{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 30 entries, Ainu to Yimas\n",
      "Data columns (total 11 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Family      30 non-null     object \n",
      " 1   LID         29 non-null     float64\n",
      " 2   LOID        30 non-null     int64  \n",
      " 3   Continent   30 non-null     object \n",
      " 4   Area        30 non-null     object \n",
      " 5   Analysis    29 non-null     object \n",
      " 6   Forms       30 non-null     object \n",
      " 7   Elim        1 non-null      object \n",
      " 8   Status      30 non-null     object \n",
      " 9   Comments    8 non-null      object \n",
      " 10  Impression  22 non-null     object \n",
      "dtypes: float64(1), int64(1), object(9)\n",
      "memory usage: 2.8+ KB\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pathlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "import pandas as pd\n",
    "\n",
    "SAMPLE = 'http://proalki.uni-leipzig.de/wiki/Project:Portmanteau_Analyses'\n",
    "\n",
    "SAMPLE_CSV = pathlib.Path('portmanteau_sample.csv')\n",
    "\n",
    "ENCODING = 'utf-8'\n",
    "\n",
    "RENAME = {'Quechua (Ayacucho)': 'Ayacucho',\n",
    "          'Tepehuan': 'Tepehua',\n",
    "          'Lakhota': 'Lakota'}\n",
    "\n",
    "\n",
    "if not SAMPLE_CSV.exists():\n",
    "    _sf, = pd.read_html(SAMPLE, header=0, index_col='Language')\n",
    "    _sf.to_csv(SAMPLE_CSV, encoding=ENCODING)\n",
    "\n",
    "sf = pd.read_csv(SAMPLE_CSV, encoding=ENCODING)\n",
    "sf['Language'] = sf['Language'].replace(RENAME)\n",
    "sf = sf.set_index('Language')\n",
    "sf = sf.rename(columns={'Languoid': 'Family'})\n",
    "\n",
    "sf.info()\n",
    "assert sf.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Family</th>\n",
       "      <th>LID</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Area</th>\n",
       "      <th>Analysis</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>12</td>\n",
       "      <td>N-C Asia</td>\n",
       "      <td>N Coast Asia</td>\n",
       "      <td>Ain/Paradigms/to X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>Aleut</td>\n",
       "      <td>18</td>\n",
       "      <td>N-C Asia</td>\n",
       "      <td>N Coast Asia</td>\n",
       "      <td>Ale/Paradigms/to X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>Bella Coola</td>\n",
       "      <td>995</td>\n",
       "      <td>W N America</td>\n",
       "      <td>Alaska-Oregon</td>\n",
       "      <td>Blc/Paradigms/to X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>Chukotkan</td>\n",
       "      <td>56</td>\n",
       "      <td>N-C Asia</td>\n",
       "      <td>N Coast Asia</td>\n",
       "      <td>Ckt/Paradigms/to X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>Indo-Iranian</td>\n",
       "      <td>1399</td>\n",
       "      <td>S/SE Asia</td>\n",
       "      <td>Indic</td>\n",
       "      <td>Dry/Paradigms/to all/Npst</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Family   LID    Continent           Area  \\\n",
       "Language                                                      \n",
       "Ainu                 Ainu    12     N-C Asia   N Coast Asia   \n",
       "Aleut               Aleut    18     N-C Asia   N Coast Asia   \n",
       "Bella Coola   Bella Coola   995  W N America  Alaska-Oregon   \n",
       "Chuckchi        Chukotkan    56     N-C Asia   N Coast Asia   \n",
       "Darai        Indo-Iranian  1399    S/SE Asia          Indic   \n",
       "\n",
       "                              Analysis  \n",
       "Language                                \n",
       "Ainu                Ain/Paradigms/to X  \n",
       "Aleut               Ale/Paradigms/to X  \n",
       "Bella Coola         Blc/Paradigms/to X  \n",
       "Chuckchi            Ckt/Paradigms/to X  \n",
       "Darai        Dry/Paradigms/to all/Npst  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sf.loc[sf['Status'] == 'ready', :'Analysis'].drop('LOID', axis=1)\n",
    "\n",
    "df['LID'] = pd.to_numeric(df['LID'].fillna(0), downcast='integer')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2913 entries, 199 to 3213\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   ISO639.3           2846 non-null   object \n",
      " 1   language           2913 non-null   object \n",
      " 2   alt.language.name  2873 non-null   object \n",
      " 3   language.search    2913 non-null   object \n",
      " 4   lsbranch           301 non-null    object \n",
      " 5   ssbranch           529 non-null    object \n",
      " 6   sbranch            1322 non-null   object \n",
      " 7   mbranch            2070 non-null   object \n",
      " 8   stock              2913 non-null   object \n",
      " 9   alt.stock.name     366 non-null    object \n",
      " 10  stock.search       2913 non-null   object \n",
      " 11  longitude          2913 non-null   float64\n",
      " 12  latitude           2913 non-null   float64\n",
      " 13  area               2913 non-null   object \n",
      " 14  continent          2913 non-null   object \n",
      "dtypes: float64(2), object(13)\n",
      "memory usage: 364.1+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISO639.3</th>\n",
       "      <th>language</th>\n",
       "      <th>alt.language.name</th>\n",
       "      <th>language.search</th>\n",
       "      <th>lsbranch</th>\n",
       "      <th>ssbranch</th>\n",
       "      <th>sbranch</th>\n",
       "      <th>mbranch</th>\n",
       "      <th>stock</th>\n",
       "      <th>alt.stock.name</th>\n",
       "      <th>stock.search</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>area</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>naq</td>\n",
       "      <td>Nama</td>\n",
       "      <td>Nama, Khoekhoe</td>\n",
       "      <td>Nama, Nama, Khoekhoe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Khoekhoe</td>\n",
       "      <td>Kwadi-Khoe</td>\n",
       "      <td>Central Khoisan</td>\n",
       "      <td>Kwadi-Khoe, Central Khoisan</td>\n",
       "      <td>18.00</td>\n",
       "      <td>-25.50</td>\n",
       "      <td>S Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>knw</td>\n",
       "      <td>!Kung</td>\n",
       "      <td>!Xu, !Kung, \\t!Hu, !Khung, !Ku, !Kung, !Xu, !X...</td>\n",
       "      <td>!Kung, !Xu, !Kung, \\t!Hu, !Khung, !Ku, !Kung, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ju</td>\n",
       "      <td>Northern Khoisan</td>\n",
       "      <td>Ju, Northern Khoisan</td>\n",
       "      <td>18.00</td>\n",
       "      <td>-19.67</td>\n",
       "      <td>S Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>hts</td>\n",
       "      <td>Hadza</td>\n",
       "      <td>Hadza</td>\n",
       "      <td>Hadza, Hadza</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hadza</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hadza</td>\n",
       "      <td>35.17</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>S Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>sad</td>\n",
       "      <td>Sandawe</td>\n",
       "      <td>Sandawe</td>\n",
       "      <td>Sandawe, Sandawe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sandawe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sandawe</td>\n",
       "      <td>35.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>S Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>kwz</td>\n",
       "      <td>Kwadi</td>\n",
       "      <td>Kwadi, Cuepe, Curoca</td>\n",
       "      <td>Kwadi, Kwadi, Cuepe, Curoca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kwadi</td>\n",
       "      <td>Kwadi-Khoe</td>\n",
       "      <td>Central Khoisan</td>\n",
       "      <td>Kwadi-Khoe, Central Khoisan</td>\n",
       "      <td>12.00</td>\n",
       "      <td>-16.00</td>\n",
       "      <td>S Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ISO639.3 language                                  alt.language.name  \\\n",
       "LID                                                                        \n",
       "199      naq     Nama                                     Nama, Khoekhoe   \n",
       "148      knw    !Kung  !Xu, !Kung, \\t!Hu, !Khung, !Ku, !Kung, !Xu, !X...   \n",
       "94       hts    Hadza                                              Hadza   \n",
       "347      sad  Sandawe                                            Sandawe   \n",
       "151      kwz    Kwadi                               Kwadi, Cuepe, Curoca   \n",
       "\n",
       "                                       language.search lsbranch ssbranch  \\\n",
       "LID                                                                        \n",
       "199                               Nama, Nama, Khoekhoe      NaN      NaN   \n",
       "148  !Kung, !Xu, !Kung, \\t!Hu, !Khung, !Ku, !Kung, ...      NaN      NaN   \n",
       "94                                        Hadza, Hadza      NaN      NaN   \n",
       "347                                   Sandawe, Sandawe      NaN      NaN   \n",
       "151                        Kwadi, Kwadi, Cuepe, Curoca      NaN      NaN   \n",
       "\n",
       "    sbranch   mbranch       stock    alt.stock.name  \\\n",
       "LID                                                   \n",
       "199     NaN  Khoekhoe  Kwadi-Khoe   Central Khoisan   \n",
       "148     NaN       NaN          Ju  Northern Khoisan   \n",
       "94      NaN       NaN       Hadza               NaN   \n",
       "347     NaN       NaN     Sandawe               NaN   \n",
       "151     NaN     Kwadi  Kwadi-Khoe   Central Khoisan   \n",
       "\n",
       "                    stock.search  longitude  latitude      area continent  \n",
       "LID                                                                        \n",
       "199  Kwadi-Khoe, Central Khoisan      18.00    -25.50  S Africa    Africa  \n",
       "148         Ju, Northern Khoisan      18.00    -19.67  S Africa    Africa  \n",
       "94                         Hadza      35.17     -3.75  S Africa    Africa  \n",
       "347                      Sandawe      35.00     -5.00  S Africa    Africa  \n",
       "151  Kwadi-Khoe, Central Khoisan      12.00    -16.00  S Africa    Africa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUTOTYP = 'https://www.autotyp.uzh.ch/download/release_2013/autotyp.csv'\n",
    "\n",
    "AUTOTYP_CSV = pathlib.Path(AUTOTYP.rpartition('/')[2])\n",
    "\n",
    "AUTOTYP_FORMAT = {'encoding': 'utf-8',\n",
    "                  'na_values': '', 'keep_default_na': False,\n",
    "                  'index_col': 'LID'}\n",
    "\n",
    "\n",
    "if not AUTOTYP_CSV.exists():\n",
    "    _af = pd.read_csv(AUTOTYP, **AUTOTYP_FORMAT)\n",
    "    _af.to_csv(AUTOTYP_CSV, encoding=AUTOTYP_FORMAT['encoding'])\n",
    "\n",
    "af = pd.read_csv(AUTOTYP_CSV, **AUTOTYP_FORMAT)\n",
    "\n",
    "af.info()\n",
    "assert af.index.is_unique\n",
    "af.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>nunique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>2913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stock</th>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mbranch</th>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sbranch</th>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           nunique\n",
       "language      2913\n",
       "stock          399\n",
       "mbranch        236\n",
       "sbranch        154\n",
       "area            24\n",
       "continent       10"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "af[['language','stock', 'mbranch', 'sbranch', 'area', 'continent']].nunique().to_frame('nunique')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>stock</th>\n",
       "      <th>mbranch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chibchan</th>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great Andamanese</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morehead and Upper Maro Rivers</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                stock  mbranch\n",
       "Chibchan                           16        5\n",
       "Great Andamanese                    5        4\n",
       "Morehead and Upper Maro Rivers      3        1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([af.groupby('stock').size(), af.groupby('mbranch').size()],\n",
    "          axis=1, join='inner', keys=['stock', 'mbranch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "593"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "af['family'] = af['mbranch'].fillna(af['stock'])\n",
    "\n",
    "af['family'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Malayo-Polynesian</th>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bantoid</th>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indo-Iranian</th>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Semitic</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germanic</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     n\n",
       "family                \n",
       "Malayo-Polynesian  326\n",
       "Bantoid            164\n",
       "Indo-Iranian       109\n",
       "West Semitic        51\n",
       "Germanic            50"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "af['family'].value_counts().to_frame('n').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>ISO639.3</th>\n",
       "      <th>family</th>\n",
       "      <th>continent</th>\n",
       "      <th>area</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>ain</td>\n",
       "      <td>Ainu</td>\n",
       "      <td>N-C Asia</td>\n",
       "      <td>N Coast Asia</td>\n",
       "      <td>43.0</td>\n",
       "      <td>143.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>ale</td>\n",
       "      <td>Aleut</td>\n",
       "      <td>N-C Asia</td>\n",
       "      <td>N Coast Asia</td>\n",
       "      <td>54.0</td>\n",
       "      <td>-166.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>blc</td>\n",
       "      <td>Salishan</td>\n",
       "      <td>W N America</td>\n",
       "      <td>Alaska-Oregon</td>\n",
       "      <td>52.5</td>\n",
       "      <td>-126.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>ckt</td>\n",
       "      <td>Chukotkan</td>\n",
       "      <td>N-C Asia</td>\n",
       "      <td>N Coast Asia</td>\n",
       "      <td>67.0</td>\n",
       "      <td>170.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>dry</td>\n",
       "      <td>Indo-Iranian</td>\n",
       "      <td>S/SE Asia</td>\n",
       "      <td>Indic</td>\n",
       "      <td>24.0</td>\n",
       "      <td>84.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ISO639.3        family    continent           area  latitude  \\\n",
       "Language                                                                   \n",
       "Ainu             ain          Ainu     N-C Asia   N Coast Asia      43.0   \n",
       "Aleut            ale         Aleut     N-C Asia   N Coast Asia      54.0   \n",
       "Bella Coola      blc      Salishan  W N America  Alaska-Oregon      52.5   \n",
       "Chuckchi         ckt     Chukotkan     N-C Asia   N Coast Asia      67.0   \n",
       "Darai            dry  Indo-Iranian    S/SE Asia          Indic      24.0   \n",
       "\n",
       "             longitude  \n",
       "Language                \n",
       "Ainu            143.00  \n",
       "Aleut          -166.00  \n",
       "Bella Coola    -126.67  \n",
       "Chuckchi        170.00  \n",
       "Darai            84.00  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JUM = pd.Series({'ISO639.3': 'jum',  # missing in autotyp\n",
    "                 'family': 'Western Nilotic',\n",
    "                 'continent': 'Africa',\n",
    "                 'area': 'African Savannah',\n",
    "                 'longitude': 33.7494, 'latitude': 10.4349})\n",
    "\n",
    "\n",
    "jf = df[['LID']].reset_index().set_index('LID').join(af).set_index('Language')\n",
    "jf = jf[['ISO639.3', 'family', 'continent', 'area', 'latitude', 'longitude']]\n",
    "jf.loc['Jumjum'] = JUM\n",
    "assert jf.notnull().all().all()\n",
    "\n",
    "jf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>ISO639.3</th>\n",
       "      <th>family</th>\n",
       "      <th>continent</th>\n",
       "      <th>area</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>jum</td>\n",
       "      <td>Western Nilotic</td>\n",
       "      <td>Africa</td>\n",
       "      <td>African Savannah</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>kun</td>\n",
       "      <td>Kunama</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Greater Abyssinia</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>tuv</td>\n",
       "      <td>Eastern Nilotic</td>\n",
       "      <td>Africa</td>\n",
       "      <td>S Africa</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>mph</td>\n",
       "      <td>Iwaidjan</td>\n",
       "      <td>Australia</td>\n",
       "      <td>N Australia</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>wrr</td>\n",
       "      <td>Wagiman - Wardaman</td>\n",
       "      <td>Australia</td>\n",
       "      <td>N Australia</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>tee</td>\n",
       "      <td>Totonac-Tepehuan</td>\n",
       "      <td>C America</td>\n",
       "      <td>Mesoamerica</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>lkt</td>\n",
       "      <td>Siouan</td>\n",
       "      <td>E N America</td>\n",
       "      <td>Basin and Plains</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>mrc</td>\n",
       "      <td>Yuman</td>\n",
       "      <td>E N America</td>\n",
       "      <td>Basin and Plains</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>sac</td>\n",
       "      <td>Algonquian</td>\n",
       "      <td>E N America</td>\n",
       "      <td>E North America</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>ket</td>\n",
       "      <td>Yeniseian</td>\n",
       "      <td>N-C Asia</td>\n",
       "      <td>Inner Asia</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>myv</td>\n",
       "      <td>Finno-Ugric</td>\n",
       "      <td>N-C Asia</td>\n",
       "      <td>Inner Asia</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>ain</td>\n",
       "      <td>Ainu</td>\n",
       "      <td>N-C Asia</td>\n",
       "      <td>N Coast Asia</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>ale</td>\n",
       "      <td>Aleut</td>\n",
       "      <td>N-C Asia</td>\n",
       "      <td>N Coast Asia</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>ckt</td>\n",
       "      <td>Chukotkan</td>\n",
       "      <td>N-C Asia</td>\n",
       "      <td>N Coast Asia</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>yee</td>\n",
       "      <td>Lower Sepik</td>\n",
       "      <td>NG and Oceania</td>\n",
       "      <td>N Coast New Guinea</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>saj</td>\n",
       "      <td>North Halmaheran</td>\n",
       "      <td>NG and Oceania</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>jqr</td>\n",
       "      <td>Aymaran</td>\n",
       "      <td>S America</td>\n",
       "      <td>Andean</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>quy</td>\n",
       "      <td>Quechuan</td>\n",
       "      <td>S America</td>\n",
       "      <td>Andean</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>hix</td>\n",
       "      <td>Cariban</td>\n",
       "      <td>S America</td>\n",
       "      <td>NE South America</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>rey</td>\n",
       "      <td>Tacanan</td>\n",
       "      <td>S America</td>\n",
       "      <td>NE South America</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>dry</td>\n",
       "      <td>Indo-Iranian</td>\n",
       "      <td>S/SE Asia</td>\n",
       "      <td>Indic</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>thf</td>\n",
       "      <td>Remnant Himalayish</td>\n",
       "      <td>S/SE Asia</td>\n",
       "      <td>Indic</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>njb</td>\n",
       "      <td>Brahmaputran (Sal)</td>\n",
       "      <td>S/SE Asia</td>\n",
       "      <td>Southeast Asia</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>blc</td>\n",
