So you want to become a Data Scientist?.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('happiness.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.Year = pd.DatetimeIndex(data.Year.apply(lambda x: datetime.datetime(x,12,31)))\n",
    "#data = data.set_index('Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "# download wikipage\n",
    "wikipage = \"https://en.wikipedia.org/wiki/List_of_sovereign_states_and_dependent_territories_by_continent_(data_file)\"\n",
    "result = requests.get(wikipage)\n",
    "\n",
    "# if successful parse the download into a BeautifulSoup object, that allows us some easy manipulation \n",
    "if result.status_code == 200:\n",
    "    soup = BeautifulSoup(result.content, \"html.parser\")\n",
    "    \n",
    "# find the object with HTML class wibitable sortable\n",
    "table = soup.find('table',{'class':'wikitable sortable'})\n",
    "\n",
    "# loop through all the rows and pull the text\n",
    "new_table = []\n",
    "for row in table.find_all('tr')[1:]:\n",
    "    column_marker = 0\n",
    "    columns = row.find_all('td')\n",
    "    new_table.append([column.get_text() for column in columns])\n",
    "\n",
    "# massage the dat a bit, so that we get a nice lookup-table from country to continent\n",
    "continent_map = pd.DataFrame(new_table, columns=['ContinentCode','Alpha2','Alpha3','PhoneCode','Name'])\n",
    "continent_map = continent_map[['ContinentCode','Name']].drop_duplicates('Name')\n",
    "continent_map = continent_map.set_index('Name')\n",
    "continent_map = continent_map.T\n",
    "continent_map = continent_map.to_dict(orient='index')['ContinentCode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found afghanistan in afghanistan, islamic republic of\n",
      "\n",
      "found albania in albania, republic of\n",
      "\n",
      "found algeria in algeria, people's democratic republic of\n",
      "\n",
      "found angola in angola, republic of\n",
      "\n",
      "found argentina in argentina, argentine republic\n",
      "\n",
      "found armenia in armenia, republic of\n",
      "\n",
      "found australia in australia, commonwealth of\n",
      "\n",
      "found austria in austria, republic of\n",
      "\n",
      "found azerbaijan in azerbaijan, republic of\n",
      "\n",
      "found bahrain in bahrain, kingdom of\n",
      "\n",
      "found bangladesh in bangladesh, people's republic of\n",
      "\n",
      "found belarus in belarus, republic of\n",
      "\n",
      "found belgium in belgium, kingdom of\n",
      "\n",
      "found belize in belize\n",
      "\n",
      "found benin in benin, republic of\n",
      "\n",
      "found bhutan in bhutan, kingdom of\n",
      "\n",
      "found bolivia in bolivia, republic of\n",
      "\n",
      "found bosnia and herzegovina in bosnia and herzegovina\n",
      "\n",
      "found botswana in botswana, republic of\n",
      "\n",
      "found brazil in brazil, federative republic of\n",
      "\n",
      "found bulgaria in bulgaria, republic of\n",
      "\n",
      "found burkina faso in burkina faso\n",
      "\n",
      "found burundi in burundi, republic of\n",
      "\n",
      "found cambodia in cambodia, kingdom of\n",
      "\n",
      "found cameroon in cameroon, republic of\n",
      "\n",
      "found canada in canada\n",
      "\n",
      "found central african republic in central african republic\n",
      "\n",
      "found chad in chad, republic of\n",
      "\n",
      "found chile in chile, republic of\n",
      "\n",
      "found china in china, people's republic of\n",
      "\n",
      "found colombia in colombia, republic of\n",
      "\n",
      "found comoros in comoros, union of the\n",
      "\n",
      "found costa rica in costa rica, republic of\n",
      "\n",
      "found croatia in croatia, republic of\n",
      "\n",
      "found cuba in cuba, republic of\n",
      "\n",
      "found cyprus in cyprus, republic of\n",
      "\n",
      "found czech republic in czech republic\n",
      "\n",
      "found denmark in denmark, kingdom of\n",
      "\n",
      "found djibouti in djibouti, republic of\n",
      "\n",
      "found dominican republic in dominican republic\n",
      "\n",
      "found ecuador in ecuador, republic of\n",
      "\n",
      "found egypt in egypt, arab republic of\n",
      "\n",
      "found el salvador in el salvador, republic of\n",
      "\n",
      "found estonia in estonia, republic of\n",
      "\n",
      "found ethiopia in ethiopia, federal democratic republic of\n",
      "\n",
      "found finland in finland, republic of\n",
      "\n",
      "found france in france, french republic\n",
      "\n",
      "found gabon in gabon, gabonese republic\n",
      "\n",
      "found gambia in gambia, republic of the\n",
      "\n",
      "found georgia in south georgia and the south sandwich islands\n",
      "\n",
      "found germany in germany, federal republic of\n",
      "\n",
      "found ghana in ghana, republic of\n",
      "\n",
      "found greece in greece, hellenic republic\n",
      "\n",
      "found guatemala in guatemala, republic of\n",
      "\n",
      "found guinea in equatorial guinea, republic of\n",
      "\n",
      "found guyana in guyana, co-operative republic of\n",
      "\n",
      "found haiti in haiti, republic of\n",
      "\n",
      "found honduras in honduras, republic of\n",
      "\n",
      "found hungary in hungary, republic of\n",
      "\n",
      "found iceland in iceland, republic of\n",
      "\n",
      "found india in british indian ocean territory (chagos archipelago)\n",
      "\n",
      "found indonesia in indonesia, republic of\n",
      "\n",
      "found iran in iran, islamic republic of\n",
      "\n",
      "found iraq in iraq, republic of\n",
      "\n",
      "found ireland in ireland\n",
      "\n",
      "found israel in israel, state of\n",
      "\n",
      "found italy in italy, italian republic\n",
      "\n",
      "found jamaica in jamaica\n",
      "\n",
      "found japan in japan\n",
      "\n",
      "found jordan in jordan, hashemite kingdom of\n",
      "\n",
      "found kazakhstan in kazakhstan, republic of\n",
      "\n",
      "found kenya in kenya, republic of\n",
      "\n",
      "found kuwait in kuwait, state of\n",
      "\n",
      "found latvia in latvia, republic of\n",
      "\n",
      "found lebanon in lebanon, lebanese republic\n",
      "\n",
      "found lesotho in lesotho, kingdom of\n",
      "\n",
      "found liberia in liberia, republic of\n",
      "\n",
      "found libya in libyan arab jamahiriya\n",
      "\n",
      "found lithuania in lithuania, republic of\n",
      "\n",
      "found luxembourg in luxembourg, grand duchy of\n",
      "\n",
      "found macedonia in macedonia, the former yugoslav republic of\n",
      "\n",
      "found madagascar in madagascar, republic of\n",
      "\n",
      "found malawi in malawi, republic of\n",
      "\n",
      "found malaysia in malaysia\n",
      "\n",
      "found mali in mali, republic of\n",
      "\n",
      "found malta in malta, republic of\n",
      "\n",
      "found mauritania in mauritania, islamic republic of\n",
      "\n",
      "found mauritius in mauritius, republic of\n",
      "\n",
      "found mexico in mexico, united mexican states\n",
      "\n",
      "found moldova in moldova, republic of\n",
      "\n",
      "found mongolia in mongolia\n",
      "\n",
      "found montenegro in montenegro, republic of\n",
      "\n",
      "found morocco in morocco, kingdom of\n",
      "\n",
      "found mozambique in mozambique, republic of\n",
      "\n",
      "found myanmar in myanmar, union of\n",
      "\n",
      "found namibia in namibia, republic of\n",
      "\n",
      "found nepal in nepal, state of\n",
      "\n",
      "found netherlands in netherlands, kingdom of the\n",
      "\n",
      "found new zealand in new zealand\n",
      "\n",
      "found nicaragua in nicaragua, republic of\n",
      "\n",
      "found niger in niger, republic of\n",
      "\n",
      "found nigeria in nigeria, federal republic of\n",
      "\n",
      "found norway in norway, kingdom of\n",
      "\n",
      "found oman in oman, sultanate of\n",
      "\n",
      "found pakistan in pakistan, islamic republic of\n",
      "\n",
      "found panama in panama, republic of\n",
      "\n",
      "found paraguay in paraguay, republic of\n",
      "\n",
      "found peru in peru, republic of\n",
      "\n",
      "found philippines in philippines, republic of the\n",
      "\n",
      "found poland in poland, republic of\n",
      "\n",
      "found portugal in portugal, portuguese republic\n",
      "\n",
      "found qatar in qatar, state of\n",
      "\n",
      "found romania in romania\n",
      "\n",
      "found russia in russian federation\n",
      "\n",
      "found rwanda in rwanda, republic of\n",
      "\n",
      "found saudi arabia in saudi arabia, kingdom of\n",
      "\n",
      "found senegal in senegal, republic of\n",
      "\n",
      "found serbia in serbia, republic of\n",
      "\n",
      "found sierra leone in sierra leone, republic of\n",
      "\n",
      "found singapore in singapore, republic of\n",
      "\n",
      "found slovakia in slovakia (slovak republic)\n",
      "\n",
      "found slovenia in slovenia, republic of\n",
      "\n",
      "found somalia in somalia, somali republic\n",
      "\n",
      "found south africa in south africa, republic of\n",
      "\n",
      "found south sudan in south sudan\n",
      "\n",
      "found spain in spain, kingdom of\n",
      "\n",
      "found sri lanka in sri lanka, democratic socialist republic of\n",
      "\n",
      "found sudan in south sudan\n",
      "\n",
      "found suriname in suriname, republic of\n",
      "\n",
      "found swaziland in swaziland, kingdom of\n",
      "\n",
      "found sweden in sweden, kingdom of\n",
      "\n",
      "found switzerland in switzerland, swiss confederation\n",
      "\n",
      "found syria in syrian arab republic\n",
      "\n",
      "found tajikistan in tajikistan, republic of\n",
      "\n",
      "found tanzania in tanzania, united republic of\n",
      "\n",
      "found thailand in thailand, kingdom of\n",
      "\n",
      "found togo in togo, togolese republic\n",
      "\n",
      "found trinidad and tobago in trinidad and tobago, republic of\n",
      "\n",
      "found tunisia in tunisia, tunisian republic\n",
      "\n",
      "found turkey in turkey, republic of\n",
      "\n",
      "found turkmenistan in turkmenistan\n",
      "\n",
      "found uganda in uganda, republic of\n",
      "\n",
      "found ukraine in ukraine\n",
      "\n",
      "found united arab emirates in united arab emirates\n",
      "\n",
      "found united kingdom in united kingdom of great britain & northern ireland\n",
      "\n",
      "found united states in united states minor outlying islands\n",
      "\n",
      "found uruguay in uruguay, eastern republic of\n",
      "\n",
      "found uzbekistan in uzbekistan, republic of\n",
      "\n",
      "found venezuela in venezuela, bolivarian republic of\n",
      "\n",
      "found vietnam in vietnam, socialist republic of\n",
      "\n",
      "found yemen in yemen\n",
      "\n",
      "found zambia in zambia, republic of\n",
      "\n",
      "found zimbabwe in zimbabwe, republic of\n",
      "\n",
      "did no find: ['Congo (Brazzaville)', 'Congo (Kinshasa)', 'Hong Kong S.A.R. of China', 'Ivory Coast', 'Kosovo', 'Kyrgyzstan', 'Laos', 'North Cyprus', 'Palestinian Territories', 'Somaliland region', 'South Korea', 'Taiwan Province of China']\n"
     ]
    }
   ],
   "source": [
    "country_map = {}\n",
    "not_found = []\n",
    "for country in data['Country name'].unique():\n",
    "    for wiki_country, wiki_continent in continent_map.items():\n",
    "        if country.lower() in wiki_country.lower():\n",
    "            country_map[country] = wiki_continent\n",
    "            print(f'found {country.lower()} in {wiki_country.lower()}')\n",
    "            break\n",
    "    else:\n",
    "        not_found.append(country)\n",
    "        \n",
    "print(f'did no find: {not_found}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "not_cound_countries = {\n",
    "    'Congo (Brazzaville)': 'AF',\n",
    "    'Congo (Kinshasa)': 'AF',\n",
    "    'Hong Kong S.A.R. of China': 'AS',\n",
    "    'Ivory Coast': 'AF',\n",
    "    'Kosovo': 'EU',\n",
    "    'Kyrgyzstan': 'AS',\n",
    "    'Laos': 'AS',\n",
    "    'North Cyprus': 'EU',\n",
    "    'Palestinian Territories': 'AS',\n",
    "    'Somaliland region': 'AF',\n",
    "    'South Korea': 'AS',\n",
    "    'Taiwan Province of China': 'AS',\n",
    "    # there is a wrong mapping of Georgia to AN, which would become apparent later, I am going to fix it here\n",
    "    'Georgia': 'AS'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for country, continent in not_cound_countries.items():\n",
    "    country_map[country] = continent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Continent'] = data['Country name'].map(country_map)\n",
    "data['Continent'] = data['Continent'].replace({\n",
    "    'NA':'North America',\n",
    "    'SA':'South America',\n",
    "    'AS':'Asia',\n",
    "    'EU':'Europe',\n",
    "    'AF':'Africa',\n",
    "    'OC':'Oceania'\n",
    "})\n",
    "data.to_csv('happiness_with_continent.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data into a DataFrame (you can see that we use pd. instead of pandas.)\n",
    "data = pd.read_csv('happiness_with_continent.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Life Ladder</th>\n",
       "      <th>Log GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy at birth</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Positive affect</th>\n",
       "      <th>...</th>\n",
       "      <th>gini of household income reported in Gallup, by wp5-year</th>\n",
       "      <th>Most people can be trusted, Gallup</th>\n",
       "      <th>Most people can be trusted, WVS round 1981-1984</th>\n",
       "      <th>Most people can be trusted, WVS round 1989-1993</th>\n",
       "      <th>Most people can be trusted, WVS round 1994-1998</th>\n",
       "      <th>Most people can be trusted, WVS round 1999-2004</th>\n",
       "      <th>Most people can be trusted, WVS round 2005-2009</th>\n",
       "      <th>Most people can be trusted, WVS round 2010-2014</th>\n",
       "      <th>region</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.723590</td>\n",
       "      <td>7.168690</td>\n",
       "      <td>0.450662</td>\n",
       "      <td>50.799999</td>\n",
       "      <td>0.718114</td>\n",
       "      <td>0.177889</td>\n",
       "      <td>0.881686</td>\n",
       "      <td>0.517637</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AS</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2009</td>\n",
       "      <td>4.401778</td>\n",
       "      <td>7.333790</td>\n",
       "      <td>0.552308</td>\n",
       "      <td>51.200001</td>\n",
       "      <td>0.678896</td>\n",
       "      <td>0.200178</td>\n",
       "      <td>0.850035</td>\n",
       "      <td>0.583926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.441906</td>\n",
       "      <td>0.286315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AS</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>4.758381</td>\n",
       "      <td>7.386629</td>\n",
       "      <td>0.539075</td>\n",
       "      <td>51.599998</td>\n",
       "      <td>0.600127</td>\n",
       "      <td>0.134353</td>\n",
       "      <td>0.706766</td>\n",
       "      <td>0.618265</td>\n",
       "      <td>...</td>\n",
       "      <td>0.327318</td>\n",
       "      <td>0.275833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AS</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.831719</td>\n",
       "      <td>7.415019</td>\n",
       "      <td>0.521104</td>\n",
       "      <td>51.919998</td>\n",
       "      <td>0.495901</td>\n",
       "      <td>0.172137</td>\n",
       "      <td>0.731109</td>\n",
       "      <td>0.611387</td>\n",
       "      <td>...</td>\n",
       "      <td>0.336764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AS</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.782938</td>\n",
       "      <td>7.517126</td>\n",
       "      <td>0.520637</td>\n",
       "      <td>52.240002</td>\n",
       "      <td>0.530935</td>\n",
       "      <td>0.244273</td>\n",
       "      <td>0.775620</td>\n",
       "      <td>0.710385</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AS</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country name  Year  Life Ladder  Log GDP per capita  Social support  \\\n",
       "0  Afghanistan  2008     3.723590            7.168690        0.450662   \n",
       "1  Afghanistan  2009     4.401778            7.333790        0.552308   \n",
       "2  Afghanistan  2010     4.758381            7.386629        0.539075   \n",
       "3  Afghanistan  2011     3.831719            7.415019        0.521104   \n",
       "4  Afghanistan  2012     3.782938            7.517126        0.520637   \n",
       "\n",
       "   Healthy life expectancy at birth  Freedom to make life choices  Generosity  \\\n",
       "0                         50.799999                      0.718114    0.177889   \n",
       "1                         51.200001                      0.678896    0.200178   \n",
       "2                         51.599998                      0.600127    0.134353   \n",
       "3                         51.919998                      0.495901    0.172137   \n",
       "4                         52.240002                      0.530935    0.244273   \n",
       "\n",
       "   Perceptions of corruption  Positive affect  ...  \\\n",
       "0                   0.881686         0.517637  ...   \n",
       "1                   0.850035         0.583926  ...   \n",
       "2                   0.706766         0.618265  ...   \n",
       "3                   0.731109         0.611387  ...   \n",
       "4                   0.775620         0.710385  ...   \n",
       "\n",
       "   gini of household income reported in Gallup, by wp5-year  \\\n",
       "0                                                NaN          \n",
       "1                                           0.441906          \n",
       "2                                           0.327318          \n",
       "3                                           0.336764          \n",
       "4                                           0.344540          \n",
       "\n",
       "   Most people can be trusted, Gallup  \\\n",
       "0                                 NaN   \n",
       "1                            0.286315   \n",
       "2                            0.275833   \n",
       "3                                 NaN   \n",
       "4                                 NaN   \n",
       "\n",
       "   Most people can be trusted, WVS round 1981-1984  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "3                                              NaN   \n",
       "4                                              NaN   \n",
       "\n",
       "   Most people can be trusted, WVS round 1989-1993  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "3                                              NaN   \n",
       "4                                              NaN   \n",
       "\n",
       "   Most people can be trusted, WVS round 1994-1998  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "3                                              NaN   \n",
       "4                                              NaN   \n",
       "\n",
       "   Most people can be trusted, WVS round 1999-2004  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "3                                              NaN   \n",
       "4                                              NaN   \n",
       "\n",
       "   Most people can be trusted, WVS round 2005-2009  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "3                                              NaN   \n",
       "4                                              NaN   \n",
       "\n",
       "   Most people can be trusted, WVS round 2010-2014  region  Continent  \n",
       "0                                              NaN      AS       Asia  \n",
       "1                                              NaN      AS       Asia  \n",
       "2                                              NaN      AS       Asia  \n",
       "3                                              NaN      AS       Asia  \n",
       "4                                              NaN      AS       Asia  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview the data: first 5 rows\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Life Ladder</th>\n",
       "      <th>Log GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy at birth</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Positive affect</th>\n",
       "      <th>...</th>\n",
       "      <th>gini of household income reported in Gallup, by wp5-year</th>\n",
       "      <th>Most people can be trusted, Gallup</th>\n",
       "      <th>Most people can be trusted, WVS round 1981-1984</th>\n",
       "      <th>Most people can be trusted, WVS round 1989-1993</th>\n",
       "      <th>Most people can be trusted, WVS round 1994-1998</th>\n",
       "      <th>Most people can be trusted, WVS round 1999-2004</th>\n",
       "      <th>Most people can be trusted, WVS round 2005-2009</th>\n",
       "      <th>Most people can be trusted, WVS round 2010-2014</th>\n",
       "      <th>region</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.184451</td>\n",
       "      <td>7.562753</td>\n",
       "      <td>0.765839</td>\n",
       "      <td>52.380001</td>\n",
       "      <td>0.642034</td>\n",
       "      <td>-0.048634</td>\n",
       "      <td>0.820217</td>\n",
       "      <td>0.725214</td>\n",
