scripts/04_metaphone_lstm_fl_voter_name-evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soundnames: Learning from sequence of sounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SAMPLE = 100000\n",
    "# Florida voter\n",
    "df = pd.read_csv('/opt/data/fl_voter/fl_voter_name_gender_race.csv', usecols=['last_name', 'first_name', 'race'], nrows=SAMPLE * 1.5)\n",
    "df.dropna(subset=['first_name', 'last_name'], inplace=True)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmap = {1: 'aian',\n",
    "        2: 'asian',\n",
    "        3: 'nh_black',\n",
    "        4: 'hispanic',\n",
    "        5: 'nh_white',\n",
    "        6: 'other',\n",
    "        7: 'multi_racial',\n",
    "        9: 'unknown'}"
   ]
  },
  {
   "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>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Palmer</td>\n",
       "      <td>Alton</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mc Cleod</td>\n",
       "      <td>Alicia</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Dale</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Collier</td>\n",
       "      <td>Seth</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Martin</td>\n",
       "      <td>Robert</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Turner</td>\n",
       "      <td>Sharon</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Wood</td>\n",
       "      <td>Lilianna</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Planck</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Burdge</td>\n",
       "      <td>Jeffrey</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Zofnas</td>\n",
       "      <td>Scott</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Fogarty</td>\n",
       "      <td>Kjaristy</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Evans</td>\n",
       "      <td>Claretha</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lake</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Paule</td>\n",
       "      <td>Joan</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>McCartney</td>\n",
       "      <td>Wayne</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Estores</td>\n",
       "      <td>David</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bolton</td>\n",
       "      <td>Joe</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Fairchild</td>\n",
       "      <td>Mark</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Kung</td>\n",
       "      <td>Michael</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Katz</td>\n",
       "      <td>Robyn</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Fletcher</td>\n",
       "      <td>Omar</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Feathers</td>\n",
       "      <td>Ronda</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Arduine</td>\n",
       "      <td>Steven</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Potter</td>\n",
       "      <td>Tanda</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Long</td>\n",
       "      <td>Marcus</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Dreaden</td>\n",
       "      <td>Jenny</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nadeau</td>\n",
       "      <td>Donna</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Baker</td>\n",
       "      <td>Antonio</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149970</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>Cheryl</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149971</th>\n",
       "      <td>Ritzinger</td>\n",
       "      <td>Fernando</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149972</th>\n",
       "      <td>Miller</td>\n",
       "      <td>Angela</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149973</th>\n",
       "      <td>Houder</td>\n",
       "      <td>David</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149974</th>\n",
       "      <td>Mejia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149975</th>\n",
       "      <td>Stirling</td>\n",
       "      <td>Jahala</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149976</th>\n",
       "      <td>Strickland</td>\n",
       "      <td>Warner</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149977</th>\n",
       "      <td>Brasfield</td>\n",
       "      <td>Gregory</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149978</th>\n",
       "      <td>Rosias</td>\n",
       "      <td>Richard</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149979</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>William</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149980</th>\n",
       "      <td>Drandakis</td>\n",
       "      <td>Mary-Ann</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149981</th>\n",
       "      <td>Caro</td>\n",
       "      <td>Gonzalo</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149982</th>\n",
       "      <td>Brinsko</td>\n",
       "      <td>Andrea</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149983</th>\n",
       "      <td>Wilson</td>\n",
       "      <td>Betty</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149984</th>\n",
       "      <td>Neelands</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149985</th>\n",
       "      <td>Stroud</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149986</th>\n",
       "      <td>Runyon</td>\n",
       "      <td>Leta</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149987</th>\n",
       "      <td>Fugate</td>\n",
       "      <td>Michael</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149988</th>\n",
       "      <td>Brown</td>\n",
       "      <td>Sam</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149989</th>\n",
       "      <td>Pincus</td>\n",
       "      <td>Joan</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149990</th>\n",
       "      <td>Daugherty</td>\n",
       "      <td>Natalie</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149991</th>\n",
       "      <td>Toro</td>\n",
       "      <td>Rita</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149992</th>\n",
       "      <td>Tillman</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149993</th>\n",
       "      <td>Elmahdawy</td>\n",
       "      <td>Ahmed</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149994</th>\n",
       "      <td>Limkeman</td>\n",
       "      <td>Karen</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149995</th>\n",
       "      <td>Harrison</td>\n",
       "      <td>Faye</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149996</th>\n",
       "      <td>Hall</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149997</th>\n",
       "      <td>Griffin</td>\n",
       "      <td>Shawn</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149998</th>\n",
       "      <td>Murphy</td>\n",
       "      <td>Jesse</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149999</th>\n",
       "      <td>Brown</td>\n",
       "      <td>Lee</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          last_name first_name      race\n",
       "0            Walker  Elizabeth  nh_white\n",
       "1            Palmer      Alton  nh_white\n",
       "2          Mc Cleod     Alicia  nh_black\n",
       "3       Scarborough       Dale  nh_white\n",
       "4            Walker     Daniel  nh_white\n",
       "5           Collier       Seth  nh_black\n",
       "6            Martin     Robert  nh_white\n",
       "7            Turner     Sharon  nh_black\n",
       "8              Wood   Lilianna  hispanic\n",
       "9            Planck    Barbara  nh_white\n",
       "10           Burdge    Jeffrey  nh_white\n",
       "11           Zofnas      Scott  nh_white\n",
       "12          Fogarty   Kjaristy  nh_white\n",
       "13            Evans   Claretha  nh_black\n",
       "14             Lake      James  nh_white\n",
       "15            Paule       Joan  nh_white\n",
       "16        McCartney      Wayne  nh_white\n",
       "17          Estores      David     asian\n",
       "18           Bolton        Joe  nh_white\n",
       "19        Fairchild       Mark  nh_white\n",
       "20             Kung    Michael     asian\n",
       "21             Katz      Robyn  nh_white\n",
       "22         Fletcher       Omar  nh_black\n",
       "23         Feathers      Ronda  nh_black\n",
       "24          Arduine     Steven  nh_white\n",
       "25           Potter      Tanda  nh_white\n",
       "26             Long     Marcus  nh_black\n",
       "27          Dreaden      Jenny  nh_white\n",
       "28           Nadeau      Donna  nh_white\n",
       "29            Baker    Antonio  nh_black\n",
       "...             ...        ...       ...\n",
       "149970        Lucas     Cheryl  nh_black\n",
       "149971    Ritzinger   Fernando  nh_white\n",
       "149972       Miller     Angela  nh_black\n",
       "149973       Houder      David  nh_white\n",
       "149974        Mejia   Victoria  hispanic\n",
       "149975     Stirling     Jahala  nh_white\n",
       "149976   Strickland     Warner  nh_white\n",
       "149977    Brasfield    Gregory  nh_white\n",
       "149978       Rosias    Richard  nh_white\n",
       "149979       Thomas    William  nh_white\n",
       "149980    Drandakis   Mary-Ann  nh_white\n",
       "149981         Caro    Gonzalo  hispanic\n",
       "149982      Brinsko     Andrea  nh_white\n",
       "149983       Wilson      Betty  nh_black\n",
       "149984     Neelands      James  nh_white\n",
       "149985       Stroud    Barbara  nh_white\n",
       "149986       Runyon       Leta  nh_white\n",
       "149987       Fugate    Michael  nh_white\n",
       "149988        Brown        Sam  nh_white\n",
       "149989       Pincus       Joan  nh_white\n",
       "149990    Daugherty    Natalie  nh_white\n",
       "149991         Toro       Rita  nh_white\n",
       "149992      Tillman   Clifford  nh_white\n",
       "149993    Elmahdawy      Ahmed     other\n",
       "149994     Limkeman      Karen  nh_white\n",
       "149995     Harrison       Faye  nh_black\n",
       "149996         Hall      James  nh_black\n",
       "149997      Griffin      Shawn  nh_white\n",
       "149998       Murphy      Jesse  nh_black\n",
       "149999        Brown        Lee  nh_black\n",
       "\n",
       "[150000 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['race'] = df.race.apply(lambda c: rmap[c])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race\n",
       "aian              553\n",
       "asian            5354\n",
       "hispanic        10129\n",
       "multi_racial     1508\n",
       "nh_black        23223\n",
       "nh_white        93525\n",
       "other            5245\n",
       "unknown          1570\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['race']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evalulate"
   ]
  },
  {
   "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>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70327</th>\n",
       "      <td>Stamm</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113593</th>\n",
       "      <td>Hill</td>\n",
       "      <td>Ralph</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126263</th>\n",
       "      <td>Hansleat</td>\n",
       "      <td>Christopher</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107149</th>\n",
       "      <td>Nixon</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76878</th>\n",
       "      <td>Rolle</td>\n",
       "      <td>Philip</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32765</th>\n",
       "      <td>Weintraub</td>\n",
       "      <td>David</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80271</th>\n",
       "      <td>Overacker</td>\n",
       "      <td>Blake</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46545</th>\n",
       "      <td>Thomson</td>\n",
       "      <td>Michael</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117598</th>\n",
       "      <td>Ngwafor</td>\n",
       "      <td>Yolande</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67616</th>\n",
       "      <td>Martin</td>\n",
       "      <td>Bryan</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131848</th>\n",
       "      <td>Zipper</td>\n",
       "      <td>Adam</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41630</th>\n",
       "      <td>Young</td>\n",
       "      <td>Tracey</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91120</th>\n",
       "      <td>Patterson</td>\n",
       "      <td>Burton</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116486</th>\n",
       "      <td>Levy</td>\n",
       "      <td>Maya</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89834</th>\n",
       "      <td>Bembry</td>\n",
       "      <td>Marianne</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148983</th>\n",
       "      <td>Zahedi</td>\n",
       "      <td>Zahra</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96863</th>\n",
       "      <td>Bassett</td>\n",
       "      <td>Leslie</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28239</th>\n",
       "      <td>Hogan</td>\n",
       "      <td>Eric</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22262</th>\n",
       "      <td>Halbrook</td>\n",
       "      <td>Robert</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52954</th>\n",
       "      <td>Spooner</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64421</th>\n",
       "      <td>Charudattan</td>\n",
       "      <td>Savitar</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102415</th>\n",
       "      <td>Nyquist</td>\n",
       "      <td>Samuel</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49173</th>\n",
       "      <td>Bond</td>\n",
       "      <td>Robyn</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97266</th>\n",
       "      <td>Bowles</td>\n",
       "      <td>Jarrett</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129269</th>\n",
       "      <td>Carter</td>\n",
       "      <td>Koby</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99544</th>\n",
       "      <td>Swain</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46356</th>\n",
       "      <td>Crisp-Maradie</td>\n",
       "      <td>Aisling</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119170</th>\n",
       "      <td>Gage</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130172</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>Jamel</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70555</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93718</th>\n",
       "      <td>Gibby</td>\n",
       "      <td>Allyson</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4810</th>\n",
       "      <td>Kirby</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117313</th>\n",
       "      <td>Forrester</td>\n",
       "      <td>Marie</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52721</th>\n",
       "      <td>Visvanathan</td>\n",
       "      <td>Kaushik</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59853</th>\n",
       "      <td>Harbin</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106141</th>\n",
       "      <td>Hassani</td>\n",
       "      <td>Nabil</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124399</th>\n",
       "      <td>Roughton</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94226</th>\n",
       "      <td>Greene</td>\n",
       "      <td>William</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129967</th>\n",
       "      <td>Navarro</td>\n",
       "      <td>Raquel</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18546</th>\n",
       "      <td>Lowe</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124058</th>\n",
       "      <td>Williamson</td>\n",
       "      <td>Homer</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120549</th>\n",
       "      <td>Terry</td>\n",
       "      <td>Diane</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6911</th>\n",
       "      <td>Lunger</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98620</th>\n",
       "      <td>Antoine</td>\n",
       "      <td>Rachelle</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16322</th>\n",
       "      <td>Poole</td>\n",
       "      <td>Shawn</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15083</th>\n",
       "      <td>Barnum</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29691</th>\n",
       "      <td>Mason</td>\n",
       "      <td>Dinah</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56274</th>\n",
       "      <td>Antunes</td>\n",
       "      <td>Karoline</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28814</th>\n",
       "      <td>Everitt</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101125</th>\n",
       "      <td>Meeks</td>\n",
       "      <td>Gabrielle</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>Hanks</td>\n",
       "      <td>Kristy</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73853</th>\n",
       "      <td>Castellanos</td>\n",
       "      <td>Robin</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117930</th>\n",
       "      <td>Ellis</td>\n",
       "      <td>Kenneth</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17819</th>\n",
       "      <td>Lipka</td>\n",
       "      <td>Thesia</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96606</th>\n",
       "      <td>Keaton</td>\n",
       "      <td>Shanieka</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87258</th>\n",
       "      <td>Wear</td>\n",
       "      <td>Scott</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79970</th>\n",
       "      <td>Caroline</td>\n",
       "      <td>John</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107004</th>\n",
       "      <td>Prado O' Hearn</td>\n",
       "      <td>Monica</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87473</th>\n",
       "      <td>Altiere</td>\n",
       "      <td>Nell</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             last_name   first_name      race\n",
       "70327            Stamm     Patricia  nh_white\n",
       "113593            Hill        Ralph  nh_white\n",
       "126263        Hansleat  Christopher  nh_white\n",
       "107149           Nixon        James  nh_black\n",
       "76878            Rolle       Philip  nh_black\n",
       "32765        Weintraub        David  nh_white\n",
       "80271        Overacker        Blake  nh_white\n",
       "46545          Thomson      Michael  hispanic\n",
       "117598         Ngwafor      Yolande  nh_black\n",
       "67616           Martin        Bryan  nh_white\n",
       "131848          Zipper         Adam  nh_white\n",
       "41630            Young       Tracey  nh_black\n",
       "91120        Patterson       Burton  nh_white\n",
       "116486            Levy         Maya  nh_white\n",
       "89834           Bembry     Marianne  nh_white\n",
       "148983          Zahedi        Zahra  nh_white\n",
       "96863          Bassett       Leslie  nh_white\n",
       "28239            Hogan         Eric  nh_white\n",
       "22262         Halbrook       Robert  nh_white\n",
       "52954          Spooner       Joseph  nh_white\n",
       "64421      Charudattan      Savitar     asian\n",
       "102415         Nyquist       Samuel  nh_white\n",
       "49173             Bond        Robyn  nh_white\n",
       "97266           Bowles      Jarrett  nh_black\n",
       "129269          Carter         Koby  hispanic\n",
       "99544            Swain     Patricia  nh_white\n",
       "46356    Crisp-Maradie      Aisling  nh_white\n",
       "1268           Johnson        Tyler  nh_white\n",
       "119170            Gage      Timothy  nh_white\n",
       "130172          Jordan        Jamel  nh_black\n",
       "...                ...          ...       ...\n",
       "70555           Walker      Alberta  nh_black\n",
       "93718            Gibby      Allyson  nh_white\n",
       "4810             Kirby      Barbara  nh_white\n",
       "117313       Forrester        Marie  nh_white\n",
       "52721      Visvanathan      Kaushik     asian\n",
       "59853           Harbin      Matthew  nh_white\n",
       "106141         Hassani        Nabil  nh_white\n",
       "124399        Roughton        James  nh_white\n",
       "94226           Greene      William  nh_white\n",
       "129967         Navarro       Raquel     asian\n",
       "18546             Lowe       Thomas  nh_white\n",
       "124058      Williamson        Homer  nh_white\n",
       "120549           Terry        Diane  nh_white\n",
       "6911            Lunger      Barbara  nh_white\n",
       "98620          Antoine     Rachelle  nh_black\n",
       "16322            Poole        Shawn  nh_white\n",
       "15083           Barnum     Patricia  nh_black\n",
       "29691            Mason        Dinah  nh_white\n",
       "56274          Antunes     Karoline  hispanic\n",
       "28814          Everitt    Alexander  nh_white\n",
       "101125           Meeks    Gabrielle  nh_black\n",
       "654              Hanks       Kristy  nh_white\n",
       "73853      Castellanos        Robin  nh_white\n",
       "117930           Ellis      Kenneth  nh_white\n",
       "17819            Lipka       Thesia  nh_white\n",
       "96606           Keaton     Shanieka  nh_black\n",
       "87258             Wear        Scott  nh_white\n",
       "79970         Caroline         John  nh_white\n",
       "107004  Prado O' Hearn       Monica  hispanic\n",
       "87473          Altiere         Nell  nh_white\n",
       "\n",
       "[100000 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf = df[df.race.isin(['asian', 'hispanic', 'nh_black', 'nh_white'])].sample(SAMPLE, random_state=10)\n",
    "sdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from ethnicolr import pred_fl_reg_name, pred_fl_reg_soundname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method pred_fl_reg_name in module ethnicolr.pred_fl_reg_name:\n",
      "\n",
      "pred_fl_reg_name(df, lname_col, fname_col) method of builtins.type instance\n",
      "    Predict the race/ethnicity by the full name using Florida voter model.\n",
      "    \n",
      "    Using the Florida voter full name model to predict the race/ethnicity of\n",
      "    the input DataFrame.\n",
      "    \n",
      "    Args:\n",
      "        df (:obj:`DataFrame`): Pandas DataFrame containing the last name and\n",
      "            first name column.\n",
      "        lname_col (str or int): Column's name or location of the last name in\n",
      "            DataFrame.\n",
      "        fname_col (str or int): Column's name or location of the first name in\n",
      "            DataFrame.\n",
      "    \n",
      "    Returns:\n",
      "        DataFrame: Pandas DataFrame with additional columns:\n",
      "            - `race` the predict result\n",
      "            - Additional columns for probability of each classes.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pred_fl_reg_name)"
   ]
  },
  {
   "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>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race_orig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70327</th>\n",
       "      <td>Stamm</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113593</th>\n",
       "      <td>Hill</td>\n",
       "      <td>Ralph</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126263</th>\n",
       "      <td>Hansleat</td>\n",
       "      <td>Christopher</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107149</th>\n",
       "      <td>Nixon</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76878</th>\n",
       "      <td>Rolle</td>\n",
       "      <td>Philip</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32765</th>\n",
       "      <td>Weintraub</td>\n",
       "      <td>David</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80271</th>\n",
       "      <td>Overacker</td>\n",
       "      <td>Blake</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46545</th>\n",
       "      <td>Thomson</td>\n",
       "      <td>Michael</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117598</th>\n",
       "      <td>Ngwafor</td>\n",
       "      <td>Yolande</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67616</th>\n",
       "      <td>Martin</td>\n",
       "      <td>Bryan</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131848</th>\n",
       "      <td>Zipper</td>\n",
       "      <td>Adam</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41630</th>\n",
       "      <td>Young</td>\n",
       "      <td>Tracey</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91120</th>\n",
       "      <td>Patterson</td>\n",
       "      <td>Burton</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116486</th>\n",
       "      <td>Levy</td>\n",
       "      <td>Maya</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89834</th>\n",
       "      <td>Bembry</td>\n",
       "      <td>Marianne</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148983</th>\n",
       "      <td>Zahedi</td>\n",
       "      <td>Zahra</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96863</th>\n",
       "      <td>Bassett</td>\n",
       "      <td>Leslie</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28239</th>\n",
       "      <td>Hogan</td>\n",
       "      <td>Eric</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22262</th>\n",
       "      <td>Halbrook</td>\n",
       "      <td>Robert</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52954</th>\n",
       "      <td>Spooner</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64421</th>\n",
       "      <td>Charudattan</td>\n",
       "      <td>Savitar</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102415</th>\n",
       "      <td>Nyquist</td>\n",
       "      <td>Samuel</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49173</th>\n",
       "      <td>Bond</td>\n",
       "      <td>Robyn</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97266</th>\n",
       "      <td>Bowles</td>\n",
       "      <td>Jarrett</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129269</th>\n",
       "      <td>Carter</td>\n",
       "      <td>Koby</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99544</th>\n",
       "      <td>Swain</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46356</th>\n",
       "      <td>Crisp-Maradie</td>\n",
       "      <td>Aisling</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119170</th>\n",
       "      <td>Gage</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130172</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>Jamel</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70555</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93718</th>\n",
       "      <td>Gibby</td>\n",
       "      <td>Allyson</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4810</th>\n",
       "      <td>Kirby</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117313</th>\n",
       "      <td>Forrester</td>\n",
       "      <td>Marie</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52721</th>\n",
       "      <td>Visvanathan</td>\n",
       "      <td>Kaushik</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59853</th>\n",
       "      <td>Harbin</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106141</th>\n",
       "      <td>Hassani</td>\n",
       "      <td>Nabil</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124399</th>\n",
       "      <td>Roughton</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94226</th>\n",
       "      <td>Greene</td>\n",
       "      <td>William</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129967</th>\n",
       "      <td>Navarro</td>\n",
       "      <td>Raquel</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18546</th>\n",
       "      <td>Lowe</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124058</th>\n",
       "      <td>Williamson</td>\n",
       "      <td>Homer</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120549</th>\n",
       "      <td>Terry</td>\n",
       "      <td>Diane</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6911</th>\n",
       "      <td>Lunger</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98620</th>\n",
       "      <td>Antoine</td>\n",
       "      <td>Rachelle</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16322</th>\n",
       "      <td>Poole</td>\n",
       "      <td>Shawn</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15083</th>\n",
       "      <td>Barnum</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29691</th>\n",
       "      <td>Mason</td>\n",
       "      <td>Dinah</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56274</th>\n",
       "      <td>Antunes</td>\n",
       "      <td>Karoline</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28814</th>\n",
       "      <td>Everitt</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101125</th>\n",
       "      <td>Meeks</td>\n",
       "      <td>Gabrielle</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>Hanks</td>\n",
       "      <td>Kristy</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73853</th>\n",
       "      <td>Castellanos</td>\n",
       "      <td>Robin</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117930</th>\n",
       "      <td>Ellis</td>\n",
       "      <td>Kenneth</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17819</th>\n",
       "      <td>Lipka</td>\n",
       "      <td>Thesia</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96606</th>\n",
       "      <td>Keaton</td>\n",
       "      <td>Shanieka</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87258</th>\n",
       "      <td>Wear</td>\n",
       "      <td>Scott</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79970</th>\n",
       "      <td>Caroline</td>\n",
       "      <td>John</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107004</th>\n",
       "      <td>Prado O' Hearn</td>\n",
       "      <td>Monica</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87473</th>\n",
       "      <td>Altiere</td>\n",
       "      <td>Nell</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             last_name   first_name race_orig\n",
       "70327            Stamm     Patricia  nh_white\n",
       "113593            Hill        Ralph  nh_white\n",
       "126263        Hansleat  Christopher  nh_white\n",
       "107149           Nixon        James  nh_black\n",
       "76878            Rolle       Philip  nh_black\n",
       "32765        Weintraub        David  nh_white\n",
       "80271        Overacker        Blake  nh_white\n",
       "46545          Thomson      Michael  hispanic\n",
       "117598         Ngwafor      Yolande  nh_black\n",
       "67616           Martin        Bryan  nh_white\n",
       "131848          Zipper         Adam  nh_white\n",
       "41630            Young       Tracey  nh_black\n",
       "91120        Patterson       Burton  nh_white\n",
       "116486            Levy         Maya  nh_white\n",
       "89834           Bembry     Marianne  nh_white\n",
       "148983          Zahedi        Zahra  nh_white\n",
       "96863          Bassett       Leslie  nh_white\n",
       "28239            Hogan         Eric  nh_white\n",
       "22262         Halbrook       Robert  nh_white\n",
       "52954          Spooner       Joseph  nh_white\n",
       "64421      Charudattan      Savitar     asian\n",
       "102415         Nyquist       Samuel  nh_white\n",
       "49173             Bond        Robyn  nh_white\n",
       "97266           Bowles      Jarrett  nh_black\n",
       "129269          Carter         Koby  hispanic\n",
       "99544            Swain     Patricia  nh_white\n",
       "46356    Crisp-Maradie      Aisling  nh_white\n",
       "1268           Johnson        Tyler  nh_white\n",
       "119170            Gage      Timothy  nh_white\n",
       "130172          Jordan        Jamel  nh_black\n",
       "...                ...          ...       ...\n",
       "70555           Walker      Alberta  nh_black\n",
       "93718            Gibby      Allyson  nh_white\n",
       "4810             Kirby      Barbara  nh_white\n",
       "117313       Forrester        Marie  nh_white\n",
       "52721      Visvanathan      Kaushik     asian\n",
       "59853           Harbin      Matthew  nh_white\n",
       "106141         Hassani        Nabil  nh_white\n",
       "124399        Roughton        James  nh_white\n",
       "94226           Greene      William  nh_white\n",
       "129967         Navarro       Raquel     asian\n",
       "18546             Lowe       Thomas  nh_white\n",
       "124058      Williamson        Homer  nh_white\n",
       "120549           Terry        Diane  nh_white\n",
       "6911            Lunger      Barbara  nh_white\n",
       "98620          Antoine     Rachelle  nh_black\n",
       "16322            Poole        Shawn  nh_white\n",
       "15083           Barnum     Patricia  nh_black\n",
       "29691            Mason        Dinah  nh_white\n",
       "56274          Antunes     Karoline  hispanic\n",
       "28814          Everitt    Alexander  nh_white\n",
       "101125           Meeks    Gabrielle  nh_black\n",
       "654              Hanks       Kristy  nh_white\n",
       "73853      Castellanos        Robin  nh_white\n",
       "117930           Ellis      Kenneth  nh_white\n",
       "17819            Lipka       Thesia  nh_white\n",
       "96606           Keaton     Shanieka  nh_black\n",
       "87258             Wear        Scott  nh_white\n",
       "79970         Caroline         John  nh_white\n",
       "107004  Prado O' Hearn       Monica  hispanic\n",
       "87473          Altiere         Nell  nh_white\n",
       "\n",
       "[100000 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf.columns = ['last_name', 'first_name', 'race_orig']\n",
    "sdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ethnicolr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\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>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race_orig</th>\n",
       "      <th>race</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70327</th>\n",
       "      <td>Stamm</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001412</td>\n",
       "      <td>0.010637</td>\n",
       "      <td>0.046354</td>\n",
       "      <td>0.941598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113593</th>\n",
       "      <td>Hill</td>\n",
       "      <td>Ralph</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.008628</td>\n",
       "      <td>0.258736</td>\n",
       "      <td>0.728802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126263</th>\n",
       "      <td>Hansleat</td>\n",
       "      <td>Christopher</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.004957</td>\n",
       "      <td>0.015766</td>\n",
       "      <td>0.036128</td>\n",
       "      <td>0.943149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107149</th>\n",
       "      <td>Nixon</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002323</td>\n",
       "      <td>0.001857</td>\n",
       "      <td>0.232952</td>\n",
       "      <td>0.762868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76878</th>\n",
       "      <td>Rolle</td>\n",
       "      <td>Philip</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006509</td>\n",
       "      <td>0.027061</td>\n",
       "      <td>0.168978</td>\n",
       "      <td>0.797452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32765</th>\n",
       "      <td>Weintraub</td>\n",
       "      <td>David</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001058</td>\n",
       "      <td>0.008580</td>\n",
       "      <td>0.001835</td>\n",
       "      <td>0.988527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80271</th>\n",
       "      <td>Overacker</td>\n",
       "      <td>Blake</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.000873</td>\n",
       "      <td>0.010072</td>\n",
       "      <td>0.010636</td>\n",
       "      <td>0.978419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46545</th>\n",
       "      <td>Thomson</td>\n",
       "      <td>Michael</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002091</td>\n",
       "      <td>0.006707</td>\n",
       "      <td>0.109481</td>\n",
       "      <td>0.881721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117598</th>\n",
       "      <td>Ngwafor</td>\n",
       "      <td>Yolande</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.553349</td>\n",
       "      <td>0.024487</td>\n",
       "      <td>0.277182</td>\n",
       "      <td>0.144982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67616</th>\n",
       "      <td>Martin</td>\n",
       "      <td>Bryan</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003680</td>\n",
       "      <td>0.028832</td>\n",
       "      <td>0.087668</td>\n",
       "      <td>0.879820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131848</th>\n",
       "      <td>Zipper</td>\n",
       "      <td>Adam</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001708</td>\n",
       "      <td>0.005752</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>0.991450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41630</th>\n",
       "      <td>Young</td>\n",
       "      <td>Tracey</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003558</td>\n",
       "      <td>0.004002</td>\n",
       "      <td>0.319959</td>\n",
       "      <td>0.672481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91120</th>\n",
       "      <td>Patterson</td>\n",
       "      <td>Burton</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002981</td>\n",
       "      <td>0.015022</td>\n",
       "      <td>0.190035</td>\n",
       "      <td>0.791962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116486</th>\n",
       "      <td>Levy</td>\n",
       "      <td>Maya</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.055772</td>\n",
       "      <td>0.086716</td>\n",
       "      <td>0.273967</td>\n",
       "      <td>0.583545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89834</th>\n",
       "      <td>Bembry</td>\n",
       "      <td>Marianne</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.004096</td>\n",
       "      <td>0.009856</td>\n",
       "      <td>0.063508</td>\n",
       "      <td>0.922540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148983</th>\n",
       "      <td>Zahedi</td>\n",
       "      <td>Zahra</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.126370</td>\n",
       "      <td>0.184613</td>\n",
       "      <td>0.060738</td>\n",
       "      <td>0.628279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96863</th>\n",
       "      <td>Bassett</td>\n",
       "      <td>Leslie</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>0.015382</td>\n",
       "      <td>0.067147</td>\n",
       "      <td>0.914641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28239</th>\n",
       "      <td>Hogan</td>\n",
       "      <td>Eric</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003634</td>\n",
       "      <td>0.007754</td>\n",
       "      <td>0.076894</td>\n",
       "      <td>0.911718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22262</th>\n",
       "      <td>Halbrook</td>\n",
       "      <td>Robert</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>0.018786</td>\n",
       "      <td>0.977048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52954</th>\n",
       "      <td>Spooner</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002368</td>\n",
       "      <td>0.010390</td>\n",
       "      <td>0.065687</td>\n",
       "      <td>0.921555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64421</th>\n",
       "      <td>Charudattan</td>\n",
       "      <td>Savitar</td>\n",
       "      <td>asian</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.908178</td>\n",
       "      <td>0.007267</td>\n",
       "      <td>0.008689</td>\n",
       "      <td>0.075866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102415</th>\n",
       "      <td>Nyquist</td>\n",
       "      <td>Samuel</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006289</td>\n",
       "      <td>0.030441</td>\n",
       "      <td>0.029402</td>\n",
       "      <td>0.933868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49173</th>\n",
       "      <td>Bond</td>\n",
       "      <td>Robyn</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003069</td>\n",
       "      <td>0.005616</td>\n",
       "      <td>0.027489</td>\n",
       "      <td>0.963826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97266</th>\n",
       "      <td>Bowles</td>\n",
       "      <td>Jarrett</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.000959</td>\n",
       "      <td>0.005905</td>\n",
       "      <td>0.166097</td>\n",
       "      <td>0.827039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129269</th>\n",
       "      <td>Carter</td>\n",
       "      <td>Koby</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.008078</td>\n",
       "      <td>0.012652</td>\n",
       "      <td>0.180947</td>\n",
       "      <td>0.798323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99544</th>\n",
       "      <td>Swain</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001622</td>\n",
       "      <td>0.011284</td>\n",
       "      <td>0.019899</td>\n",
       "      <td>0.967196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46356</th>\n",
       "      <td>Crisp-Maradie</td>\n",
       "      <td>Aisling</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.167100</td>\n",
       "      <td>0.094908</td>\n",
       "      <td>0.362198</td>\n",
       "      <td>0.375793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002274</td>\n",
       "      <td>0.008617</td>\n",
       "      <td>0.128224</td>\n",
       "      <td>0.860885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119170</th>\n",
       "      <td>Gage</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.005243</td>\n",
       "      <td>0.030062</td>\n",
       "      <td>0.963695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130172</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>Jamel</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.002271</td>\n",
       "      <td>0.002076</td>\n",
       "      <td>0.766653</td>\n",
       "      <td>0.229001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70555</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.006479</td>\n",
       "      <td>0.024083</td>\n",
       "      <td>0.671138</td>\n",
       "      <td>0.298300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93718</th>\n",
       "      <td>Gibby</td>\n",
       "      <td>Allyson</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002130</td>\n",
       "      <td>0.007453</td>\n",
       "      <td>0.037111</td>\n",
       "      <td>0.953306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4810</th>\n",
       "      <td>Kirby</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001567</td>\n",
       "      <td>0.006913</td>\n",
       "      <td>0.034497</td>\n",
       "      <td>0.957023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117313</th>\n",
       "      <td>Forrester</td>\n",
       "      <td>Marie</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.008028</td>\n",
       "      <td>0.004680</td>\n",
       "      <td>0.564960</td>\n",
       "      <td>0.422332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52721</th>\n",
       "      <td>Visvanathan</td>\n",
       "      <td>Kaushik</td>\n",
       "      <td>asian</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.932509</td>\n",
       "      <td>0.002146</td>\n",
       "      <td>0.045917</td>\n",
       "      <td>0.019429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59853</th>\n",
       "      <td>Harbin</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002334</td>\n",
       "      <td>0.010585</td>\n",
       "      <td>0.025707</td>\n",
       "      <td>0.961373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106141</th>\n",
       "      <td>Hassani</td>\n",
       "      <td>Nabil</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.592280</td>\n",
       "      <td>0.059671</td>\n",
       "      <td>0.009719</td>\n",
       "      <td>0.338329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124399</th>\n",
       "      <td>Roughton</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002367</td>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.124546</td>\n",
       "      <td>0.870988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94226</th>\n",
       "      <td>Greene</td>\n",
       "      <td>William</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.000729</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.045775</td>\n",
       "      <td>0.949097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129967</th>\n",
       "      <td>Navarro</td>\n",
       "      <td>Raquel</td>\n",
       "      <td>asian</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.020107</td>\n",
       "      <td>0.923608</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.045825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18546</th>\n",
       "      <td>Lowe</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002090</td>\n",
       "      <td>0.003674</td>\n",
       "      <td>0.032203</td>\n",
       "      <td>0.962033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124058</th>\n",
       "      <td>Williamson</td>\n",
       "      <td>Homer</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.005782</td>\n",
       "      <td>0.007955</td>\n",
       "      <td>0.090098</td>\n",
       "      <td>0.896166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120549</th>\n",
       "      <td>Terry</td>\n",
       "      <td>Diane</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.007299</td>\n",
       "      <td>0.128881</td>\n",
       "      <td>0.861786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6911</th>\n",
       "      <td>Lunger</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001286</td>\n",
       "      <td>0.008791</td>\n",
       "      <td>0.010980</td>\n",
       "      <td>0.978943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98620</th>\n",
       "      <td>Antoine</td>\n",
       "      <td>Rachelle</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.006417</td>\n",
       "      <td>0.023499</td>\n",
       "      <td>0.571437</td>\n",
       "      <td>0.398646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16322</th>\n",
       "      <td>Poole</td>\n",
       "      <td>Shawn</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003129</td>\n",
       "      <td>0.002525</td>\n",
       "      <td>0.062497</td>\n",
       "      <td>0.931849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15083</th>\n",
       "      <td>Barnum</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001868</td>\n",
       "      <td>0.011281</td>\n",
       "      <td>0.035789</td>\n",
       "      <td>0.951062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29691</th>\n",
       "      <td>Mason</td>\n",
       "      <td>Dinah</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.007400</td>\n",
       "      <td>0.030338</td>\n",
       "      <td>0.086016</td>\n",
       "      <td>0.876247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56274</th>\n",
       "      <td>Antunes</td>\n",
       "      <td>Karoline</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.038044</td>\n",
       "      <td>0.314749</td>\n",
       "      <td>0.144770</td>\n",
       "      <td>0.502437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28814</th>\n",
       "      <td>Everitt</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.011950</td>\n",
       "      <td>0.052154</td>\n",
       "      <td>0.093300</td>\n",
       "      <td>0.842596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101125</th>\n",
