGPzWufjRtbaGqyqlK4oKlJpr1dpr1dmTESZKF0dqp0dqr09/scO+bUtDjU2lodd4aOjg6eeOIJfuVXfoXvfOc7dHV1cfzl41w6PciG21vmJQxF6sNc6b3MlSsv0Na2/J5U/f3l9DDDKBfZOHZsamqubcPRozYHDtTGdVcgECwBSS4vtUTNRS6tPEIUEghqjFp+0BWsXmLROI0NSQYHLhBtCM+4nSxLIEsoqoxruTiWh2bM7vlQy9iXe2jZfQ+6pi8ohcxzfYbOpynlXB588LMkk3deW+dBKDzzd7jSaHoCxx2bEDpc20VR1CWnYY2XkM/lLhC4wbQl3itJEAQEXoCi6shy5c+5SCRJqTiA700vdnqOh6IpU6KEAPRQCEVR2Lx+y4zm5dpVvyLHdOflT1XMmkTqQvMef10yyrf+/lu8ee5NfuuJ36KlpWXOfTo7FY4eXbl7iq7DgQPGshoHt7bKHD5s1LQ4NFN63EoRi8V47LHHAMjn8/zL//dfzksYylzJc6U3zeOPP86ePe/kySetSRE7y4Ftw8mTzpzpYWfPeuzbF1T9uxUIBEukBo2ma248VaDGZDqBQJDN1t4DrmD1s3nDFux8Fm94cN77yJqC58zPqLhWsc+fwbMtWpvXz3sfz/XpO3MF2Uvw4Nv+/SRBCMqikB6a/2R+uQmFGvBsD7tg4/s+vu1jGE0VaTsSSdLYeDsEKlbOwnOX53zwfR8zY+K5HolEx6LS3ubCdS0cx5w+SigI8D1/xlQ5PRxCUVQi4Qi6Nn20wrgQ5NgzR9h5rk/q8hhvvtTLxdcH6DnRh1Wce9YtKzLxphgb72ih6/w5Pv3pT8+5D2yY8zwAACAASURBVMCuXSoPPaTR1LT8j3utrTIf+lB4xSpJjYtDhw8bVYvMmQ5dn9knpxrEYjH++I/+mGTDGi6dHpzRDD1zJc9A9zCPP/44hw4dIpmUefTRlcnXOnnSnZeZ9NGjtZG2KxAIlsC4KFRryy1O7dy1BAIBIFLHBJWhtW0vY0PZib/b1rZhz1KefTpUvSwK+f7qPieLfb1s3jC/MuhW0eb8q5fR5DW87W2fI5Fon7KN6wZoNZI+BmVRqCl5B7JkYGXLwk34apRPJdC0CI2NtxMy1mDlrAkz5koiyzJ6VEeSZdKp1xkbO4/rVrbkkGWNISvyhHfQdAQznOt6JExqdJg3zp2aNeJMVmSKWXPKxNsxXQa6hjl37AJjQ1maNyfpuHsDWkil50QfQ73pWSuXhaI6VsEuC08BPProo3Mc7TU6O1Xe977lFU/279c4fNioiudLLYlDmzfLvO99oZoShWBuYehGQWic1laZhx5afoPn8WihucTLnh6fI0dsUY1MIFj1SDW43NqI9DGBoMZIpUSkkGDprGvZz4mTP574e9P6dob/4W/nvb/v+dhFB1lZ/TdKp/sMmx98ZM7tMlfyDF8co631Qfbu/cSM25UjhWrL8bQs3OygWExRLAxW1KgZyiXr6xIb0Y04mcx5fPdqVI3E1Yp1S58EK5pCOKHgWi6WOYpZShMKNxGLtVXEX8j33Rmf+yRJQlbliRSyG9HDYUx3ZjHIMV0uvjGA7/mkLo2SujS1Ity48fv1VaHWb2+mmDXpP3eFzJUcze1JPNfH93yKmXKFu2LWvLp/GKtoYxYt7r///oUcOnBNPMnlAn7wA5ve3qXfa5qaZA4c0GpCBLk+rezMGZeeHm/ZU5/GaWqSeeABreqi1GyMC0PTpZKNDeb4hV/4hUmC0DidneWpwrPPVl4Mvp6TJ92Jvmbj7FmPVMqqmggpEAiWhiQic2oSIQoJBDVGLlftEQhuBlpb7+Xll79AMWvSsa0DKKdSzQfbdHBKLoquYET0VX/vNs+fofHhwzQ1JEmPlqtDWUUbs2BjFiw8KyA7Uv7h7dr1z9m69admbc/zQJ2hPHk18X0X2xpBkpYvsisUakBV7ySfu4CZz0xaJ0llA2ctvLTIAtVQUQ11kjgUiTYTi7UuKa3M8ywkefLJLMkShmZgORaKruCa06d+GeEQTrFQbsf1yQzn8b1rokouXUAzVDbuWIcWUieEnHEUVcaYxqsIymLR1j0bSV0aY6BrGFVX0EMaekRDVdUJw3gjojPYk2Ljpo3z8hOaiXi8XPa9t9entVWmtVXGspgwpM5mg3mVf9+5U+WBB2qvTHj5mMrfdU+PT0/P8glEug779mkT1bNqneuFoXPHLhCOhvADH3xpwoNoOjo7VXp6vIoIiTNh20xrMj0d6bTPk09aPPqoLjyGBIJVh7Ss/oSLodbGUw1Wx11MILiFqEalGMHNx/WlhJsakpjpK/PazzZdXNPFiOmo00RMrEYCq4Rju+SHS/R3pyYEIE0PkUi007x2N3fduYtEon1eJZhtG3RDQ9F1vJUKRZgDxykyNvomSOUok1yuj3i8bVn6UlWD+obbJv52XQvft3CcErnsxRmrey24n+vEoVJpmFLxCtFYK5HI2kWJQ75nTYloUiRl4r+KpuAUHRzLmVJtLVpfTzF9Cato039uGM/10EPXtlFUmbbtzRORFwsxkB4nuaGe5Ib6Wbexcg7v+9D7Ftz29eRyASdPlsWvXbu0WT2AUil/QkxJpXwsqywWtbUpNR0VM065AtrCBaLWVpl4XCIel8jlAnK5YIpYtnOnyr59q69MeiwW48tf+jLd3d10dXXR3d1NLBYjFovNut+BAwZf/ao5L8FwsZw9O3/PsnTa56tfNTl82KiJSDWBQDBPajFSqNbGUwWEKCQQ1BAidUywHBi6QWCV5rdxECAp8k0jCAH4fsDYhYu0NN2OlbuTHbdtIZHYQjS6dlHtFQrl32lDawup3ouVHOq8GEmfBglk2UBVQ/i+S6l4BUVX0MI6vutTyPcTCjVUPI1sOsqpXQa6XockKWQzPcjq7N49C2r/OnGoUBigkO9flDgkST43Tmddz8W9Wo1MlmX0iI5TcvAdv+xxJEkEfkA0nuBi9yUunOonkgizsWPdvMp7z4VjuuRGixQyRfSQRihqEImHpq1eNjKQpVQw2b9//6xtnjzp0t/vTfJdGRc2ricWk+Y0hb5+sr0aRKDZmE4gyuWCCWPoZFIhmZTmjDxJpXwMY+7tap2Ojg46Ojrmvb1hwMGDOk8+WTuGPrYNTz5pCWFIIFhN1KIoJBCikEBQS4jUMcFyMd+MoiBY3abS0+E5PorrcdfujxCNLL16luOAY3skmpurIgrJioFljSArJVxPwnM81LA6Ed2iaAqqrpLNdNOU3LmiY4tEkth2Fis3ghE3KiYMwQ3iUL6fUvEK9Q23zVv4su0SenT2tD/VUJFVGatgUxorC6mJdc3YJZf+i4M0rps7mmc2PNenmDXJjxSwCi5m0SQSaSSZvJdiaZDUxR4cexgARVWpa6jD9z0sy8TMW3zsYx+bNaLjyBF73tEWnZ03j/C7UK4XiBbKrSw+tLbK7N2rcvz4zBX2KsHmzfK8UtViMYl9+9Rb+t9EIFid1JooVGvjWXmEKCQQ1BCp1Oou/y2oHRynMPH/bWvbMHvenNd+vhdUJAKilvAcD0Ovp5I2qYWCT8O6xfu6LAVdr8MyRzBiM+etaBENM2MuaxrZTNTXb2Fs1MXMZlBUBVmXUTSlImbUUBZuFF3BKTmkU68Tr9tINNo8535BEMzruU9WZPSIhpWzMOIGrZ3tRBIhGjbOnVo4EyMDWfLpIsVcCU0PsWbNnWxadzfr1t0zJWKtULhCqXSFQmGIQmEIgOZmmX/2zx6d0UvIsuDppy36++cfbTofU1+B4Eb27dPo6fGXLdVd1+HRR8vXtplS/sbFIHEOCwSrkFqMFKq18VQBcTUVCGoIUY5eUCn6+1/ACBlE6kLIU5Jmbh0cy8NzPKTUGI13Vq7dkinTtH595RpcAKFQPdlMgOd6KOr00R6SJKFH9RVNI7ue+obbcJwipWIKy0rjFE1UTUG7mpK1VCRJQo/oyIpMPncJx85Ql9gyazqZJMsEXjC/J5+rPxlFVWhobWYsM7Kk8Y70ZSHQefDBz5JMzn4iRqNriUbXTtpu506VlpbpTZ0tq5xCs5BJ+ubN8qpPfxJUjwMHNJ580loW8+7ro36uj+hKpXzOnPFIJiUhBgkEqxhJqj2jaSEKwc31OlggWOUIUUhQKbrPf4NwfflhWlMNAtucY48ygR8gVSiio9r4no9dtEkkNhEK1SNTuZSHYjGgsW1lI3DGkWUVVQvh2bNHFo6nkeVyvSszsBvQtAh1iY2sWXs3Tck7CHxpxupec+G5Hr4/VfRQDRUjbmC7OdKpUzhOcZbxhAn82a+xQRDgmA5W3kK+GjHXsK6FXDE/5xhzI0VSl8amXbdhx1qQbHp7n5mznek4e9ad5BE0Tirl87WvmQuO2mhvv3VTxwRLJ5mUOXzY4PBhg717VbZvX/7zKZmUeeABTQhCAsGqR7oWLVQji9CERKSQQFBTLCT0XyCYiULhCmOjl2jfXRYtDEPHnG5GOQ2BHyArq//u6Hs+Zs4mHG4iHF5T8fazWbh9+4aKtztfDKMR0xyacztJlqAGfKI0LUI0toFcthc1pC7oLaHneli58vkrK2UDa0mVyqlpV/82YgZ20Z41nUxVYpj2MBozl1F3TRfXctEj+kQFtXW3dZAeTc0xRp/B7jSe66IZKom1k31/jIjOhh1rufTG8xw/Dnv3fnzexw9lQ92jR20OHLjmg9PT43PkyLVojZ071YlKWPG4RF3dzOLuajeNFlSf8Yie8XOptdXl2WcrmaQrEAhuTiRqz8On1saz8ghRSCCoEUSUkKBSXLjwDHpIw4iUJ5C5fA4jnphzP6tgl1+ayKv75jguCIVCjSQS7dc+RwUq49uVTvuEIiEaN7QxcqmvIm0uBMOIU8j3z2/joDaiQiKRJIX8ZVzTRQvPLMzciGu6hMKNRKPrsO0cjlPAsTI4RRNJktDCWjliKGrgqA753CXC4aYpqWSKaoBVjgaaSZTyfR9ZkycEIUmRWb+1g9eff3rWMaYujhKLtbBp46OcPPkXhGL6xO9vnOuFoZdf8djzln817+8AyuW6d+3ySSZlTp50OXr02gT8oYdEBIWguoyff0sVhoRgKRDc5EjUYLpWrY1n5RFPEAJBjSBEIUGluHTpu9S31E387c/j5uu5Pq7tEa4zkFexKOT7AWbOIhRqmCQIqWvXM1rB6n6OA4W8y/odt1dFFJovnusRDsWrPYwJorH15LK9BH6AGlLnrE7muWVPqIb69aiqMckbyXUtLGuMXPYiklKOHNIMDbfkUiqlp0QLGUY9ZmkIK2ehR/Vp+w78YJJP07ptW5EVlaHhgdnH6XnEoi1s3fpTjI11cfH159i8s3VKaflxYeji60cBFiwMHT3qEI9LkyqMbd+uCEFIsOKcOeOSz5cLZFhWOZVxOTyGBALBTUZNGk1XewDVRzxFCAQ1Ql+fqDwmWDqOU6BQSLN26zW/m9Roii1r1826n+f6yIpU0RLi1cAuOWhalESiY9LnciiKW5hhp0UylpHZfNfdnPzHxfnErASVqvhVKSKRJACWOYyZzaOoCkZ85ipqnukSCjehqlO3UVUDVW3GcQpY+ZGJdoIgwDCmlo1XVYOGxh1ksxcwsyNoEQ3NuBax5Hs+vuujha59tuHO20mPpnDc2aMf4k0x+s4ex3EK7N37CYrPDzLQ1Uty4+RxmAWb7FAez3Wv/lavTKk+Nhs3phhv365MSikT3DoEvl9V/7ezZz2R8i4QCBaOMJquSYQoJBDUCPO0fBEIZmV4+DWASakrtmNBfOokeRJBsOpvikEAvuMTq0tOWedT+RSqkZGAjjtvr3i7lcRzPJTwzKJLNYhEkkQiSVzXIp0+hWu5E+la1+M5Hq7jUV8/u6F3ff0WxsbAyo0gKRK6EZ1WRIKySXd9fQdFPUEud4HAAz2iEQQBVt5C0RUU7dq5svmuOxmYI0oIIN4YQdU0+vtfYNOmg9x37+/y/POPc/H1yVFkkUgjmzf9EzZtenhBYtB0NDXJ7N8vBKFblZulIMCNHD/ucvy4SzwuEY9LGAY88khtXcMEAsHikSSJoOaeN2ttPCuPEIUEghphodVjBILpGBs7T7whNuXzuW7AnuujqKt7kuGYDrKsTmssHV3XTPrFylUfAxgdDYjsjFLXvJbs0JWKtl0pgiBAVWtTOFBVg0ikhVJpcFpRyCk6M0YJ3UhZGAowSyOE5iG2RCJJZFlhbLQLPaJhF2y4WuZ+Akliw/bb+P6Lz87reCIJnYHBo2zadBBNi3LgwJ/Oa7/F0NRUrv5kiLmy4CYllwtEWr1AcFMijKZrkdU9AxAIbiJSKSEKCZZGJtNDV/c3CScmzxSLpSLJzdtQlMkTb9/zsU0Xq2Dje6v//PNsn3B4apTQclEo+BQLHlvfum/F+lwIwdWqY5JUu+9/otFmggBca7JgFwQBsipjltJksxfx/bkFvfr6DiKR5okUtbmQ5XJEkF208V0fI6pPCmlff2cniqpxeeDivNqLN8Xo7zs+r22Xgq7Do4/qQhAS3BII42mB4CakBsrQT16q/YVUH3GlFQhqAMtCGDQKlkQm08Nzz32SSEKlcd01k+kD97+Lt7/1IZqTLWzauZd4rLwuCMDMWbiWQwBoIW2Sl8pqw7E8fN8nEm2Zss7o6ARgZKTywlf/AOx+18MVb7cSjAt915sz1xqyrBKJtOCak0UfSZLQozpGzMA0h0mnTmHb2Tnbq0tsnHffqhohFG4qp6+Fp5pe73rnQ1zsvzCnn9A48cYIiqry8iv/ad5jWCi6DocPG8Tj4glWUF1Wyk9InOsCwU1G1QWgqUvNeRxVASEKCQQ1gEgdEyyFcUEo1qjRuu1a6kxTQ5Ktm7fhWkWsUo5I5100JBqB8n2wnLIjEYrq6CF1VVsKeY5XLkMuTfUOMrbuYmhoeX5jfX0+zRtbSaybKkYJ5kc02kzA1GghAEUrG1FLasBI+ixjo+fmFTU0H8r+QlvQNANuOD1kVWHHfffQ1fvmgtrceEczfX0/5MSJP6/IGG/kwAGDZFI8ugluHYQoJBDcZFwVYWppWdUPwBVCPFkIBDWASB0TLIWXX/kckYRK85amSZ/r+tVqTI5FLjdGaEO5RPt4GpkW0gj8ANdZ3ZXvfM/HczyisdZp1xtt7csmChWLAdlswJ0HHlqW9peC7/hlwaPGmYgWstyJlLfrka56/YTqQpjmGLadq2j/ihqbkj7Zse8tIMtc6OtZUFtGRKf1tiTd3X/PhQvfq+QwAejpqawvlkBQ67S1Vb5IgEAgqCZSjS63NkIUEghqAMsSZoqCxTM2eolE81Rzadsul7STdQPLMtEa1yLFE3heeWIpSaDoCo65uieanlv+/cjyVO8cZU0LeizCyMjy/cb6+312HawNUcgpOVh5C8d08D0fSVodKYHRaDMS8pQ0sklcfWZTlMoKXZFIEs/x8K4TR3e+8yF6L/fgeQsXTCN1IdZ1rOHll79Af/8LlRwqZ896HDkico0F1aWnZ+VeZImy9wLBTUYNpItNWQRCFBIIagHx0CNYLJlMOZJB06cKIunRFC+fOoakhXBlDcexKTZONuHVwxq+669qo2nNUJBlmWJhcMq6UMcOclmPUmn5RKGBAY9EU4LmbR3L1sdMXJ9yZRcdXMvD0JJIvo7vBcgVFlCWC1lWicY2zBgtBBD45c8r7ZGk63VEY63YBbtcrS1ksO3uu6ZNHTuw/1189IO/yn173jZrm1pIRVFVurqequhYoSwMPf20hWVVvOnaZobzQrDy9PWtXHTpsWMOX/mKydmzq/vlhUAgKFPWYaqfMjYlhewWp3ZLkggEtxCi7KpgsRQKQ6iaihaa/nL+8smXePnkS6xrbmP7lttpWzfZiFeWJWRFwjZdQtHaLF0+H9SQSrEwRCTaMslXKNSxk8vLXC3eNOHKlYB73vNenvrDf7+8nV1lXMgo5PsB8L0Az/ZobLq9po2lZyMSSVLIX8Yu2hjRqWLWcqbDxeNtWOYIdsHmnp89TLGU53L/1Kpjbc1tQEDLmnUztjXUm2Z0IEtHx7vZvfujyzLenh6fbNa6pcrS+46JJCtI6uq9Tt0MWBb09q5synEuF3DkiMOxYy779qls3y6mLwLB6qUW07VqbTwrj4gUEghqACEKCRbL2Nh5wrHQnNsNDPVx/OSLRFs3oa3fPGmd7wco6ur2bdAMBSQmRQuFd9yNlqjn8uXlj4Lq6vK4/d63EF8zv3LolSAeb6Mu0Y5dtFe9IDSOpkXw3WvXQ9/3cSwHu2jj2h6yHF62vusbbkNWNPY8+iivvP4KAVOvy8+/9P/xZs+bvPDq0WnbsIo22eECa9buWDZBaJx02r+lPIYC1yZwRepcNbEsePJJq2rPLOPi0Fe+Ylalf4FAUCGqbR8kLIWmIEQhgaDKiNQxwVLIZHpQjPndzfKFHG+eP0Pknmv+N47lIQGqvrpFIbgWLeQH5bfYsXsO0tMbLGvq2DiZjM/oqM8DH/rQsvd1PZFIksam7TeFIFQoDGFZWYzYtUgQp+DgWwFyECYSaSEaa162/lXV4G0/9y+wLY8f//jVabfpvXye7//oGQaG+qZdb0R0Nt7RwsjIm8tamh4gFpPo7LxFIiaCACVShxyKijSyKpHLBXzlK6WaqJYqXqQJBKuZaqs/QhGaDiEKCQRVJput/gOWYPUiyR6+O/9z6PhrLxFa00L49rsBcG0XWVNuCp893/XwAx/XKRK56z7kUJSenpVLc+ju9tn14L1EGhpWrE8op5KtdkEIIJ+7hBpWkZXyo4nneniuR0PjDhqbOonH29D1umXrX1ZVHv7wP+f06TEGu1J4C/hdXU9ZGGrm8uWjyyoM7dt3iwhCcNUI9OpyM1ysViHxuIQtArUEAsESkVZS65nnIm4rQhQSCKqOeOMlWArrWvZTzNjznsDmCzlOnD1J7J534PsBvuujz+BHtJqwCja+G9CU3IERX0Pi3oP0XghwVzC7JpXyyeYCHvjg+1eu05sE0xwtmzxfZ5jumS6hcBOqujKmOTvfeQAtFALejixFGb40sui2llsY0nVunSghQc2gCzsngUCwZGpABRLRQlMQopBAUGVSKREpJFg8mzYdRNdjZIbz897ntdM/RgqF0Dr3ICvSRGTGasUuOvhOQENjJ5oaIXrXfdiezLlzK2uGCnD6tM/dD7+d+rbWFe97NWNbOVRdnagA4rkeruMRi7WtSP9ayOAdH/ogXV0+ihJl797HGRvMkRspLrrN64WhI8/+KoVC5RzPd+0SgpBg5UkmV/e9QiAQ1ALVFn+EKDQd4uouEFSZXK7aIxCsdjZuOMSV3jT9bw4zMpDFMWcPj7Edm2OvHSN899vQWmauolTrBAGYeRvP9mloKgtC2vrNJO55B2+crk4E3uioz+CQz0//q9+oSv+rFcsaQVKvPZS5pkso3LhiUUJv/8g/xUent7cs0ieTd9LZ+X4Gu9OLTiODsjC05e423CDNkSO/xoUL31vyWHUddu3SltyOQLBQmprEtEEgECyRq+latbQIhCgkEFSdWjBtFKxutm07zL33for66D7yVwK6X7005z6nzpzgUuoysYd/FpfVOcF0bY/AC2hM3oGmRlDWtND07g/R2+sxNFS939Xp0x7Nm9bT+eADVRtDLeL7LradpVhMkc/3MzpyjtGRNxgafBnPc1C0stm553p4jkcstn5FxtW0cQN7Hz3Ia6cmC4k7dnwQWVbJLyFaCEBRZdbf3kxDW5SXX/4CJ1/7kyW1t327esuUoRfUFlu2rP6CBAKBoBaodlSQiBS6ERF/LBBUEeEnJKgEmhaltfVeWlvvJZU6xXPPfWpe+x1//QUe2f9uooc+QPEf/gpFXV3vCXzXwzAaUBUDyQjR8K6fJT2mcPp0dct0WxZ0dXsc+uV/Tvex4zilm6N8sm1nGUmfRVFVVMUgCGRULYIsq0iSgqaFr9s2j++7uG6BwHdwHGtinazIyLJEIIHv+ChKCNctIcvl82+lo4R++vFP0N/vMTo6VUhMNu2mkDlFYm1syf00rqvDd32y2TeX1E5/v08uFxCPi4dYwcrS2ioTi0nk89V/drEshDgqEKxGajE8p8aGUw1W1wxAILjJEKKQoJrYjs3TR/8etb6RyNt/qtrDWRBBUI4UMkL1ACTe+R5srZ4TJ6orCI3T0+MToPGOj3y42kOpGJaVQ1EVFF3Gl2wC2cS0hjHNQYrFPkbSZycW0xrCtIYJJAs0HyNuEKoLEWmIEKoLoccMtLBGEAREoi0AeI6HXbRXNEpo16GHSa5v5cyZ6SPL1qzZhV2o3DmlGSqF/NiS2kinfb76VVP40QmqQq1UvRNR1gKBQFA5hCgkEFQR8VAvqDbDfSN88UtfInHnLsJ33Vft4cwbz/WQZZmQ0UDinf8EY8NWTr7GilYbm40ggFOv++x55CDrd95R7eFUBM8tICkSqqGihTW0sEYoXhZ4jLhBpCEysehRnVA8VN7O0FBUZYqhuWu6qFoIVS2XNLKLDnIQoi7RviJRQvE1SR7+fz7CuXM+ljW9QL9mzZ2YJWvBvkKDPSm6jl9ioGsYq3itjrcWUikUFl/VbBzbhieftMQ9RLDidHaqxGLitbpAIFgcUrUNhISx0LQIUUggqCIzTUQEgpXAc32GelPYUomjr/yAxrcdIv7AI9Ue1rzwHB9dj5N4+GcIb93Fy6/4ZLO1NUFOpwMuXfJ572//JuH6RLWHs2QsKzvh+1MJfNcnHF6LqkZoSt5Bc/MeGptuJxJJVqyPmZAVhff9m9+hUIDe3pmr1CUS7Wh6iGJ2YSmA4WgI13HR2EDPiT56f9zPUG96ThP4hTAuDPX319Z5L7j56eysvreQEEQFgtVKtb2DhKfQdAhRSCCoIuJhXlBpwuG1AJOiE6bDc30uvj6AaqgkN9Tz5vkzfOt7f0f4zreQ+Mn3IxnhWfevFuNpY76k0vqRTxHatpsXXvIZGanN39Lrr7vYrsZ7fueTq/pNlOMUCYIASa7MMXiOh+/5hMNNyLKKpkUq0u58OfjRXyKxdi2vvBoQzKHNJxLtFDILM5tOrI1hhA2STbs5dOiL3Lbtw/ilOga6h5cw6qmMC0NnztRIiJzglmDXLg1dr+4YzpyZWcwVCAQ1TrX1H6EHTUGIQgKBQHATUSpdAcqlsGejv+sKAQGb7mid+GxgqI+nnvkmTkOShsc+glxXv6xjnS++H2CbLmbOojhWwo810faRTyPXdfDs992aixC6niCAV14NaNnSztt+4QPVHs6iCQIXSZaxchaO6RDMpaTMgWu7hEIJZHnl/Um27N3DWw49zKs/Losqc7Fp47vIpUoL7qextY7u7r9D16Ns3fpTHDzwJxw8+AU6Ot69iFHPztGjDpY193YCQSUwDDhwwKC1tXrTiHTaF76MAsGqpNoKkFCFpkP5zGc+85lqD0IguFV59lmn2kMQ3GT09j6D5fVSl4zOuM1gT4rCaIkNt7egGZMn5SWzSNeFc2zYtJW1+95G6WIXQTG/3MOeEatoYxccpEAhZDSx9i2HaP/AvyZbaubHJ2rHQ2g2XBcKBXjrgdvpe/McYwND1R7SglEUg1isFVnWsUtZ7JJF4AdIqlT2B1ggTtEhEl234hFC8TVJfv6z/5bz54N5R2pGo810n38KRZUIRecXHmEVbVzHI5PKIssaa9bsBCAUaqClZc+ixz8TngeKAm1t1U/rEdwaNDRIdHaq7NunkUwqhMMSngel0soJNfG4RHOzeL8tEKwmvGKWwK+tSD9J0VAi8WoPo6rURgkBgeAWRLzVtHOCjQAAGiNJREFUFSwH6ZFXUYzZJ+mqqhKK6jNGE9mOzVPPfJN33Pcwt73/l8mcOUHxxWfxs0urmrRQXMfDtTzqGzqIrGknet8h6rbvoKvL49y52nqgmIuhIZ/eXonHPvk4f/34b5O+eHnZ+pIkiMbKYpRtQyWfvSKRJJFIEtMcpVgcwMwUUHUVxVBQ1PkLErIqY9vZFfEPGseIx/jAH3yGTFaiu3v+0WWaFmVdy/2MpF+ctTS9VbRJX8qSHcmV99NDJNfcjq6vzIPm8eMunZ2qKFUvWHHa22Xa28vijGXB2bMuR48u/0uvM2c8du0SUxmBYFUhsaiXSctKrY2nCogrqUBQJUQ5VcFyMHzlLG3bmyvS1vd/9Axnz59m/563seYDv0z+1RconniRwFp4Ks1C8T0fu+AQb2pnzcMfom73WxkZ8XjxRbdm/YPm4swZj3hM4UOf/X2+/K9+i+zQlYq2LytQVycTjYF83ctz1wXHhmzWx66QGB0KNRAKNWDbWYqFQcxcBlVTUAx1XmbUiqZglUbw/Y0rkkKmhUP8/Gd/Dy3SwPEXFn7+bNv2M3zve8/imC5aaPrxGhEdq2SzceND3H77zxONrl3qsBfMsWMOBw5U2exFcEtjGLBrl0oyKfPtb1vzStFcLOm0Tyrlk0yKaCGBYPUgUrZqEZE+JhBUib4+j97e1Tm5FdQmmUwPPT1P07IliTyLIXAxZ5IZzmOXXKxS+W2urMjT7pMv5Hjj3CkKZon1O/dSv2c/gaLg9F1YtuMIAnBcmYZ7HqX9/Z+CxjZefsWjq8tf0dSE5WBwMKC5RePuh/fTc/x5VNnGCEnTLp4P/jwuEaoG9Q0yTUkJw5j6wkuWQdPAc6ePUFzKCzJFMQiFmwiFk7iug1XMlU2kXZ+AsjH1dG8EJUXCMR2MUAJFWd7y87Kq8sF/9xnqW9p44UV/USmHoVAD/QPPUzIzxBpmTnmzShZWscj22963hBEvnnQ6oLNTxZgjWlAgWG7icYlt21T6+pb3uv2Wt2jifBcIVhFeKQeBV30boesWWdVQwjNHAt8KCFFIIKgSPT2eqD4mqCiXL/+AsezraCGVsaEsuSsm+dE8IGGENTzXZ7ArRX7Epn3zI0RDmylkRhm62MdIf4ZS1qGYLaKoyhSvofRoitfOnsTxfTbfdS/19z2MsqYFFBXfNgmshZXsng65rp5w510onW8l+c73Ud/xEGfOKZx8zVv1YtA4QQDxOpm77o6y9533cu5Hz6MqDqGQNGUZTwOaKdVUN6ApKVPfIM2rEpBlTm5rfH9NkzDNpX2/sqwSCjUQiTYjSRr4PrZl4pScab2HfNfHdwMSifYl9TsXkizznt/5LVpv28aLL7Gk41TkEBd7fkhTW2LGbYywzkDPJVpb7yUUalh0X0vBsgLa24W3kKD6GIbE1q0qxWJAOl35a7iuw733ahVvVyAQLB9eKX/1jVcNqEFXF0lRb3lRSAqWWkJEIBAsim9/2xKRQoKK8qMf/TsGBl4EILnmdpJNu8nmuujvO46qqaiqgmGsYe+e35wyGU+lTjE21sOFC98l0IZp3TZ76svm9VvYvL6d9c2tRKJ1mOkhzHNvYPf3AuDlMrN6EMl19Sjx8uTa2NKJvmEL4aZmxlJpXn3lderrf5JCYd0Svo2VZ7wSTzYbkM9PvbW2tsocOKBPiD2+5zHYc4H//enfxywUZmzXMstihixfE3+M0MLHN3zFxyyVI4saG+WJNkpFSA0vz7XItrNksxfwXAs1rKIZ5QmcVbDQlAT19R3L0u84P/2bH2f7fffy4osemczSjtFxCjz9jx+hoTVG47q6Gbe7cGqQ1rUH2b37o0vqbyl86EMh4S0kqClOnnTp6fFIpfxFp5Q1NckkkxKtrTLJpCzSxgSCVYg9MkDgLmNe6SKQ9RBafWWsF1YrQhQSCKrEk09aIlJIUFG6ur5FfX07yeSdkz53nAL9/S9QKAyxbdthNG3mymT9/S9w/OX/wNa9G+bdb1NDks0btrC5dTP1dY0Tn0uShKzIFPsuIAUBUihMKDn1pjsymuJ012nOvdnFyRdeZ8+eX2fTpoPz7n85aGqSMYxr/zUMacoEZLZyzKmUPzEJSiZl9u3Tpt0+8H1GBgb5yu/8W/IjoxU/jutJp3xCobLn0DiODUODPsv9JFAoDJHPX0aWJdSwip23iddtXlaj6ff+7qfYumc3L73kVCxKoavrW5w8+RfUNcVYs7FhWn+hzJU82UGPQ4f+qiJ9LobWVpnDh5c3LW+lOHnSJZmUq1r+XFBZLKvsB5TNlsvK9/f7U8R0XWfi372tTZm4FgsEgtWNEIVqEyEKCQRV4k//dPnNegWCheI4Bb71rQ/Qtr2ZeOP8y4XnRooMdqfxrhq21NfX09BQTp/ZvHkzAKZpMjg4CMDo6ChjY1MjiTo63r0iERbjE454XJpY6uqu/b2S+J5HIZPlf/72v2F0YHDl+vXLgpC7/EWCrvbnks1ewCyNALC2+e5lMZnWwiHe/3u/y9r2do4d85ccIXQjmUwPx1/+jxQKgzS2JaZEDXmuz7ljF7j33k/R2npvRfteCLt2ze4t1Nq6cKHlzBmXfH7yZ/E4xOPldio9cT9yxObsWe9qPxLt7QrbtytTBNpcLpjYRrC6SaXKv1cRBSQQ3Jw4o4M1JwpJmiFEISEKCQQrj2XBl74kRCFBbfLCi79H3jo9ZwoZlCfAQ+fTZNN5Ojvfz44dH5xY5zgFMpmeefWpadFl95dpapLZtUuhvV2tuTfOvudhmxb/83f+LVfOz+87WyqpYZ9ScUW6moRtZzFLY9QlNla87f+/vfuPjbu+7zj++n7v7nuO74dJfDHJuSQ+EnqGhANGzC9nAcy0JahAJzqrsHRo1doJaVLVtZq0apv2Q2rXSe20rRVS106VxqBlGowwDToNsrGEH0mgxIFggqlJgs8OPsc/zj/vx/f2x9UhBif+kfvxvfs+H1IU5WKfP7aS74/X9/15v9eEw/rtb/2lQpEWvXrI1tRU+S5x+vqe0du9/yx/o6nLY80LqoYG302pKXCDdtz49bJ9/VJIJLzascO3rP8PyzlvhUKGdu+2SnJDf+BAVseOLd4VPBQqbiFKpwvnQoTubrbMAYDTEQo5E6EQUAXJpK2nny7RbGigxE6efF5He76/5Bay+eqgYHDDon2KnMCypHjcq+uu8zr+hrFg28rn83r8z7+lU8feLOvXGj1razJd1i9RceENl2vvN/9CHn9Ihw4XLrl59nJMTX2o117/jsbG+hS6LKTR4bML/v6eex6/6HZNJwiFDHV1WUtWDfX05HTw4NJlZZYldXb61N6++iqw3t6c9u9ffgnb7t1+xWJUlgCA0xVDoQqVKC9TMRRa+kFoPSMUAqpgpRe8QCUVG+p+UaF1llpi6xb9mNTpMaU+GP1EdZBTRKOmEglfTd4oFmxb//3jf9Ghf3+mbF/jzJCtTB3l0rEbb9Bv/tFXNTPr1eEjeWUrfHg9efJ5TU2dUUtLYsHrTU0xx4dC85aqGnr00dlz27SWIx73qKtrGWPxPmal58fVfh0AQOVlR4dUyC9eBVoths8vX9P6ai+jqgiFgCo4fDirI0ecdUAEzjc+3q/nn/+KNm5Zr6aWhWM6xz+c1OB7w1XvmXIhnZ0+JRKl71VTUYWC3n/zuJ769t9qemKi5G9fL6GQ6fHoji8+pJs+8xv64ANbb72VK066xaqEQoY6Oy3FYqZSKVvJpK1UylYqVdDIyMp/sM3NpvbssS5YpXf+JKpk0tbcXEE9PSs7NxIKAUDtIBRyJkIhoArOb54JONXJk8/rtdf+Ti1tzQo0NcjfaGluOqP+owOOmBD2cZYl7dnjr5spRQXb1tz0jH76V9/WB2/3lvS96yEUCrWs12/96R+rORpVz7G8hoZIg5zIsqRYzHOuyuhSRpJfCNvHAKA2ZMfOODAUsuQLEwoRCgEVxjh61Irjxx/TqdM/0/RUcVy61+dTdONO7djx1SqvbKGlKhJq1fwp+sXH/1UHn3hShRKVwQwmKzN1bONVm+S1fDr91nule1PD0I333K3b9z6gTMbUa68XND3N8dTNLItG0wBQC7JjHzo0FIpUexlVRSgEVMGPfjRT8ielQDnNTxIbG+vX1q33VHs5kopBUCRiKBIxa3+72BIKhYLODg5p33e/p+SJdy/5/bKZYrVQua8A1rW2yN/YoMF3T5Xk/SJtm3Xv176ilk2tbBfDAs3Nprq7HTZWEACwQHbcgaGQl1CIUAiogkceYRw9akM87vnE0/e5OV1wVHS5nB8ARSJm3WwRW4lCoSDDMPT2S6/qv37wT5o8O3pJ71epYKgUfA1+3f47e3Xj3b+u2ZmCjr2Z08hIDSz8l7LZKb344je0a9c3a6bxdK0JBg194QsN1V4GAOAisuPDDg2Fmqu9jKqq70ergAOxbQy1Ihg0LtjANRIxKjJBLxg0dNddS4/LdgPDKIZz7bd06KqOX9GBJ57Uq0/uU26Vo7Z8lhQKG5oYd3a4ctWtN2v3w19WQyCgEyfy6u/P10SQdb7h4WMaH+/Xc8/9rm699c8UiWyv9pIcLxg05Pcby25w3d7uKfOKAACoT4RCQIXN1XhzV7hHR8eFTxHt7cW/K2cwdO21XnV0XHhEtlsZpimvaeqOB7t1y2c/o//76ZP6+X/+TNlV7EnNZByarhiGPn3bLbqtu1sb2qIaHLT16mvZmj1+JpMvKxwJyPSaevHFb+iaax5Ue/vnP/FxU1MfSpICgZZKL9ExolFT8bhHk5PFSZ3LNX9MAgA4mPHRQy6ncNhyqoLtY0CFMY4etWC5WzGSSVvPPjv3iR5ZwaChcLh4ll3ptCHLkrq6mCa0XIVCQdm5jF5+ap8OPbVPmdnlJydOm0LmsSxd+2t3qbP7foXWhjQ0ZKuvL39uclWteuaZbkU2hdTUElT67LTO/GJEwUCrfFZAppnRmaG+T3xOILhOgUCLPGaT1q7dsmiIVIvmjw3nV83OTyjr6PDJsgwdPLiyCZ3RqKn77iM9BgCny06kJAduH/OG1lV7GVXFYxWgwmr95gbucLEqofNFo6a6uxuUThdkWVIksniQk0zaGhjIq7/fvuh2kLY2U11dfqqDVsAwDFkNft3++c+p83Of1cCJPh3e96z6Dh9RPnfxC6+sAxreG6ZHm69P6Nq77tJVHdfL4/UqmZReO5rVzEztHy+TyVeUzc5qfFjKZnJqDDfoimsu1+iZsyp4J5SXtGnbRkmSz/LKtm3lc7ayszll585oevqUBo+/qjVrWjQ83KPBwZeUSPy+Nm++q7rf2CpdbDtoOl3Q00/PLXvLGACgthiGVHBaaY7DllMNhEJAhREKwemKT+2Xf3oIhYwlR0FHo8Xm0B0dxS2UyaSt/v7cgv8PiYSP6qBLYRjy+nzavO1qbd52tXLZrHpfPqQjzzyrgXdOLPop1awV/tS127T9jjt0defN8q/xa3y8oHf7bA0O5pTN1s9xsqnpSiUSX9LYWJ8mJk7o1OkBSVJDY4M8/pzWBIsVef5GSx7vef/+w8XfUqfHNHl2Wkd7vq9AU4Oy2Vlls9OV/jZKYv44cCH9/flVBUKpFCESANQGJyYwTlxTZREKARXGxSucrtx9fPx+KRYzFYst3sQapeH1+bR9V6e27+rU7NSU+l4/qr7Dr+v9oz2aGh3T3Gxl17P2U63adO02xa6/Tm2J7WpobNDsbEEDSVunT2c1PV0/QdD5AoEWbd16z4LXUqk3NTbWvyAoagw3nKsYmpfP2ZIhRa5Yq8Zwg2anMlJK2rjx5kp+C6tmWdKePf6LVhGeLx736uDBlfcpy2SKYTMVhgDgdIbzmvg4bT1VQCgEVNgqesECFdPcbCqR4NRQbxoCAW3/1dt0zW03y/R4NDk6prcPvaUj+49rYnhY6eGUxoaGlJu79ANUQzik8PqImlpa1NSyXq3XbNPmbe1qDDUqM5fT6KihX7xf0OhoThMT7gzJI5HtCyaQHT/+mN7te0Jn+sY1MTpxwW1/jYG12rR5V800os5kpIkJe9lNoDOZgkIhY1UVtSMjNlMKAQBYBa78gQqiSghOt3Onr9pLQBmZnuLY7uDay7Q+fotuv+JmNTZ+9IQsMzun8eERTaSGNT2e1kw6renxCc2kJzWTTmsmPamGQKPWhENqDIfVGA5rTTikwGVhhSPr1dQSkc/66N/Q9LStdNrQyQ9snT3r3hBoKYHA5bpqa7ckKb7lSllWUJLk8wXU1BSr5tIu2ZEjuSVDoXS6oMOHsytqLv1xqRShEAA4nuHASiEQCgGVRJUQnKyt7eL9PlA/entz2r//o2064bCpQEAKBr0KBjdoXdtGrfcU5DELMs2CPF5DHo8pj8eQnS8ol7eVzxWUz0u2LdkFj2ZnpVOnbaXTOU1NiQBoBWq1afRypNMF9fYuHgyVIgyaNzdXn9sPAaD+OC0Uctp6Ko9QCKiggYFLv/AFysGypJ076fHjBh8PhKRigDMxsdRnXuz4RQCEC9u/P6uenrxaW03F4x75/UbJwqB5DHEAAGB1CIWACpqbq/YKgMUlEt4lJ4ih9i0WCAGVMDJia2TEVk/P4v2SLlVHB1tfAcDxDEOG07aPOW09VUAoBFTQakbtAuUWDBpKJLihqncvvJApaWUG4BTxuIdQGwCAVSIUAiqI8nY40c6dFqOc61w6XSAQQt2iSggAaoQDG00vvpys3nzuJ/qbH7+k3qGMQhu26P4vP6QvdbWqHs84dBQFKohQCE4TjZqKxTgV1LtQyFAw6KyLMKAUqBICgFpiOPTXQkOHHtXXvveOdv3Bn+j5//gH/eMfxrX/u9/RDw9NlvbH4RDcCQAVwjh6OE1bm6nduykRcovWVk75qD9UCQFA7TCqnf0sKxOa0f/se0XRrgf0eztbFQo26sqb7tfX723UM4+9oiXnctQgrhCBCmEcPZzCsqTdu/3as8fPtjEXicXYMY76QpUQANSaaqc/y0mFhvTzPlvbb9qy4NWrr9+iyb5eDZTk5+AsXCECFUKlEJygrc1UVxdhkBtFozwHQn1pb+cyFgBqiWk1SAWHtdP4RFOhCU2cXaNPB60Fr3rXBRTMDGpssiDV2ZZ8zqZAhczNOewACFcJBg3t3GnRP8jF/H7p4YfXnPtzOl3Qo4/OVnFFwOpZFkEnANQaw1MbW34Na+mPqSeEQkCF0GQa1WBZUiLhVSLhozoIC4RChuJxD1PJUJMIhAAA5RFWc3BGI2czkhrOvZo7O6VJK6xwHQZGnFGBCiEUQqXt2OHV3r1r1NFBIITFsf0GtSoW81R7CQCAurRBN1zl19tvvLfg1bffeE/Bre26wqqvrWMSoRBQMYRCqJR43KO9exsIg7CkaNRkVD1qEo3TAQDlsUZdD9yq4Rce1w8PDGgyM63+N/5Nf79vRHfee6PC1V5eGRiFgtM6PQH16ZFHZqq9BNS5aNRUV5fFNB6sSCpl6+DBrJJJmuGjNjQ3m+ruJvEGAJRP34Gf6K9/8L/qOZVRaMMm3f3gQ3r43jY1VnthZUAoBFQADV1RbtGoqfvu4yYJq5dM2jp8mHAIzrdjh1cdHbXRrBQAAKej9haoALaOoZyam03t3k0ghEszHyym0wX19ubU25vX5CTHLjhLsXk+gRAAAKVCKARUwMQET95RHpYldXXROwilEwoZ6ujwqaPDp1TKVk9PTv39eWUy1V4ZIMXjXo53AACUEKEQUAFUCqFc9uzxKxJhZgDKIxIp9qmSpN7eYjgUCplqbfUoFjOVStlKJm0NDOSVTNp1Gxw1N5uKRovfdyqVVyplK52WRkYI/CvtyiuZOgYAQCnRUwiogAMHsjp2LFftZaDO3Hmnj5HicJT5kKi3N1/zgUlbm6nrrvOpudm8aGVKMmlrYsJWKlXQyIhNT6Yye/jhNdVeAgAAdYW7CaACav3mCM4Tj3sIhOA4kYipSMRUIuH9ZTiU0zvv5Ku9rBVbSeAajRariM7X32/ruefmyrE0VwsGmawIAECpcUcBVMAc9wYosUSCwzecrRiWWOrslN55J6ejR3OOb1xtWcUtmR8PeVYqFisGRVQNlVY4TCgEAECpsX0MAAAAAADAhehOCgAAAAAA4EKEQgAAAAAAAC5EKAQAAAAAAOBChEIAAAAAAAAuRCgEAAAAAADgQoRCAAAAAAAALkQoBAAAAAAA4EKEQgAAAAAAAC5EKAQAAAAAAOBChEIAAAAAAAAuRCgEAAAAAADgQoRCAAAAAAAALkQoBAAAAAAA4EKEQgAAAAAAAC5EKAQAAAAAAOBChEIAAAAAAAAuRCgEAAAAAADgQoRCAAAAAAAALkQoBAAAAAAA4EKEQgAAAAAAAC5EKAQAAAAAAOBChEIAAAAAAAAuRCgEAAAAAADgQoRCAAAAAAAALkQoBAAAAAAA4EKEQgAAAAAAAC5EKAQAAAAAAOBChEIAAAAAAAAuRCgEAAAAAADgQoRCAAAAAAAALkQoBAAAAAAA4EKEQgAAAAAAAC5EKAQAAAAAAOBChEIAAAAAAAAuRCgEAAAAAADgQoRCAAAAAAAALkQoBAAAAAAA4EKEQgAAAAAAAC5EKAQAAAAAAOBChEIAAAAAAAAuRCgEAAAAAADgQoRCAAAAAAAALkQoBAAAAAAA4EKEQgAAAAAAAC5EKAQAAAAAAOBChEIAAAAAAAAuRCgEAAAAAADgQoRCAAAAAAAALkQoBAAAAAAA4EKEQgAAAAAAAC5EKAQAAAAAAOBChEIAAAAAAAAuRCgEAAAAAADgQoRCAAAAAAAALkQoBAAAAAAA4EKEQgAAAAAAAC5EKAQAAAAAAOBChEIAAAAAAAAuRCgEAAAAAADgQoRCAAAAAAAALkQoBAAAAAAA4EKEQgAAAAAAAC5EKAQAAAAAAOBChEIAAAAAAAAuRCgEAAAAAADgQv8PViRHZwiU+bUAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 32,
"metadata": {
"image/png": {
"width": 600
}
},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image('./assets/active_cases_eu.png', width=600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Focus on the epidemiological trajectories in Norway"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>Country/Region</th>\n",
" <th>Province_State</th>\n",
" <th>Confirmed</th>\n",
" <th>Deaths</th>\n",
" <th>Recovered</th>\n",
" <th>Lat</th>\n",
" <th>Long_</th>\n",
" <th>Active</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>452</th>\n",
" <td>2021-04-01</td>\n",
" <td>Norway</td>\n",
" <td>NaN</td>\n",
" <td>96770</td>\n",
" <td>673</td>\n",
" <td>17998</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" <td>78099</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4435</th>\n",
" <td>2021-04-02</td>\n",
" <td>Norway</td>\n",
" <td>NaN</td>\n",
" <td>97410</td>\n",
" <td>673</td>\n",
" <td>17998</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" <td>78739</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8418</th>\n",
" <td>2021-04-03</td>\n",
" <td>Norway</td>\n",
" <td>NaN</td>\n",
" <td>98102</td>\n",
" <td>673</td>\n",
" <td>17998</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" <td>79431</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12401</th>\n",
" <td>2021-04-04</td>\n",
" <td>Norway</td>\n",
" <td>NaN</td>\n",
" <td>98676</td>\n",
" <td>673</td>\n",
" <td>17998</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" <td>80005</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>2021-04-05</td>\n",
" <td>Norway</td>\n",
" <td>NaN</td>\n",
" <td>99249</td>\n",
" <td>676</td>\n",
" <td>17998</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" <td>80575</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date Country/Region Province_State Confirmed Deaths Recovered \\\n",
"452 2021-04-01 Norway NaN 96770 673 17998 \n",
"4435 2021-04-02 Norway NaN 97410 673 17998 \n",
"8418 2021-04-03 Norway NaN 98102 673 17998 \n",
"12401 2021-04-04 Norway NaN 98676 673 17998 \n",
"16384 2021-04-05 Norway NaN 99249 676 17998 \n",
"\n",
" Lat Long_ Active Latitude Longitude \n",
"452 60.472 8.4689 78099 60.472 8.4689 \n",
"4435 60.472 8.4689 78739 60.472 8.4689 \n",
"8418 60.472 8.4689 79431 60.472 8.4689 \n",
"12401 60.472 8.4689 80005 60.472 8.4689 \n",
"16384 60.472 8.4689 80575 60.472 8.4689 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df0 = df[df['Country/Region'] == 'Norway']\n",
"df0.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>Country/Region</th>\n",
" <th>Province_State</th>\n",
" <th>Confirmed</th>\n",
" <th>Deaths</th>\n",
" <th>Recovered</th>\n",
" <th>Lat</th>\n",
" <th>Long_</th>\n",
" <th>Active</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
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" <th>171721</th>\n",
" <td>2021-05-14</td>\n",
" <td>Norway</td>\n",
" <td>NaN</td>\n",
" <td>118740</td>\n",
" <td>774</td>\n",
" <td>17998</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" <td>99968</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175704</th>\n",
" <td>2021-05-15</td>\n",
