Mass shootings in USA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('mass.csv', encoding = \"ISO-8859-1\", parse_dates=[\"Date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>S#</th>\n",
       "      <th>Title</th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Incident Area</th>\n",
       "      <th>Open/Close Location</th>\n",
       "      <th>Target</th>\n",
       "      <th>Cause</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>...</th>\n",
       "      <th>Total victims</th>\n",
       "      <th>Policeman Killed</th>\n",
       "      <th>Age</th>\n",
       "      <th>Employeed (Y/N)</th>\n",
       "      <th>Employed at</th>\n",
       "      <th>Mental Health Issues</th>\n",
       "      <th>Race</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Texas church mass shooting</td>\n",
       "      <td>Sutherland Springs, TX</td>\n",
       "      <td>2017-11-05</td>\n",
       "      <td>Church</td>\n",
       "      <td>Close</td>\n",
       "      <td>random</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Devin Patrick Kelley, 26, an ex-air force offi...</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Walmart shooting in suburban Denver</td>\n",
       "      <td>Thornton, CO</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>Wal-Mart</td>\n",
       "      <td>Open</td>\n",
       "      <td>random</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Scott Allen Ostrem, 47, walked into a Walmart ...</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Edgewood businees park shooting</td>\n",
       "      <td>Edgewood, MD</td>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>Remodeling Store</td>\n",
       "      <td>Close</td>\n",
       "      <td>coworkers</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Radee Labeeb Prince, 37, fatally shot three pe...</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Advance Granite Store</td>\n",
       "      <td>No</td>\n",
       "      <td>Black</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Las Vegas Strip mass shooting</td>\n",
       "      <td>Las Vegas, NV</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>Las Vegas Strip Concert outside Mandala Bay</td>\n",
       "      <td>Open</td>\n",
       "      <td>random</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Stephen Craig Paddock, opened fire from the 32...</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>585</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unclear</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>36.181271</td>\n",
       "      <td>-115.134132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>San Francisco UPS shooting</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>2017-06-14</td>\n",
       "      <td>UPS facility</td>\n",
       "      <td>Close</td>\n",
       "      <td>coworkers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jimmy Lam, 38, fatally shot three coworkers an...</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Asian</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   S#                                Title                Location       Date  \\\n",
       "0   1           Texas church mass shooting  Sutherland Springs, TX 2017-11-05   \n",
       "1   2  Walmart shooting in suburban Denver            Thornton, CO 2017-11-01   \n",
       "2   3      Edgewood businees park shooting            Edgewood, MD 2017-10-18   \n",
       "3   4        Las Vegas Strip mass shooting           Las Vegas, NV 2017-10-01   \n",
       "4   5           San Francisco UPS shooting       San Francisco, CA 2017-06-14   \n",
       "\n",
       "                                 Incident Area Open/Close Location     Target  \\\n",
       "0                                       Church               Close     random   \n",
       "1                                     Wal-Mart                Open     random   \n",
       "2                             Remodeling Store               Close  coworkers   \n",
       "3  Las Vegas Strip Concert outside Mandala Bay                Open     random   \n",
       "4                                 UPS facility               Close  coworkers   \n",
       "\n",
       "     Cause                                            Summary  Fatalities  \\\n",
       "0  unknown  Devin Patrick Kelley, 26, an ex-air force offi...          26   \n",
       "1  unknown  Scott Allen Ostrem, 47, walked into a Walmart ...           3   \n",
       "2  unknown  Radee Labeeb Prince, 37, fatally shot three pe...           3   \n",
       "3  unknown  Stephen Craig Paddock, opened fire from the 32...          59   \n",
       "4      NaN  Jimmy Lam, 38, fatally shot three coworkers an...           3   \n",
       "\n",
       "      ...      Total victims  Policeman Killed  Age Employeed (Y/N)  \\\n",
       "0     ...                 46               0.0   26             NaN   \n",
       "1     ...                  3               0.0   47             NaN   \n",
       "2     ...                  6               0.0   37             NaN   \n",
       "3     ...                585               1.0   64             NaN   \n",
       "4     ...                  5               0.0   38             1.0   \n",
       "\n",
       "             Employed at Mental Health Issues   Race Gender   Latitude  \\\n",
       "0                    NaN                   No  White      M        NaN   \n",
       "1                    NaN                   No  White      M        NaN   \n",
       "2  Advance Granite Store                   No  Black      M        NaN   \n",
       "3                    NaN              Unclear  White      M  36.181271   \n",
       "4                    NaN                  Yes  Asian      M        NaN   \n",
       "\n",
       "    Longitude  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3 -115.134132  \n",
       "4         NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df.loc[15, 'Location'] = 'Forestville, Maryland'\n",
    "df.loc[16, 'Location'] = 'Halifax County, Virginia'\n",
    "df.loc[18, 'Location'] = 'Chicago, Chicago'\n",
    "df.loc[19, 'Location'] = 'Texas, Texas'\n",
    "df.loc[20, 'Location'] = 'Alabama, Alabama'\n",
    "df.loc[21, 'Location'] = 'Long Beach, California'\n",
    "df.loc[22, 'Location'] = 'Albuquerque, New Mexico'\n",
    "df.loc[23, 'Location'] = 'Memphis, Tennessee '\n",
    "df.loc[24, 'Location'] = 'Chicago, Chicago'\n",
    "df.loc[25, 'Location'] = 'Albuquerque, New Mexico'\n",
    "df.loc[29, 'Location'] = 'Greenhill, Alabama'\n",
    "df.loc[34, 'Location'] = 'Atlanta, Georgia'\n",
    "df.loc[35, 'Location'] = 'Florida, Florida'\n",
    "df.loc[36, 'Location'] = 'Elberton, Georgia'\n",
    "df.loc[37, 'Location'] = 'Trenton, New Jersey'\n",
    "df.loc[38, 'Location'] = 'Detroit, Michigan'\n",
    "df.loc[40, 'Location'] = 'Wilkinsburg, Pennsylvania'\n",
    "df.loc[42, 'Location'] = 'Lafayette, Louisiana'\n",
    "df.loc[43, 'Location'] = 'Kansas, Missouri'\n",
    "df.loc[45, 'Location'] = 'Roswell, Georgia'\n",
    "df.loc[46, 'Location'] = 'Wichita, Kansas'\n",
    "df.loc[47, 'Location'] = 'Detroit, Michigan'\n",
    "df.loc[48, 'Location'] = 'Riverside, California'\n",