       "      <td>Meeks</td>\n",
       "      <td>Gabrielle</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.007446</td>\n",
       "      <td>0.020171</td>\n",
       "      <td>0.241123</td>\n",
       "      <td>0.731260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>Hanks</td>\n",
       "      <td>Kristy</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.010387</td>\n",
       "      <td>0.017741</td>\n",
       "      <td>0.018548</td>\n",
       "      <td>0.953324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73853</th>\n",
       "      <td>Castellanos</td>\n",
       "      <td>Robin</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.026423</td>\n",
       "      <td>0.326437</td>\n",
       "      <td>0.010010</td>\n",
       "      <td>0.637130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117930</th>\n",
       "      <td>Ellis</td>\n",
       "      <td>Kenneth</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.000902</td>\n",
       "      <td>0.002894</td>\n",
       "      <td>0.242056</td>\n",
       "      <td>0.754148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17819</th>\n",
       "      <td>Lipka</td>\n",
       "      <td>Thesia</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.031543</td>\n",
       "      <td>0.076913</td>\n",
       "      <td>0.069105</td>\n",
       "      <td>0.822440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96606</th>\n",
       "      <td>Keaton</td>\n",
       "      <td>Shanieka</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.001077</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.993908</td>\n",
       "      <td>0.004875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87258</th>\n",
       "      <td>Wear</td>\n",
       "      <td>Scott</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.000522</td>\n",
       "      <td>0.002279</td>\n",
       "      <td>0.017176</td>\n",
       "      <td>0.980023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79970</th>\n",
       "      <td>Caroline</td>\n",
       "      <td>John</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001699</td>\n",
       "      <td>0.006172</td>\n",
       "      <td>0.031807</td>\n",
       "      <td>0.960322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107004</th>\n",
       "      <td>Prado O' Hearn</td>\n",
       "      <td>Monica</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.020216</td>\n",
       "      <td>0.748698</td>\n",
       "      <td>0.019251</td>\n",
       "      <td>0.211834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87473</th>\n",
       "      <td>Altiere</td>\n",
       "      <td>Nell</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006803</td>\n",
       "      <td>0.025914</td>\n",
       "      <td>0.127528</td>\n",
       "      <td>0.839754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             last_name   first_name race_orig      race     asian  hispanic  \\\n",
       "70327            Stamm     Patricia  nh_white  nh_white  0.001412  0.010637   \n",
       "113593            Hill        Ralph  nh_white  nh_white  0.003834  0.008628   \n",
       "126263        Hansleat  Christopher  nh_white  nh_white  0.004957  0.015766   \n",
       "107149           Nixon        James  nh_black  nh_white  0.002323  0.001857   \n",
       "76878            Rolle       Philip  nh_black  nh_white  0.006509  0.027061   \n",
       "32765        Weintraub        David  nh_white  nh_white  0.001058  0.008580   \n",
       "80271        Overacker        Blake  nh_white  nh_white  0.000873  0.010072   \n",
       "46545          Thomson      Michael  hispanic  nh_white  0.002091  0.006707   \n",
       "117598         Ngwafor      Yolande  nh_black     asian  0.553349  0.024487   \n",
       "67616           Martin        Bryan  nh_white  nh_white  0.003680  0.028832   \n",
       "131848          Zipper         Adam  nh_white  nh_white  0.001708  0.005752   \n",
       "41630            Young       Tracey  nh_black  nh_white  0.003558  0.004002   \n",
       "91120        Patterson       Burton  nh_white  nh_white  0.002981  0.015022   \n",
       "116486            Levy         Maya  nh_white  nh_white  0.055772  0.086716   \n",
       "89834           Bembry     Marianne  nh_white  nh_white  0.004096  0.009856   \n",
       "148983          Zahedi        Zahra  nh_white  nh_white  0.126370  0.184613   \n",
       "96863          Bassett       Leslie  nh_white  nh_white  0.002831  0.015382   \n",
       "28239            Hogan         Eric  nh_white  nh_white  0.003634  0.007754   \n",
       "22262         Halbrook       Robert  nh_white  nh_white  0.001099  0.003067   \n",
       "52954          Spooner       Joseph  nh_white  nh_white  0.002368  0.010390   \n",
       "64421      Charudattan      Savitar     asian     asian  0.908178  0.007267   \n",
       "102415         Nyquist       Samuel  nh_white  nh_white  0.006289  0.030441   \n",
       "49173             Bond        Robyn  nh_white  nh_white  0.003069  0.005616   \n",
       "97266           Bowles      Jarrett  nh_black  nh_white  0.000959  0.005905   \n",
       "129269          Carter         Koby  hispanic  nh_white  0.008078  0.012652   \n",
       "99544            Swain     Patricia  nh_white  nh_white  0.001622  0.011284   \n",
       "46356    Crisp-Maradie      Aisling  nh_white  nh_white  0.167100  0.094908   \n",
       "1268           Johnson        Tyler  nh_white  nh_white  0.002274  0.008617   \n",
       "119170            Gage      Timothy  nh_white  nh_white  0.001000  0.005243   \n",
       "130172          Jordan        Jamel  nh_black  nh_black  0.002271  0.002076   \n",
       "...                ...          ...       ...       ...       ...       ...   \n",
       "70555           Walker      Alberta  nh_black  nh_black  0.006479  0.024083   \n",
       "93718            Gibby      Allyson  nh_white  nh_white  0.002130  0.007453   \n",
       "4810             Kirby      Barbara  nh_white  nh_white  0.001567  0.006913   \n",
       "117313       Forrester        Marie  nh_white  nh_black  0.008028  0.004680   \n",
       "52721      Visvanathan      Kaushik     asian     asian  0.932509  0.002146   \n",
       "59853           Harbin      Matthew  nh_white  nh_white  0.002334  0.010585   \n",
       "106141         Hassani        Nabil  nh_white     asian  0.592280  0.059671   \n",
       "124399        Roughton        James  nh_white  nh_white  0.002367  0.002100   \n",
       "94226           Greene      William  nh_white  nh_white  0.000729  0.004398   \n",
       "129967         Navarro       Raquel     asian  hispanic  0.020107  0.923608   \n",
       "18546             Lowe       Thomas  nh_white  nh_white  0.002090  0.003674   \n",
       "124058      Williamson        Homer  nh_white  nh_white  0.005782  0.007955   \n",
       "120549           Terry        Diane  nh_white  nh_white  0.002034  0.007299   \n",
       "6911            Lunger      Barbara  nh_white  nh_white  0.001286  0.008791   \n",
       "98620          Antoine     Rachelle  nh_black  nh_black  0.006417  0.023499   \n",
       "16322            Poole        Shawn  nh_white  nh_white  0.003129  0.002525   \n",
       "15083           Barnum     Patricia  nh_black  nh_white  0.001868  0.011281   \n",
       "29691            Mason        Dinah  nh_white  nh_white  0.007400  0.030338   \n",
       "56274          Antunes     Karoline  hispanic  nh_white  0.038044  0.314749   \n",
       "28814          Everitt    Alexander  nh_white  nh_white  0.011950  0.052154   \n",
       "101125           Meeks    Gabrielle  nh_black  nh_white  0.007446  0.020171   \n",
       "654              Hanks       Kristy  nh_white  nh_white  0.010387  0.017741   \n",
       "73853      Castellanos        Robin  nh_white  nh_white  0.026423  0.326437   \n",
       "117930           Ellis      Kenneth  nh_white  nh_white  0.000902  0.002894   \n",
       "17819            Lipka       Thesia  nh_white  nh_white  0.031543  0.076913   \n",
       "96606           Keaton     Shanieka  nh_black  nh_black  0.001077  0.000140   \n",
       "87258             Wear        Scott  nh_white  nh_white  0.000522  0.002279   \n",
       "79970         Caroline         John  nh_white  nh_white  0.001699  0.006172   \n",
       "107004  Prado O' Hearn       Monica  hispanic  hispanic  0.020216  0.748698   \n",
       "87473          Altiere         Nell  nh_white  nh_white  0.006803  0.025914   \n",
       "\n",
       "        nh_black  nh_white  \n",
       "70327   0.046354  0.941598  \n",
       "113593  0.258736  0.728802  \n",
       "126263  0.036128  0.943149  \n",
       "107149  0.232952  0.762868  \n",
       "76878   0.168978  0.797452  \n",
       "32765   0.001835  0.988527  \n",
       "80271   0.010636  0.978419  \n",
       "46545   0.109481  0.881721  \n",
       "117598  0.277182  0.144982  \n",
       "67616   0.087668  0.879820  \n",
       "131848  0.001090  0.991450  \n",
       "41630   0.319959  0.672481  \n",
       "91120   0.190035  0.791962  \n",
       "116486  0.273967  0.583545  \n",
       "89834   0.063508  0.922540  \n",
       "148983  0.060738  0.628279  \n",
       "96863   0.067147  0.914641  \n",
       "28239   0.076894  0.911718  \n",
       "22262   0.018786  0.977048  \n",
       "52954   0.065687  0.921555  \n",
       "64421   0.008689  0.075866  \n",
       "102415  0.029402  0.933868  \n",
       "49173   0.027489  0.963826  \n",
       "97266   0.166097  0.827039  \n",
       "129269  0.180947  0.798323  \n",
       "99544   0.019899  0.967196  \n",
       "46356   0.362198  0.375793  \n",
       "1268    0.128224  0.860885  \n",
       "119170  0.030062  0.963695  \n",
       "130172  0.766653  0.229001  \n",
       "...          ...       ...  \n",
       "70555   0.671138  0.298300  \n",
       "93718   0.037111  0.953306  \n",
       "4810    0.034497  0.957023  \n",
       "117313  0.564960  0.422332  \n",
       "52721   0.045917  0.019429  \n",
       "59853   0.025707  0.961373  \n",
       "106141  0.009719  0.338329  \n",
       "124399  0.124546  0.870988  \n",
       "94226   0.045775  0.949097  \n",
       "129967  0.010460  0.045825  \n",
       "18546   0.032203  0.962033  \n",
       "124058  0.090098  0.896166  \n",
       "120549  0.128881  0.861786  \n",
       "6911    0.010980  0.978943  \n",
       "98620   0.571437  0.398646  \n",
       "16322   0.062497  0.931849  \n",
       "15083   0.035789  0.951062  \n",
       "29691   0.086016  0.876247  \n",
       "56274   0.144770  0.502437  \n",
       "28814   0.093300  0.842596  \n",
       "101125  0.241123  0.731260  \n",
       "654     0.018548  0.953324  \n",
       "73853   0.010010  0.637130  \n",
       "117930  0.242056  0.754148  \n",
       "17819   0.069105  0.822440  \n",
       "96606   0.993908  0.004875  \n",
       "87258   0.017176  0.980023  \n",
       "79970   0.031807  0.960322  \n",
       "107004  0.019251  0.211834  \n",
       "87473   0.127528  0.839754  \n",
       "\n",
       "[100000 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edf = pred_fl_reg_name(sdf, 'last_name', 'first_name')\n",
    "edf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race_orig</th>\n",
       "      <th>race</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107149</th>\n",
       "      <td>Nixon</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002323</td>\n",
       "      <td>0.001857</td>\n",
       "      <td>0.232952</td>\n",
       "      <td>0.762868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76878</th>\n",
       "      <td>Rolle</td>\n",
       "      <td>Philip</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006509</td>\n",
       "      <td>0.027061</td>\n",
       "      <td>0.168978</td>\n",
       "      <td>0.797452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46545</th>\n",
       "      <td>Thomson</td>\n",
       "      <td>Michael</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002091</td>\n",
       "      <td>0.006707</td>\n",
       "      <td>0.109481</td>\n",
       "      <td>0.881721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117598</th>\n",
       "      <td>Ngwafor</td>\n",
       "      <td>Yolande</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.553349</td>\n",
       "      <td>0.024487</td>\n",
       "      <td>0.277182</td>\n",
       "      <td>0.144982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41630</th>\n",
       "      <td>Young</td>\n",
       "      <td>Tracey</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003558</td>\n",
       "      <td>0.004002</td>\n",
       "      <td>0.319959</td>\n",
       "      <td>0.672481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97266</th>\n",
       "      <td>Bowles</td>\n",
       "      <td>Jarrett</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.000959</td>\n",
       "      <td>0.005905</td>\n",
       "      <td>0.166097</td>\n",
       "      <td>0.827039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129269</th>\n",
       "      <td>Carter</td>\n",
       "      <td>Koby</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.008078</td>\n",
       "      <td>0.012652</td>\n",
       "      <td>0.180947</td>\n",
       "      <td>0.798323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8257</th>\n",
       "      <td>Brown</td>\n",
       "      <td>Murray</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.002375</td>\n",
       "      <td>0.004668</td>\n",
       "      <td>0.573051</td>\n",
       "      <td>0.419906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66423</th>\n",
       "      <td>Terrell</td>\n",
       "      <td>Gabriel</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.012548</td>\n",
       "      <td>0.310676</td>\n",
       "      <td>0.134175</td>\n",
       "      <td>0.542601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148930</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Kirstin</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.007258</td>\n",
       "      <td>0.014146</td>\n",
       "      <td>0.067739</td>\n",
       "      <td>0.910856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25404</th>\n",
       "      <td>Fegins</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003001</td>\n",
       "      <td>0.023684</td>\n",
       "      <td>0.099448</td>\n",
       "      <td>0.873867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68783</th>\n",
       "      <td>Coronel</td>\n",
       "      <td>Bradley</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.132714</td>\n",
       "      <td>0.012440</td>\n",
       "      <td>0.851565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102610</th>\n",
       "      <td>Cohen</td>\n",
       "      <td>Zacharias</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.017844</td>\n",
       "      <td>0.266580</td>\n",
       "      <td>0.069537</td>\n",
       "      <td>0.646039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109950</th>\n",
       "      <td>Van Steenburgh</td>\n",
       "      <td>Melanie</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006537</td>\n",
       "      <td>0.020603</td>\n",
       "      <td>0.003942</td>\n",
       "      <td>0.968919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73909</th>\n",
       "      <td>Cruger</td>\n",
       "      <td>Kenneth</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001169</td>\n",
       "      <td>0.004573</td>\n",
       "      <td>0.211129</td>\n",
       "      <td>0.783130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14221</th>\n",
       "      <td>McCoy</td>\n",
       "      <td>Lafawnda</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.002657</td>\n",
       "      <td>0.005162</td>\n",
       "      <td>0.947310</td>\n",
       "      <td>0.044871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24797</th>\n",
       "      <td>Reynolds</td>\n",
       "      <td>Wilfred</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.018307</td>\n",
       "      <td>0.446174</td>\n",
       "      <td>0.531758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93979</th>\n",
       "      <td>Hatcher</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002819</td>\n",
       "      <td>0.001882</td>\n",
       "      <td>0.156048</td>\n",
       "      <td>0.839250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101230</th>\n",
       "      <td>Rivas</td>\n",
       "      <td>Steven</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.009003</td>\n",
       "      <td>0.674322</td>\n",
       "      <td>0.007249</td>\n",
       "      <td>0.309426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35759</th>\n",
       "      <td>Boyd</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002546</td>\n",
       "      <td>0.002274</td>\n",
       "      <td>0.134031</td>\n",
       "      <td>0.861149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89329</th>\n",
       "      <td>Lackey</td>\n",
       "      <td>Andria</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.007762</td>\n",
       "      <td>0.047902</td>\n",
       "      <td>0.380618</td>\n",
       "      <td>0.563718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104045</th>\n",
       "      <td>Eder</td>\n",
       "      <td>Joean</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.004281</td>\n",
       "      <td>0.016747</td>\n",
       "      <td>0.038774</td>\n",
       "      <td>0.940198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138804</th>\n",
       "      <td>Martinez</td>\n",
       "      <td>Evelyn</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.006776</td>\n",
       "      <td>0.869072</td>\n",
       "      <td>0.005672</td>\n",
       "      <td>0.118480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30936</th>\n",
       "      <td>Ginory</td>\n",
       "      <td>Almari</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.013781</td>\n",
       "      <td>0.078765</td>\n",
       "      <td>0.591831</td>\n",
       "      <td>0.315624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112844</th>\n",
       "      <td>Carter</td>\n",
       "      <td>Mauricio</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.004041</td>\n",
       "      <td>0.342353</td>\n",
       "      <td>0.522892</td>\n",
       "      <td>0.130714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117291</th>\n",
       "      <td>Oates</td>\n",
       "      <td>Alexis</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.004619</td>\n",
       "      <td>0.032604</td>\n",
       "      <td>0.463219</td>\n",
       "      <td>0.499558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97257</th>\n",
       "      <td>Conerly</td>\n",
       "      <td>Albert</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001255</td>\n",
       "      <td>0.008268</td>\n",
       "      <td>0.255884</td>\n",
       "      <td>0.734593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112394</th>\n",
       "      <td>Albritton</td>\n",
       "      <td>Evelyn</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.007516</td>\n",
       "      <td>0.034918</td>\n",
       "      <td>0.368678</td>\n",
       "      <td>0.588888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105095</th>\n",
       "      <td>Rhodes</td>\n",
       "      <td>Candace</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.005207</td>\n",
       "      <td>0.009166</td>\n",
       "      <td>0.235040</td>\n",
       "      <td>0.750588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77475</th>\n",
       "      <td>Albistor</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003673</td>\n",
       "      <td>0.032984</td>\n",
       "      <td>0.125138</td>\n",
       "      <td>0.838206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3937</th>\n",
       "      <td>Hill</td>\n",
       "      <td>Phoeby</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.009833</td>\n",
       "      <td>0.012281</td>\n",
       "      <td>0.280006</td>\n",
       "      <td>0.697879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74901</th>\n",
       "      <td>Hunt</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006932</td>\n",
       "      <td>0.016531</td>\n",
       "      <td>0.084982</td>\n",
       "      <td>0.891555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133600</th>\n",
       "      <td>Toyloy</td>\n",
       "      <td>Monique</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.020436</td>\n",
       "      <td>0.176052</td>\n",
       "      <td>0.257847</td>\n",
       "      <td>0.545664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50519</th>\n",
       "      <td>Campbell</td>\n",
       "      <td>Gwendolyn</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.005208</td>\n",
       "      <td>0.008410</td>\n",
       "      <td>0.750798</td>\n",
       "      <td>0.235583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143135</th>\n",
       "      <td>Pomeranz</td>\n",
       "      <td>Ilene</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.017881</td>\n",
       "      <td>0.578683</td>\n",
       "      <td>0.008575</td>\n",
       "      <td>0.394861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27807</th>\n",
       "      <td>Kirk</td>\n",
       "      <td>Shirlene</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.005087</td>\n",
       "      <td>0.008138</td>\n",
       "      <td>0.092680</td>\n",
       "      <td>0.894095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15773</th>\n",
       "      <td>Chavez</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.005897</td>\n",
       "      <td>0.763884</td>\n",
       "      <td>0.006487</td>\n",
       "      <td>0.223732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14507</th>\n",
       "      <td>Frankenfield</td>\n",
       "      <td>Jarrod</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001914</td>\n",
       "      <td>0.003565</td>\n",
       "      <td>0.131624</td>\n",
       "      <td>0.862897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95914</th>\n",
       "      <td>Cubas</td>\n",
       "      <td>Melanie</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.032778</td>\n",
       "      <td>0.588289</td>\n",
       "      <td>0.019678</td>\n",
       "      <td>0.359255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88446</th>\n",
       "      <td>Appadoo</td>\n",
       "      <td>Cathaerina</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.078784</td>\n",
       "      <td>0.090062</td>\n",
       "      <td>0.101946</td>\n",
       "      <td>0.729209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47833</th>\n",
       "      <td>Veloso-Madison</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.030236</td>\n",
       "      <td>0.265275</td>\n",
       "      <td>0.182387</td>\n",
       "      <td>0.522103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26176</th>\n",
       "      <td>Salazar</td>\n",
       "      <td>William</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.009860</td>\n",
       "      <td>0.395829</td>\n",
       "      <td>0.005493</td>\n",
       "      <td>0.588818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40095</th>\n",
       "      <td>Miller</td>\n",
       "      <td>Jeffery</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002545</td>\n",
       "      <td>0.005137</td>\n",
       "      <td>0.048592</td>\n",
       "      <td>0.943727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136866</th>\n",
       "      <td>Strawder</td>\n",
       "      <td>Thine</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.017952</td>\n",
       "      <td>0.009242</td>\n",
       "      <td>0.398772</td>\n",
       "      <td>0.574033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30884</th>\n",
       "      <td>Cherry</td>\n",
       "      <td>Christopher</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.007536</td>\n",
       "      <td>0.013974</td>\n",
       "      <td>0.213117</td>\n",
       "      <td>0.765373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144602</th>\n",
       "      <td>Nobles</td>\n",
       "      <td>Tonia</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.008511</td>\n",
       "      <td>0.053910</td>\n",
       "      <td>0.448074</td>\n",
       "      <td>0.489505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49071</th>\n",
       "      <td>Cosme-Stroff</td>\n",
       "      <td>Hexsandmarie</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.010900</td>\n",
       "      <td>0.154225</td>\n",
       "      <td>0.016675</td>\n",
       "      <td>0.818200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73969</th>\n",
       "      <td>Drake</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.008274</td>\n",
       "      <td>0.047169</td>\n",
       "      <td>0.015638</td>\n",
       "      <td>0.928918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8881</th>\n",
       "      <td>Randolph</td>\n",
       "      <td>Ella</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>0.025476</td>\n",
       "      <td>0.407537</td>\n",
       "      <td>0.561815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123164</th>\n",
       "      <td>Ellis</td>\n",
       "      <td>Tiarah</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>0.001892</td>\n",
       "      <td>0.934385</td>\n",
       "      <td>0.061924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112137</th>\n",
       "      <td>Sutton</td>\n",
       "      <td>April</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003013</td>\n",
       "      <td>0.003699</td>\n",
       "      <td>0.157868</td>\n",
       "      <td>0.835420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1618</th>\n",
       "      <td>Jones</td>\n",
       "      <td>Janet</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002223</td>\n",
       "      <td>0.005141</td>\n",
       "      <td>0.159038</td>\n",
       "      <td>0.833597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10872</th>\n",
       "      <td>Green</td>\n",
       "      <td>David</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001909</td>\n",
       "      <td>0.005497</td>\n",
       "      <td>0.174284</td>\n",
       "      <td>0.818310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56475</th>\n",
       "      <td>Hoare</td>\n",
       "      <td>Kishalay</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.100128</td>\n",
       "      <td>0.021515</td>\n",
       "      <td>0.743659</td>\n",
       "      <td>0.134698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117313</th>\n",
       "      <td>Forrester</td>\n",
       "      <td>Marie</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.008028</td>\n",
       "      <td>0.004680</td>\n",
       "      <td>0.564960</td>\n",
       "      <td>0.422332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106141</th>\n",
       "      <td>Hassani</td>\n",
       "      <td>Nabil</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.592280</td>\n",
       "      <td>0.059671</td>\n",
       "      <td>0.009719</td>\n",
       "      <td>0.338329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129967</th>\n",
       "      <td>Navarro</td>\n",
       "      <td>Raquel</td>\n",
       "      <td>asian</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.020107</td>\n",
       "      <td>0.923608</td>\n",
       "      <td>0.010460</td>\n",
       "      <td>0.045825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15083</th>\n",
       "      <td>Barnum</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001868</td>\n",
       "      <td>0.011281</td>\n",
       "      <td>0.035789</td>\n",
       "      <td>0.951062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56274</th>\n",
       "      <td>Antunes</td>\n",
       "      <td>Karoline</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.038044</td>\n",
       "      <td>0.314749</td>\n",
       "      <td>0.144770</td>\n",
       "      <td>0.502437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101125</th>\n",
       "      <td>Meeks</td>\n",
       "      <td>Gabrielle</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.007446</td>\n",
       "      <td>0.020171</td>\n",
       "      <td>0.241123</td>\n",
       "      <td>0.731260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16920 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             last_name    first_name race_orig      race     asian  hispanic  \\\n",
       "107149           Nixon         James  nh_black  nh_white  0.002323  0.001857   \n",
       "76878            Rolle        Philip  nh_black  nh_white  0.006509  0.027061   \n",
       "46545          Thomson       Michael  hispanic  nh_white  0.002091  0.006707   \n",
       "117598         Ngwafor       Yolande  nh_black     asian  0.553349  0.024487   \n",
       "41630            Young        Tracey  nh_black  nh_white  0.003558  0.004002   \n",
       "97266           Bowles       Jarrett  nh_black  nh_white  0.000959  0.005905   \n",
       "129269          Carter          Koby  hispanic  nh_white  0.008078  0.012652   \n",
       "8257             Brown        Murray  nh_white  nh_black  0.002375  0.004668   \n",
       "66423          Terrell       Gabriel  nh_black  nh_white  0.012548  0.310676   \n",
       "148930           Adams       Kirstin  nh_black  nh_white  0.007258  0.014146   \n",
       "25404           Fegins       Jessica  nh_black  nh_white  0.003001  0.023684   \n",
       "68783          Coronel       Bradley     asian  nh_white  0.003280  0.132714   \n",
       "102610           Cohen     Zacharias  hispanic  nh_white  0.017844  0.266580   \n",
       "109950  Van Steenburgh       Melanie     asian  nh_white  0.006537  0.020603   \n",
       "73909           Cruger       Kenneth  nh_black  nh_white  0.001169  0.004573   \n",
       "14221            McCoy      Lafawnda  nh_white  nh_black  0.002657  0.005162   \n",
       "24797         Reynolds       Wilfred  nh_black  nh_white  0.003761  0.018307   \n",
       "93979          Hatcher         James  nh_black  nh_white  0.002819  0.001882   \n",
       "101230           Rivas        Steven  nh_white  hispanic  0.009003  0.674322   \n",
       "35759             Boyd         James  nh_black  nh_white  0.002546  0.002274   \n",
       "89329           Lackey        Andria  nh_black  nh_white  0.007762  0.047902   \n",
       "104045            Eder         Joean     asian  nh_white  0.004281  0.016747   \n",
       "138804        Martinez        Evelyn  nh_white  hispanic  0.006776  0.869072   \n",
       "30936           Ginory        Almari  hispanic  nh_black  0.013781  0.078765   \n",
       "112844          Carter      Mauricio  nh_white  nh_black  0.004041  0.342353   \n",
       "117291           Oates        Alexis  nh_black  nh_white  0.004619  0.032604   \n",
       "97257          Conerly        Albert  nh_black  nh_white  0.001255  0.008268   \n",
       "112394       Albritton        Evelyn  nh_black  nh_white  0.007516  0.034918   \n",
       "105095          Rhodes       Candace  nh_black  nh_white  0.005207  0.009166   \n",
       "77475         Albistor       Jessica  hispanic  nh_white  0.003673  0.032984   \n",
       "...                ...           ...       ...       ...       ...       ...   \n",
       "3937              Hill        Phoeby  nh_black  nh_white  0.009833  0.012281   \n",
       "74901             Hunt      Brooklyn  nh_black  nh_white  0.006932  0.016531   \n",
       "133600          Toyloy       Monique  nh_black  nh_white  0.020436  0.176052   \n",
       "50519         Campbell     Gwendolyn  nh_white  nh_black  0.005208  0.008410   \n",
       "143135        Pomeranz         Ilene  nh_white  hispanic  0.017881  0.578683   \n",
       "27807             Kirk      Shirlene  nh_black  nh_white  0.005087  0.008138   \n",
       "15773           Chavez       Anthony  nh_white  hispanic  0.005897  0.763884   \n",
       "14507     Frankenfield        Jarrod  nh_black  nh_white  0.001914  0.003565   \n",
       "95914            Cubas       Melanie  nh_black  hispanic  0.032778  0.588289   \n",
       "88446          Appadoo    Cathaerina  nh_black  nh_white  0.078784  0.090062   \n",
       "47833   Veloso-Madison      Mitchell  hispanic  nh_white  0.030236  0.265275   \n",
       "26176          Salazar       William  hispanic  nh_white  0.009860  0.395829   \n",
       "40095           Miller       Jeffery  nh_black  nh_white  0.002545  0.005137   \n",
       "136866        Strawder         Thine  nh_black  nh_white  0.017952  0.009242   \n",
       "30884           Cherry   Christopher  nh_black  nh_white  0.007536  0.013974   \n",
       "144602          Nobles         Tonia  hispanic  nh_white  0.008511  0.053910   \n",
       "49071     Cosme-Stroff  Hexsandmarie  hispanic  nh_white  0.010900  0.154225   \n",
       "73969            Drake     Elizabeth  hispanic  nh_white  0.008274  0.047169   \n",
       "8881          Randolph          Ella  nh_black  nh_white  0.005172  0.025476   \n",
       "123164           Ellis        Tiarah  nh_white  nh_black  0.001799  0.001892   \n",
       "112137          Sutton         April  nh_black  nh_white  0.003013  0.003699   \n",
       "1618             Jones         Janet  nh_black  nh_white  0.002223  0.005141   \n",
       "10872            Green         David  nh_black  nh_white  0.001909  0.005497   \n",
       "56475            Hoare      Kishalay     asian  nh_black  0.100128  0.021515   \n",
       "117313       Forrester         Marie  nh_white  nh_black  0.008028  0.004680   \n",
       "106141         Hassani         Nabil  nh_white     asian  0.592280  0.059671   \n",
       "129967         Navarro        Raquel     asian  hispanic  0.020107  0.923608   \n",
       "15083           Barnum      Patricia  nh_black  nh_white  0.001868  0.011281   \n",
       "56274          Antunes      Karoline  hispanic  nh_white  0.038044  0.314749   \n",
       "101125           Meeks     Gabrielle  nh_black  nh_white  0.007446  0.020171   \n",
       "\n",
       "        nh_black  nh_white  \n",
       "107149  0.232952  0.762868  \n",
       "76878   0.168978  0.797452  \n",
       "46545   0.109481  0.881721  \n",
       "117598  0.277182  0.144982  \n",
       "41630   0.319959  0.672481  \n",
       "97266   0.166097  0.827039  \n",
       "129269  0.180947  0.798323  \n",
       "8257    0.573051  0.419906  \n",
       "66423   0.134175  0.542601  \n",
       "148930  0.067739  0.910856  \n",
       "25404   0.099448  0.873867  \n",
       "68783   0.012440  0.851565  \n",
       "102610  0.069537  0.646039  \n",
       "109950  0.003942  0.968919  \n",
       "73909   0.211129  0.783130  \n",
       "14221   0.947310  0.044871  \n",
       "24797   0.446174  0.531758  \n",
       "93979   0.156048  0.839250  \n",
       "101230  0.007249  0.309426  \n",
       "35759   0.134031  0.861149  \n",
       "89329   0.380618  0.563718  \n",
       "104045  0.038774  0.940198  \n",
       "138804  0.005672  0.118480  \n",
       "30936   0.591831  0.315624  \n",
       "112844  0.522892  0.130714  \n",
       "117291  0.463219  0.499558  \n",
       "97257   0.255884  0.734593  \n",
       "112394  0.368678  0.588888  \n",
       "105095  0.235040  0.750588  \n",
       "77475   0.125138  0.838206  \n",
       "...          ...       ...  \n",
       "3937    0.280006  0.697879  \n",
       "74901   0.084982  0.891555  \n",
       "133600  0.257847  0.545664  \n",
       "50519   0.750798  0.235583  \n",
       "143135  0.008575  0.394861  \n",
       "27807   0.092680  0.894095  \n",
       "15773   0.006487  0.223732  \n",
       "14507   0.131624  0.862897  \n",
       "95914   0.019678  0.359255  \n",
       "88446   0.101946  0.729209  \n",
       "47833   0.182387  0.522103  \n",
       "26176   0.005493  0.588818  \n",
       "40095   0.048592  0.943727  \n",
       "136866  0.398772  0.574033  \n",
       "30884   0.213117  0.765373  \n",
       "144602  0.448074  0.489505  \n",
       "49071   0.016675  0.818200  \n",
       "73969   0.015638  0.928918  \n",
       "8881    0.407537  0.561815  \n",
       "123164  0.934385  0.061924  \n",
       "112137  0.157868  0.835420  \n",
       "1618    0.159038  0.833597  \n",
       "10872   0.174284  0.818310  \n",
       "56475   0.743659  0.134698  \n",
       "117313  0.564960  0.422332  \n",
       "106141  0.009719  0.338329  \n",
       "129967  0.010460  0.045825  \n",
       "15083   0.035789  0.951062  \n",
       "56274   0.144770  0.502437  \n",
       "101125  0.241123  0.731260  \n",
       "\n",
       "[16920 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edf[edf.race_orig!=edf.race]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "import numpy as np\n",
    "from scipy import interp\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "def plot_roc_curve(df, title=''):\n",
    "\n",
    "    class_labels = df.race_orig.astype('category').cat.categories\n",
    "    n_classes = len(class_labels)\n",
    "\n",
    "    y_test = np.array(df.race_orig.astype('category').cat.codes)\n",
    "    y_test = keras.utils.to_categorical(y_test, n_classes)\n",
    "    y_score = df[class_labels].to_numpy()\n",
    "    \n",
    "    # Plotlinewidth.\n",
    "    lw = 2\n",
    "\n",
    "    # Compute ROC curve and ROC area for each class\n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "    for i in range(n_classes):\n",
    "        fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
    "        roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "    # Compute micro-average ROC curve and ROC area\n",
    "    fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test.ravel(), y_score.ravel())\n",
    "    roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
    "\n",
    "    # Compute macro-average ROC curve and ROC area\n",
    "\n",
    "    # First aggregate all false positive rates\n",
    "    all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
    "\n",
    "    # Then interpolate all ROC curves at this points\n",
    "    mean_tpr = np.zeros_like(all_fpr)\n",
    "    for i in range(n_classes):\n",
    "        mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
    "\n",
    "    # Finally average it and compute AUC\n",
    "    mean_tpr /= n_classes\n",
    "\n",
    "    fpr[\"macro\"] = all_fpr\n",
    "    tpr[\"macro\"] = mean_tpr\n",
    "    roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
    "\n",
    "    # Plot all ROC curves\n",
    "    fig = plt.figure(1, figsize=(12, 8))\n",
    "    plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
    "             label='micro-average ROC curve (area = {0:0.2f})'\n",
    "                   ''.format(roc_auc[\"micro\"]),\n",
    "             color='deeppink', linestyle=':', linewidth=4)\n",
    "\n",
    "    plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
    "             label='macro-average ROC curve (area = {0:0.2f})'\n",
    "                   ''.format(roc_auc[\"macro\"]),\n",
    "             color='navy', linestyle=':', linewidth=4)\n",
    "\n",
    "    cmap = plt.get_cmap(\"tab10\")\n",
    "    colors = cycle([cmap(i) for i in range(n_classes)])\n",
    "\n",
    "    for i, color in zip(range(n_classes), colors):\n",
    "        plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n",
    "                 label='ROC curve of class {0} (area = {1:0.2f})'\n",
    "                 ''.format(class_labels[i], roc_auc[i]))\n",
    "        if i >= 19:\n",
    "            break\n",
    "\n",
    "    plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title(title)\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:39: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
     ]
    },
    {
     "data": {
      "image/png": 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uhKiTUrYdqjZpNBqNRqM5jLieEmyuL9yKrMLCrSsBjqeWkiDMiOYv8+k26E6C7YHnwWtmQE0ekTeQhru2qHr7UkrI/c9r8pf54G54un1UYL5uJpxUn9/2K49BKkuQXnsahPPIqzs2wDeehOOnKTF6Yh1cckz+Mi9/FNZ1K6EJ8M93w9HV4+12DsB/3aPqlUBjKdz55jEm0vPAdZHX/ht5xzr13vPgMyuQ75qvrpnnjdq5Lr2n/prhjEcaj7SExj+8lWisCCn9c0RCb5LUf9/PP/uHcYCU9AguqOSt15+J9KQSx0hkJoO0bZ647GEe292HAzhScvq7F/HaC45CSomUEsdzkNLDW9PBl7/1GH24OEg8E7553ZmURgNkvAye5+F6Dq50eeIXT3Pfrm4MJIaQzK+N8NYLjwLPRXoOOGnwXOxnWnl0RxeDpo1peIBg+Ls1lJSH8FwPifTb4ZHpTrC9bRDHdGjrMPN/PpNAKH17aPAF9j1SyqV59t0DfEtK+bi//k/gi1LKlROV2dzcLFeunNBEo9FoNJqXDikh44HtKs+fKwt78bb2wVBGCU1HQkO0sHD841ZIOkpguhLeuwhCef7w13fDnZuUjSthSRX81+L8ZX7vGXi+S4kx14MvngBH5RFu+4bhA/crG1fCtGL4zZsKl/mztb54Bj53HFy8PL/tnJ+o8x9h+4ehNDTe7v82wyX/GF1/+zy4+az8Zb7lbvjXvtH1u94CpzXsX5WeB46D3NqD/I87kHhIPKgM4N33ViUuXU+JMsdVrz95Du7egivSCCy4YAHy7Nn7baTr4HR1YUajyC88BGkHKfxyLz+eTNterGm1SMcBx1Wvv1pLKt1O0C1TdjNLkMdNGy3Tc8F2kI6D82wb6aEEnvDw8DBnRrGKTdzN2xCzG/EcB+naeGmbdF8SpMTAwxAelmUgPInwPIR36DTekcyaZJKrOtpZFi7izoH+VVLK5oMt41CGiEwZQoiPAB8BaGxsPMyt0Wg0Gs2LxpNKjI54J/PRNgx7h5R4dTwlRueUj7fb3gcP71GC1XaVJ/PDR+cvd1U73PCssnE8WFELXygQnfj5h+GJ1lGR+YPXwWvydD+3DEFzVjRkfQmsuWi83S/XqWV99+i2K06EzxT47/7iI9CbGl1/cxOEisbbbe+HH60eXT93TmGB/eQ+da1G+O+j8tvZHjzbMbo+mM5vB8h4BrqH8XCRwsHr3Ie3JYIXjyPTaaTtIB1bCUdzDWbQQApXCd3f3oEMiP1eTunYSgxv6iYdfZaAF8UTDnLdY3gfuUvt80WozGRIrV9PMFSDLE9hm4MYXgA+eYsq23XBcXxPqk9lTuPP+WnB86Is6/1f/CUfAX8Z4fonCpcZgESgVb3vAO4pbIqlBsoZAHu7cf3NcmcLAhD+vjFCTqK+Lzm4AlwDgn4hw2HwBHhGzqu/1PdBdykkgqoiz+9k8PyKJSCzX/332bZqu0ACVYOSeBiGw6O9FfuP8Y8DMBj7QCCy3gjA9o8pknL/+Q8bgmJPjhtUKIUqz8H0L40qzRQetjRVmULgSYlpjB7dNpTmUw/uRgK95vhrOVkOp8BuBRqy1mf428YhpfwJ8BNQHuxD3zSNRqN5BeN66l/IKjCOfX236oK3fa/rifX5u7R3D8JNz0FXUsVLziyF9y0Zb9ebhDfdrbrlM54SzledrLrqc+lMwNG3KMEKUF0EGz6Yv523bYD/fXp0/bPHwZdyxPDTbbC6E374LLTFR7e/Z2F+gd2VhHt3jK4XukYArUPK4zxCPJPfLphThpvnT1lKWFgJZ88eK7Dz2Y5g5oROOAX+/nLOQdoO3tAQXjyONzyMl0wh7YwSscOb8YL7kLhI4eI9YONtK0GmUqS3bcOqipFcv55AWRUyup2U1YXllUBCIN90F9K28dJpVVYmgzc0pCqNZTXgDn/Je05AtsP+/z1S+PyzP74e4NH8Zpl0J76GwjNsSNnjbBwDLP9SDxYpsemaICTEhqClyheZvtB0/VcB1PbBrlqxX4i6vp0roK5XsrtWqPKMrP0CqgahNSbwDFX/SNnRpKSrLKc8MXr8iK3jmLjSwEVgmSbhSBACJsIQdMo+aoNVpN0k6X6PYtfDNCTl4STxKDTZNpbMIAx1aQyUADUlRKSHJdU2IdV+4duYUr3f6l+3lBBUuS6WHDleYgEOUOF5BKUkICEkJYZvI4BeWUqVGKDbK6NTVuJgUopJRJpERJqEF6JNVmNj0VDsMSRKKa+qRxoWMgOPPtbKoCNRESkGzWefTEVlhC4RY/a0KoQVgnApm3+zhat+u46BtIM9lOG81zRy3W/egSEEpjG6BE2Dq5t/zpMbOgnWFGOagit+9RZOPmPWuHtl8J+7+MHKCwibJVx4xvv54h8uKHyPTsDhFNh/Bj4hhPgtanDjgI6/1mg0r1hGwgQyfpgAQCyPt7EnCas6VHxpylEhAG+YBQtyXWuofR+83xfCHgQM+P1b8td/8xr4+r9U3RL42HK4+pT8thfdrwYcjbDyQiWec9naBz9/fnT91Bn5Bfat65XXNtsz2pMabwdgiVFxDeq8ChHIEa4j1zWb2mL4yDL48eqx2wuVmytaJ6rfzKm/kMAN5IRt5OmWl4A8Job852Y8YwhP2Mrj27oZ77EMMp3CSyTw4nHS27ZjVlYgWYWMpHCMOBIX45u7cIb6cPv7MaLR/fayP45XMYTExhMOrJJwXOHTGiNw734or0kK9gtcx0ioE9jakdd2/zEBgScETsgkU2wxaGaQoSCuKRj2kgjLwvE8avo9dtYKHBMcU+KaYJvgmOAaYr/4dQVE0tBRDrYFGcu3McExxH5x6hqQCo7uV+WMClaZFeMtPYF0BUbAw02Z1IbKCUVDGMLEFCaGMDCEwfa+7QxvjiEdA+kJyqZnOPu4k7GEhWVYmMKkI9GBG1vC3bc+xfObMrjxIAiTS95Ty9JFJZQYYaZJieFBOuOQ6kvy2z9uwopaBHtMZkUTzJzXSInwsNw0c9JbqXB76M9YBL0My62d9MgoQRxCZAimXP+DGaFl9O3IrZoixwY6ZDm1YoCnvIWkZQCBpEhk2OQ1kCaAjUmVGGS9N4sMFmXFEYaHMyQSDr1eGdIVNDaUE6uKYhgm0jAxDAMMk8Gky6rn0xgygAiEIRjh3e9aRmMsQsI0iYRMlkZDWIbB9m09PL+2g1DQIhAwOLapksWL84QnAfPPHUBKCAQMgkGT8vIwZu73ETjjGw189Buvm/C+HOGbqz+cd/vWrVv5xCc+wZVXXslpp51G6etmsbX/cYR/33xRvMwEthDiN8AZQJUQYi/wP/idKFLKHwP3AecA24AE8IFD1RaNRqMZw45+2NCjRHHKhTllsGJaftvPPqQE5HHTlHC66Uzlzc3lqTblxR3hxHr4y9vH2uwehFvXQeuwGmw1wrTi/AJbSvjH7tH1fPG32bbpLAE6kXDM9bimnfx2Xk4ZrUP57S5eDpt6x27LJ4YBgjnnkKc7ez+5AtvJYzvyYJD751vo/HPLnIQH2cPFExm89n14G1K4g4OkN28Gw1Qit28AL7qSlNWJJaJ4pof3Xw+pfcNx7JaWseVWZL2/318Kkf2M9rfthe2yTksKgfDDIjIzqkkHlZgdkikQJsOmTcK0SZuSIctGhkOkTY80DiFbsi8mCNoqRMC2lJd3uEiVMSKEs8WsbYFnZD+4SMDG79QfuZj+dsF+d3MWZaEygkaQYXuYiBXBkx67nihCBkwC4QTpXTE+eMkSGqINBM0gpcFSIoEI1UXV/PHuLXznW08CAi9pccH5y7n2m6/HNJRoNg2TgBEgZIY49pifsGbN6IPCbas+wpKja3AdByc5gGfbeK7N82taufjqP1FZ7lAUdFk82+LcmhLqB56jNWEyw91L2B2mz/4X55YM0Xh8JwkZIiLS0I5a8nD+gpwNfXmMRuI/gJgY/53rNauxRRATh0EzhpNK8qyziIiRZjeNlNbE6C5dQF+onrRRzEDK5ajpZbiOR1dnnFDAJBQyCYcsqmMRFlZFCFkmJ5WFqS/P4xSYIpYurmHp4ppJ2TY05PmNnWLS6TTf/va3ueaaa0in0ySTSR59VHWTiHwDbw+SQ5lF5L0H2C+Bjx+q+jUazRHCum7lSR3OKI/vSdNhaZ7USc91wJm/V93wSUeJzefzPLd7Eq5bCb/dNLrtA0sLC+xb16vXZ/x/zEKxqLnCMZVHtDquirn97MM5tgXEaK6gnlA0H4RtrshMFhDYlTl/tjsG8tuFrcl5m2FsOEPQgNAEIRr1JdBcC5apyp81wZ9uc60KN2mepmzL1MA56bo4PT04HZ3IVBJ7Xwt8MoJ0M7jpBKmuB7Gu3YiXTOHF4ySefRazrIz0pk2YZeV40xJI2w8N+d9fFK5/fzhDu9KUBcbiG2VlGOEwRiSCCIXwQgHccAAnYOCFAiSCkLQ87K5OButLaU93U1QURabTxC2XrXTi4pIMwEDYI+l7bjMB9ZoOgDvGS59PwWVjojr8YUSlh8wQxYFiehJ9eEmLTHcxMmFw/PJZ1FZUkHJTzC6djWUob25/p8sPvrUGaZuIgMvM2HR+dMObsAwLgaAiXEHIDBEyQ3z5S4/ws5+s2e9J/uEN5/Lxjx0/rlVSSoLv+waO4yGQhCyHL9/4KYLxPZDogcF9YHeBtw0v04LduBXL8FhR10Ex26h54N94dhppJ5DJfjLpJEknw+q3Pk/7OaUEDJeA4WH96UbMP9mExNjvy+nAxvfnNGqbemnK2lQ/etmUuM7iaXEUYOAJAylMhjOSaCiAECY2gtKiMDXePoZqj8cKhsEKYgKZsll4FbOZUVNFJFoB4VIwQ2AGwTDGhJGP/BLOP8CnrBnLgw8+yCWXXMKWLcrRcdFFF/Gd73xnSut4RQxy1Gg0rxC298H2AeURTbvwH435wyR2DagBZCMhBa9thN+9ebwdKIF7S1aYwrWn5RfYj+1Vr9meVNsd331vCDihfqzALiQwQaXGWts1ur53CJbl8cLkCtx8XuEm33WZK5wLeZBNQ3lRR0IqRtKAGXm8K9kCV5A3TGE/S6pUerSAL1yLCvwV1JfAp1ao8tZ2wdsn+Bu/ZLnK3DAjqs4vX8gJqH1tH1PndSAv0dvmqyULL5PBGxjAHRjA6e3F3rMXadvYc/eSCbRgr2/FiERwLuzBaW/HSyQmrgPg8bGrTpuKVnQH+tUGy8IsKUFEijCjpRjREqyqaty+PoqOWoqIRPAiYboZpruvlX2lLm1uL2ZJCdsz++gNpOkQw4SLShjKDNGT6sEU/biywEPIOPKHZggMJJIiq4hUTwBZMkhieyXSNlh+YiVFVpjpJdMJmAEG0gMsrFzIjudT/PQ725GuwB0O8c43L+OH33sbASNAwAxgCWu/9+7EE3/GU0+NDo365aMXceqpM8e1Y9Ombq565Mb961XzKjipthncjFoycYj3w+A+Tg4/T3z+FsrCGRZV9fAfPavhNxElHj0HPAfpOXjxXuwrnyXtmoRM/zpdd0Pe69AMNI+JEtgOz431k2ePQZwWHCx4pXuJ4mLhYOJgYXswy+hkXXgFSauUPbKG2eUmrUULaagqwYvWYwZLqK8uI1hWT1lZGcJQNY9/bMhPgUd7zSGgu7ubz3zmM9x2220ALFq0iJtuuonTTz99yuvSAlujeTXjSZULdiijBqjliXFjTSfcsVHlkB1MwzG18PkCfx1vvFvlox3hT2+Dk/NkXXC9sfG6ewqEHoCaZCCbfGJYSvj4MfD9VWNTgKXyCGyAaM7gt0IeZIDSHNt8KcVACceAoYR2yIRoATtQIrSqCN7UpLy/S/PHIQJw67kqbjlgTjwg7z0L4Z0LlFc43+eYzY1vmHj/CPUl8OWTJme7uEotB0IIdT4oL6VMJJSHuaeH9NatyIyN09NNcvVqzPJy3F4Vc2zv3YsXjx+g8PyYZWWISAQ8j1BTE05XF+GjjsIoKcYsKcEdHCI0twkRCmNWlJMMQmswzrZMK3YkSGumk7ZEO7ZnEzACbOrdRElA3Wcbe381trIR96LFaJTEyC1rjw5udKWL9ATCUA9ClrBoKm+iPFxOT7KHmaUz2bx6iGfuTyM9Qaa7mEs+fAIffO/JVIYrKQ+VK6zoPTMAACAASURBVA+xL4Zra79LZ+fo9Xmm+/PEYuNTBd61YwPXrfv9/vXh1iLKQvl7BmpqiikNpakridNQNkRo9z+gMgbJPuhvga4tEIrSODhAzxceZGdfGSvqO9XBX/9K3jLfH4H3Z0dOucDmsTbZQSQj4jotA6SxGKSYVlnFgCymS5aTJuCLYRMHA1ealIthNssGXBHAM4IkzBL6bAvDCtNQUw6BYvrdEG9rnsXi6ZXUVUYJBELjvMPZHO2/jgyxXVHATvPyRgjB/fffTzgc5itf+Qqf+9znCAYLZDF6kWiBrdEcSXTEVTd+T1It8yryC1yAE29T6b1GeP4imFYy3m7vEPwiy4M8UR6fXE9oVzK/XXHOD1pfCh7dMyZ3bcEy84VeCKE8oqXBsQI76YwX0wAzSlS6tW196rV5Ah/S5SfCYEY9iEwrhkWx/HbzKmDfxwqXk82lx6plMpw5a3J2AXOsm+4wIm0bp68Pp60Nu60dt7+f5Jo1iGAQu7UVp7MTp7sbd2gI7PEZHw6EEYngJRIULV+OUVaKTKaIHHccVm0NRlERsjiCHSslGSsmERYkvDT96X6GMkNs7t1M0plF2k3TOryVoBmkJ9nD5r7fQhoo7NycFOn2EpyhEKcsW8jRs+ayoHIBEkltpJZYOMZgr8sJK27BGQ6BK2hoKKOl5TN5y/rGE4/y13+MDkI09zawoDI3iFcRyukVSSRsYnlu1UiRRSRgM60kzrLaLs4q2QIP7wLPBicFLU+BFYJdj/PnYyVk36bb7tofJjGmTCBSBJVFnWO220aYtLRwhUWZ188maxGVbhdPO3MZlmEkAg+DNlnJPhkjThjXF8vCtEg4JmbNPIatSoqCFgumRbEMg4Al6I/brJhVQYn/0DkzVkwkaBIwDQKm4IJomOBED6SaVw3r1q1j3rx5hEIhYrEYd9xxB01NTTQ1NR344BeBFtgazcuduA13b4H+NNy1WcUgX3taftvbN8K1T46uf/yYwgLbzVHKwwWETnmOJzY+gSA6oW6sN/rZdnjL3PF2JTlKsDtZOBfyopjy9JYElTc7X3jGCLeeq9pXV6w8w1UFBuysmAZ/fFvhcrI5vm5ydq8CpOfh9vbi9PbitLXhDg7i9PRg723F3rsXp7OTzN69eIOTV6kiFMKKxTBjMazqarx4nMiKFZjl5WTcNE5jHXZpEQNhSbxIsDm5k4xns6l3E/uG9xErMljduYWUuxYjbZBJZqAX2HPAqiekyCoi49gMbionsaMSYXo01lZz5SfeSFmwDE96VEeqCZkhykJlfOpDj/Db32zYf/w373g77z1ufI7pRMjGGRi9L/ftG8J1vbwZEkpzekva2+Oqt8ZzwE4oT7KnJkY5qrabmN2PZXg0VSextt4L21vU/tW3Q6gUujZyNhC/IqeihydKxqzYHZpPzG7jEXsRaYKYeAzJIvqIstFrJE2AFEHaZSV9MkovBUKEcljeUE7KdjllbhUn15Vy1PQyZlcVa3GsedEMDw9z1VVX8f3vf5+rr76aK6+8EoAzzzzzJalfC2yN5nDxt50q88S+YeV5/uQKFbOci+vBZVmptLb3wzWn5o9fzRXDwwXy9sL40IdCwjk3JGIigT2/UmXb6Espz3ch72txQM0KVxaE8rBqS76pjQHePFctkyHfVMKaSSOlxO3rw25pwentI7VhA9KxsVv2kNq8mczu3WrijgMhBEhJaOFCAtOnY1aUY8WqkJ6LtWwJQ6VBtlm9tBmDbBzaRlmojJSbYmvfVtrj7XQkVqmeEgPY+8LPpyZSQ2eik6OrjiYSiFARqqA8XM4dt62nfYvAjQeQjskt3/8vmhc3EQlEKA2VEjDUA+Azz7Ry/EU/219e/bF1vPm6/GMFKsrH3r9tbcN57YqKVIoy2x+E6rqSzo5h6mIC4l3QuxPSg9C7k3dbT7Pss0mainZRbvYTNlLIq9+PyNONdO+5ORse/UPOhrHTTHTIcvbJKkxcemQp6+RshmURGSwEsE3Ws0tOo0XWAGJc6jeA4qCJZRpYhmpRbzzDCbMrmWYK5Wk2BWnH4w2La1lQGyVgGQRNg4BpMMvPXKHRHAr+9Kc/cemll7Jnzx6EEAwMFBigfQjRAlujmUo64vDr9fBgi5p1blEMPl1glra/7xrNUAFwblN+gR0NQtgcjRNOuWqyDokKWcjmYLzNfpYFIgElePOlQAN1Ht88VdlPJIRBzUhXaFa6bISA148fLKU5tEjXVV7m3btJ79iBvbeVxFNPYRQXY7e1Ybe3HzBkwygrwxsYIDR/PiIQoGj5cqzaWgIzGxkuDbCvxKY74tCaaMMSFr3pXh7Z8wiO59AebyfTmikwpVhhMp3FiICLtC3efPwpOJ7D3Iq5BI0g1ZFqppdM56/37ORrX1iFGw8iMyYXXricX/3qrXnLu/9zt7D+8dHUeZXpWTSUjg9PKisLj1nv7y+Q2xso879PlmUQyxrYm7Jd1m7dgd22gaKh3VT2rGLv1c9Tk95O2izB8GwCP/l/ecusBWqzo7b8r6gjDTIEiIg0e7xqMljYWLgYKrwCSSkJnpNzKWeYp7xF9BLlcfco+ikhwdjzCpoGQcvAdj1cT9I8q4JExuWoighnVxYpcewL4+pokEV1pUSCWj5oXn60tLRw6aWX8uc//xmAFStW8OMf/5jm5oOe6fxFo78hGs1k6Iir0IddA7BzAM5bkD9lWHscvu3PPPdMOyzqKSywa3OEameBbAdCQHVkbOjFJx6AX54z3nYkrVmsSC0n1Rc+p1+do+KbD9QVWx5WE3loXtZIKfHiCezWVtzeHuyODjK7dpF4ZiUISK5cNalyjGiUQF0dVl0dbnkxaSdNfHEj6YZqdsUkg1aGuB1nU+8mXOmSsNfRnXyYjvaOgrl/swkaQTJehvjmGKm9ZciMyamnN/LO155MkVVEVVEV9SX1RINRKkIVVFd/h56e0Vj+y3u+QGVu+kBgX3UUp2/j/vXBCab2Li8fKzB7e/OPFaiqinDOOfMoLQsRLg9RM62EXZ2DyHgXZv8uvO4tDCQdhJPivLrnePONpcRSu+iLpwmm72fwfzYQwuZ4kf+hJeSOerkdaWAJjz1eNUmCtMoqBimmQ1aw3ptJhgBrvCb6zBhJV1AcNJlTVULAFCyuLyVgGqRsj/qyMLWlKv7Y9SQlsQjnWAamIfhvQ2AKgWEILENQFDCpjoamJOevRnO42bhxI83NzSQSCaLRKNdccw2XXHIJpnl4ekq0wNZoQKWU60+pWeHy8dG/wxNZbreFlfkFdlP52PVt/UqQ55uYJLeurgnSiV2wWGXwqAirWOmLliqvcy4n1sP95xUuJ5t8g/80L2u8ZJJMSwupDRtx2ttIPPscXjJBeus2vIPoArWa5uAunkN/ZYhkRYRBK8PG0jhdJR5O2OLJtieBdhJO1j3Z5i8HINUaRboGgWQpJ59eR3NtM3UlddQV17EktoSSYAlXXfUwV187OkX23Dmn8K4F+WdjKy0NjRHY7e3DeQV2TY36Ppkhk+r6KOFYmPX7BmgfSGG7Hts6h7FdiQTmv7Gez54RIGS6ROQAKzffS9/1v2Np/Ek6EpJiSyKkiyFdLl/hsEJspk1WMsdrhx9N5gqjsmPk6Nb1xSfgiQB9wTo2l59Cb7gRO1BKcXExVdEiQpaJJyVNNSWUG4Jq02CBKXidaTCjooigaWgxrNEUYOHChRx//PHU1NRw3XXXUV8/gYPpJUALbM2rl009cM2TsLlXzbC3pAr++e78tk3lYwV2a/74SkqCyts8IpZtT3m98wns46apSUeqI2pQXq44z+azE817rDmSkK6L3dZGau1aMi0t2K37yLS0kHjqqUkdLyIRZCLB8CnL6Ko0GK4qZpvXTntNkG2hPva5vbiyhTHTLAMk/SUPiR0VBCqTJLZVsrS5jOPnLaYmUkNdcR1zy+cStsJUiBqqK76//5hg0GTDN6/MKwjr6sZmqynkQYbRgX7l5WFqa4uJJ23+b9VeVu3uwzQg43g83zpIvdfOBZeHOFpsp0F0IRFkfvw1SjHwMHgNDnNFK1FRoK6R6I+RCQdhzIx6c8Soez5OEe1GLQmjBM9J0128gGAojJeJE5i+jIDwsKON1FRXUx2rpGz6QgiXkj3JfIFhyhqNZpL09fVx5ZVXctlllzF37lyEENx3330UFR262SgPBi2wNUcefSnY0gc7+1XKuvMXFZ4B7v6do+/bJsixOzdH/BaaMhrgQ0dDPKP+pBtL4ZgCWS8mmzdYc8ThDg2R2bWbTMtuvMFBMrt2kdnbit2ym8yu3cgDxEEXv+Y1SNel5LTTSFYU8XhpO/8YeoanB9YCGdRP+/rxB/rjE4MiREamSbdFkR4Udc3ik//9WgxhEAvHmFY8jRnRGdQX13Pppfdz443P7C/is98/i0+dfOK4oqWURCIBEgnV9kzGpb8/RUXF+D+7uroo5dURph9dTUVtMYONxXz9ng2YhsBxJY7n4XgS15UUvWMmV3zAYl5mPfv6k3T+8U9kZDHHCZsVYgsNogtD+Ir4IDtl+iOzcI0QJalWOqtPxqhoRIYrKZo2j2AwhGlZWIEgphnAsAKIipkQraPYMDm0Cb40Gk0hpJTcfvvtXHbZZXR1ddHS0sI996hMOC8XcQ1aYGuORD79INy3Y3R9cSy/wJ5TruKPRwb3dSfyz/wHsKASlvjlNJbCGXnyNY9w2Us/mELz8sRLJklv30Fm507S27aR3rSJ4SeeOGAmDrO6itDcuYQXLiIwYzpWXR2dUcnzVi87U+3sSm5lS98WbO82Ogc7x+VudgZDmMUZTq14A0UVkqVVS6kqqmJZ9TKqi6pp35Omqen6/fYNDaV86LoP5W1LRcX4gX6eJ+kaTu8Pv8g4Hpvah2h8bQNp26O0LES0NMRPHt/Jlp44sZIgtivJuB6dfUPs27OT4z4YpVG0UCGGmJHoJvSkTRCbZcZ20jLAscZWPAw1/fTIbN+TyPOdnnMWIWcIZp0ClXNUSrvyRggUqdkCI5VQ1gBCkP3YPME3WqPRvEzYvHkzl1xyCQ89pDJrnXbaaVM+xflUoQW25pVBXwr+bzOs61ZTYQdN+Mvb89s2Rseu7ygQmxo0VVjGZn9q7WnFKtd0dZ4sGa+dqRaNJg9SSpyODpJr15Jau5bUhg3E//XvCY8RwSDhRYswSkuJrFhBYGYjgxUh2qpNWr1edg/u5pG9j5BJCFr2bJ2wrGNrjmV22Wwe+bXNY7dJ3GEVVnHV78/jnWcsHmdfXT32IXIkRMPzJEMph1UtvXQNpXl6Zx87QtDwjnmEwhZGeZBbknFuueK+fFeBmUszLBG7WGzsZqFoIfF0mLNEBzWin3qRNYV9OM/hByAROwqvqAKr8XjCXgKmHQVWGIQBjSdBSQ0IwQTzZ2o0mlcoqVSKa6+9lm9961tkMhlisRjf+973uPDCC1+24xK0wNa8MhjMwBWPja6XTfA32pgzwcGO/vx2oCZsiQagqUIP+tNMCum6pLduJbV+A/EnnsDt7yO5fkPBQYaBGTMIL1pIcOZMzDmz6G4opaM6wO/vXcvGxGqGk0PEh34HnV3Qybhpo7NJdxQTkaV84px3URYqY2Z0JsfWHotlqJ/yj99+Lw8PrwRABA2e2tHD1vs2qnALT4VeuJ7EcSWzPrEMy5WYAYNUkcX8K+8n4+ZL1SiJzA0TYxBLDDFTdBAmQ4Q0s402Lgg+hoVDuTfB9yxfqeEyRMVssJPQ9FolkK2Q8jJ7DtQsgtg85XEOFDFBckiNRnOEs2fPnv3i+oMf/CDf/va3ieWbqvRlhJByonmPX340NzfLlStXHu5maKaKPYMqZ/RTbbCuCz5wlFpy8STMullNfT3C1g+pFHK5PLgbrnpCiebZZSqzxmSnm9ZosnB6e0lv2YrT0U5yzVpSmzaR3rQJLzE+44sRjRJevBhj0VL6YzNptaqxZ0XordnIgy0PknSSbOzdmKeWsUQDUaaVTOPYmmOJBqOIeDFf+u9dSC9G0axSonPKec3x09nQNkh/wiYatnB9AW07HgWymQNQyjBLjV1ESdAk2hBITDyCwma+2EsGC0tIThbPkxEhqujHw8CYsNQcgiUqNKNmEVTNg5rFYASgvAGKqyESyz9Jkkaj0WTR3d1NLBbb76G++eabWbx4MaeeeupL2g4hxCop5UHHfmoPtubw8uM18JM1o+uFvM2GUOEc67pHt+0ezC+wdTiH5gUgbZvkmjWkNm8muXqNyuKxe3de20B9PeElSwjObcKJldF5VD17ilP85Mk72Nb7W+SwQaAyqRJ15CTrMIRByAzhtcfYtzqEmwiR6ZrJBe85k4ULZ/J86wDFKYvf/62TurIwm9qHqHnH2Ona/7W9Z7S8VD9Ljd2UEmeBuZcUAWaKTkpFAhuTZnMb1cYgRd4EaSDzosJGDDzlVXYzUDFL7bJTKsY5EoOy6VC9EOqPUV5ojUajeRG4rsvNN9/MFVdcwY033sgFF1wAwEc/+tHD3LKDQwtszaFjMA2rOmBNZ+HJVpbn/CHvmSA7xwWLlQf7qCo16DB3FkONZpJIKbFbW0mtW0fi6afJtOwh/q9/gZfjqQ0ECEybxs4ej0e8Cp4rstkx06HxHQ6dqcdwvIdUvuPVo4dYOeNpT5l+CosqF2FRQrl3PH1DYf61vZsnO3uwql2CQZOieXBPS5p7WzYxU3QwV+zjJFxquvo42xrElSZzjDbqoyZ1oodyOUg0sWfyJ5zrgC6uUWEZ8S4VyxwsASsImThUzYdQKYRKoGwGROvAnMToQo1Go3mRPPfcc1x88cU8/bSasO2BBx7YL7BfaWiBrZl6pIQ33Q1Pt43mk33vovyTuOQK7L0HSH+n0bwAnL4+0ps3M/zQQ6R37iS9aTNOZ+c4u0B9PcHmYxmaU8PeWcX8n/M0z/SuxnU9MDoA9aO5L48zuDGylEBqNs8+04fdMx2rcgZGUSX/3BLgftfDwmG6eIrZop1FopW3BfdgB0yWGjsJ4LLQmIRgnsgJXdYA9cvByfivaSWQi6tVbHP1AiifCcbhmdVMo9FoCjE0NMRXv/pVrr/+ejzPo76+nuuvv563v71AMoNXAFpga6YeIVTMdHZ4/5Nt8Ja5422bylV8dPM0NfHKUp0XWvPisDs7SW/ZSmrjBjLbtpNcu5bMrl3qwS+LTKiYlnSQjZEy9jRJNi7zGJ7pEPf+qgw6sowN8JwivFQ9yAAkGilO1BJ1AtR4NrNEJyYedaKV95X1sKT8MVIECOASxCFkZmg0uiZ/EsESFY5Ru0R5lUtqoHQ62AmIzYVwmdpfWg/hch3TrNFoXrFs2LCBM888k9bWVgzD4NOf/jRf+9rXiEajBz74ZYwW2JqDw/VgfQ88sBtiYXj/0vx2x02DZ0ZnPuPJffkFtiHg9jcemrZqXhXYnZ0knnlGLU8/Q2bHjnE2rmGyrbKefdUlbKt1WVcHbQ17wBoCsnpN/FAKyy4m6sL8lEFTIsy70zuYQws9ch8xkWX/Qn5ByxshUqXil6sXQN1yCISVmC6uUaEZGo1G8yqhqamJ4uJijjvuOG6++WaOOeaYw92kKUELbM3k2TkAb7wLOv1+6lmlcOGS/N6z4+vgR6tVrHTzNJ3FQ/OikVLSG8+wcc0WNt7/CFU7N1K7cwMVve1j7FJmgB1l9WyvqKalLkFLjWTXjH5kcVvBso/uN/nPTB/HZ+I0Og5FBbIrjRHXoLzIfbvVgL9ED8w5AxBQUg0l01QmjZHUc5EYFJWPL1Sj0WheRdi2zQ033MBFF11EZWUloVCIBx54gPr6ekzzyAlh0wJbM3lmlEAqK03erkF4uh1OqBtve0YjrPtA/rhrzasaKSVpx6NzME37YIpHt3Txz02dVBYHcNyRfM0qZ/OGvf0sSXTQ0LOHBV07OKpnB3WJXk7JKi9hhdhQ2cj6+igbZyfZXmshorvBah1X9xJH0JQcosSTvHl4mDl2YTHNvLMgWguVTcrrHC5TaeciMbCKwDAOzQXSaDSaI5THH3+ciy++mPXr17Nx40Z++tOfAtDQcOTNpaoFtmYs/SmI2zA9T+xTwITXzYQ/ZM0q9/tN+QV2cUAtmlcNjqumy97QNrh/+uyBpM261gHaBlIkbRcpYWd3fMJyStNxjunawrGdW/hy+3rKMmNH9sWtMOtLGti3qA6xPM268l2sZxeOH/Q/0p9iSsmxqTRzbJvXxpMck06PF9NFlbD07dBwIgSLVchGrGmqLolGo9FogJ6eHr74xS/y85//HFBhIeedd95hbtWhRQtsjWJNJ9y6Xk1HfuZs+OlZ+e3OmgX3bFdC+4wGeNu8l7SZmsODlJKd3XHW7h3A8SS262G7Hru6EyQyDnc9uxfbPbhJqyoiAfoSNuzqY0HnDo5p38zbavsobds5xs6ImASrwmwzknQfO8StDdAT3k7SGGs3O2NT4bk0ZWzOHU6wxA6RrmmmuK4Wyx6ExhMhFFXhHOUzVVo6jUaj0RwypJTceuutfO5zn6O7u5tAIMCXvvQlLr/8coqKig538w4pWmBrYEsvvP7O0fV7tkP7MEzLM9jqnCb4zznaO30E43mSzqE0f3iulU3tg/QMZ3h8W/eBD/RZ3lDOYNLmNXOrSMUzxOM2S+bFmBmLML82SiRoUj3QztAf72TD/X+loq8TayTlTBsIE6xpgsFpwzw0z+KpugB7A5Ih0wBGk0zXOQ5Hp9LUOy7vrTmeunmnqfCNqvlq4KBpkWcaIo1Go9G8RKxatYqLLroIgDPOOIObbrqJhQsXHt5GvURoga2B+ZVwdDWs9dOIOR78cj186YTxtkX6ljkSGEjY3LeujR1dw7T2J7EMgwc2dhAJmnQPZwoeV1saImSZnNwUwzIFAdNgIGFzTGM5c2uinDC7kpaWAd7whl/znd29WGQ476Q4H/tqjMzTOxn4+2MM7zHZ0acCOar9cp1Kh22NkudnG/yxKYgdEGSLaYAwQZZZ8zh9xvGcOv80ZlUfrb3QGo1G8zLDcRwsS2mF5uZmLrvsMpYtW8b73ve+/dOevxoQstAAn5cpzc3NcuXKlYe7Ga9M2uOFZz+8dR189mH1vq4YPnEsfGTZS9Y0zaFBSsneviR/XrOPv65rp3MoRTLjMpg9WLUAQihv9HkrGphdVUzzrAoCpsHQUJrnnmunuqqIRbMD0LsDujbDjodh3V1IK4SXSoAD8fYQfduKyQxaOKms0eGmJNkI6+cKfrrAoC+af8DgCXUnsDi2mLNmnsWCygVYhn7A02g0mpcrf/vb3/j4xz/OLbfcwqmnnnq4mzMlCCFWSSkLTEddGP1vdaQjJTzYAjethk098Nz71WDFXN4+H/6yXaXd+8/Z+W00Lzs8T7KzJ86mtiESGYd/7+ihKGDywMYOXE9O6I0GOGNBNbOripldVUxtaZiyogBzqoqpKc0KrnAysPMRnvzBPyjedS/VwV4WCklNcXJceXbcZGC3YLg1RrI3AHKst+KJJQYPHgUbGwSONbovGohyTO0xrKhdwekzTmdO2ZxXladDo9FoXsns27ePz3zmM9x5pwo3veGGG44Ygf1C0QL7SOeDf1XCeYT7duaf8KUkCL9/y0vXLs0B6RlO0xvP8MDGToZS9v7BhY4rSTsud67cO+mywgGDmZXFfOYN8zhxToxwwCRkGWNFbCYBXZtg5xbY8QjsehwS3Wr2QOBEgMqx5UoJjpjG0G6L4Z4Y8S2jsxW6wKY6k9ULJGtnCXZNA+nXV11Uzeyy2SyoXMDJ9SfzmvrXaEGt0Wg0rzBc1+Wmm27iyiuvZHBwkEgkwlVXXcWnP/3pw920w44W2Ec68yuBLIH987X5BbbmsCKlZCjt8NCmTv6+voN7ny88KUo+ZsUi2K7kdYtqcDzJMQ3lVESCNNWUMCsWGRWvw52w50HY96xSx3YC+e8fIUamMJyAZPEcvvyHGbQPRgj0w4kxj9dG23G7u1FTIHbhGPDsXMETiwSrmwTJkAAEs8tmc1xRNRcuvpBl1csoD+sJVzQajeaVzNatWzn//PMZCdt905vexA033MDMmTMPc8teHmiBfaTztnnwvWfGbkvYENFZQF4KEhmH3T0J9vYl6Y2neXZ3P0HLIGX/f/buOy7q+g/g+OuA4wCRDYK4xYF7j9yaI2fOnFmZ28yRWpnbtDJNy4GjtOVuaO40c+TeA0UFQRGZsvfdfX9/nD8coDI9wPfz8egB9x2fe58Yvu9z78/7o+OgTxjF7Sy4FBj9zPuLasxwttEQk6hlaLOymJmaoDZVYWpiWGBYqVhRapZ8LFmNDYaIW6ANMZR2+NyE1CS4/hcEX87wOZ6eN44uUgVbaxMo2xzKNDO0t7NyQHvlGppFnzK0yA2KWSdBMuiSIUkN10uoOO6p4qyHipgihhG7lu9Kq5KtaOTWCGtz2f5bCCEKE3t7e27fvk2JEiX47rvv6Natm3wS+RhJsAsDRcl4u3IwbFVexRHK2sIHdaBOsWdfK7Ls7oMEzt+NIilFx/aLQcQlazE1UZGi1XP53rMT5/8Lj0tOd6xiMWvaV3WlZ50SlHHKYFFqUgxE3YHY+xAVDb6+cHMv3Dub+cBdqho2VClRj42/+7FuWzSHA0qQqFUzb15rPhlpqJ1LunGD8BVreLB3N+o7wQx62NgjzAZOVFZxorIJN4sDKhUN3RoypXxX6herj5t1BpsPCSGEKLAURWHHjh20b98ec3NznJyc2LlzJ1WrVsXaWiZRniYJdkGmKIae1Z+fgKWvQz3XjK/7qwfYaF5ubIVIQoqWa/djuRkSS7JWz9mASFJ1enZfCc70GLVK2hEWm0yLSs4kp+qpX8YeC7UpluamlHUqgp2l+smFhXodRPjC+dNw7wxEBxrqojVFDXXRz2NmSaJLPS55RxEakYqlKoFAy0a8M2UAlGwI6ieb+0ddPsNe351pj/1PXSdo6TkCdv+OnW8oov5/RAAAIABJREFUAGog0Ry8S6r4s7EJqVXLUdnRk0FujahXrB4lbQrfNrdCCCEM/Pz8GDNmDLt372bu3LlMnToVgIYNM2jnKwBJsAuuuzHQYSuEPtxGetBO2NXLMFP9NEmuM02r07PnajCRCansuXKf+9FJ+IU9f2tvgGYVnCjjaJhtblulGLaWaszNTHApqsHR+gV//qmJcNLLkER7/wlJ0aB7RvePhIcz3rYlDYsPXaqApR2oi4BtCXjtA7C0QxeXQlP7L9FqDbXVJiYqWn5SizLq9DtnNajnxodvWFLH7gLl7vrgeC2B6GtghyGpPlFJxaVaNtg0bIynaw2+K9NBZqiFEOIVkJKSwsKFC5k9ezZJSUnY2tri6vqMyTzxBEmwC6qi5hCf+uhxeCJ0/g2O9gd72b8uM0Jjk4iIS2G/dwh7rgZzNSjmudc3LudIRHwyrSsXIylVR+PyjlRxs6Gkg9WLn0xRICUeQq6Cz05DXXR8mKGH9P0Lz75PYwuu1cCpAhSrhrZYLfwT3Tl+Oox9+/zo0KE8A96qke42a2tzGjUqwdGjdwBDO79ly06xYEE7QzhaLVcO/obvHz/j8a8vwx9b45ikhstlVBzzVNG6/xQGVelOUfOiL36NQgghCo3Dhw8zYsQIrl27BsCAAQNYuHAhxYoVM3JkBYMk2AWVnQXMaQoTDj46VrcYaKR/9bMoisKhG2EsP+jLKf8Hz73WzdaCzjXcKGqhpnlFZ6q722JqkoXa9cQow2y09zYIPAvJL67HpmxzqNEXrBwM35unr7/+c6s3vXuvTnscH5/CgAHpE2yA118vy9Gjd7C0NKNZs9JUqOjAOd+j+KxbSsm9l3GM0VPp4bUPrOFWcRWa3t2o3KYXPZwq8bYsTBRCiFfSf//9R4sWLQCoUKECK1asoE2bNkaOqmCRnRwLMp0e2m8xzGbPbgrVnV98zytCr1fwvh/D6iN+HL0ZTrJWT1xyxrsX1ipph42lmp513GlczvHJWugXPxFoEyEuBK78Bhc3GVrhPSuhVlsZSjvqvA0l6hsel34NbIqnXRIZmUjRohrMzNLvbnjzZgQVKy5Ne2xrqyE8fHKG1wYGxhAZmYi5SyRn964lfPdfNLmqYPWw+iSqCFyr7YhZ2xa07zyWYkVkVkIIIYRhQuqNN96gUaNGfPzxx1hYvLqfjMtOjoWRosDaK1DeDlpksIjM1MSwOYyd5pXvDBKblMq+qyH8eeEeJ/0ekKJ7dl9nN1sL2ld15YPWHi+ujwbDzyH2Ptw6AImRD0s9rsD1HWCiBn3qs+91qwWVO4NnF3Cu9Myf05UroezYcYNdu25y7NhdDh4cTLNm6XuJli/vgJWVmoQEw3NGRydz+vQ9Gjd+8u/H3Zi7/HNiFbq/D1HxXBi1Yx+dCyhtiapXJ1q+/QmNNZkobxFCCFGoXb16lXHjxrFixQo8PDxQqVTs3r1b2u7lgCTY+VWqDj44AL/dgBrO8HcfyKhE4RWrt05K1REYmcDVoBhO+D3gjP8DbobGZXitSmXIjd9rUpaedd0p72ydfvfCx+n1EHoVfPZAVAAEX4L7F58f0P+Ta0sHsHYxzEa71TIk1FYOz7837WkVevXajI9PRNqxnTtvZphgm5ioaN26LHFxKTRtWpJ27cpTt65h9jsuJY6Fp74i+fR5qv3tSwO/R59OPXDSkNS4Bg0HT8KzWvVMxSWEEKJwS0hIYM6cOXz99ddotVqmTZvGhg0bACS5ziEpEcmP4lLgnd1w6O6jY993gK6v7g6MlwOjWfi3D//6hD33ujaVXWhc3pH2VV0zt/gwPgIOzIKLG57dueP/TNRQxAlq9jW0y0MFFTtAsSovfBq9XsHHJxxPz4zLeFauPMOIEY9a5VWv7sKlSyNfOG6qLpW/A/7m9JntFN90hKZXH83cp6hVJLZvTLnuA3Fu1ByVqdTnCyGEMNi1axejR4/G398flUrF8OHDmTdvHvb29sYOLV+REpHCRGMKSU/VCy85C13KvzKlILdC47h2P4ZlB29xPTg23fkaJWzR6hRql7KjeUVn6pdxwKGI+YsHjgyAW3+D379w7zzEBKa/pnRTwxaFnl3AvgyUbmJIrLNh27br7Nhxg507bxIcHEdIyEc4O6dfvPj22zX57LODhIcb2i4qimEBY5EMXlNkUiQng0+yy28X908dpunFFPqcf/KNsqZbRzwmTEYtq72FEEI8JigoiA8//JCtW7cCULNmTby8vGjUqJGRIytcJMHOj9SmsKY9tN4EYYlQxgZWtiuUyXVSqo47DxK4GRLH5jN3OeP/gPgU3TOvf79pWSa2q4SleSZmY1MTIcQbrmyFsOuGjVqUDMZ2KG/o2lH/fUNLvFw0a9Yhzp9/tCHN7t23ePvtmumus7RUs2hRO+LiUujYsQKlS9s9cf5Y0DE2Xt/IifsnSEpNoIGPQudTeirde3SNqkFt3AcPwbpVK1Qm6Rc9CiGEELGxsWzfvp0iRYowe/Zsxo4di5mZpIO5Tf5E8ytXa/BqB/NPwNqO4JrBltkFmHdQDBM2X8hwdvpx3Wu742KjYVybiplLqhMj4eyPcO4neOCb8TXmRaFEXSjVGGq8BQ5ls/EKDBRFISgoFnd3mwzPd+pU4YkEe8eOGxkm2ACDBj15PDg+mFnHZ3El/ApRyVGUD1Lof0nPa9cUiiY9fH5LDfZvdse+Tx8sPD2z/TqEEEIUXt7e3nh6eqJSqahUqRI///wzjRs3pmRJ2YU3r0iCnZ81LwlNS2S8uLEASdXpuX4/lg2n7+AbGsfJ2+l7UNcsYUuKTqFzDTdaVHSmYrGimGfQei5DEb7gswtOroTou+nPu9UCK0eoM8hQ7mHtksNXBP7+Ufz44wW+//48er3CnTvjMcng59S5c0Xmzj0CGDZ/sbZ+cRlLYGwg666uY+uNreh1WhpdV+h9VE+JiMcuUqlwHjcO+wH9MbWWftVCCCHSi46OZurUqSxfvpyff/6ZAQMGANCnTx8jR1b4SYJtTHoF9tyGN8o+u/yjACbX0YmpLP/3Fg/iUthyNoMa58cs61+HTjUyue12UjSEXoNIf7h3Fk6vASWDdnxF3aBSR6g7GNwyni3OiejoJCpW/I7U1EfPffZsEPXru6e7tn59dyZPfo3XXy9H8+al0WjS/y+nV/Tcjr7Nlhtb2OKzhRR9CiZ6hWZXFPqd0eAQkgiASq1GU8UTh7ffxqZDB1m0KIQQIkOKorB582bGjRtHcHAwpqam3Llzx9hhvVIkwTaWFB2MfdiGr1dFWNQaLAvmj0OnV/jrYhCHb4Tx16UgUnUZd6apVKwoRTSmTOlQmeolbLEyf87rDTxr2Akx6Dz4H3lxEEVcoMEwQ5u8Mk2y+Uoyx9bWgjfeqMD27T5px/7443qGCbaJiYovv2yb7niqLpWj946y3Xc7++/sTztuH6vw5nk9ba9rsI1IAhIxdXLCccgQ7Hp0x9TWNk9ekxBCiMLh1q1bjB49mn379gHQuHFjvLy8qFEj411/Rd4omBldQZeig4E74eDDd5Nbb4BvFPzU0VB7nc/FJWv5cMN5AiMTCY9LJiI+4/Z2r5V35K36JfFwsaZq8UwkhooCF36FHRMMXTyeRWMLlTqAhR2Ubw0V2+fqAtBjx+7yxx/XCAyMZcOGnhleM3hwzbQE29LSjKSnu75kQKvX8nfA3+zw28HhwMNPnHOLUBh01Z7aZ6IxTdYBSZi5ueE49H3sevXCxDwTHVKEEEK80o4cOULbtm1JTk7G3t6eL7/8kiFDhmAiC99fOkmwjSFJCxGJTx4LioPI5HybYIfGJPHryTusOuxHYmrGXT5cbSwY1ao8jcs5UtapCGamL/gfWq+D+xfAeztc2w4P/J48r7Yy7IJYrSe41zXUUefhL4mUFB3Nmq3l1ClDaw6VCr79tkOGbfU6dapA+/blefPNyvTtWw07u2dv+BMcH8y6q+v4y/cvYlJi0o5rTDW0e+BO15N6bM/5ghIOgGW9ujgMHEjRNm1QqdW5/CqFEEIUVg0aNKB06dI0bNiQr7/+GheXnK85EtkjCbYx2Ghgazfo8SdcCYfSNrCpq2FL9HxCq9Pz/dHb/OcbweEbGW/uMrhxad5vVg4LtSlO1ubP3vVJUSD4Mtw+ZKifDjhmaJuXUf00gFNF6LkmT+qnn8fc3BS9/lF5i6LAvn2+DBiQ/mM1jcaMPXsGPnMsRVE4HnScDT4bOBx4GP3D1+pg4UDPCj3pQg3Mvt9K3IEDaffYdO2CbbduWDfJ2xIXIYQQhUNoaCizZs1izpw5ODg4oNFoOHPmDEWLFjV2aK88SbCNxd4CfusGEw7C/Obglj9mri8FRjH857Pcj07K8HyzCk5M71wFDxfrF2+jmhwHZ9fCyVUQ/YzFFVaOYKqByp2gQltDyYdp3s7aKoryzNi7dKnImTNBaY/37s04wX6WhNQEll9Yzr+B/xIQE5B2vJpjNYbXHE5DXWkiV3gRvW2F4YSJCY7vv49dr56YlyqVvRckhBDilaLX6/n++++ZMmUKkZGR6HQ6vLy8ACS5zickwTYmB0tY19HYURAcncSqw35svxhEeNyTtc+lHa0Y3LgMr3sWo5RjJrYeP7sOrv0Ft/anP1e8DljaQ9lmYF8WStQHm+IvbQOd27cjWbnyLAcP+nPixJAMk+wuXSoyY8a/tG9fnvfeq03btuUyNXaqPpVfvX9l+cXlJGoN5T+2Glt6ePSgm0c33O+nEPb5t/j/+2/aPdYtW+IyeRKacpl7DiGEEOLy5cuMGDGCY8eOAdCuXTs++ugjI0clniYJ9itIp1eYs8ObGyGxHPONyPCaFQPq0K6qK6YvahOYkgB3T8A/n8O9Mxlf03AENJ8MRRxzGHn26HR6evbczPbtPigPK0COHr1Ds2al011bq5Yr/v4fpttJ8VmuRlxl9aXVnA4+nVZf7WjhyNtV32ag50BST58ncuZSbu/bB3pDmYhNx444Dh+ORaWKufMChRBCFHrx8fHMnj2bRYsWodVqcXV1ZfHixfTp0+fFnyiLl04S7Lz2XyDs9IMZTUBjvL7Fccladl26z7pj/njfj8nwmpaVnJnUvtKLO34kRRtmqi9sgLBr6c9X6wVVu0OpRlDEKefB55Dpw8WWymPdA9euvZBhgq1SqV6YXGv1Wjb5bGKzz2b8oh8tzLTX2PNOtXcYVGUQSlAoIWMnPFFjbfvmmziP+xC1q2sOX5EQQohXzblz5/jqq69QqVSMHj2auXPnYmeXf9ZuiSdJgp2X/KPhvT3wIAlO3ofV7aHcy/2f4aRfBPN2XeNiYPQTx1UqaOrhxJCmZaldyh5byxfUPev1cGoV7J8J2sT052sNgGJVof5QMMt/LeVGjarPtm2P+lbv2+eLTqdPS74zI1Wfyi6/Xcw9MZck3aMa9a7lu/Kmx5vUdalL/D//EPTV+yScOgWAysICh8GDsevZQ2qshRBCZElUVFRaEt2sWTPmzp1L27ZtadCggZEjEy+iUpSMNwXJr+rVq6ecOfOMUoT8JCIRuv4ONyIfHbMxh5ODwMkyT55Sp1fYcSmIQz5hmJqoOOgTlq6muk+9EoxoUZ6SDlaoX5RcalPg1t+GpDr8Rvrzlvbw1q95vrHLi2i1ejZtusIPP1xgx45+WGbwZkGvV6hceSnW1uaMHl2ffv2qY2X14sWUekXPgTsH+O/ef+z130tcahwAZiozhtUYRr/K/bDV2BL3zz+EfvMNKbd8AVBpNBRt0xqXiRNRu6ffgEYIIYR4Fq1Wy7fffsuMGTPYtWsXzZo1M3ZIryyVSnVWUZR6Wb1PZrDzyv14Q7/rxw2tmSfJtaIofPfPLRb9nUES/NBXvWrQrVZxNGYvKFPR6+HmPrjyG3hvS7/hS8mG0OsHsC2RC5Hn3OrVZ5k37yj+/lEA/PDDeUaPTv/O3sRExX//vYeTk1WmatXiUuL49dqvrL68muTH/gwcLRwZXHUw/T37ozHVkOzri//Hw0i6fBkAUwcHHAYNxK5vX8zs7XPpVQohhHhVnDx5kuHDh3Px4kUAduzYIQl2ASQJdl6p5gT/9oNPDsOm69C5PEzO3Y90boTEsuJfX/44f++J4yXsLXndsxj1ytjj6WZDeefntACM8IXAMxAVAHdPGWasH2eqgeq9waM1VHkTTIxXR56R8+eD05JrgAULjjFsWF3U6vRxZrRhzNP0ip7vL3/P6sur07qBADR2a8ywGsOoW6wuKpUKfUICId99xYO1aw0XmJnh/OFYHAYOxMQybz6hEEIIUXhFRkby6aefsnLlShRFoUyZMixdupROnToZOzSRDVIi8jLs84fmJcAid97P+IbF8dGWi5y/E/XE8bql7fm2X23c7V6Q4KUmwckVhtKPZ7G0h1ZTof77L62NXnbcuxeDh8d3aVuVm5ioOHhwMM2bp1/A+DzxqfFs9tnMj1d/JCLJ0FmlmmM1+nv2p32Z9pibPqorT/L25u7IUWhDQgDDzovuX34ppSBCCCGy5fjx47z55puEhoZiZmbGpEmT+Oyzz7CyykR7XJGnpEQkP2tXJleGuRQYRdel/6U7Prx5OfrUL/n8mWoAbTIcXmD472k1+4F1MSjT1ND9Q5N/GtWfORNEjRrFMDdPPyvt7m7DqFH1WLr0NO+8U5NJk5rg4eGQ6bFjU2JZd3Udqy6tSjvmaOHI0BpDGeA54IlrteHhhK/wInLDBtDrUbu74zJpEkXbt5MWSUIIIbKtQoUK6HQ6mjZtipeXF1WrVjV2SCKHZAa7gNh7NZjhP59Ne2yigjlvVmNAw0zM1Ebdhf+WwOnVTx5vOALqDQHn/NePWVEUDh0KYMaMfzl8OIA5c1rx2WfNM7z2wYNEkpK0FC+e+TcFKboUtt7YysIzC0nRpwBgqjJl1muz6FSuE2YmT773jN62jfszZqIkJYFKhW23bhSb+immsmOWEEKILEpOTsbLy4uRI0dibm74hPTWrVuUK1cOE5PMd7cSeU9msI1thy+0KQ2Wuf9HuvPSfUavP5f2+NSnbXCxsXj2DTot7J8BF9ZD4oP05+sMho4LwEyT67HmlpMn79Gq1Y9pj+fMOUzv3lWoVCl9X20Hh8zXPMekxPCL9y+sv76e6GRD68IyNmV4t9q7dPfonm4mOu7wYcJXriLxrOHNTZEWzXEZPx6LypWz87KEEEK84g4cOMCoUaO4ceMGcXFxTJ06FQAPDw8jRyZykyTYueFAALy7Gyraw/K2UNMlV4Y97hvBmPXniIhPSTt2cXo7bDNqL6fXg+8/cOEXuPpHxgPW7GdIrPNR+cezNGzoTpUqznh7hwGQkqJj2LAdHDw4GJMX7S75DH8H/M30/6antdorVbQUI2qOoEv5LumuTfLxIXzpUmL/frTlu8ukSTgOeS9bzy2EEOLVFhISwsSJE/n1118B8PT0lO4ghZgk2DmVqIVJ/xq+vxEJHbbC583gverZHjJZq2PcxgvsvhKcdsyxiDn/TGz5ZHKt18GV3+Gf2RB1J/1Anl2h/TxDS70CViNs2KmqPqNH7wKgZcsyfPpp0yy/DJ1ex+mQ02z22czfAYYOKS6WLnza8FNal2qdbsZaFxdP6IIFRG3aZDhgZoZdjx44jx8nbfeEEEJkmV6vZ/Xq1Xz88cdERUVhYWHB9OnTmThxYlp5iCh8JMHOqeXn4W7so8eKYmjRl013IhJovuDgE8e8BtahXRVXw8ytosDtw3DsO7hzHFLiHl1oag5WTvDaGKjxVr7YpvxZFEVh61Zvtm3z4eef05dmAAwaVIODB/2ZMKERjRuXzNL4MSkxrL2ylj9u/pHWFQRgSLUhfFD7A0wzaDcYvXMnIbPnoIs2lI7Y9euL0/vvS3cQIYQQ2bZjxw5GjBgBQIcOHVi2bBnlypUzclQir0mCnVP9POFmJPz2cJOXITWggVuWhtDrFU74RfDFnutcemxL8151S7CgVw1D8hl6HXaMMyTVT2s8Bqr1BPc6OXklL83Bg7f5/PMjHDhwG4D+/avTsWOFdNcVLaphy5beWRpbURQWnV3ElhtbiE+NN4yjLkqj4o0YX2c8JW3SJ+qpQUEEfTqVhBMnANBUqIDbnNlY1qqV1ZcmhBBCoNfr0xYrdunShX79+tG9e3d69eolXadeEdJFJLecvA8LT8Pq9mCb+cWDSak6Oi45gl94/BPHfx/1GnVK2RtmrLe8A95/PnmjpT1U7wNtZ4P6OQse86H33tvG2rUX0h6/9lpJjh59N8e/dE4Hn2b8v+PTFi/aa+yZ23QujYs3Rm2Svm5d0el48ONPhC9fjj4uDpWVFc5jxuDwzmBUsopbCCFENvz5559MnjyZXbt2ycLFQiC7XUQkwTYSvV5h05m7fPbnFXR6w8+gipsNHaq5Mqx5OSxM9HDoSziyCBSd4SZTDXT8CmoPync7KmbFlSuhVK++Iu2xSgVXroyiShXnbI2Xokth1vFZbPfdDoC5iTlTG02lR4Uez74nMJCgSZNJPH8eAMu6dXFftAh1sdxZoCqEEOLVEhAQwNixY9m+3fBv0dixY1myZImRoxI5JW36CpDg6CQazT/wxLFFfWrSo04JSIqBvR/Brf1PLlxsOgFen/GSI82ZpCQtFhnsXlmtmgvt25dn715fWrcuy7JlHalcOXv14seDjjPs72EAmKnM6Fy+M5PqT8LG3OaZ90T9+Sf3P5sGWi0mtra4zZolm8UIIYTIltTUVBYvXszMmTNJSEigaNGizJs3j5EjRxo7NGFEMoP9km0+fZfJv11Ke1zSwZI1g+pRKf4U/PM5BJ178oaWn0L9Ifl6weLTTpwIZMqU/Wi1ev77L+O2dufO3Sc2NpnmzUtnK7G9F3ePeSfncTjwMABmJmasaruK+q71n3lPir8/4V4rif7TUG5TpGlT3ObOQe3qmuXnF0IIIS5evMigQYO4fPkyAH369OGbb76hePHiRo5M5BaZwS4A9l4NfiK5/rZPNbpaXoKVTy28c64MxWtD2zlgnb2yCWMIDY1nypT9rFtnqK82NVURHZ2ErW36GvE6dbK2EPRxR+8d5eMjH6fVWg+qMoixtcdiYZZxLbqSmkrEmjWELVsOWi0AjkPfx2XixGzHIIQQQlhYWODj40O5cuVYtmwZHTp0MHZIIp+QBDurdvnBotPQpzL0qAhOmdtFcNqfV/j5RAAATYuG8GOxDZhuP/XkRU6VoMsSKN04t6N+KZKTtWzefDXtsU6ncPCgP2++mTu7HiakJjDj2Az2+O8BwMPOg9mvzaa687N7jsfu30/wnLloQ0IAsG7TBpcJ49GUL58rMQkhhHh1KIrC3r17ad++PSqVikqVKrFr1y5ee+01LC0zv6uwKPwkwc4KrR7mHje05bsYBjP+g08bwQfPbo+XqtPz/o9nOHQjjEYm3swzW0O51GAIfOyiGn2h/ecFqgwkIyVL2vLpp0357LNHfbx9fMJzPK6iKGz22cyS80uITTH0HB/oOZAP63z4zFlrfVIS4cuWEbF6DQBqd3dcJk7ApmPHHMcjhBDi1ePj48PIkSM5ePAgv/zyCwMGDACgTZs2Ro5M5EeSYGfF95cNyfX/6fTQqtQzL79wN4o3l/2HKTq2ms+hnsmNRyedK0PDEVD3nQK3y+LzTJz4Gt9/fx5LSzXfftuBNm1y1kz/37v/suzCMq4/uA5AWduyTK4/mabuTZ95T9L16wSOHkPqvXsA2A8aRLEpk1GZyV93IYQQWZOYmMj8+fP58ssvSUlJwcnJSXZgFC8kGUdWKArYmENMiuHxW5WfuWvjsoO3WLDXhyoqf7aZT0Otethqz0QNvddC5c4FMrFWFAU/v0jKl3fI8LyFhRl79gykbFk71OrstxIMjg9mwekF7AvYl3ZsWqNp9KrYCxNVxj2qFUUh7NtviVjhBRhmrV1nTMe6efNsxyGEEOLVtW/fPkaNGoWvry8A77//Pl988QWOjo5Gjkzkd5JgZ8WIWtDfE364DL96w2fpa6X1eoVhP5/l8LVAflB/Q2vTRxuq0OVbqDv4JQacuyIiEhgyZDu7d99i7dpu9O+fce1zxYrZ/8UTkRjBe3vfwy/aL+1Y+zLtmVJ/Cs5Wz17wqY+PJ+jjj4n9ez8ANl264Dp9GqZFi2Y7FiGEEK+urVu30ru3YTfhqlWr4uXlRdOmz/70VIjH5WmbPpVK1QFYApgCaxRF+eKp86WAHwG7h9d8rCjKrueNmW/a9OkVMEk/Az1n+0UcT33NENPdaFSpj04MOwTFC+7W2zduRNCo0RoiI5PSjs2c2YLp01vkWv/oY0HHmHx4clp3kNdLvc6Y2mMob/f8BYnxJ08RPH06KQEBmFhZ4TZ/Pjbt2+VKTEIIIV5NycnJNGrUiL59+zJhwgTU6vQ7AovCL9/t5KhSqUyBG0BbDEv6TgP9FEXxfuyaVcB5RVFWqFSqKsAuRVHKPG/cfJNgP+Xg9VAW/riJHZrPHh20cjS02qs9wHiB5RK9XqFJkx84ceLR6kyNxpQjR96lfn33HI2t1Wv59ty3rL26FgAHCwdWtV1FJYdKz48pPp6gTz4ldp+hjMSkSBFK//oLFpVzp2uJEEKIV8e5c+f47LPP+OWXX3BwMJRB6nQ6TE0L7s7JIueym2BnXMyaOxoAtxRF8VMUJQXYCHR76hoF+P+We7ZAUB7Gk2cuBUax9+cvn0iuU6r1hUm+hSK5BjAxUbFkyaP+ntWquXD+/PAcJ9d+0X703N4zLbnu7tGdnd13vjC5Tjh3Dt+OnYjdtw+VWo3924Pw+PegJNdCCCGyJDY2lvHjx1O/fn12797NvHnz0s5Jci2yKy9rsN2Bu489DgQaPnXNTGCfSqX6ACgCvJ7RQCqVahgwDKBUqWd37XjZUnV6hv90hhq+K/hC/TsAegt7TMacwtzaxcjR5b4GDdz54IMGuLsXZfz4xpibZ/8XT6o+Fa+LXvxw+Qe0ihajxm8UAAAgAElEQVRbjS1zm8ylZcmWL7w3/tQp7rw3BLRaNBUr4jZ3DpY1amQ7FiGEEK8eRVH4/fff+fDDD7l37x4mJiaMGzeOGTNmGDs0UQgYe5FjP2CdoigLVSpVY+BnlUpVTVEU/eMXKYqyClgFhhKRlxrhD5ehlgvUKfbEYZ1eofc3O5gbM41qZv4AJDjXwmrYXlBn3Ju5INiy5SouLkVo0aJMhue//faNHD/HDr8dLD2/lHtxhjZ6TdybML/pfOwt7J97n6IohC1eQsTKlQBY1atHqbU/oJK6OCGEEFng7+/PmDFj2LlzJwD169dn5cqV1K5d28iRicIiLxPse8Dje4CXeHjscUOADgCKohxXqVQWgBMQmodxZd6DRJj5HyRq4c0KMLURlLFFr1foufAv/owflFZko20zC6tm44wbbw7cuxfDhx/u4bffrlGmjB2XLo2gaFFNrj5HaEIoc0/M5eBdw0Y0pipT5jadS+dynV94ry46mnuTJhF/+AgANp064fb5XEmuhRBCZJm/vz87d+7ExsaG+fPnM3z4cCkHEbkqLxPs00AFlUpVFkNi3Rfo/9Q1d4A2wDqVSuUJWABheRhT1vzsbUiuAf68CRdCub2rO+8vWs/v5jPg/80zBu/ArGwzo4WZU1FRSdSo4cWDB4kA+PtHMWnS33h5vTjxzawdfjv45MgnaY8bujVkbpO5uBZxfeG9CefPc2/8BLTBwZhYW+M2fx42bdvmWmxCCCEKv1u3buHh4QFAy5YtWblyJV27dsXV9cX/DgmRVXm2yFFRFC0wBtgLXAM2K4pyVaVSzVapVF0fXjYRGKpSqS4CG4B3lLzsG5gVOr2hPOQxV96qQN+Ff7DNfBq2qgT0qOC9fVCAk2sAOzsL3nvvyRaCa9de4M6d6ByPHZcSx/yT89OSaw87D4ZUG8KadmsylVxH/fEnAf0HoA0OxszVlTKbN0lyLYQQItMiIiJ4//33qVixIkeOHEk7PmzYMEmuRZ7J0xrshz2tdz11bPpj33sDTfIyhmxTgE8awqpLcDmMXR7WTAy/xw7zz7FWJZFgUQzLwVvAraaxI80Vs2e3Yts2H27efEDdum6sW/cmpUrZ5mhM3yhfxv4zljuxdwDoV7kfnzT4JFN9sxVFIWrjRoLnfg6KQtEOHSj+xXxMLApufbsQQoiXR1EUfvzxRyZNmkR4eDjm5uZ4e3vTrFnBnhQTBYOxFznmX2Ym0NcT3qrMiO+OcSAonD3mUyhvcp9Uc3usRv0LNsWNHWWusbRU88MP3ThzJogPPmiAqWn2P9yIS4ljypEpHA48DBj6Ws9oPIPWpVpn6n5Fq+VGg4boExIAsO3ZA7c5c1CZ5GVXSSGEEIXFtWvXGDFiBIcPG/4datWqFcuXL6eytHIVL4kk2C9wyj+SvUEP+FG9gPIm9wFQ9/upQCbXAQFRXLoUQpcuGfeYbtq0FE2b5qwNol+UH2P+GcPdWEOHxjal2jCz8UzsLOwydb+i1RI4blxacu04bBjO48fl2m6RQgghCrc//viDt956i9TUVJydnVm0aBEDBgyQf0fESyUJ9nPcDo+n38qj7DL/FE+Thy29+2+Bss2NG1g2HDkSQI8em4mNTc6V3Rcz4h3hzcBdA0nVp+Ju7c7CFgup6lQ10/enhoZyb9x4Es+dA6D4119j27lTrscphBCi8GrWrBm2trZ0796dL774Im1XRiFeJvnM/RkURaH/6hO8Y7rnUXLdbRlUbGfcwLLh++/P0br1T4SHJ5CcrOPNNzdx/35srj6Hd4Q3ow+MJlWfSjnbcmzusjnTybWiKDz46Wd823cg8dw5TGxtKblmjSTXQgghXigoKIgJEyaQkpICgJOTEzdu3GDVqlWSXAujkQQ7A4qiMHr9OWrEHmaa+lfDwRZToPZA4waWTWq1KVrto717goJiWbHiTK6MHZ8az6Kzi3hrx1uEJ4bj6eDJhk4bsDG3ydT9SkoKQR9NImTePJTERKwaNaLspo1YN82fa1+FEELkDzqdju+++47KlSvzzTffsHDhwrRz9vbP37hMiLwmJSJP23aLb+5HsNs/lCOaXwzHnCpCi4+NG1cOvP12TS5dCmHhwuMAzJzZgmnTWuR43KikKAbuHkhATAAANZxr4PW6F1Zqq0zdrw0L487QYSRfv45KrcZ11izsenTPcVxCCCEKt7NnzzJ8+HDOnj0LQJcuXejf/+mtNoQwHkmwH6coHFp1lm+rWzPT7CdKqMJRTC1QvbMLCngHiy+/fB1//yj69atGz55VcjxecHwwI/ePJCAmAEszS75u8TXNS2S+Nl374AEBg98hxc8PUzs73BcvpkijhjmOSwghROEVExPDtGnTWLp0KXq9npIlS/Ldd9/RrVs3Y4cmxBMkwX7MxX/9GVzdmtdNzvKO2T4AVK2/BmtnI0eWc6amJmzd2idXxvJ54MPI/SMJSwzD3dqd1e1WU7JoyUzfn+TtzZ3hw9GFhWNWrBilf/0V8xK5v+hSCCFE4bJjxw6+/fZbTE1NmThxIjNnzsTa2trYYQmRjiTYQKpOz5L9N1l68BbORLFKvchwIqYJNBlk3OAySafTM2vWIapVc6FPn8x37siq08GnGX1gNInaRMrZlmN1u9W4WLlk+v64I0cJHDMGJTkZs+JulPn5Z9TuklwLIYTIWFxcXFoS3a9fP06dOsW7775LzZqFY6M3UTi98gm2Tq/QeP4/hMclA7DO/EtMVArE2EHThS+4O39ITdUxaNAfbNp0FY3GFHf3ojRpkrN+1k9TFIX119ez5NwSErWJ1HCuwfI2y7HVZH63x7hDh7g7ajTodJh7lKfMxo2YysyDEEKIDKSkpPD111/z9ddfc+rUKTw8PFCpVCxevNjYoQnxQgW7sDgXfPfPzbTkelGpY1Q1MSzYw246dKxgxMgyb8qU/WzadBWA5GQdXbtu5ObNiFwbPyYlhnf3vssXp74gUZtIq5KtWNd+XZaS68iNG9OSa5uuXSj3+++SXAshhMjQ4cOHqVWrFlOnTiUyMpJt27YZOyQhsuSVTrAv3I1i8f6bAHz8RmV6qE8YTtTsD9OHgEXBmOAfMqQ2ZmaPfpSKohAQEJ0rY9+Nvctbf73F2ZCzmJmYMa3RNBa3WozaVJ2p+xVFIXzFCoJnzgKdDvsBAyj+xReozM1zJT4hhBCFR3h4OO+++y4tWrTg2rVrVKhQgf379zNx4kRjhyZElryyCXaKVs+QdacB0JiZ8H7ZCLh3BlQm0PErI0eXNVWrujB+fCMAypa14/z54bz+erkcj3sz8iYDdw0kMC4QF0sXNnbaSJ9KfTBRZf6vTcjczwlb8i0ATqNG4TrtM1QFvCOLEEKI3Ldnzx4qVarEunXr0Gg0zJo1i0uXLtGmTRtjhyZElhWMKdo8MOrXc0TEG3Z9OjChOWZbH+4aWKs/aIoaMbLsmT69BQEB0Xz33Ru4uBTJ8XhXI64ydN9QYlNiqexQmZVtV+JgkfkdsbTh4YQu+obo338HExNcZ87Avk/udDERQghR+JQtW5a4uDjatGnDihUrqFChYJRpCpGRVzLBDo1JYv+1EACmdvSkRPRZCDoPZpbQZqZxg3uOuLgUrKzUmJio0p2ztjZn06ZeufI8p4NPM+bAGBK0CdR2qZ2lzWMAUkND8e/dB22I4c+4+Px52EqPUiGEEI9JSEjgl19+YejQoahUKipVqsSZM2eoVq0aKlX6f+eEKEheyc/qVx/xA6CIuSlDGpeA3z4ynGg4LN/2vL5/P5YGDVYzb96RPH2ezT6beW/veyRoE/B08GRl25VZSq61kZHceeddtCEhDzuFbJDkWgghxBN27txJ1apVGT58OBs2bEg7Xr16dUmuRaHwyiXYcclaVh+5DcD4thUx2TMH4nwgWQOLSsKs/0CnN3KUT4qJSaZTp/VcuxbO9OkH2bnzRp48zx83/2DuibkAuBZx5eeOP2NpZpnp+7WRkQQMGEiKnx/mpUtTet06LGvVypNYhRBCFDyBgYH07NmTzp074+/vT82aNfHw8DB2WELkulcuwZ6/6xpgWNg4sFFpuPKw9c+t6nAmGf69C6b5548lMTGVbt02cv58MACKAgMG/I6v74NcfZ7lF5Yz/dh0FBQGeA5gX899aEw1mb5f++AB/r37GLY+t7en5OpVmDk55WqMQgghCiatVsuSJUvw9PTk999/p0iRIixatIgzZ87QoEEDY4cnRK7LP5nkSxCVkMKvJ+8AML9HdSwC/oXkh32vj3UwfG2Vuxu05JSZmQkNGhR/4lijRiUoXjz3FmIuOruIFRdXANC1fFc+bvBxlj6i0z54gH+ft0gNDMTMxYUyGzdgXip//TkKIYQwnlWrVjFu3Dji4uLo3r07165dY/z48ZiZvZJLwcQr4JVKsE/dNsz6uttZ0qNOCTix3HAi4HVIfVgK0S1/fVSlVpvy5Zdt2b17AM7OVlSt6syWLb2xtMxcH+oX2R+wn7VX1gIwqtYoPm/6eZbu14aHc7tXL1IDA9Nmrs1Ll86V2IQQQhRciqKkfT9kyBDatWvH9u3b+f333ylZsqQRIxMi771Sbx1/Om6YrW5e0RmSosH3oOHEN9/AkmJwLgRq5M9Fjh06eHDx4gg0GjOKFs186cbz7PTbycdHPgZgoOdARtYcmaX79QkJ3B02HG3Qfczc3Cj980+YlyiRK7EJIYQomBRFYdOmTcyfP5+DBw/i4OCARqNh7969xg5NiJfmlZnBDo5O4uitcADaVS0G13eBogO3WmBXCmw00LIU5OPVy25uRXFwyPyiw+d5PLnu7tGdj+p9lKX7U+/d43av3iR5e6MyN6fkiuWSXAshxCvu1q1bdOjQgX79+nHp0iVWrlxp7JCEMIpXJsFesNcHgNaVXWhVyQUurjec8HjdiFE9Sa9XOHIkIE+fIyE1gWn/TUtLrtuVbses12ZhamKa6TFS793Df9Agw4JGZydKb1iPReXKeRWyEEKIfC45OZk5c+ZQrVo19u3bh729PatXr2bKlCnGDk0Io3glSkRCY5L443wgAJ1ruEFsMAQcM5ys3MmIkT2iKAoff7yfBQuOsXx5R0aOrJ/rzxGdHE3LTS3RKloABlcZzMR6E7O0oFFRFALHT0grCym7dQtmjo65HqsQQoiC4dixY7z33nv4+Bgmst5++20WLFiAi4uLkSMTwnheiQR74+m76BVws7Wge2132P4B6LVQ6jVwr2Ps8ACYPfsQCxYYkv5Ro3aRnKxj3LhGuTZ+dHI0Yw6MQatoMVWZsrbDWmq71M7yOBErV5F06RImNjaUWf+rJNdCCPGKS0lJwcfHh0qVKrFixQpatWpl7JCEMLpXokTkhF8EAKNbeaDSJsH5nw0nag42YlSPhIXFs2TJySeOzZt3hMjIxFwZX6fXMfafsVwIu4CVmRVbumzJXnK9bh1hixcD4PLRRNRubrkSnxBCiIJDr9dz6NChtMctW7bkzz//5OLFi5JcC/FQoU+w45O1ae35XvcsBtd3PjrZOwpG/w37bkOqzkgRgrNzEbZt64uFheEDBXt7C3bvHoC9fe4saPz85OecCz2H2kTNug7rqGBfIctjRP32G6FffAmA4/tDsO/TJ1diE0IIUXBcunSJpk2b0rJlS44cOZJ2vFu3bmg0udPhSojCoNAn2OuO+aPVK1Rzt8HV1gJOehlOHO8AsVrY7AMDd0JsilHjbNasNBs39sTKSs22bX2pW7f4i2/KhDWX17DlxhYAlr++HE9HzyyPEbN3H/enfgaATZcuuHyUtY4jQgghCrb4+HgmT55MnTp1OH78OK6ursTFxRk7LCHyrUJdg52i1ad1D+leu4Rhn/Eww2N8H0s0qzhBLrW/y4lu3Srj7/8hzs5FcmW8rTe2suTcEgCm1J9CI7es13TH7NnLvXHjALDt1RO3OXNyJTYhhBAFw19//cWYMWO4c+cOKpWK0aNHM3fuXOzs7IwdmhD5VqFOsCduuZj2/TuvlYGQK5AcAya2kOoGpBpONs6d2eLckFvJ9YE7B5h1fBYAfSr2YWCVgVkeI/HKVe5NmgSAVb16uE2fnqWOI0IIIQq2pUuX8sEHHwBQu3ZtVq5cSf36ud/lSojCplCXiNwMiQVgeItymJqo4NJmw4mq7eDW+7CnF0xrDD2yXpOcEwcP3s7T8S+GXeSjQ4YyjibuTZjaaGqWx9BFRXF3+HBITcWyTh1KrvRCZW6e26EKIYTIx/r27UuZMmVYvHgxp06dkuRaiEwqtAn2lXvRXA82JNgftH6YQN85bvhatTuoTaGuK4ytC/VfXjeMpUtP0br1T0ydegBFUXJ9/PDEcMYfHI9Wr6V5ieYsa70ME1XWfsxKSgoB77yLLiIC8/LlKfX9GkyK5M7MuhBCiPzr5MmTvPXWW6SkGNYlOTk5cePGDT788EPMzAr1h95C5KpCm2AvOXATgGruNlhrzCAmCAJPg8oUSr9mlJhu3XrA5Ml/AzBv3lEmTNibq0l2UFwQ7+55l7DEMKo6VmVB8wVZ2qERQJ+Swt0PPiD5+nUASi5fhoml8evThRBC5J3IyEhGjhxJ48aN2bx5M8uXL087p1arjRiZEAVToUywFUXhb+8QAMa/XtFw0O9fw9eSDcHS/qXHpNPp6dFjE4mJ2rRjXl5n8fePypXxr0Zcpdf2XvjH+ONo4cg3Lb/BSm2VpTEUReHu8OHEHzoMpqaUXL0a89KlcyU+IYQQ+Y+iKKxfv57KlSvj5eWFqakpn3zyCcOGDTN2aEIUaIUywT7h9yDt+9aVH27V6nvQ8LXSG0aICExNTfjuuzdwdHw0G7x8eUfKls15sn8n5g4j/x5JbGosHnYebOy8ETfrrJW9KHo9gR98QMLxE6gsLSm5ciXWzZrmODYhhBD5040bN2jbti0DBgwgNDSUpk2bcuHCBebNm4eVVdYmaIQQTyqUCfZ/t8IB6FaruKHrhV4PtwylGZRvbbS4WrQow+nTQ6le3YXevavwzju1cjzmwTsH6fVXLyKTI/Gw8+CXjr/gWsQ1y+OEzP2cuP0HQK2mxHffYd20SY5jE0IIkX+dOHGCAwcO4ODgwPfff8+hQ4eoWrWqscMSolAolCsWDt8MA6BVpYez1xE3ITESrFxgwBVoHAldPKBuMTB7ue8xypa159ixIajVJjlueXc6+DQfHvwQBYUS1iX46Y2fKKLO+mLEmD17iFy/HoASS5ZIci2EEIXUnTt3KFWqFACDBg3i/v37DBkyBCcnJyNHJkThUuhmsENjk7gUGA1AswoPf2GEGRbsofKAS+Gw8iJ0/g16/GmUGK2tzdFocvbe5kbkDSb8OwEFhcZujfnzzT8pal40y+Mk37xJ0KTJADgOH07R1q1yFJcQQoj8JyQkhAEDBlCxYkVu3jQ0AVCpVEyZMkWSayHyQKFLsL/5+wYAFYtZ42itMRw8/4vha9RTG8pUccyzOPT63G/B938n759k8O7BRCVH0cS9CUvbLEVjqsnyOPrERO68NwQlNZUirzXGeewHeRCtEEIIY9Hr9Xh5eVGpUiXWr1+PSqXi/Pnzxg5LiEKv0JWIeN839L52tX2stVzUHcPXsKfepdtkPSnNrA8/3I2iwDfftEetzlqrvOfxjvBm7D9jSdAm8Frx11jUYhHmplnfAEZJSeHO+0PRhoVh6uCA2xdfoDLNvTiFEEIY18WLFxkxYgQnTpwA4I033mDp0qWUK1fOyJEJUfgVqgRbq9PjExwDwPjXH24uo01+VCIyaCjUj4eT92HbLehVMU/i2LTpCkuXngbg+vVwNm/ujYNDzntJX424yrt73iVRm0jzEs1Z0moJZibZ+xHeHTOGxLNnwcyM4l9+idrFJcfxCSGEyB/WrFnDiBEj0Ol0FC9enCVLltCzZ88cr/0RQmROoSoRuRoUQ1KqHlcbC2qXetj+7o7hnTuOHtDMA4bWhDUdIGwMVHTI9Rju349l6NC/0h4fOHCbHj025Xjck/dP8t6e90jUJlLHpQ4LWyzMdnId/ddfxB8+AkDJlV7Sjk8IIQqZZs2aYW5uztixY7l27Rq9evWS5FqIl6hQJdhbzwYCULuU3aOD/98evUSDlxKDr28kZo91JjEzM2H27JwtHNwfsJ9R+0eRoE2gZYmWeLX1wsLMIltjJV69yv3PpgHgNHo01k2kY4gQQhR0AQEBzJgxI2134EqVKuHv78+SJUuwsbExcnRCvHoKVYJ9wi8CgGruto8OpiYYvpq8nJfatGkpvL1H07t3FQC++up1mjfP/m6Id2PuMvnwZFL0KXQs25FvWn2DpVn2yk204eEEjhqNkpxMkWbNcBo1MttxCSGEML7U1FS++uorqlSpwuzZs9mwYUPaORcp/RPCaApNDXZYbDI3Q+MAeLdJmUcnAs8avlbu/NJicXW1ZvPm3uzefZM2bbK/mCRFl8KoA6NI1adS2qY0nzf9PNtlIQAhX36FNiQETcWKuH+zSBY1CiFEAXbs2DGGDx/OlStXAHjrrbdo2bKlcYMSQgCFaAb7tL9he/TapeywMn+YhCoKBF82fO9a/aXH9MYbFTA3z14SqygK049Nxz/GHxdLF75v932OkuvYAweI+ctQG+72+VxMra2zPZYQQgjjefDgAcOGDaNJkyZcuXKFcuXKsXv3bjZu3Ejx4sVfPIAQIs8VmgR70+m7ABQr+lhtcvRdSI4GjQ2cSIFbkUaKLutWXFzBTr+dqFCxuNViihUplu2xtBERBH06FQDHoUOxrP7y32wIIYTIHatWrWL16tWo1Wo+++wzrly5QocOHYwdlhDiMYWmRORqkGH3xnpl7B8dDDZ8bIZFRRi4y/C9syW0KgXL2ubK84aGxmNvb5Grva6PBx1nxcUVAMxpMofqzjlLiO8OHYY+OhqLqlVx/mBMboQohBDiJUpMTMTS0rD+Zvz48fj4+DBlyhQqV65s5MiEEBkpNDPYGjNDgtuw7GO7M4ZcNXzVPlYHHZYIidpceU6dTk/Xrhto3nwdfn65MzsemhDKR4c+AqBXxV508+iWo/HiT5wgydsbgOJffoHKPOub0gghhDCOxMREpk2bRoUKFXjwwFAKqdFoWLt2rSTXQuRjhSLBDolJ4l5UImYmKioUe6y2ONzH8DXsqS3RXaxy5XmnTz/IyZP3OHEikFq1vFi//nKOxtMreqYcnkJMSgw1nGvwacNPczSeotUSMv8LAOx690bj4ZGj8YQQQrw8+/bto3r16sydO5d79+6xY8cOY4ckhMikQpFgrzrsB0ApRyssHi/V+H+JiLMn1HB+dLxN9tvm/d/Jk4HMn3807XFsbAqbN19N60GaVYqiMPnwZM6EnEFjquHzJp+jNlHnKMbQBQtI9vFBZWGB0xgpDRFCiILg/v379O3bl/bt2+Pr60u1atU4evQob7/9trFDE0JkUqGowU5I0QFPLXDU6yHipuH7sT1hkjWk6OBmJJTOedP9mJhkKlVy4vr1cMDQms/Lq3O2d8rafXs3e/33ArCo5SLK2JbJUXxxR47y4MefACj+xXzUxaQfqhBC5HebNm1i2LBhxMTEYGlpycyZMxk/fjxqdc4mXIQQL1ehmMG+HhwDwNDmZR8djLwNei1YOYHmYdmIuSlUdQLrnNcht21bnqtXR/Hbb32oW9eNH398E1fX7LW+S0hNYO7JuQB8VO8jmpdonqPYFEUheNYsAKxbtsRGVpcLIUSBUKxYMWJiYujUqRPe3t5MnjxZkmshCqBCMYN9OzweAE+3x2am7180fHWrmWfPa2KiokcPT7p3r5ztmWuA9dfXE5sSSxmbMgz0HJjjuKI2biQ10LBtvOv0aTkeTwghRN6IiYlh586d9OvXD4CWLVty+vRp6tatm6N/V4QQxlXgZ7CjE1OJSkjFUm2Kq81jJSIRtwxfHbK/k2Jm5eSXYHRyNGsurwFgYr2JmJrkrN2fkppK8KzZALjOmI5aNh0QQoh8R1EUtm7diqenJ/379+fIkSNp5+rVqyfJtRAFXIGfwfYLM2yPXtrR6slfSHEhhq+O5Y0QVeYtPreY+NR4arvUpmXJljkeL3ju5wCoS5XCrm/fHI8nhBAid92+fZv/sXff4VGV2QPHv++kTHogDQi9SUJvUqTFuuiydtRFQVlXBRRFEFHUVRFUFMXCUtQFRRd3RVDQHyIuKIIK0nuRGgIkhPSeycz7++NOQkJJJplMJhPO53ny3Dsz975z2GeFw8u55zz22GOsWGHMZ7jyyisJDQ11c1RCiOrk8TvYvx81+oK2igws+0Fxgh1U9QmI5ysoqJ7+2cUOpB5gycElAIztNtbp9XJ++430//4XgIhRo2QHRAghapHCwkJef/11OnTowIoVKwgNDWX27Nn89ttvdO7c2d3hCSGqkccn2AcSswAosNjKfpBitO7j7T9g1Cp4fSN8vg8KrFX6nh07EmnZ8l0WLtxR5VZ8pVmsFib9PAmN5o62d3BlwyudWs9WWMjpF18CIOSmG6l3+21OxyiEEKL6TJkyhWeffZa8vDz++te/sn//fkaPHo2XV/VNAhZC1A4en2BvO5EOQNsGwWU/OGOf4rg+B5YchLc2wZNrwKtqu7oTJ/7A6dPZ3H//1wwYsIBdu5KcCZuPdn3E4YzDhPmF8WSPJ51aC+wPNsbHYwoOptG0aU6vJ4QQwnmlN2TGjRtHnz59WLVqFYsWLaJhw4ZujEwI4Uoen2AH+xll5F2b1jv3ZkHWufPM+ufOW9UD78r/kr///hA//HCk5PUvv5zg2LH0Sq9T7IfjPzB7x2wApvabSqjZudq7orNnOTPzHQAixz2Byd/fqfWEEEI4R2vNxx9/zKBBgygsLAQgIiKC3377jeuvv97N0QkhXM2jE2ybTXPojPGQY88WpRLpjJPG0bcxWEv1D21VtUR2z55kvEsl5tdc05IhQ66o0loZBRk8v/55AIbFDGNAkwFVWkOh4mkAACAASURBVKeY1ppTzzyLzsvD3D6W+nfd5dR6QgghnLNv3z7i4uIYOXIk69at4/PPP3d3SEKIGubRCXZ8ai65hVYigsxEBJnPfVA8wbFxDHx7B7x9NYzuCn9qefGFKjB+fF/27XuUoUPboxS88cZ1VX6A8M1Nb5JblEvnyM480+uZKq1RWtIrr5Czfj3Kx4fo115HyUACIYRwi9zcXJ577jm6dOnCzz//TGRkJJ9++qmMOBfiMuTRbfp2ncwAoGGouewHyQeMY8NY6N3I+HFSmzZhfPHFUA4dSqVNm7AqrbHy6EqWHV4GwGNdH3O6y4c1M5O0RcbOSOSE8fi1q9quuhBCCOf873//4+GHH+bo0aMAPPLII7z22mvUr1+/gjuFEHWRRyfYx1OMCY5N6gWU/eDsQeMYUf0JZ1WT6yPpR3h2/bMAjOwwkr7RfZ2OJfmdd0rOw+6/3+n1hBBCVM2pU6c4evQonTp1Yt68efTt6/zv8UIIz+XRCfaW42kAXNf+vF7XLkywq2rKhikU2YroHtWdcT3GOb1e9tq1JbvXjd+ZKT2vhRCiBlmtVrZu3cqVVxotVocPH46Xlxd33XUXPlKqJ8Rlz+EabKVUQMVX1awfDyQD0LVpqYcXtYbTO43zyHZuiOpCG09vZEvSFnxMPrwx8A1MyrnSd2t2DqeengRA/XvvJWTw4OoIUwghhAM2b95M7969GTBgAIcOHQJAKcW9994rybUQAnAgwVZKXaWU2gvst7/uopSa7fLIKpBvOTcwpnl4qSmOBZmgreDtBwFVK+ewWm3MmbOJjIx8Z8Ok0FrIi7++CMCoLqNoEOj8ZMmk117FmpGBT3Q0DZ6Z5PR6QgghKpaRkcHYsWPp1asXW7ZsISoqisTERHeHJYSohRzZSp0J/AlIAdBa7wAGujIoR5xMzwMgOtQPH69Sv4w8o2yEonzYcAqScoxd7UpYtuwAY8asoGnTmUyY8D3x8RlVjvOTPZ9wMvsk0YHRPNDhgSqvUyx96VdkLFkKStHo1WnSNUQIIVxMa83ixYuJjY1l1qxZmEwmnnrqKfbu3Uv//v3dHZ4QohZyqAZba33ivBrfqs0br0ZpOUbj/rP2Y4ncVOOoW8BflhrngT7wSn8Y3qHCdYuKbLz88loAsrIKefvtDaSl5TN//i2VjjHXkstHuz4CYGz3sfh6+VZ6jdIsp09z+kVjNzxizBgC+/Rxaj0hhBAV+8c//sHUqVMB6NOnD3PnzqVLly5ujkoIUZs5soN9Qil1FaCVUj5KqaeAfS6Oq0KJmUb5xsC2kWU/yD5jHPNKlY3kWMDfsec5P/10Bzt3lh2D/uSTVUtkP9j5AblFucSGxTKk1ZAqrVFa6sefgMWCX6dORDz2qNPrCSGEqNh9991HgwYNmDdvHr/88osk10KICjmSYI8CHgUaAyeBrsAYVwbliLNZBcBFemCf2GgcM4LKvt/SsSmOw4d3Ye7cP9OggZGgDxvWiU6dKl83nZKXwqd7PwVgTFfn/+eyZueQ/uWXAERNfEq6hgghhIusXbuW0aNHo+3lhe3ateP48eM8/PDDmEwePZ9NCFFDHPmdop3W+l6tdQOtdZTW+j4g1tWBVWRHglEXHR54XoJttZeM1A+BzpEQYK9RblXPoXW9vU088khP/vhjLC+9NIi33rqhSvHN3z2fQlshV0VfRVzTuCqtUdqZt2Zgy8nBt2VLAuxtoYQQQlSf5ORkHnjgAeLi4pg7dy5Lly4t+cxsNpdzpxBClOVI3cT7QHcH3qtR/r5eAFistrIfpB0zjsPugtfvMh5wTMyB+n6VWj842MyLL8ZVKbYj6Uf4bN9ngNE5xFmFCQmkf7EYgKhJT8vutRBCVCObzcaCBQt4+umnSU1NxWw2M3nyZIYMcb60Twhxebpkgq2U6gtcBUQqpcaX+igE8HJ1YBU5dtaY4til6Xk70ylGT1LqtzSOSkGj88pFXGzuzrnYtI0bmt9At6huTq+X8uFHYLUS0KsXwXFxzgcohBACgN27dzN69GjWr18PwHXXXcfs2bNp27atmyMTQniy8kpEfIEgjCQ8uNRPJnCnI4srpQYrpQ4opQ4ppZ65xDV3KaX2KqX2KKUWORr4H2eyAWgbdV7ynB5vHCPaOLpUtdp4eiPfHf0OgEe7Of8gouXUKdLt/0wZ9dQEp9cTQghxztKlS1m/fj0NGjRg0aJFrFq1SpJrIYTTLrmDrbVeC6xVSn2stT5e2YWVUl7AP4HrgQRgk1JqudZ6b6lr2gLPAv201mlKqShH1rZYbSTbH3KMrud/7oOCLLDkgskb/Byrua5OWmtmbzdm8Nx1xV20Cm3l9JqJ014Fi4XAq/ri16mT0+sJIcTlLjExkYYNGwIwadIkioqKGD9+PPXq1fyfG0KIusmRhxxzlVJvKqVWKKXWFP84cF8v4JDW+ojWuhD4D3B+M+mHgH9qrdMAtNZnHAn6jD25Dg/0xc+nVLXKmf3GsX4LozTEQS+//BN///tyNm06WfLUeFV8fehrtp7ZCsBDnR+q8jrFstb8SPbq1aAUDV96SWqvhRDCCQkJCdxxxx107tyZ1FRjZoLZbGbKlCmSXAshqpUjCfa/McaktwReBo4Bmxy4rzFwotTrBPt7pV0BXKGU+kUptUEpNfhiCymlHlZKbVZKbU5OTuZAYiYA6XmWshcWt+gzBzsQniE7u5D33vudf/1rG716fUSPHh9w8GCKw/cXK7AW8P629wF4ptczNAxsWOk1zpcy/18AhNw4GN9mzZxeTwghLkdFRUW88847xMbGsnTpUnJzc9myZYu7wxJC1GGOdBEJ11r/Syn1RKmyEUcSbEe/vy0QBzQBflZKddJap5e+SGv9AfABQM+ePfWJVGNMequIwDKLlYxJ3xcEf18JTYIhOsiY4HiJQTPz5m0m1b4ewJEjaTRpElLpX8g7W94hOS+Z1qGtGRYzrNL3ny977VryNht/AIQ/8ojT6wkhxOXo999/Z9SoUWzbtg2A22+/nXfeeYemTZu6OTIhRF3mSIJdvE18Win1Z+AUEObAfSeB0r+DNbG/V1oCsFFrbQGOKqUOYiTc5SbwVptRxtEw9LzWe4VGZxEOesPv9m4iJgUjO15yrW++OVjm9WOP9SKguHe2g9Lz01m033g+88keTzpdymErKODk05MAqD98OH7t2jm1nhBCXI6mT5/Os88+i9aa5s2bM2vWLGm9J4SoEY6UiExVSoUCE4CngI+AcQ7ctwloq5RqqZTyBe4Blp93zdcYu9copSIwSkaOVLRwUpYxJr13y/Py/Jxk45gefu69MD/wuXRXwTVr7mfFimHceGMbgoJ8eeKJ3hV9/QVmbJ6BTduI8I9gUNNBlb7/fMeH3YstIwPvqCjpHCKEEFXUu3dvvL29mTRpEnv27JHkWghRYyrcwdZaf2s/zQCuBlBK9XPgviKl1GPA9xh9s+drrfcopaYAm7XWy+2f3aCU2gtYgYla6woLoFOyjWmN4UHnTdbKTjKOeaVa9zU8r4zkPCaT4sYb23LjjW1JTc0jLMy/3OvPdyT9CMsOLwPglX6vVOrei0n/6mvy9+wBIHrGm5hkepgQQjjk0KFDrFixgscffxyAuLg4jh49SuPG5z/+I4QQrlXeoBkv4C6MBxNXaq13K6WGAJMBf6DCCSpa6xXAivPe+0epcw2Mt/84LLewCIBgv/PCT7N3E3zzbkgJg/hMCHE8Qa1scm3TNiavnwzAoCaD6N+4f6XuP5+2WEh+910jlr/9jcBevZxaTwghLgcFBQVMnz6dV199lYKCArp168aAAQMAJLkWQrhFeTvY/8Koof4deE8pdQroCTyjtf66JoK7lNxCKwBm71KlHzYrZNpLvAd2A5/KJctV8XPCz+xJ2UOgTyBT+011er20/35BUWIiXuHhRD4+thoiFEKIum3NmjWMHj2agweN52nuv/9+YmJi3ByVEOJyV16C3RPorLW2KaX8gESgtSMlHK62MyEDAF/vUiXkeWmgreAXWiPJNcBHuz4C4P4O91OvGgbbnHnrLQDCRz6Ayc+vgquFEOLydebMGSZMmMBnn30GQLt27Zg7dy5xcXHuDUwIISj/IcdCrbUNQGudDxypDck1QFSwUfYR6FtqBzs/o+zRxTYnbmZH8g68lXf1tOVbtw6dZ7QLDL39dqfXE0KIuuzll1/ms88+w2w288orr7Bjxw5JroUQtUZ5O9gxSqmd9nMFtLa/Vhjl051dHt0lpOUaDzmWGZNe/IBjw4rDWrXqMB98sIUbb2zD4MFtaNy48n2v/7XbGALTo2EPQs2hlb6/NG2zcWbmTABCb7kF7zBHuiAKIcTlpbCwEF9fX8BIsFNSUpg6dSpt2rRxc2RCCFFWeQl2bI1FUUlJmcao9EDfUuEX98DOOVvh/Z9+upMlS/axZMk+AKZOvZrnnhvo8PcfSD3A+pPrMSkTrw943fHALyHj62UU7N2H8vEhckKlnvcUQog6Lycnh5deeonvv/+ezZs34+vrS0REBP/5z3/cHZoQQlzUJRNsrfXxmgykKvxLl4gk7TaOIVfCqWxoEABeF1bA5OQUsnTpvjLv9e1buYleH+/5GIDb2txGhH9Epe69mNOTjU4kYX9/EJ+oKKfXE0KIumL58uWMHTuW+Ph4lFKsWbOGwYMHuzssIYQolyODZmoVm9Yl52UeciyuvV51Crp8DI3nwNBlF9y/bl08ubmWktdNmoQQF9fC4e8/lHaIlcdWAsbDjc7K27Gj5DxsxAin1xNCiLogPj6eW2+9lVtuuYX4+Hi6devGxo0bJbkWQngER0al1yrFY9IvUJhrHLPt9dRWDb4XTnD8059as3PnKJYtO8Dy5Qfo3bsxJpPjo82nbZxGka2Im1vfTMvQlpUN/wInn5oIQOBVV+Fdv77T6wkhhKf74IMPGD9+PDk5OQQHBzN16lTGjBmDt7fH/ZElhLhMOfS7lVLKH2imtT7g4ngqVJxfB5nPHzJz1DhmlkpSo4M4n1KKTp0a0KlTA55/fiBWq83h7z6SfoTNSZvxVt5M6On8CPOCP/7AcuIEAA2em+z0ekIIUReYzWZycnK48847eeedd2RYjBDC41RYIqKU+guwHVhpf91VKbXc1YFdiraXiDSpf16v6wz7kJmG9SHC3+h1UsGYdACvi9RpX8rCvQsBGNxyMGF+znf6SPnkEwACBw7A3Lq10+sJIYQnSktLY8WKc0N/R4wYwbp161i8eLEk10IIj+TIDvZLQC/gJwCt9XallPO1EVVUZN/CLjq/VCTlkHF86zZo0Q+KbGBxfHe6IpmFmXxz+BsAHuz4oNPr2QoKyPhyCQCRjz7q9HpCCOFptNYsWrSI8ePHk5GRwe7du2nTpg1KKfr37+/u8IQQosocSbAtWusMpcrUKV+iELrm5NnHpZewGq37CLJ34fA2GT/VZM72ORTaCrmy4ZW0qe98z9XUBQsA8AoNxa+z21qKCyGEWxw8eJAxY8awevVqAAYMGFDyL5RCCOHpHMlA9yilhgFeSqm2Sqn3gV9dHNclWew10zENg8+9aSu1Ux3cqNq/MzU/lf8cMPqtju/hfJ9qXVhI6mf/BqDBCy9w3l9ehBCizsrPz+ell16iU6dOrF69mvDwcObPn8/atWtp27atu8MTQohq4UiCPRboABQAi4AMYJwrgyqPwkhGT6bnnXszt9QEd/OFDzYCrF17jFdeWcuvv57AYrFe9JpL+XDnhxTZiujbqC8dIzpWOubzpX76GdazZ/Fu2JCQG6XllBDi8jFmzBhefvllCgsLGTlyJPv372fkyJGy0SCEqFMcSbBjtNbPaa2vtP88r7XOd3lkl1DcB7tLk3rn3sxLM45+lx5Z/p//7OYf//iJfv3mEx7+Bv/611aHvu9M7hk+2/cZCsWj3aqnVvrMm28CEDFqFMrrwlaCQghRV02aNIlu3bqxdu1a5s+fT0SE88O6hBCitnEkwX5LKbVPKfWKUsr57VsnFRYZ5SDeXqV2O/LTjWP4xWujtdasXHm45HVWViHR0cEXvfZ8q+ON+sAWoS3oEtmlChGXlbd7T8l56F+GOL2eEELUVjabjblz53LnnXeW1Fe3a9eOLVu2MHDgQDdHJ4QQrlPhQ45a66uVUg2Bu4B5SqkQ4L9a66kuj+4iiofCZOUXnXszPd44HrHAP9YbbfpiwuGGFgDs3JnEsWPpJZf7+XkzaFALh75v9XEjwX6gwwPOhg5A6vx/AWBu2wZTYMVtBIUQwhNt376dUaNGsXHjRgBWr17NddddByDlIEKIOs+hNhta60St9XvAKIye2P9waVTlKH7IsU1UqVprm72mOusMzNkOr/wGn53bKW7YMIh584YwbFgnGjcO5pprWhIQ4FPhdyXnJvN74u8ADGoyyOnYbfn5ZP+0FoD6997r9HpCCFHbZGdnM2HCBHr27MnGjRuJjo5m8eLFXHvtte4OTQghakyFO9hKqVjgbuAOIAX4L+D8GMMqKu7iVFBU6kHF4hrs083OvRcZUHLaoEEQDz/cg4cf7oHWmtxci0Pf9eneT9Form12LeH+4c6GTuKUV7Dl5oJShN52m9PrCSFEbbJ8+XIeffRREhISMJlMPPHEE0yZMoWQkBB3hyaEEDXKkT7Y8zGS6j9prU+5OJ4KFf/LYv0A33NvFmbZj6WmO0acN+mx5H5FYKDvRT8rLdeSy5d/fAnAiPYjqhRrabqoiIylSwFo+I8XMJnNTq8phBC1yfbt20lISKBnz57MnTuXHj16uDskIYRwC0dqsPvWRCCOstknONYrnWBb7C37/hQD1/eF5Fzo5Vw/7O+OfkdWYRYdwjvQvUF3p9YCSFv0OQDKbKbePfc4vZ4QQribxWLh4MGDdOjQATA6hDRt2pQRI0bgJR2ShBCXsUsm2EqpL7TWdymldlF2cqMCtNbaLeMH8y02/AGf0l1E8uwPMF7ZGnpXz47J/x39PwBubXOr02tprUl69VUAGjz7rDzgI4TweL/88gujRo0iKSmJ/fv3ExYWhtlsZuTIke4OTQgh3K68Hewn7Mda1UuuOLEuM1H3zF7j6FfvwhuqYMPpDWxK3IS/tz83tbrJ6fUyV6woOQ+9XWqvhRCeKzU1lUmTJvHRRx8B0KpVK06cOEFYWJibIxNCiNrjkl1EtNan7adjtNbHS/8AY2omvAtZrEZmHRlcqobZWmgcvcvWVhv9rw+Rnl65uTjTNkwD4ObWNxPi69zDObqoiFMTngIg7G9/w+Rbcf23EELUNlprFi5cSLt27fjoo4/w8fHh+eefZ/fu3XTp4vyMACGEqEscadN3/UXeu7G6A3FUkc1o02cqXWaRc9Y4RsaUufbAgRRuvPHfhIVNp3PnOTz99A8Vrn827ywJ2QkADIsd5nS86UuMBxtNgYFEjXuigquFEKJ2euSRR7j//vs5e/YsgwYNYufOnbzyyiv4+1/8gXIhhLicXTLBVkqNttdft1NK7Sz1cxTYWXMhluVlHzQT6l+qj3VuinEMaVzm2l9+MQbQaA27dp1hx46kCtdffHAxRbYiejfqTavQVk7FqouKODtnDgD17rwTJbvXQggPNWzYMCIjI/nkk0/48ccfiYmJqfgmIYS4TJVXg70I+A54DXim1PtZWutUl0ZVjuLSa7OP/e8GNhsUZhvn3gFlrl23Lr7M6/79m5a7tsVq4YsDXwDV05ovffFiihIT8apXj8gnxzm9nhBC1JTvv/+eX3/9lZdffhmAuLg4jh49SqBMoBVCiAqVVyKitdbHgEeBrFI/KKXc9jRLYZFRIuLrZQ+9INP+gRkaz4MrPoS/GGUZHTpE0r17o5Le2f37Nzt/uTKW/rGUs3lnaRHSgv6N+zsVp9aalPkLAIia+BQmPz+n1hNCiJpw+vRp7rnnHgYPHsyUKVPYsGFDyWeSXAshhGMq2sEeAmzB2Dgu3VtOA87VT1RRcRDexW368u0t+gr8wKYhrQByjEmNEyf2Y+LEfpw9m8uaNUe56qpL72BrrVl8cDEAIzqMwKQcmiJ/SRnLlmE5cQJTSAihf/mLU2sJIYSrWa1W5s6dy+TJk8nMzMTf35+XXnpJhsUIIUQVXDLB1loPsR9b1lw4FTPZa7DN3vYhBln2uuqCUg/ahJfdLY6ICOCuuzqUu+6mxE0cSDtAmF8YQ1o515lQFxVxZsZbxnePGS2110KIWm3r1q2MGjWKTZs2ATBkyBDef/99WrRo4d7AhBDCQ1W4TauU6qeUCrSf36eUelspVX6thQsV978uftiRjBPGMSD33EVRZWuxHfH5fmPS4l3t7sLf27mn4rPXrcN69ixe9eoRNsL5Wm4hhHClf/7zn2zatInGjRuzdOlSli9fLsm1EEI4ocJR6cAcoItSqgswAfgI+BQY5MrAKuJdnGDnJBvHNm3g5GhIy4dKTkrMKsxibcJaFIqhVwx1Ora8HTsAMLdtizI5V2oihBDVTWtNamoq4eHhAEyfPp2oqCgmT55McHCwm6MTQgjP50j2V6S11sAtwCyt9T8Bt/0OrO1b2CU72Ln2hiZRseDrBQ0CK72DvfLYSiw2Cz0b9iQqIMrpGFPmzgMg9I7bnV5LCCGq09GjRxkyZAj9+/ensNAY0hUREcFrr70mybUQQlQTRxLsLKXUs8Bw4P+UUibAp4J7XKa4TV/JDnbmKeNYaky6xWItScQd8dUfXwFwWxvnx5jnbt1ach7Yp4/T6wkhRHUoLCzktddeo0OHDqxYsYLTp0+za9cud4clhBB1kiMJ9t1AAfA3rXUi0AR406VROaBkB1tbjaNfaMlnb7/9G02bzuTee5cyb95mTpzIuOQ6285sY9fZXQT6BHJd8+ucjitj+XIAfKKj8WnY0On1hBDCWevWraNbt25MnjyZvLw8hg0bxv79+6VDiBBCuEiFCbY9qf43EKqUGgLka60XujyycpgUqOI666J84xh6borjzp1nOHkyi0WLdjFq1P+xbNmBS6717eFvAahnruf0w43WzEwyln8DQPQb051aSwghqsPEiRMZOHAge/fupU2bNqxatYp///vfNJQNACGEcBlHuojcBfwODAXuAjYqpe50dWDlKWnRB+fa9IWe63G9a1fZkeidOl28rtpqs7I6fjUAY7qOcTquk089hc7Nxa9TJwJ69nR6PSGEcFbHjh3x9fXlxRdfZNeuXVx//fXuDkkIIeo8R7qIPAdcqbU+A6CUigT+B3zpysDKUzJkBiDLXoO9Ih3C9mIJ8GH//rNlru/UqcFF1/k54WdS8lOI9I/kzy3/7FRMtsJCcn5eB0DUhAlOrSWEEFW1d+9etm7dyn333QfAiBEjGDRokLTdE0KIGuRIgm0qTq7tUnCsdttlSh5wBEg7Zhzf2Ad5J/DxNnH61Hh27U5m164kjh/PICzs4qUfC/YYo8zvibkHL5PXRa9xVOqCj0vOA/v0dmotIYSorNzcXKZOncqbb76Jl5cXffr0oU2bNiilJLkWQoga5kiCvVIp9T3wuf313cAK14VUMW8ve35vtZx7My/QONb3IzwikLi4QOLiWlxyjYSsBLad2Ya38ubm1jc7HVP6kiUARIx9zOm1hBCiMr777jseffRRjh49CsCDDz5Y0uNaCCFEzaswwdZaT1RK3Q70t7/1gdb6K9eGVT6f4h3swmzjWGAG7O/VMzu0xq+nfgWge4PuNAx07mGfrB9/xBIfD0D9u+5yai0hhHDUyZMnGTduHF9+aVTsde7cmblz59K3b183RyaEEJe3SybYSqm2wAygNbALeEprfbKmAivPqQx755ACe4LtFwwPdoKMAoeHzKw5sQaAv7T+i9PxpH78CQD1hw3DOzLS6fWEEMIRjz76KMuWLSMgIIApU6bwxBNP4O3tyD9MCiGEcKXyfieeDywEfgb+ArwP1IrRhC3C7Ul08Q52eBg85vjk9hxLDlsStwDQu6Fz9dLW9HRyN24EZHKjEML1ioqKSpLo6dOn4+Pjw1tvvUWzZs3cHJkQQohi5SXYwVrrD+3nB5RSW8u5tkaVDJkp3sH2DarU/RtObSDfmk+niE40CmrkVCyZ368yQmjVCv8OHZxaSwghLiUjI4Pnn3+egwcPsnLlSpRStGvXjsWLF7s7NCGEEOcpL8H2U0p1o6S4Gf/Sr7XWbku4TcVDZgoyjaM5GIADB87y44/H6Ny5AR07RhEScvF67B9P/AjAwCYDnY4l7XPj2c+QP9/k9FpCCHE+rTWLFy9m3LhxnD59Gi8vL7Zv3063bt3cHZoQQohLKC/BPg28Xep1YqnXGrjGVUFV5FyCnWUc/UIA+OGHI4wd+13JdY8/3ot3373xgvu3JBnlIX2jnXsQKG/nTgr270f5+RF2/wNOrSWEEOc7fPgwjz32GCtXrgSgb9++zJ07l86dO7s5MiGEEOW5ZIKttb66JgOpjOL8mrxU4+hXD4Ddu8+UuS46OviCezMKMkjITsBbeRMbFutUHKefew6AkCF/xiso0Km1hBCitBkzZvDCCy+Qn59PvXr1mD59On//+98xmdw6hkAIIYQDPPJx85Ia7Lw04+hfH4AtW06Xua5jxwtHpO9I3mF8FtERXy9fp+IoSjYmRgZ07+HUOkIIcb7c3Fzy8/MZPnw4M2bMICrqwt/PhBBC1E4euRVyQYnIR4dh0OfclmXlzsFt6NAhEh8fEz17Rl9w708nfgKgW5Rz9YuF8fFY09MBCBn8J6fWEkKI5ORk1q9fX/J60qRJrF27loULF0pyLYQQHsYjd7BLJqVb7P2wUzXsTWEywLc3QZv6WCxWfHzKjj/XWpck2Nc3v96pGNKXLAXAv3t3TAGO9d4WQojz2Ww25s+fz9NPP423tzf79+8nLCwMs9nMwIHOP4gthBCi5lW4g60M9yml/mF/3Uwp1cv1oZUbk3Fy2ij3oKjU3xOCjbKP85NrgH2p+0jOSyYq+kLVKgAAIABJREFUIIqOER2diiFl3jwA6v/1r06tI4S4fO3evZuBAwfy0EMPkZaWRteuXcnNzXV3WEIIIZzkSInIbKAvUJxJZgH/dFlEDijZwQ5tbBx9LOc+DL50XfXKo8aT+AObDDyXpFdB/oEDJedBV9faZ0GFELVUTk4OkyZNolu3bvzyyy80aNCAzz//nO+//54mTZq4OzwhhBBOcqREpLfWurtSahuA1jpNKeXc04FOKnnIsTDHOL5wG4RebYxK97/0L+mH4z8A0Cmik1Pfnzp/AQABffpI9xAhRKXdeeedJcNixowZw7Rp06hXr567wxJCCFFNHEmwLUopL4ze1yilIgGbS6OqwLkE2z7JMToC2pT/EFBKXgoJ2QkAXNO06i28C+PjyVi2DIAGk5+t8jpCiMvXpEmTSEpKYs6cOfTu3dvd4QghhKhmjiTY7wFfAVFKqWnAncDzLo2qAj5e9soWexcR7RcKWpdb9rE6fjUALUJaUM+v6jtFKR9+BBgPN/pdcUWV1xFCXB6Kiop4//33OXbsGO+++y4AcXFxbN68WXpaCyFEHVVhgq21/rdSagtwLcaY9Fu11vtcHlk5ShJpSx4Aadkmmoe8Ttu2YVxxRTidOzdg8uQBZe5Zm7AWgHtj73XquwsOHgQg6Oo4p9YRQtR9v//+O4888gjbt28H4OGHH6ZDhw4AklwLIUQd5kgXkWZALvANsBzIsb/nNiUPOdp3sE8kFZGdXci2bYn89797WLRoV5nrtdb8nPAzAAOalE28K0PbbOTtMDqXBMvDjUKIS0hPT2fMmDH06dOH7du307x5c7755puS5FoIIUTd5kiJyP9h1F8rwA9oCRwA3PYnRcmgGftDjkdOFpX5vGXL+mVe70s9t+EeHXjh8BlHZa1caQ/AhG/r1lVeRwhRd/3nP/9h3LhxJCUl4e3tzYQJE3jhhRcIDJQHooUQ4nLhSIlImZYbSqnuwBiXReSAkh3sQqNf7OG1SWU+b9WqbI3176d/B6B7VHen2vMlv/seACFD/uzUOkKIumvVqlUkJSXRr18/5syZQ6dOznUtEkII4XkqPclRa71VKeXWx96VUmC1gK0QbIonl5zg7vr1OHZzK45e04wrrggvc/2mpE0AxDWNq/J32vLyKDx5EoB6t95a5XWEEHVLQUEBJ0+epFWrVgC88cYbDBgwgPvvv1/qrIUQ4jJVYYKtlBpf6qUJ6A6ccllEDjApwGKfdmbxxUuZaOoFTbs0YsCILhdcfzzzOAD9Gver8nfmbNwIRUV4N2hA4FVXVXkdIUTdsWbNGkaPHo3JZGLHjh34+voSERHByJEj3R2aEEIIN3JkeyW41I8Zoyb7FlcGVRGTUiUPOFLkc+6Di0xxTM5NJj4zHh+TD02Cqj4hLefXXwEI6O3WKfFCiFogKSmJ4cOHc+2113LQ3lkoISHBzVEJIYSoLcrdwbYPmAnWWj9VQ/E4xGQqlWAHZkP/xpBtgeigC67dcHoDGk2vRr0I8Amo8ndmfLkEgOBrqj6kRgjh2Ww2Gx9++CHPPPMM6enp+Pn58fzzzzNx4kR8fd064FYIIUQtcskEWynlrbUuUkpVva7CRUxKQVai8aJBJ/jqtkteu+r4KgB6Naz6zrMtNxdbrlGS4te+fZXXEUJ4tttuu43ly5cD8Kc//Yl//vOftJaOQkIIIc5T3g727xj11tuVUsuBxUBO8Yda66Uuju2STArQVuNFfnq51+5KNnpitw+vemKctXpNyblvM7e2ABdCuNHtt9/O77//zrvvvsvQoUOlm5AQQoiLcqSLiB+QAlzDuX7YGnBjgq3Akg9AZsAVfLtoF02bhtC0aSiNGwfj4+MFQGJOIin5KQB0CK962+5TEycCEHqLW0vPhRA1bPny5SQkJDBmjNGZdMSIEdx+++0EBwe7OTIhhBC1WXkJdpS9g8huziXWxbRLo6rAkeTski4iJxILuXfUuVx/woS+zJhxAwC/njIeTLyy4ZUE+1btD8TC+PiS88gJ48u5UghRV8THx/P444+zbNkyzGYzgwcPplWrViilJLkWQghRofISbC8giLKJdTG3JthtGwSXTHE8m1W2EUrLlueGzBxOPwxAkM+FDz866uzsOQD4de6MT1RUldcRQtR+FouF9957jxdffJGcnByCg4OZOnUqzZs3d3doQgghPEh5CfZprfWUGoukEny8FBQVAJCcZivzWYsW5xLsXWeN+utbWle9tCNrlfGQZPB111V5DSFE7bdhwwYeeeQRdu7cCcDQoUOZOXMmjRs3dnNkQgghPE15CXatfXpHKQVZRpu+JrkmhrYO52iBhSO5Ftq1iwDApm1sO7MNgNjw2Cp9jy4qKukeIsNlhKjbXnjhBXbu3EnLli2ZNWsWN910k7tDEkII4aHKS7CvrbEoKsmkgPRUAPqk+fBFBtAsDLbcX3LNiawTAHgrb6KDoqv0PZkrVgDgFRaGX/uqJelCiNpJa01WVhYhISEAzJo1i4ULF/Lcc88REFD1nvlCCCHEJSc5aq1TazKQyjApBXn2joFF9uEOoX5lrikuD+nWoFuVvyf1088AMLdpgzI5MvRSCOEJDhw4wHXXXcftt9+O1sYjJe3atWPatGmSXAshhHCaR2aNJqUgxUigKbJvwoeUnaK28fRGALpGdq3y91gzMwCoP+yvVV5DCFF75Ofn8+KLL9K5c2fWrFnD9u3bOXbsmLvDEkIIUce4NMFWSg1WSh1QSh1SSj1TznV3KKW0UqqnI+ualIKGLYwXI1rDvBtgXI8y1+xJ2QNA3+i+VYrdmp6O5Xg8mEwExcVVaQ0hRO3xww8/0KlTJ6ZMmUJhYSF/+9vfOHDgAC1btnR3aEIIIeoYRwbNVIlSygv4J3A9kABsUkot11rvPe+6YOAJYKOja5sUYCoyXvTuAh2uKPO5xWohPtPoXx0TFlOl+NO//BIA/86dMfn5VXC1EKK20lrz4IMPsmDBAgDat2/P3LlzGTBggJsjE0IIUVe5cge7F3BIa31Ea10I/Ae4WL+8V4DpQL6jC3uZFFgL7S/MF3y+7cw2CqwFNAtuVqUBM1przsx4C4CgQQMrfb8QovZQStGiRQv8/f157bXX2LZtmyTXQgghXMplO9hAY+BEqdcJQO/SFyilugNNtdb/p5Sa6OjCSik4uQWAMY//j9OBWTRvHsqjj15J27bhnMw+CUCIb0iVAs9a9YPxPT4+hD3wQJXWEEK4z/bt2zl9+jQ33ngjAJMmTWL48OFSDiKEEKJGuO0hR6WUCXgbmODAtQ8rpTYrpTYbr4GwVgAcOZLK11/v5913N5KRYQyfWXXcGA5zVeOq9a5Otf9TctC112Ly96/SGkKImpeVlcX48ePp0aMH999/P6mpRjMks9ksybUQQoga48oE+yTQtNTrJvb3igUDHYGflFLHgD7A8os96Ki1/kBr3VNr3RMgIS0PEjYDkJgdeO4Lmhg71lmFxhCatvXbVjpoS2Iiedu3AxA59rFK3y+EqHlaa7766ivat2/PzJkzARg2bBg+Pj5ujkwIIcTlyJUlIpuAtkqplhiJ9T3AsOIPtdYZQETxa6XUT8BTWuvNFS3cMjwA7R+Jyk0k396mz2RSREYa/WtPZZ8CoEN4h0oHnbrwU2O9oCDMrVtX+n4hRM06fvw4jz32GN9++y0APXv2ZN68eXTv3t3NkQkhhLhcuSzB1loXKaUeA74HvID5Wus9SqkpwGat9fKqrq2Ugsw88IZPfMNJCA4k7cFOeHmZyCjIIDkvGV+TL9GBlZ/gaM1IB8CvY8eqhieEqCFaa+644w62bNlCSEgIr776KqNGjcLLy8vdoQkhhLiMuXIHG631CmDFee/94xLXxjm6rlKglFFv3VsF0NtshjvbA7AzeScA0UHReJkq94esLioiY8lSACIefqhS9wohao7NZsNkMqGUYsaMGcydO5eZM2fSqFEjd4cmhBBCuDbBdhUTgMnepq/IXmMZZExy3H12N2Ak2JWV+d1KALwbNiSgb9UG1AghXCclJYVnnjFmVn344YcAxMXFESfDoIQQQtQiHjkq3UtbQdnAZoIQo+6aEKMf9vfHvgdgSKshlV4357ffADAFBhplKEKIWkFrzSeffEJMTAwfffQRCxcuJCEhwd1hCSGEEBflkTvYPth3r83+8MdDYLGClwmtNYczDgPQu1Hvcla4uIylRnlI/buGVlusQgjn7Nu3j9GjR7N27VrA2LGeM2cOTZo0cXNkQgghxMV55g62zZ5gextlIfh4gUmRnJdcck1UQFSl1rQVFJSchwyp/O63EKJ6aa154YUX6NKlC2vXriUiIoJPPvmENWvWEBMT4+7whBBCiEvyyATbbMsBIF/7c+hQKllZBWit2Z+6H4BuUd0qvWbOr78CYAoNxTs8vPqCFUJUiVKKkydPYrFYeOihhzhw4AAjRoyQ8i0hhBC1nkeWiHhhBeDEyVyuaPs+AD/8MJwDEbsAaBHSotJr5u3YAYBP48o/HCmEqB6nTp3i7NmzdO7cGYA33niDBx98kH79+rk5MiGEEMJxHrmD7aWNBNtiOxd+gwaBJOcaJSL1/epXes2ixCQAQm74UzVEKISoDKvVyqxZs4iNjeWee+6hsNAoA4uIiJDkWgghhMfx7ATbWjrBDiqZ4FiVEpGCI0cA8O/cqRoiFEI4auvWrfTp04exY8eSmZlJ69atyczMdHdYQgghRJV5ZIKtMowabF9vH5rV8yPA7EV4uD/xWfEANA5qXKn1rNnZ5O80BtT4Nm9evcEKIS4qMzOTJ554giuvvJLNmzfTpEkTli5dyvLly4mIiHB3eEIIIUSVeWQNtm+mkWDHWnw47h2IHhxNjjWXk9knMSlTpWuwM5YsAcAcG4tP48ol50KIytNaM3DgQHbs2IGXlxfjx4/npZdeIjg42N2hCSGEEE7zyB1s33z7Px8XGsNlVD0/DqUfAqBZcDN8vHwqtV7mDz8A4BMtDzgKUROUUjz55JP06tWLzZs389Zbb0lyLYQQos7wyB1sn8Jc46TI3gc71MzxzOMAtK3fttLr5e0wykOCr7uuWuITQpRVWFjI22+/jZeXFxMnTgRgxIgR3HfffXh5ebk5OiGEEKJ6eWSC7Rtmg1wgphG07wmdIzmYZkxhbBXaqlJr5R84ABYLKiCA0Jv/4oJohbi8rVu3jlGjRrF3717MZjMjRoygQYMGKKUkuRZCCFEneWSJSIBflnHSrh482wf+3LpkB7tNvTaVWivrf/8z1uzRAyV/2AtRbc6ePcvf/vY3Bg4cyN69e2nbti3ffvstDRo0cHdoQgghhEt5ZIJd5OVvnFjPjTdPyjX6WFe2g0jq/AUAhA75c/UEJ8RlTmvNggULiImJYcGCBfj6+vLiiy+yc+dOrpMyLCGEEJcBjywROXI4iSuB7zdrft65mltubceJrBMANApqVKm1bDlGRxK/Tp2rO0whLlufffYZKSkpXHPNNcyePZt27dq5OyQhhBCixnhkgh2SGQ8BsGHzWV79aT0hrbPJ8coh0j+ScL9wh9ex5eWVnPu2bFH9gQpxmcjNzSUjI4NGjRqhlGL27Nls2rSJe++9F6WUu8MTQgghapRHlohkFRh/L4gIMBJkFWrsQmt0pf4wLzx+vORckgAhqua7776jY8eODB8+HK01AO3ateO+++6T/66EEEJcljwywS602AA4lh4CwBlvowf2gMYDKrVO/p49gPS/FqIqTp48ydChQ7nppps4evQoycnJpKSkuDssIYQQwu08MsFu723skv3ZN4QpDUKx5pwBIDIgslLr5G7aDIBfh/bVG6AQdZjVauW9994jNjaWL7/8ksDAQN566y22bNkiI86FEEIIPLQGO9qcBkCc1Yc4qzf3mE4B0C+6X6XWyfj6awDM7WKqN0Ah6iibzcagQYP45ZdfALj11lt59913adasmZsjE0IIIWoPj9zBVoUW48S7iDwfC3sK/gCgdb3WVVovsG+f6gpNiDrNZDJxww030LRpU5YtW8ZXX30lybUQQghxHo9MsG3YR6TnB3Ag+lzNZ6g51OE1LKdOlZz7d+lSbbEJUZdorfnvf//LkiVLSt6bNGkSe/fu5eabb3ZjZEIIIUTt5ZElIt5XBEAyMOtWknLy4MgymgVXbhctc9WqknPl7ZH/MwjhUocPH2bMmDGsWrWKyMhIrrnmGurXr4/ZbMZsNrs7PCGEEKLW8sgdbO+ibOMkIpxT9Y2x6b0a9arUGkWnTwPg17FjtcYmhKcrKChg6tSpdOzYkVWrVlG/fn2mTZtGaKjj/0IkhBBCXM48c+vWYh8Q4xvImVyjg0jz4OaVWiJt8ZcABF0dV52RCeHRfvrpJ0aPHs3+/fsBGD58ODNmzCAqKsrNkQkhhBCewyMT7ORTqdQPgc7d5hP0+CmIrlyLPltODjo3F4B6Q4e6KkwhPIrVamXMmDHs37+fdu3aMWfOHK6++mp3hyWEEEJ4HI9MsK8IMR5szMpX+AQnARAd5PiwmKyffio595GdOXEZs9ls5OfnExAQgJeXF3PmzOHnn3/m6aefljprIYQQooo8sgb7TH4gAAVFXhQGG8l246DGDt+f8+uvAPhIezFxGdu1axcDBgxg7NixJe8NGjSIF154QZJrIYQQwgkemWD7eRUBkKtUyXsR/o5PkLPZy0OkPZ+4HOXk5DBp0iS6d+/Or7/+ynfffUdaWpq7wxJCCCHqDI9MsEN8CgDYHG3sZEcHRmNSjv1StNVK3uYtAIT+ZYhrAhSilvrmm29o3749b7zxRknN9d69e6lfv767QxNCCCHqDI+swS6WFVwIQLh/uMP3FCUmUpScjCk4mMABA1wVmhC1SlFREXfffTdLly4FoGvXrsybN49evSrX3lIIIYQQFfO4HWwTtpLzMyFGqUekv+MdRLLWrgXA3KYNqlSJiRB1mbe3N6GhoQQFBTFz5kw2bdokybUQQgjhIh63g63QxoktkJN9fYDK1V8XHDhonGhd3aEJUats3LgRgN69ewPw5ptvMmXKFJo0aeLOsIQQQog6zwN3sO2JcUgwx270A6CeXz2H77empwMQFBdX3aEJUSukp6czevRo+vbty8iRIykstJdShYdLci2EEELUAM/dwc5OLHnP39vf4fvz9+4FILDfVdUalxDuprXm888/Z/z48SQlJeHt7c3NN9+M1Wp1d2hCCCHEZcVjE+wMU0O2HzoMQOvQ1g7da83OxnLiBJhMmNu1c1mMQtS0P/74gzFjxvC///0PgH79+jF37lw6duzo5siEEEKIy48HJtiGE4kWjjTdiTJB85DmDt1bsH8/AKbAQEy+vi6KUIiaZbFYuOaaa0hISCAsLIw33niDkSNHYjJ5XAWYEEIIUSd4XIKNfQfbYjNR3Po6KsCxced527cD4N9JdvWE59Nao5TCx8eHadOm8eOPP/LGG28QGel4Vx0hhBBCVD+P2+Iq3sHOKhV6oE+gQ/fm2zuI+LVvX91hCVFjkpKSGD58OFOnTi15b8SIESxYsECSayGEEKIW8LgEu7iLiI/fuRIPR/tZZ33/PQCBAwdWf2BCuJjNZmPevHnExMTw2Wef8fbbb5OVleXusIQQQghxHo9LsJV90EyQvbolUjvWoq8oNRVtb1fm176Da4ITwkV27NhBv379GDVqFOnp6QwePJgtW7YQHBzs7tCEEEIIcR4PTLANid5eAAQox1r0Za/9GQDfli3xCnKspEQId7NYLDz11FP06NGDDRs20KhRI7744gtWrFhBq1at3B2eEEIIIS7C4xLs4hKRU2Yj1Q60mh26r+DQHwB4hYe5JjAhXMDb25tt27Zhs9kYO3Ys+/btY+jQoQ6XRQkhhBCi5nlcF5HiPthZzYOBAsIiGjp0X/qXSwAIGzHCVaEJUS3i4+OxWq20bNkSpRRz584lIyODnj17ujs0IYQQQjjA43awixPsdD8j9LbRsY7daDNqt/1kwIyopSwWCzNmzCA2NpaHHnoIrY3/r7dt21aSayGEEMKDeOwOtsXHBIVQz6/ihxy11YotJwcA7yjHemYLUZN+++03Ro0axc6dOwEICwsjNzeXwEB5XkAIIYTwNB64g23YdTIdgObBFU9xLDp7Fmw2TMHBmPwdeyhSiJqQlpbGI488wlVXXcXOnTtp2bIlK1as4IsvvpDkWgghhPBQHruDna6KAMd2sPP37AXA3Lq16wITopIKCgro2rUr8fHx+Pj4MHHiRJ577jkCAgLcHZoQQgghnOCxCXZyhD3BNlecYKd99ikAvi1auCwuISrLbDbz4IMPsnr1aubMmUN7mTAqhBBC1AkeWyJizjNCD/EKqvAea6Yx7c6nWVNXhSVEhfLz83nxxRdZtGhRyXuTJ0/mp59+kuRaCCGEqEM8bgfbrArRgM3XPtExUUM5w+y0zUb+7t0A1LvllhqIUIgL/fDDD4wZM4ZDhw4RFRXFbbfdhr+/P97eHvefoBBCCCEq4HE72BbtTY5SWLzAv8Ab/wpGRefv2lVy7h0d7erwhCgjMTGRYcOGccMNN3Do0CE6dOjAkiVL8JeHbYUQQog6y+MSbIB0LyPs0Fw/8PMq99o8++61X8eOMv1O1Bir1crs2bOJiYnh888/x9/fn9dff52tW7fSv39/d4cnhBBCCBfyuH+fVmgyTfYE2+IP5vJ/CYVHjwHg06SJq0MTooTVauX9998nIyODm266iVmzZtGyZUt3hyWEEEKIGuBxCTZAhj3BDuzYFPzL/yVkrV4NQOgtN7s8LnF5y8rKwmq1Uq9ePXx9ffnwww9JSkri9ttvl389EUIIIS4jHlciotCkexllIeH+4eVeW5SWRtHp0wAE9u7t8tjE5UlrzdKlS4mNjWXChAkl7/fv35877rhDkmshhBDiMuOBCTakFe9gq5Byr8346msAzLGxmGR4h3CBY8eOcfPNN3PHHXdw8uRJdu/eTX5+vrvDEkIIIYQbeVyCDZo/fH0AyEou/8r8PXsAMEvtq6hmFouF6dOn0759e7799ltCQkKYNWsWv/76K35+fu4OTwghhBBu5HE12Arw08Y0x0JTXrnX5u3YAUCAlIeIapSbm0ufPn3YZW8Bec899/D222/TqFEjN0cmhBBCiNrA4xJsP1XIKftwjqYBzcu91pKQAIB/t64uj0tcPgICAujZsye5ubnMnj2bG264wd0hCSGEEKIW8bgEuwAfCu0PjZn3noXBF7+uKDW15NzcqlVNhCbqKK01CxcupHXr1iU9rGfOnImvr68MjBHiMmOxWEhISJBnLYSoY/z8/GjSpAk+Pj7Vsp7HJdgKXVIi0nZ3wSWvy1m3DgDvRo1QMo5aVNG+ffsYPXo0a9euJTY2lu3bt+Pr60toaKi7QxNCuEFCQgLBwcG0aNFCOgQJUUdorUlJSSEhIaHaZlZ43EOOCsgxGb+phVgu/TBZ9tqfAQiQ8hBRBXl5eTz//PN06dKFtWvXEhkZybPPPlttf7MVQnim/Px8wsPDJbkWog5RShEeHl6t/zLlgVu7mgO+vgD4HMm+5FW2QmN3269DxxqJStQdK1eu5NFHH+XIkSMAPPTQQ7z++uuEhYW5OTIhRG0gybUQdU91/3ftkTvYUUVWAMyje13yusJDhwHw79K5JsISdUR2djbDhw/nyJEjdOzYkfXr1/PBBx9Ici2E8CjLly/n9ddfd3cYbvfxxx8TGRlJ165diYmJYebMmWU+/+CDD4iJiSEmJoZevXqxfv36ks8sFgvPPPMMbdu2pXv37vTt25fvvvuupn8JFRo3bhw///yzu8O4pC1bttCpUyfatGnD448/jraX+ZaWlpbGbbfdRufOnenVqxe7d+8u+Sw9PZ0777yTmJgYYmNj+e233wB46qmnWLNmTY39OirL4xLsAArYbzZ2sOsP6XbRa7TWWBITAfBtXn6nESGsVisWiwWAoKAg3n33XaZPn87WrVvp16+fm6MTQojKu/nmm3nmmWcculZrjc1mc3FEl1ZUVOTS9e+++262b9/OL7/8wrRp0zhx4gQA3377LfPmzWP9+vXs37+fuXPnMmzYMBLt+cMLL7zA6dOn2b17N1u3buXrr78mKyurWmOzWq1O3Z+SksKGDRsYOHCgw/e4+n/v840ePZoPP/yQP/74gz/++IOVK1decM2rr75K165d2blzJwsXLuSJJ54o+eyJJ55g8ODB7N+/nx07dhAbGwvA2LFja/VfIj0uwc7HhzD7/yGDfIMufs2evej8/P9n77zDsirfB/45DAE1HKnkClGR9Q6muBeBlYi5wlHO7KfmyMyRI00bRqa5TRtmztJwfrUiMFO/pkAgiAh+DQcS4gJfhqzn98crJ99eHJWK1PO5rnNdnHOe8zz3ec4B7uc+98Discewqlv3YYonqWTExMTg7+9v8ks6YMAAJk+eLP2tJRLJ3am71HS7HWsTTdu99tcsb2lpabi6ujJkyBBatGjBwIEDiYiIoG3btjg7O3PkyBHAaLkdM2YMAJmZmfTs2RO9Xo9er+fQoUOkpaXh4uLCoEGD0Gg0nDt3jkmTJqHRaNBqtWzevLnc8Y8cOULr1q3x8vKiTZs2nDx5EoBWrVpx/GZxN4BOnToRHR1Nbm4uw4YNo2XLlnh5ebF9+3ZVvpCQELp06UJAQAAGg4GAgAC8vb3RarVqO4C5c+fi4uJCu3bt6N+/P/Pnzwfgf//7H08//TQ+Pj60b9+e5OTkO87d448/TvPmzcnIyADg/fff54MPPqBOnToAeHt7M3jwYJYtW0ZeXh6rV69myZIl2NjYAODg4MDzzz9v1u/Ro0dp06YNer2eli1bcv36dZP5BwgODmbfvn2A0ZAzceJE9Ho97733Hn379lXb7du3j+DgYAC+++47Wrdujbe3N3379sVgMHeL3bp1K08//Xs6tTlz5uDn54dGo+Hll19WrcWdOnWL2yqvAAAgAElEQVTi1VdfxdfXl0WLFhETE0PHjh3x8fGha9eu6pysXr0aPz8/9Ho9vXv3Ji8v745zejcyMjLIycmhVatWKIrCoEGD2LZtm1m7pKQkunTpAoCrqytpaWlkZmaSnZ3N/v37GT58OABVqlShZs2aADg6OnL58mV1QfSoUekUbAUwKEax7auUXyo97+YfGGtZ+ENyG3Jychg/fjwtW7YkJiaGL7/8UrViSyQSyaPMqVOnmDhxIsnJySQnJ7NhwwYOHDjA/Pnzeffdd83ajxs3jo4dOxIfH09sbCweHh4ApKamMnr0aI4fP050dDRxcXHEx8cTERHBpEmTVKXrVlxdXfnpp5/45ZdfmDNnDtOmTQOMVuKvvvoKMCpVGRkZ+Pr68s4779ClSxeOHDlCVFQUkyZNIjc3F4DY2Fi2bNnCjz/+iK2tLeHh4cTGxhIVFcXEiRMRQnD06FG2bt1KfHw8e/bsITo6WpXl5ZdfZsmSJcTExDB//nxGjx59x3k7e/YsBQUF6HRG19Hjx4/j4+Nj0sbX15fjx49z6tQpnnzySezty9czyigsLCQ0NJRFixapc3e39K25ubn4+/sTHx/P1KlT+fnnn9U52bx5M/369ePSpUu8/fbbREREEBsbi6+vLwsWLDDr6+DBgyb3MGbMGI4ePUpiYiL5+fns2rXLRNbo6GjGjRvH2LFj2bJlCzExMQwbNozp06cD0KtXL44ePapaij/99FOzMaOiovD09DTb2rRpY9Y2PT2dRo0aqfuNGjUiPT3drJ1er+ebb74BjIu4M2fOcP78eX799Vfq1q3L0KFD8fLy4qWXXlLnCoyLooMHD95xviuKShfkKBAUWihQomBrVX4WkeIsYw116X8t+SNCCLZs2cL48ePJyMjA0tKS1157jbfeektarCUSSaXAyckJrVYLgIeHBwEBASiKglarJS0tzax9ZGQka9euBcDS0pIaNWpw9epVHB0dadWqFQAHDhygf//+WFpa4uDgQMeOHTl69CghISEmfWVnZzN48GBSU1NRFEU1TDz//PMEBQXx1ltv8dVXX9GnTx/AaIXdsWOHanUuKCjg7NmzAAQGBqrxLUIIpk2bxv79+7GwsCA9PZ3MzEwOHjxIjx49sLW1xdbWlu7duwPGeJlDhw6ZWH9v3Cg/de/mzZvZv38/ycnJLF26FFvb22cg+7OcPHmS+vXr4+fnB3BXhRyMz6B3794AWFlZ8fTTT7Nz50769OnD7t27CQsL48cffyQpKUl1UywsLKR169ZmfWVkZFD3li/1UVFRhIWFkZeXx5UrV/Dw8FDnLDQ0VJU5MTGRwMBAwOimUlaJODExkRkzZnDt2jUMBgNdu3Y1G7Nz587ExcXd8xzdC1OnTmX8+PF4enqi1Wrx8vLC0tKS4uJiYmNjWbJkCf7+/owfP5558+Yxd+5cAOrVq8eFCxfuqyz3i0qoYBtRCi1v2+bGqVMA2Di3eAgSSSoL169fJzQ0VA1S8ff3Z+XKlXh6ylSOEomk8lDmsgBgYWGh7ltYWPwp/9pq1ardtc2yZctYvXo1AP/5z3+YOXMmnTt3Jjw8nLS0NDp16gRAw4YNefzxxzl27BibN29m5cqVgFFx3rp1Ky4uLib9/vzzzybjr1+/nqysLGJiYrC2tqZJkyZ3TJlWWlpKzZo170nRCw0NZenSpURHRxMUFERISAhPPPEE7u7uxMTEqK4JYHQb9PDwoHnz5pw9e5acnJx7Upr/iJWVlYlf+633Ymtri6Xl7zpMv379WLp0KbVr18bX15fHHnsMIQSBgYFs3LjxjuPY2dmpfRcUFDB69Giio6Np3Lgxs2fPNhm3bL6FEHh4eKjBgrcyZMgQtm3bhl6vZ82aNapby61ERUUxYcIEs+NVq1bl0KFDJscaNmzI+ZtVtcGYR75hw4Zm19rb2/P555+r8jk5OdG0aVPy8vJo1KgR/v7+APTp08fEpbOgoOCRLfhW6VxEhGJUsYXd7f+I3EhJAWSJdIkp1atX58aNG9SoUYMVK1Zw6NAhqVxLJJK/R9YY0+12DNKYtlvQ5fZt7zMBAQGsWLECMFors7Ozzdq0b9+ezZs3U1JSQlZWFvv376dly5a88sorxMXFERcXR4MGDcjOzlYVpDVr1pj0ERoaSlhYGNnZ2aobRteuXVmyZInqC/zLL7+UK2N2djb16tXD2tqaqKgozpw5A0Dbtm3ZuXMnBQUFGAwG1eXB3t4eJycnvv76a8ColMXHx99xHnx9fXnxxRdZtGgRAJMnT2bKlClcvnwZgLi4ONasWcPo0aOpWrUqw4cPZ/z48RQWFgKQlZWljleGi4sLGRkZHD16FDAacoqLi2nSpAlxcXGUlpZy7tw51Te+PDp27EhsbCyrV6+mX79+gNGn/eDBg5y6aTDMzc0l5aZucytubm5qmzJluk6dOhgMBrZs2VLueC4uLmRlZakKdlFRkeo/f/36derXr09RURHr168v9/oyC/Yftz8q1wD169fH3t6ew4cPq1WRe/ToYdbu2rVr6jx/8skndOjQAXt7e5544gkaN26s+vr/8MMPuLu7q9elpKSg0Tya6ZgrnYJdRp3L1eAd89VXicFAcWYmWFhge8tDkPw72b9/P6mpqYAxx+Vnn33GyZMnGTlyJBYWlfb1l0gkkntm0aJFREVFodVq8fHxISkpyaxNWYo0vV5Ply5dCAsL44knnjBrN3nyZN544w28vLzMrOV9+vRh06ZNJoGAM2fOpKioCJ1Oh4eHBzNnzixXxoEDBxIdHY1Wq2Xt2rW4uroC4OfnR0hICDqdjmeeeQatVqtW0l2/fj2ffvoper0eDw8Pk8DI2zFlyhQ+//xzrl+/TkhICMOGDaNNmza4uroyYsQI1q1bp7pLvP3229StWxd3d3c0Gg3BwcFm1uwqVaqwefNmxo4di16vJzAwkIKCAtq2bYuTkxPu7u6MGzcOb2/v28pkaWlJcHAwe/bsUQMc69aty5o1a+jfvz86nY7WrVuXG8TZrVs31cpcs2ZNRowYgUajoWvXrqrbyh+pUqUKW7ZsYcqUKej1ejw9PVXleO7cufj7+9O2bVv1Gfxdli9fzksvvUTz5s1p1qwZzzzzDAArV65Uv3ScOHECjUaDi4sLe/bsURdBAEuWLGHgwIHodDri4uJUv/+ioiJOnTqFr6/vfZHzfqOUl4/wUUbT0Eoo77rh8lsNthR/ANNNfZLyYmM5M2Ag1o0b0/z77ypISklFc+nSJSZPnsznn39OQEAA33//vSwOIZFI/jYnTpxQ04RJHg4Gg4Hq1auTl5dHhw4dWLVq1R0V1n8b7dq1Y9euXWp2jX8LZUGxZf7Y94Pyfr8VRYkRQvxpLf6BmvAURXlaUZSTiqKcUhTFLCGnoiivKYqSpCjKMUVRflAU5a5JqwsVo9t41UIrsDH3wy48YwyesKhefgo/yT+b0tJSPvvsM1xcXPj888+pUqUK7du3/9u5RiUSiURSMbz88st4enri7e1N7969pXL9Bz788EM1cPTfRHFxMRMnTqxoMW7LAwtyVBTFElgGBALngaOKouwQQtz6beoXwFcIkacoyiggDAi9U7+lN8Mcq96oAjXNxVf9r2UGkX8dx48fZ9SoUfz000+A0e9w+fLltGghg10lEomksrJhw4aKFuGRpiwA8N/GrRlkHkUeZBaRlsApIcRpAEVRNgE9AFXBFkJE3dL+MPDC3Totc2ip3t4ZOpgr0de/M7qF2DRt+lflllRCsrOzadWqFQaDgXr16rFgwQIGDBgg3UIkEolEIpE8dB6kgt0QOHfL/nngTsus4cCeu3VaelNfeqxqDbAzF7/44kUArJ988p4FlVRehBAoikKNGjWYMmUK6enpvPvuu9SqVauiRZNIJBKJRPIv5ZHIg60oyguAL9DxNudfBl4GcGxkLAZia1m13L7EzaT3tvcp+lXyaJKens748ePp0aMHL774IgDTp0+XFmuJRCKRSCQVzoMMckwHGt+y3+jmMRMURXkKmA6ECCHKLcMkhFglhPAVQviWWbC/2XjGrF3hLRWsrBwc/rrkkkeW4uJiFi1ahKurK1u3bmXWrFlqAKNUriUSiUQikTwKPEgF+yjgrCiKk6IoVYB+wI5bGyiK4gV8jFG5vngvnZZgVKKUEvPKPRc/MuZNtHV3l8rWP5CjR4/i7+/Pq6++isFg4LnnnuPHH380qYglkUgkEkl5pKWlYWdnh6enJ+7u7gwaNEgt9Q7GcvEtW7bE1dUVV1dXVq1aZXL92rVr0Wg0ainvsvLvjxLbtm1jzpw5FS3Gbbly5QqBgYE4OzsTGBjI1atXy203ZcoUNBoNGo2GzZs3q8eXLl1K8+bNURSFS5cuqcd37drFm2+++cDl/zM8MAVbCFEMjAG+BU4AXwkhjiuKMkdRlJCbzT4AqgNfK4oSpyjKjtt0p1JWeNSq1Nb8ZIkx6b11OWU4JZWX3NxcxowZg7+/P7GxsTz55JNs376d8PBwGjdufPcOJBKJRHLPVGRaUyGESYnx+02zZs2Ii4sjISGB8+fP89VXXwHw22+/MWDAAFauXElycjIHDhzg448/Zvfu3QDs2bOHjz76iO+++46EhAQOHz6sFry5X/yZMve3IywsjNGjRz/UMf8M8+bNIyAggNTUVAICAkzKnpexe/duYmNjiYuL4+eff2b+/Pnk5OQAxsqeERERODqaZnXu1q0bO3fuJC8v76Hcx73wQPNgCyH+I4RoIYRoJoR45+axN4UQO27+/JQQwkEI4XlzC7lzj78HOdpkAxdNJ7L4YhYA9jcrIUn+GVhZWREREYGFhQWTJk0iKSmJkJC7vioSiUTywFGUt0y227FqVYxJu5df3vmXxktLS8PV1ZUhQ4bQokULBg4cSEREBG3btsXZ2VktyX3kyBFat26Nl5cXbdq0UUtNl5SU8Prrr6PRaNDpdCxZsgSAJk2aMGXKFLy9vfn666/ZuHEjWq0WjUbDlClTypXFYDAQEBCAt7c3Wq1WraQ4depUli1bprabPXu2au394IMP8PPzQ6fTMWvWLPWeXFxcGDRoEBqNhnPnzjFq1Ch8fX3x8PBQ2wH85z//wdXVFR8fH8aNG6dWPszNzWXYsGG0bNkSLy+vu1Z1tLS0pGXLlqSnGz1Xly1bxpAhQ9Qc23Xq1CEsLExVAN977z3mz59PgwYNALCxsWHEiBFm/WZmZtKzZ0/0ej16vZ5Dhw6RlpZmUs57/vz5zJ49G4BOnTrx6quv4uvryzvvvIOjo6O6wMjNzaVx48YUFRXxv//9j6effhofHx/at29fblXHlJQUbGxsqFOnDgA7d+7E398fLy8vnnrqKTIzM9Xn8eKLL9K2bVtefPFFsrKy6N27N35+fvj5+XHw4EHg9u/Q32H79u0MHjwYgMGDB7Nt2zazNklJSXTo0AErKyuqVauGTqdj7969AHh5edGkSROzaxRFoVOnTuzatetvy3jfEEJUqu0xR1uhWaMRUR0mCnHgvLiVlHbtRZKLqyg4fVpIKjenTp0Sly5dUvePHDkijh07VoESSSQSiRBJSUkm+zDbZLsdH38cbdJuxIgdf2n8X3/9VVhaWopjx46JkpIS4e3tLYYOHSpKS0vFtm3bRI8ePYQQQmRnZ4uioiIhhBDff/+96NWrlxBCiOXLl4vevXur5y5fviyEEMLR0VG8//77Qggh0tPTRePGjcXFixdFUVGR6Ny5swgPDzeTpaioSGRnZwshhMjKyhLNmjUTpaWlIjY2VnTo0EFt5+bmJs6ePSu+/fZbMWLECFFaWipKSkpEt27dxI8//ih+/fVXoSiK+O9//6teUyZXcXGx6Nixo4iPjxf5+fmiUaNG4vTN//H9+vUT3bp1E0II8cYbb4gvv/xSCCHE1atXhbOzszAYDGZz5+HhIYQQIj8/X3Tq1EnEx8cLIYTo2bOn2LZtm0n7a9euiVq1agkhhKhVq5a4du3aXZ/P888/LxYuXKjKfu3aNZNxhRDigw8+ELNmzRJCCNGxY0cxatQo9VxISIiIjIwUQgixadMmMXz4cCGEEF26dBEpKSlCCCEOHz4sOnfubDb2Z599Jl577TV1/8qVK6K0tFQIIcTq1avVc7NmzRLe3t4iLy9PCCFE//79xU8//SSEEOLMmTPC1dVVCHH7d+hWcnJyhF6vL3c7fvy4WfsaNWqoP5eWlprsl/Htt9+KNm3aiNzcXJGVlSWcnJzE/PnzTdo4OjqKrKwsk2Pr1q0TY8aMMevvz/DH328hhACixV/QVx+JLCJ/hio3S7sXW5SC9e8GeFFcTHGW0YJtfXOFKal83Lhxgw8++IB33nmHgQMH8sknnwDg5+dXwZJJJBLJo4GTkxNarRYADw8PAgICUBQFrVZL2s1g/+zsbAYPHkxqaiqKoqi+xhEREYwcORIrK+O//9q1a6v9hoYa67wdPXqUTp06UbduXQAGDhzI/v37ee6550zkEEIwbdo09u/fj4WFBenp6WRmZuLl5cXFixe5cOECWVlZ1KpVi8aNG7No0SK+++47vLy8AKMFPDU1lSeffBJHR0datWql9v3VV1+xatUqiouLycjIICkpidLSUpo2bYqTkxMA/fv3V/2kv/vuO3bs2KFaygsKCjh79qxZ2ev//e9/eHp68uuvv9KtWzd0uvtblC4yMpK1a9cCRit5jRo1butnXEbZvJf9vHnzZjp37symTZsYPXo0BoOBQ4cOmRRWuXHDPCdERkaG+swAzp8/T2hoKBkZGRQWFqrzBhASEoKdnTGWLSIigqSk32sA5uTkYDAYbvsO3cpjjz1GXFzc3aalXBRFKTdeLigoiKNHj9KmTRvq1q1L69at7ynWql69ely4cOEvyfIgqHQKdhm1cu1MSqWX5b8GsLCxqQiRJH+Tffv2MWrUKPXTV3FxMSUlJTKIUSKRSG7B5pb/cRYWFuq+hYWF6lM7c+ZMOnfuTHh4OGlpaXTq1Omu/VarVu2O53/++Wf+7//+D4A5c+Zw5coVsrKyiImJwdramiZNmlBQUAAYq+xt2bKF3377TVUghRC88cYbah9lpKWlmYz966+/Mn/+fI4ePUqtWrUYMmSI2u/tEEKwdetWXFxc7tiuzAf70qVLtG3blh07dhASEoK7uzsxMTH06NFDbRsTE4OHhwdgXMjExMTQpUuXO/ZfHlZWViZ+5X+8l1vvPSQkhGnTpnHlyhV1vNzcXGrWrHlXRdbOzo7s7Gx1f+zYsbz22muEhISwb98+1S3lj2OWlpZy+PBhbG1NY9vGjBlz13fo+vXrtG/fvlx5NmzYgLu7u8kxBwcHMjIyqF+/PhkZGdSrV6/ca6dPn8706dMBGDBgwD1VZC4oKFAXDY8CD9QH+0FQVsnRqp0j1P79ZchPSATAVpZIr3RcvHiRwYMH07lzZ5KTk3FxcSEyMpI1a9ZI5VoikTzSCDHLZLsdL7/sY9Ju1aruD1Su7OxsGt4M+F+zZo16PDAwkI8//lhVxK9cuWJ2bcuWLfnxxx+5dOkSJSUlbNy4kY4dO+Lv709cXBxxcXGEhISQnZ1NvXr1sLa2JioqijNnfk+fGxoayqZNm9iyZYtqee3atSufffYZBoMBMNYzuHjRPIFYTk4O1apVo0aNGmRmZrJnj7EGnYuLC6dPn1at9Ldml+jatStLlixB3PzK/csvv9xxfurUqcO8efN47733AHjllVdYs2aNqsRevnyZKVOmMHnyZADeeOMNJk2axG+//QZAYWGh+oX1VgICAlixYgVg9HfPzs7GwcGBixcvcvnyZW7cuHFHP+Hq1avj5+fH+PHjCQ4OxtLSEnt7e5ycnPj6668B42IiPj7e7Fo3NzdOnTql7t/6DnzxxRe3HTMoKEj1xQfUObjdO3QrZRbs8rY/KtdgXECUyfLFF1+YLGjKKCkp4fLlywAcO3aMY8eOERQUdFv5y0hJSTHxda9oKq2CbT29HTS2V48Xnv6f8fyNwgqQSvJXuXTpEm5ubqxduxYbGxvmzJlDfHw8nTt3rmjRJBKJpNIyefJk3njjDby8vEwyRbz00ks8+eST6HQ69Ho9GzZsMLu2fv36zJs3j86dO6PX6/Hx8SlXERo4cCDR0dFotVrWrl2L6y0F3jw8PLh+/ToNGzakfv36gFGRGzBgAK1bt0ar1dKnTx+uX79u1q9er8fLywtXV1cGDBhA27ZtAaOFdvny5Wqw32OPPaZm8pg5cyZFRUXodDo8PDyYOXPmXefoueeeIy8vj59++on69euzbt06RowYgaurK23atGHYsGF0725cCD377LOMGTOGp556Cg8PD7y9vdXMFreyaNEioqKi0Gq1+Pj4kJSUhLW1NW+++SYtW7YkMDDQZJ7KIzQ0lHXr1pm4jqxfv55PP/0UvV6Ph4dHuUGcHTp04JdfflEXGbNnz6Zv3774+PiogY/lsXjxYqKjo9HpdLi7u7Ny5Urg9u/Q32Hq1Kl8//33ODs7ExERwdSpUwGIjo7mpZdeAqCoqIj27dvj7u7Oyy+/zLp161SXpsWLF9OoUSPOnz+PTqdTrwGIioqiW7du90XO+4FS9iAqCzWb2IrGbzmzNWQrLWr9/sng8mefczEsjOqdOtF45YoKlFDyZxk2bBjnz59n+fLlNG/evKLFkUgkktty4sQJM79eycPDYDBQvXp1hBC88sorODs7M2HChIoW65Fh/PjxdO/enaeeeqqiRXmoZGZmMmDAAH744Ye/1U95v9+KosQIIXz/bF+V0IJtdIiPj75kcvza1q0A2N0MnpA8muTm5jJlyhT279+vHlu+fDnffvutVK4lEolEckdWr16Np6cnHh4eZGdnm/lz/9uZNm3aI5UL+mFx9uxZPvzww4oWw4RKF+RYZm+P/P4sfQPbqMfLMohUKSc/ouTRYOfOnYwZM4azZ8+ye/dujh07hoWFhVlghUQikUgk5TFhwgRpsb4DDg4O/8o6EY9iprFKZ8Eui8O1VqqYHr/pC2WnfXQc3CVGzp07R69evQgJCeHs2bN4eXnx+eefY2FR6V4/iUQikUgkkrtS6TSckpspE60tf1ewi25WJwKweuKJhy2S5DYUFxezYMEC3NzcCA8Pp3r16nz00UccOXLkkVxtSiQSiUQikdwPKp2CXUbHiCwoNTqMFKb9nhpIkVbRR4acnBzee+89cnNz6d27NydOnGD8+PFqNLBEIpFIJBLJP5FKqelYCkFIWi5YGM3ZN1JSAHjsHvIkSh4s165dw87ODhsbG2rXrs3HH3+MjY3NI5U6RyKRSCQSieRBUinNvVZC+T3aEcjZvRsAxVZWcKwohBBs2LABFxcXwsLC1OO9evWSyrVEIpFIJJJ/FZVTwS413VesrQGwcXKqAGkkKSkpBAYGMnDgQC5evMj+/fupbPnVJRKJpLJgaWmJp6cnGo2G7t27c+3aNfXc8ePH6dKlCy4uLjg7OzN37lyTv8d79uzB19cXd3d3vLy8mDhxYkXcwl+if//+6HQ6Fi5ceE/tq1ev/oAlujfatGlz90b3yEcffcTatWvvW3/3m19//RV/f3+aN29OaGgohYXmxf8KCwsZOnQoWq0WvV7Pvn371HNPP/20Wkxn5MiRlJSUAPD6668TGRn5sG7j/iCEqFSbbRNboVmjEeLwBVFGSrv2IsnFVeQnnxSSh0d+fr6YPXu2qFKligBE7dq1xaeffipKSkoqWjSJRCJ5ICQlJVW0CKJatWrqz4MGDRJvv/22EEKIvLw80bRpU/Htt98KIYTIzc0VTz/9tFi6dKkQQoiEhATRtGlTceLECSGEEMXFxWL58uX3VbaioqL72l8ZGRkZolmzZn/qmlvn6Z9AUVGR0Gq1f2qOH9TzuB19+/YVGzduFEII8X//93/lvl9Lly4VQ4YMEUIIkZmZKby9vVW9ITs7WwghRGlpqejVq5faV1pamggMDHzg8pf3+w1Ei7+gr1ZKH+y6whL8jaVXiy9fpjgrC8XGBhunJhUq17+J3377jQ4dOpCamgrAkCFD+OCDD+5YjlUikUj+STSZuvuB9Js2797d6lq3bs2xY8cA2LBhA23btiXoZjxS1apVWbp0KZ06deKVV14hLCyM6dOnq6W6LS0tGTVqlFmfBoOBsWPHEh0djaIozJo1i969e1O9enUMBgMAW7ZsYdeuXaxZs4YhQ4Zga2vLL7/8Qtu2bfnmm2+Ii4ujZs2aADg7O3PgwAEsLCwYOXIkZ8+eBYzW2LIy6GUUFBQwatQooqOjsbKyYsGCBXTu3JmgoCDS09Px9PRkyZIltG/fXr0mMzOTkSNHcvr0aQBWrFhhYjU2GAz06NGDq1evUlRUxNtvv02PHj3Izc3l+eef5/z585SUlDBz5kxCQ0OZOnUqO3bswMrKiqCgIObPn28i45EjRxg/fjwFBQXY2dnx+eef4+LiwvHjxxk6dCiFhYWUlpaydetWnJ2d1Xm7nRxpaWk888wztGvXjkOHDtGwYUO2b9+OnZ2dybiRkZF4e3uriQJWr17NqlWrKCwspHnz5nz55ZdUrVrV7Hm88sorvPLKK2RlZVG1alVWr16Nq6srO3fu5O2336awsJDHH3+c9evX4+DgcM/v3h8RQhAZGcmGDRsAGDx4MLNnzzZ7x5KSkujSpQsA9erVo2bNmkRHR9OyZUvs7e0BYxaywsJCFMUYa+fo6Mjly5f57bffeKKSZIurlAp2lVs8W27cVPCqPPkkSpUqt7tEcp9xcHCgcePGWFlZsWLFCjp27FjRIkkkEsm/ipKSEn744QeGDx8OGN1DfHx8TNo0a9YMg8FATk4OiYmJ9+QSMnfuXGrUqEFCQgIAV69eves158+f59ChQ1haWlJSUkJ4eDhDhw7l559/xtHREQcHBwYMGMCECRNo164dZ8+epWvXrpw4ccKkn2XLlqEoCgkJCSQnJxMUFERKSgo7dqrC/G4AACAASURBVOwgODiYuLg4s7HHjRtHx44dCQ8Pp6SkRF0ElGFra0t4eDj29vZcunSJVq1aERISwt69e2nQoAG7b8ZxZWdnc/nyZcLDw0lOTkZRFBP3mzJcXV356aefsLKyIiIigmnTprF161ZWrlzJ+PHjGThwIIWFhap7w93kAEhNTWXjxo2sXr2a559/nq1bt/LCCy+YXH/w4EGT59urVy9GjBgBwIwZM/j0008ZO3as2fMICAhg5cqVODs78/PPPzN69GgiIyNp164dhw8fRlEUPvnkE8LCwsyqIZ48eZLQ0NByn/m+ffvURRTA5cuXqVmzproAaNSoEenp6WbX6fV6duzYQf/+/Tl37hwxMTGcO3eOli1bAtC1a1eOHDnCM888Q58+fdTrvL29OXjwIL179y5XnkeNSqlgF+UXs2FDAgMGaCk4fhwAUY6fj+T+UVpayurVq+ncuTMtWrRAURQ2bNhArVq1qCIXNhKJ5F/In7E030/y8/Px9PQkPT0dNzc3AgMD72v/ERERbNq0Sd2vVavWXa/p27cvlpaWAISGhjJnzhyGDh3Kpk2bVAUtIiKCpKQk9ZqcnBwMBoOJr/SBAwdUJdHV1RVHR0dSUlJUy2Z5REZGqn7JlpaW1KhRw+S8EIJp06axf/9+LCwsSE9PJzMzE61Wy8SJE5kyZQrBwcG0b9+e4uJibG1tGT58OMHBwQQHB5uNl52dzeDBg0lNTUVRFIqKigDj14R33nmH8+fP06tXL5ydne9JDgAnJyc8PT0B8PHxIS0tzWzcjIwM3Nzc1P3ExERmzJjBtWvXMBgMdO3a1ex5GAwGDh06RN++fdVzN27cAIxKeGhoKBkZGRQWFuJUThybi4tLuYuav8OwYcM4ceIEvr6+ODo60qZNG/XdAfj2228pKChg4MCBREZGqu93vXr1uHDhwn2V5UFSKYMcbSkhJ8f4ghScPAmA1d/4rCG5M/Hx8bRt25aRI0cyevRoNWDGwcFBKtcSiUTykLGzsyMuLo4zZ84ghGDZsmUAuLu7ExMTY9L29OnTVK9eHXt7ezw8PMzO/xnKPteD0ZXjVqpVq6b+3Lp1a06dOkVWVhbbtm2jV69egNFQc/jwYeLi4oiLiyM9Pf2hBCKuX7+erKwsYmJiiIuLw8HBgYKCAlq0aEFsbCxarZYZM2YwZ84crKysOHLkCH369GHXrl08/fTTZv3NnDmTzp07k5iYyM6dO9W5GDBgADt27MDOzo5nn33WLCjvdnIA2Nj8ngXN0tKS4uJis3Ht7OxM5n3IkCEsXbqUhIQEZs2aZXKu7HmUlpZSs2ZNdc7j4uLUrwZjx45lzJgxJCQk8PHHH5s9UzBasD09Pcvd/mjdf/zxx7l27Zoq+/nz52nYsKFZn1ZWVixcuJC4uDi2b9/OtWvXaNGihUkbW1tbevTowfbt29VjZS45lYVKqWAXF1phaWn8RS86ew6AKk2aVKBE/0wMBgOvv/46Pj4+HD58mAYNGjBy5MiKFksikUgkGH2sFy9ezIcffkhxcTEDBw7kwIEDREREAEZL97hx45g8eTIAkyZN4t133yXlZu2I0tJSVq5cadZvYGCgqrTD7y4iDg4OnDhxgtLSUsLDw28rl6Io9OzZk9deew03Nzcef/xxAIKCgliyZInarjzLaPv27Vm/fj1gzFB19uxZXFxc7jgPAQEBrFixAjC6zWRnZ5ucz87Opl69elhbWxMVFcWZM8bidBcuXKBq1aq88MILTJo0idjYWAwGA9nZ2Tz77LMsXLiQ+Ph4s/Gys7NVxXHNmjXq8dOnT9O0aVPGjRtHjx49VN/4u8lxr7i5uXHq1Cl1//r169SvX5+ioiJ1zv6Ivb09Tk5OfP3114DRil52T7fexxdffFHu9WUW7PK2W91DwPjcO3fuzJYtW9Q+e/ToYdZnXl4eubm5AHz//fdYWVnh7u6OwWAgIyMDMPpg7969W40XAOP7oNFo7j5RjwiVUsG2LFGwumZ0CSm5fh2Aqj7eFSnSP45t27bh5ubGhx9+iBCCsWPHcuLECfr06WNixZBIJBJJxeHl5YVOp2Pjxo3Y2dmxfft23n77bVxcXNBqtfj5+TFmzBgAdDodH330Ef3798fNzQ2NRqMGBt7KjBkzuHr1KhqNBr1eT1RUFADz5s0jODiYNm3aUL9+/TvKFRoayrp160z8dxcvXkx0dDQ6nQ53d/dylfvRo0dTWlqKVqslNDSUNWvWmFh3y2PRokVERUWh1Wrx8fExcUMBGDhwINHR0Wi1WtauXasqbQkJCbRs2RJPT0/eeustZsyYwfXr1wkODkan09GuXTsWLFhgNt7kyZN544038PLyMrE0f/XVV2g0Gjw9PUlMTGTQoEH3JMe98swzz7B//351f+7cufj7+9O2bds79rV+/Xo+/fRTNf1dmVV49uzZ9O3bFx8fn/uWoOD9999nwYIFNG/enMuXL6vxATt27ODNN98E4OLFi3h7e+Pm5sb777/Pl19+CUBubi4hISHodDo8PT2pV6+eatQrKiri1KlT+Pr63hc5HwZK2ef+yoKdk514fowbi3ZNomZUf064Gv2Rmv5nNzZNm1awdP8M0tPTadasGTdu3MDHx4eVK1dWqpdaIpFIHhQnTpww8YOVSB4mPXv2JCwszMy/+59OeHg4sbGxzJ0794GOU97vt6IoMUKIP60EVcogR1uhUNPOmtL8fPVYlUaNKlCiyk9RURFWVlYoikLDhg155513qFKlCqNHjzYJPpBIJBKJRFIxzJs3j4yMjH+dgl1cXFypiiJBJXURuWBXAjaW3LjFF0mm6PvrHDp0CB8fH9atW6cemzhxImPHjpXKtUQikUgkjwguLi506NChosV46PTt29fM5/tRp1Iq2K55tvCkPTdSjQq2bSVyen+UuHLlCv/3f/9H27ZtSUhIYPny5bLEuUQikUgkEsnfpFIq2NYN7GFRAMWZvxn361eOqj6PCkIIvvzyS1xdXVm1ahXW1tZMnz6dyMhIGcAokUgkEolE8jeplD7YNhZGsYtuJhy3rFW7IsWpVGRmZtK/f381Krxjx46sWLFCBu1IJBKJRCKR3CcqpQXbIvsaV67kg2IUX7G9cwofye/UrFmTjIwM6tSpw5o1a4iKipLKtUQikVQiLC0t8fT0RKPR0L17d5OCH8ePH6dLly64uLjg7OzM3LlzTVz/9uzZg6+vL+7u7nh5eVWqwLH+/fuj0+lYuHDhPbV/UEVsZs+ezfz588s916ZNmwcy5h+5cOGCSRnxe+HVV181SfP3qBETE4NWq6V58+aMGzeuXJfVq1ev0rNnT3Q6HS1btiQxMREwFqFp2bKlmopw1qxZ6jX9+vUjNTX1od1HGZVSwU5KK+XIkXQKkpMBsHV3r2CJHm2+//57Ll++DBirRX399dckJyczePBg6RIikUgklYyySo6JiYnUrl1bLQqTn59PSEgIU6dO5eTJk8THx3Po0CGWL18OGEtrjxkzhnXr1pGUlER0dDTNmze/r7KVV4HwfvDbb79x9OhRjh07xoQJEx7IGPeDQ4cOPZRxGjRooBZ0uRcuX77M4cOH/1SA5IN6lrdj1KhRrF69mtTUVFJTU9m7d69Zm3fffRdPT0+OHTvG2rVrGT9+PGDUbSIjI4mPjycuLo69e/dy+PBhtd+wsLCHei9QSRVsSi2wsFAouFnu01qWSS+XjIwM+vfvT1BQEFOmTFGPazQatbKWRCKRSCovrVu3Jj09HYANGzbQtm1bgoKCAGOlx6VLlzJv3jwAwsLCmD59ulqUxNLSklGjRpn1aTAYGDp0KFqtFp1Ox9atWwFTi/CWLVsYMmQIYCzZPXLkSPz9/Zk8eTJNmjQxsao7OzuTmZlJVlYWvXv3xs/PDz8/Pw4ePGg2dkFBgTq2l5eX6s4YFBREeno6np6e/PTTTybXZGZm0rNnT/R6PXq93kzJNRgMBAQE4O3tjVarVQut5Obm0q1bN/R6PRqNhs2bNwMwdepU3N3d0el0vP766+XOe1JSEp06daJp06YsXrxYPV42RxkZGXTo0EH90lAmc/Xq1ZkwYQIeHh4EBASQlZUFwOrVq/Hz80Ov19O7d2/y8vLUuR03bhxt2rShadOmqlKdlpamVjUsKSnh9ddfR6PRoNPpTKpllrF161aTsu9z5szBz88PjUbDyy+/rFqLO3XqxKuvvoqvry+LFi0iJiaGjh074uPjQ9euXdVKi7eT96+SkZFBTk4OrVq1QlEUBg0axLZt28qd9y5dugDg6upKWloamZmZKIqizn1RURFFRUWqAbF9+/ZEREQ89AVDpfTBpkRBUYCiIgCqODpWrDyPGCUlJXz88ce88cYb5OTkYGdnh4uLC0IIabGWSCSS+8XsGg+o3+y7t8H4t/6HH35Qq+UdP34cHx8fkzbNmjXDYDCQk5NDYmLiPbmEzJ07lxo1apCQkAD8Xir9Tpw/f55Dhw5haWlJSUkJ4eHhDB06lJ9//hlHR0ccHBwYMGAAEyZMoF27dpw9e5auXbty4qahrIxly5ahKAoJCQkkJycTFBRESkoKO3bsIDg4uNzy6uPGjaNjx46Eh4dTUlKCwWAwOW9ra0t4eDj29vZcunSJVq1aERISwt69e2nQoAG7d+8GjKXDL1++THh4OMnJySiKYrJQuJXk5GSioqK4fv06Li4ujBo1Cmtra/X8hg0b6Nq1K9OnT6ekpERVQHNzc/H19WXhwoXMmTOHt956i6VLl9KrVy9GjBgBGCtpfvrpp4wdOxYwKp8HDhwgOTmZkJAQM9eQVatWkZaWRlxcHFZWVly5csVM3oMHD5pcN2bMGLWy4osvvsiuXbvo3r07AIWFhURHR1NUVETHjh3Zvn07devWZfPmzUyfPp3PPvvsjvKWERUVVe7XhqpVq5otgtLT02l0Sz2TRo0aqQvHW9Hr9XzzzTe0b9+eI0eOcObMGc6fP4+DgwMlJSX4+Phw6tQpXnnlFfz9/QGwsLCgefPmxMfHm/1+PEgqpYJtb21N7eiz6r7VXUq2/puIjY1l5MiRHD16FIBu3bqxdOlSmjRpUrGCSSQSieS+kJ+fj6enJ+np6bi5uREYGHhf+4+IiGDTpk3qfq1ate56Td++fdW6CaGhocyZM4ehQ4eyadMmtVx6RESESRnznJwcDAaDiWX8wIEDqqLm6uqKo6MjKSkp2Nvb33bsyMhI1q5dCxit8jVqmC58hBBMmzaN/fv3Y2FhQXp6OpmZmWi1WiZOnMiUKVMIDg6mffv2FBcXY2try/DhwwkODiY4OLjcMbt164aNjQ02NjbUq1ePzMxMEwXRz8+PYcOGUVRUxHPPPYenpydgVPbK5uOFF16gV69egNF9Z8aMGVy7dg2DwUDXrl3Vvp577jksLCxwd3cnMzPTTJaIiAhGjhyJlZVRpatd2zzxQ0ZGBnXr1lX3o6KiCAsLIy8vjytXruDh4aEq2GXynTx5ksTERPX9Kikpof5NfetO8pbRuXPnchdEf4epU6cyfvx4PD091a8cZe+dpaUlcXFxXLt2jZ49e5KYmKha+evVq8eFCxekgn039MUWuJ8+QxqAtbW0yt4kLS2Nli1bUlJSQsOGDVm8eDE9e/aU8yORSCQPgnu0NN9vynyw8/Ly6Nq1K8uWLWPcuHG4u7ubBbGdPn2a6tWrY29vj4eHBzExMej1+r807q3/SwoKCkzOVatWTf25devWnDp1iqysLLZt28aMGTMAKC0t5fDhw9ja2v6l8f8q69evJysri5iYGKytrWnSpAkFBQW0aNGC2NhY/vOf/zBjxgwCAgJ48803OXLkCD/88ANbtmxh6dKlREZGmvVpY/N7cgVLS0sz94MOHTqwf/9+du/ezZAhQ3jttdcYNGiQWT9lczpkyBC2bduGXq9nzZo17Nu3r9yx/mqtCjs7O/WZFRQUMHr0aKKjo2ncuDGzZ882eZ5lz1IIgYeHB//973/N+ruTvGX8GQt2w4YNOX/+vLp//vx5GjZsaHatvb09n3/+uSqfk5MTTZs2NWlTs2ZNOnfuzN69e1UFu6CgADs7u3Ln5kFRKX2wrUssuJFnXMVVu/kJQAJNmjRh6NChTJgwgRMnTtCrVy+pXEskEsk/lKpVq7J48WI+/PBDiouLGThwIAcOHCAiIgIwWrrHjRvH5MmTAZg0aRLvvvsuKSkpgFHhXblypVm/gYGBauAk/O4i4uDgwIkTJygtLSU8PPy2cimKQs+ePXnttddwc3NTY36CgoJM/IPLs262b9+e9evXA5CSksLZs2dxcXG54zwEBASwYsUKwGhlzc42XfhkZ2dTr149rK2tiYqK4syZM4AxE0fVqlV54YUXmDRpErGxsRgMBrKzs3n22WdZuHAh8fHxdxz7dpw5cwYHBwdGjBjBSy+9RGxsLGCc8zI/6g0bNtCuXTsArl+/Tv369SkqKlLv/14JDAzk448/VpX88lxE3NzcOHWz+nWZMl2nTh0MBsNtgyVdXFzIyspSFeyioiKOHz9+z/KWWbD/uJUXCFq/fn3s7e05fPgwQgjWrl1Ljx49zNpdu3aNwsJCAD755BM6dOiAvb09WVlZqjtPfn4+33//vRprAMZ3SfOQixJWSgW7SoklhXnGwIDSP/ha/ZtIS0uje/fu/Pjjj+qxVatWsWDBAh577LEKlEwikUgkDwMvLy90Oh0bN27Ezs6O7du38/bbb+Pi4oJWq8XPz48xY8YAoNPp+Oijj+jfvz9ubm5oNBpOnz5t1ueMGTO4evUqGo0GvV6vBhrOmzeP4OBg2rRpo7oK3I7Q0FDWrVunuhsALF68mOjoaHQ6He7u7uUq96NHj6a0tBStVktoaChr1qwxseCWx6JFi4iKikKr1eLj42PihgIwcOBAoqOj0Wq1rF27VlW8EhISaNmyJZ6enrz11lvMmDGD69evExwcjE6no127dixYsOCOY9+Offv2odfr8fLyYvPmzWq2i2rVqnHkyBE0Gg2RkZGqH/TcuXPx9/enbdu2JorhvfDSSy/x5JNPotPp0Ov1bNiwwaxNt27dVCtzzZo1GTFiBBqNhq5du+Ln51duv1WqVGHLli1MmTIFvV6Pp6enqhz/HXlvx/Lly3nppZdo3rw5zZo145lnngFg5cqV6rty4sQJNBoNLi4u7Nmzh0WLFgFGF5jOnTuj0+nw8/MjMDBQde/JzMzEzs6OJ554uEUJlcpWGtvOyU589lowAWlaLu/eiH1wMA3nf1DRYj1UioqKWLBgAW+99Rb5+fm0atWq3E84EolEIrm/nDhxQtYOkPxlqlevbhaE+bBo164du3btombNmhUyfkWxcOFC7O3t1WDgO1He77eiKDFCCN8/O26ltGBbNarB9SRjfsOq/i0rWJqHy4EDB/Dy8mLq1Knk5+fTr18/vvnmm4oWSyKRSCQSySPMhx9+yNmzZ+/e8B9GzZo1GTx48EMft1IGOVra2GNdv5TCX3/Fwq5qRYvzULh69SqTJk3i008/BYypl5YvX67mO5VIJBKJRPJoU1HWa0BNW/dvY+jQoRUybqW0YP/3QAa5h4wuEdYNGlSwNA+H0tJStm/fjrW1NTNnziQhIUEq1xKJRCKRSCSPIJXSgp32v2yEoqAIgUW1f64FOzk5GScnJ2xsbHj88cdZv349Tz755H0LKJBIJBKJRCKR3H8qpQVblFqg3AzOtConoXplJy8vj+nTp6PT6QgLC1OPBwUFSeVaIpFIJBKJ5BGnUlqwKf49t7PlP0zB3rt3L6NHj+bXX38F4NKlSxUskUQikUgkEonkz1ApFexutxjelZslMis7Fy5c4NVXX+Xrr78GQKvVsnLlStq0aVPBkkkkEolEIpFI/gyV0kWkRWFJRYtwX0lJScHV1ZWvv/6aqlWrEhYWRkxMjFSuJRKJRGKGpaUlnp6eaDQaunfvrlawAzh+/DhdunTBxcUFZ2dn5s6da1Jee8+ePfj6+uLu7o6XlxcTJ06siFv4S/Tv3x+dTsfChQvvqX316tUfsESmpKWl/alqgZ06dSI6Otrs+Jo1a9TiQH+We7nn/Px8OnbsSEnJo6tLvffeezRv3hwXFxe+/fbbcttERkbi7e2NRqNh8ODBaiVLIQTjxo2jefPm6HQ6tYpmVlYWTz/99EO7h0qpYFuWFAFg1eDOlaQqC87Ozvj5+dG9e3eSkpKYNGkS1tbWFS2WRCKRSB5B7OzsiIuLIzExkdq1a6tlzfPz8wkJCWHq1KmcPHmS+Ph4Dh06xPLlywFITExkzJgxrFu3jqSkJKKjo2nevPl9la1Mybnf/Pbbbxw9epRjx44xYcKEBzLGv4XPPvuMXr16YXmPHgBCCEpLSx+wVL+TlJTEpk2bOH78uOo2+8fFQGlpKYMHD2bTpk0kJibi6OjIF198ARgXkampqaSmprJq1SpGjRoFQN26dalfvz4HDx58KPdRORXsAuODtnWtnNW0cnJyePXVV0lJSQFAURR27NjBjh07cHR0rGDpJBKJRHIvaL/QPpDtz9C6dWvS09MB2LBhA23btlVTuFatWpWlS5cyb948AMLCwpg+fboaLG9paakqH7diMBgYOnQoWq0WnU7H1q1bAVPr6JYtWxgyZAgAQ4YMYeTIkfj7+zN58mSaNGliYlV3dnYmMzOTrKwsevfujZ+fH35+fuUqOgUFBerYXl5eapn2oKAg0tPT8fT05KeffjK5JjMzk549e6LX69Hr9Wo571vvJyAgAG9vb7RaLdu3bwcgNzeXbt26odfr0Wg0bN68GYCpU6fi7u6OTqfj9ddfN5Nx9uzZDBs2jE6dOtG0aVMWL16snispKWHEiBF4eHgQFBREfn6+2fW38uWXX6pfI44cOWJ2fufOnfj7++Pl5cVTTz1FZmamek/lPaMyLl26ROvWrdm9e7dZn+vXr6dHjx53nJu0tDRcXFwYNGgQGo2Gc+fO8cEHH+Dn54dOp2PWrFlqf8899xw+Pj54eHiwatWqO97vvbB9+3b69euHjY0NTk5ONG/e3GxuLl++TJUqVWjRogUAgYGB6hxs376dQYMGoSgKrVq14tq1a2RkZKiyrl+//m/LeC9USh9sK2d7+A0Uy8q1PhBCsGXLFsaPH09GRgbJycns3bsXgGrVqlWwdBKJRCKpTJSUlPDDDz+oJaCPHz+Oj4+PSZtmzZphMBjIyckhMTHxnlxC5s6dS40aNUhISACMhc7uxvnz5zl06BCWlpaUlJQQHh7O0KFD+fnnn3F0dMTBwYEBAwYwYcIE2rVrx9mzZ+natSsnTpww6WfZsmUoikJCQgLJyckEBQWRkpLCjh07CA4OJi4uzmzscePG0bFjR8LDwykpKTEr5mJra0t4eDj29vZcunSJVq1aERISwt69e2nQoIGqhGZnZ3P58mXCw8NJTk5GURSThcKtJCcnExUVxfXr13FxcVEXKqmpqWzcuJHVq1fz/PPPs3XrVl544YXbzlteXh5xcXHs37+fYcOGkZiYaHK+Xbt2HD58GEVR+OSTTwgLC+PDDz+84zPKzMwkJCSEt99+m8DAQJP+CgsLOX36NE2aNLnj3JTdyxdffEGrVq347rvvSE1N5ciRIwghCAkJYf/+/XTo0IHPPvuM2rVrk5+fj5+fH7179+bxxx83GXfChAnqYulW+vXrx9SpU02Opaen06pVK3W/UaNG6iKyjDp16lBcXEx0dDS+vr5s2bKFc+fOqdc3btzY7Pr69evj6+vLjBkzbvs87ieVUsEWj9kBYGFvX8GS3DunT59mzJgx7NmzB4BWrVrx/vvvV7BUEolEIvmrJAxOqJBx8/Pz8fT0JD09HTc3NzMl6u8SERHBpk2b1P1atWrd9Zq+ffuqLgehoaHMmTOHoUOHsmnTJkJDQ9V+k5KS1GtycnIwGAwmlvEDBw4wduxYAFxdXXF0dCQlJQX7O/y/j4yMZO3atYDRKl+jRg2T80IIpk2bxv79+7GwsCA9PZ3MzEy0Wi0TJ05kypQpBAcH0759e4qLi7G1tWX48OEEBwcTHBxc7pjdunXDxsYGGxsb6tWrp1qWnZyc8PT0BMDHx4e0tLQ7zlv//v0B6NChAzk5OWYK/fnz5wkNDSUjI4PCwkKcnJzUuSzvGRUVFREQEMCyZcvo2LGj2XiXLl2iZs2ad50bAEdHR1XR/e677/juu+/w8vICjJbv1NRUOnTowOLFiwkPDwfg3LlzpKamminY9+o3f68oisKmTZuYMGECN27cICgo6J5cXurVq8eFCxfuqyy3o3KZgG9ilWhcpdi6PPo5oQsLC3n33Xfx8PBgz5491KxZk5UrV3Lw4EH0en1FiyeRSCSSSkaZD/aZM2cQQqg+2O7u7sTExJi0PX36NNWrV8fe3h4PDw+z838GRfk9RW5BQYHJuVu/wrZu3ZpTp06RlZXFtm3b6NWrF2D0mz18+DBxcXHExcWRnp7+UAIR169fT1ZWFjExMcTFxeHg4EBBQQEtWrQgNjYWrVbLjBkzmDNnDlZWVhw5coQ+ffqwa9eu2wbF2djYqD9bWlqqvue3O347bp3T8vbHjh3LmDFjSEhI4OOPPzab9z9iZWWFj4/PbQMD7ezsTPq43dyA6TMVQvDGG2+oz+7UqVMMHz6cffv2ERERwX//+1/i4+Px8vIqV8YJEybg6elptpW5L91Kw4YNVWs0GBcZDRs2NGvXunVrfvrpJ44cOUKHDh1Ud5E7XV9QUICdnd0d5/B+USkV7JL/b+/e46Is08ePfy7QRCzJDlpm6/kAMgN4YNUVsyzwq0S5Hviabt/MLLfUVg1zXbJSv2a1iqd+mu66Zrrqrv5Mt4OVRatkZoh4SAn7eaZSM0PxlMD9+2OGZwcYYIxhYOx6v168ZOa5n/u55uEWrrnnPhxzrA0tLg25pjp27BhTpkzh0qVLDBkyhKysLJ544gkCAvzy1iullKohgoODIPIU2AAAHH1JREFUmTt3LjNnziQ/P58hQ4aQlpbGpk2bAEdP95gxY5gwYQIASUlJTJ8+3Zr/U1hYyMKFC0vVe99991lJO/xn+EGjRo3Yv38/hYWFVo+lOyJCv379GDduHKGhoVZvZmxsLPPmzbPKuRvuERMTY42Rzc7O5ujRo7Rt27bc+9CrVy8WLFgAOIbN5ObmFjuem5tLw4YNqV27NqmpqRw5cgRwLI8bHBzM0KFDSUpKIiMjg7y8PHJzc+nTpw8pKSns2rWr3GtXVtG477S0NEJCQkr1vufm5lrJYdEkPij7ZyQiLFmyhKysLLefkjdo0ICCggIrCS7r3pQUFxfHkiVLrOE3OTk5nDx5ktzcXBo0aEBwcDBZWVls27bN7fkpKSlWcu76VXJ4CEBCQgKrVq3i8uXLHDp0iAMHDhAdHV2q3MmTJwG4fPkyL7/8MiNHjrTOX7ZsGcYYtm3bRkhICLff7lgUIzs7+6pWeqkMv8zygoxzkmP79tUciXtnzpyxlkVq2bIlc+bMYdOmTSxfvpxGjRpVc3RKKaWuFVFRUdjtdlauXEndunVZv34906ZNo23btthsNjp37mwt+Wa325k9ezaDBw8mNDSU8PBwDh48WKrO5ORkzpw5Q3h4OBEREdbY2RkzZhAfH0+3bt2shKUsiYmJLF++3BoeAjB37lzS09Ox2+2EhYW5Te6ffPJJCgsLsdlsJCYmsnTp0mK9wu7MmTOH1NRUbDYbHTt2LDYMBWDIkCGkp6djs9lYtmyZNclzz549REdHExkZyYsvvkhycjLnzp0jPj4eu91O9+7dmTVrVrnXrqygoCCioqIYOXIkf/3rX0sdf+GFFxg4cCAdO3bklltusZ4v62cEjp7zlStX8vHHH1sryLiKjY0lLS0NKPveuDvnoYceomvXrthsNgYMGMC5c+fo3bs3+fn5hIaGMnHixGJjp3+u9u3bM2jQIMLCwujduzevvfaaNfyjT58+1hCPV199ldDQUOx2O/fffz/33HOPVaZFixa0atWKESNGFLsHqamp9O3bt9IxekJc18f0B8HN6podQc0AaLszgwAfdfV7orCwkKVLl5KUlMTs2bP53e9+V90hKaWU8qL9+/cTGuqfK1gpBZCRkUFKSgpvvvlmdYficz169GD9+vVlzitw9/9bRHYYYzpd7bX8rge7lstSiDUpuf7yyy/p2bMnw4cP54cffrAmMyqllFJK1RQdOnTg7rvvrtEbzVSFU6dOMW7cOI8m7XqD/yXYzrXOg25vUb2BOF24cIE//vGP1tqcDRs2ZMWKFT5bZ1EppZRSNddTTz1VanLf3/72t2qN6dFHH/V4o5lrxa233sqDDz7os+v53TJ9gc43XIX5P1VvIDgGy8fFxXH48GFEhJEjRzJ9+nSfvTtSSimlVM3mOhlR/XL4X4Lt7MGu1eDm8gv6QNOmTQkKCiIiIoKFCxd6ZXC/UkoppZTyb343RCTAOSez1m0NfX7t/Px85s+fz+nTpwHHepcbN24kPT1dk2ullFJKKQX4YYJd54rj31rNy18iyNu2b99OdHQ0o0eP5tlnn7Web9q0KbVq+d0HAUoppZRSqor4XYJd27kpUsANvtkmPTc3l1GjRtGlSxd27tzJr371Kx544AGfXFsppZRSSvkfv0uwr3Mm2AUtyt/ZqbKMMaxatYp27dpZi5xPmDCBffv2cf/991fptZVSSqmyBAYGEhkZSXh4OPfffz8//vijdezLL7/knnvuoW3btrRu3ZqpU6fiut/Fe++9R6dOnQgLCyMqKorx48dXx0v4WQYPHozdbiclJcWj8r7Yht3V4cOHvbpL4COPPMKaNWtKPf/NN98wYMAAwLEb5rvvvnvVdX/77bfEx8dXOsaqYoxhzJgxtGrVCrvdTkZGhttyK1euxGazYbfb6d27N99/79jpOzMzky5duhAZGUmnTp3Yvn07AG+//TaTJ0/2yWvwuwQ737mqTF79qt0RcdeuXQwePJjvvvuObt26kZGRwcsvv0y9evWq9LpKKaVUeerWrUtmZiZ79+7lpptuslapuHjxIgkJCUycOJGvvvqKXbt2sXXrVmsnu7179zJq1CiWL1/Ovn37SE9Pp1WrVl6NLT8/36v1Ffnuu+/44osv2L17N2PHjq2Sa/iLxo0bW4n3z02wZ82axYgRIzwuX1U/17K89957HDhwgAMHDrBo0SJ+//vfu43p6aefJjU1ld27d2O325k/fz4AEyZM4PnnnyczM5MpU6YwYcIEAPr27cu//vUvLly4UOWvwe8S7KKNZgJuvNHrdbsuuh4ZGcnYsWNZvHgxW7ZswWazef16Siml/Nf+dqFV8nU1unbtSk5ODgB///vf+c1vfkNsbCwAwcHBzJ8/nxkzZgDwyiuv8Kc//cnaDjswMNBt4pKXl8ewYcOsnsG1a9cCxXuE16xZwyOPPAI4elpHjhzJr3/9ayZMmECzZs2K9aq3bt2aEydOcOrUKfr370/nzp3p3Lkzn376aalrX7p0ybp2VFSUtQV4bGwsOTk51p4Trk6cOEG/fv2IiIggIiKCrVu3lno9vXr1okOHDthsNtavXw/A+fPn6du3LxEREYSHh7N69WoAJk6cSFhYGHa7nWeeeaZUjC+88AKPPvooPXv2pEWLFsydO9c6VlBQwIgRI2jfvj2xsbFcvHix1PkAJ0+epGPHjoCjQ09EOHr0KAAtW7a0EsDNmzfTrVs3WrRoYSXVRT3lP/30E5MnT2b16tVERkayevVqzp8/z6OPPkp0dDRRUVHWay1p7dq19O7d26ovJiaGDh060KFDB+v+ffLJJ8TExJCQkEBYWBgFBQUkJSXRuXNn7HY7r7/+ern3tzLWr1/Pww8/jIjQpUsXfvzxR7799ttiZYwxGGM4f/48xhjOnj1L48aNARARzp49CziG+ro+37NnT95+++1Kx1gRv52dJ8He7UlOTU3lySef5PXXX6dHjx6A4x2eUkopVRMVFBTw0UcfMXz4cMAxPKQoaSvSsmVL8vLyOHv2LHv37vVoSMjUqVMJCQlhz549AJw5c6bCc44fP87WrVsJDAykoKCAdevWMWzYMD7//HOaNm1Ko0aNeOihhxg7dizdu3fn6NGjxMXFsX///mL1vPbaa4gIe/bsISsri9jYWLKzs9mwYQPx8fFkZmaWuvaYMWO46667WLduHQUFBeTl5RU7HhQUxLp166hfvz7ff/89Xbp0ISEhgY0bN9K4cWPeeecdwJGInT59mnXr1pGVlYWIFHuj4CorK4vU1FTOnTtH27ZtrTcqBw4cYOXKlSxevJhBgwaxdu1ahg4dWur8hg0bcunSJc6ePcuWLVvo1KkTW7ZsoXv37jRs2JDg4GDAMZQjLS2NrKwsEhISrKEhANdddx1TpkwhPT3d6rmdNGkS99xzD0uWLOHHH38kOjqae++9t9in74cOHaJBgwbUqVPHiuXDDz8kKCiIAwcOMHjwYNLT0wHHtup79+6lefPmLFq0iJCQEL744gsuX75svZm788473d5fESn2mhMTE/nqq69K3Ytx48bx8MMPF3suJyeHO++803rcpEkTcnJyuP32/yxwUbt2bRYsWIDNZqNevXq0bt3a+jRn9uzZxMXF8cwzz1BYWFjsTVfRvR40aJDbn623+GWC/ZPU4rZc72w0c/LkSZKSkli2bBngSKqLEmyllFKqLKFZ+ysuVAUuXrxIZGQkOTk5hIaGct9993m1/k2bNrFq1SrrsSebpw0cONDaGTAxMZEpU6YwbNgwVq1aRWJiolXvvn37rHPOnj1LXl5esZ7xtLQ0Ro8eDUC7du1o2rQp2dnZ1K9f9sIGH3/8sfU3PDAwkJCQkGLHjTFMmjSJzZs3ExAQQE5ODidOnMBmszF+/HieffZZ4uPjiYmJIT8/n6CgIIYPH058fHyZ45T79u1LnTp1qFOnDg0bNuTEiRMANG/enMjISAA6duzI4cOHy4y7W7dufPrpp2zevJlJkyaxceNGjDHExMRYZR588EECAgIICwuzrlGeDz74gA0bNvDnP/8ZcHwicPToUUJD//PJyLfffsutt95qPb5y5QqjRo0iMzOTwMBAsrOzrWPR0dE0b97cqnv37t1WT3pubi4HDhygSZMmbu/vbbfdViy2ok8IvOXKlSssWLCAnTt30qJFC0aPHs1LL71EcnIyCxYsICUlhf79+/OPf/yD4cOHs2nTJsDxhuKbb77xaizu+N0QEYDrTD4N6tauVB2FhYUsXryYdu3asWzZMurUqcPUqVO93gCUUkopbyoag33kyBGMMVavXVhYGDt27ChW9uDBg1x//fXUr1+f9u3blzp+NVx7JC9dulTsmGsPadeuXfn66685deoUb731Fr/97W8Bx9/dbdu2kZmZSWZmJjk5OT6ZiLhixQpOnTrFjh07yMzMpFGjRly6dIk2bdqQkZGBzWYjOTmZKVOmUKtWLbZv386AAQN4++23rWEUJRX1/oIjqS8ao1zW8+706NGDLVu2cOTIER544AF27dpFWlpasQTbtT7XyaplMcawdu1a6x6XTK7B0X5cf34pKSk0atSIXbt2kZ6ezk8//acD0/Xnaoxh3rx5Vt2HDh0iNja2zPtbUmJiYqkt4yMjI603R67uuOMOjh07Zj0+fvw4d9xxR7EyRZ9mtGzZEhFh0KBBVk/1G2+8YbW7gQMHWpMcwdF269atW+G9rCy/TLANAVDio4ercejQIWJiYnj88cc5c+YMsbGx7N27l+Tk5GKNWSmllKqpgoODmTt3LjNnziQ/P58hQ4aQlpZm9dRdvHiRMWPGWBO8kpKSmD59utVDWVhYyMKFC0vVe9999xXb3rtoiEijRo3Yv38/hYWFrFu3rsy4RIR+/foxbtw4QkNDuflmx87LsbGxzJs3zyrnbrhHTEwMK1asACA7O5ujR4/Stm35q4b16tWLBQsWAI5hM7m5ucWO5+bm0rBhQ2rXrk1qaipHjhwBHKtxBAcHM3ToUJKSksjIyCAvL4/c3Fz69OlDSkoKu3btKvfalRETE8Py5ctp3bo1AQEB3HTTTbz77rt0797d4zpuuOEGzp07Zz2Oi4tj3rx5VjK+c+fOUue0adOmWM96bm4ut99+OwEBAbz55pvF5qO5iouLY8GCBVy54tiQJDs7m/Pnz5d5f0tavXq1lZy7fpUcHgKQkJDAsmXLMMawbds2QkJCig0PAUcSvm/fPk6dOgXAhx9+aL2ZaNy4Mf/+978BxyccrVu3ts7Lzs726movZfHLBFtq3QwBPz/Brl+/PtnZ2dx2222sWrWKjRs3en0mtVJKKVXVoqKisNvtrFy5krp167J+/XqmTZtG27ZtsdlsdO7cmVGjRgFgt9uZPXs2gwcPJjQ0lPDwcA4ePFiqzuTkZM6cOUN4eDgRERHWRMMZM2YQHx9Pt27dSiU7JSUmJrJ8+XJreAjA3LlzSU9Px263ExYW5ja5f/LJJyksLMRms5GYmMjSpUsr7PiaM2cOqamp2Gw2OnbsWGwYCsCQIUNIT0/HZrOxbNkya5Lnnj17iI6OJjIykhdffJHk5GTOnTtHfHw8drud7t27V+lcrGbNmmGMsYaldu/enRtvvNGjITlF7r77bvbt22dNcnzuuee4cuUKdrud9u3b89xzz5U6p169erRs2ZKvv/4acNzzN954g4iICLKysspcLe2xxx4jLCyMDh06EB4ezhNPPGG9sXN3fyujT58+tGjRglatWjFixAhrJRzAGoLTuHFjnn/+eXr06IHdbiczM5NJkyYBsHjxYsaPH09ERASTJk1i0aJF1vmpqan07du30jFWRDz5yKEmCQ+qa/4Z053QDe9BXc+HkL///vv07NnT+o/62WefERYWVmqsllJKKVWW/fv3l/rIXSl/s27dOnbs2MG0adOqOxSfOnHiBA899BAfffSR2+Pu/n+LyA5jTKervZZf9mDTpqnHyfWxY8fo168fvXv35tVXX7We79q1qybXSimllPrF6devH82aNavuMHzu6NGjzJw50yfX8stVROS66yosk5+fz9y5c5k8eTLnz5/n+uuv56abbvJBdEoppZRSDk899VSpNb+ffvpphg0bVk0ROTz22GPVev3q0LlzZ59dyy8T7DWbTzLy+wvcckuw2+Pbtm1j5MiR1uSE/v37M2fOnFIzUJVSSimlqpLrhFH1y+GXCfYPZ69QWOh+7Pjnn39Ot27dMMbQrFkz5s+f75PB7EoppX4ZjDGlNtFQSvk3b89J9MsEOxBT5ip90dHRxMXFERUVRXJysrUbklJKKVVZQUFBnD59mptvvlmTbKWuEcYYTp8+TVBQkNfq9MsE+1hhPSS/EHBsSzp27FhmzZpFmzZtEBHeeecdAgL8c/6mUkqpmqtJkyYcP37cWntXKXVtCAoKokmTJl6rr0oTbBHpDcwBAoG/GGNmlDheB1gGdAROA4nGmMMV1fuHOnUJvniZF1+cxUsvvcTly5cJCgqytu/U5FoppVRVqF27trV1tFJKlaXKEmwRCQReA+4DjgNfiMgGY4zrCvDDgTPGmFYi8t/Ay0Bi6dqKO3DhB/4nthvZ/+8AAMOGDeOVV17x+mtQSimllFLqalXZRjMi0hV4wRgT53z8RwBjzEsuZd53lvlMRGoB3wG3mnKCujEw0OQWOoaHhIaGsnDhQmsXJKWUUkoppbylJm40cwdwzOXxcedzbssYY/KBXODm8io9W1hInYBaTJ88hczMTE2ulVJKKaVUjeIXkxxF5HHgcefDy5cL8/dOmjKZSVMmV2dYqma5Bfi+uoNQNY62C+WOtgvljrYL5U7bn3NSVSbYOcCdLo+bOJ9zV+a4c4hICI7JjsUYYxYBiwBEJP3ndNWra5u2C+WOtgvljrYL5Y62C+WOiKT/nPOqcojIF0BrEWkuItcB/w1sKFFmA/A/zu8HAB+XN/5aKaWUUkqpmq7KerCNMfkiMgp4H8cyfUuMMV+KyBQg3RizAfgr8KaIfA38gCMJV0oppZRSym9V6RhsY8y7wLslnpvs8v0lYOBVVrvIC6Gpa4+2C+WOtgvljrYL5Y62C+XOz2oXVbZMn1JKKaWUUr9EuuWhUkoppZRSXlRjE2wR6S0iX4nI1yIy0c3xOiKy2nn8cxFp5vsola950C7Gicg+EdktIh+JSNPqiFP5VkXtwqVcfxExIqIrBfwCeNIuRGSQ83fGlyLyd1/HqHzPg78jvxKRVBHZ6fxb0qc64lS+IyJLROSkiOwt47iIyFxnm9ktIh0qqrNGJtgu26z/FxAGDBaRsBLFrG3WgRQc26yra5iH7WIn0MkYYwfWAK/4Nkrlax62C0TkBuBp4HPfRqiqgyftQkRaA38EfmOMaQ/8weeBKp/y8PdFMvAPY0wUjsUX/o9vo1TVYCnQu5zj/wW0dn49DiyoqMIamWAD0cDXxpiDxpifgFXAAyXKPAC84fx+DdBLRMSHMSrfq7BdGGNSjTEXnA+34Vh/XV3bPPl9ATAVxxvxS74MTlUbT9rFCOA1Y8wZAGPMSR/HqHzPk3ZhgPrO70OAb3wYn6oGxpjNOFazK8sDwDLjsA24UURuL6/OmppgV8k268rvedIuXA0H3qvSiFRNUGG7cH6cd6cx5h1fBqaqlSe/L9oAbUTkUxHZJiLl9WCpa4Mn7eIFYKiIHMexEtpo34SmarCrzT/8Y6t0pa6WiAwFOgF3VXcsqnqJSAAwC3ikmkNRNU8tHB/59sTxaddmEbEZY36s1qhUdRsMLDXGzBSRrjj26wg3xhRWd2DKf9TUHuyr2Wad8rZZV9cUT9oFInIv8CcgwRhz2UexqepTUbu4AQgHPhGRw0AXYINOdLzmefL74jiwwRhzxRhzCMjGkXCra5cn7WI48A8AY8xnQBBwi0+iUzWVR/mHq5qaYOs268qdCtuFiEQBr+NIrnU85S9Due3CGJNrjLnFGNPMGNMMx9j8BGNMevWEq3zEk78jb+HovUZEbsExZOSgL4NUPudJuzgK9AIQkVAcCfYpn0apapoNwMPO1US6ALnGmG/LO6FGDhHRbdaVOx62i1eB64F/Oue8HjXGJFRb0KrKedgu1C+Mh+3ifSBWRPYBBUCSMUY/Cb2GedguxgOLRWQsjgmPj2gH3rVNRFbieLN9i3Ps/fNAbQBjzEIcY/H7AF8DF4BhFdapbUYppZRSSinvqalDRJRSSimllPJLmmArpZRSSinlRZpgK6WUUkop5UWaYCullFJKKeVFmmArpZRSSinlRZpgK6XUVRCRAhHJdPlqVk7ZPC9cb6mIHHJeK8O5s9zV1vEXEQlzfj+pxLGtlY3RWU/RfdkrIv8SkRsrKB8pIn28cW2llKppdJk+pZS6CiKSZ4y53ttly6ljKfC2MWaNiMQCfzbG2CtRX6VjqqheEXkDyDbG/G855R8BOhljRnk7FqWUqm7ag62UUpUgIteLyEfO3uU9IvKAmzK3i8hmlx7eGOfzsSLymfPcf4pIRYnvZqCV89xxzrr2isgfnM/VE5F3RGSX8/lE5/OfiEgnEZkB1HXGscJ5LM/57yoR6esS81IRGSAigSLyqoh8ISK7ReQJD27LZ8Adznqina9xp4hsFZG2zh30pgCJzlgSnbEvEZHtzrKl7qNSSvmLGrmTo1JK1WB1RSTT+f0hYCDQzxhz1rnd9jYR2VBi57eHgPeNMf8rIoFAsLNsMnCvMea8iDwLjMOReJblfmCPiHTEsZPYrwEBPheRfwMtgG+MMX0BRCTE9WRjzEQRGWWMiXRT92pgEPCOMwHuBfweGI5jW+DOIlIH+FREPjDGHHIXoPP19cKx2y5AFhDj3EHvXmC6Maa/iEzGpQdbRKYDHxtjHnUOL9kuIpuMMefLuR9KKVUjaYKtlFJX56JrgioitYHpItIDKMTRc9sI+M7lnC+AJc6ybxljMkXkLiAMR8IKcB2Onl93XhWRZOAUjoS3F7CuKPkUkf8LxAAbgZki8jKOYSVbruJ1vQfMcSbRvYHNxpiLzmEpdhEZ4CwXArTG8ebCVdEbjzuA/cCHLuXfEJHWOLadrl3G9WOBBBF5xvk4CPiVsy6llPIrmmArpVTlDAFuBToaY66IyGEcyaHFGLPZmYD3BZaKyCzgDPChMWawB9dIMsasKXogIr3cFTLGZItIB6APME1EPjLGlNcj7nruJRH5BIgDEoFVRZcDRhtj3q+giovGmEgRCQbeB54C5gJTgVRjTD/nhNBPyjhfgP7GmK88iVcppWoyHYOtlFKVEwKcdCbXdwNNSxYQkabACWPMYuAvQAdgG/AbESkaU11PRNp4eM0twIMiEiwi9YB+wBYRaQxcMMYsB151XqekK86edHdW4xh6UtQbDo5k+fdF54hIG+c13TLGXADGAONFpBaO+5PjPPyIS9FzwA0uj98HRouzO19Eosq6hlJK1XSaYCulVOWsADqJyB7gYRxjjkvqCewSkZ04eofnGGNO4Ug4V4rIbhzDQ9p5ckFjTAawFNgOfA78xRizE7DhGLucCTwPTHNz+iJgd9EkxxI+AO4CNhljfnI+9xdgH5AhInuB16ng009nLLuBwcArwEvO1+56XioQVjTJEUdPd21nbF86HyullF/SZfqUUkoppZTyIu3BVkoppZRSyos0wVZKKaWUUsqLNMFWSimllFLKizTBVkoppZRSyos0wVZKKaWUUsqLNMFWSimllFLKizTBVkoppZRSyos0wVZKKaWUUsqL/j//AcKITs3ypwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(edf, 'ROC Curve -- Ethnicolr Florida voter name LSTM Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Soundnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\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>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race_orig</th>\n",
       "      <th>race</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70327</th>\n",
       "      <td>Stamm</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.013371</td>\n",
       "      <td>0.039493</td>\n",
       "      <td>0.092745</td>\n",
       "      <td>0.854392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113593</th>\n",
       "      <td>Hill</td>\n",
       "      <td>Ralph</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003276</td>\n",
       "      <td>0.010261</td>\n",
       "      <td>0.098314</td>\n",
       "      <td>0.888149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126263</th>\n",
       "      <td>Hansleat</td>\n",
       "      <td>Christopher</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.019412</td>\n",
       "      <td>0.053285</td>\n",
       "      <td>0.207894</td>\n",
       "      <td>0.719408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107149</th>\n",
       "      <td>Nixon</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.003627</td>\n",
       "      <td>0.007037</td>\n",
       "      <td>0.936663</td>\n",
       "      <td>0.052673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76878</th>\n",
       "      <td>Rolle</td>\n",
       "      <td>Philip</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.009926</td>\n",
       "      <td>0.141765</td>\n",
       "      <td>0.265204</td>\n",
       "      <td>0.583105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32765</th>\n",
       "      <td>Weintraub</td>\n",
       "      <td>David</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.017998</td>\n",
       "      <td>0.253081</td>\n",
       "      <td>0.277360</td>\n",
       "      <td>0.451561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80271</th>\n",
       "      <td>Overacker</td>\n",
       "      <td>Blake</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.009132</td>\n",
       "      <td>0.023917</td>\n",
       "      <td>0.300354</td>\n",
       "      <td>0.666597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46545</th>\n",
       "      <td>Thomson</td>\n",
       "      <td>Michael</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.048354</td>\n",
       "      <td>0.017653</td>\n",
       "      <td>0.495880</td>\n",
       "      <td>0.438113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117598</th>\n",
       "      <td>Ngwafor</td>\n",
       "      <td>Yolande</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.026524</td>\n",
       "      <td>0.059345</td>\n",
       "      <td>0.208490</td>\n",
       "      <td>0.705641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67616</th>\n",
       "      <td>Martin</td>\n",
       "      <td>Bryan</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.005361</td>\n",
       "      <td>0.099264</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.716315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131848</th>\n",
       "      <td>Zipper</td>\n",
       "      <td>Adam</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.017689</td>\n",
       "      <td>0.025597</td>\n",
       "      <td>0.112789</td>\n",
       "      <td>0.843925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41630</th>\n",
       "      <td>Young</td>\n",
       "      <td>Tracey</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.626420</td>\n",
       "      <td>0.009172</td>\n",
       "      <td>0.330633</td>\n",
       "      <td>0.033775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91120</th>\n",
       "      <td>Patterson</td>\n",
       "      <td>Burton</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.161004</td>\n",
       "      <td>0.023201</td>\n",
       "      <td>0.496568</td>\n",
       "      <td>0.319228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116486</th>\n",
       "      <td>Levy</td>\n",
       "      <td>Maya</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.017577</td>\n",
       "      <td>0.057037</td>\n",
       "      <td>0.406073</td>\n",
       "      <td>0.519313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89834</th>\n",
       "      <td>Bembry</td>\n",
       "      <td>Marianne</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.005213</td>\n",
       "      <td>0.010724</td>\n",
       "      <td>0.147011</td>\n",
       "      <td>0.837053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148983</th>\n",
       "      <td>Zahedi</td>\n",
       "      <td>Zahra</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.650308</td>\n",
       "      <td>0.027868</td>\n",
       "      <td>0.058930</td>\n",
       "      <td>0.262894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96863</th>\n",
       "      <td>Bassett</td>\n",
       "      <td>Leslie</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.240495</td>\n",
       "      <td>0.088697</td>\n",
       "      <td>0.167632</td>\n",
       "      <td>0.503176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28239</th>\n",
       "      <td>Hogan</td>\n",
       "      <td>Eric</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.015892</td>\n",
       "      <td>0.076865</td>\n",
       "      <td>0.333513</td>\n",
       "      <td>0.573729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22262</th>\n",
       "      <td>Halbrook</td>\n",
       "      <td>Robert</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.013463</td>\n",
       "      <td>0.046182</td>\n",
       "      <td>0.263052</td>\n",
       "      <td>0.677303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52954</th>\n",
       "      <td>Spooner</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.025712</td>\n",
       "      <td>0.108648</td>\n",
       "      <td>0.160514</td>\n",
       "      <td>0.705126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64421</th>\n",
       "      <td>Charudattan</td>\n",
       "      <td>Savitar</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.142903</td>\n",
       "      <td>0.001958</td>\n",
       "      <td>0.836754</td>\n",
       "      <td>0.018385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102415</th>\n",
       "      <td>Nyquist</td>\n",
       "      <td>Samuel</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.048053</td>\n",
       "      <td>0.119069</td>\n",
       "      <td>0.577847</td>\n",
       "      <td>0.255031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49173</th>\n",
       "      <td>Bond</td>\n",
       "      <td>Robyn</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.035891</td>\n",
       "      <td>0.177987</td>\n",
       "      <td>0.279598</td>\n",
       "      <td>0.506524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97266</th>\n",
       "      <td>Bowles</td>\n",
       "      <td>Jarrett</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.020016</td>\n",
       "      <td>0.181205</td>\n",
       "      <td>0.285744</td>\n",
       "      <td>0.513034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129269</th>\n",
       "      <td>Carter</td>\n",
       "      <td>Koby</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003947</td>\n",
       "      <td>0.041693</td>\n",
       "      <td>0.386461</td>\n",
       "      <td>0.567899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99544</th>\n",
       "      <td>Swain</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.181405</td>\n",
       "      <td>0.105994</td>\n",
       "      <td>0.116426</td>\n",
       "      <td>0.596175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46356</th>\n",
       "      <td>Crisp-Maradie</td>\n",
       "      <td>Aisling</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.106954</td>\n",
       "      <td>0.221042</td>\n",
       "      <td>0.278690</td>\n",
       "      <td>0.393314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.011509</td>\n",
       "      <td>0.016761</td>\n",
       "      <td>0.657754</td>\n",
       "      <td>0.313975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119170</th>\n",
       "      <td>Gage</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.021663</td>\n",
       "      <td>0.024126</td>\n",
       "      <td>0.075133</td>\n",
       "      <td>0.879078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130172</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>Jamel</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.012186</td>\n",
       "      <td>0.048946</td>\n",
       "      <td>0.319936</td>\n",
       "      <td>0.618932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70555</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.008978</td>\n",
       "      <td>0.084494</td>\n",
       "      <td>0.529022</td>\n",
       "      <td>0.377506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93718</th>\n",
       "      <td>Gibby</td>\n",
       "      <td>Allyson</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.112069</td>\n",
       "      <td>0.037440</td>\n",
       "      <td>0.216914</td>\n",
       "      <td>0.633577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4810</th>\n",
       "      <td>Kirby</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.032686</td>\n",
       "      <td>0.045276</td>\n",
       "      <td>0.396767</td>\n",
       "      <td>0.525272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117313</th>\n",
       "      <td>Forrester</td>\n",
       "      <td>Marie</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.003301</td>\n",
       "      <td>0.019788</td>\n",
       "      <td>0.609891</td>\n",
       "      <td>0.367020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52721</th>\n",
       "      <td>Visvanathan</td>\n",
       "      <td>Kaushik</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.035001</td>\n",
       "      <td>0.145977</td>\n",
       "      <td>0.068166</td>\n",
       "      <td>0.750857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59853</th>\n",
       "      <td>Harbin</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.032371</td>\n",
       "      <td>0.017392</td>\n",
       "      <td>0.177411</td>\n",
       "      <td>0.772826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106141</th>\n",
       "      <td>Hassani</td>\n",
       "      <td>Nabil</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.246098</td>\n",
       "      <td>0.036387</td>\n",
       "      <td>0.094666</td>\n",
       "      <td>0.622850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124399</th>\n",
       "      <td>Roughton</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.359739</td>\n",
       "      <td>0.045542</td>\n",
       "      <td>0.336647</td>\n",
       "      <td>0.258071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94226</th>\n",
       "      <td>Greene</td>\n",
       "      <td>William</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.012118</td>\n",
       "      <td>0.040277</td>\n",
       "      <td>0.196451</td>\n",
       "      <td>0.751154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129967</th>\n",
       "      <td>Navarro</td>\n",
       "      <td>Raquel</td>\n",
       "      <td>asian</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.043585</td>\n",
       "      <td>0.707592</td>\n",
       "      <td>0.104126</td>\n",
       "      <td>0.144697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18546</th>\n",
       "      <td>Lowe</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.667321</td>\n",
       "      <td>0.013026</td>\n",
       "      <td>0.075342</td>\n",
       "      <td>0.244312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124058</th>\n",
       "      <td>Williamson</td>\n",
       "      <td>Homer</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.229479</td>\n",
       "      <td>0.082544</td>\n",
       "      <td>0.330838</td>\n",
       "      <td>0.357139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120549</th>\n",
       "      <td>Terry</td>\n",
       "      <td>Diane</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.027679</td>\n",
       "      <td>0.053028</td>\n",
       "      <td>0.169704</td>\n",
       "      <td>0.749588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6911</th>\n",
       "      <td>Lunger</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.452174</td>\n",
       "      <td>0.102647</td>\n",
       "      <td>0.072305</td>\n",
       "      <td>0.372875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98620</th>\n",
       "      <td>Antoine</td>\n",