       "      <td>Salishan</td>\n",
       "      <td>W N America</td>\n",
       "      <td>Alaska-Oregon</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>sis</td>\n",
       "      <td>Siuslawan</td>\n",
       "      <td>W N America</td>\n",
       "      <td>Alaska-Oregon</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>kyh</td>\n",
       "      <td>Karok</td>\n",
       "      <td>W N America</td>\n",
       "      <td>California</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ISO639.3              family       continent                area  \\\n",
       "Language                                                                       \n",
       "Jumjum           jum     Western Nilotic          Africa    African Savannah   \n",
       "Kunama           kun              Kunama          Africa   Greater Abyssinia   \n",
       "Turkana          tuv     Eastern Nilotic          Africa            S Africa   \n",
       "Maung            mph            Iwaidjan       Australia         N Australia   \n",
       "Wardaman         wrr  Wagiman - Wardaman       Australia         N Australia   \n",
       "Tepehua          tee    Totonac-Tepehuan       C America         Mesoamerica   \n",
       "Lakota           lkt              Siouan     E N America    Basin and Plains   \n",
       "Maricopa         mrc               Yuman     E N America    Basin and Plains   \n",
       "Fox              sac          Algonquian     E N America     E North America   \n",
       "Ket              ket           Yeniseian        N-C Asia          Inner Asia   \n",
       "Mordvin          myv         Finno-Ugric        N-C Asia          Inner Asia   \n",
       "Ainu             ain                Ainu        N-C Asia        N Coast Asia   \n",
       "Aleut            ale               Aleut        N-C Asia        N Coast Asia   \n",
       "Chuckchi         ckt           Chukotkan        N-C Asia        N Coast Asia   \n",
       "Yimas            yee         Lower Sepik  NG and Oceania  N Coast New Guinea   \n",
       "Sahu             saj    North Halmaheran  NG and Oceania             Oceania   \n",
       "Jaqaru           jqr             Aymaran       S America              Andean   \n",
       "Ayacucho         quy            Quechuan       S America              Andean   \n",
       "Hixkaryana       hix             Cariban       S America    NE South America   \n",
       "Reyesano         rey             Tacanan       S America    NE South America   \n",
       "Darai            dry        Indo-Iranian       S/SE Asia               Indic   \n",
       "Thangmi          thf  Remnant Himalayish       S/SE Asia               Indic   \n",
       "Nocte            njb  Brahmaputran (Sal)       S/SE Asia      Southeast Asia   \n",
       "Bella Coola      blc            Salishan     W N America       Alaska-Oregon   \n",
       "Siuslawan        sis           Siuslawan     W N America       Alaska-Oregon   \n",
       "Karuk            kyh               Karok     W N America          California   \n",
       "\n",
       "               n  \n",
       "Language          \n",
       "Jumjum       375  \n",
       "Kunama        61  \n",
       "Turkana      190  \n",
       "Maung        108  \n",
       "Wardaman     108  \n",
       "Tepehua      173  \n",
       "Lakota        68  \n",
       "Maricopa      68  \n",
       "Fox           69  \n",
       "Ket          113  \n",
       "Mordvin      113  \n",
       "Ainu          27  \n",
       "Aleut         27  \n",
       "Chuckchi      27  \n",
       "Yimas        163  \n",
       "Sahu         240  \n",
       "Jaqaru        47  \n",
       "Ayacucho      47  \n",
       "Hixkaryana   192  \n",
       "Reyesano     192  \n",
       "Darai        203  \n",
       "Thangmi      203  \n",
       "Nocte        216  \n",
       "Bella Coola   59  \n",
       "Siuslawan     59  \n",
       "Karuk         47  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(jf.merge(af.groupby('area').size().to_frame('n'),\n",
    "          left_on='area', right_index=True)\n",
    " .sort_values(['continent', 'area'])\n",
    " .drop(['latitude', 'longitude'], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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></th>\n",
       "      <th>language</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th>area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <th>N Africa</th>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <th>S Australia</th>\n",
       "      <td>81</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">NG and Oceania</th>\n",
       "      <th>Interior New Guinea</th>\n",
       "      <td>81</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S New Guinea</th>\n",
       "      <td>67</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S America</th>\n",
       "      <th>SE South America</th>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">W and SW Eurasia</th>\n",
       "      <th>Europe</th>\n",
       "      <td>142</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greater Mesopotamia</th>\n",
       "      <td>131</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      language Language\n",
       "continent        area                                  \n",
       "Africa           N Africa                   28      NaN\n",
       "Australia        S Australia                81      NaN\n",
       "NG and Oceania   Interior New Guinea        81      NaN\n",
       "                 S New Guinea               67      NaN\n",
       "S America        SE South America           32      NaN\n",
       "W and SW Eurasia Europe                    142      NaN\n",
       "                 Greater Mesopotamia       131      NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(af.groupby(['area', 'continent'], as_index=False)['language'].count()\n",
    " .merge(jf[['area']].reset_index(), how='left', on='area')\n",
    " .set_index(['continent', 'area'])\n",
    " .sort_index()\n",
    " .query('Language != Language'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>present</th>\n",
       "      <th>left_only</th>\n",
       "      <th>both</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>languages</th>\n",
       "      <td>562</td>\n",
       "      <td>2351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "present    left_only  both\n",
       "languages        562  2351"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(af.groupby('area').size().to_frame('languages')\n",
    " .merge(jf.groupby('area').size().to_frame(),\n",
    "        how='left', left_index=True, right_index=True, indicator='present')\n",
    " .groupby('present', observed=False)['languages'].sum()\n",
    " .drop('right_only')\n",
    " .astype(int)\n",
    " .to_frame().T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>nunique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           nunique\n",
       "family         593\n",
       "continent       10\n",
       "area            24"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "af[['family', 'continent', 'area']].nunique().to_frame('nunique')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>nunique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           nunique\n",
       "family          26\n",
       "continent        9\n",
       "area            17"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jf[['family', 'continent', 'area']].nunique().to_frame('nunique')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA68AAAHiCAYAAAAHwfpyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3wUdf7/XzPbe8kmm2TTQxr9FPUs2LCLoucpFuQ4G4qAiHKn4klEvVM8VBDxLEhRTkWPwwIqeorKebb70qQEEtJ7tvc28/sjv/lcNtkkm7qbZJ6Pxz6SbGZnPrM7+5nPu73eFMuyLHh4eHh4eHh4eHh4eHh4Ehg63gPg4eHh4eHh4eHh4eHh4ekN3njl4eHh4eHh4eHh4eHhSXh445WHh4eHh4eHh4eHh4cn4eGNVx4eHh4eHh4eHh4eHp6EhzdeeXh4eHh4eHh4eHh4eBIe3njl4eHh4eHh4eHh4eHhSXh445WHh4eHh4eHh4eHh4cn4eGNVx4eHh4eHh4eHh4eHp6EhzdeeXh4eHh4eHh4eHh4eBIeYbwHwMMz2vD5fGhoaEBDQwPq6+sjfnK/t7a2wuFwgGXZqPuQy+VQq9WQSCQQi8UQiUQQiUQRv3N/Hzp0CFOnTh3ek+Th4eHh4RmhhMNhBINB8ggEAl1+DwQCcDqdcDqd3e5HpVLBYDAgPT0d6enpMJlMET+53xUKxTCeHQ/P6IZiu1s98/DwdIvD4cCJEydQXl7e5WdLS0vEtjKZDCaTKeJmlpKSAp1OB61W2+Wh0WggFovjdGY8PDw8PDw8HKFQCHa7HTabrcvDarWira2ti5Pa4XBE7EOn02HcuHEYN24cCgoKUFBQQH7X6/WgKCpOZ8fDM/LgjVcenh6wWq04ePAgDh48iAMHDuDo0aNdDNSkpKSIG1F2dnaE91WtVvM3Jh4eHh6eUQPLsmAYBuFwOOLBLSk73vO433v7SdM0xGIx+ZthGLI/gUAwpPdRlmURCATg9/tBURQoigJN0+R3iqIQCATgdrsRCAQizk0gEECpVEaMz+VyobGxEY2NjWhoaEBtbS1OnjxJHs3NzWRbrVaLgoICFBcXY/LkyZg8eTKmTJkCo9E4ZOfLwzOS4Y1XHh603yTLy8tx4MAB8jh48CBqamoAAGKxGCUlJSguLiZe06KiIowbNw4ajYakG4VCIXLTo2kaQqEQEokENM2Xl/Pw8PDwJCYsyyIUCiEUCiEYDMLn85H7md/vRzAYBABiyHU2WkOh0IDHQNM0WJYFy7IRRiP3kEgkkEqlkEqlUKvVkEql8Pl8CAQCCAQCCIVCEQY1wzCQSCSQyWSQyWQIBALkec4w5n7vbKx2NGa5c2MYBoFAAAzD9Om8hEIhFAoFMcRZloXL5UJ1dTVqampQVVWF6upqVFZW4vjx4/B4PAAAo9FIDNkpU6Zg8uTJGD9+PIRCvuKPZ2zDG688YxK73Y4ffvgB//nPf/D999/j+++/h81mAwCkpKSgsLAQxcXFGD9+PAoLC5GVlUUMUM4oFYlE5GbEPScWiyMeAoEgXqc4ILjFQ2/P8fDw8PCMTBiGgdlshsvlAtBumHK6ChRFQSQSgaZpeL1eeDwe4qDl7nderxcCgQBCoRACgQAsy8Lv94NhGBJFlUgkCAaD8Hg8CIVCCIfDMY2NOwZN0xCJRJDJZFAoFJDL5QiFQnA4HGRf3Phi3XciwzAMGhoaUFZWhhMnTuD48eM4efIk6urqALTrYZx22mk488wz8etf/xpnnnkmUlJS4jxqHp7hhTdeeUY9LMvixIkT+Pbbb4mxeuTIEbAsC61WiwkTJmDSpEmYOHEiioqKkJycDIVCQW68nGiSQCCAQCCI8M4mGpz3nPM+c4sPznsuEokgEAjg8XjAMAzxBjscDvI6j8cDh8MBiUQChUJBvPDcvmiahkwmg1arhVQqjfcp80SBEyPhIikdH+FwmHyO3DWg0+kS8nrm4eEZOsLhMGw2G4myhkIheDyeiLRYAMSQFYvFEAqFCAQC0Gq1UCqVYFmWRD+5qC33OxdF5R6x0NEJLBQKoVaroVariSM4FArB5XIhGAxGRGXD4TDJlBqNuFwuHD9+HIcPH8bBgwdx+PBhUr6Ul5dHjNlzzjkHkydP5rO9eEY1vPHKMyqpq6vDl19+iX/961/417/+hfr6etA0jXHjxmHixImYNGkSJk+ejIKCAkilUmKkdnyMxMm/paUFtbW15G+ZTAaRSAS32w2WZZGUlAS73Y5AIECMF6FQCKPRiOTk5KhpYB3VjXkDZ2TApbz5fD6ysOy8iGRZFjRNIy0tDUB7NoLX64VUKiWODoPBwKeo8fCMIILBIFwuF/x+P3FcBoNB4nzlIqVyuTxqZpDf7yfGK5c26/f74fP54Pf7u0Q3udKYYDAIlUoFpVIZoeLLjcnr9XZJt6VpOuq9VyKRkKhrtAwgj8cDp9NJamy5R2tr62C+lQkNy7Jobm7GwYMHcejQIRw+fBhHjx5FMBiEXq/HBRdcgBkzZmDGjBkoKCjg7908owreeOUZFVgsFnz11VfEWD1+/DgoikJxcTFOPfVUnHnmmTjjjDOg0Wig1WqhUqkihCFGA+FwGJWVlWRxQlEUQqFQhBddLBZDqVRCLpdDKBSSBQLP2INL8eMWuh0XttzvXAohDw/PyMTv95P0XoZhIjIwRCIRqSHl7hndwUVlvV4vMRa5eUKhUJD6U7PZDLlcThyjTqcTbW1t5HhcVlA0uNpQrlWcQqGIGJPX64XZbCYpzMFgcFSkCg8Wfr8fhw8fxk8//YSffvoJv/zyC0KhEDIyMnDhhRcSY9ZkMsV7qDw8A4I3XnlGJCzL4tixY/joo4/w0Ucf4bvvvgPDMMjOzsa0adNw2mmnYdq0adBqtQD+5+FVqVRISUkZdYYry7JobGyE2+0mnnK5XA6lUkkevBHSf0ZrvS+3GO0cWVEoFFCpVFCr1XzEnYdnGLDb7XC73URQCACp9zQajXH7DnJj6awM3NP2nMCT2+2G2+2OyPIxm80A2tWDVSoVpFJphHiSTqeDTCYjNa+xjI/LMvF6veR5bmnr8/lgs9liTlseTbjdbuzfvx8//fQTfv75Zxw7dgwAMHXqVMycORNXXXUVpk2bxjuweUYcvPHKM2IIBAL49ttvicF68uRJSKVSnHHGGZg+fTouueQSTJw4kdTzdadWOFoX4izLwm63g6ZpKBSKESsWNVRwqbTcYqxj+iwAUq/VsUk9t5jS6/Uj9v1kWZakgnO9BymKgtPphMVi6fG1XCSEW2B2VOpkGAYymQw6na5Lm4hEprMjYrQ6JnhGBsFgELW1tbBareQ5rVYLg8EAh8MBoVCItLQ0hEIhOJ3OiFTbztet2+1GXV0d1Go10SSI5dpmGCZi7qNpGi6Xi+zD4/HA7XbD5XLB6/VCLBYjJSUFKpUKDocDPp+PtIsRCoUkdZlrL8NFaYPBIPx+P5RKJZlPaZpGKBSC1+uF1+tFIBCAQCCARCJBamoqdDpdzO8lZwBzc7nZbIbFYokwwDsveWUyGQQCQUS5TF/VhEcKNpsNP/zwA/bu3YvvvvsOdrsdqampuOKKK3D11VfjoosugkKhiPcweXh6hTdeeRIan8+HTz75BNu2bcOuXbvgcDhgNBpxzjnnYPr06Zg2bRqkUikEAgFkMhn0ej10Ot2Q94TjSUzC4XBEnVbHB7eI4qIZnGJ0x987P5coBitngHYWXwoEAnA4HOTcAJDvQkfxlI69Fwd7ypfJZBCLxcjIyEhIAS+WZWG1WuFwOEidnEajgUajgcfjQVtbG6nb41pxdPzJwzOUcIafUCgkKb4ajSYiGsal+DscDrS1tcHr9YKiKJLyK5VKIZPJIJVKSRsWp9OJUCgEhUIBiUSCQCAAj8cDiUSCjIyMLvsPBoNwOp1EFdjlciEQCEAoFEKr1UKn00GlUnW5rzIMA7vdDovFQpynXD0tl/XDMAwEAgGZV7l5iJvTOHE57ic3B3NlCwPJGuLOjTNKuVKZcDjcbUcAztHp9XqJbgD3+2gxbEOhEA4ePIhvv/0We/fuRWVlJSQSCWbMmIHrr78e11xzDclc4+FJNHjjlSfhCAQC2L17N95991188MEHcDqdKCoqwvnnn4/p06ejqKgIYrEYWq0Wer2e3AC5FEeesYXP50NVVVVEPz4AZPGkUCjIAo5TTE5EOBXg1tZWuFyuLoZqok3V3IJZJpMhOTk5oYWdHA4H6urqukRZO6dCSiQSqNVqYggk6rUyVFAUhX/+85+45pprhuwYOTk5WLJkCZYsWdLn11ZVVSE3Nxf79u3D1KlTo26zadMmLFmyhLQ+Gw2wLAu3201qPTkHjEwmQ2trK+lzymWMcHMF116m43eTZVloNBqoVKpuj8cwDBwOB5mHgPZrQ6/X9/i6UCgEm80Gu91OnIYdjT2pVEoiu5zYUmdEIlGX+S4nJwdyuTzmSPJQwRnCnCHb8edIN2pramrwzTff4JtvvsG+ffsgEolw6aWXYvbs2bj66qt7/Nx5eIabxF1t8IwpgsEgvvzyS7zzzjvYsWMHbDYb8vPzcfPNN+PKK6/E1KlToVaridIgy7IknTGRF808g0tHFV2Xy0WUdH0+H1JSUkjkjzNWR1L03WKxoKGhIcIATwQEAgHkcjkxVLnHSDLs1Go1xo8fH+9hxJ2mpiY89dRT2LlzJ+rr65GSkoKpU6diyZIlmDFjRryHNyjMnj0bV1xxRbyHERPhcBhWqxVerzdC6V0sFkdEU7m/WZYldaLc3JaamkqMVq7Ugfu+9vc7StM0tFptnyNvQqEQBoMBBoMBwP8yRjhBOK78wOPxwOfzAUCX0h6xWAyZTEYyaLjzEYvF/TqXwYQbn1gshkajIc9zRm1tbS3C4TBcLlfCORt7IysrC3PmzMGcOXPQ0tKCL774Al988QXmzJkDqVSKK664ArNnz8bMmTMhl8vjPVyeMQ6/6ueJK4cPH8bGjRvx5ptvoqWlBXl5ebjxxhtx0UUXIScnh6REBQIBiEQiyOVyPpVlDBIKhWC1WmG1WuF2uyGRSKDT6aBWqyGVShOy5pJbxHD1oVw9VsdHx+e4CGs8oSgKGo2GOIY4sSan04mWlha0traCZVno9XoEAgHk5eWRGjduQUpRFGiajlAslslkPR6XUxGVSqXQaDR8BsUQUFVVhbPPPhtarRbPPvssJk2ahGAwiM8++wz33nsvEXMZ6XDOlZGAQCBAUlISvF4vTp48Cb/fH3W7zun+XK9tuVwOhUIBvV4/ZOfMGWYWiwUejyfC0KRpGunp6RAIBAiFQmhsbCS1ux17oXO/p6WlkRp5rkcsl7ofCoXIvJGWlkbmgEAgAKfTiUAgQAx5l8tF3geu9zrX/iceTjXOqM3Pzwfwv/uV2WyG2+0e9vEMlJSUFNx88824+eab0djYSAzZ2bNnQ6lUYvbs2fj973+Ps846K+HuuzxjAz5tmGfYsdlsePvtt7Fx40b89NNPMBgMuOWWWzB37lz86le/IjL+XM0hPzmOLThvfTAYRFNTEzHqhEIhVCoV5HI5afUzmMd0uVwIh8MRCzNOzKPjdrFcjyzLorKyMkKEJdHhaug4QzqWFhQSiaTXbXU6HRQKBVH47iioFggE0NraCqfTGfEaLmWX+ywEAgGys7MH1ajtOM9E6yc52rjiiitw8OBBlJWVdRFlsdls0Gq1oCgKr732Gnbu3InPPvsMJpMJq1evxtVXXw0gekrujh07cO2110YYVx999BFWrlyJQ4cOQalUYvr06fjnP/8JoGva8Ouvv44HH3wQ//jHPzBjxgwwDIO//vWvePXVV1FbWwuj0Yj58+dj+fLlJG34H//4B1588UX88MMPKCgowN/+9jeceeaZ3Y4x0WFZFgcPHuyT80ooFCIpKQkSiYT0PVUqlcjKygLLsuQ7JRaLIZFIyBzHOYQ5cadQKASpVEpa1CiVyggDMBQKobW1lXz3wuEw6urqALQ7CjhDk3t4vV7U1dURwaaO55ifnx/hfO7cl5wjNzcXQPt16fF4Iox6iqKgVCrJ3MPVpFIURe4RarU6YcqIfD4fLBYLUV4eqa19JBIJWltb8dlnn+Gdd95BdXU1CgsLMW/ePMydO5dvv8MzrPCRV55hgWEY/Otf/8LGjRuxfft2hEIhXHbZZfjHP/6BmTNnRqQEDVSggWdk0rHvqM1mg9PpBMMwSEpKQlZW1pC0N+IUmhsbG+HxeCL+x6ldJiUlgaIoMAwDm80GvV5PtulY78QtqACQ1jOJBvc946K+XOQXaH8vOraaiIXuIkUd4SLmHB2dA5yjojOd37ukpKRBqSnjUhbtdjtpocGNiet7zIl2SSSSiFRHTuwrUVIY+4LFYsGnn36Kp556KqqaaEeD4vHHH8eqVavw7LPP4sUXX8Qtt9yC6urqiOu+J3bu3Ilrr70Wy5cvx5YtWxAIBLBr166o265atQqrVq3C7t27cfrppwMAHn74Ybz22mt4/vnncc4556CxsbFLVHj58uX461//ioKCAixfvhw33XQTysvLR2wJCScoFAtcjXlLSwuam5sj/uf3++F0OiPqXoH2e2pOTg4cDgdqa2shEAig0+mQlpZGFHm574JQKER6ejoMBgNxHKnVarhcLvLg4CLGHEKhEIWFhSgsLITNZkNDQwOEQiGysrKiRoaTk5MRDofR0NAQ8XxdXR1SU1ORkpKCtrY20DQNn89H5iyn00mM87y8PKJQLJVK0dzcjNbWVrS0tJDvdWpqKpRKZUzv72AjlUqRnp4OACTS3NraSloGcW2EEh2/3w+1Wo2bbroJ8+bNw3fffYft27dj5cqVePTRR3HJJZfg97//PWbNmsUL3fEMOXzklWdIsVqt2LRpE9avX4/y8nLk5eVh5syZmDVrFkpKSpCamjrqIx48PeP3+1FTUwOPx9NFxEMmk6GkpGTQrxHOEG1sbOxiKMlkMqSmpkKr1ZKIhsPhgNvtRnZ2NsRiMaxWK0mn7WjwicViqNVqKBQKUBQFn89HUu6Ge6rl0um4h1AoTLhaLK5FRU/I5XJkZWV1MboYhkFzczNomkZycjJZBHIR1Y51gVxKot/vx/Hjx2MyFEwmE5qamrqMTyKRkPTHkVL3++OPP+KMM87A9u3bce2113a7HUVRePTRR/HEE08AaG+9olQq8cknn+Cyyy6LKfJ61llnIS8vD2+99VbUY3CR18bGRrz55pv4/PPPMWHCBACA0+lEcnIy1q1bhzvuuKPLa7nI6+uvv47bb78dAHDkyBFMmDABR48eRXFx8YiMvIbDYaLlwDnvojmSpFIpxo8fD4qiUF9fT+r9Ozve+gJXHtB5bpBIJEhPT4dOp+sSQeWcjNyDc2JxtaCZmZnE4ef1etHc3AytVguNRhNV3betrQ3V1dVdnheJRERwiptvO8/X3Ng6Kqp3/Btor3nnnktJSYFGoyHXtt/vJwr0wwXDMCgvLyfiVXK5PKIv7UA+z+GC69Pr9XqxY8cObN++Hfv27UNKSgruvPNOzJ8/H5mZmfEeJs8oZWS6KXkSngMHDuCll17CW2+9hVAohBkzZuChhx7C1KlTYTAYiNohb7j2H64WiatL5FJaOSMwEetAoyEWi5GXlweaplFdXU080kC7Z7+hoWHAKUksy8JiscDlcsHtdvcYYczIyIBKpUJZWVmXeiXu/ZbL5Whubu6ykAoEAmhra0NbWxt0Oh1SU1Nht9vjYjBy4i+JTE/j44xRj8eDY8eOQa/Xw2QyQSQSwWq1ksU7ADQ2NnbZn1AoJDXRXGSnL8ZmfX191OcDgQBqa2vR2tpK1KyVSiVZ/HZua8SpSHM1wNxjOL+bfbn+Jk+eTH5XKBRQq9VoaWmJ+fX79+/HnXfe2eM2q1evhtvtxs8//4y8vDzy/NGjR+H3+7sVj+K+b3q9HlarlaTOAu0pqMXFxTGPM5HgDAGg3dBKT09HIBCAzWaDy+WCRCIh5RIc3JzIKfx6PJ6INmGxfuZc2m1n/H4/Kisr0dTUhPT0dKhUKnLdcqnHnDAT179VKBRGtMuiaRoWi4VkX1AUBZVKBY1GA61WSzIYuDVBKBSCx+MhAlac6nAwGARN09BoNKAoKmL+7nye0c6b628NgKRXu1wuJCUlEeObq5nNz88nLXWGCpqmUVhYSP7m5ggAJGrORcRjyW6JB+FwGDabDTRNY86cOfjd736HsrIyvP3221i7di2efvppzJo1C/feey8uuOCCEbEW4Rk58MYrz6ARCASwfft2vPTSS9i7dy9MJhP++Mc/4uqrr0Z6ejp8Ph9UKlXM6Wc8XeHSjrg0L5qmibebYRhYrVYEg0EIhUKEQiGoVCokJyfHLWUqFrjUNIZhIhQuOy5CGIbpd5SLZdkuRnF3dKyz1uv1XYzX7gyazhiNRqSmpsJsNkekxJWWlsLpdGL16tXkuS+++AKPPfYYFixYgDlz5vS432nTpuGvf/0rzj///JjGMZLg0og5Oi9CuUWwVCrt4nyIZgSHQiFYLJaI5/qbnsf14WQYhjgFuBo2DpFIBJZlY07/NBgMyMzMHJbobUFBASiKikmUqXMEiougAV0/I6B9sd2RWISDpk+fjp07d2Lbtm146KGHurzW5/PBbDaDoig0NjYSUbOamhoA7U6tqqoqMAxD0kcrKyuRlJSExsZGkgIvFosTvp6ZS93vHJEUi8VISUlBSkpKj68XCoXQ6/UQCoXkHttZwZeiKNLzmet53bmNTXd4vV5UVFSQMXVWHecit91FLpOTk6FWq1FbW0t61XLpy5wAo1arJfuRSqXkHsf1vQXaI8Qd/44FsVgMnU4Hq9VKHF0ASOpzx3tCKBSC1+vFsWPHoFAokJycjFAoRNKPuXTtoYjQcp89wzBEoIr7LJVKZUQbpERLMWYYBhRFQavV4owzzsAZZ5yBJ598Etu3b8f69esxY8YMlJSU4N5778XcuXP5ljs8gwJvvPIMGKfTSWqU6urqMG3aNKxatQpXX301UlJSoFaro6YK8UTC1dVRFEUWxWazGR6PByqVinixuZuwQCAgaWOcZ5miKOTm5hJPulqthkQigcPhGHSRo8GGa8/QGwzDIBgMxlQDyzAMTp48Cbvd3ut+ZTIZ8vPzIRKJUF9fj6ampliHHoFQKIRGoyHRQYFAEFFb2pEdO3bgmWeewcMPP0xEccYiYrE4pl62/anLHQy4lOOe6GzE9UZbWxtcLhfy8vL6pRTbWTzM5/OhpaUFEomEiPRw3xGVSoWLL74Y69atw+233w6NRhPx/eEEm3ojOTkZTqcTbrebpHHv378/YpvJkyfjX//6F37/+993u5/TTz8dCxcuxGWXXQahUIgHH3wQQLvTRyqV4u9//3uvvWY7f5+4CCJnlB05ciTi/1yddbSf0Z7jDCmuZc1gzJ1c/1SbzYbMzEwIBAIEg0FYrVbYbDao1WrodDoSvbTZbFAoFBEGU0dHAkfHuZNzYnICdB0dLh1/7082CGdEdZxPOUefwWDo4iTlrlGNRgOpVIry8vKIqLDH4yEpsgaDgWQRcb25ufPpGAn1er3EGOWcVh3PhVP+5RwtQqGQCEDZ7XYSAY4WbebaDXm9XlgsFvL5ACCZFHa7HRKJZEhUnmmajlBi7thTtuP17vF4Eiotnss2Atrncr1ej9mzZ+PKK6/E7t27sW3bNtx3331Yvnw57rnnHixevBhpaWlxHjXPSCZxV7I8CU9TUxPWrl2L9evXw+1245prrsHixYsxbdq0uDcTT3S42iZO/r+qqgoSiQQMw8DtdndZmEVbsEeLNrEsi5qaGqLOyy0yuNQ6kUhEUhgT2ZDtjpaWFtTX1xNvb3dRAO7a8/v90Gq1UCqVaGho6HbBplarkZeXh2AwiLKyMni93h6Nzp4IhUI4fvw4AET0a+wc+d28eTNeffVV/PnPf8YFF1wAANizZw9ee+01VFZWIjk5GVdeeSVuu+02CIVCXHXVVQBAFvppaWn46KOP+jS2RIMzEjpGRRKRoWph5PP5cPToUWRlZUGtVqO1tZVkVHCPjrXLnBOQW0Dn5uYSIyYUCsHlcqG1tTXqsRYsWIDbb78dZ555JubPn4/i4mKIxWL89NNPeOutt3D48OFex3vGGWdALpfjkUceweLFi/HDDz9g06ZNEdusWLECM2bMQH5+Pm688UaEQiHs2rULf/zjHyO2O+uss7Br1y5cfvnlEAqFmDdvHurq6jB37lysXbsWQqEQU6dOhdVqRUVFRa/GbG90Jw4WK1xKbMeHTCaLmG+AdgOopaUFLpcLJpMJNE0Tg89qtSIcDkMmk6GlpYWk+3K4XC40NDRAKpVCp9PB4/GgtrYWJpMJXq8XTqeT1MZ27gXLPbhIrEQigdlsRigUIlHX/hisUqkUarW6S30/l7ItFoshEAggk8miqrGXlZURhWCj0Uj0ALjor9lsRktLC0kD55x/Wq2WXPsd98nN9ZzYXnNzM4mkisViGAwG0lJHIpFE3OeUSiURT/J6vcRRbLVaI1KLgf8ZzR0/G7fbDaFQiHA4TFSEJRIJUWsezDVPdz1lgfZruby8vMuYE4FAIBDh+D311FNx6qmnoqmpCe+++y7WrVuH5557DnPmzMEf/vAHFBUVxXG0PCOVkbd65Yk7ZWVlWL16NTZv3gyJRII777wT999/PzIyMuI9tISH84o3Nzd3MUgHS52WW2h37BvKKeJ2RKVSISkpiSwSeiPWNjG9wUVOgf+l6XYmEAjA4/HA7XYjNTWVqHF29EBzKdSdxS0UCgWys7MjjNpQKESElzojEomQl5dH0oUzMjKIB55hGBJF7U8klotUdE4nXbt2Ld5//308//zzRGV13759WLFiBZYtW4apU6eirq4Of/7znwEAd911F7Zs2YKLL74YK1aswJlnnjnisxm4hWmiG65A32pGY0EsFhPHSDgcjipW0xterxf79++HTCYjbU7S0tJQW1sbNQqckZGBrVu3YsOGDXjhhRdIXXZxcTGWLl2KAwcOAACam5vR1tYGlUrVRTVUr9fjrbfewrJly/Daa69hxowZKC0txV133UW2Of/88/Hee+/hiSeewNNPPw21Wo1zzz03Yj9cG6zc3Fxs3LgRv/vd71BfX48bb7wRd9xxBwQCAV555RW0trbCYDDguuuu6/P7M9hwzoGOartAe7Sso/EoEong9/vh8XhQVlYWdV9er7fHDAKfz0fquIH2lOjOcHNL5zlNKBRGGLRcZJCbv7kWM01NTd3ec4RCIcRiMYxGI3Q6HYB2h4nX64VQKCQiR5xhyTldO6eEcr1dHQ4HPB4PrFYrWJaFSCSCWq2GyWQi3wO73U5SwK1WK7RaLZKTk6FSqVBfXw+HwwGFQgGDwUAMRS7l2Gq1orGxkWgkcKhUKmRkZEAmk0X0nAVA6odlMhm0Wi2am5shFArhdDpJ1LozXDSU62stkUig1+u7GJdDDUVRGDduHILBINxuNyorKxNKiC8aqampuO+++3D77bdj+/bteO+997Bx40bMmjULf/jDH0irKx6eWODVhnli5uDBg1i5ciW2b98Oo9GIJUuWYP78+TGlm401uJscy7KwWq1EQTAeKY+9QdM0dDpd1LSvjoTDYVRUVBAFSIVCAZlMFrVmr2P/Pa5tDCcqJZPJSBsGTsijo2HNLQ44VCpVlz6gvUFRFIxGI/R6PWlL0F0EVSAQICcnB1qtFq2traSuDmi/4ZpMJtjtdpSXl/dpDBwKhYJES0pLS/HZZ58hGAzi5ZdfxmmnnUa2W7BgAU477bSIdMtdu3Zh7dq1+PTTTwGMnppXkUhE0hjHCpxQEzc3AP9LZeVKBvqLx0Nj27Zk1NdLYDL5ccMNrZDLB/7ecurZKpUKKpVq0Or96uvr0dbWNmQR7USCMwQTRUFWLpfDaDRCq9XC6XSSdjJcNk7HSCcX3WxpaSH9YF0uFxQKBZKSkiIUwDlF4O7weDxobm6G3++HQCCAXq8n7cU8Hg9JPe38PeCi0B2NeU68jRN86m5+FggESEtLI2rkscKyLHw+H4RCISwWC9ra2np1LnORcI1GQ+5vQ5V9FgwG0dbWRkSm3G531H65ww3XZox7/4B25wBXosCtG+RyOUKhELZu3Ypnn30Wx44dw/Tp07FixQpceOGFfNYeT6/wxitPrxw6dAgrV67E+++/j9zcXDzyyCO49dZb+V5e3eDxeNDU1ASr1QqRSNTnWrh4MnXq1KgRPU4shRNR4SKanOAMy7Jwu92or6+Hz+fr4rXmDNdEQiQSwWg0wmAwgKZp2O12IkzCkZOTQ9L9+gMXYeOugdLSUpw8eRI2mw0pKSlYu3YtiQBcdNFF8Hq9EYsshmHg9/uxd+9eSKXSEW+8ctdOoizkhxrOEEhPT4fD4Yip9rqveDw05s0rRlWVFDTNgmEo5OT4sGnTsUExYDvCpZCqVCqo1eoBiU1xC1wumsn1Jx2NaLVaCIVCUhcYTyQSCVJSUrqkpHecx10uFxiGIZ+zTCaD0+lEeXl5t/M4TdNQq9XQaDQRRi23PcuyqKurQyAQgEajIZ/3hAkT0NTU1CdFa+54qampMBqNoGkatbW13e5DJBJBp9NBr9dDLpf32Tjisnza2tpgsVh6dbpxDgu5XE6+M16vFwqFYtDWTQzDoKKigggdhkIhIiIXb6egyWRCSkpKTAY8wzD46KOP8OSTT+Lnn3/G9OnTUVpayisU8/QIb7zydMvhw4fx+OOP47333kNOTg7+9Kc/4dZbbx3WfmgjhVAoBLPZDLvdTnp7DifRetv1h4KCAtITjyMQCKCiogLBYBCFhYWQSCTEGG1tbSU1qNHgFkSJNM1IJBKkpqZCr9eDYRiYzeaYPOt9JZqhxqkNP/jgg7j77rthMBiwdu1