       "      <td>...</td>\n",
       "      <td>0.601080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.082942</td>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>3.703191</td>\n",
       "      <td>7.556052</td>\n",
       "      <td>0.735800</td>\n",
       "      <td>53.799999</td>\n",
       "      <td>0.667193</td>\n",
       "      <td>-0.097354</td>\n",
       "      <td>0.810457</td>\n",
       "      <td>0.715079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.655137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.082942</td>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.735400</td>\n",
       "      <td>7.538829</td>\n",
       "      <td>0.768425</td>\n",
       "      <td>54.400002</td>\n",
       "      <td>0.732971</td>\n",
       "      <td>-0.068105</td>\n",
       "      <td>0.723612</td>\n",
       "      <td>0.737636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.596690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.082942</td>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2017</td>\n",
       "      <td>3.638300</td>\n",
       "      <td>7.549491</td>\n",
       "      <td>0.754147</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.752826</td>\n",
       "      <td>-0.069670</td>\n",
       "      <td>0.751208</td>\n",
       "      <td>0.806428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.581484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.082942</td>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2018</td>\n",
       "      <td>3.616480</td>\n",
       "      <td>7.553395</td>\n",
       "      <td>0.775388</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>0.762675</td>\n",
       "      <td>-0.038384</td>\n",
       "      <td>0.844209</td>\n",
       "      <td>0.710119</td>\n",
       "      <td>...</td>\n",
       "      <td>0.541772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.082942</td>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country name  Year  Life Ladder  Log GDP per capita  Social support  \\\n",
       "1699     Zimbabwe  2014     4.184451            7.562753        0.765839   \n",
       "1700     Zimbabwe  2015     3.703191            7.556052        0.735800   \n",
       "1701     Zimbabwe  2016     3.735400            7.538829        0.768425   \n",
       "1702     Zimbabwe  2017     3.638300            7.549491        0.754147   \n",
       "1703     Zimbabwe  2018     3.616480            7.553395        0.775388   \n",
       "\n",
       "      Healthy life expectancy at birth  Freedom to make life choices  \\\n",
       "1699                         52.380001                      0.642034   \n",
       "1700                         53.799999                      0.667193   \n",
       "1701                         54.400002                      0.732971   \n",
       "1702                         55.000000                      0.752826   \n",
       "1703                         55.599998                      0.762675   \n",
       "\n",
       "      Generosity  Perceptions of corruption  Positive affect  ...  \\\n",
       "1699   -0.048634                   0.820217         0.725214  ...   \n",
       "1700   -0.097354                   0.810457         0.715079  ...   \n",
       "1701   -0.068105                   0.723612         0.737636  ...   \n",
       "1702   -0.069670                   0.751208         0.806428  ...   \n",
       "1703   -0.038384                   0.844209         0.710119  ...   \n",
       "\n",
       "      gini of household income reported in Gallup, by wp5-year  \\\n",
       "1699                                           0.601080          \n",
       "1700                                           0.655137          \n",
       "1701                                           0.596690          \n",
       "1702                                           0.581484          \n",
       "1703                                           0.541772          \n",
       "\n",
       "      Most people can be trusted, Gallup  \\\n",
       "1699                                 NaN   \n",
       "1700                                 NaN   \n",
       "1701                                 NaN   \n",
       "1702                                 NaN   \n",
       "1703                                 NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 1981-1984  \\\n",
       "1699                                              NaN   \n",
       "1700                                              NaN   \n",
       "1701                                              NaN   \n",
       "1702                                              NaN   \n",
       "1703                                              NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 1989-1993  \\\n",
       "1699                                              NaN   \n",
       "1700                                              NaN   \n",
       "1701                                              NaN   \n",
       "1702                                              NaN   \n",
       "1703                                              NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 1994-1998  \\\n",
       "1699                                              NaN   \n",
       "1700                                              NaN   \n",
       "1701                                              NaN   \n",
       "1702                                              NaN   \n",
       "1703                                              NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 1999-2004  \\\n",
       "1699                                         0.116683   \n",
       "1700                                         0.116683   \n",
       "1701                                         0.116683   \n",
       "1702                                         0.116683   \n",
       "1703                                         0.116683   \n",
       "\n",
       "      Most people can be trusted, WVS round 2005-2009  \\\n",
       "1699                                              NaN   \n",
       "1700                                              NaN   \n",
       "1701                                              NaN   \n",
       "1702                                              NaN   \n",
       "1703                                              NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 2010-2014  region  Continent  \n",
       "1699                                         0.082942      AF     Africa  \n",
       "1700                                         0.082942      AF     Africa  \n",
       "1701                                         0.082942      AF     Africa  \n",
       "1702                                         0.082942      AF     Africa  \n",
       "1703                                         0.082942      AF     Africa  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview the data: last 5 rows\n",
    "data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Life Ladder</th>\n",
       "      <th>Log GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy at birth</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Positive affect</th>\n",
       "      <th>...</th>\n",
       "      <th>gini of household income reported in Gallup, by wp5-year</th>\n",
       "      <th>Most people can be trusted, Gallup</th>\n",
       "      <th>Most people can be trusted, WVS round 1981-1984</th>\n",
       "      <th>Most people can be trusted, WVS round 1989-1993</th>\n",
       "      <th>Most people can be trusted, WVS round 1994-1998</th>\n",
       "      <th>Most people can be trusted, WVS round 1999-2004</th>\n",
       "      <th>Most people can be trusted, WVS round 2005-2009</th>\n",
       "      <th>Most people can be trusted, WVS round 2010-2014</th>\n",
       "      <th>region</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1632</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2009</td>\n",
       "      <td>5.260721</td>\n",
       "      <td>8.298897</td>\n",
       "      <td>0.904678</td>\n",
       "      <td>62.759998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012725</td>\n",
       "      <td>0.610258</td>\n",
       "      <td>0.735559</td>\n",
       "      <td>...</td>\n",
       "      <td>0.319250</td>\n",
       "      <td>0.265871</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.139224</td>\n",
       "      <td>AS</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>2013</td>\n",
       "      <td>5.402427</td>\n",
       "      <td>8.045762</td>\n",
       "      <td>0.850716</td>\n",
       "      <td>62.360001</td>\n",
       "      <td>0.755037</td>\n",
       "      <td>-0.063735</td>\n",
       "      <td>0.899560</td>\n",
       "      <td>0.722004</td>\n",
       "      <td>...</td>\n",
       "      <td>0.374307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.165868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.377254</td>\n",
       "      <td>AS</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>2009</td>\n",
       "      <td>4.110626</td>\n",
       "      <td>7.790645</td>\n",
       "      <td>0.818258</td>\n",
       "      <td>57.700001</td>\n",
       "      <td>0.937233</td>\n",
       "      <td>0.151726</td>\n",
       "      <td>0.964779</td>\n",
       "      <td>0.796208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.391147</td>\n",
       "      <td>0.119404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AS</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>2013</td>\n",
       "      <td>5.756059</td>\n",
       "      <td>8.421094</td>\n",
       "      <td>0.802883</td>\n",
       "      <td>62.259998</td>\n",
       "      <td>0.657734</td>\n",
       "      <td>-0.021719</td>\n",
       "      <td>0.940632</td>\n",
       "      <td>0.581804</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.218496</td>\n",
       "      <td>0.141357</td>\n",
       "      <td>0.175908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EU</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.000517</td>\n",
       "      <td>7.437034</td>\n",
       "      <td>0.739956</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>0.772344</td>\n",
       "      <td>0.069052</td>\n",
       "      <td>0.815770</td>\n",
       "      <td>0.703376</td>\n",
       "      <td>...</td>\n",
       "      <td>0.640637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.075872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country name  Year  Life Ladder  Log GDP per capita  Social support  \\\n",
       "1632   Uzbekistan  2009     5.260721            8.298897        0.904678   \n",
       "819    Kyrgyzstan  2013     5.402427            8.045762        0.850716   \n",
       "235      Cambodia  2009     4.110626            7.790645        0.818258   \n",
       "998       Moldova  2013     5.756059            8.421094        0.802883   \n",
       "1565       Uganda  2017     4.000517            7.437034        0.739956   \n",
       "\n",
       "      Healthy life expectancy at birth  Freedom to make life choices  \\\n",
       "1632                         62.759998                           NaN   \n",
       "819                          62.360001                      0.755037   \n",
       "235                          57.700001                      0.937233   \n",
       "998                          62.259998                      0.657734   \n",
       "1565                         55.299999                      0.772344   \n",
       "\n",
       "      Generosity  Perceptions of corruption  Positive affect  ...  \\\n",
       "1632    0.012725                   0.610258         0.735559  ...   \n",
       "819    -0.063735                   0.899560         0.722004  ...   \n",
       "235     0.151726                   0.964779         0.796208  ...   \n",
       "998    -0.021719                   0.940632         0.581804  ...   \n",
       "1565    0.069052                   0.815770         0.703376  ...   \n",
       "\n",
       "      gini of household income reported in Gallup, by wp5-year  \\\n",
       "1632                                           0.319250          \n",
       "819                                            0.374307          \n",
       "235                                            0.391147          \n",
       "998                                            0.355757          \n",
       "1565                                           0.640637          \n",
       "\n",
       "      Most people can be trusted, Gallup  \\\n",
       "1632                            0.265871   \n",
       "819                                  NaN   \n",
       "235                             0.119404   \n",
       "998                                  NaN   \n",
       "1565                                 NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 1981-1984  \\\n",
       "1632                                              NaN   \n",
       "819                                               NaN   \n",
       "235                                               NaN   \n",
       "998                                               NaN   \n",
       "1565                                              NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 1989-1993  \\\n",
       "1632                                              NaN   \n",
       "819                                               NaN   \n",
       "235                                               NaN   \n",
       "998                                               NaN   \n",
       "1565                                              NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 1994-1998  \\\n",
       "1632                                              NaN   \n",
       "819                                               NaN   \n",
       "235                                               NaN   \n",
       "998                                          0.218496   \n",
       "1565                                              NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 1999-2004  \\\n",
       "1632                                              NaN   \n",
       "819                                          0.165868   \n",
       "235                                               NaN   \n",
       "998                                          0.141357   \n",
       "1565                                         0.075872   \n",
       "\n",
       "      Most people can be trusted, WVS round 2005-2009  \\\n",
       "1632                                              NaN   \n",
       "819                                               NaN   \n",
       "235                                               NaN   \n",
       "998                                          0.175908   \n",
       "1565                                              NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 2010-2014  region  Continent  \n",
       "1632                                         0.139224      AS       Asia  \n",
       "819                                          0.377254      AS       Asia  \n",
       "235                                               NaN      AS       Asia  \n",
       "998                                               NaN      EU     Europe  \n",
       "1565                                              NaN      AF     Africa  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview the data: 5 random rows\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>Year</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Life Ladder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2013</td>\n",
       "      <td>North America</td>\n",
       "      <td>6.201498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2006</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>7.305014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2015</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5.265249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>Africa</td>\n",
       "      <td>4.074250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2009</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5.309526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year      Continent  Life Ladder\n",
       "50  2013  North America     6.201498\n",
       "10  2006        Oceania     7.305014\n",
       "60  2015           Asia     5.265249\n",
       "6   2006         Africa     4.074250\n",
       "25  2009           Asia     5.309526"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['Year','Continent'])['Life Ladder'].mean().reset_index().sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_by_year_and_continent = data.groupby(['Year','Continent'])['Life Ladder'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Life Ladder Development by Region')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YM2cONm/ejM2bN+Oiiy7yqhbyx4IFC7Bo0SLs27cP//jHPwD4vi9kWca8efOwZcsWj5DVYrHgk08+6fJ+582bh/r6evz73//22P7222+joaHB6+dOREQUDlgBRUREFGR/+tOfEBkZ6bV94cKFnfYIau/+++/Hjh07cO211+Laa6/F4MGDsWnTJmzcuBGLFy/2qBTpzPvvv49du3Z5bR8zZgyWLFmC2bNn47PPPsOjjz6KcePGoaioCKtXr0ZTUxMAoKGhAYCz0fXHH3+MX//617jhhhsQHx+PVatWeS0j8nXcbbfdhrVr1+LOO+/ENddcg1GjRuHHH3/Ehx9+iNmzZ2P27Nk+7aOOHDx4UA28HA4HKioqsG7dOuTn52Pp0qUePar8nccll1yC5cuXq1/3xmNyXe+ee+7BkiVLMHToUGzfvh1r167FhRdeiDlz5niMf//991FfX49zzz0XW7duxcaNG3HnnXciLS2tx/vMpTeOOXeuvkUfffQRhBC44oor1H5UHfn4449RX1+Pc845B5s3b8bGjRtxyy23eFV3LVq0CHfffTcA4IknnvDzUXpbvnw5tm7dir/85S/42c9+hiFDhvi8L+6++25s3rwZixcvxvXXXw+9Xo933nkHFosFQOfLHq+++mq8//77WLFiBQ4dOoTc3Fzs3bsX7733HsaPH4+rr776rB8XERFRsDGAIiIiCrLOqh+GDx/ucwA1ZMgQrF69Gi+88AJWr16NxsZGZGRkYPny5erZwbrz3Xff4bvvvvPaPn/+fCxZsgSPPfYYIiIisGHDBnz44YdISUnB5ZdfjgsuuABLlizB9u3bMXbsWJjNZvz73//GM888g1WrVsHhcODiiy/GqFGjPAIAX8fFxsZi1apVeOGFF/DFF19g1apVGDx4MO644w7ceuutZ9Xv5quvvsJXX30FwNk3Jzo6GmPHjsWf/vQnXHzxxR5j/Z3HhRdeiMceewwjR470ajLd08fkut6f//xnfPbZZ6itrUVGRgYefPBB3HjjjV7jV65cib/85S9Yu3YtMjIy8Pjjj+OXv/xlj/eXu9445tyNGDEC119/Pd577z3s2bMHU6dO9VhO196rr76KJ554Ap988gmSk5OxfPnyDvfB3LlzYTabYTabe2WpWmJiIh544AE88sgjePTRR/HGG2/4vC+GDBmCf/3rX3jqqafw8ssvw2Aw4PLLL4dGo8Frr73WYX8owNk36o033sBf/vIXfP755/joo4+QkpKC2267DbfffvtZVXURERGFiiS66nJIRERERH3eiy++iJUrV2L9+vVIT08P9XRCymq1YsaMGVi8eDEeeOCBkM6lsrIS8fHxXpVOjz/+OP7zn/8gPz+fYRIREQ0Y7AFFRERERP3Gp59+irq6Olx55ZWhngruvvtu/PznP/foc9XU1ISNGzdi9OjRDJ+IiGhA4RI8IiIiIgp7//jHP/DTTz9hy5YtmDt3LkaMGBHqKeGyyy7DI488gltvvRXz589HS0sLPvroI5SWluL3v/99qKdHREQUVAygiIiIiCjsORwOfP3118jLy+uV5uO94eqrr4bBYMA///lPPPPMM5BlGbm5uXjjjTcwZcqUUE+PiIgoqNgDioiIiIiIiIiIAoo9oIiIiIiIiIiIKKAYQBERERERERERUUAN2B5Q1dUNUBSuPhyoEhLMqKysD/U0aIDg8UbBxOONgonHGwUbjzkKJh5vFEz94XiTZQlxcZGdfn/ABlCKIhhADXD8+VMw8XijYOLxRsHE442CjcccBROPNwqm/n68cQkeEREREREREREFFAMoIiIiIiIiIiIKKAZQREREREREREQUUAygiIiIiIiIiIgooBhAERERERERERFRQDGAIiIiIiIiIiKigGIARUREREREREREAcUAioiIiIiIiIiIAkobqjvesWMHli5d2un3V6xYgSuuuMJr+4cffogHH3zQa/u1116LRx99tFfnSEREREREREREZy9kAVROTg5WrVrlsU0Igd/+9rdobGzEnDlzOrxeQUEBMjMz8fTTT3tsT0xMDNhciYiIiIiIiIio50IWQJnNZowfP95j25tvvonjx4/jnXfeQXx8fIfXO3jwIHJycryuS0REREREREREfVOf6QFVXl6O559/HkuWLEFeXl6n4woKCpCdnR3EmRERERERERER0dnoMwHUiy++CFmWcc8993Q65syZM6isrMT+/fvxs5/9DDk5ObjooovwwQcfBHGmRERERERERETkj5AtwXNXWVmJDz74ADfddBOio6M7HVdQUAAAKCoqwgMPPACDwYAPPvgADz30EBwOB6666iqf7zMhwXzW86bwlpQUFeop0ADC442CiccbBROPNwo2HnMUTDzeKJj6+/EmCSFEqCfx0ksvYeXKldiyZUunvZ8AoKqqCvn5+Zg8eTLM5rYAadmyZTh69Ci2bNni831WVtZDUUL+0ClEkpKiUF5eF+pp0ADB442CiccbBROPNwo2HnMUTDzeKJj6w/Emy1KXxT59Ygnel19+iVmzZnUZPgFAfHw85s6d6xE+AcCcOXNQVlaGqqqqQE6TaECSZQlajQydRgON5Pys1ciQZSnUUyMiIiIiIqIwEfIleGVlZdi/fz+WLVvW7didO3fiyJEjuPrqqz22t7S0QKvVIiqqf5erEQWbViOjpcmOreuO4ODeUiiKgCxLyM5NwawFI2EwaWF3KKGeJhEREREREfVxIQ+g8vPzAQATJ07sduyuXbuwYsUKjBs3DqNHjwYAKIqCL7/8Eueeey50Ol1A50o0kGg1Mk4ercJHq/IBt9WqiiJwYHcJDuwpwaWL85A5Ip4hFBEREREREXUp5EvwDh06BJPJhLS0NK/vVVVVYdeuXaivrwcAXHnllRg8eDDuuusufPLJJ9i4cSNuu+02HDp0CPfff3+wp07Ub8myhJYmu1f45EEAH63KR0uTncvxiIiIiIiIqEshD6AqKio6PfPdpk2bsHjxYuzbtw8AEBMTg3/9618455xz8Mc//hH33HMPGhsb8cYbbyAvLy+Y0ybq12RJwtZ1RzoPn1wE8PX6IwygiIiIiIiIqEt94ix4ocCz4A1s/eEMA4Gk02iw8o8bffodkWUJdy2fC5vDEYSZhScebxRMPN4omHi8UbDxmKNg4vFGwdQfjrewOAseEfUN9bUtKNhTioZ6q88BraIINNRbsW/naVSVN2CAZtpERERERETUhZA3ISei0GlusuH0qRoUnahB0clqWKqaAABD7oiHLEs+V0DZbQ5s+fIwAMBo0iIlPQap6TFIzYhBYrIZGg2zbiIiIiIiooGMARTRAGKzOVBaZEHRiRoUn6xGeWl9h+OOHapAVk4yCvaUdnubWTnJOHaoXL3c3GTHicOVOHG4EgCg1clISYtWA6lBg6Oh02l65wERERERERFRWGAARdSPKYrAmZJaFLdWOJUW10JxdF7VpNFISEmPAWTgvPkjUbC3tOtG5JJz3JGDZRg2KgElRRY0N9k9hthtirPC6kQNAGfFVFJqlBpIpaZHw2DU9cbDJSIiIiIioj6KARRRPyKEQFVFI4pPVKPoZA1KCmtgbem8ObgkAUkpUUgbGov0zDikpEVD21qdpNXIuHRxHj5ald9xCCUBly3OgylShzF5gzEmbzCEEKiubERJoQUlRRaUFFpQX9vicTVFESgrrkVZcS127SgEACQkRTrDqAzn0r3IKEOv7RMiIiIiIiIKPQZQRGGuztKMohPVKD5Zg+KTNWhssHY5Pi4hAmmZsUgfGofBQ2JhMHb8NGB3KMgcEY9b752Fr9cfQcGeUiiKgCxLGD0uBefNHwmDSQu7Q1GvI0kS4hMjEZ8YiZwJg9X5uQdS1ZWNXvdVWd6AyvIG7P3pNAAgOtaohlGpGTGIiTNBkqSe7iIiIiIiIiIKMQZQRGGmqdHVOLwaRSeqUVvT3OX4yCgD0jNjkTY0DumZsX5VF9kdCnRGDeYvGo0Fi8aoAZSQBBRFeIRPnYmKMSIqxois3OTW+VtRUliLkqIalBTWoqKsDu1PnFdb04zammYc3FMGAIiI1HsEUvFJkZBlBlJEREREnZFlCbIkQYLU9j8cBBQhfD7bMRFRb2IARdTH2awOlBRZnFVOJ2pQcabjxuEuBqMWg4c4K5zSh8aedfWQoggobmvwHJ2v6POJKUKP4dmJGJ6dCACwtthRdrrWWSVVaEFZSR0cds9gq7HBiqMF5Tha4Gx2rjdoPM60NyglChotz7RHREREBDhbKbQ02bF13REc3NtWxZ6dm4JZC7yr2ImIgoEBFFEf43AoOFNSpwZOZadru3yXSquVkZoRoy6rSxhkDqvqIL1Bi4xh8cgYFg8AcNgVlJfW4XTrsr3SIotXHytriwOnjlbh1NEqAIBGKyM5NUrtI5U8OBp6A5/eiIiIaODRamScPFrl1cdTUQQO7C7BgT0luHRxHjJHxDOEIqKg4is0ohATQqDyTAOKT7oah1tgs3bdOHzQ4GjnsrrWxuH9qfpHo5WRkh7jPBsfnP8sVZU3ePSRat/nymFXcLrQgtOFFgDOfZSYHIXUjGikpsciNSMapgh90B8LERERUTDJsoSWJnvnJ5EBAAF8tCoft947CzqjhsvxiChoGECFIa7nDn+1NU0oOlGjhk7NjbYux8cnRTornDJjMXhI7ICq7pFlCYnJZiQmmzFuUhqEEKitacbpUzVqINW+D5YQQHlpHcpL67D7+2IAzubr7n2komKMoXg4RAHh/nehproROo2GfxeIiAYgWZKwdd2RzsMnFwF8vf4I5l8ymn8niChoBs6r2H6C67nDU2ODtfUsddUoOlGDOkvXjcOjog3OpuFD45CWGYuISFbvuEiShJg4E2LiTBiTlwoAaKhrUcOokkILKssbvK5XXdmI6spG7N9VAgAwRxvUQGpwRgxiEyJ4pj0KS/y7QEQ0MAkhYG2xo7amGXWWFtRZmpGTNxgH95b6dP2CPaVYsGhMgGdJRNSGAVQY4Xru8GFtsaOk0IKi1sCpqoNAxJ3RpEVaprNpeFpmHKJjjQxD/BAZZcDIMYMwcswgAEBLsw2lRbVqH6nykjqvd/fqa1tweN8ZHN53BgBgNOmQmh6t9pFKTI4Kq15aNDDx7wJReGEVO/lDCIGWZjvqLM1uHy1tX9c2e/XJHDV6kM/HkqIINDZYcexwuVodzv8/iSiQGECFCa7n7tscDgVlxbXOxuEna3Cmg8DDnVYnY3BGrFvj8Ej+we9FBqMOmSMTkDkyAQBgszlw5nQdSgqdy/ZKi2tht3m+GG9usuH44UocP1wJANDpNUgeHK1WSSUPjoJWp/F5DlwSRYHGvwtE4YXVitReZwFTrdvlrvqCdkaWJZ+e72VZgs3qwIZPDwIAIs16pGbEqGcajk+K5JtxRNSrGECFCX/Wc29ddxjjp2Sg/Ew9DEat+mE06aA3aPmHpBcIIVBRVo+ikzUoPlGNkiKLV6DhTpYlDBochfTMOKQNjUXy4GhoNP2ncXhfp9NpkJbpDPwAZ2BYeaYeJa2Ny0uLLGhusntcx2Z1oOhENYpOVANo/Rm6zrTX2iTdYOz4KZQvMuhsidagUnG0flYEFEWBaP3a4RCIMhvwNft8EIUFVisOTO4Bk3OZnNtHbUuPAyZ3Wp2MqBij+tHcbEV2TjIO7Ol+GV5WTjKOHSpXLzfUW3HkQDmOHHBu0xs0SEmLQUprhfig1Gho+9GJb4go+BhAhQkJks/ruQ/uLcOU84Zh3UcHOvy+3qCBwaCFwaiDXg2nXEGVziO0ct8W7uHV2VSkCCFgqW5C8ckaFJ2oxulTNV6BRXsJgyLVwGlwRix0et+rZyiwNBoZg1KjMSg1GnlTMiCEQHVlo9pDqqTIgvraFo/rKIpAaXEtSotrsROFAJw/Y1dT89SMGESaDXyREURqSKMINZhpC2yUtuCmXYijOITzuh7bBRSH4nnZbbvw2tbuNjsLi9rfR7v7EoqAo3Wcc06t233IiW64YzoO7ivzaV8V7CnF1NnD8Pn7e2E06mA06WAwOd+YcD7/Oz87t+tgNGr9qvgjos6xWrH/EkKgual9BVNgA6aoGCOiY4yIijEgKsYIo0nnUUUvyxJmXTAKB4O8qUcAACAASURBVPaWdv0GhQTMnD8SB/eWImNYHEqLa73mam1x4NSxKpw6VuW8bY2EQSlRblVS0TAYdWf1+IhoYGEAFSZcL1p8HdsVa4sD1hYH6tq9wPaF3qDpIKTqKLRqfSHT+rXeoA3pErOeVKQ01ltRdLIaxSdqUHSy2iuQaC861ujWxykWpgg2Dg8XkiQhPjES8YmRyJkwGABQZ2lWK6RKiiyoqWz0ul7lmQZUnmnA3p9OAwBSM2Jw0WU5fepFhhDOMKPDgKVd6OEdrniHOMI9dOkoXOkgxGkLYtrdnuh4u1fY08F9ucYMdP78XVAcAqVFtT7ftlYrq2GUweQeUGk9Q6zW8MrQ+pwfTtWd7MdDwcCzkp29UC1r7ypgci2T66oC3heugCm6Xcjk+jCa/PsfWlEEDCYtLl2c1/n/IxJw2eI8RETqMG5SGsZNSoOiCGd1eJEFpUW1KCm0oLHB6nnbjrY349D6Zlx8UmRrZXg0BmfEwBzNswwTUecYQIUJWZb8Ws+tM2gwYnQSWpptaG6yo6XZ+WFt6bpqpztqeGXx/7p6g+/VVu6X9QbNWYVXvlakpGXG4viRCjVwqq7wDhzcmSJ0ag8nV+Nw6j9c//hl5SYDAJoarSgprEVJUQ1KCi2oKKv3qlIZmT0I2zcf8+lFxpZ1h5GTl4rDB874VHXjUIOZzsIizyVa7tspPLme92WNBElyfpZl2WO7P38X/GW3K7DXtaChzr83K1xvVKiBldGzssqz+so5LhRvUnCpLAWD3a5A9qOKvWBPKabNHo4vPtwHnV7j/NBpoNPLrZ/dt3l/rW39WquV+01vyUD+rjoDJluHvZfqejFgio41ISra0CsBky/sDgWZI+Jx672z8PX6IyjY07bfRo9LwXnzvfebLEtISolCUkoUzpnk3De1Nc1qZXhJkQWWqiav+6oqb0BVeQP27XS+Ged+luHU9BjEJfIswwMF39QhX0hC+FLo3/9UVtaH1S+CViNj3ccFOLC7pNuxY/NSMf+S0bDbvf9gKoqAzeoMo5zBlE0Np9o+Ot7W/iwbwSJJbeFVZ9VWBpOudVmh5zijSQt7i4JX/rS12zLk62+bhvf/vRP1nbzY0uk1GJwRg7ShcUjPjEV8EhuHD2TWFjvKTteqVVJnTtfiutum4a2XtvscCFz/P9Pw5l+/DcJs+y9JAmSNWygjdxDUyBIkt8DG+SE7L0sdb5fcb0+9XbmDbc7reI3v6L7az6eD7a77kSR0+/ziz9+FMXmpmDlvBMrL6tDcZFOf/9WvWz83N9vQ0mRHc5MtqH8jJQnqc7mxm8DKFWgZTTpodT17kd3ZGxNtEwKXyvooKSkK5eV1oZ5Gn9DUaEXlmQZUlNWj4kw9KsrqUVPZiKW3T/fruf6GO/wb3xmvgKqD0ErrHmx1E3BpdZqgt2I4299VIQSaGm1ey+J6M2DS6TXOMKldwBQd6/xsMIZuFUCHgYDU8zeoGhuszuqoohqUFtWivLSu2yXjBqNWXa6XmhGDpJSosKqU5XOcb/imTu/oD8ebLEtISDB3+n1WQIUJRQjMWjASB/aUdBuknDd/JByOjgfJstQazugQHevnHBQBa0vHIVVzk3doZW0d19xsP6v170JAvU2g2a/rzlowCuVldT5VpHy/7QQmTM1wlsnDua9S0qLVwCkpNbz+YFJg6Q1aZAyLR8aweACAw65AI8m9tlS2N0kSPAIUSZag8QhmOghwJO9wReMRzHhX4shd3WZnIU431T0e96VxD29avx7AIbA/fxdmzR8JrUGDwUN8e+IXQsBuU1oDKpv6PN/c1BZQeYRYzc4Qq6XZ7lP/Ku/7gzMAa7LDnwJbWSPBaPReCthWfeVdiRURqUeLlf14qOdcfSHdw6bKsno01Fs7vU4gqxU7Y7M6zrr/UHtarQxtZ2GVuk3usCqrs4Crs/+t/Omddcvd5+F0cQ2qyhtQZ/EMmTp6Q9YfasDUrveS6yOUAVN3FEVAcdt5jrM8HCIi9RienYjh2YkAnMeY6824kiILyk57n2W4pdmOk0cqcfKI8yzDGq2M5NQopLhO6pIWDb2BL0nDGfufkj/42x4m/FnPHaiEWZYl9V1nwOTXdRVFdFJd1UG1lSvMag27zuafp+FZidi24YhPYw/tK8P080egrq4ZaZlxSE2PYeNw8plGK0On0fj1IsNg0mHGvBEdhisajewWzHRUWeMWJnlc1zvE6av/GNPZCeTfBUmS1BeHUTG+Ly8WwvlGRXNTB4FVs6vSyj3QOrsKW8Uh0Nhg9epT0hV/3phgPx6y2RyoKm9A5Zl6VJS1fj5T71fVTHFhDbJyklHgw1nJsnNToNHJuOSac5wBks2hBknq1zYH7F7bFI9xjrMMXTpjtyuw2xU0w9ZrtynLUofVVhOmZuBoQbnPv6tJyWZs33Tc7/vX6TWd9F8y9PmAKdR0eg3Sh8YhfWgcAM+zDJcU1aKkyILmRs9jxWFXcLq1ehxwvkmWkGRubWwerZ7UhcIDT7Jw9kLV4y5UGECFkZ6s5+4rZFmCKUIHU4T/Z8pwOBT1BY17aGV1r8DqZNkg4F+T3kizHtPnjvB7jkQAICCQnZvi05Ko0eNSYDBpkDclPQgzo/6qr/1dkKS2KtuYON/fqHA4FPUNiPZLAT2+di0XbN3Wk8oGf96YKNhTimlzhuPbzUcRE2dSP6LjTNDxLIH9TmODtTVoqkfFmQZUltWjpqrR56o+jVZGQlIkEpPNSBhkRmJyJBKSzDAYtRg+KhEFPpyVbNYCZ7Wi6wV9TzlbLrQFVPaOgqwuAi6b1QG7VfHaFghtb1J69im9YNEYfL5mr0+3cWhfGabOGqZWsbvTG1xL5LwDpuhYY8hPlNOfeJ5l2PmmRE1VE0qL2s4yXFvjuZpBCDiXrJ6px54fiwE4T+yTmh6jVknFxpv4M+pjXMtbdVoNtvl4koUt6w5j3LlpKD5VA61OhkYrO6sqtTK0Omc1pOd2jfNrndxv31AdiEsXGUCFGbtDgc6owfxFo7Fg0Riv9dz97QAFnH/MTBH6Hp1VTutnRYosS2ddnkwDV28tlSXyR/u/C5Lk/Ic+nP4uaDQyIiL1iIj073nebnN497NqPfmGZ/VV29eAf29MOOwKDuR7V65EmvWIdgul3D9YPdu3KYprCV292xK6Br8q6UwROiQmm9vCpkGRiImP6HAJXSiq2J0tF5x9MXuLa2luVwGWvduAq931rV0v2/Xnd1WrkzFuUprXGeV6cx+QfyRJQlxCBOISIjAmLxUA0FDXgtLitmV7lWe8T+pSW9OM2ppmHNxbBgAwmnRqD6mU9BgkJpvZFiNAhHAGwg31VjTWt6ChzoqG+hY01lvRUN/2dWO9FYoicMMd03FwX5lPt31obxmmnjcM32w46ve8JAmtwZTGI7jS6LzDKq1WdguzNG4hl+dljftnncZjWzB63g3UpYt8Rg5Dvb2eu3/zryJFSAwEqOf6wlJZGpjc/y70hwaWvtLqNDDrNDBH+75cw9+lsp1paP1nvKTQu2tVRKTerVrKiNj4CETHGhETZ2KvkyCzWR2oLG9bOldZVo/K8ga/ltDFJkQgcVBka1WTGYmDzIgw+xmW9rFqxZ5wX5qLyN65TSGcZ23tKLSKMOv9+l2NNBtw3oKRvTMxCpjIKANGjE7CiNFJAJwndSktdi7XKy20oKykzmsJaXOTDccPV+L4YWcfKa1ORvLgaOeZ9jJikDw4msG/D6wtbsFS69+wxroWj2Cpod7q9xLeYPQ/FQKw25SzPmmAP2RZgkbbrlJLq3ELvWTvUKx9BZfXWGcIZjRqEWk2DMili/wviPo1VqRQsPWHFxlE/Zk/S2Wzc1MASeC8BSNhqW5SP+oszV3+I+jqS1VS5B1OmSKdyxNjYk2IiXdb1hdrYqXGWWqst6pnn3NVN9V0cNr4zmh1ziV0Ca0hU2KyGfGJkb32wnYgVrF3R5IkaLTOF3nOHqNttBqZbyIOAHqDFkOGx2PI8NaTujgUlJfWOXtIFVpQWmTxWp5ptykoPlmD4pM1AJzVMYnJUUjNcIZSKekxflfUhjO7zYHGBqtareQRMrlVMPX2MlqDUav2G/U1KNYbtRg3MQ12uwKH3dH6WVH7yzlsCuyt213fc9iVkIQviiKgBOBkDsDA7kcpCdGT89WEv8rK+n7zQ6Su+XIK38sW52FIPytvpNBybyjYfkkUn3sokAZSBVRPyLIEW7MDr/xpa7dvTNx67yxoDRq0/1fJ4VBQX9viEUrVVjehproJdTVdh1NdMUboOlzSFxPXd8OpUBxviiJgqWpERetZ6FzVTU0NvjfGjjDrkTjIjITkSDVsio41BXzJBfmuN35XKfwJIVBd0YiSIotaJVVX29Lt9WLiTc4KqdYqqehYo889hNz/h4MEQCAkTaEdDgVNDVY1SGoLlTyrltoHdGdLq5MRGWVApFmPSLMBkVF6RJjbXY7UO5etaWSs+7jAp6B4bF4q5l8yukf9GxVFwG5rF1i1nnDBK8SyO5zVUm6XHXbRFmzZXOMdbaGXo3VbawDmsCs9Oquvr264Yzreemm7z8HdXcvnwhYmy55kWUJCgrnT7/fN/2aIehErUigUBuqSKKK+rjeWymo0shoMdXT79bXNHuGUpaoJlppm1NY0Qemi0ra50YbmRhvKimu9vmc0aT2aoLuHU+0rR/oTm9XRGjA1qFVNVeUNPr+AkSTnEjpXnyZXz6aBVB0RrrisnQBnlVx8UiTikyKRM2EwAKC+tlk9y15JoQVV5Q1e17NUOZ97C3Y7e/hFROrVs+ylpscgYZC5w8A5GE2hFUWgudGmViu5AiVXwNTYWrXU1Nh7Z5sEAI1GcgZJUa1BklmPCDVoaguZ/FkqHqzVJrIsQW/QQh+kEySK1rCxLaxqq9hSAy9bu8vttntWdjmcIVdrMOZr1RiAfvfmNSugaMBgRQqFCgMoCiYeb75xvcgI5hsTznCqBbU17cMpZwVVT/8xNxg9w6lYt5DKaOr9M3wFojpACNFuCZ2zuslS7d8SOmdVk2sJXSTiEiN5xsIwF4rfVQovLc02lLoCqSILzpTUdRn2A4BOr0FKWtuSveTBUTAadd2umuiqKbTawLuuLVhq37zb9bk3X4HLsoQIsx4RZrdgqYPqJYMxMGd85GoT/+k0Gqz848YBWQHFAIoGJL5Ao2Di8UbBxOPNd+5BSvt+PMH+H0EIoS7rq61pQk2VM5Sy1DirqfxtCuuiN7jCKaPnsr54Z+WUvy9GeqM6QFEEaiobPfs1nWlAsx/v9kdG6T3CpoRBkYiJ46na+yu+iUj+sNsVnCmpdYZShRaUFltgben6xXt0jBGXXzsB//zbt91W8tz065k4cvAMqisavc4Q113w5a+ISFeYpEdklME7ZIoywBTh/3N5b2NQ7J9gLV0MBQZQnWAANbDxBRoFE483CiYeb/2PEAINdVbPZX1uvad6+k+p3qBBdKwJsfGtFVNujdE7ekHjy7vc7asDrC1251noyupbA6cGVJXX+1ztJUlAXGIkEga19WpKGBQJUwSX0A1UfI4jfymKQHVFA0oKLWqVVEOd1WOMqyl0wZ7Sbm9v9LgUJCWbsXXdkR7PyWjSevRViojSt33tCpsi9WHVl64vvanT1/XnHnfsAUVEREQUxiRJgjnaAHO0AWmZsR7fcy1d6yicslQ3dXnKamuLAxVlziqk9nR6jduyPiOSkqOQMTTep1NGL7trBr7behyFJ6pRW9Ps8+PU6TUeQVNishlxiZHQamWfb4OIqD1ZlpAwyNn/LXdiGoQQqLO0OJuat/aRGp6ViG0bfAuUDu0rw9RZwzoMoPQGjVuw5Na8O8qgXo4w6/vl85p7/1MACJMVYyExkHvcMYAiIiIiClOSJDlf2EQZMHhIB+FUg7W1UqoZNdWNqK1ua5De1amlbVbPcGrWglHYtv6IT6eM/nbTMSQlm7FvV+dLC8zRBq/G4P6cpYqIqKckSUJ0rBHRsUZk5yYDAGT41xRaZ9Bg+tzhbsGSs3pJp2fPOfLNQD1RFgMoIiIion5IkqTWXiEGDM7w/J4QAk2NNq9G6K5wqn2/lJ5WB8iyhDjXWeiSI1s/m/v1mfuIKPxoNLLPZyaTZQkREXqMn5rR7ViirtgdCnRGDeYvGo0Fi8Z49bjrb+ETwACKiIiIaMCRJMnZ3DZSj9T0GI/vCSHQ3OQWTlU3QavT+FUdYDTp8Isbz0V8YiQ0/XCpCRH1LwIC2bkpPjWFHj0uBUIKj3481Pe5L10cCD3uGEARERERkUqSJJgi9DBF6JGS5gyndBqNX9UBBqMWSSlRgZ4qEVGvUITArAUjcWBPSbdNoc+bP9LnEykQkSe+JUVEREREXXJVB/iC1QFEFG7cm0Kjs1Z0bk2hw+WMZER9DQMoIiIiIuqSqzqg0xdmLqwOIKIw5d4UemxeKmTZ+YQnyxLG5qXi1ntnYciI+H7Zl4coWLgEj4iIiIi6NJBPGU1EA8dAbApNFEysgCIiIiKibrE6gIgGAlfQZHM4EBMXAZvDAbtd8flEDETUOVZAEREREZFPWB1AREREPcUKKCIiIiLyGasDiIiIqCcYQBERERERERERUUAxgCIiIiIiIiIiooBiAEVERERERERERAHFAIqIiIiIiIiIiAKKARQREREREREREQUUAygiIiIiIiIiIgooBlBERERERERERBRQDKCIiIiIiIiIiCigGEAREREREREREVFAMYAiIiIiIiIiIqKAYgBFREREREREREQBxQCKiIiIiIiIiIgCigEUEREREREREREFFAMoIiIiIiIiIiIKKAZQREREREREREQUUAygiIiIiIiIiIgooBhAERERERERERFRQDGAIiIiIiIiIiKigGIARUREREREREREAaUN1R3v2LEDS5cu7fT7K1aswBVXXOG1vaGhAc8++yzWrl2LxsZGTJo0Cb/97W8xdOjQAM62b5FlCXrYoVXsEA4HJI0GdlkLK7RQFBHq6REREREREREReQhZAJWTk4NVq1Z5bBNC4Le//S0aGxsxZ86cDq937733Ys+ePXjwwQcRGRmJlStXYunSpfj0008RFRUVjKmHlEFSINVWo2j1f1G94zs1gIqbOgXpv/wFRFQcWgQL24iIiIiIiIio7whZAGU2mzF+/HiPbW+++SaOHz+Od955B/Hx8V7X+eGHH7B582a8+uqrmD17NgBg0qRJmD9/Pv7zn//g1ltvDcrcQ8UgKWjavRNHXlgJiLZKJ+FwoOqbb1H17XaM/N+7YDpnAkMoIiIiIiIiIuoz+kxKUV5ejueffx5LlixBXl5eh2O2bduGyMhIzJw5U90WHx+PyZMnY8uWLcGaakjIsgSprtorfPIgBI68sBJSXTVkWQruBImIiIiIiIiIOtFnAqgXX3wRsizjnnvu6XTMsWPHkJmZCY1G47F9yJAhOH78eKCnGFJ62FG0+r+dh08uQqD43TXQC0dwJkZERERERERE1I0+EUBVVlbigw8+wHXXXYfo6OhOx9XX18NsNnttj4yMRH19fSCnGHJaxY7qHd/5NLZq+w5oFFuAZ0RERERERERE5JuQ9YBy9+6770JRlC7Pigc4m5R3Rpb9y9ISEryDrL6s+UwzhMO3qibhcECGQFJS/2/Kfja4fyiYeLxRMPF4o2Di8UbBxmOOgonHGwVTfz/e+kQA9eWXX2LWrFkdNh53ZzabUVRU5LW9oaGhw8qorlRW1kNRulnO1oeYJQmSRuNTCCVpNFAgoby8LggzC09JSVHcPxQ0PN4omHi8UTDxeKNg4zFHwcTjjYKpPxxvsix1WewT8iV4ZWVl2L9/PxYuXNjt2GHDhqGwsNCrEurkyZMYNmxYoKbYJ9hlLeKmTvFpbPy0qXDIugDPiIiIiIiIiIjINyEPoPLz8wEAEydO7Hbseeedh9raWnzzzTfqtqqqKvzwww+YMWNGwObYF1ihRfovfwFI3ZzdTpKQdvVVaIGm63FEREREREREREES8gDq0KFDMJlMSEtL8/peVVUVdu3apTYYnzx5MqZMmYLf/OY3ePfdd/HVV1/hxhtvRFRUFJYsWRLsqQeVogiIqDiM/N+7Og+hJAkj//cuiKi4LvtlEREREREREREFU8gDqIqKik7PfLdp0yYsXrwY+/btU7etXLkS8+bNw9NPP42HH34YKSkpeOONNxATExOsKYdMi5BhOmcCzvnTs0iYOQOSxlnlJGk0SJg+DTm/fxT6EVloESH/sRIRERERERERqSQxQEtlwq0JuTtZlqCHHRqHHVAcsNfWovq771G2bj3MU2cg6Re/DPUU+7z+0OCNwgePNwomHm8UTDzeKNh4zFEw8XijYOoPx1ufb0JO/lMUgWZFgwbJgAZNBCpPlqDo3TWwVdegdtvXEHZ7qKdIRERERERERKRiABWGZFmCTqtAr7VBJzcjfmIO0m68Drq4WDjqalG/86dQT5GIiIiIiIiISKUN9QTIPzqtAltTLb7b+BGO798JRXFAljUYNmY8xj/8GzTu+BE1mzciavKUUE+ViIiIiIiIiAgAA6iwotMqKDm2F+vXvAa4te5SFAeO7vsRR/f/hLkLlyAGgLW0FPqUlNBNloiIiIiIiIioFZfghQlZlmBrqvUKnzwIgY2f/wcRUyei4YcdwZ0gEREREREREVEnGECFCY3swA8bP+o8fHIRArt+2Ah9lAmKzRqcyRERERERERERdYEBVJiQ4MDx/Tt9Gnv8wC6Yc8eg/qcfAzwrIiIiIiIiIqLuMYAKE0JxQFEcPo1VFAeEJMGyaWOAZ0VERERERERE1D0GUGFCkjWQZY1PY2VZA0kINB0+hJbTxQGeGRERERERERFR1xhAhQkBDYaNneDT2GFjxqPup3wAgGXLpgDOioiIiIiIiIioewygwoRD0WDS3EsBSep6oCRh4pyfo/zLdQCA2m+2QbGyGTkRERERERERhQ4DqDChKAI6UzTmX3Vz5yGUJGH+VTdDFxEH6HTO6zU2ou7774I4UyIiIiIiIiIiTwygwojNLiN1eC4W3/V7jBg3We0JJcsajMiZiMtuuh+xyZmwKxrEzJqjXo/L8IiIiIiIiIgolLShngD5x2aXIRviMH3htZixcDGEoqClqR7H9+/EV6tfQeqwXExasBjRM2eh4sP3AYcDzUePoKWoEIb0jFBPn4iIiIiIiIgGIFZAhSFFEbDZZVjtetgUIyrOVOOHjR+jsc6Ckwe+h7WlCdqYGJgnTFSvU7N5U+gmTEREREREREQDGgOofiApfSSiE1IBAHabFSf2O3s+xc45Xx1Tt/0bKC0toZgeEREREREREQ1wDKD6AUmSMGr8eerlI/lfQwgB0+gx0CUnAwCUpibUfbc9VFMkIiIiIiIiogGMAVQ/kTlmMrR6AwCgrqoMZwoPQZIkxMw+Xx3DZXhEREREREREFAoMoPoJnd6IoWOnqpcP79oKAIiZcR4krbPXfMuJ42g+dTIk8yMiIiIiIiKigYsBVD8yKq9tGd7po3vQWFcNTVQUzBMnqdstmzeGYmpERERERERENIAxgOpHohNSMChjFABACIGju7cBAGLmzFXH1G7fDqW5KSTzIyIiIiIiIqKBiQFUPzMyb5b69dE938Jht8E0Kgv61MEAANHSjNodbEZORERERERERMHDAKqfSRs5DiZzDACgpbEORUd2O5uRzzlfHWPZvAlCiBDNkIiIiIiIiIgGGgZQ/YwsazDinJnq5SOtzcijp8+EpNMBAFpOnUTLieMhmR8RERERERERDTwMoPqh4eNmQJY1AICK08dQXV4MTWQkoiZNUcfUsBk5EREREREREQUJA6h+yBQZjfRReeplVxWU+zK8uu92wNHYGOypEREREREREdEAxACqn3JvRn7ywA+wNjfCOGIk9GnpAABhtaJu+zehmh4RERERERERDSAMoPqpxLThiEl0nvnOYbfi+P4dkCQJsW5VUDVsRk5EREREREREQcAAqp+SJAkjx7dVQR3Z9TWEUBA1bQYkvR4AYC0uQvOxo6GaIhERERERERENEAyg+rHM0ZOg0xsBAPU15Sg7dQiaiAhETZmqjrFsYjNyIiIiIiIiIgosBlD9mE5vwNCctrBJbUY+e666re6H7+BoaAj63IiIiIiIiIho4GAA1c+NzDtP/fr0sb1oqK2CcdgwGIZkAgCEzYbab7eFanpERERERERENAAwgOrnouOTkTwkGwAghMDR3dsgSRJi3JqRW9iMnIiIiIiIiIgCiAHUAOBeBXVsz7dw2G2InjoNksHZH8pachpNhw+FanpERERERERE1M8xgBoABo/IRURUHACgpakehYd3QTaaED11mjrGspnNyImIiIiIiIgoMBhADQCyrMGIc2aol9Vm5Oe3NSOv//EHOOrqgj43IiIiIiIiIur/GEANEMNzp0OWNQCAypITqD5TCOOQTBiGDgMACLsdlm++DuEMiYiIiIiIiKi/YgA1QBgjo5GeNV69fLi1CirWvRn5FjYjJyIiIiIiIqLexwAqDMmyBFmvACYbHIYWwGSDrFcgy1KX1xs1fpb69amCH2FtbkTUlGmQTSYAgK2sDE0FBwI6dyIiIiIiIiIaeLShngD5R9YL1CkWrNn7GX4ozodDKNBIMial5eGqsRcjShsNxdpxEJWQOgyxSWmoKS+Gw27D8X3bkT1xHqKmTYdl4wYAQM3mTYgYMzaYD4mIiIiIiIiI+jlWQIURWS+wp3IfHlr7JHYU7YRDKAAAh1Cwo2gnHlr7JPZU7oOs73gZnSRJGJnXVgV1ZNfXEEJB7Gy3ZuQ7f4S9tjawD4SIqBOyLMEoO2BGCyIdjTCjBUbZ0W2FJxERERER9W0MoMKELEuoU2rxt+/fgkDHAZOAwN++fwt1Sm2nL9Yyx0yEzuBccldvqUDpyYMwZGTAOGKkc4DDgdptWwPyGIiIumKQjKF0/wAAIABJREFUFOhrK1D0979j5213qB9Ff/879LUVMEhKqKdIREREREQ9xAAqXGgdWLP/s07DJxcBgff2fw5oO36hptUZMCxnqnr5SGsz8pjZ56vbLFs2QSh8oUdEwWOQFDTt3ond996Pqm++hXA4AADC4UDVN99i9733o2n3ToZQRERERERhigFUmFA0DvxQnO/T2O+Ld0GR7Z1+f2TeeerXp4/tQ4OlElGTp0COiAAA2MrL0Xhg/9lNmIjIR7IsQaqrxpEXVgKdnYlTCBx5YSWkumouxyMiIiIiCkMMoMKEQ1HUnk/djhUKlC7GRsUNQkrm6NZLAkd2b4Os1yN6xkx1jGXzxrOZLhGRz/Swo2j1fzsPn1yEQPG7a6AXjuBMjIiIiIiIeg0DqDChkWVoJN9+XBpJhtzN2JHj25qRH9vzLRx2G2Lcm5Hv2gl7TU3PJktEkGUJsl4BTDaUN1QCJhtkvcLqnQ5oFTuqd3zn09iq7TugUWwBnhEREREREfU2bagnQL6RHRpMSsvDjqKd3Y6dnDYesqJFV/VSqcNyEBEVh8a6alibG3Dq0E4MGzsFplFZaDp8CFAUWL7egoRFl/begyAaIGS9QJ1iwZq9n+GH4nw4hAKNJGNSWh6uGnsxorTRUKwMolyEw6H2fPJlLBQHoAnwpIiIiIiIqFexAipc2DW4auzFkND1i1YJEhaOmguHteulLLIsY0Re25I7tRn5nPPVbZYtm9mMnMhPsl5gT+U+PLT2Sewo2qkunXUIBTuKduKhtU9iT+U+yPpulpsNIJJGA0njW6IkaTSAxPCOiIiIiCjcMIAKE4oiECVH4/bJ13caQkmQsOzcX+KH0/l4fe9/uuwDBQDDc6dDbn3RV1V6ElVlp2CeOAmy2QwAsFdVomHvnt59IET9mCxLqFNq8bfv3+r0jJUCAn/7/i3UKbVcjtfKLmsRN3WyT2Pjp0xGxZatqPtuB0R3PaOIiIiIiKjPYAAVRhSrhHEJOXjqwv8P09LPVXtCaSQZ09LPxR/m3Y/yhkp8fHAdfjyTj38deLfLEMoYEYWMrAnq5SO7tkLW6REzo+0seZYtmwL2eIj6Ha0Da/Z/1mn45CIg8N7+zwEtKwwBoLaoBKkLf9Z9ZZMkIWXhRSj95DOUvPI3nH7xz7BVVgRnkkREREREdFYYQIUZxSohSonFDbm/xAsLH8eLCx/HCwsfxw25i5EgJaGuoUkdu6P0R7xz8P0uqwRG5rU1Iz9V8BNamhoQM/t8dVtD/i7YqqoC8liI+htF48APxfk+jf2+eBcU2R7gGfV9LaeLUfj0U6j+8ScMu+WmzkMoScKIO29Hzf4DsLWeIKFhdz5OPPpbVH/1JZcLExERERH1cQygwpCiCChWGWjWQW4xAM06KFYJQgC/GHUpZg6eqo7ddnoH/nv4o05DqITUoYgblA4AcDhsOL5vO/QpKTCNHuMcIAQsWzcH/DER9QcORVF7PnU7VijdLpPt76zlZ1D0/57B/8/efYfHUV0NHP7NbJNWq9673LssuVewsYFA6L2EYiAEQk2oCRAMhPYBoQRiCB1CwIABm2ADNtgG9967bPXe69aZ74+VR5IlWZK92pXk+z6PH2nuTrkrz87Onj33XFdtDfnfLMJeWUnqS/9H+NQpWk0oSacjfOoUUl9+EXP6OPymzCB45hlaoEq12SiZ/ynZzzyFNTvLl09HEARBEARBEITjEAGoPkaSJK4acjETY8ZqbStyV7MwY0mbQShJkhiY1pQFdWj7KlRVIaRZFlT1ql86PUOVIJzKdLKsDY3tcF1JRu7kun2Ro6KCvJdewNWYzSSZTMiDhmMPiSL+5ptJe/NfpL/1L9Le/BfxN92CPSgCmyqjM5uJvvZ6Eh/6K8a4eG1/tswjZP/9CUq+/BzFZvPV0xIEQRAEQRAEoR2n7qefPkyWZK4dehljo0ZrbUuzV7D4yNI2108aMhajyQxAXVUZhZn7sIwZiy4wEABnRQV1O3d0f8cFoZeTXTrGxY/ueEVgfHwasqLv5h71TM6aavJe+j8cpSUASHo98Xfdi3//ASiKilXRUSeZqNOZqZNMWFUZRWkZQPcfOIjkvz1B+EWXIOkb/46KQsX3i8l6/FHqdu/y9tMSBEEQBEEQBOE4RACqj9LJOm4YfhWjI0ZobYszl/Fj5vJW6+oNRlJGNg3bO7jtVyS9nqCpTZlRVStbbycIwjGcOi4Zdk67M1UeJSFx8bBzUB2n3iXYVV9H3ssvYS8scDfodMT+8U7MR4f9doGk1xN+3gUkz/07/kOGau2O0hLyXn6RgnfewllT7amuC4IgCIIgCIJwEk69Tz+nEJ2sY87IaxkeNkRrW3h4CT/n/Npq3YGpTTPfFRzZQ21VWcti5Lt2itmmBKEDiqJypCyHG8dc3m4QSkJizpgrOFyWfdwJAvoixWYj79WXsR2t1SRJxN7yByypaSe1X2NMDAn3P0T0jTchmwO09pp1a8l87K9Ur1l9yv2tBUEQBEEQBKGn0c2dO3eurzvhCw0Ndk6FzyM6SSYtchRHqrMps7pns9tbfoAgo4XkoERtPZN/AGUFmdRWuoNMOp2e+OFjaTh0EEeJe5iMbDJhHjbc68+hOwQEmKivt/u6G0Ifs6dsP/O2fUBiUCxXjDofu9NOQU0xKio6SWZCQpo7+FSexX93fUOkfzjxllhfd9srFIed/H++RsOB/Vpb9I03ETR5ikf2L0kSfknJBE2ZhrOyEnteLgCq3U7t1i1YDx3Eb8BAdBaLR47Xk4nrm+BN4nwTvE2cc4I3ifNN8Ka+cL5JkoTZbGz3cRGAOgXoZB3pUaM4VHmYCpu74O+usn2EmUJIDGwq4ms0+ZO9bzMA1eVFDEo7DZ2/mdpNGwCwFxcROutMJLn3J871hRe30LNYnVbe2P4eVpeVA2WHwSnzu5GX8pvBM/jtkFmcM/AM0iNT+f7ACn44vAKA/eUHGRudhtng79vOdzPV6aTgrX9Rv6upllzkVdcQMuMMjx9L9vMjcOw4/Pr1p+HQAZSGBsA9LK/q15VIsoxfv/594jrWHnF9E7xJnG+Ct4lzTvAmcb4J3tQXzreOAlB99w5caMGkM3L76JtaZD19su9LNhVu1ZZjUoYTEBQGgN1aT86BrVhGp6ELDgbAVVVF7fZt3u24IPQSiw7/oAV4Awxmzk05E8Uug9VAZEAYWA0odonfppxFhJ/7dWZ12fhg96e4lL47y6SqKBS+/w5125quNeEXXULo7LO69bgBo1JJeeJpQs88GyT3cEjV4aD0qy/J+vsTNBw+3K3HFwRBEARBEAShJRGAOoX46/24c/TNJFjiAFBR+XDvfLYV7wRAlmUGpE7V1j9ajDx42mlaW9UvK7zaZ0HoDQ5XZfJL7hpt+bJBFxBobHuol5/ejzkjr0GW3JffI9VZLMn8ySv99DZVVSn+5CNq1q/T2kLPPoew357vlePLfn5EXnk1SY88jikpWWu35+aQ8+xTFH/6CYq1wSt9EQRBEARBEIRTnc8DUBs3buTqq69m9OjRTJs2jaeeeoq6urp211+4cCFDhgxp9e/JJ5/0Yq97L7PBzF1pvyc2IBoARVV4b/d/2VW6F4D+oyYj69xTmlcUZVNWmEXwaadrGQT1u3dhLy72TecFoQdyuBz8Z++XqLjH9A4PH8L46PTjbpMSlMRv+zVlAH2f+ROHKo90az+9TVVVSr+YT9XKFVpb8OkzibjsCiTp+LMEeppfSgpJj/yNiMuvRDIaj3aQyp+WkvnYI9Q2y84SBEEQBEEQBKF7+DQAtW3bNubMmUNkZCTz5s3jjjvuYNGiRTz66KPtbrNv3z6Sk5OZP39+i3833XSTF3veu1mMAdydfitR5ggAXKqLt3d9zN7yA5j8LSQNGaOte2jbrxjCIzCPGKW1iSwoQWjyfdbPFNW7g7ImnZGrh1zSqQDLWckzGBTSH3BnI36w+1PqHX0nG6f8f4uo+PF7bTlw0mSirr3O68GnoySdjrCzzyHliacxjxiptTsrysl//VXy33wDZ2WlT/omCIIgCIIgCKcCnwagXnzxRdLS0nj11VeZMmUKV199Nffeey87d+6koaHtD2L79+9nxIgRpKWltfiXkJDg5d73bkHGQO5J/4NWi8apOHlrx4ccrMhg4Ojp2nrZ+7dga6glZMZMra169SpUp9PrfRaEniavtoAfs5ZryxcOOJcwv9BObStLMjcMvwqz3l2AvMJWyaf7F6D2gdkRKpb+QNnCr7XlgPQxxMy5pUcU/jZERhJ/733E/P4P6AIDtfbaTRvJfOwvVK5cgaooPuyhIAiCIAiCIPRNPvs0UF5ezqZNm7j66qtbfCN+7bXXsmzZMvz9254Vat++fQwZMsRb3ezTQkzB3J3+B0JNIQA4FAfzdrxPlRlCo5MAUFxODu9aR8CoVPSh7g/Wrppqardu8Vm/BaEncCku/rP3CxTVHazoH5zC9PhJXdpHqF8I1wy9TFveUryDdYWbPdpPb6v6ZSUl8z/Vls3DRxB76+1IOp0Pe9WSJEkETZxMylPPEjRlmtauNDRQ/PEH5L7wHLb8fB/2UBAEQRAEQRD6Hp8FoA4cOICqqgQHB3PvvfeSlpbG2LFjefzxx7FarW1uU1xcTFlZGXv27OE3v/kNI0aM4Oyzz+abb77xcu/7jnD/UO5Ov5VgozsTwOay88a2dwkbMkJbJ2P7KlRJIqhZMfLKlctb7UsQTiXLc1eRXZMLgF7W87uhl2mFxbsiPWoUU2InaMufH/iG4voSj/XTm2o2rKfo4w+0Zb+Bg4i7425kg8F3nToOncVCzE23kHDfgxiiorX2hoMHyH7yb5Qt+gbF4fBhDwVBEARBEASh7/BpBhTAww8/TGhoKPPmzeOuu+5i4cKFzJ07t81t9u3bB0Bubi4PPPAAb731FqNGjeKhhx5iwYIF3up6nxNljuDu9FuxGAIAsLqsfFG7Dr3JD4C66nIKM/cQPL2pGHnDvr3YCwt91mdB8KXi+lL+d/gHbfmclNlEB0Sd8P4uG3yBVpPN7rLz/u5PcSq9a5hr7batFLz7b2gcQmhKSib+7j8hm0w+7lnHzMOGkzz3KcLOPQ8aM7VUp5OyRd+Q/cTfqD+w38c9FARBEARBEITeT1J9VHBk4cKFPPjgg5x99tm89tprWvv777/P888/z9KlS0lMTGyxTXl5Odu3b2f8+PFYLE1TnM+ZM4eMjAx++eUXr/W/L8qqzOWJ5a9Qa3fPQji0SCKqwF2LK3nwKM6f82f2Pv0c5Rs2AhB30QX0m3ODz/orCL6gqipPrniF3cUHAEgOSeDZMx9GL5/cELPD5dk88tP/4VJcAFw07GyuSb3opPvrDZU7drLnyadRG7OF/BMTGPXMUxiCgnzcs66ry8zk0BtvUnvgYIv26LPPJOX669BbAnzUM0EQBEEQBEHo3fS+OnBAgPsm/rTTTmvRPm3aNJ577jn279/fKgAVFhbGzJkzOdbpp5/OmjVrKC8vJywsrFPHLyurRVF6f7FfTzITzB2pN/Patn/T4LSSGeIisgAkIOvALo4cPIzfpGnQGIAqXPYz5rPPQzYYfdvxExAZGUhJSY2vuyH0Qqvz1mvBJ1mSuWrQJVSU1R93m86cb4GEckH/3/D1oe8AWLj3R5L9khkcOtAzHe8mDRmHyP3HC1rwyRAZSew991Fpk6A3vsYCwom9/y9UrviZ0gVfotrcQ8KLflhK6boNRF39Oyxjx/lsNr/OENc3wZvE+SZ4mzjnBG8S55vgTX3hfJNlifBwS/uPe7EvLaSkpABgt9tbtDsaP8S0dXO/detWvvjii1btNpsNvV5PYLMZjYQTkxSUwB9H34xJZ8RqkikPOnqKqBzavpqAkaPQh4UDoNTWUruldxdMFoSuqLRV8VVjgAhgVuJpJAV6bgbOMxKnMzR0EAAqKh/umU+to85j+/c0a3YWea/+A9VmA0AfGkrCnx9EH9K5mQB7KkmWCT1jNilPPU1AWrrW7qqqouDNN8h//VUc5WU+7KEgCIIgCIIg9D4+C0ANGDCA+Ph4Fi9e3KJ9+fLl6PV60tPTW22zbds2Hn30Ua0WFICiKPzwww+MGTMGQw8tdNvb9A9O5vbUmzDIBvIjmoYVZexai8vlJPi007W2qhWiGLlwalBVlc/2f43V5c6IifQP59x+Z3r0GLIkc/3wK7V6bJW2Kv67bwE+Gil9XPbCAvJefhGl3p39pbMEkvDnBzBERvq4Z55jCAsn7o67ib39DnTBIVp73fZtZD72CBXLlqIqig97KAiCIAiCIAi9h25uexW/u5kkSURGRvL++++TmZmJxWJhyZIlvPHGG/zud7/jrLPOory8nP3792OxWDAajfTv35/vvvuO77//nrCwMPLy8nj22WfZvn07L730EjExMZ0+fkODnR74ma7HCPcPJTkokTWVO4kod2JwgeJyogsIIH70RCqWLQVVxVlehmXcePSBvavWS0CAifp6e8crCkKjLcU7+CHrZ2351lHXa4XDO9KV881PbyImIIpNRdsAKKovJtgURFKQ5zKtTpajtITcF57HVVUFgOzvT8L9D2JKSOxgy95HkiRMcfEET5+OUt+ALSvT/YDLSf2undTv2olfv/7og4N92s/mxPVN8CZxvgneJs45wZvE+SZ4U1843yRJwmxuv0SPzwJQAIMGDWLo0KH88MMPfPTRR2RkZDBnzhzuueceJEliyZIl3HbbbUybNo2EhAT8/Pw488wzOXz4MJ999hmLFi0iNDSU559/njFjxnTp2CIA1bFI/3ASAuPYWriD0Bp3YeTsksMMGj8LCgqxFxQAIOl0BIxM9WVXu6wvvLgF76l11DFv+3vYFfcQ4WlxEzk9cWqnt+/q+RZtjqTWXktWTS4A+ysOkRY5Eoux/fHU3uKsrCT3hedxlpUCIJlMJNx7H/79+vu4Z91LNhixjE7DPGw41sMZuGrd4/OdlRVUrfoFxW7Hf+AgJN3JFaP3BHF9E7xJnG+Ct4lzTvAmcb4J3tQXzreOAlA+mwXP10QR8s7bnLOR/V9+jK7xz5WfGscNkbMp/efrAMhmM/1ffAXZ2HuKkfeFAm+C93y0Zz7rC931zkJMwTw68c/46/07vf2JnG92l4PnN71GYV0RAAmWOO4fdycG2WdzR+CqqSHnhWex5+cDIOn1xN/zZ8zDhvusT76gOBxUfL+Y8u++RXU6tXZDZBRR191AwPARPuyduL4J3iXON8HbxDkneJM43wRv6gvnW48tQi70HmMTxxPSr2kmLl1OIe/Y16CLcA8/Uurrqd200VfdE4RutbtsvxZ8ArhqyMVdCj6dKKPOwE0jrkHfGHDKrc1nUcaSbj9ue1wNDeS+8pIWfEKWib3tjlMu+AQgGwyEn38hyY8/if+gwVq7o6SYvH+8QOF7b+Oq6d03D4IgCIIgCILgaSIAJXTKpEkXab9HVirkV+Swa4Cf1la5UhQjF/oeq9PKp/sWaMvjotMYFeG9gEu8JZaLBpyrLf+c8yt7yw547fhHKTYb+a+93FT/SJKIuflWLGmtJ4s4lRhj40h44GGir5+DbDZr7dVrVpP52F+pXrumRxaQFwRBEARBEARfEAEooVPCYpIIi0kGQFYhpszFqjgriiwBYM04hC03x5ddFASPW3T4BypslQAEGMxcNugCr/dhRsJUhocP0ZY/3PsZNfZarx1fcTjI/9c/aTjYFPiKuu4GgiZO8lofejJJlgk+7XRSnnoGy7gJWrurtobCd/9N3isvYS8p9mEPBUEQBEEQBKFnEAEoodMGpk3Xfo8tdVLvJ3EooanuU+XKFT7olSB0j4zKTH7JXaMtXzboAgJ9UARckiSuH3alduwaey3/2fu5VzJrVJeLwrffpH73Lq0t8oqrCDltRrcfu7fRB4cQd9sfibvrXvRhYVp7/e5dZD3+KOXfL0Z1uXzYQ0EQBEEQBEHwLRGAEjotaXA6Jv8AAPwcEFatsHNgUy2cmrVrUGw2X3VPEDzG4XLwyb4vUXEHeUaED2V8tO+GmwUaLVw37EpteVfZPlbmrTnOFidPVRQKP3iX2i1N9a/Czr+Q0LN+063H7e0so9NIefIZQmafBZI7Q1S12yn98nOy//4E1swjPu6hIAiCIAiCIPiGCEAJnabTG+g3crK2PLouhNxoAxWB7mnHFWsDVevX+qp7guAx32f9TFG9e9iUSWfkqiEXIzUGE3xlRPgQZiZO05a/PvQd+bWF3XIsVVUp/vQ/1KxtCnKFnnk24RdcdJythKNkPz+irrqGpL8+hikxUWu35WST/fSTFH/2XxSr1Yc9FARBEARBEHoCWZaQjQr4OyipKwN/B7JRQZZ9+9mju4gAlNAlA1KnAu4Xg6u4hFnhE1pkQR354WsUVfFR7wTh5OXW5PNjVlNR/YsGnEuYX6gPe9TkwgHnEm+JBcCpOHl/93+xuxweP07pV19StfxnbTlo+mlEXHGVz4NwvY1fv/4kPfI4EZdegWQwuBtVlcplP5L5t0eo3bHdtx0UBEEQBEEQfEY2qtTIlXywaz53L36MO/73KHcvfowPds2nRq5ENva9yWxEAEroEktwOHH9m2YB61epI2zqaTgbzyRLURULf3lfzPwk9EouxcUn+77QgqgDglOYFt9zim0bZD1zRlyDQXYHM/LrCvkm4zuPHqPsu2+pWNK0z8AJk4i+7kYRfDpBkl5P2DnnkvzE05iHjdDaneVl5L/2MgVv/QtnVaUPeygIgiAIgiB4m2xU2Vm2m4d+fIb1uVtxNX7+cKkK63O38tCPz7CzbHefC0KJAJTQZQNHNxUjP7J7PecPv5CqIfFam7J2I18cXCSCUEKvszx3Fdk1eQDoZT3XDr0MWepZl8nYgGguHXSetrwydw07S/d4ZN8VPy2l7OsF2nLA6DRibroFSe5Zf4PeyBgVRfyf7yfm5t8jW5qK2dds3EDmY3+l6teV4popCKcAWZYw6BWMegcG2er+qe+7Qy0EQRCE1mRZokapZt7Gj7Was8dSUZm38WNqlOo+9R6hmzt37lxfd8IXGhrsiHv9E2MJCSdr32bs1noUl5OA4HAGDJtIzZrVAIRWu1gYU4IVJ0NDB/XIzImAABP19XZfd0PoQYrrS3l313+07Kfz+p3F6KiRHtm3p8+3pMAEcmsLKKovAWBf+UEmxIzBT2864X1Wrf6V4o8/1Jb9hw4j7s67kY8OHRNOmiRJmBKTCJ46HVd1NbbcHABUh4O67dto2L8P/wED0VkCT+o44vomeJM43zrPoFdwWavYsPQLfln4MdtX/8iutT9RW1lCbGIyBpMRRel590w9jTjnBG8S55vQHSSDwqe7vyG3uqDDdWtsdaTFjEB19Y73B0mSMJuN7T4uvtYWukySZAaOnqotH9r2K/6DhmCIddemMTpVhmTaWJa9ku+OLPVVNwWh0xRV4b/7vsShOAFIsMQxO+l0H/eqfZIkce3Qywg2BgFQ66jj472fn3D9tZpNGyj64D1t2W/AQOLvvAfZ0P6bh3DidIGBxNz8e+L//ACGyEitveHAfrLmPkbZtwtRnU4f9lAQBE8z6BUKDu9i/uuPk7FrE4riAkBRXGTs2sT81x+n4PAuDHpRR1MQBKGvqbXXcaAigxU5q/nvvgWUO8rZlNe5WqAb87ahyH3nvlAEoIQT0m/EJHR6d2ZEZUke5YWZhJw+U3t81KEGUFWWZC7jh8yf29uNIPQIa/I3cLDyMACyJHPtsMvQyTof9+r4LMYArh9+JVLjpAB7yw+wPGdVl/dTu2M7BW+/xdGUUFNiEvH3/AnZz8+j/RVaCxg+guS5fyf0N+dC4zBH1emkbOHXZD35NxoOHfRxDwVB8ARZlnA0VPPTgndpN/1eVflpwbs4GvrWUAtBEIRTic1lJ6s6h7X5G1lw8Fv+ufVt/rLqKR5a9QSvbn2LLw4uZHX+ehyKQ6v51BGXqvSpSb70vu6A0DsZ/cwkDR3LkV3rADi0bRXjT7+U0gVfoDocRFU4iS53UhRuYNHh7zHoDJyROL2DvQqC91Xaqvj60GJteVbiaSQFJviwR503NGwQs5NOZ2n2CgAWZixhcOgAEgPjj79ho/r9+yiY9zq43N/EG2Niif/T/ejMAd3VZeEYsslE5GVXEDRxEoUfvo8t8wgA9vx8cp57muAZZxBxyWXozGYf91QQhM5SVQWHzYrdWofNWo8l0MzO1YvbDz41bcimFd8y+ZxrURTxHbEgCEJP5VJcFDeUkl9bSH5dIQW1heTVFVLWUN5uTaeWJHSS3KkglE6Se1xN2pMhAlDCCRs4eroWgMo5uJW0GRcTOG4C1WvdtaCmZhv4Kty97oKD36KX9JyWMNlX3RWEVlRV5bP9X2F1WQGI8o/g3H5n+rhXXXNe/7PYX3GQ7Jo8XKqL93d/ykPj78akO/7wuYbDh8l77RVUhwMAfUQE8X9+AH1QkDe6LRzDlJhE0l8fo/LnZZR+vQDVZgOgasXP1G7dQtQ1vyNw7Dgf91IQ3GRZQie7kHBRXVGKUS+josOl6FCUvlNgU1UV7NYG7Nb6xn912K312Bp/am0NLdsctvoWkwpcfOvDHNmzrVPHPLJ7C5POuhRF8UMWE0AIgiD4lKqqVNgqtUDT0Z9FdcU4VVen92OQ9cQGRBMbEEOcJQa9qmdc/GjW527tcNvx8WnIip6+kgMlAlDCCQuLTiQ8NoWygkwUl4vDO9fS7/QZWgAqKaOSIZNHst+aC8D8A19jkPVMjhvvw14LQpMtxdvZWbpXW75m6GUYdb2r6LZe1nPjiGt4bsMr2BUHRfXFLDj4LdcMvbTdbWy5OeS98hKqzR140wWHkPDnBzGEhXmr20IbJFkmdPZZWNLHUvzJR9TtcNcGcFVVUjDvdarTxxB19e/E/5PgUwa9gqOhmg3LF3Fkz1YUxYUs6+g3PJ1xMy/A4B8zDbDQAAAgAElEQVSEw9mzAieKouCwNWgBJC2I1FDfoq1lcKkOu7UBOvVNdgdUVav51HFfXdRXV/DtBy8TGp1EeEwyYTHJhMcm428J6ZETuwiCIPQFtY66FoGmgrpC8muLtC+qO0NCIsocSVxANHGWGOIaA04R/uEtsphkWeLS4eeyIXfbcTOmJCQuGX4OqkPGI+9HPYAIQAknZeDo6ZQVZAKQsWM1Q246A2N8Ava8XFS7navqB/NBiERWtXu2p0/2fYle1jM+Jt13nRYE3G8ynx9YqC1Pi5/EoND+PuzRiYs2R3L54Iv4ZN8XAKzOX8/w8CGkRbaexc9eVEjuP15Aqa8DQLZYSLjvAYxRUV7ts9A+Q3g4cXfdS+3mjRT/9z+4qqsBqNu6hay9e4i45DKCZ5yBJLIjTlrzTB5VcSHJuj6ZyeMpRwtpH1vL6Ggh7Yzdm5l16c3E9h/ZLUGo5oEkW0NdG5lJzQJKzR+3eSiQ1AUGox9GPzNG/wD0BiOyrOtUEEpurD/odNgpyT1ESe4h7TG/gCB3QCo22f0zOgmDyb/bnoMgCEJfZHfZKagrapXVVG2v6dJ+QkzBTUGmxkBTjDkKQye+zFYUlUB9ELePv455Gz9uMwglIfHHCdcTKAehOPvOPYmkqh0NSO+byspqxc2lB7icDr59+3FsDbUATL3gFizZxRT/9z8AGOMTiHzkL7y27W1ya/MBd5Hnm0ZcS3rUKJ/1OzIykJKSrl1khL7lwz2fsaFwC+B+A3l04n3467un8LY3zjdVVXl39ydsLd4BQIDezF8m3EuoX4i2jqOsjJznn8ZZXg6A7O9Pwn0P4ZeS0q19E06cq66O0gWfU/XLyhbtfv0HEH39jZgSEgF3IMWIE73iRFJVVEnCKeuxoxfvde04msmzqRdl8viSLEsotgrmv/748WsZSRJX3vkEsim03XNPUVw4bA2tgkhNWUnN25qCSg5bQzc9u/YZTP7uQJKfGZNfgPa70S8Ao7/7p6l5m58Zo8mMrGuayMKgV1i7+D9k7NrU4fEGjBhLZHwK635c0IneSQSFRREWm6JlSoVExLU4dl8k7uEEbxLnW+/lUlyUNJSSp2UzuQNNpZ2u0+Rm1vu3yGaKbQw4mQ0n/wWAbFSpUar5as8SNuZtw6Uq6CSZ8fFpXDL8HHfwyd67Ml9lWSI83NLu4yIAJZy0Hau+Ze+GpQBEJw1h+rlzOHz/vah2OwCJf3kUV2Isr2x9k4K6IgB0ko5bR13PyIhhPumzeDM5te0u28+/tr+rLd+eOqdbz0VvnW/1jnqe2fAKFbZKAAaHDOCu9N8jSzLOqkpynn8WR7H7NSgZjST86X78Bw3u9n4JJ6/+wH6KPnofR2FhU6NOR9hvziXuovPRWeupyTyCIToSFZAAR1EJgSn9UMxB2FQRSGmuvUwejSR1ayZPb9SlIMrIcQxMnULG7i1tDG3zdSDpaMCoWTDJ/2hwqXmAyf1P9sCMqF0N3knGUOqqKygvzKKsMIvywizKC7NxOmwdHkunMxASnUB4TGOWVGwKAUFhfWronriHE7xJnG89X3fVaToacAo2BnXrNVSWJdC7UGQXyIACsqIHp9wr4xUdBaDEEDzhpA1Incq+jctQVZWi7P3UWWsInDCR6lW/AlC1cjkxN/2eu9Nv5eUt8yiuL8Wlunh718fclnojw8LEB2DBe6xOK5/ua/pmeVx0ms8CoZ5mNpi5YfhVvLr1LVRUDlRmsCxrJbMixpP7jxebgk96PXF33N0jg09iSFTbzIOHkPz4U5Qv/h/li//nnrnQ5cLPIOEszqfeILM1azuZP2zXMnlSho4mPTocv/o6TBGxIgjVSJYlHA3V7QefAFSVnxa822Emz4lSVQXF5UJRXNpPVXGhuJwoytHHnCguF6qiaL8fXV9VXLgUF+ox+1AUZ2Ob0rivpt/VFus17afF9o3Hb+pL0/bn33AvR/Z0XCwV4MieraROnsWh7as8+ncDdyCpdSZS8yykpuCSyd/902Dy90gg6UQpiorBP4hZl97cYdDTnXmnYg4MwRwYQsKg0Y37UKgpL6KsMJPywmzKCjKpKi1APWYGJZfLQVn+Ecryj2htJn+LVkcqrDEwZfQTM2sKguBZWiBF58KlKOhkGdmlA6fn7uFqHXXajHMFWsDpROo0RRAXEEOsJYb4xp+Rx9Rp8hZFUcEuA7IW8HRf2fvmfa8IQAknLSAojNj+I8jP2AXAoe2rGHbaTC0AVbNxA5FXXkNQQCD3pP+BlzfPo9RajlNx8taOD7lj9E0MCh3gy6cgnEIWHf5eyxAKMJi5bNAFPu6RZw0K7c/ZKWfwfeZPAHx/YAn9PvoJNc89GQCyTMyttxMwonV9KF/rjcWNvUk2GIi48GICx0+g6KMPcJWVEH7GDHJyDrB88aetavIc3rOFw3u3MvPcq0kKDUHWW/p0EO9oUMflcrqDJy4nLqcTl8vRYjkkLJSdaxYfPxPFvUM2/byI/iPHc3jvjuMEbZq3NQaQGn9XWwSCmtbtjcnnqqp0qZD28UkYmw1tM/oHtJ+dpAWSAhoDSb3zGuBwysT2H8mVdz7BphXfcmT3lqZr3IgxjJtx/nGvcbIsExwRS3BELP1HumcUdjrsVBTnuDOlCtzZUvXV5a22tTXUUnBkNwVHdmttlpDIZgGpFEIi49Dpe9ckHIIg9BzuoWRVLNi1mE1527WhZOPiR3Pp8HMJ1HdtKFlbdZoK6gqp8mKdJqF7iACU4BGDRk/XAlCZezYwaupvMSUlY8vOQnU4qF67mtDZZxFiCubu9D/w8pZ5VNgqcSgO5u14nzvTfk//4GQfPwuhLc1ry6guF5JO12try2RUZvJL7lpt+fJBFxJobD9FtLc6N2U2+8sPkl2exXkrKlGLHdpjMXNuIXDMWB/2rm2+Lm7cm5ji4kl88C/oivNpcFpbBZ9aUFWWL/6Uy29JJiDYTAOeSSFXVRVVUVoGe5r/dLZs8/jjjes0f7yzwZGLb32YI3u2dWrdI3u3kjplFhk7Vp/Mn6tvkKQuFdI2+gWQPuPSZsPbmrKTenMg6WQ4nDKyKZTJ51zLlHOuRFUUJFlGRY9L0eHoYpFZvcFIZPwAIuObvsSz1lU3DtvL1rKl2hryWFtZQm1lCVl73UMqZZ2OkMgELUMqPDYZS0hknxq6JwhC95CNKjvLdrcqpu1SFdbnbmVD7jZuH38do8JHtApCHa3TlF9XRH5tgfazq3Wa/PX+xAXEEG/xfJ0mwbNEAErwiOjkIVhCIqmtLMFhayB732bCT59B8ccfAlC1cgUhs85EkiTC/UO5O/1WXtkyjyp7DTaXnTe2vcs96beSFJTg42ciNGeSFKTqCnI//5KK9Ru0AFToxAkkXHEZamBorxnW43A5+GTfl9qb2YjwoYyLTvNxr7qHTtZxw5DL2fbi4yQ0Cz5FXXs9QZOn+LBnbesJQ6K6m6oqqIrq/qkqWgBHVdVjlpt+B/dPpbGNY/YRERrI5pXfdSqTZ8uaHxmWPpUjGQdPKsjjatbWa1PDVdWDmTwnTtbpkGUdkqxD1umRZRm58Xd3m/txudnvLdqPs70sy0g6HbKsb7muToeujf1ILY6jb/wpN/VF1uFvNtBveHqnakD1GzEGg38Qg8ec3m1/v95KUVQURQaMjQ1HH/HM68kvIIj4AaOIH+Ce6EVVFWoqSrQsqfLCLCpL8lqd24rL1VhrKouj8+4ZTWbCYpIah++lEBaThJ850CP9FAShb5BliRqlqt2Z3ABUVOZt/JjnzvwL+ZWlZFblkF9bRH5dwQnVaYoJiNaymbxVp0nwHBGAEjxCkmQGpk1j24qvATi47VdSLr+bks/no9qs2AvyaTh4APPgIQBEmSMaa0K9Sa2jDqvLyuvb3uGeMX8g3hLry6ciNDJJCg07tnLotddbfMBVXS7K16ylfO06Bt59J/6p6b0iCPV95k8U1RcDYNIZuXrIJX32jUp1uXB88iXJeU3j4X9NCyBteBg9L/cJdLKLDcsXdW5I1PJFDEidTPbBve6ADccEchQFWgV1mgV+lGaPNWujsU1p3IcW8Dk2MNTsOC3atOXG4x8TbOoOF9/6MJn7tndq3SN7t5E6ZTa71y7plr70FLLOHYDR6fTuQEwbv+sNxi5l8pj8LYw54/IWgZw2gz+NwRvpaPBGC+S0DvJIktzrrj+qJDFu5gVk7N7cYSHtcTPOx+nS0WuDlH2IJMkEhUUTFBZNyvAJgHsG44ri3BZZUrWVJa22tdvqKczaR2HWPq0tIDhcy5IKi0kmNCoBvcHotecjCEIPo3exYNfiDrOVVFS+2ruE+KAYFmZ0fC/S0+o0CZ4jAlCCx/QbPpGdq77D5bRTVZpPeXkBQRMnUfXLCsBdjPxoAAogJiCau9Nv5dUtb1HnrKfOWc9rW//Nn8bcRkxAtI+ehQDubzOk6opWwacWVJVDr71O6ssvIgdF9OiMlNyafH7MXqEtXzTgt4T6hfiuQ91IVRSKPnyf2k0btbb1I8xsGR7A3v1fkRKUTLh/qA972JqEq8vFjfdtXNbNveoFfJzJI0lSu0EeWd+yva2AkE5vaFxuDOLo21mveZu+/f3JOn2ngjoGvdKlTB69XyCD0qZ74k/Wq51IIW2hZ9LpDUTE9SMirh/gzlKzNdS1mHWvrCALu7Wu1bZ1VWXUVZWRs38LAJIsExIR1yJLKigsGkl8QBSEU4JLdrEpz/1lWIhfEOckTmFs+FAkRUWVJTaV7eP7nDVUWqvZlLedcwbN4Ks9LQNQIabgVhlNok5T3yUCUILHGP3MJA8bx+GdawA4tO1X0mfM1AJQtZs34bqqBl1gU/p2vCWWO9Nu4bVt/6bBaaXWUcdrW//NvWNuJ8oc4YunIQBGnOR+/mWnMlLyvlhA/E23YKVn3my6FBef7PsCpXGmoAHB/ZgWP9HHveoeqqpSMv9Tqtc0zTxlmTmTgyn5YKugwWnlwz2fck/6H9D5cEaoYx0t4NwZ3TkkqrtJkowkS43ZL+4MmOZtaMvNHpOkxuWjbU3r6LqYyWPQGYgpsWEIDSNg0BD8k5Ld+2gnyNNREMiXs4qdDJeiE5k8J+hkC2kLPZfJP4DYfsOJ7TcccL+f1FWVUtY44155YRYVxbkoLmeL7VRFoaI4l4riXK1Wmt5oIiy65ax7/pbgk+pf8xlSqytKMeplMUOqIHiZoioU1hWTWZ1DVnU2WdU53Dz+KlyqwiUDZnFa4FAqvl5M7oZPtLIdQyaMY9LF1/FLzT6+yvgJvWzgtPjJ7hpNlhjiAqIxG8SsnKcSEYASPGrg6GlaACr34HbSZlyCKaUftswjqE4nVWtWEXb2OS22SQpK4I+jb+af297G7rJTZa/RMqHC/cN88TROeXrFScX6DZ1at3zdepJuvAEkUzf36sT8nPMr2TV5AOhlPdcOvbTPpu6WffMVlT8t1ZaDpk0n+urruLEmh5e3zENRFTKqMvkxaznn9Jvtw562dHRoUqeLG/sHMGraeU2BHFlC4pjgjiwdE+RpHdSBY4I7LdY5uo/my62DQE2Bo5Zt2nrNlj3NaFA7n8kzfAwNm7cTW2KDkgI4UIA+LIyQmbMInn46OkvfK8bfHpHJc3KOLaQtS6ConHAhbaFnkiQJS0gklpBIkoe6B2+7XE6qSvO1WlJlhVnUlBe12tZpt1Gcc4DinANam78lhPBY94x7YTFJhEYnYTB27r5BzJAqCN6nqiqVtqrGYFMOmdXZZNfkYnPZW6176YDZTCwzc+Tvc1uV7ahcu57KdRuYdPvNSANmE2oK5sohF3vviQg9jqT2xrmAPaCsrFZ8Y9JNln32MmX5RwAYOeW3JDj8KPrwfQAM0TGk/P3ZNj+MHazI4I3t7+FQ3EWTI/zCuHfMbd0yVCoyMpCSkq5N43kqCXDVs/UPf+z0+qkvvUBlVT1+ST1rJsPi+hKe2fAyDsX9je2F/c/hrJSZXu+HN8638iXfUbrgC23ZMm48sbfe3hhogSVHlvG/Iz8CIEsyfxpzG/2DU7q1T51l0CusWfwxh3dt7nDdAaPGM/mca8WHDdwZAYqtgvmvP95hJs+VdzxOyZffUbFyBbhaBvoko5GgSVMImXUmpvj47u10D3L0Q63I5Dk54v301Ga31lNelEN5YaZ7+F5BFtb6js8HSZIICo/VakmFxyYTFB7TKrOyvRlSm+1IzJAqdKtT5RpX72gguyaXzOpsLehUbe/4eV+behFT9MkceODRDu9FBr/wNLroOFw2D3a8j+kL55ssS4SHt//FpghACR6XtXcT65Z8BLi/8Tr3uofJfPA+lAb3NMAJ9z+EeeiwNrfdW36AN3d8gLMxYBBljuDe9NsJNnl21pW+8OLuThZsbP3DH1FdHWekSDodI558nF2P/A1TYhJBU6cTNGmyzzMqFFXhta3/5mDlYQASLHE8OO4unww96+7zrXL5TxR/8rG2HJA6mrg/3oWkb0pyVVSFV7a8RUaVOzgc7hfKXybci7/e99PTHtr2Cwn9+rHwvRc7DqTc+QSSMbTbCnv3Nu4PZzv5acF77X44m33pzcQ0fjhzVlZQuWI5VSuX46ppfU6ahw0nZNaZBKSO1oKXfVnTsB4nqqIgybKWySPuETpHvJ8KzamqSn1NRYtZ98qLcnA5W2dNHEtvMBIalajVkopK6I/JoHYuyN5LZ0gVer6+eI1zKE7yavO1QFNWdQ5F9a0nImhLsDGQlKAkkoMSSQ5KZGR4MoXvvkf5mnUdbhs+dTLxN/8eq9L37y9OVF843zoKQOnmzp0713vd6TkaGuwdlrcRTkxgaCQZO9ficthx2q2ExSbjLxmwZbo/+Cp2B4Hjxre5baR/OAmWWLYW70RFpc5Rz57y/YyJGo1R57lZVgICTNTXd3wzdKqSZQlHcSENObkdrhs+aSKu+nqq9+zFVV1F/a6dVCz9AVtuDpLJhCEi0icfZFfnr2dlnns4qCzJ3Db6Rp8VHu/O86167WotwxDAf8hQ4u68B9nQsnCjJEkMCRvIuoJNOBUnDU4r5dZK0qNGdUu/OkNVVXau+pYdq/+H2RLE4NSJZB3c1fbKjd9yB0cl4lJ61+xh3UlRJILDoxg2ZgrWhlqqSotQVRVZ1tF/5DjOuuIPBEclapkBsp8/5qHDCJk1G0NUFM6yMlzVVdr+HKUl1GxYT826tYCKMTau1bnUl6iq+2/oUnQo6tHAkyTuD7pAvJ8KzUmShNHkT3B4LDEpQ+k3chJDx88iYVAaoVEJmMyBqC4XtobWBc4VxUV9TQWl+UfIObANS5CFjF2bqCjO7/C41oZakgaPRBHvD4KH9fZrnKIqFNeXsLtsH2sKNvDdkaUsOLCIVfnr2VO2n7zaAuoc9W1ua9IZGRCcwpio0cxKOo1LB53Hb/ufxdjoNAaG9CPCPxwzTo68+XbHdWMBa34Bcef+BockqgC1p7efb+B+HzCb2//cLjKghG6xc/X/2LPePdwnKnEwUyZdSNYTj7kf1Ono/8LL6IOC2t1+W/FO3t39iVY4OsESxz3pt3qsSF1fiC53J1tOFha9yu6/PdHht46jXnie3CU/UPnLL6gOR6tVdCEhBE2eSvDU6RhjYrqx100qbVU8te4lrC4rAGclz+TCAed0sFX36a7zrWbzJgrefEP7P/Lr15+E+x5A9ms/q2lz0Xbe2/2JtnzD8KuYEDPG433riKoqbFn+FYe2/aK1TTvvGuL6DWHziv+JIVFd1DyT59iaPMd7r1NVlYaDB6hc9iO1W7e0er3Lfn4ETZ1OyBmzMUaL2UmF1sT7qXAiHHYrFUU52rC9ssIsGmorW6xz8a0Ps/CdFzpdH/Da+5/H7vTcl5WCAL3vGldlq9EKhGdW55BVk0OD09rhdrIkE2+J1bKbUoISiTZHtls3VbE2ULdjBxGDUtj5wEOd7t/Ip59k/8v/xJiQgCkxCVNCIqbEJAwREadE5nVHetv51paOMqBE+FHoFgNSp7J3w1JUVaU45wC2Mwz4DRiINeMQuFxUr/6VsHN+2+72aVGjuEG5kg/2fIaKSm5tPq9vf5e70n6Pv97Pi8/k1GMvKiT3pReJnj2TfrfcxJF32h/aM/DuOyE0gsirryPswkup2biB6tW/YD18WFvNVVlJxZLvqFjyHX4DBxE8dRqB4yccN0hyMlRV5bP9X2nBpyhzBOek9JyC255St2snBf+ep/3fGBMSib/nzx3+XcdGj2ZP+X7WFbgLV8/f/zX9gpKJNId3e5+PUhQXG5d+Rubu9VpbXP+RxPQfi85o1IobHzskShQ3bp+iqCiKDBiPuXk5/t9MkiTMg4dgHjwER2kJlct/ourXX1Dq3d+GKlYrlT8tpfLnZQSMSiVk1pmYh4/olqLqgiCcOgxGP6ISBxGVOEhra6it0mbcKyvMAjo/86miuLDV15JzJJPw2BTMgaHiOiX0eVanjeya3KZgU3UOFbbKjjfEPerEHWhyB5wSLHEYdcfPeHbV1VG3fRs1WzZRv2snqtNJ+DNPIel0nS7bAe5sa0dpCXXbtjY9ZvLDlJDgDkglJGJKTMSUkNBtnxcE3xEZUEK3WbXoHfIO7QBgUNppDDDFUPT+OwAYIiNJefr5DiPdaws28Z+9n2vLA4JTuCPtFkwnORyvL0SXu4OzqoqcZ/+Oo9Q9Djz+8ssInzyRvAVfU75uvTalatikicRffilqYCg2tfX/oS0/j+rVq6heuxpXdXWrxyWjkcBx4wmaOh3/wUM8epO4uWgb7+3+r7Z8b/ptDArt77H9nwhPn2/1B/aT98pLqHZ3iq4hOprEB/+KPrhz01xbnTae2/gKJQ1lAKQEJfHnMbd7pT6Wy+Vk3eKPyD24TWtLHDKGSb+5Dlnn/fpcfdHJnm+KzUb12tVU/rQMe0HroS/GuDhCZp1J0KQpyKaeOful4D3i/bRrZFnCiBO94tTeU52yHjt6cV96DKPezicvPtTpDKgLb76fr99+HgA/cyBhscmNRc7dNaWMJvFBVui6nnKNcyku8usKW9RtKqgrQu3giyYAiyGAlMaaTclBSSQHJWAxBHTquM6aamq3bqF28ybq9+1tNZFJwhWXUZ+dQ/m69e3soUn45En4JyaQ+/mXnTo2uD8zmhKSMCUmYmwMTBnC+262VE85306GKELeDhGA6n6FWftZueANwP1N13k3Pkb2Xx/SvlmP/9P9BIwY2eF+fs1by2f7v9aWB4cO5PbUOR1G6Y+nL7y4PU2xNpDzf89hy3Z/6ygZjSTc9yABgwZhxInO5QTFBbIOl2zALnVcpFd1OqnbtZPq1auo3bGt1ZsWuN9YgqZOJ2jKVAxhJ5eFU2uv46n1L1LrcNeWmB4/mat6wFSvnjzfrJlHyH3xeRSrO8NLHxZO4kN/xRDetb9dVnUOL25+Qxvm+puUWZzf/2yP9LE9ToedNd++R0HmHq2t38hJjJt9FXIfvZHwBU+db6qqUr9nN5XLfqRu545Wj8vmAIJPO52QmbO6fP4JfYd4P+08k6Qg1VSQ+/mXVKzfoAWgQidOIOGKy9r9UudUZdArrF38HzJ2bepw3QEjxhIaFcem5d+2s4ZEUFgUYbEphDfOuhccEddq1j1BOJYvrnGqqlJmLdeCTZnVOeTU5GkzhR+PQTaQFBjfYihdmF/XMgKdlRXUbNlM7eZNNBzY3245DmNCIiHTphMzIZ0d9z3YYdmO1JdfxGYOwZqXhy0nB1tuTuPP7DYnRmmP7OfnDkZpmVKJmOITkP16/yiZvvCeKgJQ7RABqO6nqipLPniamopiAMbOuoKgvVlULlsKgGXMWOL+eFen9vVzzq8sONh0UzE8bAi3pt6AQT6xUaR94cXtSarTSd4/X6F+d2MBaEki7o67saSle+wYzupqatatpWr1r9jz2ihuLkmYhw0naNp0LOljkA1dz3L7YPdnbCzaAkCIKZhHJ97XI4Zseup8s+XlkfPCsyi1tQDogoNJfPCvJ1yb58es5SzMWAKAhMQ96bcyKHTASfezLQ67lVXf/Jvi3ENa26D000mfcYkYJuFh3XF9sxcWUvnzUqpWr0a1HVNLQpaxpI8hdPZZ+A0cJP4/TzHi/bRzTJJCw46tHHrt9eMOa/dPTRdBqEayLKHYKjo1C97ltz/GgZ1bKczaT1lhFk57x/O86/QGQqMTCYtJbgxKiaF7QmveuMbV2uvIqskhsyqbzBp30Km9wuDNSUjEWWJIDkzUMpxiA6JPKKPdUVpCzeZN1G7Z7C6Z0g5TSj8Cx47DMmacdv95stc3VVVxVVe5g1GNASlbbq47C1tROvcEJAlDZFRTQKoxOKUPj+hVr+m+8J4qAlDtEAEo7ziwZSVbVywAIDg8lhmzryf78UfcD8oy/f/vH+hDOjcz2Y+Zy1l4eIm2nBoxgltG/u6ELrJ94cXtKaqqUvT+O1SvWa21RV13IyGnz+i249myMqla/Ss169dpGXHNyWYzgRMmETxtOqbklE69cewu28e/tr+nLd+eOoeREcM82vcT5YnzzV5cTM7zz+Cqco/tlwMCSHzwL5jiE054n4qq8M9t73Cgwn2jEWIK5pEJf/JYsf+jbA11/PL1m5Q31vQAGD7xbEZOObdX3RT0Ft15fXPV11O96lcqf16mDdVtzpSUTMisMwmcMLFPz54nNBHvpx2TZQljdSk7/nR/pzIE7EER4h61kUGvUHB4Fz8teLfdD7azLr2Z2P4jtUkqVFWhuryIsoIsdz2pgkyqSgtQ1Y4/yDYN3UshPDaZsOgkDGLo3inN09c4u8tBTk0eWdXZWoZTqbW8U9uG+YVqWU3JgYkkBsbjpz/xofD2wkJqt2yiZvMmbFmZba8kSfgPHIRlzFgsY8a1m/F8NMMz74sFXSrbcTyKw4G9IL9ZtlQ2ttwc7YvYzpD9/TElNA3f07KlemgJgb7wnioCUO0QASjvsNsa+Pbfj+F0uGvVzLzibqlWKUUAACAASURBVGyffE7DwQMAhF90CeHnXdDp/f3v8I8syVymLY+JSuXG4Vd3OQjVF17cnlL69QLKv2vKLgs7/0IiLvTOsDXFYad26xaqV6+ifs/uNm8ujfEJBE+dTuDkyegD25450eq08vf1/9AKL46LTmPOiGu6te9dcbLnm6O8nJznn8ZZ5q7ZJPv5kXDfg/j1O/naVpW2Kp5Z/zJ1TncgMD1yFDeP/J3HAkPWumpWLPgXVaVNtYRSp1/AsPF9rzB8T+GN65uqKNTt2E7Fsh9p2Le31eO6wCCCZ8wkZMZM9MGd+5JB6J3E+2nH/GQXue+8Q/matR2uGz51CvE33YJVZEFpDHoFR0M1m1Z8e8IzpDod9sZZ9xqLnBdkUV9T0YmjSwSFRzfLkhJD904FsiyB3oWic4Gkgiohu3Tg7Lj8RHOKqlBYV9wYaHLPTJdXV6iVPzgef72/ltV09GeQMfBknhaqqmLPy9UyndockQAgy5iHDMUyZhyW9DGdThY4WuPuRMp2dOU5uKoqWw3hsxcWdi1bKiraXfRcm4kvEX1YuM+/GO0L76kiANUOEYDynk3L5pOxw51dkzg4nZERQyh8598A6MPD6ffsC50uJKeqKgszlrA0e4XWNiFmDNcNu6LdaULb0hde3J5Qufxnij/5SFsOmjad6Btu8snF11FWRvXa1VSv/hVHSevMCnQ6AlJHEzx1OgGjUrWZNADm7/+GX/LWAO5Ci49OvI9AY/sXPm87mfPNWV1Nzv89g6OwEADJYCD+T/djHjzEY/3bVrKLt3c2nQfXDr2cKXHjT3q/9TUVrPjyDW0YLsDYWZczcPT0k9630D5vX99suTlU/LSUmnVrUR3H1KfQ6QgcP8E9PC+ln9f6JHiPeD9tm6uhAeuRw1gzDhE3fRK7Hvprp2eJSv3HC5QezsWUlNzpySX6OlmW0MkuJJzIEigq2gypJ3o/31Bbpc24V1aQRXlR14buaVlSMcli6F4fIhtVapRqFuxZzKa87bhUBZ0kMy5+NJcOP5dAOQjF3vr/WlVVKm1Vzeo2ZZNVk4vdZe/wmHpJR0JgvBZsSglKJNLfM0PHjo48cAedNuEoKmp7RZ0O87ARBI4diyVtDLrAkwt2eZvisGPPz28xhM+Wk41SV9fpfchmc+PwvYSmwudx8V7NluoL76kiANUOEYDynsqSfH74+DkAJFnmtzc+SsHcuVr6ZNzdf8KSOrrT+1NVlS8PLmJFbtOQsalxE7h6yKWdvlD3hRf3yardupn8fzWN1Q4YlUrcHXcj6U+srpanqIpCw8EDVK/+lZpNG7WZ3prTBQURNHkKQVOnk2O288qWN7VZQG4cfjXjYzxXu8oTTvR8c9XVkfvic9hyctwNOh3xd91DwMhUD/cQPt23gFX57hlMjLKBh8ffQ3RA1Anvr7ayhOVfvkF9tTutXJIkxp99Lf2GT/BIf4X2+er65qqpoerXlVQu/wlnRevMAr8BAwmdfRaW9DE+v84IniPeT933JY7iIqwZGTRkHKQhI8OdWdD4/jry6SfZ9cjfOr2/5uvrgkPwS07GlOT+55ec3CO+pfel7jrnFEWhpryIskIxdO9UJRtVdpbtZt7Gj9ucXU5C4vbx1zEqfAS1dVaya3LJbDaUrtreufMy2hylBZqSgxKJt8SiP8G6tm1RFQXr4QxqN2+iZssmLYO+1fMxGDCPHEXgmHEEjB6Nzty5mfF6C1VVcVZUYMvNwa4N4cvFXlhw/OHQzUkShuhoLSClZUuFhnnsOtx8dlRJVVElqVfPjioCUO0QASjv+nn+q5TkZQAwcvI5ROVUUfHj9wAEpKUTf+c9Xdqfqqp8uv8rVuc3Tfl5esJULh90QacuBqf6DXPDoYPkvvR/WraCKaUfiQ883OPGQyvWBmo2bqBq9Sqshw62uU5ppD/bU/QcSDYxOHY4t6XO6XE35idyvilWK7n/eAHrYffrBkki9rY/Ejj25DOT2mJ32Xlu42sU1buzlZIC47lv7B0ndENUVVrAigVvYK2rBtzTY0869wYSB6d5tM9C23x9fVOdTmq3bKbip6VtFjLVh4YRMvMMgk+bgc7SczIVhRPj6/PNFxSbDWvmEawZh2jIOIQ1IwNXbft/g5HPPMXux+Z2OgNqxJOPHzdgJZsDGoNSSZiSUvBLSsIQHdNnpyU/ljfPOafDRkVRLmWFmVpNqa4M3QuPSW6ceS9JDN3r4WRZokau5KEfn2kz+HSUhMTfZt7Lq2vfpdJa3eF+g42B2ox07n8J+Os9H5xUXS4aDh5wZzpt3YyrsrLN9SSTH5bUVCxjxxEwMrVPzBzXVYrdjj0/r2kI39HaUm3UpW2PbA5oGsLXGJgyxsUjG7s2iVJfnB1VBKDaIQJQ3pW9bzNrF38IgH9AMGedfxvZf2ssRi5J9Hv+JQxhYV3ap6Iq/GfvF6wv3Ky1zU46nYsGdFzY+FS8YT7KXpBP9nNPaymphsgoEv/yKPqgtusr9RT2wgKqVq+ieu3qNt9UnTqJgDFjiDxtFv5DhvaoG/Gunm+Kw07eqy+3qK0TPecWgqdO647uaXJq8nhx0+s4VfeHpDOTZnDRwHO7tI/yomxWLpiH3eo+v3Q6A1MvuJnYfsM93l+hbT3p+mY9ctg9PG/jBjjmw7dkMBA4aTKhs886qWL6gm/1pPOtO6iqirO8jIZDh7SAky03p9X53IokYUpIxG/AQGJmzaRkyXeUr1nX4fHCp0wiZPIU8r5ZhC0nu80s4DYPZzJhSkzCrzEoZUpKwhQX3yezDX19zomhe32TbFT4YNd81udu7XDdiQnpxAfF8NWeJS3aTTojyYEt6zaF+nVfHUTV6aR+3x5qNm+ibuvWdgPhstmMZXQ6lrHj+H/2zjs+jvJc29fMdkmr3nuzjeVuSe6AGwYDoZl2aCEhMRBICCThg3By4ISTAAkt9FACCQnFYDoYsME24CrLVe6yZPXepdXWme+PkVaS1Vayykqei59/u9NfDe/OvHPP89yPz5Qpg6o0Pd5RoqVqOwlSxdiKC5WUxQFES+kjozoEqdg4DHHxaAMDe/ydj9fqqKoA1QuqADWyuFxOPnvlQawW5cK44OKfwCcd5rUhl1xGyCWXDXy/kos3Dr3N7sr97nkrE5dzcfKKPrcb7cHLaOGsr6fwkYfdobgas5m4+/7bXUZ1LCC7XLQczKF883ocB3LQ9BAZrw0JwX/BIgIWLkIXGjbyjTyFgfQ32emk9MXnaNm3t2P7624gaOnImHZ/W/gda3M/c0//cubPOSt4gkfbVpXk8f2HL+GwWwHQ6gycfdlqwuM8215laPDG65uzvp76zRtp2LQRV1P3N8amsyYTtHwFvtNneJV4rNI/3tjfTgfJ4cBWWOAWm1pzc93VR/tC9PHFmJyCKTUVU0oqxqQkRKMS5TDQKng2cyiyLCNLEvbycmxFBdgKCrAWFmArLPD4Lb2g1aKPjsGQkICxXZSKjfO6aOeB4m19riN1ryNKqqG6FE8esYy+/m1RUorJuZq6N/w4XA5qrLVUtdZQZalWPltruG7mJTy08UlcHqRcagSRPyz+Na/terdNbIon0T+OCJ+wAXnSDgbJbsdyMIem3bto2be31+uBxmzGb9Zs/GZn4HPW5HEpRo8Eks2mREud6i3V2urxPkQ/vzZvqY4UPmNsDMbWxnFZHVUVoHpBFaBGngNbPufQjq8ACI9NJSNxLmUvvwiANiiIpEcf72Is7SkuycWrOf9mf/VB97xLki/g/MSlvW7jbYOXkcDV2krxXx7BVlQIgKDXE/e7+4akktpI45Jc/DX7OaqqiphUYGVWgURAdc83YNNZkwlYeDZ+s9NHbdDtaX+TJYnyV/9O086O1NLQK64k+MKLh7N5XZBkiRf2/YPDtUqlygC9md/PuQc/fd++AOUFR/jh41dxOZW39XqDD+dccTshUQnD3maVrnjz9U1yOGjauYP6b9ZjKyzotlwXFkbg0uX4LzwbjY/PKLRQZaB4c3/zBGd9Pa0njrf5N+ViKziJ7HT2u50+Ohpjciqm1FSMyanoI/tOgRuqN92yLOOsrnaLUbZCRZhyNTR49PciCOijohQ/qfh2b6n4MeX9Mhb63NCl7iUQEBo16NS9DvN2F7LkQhA1yGhOy7x9LGB32d3CUmeRqcpSTb2toccUu4eW3M1DG5/y+BjPrPwjGtvIpK9JVistB/YrkU4H9iHbeo640wQE4jc7HXN6BqYJEwf1XKXSP+2RsR2V+JQUPkdlpcfRUrHXXIWloJDa7Tv6XXesVUdVBaheUAWokcfSVM9nrz7kNnM8//p7qXn0MVxNyiAi+s678Js5OPNoh+Tk5f3/5FDtUfe8VRN+xNK4nqttjYXBy1AiO52U/O0pLIfbRDpRVM73AMzfvYn1BZv46MQXAGhFLb+fczcB1RYaf/iexh3beqx4IZpMmDPn4r9wEcbklBENefekv8myTMW/Xqfx++/c84IvvJjQK64c7uZ1o8HWxJ93PkmzQzmP00OnsHraTb2es5Lc/Wz9/HWktpQUo4+Zc1fdQWBY9Ii1WaWDsXB9k2WZ1uPHqP9mPc27s7sN2ASDkYCFiwhcthx9ROQotVLFE8ZCf2tHdjqxFRd3EpyO92rO2xnBYMSUnIIxRYlwMialoPEduGDT7vVR8t5aarfvcHt9BM+bS8xVq07L68NZX99NlHJWV3u8vS4srIvRuSHOeyvwjaU+15nW5gbF4LzsZJvReSFOhyepe3qCI+IIjkwYUOqeTivhaG1k18ZPyD+0B0lyIYoaktJmkbHkEnQmfxzOsfFA2xOtTivV3USmaqosNTTY+/dmOpWHltzDw5ue9jgC6pmVD4NVN5ime4TL0kLLvn00ZWdhOZjTvcpsG9qQEMyzM/BLz1DGt2oU8agh2WzYSordwpS9WPnsKVpqoN6AM196gRZhbESvqgJUL6gC1Oiw5dPXKD6+D4DUGYuIr4O6LxUhwXfadGLuumfQ+7a7HLy4/3WO1XWY3l4z8XLOiZ3fbd2xOngZDLIkUf7aKzTt2OaeF3HzLQQs6lmc83YqLVX8eedTOCTlDfWlyStZkbjEvVxyOGjZt4eGH37AcvBAj28i9FHR+C9chP/8BWgDhi83v53++pssy1SteYf69V+55wUsWUb4dTeMmjdETvVhXtz/unv62kmXc3ZM999SwZFsdqx70y0s+5iDWHzlHZiDBl9BT+X0GGvXN0dNNfXffkPD95t7TCXwnTadwOUr8EmbonqleCHe3N9cTU1KGt0Jxb/JejLfI08lXXiEkkaXkoIpZQL6mJghe6hrr3akcTlBcoGowSXqsAtDH5Hiam7GVlTYIUwVFGCvKPf4Db0mMNAdJdVeic8bKvB5c58bCEOXupdIcERcl9Q9nVaiLC+Hb9a+1mvE3bJVtxCVPNWrRSiLw9I9iqlNZGpyNA9qnwICwcZAwkyhhPqEEGYKIcwUyoTweN4/+plHHlDzYmfz46nXINmH9rfgamqiee9umrKzlZfGvYgTuohIzOkZ+M3OwJCQMOq/SZXekWUZZ011R7RUm/H5pLvuGFB11Fl/f4EWzdiIDFcFqF5QBajRoaLwGJvefw5Q/GEuuOJXlDz4B2WhIJD06F/RhYQOev82l53n977KiYaT7nk3nHUV86O7Vg4bL4MXT6h6f41b5AMIuewKQi6+ZBRbNHgkWeKZPS9zvD4PgDi/aH6X8Us0vYSmO+rqaNq2hYYt3ysmgqciivhOm47/wrPxmz5j2PLj++tv1R9/SO2nH7un/RcsJOLmW0b9LdaaYx+zuXgLADpRy//LvIso3w6/sBP7t7Jrw7vQFsruFxjG4ivvwNd/YAUFVIaWsXp9k2w2GrdvpX7Deuxlpd2W66OiCVy2HP/5C8e8h814wlv6myxJ2EtLFLEpN5fWvNyer/unIOj1GBOTMKa0eTclp3h9UY7TQbJalQegtigpW0EBttKS/k3V2xB9fTtS9xKUND5deMSI3q+8pc8NB06HjdqKImrLCtzCVGtz/x5knVP3IhImEZeUxHsv/LFfb5lr7vxfREPQqD0TybJMs6OlexRTaw3VlhpanJ5XJeuMKIiEGIMIM4US5qMITIrQFEKwKRhdDxV+B1IF77EVv8fPFeiRWNgfzvp6mvdk05S9i9ZjR0HqOQJLHxOriE7pGeijY1TRaYzjK1vZe9sdagTUmYQqQI0Osizz5T//TGOtMiicvfQqTN9ux3JISQ0LvvhHhF626rSO0eq08uzeVyhoLAKUG8WP064lM7IjvW88D146U/fNeqre/o97OuDcxYTf8OMxe9P6vmQ77xz9AFAGF/dm/JI4c0y/28myjDX3OA1bvqcpKwvZZu22jsbPjHn+AgIWLsIQGzek7e6rv9V+tY7q9951T/ulZxC1+navyNt3uBz8ZdezlLaUAxDjF8Xv0u9Ep9FxNPtb9m7+yL2uf0gUi1f9ApOfd6ZsnEmM9eubLMtYDh2k/pv1tOzf12256ONDwNnnErh02Wm9sFAZGkarv7ksLVjz8jqim/JOIFm7X9tPRRsc4vZtMqWmYoiNO+PNeSWHQzHZbRelCgsHWIHPqJjqdoqW0kdFj9pLnfHGQFP3Mpb+iNqKUvIOZve6Tjsp0zKZv/L6YY2CkmWZRntTjyJTlaUGq6v/321PaAUNIW2iUleRKZRgY2CvLyf7QtTLHKg5yItZb/YoQgkI/GLOTUwNTjut6CdHTTXN2dk07d6F9URur0KhISHRHemkj1TT0ccTRtFF8auvUrt1W7/rqh5Q4wRVgBo9ju/5jt0b3wfAPySShVPOo/yl5wHQBASQ/NgTpz1osTgs/G3PyxQ3K2/RRUHkthk3My1yIpLGBYIMsoDo0oBzfBoxNmVnUfbSC+6bmu/MWUTffqdXCBuDoc5az//teNI9UFmRsIRLU1YOeD+S1UpTdhaNW35Q3jT1gCEhkYBFZ2OeM29QPh+n0ttguX7zJirffMM97TN1GtF3/ApRN3yeAgOltLmcx3Y9g7Mt5XFx7ELSavTkbOsoPRwUEc+5V9yOwTR2TGzHM+Pp4cxeUU79Nxto2PJDd+FYEPCbnU7gsvMUs9UxKqyPdUaiv8myjKO8jNY23ybriRNKlFw/Q1hBq8UQn9CWTqf80wUFDWtbxwtKBb4yd+qetagQW8FJjys/CVot+phYt5+UISEBQ0zsoKMX21MXtZITQZaRBQGnqMWOdlyO4fpCkiQaa8vboqQKqCk7SWNNmTsa5/LV9/Hxq39FkvqPrBBFDdf/9jHsTv3ptUmWaLA1utPjuopM1dilnv2L+kMn6tyRS6GniExBxoBhqTgn6mWapEY+OLSOrJK9uGQJjSCSGTOTK9JWYhb9ByU+2SvKac7eRdPubGwn83tdz5iS2iY6pXtFJWeV4WGw1VHHAqoA1QuqADV6OGytfPLy/7jf3iy+4hc0P/uiu4JL1O13Yk7POO3jNNtbeHrPS5S1VPCjScuZGTWFr49/x67Sfe6bSUbMDFalXTjom4m3Yjl2lJIn/+qu5GNMTiH2N/eO2bQVWZZ5af8b5NQcBiDcJ5TfZ96NTnN6Qo29ooLGrT/QuHULzrrabssFrRa/2en4Lzwbn8lpg04x6OkBrXHHNspffdl90zFNnETMXfd45f+jzcVbWXPsI5BlkkqdxFV1DGpDY5I557Jb1bLRXsR4EqDacVksNG75nvpvN+Coquq23BCfQOCy5ZjnzEXUnd6DlMrAGI7+JlmtWPM7opta8070WFziVDQBAW6xyZSSiiEhQe0PQ0hHBb6TiihVWIit8CSuRg8NnwUBfVQ0hvh4jPGJiigVF99vxct28/biNe9Tt2On27w9aO4cYq++8rTM28cLnVP3JkybzvsvPOzxtjf89i84pP6rubkkF3W2hk4iU4cvU3VrjftF1UAxaPQdwpJPR6pcmE8o/nrzsIhM/SGKAmhdSKILREACUdKCU/T4+VGWZeylpTRnZ9GUvQt7SXHPKwoCpklnYZ6djt/sdLSBqkh+pjBU1VG9DVWA6gVVgBpddn2zhhP7fgAgdsJMJjkDqP38UwB8pkwl9u7fDslxGu1N7Kvfh0lv5I3d7/UaTnt75o1MC5kyLkQoW0kJRY/9yW3mq4uIJP6+B9CYzaPcssGzq2Ivrx98yz199+zbSQ1MGrL9y5KE5dBBGrd8T/Oe3T2W4NYGBeO/cCH+C85GHz4wg+1TH9Ca9+ym9MXn3Hn+hsQkYn9zLxqTd4o4sizz0r7Xse47QHRNh/gUmXAWCy+5Ba3O+0SzM5nxKEC1I0sSLfv3Uf/NeiyHD3VbrjGbCTh3CYGLl6INHP4CA2cqGo2ITrCjdTndYoBTo8Uh63G5+q8g1RlZlnFUV7l9m6y5udiKi/o3yhZFDHHxmFJSMKZMwJSSgjYkVI2EGwWUCnwnldS9ggKshSc9qi7Yji4sXBGlEhIxxMdjiE90+3CN1we04USvdfCfx+/1OAJq1W0PUF5aQWTiWUjI1Frr3OlxHSJTNTWtdbhkz7zCTsWkNXURltqjmMJ8QjDr/Lz6dzuQe6osy9gKC9oinXbhKC/veUWNBp/JaZhnZ+A7axZa8/j1nVPpm+GsjjpaqAJUL6gC1OjSUF3Gl/96BABBELlg1a8of/hh9+Ai8ZG/oA87/SpaoijQKNRx3/pHPDIUNEuBY7pfOGprKXrk/9zRPBp/f+Lv/wO6sLEbwttsb+HhHY/T7FDefp8TM59rJl0+bMdzNTfTtHM7DVt+wFZwssd1TBMn4b/wbMwZmb1GLPWWLlB/7ARFj//FLXLpY2KJ+919aPx6v1CPNpLkYssX/6T02F73PFtYANdd+z9ovShdUEVhPAtQnbGVFFP/zXoat23tXp5ao8GcMYeg5edhTEoenQaOU4w6F3KdEo1SvyPLPVgOnJtJ7NVXIgQFYXX0nuot2e3YCk7S2klwcjX1H0Ej+vlhSk7BlDoBY3IKxqRkr4wYVVHoUoGvQKnCN5AKfNqgIHwnpxF/6UUcuO8Bj1JU7P6hY3oMN1TodTJbP3+TEzm7+l03ZUo6QeHR7Nr4KQ69SFmQSFmwiM0w8AdeP51vJ9PvkC4G4L66sVG9q52BpHzKkoQ1P88tOjmrq3vcp6DV4jN1Gub0DHxnzETjo9oWqCh0ro4qIiMhDFt11JFAFaB6QRWgRp9v1zxDVXEuAFPmXUDgziNYcvYDEHTBhYRdefVpH0PUS7yR8+6ollQdKVyWFooee8Qd4isYjMTdex/GhMTRbdhp8sbBd8iq2A1AoCGA/577G0za/kPFhwJbUZFiXL59G67m7g/0gsGIOTOTgIXnYExNdb/B6z1dIJOolRdQl72b0o8+QRceQdy993t1pIbL6WDb5/+k5MR+97yKIJGj8TqunHgpS+IWjWLrVHriTBGg2nE1N9Pw3SbqN37bYyqtMSVVSc+bndHNX7DzQ4Y7kucM9ZXxBKPORVN2NvnPvthrNErSL2/HnJ7uFqEctbVKGl27WXhhQf8V1wQBfXRMl3Q6XUSEV0dJqPSPZLViKyrCWtQhSvVVgS/26iuxFBZRu31Hv/seaya9w4VLciHrnTib6/j4xUf6Fe4u/elvWf/u37E0d4jAMlBvFikL1lATICKLHb+7AL2Z0FMry7UJTiatd0ZxDxRPUj6tLmg9dpTm3bto3rMbZ11dj/sSDAZ8p01XIp2mT0c0jo9zpDJ8jIcxnCpA9YIqQI0+Rcf2sPWz1wEw+vqzJPNyyl94FgCN2Z/kvz55+hVUTA5+9cUfcMn9pwRoBJFnVj4M1rEX0SE5HJQ8/QStR48oMzQaYn51N75Tpo5uw06TgzVHeGHfP9zTt0//CVNDJ494O2Snk+Z9e2nc8j0tOQd6LJGri4gkYOEiIpYtwXbsSJ/pAkk/+ymOpmZ0szK9uoqX02FnyyevUl5wxD1PTIhlU2AVCAJaQcO9mb8ixi9qFFupcirjYfAyGGSnk+Y9u6nb8LVSVegUtEFBBCxeSuA5i9GYzaqvzADRaES0DZUcuPt3/T7UTn3sT5SsW0/T3n09ioKnIppMGJNTOszCk5L79QVSGR+4K/AVtFfgK8BWXIRstzP1zw9z8A8PeVymfMYzT9Hk1AxJ8RBvw+ay02BrpNHeRKO9yf391M9mRwsPLbmHfcUHmOTwZ+vHb/U6Fllw6fWcEOoxFFaRu28H+h4snAS9nsDkiSRNm09c1ESM2vEddehJymfybaux1dRQsub9Hvchmkz4zpiJOT0DnynTEPWqD52K54yHMVx/AtSZXXdWZVSJSZmO0dcfa0sj1pZG6k0y2qAgnHV1uJoaad6zG3PmnNM6hkuSPBKfAFyyhCRLjLXHDVmSqPjHKx3iExB580/HvPhkdVp5+8gH7unMiFmjIj6BEjZtTs/AnJ6Bs76exm1badzyPfbyMvc6jopyGjZuIGLmlN4HLgCyTP6r/2D6E3/BHhjmtUK43dbK9x/9neqSPPe8SelLSVt0EbnZz1PcXIpTdvGPg2/x/zJ+hf40DeFVVE4XQavFnDkHc+YcrCfzqftmPU07d7ijK5x1ddR8uJbazz4h8bbbcCFx4rkXuvxWZZeL2q3bqN22/Yz2lZFlGVwuZKfT/U/vq6Hk3ff7T6GSZco+/ATf2BjqehGfdJGRmJJTMaYq0U36qOhBF3lQGduIOh3GhESMCYkEtM2TXS7sFeVofH08Ep/at3HW13Pigf9BFxGBMTEJY1IyxqRkDHHxXikCyLKMxdnaTUTqIizZG2m0NWF12QayZz48vp5LU5dz4erfcfiHbyg4vBdJciGKGhImz2TyomVsqznEx7kb+MuKB/CZNA2hqo7aYwepKDgKbbYVst1O3ZEc6o7kUBCVSPK0BcRNnIVOP/6EKFEUEBrr+h3D5b30MlP+93+oXL8BR129sq2fH34zZyui0+S003+BrqIyjlF/V9N/WQAAIABJREFUHSqjhqjRkDJtAQe3fwlA7v4tTF90DrWffgxA/eaNpy1AaUQRjSB6HAHlkBzoZf2YCvOvfu9dmrJ2uqdDr7gS//kLR7FFQ8PHJ76kzqbc2P10vlw54ZJRbpGCNjCQ4JUXEnTBSqx5J2jc8j1NO3cgWa1EnLecsi++9OgBrWTth0q6gBdKnrbWFjZ/8AJ1FUXueVPnryRt3gUIgsBPplzHo1l/wyE5KG+p4MPcz4bVl0tFZaAYE5OIumU1YVdeTf2mjTRs2uj2GdL6+eITFszBB//Y50NG7jPPMf2pxxGHwVdGlqQOccflRHYon3QSfGSnS1nmdCjfnZ2+d9qms0iE04nkdMIp87v8c7mQHQ7ls4d9tm97KlP//DB1O7M8+vtqd2Yx5eILKX5vLS6dBmdMOLqkJAImphF61nR0quGuSh8IGg2G6Bg02BA0Go8joNpxVFTgqKigacd2ZYZGgyEmtk2USsKYmIw+OrrLNkOJJEs02Zvd4lHHpyIuNdoa3d8HWzmuNwQEREGDRhD5OHcDm4t3smLaQlYsOQ9BBlmAPVVHeHTfG9RbG9EIIr5aH2ZETodIYNq5tDTWkn9wB/k527E0daSW1ZSdpKbsJHs2rSV+UjrJ0+YTHBE/psbMfaG1t1K85j2PxnDl674i8qILaSwpxzw7A9PEScPWn1RUxhuqAKUyqqRMX8ihnV8jSxJVJSfg0hXw2Scgy7QeOYy9vBx9ZOSg9y+6NGTEzPDIAyojZgY/FGVxqCyXqyZeQqRvxKCPO1LUff0ldeu/ck8HLFlG0MqLRrFFQ0NufT7flWx1T1814RL89N4VUi8IAqY2b5Kwa66jefcugs5KoWTthx5tX7t9B/E3/xgE73qL2NrcwKa1L9BY0xHdNfPcy5iUvtQ9HekbzlUTLuGto2sB+K5kG5ODJzI9bMqIt1dFpS+0AYGEXno5wRdeTHPWTuo2fE3YvEzPheI17xG8eAk1+3K6iUXyqWKR09EpaqizaNRJaGoXinpI4/V6ZHlA0SiCrw9vXRBEdaAWWXQBudCUiy57HeE+oYT7hBHR6V+4T9iI+fupjA2copaguXOo3bqt33WD52bSlHcSNJrunlIul5LaV1hAw3ebACW1zJiQ6I6UMiQloQsN61NMcbgcbVFJnUSkts8GeyNNbSJTk725z8I3g0EraDDrzQQY/AnQm/Fv/9Sb8TeYCdD7428wY9b5odML7rFvvbWRNUfWsYZ1Pe43M2YmoqSl8xXJ1z9Yeek093wqCo+Sd2AbJSf2I7ddt5x2G3kHtpJ3YCsBodEkT5tP4uRM9MaxkTYr2WzYy0qxFRdjLynGVlKMraSEyf/vN9Tt8Fxkj//pTzB52RhORWUsoApQKqOKyS+A2NTpFLVV1zqZv5+oGTNp2asIRg3fbSLs6msHfwCnhlVpF7KzeG+/VfBWpJ7DM9tfp8HayJ92PsXSuLNZmbgMo5cOiBt3bqdqzTvuab9Z6YT/1/Vj/k2Uw+XgP0fec09PDZlMesTMUWxR/4gGA/7zF6J1WQb0gIbkAi96YdbSWMum95+nub6qbY5AxvKrSZnePaJuQfQcDtYeZV9VDgD/PvIev/ePJdAQ0G1dFZXRRtTp8F+wEPP8Bfg6Wtj3y7s82q52RxZRF11I7eefDnMLvQ+XAJIILo2ASxRAoxlQNIrga6IquHtqrkNyUNJcRklzWbdlZr1fF0Gq/XuIMRiN6EUXS5URwY6W2KuvpHbb9n59x2KuvgqbOZTUjHmK0Xl+HtaT+Vjz83CUl3fbRLbbaT1+jNbjxzpm+phwxIZjiQyiLtyXqhAd1Vo7jW3pcBZn65D/jQaN3i0edflsE5vaBSZfrY/n4zun4PHY94q0lcgOEXpYTxRFohInE5U4GauliZOHdpJ3YBtNdZXudRqqS9mzcS37vvuYuAkzSZ42n7DYVK8YiyqpnBXYS4vbxKYSbCXFOKoqe+5PAxTZvW0Mp6IyVlAFKJVRJ3XG2W4BquBwFikLr+kQoLb+QMjlVyDqBpe7L0kyZq0/t2feyItZb/Z4IxYQuD3zJupaGmm0KqZvkiyxoXAzWeV7uCL1ItIjZnrFzbQdy5HDVPzjVfe0MXUCkT+/dVx4aKw7+Q2VFqWErVFj4NpJl3vVue8LYYAPaHjRA1VTXSWb3n/eHW4vCCJzL7iBhMkZPa4vCALXnbWKgsYi6m0NtDgsvHloDXfMvAVRGPv9UGV8IggCgkYc2EPGcLVFq1V8Qto+Ba0WQdPpe5f5GgSdrm25BkGrUz41WgSdThGHetquh32i0VDnbKLQUk6BpZT8lhLqnE24RAFJAy5RwCUCna67OlFHsr9I4NxM6rZu7/dvC5ybiVOjYfW0H1NpqaLCUuX+bHa09Lpdk72ZJnszufX5XeZrBA2hphDCfUK7CVR+Ot8xc49QGTi2QBMxt99CyYuv9WoKHfOLn2ELNCFIIOr0mJJTMCQloXVYkOxNtNZX0nIyD1dBIWJxOaayWgzNPXgqWVrRHSsg4FgBAUAi0OgrUhGsozxES0WIjspgLQ5d//c4P52vIh51EpJO/fTXm4fF1NvTse8v5tyEWfRHcvYfrWX0MXNWxjImpS+lujSPvAPbKDq2B5fToRzT5aTgyC4KjuzCLzCM5KnzSEybg8lv+F9KybKMs662k8hUhL2kGHtZWY/pxL0iCGN2DKeiMpbwuAre2rVrSU9PJzExcZibNDKoVfC8B1mW+fJfj7pTfmYtXoX2nU9w1tYAEPnzW/GfO/+0jiHqZZqkRj44tI6skr24ZAmNIJIZM5Mr0lYqN2C7QHFTKWuOfcSJhpNdtp8QmMzVEy8j2m/w6YBDha24iKLH/ozUqryJ00dGEXffA2j8eq82MFYoairlL7ueQWrz7Lp20uWcHXN6/+9HEqPoovjVVz1KF/CmktH1VaVsXvs8VosiwIoaDQsu+gkxqdP73fZYXS7P7HnFPcC9PPUilsefO6ztVemb8VBBZTjxw8aeW3/h8UPGtCf+QsnGH7qLOjrlkz6Fn+5ikaDRKILRCIkmsixT1lJBbn0ex+vzyK3Pp9Hed//Qa/SkBCSSGpjMhMBkEvxjMej0HlfBm/bUX3H4hyP1kGrY4rC4xajOwlSVpRqnPHDBz0dr6iJItX+GmULQqcURxjSiXuKNnHeJMQRzjvks6j9aR/2OLHfFysC5mQRetpLvmo5QYqtlZkQa645tdht5S334f/paXETUOomocbT9c2J09P9cIANNQUaaIwNwxIRBXAzG2Dj8fYIIaIteMuv90Iqj/47f07HvYLHbWik8kk1ezrYunpHtCIJIdPIUkqfNJzJxMuIQCDau5mZspSXYi4vcqXP2kmL3mNgjBAFdRASGmFgMMbHo2z7NUaGUvPbamBvDqYwvxsMYrr8qeB4LULNnz+aWW27hjjvuGLLGjSaqAOVd5O77nuxvlLQr/+AIMkOmUvux4qVjmjiJuHvvP+1jiKIAWheS6AIRkECUtOAUu/QFWZbJqtjDh7mfdxmki4LI4tiFXJi0HJPWdNrtGQyOmhoKH3kYV71izq0JCCT+9/+NLiR0VNozlLgkF3/Nfo6iphIAUgOTuGvWrWMqmkYUBfSN1ey/+7f9PqBNf+pxbOZQPLwEDxs1ZQV898GL2G0WADRaPYsu/RmRCWd5vI+PT6zj64KNyvaCht9m3EG8OXZY2qvSP+Nh8DKcjFWh2FMkWaK0ubxNbFIEp76ijgCMGiMpgYlMCEwmNTCZeHNMjylvRp2Lpuxs8p99sddolKRf3o45PR2rY2APm5IsUWuto8JS3U2gqrc1DGhfoER4BBuDukVMhfuEEmgIUKOmvABJlrA4W2mxt9DssNDsaKHF0UJz27/zJi3kgQ2P4ZIlAo3+XBC3gPSQSYiSkh66q+YIXxVtc5tp/2HxXTy08akBt0Mn6gjQ+RFlNRBd6yKkuhVzRROG8loEpwdCtVaLIT6+o/JeYhK6iEiviErvPPaVZAlREHsc+54udZVF5B3YRsGRbBy27mKQyS+ApCnzSJo6D7+AkH73p/g0lbVFM5W0iU3F7vGvp2iDgtHHxHSITbGx6KOiesysGItjOJXxx3gYww2ZALVo0SJuvvlmfvaznw1Z40YTVYDyLhx2K5+8/AecdiUk+uwLfkzz08+7jVoT/vgnDNExQ3Y8T37crU4rX+SvZ1Pxli5v0cx6Py5PuYg5kbNHdADrammh6LE/YS8tBUA0mYi7934McfEj1obhZH3BJj468QUAOlHL/XPuJsInbJRbNXAMgkTr/j29l/EVBK8p715ZdJzvP3oZp0P53en0Rs6+/FbCYlIGtB+X5OKJ7BcoaFLegIb7hHJf5q8xaLyv7PWZwHgYvAwn4+0hQ5IliptKOd4W4XSiPr9frxofrYmUwCQmtEU4xZqjPRb7jToXcl0dxWve7xaNEnv1lQhBQQMWn/rD6rRR2VpFZUubKNVa7Rao7C77gPen1+iJMHU1Qg/3DSPcFDYsKVFnArIs0+q0niIiWZTv9vZ5li6fLQ5Lnx5FDy25e0CC0qnrm7SmrobdXfyVzPjr/QkwmDFqjD2O52SnE1tpieInla/4SdlLS/ovYIAyRjMmJmHoJEppg4LGvfDpdNgpPr6XvAPblOJCPRARP4nkafOJSZmGKIg4KiuwtYlM7abgjspefJp6QfTx6RLN1C46aXwHVsBmLI3hVMYn42EMN2QC1IYNG3jwwQf56U9/SkZGBsHBwWh6KDcZHR09+NaOIKoA5X1kf/Meufu+ByA2dQZJxRaad2cDELj8PMKvvX7IjjWQH3dpczlrjn3E8fq8LvNTAhK5euJlxJqHv89LDjslTz7eYZap0RD769/gMzlt2I89ElRYqnhk51M42soRX5qykhUJS0a5VYPHIEgITXWUvLeW2u073A9owfPmEnPVKmRz0KgPXMryD7Hlk9dwuRT/Br3Rl3NX3U5wxOAEzUpLNY9kPe1+GFwQNYfrJ185ZO1V8ZzxMHgZbsbyQ4ZLclHUXMLxunbB6SRWl7XPbfx0vqQGJrlT6qL9Ik8rulSjEdEJdjTtFf1EEUmnwy7pcLlGrsKfLMs02BupaOmazldhqaLWWjeoSmSBhgDCTaGE+3at0hdsDBqSiFx3RIrGhUuS0IgioksDTo3XjEtlWcbmsncXkhwtbdFKXec1t4lJfaW8DYaHltzDw5uexuXBfjWCyF9X/IGS6ir820Qm/TCkYEo2G9aCk1jz87CdzMean4+juqr/DVGi1o1JSV0ipQYqkIwlGmsryM/ZTv6hndgs3e9JWlkguN5OcK0Nk82zviNoteijY9zRTIaYGPQxcWgDA4dM3BsLYziV8ct4GMMNmQA1ffp0nE5nv28BDx8+PKAGZmVl8eSTT3Lo0CHMZjPnn38+99xzD769XJBbWlp4/PHH+frrr7FYLGRkZPDAAw8M2JtKFaC8j4aaMr785yOAkje+9JxrqX3xJUB5s5H8+NOI+qGJqBjoj1uWZbIr9/HB8c9osDe65wsInBM7n4uTzsdHNzxpebIkUfb3F2jO3uWeF/nz2/CfO29YjjfSSLLE3/b83W06G2eO4Xfpd475ikeiKKDHicblRERGQsAl6rALo/+QUXRsL9u/+CeSpKQWGH39WbzqDgJCo05rv9vLdvHm4TXu6Z9NvZFZ4dNOa58qA2c8DF5GgrHykOGUnBQ2FbsFp7yGk9j6ifox6/3c0U2pgclE+oYPWzqzt/Y3h8tBVWtNj35Tg6lmphW1hJlC3Ol8naOnfHWelZ9v9+RZe+gLdpXsc3vyZMTMYFXahaftydMbdpfjlAikzmJSdyGp2dGCUxqAefNpYNKa8NP54KfzxVfnq3zqlelpUZNYn7+ZHSV7+t3PvNjZ/HjqNcNy/vrD1dSkVNxrq7pnzc/D1eTZb0IXEdEhSCUlY4iLH7Kx7mjhamnpiGYqLsZaWkxlXSnVJolGP22XAgft+FqchNQ7CGxwoJFRfJrCIzDExiqCU6wS2aQLjxiR1EZvHsOpjG+89Z46EIZMgHr22Wc9UpbvvPNOjxu3d+9ebrjhBpYuXcq1115LQUEBTz75JIsWLeKpp3oOuV29ejUHDhzg3nvvxdfXl+eee476+no+//xzzGazx8dWBSjvZON7z1JZdByAyXNWYP58M44q5c1S5E9/jv+C7uXgB8Ngf9xWp40vT37DN0XfdXnT56fz5dKUC5kXlT6kg3xZlql6+z/Uf7vBPS/0qmsIPn/lkB1jtPm+ZDvvHP0AUHy27s34FXEjEFU2knjTzST/0E6yvvqP+2WCj38wi1fdgTno9NMdZVnm9YNvkV25T9m31sTv59xNkDHwtPet4jne1N+8nc4PGUguEDWj/pDhcDk42VjkNg3PayjAITn63CbQEEBqp5S6cJ+wEUv1GWv9TZZlmh0tbkGq0tKRzlfdWoNrEEbofjrfrul8PmFE+IQSagpxm1GLepkDNQf7qch7I9NCpvQpojglp1skaheVOlLcuopIzfYWWpyWQaUpDgajxoCvzqdDSNL54qfvKi75tS/X++Kr9enzZZMoCjSJ9fy/r//cZzSbgMBjK36PnyvQK9JlZVnGWVvTJXXPWlCAbOs7UhEAjQZDTGybKJWEMTEZfXS0UrxgELRf47SS0y2yO0UtdrSnfY2T7HbsZaVK9bnS4rbPEpx1db1uY9cK1ATqqAnU49B3Hy9rRC2x8ZNJzVxCSGyKV6QsjrVrnMrYZjz0tyEToIaDG264AYA333zTfYH5z3/+w+uvv86nn36KydQ1omTXrl1cf/31vPLKK5xzzjkA1NbWsmzZMm6//XZWr17t8bFVAco7KTq2l62f/QNQSr4ujJtL7YeKOGFMSSX+/v8ekuOc7o+7vKWS9459zJG6413mJ/nHc/XEy4j3HxoD5tp1X1C9tiOiJHD5CsKu+S+vuCEPBXXWev5vxxNYXYoH0YqEJVyaMn7EtXa85WZyfO/37P72Pfe0OSicxVfegY85aMiOYXG08uedT1FnU4xCJwQm86tZq8eUmfxYx1v6m4pn2F128hsK3abh+Y2F/UafBBuD3NFNEwKTCTUFj9p9YTz1N5fkosZa2y1iqtJS3W/lwJ4QBZEQYxApgQlcNHkp//PtE/0KKf+37HfsLz5KlaW2Q0jqFK3UX7rlUKETtfjp/LoKRm0CUhdBSe/rFp10w1D5zRPh7hdzbmJqcNqoRD95iixJ2MvKFDGqLVLKVlwEnlTj1OsxJiS6I6UMSUnoQvsXmdujPIvXvE/djp1uASpo7hxir77S4yhPWZLafJo6RCZbcTGOyoqB+TSZTBhi49BHx6CPiaHBIFNSlU/pycPuiOzOBIRGkzxtPglnZWAwjV6q4ni6xql4P+Ohvw25AHX06FE2bdpEaWkpN910Ez4+Phw7doxzzx1Y2e3a2loWLFjAE088wUUXXeTRNn/729/45z//SVZWVhf/qdWrV2OxWPj3v//t8fFVAco7kSQXn73yEK0tSsWbOYuvwvH3N9w36ISHHsYQG3faxxmKH7csy+ytymHt8U/dD9ugDIYWxszlkuQLPA7L74nGbVspf+1l97RfxhyiVt/mFVVVhgJZlnlp/xvk1Chpu+E+ofw+8+5xWTbbG24mh7M2sP/7T9zTAaHRLF71C4y+/kN+rNz6fJ7e/ZL7YeFHyRdwQeLSIT+OSs94Q39T6R2r00Z+Q4HbNLygsajfqJtQYzCpQcnuCKcQU/AItbZ/zpT+1ups7RIt1RFBVeX2L+yNVWkrKWooY2fJ3n6PMzd2FjH+kXxwaN1QNR2NoOkQknS++OpPiUY6JfXNT+eL3ouKSLSnLn5waB1ZJXvdqYuZMTO5Im3lsKUuDjeSw46tqKiLKOUoL/doW9HPD2NishIl1RYppfXvuJ8PxudOlmWc9fXYS4qwlZRgL1YMwe1lpciOvqMwu+xaq0UfFa14NEXHKml0MbG9mrBbLU0UHM7ixIFtNNVWdP9bNVpiJ8wgeep8wuNSEUb4hdaZco1T8Q7GQ3/rT4Aa0KuKhx9+mLfeegtZlhEEgQsuuIDGxkbuuusuFi9ezN/+9jcMBs+qhxw7dgxZlgkICODXv/41mzZtQqPRcPHFF3P//fdjNBq7bZOXl0dCQkI38/P4+HjWrRu6G7XK6CGKGlKmLyBnm/L/M+94NpNnpdO8aycA9Zs3EXH9jaPZRDeCIDArfBppIZP46uS3fFO4GafsQkbmh5Lt7KnczyXJF7Ages6Aoz9aDuZQ/sZr7mnTxElE3vKzcSM+AWRX7nOLTwDXn3XVuBSfRhtZlsnZ+gWHdnzlnhccmcA5l982bG8UUwOTuCBxGetOKqmjn+d/zaSgVJICxkfFRhWVgdDqtHKiPp/c+nyO1+dR2FTcr1lzuE9olwgnNY119DFpTST4x5Hg3/UlmCRL1FkblGip1raoqTZT9PaXU9MiJvPR4a962m03dpXsY+WExb0KUAKCW0Ty1frgp+9BSNK1z1cilYwaw5iOnJbsAmYxkB9PvZobp68CEZBAlLTgFJGcY/OFsqjTY0pOwZTcUXnWZWnBVlDQkb53Mq/HlDapuRlLzn4sOfvd87QhIRgTk/BNSyN8xtTexScAWSb3meeY9tifqdm8BcuxY9hKSpAsLZ7/AYKALjy8S/U5Q0yM4tM0gJRBo4+ZSelLmTh7CdWl+eTlbKPo6G5cTkX0klxOCo9kU3gkG7+AUJKmzSMpbS4mvwDP26qiouI1eCxA/etf/+I///kPt956K8uWLePqq68GYP78+dx888288cYbvPLKKx57QNXW1gJw3333cd555/Hiiy9y9OhRnn76aWw2G48++mi3bZqbm/Hz666m+fr60tzc7OmfAtCnKqcyumQuXsGhHV8jSS6qS/Lwv+BGtwDVvGMbk2/7KZoeBMqBEhbmuWdYf9wSeRUXTjmX13e/y97yQwC0OCy8ffQDdlZmc0v6taSGJHq0r+a8PHJffM4d9eUTH8e0Bx9A6zd+KqU02pp5f0tHNM6K1HOYP2H6KLZo+BnK/uYpsizzw2dvc2jHeve8mOSzuOimX6E3DI9pfjs3hVzGiaY8jtXkIckSbx55h7+c/wAm3en/dlX6ZzT6m4pCs72FI1W5HKo8zqGq4+TXF/XrSxPrH0Va2ATSwicwOWwCQaax9WB1pve3CAI4i+4Cu81pp6ypEq2o8aiSG4BLlvDRmbhhxuWY9X6YDX74G5RPs8EXH51JTWke15ghIRLOmeueY6uppTk3l+bjyr+m47m4WroLRc6aGppraghMSaTk/bX9p8fJMqUffowpNoaa9irLvaAPDsYnPg6fxAR84uPwTUjAFBeLxsPAA08JD59B2swZ2KwWju/bwaGs76gsOele3txQzYEfPiNn6xcknjWDtIxzSJg4DXGQHlmecqZf41RGlvHe3zwWoN555x0uuOAC7r77buo6KfH+/v7cd9991NbW8tlnn3ksQDnaQjlnz57Ngw8+CChilizLPPbYY9xxxx3ExXV9y9TXAE4cYGSImoLnzWiImTCDoqO7AThccoLQiAgcFRW4LBby131DwKJzTusIwxHeqMXEzyb/mP1hh1h7/BNqrMrv5ERdAb/f8BgLojK5JGUlZn3v4qejuorCR/4Pyap4PGiDgom4827qWiVoHdvhmJ154+DbNNkU0TjIEMiK6OVjPty0L0YjnFaSJHZteIf8nO3ueVGJacy76Kc0NDqB4W/P9ROv5pGdT2N1WaloqeaFrf/mprRrhv24ZyKdS7sjyCALXlfa3RvpfN5ckoRGFAd83prtLW7D8OP1eZQ2l/fp8wMQ4xfljm5KDUzqcl9wNkNV89i5Ho6HdIHhxJcAMDnQCKJHIpRGEPHV+jI/ZH7HTBmwQqtVopUBRKiMU868PqeD5Mn4JE/G53wIk2UclRWdUvfysRWcRHYqqaCBM2dQsvZDj/ZcuzOLKRdfSPF7awHFp0nfFsnUObJJc0oAQCvQ2mgHhs/gPiI5g4jkDOoqi8k7sI2CI7tw2JQKlrIkkX9oD/mH9mDyDSBp6lySpszDLzB0yNtx5vU3ldFkPPS3IUvBKyoq4qabbup1eUZGBl995Vl4MShRS4DbTLydRYsW8eijj3L06NFuApSfnx/FxcXd9tXS0tJjZJTK2GXCjLPdAlTB4V3ELzyHhg8UM/L6TRtPW4AaLgRBYEbYFCYHT2R9wUa+LtzkNpPdWpbFnqocfpR8PmfHzOv2BtPV3Ezx00/galD8r0STiZhf34Mu2Hu8PoaCnOrDZFV0lFS+dtLlmLRqVMxQIrlcbP/yTfdvCCB2wkzmXXgTGs3Qm8T2RqgpmP+adDmvH3obgB3l2aQFTyQjctaIteFMQPFHaWBtTi+l3bVj0x9luBnseWu0N3G8Ls8tOpW1dPcs6YyAQKw52p1SlxqYdFr+gCpjD9GlISNmBjuK9/S7bmbMTERJi2fxUipnIoIgoI+IRB8Rif+8BQDITie20hKs+XmIegOyB+bmALLLhejrS/Sv7sYQG4s2aPQKGvRGUHgs6cuuYsY5l1J8fB95OduoKs51L29taeDQjq85tONrIuInkjx1PjGp09FoVVsHlbGBKApoRBcCLhrrqtFrRWQ0uKTx+RLR4yeRoKAgyvswxjt+/DgBAZ6HjCcmJgJgt3dVztsjo3q6+CUlJbFt2za3B1U7BQUFJCUleXxsFe8nNCaZgNBoGqpLcTnt1AUZ0Gi1yg32ZD7WwgKM8Qmj3cxe0Wt0XJS8grlR6bx//BMOVCteR63OVtYc+4itpTu5ZtJlJAckAkop25Jnn3abTwpaLdF33oUhZmiq6XkLrU4r7xzteCuXGTGLqaGTR7FF4w+X08HWz16nNC/HPS8xbQ6ZK/4LsY9y18NFRuQsDtYeZWe5Ioa9ffRDkgISvMpEeSzTW4Uolyyxo3iBSmTbAAAgAElEQVQPO4v3elTa/UxjIOettqmR43V57ip1FZaqvvctiMSZY9yG4SmBiZi0w5vyquLlODWsSruQncV7+62Cd0XaSmSHCP1E0amodEbQajHGJ2CMT0CLDUGj8UiEEjQaNL5++E2fMQKtPD20Oj2JaZkkpmXSVFdJXs52Th7cgdXSES1SUXiMisJj6I0+JKbNIWnqPAJDo0ex1SoqfaPTSjhaG9m58RPyD+1BklyIooaktFlkLLkEnckfh3N8pV17/Necd955vPXWW+TmdijO7SLQ5s2beffdd1myZInHB05JSSEmJoYvvviiy/yNGzei1WqZNav7G/JFixbR2NjI1q1b3fNqa2vZtWsXCxYs8PjYKt6PIAikzjzbPZ13JAvf2enu6YbNG0ejWQMm1BTCbdN/wu3Tf0KoKcQ9v7i5lCeyX+Bfh96lwdpA2csvYj3R9tsSBCJvWY3PpLNGqdXDxycn1rlNWf10vlw54ZJRbtH4wmG38f1Hf+8iPqXOOJs55183KuJTO1dPvIxQoyI4WV1W3jj0Nq4eSi6rDAxRFGiSGnstTw4gI/Ni1ps0SY1KupnKgM5bua2cv2Y/yxuH3mZL6Y4exSdREEnyT2BFwhLumHELfz37Ie7N+CWXp17E1NDJqvikgiTJmEV/bs+8EYGef4cCAr+YcxNm0b9fzzAVlb5wilqC5s7xaN3geXNxiWMvUsgcFM6Msy/hRz//Iwsv+RlRSVO6BCfYrRaO7d7EV/96lA1vP0negW047LZRbLGKSnd0WomyvBzefe5BTuTsQmobG0uSixM5u3j3uQcpy8tBpx1fMbGC7OFdrrGxkeuvv56CggImTJjAoUOHSE9Pp6WlhSNHjhATE8OaNWsIHkC60BdffME999zDxRdfzBVXXEFOTg7PPPMM119/Pffffz+1tbUUFhaSmprqTrG78cYbOXbsGL/97W8JDAzk2Wefpb6+nk8//XRAEViqB5T347Db+PTlP+CwK35I8+Zegu31fwMgGIykPPEUonFwA/vRyK91uBxsKPyOrwq+xSG1lbOVZZZntzLlWIeJfti11xG0fMWItm0kyK3P56ndL7qnfzLlOjIiZo5ii0aOkehvdquF7z58iZqyk+55kzOXM23Rj7winD6/oZAnd7/grv51YeJyLkoef/18JBH1Em/kvOtRWs+82NlcfdYlNDRbABlZVqQX2f1duR9Kstxpedt/ctf1ZNm9Zceyzvts+3/cZRvosr2MDLKM1HbcrstARgKZtuWd9y2fcjzZ/bDevc2Se5vO+5gSNYEtRTvZUdL/eZsbO4sY/8guFcm0goYE/3gmBCkRTkkBCRi8qGT9SDMe/CpGCiXts5EPDq0jq2SvO+0zM2YmV6StxCyq6bKeoPa5vhFFAX1jNfvv/m3fRuSCwPSnHsdmDh0XoqelqY78gzvIy9mOpbG223KtzkD8pNkkT5tPcGSCx2Mjtb+pDAeiKCDZ6nj3uQf7/Z1ec+f/IhqCxox20Z8HlMcCFEBrayuvvvoqX3/9NUVFRbhcLmJiYli6dCm33nrrgASgdjZs2MDzzz9Pbm4uISEhXHPNNdx6662IosgHH3zA/fffz7/+9S/mzlWqQTQ0NPDoo4+yYcMGJEkiPT2d++67j+Tk5AEdVxWgxga7v32f43u/AyAmZRqxu/Kxl5UCEH7jzQSeu3hQ+x3Nm0lNax1rcz9lX1UOmQdbWLCvk6HoufOZeOOto9Ku4cThcvDnrKeotFQDMDVkMrdNv9krhJGRYLj7m9XSxOYPXqS+ssMjb9rCi0mb610Cz5cnv+HTPMUrUEDg7tm3kxKYOLqNGqNIsoRsdHD3lw96bGz8h8V38dDGp0agdd7NQ0vu4eFNTw/gvP2at/Z8TGqb4JToH49eM/YiBoYL9eFsYLiN70UXkiwhCiKipAWnqI5LPUTtc/1jECRa9+8h95nnen64FQRSf3UnpumzsMnjK71HliUqCo+Rd2AbJbn73VElnQkIjSZ56nwSJmdgMHWvMt3Zk0dAQmZ8e/IMFZ3Pmyy5EESNet56QaeV2PbFvzmRs6vfdVOmZTJ/5fVjJhVvSAWo8YQqQI0NGmsrWPfGnwAlLW/RpKU0f6B4CBniE4j/w0ODEjG8YfBy+Kv30Lz3uXv6aIKBLxf4kxk5m8tSLyTQMLbKcPfFxyfW8XWBkjZp1Bj477m/IcgYOMqtGjmGs79ZmurZvPZ5Gms7jJBnLV7FxNnnDsvxTgdJlvjbnr+TW58PQLAxiPszf42PTk1R6g1JlqhpraOspZzylkpKWyoot1RQ3lLJA+feOSBB6aEld6sCFAM/D8+s/CMam1oooTe84X6qcmah9jnPMAgSQlMdJe+tpXb7DmSXC0GjIXjeXGKuWoVsDhp34tOpWC1NFBzOIu/Ati7jpHZEjZbY1BkkT5tPeFwqgiC6PXl2nUGePEPBmXTeXC4nTrsVh92Gw27FabfibPvu6PTd2Wm5w25r+1T+nX/trXz0ymM9CqSnIooarv/tY9idYyPaetBV8EpLSwd1wOho1ehNZejwD44gIn4iFYXHkGWZKpMLH50O2eHAVliA7WQ+xqSBRb95Ay05+9Gs7UjpKI40sH6ePwgCWRV72F99kAuTzmNJ7CI0o+jdMxQUNZWyoXCze/qy1IvOKPFpOGluqGHT+8/R0lADKCJtxnnXkjx1fj9bjg6iIHJz2n/xp51P0epspdZaxztHP+AnU647Y6LheqNHoamlnHJLVUfKbjeEAZV2FwWN4sUlCIgIbedc+Wz71va9bbrL8rb/BNqXIrb9PxMEEZH270Kn/XTsQ0SAU/YtuNvQtoZwahs6Ha+tzZyyb/ceBRDbbC0Foe/j+ep8B3TeNMLYvgarqKicmdhkEdE/lJhbbiH+5h+D5AJRg0vUYRfOjIgUo4+ZSelLmTh7CTVl+eQd2Ebh0T24nEoRLMnlpPBoNoVHs/ENCGHRRdfhsFn49oN/dIkca/fkOXEwm2WrbiEqeeq4EVOGgnYvo2/WvuaV502WZSSXs4sY1C4QdYhCHd+7CEgOKw6bFafD1vZpRfKwymSfbZJcHolPoJxHWRo/PlC9ClBLly4d1APB4cOHT6tBKiqnkjrjbCoKjwGQf2QXc9IzaN6+DYD6zZuIHGMClPXkSUpffB7aLiT62Dhm3PUL8ku+YXflfgBsLjsf5n7OttIsrp54GZOCU0ezyYPGJbn4z+E1bt+fCYHJLIz2zBhTpW8aayvY9P5ztDY3ACCIIvNW3kT8pNmj3LK+CTIGct1Zq3gtR/Fzy67cR1rIJOZFZYxyy0YGSZaobq2lvKWCspYKyloqPRCaeuZIdS7p0dPZWbK333UzY2YSpg/jfxfcN9imjxtEUSIjZoZH3lmZMTMRJS3jZ9inoqJyJiFJMlY0IGigXUtXzPFGs1kjjiAIhEYnExqdzKzFV1B4dDd5B7ZRW1HoXkeWnBhNRr56u5e0RQBZ5pu1r405T57hRBQFHK2N3cSnLgzivMmyjMvp6F0gcthw2qxdoo6cbWKRe5mjYxuvE3AEAVHUeBwBJYgi42Uw0qsAdccdd3QRoBwOB2+++SZGo5GLLrqI5ORkJEmiqKiITz75xL2NispQE50yFR9zEJamOmytzbRMjoHtyrKmndsJu/paND4+o9tID7FXVVLytyeRbUolDm1wCLG/vgdtYBC3BN3AwtrjvHfsY8otlQCUWyp5Zu/LzA6fzhWpF4+5yKFvir6jqFmJptSJWq47axWioL4xOl3qKovZvPYFbK2Keb2o0bLwRz8lOnnqKLfMM2aHT+dwVCZby7IAWHPsI5IDEgn3CR3llg0d7UJTWUuFW2wqb6mg3FKJQ3IOaF9mvR9RvpFE+YYT5RtBlG8kkb7h+Bv8mB1VT1bJPrW0+0BwaliVdiE7i/eq501FRUXlDENnMJEyfSEp0xdSV1VC/oFtnDycRVrmORzY/m3/4pwss+vbj4mbOJ3j+7MRxLZoXkHsiOwV2qJ13d/FTtNi12Xtkb2i2DXyuKd1u033cmyxY7rrumKX7932g4frdVpm9vcle+Mnnp23jZ8wcdYiCo4d6iV9ravIJHsQqTwaCKKITmdEazCi0xnQ6g3o9Ea0eiM6vaHt09g2r2N553UMvv4kpc3yyAMqacps5N5lmzFHr3/JL3/5yy7Tjz32GMHBwT1Wurvjjju45pprOH78+PC0UuWMRhQ1pExfwIEtil9SQdlxUmNisZcUI9vtNG3fSuDS5aPcyv5xNjVS8vQTuJoaARB9fIn59W/QBga51zkreAK/n3M3G4t/4Iv89dhcSojw7sr95FQfZmXicpbGn41W9P6LUIWlis/z17unL0paQbhP2Ci2aHxQXZrPdx++hMPWCoBWp2fRpauJiJ84yi0bGKsmXEJuQz6VlmpsLjtvHHyb36T/YsylnPYkNJW1VFBxWkJTRJvYpAhNfrruBqnQVtpdq5R2fzHrzR7FlM6l3SWnKqKAet5UVFRUVBSCwmIIWnol08++BL3GxrvP/I9H2+Uf3sv0BcspObF/mFvo/Vy++j7yD/UfUQyQf2gP0+cv49COr4e5Vd0RRU03gahDODJ0EYy6i0kd87Q6Axqt7rStIwRRIGPJJZw4mN1vFbyMxT/C6dIwXl6GefwU++GHH7J69epu4hOA2Wzmmmuu4fnnn+ehhx4ayvapqACQPHU+B7d9iSS5qCnLZ2LmuVCiVPyq37yJgCXLvNpDRrLZKH3maRwVigGioNMR88tfY+jBM00jalgefy4ZETP5MPdzdlUo6TV2ycHHeevYVp7F1RMuY3KI9woOkizx1pH3cbY9gMeZY1gad/Yot2rsU1F4jB8+fhmnQxEmdQYT51x+G6HRSaPcsoFj1Br4Sdp1PJ79PC7ZRUFTEZ/lf82lKStHu2k9oghNNZS1VHYRmwYjNPnrzUT6RrQJTcq/voSmPttlF5gWMoXHVvxeLe0+ANTzpqKioqLSjlanRyNKA/LkUWlDloftvIkabZ8CUcd8A1qdEZ1BEYh0hjYBqf17m2jkTUiSjM7kz7JVt/SevigI/H/27ju+zereH/jnnEfTGt52bMfOHmTvhISQhBRCgKQhCaSFC5SGDiCUclva3o70d8vtZRRKaem8QGnKKhAICQ0ECAQI2ZPsPe14xEu2JWs85/z+eGR525Ita/n7fr38ip9Hjx4dKUeS9dE53zN3yXJ/AffECJ+AEAIoIQTc/mlDbamoqADnNLWG9AyTxY6+Q8bh/LHdAIBi7kSqwQDp8cBTeBH1p0/BPCg26yRJVcWlv/4J9WdOazsYQ59vfRfmIUM6vF6KMRl3j7wNV+VOw+vH16CorhgAUOq8jGf3P4dxmaOwePACpJtTOzxPNHxRtD2w0hlnHLcPvyXuRrbEmqLTB/HFuhcgVC3sMJqtmLXkPqRm9Y1yy7quwN4XCwbOw5pT6wEAH57bhCvShmJo6qCotakxaCrxh01aUfCuBk1auNQ8bLLowztlWHgYbDwFd426FXeMWQJwAAKNS7sn0B8t4dTycRNSgDNOjxshhPRCjCsh1eQxWZIxY8FySCkgpQSkhJQysN1sHyTQan8bx8kWx0FCisZt7TxtHyulaHFZ27fd7nFNz9fkdtGw3c6xXNGF9LjpjUkYdeV8//S1xtCocdqaMRAuKUrsz/joDq+PI2fgKCxb8d/YtWkdzhza07h64MgJmDR7QUKtHtgg6P/VqVOn4sUXX8ScOXMwfPjwZpdt3rwZq1atwrx588LeQEIaDB43MxBAnT+5DzmTJsK1RStGXv3pJzEZQEkpUfLSP1D35f7Avqyv3w7bhIlBn2NI6kD8ZPKD+KxwK949/QHq1XoAwL6ygzhUfgzz+l2DrxRcDb0SG8l+ZX0V1pxcH9i+tmA28m20OmZ3nD+2B9veWxUooGi2JmP20hWwp2VHuWXdN7fgahypOI5jlSchIbHm1L/xg2nfhaJnUIWAwjm4qgC+8K7Y0zxoajp1riwwci9YDUFTy7Ap3EFTR4SQgIcD4IElyrXeQiFKR5o+bg1/3tHjRgghvY+EElJNHsWQhL5DxkagZbFNrxMhPW6GpGSMvDI2R7tHg9fHwY2puHL+7Zg+fxk4A4QEJHRQhZJQI58aBB1A/ehHP8KyZcuwePFijB49Gvn5+XC73Th79ixOnjyJgoICPPzwwz3ZVtLLZeQOQEpmHqrKCqH6vKjumw6D/7KanTuQuew2KJbQp7D0pIp178Dx+WeB7bQbbupSvSqFK5iTfxUmZo/FmpPrsb1YC+K8wot3z2zAtuJduGXIQozKuCJsbe8KKSVeO/YW6lVttGR2Uibm958b1TbFu9MHt2LXh6/5vwkDLMnpmL10BazJ6VFuWXhwxnHniGX43x1P45qB0zEuZyReOvQmdhd9GZgSNSlvLJaMuAE2XehTooQUKHOVN6vP1NWgKbnJ1LloBU2EEEIICT9VKL22Jk930OPWfUJICMEBGAJfImoS83FiUnbUU5orLy/Hc889h88//xyFhYUAgPz8fFx33XW4++67YYmxD/8dKS+vpaUz49CpL7dg10evAQCsKZkYedELz3ltCdXMr92G1K9cF9R5mj+5e0b1Z5+iZNXfA9v2K2cg+5v3hKVW1enqs/jXsTW46F9hrsHojCuwdMhCZJijE07sKt6Lvx9+NbD90IR7MTgl/uoThVtX+9vxPZ9i76bVgW1bWjZmL7kfSbb4Wg0xGGXeEpyru4AX97zRblHoeyffgdHpI9sMoRqCptbFwLsWNDUUAG8aNsVL0BSJ1zdCGlB/I5FGfY70FL1O4NLpg53W5MkZOCrhpkV1Bz1u4ZMIr2+cM6SnW9u9PKQAKpFQABWffF431v5tZWAFsIlDpkO88z4AwJCTi36/+nVQAU9PP7lr9+9D0R9/D/inTCWNHIW8B74PpgvfXGYhBTYXbsPa0xvg8rkC+3Vch2sLZuO6fnNgiOC0vFpPHR7Z/iRqvXUAgKvzpmPZsEURu/1Y1pX+dnj7BzjwxbuB7ZTMPMxach9MSbZwNy/qOGeo4VX48Qf/22b41ICB4fFrf4p6p4oLjiJcqi1BsbP7QVNDEXDt9ywkxUnQ1J5E+OOFxA/qbyTSqM+RnqTXCXhdjl5Vkycc6HELj0R4fessgGr303BRUVF7F3Uot41VvQgJF53eiAEjp+L4nk0AgEveKvQxmiDd9fBcKoLrxHEkDR0W1Ta6Tp/Cpb/+KRA+GQv6Iffe+8MaPgHa1KWr+07H+KwxWHvqPWy5tBMA4BM+vHf2I+wo3o0lQxZiTMaIiKwQ+OaJtYHwKdWYgq8Our7HbzMRSSlxYPM6HNn5UWBfek5/XH3zd2EwxXcw0i6ditUH13cYPgGAhMTqI+uRZ++Dtw6/F/TpEzVoIoQQQkh49caaPOHQ8nGTQoBxTo8baaXdT8TXXHNNlz60HjlypFsNIqQzg8de1RhAnTuCgknj4f6ioRj5pqgGUJ6SYhT9/neQHg8AQJeRgbwHHwI3mXvsNm0GK26/4hbMyJuKfx1bg/M1FwEA5fWV+NuBf2BE+jDcMmQhspIye6wNBy8fwc6SvYHtrw9fDJPO1GO3l6ikFNjz8Wqc3P95YF9W/lBc9dVvQW8wRrFlPUsoKnYV7u/8QAC7Cvdj/pDZbQZQyQZ7s9XmtKlzFDQRQgghJHi9rSZPuDR93LQdDZfQ40YatRtA3X///c0CKK/Xi3/+858wmUy48cYbMXDgQAghcOHCBaxduzZwHUJ6mi01C9n9hqHk3DFIKVGeZUHDIL/a3Tuhfu02KLbIT1PyVVej8HdPQa3V3qS41Yq+3/8BdMmRqdfT316AhyetwJaiHVh76n3U+ZwAgMPlx/Drit9ibsEszOt/DYyKoZMzhcblq8erx94KbE/OnoCR6cM7uAZpixAqdn7wKs4e3hHYlztwFKbfdDcUXWyscNhTVCGgStH5gQBUKcAZx/DUIY1hkzUbfZIoaCKEEEIIISSWtRtAPfDAA822H3/8caSlpeH1119HWlpas8vuv/9+LFu2DCdOnOiZVhLSwpCxM1Fy7hgA4Pz5Qxjbvz+8Z89C+nyo3rIZafMiu7ynqK9H4e+fhresDADADAbkPfB9GPrkRLQdnHFclTcN47JGY93pDfiicDskJHxSxYZzH2NH8R4sHnITxmeODtu0vLWn3kOVuxoAYNVbsHTIgrCctzdRVR+2rV+Fiyf2BfblD5uAadffAa4oUWxZZCicQ2E8qBBKYRzppjQ8MP5bEWgZIYQQQgghJFyCrgT29ttv47bbbmsVPgGAzWbDsmXLsH79+rA2jpD25AwchSRbKgDA7aqDa+TAwGXVn32KSNbWlz4fiv7yR7jPndV2MIacb98L86DBEWtDS1a9BV8fthg/mvQA+tsLAvsr3VV4/uBLeHbfcyiuK+327ZysOoPPCrcGtm8Z+lVYDfGzGmYs8Hk9+OKd55qFTwNGTcO0+Xf2ivAJALiqYFLe2KCOnZw3DlyEt54aIYQQQgghpOcFHUAJIeB2u9u9vKKiApxTZftI4JzBrFNh17thV5yw690w61Rw3vOFpmMF5xyDxs4IbBc6S8HNWp0lb0kxXMeORqQdUkqUrHoRzoMHAvuy/uNOWMeNj8jtd6bA3hc/mHgf/mP4LbDqG4Oho5Un8Osdv8XbJ/+Nel99l87tVb14+egbge3RGVdgYlZwIQLReD31+Oztv+DS2cOBfUPGz8Lka7/eu15PfQqWjLgBDB2/hjEwLB4xH9Lbix4bQgghhBBCEkTQf8VPnToVL774Io4ebf3BfvPmzVi1ahVmz54dzraRNph0AhbvZdR+/BwK/3xf4Kf24+dg8V6GSRdcHZVEMHDUlYERIhUl54GJYwKXVX/6SUTaUP7OW3Bs2RzYTrtpIVJmzYnIbQeLM44rcyfjl9Mexqy+0wMf8oUU+Oj8p/jVtiexq2RfyKPG1p/9CKXOywAAk2LCsqE3R2S1vUThdtVh05t/RNnFk4F9I6bOw/jZi3vd4yiEhI3bce/kO9oNoRgY7ptyJ2zcHtERjoQQQgghhJDwYDLIv+QvXLiAZcuWoaqqCqNHj0Z+fj7cbjfOnj2LkydPoqCgAK+88grS09N7us1hUV5eCyHi60OMSScgzu9F+bvPou3VBBjSb1oBXjAe9b7eMUJg23urcO7ILgBAQf/RSF//hXaBomDgb56Gzm5v83rNV7TomqpNH6P0pVWBbfuMmcj+xjdjPjy4WFOE14+vwanqs832D00ZhFuGfhW51j6dnuNCTSGe2PUHCH/Nnq8PW4yr8qb1RHMTQsv+Vl/nwKbVf0L15aLAvjEzF+KKyV+JRvNiBjdI1AgH3jr8HnYW7oMqBRTGMTlvHBaPmA8bt0N4Yvv5FQvC8fpGSLCov5FIoz5HIon6G4mkROhvnDOkp1vbvTzoAAoAysvL8dxzz+Hzzz9HYWEhACA/Px/XXXcd7r77blgs8VP7Jd4CKM4ZLN7LKHr+h+h4KUuG3OVPok6fEVf3r6suF53BxteeBgAoih7jnclQT50GAGQsuQVp829s83rdfXLX7t2Doj/9AfA/fZJGjUHeiu+B6eKjNo2UEjuK9+DtU/9Gjac2sJ8zjtl9Z+CGAdfCrDNp+zgDdCqEokIVWiCws3Af1hzbgKp6B4akDMT3xn8bnPWO0DNYnDMoXAWDCgYBCQ4JBbWOGmz81x9QU9lYg2vCNbdgyLiZUWxt7Aj0N65C+Fe840IH+HiveE0Lh0T444XED+pvJNKoz5FIov5GIikR+ltYA6hEEm8BlFmnovbj5+A8urXTYy3Dp8My9x64ekGdFCklPnj5N6gqvQgAGFYwFknvfw4A0Gdmov+vHwdro5ZOd57crlMncfHJxyG9XgCAsf8A5D/8E3CjsYv3InpcPhf+feZDfHpxS2A0EwDYDFbcPOhGTCsYj1pZg9WH12NX4f7AiJSJuWMwb8gsfFl8BONSxiIrKTOK9yL26HUCXpcDuz5ZizOH90IIFZwrGDBiPEZPm4tzx/Zj7+fvgzGGyfNux4ARU6LdZJJAEuGPFxI/qL+RSKM+RyKJ+huJpETob50FUCEP13A4HHA6nRCi8cOqqqqoq6vDtm3b8I1vfKNLDSUd0zMfnMd3BHVs3fHtSJl7F1yIv0AkVIwxDBk7Ezs/fBUAcLH6IoYlJUE6nfCWlcF55DAsI0eF7fY8xZdQ+PunA+GTPjMLed97KC7DJwAw68xYOmQhpudMwevH1+BElTZ6rMZTi0pRjt1l+/DCnn9BNhl1p0qBHYX7sLNwP7416Tb0ScmA8ETrHsQevU7g0umD2Lj6+cAIOQAQQsWpg7tw6tBuXH3TbRg/cz7MyTnIHzouiq0lhBBCCCGEkMgIOoAqKSnBAw88gAMHDnR4HAVQPUSo2k+4j00ABcMnYv9n78DjdqKuuhy+CSOhbN4JQCtGHq4AyldVhYtPPwlRVwcAUGw25H3/B+3WmYonudY+eHD8d7C7dD/eOvEuGAfG5YzE/2z6fbPwqSkJif/b9Qoev64/bDwlrkYU9hTOGbwuR6vwqRkp8dm7r+CWe38BXVImPW6EEEIIIYSQXiHoOVpPPPEEDhw4gBtuuAGLFi2ClBLf/va3sXTpUtjtdhiNRrz66qs92dbejSvaT7iPTQA6vQH9R00NbJeYfIHfa/ftha+qqtu3obpcKHzmt/CVlwMAmMGAvO89BEN2drfPHSsYY5iUPQ4rp/0Q3xz7NWw48Wm74VMDCYm3Dr8H9KLVFzuicBW7PlnbfvjUQErs+fw9KLz3BMWEEEIIIYSQ3i3oAGrr1q1YtGgRnnrqKfzsZz8DYwwzZ87EI488gjVr1iApKQkffvhhT7a1V/NKHZKGBlcnxjJ0KrzQ93CLYsvgMVcFfi8pOg02ZKC2IQSqN3/WrXNLnw+X/vQs3BfOazs4R1pDKNEAACAASURBVM5374dpwMBunTdWmXQmDMroh91FXwZ1/M7CfRDc1/mBCUhKCberFmWFp3H64DZ4XQ6cObw3qOueObQHDL3zcSOEEEIIIYT0PkFPwXM4HJgwYQIAwGq1Ijc3FwcPHsSkSZOQk5ODW265BR988AEefvjhHmtsb+YWOqTMWArn0W3obBU8+5WLUOdTOjkusdhSM9Gn33AUnzsKQKKyXwZSTmj1jKo//xRpN9zUZjHyzkghUPz35+E8ciiwL/vOb8A6Zmy4mh6TVCGgyuBGNalSaKuV9XCbosnn9aC2qgw1laX+nzI4KkpQW1kGj9sZOK5vvx9DBDn9VQgVUtDIMUIIIYQQQkjvEHQAlZycDJfLFdguKCjAsWPHAtv5+fkoLi4Ob+tIgBASXmMq0m9agfJ3n0Xb4RJD+rzlcB7fAbXvZPDkxJkeFozB42b6AyigsOI8UqxWoLYWvvJyOA8dhGX0mJDPefmtN1GzvXHlwfSv3ozkq64OW5tjlcI5FMaDCqEUxsFZ/MdPQgg4aypRU1GCmsoy1FQ2/FsKZ01lcCdhDJwrQYVQnCtaKEoZFCGEEEIIIaQXCDqAmjBhAt566y3cfPPNsNlsGDp0KD788EO43W4YjUYcOHAAVmv7y+2R7qv3cZgKxiN3+ZOo/mI16o5v14qNcwWWoVNgnzgPzpN7UL19LXjyZiQt+gWY0RLtZkdMzoCRSLKnwemogKfeifqxQ2H6Yg8AoOrTT0IOoCo3fojK99cHtpOvno20mxaGtc2xiqsKJuWNxfaLnU8nm5w3Dlzo4iJHaZgy1zJgqqksRW1VGYQaek0mRWeALTUTtrRsOOvqMGDEOJw6uLvT6w0YOQEy9IVICSGEEEIIISQuBf3p595778XXv/51zJo1Cxs3bsStt96Kl156CYsXL0Zubi42b96MpUuX9mRbCbQQyqPPgOWa5UiZe1cggPJCj5qSC6jd9R4AQFQXw/XhszDf8AMw3js+5HLOMXjMDHy5eR0AoJg70d9/Wd3+ffBWVECflhbUuWp270TZa68Eti1jxyHr9jvAGAtzq2OUT8GSETdgx8V9HRYiZ2BYPGI+pJcjlqZ8+rwe1FSVodYfLjkq/CFTiylzwWKMw5KcrgVNqVnNfszW5EC/4JxhUlomTh3a03EhcsYwafYC+NTeNVWWEEIIIYQQ0nsFnUyMGDECr7/+Ol599VWkpqYiNTUVv/nNb/DUU09h7969mD9/PtV/ihAhJFxCgQv+le78gzZYWj+YZn8L9Rv/pO0uOgL35n/COPMbvSY4GTj6Shzc+h6E6kNleRHyhw+CcvQUICUcmz9D+sJFnZ7DefwYiv/vr4EAwTRwIHK+fS+Y0ntWFhRCwqaz497Jd+DPO//ZZgjFwHDflDth43YIX+RDFCEEnI6KJnWZSkOfMteCKcnWPGBK0/61JKdDUTp/uRRCQm+2Y+6S5di4+vm2QyjGMHfJcujNdnij8LgRQgghhBBCSDQwKTtbLzwxlZfXQojEvOvuPWvh2fVWYNs4bRkMY+ZHsUWRtf39l3D28A4AQF7WQGRt2gcA0KWmYcBjvwFTFGRm2lBWVtPquu6iQlx47NcQTm2UjD47GwU/+TkUmy1ydyCGcINEjXDgrcPvYWfhPqhSQGEck/PGYfGI+Vr45Om5cLNxylyTgKmiFDVVXZ8yp9MbYPUHTPbULFibjGoyGM1habdeJ+B1ObBr0zqcObQHQqjgXMGAkRMwafYCf/gU/3WzSGxq7/WNkJ5A/Y1EGvU5EknU30gkJUJ/45whPb390kxhm5v12muvYfPmzXj22WfDdUrSRYbxCyCqi+E7sQUA4N72Opg9G/r+E6LcssgYPHZmIIAqLj+PzGQbWHUNfJUVqDvwJazjxrd5PW9lJQp/91QgfFLsduR9/we9NnwCAOFhsCupWD7qFiwfuRhSqGBcAZgOqqqD6glP5aemU+Yapss1/Hjdrs5P0ELjlLmsVtPmmk6Z6yleHwc3puLK+bdj+vxl4AwQEpDQQRUKjXwihBBCCCGE9DphC6COHDmCjRs3hut0pBsYYzBdfTdcNZehFh8HIFH/8V/AF/4MSka/aDevx6X1KUBqdj4qSy5AVX2oGTkA9i1fAgCqP/2kzQBKdTpR+Lun4KuoAAAwowl5D/4nDJlZEW17rNFG8lRh+ydrcebw3saRPCPGY9KchSGN5Gl7ypw2qqnLU+YsdthSMptNlwtlylxPEkJCCA7A0OLbDAqfCCGEEEIIIb1P76hO3QsxRQ/TdQ/A+favIGvKAJ8Hrg2/Q9KileCW1Gg3r0cxxjB47Ezs/EArIn7JVwUbAAag7uABeMsvA5mNo5qE14uiP/0BnsKL2g5FQe59K2Dq1z/ibY8lep3ApdMHW9UyEkLFqYO7cOrQbsxdshw5A0cFQqhWU+YqSgMrztVWX+7ylLmWhb9t/qlz4ZoyRwghhBBCCCGkZ1EAlcC4yQbz9Q/B+c4jgMcFWVephVALfgqmN0a7eT2qYNgE7P9sDTz1Tjhrq+AeMRC2SxXoc921sBs56kvLYGUMXqbg4huvwnX0SOC6fe76JiwjR0Wx9dHHOYPX5Wi/kDYASImNq5/Hku/8DAd2fI7LRWfDM2WuYSRTSibsadkwWey9pog+IYQQQgghhCQqCqASnJKaC/NXVsD13lOAFBCXz6H+k7/CdO0KMJa4RZB1egMGjJyGY7s/BgBU5dox8Wt34NL693HxzbcgVRVMUZA6ZTJy58+D2W5F0Zq1yFi8FPbpM6Lc+uhTuIodn6xtP3xqICX2ff4eUrNycXzPuU7Pa7LYm4xiyoypKXOEEEIIIYQQQnoOfeLrBXR9R8I44w64N/8DAOA7uweeHW/COPXWKLesZw0eexWO7f4EgMSlotPY8z+PwNikaLZUVVRs3YaKbdsx4J5vYsD3H4Ru5LjoNTgKvB43aqsvo7ayDLVVZaituozaqjLMvOlWnDm8N6hznDmyD2OmfwW7PlkHANDpja0KfzcETnqaMkcIIYQQQgghvVK7AdSaNWtCOtGZM2e63RjScwwj5kBUF8N7YAMAwLN/PVhyNgzDZ0W5ZT3HmpKB3AFXoOjMYQDA5RQ98krdrQ+UEmeeewFjnn4SHoVDiMQqEu1xuwLBUm1VGWoCv19GfZ2jzesIVYUQwdVrEkKFwWzFnFsegC01i6bMEUIIIYQQQghppd0A6ic/+UlIHyKllPShM8YZpy6DqC6Ben4fAMD9+SpwexZ0uVdEuWU954rxMwIBVHmKHjllbvC28iUpUfjGauR98x7UI/6mJrpdde2GTG5XbegnZAycK0GFUJwrMJjMyMof0oWWE0IIIYQQQgjpDdoNoB599NFItoNEAOMc5rnfhXPtryHKLwBShevDZ2H56i/AU/pEu3k9oqBgMAweAY+BQ9VxVNr1SK/2tnlsxbbtKPjGXQCLvQLtDavLBcKlyrImgdNleNzOLp2XcwWW5HRYUzJgTcmENSUDtpRM6E12DBgxHqcO7ur0HANGToCk2byEEEIIIYQQQjrQ7qfGm2++OZLtIBHC9CaY530fzjWPQDqrAHcdnO8/DcuiX4CZrNFuXtgxKZFR6UFRtgkAUJZmQFq1F22N1ZOqCggVUCLbxsDtS4n6OgdqqprXY2oYzeTztDF9MAhc0fkDJi1ksgXCpkwk2VLAees7zDnDpDkLcerQ7o4LkTOGSbMXwKcqABJr6iIhhBBCCCGEkPChYQu9ELemwzzvQTjXPgqoHkhHCVwf/gHmGx4GS7CVyJiiIMOh4lKmhOQMLrMCp4nDUi/aPBZthDHhJKWAs6aq2eilmkDYdBmqz9Ol8yo6Q2D0UrPRTKmZMFuTQ17xUAgJvdmOuUuWY+Pq59sOoRjD3CXLoTfb4fVR+EQIIYQQQgghpH2JlTaQoCmZA2C65tuo//BZAIB66RjqP/8HTLO+mVC1vHxch8zJU5B6di/q8zMxcvIs9Ft+BRRVggGo2bMfZRs+hLeyCmnTpkLl+m4P5BFChdNR2aoWU21VGWqryyFUX5fOqzMY/QFTZrPpctaUjB4p/O31ceQMHIVlK/4buzatw5lDeyCECs4VDBg5AZNmL/CHT/FXM4sQQgghhBBCSGQxKTuaX5O4ystrE261s65w73sXnh1vBrYNU26BcdyNUWxReHHOYHBcRuGWz2GZOhEHtn2Ms0f2BYKU/sPHYvzka+DcvhtZV18Nty0DwTwlhKqizlHefARTZRlqqspQ5yiHFK1HWAXDYEpqFS41bBvN1qiEg5wzKFwFgw9SCDDOIaGDKhR6DgUpM9OGsrKaaDeD9BLU30gkUX8jkUZ9jkQS9TcSSYnQ3zhnSE9vv7QPjYDq5Qxjb4SoKoHv+OcAAM+ON8CTs6EfMCnKLQsPISSQkgI5pADvvPBks6lkQqg4fXgPTh/Zi7mL7tKOazKVTPV5UVdd3mSKXOOUOaejElJ2LWQyJtnaDJisyRkwmi3dvs/hJoSEEByAwb+j4RIKnwghhBBCCCGEBIcCqF6OMQbTzLvgqimFeukYAKD+47+BL0yHkjkgyq3rPs4Z6l012LjmH+0X05YSG9f8A0u+8zMc3r0V5cUXUFtVBmdNFboaspgtyY3hUmqTsCk5A3qjuet3iBBCCCGEEEIIiUMhB1AVFRXYsmULioqKcMMNNyApKQmVlZUYNGhQT7SPRABTdDBf+wDq3nkEsroEUD1wbXgGSYtWglvTot28blG4ih2frO14JTcAkBL7Pn8PqVm5OHLheFDnTrKlNi/4HRjRlAGd3hiG1hNCCCGEEEIIIYkhpADqhRdewDPPPAO32w3GGEaPHo26ujo88MAD+NrXvoaVK1cmVAHr3oSZrEia9xDq3nkEcNdBOqvg2vA7JC38KZjeFO3mdRmDijOH9wZ17Jkj+zBm+lew65N12nUZQ5I9LRAy2ZqETRZ7OnR6Q082nRBCCCGEEEIISRhBB1Dr1q3DE088gRtvvBHXXXcdHnzwQQDAyJEjce211+K1117DgAEDcOedd/ZYY0nP4il9YL52BVz/fhKQKkT5ebg2/gXm674HxuNzpTMpVAihBnWsECoMJgtmLvoObKmZSLKnQVFoliohhBBCCCGEENJdQacKL7zwAmbMmIGnnnoKU6ZMCezPycnB73//e8yaNQtvvPFGjzSSRI4u9woYZzaGiOr5fXBv/1cUW9Q9jCvgXAnqWM4VGMxJyB04ErbULAqfCCGEEEIIIYSQMAk6gDp16hSuueaadi+fM2cOLly4EJZGkegyDJ8F/Zj5gW3vgQ3wHNkUvQZ1g4SCASPGB3XsgJETIKkuPyGEEEIIIYQQEnZBB1AWiwU1NTXtXl5UVISkpKSwNIpEn3HqLdD1nxDYdm9eBd/FQ1FsUdeoQsGkOQuBzmqTMYZJsxfApwY3WooQQgghhBBCCCHBCzqAmjlzJl555RWUl5e3uuzo0aN4+eWXMX369LA2jkQPYxymOd8Bz+in7ZACro+ehVpZFN2GhUgICb3ZjrlLlrcfQjGGuUuWQ2+2Q3a2Wh4hhBBCCCGEEEJCxmSQn7hLSkqwdOlSeL1eTJ48GR999BHmzZsHn8+HTZs2wWq14o033kB+fn5PtzksystrIQSFDZ0RdZVwvv3fkM4qAACzZSLp5pXgJluUWxYavU7A63Jg16Z1OHNoD4RQwbmCASMnYNLsBdCb7fD64rPQOol9mZk2lJW1P4KUkHCi/kYiifobiTTqcySSqL+RSEqE/sY5Q3q6td3Lgw6gAKC0tBS//e1vsXHjxsB0PLPZjKuvvho//OEP4yZ8AiiACoV6+Syca/8X8HkAAEqfoTDf+DCYoo9yy0LDOYPCVTD4wBkgJCChgyoU6gukRyXCmwmJH9TfSCRRfyORRn2ORBL1NxJJidDfOgug2q24/PDDD+OWW24JrHhXVFSEtLQ0PPbYY5BSorKyEqqqIi0tDYpCdXMSmZLRH6Zrvov6D/4AQEItPo76T1+Aac63wTqrrRRDhJAQggMwtHhyU/hECCGEEEIIIYT0pHbnHG3YsAHnzp0LbM+dOxcfffQRAIAxhrS0NGRmZlL41Evo+0+AceqtgW3fya3w7F0XxRYRQgghhBBCCCEkXrQ7AiozMxN///vf4fF4YLFYIKXEzp074fP5OjzhokWLwt5IEhv0Y66HqL4E79HPAACeXW+BJ/eBftCUKLeMEEIIIYQQQgghsazdGlDr1q3DT3/6U3i9Xu1AxjpdIYwxhiNHjoS/lT2AakB1jRQ+uNY/BbXI//+s6JG04CdQsgZFt2EhSoT5tSR+UH8jkUT9jUQS9TcSadTnSCRRfyORlAj9rcs1oBYsWICZM2fizJkz8Hg8uOuuu/Dd734X06dP75GGkvjAuA7ma1fAueYRiOpiQPXCteEZJC1aCW7LiHbzCCGEEEIIIYQQEoPaDaAAICUlBePHjwcA3HzzzZgzZw7Gjh0bkYaR2MWMFpivfwh1a34FuOsgXQ64NvwOSQt/BmYwR7t5hBBCCCGEEEIIiTEdBlBNPfroo2G/cZ/PhwkTJsDtdjfbn5SUhL1797Z5nXfeeQc/+tGPWu2//fbbsXLlyrC3kbSNJ2fDfN334Pr3E4BQISouwrXxzzDPexCMU2F6QgghhBBCCCGENGo3gLriiivwxBNPYMGCBQCA4cOHgzHW4ckYYzh8+HDQN37mzBm43W48/vjj6N+/f2A/5+0uzoejR4+iX79+eOKJJ5rtz8ig6V+RpssZBtPVd6N+03MAAPXCl3Bvew2m6bdHuWWEEEIIIYQQQgiJJe0GUIsWLUJBQUGz7c4CqFAdPXoUnHPMmzcPZnNwU7eOHTuGkSNHYty4cWFtC+ka/dCrIKqK4dn3LgDAe/BD8OQ+MIycG+WWEUIIIYQQQgghJFa0G0C1nHL32GOPhf3Gjxw5goKCgqDDJ0ALre68886wt4V0nWHyYojqYvjO7AIAuLe8DG7Pgi5/dJRbRgghhBBCCCGEkFjQ/ly3EG3fvh2rVq0K6TrHjh2DwWDA8uXLMX78eEyePBkrV65EbW1tm8eXlpaivLwchw8fxvXXX4+RI0di3rx5WLNmTTjuAukixjhMc74FnjlA2yEFXB/9CWpFYXQbRgghhBBCCCGEkJgQtgBq/fr1IRcqP3r0KM6fP49Zs2bhb3/7G+677z68++67uPfeeyGlbPN4ALh48SIefvhh/PWvf8Xo0aPx4x//GKtXrw7L/SBdw3RGrQC5JU3b4XXBteFpCJcjug0jhBBCCCGEEEJI1DHZVtLTBb/85S/x+uuv48iRI0FfZ8eOHUhOTsawYcMC+9auXYuHH34YL7zwAmbMmNHs+IqKCuzfvx+TJ0+G1WoN7L/77rtx6tQpfPbZZ92/I6Rb3CVnUbTqZ5CeegCAMW8Ycv7j/4HrDFFuGSGEEEIIIYQQQqKl3RpQkTBlypRW+2bPng1AG+3UMoBKS0vDnDlzWl1n1qxZ2LJlCyoqKpCWlhbUbZeX10KIsGRvpCmeDtOc78L1wTOAlHAXHsPFN5+B6ZrvhL2IfXdkZtpQVlYT7WaQXoL6G4kk6m8kkqi/kUijPkciifobiaRE6G+cM6SnW9u/PIJtaaa8vBxvvPEGLly40Gx/fb02ciY1NbXVdfbu3Ys33nij1X632w2dTgebzdYzjSUh0fUbB+O0rwW2fae2wbOb6nQRQgghhBBCCCG9VdQCKMYYVq5ciZdeeqnZ/vXr10NRFEycOLHVdfbt24ef//zngVpQACCEwIYNGzBhwgTo9foebzcJjn7UddBf0ThazbPnHXhPbo1iiwghhBBCCCGEEBIt7U7BKyoqCulEdXV1IR2flpaG22+/Hf/85z9htVoxadIk7N69G3/5y19w++23o1+/fqioqMD58+cxePBgWK1WLF68GKtWrcKKFSvw/e9/HxaLBa+88gqOHz+Ol19+OaTbJz2LMQbjjNshHKVQCw8BAOo/fR7cmgGlz5Aot44QQgghhBBCCCGR1G4R8uHDh4dUs0dKCcZYSEXIvV4vXnzxRaxevRqFhYXIzs7GrbfeinvuuQecc7z11lv4r//6L6xatQpTp04FABQWFuKpp57C9u3bUVtbi1GjRuGhhx7CpEmTgr5dgGpARYp018H5zq8hqrRAk5lsSFq0EtyeGdV2JcL8WhI/qL+RSKL+RiKJ+huJNOpzJJKov5FISoT+1lkNqHYDqJ/85CddKhr96KOPhnydaKAAKnKEoxTONY9A1mtPJp6ai6Sv/hzMkBS1NiXCk5vED+pvJJKov5FIov5GIo36HIkk6m8kkhKhv3UWQLU7Be+xxx7rkQaR3ofbs2C67ntwvfs4IHwQlUVwffQnmK9/CIwr0W4eIYQQQgghhBBCeljUipCT3kXXZwhMs74Z2FYvHoR7y8toZwAeIYQQQgghhBBCEggFUCRi9EOmwzBhYWDbe/hjeA99FMUWEUIIIYQQQgghJBIogCIRZZh4M3QDpwS23Vtfge/8vii2iBBCCCGEEEIIIT2NAigSUYwxmGbfA541SNshJVwb/wK1/EJ0G0YIIYQQQgghhJAeQwEUiTimM8B83ffArOnaDm89XO8/DeGsim7DCCGEEEIIIYQQ0iMogCJRwZOSYb7+IUBvAgDIugq4Nvwe0ueJcssIIYQQQgghhBASbhRAkahR0vrC/JX7AMYAAKLsNOo3/R+kFFFuGSGEkN6AcwazToVd74ZdccKud8OsU8E5i3bTCCGEEEISji7aDSC9my5/DIxX3g73lpcAAL7TO+FJ7gPj5CVRbhkhhMQPzhmM3Ac988FbXQ+7nsErdXALHYSQ0W5eTDLpBPTuSlR98Sacx3cAQgW4gqShU5AyYym8xlTU++h7OkJiAb3GEUJIYqAAikSdYdRXIKovwXtoIwDAs3cdeHIf6IfOiHLLCCEk9lGQEjqTTkCc34uid58F0OTDq1DhPLoVzqPbkH7TCpgKxtNjR0iU0WsciTQKPAnpOUxK2SufReXltfQCEkOkUOHa8AzUC19qO7gC840/gi5nWI/cXmamDWVlNT1ybkJaov5GekpDkFLeMkgJYEi/aQU4BSkBnDNYvJdR9PwP0fZj1oAhd/mTqNNn0N8LHaDXN9KT6DWORBoFniSaEuE9lXOG9HRru5dTAEVihvS44Hzn1xCVFwEAzGhF0s0rwe1ZYb+tRHhyk/hB/Y30hFCDlFpdGoQqtGMl/P/Kdrb95/P/G6jN1+blDb+LxvM0XOa/XDbcRuD40Nog27rNNrebnKdhn9Ra0HCZNasPnLvXwXl0W6ePsWX4dFjm3gOXlz5stIde30hPobCYRBoFniTaEuE9tbMAiqbgkZjBDGaYr/8+nGt+BelyQLpr4Xr/aSR99edgRku0m0cIIVEjhQ/SVQPpqoZ0OiDrHbBkZKPqwEZ0/MEMACSqN78BfUYeHF+sjkRzY1rqHY/AeXxnUMfWHd+OlLl3wQVjD7eKENKSkftQ9cWbCOo17ovVWlgsKBQgXcM5g95d2XpqdjMS5e8+i9zlT8JDgSchXUIBFIkp3JYB83Xfg/PdxwDVB1F1Ca6P/gjz/P8E49RdCSGJQ/o8WqDkckA6HRD1Dkinf9vlCFwmXA7AXdfq+ql3PILyE0EGKSd2ImfyDaiiAEobFSXU4I4VKqTLAV9xKZScYWAKvQ8R0pOkxwW17AzUkpOwjZ6qTYEKQt3x7UiefjM8R/aCp+aBp+WBmZPBGK1oSTonpYDBV4eqL95AcIHnm0i65h7UCyUSzSMkodBfUiTmKNmDYZp1D+o//gsAQC08DPfml2CceRf9IUFIgmta+LOh7kK8FP6UUgLeekhXNYSrZZjk8IdJjfvgre/uDYYUpAAAGAPg/2FtbIM17muyzQL7m1wnqO3G87CW5204ptV2w+t8Qxu5f6utyxtukze57Ta2G26fMTC9CeBKcI8dVyA9LrjW/wbQm6HLHw1d//HQ5Y+hkbmEdJMUAqKqEGrpaYiSU1BLT0FUFqEhAJDDx4UWFntccG99pXGf0QIlra8WSKXmgqf2BU/LAzfZwn9nSFyQqheiuhii6hJE5SWIqiLt96pi2G77RQijY3fAPvkG1L37Z3B7Npg9Czw5G9yepW1b08E4jcYjpC0UQJGYpB88DaK6BJ7dbwMAvEc3gaf0gWHM9VFuGSGkp8Ri4U8pBeB2NgZHLUKlpoGSdDkA1dszDWEMzGQDM9u1b/XNNi0ACSFI4cnZsH3r7z3Tvjji06lIGjoFzqNbOz3WMmQyXGf2axteF3ynd8B3egfAOJScYdD1Gwddv/E9UquQkEQjnNUQpaehlmphk1p2puMgnrGQXuNacddBvXQM6qVjzU9rtvtDqTxwf0ClpOZSqJxApLvOHzIVQa1qDJpkTVlj/cBWVwrxSx0pISqL/KFpC1wBs2VqgVSTYIonZ4HZMmhWB+nVqPeTmGWYsBCiuhi+k9qHBPe2f4Hbs6HrPz7KLSOEhFtD4c9WtReECufRrXAe3Yb0m1bAFIbCn1KokPU1jcGRs3GqW9Opb9pPDSCD/IM0VFzxB0oNoZIdPLDt35dkBzPZtfCpxbepIQUpQ6fCC33P3I844xY6pMxY6i9C3nFhY/v0xaj4cjOYNR2ytrzxIimgFh2BWnQE7q2vgqfmQddvPHT9xoFnDQRj9M036d2k6oW4fA5qk8BJ1lzu/IqMgaf1hZI1CF6vQNLQycEtGDB0CjxuD/TDZkKtLNRCgXbCLelyQHU5oBYdaX7TltRAMKX4p/HxlFwwgzmo+0wiS0oJWVfZZBSTFjiJqiLtC6FQcaV7gWdTQoWsLoZaXQz1QovLGAezZTQLpbg9Gyw5C9yWCabQezVJbLQKHolp0ueB899PQJSc1HbojEha+FMoGf26dd5EWGGAxA/qbx0Lx0pHUvU2r53kbBootQiV6ms7uZ1uUAxa7XP9owAAIABJREFUaGS2g/tDpZYhE0vSLoMhqVvTikNfBS8DvfQtv5VgVjrKWLACLF8LPKWUEJUX4Tu7F75z+yDKTrd7bma2Q1egjYxS+o4A0yV2AXN6fSNSSsiay40jm0pPQVw+Dwhfp9dl5mQo2YPAswZByRoEJbO/Nk0W3XuNk1JA1lZAVF6EWlEEUVkIUXkRovISoHpCun/Mmq6FUg3T+dLywFNyEv65HSuk8EFUl/pDpiZhU9WlLkxlZ1r4k5oLnpIDnpIDJUX7PclqRu3HzwX3pc7w6UiadRfqyi5pbXOUQFSXQjpKIBylkM6qrt1ZMDBrWvPpfP6Aitszqc/1AonwntrZKngUQJGYJ1wOONc8og2bhfYNVdKileCW1C6fMxGe3CR+UH/rmFmnBv9H37BpMA6bDseBz5qMWqoGPK6ea6DB3Eag5J8GZ07WRi0l+S/zf3CKlFCDFNKoYcpn9RerUXd8e2DKp2XoVCTPWNLhlE/hrILv3D74zu2FWni4/amXih5K3kitblTBWPCklB68R9FBr2+9T6BQeOkpqCWnIEpPQdYH0QcUHXhGfy1oyhoEJXsQmCWtwyA+3K9xUgjI2stQKy5qoVRFofZv1aWgArOmt8vsmdpIqSZT+XhKHxrB0kXS42oWLgXCpurS0EciKzrw5JxAyKQFTrngydlgOkObVwnnlzrSWw/hKIWo9gdS/oBKOEoh6ypCuy9Nb9mS2kYwlQVuz6KRegkiEd5TKYBqBwVQ8UWtLIRzzf8AXu1DJs/oj6QF/wWm79o3AYnw5Cbxg/pbx+x6Nwr/fF/Qw95zbvslLr20shu3yMBM1hajk+yNYVLDT1KyNvWtnT9WY0V3gpTers2i99DDrSpB/40gvW74Cg9BPaeNjurogzjPGuifqjcePDUvIRbWoNe3xNa6UPhpiMpCBDOKlNmzoWQNDIRNPC2/SytJRuI1TgpVG8VSUeiv6+MPqKpKQgs/GNdGrwSCKf+/ydlU9wf+0XKu6hZT5rTfuxTMGC2No5hSGwKnXDBrRpeKgEfiSx3p80A4yiAcJc2CKeEo0aZ6d/GjOTPbWwdT/pFUVN8sfiTCeyoFUO2gACr++C4ehOu93wJSAAB0/SfCdO39Xaq1kQhPbhI/qL91zM7rUPiX+4M+Puc/ftU6gGK8jUCpyailhjCp4d/O6jfEmaZBCmcSQrKQgxTSfVIIiLLT8J3bC9+5vW0Xp/VjtsxAEXMlZ2jcfjil17fEEnKh8AYGM5TMgVCytdFNPGtgWFebi9ZrnFR92qpplYUQFRe1otaVFyEdpaEFBVzRRuSk5jYpfJ4HZs9KyNXSpBCQNWUQVUVQK5uPaILHGfL5mDW9cTSTf8ocT83V3s/DHORH80sdqXohay4HpvQJ/5Q+UV3qL6AuunReZrS2HUwlZ2uXhekxjOeVjGNFIrynUgDVDgqg4pPn8Mdwb14V2DaMvQHGqbeGfJ5EeHKT+EH9rTWp+qBeOADvyS3InLEQl17+f0GPgMr95pOoOH3MHyZpNZWY0UKFn/2ov8UOUV3SOFWv+Hj7Hx4MZujyx2qBVP7ouPq2mvpb/ApHoXAtbBqkTT2L0GtwLPQ56fP4R/BoU/hU/1S+hnIRQVP0/jBFGy2lTenrC2ZLD8vj2dOBgPS5IaqKm0+Zq7wEUV0c4pRGBFZrDQRMgbCpT8Snt8filzpS+LS6ZtUlzQIq6SiFcJSF/ng3MJjbCKb6aP+a7UGHU7G4knE8ioXXt+6iAKodFEDFr/otr8B78IPAtmnWcuiHzQzpHInw5Cbxg/qbRkoJteQkfCe2wHt6B+CuAwCkXLUUnrILcB7b3uk5LMOnwzL3Hri89EdMe6i/xSZZXwvfhS+1QOrCl+2PKmEKlNxh2lS9gnHg9szINjRE1N/iQ08VCo+GWO5z0lvvn8JX6F+NT6szFfL0Mp1RGy3VdEW+1LxOa2Y1Fc5AQNTXNJsu1xA2yZpyhLyoh96kBUtNpswpKblg9oyYHAkay/2tgRQCsq6iSd0pfzDlD6narVPYGb0pUGOKJ2eDBVbuy9a+BPSHpMFMXUy/aQV4GFYyTnTx0N86QwFUOyiAil9SCLg+eAbq+f3aDqbAfOMPocu9IuhzJMKTm8SP3t7f1Koi+E5shffktja/HVasKcha9J+49NIvQau5dV9v72/xQKo+qJeO+qfq7dPqfrSDp/VtrBuV2T/mRvpRf4tNkSwUHmnx2Oekx+mfvtek8HllYeirpenN/pFSueCpjavyMXNys/+jrgQC2sqB5doIphYrzgXVd1reQlJKqylzPCUHLCklpvpTZ+KxvzUlpYB0VrcIphpHUcHn7tqJFQO4PQu67IFIn3I9Lq36Gbq6kjFpFO/9DaAAql0UQMU36XHBufZ/ISouaDuMFli++gvwlD5BXT8RntwkfvTG/iacVfCd2g7via0Ql8+2eQyzpkM/+ErohlwJS2YOreYWJr2xv8UzKSVExYVAGCXKzrR7LDMnN9aNyhsREwXyqb9Fn1YovEgb1RSlQuGRlEh9TtbXNhspFQimQg18jJbAiny6PgOR0n8YLv39x+gsEMi569co37URvuITEFXFgOoJ7XYZB7NnQWm22pz2OzMkhXauGJVI/a2lQFF4RylkdUmzlftEdUlg8aeOhDaKfRrM429EXXV1Y73OBKzL2R2J0N8ogGoHBVDxT9SWw/n2r7Ql2AGw5GxYvvoLMFP7Hb5BIjy5I4UKCnZfb+lv0lsP35nd8J7cCrXwUNsFWo0W6AdMhm7IlVD6DGk2moNWcwuP3tLfEpWoq2ysG1V0GFDbmSKlGKDrO1ILowrGgiclR7ahftTfQhOO99RYLRQeKb2hzwmXo0nhc21lPrXiYlAFvEMKBIZNgz4jD1VfrO74QJ2hdRHwlFzw5CwwRR/s3YpLvaG/tUVKCemubR1MOUogq0sh3bUAgJw7HgmpjmdbKxkzY8PKxLbmi8mYmi4qYwMzJwN6U1yNoAtVIvS3zgKo2P6Kg5AOcGs6zPMehHPdo4DqhawugevDZ2G+4Ycx/+1dvKCCgqQzUvigXjwE74mt8J3bA/ja+PaU67RRG0OuhC5/TLt/rNb7ODz6DFiuWY6UuXc1fjiDHnWqAuGjwJMkPm5JhWHEHBhGzNFC3YuHtDDq/P7moyJUT2C1PYCBZw8KjI7iKbkJ/Qd6vOrKe6pUvRDl56GWnIqrQuGke7jZDm62A03KS0gpIZ1VzUZLqZXaynxNA0jzgLGo2vJ2ULdTd2IncibfEAigmMnWbBRTQ9jErGnUd3oZxhiYyQaYbFCyB7e6XLrrIBylYEZrcOET0O5x0l2rBVrBzEhVdM2CKeZ/rjCzrdV+ZrbFZF2xlpp+MeGtroddzxL6y/7Y/x8hpANK1kCY5nwb9R/9EQCgXjoK9+Z/wHj1N+mP725qqB9Q1HJKlFDhPLoVzqPbkH7TCpiooGCvI6WEKDsN74kt8J3a0e5UASVnOHRDroR+wKSgV/USQsIlFLjgH44d+Fsl8d6ACekM05ugHzAR+gETIYXQAgh/6CSqLjU5UkKUnISn5CQ8O94Es2f560aNg9JnKE1viAHBvafeD2P2MNSeOdTFQuGDwRum00W5UDgJP8YYmCUV3JIK9B0V2C+l1ApQ+0dLMZ0hpECAmawwL/wZlJScoGYREAIAzGiBkjkATO8GuBL0CCimN0HJvQLS5dB+6msR0t94qg+yriL4wv5GC7ipxciqptP/zHZwczKY2QYYkiL++bE3ftlPU/BIQnDvXQfPzsbhw4Ypt8I47oZ2j0+E4Y09iXMGi/cyip7/IaigYPclSn8T1SXwntwK74mtkI6SNo/hqX210GnwNHBreoRbSIDE6W+kY6KqGL7zWt0otfh421NeAcBogS5/jBZI5Y8Ke10W6m+dC+U9Nee2lShd+wzU2g6GAjQtFO6fThdrhcJ7EvW5jtn1bhT++b6gA4G8e/8Eh9fY8w2LU9TfOmbWqaj9+Dk4j27t9Ni2VjKWQoWsr4Wsd0C6avzBVHXgd+FyNLmsuu2R9uHClSZT/7Qpf8xs84+w8u9PanJ5N6efJurqgTQFj/QKhnE3QVQXw3f8CwCAZ8cb4MnZ0A+YGOWWxR/pc0PP6lH1xRvo/BsJieovVmtvJiJ+XhhJ8ITLAd+pHfCe3AJRerrNY5glFbpB06AfMh1Ken6EW0hI78RT+sCQMh+GMfMh62vhO79fm5J38WDzekDuOvhOboXv5FaAK9rIRP/oKG7LiN4dSFBSCEhPnfZhqV77saRloGr/+wjmPdWxewNsY+c2q8kTj4XCSfR4pQ5JQ6cEFwgMnQovEruGE+lZbqFDyoylcB7dhs4C9uQZS1DrU5odx7gClpQMBFnHUHrd/pFTjsaAKvBTo+13+i+vr2n/y5m2CBWyrhKyrjK44w1mbQRVW1P/mgRW3GQHjEnNprFyzqB3V7YeFdv83qL83WeRu/xJeBLoy3569yIJgTEG08xvwOUo074JhkT9x38FX/hTKJn9o928mCGl1P4gri2HqLkMWVsBUdv8X1lfA9sdj8B5fGdQ56w7vh3JVy2FWlYBntEv4YtR9gbS54bv7F54T2yBevEgIEXrg/Rm6AdOgm7wlVByhoNxCiAJiRZmskI/dAb0Q2dAql6oRUcDq+o1m6YgVKiFh6AWHoJ7y0vg6fn+MGq89vpNNV6akVICPo//g0yt9v7pqmn8vb7F764aSHcdWn6YSL3jEZQH+556YidyrlwEp8sb14XCSfR0NxAgJBRCSHiNqUi/aUWnKxl7jamQ3aznyfRGMH0mYM/s9FgphFZfyj96SguuapoEVk0CrPqa4BZzaMrjgvS4oFa3PSugecMVfzCljaxKHncNqk7sQG/8sp8CKJIwmKKH6boH4FzzCKSjFFA9cG34HZIWrQS3pkW7eREhVR9kXWXzUKmmHKJW+5G15YDqDeJEMqT6AbK+Fs53/gdQdFAyBoBnD4bSZzCU7CFaEU0S86QQUIsOa8XEz+5u+02YK9pUniFXQlcwLiaWgCeENMcUPXT5o6HLHw054w6I8vOBYuXi8rlmx4ryC/CUX4Bnz1qwpJRAEXMl94oOn9/xWjBVChXS3Xx0UpshUpPtoN4zO73h0N5TudkO46TF3b9d0itFOhAgpN7HYSoYj9zlT8bUSsaMczCzHTDbAeR1erz0uZtMA/QHVE2m/zW7rL6m7S9o2z25CumsgnRWAbgA45xlqDgR/Jf9KXPvgguJMVWWAiiSULjJhqTrH0LdmkcAjxPSWaWFUAt/mhDFOKXHqQVJNY2BUtNwSdZVodvfYnEF4LqQCgoGqD6oJSeglpyA90ttF7NnQ8keDKXPEK1AamoufcseI6SUEOXntNDp5DZtbn0blOwhWl2ngVOoQCkhcYQxBiWjH5SMfjBOXARRWx6YqqcWHmlW3Fo6q+A9sgneI5sAnRG6vqO0IuYFY5t9kRArBVOllIC3vnWA1CJEEk0uh7uux9sFADBawExWbUUxkw3MYA7tPZUrTRZgICR0sRoIkMSVCCsZM50RzGYEgpieLqXwf6HRZPpf0/pV9Q2jq/y1q1p+sRviFxNBHxsHKIAiCYen5MB87Qq41j8FSBWi/DzqP/4rTNc+ENPThKQUkM7qxlCpphyy9nKzoAkeV/dvyGAGt2aAWdP8/6aD29LBrelg1nQwczJUgwyhfsBkuKvLwezZbRamlo4S+Bwl8J34InD7SvZg/88QKFkDEyIcjCfCUQavvyZM85W0GvHkPtANmQ794CvBgxjmTAiJfdyaDsOIa2AYcQ2kxwXfxYPwndsH3/l9zcMZnxu+s7u10ZBg2ut1v/Gwj5oBUXQCRe/+EeFeHVUKX5MgqbbjUUr+y4NZHa7bGpb8NtkCoZK23Lf/98B+u/9fa6tVB306lWrykIhLhECAxJfetJIxYxzMZANMNiA1mNFVnmbT/5jZ3mu/mKAAiiQkXd4IGGfeCfdnfwcA+M7thXf/OiRPvilqUwakzxOYFtc0VAqMZqqrCEO6zcAsKVqoZG0Mlbg1HawhZApiBSS3YCHUD7gFtboMWL82HsLlgFpyEmrxCYiSk1AvnwHUFh8QPC6oFw5AvXDAfwoGnlbQbJQUs6b3mtV8IkXW18J7egd8J7f566S1xsz2QDFxrR4M/R8QkqiYwQz9wMnQD5wMKVSoJScDU/Vks3oWEmrJCaCuDPrBV+BSy/CpmcaCqW5pgVpX3SpAEoHRSQ11lbTfw/IFS+f3GqxhdJLZ3kaIZGsVMEFn7PZrIdXkIdHSmwIBQmIZ0xnArOmAf4Xo3vzFBJMylNLwiaO8vDam6xR0hHMGyRgEGFQhoHAODgkmZdzep55Sv+01eL98H8lTFyJp0HhU79mgFdcO85QBKSXgrtPCpZom4VLT6XEuR/fvkKJvDJdsDeFSk9FMltSwrYwTzNKgGQtWgOW3/023VL0Ql89p0/KKT0ItORHU48CSUpoEUkPA0wviesWfaC3hK30ebbrNiS3wXfiy7YBTZ4RuwERtBbvcK1p9c0/iDy0ZTbpLVF0KFDFXS04AUiLlqqXwlF2A89j2Tq9vGTYN+oy8Ziu59Qidoe0Aqb2AyWiN2kjocLynEg29xpFIov5GegLnDBbvZRQ9/0N09sVE7vInUavLQLzENpwzpKe3X7KDAqg4wxQOh8uH1z86jm0HL0EVEgpnmDYqB8u+MhQ2sw5SDaEgWoKTQkB/YRu4VFH+wQto74++9JtWgHcwZUD6l+UMhEptrCAHn6fb7WUmW5OpcU3+tWlT5ZjJFtFRKQ21PsJVP0BKCVlTFhglpZachKi4iE6/iVP02hLUTabuxVMtokj+8SKlgHrpGHwntsB7ehfgbWNUAeNQ+o6Cfsh06PqNB9MnRlFDoqE/lkk4CZcD6oUvkfb/2bvv8Cir/P3j72dmMikz6QESWkJvoUoTcFFAsaDrD0UUUVexLaLoqri2XcsqFnZdFFfXdV3FjsKCCAiKq36l2CjSeyeQ3uvM8/z+mDAQkpAAISHJ/bouLpinzZnMYTJzz+ec06YjB995uNrDBeLG/Zmk9/5U/TsyDF9AdKQCKdBdWolUUXVS6TZH/XrtqunfqY2VXuOkNqm/yZnSUL+YUABVifoYQBl2G2t3pDH949VU9KwZBkwe25ue7aIVQpU6mXQ57pYXyE5OxZOZVH54XH4GFf7QT4Zhw3BF+sOkcsPjXNFnZRBw7GpHx84fUOS118j/Iau4AG/yDn8g5U3eUa1lUG3hsdiadTi62l5E7Fk7uXltvHnxpu2jZNtyPDt+KLvs+jFsTdsS0P5cHO0GaHXCBkxvluVMCLPnc+C1idU+Pu6Gpzk0d3qVQ9xspdsIDDlrX8Nr0pn+ndoY6DVOapP6m5xJDfGLCQVQlahvAZTNZpBd6GXyS9+cMAcxDJh+3/mEBemNDECww0vu129Wb3zt6Q4ZCAiqeM4ldww2dxRGSORZPQn62cIyTcyM/WWqpKyclKpPDHRhb9rOP4+UvUnbsybQO1NvXszcNEq2/4Bn+/LSSrLyjLBmBLQfSECHc7GFx9Z4G+TsozfLciaEBRT5AqhqVkC1+P0/yC45O16DpWHRa5zUJvU3OdOO/WLCZliYllGvv5ioKoCqv5OqNDKWYfDxV1urLMKxLJi1dCsTLu8K9bDD1rQAw+NbJroa8rb9RFy/SysNoIzg8GNCpSOTfMf4t+EM0aTNNcCw2bBHt8Ye3Rq6DgPAzM88WiF1eDtm6u7yH4KK8vDu+xXvvl+PXAhbTHyZYXs2d1TtPpgzwCrKo2TXz3i2rcCbtIWKKvuMoFAc7fr7JhNv0lb9UkROW4nlaLQTpoqIiJwpxy4WUDbwbJif5RVA1RMmBivXV7xc+vFWrEvi5lFd0UdOfCFFdVeWM73gCMDR8TxsxwVNhjsKw64303XFFhKBrXS1JvBNru1N3Y330HbMw6VVUoXHfTtlmZgpuzBTdlGy/ksADFfUcZObt6oXk25b3hI8+37Fs22Fb7n041cWBLA7cST0IaDDQOwtEzFsenkXkZqjldxERETkdOkTSj3hNU281axo8poWXtPSkwtgs/v+VHPIgM0VRfD5E858u+S0GA4njtiOOGI7AqWTm2cfLju5ecaBcudZeel4dv6IZ2dpVZzD6Ru256+Sao8R6KrNh1IpyzLxHtqGZ/sKSnb+BEV55Q8yDOwtuvnmdUrog+EMrv2GikijYJoWJYGRRI+aVOWEqSWBkVgehU8iIiJSljKKesJus2G3GdUKoew2A7vNAK/e/GnIQONgGAZGeCy28FgCOg4BfEPVvId34D18ZHLzneApKnuipxjvwU14D27yb7JFNvcP2bM364AR3uyUhrAdO567JKuQsACDEstBkek44Xhub8YBPNtWULJ9BVZuWsXXjoknoP0gHO0HYAuJOOm2iYicikKPjaDWvWk+YVqDmjBVREREaocCqHrChsXAxDiW/XqwymPP7R6HzVDhO2jIQGNmBLpwtO6Bo3UPACzTi5m+D++h7f5QqqKAx8w4iJlxkJLN35Vex43NP2yvPfYmbTAczhPe95EVLTKXfeqbg6z0A1pIx/5EDL663Ac0Mz8Tz/aVlGxbgZm2p+LH444moMMgHO3PxR7Z/FR/LCIip6XQY6M4IAbXsAlEDL+pzISpeV47piqfREREpBJaBa+eONlV8EID7TTSp7acIIeJuXd1lUMGjFa99a1tI2PmppdObF46bC91L1hVDNe02UsnN+/gn0/q2Cqk6vS36FGTMFokkrvlJ0q2rcB7cCMV/scOdBHQtj+ODuf6hgc2giXK5fRpxR6pTepvUtvU56Q2qb9JbWoI/a2qVfAUQNUjht3G2h1pTP94dYWfVQ0DJo/tTc920Vhes/YbeBY7UpGiIQNyIpanCG/yrjKhVIVzLx3HCI3B3qw9Aa26EZHQmaT/TKGqiru46/9M8ry/483NLLvL7sDRuheODoNwtOqBYVehqpychvDmReoP9TepbepzUpvU36Q2NYT+pgCqEvUxgAJfCJVT4GHW0q2sWJeE17Sw2wwGdIvl0sFt8JR4aN2k8ie8MTt2Tp5jhwwUee31si/ImWdZJmbWIbyHtmEe3u6rksqsfDXKiCFXU5yyj/wtP1R5bVengQTEtCBz2WzAwN68s28y8bZ9MZwhNfgopLFpCG9epP5Qf5Papj4ntUn9TWpTQ+hvVQVQ+mq9nrG8JmFBdiaM6srNo7riNS2Kir3839qD/PX9X7DbDJ69fSAOu6p5jmeaFgWmnQLsx/3nVvgkFTMMG/aI5tgjmkPnoQBYhbmlFVKlVVLJO8FbAkBwm55kLv9vta6dt+0n4gZeQX6JnYD2A7C5o8/Y4xAREREREalrCqDqIV+1joWB7wksMU0WfL+TvEIPAD9sPMzg7nF12USRBssIcuOI74UjvhcAlteDmbYX7+HtGHanb3hndZhebCERBPa69Ay2VkRERERE5OygMpkGIDjQwUX9Wvlvf75ij4aUidQSw+7A3rQtzu4XYbgiwGav3ok2e/WPFRERERERqedUAXUCBQV55OZm4S0dXnM2O6etjbZNWvpXvtu3fxeBTn24rUxysg3TrH8TtdvtAbjd4QQHu+q6KVKBEstBSMf+5G9eUeWxro4DKCGgFlolIiIiIiJS9xRAVaKkpJicnAwiImIICAjEMIy6blKVgt1FZOUWAeBw2GgW46oX7a4LDocNj6d+BVCWZVFSUkRmZioORwABAc66bpIcp8h0EDH4avI3r6SqVfDCB19FrsdexXEiIiIiIiINg4bgVSInJxO3OxynM6jehDhhrgB/W0s8JvlFnjpukdQkwzBwOoNwucLJzc2s6+ZIBUzToiQwkuhRk4DKXjcMYi6fRElgJI10EVIREREREWmEFEBVwuMpJjAwuK6bcVLsNhuhIUeH9GTlFusDbgMUFBRMSUlxXTdDKlHosWFr3ZvmE6bh6jzo6DxPNjuuzoNoPmEaRqveFHr08isiIiIiIo2HhuBVwjS92OrhBMFhLic5+SVYlkVxiZeCIg8hQZpnpiGx2eyY1V1pTepEocdGcUAMrmETiBh+EzbDwrQMSgggz2vH9CgYFhERERGRxkVfwZ9AfRl6dyyH3YZbVVANWn3sl42RaVoUeOxklwQSEN6E7JJACkpsWqFSREREREQaJQVQDVC4y+mffqaoxEth8dlRLXO2BWFnW3tEREREREREGioFUA2Qw27DHXx0hbTM0pXxjrVhw3qeeOJRRo++jGHDBjN27JX87W/Pk5KSXOPtSUlJZsqUezl0KMm/7eqrL+e5556u8fuqjtzcXJ566s+sXbu6Tu5fREREREREpLFRANVAlamCKvZSWHx0RbxZsz5k4sQJ5OTk8Pvf3820adO59trxrFixjFtvvZF9+/bWaFtWrfqZ5cu/L7Pt2Wdf5IYbbq7R+6muHTu2s3DhfEzTrJP7FxEREREREWlsNAl5AxXgsOEOCiC3oASAzNxiYqMc/PrrGmbMeIlrrhnHpEn3+o/v06cvQ4b8hptvHse0ac8xffo/zmj7OnbsfEavLyIiIiIiIiJnjzoNoDweD3369KGoqOwQsZCQEFavrnh4VF5eHtOmTWPJkiXk5+fTt29fHn30URISEmqhxfVLuNvpD6AKizwUFXv58MN3CQsL57bbfl/u+CZNmnLXXfeSmpqCx+OrmJozZxaffTaXgwcPEB0dw6hRVzB+/O+w230rBE6adDutW8cTGxvH3LmzyczMoFOnzkye/ACdO3dl4cL5PPvskwCMGXMFl1wyikcffYKrr76cvn3788c/Pk5S0kHGjLmCZ555gcWLF/HTTytxOAI4//zhTJ58P0FBQf42fvbZf5k16wMOHNhPTEwTrrhiNOPH3+SfmPuZZ54gLS2NYcOG895773D48CHi49vw+9/fzYAB57LOjFTxAAAgAElEQVRq1c/cc8+dANxzz5306tWHGTPeOHNPgoiIiIiIiIjUbQC1a9cuioqKeP7558sESDZb5SMD77vvPtatW8eUKVNwuVzMmDGDG2+8kQULFhAaGloLra4/Ahx2XMEB5JWGUBk5hfzww0rOO28ogYGBFZ5zySWj/P/+y1/+zNKlS7jhhpvp3r0Hv/66lv/8518cPHiAhx/+k/+4r7/+koSEttx334OYpsWrr/6dxx57iFmz5nHuuUO45ZbbeeutN3jmmRdp375Dpe197rm/cNllVzB16l/ZtGkDb7zxD6Kiovxh2bvv/oc33vgH11xzHQMGDGLTpg38+9+vk5mZwd133+e/zsaN60hOPsStt96Jy+XmzTdf57HHpvDf/y6iU6fOPPjgI7z44rP84Q8P0bv3Oaf1MxYRERERERGRqtVpALV582ZsNhsjR44kODi4yuN//vlnvv32W/71r3/xm9/8BoC+ffsyfPhwPvzwQ26//fYz3eR6J9zl9AdQySlpFBcXERsbV+V5O3fu4IsvFjBx4mTGjbsBgH79BhIUFMRrr73C2LHjaNu2PQBer8nf/vYKISEuAPLz83jmmSfYsWM7HTp0pEWLlgB07NiJuLjmld7n4MHn+YcF9u3bn59++oHly/+P2277Pbm5ubz99puMHj2Gu+/+AwD9+w8kODiEV1/9O2PGXEdsbCzgm2T8rbfep3nzFgAEBwczadLtrF79M+eddz4JCW0BSEhoQ5s2bU/uByoiIiIiIiIiJ61OJyHftGkTrVu3rlb4BLBs2TJcLheDBw/2b4uKiqJfv3589913Z6qZ9ZozwE5IkC9nPDJsrjqTbx9ZIe7CC0eW2X7RRZcAsHr1Kv+2du3a+8MngKZNmwFQWFhwUm3t3r1nmdtNmjSloKAQgPXrf6WoqIghQ36Dx+Px/xk8+Dy8Xi+rVv3kPy86OsYfPh25DuC/loiIiIiIiIjUrjqtgNqyZQtOp5MJEyawatUqHA4Hl1xyCVOmTMHtdpc7fufOncTHx/uDlCNat27NokWLaqvZ9U64O5D8Qg8udyhBwSEcPHiw0mPz8nIByM7OAiAyMqrM/iO3jxwHEBgYVOaYI/MxmaZ1Uu08dq4n8A3FtCyzTHvuu29Sheempqac8DqA/1oiIiIiIiIiUrvqfAhebm4uY8aM4c4772T9+vW88sor7Nq1i5kzZ/qDjCNyc3MrDKZcLhe5ubnltotPYICd4EAHBUUeevTsy+rVP1NUVFThPFCzZn3I22+/yaRJvjmVMjLS/RVEAGlpqQCEh0fUTuNLuVy+5/3JJ5/1D+k7VkxMk1ptj4iIiIiIiIhUX50GUC+99BLh4eF06tQJgH79+hEdHc2DDz7I8uXLywy1A7CsyitqTjRxeUWio8sHWcdKTrbhcNTpCMUaFR0RzP7DOVwyagw//fB/vPnv15l8z31ljjl0KIk5c2bRtWsi/fv3B+Drr5dw/fU3+o/5+uslAPTp0weHw4ZhGBgGZX5Wdrut9G8Dh8NGQIDDv/3Y4wzDt//I8TabUW7/kWv37NmDgIAAMjLSGDnyYv8x69at5V//+icTJ04iNrbZCdtz5PpOp73C9tQnNpuNJk006X59oudLapP6m9Qm9TepbepzUpvU36Q2NfT+VqcB1JGQ41jnn38+4KuOOj6Acrvd7N+/v9w5eXl5FVZGnUhaWu4Jh4iZponH03CGbDlsBkGBDjp06saVV9/Ahx/MZM/u3Vx88WWEh4ezbdsWPvzwXQzDxuOPP0Xz5i0YOfISXn/9VfLzC0hM7M769euYOfMtRo68hFatEvB4TCzLwrIo87Pyes3Svy08HpPgYN/8UF9//RXnnjuE+PgEwBcoejym/3jTtMpc59hru93hXHvteF57bQbZ2Tn07NmbQ4eSeOONf+B2u2nVqk2V7Tly/eBgX1/5/vv/IyTETYcOHc/cD/4MMU2TlJScum6GVFOTJqF6vqTWqL9JbVJ/k9qmPie1Sf1NalND6G82m3HCYp86C6DS0tL4+uuvGThwIK1atfJvLyz0TRQdGRlZ7pw2bdqwYsUKLMsqMzxvz549tGnT5sw3up6LcDs5VOThqmt+R5t2Hfm/pZ8zffo0cnJyaNq0GRdcMILx428mJiYGgIcf/jMtWrRiwYLPmDnzLZo1i+WWW25n3Lgbq7insnr3Pod+/Qbwz3++yqpVv/DCCy+dUvtvv30i0dHR/Pe/n/Luu/8hLCycAQPO5Y477qpwOGFlWreO56KLLmH27Fn8+OMK3nnno1Nqj4iIiIiIiIhUj2GdaFzbGZSens7gwYO58cYbefjhh/3b3377bV544QUWLVpEfHx8mXN++uknxo8fz1tvveWvjkpPT2f48OHccccd3HnnndW+/6oqoA4d2kNsbHyl++urpLQ8ioq9ALhDnMSEB1VxRsPkcNjqdYVbQ+2fDVVD+DZD6g/1N6lN6m9S29TnpDapv0ltagj97aytgIqKiuL666/n3Xffxe1207dvX3755Rdef/11rr/+euLj40lPT2fv3r20b98et9tNv3796N+/P3/4wx944IEHiIiI4JVXXiE0NJTrrruurh5KvRLhDuRwej4AuQXFRLidOOz1cx4kEREREREREakf6nQOqIceeohmzZoxe/Zs3njjDZo1a8Y999zDrbfeCsA333zDww8/zMyZMxkwYAAAM2bM4LnnnuOFF17ANE3OOecc/v73vxMeHl6XD6XeCHLaCXTafVVQFmTlFRMd1jiroERERERERESkdtTZELy61liH4AHkF5aQnFEA+Faaa9HE1eiqoDQET2pTQyinlfpD/U1qk/qb1Db1OalN6m9SmxpCf6tqCF7jSh0EgOBAB84AO+BbaS47r7iOWyQiIiIiIiIiDZkCqEbIMAzC3U7/7Zz8Erxm/a0GEhEREREREZGzmwKoRiok0EGAw/f0+6qgSuq4RSIiIiIiIiLSUCmAaqQMwyDCHei/nZ1frCooERERERERETkjFEA1YiFBDhxHqqBMi5x8VUGJiIiIiIiISM1TANWIGYZBhOvoXFDZecUnXBlQRERERERERORUKIBq5FzBATjsvm5gmhY5+VoRT0RERERERERqlgKoRu74FfGyVAUlIiIiIiIiIjVMAVQj9thjUxgypC9Ll8zHfkwVVG5BCampKUyadDvDhg3i8ssvori44sqoZ555grFjr6zNZouIiIiIiIhIPeOo6wZI3cjKymTZsv+jXbv2fPbZHIZdeDnp2YW+fXlFzJ/9IevWreVPf/oLTZs2xel0Vnid3/3uVvLz82qz6SIiIiIiIiJSz6gCqpFasuQLnE4nd955N1u3bmHf7q3YbQYAXq9FWnoGMTFNGD78Qrp371npdVq0aEmHDp1qq9kiIiIiIiIiUg8pgGqkFi78jH79BjJgwLnExDRh/vz/Ela6It69E69j8aL5HD58iCFD+vLvf/+TVat+ZsiQvsybN4fRoy9j5MihrFmzqtwQPMuy+Pjj9xk37iqGDRvMtdeO5tNPPypz33PnzuaWW8YzYsQQhg0bzC23XM833yyt1ccvIiIiIiIiIrVHQ/BOwrJ1SXz/a1JdN4MhPeIY3D3ulM/ftm0L27ZtZcKEO7DZbIwceSlz5szirrvuxWYzuPfBp5gz6x127dzC1Gen0bRpU/bv3wfA22+/yb33PkB+fj5duyayYMFnZa79j3+8zKxZHzBu3I306dOXdevWMn36X7HZ7IwePYZPPvmIGTNeYsKEO0hM7EFOTjbvvfcOTzzxKJ980p0mTZqe1s9GRERERERERM4+CqAaoQUL5hMZGcXAgYMBuPTSy3n//Xf48ssvGHbRFSS06UBoWAR2u4Nu3RIxDMMfQI0efQ1Dhw6r8Lo5OTnMmvUB11wzjjvuuAuAfv0GkJKSzJo1qxg9egxJSQcYN+5GbrzxFv95sbHNmTBhPOvW/cqwYSPO8KMXERERERERkdqmAOokDO5+epVHZ4OSkhK+/HIRF1wwgoKCAgCioqLp1KkL8+bN4corryY77+iKd3mFHtzBAf7bHTp0rPTaGzasw+v1MnToBWW2P/TQY/5/33PP/YAvrNqzZzcHDuxj1aqfAfB4Sk7/AYqIiIiIiIjIWUcBVCOzbNl3ZGVlMXfubObOnV1u/8aN62kRfzRkysotwhV0tJsEB4dUeu3s7CwAIiOjKj3mwIH9vPDCs/zyy48EBATQunUC7dt3AHzzR4mIiIiIiIhIw6MAqpFZuHA+LVu2ZsqUR8ps93o9PPTQH/jsszk89MfHMXwL4lHiMckv9FTr2i6XG4CMjAxatGjp337gwH6Skw/Ts2dvHnxwMk5nIG++OZP27TvicDjYtWsnixcvrJkHKCIiIiIiIiJnHa2C14ikpqbyww8ruPDCkfTp07fMn379BjJo0HksXbqE/Lw8AhxHu0bWMUPyTqRr10QcDgfLln1XZvvMmW8xdepTZGVlsnfvHi6//Eo6d+6Kw+HLP1euXA6AaZo19EhFRERERERE5GyiCqhGZPHiBXi9XkaMGFnh/osvvoxvvlnK4sULcB4TQBWXeCkq9lZ5/cjISK66aiwffvguDoeDnj178+uva1i06HMeeugxIiOjiItrzqeffkRMTBNcLhc//riCWbM+BKCwsLBmHqiIiIiIiIiInFVUAdWILFw4n/btOxIfn1Dh/oEDBxEREcm8eXMwDAPbkXF4QG5B9SYIv+uuydx6650sXryQKVPu5X//+4qHH/4Tl112BQDPPjuN6OgY/vKXP/PnPz/Mhg3ref75l4iPT2Dt2tWn/RhFRERERERE5OxjWI105ue0tFxMs/KHfujQHmJj42uxRWcfj9dkf0oelHaRZlEhBAc2jKI5h8OGx1N/h/ypf9YvTZqEkpKSU9fNkEZC/U1qk/qb1Db1OalN6m9SmxpCf7PZDKKj3ZXvr8W2SD3jsNsIDQ7w387MLdJKdSIiIiIiIiJy0hRAyQmFu5xQOhKvqLh6c0GJiIiIiIiIiBxLAZSckMNhw12mCqp6K+KJiIiIiIiIiByhAEqqFO5y+v9dWOyhsNhTh60RERERERERkfpGAZRUKcBhx3VMFVSWqqBERERERERE5CQogJJqCXcfrYIqKPJQVKK5oERERERERESkehRASbU4HXZCgo6tgiqqw9aIiIiIiIiISH2iAEqqLeKYKqj8Qg/FqoISERERERERkWpQACXV5gywExzk8N/OzNNcUCIiIiIiIiJSNQVQclIi3IH+f+cXlFDsURWUiIiIiIiIiJyYAqhG7LHHpjBkSF/mzp1d7XMCA+wEBzr454zn+cOk8VoRT0RERERERESq5Kj6EGmIsrIyWbbs/2jXrj2ffTaHK6+8qtrnhrudXHn1eAoLCsgrKCHCHUiAQ1mmiIiIiIiIiFRMqUEjtWTJFzidTu688262bt3Cpk0bqn1ukNNBfOt44tu0ByArTyviiYiIiIiIiEjlVAHVSC1c+Bn9+g1kwIBziYlpwrx5c+jSpZt//+bNm3jttZfZvHkjpmnRtWsit932exITuwPwz1efY+3aNfxtxnvkFpQQaDd5d+a/+e67/3H48CECApwkJnZn4sTJtG/foa4epoiIiIiIiIicBVQB1Qht27aFbdu2cvHFl2Kz2Rg58lKWLl1CXl4uAHl5uTzwwN2Eh0fwl7+8wJNPPkthYQEPPHC3/xi7zcAwDN8FLXjyycf54osF3HDDzfztbzO4++772LFjO08++SiWZdXVQxURERERERGRs4AqoE5CydZllGz5rq6bQUCn3xDQcfApn79gwXwiI6MYONB3jUsvvZz333+HxYsXMXr0GHbt2kVmZiZjxlxL9+49AYiPT2DevDnk5+fjcrkxDAO7zRdAFRcXk1dQwD2TH2DE8AsB6N37HPLycpkx4+9kZmYSGRl5mo9aREREREREROorBVCNTElJCV9+uYgLLhhBQUEBAFFR0XTq1IV58+YwevQY2rZtR0REJFOm3MewYSPo3/9c+vcfyMSJ95S5lmGAM8AOOHno0ecJczlJSUlm37697N27h+XLvwfA4ymp7YcpIiIiIiIiImcRBVAnIaDj4NOqPDobLFv2HVlZWcydO5u5c2eX279+/ToSE7vzj3/8i3fe+TdLl37JvHlzCAwM5OKLL2Py5AdwOp3+4yPcTpIzCvh1zU+89/arHDywl5AQF+3bdyA4OARAQ/BEREREREREGjkFUI3MwoXzadmyNVOmPFJmu9fr4aGH/sBnn80hMbE7rVsn8PjjT+P1etm0aQNffLGQuXM/pWXL1lx33Xj/ecGBDtJSD/HSC4/Tb8B5PPn0i3Tu0BbDMJgz5xN++GF5bT9EERERERERETnLKIBqRFJTU/nhhxXceOMt9OnTt9z+QYPOY+nSJQwe/BtefPEZ3nnnI6KjY0hM7EFiYg+++moxycmHy5xjGAaHD+ygpKSYK0aPIySsKaZlYTcMVq70hU+mqQooERERERERkcZMAVQjsnjxArxeLyNGjKxw/8UXX8Y33yxlx45tmKbFww8/wPjxv8PlcrF06RLy8/MYOvSCcud179YNu93Oh+++wSWjrmaL3eLb/y1ixQrfHFCFhQVn9HGJiIiIiIiIyNnNVtcNkNqzcOF82rfvSHx8QoX7Bw4cREREJP/731f87W8zcLtDee65p3nwwXvZunULf/nLC/Tq1afcea1ateaPjzxFWuph/vrco7zy8gtYFrzyyj8xDIO1a1ef4UcmIiIiIiIiImczw2qkM0SnpeWecGjYoUN7iI2Nr8UW1W+WZXEgJQ+P1wQgIjSQCHdgHbeqcg6HDY/HrOtmnDL1z/qlSZNQUlJy6roZ0kiov0ltUn+T2qY+J7VJ/U1qU0PobzabQXS0u/L9tdgWacAMwyDcfXR1vOy8Ys39JCIiIiIiIiKAAiipQe7gAOx2X5cyTYucguI6bpGIiIiIiIiInA0UQEmNMQyDcNdxVVCNc4SniIiIiIiIiBxDAZTUKHdIAHa7AYDXa5GbX1LHLRIRERERERGRuqYASmqUzTAIcx2dfDwrr5hGOs+9iIiIiIiIiJRSACU1LjQ4AJvtSBWUSW6BqqBEREREREREGjMFUFLjbLayc0Fl5aoKSkRERERERKQxUwAlZ0RoiNNfBeXxmuQVqgpKRERERERE5AibzcCw27DsdpIz8rHsdgy7zf9ZuqFx1HUDpGGy2QzCXE4yc4oAXxWUKygAw2iY/5FEREREREREqsuw28gq8DDrq62sXJ+E17Sw2wwGJsYxdkRHQoMdWF6zrptZoxRAyRkTGhLgm4TctCjxmOQXenAFB9R1s0RERERERETqjGG3sXZHGtM/Xs2xs9V4TYtlvx5k+bqDTB7bm57tohtUCKUAqpGZNOl21qxZVen+/v3P5W9/e6VG7stusxEWEkBWbjEAmblFhAQ5VAUlIiIiIiIijZLNZpBd4CkXPh3LsmD6x6uZft/5hAXZMc2GMaeyAqhGqEuXrkye/GCF+0JDQ2v0vsJCnGTnlWBZviqogiIPIUGqghIREREREZHGxzIMPv5qa6Xhk/84C2Yt3cqEy7uCAiipr0JC3CQmdq+V+7LbbYSGBJCdd6QKqpjgQFVBiYiIiIiISONjYrByfVK1jl2xLombR3WloXx61ip4Uk5S0kGGDOnL4sULy2x/5pknGDv2Sv/tq6++nFdeeYm7776DYcMG89e/Pg9AcvJhnn76T1x55SUMHz6Yx/44ic0b1wJQXOJl1959DBnSl6VLv+T+++9h+PDBjBnzW2bN+rDM/ZmmycyZb3HNNb/lggvOZdy4q/j887ln+NGLiIiIiIiI1JziEi/rd6Xx0dJtpGUV4K1mRZPXtKp9bH2gCqhGycLj8VS4x263n9SVPv30I8aNu5Hx439HaGgoqamp3HbbjYSEuJg4cTIhISHMmfMJU596gCmPPk+37n3IKa2GevHFZxk69AKeeeZFVq5cxssv/xXDMBgz5loApk2byqJFn3PTTRPo2jWRH39cyfPPP0NhYSFXX33t6f0IRERERERERM4Ay7I4kJLH+l3pbNidztZ9mZR4fJOJX9CvNXabUa1gyW4zsNsM8DaMEEoB1En4IekXViT9VNfN4Ny4fgyIO+eUz//ll584//yBFe6bNu1l4uMTqn2tFi1acscdd/lvv/rqdHJycnnjjXdo1iwWgEGDhnDTTdfx0fv/4unnXqO4xPcfr1u37jz88J8AGDhwEKmpKcyc+RZXXz2Wffv2Mn/+XCZOnMx1140HoH//gZimlzfffJ1Ro64kKCjoVB6+iIiIiIiISI3Kzi9m4650NuxKZ/3udP9iXMdbszWF/t1iWbGu6mF453aPw2ZAw4ifFEA1Sl26dOP++x+qcF+rVq3Jzs6u9rU6dOhY5vaaNavo0aOnP3wCsNlsjBhxEW+++ToFBfn+7RdeOLLMuUOHDuObb75m7949rF79M5ZlMXjweWWqtYYMGcqsWR+yceN6+vTpW+12ioiIiIiIiNQUj9dk+/4sNuxOZ/3OdPYczjnh8XHRIXRLiCK+qYtBPeJYuT7phBORGwZcM7xjg6l+grMogJo0aRJbtmzhyy+/rPSYefPmMWXKlHLbr7/+ev70pz+dyeYBMCDunNOqPDpbhIS46Ny5a6X7TyaACg4OKXM7Jyeb1q1blzsuKioay7IoPCaACouIKnNMZGSU/xpZWVkAjBt3VYX3m5qaWu02ioiIVMRmM7AMAxMDr2lit9mwYWFYVoNZ7lhERERqhmVZHM4oYP3ONDbsSmfz3kyKSryVHu8KctAlIYrENlF0S4giOvzoCB7DbmPy2N5M/3h1hSGUYcDksb0JDXZgec0z8XDqxFkRQM2bN48vv/yywuDiWJs3byY+Pp4XXnihzPaYmJgz2bxG58gKdaZZtqMfW71UmdDQUNLS0sptT0vzBUbNmkZz4OBhAA4llz0uPT0d8AVRbrcbgBkz3qhwqF1cXPMq2yIiIlIZw24jq8DDrK+2snJ9El7Twm4zGJgYx9gRHRvcGz4RERE5eXmFJWzaneGby2lXOmnZhZUeazMM2rYI8wVObaJoExuGzVbx+nWW16Rnu2im33c+s5ZuZcW6o+9Fzu0exzXDG+Z7kToPoA4fPswzzzxDbGxslcdu2bKFbt260atXr1poWeMVEuIC4PDhQ/5tHo+HjRs3EBAQcMJze/U6h9mzZ5GcfJimTZsBviDr66+/pEuXrjSJdPsDqB9XLuOyiy8h0Omb+Pybb5YSGxtHixYt6dmzD+CrxurVq4//+t9++zXz58/lgQceITy85h6ziIg0HobdxtodaeW+dfSaFst+PcjydQeZPLY3PdtFN7g3fiIiIlI5r2my62AO63elsWF3OjsPZp9wmFxMeBCJbaPplhBFl/hIQoKqH7FYXpOwIDsTRnXl5lFdfWVPloXNAMO0MBvge5A6D6Aee+wxBg8eTGBgIL/88ssJj928eTM33nhjLbWs4crPz2X9+nUV7jMMg27dEunevQeffPIhzZu3JCwsjE8++Yji4qIqA6ixY6/niy8WMHny77nlltsJCXHx3/9+wp49u3nxxekEOOz+/5TLv1/Ky6/EMPS8wXz//bd8993/+NOfngagffsOjBgxkqlTn+Lgwf107NiZnTu388Ybr9GpU+dqBZYiIiLHs9kMsgs8lZa8A1gWTP94NdPvO5+wILuG44mIiDRgqZkFrN+dzoad6Wzck0FBUcUrxgMEOu10aR1JYltflVOzyJBKj60O33sMCwNo0iSUlJQcfFsapjoNoD755BM2bNjA559/Xm5Y3fGSk5NJS0tj48aNXHzxxezbt4+WLVvy+9//niuvvLKWWtwwbNq0kTvvvLnCfXa7nW+//YFHHnmCl156keeffxqXy81ll11Bjx49Wbhw/gmvHRMTw2uv/ZvXXnuZadOmYpomnTt35aWXXvVPGh4a4guxrr72Ftav/ZlFC+bQokULnnjiGUaMODox+WOPPck77/yb2bM/ISXlMFFR0YwadQW33npnDf0kRESksbEMg4+/2nrCbzPBF0LNWrqVCZd3BQVQIiIiDUZhsYfNezL9q9UdTq98qhkDSIgLpVubKBLbRNO2eRgOu632GtvAGJZV1VuwM+PAgQNcfvnlTJ06lZEjR/LHP/6RX375pdJJyL/77jtuu+02unXrxl133UVgYCBz585l/vz5PPvss1x1VcWTVZ+qDRs20rx5fI1eU3wOHjzI6NGjuPPuhxnymwtxhQQQF+2q62bVKwcP7qFbt8onkhcRkYqlZRUw4S9f4q1GqGS3Gbxw93l8/fM+WjRx06Kpm5ZN3cSEB1c6p4OIiIicXUzTYueBLFZvTWbVlmQ2707Hc4KV5aLDg+jTqSm9OzalZ8cmhLmctdjahq1OKqAsy+KRRx5h6NChjBw5suoTgMTERF5//XX69evnn6B6yJAhpKWlMX369JMOoNLSck9YUm+aJh5PwxtzeTbwHjeWNS+/hPyQEpwB9lprg8Nhq9fPr2mapKSceJlPOXscKacVqQ3qb+XlF5awflc6a7ancuX5HaoVPoFvTqjiEi8Llu0qs93psBEbFUJsdIj/77goF82iggly1vnsBrVK/U1qm/qc1Cb1t/orI6eIDbvS2bDbN3l4bkFJpcc6HTY6to4gsU003dpE0Tw6xL8wV1F+ESn5RbXS5obQ32w2g+hod6X76+Rd0vvvv8+WLVuYP38+Ho9vfOWRQiyPx4Pdbvc/4UdERUVxwQUXlLvW0KFDWb58Oenp6URFRZ35xkuNcTqOBk5ZecU0iQiuw9aIiEhDcjgjn7XbUlmzPZVt+7P8odNvh7bHbjOqXQFVkWKPyd7kXPYm55bbFxkaSGxUCHHHhVORYYHYDFVNiYiInAnFJV627s9k/U5f6HQgJe+Ex7dq6qZb6Wp1HSWLycgAACAASURBVFuGE+CovWKIxqxOAqjFixeTkZHBkCFDyu3r1q0bU6dOZfTo0WW2r169mu3btzNmzJgy24uKinA4HISGhp7RNkvNiYtrzvff/0xhsYdDab7xtnkFJUS4nfqPLyIip8Rrmmzfn8XaHWms3Z5KUlrF8zms2ZpC/26xrFiXVOU1BybGYcNizAXtOJSWT1J6PofS8k/4LWpGThEZOUVs2pNRZruqpkRERGqOZVkcSMljfWmV09Z9mZScYIRLWEgAXdtEkdgmiq4JUUS4A2uxtXJEnbzjefLJJ8nLK5tIvvrqq2zatIkZM2bQsmXLcuesWbOG5557ju7du9O5c2fANwxp8eLF9OnTp8rV2eTsE+R0EBTooLB0lYGsvGJiwlUFdbax2Qwsw8DEwGua2G02bFgYlqWVoUSkTuUXeli/K40121NZtyONvMLKV62Jjw2lV/sYuidEMrhHc1auTzrhROSGAWNHdCQ00M4lA8rOCZlbUFIaSOVxKC2fQ+n5JKXlk5JZUGlllaqmRERETk92fjEbd6X7Jw/Pyi2u9FiH3aBDy4jSycOjaNnUrd+pZ4E6CaDatm1bbltERAROp5Pu3bsDkJ6ezt69e2nfvj1ut5vRo0czc+ZMJk2axL333ovL5eKDDz5g69atvP/++7X9EKSGRLicHCoNoHILSgh3BRLg0KoCZwvDbiOrwMOsr7aycn0SXtPCbjMYmBjn+2AW7MDy1t+5tESk/qlsaN3xnA4bXROi6Nk+mh7tYogMPfpNp2G3MXlsb6Z/vLrCEMowYPLY3pW+xrmDA2jfMpz2LcPLbPd4TVKzCklKy+NQabXU6VZNNasgmFLVlIiINAYer6+6ecPudNbvTGfP4RPPjxQXHeIPnDq1iiTQqdE1Z5uz9t3LN998w8MPP8zMmTMZMGAA4eHhvPfee/z1r39l6tSp5ObmkpiYyNtvv03Pnj3rurlyioICHQQ67RQVe8GC7LxiosOD6rpZgu8D2todaeU+oHlNi2W/HmT5uoNMHtubnu2iFUJJjVHFnRyvukPrACLcTnq2j6Fn+xi6xEcSWMniFpbXpGe7aKbfdz6zlm5lxbqjAfu53eO4ZvipBewOe+kwu6iQcvsqqpo6lJ5PcsaJq6b2Jeey7wRVU75QSlVTIiJS/1mWxeGMAl+F0840Nu/NpKjEW+nxriAHXRJ8gVO3hCh9jqwHDMs6UQF6w1XVKniHDu0hNja+0v1ScwqKPBxOL/1AYUDLJm4c9jNbBVXfV8E70/3TZjPILvQy+aVvqhyiMv2+8wkLsiscOIGGsKJFbTDsNrJVcXfaGkJ/O5Whdb3ax9C6mbvcIiYncjTwxN/fbAYYZu0FnqdaNVWZclVTUSHERdds1dSxQTEGYKGgWGpNQ3iNk/pD/a36TvVLxLzCEjbtzvBXOaVlF1Z+H4ZBuxZh/snD28SGYatkwZD6qCH0t7NyFTyRYwU57TgD7BSX+KqgsvKKiQ5Tel2XLMPg46+2njB8ArAsmLV0KxMu7wr60CGnQRV3cmRo3dodaWzdl3lKQ+tOlu8NsYVB6Rsir4VvS+2p66qp2KgQosKCql01paHZIiJyvJP53eA1TXYl5bB+Zxobdqez82D2CT9zxIQHkdg2msQ2UXRuHUlIkCKM+kzPntQ5wzCIcDtJzigAIDe/hAiXE/sZroKSypkYrFxf9QpRACvWJXHdRZ0oLigmXKtJyCmw2QyyCzyVzscDvrBz+serVXHXgHhNkx0HslmzPbXGhtY1NFXNNXVsOFWjc01VUjWloFhERI5Xnd8N91zTiwi3k8//bxcb92RQUFR5ZXOQ006X+Ej/XE5NI8t/QSP1lwIoOaMsy6rWcIjgQIe/CsqyLLLyi4kKVRVUXfGaZqXfrpc/1iI7r5iH/7GMFjEuusRH0iUhkk6t9A2FVI8q7hqP2hpa19AdWzXVi5gy+8pUTZWGUjVRNdWuRTjn922loFhERPyq+yXiy7PW8PQdg9iZlF0ufDKAhLhQurXxVTm1bR52xqdjkbqjT4eNzKRJt7Nu3VrefHMmHTp0Krd/6NAB3HjjLUyYcMdp3U9JSQmvvz6DTp06c9FFlwDwzDNP8Ouva/j447nljjcMg3C3k5TSKqicvBLCXU7strIvPjNm/J2PPnqP8eN/x513TjqtNtaEpKSDjBlzBY8//hQjR15a182pMR6vid1mVCuEsh8z7vpAah4HUvP46pf92AyDhLhQusRH0jUhivYtwghwNI6qBameEo+XXUk5tIgNO6mKuyt+045ZSzYTF+2ieYzLX7XhbCRVMfVNXQyta8xOpmrqUHo+SdWsmurRoQmz/7ddQbGIiPidzJeIC5ft4sIB8cz6aiuRoYH+CqeuCVG4gwNqp8FS5xRANUJer5epU5/ijTfeweE4M10gIyOdjz9+n0ce+XO1zwkJdBDgsFHiMbEsi+y8kjIfQDweD0uWLKJdu/YsXDifW2+984y1v7qio2N4/fX/0LJlqzptR03JLSjhvSVbaNMigv7dYlmxrupQYEBiLHuSsssFVqZlsfNgNjsPZrNgxR4CHDY6tAz3B1LxzUIb1KSBUrW8whK2789i2/4stu3PZFdSNh6vxdSJg0+q4s7rNfl5SwqQ4t9uADERQTSPdhFXGko1j3YRF+1SJV4t09C6s9PpVk316tiET7/eVq37WrEuiZtHdUWv8CIiDZvHtKr9JeIPGw5x3UWd6J4QSfPoEFU2N1J6V94Iud1utm7dwvvvv8NNN02o6+b4+aqgAknN9FVBZecXE+Zy+itsVq5cRkZGOk8//Tx33XUr3333DcOGjajLJuN0OklM7F6nbagpa7al8vYXm8nOK2bb/izuv/4cVq5PqnIVvGtHdCI00E7Ptr9h2/5MNu7OYNPuDPYezikzkW+Jx2Tj7gw27s5g9rc7CQl00Kl1BF0TouiaEElslH4RNTSpWQWlYZMvcDqQklfpsadScXcsC0jJLCQls5C1O9LK7ItwO33VUtEu4mJC/JVTYSEB6nM1REPr6rfqVE2FBAWcXFBsWnqTKSLSAKVnF/Lz5mR+2pLMrb/tflK/GwKddlrEuM5wC+VspvcGjVCnTl2JiIjgnXf+zdChw0hIaFPpsUVFhbz77tt89dUSkpMP0bx5C8aMuY7f/na0/5irr76coUOHsXXrZjZsWM855/RlxYplADz77JO89dYbfPrpfP/xn38+l/fee4fk5MO0bp3AxIn30L//QABcQQ4yHTY8HhPLtMjJLyaidGLrBQvm06lTF3r27EXXronMmzenXAA1adLtJCS0JTIyknnz5lBQkM+gQUN46KHHmD37E2bP/piCgnz69RvAgw8+Qnh4BACmafLee2/z+efzSElJJi6uOePG3cCoUVeWuXZsbBx5eXn8+OMKzjmnP/fd92C5IXh79+7mtddmsHr1LxiGQa9evZk06T5atGgJwIED+3nrrX/y888/kpmZSVhYOAMHDuLuu/9AWFjYKT+vpyq/0MOHS7eybN0h/7aMnCJS0vO455pevDxrTYUhlGHA5LG9/ataBAbYSWwTTWKbaMD3jfrmPRls2pPBxj0ZHE4vWwWRX+Rh9bZUVm9LBXwhQZd4XxjVJT6SKK2EWK+YpsWB1Dy27c/0B07p2UVVntcsKoSUjHwGdItleTUq7gZ2jyM4wM4tl3YhKS2PpLR8DqblkZJZUGlYmplbTGZucbmJl11BDuKifdVSR0Kp5tEhRIVXf0WwxuxwRj5rt6exdntqtYbW9WgfTU8NratXjq2asuz2kwqKTdNCJVAiIg3DsaHTjgPZZfadzO8Gu80Ar4ZnN2YKoBqpe+99kF9++ZGpU5/itdf+jc1WfqI3y7J44IHJbNmymVtvvYOEhLYsX/4906ZNJSMjnd/97lb/sZ9++hHjxt3I+PG/w+0O5be/vYo//vEP3HTTBIYOvcB/XFLSQT744F1uu20iwcHB/POfr/LIIw8we/bnhIdH+KqgXE7SsgoByM4rJizESVZWJitWfM9dd90LwKWXjuKvf32e/fv3lRv+tmTJIrp1S+Sxx55k587tzJjxd7Zt20pMTBMeeugxkpIOMn36NKKjY7jvvikATJs2lUWLPuemmybQtWsiP/64kueff4bCwkKuvvpa/7W//PILLrlkFM8//1KF39qnpCRz++0306xZLFOmPIrT6eRf/3qNe++dyMyZH2MYBnfffQfR0THcf//DuN1u1q1by1tvvUFgYBAPPPDH03hWT96G3en8Z+GmMkFBuNvJ7y7uTM+20Rh2G9PvO59ZS7eyYt3RJVXP7R7HNcNPvNy2OziAvp2b0rdzU8D3i2vj7gw27Uln454MsnKLyxyfmVvMig2HWLHBF4Q1iwqha3wkXRMi6dQ6UmPDzzLFJV52JWX7K5y2H8g64YomADbDID7WTYeWEaV/wglzObHZDBLiwllRjYq7scM7EhpoZ0iPuDL7SjxeDqUXkJSWx8FUXzCVlOYbTuSp5I1OXqGH7Qd8bT+WM8D3ofvIcL7mpQFV08jgRj0ppobWNW42LAYmxrHs14NVHjugWyxf/7KfpOQcxlzQXq/fIiL1UFpWIT9vSebnzcnsOJhd4TFrt6ZUe9qOc7vHYTNA8VPjpgDqJGQvX0bW99/VdTMIH/IbwgYNPq1rREZGcu+9D/LEE48ya9YHXHvt+HLHrFixjNWrf+Hpp5/jggt8lUb9+w/E4/Ewc+Zb/L//d7W/gqhFi5bcccdd/nOTkw/7t3fs2Nm/3TRNnn/+JVq1ag34hrDde+9ENm5cz7nnDgF8wUVWbjEer4lZWgW1ZMlCAC688GIAhg8fycsvv8Rnn81h4sTJZdptWRbPPPMCISEu+vcfyMKF8zl06BBvvPEObrcbgB9+WM769esA2Lt3D/Pnz2XixMlcd914/+M0TS9vvvk6o0ZdSVCQrxonMDCI++//IwEBvjfTSUll34h//PEHeL0e/v73V4mMjAKgdet47rvvLrZu3UxQUDCxsXE8/vhTxMU1B6BPn75s3LieNWtWVeu5qwlFxV5mfbOd/606UGb7gK7NuP7Cjv4PC5bXJCzIzoRRXbl5VFd/AGUzwDAtzJNYZjsqLIghPeIY0iMOy7JISsv3VUftTmfz3sxy4cXh9HwOp+fzv9UHMIDWsaF0LV1hr0PLCH2grWW5BUfmb/JVOO0+lF1psHNEoNNO++Zh/rCpbfNwAp3lnzfTtAgNdjB5bO9KV1E5vuLueAEOO62aumnV1F1mu9c0Sc0s5OCRaqnUPF9IlZZPUbG3wnYXl5jsPZzL3sNlVwSz2wyaRgaXHcoX7SI2OqTB9kcNrZMjDMti7IiOLF93sMqg+NLBbZj2/i9k5hSxelsqV5/fjiE94lRZKCJylkvNKuDnzSn8vCWZnZWETjbDoEt8BH07N6Vfl6YM6tG8WtN2XDO8o6qfRAFUYzZixEi++moxb775Ouedd75/iNgRa9asIiAggKFDh5XZftFFFzN37qds2LCeQYN8oVGHDh2rdZ/R0dH+8AnwhzA5OUc/6B1ZEe9IFVRWXjELFsynX78B2O12cnJyABg4cBALF37ObbdN9AdCAAkJbQgJOTq2ODIyGqcz0B8+AYSHR7B79y4AVq36CcuyGDz4PDyeox+uhgwZyqxZH7Jx43r69Onrv/ax93W8X39dQ/fuPf3hE/gCqNmzP/ff/sc/3sQ0Tfbt28v+/XvZtWsXe/bsrsZPr2Zs3ZfJWws2kVw61xb4Qr8bRnaiX2m10rF8y2hbGJS+YHgtfFtOnWEYvuFOMS6Gn9MS07TYcziHjbvT2bg7g+0HsijxHA0ZLGDPoRz2HMph0Q97cdgN2jUP9w3XS4iiTVxouRUT5dRZlkVqVuExw+myOJha+fxNR4S7nHRoGU6HVhF0bBlBy6auaj8vltekZ7voU664q4zdZqNZVAjNokLo3aHsY8zIKSoXSiWl5ZGTX/GKYF7TKq2syoetR7cbQHR4kH84n28ony+kcgXVv8qP5Ix81mhonRynukHxPdf05kByDpk5vsra3IIS3l60me9/TeKGkZ3KhcQiIlK3UjML+HlLCj9tTmZX0glCp4RI+nVuSu8OMYSGOP37DLvttL5ElMZFAdRJCBs0+LQrj842DzzwMOPHX8Nzzz3Nyy+/XmZfTk42kZFR5YbnRUWVzvGTezQ0Cg4Oqdb9BQUFl7l95NqWVfbFyBUcQGZuMV6vyY5tW9i5czs7d27nkksu4Hjffvs1I0aM9N8+Nnw6er+VzyeUleUbfjNu3FUV7k9NTfX/u6rHmZ2dRatWJ14R76OP3uPdd/9DVlYWUVHRdO7chaCgYAoKKh/OUhNKPF7++90uFv+4t0x41LtDDDde3Jlwl7PSc880m82gTVwYbeLCuOzcBEo8Xrbvz2Jj6RxSu5Kyy/xC83gttuzLZMu+TP77f7sIctrp1CqCLglRdI2PpEUTl6ovToJpWuxPyfXP3bRtfxYZOVXP3xQXHeILnEornJpEBJ/Wz70mK+6qYhgGUWFBRIUF0a1NVJl9OfnF/rmlklLzS+eayiOtkjmtLCA1q5DUrELW7Sw7AXqYy+kfwtc85uh8UxFuZ432UZvNwDIMTAySM/LBbseGhWFZpSFy5TS0TqrrZILigKt68MFXW0kt/TJp+4EsnvzPT4zo25LfDmlDcKDegoqI1JXUzAJ+Kh1etyspp8Jj7DaDLvGR9O3clD4dm1Q6nPpMfYkoDZN++zdyMTFNmDTpXp577mnmzp1dZl9oaCgZGemYplkmhEpLK500OiLijLXLVjoXVHp2Id/9bxHBIS6mTp1Wrnz/qaceZ968OWUCqJN1pDJqxow3KgyqjlRpVYfL5SYjI7Pc9h9/XEl8fAJr165hxoy/M3HiZC699HL/z/Dxx//I1q2bT/ERVG1XUjZvfr6xzAfL4EAH11/YgXO7xZ51YU2Aw06XhCi6JPiCgfzCErbszfQHUsdX4xQWe1m7I82/+llYSACd4yN9K+zFRxITEVzuPhqzohIvuw5m+8Om7QeyKKxkONoRdptBQmyoP2xq1zKcsJCaDy3PRMXdyQoNcRIa4qRjq7KvcYXFHv/cUkcqpw6m5ZOSUYBZSd15dl4x2XnFbN5b9nUhONDhq5YqM5wvhJjwYGyVrPRXGcNuI6vAw6yvtrJy/dE3fQMT4xg7ouI3fRpaJ6fq+KAYwwDLKhcU9+oQQ5eESBas2M2ilXvxmhamZbHkp338uOkw1w7vQL/OTdWfRERqSUpmgW8i8c3J7D50gtApIZJ+nZrS+wSh0/Fq80tEqd8UQAmjRv2WpUuX8Nprr2CaR18cevU6hw8+eJdvv/3aPwcUwJdfLiYgIIAuXbpVek2b7fS/FXcHB5CSkcPy77/mnH6D6dSlZ5lyT/DNCfXhh++yd+9uWrdOOKX76dmzDwDZ2dn06tXHv/3bb79m/vy5PPDAI4SHV3Z2WT169GLBgs/Izs4iLMx30qFDSdx//908+ugTrF+/joiICMaNu8F/Tn5+Pr/+ugans+aHr3i8JvOX7WbBij1lPiB3axPFzZd0rjcrzYX8f/buO76pen3g+Cej6Ui6N5RRKClQ9iqjCggoIOh1IirXAYh4RUSu84fXxRWF60BBGS7cAiqgoLgFWYogS6DM0kKhM91tmuT8/ggNDW1pC23atM/79eIFnJycfE95SHKe83yfr5cHPY2h9DSGAmDKL2F/Ujb7j2fzd1JWhdXWcgtL+X1/Gr/vTwMgNMDLscJexzaB9ZI4aczyCs1n+zfZK5yOn86rdrUSL52GmJb+jgqn6BZ+zb7axUundVTqlVdqsZGWXVguKWVPUJ3OKnSaSlpeUYmFo6dyK/RX8NDaG6CfS07ZE1PhQT6VNkBXadTsOpJZoezdalPYtPsUm/ecYvq4nnRvH8yZjHyZWifqRPlEcWioL+npeZUmij09NFx/eXsGxEXw4XeJjpUoTflmFq3ex8bdqdw+wkh4UM2qqIUQQtROWrmkU9IFkk6d2wbRp2MoPTvUPOl0vsZwE1E0fpKAEgA8+ugsJkwYh1LuCqZ//4H06NGLF154jvT0NKKj27FlyyZWr/6cf/7zbnx9fas8nl5vnwL155+/06ZNNHFxXWo9JrVaxf7d2ygoyGPAoCvIyTdj8PZwuls6cuTVfPLJB6xe/SXTps2o9WsAxMR0YPjwq5gz51lOnUrBaOzI0aOHWbLkTWJjOxIREVHjY40bdxvffruWhx6axoQJd6FWq3jnnSW0adOWwYOvwGazsWrVSt54Yz4DBiSQnp7GJ598QFZWJgEBgRc1/qqkpOXz1tq/nRope3poGHdFDIN7tHDru84BBk8GxEUwIC4CRVFIyy6yV0edbWieX+TcwyfdVEy66RQbdtmbxkeFGujc1r7CnrFVAF66pvNWqCgK6aYip+l0F5pSVSbAYK/4Katwigo11LoSp7ny0KppGWqgZahzbxubTSEjt9jRY6psOt+pzMIqVwwstdhITssnOc25AbpapSI00LvcdD4fWof74u/vXWXPBQBFgfmf7eS/9w5i3sc7q5xaKVPrRH2KDNbz71t6sO3vM3z602FyC+yroO47lsWTb//O6P6tuXpAGzy0EndC1KXy07OtNhsatbrG07OF+0rLLuSPA2lsP5BO0pmqk05x0UH0iQ2jpzHELXtWCvfUdK66xCWJiIjk3nv/xSuvzHNsU6vVzJ37KkuXvslHH71PXl4uUVGt+Pe/H+faa6+/4PG8vb255ZbbWbVqJVu2bGbNmvUXNa6ff/wGXz9/unTrjcVqo6CoFEO56pX27WPo0MHIt99+7bQKX23NmvUMy5a9zeefryA9/QxBQcGMGXMNkybdW6vjREREsHDhUt588zVmz/4Pnp6e9O7dj3/9azre3t6MGjWG1NRTrF27hpUrlxMaGsqAAQlcd91NzJ37X06cSKJ16zYXfR5gv+j99vcTrNp41GmVMmOUP3eP6UxYE5uOplKpHE2mh/ZsiU1RSD6Tb19hLymLxGQT5lLnKpSU9HxS0vP57o9kNGoV0S387CvstQmkfUv/SitNGiurzUZKWgGJjobhJnLyzdU+r0WI/mx1k73CKcTfy62Tko2RWq0iLMCbsABvesSEOLYrioIp33xuKl9mAalnp/OVXZifz6YojpUhdx6yT4MeN9zIiTPJF1x1xv568PVvRxkR34blP5zrni5T64QrqVQq+sdF0K19CF9uPMpPO1JQFHul7ppNx9m67wy3XWmka7vghh6qEE3CxUzPFu7rTHaho9Lp/FV8y5Qlnfp2DKNHB0k6iYahUpTqvro2TZmZ+RfM/J8+nURExKUlAkTdMOWXOFbT0WrVtAy59AbTWq0aSxVTY9xBVfF5OquQt7/+myPlpvVoNWpuHNyO4X1bNcslsC1WG0dO5pxNSGVz7FTuBaef6TzUGKMC6NQ2kM5tgmgVbrjkn1vZFJW6UGK2cvTUuel0h0/lUlKD/k3RkX6OZFNMlP9Fl1eL+lVQXEpqhj0pZa+csldNlTVyLm/OfYOYtWhztdMpwR4Ds+8dyGffHZSpdaJOXez7W9LpPN5ff7DCikt9YkO5ZVgHt5kiLlyvLj9Tm6qqpmc7Hj+7Kln39sGShKpGY463miSdtBoVcW2D6HN29TofSTo1ao053mpKrVYRHFz1ireSgKqCJKAaD6tN4WT6uX+vkADvS754bmoJKJui8OOfKXz+yxHM5c4rOtKXiVd3pkVIxZUBm6uiEguHUkz8fdze0Pz8qU7nM3h70LH1uRX2wgJrttJb+bJ3VIDCRZW95xaYnabTnThTff8mb08NMS0DHBVO0ZF+6GRKlVsrKbVyuqxa6ux0vnFXxvLIgt9qfIwljw1DK50YRB27lC/LNpvChl2nWPnLEQrLTUv19NBwbUI0w/tEuVVFqnCNpnCBVp/UahW5xVamv/LLBStkVSqYP2MIfl4amY53AY0t3s5klU2vS+NEFd9htRoVXaKD6dMxlB4xofh4yaQnd9HY4u1iVJeAkmgUjZ5GrcLXR0dOvr0KKie/BL2XVqaLnJVhKuKddfudVtnSqFVckxDN6P6t0ajly3t53p5aurUPoVt7+5Qo+wpl2WcTUlmkm5wrTfKLStl+MJ3tB9MBCPLzpNPZFfY6tQkkwFCxiuRiy94VRSHNVERictl0uhzOZFXfvynQ1/Ns/yZ7hVPLEL30b2piPD00tInwpU3Eud57ikaDRq2qcQWURqMCq1xkiMZDrVYxpGdLehlDWfHzYTbtPQ3YE67Lfz7M5r2pTLgqlg5R9bfqrhBNjaJS8dkPiTWanr38x0Qmju0MkoBq1E6XSzpVdeO0LOnUt2MY3WNCJOkkGi2pgKqCVEA1LmV9bsrCNTTQ+5LmLTeFCqjw8NZs3J3KJz8ecpqCFRVqYNKYTrQOr7pJvKhahulsQ/OzTc1zC0svuH+LEL09IdUmkNjWgej1uhqXvVtKLZw4k+9U4VRVD6DyWobqHc3CO0T5E+LftPp6iZpRadS89dXfbNp9qtp9E7q3YOLYzihu/L4nGqe6vFt78EQ2H36XyMmMAqftCV0juWlo+wor4YrmqSlUCNQnRaPhnjk/1PjmxJuPXoHaZmuWbRpqoqHiLTWz4Oz0unRS0qtKOqnpcrankySdmoam8P4mFVCiSdCo1fjqPcg921w5J9+Mj2fzrYKy2hReXbGbPUczHdtUKhjdvw3XDIrGQytVTxcrJMCbywO8ubx7CxRF4WR6gWOFvYPJJorP67d0KsPeq+fHP1MI8vPk4dv71GhVsuenDuLVT3dWW+Gk1ZT1b7InnNq3lP5Nwk6lKIwbbmTznlPVTrO4eZhRqp9EoxfbOpCn7urL99uTWf3bMccCEr/tSWXnoXRuHNKey7q3kAtlIcqxKQqn0gs4mGziYLKJG4cZa5R8Avv3ycycYp5c4HmUeQAAIABJREFUvIWwQG/CA70JD/QhLMj+e3igNwG+nvJ/zkVSMwsclU4p6QWV7qPVqOnazt7TqUdMCN6ecjkv3ItErHAb/j468gpKURQFc6mVohJLs2ukpygKBcUWcvLNTsmn8CAfJo3pRPsW/g04uqZHpVIRFWYgKszAlX1bYbHaOH46j/3Hs9iflM3hkzlOKw2O6NeGNRuP1qjs/auNRxncK8ppVTIAH08tMWcrm4ytAmgb4StLk4tK2WwKvt5apo/rWW3Fnax2JNyFVqNmVHwb+nUM55MfD7Ej0T79uaDYwrJvD/Lbbvu0PKnyFc2V1WbjxJl8EpNNHDxh4lCKiYLicz3UbriiQ62mZ4N9wZayG2rn02nVZ5NTzomp8CAf/PW6ZnszuK6cyjhb6XQwjZPVJJ3KKp0k6STcmUSvcBsajRqDjwd5Z6comfLNeDejKiir1UZmbjGFxRZs5a40R/RpxfWD2+EpTabrnVajJqalPzEt/Rk7KJqSUiuHUkzsP25fYa+HMZSVPx2q0bG27TvN2Mva8ePvJ+jQyt9R4dQiRC93GkWNKVYb3dsHM3/GEJb/mMiWPed6jg3oGsnNw2SpbeGegv29uP/6ruw6nMFH3yc6VoI8ciqXZ977g2G9o7jusnZyISaaPIvVxrHUXHvCKdnE4ZScCtXY5f2VmE6/uAi27Emt9tjxcRHsO5Z5wX3MFhsp6QWVVuR46jSEB3gTFuTjqJ4KP5uk8vXxaDbf0Wvr5Nmk0/YDaRWmHJfx0Krp2s7eSLx7e0k6iaZDIlm4FX+9jrxCMyhgLrVSbLY2izfkguJSMnOKnfqWhfh7cffoTnRsE9iAI2vePD00dIkOpkt0MACl1OyOI9jL3gN9PZl338D6HKJoBhSrDT8vDRPHdOauMZ3tZU+KgloFKpuCTZJPwo11jwmhU5tA1m5J4pttSVisCooCP2xP4Y8DaYwf1oG+HcPkQlc0GSWlVo6eyuXgiWwSk00cOZVLaTX9+3x9PIhtFYCxVQCdowMZ2K0FW/emVjs9+5YRsfh6augfG8aZ7ELOZBeSllV09s9FnMkqdKquqjBWs5UTafmVrsbm7akhLLBiYio8yKdZthI4mZ5vn153ML3SSjOwJ526tQumT8cwurUPbhbXOKL5kagWbkWrUePrfTYJBZjyS5r0m7PVZiMrt4SCIudG2F46Dc/c3a9Jn7s70mrUtSp799CqQZIDog7Yk9MKKs41sLRvEcL96Tw0XHd5O/rHhfPhd4nsT8oG7P0gF63ex4Zdp7j9ylgignwaeKRC1F5RiYXDJ3McU+qOpeZW+z0i0NfTnnBqHUBsqwAignyckrAqjbpW07N9vLRER/oRHelXYd/8otJKE1NnsosoKqk6OVVUYiXpdB5Jpys2VNZ7ae3JqfOm9IUHejep9hplSac/DqSRmll5z0+dVk3X9vbV67q1D8ZLJ9/tRdMmq+BVQVbBa7xKLTZOZuQ7rqwignzwqmUixh1WwSsqsZCRU4y1XIJCo1ER7O9NbvZJic9GSFYlE41BU1hBRbgPV8eboij8vj+NT388RE65VUO1GhWj4ttw9YA26GRKepPm7u9x+UWlHDo7nS4x2UTSmbxqe0eGBXhjPFvhFNs6gBB/r2qr/lQaNXlFlnqbnq0oCnlFpeUSU4WcKZekKrnANMELMXh7VJKY8iEs0LtBbrzWJt4URXFMr6su6dSt/blKJ0k6iTLu/v4G1a+CJwmoKkgCqnHLyCkiv9BeFeTlqa31Xc/GnICy2RSy8ood51dG7+1BkJ8nGrVa4rORUqtV5BZbmf7KL9WWvc+fMQRfTw3N9C1Y1KOm8OVFuI+GirfCYgurNh7lxx0pTu+3oQFe3DYilm7tg10+JuEa7vYel5NfQmJKjmNKXVWrm5UXGexDbOtAjK38MUYFEOTndVGvrVarUFQqbOBIQDmmZ9ewZcDFUBSF3AKzU7VUWYIqzVToWOGytvz0ugpT+soapHvq6i7xfO7npgIVoIAaBZVS8edWtmKyfXrdBZJOHmq6tQ+xVzq1C67T8Yqmw93e3yojCagqSALKvZVarE4rRUQE+9Tq7kFjTUAVn616spS7I6VWqwj290JfriRZ4rPxUmnU7DqSWW3Ze/f2wdIYWtSLpvDlRbiPho63pNN5fPDdQY6eynXa3tsYyvjhHS76wl00Xg0dc9XJzCl2NAxPTDZxOqvyhEQZFdAqzOCobuoQFYCfXueawTYARVEw5ZvPJqbOTelLyy7iTHaR03fg2gj09SQ80LvC1L6wQO9arSas0qjJLbKw/IdEtu49VznWv0sk44bbK8dsFispZUmnA2lV/hvrPNR0P5t06ipJJ1EDjf39rSYkAVWF5p6A2rFjO8uXf8zevXsoLCwgNDSMAQMGMW7cbURGtmjo4VUqNfUUN910DU8++SxXXTWadFORozeSt6eW8FpUQTW2BJTNppCdX+JY4a+Mj5eWYD8vNBq10/amHp/urr7L3oW4kKbw5UW4j8YQbzZFYeOuU6z85YhTw2RPDw3XJLRlRJ9WaM/7HBXuqzHEXBlFUUgzFXHwhD3ZlJhscqzYWBW1SkXbSF/HlLoOUf5ONxmbM5uikJ1bUkliyv57TRd6KU8FBPl5nk1MOTdFDw3wdnpvqMlNxPtu6E5Ofgkffnug0tdzSjq1D5ZVqkWtNKb3t4slCagqNOcE1DvvLOGdd5YwcOBlXHXVaAIDA0lKOs7KlZ+SmZnBc8+9SN++8Q09zArMZjOJiQeJimpFQEAA5lKr0yoSkSH6Gr/JN6YEVLH5bNWTxbnqKcjPC72XttI5/k05PpuK8mXvFVYlq8eydyGawpcX4T4aU7zlFppZ+fMRfjtv+fmWIXomXBWLsVVAA41M1KWGjDlFUTiVUeCocDqYbCIn33zB52g1KtpF+mFsHUhsqwDat/STnj8XwWqzkZlbQlpWYYWpfRmmYmwXcUmrUkGwnxfhQT60i/RjcO9WPLxgY7VtFJ6bMpCXPvqT7LwSwJ7s7h4TTJ9YSTqJS9OYPlMvliSgqtBcE1AbNvzCE0/8m8mTp3LHHROdHisqKuLhh6dz9OgR3n//M0JCQhpolDWXll1EYbG9Csrn7IoaNdEYElA2RSEnv6TCFxdvTy3B/l4XvFvbVOOzqWoKHybCfUi8CVdqjPGWmGzig+8OOk3VBxjUJYKbrojBz6fpTm9qDlwZczabQnJavmM6XWKyifzzViY+n85DTUxLf/uUulYBtGvhV6spYKL2LFYbmTnFFRqhn8kqJDO3uNom7wDjhhs5cSaPLeclsCszsGskbSL9OJKc7ZheJ4sfiLrQGD9Ta6u6BJSk35uZ9957i7ZtoysknwC8vb159NFZjB9/PZ9//hlTpvyLgoJ8lix5g19++YmCgnzatYth8uSpThVSa9Z8yfLlH3PyZAohIaFcc8313H77HU6VO6tWfc6aNV9y4sRxbDaFtm3b8s9/3s2QIcMAWLfuK/73vznMn/8mr732MkeOHCIwMIgbb7yF8eNvBypOwQM4fngP77z7NkePHKSkuJiwsDBGjx7LHXdMRK1uvOX2JaVWMkzFlFrOrRCiUqkI8vPE4O1R7comQgghhKicsVUAT93Zlx+2p7D6t2OUlNo/azftPc1fhzO4YXB7Lu/RArV81orzWKw2kk7nORJOh1JMFJVceDU3b08NHaLsySZjqwDaRPjKlE8X02rU9ul1QT7Q3vkxi9VGuqmoQmIqLbuQrNySskW16WEMZeVPh2r0etv2nebusXGM6NWybk9EiGZAElC1cHDPaQ7sPt3Qw6Bjtwhiu0bU+nkmk4nExAPceuuEKvdp1ao1HToY2bx5I5Mm3cuMGfdz8mQykyZNJSqqFWvWfMkjjzzI4sXvYjR25IMP3mXJkje4+ebxxMcPZP/+fbz99iJMpmymTZsBwIoVn7JgwStMnDiFLl26kZeXy4cfLuPpp/+PFSu6EhoaBoDFYuGpp55g/PgJtG8/ja++WsXCha/SoYORPn36VRjrwYMHePjf0xh02TCuvuY/9qWZN//E228vpnXrtgwbNqLWP6P6pigKOQVmTPklUO5ujJdOQ7C/Nx5a+cIihBBCXCqtRs3I+Nb06xTGpz8eYvvBdAAKii28v/4gG3en8s+rYmkT4dvAIxUNqdRi5eipXPt0uhMmjpzKqXaFNoO3h6N/U2yrAFqFGVCrJZnZWGk1aiKD9UQG6ys8VmqxOpqf+3h51LjHlNWmYFMU5Fu7ELUnCahm5PTpUwBERFy4yXjLllH88cc2tm7dzN9/72Xu3FcZODABgJ49e3PPPXewY8d2WrSI4r333uL6629i2rSHAOjXrz/e3j4sXPgqN900noiICFJTT3Lrrf/kn/+82/EaEREtmDjxdvbs2c0VVwwHwGazMXHiFK6++hoAunTpxq+//szmzRsrTUAdPXqY+PgB/OfJZx2rT3Tp1ps/ft/EX3/taHQJKLPFXvVkLnWuegr09cTXR6qehBBCiLoW5OfFfdd1ZfeRTD76/iDpJnuD6GOpuTy77A+u6BXFdZe1w8dLvhI3Zuf6KqpIyy4EjQY1Ciqldn0Vi80WDp/MsU+nO2HiaGouFuuFn+9v0BHb6lyFU2SIXqrnmggPrYaWoQZahhpQNBo0alWNklAatQqNWgXVxI4QoiL5tK2F2K4XV3nUWJS1+9JqL/zPrtFoUBTYvfsvdDodAwYMcjym1Wp5552PANi6dTMlJSUkJFyOxXJu1ZlBgy7jtddeYseOPxg9eiwPPDATgLy8PJKSjnPyZDI7dmwHwGJxnkfftWt3x591Oh0BAQEUFVW+msioUWMYNWoMJSUlnD6VRHJyEsePHcZqtVY4bkNSFIXcQjOmPDPlW655emgIDvBCJ30BhBBCiHrVrX0wHVvHs25rEuu2JmGxKigK/PhnCtsPpDFuWAzxncLlZlAjpNKoySmysPyHRLbuPbeybP8ukYwbfuGVZQuLS0lMySHxhL1heNLpvGqbVYf4ezmqm4ytAwgL8Ja4aAbUKPTvEsmm3aeq3XdA10jUKqfJDEKIGpIEVDMSEREJ2HspXcipUycJDw8nNzeHgIDAKj90c3NzAJgx4/5KH8/IsJe7nzyZwty5z/Pnn7/j4eFB69ZtiYnpAMD5PfC9vLyc/q5Wq1GUyr9UlJQU88or81i/fh0Wi4XQsAg6GONQazRYG8kS96UWGxk5RZSYy/UPUEGAwRN/vU6+0AghhBAuovPQ8I/L2jEgLoIPvzvIvuPZAOQUmFmy5m827krl9iuNlU7VEQ1DpVGz60gm8z/b6dRI2mpT2LT7FJv3nGL6uJ50bx+MYrWRW2B2NAtPTDaRnJZfbZIgIsjnXMKpVQDB/l7VPEM0RSpFYdxwI5v3nKp2Fbybhxml+kmIiyQJqGYkMDCIuLiubNjwM5MnT620SXdq6ikSEw8yfvwELBYLJpOpwj779+/Dw0OHXm/vbv/MM8/TsmVUhf1CQkKx2Ww8/PB0dDpP3nrrfWJijGi1Wo4dO8r69esu6XxeffUlfvnlJ5577gV69+5HTqGNYrOV+yZej9ly4YaR9U1RFPKKSsnOLXFKsuk8NIT4e8lKGUIIIUQDCQ/y4aFxPfjjQBqf/HjIsRrt/qRs/vP274zq35qrB7SVpdQbmFqtIrfIUiH5VJ6iwPzPdvLCvxJ4d81eDpyo+L31fFGhemJbBWJsHYAxyh9/g2cdj1y4I5tNwddby/RxPauMOZUKpo/recGqOyHEhUnvtGbmrrsmk5R0nLfeWlThsZKSEl544Tm8vX247rob6datB2ZzCX/8sdWxj9Vq5ZlnnmTFik+Ii+uKh4cHmZkZdOzY2fHLYrGwePFCMjIyyMkxceJEEmPH/oOOHTs7pv9t3boZsPd9ulh79vxFnz79SEgYjLe3N/4GT44dSSQ314S51IqlgT4YLFYbZ7KLyMopdko++Rs8iQz2keSTEEII0cBUKhX9OoXz/OT+jOjTirKCZKtN4evNSTz51jZ2Hc5o2EE2c4pKxWc/JF6wGgXsSajVvx6hmzGswmMqFbSN8OXKvq2YdkNXXpt+Gc9OjOe2K4307RgmySfhRLHa6N4+mPkzhpDQvYW9zxP2nk8J3Vswf8YQR7WdEOLiSAVUM9O//0CmTp3GokULOHw4kVGjxhAUFExychLLl3/CmTOneeaZOYSHRxASEkqnTnE899xTTJ48lYiICL76ajUZGWmMG3crAQEB3HLL7SxevJD8/Hy6d+/J6dOpLFnyBgaDgejodnh6ehIZ2YKVKz8lJCQUvV7P779vYfnyTwAoLq68v1NNdOoUx88//8Dq1V/QunUbDh9O5N333kalUlFSXExOgZlgP9eVUSuKQkFRKZl5JSjlGhh6aNWE+HvjqZPEkxBCCNGYeHtqGT+8A4O6RvDBdwc5cjIXgIycYuav3E3PDiHcOtwo07JcrMRsRe2hYuve1Brtv23facZe1o7PfzpEdAs/x3S6mJb+eHvK5Y6oOcVqw89Lw8QxnblrTGd7FlNRUKtAZVOwSfJJiEsi78jN0G233UHXrt1Zvvxj5s9/idzcHEJDwxgwYBA333wrLVq0BOzNyF9+eQFvvvkaixcvpKSkmNjYTrz66hu0axcDwD333EdwcDBffrmSDz54Fz8/f+LjBzBlyr/w9LTfVXr++f8xf/7/mD37KXQ6D9q2bceLL77Ca6+9xK5dO7nuuhsv6jymTZuBxWJhyZKFmM2ltGjRgttuv4sDBw+xa+c2cvNL8Nfr0Grqv9DPYrWRmVtMUbHFabufXkeAr6esliKEEEI0Yq3DfXn89t78tjuVFT8fpuDs5/nOQxnsO57FNYOiubJvK5d8p2hOFEUhM6eY5LR8ktPzSUnLJzktn7TsIp6/b1CNViQDe+Wan17HghmXy9RJccnsKysqqIDQUF/S0/OwbxFCXCqVcn4X6GYiMzP/gsu2nj6dREREGxeOSNQFRVFIzSzEXGrvAeWn1xFUSRWUVqvGYqmbOxgFRaVk5hY7xZNWoyYkwAsvXf3keCU+3UvZlxchXEHiTbhSU4y3vEIzK385wsbdztU3LUL0TLjSSGzrwAYamXsrNltISS+wJ5nS7Ymmk+n5FJVU3rdzzn2DmLVoc42SUBq1iiWPD0Ml1SmijjXF9zjReDWFeFOrVQQHG6p8XCqgRJOiUqkIMOhIyy4CIK+wFH+9Dk093LG02mxk5ZZQUFTqtN3XR0egrydqtVQ9CSGEEO7G10fHXaM7kdAtkg/WHyQlvQCAUxkFvPjxTgZ2ieDmoTH46XUNPNLGyaYoZJiKSE4rIOVsoiklLZ80U1GNj6FSwaHkbOLjIti8p/ppeAO6RqJWSYWKEEI0dpKAEk2Ot6cWD62aUosNRVHILTQT6Fu3vRsKi0vJzCl2uiun0agJ8feSXgNCCCFEE9AhKoCn7urLj9tT+PK3Y5SY7ZU6m/ee5q9DGdwwuB2De7Rs1jecikosTkmm5PR8UtILHD+rmtB7aWkVZiAqzECrUPvvLUP0eHlq6dEhjC17Uy/YiFylgpuHGcEq6SchhGjs5EpZNDn2KihP0s/eacstLMVPr0OjvvQqKJtNISuvmPxC56onvbcHQX6edfIaQgghhGgcNGo1V/ZrTZ+OYXz602G2H0gDoLDEwgffJfLbnlQmXBVL2wi/Bh5p/bLZFNJNRfZeTWn5jqRTRk7NF5NRq1REBvsQFWYgKlRPqzADrcJ8CTDoUFXSK9NmU/D11jJ9XE/mf7az0iSUSgXTx/XE11srK5MJIYQbkASUaJJ8vLSOPk+KTSGvsJSAS1xqt6jEQkZOMdZyX3DUahUh/l74eHlc6pCFEEII0UgF+Xlx3z+6sPdoJh9+n+iY6n8sNY/n3tvO0F4tuf7ydk3i+0BhcenZJFOBI+F0MiMfc2nNEzwGb4+zCSb7r6hQAy1CfPDQ1q5BuGK10b19MPNnDGH5j4ls2ZOK1aagUasY0DWSm4cZJfkkhBBuRJqQV0GaPLu//CIzGSb7nTm1WkVUqMFRJl+bJuQ2m0J2Xgl5hWan7T5eHgT7edZLf6nqSHy6l6bQUFC4D4k34UrNMd5KLVbWbT3B2i1JWMolPvz0OsZdEUP/zuGVVvQ0NjabwpnswnNVTWcrmzJzS2p8DI36XFVTq3JT6Pz1lVc1XSy1WoWiUmEDe9mToqBWgcqmXPD7vBCXqjm+x4mG0xTiTZqQi2ZL7+WBSWPGYrVhsynkFZrxr2UVVLHZXvVUPlmlVqsI9vNC7+3+dzmFEEIIUTseWg3XJkTTPy6cj75LZO+xLAByC8ws/epvNu46xe1XxtIiRN/AIz0nv6jUuU9TWj4nMwoorcWKwH56Xbkkk/5sVZMerQtuxNmTTAoqzl2g2bcIIYRwJ5KAEk2WSqXC36Aj82x/gpwCM74+uho1C7UpCqb8EnLznauevD21BPt7ueTLlhBCCCEar/BAH2bc3J0/D6bzyY+HyM6zVw4dOGHiqXd+Z2R8a8YMbIunR+2mnV0Kq83G6czCs0mmAke/prKx1YRWo6JFsP5sryYDrcLtv/vLqn9CCCEukSSgRJNm8PbAlG/GerYKKr+otNplk0tKrWSYipzuCqpUKoL8PDF4e7hFWb0QQggh6p9KpaJPxzDiooNYs+kY3/+Rgk1RsNoU1m5JYuu+M9w2wkiPDiFA+alkKqw2Gxq1GjUKKqX2U8lyC832aXNnp9Alp+dzKqPQaVpgdQIMOsfqc2Ur0UUE+ciNNiGEEPVCElCiSVOpVPjrdWTlllVBlWDwqXzqnKIo5OSbMRWUONV0e+m0hPh7odXKlzEhhBBCVOTtqWXcFR0Y2CWSD9Yf5PDJHAAyc4t57fPd9IgJ4Z5ru1CqwPIfEtm691wz7f5dIhk3vOpm2hbruaqm8tPocs6r0r4QrUZNyxC9I8nUKtRe4eTrI1VNQgghXEcSUMJlFEVpkOohg48HOQUlgAo/vQ4FHNVNZaMxl1rJyCnGXGp1PE+lUhHo64mvj1Q9CSGEEKJ6rcIMPHZ7LzbtSWXFz0fILyoFIDrKn7+OZrJ01R7KL/9jtSls2n2KzXtOMX1cTzq2DiAxKcsxfS45LZ/UzAKstaiOCvT1dFp9rlWYgfAgbzRquZEmhBCiYUkCqhk6cuQw77//Njt37iA3Nwd/f3+6d+/FhAl30aGDsc5fLz09jXnznmfGjEeIjGwBwI03jqVPn3489tiTF33ciRMncPDgfl566XXi4wdUuZ9apSIs0AeVSkVOfgnJZ4rtFU4q0Ht7EGjwpLDE4pR88tRpCPH3qtVywevWfcXzzz/DF1+sJSws/KLPSwghhBDuS61ScVm3FvTsEMrKX46w52gmvTuG8+TizVS19rSiwPzPdvLclIG8u+5AjXo26bRqWobqHUmmVmEGWoYaMMgiKUIIIRopSUA1M4cPH2Lq1Lvp2rU7M2Y8TEBAIOnpaaxY8QlTptzFa68tokuXrnX6mjt2bGfz5t+YMeOROjvmkSOHOXhwP+3atWf16i8umIAC+x3GtKwC540KFBSWUlBYSkigNwG+npjySwg0eOJ3EcsHDxiQwKJF7xIYGFTb0xFCCCFEE2Pw9uDOUR3JKSpl+U+Hq0w+lVEUWLfpGCPi27D8h0Snx4L9vM5NnwszEBWqJzzQp0YLqwghhBCNhSSgmpnlyz8mMDCIefPmo9Gcq+5JSBjMbbfdyLJlbzFv3vwGHGHNrFu3hrZt23HTTeP53//mkJGRQUhISKX72pNPhRc8XkZ2ES3DDOi9tLWqeiovMDCQwMDAi3quEEIIIZomP4MXv+87XaN9t+07zXVDYziTke+obIoK1ePjJVVNQggh3J8koJqZ7OwsFMXej6k8Hx8fHnjgIYqLi522f/vtWpYv/5gTJ5Lw9fVj2LArmTz5Xjw9vQC4//570Gi0zJ//huM5O3Zs54EH7mXhwrc4eTKZ559/BoCbbrqGUaPG8H//9zQApaWlvP76K3z33TcUFRXSrVsPZs58jJYtoy54DhaLhfXrv2HUqDEMGTKMV16Zx9dfr+LOOyc57ZeQ0IeHH36CbX/8we9bf8PDQ8eVo67jqqtv4MP3FvLH1o3odDouG3IV426bjCm/hFB/b3JyTCxatICNG3+lsLCQ2NiOTJ06jW7dejgde9Kke9mw4ReOHz/G3XdPJigouMIUvC1bfmPZsnc4fDgRg8GXIUOu4J57/oWPj4/jZ/XBB++yf//fFBcXERoazujRY7jjjomopVeDEEII4fasNluNezhZbQr+eh13jOxYz6MSQgghXE+ucJuZ/v0Hkpp6knvvvZsvvlhBUtJxx2NDhw5n1Kgxjr+//fZi/vvfp+nRoxfPP/8/xo27ldWrv+CRRx6qkMCqyoABCdx99z0A/Pe/85ySRN9//y0nThxn1qxnmDnzMfbv/5tnnplV7TE3b/4Nkymbq64aja+vL5dddjlff70am63iyjELF87HW+/LjEeeo0fv/ny+/D2eevw+PHWeTH/4GfrEX8bXqz/lj20bKSgqpbikhOnT72Pz5t+4995/MXv2i/j6+vHgg/exf/8+p2O///47jBw5mmefncNllw2p8NqbNm3kkUdmEBISynPPvcDkyVNZv/4b5sx5FoCDBw8wY8a/CAoK4rnn5vDii6/QrVt33n57MT///GONfr5CCCGEaNw0ajWaGk6V06hVNd5XCCGEcDdSAVULx/7+nWN7tzb0MIju0p/ozv0u6rnXX38zmZmZfPrpR7z88osABAQEEh/fnxtvvIVOneIAyM3N4aOPlnHddTfywAMzAejXrz+hoeE89dTjbNmyiYEDE6p9vcDAQEdFk9EY62hCDhAeHsGcOS+h1drDMCUlmWXL3qa4uBgvL68qj7lu3RqMxlhiYjoAMHr0Nfz44/ds27aZAQOcx2Q0xjLhzvsBaN22PRt/WY8AvBVAAAAahklEQVSffwB3TJoOQFyXnmze+COHE/fRr//lrF+/jiNHDrF06TI6duwM2JN2kyffweLFC3n11XOVXj169Obmm291/H3fvj1Or/3OO0vo2LETs2e/6NimKAqffvoRhYUFHD16mPj4Acya9ayj31TfvvFs2rSBv/7awbBhI6r9+QohhBCicVOj0L9LJJt2n6p23wFdI1Gr7GulCCGEEE2NVEA1MyqVinvuuY9Vq77hqadmM2bMtfj4+LB+/Tfcc8+dfP75cgD27duL2Wxm+PCrnJ4/dOgwPDw82Lnzz0seS+fOXRzJJ8CRnMrPz6vyOVlZmWzZsomhQ4eTl5dHXl4enTp1JigomNWrv6j0NTh7I9HX1x+1Wk37mE6Ox1UqFXqDgYKCAlDBn3/+TmhoGDExRiwWCxaLBZvNxsCBCezatZPS0lLHcy+0YmBJSTGJiQe4/PKhTtvHjLmWDz9cjo+PnlGjxjB37quYzWYOHz7Er7/+xNtvL8ZqtWKxlFZxZCGEEEK4E5WiMG64kerWNlGp4OZhRrBK+kkIIUTTJBVQtRDdud9FVx41Nn5+fowYMZIRI0YCkJh4gOee+w8LF85nxIiR5OXlAhAc7NzYW61WExAQSH5+/iWPwdvbu8KxgUqn0pVZv/4brFYrixcvZPHihU6PbdmyifT0NEJDwxzbfHx80Ht7UFB4LqHjWUV1lcHbg9ycHNLSzjBkSP9K98nJMRESElrp+MvLzc1FUZQLNiUvKSnmlVfmsX79OiwWC5GRLejatRsajbbGUxyFEEII0bjZbAq+3lqmj+vJ/M92VroankoF08f1xNdbi2Kt+nuQEEII4c4kAdWMpKWdYfLkO5g8+V7GjPmH02NGY0cmT76PJ574N6mpp/D19QUgMzPDqSm4zWYjOzuLgIAAwF5BZLNZnY5VVFRUb+ewbt0aunfvycSJU5y2Z2dn89RTj/PVV6scPafKBPl6OSWgqhLo64XBYKBt22hmzXqm0n38/QNqNE693nB2XCan7YWFBezZs5u4uK4sXDifX375ieeee4Hevfs5ElpjxsjUOyGEEKIpUaw2urcPZv6MISz/MZEte1Kx2hQ0ahUDukZy8zCjJJ+EEEI0eTIFrxkJDg5Bo9HwxRcrKCkpqfD4iRPH8fLyomXLKOLiuqLT6fjhh/VO+/z8849YLBa6desOgF6vJy3tjNM+u3f/5fT3ulrNbf/+fRw7dpTRo8fSq1cfp1/Dho2gY8fOfP31aqxW54SYRq0iLMjngsf29tSgUavo0aMXp0+nEhISSseOnR2/Nm78lRUrPnWaMnghPj4+xMQY2bx5g9P2DRt+YebMaeTn57Nnz1/06dOPhITBjuTTgQP7MZmyL1gFJoQQQgj3o1ht+HlpmDimM0seH+b4NXFsZ/y8NJJ8EkII0eQ1mgTU/fffz4gRF678KCgo4JlnnmHQoEH07NmTyZMnc/z4cdcMsAnQaDQ89NAjHDlymEmTJrBq1Up27vyTLVs28dprL7F06ZvcffcUDAYDfn7+jB8/gS+/XMlrr73EH39sZfnyj5k7dzY9evQiPn4gAAMHXsbJkym8/vor7Nixnffee4tvv13r9LoGg72a6tdff3Jada+21q79Cp1Ox+DBV1T6+MiRo0lLO8OWLZsqPObjqaVVuC+OhlDY/2jw8UCrUaPV2P8rjB59DSEhYTz44H2sX7+OHTu28/rrr7Bs2du0aNHS0Sy8JiZNmsLevXt49tkn2bZtC19/vYoFC15h5MiriYiIoFOnOLZu3cTq1V+wc+efrFjxKf/+9wOoVCqKi4tr9bMRQgghRONnsykoVhsqqw2toqCy2lAsNmw2mXovhBCi6WsUU/BWr17N999/T+vWrS+434wZM9izZw+PPPIIer2eBQsW8M9//pO1a9c6poyJC0tIGMzixe/xySfv8/7775KdnYVOp8No7Mizz77A4MHnmmZPnjyVoKAgPv98OV9+uZLAwCCuvfZ67r57iqOq6eqrr+HkyRS++eZrVq1aSY8evZk9+0WmTp3oOE7Pnr3p2zeexYsXsmPHn8yd+0qtx11SUsIPP6wnPn4ABoOh0n2GDx/JggWvsmbNFyQkXF7hcY1ahUoFAQZP2kT6AU7pKMBeufTGG0tZtGgBr7/+CoWFhbRo0ZIZMx7mhhvG1WrMCQmDmTPnJd59dymPPz6TgIBAxo69jjvvnATAtGkzsFgsLFmyELO5lBYtWnDHHRM5duwIW7duxmaz1Vn1mBBCCCGEEEII0ZBUSgN3Oz5z5gxjx47F29sbnU7H999/X+l+27dv57bbbmPp0qVcfrk9uZCVlcWwYcOYOnUq99xzT6XPq0pmZv4F7zadPp1ERESbWh1TuA+tVo3F4r6l7hKf7iU01Jf09KpXdxSiLkm8CVeSeBOuJjEnXEniTbhSU4g3tVpFcHDlBSPQCKbgzZo1i0GDBjFgwIAL7rdp0yb0ej2DBg1ybAsKCqJv375s2LDhAs8UQgghhBBCCCGEEA2pQRNQK1asYN++fTz55JPV7nv06FHatGmDRqNx2t66dWuOHTtWX0MUQgghhBBCCCGEEJeowXpAnTx5kjlz5jBnzhyCgoKq3T8/P7/S3j96vZ78/Pxav/6FysIA0tLUaLUNXiAm6pE7//uq1WpCQ6XvmTuRfy/hShJvwpUk3oSrScwJV5J4E67U1OOtQRJQiqLwxBNPMHjwYK666qoaP6cqF9OouboeUDabza17BIkLc/ceUDabze3nBzcnTWE+t3AfEm/ClSTehKtJzAlXkngTrtQU4q26HlANkoD66KOPOHjwIF999RUWiwU4l2CyWCxoNJoKy90bDAZSUlIqHKugoKDKVdGEEEIIIYQQQgghRMNrkATU+vXryc7OJiEhocJjcXFxzJkzh+uvv95pe3R0NFu2bEFRFKfkVFJSEtHR0fUyzvNfS4jGoIEXrhRCCCGEEEIIIWqtQRJQzzzzDAUFBU7bFi5cyP79+1mwYAFRUVEVnpOQkMCiRYvYvHmzYyW8rKwstm/fzpQpU+p8jBqNltJSMzqdZ50fW4hLUVpqRqNpsPZtQgghhBBCCCFErTXIVWy7du0qbAsICECn09G1a1fAnlw6ceIEMTExGAwG+vbtS79+/XjooYf497//TUBAAK+//jq+vr6MHz++zsdoMARgMqUTEBCKh4dOKqFEg1MUhdJSMyZTOr6+gQ09HCGEEEIIIYQQosYabRnFL7/8wuOPP877779PfHw8AAsWLOCFF15g7ty52Gw2evfuzauvvoq/v3+dv763tx6AnJwMrFZLnR9fNCy1Wo3N5n5NyDUaLb6+gY74FEIIIYQQQggh3IFKaaYNZapbBU80bU1hhQHhPiTehCtJvAlXkngTriYxJ1xJ4k24UlOIt+pWwVO7cCxCCCGEEEIIIYQQohmSBJQQQgghhBBCCCGEqFeSgBJCCCGEEEIIIYQQ9UoSUEIIIYQQQgghhBCiXkkCSgghhBBCCCGEEELUK21DD6ChqNWqhh6CaGASA8KVJN6EK0m8CVeSeBOuJjEnXEniTbiSu8dbdeNXKYqiuGgsQgghhBBCCCGEEKIZkil4QgghhBBCCCGEEKJeSQJKCCGEEEIIIYQQQtQrSUAJIYQQQgghhBBCiHolCSghhBBCCCGEEEIIUa8kASWEEEIIIYQQQggh6pUkoIQQQgghhBBCCCFEvZIElBBCCCGEEEIIIYSoV5KAEkIIIYQQQgghhBD1ShJQQgghhBBCCCGEEKJeSQJKuB2bzcYnn3zC2LFj6dmzJ8OHD2fOnDnk5+c79tmzZw8TJkygZ8+eJCQk8PLLL1NaWup0nOPHj3PvvffSp08f4uPjeeqpp5yOAVBcXMyLL77IZZddRo8ePbjpppvYtGmTS85TNB6ujLnk5GTuv/9+4uPjGThwIDNnziQtLc0l5ykah7qKtzIWi4Vx48bxxhtvVHgsIyODmTNnEh8fT+/evXnooYdIT0+vt3MTjY8r4y0pKYnp06eTkJBA7969GT9+PFu3bq23cxONjyvjrby9e/cSFxfH6tWr6/R8ROPmyniTawbhynhz5+sFSUAJt/PWW2/x3HPPMWTIEBYuXMhdd93FqlWrmD59OmD/gnvnnXfi6enJq6++yt133827777LnDlzHMfIycnhjjvuICMjgxdffJGZM2eybt06Zs6c6fRaDz74IJ9//jkPPPAACxYsIDQ0lHvvvZcDBw649JxFw3JVzBUVFTFx4kQOHTrEs88+y3/+8x92797NPffcU+WHk2h66iLeypjNZh599FH++uuvCo9ZLBYmTpzI7t27efrpp3n66afZsWMHkyZNwmKx1Pt5isbBVfFmMpmYMGECR48e5YknnuCVV14hODiYu+66i+3bt9f7eYrGwVXxdv5+jz32mLyvNUOujDe5ZhCuije3v15QhHAjNptN6du3r/L00087bV+7dq1iNBqVv//+W3niiSeUwYMHKyUlJY7HP/roI6VTp07K6dOnFUVRlIULFyo9evRQsrKyHPv88ssvitFoVP766y9FURRly5YtitFoVDZv3uzYp6SkRBk5cqSydOnS+jxN0Yi4MuY2btyoGI1G5ffff3fss3nzZsVoNCrbtm2rz9MUjURdxZuiKMquXbuU6667TunXr59iNBqVhQsXOh1z1apVitFoVA4fPuzYdujQISU2NlZZu3ZtPZ2haExcGW/vvvuuEhcX5/Qci8WiXH311crUqVPr6QxFY+LKeCvvxRdfVC6//HLFaDQqq1atqvsTE42SK+NNrhmEK+PN3a8XpAJKuJWCggKuueYaxowZ47S9Xbt2AJw4cYJNmzYxdOhQdDqd4/GRI0ditVr57bffANi0aRN9+/YlMDDQsU9CQgJ6vZ5ff/0VgO+//57o6GgGDBjg2Een0/HNN98wadKkejtH0bi4MuZKSkoA0Ov1jn3K9jeZTPVwdqKxqat4A/vd2KCgIFauXFnpa23atImYmBjat2/v2Fb297KYFE2bK+MtIiKCO++8k/DwcMc2jUZDmzZtSE5OrsvTEo2UK+OtzI4dO/jwww/5z3/+U4dnItyBK+NNrhmEK+PN3a8XtA09ACFqw2AwMGvWrArbf/jhBwDat29Pamoq0dHRTo8HBQVhMBg4duwYAEePHuWaa65x2kej0RAVFeXY5+DBgxiNRr788ksWLVpEcnIyMTExPP74404fMKJpc2XMJSQk0L59e/73v/8xe/ZsVCoV8+bNIzQ0lIEDB9bH6YlGpq7iDWDRokUYjcYqX+vo0aMVjgPQunVrp+OIpsuV8TZy5EhGjhzptC0nJ4c//viDQYMGXcppCDfhyngD+zSVxx9/nClTphAbG1sHZyDciSvjTa4ZhCvjzd2vFyQBJdzerl27WLJkCcOHD8fPzw+wvwmcT6/XO5rA5eXlVbtPVlYWx48fZ+/evcyYMQN/f3/eeecdJk+ezJo1axwZbdH81FfMeXp6Mnv2bKZOncrQoUMB8PPzY9myZZU+VzQPFxNvQLUXZ3l5ecTExFR6nKSkpEsctXBX9RVv57PZbDz55JMUFBQwceLESxu0cFv1GW8vvfQSPj4+TJkyhdOnT9fdoIXbqq94k2sGUZn6ijd3v16QKXjCrf35559MmjSJqKgoZs+ejaIoF9xfra4+5Mv2KS0tJSMjgzfffJOxY8dy+eWX8+abbxIQEMDixYvrZPzC/dRnzG3bto077riDLl26sGTJEhYtWkSnTp2YNGkSR44cqZPxC/dSH/FWE3V1HOFeXBVvpaWlPPzww6xfv54nnniCLl26XNRxhHurz3jbtm0bn332GXPmzEGrlfvton7jTa4ZxPnq+/3Nna8X5BumcFvr1q3jrrvuIjIykvfee4/AwEBH1regoKDC/vn5+fj6+gL27HNV+5QdQ6/XExER4VS27e3tTa9evTh48GB9nJJo5Oo75hYvXkyLFi148803GTx4MEOHDmXp0qV4e3szf/78ejwz0RhdSrzVRE1iUjQf9R1vZXJzc5k4cSLr1q3jySef5LbbbrvksQv3U5/xVlBQwOOPP87kyZOJiYnBYrFgs9kAUBQFq9Vadyci3EJ9v7/JNYMor77jzd2vFyQBJdzSu+++y0MPPUSPHj346KOPCAsLA+wfAOHh4RWmj2RmZlJQUOCYdxsdHV1hH6vVSkpKimOfNm3aYDabK7x2aWkpKpWqPk5LNGKuiLmTJ0/SpUsXp+aEnp6edOvWjUOHDtXn6YlG5lLjrSYqi0mwN8qszXGE+3NFvAGcOXOG8ePHs3PnTl5++WVuv/32OjsH4T7qO9727t3LyZMnWbhwIXFxccTFxTFixAgAHn30UcefRfPgivc3uWYQZVwRb+5+vSAJKOF2VqxYwQsvvMCoUaN46623KmSMBw0axM8//+z0QbB+/Xo0Gg39+vVz7LNt2zanlQJ+++03CgsLHc3bLrvsMjIzM9m6datjn7y8PHbs2EHv3r3r8xRFI+OqmIuOjmb37t2UlpY69jGbzezbt4+WLVvW5ymKRqQu4q0mEhISOHToEEePHnVsO3z4MEeOHHGLJpaibrgq3goKCrjrrrs4ffo07777LqNGjaqzcxDuwxXxFhcXx8qVK51+vfnmmwA88MADjj+Lps9V729yzSDAdfHm7tcLKqW6CYlCNCKZmZkMGzaMoKAg5s6dW2Fef+vWrcnOzua6666jV69e3HHHHRw/fpyXX36ZG264gaeffhqwNwscPXo0ERER/Otf/8JkMjFv3jy6d+/O0qVLAft/5Jtvvpn09HRmzpxJUFAQixYt4vDhw3z11VdERka6+vRFA3BlzO3evZtbb72V+Ph4JkyYgM1m48MPP+T3339n2bJl8iWmGaireDtfbGws06dP57777nNsM5vNXHPNNZjNZh566CHA3rTXYDDw5ZdfSt+UZsCV8fbiiy/yzjvvMG3aNBISEpz29/T0pFOnTnV+fqJxcWW8nS8lJYVhw4Yxd+5crr322ro8LdFIufrzVK4ZmjdXxpu7Xy9IAkq4lVWrVvHoo49W+XjZF4vt27czd+5c9u/fT2BgIP/4xz+YNm0aHh4ejn0TExN5/vnn2blzJ3q9nuHDh/PII4849T4xmUy89NJLfP/99xQXF9OzZ08ee+wxWc63GXF1zO3cuZNXX32VXbt24enpSZcuXZgxY4Y06W0m6jLeyqvqAi01NZX//ve/bNq0CZ1Ox6BBg3jsscccJeOiaXNlvA0bNoyUlJRK92/dujXff//9pZ2MaPRc/f5WniSgmh9Xx5tcMzRvro43d75ekASUEEIIIYQQQgghhKhX0gNKCCGEEEIIIYQQQtQrSUAJIYQQQgghhBBCiHolCSghhBBCCCGEEEIIUa8kASWEEEIIIYQQQggh6pUkoIQQQgghhBBCCCFEvZIElBBCCCGEEEIIIYSoV5KAEkIIIYSoYw8++CCxsbF8/PHHVe7z2WefERsby+zZs104MiGEEEKIhqFSFEVp6EEIIYQQQjQlZ86cYfTo0Wg0Gr799luCgoKcHs/KymLUqFF4e3uzdu1a9Hp9A41UCCGEEMI1pAJKCCGEEKKOhYeHM336dHJycpg3b16Fx+fOnYvJZOLpp5+W5JMQQgghmgVJQAkhhBBC1IPbbruNuLg4vvzyS3bs2OHYvn37dr788ktGjx7NkCFDGm6AQgghhBAuJFPwhBBCCCHqye7duxk3bhyxsbF88cUXKIrCP/7xD9LS0li7di0hISGOfbdv387rr7/O7t27AejVqxcPPvggXbt2deyjKAoff/wxX3zxBUePHsVisdCyZUtuvPFGJk6ciEqlAuDyyy9n6NChlJSUsHbtWoKCgli9ejUBAQGu/QEIIYQQQpylbegBCCGEEEI0Vd26deOWW27h448/ZtWqVRQVFZGYmMjzzz/vlHzasGED9913H506dWL69OmYzWY+//xzbrvtNpYtW0bPnj0BeOmll1i6dCnXX38948aNo6CggC+++IJ58+bh6+vLuHHjHMdcs2YNMTExzJo1i4yMDEk+CSGEEKJBSQWUEEIIIUQ9ysvLY9SoUajVaiwWCx06dGDZsmWOx61WKyNGjKBFixa8//77qNX2DgkFBQVce+21+Pv78/nnn2M2m4mPj2f48OFOfaVyc3MZMGAAQ4cOZcGCBYC9AiojI4MNGzY4JbqEEEIIIRqK9IASQgghhKhHvr6+PPbYY5w5c4b8/HyeffZZp8f37t3LyZMnGT58OCaTiaysLLKysigpKWHIkCHs3buX9PR0dDodW7Zs4amnnnJ6vslkwmAwUFhY6LQ9Ojpakk9CCCGEaDRkCp4QQgghRD0bM2YMM2fOpHv37rRp08bpsRMnTgAwZ84c5syZU+nzU1NTCQ0NRafT8fPPP/PTTz9x7NgxkpKSyM3NBcBmszk9JygoqB7ORAghhBDi4kgCSgghhBCiAVmtVgAeeughp4bj5UVHR6MoClOmTGHDhg306dOHXr16MX78ePr27cutt95a4TkajaZexy2EEEIIURuSgBJCCCGEaEBRUVEA6PV6Bg4c6PTY7t27ycnJwdPTk61bt7JhwwamTZvG/fff79intLSUnJwcl45ZCCGEEKK2pAeUEEIIIUQD6tatG8HBwbz//vtOfZzy8vKYPn06s2bNQqPRYDKZAIiJiXF6/qefforZbHZUUgkhhBBCNEZSASWEEEII0YB0Oh2zZs1i5syZXH/99dxwww3odDpWrFhBamoqL7/8MhqNht69e6PX65k9ezYpKSkYDAa2bt3KN998g6enJwUFBQ19KkIIIYQQVZIElBBCCCFEAxs9ejT+/v4sWrSIN954A41Gg9FoZPHixQwePBiAsLAwFi9ezEsvvcTChQvR6XT8fzt3UAMwDMRA8AollEMiYMIlFC4orFbVDAK/9+ExRs05a+9da6065zgfBwA+6enufnsEAAAAAP/lAwoAAACAKAEKAAAAgCgBCgAAAIAoAQoAAACAKAEKAAAAgCgBCgAAAIAoAQoAAACAKAEKAAAAgCgBCgAAAIAoAQoAAACAqAsdX7rgQenBXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(\n",
    "    font_scale=1.5,\n",
    "    rc={'figure.figsize':(20,10)}\n",
    ")\n",
    "\n",
    "ax = sns.lineplot(\n",
    "    x='Year', \n",
    "    y='Life Ladder', \n",
    "    hue='Continent',\n",
    "    marker=\"o\",  \n",
    "    markersize=12,\n",
    "    lw=3,\n",
    "    data=grouped_by_year_and_continent\n",
    ")\n",
    "\n",
    "ax.set_title('Life Ladder Development by Region')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>Continent</th>\n",
       "      <th>Country name</th>\n",
       "      <th>Life Ladder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>6.087859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>5.614522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Austria</td>\n",
       "      <td>7.249100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Taiwan Province of China</td>\n",
       "      <td>6.263030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Poland</td>\n",
       "      <td>5.865575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Continent              Country name  Life Ladder\n",
       "57       Asia                    Cyprus     6.087859\n",
       "91       Asia              Turkmenistan     5.614522\n",
       "97     Europe                   Austria     7.249100\n",
       "87       Asia  Taiwan Province of China     6.263030\n",
       "125    Europe                    Poland     5.865575"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_country_and_continent = data.groupby(['Continent','Country name'])['Life Ladder'].mean().reset_index()\n",
    "grouped_by_country_and_continent.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(grouped_by_country_and_continent, col='Continent', col_wrap=3, height=4.5)\n",
    "g = g.map(plt.hist, \"Life Ladder\",bins=np.arange(0,10,0.5))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Blogging: Data Extraction",
   "language": "python",
   "name": "data_extraction"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}