       "      <td>Rachelle</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.017185</td>\n",
       "      <td>0.289851</td>\n",
       "      <td>0.463042</td>\n",
       "      <td>0.229921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16322</th>\n",
       "      <td>Poole</td>\n",
       "      <td>Shawn</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006161</td>\n",
       "      <td>0.060222</td>\n",
       "      <td>0.146635</td>\n",
       "      <td>0.786981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15083</th>\n",
       "      <td>Barnum</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.009041</td>\n",
       "      <td>0.088999</td>\n",
       "      <td>0.136601</td>\n",
       "      <td>0.765359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29691</th>\n",
       "      <td>Mason</td>\n",
       "      <td>Dinah</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.019291</td>\n",
       "      <td>0.026949</td>\n",
       "      <td>0.089073</td>\n",
       "      <td>0.864686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56274</th>\n",
       "      <td>Antunes</td>\n",
       "      <td>Karoline</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.027951</td>\n",
       "      <td>0.609847</td>\n",
       "      <td>0.064450</td>\n",
       "      <td>0.297753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28814</th>\n",
       "      <td>Everitt</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.005993</td>\n",
       "      <td>0.008588</td>\n",
       "      <td>0.212610</td>\n",
       "      <td>0.772809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101125</th>\n",
       "      <td>Meeks</td>\n",
       "      <td>Gabrielle</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.012605</td>\n",
       "      <td>0.041110</td>\n",
       "      <td>0.241873</td>\n",
       "      <td>0.704412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>Hanks</td>\n",
       "      <td>Kristy</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.088138</td>\n",
       "      <td>0.059136</td>\n",
       "      <td>0.340304</td>\n",
       "      <td>0.512423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73853</th>\n",
       "      <td>Castellanos</td>\n",
       "      <td>Robin</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.011348</td>\n",
       "      <td>0.697746</td>\n",
       "      <td>0.235790</td>\n",
       "      <td>0.055116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117930</th>\n",
       "      <td>Ellis</td>\n",
       "      <td>Kenneth</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.015391</td>\n",
       "      <td>0.069490</td>\n",
       "      <td>0.344155</td>\n",
       "      <td>0.570965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17819</th>\n",
       "      <td>Lipka</td>\n",
       "      <td>Thesia</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.099590</td>\n",
       "      <td>0.231785</td>\n",
       "      <td>0.273994</td>\n",
       "      <td>0.394630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96606</th>\n",
       "      <td>Keaton</td>\n",
       "      <td>Shanieka</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.009100</td>\n",
       "      <td>0.021295</td>\n",
       "      <td>0.507655</td>\n",
       "      <td>0.461950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87258</th>\n",
       "      <td>Wear</td>\n",
       "      <td>Scott</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.013599</td>\n",
       "      <td>0.292947</td>\n",
       "      <td>0.165856</td>\n",
       "      <td>0.527598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79970</th>\n",
       "      <td>Caroline</td>\n",
       "      <td>John</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.011154</td>\n",
       "      <td>0.161333</td>\n",
       "      <td>0.142277</td>\n",
       "      <td>0.685236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107004</th>\n",
       "      <td>Prado O' Hearn</td>\n",
       "      <td>Monica</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.018872</td>\n",
       "      <td>0.467786</td>\n",
       "      <td>0.210948</td>\n",
       "      <td>0.302394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87473</th>\n",
       "      <td>Altiere</td>\n",
       "      <td>Nell</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002477</td>\n",
       "      <td>0.011455</td>\n",
       "      <td>0.213243</td>\n",
       "      <td>0.772826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             last_name   first_name race_orig      race     asian  hispanic  \\\n",
       "70327            Stamm     Patricia  nh_white  nh_white  0.013371  0.039493   \n",
       "113593            Hill        Ralph  nh_white  nh_white  0.003276  0.010261   \n",
       "126263        Hansleat  Christopher  nh_white  nh_white  0.019412  0.053285   \n",
       "107149           Nixon        James  nh_black  nh_black  0.003627  0.007037   \n",
       "76878            Rolle       Philip  nh_black  nh_white  0.009926  0.141765   \n",
       "32765        Weintraub        David  nh_white  nh_white  0.017998  0.253081   \n",
       "80271        Overacker        Blake  nh_white  nh_white  0.009132  0.023917   \n",
       "46545          Thomson      Michael  hispanic  nh_black  0.048354  0.017653   \n",
       "117598         Ngwafor      Yolande  nh_black  nh_white  0.026524  0.059345   \n",
       "67616           Martin        Bryan  nh_white  nh_white  0.005361  0.099264   \n",
       "131848          Zipper         Adam  nh_white  nh_white  0.017689  0.025597   \n",
       "41630            Young       Tracey  nh_black     asian  0.626420  0.009172   \n",
       "91120        Patterson       Burton  nh_white  nh_black  0.161004  0.023201   \n",
       "116486            Levy         Maya  nh_white  nh_white  0.017577  0.057037   \n",
       "89834           Bembry     Marianne  nh_white  nh_white  0.005213  0.010724   \n",
       "148983          Zahedi        Zahra  nh_white     asian  0.650308  0.027868   \n",
       "96863          Bassett       Leslie  nh_white  nh_white  0.240495  0.088697   \n",
       "28239            Hogan         Eric  nh_white  nh_white  0.015892  0.076865   \n",
       "22262         Halbrook       Robert  nh_white  nh_white  0.013463  0.046182   \n",
       "52954          Spooner       Joseph  nh_white  nh_white  0.025712  0.108648   \n",
       "64421      Charudattan      Savitar     asian  nh_black  0.142903  0.001958   \n",
       "102415         Nyquist       Samuel  nh_white  nh_black  0.048053  0.119069   \n",
       "49173             Bond        Robyn  nh_white  nh_white  0.035891  0.177987   \n",
       "97266           Bowles      Jarrett  nh_black  nh_white  0.020016  0.181205   \n",
       "129269          Carter         Koby  hispanic  nh_white  0.003947  0.041693   \n",
       "99544            Swain     Patricia  nh_white  nh_white  0.181405  0.105994   \n",
       "46356    Crisp-Maradie      Aisling  nh_white  nh_white  0.106954  0.221042   \n",
       "1268           Johnson        Tyler  nh_white  nh_black  0.011509  0.016761   \n",
       "119170            Gage      Timothy  nh_white  nh_white  0.021663  0.024126   \n",
       "130172          Jordan        Jamel  nh_black  nh_white  0.012186  0.048946   \n",
       "...                ...          ...       ...       ...       ...       ...   \n",
       "70555           Walker      Alberta  nh_black  nh_black  0.008978  0.084494   \n",
       "93718            Gibby      Allyson  nh_white  nh_white  0.112069  0.037440   \n",
       "4810             Kirby      Barbara  nh_white  nh_white  0.032686  0.045276   \n",
       "117313       Forrester        Marie  nh_white  nh_black  0.003301  0.019788   \n",
       "52721      Visvanathan      Kaushik     asian  nh_white  0.035001  0.145977   \n",
       "59853           Harbin      Matthew  nh_white  nh_white  0.032371  0.017392   \n",
       "106141         Hassani        Nabil  nh_white  nh_white  0.246098  0.036387   \n",
       "124399        Roughton        James  nh_white     asian  0.359739  0.045542   \n",
       "94226           Greene      William  nh_white  nh_white  0.012118  0.040277   \n",
       "129967         Navarro       Raquel     asian  hispanic  0.043585  0.707592   \n",
       "18546             Lowe       Thomas  nh_white     asian  0.667321  0.013026   \n",
       "124058      Williamson        Homer  nh_white  nh_white  0.229479  0.082544   \n",
       "120549           Terry        Diane  nh_white  nh_white  0.027679  0.053028   \n",
       "6911            Lunger      Barbara  nh_white     asian  0.452174  0.102647   \n",
       "98620          Antoine     Rachelle  nh_black  nh_black  0.017185  0.289851   \n",
       "16322            Poole        Shawn  nh_white  nh_white  0.006161  0.060222   \n",
       "15083           Barnum     Patricia  nh_black  nh_white  0.009041  0.088999   \n",
       "29691            Mason        Dinah  nh_white  nh_white  0.019291  0.026949   \n",
       "56274          Antunes     Karoline  hispanic  hispanic  0.027951  0.609847   \n",
       "28814          Everitt    Alexander  nh_white  nh_white  0.005993  0.008588   \n",
       "101125           Meeks    Gabrielle  nh_black  nh_white  0.012605  0.041110   \n",
       "654              Hanks       Kristy  nh_white  nh_white  0.088138  0.059136   \n",
       "73853      Castellanos        Robin  nh_white  hispanic  0.011348  0.697746   \n",
       "117930           Ellis      Kenneth  nh_white  nh_white  0.015391  0.069490   \n",
       "17819            Lipka       Thesia  nh_white  nh_white  0.099590  0.231785   \n",
       "96606           Keaton     Shanieka  nh_black  nh_black  0.009100  0.021295   \n",
       "87258             Wear        Scott  nh_white  nh_white  0.013599  0.292947   \n",
       "79970         Caroline         John  nh_white  nh_white  0.011154  0.161333   \n",
       "107004  Prado O' Hearn       Monica  hispanic  hispanic  0.018872  0.467786   \n",
       "87473          Altiere         Nell  nh_white  nh_white  0.002477  0.011455   \n",
       "\n",
       "        nh_black  nh_white  \n",
       "70327   0.092745  0.854392  \n",
       "113593  0.098314  0.888149  \n",
       "126263  0.207894  0.719408  \n",
       "107149  0.936663  0.052673  \n",
       "76878   0.265204  0.583105  \n",
       "32765   0.277360  0.451561  \n",
       "80271   0.300354  0.666597  \n",
       "46545   0.495880  0.438113  \n",
       "117598  0.208490  0.705641  \n",
       "67616   0.179060  0.716315  \n",
       "131848  0.112789  0.843925  \n",
       "41630   0.330633  0.033775  \n",
       "91120   0.496568  0.319228  \n",
       "116486  0.406073  0.519313  \n",
       "89834   0.147011  0.837053  \n",
       "148983  0.058930  0.262894  \n",
       "96863   0.167632  0.503176  \n",
       "28239   0.333513  0.573729  \n",
       "22262   0.263052  0.677303  \n",
       "52954   0.160514  0.705126  \n",
       "64421   0.836754  0.018385  \n",
       "102415  0.577847  0.255031  \n",
       "49173   0.279598  0.506524  \n",
       "97266   0.285744  0.513034  \n",
       "129269  0.386461  0.567899  \n",
       "99544   0.116426  0.596175  \n",
       "46356   0.278690  0.393314  \n",
       "1268    0.657754  0.313975  \n",
       "119170  0.075133  0.879078  \n",
       "130172  0.319936  0.618932  \n",
       "...          ...       ...  \n",
       "70555   0.529022  0.377506  \n",
       "93718   0.216914  0.633577  \n",
       "4810    0.396767  0.525272  \n",
       "117313  0.609891  0.367020  \n",
       "52721   0.068166  0.750857  \n",
       "59853   0.177411  0.772826  \n",
       "106141  0.094666  0.622850  \n",
       "124399  0.336647  0.258071  \n",
       "94226   0.196451  0.751154  \n",
       "129967  0.104126  0.144697  \n",
       "18546   0.075342  0.244312  \n",
       "124058  0.330838  0.357139  \n",
       "120549  0.169704  0.749588  \n",
       "6911    0.072305  0.372875  \n",
       "98620   0.463042  0.229921  \n",
       "16322   0.146635  0.786981  \n",
       "15083   0.136601  0.765359  \n",
       "29691   0.089073  0.864686  \n",
       "56274   0.064450  0.297753  \n",
       "28814   0.212610  0.772809  \n",
       "101125  0.241873  0.704412  \n",
       "654     0.340304  0.512423  \n",
       "73853   0.235790  0.055116  \n",
       "117930  0.344155  0.570965  \n",
       "17819   0.273994  0.394630  \n",
       "96606   0.507655  0.461950  \n",
       "87258   0.165856  0.527598  \n",
       "79970   0.142277  0.685236  \n",
       "107004  0.210948  0.302394  \n",
       "87473   0.213243  0.772826  \n",
       "\n",
       "[100000 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edf2 = pred_fl_reg_soundname(sdf, 'last_name', 'first_name')\n",
    "edf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race_orig</th>\n",
       "      <th>race</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76878</th>\n",
       "      <td>Rolle</td>\n",
       "      <td>Philip</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.009926</td>\n",
       "      <td>0.141765</td>\n",
       "      <td>0.265204</td>\n",
       "      <td>0.583105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46545</th>\n",
       "      <td>Thomson</td>\n",
       "      <td>Michael</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.048354</td>\n",
       "      <td>0.017653</td>\n",
       "      <td>0.495880</td>\n",
       "      <td>0.438113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117598</th>\n",
       "      <td>Ngwafor</td>\n",
       "      <td>Yolande</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.026524</td>\n",
       "      <td>0.059345</td>\n",
       "      <td>0.208490</td>\n",
       "      <td>0.705641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41630</th>\n",
       "      <td>Young</td>\n",
       "      <td>Tracey</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.626420</td>\n",
       "      <td>0.009172</td>\n",
       "      <td>0.330633</td>\n",
       "      <td>0.033775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91120</th>\n",
       "      <td>Patterson</td>\n",
       "      <td>Burton</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.161004</td>\n",
       "      <td>0.023201</td>\n",
       "      <td>0.496568</td>\n",
       "      <td>0.319228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148983</th>\n",
       "      <td>Zahedi</td>\n",
       "      <td>Zahra</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.650308</td>\n",
       "      <td>0.027868</td>\n",
       "      <td>0.058930</td>\n",
       "      <td>0.262894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64421</th>\n",
       "      <td>Charudattan</td>\n",
       "      <td>Savitar</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.142903</td>\n",
       "      <td>0.001958</td>\n",
       "      <td>0.836754</td>\n",
       "      <td>0.018385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102415</th>\n",
       "      <td>Nyquist</td>\n",
       "      <td>Samuel</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.048053</td>\n",
       "      <td>0.119069</td>\n",
       "      <td>0.577847</td>\n",
       "      <td>0.255031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97266</th>\n",
       "      <td>Bowles</td>\n",
       "      <td>Jarrett</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.020016</td>\n",
       "      <td>0.181205</td>\n",
       "      <td>0.285744</td>\n",
       "      <td>0.513034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129269</th>\n",
       "      <td>Carter</td>\n",
       "      <td>Koby</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003947</td>\n",
       "      <td>0.041693</td>\n",
       "      <td>0.386461</td>\n",
       "      <td>0.567899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.011509</td>\n",
       "      <td>0.016761</td>\n",
       "      <td>0.657754</td>\n",
       "      <td>0.313975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130172</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>Jamel</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.012186</td>\n",
       "      <td>0.048946</td>\n",
       "      <td>0.319936</td>\n",
       "      <td>0.618932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57607</th>\n",
       "      <td>Anderson</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.020357</td>\n",
       "      <td>0.011531</td>\n",
       "      <td>0.856285</td>\n",
       "      <td>0.111827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53588</th>\n",
       "      <td>Doak</td>\n",
       "      <td>Alicia</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.012738</td>\n",
       "      <td>0.613347</td>\n",
       "      <td>0.201196</td>\n",
       "      <td>0.172719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144872</th>\n",
       "      <td>Johns</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.023942</td>\n",
       "      <td>0.498598</td>\n",
       "      <td>0.471459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66423</th>\n",
       "      <td>Terrell</td>\n",
       "      <td>Gabriel</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.004553</td>\n",
       "      <td>0.067958</td>\n",
       "      <td>0.433445</td>\n",
       "      <td>0.494044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148930</th>\n",
       "      <td>Adams</td>\n",
       "      <td>Kirstin</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.007680</td>\n",
       "      <td>0.013550</td>\n",
       "      <td>0.355171</td>\n",
       "      <td>0.623599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26388</th>\n",
       "      <td>Tolson</td>\n",
       "      <td>Melissa</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.040870</td>\n",
       "      <td>0.060595</td>\n",
       "      <td>0.801604</td>\n",
       "      <td>0.096931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30998</th>\n",
       "      <td>Oulman</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.037518</td>\n",
       "      <td>0.320782</td>\n",
       "      <td>0.362121</td>\n",
       "      <td>0.279578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127948</th>\n",
       "      <td>Mccray</td>\n",
       "      <td>Marniqua</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.008944</td>\n",
       "      <td>0.062526</td>\n",
       "      <td>0.207198</td>\n",
       "      <td>0.721331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25404</th>\n",
       "      <td>Fegins</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.470409</td>\n",
       "      <td>0.044255</td>\n",
       "      <td>0.062835</td>\n",
       "      <td>0.422501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68783</th>\n",
       "      <td>Coronel</td>\n",
       "      <td>Bradley</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.014628</td>\n",
       "      <td>0.108030</td>\n",
       "      <td>0.502919</td>\n",
       "      <td>0.374423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102610</th>\n",
       "      <td>Cohen</td>\n",
       "      <td>Zacharias</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.067027</td>\n",
       "      <td>0.042084</td>\n",
       "      <td>0.311105</td>\n",
       "      <td>0.579784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109950</th>\n",
       "      <td>Van Steenburgh</td>\n",
       "      <td>Melanie</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.035652</td>\n",
       "      <td>0.051950</td>\n",
       "      <td>0.084418</td>\n",
       "      <td>0.827980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101644</th>\n",
       "      <td>Hazel</td>\n",
       "      <td>Cozette</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.005590</td>\n",
       "      <td>0.017584</td>\n",
       "      <td>0.278920</td>\n",
       "      <td>0.697906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44791</th>\n",
       "      <td>Chambers</td>\n",
       "      <td>Melissa</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.022979</td>\n",
       "      <td>0.002692</td>\n",
       "      <td>0.928257</td>\n",
       "      <td>0.046071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144007</th>\n",
       "      <td>Tjiam</td>\n",
       "      <td>Francisca</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.014619</td>\n",
       "      <td>0.022595</td>\n",
       "      <td>0.831071</td>\n",
       "      <td>0.131716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146663</th>\n",
       "      <td>Roskos</td>\n",
       "      <td>Katherine</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.022056</td>\n",
       "      <td>0.727249</td>\n",
       "      <td>0.086819</td>\n",
       "      <td>0.163877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149542</th>\n",
       "      <td>Brinkley</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.027283</td>\n",
       "      <td>0.056831</td>\n",
       "      <td>0.763463</td>\n",
       "      <td>0.152423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100025</th>\n",
       "      <td>Jacobs</td>\n",
       "      <td>Chaddrick</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.012826</td>\n",
       "      <td>0.047777</td>\n",
       "      <td>0.071592</td>\n",
       "      <td>0.867805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17157</th>\n",
       "      <td>Legrand</td>\n",
       "      <td>Carolyn</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.032599</td>\n",
       "      <td>0.015881</td>\n",
       "      <td>0.844631</td>\n",
       "      <td>0.106889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73969</th>\n",
       "      <td>Drake</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.058248</td>\n",
       "      <td>0.043043</td>\n",
       "      <td>0.262318</td>\n",
       "      <td>0.636391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146569</th>\n",
       "      <td>Landry</td>\n",
       "      <td>Kelly</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.016998</td>\n",
       "      <td>0.038854</td>\n",
       "      <td>0.477694</td>\n",
       "      <td>0.466454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8881</th>\n",
       "      <td>Randolph</td>\n",
       "      <td>Ella</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.009832</td>\n",
       "      <td>0.056049</td>\n",
       "      <td>0.369286</td>\n",
       "      <td>0.564833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120658</th>\n",
       "      <td>Esposito</td>\n",
       "      <td>Rocco</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.115734</td>\n",
       "      <td>0.560447</td>\n",
       "      <td>0.106016</td>\n",
       "      <td>0.217803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112137</th>\n",
       "      <td>Sutton</td>\n",
       "      <td>April</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006981</td>\n",
       "      <td>0.016262</td>\n",
       "      <td>0.114856</td>\n",
       "      <td>0.861901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43638</th>\n",
       "      <td>Newman Gutierrez</td>\n",
       "      <td>Yoana</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.013558</td>\n",
       "      <td>0.201405</td>\n",
       "      <td>0.672096</td>\n",
       "      <td>0.112941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78912</th>\n",
       "      <td>Stephens</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.003629</td>\n",
       "      <td>0.028746</td>\n",
       "      <td>0.558788</td>\n",
       "      <td>0.408837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22308</th>\n",
       "      <td>Poole</td>\n",
       "      <td>Leonves</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006161</td>\n",
       "      <td>0.060222</td>\n",
       "      <td>0.146635</td>\n",
       "      <td>0.786981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105744</th>\n",
       "      <td>Wilding</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.019314</td>\n",
       "      <td>0.032655</td>\n",
       "      <td>0.691322</td>\n",
       "      <td>0.256709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>Shehan</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.446029</td>\n",
       "      <td>0.036298</td>\n",
       "      <td>0.075569</td>\n",
       "      <td>0.442105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10872</th>\n",
       "      <td>Green</td>\n",
       "      <td>David</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.012118</td>\n",
       "      <td>0.040277</td>\n",
       "      <td>0.196451</td>\n",
       "      <td>0.751154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51864</th>\n",
       "      <td>Frazier</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.037019</td>\n",
       "      <td>0.124502</td>\n",
       "      <td>0.489238</td>\n",
       "      <td>0.349241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91747</th>\n",
       "      <td>Warrington</td>\n",
       "      <td>Carolyn</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.037508</td>\n",
       "      <td>0.020950</td>\n",
       "      <td>0.816851</td>\n",
       "      <td>0.124691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40927</th>\n",
       "      <td>Brouillette</td>\n",
       "      <td>Jason</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.011832</td>\n",
       "      <td>0.319161</td>\n",
       "      <td>0.397120</td>\n",
       "      <td>0.271887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34791</th>\n",
       "      <td>Davies</td>\n",
       "      <td>Wendy</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.003307</td>\n",
       "      <td>0.008456</td>\n",
       "      <td>0.687152</td>\n",
       "      <td>0.301085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117485</th>\n",
       "      <td>Romine</td>\n",
       "      <td>Bonnie</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.088712</td>\n",
       "      <td>0.509925</td>\n",
       "      <td>0.064147</td>\n",
       "      <td>0.337216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56475</th>\n",
       "      <td>Hoare</td>\n",
       "      <td>Kishalay</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.004937</td>\n",
       "      <td>0.048879</td>\n",
       "      <td>0.152174</td>\n",
       "      <td>0.794010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67162</th>\n",
       "      <td>Henderson</td>\n",
       "      <td>Jarrett</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.006050</td>\n",
       "      <td>0.014824</td>\n",
       "      <td>0.805079</td>\n",
       "      <td>0.174046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42759</th>\n",
       "      <td>Wilcox</td>\n",
       "      <td>Andrew</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>0.062117</td>\n",
       "      <td>0.707445</td>\n",
       "      <td>0.222501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23904</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Dal-Tonya</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003465</td>\n",
       "      <td>0.013752</td>\n",
       "      <td>0.286978</td>\n",
       "      <td>0.695804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117313</th>\n",
       "      <td>Forrester</td>\n",
       "      <td>Marie</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.003301</td>\n",
       "      <td>0.019788</td>\n",
       "      <td>0.609891</td>\n",
       "      <td>0.367020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52721</th>\n",
       "      <td>Visvanathan</td>\n",
       "      <td>Kaushik</td>\n",
       "      <td>asian</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.035001</td>\n",
       "      <td>0.145977</td>\n",
       "      <td>0.068166</td>\n",
       "      <td>0.750857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124399</th>\n",
       "      <td>Roughton</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.359739</td>\n",
       "      <td>0.045542</td>\n",
       "      <td>0.336647</td>\n",
       "      <td>0.258071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129967</th>\n",
       "      <td>Navarro</td>\n",
       "      <td>Raquel</td>\n",
       "      <td>asian</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.043585</td>\n",
       "      <td>0.707592</td>\n",
       "      <td>0.104126</td>\n",
       "      <td>0.144697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18546</th>\n",
       "      <td>Lowe</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.667321</td>\n",
       "      <td>0.013026</td>\n",
       "      <td>0.075342</td>\n",
       "      <td>0.244312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6911</th>\n",
       "      <td>Lunger</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.452174</td>\n",
       "      <td>0.102647</td>\n",
       "      <td>0.072305</td>\n",
       "      <td>0.372875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15083</th>\n",
       "      <td>Barnum</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.009041</td>\n",
       "      <td>0.088999</td>\n",
       "      <td>0.136601</td>\n",
       "      <td>0.765359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101125</th>\n",
       "      <td>Meeks</td>\n",
       "      <td>Gabrielle</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.012605</td>\n",
       "      <td>0.041110</td>\n",
       "      <td>0.241873</td>\n",
       "      <td>0.704412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73853</th>\n",
       "      <td>Castellanos</td>\n",
       "      <td>Robin</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.011348</td>\n",
       "      <td>0.697746</td>\n",
       "      <td>0.235790</td>\n",
       "      <td>0.055116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43322 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               last_name first_name race_orig      race     asian  hispanic  \\\n",
       "76878              Rolle     Philip  nh_black  nh_white  0.009926  0.141765   \n",
       "46545            Thomson    Michael  hispanic  nh_black  0.048354  0.017653   \n",
       "117598           Ngwafor    Yolande  nh_black  nh_white  0.026524  0.059345   \n",
       "41630              Young     Tracey  nh_black     asian  0.626420  0.009172   \n",
       "91120          Patterson     Burton  nh_white  nh_black  0.161004  0.023201   \n",
       "148983            Zahedi      Zahra  nh_white     asian  0.650308  0.027868   \n",
       "64421        Charudattan    Savitar     asian  nh_black  0.142903  0.001958   \n",
       "102415           Nyquist     Samuel  nh_white  nh_black  0.048053  0.119069   \n",
       "97266             Bowles    Jarrett  nh_black  nh_white  0.020016  0.181205   \n",
       "129269            Carter       Koby  hispanic  nh_white  0.003947  0.041693   \n",
       "1268             Johnson      Tyler  nh_white  nh_black  0.011509  0.016761   \n",
       "130172            Jordan      Jamel  nh_black  nh_white  0.012186  0.048946   \n",
       "57607           Anderson      Nancy  nh_white  nh_black  0.020357  0.011531   \n",
       "53588               Doak     Alicia  nh_white  hispanic  0.012738  0.613347   \n",
       "144872             Johns      James  nh_white  nh_black  0.006000  0.023942   \n",
       "66423            Terrell    Gabriel  nh_black  nh_white  0.004553  0.067958   \n",
       "148930             Adams    Kirstin  nh_black  nh_white  0.007680  0.013550   \n",
       "26388             Tolson    Melissa  nh_white  nh_black  0.040870  0.060595   \n",
       "30998             Oulman      James  nh_white  nh_black  0.037518  0.320782   \n",
       "127948            Mccray   Marniqua  nh_black  nh_white  0.008944  0.062526   \n",
       "25404             Fegins    Jessica  nh_black     asian  0.470409  0.044255   \n",
       "68783            Coronel    Bradley     asian  nh_black  0.014628  0.108030   \n",
       "102610             Cohen  Zacharias  hispanic  nh_white  0.067027  0.042084   \n",
       "109950    Van Steenburgh    Melanie     asian  nh_white  0.035652  0.051950   \n",
       "101644             Hazel    Cozette  nh_black  nh_white  0.005590  0.017584   \n",
       "44791           Chambers    Melissa  nh_white  nh_black  0.022979  0.002692   \n",
       "144007             Tjiam  Francisca     asian  nh_black  0.014619  0.022595   \n",
       "146663            Roskos  Katherine  nh_white  hispanic  0.022056  0.727249   \n",
       "149542          Brinkley    Timothy  nh_white  nh_black  0.027283  0.056831   \n",
       "100025            Jacobs  Chaddrick  nh_black  nh_white  0.012826  0.047777   \n",
       "...                  ...        ...       ...       ...       ...       ...   \n",
       "17157            Legrand    Carolyn  nh_white  nh_black  0.032599  0.015881   \n",
       "73969              Drake  Elizabeth  hispanic  nh_white  0.058248  0.043043   \n",
       "146569            Landry      Kelly  nh_white  nh_black  0.016998  0.038854   \n",
       "8881            Randolph       Ella  nh_black  nh_white  0.009832  0.056049   \n",
       "120658          Esposito      Rocco  nh_white  hispanic  0.115734  0.560447   \n",
       "112137            Sutton      April  nh_black  nh_white  0.006981  0.016262   \n",
       "43638   Newman Gutierrez      Yoana  hispanic  nh_black  0.013558  0.201405   \n",
       "78912           Stephens     Gerald  nh_white  nh_black  0.003629  0.028746   \n",
       "22308              Poole    Leonves  nh_black  nh_white  0.006161  0.060222   \n",
       "105744           Wilding    Patrick  nh_white  nh_black  0.019314  0.032655   \n",
       "1028              Shehan   Victoria  nh_white     asian  0.446029  0.036298   \n",
       "10872              Green      David  nh_black  nh_white  0.012118  0.040277   \n",
       "51864            Frazier      James  nh_white  nh_black  0.037019  0.124502   \n",
       "91747         Warrington    Carolyn  nh_white  nh_black  0.037508  0.020950   \n",
       "40927        Brouillette      Jason  nh_white  nh_black  0.011832  0.319161   \n",
       "34791             Davies      Wendy  nh_white  nh_black  0.003307  0.008456   \n",
       "117485            Romine     Bonnie  nh_white  hispanic  0.088712  0.509925   \n",
       "56475              Hoare   Kishalay     asian  nh_white  0.004937  0.048879   \n",
       "67162          Henderson    Jarrett  nh_white  nh_black  0.006050  0.014824   \n",
       "42759             Wilcox     Andrew  nh_white  nh_black  0.007937  0.062117   \n",
       "23904            Douglas  Dal-Tonya  nh_black  nh_white  0.003465  0.013752   \n",
       "117313         Forrester      Marie  nh_white  nh_black  0.003301  0.019788   \n",
       "52721        Visvanathan    Kaushik     asian  nh_white  0.035001  0.145977   \n",
       "124399          Roughton      James  nh_white     asian  0.359739  0.045542   \n",
       "129967           Navarro     Raquel     asian  hispanic  0.043585  0.707592   \n",
       "18546               Lowe     Thomas  nh_white     asian  0.667321  0.013026   \n",
       "6911              Lunger    Barbara  nh_white     asian  0.452174  0.102647   \n",
       "15083             Barnum   Patricia  nh_black  nh_white  0.009041  0.088999   \n",
       "101125             Meeks  Gabrielle  nh_black  nh_white  0.012605  0.041110   \n",
       "73853        Castellanos      Robin  nh_white  hispanic  0.011348  0.697746   \n",
       "\n",
       "        nh_black  nh_white  \n",
       "76878   0.265204  0.583105  \n",
       "46545   0.495880  0.438113  \n",
       "117598  0.208490  0.705641  \n",
       "41630   0.330633  0.033775  \n",
       "91120   0.496568  0.319228  \n",
       "148983  0.058930  0.262894  \n",
       "64421   0.836754  0.018385  \n",
       "102415  0.577847  0.255031  \n",
       "97266   0.285744  0.513034  \n",
       "129269  0.386461  0.567899  \n",
       "1268    0.657754  0.313975  \n",
       "130172  0.319936  0.618932  \n",
       "57607   0.856285  0.111827  \n",
       "53588   0.201196  0.172719  \n",
       "144872  0.498598  0.471459  \n",
       "66423   0.433445  0.494044  \n",
       "148930  0.355171  0.623599  \n",
       "26388   0.801604  0.096931  \n",
       "30998   0.362121  0.279578  \n",
       "127948  0.207198  0.721331  \n",
       "25404   0.062835  0.422501  \n",
       "68783   0.502919  0.374423  \n",
       "102610  0.311105  0.579784  \n",
       "109950  0.084418  0.827980  \n",
       "101644  0.278920  0.697906  \n",
       "44791   0.928257  0.046071  \n",
       "144007  0.831071  0.131716  \n",
       "146663  0.086819  0.163877  \n",
       "149542  0.763463  0.152423  \n",
       "100025  0.071592  0.867805  \n",
       "...          ...       ...  \n",
       "17157   0.844631  0.106889  \n",
       "73969   0.262318  0.636391  \n",
       "146569  0.477694  0.466454  \n",
       "8881    0.369286  0.564833  \n",
       "120658  0.106016  0.217803  \n",
       "112137  0.114856  0.861901  \n",
       "43638   0.672096  0.112941  \n",
       "78912   0.558788  0.408837  \n",
       "22308   0.146635  0.786981  \n",
       "105744  0.691322  0.256709  \n",
       "1028    0.075569  0.442105  \n",
       "10872   0.196451  0.751154  \n",
       "51864   0.489238  0.349241  \n",
       "91747   0.816851  0.124691  \n",
       "40927   0.397120  0.271887  \n",
       "34791   0.687152  0.301085  \n",
       "117485  0.064147  0.337216  \n",
       "56475   0.152174  0.794010  \n",
       "67162   0.805079  0.174046  \n",
       "42759   0.707445  0.222501  \n",
       "23904   0.286978  0.695804  \n",
       "117313  0.609891  0.367020  \n",
       "52721   0.068166  0.750857  \n",
       "124399  0.336647  0.258071  \n",
       "129967  0.104126  0.144697  \n",
       "18546   0.075342  0.244312  \n",
       "6911    0.072305  0.372875  \n",
       "15083   0.136601  0.765359  \n",
       "101125  0.241873  0.704412  \n",
       "73853   0.235790  0.055116  \n",
       "\n",
       "[43322 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edf2[edf2.race_orig!=edf2.race]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:39: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
     ]
    },
    {
     "data": {
      "image/png": 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mpGCcMUk0J2fgDIkj48nF2Jv7ccHYq3hu5U98n89h9GXA/gC4TSn1JsbFjbVSfy2EEEKII6qkwajXbfSM+I5P6HKhGwD/KzDCcJPDCMSzM+Dmkd6PecF7sLOqbXv1lTA4umu7A43wx3ajveMSfAfsRgeU29q2m30E14BOAdfmNGqcvZ1TkH/HbfthwrC1U0R0+AjYnYO4w23UUAf7H+qD1pqalhqWZq7nq5TNVITa2JFSTkDZ32n+yO79uF6mvYgPiicxJJFwSzhh1jDWflbOto21BDlhSLydKalOxh8so//echwFFtCdXwMjWDcH+LMrbRAhcy8ketQwHr7/lzxww91MnTIFrTXn/Oo+lKfvz6ljLGArpd4ApgExSqli4DeAP4DW+kVgCTAH2AM0ATccqb4IIYQQ4gT29i5Y5imTaHbCj4bBeQO8tx27qGNYLJrnfbS5oA7e2922nRHh+/k7h1Gbj9KHTiO4NDm8t4OuwbnFRxj2N4O/qe2c3Npo6+2cOj+/r2MCxAVBQjBYTOBnMi5I7MTldlESXc/2+zX1/s3UW5ppCCmnZu0GavwaqW2ppbqlmpKGEqNko9Nb0oyd2MBYsiKzsJqtlDeVMyBkMK++sovaA/5oRzhmHc9v5l/A9qImtu6upcDfDHY7sTWlXFO5h+nWjVgdDpq2WbF/ZZyfA6vxZ4wfptAA7CERHGgJZHV9FoFDRzPotBFMm57Kyy8/x2O3/h8tLS3YbDZWrVp1KFj/UEdyFpErv+NxDdx6pJ5fCCGEEMegBjtUNRvhsskJ0YHQP8x729+tgR0VRvsWF7x+LvQL6doupwLe39O2PTWla5tWQf5Q29K23eTwHkaDO432Hi4Mdy6T8BWwgzs9T6OPdt6O2XyYtud7kmugHwT6G3XY3kxPhQ8ugiA/4/wiAnwf8825HTbr7HW8n7OYAw0HKKwvpLCukOKGYpxuJ0S1a+jAa8FviH8IySHJJJkHkFw3jknZaQxJSSMqwNh5ZW4572wqZnN+A87QEYSO8SPS3Mggiqhe8gIXF+Vwa3ElZpsL1aRRnnO0YcGG5dDzuIOsWKafRvJNPyNwUNtsImOAcz0//+9//2PatEvJzc0F4Prrr+eJJ3yUoHxPx8VFjkIIIYToY1obo6PeplVrcsDTG43gWm83Zoh4eZb34yzaAb/5sm37llEw/3TvbT/KM2amaNXoI+R2Hpn1FXDBCJftA7bPMNwpYPt6bjBGexNDjFBs9YwoexMdCHdlQ5jFCPVRhwm4d4+H28caI9lWP2Mk2ZeFM30/1rmfcUHda+tRYasgryaPx9Y9Rn5tftdDBsaREpZCbUstpyWeRoglhHBrOJHWSMKt4URYI0gITuCvz+fwm4c+x2ZzgtrG755NwJa9h+bib6gv2U1V6X6m11dxSW0NYbVNhNQ0oWvBZTd1LfVQGv8QF/4RZnaSQXXSeCKGZJE1dTQDJg31fS4VFdx11128+uqrAAwZMoSFCxcyderUHr0m3SEBWwghhDiZaG3U9pY0GH9aTDDFx4jvY2uNMonaZqi1w/NnwaVeLrQzm+BPm9q2TQr+MrN7dcCHC66dQ66vkobATu0ON9qbGmYE1mC/rn1pb3Q8LJxh9CHI3yiX8OVvZ/t+rL2IALh/Yvfahlu7166XaK2xOW3U2+vJq8ljd81uVhSuYEvZlkNtMsIzmJs5l+SQZDIiMkgOSSbIP4iqKhurVxew49Ny5t0/GYDGFicltc3sKy5nR1UuZY48rrihkfHBeUwKyCW98kXCPmnC3mCmfFsodUWB4G77fXHSNoKvrGb8I0IJmTiKkNPGEzRyGCo0BqLS6d+Dc1RKsXTpUgICAnjwwQe5++67sVgs373j9yABWwghhDgRfFMOK4ugrsUovbhjDMR7CYU5lTDtzbbtodGw0kdVZ02zMQ1b+21vrGZjFLl1NNitjT50DsjQszrkzgHYV8CelQbJIUbQDjBD/3Dfx/zoYt+PtZcYApd4+TBxnHNrN/tq91Fhq+CrA1+RU5lDaVMpJQ0lNLu6vr8B5gDSw9MZHDWYX2T/gnBr22urtWbQoOfJza3EFGAieaiFuNQDBBxYTXT9twxRhZxlOgjAJQCxrftBTV4QuTvjcTW2BWlLQiTW9ERMqf0JHjWGoPHT8IuNRf2AELx9+3aysrKwWq1ER0fz+uuvk5mZSWZm5vc+ZndIwBZCCCGOVeVNsLEU9jfAgQZICoEbR3hve8/nsPFg2/bcTO8BO6xTWGnwMYsDdB1FbV9a4a1t+3KLerv3gB1pNZaoDvTUAaf4qL8Go3zk2lMgNhCiAo3p77wZGGXchE9lTWXsqdnDs5ueZWfVTq9tAswBBPsHkxiSyMDIgYxPGM+kuNNxNfsRExMEthoo+ZrGg3nUleTRUp7PS+d8Q/yFlaRaqwhWLcbUFUDrALQTPxr9InCaAshbY6K2MpDIBgfB9rYwbx06hKSnnsKant5r59vQ0MDDDz/Ms88+yyOPPMIDDzwAwMyZ3Syn+YEkYAshhBBHU4sLNpZAaSMcbDJKNX6WDTFeVo3bWArXLWnbnpToO2CnhXcM2L7mOA7pFLAPV6IR0SlgH67tL8aB020E+BBL1yDfama6ceuOszO610504HK7+PLAl+RW55Jbncuuql0d6qejAqJICU0hJTSFUxNPZWDkQPqF9CPMYnzYqSir4/2/vEnJ3kVsUL8mJQH8/UoJpxGAYM8NIK3d56NaVyBVpngCU4cQNWw22pxEc2Edtu051K/4jNCSEkIxVnYxx8QQfeONhEyb2qvBGuD999/n9ttvp6ioCKUUtbXff8nz70sCthBCCHG0uDVc+j6sOdDx/ksGeQ/YiZ1mzKj2UaIBXUerDzZ6bxfsb9wSQ4xb9GEutLt8CMxKN0anI6zGlHC++FrcRBwVlbZKFmxZQGljKfm1+ZQ0dlxaJMQ/5FCgvmnETfhjRbs1FlcduqEM5961NOcu52D+NmKrt3Ajto7LAQI2baFIx1Kk49hPHNbYDPJrQlm22U2TK5bLQho407oX5+qdVJc9Bc6OtfB+if2wZmURccklhE6fjvLr3RhaWFjI7bffzgcffADA2LFjefHFF8nO9rFa5REkAVsIIYToDreGSlvbRW+d1TTDlR9Cnd0opQjwg43XdWxjUvDnGXDev6Go3RrNFTa8SgrtuF11mIA9PAZm9Dcu4BsYaSxi4o3FDPtu9n2c9mICvQd/cUxodjbz6s5XWVuyls0HN+Nwt33DkBCcwBnJUxkc1I/QRoiptFG0Zx/1X6xj6Wv/JdJUx8iQ/URTg8JYqMQfDl00uNcdz+qWoZQ0h7GnOpJJF13GpPHDSIwMIsNixs8zm0zlyi+5xraU5nVv4dxthPrWWO0XF0fAiOEEDh9B0NgxBI4ZgzIdZjaUH2Dnzp1kZ2fT1NREaGgojz32GLfccgtm82E+FB5BErCFEEKIw7lvFXycZ8y44XTD4nNgtpevtC3mjiUanecxbpUcCv++wAjZJZ5RZl8BOzrACM3xwW0jzr5cPMj7UtrihKO1ZlnBMp7e+HSHkepAk4VLA8eSXVXKyKLtRG1f32G/UQCBnptHvQ6kXIdTTRhb9QAqIkcz/rSz+NkvtrDty2IAUlLCuPL6RIYkGhc4OkpLKX/nXapffRVXTds0in6xsURcfjlhs2bin5qKyXr0ZkIZPHgw48ePJy4ujmeeeYbExMSj9tzeSMAWQghx8ilthNNfh5GxsKoYXjgLLhvsvW2DvS0IA5T5KL0I9DNWu3N5VsBocRnTxXlbxCQ2yFgie1+t8fPIWO/HVApen+v9MXFSaak7wAff/JV9Nfl83VDI1y3lAAxwuLmlqpoEl4OBdgcBek+H/cp0BPt1DPUBiTS4o1i3rZmyxkDK6gKIzBjOw09fT0KIlXR/M2PbTat40xVO/K4ZyezZA0hLMy4ute/bR+Urr1Dzn/fB0TZaHj3vZkLPPJOAoUNRR2nEuLq6mgceeICf//znDBgwAKUUS5YsITDw2PjGRQK2EEKIE0e9HfbXQ3WLUYOcFQmnxHRttzTfKONYZYzQ8dYu3wG7S22zj4sHlTIu7KtuN9NGnd33KoE3j/zu8xEnB5cDqvfBvi+g/Fuo2E1V40E2OarId9uoVLAsJIiqduE10uXijuoaLqxvRGlFCVF8rWMp1jH4xw6kInE6A4aNIyUmjKxQKyFWP7ZuLWX+gy8dOsZUUzDJUd7n977ttvEAuG02aj/8kMqX/0pLXh64XKAUoWfPJuzsswmeMAFz+GGmRexlWmtee+01fv7zn1NeXk5hYSEfffQRwDETrkECthBCiONJg90IzukRRj1zZ79dA3/b1rZ930TvAfuCLPjVyrbtlUWwqdSose48w0V8u5XvwixtI9TevHWeUQMdZjEuDPQ2TZ04edUUQfEGI0TXl0BNIVTthdpi0G1zfK8PsHJrfCzNgX5AWx1+qt1NRl0UJmcoYfaBbA7KZEd6Bp8uq2XTV6U46+w4q1t47bWL+L9Lhnd5+iFDYpg3byznnz+YiROTifCxVLp2u2nasJHa99+nftky3I1t39oET5lM/H339frMH92xa9cubrnlFj777DMApkyZ0utLnPcWCdhCCCGObc1OmPMuFNVBjWd0OP8mCPUyDVxqp4sCfc2kERlgLEjS3G7hktnvQO6Pu7a9eCDMSDOWmD7cyn8Ao+IP/7g4ObicULELDu6AhjKoyoONf/PZ3I2iXMUYFxbqAbwZswu7qQlTUz/MzRlQF0BEcwa/+NHlpEQGkRgRSLC1LcK17FrBl2/sPrT9ySd5XHVV14BttfqxcOG5PvvhrKig6p+LqH799Q6hOmDkCMLPnUvojLPwT/Bx8ewR1NzczO9//3sef/xx7HY70dHRPPXUU1x33XUob6uFHgMkYAshhOgbDXZYuhc+2Qe7KmHeKLhqaNd2AX5QUGuUW7Sqt3sP2MmdFi3xNRc0wLNnGOUcw2NhcJTvpamjPIucCNGZ2w3Ve6FwLdQWGSPRFblQ8g04vV+4WuSO5QP3JEp0NPt1DAU6lpLwAgjNRZlsmIOMpclj/Qbw5W8nYj9gzBwTEWFjyqOx+Pl1nYVj9OgELBYzI0fGM2dOFpdc4uXvUSfO8nIa162neccO7AUFtOTtwVFUbJwTgMlE2OzZRM+7mYCBA7/nC9Q7ioqKDoXrG2+8kT/84Q9ER0f3aZ++iwRsIYQQR5bWRn1yZysK4aeftm3vqenaplVKKOyobNuua/E+o0ZaGGSEQ3QgRAXAhH6+jykzbojucjRD3X4o2WqMStcdgKp8OJgD9nqvu1RaEvmWdHJtoRzQwawxpbLbLxy3tRZT4H4CgwrBvBPM1fipjmE8MTiRl2Y8w+n3/Jv9GAG7pqaZtWuLOf301C7Pdf75g2louA//w81TjlG/XLdkCTVv/oumjRuNv5vt+fsTNH484XPPJWzOHEx9WNNcUVFBdHQ0SimysrJ47rnnGDp0KJMnT+6zPvWEBGwhhBC96+mN8HUZFNYZt2WXwoDIru3a1zaD0daXlDAjYFtMxkWHTh910CPjYN2137/vQjSUGRcb7t9kjExX74OmCp/Nq02RbHEPIMeZSJmOYK/uxzZ3OvWqHHNQAX6ROzEH5QAbaP+dS7viJOKD4rl26LUMiRpC/7D+xATGYDaZGTcuif37vz3UbsWKfK8B22I5fLBu2riRmnfepfY//zl0n7JYCBo/nsAxo7FmZGBJz8CS1v+oTq3njcvl4qWXXuL+++/nhRde4Oqrrwbg5pu7OXf7MUICthBCiO6rtBkzbzjdcKmPEeDPCzuuVFhQ5z1gx3YK2CsK4NtKGOzlq9/fTYYnpkJcsPeLG4X4Li0NRmAu/9aoj7bVQEs9tNQZfzZ7/nR0rdt3YabGL4YSHU21w8yXrlP4WmeS606mEmMGjbBgB6Hxq3H5b8Jq+gwXbfNT+5n8SAhKIDYoFktLOB/8rRxbfiTO+gCSImP5dMt9XmuJZ8/OxM/PxJQpqZx+eiojR3av/tnV0EDtf96n4bPPaNm9G2dZWYfHY392B5HXXIM5NNTHEfrGli1bmDdvHuvXG/N3L1++/FDAPt5IwBZCCPHd6u0w4y3I85Rx9Av2HUtsXBEAACAASURBVLBTwzoG7CLvX6ETF2TUPmdGGKH6yiHQP8x721Qf9wvhja0aSrcbYbruABzcDrs/6d6uBLBNDeIbnc4X9oHsdKdSTgRujNpni9nEsKQwRqZEMDa4hkbzNvY2bWRr2Ubq2o1Lm/FjasoUzk4/mynJUwjyNz5QOhwuXr3i97S0GG3zCh2UlDSQmNg17N58czY339z9Zb61203Nu+9S+uBDHe5X/v6EzphB8GmnETZ7FqZg71Pz9ZX6+noeeughnnvuOdxuN4mJiTz33HNcdNFFfd21700CthBCCCM4bz5o1DWfltT18RD/joutlDaC3WWsXthZ55Dsq/Qj1AKrr/r+fRZCa2iqNMo49m+G3KWQ97/D7lKTdTFNsSPZ7wzlPzl15NaYqHZZqddB1BNEE1agbTQ5LtTKZYPjsFptWMI302I6yIHGYr6sK6Skqm2UGreJhpw4atam0lwUzl3Xz+SxH83p8vz+/mZGjUpg3br9AJjNiq1bS+nXL+R7z4jhOFhG/SefUPPOO7Ts2mU8T1ISEZdcTOjMmVhSUlAWLxcFHwNycnKYOXMm+/fvx2QyceeddzJ//nxCj7HR9Z6SgC2EECezf+fCze1G9q4c4j1gKwXpYW0XGmrgQAOkeVlgYk6GcaFhSpgx8hwX1LWNED2ltTFDx77VsGsp1HnmkfZxkSFBMTDobOoDEqi2JFIQPIJr/30QtrVvZEyrmBoVRFZUEGP6RzIxPYrU6CBCrf4EW834mU1sObiFny2/l+rKjrXYof6hnJZ0GlOSp7Dvs3DuePKzQ499vcH3RbtPPjkTi8VMcnIYsbFB33lxYpeXwuWi/tNPsW39msZ162jZufPQY36xscTdew9hc+Ycs1PYtZeZmUlwcDDjxo3jpZdeYvTo0X3dpV4hAVsIIU5UWhszc3xbCZOSIMbLjADmTlN+5R1mJo+0cCNgt45y+3edLgwwFnbxtriLED3haIbK3VCwxrPK4Woo/aZrO2sYRKZBdCbNaWewP3AI6+oj+csXRRSvs+F0t14Qe/DQLukxwcSGWpk5NJ45w/uRGOF9tgy32829bz/Nx/X/BLObpt3R3Hvh9QyKzyAlNIXk0GT8Tcbc6Ht0FdAWsDds2I/W2mvI9XahYndorWn86ivKn/0TzdvaPikof3+CJ08mdMYMwuac3ecXKh6Ow+FgwYIFXH/99URFRWG1Wlm+fDmJiYmYj9Iy60eDBGwhhDgR/WolvJHTtpDKojlwdkbXdmd2+o9+fUnXNq1+NxmemGYE9eNgZEwcJxrKoHSbMRpdvsuYV7p8F9QUgHZ3bBsQjjvjDBrix/EtaexVyTSbw1i9p5KcPbUc2NQMlHlubcwmxdj+kQxPCucnkzNICPe+gqHL7aKgvoCdlTv5KP8jNpVuxuZqAjNU/DeL0reHEzduIlPGDu6yb2ZmJNdfP4qBA6MYOjS22xckdpe9eD8lD/6apjVrAVABAURddx3WrCxCz5h+zNVVe/PFF18wb948duzYwc6dO3n55ZcBSElJ6eOe9T4J2EIIcbxwa9hRAd+Uw55qsDnh8ane21pNHVcp3FXlPWCHWIyR6X21xva8kcbzeJupI+n4rokUx4iDO2DzItizApzNxgIt3igzRGehYwdRrwPZYEviicpTyd3i5NCgNMUddrH4mUiNCiI1Koh+4QGMSY1kUma0zxHqvMp9fLThS7YfzMGSXMPWsq3U2TteM+BnDyb/5WHUbTBC4Oef7+P887sGbKUUf//7+T16KbrDcbCMoh/fSMvuPZ4O+RF7++1EXHYpfpFeZuc5BlVWVnLPPffwyiuvAEZZyKWXXtrHvTqyJGALIcTxYl0JnPfvtm2LCR6b4j0Md66Nzq32fdy3zjMWcvGyQpwQP4jTbpR5HMyBsh2wd5Uxv3R7foGQNBYdkUJzRBbl/ol86+zHG3vM7K12UvxN+zIPJwBhAX7UNTu5cnwqJgWhAf7MHpbA8KRwzN2YxtHt1lz+6z/w7aDX2u70ZPWE4AQGRw0mOz6bM1LPQFeHMvD251HKxbnnDmTSpCM/2tqydy/1y5Zh37uX2iVLweEAIHjqFPrNn49/fPwR70Nv0FqzaNEi7r77bioqKvD39+fee+/lvvvuI7APF7E5GiRgCyHEsabBbowsd5YdD8H+0Gj8Z4vdDWWNkOBlRcP+noAd4g9j4mH4YWqi071cqChET2gNFbthz6dQtA7cLmOlw4pccDs7NHX4hVCUfC57k86lmnB2NISys7yZHV/XUd/c2rbjXNSxoVYmZUQz65QEzhwSR0APLwpsr8JWwd+2/+1QuG4pDaF2fTIPzbuIuRNOIzWsU9lUKLz00rmMHt2PESOOXLB1NzVRtfhVqv75T1w1NW1LlmME6/i778aalXXEnv9I2LRpE9dffz0A06ZNY+HChQwe3HX0/0QkAVsIIY4F28phwWZjqrySBii+pWuds78ZJifDf/e23VdQ7z1gn54Ee34C4cfuxU7iOOZywsFtULgO9iyH4g3Q3PUCWY2iMTiVYv80Njb1Y3VDP1Y2j6T5Wyt8C1DvuRmCLGZSIoNIjgzk1AExnJoZTVJkIGEB/j3qntaavXtrKC6uY8qU/uTX5LOvbh/bK7bz6s5XsTmNpclteyPJe3Q6uE1YZg8kdYb3iw9/9KNRPXr+nvTTnp9P/fIVVL/+Os6DbRdihp9/HoFjxhIweBCBI0cekec/EpxOJ35+RrzMzs7m5z//OSNHjuTaa689LmY16S0SsIUQoq9pDWf8q+N9zS4I9PJP9JwMcLmNlRGzIn2PPgf4GTchvq+qfCjbaVx8WFtsLNjSeqs/0GVkmpB4SBgBIXG44k7hnbJEHl7rxtbcdkFhkMXM+LQoIgL9sfiZ8DebCPQ3MzIlnEkZ0cSGWn9QCMsp2svPn1hEbtlemq3VhCW3kFFhoqi+Y533tORp9CuezAPzc4iKDGLmzEzS0iK+9/N2h6u+HtuWLbTk5tKcm4s9L5/mHTs6tLEOGUL4OXMIv/ji46a+ur1ly5Zx66238ve//53JkycD8NRTT/Vxr/qG/OsrhBBHUpXNmNpu7QFICIZrT+naRik4JwM+zm+7r7bFe8C+cohxE6I3VXlm7qj3BOiCr6Dgy8PvE5mOK2UilTHjqE04lcaABGqbnXy5p4J/fVpErc0oZQqymPnJ5AwmZUYzJjUSSy/W+jvdTrZVbGPtgbWsL13PxoMbYSwEY9zAWEg00C+QsfFjSQxOZHb6bMYljKOoqJaoP2dw3XUjCQrq2Qh5T9gLC6l+7XWq334b3dTUtYGfH4HDhhF9008ImTYNZTr+roU4cOAAd911F2+99RYACxYsOBSwT1YSsIUQ4kjZVt5xZHpMvPeADTA6vmPAbrTTFhGE6AVaQ1OVcdFhRa5RM527DBrLjKXFO7OEQtJoY8GWfiMgLAnCEqk0x7B0HyzLrWX1+taFV/I9tzbhgf7ceHo6P56cTpDlh8eN6mobi17bwlufr+T8G+Lxi6/hg7wPqGyuPNRGoTAfSKF0mwV7eQiOiiAe+cVc5l1+JoF+HS+qS0kJZ9687i9D3lPabqf0sceoebPt34DAkSMJGDYM68CBWLMGYElPPy5Hqlu5XC4WLlzIAw88