{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cs224 \n",
      "last updated: 2020-08-03 \n",
      "\n",
      "CPython 3.6.10\n",
      "IPython 7.15.0\n",
      "\n",
      "numpy 1.18.5\n",
      "xarray 0.15.1\n",
      "scipy 1.5.0\n",
      "pandas 1.0.5\n",
      "sklearn 0.23.1\n",
      "matplotlib 3.2.2\n",
      "seaborn 0.10.1\n",
      "pymc3 3.9.2\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'cs224' -u -d -v -p numpy,xarray,scipy,pandas,sklearn,matplotlib,seaborn,pymc3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np, scipy, scipy.stats as stats, scipy.special, scipy.misc, pandas as pd, matplotlib.pyplot as plt, seaborn as sns, xarray as xr\n",
    "import matplotlib as mpl\n",
    "\n",
    "import pymc3 as pm\n",
    "\n",
    "import theano as thno\n",
    "import theano.tensor as T\n",
    "\n",
    "import sklearn, sklearn.linear_model\n",
    "\n",
    "import datetime, time, math\n",
    "from dateutil import relativedelta\n",
    "\n",
    "from collections import OrderedDict\n",
    "\n",
    "SEED = 42\n",
    "np.random.seed(SEED)\n",
    "\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "# pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "np.set_printoptions(edgeitems=10)\n",
    "np.set_printoptions(linewidth=1000)\n",
    "np.set_printoptions(suppress=True)\n",
    "np.core.arrayprint._line_width = 180\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:70% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "\n",
    "from IPython.display import display_html\n",
    "def display_side_by_side(*args):\n",
    "    html_str=''\n",
    "    for df in args:\n",
    "        if type(df) == np.ndarray:\n",
    "            df = pd.DataFrame(df)\n",
    "        html_str+=df.to_html()\n",
    "    html_str = html_str.replace('table','table style=\"display:inline\"')\n",
    "    # print(html_str)\n",
    "    display_html(html_str,raw=True)\n",
    "\n",
    "CSS = \"\"\"\n",
    ".output {\n",
    "    flex-direction: row;\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "def display_graphs_side_by_side(*args):\n",
    "    html_str='<table><tr>'\n",
    "    for g in args:\n",
    "        html_str += '<td>'\n",
    "        html_str += g._repr_svg_()\n",
    "        html_str += '</td>'\n",
    "    html_str += '</tr></table>'\n",
    "    display_html(html_str,raw=True)\n",
    "    \n",
    "\n",
    "display(HTML(\"<style>.container { width:70% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "%aimport covid19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Source: Bayerische Landesamt für Gesundheit und Lebensmittelsicherheit (LGL): [Übersicht der Fallzahlen von Coronavirusinfektionen](https://www.lgl.bayern.de/gesundheit/infektionsschutz/infektionskrankheiten_a_z/coronavirus/karte_coronavirus/index.htm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[web-scraping-tutorial-python](https://www.dataquest.io/blog/web-scraping-tutorial-python/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import urllib.request\n",
    "import time\n",
    "import bs4\n",
    "import re\n",
    "# from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import decimal\n",
    "import locale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dateparser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.lgl.bayern.de/gesundheit/infektionsschutz/infektionskrankheiten_a_z/coronavirus/karte_coronavirus/index.htm'\n",
    "response = requests.get(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = bs4.BeautifulSoup(response.text, \"html.parser\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# soup.find_all(class_=\"accordion\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "tableFaelle = soup.find(id=\"tableFaelle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tableFaelle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dateparser.parse('01. Mrz 2020', date_formats=['%d. %b %Y'], languages=['de'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dateparser.parse('01. Mar 2020', date_formats=['%d. %b %Y'], languages=['de'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dateparser.parse('01. März 2020', languages=['de'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "locale.setlocale(locale.LC_ALL, 'de_DE.UTF8')\n",
    "df = pd.DataFrame(columns=['report_date', 'new_confirmed'])\n",
    "for i, tr in enumerate(tableFaelle.find_all('tr')):\n",
    "    if i == 0:\n",
    "        continue\n",
    "        \n",
    "    td    = tr.find_all('td')\n",
    "    dtstr = td[0].text + '2020'\n",
    "    dtstr = re.sub('Mrz','März', dtstr)\n",
    "    dt = pd.to_datetime(dateparser.parse(dtstr, languages=['de']))\n",
    "    ct = locale.atof(td[3].text, decimal.Decimal)\n",
    "    \n",
    "    df.loc[i] = [dt, ct]\n",
    "df   = df.set_index('report_date')\n",
    "df['confirmed'] = df.new_confirmed.cumsum()\n",
    "df = df[['confirmed', 'new_confirmed']]\n",
    "df = df.astype(np.float)\n",
    "# df = df[df.index <= pd.to_datetime(datetime.datetime.now().date() - datetime.timedelta(days=2))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-08-03'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_string = datetime.datetime.now().date().strftime('%Y-%m-%d')\n",
    "today_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "bavaria_df = df[df.index <= pd.to_datetime(datetime.datetime.now().date() - datetime.timedelta(days=2))].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(\"bavaria_covid19_data.xlsx\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_excel(today_string +\"_bavaria_covid19_data.xlsx\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>new_confirmed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>report_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-27</th>\n",
       "      <td>50632.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-28</th>\n",
       "      <td>50732.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-29</th>\n",
       "      <td>50845.0</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-30</th>\n",
       "      <td>50944.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>51063.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>new_confirmed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>report_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-27</th>\n",
       "      <td>50632.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-28</th>\n",
       "      <td>50732.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-29</th>\n",
       "      <td>50845.0</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-30</th>\n",
       "      <td>50944.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>51063.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\">"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_side_by_side(df.tail(), bavaria_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "bavaria_df['recovered'] = 0\n",
    "bavaria_df['death']     = 0\n",
    "bavaria_df['new_recovered'] = 0\n",
    "bavaria_df['new_death']     = 0\n",
    "cbr_bavaria = covid19.CasesByRegion('Bavaria', df=bavaria_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bavaria first dead person date: `2020-03-12`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85a1deeda0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2560x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(32,8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1,1,1)\n",
    "cbr_bavaria.plot_daily_stats(ax=ax, days=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Liste der deutschen Bundesländer nach Bevölkerung](https://de.wikipedia.org/wiki/Liste_der_deutschen_Bundesl%C3%A4nder_nach_Bev%C3%B6lkerung)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15751439368619405"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "einwohner_deutschland = 83019213.0\n",
    "einwohner_bayern      = 13076721.0\n",
    "prozent_bayern        = einwohner_bayern / einwohner_deutschland\n",
    "prozent_bayern"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Bevölkerung Europa](https://www.indexmundi.com/map/?v=21&r=eu&l=de)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10591969837150829"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "einwohner_österreich = 8793370.0\n",
    "prozent_österreich   = einwohner_österreich / einwohner_deutschland\n",
    "prozent_österreich"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bavaria_new_confirmed_threshold = 100.0 * prozent_bayern // 1 + 1\n",
    "bavaria_new_confirmed_threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| prediction date | predicted day for reaching threshold | predicted max | growth rate  | steady state rate  |\n",
    "| :--- | :--- | ---: | ---: | ---: |\n",
    "| 2020-04-02 | 2020-04-20 | 23948 | 0.059 | - |\n",
    "| 2020-04-03 | 2020-04-21 | 24967 | 0.062 | - |\n",
    "| 2020-04-04 | 2020-04-24 | 28251 | 0.063 | - |\n",
    "| 2020-04-05 | 2020-04-27 | 30820 | 0.060 | - |\n",
    "| 2020-04-07 | 2020-04-29 | 33170 | 0.049 | - |\n",
    "| 2020-04-08 | 2020-04-29 | 33148 | 0.042 | - |\n",
    "| 2020-04-09 | 2020-04-29 | 33534 | 0.036 | - |\n",
    "| 2020-04-10 | - | - | 0.046 | 1211 |\n",
    "| 2020-04-11 | - | - | 0.043 | 1195 |\n",
    "| 2020-04-12 | - | - | 0.039 | 1129 |\n",
    "| 2020-04-14 | - | - | 0.030 |  817 |\n",
    "| 2020-04-15 | 2020-05-05 | 37238 | 0.017 |  - |\n",
    "| 2020-04-16 | 2020-05-05 | 37391 | 0.014 |  - |\n",
    "| 2020-04-19 | - | - | 0.017 |  500 |\n",
    "| 2020-04-21 | - | - | 0.015 |  492 |\n",
    "| 2020-04-22 | - | - | 0.015 |  499 |\n",
    "| 2020-04-23 | - | - | 0.014 |  485 |\n",
    "| 2020-04-24 | - | - | 0.014 |  481 |\n",
    "| 2020-04-26 | 2020-05-23 | 42926 | 0.009 |  - |\n",
    "| 2020-04-27 | 2020-05-25 | 43216 | 0.008 |  - |\n",
    "| 2020-04-28 | 2020-05-26 | 43529 | 0.008 |  - |\n",
    "| 2020-04-29 | - | - | 0.012 |  405 |\n",
    "| 2020-04-30 | - | - | 0.012 |  364 |\n",
    "| 2020-05-02 | - | - | 0.011 |  355 |\n",
    "| 2020-05-03 | - | - | 0.010 |  347 |\n",
    "| 2020-05-04 | - | - | 0.010 |  339 |\n",
    "| 2020-05-05 | - | - | 0.009 |  322 |\n",
    "| 2020-05-07 | - | - | 0.009 |  285 |\n",
    "| 2020-05-08 | - | - | 0.008 |  274 |\n",
    "| 2020-05-09 | - | - | 0.008 |  265 |\n",
    "| 2020-05-11 | - | - | 0.007 |  262 |\n",
    "| 2020-05-12 | - | - | 0.007 |  256 |\n",
    "| 2020-05-13 | - | - | 0.007 |  248 |\n",
    "| 2020-05-15 | - | - | 0.004 |  157 |\n",
    "| 2020-05-17 | - | - | 0.004 |  162 |\n",
    "| 2020-05-19 | - | - | 0.004 |  161 |\n",
    "| 2020-05-20 | - | - | 0.003 |  152 |\n",
    "| 2020-05-23 | - | - | 0.003 |  128 |\n",
    "| 2020-05-29 | - | - | 0.003 |  119 |\n",
    "| 2020-06-07 | - | - | 0.003 |   99 |\n",
    "| 2020-06-14 | - | - | 0.002 |   87 |\n",
    "| 2020-06-24 | - | - | 0.002 |   73 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/local/cs/workspaces/covid-19-data-analysis/covid19.py:1526: UserWarning:Exception in curve_fit: Optimal parameters not found: Number of calls to function has reached maxfev = 600./ None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigmoid+asymmetric+linear: seor: 165.40695572785646; growth-rate: 0.001, date:2020-09-08 00:00:00, projected value: 73.48845146958611\n"
     ]
    },
    {
     "data": {
      "image/png": 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JnMb48TsPZeR7vEWLFvHhhx9yzz33EA6HAZg4cSJ33nkn6XQ62+Lio48+4qtf/SoAkyZNYuXKldnHaWxsZPPmzUyaNCl7/5UrVzJt2rTsfauqqvZZawtQ6EFEBrHx/glYWNlKDwAWFuP9E/pxViIiIiIiIiIiIiIiItKfPM/LWz0BL42Hx09+9KNM8CC6g2/9n7mtIYSt4Hlc+OU5XHj+7Ox9Ug3r2x+jk1OPPoxTj761w5a5meNHd3DG547jjM8dh5doAQxuuW4RGJlQAaaPL55+BmeecSYYRmZ79jJz3QCMZIrgaQvw+YNgBzCsIMGS0j69Jtdccw3vv/8+99xzD4WFhdntxxxzDH6/n7vvvpt58+bx3HPP8emnnzJr1iwAZs+ezdy5c3nttdeYOnUqt956K0ceeSRjxowB4LzzzuOuu+7ipJNOIhQKcccdd3D++ef3aY59pdCDiAxaM0In8ph/CTXJalzc1sDDRGaE8ldBERERERERERERERERkQHG8/DSabyU2yWk0F3rB9jF/jwBhZ7rFEIwLTBMjG7CCdmQQqfbHS8Nw+j9NNJpDGs7VsX4bAUGAMPf+1P8Gzdu5IEHHsDv93PyySdnt1988cVccskl3HHHHSxcuJBbb72V0aNHc/vtt1NamglXjB8/nuuvv56FCxdSX1/PUUcdxU033ZR9jLlz57Jx40bOO+88XNflrLPOYv78+b1/vrvB8DJ1OD7zmppiJBLtZfDT6TRbtmygomJ0zheJDCz98XlaeOcbAFx30bH75Hiye1zPZVnsZTZb6xnujmFG6EQsQ3kuEdl/GQaUlRVRXx9h//gtT0Rkz9D6KSLSTmuiiEjfaP0Ukf2Bl3LBieO5CTwnDk6HSzeO5yRIOwm2l4yhfGgFZod2EF634YTdWDS7CRpkAwjsYv8uAgr33Xc3f/jD/+ty2BEjRnHvvQ/2fd490N150kDAorg4tFePPdjozKCIDGqWYXFywSn6Y0JERERERERERERERKSV53mQcvDcBCTjeG57QMFzEtngAq23OwYXcsa5HffHIZ3a9bF9Nt6J8/FijXgdKxzkVE8wcwIIpmmSxshbRcHAyIzvXIHBMPpWQaEX5s37NvPmfXuvHkN2n0IPIiIiIiIiIiIiIiIiIiL9xPM8cJOZYIHbKZjQGjbwOgUSssGEDsGFzhUY+tziwTDBDmK0fYSKMYsrwAq0bgu07g+Albk07CC0Xfr8tKQtzCEjMH12a8WFnQcULMvEdXenJcVnkd7p21MKPYiIiIiIiIiIiIiIiIiI9ICXTmeDCR1bOnQXPPCcWKfKCvmqLSTo8wlun4VhtQcOjIIhmHYQwwpkgwvZfR1CCu2hhq7BBUxrtysoWFs30hKLUVhotz41LxPu6EY6nWnnIG08mpub8PmsnNYWkp9CDyKy33A9l2Wxl6lJrmG8fwIzQidiGVoGRUREREREREREREQ+i7y027eWDjnBhU6hhlSy7xOy/JlgQWvFBEJFnQIKnSomtO7L3RbAyAYXAhjmwDzPMWRIBdu3byEaberReNM0FXroxOezGDKkor+nMSgMzO8CEZE9zPVc/n3LAmqS1bi4WFg85l/CjRW3KPggIiIiIiIiIiIiItKPPM+DLgGFWJ9aOuQEGVJuH2dk5IYM/GGMgiE7benQMbiQU0WhbZ8VwNiP3rFvWTbl5SN7FGQwDBg2rJCGhmZ2Ugxiv6MKDz2nM30D3Pbt2/nP/7yCVatWEotFOeOMs7jyyqv7e1oig06mwkM1STIJzCRJapLVLIu9zEnhU/p5diIiIiIiIiIiIiIig4PneZBK7qKlQzy3SoLbubJC12oLeKm+TcgwO7RvCGKEijGKAjtp6RDIrbaQb5vl3+32DpLRkxP3hgE+nw/TNBV6kD5R6GGAe/zxJYRCYZ577q85i+vmzZuYO3c2L720jEAg0I8zFBkcapJrcMlNdLq41CTXKPQgIiIiIiIiIiIiIp9JnpfuNmTQXUsHWgMLHUMNnastQB/PTJtWThjBCJdi2rnhg123dOhUQcFnK6Agsp9T6GGA27x5E+PGHajFWmQ3jfdPwMLKVnoAsLAY75/Qj7MSEREREREREREREcnw0ilwE3jJeM9aOuRr/eB23B8HN7nrA3fH588NGQSLMDuHEaz2Cgs5lRU6tXvIjvXp1KSI7HlaWQawa6+9ihdffB7DMFi69GGGDx/BuHEHsmjRYi655NsAnH32qa1jb+C4407oz+mKDGgzQifymH8JNclqXNzWwMNEZoRO7O+piYiIiIiIiIiIiMgg46XcvC0dMpex/C0dnHgm1NBNtQVSTt8nlBMyCGKES1urI3TX0qFT6wd/EMPqFGboQVsCEZGBQKGHDhJvP4679o29fhzrwGMJHDVnl+OuuupaTNPH0KHDmD//h9x11+9Yv/5jAH7727uZO3c2Tz31otpbiPSAZVjcWHELy2IvU5Ncw3j/BGaETsQytAyKiIiIiIiIiIiIfFZ5ngcpp4ctHdrCCB2uu/mrLZBO9W1ChpEJGbSFDgKFGIXDet3SoX1cCCwbw1BAQUT2XzrbJyL7DcuwOCl8CieFT+nvqYiIiIiIiIiIiMgAt74uwuPL1rGhrpnRlYXMmTGOMZVF/T2tzzTPS4Ob7H1Lh5xxuWNx4+B5fZuQ4QN/e8jACJdg9ralQ2twIRtk8NlqaS4isocp9NBB4Kg5ParAICIiIiIiIiIiIiIiA0dvAgq7Gut5HtUbGrnxT++STnl4QENTnBU1DSycN13Bh1ZeOp1bBcGN4yXjO2np0LmyQv5t0MeAgs/ODRkUDsPMtm/I09LBDmWCCVanygodgws+nUYTERkMtFoPUkoBioiIiIiIiIiIiMhnWU+DDOvrIlx331uk0h6elz+g4KbSNMccqjfs4PdPfkgqnTmxXt8Y573qeiaNKsVJpWlsTtIUTeK46ZxjeB6k0h5PLPuYS887bO8/+T3MS7t7pKVDTgWFVLLvE7ICOS0bjFBxr1s65Oy3Ahimb8+9YCIiMqgo9DBIlZYOwTRNNm78lAMPHN/f0xERERERERERERER2aU9EWQYXVFIPJmiKZqkqSXJQy+twU21VwfwPHBTHjc88A4FQZtIzCGRTHU7J8+DVRt2UFrop6QgwIiyAlZv2EHCSXUZt74usudejLxz8SDl9KGlQ6fWDp2qLZB2+zgjIzeMEAhjFA7t0N6hZy0dcscFMAxzj75uIiKyf1PoYZAKBoN885vf4bLLLsF1HRYtWsyxxx7f39MSERERERERERERkc+APdkuouO4fEGGf/uXIxhSFKQl7hKNO7TEXZ5+/eO8QYbr//B26/V0l8fvLJFMUV4aonJIiIKQTVHIz2v/rCWa6BoAKCsJ8ov5J2Rv37Z0Oe9V1+N16LRgGHRpg0EqmQ0c4MaJx3y49dtIJ/O0dMgTRmgPLcTbAwrerp9bXobZGjxor55gFpV3CB4EO1RG6BRQ6BRcyF76/Ko8LSIiA57heV4fmyMNLk1NMRIdfpFJp9Ns2bKBiorRmKYShQNVf3yeFt75BgDXXXTsPjme7D7DgLKyIurrI+wfK5qISPe0JoqI9I3WTxGRdloTRaRzOMEwwGcaOe0iuh0LmKbBlz53IKGArzXI4NISd1he00Bjy260RAAsn8EhY4dSFLYpDvspCvt548M61tdF6LhkGQZMnViebUXheWlwEtz1xLtUr9uCH4eA4RIwXIKGw8SqIKccXp4NHjQ1Rli+aiM2DgFcAoZDwHQZXuzD8pz2gAJ9XCh9Vk4lhG6DB9mWDh3DCPmDC5iWAgoiMijp98/eCQQsiotD/T2NAUWVHkRERERERERERERE9hM9qcqw9OW1pFJe9nR+W5WF25YsZ2xVMS1xpzXI4LK9OU66Q2ECD0ilPR79W02P5xT0+/j8tJEUBG3CQYuCgMnf3lzHps0N+I3WwIHhEjBdDhoe4rTDzJyWDkeNjLCiZRN+HPytY4OGS2XUR/MfkpmAgpsJW3wFoCTPJJoh8fcOcwKO8WeuJz2LlOnHHwpjB4Jgl2Dmaelg2AEKSkuIJskGEshWWMi0e8hsC2CYOj0jIiKyp+inqoiIiIiIiIiIiIjIINbr9hKtgYb6xjjvVzcwbVI5STdFQ1OcbU1xYolU3uM0NCXY1rSVUMAiHLQoDNm0tMQwjUS2akKgtYJCSdDjjKMqCbZu95NkxapNNDZG8NMhyGA4lAQ8hm6itd1DHFIukwGG5JlEBBIv524KAke3BhQSnk3K9BMIh7GDYQx7aJeWDk1Jg482xtjSnKaouJAjJo+kvKy0PcDQsQKDFcDoYRViw4DSsiJcvVNZRERkn+pR6OGKK67gqaeewrbt7LY///nPjBgxAoDVq1ezcOFCVq1axejRo7n66quZPn16duyzzz7LjTfeSENDA9OmTWPx4sVUVlZm999888089NBDpFIpzjrrLBYuXJg9VlNTE1dddRUvv/wyhYWFXHLJJVxwwQV75MmLiIiIiIiIiIiIiOwrPQ0n9GZs5/YSDY1xlq9p4Ixjx2AYBjuaE5mPpgT125oIeA4Bsz1wEMAlufZjQr4Uh4egdIhHMh4llYxn2zr4W4MLQ4IeFUW+TJUFN1NlgZL8AQkAVrRf9YBDAQKZ22nPIOFZJLApChViBMMYRWXtLR38QSJJk1Wb49S3pCkoKuSwSSMYNqwkp91DNphgB8GyKTJ2HVAIAhW7HCUiIiKDRY8rPVx44YVcfvnlXbY7jsP8+fP5yle+wv33388zzzzD97//fV544QVKSkqoqanhJz/5CbfffjvTpk3jhhtu4Ec/+hH3338/AI888ghPP/00S5YsIRwOc/HFF3PHHXdw2WWXAXDNNdeQSqV45ZVXWL9+Pd/61rcYP348xx133B56CURERERERERERERE+qbP4YSmOCtqGlg4b3qX8Ws3bue/HngTK51p17CleRN/WPchx04swY+LE4+RTmY+Yi1RZgeSrdUUnPaKC/90cltD4GKW9qD8QBIwySQDWrmeScKzCfrDGEYIwqWYrYGDlpSPd2qaiHtWJsTgWTiGny/OmMSwspIOLR0ylxu3uzz5Zi0fb4kyprKY2TPGUtpN8CMIlPf8UyEiIiL7qd1ub/GPf/yDeDzORRddhGmazJkzh3vvvZfnn3+euXPn8sQTT3DiiSdywgknALBgwQJmzJjB+vXrGTNmDEuWLOHCCy9k1KhRAMyfP59FixZx2WWXEY1GefbZZ3nssccoLCzkkEMO4Utf+hJLlizpEnrYsmULW7du7TK/4uJiRo8ejWFkSku1aatG5anG1ACX+fx0/vztC/v6eNJ3bZ8rfc5ERLQmioj0ldZPEZF2WhNlsFlfF+HxV9exvq6ZMZWFzJm5i+oJPRjbm3E/u+8f+NIOflw2Nddxz7rVzDt1LMWBNE4sRjIWxU3EWFlTyxn+aE5bhwAuTf/zLGt9Lr60g42DH4dyw2VxSZ4nUJtnWyD3ZtLzkfBsHMOmpKQIXyCEzx/EsIOs2hxlc2OKhGdnqywkPYsRlUM5bcbE9soJdpBNO1yefquOtVvjjKosZc7MsQzP8xqEgYPrIjz+6sesr4swprKo27EAY4rhBwcMz7tvsNP6KSLSN1o/e0evU1c9Dj08/PDDPPzww1RVVTFv3jy+/OUvA1BdXc2kSZMwO/S0mjx5MtXV1UCm9cXhhx+e3VdaWsrw4cNZvXo1Y8aMobq6msmTJ2f3H3zwwdTW1hKJRNiwYQMAEyZMyHnse+65p8v8/vSnP/HrX/+6y/azzz6bm266iaKiEEWdfseKRBqIRBooLh6CZVmAvkIGEs/ziMUiBIMBKiry/Ya/d/h8ma/lsrL8v5TLwDVs2J75nDmew1+2/4VVsVUcFDqIzw/5PLZh7/qOIiIDyJ5aE0VE9jdaP0VE2mlNlP60dmMjDz6/knWbmhg3opivnT6ZcSO6/o9w7cZGrrvvbdxUur16wtpt3LTgxC7j125s5Gf3vYkvnSCAw6bmWu79eBULzj+EymIfbjyKE4+yZct2Xn+jhlE4jMchsNFl/cMulPmxvSS4CUw3gZlKUJBKcmO+9g5vZC5sMqEAgJkAodxhbZUR3LQf1/STMAtJ+3Pm7yAAACAASURBVAI0RD1iKV/r/kxAIenZWMEQXz79UMJFhQTDBfgCIX731CreXN2YrbTgYWIacOyhw/m/Fx6Tc7zUpkbuuOVlUqk0aQ9MI/P/0JvmnsjwTq9XJTD16Ck9+nyVlRUxbcqIHo3dH2j9FBHpG62f0lc9Cj184xvf4Mc//jElJSW89dZbXHbZZRQVFXH66afT0tJCUac0QXFxMZFIBIBoNJp3f0tLS979bddbWlqIRqMUFBR0e9+OvvKVr3DKKad02V5cXAxAJBIjkXBz9hUWDqOpaQdbtmymraKADCw+n8XQoRXU10f22TFTqTTAPj2m7B7DyPwgbGiIsLvFW1zP5fK6BdQkq3FxsbC4b9P9/LLyFixjt4vjiIjsdXtyTRQR2Z9o/RQRaac1UfaW3lRPuPbeNzHSKfw4VDdu4RerVnLxmRMYFjZIxlpIxqIkYzHeX7mRU+1mAraLv0MFhbV3P09DwMOXTuJLJ7E8Byud5BfF6a4Te+7P1HW4GQTOCOYOSXsGiR0WCTIhhKRnkfCKs6GFhGeToC2cYJHy+Rk3uhyfP4QVCGGHQry+egfrtiSIt4YYHCwwDKZNLOfS8w/LOd5tS5bzbnV9zvegYcC0A8oJjj+MNBBt3T7zhBB/qX6LlOfhtY4zTYMzjh7V5X+cRX6ThfOO6lKVochv6v+he4DWTxGRvtH62TuBgEVRUWjXA/cjPTqDN2VKe5rz2GOP5YILLuDZZ5/l9NNPp6CggObm5pzxkUgkG1YIh8O92t92vaCggHA43CXg0PG+HVVUVFBRUdHtc/A8unyTGIZJSclQiouHqM3FANVWQaQ/Pj36khh88n2f99ar0ZepSVaTJAlAkiQ1yWpejb7MSeGuwSoRkYFqT6yJIiL7I62fIiLttCbuv9bXRXh82To21DUzurKQOTN21S5iLbVbdjC23M8Xp1UyvMTCcxLgxPGcOLgJtjU08uo/aqjC4QDDJbDRYd3DLlZVENtzSLeOM9wEQSfBL4odfEanL8BXwCFTrzfQ+pGvekLKM4h7NsmYRQybhOcnSQGJdMeAQnsFBdfwU15eClYAzwrwwfoWIo6ZHZv0LBx8FBcEuPS8wwgHLIYFLMJBi98/8U/eW9M1nDB1Yjkzz80NMgwdH+G6+3LDCT7T4JwZY7t8r82eMY7lNQ2k0h6et/OxoyuKWDhvOk8saw8yzJ4xltEVRXm/h0dXFHHpeblz0/f6nqX1U0Skb7R+9oxeo6769LZl0zSzIYGJEydy5513kk6nsyeoP/roI7761a8CMGnSJFauXJm9b2NjI5s3b2bSpEnZ+69cuZJp06Zl71tVVUVRURFjx44FoKamhvHjxwOwcuVKJk6c2Jdpd8swDAw1PxERoCa5BpfcqjAuLjXJNQo9iIiIiIiIiMiA09OAQtu4T+uaGFce4KzpwxlZauG58Q4BhQQNDTv462trKMNhJA6BT12qH3EpGFtEyJfKBhk8J0EqESMUjzIPF9MHbANebK9A0FEYODvPGxKTDb7WqgmZAEKCAEmvMKetQ1trB9e0qaoYgukP4QuEsAJB3v+kmU8aHJIdQgxpw8eUsUO56JxD8FsmfsuHaRrctnQ57+WpnjB1YjkXdggBrFu6nOo84yaMLGHCyNwWEHNmjmPF2q7hhNkzxnZ5rmMq84cT8n2+ejO2bXznIIOIiIjI/qJHoYdnnnmGz33uc4TDYd555x3uv/9+rrrqKgCOOeYY/H4/d999N/PmzeO5557j008/ZdasWQDMnj2buXPn8tprrzF16lRuvfVWjjzySMaMGQPAeeedx1133cVJJ51EKBTijjvu4PzzzwcyVSBOP/10brnlFn72s5/x6aefsnTpUn71q1/tjddCRAYI13NZFnuZmuQaxvsnMCN04j5rLTHePwELK1vpAcDCYrx/wj45voiIiIiIiIh8NvW6esKydWysa2JcuZ8zp1cxotSCZBzPzQ0ovPz6GipwGdUaUFjziEvBAYWEfKlMlQUngROPYTU38zXDwe9LZcIJz0PXJsJQAHwpX7XkTZAwbBzsbMCgxQ0STxfmVE3IDTDY2cBCThsIzyLZui8Y8POlEw+kKGxTGLIpDdk8+tca/vnxtrzhhK91OrE/sq61ekKn0MGXTx5PcdifM3bOjHGsyFM9oXNAoafjYO+GExRkEBEREekZw+tBX4cLLriAVatWkUqlGDFiBF//+tezlRwAVq1axcKFC1m1ahWjR4/m6quv5uijj87uf+aZZ/jlL39JfX09Rx11FIsXL6ayshIAz/P41a9+xUMPPYTrupx11llcddVV2LYNQFNTEwsXLuSVV16hoKCA+fPnc8EFF/T6iTY1xUgk3F0PlP3ewjvfAOC6i47t55nsn1zP5d+3LKAmWY2L2xo4mMiNFbd0G3wwDCgrK6K+fvd7PfXl+CIiA8meXBNFRPYnWj9FRNppTey59iBDhAMqQsw+ZkSmeoKTgNYKCp4Tp6F+B88sW43tOfgNh4DhEjRcjhpfTIEvhecmcgIK8ZYW/IaDbaT7NK+0B65hk8TGMWyiro9YukP4ADtvJYXcfR3DCjZJfHiYmIZBOJhp7bCtKY6b6vpFUhS2+e45h1AU8lMYsikM2/z+yX92W2Wh84n99d0EGRbOm95tFYuehg56OrY3jynSRuuniEjfaP3snUDAorg4X0p1/9Wj0MNngUIP0lMKPfSvv0X/l5safp5TacGPnx8Nu6Lb9hJ7+odhf1aaEBHZXfoDQUSkb7R+ioi0+yyuiTlVFioKmHPcKEYNtbMVE3Di2eBBQ/0Olq/eREukmbICg8kjwhTZ6daWDplggucmcGJRIk0R/LgEDAef0bcXK42BY/hJYpP0LKKuj3jnQEKXkEKHS7pWUnANH0XhAGZrWGB7JEE6z/T8lsm0g8oJ2j4Cfh8B28fbq7aysT63BoRhwJSxQ5l/7qEE/b5sq+CdtYvYl0EGkYHis7h+iojsC1o/e0ehh650Fk9EBpSa5BpccgNKLi41yTXdhh72NMuwOCl8yj47noiIiIiIiIi023lAoUNIwYnT0NDIitUbaYm0UFZgcNDwUG5AwU2QiEZxtjVyruEQNF389Q7mn/O3doBMe4fjAUwgBtSA07ovbVikfH5Shk1T0iSaDrdXQmi9dIxMSCGa6tTqIRtSyL2dwsTymYQDFqGgTUNLLG/1hMKQzVdnTcyMC2QqLTz8v2tY3U0biI6hg+7CCYceOIzvnTMl5zjTJpV32y4iFMj9d/JAaQMhIiIiIvs3hR5EZEAZ75+AhZVT6SHTYmJCP85KRERERERERHbF8zxIJXNCCW3Xt9bv4N2PPqW5KUJFockhowoo9qczVRN2M6BwHLQHFNa2BxTw+fGsANGkgeuZtKQLSdAeQjD9QYqKioimfcRSPlpcH1ua07S4vg4hhtxWEGnMXb4Ols/gkLFDM8GEgEVp0OK96vq81RMOPXAol553GLbly27vLqAwaXQpx0+pynmML588npX3bd9l6GBvhRMUZBARERGRgUChBxEZUGaETuQx/xJqktW4uK2Bh4nMCJ3Y31MTERERERER+czoLqCwZet23vpgPc2RFioK4LAxhZQGaW/r0Cmg0NLcjOEmCJoutudgkL8ecSHwOciEE6LA6g7hBMAzbdK+AC2OmQ0oxDu0bfD5g5SUlrRWUfCT9Cw+aXDYFoOEZxHvUGnBNWzino+Ut4uAQguwPXO1LaCwI5EglacPREHQYt7nJxBurbAQDlo8+tcaPvp4e84zNgw4fHxZlxP7R0+uyFs94fyTxucEHmDvBBT2ZjhBQQYRERER6W+G5+0fnVGammIkEu6uB8p+b+GdbwBw3UXH9vNM9l+u57Is9jI1yTWM909gRuhELKP7jJZ6PYmItNOaKCLSN1o/RWQg21VAoamxmcoig8O7CSjEo1EadzRlwgk+l7CZwnAT0E1AYZd8flI+PzvikEi3tWzIBBTKy0vBDmaCCGmLWNrik/okOxJGNsDQMaCQuZ/VowoKPWX7TA4fP4xQMBNkWLG2gc0N0ZwxhgFTxg7lkjlTCPotTNMAuq+y0LldBGTacOQLMiycNz1vmGB9XaTHoYPejBWRgUO/U4qI9I3Wz94JBCyKi0P9PY0BRZUeRGTAsQyLk8KncFL4lP6eioiIiIiIiEiv5Aso1NZt5/Xln9C4vYmqEh9HjSuiNEQ2mNDXgEIR8HnIVE9oAT6iQ7PI1vn4bOKOD1pbNTS5YRLYVFUMwRcI4hg2SWwSnk11bZytLR4Jz8oJKBh2AH8oTCRpEkkaxJyd/Ce6ueevVcD2ccJhVYQDFqHWj1fe38QntZEu1ROmjB3KhV+cjOUz8fkMLNPkd0/+k/fXdA0oHDZ+GD/oEFA44dCqvOGEL588nnDQzpnT3moD0TZe1RNERERERPY8hR5ERERERERERGS/1DmgsLm2gVff/Zjt25oYUerjmAmlDA2B58a7BBRiLc00NDTiOQnCVooiO42ZSoDTNaBQDJwG4CMTCljRNZwA+QMKSWxGjhiKHQyTMv2kDD+uabP8kxY2NaW6BBSC4TDDhpXSlITGuMGWbQncVJ6QQqTnr5OZhhFFBRSHLSr8PoJ+HyvWbiPhpLqMLS7wc+mXDiMU8GWDDHc+9SHv5QknTBk3lG+cdlDO/cePKO42oDC0OJgz9tyZ4/hg7a4DCr0JJ+zNIIOIiIiIiOwdCj2IiIiIiIiIiMiA19eAQkskQt3WHaSTcQqsFEOCYHnJ7P6OAYVi4EzIhBMiwLuQ6HY+PgpawwYtCYttCT/FJWWYBQEcw99aQcFifb1DQwwS2DkBBcMOUlBcSNT10eL6iLomjbEU6XSegMKqXrxQEVgfcwgHLcLB7v/1VxC0+MopEwm2hhiCfov/ebmGlet3dKmycGSe1g7dtYGYMLKECaNKcsbOmTmOFT0IJ8DeCyioyoKIiIiIyGeXQg8iIiIiIiIiIsL6ugiPL1vHhrpmRlcWMmfGuG7f3b6rsR0DCps2N/DXt9ayraGRkUMsTjhoCGUFBl4yvk8CCn6g0vOR8GwSSYu6pE1paTG+cCWu4cdtDSjUbIlT3wIJz8oJKBh2AH+4gKaEwY4ENDs+kp5FGrPrwZp6/nobKRjmD2JbJnbQZJjlozkeIZ2njUUo4OP0Y8bgt3zYlonfMvnLuxv5uDa3XINhwJETyvjh+Ydnt3UXTjhozBBmHj485/5f+cLEvFUW8oUT1AZCREREREQGCoUeREREREREREQGkT0ZToBMQGHDpm386sE3sDwHPw6NLS6PfLKcr518QJeAQlNjhNUrN3IESY7GJbDZpXmpw45iK29AoQSYA5lwQhPwJsS7eW75AgoFRYUY4XJcw8YxbBxs1m9z2B4z8gYUAuECWlI+WhyTbVGIpX1dAwqNPX+9DRcqgmEKiiyqyi0Kgjbvr6knnszf2uHicw7Btn34LRPbMnnwxWr+uW5bl+oJU3tRPeHgA4Yye8a4nLEHVBXlDSjMmZk7rq/hhI31LYwsK1AbCBERERERGfAUehARERERERGR/d6eDhLsrbHr6yIsvq89nFDbXMt9H6/k27PGUV5oghPHa/1o3NHEihXrOQiHw3EIbHbZtsSlcKiNjZNtE4GbYAgei4rzTCpPQCEIHOvPXE+2BRQ8i60RD6wgCYpJGhYJLJoSJtFU+5jOLR7ink3SszL7sfJXUOhFQMF0oTwYJmCbBMI+kvEI6XS6y7hw0GLOjHEE/D4Cto+A38czr3/Cmk8bdyucMGFkCQePHZoz9ssnj2fl+u39Vj2hL+GEH55/GGVlRdTXR3KeY76xCjKIiIiIiEh/MzxvZ3+6fHY0NcVIJNz+noYMAgvvfAOA6y46tp9nIj1lGPTonzEiIvsDrYkiIn2j9bP/9TQcsDcCB+vrInnfMb9w3vS8oYPejX0TX9rBj0vQdAiZKb5z2jiGhCEVj5FKxEklYzTtaGLF6k34cQgYDgHDJWg4VBWb2LiYbgJfOoGRSmDR9SR+T3QMKLQFDeK0BxASnS/7EFAoDNn4fAa2z8TymWzdESOV7vpN5bdNjppUgW0ZWK1jX1m+iVgiT/WEsM1FZx+C3/bht038lo8HX6rmwx5UT+gunJAvyLC3vg7axvc0dNCbsXuL1kQRkb7R+iki0jdaP3snELAoLg719zQGFIUeRDpR6GHw0Q9DEZF2WhNFRPpmf18/+7vKQU9PIHc37l/nHkHFkBCplIeTSpNKeXy6tZl7nllJOu1lT4y3lf4vLQyQSqdJpT1SKY+X39/ExvoWAEzS+A2XAA7DS0wOHhHGTCVaP5I0NOwglYwTMByCuK0BBYewlaLQSmN7SWwc/F4SC4cALqbR+y+qtEc2XJAJIeQGEjIBhExooW2/5wuQ8vlJmX7SvgC1TelMlQXsnIBCwPZx/JRKrNb2C37Lx5srt7Cp9TVoYwDjR5Zw/kkHZsb6MuMfeLFngQPoXehgbwQUPuvhhL1lf18TRUT6SuuniEjfaP3sHYUeulLoQaQThR4GH/0wFBFppzVRRKRv+nv9/KxVOUinPdZuauIXD75DKpUJHRiAaRqcd9KBFARtonGXeNIlmnB5Z/VWtjUluswtYPsoLrBxUx5uKk1L3CG3U4GHRTobPAgaLn7DIWhkwgZtFRNyL1urKHSoqNDx0m90rTTQE65nksQmiY1j+HGwiTgm8U5VEuKeTcr0M3L4MLACYAcx7ADLVu5gR8LMjnHwAQbFBX4unj0FuzV08PBf1rDyk+17PHCwd6td9G/1hM9yOGFv6e81UURksNL6KSLSN1o/e0ehh64UehDpRKGHwUc/DEVE2mlNFBHpm72xfu6rKgddAgeex8ebm/j5H99pHwuYPoPvnT2FYSVBHDeFk0rjuGmeXPYxH9dGusyrckiIw8YPw3Uz45xUmlXrd9DYkuwy1rZMgn5fZpybztvOAMDAw98ljNB9WCFoOgwJkrmNQ9qJZ9o/0H5/Xx+qKACkfX48XwDPCrA9btCUMLIBhYSXCS8UlxRx5CGjMO0gZiCEzx/kyX/U8uHGaKexFodPrNonVQ4GQohgoIyVvUe/U4qI9I3WTxGRvtH62TsKPXSl0INIJwo9DD76YSgi0k5rooh8lu3NFgxPLFvHxvooI8vCzN7L1RNGlBUQjWcqHLTEHf70UjVrNjZ1eYyykiAHjS4l7qRIOCnWbWqiJd7171rTNLB8BunWVg17cvnPhBQyYYRgWxAhWy0hs81vuIRNl/JCM3s7QJJENIpN+5i2+/dtIibYQZpdH82t1ROSHaonFJUUc+ik4Rh2CMPOVE94/r0trNocJ55ub/+QxOKgA4dz8flTMQwz+/ADoXJBb8cqRCB7k36nFBHpG62fIiJ9o/WzdxR66EqhB5FOFHoYfPTDUESkndZEERls+rMFw87GXv5/plI1NIybSmcrIny6pZm7/vwR6XR7sMA0Db547BjCQYukkybhpEg6Kd5fU09DnnYNhkGf1me/beK46bz3tX0mBx1Qis8w8PlMLCNN9drNGKlkbkABh2J/mqljC7FJ4vccLC/J1q3bcOKxbCihLcwQNl1snN5PFsBnE0tbtLi+1sBBa/AAm8KiQg6eMBzDDmZbO2yLwSOvbiSe9hFrrbLgYvODf5nOqBFl4LMxDENVDkT2Ef1OKSLSN1o/RUT6Rutn7yj00JVCDyKdKPQw+OiHoYhIO62JIrI37elKCzs7KV1eGspWQ4jGHR75aw1rN3WthlBeEuTgsUNwXA83lcZNpVmzsZFItOvJ+oDto6TQn6mIkPZIpz2aY063rRj2BtsymTqxjIKgTThoEQ5avPFBLbVbd+RUUQiZDpOqgnzxqEp86QSGE+eN5Z+wY3tTa2uH1uoLpkNpAIaFgWQMz4lDuvd/+7qeSaK1ckJbOGHUiDJCBQVghzD8QQw7yI6EydNv1xJL28TTFklsHMPPt2YfyYiqYdkqC4bp22uhA4UIRPY+/U4pItI3Wj9FRPpG62fvKPTQlUIPIp0o9DD46IehiEg7rYki0lt7o9LCJ7VNXP+Ht7OtFgwyFRHmzBxHKGBlgwxvrdyStxrC7vCZRrchBp9pMLKsANM08JkGpmnw8eYmnFTX8UG/j+MPrcL2mVg+E9syeeGtDURb20sYpAm2VkUoC8PXP38AfhJYnoPtJXnjvU9o2LYjJ6AQMFzKC2BUqQVOHM+J4zkxvGQcow9NKTLVEywKioqwg2EMf1vlhBAtro+/r95BPG0RT2dCDI5hc+4ph1BRMRTDDrZXWvCH2FAfV+UCEcnS75QiIn2j9VNEpG+0fvaOQg9dKfQg0olCD4OPfhiKiLTTmigisGeDDAknRVNLkrv+/CGrNzR2eYwhhQFGlhfQ0lqVIRZ3iESdPpzCb+e3TY6ZXJmthBAOWCxbsZlP6ppzxhkGHDpuKN+bPSUTTPCZmKbBbUuX8151fc46aBgwdUIZPzhnIl4yipeMgxPjsb98yMZN9QRxslUWgqbDyFKLKSPDeE4sE1BIxtmxvRHceGsLiN7/fZn2DLCD+AKhnMCBYQdpSflYtzVJQxSChYUcPL6KocNKW0MMmXHYQTY1pnj6rTrWbk0wurJEgQMR2Sv0O6WISN9o/RQR6Rutn73Tl9DD/fffz9KlS1m9ejWzZs3i5ptvBuCtt97iu9/9bs7YaDTKFVdcwbe+9S0+/fRTvvCFLxAOh7P7zznnHK655prs7ZtvvpmHHnqIVCrFWWedxcKFC7FtezeeYe9Z+/RoIiIiIiIi0id9DTI0NMVZUdOQE2RIpdM0tTg8+FI1bocqB54HbsrjxgffJRy0aGpxSDipnc5re3OCaNIlHMgEFKqGhoknIyTddJexxWGbS+Ycmg0y3P/CalasbegSTjh03DC+fdbB7fNKpzmoys9tD/0D20sSNBxChkPY53DeAUns1ZvxknGSyRg4Mb5mRzi+sK69BUTbR4ND8z25czoNoDDPE4uDWwOYVjZwEC4Ms2Gbn7hnEWutnpDE5pjDDqB0SHFOGwjsIHURjxeX1/NxfZLKiqGcOWMCY6qK876OIaBsp690xughcPHYMT0YCWMqi7j0vMN6NFZEREREREREPrsqKir4/ve/z9///ne2b9+e3T59+nTefffd7O2amhrOPvtsTj/99Jz7v/766wQCgS6P+8gjj/D000+zZMkSwuEwF198MXfccQeXXXbZ3nsyeSj0ICIiIiIi0k/2ZJDBTaVpibs8/L9rsm0loD3IcPPD7zOkKMD25gRNLcmdvnMimnAZWhykYkiY4rCfkgI/K9Y2sLG+JWecYcCRE8r44fmH52xvq7RgeOn2wIHpcHhlmAnGerxIDK8hxleH72DM5nX4SWbHhQyHA10/LX/6Y6b9QzIGboJS4Kp8eYH3oXODDD8wKRQkmraIpiySdgkFQ0qwCgtbAwyhTIUFfxD8YRqi8NrqRjY3pSgtLeGkow9k5PCyTJUFX+6fzZE81RMquqmeMBL45kHdv84iIiIiIiIiIn1VU1NDPB7vsr28vJyKioqcbaeddhoAH330UU7oobNHH32U448/nhEjRvRoDkuWLOHCCy9k1KhRAMyfP59FixYp9CAiIiIiIjLY9STM0F2Q4T++No0hRQFa4i4tMYeWuMOTyz7OW5HhhgfeIRywaI67JJI7r8jQ2JLE5zMoKw4yYUQJpYUBPvpkG5saojnjsm0gZk/CS0QzoYNklJllHo//7zr8XpJAW6UF0+HocDGx5/+Kl4xlQwrz4lG+VtqC3+g0p0aIPdt+MwzMCrbf9gDPCuFzQuAPYRSVYdpBDH84E1BoCyvYIfAHs9cNf+v4tut2AMMwyV9ToavhwPlH9ayUpqoniIiIiIiIiMhAcPnll/Phhx922X7ppZfywx/+sNeP57ouTz75JFdccUWXfbNmzSKdTnP00Ufz4x//mOHDhwNQXV3N5MmTs+MOPvhgamtriUQiFBXtuxabCj2IiIiIiMh+q6eVFnoz9pPaJq7/w9vZagsNjXGWr2ngjGPHEApYROMusYTLe2vq8wYZrv/D2z2efyKZonJImKphBRQELQpCNh9+vJ2GbRFCRpKQ0doKwkxyyIggp08th2RTNszQPKqJ6uRmgiQJto4NG0lC21ya/19ue4oi4OvhPJPYAK5hdKieEMIurSTl2azbkWJ73CBQUMCBB1RROqQk2yqibWzH+2H5MQyzx89fRERERERERGR/9ctf/rLbSg998be//Y1EIsGsWbOy24YMGcKjjz7KwQcfTCQS4cYbb+SSSy5h6dKl+Hw+otFoTrih7XpLS4tCDyIiIiIiIntbT1pGtKnZ2MgND7yTDTLUN8Z5v7qB46dUAtAYTdLUkvnY0ZzMua8HpNIef37tkx7NK2QbnHzoEErsNEW2S4Hp8M/qjTRt29HaAiJJyEwSMhyqigzGl9uZKgvxKDTFmGNGMYa6XR84AomXczcFgCl+kwT/P3v3Hh9VYef//3XmmpnJ5E4SSAiBXATkDooahYqXXrSoeKvb1m5367fFusX+rN9tv1/21+3Wbi9oldYuu9vaba2tWkUBK0XbegFTRZH7PQmEXICE3Ce3mTlzzvePxEBMrIDAJOT9fDxihnP9zMCcxMd5z+fjodNyY7v8uFNH404M9nRO8Ph7v3wD/nxid4WesILR79h+IO2knrGIiIiIiIiIiJyqgoKCM3q85557juuvvx6v19u3LBAIMHVqT8fL1NRU/vVf/5VZs2ZRWVlJQUEBfr+f9vb2vu3fexwIBM5obR9GoQcRERERETmvnGxHhufWH+gLMcDxTguP/H4bWWl+OrqjdHSbtHdFiZrWgP0t26Z051EAPC4HSQEPaUkJdHZHMcwwfkekp2uC0fM9LSHGNdPT8VphXFYXByqP0t0RwkcEv6On04LPiOI1THhfPmI8QOIgTzYCsXofhrc3iJCchYmxjAAAIABJREFUjMPjo9Nyc7AhSkOnQUJikElFY0hPT+3dzt8vwIDLQ9L7AgsiIiIiIiIiIjJyNDY28vrrr/Pkk0+e0n5FRUXs3buXWbNmAbBnzx6ys7PPaZcHUOhBRERERESGgZMNMgzWvWF7eSM3zZtALGZxtKmL+uZOjjZ10tE9SDcEoKUjgmXFSE+IMSEQIznN4lhdAy6ruy/E8F6QIdljMiUnASPahR3uwI50YiV14sAe9NicMGaxAIi6HHTaXrpsDy1WgKO2hwn52QSCSb3BBF9fUKGhEzbsbaWqySQ9I42rLi1ibE7GoOMg/EDGabzOIiIiIiIiIiJy/jFNk1gshmmaWJZFOBzG4XDgdrsBWLNmDePHj+/r6vCebdu2kZiYyPjx42lvb2fZsmWMGzeO/Px8ABYtWsRjjz3G/Pnz8fl8rFixgptvvvlcPz2FHkRERERE5Mw6lYDC6QYZdlQ08sVPTSKQ4CbUGSHUGSXUFeHt3XWYsb7eDbhtk4AR5s0NbxMwIviNMPkJMS5OjmEndGBEO/H3Lvf3jo1IdERxE+05RKT3y//Bz9du8IHXj+H140hMJ2J72FrVSYflodPy0IWHMF4WXjmZrKyM3m0DGB4/1Y1h1pRWUlUXIi87yMKSfNIHeQ0ARgO3TT+lvwoRERERERERERFWrFjBo48+2vfndevWcdNNN/GDH/wA6BltMVhYobq6mocffpjGxkYCgQCzZ8/mP//zP3E6nQDceuut1NbWsmjRIkzT5LrrrmPx4sXn5kmdwLBt+wM+gnR+aWvrIhwe/JNcIida+ouNADzwpblxrkROlmFARkaQhoYQI+OKJiLywXRNlLPldAMKhgFOh8HSO+eQlxXEtm2ipkX54VYe+f22vvESBuBwGHz84jy8bgcd3Sad3SYd3VH21zRjdnXjd4QJGOGegIIj0vc4YETwO8J9wYXACY9dxsCxFO8Xsw26bA+dtocu28uY0Rn4gkk9HRa8AfAEaA47ePavR+iw3L1BBi9hw8t9n72EvNEpg74OfWGGrJ4ww2Cvlwwdun6KiByna6KIyOnR9VNE5PTo+nlqvF4XSUm+eJcxpKjTg4iIiIjICPVROy3cdf2FJPpchLqidHRFCXVFKd1+5IROC2DbYMZsHnh8Ey6ng2g0ip8wiY5u8o0wie7uvpBCwBHGv+MN/EaYvBODDQlhnL4P/z9e23DQTQJtppuGWJAO29s3OiIpLY1LZxX0dFjwBjC8iRgJAWpabdZsPEpVfXtfOCFtkNcgG1iY3xNkaDyJIENeVpB7Fk0ddJ2IiIiIiIiIiIicOQo9iAxjpm1S2rWeikg5BZ5CSnzzcBl6W4uIiMiH+6Agw+Ibp+DzuGhpD9PSHqE5FOadvXWDBhlWrN6Bz4gSMLoJGD1BhglGmKkJ3SQaYQKO48sTjTBJzjBeIh9am204wJuI4Q3gSMikstniSAg6LS8dtofO3jDDmDGZ3Hj1lL4QA+4EWurb+dFgnSaumYNnkIBCXhDuyc08qddMQQYREREREREREZGhR3dHRYYp0za5v34JFZEyTExcuFjlWcmyzOUKPoiIiIxgf6t7g23bhDqjHGns4Hd/LhsQZLBjJo8/t5FERzcBo5tER5iA0c2ljjCJ/p4AQ8ARJtHo7vvuNP52BwbTdtBhe4m5A/izcjESghgJibxzsJPyRot2y0uH5aXD9tKFl8LxOXz55lkYhtF3jGBdiOWDBBluuHIOzoz+QYa8rCBL75yj0RIiIiIiIiIiIiIjhO6MigxTPR0eyoj0floyQoSKSBmlXeuZ718Q5+pERETkTDrdMRRNrV2Ul9dyUb4Xq6uNcFsLHrODoKOLEqObYGJ3bxeGnu8JhvmhtXRYHjpsL01WIq3+MRSMH9MbZOgJMzR0u3jsL9W0xby0W17CuHE6HCy9cw7+E2oeVxTiN4MEGT51RXG/wAOcepBBHRlERERERERERERGDoUeRIapikg5Jv1vTJiYVETKFXoQERE5j7w/yNDS1kHVgSru/fQEAnTR1thAZ2sTkVALrY2N3OXvIGgcDzM4DRtaeg/m7f3qZdoO2u0EQlYCDWaQdisBf3IKM6bk9wsyHO1w8uPV5YRiHmK24/jIiJvnkPC+4MFo4M7sKX0BhckfEFBQkEFERERERERERETOBIUeRIapAk8hLlx9nR4AXLgo8BTGsSoRERE5Wf26N2QGuGHuaHISTezOVuzOFuzOFqzOVhr3VvJlfwtJji6CRhcBR+/P/td7vqX2fgHghG6Hi5DloymWyCE7g5Dlw/QEuObyC3H4kzB8yTh8SdSGHHz3qV3ELPp1Wlh62xy87wse5AL3fn70GQ8oKMggIiIiIiIiIiIiH5VCDyJDjGmbvaMryinwFFLim4fLGPhWLfHNY5VnJRWRMkzM3sBDESW+eXGoWkRERAZj2xZ2d/uAIENbwzGq9lcyz+gkyegiqaELz9oYnYMcoxDodLpps/0cjqUSivoI2Ql04iM7JxtfcirB1HRSMzN5dmM9mytase3j+xsGzMwdhXdK/3DB2BRYeqdfnRZERERERERERERkWFPoQWQIMW2T++uX9AsyrPKsZFnm8gHBB5fhYlnm8pMKSIiIiMhH068rQ1YiN5SMZ2y6F7ujGaujqfd7M23H6qirrcUVbiPF2YXf7sSwrQHHSwCmuyFkJdBm+aiIJtFm+2izfLRaPtosP10OP97kNOq6PNSHYv32NwyYWTSK294XQvi0J4VtB4+Pwnive8PCkvxBn5eCDCIiIiIiIiIiIjLc6e6oyBDSE2Ao6xtZESFCRaSM0q71zPcvGLC9y3Ax379g0HUiIiLytw0WZMjLCmLbNkS7sNqbsTuaaDhyhI1v7abY6OBio5Pko514nu+k3REecMwEYKxt0Gb7aIz4OGil4ElKo83ycbTLzZFOd1+wIWT7sHD02z+Q4OJLn57MmIwA6ckJOAyDqroQDzx+ckGGvKwgS++cc9LdG0RERERERERERESGO4UeRIaQikg5Jma/ZSYmFZFyBRtERETOEDvaTW1lFc/+YSPJtDPX0UlKbSfHVnaSlBLD2d0K5vFAQwD4RELP46jtoNXycySWQoedSNSTRFPMT2M0gSNdXlpifkJ2Qv8wQzt43U4yU31kZfsYl+Znx4FG2ura+9VlGHBBXirTCzP6LT/VIIO6N4iIiIiIiIiIiMhIotCDyBBS4CnEhauv0wOACxcFnsI4ViUiIjJ8VB1tY90be2g/VkdBSpRLxrlJph27vRGrvQE71IgdbicZuCvQf98uy01Dm582O51G00er5afFCtBi+Xsf++mwvYDRt09q0Ivf68KX7OJwV4joIKMs0pK8LFt8GYZxfL+LJmaedPcGUJBBRERERERERERE5IMo9CAyhJT45rHKs5KKSBkmZm/goYgS37x4lyYiIjIk2LaF3dmKHWrAOiHIYLU3Emmpx9/WwB2GCU4gBOyEKGBj0OkM0mwFqY9mccz002wFaIol0tIbaAjjweEwKByTRFKil+SAh+yAh5q99Ryub8c+oQ7DgJlFo/oFEX763Ha2ljVg2/23y89O6hd4AI2hEBERERERERERETlTFHoQGUJchotlmcsp7VpPRaScAk8hJb55uAy9VUVEZOSwo91YbfVYbfXYbfW01dXSWFtNQqSFZKMDJ7GB+zhctNkB6s1RNMUCNFmJPaEGK0Cz1RNssHCQFPAwJt1PQ2s3Da3d/Y5hGDCjMGNAR4VpBekn1ZXhhpLx7KhoVPcGERERERERERERkXNId1JFhhiX4WK+fwHz/QviXYqIiMhZYds2dncIu60eq7WuL+BghY5ht9Vjd7X1294LpNsuGmOJHLZG02IHyMrNpZUglSEP+5ocHOt2Y2MMer5En5tv3jyN0Rl+AgluAKrqQic9XuJkuzKoe4OIiIiIiIiIiIjIuafQg8gIYNqmukeIiMg5ZVsx7Pam44GG3q4NVqgeq+0YRLsH7GMkBDGSMnHmXIgjKZOoL4NnNrWy9ahByE6AE0MN+3q++bxOckclcmFmIuU1rdQMMoaieGwKhbnJ/c51qgGFk+3KoO4NIiIiIiIiIiIiIueW7nqKnOdM2+T++iVURMowMXHhYpVnJcsylyv4ICIiH5nVHcJqOdL/q/UodlsD2O8bQ2EYGInpRJPz2FQLx2KJNMSCNNpBmu0gt186la7uGAePtlFZHuJoUyfgH/S8SX4PS78wm/SkBAyjJwxxKt0bQAEFERERERERERERkfOB7niKnOd6OjyUESECQIQIFZEySrvWa4SGiIicFNuKYYcaBoYbWo5gh9v7bRvDQZOdRJd3HJm5eSRn5+JIzsSRlImRmAEOJ//9+23s6mjq15EB4Jcv7u17nJni4+JJmdQe6+BwQ8eA7g2FuclkJPv67a/xEiIiIiIiIiIiIiIjj0IPIue5ikg5Jma/ZSYmFZFyhR5ERKQfO9I1oGOD1XKEWGsdhtX/ZwneAM6UMThSRuNIGU0jKfzkz/U0mAFitqOny8Ixg1uvLMTssDi8vYnDDTUcaeygOxIb9Pz+BBeLb5jCuOwgiT43oO4NIiIiIiIiIiIiIvK3KfQgcp4r8BTiwtXX6QHAhYsCT2EcqxIRkXiyukMcrShj19YdOEN15HhDZDvbcHS39t/QMIj5M9gXzuZoLIm6WDL1sSQaSeHrn7+czFQfDa3dNLR289zrFdRFj3dUsG0wYzZP/rmsb1lSwEN+drBvn/ediol5qVw4Pq3fcnVvEBEREREREREREZG/RaEHkfNciW8eqzwrqYiUYWL2Bh6KKPHNi3dpIiJyhlXVhVhTepDahk5yMvwsvHg0Oe5WYs21WE01WL3f7a42gsAlAA7ojrioiSWTlX8RidljMQOZdHkzaHel8uSrBykPtQ0413d/3dN94cMk+tz8081TGZ0eUPcGERERERERERERETnjFHoQOc+5DBfLMpdT2rWeikg5BZ5CSnzzcBl6+4uInC9sy6L2QAV/+MMGxhhNzHa2MLquhdS17XSesJ3l9BD2Z1FmjKGiPZEjsRSOxlJotX2AgXOngbXdxqYTqOr9GpxhwKUXZpGe7GNUcgJvbD9CeW0r9vu2KR6bQlFuSr991b1BRERERERERERERM4U3fUUGQFchov5/gXM9y+IdykiInKKqupCrC49SHVdO2OzErnh4mzGuFqwGquwGquJNVZjNdWQHIvweX/PPqbtoD6WxLvhfI7aadRGkzkSS6HZSsTG+MBzOQyYUTyKgM9FIMFNwOfm7d11VNW399vOMGBaQQZ3ffrCvmXjsoPq3iAiIiIiIiIiIiIi55xCDyIiIiJDVPWhWp597hVyHI3MdDaRU9dE6tp2uk7YphMfNWYG1dEUamNpHI6lUhdLxsIBgMtpMKMwg1lBL6lBL6mJXl7dUkt5zcCuDFMLMvjq+4IIU8annVSYQd0bRERERERERERERCQeFHoQERERGQKsjmashkpiDYeIHavEaqgkpbOFuwK9622Dequne8PhWCo1sTQOm6kY/mTGZCTS0NrFsZbufsd8ryPD3Tf1DzKMyQicdFeGUwkzqHuDiIiIiIiIiIiIiJxrCj2IiIiInGPVVYfZVLoRT2s1473N5DgacYTb+tZbOKizUjgULaTaTKPaTOdwLJVo769uiT43X711CjmjEkn0uYGeMRhnI8jw3vYKM4iIiIiIiIiIiIjIUKTQg4iIiMhZZJthYg2HsOoPEKs/QPhIOSldTVwN4AAz4qA2lkJNrIgqM50aM42jVhqjM5PptKMc6xjYvaF4bAoX5KX2W35ikKG2oYOcjICCDCIiIiIiIiIiIiJy3lPoQUREROQMqKoLsfqNA3TXVzMluY2LMjrwhaqxmmrAtgCwMWi0kjkYLeCQmcEhM4MjsRRiOEkOeLj6klyuyEkmPzsJr8d5St0boCfI8E83TyUjI0hDQwjbPocvgIiIiIiIiIiIiIhIHCj0ICIiInKa7Gg3sfoDNFXs4uiOrdzurMfnjEI70A6dzgBHnfns7UylojuNqlg63bZn0GO5XQ6uuzS/37JTHUMhIiIiIiIiIiIiIjLSKPQgIiIicpKs9kZiR8uI1ZUTqyvDaqwG28IHFLkcVJnpHAyP4pA5ikOxDFosPy6nkwmjgxTmpvDJ3GRe3VLLjgON/bowGAYaQyEiIiIiIiIiIiIichoUehAREREZhG3bWC2HObZvG/V7t5EWriHZ6Di+3ptIS/IFlEdG8cZRP9VmOjGc/Y6Rkujhh1+5DLfL0bcsNehld2XTSY+sEBERERERERERERGRD6bQg4iIiAhg2xZWUy2xI3uJHdlH7Mg+7O4QAWA8cCSWwk6ziMpYJo2eHCqavICBYYDf6yJmmv2OZxgwYUxyv8ADaGSFiIiIiIiIiIiIiMiZpNCDiJwWM2bREgrTFApj2zaj0wMkBQafUy8iMhTZloXVVEXs8D5iR/ZiHt0P4d5ODoaBkZbHVrOAd5pTOGBm0Wl7+/Z1xxzMm57