    "df.loc[53, 'Location'] = 'Belfair, Washington'\n",
    "df.loc[55, 'Location'] = 'Missouri, Missouri'\n",
    "df.loc[56, 'Location'] = 'Houston, Texas'\n",
    "df.loc[59, 'Location'] = 'Florida, Florida'\n",
    "df.loc[60, 'Location'] = 'Kalamazoo, Michigan'\n",
    "df.loc[61, 'Location'] = 'Vallejo, California'\n",
    "df.loc[62, 'Location'] = 'Michigan, Michigan'\n",
    "df.loc[65, 'Location'] = 'Tampa, Florida'\n",
    "df.loc[66, 'Location'] = 'Los Angeles, California'\n",
    "df.loc[67, 'Location'] = 'Texas, Texas'\n",
    "df.loc[68, 'Location'] = 'New Orleans, Louisiana'\n",
    "df.loc[69, 'Location'] = 'Glendale, Arizona'\n",
    "df.loc[70, 'Location'] = 'Caroline County, Virginia'\n",
    "df.loc[72, 'Location'] = 'Perris, California'\n",
    "df.loc[73, 'Location'] = 'Florida, Florida'\n",
    "df.loc[74, 'Location'] = 'Los Angeles, California'\n",
    "df.loc[75, 'Location'] = 'Gloucester County, Virginia'\n",
    "df.loc[76, 'Location'] = 'Wilmington, Delaware'\n",
    "df.loc[77, 'Location'] = 'Memphis, Tennessee'\n",
    "df.loc[78, 'Location'] = 'Lakeland, Florida'\n",
    "df.loc[163, 'Location'] = 'Washington, D.C.'\n",
    "\n",
    "df = df.drop([17, 64])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['State'] = df.apply(lambda row: row['Location'].split(', ')[1], axis=1)\n",
    "df['City'] = df.apply(lambda row: row['Location'].split(', ')[0], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'TX': 'Texas', \n",
    "     'CA': 'California', \n",
    "     'CO': 'Colorado', \n",
    "     'MD': 'Maryland', \n",
    "     'NV': 'Nevada', \n",
    "     'PA': 'Pennsylvania',\n",
    "     'WA': 'Washington',\n",
    "     'LA': 'Louisiana'\n",
    "    }\n",
    "df['State'].update(df['State'].map(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa42955c88>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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vAPZt2nZKMivJecBtTdv85vuTSW5qPr9LclLT/i9J5jWfw5q2yUl+kuSEJLcmuTjJas21dyW5PsnNSc5Ksvryv3tJkiRJ6j49ldQCbwYurKqfAfcn2bJp3wJ4f1W9vL1zVR1eVdOAnYAHgC83Yw4G/gbYGnhXklc1Q6YAx1bVVOAhYK+m/TtVtVVVbQ78hFZS/RxJZiSZnWT2A/OfHKFbliRJkqTu1WtJ7X7AGc3xGTy7Bfm6qvrFYAOSBPgG8PmqmkOr0nt2VT1aVfOB7wA7NN1/UVU3NcdzgMnN8SZNNXgusD8wdbC1qmpmVfVVVd8641ZZ4puUJEmSpF7RM3/SJ8k6wC7ApkkKWBEo4HvAo4sYegTw26o6aRjLPNF2vABYrTk+Gdi9qm5OchCtyq8kSZIkaSn1UqV2OnBKVW1YVZOr6iXAL3i2yvocSd4E7Aoc2tY8C9g9yepJ1gD2aNoWZU3gD0lWplWplSRJkiSNgF5KavcDzh7QdhaDvAW5zb8A6wPXNS+L+mRV3UCr8nodcC1wYlXdOMTa/970vRK4fQlilyRJkiQNIlXV6Rg0iE03WKvO/dBOnQ5D0ii20fvO6XQIkiRJy0ySOVXVN1S/nnmmdqx53gte5j9YJUmSJGkIvbT9WJIkSZLUZUxqJUmSJEljltuPR6nH7v0/5h23W6fDkDQCNvmn8zodgiRJUteyUitJkiRJGrOWeVKbpJIc1Xb+wSRHLOYcOyXZtu385CTThzHuhUnOSPLzJHOSXJDk5Yt1Awuf+5dJJjTHV43EnJIkSZKkxbM8KrVPAHv2J4CLK8lKwE7AtkN0HTgutP4u7WVV9VdVtSXwr8Ck4Y5PMqzfp6oWKzZJkiRJ0shYHknt08BM4AMDLySZnOTSJLckuSTJBk37yUmOT3It8C3gEOADSW5KskMzfMckVyW5cyFV252Bp6rq+P6Gqrq5qmYlGdesd0OSuUne3BbPT5N8HZgHvCTJfk2feUk+M9gNJpnffO+U5LIk305ye5JTm+SaJIcnub6ZZ2Z/uyRJkiRpyS2vZ2qPBfZPMn5A+5eAr1XVZsCpwDFt114MbFtVewLHA0dX1bSqmtVcfxGwPfBG4NODrLkJMGch8TwO7FFVW9BKfo9qSzKnAMdV1VTgKeAzwC7ANGCrJLsPca+vAg4DNgY2ArZr2r9cVVtV1SbAak3ckiRJkqSlsFyS2qp6BPg6cOiAS9sApzXHp9BKUvudWVULFjHtOVX1TFXdxjC3FLcJcGSSW4AfAuu3zfGrqrqmOd6K1vble6vqaVqJ945DzH1dVf22qp4BbgImN+07J7k2yVxaSfLU5wSVzEgyO8nsB+c/uZi3JEmSJEm9Z3m+/fgLwDuANYbZ/9Ehrj/RdjzYVt5bgS0XMnZ/YCKwZVVNA+4GVh3mukNpj2sBsFKSVYHjgOlVtSlwQtt6f1ZVM6uqr6r61h63ylKGIUmSJEndb7kltVX1AK3nY9/R1nwVsG9zvD8wa+C4xh+BNRdzyUuB5yWZ0d+QZLPmmdzxwD1V9VSSnYENFzLHdcDfJpmQZEVgP+DyxYwDnk1g70syDhjyzc2SJEmSpKEt779TexTQ/hbk9wEHN9uADwDev5Bx3wX2GPCiqEWqqgL2AHZt/qTPrcB/AXfR2kbc12wFPhC4fSFz/AH4KPAj4GZgTlWdO5z1B8zzEK3q7DzgIuD6xZ1DkiRJkvRcaeV+Gm2mbrhWffMjQz2+K2ks2OSfzut0CJIkSWNOkjlV1TdUv+VdqZUkSZIkacSs1OkANLjVJr7M6o4kSZIkDcFKrSRJkiRpzDKplSRJkiSNWW4/HqUevff/uP5/3tTpMCSNgK3e/d1OhyBJktS1rNRKkiRJksasrkxqk+yepJK8ojmfnGTeEGN2SnL+8olQkiRJkjQSujKpBfYDftx8S5IkSZK6VNcltUnGAdsD7wD2HeT65CSzktzQfLZtu/z8JN9L8tMkxydZoRnzlSSzk9ya5BNtc/0yyX8luam5vkWSi5L8PMkh/fEkuaRZa26SNy/bX0CSJEmSekc3vijqzcCFVfWzJPcn2RK4v+36PcBrqurxJFOA04G+5tqrgY2BXwEXAnsC3wb+raoeSLIicEmSzarqlmbMr6tqWpKjgZOB7YBVgXnA8cDjwB5V9UiSCcA1Sc6rqlp2P4EkSZIk9Yauq9TS2nJ8RnN8Bs/dgrwycEKSucCZtJLYftdV1Z1VtYBWsrt90753khuAG4GpA8ac13zPBa6tqj9W1b3AE0nWAgIcmeQW4IfA+sCkwQJPMqOp+M5+aP6Ti33jkiRJktRruqpSm2QdYBdg0yQFrAgUcGxbtw8AdwOb00rqH2+7NrB6WkleCnwQ2KqqHkxyMq1KbL8nmu9n2o77z1cC9gcmAltW1VNJfjlg/LOLVc0EZgK8csO1rORKkiRJ0hC6rVI7HTilqjasqslV9RLgF8BL2vqMB/5QVc8AB9BKfPu9OslLm2dp96H1sqnnA48CDyeZBLxuMWMaD9zTJLQ7Axsu0Z1JkiRJkp6j25La/YCzB7SdBfxr2/lxwNuS3Ay8glbC2u964MvAT2glw2dX1c20th3fDpwGXLmYMZ0K9DXbnQ9s5pEkSZIkjYD4vqLR6ZUbrlVf/9gOnQ5D0gjY6t3f7XQIkiRJY06SOVXVN1S/rnqmtpusMfFl/kNYkiRJkobQbduPJUmSJEk9xKRWkiRJkjRmuf14lJp/3/8x64Q3djoMqafs8K7zOx2CJEmSFpOVWkmSJEnSmGVSO0xJXpzk3CR3JPl5ki8mWSXJQUm+vJAxFyRZa3nHKkmSJEm9wqR2GJIE+A5wTlVNAV4OjAM+tahxVfX6qnpoOYQoSZIkST3JpHZ4dgEer6qTAKpqAfAB4O3A6sB6SS5sqrif7R+U5JdJJjTH/5JkXvM5rAP3IEmSJEldxxdFDc9UYE57Q1U9kuTXtH7DacCrgCeAnyb5UlX9pr9vki2Bg4G/AQJcm+Tyqrpxed2AJEmSJHUjK7Uj45KqeriqHgduAzYccH174OyqerSq5tPayrzDwEmSzEgyO8nsh/745LKPWpIkSZLGOJPa4bkN2LK9IcnzgQ2Ap2lVaPstYAkr4FU1s6r6qqpvrTVXWdJYJUmSJKlnmNQOzyXA6kkOBEiyInAUcDLwp2GMnwXsnmT1JGsAezRtkiRJkqSlYFI7DFVVtBLRtyS5A/gZ8DjwsWGOv4FWAnwdcC1wos/TSpIkSdLS80VRw9S8+OlNg1w6ufn093tj2/HktuPPA59fZgFKkiRJUg+yUitJkiRJGrOs1I5S4ya8jB3edX6nw5AkSZKkUc1KrSRJkiRpzDKplSRJkiSNWW4/HqUeue8Ofnji6zsdhtRTdn3nBZ0OQZIkSYvJSq0kSZIkaczqqqQ2SSX5Rtv5SknuTXJ+c75bko8uYvzkJPMWcu2TSXYd+aglSZIkSUuq27YfPwpskmS1qnoMeA3wu/6LVXUecN6STFxVh49MiJIkSZKkkdJVldrGBcAbmuP9gNP7LyQ5KMmXm+NJSc5OcnPz2bbptmKSE5LcmuTiJKs1/U9OMr05fn2S25PMSXJMWyX41UmuTnJjkquS/HXbut9JcmGSO5J8dvn8FJIkSZLU3boxqT0D2DfJqsBmwLUL6XcMcHlVbQ5sAdzatE8Bjq2qqcBDwF7tg5p5/wd4XVVtCUxsu3w7sENVvQo4HDiy7do0YB9gU2CfJC9Z8luUJEmSJEH3bT+mqm5JMplWlXZRrzLdBTiwGbMAeDjJ2sAvquqmps8cYPKAca8A7qyqXzTnpwMzmuPxwNeSTAEKWLlt3CVV9TBAktuADYHftE+cZEb/XC9YZ9Vh3K0kSZIk9bZurNRC67nZ/6Zt6/FieKLteAGLl/j/B/CjqtoEeBPQnpkOOW9VzayqvqrqG7/mKouxrCRJkiT1pm5Nar8KfKKq5i6izyXAewCSrJhk/DDn/imwUVMNhtaW4n7jefbFVAcNN1hJkiRJ0pLpyqS2qn5bVccM0e39wM5J5tLaZrzxMOd+DPgn4MIkc4A/Ag83lz8L/FeSG+nCrd2SJEmSNNqkqjodw5iTZFxVzU8S4Fjgjqo6eiTXePnk8XXc/9tuJKeUNIRd37mox/AlSZK0PCWZU1V9Q/XrykrtcvCuJDfRemPyeFpvQ5YkSZIkLWdWakepvr6+mj17dqfDkCRJkqSOsFIrSZIkSep6vsxolHr4vjv43ldf1+kwpJ7yhrd/v9MhSJIkaTFZqZUkSZIkjVkmtZIkSZKkMasrktokleSotvMPJjlihOZeNcntSTZta/tQkmG/8TjJy5q3JUuSJEmSRlBXJLXAE8CeSSaM9MRV9ThwGHBcWtYHDgE+OpzxSXxuWZIkSZKWkW5Jap8GZgIfGHghycQkZyW5vvls17TPTbJWk6jen+TApv3rSV7TPkdVXQj8ATgQOBo4oqoeTLJCks8nmdfMN72ZY9cklyU5H5g7IJ6XJbkxyRbL4HeQJEmSpJ7SLUktwLHA/knGD2j/InB0VW0F7AWc2LRfCWwHTAXuBHZo2rcBrhpk/sOATwETq+qUpu0twCuBzYHXAEcneUFzrQ/4p6p6Zf8ESV4JnAkcWFU3DFwgyYwks5PMfnj+k8O/c0mSJEnqUV2zNbaqHknydeBQ4LG2S7sCGyfpP39+knHALGBH4FfAV4AZzdbiB6vq0UHm/32SS4Hz25q3B06vqgXAXUl+TCuZfRK4uqp+3dZ3EnA2sHtV3b6Qe5hJq+LMlMnja7F+AEmSJEnqQd1UqQX4AvAOYI22thWAratqWvNZv6rmA1fQqs7uAFwG3AtMp5XsLswzzWc4BibGDwG/A7Yd5nhJkiRJ0hC6KqmtqgeAb9FKbPtdDLyv/yTJtKbvb4AJwJSquhP4MfBBWsnucM0C9m2erZ1Eazvz7IX0fQJ4M/DOJHsvxhqSJEmSpIXoqqS2cRStZLXfoUBfkluS3EbrzcX9rgV+1hzPAtanldwO17eB24FbgB8C/1JV9yysc1MhfiPwkSRvWIx1JEmSJEmDSJWPbo5GUyaPry8c7k5laXl6w9u/3+kQJEmS1Egyp6r6hurXNS+K6jbjJ0zxH9iSJEmSNIRu3H4sSZIkSeoRJrWSJEmSpDHL7cej1EP33cF3TvqHTochjTp7Hnxhp0OQJEnSKGKlVpIkSZI0ZvV8UptkfvM9Ocm85ninJOc3x7sl+egSzr1ekm+PXLSSJEmSpHZuPx5CVZ0HnLeEY38PTB/ZiCRJkiRJ/Xq+UjuUJAcl+XJzfHKSY5JcleTOJNOb9iT5XJJ5SeYm2adpb6/+Tk1yXZKbktySZErn7kqSJEmSuoOV2sX3ImB74BW0KrjfBvYEpgGbAxOA65NcMWDcIcAXq+rUJKsAKy6/kCVJkiSpO1mpXXznVNUzVXUbMKlp2x44vaoWVNXdwOXAVgPGXQ18LMlHgA2r6rGBEyeZkWR2ktkPz39yWd6DJEmSJHUFk9rF90TbcYY7qKpOA3YDHgMuSLLLIH1mVlVfVfWNH7fK0kcqSZIkSV3OpHZkzAL2SbJikonAjsB17R2SbATcWVXHAOcCmy3/MCVJkiSpu/hM7cg4G9gGuBko4MNVdVeSyW199gYOSPIUcBdw5PIOUpIkSZK6Taqq0zFoEC+bPL4++/FtOh2GNOrsefCFnQ5BkiRJy0GSOVXVN1Q/tx9LkiRJksYstx+PUmtNmGJFSpIkSZKGYKVWkiRJkjRmmdRKkiRJksYstx+PUg/cfwenn/zaTochjTr7HXRRp0OQJEnSKGKlVpIkSZI0Zo3JpDbJ0UkOazu/KMmJbedHJflYkm93JkJIckGStTq1viRJkiT1gjGZ1AJXAtsCJFkBmABMbbu+LXBpVU3vQGwAVNXrq+qhTq0vSZIkSb1grCa1VwHbNMdTgXnAH5OsneR5wCuBB5LMA0gyNcl1SW5KckuSKU37gc35zUlOadomJ7m0ab8kyQZN+8lJjklyVZI7k0xv2l+U5Ipm7nlJdmjaf5lkQjPfT5KckOTWJBcnWW15/liSJEmS1K3GZFJbVb8Hnm4Szm2Bq4FraSW6fcBc4Mm2IYcAX6yqac313yaZCvw/YJe2jiSyAAAgAElEQVSq2hx4f9P3S8DXqmoz4FTgmLZ5XgRsD7wR+HTT9lbgombuzYGbBgl5CnBsVU0FHgL2Guy+ksxIMjvJ7D/+8cnBukiSJEmS2ozJpLZxFa2Etj+pvbrt/MoBfa8GPpbkI8CGVfUYsAtwZlXdB1BVDzR9twFOa45PoZXE9junqp6pqtuASU3b9cDBSY4ANq2qPw4S6y+qqj/ZnQNMHuyGqmpmVfVVVd+aa64y1P1LkiRJUs8by0lt/3O1m9LafnwNrYR0W1oJ759V1WnAbsBjwAVJdlnCNZ9oO04z9xXAjsDvgJOTHDjEuAX4p5QkSZIkaUSM5aT2KlrbgB+oqgVNpXUtWontXyS1STYC7qyqY4Bzgc2AS4G3JFm36bNO27z7Nsf7A7MWFUSSDYG7q+oE4ERgixG4N0mSJEnSMIzliuFcWm89Pm1A27iqui/JuLb2vYEDkjwF3AUcWVUPJPkUcHmSBcCNwEHA+4CTknwIuBc4eIg4dgI+1Mw9HxisUitJkiRJWgZSVZ2OQYPY6KXj61Mf37rTYUijzn4HXdTpECRJkrQcJJlTVX1D9RvLldquts66U/zHuyRJkiQNYSw/UytJkiRJ6nEmtZIkSZKkMcvtx6PUfff/jJO+9vedDkNaYge/7eJOhyBJkqQeYKVWkiRJkjRm9URSm+RHSV47oO2wJCcl+fYSzHdiko2H6HNIEv+8jyRJkiQtQ72y/fh0YF+g/XXC+wIfrqorBnZOslJVPb2wyarqnUMtWFXHL0mgkiRJkqTh64lKLfBt4A1JVgFIMhlYD/hNknlN20FJzktyKXBJkhWSHJfk9iQ/SHJBkulN38uS9DXH85N8KsnNSa5JMqlpPyLJB5vjdyW5vulzVpLVl/cPIEmSJEndqCeS2qp6ALgOeF3TtC/wLaAGdN0CmF5VfwvsCUwGNgYOALZZyPRrANdU1ebAFcC7BunznaraqunzE+AdS343kiRJkqR+PZHUNvq3INN8nz5Inx80CTDA9sCZVfVMVd0F/Ggh8z4JnN8cz6GVCA+0SZJZSeYC+wNTB5soyYwks5PMnv/Hp4a8IUmSJEnqdb2U1J4L/F2SLYDVq2rOIH0eXYJ5n6qq/orvAgZ/Tvlk4J+ralPgE8Cqg01UVTOrqq+q+satufIShCJJkiRJvaVnktqqmk+r2vpVBq/SDnQlsFfzbO0kYKelWH5N4A9JVqZVqZUkSZIkjYBeeftxv9OBs3l2G/KinAX8HXAb8BvgBuDhJVz334FrgXub7zWXcB5JkiRJUps8u3NWAyUZV1Xzk6xL60VT2zXP1y5zk1/6/Pr4EVsvj6WkZeLgt13c6RAkSZI0hiWZU1V9Q/XrtUrt4jo/yVrAKsB/LK+EVpIkSZI0PCa1i1BVO3Vq7QnrvtxKlyRJkiQNoWdeFCVJkiRJ6j4mtZIkSZKkMcvtx6PUvfffwf+c8tpOhyEtsXcfcFGnQ5AkSVIPsFIrSZIkSRqzOpbUJlmQ5KYk85KcmWT1TsXSxDN/Gcx5YpKNR3peSZIkSVJLJyu1j1XVtKraBHgSOKSDsSwTVfXOqrqt03FIkiRJUrcaLduPZwEvA0jyj0mua6q4/5NkxaZ9fpJPJbk5yTVJJjXtJyc5JslVSe5MMr1pf1GSK9qqwTskeXuSL/QvmuRdSY5uDyTJGUne0HZ+cpLpSSYnmZXkhuazbXN9pySXJfl2ktuTnJokzbXLkvQ1x19JMjvJrUk+sUx/TUmSJEnqER1PapOsBLwOmJvklcA+wHZVNQ1YAOzfdF0DuKaqNgeuAN7VNs2LgO2BNwKfbtreClzUzLM5cBPwLeBNSVZu+hwMfHVASN8E9m5iWwX4O+B7wD3Aa6pqiybGY9rGvAo4DNgY2AjYbpBb/beq6gM2A/42yWZD/zqSJEmSpEXp5NuPV0tyU3M8C/hfYAawJXB9U+xcjVYyCa0tyuc3x3OA17TNdU5VPQPc1l/BBa4HvtoksOdU1U0ASS4F3pjkJ8DKVTV3QFzfB76Y5HnAPwBXVNVjScYDX07Sn2y/vG3MdVX122b+m4DJwI8HzLt3khm0fvMX0UqAb2nv0FyfAbDOuqsO/qtJkiRJkv6sk0ntY00V9c+abbtfq6p/HaT/U1VVzfEC/jL2J9qnAaiqK5LsCLwBODnJ56vq68CJwMeA24GTBi5SVY8nuQx4La2K7BnNpQ8Ad9Oq+q4APL6Q9QfGRpKXAh8EtqqqB5OcDDwna62qmcBMgA1fOr4GXpckSZIk/aWObz8e4BJgepIXACRZJ8mGSzJRM+7uqjqBViK7BUBVXQu8hNb25NMXMvybtLYm7wBc2LSNB/7QVIQPAFZcjHCeDzwKPNxUkl+3eHcjSZIkSRpMJyu1z1FVtyX5f8DFSVYAngLeC/xqCabbCfhQkqeA+cCBbde+BUyrqgcXMvZi4BTg3Kp6smk7DjgryYG0Et1HhxtIVd2c5EZa1eHfAFcuzo1IkiRJkgaXZ3f09o4k5wNHV9UlnY5lYTZ86fj62Ce37nQY0hJ79wEXdToESZIkjWFJ5jQv212kUVWpXdaSrAVcB9w8mhNagInrTjEpkCRJkqQh9FRSW1UP8ZdvLZYkSZIkjWGj7UVRkiRJkiQNW09VaseSex64gy+e+tpOh6Ee8P793eYuSZKksctKrSRJkiRpzOq6pDbJC5OckeTnSeYkuSDJoM/RJpmcZN4yiuOIJB9cFnNLkiRJklq6KqlNEuBs4LKq+quq2hL4V2DSCM3vdm1JkiRJGkW6KqkFdgaeqqrj+xuq6mbgx0k+l2RekrlJ9hk4MMmqSU5qrt+YZOem/aAk5yW5FLgkybgklyS5oen75rY5/i3Jz5L8GPjrtvZpSa5JckuSs5OsvSx/BEmSJEnqFd1WedwEmDNI+57ANGBzYAJwfZIrBvR5L1BVtWmSVwAXt21b3gLYrKoeaKq1e1TVI0kmANckOa/ps2+zzkrADW2xfB14X1VdnuSTwMeBw0boniVJkiSpZ3VbpXZhtgdOr6oFVXU3cDmw1SB9vgFQVbcDv+LZv2n7g6p6oDkOcGSSW4AfAuvT2t68A3B2Vf2pqh4BzgNIMh5Yq6oub8Z/DdhxsCCTzEgyO8ns+Y88udQ3LUmSJEndrtuS2luBLZfBvI+2He8PTAS2rKppwN3AqiOxSFXNrKq+quob9/xVRmJKSZIkSepq3ZbUXgo8L8mM/oYkmwEPAfskWTHJRFqV0usGjJ1FK2Gl2Xa8AfDTQdYYD9xTVU81z91u2LRfAeyeZLUkawJvAqiqh4EHk+zQ9DuAVqVYkiRJkrSUuuqZ2qqqJHsAX0jyEeBx4Je0nl8dB9wMFPDhqroryeS24ccBX0kyF3gaOKiqnmi9UPkvnAp8t+k3G7i9WfuGJN9s1rgHuL5tzNuA45OsDtwJHDxiNy1JkiRJPSxV1ekYNIgNNhpf/99/bN3pMNQD3r//RZ0OQZIkSXqOJHOqqm+oft22/ViSJEmS1EO6avtxN3nBOlOsoEmSJEnSEKzUSpIkSZLGLJNaSZIkSdKY5fbjUequB+7gM2e8ttNhqAd8ZF+3uUuSJGnsslIrSZIkSRqzuiKpTTJ/BOZYL8m3h+hz1dKuI0mSJEkaOW4/blTV74HpQ/TZdjmFI0mSJEkahq6o1A4myeQklya5JcklSTZo2k9OMr2t3/y2/vOa46lJrktyUzN+yoC+45o5b0gyN8mb2+b4SZITktya5OIkqzXX3pXk+iQ3JzkryerL9xeRJEmSpO7TtUkt8CXga1W1GXAqcMxijD0E+GJVTQP6gN8OuP44sEdVbQHsDByVJM21KcCxVTUVeAjYq2n/TlVtVVWbAz8B3rEkNyVJkiRJelY3bz/eBtizOT4F+OxijL0a+LckL6aVjN4x4HqAI5PsCDwDrA9Maq79oqpuao7nAJOb402S/CewFjAOeM4rZ5PMAGYArDVh1cUIV5IkSZJ6UzdXahfmaZr7TrICsMrADlV1GrAb8BhwQZJdBnTZH5gIbNlUc+8G+rPQJ9r6LeDZ/zg4GfjnqtoU+ERb//Z1Z1ZVX1X1rbHmc8KSJEmSJA3QzUntVcC+zfH+wKzm+JfAls3xbsDKAwcm2Qi4s6qOAc4FNhvQZTxwT1U9lWRnYMNhxLMm8IckKzfxSJIkSZKWUrdsP149Sftzr58H3geclORDwL3Awc21E4Bzk9wMXAg8Osh8ewMHJHkKuAs4csD1U4HvJpkLzAZuH0aM/w5c28RyLa0kV5IkSZK0FFJVnY5Bg3jxRuPrfUdu3ekw1AM+su9zHu+WJEmSOi7JnKrqG6pft1Rqu84L15lisiFJkiRJQ+jmZ2olSZIkSV3OpFaSJEmSNGa5/XiU+v2Dd3D4t/6h02FoFPnk3hd2OgRJkiRp1LFSK0mSJEkas0xqJUmSJEljVs8ktUkqyVFt5x9McsQyXvOXSSYsyzUkSZIkqZf1TFILPAHsaZIpSZIkSd2jl5Lap4GZwAcGXkgyMclZSa5vPtslWaGptK7V1u+OJJOSvCnJtUluTPLDJJOa6+smuTjJrUlOBNI29pwkc5prM5bD/UqSJElS1+ulpBbgWGD/JOMHtH8ROLqqtgL2Ak6sqmeAc4E9AJL8DfCrqrob+DGwdVW9CjgD+HAzz8eBH1fVVOBsYIO2Nd5eVVsCfcChSdYdGFySGUlmJ5n9p0eeHKFbliRJkqTu1VN/0qeqHknydeBQ4LG2S7sCGyd/Lqw+P8k44JvA4cBJwL7NOcCLgW8meRGwCvCLpn1HYM9mre8lebBtjUOT7NEcvwSYAtw/IL6ZtKrJrPdX42vp7laSJEmSul+vVWoBvgC8A1ijrW0FWpXXac1n/aqaD1wNvCzJRGB34DtN/y8BX66qTYF3A6suasEkO9FKnLepqs2BG4caI0mSJEkaWs8ltVX1APAtWoltv4uB9/WfJJnW9C1a24g/D/ykqvorq+OB3zXHb2ub5wrgrc0crwPWbuv/YFX9KckrgK1H8p4kSZIkqVf1XFLbOApofwvyoUBfkluS3AYc0nbtm8A/8uzWY4AjgDOTzAHua2v/BLBjkltpbUP+ddN+IbBSkp8AnwauGcF7kSRJkqSe1TPP1FbVuLbju4HV287vA/ZZyLjZtL3FuGk7l9ZLpAb2vR/4+4WE8LrFj1qSJEmStCg9k9SONeutPYVP7n1hp8OQJEmSpFGtV7cfS5IkSZK6gEmtJEmSJGnMcvvxKPWbB+/g/Wf9Q6fD0Cjyxb3cji5JkiQNZKVWkiRJkjRmjYlKbZJ1gUua0xcCC4B7m/NXV9WTHQlMkiRJktRRYyKpbf5UzjSAJEcA86vqvzsalCRJkiSp48b89uMkb0tyXZKbkhyXZIUkKyV5KMmnk9yc5OokL2j6fyPJF5NcleTOJHu0zfXRZq5bkhzetK2Z5PvNPPOSTG/aP5fktqbvZ5q2SUm+k2R2M8/WTfu4JCc3bTcmedPy/6UkSZIkqfuM6aQ2ySbAHsC2VTWNVuV53+byeODyqtocuBp4e9vQFwDbAbsD/9XM9XpgA+BvaFWFt02yLfB64JdVtXlVbQL8IMmkpn1qVW3WPwdwDPDZquoD9gZObNoPBy6sqlcDuwBHJVl1ZH8NSZIkSeo9Y2L78SLsCmwFzE4CsBrwm+baY1X1/eZ4DrBD27hzqqqAW5Ks37T9PfA64MbmfBzwcuBa4NNJPg18t6quTPIn4BnghCTfA85vi+evm1gA1k6yWv/cST7atK9KK4H+WfvNJJkBzABYc4I5ryRJkiQNZawntQG+WlX//heNyUpA+8ujFvCX9/rEgDn6v/+zqv73OYskfbQqs59O8v2qOrJpew3wFuA9tBLXMMiLq9LKcnevqp8v6maqaiYwE2DSX42vRfWVJEmSJI3x7cfAD4G9k0yA1luSk2ywhHNdBLwjyRrNXC9OMqGp5M6vqlOAo4AtkqwJPL+qzgc+ALyqLZ7/n707D9OrLu8//v6wBgxJQKgLopEQpSRAgIHKaqC4ggISRUpV3CK1P6lYFGothroiUmVxaaSIVBRlR0CxKgEEJSSQDUSQxYqoBWVfwnb//njOlMfpJDNJZjIzz7xf1/Vcc859vud77vP8d8/9Pef5++4Jk0xrm/sDbfHu8ZIkSZKkVTCiO7VVtTjJscCPkqwBPAkcBty9EnNdmmRL4OfN8uGHgL8BtqLVoX2GVvf3MFrP656XZF1a/xj4UDPN3wNfSfJOWt/t5U3sWOCLSRY3438F7Ldydy1JkiRJ6pbWo6Uabp43aXy99XM7D3UaGkZOPPAHQ52CJEmStNokmd+8hHe5RvryY0mSJEnSKDailx93ss02nGxnTpIkSZL6YKdWkiRJkjRiWdRKkiRJkkYslx8PU7fffysHXfDaoU5DK+E7+7tsXJIkSVpd7NRKkiRJkkasEVvUJnlRkguT3JrktiQnJlknyaFJTull/KwkR/Yx5+lJZgxe1pIkSZKkgTQii9okAc4DLqiqycDLgLHAp