QF1dHUFBQTz88MPceeedfd21PicBWwghfgi3Bqfb+5LhGRHGisutEyB8XQb1dmOJ8M7GJRglH6PijPmmI73P0ytEt9mboHANfPsx7PzQWFJcu7y39Q+C1IkQngyhicbCLYPPgQBj2Xuny82Wohp+9c437K3I67L74IRQLH4mAvzMjEqNYPqgOLLTIvHvvJDR9/T0nz/j8XcXEzErB+vcRhZXAJ5snxKawuSkyQyNHsq0lGk8/vAGHn/jSwID/Zg4MZkB4VldwnVvczc305yzE2dZGfbCQhpWrsSen4+ruhpMJgKGDaPfw78hYOjQI9qPo2n37t1cddVVbNy4EYC5c+eyYMEC+vfv38c9OzYorfV3tzqGZGdn69Y3Uwgh+kyTAx7+Cv61E349CX7i4yKkia92XB3xjblwlvwHJI6Qvath3Yuwf7NR7tGZJRRiBkDMQAhLhIxpED8crKHg1/GDX3WjnR0H6rjhH+txuXW7uadhVEoEk7NimJwVS3b/SEzdmBqvJ6qaq/is8DOKG4pZWbySPdV70J5PqvbyIJzlYUw4PZ47s+9gXMK4DnXbeXlVVFbaGD06ocdLkPeE226nYuFCmjZswLZ5S4dZP1r5JyeT+Mc/EjTmxFj+u72KigoGDx5MYGAgCxYs4Pzzzz8hL2JUSm3SWvf4qw4ZwRZCiJ5aXQwX/adte12J74A9PBaanUYJyIz+MCL26PRRnJhaGqC+BOr2Q12JEaJriowLEavyjCXFW5n8ISodAqNg9u8hfliXEN1ZfbOD3IMNPP3pLr7cU9nl8TnDE7h8XCpTB/bO77HbrVmxIp9PP81n06YSfvnLUwkbUcb9q+/vUPphUiachXGULk+ienUauE38evB5jO/XNbhmZkaRmdkr3fNKOxxUvPwyNW+8ibO8/ND9lvR0LJkZ+MXGYk1LI+SMM/BPTESdIMt/a6356KOPmDVrFhaLhZiYGD7++GNOOeUUQkK8zGR0kpOALYQQ3jTYocUF0V6+Ws5OgCB/YxQbjICttfflw/8yU5YVFz1jb4K9K43QXFNorHxYd8AI1i11h983IBzG3gBjroOI/mD2/d+81pq88kaWbCvh811l5JU3HrowsdWY1AiG9AvjtAExzBnerzfOroNLLnmL99771tgwuwmYsZHCg58BMCp2FBP6TWBAxACmpUzjmSc28HLhZn4xfzSXXXYKmZlRvd6fw9EuF02bNlH+3HPYNhqL5Vizsoi4/HLCZs3EL/bE/fCcn5/PbbfdxtKlS/ntb3/LAw88AMCECRP6uGfHLgnYQgjRyuGCRTtg/hqwOeDHI4yVEjsL9DOmx9vhKQItbYRyG8QFdW0r4Vp0l9aw/V349CFjhNobvwAI7WeUd4T2g7B+EJ4KEakQkWKUfpgPPyNGaW0zS7eX8NfVe9lfY+vwWIC/iaggCy6tef6qMYxLO7Ihds6cLD5atYl+124hMKWWwmgbJmXipyN/yo+H/xizqW309667JnHPPaf3ejlKd5QveJ7KV15BNzcDYI6IoN/vHzNm/TiB/47b7Xaeeuop5s+fT3NzM+Hh4SQkJPR1t44LErCFEAKMcHPLp/D+nrb71pf4bp8WZgTscQlGXbUs6CJ+iANbYOm9ULTW2I4dAv0nGUE6fpgRoEP7QWBkjz+05Zc3sLGgmtLaZj7NOci2/bWHHvM3K84e1o/pg2M5LTPmB89D7Y3L5cbhcBPgZV72Cy7L4Kna1fjFNABgrwjiTzOfYPbwrh9sve1/NFT9859UvPACAH79+hFx4YVEXXct5ogjO292X1u1ahXz5s1j586dAFx99dU89dRTxB8ny7T3NQnYQggBUNMCD50KS/ONJcgBvi43ptob7uWr3+fOhFdmQy/NkiBOQk67MWK95VUo+MK4LzgWznwIRl0Npu7V7hZVNXGwrhm7y43TpXG63ZTVtVBW38Kq3HI2FnScgi/A38TkrFimDozl0uxkrH69XyPsdLpZu7aYf/1rO++8s5N77jmNO++c2KXdc9uexi+mAX9bGBeE/oTL557DwKyYXu/P96G1pvzpZ6h8+WUAEh55hMjLL+vjXh0dX375JVOnTgUgKyuLhQsXcuaZZ/Zxr44vErCFECeHuhbYVgH76+GCrK7T6kUGGLcrhhhlIq38fQToMBmxFt9DSz0czIGc9+HrN8BWZdxvtsL4n8DUXxl11F7YnW62FtWwp6wBu9OF3eXmm+JaPt5WwndNCGY2Ka4/NY1xaVFMGxRLwBGcXQNg69ZSJk/++6Htt97a0SVgLy9Yznt73sNqtvKvKxaRGXEEr0zsJq01jatWUfPv92hatw5XjTEDUNzdvzhpwjXAqaeeyqxZs5g4cSL33nsvAQEybWhPScAWQpwcZr5tTJeXHGrMXT13AAR7qVW9bQxsKYPHp8D43r+oS5wk6kuhdDvsW23MP91YDuXfdpzlAyBmEIz9EYy4HIK7jtw2tDj5pqiGrcU1LPw8j/pmZ5c2ABmxwcSGWLH4mfAzKQItZlIigxjcL5QZQxMIsfb+f/clJfXExAR5nQpvyJAYlOJQ8F+zppjCwlpSU40PD2VNZTy85mEA7hp7V5+Ha0dZGVV//we1H32Iq7zi0P1+sbHE3XsP4eec04e9O/J27NjBnXfeycKFCxkwYABKKZYuXXpC15cfaRKwhRAnh2ExRsAurofbV8B9q+BHw+Dh0zq2Sw+H/13eN30Ux7eqvbDyD5C/0vsc1ABmixGqUycYZSCJo73WVNudbj7JKeW217d0uD8jJpjRqZGEWM1Y/Ez4m02MSolg5ilH58KznTvLeeON7bzzTg47d1awdOnVzJ49oEu74GALGRmR5OUZ5SmhoRa2by8jNTWcJkcTtyy/hdqWWk5LOo2rBl91VPruTd3SpVQtWoxt61bafw0QMn06MbfdSsDQoSd0yGxqauLRRx/lySefxOl08uCDD/LGG28AnNDnfTRIwBZCHN8aHbBsL2wohU2l8PHF4O3r7+GxHS9gbHQYYVqI3lC+C/42u63kw+QP8adAxlSITDcWcokdDLGDDjvLh9Pl5t9b9vOn5bs7zPBxWXYyk7NiOXdEvz4NPu+/v4tHH111aPuNN7Z7DdgA06alMX58EpdddgpnzUxjXfmX3LriWbYc3EK9o55waziPnvroUT8f7XJRtXgx9Z8ux7bJmG5PWSwEn3Yalv79ib75puN6+fLuWrJkCbfeeiv79u1DKcW8efN47LHH+rpbJwwJ2EKI49vUN6Cg3dzAX5cb81R3NioOBkXB8BgjbJ+RCoOjj14/xYmppcFYjvyDO4xw3f90mH4/pIw/bJDOOVDH7rJ6qhvt1Nqc1Njs1NocbC2sIb+iEYABcSFcnp3CBaOTiA09Nmr+p07tuArpe+/t5KWXzu0yw4fT7eTeJ4ayuWwzaw7+nQUfb6SsqezQ48NjhvObSb8hNujozB2ttca2aRP1K/5H3ZIlOA8eBED5+xP3y7sJv+hizCHBR6Uvfe3AgQP87Gc/45133gFg5MiRvPjii0yc2PUiVPH9ScAWQhzfJiZ2DNirir0H7Kkp8EXffRUtjnMup7HIS00BFK2Hkm/gwGajrlp7Zp1JmQhXvw2WtvnQ99fYWJdfyarcckwmRXl9CxUNdnaW+F4wJjUqiLtmZHHeyCTMR2nO54qKJtav38+ePVXk5JTz/PNz8PPreoFvdnYiQUH+NDU5MJkUQ4bEUlBQw6BBMeRW5/LaztfYWbmTvJo87G57h33TwtK4bNBlnJF6BkkhSUflvLTWNH/9NWXP/ommtWsP3W8KDibmpz8l4uKLTvjp9jqrr6/ngw8+IDg4mPnz53PHHXfg5ydxsLfJKyqEOHbVNMNrOfBZESycAbFeFnKZnAz/+rZt+8ti+Hn20eujOHE1VUHxRvj2I2PGD5e9axuTnzFPdepEmH4/pTYTr32+i5W55VQ22DlQa/M6w4fFz8T0QbHEhwUQHuhPeKA/YYH+xIZaOX1ADP5HefrHL74o5MIL/3Voe+TIeG65ZVyXdv7+Zu6//3QSEkK4+OIhNPvXsrt6J++uW807ue/gcLetBJkUksTQ6KFMSJhAVmQWo+JGYVJH57wcJSXUf7qc2g8/pHnbttbOE3rWmURcdBFBEyZgshx+2fgTSU5ODkOGDEEpxaBBg1i8eDGTJk0iJSWlr7t2wpKALYQ4Ni3bC9d83La9vACuHNK13ZRkSAuHEZ6yj7l9P9WXOE7ZGyF3GWx7G5TJCNbtWUIgNAGSx0HiGEgcBQnDKbMpNhZUs/LjIt7bsh+7y31oF7NJMX1wLEkRgQRYzExMjybIYiY9Npi40KM79Vljo52gIH+vNc9paR1HcR966HOuvHI4ERFtfSxvKmdV8Sqap23l0+rdvPRxPjZnx5UgZ/SfwTVDrmFA5ADCLGFH5kQOQ2tN7bvvcvDxP+BuMBavMUdEEH7BBUReeQWW/v2/4wgnltraWh544AH+/Oc/s3jxYq6++moALrvs5JlysK9IwBZCHHuqbFDXabTwk33eA3a/ENhw7VHpljgBaQ05/4ENrxi11O5O0+CFJEDcEDjzQUgaC0CL08X2/bWs2V1J/ppdfPj1ARyutmHqfuEB3DItkwGxIYxKjSDI0nf/1e7aVcE///k1S5fuYceOMjZuvIkRI7quxNc5YFdUNPGnP63lvvtP443c11mcs5iDTQe77BdpjSQtPI2x8WPJjs/m1MRTj+pFi1prHMXFNK1bR+P69TStWYuzvByA4FMnETb3PMJmz8IUGHjU+nQs0Frz1ltvceedd1JaWorZbKawsLCvu3VSkYAthOhbjY6u81FHBcKlg+D5zZBTadz3UR44XN5nCBGipxw22L8ZFl8Irpa2+yNSISoTMqdD1kyIG8Jnu8rYsqOavJWbyS2tZ29FI053x7qPUSkRnDk4jvHpUYxOjcTipX65LyxYsJ4XXthwaPuDD3Z5DdgREQGcc04WcXHBpKVFMHRUOAeT13DxR0+xr24fAEF+QYyOG83w2OEMjBzI+ITxhFv7ZiYeV0Mj5c88Q/3//oezpKTDY+bISCKvvJKYW3+KMp98/17s2bOHW2+9lU8++QSASZMm8eKLLzJixIg+7tnJRQK2EKJvbD0I89dAVTN8drnXuYCZltIWsJddKuFafH9NVVCyFQ5shYKvYO/KtppqZTZm/hh6PnvcCXyw9QBV5XbK8hrZUrSc8vqWDodSCtJjghkUH8qkzGiy4kOYlBHdJ9PnOZ1uVq8uYNq0NK/PP3FicoeA/eGHufz611O8Huv9Dy6nsrmSgroCfrf2YfK25QFGLfVNI27iggEXHLUaal+c5eVUv/UWtf95H0dREQDm8HCCxo8/dLNmDUCZjo0POEfb6tWrmTFjBi0tLURGRvKHP/yBG2+8EdNJ+nr0JQnYQoijS2t45Ct4od0CGmsOwKleZhW4aCCMjDP+FKK7nC1QlmMs+FKxG/I/h7riTo2UMS91/CnUn3YP7+RbyPmsjve2rO4yOg1gUvCHi0cwOCGMAXEhBFr69sPesmV7WLFiL2++uZ2iojp2776dAQOiurSbNCm5w3ZVlQ2bzUFgoD82p42VxStZXrCc0sZSdlfvpsnZdKhtWlgaPxvzM6amTMXf5HvKwaPBbbdT9sSTVC9efOg+S1oaCQ//hqDx40/aQN3Z+PHj6d+/PxMmTODJJ58kLi6ur7t00pKALYQ4ur7ab8xF3d5fv/EesEfGGTchvktTFWx8xQjV+zeDo7Hj4/7BEDsQAqMgMo2GSXfzxBdVfLGngryNbQsQKQWXjk1mWFI40SEWUiKD6B8dRETQsTXjxOOPf8nnn+87tL1uXbHXgJ2REckVVwxj9uxMzj13IH4hDpYX/5fV+1ezsmhlh0ANEBUQRVRAFJOTJjNv5DyC/L3M3HOUOauqKL7tdmybNwNgSU8n+sb/I/y881An0Uwg3pSVlfHII4/w6KOPEhUVhdVqZePGjYSGhvZ11056ErCFEEfX+H5GqccTG4ylywGqm8HphmOkblUcJ+pLYemvoHofHMyBdlPEET0AYgYas35k/x/EnYJTw+o9Ffx7834+fnIL7QeqY0KsnDE4lhtOS2dIv6M/+0VPTZvWv0PAXr9+P1df3bXGVinF714czedFn/PA5hdZV7IOp267kHN4zHDOTj+bwVGDSQ9PJyYwpssxjjatNc6yclr27Kbugw+oXbIUHA5MwcHEP/hrws8//6RfxtvtdvPKK69wzz33UF1djcvl4sUXXwSQcH2MkIAthDgybE74bz7MyQRru6/TW+uosxOMgH3fRJm3Wnw3raEi15jpo3AdFG+Ayt0d22SeCdk3QMoECDG++fi6qIY3vyri05wVtDjd1De3hctxaZFMyojmpqmZBFvMx0xoc7s1K1bks2LFXh5//CyvbaZPT+fhh1cCYLWaCQ7uOJJbYavgv3v/y4f5H5JTmXPofrMyMyFhAlNTpjIteRopYX0zD7LWmoaVK3HX1eGqr8dd30DTls24a+toyc/HXddxIZ6gcePo9+h8LGlpfdLfY8m2bduYN28eX331FQAzZ87k7rvv7uNeic4kYAsheteeanhrFzyz0dh+53xjFcXOZqbBpES4euhR7Z44jjhbYPMi2PIq1BQaS5G35xcImWfAkLkweA4EGDNaNDtcfLChiFfXFfBNcW2HXTJjg7lwdBLnj0oiJarvyx/a01qzYMF6nn9+Pbt3G+d64YWDmTAhuUvbCROSuOWWbCZPTuXsczKpcpeyomAFm8o2sb1iO9+Uf4NLuwAI8Q/hjNQzmNBvApOTJhMZEHlUz6szx8EyCm+4AXt+vs82prAwrAMGYM3KImz2LIInTTqKPTw2NTY2Mn/+fJ5++mmcTicJCQk8++yzXHbZZcfMh0PRRgK2EKJ3vb4TFmxu216+D05N7DoDyHkDjmq3xDGuqcqY3aO2GKr3GkuQl3zTMVQHxUD6ZEidZCz2En8K+Fmpb3awJq+ST3P2UVxtY01+5aFdIoL8uWRMMsOTwxmWFE5mbEgfnFz3KKVYunTPoXAN8Nxz63ntta4B2212cOm9QSzZu4iFS9ZR3VLd4XE/5ce0lGmcm3EuU5OnEuB3dBe18aVpwwaKbrsdd63xwSdo/HgsmRmYQ0IwhYRiSUkmMDsbv9hYCY2dbN68mT/+8Y8opbj11lv57W9/S8RJtsz78URpb2u4HsOys7P1xo0b+7obQghfNh+EWW+3bQ+IgD+dadReC9GqoRxylxpLkZfvgqK13tvFDjHmpD7lIkjOxuHWNNld1DY5yCmp4/2t+1m6Cg7hTwAAIABJREFUvdTrrk9cMoK5/8/efcdVVf4BHP/cwWXvjSKKKCIg4sRtOdJKzVWONM1tzsy0tNIsNTPNrVn9NE2z0tLc5cI9UHGDE0WQDbIv3Ht+fxwDEVoKXMbzfr18xXnOuZevdrl873Oe5/sNcMOkHJV33L37Bp07f593rFYruXdvIi4uFmh1Wi4nXGbNpTUcjDyIXsrvGOli7oKHlYdcp9rBH38Hf4PPVD8uNy6OmHmf83DHDtDrMfH3x+XDDzD19zd0aGVacnJygST6008/pUOHDjRp0sSAUVUuCoUiRJKk/7yOUcxgC4Lw352MBhdz8ChiM1igE1SxgPtym2KsjMG7cHUDoZLR6yDxFtz4A65uh7vH4LEEEZCbvHi1BxsPeYOic11yLN0JjUzhaFgCWzcd4lZ8etHPDwxtWYMWXg542JvhYW+OSlm2ZkCTkjLZvfsGGzZcYu3aV7CzK9xdsGPHmtSp48DNm4n07FmXkSMbcin7BG/v+JbwpHCyH2uK42ntSasqrehQvQP1HOqV2Rnf1AMHiJ37GdqICABsX38d5ynvojAybOm/siw3N5fFixfz0UcfsXPnTlq1agXAtGnTDByZ8G+JBFsQhH9PpweX5fLXb/jC/OcKX6NQwKTG8tctq0INw3R6E8oAvQ6u74Uz38K9k5D12HpopZG8KdGrndyK3Nod7DwJj03jj6sxRF7NJOp4LKdvXyNdq8t7mEIBFho15sZqajlb4FfFGv8q1rTzccJYXXZnqjdsuMjAgb+ge9RS/fvvLzB2bNNC1ymVChZ90wq9TSKp6jh2PljBzoM7885Xt5Lbkveu3RtfB99Si/+/yE1IIP3oUXKiokndt4+sixcBULu4UOWL+Zg1bGjgCMu2kydPMmLECEJDQwHYvn17XoItlB8iwRYE4d/z/ib/65/C4IPmYKYuvL56QNn8xS+UAkmChJtw6wCcWAGJN/PPmdrJa6h9ukKtDmBizb3EDKZuucC9xLukZd8iMV1b6ClrOprTwsuBNrUdaVPbEbWq/JVztLc3zUuuAVavPsuYMU3I1mVzJeEKcZlxnI89T1hSGKcfnC7wWKVCyZj6Y+hduzc2JmVzza2k15O2fz/JP/1M2qFDhc47jB2D/ZAhKE3KxlrwsigpKYn333+fVatWIUkS1atXZ+nSpbz00kuGDk14CiLBFgTh39HpoaoFpDy6RZ2RC/22w7buho1LMKw/y+fdOSL/iTgKaTH5563doclwqNtVXvrxaBlDdEom3+67wurDt4t82pldfXG0NKZBNVtcrMt+UhYTk8Yff9wqshY1UKgJzLXo23ywZy674n5Gqy/4ocJUbYqPnQ/ulu5Us6pGqyqt8LH3KbHYn4U+K4vUvXtJ+n4DmY9mXAGU1tbY9OyJkasrlu2ex8jNzYBRln3Hjx/nlVdeITY2FrVazeTJk5k+fTpmZmWr0o3w74kEWxCEgrJy5aYvFk90SFMoYGcvqPstpD9q6NHQGcrhbKLwDHKz5YQ6LkyuRX1pC6THFrzGzIEc9+ZctW7Fb7ogou7kEnMhitjU26Rn55KVoyuw7KO+uw1TO9ehur05xmolNmZGZXY98ZNu305i3boLfPHFcdLStPj5OREQ4FLoOg8PG4zM9NTqnIJz22jiLK+w9dHnkFq2tahuVR1bY1s0Kg3D6w0vUxsUi5J5+TIJK1eRfuIE+tRUAFQ2NtgNeRPrbt0wEi26/5NatWqh0+lo2bIlK1euxNdX3AUs70SCLQiCLE0Lc07C+iswqRGMe2KdpFIBZkbwQnXYch2mNIF3xE72SiMjEY4thtPfQnbB2tJYOINHC8JN6/F7Rm12RFly9UIqcpGqe0U+nVIBz9dxpn9QNVrXcixzGxL/rTVrzvPxx8F5x+PG7ebgwTfyPiDk6nP5+uLXHI48TL2VYWTrs4kD1Eo1jZ0bMyJgBA2dy9ea5MjxE0jdsyfv2MTfH5uePbF66UVUoovgv5Kdnc3KlSsZNWoUGo0GBwcHTpw4gaenJ0qlmLSoCESCLQiVnSTBpydg+TnIeVTVYcNVGNsg73Z+AaPqwxt+0LxK6cYpGEZ6PJz5Hxz4JH/M3gsc68iVPrzag0dzvjl6h1nb/+wYmIpGpaS+uw0NPGzxcbXE2coEZysTLIzVGBspMVGr0KjLfyLRu7dvgQQ7ODiCbdvC6PiSB2svr2XZ+WUFrg9wDODFGi/SsXrHMtGW/N+QJInkTT+SemA/OXci8quB9O+PdY/umIrZ1v9k3759jB49mvDwcNLS0vIqg3h5id4AFYlIsAWhsotKgz8i8pNrkFuYn34gl9x7cgNjfefSjU8oXWmx8jrqiONyKb0HF/PPmdpB1yXg8zK7Lkbz+9UYYv7I4tL930nJlJcNeTqY82l3fwKr2ZSr+tN/58KFGKKiUunUqXAC5OvrSJ06Dly7Fo+tu56XpmXxk/oTZmy8TK4+vy37x80/pq172zK/9ONJ6adOkbzpR7l+9SMKjQbH8eOwHzLEgJGVPzExMUyaNInvv5frnPv4+IjqIBWYSLAFobKrYgkHXoM9d2D+KQiNk8dPRstrrIXKQZsOwZ/DsSXwWGKI0gjc6oNnW2g9GUml4Z0fQ9l8NrLQU6iUCraOaYGlSfmvb5yeruW338L55ptz/PHHLVq1qlZkgq1QKHh7agOOan/jsunvnNdlQTwoUFDHrg69avWit3dvlIryNVuvS03l/oSJpB89Kg+o1bhMn45pPX80Hh4ozc0NG2A5otfrWb16NVOnTiU5ORkTExM+/PBDJk2ahEaj+ecnEMolkWALgiAvBelUAzJz4FCkXOM6UCTXFZpeL3dSDNsJ8TcKdlK0qwkBfeSW5FUagsaMe4kZLP01jF/P3yc7V77bMbVzHeq4WFLV1hQ3G1PMNBXnV0psbDp9+27OOz5xIpKMjBzMzPI/PGTkZLDt5jbWm6wgkUTQwfPuz9Ozdk8CnQKx1JSv9ci65GSyb93i4a7dPNy5E11CAkozMyw7dsS2f39M/f0MHWK5tH37dkaOHAlAp06dWLZsGZ6engaOSihpFefdUBCEf5aVK29i7FELiugiR7da0L126ccllJ7sVLjwo1yjOuF6/rhCJa+tbjEeAvsDcCM2jbh7mey+dIu1xyPyLjXTqOjs58rINjVLO/pSU6OGLX5+Tly6JFdIycnRc/ToXTp0qMm2m9vYcHUD1xKvoZPkaigBjgFMajSJQKdAQ4b9r+mzskjesoXs8HC0N2+RfesWuoSEAteY+PlRZcEXaKpVM1CU5Zder8/brNilSxf69u1L9+7d6dWrV7mpkCM8G5FgC0JlEJ8JfbbBjWS5xJ5aCYOKmI0qp5UchH+QngDnv5e7Kt4PgZwMedyqirxJ0bMtF40D2R+Ry4PbWaRePcv2C9FFPtX/BjWmVS2Hctns5U+SJHH2bDS//HINPz8n+vQpema2S5faeQl2w4auWFubEJYYxrQj8qY0BQrqOdTjtTqv0cWzS7lJnHITEogcO47Ms2cLjCvMzDCuUQNNTU/MmzTBumfPcvN3Kkt+/fVX3n33XXbu3ImXlxcKhYINGzYYOiyhlIkEWxAqurBEaPnEm/vm8KITbKFiycmEkyvh8ALIfpg/7tECGg+ROyqqjFiy7zpf/H6lyKcwNVLR1tuRl+u50dnPBWU5/xB26VIsL720gbt35VKDPj4OvPaab5GJ5Kuv+pKTo6N//3rUC3DicvxlZh6fA0DXml2ZHjQdU3URd4LKsIzTp4kYMDDv2HH8OEz8/DGu6YnaxQWFKBH31CIiIhg3bhzbtm0DYMmSJSxatMjAUQmGIhJsQajoqljAF21h2mHIetTc40QURKZC1fK1RlT4BzlZcPc4JNyAxFtw9TdIketQp7i1JMExiLu2QdxSe5FwP5uEsKvcS8rg6A15aUB7HyfaeDthaazG0dKYwGo2FWpdNUCNGjbEx2fkHV+9Gs+ePTeL3MBYv74L9QKcCI4MpvvWEdxKuZV3brDv4HKTXOuzs+Vuixs2knnuHABGHtWoungxJt7eBo6u/MvJyeHLL79kxowZZGRkYGlpyezZsxk1apShQxMMqGK9cwqCUJiFBgb6gb8jDNgBMRnQszZYlP9KDwJyA5iwXXBtO4TvAUlX4HSWfV0mJfVix606cAtACxScrdaolLSv68Ty/uWr4cnf0eulImfbzc01dOvmzcaNl/LGVqw4U2SCna3LZtqRaey5IzdVcTJ1opFLI55zfw4v27Jbs1ifkUHSxh/IDA0lNyaG7Js30aelAaA0N8eibVtcZs5EZSEqgTyr0NBQBgwYwMWLcjnLV199lYULF+ImWsNXeiLBFoSK5EQUeNuBrUnhc3Xs4cPm0N6j6A2OQtmn18l1qaPPQ+QZuV514q0Cl8SrXblqXI+QDAfOZ7sRfD8APfJt/1a1HLA2NcLeXIO9hTH2FhrszTX4V7Whik35f01IksSJE5HMmhWMt7c9Cxd2KvK6fv38+fXXa7zwghfdu9eha9fCs7gp2SmMPzCekJgQTNWmvO7zOkP8h2BuVLaT0r9aX23i64vNq69i/fJLosReMTIxMSEsLAxPT0+WLVtGp05Fv+aEykchyb1sy41GjRpJZ86cMXQYglC2ZOTA4rOw4LScRI9pYOiIhOKi18GtA3B2nTxDnZtZ8LSkQKmQWJbblU2657grOQHyzK2pkQpXaxMcLI2Z2L42zWraG+AvUDpOnoxk8OCtXL0aD4CpqZo7dybg5FQ4mczJ0ZGWpsXWtuCHCp1ex9XEqxy+f5jN4ZuJyYjBydSJ5e2X421XtpdS6LOySPxuHQmrV6NPTUXt6orj+HFo3N0xcnXFSMyoFgtJktizZw8vvPBC3rr9ffv20bx5c0xNy/+HVKEwhUIRIklSo//6ODGDLQjl3Z0UaLwu//ibizCyvlwpRChf9HqID5dnqB9chPDd8nrqxySonDiqrcl5vRfn9TW5InlgZGxOyzoOjKjlgI2pBpVSgbeLJdXtzSpNFYjate2JjU3PO87MzOXLL08we3a7QtcaGakKJddrL69lRegK0nPyn8Pb1pslzy/B1cK15AJ/RpIkkbLlF6IftdsGMGvaFLc5s0VSXczCwsIYNWoUBw4cYP369fTvL5ezbNeu8GtMEESCLQjlnala3sh4X15jSWSqXCWkmxeYiB/xcuPBRdg5Wd6k+IRYlQvfZbVis6410eTPQp+a1g5TIxWmRqpyXTbvv0hNzcbS0rjQuK2tKZ988jyjRuW39D57NhpJkv7yQ4YkSYTGhbLm8hr23d0HgLWxNfUd69PPpx9BrkFlugOjlJtL1LtTeLhzZ96Y8/Tp2PbvV2k+WJWGzMxM5syZw2effYZWq8XBwUF0YBT+kfjtKwjlnbM5fNAMRv6eP6ZRiuS6PJAkSI+H7RPkTYqPXFbVJTcni09z+nNB8iQLOaG0MFYzq5M3tZ0taehhW2mS6uTkLDZvvsL331/k/PkHREVNwqSI1/ewYQ34+uuzODtbMGlSM55/vkaB83pJT0p2CkfuH+Fc7DkO3TtEbGZs3vmBdQfyTqN3ynxymhMTQ9IPP5CwYqU8oFLhOnMG1j16iDJ7xWzv3r2MHj2amzdvAjB06FDmzp2LvX3FXW4lFA/xG1gQyosLceBmAQ5FrPPrURtWX4CQGAjuCz7izb/Myk6TG75c+RXpdjCKzKS8U9t1TZmd058oHADwdDDnpWq22JoZ4eFgzquNqmKsVhkqcoOQJImAgJV5dasBdu68To8ePoWuVamUnDw5FNUTHzxuJt9kZehK/oj4g1wpt8A5G2MbutXsRhv3NjR2aVwyf4liIkkSD3fs5MGsWehT5H8Plb09LjM+wqpDBwNHV/H8/PPP9O7dGwBfX19WrlxJy5YtDRyVUF6UaIKtUCg6AYsAFfC1JElznzhfDVgL2Dy6ZqokSTsLPZEgVHa/Xodhe2BGc3iriA2MCgWsfxnsTEQ3xjLoQUoW16PisTi5EJ+7GzDRy3WYFUCmpOGe5MhHuYO4bdGQjo2ccbc1o46rJS29HMr8bGpJUygUdOvmzZIlp/LGvv/+YpEJNlAgub6VcouVoSvZfXs3EvKGfksjS9yt3Gnu1pzn3Z/Hx94HtbLszzVJej13B75BxqNN/qaNGmLbpy+W7Z5HKTbXlYguXbpQv359+vTpw9tvv42RkShtKvx7JfauolAoVMAyoAMQCZxWKBTbJEl6vADrdOBHSZJWKBSKusBOoHpJxSQI5Y4kQa+tEBwpH/9wDUYHygn1k4qa2RYMR5fDifOh7Ny9iwZZx2iivIabIhGAi/rq7NQFEaz3J8bcm75NqvFmVRva1HZEUwk3p0ZGPmTDhotMmtSs0OwzQP/+/gUS7JiYtL+sc52mTePH8B8JTwpn562dSEiolWp61urJwLoDqWZVrUT/LiUhNymJ+xMm5iXXjhMnYj9sqFgOUszOnj3L9OnTWb9+PXZ2dhgbG3PmzBlUqsp110goHiX5sb0JcEOSpFsACoXiB6AbBTscSIDVo6+tgagSjEcQyp89d/KTa4BrifIykAbOYqa6LJEkeZNi7FW4uR+iQ9HHXydIyiUI5PtzwEO1HYe9JpNR82X8TYx43d0GN2uTSjtLnZCQwbJlp5k37yjp6Tm0bu1BUFDVQtc1aVKFDh08CQqqSv/+/nh7OxS6RpIkTj44yYxjM7ifdh8AtVJNd6/uDPMfVqYrgfyd5F9+JXbePHRJSSjNzXGa8i62r75q6LAqlNTUVD788EMWL16MXq9n9uzZzJ8/H0Ak18JTK8kEuwpw77HjSKDpE9fMAPYqFIqxgDnQvqgnUigUw4HhANWqlb/ZB0F4ai9Uh3EN5BrXf7qVDI1cDBaS8IScLPhlBFz5teC4pCAKO2Kxp4Z/M6yb9MeqSkNeUonbzH/q2HE9Z89G5x3v3n2jyARboVCwd++Av3wevaRn9snZbArblDfW1LUp05pOo4Z1jb98XFkXt3gJ8cuXA/Ja62rffiNamxcjSZLYsmUL48eP5/79+yiVSiZMmMBHH31k6NCECsDQC8/6AmskSfpCoVA0A9YpFAo/SZL0j18kSdJXwFcgN5oxQJyCUPJ0enjy9rhCAdObyRVB5p2C4QHwah3DxCfky06FiONw9zjS+e9RpMWgV6jYp2/AxVwPDujrc0NyQ6ExZ/XARlh7FZ5xrSz+bGZW1Cz9qFGNGDbst7zj3btvMGNG2//03OuurGPL9S3cTLmJSqHiDd836O7VnerW1Z81dINK+N8aOblWqXB+dzK2/fujUBv6V3bFcefOHcaMGcOOHXJZx8aNG7Nq1SoCAwMNHJlQUZTkT+t9wP2x46qPxh43BOgEIEnScYVCYQI4ALEIQmURmQqv/AILnoPW7oXPKxQwsRH09obq1qUfnyDPUj+4CMkRcGkLhO+CR/MACiBcX4XpOW9ySpI33r3XuQ7tfJzwsDfHqJKU0nucTqfn11+v8dtv4ezde5Ndu/oTEFD4rsuAAfX48MMDREenYW1t/JcbFx93Pek6N1NuEpESwYF7B7iccDnv3MoOKwlyDSrWv0tpyr51m/Tjx0g/dpy0fXJdbsfx47F74w0DR1bx3Llzhx07dmBlZcWcOXMYMWKEWA4iFKuSTLBPA7UUCkUN5MS6D9DviWvuAu2ANQqFwgcwAeJKMCZBKFuC70HPrfLXb/0BB/uAfRGbFdVKkVyXNm063Dood1M8+12h07m2NVkbV5v9+voc1fvR3seFz32d6eTngqVJ5V4G0qPHj2zbFpZ3vG/f7SITbGNjNTNntiU5OYvhwxtibW1S5PM91D7kx7Af2XFrBzeSC3a2tNJY8YbvG3St2RUX8/K5dCo3Lo6oKVNIP1awyZBVly7YDxtqoKgqnhs3buDl5QVA27ZtWbVqFV27dsXFpXy+boSyrcQSbEmSchUKxRhgD/IWn28lSbqsUCg+Bs5IkrQNmASsVigUE5E3PA6S/ryfKAiVwc3k/K8fpMOE/fDdi0VXCRFKljYDIo5CzCW4cxRuB4Muu+A1NVpz374ZE654czo6PxncNDyIpp6i9vifGjRwKZRgv/12syKvHTas4V8+T1hiGHNPzeVS/CWydFkAWGosaezcGFsTW4Lcgmjh1gJLjWXx/gVKkT49nbtDh5EdFgYqFRZt2mD5/HOYN28uWp0Xk4SEBKZMmcK3337LoUOHaNWqFQDDhw83cGRCRVaiC7oe1bTe+cTYh499fQVoUZIxCEKZ5vvE2tzdtyE2Q+7OKJSsnCw5oU6NhsgzEPK/Jy5QQJVG4N0JandC7+jLR79dYd2RiLwrHC2NWTekCXVcrBDyDR/ekE8+OUxurryMJiQk6i/L6hUlV5/LH3f/4MOjH5KZmwlAgGMAg/0G06pKKzSqitGmOuP0aSLHT0CXmIja0ZHqP27CyLV8VjspiyRJYu3atUyePJn4+Hg0Gg1XrlzJS7AFoSSJHROCUBoycuSNik8mGHUfm/XsXANWdATzyr28oMTp9fKyjz3vQ9LtwucbD4UqjUhya8XuO3rSs3O5eDCFXRf3oNXJCWOHus6809Gb2s4WlbLEniRJ/PjjZQIDXaldu/DMvaurJb161SU9XcvEiUG0aFHtXyXXcRlxbLy2kZ/CfyI5W76707lGZ95p9A5OZk7F/vcwFH1GBtHTP+DhTnn+SePpidtnc0VyXYyuXr3KyJEjCQ4OBuC5555j+fLl1KkjNokLpUMk2IJQkiQJ5p+GFefh995Q07bgeQsNTAuCFz2htp1hYqwMJAnCdslrqe+fgfRHWz3sPMGtAWmWntzQO7Mvsw6nIlXcv5RJZFJooadxtTbhg5fr8qJ/5U2EEhMzGTNmJxs3XmLIkEC+/rprkdetXfsKGs2/2zSWnJXM9KPTOXz/MPpHm0ftTOwYUHcAQ/yGVKgPMZmXLnNv+HB0iYkojI2xe+MNHMeNFRVCitEvv/zCa6+9Rk5ODo6OjixYsID+/ftXqNeRUPYpytuS50aNGklnHnWzEoQyLSkLJh2A327Kx2tflBNpoXRd2QZHFkDUufwx62rQdATahsN4Y+05jt9KKPKhDhbGBFazwdfNiu6BVfCwr9xLd9LTtVStupDkZHk9tEaj4vbt8bi5Pf0a6BxdDmP3j+Vo1FEAWldtzVD/odR3rF+hEiJ9VhbJW7YQM+sT+QMfUGPrVky8axs4soonPj4eHx8funfvzty5c7GzE5MXwtNTKBQhkiQ1+q+PEx+ZBaEkXIqHV7dBXEb+2LVEkWCXpqQ78PMQecYawMQGmo8F3+7yzLVCweGrMQWS6y4BbliaqBnWyhMbUyNszSvGWt/iYm6uISDAmUOH5HXoWq2ORYtO8NlnHZ7q+XL1uQzcNZBLCZcAWNNpDQ2d/3rTY3mUffs2id99R8rmLUhaLQBmQUG4zZktloQUk6ioKObPn8/cuXPRaDQ4ODgQHh6Ora3tPz9YEEqISLAFoSSkZIGndcEEe8dNGOQLdkWU4ROKhyRBdChc2SrPWgMo1VCzHfT4CkxtyMrR8cOxO5yOSCI4XF4q0snXhZUDKlZi9ywkSfrL2eO+ff3yEuyAAGfeeKP+U3+fVRdW5SXXYwPHVojkWtJqyQgJIe3gQdIOHkIbkb8pVlOzJg4jhmP18ssolJWvPnpx0+l0LF++nGnTppGamoqjoyPvvfcegEiuBYMTCbYglIQWVWF7VbgQByvOyf9d2VEk1yUhJ1NeW31jnzxbnVFwucfpLvu5mG7F8Z9ucD0mlaiULLS5+c1i67hY8nE339KOuky6fTuJWbOCCQtL4PDhwUVuTOzVqy4zZhzi7beDGDu2KSYmT/dr5Jfrv7AydCUAi55bxPPVnn+m2MuCzNBQ7o0Zgy4uPm9MaWWFRatWWHftgnnLlihEM5NiERISwogRIwgJCQGgS5cu9Ov3ZKsNQTAckWALQkmq5wijA8HTRlQHKW6Jt+HYYri4GbJT8oZ1Zg7ccXiOXQnOrE6oR8qmyEIP9a9izYAgD2q7WOLrZlUpuy0+aePGiwwevJXsbB0A330XyqBBhWen7e3NiIiY8K83MD4pIyeDJeeWsP7qegCmNZ1WrpNrSa8n/egxEtevI/2QXLHCyN0dq04vYNGmDab164sNjMXo4cOHfPDBByxduhS9Xo+7uztLliyhW7duhg5NEAoQP/WC8Kz0EuTowfgvEg5/x9KNp6JLuAkxl+HHAfljboHcU3sw+HpzbmRVgcT8mVcTIyU9G1TFzcaUBtVsCXC3xkwj3vqetH379bzkGuC99/bRq1ddLCwKr0N/2uT6j4g/mHd6HtHp0QAM9htMnzp9ni5gA9Onp5O8dStJ679He+sWAApjY6x7dMf5vfdQasT6/ZKwfft2Fi9ejEqlYtKkScyYMQMLCwtDhyUIhYjfMoLwLBaHwKzjcLgv1BGd/EpUzBXYPwvC8ntX6S3d2FFvMTse2LH78oO8cVMjFYNaVKdbfTe8nS0rVDWKkvLFFx3ZsSOclBS5e6WRkZKQkCjatKn+zM+9584evrn4DVcTrwJQx64OM5rNwNeh/C3NkSSJlF+3EjtvHrqkJADULi7Y9uuHTe9eqMXa32KXlpaWl0T37duXU6dOMXjwYAICAgwcmSD8NVGmTxCe1sarMG5f/nEDZ+hRG16vK5aDFJeU+xAfDhc2QegPgESu0oTrxr5cUfswLy6IGPJLcL3TsTZvPeclEuqntHLlGcaO3cXSpZ0ZOrQBqmdYOnM75TYbr20kODKY+2n3ATBTmzGuwThe834NtbL8ze9IWi33xowhPfgwAKYBAdgNHoRlu3YojMTPfHHTarXMnz+f+fPnc+rUKby8vAwdklAJiTJ9glCa9BI8munLczZG/jPYzzAxVRTadDiyUG4ME3Mpb1hSGrHLuDMfJnUmPsO6wEN6NaxK/6bVCKwmZg//SkZnGxpJAAAgAElEQVRGDhs3XuThw2wmTmxW5DXDhzckMNCFpk2rPtP3epD+gP47+pOakwqASqFiYN2BDK03FCtN+WwrL+XkcH/KFNKDD6MwMsJl1sdYd+smPsyVkODgYEaOHMnVq/Jdj61btzJp0iQDRyUI/55IsAXhaSgVMLI+xKTD0scamBzrD0+5PrXSysmEO0fg3km4dwruh4A2Le90uFEdQnKqsyyzE5EZcrvs9zrXwcfVCnNjFV5OllibitnDv6LT6Vm48ATz5h0lLi4DpVJBu3ae1KvnXOhapVLxVMm1JEkcjz7OudhzPEh/wPZb28nV5xLoFMikRpPwtPbEUvP0zWgMSdLrSdmyhdj5X6BLltu3O4wdi80rrxg4soopPj6eyZMns2bNGgBq1arFihUraNeunWEDE4T/SCwREYRnoZfAazX094FZrQwdTfmi10Pw53BiGWSlFDiV7RTAuOjOnMnxIAF5ttrGzIh6VW348GUfvJzKZ7JmCDExafj5rSA+Pr8me8uW1QgOHlQss69xGXFMDp5MSExIgfEa1jX49oVvcTB1eObvUZp0aWlkXbhAxvnzZJ47T8bp00hZcudKlEqcJk/GbuAAUW6vBOzevZv+/fuTmJiIsbEx77//Pu+++y4mJiaGDk2oxMQSEUEwhBw9XBoMZmIG9W/p9RB9Tp6pjg6V11U/uJh/3tIV/HuDexN0bo14feNtTuck0aqWA2+2rIGPixXOVsbidvxTcHa2YO3aV3jppQ15Y0eO3OXYsXu0aFHtqZ9XkiQO3z/Mh0c/JCErATsTO7rV7IarhSvVraoT5BpULv5/6TMzebhzF5kXLpB5/jzZ16/Lr9fHqJ2dMWvUCLfP5oqSeyWoRo0apKWl0a5dO1asWEGtWrUMHZIgPDXxTiEI/0Z4IlSzgiebahirADGTVUhGIlz5Va78ce9EwWT6SX49kV76ggMRWradj+LXtWcBcLQ0ZknfQGzMRLmzf6LT6dmy5SqdO9cqsqzeiy/WYsKEpnzzzTlGjWrEG2/Up27dpy8fmZmbyZTgKRy4dwCAJi5NmNd6Hvam5aeSji45mbTgYOKWLSMn4m7+CbUaEz8/TOsHYBYYiGn9+qKleQnJyMhg/fr1DBs2DIVCgbe3N2fOnMHPz69cfDgThL8jEmxB+DuSBOuvwLTD0Ks2LCi/DTFKRVwY/P6h3FVRn1P4fODrUK0ZOHiDgxeSiQ2L991g85Lz3E3MKHDp+Ha1RHL9D+LjM/jhh0ssXXqKsLAEFizo+JcbGOfObc+UKS1xcXm2msHJWcmM2T+G0LhQ1Ao1o+qP4k2/N8tNVZCc6GgS135H8o8/os+QX3NqN1dsX+uDWaOGmPj6ohRLEkrcjh07GDNmDHfu3MHCwiKvC6O/v7+BIxOE4lE+3hEFwRCydfDmLth7Rz5edwUCnWFA+avdW6KS7kDsVTi2BCKO5o/XbAde7cHKDSycwD0IlPll37S5ep777AD3kzPzxlrXdsTN2oSeDavSuLodwt+LjU1n7Nhdecfz5x9n9OjGGBsXfms3NlY/U3Kdpk3j1xu/su7KOqLSo3Axd2FV+1V42ng+9XOWtuzr17nVpWvescbTE5uePbF57TVUFuYGjKzyiIyMZPz48WzZsgWAgIAAUX5PqJBEgi0If0WSwOOJkmJTD0Fr98LjlZEuF77rBhFHCo7XfQXavgdOdQo9RJIk9lyO4bfQKI7djCcpQ57lblDNhmX9G+BqbVoakVcYdeo4YG1tnNccJioqle++C2XYsIbF8vy5+lwuxV9i3ZV17Lu7D50kd3r0svFiRfsVuJi7FMv3KWmSTkfMp5+StGEjAMbe3rh+MgsTsRSh1OTm5rJs2TKmT59OWloa5ubmzJo1i7Fjx6IW69qFCki8qgXhr5ioYWwDWHsJtHrQKGFeW5Fcg1yr+tdR+cm1kRkEjYagUWAuV41Izcrh9J1ETt5OJFOr415iBidvJ5KhzW/HXd3ejNk9/Gles3xVmiht6elazM0LL5f5s6ze3r03UakUvP56PVq39nim73Uu9hxfhnzJ1cSrZOZmFjjn7+DP89Wep2+dvpgblf0ZXyk3l7jFS0hYvVr+wAxYtGtHlc/noTQzM3B0lctXX33FhAkTAOjevTuLFi3C3d3dwFEJQskRCbYg/B1XC+jvC2cfwPIOULuSLlvISJSXgcRdg7Nr5UogABoLGPALuDcpcPnmkEgm/RRa5FOZa1S0rOVAy1qO9GtSDZVSzCAWJSkpk127brBhw0Xu3Enm4sVRRc62Dh5cnzZtPOjf3x8PD5tn+p5XE64ydM9QtHotAEqFEmuNNc3cmjEqYBTVras/0/OXpozTp4maNp2cu/kbGKsuW4qlqKdcaiRJynvNDhkyhK1btzJmzBi6dOli4MgEoeSJBFsQ/qSX5AYyT5oWJM9mG1fSaiEha2DHO0VvWuzzfV5y/SAli1txaZy7l8zne8LyLhn7vBf25hpMjFQ4W5nQprYjSpFU/62ffrpMnz6b0evz+xScPHmfoKDCTWD69Cm+zqFLzy9Fq9fSwKkBC9ouwM7ErlwuoXi4dy9R70xG0mpROThgP3gQVl26YOTkZOjQKgVJkti0aRNz5szhwIED2NnZYWxszJ49ewwdmiCUGpFgC4JeggE7wNms6Coh1salH1NZIElwciXsniofu/iDYx2o0RpqdwYLucxbSmYOyw7c4OvDt3gsH6RpDTu+7FNfrKt+Cl27euPv70RoaEze2Ndfny0ywS4uF+IuEBwZjKnalIXPLcTOpPzdrdGlpBC3aFHeWmvr7t1xmfERSuNK+jNsADdu3OCtt95i7969AKxatYr33nvPwFEJQukTCbZQuWXroNdWOBElz1C/3wwcKnFCmKuFO8Fymb07R+DBBXn8hTnQbDQgz07djEtjX8hN9l2LJSQiCd2jzLqanRkNPWzpUNeZF3xdxPKPp2RsrGbjxp40bPgVmZm5AFy9Gl/glntxWx66HIB+dfqVy+Q649w57k+aRG5UNKjVOI4Zg/2I4eVyBr48ys7OZt68eXz66adkZ2dja2vLvHnzePPNNw0dmiAYhEiwhcpLkmDifjm5BjnZ/t9FGB0I5pWwM2NWCnz3CkSdLTjefRUE9EGSJPZfi+Xj7VeISMivWa1SKmhaw46X67nyepCHSGj+pbi4dNasOc+rr/oWuXbax8eRxYs7c/16At27+9CkSZUS+be9HH+Zfjv7oZf0mKnNGOQ7qNi/R0nSp6cTt3gJiWvXAmDk7o7b7E8xa9zYwJFVHseOHePNN98kLExeGjZw4EA+//xznMSSHKESEwm2UHll6yA5q+DYvFPQqQb4P32Xu3IpLQ429pGTa6sq4NOFZGNXzlg8z8HbCi4cPsLtuHRSs+XZVFMjFZ6O5oxqW5NWtRyxNq2EH0ieUnR0KgsWHGf58jNkZOSQkpLNJ58U3cBo6NAGJRqLJEm8f+R99JLcGnxM4BhsTJ5to2RpkCSJrEuXSP7xJ1K2bUPKlssU2r3xBg5vjUZlJSr9lCatVktYWBje3t6sWLGC5557ztAhCYLBKSRJ+uerypBGjRpJZ86cMXQYQkWhl2DuSVh4BmpYw+oXIKCSzLpEhsCpryA5Au4el8esqpL02q90XBNBXGp2oYe4WJkwtFUNBjTzwFhdSTd9PqMffrhE376b845dXS2IiJiAkVHp/3vuu7uPCQfk0mn/e+F/NHJpVOox/Fepf/xB3JKlZIflb6Q1DQzEYdRILFq3NmBklYder+fw4cO0adMmb2zr1q106tQJY7HeXahgFApFiCRJ//nNUcxgC5WbUgHvB0E9R/Cxg5q2ho6oZOly5FJ7e6bB7UMFz7k3JbXTIl7f/CAvubYxM6J/02q0qOmAt4slduYasQTkGXXvXgdbWxOSkuS7J9HRaezYcZ1XXincmKck3X14Ny+5HuQ7qMwn15IkEb9sOfFLl+aN2Q0ahE2vnhiLToCl5sKFC4wcOZLjx48THBxMq1atAOjWrZuBIxOEskUk2ELlkaMDPUWX23u5ZqmHU6okCfZ/IlcF0abJYyoN2HlCpzncV7kzflccZ5bcAuQGMD+OaIaTlYkBg66YjI3V9O3rx/LlZ7C3N+Xdd1vQokXpNNy4knCFH679wLXEa1xLvAaAp7Unb/qV7Y1o2Tdu8GDWJ2ScPAmAzauv4jhxAmrbCv6BuAxJT09n5syZLFiwAJ1Oh4uLC2lpaYYOSxDKLJFgC5XDjKNwMxnWvmjoSEqXLhcOfQbXdkDsZXnMwoWM6u35LOdVQuKVXFr9EAjPe4iRSsH6oU1Fcv2U9HqJXbuuY2yspn17zyKvGT68ITVq2DJyZCMsLAp3aCxuUWlRLDm3hO23tueNqZVqWri1YHar2Vhpyt6aZX12NllXrpC6Zy9JmzYhZWaisrbG+cMPsH7pJUOHV6n89ttvjBkzhrt376JQKHjrrbf45JNPsLEp++v1BcFQRIItVGyJmdB3O5x9VE944n5Y8ByolIaNq6TFhcGp1XB9DyTLnez0ajPON/mcHdoGfHPkNlBw9qlJdTuCatozuHl1bItoyy38s0OH7vDWWzu5fDkOGxsTjh59k7p1C2+YDQhwISDApURj0eq0bL+1nW8vfUvEwwgANEoNnWp0opFzIzrV6ISpumyWpEw/dYr7kyahi4vPG7N84QVcZ85AJZK6UrV06VLGjh0LQGBgIKtWraKxqNAiCP9IbHIUKi5Jgi5b4GR0wXF/R/jllYrbQObGH7C+Z95hlpEtCzM6sV7XnnTyEypTIxWvB1Wja0AVajlbYGKATXYVTfv237Fv3+28Yw8Pa06cGIqLi0WpxhGZGsnbB9/mauJVAMzUZrSu2ppxDcbhblk6y1GeVtrRo9wbMhQAlbU1Vl27YtW5E6aBgWL9vwHEx8fTuHFjJkyYwFtvvYVaLeblhMpFbHIUhCcpFPBeU/jsFByPyh+/GAeWFXCGNvYa7PsYwnY8GlCg7fsTHX/WclcHng7mOFkZU8PBnC713Gju5WDQcCuiOXPa0aTJ13nHd++msH79Bd55p3mpxRAcGczUw1NJ1aZSxaIKo+uP5sUaL6JWlt23e0mnI/3IEZJ/3kzq778DoDAywutwMEpNBfxZLcNOnjzJggULWLduHRqNBgcHB8LDwzEyEqU4BeG/KLvvuIJQHFpUhW1V4UgkDNkNbwXCuIaGjqr4pcfLs9YPI+XjJiN46DeAL0NV3E29jbezJbsntBIzgCWsceMq9Ozpw+bNV3nxxVrMmvUcDRq4lvj3zdXnsvfOXk7HnObn8J8BaFu1LZ+2+rRMrK+WJAn9w4fkREfn/cmNjiYnKpqcBw/IDAkpcL3t66/jNOltkVyXoqSkJN5//31WrVqFJEk0a9aMCRPkKjMiuRaE/04k2ELl0NgVLg4GTQVaBqHNgHPr4PQ3kB4LmUlgVxP6bmTnAysmfnWe7Fy5gcjw1p4iuS4mu3ZdJzMzlx49fIo8P3due95+uxnNm5f8Uox7qfe4knCFby5+k7ccRKlQMjZwLG/6vYlSYZi9BrrUVOJXriQ7/Do50VHkRkWjz8j4x8c5vv021l27YORSsuvThXySJLFx40YmTpxIbGwsarWayZMnM3z4cEOHJgjlmkiwhcqhqNJ85ZFeD6dXw9Xf4N5J0GnzT9nX4mDTr/n9sJYfTp9FksDJ0ph2Pk50D6xiwKArhpCQKD766CA7dlzH2dmc9u09sbIqvI7fy8sOLy+7Eo9n562dTDk8Je/YTG1GiyotGBkwktq2tUv8+/8VSZK4P2Ei6UePFhhXmJlh5OaKkasbRq6uGLm5onZxkY/dXDFydUUh1veWqvDwcEaPHs2+ffsAaNmyJStXrsTX19fAkQlC+SfezYSKJz4TbI0rVqUQvR7iw+HAJ3JyDYAC3AKh4SA2R1oz+bga/Zb7eQ95tVFVPutZT8xcFwOdTk+PHj9y924KADEx6cyZc5g5c9qXeiwx6TF8e+lbNlzbAICHlQetqrTiVe9XqWFdo9TjeZw2MpJ7I0eivXETgCpffommugdGrq4orazEa7GMOXHiBPv27cPOzo7PP/+cQYMGoVRWoPdNQTAgkWALFcu6y2BuBD0MN4NXbPQ6CPkfXNkKd46AJC/3QGMJL83ngXMrTkTD/muxbAvN38Tp42rFx918aVy95GdRKwuVSsm4cU14553f88YWLTrJ5MktsLMrnVJ3d1LusOHaBjaHb0arl+9c+Nn7sabzGoxVhq+Ik5uYyM32HQB5g6LjpLex6vSCgaMSnnT37l2qVasGwIABA4iOjmbIkCE4OIhNz4JQnESZPqHiGLUXfn7UMOXlmjC3NTibGzamp5UUAT8PhvsFN3/h2wPavMuG2+Z8uPUSufr8n98Adxt+HtkMo4o0c1+Kbt9OYu3aUKZMaYGpaeFNXSkpWbi7LyQ1VUuLFu4sXty5VDYw6vQ6BuwawMX4i3ljHT06MiJghEGXgjwu4/RpIie+jS4+HoVGg+dv29B4eBg6LOExMTExvP3222zevJmLFy9Sq1YtQ4ckCOWCKNMnVG77I/KTa4DtN+U/YUOglGYYi0XMZblBzOUtkJUClq5QpSG0mECKfT0W7bvJ+kV30Ork2exWtRyo62ZFx7ouNPQQbaOfRlqaliVLTjJz5iGys3X4+TnRq1fdQtdZW5swf35Hqla1onNnrxJf7pCVm8W52HMsDFmYt4GxgVMDpgVNKzOJNUDq/v1Ejn4LAE316lRbuwYjZ2cDRyX8Sa/X89VXXzF16lRSUlIwMTHh3LlzIsEWhBImEmyhYnAxh9q2EJ6UP2ZrXH6S6/jr8NsEiDiSP1a7E3RfybH7Ovaei2FzyEFSs3PzTn/czZeBzaqXfqwVSFZWLvPmHWXWrOC8se+/v1hkgg1yi/OSpJf0HI86zprLazgRfaLAucXPLea5as+V6Pf/L/TZ2dyf+DZp+/cDYNm5Ey4ffIDaTixNKitCQ0MZOXIkJ07Ir6XOnTuzdOlSPD09DRyZIFR8YomIUHFk5MD0w7DuCkxoCO8Hyc1myiJJgvDdEHkaYq5A+C553Mgc/HpAw8Gk2Pqz8cw95u66lvewZp72vNHcg7beTqLzYjGJj8+gSpUFaLU6ADQaFQ8eTMLWtnQ/nIXGhTLp4CRiMmIAUClUVLGoQjevbvSu3Rtbk7J1h+L+O5N5uH07APajRuI4bpzYxFiGfP3114wcORKdToebmxuLFi2iZ8+e4v+RIPxHYomIIJgZwZw20LEGdDJsNYW/9DAKrmyDM9/IVUEek6ayYZ7X9zzMNCdhj5bD1/M31NV1tWLaSz60EN0Xn4okSYSHJ+DtXfjfz8HBjO7d67Bp02VUKgVTp7bA2Lj03hrvp91n07VNbLy2kSxdFpYaS3rV6sUQ/yFYG1uXWhz/Reoff/Bw924Aqq1Zg3lQUwNHJDypVatWaDQahg0bxqxZs7CyMnzDIUGoTESCLVQsxqqyl1zr9RB3FY58CRd/zBtONXLgsOnzBCdYcV7vxTWpGpxLAVLyrlEpFXw1oCHtfMSa1qchSRIHD95h5sxDnDx5n5SUqWiKaDY0bFgDEhMz+fzzDgQElF6Tk+DIYD44+gGJWYkAdPDowGetP8NIWTY752WFhxPzyadknDoFgP3QISK5LiMiIiL49ttvmTFjBgqFAm9vb+7cuYOTk5OhQxOESkkk2EL5lKODsr5E4n4IHFsCtw9DRnze8AOz2nyc3Im9WY3ITZV/BGs7W9DUTEMLLweq2JiiUSuxt9DQzNNe3NJ9SqGhDxg9eifHjt0rMNa4ceGmO+3aedKuXemtS03ITODTk5/ye4R8l8LX3pdh/sNo494GtbJsvi1nnj/PvVGj0SUlobSwwG7gQBzGvGXosCq9nJwcFi5cyMyZM8nIyMDb25t+/foBiORaEAyobL6TC8LfOREF0w7DN52gehm8hZ4SCb+OhohjoM+RxyzdoEZrsv360HZdFll6PY08bOla3432Ps642ZSTzZjlyOnTUZw4EVlg7Nixe0Um2KVFkiR239nN7JOzSc5OxkxtRn+f/gz2G4ylxtJgcf2TtMNHiBw9GiknB42nJ9U3/YDKsuzGW1kcO3aMESNGcOnSJQBee+012rZta9igBEEARIItlDeJmdBli/x1u02wtD10LgM74iUJIs9A8OdwfU/+eEBfaD2ZDItqrD1+l8++lTcsmhqp2Dg8SNSsLkFDhzbA2dmcPn02k5Ehf9CJj88wWDx6Sc/Hxz9m8/XNADR1bcrM5jOpYlE229hLkkTWpcuk7t1L4nffIeXkYNmxI27zPkNpYmLo8Cq1xMREpk6dyurVqwHw9PRk2bJldOrUycCRCYLwJ5FgC+XL+P35Xz/UwsCd8P3L0LG6wULi+DI4shDS4+RjpRF4d0bbcCh/ZNbm5JEEfj1/gJTMnLyHLOsfKJLrYiJJEunpOVhYaAqd69LFm/37BzJ79hGmTGlB8+buBogQjt0/xvLQ5YTGhaJAwfSg6fSu3btMLv+RtFpS9+0j9ssvyYm4mzdu0+c1XD76qEzGXNl89dVXrF69GiMjI6ZMmcL777+Pqam4CyYIZYko0yeUL8fuQ5/fIPNRPWh3SwgZaLhyfOfWw1Z5HapkZk9crdfYbt6DY9Fw5EY8WTn6vEtrOJjzcj1Xhrb0xNqsbG5iK08SEjKYM+cImzdfpVEjN376qXeR10mSZNCk8Jfrv/DRsY+QkDBVm7Ks3TIauzQ2WDx/J+PsWSJHv4UuORkApZkZ1q+8gmWnFzBr3Fgk1waUmZmZl0RnZ2czcuRIpkyZQp06dQwcmSBUbE9bpk8k2EL5sz8C+m0HnQTXh4KNAW5X3zkqz1yH7QDgmkUQvVMnkpqtK3CZo6UxHeo607thVeq724gEpZgcPhxBr14/ERubDoCxsYoHD97BxhCvhb/xY9iPzDoxC4A+3n140+9NXC1Kvr3608gICSGi/+t5x9Y9e+A8dapYa21gmZmZzJ49m//9739cuHABO9HIRxBKlaiDLVQez3vAio5Qx670kmtJkutWH5oHN/6ArOS8U1GOLeh6bzhadFSxMaV1bUcaedjSpIYdVW1NRVJdAry87Mh+rKtldraOn366zLBhJdtp8d/SS3oWnFnA2itrAZjUcBKD/AYZNqi/kJuURNzCL0neIu9tMA0IoOqypagdRM11Q9u7dy+jR4/m5s2bAGzfvp2BAwcaOCpBEP4NkWAL5VP3WqXzfbJSYPtEuHUQMhLyx9WmUOclIgLG03ldFFp0zHrFj9ebVhMJdSlwdbXks8/aM3KkfAdBpVIQEZHyD48qHdcSr/HpiU85H3cegKlNptLfp7+Boypa9vXrRM+YSWZICABGbm5UW/cdSk3h9exC6YmOjmbixIls2rQJAD8/P1auXEmLFi0MHJkgCP+WSLAFoSjxN+DgHAjbBTnyMgTMHKDm8+DdCep2J1eCLrN+J0Oro72PMwOCPAwbcyUzbFhDfv75KkFBVZg0qblBl4ckZiVy6sEpTkSdyKsSArCq/SqaV2lusLj+Ss6DB8Qt/JKUbdtAklAYG+MyYwbWXV5GoRa/Fgxp06ZNDB8+nIcPH2JqasqMGTOYOHEiRkZi34YglCfinVQou945AAFO8mx1ERUiSkxmMnzVFrSp8rGzH7yyHJz9QSlX/ohISOeVZUd5mJWLiZGSGV3rll58lUR4eALz5h1l2LAGNG1atdB5pVLBnj2vo1Qa7o5Brj6XTWGbmHtqboFxtULN4ucXl8nk+uHvvxM1+V2krCwURkZY9+yB/dBhaKqWzXKBlY2zszMPHz7kpZdeYunSpVSvXt3QIQmC8BREgi2UTcH3YO1l4DJ8eAR6ecOc1qAuhdJ2l37OT65f3wyez+cl1vcSM/hkxxUOhsWRnStXCJnd3Z+qtmYlH1clIUkSixadZMqUP9BqdVy/nsjBg28UufTGkMl1jj6HoXuGcjb2bN7YuMBxtKzSEm87b5SKsleG8eHuPUS9+y6SVot5m9a4TJ+Oxt0wpQsF2cOHD9mxYwd9+/YFoG3btpw+fZqGDRuK5WaCUI6JBFsoeyQJZp/IP07LgdspJZ9c6/VwYjnsmykft30fvNoDEJ2SybIDN1h/Ir8ucEsvBxb1qY+9hXHJxlXJrF0bysSJ+c16goMj2LHjOi+/XNuAURWUrctm7L6xnI09iwIF/Xz6MaLeCGxNbA0d2l+KW7aM+CVLATBv1Qr3lStFAmdAkiSxefNmxo8fT1RUFFWrVqVVq1YANGr0nwsWCIJQxogEWyh7HmphXEMYtBP+rCI5qn7JfT9dLiTdlpvFnP9eHvNoSXjNN1i04Sw7LkTnXapQwEv+rvRqWJU2tR1FglIC+vXzZ9WqkAJtzlevPltmEuwcXQ6fnfqM49HHsTW2ZVm7Zfg7+hs6rL8kSRJphw4Rv2w5ANY9euD68Uzx2jWg27dvM2bMGHbu3AlA48aNsba2NnBUgiAUJ5FgC2WPtTG86AkfNoeZx6ChM7QrgQ2Euhy4shU2D8kfU2nI6bKU5XH1WbbqHNrc/EYxTWvY8ckrftRyFnWBS5JGo+Knn3rTsOFXZGfnsmhRJ/r1M3wCm56TzsZrG9lyfQv3Uu8BML/N/DKbXGecOUPcl4vIunIFfYbcIt66V09cZ80SybWBaLVaFixYwMcff0xmZibW1tbMmTOH4cOHo1KpDB2eIAjFSCTYQtn1ViDcS4Vh9YrvOXO1EL4bbh+Cq9sh7UHeqSQbP1aZDmHlDxbADQA61HWmT2N3Gteww8pE7OIvLrm5en766TK9e/uiLmLpT9WqVmzZ8ipOTubUqmVvgAjz3Uy+ybzT8zgWdSxvzMnMielNp9PEtYkBIyuapNUS9f40Hm7fnjemsrHBps9rOI4fL5JrA/r444/59NNPAejbty8LFizAxcXFwFEJgpy4A1sAACAASURBVFASRIIt/J+9+46K6mjjOP69LE2KIGAX7N1YsfeW5NVoEnuviQV7i8YSE0vsvQRrNDFRY4sae4ldo9h7wwKISBPpsOy8f1yDGhFRgbsL8znH497Zu8uDhf3t7NxnjJeiwLS6qfd8JxfBKU8Ie7GOWp+tCN75W7HkaVU23Yh+5fTprcrSxl1eAJaa/Pye8dNPXqxadQE/v3AePAhj1KhaSZ5bs6ZbOlf3Ot9wX77e+zWB0YEAFHQoSJ+yfajvVp8s5lk0ru51Qq/Hd8BAIg4fRrGywqlHd7K1a49Fzhxal5ZpCSES39QMHjyYAwcOMGHCBBo3bqxxZZIkpSW5VbqkvWO+UC1P2l7E6H8JlqgXEAlLO7yLdGP0ldz8E1cAUF/8rMzN6FWnEA1L5qSgsy0ONnLGOrUNGbKbuXP/STy2tNRx9mwvypQxvgAYFB1E111deRj+kHLZyzG19lTy2b/eLtBYJISF4TtwEFH//ANmZrit+hnbKsY3w55ZCCFYvXo1K1euZP/+/VjKzXskySTJrdIl0zTuKHhehG5lYHpdddY6Lfz9IwAhLu64+w7GcO5FmC+c3Zb6xXPQs3ZBcjsY36xkRjJhQn02bbqOj88zAOLiEujffyeHDnXTtrDnhBB4h3lz/9l9PC968jD8ISWdSuLZyBM7Szuty3sjfXAwt+vVh/h4dC4u5J44QYZrDV2/fp0+ffpw5MgRANauXUvXrl01rkqSpPQkA7aknRN+argGWHUF3LLCgIqp/mUCrh0j561dxCjWNPbtiQE1XLev4kq7ym6Uc3VM9a+Z2cXE6LG2fv3Hi729FUuWfEaTJr9ja2tBhw4fMX58Ki4D+gAx+hgG/T3olbXWBbIW4KdGPxltuBYJCYTv3cvjSZMhPh4LNzfcli3FMr/cVVQLUVFRTJ48mRkzZhAfH0/27NmZPXs2HTt21Lo0SZLSmQzYkjai4uFWKNR1hcNqRwYmnIA2xSGn7Qc/fVBELH+e92PW3lssYRI5dbAi/mOe6RwZUr8ofesVxjI9Nq3JZK5cecIPPxzm/v2nnD79VZIX1P3vf0XZsaMDtWu7YW9vHD3Eo+KjGHZ4GCcencDe0p7y2ctT2LEwnUt1xjmLthdZvokhOpr7HTsSe+06ABZ58+Lq+ZMM1xrZv38/vXr14t69ewD07t2bKVOmkC2b8fZGlyQp7ciALWkji7m6LMQ/4kXA/qLoB4frA9cD6Ln6xRr9qsp16lhdJlKxQakxkH1VSlHA5cMDvPSqx48j6Nx5C/v3eyeObd16ky++KJHk+U2aFE2v0pJ19+ldJpyckLgbo6OVIz9/8jNFshXRuLLkGaKjCZgyVQ3XZmbkGDGCbG1aY2Yr/21r5dGjR9y7d4+PPvqIJUuWUL16da1LkiRJQzJgS9r4d2azUk7198q5YEHDD3rKI7cCXwnXtfKa4RnzC0SCbd3BeNSr/EHPL72Zs3MWQkJe7cLy3Xd/07x5cU23M3+TI75HWHtjLaf8T6E36DFXzCngUIDJtSYbfbgWQvCwR0+iz58HwHXZUuxq1tS4qswnISGBc+fOUbmy+nOlc+fO6HQ62rRpg4WFvEBakjK7FH9GriiKTVoWImVSFXNB949gy5eQxJrdlPAJiWL+gdt0WXk6cWxbGyfWhHbCLvIB2OWCan1Tq2IpCRYWOn755QusrF5slmFubkZAQISGVSVt7/299D/Qn2N+x9Ab9LQu1poj7Y6w5fMtlHIupXV5yYrz8eHely2IPn8eM1tb8i1eLMO1Bry8vKhatSq1a9fmzh21Z76iKHTs2FGGa0mSgBTMYCuKUgNYDtgBboqilAN6CyE80ro4KRNwyaJ2D3lHQghO3wthxMZLPAyJShzPSgQHGvqRffskEAnqYNftYJ01tSqW3qB06Rz8+GNDNm26zrBh1fnyyxJGs6mJf4Q/yy8v51rwNa4EXwGgvmt9hrsPxy2r9v22UyL+8WMejRxF7I0bmGXNSt4Z07GraxwXiGYWYWFhjB07lkWLFiGEwNXVlcePH1OkiHF/6iFJUvp7ax9sRVH+AVoB24QQFZ6PXRFClEmH+l4j+2CbsKh4dWlIlvdfmRSfYODMvRBWHr/H/utPEsfrmV1gTLb9FIm6iPJvsAYYchUcjLd3sSmKjdVjZZX03+G/P0+MJVgbhIEd3jsYc2wMArU2K50VAyoMoEupLkZT55sYYmN5+scGnu3ZTbTXWQDM7OwotH0bFrlza1xd5iGEYOPGjQwaNAh/f390Oh1Dhgxh/Pjx2NkZZ4cZSZJSR5r2wRZC+PznhSjhTedKUpLiE6DVVljd5L0Dttf9EHr/epbgyLjEsZpFnBlo9zdVb0yHSEDRgVt1KPYpVOkFlnJlU2qJjIxj8eIzzJ59iuPHe1Co0OvdEYwlsN4Pu8+u+7vYfnc7PuHqRbRVc1WlYf6GNC3UlKyWxv2JhhCC8H37CFq4iNhbtwBQLCywrVGDHN+MkOE6nX333XdMmjQJgGrVquHp6Um5cuU0rkqSJGOWkqTj83yZiFAUxQIYBFxP27KkDMUgoMF6uBECZVdB1dzQOD/0KgcWumQfmmAQ3AuKYP/1J8zZd4tYvQFQe1j3ruJMgfMz4OzP6sl1R0HV3mDjlMbfUOZz8eJjWrT4A2/vUAA8PHawa1dHownUoIbSP+/8yZrra7gVeuuV+/qV70fvsr2Nqt7kPP7hB56uWw+AzsmJHCNGYN+4ETo5W6qJTp06sWzZMiZMmMBXX32FmZls8SlJUvJSErD7APOAvIAfsBeQ66+llBt1RA3XAHoDHPcDn2fgUeGNDwmJjGP5UW9WHLuXGKoBstlYsL59fooF7oU1MyHmqXpHhc5Q/9u0/C4ytfh4Q2K4Btiz5y7r11+lXTtNVool6ZdrvzDTayYA9hb21HerT0O3htTIUwNrc2uNq0sZIQRBCxaq4drcHJdevXDu2UO230tnhw8fZt26dSxevBhFUShevDgPHjzAyso4+rZLkmT8UhKwiwshXtmGSlGUmsDxtClJynCKOIKjFTyNVY+tdWrXkCRmEw0GwfANF9l+6RHxCeqaWRc7K+oUc6FuUWeaPJiBxW+rXzzAtRo0Gg/5a6THd5JpubvnoUmTouzceRsAe3tLoqPjNa5KFZsQy977e5l7di4Ao6qMonWx1ljqLDWu7N08272HoEULib2tdqXINXYs2dq11biqzCUwMJARI0awerX6M6ZRo0a0bNkSQIZrSZLeSUoC9gLgv/tXJzUmSUnrVQ7aloD5Z2H+OdjeQt0W/T+CImKZvOM6W877AVClgBNf1S7Ix6VzQUQg7BwO1/4EFMhdDmoMgNJfglnyy0yk1DFuXB127rxN377uTJhQHxcX7de3Tzs9jTXX1yQetynWho4lTW9b6tANG3g87jsAdM7O5BgyGMdWrTSuKvMwGAz8/PPPfPPNN4SEhGBlZcXo0aP57LPPtC5NkiQT9caArShKdaAGkF1RlKEv3ZUVkIlGejcOVjCqKnQpA/lfhGshBNsv+fPXxUfsvRaQOP5Z2dwsbF8B7uyHjaPhyib1DsUMOvwBRRun93eQKVy/HkiJEi5JrlWuVi0fx4/3oEYNVw0qe1VwdDArr6xMDNfFsxWncf7GdCvTTdvC3oM+NJTAufMAyOJeifwrV6JYmtbsuym7cuUKffv25dixY4A6a7148WKKFjWO3UYlSTJNyc1gW6L2vjYH7F8af4batu+tFEX5FHX9tg5YLoSYmsQ5bYDvAQFcFEJ0SFHlkumx0L0Srq89esbXv3jh9/TVHQB/7V6JWr7LYE5neOb7fFSBIg2h/mjIWykdi84cwsNjmTjxCLNmnWTFiuZ061Y+yfOMIVyHx4XTcWdH/CLUTzq+qfwNnUt11riqdyf0egJ+nELYtm0YIiKwLl2a/KtWoZjLDXbT0+bNmzl27Bg5c+Zkzpw5tGvXzmQuhpUkyXi98Se5EOIwcFhRlFVCiAfv+sSKouiARUBjwBc4oyjKNiHEtZfOKQp8C9QUQoQqipLjnb8DybgIAVF6sE1+N7OAZzG0WXKSiFg9ADaWOma0KEUTi3Mo68uA4fn6XgsbdRfGSt3BUftwlxGdOeNHkya/ExSkbtgzcOAu6tbNT8GCr7fhMwZTT0/FL8KPvHZ5+bbKt9TJV0frkt6ZIToa3379iDxxEgCL/G7kW7hAhut08vjxY3LlygXAyJEj0ev1DB06FEdHR40rkyQpo0jJT/MoRVFmAKWBxEvxhRAN3vK4KsAdIYQ3gKIo64DPgWsvnfM1sEgIEfr8OZ+89iySaZl0Ehadh42fQ62kN3gRQjBpx3UiYvUUz2nPvPblKWEfBwsrQ3TIixML1IYu20C2xEpTpUplR6d7MWMXHh5Ht25bOXSoq1HN5IXFhrHw/EK23d2Gtc6anxr9REGHglqX9c70oaHcb9eO+AcPAcgzayZZP/0URSdX3qU1X19fBg0axNGjR7lx4wZOTk5YWVkxYcIErUuTJCmDSUly+Q24ARQEfgDuA2dS8Li8gM9Lx77Px15WDCimKMpxRVFOPV9S8hpFUXopiuKlKIpXYGBgCr60pIlDD9WLGBMEfPknjD0K0frEu3df8Wf81iu09jzJ9ouPsLYwY3lXd0rktIczy1+E68INoPtu6PaXDNfpwNbWkjFjaice58uXldGjaxlVuL4afJU229uw7uY6FBRGVRllUuE6PuAJoWvX8rDnV9yuXoP4Bw8xy5qVgps34dC0qQzXaUyv1zN37lxKlizJ5s2biYqK4uzZs1qXJUlSBpaSGWxnIcQKRVEGvbRsJCUBO6VfvyhQD8gHHFEU5SMhxNOXTxJCLAWWgrpVeip9bSk1RcTBwAOvji25CF3L8MzVjrLf733lrqzW5kz6ogyuEZfgzx/g4Qn1jsYToOagdCpa+levXpVYsuQsLVqU5JtvamJnZzwX2W25vYVJpyYRZ4ijlHMpvqv2HaVdSmtdVoo93fInj7//HhH7vE2lmRk27u64ePTFulQpbYvLBE6fPk2fPn04f/48AC1atGDu3Lm4usolZ5IkpZ2UBOx/m936K4rSFHgEpGSrPD/g5Z9g+Z6PvcwX+EcIEQ/cUxTlFmrgTq0AL6WnX5tCx78gQF3LS6P8PMmZhXaLXm2ZPr1VWZrY3cJuX3MIVnv+YmYOlbpBld7pW3MmERERx4wZx2nWrDju7nleu9/KypyLF/ug0xnXJwZbbm/huxNq+7rWxVozqsook+pvHbhgIUGLFgFg/dFHZGvfHrv69TDPZpzr2zOaadOm8e233yKEIH/+/CxcuFC23pMkKV2kJGBPUhTFARiG2v86KzA4BY87AxRVFKUgarBuB/y3Q8ifQHvgZ0VRXFCXjHinsHbJmNhZQrkcsKc1NN4AtfISMKUW7ZeewjsoElenLLSq6MrABoVRjs+BHZNAGMAuJxT7BKr1gxwltP4uMhyDQbB48Rl+/PEo/v4R/P33fQ4f7pbk8g9jCdcx+hjOPTnHX3f/Yrv3dgBGuI+gS+kuGleWckKvx//77wnbqLaXzNalM7lGj9a4qsynatWqmJubM3ToUMaNG4et3BFTkqR0ogjx7isuFEWpKYR4606OiqI0AeaitulbKYSYrCjKBMBLCLFNUV/lZwGfAgnAZCHEuuSe093dXXh5eb1zzVI6CovlWlgUvdaeJzA0jP+5BDLlI3+yhN6Cx5fh6fOmNHVGQN1RoJOdE9LK6NEHmDLl2Ctjmza1oUWLkhpVlLSw2DDW3VjH7ae3Oe1/mtDYF9uyN3JrxMy6M9GZyIZC+sBAfAcNJvrcOVAUsrVvR85x44xqTXtGdefOHXbu3MnAgQMTx/z8/Mib97+X/0iSJKWMoihnhRDu7/y4NwXs52322qBemLhbCHFFUZTPgNFAFiFEhQ8p+H3JgG3c9AkG+v9+nv1XfRlkvpkB5n++fpKFLbRYAiWbpX+Bmczt28F89tlabt0KThyrUcOV48d7aFjVqy48uUDvfb2J0kcljpmbmdOtdDe+LPIlblndNKzu3RhiYvD56muivLwwz56d3FOnYFezptZlZXixsbFMmzaNH3/8kdjYWI4cOULt2rXf/kBJkqS3eN+AndzU4QrUNdSngfmKojwC3IFRQogkUpOU6dwNhUKO8HxmLipOT2vPkwQ88mGN5XyqmV1/cW6B2upSkLyVIGdpsHbQqOjMpWhRZ06d6kmrVhs4ePAeo0bV5NtvjSN4nHh0gqmnp3Iv7B4Arvau5M+an49cPqJb6W7YWGi/Ffu7iA8IwKdPX2KvX0fn4ECBDX9g8bzXspR2Dh48SN++fbl16xYAXbt2pUQJudxMkiRtJRew3YGyQgiDoijWwGOgsBAiOJnHSJnF8L8h3gATaqnboAOL/thBj8CltLR+viTBLie0WAaF6mpYaOYQG6vHwkKHmdnryxCyZcvC7t0duXs3lBIlXDSo7lW3Qm+x8spKdnjvSBxrWqgp46uPJ4t5Fg0rez9CCCKPHePx+O+Jf/QIi7x5yTtvngzXaezJkycMGzaMNWvWAFC8eHE8PT2pV6+etoVJkiSRfMCOE0IYAIQQMYqieMtwLQEQEAmrr6q3t9yGz22h/F6G+WzHTPd8yZGVA/Q+AvYyZKS1y5cDaN16AzNmNKZZs+JJnmNhoTOKcL359mYmnJxAgkjA3MycFkVaMKDCABytTXMHPaHX4z9mDGFbtwFgXbo0rsuWYu6UkkZL0of44YcfWLNmDVZWVowdO5YRI0ZgZWWldVmSJElA8gG7hKIol57fVoDCz48VQAghyqZ5dZLxCYmG/21Ub+d8CKXPgPM58FF3LTpvKELx/3lgU7W73CQmjUVExDFy5D4WL1avSWjTZiN793aidu38Glf2Or1Bz5pra5h3bh4JIoHmhZvTp1wfXO1Ntxdx/JMn+I8ZS+TRowA4tm9HrtGjUSwsNK4s44qLi8PSUm3T+MMPPxAcHMykSZMoUqSIxpVJkiS9KrmAbVxtBiTjEJsADQwQuhpcbyUOx+euRM8HjbhgWYlL1T/RsMDMw8bGgtOnHyUex8ToadZsLceP96B06RwaVqZKMCRwIfACh30Os/fBXvwi1Db4JZ1KMrnWZI2re3/CYODpH3/wZNZsDOHhKDY25J01E/v69bUuLcOKjIzk+++/Z8+ePXh5eWFpaYmLiwvr1iXbdEqSJEkzbwzYQogH6VmIZAJinsH5SWC3FOwEGKzArzp8N44e+xM4agiitJNpXZhmyszMFObM+YTatX9OHGvXrgyFCmm7icnjyMd4XvTk4MODr7Tby581P1VzVWVgxYHJPNq46QMD8R0yhGgvdZttu3r1yPXdOCzyvL55j5Q6tm3bxoABA3j48CGKonDw4EE+/fRTrcuSJElKlmxALKVMbDh41oSnD9Xj8j14FteOoy3y8cvuh/xzLwSAsvlkd5D0VKuWG61bl+LQoftMm9aI7t016Z4JqBf7XQu5xojDI/AJ9wEgm1U26rvVp4FrA2rlrWUyvayTEnPzFj59+6B/5I8uuwu5xozF/pOPZX/rNPLw4UMGDhzI1q1bAahQoQJLliyhcuXKGlcmSZL0djJgSynzR1c1XNvnhvZruWtRlKbzjxJzLijxlPZVXPmheRkNi8x4EhIMeHp64exsQ7t2Sf/ZLl3aDBsbCywttQuvlwIvMefsHLwC1PXgOW1yMq7aOJMP1aAuCQlauIjgZcsQ8fFkKVeOfIsXYe7srHVpGdbSpUsZOnQokZGR2NvbM2nSJDw8PDA3ly9ZkiSZhhT9tFIUJQvgJoS4mcb1SMYiOhSOz4Pb+0EfDcF31PF2v0OeCizdeImYeAO5HawRAkZ8UpyWlfJpW3MG8/RpDB06bGLXrjvkymVH06ZFsbd/vUuCo6O1BtW9oDfoGX54OP6R/tiY2/B5kc/xKOdhup1BhCDu3j0iT50i6tQ/RJ46heHZMwAcW7ci55gxmFlr+2ee0VlZWREZGUmrVq2YO3eu3IlRkiST89aArShKM2AmYAkUVBSlPDBBCNE8rYuTNBAfDXvGwLWtEBX06n2NJ/LEvhRjfvFi37UAFAV+/7oaBV1stak1AxNCUK/eKi5eDADg8eMIpk07zqRJDTSu7HX7HuzDP9Ifa501O1vsxDmLac7sxty8RfDy5USdOoU+MPCV+8zz5CbH4ME4NJc/9tJCaGgoJ0+epEmTJgB06dKFwoULU6tWLY0rkyRJej8pmcH+HqgCHAIQQlxQFKVgGtYkacX7EPzWGhLi1ONnjnCjIjzJC+HF2aory6DtBxJP71WnkAzXaURRFLp0KcewYXsTx5YsOcvo0bWxsTGeNnBCCFZdXQXAiMojTDZcGyIjedi9Owkh6rUEOhcXbKtWxaZaVWyrVcMiXz651joNCCH4/fffGTp0KGFhYVy5coUiRYqgKIoM15IkmbSUBOx4IUTYf15cRBrVI2kh6A4cmgJXNr4Ys+gIvxUDgzlnc1jRoVkuYiMjEu/+5tPieNSTvWfTkodHZebOPYWPzzPKl8/F9u3tjSpcA3gFeHEt+BrZrLLRvLBpzu7qQ0J4NHIUCSEhWBYqRL55c7F8HvKktHPr1i08PDw4cEB90167dm2EkC8tkiRlDCkJ2FcVRekA6BRFKQoMBE6kbVlSugi4BkdnwtUtIAyg6KBaX6gxACLswHAJVl9lfHVnYs3VTWM6VHVjdJOS2FnJi40+hMEg2LfvLgsWnGbgwKp8/HHh186xtjbnxx8bcvbsIyZNaoCtraUGlSZv9dXVALQr0Q5rc9Nal2yIiyNs0yYe/zAhccy5Z0+sihbVsKqMLyYmhqlTpzJlyhTi4uJwdnZmxowZdOvWTb6pkSQpw0hJShoAjAFigd+BPcCktCxKSmPeh+DkYri9Rz02s4AKnaHWYHAqpI7Zg2FMdbY1ysuVrVcA2D+0DkVy2GtTcwYhhGDFivNMm3acO3fU5QgxMfokAzZAp05l6dTJODdN9Q7z5rDvYax0VrQt3lbrclJMHxpKwKTJRJ48mbgkRLGywuHzz3Fo8aXG1WV8Hh4e/Pyz2ru9e/fuTJ8+HRcXF42rkiRJSl0pCdglhBBjUEO2ZOrO/wbb+qsz1ubWULEr1BwIDi86gBgMgkO3njB5x3XuBkYCan9rGa4/XEyMnt9/v5wYrgEOHLjHuXP+VKyYW8PK3t2/s9fNCzc3ibXX8X5+hK5dS/Cq1aDXA2BVrBguHh7Yf9wYxcxM4wozh5EjR3LhwgXmzp1LnTp1tC5HkiQpTaQkYM9SFCUXsBFYL4S4ksY1SWkh6Db8PVldDgJQqD58uQTscyae8o93MMuO3uOCTyhBEXGJ491qFKB/A7neOjVkyWLBzp0dadNmA9u3v9hqfvPm6yYTsOMS4vA44ME//v8A0LlUZ40reruII0fw8eiXGKyzVKxI9kGDsKnsLoN1GjIYDCxdupT9+/ezYcMGFEWhePHinD17Vi4HkSQpQ3trwBZC1H8esNsASxRFyYoatOUyEVMRHQrLG0HMU/W48lfwv+nwfAOQkMg4vt92lW0XHyU+JGdWK2oUdqFXnUKUzJ1Vi6ozLGtrczZtakOnTluwsDDDw6My1asbfw9xIQQ/X/2ZtTfW8jjyMQBDKw2loIPxNhUScXEEr15N0PwFoNeTxb0SOUeMIEu5clqXluFduHCBPn368M8/6huxAwcO0KhRIwAZriVJyvCUd7lqW1GUj4BvgLZCCE2uuHJ3dxdeXl5afGnTlKAHz1oQeB1ss0PL5VCwLjx/gXsQHMlnC44RHqPO7JXJZc+c9hUoksNOvgi+p5s3g/D09CI4OJrVq794459jfHwCFhams8vhn3f+ZNzxcQDkss3F1NpTqZSzksZVvVm8vz8Pv/6auDt3Acja5H/kmToVxdL4LhbNSCIiIhg/fjzz5s0jISGBPHnyMG/ePFq2bCl/pkiSZHIURTkrhHB/18elZKOZkkBboCUQDKwHhr1zhVL6Cw+A9R3VcG1uDW1/A7eqiXdf9HlK26UniYk3ADDlWBDtfHxR7uuhb3ko7qRV5Sard+/tLF16LvG4SpW89O9fJclzTSlcP4t7xpyzcwBoWbQlo6uOxlJn3EHV/7vxxN25i5mDA3lnzcKuVk2tS8rwtm3bRr9+/fD19cXMzIxBgwYxYcIEsmaVn4JJkpS5pGQN9krUUP2JEOLR206WjIAQ4HcWljd8MdZlK7hV5cTdIHZc8uf2kwguPHxKXIKB7ChMOPCYT+9HoQD8dg2aFZYB+z2UKpX9leNhw/ZSrVo+3N3zaFTRhzMIAz/+8yMhMSFUzFGR8dXHG/1M5NNNm4k8ehSAghv+wNLNTeOKMocLFy7g6+uLu7s7np6eVKpkvJ9wSJIkpaWUrMGunh6FSKnowA9wbM6LY49/CLYpyK/7bzF3/+1XTm3nno/vF1zF+n7Ui8Fi2aC+DCTvo2vX8nz77QGio9UlN1mzWnH//lOTDtizvGaxw3sH1jprRlcdbdThWgjB040beTzuOwCyDx0qw3Uaio+P59atW5QuXRpQO4S4urrSpUsXdDrT+YRGkiQptb0xYCuK8ocQoo2iKJd5dedGBRBCCONszivBLbW/tSjSmI32nVjx+xNuPL6beLebkw2jm5SgcgEnnO2soEVZ2OkNs87AlSAYXxPMjDdEGYOEBAM63evdJxwdrenQ4SNu3Aiib193WrUqhZUJb8pz5vEZ1lxfg5lixqKGiyjuVFzrkt4o9vZtfAcNJs7bGwCnrl1x6fW1xlVlXMePH6dPnz4EBARw48YNnJycsLKyonv37lqXJkmSpLnkXvkHPf/9s/QoREol+jgIugUoHCw7gxG/XwPCMVPA1tKc4Z8Up2uNAq8+xkyBzwpD00JwKRDK5dCgcNNw5coT5s49xZkzjzh/vjdmSbwRWby4KZaWpj97FxQdxKijH3oNyQAAIABJREFUozAIA19/9DVVcie9llxr+tBQwnfvTtyRUefsTLa2bXDp21fjyjKmkJAQRo4cyfLlywEoVKgQPj4+ODnJJWWSJEn/emPAFkL4P7/pIYQY+fJ9iqJMA0a+/ihJc1c3g0FPjH1+Rmy9A0CdYtmZ2bosOezfspW1oshw/QbR0fG0br2BHTteLLHZtes2TZsWe+3cjBCub4bcpMeeHjyLe0aFHBXwKO+hdUmvMcTF4T9qFM/27kvsbw1QeOcOdA4OGlaWMQkh+PXXXxk2bBhBQUFYWFgwcuRIRo8eTZYsWbQuT5IkyaikZIeFxkmM/S+1C5FSgc9p2K5+8LD7aT5CIuMo7+rIkk6V3h6upWRlyWKBXm94ZWzOnFMaVZN2DMLAn3f+pPe+3jyLe0aBrAWYVnsa5mbGtcwl/tEj7jVrzrOdu0Cvx7ZWLXJPnkwxLy8ZrtNI79696dq1K0FBQdStW5dLly4xceJEGa4lSZKSkNwa7L6AB1BIUZRLL91lDxxP68KkdxQeQMLGnuj0MRyiMhPiO5Pd3opV3SuTJQPMqBqDH39syJ49L9ay370bSlhYDA4OGePNy72we4w9NpZLQep/9yq5qrC40WKsdFYaV/aqWO97+PTtQ/yDh5g5OOD600/YVKygdVkZXocOHfjzzz+ZOXMmnTt3NuqLXSVJkrT2xo1mFEVxALIBU4BRL90VLoQISYfakiQ3mklCgp6E+RXQhT0kTNhQO3YelnbZ2DukLk62SfQqDo1RL2asmhtk+H4nbdpswMfnGcOHV+fzz0tgbm7622z7PPNh+pnpHPM7hl7osdJZMb76eJoWaoqZYlzfX/ihQzz6ZiSGZ8+wKlECt2VLMc+e/e0PlN7Znj17OHHiBD/88EPiWGRkJLa2thpWJUmSlL7ed6OZ5AJ2ViHEM0VRkrxyRauQLQP2fzy6gNjQDSX0HgD/i51C9Rr16F6zAK5ONkk/ZvF5GH8c7C3VdnztSkDjAulXsxFLSFCXgSTVIQQgMjIOGxuLDDN7FxQdRP0/6iceN3BtwNhqY8luY3yhNfzgQXw9+gFg17AheadPw0yGvVTn7+/PkCFDWL9+PQAnT56kWrVqGlclSZKkjfcN2MlNT/3+/PezgNfz38++dCxpzfcsLK2bGK47xI2mfOXafNes1JvDdXwC/HxFvR0eB9vuwLXgdCrYuG3ceI1y5TyZM+cUBkPSbzxtbS0zTLj2fuqdGK4tzSz547M/mNdgntGFayEEIWt+exGuGzQg34L5MlynsoSEBBYtWkSJEiVYv349WbJkYdq0aXKzGEmSpPeQXBeRz57/XjD9ypFS7OoW+PNFZ4dGsdO5I/Ixr/HrXS1esekW3A97caxToNVbHpMJHD/+kNatNwAwYsQ+9u3zZvXqL8iVy07jytLGrnu7GH1sdOLxxuYbKehgfP/VI0+eJHDBQqLPqdvPu/Tvj0vvXihmxrV0xdSdO3eOPn36cObMGQA+++wzFixYQIECBbQtTJIkyUS99VVKUZSaiqLYPr/dSVGU2YqiyK3RtCIE0eu/hg3dID6KIwkfUTpmBXdEPu5M/h/Z7d9yQVrLYjCjHuR8PsPdqRTktU/rqo1eiRIurxzv3XuXpk1/f+NMtin7++HffHv0W/QGPY3cGrHjyx1GGa79hg7lYfceRJ87h5mDA7knTSR7/34oFhZal5bhLFq0iDNnzpA3b142b97Mtm3bZLiWJEn6ACnpvfUTUE5RlHLAMGA58CtQNy0Lk5L2zP82Wa//AcDU+HZ4JjSjb70ijPy0RMqewEIH3cpAm+Kw4rKcvX7O2dmGQoWy4e0dirm5GW3blmb69MZJbiRjyg48PMA3h78hQSTQs0xPBlcarHVJSQrbtk1twQfY1qlN3tmz0dllzE8TtCCEICQkBGdnZwCmTZtGjhw5GD16NPb28g23JEnSh0pJwNYLIYSiKJ8DC4UQKxRF6ZnWhUmvi9frifulJQB/JVTFvtEI9pXKSdGc7/GCaGMBAyqmcoXGTwjxxjXU7u55ANiwoTUVK+ZOz7LS1IUnFzjse5jjfse5HnIdgLbF2zKo4qC3PFIbz3bv4dGYsQDkGD4Mp549M8y6d2Nw7949+vfvj7e3NxcvXsTS0hIXFxemTJmidWmSJEkZRkoCdriiKN8CnYHaiqKYAfIz2nS25qQ3RfZ0pRoP1YFqHvSrX0TbokxIQoKBadOO4+X1iE2b2iQZ2KZObUju3PZYWxvXpiofIjg6mJ57ehJniANAp+gYXHEwXUt3NbrQGvfwIU9mzCR83z4AsjZpIsN1KoqLi2PWrFlMnDiR6OhoHBwcuHz5sryIUZIkKQ2kJEm0BToAPYQQj5+vv56RtmVJL5u64zLZTk6hmrm6AcjPulZ8VucdNtP0C8/U66wDAiLo2HEzBw6o3VY2bLhGmzalXzuvYMFs6V1amvEN9+Uv779Ye2MtcYY4bMxtmF1vNqWdS+No7ah1ea+JvXeP+y1bYYiKwszGhuzDh5GtXTsZrlPJ0aNH6dOnD9euXQPUTWNmzZpFrly5NK5MkiQpY3pjH+xXTlKUnEDl54enhRBP0rSqZGSmPthCCJYcvkPJAz2oq1PDtaH5Qswqdk75k/x2DfwjYEAlsMp8m8oIIahSZTleXo8Sx3LlsuPGjX4ZZgfGl90Mucmoo6O48/TOK+Nrm66ljEsZjapKnj40lAcdOxHn7Y1tjRrknjQRizx5tC4rwxgxYgQzZ84EoEiRIixevJjGjRtrXJUkSZJpSIs+2P8+cRvgNNAaaAP8oyhKq3cvUXpXq0/cx7B/AnV1l4hXLKHZ/HcL13EJMPggTDsNVX6FFZcgRp92BRshRVGYO/cTLCxe/FOPjIzj2rVADatKfVeDrzLo4CDa/tWWO0/vYG9hT167vHQq2YlDbQ4ZbbgWcXE8GjacOG9vrIoWJe+c2TJcp7IyZcpgaWnJ+PHjuXz5sgzXkiRJ6eCtM9iKolwEGv87a60oSnZgvxCiXDrU95qMPoMdGB7LutMP+edeCI/vXmC/1TcA6FutxrzMF+/2ZHO84MdTr46tbwYN8qdStaZj5crz9Oy5jTp18rN2bUvy5Mk4S2ZuhNygx54ehMeFA/BFkS8YU3UM1ubGPUMf7+eH75ChxFy6hJmdHYW2bZXhOhVcu3aNc+fO0alTJ0D9FOfBgwey7Z4kSdJ7eN8Z7JSswTb7z5KQYFIw8y29u9sB4TSecyTxuLfFZQBE0U/ePVwLATu9Xx0rli1ThmuAHj0qkC2bNc2aFcfcPGP88/V55sOoY6O4FKguH6qYoyITa07ELavxt6l/tmsX/t//gCEsDPM8uck3Z44M1x8oKiqKSZMmMWPGDHQ6HdWqVaNIkSIoiiLDtSRJUjpLScDerSjKHmDt8+O2wM60Kylzmrv/FnP33wbAMYs54/Oe5UvfXwFQ8lVO7qFJUxTY0RI231KXiPiGw/JPU7NkoxIXl8Ds2ScZObLmGy+M+/LLkulcVdrweuzFupvr2PdgHwZhQKfoqJGnBt9V/45ctsZ90VrMzZsEzptPxMGDANjVrUueaVPRORrfhZemZNeuXfTr149799QLeXv27JnY41qSJElKf28N2EKIEYqitABqPR9aKoTYkrZlZR6x+gSGrr/Ijsv+AGSzsWBXaztyrZ+mnuDgClW+fr8nt9RBu5LQohjcCYWSGfMF988/bzB48G4ePAgjKCiKGTMaZ9juE7vv7WbEkRGJx00KNmFE5RG4ZHFJ5lHG4dnu3fgNHQYGA4qlJS4eHjj3+lpue/4B/Pz8GDx4MBs3bgSgbNmyeHp6Ur16dY0rkyRJytzeGLAVRSkKzAQKA5eB4UIIv/QqLLP4+8aTxHBdv3h2VnarjHJUveKfoh9D+/XwoQHEUgeljD+AvY8//rhK27YbE49nzTqJhYUZP/7YMMOEbCEEs8/O5szjM1wNvgpAIYdCzKg7g2LZTGMnzujLV/AbPAQAh1YtyTF4MOYuGfPfZHrq168fW7duxcbGhgkTJjBo0CDMzTNOH3dJkiRTldxP4pXAL8ARoBmwAGiRHkVlJmcfhALwWdncLOzwfGfFe8/XYeep+OHhOoP7+OPClCuXk4sXAxLH5s8/Td++lXFzc9CwstSz4dYGVl1dBYCVzoqhlYbSvkR7k3kDEXvnDr4eHgBkbd6M3BMnmkztxkiv1yeG6GnTpmFhYcGsWbNwczP+tfeSJEmZRXLpzV4IsUwIcVMIMRMokE41ZQr/eAfTd81Zlh1V10y2dneFhHjY0O1FwC7ZTLsCTYSjozV79nSiSBEnALp2Lcfly30zTLiOiItg0YVFADRwbcCxdsfoULKDyQRUfWgoPn090AcGYl26NLnGjDGZ2o1NWFgYAwYMoGnTpvzb/al48eJs2LBBhmtJkiQjk9wMtrWiKBWAf18Ns7x8LIQ4l9bFZVQJBkH3VWeIiktIHKvsagu/fA4PjqsDNQZCztd3G0xWRJx6caNt5trJPmdOO/bt68zduyE0bFhI63JSza3QW8w9O5eQmBAq5KjA3PpzTSacCiF4unEjgbPnkBAaikV+N9xWr0ZnZ6t1aSZHCMGGDRsYPHgw/v7+6HQ6Lly4QIUKFbQuTZIkSXqD5AK2PzD7pePHLx0LoEFaFZXR3XwcnhiuV/eoQlU3e6x3DlLDtc4SvlwCZd5jNc6KyzDjNFTMCTXyQuP8UMm4u0qklBACIcDMLOmAWaCAIwUKZIxOFLdCb7Hs0jJ239+dODbcfbjJhOt4Pz+ezJrNs51qsyEbd3e1U4gM1+/s7t279O/fn9271X8L1atXx9PTk7Jly2pcmSRJkpScNwZsIUT99Cwks9AnGFh2VO1P/WWFvNQtaA8rGsNjtZcxHTdCobrv/sT77sOkk+rtk4/UXzolQwTs69cDGT58H1mzWrFiRXNsbDLmDH1YbBg/nPyBfQ/2AWCumFMkWxGGVBpC2ezGH6gSIiLwGzSYyOPHE8dyjhtLtg6ms6TFmMycOZNx48YRExODo6Mj06ZN46uvvsJMXpchSZJk9OTl5ukkPsHAwoN3mHfgduLYJ7kjYVYxiAlTBzpseL9wHZsAHf56dczCDLq84xITIzR37imGDNmTeHzlyhM2bmxN0aLOb5zNNkUHHx5kwfkF3Hl6ByudFa2KtaJb6W5G39caIPryZYKXLSfy2DEMUVEoVlbY1a2LU+dO2FR+jx7uEqBuHBMTE0Pnzp2ZOXMmOXLk0LokSZIkKYVkwE4HMfEJlBi3+5WxyZ/m5ZPTbV6E65YroNjH7/cFdAps/gL234f9D+BWKHxbDXKa/kfyn3xSGDMzBYNBvajrypUnNGjwC3fvDsTa2vT/+cYlxDHm2JjE5SD57PLh2diT/FmNf8dNIQSha34jYPp0iI8HwLpsWXJPnIh1cdNoH2hMAgMDuXnzJrVqqVsOjBw5knr16lGnTh2NK5MkSZLeleknFCMmhODOkwi+2XQpcSyHvRWb+tbA9f4miHjeWm74bbD7gNkpczOonU/9NcQdDj5UN5fJAEqWzE6XLuVYteoCAAULOrJ2bcsMEa4Bpp2exu77u7Ews6B/hf60LNoSByvT6IDyoFNnos+eBcChRQtcPDywzJdX46pMj8FgYOXKlXzzzTeYm5tz48YNnJycsLKykuFakiTJRL01pSjq4smOQCEhxARFUdyAXEKI02lenQk7eTeYQevO8yQ8NnFsaedKfFz6+Uf+fx9Wf//f9A8L1/8VFgdfFE295zMC339flxs3ghg4sAotW5bC0lKndUkfLCo+iqmnp7LlzhYszCxY+clKyucor3VZKRa4eHFiuHZs3Ypc33+PojP9v5f0duXKFfr06cPx5+vWGzduTFRUFE5OThpXJkmSJH2IlEwDLgYMqF1DJgDhwCZALq5MxpIjdxPDtbWFGQvaV6RxqZzqnUK86HVd8D3WXCcnf9bUfb50YjCIN66pzp/fkZMne6ZzRWnDN9yXVVdXsf7m+sSxARUGmFS4DvntN4LmLwDAqWcPco4Y8ZZHSP8VGRnJhAkTmD17Nnq9npw5czJ37lzatm0rLwiVJEnKAFISsKsKISoqinIeQAgRqiiKZRrXZdKEEBy6GQjAn/1qUt71P+3jAm+oy0PsckH24hpUaFz0egN1665i9OhaNG2aMZa2/NfV4Ktsub2FTbc2oRd6ALKYZ2Fyrck0zt9Y4+pSRghB5LHjBEycBED2wYNw6dNH46pMU6tWrdi9ezeKouDh4cHkyZNxdMwYbSYlSZKklAXseEVRdKi9r1EUJTvqjLaUBCEEX/+ifnSuKODmZPP6Sd7Pl4cUrKOe9L4ePAMna7A33fc7kZFx9O+/ixMnfGjefB3z539Kv35VtC4r1Xg/9Wbk0ZHcCLmROFY8W3E6lepEs0LN0JmZzrKKx+O/5+kffwCQpUIFnHv10rgi0zVy5EgCAgL46aefqFq1qtblSJIkSaksJQF7PrAFyKEoymSgFTA2TasyUZd8nzLuzytc9FU7g/SoWRAn2yTC773nAft9WvK9bP5ZsNSpG8tUzAEFHcHEWtcNG7Y38QJGg0HQv/8ubGws6N7d9Hep8wn3oefengRFB2FjbsMXRb6gQo4KNM7f2KSCddyDBwTOm8eznbtAp8OlnwdOXbqiyH7MKaLX61mwYAH3799n3rx5ANSrVw8vLy/Z01qSJCmDemvAFkL8pijKWaAh6jbpXwghrqd5ZSYmPsFA84UvNtgY27QkX9VOYtvuBD14H1Jvf+j66z33ICDqxfG8BtCh1Ic9ZzobN64O69ZdISxMXa+eJ489TZqY9kWaMfoY/vb5mxlnZhAUHUTlXJVZ0GABtham1zYx/vFj7nfsREJQEAC5J03C8csvNK7KdJw+fZrevXtz4YL6JrJXr16ULq32p5fhWpIkKeNKSRcRNyAK2P7ymBDiYVoWZmrOPQhNvL2iqzv1iv+nM4gQcHkD7PoG4qPAqTA4ur7/F/SPeDVcA7Q2vfXcefNmZc6cT+jRYxsFCjiyc2cHcua007qsd6Y36Dnmd4y99/dyyOcQ4fHhAFTMUZH59eebXLgWCQlEHD6M//jxJAQFYWZjQ8Etm7HMb/z9uY3B06dPGT16NJ6engghyJ8/PwsXLkwM15IkSVLGlpIlIjtQ118rgDVQELgJyFeK5xIMgu2XHgFQOLstDUvmfP2kDV3h2lb1trUjfDzxw75oSAy42cNDNchRIQdYGOeyg+joeLZsuUGNGq4UKPD6hVzdupUnOlpPly7lsLMzrfXk0fpo/rj5B+turMM3wjdxvKRTST4v8jltirfBwsx0tnYXCQmErFpN6O+/E+/nB6jrrV1/WoxOXoSXIuvWrWPw4MEEBARgbm7OsGHDGDduHLa2pvUmS5IkSXp/KVki8tHLx4qiVAQ80qwiE6NPMFBtykGCItQlDjWLuLx+0tHZL8J10Y+h3VrQfeBGKaVd4HhHWHIR5noZ7cYya9deZvDgPTx5Esn06Y0YMaLma+eonRRMr+tjvCGeAQcG8M/jfwDIkSUH7Uq0o6FbQwo5JrE8yMjF3r2Lb7/+xN2/D4BFvnxka9+ObB06YJYli7bFmZC9e/cSEBBAzZo1+emnn/joo4/e/iBJkiQpQ1GEEO/+IEW5/N/gnV7c3d2Fl5eXFl/6NdcePaPrz6cJfN7vun7x7MxpWx5Hm5dmYX3OwMqPQRjgkx+her/UL+RJFNiYg5HN/vbqtZ1ly84lHteq5cbRo901rCj1nPY/Tf+D/YnWR+Ng5cAI9xE0LdQUczPT22HSEBNDwJSpPN2wAQwGsLAg56iRZGvfXl7ImAKxsbH4+flRqJD6piooKIjt27fTtWtXuc5akiTJxCmKclYI4f6uj0vJGuyhLx2aARWBR+/6hTKaBIOgyfyjicdrv65G9cLOr550ZjnsGKbeLvV52oRrgBxJtALUmBCCbt3Ks3LleRIS1DdxJ074EBQUhYuL8dWbEhFxEfzj/w/HHx1ny+0t6IUeJ2snZtadSeVcpjcD/6+ghQt5un496HQ4tm2LSz8PLHKk4u6iGdjBgwfp27cvZmZmXLx4EUtLS1xcXOjePWO8kZQkSZLeT0qm2+xfuq1HXZO9KW3KMQ3hMfGM33o18fjEqAbkcfzPR+jBd2HXKPV2gdrQZFY6Vqg9RVGoUcOVESNqMHWq2l2lb193DIZ3/8TEGBz1Pcqww8OI1kcnjjUv3JwJNSaYVMu9/wpatozg5SsAyL96FTbu7/wmPVMKCAhg+PDhrFmzBoASJUrg6+ubOIstSZIkZW7JBuznG8zYCyGGp1M9Ri8kMo5WP53AOygSgB+al349XANs7gWGeHCtCt3+Sp0vfisE8tgZ3VKQ5Hz3XV1OnPBl0KCqtGhRUuty3suZx2cYcmgIsQmxlHQqSZ18dWjo1pCSzqb5/fwrbOtWAmfNBkUhx8hvZLhOAYPBwLJlyxg1ahRPnz7F2tqasWPHMmLECCwtTef/pSRJkpS23hiwFUUxF0LoFUV5/aq0TOp2QDiN5xxJPJ7Xrjyfl8/7+omx4eD3fJ14xw2p88UfPINhh6BPOWhcQN1gxgRkyWLBoUNdUT5kx0oNXQq8RP8D/YlNiKVVsVZ8V+07k/1eXhZ17hyPRqqfsGQfOgTnbt20LchEfPnll2zbtg2ATz75hEWLFlG4cGGNq5IkSZKMTXIz2KdR11tfUBRlG7ABiPz3TiHE5jSuzagER8S+Eq6Xd3GnUakk2vEB3Nih/u5UCKwdUqcAzwtw6pH6y9la7Xn9fU3QGcdFVM+exZI1q1WS95liIBVC8PPVn/G86Em0PpqmhZoytupYk/xe/iv66lV8PdTrAWxr1sT5q680rsh0tGjRgtOnTzNv3jxat26dIf49SJIkSakvJWuwrYFgoAEv+mELINMEbINB8MncFxc07hxYm1J5siZ9clQI7B2n3i7TKnUKCIyC5ZdeHAfHqFuiG0G4jo9PYOzYg1y/HsTEifUpWzanyYcO7zBvZnnN4oiv+oaqkVsjJtWcZNJrrUF90xCyejWB8xcgoqLIUqkSeefOMfm/r7S0bds2fH198fBQO5N26dKFFi1aYG9v/5ZHSpIkSZlZcgE7x/MOIld4Eaz/ZZpXqr2H+AQDX632SuxzvXtwbUrkekO4vrwRNvVUbzu4QY0BqVPEiUdgYwFR8eqxrQV8Wy11nvsDtW27kS1bbgCwffstSpRwYdiw6nz1VUWNK3t398Pu43nJk933dpMgEgAYUGEAX330FWaK9m9mPlTI6tU8mToNAIfPm5Nr4kTM5LrhJD18+JCBAweydetWrKys+PTTTylUqBCKoshwLUmSJL1VcgFbB9jxarD+V6YJ2I1mH+ZBsLol+bjPSr05XAMcnv7iductYJ3Mue/i8yLQwA3WXVdnslsXB2vj6Lfs7p4nMWAD3LgRhE5nWjOi0fpozjw+Q78D6rIJnaKjWaFmdCjZgTIuZTSuLnVEnTtP4Jy5AGQfNBDnPn3kzHUS4uPjmT9/PuPHjycyMhJ7e3smTZpEfrlFvCRJkvQOkktp/kKICelWiZERQvD1L16J4XpKi49oX8XtzQ8IvAlBN9Xbox6m3trrf9lbQufSUCkXlM2eus/9Adq3L8OYMQcBcHGx4aefmtKqVSmNq0oZgzAw5+wcVl1d9cr4n5//SQGHAprUlBYiT57Ep19/RGwsWZs2xbl3bxmuk3Dq1Cl69+7NpUvqcqzWrVszZ84c8uZN4kJmSZIkSUpGcgE7U78Cn7gbzP7rTwBwdcqSfLiGF7PXFTqnfrj+l5kCFd9wYaVGChbMRo0arhQunI1Zsz4me3ZbrUtKESEEM87MYM11tY+xk7UT9V3r06pYqwwTroXBQPCKFerMtcGAZaFC5J48Se7O+Abjxo3j0qVLFCxYkIULF9KkSROtS5IkSZJMVHIBu2G6VWGE+qw5C0AhF1sODq+X/Ml+5+DKRvV2nTRsGW6krfkOHuyClZVxLFlJifC4cBZdWMRv139Dp+gYU20MrYq2ynCzuqG//a72uQayNvkfeaZPRzE3nb+ntCaEIDw8nKxZ1aVcCxcu5JdffmHMmDHY2JjmbqOSJEmScXjjVJYQIiQ9CzEmj8NiCI/RAzC7bfnkT44OhWX11dv5qkC2AmlbnAb27/emZ8+tREfHJ3m/KYXrM4/P8NmWz/jt+m+YKWZMrDmR1sUyVrs1kZBAwIwZBEyeDEDO78aRd/ZsGa5fcvPmTRo1akSLFi0QQr2kpHjx4kyePFmGa0mSJOmDyVfcJGy/+AiAWkVcKO/qmPzJPqef31Cg/brUK+JWCGQxB9dUulDyPR09+oDmzdcSHa3nwoUAtmxpi5tbGi2BSWO/Xf+NqaenAlDSqSTjqo3jo+wfaVxV6jFERhLy66+E/PYbCYFBAGTr3Jls7dtrXJnxiImJYcqUKUydOpW4uDicnZ25f/8+BQsW1Lo0SZIkKQNJ04CtKMqnwDzUjiTLhRBT33BeS2AjUFkI4ZWWNb3NtUfPmLzzOgAV82dL/mSDAbYNVG/X6A+2zqlXSK89cDUYCjhAnXzQpzwUfUs9aaBfv51ER6uz+efO+ePuvpQbN/rj5JTE9vBGKjYhll+v/cqSi0sAqJ23NjPrzsTGImPMVBpiYghesYKQn1dhiIgAwMLNjRxDh5L10080rs547Nu3Dw8PD+7cuQNAjx49mD59Os7Oqfj/VpIkSZJIw4CtKIoOWAQ0BnyBM4qibBNCXPvPefbAIOCftKolpe4GRtBmycnE4/71iyT/gMsbIOKxert0i1QsJFQN1wD3w9Rf/Sqk3vO/gwEDqtCr11+Jx/37VzGpcH3g4QFme83mYfhDQN00ZmbdmSa/aczL/MeO49lfL/6OXJcuwbZ27Qy17OVDCCHo2bMnP//8MwClSpXC09OT2rVra1yZJEmSlFGlZTsqh5RsAAAgAElEQVSBKsAdIYS3ECIOWAd8nsR5E4FpQEwa1vJWMfEJDP3jIhGxevI723D5+4+xNE/mj+epD+z6P3v3HVd19QZw/PNlgyCKgCJuQVBAUBHcgObKIk3LPdIst7acmaU5UtPMkSNnNjTLmf40c2sOQHBvEUFERNnrcu/5/XH1Ju5x4TLO+/Xiddf3fs/DBfS55z7nOZ9prweOAWc9bqzy9zUY+8BGMnUcodozSlXySI8etSlTRptQDx/uzxdfBBgkjhe17sI6vFZ6MWL3CKJSonC2dmZa02nMCJhRZJJrkZND7Pj/kmvHUaNwP3Ma62bNZHL9AEVRqFKlCpaWlkydOpXjx4/L5FqSJEnKU3lZIuIMXH/gdjTg/+ABiqLUBSoKIf5SFOWzPIzlmYb8cpyI64nYW5uxcXBjbCxMn3xwTjb83hsyk6BqM2iq584hvT1BrYE5oZCmgvau+j3/Q1QqNbGxqY+trba0NOWrrwKxtjajVy/vPI1DH4QQLIhYwMKIhbr7+nn2Y6DPQMyNzQ0YmX4JIbg1YwaJv68DExMcP/6YMu/1MXRYBUZ4eDixsbG0bdsWgFGjRtGzZ09Zay1JkiTlC4MtclQUxQiYBfR5jmM/AD4AqFTpGf2oX0JiejY7z8YBsKqvP6WsnrF99I7PISYUbCvCOyvBWM8vo+W983VwhTXnIPgZpSqvIDz8Jn37bsTCwoSDB/s+duZz8GC/PBtfXzRCw4GYA/wQ/gOnEk4B0KNmDz6q9xFmxkVrO3AhBLGjx5C0cSMA9gMGUKbvewaOqmBISUlhwoQJzJkzhzJlynDu3Dns7OwwNzeXybUkSZKUb/IywY4BKj5wu8K9++6zATyBPfeSunLAJkVRgh9e6CiEWAwsBvD19dX7Nu1hUXcBqF+lNLXKP6Nrx+n1cHQRGJlqk2srO32H85++XvBmdahgo/dTZ2XlMGHCHmbMOIRGo31Jt227xOuv5+1seV6IT4+n+e/Nc933bo13GeU3ykAR5R0hBPGzZumSa4ePP6bM+/0MHJXhCSHYsGEDw4YNIzo6GiMjI7p164ap6VM+iZIkSZKkPJKXCfYxwFVRlKpoE+suQLf7DwohkgD7+7cVRdkDfGqILiLHoxKB5+gaosqATcO111tPgQr18jYwr7zbEt3Y2IidO6/okmuA8eN307atS6Gq39UIDWMOjNHd7uDSgcE+gylbomDteKkvt+fOJWHJj2BigvO331KydStDh2Rw165dY8iQIWy5V4vu6+vLokWLqFtXj+siJEmSJOkF5FmCLYTIURRlCLAdbZu+ZUKI04qiTARChBCb8mrsF5WQlg1AhVLP6I5xcA5kJUEZF/Drnw+R5R0TEyOWLg3G13cJOTkaAKpVK01mZg6WloVn1m/ZqWUciT2CjZkNa95YQ0Wbis9+UiGkTk4m9osJpPzvfwA4fTlBJtdoZ647duxIaGgoJUuWZMqUKQwYMABj46KxkFWSJEkqnPK0BlsIsRXY+tB9Xzzh2MC8jOVpMlVqACxMn/KfclYKhGjbfGFfA/Q9y6vWwIl4qFkGLPKnNN7buxxjxjRh2bLjzJrVmnfeqVWoZq93Re1iTtgcAKY2mVpkk+usK1eJHjiQ7GvXUMzMKNm2LaU6dTJ0WAal0WgwMjJCURRmzpzJwoULmT17Nk5OToYOTZIkSZLkTo4AWSrtDO5TE+zjP//X87rTcv0H8fofEBYHC1pCexd4Wix6NG5cUz75pCG2thb5Mp6+XE++zvDd2nKdzm6dCahYONoHvqjE9RuI/fxzUKsxd3Whwrx5mFWubOiwDCYhIYHRo0cDsGTJEgACAwMJDAw0YFSSJEmSlFte9sEuNBLSsoBnJNgXt2svg+eCqZ6TUSG0yTXAoL/BfzX8c02vQ2Rm5jz2fnNzk0KVXGers1kQvoCuW7XbfztbOzPGb8wznlU4pfzzD7HjxoFajZWvL5VXry62ybUQgpUrV+Lu7s6PP/7IqlWriI6ONnRYkiRJkvRYxT7BzshWc/jKHQAsn5RgJ9+Ay7sARdv3Wt/O38l9+3oKuOhvW/TQ0BtUqzaHvXsj9XZOQ5l6dCo/RPxAUlYSAAteW1BkNo65T52axq1vZxE9eAhoNNj17k3l1T9hbPton/Li4OzZswQFBdGnTx9u375NYGAgERERVKhQwdChSZIkSdJjFfsEe8GeS7rrdSo9YbfEXV9rL2u0htJV9B+EuQm4PpRQV35Gu8DnIIRg9eoTBAWtJDY2laFDt+kWNBZGl+5e4s+Lf2KsGPNVo68I6RFCNdtqhg5Lr7Kjo7nWtSsJ98ofbN9+G8dPPzFwVIYhhGD8+PF4e3uzd+9e7O3tWblyJbt27cLd3d3Q4UmSJEnSExXrBFsIwa9HowCY+rYXJcwfU5KeEgen/tRebzgkbwKpagu7u8Cn9cHUCMY1ePZznkEIQf/+m+nZcz0pKdouKSdP3mLRonzvgvjK7mTeYdvVbXT5qwsaoaFTjU687fp2kdmZUZ2YSNKmTUQPH8Hl11qSdfEips7OVFnzG+WnTEYppr2cFUUhJiYGlUpF//79OX/+PL169SpUC3ElSZKk4qlYL3L8MyyG26nZmBgpvOv7hA4UOz6HnAxwex2qNs27YMyNYZQ/vOUCDlavfDqNRhAYWIVjx25w4kSc7v6QkNhXPnd+EULw67lfmXZ0GgJtv25na2cG+ww2cGSvLuvKVVJ37yZ1927Sw8JAk/uThcq//Ixp2aLZy/tpbty4we3bt6lduzYA06dPp1+/fjRu3NjAkUmSJEnS8yvWCfapG9o63ubujhgbPWZWbOtIOLlWez1oXP4E5V5GL6cxNjaiR4/adO/uxc6dVxg7dhevv+7C+PGFo9tGbGosQ3YN4cLdCwDUtKtJw/IN6ereldIW+qtPz0/qpCQSli4jZft2sq89sIjVxAQrfz9sgoKwDgrCrGLRbDf4NGq1mh9++IFx48bh7OxMeHg4ZmZm2NvbY29v/+wTSJIkSVIBUqwT7Ox79chNXR/zH3hshHZLdIBytaGsRz5Gpj+KovDaa9Xw8SmHg0MJQ4fzXBIzE2m3vh0qjQojxYjxDcbTqUbh7vucvH0HsWPHoklLA8DY1pYSAc2wCQqiRJMmGNvYGDhCwwkLC+PDDz8kJERbvtSsWTOSk5NlYi1JkiQVWsU6wVaptQm2qfFjStHPb9NeKsbw4T79byyjEdovk7wvg1cUpdAk1xqhYdjuYag0KgC2vr0VZ2tnA0f1ajLCw4n55BPIycGiVi3KfPABNq+1QDEp1n9+JCcnM378eObNm4dGo6FChQp8//33tG/fXtZZS5IkSYVasf4fXqXW1vWaPZzk3o2EPVO1199erP/kGrR9rkuag29ZeFyC/4Lmzz+KWi0YNsxfD8EZzs9nf+b4reOYKCb8+savhT65vrNqFbd/WAg5Odi0bo3zd7Nl8oi2vr5Zs2ZERERgbGzMxx9/zJdffolNMZ7JlyRJkoqOYp1g3y8ReWQG+8S9umsnH/DokDeDf3MEIuLBwRJaV4WhdaHaE9oEPsOyZccZMmQbxsYKtWo58NprhbN13fk755kVMguAqc2m4m5XeFuxqVPTuD1/PneWa3f9tPStR/mpU2RyfY+iKHz00UcsWLCARYsW4ePjY+iQJEmSJElvinWbvuzHlYgIARG/aa83Ggp5sYnJzTRtcg0QnwGrz4DVy73XmTRpL/36bQJArRa8++7vXLp05xnPKpjWXVhHjsghoEIArSu3NnQ4L0WTnc3thQu52KSJNrlWFOwHDaLyTz9hZPXq3WEKq+zsbKZNm8aMGTN09/Xq1YtDhw7J5FqSJEkqcor1DPb9GmzzB0tEIn6DO5fBuizUap83A884mvu2twOUs36pUzVrVhkzM2Oys9UA3L2byYkTcbi42L1qlPkqW53N1qtbARhaZ2ihm+nVpKeTuG4dCUuXkROnbYto6eODXZ/elGzTxsDRGdb+/fsZMGAAZ86cwdzcnF69elG2bFkURcHYuGjtwilJkiRJIBNs4KEZ7CM/aC99+4JxHr08ratoFzceuwkn48HT4aVPFRBQheXL36J7d+1mOAsWvM7bb9fUU6D5Z/f13SRnJ+Nu546bnZuhw3lumWfPcve3NSStX4/I1m7oY16jBmXHjqFEg1ffMKgwu337NiNHjmT5vTIZV1dXFixYQNli2N9bkiRJKl6KdYL9Xw32vdnSm6e07fkA/Afk3cCtqmq/EjLgwx3wWf1XOl23bl4kJKTj6elIUFBVPQWZvzZe2ghAe5c8+tRAz0R2NrHjvyBp40bdfRaentgPHIB1UBCKUfGtvhJCsGLFCj777DMSEhIwMzNjzJgxjB49GgsLC0OHJ0mSJEl5rngn2A93EdlxbzOZen3A8uUWHL6QdBX81A4sX/3HMHRo4e0eciv9FgdvHMTEyITXq75u6HCeSJOWRsLKlaTt20/m2bOIrCwASvfoQekunTF3cTFwhAXH6tWrSUhIoHnz5ixYsAA3t8LzqYQkSZIkvapinWCrHuwikngdruzRPuD+Zv4EULHkCx2+a9dVAgOrYPS4XScLqevJ1+mwqQMaoSGoYlCB3aVRdesWMUOHkRERobvPrFo1nL6ehFXdugaMrGBIT08nKSkJJycnFEVhwYIFHDt2jO7duxe6enpJkiRJelXF93Ns/usiYmZi9F/f6wr1wfW1vBnw3ngvY+rU/bRosYrRo3fqMSDD2h65nY6bO5Kl1s4E9/bobeCIHiWEIG7GDC41b0FGRASKpSXlZ8ygxpHDVN/6l0yugW3btuHp6UnPnj0RQvupkJubGz169JDJtSRJklQsFe8Z7AcXOcaEau9sODhvBlt3HrZdhSWt4QVnoLdsucDYsbsAmDHjEGXLluCTTxrlRZT54nrKdb4P+57/Rf4PAM8ynkwPmE5Fm4oGjuxRt6bP0PWytmn5GmU//xxTuUgPgJiYGEaMGMG6desAsLGxISEhQW5xLkmSJBV7xXoG+36JiNXdsxB/DixKgVse1ACnq2DCQdh0Cb46+EJPvXs3g759N+a674sv9nDjRoo+I8wXQghO3T5Fj609dMn1YJ/B/NLulwKZXKeHhOh6WTt/9x0V5s6VyTWgVqv5/vvvqVmzJuvWraNEiRJ8++23hIaGyuRakiRJkijmM9j3FznanLu3c6NXJzAx1/9AP56AW+na6wvCoWwJGOjzXFuwlyplwcyZrRg8eCupqdkYGyts3dqN8uULz5bS/974l/UX1xN2K4y4dG2P6Hpl6/FFgy+oVqpg7jqpTkwk9vPxANi99x4l2xTOjW/0TaPREBAQwMGD2jeK7du3Z86cOVSqVMnAkUmSJElSwVG8E+wcNT7KJaxCF2nv8O6m/0GikuHbY7nvu53xXMk1aLeU7tXLmyZNKtGjx5+8/rorAQFV9B9nHll6cinfhX2X677g6sGM8x+HlWnB3Nkw88wZoocOQxUTg1mVKtgP+NDQIRUYRkZGtGrViqioKObNm0dwcLChQ5IkSZKkAke5vyipsPD19RUhISF6OVej8WvZZjQcWyUd7GvA4KPPnfg+t8wc+PMCLAyHs3fA3BiO9waHF08uc3I0KAoYGxeOyp4Ldy/QcVNHABqVb8Qb1d6gVZVWmBvnwacEeiCE4O5PP3FrxkyESoVFrVpUWDAf03LlDB2awQghWLt2LSYmJnTsqP1ZZmVloVKpsLZ+ud1HJUmSJKmwUBQlVAjh+6LPK7Yz2AmpWQwVv2CrpCNsnFB6btB/cg1gYQLdakHXmrD3OsSkvlRyDWBiUjgSa4CrSVfpv6M/AF72XixqucjAET2dOjmZmE8+JW3/fgBKdelM2dGjMSrGG6NcvnyZQYMGsWPHDhwcHGjevDmlS5fG3Nwcc/OC+SZJkiRJkgqCYptgh525SFeT3QAotTuDrXPeDqgoEFAxb5L4AmhWyCzuZN6hjmMd5rWYZ+hwnkp14wZR7/cn+8oVjGxtcZo4kZKtWxk6LIPJyspixowZTJ48mczMTEqXLs3kyZOxtbU1dGiSJEmSVCgU2wT71rkHunk0Hp4/gz4jub5+PYnx43fz9ts1adfOtdCUgjzscuJl9kTvwdzYnG8DvqWk2YttqJOfNGlpXGrxGgiBsb09lVetwrxa4dxuXh/27NnDwIEDOXfuHAA9e/Zk5syZODo6GjgySZIkSSo8CmcGpwdlb2pnr2Nce4CVnYGjga1bL1KnziJWrozgrbd+o0aNecye/S+ZmTmGDu2FrTi9AoD2Lu1xsHIwbDBPIYTgxugxcG8dQqWlPxbr5FqtVjNo0CDOnTuHm5sbu3btYtWqVTK5liRJkqQXVDwTbCF4LX0bAGa12+fNGJoXWzx6/HgsCQkZuttXrtxlyZKwQrUt+pWkK7y//X02XNqAgkKvWr0MHdJT3f1pNSl//41iZkalZUuxcHMzdEj5TqPRkJ6ubSFpbGzMDz/8wMSJE4mIiCAoKMjA0UmSJElS4VQsE+yMPbN11+1rBuh/gHMJUGclLIqANNVzPWXMmKa0bl1dd7tBgwps29YdMzNj/ceXR+aGzeXIzSMAvO/1PpVKFszeyNnXrhHz8cfETZkCQJkBH1KiUeHdGfNlnTx5kqZNmzJ06FDdfQEBAYwfP14uYpQkSZKkV1AsE2zLvV8BcMTUD8XETP8DfHMUbqTC5/uh3kr46fQzn2JkpPDTTx2oVMmWKVOac/BgXypXLqX/2PLIoZhD7IzaCcCfwX8yrO4wA0f0KJGdTeKf67nasRPJW7ehmJpi26ED9gMGGDq0fJWWlsaoUaOoW7cuhw4dYtu2bdy9e9fQYUmSJElSkVHsFjlmx57ifkr9V5Wx+Ot7gFvpsPXKf7cTMp+7c4iDQwnOnh2MlZWpvqPKU4siFjEvXNspxK+cH66lXQ0cUW7qlBTurFxF4tq15Ny6BUCJRo1wmvw1pk5OBo4uf23evJkhQ4YQFRWFoigMGjSIyZMnU6pU4XkzJ0mSJEkFXbFLsI/9s57GwHlNBd5q4qP/AXZG5q6/Lm8N3Ws+99MLW3L997W/dcl1TbuaTGkyxcAR5ZYdHU1U336ooqIAMHd1xa5vX2zfCkYxKj4f4OTk5NC5c2f+/PNPAHx8fFi0aBF+fn4GjkySJEmSip7ilWBnp+MWuRqAu26daVC5tP7HaFsNnG3gbAKcuwP1yj4yg33nTgZ2dpb6Hzuf/XjyR+aEzQGgiXMT5reYj5FSMJJWTUYGCUt+JGHpUkRWFiYODpQdMxqbtm1Rikkv8geZmJhga2uLtbU1kyZNYsiQIZiYFK8/f0mSJEnKL8Vrq/R/JsH+mVzVlCXzg0PUrGCv3+CeQ1JSJpUqfUezZpX55JOGBARULlQJn0Zo+PXcr2y8tJGzd84C0MWtCyPrj8TUuGDMvqcdOUrsmDGobtwAoOSbb+L42aeYFrN2c0eOaBec+vtrC6ESEhLIyMigQoUKhgxLkiRJkgoNuVX6s2jUiOM/owBfq3uzoFwezF4/hy1bLpCcnMWWLRfYsuUCr7/uyl9/dTNILC/qdsZtem3rxfWU6wCYG5szwHsA73u9b+DI/iOys4kZNgx1UhJm1arhNPErrHxf+O+iUEtMTGTMmDEsWrQId3d3wsPDMTMzo0yZMoYOTZIkSZKKheKTYJ/6EyU1lmsaR66WboS5Sf63v9NoBOvWnc11n4dHwd2I5WH/XPtHl1xPbzadoIpBWJhYGDiq3OLnL0CdlIRiYUHVDesxMsuDLjEFlBCCX3/9lY8//pi4uDhMTEwIDg5GrVYbOjRJkiRJKlYKRsFsXtNo4JC2Vvioxp0aZQ2zdbeRkcK337bC2vq/pK9bNy+DxPKiNEKjW8w42m80bau2LXDJddrhI9xZtgwA59mzilVyffHiRVq1akX37t2Ji4ujcePGHD9+nGnTpmFpWfjr/SVJkiSpMCkeCfb2sXDzJACTcnpQo5yNfs9/JwO2XXmu3RurVSvN7NmtARg61A8fn3L6jSWPHIw5SGJWImZGZrSo1MLQ4eSSdeUK14cMIapPH4RKhU3L17ApRrsQqlQqmjdvzs6dO7Gzs+PHH39k3759eHp6Gjo0SZIkSSqWin6JiFoFIUsB+KnMcJJjrKlR1lq/Y8wNg3nHwbU0DKoD77iB+ZNLUPr1q0NCQjrDhzfQbxx5JD49nlH7RwHQ16sv5UoUnDcF6ceOce29vpCTg2JpiV2f3ti/X3BqwvOSEAJFUTA1NWXy5Mns3r2b6dOn4+BQeMqOJEmSJKkoKvoz2HeugDobSjiwJEM7q1mjrB5nsBMyYPkp7fWLd+GjXbAw/KlPURSFUaOaYGFR8N/fnE44TafNnUjJTsHGzIZu7gVnQabq1i1iPx8POTlYt2hB9e3/w3H4cIxKlDB0aHkqLi6Onj178vXXX+vu69WrF8uXL5fJtSRJkiQVAEU/wT6xFgB1eV+u303HxEihShk9JmB/R0Ka6r/bJc2gtwe//XaKS5fu6G+cfJatzmZ26Gy6bOnCncw7OFo5srrtakpbGKb7ysMyTp8m8p13yb52DXM3N5ynf1Pk2/BpNBpdZ5DVq1cza9YsUlJSDB2WJEmSJEkPKfoJdvQxAK6Xqo8QUNW+BGYmevy2O7vD9ACwuFcS8o4bB0/folu3P6hVaz6jRv1NcnKW/sbLB0lZSbRc15Jlp7QLBkuZl2L9W+upVqqagSPTSt1/gGs9e5ETF4dl3bpUWr6syM9aR0RE0LhxYwYMGEBiYiJt2rQhNDQUGxs9ryeQJEmSJOmVFf0EO+ESAN9edgbA3UnPHUQUBd7zgn86Q2NnGFaPESO2IwSoVBqmTz9E48bL0DzHAsiCIC4tjvYb23MnUzv7PqXJFPZ13kdJM8N0XnmYJj2dGyNHItLTKfnmm1RasRwTOztDh5VnVCoVn376KfXq1ePw4cM4OTmxdu1atm7dSrVqBeMNjyRJkiRJuRX9BDsjEYCQO+YAtPcpnzfj1LCDDR24mqXi9OlbuR6aNCkII6OCv1vjT2d+4rV1r3E74zYAi1ou4s3qbxaYnSY16elEduuO+u5dzF1dKD/9myLfis/ExITjx4+j0WgYOnQoZ8+e5Z133ikwPxNJkiRJkh5V8FfZvQq1ClRpCMWImxkmONqY06Jm2TwdsmrV0pw4MZDXX/+Zixfv8Npr1Wjf3j1Px9SHm2k3+TbkWwDqOtYluHowjco3MnBU/0ndv5+Y4SPQpKdjbG+P07RpRTbJjIqKQq1WU7VqVRRFYeHChSQlJeFbzHaklCRJkqTCqmjPYGcmAZBtYoPACC9n23wZ1sXFjkOH+tG2rQszZrTMlzFflhCCrw9/TZs/2qAWanwcfFjRZgUda3Q0dGgACJWKhB9/5PrAQbrkuuL8eVh6eBg6NL1TqVTMnDmTmjVr0r9/f4TQlhW5urrK5FqSJEmSCpGiPYMddxqATBPtQrAq9vm3EM7e3or16ztjbl5wX+KEjAQmHJrA3ui9ANQvV5/P/T8vMDPDQgiuDxpM2v79ANi0bInzd7NRjPN/m/u89u+//zJgwABOnDgBgJ2dHenp6ZQo4os3JUmSJKkoKrjZnz6c+gOABLMKAJSyNNXPebdchvBb8GZ1qO2gXej4GAU5uT4Qc4BP9nxCek46liaWjPYbzduubxs6LB0hBDc+/UyXXDtNnoxth/YoRkXrQ5e7d+8yevRoFi9eDEDVqlWZP38+bdu2NXBkkiRJkiS9rIKbAepD2EoA/rHvAXFga6WnBHvBcTh2k9uzjzHbTMPYRe0oEeyqn3Pnkz8v/kl6Tjrmxub82u5XqpeqbuiQdDSZmdyeP5/kv/7CyMoKp2lTKdmqlaHD0rusrCx8fHyIiorC1NSUzz77jHHjxmFlZWXo0CRJkiRJegVFN8FOitZdDRNuQDylrPTQcSI2FY7dJEGjISgpmVNqNb8P38rqcp3w83N+9fPnk6tJVwFY2WZlgUqusy5dInrwELKvXQOg7LhxRTK5BjA3N6dfv378888//PDDD9SqVcvQIUmSJEmSpAdF6/P2B906q72s2IDz8ekAVNNHDfbBGLKFoGNyCqfUagAuRibSqNFStmy58OrnzwdqjZprydoEtqptVQNH85+Uf/7hyhtvkn3tGmbVquE0dSqlOhacspVXlZmZyYQJE/jll190940dO5Y9e/bI5FqSJEmSipCiO4O9eQQAORX8ibyUhrGRgouj9auf19OerI/rY/nDYYhO0t3doEEFmjcvOMnq09xIvYFKo6KsVVmsTA1fjiCys0lYtoz47+YAYFW/PhUXL8LI0tLAkenP33//zaBBg7h06RKOjo506NABS0tLTEyK7p+gJEmSJBVXRXMGOzEKkrUlIrcqvY5GgJOtBRameug+4V4Gm7EN2Hx1GCNG+APQtGklduzoiZW+arzz2NVkbXlIQZi9Vt24QWTXbrrk2rZ9eyotW1pkkuubN2/SrVs3WrVqxaVLl/Dw8OCPP/7Asoh8f5IkSZIkPapoTp9d2K69rBZElLkrkEC5khZ6HcLExIjZs9tQr1552rRxKTTJNfxXf23oBDv5f9uJ/eILNMnJmDg6Un76N5Ro0MCgMemLWq1m0aJFjB07lqSkJCwtLZkwYQIfffQRZkV890lJkiRJKu6KZoJ9VdvXGQd3biZlAlDOVr8J9n09etTOk/PmpYKQYMdN+4Y7K1YAYB0YiNOUyZjY2RksHn1Tq9XMnTuXpKQkXn/9debNm0fVqob/xECSJEmSpLxXNBPs25e0l54duXnlXoKtjxnsHA2YFP6qGkMl2Kpbt0jZuZPkrVvJCAkFoOz4zyndrVuB2dzmVaSkpKBWqylVqhRmZmYsWbKEuLg43n777SLx/UmSJEmS9HyKXoKdfgfiz4KJBTjV5uZxbbL9KjPYf/x+hqZHbuLYpRb4ltNXpAajS7BL5k+CnTXHDBIAACAASURBVB4Swq3vviMjNAzubf+NqSllP/0Eu+7d8yWGvCSEYP369QwbNozWrVuzdOlSAJo0aWLgyCRJkiRJMoSil2BfP6q9dK4HJuavXCKybt0Z3u38O6VRmHUpgV6TA1E8HPQVbb67m3mXu1l3sTKxwtHKMc/HSzt8hKg+fXS3rZs3p2TrVlgHBWFcsmSej5/XIiMjGTp0KFu2bAHg1KlTZGZmYmGRNyVJkiRJkiQVfEUwwT6ivayo7fBxM/nlS0RCQ2/Qo8efCAF3EPTZeIbt/7vEL6ObwZeN9RZyfopMjgS05SF5VbYghODuT6tJ2ryZzJMnATB1dqbq+j+LRFINoFKpmDVrFl999RUZGRmULFmSKVOmMGDAAIyN9dCtRpIkSZKkQqvoJdg3jmsvy/sA6Gawy75Egq3RCGxKmJGVlQGAMfCeuTl4F94Z7B2RO4C8q7/WZGcTN3Uqib/+pr3D1JQS9X0p/+23RSa5Tk9Pp0GDBpy89+ahS5cuzJo1CycnJwNHJkmSJElSQVD0EuzYCO2lcz3UGkF8ahbwcgl2/frO/DaiEa2++AcNsNi6BC1Ll4BgFz0GnD9Ss1P54tAX/H3tbwCCKgbpfQyh0RA9YABph/4FY2McP/6I0l27YmRl+M1s9MnKygpfX1/S09NZsGABrYroVu6SJEmSJL2copVg52RBxh1QjMGmPLdTs1BrBPbWZpi9ZPePFuObMM0MQnZepW+gC5SxBOPC1UlEIzRMOjyJv6/9jamRKV3cu9Cycku9jiFUKm5OnEjaoX8xLl2aij8swNLHR69jGIoQglWrVlG9enXdwsXZs2djZmYmN4yRpGJGpVIRHR1NZmamoUORJEmPLCwsqFChAqam+tnXpGgl2Mkx2ktrRzAyIlZPPbA/HdkY1UcNQaUBq8L1kl1JvML4Q+M5EX8Cc2Nzfmv3Gy6l9TcDL4Qgdfce4r//nqxz50BRcJoyucgk12fPnmXgwIHs3buXmjVrEh4ejpmZGba2toYOTZIkA4iOjsbGxoYqVarI9puSVEQIIUhISCA6Olpve1YUrmzxWY4s0l6WLA/8V3/9qj2wFUXBzMwYzArX4rXTCafpv70/KaoUzI3NmdFsht6Sa6HRkPL3Tm4vXEjW2bMAmJYvj9O0qZTw89PLGIaUkZHB5MmTmT59OiqVCgcHB8aMGaO3d7aSJBVOmZmZMrmWpCJGURTKlClDfHy83s5ZtBLsy7u0l95dAYhLfrEFjjk5GoyNlSLxD2d8ejzDdg0jRZVCQ6eGTGs2DTsL/eyUqE5MJOr9/mSeOgWAiYMDdv36UrpzZ4yKQMnE//73PwYPHsyVK1cA6N+/P9OmTcOuCO00KUnSyysK/0dIkpSbvv+uC1cx8dOk3oLbF8DMBur1AdCViDg9Z4nI4MF/0avXBjIyVHkVZb7498a/9N3el1vpt6jrWJf5LebrLblWxcUR2bkLmadOYWxvT9nxn1N959+U6dOnSCTXqamp9OzZkytXruDp6cmBAwdYvHixTK4lSSpUNm3axLRp0wwdhsGtWLECBwcHfHx8cHd3Z/bs2bkeX7x4Me7u7ri7u+Pn58eBAwd0j6lUKkaPHo2rqyt169alYcOGbNu2Lb+/hWcaMWIE+/btM3QYTxQaGoqXlxcuLi4MGzYMcX/DuQckJSXx5ptv4u3tjYeHB8uXLwcgPDychg0b4uHhQe3atVmzZo3uOV26dOHixYv59n28qKIzg512b1rfphwYaz/Gf5EZ7NWrT7B4cRgA587dZv2ctlRo6AyFbKbiTMIZBu4ciFqoKV+iPLMCZ2FqrJ+yhuzISKI++BBVVBRmLtWptGgRps7Oejm3IanVajQaDaamplhbWzNnzhyio6P56KOPZEmIJEmFUnBwMMHBwc91rBACIQRGRoaZc8vJycHEJO/Skc6dOzNv3jwSEhJwc3OjU6dOVKxYkS1btrBo0SIOHDiAvb09YWFhtG/fnqNHj1KuXDnGjx9PbGwsp06dwtzcnLi4OPbu3avX2NRq9SvtnZCQkMDhw4f57rvvnvs5ef16P2zgwIEsWbIEf39/Xn/9df73v//Rtm3bXMfMnz+fWrVqsXnzZuLj43Fzc6N79+5YWVmxatUqXF1duXHjBvXq1aN169aUKlWKgQMHMn36dJYsWZJv38uLKDoz2LfvvYsx/S+Zjk3S9q9+1iLHEyfieP/9TbrbISE36NxyNeKtP+HLg7DhIsSn6z9mPVt/cT2dt3RGLdQEVgjk1zd+pYxlGb2cWwjBzclTUEVFYe7qQuVVq4pEch0aGoq/v3+umZ5u3boxcuRImVxLkvRsDvNyfz3JqlO5j/t410sNFxkZibu7O3369KFGjRp0796dnTt30rhxY1xdXTl6VLub8YoVKxgyZAgAcXFxdOjQAW9vb7y9vTl06BCRkZG4ubnRq1cvPD09uX79Op999hmenp54eXnlmil80NGjR2nYsCF16tShUaNGnD9/HoAGDRpw+vRp3XGBgYGEhISQlpZG37598fPzo06dOmzcuFEXX3BwMM2bN6dFixakpqbSokUL6tati5eXl+44gEmTJuHm5kaTJk3o2rUrM2fOBODy5cu0adOGevXq0bRpU86dO/fU165MmTK4uLgQGxsLwDfffMOMGTOwt7cHoG7duvTu3Zv58+eTnp7OkiVLmDt3Lubm5gCULVuWd99995HzHjt2jEaNGuHt7Y2fnx8pKSm5Xn+AN954gz179gBgbW3NJ598gre3N1OnTuWdd97RHbdnzx7eeOMNAHbs2EHDhg2pW7cu77zzDqmpqY+M/ccff9CmTRvd7YkTJ1K/fn08PT354IMPdLPFgYGBjBgxAl9fX+bMmUNoaCgBAQG6hPX+a7JkyRLq16+Pt7c3HTt2JD391XKf2NhYkpOTadCgAYqi0KtXLzZs2PDIcYqikJKSom2ckJqKnZ0dJiYm1KhRA1dXVwDKly+Po6Ojrk66adOm7Ny5k5ycnFeKMa8UnQRbpU2mKVVZd1dcsrYH9rNKRE6ejMPU9L93kGbGCgvMLFD+jYX5x+HDHZCUpf+Y9SBbnc0/Uf/w7uZ3+eLQFwBUtKnIN82+0VtZiBCCuKlTSdu/H8XUlAoLFmBSyEsmkpOTGT58OH5+foSGhvLTTz+hUhXu0iBJkoqHS5cu8cknn3Du3DnOnTvHL7/8woEDB5g5cyZTpkx55Phhw4YREBBAREQEYWFheHh4AHDx4kUGDRrE6dOnCQkJITw8nIiICHbu3Mlnn32mS7oe5O7uzv79+zl+/DgTJ05k7NixgHaWeO3atYA2qYqNjcXX15fJkyfTvHlzjh49yu7du/nss89IS0sDICwsjHXr1rF3714sLCxYv349YWFh7N69m08++QQhBMeOHeOPP/4gIiKCbdu2ERISoovlgw8+YO7cuYSGhjJz5kwGDRr01NctKiqKzMxMateuDcDp06epV69ermN8fX05ffo0ly5dolKlSpR8xgZp2dnZdO7cmTlz5uheu2e1b01LS8Pf35+IiAhGjx7NkSNHdK/JmjVr6NKlC7dv3+brr79m586dhIWF4evry6xZsx4518GDB3N9D0OGDOHYsWOcOnWKjIwMtmzZkivWkJAQhg0bxtChQ1m3bh2hoaH07duXcePGAfD2229z7NgxIiIiqFmzJkuXLn1kzN27d+Pj4/PIV6NGjR45NiYmhgoVKuhuV6hQgZiYmEeOGzJkCGfPnqV8+fJ4eXkxZ86cRz5ROXr0KNnZ2VSvXh0AIyMjXFxciIiIeOrrbShFp0QkK1l7aVMOgEyVmqu3tb+wzyoR6d69Nq1buzB9+kHmzDnCN2Vs8FY98JFNm6rgUjpPwn4Vq8+sZuGJhSRlJQFgY2rDe57v0c+rH0aKft475dy+zc2Jk0jZsQPF1BTnOXMwq1hRL+c2BCEE69atY/jw4cTGxmJsbMzHH3/MV199JWesJUkqFKpWrYqXlxcAHh4etGjRAkVR8PLyIjIy8pHjd+3axapVqwAwNjbG1taWu3fvUrlyZRo0aADAgQMH6Nq1K8bGxpQtW5aAgACOHTv2SJlJUlISvXv35uLFiyiKopuYePfdd2nVqhVfffUVa9eupVOnToB2FnbTpk26WefMzEyioqIAaNmypW59ixCCsWPHsm/fPoyMjIiJiSEuLo6DBw/y1ltvYWFhgYWFBW+++SagXS9z6NChXLO/WVmPnwhbs2YN+/bt49y5c8ybNw8Li1frLPag8+fP4+TkRP369QGemZCD9mfQsWNHAExMTGjTpg2bN2+mU6dO/PXXX0yfPp29e/dy5swZGjduDGiT44YNGz5yrtjYWBwc/ttdevfu3UyfPp309HTu3LmDh4eH7jXr3LmzLuZTp07RsqV2Pwy1Wq3bifjUqVN8/vnnJCYmkpqaSuvWrR8ZMygoiPDw8Od+jZ7H9u3b8fHxYdeuXVy+fJmWLVvStGlT3esZGxtLz549WblyZa7E29HRUVc6UtAUnQQ7816Cba79YXyx8ZTuIRuLZydO9vZWTJ/ekk+H++Pw50XYcgXC4rQPtsmbbcVfxY8nf2RO2BwAHK0c6erelW7u3bAy1d+uiUmbNnFz8hQ0SUkoVlZUmD0L64AAvZ0/v6WkpNC5c2fdIhV/f38WLlyITxHp2S1JUvFwv2QBtLN4928bGRm90MflJUqUeOYx8+fP19W4bt26lfHjxxMUFMT69euJjIwkMDAQAGdnZ8qUKcOJEydYs2YNCxcuBLSJ8x9//IGbm1uu8x45ciTX+D///DPx8fGEhoZiampKlSpVnrqZj0ajoVSpUs+V6N2vwQ4JCaFVq1YEBwdTrlw5atWqRWhoKM2bN9cdGxoaioeHBy4uLkRFRZGcnPxcSfPDTExM0Gg0utsPfi8WFha56q67dOnCvHnzsLOzw9fXFxsbG4QQtGzZkl9//fWp41haWurOnZmZyaBBgwgJCaFixYp8+eWXuca9/3oLIfDw8ODff/995Hx9+vRhw4YNeHt7s2LFCl1Zy4N2797NRx999Mj9VlZWHDp0KNd9zs7OREdH625HR0fj/Jjy0uXLlzN69GgURcHFxYWqVaty7tw5/Pz8SE5Opl27dkyePFn3hvC+zMzMArvhW9EpEbk3i4uF9g/hVor2nWwTF/sXOo2jc0mUofVg+zsQ1gsGeEPXmnoN9VWdun2K78O+B6BTjU7s7LST973e11tyLdRq4qbP4MbIUWiSkijRpAnVN28q1Mk1aOvesrKysLW15YcffuDQoUMyuZYk6dXED8n99SS9PHMfN6v5k4/VsxYtWvDDDz8A2tnKpKSkR45p2rQpa9asQa1WEx8fz759+/Dz82Pw4MGEh4cTHh5O+fLlSUpK0iVIK1asyHWOzp07M336dJKSknRlGK1bt2bu3Lm6WuDjx48/NsakpCQcHR0xNTVl9+7dXLt2DYDGjRuzefNmMjMzSU1N1ZU8lCxZkqpVq/L7778D2qTxWaUCvr6+9OzZkzlztJNTI0eOZNSoUSQkJADajhUrVqxg0KBBWFlZ0a9fP4YPH052djYA8fHxuvHuc3NzIzY2lmPHjgHaiZycnByqVKlCeHg4Go2G69ev62rjHycgIICwsDCWLFlCly5dAG1N+8GDB7l06RKgLSu5cOHCI8+tWbOm7pj7ybS9vT2pqamsW7fuseO5ubkRHx+vS7BVKpWufj4lJQUnJydUKhU///zzY59/fwb74a+Hk2sAJycnSpYsyeHDh3W7Ir/11luPHFepUiX++ecfQLtm4Pz581SrVo3s7Gw6dOhAr169dJ+KPOjChQt4eno+Nk5DKzoJduot7aWVdlHfqRjtPyCj27q//DkrloRJTV81Mr3K0eTw1b9fIRD0rNWTCQ0n6LV3ozo1lesffMidZcvAxIRyE76g4pLFhXZB4759+3RtfBRFYdmyZZw/f54BAwYYbMW8JElSfpozZw67d+/Gy8uLevXqcebMmUeO6dChA7Vr18bb25vmzZszffp0ypUr98hxI0eOZMyYMdSpU+eR2fJOnTrx22+/5VoIOH78eFQqFbVr18bDw4Px48c/Nsbu3bsTEhKCl5cXq1atwt1d+393/fr1CQ4Opnbt2rRt2xYvLy/dTro///wzS5cu1bV2e3Bh5JOMGjWK5cuXk5KSQnBwMH379qVRo0a4u7vTv39/Vq9erSuX+Prrr3FwcKBWrVp4enryxhtvPDKbbWZmxpo1axg6dCje3t60bNmSzMxMGjduTNWqValVqxbDhg2jbt26T4zJ2NiYN954g23btukWODo4OLBixQq6du1K7dq1adiw4WMXcbZr1043y1yqVCn69++Pp6cnrVu31pWtPMzMzIx169YxatQovL298fHx0SXHkyZNwt/fn8aNG+t+Bq9qwYIFvP/++7i4uFC9enVdB5GFCxfqPukYP348hw4dwsvLixYtWvDNN99gb2/P2rVr2bdvHytWrNDVet//1CIuLg5LS8vH/p4WBMrj+hEWZL6+vuLBRQ46iwIgNhx6byaujB/+U7TvhI6Pb0npEmb5HGXeWXl6JTNDZlK+RHnWv7Vef7PWGg3xs78jYcUKUKkwsrGhwpzvKPGYRQuFwe3btxk5ciTLly+nRYsW/P3333JzCEmSXtnZs2epWbNgfapZ1KWmpmJtbU16ejrNmjVj8eLFT01Yi5smTZqwZcsWSpUqZehQ8tXs2bMpWbIk/fr109s5H/f3rShKqBDC90XPladTeIqitFEU5byiKJcURRn9mMc/VhTljKIoJxRF+UdRlMqPO88zXftXm1yjgKMH83ZpPy6pal+iSCXXMakxzA+fD8C4BuP0llynHT7M1U6dSFiyBFQqTCtWpNLy5YUyudZoNCxbtgw3NzeWL1+OmZkZTZs2Ra1WGzo0SZIk6SV88MEH+Pj4ULduXTp27CiT64d8++23uoWjxUmpUqXo3bu3ocN4ojxb5KgoijEwH2gJRAPHFEXZJIR48LOp44CvECJdUZSBwHSg8wsPdnq99rL2u1CiDGlZ1wFwtDF/4lOGDdvGpk3ncXKywcnJmuHD/QkIqPLCQ+cXIQST/p1ERk4Grau0plmFZq98TlVcHPGzZpG0UdsDXLG0pPyUyZR8qAF8YXH69GkGDhzI/v37AW3d4YIFC6hRo4aBI5MkSZJe1i+//GLoEAo0f39/Q4dgEO+9956hQ3iqvOwi4gdcEkJcAVAU5TfgLUCXYAshdj9w/GGgx0uNdP2w9rKO9ulJ97Y6f6/xk7t/XL2ayLVrSVy7pq3V7v6Oh7ZrSG0HMCl4tbnbr23n4I2D2JjaMKr+qFc6l1CrufPTT8R/PxeRno5iaopdnz6U+fADjK2t9RRx/kpKSqJBgwakpqbi6OjIrFmz6NatmywLkSRJkiQp3+Vlgu0MXH/gdjTwtLdZ/YBtLzxKdjrERoBiDM7aPoiJ9xLsMtZPLg+5eTP3jkhOtzJg2O9gbQr+5eHN6tC91guHk1e2XtkKQC+PXjhYOTzj6McTQpB++DC3Zn5L5r0VwzYtX8Nx5MhC29taCIGiKNja2jJq1ChiYmKYMmUKpUsXvL7lkiRJkiQVDwWiD7aiKD0AX+CxfeAURfkA+AC0rVxyuXZQe2ntCGbaHo+J6dqWOraWT+5/HRubkuu20783tFdSVfDPNXAqUWAS7HRVOoduaFf4dnDp8FLnSDt8hFszZ5J5Stsf3NjBHqeJE7EJCtJbnPkpJiaG4cOH89Zbb9GzZ08Axo0bJ2esJUmSJEkyuLxMsGOAB6dFK9y7LxdFUV4DxgEBQojHbsMkhFgMLAZtF5FcD8bfa1vj+F8ynJShbR1U6ikJ9pkzg4mNTSE2NpWbkYmUH/9Qw/U3qj/xufnt0I1DZKmz8LL3omyJsi/0XCEEKTt3EjNsOAiBcenS2PXuRekePTG2fvYmAwVNTk4O8+fP5/PPPyc1NZWwsDC6deuGsbGxTK4lSZIkSSoQ8rLY+BjgqihKVUVRzIAuwKYHD1AUpQ6wCAgWQtx6qVESLmsvXVsB2oQyKUM7g13yKQl2yZLmuLnZExhYhS7Bbpi/Xxsq2WgfLGsFQZWe+Nz89k+UtuVg80ovvjHB7bnziBk6DITAvFZNXHbvwn7AgEKZXB87dgx/f39GjBhBamoq7du3Z+/evbl2xJIkSZKkx4mMjMTS0hIfHx9q1apFr169dFu9g3a7eD8/P9zd3XF3d2fx4sW5nr9q1So8PT3x8vKiTp06uu3fC5INGzYwceJEQ4fxRHfu3KFly5a4urrSsmVL7t69+8gxu3fv1vW89vHxwcLCgg0bNgBw9epV/P39cXFxoXPnzrpNgObNm8eyZcvy9Xt5ljxLsIUQOcAQYDtwFlgrhDitKMpERVGC7x02A7AGflcUJVxRlE1PON2TpWt3YMLaUXszW41KLbAwNcLC9DkTLztLmNAYQnrBzndhVhAYFYzZUJVGxd7ovQC0qNTihZ57Z/XP3F6wAIyMsB82lMorVmBkYZEXYeaptLQ0hgwZgr+/P2FhYVSqVImNGzeyfv16KhbS2nFJkqSCypBtTYUQubYY17fq1asTHh7OyZMniY6OZu3atQDcvHmTbt26sXDhQs6dO8eBAwdYtGgRf/31FwDbtm3ju+++Y8eOHZw8eZLDhw/rNrzRlxfZ5v5Jpk+fzqBBg/J1zBcxbdo0WrRowcWLF2nRogXTpk175JgHd4rctWsXVlZWtGqlnUQdNWoUH330EZcuXaJ06dIsXboUgL59+zJ37tx8/V6eJU/bZQghtgohagghqgshJt+77wshxKZ7118TQpQVQvjc+wp++hkfI/veYsV7W6TfX+D4tPrrJ1IU8HaEVk/uPpLfjt08Rkp2CtVsq1HV9vniUsXEcH3gIOK+/hqAsqNH4zBoEMYP7UBVWJiYmLBz506MjIz47LPPOHPmDMHBL/6rIkmSpG+K8lWurydZvDg013EffLD5pcaLjIzE3d2dPn36UKNGDbp3787OnTtp3Lgxrq6uui25jx49SsOGDalTpw6NGjXi/PnzgDZ5/vTTT/H09KR27dq6pKRKlSqMGjWKunXr8vvvv/Prr7/i5eWFp6cno0Y9vnNVamoqLVq0oG7dunh5eel2Uhw9ejTz58/XHffll1/qZntnzJhB/fr1qV27NhMmTNB9T25ubvTq1QtPT0+uX7/OwIED8fX1xcPDQ3ccwNatW3F3d6devXoMGzZMt/NhWloaffv2xc/Pjzp16jxzV0djY2P8/PyIidFWrs6fP58+ffroemzb29szffp0XQI4depUZs6cSfny5QEwNzenf//+j5w3Li6ODh064O3tjbe3N4cOHSIyMjLXdt4zZ87kyy+/BCAwMJARI0bg6+vL5MmTqVy5su4NRlpaGhUrVkSlUnH58mXatGlDvXr1aNq06WN3dbxw4QLm5ubY29sDsHnzZvz9/alTpw6vvfYacXFxup9Hz549ady4MT179iQ+Pp6OHTtSv3596tevz8GD2rVtT/odehUbN27U9a7u3bu3bmb6SdatW0fbtm2xsrJCCMGuXbt0W6Y/+HwrKyuqVKny1C3p850QolB91atXT+SyKFCICSWFiDoihBDiZHSiqDxqi2g9e68oCib9O0l4rvAUc0LnPNfx6sxMcfmNN8QZN3dxtra3uDV3Xh5HmDcuXbokbt++rbt99OhRceLECQNGJEmSJMSZM2dy3YYvc309yaJFIbmO699/00uNf/XqVWFsbCxOnDgh1Gq1qFu3rnjvvfeERqMRGzZsEG+99ZYQQoikpCShUqmEEEL8/fff4u233xZCCLFgwQLRsWNH3WMJCQlCCCEqV64svvnmGyGEEDExMaJixYri1q1bQqVSiaCgILF+/fpHYlGpVCIpKUkIIUR8fLyoXr260Gg0IiwsTDRr1kx3XM2aNUVUVJTYvn276N+/v9BoNEKtVot27dqJvXv3iqtXrwpFUcS///6re879uHJyckRAQICIiIgQGRkZokKFCuLKlStCCCG6dOki2rVrJ4QQYsyYMeKnn34SQghx9+5d4erqKlJTUx957Tw8PIQQQmRkZIjAwEAREREhhBCiQ4cOYsOGDbmOT0xMFKVLlxZCCFG6dGmRmJj4zJ/Pu+++K2bPnq2LPTExMde4QggxY8YMMWHCBCGEEAEBAWLgwIG6x4KDg8WuXbuEEEL89ttvol+/fkIIIZo3by4uXLgghBDi8OHDIigo6JGxly1bJj7++GPd7Tt37giNRiOEEGLJkiW6xyZMmCDq1q0r0tPThRBCdO3aVezfv18IIcS1a9eEu7u7EOLJv0MPSk5OFt7e3o/9On369CPH29ra6q5rNJpctx8nKChIbN68WQjx3+/YfVFRUble16+//lrMnDnzqed7lof/voUQAggRL5GvFoguIq8k6V4nQNsKAJy7qe0OYveYHRy3bLlAxYol8fYumPvWP0ytUbPm/Brg+cpDhEpF/Pffk3XxEqbOzlT+eTWm5QrH93pfVlYWM2bMYPLkyXTv3p0ff/wRgPr16xs4MkmSpIKhatWqeHl5AeDh4UGLFi1QFAUvLy8iIyMB7d4AvXv35uLFiyiKoqs13rlzJwMGDMDERPvfv52dne68nTtr93k7duwYgYGBODhoW8J2796dffv20b59+1xxCCEYO3Ys+/btw8jIiJiYGOLi4qhTpw63bt3ixo0bxMfHU7p0aSpWrMicOXPYsWMHderUAbQz4BcvXqRSpUpUrlyZBg0a6M69du1aFi9eTE5ODrGxsZw5cwaNRkO1atWoWlX7aW7Xrl11ddI7duxg06ZNupnyzMxMoqKiHtn2+vLly/j4+HD16lXatWtH7dq1X/GnkduuXbtYtWoVoJ0lt7W1fWyd8YPuv+73r69Zs4agoCB+++03Bg0aRGpqKocOHeKdd97RHZeV9WhPmyT+NQAAIABJREFUiNjYWN3PDCA6OprOnTsTGxtLdna27nUDCA4OxtLSEtD+Tpw5898egMnJyaSmpj7xd+hBNjY2hIeHP+tleSxFUZ7anCA2NpaTJ0/SunXr5zqfo6PjY2f2DaVwJ9jZaZAWD8ZmYK1NJLedjAWgbMnctcY5ORoGDNhCTEwKTZtWYsgQPzp0cMf0eeu081m6Kh3/X7Rtw8uVKEetMk9vGZiTkEBkl66ort/bxXL0qEKXXO/Zs4eBAwfq/kBycnJQq9VyEaMkSdIDzM3/26XYyMhId9vIyEhXUzt+/HiCgoJYv349kZGRBAYGPvO8JUo8ffH7kSNH+PDDDwGYOHEid+7cIT4+ntDQUExNTalSpQqZmZkAvPPOO6xbt46bN2/qEkghBGPGjNGd477IyMhcY1+9epWZM2dy7NgxSv+fvXOPy/H+//jz7qCDlGONnJV0uO9K5XycKSOZ01qYidnX2WLOZoZt2BznbCzmuPFtzMa+Q0bMoSwKKT9CSSU6l+ru+v1x61q3uwiJvt/P8/G4H9t9XZ/D+7quG6/P+3p/3u8aNRg2bJg8bmlIksTevXuxs7N7YruiGOx79+7Rvn179u/fj4+PDw4ODoSFhdGnTx+5bVhYGI6OjoBmIRMWFsabbz57sgEDAwOtuPLHr6X4tfv4+DBz5kzu378vz5eVlUX16tWfKmRNTExIS0uTv48fP55Jkybh4+PDsWPH5LCUx+csLCzk9OnTGD+2R2vcuHFP/Q1lZGTQsWPHEu3ZsWMHDg7a2sXKyoqEhATq1q1LQkIClpaWpV7Pjz/+SN++fTE01IT81qpVi9TUVAoKCjAwMCAuLg5ra2u5fW5urrxoeB14/UoWPgupRd7rBqCnuZT41BwA+rW01mr6++/XiI/XeLdPnLjF8OH7yLl6H66kgKSd+e9Vc/X+VVlcA0z3mP7EVV5hbi4Jcz4j//Zt9C0ssF66BPPu3SvC1HIhKSmJDz74gK5duxIVFYWdnR1Hjx4lMDBQiGuBQPBaI0mfaX1K46OP3LTabdjQ+6XalZaWJouPwMBA+Xj37t1Zv369LMTv37+v07dVq1b8+eef3Lt3D7Vazc6dO+ncuTOtW7eWN5/5+PiQlpaGpaUlhoaGBAcHc/PmTXkMX19fdu3axZ49e2TPq5eXF5s3byYzU7N3Kj4+nqQk3QRi6enpVK1aFQsLCxITEzl4UFODzs7OjuvXr8te+t27d8t9vLy8+Pbbb5Ee/Xv+999/P/H+1K5dm4ULF/LVV18BMHbsWAIDA2URm5KSwrRp05g6dSoAM2bMYMqUKdy9exeAvLw8+Q1rcbp168batWsBTbx7WloaVlZWJCUlkZKSwsOHDzlw4ECpdpmZmeHh4cHEiRPx9vZGX18fc3NzmjRpwk8//QRoFhMXLlzQ6Wtvb8+1a9fk78V/A1u2bCl1Tk9PT60NgkX3oLTfUHGKPNglfR4X16BZQBTZsmXLFq0FzePs3LkTPz8/+btCoaBr167s2bOnxP7R0dFase6vmsotsHMevXapWls+lJqteYXRtI52ye/AQO0f43vvOWH+41XotBNctkDAUQhPfLn2lgF1oZp5f2lS7BjpG7Gj5w66NSo9PESdmkqs73tkHjkCBgY02LwJ8549K8rcF+bevXvY29uzdetWjIyMmDdvHhcuXKBrJS2AIxAIBK8DU6dOZcaMGbi6umplivjwww9p2LAhKpUKZ2dnduzYodO3bt26LFy4kK5du+Ls7Iybm1uJQmjw4MGEhoaiVCrZunUrLVq0kM85OjqSkZGBtbU1devWBTRCbtCgQbRt2xalUsmAAQPIyMjQGdfZ2RlXV1datGjBoEGDaN++PaDx0K5Zs0be7FetWjU5k8enn35Kfn4+KpUKR0dHPv3006feo3feeYfs7GxOnDhB3bp12bZtGyNHjqRFixa0a9eO4cOH07u3ZiHUs2dPxo0bx1tvvYWjoyMtW7YkPT1dZ8wVK1YQHByMUqnEzc2Ny5cvY2hoyJw5c2jVqhXdu3fXuk8l4evry7Zt27RCR7Zv386mTZtwdnbG0dGxxE2cnTp14u+//5YXGXPnzmXgwIG4ubnJGx9LYuXKlYSGhqJSqXBwcGDdunVA6b+hF2H69On88ccf2NracvjwYaZPnw5AaGgoH374odwuNjaW27dv07mzdv3BRYsWsXTpUmxsbEhJSWHEiBHyuZMnT9L9NXIuKqTXzHv7NNzd3aXQ0FDNl5jDsL0/NOsG7/+brIcFOH72OwCXPveiqtE/ETA//HCBjRvPc+LELQBOnhxOuw//gOTsfwbf1AN8bCrsWopTKBXy243f2By5mZgHMdQ0rsmBvgeoVqVaqX2kggJujxlD1vETGDZqiPU3SzBRvj6rt7IyfPhw4uLiWLNmDTY2r+b+CwQCQVm4cuWKTlyvoOLIzMzEzMwMSZIYO3Ystra2BAQEvGqzXhsmTpxI7969eeutt161KRXK33//zdKlS/nhhx9eaJyS/nwrFIowSZLcn3WsSh6D/Wjl+6hEeujNfzYSmFbRDi14/31n3n/fmWvX7vPzz1G0Vehpi2sF0Kn+y7a4RLLzs+n5756k5GpyelerUo1vOn/zRHENkPztKrKOn0DP3JxGmzdjaG39xPavA1lZWcybN49evXrRqVMnANasWYORkZGoxCgQCASCJ7Jx40a2bNlCXl4erq6uOvHc/+vMnDmTM2fOvGozKpx79+4xf/78V22GFpVcYGdp/mukEaL7wjX5LPu6Wpcq1mxsavLJJ+3gwP9BB2uIfgBJ2eD+BlSv+CIsYYlhTDo2ifu5mji4ZhbNCOwRSHXj6k/sl/J9ICnr14O+PtZLl1YKcf3LL78wbtw4bt26xa+//srFixfR09PT2VghEAgEAkFJBAQECI/1E7CysvqfrBPxOoWGFFG5BfbDR0VmHnmwDR9tdDQoSxVG72aaD0BiFiRmP7n9S0BdqObTk59yP/c+NtVtmOIxhXb12j21X+ref5O0aBEAdcaPw6xD+5dt6gtx+/ZtJk6cSFBQEACurq6sX78ePb3KvQVAIBAIBAKBoCQqt8DO0xbYmQ81QfidmtcprUfJWFXVfCqYkPgQbmfcpl7VevzY+0cM9Z5efbIwJ4f7gd8DYNGvH7VKqCT1ulBQUMDKlSuZM2cOWVlZmJmZsWDBAsaOHSvnYBUIBAKBQCD4b6NyqxxZYGsyhqTnajKIVDOuHJe1/cp2APxa+JVJXOcnJhI3ZiwPY66hX706VjOmo3iN09ilp6fz1VdfkZWVRf/+/Vm+fDn167+aOHeBQCAQCASCiqJyKNHSKIrBfiSwM3I1HuwigX38+E2ysvLo0qUxJiZPF7AVyf+l/h9/JfyFiYEJfW37PrX9w+s3uDVsGAVJSRjWr0+DtWvQr/bkTZCvgtTUVExMTDAyMqJmzZqsX78eIyMjevXq9apNEwgEAoFAIKgQKncQbFEMtpFGYBeFiFQz1ojpRYtO0rPnDmrVWkzv3jsJD7/7Ssx8HEmSWH9xPQC9m/bGwsjiie3z4+O5OXQoBUlJmLi70finHzGyta0IU8uMJEns2LEDOzs7Fi9eLB/v16+fENcCgUAgEAj+p6jcArtYDPaDrDyuJWm+mz8S2KGhdwDIySngwIFo9PUfbX48mwDqQp3hKoL8wnxmhczi4I2DKFAwyH7QE9vnXo0mdtBg1PfuYaxS0XDjRgxq1Kgga8tGdHQ03bt3Z/DgwSQlJXH8+HEqW351gUAgqCzo6+vj4uKCk5MTvXv3JjU1VT536dIl3nzzTezs7LC1tWX+/Plafx8fPHgQd3d3HBwccHV1ZfLkya/iEp4LPz8/VCoVy5YtK1N7MzOzpzeqANq1e3rygrKyfPlytm7dWm7jlTc3btygdevW2NjY4OvrS15enk6b7du34+LiIn/09PQIDw8nIyND63jt2rX5+OOPAVi1ahWbN2+u6Mt5MSRJqlQfNzc3SWaLjyR9Zi5JMX9IX/52WWo07YDUaNoBqbCwUEpOzpJgrvwxMpovFRSoJen4bUmq/a0kef0oSdP/lKQ9UVJFkZ2fLU04MkFyCnSS3H9wl/Zf2//E9plnzkhR7h7SZbsW0o3Bg6WC1NQKsrRs5OTkSHPnzpWqVKkiAVLNmjWlTZs2SWq1+lWbJhAIBC+Fy5cvv2oTpKpVq8r/P3ToUGnBggWSJElSdna21LRpU+n333+XJEmSsrKypB49ekirVq2SJEmSIiIipKZNm0pXrlyRJEmSCgoKpDVr1pSrbfn5+eU6XhEJCQlSs2bNnqlP8fv030B+fr6kVCqf6R6/rOdRGgMHDpR27twpSZIk/etf/3rq7+vixYtS06ZNSzzXsmVL6c8//5QkSfNbdnFxKV9jS6CkP99AqPQcerVyx2DnaFbtqZIZ34fEAvD1ABUKhYKCgkJGj3bnt99iuHkzDQeHOujr68GPUZq+YYmaT6gl9Ld7qWYWSoUsOruIX67/QkZeBtWqVGP9W+tR1lGW2if90CHuTJmKlJ9Pte7dqffN1+gZGb1UO5+Fu3fv0qlTJ2JiYgAYNmwYX3/99RPLsQoEAsF/E42n//pSxo1dWPawurZt23Lx4kUAduzYQfv27fH09ATA1NSUVatW0aVLF8aOHcvixYuZNWuWXKpbX1+f0aNH64yZmZnJ+PHjCQ0NRaFQ8Nlnn9G/f3/MzMzIzNS8Kd6zZw8HDhwgMDCQYcOGYWxszN9//0379u3597//TXh4ONWra+o52NraEhISgp6eHqNGjeLWLU1F5eXLl8tl0IvIzc1l9OjRhIaGYmBgwNKlS+natSuenp7Ex8fj4uLCt99+S8eOHeU+iYmJjBo1iuvXrwOwdu1aLa9xZmYmffr04cGDB+Tn57NgwQL69OlDVlYW7777LnFxcajVaj799FN8fX2ZPn06+/fvx8DAAE9PT7755hstG8+ePcvEiRPJzc3FxMSE77//Hjs7Oy5duoS/vz95eXkUFhayd+9ebG1t5ftWmh2xsbG8/fbbdOjQgVOnTmFtbc2+ffswMTHRmvfo0aO0bNlSzsK1ceNGNmzYQF5eHjY2Nvzwww+YmprqPI+xY8cyduxYkpOTMTU1ZePGjbRo0YJffvmFBQsWkJeXR61atdi+fTtWVlZl/u09jiRJHD16lB07dgDwwQcfMHfu3BJ/Y0Xs3LmT9957T+d4dHQ0SUlJ8nM2NTWlcePGnD17llatWj23jRVJ5RXYkgQZmpjq61lG5KkLaVTLlAFumiwVb7xhxpo1vZAkiaioeyQmPtoQGfpYHHbrui/VzBtpN5h5YiaRKZEA1DSuyfru62lRs0WJ7aW8PBK/+YYHWzXlPmsM8sNq1qzXLluIlZUVDRo0wMDAgLVr19K5c+dXbZJAIBD8T6FWqzly5AgjRowANOEhbm5uWm2aNWtGZmYm6enpREZGlikkZP78+VhYWBAREQHAgwcPntID4uLiOHXqFPr6+qjVaoKCgvD39+fMmTM0atQIKysrBg0aREBAAB06dODWrVt4eXlx5coVrXFWr16NQqEgIiKCqKgoPD09iY6OZv/+/Xh7exMeHq4z94QJE+jcuTNBQUGo1Wp5EVCEsbExQUFBmJubc+/ePdq0aYOPjw+HDh2iXr16/PqrZqGUlpZGSkoKQUFBREVFoVAotMJvimjRogUnTpzAwMCAw4cPM3PmTPbu3cu6deuYOHEigwcPJi8vD7VaXSY7AGJiYti5cycbN27k3XffZe/evQwZMkSr/8mTJ7Web79+/Rj5KFXv7Nmz2bRpE+PHj9d5Ht26dWPdunXY2tpy5swZxowZw9GjR+nQoQOnT59GoVDw3XffsXjxYpYsWaI159WrV/H19S3xmR87dkxeRAGkpKRQvXp1eQFQv3594uPjS+xbxO7du9m3b5/O8V27duHr66tVNNDd3Z0TJ04Igf3SSb8DmXfB2II4agN3cKpnoVPBUaFQYG9fB3v7OhpR7mwJD3IhJVfTYFrrl2diXjr+h/xJyU2hlnEtRjuPZkDzAejrlSyWH167RvzUqTy8rPkLp05AALU+GvlalBAvLCxk48aNdO3alebNm6NQKNixYwc1atSgSpUqr9o8gUAgqHCexdNcnuTk5ODi4kJ8fDz29vblXsXu8OHD7Nq1S/5eowz7fgYOHIj+I0eQr68v8+bNw9/fXxZKReNevnxZ7pOenk5mZqZWrHRISIgsElu0aEGjRo2Ijo7G3Ny81LmPHj0qxyXr6+tjYaGdOECSJGbOnMnx48fR09MjPj6exMRElEolkydPZtq0aXh7e9OxY0cKCgowNjZmxIgReHt74+3trTNfWloaH3zwATExMSgUCvLzNSmC27ZtyxdffEFcXBz9+vXD9rFkBKXZAdCkSRNcXFwAcHNzIzY2VmfehIQE7O3t5e+RkZHMnj2b1NRUMjMz8fLy0nkemZmZnDp1ioEDB8rnHj58CGhEuK+vLwkJCeTl5dGkSROdOe3s7Epc1JQHZ86cwdTUFCcnJ51zu3bt4ocfftA6ZmlpSVRU1Eux5WVQeTc53te8CiI3jeRMTfaQOtWeEkKhUMA6T7g8Av58D5a/CdVenjhcHraclNwULE0tCeoThG8L31LFdeqePdzoP4CHl69gaG1No+3bqP2vj14LcX3hwgXat2/PqFGjGDNmjLxhxsrKSohrgUAgqGBMTEwIDw/n5s2bSJLE6tWrAXBwcCAsLEyr7fXr1zEzM8Pc3BxHR0ed889C8X+PcnNztc5VrfpPsba2bdty7do1kpOT+fnnn+nXrx+gcdScPn2a8PBwwsPDiY+Pr5CNiNu3byc5OZmwsDDCw8OxsrIiNzeX5s2bc/78eZRKJbNnz2bevHkYGBhw9uxZBgwYwIEDB+jRo4fOeJ9++ildu3YlMjKSX375Rb4XgwYNYv/+/ZiYmNCzZ0+OHj1aJjsAjIqFgOrr61NQUKAzr4mJidZ9HzZsGKtWrSIiIoLPPvtM61zR8ygsLKR69eryPQ8PD5ffGowfP55x48YRERHB+vXrdZ4paDzYxTceFv887t2vVasWqampsu1xcXFYW1uX+lx27dqFn5+fzvELFy5QUFCg8zamKCSnslB5BXbOfc1/zaxIztCsxp4qsIvQU4BDbRjs8JKM0xB8OxiAma1mUsO4ZA9AXlw8sYOHkDD7U6SHD7Ho348m+37G9LEf1qsgMzOTTz75BDc3N06fPk29evUYNWrUqzZLIBAIBGjiUleuXMmSJUsoKChg8ODBhISEcPjwYUDj6Z4wYQJTp04FYMqUKXz55ZdER0cDGvG1bt06nXG7d+8ui3b4J0TEysqKK1euUFhYSFBQUKl2KRQK+vbty6RJk7C3t6dWrVoAeHp68u2338rtSvKMduzYke3bNUXYoqOjuXXrFnZ2T94n1a1bN9auXQtowmbS0tK0zqelpWFpaYmhoSHBwcHcvHkTgDt37mBqasqQIUOYMmUK58+fJzMzk7S0NHr27MmyZcu4cOGCznxpaWmycAwMDJSPX79+naZNmzJhwgT69Okjx8Y/zY6yYm9vz7Vr1+TvGRkZ1K1bl/z8fPmePY65uTlNmjThp59+AjRe9KJrKn4dW7ZsKbF/kQe7pE/x8BDQPPeuXbuyZ88eecw+ffqUOG5hYSE//vhjifHXO3fuLFF4R0dHl+jtfl2pvAK7KAd2k07/CGyz12MToCRJfH3ua+7l3ENPoUebem1KbJeflETc6FHkhIWhV7UqltOmUe+LL9B/DVIL/fzzz9jb27NkyRIkSWL8+PFcuXKFAQMGvBZedYFAIBCAq6srKpWKnTt3YmJiwr59+1iwYAF2dnYolUo8PDwYN24cACqViuXLl+Pn54e9vT1OTk7yxsDizJ49mwcPHuDk5ISzszPBwRpn0cKFC/H29qZdu3bUrfvk/Uu+vr5s27ZNK3535cqVhIaGolKpcHBwKFHcjxkzhsLCQpRKJb6+vgQGBmp5d0tixYoVBAcHo1QqcXNz0wpDARg8eDChoaEolUq2bt0qb/KMiIigVatWuLi48PnnnzN79mwyMjLw9vZGpVLRoUMHli5dqjPf1KlTmTFjBq6urlqe5h9//BEnJydcXFyIjIxk6NChZbKjrLz99tscP35c/j5//nxat25N+/btnzjW9u3b2bRpE87Ozjg6Osoxz3PnzmXgwIG4ubmVW4KCRYsWsXTpUmxsbEhJSZH3B+zfv585c+bI7Y4fP06DBg1o2rSpzhg//vhjiQL75MmT5R4O9TJRFL3uryy4u7tLoaGhcHodHJoGHiMZmvgux6OT+X6YBzUfFlK9ujENG+rGY1cEeeo8vj73NbuuauLXAtwCGO40XKedVFDAreEjyD57lipNmtBo+zYMatasaHNLJD4+nmbNmvHw4UPc3NxYt24d7u7ur9osgUAgeOVcuXJFKw5WIKhI+vbty+LFi3Xiu//b+fvvv1m6dKlOXHZ5U9Kfb4VCESZJ0jOLoMq7yfFhhua/RtW0QkTG/iuI48dvUq9eNdq1a8CCBV2xs6uY1HFR96P49OSnRN2PQl+hT4BbAB84fqDTTpIkEj6dQ/bZs+jXqEGjrVteubjOz8/HwMAAhUKBtbU1X3zxBVWqVGHMmDHyxhWBQCAQCASvjoULF5KQkPA/J7Dv3bvH/PnzX7UZz0TlDRHJ0FRpxMxSFtjmRgacPatJCXPnTgZ79lzGxERT1ZHCl+epT89LZ9KxSQz8ZSBR96OwNrNmy9tbShTXALmXLpP2KH6t/upVGNSp89JsKwunTp3Czc2Nbdu2yccmT57M+PHjhbgWCAQCgeA1wc7Ojk6dOr1qMyqc7t2707hx41dtxjNReQV29qNNjlXrkPlQkyInNjqF3Nx/4qGsravRoIE5ZOWD/Sbo8RPMOA57rsJDdUmjPjOFUiFT/5zKHzf/wFjfmCH2Q9jrsxfnOs6l9sk4otmAYjGgP6YtW5aLHc/D/fv3+de//kX79u2JiIhgzZo1osS5QCAQCAQCwQtSeQV2zqPE9yY1yFdrRKGTQx0WLOhK9erGALRr10ATh30iDu7naio3fncRPjsJ+i8eny1JEtOPT+fknZNU0avCj71/ZFqraVQ1rFpqn9zoaO5/twkA82I5KysSSZL44YcfaNGiBRs2bMDQ0JBZs2Zx9OhRsYFRIBAIBAKB4AWpvDHYuZr8i4XGNVAXJgBQw8KYWbM6MW5cK1asOIOdnSY1ECfjtPt6NwODF1tbFEqFDP99OGGJYRjqGbKo0yKaWOgmaS9OXlwct0eN0pQ/9/KiaocOL2TD85CYmIifn5+8K7xz586sXbtWbNoRCAQCgUAgKCcqvQc730hTsclQXyF7Xy0sjJkzpzO+vo/yJSZkafftWP+Fp59zcg5hiZqE/Z+1/Yy3Gr31xPaSWs2dyZ9QcCcBExcX6s6f90q8xdWrVychIYHatWsTGBhIcHCwENcCgUBQidDX18fFxQUnJyd69+6tVfDj0qVLvPnmm9jZ2WFra8v8+fO1Qv8OHjyIu7s7Dg4OuLq6lql0+uuCn58fKpWKZcuWlan9yypiM3fuXL755psSz7Vr1+6lzPk4d+7cYcCAAc/U5+OPP9ZK8/e6ERYWhlKpxMbGhgkTJpQYsvr111/LhW6cnJzQ19fn/n1NyPCyZctwdHTEyckJPz8/uXDOe++9R0xMTIVeC1Rqga35CyU2S7OJ0VD/CZfyXQ+IGgE7vGGyB3i88dzT5hfmM/i3wez7P00eSb8WfvSxKTmRenFSNm4k58IFDOrUocGG9eg/oexrefPHH3+QkpICaKpF/fTTT0RFRfHBBx+IkBCBQCCoZBRVcoyMjKRmzZpyUZicnBx8fHyYPn06V69e5cKFC5w6dYo1a9YAmtLa48aNY9u2bVy+fJnQ0FBsbGzK1baSKhCWB3fv3uXcuXNcvHiRgICAlzJHeXDq1KkKmadevXpyQZeykJKSwunTp59pg+TLepalMXr0aDZu3EhMTAwxMTEcOnRIp82UKVPkQjdfffUVnTt3pmbNmsTHx8t51iMjI1Gr1ezatUsed/HixRV6LVBZBba6AB6mAwrOJWg2KxroPUUo1jKB7o1hemuwKj1G+mn8decvLiZrqjMNcxzGzNYzn9onPz6e5G9XAVD3yy8rTFwnJCTg5+eHp6cn06ZNk487OTnJlbUEAoFAUHlp27Yt8fGa7Fk7duygffv2eHp6AppKj6tWrWLhwoUALF68mFmzZslFSfT19Rk9erTOmJmZmfj7+6NUKlGpVOzduxfQ9gjv2bOHYcOGAZqS3aNGjaJ169ZMnTqVxo0ba3nVbW1tSUxMJDk5mf79++Ph4YGHhwcnT57UmTs3N1ee29XVVQ5n9PT0JD4+HhcXF06cOKHVJzExkb59++Ls7Iyzs7OOyM3MzKRbt260bNkSpVIpF1rJysqiV69eODs74+TkxO7duwGYPn06Dg4OqFQqPvnkkxLv++XLl+nSpQtNmzZl5cqV8vGie5SQkECnTp1kT2uRzWZmZgQEBODo6Ei3bt1ITk4GYOPGjXh4eODs7Ez//v3Jzs6W7+2ECRNo164dTZs2lUV1bGysXNVQrVbzySef4OTkhEql0qqWWcTevXu1yr7PmzcPDw8PnJyc+Oijj2RvcZcuXfj4449xd3dnxYoVhIWF0blzZ9zc3PDy8iIhIeGJ9j4vCQkJpKen06ZNGxQKBUOHDuXnn39+Yp/HKz4WFBSQk5NDQUEB2dnZ1KtXD9BUBz18+HCFLxgqZwx2rqYMqmRSnV8u3gVgVJdmFTJ11P0oAAbbD2ay+9NfrWWeCOHOjBmgVlPt7R6YdXz5cddqtZr169czY8YM0tPTMTExwc7ODkmShMdaIBAIyou5Fi9p3LSnt0Hzd/2RI0fkanmXLl3Czc1Nq02zZs3IzMwkPT2dyMjIMoWEzJ8/HwuNXSS2AAAgAElEQVQLCyIiIoB/SqU/ibi4OE6dOoW+vj5qtZqgoCD8/f05c+YMjRo1wsrKikGDBhEQEECHDh24desWXl5eXLlyRWuc1atXo1AoiIiIICoqCk9PT6Kjo9m/fz/e3t4lllefMGECnTt3JigoCLVaTWZmptZ5Y2NjgoKCMDc35969e7Rp0wYfHx8OHTpEvXr1+PXXXwFN6fCUlBSCgoKIiopCoVBoLRSKExUVRXBwMBkZGdjZ2TF69GgMDQ3l8zt27MDLy4tZs2ahVqtlAZqVlYW7uzvLli1j3rx5fP7556xatYp+/foxcuRIQFNJc9OmTYwfPx7QiM+QkBCioqLw8fHRCQ3ZsGEDsbGxhIeHY2BgIIdMFOfkyZNa/caNGydXVnz//fc5cOAAvXv3BiAvL4/Q0FDy8/Pp3Lkz+/bto06dOuzevZtZs2axefPmJ9pbRHBwcIlvG0xNTXUWQfHx8dSv/0/4bv369eWFY0lkZ2dz6NAhVq3SOC+tra355JNPaNiwISYmJnh6esoLTT09PWxsbLhw4YLOn4+XSeUU2I/ir9Mx48yN+1gYG/BW44rxyBYJbIdaDk9tm7x6Nfceea4N6tTBavqMl2obwPnz5xk1ahTnzp0DoFevXqxatarS5Y8UCAQCQcnk5OTg4uJCfHw89vb25V4++vDhw/LrdYAaNWo8tc/AgQPlugm+vr7MmzcPf39/du3aJZdLP3z4sFYZ8/T0dDIzM7U84yEhIbJQa9GiBY0aNSI6OhrzJ7z5PXr0KFu3bgU0XnkLC+2FjyRJzJw5k+PHj6Onp0d8fDyJiYkolUomT57MtGnT8Pb2pmPHjhQUFGBsbMyIESPw9vbG29u7xDl79eqFkZERRkZGWFpakpiYqCUQPTw8GD58OPn5+bzzzju4uLgAGrFXdD+GDBlCv379AE34zuzZs0lNTSUzMxOvYlnG3nnnHfT09HBwcCAxMVHHlsOHDzNq1CgMDDSSrmYJhesSEhKoU6zmRnBwMIsXLyY7O5v79+/j6OgoC+wi+65evUpkZKT8+1Kr1dStW/ep9hbRtWvXEhdE5cEvv/xC+/bt5Wt98OAB+/bt48aNG1SvXp2BAweybds2hgwZAoClpSV37twRAvupPBLYNx7FXzeX9LFrshInJ0u6d2/KkCEqWrasW+7TPsh9wOmE0wDY1bB7YtvMEydkcV194ADqTJqEQRn+knoRYmNjadWqFWq1Gmtra1auXEnfvn2F11ogEAheBmX0NJc3RTHY2dnZeHl5sXr1aiZMmICDg4POJrbr169jZmaGubk5jo6OhIWF4excep2GJ1H835KiDWRFVK36T+hl27ZtuXbtGsnJyfz888/Mnj0bgMLCQk6fPo2xsfFzzf+8bN++neTkZMLCwjA0NKRx48bk5ubSvHlzzp8/z2+//cbs2bPp1q0bc+bM4ezZsxw5coQ9e/awatUqjh49qjOmkZGR/P/6+vo64QedOnXi+PHj/PrrrwwbNoxJkyYxdOhQnXGK7umwYcP4+eefcXZ2JjAwkGPHjpU41/PWqjAxMZGfWW5uLmPGjCE0NJQGDRowd+5credZ9CwlScLR0ZG//vpLZ7wn2VvEs3iwra2tiYv7J+NbXFwc1tbWpV7Prl27tMJDDh8+TJMmTeRFRL9+/Th16pQssHNzczExMSl1vJdB5YzBfiSw0yTNqjf+6C0AIiOTWLbsNOfOPXqtkJlXLtPlq/NZcHoBnns8ycjLoKphVZpaNC21fW5UFLdHaeLaavr7U3f+/JcurgEaN26Mv78/AQEBXLlyhX79+glxLRAIBP+lmJqasnLlSpYsWUJBQQGDBw8mJCSEw4c1xcxycnKYMGECU6dOBTQbxL788kuio6MBjeBdt26dzrjdu3eXN07CPyEiVlZWXLlyhcLCQoIeVSMuCYVCQd++fZk0aRL29vbynh9PT0+t+OCSvJsdO3Zk+/btAERHR3Pr1i3s7J7s0OrWrRtr164FNF7WtDTthU9aWhqWlpYYGhoSHBzMzZs3AU0mDlNTU4YMGcKUKVM4f/48mZmZpKWl0bNnT5YtW8aFCxeeOHdp3Lx5EysrK0aOHMmHH37I+fPnAc09L4qj3rFjBx0epevNyMigbt265Ofny9dfVrp378769etlkV9SiIi9vT3Xrl0D/lkc1a5dm8zMzFI3S9rZ2ZGcnCwL7Pz8fC5dulRme4s82I9/StoIWrduXczNzTl9+jSSJLF161b69Ck5gURaWhp//vmn1vmGDRty+vRpsrOzkSSJI0eOaGVIi46OlmPWK4rKKbAf5cBOxQwHSzP+Crmtdbp790fx2IMOQPvt8NVpuJgMz7jykySJ0Luh+Pzsw+6ru8lV5+JUy4l1b63DUN+wxD65UVHc/GAYqNUYNW+O5ZSSN0iUB7GxsfTu3Zs///xTPrZhwwaWLl1KtWrVXtq8AoFAIHg9cHV1RaVSsXPnTkxMTNi3bx8LFizAzs4OpVKJh4cH48aNA0ClUrF8+XL8/Pywt7fHycmJ69ev64w5e/ZsHjx4gJOTE87OzvJGw4ULF+Lt7U27du3kUIHS8PX1Zdu2bXK4ASBneVCpVDg4OJQo7seMGUNhYSFKpRJfX18CAwO1PLglsWLFCoKDg1Eqlbi5uWmFoQAMHjyY0NBQlEolW7dulTd5RkRE0KpVK1xcXPj888+ZPXs2GRkZeHt7o1Kp6NChA0uXLn3i3KVx7NgxnJ2dcXV1Zffu3UycOBHQeIfPnj2Lk5MTR48eleOg58+fT+vWrWnfvr1sX1n58MMPadiwISqVCmdnZ3bs2KHTplevXrKXuXr16owcORInJye8vLzw8PAocdwqVaqwZ88epk2bhrOzMy4uLrI4fhF7S2PNmjV8+OGH2NjY0KxZM95++20A1q1bp/VbCQoKwtPTU+utSevWrRkwYIC8kbWwsJCPPvoI0GyCNTEx4Y03nj+D3POgqGylsd3d3aXQ1SPh4FS2FHQntOEUzm6KICzsDmq1RNu29Tl1agTczwH7zVBY7PpCh0KjsmXwKJQKmXRsEkduHQHA0sQSfyd/BtsPLtUrnH3uHLdHj6EwM5Oq7dpS96uvMLSyeuFrfpz8/HyWLl3K559/Tk5ODm3atCnxFY5AIBAIypcrV66I2gGC58bMzExnE2ZF0aFDBw4cOED16tVfyfyvimXLlmFubi5vBn4SJf35VigUYZIkuT/rvJU6BjsVM9ydrPj2TGsyMh7y119xWFg8WuluvaQtrlvULLO4BjgZf1IW10PshzDWZSxmVUpPWp+fmETchIkUZmZSrUcP6i1aiN5TVt3PQ0hICKNGjZJf07z33nvPvcIWCAQCgUDwv8GSJUu4devW/5zArl69Ou+//36Fz1upBXa6VBXbahoRW62aEZ6exVL1HdMOG8HryWXMi1MoFfJ16NcA+Nr5Mq3VtCe2z09IIP7jANQPHlC1XTusly5BoVe+0TcPHjxgypQpbNq0CdCkXlqzZo2chkYgEAgEAsHrzavyXoMmjOJ/EX9//1cyb6UU2Nnp9zAFUiUzrMxL2Y0c9A4kZsGV+xCVAu/Ylnn8kPgQbqTdwMLIgikeU0pt92DXLh7s2MnDRxtG9GvWpN6iheUurkGzMWLfvn0YGhoyffp0ZsyYUeE7YgUCgUAgEAgET6fSCWxJgsiYG7QCrN54A5cGpbzqUCjgDTPNp2vDMo+fW5DL1+c03usRTiMw0i85zCPt11+5O/dzzVRGRlRt0warGdMxKJZn8kWJioqiSZMmGBkZUatWLbZv307Dhg3LbUOBQCAQCAQCgaD8qXRZRLLyCjDJSwFgTK926D+tRPozIEkSc/+aS2x6LLVNatO/ef8S2z28do2E2Z8CYPnJZJqfO0uD9euoUk7FXLKzs5k1axYqlYrFixfLxz09PYW4FggEAoFAIHjNqYQebAmlXiwAOVSn9G2Hz0ZGXga+B3y5naGJ3V7SeQnmVXQ3RebfvUvc2HFIOTmYe3tTc8SIcs01fejQIcaMGcONGzcAuHfvXrmNLRAIBAKBQCB4+VQ6D7YkSRRKGkFb3247HTt+z5o150hOznruMY/eOsrAXwbK4vp9h/dpadVSp136f/7D9T7vkHfzJlUaNaLu53PLTVzfuXOHd999l7fffpsbN26gVCo5efIkK1asKJfxBQKBQCAQCAQVQ6UT2PoFOegpJG4p6pOn1ick5BZjx/5Gnz67nmu82LRYJgZPJD4zHpvqNvzyzi9M9Ziq1UaSJJK++Yb4CRMpTEujaqeONNq+Db1iSc5fhOjoaFq0aMFPP/2EqakpixcvJiwsjHbt2pXL+AKBQCD470FfXx8XFxecnJzo3bs3qamp8rlLly7x5ptvYmdnh62tLfPnz9cqr33w4EHc3d1xcHDA1dWVyZMnv4pLeC78/PxQqVQsW7asTO3NzMrrHXfZiI2NfaZqgV26dCE0NFTneGBgoFwc6FkpyzXn5OTQuXNn1Gr1c81REXz11VfY2NhgZ2fH77//XmIbSZKYNWsWzZs3x97enpUrV8rnjh07houLC46OjnTu3BmAvLw8OnXqpFPW/mVR6UJEFJLmxtxUa1fkadnyUVWp6X/C/6VCm3rQpi60fANMSr5MdaGaT/7UVFr0eMODNd3WYGygnZVEKiggYe5c0vbsBcBy6lRq+g8r17AQW1tbPDw8qFq1Kt9++y2NGjUqt7EFAoFA8N+FiYmJXGb8gw8+YPXq1cyaNYucnBx8fHxYu3Ytnp6eZGdn079/f9asWcPYsWOJjIxk3Lhx/Prrr7Ro0QK1Ws2GDRvK1baCggIMDMpfWty9e5dz587J5b4Fz8/mzZvp168f+vr6ZWovSRKSJKH3EjKklcTly5fZtWsXly5d4s6dO7z11ltER0fr2BsYGMjt27eJiopCT0+PpKQkAFJTUxkzZgyHDh2iYcOG8vEqVarQrVs3du/ezeDBg1/6dVQ6D7ZC0qy4UvO0s3s4O1tBdj5sitDkwF54Bt75Gc7fLXWsZWHLuPrgKiYGJixov0BHXAPc6D+AtD17URgbY/3tSmoN939hcZ2ens7HH39M9KP0fgqFgv3797N//34hrgUCgaCSoNyifCmfZ6Ft27bEx8cDsGPHDtq3by/XRzA1NWXVqlUsXLgQgMWLFzNr1ix5s7y+vj6jR4/WGTMzMxN/f3+USiUqlYq9ezUOpuLe0T179jBs2DAAhg0bxqhRo2jdujVTp06lcePGWl51W1tbEhMTSU5Opn///nh4eODh4cHJkyd15s7NzZXndnV1lcu0e3p6Eh8fj4uLCydOnNDqk5iYSN++fXF2dsbZ2Vku5138erp16yaX0d63bx8AWVlZ9OrVC2dnZ5ycnNi9ezcA06dPx8HBAZVKxSeffKJj49y5cxk+fDhdunShadOmWp5TtVrNyJEjcXR0xNPTk5ycHJ3+xfnhhx/ktxFnz57VOf/LL7/QunVrXF1deeutt0hMTJSvqaRnVMS9e/do27Ytv/76q86Y27dvp0+fPk+8N7GxsdjZ2TF06FCcnJy4ffs2X3/9NR4eHqhUKj777DN5vHfeeQc3NzccHR3LZcG2b98+3nvvPYyMjGjSpAk2NjYl3pu1a9cyZ84cWfhbWloCmj8H/fr1o2HDhlrHi2zdvn37C9tYFiqdBxupEAAzyzqcOzeSCxfucvFiIu3aNdBUbyyOgR6oLEsYBJKyk9gRtQOA2W1mU8+snk6bhzExPLx6FYBGgd9j4uLyYqZLEnv27GHixIkkJCQQFRXFoUOHAKhaTuEmAoFAIPjfQK1Wc+TIEbkE9KVLl3Bzc9Nq06xZMzIzM0lPTycyMrJMISHz58/HwsKCiIgIQFPo7GnExcVx6tQp9PX1UavVBAUF4e/vz5kzZ2jUqBFWVlYMGjSIgIAAOnTowK1bt/Dy8uLKlSta46xevRqFQkFERARRUVF4enoSHR3N/v378fb2lj33xZkwYQKdO3cmKCgItVqtU8zF2NiYoKAgzM3NuXfvHm3atMHHx4dDhw5Rr149WYSmpaWRkpJCUFAQUVFRKBQKrYVCcaKioggODiYjIwM7Ozt5oRITE8POnTvZuHEj7777Lnv37mXIkCGl3rfs7GzCw8M5fvw4w4cPJzIyUut8hw4dOH36NAqFgu+++47FixezZMmSJz6jxMREfHx8WLBgAd27d9caLy8vj+vXr9P4Udaz0u5N0bVs2bKFNm3a8J///IeYmBjOnj2LJEn4+Phw/PhxOnXqxObNm6lZsyY5OTl4eHjQv39/atWqpTVvQECAvFgqznvvvcf06dO1jsXHx9OmTRv5e/369eVFZHH+7//+j927dxMUFESdOnVYuXIltra2REdHk5+fT5cuXcjIyGDixIkMHToUACcnJ86dO1fq8yhPKp3AVhRqPNgPDczo6l4Pd/diwvg77R8mXo2hWhWdMZKykxjx+wjyC/Pp2qArPs18dNoU5uVxZ9ZsAKr16PHC4vr69euMGzeOgwcPAtCmTRsWLVr0QmMKBAKB4NUR8UHEK5k3JycHFxcX4uPjsbe31xFRL8rhw4fZteuffU01atR4ap+BAwfKr/B9fX2ZN28e/v7+7Nq1C19fX3ncy5cvy33S09PJzMzU8oyHhIQwfvx4AFq0aEGjRo2Ijo7G3Fw3q1cRR48eZevWrYDGK29hYaF1XpIkZs6cyfHjx9HT0yM+Pp7ExESUSiWTJ09m2rRpeHt707FjRwoKCjA2NmbEiBF4e3vj7e1d4py9evXCyMgIIyMjLC0tZc9ykyZNcHmkF9zc3IiNjX3iffPz8wOgU6dOpKen6wj6uLg4fH19SUhIIC8vjyZNmsj3sqRnlJ+fT7du3Vi9erUce1yce/fuaZVKL+3eADRq1EgWuv/5z3/4z3/+g6urK6DxfMfExNCpUydWrlxJUFAQALdv3yYmJkZHYJc1bv5ZePjwIcbGxoSGhvLvf/+b4cOHc+LECQoKCggLC+PIkSPk5OTQtm1b2rRpQ/PmzdHX16dKlSpkZGRQrVq1crepOJUwRETjwX6oX0Ig/7I3IeZD2N8PvuoEs9qWOMbCswuJTY+lsXljZrWeVWKb5GXLyb14EYN6dXnjsznPbW9eXh5ffvkljo6OHDx4kOrVq7Nu3TpOnjyJs7Pzc48rEAgEgv9NimKwb968iSRJrF69GgAHBwfCwsK02l6/fh0zMzPMzc1xdHTUOf8sFA+PzM3N1TpX/C1s27ZtuXbtGsnJyfz888/069cP0FQkPn36NOHh4YSHhxMfH18hGxG3b99OcnIyYWFhhIeHY2VlRW5uLs2bN+f8+fMolUpmz57NvHnzMDAw4OzZswwYMIADBw7Qo0ePEsc0MvonTFVfX1/eOFfa8dJ4POT08e/jx49n3LhxREREsH79ep37/jgGBga4ubmVujHQxMREa4zS7g1oP1NJkpgxY4b87K5du8aIESM4duwYhw8f5q+//uLChQu4urqWaGNAQAAuLi46n6LwpeJYW1tz+/Zt+XtcXBzW1tY67erXry//tvr27cvFixfl415eXlStWpXatWvTqVMnLly4IPcrEuYvm0onsHkUg52nX0pIRXVjaFsPPlSBre6q+3jccQ7fPAzAFx2+wKqqlU6bggcPSN2zBwDrRYswKMPqvTRu377NvHnzyM3NZfDgwURFRfGvf/2rwjYLCAQCgeC/E1NTU1auXMmSJUsoKChg8ODBhISEcPiw5t+4nJwcJkyYwNSpmsxYU6ZM4csvv5T3/xQWFrJu3Tqdcbt37y6Ldvgn/MDKyoorV65QWFgoeyxLQqFQ0LdvXyZNmoS9vb3szfT09OTbb7+V25UU7tGxY0c5RjY6Oppbt25hZ2f3xPvQrVs31q5dC2jCZtLS0rTOp6WlYWlpiaGhIcHBwdy8eRPQpMc1NTVlyJAhTJkyhfPnz5OZmUlaWho9e/Zk2bJlWsLsZVAU9x0SEoKFhYWO9z0tLU0Wl1u2bJGPl/aMFAoFmzdvJioqqsS35DVq1ECtVssiuLR78zheXl5s3rxZDr+Jj48nKSmJtLQ0atSogampKVFRUZw+fbrE/suWLZPFefHP4+EhAD4+PuzatYuHDx9y48YNYmJiaNWqlU67d955Rw47+fPPP2nevDkAffr0ISQkhIKCArKzszlz5gz29vYApKSkULt2bQwNDUu0szypdCqvyIOdW5IH+yncSr/F9BPTkZAY6jAUVR1Vie3S9++nMCMDk5YtMXF3f+Z5Hjx4IKdFatasGStWrODw4cNs27YNKytdQS8QCAQCwfPg6uqKSqVi586dmJiYsG/fPhYsWICdnR1KpRIPDw855ZtKpWL58uX4+flhb2+Pk5MT169f1xlz9uzZPHjwACcnJ5ydnWURs3DhQry9vWnXrh1169Z9ol2+vr5s27ZNDg8BWLlyJaGhoahUKhwcHEoU92PGjKGwsBClUomvry+BgYFaXuGSWLFiBcHBwSiVStzc3LTCUAAGDx5MaGgoSqWSrVu3yps8IyIiaNWqFS4uLnz++efMnj2bjIwMvL29UalUdOjQgaVLlz5x7hfF2NgYV1dXRo0axaZNm3TOz507l4EDB+Lm5kbt2rXl46U9I9B4znfu3MnRo0dZs2aNzpienp6EhIQApd+bkvoMGjSItm3bolQqGTBgABkZGfTo0YOCggLs7e2ZPn26Vuz08+Lo6Mi7776Lg4MDPXr0YPXq1XL4Uc+ePblz5w6g2Yy6d+9elEolM2bM4LvvvgPA3t6eHj16oFKpaNWqFR9++KGcPjE4OJhevXq9sI1lQVE8P2ZlwLmhhXRhOHzXcDEfDv9Xmftl52cz+LfBXEu9RpcGXVjRdQV6Ct31xYMff+Tu/AWQn0/dBfOpPmBAmecoLCwkMDCQKVOmsHz5ct5///0y9xUIBALB68+VK1dkb5hAUBk5f/48y5Yt44cffnjVplQ4/fr1Y+HChbK3+3FK+vOtUCjCJEl6Zm9rpfNgS482Od5OgaSkslVvzFfnM/rwaK6lXqOJRRO+6vCVjriW8vJI+Pxz7s75DPLzqTH0fSz69y+zXZcuXaJLly6MGDGC+/fvy5sZBQKBQCAQCF4XWrZsSdeuXV/rQjMvg7y8PN55551SxXV5U+kEdlGIyOE/k7Gy+oa2bTdBzAPIyi+xfVJ2Ep57PTmfdB6Arzt9jVkV7fASKS+Pm+8PJXXnLhSGhtT98kvemDmzTPmus7OzmTFjhpyb09LSku3bt1dYnkWBQCAQCASvL2PHjtXZ3Pf999+/UpuGDx9e5kIz/y1UqVJFTtdXEVS+NH2SGjAkKU4jtGvVMoFhv8GNNGhpBR3qw0cqpBrGrDi/gm1XtvFQ/RCApV2WYldTe7OEpFaTMPdzci5cQM/UlAYbN2D6WB7R0oiOjsbLy4vY2FgUCgWjRo3iyy+/LFNKI4FAIBAIBP/9FN+MKPjfodIJbD00wjo9ywDIx9mmJmx/VDr1TAKE3oWxrnx55kt2XdXkiGxXrx1fdPiC2ia1tcYquH+fO59MIevUKRSGhjT47jtMW7qW2ZZGjRphbGyMs7Mz69atK5fgfoFAIBAIBAJB5abShYgUCeza9TQVGl2NHisk42LJPYN09v/ffgDa1G3D+u7rdcR1yubviWnXnqxTp9C3sKDBpqeL64KCAlatWkVKSgqgyXd56NAhQkNDhbgWCAQCgUAgEACV0IMNkC6ZsHP7AMzyJGqdiofGcRCbrjnZqi4bL24kuyCb9tbtWfeWbhqglO++I+mbJQAY2drSYN1aDEtIYl6cs2fPMmrUKP7++2/Cw8PldDCNGjUq34sTCAQCgUAgEFRqKqXALkAfIwN9mtavBk1rwBAniM+Av+5QYGPOb1EzABilGqXTN//OHVlc1/l4IrU++gjFE4q+pKWlMWvWLNasWYMkSTRs2JA+ffq8nAsTCAQCgUAgEFR6Kl2ICIApDzEyeMx062owwI7gGpGkPkyliUUTnOvoliJPXLQYALM336T2qFGlimtJkti1axctWrSQk5xPnTqVy5cv07t373K/JoFAIBAIyoK+vj4uLi44OTnRu3dvUlNT5XOXLl3izTffxM7ODltbW+bPn0/xehcHDx7E3d0dBwcHXF1dmTx58qu4hOfCz88PlUrFsmXLytS+IsqwFyc2NlYuaFIeDBs2jD2PqkoX586dOwx4VKMjPDyc33777ZnHTkhIwNvb+4VtfFlIksSECROwsbFBpVJx/vz5Etvl5eXx0Ucf0bx5c1q0aMHevXsBTTl0X19fbGxsaN26NbGxsYCmuNCwYcMq5BoqpcC+IDWjyuMCG80DCYwMBMDXzlcnzV7K94Fk/P47KBTUmTjxyXNcuICfnx93796lXbt2nD9/nkWLFlG1aikl2gUCgUAgqABMTEwIDw8nMjKSmjVrylkqcnJy8PHxYfr06Vy9epULFy5w6tQpuZpfZGQk48aNY9u2bVy+fJnQ0FBsbGzK1baCgoJyHa+Iu3fvcu7cOS5evEhAQMBLmaOyUK9ePVl4P6/AXrp0KSNHjixz+5f1XEvj4MGDxMTEEBMTw4YNGxg9enSJ7b744gssLS2Jjo7m8uXLdO7cGYBNmzZRo0YNrl27RkBAANOmTQNAqVQSFxfHrVu3Xvo1VEqBnSmZ6Hiw1YVqloQu4eK9i1Q3qk5fm77yucK8PFI2bSZp0SIA6i38CmM73UTjxZOuu7i4EBAQwMaNGzlx4gRKpfIlXY1AIBAIKiNXWti/lM+z0LZtW+Lj4wHYsWMH7du3x9PTEwBTU1NWrVrFwoULAVi8eDGzZs2Sy2Hr6+uXKFwyMzPx9/dHqVSiUqlkr2Bxj/CePXtkT+CwYcMYNWoUrVu3ZurUqTRu3FjLq25ra0tiYiLJycn0798fDw8PPDw8OHnypM7cubm58tyurq5yCXBPT0/i4+PlmhPFSdaaSkIAABZTSURBVExMpG/fvjg7O+Ps7MypU6d0rqdbt260bNkSpVLJvn37AMjKyqJXr144Ozvj5OTE7t27AU0JbgcHB1QqFZ988omOjXPnzmX48OF06dKFpk2bsnLlSvmcWq1m5MiRODo64unpSU5Ojk5/gKSkJNwepQS+cOECCoVCFn3NmjUjOzsbgOPHj9OuXTuaNm0qi+oiT3leXh5z5sxh9+7duLi4sHv3brKyshg+fDitWrXC1dVVvtbH2bt3Lz169JDH69ixIy1btqRly5by/Tt27BgdO3bEx8cHBwcH1Go1U6ZMwcPDA5VKxfr16594f1+Effv2MXToUBQKBW3atCE1NZWEhASddps3b2bGDE1YsJ6enlxOft++fXzwwQcADBgwgCNHjshvcnr37s2uXbte2ManUSljsLMxwshQO0G6/+/+/J30NwYKA2a0moGpoSmFOTmk/vgjKZs2U5CUBIDVjOlYlBBDHRwczJgxY1i/fj2dOnUCNCs8gUAgEAheR9RqNUeOHGHEiBGAJjzE7bE6Ds2aNSMzM5P09HQiIyPLFBIyf/58LCwsiIiIAODBgwdP7RMXF8epU6fQ19dHrVYTFBSEv78/Z86coVGjRlhZWTFo0CACAgLo0KEDt27dwsvLiytXrmiNs3r1ahQKBREREURFReHp6Ul0dDT79+/H29ub8PBwnbknTJhA586dCQoKQq1Wk5mZqXXe2NiYoKAgzM3NuXfvHm3atMHHx4dDhw5Rr149fv31V0Cz5yolJYWgoCCioqJQKBRaC4XiREVFERwcTEZGBnZ2dvJCJSYmhp07d7Jx40beffdd9u7dy5AhQ3T6W1pakpubS3p6OidOnMDd3Z0TJ07QoUMHLC0tMTU1BTShHCEhIURFReHj4yOHhoCmcMq8efMIDQ1l1apVAMycOZM333yTzZs3k5qaSqtWrXjrrbe03r7fuHGDGjVqYGRkJNvyxx9/YGxsTExMDH5+foSGhgKasuqRkZE0adKEDRs2YGFhwblz53j48KG8mGvQoEGJ9/fxKAJfX1+uXr2qcy8mTZqkUwAmPj6eBg0ayN/r169PfHw8devWlY8VPZtPP/2UY8eO0ez/27v3+CirO4/jn18gEhDBFyBIxRKUiwkkgCBbkShKJRQQpNamrrYvWbwL2lUjLRupWmpBBcvFxSqwXFdo7aLsSsFyE7GichfBRhbLTSsU2SggSMhv/3ieTHOZJINMbvh9v155Mc/Mec7zm5nDzG/Oc85zLr6YKVOm0KJFi2L7161bl8aNG3Pw4EGaNWtG9+7dGTt2LA8//HDU9zZeamWCfcSTyP7xy/z0nu506J3M2HfGsnH/RgDG9x7PVU168PfnX+CzmTM5+dlnANTr0IFm995Do/CXfaH9+/eTnZ3N7NmzgSCpLkywRUREypLywfaKC1WCL7/8ki5durBv3z5SUlK49tpr41r/smXLivXwxbJ42o033hhZGTArK4vHH3+coUOHMn/+fLKysiL1btu2LbLP559/zuHDh4v1jK9Zs4YRI0YAcMkll9C6dWtyc3Np1KhRmcdesWJF5Du8Tp06NG7cuNjj7s6oUaNYvXo1CQkJ7Nu3j08//ZS0tDQefPBBRo4cycCBA8nIyCA/P5+kpCSGDRvGwIEDyxynPGDAAOrVq0e9evVo3rw5n376KQBt2rShS5cuAHTr1i0y9jeanj178uabb7J69WpGjRrFkiVLcHcyMjIiZa6//noSEhJITU2NHKM8r732GosWLeLpp58GgjMCu3fvJiXlH2dGPvnkE84777zI9okTJxg+fDibNm2iTp065ObmRh7r0aMHbdq0idS9ZcuWSE96Xl4eH374Ia1atYr6+p5//vnFYis8QxAv+fn57N27l549ezJhwgQmTJjAQw89xJw5c8rdr3nz5nz88cdxjSWa2plgk8Rzv3+fG/5yiBk/WcPiZsHg9wfSRtB5+W7+97lRnMzLAyApPZ1md91Fw6t7F/s1VVBQwPTp0xk5ciSHDh2iXr165OTkkJ2dXS3PSUREJBaFY7CPHj1KZmYmzz77LPfddx+pqamsXr26WNmdO3fSsGFDGjVqRMeOHVm/fj2dO5e+AEAsin6HHjt2rNhjRXtIL7/8cnbs2MGBAwd4+eWXycnJAYLv3bVr15KUlPS1jv91zZs3jwMHDrB+/XoSExNJTk7m2LFjtG/fng0bNrB48WJycnLo06cPo0eP5p133mH58uW89NJLTJkyhRUrVpSqs7D3F4KkvnCMcsn7yxoiAnDllVfyxhtvsGvXLgYPHsy4ceMwMwYMGBD1OEUnq5bF3fnDH/5Ahw4dyixTv379Yu/fM888Q4sWLdi8eTMFBQXF3p+i76u7M3nyZDIzM4vVN3PmzKivb0mn0oN9wQUXsGfPnsj23r17uaDE5ZSbNm1KgwYN+P73vw8EP/KmT59ebP9WrVqRn59PXl4eTZs2BYK2W79+/TJfn3ipnWOwTwYNbkX6qkhy3e1oG3r/6jX2jxvHybw86nfvxoXTp5G8YD7nXHN1sQ+Gjz76iIyMDO644w4OHTpE37592bp1Kzk5OcUas4iISE3VoEEDJk2axPjx48nPz+fmm29mzZo1LFu2DAh6uu+7777IqfDs7GyeeOKJSA9lQUEBzz1Xeq2Ia6+9ttjy3oVDRFq0aMH27dspKChg4cKFZcZlZgwZMoQHHniAlJSUSGLTt29fJk+eHCkXbbhHRkYG8+bNAyA3N5fdu3eXmywC9OnTh6lTpwLBsJm8sIOtUF5eHs2bNycxMZGVK1eya9cuILgaR4MGDbjlllvIzs5mw4YNHD58mLy8PPr3788zzzzD5s2byz326cjIyGDu3Lm0a9eOhIQEmjRpwuLFi+nVq1fMdZxzzjl88cUXke3MzEwmT54cScY3btxYap/27dsX61nPy8ujZcuWJCQkMGfOnGLz0YrKzMxk6tSpnDhxAgjenyNHjpT5+pa0YMECNm3aVOqvZHINMGjQIGbPno27s3btWho3blxseAgE7ey6665j1apVACxfvpzU1NTI/rNmzQKC+QLXXHNNJA/Mzc2N69VeylIrE+wjBUESvCE9eBN/vvAKnjzSm2Pvv0/dli258LfP0XrOHBpecUWpMUAAjRo1Ijc3l/PPP5/58+ezZMmSuM+kFhERqWxdu3YlPT2dF198kfr16/PKK68wZswYOnToQFpaGpdddhnDhw8HID09nd/85jfcdNNNpKSk0KlTJ3bu3FmqzpycHA4dOkSnTp3o3LlzZKLh2LFjGThwID179iyV7JSUlZXF3LlzI8NDACZNmsS6detIT08nNTU1anJ/zz33UFBQQFpaGllZWcycObPCjq+JEyeycuVK0tLS6NatW7FhKAA333wz69atIy0tjdmzZ0cmeb733nv06NGDLl268Nhjj5GTk8MXX3zBwIEDSU9Pp1evXpU6Fys5ORl3jwxL7dWrF+eee25MQ3IKXX311Wzbti0yyfGRRx7hxIkTpKen07FjRx555JFS+5x99tlcfPHF7NixAwhe81mzZtG5c2c++OCDMq+Wdtttt5Gamsqll15Kp06duPPOOyM/7KK9vqejf//+XHTRRbRt25bbb789ciUcIDIEB2DcuHE8+uijpKenM2fOHMaPD9Y5GTZsGAcPHqRt27ZMmDAhMtEXgjl3Rc8SVBaL5ZRDTdL9W3V8yNAR1J/XlP947Hc0OJbI4rl92X/gFTDjwmkv0PCKK0rtt3TpUnr37h35j/rWW2+RmppaaqyWiIhIWbZv315sPKtIbbRw4ULWr1/PmDFjqjuUKnX8+HGuuuoq1qxZQ926pUdJR/v/bWbr3b37qR6rVvZg06Ah7ZYFv2Cu5hIOHAquAdl85MOlkus9e/YwZMgQ+vXrx1NPPRW5//LLL1dyLSIiIt84Q4YMITk5ubrDqHK7d+9m7NixUZPreKuVkxzz69Rn2tZpAHxvcz6ef4KGffrQJLzmIQSzSydNmsTo0aM5cuQIDRs2pEmTJtUVsoiIiHwD3XvvvaWu+X3//fczdOjQaooocNttt1Xr8atDu3btaNeuXZUcq1Ym2HlnnWBn3k7O/8xpvno7JCTQ4uHsyHjrtWvXctddd0UmJ9xwww1MnDix1AxUERERkcpUdMKofHPUygT77w0PkHjCefSVJMg/QuPrr+es1q0BePvtt+nZsyfuTnJyMlOmTKmSwewiIvLN4O5RJ9CLSO0V7zmJtTLBzi14ndELTtLkb0eAYOx1oR49epCZmUnXrl3JycmJrIYkIiJyupKSkjh48CBNmzZVki1yhnB3Dh48GNdrtNe6BHtPYl3uXu902AckJXHyyXFc/+MfM2HCBNq3b4+Z8eqrr5KQUDvnb4qISM3VqlUr9u7dy4EDB6o7FBGJo6SkJFq1ahW3+io1wTazfsBEoA4wzd3Hlni8HjAb6AYcBLLc/a/l1flVfgKD1xbwVUEBC1I6MmHQII4fP05SUlJk+U4l1yIiUhkSExMjS0eLiJSl0hJsM6sDPAtcC+wF3jWzRe5e9Arww4BD7t7WzH4EjAOyStf2Dy0PwcaEIzz+t8/Y9WGwGtXQoUN58sknK+V5iIiIiIicisrs6u0B7HD3ne7+FTAfGFyizGBgVnj7JaCPVTCo7ePjXzFs7x525R8hJSWF119/nRkzZtCsWbO4PwERERERkVNVmQn2BcCeItt7w/uilnH3fCAPaFpepZ8XFFDH6vDzfnezadOmyBKjIiIiIiI1Qa2Y5GhmdwB3hJvHT/rJrb9eMpVf15tanWFJzdIM+Ht1ByE1jtqFRKN2IdGoXUg0Hb7OTpWZYO8DLiyy3Sq8L1qZvWZWF2hMMNmxGHd/HngewMzWfZ014eXMpnYh0ahdSDRqFxKN2oVEY2brvs5+lTlE5F2gnZm1MbOzgB8Bi0qUWQQUrm/+A2CFx/tK3yIiIiIiVajSerDdPd/MhgNLCS7TN8Pd3zezx4F17r4ImA7MMbMdwGcESbiIiIiISK1VqWOw3X0xsLjEfaOL3D4G3HiK1T4fh9DkzKN2IdGoXUg0ahcSjdqFRPO12oVpRIaIiIiISPxoyUMRERERkTiqsQm2mfUzs7+Y2Q4z+1mUx+uZ2YLw8bfNLLnqo5SqFkO7eMDMtpnZFjNbbmatqyNOqVoVtYsi5W4wMzczXSngGyCWdmFmPww/M943s/+s6hil6sXwPfJtM1tpZhvD75L+1RGnVB0zm2Fm+81saxmPm5lNCtvMFjO7tKI6a2SCXWSZ9e8BqcBNZpZaolhkmXXgGYJl1uUMFmO72Ah0d/d0gtVBn6zaKKWqxdguMLNzgPuBt6s2QqkOsbQLM2sH/By4wt07Aj+t8kClSsX4eZED/M7duxJcfOHfqzZKqQYzgX7lPP49oF34dwdQ4UIsNTLBppKWWZdar8J24e4r3f1ouLmW4PrrcmaL5fMC4JcEP8SPVWVwUm1iaRe3A8+6+yEAd99fxTFK1YulXTjQKLzdGPi4CuOTauDuqwmuZleWwcBsD6wFzjWzluXVWVMT7EpZZl1qvVjaRVHDgD9WakRSE1TYLsLTeRe6+6tVGZhUq1g+L9oD7c3sTTNba2bl9WDJmSGWdvEocIuZ7SW4EtqIqglNarBTzT9qx1LpIqfKzG4BugNXVXcsUr3MLAGYANxazaFIzVOX4JRvb4KzXavNLM3d/69ao5LqdhMw093Hm9nlBOt1dHL3guoOTGqPmtqDfSrLrFPeMutyRomlXWBm3wX+DRjk7serKDapPhW1i3OATsAqM/sr8B1gkSY6nvFi+bzYCyxy9xPu/hGQS5Bwy5krlnYxDPgdgLu/BSQBzaokOqmpYso/iqqpCbaWWZdoKmwXZtYV+C1Bcq3xlN8M5bYLd89z92bunuzuyQRj8we5+7rqCVeqSCzfIy8T9F5jZs0IhozsrMogpcrF0i52A30AzCyFIME+UKVRSk2zCPhJeDWR7wB57v5JeTvUyCEiWmZdoomxXTwFNAR+H8553e3ug6otaKl0MbYL+YaJsV0sBfqa2TbgJJDt7joTegaLsV08CLxgZv9KMOHxVnXgndnM7EWCH9vNwrH3vwASAdz9OYKx+P2BHcBRYGiFdarNiIiIiIjET00dIiIiIiIiUispwRYRERERiSMl2CIiIiIicaQEW0REREQkjpRgi4iIiIjEkRJsEZFTYGYnzWxTkb/kcsoejsPxZprZR+GxNoQry51qHdPMLDW8ParEY38+3RjDegpfl61m9t9mdm4F5buYWf94HFtEpKbRZfpERE6BmR1294bxLltOHTOB/3H3l8ysL/C0u6efRn2nHVNF9ZrZLCDX3X9VTvlbge7uPjzesYiIVDf1YIuInAYza2hmy8Pe5ffMbHCUMi3NbHWRHt6M8P6+ZvZWuO/vzayixHc10Dbc94Gwrq1m9tPwvrPN7FUz2xzenxXev8rMupvZWKB+GMe88LHD4b/zzWxAkZhnmtkPzKyOmT1lZu+a2RYzuzOGl+Ut4IKwnh7hc9xoZn82sw7hCnqPA1lhLFlh7DPM7J2wbKnXUUSktqiRKzmKiNRg9c1sU3j7I+BGYIi7fx4ut73WzBaVWPntn4Gl7v4rM6sDNAjL5gDfdfcjZjYSeIAg8SzLdcB7ZtaNYCWxfwIMeNvMXgcuAj529wEAZta46M7u/jMzG+7uXaLUvQD4IfBqmAD3Ae4GhhEsC3yZmdUD3jSz19z9o2gBhs+vD8FquwAfABnhCnrfBZ5w9xvMbDRFerDN7Alghbv/Szi85B0zW+buR8p5PUREaiQl2CIip+bLogmqmSUCT5jZlUABQc9tC+BvRfZ5F5gRln3Z3TeZ2VVAKkHCCnAWQc9vNE+ZWQ5wgCDh7QMsLEw+zey/gAxgCTDezMYRDCt54xSe1x+BiWES3Q9Y7e5fhsNS0s3sB2G5xkA7gh8XRRX+8LgA2A78qUj5WWbWjmDZ6cQyjt8XGGRmD4XbScC3w7pERGoVJdgiIqfnZuA8oJu7nzCzvxIkhxHuvjpMwAcAM81sAnAI+JO73xTDMbLd/aXCDTPrE62Qu+ea2aVAf2CMmS139/J6xIvue8zMVgGZQBYwv/BwwAh3X1pBFV+6exczawAsBe4FJgG/BFa6+5BwQuiqMvY34AZ3/0ss8YqI1GQagy0icnoaA/vD5PpqoHXJAmbWGvjU3V8ApgGXAmuBK8yscEz12WbWPsZjvgFcb2YNzOxsYAjwhpl9Czjq7nOBp8LjlHQi7EmPZgHB0JPC3nAIkuW7C/cxs/bhMaNy96PAfcCDZlaX4PXZFz58a5GiXwDnFNleCoywsDvfzLqWdQwRkZpOCbaIyOmZB3Q3s/eAnxCMOS6pN7DZzDYS9A5PdPcDBAnni2a2hWB4yCWxHNDdNwAzgXeAt4Fp7r4RSCMYu7wJ+AUwJsruzwNbCic5lvAacBWwzN2/Cu+bBmwDNpjZVuC3VHD2M4xlC3AT8CTw6/C5F91vJZBaOMmRoKc7MYzt/XBbRKRW0mX6RERERETiSD3YIiIiIiJxpARbRERERCSOlGCLiIiIiMSREmwRERERkThSgi0iIiIiEkdKsEVERERE4kgJtoiIiIhIHCnBFhERERGJo/8HYv8d90uePzgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(edf2, 'ROC Curve -- Soundnames LSTM Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average Soundnames + Ethnicolr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>race_orig</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>Walker</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.005717</td>\n",