" <td>Norway</td>\n",
" <td>NaN</td>\n",
" <td>119052</td>\n",
" <td>774</td>\n",
" <td>17998</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" <td>100280</td>\n",
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" <th>179687</th>\n",
" <td>2021-05-16</td>\n",
" <td>Norway</td>\n",
" <td>NaN</td>\n",
" <td>119299</td>\n",
" <td>774</td>\n",
" <td>17998</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" <td>100527</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183670</th>\n",
" <td>2021-05-17</td>\n",
" <td>Norway</td>\n",
" <td>NaN</td>\n",
" <td>119500</td>\n",
" <td>774</td>\n",
" <td>17998</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" <td>100728</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187653</th>\n",
" <td>2021-05-18</td>\n",
" <td>Norway</td>\n",
" <td>NaN</td>\n",
" <td>119814</td>\n",
" <td>774</td>\n",
" <td>17998</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
" <td>101042</td>\n",
" <td>60.472</td>\n",
" <td>8.4689</td>\n",
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"</table>\n",
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"text/plain": [
" date Country/Region Province_State Confirmed Deaths \\\n",
"171721 2021-05-14 Norway NaN 118740 774 \n",
"175704 2021-05-15 Norway NaN 119052 774 \n",
"179687 2021-05-16 Norway NaN 119299 774 \n",
"183670 2021-05-17 Norway NaN 119500 774 \n",
"187653 2021-05-18 Norway NaN 119814 774 \n",
"\n",
" Recovered Lat Long_ Active Latitude Longitude \n",
"171721 17998 60.472 8.4689 99968 60.472 8.4689 \n",
"175704 17998 60.472 8.4689 100280 60.472 8.4689 \n",
"179687 17998 60.472 8.4689 100527 60.472 8.4689 \n",
"183670 17998 60.472 8.4689 100728 60.472 8.4689 \n",
"187653 17998 60.472 8.4689 101042 60.472 8.4689 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df0.tail()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
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" Confirmed Deaths Recovered Active\n",
"date \n",
"2021-04-01 96770 673 17998 78099\n",
"2021-04-02 97410 673 17998 78739\n",
"2021-04-03 98102 673 17998 79431\n",
"2021-04-04 98676 673 17998 80005\n",
"2021-04-05 99249 676 17998 80575"
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},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"df1 = df[df['Country/Region'] == 'Norway'].groupby('date')[['Confirmed', 'Deaths', 'Recovered', 'Active']].sum()\n",
"df1.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
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" Confirmed Deaths Recovered Active\n",
"date \n",
"2021-05-14 118740 774 17998 99968\n",
"2021-05-15 119052 774 17998 100280\n",
"2021-05-16 119299 774 17998 100527\n",
"2021-05-17 119500 774 17998 100728\n",
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"execution_count": 36,
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"source": [
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Case fatality rate [CFR](https://en.wikipedia.org/wiki/Case_fatality_rate)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"def fatality_rate_given_country(csse_daily_df, country):\n",
" dfc = csse_daily_df[csse_daily_df['Country/Region'] == country]\n",
" last = dfc['date'].iloc[-1]\n",
" cfr = dfc[dfc['date'] == last]['Deaths'].sum() / dfc[dfc['date'] == last]['Confirmed'].sum() \n",
" active = dfc[dfc['date'] == last]['Active'].sum()\n",
" confirmed = dfc[dfc['date'] == last]['Confirmed'].sum()\n",
" return last, cfr, active, confirmed"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Case fatality rate (accumulated Deaths/accumulated Confirmed) for given country:\n",
"\n",
"Norway (upto 2021-05-18) = 0.65% (confirmed=119814, active=101042)\n",
"Sweden (upto 2021-05-18) = 1.37% (confirmed=1047143, active=1032842)\n",
"Denmark (upto 2021-05-18) = 0.93% (confirmed=268956, active=13068)\n",
"Iceland (upto 2021-05-18) = 0.44% (confirmed=6549, active=58)\n",
"China (upto 2021-05-18) = 4.71% (confirmed=102784, active=378)\n",
"Italy (upto 2021-05-18) = 2.99% (confirmed=4167025, active=315308)\n",