NlPFpTM5PpaG1mwce33TS3Rs0skJERERERERERERE5MxQ6EFEBrBtm8a2bprawjSFumkOhWlqC/d+7/lzW0cE+337JfrcjMkIMCYjQE5GgDHpfsZk9IQhDMOIy3MREXmPbdvYrXWYtbsIHdiGfXQfXjvcs85w4MzIpyt5AgesbN5tSmZnVSdd4digx0oOePj7T07s+3NeglvdG0RERERERERERERE4kChBxHpp7Pb5LEXd7OlrGHAOsOAlEQvGckJFOUmk5aUQGqw55PPRxo7qG3ooLq+nf3VLf32CyS4+sIQhTnJzCoehc+ry4+InFlVdSFWlx6kuq6dsVmJ3FAyntxgjFjtbsza3cRqd2N3NAHgsg2qYumURYsoN7OoNDNxdvjo6O4ZUeF0tDN+dBLtXVHqmjr7hbwMg0HDDOreICIiIiIiIiIiIiJy7umuo4j0qTnWzs+e20FdcxczizIoHpvSF2xIC3pJTvTgdDj+5jFs26alPcLhho6er94wRO2xDspqWnl962F+vW4f0wvTmTspi+mF6bhdznP0DEXkfFVVF+KBxzfhsiJMcNWRX3OE7ueO0OE8HsKK+DNpzJjL+rpktrSl0WX3H8njs22uvWgsk/NTKcpNwed19R33ZMdWiIiIiIiIiIiIiIjIuaXQg4gA8PaeOn65dg+xmM1nrylmwayc0xpJYRgGqUEvqUEvF45P61v+Xhhie0UDG3fXsXnfMd7dd4wEj5PZxaOYOzmLSfmpHxqqkI/GtE1Ku9ZTESmnwFNIiW8eLkM/CmT4sm0bq7mW8j+9xJf9ZUxw1eMyLABaLR/vRgrYG81mX2Q0rU3+v3msQIKbz1xV1G9ZXlZQYytERERERERERERERIYw3ekSGeHMmMWzr1Xw8jvVJAc83H3TFIpyU874ed4LQ8yfkcP8GTk0h8K8s6eOjXvqKN15lNKdRwn63Vw0MZO5k7MoyEnGcRqhC/lgpm1yf/0SKiJlmJi4cLHKs5JlmcsVfJAha7CRFWNTXZi1u+k6uBWzegfucAsXARGXk33R0eyLjmFvdDR1VjJOh4OZRRlcnuYnM9VHVqqfF9+sZOfBJuwTZlZ80MgK0NgKEREREREREREREZGhTHe5REaw1o4I/7lqJ/uqWyjMTebuG6eQkug9J+dODXq59uI8rr04j7qmTjbuqWPj7jpe2VzLK5trSU/y8rGZOVw9eyxej8ZfnAk9HR7KiBABIEKEikgZpV3rme9fEOfqRAY6PlrCItvRQlq4loYjvyPZXY8DCwNoiiWxJzqJ/bEc9ocziZ7wq41hwPTCDO6+qX9gIcFTwJ5DzRpZISIiIiIiIiIiIiJyHlDoQWSEqqht5T9W7aQ5FOaq2bncvqAQlzM+oyWy0vwsLBnPpy/Lp7q+nY2763hrdx0rXz/AnzbVcP2l45g/Iwe3S6MvPoqKSDkmZr9lJiYVkXKFHmTIsWMmb7/yGjd4dzLFXUOaswOAiO1kT2Q01a5xxLIvZMz48Vw0NoXp0RgPPL4J4ySCDBpZISIiIiIiIiIiIiJy/lDoQWSEsW2b17Ye5nd/2o/TYXDX9ZO5dEp2vMsCekZg5GUFycsKctO8CWzYfoQ1pQf53Z/LeOntKhZePp7LpmTjdCj8cCLTNnu7OJRT4CmkxDdv0HEVBZ5CXLj6Oj0AuHBR4Ck8l+WKfCA70olZtZ3wgc1Eq7fz8Vg3JEBDLJHXuiexJzqG8mg2SUl+HvxKyYD9TyXIoJEVIiIiIiIiIiIiIiLnB4UeREaQSDTGb17eR+mOo2QkJ3DPoqlD9pPNLqeDK2fmcNmUbF7ZXMPaNw/xP2v38se3qrhp3gRmXzAKh2HEu8y4M22T++uXUBEpw8TEhYtVnpUsy1w+IPhQ4pvHKs/KftsWeIoo8c2LU/UyUlXVhVhdepDqunYmZlhcl9tCsGkP5uE9GLYFQK2Zzs7oRHZExnIklgL0vN8NA/KzkwY9roIMIiIiIiIiIiIiIiIjj0IPIiNEa3uYR57ZzqG6EFMnpHPXpyeT6HPHu6wP5XU7+eTcccyfnsPL71Tx0jvVrFi1k7ysRBbNK2DqhDSMERx+6OnwUNbXvSFChIpIGaVd6weMrHAZLpZlLj+prhAiZ0tVXYj/fuIvTHVVcpW7irHNTdAMYRzsj4xmR3QsLckTuejiIuaMCvDK77ZgGB8+skJEREREREREREREREYm3ekSGQEs2+YXf9jNoboQn74snxuuGD/suiT4E1zceMUEFszOZe2bh3hlcy2PPLONotxkbp4/gYyModmx4myriJRjYvZbZmJSESkfEHqAnuDDfP+CQdeJnE2x5lrMA+/A1g38c7ARgA7LwzvhCeyIjGWfOYaLpuZx1cwcxo8+3snhVEZWiIiIiIiIiIiIiIjIyKPQg8gI8JdNNeyqbOaKaaO5ad6EeJfzkST5PXzmqiKuvWgsL/y1kg3bjvCD325h3u56bp0/gUDC0O9ecSYVeApx4err9AD0jq0ojGNVIj1iTbWYB97GPPgOVvNhADy2l7+Gi9gaGUeZmY2FA4D0pAS++KlJA46hkRUiIiIiIiIiIiIiIvK3KPQgcp6rPdbOM69VkJni446ri+JdzhmTlpTAFz4xkU/MzePpv5Szfkst28uO8cVPTWLqhPR4l3fOlPjmscqzkopIGSZmb+ChiBLfvHiXJiOQbdtYvR0dzAPvYLX0BB3wJnJs1EX8+VgWbzen9AUd3mMYMC5b3RtEREREREREREREROTUOT58k/6am5uZO3cut912W9+y/fv3c9tttzF9+nSuv/56Nm3a1G+fdevWcdVVVzFjxgz+4R/+gbq6un7rH374YebOncucOXP49re/TTQa7VvX1tbGkiVLmDlzJldccQW//e1vT7VkkREralr89wu7sSybuz49mQTP+Zdzykr187VbprLk9pmEozEe/v02Hl+3l+6I+eE7nwdchotlmcu5L/2b3BL8DPelf5NlmctxGeff37UMXVZ7I3UbVlL9y2/Q+exSIptXY3aFiBXO453cz7G0+RYe2DeJbR2ZXDJlDC6nwXsTdgwDnA6DhSX5cX0OIiIiIiIiIiIiIiIyPJ3yXbEf/vCHFBcXEw6HAYhGoyxevJjbb7+dJ554gj/+8Y/cfffd/OlPfyI5OZmKigq+9a1v8bOf/YxZs2bxwx/+kPvuu48nnngCgGeeeYa1a9eycuVK/H4/X/7yl1mxYgVf+9rXAPi3f/s3YrEYGzZsoKqqii9+8YsUFBRwySWXnMGXQeT8tGrDAarr21lYkk9BTnK8yzlrDMPg6ovzGJvu47EX9/Da1sPsqmziH6+bTPHYlHiXd9a5DBfz/QuY718Q71JkBLG724ke3IRZ9ldiR/fjB2zLwxuRYrZG8qmIZcJRB5YNWaleFl4xlpKp2SR4XFx70VjWlFZSVRciLyvIwpJ88rLU6UFERERERERERERE5Gx44okneO6559i/fz/XXHMNDz/8cN+6BQsW0NDQgNPpBGDMmDG8+OKLfevXrVvHsmXLaGxsZNasWXz/+98nKyurb/3DDz/MU089RSwW47rrrmPp0qW43ed2HP0pdXrYuHEjVVVV3HjjjX3L3n77bbq7u/nSl76Ex+PhhhtuIDc3l5dffhmANWvWMG/ePC677DISEhJYsmQJW7ZsoaqqCoCVK1fy93//9+Tm5pKWlsbixYtZuXIlAJ2dnaxbt457772XxMREJk+ezE033dS3XkQ+2N5DzazbWMX40Ulcf1l+vMs5J9KTE7jvMzP4u6uLaGmP8MPfbub3r5YTNWNn7ZymbfJ65yv8suW/eb3zFUx7ZHSYkJHJNiNED7xD10vLaX9iCeENvyJ27CAHPRfw8/Yr+ZeWW3mm8xLKzGws20HA52bJLdP43v+6hKtm5/Z1m8nLCnLPoqn8aPFl3LNoqgIPIiIiIiIiIiIiIiJnUWZmJnfffXe/aQ4nevTRR9myZQtbtmzpF3h4r8HBd7/7Xd566y3GjRvHfffd17f+xAYHL7/8Mrt372bFihVn/fm830l3eohEInz3u9/loYceYteuXX3Ly8rKKC4uxuE4np+YOHEiZWVlQM/oi2nTpvWtS0lJYfTo0ezfv5+8vDzKysqYOHFi3/pJkyZx9OhRQqEQ1dXVABQWFvY79q9+9asB9dXX13Ps2LEBy5OSkhg7diyGQV8rbZGTMZz/vXR2R/nFi7txux38r4WTcbtOeZLNsPL+NvnXXDSWKRPS+MULe1i3sYodFY3c9enJjMs+szdWTdvk/volVETKMDFx4WKVZyUPZmm8hJw/bNsidngv0bI3iR7cBJEuMAycYybjLroUK3cGv/j5Ftoj0QH7et1OZhRlxKHqke3Ea6KIiJw8XT9FRI7TNVFE5PTo+ikicnp0/Tw1771OFRUVdHd3D1g/atTtwewpAAAgAElEQVQoMjMz+y279tprAdizZw/Nzc0nfa4TGxwALFmyhJKSEqqqqsjLy+vX4ABg8eLFfOc73+mb6nCunPRduf/6r//i8ssv54ILLugXeujo6CAY7H8jMSkpiVAoBPR0axhsfUdHx6Dr33vc0dFBZ2cngUDgA/c90dNPP82jjz46YPn111/PQw89RDDoI6gPkspJcDp7AgIZGcP3H8xDv32XprYwX71lOlOKsz58h/NEevrxv7OMjCA/Lszk2VfKePLlfXz315u449oLuGVBUd/f8Uf1ctPLHIiWEyECQIQIB6LlbHe+w7Vp156Rc4jES7S1nvZtrxHa/gpma0+o0JM9gcQp80icfDkdhp8XSw/y4mNbae8aGHhwGFCUlzqsr6XD3YnXRBEROXm6foqIHKdroojI6dH1U0Tk9Oj6eWq+8Y1vsHv37gHL77nnHv7pn/7plI71zW9+E8uyKCoq4t5772X27NnAR2tw8P6MwNl0UqGHyspKVq9ezerVqwesCwQCtLe391sWCoX6wgp+v/+U1r/3OBAI4Pf7BwQcTtz3RLfffjsLFgycZ5+UlNS7XxfhsNrOy4eLxSwAGhpCca7k9GzcXcdrm2uYXpjO7MK0Yfs8ToVh9PwgbGwMYdv91101cwyFo4P8/A+7eWLdXv667TCLb7yQjBTfRz7v1pYdRO3+N3ujdpStTTuYZV36kY8vcq7ZZgSzcjORvRuI1e4GbDodATZbMziWNoMrL5lNq9vJS6sreGPHEaKmxaiUBD51yThefqeKmGVj2z3vSYfD4BMX5Y6Ia9BQ87euiSIi8sF0/RQROU7XRBGR06Prp4jI6dH189R4vS6CQR8PPvjgB3Z6OBU/+tGPmDJlCgDPPfccd911Fy+88AI5OTkfqcHBkAs9bN68mbq6ur5QQSQSIRKJMHfuXL73ve+xf/9+LMvqG3GxZ88e7rjjDgCKi4vZu3dv37FaW1s5cuQIxcXFABQVFbF3715mzZrVt292djbBYJD8/HygpzVHQUEBAHv37qWoqGhAjZmZmQPadJzIttGbRE7JcPz30tTWzePr9pHkd/PFT04CjGH5PE7XB73P87KC/P9fuIhVGw6wbmMV3/nVJu5ZNJXisSkf6XwT3IW4cPV1egBw4WKCu3BEve4y/MUaKonu3UC0/E2IdILDSWT0dH5dns6eyGhitgOjxeL1g+/0/dvOzw7yyUvGMbt4FA6HwcWTMllTWklVXYi8rCALS/IZmxnUeyGO9LuPiMjp0fVTROQ4XRNFRE6Prp8iIqdH18+T895r9N79849qzpw5fY//7u/+jrVr17J+/XruuOOOj9Tg4Fw6qdDDJz/5yb45HQDr1q1j9erVrFixgvT0dDweD7/85S+58847eemll6ipqeGaa64BYOHChdx66628+eabzJw5k5/85CfMmDGDvLw8ABYtWsRjjz3G/Pnz8fl8rFixgptvvhnoeZE+/vGPs3z5cv793/+dmpoannvuOR555JEz/TqIDHuWbfOLP+ymM2zytVumkRTwxLukIcXtcnDrlYUU5ibz32t2s+zJLdz5iQu4YtqY0z5miW8eqzwrqYiUYWLiwkWBp4gS37wzWLnI2WF3txMtf5Povg1YjVUAOFJzcM+6AVfRpfzqj5XsijT0/fJk9/4nKeDmKwuncEFeCsYJA9bysoLcs2jquX8iIiIiIiIiIiIiIiJyxhiGgd17c+CjNDg4l04q9ODz+fD5jreCT0pKwu12k52dDcCKFStYunQpP/nJTxg7diw/+9nPSEnp+QR1QUEB3/ve91i6dCkNDQ3Mnj2bhx56qO9Yt956K7W1tSxatAjTNLnuuutYvHhx3/pvf/vbLF26lCuuuIJAIMDXvvY1Lr1UbeNF3u9P71Szt6qFj80Yw4zCjHiXM2TNLBrF//n8bH7y7Hb+Z+1eao91cNuVhTgcxofv/D4uw8WyzOWUdq2nIlJOgaeQEt88XMZJXVpFzjnbtrHqK4jsfgXzwNsQM8Htwz3pY7gvmIdj1HgMw+DQ0RB7KpsHTdR6XE4mjks998WLiIiIiIiIiIiIiMhpMU2TWCyGaZpYlkU4HMbhcHDs2DEOHz7MtGnTAFi1ahU7d+7ke9/7HvDRGhycS4Ztj4wmIW1tXYTDZrzLkGFg6S82AvDAl+bGuZKTV13fznd//Q7pSQn86xcvxutxxrukc8owICMjSEPDyc96auuI8OjzOyivaWVaQTpfXnghPq/CCnJ+sqNhohVvEd31ClbjIQDqXGN4M1JMaNRUrr+8mLSkBN7adZQ3th+hqr590OMYRk9wSB0dhrbTuSaKiIiunyIiJ9I1UUTk9Oj6KSJyenT9PDVer4ukJN+Hb3iCn/70pzz66KP9lt1000186Utf4r777qOqqgq3201BQQH33nsvc+cev0/6xz/+kQcffLCvwcH3v/99srKygJ4PWz7yyCM89dRTfQ0O/uVf/gW32/3Rn+gpUOhB5H2GW+ghasb47q83cbihk//z+dlMGJMU75LOudP9YRg1LR5/aS+lO44yJiPA126ZRmbKqf2QEBnKYi2Hie5+lej+NyDSBe4Ewjlz+OmOVGrMVGwbDAADHAbELPB5ncydnE1RbjL/s3YPMcvu2c4Ap8Ng6Z1zyMs6t22p5NTofxBERE6Prp8iIsfpmigicnp0/RQROT26fp6a0wk9nO/0sWaRYW7l6weoOdbBjVeMH5GBh4/C7XLwD5+aRE5GIs+8Ws4Dv97EV2+awgV5at0vw5dtxTArNxPd/Qqxw3sAcKTm4L5oAe6iy/jVC2XURBt47/dGu/c/vgQXd1xVzKwLRuF193SLyckIsKa0kqq6EHlZQRaW5CvwICIiIiIiIiIiIiIiQ4pCDyLD2IHDbbz8TjUFY5K47tJx8S5nWDIMg0/MzWN0up//WrOLB5/ayueuLWb+jJx4lyZySqqrDnNw/QsUdGwjyejENhy4J1yM+8KrCKdMYPuhZrb/qZKt5ccDDydK8Li4dEp2v2V5WUGNshARERERERERERERkSFNoQeRYez59RUYwN9/ciJOhyPe5Qxr0wsz+L+fn83yZ7fz63X7qG3o4PYFhXpdZciLNdXS/M4fCFRuZKZh0Wz5eTEyg7cjxVxsFFH1aoiymjeIWT1RhwSPk+5IrN8xDAN1cBARERERERERERERkWFJoQeRYWp/dQu7Kpu55MIsckYlxruc80LOqET+5Qtz+NnzO/nzphqONnay+MYp+Ly6VMrQYts2sdpdRHa8RKx6B16gMpbBq12T2R7Nw6InrPPyO9V4XA4uHJ/G9IJ0pk5IpzNs8sDjm4hZNrbdE3hwOgwWluTH9TmJiIiIiIiIiIiIiIicDt3JExmGbNvm+fUHcBgGN5SMj3c555Wg38M3PjODJ17ex/ptR/jx01v5+m3T8Se4412aCLYZwSx/i8iOl7Gaa7ANg+bUKfy2Jo/ycMaA7ZMCHn70lUvxuJ39li+9cw5rSiupqguRlxVkYUm+Oj2IiIiIiIiIiIiIiMiwpNCDyDC051Az+6pbuHzqaLLS/PEu57zjcjr4wicmkujzsPatQ/zoyS3cd/sMgn5PvEuTEaaqLsTq0oM01h1jQbCc6eZOHJEQpsPDFqaxtrmApsbBwwqGAYU5yQMCD9AzyuKeRVPPdvkiIiIiIiIiIiIiIiJnnUIPIsOMbds8v+EATofBp9WO/qwxDIOb50/A63bw/IaD/Oh3W7jvMzNISfTGuzQZIarqQqx44hU+5t7JRd4K3J0WTbEAr4fn8Fa4EI8/kWkXpjOtIJ2kgIcHn9qikRUiIiIiIiIiIiIiIjLiKPQgMszsONBERW0bH5uZw6gUX7zLOa8ZhsGnS8bjcTt5+pVyfvjbzdx/x0zSkhLiXZqc58zGaur/8Bu+mViGw7CpNDN4tXsy2yN5ZKUn8r8/M5m8rCAOw+jbRyMrRERERERERERERERkJFLoQWQYsW2bVRsO4HI6uP7ScWflHKZtUtq1nopIOQWeQkp883AZI/tS8fGL8/C4HPzm5f18/4nN3H/HDDJTNVZEzrzWQ/toevN5Mtr2cgGw1xzNn7qmUm5mAT0Bh6hpkZ+dNGBfjawQEREREREREREREZGRaGTfyRQZZraWNVB5NMTVs3PPSrcB0za5v34JFZEyTExcuFjlWcmyzOUjPvhw5axcPG4nv1y7hx/0dnwYnR6Id1kyDFXVhVhdepDqunbGZiWy8LJ8oof3YG1by+joITKA3bFxbHTMYlsoiH3CvoaBujeIiIiIiIiIiIiIiIicYGTfxRQZRizb5vkNB/G4HFx3lro89HR4KCNCBIAIESoiZZR2rWe+f8FZOedwUjJ1NG6Xg5+/sJsf/HYz990+Qzeg5ZRU1YV44PFNxCwb27bJ6iyj9ejjjHc1ELMN9nsm4pr2KWbPmMLohg529m3bE3hwOgwWluTH+2mIiIiIiIiIiIiIiIgMGQo9iAwT7+47Rs2xdj5xcR7Jid6zco6KSDkmZr9lJiYVkXKFHnpdPCkLj8vJf6zawbInt/D122YwYczAUQMig1ldehArZjHdfYhrfTvIcTVj2g4225OY/Kk7mD02r2/bvKwgS++cw5rSSqrqQuRlBVlYkq+gjYiIiIiIiIiIiIiIyAkUehAZBizLZtWGA3jdTj5xSd6H73CaCjyFuHD1dXoAcOGiwFN41s45HM0oymDJLdP56crtPPjUFu69dTrFY1PiXZYMcWYshrN2G99I2kyOq5mw7eLVrsm80j0ZT1Ia88cOfG/nZQW5Z9HUOFQrIiIiIiIiIiIiIiIyPDjiXYCIfLiNe+o40tjJ1XNySfJ7ztp5SnzzKPAU4cGDAwcePBR4iijxzTtr5xyuLhyfxtdvm44N/PjpreyqbIp3STJE2bbNoc1/5cBj3+Tz3r8wytnGX7om852WRazqmkMIv7o3iIiIiIiIiIiIiIiInCZ1ehAZ4mKWxZo3DuLzOvn4xWevywOAy3CxLHM5pV3rqYiUU+AppMQ3D5ehS8VgLshL5f7PzOTHT29l+TPb+fpt05k0LjXeZckQ0lK+jaYNTzMqehjTdlAenM0ThwtoiSVg22AY4HQYLCzJj3epIiIiIiIiIiIiIiIiw5LuZIoMcX/deZS65i5uuHw8iT73WT+fy3Ax37+A+f4FZ/1c54MJY5K4/46Z/OjJzfxk5Xb+9x0zGT86Kd5lyTlWVRdidelBquvaGZuVyI3FMYztq0luryTNNtjjnUr+1bczMzeX9LoQa0orqaoLkZcVZGFJvjo9iIiIiIiIiIiIiIiInCaFHkSGMDNm8UJpJYEEF9fMGRvvcuQDjMsOsuSW6fz46a38+OmtfPOzs8gZlRjvsuQcqaoL8cDjm4hZNmMdDcyNbSWl4TCWbbDVLiblspu5aGoxhmEAkJcV5J5FU+NctYiIiIiIiIiIiIiIyPnBEe8CROSDvbH9CA2t3Xxibh7+BGWUhrLisSl8ddFUuiMxHnx6K/UtXfEuSc6R1aUHSaOVLwZe477ktVzgPsym8Hh+btzO3H/8Z6ZOu6Av8CAiIiIiIiIiIiIiIiJnlkIPIkNU1Izxwl8rCfrdXDU7N97lyEmYOiGd/7XwQto6Ijz45BaaQ+F4lyRnmdXVxoX16/hm0mqme6rYFsnjh20L+U3HFdTHkvC4nfEuUURERERERERERERE5Lymj46LDFGvbT1McyjMbVcWkuDRW3W4uGhiJt3hifzPH/fyUO+oi0SfO95lyRlmm2Ei218ivHUtFzm6ORgdxequ2Rw0MwEwjJ4xFiIiIiIiIiIiIiIiInJ26U6qyBAUjsZ48c1DJCd6uHJWTrzLkVN0xfQxdIVNnnqlnB8/vZX775iJz6vL7fnAtizM/W8Q3vQcdmcLjVaQNR1z2W7mAT0jLAwDnA6DhSX5ca1VRERERERERERERERkJNBdOJEh6NXNtbR1RPjsNcV41R5/WLr24jw6uk1e+GslP3l2O1+/bbpGHQxjtm0Tq9lBeOPvsZpq6DJ8vNhxEXs8U/nsDZO5KehlTWklVXUh8rKCLCzJV6cHERERERERERERERGRc0ChB5EhJhyNsfatQ6QleZk3fUy8y5GP4MYrxtMZNvnLuzWsWLWTry6aisvpiHdZcopiDYcIb3yaWO1uLMPFa5FpvNQxmTlTx/GvCwrxJ/SML7ln0dQ4VyoiIiIiIiIiIiIiIjLyKPQgMsS8teso7V1RPnt5MW6XbpAPZ4ZhcMfVRXSFTf668yiPvbiHu66fjMNhxLs0OQnVh2qpf+13FHXvAgP2OCfyVMOFOBLTWHzrRKZOSI93iSIiIiIiIiIiIiIiIiOeQg8iQ4ht2/z53RoSPE4um5Id73LkDHAYBl/81ES6wiYbd9fh87r4/LXFGIaCD0OVbZnUv7UW744XKDai7Dezeb5zDodjacwqHsU/fGoS/gT9+BQRERERERERERERERkKdNdGZAjZV9VC7bEOrp6Ti8+rt+f5wulw8JUbprD82W28tqUWv9fFLR8riHdZMgizdjfhvz6Bv/kwTVaAJzsvY1s0DzB4L6aiwIOIiIiIiIiIiIiIiMjQoTs3IkPIn9+tAeCqWblxrkTONLfLwT2LpvLQU1tZ+9Yh0pMTuHJmTrzLkl5WqIHwW09hHtwETjevmjN5sW0S0RN+TNpAVV0ofkWKiIiIiIiIiIiIiIjIAI54FyAiPRpau9hSdoxpBelkpfnjXY6cBQkeF1+7ZRqZqT6eeHkf2ysa4l3SiGebEcLvrqLj99/CPLgJZ/5s3hx/N6vapvYLPAAYBuRlBeNUqYiIiIiIiIiIiIiIiAxGoQeRIeLVzbXYNlw1W10ezmdBv4ev3zqdQIKbFat2ceioOgfEg23bRA9uouP33yLy7iocwVHErlzCow0lPPV2M5mpPpxOA6N3poVhgNNhsLAkP651i4iIiIiIiIiIiIiISH8abyEyBISjMdZvO0xWmp8Lx6fFuxw5y7LS/NyzaCoPPrWF5c9uY+mdc0hLSoh3WSOG1XKU7tLfEKvdBW4f3kvuYL9/Jj9fu49QZ5QrZ+XwmQWFHGnsZE1pJVV1IfKygiwsyVenBxERERERERERERERkSFGoQeRIWDj7jo6uk1uvGICjvc+Wi7nteKxKfzjdZP5rzW7eOSZbXzrc7PxeXVJPpvsWJTItrVEtrwAMRNX8eW4Zt/Mqncb+ePanfi8Lu6+