aKBQKnH322bjrrrtw4YUXdtkXVzs30o1XTkCsu7rM0cRwOWw2bTJi/XoTGOZ/Cz2aZrFgQT3mzWsesuMqlUrk5+eDoiiEw2EStesvDMOgrq6OGAeJNGcMBlx9t1wuh0QiIWm/gUAAYrGY9ERNRAQCAdRqNWn91draOmj3OM64i6YOHgtisRgmkwkUReHkyZO9bp+WlkbqXvtDOByGxWKJyNDpC1wLNS7dfCCEQiH4/X60tbWRVkmJkM2QmZnZq0p2Z1iWxa5du1BaWoqff/4Z5557Lh5//PERe6/jGVr4mleeLhw9ehSPP/44tm3bhqysLLz22mv43e9+xxutHeA8sZz8f3/VGwdzPCaTCRqNhqQncz97uplxQhoajYYoQnbE4XCgsrKS7KOxsZGkOfZWt8W1vxmO94VTBuXEOTweDxiGIYIqYrEYIpEIEokEKpUKDocDVVVVsNlsQzY+Lp0rGmlpaXjllVdw9913Y9GiRXjxxRdRVFSE6urqHhu7c2IhI5VodXGjleGaD+rrJSTiykHTLOrrhzYzxuVy4fDhwxFCSFzqqUwmQ1JSUp+isxRFITMzE1lZWQBA6sTLy8tHRaSeZVlirHaG6x/NqcxG0yjoDk4siatv5R5+vx/V1dVkjubap/WHcDhMFH61Wi30ej2amwfHMdJdn9lYCQQCUWuCgfaSE51Oh3A4TFKfud60/UUgEEChUEAgEMQ0F0skEvLZAyClMkKhECqVKkJLwmg09ikyzAlcyeVyIn7V2toKsViMjIwMtLS0DEm2R2/U1dVFKEbHAkVRuPLKK3HFFVdg586dWLFiBS644AKcd955WLlyZZe6eZ6xDW+88hAaGhpQWlqKDRs2ICMjA+vXr8fcuXNhs9lgt9uh1+tJJI2LKgQCAVAURZp80zQNh8OBtrY2IpTACeGMdLhFhd1uh9lsTqjm4dyNlOvDx9FRbp8zaH0+H+RyOTQaDdRqNRH9qKurg9frRXJyMpKTk0lUtSOxesZ7WyhxBvBA4FIyO4podF5QsCwLqVQKmqYhFArhcrlQVVU1LJFxt9tNxhhtkZOamhphwM6dOxd//OMfkZqaihkzZoCmaRw/fhwVFRVYsGABACA9PR0//fQTpkyZQuoRRxJjrc51ODCZ/BGGKwAwDAWTaeDzU0pKCqRSabdRplAoFDG3c21Y/H4/bDYbqW00Go1R0yVDoRBJp3Y4HBCJRMjKyoJUKiUZGyaTCQ0NDaRufLTSsda/c7lBZyQSCTIzM0kE1+/3k16rEokEAoEAoVAIRqMRLMtCIBBAo9Hg2LFjA4ruduxtPBhz+FDB3QfVajXC4TBEIhEKCgrAMAypk+0vDMOgsrKyT05Eg8FAUodtNhv5vBwOB9mPRCKBy+Uijta+wAlxcUrLXBbDcK1RuJ64VquVpI/311FCURRmzpyJK6+8Eh999BFKS0tx3nnnYebMmXj66acxYcKEQR49z0iETxvmgcPhwLPPPovnnnsOUqkUS5cuxbx584ihwtWoaDQaMvFyjb8FAgGJPHYmWgrqSCQcDsNsNsNms/VZOKgjXO85tVoNtVpNVBa5Hn8DhaZpZGRkELGI7mBZFk6nkxw32k1mICmPYrG4S3Sh8/4GuvDhIqyJkCLVGxKJhPSlfeSRR2C327F69Wry/5aWFtx9993QaDT43e9+hy1btqCsrAxCoRA5OTm45pprcO211wIAvvnmGzz//PNoaGhASkrKiGyVk5SU1KVtEE//Gcqa15SUFGRmZuLIkSMxGz0dW5NwPUq52nigff6x2Wxobm7uNjOBpx2hUNhtix/OcOXmVbFYjOzs7C73XI/HA4fDQdrT9LW2NFGhaRoKhYI4OsLhMFGpZ1kWNE1j8uTJxJmiVCoHzYnOpQ6bzeZer2GappGWloaUlBRyH+BavR0/fpy8XiaTIT09HUqlckBt7LjrweFwoLq6GsFgEHq9nggkDrbDgROiS0lJIZkXKpVq0NTwWZbFe++9h4cffhhVVVWYN28eVq5cCZPJNCj75xmZ8MbrGCYQCODVV1/FypUr4XQ6cd9992HZsmUkrdJiscDj8RBl0L5EqyiKwuTJk0dkL1EO7gbV2Ng4oEidUqmEQqFAWlpal551AHDixIkB92tLSkoiTe8BkBYcnT24LMvi8OHDQ+aRjSbQMNg3Sy51NtGmrmiKrzqdKOK9Hkjq3mhBrVaDYZguLUd4+s9QqA0LhULk5eVBpVKR+Y8T9rHb7aipqel2XkxNTUVaWhp5TTTC4TBsNluPLVt4QNSAO853nXuQAu0KvFlZWST7pL6+Hnq9HiKRCOXl5WPiPRYIBMSY5Vr9KJVKNDc3w2KxkLIYTj2bU8HtjaamJrjdbtI7llsPBQKBbtvqdIab+6VSKdLT06HVakFRFGnjZrfbwTAMTCbToIo6cSn3J0+eRDAYHNSIOWe0pqWlDXl2XSAQwCuvvIKVK1fC7XZjyZIl+OMf/zjsbYp4EgPeeB2DsCyLDz/8EA8++CAqKiowb948LF++HHK5nETiQqFQnyc4rn4yKSmJePpGEtxXwel0orW1FXa7fcAGklwuR2FhYRcvZCAQgM1mg9VqHfAiXiQSYcKECeQYXq8X5eXlCAQCUCgU0Gq1kMlkpAazs6LuSCJR1ZujRb/y8gJ4440jEUaESCQijoWxjFqthl6vh8ViGbDjhqd/0DRN2lh4PB5IpVIixkZRFJKTk3tMsQyHw2hqaiL9P4H/tagyGo0xOS5ZlkVzczMaGxt7vN9wyqUWiyUhv//DDddiSSAQkJZsHFyrklAoBKVSOaadRBKJBBMnTgSAqGrEYrGYGLKcMRuNUCiEI0eORL32ZDIZUYXvS8YSJ1Sl0Wjg9Xqh0+kgEomIgNdgr598Ph9aWlrQ2tpKUqq5TLr+QtM06cfL9d4dahwOB1atWoXnnnsOcrkcjz/+OObPnz+iAyU8fYc3XscYx44dw5IlS/DZZ5/h0ksvxbPPPotJkyYBaDeouGbfsSyuOTVVrn5yMFNFhhOfzweLxQKr1QqFQgGHwzHgBZJYLEZSUhJSUlLIpMq9v1ardVDT5QQCAYxGI5KTk8mxbDYbTp482eVG2vHmMhK/+tFSkhOBviq+JmLroOGG89pz6txjeZEdLzpmAnDzCJf+1xe46JFQKITVau3SA7Q3uPrWcDiMUChEame5+UyhUBDHW+e5mf8ujS041ebu7gMikYj0UxYKhSguLgbQfo1VVlZGbXvGRSMzMjJgNBpJWm/H+yXXs7xj6x/u947ZCVxKfF+d/zRNIysrC1qtdsjWUVzf+VAoBIVCQfRMBqpyLRQKkZycDKPROGxrwPr6eqxYsQJvvPEGJk2ahLVr1+K8884blmPzxB/eeB0jOBwOPPHEE3jhhReQlZWF559/HldddRWAdqPK6/USg9VsNnep7eysZNjRSz8S4epY7XY7qQXy+XwDWgQJhUJoNBokJSWR+hq/308M1qFWzKRpGgaDoceWA4nYuiZW1Go1nE5nQo79qaey8NFHSQiF/pciKRQyuOoqM5Yv719LhdEEZ2BwafPc39ycw9WvcQSDQZIBwjO8qFQqjBs3bkBtb4YKhmEiRId4w3VsotPpQNM0mScEAgHpb8oZoNGora2FxWKBUCgkadQURaGkpAQMw5AUdp/PB7FYDK1WS9qU9YVQKETa1/j9fvh8vi7GLJfeHS2bSKPRID09nfQAHwoYhoHb7UZ1dTVxGA0UTkhMrVYP29rw559/xqJFi/D999/jxhtvxLPPPouMjIxhOTZP/OCN11EOwzDYunUr/vCHP8DhcOCRRx7BAw880Gt/MbfbDa/XS4zVRFzI9BW3243W1lZ4vV5SuxIIBEitSX9QKpUk9Ucmk4GiKPh8PmKw9ubRHO60rkRNu+0JTkUzkdtlxKvX5mhEp9NBoVDA6XSCpmmiUMxF4xiG4Y3aIUatViM/Pz9u8z7XYotLi62uriZpmR3n1ERWvOUZPjinLJcFZjQao167HdvSOJ1O1NXVwWAwIDk5OWKbYDAIv9/fxanWH0KhEBoaGqL2tubayRgMBrjdbjgcDiKuxal0D1QdORbsdjvsdjs8Hs+AssI61/MOFwzD4M0338Qf//hHOJ1OLF++HEuXLh1wH12exIU3Xkcxhw8fxvz58/Hvf/8bN9xwA/7617/22ENytMHV/XBtYhoaGga00OEim+FwmKRJc2ltwWAQra2tsFqtMQtjaLVaaDQaVFdX92kcAxH96Um9MhHhxEoS3eAeSsXXsUhGRgbq6uqi/o+r9+ssYMMzuGg0GuTn58clu6a+vh5NTU1EHCfafCeRSBKqXVmiMZCodCyvTUpKIhFMLqU2nlAURfqv5ubmxpS+yp3jcFzj4XCYrEU69mFnGAYCgQA6nQ56vR5KpRJer5doAej1+mExYDna2tpgtVr7pUUglUqh1+sjSpiGE4fDgZUrV2LNmjXIzs7Gyy+/jIsvvnjYx8Ez9PDG6yjE5/PhqaeewjPPPIP8/HysX78eF1xwQbyHNeRwl3JVVRWcTueg9pSUy+XIzc2N6sljWRbHjx/vUwQ1NzcXFouFeHcNBkO3vRSB9purXC5HKBQibXba2tr6dS5c2uZIWfB3FCZJZIZC8XUsIpPJUFJSgn379sW0+B7NCs4ZGRlQq9UIBoNkUTmYcCJ7KpUKUqmUlE/4/X7IZLK4ODuDwSAOHTrU62cvk8kQCARGjCNuuBmIo5ITMhKJRHC5XHC73eTzkMvlyMjIgNvtJq1pEqXlkVarRW5u7rBlDITDYVRVVYGmaUgkEkilUlJSJRAI4PP54HK5oNPpohrTLMuSsi3OmA2Hw1AqldDr9UQ5ebgdSD6fD21tbfB6vXC73TFfQxRFQavVwmg0Qi6Xx62s7NixY1iwYAG++uor3HrrrVi9enVEdJ1n5MMbr6OMPXv24K677kJVVRWWL1+Ohx56aNBk1xORUCiEmpoaeL1e0DSNUCg06II+XNuH7m6IjY2NaGho6NM+RSIRUSG22+2oq6vr8QaRnp6O5ORk+P1+iMViHD16NOGNucFEIBBAJBKNiXYPY5309HQ4nc6YeyonqohXb1AUFVG+EO3azsvLg0wmw+HDhwftuAqFAiqVCmq1GgqFIuFKQmpqakiKJee44uqlBQIBvF4vKdUwm80JXU6QCHAGRH+WemKxGJmZmVCpVKQljF6vB8MwqKioSCilcIPBgKysrGE1mFiWhcPhQENDQ5frsKPqM03T0Ol0MBgMManyhsNh+Hw+UBQFqVQa1++ow+GAy+VCW1tbn9YcSqUS+fn5cVMBZlkWmzdvxtKlS0HTNJ577jnceuutI1anhScS3ngdJVgsFixbtgxvvPEGzjnnHLz66qsoKSmJ97CGlHA4jIqKCjidTiJ+MJhRGJFIhNzcXKhUqm63sdvtKC8vj2l/AoEAJpMJLpeL9Hdzu91QKpXQ6XQIhUKora2N+lqNRoNx48YBAOrq6tDcPPbqKGmaJu1++Glr9CCVSkmrCq4/ZazfYZqmIRQKE954pSgKKSkpJCojkUggEomIqNvRo0eJ84qrGxOJRJBIJPB4PDHPMT1hMBjIfhMVhmFw6NChbj9/kUhE6qAHO9rOqecLBAK4XC5+jgGQmZmJlJSUiOd6Uu2NNwUFBVCr1cN+XJZlYbfbY2pDJ5VKoVAoIBKJkJ6ePmKMKb/fD4vFQjLGYvl+pKSk9EvwajBpaWnB/fffj7///e+YMWMG/va3v5G1FM/IhTdeRwHbt2/HPffcA5/Ph1WrVuHOO+9MOG/6YOPxeNDY2AiXywWhUIjs7Gw0NDQQxcGBotVqkZ2d3a3XMBwOo66urk+puwKBACqViqRade7PCrSnu3ROweLqYZKSkmC329Hc3DxmF1Y0TSM3N5fcPDu3Lej8XDgcJh5xXr02MZBIJFAqlRCLxcTZ5PP54PV6+xxZl8vlCR95E4vFyMvL61b4paWlBWazGX6/H2lpaaSnKUdzc3O39b+xwCmmjhTxkmAwCLPZDJfLBbvdPizH5D4jrp0ZlxIrl8uh1Wphs9kgFArhcrlGTLlFf+EcQtz9SqPRkGsnEAigra0NZrM5zqOMzsSJE+OaafbLL7/0qfaXy4CQy+VEGJNr8ZPoOBwO1NXVEWFPzvnY8doQCAQwGAwwmUxxN9I//fRT3HPPPWhqasLKlSuxdOnSEdnakacd3ngdwVitVixatAhbt27FrFmzsH79eqSnp8d7WEMKy7JEuW+oap3EYjFSU1O7rZEIhUI4duxYTDcpkUhE6mBEIhGam5vJuIVCIaZMmRKx38bGxi6N1Hki4dIt+yoQwqVocgqlsaaljlU6LjY4AZeebhfcgiva4p4T3hIKhQiHwzF77nsbHxeJT1Q0Gg1ycnJ6XYy63W6IxWKSauj3+0nqtMPh6HGuE4lE5LUMw5BUTqVSCZPJBKVSOajnNJwEg0E0NDTAZrMNmeNJLpcjLS0NlZWVXa5dvV6PlJQU4ngIh8NoaWlBU1MTKIri620TjF/96ldxddwHAgEwDIPa2tp+pVQLBAKIxWIUFBQkdIYEB8uyRHAKaJ/7W1paEAwGYbfbEQwGieM9EUoU3G43/vSnP+GFF17AWWedhU2bNvFR2BEKb7yOUD799FPcfvvtcLvdePHFFzFnzpy4e7aGA5vNhsbGxohG9kOBRCLBxIkTu/2/2+2GxWJBW1sbWJaNEGvo+Ojo2fP7/WhpaYHFYiF1MFOmTIHdbofFYoHdbh+zEdW+wKWIDwShUAiGYUZ9FKW/RFMb5URJOvZD5hYjAoEAGo2m3yJi/R0j0L9avuEgPT0dqampfZqXOQEYm81GnuPq3rgauNTUVLJttIiG1WpFY2MjSkpKRsU9obq6mlxXYrEYUql00Got1Wo18vLy4Ha7ceLEiYj/SSQShEIhyOVyFBYWRn292+1GXV0dEd4TCATkZzgcjhDx49v6DD1paWkJ4cBnWRatra2oq6vr8/zERbxjEZ6qqKhAeno6ZDLZQIY7JHDthjjBKqFQSPp8x5tvvvkG8+bNQ3NzM5599lncfffdcTesefoGb7yOMJxOJx588EG8+uqruOSSS7Bhw4Yx0ZCZiyi0trb2mrolFosHZNgKBALo9XpkZWX1OB6XywWJREJk7Ln+gx0FFrxeL9ra2kjKaigUQjAYJMIHg2GIjRU63vQGY9oaqUI/8UahUBCV17G2GOdqVt1ud4+RQJPJRIzMWPH5fKioqIhInRaJRBg/fjxJWRWLxRCLxQgGgxAKhd0uBBmGGTWLMW6ulcvlJILNtdIZCElJScjOziZ1x2azGQ6HA263GxRFobCwEAqFgohF9YdAIACz2UzE5kKhUMKm3I4W4lX3Go1oZUCxolKpkJ+f32Nqa11dHcxmMxGDksvl/R3qmMPlcmHZsmX429/+hosuughvvPHGmGolOdLhjdcRxN69ezF37ly0tLRg9erVuOuuuxLCizUccHU2fr+/R8W7lJQUBIPBfotJpKWlIS0trdf31ev1wul0wuPxwOPxRESjAJBIrMPhSNjI0EhhqFr7cNFCiqLgcDjGlHrzWEcgEEAqlZJUZqFQCJFIRIxEgUAApVKJYDCIQCCAQCCAUCiE7OxsiMVi0uLC7XbDbrfDZrNFXJ+cAysvLy+mGjy73Y7Kysoujqxx48ZBo9EM+vmPdFiWRXV1dUyG4M8//4y7774bX331FRHfU6lUKCgoiJjng8Eg6uvroVaroVarB73u0OPxoK2tDRaLpVuH5Whu+zRcCIVClJSUDGtv1O44efLkgIStFAoFdDodkpOTu3WgWCwWVFVVgWVZyOVyJCcnIykpacysDQfKZ599httvvx1OpxPr1q3DrbfeGu8h8cRA4leF8yAcDuOpp57C448/jjPPPBOff/458vPz4z2sYYFlWTQ2NpL6os4Ghk6ng9FohFQqRTgcRltbW59qRkUiERGkUCqVMfUm83q9OHbsWI/GlN/vj3vT9tHEUET4wuEwLBYL1Go1CgsL0dLSQlp08IxeehNjS0pK6nUfFEVBIpHAZrOBoigYjUaIRCLy4MSoeptLWJZFc3Mz6uvru/zPYDDwhms3cPXOXE2dRCLBp59+ihtvvBFnn302tmzZAovFAr1eH7V20O12o6amBmq1GjqdDkD7vSAnJyem41dVVSE3Nxf79u3D1KlTY3qNXC5HVlYWMjIyYLPZ0NbWBqfTSVSOaZomKZaJqOQ7UgiFQjh+/DgyMjKgUqkQDAZJq7XhZqAGtNvthkQigcViIXXXnG4D0H4P4/Q0OCHIxsZGtLa2QqPRwGg08qJEvXDppZfil19+waJFizB37lzs3r0b69ev77HLBE/84Y3XBKeurg633HIL9u7diz/96U949NFHE1KJzmKxoKmpCSzLQiwWIzk5mUS1BgI3GXeEpmkYDAYYjcaIm4PX643Z+MjMzITBYOhzOhgnxsBHU6Nz1113oaioCA888EBM25eWlsLpdGL16tXdbjPU77XD4cCRI0eQkpKClJQUXjBrlELTNDIzMwc1KmE0Gvv9WoZhUFVVFdVQEYvFY6IcZCAYDIaINi6fffYZ7rnnHmzatAkikQiTJ0+Gx+OJmlHBMAza2tqg1+uHc8gA2q9DvV4PvV4Pv99Psorkcjl0Oh08Hg9MJhMRheIjsX3H7/ejoqICCoUCHo8HLMvGpW1LamoqpFIprFZrv+u0ufY0HEajEXK5HBqNBg6HA9XV1REGKpcp4vF4SAZYd0rnPO1otVq8+eabuPTSS3HPPffgP//5D9555x1MmzYt3kPj6YbRURQzSvnggw8wZcoUnDx5El999RVKS0sTznBlGAY1NTWorKwERVFIS0vDuHHjoNVqB2WBqFAokJ2dTSKsWVlZmDx5MjIzMyMMV5Zl4fP5YqofNZlMSElJ6ddNjKbpPouwxIPS0lJMmzYNf/7zn7v875lnnsG0adNQWlo6/APrxIMPPpgQ4+AiYFarNabIG8/IQi6Xo6SkBAaDIa7fXZZlEQqF4HQ6cezYsW4jbEajcdTUrA4VHd8fl8uFd999F4sWLcLMmTOxefNmCIVCyGSyLp83RVE4fvw4Fi5ciJSUFGRmZmLx4sURtYkURWHHjh0Rr9Nqtdi0aRMAIDc3F0C7ui1FUTj//PP7dQ4SiQQmkwmTJk1CTk4O8vLyMGHCBKSkpGDKlCkoKCiARqNJuPv+SKFjT/CWlhYcP358WHUOhEIhDAYDCgoKMGXKFOTk5Ay4LpXL1Dhy5AhaWloQDoe7Paeqqiq0tLSgsbGRd7jHwJw5c7Bv3z7odDqceeaZ+Otf/zrmdB1GCvzdMQHx+XxYuHAhrrnmGkyfPh379+/HueeeG+9hReD1erF//34cPHgQHo8H2dnZyMrKglAohM1mG9SUWYPBgLy8PGRkZJDaD6/XS1R7gfaaserq6l4naK1WG1O0hFMGrqqqivDcm81mnDhxYkRMaEajEbt3744QgPH7/fj000/7LCbTEW4BPhgolcqESs8JBoNwu90wGAzxHgrPIJGamori4uK49zltbW3FgQMHcODAARw/fpwIvEWjtrYW+/btQ0NDA7/ojIFt27ahuLgYRUVFmDNnDt544w2wLEvKQjj0ej2USiXuvPNOzJ49GwcPHsS7776LvXv3YuHChTEf78cffwQAfPHFF2hsbMT27dsHNH6ulRT3O6fKKpfLMW7cOGRkZIxIZ0ZpaWlEFs5dd93VY5bNUON2u3HkyJGY+gdXVVWBoijs379/UI4tFAqRlJSEoqKiAUf8uehqRzXr7rBYLGhoaMCBAwd4IzYGxo0bh3//+9+4//77sWzZMlx++eVobm6O97B4OsG78xKMyspK/Pa3v8Xhw4fx0ksv4Z577km4KF84HI4QF3G73cRrLZFIukRFBwOub5jD4YDT6SQRWe6G7/F4et1Hampqr2JMLMuiqakpYpI3GAxEJXIkTWLFxcWoq6vDV199hcsvvxwA8NVXXyE1NTWinUAgEMCaNWuwe/duuN1ulJSUYOnSpZgwYQKA/wmerFmzBi+//DLKy8uxbt06TJgwAX/5y1/w1VdfQS6XdxE6eOmll/Djjz9i8+bNEc/fdNNNuPDCC3HnnXd2SRu+6667UFBQALFYjA8++AAikQi/+c1vMH/+/KF8qyLw+XwjcqHIE4lIJEJubm5COEcCgQDq6ur65PQaKVkeicCGDRswZ84cAMBll10Gu92OPXv2YMKECaSfs9FoRE5ODu68807ccsstWLJkCYB2ddq1a9fivPPOw8svvxyTk4PrAZ6UlDQgR2Cs6PV6OByOiPTRoaS0tBQff/wx+Vuj0WD8+PFYvHgxCgoKhmUMPVFbW4s33ngDP/zwA6xWK5KTkzFx4kTMmTMH48eP7/G14XAY5eXlRNTJbrfD5XJBoVBAq9UOeZSbpmnk5ORAJpNFrXUfKsLhMBobG+F0OqFWq6HVauPu0EtUxGIxVq1ahYsuughz587Fr371K2zbtg3nnHNOvIfG8//hV2gJxCeffIJTTz0VVqsV33//PRYsWJBQCxefz4fGxkYcPny4S9SApmlkZGRg/Pjxg1Lr2hGPx4OjR4+iuroaVqsVGRkZyM/PjxBg6CkViEsZ7K3exe124+jRo12iHeXl5Thy5MiIMlw5rr76anz00Ufk7w8//BBXXXVVxDZr167Fl19+idLSUrz11lvIzMzEokWLunin161bh4ULF+L9999HQUEB1qxZg//7v//D6tWr8dJLL+G///0vysrKyPaXXXYZDh8+jLq6OvJcRUUFTpw4gcsuu6zbMX/88ceQyWTYtGkTFi1ahNdffx3ff//9QN+KPtFTVIwn8dHpdBg/fnxCGK5Ae6/SvmZrhMNhXkAsBsrKyvDjjz/ipptuAtAe4br22mvx3HPPoba2lrzvnOPywIED2LRpE5RKJXlceumlYBgGlZWV8TyVbqEoiqQVK5XKYTnmWWedhU8//RSffvop1q9fD4FAQAz+eHLkyBHMmTMH1dXVeOSRR/Dee+/h2WefRU5ODl544YWY93Py5EkcOHAAVVVVaGtrQ3V1NcmKaG1tHVL1ea5fc15e3rCu8ViWhdPpRENDA8rLy/k2fb1wySWXYN++fRg3bhwuuOACrFmzho9cJwi88ZoAMAyD0tJSXHnllTjrrLPw3//+N2YFw+FELBaDYZiISV2hUCA3NxeTJk0akjoth8OBsrIyckxu/9yE7/f7UVtbG7V2jKIomEwmFBcX91pn0tbWhmPHjkU1WkbCBB/tBkhRFGbOnIn9+/ejsbERjY2NOHDgAK644gqyjdfrxfvvv4/77rsPZ599NvLy8vDoo49CIpHggw8+iNjf/Pnz8etf/xoZGRkQiUT44IMPsGTJEpx++ukYN24cSktLI9KJ8/PzUVhYiE8//ZQ89+mnn2LixIk99lMrKCjAXXfdhaysLMycORMlJSX46aefBvL29Bn+BjUyEQqFyMnJQW5ubsLUCQYCgX6LtTQ2NvKCPb2wYcMGhEIhpKenk7ZHGzZswOeffw6Px9NF+MrlcmH+/PnYv38/eRw4cAAnTpwgKv4URXWZA+LdSouiKOh0OhQVFWH8+PH9EhzsCyKRCAaDAQaDAUVFRZg3bx7RBeBoamrCQw89hPPPPx8XXnghli5dioaGhpiPsXPnTtx6660499xzcemll2L58uU9RpdZlkVpaSmysrLw+uuv45xzzkFGRgaKioq6pCSXl5fj7rvvxtlnn40ZM2bgqaeeisjQ8nq9eO2113DFFVfgzDPPxM0334zvvvsOTqcTNTU1OHbsGID2UiGur/Xtt9+O3NxcyGQyFBUVYc2aNX15S7ug0+lQXFw87EEKlmURDodhs9n4e10vpKWl4V//+hcWL16MJUuW4KabboopXZtnaEmMu/sYxmKx4JZbbsFnn32GlStX4pFHHknIlEWPxwOLxUJqWQUCAemHp9FohkSOnfOGAu21kQaDAVqtlhyLYRicOHGCjImmaZhMJtJCQSaTxZQW43K5UFNTM+jjH06iLbZYloVWq8W5556LnTt3IhwO4+yzz4ZWqyXb1NXVIRQKYcqUKeQ5oVCICRMmdIlCdEzHqqurQzAYxMSJE8lzGo0G2dnZEa+57LLL8OGHH+KOO+4Ay7L47LPPcPPNN/d4Lp3T0gwGw7Cly/GMTLh2NampqQnXGqKjEFCsSKVSZGZmQigUJuT9IFEIhULYsmULnnnmGVx++eUIBAIoLy8HACxbtgz79+/HpEmTIl5zyimn4MiRIxg3bly3+01OTo5QuT9x4kSE4cOVxcTLsSmTyZCdnQ2/3w+lUkmEe4YKj8eDXbt2ITMzk9QQh0IhLFq0CJMmTcLrr78OgUCADRs2YNGiRXjnnXdiak0TCoVw9913Izs7G1arFc8//zxKS0uxdu3aqNuXlZXh5MmTePLJJ6N+L7hMC6/Xi4ULF2LSpEnYvHkzrFYrnnzySaxatYoIBL799tt466238Mgjj6CoqAgffvghli5dim3btiErK4vss7m5GYcOHYJIJIJWq8XWrVuRlpaG7777DnfddRfS0tJwww039PUtJcjlctLqZjgJhUKoqqpCQ0MDcnNzIRaLIRKJEirbL1EQiURYvXo1fv3rX+O2227D6aefju3bt6O4uDjeQxuz8HfFOHLw4EGceuqp+PHHH/HJJ5/g0UcfTbiFCsMw8Pv9cDgcaGlpgc1mA9B+07ZarWhoaBj0yY5lWTQ0NKC6uhoKhQITJkxAUVERkpKSIhamDQ0NxHBVqVQYP348UlJSoFQqodPpejVcWZZFIBDAyZMnR7z3sbuURJZlMXPmTHz44YfYuXMnrr766n4fQyaT9fk1l156Kaqrq3Hs2DEcOHAAzc3NuOSSS3p8TeeIWTTDnCc2uLrP0bwg0ev1mDBhAkwmU8IZrgD65aWnKApqtRpyuTzh7gmJxIcffgiLxYLzzjsPwWAQycnJuPrqqzFjxgzccMMN2LJlS5fX/PGPf8R3332HhQsXYv/+/Thx4gQ++OCDCMGmCy+8EOvWrcO+fftIzX9HYywlJQUymQyffvopmpubYxIAGmwYhkFWVhbS09OJIT6Y3/O9e/di+vTpmD59Os4991x88803+Mtf/kKux927d4NhGPzpT3/CuHHjkJubixUrVqCpqQn//e9/YzrGrFmzcPbZZyMjIwOTJk3Cgw8+iO+++65bDYva2loA6LUf76effopAIICVK1di3LhxOO2007Bs2TLs2rULZrMZAPDWW2/hd7/7HS699FLk5ORg8eLFKCwsxNtvvx11n8FgEDfffDMkEgm8Xi8uvPBC3Hrrrdi2bVtM59oT8Zy3AoEAysrKcOjQIRw9epTvMdwD119/PRFrO+2007ookvMMH3zkNU7s2LEDc+bMQWFhIfbs2dMlYjWcsCwLj8cDmqZhNpuhUCgQDoeh1+uJIdld5Cs1NXVQF1dc6x2z2QytVovc3Nxu98/15QTQZ48h56WurKyMezrYUHPmmWciGAyCoiiceeaZEf/jUoAPHDiAtLQ0AO0e2SNHjpAasmhkZGRAKBTil19+IYIlDocDNTU1OPXUU8l2RqMRp5xyCj755BP4/X6cccYZcemtOBYRCAQoKCiA0+kclca/QqFAZmZmwvcw7E/klU8V7h2WZfHyyy/jnHPOweTJkyEWi4kRIJPJcMMNN2D16tU4ePBgxOsmT56Mr7/+GsuXL8f06dPBsizy8/Mxe/Zsss3q1avx+9//HtOnT0d6ejrWrFkTYZAJhUKsXbsWK1euxGOPPYbp06djz549w3LeHDRNEwetQqGAVCqFTCYbNOPj1FNPxcMPPwygfW5///33sXjxYmzevBlpaWk4ceIE6urqunRC4MTJYuHo0aN49dVXcfz4cTidTuKEbWpqQl5eXpftY53HKisrUVBQEOFwnTp1KhiGQXV1NSQSCVpbWyMyjgBgypQpOHHiRLf73bZtGz788EM0NTXB7/cjGAyiuLgYtbW1JEuCU4umaRpCoRBqtbrHsTqdzoRZg3i9XtTU1EAikcDtdkMqlUIqlcYURR8rlJSU4IcffsDvf/97XHvttXjqqafw8MMPj2oHcSLCG6/DDMuyePrpp/HII4/guuuuw+bNm+O2+GJZFg6HAw0NDfB4PBERLpVKBYPBgObmZqjVauj1eni9XrS2tpL0Fq4mZjDH43Q6YTaboVKpehUz6EnROBwOk0Vjx5uHxWIhCn8URQ1qS59ERSAQ4L333iO/d0Qmk+G3v/0t1qxZA7VajdTUVGzZsgU+nw+zZs3qdp9yuRyzZs3CmjVroNFooNPpsH79+qiOhssvvxyvvPIKgsEgli5dOrgnxxMViqIwbtw4SCSSHhdjIxWJRIKCggIIBAKwLJvQCwduIdgX+GhrOy4XsG4dUFkJ5OYCCxcCnF4RwzDYtWtXtwvr008/ndzPFi9eHPG/0047Dbt37+72uOnp6fjss88inuOyjjjuuOMO3HHHHX08o6GBE3SSSqVgWbbLWPuDTCaL0CYoLi7G+eefj3/+859YsGABPB4PiouL8eSTT3Z5rU6n63X/XGrvmWeeiSeffBI6nQ5NTU1YuHBht8Ycl85bVVU17Cmbn332GdasWYMlS5Zg0qRJUCgU2LJlCw4fPoyWlpZuX5ebmxvVYevxeFBfX9/vevihIhQK4ejRowDa5yGWZWEwGBAOh6HVaqFWqxMyw2U4UalU2LZtGx5//HEsX74chw8fxuuvv96v7DSe/sEbr8OIz+fDHXfcga1bt+Kxxx7DihUr4rpICQaDsFqtJEWno+HKReFSUlJAURSqqqpIug0HRVEIhUKD1hYnEAiQtgahUAgulwssy5I6lkAgALfbDZlM1mWSCAaDcLlc5MGdk0AgQGZmJrxeL0KhENxu97DXliQCPSlULly4EAzD4LHHHoPH40FJSQlefPHFXj3G9913HzweD+6//34oFArccsstUVMkZ8yYgVWrVoGmaZx//vkDPRWeGOBUSZuamhLGqz+Y+P1+/PLLLwDaF9mFhYXEiA2HwyRyKRaLuxi2TqcTMpls2ASdFApFTDXbIpEIOp0OOp0u4aPJw4HLBfz618DRo4BAAITDwFtvAd9/327ACgSCMb+I7ohCoYDP50MgEBiSUgsuosg5fIuLi/H5559Dp9P1SwG5qqoKdrsdCxcuJNk7R44c6fE1RUVFyMvLw9atW3HJJZd0WT85nU6oVCrk5ubi448/htfrhUKhAMMw2L9/P2iaRnZ2NpRKJZKTk3HgwIGITKEDBw6QFnGdOXDgACZPnozrr7+ePBdLq5vq6mpIpdIuopEde/smKlwknFM9dzgcMBgMCaktMNzQNI3HH38c48ePx7x581BeXo4dO3aQtTPP0JLY35xRRFNTE6655hocOHAA77zzTkSK0nARDAZx6NAhUBQFiqLAMAy5wVEUhbS0NCQlJUUYo9xNMNqNMBAIoKWlpYuaY39gWRY+n4+0o/F6vSgvL4der0dLSwvcbjdZkMrlchQXF8Pj8aCtrQ1Op7PbCGo4HEZVVdWAx5dIeDw0tm1LRn29BCaTHzfc0Aq5PLLmlROl6I6OqowSiQTLli3DsmXLom47bdo0/Pzzz12el8vleOKJJyKemzt3bpftVCoVvvvuu6j77jzOV199tcex8vROVlYWtFotLBYLmpqa4j2cIYObD6RSKZqamqBSqSCRSFBZWUmiGXq9HtnZ2RGLXI/HA4FAMKzGa28kJSUhOzs7oSPIw826de2GK8O0P4D2v9etAx56KL5jS1SkUilKSkrQ2tpKRAhpmu5zmyagfb3Q1tYGoN0o3LZtGzweD6ZPnw6gPaPmzTffxAMPPIC7774bKSkpaGxsxFdffYW5c+fCaDT2uP/U1FSIRCK8++67uO6661BRUYHXX3+9x9dQFIXHHnsM9957L+644w7cdtttyMnJgdfrxTfffIMffvgBr776Ksn2WbFiBRYsWIBAIIBnn30WV1xxBZKSkgAAt956K1555RVkZGSgsLAQH330EY4fPx41kgy0z6s7d+7Ef/7zH6Snp2PXrl04fPgwTCZTj2NmGAYVFRUoKSmJmHNkMhlyc3ORlpaG8vLyhM8C41KgrVYr3G43VCpVTNoio53Zs2cjPz8fs2bNwumnn44PPvgAp5xySryHNerhjddh4PDhw7j88ssRDofx7bffYtq0acN6fC49uKWlJaohSlEU8vPziYpgRxiGQVVVVZc6GoFAgLS0NNKsva/4fD7YbDYwDAOtVouKigqoVKqIGy3DMOTm2RGPx4PKysoxKfPu8dCYN68YVVVS0DQLhqGwa1cSNm061sWA5RlbiEQi5OTkQCKRkBqysUBbWxskEgn8fj8sFkvEQt1iscDr9SI/Px8SiQThcBgCgaBfi/n+IpPJYoqE8YZrJJWV7RHXjh+VQND+PE/P6PV6+P1+WK1WaDSafvUL/u6770g/boVCgezsbDz99NNk/SKVSvHqq6/ixRdfxLJly+DxeJCcnIzTTz89JoeNTqfDihUrsH79erz77rsoLi7GkiVLei0tmThxIrZs2YI33ngDTz31FGw2GwwGAyZPnkxeK5VKsW7dOrz44ouYM2cO5HI5rrzyStx5551kPzfeeCNcLhdeeOEFWCwW5OXl4bnnnotQGu7Ib37zG5SVlZH6xksvvRTXX399t47ZjnDCkAUFBV2+51KpFEVFRThx4kRC9xenKIqs2fx+PynxysnJQXNzMxHLHIvz2LRp0/DTTz/hmmuuwfTp0/Huu+9i5syZ8R7WqIZix9rqf5j5+uuvcc011yArKwu7du3q1Us3FASDQdhsNjQ3N3fx7nU2XFmWhcvlgtfrRTAYRCAQgNVq7bLwUqvVyM/PjyntmWEYWCwWWCwWCIVCSKVSuFwusrjur2d4LLJpkxHr15vAMP+7QdA0iwUL6jFvXnMcR8YTT3Q6HbKysojoWm1t7Zhz7HQHRVEQCAQoLCzEsWPHwDAM8vPzI1pGDTXHjh3rse5Vo9H02LplLPL008Dy5ZHGK00DTz3FR15jJRQKgWVZ1NXVkdT1WDJ3RgsymQypqanQarWgaRpWqxWVlZVxmxtTUlK67XEeCoVQXl7eL4G3REGtVndpdTeW8Hq9uOWWW/DBBx9g/fr1mD9/fryHNGrhI69DyLZt23Drrbdi+vTp+Mc//hE1sjnUcI2oW1tboxqueXl5EX3bbDYb6a3aHRRFQSKRoK6uDgqFgqThdIRlWdjtdmg0GjQ3N/fYuJw3XGOnvl5CIq4cNM2ivl4Sx1HxxAuappGVlQWFQoHGxkaYzea49Z5MNIRCIViWhVAoREFBATweD5lrhlvNV6FQdFmUmkwm6HQ6vnazGxYubK9x7VjzWlLS/jxPbHBpqrm5uTAYDDh2rA7z5uWMmcwdr9eLyspKmEwmpKamku9bRUVFXNYdLS0tpESr83eem6dOnjyZcCJOseJwOOBwOKBSqcZkBFYmk+G9997DkiVLcPfdd6O2thZPPPHEmHwvhhreeB0innvuOTzwwAO45ZZb8MYbbwyaqFGscDWkTU1N8Hg83RquXPSB2z4WmX2WZUkaUrT0oEAgQOptGIbh+4YNIiaTP8JwBQCGoWAyJXa9DM/go9VqoVKpYDabR11d92AQCoUgFArh9/vR2toasYAYbgM/2jzJsiwkEt7p1B1KZbs4U3dqwzx9Q6lU4p//NKGqSgqGoch9pKpKim3bkkd15g7Xj95oNEKtVqOwsBDl5eVxaUnV3NyMtrY2pKamIiUlJSJ7TSAQID8/n5RFjUROnjyJzMxM6PX6MWm0CQQCrF27FllZWfjDH/6A2tpavPbaa8NuA4x2eON1kGEYBg888ABeeOEFPPTQQ3jqqaeGXVE4HA6jurq6i9EoEomgUCigVCqhUqkgl8sRCARgsVjQ1tbWJ8EAlUqFzMzMCNVflmVhNptRV1dHFofxaN4+mrnhhlbs2pUU4TnPyfHhhhv6XtPEMzKRSqXIzMyE1WpFbW1tvIeT0AgEAoRCIbS2thJFU2D4jdfOaqwCgaBXNW+edkOVTxEePNraVKDpzqnYoz9zh0udttvtyMzMRDgcJnWm8eg+EA6HUV9fj+bmZqSlpcFgMJB1Ik3TyMnJwf79+4d9XIMBJ5LpcDiQk5MzJg1YiqKwbNkymEwmzJs3D42NjXj//ff5OX8Q4Y3XQSQUCuH3v/89tm7dinXr1uHee+8d9jFwkU632w2FQkGMVYVC0cXzwzAM8QJyKTRisRgMw3TrkRSLxcjIyIBWq+0SyeAEIvi0xaFDLmewadOxMVOzxNOOTqcjDieKoojDiSc6IpEIqampRBHdZDJBq9WS8oXhnqNEIhGEQiFCoRCpv+3cOoOHZyhpz7YCGCay3nMsZe44nU7SjicvL48YsD6fLy7jCYVCqK2tRXNzMzIzM0kmXLzGM5iIxeKIbhZjkZtvvhlpaWm45pprcNFFF+GTTz6JWmbH03d4waZBwufz4cYbb8TOnTvx1ltvxaUVDkcgEIBQKIw54hsKhWC1Wknf1+4mToVCAa1WC6PRSCYjLt24vr6ej7Ly8AwhMpkMwWAwLqluIwWhUAiFQgGJRAKv1wu32w2lUolx48aBoiiUlZXB5XJBpVKhsLBwWMdWUVEBl8uFwsJCvpk9T1z4X+9clkRgc3J8o7bmtSc47Q6DwUAc/vGEpmkUFBRAoVCgpqaGzF8jBYFAQOZeiUSC5ORkNDQ0gGVZZGRkjFkDFgD27duHSy65BEajEbt370Z6enq8hzTi4Y3XQcDlcuGaa67Bv//9b7z//vu48sor4z2kXgkEAmhqaoLZbCbese4uBalUCpVKBaVSSWrIvF4vPB4PvF4vL7jEw8MTVwQCAWmF07H8QSgUYvz48RCJRACA2tpatLS0AAAmTZo0rHVIZrMZCoVizPdF5IkvLhfw4ossKioYpKV5MXt2GwIBy5hVJ+fSdE+ePBnvoQBoN6opihpx6yqj0YiMjAzyN8MwOHjwIMLhMLKysvrdVnG0cOzYMVx00UWQSCT44osvkJubG+8hjWh443WAWK1WXHHFFTh8+DA++ugjnHfeefEeUo8EAgE0NzejtbUVLMsiJSUFCoUCNE1HPCiKAk3TEAgERLGwY+NzHh4ennhD0zS0Wi2cTieCwWCX/3MqoxwVFRVECKXz/3h4xgKc4CLLsqBpGhKJhDizQ6EQaJomLXbGCkqlMkKNnKfvdDZQvV4vSdGOR6ZLIlJVVYWLL74YHo8Hn3/+OcaPHx/vIY1Y+JrXAdDS0oKLL74YdXV1+PLLL0nz7kQkGAyipaUFNpuNiIXo9Xro9fpeX8uyLBwOBy8Ow8PDk1Bw81c0wxVAFzXfjtuZzeaIEggenrEARVFITk6G3+/HiRMniMaFUCiEXC5HcnIyaJpGeXk5KIoaE0asy+WK9xAShtTU1JjmxM7XRWdROplMBqPRiObmZrhcLoTD4THfEiwnJwfffvstLr74Ypx77rnYvXs3TjnllHgPa0TCR177SUtLCy644AJYLBZ88cUXmDBhQryH1CMsy3Y7IXX3v3A4DIvFgtbWVni93qEeIg8PD0/MGAwGuN3uHuemoqKiiEXVwYMHIwzY4uLiqG1seHjGCgzDoK6ujrS/41rK+Hw+JCcno7GxkTfuxhBTp04dNCOTYRgcOXIEfr+ftM5RqVRjXrTIYrHg8ssvx4kTJ/DFF1/wBmw/GN4eLqOEjobrnj17Et5wBXpWe6utrUVZWRlaW1sRDAbh9XpRU1ODgwcPEuEAHh4enkTC5/P1Ojdxta5Au5Ouc4TWYrEMydh4eEYKNE0jKysL48aNg1AoBMuyaGpqAsuykMlkyMrK4uvz+sBHH32E888/P97D6DeDGc/iovxAe4md2WyGy+VCa2sr3G43XC7XmEzV1uv12L17NwoKCnDRRRfh//7v/+I9pBEHnzbcRzobrkVFRfEe0oAIhUJoa2sDy7JwuVx8TSsPD0/CI5VKY4oGdTReOcNVIpEgKSkJEokEVqu1x6wUHp6xgkajwYQJE1BdXU0MiqNHjyI3Nxc6nQ7Nzc3weDxxHuXwUFpaCqfTidWrVw/7sX/++Wfcfffd+Oqrr6BSqYb12MnJyRAIBGBZFmazGV6vF0KhEAKBAFKptM99SimKQjAYhEAgIO3JuBZvCoWC/I+maWg0GqSlpQ36OSUqGo0Gu3fvxiWXXIKLLrqIj8D2Ed547QMtLS248MILYbFY8NVXX40owzUQCMDlcsHtdiMtLS1ChInPHOfh4RkJaLVaKJVKNDY29rqtXq+PaBfGGa+pqakwGAwAALVaDb/fzysA8/CgXZ07Pz8fQLuR4Xa7cfz4caSmpqKwsBA2mw319fXd1pjz/A+appGRkQGFQgG/3w+fzwen0wmn0xnvoUUlKysLSUlJaGtrQ1NTEwKBQJdt0tLSkJaWFrOzLxgMorm5Oer/OrcBYll2TBmvAG/ADgTeeI2R1tZWXHjhhTCbzfjqq69QXFwc7yHFhNfrRXl5ecRE5PP5EAqFEAgE+J6RPDw8CQ1FUdBqtRCJRHA6nUQtuCckEgmysrIinuPmQK1WS54TCoXEkcfDw/M/DAYDdDodSe30+XxISkqCSCTCiRMn4j28uPHWW2/ho48+Qn19PTQaDaZPn47FixdDLpeTbSiKQkpKCmQyGdxuNy6//HJkZmbi7bffRkNDAx555BHs3r0bbrcbJSUlWLp0KSZMmICGhgbcfffdAIALLrgAADBz5kyUlpbiu+++w4YNG1BRUQGBQIBJkybhwQcfjGhP0x8EAgHy8vKgVqtRU1NDap+j0djYCKFQiJSUlJj2zbIssrKy4HK5ei3R8Hq9aGlpgVAohFarjXA8jmZ4A7Z/jI2rY4DYbDZccsklaGtrG1GGazgcRllZWRcPmsPhgMfj4Q1XHh6ehIVTRVer1bDb7WhpaYmp/p6iKOTl5XURHQkEAlCr1byxysMTIzRNo62tDSdPnkR5eTmOHTsGADF1KRit0DSNZcuWYdu2bSgtLcVPP/2EtWvXkv9zbQZNJhOsViumT5+OiRMn4v3334dUKsWaNWvw9ddf4y9/+Qu2bduGrKwsLFq0CHa7HUajEatWrQIA/OMf/8Cnn36KBx98EEC7cXfLLbfgzTffxPr160FRFB588MEB14xyKtNerzeq4cq1U1IqlUhKSiJZK7EgFouh1WrhcDh63ZZlWdTW1sJut5N047ECZ8AWFBTgkksuIS2GeLqHN157we12Y+bMmaiqqsLu3btHjOEKtBuvycnJUKlUY8aLxcPDM3KhKAoSiQQpKSmQy+VwOByw2+19WqBlZmZGREE4gsHggNKD9+zZA4qiYor88owtKIrCjh07BmVfiXSdURSF7OxsiMVihEIhuN1umM1mKBSKMdv25Oabb8a0adOQnp6O0047Dffccw8+//xz8n9OSbesrAxnn302Lr30UmzcuBECgQButxsvv/wyVq9ejfnz52PWrFnYsmULJBIJPvjgA+KwA9odBAaDgailz5gxAxdeeCEyMzNRVFSEFStWoLy8HCdPnhzQ+fj9fjQ0NHTbCpFhGPj9fgSDQej1ejQ1NfVp/xRFQalUQq1WIzMzE8nJyVAoFKAoCmq1mmTICIVCiEQiSCQSOBwO2Gw2NDc3k1rZ0Y5Go8Enn3yC9PR0XHzxxaisrIz3kBIa3gXdA36/H7/5zW+wf/9+fPHFF5g8eXK8h9QnxGIxTCYTgHav1rFjx8aM4EK8GSv98Xh4BguRSISUlBQwDIPm5uZ+RRS0Wm23kYFAIAC73Y7U1NQIIafO/Oc//8E555yDyy67DDt37iTPn3XWWWhsbIRGo+nzuHhGNq2trXjsscewc+dONDc3Q6fTYcqUKXjsscdw9tlno7GxETqdLt7DHBKEQiFyc3NRVlYGoD0TjWEYCIXCMWNYdOSHH37Apk2bUFVVBbfbjXA4TGpaMzIyoFQq4fV6MX36dNx888144YUXyFqgoqICwWAQZ599NtmfVqvFaaed1quxUlNTg7/97W84fPgw+QwAoKmpCePGjRvQOYXD4V5rcbm+wAD6VPfasY66I9x7wjAM1Go1BAIBDh8+DL1eD5FIhCNHjiAQCMDr9SInJ6dvJzRC4VSIp0+fjosuugjffvst0tPT4z2shIQ3XrshFArhlltuwddff41du3bh17/+dbyHNCACgQBvuA4RFEVBJpNBoVBALpdDLBajpaUFdrt9SI8rlUqh0WigVCrJDTQQCMBisfCGM8+IQSAQQCaTQSAQoLGxsd9pcGKxGNnZ2d0uqoLBIBiGQWNjY5d62I5s2LABixYtwoYNG9DQ0EAWD2KxGKmpqf0aG8/I5rrrrkMgEMDmzZuRl5eH5uZm/Otf/4LZbAaAUX9dKJVKZGRkoLW1FX6/Hw6HY0y2OGloaMD999+P6667DgsWLIBarcb+/fvxxBNPQCgUIj09HX6/HxKJBBdddBE+/vhjzJ49G8nJycjPz+/W2BeLxRCLxT0e+/7770daWhqWL1+O5ORkMAyD2bNnD7j8i6KoPotIVVZWQqFQwGg09rot935EOy7QPv8LBAJYLBZotVpIJBJSMxwMBmE2m8eUInxqaiq++OILnHPOObjkkkvw9ddfj/m+uNHgc0mjwDAM7rzzTuzYsQPvvvsuLrzwwngPacDw/Qx7R6VSISMjAwUFBSRi3R0ajQZZWVkoKSnB1KlTUVhYCJqm4ff7UVlZOWSGq0KhQEZGBiZOnIgJEyYgIyMDWq0WSUlJSE9PR3Z2do9Rpd6QyWQwGAzEoBhuZDIZcnNzkZOTQ+prDAYD1Go1OS8utZRndBAOh+FyufqcHtyZvLy8HutZg8Eg0tPTEQgE4Pf7o27jcrnw7rvv4p577sGVV16JTZs2kf91TufctGkTtFotPvvsM5SUlECpVOKyyy6LUEI+//zzsWTJkohjXHPNNZg3b15/T5NnmLHZbPj222/xzDPP4IILLkB2djZOP/10PPzww7j66qsBRKYNR0v73b9/PyiKQlVVFQCguroaV111FXQ6HRQKBSZMmIBdu3ZFPb7ZbMZNN90Ek8kEuVyOSZMm4e233yb///jjj6HVaolhxB3roYceItvccccdmDNnTkz7A9qv28WLF+MPf/gD9Ho9UlNT8fLLLxOhnrFouALtrYMYhsH999+PSZMmITs7m9SJBgIBHDx4EI2NjaAoCi+88ALy8/Nx4403ory8HIcOHYLb7YZYLMbXX38Nl8uFtrY2VFZW4j//+Q8mTpwIlUpF7nMdDV2bzYbq6mrcfvvtOP3005GbmztoqsU0Tfe5vtRqtaKuro44b3qis2Bod+j1+gjnY0pKCtLT06HT6WJ6/WgiOzsbn3/+OVpaWnDZZZfFVDM81uCN1ygsX74cmzZtwqZNmzBr1qx4D2dQSEpKiosxMlLIyclBYWEhUlJS4HK5YLVaIRKJIrx9nGFXVFSEcePGITk5GXK5HDRNg6ZpOBwONDU1DZkQVlJSEoqKimA0Grs13urr6/s10UskEkgkEhQUFCArKwupqanw+XwDHXLM0DSNnJwc5OTkoK2tDVVVVTCbzWhra0NbWxsCgQCUSiVSU1PH5M2Mp2e4lhTdwbIs9Ho90tLSMG7cuG57xG7btg3FxcUoKirCnDlz8MYbb/SYxeDxePDXv/4Vb775Jr755hvU1NQQgRWe0YFSqYRSqcSOHTu6dXr0lXvvvRd+vx/ffPMNDh06hGeeeYbUNnbG5/Ph1FNPxc6dO/HLL7/grrvuwq233ooff/wRADB9+nQ4nU7s27cPAPD111/DYDBgz549ZB9ff/01zj///Jj2x7F582YoFAr88MMPWLVqFVauXIn//Oc/Y0Y/w+VyoaysLOKh1WoRCoXw7rvvoq6uDjt37sT27dsBRBqbDMOgtrYWK1euREFBAe6++240NjZCJpPhuuuuw4MPPoiNGzfiq6++wuLFi0masc/nQ35+PiiKwqFDhxAOh0larUajwfbt21FbW4uffvoJzz333IDPUS6XDyj1u7q6Gm1tbd06M0KhEHw+H2pra/tsbHPCV1lZWWPSWV1cXIzPPvsMx48fJ5kfPP+DTxvuxPr16/H0009j9erVxFM5GrDZbDEpdY415HI5MjIySDNwiqKQnp5OUgXr6+vR1NQEo9GItLQ00DTdJX0lHA6jurp6yN5fmqZhMBiQmZnZ43atra3d9lTrDk50IysrC0qlEjRNk3MeLuRyOXJzc9Ha2koiE53x+XzDakzzjBxSUlJ6bd3AsmxEilt39YkbNmwg8/5ll10Gu90esfDvTDAYxN/+9jdS07Vw4UKsXLmyH2fBk6gIhUJs2rQJd955J/72t7/hlFNOwXnnnYcbb7yx3zoYNTU1uO666zBp0iQA7VkD3WEymSIcIosWLcJnn32Gbdu24fTTT4dGo8HUqVOxZ88eTJs2DXv27MH999+Pxx9/nGQ0lJeX47zzzotpfxyTJ0/GihUrAAAFBQVYt24dvvvuO2RnZ/frnEca//3vf3HLLbdEPDdr1izcf//92Lx5M9atW4dTTjkF9957L3mfOiMUCvHUU0/h4Ycfxj333INXXnkFCxcuBMMweOyxx+DxeFBSUoLNmzfjjDPOQDAYhMlkwiOPPIJnnnkGzc3NmDt3LjZu3IitW7di6dKluPHGG5GdnY0HHngA8+fP7/f5aTSaAWeIsSyL6upq1NfXQ6fTwWAwELG8YDCIQCAAiUQCkUiEqqoqZGZmRrQq4+mZX/3qV9ixYwcuu+wy3Hnnndi0adOYSZ/uDd547cAHH3yARYsW4b777sP9998f7+EMGh6Pp1sluaFGLpdDo9FAoVCgvLw8LmOIhkQiQWZmJtRqdZfJIBQKwWKxwGw2kzphpVIJiqK6bOv3+1FRUTHohmtqaiqSk5MhFApj8nR7vV7U1NT06RgqlQoFBQVdzqlz8/ChxmAwgGEYtLS0DOtxeUY+er0eGRkZMd3QO6qjRvtOlZWV4ccff8Q///lPAO0Lz9mzZ2PDhg3dGq9yuTxCjCQtLY2/jkch1113Ha688kp8++23+P777/HJJ59g1apVeP311/uVAr548WLcc8892L17Ny666CJcd9113RrC4XAYf/7zn7Ft2zaSWeP3+yMUtc877zzs2bMHDzzwAL799lvShmXv3r2wWCxIT09HQUFBzPsD0GU8aWlpaG1thUqlSggl5KGktLQUpaWl3f6/s1F75ZVXkt+vuuoqXHXVVeRvoVCIZ599NmL7ZcuWYdmyZZg4cSJcLhepaRSJRKisrMR9992HFStWRJQAXXnlleQ4oVAI1dXV+Pnnn7sdIzcnRlu3aLXaQSslk8lkMBqNqKqqQmtrK6RSKUKhEMlA02g0EAgEKCoq6rWul6crF1xwATZt2oSbb74ZmZmZePLJJ+M9pISAN17/P99//z1uuukm/OY3v8Fzzz03arwbwWBwwFLq/YGmaYwfP56kezQ2NkIgEEAkEiVEBC07O5tEWzvCsiwYhoHT6YwQuGpsbITD4YBQKIRarYbH44Hb7Ybdbh90xcXU1NRea259Ph9qamrAsixJWxYIBDGPRSqVIi8vL+p1npOTg2PHjg1bnzWn0zlo9Ts8YweNRoOcnJyY5urGxkYoFIoevf4bNmxAKBSKUHdkWRYSiQTr1q2L+prO9eWdVcZpmu6SdjyW+heOJqRSKS6++GJcfPHF+NOf/oQ77rgDK1as6GK8co6Rjp9758/8jjvuwKWXXoqdO3di9+7d+Mtf/oLVq1dj0aJFXY777LPPYs2aNXjhhRcwadIkKBQKLFmyJCKN8Pzzz8cbb7yBAwcOQCQSobi4GOeffz727NkDq9VKoq6x7g+Ifm0zDDMmjNfhQiwWR4jxUBQFj8cDh8OBmpoaKJVKpKenR6xVOKXn/Px8tLW1oba2NmraLnf9dZ5/ZDIZbDbbgEQdJRIJ/H4/kpOTkZGRgVAoBLVaTcZZX19PtrXb7XC5XNBoNLzx2k9uuukm1NXV4Q9/+AMyMzMHFHEfLYyN4oVeOHHiBK666iqceuqpePPNN0dNTUc4HEZ5efmg1en0hY51mSzLgmVZTJgwIWr/xeFCIBCAoigYjcaohivwPy9lx3QamqYhl8tJzZxSqSTR2XgYrl6vF8eOHYPT6YTL5SI90WIdi0AgwLhx47oVtwmFQgMSfeordrsdVqt12I7HM/JRqVTdOl+i4XQ6cfLkyW6FL0KhELZs2YLVq1dj//795HHgwAGkp6d3EbSJleTk5AgBp3A4jF9++aVf++JJLMaPHx81SyU5ORkAIj73/fv3d9kuMzMTd999N7Zv344HHngAr732WtTj/Pvf/8asWbMwZ84cTJkyBXl5eTh+/HjENlzd6/PPP08MVc543bNnT0TmQCz76wmaVmPTJiOeeioLmzYZ4fGMjvXScMOtRzoamcePH4+4L7tcLlRVVUXc21mWJWrPBoMhIkgQyzEDgcCA1y0SiQRTpkxBVlYWaJqG2+2Gw+FAQ0MDKV0Si8VISUlBYWEhpkyZ0m1NN09sPPjgg1i4cCEWLFiAjz/+ON7DiTtjPvJqsVhwxRVXwGAw4IMPPhhQE/tEgmEYlJeXx6U9jkAgiKgv4+pIAcStLxxFUcjPz4dcLu+1ubrH4yGNtQ0GA7RabReHRlpaGliWHdTaUJVK1aPharfbYbPZIJVKQdN0v9/LpKSkbm92TU1NEV7T4WCsKlfy9A8uVTdWJyPDMPB4PGBZFhUVFSgsLOwi7vTxxx/DarXi9ttv79LH9brrrsOGDRu6pP7FwoUXXoilS5di586dyM/Px3PPPcdHrUYYZrMZ119/PW677TZMnjwZKpUKP//8M1atWhVV0HHcuHHIzMxEaWkpnnrqKRw/fhyrV6+O2GbJkiW4/PLLUVhYCKvViq+++golJSVRj19QUID3338f3333HXQ6HZ577jk0Nzdj/PjxZBudTofJkydj69atJEvg3HPPxQ033IBgMBgReY1lf90RDALnny/B0aMm0DQLhqGwa1cSNm06Brl85M3jHg+NbduSUV8vgcnkxw03tA7beYTDYezbtw8ZGRlITk5GOByGz+frcj8MBAJoaGggmhcCgQBKpRLV1dVQKBSQyWQxl00wDNPviKtIJIJYLIbH44FGoyFGts/nI1oVLMtCKBQSHYue1N95+ganYF1fX4/Zs2dj7969+NWvfhXvYcWNMX1lBYNBXH/99bBarfjxxx+h1+vjPaRBgWVZnDx5sltFzf4iEAiQkpICg8FAxIGiTYRJSUndGohDpcTbGx1FmXpDo9FgypQpPS6OOYOcZdk+iyR1R2/RToFAAIfDgba2tgEdJ9p1zjAMqqur+ZZKPAmNVCpFQUFBrw6ojrjdbjJPMQyDEydOoLCwkGSBuN1uvPbaa7jooou6GK5Au/G6atUqHDx4sM/jve2223DgwAHMnTsXQqEQ999/Py644II+74cnfiiVSpxxxhl4/vnnUVFRgWAwiMzMTNx555145JFHumwvEonw9ttv45577sHkyZNx2mmn4cknn8T1119PtgmHw7j33ntRV1cHtVqNyy67DM8//3zU4z/66KM4efIkLr30Usjlctx111245ppruojtnHfeedi/fz+Jsur1eowfPx7Nzc0oKirq8/6icewYcPQoBYYBGKbdYKqqkmLbtmTMmzc498HhwuOhMW9eMaqqpHEzxLkSJU7fIi0tLarz2Gw2Rwg20jQNqVTaq5YJTdPQarUwGo1obm4e0P1dr9fDZDIhHA5HGKUikYgYxkC7hkUs/V95+o5AIMDWrVtx7rnn4uqrr8aPP/6ItLS0eA8rLlDsQBLfRzAsy2LBggXYsGEDvvjiC5x77rnxHlIEDMNELbTvDZZlUVVVNahGiEgkgtFoJP0/OZxOZ9R0I4FAgOLi4qhR7MOHDw97zatGoyHy84MNy7Koq6sbFJEWg8GAjIyMHhfmR48eHVA0XSwWo7i4GAzDQCAQkJtQc3Mz6urq+r1fHp6hRiwW90v0o7GxEQ0NDV2iLLfe6oDBIIXdbsfUqVNHjc4BD89QMX8+sHFjewSWQyhkcNVVZixf3jfBwHizaZMR69ebiBEOADTNYsGC+mE1xIVCIaZMmQKgfd13+PDhLvXHCoUCRqMRtbVWfPhhFmprhTCZArjggsNdDG2u7R23fqRpGrm5ubBarf3WPzEajaQncTQsFgsqKysBAPn5+byi8BDT0NCA0047DZmZmdizZ8+oyRjtC2M28vrSSy/hb3/7G15//fWEMlz9fj9aW1thNpuh0WiQlJQUYcR2/r3zc01NTX0yXOVyOSQSSdQCfolEgtTUVOj1+qhRyO6ER8LhMCorK6OmQXVWohtqJBJJzKIu/YGiKGRkZIBlWdKsvL94PB4ibNAdAzX8uUbqQHvLBJVKBbfbzacy8iQ0EokE48aN65fgh8vl6jHKotUKecOVhycGcnOBztUqDEPBZBp+XY2BUl8vIXMBB02zqK8f3p6ioVAIHo+H9IzPyMjoYmS63W788kvV/5/DBBAIWITDYmzZUoyNGyMjxU8//TS2bt3a5TjHjx+HUqnsc0aeTCaDyWTqcY7U6/VQKBRobm6GTCbr0/55+k56ejo++OADnHvuubj99tvx1ltvjbl72Jg0Xj///HMsWbIES5Yswe233x7v4YBlWdjtdrS2tkYIipjNZpjN5iE5pkajQUZGBvHYBINBtLW1obW1laSvaLXaHr8QPbVU6c445dpKlJeXD7jHWG8kJSUhMzOzTymG/YGiKGRmZoJl2QGl9HKKfT39fzAZ7tpWHp7uEIlESElJQUNDQ4QTTSKRIC0tDXq9vk83Z6/XS+qwvF4vtm1LQVWVFAxDRaQ7/uMfaVi2LAy/3w+xWDzmFgA8PH1h4ULgrbeAo0fx/w0/ICfHhxtuGJjjNh6YTP4IwxWInyFus9lIGYNOp4tqZG7bltxhDmt/rrIyMmWboih4vV6ce+65ePbZZyPmM85I7iudo8DdIZFIkJWV1ef98/SPadOmYdOmTZg9ezYmTJgQtYRhNDPmjNcTJ07ghhtuwMUXX9wvAY6hwOl0oqKiYtiOl56ejtTU1IiJTSQSIS0tDampqQAQ0yKuJ+O1p3pRlmWRnJw8ZMarUChEdnb2sKauUBSFrKwssCzbb4eD0Wjs8X0XCoWYOHEiWlpa0NraGjfxKx6ewSYrKwtarRZqtRonT54ERVFIS0uDTqfrV+mE3W6PWKhFj7IAHo8RJhNvsPLwxIJSCXz/PbBuHXDokAt6vX1YRY4GkxtuaMWuXUkR2RjxMsQbGxuhVquJIm9qairKy8sjtoklUsyyLMLhMBQKBZKSkiIyqjweD/773/9izZo1OHHiBNRqNWbOnIl77rkHQqEQH3/8MZ555hls3bqVGKFPP/00fvrpJxw4cKDHjDCe+HDDDTfgyJEjWL58OcaPH49rrrkm3kMaNsaU8ep2u3HttdciJSUF77zzTsIooQ1nax6ZTNbFcO1IrAtFlmV79OL1Jnak0WgwYcIE1NXVDaoRq9VqkZ2dHZfPlqIo6HS6fhuvPUVdOTgng9Pp7NF5wMMzEkhJSYFcLieOJrlcjvHjx/er3p+jtrYWTqcTEokELMsiLS0NeXls1ChLXt5Az4CHZ2yhVAIPPQQEg1IcPlyBcJiJq2pvLFx11VW46aabcPPNN5Pn5HIGmzYdS5hxnzx5EiUlJRCJRFHbynQXKc7ICEKj0UAgEEAqlUKj0cBut3cpA2tpacF9992Hq666CitXrkRVVRWefPJJiMVizJ8/HzNnzsTevXvx6KOP4o033sD333+PHTt2YOPGjaOmfeRo5LHHHsMvv/yCuXPn4qeffooQZxvNjBnBJpZlccstt+DDDz/Ejz/+GJMs/HDR0NAQ0RNuKMnNzR0UVWWWZVFdXd2toaZUKmP6EgUCARw6dGjA4+nI1KlT+5wqzLIsGIYhSnr9nawH8lnG+tlUVFTAZrORRuE8PCMRiUSC/Pz8Qa+Rqq6uBsuyyMjIAEVREAgEcDpZnHEGg7IyGjTdvugrKWmPIvHtB3l4+kdbWxuOHq3tUk+ek+ProtpbWloKp9MZ0Tboiy++wGOPPYYFCxZgzpw5QzbOaMZrIqJSqVBQUACKorqIM0ar28/PD+CHH2jodP/rVDB37lz8/e9/j9AHOOuss5CVlYUvv/wS77//PnEMvvfee3jxxRexZ88e0DQNh8OBm266CdOnT8dXX32F2bNn47bbboNMJkNubi5fz5qgOJ1OnH766aBpGj/88MOY6KmbGKHHYeDFF1/E22+/jXfffTehDFeHwzEshitN09DpdNDpdIOyP4qikJ2djUAgAKfT2eX/LpcLhw4dgkQiQXp6erdfJrFYDLlcPqj9aP1+P6kf6Qmv14vm5mbYbLaIFNykpCTk5OT0+bgsy0Z9L2LF5XL1arwGg0G43W5MnDgREokER44cgdfr7fcxeXjiQXJyMjIyMobEo0/TNFpaWhAIBEiNvUpF4ccfBXjxRRaVlUBeXnv93hi4x/PwDBlJSUnYsYONWk/eW/ucHTt24JlnnsHDDz+Mq6++us/HDofDRE13tOB0OtHQ0BC1hKhzpHjatCQsXiyJmMOcTidsNhtOPfVUPPzww+R5mUyGZ555BpMnT47Y75QpU+DxeNDS0oLU1FSo1Wr86U9/wsKFCzF58mTMmzcPQLtQ5NGjR4lDMDk5eUjfB56+oVKpsH37dpx++um444478Pbbb496/YYxYbzu3bsXDzzwAJYuXYobbrgh3sMhBAIBIi8+VCgUChgMBuh0ukEXLqIoCvn5+SgrK4tqQAUCASgUim7l1YF2430wDVegZ+OVq4draWnp1tA0m81QqVRISkqK+ZgMw6CqqmpAvXVjeW0wGERqaiokkvY6F51OxxuvPCMKTuBsqG6unJNOKpUiHA6TeU+pBB5+eHTf0Hl4hhOKomC36/qs2rt582a8+uqr+POf/0z6Hr/11lv46KOPUF9fD41Gg+nTp2Px4sXkXv7RRx9h9erVePzxx7Fu3TrU1NTgn//8J0pLS1FUVIQHHniA7P+BBx6ASqVCaWlp1OPv2LEDL7zwAlatWoXTTz99EN6JwaOpqalbTQu5nCEOAYGgHnV1Ukil7Q+GYdDY2AiWZSGTySL6wvaF//u//4NAIIDZbIbX64VCoYBIJIJarYbX64VSqQTLsqPeOBpplJSUYOPGjbj++utxxhln4P7774/3kIaUUW+8NjY24vrrr8dZZ52Fp59+Ot7DIbAsi5MnTw5JyxiRSISkpCQkJSX1u/8T1zwbQI+F+gKBAOPGjcOxY8eits5xuVwIBoNRW1wEg8EhMd79fj/MZjMYhoFQKCQPp9OJlpaWmFJta2pqEAwGoVKpIJfLu52oA4EAzGYz2traYlbl6w6v14tQKNRjvS5FUTAYDORvnU6HhoaGAR2Xh2c44XoQDhUKhQJmsxk+nw81NTWYOnXqqIrO8PAkEuPGCcEwkdVnPan2rl27Fu+//z6ef/75CMORpmksW7YM6enpqK+vx9NPP421a9fioYceItv4fD5s3rwZjz76KDQaTb9KoDZv3owtW7Zg3bp1mDhxYp9fPxzEIsYYDofhdrtj1r7IycnBl19+GWF4HjhwAAqFAikpKeTvzZs347nnnsO6devwwgsv4OWXXyZBCN5gTWx++9vf4sEHH8SyZctw6qmnJlQb0MFmVBuv4XAYN998MyiKwrvvvguRSNT7i4aJurq6IRHcSUtLQ1pa2oAmmcbGRjQ1NYFhGKSmpvaqMicWi1FQUIBjx46BYSLFDoLBIE6cOIGioqIIo4xlWVRWVsZkvMtkMjJ5ymQyeDweWK1WYlzr9XoYDAbQNE1q3E6cODGgelCGYUgrGZqmoVKpiAAMN/5AIBDR2mgwcLvdPb7fZrMZMpkMSUlJcDgcaG4evmbqPGMTpVJJHGFlZWUD3l9PmRiDQUtLC+rq6iCVSsGyLHw+X0xlBDw8PH1n4UJgwwY/Tp6U9Kra+9133+Hrr7/Gyy+/jNNOOy3ifx3rUdPT03HPPffgL3/5S4TxGgqF8NBDD6GwsLBfY127di127dqFV199lZQUjBWuv/56vP3221i1ahVmz56NqqoqvPLKK7j55ptB0zTcbjcee+wx3HjjjZg1axYuuOACnHbaafj222/x29/+Nt7D54mRv/zlL/j5558xe/ZsHDhwgDgmRhuj2nj985//jG+++QZffvklaQGTCFitVrS0tAzqPoVCIXJzc2NSrO2JQCBAUk+A2JWQZTIZ8vPzceLEiYjnKYqCz+dDRUUFCgoKyP6ampq6TdulKAqpqakwm80IBALIysqKqJnlUqEtFgtJZ+Hwer04efJkFyN6IDAMM+Q9aTkaGhogFAqjLvDdbjcxVpuamuDz+YZlTDxjl6SkJGRnZxNnmEKh6LfTTSAQICMjo0/p+H2FYRiSicB9PzweD2+88vAMEUol8N57ddiwQdqram9BQQFsNhteeeUVTJgwIeJ7+cMPP2DTpk2oqqqC2+1GONzef9nn85EMMpFIhIKCgn6Nc+vWrfB6vdiyZQsyMjL6d7IjmJSUFKxZswZr1qzBTTfdBLVajVmzZuH2228HAPz1r3+FTCbDvffeS+b9P//5z5g/fz7OPPNMmEymOJ8BTywIhUL8/e9/x5QpUzBv3jx8/PHHozLzaNQar99++y1KS0vxpz/9Ceedd168h0Pw+Xyoqqoa1H0qlUrk5uZGTc3tK01NTcRwlUgkffLaqNVq5OTkkPMTCoUoLCyEUChEW1sbqqurkZubCwA91rmKxWKkp6dDJpPB6/V2MURZlkVDQwOam5tJCgwn3MD1ORuJdDbEO9NR2Is3XHmGmuTk5IjaVIfD0W/D1WAwwGQyDXkLK7fb3WW+4GvCeXiGlry8FMybd6LX7ZKTk/HMM8/g7rvvxqJFi7B27VooFAo0NDTg/vvvx3XXXYcFCxZArVZj//79eOKJJxAMBonxGq3kgLvvdyRaRtfUqVOxd+9efPHFF0SIaDTSXZ0vAJx66qnYsmVL1P+tWLGC/N7U1IT8/HwsXboUS5cuHewh8gwxaWlp2LJlCy6//HI899xzePDBB+M9pEFn9JnjaE+tvPnmm3HOOefg0UcfjfdwCD6fD8ePHx/UqGBqaioKCwsHxXANBoNoa2sjf+fm5vZZ5Emv10MoFEIkEqGoqAgymYzU4HZUOu4pEsItcLVaLYxGY4RBx9UKdzSyuTY3oVBoxBquQHtNislk6jatMj09vV8qyDw8fSU1NTXCcPV6vaioqCD/l0qlSE9Ph16v77GuXiaToaioaNh6L4tEoi5p94MtCMfDwxOJWq2O0GLoibS0NLzyyiswm81YtGgR3G43jh49CoZhcP/992PSpEnIzs5Ga2vXtONo6HS6iHVLOByOmKs4JkyYgLVr12Ljxo148803YzuxMYrdbo/5/edJTC677DIsW7YMDz/8MH788cd4D2fQGXXGK8uyuO222+DxeLB169ZhWTDFgsfjQVlZWVRRo/5iNBphMpkGrYi+o0GYnp7er9q0YDCIUCgEuVwesagVi8XQarXk756MV642matf7QhXY9pfIapERa1W95ryHQgEhqROmoenIyaTKWJeCQaDKC8vB8MwoGkaGRkZKCkpQVpaGnJzczFhwgT86le/ipoOXFhYOKw957g6147wWQo8PENPRkZGzLoiqampeOWVV2C1WrFo0SJkZmYiFArh3XffRV1dHXbu3Int27fHtK9p06Zh79692Lt3L6qqqvD00093W5I0ZcoUrFmzBq+99hr+/ve/x3xuYwGRSASapslnyK81Rj5PPvkkTjnlFNx4443DVvo2XIw64/Wll17Chx9+iE2bNiVMXYPL5cLx48cHVVlYJpMhPT190PYXDAZJi5iMjIx+1whzE15v0Y7ujFepVNpjTZxGo4HJZBqQGFOiIZVKkZWV1et2DMPwNxSeQUcgEEAqlUKlUiE7O7vLd18gECA1NRV6vR4TJkyA0WjsUkMTDodhtVojnpNIJIPenqs3oomo9SY4x8PDM3AEAkGfMoOMRiNeeeUV2Gw2/OUvf8Fdd92FzZs3Y/bs2fj0009x7733xrSfWbNmYebMmVixYgXuuusumEwmTJs2rdvtp06dSlR033nnnZjHO5oRi8UoLi4GwzCQy+VQKpWQyWTxHhbPABGLxXj77bdhNpsxf/78Lo7dkQzFjqKzOXr0KE455RTccccdePHFF+M9HADtbVvq6upgs9kGdb9KpRJFRUWDtr9QKBQ10tlXamtr4Xa7YTAYkJSU1G1UmGEYlJWVRRi5Wq0WqampPUZ8WZbFsWPHRk0qoFqtRl5eXszve11dHa8wzDOoTJ48uceIicfjAcMwPUZQm5ubUVdX1+V5hUKBvLy8QSlriIWGhoaI2nCgPfqrUqmG5fg8PGOdmpoaPuV0BJKRkUHm8IyMDOh0umGbt3mGlnfeeQc33XQTtm7dGqHqPZIZNZHXYDCIuXPnIjs7G88880y8h0MIBoOD3k5FJpPBaDQO6j6FQuGADdfW1lYwDIOioiIYDIYe05nD4XAXA9Ttdvf4GqfTCZ/PN6rSAA0GQ5/e98G+lnjGNlwP5J6QyWQ9Gq5+vx/BYDCqoqHb7Y7aQmsoYFk2ovYNaPc8D2faMg/PWCczM5PPdhhhqNVqNDY2IisrCzRNo76+fsB963kShxtvvBE33XQT7r333qhO5pHIqDFen3rqKezbtw9vvvlmQrVFCIfDgxqqz8rKQklJSUT96HDD9Th1u92w2Wwwm81obGyE2+1GVlZWTDW4QqEQycnJMBqN0Ov1UCqVYFkWZWVlUXPzWZZFfX09ampqhmUhPFz0JTXH6/Xyyqk8gwonbtLY2NitU6in7zPLsqiurkZzc3O338tgMDjomSfRcDgcEZoCGo0GGRkZg6YJwMPD0zsURSE3N3fI+znzDB4Mw8BkMiEQCKCwsBACgWDQ2znyxJeXXnoJcrkct91226hYQyeGmtEA+emnn/Dkk0/i0Ucf7dL4Op6Ew2HU1tYOmvGampqK5OTkQdlXX6mtrYXT6SSCTB3Jzs4GTdMR/SB7o7W1Fa2traAoCiqVClqtFjRNw2q1Rn2/KIpCUVER6urq4HK5BuWc4g1N030SnjKbzUM4Gp6xCMuysNvtsNvtYFk2oo6eYRjShqo7KIpCXl4e6uvrIZfL0dbWFjWlv62tDXq9fkjOAWjPyujcgqw/auk8PDwDRyAQYNy4cXA6naBpGhUVFaOq3m604XK54HK5IJPJIJPJUFxc3KX8gmdko9PpsHHjRlx66aV4+eWXY64pT1RGfM2rx+PBKaecApVKhe+++y5mtbvhYDBqPyiKgkwmg0qlGlRl4b7AsiwOHDgQtQ1Neno60tLS+rzPw4cPk0iPUCgERVGYOHEiQqEQRCJR1PMMhUI4ePDgqLkJSqVSjB8/PqbPlGVZHDp0aFDVqnl4OmIwGJCdnQ2GYdDS0oKmpiYIBAJoNBpotVoolcpem52zLIsTJ05EVfucOHEiJBLJoIyV6+8MtBvGNTU1XeaFX/3qV6OyOTsPz0jC4XCgubmZL3kZIUgkEkyYMAFtbW1xC5bwDB0LFy7EG2+8gX379g2qbs5wM+Lv7I8++iiqq6vx5ptvJpTh2rFPlkgkglKp7HPtlUQiQUlJCUpKSoYs/Y1lWbhcrh5TRCiKQnFxcZfx6/X6fqsSdzSE09LSMGnSJADtNWrdnadQKOxRiXik4fP5cPTo0ZgiyVzUm4dnqAgEAjCbzTh8+DDq6+sRDocRCATQ2tqKEydO4NChQ72mG3HOtmh0rkftDyzLorKyEsFgECzLoq6uDtXV1d1ma/Dw8MQXpVLJq+SPIEKhEMLhMC9yN0pZtWoVMjMzMW/evKgBqZHCiE4b/v777/HCCy/g2WefRXFxcbyHEwFn8EmlUpK65nA4cOLEiZher1arkZubG3OfWpZl4ff7wTAMZDIZWbgxDAOr1Qqr1UqEV+RyOalXtdvtJA04KSkJPp8PMpmsS8RCKpWisLAQra2tqK+vh0wm61OacGfEYjFomobf7yfj7W1ffr8fQqEQMpksau0nTdMjLpff6/WirKwMSUlJyMjI6Pbz5lOGeYYah8MRNTqiUqmgUChAUVSvkUybzdZtZoTNZoPJZBrwGFmWhUAgQEVFRbe96/iIKw9PYkDTNPLy8lBeXj5qsqZGM+FwGCdPnkRKSkqfypp4RgZyuRwbN27EOeecg3Xr1uG+++6L95D6xYhNGw4EAjjllFMgk8nwn//8J2YjL574/X788ssv5G+ZTIbk5GSy0OKMN5qmodFoujXmwuEwEe/xeDzkd85wE4lE0Gg0EAgEMJvNMfeX/X/snXmYU9X9/9/3Zt8myWSbmcy+swuiFtAWoVZaxVqtUHfcFbVCReqKWFqhKi6oiFsdqlhLq10Qxbph6/K1gAoMwwyzL5kts2RmMtlz7++P+eWWkMxMksk6c17PwwPJvffck5DknM/2/shkMuTl5Y0rIuR0Ov2aWUcCy7JoaWkBMJJ6HMpYHo8HLpcLYrEYnZ2dfjUZaWlpKC4uhsPhGLWnrq83bldXFwQCAUQiETo7OyN+DdGGx+PBaDRCIBDA6XRyfxwOB1H+I8QdtVqNzMzMkEXFWJbFsWPHRu3BLBQKuQyLSGloaEB/fz94PN6YXuOMjIwJG8oEAiF6WCwW1NfXJ3oahDAoLi4mytGTlF/+8pd49dVXUVlZiYKCgkRPJ2xS1nh95JFH8Nvf/haHDh3C7NmzEz2dkGBZFt9++y34fD6MRiPS09PDilza7Xa0tbXFpHaEx+OhtLQ0rkrNHo8HNE1HFCVhWRZ1dXUYHBwEn8/H9OnTOQPYbrejtrbWL81Wo9FwMvC+e1dVVZFUXAJhFCKpUe3r60NjY2PQYxM1Xr1eLw4fPjxu9IaiKMyePTslHJoEwlSit7c3QFiNkLwoFAoUFRUR4btJiNVqxYwZM1BaWop//etfKVdmk5Kre2VlJX73u9/h3nvvTRnDFRjZVOXm5iI9PT0sg83tdqO9vT0qNWOjkZGREfcWQxPZXPpUTt1uN5eC7EMikaC0tBQWiwVSqRRSqTTgXq2trcRwJRCCIJVKUVBQEFGD+rS0NCgUiqCCTRNdHEdTIj8VlmXh8XiI8UogJBkajYbrwkBIbuRyOfR6PRdkSDXjhjA2crkcL774In784x+joqIC1113XaKnFBYpF3n1er1YtGgRBgYG8N1330VNvTLWDA8Po6mpCSqVatx0NpZl0d/fj8HBQTgcDr+U4FiRmZnp1yZjMtPf34+GhoZET4NASDpomkZJSUnY4nI+bDYbjh8/HvSY0WgcU+DN1z+aZVkwDAOWZf3+mEymoG14fFAUBT6fD7fbjby8PGi12oheA4FAiC2dnZ0wmUyJngYhBGQyGYqLi4kzcJJyzTXXYM+ePaiqqoqoc0iiSLlP444dO/D111/j888/TxnD1eFwoK6uDh6PB4ODg8jKyoLb7eYMU7lczjX0ttlsaG1tjXsv01RWHQsX0nybQAiEoigUFxdHbLiyLDtqRIWm6THbLng8HjQ3N8NisUR0b2Ak40Kr1WJoaGhSqZITCJONjIwMeDwedHV1JXoqhFGgKApSqRQikYikDU9innrqKezbtw9r167FW2+9lejphExKRV67urpQVlaGyy67DC+//HKipxMSLpcLNTU1foI7p4qNpKenQyKRwOFwJERVViAQoKioiDOgJzO+uuMU+tgTCHGhpKQEaWlpEV9vt9tRVVXFPaYoCizLIi8vDzRNIz09Peh1w8PDaGhoCEmULJiiuEgkQk5ODgQCAYaHh6HVakmKG4GQ5JysW0FITnQ6HXJycsjv6SRn586dWLVqFT766CMsXbo00dMJiZQyXq+55hrs3bsXNTU1KZES5vF4UFNTA4fDkeipjEq4LXlSnbHSGgmEqYpQKMTMmTMntElhWRbV1dXwer2QSCTQ6/VwuVyjRkFZlkV3dzdMJlOAM0mpVIKmabAsi8HBQc5gLSoqAsuyXNq/wWCAXC7n1NlZliUbLQIhRejp6UFzc3OipxET9uzZg61bt2L//v2JnsqEICUYkx+WZfH9738fZrMZhw8fToms1pRphvfvf/8br7/+On7/+9+nxBfJ6/WitrY2qQ3XrKysKVfL0NfXl+gpEAhJh8vlQlVVFfr6+iLOSvD1YS4vL0dubi7kcvmohqvH40F9fT3a2tr87qdUKlFaWorCwkIUFhYiPz/f77jb7YZMJsPMmTMxa9YsGI1Gv7ZixHAlEFIHtVqNnJwcKBSKuN5348aNmD9/Ph599NGAY7///e8xf/58bNy4cUL3OO+88/DOO+9MaIxE4uvPmwr7bcLEoCgK27dvR11dHZ588slETyckUiLy6na7MXfuXCgUCnzxxRdJ34CeYRjU1dUFVdxMFvLz86dcXRjpM0cgjE9hYSHUanVE14Ya+TSZTFyPZZFIBI1Gw9VWNTY2gmEY5OfnA4Bf653c3Nwxa2cJBEJq4vV6MTAwgIGBAVgslpiKVG7cuBEHDhzA8PAw9u3bB7FYDGDEAbds2TLIZLIJGbCprnYuEAhQXFwc9w4UhMRy991344UXXsDx48eRl5eX6OmMSXJbgf+fbdu24fjx49i+fXtSG64sy8JisaCqqiqpDVcAKZEWEE0cDseo/ScJBML/cDqdEV8biuHq9XrB5/ORm5sLo9GInJwcsCyLlpYW1NXVwev1gmVZ2O12ThuAz+ejtLSUGK4EwiSFx+MhPT0dBQUFmDFjRlgaHDYbjYoKA373u1xUVBhgs42/TywvL4fBYMCnn37KPffpp58iIyMDZWVl3HNffvklbrjhBixevBhLly7FmjVr0NbWxh1vb2/H/Pnz8a9//Qs333wzFi5ciPfffx979uzB4sWL/e7573//G9dccw0WLlyIpUuXYt26ddyxwcFBbNiwAeeeey4WLVqEX/7yl2hpaeGO+8bbv38/fvazn2HhwoW44447OCcgALS1teFXv/oVfvSjH+Gcc87BNddcg6+//jrk9xEYaZU2bdo0YrhOQTZu3Ai1Wo01a9YkeirjkryW4P+nu7sbjzzyCG677TbMnTs30dMZFZvNhtraWtTX109o8xcv6uvrMTw8nOhpxAWv14v6+vqoe3JJimLyc/DgQcyfPz/pnUljcfPNN2Pr1q1xu1+sfr88Hg/a29tx9OhRLl04IyMDXq8XHR0d8Hq9kMvl4PP5EIvFyMjI4ARDiouL455aSCAQEoNQKERZWRkMBsO459psNFatKsf27Ubs2aPB9u1GrFpVHpIBe9FFF2HPnj3c43/+859Yvny53zl2ux1XXnklXn/9dWzfvh0URWHdunUB+4nnnnsOv/jFL/CXv/wFCxYsCLjX559/jnvuuQeLFi3Crl278MILL2DGjBnc8Y0bN+L48eN48skn8dprr4FlWdx1113weDzcOQ6HA3/4wx/wyCOP4NVXX8XQ0BDuv//+k94LGxYtWoTt27dj165dWLBgAX71q1/5GbjjkZeXB4FAEPL5hMmDQqHA1q1b8fe//x0fffRRoqczJkmf17BhwwbweDw88sgjiZ5KUNxuN0wmU0JUgieCx+PBiRMnUFBQAJVKlejpxAyWZdHU1JTUtceEyJg/f/6Yx2+66SacfvrpcZpN7Hj88cfjmoIWK+O1paUFLpcLIpGIawkmFAqhVqvR2toKPp+P4uJiMAwDhmG49gx6vT4m8yEQCMkLRVHIzs6GVCodM2tq924dmprEYBgKDDPiUG5qEmP3bh1WrRq7Fc9PfvITPP/88+jo6AAAHD58GI8++igOHTrEnXOq+urDDz+MH/7wh2hoaEBxcTH3/OWXX44lS5aMeq8//OEP+NGPfoRbbrmFe660tBTAyG/jv//9b7z66quYM2cOAGDTpk244IILsH//fvzwhz8EMLJvW79+PWbOnAkAeOSRR/Dzn/8clZWVmDlzJkpLS7kxAeC2227Dp59+is8++wwrV64c870AAJVKRSKuU5yVK1fiueeew69+9St8++23SdsmKamN16NHj+Lll1/G1q1bk64+k2EYdHV1obOzM6a1GbGEYRjU19fDaDRCLBbD4/GApmmoVKqopGc7HA709/cjLS0NUqk0IZHKrq6uCfWOHAuKokDT9JTqkZtM7Nu3j/v3hx9+iB07duDtt9/mnpNKpX6tW1IVpVIZ1/vFynjl8XiQSCSgKAo2mw0A0NzcjNmzZ0OtVsNgMIDH4yXtYkkgEOJPeno6BgYGRhVbNJlEoGmWM1wBgKZZmEzjl0ap1WosWrQIe/bsAcuyWLRoUYAzv6WlBTt27MCxY8f8anE7Ozv9jNdp06aNea+amhpcfPHFQY81NjaCx+NxRikwYkjm5eX5Ge48Hg/Tp0/nHufn50OhUKCpqQkzZ86EzWbDSy+9hM8//xw9PT3wer1wOp0hR14zMzNDOo8weaEoCk899RTOPPNMvPrqq7j55psTPaWgJK3xyrIs7r77bhQVFWH16tWJno4fFosFra2tIfUlTAVMJpPfYz6fD4PBAK1WG1HEx+l0oqOjg4tGt7e3QygUorCwMK69ZAcHBwNeWzRhGAZ8Ph9qtRoMw2B4eNgvxYcQW05WQZTL5aAoalRlxOPHj+PZZ59FQ0MDysrKsGHDBk4QqK2tDU8++SQqKytht9tRUFCA22+/HWeddRZ3/fLly/