"US (upto 2021-05-18) = 1.78% (confirmed=32997331, active=32410112)\n"
]
}
],
"source": [
"countrylist = ['Norway', 'Sweden', 'Denmark', 'Iceland', 'China', 'Italy', 'US']\n",
"print('Case fatality rate (accumulated Deaths/accumulated Confirmed) for given country:\\n')\n",
"for i, c in enumerate(countrylist):\n",
" last, cfr, active, confirmed = fatality_rate_given_country(df, c)\n",
" print('%s (upto %s) = %.2f%% (confirmed=%d, active=%d)' % (c, last, cfr*100, confirmed, active))"
]
},
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"source": [
"last, cfr, active, confirmed = fatality_rate_given_country(df, 'Norway')\n",
"\n",
"fig = go.Figure([go.Scatter(x=df[df['Country/Region'] == 'Norway'].groupby('date')['date'].first(),\n",
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},
{
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 40,
"metadata": {
"image/png": {
"width": 600
}
},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image('./assets/active_cases_cfr_norway.png', width=600)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"name": "Active cases",
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}
],
"layout": {
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
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"width": 0.5
}
},
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],
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},
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"source": [
"region = 'Norway'\n",
"\n",
"fig = go.Figure()\n",
"fig.add_trace(go.Scatter(\n",
" x=df[df['Country/Region'] == region].groupby('date')['date'].first(),\n",
" y=df[df['Country/Region'] == region].groupby('date')['Active'].sum(),\n",
" name=\"Active cases\"))\n",
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"fig.add_trace(go.Scatter(\n",
" x=df[df['Country/Region'] == region].groupby('date')['date'].first(),\n",
" y=df[df['Country/Region'] == region].groupby('date')['Confirmed'].sum(),\n",
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"fig.add_trace(go.Scatter(\n",
" x=df[df['Country/Region'] == region].groupby('date')['date'].first(),\n",
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"fig.add_trace(go.Scatter(\n",
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" y=df[df['Country/Region'] == region].groupby('date')['Recovered'].sum(),\n",
" name=\"Recovered\"))\n",
"\n",
"fig.update_layout(title=\"COVID-19 infections in {}\".format(region),\n",
" xaxis_title=\"Date\",\n",
" yaxis_title=\"Number of Individuals\")\n",
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},
{
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\"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Active COVID-19 cases\"}, \"xaxis\": {\"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"title\": {\"text\": \"Number of Individuals\"}}}, {\"responsive\": true} ).then(function(){\n",
" \n",
"var gd = document.getElementById('cad18b7b-14a1-4f54-9fa3-ad26c20e43dc');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; }); </script> </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = go.Figure()\n",
"countries = ['China', 'Italy', 'South Korea', 'US', 'Spain', 'France', 'Germany', 'Norway']\n",
"\n",
"for country in countries:\n",
" fig.add_trace(go.Scatter(\n",
" x=df[df['Country/Region'] == country].groupby('date')['date'].first(),\n",
" y=df[df['Country/Region'] == country].groupby('date')['Active'].sum(),\n",
" name=country,\n",
" opacity=0.8))\n",
"\n",
"fig.update_layout(title=\"Active COVID-19 cases\",\n",
" xaxis_title=\"Date\",\n",
" yaxis_title=\"Number of Individuals\")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 43,
"metadata": {
"image/png": {
"width": 600
}
},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image('./assets/active_cases_selected_countries.png', width=600)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hoverinfo": "x+y+z+text+name",
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