cQpzJmYCPaMs7lk0Nc5Vi4iIiIiIiIiIiIiIyN+iO2wicWbbNn/eVEOCx8llU7LjXY6cQ3MnZ9HQ2sXK1w/wH6t2suSWabicmjp0NphH9hHe8GuslsPEErN5yTmPd3YlE966g45uk/Gjk/jKDRcyKsUX71JFRERERERERERERETkFCj0IBJn+6tbqDnWztWzc/VJ/xHoU5eM41hLN+u3HeaJl/fxhU9MxFC3jzPGDncQ3vg00b3rwemia+J1fHtjKuGYAwgD4DDgc9cWK/AgIiIiIiIiIiIiIiIyDOkjxSJx9udNNQAsmJ0b50okHgzD4HPXFjNlfBrrtx1h7VuH4l3SecG2baLlb9Hx+28R3bse55hJBG5+gN8cLuoNPJywLfDim3rdRUREREREREREREREhiOFHkTiqLG1m81lx5g6IZ3sNH+8y5E4cTkdLL5xCrmjEln5+gHe2n003iUNa1bbMbr++BDdr/wnWBYJH7sL45r/j+e3dbCrsmnA9rYNVXWhOFQqIiIiIiIiIiIiIiIiH5V66YvE0StbarBtuEpdHkY8n9fFvbdO44HHN/HLF/eQFkygeGxKvMsaVmzLJLL9JSLvroZYBFdxCZ65t/NWeQfP/nwjre0REjxOwpEY9gn7GQbkZQXjVreIiIiIiIiIiIiIiIicPnV6EImTSDTG+q2HyUr1MWVCWrzLkSEgLSmBe2+djtPp4Kcrt3OksSPeJQ0bsYZDdD7/HSJvP4ORmIrv+n/mcNFt/Pvv9/PYi3uIxWzu/PgF/PPfzcLpNDCMnv0MA5wOg4Ul+XGtX0RERERERERERERERE6POj2IxMlbu+vo6DZZePl4HO/dgZURLy8ryOIbpvCTZ7fzyDPb+L93ziHJ74l3WUOWHTOJbHmB8JYXsG0otWZSZpdgl4bZXvEuTofBNXPGsvDyfAIJbgCW3jmHNaWVVNWFyMsKsrAkX50eREREREREREREREREhimFHkTiwLZt/vJuDV6Pk8unjo53OTLETCtI53MfL+bxdft4dOUO7r9jJm6XGvO8X6yxiu7Xfo7VWM2RWCq/7SihxkyDljb+H3t3Hh5Vefd//H1mJpNMVgIhYUtYkrCvIYIYCRYEFxAExaUq4g7UylN3W35at9qW+lh8RKy7uFeCLC6IgrJEVEAk7CQhkARCAmHJZJ3MzPn9QY2mcRkgZLJ8XtfFZXLOme/5nHTmnl7X+Z77BojvEM4NF/eiQ1RIrdfFxYRx+6R+/ogsIiIiIiIiIiIiIiIi9UxNDyJ+sDvvGHlFpYxK6oQjUB9Dqeu8gR0pPFLOJ9/k8fryXdxwUU8MzQgCgOl149r0Ia5vlwAmG+xDeOtgIh6sNccYQESovU7Dg4iIiIiIiIiIiIiIiDQvutsq4gcrNuYDMHJwRz8nkcZs8nkJ7D9cxtqMAmKjQxmdHOvvSH7nKc6j8osX8RbvoywohgWuVL496KhznAnkFpY2fEARERERERERERERERFpUGp6EGlgR0oq+Xb3Yfp2a037NnoKXX6exWIwbXwfHpu/kXdWZNKhTQh9urb2d6wGk1voZHF6DnmFpcRFO5jUejchWcvB9PJZRT+WHelPYGAgbcKtHCmpwvzRaw3jxDIWIiIiIiIiIiIiIiIi0ryp6UGkgX2+aT9e0+T8wZ38HUWagOCgAO64vD+PvraB5xZvZdaUZGJaB5/x87pNN+kVq8l2ZRFvTyDFkYrNaLivjNxCJ4/N34DHa9LOcpTfmF8SVlxMgbsV71SeS5uuPZjWux3949tQUFxWc6xpnmh4sFoMxqd0abC8IiIiIiIiIiIiIiIi4h9qehBpQK5qD6u+O0B0pIO+3dr4O440Ee1aBzN9Qh+eem8zT6dl8KfrkgkOOnPDt9t0c0/RTLJdmbhxY8PGInsas6PnNEjjQ6XLzWvLduLxeBgVtI2LHJuxYPJpRV+2h6Vw943JBAcF1BwfFxPGrCnJLEnfS26hk7iYMMandNFMDyIiIiIiIiIiIiIiIi2Amh5EGtDXOwoprajmknO6YDEMf8eRJqRvtzZc+ZsE3lmZxfNLt3HHZf2xWM7Me+jEDA+ZuHAB4MJFtiuT9IrVjAgeeVq1f7xkRWxMKBNSuhIXE0ZZZTXfZR7m292H2JpzhFBvCbeHpZMQUMhBTwRvlqaQ64kiym7Wanj4XlxMGLdP6nda2URERERERERERERERKTpUdODSAMxTZMVG/IJtFtJ6dfe33GkCRp9Vix5RaWkbz1I2upsJp+XcEbOk+3Kwo271jY3brJdWafV9PDjJStME4qPV7I5q5iu7cLYe9CJx2tiGHBxuyJSq1YShItVlT1ZWp5ENTYMA83eICIiIiIiIiIiIiIicpLeeOMNFi5cyO7duxk9ejRPPfUUADk5OcyePZvvvvsOl8tFz549ue++++jX78SDpvn5+YwaNYrg4B+WXr/kkkt45JFHan5/6qmneOedd/B4PIwdO5ZZs2YREFD3AdYzSU0PIg0kM/84uUWljEzqeEaXJpDmyzAMplzYg4NHyvn4q1w6tQ1lWJ929X6eeHsCNmw1Mz0A2LARbz+9JovF6Tk1DQ8AJmB6TbIPlNA/vg3J8eH0P7IcY886vIFhvHBkONtcHTEBwwCrxWB8SpfTyiAiIiIiIiIiIiIiItLSREdHM2PGDL788kuOHj1as93pdDJixAgee+wxIiIieOutt7jllltYuXJlrUaHr776isDAwDp133vvPT766CPS0tIIDg7mtttuY968edxxxx0Ncl3fszTo2URasM825gMwanAnPyeRpizAZuV3k/oRGRbIKx/tJKegpN7PkeJIJd6eiB07FizYsRNvTyTFkXpadXMOlNQ0PPxYm/Agfp8awsBdz2LsWYc1tj9hkx/j8t9eyqDEtkRFBDEosS2zpiRrpgcREREREREREREREREgOzubbdu21flXVFRU59gxY8Zw/vnnExkZWWt7//79ufLKK2ndujVWq5XrrruOiooK9uzZ41OGtLQ0pk6dSqdOnWjdujXTp08nLS2tXq7vZOhxc5EGcLy0ik27D9GnSyTt24T4O440ca1CA7l9Uj/++ua3/F9aBv/v+rOIDKvbXXeqbIaN2dFzSK9YTbYri3h7AimOVGzGqX1lHC+tYsGqbI6VuurssxpeLgnfSvmSr8BiJTDlWgJ6j8IwDOKC4fZJ/U73ckRERERERERERERERJqdu+++m+3bt9fZfvvtt/P73//+lGpmZGTg9Xrp3Llzre2jR4/G6/Vy1llnce+999K+fXsAMjMz6dmzZ81xvXr14uDBgzidTsLCGu5BVjU9iDSAL7cexOM1GTGwo7+jSDPRtX04N1zck+eXbOeZhVu4/5pBBNis9VbfZtgYETySEcEjT7mG2+Plsw35LEnPodLloVuHMPYdLMVrnljioo21lOtC1tK1vAhL604EjZyOtbU+IyIiIiIiIiIiIiIiIr/mH//4B5WVlXW2t23b9pTqHTt2jHvvvZeZM2fWNCxERkayYMECevXqhdPpZPbs2UybNo2FCxditVopLy+v1dzw/c9lZWVqehBpTkzTZHVGAaGOAAYmRvk7jjQjZ/dux/5DZXy4bh+vfryLm8f1wjAMf8cCYOueYt76LJODR8qJigji5nG9GZQYRV5RKUvS9xJe9C1jrenYTRcBfccQOORyDJvd37FFRERERERERERERESahKBOPQgAACAASURBVPj4+Hqr5XQ6ufnmmxk+fDg333xzzfaQkBD69TsxK3dkZCR//vOfSUpKYu/evcTHxxMcHExpaWnN8d//HBLSsDPfq+lB5AzLzD9O4ZFyxpwVi81q8XccaWYmpnZj/6Ey1m07SGx0KBcOjWvwDLmFThan55BXWEp0pAOPx2RX3jHsNgsTh3flgiFx2ANOzEIR2zqAmyLX4T78JUZQBEHn/R5brJawEBERERERERERERER8YfS0lJuuukmevfuzZ/+9KeTem1iYiI7d+4kKSkJgB07dtCuXbsGneUB1PQgcsat3nwAAEf3PF4+9jnx9gRSHKnYDH385PRZDINbLunNY/M38N4XWXSOCaVXl9YNdv7cQiePzd+Ax3tiyYrDx09Mo9S3ayRTL+pF6/CgmmM9R/ZT+dlcvMcOYI0bQNCIm7A4whssq4iIiIiIiIiIiIiISEvkdrvxeDy43W68Xi9VVVVYLBaqqqq46aabiI+P5+GHH67zus2bNxMaGkrXrl0pLS1l9uzZdO7cmS5dugAwadIkXnrpJUaMGIHD4WDevHlcdtllDXx1anoQOaPKK6tZv7MIa/RhXuJF3E43NmwssqcxO3qOGh+kXjgCbdw+qR+PvraBeYu38dDUs2gTEfTrL6wHi9Nz8HhMzB9tMwB7gK1Ww0P17rVUrp0PHjeBQ68koP+FjWYpDhERERERERERERERkeZs3rx5PPPMMzW/L1u2jIkTJzJ06FC+++47du3axbJly2r2v/DCCyQnJ5OXl8dTTz1FcXExISEhDB48mOeeew6r9cQM35MnT2b//v1MmjQJt9vN2LFjmT59eoNfn2GapvnrhzV9JSUVVFW5/R1DmoBZL34NwGM3Dz3tWp9/m8/ry3fjPPtDnPEba7bbsXNXm/sZETzytM8hYBgQFRXG4cNOWsaI9tO+3X2IZxZuoUu7MB64NokAm/WMns9rmsycs4ayyrpja1REEH+ffg6mu4qq9Deo3rUGIySSoFEzsLVLPKO5RFo6jYkiIqdG46eIyA80JoqInBqNnyIip0bj58kJDLQRHu7wd4xGxeLvACLN2eqMAqwBXsrjttba7sZNtivLT6mkuUrq3pZx53Rm70Enry/fzZnsaTteWsVT/978kw0PhgFxMWF4jxVQvuhRqnetwdqpL8GTHlbDg4iIiIiIiIiIiIiIiNQrza0vcobsO+hk30EniX0sFAaYeH60z4aNeHuC37JJ83Xpud3Ye9DJ2owCurUP57xBHev9HBnZxbz84XZKyqsZlBjFlj3FeLwmpnmi4cFqMbis82HK3p8L7irsyZOwDxqHYajPTkREREREREREREREROqXmh5EzpA1GQcAuDxpIEW2RLJdmbhx/6fhIZEUR6qfE0pzZLEY3HpJHx55dT1vfrqb2OhQ4jtG1EvtareXBV9k8+mGPByBNqZN6MOQXjHkFjpZkr6X3EInXaIdXBG2kcCNazEcEQRdMBNbh171cn4RERERERERERERERGR/6amB5EzwFXtYd22Qjq1DSGhQySzmUN6xWqyXVnE2xNIcaRiM/TxkzMj1BHA7ZP68ZfXNzL3/S08dMMQIkLsJ1Ujt9DJ4vQc8gpLiY0JJaVvO5as3UtuUSkJHSO4dXxvoiJOrBcVFxPG7ZP64S0pouLTuXj37sPaoRdBI6dhCa6fhgsRERERERERERERERFp+h544AGfj33iiSd8Ok53XUXOgI27D1FR5Wb4gK4YhoENGyOCRzIieKS/o0kLERcTxvUX9eSFpduZt2grd181EJvVt+UlcgudPDZ/Q82SFYePV7Jp92EAxqd04ZKULlgttWtV791I5ecvQnUl9qQJ2JMmYFi0nIWIiIiIiIiIiIiIiIj8wO121/xsmiYrV64kLCyM3r17A7Bjxw5KSkoYNWqUzzXV9CByBqzZfACb1WBYn3b+jiIt2LA+7cgpKOGzDfn8+/Msfnt+d59etzg9p6bh4ce6x0Zw6fButbaZphfXxkW4vl2CERRG0Oi7sHXqW1+XICIiIiIiIiIiIiIiIs3I7Nmza37+29/+xoQJE5g1axZWqxUAr9fL448/js3meyuDHsMVqWeFR8rZmXuMpO5tCXUE+DuOtHBX/CaB7p0i+GxDPuu2HfTpNXmFpXUaHgCOlFTV+t2sKqPikzm4vl2CJaoLwZP+rIYHERERERERERERERER8cmCBQuYMmVKTcMDgMVi4dprryUtLc3nOmp6EKlnazIKAEgd0MHPSUTAZrUw/dK+tAq189rHO8ktdP7qa9q3CamzzTBOLJnxPc/R/ZQtegRP7mZsiSkEj/8jltA29ZpdREREREREREREREREmi+73c769evrbF+/fj12u93nOlreQqQeebxe0rcUEBURRM/Okf6OIwJARGggMyb2429vfsszC7fw4NSzfnYWkuOlVRw8UlZrm2GA1WIwPqULANV7N1L5+QvgdhF4zjUE9DkfwzDO9GWIiIiIiIiIiIiIiIhIMzJt2jQeeeQRvv76a/r164dhGGRkZPDJJ59w3333+VxHTQ8i9Sgju5jjZS4mDu+KRTeBpRFJ6BjBNaO7M/+TXTy/dBv/c/kALJba79EjJZXMfuc7Dh2r5IKzYjl0vJLcQidxMWGMT+lCbHQIVRsW4vp2CUZQGEEX/A+2Dj39dEUiIiIiIiIiIiIiIiLSlF133XX06NGDt99+m0WLFmGaJl27duWll15i6NChPtdR04NIPVqzuQDDgJR+7f0dRaSOEQM7sKeghLUZBSxem8PE1G41+w4fq+Dvb2/i8PFKrhndnVGDO9V6rVlVRsUnc/DkbsYS1QXHmN9rOQsRERERERERERERERE5LUOGDGHIkCGnVUNNDyL15Kizis3Zh+nXrQ2tw4P8HUekDsMwuG5Md/IKS1n65V7iO4bTPz6KoqPlzH57E0dKqph6UU9SB3So9TrP0QNULJ+DebwQW2IKQcOvx7D5vo6SiIiIiIiIiIiIiIiIyE85cOAAS5YsIT8/nzvvvJPWrVvz9ddfExMTQ5cuXXyqYTmzEUVajvQtBZgmdW4YizQmATYrMyb2JSTIxgtLt7Mtp5i/vvktR5xV3DSuV533b/XejZQvegSz5BCB51xD0Hk3q+FBRERERERERERERERETts333zD2LFjWb9+PYsWLaKsrAyAzZs387//+78+11HTg0g98JomazMKCA+x0z9eU/5L49a2lYNbLulNeaWbp97L4Hipi9vG9+Gcvj8sy2KaJlXfLqZy+f9hWANwjL0Xe9/RGIbhx+QiIiIiIiIiIiIiIiLSXMyePZu7776bl156iYCAgJrtw4YNY9OmTT7X0fIWIvVgV+4xio5VcNHQOGxW9RJJ45Vb6GRxeg45B0oA8HpNeneJZEivmJpjTLeLytUv4876CkubOBwXzMQSqmYeERERERERERERERERqT+ZmZmMGDGizvZWrVpx7Ngxn+uo6UGkHqzZfACA4VraQhqx3EInj83fgMdjYv5o+/a9R1mz+QDDB3TAW1FCxfKn8RZmYeuSRNBvbsUICPJbZhEREREREREREREREWmeoqKi2LdvH506daq1ff369cTGxvpcR00PIqeptKKaDbsO0T22Fe1aB/s7jsjPWpyeU6fhAcBus/D68t10cpTTduNzmM7D2AdcjH3I5RiGZi4RERERERERERERERGR+jdlyhQefvhh/vSnPwGQlZXF6tWrmTNnDvfcc4/PddT0IHKavtp2ELfHS+qA9v6OIvKL9hU46zQ8ADgCbZRVuHh28XbuDi+j9YgbCeiZ2uD5REREREREREREREREpOWYMmUKwcHBPProo1RUVDB9+nSioqK44447mDx5ss91fH6E98EHH2T48OEkJSUxcuRInnvuuZp9u3fv5oorrmDAgAGMGzeODRs21HrtsmXLGDVqFAMHDuTGG2+ksLCw1v6nnnqKoUOHkpyczEMPPUR1dXXNvpKSEmbOnMmgQYMYPnw4b775ps8XJ3KmmabJ6s0FOAKtDO4R7e840oy4TTerylfy8rHnWVW+ErfpPq16VS4PFa66NQwDxrbOZpLjK4o9obwVfC3WHsNP61wiIiIiIiIiIiIiIiIivrj88sv57LPP+Pbbb0lPT2ft2rVce+21J1XD56aHKVOm8Omnn/Ltt9/y1ltvsXTpUj7++GOqq6uZPn06559/PuvXr+eWW25hxowZHD9+HIDs7GweeOABHn30Ub766is6d+7MXXfdVVP3vffe46OPPiItLY3ly5ezfft25s2bV7P/kUcewePxsGbNGv71r3/x9NNP89VXX53URYqcKXsPOsk/VMrZvdsRGGD1dxxpJtymm3uKZvJk8V9Z4HyHJ4v/yj1FM0+58cFrmjy/dBsVVR4sxolGBwCr4eXy4K8ZWvY5KW1LGNY9gi37q/hw3b56vBoRERERERERERERERGRX+ZwOIiMjMTr9db885XPy1skJCTU/GyaJoZhsG/fPr755hsqKyu5+eabsVgsTJgwgddee43ly5czefJklixZQmpqKueccw4AM2fOJCUlhdzcXOLi4khLS2Pq1Kl06tQJgOnTp/Pwww9zxx13UF5ezrJly1i0aBGhoaH07t2biRMnkpaWxtlnn10rX1FREYcOHaqTOzw8nNjYWIwf3egT8YUv75c1GQcASB3YQe8vP/r+b99c/jdIL19NtisTFy4AXLjIdmWSXrGa80JGnnS9tC+y2ZR5mKG9Y7hoaBxL0vdSWFjMNUGr6Fi9D2uHXgSP/h3XWx3kz9/IotV7iO8QTp+urev70kSkATS3MVFEpKFo/BQR+YHGRBGRU6PxU0Tk1Gj8PDnN6e9UUFDAE088wTfffFMzqcKP7dixw6c6Pjc9ADz55JO8/vrrVFRU0LFjR8aPH8/y5cvp3r07FssPk0b07NmTzMxM4MTSF/3796/Z16pVK9q3b8/u3buJi4sjMzOTnj171uzv1asXBw8exOl0kpeXB9RuuOjZsyevvvpqnWzvvvsuzzzzTJ3t48aN48knnyQszEFY2MlcrbRUVuuJ93JU1C+/YSpdbr7ZUUS3DhEk9+3QENHkV7Rp0zw+5AVVubipPauDGzcFttxffV/+t0+/3sfHX+XSo3Mk9045C3uAlf4dLRz897tUH84nbOD5RF14C4b1xNfBrJuGcudTq/jXku38884RREcG19t1iUjDai5joohIQ9P4KSLyA42JIiKnRuOniMip0fjZ8tx5552YpsmDDz5ImzZtME6xo+Okmh7uuusu7rzzTrZs2cKKFSsIDw+nrKyMsP/qJggPD8fpdAJQXl7+k/vLysp+cv/3P5eVlVFeXk5ISMjPvvbHrrzySkaOrPsEdHh4OABOZwVVVac2Nbw0bW7Tzdry1eypzqJbQALnBqdiM37+re/xnJgq5fBh5y/WXbftIOWVbs7uHf2rx8qZZRgnvgiLi52Ypr/TnL727jhs2GpmegCwYaO9O+6k3ms79x1l7oLNtIkIYvqEPpQcL8d9MIuKT+ZgVpYSOOwq6HcBxUcral5jB24c24v/S9vC4y99zf3XJhFg83klJBFpBJrbmCgi0lA0foqI/EBjoojIqdH4KSJyajR+npzAQBthYQ5/x6gXO3fuZOHChXTt2vW06pxU0wOAYRj079+fNWvW8Mwzz9CuXTtKS0trHeN0OmuaFYKDg09q//c/h4SEEBwcXKfB4cev/bHo6Giio6N/Nrdpog9JC+Q23dxTNJNsVyZu3NiwsciZxuzoOb/Y+AC//n5Zm1GA1WIwtHeM3luNRHP5nKc4UllkT6v1vo23J5LiSPX5+gqPlPPMwi0E2CzMvLw/4cF2XDnfUrliHhgGjjF3YOsyCKj7NxuU2JaLz+7MR1/t4+0VmVw3pkc9X6GINITmMiaKiDQ0jZ8iIj/QmCgicmo0foqInBqNn75pTn+jgQMHkpub2/BND9/zeDzs27eP4cOH8+KLL+L1emuWuNixYwdXX301AN27d2fnzp01rzt+/DgFBQV0794dgMTERHbu3ElSUlLNa9u1a0dYWBhdunQBIDs7m/j4eOBEt0diYuKpxpYWJr1iNdmuzJon5l24yHZlkl6xmhHBdWcG8VXx8Up27D1KUve2hAXb6yuuCAA2w8bs6Dn/ef9mEW9PIMXxyzOU/FhpRTX/XJBBeZWbmZcPoFPbUFzbV1KV/jpGYCiOC/+ANbrbL9aYmNqVPQeO8/m3+0noEMGwvu3q49JEREREREREREREREREAPjrX//KrFmzyMnJIT4+Hput9r2wYcOG+VTHpztoTqeTFStWcP755xMcHMymTZt4++23mTFjBkOGDMFut/Pyyy8zZcoUPvnkE/Lz8xk9ejQA48ePZ/Lkyaxbt45Bgwbx9NNPM3DgQOLi4gCYNGkSL730EiNGjMDhcDBv3jwuu+wy4MQsEBdccAFz5szhL3/5C/n5+SxcuJB//vOfPv+hpGXLdmXhpvayJm7cZLuyTqvp4cutBZhASv/2p5lQ5KfZDBsjgkee9PvU7fHy7PtbKDxSzm/PT6Rft9ZUrU/DtWkpRngMwRffhSX852fF+Z7VYmHahL78+ZVveG3ZTmKjQ+kUHXqqlyMiIiIiIiIiIiIiIiJSy86dO8nIyGDNmjV19hmGwY4dO3yq41PTg2EYvP/++zz++OO43W5iYmK44YYbuPbaazEMg3nz5jFr1iyefvppYmNjmTt3Lq1atQIgPj6exx9/nFmzZnH48GEGDx7Mk08+WVN78uTJ7N+/n0mTJuF2uxk7dizTp0+v2f/QQw8xa9Yshg8fTkhICHfccYfPHR0i8fYEbNhqZnoA/rNUQMIp1zRNk/QtBwkPsdOvW+v6iClSL0zT5PVPdrEz9xgjkzoyKqk9latexL07HUvbbjgu/B8sjnCf64WH2JlxaT/+9ta3zH1/C//v+rMIDjrlCYJEREREREREREREREREajz88MOMGzeO6dOnExUVdcp1DNNsTqt+/LySkgqqqty/fqA0K27TzT1FM8l2ZeLG/Z+Gh0RmR8/52aUCZr34NQCP3Tz0J/fvyj3K397axIVD4rhi5Kk3T0j9MQyIigrj8GFns1rHyFe5hU4Wp+ewO/cYZZVu4juEc98VvXCtnIcnfyvWuAE4Rs3ACAg8pfqfbcjjrc8ySerelt9N7IthGPV8BSJSn1r6mCgicqo0foqI/EBjoojIqdH4KSJyajR+npzAQBvh4Q5/x6gXSUlJLFq0qGaViFOlR3alWbMZNmZHzyG9YjXZrizi7QmkOFJ/tuHBF+lbDgKQ0q9dfcUUOWW5hU4em78Bt+eH/xdwpKgI5+JF2I7nE9BzBIHnTsGwWE/5HKMGdyL7QAlfby9k2Te5XDS0c31EFxERERERERERERERkRZs7NixrF69mmuvvfa06qjpQZo9m2FjRPBIRgSPPO1alS4363cW0bV9GB3bhtZDOpHTszg9B8+PGh6iLceZFrIC2/FS7MkTsQ8af9ozMxiGwfUX9iCvqJQFX2TTtV04PTtHnm50ERERERERERERERERacHCwsKYM2cOa9asoXv37thstdsXZs6c6VMdNT2InIQNOw9RVe3h3H7t/R1FBIB9BU6+b3noYivi1tCVBBnVLPGmck3ShHo7T5Ddxu8m9uWR1zbw3OKtPHTDECLDTm25DBEREREREREREREREZEtW7bQs2dPysvL+e6772rtO5mHetX0IHIS0rcUYLNaGNI7xt9RRKhyeahwuQHoF5DLlNA1mBi8WDqSoK4D6/187duEcNPFvXh20VbmLd7KvVcPwma11Pt5REREREREREREREREpHnzeDzcf//9dO7cmdDQ05thX3erRHxUdKyCXXnHSOoeRUhQgL/jSAvnNU1e/GA7FVUehgVmcmPoKqrMAJ5xjmG3txPjU7