Gzn/0Mra2t+Pjjj6FQKHDDDTfgkksu4c45fPgwfv/736OpqQlFRUW44YYbsG7dOuzatQtlZWU4ePAgbr31Vjz77LN49tln0dzcjFmzZuHRRx/F8ePH8dRTT8FsNuPss8/GQw89xImI3HzzzSgrK8Pdd98dg3cxEJfLBZfLBaFQGNVx5XI5eDyeX7mCTymY9BIkEAijkZ2djYGBgaCOYqPR6We4AgDDUDAaQ3PC/fSnP8Vjjz0GAFi/fn3A8bVr1yIzMxMPPPAAdDodGIbBypUrA9Z6iUQy5n18v+ex5Omnn8bXX3+NNWvWICcnByKRCL/+9a9D2peQqCvBxxlnnIGrrroKDz30EH7xi19MqP97rEjamtf33nsPH374IZ544omob6ImwsDAAOrr6yeN4RoMj8cDk8mEo0ePhtU2w+l0oqmpCZWVlQFp1C6XK64RSpfLFReBJo/Hg/7+/lEXVkJysH37dqxZswavv/46eDwefvOb33DHQq0T2rVrF6ZPn45du3bhsssuw5YtW9DU1AQAsFqt+NWvfoWioiK88cYbuPXWW7Ft27agc3nppZewfv16vPrqq+jq6sK9996LP/3pT/jtb3+Lp59+Gv/3f/+Ht956K2bvxcnQNA2pVBrgpIpFjbhGo4FSqfSLoPgEmIjhSiAQRkMgECA7OzvosRUrzMjPd4CmWfD5DGiaRX6+AytWmEMae8GCBXC73fB4PAG1qhaLBc3Nzbjhhhtw5plnoqCgIGL9hOLiYhw4cCDosYKCAni9XlRWVgbcu6CggHvO6/X6ZRM1NTVhaGiIc8QePnwYy5cvx7nnnovi4mJoNBq0t7ePOzcej4esrKyIXhdhcrJ582YMDQ0FbSeVDCSl8ep2u7Fu3TosWbIkoHg+kdhsNjQ0NCR6GnEj3M1rU1PTmLW/dXV1OHbsGOrr60P6QY0UXzp0vKOgKdB1asqyevVqnH766SgsLMS1116LI0eOcOmxpaWluPTSS1FcXIzc3FzcdtttMBqN+Oyzz/zGWLhwIS677DLk5OTg2muvhUqlwsGDBwH8L4X5wQcfRGFhIRYtWoRrrrkm6Fxuu+02nHbaaSgvL8dPf/pTfPPNN7j33ntRXl6OuXPnYunSpX41V7GEz+fDZrNx3xWKosDj8bia1GgzODjIOf5kMllcMzEIBELqotFoIJfLA56XShlUVFRj9WoTli/vxerVJlRUVEMqDW3/wuPx8Je//AW7d+8OKFlIS0uDUqnEO++8g9bWVhw4cCDiPpg33XQTPvjgA7z44otobGxEXV0dKioqAIy0APvBD36A3/3ud/juu+9w4sQJbNiwAXq93k+xmM/n4/HHH0dlZSWOHz+ORx55BLNmzeLSjXNzc/HJJ5+gpqYGJ06cwAMPPBDSvkQkEo0bOSZMLbKzs7F+/Xo89dRTSdmpIynThisqKlBdXY0//elPSeORd7lcqKurS9n61kigKCrk999ms8FqtY55DsuycDgccDgcGBwcjMjTxzAMHA4HhEIheDxe0Pm1trZyNXUEAgCUlJRw//alFvf39yMjIyPkOqGTx6AoChqNBv39/QBGajdLSkr8WlCdrCQ52jjp6ekQi8V+UQWNRoNjx45N4NWGBkVRAQ4eX5/W6dOnc8JJ0WxPZjb/LxpC2t4QCIRQoSgKeXl5qKqq8jPIiouLMTAwgOuvN0e8PwtmFAMjmSmPPvoonnjiCaxcuRJ5eXlYt26dn+hSqMyfPx9btmzBK6+8goqKCshkMr8OGg8//DCeeOIJrFmzBm63G/PmzcMzzzzjlxUjFotx7bXX4oEHHoDZbMZpp52GDRs2cMfXrl2L3/zmN7j++uuhUqlw7bXXhtRVYjKLdhIi55577sFLL72Ehx9+GH/84x8TPR0/ks54tdvt2LhxIy6//HKcdtppiZ4OgJENXUNDA9xud6KnEldYlkVPTw/UavW4Iird3d1hjc0wDL777jtOoIXH40Emk8FgMASVaXe73eju7obZbObSc3NycqDT6eDxeOB2u+F2u2G1WtHT0xPWXAiTn5M3AD6Hh2+jE2qd0KmptRRFRbRZOnUuwerK4xHFH01szOPxoLGxEeXl5VE1XFmWhdvthkgkAk3TUKvVURubQCBMfsRiMTIzM/0yt4aGhpCbm4usrCz09vaiu7t73LKujRs3jnn85NZkZ511Fv7yl7/4Hfdl3ABAVlaW32Mfy5cvD8gcXLJkyaiKxGlpaX7lLKMx1hhZWVnYsWOH33MrVqwYd0xivBKCIZPJ8NBDD+H222/HPffcg1mzZiV6ShxJZ7w+//zz6O7uDulLHC9sNtuU6Yl6Ks3NzWhpaYFSqYRarQ6qROzxeEZVAhwLr9frt3m2Wq3o7u6GVquFwWCASCSC3W5HV1cX+vr6Ajb0PuEsAmEinFwnBIx839vb28Nqs5OXl4f333/fT+goHtHTSBEIBGOm1dvtdjQ1NaGgoCBq2S8URaGsrAwWiyVqiuYEAmFqYTAY0NfXx7W/6+npQVZWFic0qdfrcfz4cdjt9gTPNHVwu90kbZgQlBtuuAFPPPEEHnzwQfzjH/9I9HQ4kmr3MDAwgM2bN+PGG2/0kyBPNKnWwzXasCwLi8WCxsZGVFdX+xmcDocDDQ0NUYsUsSwLs9nM1XRUVVWht7c36PikxpQQDSKtEzqZZcuWgWVZ/O53v0NjYyO++uorvPHGGwCST4xIIBDA7XaP+Rrlcjn0en3U507TNNLT04nhSiAQIoKmaeTl5XGPvV6vn/Pc1x92qjGRXuAtLS1TqiSOEDpCoRCbNm3CP//5T3z55ZeJng5HUu0gnnjiCdjtdjz00EOJngoHwzARRRUnK3a7HfX19XC73WhtbUVVVVXE6nvjMV7darKncSeb0UIIztq1a5GWlobrr78ea9euxYIFC1BWVhbWGHK5HE8++SROnDiBK664Atu3b8eNN94IAHFXSxcIBCgpKYFCoQg4xufzx/zeSCQSFBcXo7S0dNQ6MAKBQEgkcrncry1ad3e3nzPOJ7Q0mVi+fDn2798/6nGj0Yi0tDRkZGRApVIhLS0taAlWMJxOJ7q6uqI0U8Jk4xe/+AVmz56N+++/P2mCRhSbJDPp7u5GYWEh7rjjDmzZsiXR0+Ho6+tLSqWtqQ5N00npKfQtFlKpFOnp6TCZTJBIJBgYGEjwzAjx5v3338cjjzyC/fv3x6XH36moVCoMDQ3B6/VyKsJjpQrTNA2xWAyn0wmBQICCggLS949AICQlHo8HVVVVnDOurKzMz+HmcDiSunRjIigUCmi1WkgkEk4zxCewearTfGBgAHV1dQBGVIUpiuJSrk+GpmnMmDEjqVpTEpKHd999F8uXL8e+fftw/vnnJ3o6yWO8rl+/Hjt27EBTUxPS09MTPR0AIzWYtbW1SWkkTXXUajWn9JpMFBQU+H1+WZbF8PAwampqEjgrQjx49913kZ2dDZ1Oh9raWjz22GM4/fTTsWnTpoTNicfjQSQSwel0ht2HWCwWY9q0aSTFl0AgJCX9/f1c+0KRSBSg+N7a2hq2mGQyIhKJ4HK5AqJearU6JF2Curo6CIVCZGdng6Io9PX1obm5OWA8lUqFoqKiqM+fkPqwLItFixaBYRh89dVXCc8sTArBJrPZjOeffx5r1qxJGsN1eHiYGK5JjEqlgtVqTarUYaFQGOC1pCjKbzElTF56e3vx4osvore3F1qtFkuXLsXtt9+e0Dn5DNZwDVc+nw+tVksMVwKBkLSo1Wrk5+ejubkZTqcTNTU1KC0t5TJdMjMz0dvbG/bvXyLIzc2F3W6HVCpFWloanE4n90ev18PtdqO+vp5TUlar1SFnxhQVFfkZGxqNBgKBAPX19X57XIvFgsHBQaSlpUX3xRFSHoqisGHDBvz4xz/GRx99hPPOOy+x80mGyOv999+Pbdu2oampya+OIVH4xImI4Zq8zJo1C21tbX7RVx6Pl9BFSigUBpUSZ1kW3333Hfk8EeIORVHIzc1FV1dX0FSx0RCLxdDpdNDr9TGcHYFAIEwcq9WK+vp6eDwe8Pl8lJaWcuq53d3dKdGVwGg0wmAwjBnR8ng8aGpqglwu5wzXjo4OFBQURJTua7VaUVdX57dvEolEmD59OnFcEgJgWRZnnXUWRCIR/v3vfyc0+prwT2dfXx+ee+453H777Qk3XBmGQXNzc4A3ipBcUBQFgUDgV9/iq+lLJMHSegASfSUkDpZlMTg4GHYfP7FYTAxXAoGQEsjlcpSXl0MsFsPj8aCmpoZrb6jT6SCTyRI8w7Hh8XhQKBTjGgN8Ph9FRUXIyMhAWloaJ9AUqjDTqcjlcpSVlfntnYh4E2E0fNHXzz//HJ999lli55LoyOvDDz+Mxx9/HE1NTQndLNlsNjQ2NoYVnSAkjrKyMlAUherqagAjAgaxUj0Oh7lz5wb1WNbX18NiscR/QpMQm43G7t06mEwiGI1OrFhhhlRKnE3RZNasWUS4g0AgpBRerxcNDQ0YHBwETdMoLi6GQqGA1+tFbW0tZ9AmCyqVCjKZDCqVKiGifj5O3Z8Q8SbCaLAsi9NPPx0qlQqffPJJwuaR0MjrwMAAnnnmGdx6660JM1zdbjc6OztRXV1NDNcUwmKxQCqVgqZpSCSSpKl9HS1in8iFaTJhs9FYtaoc27cbsWePBtu3G7FqVTlstsT8lG3cuBHz589HRUWF3/P79+/H/Pnzo3KPgwcPYv78+XFzzlAUBbvdHpd7EQgEQrTg8XgoKCgAMLIW19bWwuPxgMfjoaSkJKnaf9E0DalUCoPBkPD9wan3ZxgmJVKtCfHHF3399NNP8Z///Cdh80io8friiy/Cbrdj3bp1cb2v3W5He3s7jh8/jiNHjsBkMiVN7yJCaAwMDICiKMhkMhiNRmi12qRIzR3tc5QMcwuHRCvJjcbu3To0NYnBMBQ8HhoMQ6GpSYzdu3UJm5NIJMLOnTsxODiYsDlEi8LCQsydO3fS9UgkEAhTg5MdyCzLore3F8CIYVtcXJw0YkRGoxGZmZlJsdaKxeKAjDGfeBOBcCoXXXQRZs6cid///vcJm0PCjFeXy4VnnnkGV111FbKysuJ2X5vNhurqanR0dMBms8XtvoTo4nA44PV6kZubC6VSCYPBEHZdXywYLfKq0WhQWloKtVod5xmFh28hTVZnjskkAk37z42mWZhM0XEO2Gw0KioM+N3vclFRYQgponvmmWdCo9HgtddeG/Wcjz/+GCtWrMCCBQuwfPlyvPHGG37HXS4Xtm3bhgsuuAALFizAxRdfjL///e9ob2/HrbfeCgA499xzMX/+fGzcuBHAyGfttddew0UXXYRFixbh8ssvx0cffRT5iwe4foEEAoGQalitVlitVr8Iq9ls5gSJfBHYuXPncoJOiSJZssUAID09HXl5eQHPt7S0EP0XQgA0TWPdunXYu3cvqqqqEjKHhLXKefPNN9He3h7XqKvb7UZdXR35Mk4SGIbxS3dJBlGG3t7eoM4YiqKgUCigUCiStvccRVFJa7T6MBqdYBh/44phKBiNzgmP7UtJbmoSg6ZZMAyF997ToKKiesyaWpqmcfvtt+PBBx/EL37xCxgMBr/jx48fx3333Yebb74Z5513Ho4cOYItW7ZAqVRi+fLlAEZq/48cOYJ169ahpKQE7e3tsFgsMBgMeOyxx7B+/Xq8/fbbkMlk3Gf+tddew/vvv4/77rsPOTk5+Pbbb7Fhwwao1WqcfvrpYb9+uVwOhUIR9nUEAoGQDEilUvT09MBqtXLPOZ1O1NbWoqSkhBMmomkacrk8oeURydS+h6KooE5Ln3hTZmZmAmZFSGYuv/xyPPDAA9i6dSteffXVuN8/IZFXlmXxxBNP4MILL8S0adPick+GYVBXV5dU3i7CxDj1xz8Z6lk6OjrGrU00Go1j9mejaTruNeCpYLgCwIoVZuTnO0DTLPh8BjTNIj/fgRUrzBMeeyIpyeeeey5KS0vx4osvBhzbtWsXzjjjDNx4443Iy8vD8uXLsWLFCrz++usAgObmZnz44YfYsGEDzj33XGRnZ+PMM8/Ej370I/B4PC7NLT09HVqtFnK5HC6XC6+99ho2bNiABQsWIDs7G8uXL8ePf/xjvPPOOxG9fqPRSKKuBAIhZaFpGnl5ecjOzvZ7fnh4OKAlTEZGRkId3na7PanWXJVKFTQzrLOzk+stSyD4EAqFuOuuu/DGG2+go6Mj7vdPiPG6b98+HDt2LG5RV5Zl0dTURNKEJxmnRtAFAkFSqOM1NjaO6SShaRqFhYUBrX0EAgGMRiNmzZqFnJychLf+SUakUgYVFdVYvdqE5ct7sXq1adzIaKhMNCX5zjvvxN69e9HY2Oj3fGNjI+bMmeP33Jw5c9DS0gKv14sTJ06Ax+OFFS1tbW2Fw+HA7bffjnPOOYf7s3fvXrS1tYU8DjCyCKWlpSWF84dAIBAmAkVRMBgMKCkpgVKpREFBASiK4nqa+vYNQqEQpaWl0OkSo5fgS3FOFiiKQl5eHtfJwQfDMDCZTAmcGSFZufnmmyESibBt27a43zshacOPP/44zjjjDHz/+9+P+b2cTidaW1sxMDAQ83sR4kuwtBuZTJZwL6Hb7cbg4CA0Gs2o54hEIsycORODg4MYHByEQqGAWq32E00QCARJlVqULEilDFatin4fuommJM+bNw/f+9738Nxzz3HpwKEQiZiXL93t6aefDojSh9vzz2g0Jo2ICYFAIEQDXx9UYMRhXF9fzxmwJSUloCgKNE0jNzcXMpkMzc3NcY+Ems3mpCrV4PF4kMvl0Ol0fqVNJPBDCIZSqcTNN9+MF154Affff39cP8txj7weOXIEn376Ke6+++6YpqixLIuOjg4cO3aMGK6TlGC1y8kQPeLz+SEJM/H5fKSnpyM/Px8ajYYzXH0LBZ+fsJL0KUk0UpLvvPNO/Oc//8GRI0e45woKCnD48GG/8w4fPozc3FxOAZNhGBw6dCjomD5j9GRHRkFBAYRCITo7O5GTk+P3JyMjI5yXDYZhyGeNQCBMWlQqFQoLCwEAQ0NDAXtCjUaDoqKiuM+rv78/4c72YBgMBr/9OSm3I4zGXXfdBavVij/+8Y9xvW/cjdfnn38eWVlZuOSSS2J6H4vFgvb29qSqKSBEl9Eir4lGqVSCpml4PB50dHSEvDixLIv29nbOKCcGxcSgKCqs1OtopCQXFxdj2bJl+POf/8w9d9VVV+HAgQN45ZVX0NzcjHfffRe7d+/G1VdfDQDIysrChRdeiN/85jfYv38/TCYTDh48iA8//BAAuHYKn3/+Ofr7+2Gz2SCTyXDVVVfhySefxLvvvou2tjZUV1fjrbfewrvvvhvyfJVKJbRabcjnEwgEQiqiVquRnp4OYESb4tS9YaKEPPv7+xNy37EQCoV+woNer5fspQlBycnJwc9+9jM899xzcf2MxHV33N/fjzfeeAP33ntv2Klt4eD1ekmD5SlAsMVGKpUmXHzI6RxJM+3r60N7e3tIxoHPcB0aGuLUinNycqBUKjE0NIShoaGoeD+FQiH4fH5AGlCi37NYIBQKuf+LUIlGSvKtt97KGZ4AUF5ejs2bN+PFF1/EK6+8Aq1Wi1tvvdUvtfjee+/F888/jy1btmBgYAAZGRm47rrrAAB6vR633HILnn32WTzyyCO44IILsHHjRtx2221Qq9V47bXXYDKZoFAoUF5ezl1HIBAIhP+hUCjQ19cHm82GoaEhv3IJPp8PPp8Pj8cT1zkl67rrKydpbW2F3W6H2+1OCk0RQvJxxx13YPHixfjkk0+wdOnSuNyTYuP4zXnqqafw61//Gi0tLWGntoVDsrYiIUSX7OzsgLYkAFBTU5NQIQSapnHaaaehuroaAoEAxcXFY57PsixMJhO6urqgUqmCpi+xLAun08kZskNDQ2Musnw+HxKJBBKJBGKxmPs3j8eD1WpFTU1NwJwnUwspXwr2ZHpN0YamaUyfPj2g5tbj8ZCoP4FAmHQ4nU5UVlYCGDFkS0tL/Y57vV50dXWhq6srLmuHSCRCfn5+UpQ7jYbFYkF9fT2mTZs2ZpcEwtSFZVnMnj0bxcXF+Nvf/haXe8Zth8IwDJ5//nlcdtllMTVcbTYbMVynCKMtLrGM6ocCwzAYGBiAzWbj6mxGg2VZtLW1jfuZpSgKYrEYYrEYOp0uwJjl8Xh+xupY78Gp/ioej5fywlCnGt++tG1CcHg8HmbOnBlgpLrdbhw/fhz5+flExIlAIEwqhEIhhEIhXC4XhoaGMDw87FdqxOPxkJWVBZ1Oh4aGhpg7wRUKRdIbhHK5HCUlJQnfVxGSF4qicMcdd2D16tVobm5GXl5ezO8Zt5rXDz74APX19bjjjjtidg+WZdHS0hKz8QnJxWgGVzKIC7S1tYHH40GpVI56DsuyEWcJ+IxZnU6HwsJC5OXlQa/XQ6FQjLvIiMVi5OTkJHUKkM1Go6LCgN/9LhcVFQbYbGP/VOn1ej+lZsLYlJeXB9QD+1qKud1u0u+VQCBMOiiK8lNnH60/pUAgiEsLHbvdnvTrFp/PR1paGjFeCWNy5ZVXQqFQYMeOHXG5X9y+NTt27MBpp52G733vezEZ3+PxoLa2FsPDwzEZn5B8jCaElAzGq9PpDGh9czI+w9Vs9leyjUe0UCAQQK/XQyAQgM/nJ13U1WajsWpVObZvN2LPHg22bzdi1aryMQ1Yq9XK1QoDyVtHlGgyMjJQVFQEoVAYYKCazWYMDg5CIBAkdRobgUAgRIrBYEBRURF4PB4GBga4tmOnolarx2x3Fw1sNhtZqwiTArlcjlWrVuHVV1+Ni4J2XIzXjo4O7N27FzfddFNMPPoOhwPV1dUYGhqK+tiE5KW/vz/owpOdnZ0UkSOHwwG32w2GYdDd3Y2uri709PTAYrGgubk5wHAFRoyweDlgWJZNivfpVHbv1qGpSQyGoeDx0GAYCk1NYuzePbon3Gq1ore3l0sBIxuC4FAUBZVKFeBUsdvtaGtrAzCyaUvGzwWBQCBEA5VKhWnTpkEikaC6uhotLS1wOBx+51AUhby8vJhGYFmWHdV4JhBSjRtvvBFmszmsjgeREhfjdefOnRAIBLjiiiuiPrbPcA1XVZSQ+owWNfT1dEv0BtxqteL48eOorq5Ga2sr2tra0NzcjPr6evT29o56XXNzMwYGBmJugGVkZCA3NxdisTjh79XJmEwi0LT/a6dpFiaTaJQrRrDb7dxGIBFCTfPnz8f+/fvjft9QkEgkSEtLQ2ZmZsAxhmHQ2NjIfd587SQIBAJhsiISiVBeXg6VSgWz2Yzq6uqA/QRFUcjJyQkqDBktEikuGSqLFy/GmjVrEj0NQpIzc+ZMnHXWWXjllVdifq+YCzaxLIs//OEP+PnPfw6VShXVsRmGQUNDQ9KlPRLig1gsHjW9UaVSQSKRBLSEiTdutzvsNGa73Y66ujoIBAJoNBouxTfaqNVqsCyLvr6+AK9zIjEanWAYf2OaYSgYjaM7qDZu3Mh5+3y1xsXFxTj//POxfPnyuNQV7du3L6YiR5G0M6IoCnK5HJmZmZDL5UGdFGazmTP6RSJR0guIEAgEQjSgaRr5+fmQSqVoa2tDV1eXX/kJMPIbajQaQdP0qDWyE2G8TKvxHMsPP/wwNm7cGMUZEQiRc8MNN+DWW29Fa2srcnJyYnafmO/o/vOf/6C2thY33HBDVMdlWRbNzc0k5WIKM57gUDILEoWC2+1GZ2cnurom1nd0LCiKglqtjtn4kbBihRn5+Q7QNAs+nwFNs8jPd2DFisA065NZuHAh9u3bhz179mDbtm2YP38+tm7dijVr1kRcS+z1ekOO4mq12ph95nyGa7gRcplMhpKSEigUilGvPbncgqQMEwiEqYRPxEmhUKCrqyvoWkFRFLKysmA0GqN+//GM146ODu7P008/jbS0NL/n1q1bF/U5BYNhGFKOQxiXlStXQiwWo6KiIqb3ibnx+sorr6C4uBg/+MEPojqu2WxGX19fVMckpBbj9aJMdePVR29vb0wXDbVazWVFCIXChEfepFIGFRXVWL3ahOXLe7F6tQkVFdWQSsc2IgUCAbRaLfR6PcrLy3H99ddj69at+PLLL7mo7BtvvIGVK1fi7LPPxgUXXIAtW7b4Ref37NmDxYsX47PPPsNll12GhQsXorOzE8eOHcPq1auxdOlS/OAHP8DNN9+M6upqv/vHMm3Y9/9PURRomub+SCSSMY1NpVI5rjF6snFOUoYJBMJUg6IoZGdnQ6/XB6iwn0xGRkbUo0lOp3NM52pGRgb3x/d7fvJzb731FqZNmwaxWIzy8nJs376du7apqQkUReGtt97CwoULIRaLMXPmTHz22Wd+96isrMSPf/xjyOVyGAwGXH311Whra4PZbEZjYyOGh4fR3d2N66+/HiqVCgaDAQ8//HDAfb777jvuOYvFAoqiuDXR6/XihhtuQEFBASQSCcrKyvDMM89E500kJA1paWlYuXIl/vCHP8S0fCumxuvQ0BD++te/4vrrr4+qN99qtXLiIoSpS09Pz5i1zpPFePV4PLBYLDG9R0FBAebOnYtZs2Yl3HgFRgzYVau68MADLVi1qmtcw3U0Lr74YsyYMQOffvopgJE0sXvuuQe7d+/Gxo0bceDAAWzbts3vGofDgZ07d+LBBx/En//8Z6Snp8Nms+HCCy/Eq6++ioqKCuTk5OCuu+6Ku7o5wzB+f0QiEZfmlpWVBZVKhZKSEmRkZGDmzJkh9dT2nePrE0wgEAhTDalUCqPROO5eVa/XQ6vVRvXekda97tq1Cxs2bMDvfvc7HD9+HI8++igeeugh7Ny50++8e+65B3fffTe+/fZbLFiwAMuXL+d0NywWC5YsWYK5c+fiyy+/xCuvvIK6ujpccsklfo7dd999F0KhEK+99hpWr16NTZs24R//+EfIc2UYBtnZ2fjLX/6CqqoqbNiwAffffz92794d0WsnJC833HADmpqaYqoBEtOa17/97W+w2+248sorozam2+1GQ0MDSV8ggGEY1NfXo7y8PGhN42TqS9bb2xuz9F6WZTE8PIyhoSFYrdZJo9otl8uhVCoxc+ZMHDx4EAD8ROOysrJw2223YfPmzbj33nu55z0eD+69916UlpZyz51xxhl+Yz/wwAM499xz8c033+Ccc86J8SsJji/zwGAwQCgUIj09nUstDqf2VqFQQCaTjdmTmEAgEAgjGI1G9Pf3R01vZWBgICJNmIcffhhbt27FJZdcAmDECV1VVYUXX3wR1157LXfeHXfcgUsvvRQA8MILL2Dfvn149dVXsX79ejz33HOYO3cuHn30UQwNDcHtdmPjxo1YtmwZnE4n11aopKQEN998MwAgNzcXu3fvxt/+9jecdtppIZXlCAQCPPLII9zjgoICfPXVV9i9ezdWrFgR9msnJC8LFy5EYWEhdu3ahSVLlsTkHjE1Xnft2oXvf//7yM3Njcp4LMuioaEhKfp4EpIDu92O1tZW5OXlBRxLJhGiiRLL/q8sywZtFXAyM2bMQENDQ1LWmItE/1MhpmkaUqkUFEVBq9WCx+OBZVkuFezrr79GRUUFmpqaMDw8DK/XC6fTCYfDAbFYDGBkkS0pKfG7R29vL1544QUcOnQIfX19YBgGDocDnZ2d8Xuhp5CVlYX09HS/uuVIMlwoikJmZib3+gkEAoEwOnw+H0ajES0tLVEZr6+vD9nZ2WOmLJ/K8PAw6uvrccMNN+Cmm27invd4PAGOyAULFnD/5vP5mD9/Po4fPw4AOHz4MD799FNO/JJhGC4YUF9fj9LSUggEApSVlUEqlXLRWK1Wi76+PvT09KC7uzukOT///PP4wx/+gJaWFtjtdrhcLpx22mkhv2ZCakBRFK644gps27YNzz//fEz2FjEzXjs7O/HRRx/hhRdeiNqYJpMpJWTFCfFlcHAw6POJVhqOFhRFIT8/P2bj0zSNgoICbjE7FYVC4adIm0wolUoIBAKkpaXB6XSioKAAIpEIFEVxP5jHjx9HYWEh2tvbsXbtWlx66aVYvXo10tLS8N1332HTpk1wu93c+b7rT2bjxo0YGBjA3XffjczMTAiFQlx33XVRd6QpFAoMDQ2NqiwslUohEAig1+shlUq5zc5EyzJI1JVAIBBCR6vVoqenJyr7DIZh0NHRgezs7JCv8e2FX375ZZx11ll+x8Ixgq1WK5YvX47f/va3sNvtEAgEXPmIr7Uaj8eDWq3GtGnT4PV6YbPZIJFIwOfzwePxuNrGlpYWTJ8+HUKhMGBtfOutt7Bu3Tps3boVCxYsgEKhwOOPP46vv/465LkSUocrr7wSv/3tb7F3714u6h9NYma8/vnPfwaPx8PPf/7zqIzn8XhC9u4QphajRSUni/Gak5MT86iYVCpFZmZm0FYASqUSaWlp6O3tTZq2VD6FyIyMDDAMwy24p6ZeffLJJzh69CjWrFmDmpoaMAyDtWvXcp7lDz/8MKT7HT58GL/+9a9x9tlnAxhxzkWzDjk9PR0CgQBZWVmor69HZmYmenp6/PoBKxQKv1RmAoFAICQGiqKQm5sbINwXKV1dXdBoNCHrDhgMBmRlZaGhoWHc0ryPP/4Y2dnZkMlkkEqlOHToEO644w4AwLx58/D222+jpKRkXBFMYMSQVSgUEIvFSEtLw5w5czgRq8bGRlRVVcFoNOLbb7/1u+6LL77AwoULsXr1au65+vr6kF4rIfUoLy/HvHnzsGvXrtQyXnft2oUf//jHUVOv7OnpIXWuhKCwLOuX6gJE1l81GUlLS4u6OMRoZGZmYmBgwM/op2kaKpUK3d3dSWO4AsC0adMCFnmXy4XOzk54vV50dXVh37592Lx5My688EJce+212Lt3LzweD/785z/jnHPOweHDh/HOO++EdL+cnBy89957mD59OoaHh/HMM8/4pStPBI1G4xdZLy4uBkVR6OjogEQigVKpRHp6OhFTIhAIhCQi2qKQLS0tKC0tDTmT5pFHHsEvf/lLKJVKrkb14MGD6O/vx+rVqzln9EsvvQSFQoGCggLs2rULvb29uO666wAAt99+O15++WVcfvnlWL9+PdLT01FXV4e33noLr7zyyrhRXIqioNFocMYZZ2Dnzp0wGo3473//i+effx7ASBDB6/WipKQEf/zjH/HBBx+goKAAr7/+Og4cOICCgoIJvGOEZObKK6/Efffdh/7+/qhrtsREbbiurg4HDhyImlATy7JJoYBKSE40Gk3Ajz1FUSF5EZMZPp+P/Pz8uPXd9KUn+9qxGAwGzJw5EyKRKOm+f8EEuvbt24fMzEzk5+dj2bJl+PTTT7Ft2zb84x//AI/Hw5lnnom1a9di586dWLlyJfbt24fbb789pPs99NBDGBoawlVXXYUNGzZg5cqVE3bMZWZmIicnJ6D1AkVRcDgcGBwcRHp6OoxGIzFcCQQCIcmIdnaX1WoNqwXkjTfeiFdeeQWvvfYaZs2ahR/84AeoqKhAeno6jh07hp6eHgAjgk0VFRW4/PLLcfjwYWzfvh06nQ7AiHbCF198Aa/Xix/96EeYNWsW1qxZA5VKFXSdHY2dO3eCpmlcddVV2Lp1Kyfu1NraisOHD+Omm27CJZdcgpUrV+Kss85Cb2+vXxSWMPn4xS9+AY/HE3KQIBwoNgbhzC1btmDTpk0wm81R3fQePXoULpcrauMRJgdGozFoS5C+vj40NjYmYEYTw1fvWFRUFJEC4UQZHByERCLxU2seGhrCiRMn4j6XYNA0jZkzZ4atJm21WlFTUxOjWYUHn89HSUnJqL+PLpcLR48eTdhngEAgEAhj43A4cOLEiQlnedlsNHbv1sFkEiEnx4NHHtFBpYrc+d7Z2QmTyYT29nZcdNFF2LVrF8rKyrjjOp0uakKqJ8OyLCwWC5xOJywWi18ruezsbE5EkTB1WLx4MSQSCd5///2ojhuT0NTbb7+NH//4x1GP1shkMmK8EgIYLXVHrVajr68PAwMDcZ7RxCgoKIBQKIRMJkvI/YO1WQnHGxxr8vLyImqD5GsHMzg4mPASBD6fP+bvo0AgAI/Hi1pqMoFAiB2+35N4ZckQkgO73R4Vw3XVqnI0NYlB0ywYhsK+fW4cOgT8fwHgsBnvcxirTKqTle+VSiWqqqq4Y21tbTCZTFCpVEhPT0daWlpYkV1CanLppZfi7rvvhsViiaojPuqfnJaWFhw8eDDqBbosy8a0XQghdRnNePUJKqTSD6RcLodarU6Y4RoMhmHQ39+f6GkAGGkQH2m6ru/zII90RxAlhELhuOrRFEVhxowZEAgEk6J2m0CYzFAUhfb29oQ7xQjxRS6XT3h/sXu3Dk1NYjAMBY+HBsNQqKsTYOtWZ8RjjjeneJQB+VKWT4ZlWfT396Ovr4/s56cIP/vZz+B2u/Huu+9GddyoR17feecdCIVCXHDBBVEdt7+/H0NDQ1EdkzA5GEs0QSgUwmg0orW1NY4zipxkrG20Wq0JF2sSCATIzMycsHiVUChEaWkpenp60NzcHKXZhQ5FUSgvLx81cswwDBobG2Gz2eB2u8GyLFEZJhBSgIGBATidTuTn56eUw5QQOb6e4LW1tVy7mHAxmURcxNUHTbM4dswGlhVG3LsbGKlnPXjwYMCxWHcvYBgGIpEIeXl5oGma++PTIjl5n+P1etHe3g6LxQKhUAiRSBTwt0AgIFkNKUp2djbOOussvP3227jqqquiNm7Ujde3334b5513XtDUw0jxer1oa2uL2niEycV4KaQajQZtbW0p4RVPRuM1ke8bTdPIyMiAXq+Paq1MovqaKhSKMT+vbW1tAS14oq1oSSAQoo9QKER/fz8EAkGACBth8iKXy1FaWora2tqInLxGo9PPcAUAhqFgMAzDbHZDr9eHPeZYhh7LsmhpaYl57/hQ503TNBQKBSdS6OtfezK+95gYsKnJpZdeig0bNsBqtUYt8y2q7sHOzk588cUXUU8ZHhoaIqlzhKAIheN7Jnk8XlKl4Y5FrD2ikZCoBYOiKJSWliIzMzOqhqvL5cLx48cTUk86ltFstVphNpv9nqMoCllZWbGeFoFAmCAikYhTBydMLWQyGUpKSiJap1asMCM/3wGaZsHnM6BpFvn5DqxYYYbJZIpI58Vut4953OmMPCU52lAUBZVKhczMzFHPsVqtqK+vD2rYEpKfSy+9FA6HI6qiTVE1Xvfu3QuKorB8+fJoDpvwlEVC8hKqcE80MwFigVAohF6vT7qWNEDijFej0RgTp4MvlTwR7/VYn8NgEVa9Xk8irwRCCmAwGEjK8BRGJpOhtLQ0bANWKmVQUVGN1atNWL68F6tXm1BRUQ2plAHDMGFnHXq93gAn6KkkYwRTLBaPudYNDg6ir68PDocjjrMiRIPCwkLMnj0be/bsidqYUU0b3rt3L773ve9NuC7tVIjxShiN4eFhDAwMjJsGqlQq0d7eHqdZhUdxcTHS0tKSckEBErPQpaWlRZQuFSoajWZc73QsGBoaGjW6LhAI/ASahEJh0BZQ0YZl2aT97BEIqQJxMhGkUilKS0tx4sSJsPatUimDVau6gh7r7+9HfX09aJpGVlZWQMYQy7IYGhqC1+uFUqmE2Wwes/5Wr9cnTdeOwcFBOJ1OCIVCCAQC5Ofno76+Puh7x7IszGYzzGYzVCoVjEZjUmaqEYJzwQUX4OWXX4bX641KJl3UjFen04kPP/wQ9913X7SG5CDGK2EsGhoaUF5ePma9qEQiAZ/PTzqFu/T09ITVX4ZKvA0b3yIW6/tG0m4n1HH1ej16enoC0rO6urqg1WqDvjaKopCeno6uri4olUrk5+eDz49JN7OAOXV2dkIkEkEkEkGhUHAN7AkEAoEQOlKpFGVlZaivr49aeq5PB2FoaAilpaUQi8VgGAZ9fX3o6uriopF8Pn9cjQqKoqDRaKIyr9FwOBwYGBiA1+uF1+uFWq0OWuvY19eH3t7eMcdSqVSQSqVwOBxwOp1wOBywWCyknCbFuOCCC7B582YcOHAA3/ve9yY8XtR2Rv/5z39gtVrxk5/8JFpDchDjlTAWDMOgvr4e06dPx/XXXw+LxYK///3v3PG//vWvuOqqq3D33XfjkksuSdxETyFV6hnjmQYnFotRXFwcM8PyZGJ1D4qi0NnZCa/XC4qi/DYTTqcTAwMDo/Y78zVyF4lEcXMauFwueL1e2Gw22Gy2uBjMBAKBMFmRSCSYMWMGnE4nLBYLBgYGolKv6Xa7UVNTA61Wi56engBnfCjO+a6urjHrS6OBSCSC3W7nDFOz2YycnJwAp2heXh5EIhGGh4chkUggkUggFovhdru5aHNeXl7AmuTxeMg6lWKcddZZUKvVXIbuRIna//7evXthNBoxZ86caA3JkWzRMkLy4XQ6g4p6vfLKK7j99tuxY8cOXHTRRWhqaor/5E5Bp9NBoVBAIpEkRDQoXOJlRMnlchQVFcVtUYpVmt/JKVmnGq/AyOZhrGbd8U6FOjWFLNF9cAkEAiHV8bWkycjIQEZGBqxWK+rq6iYcjPF4POjs7JzQGG63O6oiiKdCURTy8vKgVCo5J65QKAwoUaEoalRDuqCgAIODg0H3A8RwTT34fD6WLVuG9957D5s2bZrweFELqbz33nv4yU9+EvWNLsMwAa0jCIRgnLoJf+yxx3DnnXfirbfewnXXXYe1a9fi7rvv9jtn69atuPnmm7nHN998Mx5//HE888wzWLJkCc4//3y8+OKLfte88cYbWLlyJc4++2xccMEF2LJlC2w2G3d8z549WLx4Mf7zn//gkksuwaJFi7B+/Xo4HA588MEH+P73v4/CwkKsX7/ebyF7/fXXMX/+fCgUCmRkZOCKK65Ad3d3NN+iiIiH8apWq1FSUhLXRWm0iHI0Xy/DMAHjWa1WDA8PR+0eE+VUp0+y1EMRCATCZEEul6OsrCwpDK94/MZTFAW1Wg2tVguNRgOFQhHW2qpWq5GdnR3DGRLizQUXXIBvvvkGHR0dEx4rKsZrQ0MDTpw4EZOU4d7eXpI2TAiJk3+Qf/3rX2PTpk1499138bOf/QzAiLESirfx3XffhUQiQUVFBe6880688sor+L//+z/uOE3TuOeee7B7925s3LgRBw4cwLZt2/zGcDgceOutt/Doo4/i2WefxaFDh7Bu3TocOHAA7733Hl5//XW8+OKL+Otf/8pd43a7sWnTJhw+fBh///vf0dTUhFWrVk3wXZk4sTZeMzIyUFBQEHeVztEMyGgrUwerQUoGp4SPUzcyJztiCAQCgRAdJBIJysvLE65zkSoOylhGhwnxZ9myZaAoCvv27ZvwWFFxAX388cegaRqLFy+OxnAcLMsm1SaPkNz4Ikjvv/8+/vGPf+Djjz/GkiVL/M4JxetZUlLCRWNzc3Oxe/duvyLzK664gjs3KysLt912GzZv3ox7772Xe97j8eC+++7jPIfLli3DP//5T/zrX/+CQqHA9OnTce655+LTTz/FypUrAQDXX389d31hYSG2bduGM844I6qNnSMhlsarUqlMWF/EoaGhoM/L5XIMDw9HrVwhWOpwX19fUOXIeMMwTMDrJMYrgUAgxAaRSITi4mLY7XZ0dXWhr69vXJGlaENK8QiJQKPRYO7cufj4449x3XXXTWisqBmv8+fPH7OOKxIGBwfhcrlA0zT35aZpmkRiCUHxeRNnz56Nnp4ePPzwwzjzzDP9DL9QonslJSV+j7VaLfr6+rjHX3/9NSoqKtDU1ITh4WF4vV5OBc9XrygWizF79mwolUqkpaWhrKwMBQUFUCgU3DgGg8HPOXPo0CFs3LgRhw8fRn9/Pyd339LSgunTp0fwjkSHWBqviTLefO0FTkYsFoOmaWi1Wq4FUyw3FbW1tSgpKUmoARvMA69WqxMwEwKBQJg6SCQS5OfnIysrC93d3eO2uIkmJ+9DCIR4snTpUrz++usTbtE34Tw9hmHwySefYOnSpRMdKoDu7m4wzEijZpZlwbIsGIaJaf9HQuri24gbjUbs378fJpMJy5Yt44yUYI6PYB7IU6OzJ0fO2tvbsXbtWhQXF+Oxxx7D66+/jvXr1wP4X+TXYDBAJBKhoKAA6enp4PP5oCgqQN2WoihusRoeHsb555+PtLQ07Nq1CwcOHMDf/vY3v9eVKKJtvPoMxNLS0oTVtDidTr//e4FAAI1Gg/LycvD5fBQUFEStXcxoxqnT6UR1dXXC6l99zsGT4fP5cektSyAQCIQR4cDs7GzMmjULWVlZMa+JFQqFkEqlMb0HgTAaS5cuRWdnJ6qrqyc0zoSN12PHjsFsNkfdeLXb7RgcHAx43mfAarXaqN6PkPqcLDyTl5eHzz77DJ2dnZwBq9PpAlT6ampqwrrH8ePHwTAM1q5di1mzZiEvLw9ms5k7npaWNma/2dGorq5Gb28vtmzZgnPOOQfl5eVJkzIf7VrUzMxM5OXlhS3gEE1cLhfS0tK4ljTTpk1DRkYGN59oZni43e5R++p5PB6cOHGC69MXL1iWRX19PRoaGvyez8rKInVGBAKBEGf4fD4yMzMxa9Ys5ObmjpmRk5aWhszMzIjWT5VKNe51vn02gRBtzj77bAgEAnz88ccTGmfCu9KPP/4YIpEICxcunOhQfnR1dY16rKenJyIDgTC5OTWKlJOTg/3796O7uxvnn38+vve976GyshLvvvsuWlpa8OKLL6K+vj6se+Tk5MDj8eDPf/4z2trasHfvXrzzzjvc8UidKrm5uRAKhXj22WfR0NCAf/7zn1GRE48GFEVF7fsmEomiFtGcCAqFAgUFBcjLy0NBQUHQnq/RMl69Xu+YNUYMw8TdUeHbnJz8GkUiEXEKEggEQgKhaRo6nQ4zZsxAYWEhpFIpKIqCUqlEfn4+5syZg5KSEmRlZUWkXjw8PIy+vj54PB7Y7XZYLBZ0dXWhpaUFtbW1qKysxDfffINjx44lpB6XMLmRyWRYsGBB4o3XTz75BAsXLoyqMdnb28s1Nx6NwcHBhEVtCMmJx+MJ8BZmZ2dj//796OnpwZYtW/CLX/wCzz77LK655hoMDw/jggsuCOsepaWlWLt2LXbu3ImVK1di3759uP322wGMqOZGqiKo0+lQUVGBv/zlL5g+fTq2bNmCJ554IqKxYsFEa2REIhHmzZuHmTNnJkVkj6Io8Pl8KBSKUWs8o1lbP54DoLe3N64iGjRNB3j2s7OzyW8qgUAgJAG+VjPl5eU47bTTUFxcDI1G42esymQylJWVhaWbMDw8jMbGRhw+fBhVVVWor69HW1sbzGYzBgcH4XQ6AYwEAxobG1FTU5NUrd0Iqc/SpUvx6aefTmiPRbETcKv40nfvuusuPPzwwxFP4mQsFkvI0TA+n09U06YQNhuN3bt1MJlEMBqdWLHCDKnU31hVKpXIy8sLGkkzm81oaWmJ6pwkEgkMBgPUanXcW73Ek3C+l8HIzMxEVlZWFGcUe44fPx415d2ioiJYrdYxM0pyc3PjGpWuq6vDwMAAgJGN0ty5c4nxSiAQCCmG2+1GfX19TI1MvV6PnJycmI1PmDp89tlnWLx4MQ4fPozZs2dHNMaEKsOrq6vR39+PRYsWTWQYjsHBwYAarLEghuvUwWajsWpVOZqaxKBpFgxD4b33NKioqPYzYAcGBmA2m4PWg0Qz4qdUKqHX6xNatxlPJtqqRyaTRWkm8SOakdempqZxDVOfxzteiMViznhlWRZOp5NTyyYQCARCaiAQCFBaWoqGhgbuNz3adHd3IzMzM+aCUoTJzxlnnAE+n48vvvgiYuN1QqGiL774AjRN46yzzprIMBwmk4nk1xOCsnu3Dk1NYjAMBY+HBsNQaGoSY/fuQIOgo6MDlZWVfu1tgBGVvYmSnp6OGTNmoLi4GGlpaVPCcAVGshwmolCYit/raEbSvV5vUAG6kzlZcCwepKenQ6VScUa13W6P6/0JBAKBEB1omkZRUVFMs3fGW8MIhFCQSqWYN28ePv/884jHmLDxOmfOnKj1jEpU2wxC8mMyiUDT/gYQTbMwmYLXevjqNU5WcQ2WShwOOp0O+fn5UzY6NZHveSoqF0bD2XEy40Vy451JIpVKkZuby2Uk+FpKeTwektVCIBAIKQZFUcjJyYnZXjpWUV3C1GPRokX44osvIr5+wsbr2WefPZEh/FAoFKO2lCBMbYxGJxjGP8rJMBSMxrFTLVtbW7mo30SMV41Gg5ycnCkTaQ3GRIzXVIy8Rtt4HY94R16BkYi6L0PBbDajr68Phw8fRkdHBxwOR0o6HQgEAmGqQlEUDAYDCgoKor5fIU5NQrQ4++yz0dzcDJPJFNH1ERuvXV1dqKuri1q9q4+MjIyojjfZ0ev1U+I9W7HCjPx8B2iaBZ/PgKZZ5Oc7sGKFeczrBgcHuVQXmqZDrnulaRpqtRoFBQWYM2cO8vPzp7ThCkys7jUVjVe5XD7haP3JpKenj3k8ERsDj8cDlmVBURQKCgpgMpkglUphsVjQ1NSEY8eOkQ0LgUAgpBi+EqeMjIyoOWKn+h6IED18tmOk0deIK6+//vprAMCCBQsiHSIodrudMzISEYlINbxeL0QiEXg8XlQFZpINqZRBRUX1uGrDwWhtbYVCoQBN0xAKhaPW9gkEAqhUKqhUKsjl8kmtHhwJPB4PMpksIkXDVIzgKZVKDAwMBNROAyNiRyenpIfKWArpbrebMyTjhUAgwMyZM+FyuSAWi8Hj8dDT0wOFQoGuri7k5eUlRWsjAoFAIISHSCSC0WhEVlYWrFYrent70d/fH/F6TPZEhGhhMBhQWFiI//u//8OKFSvCvj5i4/XQoUNRl872eDzo6OiAXC4nheEhYrVauf6M0W4Dk2xIpQxWrRq91choOJ1OmM1mGAwGCASCAOPVJ3QwVZSDJ4JCoYjIeE3FRY/H40Eul/sZrzk5OdBoNLDZbDhx4kRY4w0ODo4bxfR6vXFXc6RpmqvjViqVoCgKUqkUPT098Hq95DtBIBAIKQxFUVAoFFAoFMjJyYHFYoHdbodcLodYLIbNZkNjY2NI4xAI0WL+/Pk4dOhQRNdGvKM8ePAgTj/99Kh+mIeHh2G324nhGgZOpxMMw0Cn02H27NlRE8+abLS3t8PtdgdNAy0oKJhSysETIdLPl1KpjPJM4sOpqdIymQw8Hi9myrzJkG2SlpYGPp+PwsJCUBQFs3ns1HwCgUAgpAY8Hg8ajQbZ2dlQqVRcxk0okD0SIZqcfvrp+OabbyLKBIjIeGVZFocOHcL8+fMjuXxUbDYbSVGLAKvVCovFgurqak4xlOAPwzBob2+HSqWCwWBAVlYWcnNzUVpaCpVK5XduRUVFwHOEEeRyedgLmEKhiGrtaDw5NQrqe+00TUOr1cJgMEQtqpxs71FaWhr0ej1kMhmnQJyKtcsEAoFAGJ1Qf9eJ8UqIJvPnz4fVag07iw2I0Hg1mUzo6urC6aefHsnlo6JSqUjkMAJaWlpQX18Pl8uV6KnEnI0bN+Luu++O6FpfLV92djYyMzOh0+mCft5WrlwZ0ZdpKkDTNGQyWVjXjCdUlMzYbLagz2u1WuTl5SE7OxulpaVBnW4nL/R8Pn9cI1ev10MikUxswjFALBajoaEBhw8fRmdnZ6KnQyAQCIQoolKpkJubO+55iSr/sdlssFqtCbk3IXbMmzcPwEgmb7hE9En05ShHO/IqkUjC3hgTxu8fSfgfoXgOJRIJ9Hp9HGaTmoTrYErlKPapxmuw6KhMJkNJSUnQ63k8HoRCIfLz88etFRaJgvcsTjQ0TSM3NxcURaGjoyMlxbcIBAKBMDo6nQ5ZWVljnpOIyKvb7UZdXR1OnDjhV1IYq9IdQvxQqVQoLi6OqO41YuPVl3oZbQwGA9LT0yGVSkmKAmFMli9fjjfffNPvuSuuuAIvvvgi93j+/Pl4++23sWbNGpxzzjmYMWMGvvrqK9TV1WHx4sWQyWRYuHAh6uvruWtOTRtetWoVLr74Yu6x0+nEqlWrcNZZZ3HpNosXL8add96JNWvWQK1Ww2Aw4OWXX8bw8DCuu+46KBQKFBcX4/3334/NmxFHwjFelUpl3AWIosnJnua0tLRRU3ulUmlA9JVlWfD5fKSnp6OhoWFco88nmpQoHA7HqArKYrEYGRkZYFl2SmR4EAgEwlQjIyMDOp1u1OPx3pMzDIO6ujq43W5IJBK/zKSWlhYSuJkEnH766fGLvB49ehSzZ8+OyQfZ12/QaDSS+ipCVHj11VexcuVK/Pvf/0Z5eTmuuOIK3HLLLbjvvvtw8OBBsCyLO+64Y9xxGIZBR0cHjh07BpfLBYZh/L4DO3fuhFarxX//+1/ceeeduO2223DZZZdh4cKF+Oabb/CjH/0IV1999aipqKmCTCYL+buf6pkUWq2WM2DHW9SDvValUomurq6QopWJjry63W5UVVWhu7s76GdUKpUCADFeCQQCYRJCURRycnKgVqtHPR4vWJZFU1MTbDYbhEIhSktLOecxwzBQKBQYGBggdkKKM3v2bFRWVob9/xiR8VpZWYmZM2dGcmlIeDwe1NXVxWx8wtRi+fLlWLBgAXg8HtasWYOmpiZceeWVOP/88zFt2jTcdddd2L9/P3e+70s0NDSE3t5e2O122O12VFVVob29nTt+aqRtzpw5ePDBB1FSUoL77rsPYrEYWq0WN910E0pKSrBhwwb09vbiyJEjcXvtsYCm6QAV3tFI9eyJgYEBMAwDPp8/rmKyz7g7GZqmQ6oT0ul0casnGk3RmKIosCyL1tbWoG0TBAIB+Hw+MV4JBAJhkkJRFPLz84NmWMWjZMRkMnFrUH9/P4ARh6mvzZzb7caJEyfQ0dGBxsZGVFZWorW1lYiVpigzZ86ExWJBe3t7WNeFnc9ns9lQX18fU+OVz+cjNzeXa6pMmNzYbDR279bBZBLBaHRixQozpNLo/Uj66hEZhuE23rNmzeKOGwwGOBwODA4OQigUorOzE16vlxNtstlscDqdcDqdfuOearzOnj3b75hGowm4DwB0d3dH7bUlCplMNiUWC9/vj1arHdcQl8vlyMnJQWtrK/ecw+GATCYbtf0XTdPIy8uLm6gVwzCw2WxBDXHf6+PxeMjOzobL5YJQKOSOy2QyzJw5kxivBAKBMImhaRoGgyFgjTebzdDpdFHPEvJFWWmaRl9fX1Ajubu7GxqNJkCc1OVyobu7G93d3SgvL0/5bK+phs+WrKyshNFoDPm6sI3X48ePg2XZmBqvwMhmUSgUEuN1kmOz0Vi1qhxNTWLQNAuGofDeexpUVFSPa8DSNB2QauDzzp3MyTWXvpo+gUAAlmVhsVjQ3NwMYMTjZ7fbA4xUX0Tq1PucaryeWg9JUZTfcz7jYDII3oS6eKVy5NXj8XBGp1arHff8tLQ0ACMLvO9zNjw8DJ1OF9R4lUgkKCwsjGutq8vlQltbW9C+xr7Pa0lJCRwOBxobG1FWVuZ3Do/HS0pFZAKBQCBED5lMBoFA4JepwzAMWltbUVRUFNW1vbu7G319fWOe09PTg4GBgTGdpz09PZzxyrIsvF4vPB4PRCKR33xZlsXQ0BA8Hg8YhoHX6wXDMFCpVGR9izP5+fmQyWSorKzE+eefH/J1YRuvlZWVAIDp06eHe2nYJEqWmxA/du/WoalJDIahwDAjPy5NTWLs3q3DqlVdY16rUqnQ09PDPbZarTCZTCHdt7e3F9XV1VxUFQC6urqCpsqo1Wo/QScAOHHixLhppJOZRIsLxQOfw0KhUIRkrPucHOXl5RgaGkJjYyPcbjcGBwfB4/HAMAxYlkV+fj5sNhuMRmPcf+NcLteoolMCgQDl5eUQCoXo6uqa0p9vAoFAmMrw+XyIRKKAMpOBgQEMDAxEtYtAKEEqiUQCjUaDlpaWUc/p6+uD2+2Gy+WC0+nkAgVpaWnIy8vzyyRiGIYTfZLJZMjNzSWGawKgaRozZszgbMuQrwv3RpWVlSgoKAi55m0iiESiKbFJnsqYTCLQtH9Uk6ZZmEzjGwtnnHEG3nvvPXz77beoq6vDxo0bg/bbDEZXV1fIwklnnHEGjh8/jnfffRctLS148cUX0dDQkNJRxYky3vdSIpFg5syZKd1yiKbpsH+DKIoCj8eDSqVCWVkZhEIhhoeHwbKsX4pwTk5O3A1Xj8cDu92OkpKSoJ9dgUAAoVAIlmUxODg4qmgHgUAgECY/vjXh1H1VNJV+nU5nSG1vVCoVNBrNqM5XYMQgHRgYgN1u98twGxwcxLFjx7gIb3V1Nerr67nXMJZTlxB7Zs6cGXvjtbq6GtOmTQv3sogQCASYNm1aSm+ACWNjNDq5iKsPhqFgNDqDns8wDPdDumrVKsybNw9r1qzBXXfdhcWLFyM7Ozvqc1ywYAFuvPFGPPvss7jmmmswPDyMX/ziF1G/TyrB5/PHdBQYDAaIRCIubScVoWnaz1MbLlKpFAaDgWuZI5PJgqbrxgOWZeF2u2EwGMa8v8fjQXV1Nbxeb9Bo82RIeScQCATC+KSlpcHlciErK8tvPXC73ejo6IjKPUJtpadSqUDTNDIyMiK6jy/lubGxMSBw4Xa7UV9fj/7+fnR2do4qakiIDdOmTeNKUkMl7LTh2tpaLFu2LNzLImaiG0hCcrNihRnvvafxq3nNz3dgxQpz0PP7+/s5A1Uul2Pz5s1+xy+88EK/x6f2j8rKygp4bv78+X7PuVyuAOXYW265Bbfccgv3ePr06X4pJierFftoamoKeG4yybqLxWIMDw8HPM/n87mo3fDwMIaGhmLiVIgHNpttQtkfvvfH5XIhPz8/5MyAaDM0NBRStgyPxwPLslyK86mGrq8uKZQaYAKBQCCkLlqtFnw+H2KxGCqVCjU1NVzNaVdXF9LT04Oq7AMjBmFvby9EIhGUSuWomUY8Hg9isXjUHuM+hoaGIBaLoVar/UQRo8Xw8DAaGhoAjLy2wsLCsHraEyKnpKQEw8PD6OrqCtk5EVbk1ev1oqGhAcXFxRFNMFI0Gg3JRZ+kSKUMKiqqsXq1CcuX92L1alNQsabBwUH85z//waFDh3DWWWfFbD6dnZ344osvUFhYOOo5AoEg4T05k4HR3oOTVZ0dDge6urpCSgtKNpxOJ7xeL1wuFywWS0QRZJ/xSlEUaJpOiPFqt9vh9XpDSlOmKArZ2dmgKCpolJXH46G5uRkWiyUGMyUQCARCMqFSqSAWiwN6rQIj6cPBHPJutxudnZ0wmUxoaGjAsWPH0NfXF/Rcm80WkoJ9Z2cnGIYJENSMJr410uPx4MSJE5Mq2JDM+GzK2trakK8JK/La0tICt9vNtR6JF3w+HwKBICU3wITxkUqZccWZfvOb36CqqgpXXnklfvCDH8RsLldddRX0ej0efvjhUc8xGAxETAzB616zs7Oh0Wi4VCCfN9VkMsXd6TVRfKlFPoGKoqKisEQqPB4PnE4neDweiouLEyLhzzAMurq6kJeXF/I1aWlpKCsr8zO0vV4vhoeHuXQqkj5MIBAIUwuRSITS0lIcO3YMwIhztqOjg4ueKpVKroODwWCAQqEARVEYHBxEY2MjzGYzSkpKuP2T3W7HiRMnQlpPXC4XamtrYbVaY/b6aJr2m4vb7SaZn3HAp15dV1eHc845J6RrwjJe6+rqACDuxiswudItCeHzxBNPxOU+H3300ZjHeTweSZn8/6Snp6O7u5uLSMpkMq6XrQ+f8TowMIChoaGUSsM5NSV6vLSmU2FZFpmZmVCr1QnLHDGbzePWuQZDKpX69Xm12+1+XlEipEcgEAhTD7FY7Jfme3Ltq1Qq5Zy+PT09KC8vB4/HQ0NDAywWC9cRIicnBw6HA7W1tWEJP8XScAVGjPOTA2VutxsCgWBKi3PGA7FYjOzs7LAir2GFj+rq6sDn85Gbmxv25CZCsNorAiERGAyGhNUtJhsikcgvvdrX5/RkTjb42tvb4zKvaHGqqEO4xqtAIEBWVlbCDFeXyxWR4cwwDBoaGlBVVQWHw4H29vaAFlSp9n9JIBAIhOigUqmC1rr61kxfthGfzwdFUcjPz4dEIgGPx0N3dzf6+/vR3t6eVMJIMpmM6/Pqszdqa2vxzTffoLm5mWQbxZji4mIuQBoKYRuvBQUFIauDRQuKohKSckcgnAxN09DpdImeRlKhUCiQnZ0NrVYbUGjvdrv96kStVmtKLQA5OTlIT0/nHodrvCYaX5uDcPB4PKitrYXFYoHX60VfXx86OjoCPN4DAwMkG4ZAIBCmIEajEbm5ucjOzkZpaSlycnL8nPoGg8EvO4fH42HatGlcunBzc3PStKYRiURQqVRgWRYmk8mvNtcXFe7p6UF1dXXK7QFSiZKSktgZry0tLWHVTkWTzMxMKJXKhNw7mviEWyiKItHkFEOn08XdcZMKGAwG5OXlBdQBB0vxSaW6dalUiqysLO576nA4Uspgi+T3pa6uzu//rbOzc9Rzk8lrTiAQCIT4IZPJoNfroVAooNfrodFoOIO1p6cnwFHtcDi4NcPr9aK3tzeu86VpGmKxmNvD8Xg8rp2fxWIJyLQ6FbvdjuPHj2NoaCge051y5ObmoqWlJeTzwzZec3Jywp5UNKAoChqNJiH3jiZ8Pp9rQ5FKG+GpDkVRAfWchLEJ5lkN1lonmRGJRFyNcyr3rA0Fp9MJm83GtfRRqVRjZhoQ45VAIBCmLic7SDMzMzFt2jSIRCK4XC50dfmLcEokEj/Bw3BqXSeKWCwGTdNwOBygKAoikQgsy8LpdIa1pvtKasjaF31ycnLQ29s7rhPBR1jGa2tra8KMV2ByiDZNhtcwFdFqtUmT5pIqBGulE+oPUzKRmZnpF32drAwMDCA9PR0ajQbTpk1DVlYWsrOzRy3ZIAs4gUAgEICRwAxN01AoFKBpOui6kZubG3fNEI1GAx6PxxmpbrcbTqcz4hImj8cTVoSQEBo+2zLUHr4hG68ulwudnZ1xF2s6mfT0dJSXl5Mem4S4Q6Ku4eNbzE4mFY1XgUDA/f93dHQkfb0ny7JgGCbsxdnlcnGRVpFIxAlXnDxOWloaFAoFFApFUIEuAoFAIExdcnJycNpppwVdHwQCQVxtCLFYjPT09KhnfBHHbfTxfS6ibry2t7eDZdmERl6BkTz76dOnp2wKcTJveqcqy5cvx5tvvjnqcY1GQxwmEUBRVIBgUDh9UpMJn8r00NAQ6urqUFlZiYGBgURPKyhWqxW9vb1he4eNRmOAt5xlWb9os1arhVqthtFoJL2OCQQCgeCHT9NlNNRqddz0a+RyeUD6cjTg8Xjo6ekh+/kokp2dDSB04zVk9RnfRijRxisw8uXIy8uD2+3G4OBgoqcDYMTDI5fLYbfbx/TykA975MyfP3/M4zfddBNuueWWqN/3VBVdQuiIRCLO+OHxeCkbwebz+dBqtdxC6HK50NjYiOnTpyddE/Ouri5YrVZ4vV4olUqo1eqQrgu24XC5XH6/WQzDQKVSweVyRW2+BAKBQJgaUBSF7OzsuDh/WZaNiY3gdrvR3NwMj8dD9odRQiQSQa/XR9949TUizsrKimxmUYaiKBQUFKC6uhpOpzPu9xcIBFAqlVwKnUAg4NL1vF4vhoaG0NTUFDDnVGoVkmzs27eP+/eHH36IHTt24O233+aeC9Z3bCx8DajHQqVS+Um+E8LDF7Hm8XgwGo0p3SP31M+B1+tFY2MjSktLk0Y53G63+20KJmpkCgQCpKeno6+vDwAwODjI1RARCAQCgRAu8Vo/YmUg+7ommEwmCASClM0ETTaMRmPIPeRDzvvq7u6GSCRKqjonPp+P4uLiuG6keDwecnJyMGvWLOTl5SE9PZ0zgCiKAo/Hg1AohEajgVarTZpN7WRAq9Vyf+RyOSiK4h6//fbbuPHGG/3Of/PNN7F8+XLu8caNG3H33Xfj1VdfxbJly3DppZcGvc/f//53LF68GP/973+RmZmJJ598ErNmzYJMJkNOTg5Wr17t106koqICKpUKH3zwAaZNmwa5XI5ly5ZxDh8AOHDgAM477zxotVoolUr84Ac/wDfffBPldyj50Gg0KC8vx5w5c1K+R26wCKvVah2znUy8OTVFyul0TkhkiqZp5OfnIycnBzqdjqtZJinDBAKBQIiEeGUgxqM7QFNTE6eD4QteeTweuN1uuFyusBWNpzJ6vR5mszmkc0OOvHZ3d0Ov1yedMSYWi1FYWIja2tqY30uv1yMzMzPkXp/Z2dlgGIaLWpCU4cRz4MAByGQyPP/880GP79y5E3/84x/x3HPPYeHChZBKpaBpGtu2bUNBQQEaGhqwevVqrF+/Htu3b+eus9lseOKJJ/D666+DpmlcddVVWLduHXbt2gUAGBoawrXXXotnn30WLMti69at+MlPfoLa2looFIq4vPZEEG40PF74vovh/J6Nlh7c3t4OhUIBuVwelblFisvl4n5rfJjNZrhcLlAUhby8vIj6FFMUBb1eD5Zl0d/fH63pEggEAoGQ8tTV1Y153Bf0Sk9PTzobKpnQ6/VobGwM6dyQdzJmsxl6vT7iScWStLQ0ZGdno62tLSbjK5VKZGdnh50+yuPxoFQqMTg4SDwvSYJEIsFDDz0UNF1427ZteO+99/DSSy+hqKgImZmZAIA1a9Zw5+Tn5+O3v/0tbr31Vj/j1e12Y8eOHSgqKgIA3HHHHfjNb37DHV+yZInfvV566SWoVCp89tlnuPDCC6P5EgkhwLIsjh49CrFYDJlMBqlUCplMBqFQOOriMlaKua/+NVHptFarFfX19UEdZL7UqeHhYZSVlUUsPkZRVMj1swQCgUAgBGOqBXK8Xi+amprQ39+P3NzcpNPJSBb0ej2+/vrrkM4NK/KazGl/er0edrsdvb29o55D0zQkEgnEYjEkEgkkEgmEQiEGBweDNscVi8XIycmZUKo0RVHEcE0iioqKghohu3btgt1uxx//+EdkZ2dDLpdzkbSPPvoImzdvRnV1NeeIcDgcsNlsXGRRKpVyhisw0hu0u7ube9zV1YUHH3wQ+/fvR3d3N7xeL2w2G+kXliBomoZQKITVavVLAReJRJg2bVpQI5TH44HH4wVtru5yubg+qYmAz+eP+Tvj+y2bqGo28RoTCAQCYSJMNePVx8DAAKqqqpCdnQ2NRkPW01PQ6XR+++axCKvmNVkjr8DIpio3Nzeoocnj8ZCXl4fTTjsN5eXlyM/Ph8FgQFpaGsRiMfR6PaZNm4YZM2YgMzMTUqkUubm5mD59+oRrfNPS0kaNxvB4PPLhjRI0TQf8IAbbzEskkqDXn3baafB6vfjoo48A/E9huKmpCRdeeCFmz56Nt99+G4cOHeJSjk8WwznVIKYoym8+1157Lb777js888wz+PLLL/Hdd99Bo9EQ1dYEEqyJutPphMlkGvWasTymQ0NDUZlXOHg8HvT29gaIw52MQqGIym9ZovDVlBMIBAKBkMp4vV40NzeHLEw0ldDr9bBYLCGdG7LxajabkzryCowYMMXFxSgqKuKMFLVajRkzZoQkniQWi5GVlYVp06ZBp9NFxbDk8Xic0U/TNNLT07mIb1paGoxGY8q2D0km1Go1ent7/QzGEydOhHz9jBkzsG3bNrz22mt46623uI3+oUOHwDAMtm7diu9973soLS2N6Efniy++wC9/+Uv85Cc/wYwZMyASidDT0xP2OIToodFoggoPmc3mUQ3RsYzXeLbtstvtqKmpweHDh9HU1DRmey6r1ZrQpupmsxm33XYbcnNzIRKJkJGRgfPPPx9ffPFFwuZEIBAIhMQwVSOvJ9PV1ZWQTinJTDgB0pDThi0WS0rUO1EUBZVKBaVSCYfDMWqkLZ7odDouTVGlUoGiKD/DeHh4OCaNlKcSp59+Ovr7+7Fz50788Ic/xJdffokvv/wyaHRtNObMmYNnnnkGa9asgdFoxJo1a1BcXAy3241nn30Wy5cvxxdffIEdO3aEPb+SkhK8/vrrmD9/PgYHB3HPPfckxWdzKiOTyVBcXIza2tqAxdRqtQYV0hKLxaPK78ezDVZ7e7tfuvNo+Gp5PR5PwupsLr30UrhcLuzcuROFhYXo6urCxx9/PGaJB4FAIBAIkxWWZdHW1gaZTAa9Xk8U/IGwMqxCfrcGBgZSKu2MoqikMQ4EAgEyMjKQnp4OmqYDIrpSqRTp6emkV9QEKCgowK9//Wv85S9/weWXX45jx47hqquuCnucCy64AHv37sWDDz6IZ599FnPmzMGTTz6J3//+95g5cyZ27dqFzZs3hz3uq6++iv7+fsybNw9XX301fvnLXyZ1Gv5UQaFQoKioKOA76fF4gnqHx/pxjdfiY7fbQ0qtkcvlKC8vR25ubsJUny0WC/7zn//g97//Pc4991zk5eXhzDPPxH333YeLLroIAMZtReVjrFZUixcv9hNWA4CLL74Yq1atiuXLIxAIBEKYiMVi5OXlTfmyOYvFgo6OjgC9nalKODYmxYYYv6coCjt37sQ111wT8cQIo8OyLOrq6uKaephs2Gw0du/WwWQSwWh0YsUKM6TS+EWz5HI5SktLp/wP6lTEYrGgvr7e7zmVSoX8/Hy/mnWfSnGwNFyRSISZM2fGfK6NjY0BLXGCQVEUSkpKEtqKyePxQK1W48Ybb8SWLVuCCkY9/fTTmDNnjl8rqiVLlnBq3hUVFbj55pvxgx/8AJs3b+ZaUc2dO5drRbV48WKcdtppePrpp7lxL774YqhUKlRUVMTjpRIIBAIhRFiWxbfffktSiAEYjUZOZ2Uq09LSgry8vJA+E2GFClIp8ppqUBQVdiueyYTNRmPVqnJs327Enj0abN9uxKpV5bDZ4hPN4vF4KCgoIIbrFEWlUgX0abVYLKiurobD4eCeG6tdTDw+O06nMyTDVSQSYe7cuQnvIczn81FRUYGdO3dCpVJh0aJFuP/++3HkyBHunDVr1uDcc89Ffn4+lixZgt/+9rfYvXu33zi+VlTz58/HvHnzcMcdd+Djjz+O98shEAgEQhSwWCzEcP3/jKVZMZVQKpUhnxuWZRDOwITwmcppw7t369DUJAbDUPB4aDAMhaYmMXbvjo9IWF5eHum9NcUJlsbtcDhQXV3tl8Y6WjucWBuvbrc75F7WTqcTZrM5pvMJlUsvvRTt7e345z//iWXLlmH//v2YN28eFxH96KOPsHTpUhiNRigUClx99dUBrcvGa0VFIBAIhNSB/H7/j+HhYWLIAwEBhLEgkdckYipH/UwmEWja/8tL0yxMpon1pQwFjUaTEmJkhNiiUqmCOjB80vY+QSapVBo0/TUW31+WZTE4OIiGhgYcPXo0ZBl5YETUKZEqwycjFotx3nnn4aGHHsKXX36JVatW4eGHH45aK6pgrbKS5bUTCAQC4X/YbLaQBAenCm63m6xXGMmADDVbLCzjNRyrmBA+U7ne1Wh0gmH8N/8MQ8FojK2UuEgkQk5OTkzvQUgNKIoatR2Yw+HgPMUURaG4uDjAoLLb7UF7C0+E2tpa1NbWor+/P2zPrNfrHbNnbSKZPn06hoeHo9aKSqfT+Qk4eb1eVFZWRnPKBAKBQJgALMvCarWitbU10VNJOHv27MHixYu5x+OlDu/fvx8URYXlwE5FQrUzwzJep3JNZjwYrbfkVGDFCjPy8x2gaRZ8PgOaZpGf78CKFbFLfeTxeCgsLPQT5CFMbbRa7aiqwR0dHXC5XGAYBmKxGKWlpeDz/9dtjGXZkOpRQ4Vl2QnXwvT19flFMGPFaIZ1b28vlixZgjfeeANHjhxBY2Mj/vKXv+Cxxx7DT3/6U79WVA0NDXj99dcjakW1ZMkS7N27F3v37kV1dTVuu+22Sb/IEwgEQirR3t6OmpqaSR913bhxI+6+++6A5w8ePIj58+djaGgI5513Ht555x3u2GR/T0IlWFZbMELu8xrOoITwYVk2Jsbrqel1PuRyOYRCIQQCASiKgt1uh8PhAE3TsNvtUZ/HeEilDCoqquOmNszn81FaWpo07ZQIyQGfz8e0adPQ0NAQ8D1gGAZtbW0QiUTQarUQi8UoKytDTU0NF3Ht6emBTqeLKIXY6XT6/ca63e4J947NzMyMSy23zWYL2lNZLpfjrLPOwlNPPYX6+nq43W7k5OTgpptuwv333w+JRMK1orrvvvvw/e9/H5s3bw5b1f7666/H4cOHcc0114DP52Pt2rU499xzo/XyCAQCgUCIGmKx2C8gODAwAL1eH1c7y+VyJZ3WS6hB0rAir8R4jR3Dw8MT3qgG49RNNJ/PR0lJCXJyciAWi2G1WtHZ2YmBgQE4nU643e6E/T9LpQxWrerCAw+0YNWqrpgZrgKBAGVlZcRwJQRFLBajvLwcWq024Fh/fz96enrQ0tIClmUDIrB2uz2inm1erxc1NTV+UVKnc+Ip8319ffB6vRMeBwiMrlqtwJYtwM03s9i0yY1gjmORSITNmzfj0KFDsFgsGB4eRnV1NTZt2sR9/9auXYv29nbYbDbs27cPV199NViW5Xrqrlq1KiCKevHFF/vNRyAQYPv27ejt7UVXVxfuvfde/P3vfydtcggEAiEJYBiG1HWexMlpwyzL4oYbbsDixYvR1dUFYGTtzs7OxoYNG4Jeb7PZ8OMf/xiLFi2CxWJBb28vLr/8chiNRkilUsyaNQt/+tOf/K5ZvHgx7rjjDqxZswZarRbnn38+rr/+elx44YV+57ndbuj1erz66qsAgH379uHss8+GSqWCRqPBhRde6NdasKmpCRRF4Z133sG5554LqVSKOXPm4KuvvuLOCWV+QOh2JkkbThJilTJ8svHK5/Mhl8vR3NyM48ePo729PSAt0ePxwOPxJJ03JloIhUKUlZWRzzJhTGiaRmZmZtBjHo8Hg4ODXIqwRCLhDFitVhuRE6qjowNutxuVlZVoaGjAwMCAX4ueSHE4HKirq5uwY2x4eNhv42G1At/7HvDAA8BrrwGPP67E977HoqNj6pY+EAgEAiE4TU1N6O3tTfQ0khKKorBx40ZUVVVh27ZtAIBbb70VRqMxqPFqsVhw3nnngWEYfPjhh1CpVHA4HDj99NOxd+9eVFZW4uabb8bVV1+N//73v37X7ty5E0KhEF988QV27NiBG2+8Efv27fPTjHj33Xdhs9mwcuVKACPr/69+9SscPHgQH3/8MWiaxs9+9rOAfcUDDzyAdevW4bvvvkNpaSkuv/xyList1PmRtOEUI5JoTSj4jNe0tDQMDw+HVAfm9XrBsiz4fH7UBWgSiUgkQmlp6aQ1zAnRRSAQgKbpUQ2/1tZWpKWlQSAQQCKRoKysDEKhMOyUYafTyYlBsSyL/v5+9Pf3T3j+PqxWKxoaGlBUVBQwt9bWVrjdbsjlcsjlckgkkoBzrFYruru7UVhYyD333HPA8eMAw4ATWjt+nMW2bQw2b47a1AkEAoEwCZhq+67PP/8c55xzjt9zYzmR9Xo97r//fjz88MPwer1477338O233/rpagBAZ2cnVq5ciZKSErz55pvc+2o0GrFu3TruvDvvvBMffPABdu/ejTPPPJN7vqSkBI899pjfmGVlZXj99dexfv16AMBrr72Gyy67jBNPuvTSS/3O/8Mf/gCdToeqqirMnDmTe37dunW44IILAACPPPIIZsyYgbq6OpSXl4c8v6gbr3w+f1QhE8LEKSwsxHfffRf11GGKojjDNZz0QYZhwLLsmJv3VEIsFqOkpGTK/YASIoeiKIjF4lEdS16vF21tbSgoKAAQeWaK3W6PWY83n/T88PAwrFYrJ0Pvi+qazWbOYPadL5PJIJfLoVAo4PV60dDQgJKSEr9xGxsBHm/EePVB00B3tzQmr4NAIBAIqYfL5UJfX9+UE9A7/fTTcd999/k9V1lZiYceemjUa374wx/i66+/xu9//3u88MILAesuAJx33nk488wz8ec//9lPbNTr9eLRRx/F7t27YTKZ4HK54HQ6IZX6r8mnn356wJg33ngjXnrpJaxfvx5dXV14//338cknn3DHa2trsWHDBnz9yjjHXgAAiEBJREFU9dfo6enhbIKWlhY/43X27Nncv32Za93d3SgvLw95flE3XonhGntiWfMaSd0by7KTonGyQCBAaWlpQGsTAmE8xjJegZG6FIfDgaysLCiVyjjOLDRomkZ+fj5omvaLqLa1tWFoaCjg++31ejE4OOjXtkuhUATI1xcUAKf+pDAMUFJCvmMEAoFAGElvPbk2ciohkUgC2jD66lnHuubYsWPg8Xiora0Nes4FF1yAt99+G1VVVZg1axb3/OOPP45nnnkGTz/9NGbNmgWZTIY1a9YEdBsIJqx4zTXX4N5778VXX32FL7/8EgUFBX5R4+XLlyMvLw8vv/wysrKywDAMZs6cGTD2yXts337DZ9eEOr9Qu3+ElTZMiB3Dw8OjKgNPBJlMBqFQmJQ9ZNPS0uIyr+zsbGK4EiIilGiqzWZDS0sLhEIhCgsLw/6sRaJMHCrZ2dkBiwHLsrDb7TAYDH7Pjfbv9PT0gHHvuAN4442R1GGaZsEwQFGRC5dfbgWgif4LIRAIBEJKQTLdQkcikWDbtm2gaRrvv/8+fvKTn+CCCy7AkiVL/M7bsmUL5HI5li5div3792P69OkAgC+++AI//elPcdVVVwEYMRpPnDjBHR8LjUaDiy++GK+99hq++uorXHfdddyx3t5e1NTU4OWXX+YM2s8//zzs1zeR+QWDGK9JAMuyGBwcRFpaGgYGBqI2rkAggFwuT6q6VR6Ph/z8fHi9XqhUKtjtdgwNDaG9vT0m95PL5VCr1TEZmzD5SU9PR1dX17iZCy6XCy6XCw6HI2zjNS0tDQqFIuqibVKpNOhn3+PxgM/nIysrK+Kx5XLg//7PV/vqQGamEw8+qIJYzAPLsjE1yAkEAoFAmCzweDzU1NRwxuO8efNwzz334Nprr8WRI0cC1vEnnngCXq8XS5Yswf79+1FeXo6SkhL89a9/xZdffgm1Wo0nn3wSXV1dIRuHN954Iy688EJ4vV5ce+213PNqtRoajQYvvfQSMjMz0dLSgnvvvTfs1zjR+Z0KyQVOAjweDzo6OqJquAL/UxeO9rgTIS8vj5Pb5vF4nHHp+3e0yc3NJRtpQsSIRCLk5+eHdK7v+xYuvrr0aHNqqrAPk8kUlTZRcjlw773A9u0Mbr65D3L5yHtAvm8EAoEwtWEYBs3NzYmeRgACgSDpNHyEQiFuu+02PPjgg9Bqtejq6sKaNWug0+lwyy23BL3mqaeewooVK7BkyRKcOHECDz74IObNm4fzzz8fixcvRkZGBi6++OKQ5/DDH/4QmZmZOP/88/0c2zRN46233sKhQ4cwc+ZMrF27Fo8//njYr3Gi8zsVig0xT1UkEkWl7yAhEKvVipqamqiPq9FowOfzx82zjyfTp08P2DgzDIPOzk709fVF9TOm1+sDag4IhEhoa2sb93uk0+mQm5sb0fj19fVRF7QQCoV+NTE+2tvbQdM0MjIyonIflmXR0dGBjIyMcTcEDMOgq6sLTqcTarUaaWlpxNglEAiESUYy1rumpaUFddx6vV7YbLaQun7YbDR279bBZBLBaHRixQozpNKJ6dXMnj0bAoEALS0tMJvNfsfEYjFmzJgxofFDwWq1wmg04rXXXsMll1wS8/uNxrJly7Bv375xzyNpwwlmeHiYa5MRTWia5nq6JhPBlMTMZrNfj6lowOfzR+3TSSCEi9Fo5BR7R8OnzB2JRzczMxMDAwNRrXl3u91BU3iDCbG53W6uRZbvj1QqDcmwpCgKGRkZsNvtQcUgfPe0WCxoa2vjBBp6e3vB5/OhUqmQk5OTVJ5wAoFAIEROMrbWPFWM8GR8yvxWq3XUddhmo7FqVTmamsT/X+uBwnvvaVBRUR2xASuTySAQCOD1eoP2wVUqlbDZbBgeHuYym1QqVUT3CgbDMOjp6cHWrVuhUqlw0UUXRW3sWBLybmEytEtJRoRCYUwkxA0GA0wmU9THnQijpWpEooQ8HkajMaA/FoEQKRRFjRtV7erqwpEjR9Da2hr276VUKoVGE12ho/z8/ADj0263o7OzE52dnXC73QBGDNfKykocO3YMVVVVqK6uhtvtDisiStN0gOT9ydhsNjQ0NAQoC3o8HvT09HBzSUby8/Px9NNPJ3oaBAKBkBLEKpswlni9XgwNDUEqlY66d9y9W4emJjEYhoLHQ4NhKDQ1ibF7ty7i+6rVajAMA4vFEnQNVSqV6OrqQktLCxoaGlBfXw+TyQSbzRYVu6ylpQUGgwFvvvkm/vCHPyR83xyqPRDyLD0eT8RRBcLoCAQCqFQqrs9iNJBKpbBarUkl1ASM7omL9jxjYQgQCBKJBDKZDMPDw6Oe4/V60d3dDb1eH7bnWaVSgaKogLShcMnJyYFYLA5aR9vX1wdgxBnZ0dGB3NxctLW1cYsgTdMoLi7m+sGGw1jG7ni/b263OyGeepZlcd5554HH4+GDDz7wO7Z9+3bcf//9OHr0KPk9IRAIhBCxWCwxCUrEg+HhYQiFwqClkiaTiIu4+qBpFiZTZGsXn8+HUqlETU0N3G53gBOXpmmIRKKA9dNXZqfVaqHVaifUTSM/Pz+pWmKGWjoYliVKal5jg1arjep4YrE46sql0SDY5tTXtiOaEJEmQqwI9bvqc8h4vV7Y7faQIotKpTKk1jxjIRKJoNPpRhWAOvl7YTabceTIEc6g5fP5KCsri8hwHQ+tVovS0lIUFhYGPT6eA4tlWfT09MBkMkU1SktRFF577TV8/fXXePHFF7nnGxsbsX79ejz77LPIyckZM6pMIBAIhP+RTMZQJLhcLrjd7oD12Gh0+hmuAMAwFIzG8G0jnyZLTU0NbDYb3G63nxEqEomQnZ2Nnp6eoOuPy+VCe3s7amtrMTQ0FFK9bioQE+PV4XBENBnC2CgUiqj1w5LL5VGN4kaTU41XlmXR1NQ0Zh1huGi12lHr7giEiaJWq0PKPvEZWC0tLaiqqgq5rn2ivwNqtXpMx82px9xuN/h8PsRiMcrKymJmpInFYigUCqjV6qCKzOMZpHV1dWhubkZnZycqKyvR1tYWNSM2JycHzzzzDNatW4fGxkawLIsbbrgBP/rRj3D11VcHpA1TFIUXX3wRF154IaRSKaZNm4avvvoKdXV1WLx4MWQyGRYuXOgnVlJfX4+f/vSnMBgMkMvlOOOMM/DRRx/5zWP79u0oKSmBWCyGwWDAz3/+86i8PgKBQCCEB8MwkEgkfmvmihVm5Oc7QNMs+HwGNM0iP9+BFStCz5by9V3v7u5GY2Mj+Hw+DAYD9Ho915ZGqVQiKysLAwMD6OjoGDPby+FwoKmpKWi9bCpCIq8pBEVRUYm+CgQCOByOpPV6+b60PhwOR0AN3ESYPn16xGqvBEIo8Hg8pKenj3ueryZFJBJBJpOF7KCZaOpsV1fXqB5Yn2jSqTAMwxlN8cAnNsHj8SCTyaBWq0eNvHo8HgwNDfk5Tn2KxUePHkVra2tUjNhrr70WS5cuxfXXX4/nnnsOlZWVfpHYU9m0aROuueYafPfddygvL8cVV1yBW265Bffddx8OHjwIlmVxxx13cOdbrVb85Cc/wccff4xvv/0Wy5Ytw/Lly9HS0gIAOHjwIH75y1/iN7/5DWpqarBv3z58//vfn/DrIhAIhHiTrHvQcBkaGoLBYOAeS6UMKiqqsXq1CcuX92L1alOAWJNEIkFJSQnkcjlKS0tRXFwMiqJA0zSEQmFAOrXH4+Her+7ubvT29mJgYAAOhyOkNpcsy3J95icDodqZYVXmEuM1dmi1WnR0dEzoSy8SiaIaxYw2DocDVqsVfX19yMjIgFAohF6vj8qchUIhKIoi6cKEmKPVatHT0zPmOQ6HA62trZBKpTAYDGhsbITT6RzXOJ1o5JVlWTidzqAR1NFSixiGQUtLC4qKiuLy/dFqtVCr1eDz+ZxIxuDgYIAyssfjwdGjR0cVpWBZFt3d3TCbzdDpdMjIyJhQ7c9LL72EGTNm4N///jfefvtt6HSji3Bcd911WLFiBQDg17/+NRYsWICHHnoI559/PgDgrrvuwnXXXcedP2fOHMyZM4d7vGnTJvztb3/DP//5T9xxxx1oaWmBTCbDhRdeCIVCgby8PMydOzfi10IgEAiJwrf+yGQyMAwT9dKwcKFpGjweD3w+H3w+H3a7PSStFY/HA4FAALFYzDlQpVIGq1b5t83j8XhgGAYURUGhUKCurg48Hg8ejwdCoRA8Hg8CgSDo++DxeAJ0LgQCwbh7jJPx1c767Ife3l4olcoJrYeJItQMX5I2nCQIBALk5eVNaIxkVuwERhRH29vbudY4hw8fRmNj44THlclkKC8vj1vkiDC1kUqlQXvFnYrVauU8qQBQWVk5rrI4j8cLyFAIl9FqVk9tRyUQCLiMj4GBgbiUG7AsC5qmuZ52NTU16O3tRX9/P8xms5+h2t3dHZKaos+I9UViI/VA6/V63HLLLZg2bdq4zdNnz57N/dvnmT+5p67BYIDD4eDaMlitVqxbtw7Tpk2DSqWCXC7H8ePHucjreeedh7y8PBQWFuLqq6/Grl27Jk0NE4FAmFpotVrk5+fDaDQmdF/mW2tomobH44HdbodYLEZ2dnbIY5jN5jEz+nzRVIqiwOfz0d3dDZZl4fF44HK5UF1dzd17NE5tX+fxeMLaz3s8Hu59NplMaG5uxrFjx9Db25tyUfCYRF6TOao3GdBoNBgeHo5YbTQVjFcfvtSJaHyxsrOzU9LDREhNfGn+ra2tIZ0/MDCAwsJCDA4OhrSQ6/V60DQNsVgMkUiE48ePc98ThUIxphhbenr6qFL3JzsfhUIhysrKAIDz8La2tkKhUET0XWJZFlarlUt5EovFkEqlUCgUAT3pOjo60N3dzf0G+BartrY28Pl8pKenc6rN4c7BF4nVarVcdkc4+Dzz43Hye+SLFgd7zmd8r1u3Dh9++CGeeOIJFBcXQyKR4Oc//zlnaCsUCnzzzTfYv38//vWvf2HDhg3YuHEjDhw4ENWefgQCgRAPNBoNWJZFQ0NDwuZAUVTAvthsNsPtdkOj0YRUJ+pwOMAwTNCMKz6fz43PMEyA4zTSgF8k+2KpVIq2tjZu3fR6vWhqakJ3dzfnED+5j7tcLkd2dnbSZSuGameGFXkdrbkvIXpkZ2dHLDiUSr14+/v7o2K48vl8ItBEiDvp6elhRUidTify8vJCMl6zsrKQkZEBlUoFiUQCiUQCPp+P0tJSlJaWoqSkZNRrx1IKPjlaLBQKIRQK/bycHo8nYtGHoaEh9Pb2ore3Fx6Ph4s6NzQ0+G0eKIpCVlaWnyK472+forDdbkdDQ0PErRZYloXZbEZlZSVaWlqSohboiy++wKpVq/Czn/0Ms2bNQkZGBpqamvzO4fP5+OEPf4jHHnsMR44cQVNTEz755JPETJhAIBAmiM1mi1orRKlUCoFAEHIfUoFAMOoaYrFYQi4zEYvFcLvdUKlUkMlkUCqVyM7ORlZWFhiGGXUfKxAI4mYz8fl8mEymoA5fm82GoaEhDA0NwWq1Ynh4GDabDd3d3Thx4kRStdT0lRGFQliR11CKhwkTg6ZpFBYW4vjx40n1oQoHiUQStxqH8dRVCYRYwOfzUVRUhNra2pin5Wi1WiiVSi6KKJPJkJ+fD4lEgubmZi6jIScnZ0zhN4VCwaUv+QzKU1N0QhGjCoZEIoHRaERaWhpnGPN4PDQ3N6OnpweZmZkB9+ns7ITdbodIJOI81ENDQ6iqqopoDqfiM2J7enqg0WiQkZGRkF6yAFBSUoJ33nkHy5cvB0VReOihh/ycje+++y4aGhrw/e9/H2q1Gu+99x4YhuGi4wQCgZBKeL1emEymqIzlS/v1rVsURQWsuz5BJIFAAKFQOG6JTltbG0QikZ9gUjCUSiUYhkFzczN3/4GBAdA0PWbAiGXZuGVDejyesDOVgJEo5/Hjx1FUVJQU7eDCye4lkdckRCgUoqCgINHTiBiNRhO3e5GUOkKiUCgUIatbh+otDoZOp/NLf+XxeNBoNJwnGhjpbazX68ccJzMzE+Xl5QD+V2KQnp7uF62NNEopEAggEAiQnp4OuVzOGa8FBQUQiURBNwc8Hg9isRj5+fkhtR+KFF9Et7Ky0m8DEk+efPJJqNVqLFy4EMuXL8f555+PefPmccdVKhXeeecdLFmyBNOmTcOOHTvwpz/9CTNmzIj7XAkEAmEieL1e1NXVhRxFG49TU3JpmoZIJIJUKuWymXg8HhwOB5cFNF7mzsDAAKxWK/h8PufUpCgKAoEAFEVBJBJBLpejr68vqKr9eJmOqRJ8crlcqKmpGbMdT7wIJ0BKsSGGDcRiMR5//HHceeedEU+MEB6dnZ1R81zFk+LiYphMpphHX3k8HubMmUMir4SE0traOq7Xs6ioKCaOlra2Nkil0pAjpgzD4NtvvwUAzJ07l/Me+xqlSyQSTJs2LS7fqd7eXqSlpXEGuNvtRk1NTdRV7W02Grt362AyiWA0OnHttVbMnVsyYWEslmXBMMyExyEQCITJgsfjQW1tbdwE5yQSCZxOJ3g8XsSOSZlMBp1Oh56eHi76JxQKk6LkJF6IxWJMmzYtpo7k8Th69Chmz54dUjZbyOEApVJJIq9xxmAwYHh4eNz0h2TDarVCLpfH3HhVqVTEcCUknOzsbD9l2WBMJPI63r0jxe12QyQScelWNpsNdrsdvb29Uek7PR6nZmjw+XzodDq0tbVF7R42G41Vq8rR1CQGTbNgGArvvefAO+80Yfbswgn9fhw7dgxOpxPTp08PSX2aQCAQJjutra1xVUq32+2QyWQTihyKxWIMDg76pa1OJcMVGBGXam9vn9CeYqKEY2OGbGKrVKq4tFIg/A+KomA0GhM9jbDxeDyQy+Uxv49arY75PQiE8aAoCoWFhWOKMcXKeJ0IJ6c1MQwDlUqF0047LS6GazAoioLBYMD06dOh1Wqj4pjavVuHpiYxGIaCx0ODYSg0NYnxyisitLS0TLivtkAgSFgdLYFAICQbichEmWjKa29vL/r6+qI0m9Slq6sroV1lwgnUhWy86nS6iFu4ECJHLBanXP9ShmHiYrwSlWFCssDj8VBcXDzqwp2MxuvJKVaZmZnQ6XRJkQIrkUiQl5eH2bNnIysra0JtsEwmEWj6VGEPFiaTCD09PWE5ZE81dPl8PjIyMhKaZkUgEAjJRKrtVwn+NDU1we12J6Q/bDiiUyHvqPR6fURqVoSJo1Kp0NnZmehphAzDMJziaKxSL8KRTCcQ4oFIJEJRURFOnDgRcCwZjEIAAY3QfcTD2RQufD4fmZmZyMjIQH9/P7q6usJORzManWAY/wguw1AwGp3//9+htxdzOBxcerDZbEZfXx+USmVY8yEQCITJDDFeUxun04kjR45wolhCoRDZ2dlx+X/t7u4OWRskZJexXq8nkdcEkWqKur4NYSw3xOQHkpCMKBQKv5oRHo8HHo+XNLXZJxtriVDdjQSKopCeno7y8nLk5OSEde2KFWbk5ztA0yz4fAY0zSI/34EVK0bWsvFaHXi9XrjdbtjtdvT392N4eBhmsxktLS3jXk8gEAhTDbI3mxwwDAO73Y6BgQG0trbG5Z5ms3ncrgk+Qg5d6XQ6EnlNEFKpNK69UyfKycZrrOoIiEAKIVnR6/Xwer1cLWQySND74PP5mD59OliWTbnMBYqiIJPJwhLnkEoZVFRU+6kNr1hhhlQ68hvV2trKNaxXKBSck4FlWXR0dKCjo8NvPN9jmqZRUFCQco5FAoFAiCXEoTf5sFqtYFk25k747u7u6BuvvrTheLwAgj8URaG0tBTV1dVRbyMRC3w/XrHsc0VEUgjJik94yJcqrFarOSEEmUwGuVwOqVSakFpJiqJS2vETrvEKjBiwq1Z1jXp8aGgIQ0NDkEqlyMjI4FTMs7KyIJVK0dTUxPUMpGmai66TCAOBQCD409U1+m8tITVhGCYubeFiZrw6nU4MDg6SOp8EYLVaU8JwBQCbzQar1QqHwxGze/T09ECn0xFHCiEpOflHnqZpWCwWWK1WTk2PoihIpVJotdqEqfumKrEy+m02GxoaGlBcXMytcSqVCuXl5VxPWplMRgSaCAQCYRR8reMSqVpLiD4ulyvmju/u7m6ceeaZIZ0b8iqcmZkJAGhvb49sVoQJoVQqUyra2NzcjOzs7Jh92O12O/HwEZIelmXR19cXEClkWRbDw8Nobm4O2tvM4/GguroaJpMpZZxW8cJgMExIgXg8Tv3/EIvFMBqNUCgUxHAlEAiEMfAp7ysUipDO37hxI+6+++4Yz4owUeKhkWEymZCVlRXSuSGvxLm5uQAQt8Jdgj8URYUcTk8GHA4HzGYzSkpKYmZ0t7e3xzS6SyBMFI/HA6fTCYVCMWrKTWNjI3p6ejA4OAi73Q6Px4PGxkYMDw+js7MTlZWVOHHiRFLVziYSHo8HnU4X9XH1ej3mzJkTtigUgUAgEP6Hz4CNpZMxmqSKeGEiiVXnEB9OpxPd3d0hr78hG69GoxEURRHjNYFoNJqkabkRCp2dnfB4PDHbDLIsG1afRgIh3ggEAmRmZqKkpASlpaVBz/F4PGhubkZtbS2qqqpw+PDhgOjf0NAQmpubE9J7LRkRi8UQCoVRHdPpdKaciBWBQCAkIzRNh10S8+WXX+KGG27A4sWLsXTpUqxZswZtbW1+51RWVuKKK67AwoULcfXVV+PTTz/F/PnzUVNTAwDwer34zW9+g4suugiLFi3CJZdcgj/96U9+Y/iiva+++iqWLVuGSy+9FAAwf/587N+/3+/cxYsXY8+ePWG++slHLDVsAHD/z6HaCyGv1AKBABkZGVyLAEL88UUcUqXnq0+xs6CgADwejxM9iSZSqTTqYxIIscAnlhSparjdbofFYoFarY7yzFILiqLA4/HC9pbTNA2GYbi/T2V4eJgIEhIIBEKU0Gq1AYrtY2G323HllVeipKQENpsNO3bswLp16/Dmm2+CpmnYbDasXbsWZ511FjZt2oT29nY88cQTfmOwLAuDwYAtW7ZAqVTiyJEj+N3vfgetVovzzjuPO+/AgQOQyWR4/vnno/Z6JzPRdhafis+2jLrx6huURF4TSyoZr8D/6sdUKhV6e3ujPj4xXgmpgkQiwfTp0+FwONDX14ehoSGIxWJIJBI4nU709PSM22ago6MDEokENE2Dx+OBpukpZWwNDQ2hoaEBfD4fcrkcQ0NDIV+bl5cHi8UCjUaDurq6gOMejwd1dXUoLCxMqQwXAoFASEaEQiGUSiUGBgZCOn/p0qV+jx9++GH88Ic/5IT09u3bB4Zh8NBDD0EkEqGoqAhdXV3YsmULdw2fz8ctt9zCPTYajThy5Ag+/PBDP+NVIpHgoYceSpnU5kQT6722z7aMifGam5tLjNcEIxQKkZ6eHrP+qdHG6/XCZrPFzHgdHByERqOJ+rgEQqwQi8VBRQmysrLQ09OD7u7uUetL7HY7jh07xj0uKChAenp6VOeXzNHHrq4ueDweeDwebo4CgWDcKKxMJgPLsigsLATLspg1axb6+vrQ19fnFwkfHBxETU0NiouLY+5pJhAIhMmOTqcL2XhtaWnBjh07cOzYMVgsFs6Z29nZieLiYjQ2NgboqMyePTtgnN27d+Of//wnOjs74XQ64Xa7A8p2ioqKiOEaIjweL+aCsa2trdBoNCEbyWEbr4cPH45oYoToYTAYUsZ4BUY2hAaDYdR0vYmQSgrMBMJY8Hg8GAwG6PV6WCwWDA0NweFwcCJOwRhPsMzpdKKxsZFTyz0Zq9UKsVgcUOfZ3d0NjUaTdPWfdrsdAwMDUKvVyMjI4Nq2icVi1NTUBAhaiUQiaLVark7YZ+RTFAWhUIiMjAxkZGSgra3NT7ncbrejvb0d+fn5cXttBAKBMBlJS0sDn88PqWZy7dq1yMzMxAMPPACdTgeGYbBy5cqw6i0/+OADPPPMM1izZg1mzZoFmUyGP/7xj35OXwBBO2FQFBWgKxHrWs9UQCqVxtyh3dLSwgkDh0JYuv8+zwf5z0wsqWaw9ff3w+PxQKVSRX1s0kaEMNmgKApqtRq5ubkoLS3FnDlzMGfOHJSWliI3Nxd6vZ5TLx7t8z80NISqqir09vZCKpXixIkTaGho4CK6VqsVJ06cQGVlJdrb2+F2u2G322G1WiEQCEZNm3W73RgaGoLZbEZrayu6urpiKiK1atUqUBSFW2+9FUKhELNnz0ZhYSGkUik2bdoEqVSK6667Dnl5eQGLq9PpRF9fH1QqFTIzM0ddfDMzMwM88Cd7/QkEAoEQGRRFQS6Xj3uexWJBc3MzbrjhBpx55pkoKCgIKAspKChAbW2t37p39OhRv3MOHz6M2bNn47LLLkN5eTlycnJgMplCmqtarUZPTw/3uKWlhXS0AOLS6aS2thbFxcUhnx+28erxeIhoU4IJ1hcymbHb7Th69GhMWn2k2ntBIEQCn8+HQqGATqdDTk4OSktLMXv27FEXFYvFArvdjo6ODi7zQafTcUaay+WCTqeDXC5Hf38/KisrYTabIRaLkZ6eHmDoeb1e1NTU4MiRIzhx4gS3Buj1+ph7ZHNycvDWW2/B5XJx83c4HHjzzTc5T61EIkFGRkbAtXa7HcePHx/TycXj8WA0Gv2e83q9YdXTEggEAiE4oaSCpqWlQalU4p133kFraysOHDiAJ5980u+cZcuWgaIo/Pa3v0VDQwM+//xzvPHGG37n5ObmoqqqCl999RWam5vxwgsvBERdR2P+/PnYvXs3qqurUVVVhc2bNyddBlK8SU9Pj0ng6VTq6upia7wCIxYyIXFYrdZETyEiYhElHRwcJO1DCFMSmqYhk8mCHtPr9cjKysL06dMxY8YMZGdnQ6FQcIZmeno6cnJyUFxcjBkzZuC0005Dbm5u0IXa4/GgtraW+92hKAr5+fnIycmJS23svHnzkJOTg3feeYd77p133kFubi7mzp3LPffdd9/hpptu4tos3H333Whra0Nubi5EIhH2798PiqJgsVj8rqEoCoODg5DJZNizZw8WL16Mr776CvPmzYNMJsN5553np5jp8Xjwy1/+EiqVChqNBr/+9a9x7bXX4uKLL475e0EgEAipxljGK8MwnPjgo48+iurqaqxcuRJPPvkk7rrrroBxnnrqKdTX1+PKK6/E9u3bceedd/qdc8kll2DJkiW47777sGrVKgwMDOCyyy4LaZ5r166FwWDATTfdhAcffBBXXXUVxGJx+C94kiAQCOLS99xut6O1tRUlJSUhXxN2zatAIEBtbS3OP//8sCdIiA5KpRLd3d2JnkZS4PF4YLfbieowgXASIpEImZmZAc97PB5YLBY4nc7/1959h0dVZn8A/97pk5lJJr33QkKVYmGpAVRWpCguCFhA92cFEdC1rgbQdVfFCq6CClIUEHQBUUQERHqRFkIggYT03qYk0+79/cHOLEMCpMzMnXI+z5MnZMp7T0Iyc899z3teREVF2ZLPayWhZrMZ58+ftzU1EovFSE5OvmbS7CyPPPIIli9fjmnTpgEAvvzyS8yYMcNuT77m5ma8/PLL6NOnD3Q6HV577TW88sorOHXq1A3HZxjGNovb0tKCVatWISsrCwKBAAsXLsRzzz2HNWvWAAD+9a9/Yc2aNVi+fDkyMjLw4Ycf4j//+Q8yMzMd/40TQoiHu975WX19PWJiYgAAt956K7799lu7+48ePWr3da9evfD111/bvi4rK7O7XyKR4PXXX8frr79ud/vMmTNt/87KymozltDQUCxevNjutqv3ffUl8fHxLpl5vnjxIgB0aOa1Q1EJhUIkJSW1uc0AcR2VStWl/SLdVURERKe2AWpqaqLklZAb4DgOVVVVtllEjuNsJw3Xevy5c+dsa34UCgVvHRofeOABvPTSS7h06RIAYN++fVi7dq3diYV1o3mrL7/8EqGhocjJyUHPnj1veAw/Pz8olUqYzWa8/PLLtp/NxIkTsXTpUlgsFgiFQnz88cd46aWXcM899wAAFi9ejB9//NFB3ykhhHgXsVjcqit8U1MTTp48iWPHjrV67Sb8Cw4ORkBAgEuOZc0pnTbzah2cyob5xTAMwsPDUVhYyHcoDhUYGAij0djhTsqNjY1trncjhPwPwzCIioqCv78/CgoKUFlZCalUitDQ0DYfbzAYbIlrcHAw4uLiIBB0aKWJw4SGhmLMmDFYsWIFOI7DmDFjEBISYveYvLw8vPbaazh06JDdnrlFRUXtSl6By69BMpnMLqkPDAxEbW0tBAIBGhsbUVlZiVtuucV2v1AoRP/+/anBEyGEXINCobBbsrFgwQLk5ORg2rRpGDZsGH+BkVZcVS5slZeXB4VCgfDw8HY/p8PJa3p6OjZu3NjRpxEHCwwMRGlp6Q33N/QkGo0GERERHU5etVotKioqEB4e7rb7UxLiLpRKJbp3747CwsLrJqPNzc0QCASIjo5GaGgo739bjzzyiK30a8mSJa3uHzt2LOLj47Fs2TJERUWBZVn07NnT1mHZ+r1euUb+6tdP6352MTExtm2KBAKBbRaa1tcTQkjH+fn52SWv7777rkPGjYqKalVaTLomISHhmjsOOMPZs2eRkZHRoXOMDl9G79mzJwoKCjy2aZC3EAgE15wx8VRNTU2Qy+WdWiBfWlqK4uJiOrkkpB2sS0CCg4Ov+Rh/f3/cdNNNLuko3B6jR4+G0WiEyWRq1XOhtrYW586dw6uvvoqRI0ciIyMD9fX1do+xvl5e2XzpxIkTbR4rPDwc8fHxSE9PR2JiIoDLM9FSqRTh4eE4cuSI7bEWiwV//PGHI75FQgjxSrS0yzOEhobC39/fpcfMzs5ud3WUVaeSVwDIycnp6FOJg4WGhvJWxucMWq0WHMd1ui13dXW13R5dhJBru1FCKhQK3SJptRIKhTh79ixycnJaXRUODAxEcHAwli5divz8fOzcuRNz5syxe0xKSgpiY2ORlZWFvLw8bN26FYsWLbrhca0/A2siO2vWLLz11lvYtGkTzp07h9mzZ6O+vt6tflaEEOJOKHl1fxKJpNW2cc7GsizOnDnj/OTVOrWbnZ3d0acSBxOJRC7Zf8lVWJaFTqdDYGBgp8eQy+UOjIgQ4mhdqY7w9/dv86qwQCDA2rVrcezYMfTs2RNz5szBM888Y/cYsViMb775Brm5uejduzf+9a9/4Y033mj3saVSKRiGwQsvvIApU6bgoYcewsCBA6FUKnHnnXf69JYKhBByPdamTe4gKysLAwYMwIABA3Drrbdi3Lhx+PDDD52ynaMncXW5MAAUFhZCp9N1OHlluE6cSaSmpmLs2LGtNhAmrldcXOxV2+ZERkYiKioK2dnZnXohuemmm1z+x0cIaR+WZZGXl4ewsDCIxYFYvBgoKAASE4GZMwGl0nHHysnJgcFgQJ8+fZxaocKyLDIyMjBp0iQsXLjQacchhBBPdvHixVbLOfiQlZWFuro6vPbaazCbzcjNzcXrr7+OiRMntrro6SvCwsJc2qTJavPmzRg/fjxKSko6NOvbqQ18evbsSTOvbsLbStWsa6nlcnmnktfm5mYoHXkGTAhxGIFAgLS0NJSUNGDUKBPy80UQChlYLMDq1cDBg45LYFmWBcuyaGxs7FI1x9UuXbqE7du3Y9iwYTAYDFi8eDEKCgowdepUhx2DEEK8TWBgoFskr8DlmWBrx/qIiAjccsstOHToEIDL7x1fffUVvv/+e9TW1iIuLg6PPvooRo0aBY7jcM8992DixIl48MEHbeOdO3cO06ZNw/fff4/Y2FhoNBp88MEH+O2332AymZCRkYG5c+ciLS0NAHD+/HksWrQIZ8+eBcMwiI2Nxcsvv4zu3bujoaEBb7/9No4fP46mpibExMRgxowZGD16tO14jz32GFJTUyGRSLBp0yaIxWLce++9ePzxx22PqaiowNtvv40jR45AIBBg4MCBeP7551v1upBKpS4vF7bKzs6GWq1GVFRUh57XqcvRvXr1wsmTJ6k5jhvwtuRVIpEAuNwEpTOampocGY4N/a4T4hgMw2DNmkDk54vAsgxMJoBlgbNngY8++t92MxzHgWXZTndUt/7NdrR7+Y0IBAKsWLECN998MwYNGoTTp09jx44dyMjIcOhxCCHEmwQEBLhlZVx+fj5OnTplK2tevnw5tm7dipdeegnr1q3D1KlT8dprr+HYsWNgGAbjxo3Dli1b7MbYsmUL+vXrZ5u9fOGFF1BXV4ePPvoIq1atQnp6Op588kk0NjYCAF599VWEhYVh5cqVWLVqFaZPnw6R6PJ8otFoREZGBj744AOsW7cO99xzD15//fVWk4Y//PAD5HI5VqxYgVmzZuHzzz/HwYMHAVxOwOfOnYumpiYsXboUS5YsQWlpKV566aVW339CQgJv/XNOnTqFnj17djiX6dTMa//+/VFVVYWysjLesnVymbclryqVCkDrLSzaq6mpqcNXcNqjuroaYWFhDh+XEF/AcRzKy8sRFhYGo9GI3FwhBAIJrtwaVSDg8McfddDp5LY3eIZhoNPpkJyc3KHXOq1Wa9sip7GxERaLxWEnTbGxsdi3b59DxiKEEF8hEAigVqtRW1vLdyjYu3cvhgwZAovFAqPRCIFAgL/97W8wGo1Yvnw5PvnkE/Tu3RsAEBMTgxMnTuC7775D//79MXbsWHz22We2Lrlmsxnbtm3Ds88+C+ByF/szZ87gl19+sU3IPPvss9i9ezd+/fVX3HvvvaisrMRDDz2EhIQEAEBcXJwttrCwMLtZ3fvvvx8HDx7Ejh077NaGpqam4rHHHrM9f/369Thy5Ahuu+02HD58GBcuXMCmTZsQEREBAJg/fz4mTZqEM2fOoEePHgAud9bns1rx2LFjuPvuuzv8vE4nrwBw9OhRSl55xHGc25RgOIo1ae3sTKdOp4PZbLZdwXIEo9GIkpISSKVSBAQEOGxcQnwBx3GoqqpCeXm5bZsalSocLGv/3sGyQGRkC3JzLwG4XIVhMpkgFovBsmy7k0+O41BcXGz3dX19va1EjBBCCD+Cg4NRXFyP9etDUVoqRXS0AZMmVcPPj73xkx2of//+eOmll9Dc3Iyvv/4aQqEQI0eOxIULF9DS0oKnn37a7vEmkwndunUDcHmnj0GDBmHz5s3o2bMn9uzZA5PJhFGjRgG4XBLc3NyMkSNH2o1hMBhQUlICAJg6dSoWLlyIH3/8EbfccgtGjRqFmJgYAJcrD5cvX45ffvkF1dXVMJlMMBqNrZoCpqam2n0dEhJiqzQqLCxEeHi4LXEFgKSkJKhUKhQUFKBHjx6QyWROmexpr4aGBuTn59tyyo7o1Bl+dHQ0wsPDcezYMYwfP74zQxAHqKurQ0tLC99hOFRtbS0iIiIQEBDQ6UZUer3eoftUCYVCcByHixcvIj09nToaE9IBdXV1qK6utrtt0qRq/PhjMAoLZRAIOLAsg4SEFkya9L/HGY1GMAyDlJSUDs2aMgyD6Oho5OXl2W6j5JUQQtyBEjNmpKOg4H+v/T/+GIwVK3JdmsDK5XJbie9rr72GKVOm4D//+Q9SUlIAAB988EGrarsruyVPmDABr732GubOnYstW7bg9ttvtyWXer0eISEh+Oyzz1od11pd+Pjjj2P06NHYu3cv9u/fj88++wz/+Mc/kJmZiVWrVuGbb77BvHnzkJKSArlcjkWLFrWqSLx6koZhmHZN/CgUCgiFQiQmJvK63aZ1f/QBAwZ0+LmdSl4ZhkH//v1x9OjRzjydOADHcSgrK+M7DIdraWmBXq9HYGBgp5JXoVBoe3FwFKFQCKlUCoPBgPz8fHTv3t0t120Q4o4aGxtbNV/z82OxYkXuDa++C4XCTl0ssjZ+u/JrjuO8bpkFIYR4kiVLGBQUyMCyDFj28utxYaEM69eHYvr0Sl5iEggEmDFjBt5//3189913kEgkqKiouO6M4KBBgyCXy7Fhwwbs378fy5Yts92Xnp6O2tpaCIXC685sxsfHIz4+HtOmTcPLL7+MzZs3IzMzEydPnsSwYcNw1113Abi8frWoqAiJiYnt/p4SEhJQWVmJiooK2+zrxYsXodFoMHjwYKSmpvK+9+7Ro0ehVCptTaw6otO1lQMGDMCnn35KJwQ8qampsa3p8jZ1dXWIiYmBRCLp8PdosVig1+uhUCgcGpO1+7HRaERjYyOCgoIcOj4h3sjadKktfn7sdU9W5HJ5q66I7XV14zaWZWGxWBy6nIAQQkjHFBQAAgHs+h0wDIedO9W8lhGPGjUKH330Eb777js88MADeO+998BxHG666SZotVqcOHECSqXStj5TKBTi7rvvxpIlSxAXF2dbHwsAt956K3r16oXnnnsOzzzzDOLi4lBdXY29e/ciMzMTSUlJ+PDDDzFy5EhER0ejsrISOTk5GDFiBIDLfRV+/fVXnDx5Ev7+/lizZg1qa2s7lLzeeuutSE5Oxt///nfMmzcPZrMZ//rXv3DzzTfjtttuc+wPr5OOHTuGfv36dWr2t9Pv5NamTcXFxXYLjYlrWBuaeKO6ujpER0cjMDAQlZUdvxLX2NjosOTVbDbDYrFAo9HYjU/JKyHXx7Iszp492+mlDRzHITw8vMPPM5vN0Ol0rW43Go2UvBJCCI8SE+0TVwCwWBicPavA+fN+vJURi0QiTJo0CStXrsTmzZsRGBiI5cuXo7S0FCqVCunp6ZgxY4bdc8aPH4/ly5dj7NixdrczDIMPP/wQn3zyCebPn4/6+noEBwejX79+CAoKglAoRGNjI15//XXU1dVBrVYjMzPTts3No48+itLSUsyaNQsymQz33HMPhg8f3qqi6HoYhsF7772Ht99+G//3f/9n2yrnnXfe6foPy0GOHj3a6aWnDNfJzjiVlZWIiIjA2rVrMXny5E4dnHTeuXPnOvSL7GlSU1MhFAqRm5vb4efK5XJ0797dIXGUlJSgoaHBruxRJBKhd+/eVHFAfJb1baO2thYcxyEkJKTV30NdXR0KCgo6fQylUmlrkNERjY2NyM/Pb3V7cnIy1Gp1p+MhhBDSNVot0K+fARcuSCAQcLBYGFx+O/nf+4dAwOGpp0o7VUas1wtc1gzq+PHjePLJJ7F169ZOVwm5WlpamsOX1nWGNYdct24dJk2a1OHnd3qlbnh4OFJSUmjLAJ5cqxTPW1RUVEAikUAqlXb4uc3Nza3W2HWWUqlsNZbZbIZer3fI+IR4Go7jUFtbi+LiYlRVVaGoqAilpaWtHtfV/VU7W91wrdlVo9EIlmWvOxM8fPhw23YHhBBCHEupBDZtqsJTT5Vi7NhadO+ug1BoP4cmEHAoLe34uZ9eL8D06en45JNobNkSjE8+icb06enQ6x3blMhoNKKyshJLly7FqFGjPCZxZRjG4UvqOsuaOw4aNKhTz+/S/+igQYOwd+/ergxBOslisfAdglNpNJpWGzJ3RENDg0PiuHTpUpu3e3PZNiHXwrIsCgoKoNFoUF1djebmZgCXr6JevW1XZ5qabdiwAUOHDoXZbLbNkmq1WojFYgwfPtzusbt37wbDMLhw4YLd7de64FVTU4PTp08jNzfXYRe3CCGEdExUlD+mT6/EK68UITOzARxnX7XDsgyiozv+Gr1+fSgKCy83gzKbBWBZxtYMypF+/vlnjB07FhqNBs8884xDx3YmlUrFa3fhK+3duxfx8fGd3m61y8nryZMn7dYDEtfw9plX4PL32NmTTEckr0ajEWazuc37KHklvqi6uhr19fVt/v4XFhbCZDLZ/mYSEhIQEhICvV6AFSvC8eabcVixIvy6V8EHDBgAvV6PoqIi27YEv//+OyIiInDo0CG7WdNdu3YhLi4OycnJdmOIRKI2E+fm5maYzWaYzWacO3fOJ15DCSHE3ahUKluFzKRJ1UhIaIFAwEEkYiEQcK22TWuv0lIpBALHzOJez9ixY3H48GGsXr261XY67syRW0h21b59+zo96wo4IHllWRaHDh3qyjCkE7x95rWrOrmU2+7511uvp9frW+25RYi3CwgIAMMwbb7+sCyL8vJynDp1CqWlpWhubkZVlb5DZVzWhPfKbdh2796N8ePHIzExEQcPHrS73bon3oABA6BSqRAREYGpU6fa9QM4evQoBgwYgH379uGBBx7AwIEDceDAAVy8eBEPPfQQlEolIiMjsWjRolbxtDX2lVt4WWd/f/75Z/Tt2xdyuRwjRoxAVVUVfvrpJ2RkZMDf3x9Tp061W2qwbds2DB48GGq1GsHBwbj77rvtZpALCwvBMAy+++47ZGZmws/PD3369MGBAwfa+T9FCCHuSSAQ2GbcrNumWcuIn3qqtNPNmqKjDbbtd6w6O4vrjdxhrStw+fz5jz/+wODBgzs9RpeS1/T0dAQGBtK6Vxe73vYT5LKAgIBOP5fjODQ2NrbZsfRKNPtKfElDQwMuXbp0zUZlAoEAOp0OHMehuroa58+fx8qVqg6XcQ0YMAB79uxBRUUFgMszrMOHD8ewYcOwa9cuAJdnUQ8dOoTMzEyYTCYsXLgQJ0+exH/+8x8UFhbixRdfbDXu4sWLMXPmTGzYsAGpqamYP38+fvvtN2zatAnbt2/H7t27bZumW7U19vTp01uNnZWVhcWLF2P//v0oLi7GpEmT8MEHH+Drr7/G1q1bsX37dnz88ce2x+t0OsydOxdHjx7Fr7/+CoFAgHvuuafV6/orr7yC5557DidOnEBaWhqmTJlyzWoQQgjxFMHBwbY9vK3bpr3yShGmT6/sdIMlR87iehuRSNSpPdOd4ciRIzCbzV2aee3SvgECgQCDBw/Gb7/91pVhSAdR4npjXSmPqKurQ01NDUQiUZuzq9axvXWfXUKuptVqceHCBYjF4mu+/rAsa5tdtM7MWsu4rrwafqMyrgEDBmDRokW4dOkSmpubcfz4cQwbNgwmkwmffvopAODAgQMwGAzIzMy026otKSkJ//znPzFs2DDo9Xq7Tdgff/xx2/52er0e69evx+rVqzFy5EgAwFdffYWYmBi7WB555BG7sT/66CPcfPPN0Gq1UCqVtvveeOMN2xvxo48+ipdeegkXLlxAUlISAOC+++7Drl278MILLwAAJk6caHecL7/8EqGhocjJyUHPnj1ttz/33HMYM2YMAGD+/Pno0aMH8vPzkZ6efs2fHyGEuDuGYZCcnIz8/PxOb6d2Nessrqu6Dbdly5YtWLRoEXbv3u2yY7aHSqVymx0yfvvtNwQEBKBHjx6dHqPLK3dHjBiB/fv32xp3EOfrakmstxOJRHYnre3Bsizq6upw7tw56PV6aLXaa5YFsyyLqKgotynBIMTZzGYzhEJhh0vlO1PG1b9/fzQ3NyMnJwebN29GcnIyQkNDMWzYMNu61927dyMpKQlxcXE4duwYxo4di7i4OKhUKowePRoAbDO3Vldun1VSUgKj0Yhbb73VdltQUFCrrXmuHnvYsGEAgKKiIrvHXblBfXh4OPz8/GyJq/W2K8uN8/LyMGXKFCQlJcHf3x8JCQk3HDcyMhIA7MYhhBBPJZVKkZ6e3mal3KlTp3DLLbdg9uzZHRrTUbO43sad1rv++uuvyMzM7FRTR6suJ68jR46EwWDA/v37uzoUaSeRSASJRMJ3GG7L39+/3VeYzGYzSktLcfr0aRQUFECr1d7w5FCr1UImk9nNvBDijawzqB35m7pSZ8q4YmNjER4ejqNHj+LYsWPo1asXLl68iODgYMTGxmL//v3YtWsXRowYAZ1OhzvvvBP+/v5Ys2YNjhw5go8++ggAWiXaHS2Zamvs77//HkDrqgtrcyng8ozClV9bb7tyxnrs2LGoq6vDsmXLcOjQIVvfiBuNC1DlDSHEewiFQiQnJyM8PNzu9k2bNmHy5Mk4fvw4qqup7Ler3CV51el0OHDggK3iqbO6nLz27NkToaGh+PXXX7s6FOkASpyuLTAwsN2PbWpqQkVFRYfXkWk0GrcpwSDE0cxmMywWC2pqanDhwgW0tLR0ah15Z5tx9O/fH8eOHcOxY8fQv39/1NfX48yZM+jfvz+2bt2Kw4cPIzMzE7m5uaitrcU///lPDBkyBCkpKSgvL79hXDExMRCJRHYNoOrr63H+/Hnb11ePnZ6e7pBZz9raWpw7dw6vvvoqRo4ciYyMjFbbDBFCiK9gGAYxMTFISEgAwzDQ6/X45ZdfMHHiRAwaNAhbtmwBx3GYMGECVq1aZffcc+fOYcCAASguLgYArF69GpMnT8bgwYMxZswY/POf/7RrlgcAJ06cwGOPPYZBgwYhMzMTM2fORFNTE4DLFxa//vpru8dPnToVn332me1rjUaDN998E3fccQf+9Kc/YdKkSfj999/tnnPgwAHcd999GDJkCGbNmoWamhrbfSzLYtmyZbjrrrswcOBATJ061akTgFKp1G0mvPbu3QuTycR/8sowDEaMGEHJq4v5QvLa0f2orFfwrPtDtkdn161aX+gI8Ubl5eWora2FXC6HTqezJXKd0ZkyrgEDBuDEiRM4d+4c+vXrB+DycomMjAx89tlnMBqNGD58OOLi4iCRSPDxxx/j4sWLWLduHT7//PN2xOSH8ePHY968edi5cyeys7Mxffp0u9ecq8fevHkzFi5c2KmfwZUCAwMRHByMpUuXIj8/Hzt37sTcuXO7PC4hhHiy4OBgpKWlYefOnUhISEBCQgLuuusubN68GQAwbtw4bNmyxe45W7ZsQb9+/RAbGwvg8nnj888/j/Xr1yMrK8uuGge4nOw+9dRTSEpKwvLly/H5559jyJAh7a5oYVkWzzzzDE6ePIkFCxZg/fr1mDVrlt17R0tLC1atWoUFCxZg2bJlqKiowAcffGC7/5tvvsHq1asxe/ZsfPPNN7jtttswd+7cVstGHMVdZl2ByyXDkZGRXe7b4JDdakeOHImjR486ZG9N0j6+kLyyLAuRSNSuGU6FQoGMjIwOJa5A55NXd7mKRYijtbS0oKqqClVVVVAqlYiPj3f5OvsBAwbAYDAgNjYWwcHBttv79+8PnU6H+Ph4NDU1ITg4GCtWrMC3336L7t2747333sOzzz7brmPMnj0bvXr1wtixYzFq1CgMHjwY/fv3t90fGhpqN/Y///lPvPvuu13+3gQCAdauXYtjx46hZ8+emDNnDt55550uj0sIIZ5OqVTi559/xrhx4wAAAwcOhFartfUfuHTpErKzswFcrhDatm2b7bHA5VnSAQMGICoqCjfffDOefPJJ/PLLL7b7V65ciYyMDLz44otIS0tDcnIyJk+e3O5zx8OHD+PMmTN45513cNtttyEmJgZDhgyx65xrNpvx8ssvo3v37khPT8ekSZNw5MgR2/2rV6/Gww8/jDvvvBMJCQl45plnkJaWhm+++aYrP7prcrfkdcSIEV2uXOxSt2GrUaNGgWVZ7N69GxMmTHDEkOQGZDIZhEKh1+/3ajabIRAIrnvyHBYWhujo6A7P1AKdT149aWNqQjrC2ujIYDCgoqKCly2hoqKi7PZ6tYqMjLTdrtPpUF5ejilTpmDKlCngOA6nT5+GyWSye+6AAQPaHMvPzw8LFy5EYmIigoKCAADPP/+83WOsY1/pytei4cOHt3ptmj59eqvtdLKyspCVlWX7etSoUcjJybnmuAkJCa3GVavVbtusz2KxdKn5BiGEAJdnRo8cOYKNGzdCq9WiqakJt99+OzZt2oSFCxdi0KBB2Lx5M3r27Ik9e/bAZDJh1KhRtucfOnQIK1asQGFhIXQ6HSwWCwwGA1paWiCTyXD+/Hm7x3cmvrCwMMTHx1/zMTKZzK5zfUhICOrq6gBc7plSXV2NPn362D2nT58+yMvL63Rc1+MuzUVra2tx/PhxzJw5s8tjOWTmNTExEWlpafjxxx8dMRxpB4ZhoFAo+A7DJViWbfMqjVAoRFJSEmJjYzuVuAKd79xMey0SbyUS/e+aZm1tbav1Qu6koqIC5eXlqKurQ319fYe7IQO05VVXVVVVIT8/n+8wCCFe4IsvvoDZbEZsbCwyMjJw6623YuPGjdi5cye0Wi0mTJiA7du3o6WlBVu2bMHtt98OmUwGACgrK8OcOXOQkpKCt99+G6tWrcLf/vY3AP9r4CeVXnubNgBtTpZceb53o+cD9u+hwOXzdb4uPCoUCre5sLht2zZwHGfbEaArHJK8AsCYMWPw448/uu2VYW/kC6XDVlf/XkmlUmRkZHSoOVNbOlpmbOWofckIcTdXvq54QmJXVlaGgoICFBQUdOr5BsO1t+0h18ZxHMrLy1FcXAytVgudTsd3SIQQD2Y2m7Fy5UosWrQIJ06cwIkTJ7Bt2zZ8/fXXCA0NxbZt2zBo0CDI5XJs2LAB+/fvtysZPnv2LFiWxZw5c9CrVy/Ex8e36lScmpqKw4cPXzMGtVpt11xJq9WitLTU7vlVVVW4dOlSp75HpVKJ0NBQnDx50u72kydPIjExsVNjXk9UVJTDx+ysrVu3ol+/frZt37rCYcnrXXfdhdLS0lb/IcR5goOD3eaKiqsFBwe36wrYjXQ2+aXklXgrR/xdeRJPSNDdlUQigVAoRGBgYKvZBkII6YgffvgB9fX1ePTRR9GzZ0/07NkTw4YNQ1paGkaMGIHNmzdDKBTi7rvvxpIlSxAXF2e3F3ZsbCzMZjPWrVuHkpISbN26Fd99953dMaZPn46cnBz885//RF5eHgoLC7FhwwZbz56bb74ZP/74I44fP478/HxkZWXZnWf3798fffv2xd/+9jccPHgQpaWl2LdvX4e6BT/44IP46quvsH37dhQWFuLjjz/G+fPnWy1R6ar4+Hi3We9qXZ981113OWQ8hyWvQ4cOhVKppNJhF5JIJLYOa77Gz8/PIeOIRKJO/XFThQHxVtY1r76CZl47h2EYBAcHo3fv3khKSvK5ix6EEMf64osvMGrUKLtt2cRiMUJCQjBixAjk5OQgLy8P48ePh8lkwtixY+2en5aWhjlz5uCrr77C5MmTsW3bNjz99NN2j4mPj8fixYtx/vx5PPzww5gxYwZ+++03W4I6ffp09OvXD88++yxmz56N4cOH261fBYC3334b3bt3xyuvvIJJkybho48+6tD+2/fffz+mTZuGDz74APfffz8OHDiA9957D3FxcR39kV1TcHAwQkJCHDZeVx06dAj19fUYM2aMQ8ZjOAeehd97772orKzEvn37HDUkuQGO43Dx4kWf6/Tcu3dviMVih4xVW1uLwsLCDj0nLi4OoaGhDjk+Ie5Cq9Xi3LlzfIfhUgzDoG/fvrRvMyGEuKGSkhJUVlbavj5+/DiefPJJbN261a4bPfmf9PR0t+qL8/LLL9u2DXJExajDZl6By+teDx48aFcvTpyLYRjExcX5VMlWQECAwxJX4PIah46euHa2QRQh7orjONtG776E4ziUlZVRNQUhhLgh6zIto9GIyspKLF26FKNGjaLE9RokEonDqhMdZevWrfjzn//ssKWODk9eOY5rtYkwcS6xWHzdtt3eRC6XO3xRu1AotCtTaQ9KXom3cffOws5UUVGB8+fP0/pXQghxM9alHT///DPGjh0LjUaDZ555hueo3FdQUJBbVRJdvHgRp06dalXm3RUOPQOPiIjAoEGDsHHjRkcOS9pBrVZ7/VUosViMlJQUpzSpsu7z2F6UvBJvYTabUVdXZ9dR0RdptVqcPXsWTU1NfIdCCCEElytjrMnr2LFjcfjwYaxevRphYWE8R+a+Ono+62wbN26ETCbDn//8Z4eN6fBa04kTJ+KFF15AU1OT23S58hWxsbHQaDReOXsQEhKC0NBQSCQSp4wfEBAAgUDQ7kX3lLwST8VxHFpaWtDY2IjGxkZotVq+Q3IbZrMZeXl5iIiIQFRUlFtdvSaEEF9jNBppSUcHREZGQi6X8x2GnY0bN2L06NEO3d7T4Wfg9957L4xGI7Zu3eroockNCIVCJCQk8B2GU5jNZqfW8DMM06GElJJX4onMZjMKCgqQk5OD0tJSSlyvwd3LiM1mM53QEUK8XnNzM98heIyAgACH7KHqSCUlJTh06BAmTpzo0HEdfgYeFxeHAQMGUOkwT1QqlVeWU2g0GqeerGk0GpjN5nY/npJX4gmu/JtpbGxETk4O6uvreYzIc2i1WrfdNkggEKCyspISWEKIV9PpdHyH4BGkUikSEhLcrlro+++/h1gsxt133+3QcZ1yBj5x4kT89NNPPtv8g2/R0dGQyWR8h+FQFovFqSfdHR2bklfi7jQaDU6fPo3y8nIUFRUhPz8fJpOJ77A8iruufxUIBBCJRCgrK+M7FEIIcRqqDroxhmGQlJTklruObNy4ESNHjoRarXbouE45A7/vvvug1+vxww8/OGN4cgMCgcAry4eLi4s7NDvaXhzHdXifXEpeiTurr69HXl4eTCYTysrKUF1dzXdIHslgMNiahbib4OBg1NfXu+3sMCGEdAXHcTQJ1g4JCQlutzUOAJSVleH333/Hfffd5/CxnXIGnpKSgptvvhlr1qxxxvCkHRQKhdvVvneV2Wx2SkdUnU7X4aSYklfirqqqqnDx4kUqKXUQd519ZRgG0dHRKC0tpYsThBCv09zc3O4mmr4qLCzM7boLW61duxYikcjh610BJyWvADBt2jT89NNPqKurc9YhyA1ERka65dWYrqipqYFGo3HomBaLpcPPoeSVuBuO41BaWori4mK+Q/Eq7pq8AkBgYCBSU1NRUVGB2tpavsMhhBCHof4M1ycSiRATE8N3GNe0Zs0ajBkzxuElw4ATk9fJkyfDYrFgw4YNzjoEuQGGYZCYmOh2C7i7qqioyKFX4zqaiDIM43U/U+K5rBdfysvLqYTUCZzdLK6r/P390b17d+h0ug4vfyCEEHdksVioouQG/Pz83PZcNDc3F3/88QemTZvmlPGdlrxGRERg1KhRVDrMM5lMhujoaL7DcKiWlhZUVVU5bLyO/vHTrCtxJ1qtFlqtlqpcnMRisUCn08FgMLhtEisUChEXFwehUIiWlha+wyGEkC6prq7uVFWcr1Cr1YiPj+c7jGtas2YNAgICMGbMGKeM79Sz8GnTpmHPnj0oKipy5mHIDYSFhUGlUvEdhkM1NjY6bCyZTEZ7vBKPZTabUVJS4raNhbxBbW0tampqUFZW5tYnVCqVClKplO8wCCGk01iWRWVlJd9huCWJRILU1FQkJydDIpHwHU6bOI7D119/jYkTJzpt5xOnnoXfc889kMvlNPvKM4ZhvK55k06nc1jpsEgkQnBwcLsfT8krcSdms5n2wnOympoaVFRUoKKiwuFr7h3NXcvICCGkPWpqapyys4Snksvltn/HxcXB39+fx2hubP/+/bh48aLTSoYBJyevKpUK9913H7744gu3LbfyFUql0qv2fnV0C/Xw8PB2P5aSV+JOaO9W1zIajXyHQAghXonjOJ+fdVWr1UhISEBISAhSUlIQFxdn+3D3xBUAvvjiCyQkJGD48OFOO4bTz8L/+te/4sKFC/jtt9+cfShyHQzDICQkhO8wHMqRm1dLpVIEBga267GUvBJ3QleoXYt+3oQQ4hx1dXU+fYEwPDwcSUlJCA4ORnx8PAICAqBUKhEaGorQ0FC3r6xpamrCunXr8Mgjjzj1XNnpZ+FDhgxBamoqvvjiC2cfitxAcHCw2//id4Qjk1eg/bOvlLwSd0LJlGvV1dU5tOqDEELIZTU1NXyHwJu4uDjExMR49Hn6unXr0NLSgunTpzv1OE4/C2cYBo888gg2bNhAbfx5JhKJ2j276Am0Wq1Dy9EVCkW7GltR8krcCSWvrmUwGJCbm4vKykpaDkMIIQ7CsqxP9W+w7gaSmJiI7t27IzQ0lO+QuuyLL77AnXfeidjYWKcexyVn4Q8//DBMJhO+/vprVxyOXIc3lQ5LpVKHnzy2Z/aVklfiTmjNq+txHIeSkhK6IEsIIQ6i1+t95oJgeHg4MjIyEBERgaCgILumTJ4qOzsbhw4dwl//+lenH8slZ+GRkZEYM2YMli1b5jO/mO5KqVR6xVYKUqkUKSkpDk8k/f39b9jYipJX4k5EIhHfIfis0tJSek8jhBAHcPRSMHckkUjQrVs3xMTEeN255Oeff47Q0FDcfffdTj+Wy35yTzzxBE6cOIGDBw+66pCkDQzDeHxpgkgkQmpqKsRiscPHZhgGERER132Mt73gEM/mTdUUnsZgMKC6uprvMAghxON5e8mwQqFA9+7doVQq+Q7F4bRaLVasWIFHH33UJfvPuuws/M4770RycjIWL17sqkOSa/Dkxk0CgQCpqalOnT0ODAxEWFhYq9uFQiFCQkIoWSBuJSgoiC6o8Ki8vBwWi4XvMAghxGNxHOfVM69+fn5ITU2FUCjkOxSnWLNmDTQaDZ544gmXHI/hXFjz9P777+OFF15AUVHRDWe3iHMVFBSgrq6O7zA6hGEYpKSkuGyfq+bmZpSUlEAoFCIoKAj+/v6UJBC3VFRURDOAPIqMjERUVBTfYRBCiEfS6/U4e/Ys32E4nFgshp+fHxISErx2iQ/HcejduzdSUlLw/fffu+SYLj0Tnz59OsRiMZYtW+bKw5I2eOLsYUJCgks3aJbL5UhNTUVSUhLUajUlrsRtefpSAE9XWVnp03sTEkJIV5SWlvIdglOIRCIkJyd7beIKAHv27EF2djZmzpzpsmO69Gw8MDAQDzzwAD799FPqkMkzT2vcpFarvWqbH0IciWEYKBQKvsPwWSzL+vT+hIQQ0llNTU1oamriOwyHE4lESElJ8dhleu21ePFipKenY8SIES47psunkp5++mmUlZXhu+++c/WhyRUYhmlzXae7amhoQElJCd9hEOJ2mpqa0NDQAIPBwHcoPo0qMwghpGOs2455o+TkZJc0L+JTcXExvv/+e8ycOdOlSbrL32179+6NzMxMLFq0iLYY4FloaCgCAgL4DqPdamtr+Q6BELcjEAhQW1sLs9nMdyg+zRndzwkhxJvV1taiubmZ7zAczleqoT788EMolUo89NBDLj0uL5eKn3/+eRw5cgR79uzh4/DkvxiGQWJi4g33NXUXFouFTtAJuYqnLQHwVt68pokQQhzNYrGgrKyM7zCcQiQSeX25cGNjI5YuXYonn3wSKpXKpcfmJXkdPXo0evTogXfffZePw5MrCIVCpKSkeEz7biqNJKQ16nTLP0peCSGk/SorK3nrfyOTyZxa0usLF5SXLl0Kg8GAZ555xuXH5iV5ZRgGzz33HH744Qfk5OTwEQK5glQqRVJSEt9htAslr4S05ufnRw3NeEavTYQQ0j5GoxGVlZUuP65QKIRarUZISAhCQ0MRGRmJkJAQqFQqSCQSh82WevsFZaPRiA8//BAPPPAAIiMjXX583jpMTJ06FVFRUVi0aBFfIZAr+Pv7IzY2lu8wbohOEAlpm7e/Wbq7+vp6vkMghBCPUFZWBpZlXX5coVBoawBaWlqK8vJy1NTUQKPRwGg0OqQXj1qtdnkZrat98803KC0txbx583g5Pm/Jq0QiwezZs7F69WqvrXn3NKGhoW6//yslr4S0TSaTuf3frzdrbGyExWLhOwxCCHFrer2elwacYrHY6ftxMwyDmJgYpx6DbyzL4t1338WYMWPQvXt3XmLgtbf/448/DrlcTmtf3QTDMIiNjYVSqeQ7lGui5JWQa4uMjPT6JhHuiuM4NDQ08B0GIYS4Lb62xmEYxiUXF0NDQ71+vevmzZuRnZ2NF198kbcYeE1eAwICMHv2bHz66aeoqqriMxTyXwKBAElJSW67NxVtR0HItUkkEo/av9nbaDQavkMghBC31dTUxMvrJMMwTi9TDgwM9PrlOxzHYcGCBcjMzMTgwYN5i4P3XdVnz54NkUhEa1/diFgsRnJyMgQC3n897DAMg+joaL7DIMStRUREeEz3cG/jrhf9CCHEHZSXl7v8mAKBwKmJq7VqMTEx0evfe7du3Yrjx4/jtdde4zUO3rOToKAgzJw5E0uWLEFNTQ3f4ZD/8vPzQ0JCAt9h2AkLC/P6cgxCukokEkGtVvMdhk/ylD2zCSHE1ViWhV6vd+kxJRKJUydixGIxunXrhrCwMK9fsmOddR08eDCGDRvGayy8J68AMHfuXADA+++/z3Mk5EruVgJB5ZCEtI9CoeA7BJ8kl8v5DoEQQtxSS0uLQ7r5doRAIIDZbHbK2EKhEKmpqT7zfvvzzz/jyJEjeO2113hP1N0ieQ0JCcFTTz2Fjz/+GHV1dXyHQ64QERHhFvtHCgQCWu9KSDv5ypupO2EYhmZeCSHkGrRarUuPJxaLnZa4MgyD5ORkn7lgaZ11ve222zBq1Ci+w3GP5BUAnnvuObAsi3/96198h0KuwDAMEhISeP8DlUqlvF/pIcRTyOVy+ntxMXqNIoSQtjU3N6O0tNSlx5RIJA5NXoVCIQICAgAACQkJXr+X65W2bt2KAwcOICsryy3e59wmeQ0LC8OcOXPw0Ucf0b6vbkYgECAlJQUikYi3GGhGg5D2YxiGZl9djO8LfIQQ4o5MJhPy8/Od3u33SkKh0OElykFBQYiKikJ0dDSCgoIcOrY7Y1kWr7zyCoYNG4Y77riD73AAuFHyClyeffXz88PChQv5DoVcRSKRIDk5mbcrLtSoiZCOiYiI4DsEn0LJKyGEtFZRUQGj0ejSYwoEAoc2h4qIiEBoaCgaGxsRHh7usHE9wdq1a3Hq1Cm89dZbbjHrCrhZ8hoQEICXXnoJn3/+OfLz8/kOh1xFqVQiLi6Ol2PTzCshHePv7+9TZU18o5luQgixx7IsamtrXXpMkUgEk8nksPFiYmLg7++PyspKREREuE0C5wpGoxF///vfMW7cOAwcOJDvcGzcKnkFgKeffhrh4eG87yFE2hYSEuLyrr8Mw0CpVLr0mIR4OnfdF5lhGK/bD1Umk9GFAkIIuUpDQwMsFotLjsUwDKRSqUPXucbHx0MqlaK5uRnx8fE+lbgCwBdffIGCggK8+eabfIdih+Fc3be6HZYtW4bHHnsMx48fx0033cR3OOQqHMfh/PnzLuscFxER4ZYn4YR4gosXL6K+vp6340skEiiVSigUCigUClszKa1Wi6qqKjQ0NPAWm6PEx8cjJCSE7zAIIcSt5OXloampyenHEQqFkEgkaG5udtiYSUlJEAqFEIlE8PPzc9i4nkKn0yE1NRWjRo3CypUr+Q7Hjlsmr2azGb169UJUVBR27Njhc1c6PIFOp0Nubq7TjyMWi9GjRw8IhUKnH4sQb6TVanHu3DmXHEsgENiSVOvHjba4MhgMqKqqQk1NjUsbejiKSCRCr169IBC4XSETIYTwxmg04vTp0y45lkwmQ0tLi8PGi4iIQEhICMRisc++tmdlZeGtt95Cbm4uEhMT+Q7HDn/tY69DJBLh3Xffxd13340tW7Zg3LhxfIdErqJQKKBWq50+axITE0OJKyFd4Owu4VKpFOHh4VAqlZDJZB2+2CiVShEbG4uoqCjU1NSgqqrK5c09ukIoFNIFVkIIuUpNTY1LjsMwjEPXuMpkMkRGRvps0goAJSUlePvttzFnzhy3S1wBN515BS6Xpt55550oLCxEdna2162R8galpaWoqKhw2vhKpRJpaWl0YkhIFxgMBmRnZzt8XIZhEBkZifDwcIe+yZtMJpw6deq6jxEIBG4zSxsSEoK4uDh6nSKEkP/iOA7Z2dkuuRDp6PeD9PR0n2/A9+CDD2L79u3Iy8uDv78/3+G04raXFRiGwXvvvYcLFy7gk08+4TscchWO46DRaJx6jNjYWDohJKSLnPE3FBAQgB49ejjl6jTHcTfcGstdts5Sq9WUuBJCyFU0Go3LKmgcOQcXHh7u84nr4cOHsXr1aixcuNAtE1fAjWderZ544gmsW7cO+fn5CA4O5jsccgWDwYCcnBynzICEhIQgPj7e4eMS4muam5uRk5PjsPFcscaT4zgUFRW1WXYmEAiQnp6Oc+fOuayLZVtUKhVSUlJ8urSMEELawmejQIZhOpXQhoWFISYmxqcvRnIchyFDhqCpqQnHjx9322V7bv+uu2DBArAsi9dff53vUMhVpFKpUxJMuVxO3YUJcYCqqiqcPXvWoWM6uky4LQzDICoqqs2TiKSkJMjlcl5nX8ViMZKTkylxJYSQq5jNZl67yN+oSeDVGIZBfHw8VfsBWLduHfbt24f333/fbRNXwAOS17CwMLz22mv497//jT/++IPvcMhVgoKCEBQU5JCxFAoFUlJSkJGR4fQmM4T4io5cgZbL5Xadgv38/Ow+FAoFQkNDnRjt/4jF4lbHiomJQUBAABobG6HX610SR1vUarVbv7ETQggfOI5DYWGhQ0t5O0Imk3WoIkckEiEtLY22OsPlUu958+ZhwoQJGDlyJN/hXJfblw0Dlxt49O3bF0qlEvv376er3W7GYrGgqKgIEokElZWVbb5oBQQEICYmBlKpFEajEUajEQaDAUajESaTCUFBQVCpVDxET4j36kjJcExMDMLDw50cUccYjUZkZ2eD4zi7xkhnz56F2WzmrStxamqq264FIoQQvpSVlaG8vJy346ekpKC4uBgGg+GGj5XL5UhJSaGGsP81b948/Pvf/8bZs2fdftmeR2SBYrEYn3zyCQ4dOoQvv/yS73DIVYRCIRITExEdHY3u3bvbndTJ5XKkpqYiJSXFto2GVCqFSqVCSEgIoqKiEB8fT4krIU4gk8kQHR19wwt+iYmJbpe4ApevigcGBsLPz69VSVdoaCgvCaRQKPS616uysjK36d5MCPFMjY2NvCauAoEASqWyXUtb1Go1unXrRonrf50+fRoffvgh/v73v7t94gp4yMyr1UMPPYQff/wR586do+ZNbozjODQ2NsJsNiM4ONjn1xAQwjeTyYSysrJWDZAEAgGSkpIQEBDAU2TXx3EcampqoNVqbXvNGY1GXLx4EVFRURAKhcjNzXVpTIGBgUhKSnLpMZ2ttrYWwOXvjSqbCCEd1dLSgtzcXF6b6AH/a/ZpsVhQU1OD6upqAJcv5MpkMsjlcshkMvj5+dG56X9xHIehQ4eiuroap06d8oiE3qOS18rKSnTr1g2TJk3C0qVL+Q6HEEI8il6vh1arhcVigdlsRmBgIJRKJd9h3RDLshAIBOA4DmfOnIFMJkNCQgLq6+tRVFTk0ljEYjF69erlVSc+LMvizJkzUKlUiIuLowSWENJuFosFubm5aGlp4TsUAEBaWprXVcc401dffYXp06djx44dbr/W1cqjklcAWLJkCWbOnIl9+/bhT3/6E9/hEEIIcRHr25U1cTx79ixaWlpcWvLqretdKyoqUFpaCqVSiW7duvEdDiHEA3Ach4KCAt62xWlLXFycyxoLerra2lpkZGRgxIgRWLt2Ld/htJvHJa8WiwWDBg2y7UHkLpvVE0IIcR2O42A2myESiVBRUYGysjKnHzM0NBRxcXFOPw4fLBYLTp06BT8/P0peCSHtUllZiZKSEr7DsAkICEBycrJXVcY400MPPYQtW7YgJycHkZGRfIfTbh5XGyQUCvH5558jPz8fb775Jt/hEEII4QHDMBCLxWAYBpGRkYiIiHD6MT1hLVBnCYVCREVFgWVZ6HQ6vsMhhLg5jUbjVokrcLkREyWu7bNt2zasWrUK7733nkclroAHJq8A0LNnT7zyyit46623cOrUKb7DIYQQwjNXrHHy9r1dQ0JCkJaWZjsh9bDCLEKIi1gb57mb0tJSet1qB41Gg8cffxyjRo3C9OnT+Q6nwzwyeQWAl156Cd26dcOjjz4Ks9nMdziEEEJ45IolJN6evAqFQlRXV9uaeLVnr0RCiG9hWRYXLlxwy3PvwMBAmnlth1deeQU1NTVYunSpR/68PDZ5lUgk+OKLL3Ds2DF8+OGHfIdDCCGERxKJxOlvwr7QhTcgIABarRY6nQ4ajYbvcAghboTjOBQXF0Ov1/MdSityudzjyl/5sH//fixevBj/+Mc/bFvQeRqPa9h0tblz5+Lf//43jh8/jvT0dL7DIYQQwpOSkhJUVlY6bXxf2YLh/Pnz0Gg0EAqF6NWrl9fPOBNCbqyxsRFFRUUwGo18h9KKUqlESkoKvVbdgF6vx0033YTg4GDs3bvXY39eHp+86vV69OvXDyqVCvv374dYLOY7JEIIITzgOA55eXkOmzGUyWRQKBTw8/OzffjC7GttbS0KCwsBADExMQgLCwPDMOA4DpWVlTAYDIiOjoZIJOI3UEKIy5w9e9YtZ1zVajUSExN94rW5q2bOnIkvv/wSJ06cQFpaGt/hdJrH/0/7+flh1apVOH78OHUfJoQQH8YwDJKSkuDn53fDx4rFYoSFhbV5n1qtxk033YQePXogISEBYWFhUCqVPnlyVFJSgjNnzqC6uhr5+fkoLS1FQ0ODx16xJ4R0nMFgcMvENSQkBElJST752txR27dvx5IlS/DOO+94dOIKeMHMq1VWVhbeeOMNHDhwADfffDPf4RBCCOEJx3Fobm5GXV0d6urqYDKZ7O4XiURIS0uD2WxGXV0dampqbPeFhYUhKirKp5OzvLw8NDU1XfP+8PBwxMTEuDAiQgif3G0/V7VajfDwcFtzOXJ99fX16NmzJ3r06IFt27Z5fLLvNcmryWTCn/70J2g0Gvzxxx/tuvJOCCHEu3EcB41Gg9raWjQ0NIBhGHTr1g1yuRwAoNPpcOHCBQiFQiQkJEChUPAcMb9MJtMNt6Dr3r277edHCPF+ubm5vO7/LJVKbUs4AgICIJPJeIvFE02dOhU//fQTTp8+7RUXHr0meQUu/3H17dsXf/3rX/Hxxx/zHQ4hhBA3YrFYYLFYIJFI7G43m83QarVQq9X8BOYmjEYjLl26dN1ZV4VCQc0RCfEhRqMRp0+fdvpxAgICEBERgcbGRhgMBrteA7S+vvPWrl2LKVOmYM2aNZg6dSrf4TiEVyWvALB48WLMmjULmzZtwrhx4/gOhxBCCHF7JpMJOTk519y7MS4uDn5+fpBKpXQiSYgPcUXJsFAoRGRkJMLDw516HF9z8eJF9O3bF3/+85/xzTffeOSerm3xuuSV4zhMmDABe/fuxcmTJ71iepwQQghxNp1Oh4KCAhgMBrvbVSqVxzf4IIR0nMFgwLlz51r1DXC0+Ph4hISEOPUYvsZoNGLIkCGorq7G8ePHERAQwHdIDuPZK3bbwDAMvvzyS/j5+WHq1KnXvIpMCCGEkP9RKBTIyMhotZdtZGQkTxERQvhiMpmQl5fn9MRVqVQiODjYqcfwRa+++ir++OMPrF271qsSV8ALk1cACA4Oxtdff419+/bhjTfe4DscQgghxCMIhUK7smClUtkqmSWEeDeLxYL8/PxWVRjOEBcX5zXlrO5i27ZteOedd/DWW2/hlltu4Tsch/O6suErLVy4EFlZWfj1118xfPhwvsMhhBDiRliW9fgtA7rCbDZDKBS2OnHMyclBc3MzACA1NRX+/v58hEcI4QHLssjPz4dGo3H6sSIiIhAdHe304/iS8vJy9OnTBwMGDMAPP/zgle9x3vcdXeHll1/GsGHDMHXqVFRUVPAdDiGEEDeSn5+PixcvQqPR4MrruHq9HgUFBU4vl+NTRUUFTp06ZTezwrIsWJZFS0sLgMtlxDTrSohvKSwsdGjiKpfLkZaWBqlUartNKpUiKCiIliQ4mNlsxtSpUyEUCrFixQqvTFwBwKtbBgqFQnz99dfo168fJk+ejB07dkAsFvMdFiGEEDcgEolQX1+P+vp628mURqOBVqsFcHkJije+ZxiNRpSVlYHjOJSUlCAgIAANDQ2IiIhAfX09OI6DQCBAdHQ0lfMR4mOut1VWZ8TFxUGpVKJ79+7Q6XSQy+XUsdxJXnrpJfz+++/YuXMnwsLC+A7HabwzJb9CREQE1q9fj/379+PFF1/kOxxCCCFuIigoyPZvg8GA8vJyW+IKwFY6621KS0ttM82NjY0oKipCU1MTzGYzoqKiEBkZiR49etCsKyE+xmw2Izg4GPHx8Q65cBcSEgKlUgkAEAgEUKlUlLg6yYYNG/Duu+/inXfewdChQ/kOx6m8PnkFgMGDB2PRokV47733sH79er7DIYQQ4gbUavV1t2ewls96E71ej7q6ula3y+Vy+Pv7QyQSISoqChKJhIfoCCF8EgqFiImJQX19fZeXTYhEIlrP6iJnz57FjBkzMHnyZDz77LN8h+N0PpG8AsCsWbMwZcoUPPLII8jJyeE7HEIIIW4gODgYQqGwzfsUCoWLo3G+0tLSVrdJpVKkpqZe8+dACPEN1mUCcrncYWNVVlaisbGxy+ORtmk0Gtx7772Ii4vD559/7hNLPXwmeWUYBsuWLUNiYiLuuece+kMihBAClmVhsVjavM/bZh+bmpparWcTiURITU31yrW9hJCOaWlpQWFhISorK7s0jkAgQEBAAPLy8lBVVeWVFwLdAcuymDFjBkpLS/Hdd9/ZSrS9nc8kr8Dlq+jfffcdqqqqcP/998NsNvMdEiGEEB6pVCqEhoba3ZaYmAihUOhVCR3Hca1mXYVCIVJTU+26gBJCfJPFYsGFCxfaXFbQUSzLIiAgAHFxcejZsyetc3WSBQsWYOPGjVi5ciW6devGdzgu41PJK3B5z7r169fjl19+wfPPP893OIQQQnjEMAxiY2OhVqsBADKZDEFBQcjIyPCamVeLxYLCwkLo9Xq726OiouDn58dTVIQQd8FxHC5evOiQdf4ikQjx8fEICAiAQqHwiTJWPqxfvx7z58/Hm2++iQkTJvAdjksx3JWb2/mQxYsXY9asWVi2bBn++te/8h0OIYQQHnEcB61WC4PBcN0mTp5Gq9WioKAARqOx1X3p6elUzkcIgVarxblz57o8TmRkJMLDw2n9vJMdPXoUQ4YMwcSJE7Fq1Sqfu0Dgs8krx3F46qmn8MUXX2DHjh1e31aaEEKIb2hubobJZIJGo0FFRYXdff7+/pDJZJBIJJDJZAgICOApSkKIO7BYLCgvL+/yOteYmBiEh4c7KCpyLaWlpbjlllsQGxuL3bt3QyaT8R2Sy/ls8goAJpMJo0ePxsmTJ3H48GEkJSXxHRIhhBDSaZWVlSgpKWnzPpVKhbS0NBdHRAhxZwUFBV1e56pWq5GUlORzM4Cu1tzcjKFDh6KiogJHjhxBREQE3yHxwufWvF5JLBbj22+/RWBgIO666y6HLFInhBBC+HC9xBWAbV0vIYQAl6sQu9qhViqVIiEhgRJXJ7NYLJg2bRpycnKwefNmn01cAR9PXgEgKCgIP/30E2prazFu3Div3JSeEEKId6uoqLhu4gpQ8koI+R+NRoPc3FwUFRV16vlisRhBQUFITk6mNa5OxnEcnn32WWzatAnr1q1D3759+Q6JV9S7GkBKSgp++OEHZGZm4oEHHsD69eshEPh8Xk8IIcQDtLS03HC9mp+fn9d0TyaEdI3JZEJpaWmrDuQ3IhQKERUVBX9/f0ilUpptdZF3330Xixcvxqeffoq7776b73B4Rxnaf916661Yu3Ytvv/+e8ydOxc+vBSYEEKIBykvL7/hvuU060oIAS5f7MrNzYVOp+vQ85RKJbp3746wsDDIZDJKXF3km2++wd/+9je88sorePzxx/kOxy1Q8nqFcePGYfHixfjwww/x/vvv8x0OIYQQckPNzc3XvV+tVnvV9j+EkPbjOA4lJSXQ6/XgOA4ajabNrbOuJzw8HGlpaVS94WK7du3Cww8/jIceeggLFy7kOxy3QWXDV3nyySdRVFSEefPmISwsDA888ADfIRFCCCHXlJycDIFAAJPJhIqKCtTX1wMAZDIZYmJiaDscQnwUy7KoqalBVVUVqqurER4ejvLy8g6PExQURDOtLnb8+HFMmDABw4cPx7Jly+jnfwVKXtvw5ptvorKyEtOnT4dKpcL48eP5DokQQghpk1QqBXC5gUpSUhKam5vR3NyMwMBAOuEhxEcZDAawLGvbSYNl2U4lrsT1cnNzcccdd6Bbt27YsGEDzXhfxaf3eb0es9mM+++/H1u2bMGPP/6IkSNH8h0SIYQQQgghN8RxHBiGAcuyOHHiRJd6uYSFhSE4OBhyuZwuiDlZYWEhBg8eDLVajd9++w3BwcF8h+R2KHm9DoPBgPHjx2Pv3r3YsWMHbrvtNr5DIoQQQggh5IZ0Oh2Kioo63FW4Lf7+/khJSaHk1YkqKiowZMgQsCyLvXv3IjIyku+Q3BI1bLoOqVSKjRs34qabbsKf//xnnDp1iu+QCCGEEEIIuS6LxQLAMZ3GJRIJEhMTKXF1orq6Otx+++3Q6/XYsWMHJa7XQcnrDSgUCmzduhWJiYm4/fbbcfbsWb5DIoQQQgghpE0sy6K+vh4ikQi1tbWdHkcgECA8PBzp6ekQiahNjrM0Njbiz3/+M8rLy/HLL78gMTGR75DcGpUNt1N1dTUyMzNRU1OD3bt3Iz09ne+QCCGEEEIIsaPX61FWVobGxsZOPV8gECAsLAzh4eGUtDpZY2Mj7rjjDuTl5WHHjh3o168f3yG5PUpeO6CqqgojRoygBJYQQgghhLgdjuNw4cKFTiWulLS6FiWunUPJawdZE9ja2lrs2rWLElhCCCGEEMI763Y4FRUVHX6uQqFASkoKJa0uQolr59Ga1w4KCwvDzp07ERQUhMzMTOTm5vIdEiGEEEII8XEmkwlisbjDz1OpVEhNTaXE1UUoce0amnntpKqqKmRmZqKurg6//PILevbsyXdIhBBCCCHEB3Ech1OnTsFsNrf7OUKhEP7+/khISIBAQPNZrlBXV4fRo0cjPz+fEtdOouS1C6qqqnDHHXeguLgY27Ztw80338x3SIQQQgghxIdYLBbU19fj0qVL7X5OaGgooqOjIRQKnRgZuVJ5eTnuuOMOVFRU4Oeff6bEtZPoMksXhIWFYdeuXUhLS8OIESOwe/duvkMihBBCCCE+xGKxwGw2QyaT3fCxSqUSGRkZiIuLo8TVhQoLCzFkyBDU1dVhz549lLh2Ac28OoBWq8WECROwb98+bNiwAWPGjOE7JEIIIYQQ4uVYlkVBQQEaGhqu+ziBQICEhASo1WowDOOa4AgAIDc3F6NGjYJUKsWOHTtoH9cuoplXB1Aqlfjhhx9w5513YsKECVi3bh3fIRFCCCGEEC/X1NQEs9kMuVx+3cdJpVIEBgZS4upif/zxB4YMGQK1Wo29e/dS4uoAlLw6iEwmw4YNGzBlyhRMmTIFS5Ys4TskQgghhBDipTiOQ0VFBeLi4uDn53fdx/r7+7soKmK1c+dOZGZmIjExEb/99hsiIyP5DskrUE9sBxKJRFixYgWCg4Mxc+ZMFBcX4x//+Ad1cCOEEEIIIQ7DcRyKi4shl8tRV1eH2traNh/n5+cHi8VC2+C42Ndff43p06dj+PDh2LhxI1QqFd8heQ36TXYwgUCA999/H3FxcZg7dy5KSkrw5ZdfQiKR8B0aIYQQQgjxApWVldDpdPDz84PRaERgYCDq6+shEAjg7+8PlUoFuVwOi8WCgIAAvsP1GRzH4Z133sELL7yAhx9+GMuWLevU3rvk2qhhkxOtX78eDz74IAYPHozvvvuOXjwIIYQQQkiXcByH+vp6iEQiWzlwVVUV9Ho9AgICUFxcDJPJBACQy+Xo3r07n+H6DIvFgtmzZ2PJkiV49dVXsWDBAlpj7ASUvDrZnj17MH78eMTGxuLHH39ETEwM3yERQgghhBAPp9PpYDQaodfrIRKJEBgYiAsXLsBgMMBisQC4vJ9rXFwcz5F6v+bmZkybNg2bNm3Cv//9bzz22GN8h+S1aDGmkw0dOhT79u1DY2Mjbr31Vhw5coTvkAghhBBCiIcTCoUoLy+HTCZDeHg4xGIxwsLC7PZvVSqVPEboG8rKyjB06FD8/PPP2LRpEyWuTkYzry5SUVGBCRMm4OTJk1i+fDnuv/9+vkMihBBCCCEejGVZAADDMMjLy4NGo7G7XywWIzo6GkFBQVTC6gRHjhzBhAkTIBAIsHnzZvTt25fvkLwezby6SEREBHbv3o2JEydiypQp+Pvf/257wSGEEEIIIaQ9WJZFU1MTysrK0NjYiMrKSjQ2NiIkJKRVgmoymVBYWIjc3FxotVqeIvZO69atw9ChQxEbG4vDhw9T4uoi1G3YhWQyGVatWoWePXvi5ZdfRk5ODlauXAmFQsF3aIQQQgghxAMwDAOFQgGJRAKTyQSpVAqj0Qiz2YygoCCYTCaYTCaYzWZIpVIoFArb40nXsSyLrKwsLFy4ENOmTcPnn38OmUzGd1g+g8qGebJp0yZMmzYNqamp+P7775GQkMB3SIQQQgghhJBr0Gg0mDFjBjZu3Ih//OMfePHFF6kc28UoeeXRqVOnMH78eDQ1NWHNmjUYPXo03yERQgghhBBCrnL27Fnce++9KCkpwapVqzBhwgS+Q/JJtOaVR71798axY8dw66234q677sKCBQtoHSwhhBBCCCFu5Ntvv8Utt9wChmFsTZoIPyh55VlQUBB++OEHvP7668jKysLYsWNRX1/Pd1iEEEIIIYT4NJPJhLlz52LSpEkYM2YMDh8+jPT0dL7D8mlUNuxGtm3bhqlTp0KtVmPjxo3UtYwQQgghhBAelJeXY/LkyThw4AAWLVqEWbNm0fpWN0Azr25k9OjR+OOPPxAYGIiBAwdiyZIloGsLhBBCCCGEuM7PP/+Mm266Cfn5+di9ezeeeeYZSlzdBCWvbiYhIQH79u3D//3f/2HmzJm45557UFtby3dYhBBCCCGEeDWj0Yjnn38eo0ePRt++fXH8+HEMGjSI77DIFahs2I1t2rQJjzzyCPz8/LBmzRoMHTqU75AIIYQQQgjxOvn5+ZgyZQpOnjyJt956C3PmzIFAQPN87ob+R9zY+PHjcfLkSSQnJyMzMxOvv/46zGYz32ERQgghhBDiNVavXo2+ffuioaEB+/fvx7x58yhxdVP0v+LmYmJi8OuvvyIrKwtvvPEGhg8fjgsXLvAdFiGEEEIIIR6tvr4eDzzwAB588EHcc889+OOPPzBgwAC+wyLXQcmrBxAKhfj73/+OPXv2oKysDH369MGnn35KzZwIIYQQQgjphJ9//hm9evXCli1bsGrVKqxcuRIqlYrvsMgNUPLqQQYNGoSTJ0/igQcewJNPPonRo0ejuLiY77AIIYQQQgjxCBqNBk888QRGjx6NjIwMZGdn44EHHuA7LNJOlLx6GJVKhU8//RQ//fQTsrOz0atXL6xcuZJmYQkhhBBCCLmOPXv2oE+fPli1ahU++eQTbN++HbGxsXyHRTqAklcPNXr0aGRnZ2Ps2LF4+OGHcc8996CsrIzvsAghhBBCCHErOp0Oc+bMwfDhwxEdHY1Tp07hySefpL1bPRAlrx4sMDAQq1atwsaNG3HgwAFkZGTg008/BcuyfIdGCCGEEEII73766Sf06NEDn376Kd5++23s3r0bycnJfIdFOomSVy9w77334uzZs/jLX/6CJ598EkOHDkVOTg7fYRFCCCGEEMKLyspKTJ06FXfddRdSU1ORnZ2N5557DkKhkO/QSBdQ8uolgoKC8Pnnn2PXrl2orq7GTTfdhKysLBgMBr5DI4QQQgghxCU4jsPy5cuRkZGB7du3Y+XKldi+fTvNtnoJSl69zPDhw3Hy5Em8+OKL+Mc//oGbbroJu3bt4jssQgghhBBCnOrs2bMYOXIkHnnkEYwdOxa5ubl48MEHaW2rF6Hk1QvJZDIsWLAAx48fR3BwMEaMGIFJkyahqKiI79AIIYQQQghxqKamJsybNw+9e/dGUVERtm/fjq+++gohISF8h0YcjJJXL9ajRw/8/vvvWLVqFfbu3Yv09HS88cYbaGlp4Ts0QgghhBDSRSzLQqfToaamBkVFRbh48SLfIbkUy7JYsWIF0tLS8Omnn2LBggU4c+YMbr/9dr5DI07CcLRBqE/QaDRYuHAhPvjgA8TExOD999/HuHHjqIyCEEIIIcQDmEwm6PV6NDc32z4LhUKEhYVBJpNBJBJBJBJBIPCNuamjR49i1qxZOHjwIKZMmYK3334bMTExfIdFnIySVx9z7tw5zJ49Gz///DPuvPNOvPPOO+jVqxffYRFCCCGEkKu0tLSgvr4eDQ0NaGlpgUKhgEQiQUtLCyIjI+Hv7+9zExGlpaV47bXXsHz5cvTq1Qsff/wxhg4dyndYxEUoefVBHMdhy5YtmDdvHi5cuICHH34YCxYsQGxsLN+hEUL+i+M4sCxLLf0JIcRHWE/Jm5ubbQmrwWAAx3FITk6Gv7+/z8yqtqWxsRFvv/023n//fSgUCmRlZeHxxx+HSCTiOzTiQpS8+jCTyYSlS5di/vz50Gg0mD17Nl588UWo1Wq+QyPEp3Ech/Pnz0On0yEwMBAhISFQKpWtrq5zHAe9Xg+5XA6BQACDwYCKigoEBgZCpVL53NV4QgjxRCzLori4GA0NDRAIBJBIJAgPD4dSqYRQKERLSws4joOfnx/fofLCaDTa1rPq9XrMmTMHf/vb3xAQEMB3aIQHlLwSaDQavPPOO1i0aBFkMhleffVVPPXUU5BKpXyHRjwUx3FoaWlBc3MzGIaBWCy2ffjyVeOOqKmpwaVLl2xfS6XSVgmswWCARqOBQCCASqWCn58fysvLAQBRUVGIiIjocAJrNpthMBhsH3K5HAEBAW6VCHMcZ/chFArdKj5CCLkei8UCvV4PnU4HvV4PrVYLk8lku59hGKSkpMDf35/HKPnHsiy+/fZbvPzyyygsLMSMGTMwf/58REdH8x0a4RElr8SmvLwcWVlZ+OKLLxAdHY2XX34ZM2bMgEQi4Ts04gGMRiPKy8ttTSSu9dIil8uhUqmgUqmgVCq9qtzHbDZDp9OhpaUFEokEMpkMUqn0ugk7y7LgOA4CgcCWgHEcZ+sc2RUCgQB+fn6Qy+W2z9ZZ2raYTCacOnWq1e0ymQwREREICAjo1P8Xx3F2yaX1a4vFgtraWnAcB7Va3eqCmdFoxKVLl9DS0mL7OVk/X4lhGEgkEkil0jY/6IIJIcQdaDQaFBcXo7m5+bqPk0qlSEtL89nzL47jsHnzZsyfPx/Hjx/H2LFj8dZbb6FHjx58h0bcACWvpJWzZ89iwYIFWLduHeLi4vDKK69g+vTpEIvFfIdG3JD16rFcLgfLsigpKbnhG7OVSCRCr169vCK5aGxsREFBASwWS6v7rImVNZk1mUy2mWmj0Wh7nEgkglAoBMuydlfhHU0mk8HPzw9+fn6QyWRoaWlBU1MTNBrNNS86XBmjNQmWyWSQy+WQSCS2xPLKJFOv16O+vh5msxmBgYEwGAzQ6/UwmUxQKBTQ6/VgWdYuroCAAKjVaphMJly6dKnNn2dHicXiNpNaiUQCo9EIg8EApVJp+z4aGxuhUCjoNY8Q0mnWi3QajQZlZWUAAK1We8PnqVQqJCQk+GTiynEcfvjhB2RlZeGPP/7A8OHDMX/+fGrGROxQ8kqu6cyZM1iwYAG+/fZbxMfH49VXX8VDDz1EJ3TElpBIpVKIRCI0NzejtLQUJpMJUqkUBoOh3WMxDAOZTGb3IRaLwTAMGIaxzUhe/dn6wReDwYCysjJbea3ZbOYtFuIYfn5+MBqNiImJQXBwMN/hEEI8VEtLC/Lz86FQKFBXV9eu5wQFBSE8PNwn17VyHIetW7ciKysLx44dw9ChQzF//nwMHz6c79CIG6LkldxQdna2LYlNTEzESy+9hAcffBAymYzv0IgLmUwmaLVaaDQaNDU1dShBdZark9lrJboBAQEIDQ11SLJrLQ1ubGxEdXW1A74L4m6EQiHUarWt8ZU3VAYQQlzDbDYjNze3Xe+RQqEQISEhCAsL88mZVpZlsWXLFrzxxhs4evQohgwZgvnz5yMzM5Pv0Igbo+SVtNvp06exYMECbNy4EeHh4Zg9ezaeeOIJ6k7spYxGoy1Z1Wq1aGlp4TukLpFKpYiJibFrPmR9+WtPUtvc3Iz8/Hy7Ml/i/YRCIQICAtrcskgkEiEkJAQWiwUtLS1oaWmxNbkKCQmhbY4I8SHWJQdlZWU3XDojkUgQFhbms68TBoMBq1evxjvvvINz585hyJAhyMrKQmZmJjXfIzdEySvpsPPnz2PRokX46quvIBaL8fjjj+PZZ59FTEwM36GR/zIajbYZUo1GA4ZhoFAooFAobGsdr/WG2ZGrxp5IJBLZNf6xzsyq1eprNiTiOA6VlZUoLS3lIWLiiQQCAYKDg6FQKGzra63l8IQQz8BxHOrq6tDc3AyTyQSRSITY2Fi7x7Asi/r6elRUVNzwIq+fnx/Cw8MRGBjok68FDQ0N+Oyzz/DBBx+gsrISEyZMwPPPP4+BAwfyHRrxIJS8kk6rqKjAxx9/jE8++QRarRbTpk3DvHnz0KtXL75DuyGO49Dc3Ay5XO70NxCNRgODwQCVSgWpVGpLnKxvhNZkybrtR2dKFM1ms12y2p7EUyaT2SW0HMdBqVQCAC5duoSampoOx+ENrNsT6PV6aDQa28zzlY2FCOkMa1fkKxt4+erMCyHu4uoKnJaWFtTV1UEul6OhocFuzWpAQADCwsJQXV0Ns9kMi8UCo9F4w8ZyEokECQkJbe7X7QuKi4vx0Ucf4bPPPoPBYMBDDz2E5557Dt26deM7NOKBKHklXabRaPD555/jvffeQ0lJCYYNG4aZM2di/PjxbtfcieM42xXS5uZmiMViBAcHQ6/XQyQS2e1Hav3gOA5arRYGg8HuMfX19WhuboZAILB9CIVCu6+tb4JWYrEYFovFLhESi8W2Tr1arRYikcg2SyMWi23/vvIzALv1p+3t7nsj1lhEIlG7m0wQQjpPIpEgLi4O/v7+PnlSSwhfOI5DaWkpqqurERISApVKhaqqKmg0mms+RyqVwmg03rAr+5UCAgKQkJDgVdvCtQfHcdi1axeWLFmCTZs2QalU4qmnnsKsWbMQGRnJd3jEg1HyShzGaDTi+++/x5IlS/D7778jKioKTzzxBP7v//4PERERTjuuxWJBXV2dbZsP66+09d9X3mYymWjNIiHELV15Ecz6ceXX1n17r7UGlxByY2az2bY+3Wg0oqamxmnnBVFRUYiIiPCpC1MajQYrV67EkiVLcPbsWXTv3h1PP/00HnzwQahUKr7DI16AklfiFCdPnsQnn3yC1atXw2Qy4b777sPTTz+NP/3pTw57EWdZFsXFxairq6OSTkKIz2AYBiqVyrZWWywWw2w2u12lCyHuhGVZmM1m28Vsa2+IyspKh59DCIVCJCUlwd/f36HjurOcnBx88sknWLlyJfR6PSZMmICnn34aw4cP96nknTgfJa/EqRoaGrBixQosWbIE+fn5SE9Px4wZM/Dggw86pGykoaEBFy9e7FAJDyGEeBO5XA6lUonY2Fg6SSROY+3VYDKZYDab7f5tsVjsKgXkcrldZ3e+cRwHnU6Huro6NDQ0wGQyOe1Yfn5+SEpKglQqddox3EVjYyPWrVuH5cuX4+DBgwgLC8Njjz2Gxx9/nJp4Eqeh5JW4BMuy2LlzJ5YvX47vvvsOJpMJo0ePxowZMzB27Nhr7m9mbaxkNBptJT7Wz+1pkkAIId4sJCQEsbGxtBctcTiLxYKSkhJoNBqYTKYOz06qVCqEhITYGhNeWQ7f0tICnU4HgUAApVLZpT1OWZZFQ0MDGhoaIBaLbc3QrF2+r06gm5ubkZeX5/AEViwWIygoCFFRUV7998iyLHbv3o3ly5dj48aNMBgMuPPOOzFjxgyMGzfOJ5J2wi9KXonLNTQ0YO3atVi+fDkOHz6M4OBgTJs2DQ8//DD69u3b6o2moKCAmgcRQshVZDIZMjIyvPpE2RtZO827y6xkW1iWxcWLF9HY2OiS40kkEiiVSigUCiiVymvuBGAt9dVqtVAoFAgMDIRWq0VzczN0Oh0aGhrsHs8wDKRSqS2hFYlEqKiocNiFb5lMBrVaDbVaDT8/P7f+P+2qCxcuYNWqVfjqq69QWFiItLQ0WyVddHQ03+ERH0LJK+HVmTNnsGLFCqxatQqVlZVISUnBhAkT8Je//AV9+/aFWCyGyWTCmTNnaJaVEOKzVCoVwsPD7bqai8ViatzkIax7gVZVVUGv10MoFMLPzw8KhQJyuRwcx9lKcAHYzVJe64NhGHAcZ6tEsnbDt97XFS0tLaioqIBOp7vh3qXOIBAIbImsRCKBVqu1df3nm0KhsCWsMpmM73Cc6tKlS1i3bh3Wr1+PY8eOQalU4v7778eMGTMwcOBAr07Wifui5JW4BbPZjJ07d2Lt2rX4/vvv0dDQgKSkJNxxxx0YM2YM4uLinLpGhRBC3IlEIoFAIEBsbCwUCgUEAgGdKPLMYrHAYDDYmv6wLNvm56tvs5a1OvoCrPX34erTOOuFDes2TB1JsKxLdaxrQ90hWeSbtUGaWq1GQEBAl0qcPUFJSQnWr1+PtWvX4siRI5DJZBgzZgwmT56MMWPGwM/Pj+8QiY+j5JW4HaPRiF9++QXr1q3Df/7zH2g0GqSlpWH48OEYOnQounXrRidxhBCvFBAQgJCQEMjlcrsZNtJ5HMfZEkeGYWxl1lf/XFmWbdVb4crPnlj9I5FIEB0dDYPBgObmZjQ3N8NgMNhm8K9cj2r9rNfrbVvPEUCpVCI0NNRrt6jiOA5nzpzB5s2b8cMPP+DAgQOQSCQYPXo0Jk+ejLFjx9IWN8StUPJK3FpLSwu2bduGdevW4aeffkJjYyPCw8MxaNAgDB06FAMGDPD6sh1CiG+Ijo5GSEgIgP8lVgzD2D7I9ZnNZjQ1NUGr1cJoNNq64V6raseayFp/ttaSXULaIhAIoFarERQUBH9/f4/9m+Q4Di0tLdiwYQN27NiBPXv2oLCwEFKpFKNGjcJf/vIXjB8/Hmq1mu9QCWkTJa/EY5hMJvz+++/YsmULtmzZggsXLkAmk+GWW27BkCFDMHDgQERERPAdJiGEdEqvXr28viTRkViWhVarRVNTEzQaDfR6Pd8hER8hEokQGBiIoKAgSKVS2zp0vlhL1Nvaysj6dVVVFX7//Xfs3r0bBw4cQGNjIyIiIjBmzBiMGzcOI0eOhEKh4O17IKS9KHklHonjOJw7d86WyO7btw8syyIuLg4DBgzALbfcggEDBtCVQ0KIx1CpVAgLC2tzf8yCggKwLAuBQNChD+vs4tW3uduskVarRWlpabu+R+vjqbSVuBORSITevXs75W/LWtLe0tICg8FgW3t9ZYJ69d+CTqfD8ePHceTIERw9ehTnzp0DAPTt2xd33303xo4di/79+1O3cuJxKHklXqGurg67d+/Gr7/+il9//dX2Ip2eno7+/fvj5ptvRt++femqIiHE7Vm3DWFZFhaLBRaLxeGzilcmg9a9MSUSiW0WCbAvWb4y4W3r9uvdB8DuRPvKkl7rv5ubmx36/RHCh6svGrX389W3AbBLVo1G4w2PbTAYkJ2dbUtWs7OzYTabERMTg5EjR2LkyJEYMWIEbWtDPB4lr8QrlZSUYNeuXbZktqSkBAzDIDU1FT169ECvXr3Qu3dvxMXF0VVHQgghhHgMjuNQUVGBU6dOITs7G9nZ2cjNzYXJZEJwcDAyMzNtCWtKSorbVVoQ0hWUvBKvx3Ec8vLysHfvXhw4cAAHDx7EmTNnwHEcAgIC0LNnT/Ts2RO9evVCWloagoKC+A6ZEEIIIQQAoNFokJeXh+zsbJw+fRrZ2dmorq4GACQnJ2PgwIG47bbbMGjQIPTu3ZsuyhOvRskr8UmNjY04fPiwLZk9ePAg6uvrAQChoaFITk5Gamoq0tLSkJqaioSEBIhEIp6jJoQQQoi3slgsKCkpQV5eHvLy8nD+/HlcvHgRpaWlAAA/Pz/ccssttmT1tttuQ1hYGM9RE+JalLwSgsvNEPLz83Hq1CmcPHkSJ0+exKlTp3Dp0iUAl9egJSUlITExEbGxsbaPuLg4+Pv78xw9IYQQQjyFTqdDcXExSkpKUFRUhOLiYly6dAl5eXm29d8RERHo3bs3+vTpgz59+qB3797IyMigC+nE51HySsh1NDQ04NSpU7ak9uzZs8jLy0NVVZXtMWq1GrGxsYiJiUFsbCyioqIQEhKC0NBQhIWFQalU0noTQgghxEfodDpUV1ejpqYGVVVVqKiosCWrxcXFqKmpsT02MDAQqamp6Natmy1J7dOnD82oEnINlLwS0glNTU3Iz89Hfn4+8vLybJ+vTmwBQCaTISwsDCEhIbakNigoCCqVyvahVCrtvhaLxTx9Z4QQQgixMpvNtq2Zrvyw3lZfX4/q6mpUV1ejtrYW1dXV0Gq1dmNYE9SUlBSkpqba/Zv6bBDSMZS8EuJgLS0tKC8vR1lZGUpLS+0+W/9dVVWFpqama+5RKJPJbEmsWCyGSCSCUCi0+7dIJIJIJMKFCxeQlpbm4u+SEEII8UzWLajMZvM1P0wmE3Q6HXQ63TXH8ff3R3BwMKKjoxEVFdXqs/WDtukjxHEoeSWEEEIIIYQQ4vaolzYhhBBCCCGEELdHySshhBBCCCGEELdHySshhBBCCCGEELdHySshhBBCCCGEELdHySshhBBCCCGEELdHySshhBBCCCGEELdHySshhBBCCCGEELdHySshhBBCCCGEELdHySshhBBCCCGEELf3/w58dwcGEyQlAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "m = Basemap(ax=ax, projection='eck4', lon_0=155)\n",
    "m.fillcontinents()\n",
    "#m.drawparallels(range(-90, 120, 30), dashes=[], linewidth=.25, labels=[1, 0, 0, 0])\n",
    "#m.drawmeridians(range(0, 360, 60), dashes=[], linewidth=.25, labels=[0, 0, 0, 1])\n",
    "\n",
    "offsets = {'Jumjum': (3e5, -1e5),\n",
    "           'Thangmi': (0, 3e5), 'Nocte': (3e5, -2e5), 'Darai': (3e5, -4e5),\n",
    "           'Wardaman': (3e5, -2e5),\n",
    "           'Siuslawan': (-2.6e6, 0), 'Karuk': (2e5, -3e5), 'Maricopa': (3e5, -2e5),\n",
    "           'Lakota': (1e5, 2e5), 'Fox': (2e5, -3e5),\n",
    "           'Jaqaru': (0, 5e5), 'Ayacucho': (0e5, -6e5)}\n",
    "\n",
    "for l, (x, y) in jf[['longitude', 'latitude']].iterrows():\n",
    "    x, y = m(x, y)\n",
    "    m.plot(x, y, marker='.', color='b', markersize=7)\n",
    "    xoff, yoff = offsets.get(l, (2e5, 1e5))\n",
    "    ax.text(x + xoff, y + yoff, l)\n",
    "\n",
    "#fig.savefig('map.pdf', bbox_inches='tight', pad_inches=.01)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}