4Y0MUmSJEnSajUii1pgL+Dxqvo6QFU9DRwBvAtYv3tQkn2S/CzJxu0nJ3lvkuuSLExybpL12w7vkeSaJLd3d23TcnySJUkWJzmoiU9PckXTMb49yWeTHJJkbjNuUjPuDUmuTXJDkh8led6gfjuSJEmSNEqM1KJ2CjC/PVBVDwL/TfPyqyQHAEcDr6+qe3ucf15V7VhV2wK/AN7dduwFwG7AvsBnm9ibgGnAtsDewPFJXtAc2xY4DPhL4G3Ay6pqJ+BU4APNmJ8Cr6iq7YCzgI/0dlNJZiaZl2Te0gef6O93IUmSJEmjVqe+/XgvoAt4dVPs9jQ1ySeBCbSWLV/WduyCqnoGuKmto7ob8O2mI/yHJFcAOwIPAtdV1e8AktwG/LA5ZzGwZ7P9IuA7TSG8DnBHb0lX1WxgNsBGW4yvFb9tSZIkSRpdRmqn9iZgh/ZAknHAi4GngNuADWg9a9ub04H/V1VbA8cCY9qOLW2fth+5tI9/pm3/GZ79p8HJwCnN9d7X43qSJEmSpJU0UovaHwPrJ3k7QJI1gRNoFauPAr8GDgTOSDKll/M3AH6XZG3gkH5c7yrgoCRrJtkE2AOYuwL5jgd+22y/YwXOkyRJkiQtx4gsaquqgAOANye5FbgFeBz4aNuYm2kVrGd3v7Cpzb8A1wJXAzf345LnA4uAhcBPgI9U1e9XIOVZTR7zgZ7P90qSJEmSVlJa9aGGm422GF+v+vzOQ52GVsJ39v/BUKcgSZIkjXhJ5ldVV1/jOvVFUSPe5hMmWxxJkiRJUh9G5PJjSZIkSZLAolaSJEmSNIK5/HiYuvX+O3ndhb4oeST6/n7fGOoUJEmSpFHDTq0kSZIkacTqmKI2ycNt269PckuSl6zEPBOSvH8V8jg0ySkre74kSZIkqf86pqjtluSvgZOA11XVr1diignAShe1kiRJkqTVp6OK2iR7AF8D9q2q25rYJknOTXJd89m1ic9KclqSOUluT3J4M81ngUlJFiQ5Psn0JBe3XeOUJIc22zsmuSbJwiRzk2zQI599kvwsyWZJ7kiydhMf174vSZIkSVo5nfSiqHWBC4DpVXVzW/xE4AtV9dMkLwYuA/6yObYlsCewAfDLJF8BjgamVtU0gCTTe7tYknWA7wAHVdV1ScYBj7UdPwD4EPD6qrovyRxgnybHtwLnVdWTA3LnkiRJkjRKdVJR+yRwDfBu4B/a4nsDWyXp3h+XZGyzfUlVLQWWJvkf4HkrcL2XA7+rqusAqupBgOY6ewFdwKu748CpwEdoFbXvBN7bc8IkM4GZAGM2ec4KpCJJkiRJo1MnLT9+BngLsFOSj7bF1wBeUVXTms+mVdX9UqmlbeOepvci/yn+/Hsa049cbqPV/X1Zd6CqrgYmNp3fNatqSc+Tqmp2VXVVVdc64/pzGUmSJEka3TqpqKWqHqW1xPeQJO9uwj8EPtA9Jsm0PqZ5iFZB2u3XtDq96yaZAPx1E/8l8IIkOzbzbpBkrbZzDgTOSDKlba4zgG8BX1/hm5MkSZIk/R8dVdQCVNWfgNcCH0vyRuBwoCvJoiQ3AYf1cf4fgauTLElyfFX9BvgusKT5e0Mz7gngIODkJAuB/6Kti9s813sIcHaSSU34TGBD4NsDdsOSJEmSNIqlqoY6h1EjyQxgv6p6W19jx2+xce1ywj6rISsNtO/v942hTkGSJEka8ZLMr6quvsZ10ouihrUkJwOvA14/1LlIkiRJUqewUztMdXV11bx584Y6DUmSJEkaEv3t1HbcM7WSJEmSpNHDolaSJEmSNGL5TO0wdev9d/H6C/5xqNPQSrh0/xOGOgVJkiRp1LBTK0mSJEkasUZlUZvk4bbt1ye5JclLhjInSZIkSdKKG9XLj5P8NXAS8Jqq+vVQ5yNJkiRJWjGjslMLkGQP4GvAvlV1WxN7Q5Jrk9yQ5EdJntfEZyU5LcmcJLcnObyJPyfJJUkWJlmS5KAmfkyS65rY7CRp4ocnuSnJoiRnDc2dS5IkSVLnGK1F7brABcD+VXVzW/ynwCuqajvgLOAjbce2BF4D7AR8PMnawGuBu6tq26qaCvygGXtKVe3YxNYD9m3iRwPbVdU2wGE9k0oyM8m8JPOeePDRAbtZSZIkSepUo7WofRK4Bnh3j/iLgMuSLAY+DExpO3ZJVS2tqnuB/wGeBywGXpXkuCS7V9UDzdg9m47vYmCvtnkWAWcm+VvgqZ5JVdXsquqqqq51xq0/QLcqSZIkSZ1rtBa1zwBvAXZK8tG2+Mm0uqxbA+8DxrQdW9q2/TSwVlXdAmxPq7j9ZLPseAzwZWBGM8/X2ubZB/hSc851SUb1M82SJEmStKpGa1FLVT1Kq8g8JEl3x3Y88Ntm+x19zZHkhcCjVfVN4HhaxWp3AXtvkrHAjGbsGsBmVXU5cFRzrbEDdDuSJEmSNCqN6k5hVf0pyWuBK5PcA8wCzk5yH/AT4KV9TLE1cHySZ2gtaf67qro/ydeAJcDvgeuasWsC30wyHghwUlXdP+A3JUmSJEmjSKpqqHNQL8Zv8fza9fOHDHUaWgmX7n/CUKcgSZIkjXhJ5ldVV1/jRnWndjibPOFFFkeSJEmS1IdR+0ytJEmSJGnks6iVJEmSJI1YLj8epm69/25ef/6soU5DK+HSA2YNdQqSJEnSqGGnVpIkSZI0YnV0UZukknyzbX+tJPckubjZf2OSo4cgr8OSvH11X1eSJEmSOk2nLz9+BJiaZL2qegx4FfDb7oNVdRFw0epMKMlaVfXV1XlNSZIkSepUHd2pbVwK7NNsHwx8u/tAkkOTnNJsvznJkiQLk1zZxKYkmZtkQZJFSSY38Q81Y5ck+WATm5hkSdvcRyaZ1WzPSfLFJPOAf0gyK8mRq+HeJUmSJKmjjYai9izgrUnGANsA1y5j3DHAa6pqW+CNTeww4MSqmgZ0AXcl2QF4J/BXwCuA9ybZrh95rFNVXVXlj89KkiRJ0gDp+KK2qhYBE2l1aS9dztCrgdOTvBdYs4n9DPhokqOAlzRLmHcDzq+qR6rqYeA8YPd+pPKdvgYkmZlkXpJ5Tzz4aD+mlCRJkqTRreOL2sZFwOdpW3rcU1UdBnwM2AyYn+S5VfUtWl3bx4BLk+y1nGs8xZ9/n2N6HH+krySranbTze1aZ9z6fQ2XJEmSpFFvtBS1pwHHVtXiZQ1IMqmqrq2qY4B7gM2SbA7cXlUnARfSWr58FbB/kvWTPAc4oIn9AfiLJM9Nsi6w7yDfkyRJkiSNep3+9mMAquou4KQ+hh3fvAgqwI+BhcBRwNuSPAn8Hvh0Vf0pyenA3Oa8U6vqBoAk/9rEfwvcPOA3IkmSJEn6M6mqoc5BvRi/xQtr1+NnDnUaWgmXHjBrqFOQJEmSRrwk86uqq69xo2X5sSRJkiSpA42K5ccj0eQJL7TjJ0mSJEl9sFMrSZIkSRqxLGolSZIkSSOWy4+HqVvv/z2vP/+4oU5DK+HSA44a6hQkSZKkUcNOrSRJkiRpxBrUojbJPye5McmiJAuS/NVKzjM9yS5t+6cnmdGP856f5KwktyWZn+TSJC9bmRx6mfvOJBs329cMxJySJEmSpBUzaMuPk+wM7AtsX1VLmwJwnZWcbjrwMNDv4jFJgPOBb1TVW5vYtsDzgFv6eX6q6pm+xlbVLn2NkSRJkiQNvMHs1L4AuLeqlgJU1b1VdTdAkr9OckOSxUlOS7JuE2/vfnYlmZNkInAYcETT7d29mX+PJNckuX0ZXds9gSer6qvdgapaWFVXJRmb5MdJrm9y2K+55sQkv0xyBrAE2CzJwc2YJUl6fcg1ycPN3+lNzuckuTnJmU1xTJJjklzXzDO7Oy5JkiRJWnmDWdT+kFZReEuSLyd5JUCSMcDpwEFVtTWtbvHfLWuSqroT+CrwhaqaVlVXNYdeAOxGqxv82V5OnQrMX8a0jwMHVNX2tIrfE9qKzMnAl6tqCvAkcBywFzAN2DHJ/n3c93bAB4GtgM2BXZv4KVW1Y1VNBdZr8pYkSZIkrYJBK2qr6mFgB2AmcA/wnSSHAi8H7qiq7iXA3wD2WIlLXFBVz1TVTbSWFK+IAJ9Osgj4EbBp2xy/rqqfN9s7AnOq6p6qego4sx+5zq2qu5plywuAiU18zyTXJllMq0ie8n+SSmYmmZdk3hMPPrKCtyRJkiRJo8+g/qRPVT0NzAHmNMXcO4AblnPKUzxbaI/pY/qlbdu9LeW9EVjWy6QOATYBdqiqJ5Pc2Xa9Va0m2/N6Glir6U5/Geiqqt8kmUUv91dVs4HZAOO3eFGtYh6SJEmS1PEGrVOb5OVJJreFpgG/Bn4JTEyyRRN/G3BFs30nre4uwIFt5z4EbLCCKfwEWDfJzLactmmeyR0P/E9T0O4JvGQZc8wFXplk4yRrAge35boiugvYe5OMZdnFtiRJkiRpBQzmM7VjgW8kuV3L4ZoAACAASURBVKlZ5rsVMKuqHgfeCZzddG+fofXMLMCxwIlJ5tHqcnb7HnBAjxdFLVdVFXAAsHfzkz43Ap8Bfk9rGXFXc/23AzcvY47fAUcDlwMLgflVdWH/v4L/ned+4Gu0Xj51GXDdis4hSZIkSfq/0qr9NNyM3+JFtevxHxjqNLQSLj3gqKFOQZIkSRrxksyvqq6+xg3qM7VaeZMnPN/iSJIkSZL6MJjLjyVJkiRJGlQWtZIkSZKkEcvlx8PUrff/gX3O++JQp9HRLnnTB4c6BUmSJEmryE6tJEmSJGnEGhFFbZJKckLb/pFJZg3g/BOTLBmo+SRJkiRJq8eIKGqBpcCbkmw81IkAJHHZtiRJkiQNAyOlqH0KmA0c0fNAkk2SnJvkuuazaxNfnGRCWv6Y5O1N/Iwkr1rWhZKsmeT4Zq5FSd7XxKcnuSrJRcBNSZ6T5JIkC5MsSXJQM26HJFckmZ/ksiQvSDIpyfVt15jcvi9JkiRJWjkjpagF+BJwSJLxPeInAl+oqh2BA4FTm/jVwK7AFOB2YPcmvjNwzXKu827ggWa+HYH3Jnlpc2x74B+q6mXAa4G7q2rbqpoK/CDJ2sDJwIyq2gE4DfhUVd0GPJBkWjPPO4Gvr/hXIEmSJElqN2KW0VbVg0nOAA4HHms7tDewVZLu/XFJxgJXAXsAvwa+AsxMsilwX1U9spxLvRrYJsmMZn88MBl4AphbVXc08cXACUmOAy6uqquSTAWmAv/V5LMm8Ltm/KnAO5N8CDgI2KnnhZPMBGYCjNl4w358K5IkSZI0uo2YorbxReB6/rzLuQbwiqp6vH1gkiuBvwdeDPwzcAAwg1axuzwBPlBVl/WYbzrwv8VwVd2SZHvg9cAnk/wYOB+4sap27mXec4GPAz8B5lfVH3sOqKrZtJZZM36LzaqPPCVJkiRp1BtJy4+pqj8B36W1RLjbD4EPdO90L/Gtqt8AGwOTq+p24KfAkcCVfVzmMuDvmqXEJHlZkuf0HJTkhcCjVfVN4HhaS5N/CWySZOdmzNpJpjT5PN7M/RVceixJkiRJA2JEFbWNE2gVq90OB7qalzrdBBzWduxa4JZm+ypgU1rFbU9r0XrDMrSWCd8EXN/8zM+/03tHe2tgbpIFtDqwn6yqJ2h1g49LshBYAOzSds6ZwDO0CnFJkiRJ0ioaEcuPq2ps2/YfgPXb9u+l9Yxqb+e9rW37GpZdxE8BbmvGPQN8tPm0m9N8uue7jFbntec1F9B6lrc3uwFfr6qnl3FckiRJkrQCRkRRO5iS/CuwH3DoIF/nfGASsNdgXkeSJEmSRpNU+T6i4airq6vmzZs31GlIkiRJ0pBIMr+quvoaNxKfqZUkSZIkCbColSRJkiSNYKP+mdrh6tb772Gf87481Gl0tEve9P6hTkGSJEnSKrJTK0mSJEkasUZdUZvkRUkuTHJrktuSnJhknaHOS5IkSZK04kZVUZskwHnABVU1GXgZMBb4VI9xLsuWJEmSpBFgtBVvewGPV9XXAarq6SRHAHckuQN4La0id03glUk+DLwFWBc4v6o+DpDkX4C/Be4BfgPMr6rPJ5kGfBVYH7gNeFdV3ZdkDnAtsCcwAXh3VV21um5akiRJkjrVqOrUAlOA+e2BqnoQ+G9aBf72wIyqemWSVwOTgZ2AacAOSfZIsiNwILAt8Dqg/XeTzgCOqqptgMXAx9uOrVVVOwEf7BGXJEmSJK2k0dap7ct/VdWfmu1XN58bmv2xtIrcDYALq+px4PEk3wNIMh6YUFVXNOO/AZzdNvd5zd/5wMTeLp5kJjATYMzGGw3E/UiSJElSRxttndqbgB3aA0nGAS8GngIeaT8EfKaqpjWfLarqP1bh2kubv0+zjH8mVNXsquqqqq51xo9dhUtJkiRJ0ugw2oraHwPrJ3k7QJI1gROA04FHe4y9DHhXkrHN2E2T/AVwNfCGJGOaY/sCVNUDwH1Jdm/OfxtwBZIkSZKkQTOqitqqKuAA4M1JbgVuAR4HPtrL2B8C3wJ+lmQxcA6wQVVdB1wELAK+T+vZ2Qea094BHJ9kEa3ncP91cO9IkiRJkka3tOo8rYgkY6vq4STrA1cCM6vq+oG8xvgtXlK7fe6ogZxSPVzypvcPdQqSJEmSliHJ/Krq6mucL4paObOTbAWMAb4x0AWtJEmSJKl/7NQOU11dXTVv3ryhTkOSJEmShkR/O7Wj6plaSZIkSVJncfnxMHXrffeyz7mnDnUaHe2SA98z1ClIkiRJWkV2aiVJkiRJI5ZFrSRJkiRpxBpRRW2SSvLNtv21ktyT5OJm/41Jjl6Jea9ZiXP+NcneK3pec25XkpNW5lxJkiRJ0rNG2jO1jwBTk6xXVY8BrwJ+232wqi4CLlrRSatql5U455gVPaft3HmArzaWJEmSpFU0ojq1jUuBfZrtg4Fvdx9IcmiSU5rtNydZkmRhkiub2JQkc5MsSLIoyeQm/nDz9wVJrmyOL0mye5I1k5ze7C9OckQz9vQkM5rtO5N8rjk+N8kWbWO+mmRekluS7NvEp3d3lyVJkiRJK2+kdWoBzgKOaYrCbYDTgN17GXcM8Jqq+m2SCU3sMODEqjozyTrAmj3O+Rvgsqr6VJI1gfWBacCmVTUVoG2unh6oqq2TvB34IrBvE58I7ARMAi7vLnh7k2QmMBNgzMYbLfMLkCRJkiS1jLhObVUtolUoHkyra7ssVwOnJ3kvzxavPwM+muQo4CXNEuZ21wHvTDIL2LqqHgJuBzZPcnKS1wIPLuN63277u3Nb/LtV9UxV3drMteVy7m12VXVVVdc64zZYzq1JkiRJkmAEFrWNi4DP07b0uKeqOgz4GLAZMD/Jc6vqW8AbgceAS5Ps1eOcK4E9aD2ne3qSt1fVfcC2wBxand5l/Xhs9WO7t31JkiRJ0koaqUXtacCxVbV4WQOSTKqqa5sXOt0DbJZkc+D2qjoJuJDW8uX2c14C/KGqvkareN0+ycbAGlV1Lq0ieftlXPKgtr8/a4u/OckaSSYBmwO/XNGblSRJkiT1biQ+U0tV3QX09ZM4xzcvggrwY2AhcBTwtiRPAr8HPt3jnOnAh5vjDwNvBzYFvp6k+x8A/7SM622YZBGwlNbS6G7/DcwFxgGHVdXjSfq+SUmSJElSn1LlathVleROoKuq7u0RPx24uKrOWdE5x0+aWLt97mMDk6B6dcmB7xnqFCRJkiQtQ5L5VdXV17gR2akdDSZvuLFFlyRJkiT1waJ2AFTVxGXED129mUiSJEnS6DJSXxQlSZIkSZKd2uHqV/f9kX3P/cZQp9HRLj7wHUOdgiRJkqRVZKdWkiRJkjRiDbuiNkklOaFt/8gkswZw/onNNT7ZFts4yZNJTlnJOQ9L8vaBylGSJEmS1D/Drqil9Tuvb0qy8SBe4w5gn7b9NwM3ruxkVfXVqjpjlbOSJEmSJK2Q4VjUPgXMBo7oeSDJJknOTXJd89m1iS9OMiEtf+zumiY5I8mrernGo8AvknT/5tFBwHf7cZ0TkxzTbL8myZVJ1kgyK8mRTXyLJD9KsjDJ9UkmNXkdn2RJk+tBA/d1SZIkSdLo1e+iNsn6g5lID18CDkkyvkf8ROALVbUjcCBwahO/GtgVmALcDuzexHcGrlnGNc4C3ppkM+Bp4O5+XOefgIOS7AmcBLyzqp7pMe+ZwJeqaltgF+B3wJuAacC2wN7A8Ule0Oe3IEmSJElarj7ffpxkF1pF3VjgxUm2Bd5XVe8frKSq6sEkZwCHA4+1Hdob2CpJ9/64JGOBq4A9gF8DXwFmJtkUuK+qHlnGZX4AfAL4A/CdHsd6vU5VPZzkvcCVwBFVdVv7SUk2ADatqvOb+3i8ie8GfLuqngb+kOQKYEfgoh7nzwRmAqy38XOX9xVJkiRJkuhfp/YLwGuAPwJU1UJaBeRg+yLwbuA5bbE1gFdU1bTms2lVPUyryNy9+cwB7gFm0Cp2e1VVTwDzgX8EzulxeFnXAdia1nfxwlW9wV5yml1VXVXVtc64DQZ6ekmSJEnqOP1aflxVv+kRenoQcul5zT/Res713W3hHwIf6N5JMq0tv42ByVV1O/BT4Ehaxe7ynAAc1VyrXa/XSfISWkXwdsDrkvxVj5wfAu5Ksn8zft1m2fZVtJYtr5lkE1r/FJjb55cgSZIkSVqu/hS1v2mWIFeStZsXIv1ikPPqdgKtYrXb4UBXkkVJbgIOazt2LXBLs30VsCmt4naZqurGqvpGL4f+z3XSWov8H8CRVXU3rWL71CRjepz7NuDwJItoPc/7fOB8YBGwEPgJ8JGq+n0f9y5JkiRJ6kOqavkDWj+tcyKt50xDq4v5D1X1x8FPb/SaMOmltdvnZg11Gh3t4gPfMdQpSJIkSVqGJPOrqquvcX2+KKqq7gUOGZCsJEmSJEkaQMssapOcDCyzjVtVhw9KRgJgiw2faydRkiRJkvqwvE7tvNWWhSRJkiRJK2GZRW33C5SSvLmqzm4/luTNg52YJEmSJEl96c+Loq6vqu37imlgTZi0ee123CeGOo2OdvEMHxWXJEmShqtVflFUktcBrwc2TXJS26FxwFOrnqIkSZIkSatmeb9Tezet52ofB+a3fS4CXjP4qS1bkkryzbb9tZLck+TiZv+NSY4eugwhyfTm930lSZIkSYNkec/ULgQWJvlWVT25GnPqj0eAqUnWq6rHgFcBv+0+WFUX0Sq+h9J04GHgmiHOQ5IkSZI61jI7tUm+22xen2RRz89qym95LgX2abYPBr7dfSDJoUlOabZPT3JSkmuS3J5kRhOfnmROknOS3JzkzCRpju2Q5Iok85NcluQFTfzwJDc138FZTWyjJBc0sZ8n2SbJROAw4IgkC5LsnuQNSa5NckOSHyV53mr6niRJkiSpYy3vJ33+ofn7C+DDbfEAnxu0jPrvLOCYZsnxNsBpwO7LGPsCYDdgS1od3HOa+HbAFFpLra8Gdk1yLXAysF9V3ZPkIOBTwLuAo4GXVtXSJBOaOY4Fbqiq/ZPsBZxRVdOSfBV4uKo+D5BkQ+AVVVVJ3gN8BPjH9iSTzARmAqy38XNX5buRJEmSpFFhecuPf9dsblFVv24/lmTLQc2qH6pqUdMRPZhW13Z5LqiqZ4CbenRI51bVXQBJFgATgfuBqcB/NY3bNYHu72IRcGaSC4ALmthuwIFNTj9J8twk43rJ4UXAd5qu7zrAHb3c02xgNrTeftzHPUmSJEnSqLe8tx//HfB+YPMey403oNXVHA4uAj5P6/nV5bU2l7ZtZxnxp2l9HwFurKqde5lnH2AP4A3APyfZegVyPRn4t6q6KMl0YNYKnCtJkiRJ6sXy3n78LVrF20XN3+7PDlX1t6sht/44DTi2qhYP4Jy/BDZJsjNAkrWTTEmyBrBZVV0OHAWMB8YCVwGHNGOnA/dW1YPAQ7T+AdBtPM++zOodA5ivJEmSJI1ay1t+/ADwAK3lvcNSs3T4pD4HrticTzQvkzopyXha39EXgVuAbzaxACdV1f1JZgGnNd3sR3m2YP0ecE6S/YAP0OrMnp3kPuAnwEsHMm9JkiRJGo1S5aObw9GESZvXbsd9YqjT6GgXzzhkqFOQJEmStAxJ5ldVV1/jlvf2Yw2hLTbcyKJLkiRJkvqwvGdqJUmSJEka1ixqJUmSJEkjlsuPh6lf3Xcf+57z3aFOo6NdPOMtQ52CJEmSpFVkp1aSJEmSNGINWlGbpJJ8s21/rST3JLl4sK7ZRz7TB/PaSSYkeX/b/sQkfzNY15MkSZIkDW6n9hFgapL1mv1XAb8dxOsNtQnA+9v2JwIWtZIkSZI0iAZ7+fGlwD7N9sHAt7sPJNkpyc+S3JDkmiQvb+JTksxNsiDJoiSTkzwnySVJFiZZkuSgZuwxSa5rYrOTpIlvkeRHzfjrk0xqLjs2yTlJbk5yZtv4O5Ns3Gx3JZnTbL+yyWNBk+cGTfzDzXUXJTm2mfuzwKRm7PHN/u7N/hG93degfeuSJEmSNEoMdlF7FvDWJGOAbYBr247dDOxeVdsBxwCfbuKHASdW1TSgC7gLeC1wd1VtW1VTgR80Y0+pqh2b2HrAvk38TOBLVbUtsAvwuya+HfBBYCtgc2DXPvI/Evj7JpfdgceSvBqYDOwETAN2SLIHcDRwW1VNq6oPN/tXNftfWMZ9SZIkSZJWwaAWtVW1iNYy3INpdW3bjQfOTrIE+AIwpYn/DPhokqOAl1TVY8Bi4FVJjkuye1U90IzdM8m1SRYDewFTmm7qplV1fpPD41X1aDN+blXdVVXPAAua3JbnauDfkhwOTKiqp4BXN58bgOuBLWkVuX3p7b7+TJKZSeYlmffEgw/2Y0pJkiRJGt1Wx9uPLwI+T9vS48YngMubLusbgDEAVfUt4I3AY8ClSfaqqluA7WkVt59slh2PAb4MzKiqrYGvdc+xHEvbtp/m2Z80eopnv4v/naOqPgu8h1YX+OokWwIBPtN0YKdV1RZV9R99fQm93VcvY2ZXVVdVda0zblxfU0qSJEnSqLc6itrTgGOranGP+HiefXHUod3BJJsDt1fVScCFwDZJXgg8WlXfBI6nVeB2F5/3JhkLzACoqoeAu5Ls38y3bpL1+8jxTmCHZvvAtlwmVdXiqjoOuI5WV/Yy4F3NNUmyaZK/AB4CNmib88/2e7uvPnKSJEmSJPVh0IvaZrnvSb0c+hzwmSQ38GzHFOAtwJIkC4CpwBnA1sDcJvZx4JNVdT+t7uwSWoXmdW1zvA04PMki4Brg+X2keSxwYpJ5tDq43T7YvIRqEfAk8P2q+iHwLeBnzbLnc4ANquqPtLq5S5oXRS0Cnm5eVnXEMu5LkiRJkrQKUlVDnYN6MWHSpNrtuM8MdRod7eIZbxnqFCRJkiQtQ5L5VdXV17jVsfxYkiRJkqRBsVbfQzQUtthwQzuJkiRJktQHO7WSJEmSpBHLolaSJEmSNGK5/HiY+tV99/OGc84f6jSGhe/NOGCoU5AkSZI0TNmplSRJkiSNWB1R1CapJCe07R+ZZFYf58xKcuQg5DInSZ+vnZYkSZIkrbqOKGqBpcCbkmw8kJMmcXm2JEmSJA1jnVLUPgXMBo7oeSDJJknOTXJd89m17fC2SX6W5NYk723GT09yVZKLgJua2AVJ5ie5McnMJrZmktOTLEmyOMkRPa67RnP8k83+V5LMa+Y4dnC+BkmSJEkaXTqpE/klYFGSz/WInwh8oap+muTFwGXAXzbHtgFeATwHuCHJJU18e2BqVd3R7L+rqv6UZD3guiTnAhOBTatqKkCSCW3XXAs4E1hSVZ9qYv/czLEm8OMk21TVogG6d0mSJEkalTqmqK2qB5OcARwOPNZ2aG9gqyTd++OSjG22L6yqx4DHklwO7ATcD8xtK2gBDk/S/QrezYDJwC+BzZOcDFwC/LBt/L8D320raAHe0nR51wJeAGwF/FlR2xyfCbDexpus6FcgSZIkSaNOpyw/7vZF4N20Oq/d1gBeUVXTms+mVfVwc6x6nN+9/0h3IMl0WoXxzlW1LXADMKaq7gO2BeYAhwGnts1zDbBnkjHNHC8FjgT+uqq2oVUEj+mZfFXNrqququpaZ9y4Fb55SZIkSRptOqqorao/Ad+lVdh2+yHwge6dJNPaju2XZEyS5wLTget6mXY8cF9VPZpkS1rLlWleSrVGVZ0LfIzWkuVu/wFcCny3ednUOFqF8gNJnge8bpVuVJIkSZIEdFhR2zgBaH8L8uFAV5JFSW6i1VXttgi4HPg58ImquruX+X4ArJXkF8Bnm7EAmwJzkiwAvgn8U/tJVfVvtLq6/wksbrZvBr4FXL1KdyhJkiRJAiBVPVfgajiYMGmL2v2444c6jWHhezMO6HuQJEmSpI6SZH5VdfU1rmNeFNVptthwgsWcJEmSJPWhE5cfS5IkSZJGCYtaSZIkSdKI5fLjYepX9z3AG8+5eKjTGBYumrHvUKcgSZIkaZiyUytJkiRJGrGGZVGb5PlJzkpyW5L5SS5N8rIhzOeDSdZv2780yYSVmGdikr8Z2OwkSZIkafQadkVtkgDnA3OqalJV7UDrN2CfN4RpfRD436K2ql5fVfevxDwTAYtaSZIkSRogw66oBfYEnqyqr3YHqmoh8NMkxydZkmRxkoMAkkxPMifJOUluTnJmUxiT5M4kxya5vjlnyyb+nCSnJZmb5IYk+zXxNZN8vrnGoiQfSHI48ELg8iSXt827cbP99mbswiT/2cROTzKjO/8kDzebnwV2T7IgyRGD+zVKkiRJUucbji+KmgrM7yX+JmAasC2wMXBdkiubY9sBU4C7gauBXYGfNsfurartk7wfOBJ4D/DPwE+q6l3NMuK5SX4EvJ1WN3VaVT2VZKOq+lOSDwF7VtW97QklmQJ8DNilqu5NslEf93Y0cGRV+eYjSZIkSRoAw7FTuyy7Ad+uqqer6g/AFcCOzbG5VXVXVT0DLKBVmHY7r/k7vy3+auDoJAuAOcAY4MXA3sC/V9VTAFX1pz5y2gs4u7vY7cf45UoyM8m8JPOeePCBVZlKkiRJkkaF4dipvRGY0eeoP7e0bftp/vy+lvYSD3BgVf2yfZJm1fJAeIrmHwZJ1gDW6c9JVTUbmA0wYdLkGqhkJEmSJKlTDcdO7U+AdZPM7A4k2Qa4Hzioee51E2APYO5KXuMy4ANtz95u18T/C3hfkrWaePdy4oeADZaR65uTPLfH+DuBHZrtNwJr9zGPJEmSJGklDLuitqoKOADYu/lJnxuBzwDfAhYBC2kVkx+pqt+v5GU+QavQXNTM/4kmfirw3018Ic++qXg28IPuF0W15Xoj8Cngimb8vzWHvga8sontDDzSxBcBTzcvlfJFUZIkSZK0itKqITXcTJg0ufY47gtDncawcNEM36slSZIkjTZJ5ldVV1/jhl2nVpIkSZKk/hqOL4oSsMWG4+1QSpIkSVIf7NRKkiRJkkYsi1pJkiRJ0ojl8uNh6lf3Pch+51w21Gn024UzXjPUKUiSJEkahezUSpIkSZJGrI4qapPsn6SSbLmM4xOTLFndeUmSJEmSBkdHFbXAwcBPm79/JolLrSVJkiSpw3RMUZtkLLAb8G7grU1sepKrklwE3NRj/OZJbkiyY9PBvSrJ9c1nl7bz5yQ5J8nNSc5MkubYZ5PclGRRks83sTckubaZ90dJntfEX5lkQfO5IckGq++bkSRJkqTO1Undy/2AH1TVLUn+mGSHJr49MLWq7kgyESDJy4GzgEOramGS9YFXVdXjSSYD3wa6mvO3A6YAdwNXA7sm+QVwALBlVVWSCc3YnwKvaGLvAT4C/CNwJPD3VXV1U3w/PphfhCRJkiSNFh3TqaW15PisZvssnl2CPLeq7mgbtwlwIXBIVS1sYmsDX0uyGDgb2Kpt/NyququqngEWABOBB2gVpv+R5E3Ao83YFwGXNfN8mFYxDK1i+N+SHA5MqKqneruBJDOTzEsy74kHH1jxb0CSJEmSRpmOKGqTbATsBZya5E5aBeVbgACP9Bj+APDftJYqdzsC+AOwLa0O7Tptx5a2bT8NrNUUpTsB5wD7Aj9ojp8MnFJVWwPvA8YAVNVngfcA6wFXL+tFVlU1u6q6qqprnXHj+33/kiRJkjRadcry4xnAf1bV+7oDSa4Adu9l7BO0lg5fluThqvoWMB64q6qeSfIOYM3lXaxZQrx+VV2a5Grg9ubQeOC3zfY72sZPqqrFwOIkOwJbAjevzI1KkiRJkp7VEZ1aWkuNz+8RO5de3oIMUFWP0OqwHpHkjcCXgXckWUir4OzZ3e1pA+DiJItoPUf7oSY+Czg7yXzg3rbxH0yypBn/JPD9/t6YJEmSJGnZUlVDnYN6MWHSy+qVx5081Gn024UzXjPUKUiSJEnqIEnmV1VXX+M6Zflxx9liw3EWipIkSZLUh05ZfixJkiRJGoUsaiVJkiRJI5bLj4ep2+57iAPOnTPUafTb+QdOH+oUJEmSJI1CdmolSZIkSSNWRxa1SSrJN9v210pyT5KLV3K+CUneP3AZSpIkSZIGQkcWtbR+Z3ZqkvWa/VcBv12F+SYAFrWSJEmSNMx0alELcCmwT7N9MPDt7gNJNkpyQZJFSX6eZJsmPivJaUnmJLk9yeHNKZ8FJiVZkOT4JGOT/DjJ9