qckfMm94xmzFmxZOUf573Ps8/IOURERERERERERERERKR5s1gsXHXVVRw9evT0a9VDHpEWIT2jAEBLW0ijsHDVHjbuPsT1cfu4KmQdZdZwXjMvJapbL2ZNSSYuJuyMnfvy8+JJ7BTBpxvy+GZH4Rk7j4iIiIiIiIiIiIiIiDRPhmHQs2dP8vLyTruWlrcQ8YHXNPlyawGRYYH07tLa33GkhVubUcBHX+3l2rbbSSrdiCWyE+0uvosHQiIb5Pw2q4Xpl/blz6+s55WPdtKpbSgdokIa5NwiIiIiIiIiIiIiIiLSPNx88808/vjj3HrrrfTq1QuHw1Frf2xsrE911PQg4oOd+45SXFLF2GGdsVgMf8eRFmxX7lHmL9vOda02kOzZgSW6G8EX3okRdHprHZ2sVqGBTJ/Qh9lvf8fc97cwa0oyjkB9pYiIiIiIiIiIiIiIiIhvZs6cCcB9990HnJj9AcA0TQzDYMeOHT7V0R0qER+s3XJiaYsULW0hflR4tJx5C7/jupC1DLTkYO3YB8eY32MEBPklT4+4SC4/L55/f57Fqx/vZNqEPjVfRiIiIiIiIiIiIiIiIiK/ZMWKFfVSR00PIr+ivNLNxl2HSOgUQbvWwf6O02S5TTfpFavJdmURb08gxZGKzdAQ5KuyymrmvreRqwM+o3fAfmxdBhM0ahqGNcCvuS4YEkv2geOs31lEQscIRp/l2zRDIiIiIiIiIiIiIiIi0rJ17NixXurojqPIr1i/s5Bqt5dzNcvDKXObbu4pmkm2KxM3bmzYWGRPY3b0HDU++MDt8fLiwo1Mql5KfEARtu7nEpR6A4bF6u9oGIbBjRf3Iv9QGf/+PIsu7cNI7NTK37FERERERERERERERESkEVqwYAHjx4/HbrezYMGCXzz28ssv96mm7jaK/Iq1Wwqw2yyc1TPa31GarBMzPGTiwgWACxfZrkzSK1YzInikn9M1bqZpsmDZJsYce5dOAUcJ6DuGwGFXYRgWf0er4Qi0cfvEvjw6fwPzFm3loRuGEBFi93csERERERERERERERERaWSeffZZRo0ahd1u59lnn/3Z4wzDUNODSH0oKC4je38Jw/q0wxGoj8upynZl4cZda5sbN9muLDU9/IzcQieL03MozMtnqn0ZMbYSbIMnEpg0HsMw/B2vjo5tQ5l6YU+eX7qdfy3eyl1XDcRqaTyNGSIiIiIiIiIiIiIiIuJ/r7/+Oq1anZg1fOXKlfVSU3ekRH5B+paDAJzbr52fkzRt8fYEbP/VY2XDRrw9wU+JGrfcQiePzd/A/qxsbrV/QIy1hIUVQznUaWSjbHj43tl92jEqqRM7c4+xcNUef8cRERERERERERERERGRRub888/nyJEjAIwaNYqjR4+edk01PYj8DK/X5MutBURFBNGjc6S/4zRpKY5U4u2J2LFjwYIdO/H2RFIcqf6O1igtTs8hmmLuCP+EcEsF80vPZXVlD5ak7/V3tF915agE4juE8/HXuWzcdcjfcURERERERERERERERKQRCQ8PZ//+/QDs378f0zRPu6bm6xf5GVtzjnCs1MX4lC5YGvHT9U2BzbAxO3oO6RWryXZlEW9PIMWRis3QEPTfTNOkNC+L28OWE2i4ean0PLZVxwInZoBo7GxWC9Mv7cufX1nPyx9tp1Pbs4hpHezvWCIiIiIiIiIiIiIiItIIXHzxxfz2t78lOjoawzC47LLLsPzMkukrVqzwqabuOIr8jPQtBQCk9Gvv5yTNg82wMSJ4JCOCR/o7SqPl8Xr56IMvuDHwY2yGlxdKf8PO6o4AGAbExYT5OaFvWocHMW1CH5589zueeX8Ls65LJtBu9XcsERERERERERERERER8bOHHnqIsWPHkpubyx//+Eeuv/56QkJCTqummh5EfoLXa7Ip8xA941rRtpXD33GkBaiocvN+2nJGOxditcCLpaPY5W4HnGh4sFoMxqd08W/Ik9C7S2smpXYjbdUe5n+yk5vH9cbQjCkiIiIiIiIiIiIiIiItXnJyMsnJyRw4cIArr7wSh+P07sf+9DwRIi1cRZUbt8fULA/SII6UVPLm60sZ41yI1WIhZOzdXHXNJQxKbEtURBCDEtsya0pyk5np4XsXnd2ZgQlRrNtWyBeb9vs7joiIiIiIiIiIiIiISIv0xhtvMGnSJPr27csf/vCHWvt2797NFVdcwYABAxg3bhwbNmyotX/ZsmWMGjWKgQMHcuONN1JYWFhr/1NPPcXQoUNJTk7moYceorq62udct99+Ow6Hg4qKCvLz88nLy6v1z1ea6UHkJ5RXuQm0W0nuEe3vKNLM5RY6WbrgQ66yfYrFaiNs3N3Y2iUSB9w+qZ+/450Wi2Fw87hePPzqet76LJPO7cLp1iHc37FERERERERERERERERalOjoaGbMmMGXX37J0aNHa7ZXV1czffp0rrzySt544w0+/vhjZsyYwaeffkpERATZ2dk88MADzJ07l6SkJP72t79x11138cYbbwDw3nvv8dFHH5GWlkZwcDC33XYb8+bN44477vAp1+7du3nggQfYvn07AKZpYhhGzX937NjhUx01PYj8l2q3l2q3l3P7tyfQbvV3HGnGMrKL+fyDj5kStBICAgm/5F6sbbv6O1a9Cg4K4HcT+/H46xt5dtEWHpp6FmHBdn/HEhERERERERERERERadKys7OprKyss71t27ZER9d+sHvMmDEA7Nixo1bTwzfffENlZSU333wzFouFCRMm8Nprr7F8+XImT57MkiVLSE1N5ZxzzgFg5syZpKSkkJubS1xcHGlpaUydOpVOnToBMH36dB5++GGfmx7uv/9+oqOjefvtt4mKijrlpdLV9CDyX8orT0y5cq6WtpB6llvoZHF6DnmFpTgCbUQe3c71oasx7A7CLrkXa1Rnf0c8I+JiwphyQQ9e+nAHzy/Zxh+uGIjFcmpfWiIiIiIiIiIiIiIiIgJ33313zQwJP3b77bfz+9//3qcamZmZ8ZdkRAAAIABJREFUdO/eHYvFUrOtZ8+eZGZmAidmYujfv3/NvlatWtG+fXt2795NXFwcmZmZ9OzZs2Z/r169OHjwIE6nk7CwX1+2fc+ePTz11FN07nx698jU9CDyI26Pl4oqN1aLQWKnCH/HkWYkt9DJY/M34PGamCYMtO9lSugazMAQwsbfj7V1J39HPKNS+rUna/9xVn13gMVrc5iY2s3fkURERERERERERERERJqsf/zjHz8704OvysrK6jQnhIeH43Q6ASgvL//J/WVlZT+5//uff6ruTxk0aBB79uxR04NIfdqcVYzXhDCH7ZSnTxH5KYvTc/B4TEwg2b6Ha0LScZpBrAy+nCnNvOHhe789P5F9B50s/XIv3TqEMyAhyt+RREREREREREREREREmqT4+PjTrhESEkJpaWmtbU6nk5CQEACCg4NPav/3P3+//9eMHz+ev/zlL+Tk5JCYmIjNVrt9YdiwYT7VUdODyI+szTgAQHCgPhpSf0rKXOzYexQTGGrP4qqQLznuDeYZ5xgw7f6O12ACbFZmXNqXh19dzwtLt/PQDWfRtpXD37FERERERERERERERERapMTERF588UW8Xm/NEhc7duzg6quvBqB79+7s3Lmz5vjjx49TUFBA9+7da16/c+dOkpKSal7brl07n2Z5AHjggQcA+Pvf/15nn2EY7Nixw6c6ll8/RKRlOOqsImNPMYEBVqxWfTTk9FVVe/jgy73c/691VLo8DPlPw8NRbyhPOy+k2AwnLsa3Qb+5iGrl4NbxfaiocjP3/S24qj3+jiQiIiIiIiIiIiIiItKsud1uqqqqcLvdeL1eqqqqqK6uZsiQIdjtdl5++WVcLhdLly4lPz+f0aNHAydmYli9ejXr1q2jsrKSp59+moEDBxIXFwfApEmTeO2119i/fz9Hjhxh3rx5XHbZZT7n2rlz58/+87XhATTTg0iNddsOYpoQHKSPhZwer2mybutBFq7ew1FnFdGRDm7qdYiE3BMND//nHMMxMxSrxWB8Shd/x21w/bq1Yfy5XVm8Noc3P93NDRf38nekn+Q23aRXrCbblUW8PYEURyo2Q+ODiIiIiIiIiIiIiIg0LfPmzeOZZ56p+X3ZsmVMnDiRv/71r8ybN49Zs2bx9NNPExsby9y5c2nVqhVwYgmNxx9/nFmzZnH48GEGDx7Mk08+WVNn8uTJ7N+/n0mTJuF2uxk7dizTp09v8OszTNM0G/ysflBSUkFVldvfMaSRMk2TP77wNWUV1YQFB2AYBo/dPNTfscRHhgFRUWEcPuykoUe03EIni9NzyCssJTYmlH5d2/DFpv3kFpUSEmRj/LldGR6eR/WqF/AEteL9oMvYcdhCXEwY41O6tLiZHr7nNU3++d5mtu45wtSLepI6oIO/I9XiNt3cUzSTbFcmbtzYsBFvT2R29Bw1Pkij588xUUSkKdP4KSLyA42JIiKnRuOniMip0fh5cgIDbYSHN5/lwz/88ENeffVVcnJyAOjWrRtTp07l4osv9rmG7tyIAJn5xyk8Us6Ys2LZmnPE33GkicgtdPLY/A14vCamCYePV7Jp92GsFrhwaBzjhnUmIH8jlZ+/gBEcSfgl93NDeLS/YzcKFsPg1kv68PAr63lj+W46x4TRuV3jaQA5McNDJi5cALhwke3KJL1iNSOCR/o5nYiIiIiIiIiIiIiISNP34osvMnfuXK699lqmTZsGwKZNm/jTn/7EwYMHufHGG32qYzmTIUWairUZBQCc27+9n5NIU7I4Paem4eHHenVuzRW/SSDgwCYqP38eI7gVwePuw6KGh1pCHQHMmNgXMJn7/hZKK6r9HalGtisLN7VnB3LjJtuV5adEIiIiIiIiIiIiIiIizcubb77J448/zl133cWoUaMYNWoUd999N48++ijz58/3uY6aHqTFq6hys35nEV3bh9Opbai/40gTkldY+pPTLB08Uk71nvVUrngOwxF+ouEhIqbhAzYBXduHc83o7hw+XsmLH2zH20jmrYq3J2D7r8mQTixxkeCnRCIiIiIiIiIiIiIiIs3L4cOH6dmzZ53tvXv35sgR32fnV9ODtHgbdhZRVe1huGZ5kJPUKtReZ5thQEqrgycaHoJC/9Pw0M4P6RqO23SzqnwlLx97nlXlK3Gb7l9/0Y+kDuhASr92ZGQX8+GXe89MyJOU4kgl3p6IHTsWLNixE29PJMWR6u9oIiIiIiIiIiIiIiIizUKfPn149dVX8Xq9Ndu8Xi+vvPIKvXv39rmO7dcPEWne1mwpIMBmYUgvPYkvvsstdJJbWAqcaHQwzRP/7WfP5zelqzCCQnCMux9Lq+bdTOM23dxTNJNsVyZu3NiwsciexuzoOdgM375iDMPgujE9yC0sZdGaHLp2CKdv1zZnOPkvsxk2ZkfPIb1iNdmuLOLtCaQ4Un2+JhEREREREREREREREfllf/zjH7n11ltZtWoVffr0AWDbtm24XC6ef/55n+topgdp0QqKy8jKP05yj7YEB+lmpvjmSEklcxZk4DVNbrioB4MS2xIVEcS4zk5uDF2FJTAYx9j7sEZ28HfUM+5EU0AmLlx48eLCRbYrk/SK1SdVxx5g5XcT+xIUaOP5JdspPl55hhL7zmbYGBE8khtb3cqI4JFqeBAREREREREREREREalH/fv3Z8WKFUybNo3Y2Fg6derE9OnT+eyzz+jXr5/PdXQHR1q0tRkFAAzv3/xvTkv9qKhy88/3MjjqrOLWS3pzdp92DB/QEXduBhXLX8YIDMYx7l6srTv6O2qDyHZl4ab2chZu3GS7shgRPPKkakVHBnPLuN48nZbBs4u2cP81gwmwqTdPRERERERERERERESkuQoJCeHqq68+rRq6myQtltvjJX3rQdq2CqJ7XCt/x5EmwO3x8uz7W8g/VMplI7pxdp92J7bnb6Xi06chIBDH2Huwto71c9KGE29PwPZf/XM2bMTbE06p3sDEKMYO60xOgZN3VmTWR0QRERERERERERERERFphJ577jnS0tLqbF+4cKGWtxDxxdY9Rygpc3Fuv/ZYDMPfcaSRM02T+Z/sYtveo6QO6MDFZ3cGwF2wi4pPngZbIMFj78XaJs7PSRtWiiOVeHsiduxYsGDHTrw9kRRH6inXnDi8G706R/L5pv18ubWgHtOKiIiIiIiIiIiIiIhIY/HOO++QkFD3QdqEhATefvttn+toeQtpsdZkHMAAUvq193cUaQKWfrmXtRkF9O3Wmusu6I5hGHiK9lCx7CmwWAi+6C6sUZ39HbPB2Qwbs6PnkF6xmmxXFvH2BFIcqdiMU/96sVgMbpvQh4dfWc/8ZbuIjQ4jNjq0HlOLiIiIiIiIiIiIiIiIvxUXFxMZGVlne0REBIcPH/a5jmZ6kBbpeJmLjOxi+nRtTevwIH/HkUbuy60FLFqTQ1xMKNMn9MVqseApzqP84yfB68Vx4R+wRnfzd0y/sRk2RgSP5MZWtzIieORpNTx8LzzYzoxL++LxmsxduIXSiup6SCoiIiIiIiIiIiIiIiKNRZcuXfj888/rbP/888+Ji/N9dnXN9CAt0rqtB/F4Tc7tr1kepK7cQieL03PIKywlMjSQ7APHaR0eyMzLB+AItOE9dpCKj2ZDdSWOC/4HW/se/o7cLMV3jOC3o7vz+ie7+NeSbfxh8gAsFi1FIyIiIiIiIiIiIiIi0hxMmzaN++67j7y8PIYMGQLAN998w7vvvssTTzzhcx01PUiLY5omazIOEBJkY1BiW3/HkUYmt9DJY/M34PGamCYcPl4JwFUjE4kMC8TrPET5h3/HrCwl6PzfYYvt5+fEzdt5Azuw76CT1ZsPkLYqm8m/qbuuk4iIiIiIiIiIiIiIiDQ9Y8eOJSIign/9618sXboUgO7du/Pcc8+RkpLicx01PUiLs+dACQXF5Zw/uBMBNq3wIrUtTs+paXj4ngF8tb2QpNgAyj/4O2bZUYJ+cwsBXQf7LWdLYRgG14zuzv7DpXz8dS5xMWEM7R3j71giIiIiIiIiIiIiIiJSD84991zOPffcXzzmgw8+YOTIkQQHB//kft3xlRZnTUYBgJa2kJ+UV1haq+EBwAQOFxZR8dFsTOchAodfT0DiOX7J1xIF2Cz8bmI/IkLtvPLRDnILnf6OJCIiIiIiIiIiIiIiIg3kwQcfpLi4+Gf3q+lBWpQql4dvdhTSOSaMuJgwf8eRRig2JrTOtmCLi6n2T/AePUDg2Vdj73Vewwdr4VqFBnL7pH54TZP/S9tCSbnL35FERERERERERERERESkAZj//cTyf1HTg7QoG3YVUenyaJYH+VnDerer9XugUc1toSto4ynCnjwRe/8L/JRM4jtEcN0FPSguqeS5RVtxe7z+jiQiIiIiIiIiIiIiIiJ+pqYHaVHWZBRgs1o4u0+Mv6NII1Tt9rIkfS8G0COuFe0ibPwhei1dbIewD7gY+6Dx/o7Y4g3v34FRSZ3YmXuMf3+e5e84IiIiIiIiIiIiIiIi4mc2fwcQaSiFR8rZnXeMob1jCAkK8HccaYTeX7OH/EOlTDi3K+OHxVLx6f/hyc0joPdI7EMmYxiGvyMKcOWoBPIOlfLZhnw6x4SR0k8zt4iIiIiIiIiIiIiIiLRUmulBWoy1WwoAtLSF/KRduUf55OtcunUIZ+zZsVR+8QKe3M3YElMITLlWDQ+NiM1qYcalfWkdHshry3aRU1Di70giIiIiIiIiIiIiIiLiJ2p6kCbLbbpZVb6Sl489z6rylbhN988e6/WapG8poE14IL06RzZgSmkKyivdvPjBdgICLNwythfur97Cnf01ti6DCRpxI4ahobKxCQ+xc/ukfhgGPLNwC8fLXP6OJCIiIiIiIiIiIiIiIifB6/X6dNy0adOIiIj42f1a3kKaJLfp5p6imWS7MnHjxoaNRfY0ZkfPwWbUfVtvzSnmWKmL8SldsOiJ/XrjNt2kV6wm25VFvD2BFEfqT/79G7s3P91FcUkVUy7sQauc5bi2r8TaoRdBI2/DsFj9HU9+Rpd24Uy9sCcvfLCdue9v4d6rB2Gz+qdBpbl8FkRERERERERERERERBrK4MGDSUpKYvDgwSQnJzNgwAACAwPrHHfbbbf9Yh3dkZEm6cTNxUxcnHi624WLbFcm6RWrGRE8ss7xq747gIGWtqhPJ9t40lh9s6OQddsKGRDfhmHWbbi+WYylbVccY+7AsNn9HU9+xbC+7dhX6GT5+jze+iyTKRf0aPAMzeWzICIiIiIiIiIiIiIi0pBeffVVNm7cyPr163nttdcoLy+nT58+JCcnk5yczHnnnedTHc3ZLk1StisLN7WXs3DjJtuVVefYIyWVbM4qpl98G6IiHA0Vsdn7ceOJF2+txpOm4khJJfOX7SIsOICpvUpwrXsLS0Q7HBf+AcOu90pTMfk38fTuEskXm/bzxab9DX7+5vBZEBERERERERERERERaWgDBgzgxhtvZN68eXz99de8++67xMbG8sorrzB9+nSf6+gRVGmS4u0J2LDVzPQAYMNGvD2hzrFrMgrwmiYjBnZoyIjN3i81nvzUbBuNjdc0eenDHZRXubl/hA3LutcwQlrjGHsPFke4v+PJSbBaLEyb0JdHX1vPm5/uJqZ1ML06RzbY+Zv6Z0FERERERERERERERMQfKisr+e6779iwYQMbNmwgIyODTp06ceWVV5KcnOxzHTU9SJOU4khlkT2t1nTy8fZEUhyptY7zeL2s3nyAyLBA+se38VPa5ulkGk8ao8825LNj31Em9vTQfvvbGHYHjrF3YwnV+6QpCnUEcMdl/Xn89Y08+/4WZl2fTExkcIOcu6l/FkRERERERERERERERPwhOTmZsLAwLr30UqZOnUpSUhLh4Sf/cLKWt5AmyWbYmB09h7va3M/lYVdxV5v7mR09B5tRu48nI7uYo84qUgd0wGrR270+pThSibcnYseOBQt27D/ZeNIY5R8qZcEX2fSNLOe8owvBYsVx0Z1YW2k2kKasY9tQpk3oS3mVm6cXZFBe6f71F9WDpvxZEBERERERERERERER8ZdLLrmEsLAw0tLSeOedd/j3v//N5s2b8Xg8J1VHMz1Ik2UzbIwIHvmL08d/sekAFsMgdYBuZte37xtP0itWk+3KIt6eQIojtU7jSWOSW+hk0ZoctuwpphUlTLV/Bp7qEw0P0d38HU/qQf/4Nlz5mwTeWZnFc4u3MnNy/zPe8NQUPwsiIiIiIiIiIiIiIiL+9sQTTwBQVFRUs8TFgw8+SF5eHgMGDOCVV17xqY7uyEizdfhYBVv3FDMwMYrIsEB/x2mWfGk8aSxyC508Nn8Dbo9JuFHOjPBPsbrKKB1yI2Ede/s7ntSj0WfFsv9wGWsyCnh3ZRa/Pb/7GT9nU/osiIiIiIiIiIiIiIiINGaVlZVkZ2f7fLyaHqRRcZvuentaetXmA5jAeYM61m9IaZIWp+fg8Zg4jCqmh31GlLWUd8qGUZXTitsH+Tud1CfDMLjugh4UHq3gsw35dIgK4byBGgdEREREREREREREREQak/vvv5+NGzeSn59PXFwcgwcP5vrrryc5OZm4uDif66jpQRoNt+nmnqKZZLsycePGho1F9jRmR8856cYHt8fLmowCoiKC+P/s3Xl8VOXd///XmZlMMpOEBAhhD5Es7JCwKUQCBhEpFCqKe9VyaxVroS3aW+/S269bf7ZKLVRLay2Khd4VRVksICoKiKDsiBCzsCRsgbAmmSSTM3N+f6DRCGgSAifL+/l48DCe68zJezIzV9Drcz5Xj8taXKTE0pDkFRTjwuSnEStp5zrJYl9f1pUnEVNQZHc0uQhcTgc/u64nT766kXkrsmjd3Eu3Ts3tjiUiIiIiIiIiIiIiIiJfioiIYOrUqfTv35+YmJhaX+fibnQuUgNnOjxk48dPkCB+/OT6s1lburrG19qaXcjpEj9DU9rhMIyLkFYaGidB7oxYTeeQo7xf2p33y3piGBDXOtLuaHKRRHrdTL6+NyEuB3956zMKTvjsjiQiIiIiIiIiIiIiIiJfmjZtGtdee+0FFTyAih6kHsn152BiVjlmYpLrz6nxtT7cegCnw+DKXm3rKp40YJl7j3NVxYf0cu/n0/LOLC7th2GA02EwNi3e7nhyEbVvFcF943riKzeZ+cZ2fGXm9z9IRERERERERERERERELrpgMMhLL73ENddcQ8+ePcnPzwdg1qxZLFq0qNrXUdGD1BsJ7kRc39pxxYWLBHdija5TcMLHzr0nSE1uRVREaF1GlAaozG+Su3wug8Ny8LVIJrPdWGKiPKQmtWLaHf3V6aEJ6J3QkpuuSuTQMR9/XbSDQDBodyQREREREREREREREZEm74UXXuD1119n8uTJOJ3OyuPx8fHMmzev2tdxff8pIpdGmiedhe4F5PqzMTG/LHhIIs2TXqPrrNp6EIBhKe0uRkxpYDYsfoN0x2aKve1pM+5X/CwkzO5IYoMRAzpy8FgJq7cd4rWVOdx6dbLdkURERERERERERERERJq0hQsX8vTTTzNgwAB++9vfVh7v2rUru3fvrvZ1VPQg9YbLcPFM7AzWlq4m159DgjuRNE86LqP6b9MKM8hH2w/RurmHrp2aX8S00hDs+XQVvY+9wylHFG3H/xpDBQ9NlmEY3H5NFwqOl/Lexv20iwlnWEp7u2OJiIiIiIiIiIiIiIg0WYWFhbRp0+as4+Xl5QRr0Llb21tIveIyXAz1ZjAx+qcM9WbUqOABYNMXRygurWBoSnschnGRUkpD4MvfRdSWVymxQgkdNRWnN8ruSGIzl9PB/df1pFV0GPNWZLFr73G7I4mIiIiIiIiIiIiIiDRZvXv35t133z3r+Lx58+jXr1+1r6NOD9KofLj1IC6nQVqvsyuCpOkInDhA6TszCGKQ3+MnXN4hzu5IUk9Eet1MvqEPv/vnRp5/awf/8+N+tI8JtzuWiIiIiIiIiIiIiIhIk/Pwww8zceJEtm3bRkVFBS+88AI5OTnk5eUxb968al9HnR6k0ThQWEJW/kn6d4kl0uu2O47YJFh8nNNLnsEZKOc97xgGXDnQ7khSz7SPCedn1/XCXxHgT/O3caq43O5IIiIiIiIiIiIiIiIiTU6PHj1Yvnw5ycnJDB8+nGPHjjFo0CAWL15MUlJSta+jogdpNFZtPQDAsNT2NicRu1jlJZQsnY6r7CTzy4cw4ocjtc2JnFP3+BbceW1Xjp0u409vbKfcH7A7koiIiIiIiIiIiIiISJNjWRY9e/Zk6NChjBw5kk6dOvHRRx/xxhtvVPsa2t5CGgV/RYCPPztM25ZekjpE2R1HbBA0/fjemQknD7DI15fkYSOJifbYHUvqsSt7t6XwVCmL1+7lb4s/54HxvXA4Ln6RjGmZrC1dTa4/hwR3ImmedFyGfh2LiIiIiIiIiIiIiEjTsnTpUh555BEcDgfNmzevMmYYBjfccEO1rqNVFmkUNmQewVduMm7IZRi6s7/JsYJBji6aQeDQF3xY1o2C1ldyW592dseSBmDclZdx9GQZ6z4/zL/ey+K2EckXdQ4xLZOHjkwh15+NiYkLFwvdC3gmdoYKH0REREREREREREREpEl59tlnueeee5g0aRJOp7PW19H2FtIofLjlACEuB4N7trE7ilxilmVR9vE8SjLX81mgM8vNy7nrB91V/CLVYhgGP/lBV7rGRbNy8wFWbMi/qN/vTIeHbPz4CRLEj59cfzZrS1df1O8rIiIiIiIiIiIiIiJS35w8eZJx48ZdUMEDqOhBGoG8giJyD55mYLdYwsNC7I4jl9iRtW9R8fn75JhtePnUIEYMiKNlVJjdseQ8TMtklW8ls0++yCrfSkzLtDsSLqeDn43vRduWXuavzGFj5pGL9r1y/TmYVH3OJia5/pyL9j1FRERERERERERERETqozFjxvDBBx9c8HXUS1savFVbDwIwLKW9zUnkUju06QMidi7moBnN34uGEcDJ0vX76JvcirjWkXbHk2+pz1s7hIeF8MsJfXjyn5v4+9s7iY4MJbF9VJ1/nwR3Ii5c+PFXHnPhIsGdWOffS0REREREREREREREpD6LjIzkz3/+M2vXriU5ORmXq+p60ZQpU6p1nWp1evD7/fzmN78hIyOD1NRURo8ezZIlSyrHs7KyuPHGG+nTpw9jxoxh48aNVR6/fPlyhg8fTkpKChMnTqSgoKDK+HPPPcfll19O//79efTRR6moqKgcO336NFOmTCE1NZUhQ4Ywb968aj0xqT8u5p3dZX6TdZ8fpmNsBJ3bNauz60r9Zx7MxLNpLieDXv5aNJwyyw1AIGixeO1ee8PJOdWXrR3ONyfFRHuYckNvDANmvrGdIyd8df690zzpJLiTcOPGgQM3bhLcSaR50uv8e4mIiIiIiIiIiIiIiACkpqZW+dO9e3fuu+++yvGMjAx69+5dOT569Ogqj/++9f7a2r59O127dsXn87F161Y2btxY+WfTpk3Vvk61bq01TZPY2FjmzJlD+/bt2bx5M/feey8dOnSgZ8+eTJo0iZtuuom5c+eybNky7r//ft59912ioqLIzc3lkUce4YUXXqBv3778/ve/Z+rUqcydOxeA119/naVLl7JgwQK8Xi/33nsvs2bNYvLkyQA8/vjjBAIB1qxZQ15eHj/5yU9ISEjgiiuuqMWPTS61i31n9yc7CyjzBxiW0g7DMOogsTQEgRMHKF0xE9Ny8Lei4ZyywivHLOvMlidS/3zX1g5DvRmXJMP3zUmXtW3GfWN78uc3t/Pc/G385o7+RHjqbtscl+HimdgZXxaA5JDgTiTNk257pwsREREREREREREREWm8tmzZUvl1IBBg2LBhjBo1qso5zz//POnpZ9+k+X3r/Rfin//85wVfA6pZ9OD1equ0jujfvz99+/Zly5Yt+Hw+ysrKuPvuu3E4HIwbN445c+awYsUKJkyYwOLFi0lPT2fw4MHAmRYUaWlp5OXlERcXx4IFC7jrrrvo0KEDAJMmTeKxxx5j8uTJ+Hw+li9fzsKFC4mIiKB79+5cd911LFiw4KyihyNHjnD06NGzsjdr1oyOHTtiGKA18Utvre/rO7uBKnd2Dwu/8EXOD7ceJDTEyaCeber89dX7pX4KlpykdNkfwSxnWehoDgaiq4wbBnRqHanXrx76rq0dLtXrVZ05KTU5hluvTmbeu1n8ecF2HrolhRCXs84yhBguhoVn1MkcKHIuX32eNA+KiNSM5k8Rka9pThQRqR3NnyIitaP5s2Yu9Oe0Zs0afD4fI0eOrNb537feXx/U6tZSn8/Hjh07uOOOO8jOziY5ORmH4+udMrp27Up2djZwZuuL3r17V45FR0fTtm1bsrKyiIuLIzs7m65du1aOd+vWjcOHD1NUVER+fj4AiYmJVa79yiuvnJXptdde4/nnnz/r+JgxY5g+fTqRkR4iI2vzbOVCHCrPO+ed3YdcecTEXNgLkpV3gn2Hixh5RSc6tm9+Qdf6JqfzzHv5QvNJ3Qv6Szm4aCZW8TGajbyPLW8HgHIM40yHB4dx5vW784c99PrVQ+Na/oD/lC3kC98XVFgVhBghdPF2YVzcDwgx6q6bwnep7px087XdKC4PsGh1Lv98N4cHb+uHw6G/bUnD0rKl5kERkdrQ/Cki8jXNiSIitaP5U0SkdjR/1kxubi5lZWVnHW/VqhWxsbHnfdybb77J6NGjCQsLq3L84YcfJhgMkpSUxC9+8Qv69esHfP96f31Q46IHy7L4zW9+Q+/evbnyyivZvn07kd+qJmjWrBlFRWfay/t8vnOOl5SUnHP8q69LSkrw+XyEh4ef97HfdNNNN5GRcfZds82aNQOgqKiU8nLzrHG5uNqacee8s7utGUdh4YVtQfD6e18AMLh77AVf65sCgSBAnV5TLpwVDFD6zgzMw7sJ7fcjXs1szsmiA4we1Ino8ALPAAAgAElEQVTDx3zsLyyhQ0w4Y6+MJ9Lt0OtXT/1/Lf7IR2Gr2V2RQ+eQRK70pnPqWBlw9i/li6Emc9LYwXHsLzjNmq0H8Lod3JSRqG10pEEwjDP/cXDsWBGWZXcaEZGGQ/OniMjXNCeKiNSO5k8RkdrR/FkzoaEuIiM9PPjgg+zcufOs8QceeICf//zn53zs8ePHWblyJfPmzaty/A9/+AM9e/YEzhRF3HPPPSxZsoT27dt/73p/fVCjogfLsnj00Uc5dOgQs2fPxjAMwsPDKS4urnJeUVFRZbGC1+ut0fhXX4eHh+P1es/6YX3zsd8UGxv7nRUrloU+JDZI86Sz0L2AXH82JuaXreyTSPOkX9DrcaKonA27jtA1LpqOsZEX5bXV+6X+sCyL8jWvYuZtx5U8hN0t0/ng3a106RjNdemdcToMYmIiKSws0me9nnPiYqg3g6F8XaR2KV+vmsxJBgb3jOnO6ZKtvPNpPs28bkZd0enShRW5QJoPRURqR/OniMjXNCeKiNSO5k8RkdrR/Fk9X/2Mnn322fN2ejifJUuW0KlTJ/r06VPleP/+/Su/vvXWW1m6dCmrV6/mlltu+d71/vqg2kUPlmXx2GOPsXPnTl555RW8Xi8ASUlJvPTSSwSDwcotLnbt2sUtt9wCQHJyMpmZmZXXOXXqFIcOHSI5Obny8ZmZmfTt27fysW3atCEyMpL4+HjgTGuOhIQEADIzM0lKSrrApy2Xistw8UzsDNaWribXn0OCO5E0Tzouo1Y7q1RauXk/gaDFiAEd6yip1Gf+rf+hInMVzg49MQbdzisvb8btcnDXD7ri0J33UgM1nZPcIU4m39Cbp+dt5vUPc4nwhjCkd7tLnFpERERERERERERERKSqr9bPa+LNN99k/Pjx33ueYRhYX1ZXfN96f33gqO6Jjz/+ONu2beMf//gHERERlccHDhyI2+1m9uzZ+P1+lixZwv79+xkxYgQAY8eOZfXq1axbt46ysjJmzpxJSkpK5f4e48ePZ86cORw4cIDjx48za9Ysrr/+euBMF4iRI0cyY8YMiouLyczMrPYLIfWHyzhzZ/fE6J8y1JtxwQUP5RUBPtxygNhoD30SY+ooZc2Zlskq30pmn3yRVb6VmJa2T7kYKrI/xr/hDRwtO+K5+mcs/CiPIydLGZ/emdbNvXbHqxG9Z+qHms5J4WEh/OrGFFo2C2POsi/Yml14iZKKiIiIiIiIiIiIiIjUjc8//5ycnBzGjRtX5fjBgwfZuHEjfr8fv9/P/Pnz2bFjB1deeSXw/ev99UG1Vp8PHDjAv/71L9xuN8OGDas8fu+993Lfffcxa9Yspk2bxsyZM+nYsSMvvPAC0dHRwJkKk6eeeopp06ZRWFhIv379mD59euU1JkyYwIEDBxg/fjymaTJ69GgmTZpUOf7oo48ybdo0hgwZQnh4OJMnT2bQoEF19PSlIVr3+WFKykzGXXmZbXf5m5bJQ0emVGmRv9C9gGdiZ1xwUYd8zTy4i7JV/8AIb4Hn2l+x+6ifdzfkk9CuGVf3b1hdPvSeadiaR4Yy9eYUfvfPTcxatIOpN6WQ3DHa7lgiIiIiIiIiIiIiIiLV8uabbzJ06FBiYqreVO7z+XjiiSfIy8sjJCSEhIQE/vrXv1YWNXzfen99YFhW09gZ5fTpUsrLdVd1Q2dZFtNe+oSTxX6m/2wwYe66Xyye9tInADx59+XnPWeVbyXTjz2NH3/lMTduprZ8mKHejDrP1BQFjh/At/hJsMA77n8INmvH/3t5A0dPlvLoTwbSPubrfYIMA2JiIiksLKq3ez3pPdM47Dl0mj/83xYchsEjt/WlQ2zE9z9I5BJrCHOiiEh9pPlTRORrmhNFRGpH86eISO1o/qyZ0FAXzZp57I5Rr1R7ewuR+uDzPcc5dMxHep+2F6Xgobpy/TmYVC2iMTHJ9efYlKhxCfpOUrr8j2D68Vzzc5wtOrJ47V4OHfMxNu2yKgUPDYXeM43DZW2b8cD4XvgrAkyfv5XCk6V2RxIREREREREREREREWnSVPQgDcqKDfkYBgzv18HWHAnuRFzf2h3GhYsEd6JNiRoPq6Kc0uV/wio+Rlj6RFztu7PvcBHL1ucR1zqCay+vP/sD1YTeM41Hj/gW3PPD7pwu9jP9ta2cLvF//4NERERERERERERERETkolDRgzQYBwpL2LHnOP2SWxETZW/LljRPOgnuJNy4ceDAjZsEdxJpnnRbczV0VjBI2cq/Eizci7vvOEKS0zADQWYv3YVhwMQfdMPlbJjTlt4zjcvAbq257ZpkCk6U8tzr2yjV9kkiIiIiIiIiIiIiIiK2sG9/AJEaem9jPgDXDLD/Tn+X4eKZ2BmsLV1Nrj+HBHciaZ50XIY+Uhei/JPXMPdtwZU4CHe/HwGwbP0+8o8UM2ZwPHGtI21OWHt6zzQ+GX07cLrEz+K1e3nhrc+YckMfQlwNsyhHRERERERERERERESkodJqmzQIxaUVfLzjMJe1jSShfTO74wBnFrGHejMY6s2wO0qj4P/8PSo+ewdn2y6EDZ2IYRgcOFrMko/30i4mnB8Ojrc74gXTe6bxGXflZZz2VfDhlgP84z87+ekPe+BwGHbHEhERERERERERERERaTJU9CANwodbDlBhBhkxoCOGoQXFxsbM20r5x/MwotrgGfFz8gvLWPTRLj7bfQwzYPGDK+J0B73US4ZhcPuIZIp9fj7ddYQITwi3jUjWPCUiIiIiIiIiIiIiInKJqOhB6j0zEGTl5v00jwylf5dYu+NIHQsU7qP0vVkY7nC81/6S/FMWT766ETNgVZ7zyrJMOrSKaNDbW0jj5XAY3PPDHpSUbWPl5gN4Ql2MT++swgcREREREREREREREZFLQLdOS723IfMIJ4v9ZPRtj8upt2xjEiw+Tuny5yAYIGzkFBxRrVm0dg+BbxQ8AASCFovX7rUnpEg1hLgcPDC+FwntmvGfdft4++O9dkcSERERERERERERERFpErSCLPWaZVm8uyEft8vB0JT2dseROmT5Syl95zks30nCht2Nq00SAPkFxVjfPteCvIKiSx9SpAY8oS5+eWMf4lpH8NaaPSz/JM/uSCIiIiIiIiIiIiIiIo2eih6kXsvef4q9h4sY3KstEZ4Qu+NIHbGCAUrfn0XwWD7u/uMJSbyicszhOHtLAMNAW1tIg+ANC2HqTSm0bxXO/A9yWLl5v92RREREREREREREREREGjUVPUi99u7GfABG9O9gcxKpK5ZlUf7xPAL523ElD8Gd+sPKscx9JzhyohQ4U+jw1T+dDoOxafE2pBWpuUivmwdvSqF1Cy9zV2SxZttBuyOJiIiIiIiIiIiIiIg0Wip6kHrr6MlSNmcdpVfnlrRtGW53HKkjFTtWULFzJc523QgbcifGl9UNJWUVvPSfnbhDHEy+vhepSa2IiQojNakV0+7or04P0qBERYTy0M0pxESF8cqyTNbvPGx3JBERERERERERERERkUbJZXcAkfN5f9N+LAuuGdDR7ihSRyr2bqZ83b9xRLfDM+IBDOfXU9DcFVkcP13Ondd2ISWpFSlJrWxMKnLhWjQL46FbUnl63mZeWrKLEKeDfl1i7Y4lIiIiIiIiIiIiIiLSqKjTg9RLpeUma7YfpH1MON3jm9sdR+pAoHAvZSv/ihEWgefaX2KEft29Y93nh/lkZwGpSTGk92lnY0qRutUq2sNDt6QS6Q3hr4s+Z3tuod2RREREREREREREREREGhUVPUi99NFnhygtDzBiQMfK7Q+k4QqWnKB0+Z/ACuIZOQVHs6+7OBSeKmXuii+ICndz56iuer2l0WnTwsuDN6fgCXXx/Js72Ln3uN2RREREREREREREREREGg0VPUi9EwxavLcxnwhPCFd0b213HLlAlllO6TszsHwnCRv6XzhbJ1aOBYMWLy3ZSWl5gImju9HM67YxqcjF075VBFNvSsHtcjBzwXay8k/aHUlERERERERERERERKRRUNGD1Dvbcgo5erKMYantcYc47Y4jF8CygpR98HeChXtx9x1HSOKgKuPLPtlH1v5TDO/bgV6dW9qUUuTS6NQmkl/e1AfDMPjT69vYffC03ZFEREREREREREREREQaPBU9SL3z7sZ8nA6DjL7t7Y4iF8i/8S3MPRtxdR6Iu9+4KmN7D59m4Zo9tG3pZcJVCTYlFLm0EtpF8YsbehMMWkx/basKH0RERERERERERERERC6Qih6kXtlz6DSZeScZ2K010RGhdseRC1CRtRb/liU4WnUmbNjdGMbX0015RYAXF+8E4N6xPdTRQ5qULnHNmXJDbwKBIM/+ews5+0/ZHUlERERERERERERERKTBUtGD1CtL1u4FYNQVcfYGkQtiHs6mbPXLGOEt8IycjOFyVxmfvzKHw8d9jB/ambjWkTalFLFPt/gW/PLGPlgWTJ+/laz8k3ZHEhERERERERERERERaZBU9CD1Rl5BEVtzCunXpRUdWkXYHUdqKXj6KGUrZoLDiWfkFBzeaODM6/vnN7fzi5lr+GDLAeLbRDJygIpbpOnqEtecX93UBwP44/yt7Np3wu5IIiIiIiIiIiIiIiIiDY6KHqTeWPLxXgB+ODje1hxSe5a/lNJ3/oRVVown4z6cMZ2AMwUPT766ka1ZhZz2VQCw/2gx+48W2xlXxHZJHaKZelMKToeDP72+jR17jtkdSUREREREREREREREpEFR0YPUC/uPFrPpi6OkJMZou4MGygoGKH1/FsETBwi9fAKu+NTKsUVr9xAIWljfOD8QtFj85XYmIk1ZQvsoHrw5BbfLwcw3PmN7bqHdkURERERERERERERERBoMFT1IvfD2V10e0uJtzSG1V77+3wTytxPSZQghvUdVGdt7qAjLqnq+ZZ3pACEicFnbZjx0Syphbid/XvAZW7KP2h1JRERERERERERERESkQVDRg9ju0LESNuw6Qs/OLbisbTO740gt+HeupGLHuzjbdiH0yjsxDKNybNMXRzlVXH7WYwwDdfUQ+Ya41pH8+pZUvGEu/vLWDjZmHrE7koiIiIiIiIiIiIiISL2nogex3X/W7cMCxg6+zO4oUgvm/h2Ur52L0SwWz4ifYzhdAASDFm+u3s0Lb31GWKgLp8Pgq1oIwwCnw2CsOnuIVNEhNoJf39qXCE8If130OZ/uKrA7UrWZlskq30pmn3yRVb6VmJZpdyQREREREREREREREWkCXHYHkKbtyAkf6z8voFun5iR2iLI7jtRQ4ORBSt97AUJC8Vz7C4ywCAB8ZRW8uGQn23OP0alNJA9c14uSsgoWr91LXkERca0jGZsWr04PIufQPiac/76tL8/83xb+tvhzAgGLQT3b2B3rO5mWyUNHppDrz8bExIWLhe4FPBM7A5ehv2qIiIiIiIiIiIiIiMjFo5UIsdV/1u0jaFm6478BssqKKV3+J6goxzNqKs7odgAcOFrMn9/8jCMnShncsw13jOyCO8RJy6gwHhjfy+bUIg1DmxZe/vvWVP7wf1t46e2dmIEgQ/q0szvWea0tXU2uPxs/fgD8+Mn1Z7O2dDVDvRk2pxMRERERERERERERkcZM21uIbQpPlfLxjsMkd4iiS1xzu+NIDVgBk9J3/4x1+gihabfj6tADgI2ZR3jy1U0cO1XGbSOS+a/R3XCHOG1OK9IwxTb38vCtfWkZFcbLyzJZ/kme3ZHOK9efg0nV7SxMTHL9OTYlEhERERERERERERGRpkKdHsQ2y9bnEQha/DDtMrujSA1YlkXZmjkEDn1BWedhvJIZQ/6qj3E6DQqOl9LMG8IvJvRWIYtIHYiJ9vDI7f3442tbmf9BDqd9fiYMS8AwDLujVZHgTsSFq7LTA4ALFwnuRBtTiYiIiIiIiIiIiIhIU6BOD2KLE0XlrNl+kM7tmtE9XovjDUnF9mWYWWuoiO3OtC0d2ZpdSOGpMgqOlwJw95juKngQqUPNI0P579v6ktg+iuWf5PHy0kwCwaDdsapI86ST4E7CjRsHDty4SXAnkeZJtzuaiIiIiIiIiIiIiIg0cur0ILZYtn4fZsBibFp8vbtjWc6vYu8myj95HUfz9sw3r8IMFGF9Y9ww4MOtB+nZuaVtGUUaowhPCFNvTmHWwh189NkhiksruG9cj3qzfYzLcPFM7AzWlq4m159DgjuRNE86LuPcf80wLbPa54qIiIiIiIiIiIiIiHwXrTDIJXequJxV2w7SqU0kvbQ43mAECvdRtvJvGGERWFc9wLY5WVUKHgAsC/IKimzJJ9LYhYY4eWB8L15euot1nxfwx/nbmHx9b7xh9eNXuctwMdSbwVBvxneeZ1omDx2ZQq4/GxMTFy4WuhfwTOwMFT6IiIiIiIiIiIiIiEiNaXsLueTe+TSfCjPIDwfXvy4PpmVyvM029ictY5VvJaZl2h2pXgj6TlL6zgwIBsnvfie//Xcu5RWBs84zDIhrHWlDQpGmweV08F9jujOif0ey8k/y+39t5lRxud2xauRMh4ds/PgJEsSPn1x/NmtLV9sdTUREREREREREREREGiAVPcglddrnZ+WW/XRoFU5KUozdcar46u7jPT3nczh+FdOPPc1DR6Y0+cIHyyyn9J0ZWCXHWddsJM+8dxp/RZBxV8bjchp8VbdiGOB0GIxNi7c1r0hj5zAMbh6eyPVDO5N/pJjfzd3EkRM+u2NVW64/B5Oq86qJSa4/x6ZEIiIiIiIiIiIiIiLSkKmPtFxS727Ix18R5Idpl+GoZ10evrr72HKeWYz75t3H39euvbGyrCBlH75E8OgePjRTeGt3S3ontOTOa7vSPDKU1KRWLF67l7yCIuJaRzI2LV6dHkQuAcMwGD0onkivmznLM/nd3M386sY+DeLzl+BOxIULP/7KYy5cJLgTbUwlIiIiIiIiIiIiIiINlYoe5JIpLq3g/U37advSS78ureyOc5bvuvu4KRU95BUUsWjtHvILihnj3UZq+Qa2+DvxrpnK3WOSGdSjTeW2JHGtI3lgfC+bE4s0Xel92hEeFsLfFn/O7/+1mcnX96ZLXHO7Y32nNE86C90LyPVnY2J+WfCQRJon3e5oIiIiIiIiIiIiIiLSAKnoQS6Z9zbmU+YPMGZwfL3r8gC6+xjOFDw8+epGAkGLviG7SXV+Qp7Zkg3Nf8Dj41KIjgi1O6KIfEu/Lq341Y19mLlgO9Nf28Z943rQN7n+FZZ9xWW4eCZ2xpfddXJIcCeS5knHZeivJCIiIiIiIiIiIiIiUnMOuwNI0+ArM3l3435aN/cwsFus3XHOKc2TToI7CSPggqCBG3eTu/t40do9BIIWnZxHuDX8Y04GvbxUdBWhHo8KHkTqsa6dmvPft/bFG+rkhTc/451P87Asy+5Y5+UyXAz1ZjAx+qcM9Wao4EFERERERERERERERGpNRQ9ySby/eT+l5SajB8XjdNTPt91Xdx9ftuNG2uwdytSWD/NM7IwmtRiXX1BMc6OYuyM+JICDF4uu4pTlJa+g2O5oIvI9OrWJ5Dd39KdNSy+vrczhnyuyCASDdscSERERERERERERERG5qOrn6rM0Kr6yClZ8mkdMVBhX9Ghtd5zv5DJctDjchw7Zo5rk3cfR7iD3RKwk3Cjjn8VXciDQEsOAuNaRdkcTkWpoFe3hNz/uR/f45ny45QAzXt9Oablpd6wLYlomq3wrmX3yRVb5VmJaDfv5iIiIiIiIiIiIiIhI3WpaK7pii7fX7aOkzOTm4Um4nKqzqa82ZR4mo3QZ7dwnWeLry2cVcRgGOB0GY9Pi7Y4nItXkDQvhFxP6MHdFFqu3HeR3czcx5YbexER57I5WY6Zl8tCRKeT6szExceFioXtBk+vCIyIiIiIiIiIiIiIi56cVaLmoCk+W8t7GfOJaRzCoZxu748h57Np3gkPvzqGH+wCn2/TneMdhxESFkZrUiml39FenBxGb1LbLgcvp4M5ru3DjVYkcPFrCk69uYvfB0xc5bd1bW7qaXH82fvwECeLHT64/m7Wlq+2OJiIiIiIiIiIiIiIi9YRuk5SL6o1VuZgBi5uuSsRhGHbHkXPYd7iITxfNZ3zYLipaJtBu9H084NTUIGK3C+1yYBgG114eR6voMP6+ZCe//9dm7hnTnf5dYy9B+rqR68/BpGqhh4lJrj+Hod4Mm1KJiIiIiIiIiIiIiEh9ok4PctHkHjzFp7uO0CehJd3iW9gdR87h8HEfC99YyrjQ9ZielkSP/gWGCh5E6oW66nLQr0ss/31bX7yhLv6ycAfL1u/DsqyLlLpuJbgTcX2rPtOFiwR3ok2JRERERERERERERESkvlHRg1wUlmUxf2UODsNgwlVanKqPThSV88q/P+Rm1/sQEkqzMVNxhGkbC5H64ru6HNTUZW2bMe2O/nRoFc7rH+byyrJMzECwrqJeNGmedBLcSbhx48CBGzcJ7iTSPOl2RxMRERERERERERERkXpCRQ9yUWzOOkr2/lMMTWlHu5hwu+PItxSXVvCXf6/nRmMZHodJxDUP4Gzezu5YIvINdd3loGVUGI/c3o+enVuwZvshnpu/jZKyiu98jGmZrPKtZPbJF1nlW4lpmd95fl1zGS6eiZ3B1JYPc0PkzUxt+XC1t/cQEREREREREREREZEzHn74YXr27Elqamrln4MHD1aOZ2VlceONN9KnTx/GjBnDxo0bqzx++fLlDB8+nJSUFCZOnEhBQcGlfgrfSUUPUufMQJDXP8wlzO1k3JWX2R1HvqW8IsDzr29hlH8prZxFhKXdhqtDT7tjici3XIwuB55QF1Nu6M1Vqe3Zte8ET8zZyP6jxec817RMHjoyhenHnuaNon8z/djTPHRkii2FD0O9GUyM/ilDvRkqeBARERERERERERERqYW77rqLLVu2VP5p1+7MDdEVFRVMmjSJq6++mg0bNnDPPfdw//33c+rUKQByc3N55JFHeOKJJ1i/fj2dOnVi6tSpdj6Vs2jlQOrcB1sOcOREKePTO9Ms3G13HAHyCopYtHYP+QXF+CtMfsBqksIKCOkxHHeP4XbHE5Fz+KrLwdrS1eT6c0hwJ5LmSb/gRX+nw8Ht1yTTLiacf7+fzVOvbuInP+jKwG6tq5x35vtm48cPgB8/uf5s1pauZqg344IyiIiIiIiIiIiIiIjIhcnNzaWsrOys461atSI2Nrba1/n0008pKyvj7rvvxuFwMG7cOObMmcOKFSuYMGECixcvJj09ncGDBwMwZcoU0tLSyMvLIy4urs6ez4VQ0YPUqZKyChZ/tIfmkaGMGNDR7jjCmYKHJ1/dSCBoYVlwVdjnDPLmUBHbjYhBt9odT0S+w1ddDuq6yMAwDIb360DH2AhmLdzBXxd9zr7DRYwf2hmn40wTqFx/DiZVuzqYmOT6c1T0ICIiIiIiIiIiIiJiswcffJCdO3eedfyBBx7g5z//+VnH58+fz/z582nTpg133HEHN9xwAwDZ2dkkJyfjcHy9SUTXrl3Jzs4Gzmx90bt378qx6Oho2rZtS1ZWlooepHH6z8f7KCkzueXqJEJDnHbHEWDR2j2VBQ89Q/IZ69nEoUAUH5jDudeh10ikKUvuGM3/3jWAvyz8jGWf5LGvoIj7xvUkwhNCgjsRF67KTg8ALlwkuBNtTCwiIiIiIiIiIiIiIgDPPvvseTs9fNuPf/xjfv3rXxMVFcXGjRuZPHkykZGRjBw5kpKSEiIjI6uc36xZM4qKigDw+XznHC8pKanDZ3NhVPQgdeboyVLe25RPXOsIrujRxu448qX8gmIsC9o5j3NHxBp8Vih/L8rA+MZCpog0Xc0jQ/n1LX35v/ey+HDrQR57eQMPjO9FWut0FroXkOvPxsT8suAhiTRPut2RRURERERERERERESavISEhGqf26NHj8qvL7/8cm677TaWL1/OyJEjCQ8Pp7i4uMr5RUVFhIeHA+D1er9zvD5Q0YPUimmZZ+0zv2BVLmbA4qaMJByGYXdE+VJstIfy0yf4acRKnAT5a/FwjluRpLaO/P4Hi0iTEOJycMe1XYlv24y5K77gd3M3cee1XXimx4yz5nqXob86iIiIiIiIiIiIiIg0ZA6HA8uyAEhKSuKll14iGAxWbnGxa9cubrnlFgCSk5PJzMysfOypU6c4dOgQycnJlz74eWjlQmrMtEweOjKlyt2/b5z8gJJdV5GSGEO3Ts3tjihfKi03OX6yiLsjP6C508fc4jT2BFrjdBiMTYu3O56I1DPpfdrRvlU4f3lrBy+9vYu9h4q4MWMYQ70ZdkcTEREREREREREREZFaWrZsGUOGDMHr9bJ582bmzp3Lb3/7WwAGDhyI2+1m9uzZ3HHHHbzzzjvs37+fESNGADB27FgmTJjAunXrSE1NZebMmaSkpBAXF2fnU6pCRQ9SY2fu+s2u3Ofdb/k5+UkSbgNuGFb9NipycVmWxez/7GSkuZL40EK2ewayJ9CD1PhIxqbFE6dOD9KInKv7jDoS1E5Cuyj+964BzFq4g/c27SfvSDGTftSTqHC33dFERERERERERERERKQWvipyCAQCtGvXjilTpjB69GgAQkJCmDVrFtOmTWPmzJl07NiRF154gejoaODMNhpPPfUU06ZNo7CwkH79+jF9+nQ7n85ZtCIkNZbrz8HErPz3sPyuhBR2oHX3U7SLqT97tzR1S9fvo1X+e/T17MUZ34/BI+4jzXDYHUukzp2r+8xC9wKeiZ2hwodaigp38+DNKcz/IIf3Nu7n8Vc2cN+4HiR1iLY7moiIiIiIiIiIiIiI1NC8efO+c7xLly68/vrr5x0fNWoUo0aNqutYdUYroFJjCe5EXF/VywQcNNtyNUFXOemDo+wNJpV27DnG3nXvM8qzHaNlJzxX/RRDBQ/SSH2z+0yQIH785PqzWVu62u5oDZrL6eDWq5O5e0w3Skor+P28Lbz98SMBMZkAACAASURBVF6CQcvuaCIiIiIiIiIiIiIiIpW0Cio1luZJJ8GdhBs3EdkDcRW3wNMnm6tbptsdTYCjJ0tZuuQDbgn/mGBYFN5rf4EREmp3LJGL5tvdZwBMTHL9OTYlalwG92zLb+8aQNsYL2+u3s3017Zyqrjc7lgiIiIiIiIiIiIiIiKAih6kFlyGi2diZ/BA+MO02DEcbwRMH3qv2sjXA+UVAV5d8DG3u9/D6XQQMeqXOMKb2x1L5KKq0n3mSy5cJLgTbUrU+LSPCee3d/RnWGp7du07waOzP2XH7mN2xxIREREREREREREREVHRg9SOy3BxfEtHzHIntw7rhtetTgJ2syyLfy3dzpjyJUQ6yvAOvxdnq3i7Y4lcdN/sPuPAgRs3Ce4k0jzqPlOX3CFO7hjZhUk/6klFwOKP87fx+gc5mIGg3dFERERERERERERERKQJ0635Uit5BUW8t3E/ndpEckWPNnbHEeC9jXl0zV9AO/dJQgZcT8hl/e2OJHJJfNV9Zm3panL9OSS4E0nzpKv7zEUyoGss8W0i+dviz1n2SR5f5J/k3rE9aBXtsTuaiIiIiIiIiIiIiIg0QVoRkhoLBIO8vCwTgLuu7YrDMGxO1HTlFRSxaO0edh84zVXBj7nCsx+j8yBCU8bYHU3kknIZLoZ6MxjqzbA7SpPQKtrDw7f15a01u1m2Po//9/Kn3DWqGwO6xtodTUREREREREREREREmhhtbyE19u6G/ew7XMTIyzvSqU2k3XGarLyCIp58dSNbswrpbu7gKs9OdpuxHO8+AUOFKCJykbmcDiYMS+RXN/UhxOlg1sIdvLo8E39FwO5oIiIiIiIiIiIiIiLShKjoQWrkyAkfC9fsJjbaw7i0y+yO06QtWruHQNAiwXWYCd5PKAxE8I/iYSxef8DuaCLShPS8rCWPTRxI9/jmfLj1IE/M2UheQZHdsUREREREREREREREpIlQ0YNUm2VZzFn+BX4zyJ2juuIOcdodqUnLLyimlXGKiREf4rdc/L04g+JgmBYbReSSi4oI5Vc3pXD90M4cPu7jiTkbWbJ2D4Fg0O5oIiIiIiIiIiIiIiLSyLnsDiANx9rPDrNr3wnS+7SlW6fmdsdp8lqFmdzA+4QZFbxYnMHhQDSGAXGtteWINA6mZbK2dDW5/hwS3ImkedJxGfq1VV85DIPRg+LpeVlLXnp7J2+t2cOW7EL+a0x32seE2x1PREREREREREREREQaKa0eSbWcKvHz2spsosLdTLgq0e44Td7BghNcW7qEGFcxr5VcQWZFewwDnA6DsWnxdscTuWCmZfLQkSnk+rMxMXHhYqF7Ac/EzlDhQz3XqU0k/3vXABZ9tIdln+zjsZc3MD69M9cM6IjDYdgdT0REREREREREREREGhltbyHV8q93sygpM7ltRDLhYSF2x2nSysorOLjoz8S7jlLQNp2yTmnERIWRmtSKaXf0V6cHaRTOdHjIxo+fIEH8+Mn1Z7O2dLXd0aQaQlwObhiWwP/c3o+WUWHM/yCHp/+1mYITPrujiYiIiIiIiIiIiIhII6PbZeV7bck+yobMI6QmxdCvSyu74zRplmWx/Y0X6cZujkb1IGHMXTxgqHZJGp9cfw4mZpVjJia5/hyGejNsSiU1ldA+iv/3kwEsWJXLexv38+jsT5kwLJGr+rbHYajrg4iIiIiIiIiIiIiIXDitlsp38pWZzF2RhSfUye3XdMHQIpWtdq54k24lGyhwtaPT+CkYKniQRirBnYjrW3V5LlwkuLW9TkMTGuLk1quTeeiWVCI9bua9m8X0f2/l2Kkyu6OJiIiIiIiIiIiIiEgjoBVT+U4LVuVyoqicCVcl0jwy1O44TdqBbevosPdtjllRtBn/IM4Qt92RRC6aNE86Ce4k3Lhx4MCNmwR3EmmedLujSS1169Scx/9rIOl92rFr3wl++49PWL3tIJZl2R1NREREREREREREREQaMG1vIeeVlX+SD7YcILljNOl92tkdp0krPpBD2Pp/UGq5cVw9mYjoFnZHErmoXIaLZ2JnsLZ0Nbn+HBLciaR50nEZ+rXVkHlCXdw1qit9k1vxyrJdvLIsk3U7DnPHtV1o2zLc7ngiIiIiIiIiIiIiItIAafVIzqnCDDBneSYup4O7RnXV3us2MosKKVn6HCEEye8+kQGJCXZHErkkXIaLod4Mhnoz7I7SIJiW2WCKRHontOTJuy/njVW7+XDLAR6d/Sk/uKITowd1IsTltDueiIiIiIiIiIiIiIg0INXa3mLu3LmMHz+enj178stf/rLKWFZWFjfeeCN9+vRhzJgxbNy4scr48uXLGT58OCkpKUycOJGCgoIq48899xyXX345/fv359FHH6WioqJy7PTp00yZMoXU1FSGDBnCvHnzavs8pYaWfLyPQ8d8jLsynjYtvHbHabIsv4/Ct/6A1yrh0+Zj6D9kkN2RRKQeMi2Th45MYfqxp3mj6N9MP/Y0Dx2ZgmmZdkc7L29YCHeM7ML/3N6P1i28LF67l//9x6fs2nvc7mgiIiIiIiIiIiIiItKAVKvoITY2lvvvv58bb7yxyvGKigomTZrE1VdfzYYNG7jnnnu4//77OXXqFAC5ubk88sgjPPHEE6xfv55OnToxderUyse//vrrLF26lAULFrBixQp27tzJrFmzKscff/xxAoEAa9as4W9/+xszZ85k/fr1dfG85TvsP1LMsvX76NAqgpED4+yO02RZQZNjS/5EeNkRPjCu4KofjcNQxw0ROYczHR6y8eMnSBA/fnL92awtXX3O803LZJVvJbNPvsgq30pbiyMSO0Tx6F0DuGFYAieKynnm31t56e2dnPb5bcskIiIiIiIiIiIiIiINR7X6Xl9zzTUA7Nq1ixMnTlQe//TTTykrK+Puu+/G4XAwbtw45syZw4oVK5gwYQKLFy8mPT2dwYMHAzBlyhTS0tLIy8sjLi6OBQsWcNddd9GhQwcAJk2axGOPPcbkyZPx+XwsX76chQsXEhERQffu3bnuuutYsGABV1xxxVkZjxw5wtGjR8863qxZMzp27IhhgNaLv18waPHKskyClsXE0V0JcVWrLqZRsuv9kldQxKI1u+l99D+kOrL42N+FATffTlioWr6fz1evlT7j0lTtrsjBpGrhgonJ7oochhlVtwf5qitErj8bExMXLha6F/Bs6xm2bYcR4nIwelAnBnaL5Z/vfMHHOw6zLaeQGzMSGdK7rQq+akh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      "text/plain": [
       "<Figure size 2560x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbr_bavaria.fit(first_date=pd.to_datetime('2020-03-09'), new_confirmed_threshold=bavaria_new_confirmed_threshold)\n",
    "fig = plt.figure(figsize=(32,8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1,1,1)\n",
    "cbr_bavaria.plot_with_fits(ax=ax, restriction_start_date=datetime.datetime(2020, 3, 22))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/local/cs/local/install/anaconda3-2020.02-Linux-x86_64/envs/py36ds/lib/python3.6/site-packages/statsmodels/tsa/base/tsa_model.py:218: ValueWarning:A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n"
     ]
    },
    {
     "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>fit_R</th>\n",
       "      <th>gp_R</th>\n",
       "      <th>kf_R</th>\n",
       "      <th>ll_R</th>\n",
       "      <th>mean_R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>report_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.294</td>\n",
       "      <td>1.267</td>\n",
       "      <td>1.281</td>\n",
       "      <td>1.192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             fit_R   gp_R   kf_R   ll_R  mean_R\n",
       "report_date                                    \n",
       "2020-07-29     1.0  1.294  1.267  1.281   1.192"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbr_bavaria.calculate_R_estimates()\n",
    "cbr_bavaria.R().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f859bc10c88>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2560x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(32,8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1,1,1)\n",
    "cbr_bavaria.plot_R(ax=ax) # , plot_start_date='2020-03-10'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-29</th>\n",
       "      <td>20832</td>\n",
       "      <td>18528</td>\n",
       "      <td>698</td>\n",
       "      <td>180</td>\n",
       "      <td>149</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-30</th>\n",
       "      <td>20935</td>\n",
       "      <td>18628</td>\n",
       "      <td>698</td>\n",
       "      <td>103</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>21110</td>\n",
       "      <td>18758</td>\n",
       "      <td>698</td>\n",
       "      <td>175</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-01</th>\n",
       "      <td>21192</td>\n",
       "      <td>18911</td>\n",
       "      <td>698</td>\n",
       "      <td>82</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-02</th>\n",
       "      <td>21284</td>\n",
       "      <td>18984</td>\n",
       "      <td>698</td>\n",
       "      <td>92</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "index                                                                           \n",
       "2020-07-29      20832      18528    698            180            149         10\n",
       "2020-07-30      20935      18628    698            103            100          0\n",
       "2020-07-31      21110      18758    698            175            130          0\n",
       "2020-08-01      21192      18911    698             82            153          0\n",
       "2020-08-02      21284      18984    698             92             73          0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = covid19.get_austria_df()\n",
    "cbr_austria = covid19.CasesByRegion('Austria', df=df)\n",
    "cbr_austria.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "austria_new_confirmed_threshold = 100.0 * prozent_österreich // 1 + 1\n",
    "austria_new_confirmed_threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| prediction date | predicted day for reaching 100 threshold | predicted max | growth rate | steady state rate  |\n",
    "| :--- | :--- | ---: | ---: | ---: |\n",
    "| 2020-04-02 | 2020-04-23 | 13860 | 0.042 | - |\n",
    "| 2020-04-03 | 2020-04-23 | 13864 | 0.036 | - |\n",
    "| 2020-04-04 | 2020-04-23 | 14082 | 0.030 | - |\n",
    "| 2020-04-05 | 2020-04-22 | 13978 | 0.025 | - |\n",
    "| 2020-04-07 | 2020-04-22 | 14033 | 0.017 | - |\n",
    "| 2020-04-08 | 2020-04-23 | 14121 | 0.014 | - |\n",
    "| 2020-04-09 | 2020-04-23 | 14229 | 0.012 | - |\n",
    "| 2020-04-10 | 2020-04-23 | 14350 | 0.010 | - |\n",
    "| 2020-04-11 | 2020-04-24 | 14488 | 0.009 | - |\n",
    "| 2020-04-12 | - | - | 0.012 | 106 |\n",
    "| 2020-04-14 | - | - | 0.011 | 126 |\n",
    "| 2020-04-15 | - | - | 0.010 | 114 |\n",
    "| 2020-04-16 | - | - | 0.009 | 108 |\n",
    "| 2020-04-19 | - | - | 0.007 |  95 |\n",
    "| 2020-04-21 | - | - | 0.006 |  83 |\n",
    "| 2020-04-22 | - | - | 0.006 |  77 |\n",
    "| 2020-04-23 | - | - | 0.006 |  73 |\n",
    "| 2020-04-24 | - | - | 0.005 |  69 |\n",
    "| 2020-04-26 | 2020-05-01 | 15208 | 0.002 |  - |\n",
    "| 2020-04-27 | 2020-05-01 | 15096 | 0.002 |  - |\n",
    "| 2020-04-28 | 2020-05-01 | 15129 | 0.002 |  - |\n",
    "| 2020-04-29 | - | - | 0.005 |  84 |\n",
    "| 2020-04-30 | - | - | 0.005 |  81 |\n",
    "| 2020-05-02 | - | - | 0.005 |  76 |\n",
    "| 2020-05-03 | - | - | 0.004 |  75 |\n",
    "| 2020-05-04 | - | - | 0.004 |  73 |\n",
    "| 2020-05-05 | - | - | 0.004 |  70 |\n",
    "| 2020-05-07 | - | - | 0.004 |  63 |\n",
    "| 2020-05-08 | - | - | 0.004 |  60 |\n",
    "| 2020-05-09 | - | - | 0.003 |  58 |\n",
    "| 2020-05-11 | - | - | 0.003 |  58 |\n",
    "| 2020-05-12 | - | - | 0.003 |  57 |\n",
    "| 2020-05-13 | - | - | 0.003 |  55 |\n",
    "| 2020-05-15 | - | - | 0.003 |  55 |\n",
    "| 2020-05-17 | - | - | 0.003 |  55 |\n",
    "| 2020-05-19 | - | - | 0.003 |  56 |\n",
    "| 2020-05-20 | - | - | 0.003 |  55 |\n",
    "| 2020-05-23 | - | - | 0.003 |  55 |\n",
    "| 2020-05-29 | - | - | 0.003 |  51 |\n",
    "| 2020-06-07 | - | - | 0.003 |  46 |\n",
    "| 2020-06-14 | - | - | 0.002 |  44 |\n",
    "| 2020-06-24 | - | - | 0.002 |  42 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/local/cs/workspaces/covid-19-data-analysis/covid19.py:1526: UserWarning:Exception in curve_fit: Optimal parameters not found: Number of calls to function has reached maxfev = 600./ None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigmoid+asymmetric+linear: seor: 60.83140122644205; growth-rate: 0.003, date:2020-09-10 00:00:00, projected value: 63.10625191231651\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2560x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbr_austria.fit(first_date=pd.to_datetime('2020-03-09'), new_confirmed_threshold=austria_new_confirmed_threshold)\n",
    "fig = plt.figure(figsize=(32,8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(1,1,1)\n",
    "cbr_austria.plot_with_fits(ax=ax, restriction_start_date=datetime.datetime(2020, 3, 15))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}