UkWJ9mvOX9ikl8k+VqSG5P8sLuwTvLeJNclWZjk3CTrr7ZvQpIkSZI6VCcXtWcBb00yBtgGuLbt2LHADVW1DfBR4Iy2Y1sCrwF2Aj6eZG3gaOC2qppWVR8GHgcOqKrtgT2BE5KkOX8y8KWqmgLcDxzYxM+rqh2ralvgF8C7B/6WJUmSJGl06di3H1fVoiQTaXVpL+1xeDeaYrOqfpLkuUnGNccuqaqlwNIk/wM8r5fpA3w6yR7AM8CmbePuqKoFzfZ8YGKzPTXJJ2ktZR4LXPZ/Jk1mAjMB1tu4t8tKkiRJktp1cqcW4CLg87QtPe6HpW3bT9N74X8IsAmwQ1VNA/4AjOnj/NOB/1dVW9PqFI+hh6qaXVVdVdW17rjxK5CyJEmSJI1OnV7UngYcW1WLe8SvolWYkmQ6cG9VPbiceR4CNmjbHw/8T1U9mWRP4CX9yGUD4HfNcuZD+pm/JEmSJGk5Onb5MUBV3QWc1MuhWcBpSRYBjwLv6GOePya5OskS4PvAccD3kiwG5gE39yOdf6H1XO89zd8Nlj9ckiRJktSXVNVQ56BebDjp5TX9c/8+1Gn02/kHTh/qFCRJkiR1kCTzq6qrr3GdvvxYkiRJktTBOnr58Ug2acMN7H5KkiRJUh/s1EqSJEmSRiyLWkmSJEnSiOXy42Hqtvse5k3nXjPUafTbeQfuMtQpSJIkSRqF7NRKkiRJkkasUVHUJnl+krOS3JZkfpJLk8xMcvEyxp+aZKvVnackSZIkacV0/PLjJAHOB75RVW9tYtsCb1zWOVX1ntWUniRJkiRpFYyGTu2ewJNVLQxHSgAAIABJREFU9dXuQFUtBK4CxiY5J8nNSc5sCmCSzEnS1Wy/Nsn1SRYm+XET2ynJz5LckOSaJC9v4usn+W6Sm5Kcn+TatnkOTrI4yZIkx63m70CSJEmSOlLHd2qBqcD8ZRzbDpgC3A1cDewK/LT7YJJNgK8Be1TVHUk2ag7dDOxeVU8l2Rv4NHAg8H7gvqraKslUYEEzzwuB44AdgPuAHybZv6ouGNhblSRJkqTRZTR0apdnblXdVVXP0CpAJ/Y4/grgyqq6A6Cq/tTExwNnJ1kCfIFWYQywG3BWM3YJsKiJ7wjMqap7quop4Exgj57JNM/5zksyb+mD9w/UPUqSJElSxxoNRe2NtDqkvVnatv00/e9cfwK4vKqmAm8Axqx8es+qqtlV1VVVXeuOmzAQU0qSJElSRxsNRe1PgHWTzOwOJNkG2L0f5/4c2CPJS5vzupcfjwd+22wf2jb+auAtzditgK2b+FzglUk2TrImcDBwxUrdjSRJkiTpf3V8UVtVBRwA7N38pM+NwGeA3/fj3HuAmcB5SRYC32kOfQ74TJIb+PPu7peBTZLcBHySVpf4gar6HXA0cDmwEJhfVRcOyA1KkiRJ0iiWVs2ngdB0YdeuqseTTAJ+BLy8qp5Y0bk2nLRl7fm50wY8x8Fy3oG7DHUKkiRJkjpIkvlV1dXXuNHw9uPVaX3g8iRrAwHevzIFrSRJkiSpfyxqB1BVPQT0+Z+E/pi04Vi7n5IkSZLUh45/plaSJEmS1Lns1A5Tt933KDPOvX6o0+i3cw7cfqhTkCRJkjQK2amVJEmSJI1YFrWSJEmSpBHLorYPSSYmWdIjNivJkUkOTfLCfswxJ8mAvEBKkiRJkvQsi9pVcyjQZ1ErSZIkSRocFrWrpgs4M8mCJOslOSbJdUmWJJmdJG1j35xkbpJbkuw+VAlLkiRJUiexqF0184BDqmpaVT0GnFJVO1bVVGA9YN+2sWtV1U7AB4GP9zZZkplJ5iWZt/TB+wY9eUmSJEka6Sxq+1YrEN8zybVJFgN7AVPajp3X/J0PTOx1wqrZVdVVVV3rjttwZfOVJEmSpFHD36nt2x+BnhXmRsAd7YEkY4AvA11V9Zsks4AxbUOWNn+fxu9dkiRJkgaEndo+VNXDwO+S7AWQZCPgtcD/Z+/e4+yq6vv/v95yMUAgXEIiCJiqQOSWAAMCCoIFW+sNFAVqFbRttD+UoqK131oBWxW11qoRbVQEb0ARsAjKTblE7gkBAqhYEaqghHCTIEQSPr8/zk45jDOZSXKSmXPm9Xw85nHWXnutz177/PeZz9r7/Bh4FNiwGbosgV2YZDxw6JpeqyRJkiSNNVYMh+etwBeS/HtzfGJV/SLJqcCXkjwO7A18GbgV+C1ww4isVJIkSZLGEJPaYaiq24EDBug/Gzi7retDzV//cfu3tRcyyDO1kiRJkqQVY1I7Sr1gk/X5zht2G+llSJIkSdKo5jO1kiRJkqSuZVIrSZIkSepabj8epe586AnedPZPR3oZw/Zfb5g60kuQJEmSNAZZqZUkSZIkda2eSWqTVJJvth2vneT+JOd3KP7+HYx1VJKZnYglSZIkSWNZzyS1wGPATknWa44PAu5ZkQBJ3I4tSZIkSV2kl5JagO8Dr2raRwCnLzuRZM8k1ySZl+TqJNs3/UclOS/Jj4AfJvl6koPb5n0ryevaLzJErHOSXJjk50k+2TbnbUnuSHI98JLV9g1IkiRJ0hjSa0ntGcDhScYBuwDXtZ37KbBvVe0KfBj4WNu53YBDq+plwFeBowCSTAD2AS7od53lxZoOHAbsDByWZOskWwAn0kpmXwrssOq3KkmSJEnqqe22VXVLkim0qrTf73d6AnBakm2BAtZpO3dJVT3YxLgiyclJNgfeAJxdVUuSDDfWD6vqEYAktwPPAyYCl1fV/U3/mcB2/defZAYwA2D9iVuu+BcgSZIkSWNMr1VqAc4D/o22rceNfwEuq6qdgNcA49rOPdZv7NeBvwLeBpwywDWWF2txW3spK/CPg6qaVVV9VdX37I02Ge40SZIkSRqzejGpPQU4sarm9+ufwNMvjjpqiBinAscCVNXtA5xfkVjQ2gb9siSbJVkHeOMw5kiSJEmShtBzSW1V/bqqPjfAqU8CH08yjyGqp1V1H/AT4GuDDBl2rCbeb4ATgGuAq5rYkiRJkqRVlKoa6TWMOknWB+YDuy17PnZN2/QFO9WBn/zOSFx6pfzXG6aO9BIkSZIk9ZAkc6uqb6hxPVepXVVJDqRVSf38SCW0kiRJkqTh6am3H3dCVV1K643FI+r5m4yz+ilJkiRJQ7BSK0mSJEnqWia1kiRJkqSu5fbjUequh//AX5/zvx2P+9XXb9PxmJIkSZI0UqzUSpIkSZK6Vs8ltUkuS/Jn/fqOTfLFkVpT2zr+30ivQZIkSZJ6Sc8ltcDpwOH9+g5v+pcrLavzOzGplSRJkqQO6sWk9jvAq5KsC5BkCrAlMDvJ+5PckOSWJCcuO5/kZ0m+DtwKbJ1kUZJPJbktyaVJ9kxyeZI7k7y2mTcuydeSzE8yL8kBTf9RSc5JcmGSnyf5ZNN/ErBekpuSfGtNfymSJEmS1It6LqmtqgeB64FXNl2HA/8FHARsC+wJTAd2T7JfM2Zb4OSq2rGq7gY2AH5UVTsCjwL/2sw/BPhIM+fo1uVqZ+AI4LQk45pz04HDgJ2Bw5JsXVUfBB6vqulV9eaB1p5kRpI5SeY88ciDHfk+JEmSJKmX9VxS22jfgrxs6/Ermr95wI3AVFrJLMDdVXVt2/w/ABc27fnAFVX1ZNOe0vS/FPgmQFX9FLgb2K4598OqeqSqngBuB543nEVX1ayq6quqvnETNh3+3UqSJEnSGNWrSe1/A3+aZDdg/aqaCwT4eFMpnV5VL6yqrzbjH+s3/8mqqqb9FLAYoKqeYng/g7S4rb10mHMkSZIkSSuoJ5PaqloEXAacwtMviLoIeHuS8QBJnptk0ipcZjbw5ibWdsA2wM+GmPNkknVW4ZqSJEmSpDY9mdQ2TgemNZ9U1cXAt4Frksyn9UKpDVch/snAs5pYZwJHVdXiIebMAm7xRVGSJEmS1Bl5epetRpOJL9ylXvfJ8zse96uv36bjMSVJkiSp05LMraq+ocb5rOcoNWXjdU1AJUmSJGkIvbz9WJIkSZLU40xqJUmSJEldy+3Ho9S9Dz/J8efe2/G4Jx6yZcdjSpIkSdJIsVIrSZIkSepaY7pSm2Qz4IfN4XOApcD9zfGeVfWHEVmYJEmSJGlYxnRSW1UPANMBkpwALKqqfxvRRUmSJEmShs3tx4NIcmSS65PclOTkJM9K8idJfp5k0yRrJbk6ycub8d9LMjfJbUn+pulbO8k3ksxPcmuSY0b2riRJkiSpt4zpSu1gkuwEHALsU1VLkswCDq+qbyf5NHAycDMwr6p+1Ew7sqoeTLI+MCfJ2cB2wMSq2rmJu/GavxtJkiRJ6l0mtQM7ENiDVnIKsB7wK4Cq+lKSNwJvA3Ztm/OeJK9t2lsBLwD+B9g+yeeAC4CLl3fRJDOAGQATNn9ux25GkiRJknqV248HFuCUqpre/G1fVf8CkGQ8sCWwFjC+6TsQ2A/Yq6qmAbcA45pndncBZgNHA/+5vItW1ayq6quqvvU32mx13ZskSZIk9QyT2oFdCrwpyURovSU5yTbNuU8BXwM+wtNJ6gTgwap6PMmOtKq8JNkcSFWdBXwY2G0N3oMkSZIk9Ty3Hw+gquYnORG4NMmzgCeBdybZFpgGvKuqliZ5Q5K3AGcBM5LcDvwMuK4JtTXw1bT2MBfwD2v8ZiRJkiSph5nUNqrqhH7H3wa+PcDQH7aNeW1b/58NEnrXQfolSZIkSavI7ceSJEmSpK5lpXaU2nLjdTjxkC1HehmSJEmSNKpZqZUkSZIkdS2TWkmSJElS13L78Si14OEn+dy593U87jGHTO54TEmSJEkaKVZqJUmSJElda40ntUk+k+TYtuOLknyl7fjTSd67EnGPTbL+AP3HJ/l4v77pSX7StL+fZOMVvNY7k7x1iDF9ST63InElSZIkSStmJCq1VwH7ACR5FjAR2LHt/D7A1SsR91jgj5Ja4HTgsH59hzf9VNVfVNXD7SfTMuh3U1VfqqqvL28xVTWnqo4Z1solSZIkSStlJJLaq4G9m/aOwK3Ao0k2SfJs4EXAjQBJ3p/khiS3JDmx6dsgyQVJbk5ya5LDkhwDbAlcluSy9otV1R3AQ0le3Nb9JpqkNsldSSYmmZLkZ0m+3qxp6yR/neSOJNcn+XKSmc2cE5Ic17QvT/KJZswdSfZt+vdPcn7T3jPJNUnmJbk6yfYd/1YlSZIkaQxa4y+Kqqp7kyxJsg2tquw1wHNpJbqPAPOr6g9JXgFsC+wJBDgvyX7A5sC9VfUqgCQTquqRZsvyAVW1cIDLnk6rOntdkr2AB6vq5wOM2xY4sqquTbIl8M/AbsCjwI+Amwe5rbWras8kfwEcDxzY7/xPgX2rakmSA4GPAW8Y8suSJEmSJC3XSL0o6mpaCe2ypPaatuOrmjGvaP7m0arcTqWVdM4HDmqqo/tW1SPDuN6ZwKHNluL/23o8gLur6tqmvSdwRVU9WFVPAmctJ/45zedcYMoA5ycAZyW5FfgMz9xu/X+SzEgyJ8mcRb97cLk3JEmSJEkauaR22XO1O9Pa6nstrUpt+/O0AT5eVdObvxdW1Veb7cS70Upu/zXJh4e6WFX9Cvgl8DJaFdIzBxn62Erez+LmcykDV7//BbisqnYCXgOMG2Sds6qqr6r6xm+06UouRZIkSZLGjpGs1L6a1jbgpVX1ILAxrcR2WVJ7EfD2JOMBkjw3yaRmW/Dvq+qbwKdoJbjQ2iK84XKueTqtKumdVfXrYazxBuBlzbO+a7Nq24UnAPc07aNWIY4kSZIkqc1IJbXzab31+Np+fY8seya2qi4Gvg1ck2Q+8B1aSevOwPVJbqL1/Oq/NvNnARf2f1FUm7NobfsdbOvxM1TVPbSefb2eVmX5LlrP/K6MTwIfTzKPEXiOWZIkSZJ6VapqpNcwaiUZX1WLmkrtucApVXXumrj2Ni+cVsd96uKOxz3mkMkdjylJkiRJnZZkblX1DTXOquHyndC8rXgccDHw3TV14Ukbr2MCKkmSJElDMKldjqo6bqTXIEmSJEka3Eg9UytJkiRJ0iqzUjtKPfDwEk475/6Oxz3y9Zt3PKYkSZIkjRQrtZIkSZKkrtUTSW2SpUluSnJbkpuTvC/Jcu8tyZQkt66pNUqSJEmSOq9Xth8/XlXTAZJMovX7thvR+h3bEZdkrapaOtLrkCRJkqRe0xOV2nZVtQCYAbwrLWsl+VSSG5LckuQd/ec0VdvZSW5s/vZp+r+Q5LVN+9wkpzTttyf5aNP+bpK5TZV4RlvMRUk+neRmYO8kuye5ohl7UZIt1sDXIUmSJEk9rVcqtc9QVXcmWQuYBLwOeKSq9kjybOCqJBcD1TZlAXBQVT2RZFvgdKAPmA3sC5wHPBdYlojuC5zRtN9eVQ8mWQ+4IcnZVfUAsAFwXVW9L8k6wBXA66rq/iSHAR8F3r76vgVJkiRJ6n09mdT28wpglySHNscTgG2BO9rGrAPMTDIdWAps1/TPBo5NsgNwO7BJU2HdGzimGXNMkkOa9tZN7AeaOGc3/dsDOwGXJAFYC/hN/4U2ld4ZAJtN3GoVblmSJEmSxoaeTGqTPJ9WUrkACPDuqrqo35gpbYfvAe4DptHakv0EQFXdk2Rj4M+BK4FNgTcBi6rq0ST7AwcCe1fV75NcDoxrYj7R9hxtgNuqau/lrbuqZgGzAP7khdNreWMlSZIkST34TG2SzYEvATOrqoCLgL9rtgCTZLskG/SbNgH4TVU9BbyFViV1mWuBY2kltbOB45rPZfMeahLaqcBegyzrZ8DmSfZu1rBOkh1X8VYlSZIkaczrlUrtekluorWNeAnwDeDfm3NfAaYAN6a19/d+4OB+808Gzk7yVuBC4LG2c7OBV1TV/yS5m1a1dllSeyHwziQ/oZW4XjvQ4qrqD832588lmUDre/8P4LaVv2VJkiRJUlrFTI02f/LC6XXCJy/peNwjX795x2NKkiRJUqclmVtVfUON67ntx5IkSZKksaNXth/3nM02XtuqqiRJkiQNwUqtJEmSJKlrmdRKkiRJkrqW249HqYcfWsI531nY8bivP3Rix2NKkiRJ0kixUitJkiRJ6lpjPqlNcnCSSjK1OZ6S5NaVjLWos6uTJEmSJC3PmE9qgSOAHzefkiRJkqQuMqaT2iTjgZcCfw0cPsD5KUlmJ7mx+dun6d8iyZVJbkpya5J9+82bmOSaJK9KMj7JD5v585O8bo3cnCRJkiSNAWP9RVGvAy6sqjuSPJBkd+CBtvMLgIOq6okk2wKnA33AXwIXVdVHk6wFrL9sQpLJwHnAh6rqkiRrA4dU1e+STASuTXJeVdUaukdJkiRJ6lljPak9Avhs0z6jOZ7Zdn4dYGaS6cBSYLum/wbglCTrAN+tqpvaxv8QOLqqrmj6AnwsyX7AU8BzgcnAb/svJskMYAbAxIlbdeQGJUmSJKmXjdntx0k2BV4OfCXJXcD7gTfRSkKXeQ9wHzCNVoV2XYCquhLYD7gHODXJW5vxS4C5wJ+1xXgzsDmwe1VNb+KNG2hNVTWrqvqqqm/CRpt14jYlSZIkqaeN2aQWOBT4RlU9r6qmVNXWwC+BrdvGTAB+U1VPAW8B1gJI8jzgvqr6MvAVYLdmfAFvB6Ym+Ye2GAuq6skkBwDPW903JkmSJEljxVjefnwE8Il+fWcD/9h2fDJwdlOJvRB4rOnfH3h/kieBRcCySi1VtTTJEcB5SR4FvgV8L8l8YA7w09VwL5IkSZI0JsX3FY1OL3zB9PrkJy7teNzXHzqx4zElSZIkqdOSzK2qvqHGjeVK7ai28SZrm4BKkiRJ0hDG8jO1kiRJkqQuZ1IrSZIkSepabj8epR55aAk/OHNhx+O+8jC3NEuSJEnqHVZqJUmSJEldy6RWkiRJktS1xkRSm+RrSd7Rr+/gJD9o2levRMyPJDlwiDGvTfLBFY0tSZIkSRqeMZHUAqcDh/frO7zpp6r26T8hyXKfN66qD1fVcn9ItqrOq6qTVnCtkiRJkqRhGitJ7Q+BqUm2AEiyAXAg8N3meFHzuX+S2UnOA25v+v45yc+S/DjJ6UmOa/pPTXJo074ryYlJbkwyP8nUpv+oJDOb9muSXJdkXpJLk0xes1+BJEmSJPWeMZHUVtVS4GzgTU3Xa4DLq+p3AwzfDfj7qtouyR7AG4BpwCuBvuVcZmFV7QZ8EThugPM/Bvaqql2BM4AP9B+QZEaSOUnm/O53Dwzz7iRJkiRp7BoTSW2jfQvy/209HsD1VfXLpv0S4L+r6omqehT43nLin9N8zgWmDHB+K+CiJPOB9wM79h9QVbOqqq+q+jbaaLPl3owkSZIkaWwltVcDWySZBuwDXDDIuMdWMv7i5nMpA//+7+eBmVW1M/AOYNxKXkeSJEmS1BgzSW1VFXAmcBrwg6p6YhjTrgJek2RckvHAq1dhCROAe5r2kasQR5IkSZLUGDNJbeN0Ws/HDrb1+Bmq6gbgPOAW4AfAfOCRlbz2CcBZSeYCC1cyhiRJkiSpTVoFTA0myfiqWpRkfeBKYEZV3bi6r7vtC6bX5z623F8MWimvPGxix2NKkiRJUqclmVtVy3tZLzDws596pllJdqD1DOxpayKhBZiwydomoJIkSZI0BJPaIVTVX470GiRJkiRJAxtrz9RKkiRJknqIldpR6tEHl3D5t+7veNz937x5x2NKkiRJ0kixUitJkiRJ6lpdmdQm+acktyW5JclNSV7cgZhTkjyeZF6SnyS5PslRbedfm+SDq3odSZIkSVLndN324yR7A68GdquqxUkmAut2KPwvqmrX5jrPB85Jkqr6WlWdR+s3ayVJkiRJo0Q3Vmq3ABZW1WKAqlpYVfcCJPlwkhuS3JpkVpI0/Zcn+URTfb0jyb5DXaSq7gTeCxzTxDgqycymvXmSs5tr3ZDkJW39lzRV5K8kubtJukny3mZdtyY5djV8L5IkSZI05nRjUnsxsHWTnJ6c5GVt52ZW1R5VtROwHq2K7jJrV9WewLHA8cO81o3A1AH6Pwt8pqr2AN4AfKXpPx74UVXtCHwH2AYgye7A24AXA3sBf5tk12GuQZIkSZI0iK5LaqtqEbA7MAO4Hziz7dnXA5Jcl2Q+8HJgx7ap5zSfc4Epw7xcBuk/EJiZ5CZaW5I3SjIeeClwRrPOC4GHmvEvBc6tqsea9Z8D/FG1OMmMJHOSzHnkdw8Mc4mSJEmSNHZ13TO1AFW1FLgcuLxJYI9McgZwMtBXVb9KcgIwrm3a4uZzKcO/712BnwzQ/yxgr6p6or2z2e280qpqFjALYPvnT69VCiZJkiRJY0DXVWqTbJ9k27au6cDdPJ3ALmyqpoeu4nWmAP8GfH6A0xcD724bO71pXgW8qel7BbBJ0z8bODjJ+kk2AA5p+iRJkiRJq6AbK7Xjgc8n2RhYAvwPMKOqHk7yZeBW4LfADSsR+wVJ5tFKkB8FPldVpw4w7hjgC0luofUdXgm8EzgROD3JW4BrmnU8WlU3JjkVuL6Z/5WqmrcS65MkSZIktUmVu1w7JcmzgaVVtaT56aEvVtX0oeYNZPvnT6///JdLOrtAYP83b97xmJIkSZLUaUnmVlXfUOO6sVI7mm0D/FeSZwF/AP52hNcjSZIkST3NpLaDqurntF4utco23HRtq6qSJEmSNISue1GUJEmSJEnLmNRKkiRJkrqW249HqcceWMK1py7oeNy9jprU8ZiSJEmSNFKs1EqSJEmSupZJbT9JFg3Sf2qSQ4eYe3mSIV85LUmSJEnqDJNaSZIkSVLXMqkdRFpmJvlZkkuBSW3nPpzkhiS3JpmVJG1T35jk+iR3JNm3GT8uydeSzE8yL8kBa/p+JEmSJKkXmdQO7hBge2AH4K3APm3nZlbVHlW1E7Ae8Oq2c2tX1Z7AscDxTd/RQFXVzsARwGlJxvW/YJIZSeYkmfPwow90/o4kSZIkqceY1A5uP+D0qlpaVfcCP2o7d0CS65LMB14O7Nh27pzmcy4wpWm/FPgmQFX9FLgb2K7/BatqVlX1VVXfxhtu1tGbkSRJkqRe5E/6rKCmwnoy0FdVv0pyAtBedV3cfC7F71eSJEmSVisrtYO7EjgsyVpJtgCWPQe7LIFdmGQ8sNw3IjdmA28GSLIdsA3wsw6vV5IkSZLGHCuJgzuX1tbi24H/Ba4BqKqHk3wZuBX4LXDDMGKdDHyx2a68BDiqqhYPMUeSJEmSNAST2n6qanzzWcC7BhnzIeBDA/Tv39ZeSPNMbVU9Abyt86uVJEmSpLHNpHaU2mCztdnrqElDD5QkSZKkMcxnaiVJkiRJXcukVpIkSZLUtdx+PEo9vnAJN395QcfjTvtbtzRLkiRJ6h1WaiVJkiRJXcukdhiSLBrpNUiSJEmS/phJrSRJkiSpa5nUroC0fCrJrUnmJzms6f9Cktc27XOTnNK0357ko037u0nmJrktyYyRuwtJkiRJ6h2+KGrFvB6YDkwDJgI3JLkSmA3sC5wHPBfYohm/L3BG0357VT2YZL1m3tlV9cAaXb0kSZIk9RgrtSvmpcDpVbW0qu4DrgD2oElqk+wA3A7cl2QLYG/g6mbuMUluBq4Ftga27R88yYwkc5LMeehR811JkiRJGopJbQdU1T3AxsCfA8sqt28CFlXVo0n2Bw4E9q6qacA8YNwAcWZVVV9V9W2y4WZrbP2SJEmS1K1MalfMbOCwJGsl2RzYD7i+OXctcCxPJ7XHNZ8AE4CHqur3SaYCe63ZZUuSJElSbzKpXTHnArcANwM/Aj5QVb9tzs0G1q6q/wFuBDbl6aT2QmDtJD8BTqKVAEuSJEmSVpEvihqGqhrffBbw/uav/5ivAl9t2k8CG7SdWwy8co0sVpIkSZLGECu1kiRJkqSuZaV2lFpv4tpM+9tJI70MSZIkSRrVrNRKkiRJkrqWSa0kSZIkqWu5/XiUemLBk9zxhfs6Hne7oyd3PKYkSZIkjRQrtZIkSZKkrtXVSW2SRSO9BoAkU5L85UivQ5IkSZLGmq5OakeRKcAKJbVJ3PotSZIkSauo55LaJK9Jcl2SeUkuTTK56T8hySlJLk9yZ5Jj2ua8NcktSW5O8o0h4rwsyU3N37wkGwInAfs2fe9JslaSTyW5oYn7jmbu/klmJzkPuH2NfzmSJEmS1GN6sVr4Y2CvqqokfwN8AHhfc24qcACwIfCzJF8EtgM+BOxTVQuTbDpEnOOAo6vqqiTjgSeADwLHVdWrAZLMAB6pqj2SPBu4KsnFTdzdgJ2q6pf9F97MmwGw5SZbdfI7kSRJkqSe1ItJ7VbAmUm2ANYF2pPHC6pqMbA4yQJgMvBy4KyqWghQVQ8OEecq4N+TfAs4p6p+naT/Gl4B7JLk0OZ4ArAt8Afg+oES2ubas4BZADttM61W6u4lSZIkaQzpue3HwOeBmVW1M/AOYFzbucVt7aUsP6kfME5VnQT8DbAerQrs1AHmBnh3VU1v/v6kqpZVah9bmZuSJEmSJP2xXkxqJwD3NO0jhzH+R8Abk2wG0Lb9eMA4SV5QVfOr6hPADbS2ND9Ka0vzMhcBf5dknWbOdkk2WMn7kSRJkiQNotu3H6+f5Ndtx/8OnACcleQhWgnrnywvQFXdluSjwBVJlgLzgKOWE+fYJAcATwG3AT9o2kuT3AycCnyW1huRb0xrb/L9wMGreK+SJEmSpH5S5aObo9FO20yrc/7h4qEHrqDtjp7c8ZiSJEmS1GlJ5lZV31Djur1S27PGTVrHBFSSJEmShtCLz9RKkiRJksYIk1pJkiRJUtdy+/Eo9Yf7nuRXn/5tx+Nu/b7ndDymJEmSJI0UK7WSJEmSpK4Vxo+sAAAcgklEQVRlUrsCkixNclOS25LcnOR9SQb8DpM8J8kZSX6RZG6S7yfZbk2vWZIkSZJ6mduPV8zjVTUdIMkk4NvARsDx7YOa36Y9Fzitqg5v+qYBk4E71uiKJUmSJKmHWaldSVW1AJgBvKtJYtsdADxZVV9qG39zVc1ek2uUJEmSpF5nUrsKqupOYC1gUr9TOwFz1/yKJEmSJGlsMakdRZLMSDInyZwHH3tgpJcjSZIkSaOeSe0qSPJ8YCmwoN+p24DdVzReVc2qqr6q6tt0g806sURJkiRJ6mkmtSspyebAl4CZVVX9Tv8IeHaSGW3jd0my75pcoyRJkiT1OpPaFbPesp/0AS4FLgZOBEiyZZLvAzRJ7iHAgc1P+twGfBz4bTP2phFZvSRJkiT1GH/SZwVU1VrLOXcv8Bf9jt80yNjpnV+dJEmSJI09VmolSZIkSV3LSu0ote7kddj6fc8Z6WVIkiRJ0qhmpVaSJEmS1LVMaiVJkiRJXcvtx6PUk7/9A7/91N0dj/uc9z+v4zElSZIkaaRYqZUkSZIkda1RkdQmOThJJZnaHE9JcmvTPirJzJFdoSRJkiRpNBoVSS1wBPDj5nPUSjLo79RKkiRJkta8EU9qk4wHXgr8NXD4IMO2TnJ5kp8nOb6Z93/V3Ob4uCQnNO3dk9zc/H1qsKpvkvOT7N+0X5HkmiQ3JjmrWRdJ7kryiSQ3Am/sZGxJkiRJ0qoZ8aQWeB1wYVXdATyQZPcBxuwJvAHYhVZi2TdEzK8B766qacNZQJKJwIeAA6tqN2AO8N62IQ9U1W5VdcZqiC1JkiRJWkmjIak9AjijaZ/BwFuQL6mqB6rqceAcWpXdASXZGNi4qq5sur4xjDXsBewAXJXkJuBIoP01wWeuxtjta5+RZE6SOQ889uAwQkuSJEnS2DaiP+mTZFPg5cDOSQpYCyjgC/2G1gDHS3hmUj5uGJccbE5oJc6DPdP72GqM/X+qahYwC2DaVrv0v2dJkiRJUj8jXak9FPhGVT2vqqZU1dbAL4Gt+407KMmmSdYDDgauAu4DJiXZLMmzgVcDVNXDwMNJllVz39wW5y5gepJnJdma1rZmgGuBlyR5IUCSDZJs13+xqzO2JEmSJGnFjWilltZW40/06zsb+Md+fdc3/VsB36yqOQBJPtKcuwf4adv4twGnNNXfi9v6r6KVNN8O/AS4EaCq7k9yFHB6kyBD6znYOwZY8+qMLUmSJElaAanq7V2uSaYA51fVTt0Ue9pWu9RFf/+9ToflOe8f8HFeSZIkSRpVksytqqFeEjzilVoNYp3nrGsCKkmSJElD6PmktqruAjpeSV3dsSVJkiRJQxvpF0VJkiRJkrTSer5S262evO8Jfvvpnw49cAU9531TOx5TkiRJkkaKlVpJkiRJUtcaU0ltkkUrOH7/JOc37dcm+eDqWZkkSZIkaWW4/XiYquo84LyRXockSZIk6WljqlK7TFOBvTzJd5L8NMm3kqQ59+dN343A69vmHJVkZtN+TZLrksxLcmmSyU3/CUlOaWLfmeSYtvnfTTI3yW1JZqzhW5YkSZKknjQmk9rGrsCxwA7A84GXJBkHfBl4DbA78JxB5v4Y2KuqdgXOAD7Qdm4q8GfAnsDxSdZp+t9eVbsDfcAxSTbr8P1IkiRJ0pgzlrcfX19VvwZIchMwBVgE/LKqft70fxMYqKq6FXBmki2AdYFftp27oKoWA4uTLAAmA7+mlcge0ozZGtgWeKA9aFPBnQHw3E227MQ9SpIkSVJPG8uV2sVt7aWsWIL/eWBmVe0MvAMYt7y4SfYHDgT2rqppwLx+cwCoqllV1VdVfZttsMkKLEeSJEmSxqaxnNQO5KfAlCQvaI6PGGTcBOCepn3kMOJOAB6qqt8nmQrstWrLlCRJkiSBSe0zVNUTtLb/XtC8KGrBIENPAM5KMhdYOIzQF9Kq2P4EOAm4tgPLlSRJkqQxL1U10mvQAKZtvVNddOx3Oh73Oe+b2vGYkiRJktRpSeZWVd9Q46zUSpIkSZK61lh++/Gots7kcVZVJUmSJGkIVmolSZIkSV3LpFaSJEmS1LXcfjxKPXnf77nvMzd1PO7k90zveExJkiRJGilWaiVJkiRJXasrk9ok/5TktiS3JLkpyYs7EPOuJBNXcM6xSdZf1WtLkiRJklZO120/TrI38Gpgt6pa3CSi647AOtYCjgW+Cfx+TV9fkiRJktSdldotgIVVtRigqhZW1b1J/jTJvCTzk5yS5NnwzApskr4klzftzZJc3FR8vwJk2QWS/FWS65sq8H82CSxJFiX5dJKbgX8CtgQuS3JZc/6I5vq3JvlEW7xFST6a5OYk1yaZvCa+KEmSJEnqdd2Y1F4MbJ3kjiQnJ3lZknHAqcBhVbUzrQr03w0R53jgx1W1I3AusA1AkhcBhwEvqarpwFLgzc2cDYDrqmpaVX0EuBc4oKoOSLIl8Ang5cB0YI8kB7fNu7aqpgFXAn+76l+DJEmSJKnrktqqWgTsDswA7gfOBN4B/LKq7miGnQbsN0So/WhtHaaqLgAeavr/tIl/Q5KbmuPnN+eWAmcPEm8P4PKqur+qlgDfalvDH4Dzm/ZcYMpAAZLMSDInyZwHH3t4iOVLkiRJkrrumVqAqloKXA5cnmQ+cPRyhi/h6eR93DDCBzitqv5xgHNPNNdeUU9WVTXtpQzyvVfVLGAWwLStd6iBxkiSJEmSntZ1ldok2yfZtq1rOvALYEqSFzZ9bwGuaNp30aq8Aryhbd6VwF82MV8JbNL0/xA4NMmk5tymSZ43yHIeBTZs2tcDL0sysXkG94i2NUiSJEmSVoOuS2qB8cBpSW5PcguwA/BB4G3AWU3l9ingS834E4HPJplDq0pKW/9+SW4DXg/8L0BV3Q58CLi4iX8JrZdTDWQWcGGSy6rqN806LgNuBuZW1X936qYlSZIkSX8sT++K1Wgybesd6uL3frvjcSe/Z3rHY0qSJElSpyWZW1V9Q43rxkqtJEmSJElAl74oaixYZ/L6VlUlSZIkaQhWaiVJkiRJXctK7Sj15ILHuO+z13U87uS/f3HHY0qSJEnSSLFSK0mSJEnqWia1kiRJkqSuZVLbSFJJvtl2vHaS+5OcP8S8viSfW/0rlCRJkiT15zO1T3sM2CnJelX1OHAQcM9Qk6pqDjBndS9OkiRJkvTHrNQ+0/eBVzXtI4DTl51IsmeSa5LMS3J1ku2b/v2XVXOTnJDklCSXJ7kzyTFt8/8qyfVJbkryn0nWWoP3JUmSJEk9yaT2mc4ADk8yDtgFaH/98E+BfatqV+DDwMcGiTEV+DNgT+D4JOskeRFwGPCSqpoOLAXe3H9ikhlJ5iSZ8+Cihzt2U5IkSZLUq9x+3KaqbkkyhVaV9vv9Tk8ATkuyLVDAOoOEuaCqFgOLkywAJgN/CuwO3JAEYD1gwQDXnwXMApi2zYtqVe9HkiRJknqdSe0fOw/4N2B/YLO2/n8BLquqQ5rE9/JB5i9uay+l9R0HOK2q/rHDa5UkSZKkMc3tx3/sFODEqprfr38CT7846qgVjPlD4NAkkwCSbJrkeau0SkmSJEmSSW1/VfXrqhroJ3o+CXw8yTxWsMJdVbcDHwIuTnILcAmwxSovVpIkSZLGuFT56OZoNG2bF9XF7zu143En//2LOx5TkiRJkjotydyq6htqnM/UjlLrTNrABFSSJEmShuD2Y0mSJElS1zKplSRJkiR1Lbcfj1JPLljEfZ+7suNxJx+zX8djSpIkSdJIsVIrSZIkSepaYzqpTbKo3/FRSWaupmttmeQ7TbsvyUA/GyRJkiRJWgFuP15Dqupe4NCmPQeYM7IrkiRJkqTuN6YrtcuT5DVJrksyL8mlSSY3/fOTbJyWB5K8ten/epKDkkxJMjvJjc3fPs35KUlubdr7Jzl/5O5OkiRJknrDWE9q10ty07I/4CNt534M7FVVuwJnAB9o+q8CXgLsCNwJ7Nv07w1cDSwADqqq3YDDALcZS5IkSdJqMta3Hz9eVdOXHSQ5CuhrDrcCzkyyBbAu8MumfzawH3A38EVgRpLnAg9V1WNJJgAzk0wHlgLbDXcxSWYAMwC22mTyqtyXJEmSJI0JY71SuzyfB2ZW1c7AO4BxTf+VtKqz+wKXA/fTelZ2dnP+PcB9wDRaCfK6w71gVc2qqr6q6tt0/MaduAdJkiRJ6mkmtYObANzTtI9c1llVvwImAttW1Z20tikfRyvZXTbvN1X1FPAWYK01tmJJkiRJGmNMagd3AnBWkrnAwn7nrgPuaNqzgefSSm4BTgaOTHIzMBV4bPUvVZIkSZLGplTVSK9BA5i2zdS6+LhZHY87+Zj9Oh5TkiRJkjotydyq6htqnJVaSZIkSVLXGutvPx611pk03qqqJEmSJA3BSq0kSZIkqWuZ1EqSJEmSupbbj0epJQt+x4LPX9LxuJPefVDHY0qSJEnSSLFSK0mSJEnqWqO2UptkM+CHzeFzgKXA/c3xnlX1hxFZmCRJkiRp1Bi1SW1VPQBMB0hyArCoqv5tRBclSZIkSRpVunL7cZIjk1yf5KYkJyd5VpK1kzyc5KQkNye5JsmkZvw3k3w2ydVJ7kxySFusDzaxbkny4aZvwyQ/aOLcmuTQpv9TSW5vxn6i6Zuc5Jwkc5o4ezX945Oc2vTNS/KaNf9NSZIkSVJv67qkNslOwCHAPlU1nVa1+fDm9ATgiqqaBlwDvL1t6iTgJcDBwMebWH8BbAO8mFZVeJ8k+wB/AdxVVdOqaifgkiSTm/4dq2qXZTGAzwGfrKo+4E3AV5r+DwMXVtWewMuBTycZN8S9zWiS4zkPLHpkZb4eSZIkSRpTRu324+U4ENgDmJMEYD3gV825x6vqB017LrBv27zvVlUBtyR5btP3CuCVwLzmeDywHXAdcFKSk4DvVdVVSX4PPAV8OckFwPlt69m+WQvAJknWWxY7yQeb/nG0Eug7BruxqpoFzAKYvs12NczvQ5IkSZLGrG5MagOcUlX//IzOZG2g/eVRS3nm/S3uF2PZ579W1Vf/6CJJH63K7ElJflBVH2v6DgLeCPwdrcQ1DPDiqrSy3IOr6hcrcY+SJEmSpGHouu3HwKXAm5JMhNZbkpNss5KxLgL+OskGTaytkkxsKrmLquobwKeB3ZJsCGxUVecD7wF2bVvP0csCJpneFvvdbf3LxkuSJEmSOqTrKrVVNT/JicClSZ4FPAm8E7h3JWJ9P8lU4Npm+/CjwF8CO9Cq0D5Fq/r7TlrP656T5Nm0/hnw3ibM0cAXk7yN1vd5WdN3IvAfSeY34/8HeN3K3bUkSZIkaSBpPWaq0Wb6NtvVxe//QsfjTnr3QR2PKUmSJEmdlmRu80Le5eq6Su1YsfakjUxAJUmSJGkI3fhMrSRJkiRJgEmtJEmSJKmLuf14lFqy4BEWzDx/6IEraNK7Xt3xmJIkSZI0UqzUSpIkSZK61ogktUn+KcltSW5JclOSF69knP2T7NN2fGqSQ4cxb2lz3duS3Jzkfc3PAw11rWGXTvuvTZIkSZLUeWt8+3GSvYFXA7tV1eIkE4F1VzLc/sAi4OoVnPd4VU1v1jMJ+DawEXD8Sq6jk2uTJEmSJA3TSFRqtwAWVtVigKpaWFX3AiT50yTzksxPckqSZzf9dzXJL0n6klyeZArwTuA9TdV13yb+fkmuTnLncKq2VbUAmAG8Ky1TksxOcmPz90fV1iR7NOt8QZJNk3y3qTpfm2SXgdaW5DVJrmvmXZpk8qp9jZIkSZKkkUhqLwa2TnJHkpOTvAwgyTjgVOCwqtqZVhX57wYLUlV3AV8CPlNV06tqdnNqC+CltKrBJw1nQVV1J7AWMAlYABxUVbsBhwGfax/bJLlfAl5XVb8ATgTmVdUuwP8Dvj7I2n4M7FVVuwJnAB8YztokSZIkSYNb49uPq2pRkt2BfYEDgDOTfBCYB/yyqu5ohp4GHA38xwpe4rtV9RRw+0pWQ9cBZiaZDiwFtms79yJgFvCKZdVlWgn0GwCq6kdJNkuy0QBxt6J1r1vQ2m79y/4DksygVTVmq002X4mlS5IkSdLYMiIviqqqpVV1eVUdD7yLJilcjiU8vdZxQ4xd3NbOcNaT5Pm0EtgFwHuA+4BpQB/PfN73N8ATwK7DidvP54GZTRX6HQxwH1U1q6r6qqpvs/ETVuISkiRJkjS2rPGkNsn2SbZt65oO3A38DJiS5IVN/1uAK5r2XcDuTbs9AX4U2HAV17M5ra3CM6uqgAnAb5pq71tobUte5mHgVcDHk+zf9M0G3tzE2p/W88K/G2BtE4B7mvaRq7JmSZIkSVLLSFRqxwOnJbk9yS3ADsAJVfUE8DbgrCTzgadoJZvQem71s0nm0KqoLvM94JB+L4oajvWW/aQPcCmt53xPbM6dDByZ5GZgKvBY+8Squo/W87pfaH6K6ARg9+ZeTuLphLX/2k5o7m0usHAF1ipJkiRJGkRaxUmNNtO32bYu/sBnOh530rte3fGYkiRJktRpSeZWVd9Q40bkmVpJkiRJkjphjb/9WMOz9qQJVlUlSZIkaQhWaiVJkiRJXcukVpIkSZLUtdx+PEotuf8hFpx8dsfjTvr/hvpJYEmSJEnqHlZqJUmSJEldqyeS2iSfSXJs2/FFSb7SdvzpJO9dTdf+SJIDV0dsSZIkSdLy9URSC1wF7AOQ5FnARGDHtvP7AFevjgtX1Yer6tLVEVuSJEmStHy9ktReDezdtHcEbgUeTbJJkmcDLwKOSXLwsglJvpXkdUnGJflakvlJ5iU5oDl/VJLvJrkkyV1J3pXkvc2Ya5Ns2ow7NcmhTfuuJCcmubGJN7Xp37yJc1uSryS5O8nENff1SJIkSVJv6omktqruBZYk2YZWVfYa4DpaiW4fMB/4T+AogCQTmnEXAEe3QtTOwBHAaUnGNaF3Al4P7AF8FPh9Ve3axH/rIMtZWFW7AV8Ejmv6jgd+VFU7At8BtunMnUuSJEnS2NYTSW3jalqJ6rKk9pq246uq6gr+//buNcaOuozj+PdHCygXQ4FKkKsSiAloClYTtDFAlKAxghEIBCJgIhJvEBKDGhPBhMQQFcMLMRAuBcGCgIK+IKBURQiXFloKFAS1RG4ttQKtLyBtH1+cWXMo3e0uLJ2Z3e8n2Zw5c+bMPJsnT848Z/7zP3BgktkMmtebq2o9MA/4JUBVPQE8AxzU7HNhVa2tqpeAV4DfNeuXAfuPEsctzePioW3mAQuaY9wO/Gdzb0xyZpJFSRb9e92rE/rnJUmSJGk6mkpN7ch9tR9iMPz4PgZXaofvp70GOBU4A7hyHPt8bWh549DzjYz+c0gj22wYY5vNqqrLqmpuVc3dbaf3TOStkiRJkjQtTaWm9l7gc8CaqtpQVWuAXRg0tiNN7dXAOQBV9Xiz7m7gFIAkBzEYGvzkJMd2D3Bic4yjgVmTvH9JkiRJmpamUlO7jMGsx/dtsu6VqloNUFUrgeXAVUPb/BzYJsky4Abg9KoavkI7GS4Ajk7yKHAC8CKwdpKPIUmSJEnTTqqq7Ri2miQ7MGh0D6uqV7bicbcHNlTV+iSHA5dW1Zyx3jNnvwPqjvMumvRY3vu1L076PiVJkiRpsiVZXFVzt7TdhO757LMknwKuAC7emg1tY1/gxuY3dF8HvrKlN8ycPcsGVJIkSZK2YNo0tVX1B2C/lo79FHBoG8eWJEmSpKlsWg0/7pMka5n8Cau0dewOrG47CL0l5q6fzFt/mbv+Mnf9ZN76a7rmbr+qmr2ljabNldoeenI848fVPUkWmbt+Mnf9ZN76y9z1l7nrJ/PWX+ZubFNp9mNJkiRJ0jRjUytJkiRJ6i2b2u66rO0A9JaZu/4yd/1k3vrL3PWXuesn89Zf5m4MThQlSZIkSeotr9RKkiRJknrLpraDkhyT5MkkTyf5TtvxaPySrEiyLMmSJIvajkebl+TKJKuSPDq0btckdyZ5qnmc1WaM2rxRcnd+kueauluS5LNtxqg3S7JPkoVJHk/yWJKzm/XWXceNkTvrruOSvCvJA0mWNrm7oFn//iT3N+eZNyTZru1Y9UZj5O7qJP8cqrs5bcfaFQ4/7pgkM4C/AZ8GngUeBE6uqsdbDUzjkmQFMLeqpuPviPVGkk8C64BrquqQZt1FwJqq+lHzZdKsqjqvzTj1ZqPk7nxgXVX9uM3YNLokewJ7VtVDSXYGFgPHAadj3XXaGLk7Eeuu05IE2LGq1iXZFvgrcDZwLnBLVS1I8gtgaVVd2maseqMxcncW8PuquqnVADvIK7Xd8zHg6ar6R1W9DiwAjm05JmlKqaq/AGs2WX0sML9Zns/gpE0dM0ru1HFV9UJVPdQsrwWWA3th3XXeGLlTx9XAuubpts1fAUcBI02RdddBY+ROo7Cp7Z69gH8NPX8WPzz6pIA7kixOcmbbwWhC9qiqF5rlF4E92gxGE/aNJI80w5MdwtphSfYHDgXux7rrlU1yB9Zd5yWZkWQJsAq4E/g78HJVrW828TyzozbNXVWN1N2FTd1dnGT7FkPsFJtaaXLNq6rDgM8AX2+GSqpnanBfht+I9selwAHAHOAF4CfthqPRJNkJuBk4p6peHX7Nuuu2zeTOuuuBqtpQVXOAvRmMBvxgyyFpnDbNXZJDgO8yyOFHgV0Bb9do2NR2z3PAPkPP927WqQeq6rnmcRXwGwYfIOqHlc29YyP3kK1qOR6NU1WtbD78NwKXY911UnNf2M3AdVV1S7PauuuBzeXOuuuXqnoZWAgcDuySZGbzkueZHTeUu2Oa2wGqql4DrsK6+z+b2u55EDiwmZluO+Ak4LaWY9I4JNmxmUSDJDsCRwOPjv0udchtwGnN8mnArS3GogkYaYoaX8C665xm0pMrgOVV9dOhl6y7jhstd9Zd9yWZnWSXZvndDCYhXc6gQTq+2cy666BRcvfE0JeAYXAvtHXXcPbjDmqmxf8ZMAO4sqoubDkkjUOSDzC4OgswE7je3HVTkl8BRwC7AyuBHwC/BW4E9gWeAU6sKick6phRcncEgyGQBawAvjp0n6Y6IMk84G5gGbCxWf09BvdmWncdNkbuTsa667QkH2YwEdQMBheybqyqHzbnKwsYDF99GDi1ufKnjhgjd3cBs4EAS4CzhiaUmtZsaiVJkiRJveXwY0mSJElSb9nUSpIkSZJ6y6ZWkiRJktRbNrWSJEmSpN6yqZUkSZIk9ZZNrSRJeluSnJNkh7bjkCRNT/6kjyRJeluSrADmVtXqtmORJE0/XqmVJGkaSPKlJI8kWZrk2iT7J7mrWffHJPs2212d5Pih961rHo9I8qckNyV5Isl1GfgW8D5gYZKF7fx3kqTpbGbbAUiSpHdWkoOB7wMfr6rVSXYF5gPzq2p+ki8DlwDHbWFXhwIHA88D9wCfqKpLkpwLHOmVWklSG7xSK0nS1HcU8OuRprOq1gCHA9c3r18LzBvHfh6oqmeraiOwBNj/HYhVkqQJsamVJEnD1tOcHyTZBthu6LXXhpY34IgvSVIH2NRKkjT13QWckGQ3gGb48b3ASc3rpwB3N8srgI80y58Hth3H/tcCO09WsJIkTYTfsEqSNMVV1WNJLgT+nGQD8DDwTeCqJN8GXgLOaDa/HLg1yVLgduC/4zjEZcDtSZ6vqiMn/z+QJGl0/qSPJEmSJKm3HH4sSZIkSeotm1pJkiRJUm/Z1EqSJEmSesumVpIkSZLUWza1kiRJkqTesqmVJEmSJPWWTa0kSZIkqbdsaiVJkiRJvfU/mqaKVK/hycsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "states = df['State'].value_counts().to_frame().reset_index()\n",
    "states.columns = ['title', 'count']\n",
    "plt.rcParams['figure.figsize'] = (15,15)\n",
    "sb.barplot(x='count', y='title', data=states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}