       "      <td>0.048452</td>\n",
       "      <td>0.340489</td>\n",
       "      <td>0.605342</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alton</td>\n",
       "      <td>Palmer</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.008869</td>\n",
       "      <td>0.066710</td>\n",
       "      <td>0.371555</td>\n",
       "      <td>0.552866</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dale</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.230684</td>\n",
       "      <td>0.019807</td>\n",
       "      <td>0.043976</td>\n",
       "      <td>0.705533</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>Walker</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.005445</td>\n",
       "      <td>0.046284</td>\n",
       "      <td>0.323591</td>\n",
       "      <td>0.624680</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Seth</td>\n",
       "      <td>Collier</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.008728</td>\n",
       "      <td>0.022563</td>\n",
       "      <td>0.087592</td>\n",
       "      <td>0.881118</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Robert</td>\n",
       "      <td>Martin</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003473</td>\n",
       "      <td>0.052829</td>\n",
       "      <td>0.116699</td>\n",
       "      <td>0.826999</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sharon</td>\n",
       "      <td>Turner</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.002303</td>\n",
       "      <td>0.004116</td>\n",
       "      <td>0.541648</td>\n",
       "      <td>0.451934</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lilianna</td>\n",
       "      <td>Wood</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.024998</td>\n",
       "      <td>0.106819</td>\n",
       "      <td>0.237043</td>\n",
       "      <td>0.631140</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Barbara</td>\n",
       "      <td>Planck</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.002566</td>\n",
       "      <td>0.435535</td>\n",
       "      <td>0.035664</td>\n",
       "      <td>0.526235</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Scott</td>\n",
       "      <td>Zofnas</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.023966</td>\n",
       "      <td>0.018383</td>\n",
       "      <td>0.271646</td>\n",
       "      <td>0.686005</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Claretha</td>\n",
       "      <td>Evans</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.004836</td>\n",
       "      <td>0.006611</td>\n",
       "      <td>0.917838</td>\n",
       "      <td>0.070715</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>James</td>\n",
       "      <td>Lake</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.025009</td>\n",
       "      <td>0.072328</td>\n",
       "      <td>0.096741</td>\n",
       "      <td>0.805922</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Wayne</td>\n",
       "      <td>McCartney</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.007284</td>\n",
       "      <td>0.015649</td>\n",
       "      <td>0.248194</td>\n",
       "      <td>0.728874</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>David</td>\n",
       "      <td>Estores</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.014508</td>\n",
       "      <td>0.679432</td>\n",
       "      <td>0.028218</td>\n",
       "      <td>0.277843</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Joe</td>\n",
       "      <td>Bolton</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.015163</td>\n",
       "      <td>0.018974</td>\n",
       "      <td>0.290512</td>\n",
       "      <td>0.675351</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Kung</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.820622</td>\n",
       "      <td>0.010780</td>\n",
       "      <td>0.039925</td>\n",
       "      <td>0.128673</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Omar</td>\n",
       "      <td>Fletcher</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.007188</td>\n",
       "      <td>0.021955</td>\n",
       "      <td>0.800998</td>\n",
       "      <td>0.169859</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ronda</td>\n",
       "      <td>Feathers</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.015252</td>\n",
       "      <td>0.010921</td>\n",
       "      <td>0.407520</td>\n",
       "      <td>0.566306</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Tanda</td>\n",
       "      <td>Potter</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.016769</td>\n",
       "      <td>0.084903</td>\n",
       "      <td>0.110922</td>\n",
       "      <td>0.787406</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>Dreaden</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006343</td>\n",
       "      <td>0.026732</td>\n",
       "      <td>0.317821</td>\n",
       "      <td>0.649104</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Nadeau</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.030033</td>\n",
       "      <td>0.125401</td>\n",
       "      <td>0.194926</td>\n",
       "      <td>0.649639</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Antonio</td>\n",
       "      <td>Baker</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.070586</td>\n",
       "      <td>0.044673</td>\n",
       "      <td>0.746369</td>\n",
       "      <td>0.138372</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Monica</td>\n",
       "      <td>Cintron</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.009558</td>\n",
       "      <td>0.727165</td>\n",
       "      <td>0.044524</td>\n",
       "      <td>0.218753</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Houlker</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.019681</td>\n",
       "      <td>0.063763</td>\n",
       "      <td>0.306527</td>\n",
       "      <td>0.610029</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Thalmann</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.008762</td>\n",
       "      <td>0.022023</td>\n",
       "      <td>0.443870</td>\n",
       "      <td>0.525345</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Joyce</td>\n",
       "      <td>Atkin</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.007800</td>\n",
       "      <td>0.014019</td>\n",
       "      <td>0.225438</td>\n",
       "      <td>0.752744</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Laura</td>\n",
       "      <td>Hoffer</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.038778</td>\n",
       "      <td>0.034211</td>\n",
       "      <td>0.088627</td>\n",
       "      <td>0.838384</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Rossi</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.008718</td>\n",
       "      <td>0.226690</td>\n",
       "      <td>0.108814</td>\n",
       "      <td>0.655778</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Nicole</td>\n",
       "      <td>Schueler</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.013692</td>\n",
       "      <td>0.051199</td>\n",
       "      <td>0.152617</td>\n",
       "      <td>0.782491</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>Sims</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.023191</td>\n",
       "      <td>0.030205</td>\n",
       "      <td>0.538129</td>\n",
       "      <td>0.408475</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149962</th>\n",
       "      <td>Arkenda</td>\n",
       "      <td>Branton</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.005403</td>\n",
       "      <td>0.029190</td>\n",
       "      <td>0.610061</td>\n",
       "      <td>0.355345</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149963</th>\n",
       "      <td>Zoe</td>\n",
       "      <td>Haraden</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.056946</td>\n",
       "      <td>0.036784</td>\n",
       "      <td>0.214562</td>\n",
       "      <td>0.691709</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149964</th>\n",
       "      <td>LETITIA</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.005530</td>\n",
       "      <td>0.004410</td>\n",
       "      <td>0.974706</td>\n",
       "      <td>0.015353</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149965</th>\n",
       "      <td>Patrick</td>\n",
       "      <td>Townley</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.004906</td>\n",
       "      <td>0.022082</td>\n",
       "      <td>0.182347</td>\n",
       "      <td>0.790665</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149966</th>\n",
       "      <td>Scott</td>\n",
       "      <td>Fisher</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.063332</td>\n",
       "      <td>0.036624</td>\n",
       "      <td>0.084526</td>\n",
       "      <td>0.815517</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149967</th>\n",
       "      <td>Dayna</td>\n",
       "      <td>Roper</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.004527</td>\n",
       "      <td>0.020064</td>\n",
       "      <td>0.139144</td>\n",
       "      <td>0.836266</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149968</th>\n",
       "      <td>Cindy</td>\n",
       "      <td>Amster</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.058026</td>\n",
       "      <td>0.038201</td>\n",
       "      <td>0.270830</td>\n",
       "      <td>0.632943</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149969</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Drew</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.015818</td>\n",
       "      <td>0.028136</td>\n",
       "      <td>0.091525</td>\n",
       "      <td>0.864522</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149970</th>\n",
       "      <td>Cheryl</td>\n",
       "      <td>Lucas</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.009917</td>\n",
       "      <td>0.073624</td>\n",
       "      <td>0.153512</td>\n",
       "      <td>0.762947</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149971</th>\n",
       "      <td>Fernando</td>\n",
       "      <td>Ritzinger</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.400779</td>\n",
       "      <td>0.223310</td>\n",
       "      <td>0.029582</td>\n",
       "      <td>0.346329</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149972</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Miller</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.004869</td>\n",
       "      <td>0.034061</td>\n",
       "      <td>0.166609</td>\n",
       "      <td>0.794462</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149973</th>\n",
       "      <td>David</td>\n",
       "      <td>Houder</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.048147</td>\n",
       "      <td>0.032836</td>\n",
       "      <td>0.052622</td>\n",
       "      <td>0.866395</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149975</th>\n",
       "      <td>Jahala</td>\n",
       "      <td>Stirling</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.023756</td>\n",
       "      <td>0.027080</td>\n",
       "      <td>0.654816</td>\n",
       "      <td>0.294348</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149976</th>\n",
       "      <td>Warner</td>\n",
       "      <td>Strickland</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.013447</td>\n",
       "      <td>0.019766</td>\n",
       "      <td>0.186829</td>\n",
       "      <td>0.779958</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149978</th>\n",
       "      <td>Richard</td>\n",
       "      <td>Rosias</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.012433</td>\n",
       "      <td>0.356670</td>\n",
       "      <td>0.148668</td>\n",
       "      <td>0.482229</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149982</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>Brinsko</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.039022</td>\n",
       "      <td>0.152176</td>\n",
       "      <td>0.057549</td>\n",
       "      <td>0.751253</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149983</th>\n",
       "      <td>Betty</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>0.007805</td>\n",
       "      <td>0.214573</td>\n",
       "      <td>0.773166</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149984</th>\n",
       "      <td>James</td>\n",
       "      <td>Neelands</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.013874</td>\n",
       "      <td>0.086911</td>\n",
       "      <td>0.063470</td>\n",
       "      <td>0.835746</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149985</th>\n",
       "      <td>Barbara</td>\n",
       "      <td>Stroud</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>0.010353</td>\n",
       "      <td>0.087568</td>\n",
       "      <td>0.900529</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149987</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Fugate</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.021979</td>\n",
       "      <td>0.214076</td>\n",
       "      <td>0.160475</td>\n",
       "      <td>0.603469</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149989</th>\n",
       "      <td>Joan</td>\n",
       "      <td>Pincus</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.027007</td>\n",
       "      <td>0.115198</td>\n",
       "      <td>0.298601</td>\n",
       "      <td>0.559194</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149990</th>\n",
       "      <td>Natalie</td>\n",
       "      <td>Daugherty</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.009807</td>\n",
       "      <td>0.097548</td>\n",
       "      <td>0.068606</td>\n",
       "      <td>0.824040</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149991</th>\n",
       "      <td>Rita</td>\n",
       "      <td>Toro</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.036459</td>\n",
       "      <td>0.335786</td>\n",
       "      <td>0.095086</td>\n",
       "      <td>0.532669</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149992</th>\n",
       "      <td>Clifford</td>\n",
       "      <td>Tillman</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.006442</td>\n",
       "      <td>0.036490</td>\n",
       "      <td>0.467056</td>\n",
       "      <td>0.490011</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149994</th>\n",
       "      <td>Karen</td>\n",
       "      <td>Limkeman</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.021399</td>\n",
       "      <td>0.018881</td>\n",
       "      <td>0.379321</td>\n",
       "      <td>0.580399</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149995</th>\n",
       "      <td>Faye</td>\n",
       "      <td>Harrison</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.006507</td>\n",
       "      <td>0.016057</td>\n",
       "      <td>0.171779</td>\n",
       "      <td>0.805656</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149996</th>\n",
       "      <td>James</td>\n",
       "      <td>Hall</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.003098</td>\n",
       "      <td>0.006102</td>\n",
       "      <td>0.133579</td>\n",
       "      <td>0.857222</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149997</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Griffin</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.005560</td>\n",
       "      <td>0.004622</td>\n",
       "      <td>0.405981</td>\n",
       "      <td>0.583837</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149998</th>\n",
       "      <td>Jesse</td>\n",
       "      <td>Murphy</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.016585</td>\n",
       "      <td>0.049813</td>\n",
       "      <td>0.388527</td>\n",
       "      <td>0.545075</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149999</th>\n",
       "      <td>Lee</td>\n",
       "      <td>Brown</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.024608</td>\n",
       "      <td>0.279732</td>\n",
       "      <td>0.690609</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_name    last_name race_orig     asian  hispanic  nh_black  \\\n",
       "0       Elizabeth       Walker  nh_white  0.005717  0.048452  0.340489   \n",
       "1           Alton       Palmer  nh_white  0.008869  0.066710  0.371555   \n",
       "3            Dale  Scarborough  nh_white  0.230684  0.019807  0.043976   \n",
       "4          Daniel       Walker  nh_white  0.005445  0.046284  0.323591   \n",
       "5            Seth      Collier  nh_black  0.008728  0.022563  0.087592   \n",
       "6          Robert       Martin  nh_white  0.003473  0.052829  0.116699   \n",
       "7          Sharon       Turner  nh_black  0.002303  0.004116  0.541648   \n",
       "8        Lilianna         Wood  hispanic  0.024998  0.106819  0.237043   \n",
       "9         Barbara       Planck  nh_white  0.002566  0.435535  0.035664   \n",
       "11          Scott       Zofnas  nh_white  0.023966  0.018383  0.271646   \n",
       "13       Claretha        Evans  nh_black  0.004836  0.006611  0.917838   \n",
       "14          James         Lake  nh_white  0.025009  0.072328  0.096741   \n",
       "16          Wayne    McCartney  nh_white  0.007284  0.015649  0.248194   \n",
       "17          David      Estores     asian  0.014508  0.679432  0.028218   \n",
       "18            Joe       Bolton  nh_white  0.015163  0.018974  0.290512   \n",
       "20        Michael         Kung     asian  0.820622  0.010780  0.039925   \n",
       "22           Omar     Fletcher  nh_black  0.007188  0.021955  0.800998   \n",
       "23          Ronda     Feathers  nh_black  0.015252  0.010921  0.407520   \n",
       "25          Tanda       Potter  nh_white  0.016769  0.084903  0.110922   \n",
       "27          Jenny      Dreaden  nh_white  0.006343  0.026732  0.317821   \n",
       "28          Donna       Nadeau  nh_white  0.030033  0.125401  0.194926   \n",
       "29        Antonio        Baker  nh_black  0.070586  0.044673  0.746369   \n",
       "30         Monica      Cintron  hispanic  0.009558  0.727165  0.044524   \n",
       "31        Charlie      Houlker  nh_white  0.019681  0.063763  0.306527   \n",
       "32        Michael     Thalmann  nh_white  0.008762  0.022023  0.443870   \n",
       "33          Joyce        Atkin  nh_white  0.007800  0.014019  0.225438   \n",
       "34          Laura       Hoffer  nh_white  0.038778  0.034211  0.088627   \n",
       "35        Michael        Rossi  nh_white  0.008718  0.226690  0.108814   \n",
       "36         Nicole     Schueler  nh_white  0.013692  0.051199  0.152617   \n",
       "38        Lincoln         Sims  nh_white  0.023191  0.030205  0.538129   \n",
       "...           ...          ...       ...       ...       ...       ...   \n",
       "149962    Arkenda      Branton  nh_black  0.005403  0.029190  0.610061   \n",
       "149963        Zoe      Haraden  nh_white  0.056946  0.036784  0.214562   \n",
       "149964    LETITIA   WASHINGTON  nh_black  0.005530  0.004410  0.974706   \n",
       "149965    Patrick      Townley  nh_white  0.004906  0.022082  0.182347   \n",
       "149966      Scott       Fisher  nh_white  0.063332  0.036624  0.084526   \n",
       "149967      Dayna        Roper  nh_white  0.004527  0.020064  0.139144   \n",
       "149968      Cindy       Amster  nh_white  0.058026  0.038201  0.270830   \n",
       "149969     Donald         Drew  nh_white  0.015818  0.028136  0.091525   \n",
       "149970     Cheryl        Lucas  nh_black  0.009917  0.073624  0.153512   \n",
       "149971   Fernando    Ritzinger  nh_white  0.400779  0.223310  0.029582   \n",
       "149972     Angela       Miller  nh_black  0.004869  0.034061  0.166609   \n",
       "149973      David       Houder  nh_white  0.048147  0.032836  0.052622   \n",
       "149975     Jahala     Stirling  nh_white  0.023756  0.027080  0.654816   \n",
       "149976     Warner   Strickland  nh_white  0.013447  0.019766  0.186829   \n",
       "149978    Richard       Rosias  nh_white  0.012433  0.356670  0.148668   \n",
       "149982     Andrea      Brinsko  nh_white  0.039022  0.152176  0.057549   \n",
       "149983      Betty       Wilson  nh_black  0.004455  0.007805  0.214573   \n",
       "149984      James     Neelands  nh_white  0.013874  0.086911  0.063470   \n",
       "149985    Barbara       Stroud  nh_white  0.001551  0.010353  0.087568   \n",
       "149987    Michael       Fugate  nh_white  0.021979  0.214076  0.160475   \n",
       "149989       Joan       Pincus  nh_white  0.027007  0.115198  0.298601   \n",
       "149990    Natalie    Daugherty  nh_white  0.009807  0.097548  0.068606   \n",
       "149991       Rita         Toro  nh_white  0.036459  0.335786  0.095086   \n",
       "149992   Clifford      Tillman  nh_white  0.006442  0.036490  0.467056   \n",
       "149994      Karen     Limkeman  nh_white  0.021399  0.018881  0.379321   \n",
       "149995       Faye     Harrison  nh_black  0.006507  0.016057  0.171779   \n",
       "149996      James         Hall  nh_black  0.003098  0.006102  0.133579   \n",
       "149997      Shawn      Griffin  nh_white  0.005560  0.004622  0.405981   \n",
       "149998      Jesse       Murphy  nh_black  0.016585  0.049813  0.388527   \n",
       "149999        Lee        Brown  nh_black  0.005050  0.024608  0.279732   \n",
       "\n",
       "        nh_white      race  \n",
       "0       0.605342  nh_white  \n",
       "1       0.552866  nh_white  \n",
       "3       0.705533  nh_white  \n",
       "4       0.624680  nh_white  \n",
       "5       0.881118  nh_white  \n",
       "6       0.826999  nh_white  \n",
       "7       0.451934  nh_black  \n",
       "8       0.631140  nh_white  \n",
       "9       0.526235  nh_white  \n",
       "11      0.686005  nh_white  \n",
       "13      0.070715  nh_black  \n",
       "14      0.805922  nh_white  \n",
       "16      0.728874  nh_white  \n",
       "17      0.277843  hispanic  \n",
       "18      0.675351  nh_white  \n",
       "20      0.128673     asian  \n",
       "22      0.169859  nh_black  \n",
       "23      0.566306  nh_white  \n",
       "25      0.787406  nh_white  \n",
       "27      0.649104  nh_white  \n",
       "28      0.649639  nh_white  \n",
       "29      0.138372  nh_black  \n",
       "30      0.218753  hispanic  \n",
       "31      0.610029  nh_white  \n",
       "32      0.525345  nh_white  \n",
       "33      0.752744  nh_white  \n",
       "34      0.838384  nh_white  \n",
       "35      0.655778  nh_white  \n",
       "36      0.782491  nh_white  \n",
       "38      0.408475  nh_black  \n",
       "...          ...       ...  \n",
       "149962  0.355345  nh_black  \n",
       "149963  0.691709  nh_white  \n",
       "149964  0.015353  nh_black  \n",
       "149965  0.790665  nh_white  \n",
       "149966  0.815517  nh_white  \n",
       "149967  0.836266  nh_white  \n",
       "149968  0.632943  nh_white  \n",
       "149969  0.864522  nh_white  \n",
       "149970  0.762947  nh_white  \n",
       "149971  0.346329     asian  \n",
       "149972  0.794462  nh_white  \n",
       "149973  0.866395  nh_white  \n",
       "149975  0.294348  nh_black  \n",
       "149976  0.779958  nh_white  \n",
       "149978  0.482229  nh_white  \n",
       "149982  0.751253  nh_white  \n",
       "149983  0.773166  nh_white  \n",
       "149984  0.835746  nh_white  \n",
       "149985  0.900529  nh_white  \n",
       "149987  0.603469  nh_white  \n",
       "149989  0.559194  nh_white  \n",
       "149990  0.824040  nh_white  \n",
       "149991  0.532669  nh_white  \n",
       "149992  0.490011  nh_white  \n",
       "149994  0.580399  nh_white  \n",
       "149995  0.805656  nh_white  \n",
       "149996  0.857222  nh_white  \n",
       "149997  0.583837  nh_white  \n",
       "149998  0.545075  nh_white  \n",
       "149999  0.690609  nh_white  \n",
       "\n",
       "[100000 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = pd.concat((edf, edf2)).groupby(level=0).mean()\n",
    "edf3 = edf[['first_name', 'last_name', 'race_orig']].copy()\n",
    "edf3 = pd.concat([edf3, probs], axis=1)\n",
    "edf3['race'] = probs.idxmax(axis=1)\n",
    "edf3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>race_orig</th>\n",
       "      <th>asian</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>nh_black</th>\n",
       "      <th>nh_white</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Seth</td>\n",
       "      <td>Collier</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.008728</td>\n",
       "      <td>0.022563</td>\n",
       "      <td>0.087592</td>\n",
       "      <td>0.881118</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lilianna</td>\n",
       "      <td>Wood</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.024998</td>\n",
       "      <td>0.106819</td>\n",
       "      <td>0.237043</td>\n",
       "      <td>0.631140</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>David</td>\n",
       "      <td>Estores</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.014508</td>\n",
       "      <td>0.679432</td>\n",
       "      <td>0.028218</td>\n",
       "      <td>0.277843</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ronda</td>\n",
       "      <td>Feathers</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.015252</td>\n",
       "      <td>0.010921</td>\n",
       "      <td>0.407520</td>\n",
       "      <td>0.566306</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>Sims</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.023191</td>\n",
       "      <td>0.030205</td>\n",
       "      <td>0.538129</td>\n",
       "      <td>0.408475</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Stuart</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.019653</td>\n",
       "      <td>0.023918</td>\n",
       "      <td>0.212609</td>\n",
       "      <td>0.743821</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Beatrice</td>\n",
       "      <td>Christian</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.008708</td>\n",
       "      <td>0.068320</td>\n",
       "      <td>0.397853</td>\n",
       "      <td>0.525118</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Delay</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.007549</td>\n",
       "      <td>0.065065</td>\n",
       "      <td>0.101495</td>\n",
       "      <td>0.825892</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Cami</td>\n",
       "      <td>Stincer</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.080655</td>\n",
       "      <td>0.021869</td>\n",
       "      <td>0.120286</td>\n",
       "      <td>0.777191</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Ashley</td>\n",
       "      <td>Jamieson</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.011313</td>\n",
       "      <td>0.027204</td>\n",
       "      <td>0.415172</td>\n",
       "      <td>0.546311</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Vilma</td>\n",
       "      <td>Jarvinen</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.131474</td>\n",
       "      <td>0.197968</td>\n",
       "      <td>0.386947</td>\n",
       "      <td>0.283612</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Hart</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.005941</td>\n",
       "      <td>0.014256</td>\n",
       "      <td>0.214668</td>\n",
       "      <td>0.765135</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>Eckstrom</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.487689</td>\n",
       "      <td>0.032199</td>\n",
       "      <td>0.472773</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>Aguila</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.034091</td>\n",
       "      <td>0.454899</td>\n",
       "      <td>0.366496</td>\n",
       "      <td>0.144514</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>John</td>\n",
       "      <td>McCraw</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.006095</td>\n",
       "      <td>0.034040</td>\n",
       "      <td>0.193961</td>\n",
       "      <td>0.765904</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Constance</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.422196</td>\n",
       "      <td>0.008878</td>\n",
       "      <td>0.117205</td>\n",
       "      <td>0.451721</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Lizzie</td>\n",
       "      <td>Stephens</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.019229</td>\n",
       "      <td>0.024191</td>\n",
       "      <td>0.665354</td>\n",
       "      <td>0.291225</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Justine</td>\n",
       "      <td>Jimenez</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.015503</td>\n",
       "      <td>0.768510</td>\n",
       "      <td>0.020671</td>\n",
       "      <td>0.195315</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Emerald</td>\n",
       "      <td>Sims</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.026729</td>\n",
       "      <td>0.029150</td>\n",
       "      <td>0.572188</td>\n",
       "      <td>0.371933</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Alyce</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.011077</td>\n",
       "      <td>0.007183</td>\n",
       "      <td>0.492264</td>\n",
       "      <td>0.489476</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Troy</td>\n",
       "      <td>Davis</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.002193</td>\n",
       "      <td>0.006168</td>\n",
       "      <td>0.397186</td>\n",
       "      <td>0.594454</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Cinton</td>\n",
       "      <td>Alford</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.008053</td>\n",
       "      <td>0.271638</td>\n",
       "      <td>0.324622</td>\n",
       "      <td>0.395688</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Ernst</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.004945</td>\n",
       "      <td>0.007094</td>\n",
       "      <td>0.400638</td>\n",
       "      <td>0.587323</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>J</td>\n",
       "      <td>Brantley</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.007167</td>\n",
       "      <td>0.015779</td>\n",
       "      <td>0.190786</td>\n",
       "      <td>0.786268</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Justin</td>\n",
       "      <td>King</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.462633</td>\n",
       "      <td>0.010932</td>\n",
       "      <td>0.154281</td>\n",
       "      <td>0.372154</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Leon</td>\n",
       "      <td>Wheeler</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.010030</td>\n",
       "      <td>0.073444</td>\n",
       "      <td>0.134283</td>\n",
       "      <td>0.782243</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>Romana</td>\n",
       "      <td>Newbold</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.050604</td>\n",
       "      <td>0.165724</td>\n",
       "      <td>0.435759</td>\n",
       "      <td>0.347913</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Walter</td>\n",
       "      <td>Reaves</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.064367</td>\n",
       "      <td>0.159891</td>\n",
       "      <td>0.148826</td>\n",
       "      <td>0.626916</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>Tracy</td>\n",
       "      <td>Snyder</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.005756</td>\n",
       "      <td>0.040685</td>\n",
       "      <td>0.032133</td>\n",
       "      <td>0.921426</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Stephanie</td>\n",
       "      <td>Koffler</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.006414</td>\n",
       "      <td>0.028487</td>\n",
       "      <td>0.124926</td>\n",
       "      <td>0.840172</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149826</th>\n",
       "      <td>Breyon</td>\n",
       "      <td>Jennings</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.013907</td>\n",
       "      <td>0.040884</td>\n",
       "      <td>0.348087</td>\n",
       "      <td>0.597123</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149830</th>\n",
       "      <td>Janice</td>\n",
       "      <td>Singletary</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.019588</td>\n",
       "      <td>0.004278</td>\n",
       "      <td>0.746299</td>\n",
       "      <td>0.229835</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149833</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Allen</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.008846</td>\n",
       "      <td>0.031768</td>\n",
       "      <td>0.178895</td>\n",
       "      <td>0.780491</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149842</th>\n",
       "      <td>Nazli</td>\n",
       "      <td>Khodayari</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.446764</td>\n",
       "      <td>0.060626</td>\n",
       "      <td>0.098077</td>\n",
       "      <td>0.394533</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149846</th>\n",
       "      <td>Sunshine</td>\n",
       "      <td>Moss</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.052204</td>\n",
       "      <td>0.056220</td>\n",
       "      <td>0.554738</td>\n",
       "      <td>0.336837</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149863</th>\n",
       "      <td>Abraham</td>\n",
       "      <td>Hunt</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.030136</td>\n",
       "      <td>0.021549</td>\n",
       "      <td>0.407746</td>\n",
       "      <td>0.540569</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149866</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Pascual</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.024577</td>\n",
       "      <td>0.135112</td>\n",
       "      <td>0.084182</td>\n",
       "      <td>0.756129</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149874</th>\n",
       "      <td>Tammy</td>\n",
       "      <td>King</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.461504</td>\n",
       "      <td>0.005805</td>\n",
       "      <td>0.145937</td>\n",
       "      <td>0.386754</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149877</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Delcher</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.005577</td>\n",
       "      <td>0.032688</td>\n",
       "      <td>0.522226</td>\n",
       "      <td>0.439509</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149880</th>\n",
       "      <td>Darin</td>\n",
       "      <td>Siepulveda</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.024851</td>\n",
       "      <td>0.471789</td>\n",
       "      <td>0.100555</td>\n",
       "      <td>0.402805</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149882</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>Smith</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.008667</td>\n",
       "      <td>0.026777</td>\n",
       "      <td>0.365516</td>\n",
       "      <td>0.599041</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149901</th>\n",
       "      <td>Allison</td>\n",
       "      <td>Berry</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.005735</td>\n",
       "      <td>0.018290</td>\n",
       "      <td>0.198833</td>\n",
       "      <td>0.777141</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149907</th>\n",
       "      <td>Georgie</td>\n",
       "      <td>Murphy</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.015914</td>\n",
       "      <td>0.048747</td>\n",
       "      <td>0.488451</td>\n",
       "      <td>0.446888</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149911</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Roe</td>\n",
       "      <td>asian</td>\n",
       "      <td>0.068267</td>\n",
       "      <td>0.025236</td>\n",
       "      <td>0.154806</td>\n",
       "      <td>0.751691</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149914</th>\n",
       "      <td>Annie</td>\n",
       "      <td>Rentz</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.003510</td>\n",
       "      <td>0.018384</td>\n",
       "      <td>0.420418</td>\n",
       "      <td>0.557688</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149926</th>\n",
       "      <td>Kimberley</td>\n",
       "      <td>Snook</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.492619</td>\n",
       "      <td>0.006185</td>\n",
       "      <td>0.036634</td>\n",
       "      <td>0.464563</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149949</th>\n",
       "      <td>Jamaal</td>\n",
       "      <td>Craig</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.006509</td>\n",
       "      <td>0.007635</td>\n",
       "      <td>0.448903</td>\n",
       "      <td>0.536953</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149953</th>\n",
       "      <td>Jonay</td>\n",
       "      <td>Whitehurst</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.021470</td>\n",
       "      <td>0.097062</td>\n",
       "      <td>0.148701</td>\n",
       "      <td>0.732766</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149954</th>\n",
       "      <td>Danmay</td>\n",
       "      <td>Abreu</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.022513</td>\n",
       "      <td>0.437588</td>\n",
       "      <td>0.063272</td>\n",
       "      <td>0.476627</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149956</th>\n",
       "      <td>Hector</td>\n",
       "      <td>Duenas</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.003735</td>\n",
       "      <td>0.489240</td>\n",
       "      <td>0.155488</td>\n",
       "      <td>0.351537</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149961</th>\n",
       "      <td>Tiffney</td>\n",
       "      <td>Hall</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.007570</td>\n",
       "      <td>0.361504</td>\n",
       "      <td>0.628137</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149970</th>\n",
       "      <td>Cheryl</td>\n",
       "      <td>Lucas</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.009917</td>\n",
       "      <td>0.073624</td>\n",
       "      <td>0.153512</td>\n",
       "      <td>0.762947</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149971</th>\n",
       "      <td>Fernando</td>\n",
       "      <td>Ritzinger</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.400779</td>\n",
       "      <td>0.223310</td>\n",
       "      <td>0.029582</td>\n",
       "      <td>0.346329</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149972</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Miller</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.004869</td>\n",
       "      <td>0.034061</td>\n",
       "      <td>0.166609</td>\n",
       "      <td>0.794462</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149975</th>\n",
       "      <td>Jahala</td>\n",
       "      <td>Stirling</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>0.023756</td>\n",
       "      <td>0.027080</td>\n",
       "      <td>0.654816</td>\n",
       "      <td>0.294348</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149983</th>\n",
       "      <td>Betty</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>0.007805</td>\n",
       "      <td>0.214573</td>\n",
       "      <td>0.773166</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149995</th>\n",
       "      <td>Faye</td>\n",
       "      <td>Harrison</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.006507</td>\n",
       "      <td>0.016057</td>\n",
       "      <td>0.171779</td>\n",
       "      <td>0.805656</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149996</th>\n",
       "      <td>James</td>\n",
       "      <td>Hall</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.003098</td>\n",
       "      <td>0.006102</td>\n",
       "      <td>0.133579</td>\n",
       "      <td>0.857222</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149998</th>\n",
       "      <td>Jesse</td>\n",
       "      <td>Murphy</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.016585</td>\n",
       "      <td>0.049813</td>\n",
       "      <td>0.388527</td>\n",
       "      <td>0.545075</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149999</th>\n",
       "      <td>Lee</td>\n",
       "      <td>Brown</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.024608</td>\n",
       "      <td>0.279732</td>\n",
       "      <td>0.690609</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19304 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_name   last_name race_orig     asian  hispanic  nh_black  \\\n",
       "5            Seth     Collier  nh_black  0.008728  0.022563  0.087592   \n",
       "8        Lilianna        Wood  hispanic  0.024998  0.106819  0.237043   \n",
       "17          David     Estores     asian  0.014508  0.679432  0.028218   \n",
       "23          Ronda    Feathers  nh_black  0.015252  0.010921  0.407520   \n",
       "38        Lincoln        Sims  nh_white  0.023191  0.030205  0.538129   \n",
       "47         Stuart      Thomas  nh_black  0.019653  0.023918  0.212609   \n",
       "58       Beatrice   Christian  nh_black  0.008708  0.068320  0.397853   \n",
       "69         Donald       Delay  nh_black  0.007549  0.065065  0.101495   \n",
       "80           Cami     Stincer  hispanic  0.080655  0.021869  0.120286   \n",
       "81         Ashley    Jamieson  nh_black  0.011313  0.027204  0.415172   \n",
       "86          Vilma    Jarvinen     asian  0.131474  0.197968  0.386947   \n",
       "90         Angela        Hart  nh_black  0.005941  0.014256  0.214668   \n",
       "91         Andrea    Eckstrom  nh_white  0.007339  0.487689  0.032199   \n",
       "100        Daniel      Aguila  nh_white  0.034091  0.454899  0.366496   \n",
       "109          John      McCraw  nh_black  0.006095  0.034040  0.193961   \n",
       "117     Constance        Shaw  nh_black  0.422196  0.008878  0.117205   \n",
       "119        Lizzie    Stephens  nh_white  0.019229  0.024191  0.665354   \n",
       "126       Justine     Jimenez  nh_white  0.015503  0.768510  0.020671   \n",
       "127       Emerald        Sims  hispanic  0.026729  0.029150  0.572188   \n",
       "136         Alyce    Anderson  nh_white  0.011077  0.007183  0.492264   \n",
       "137          Troy       Davis  nh_black  0.002193  0.006168  0.397186   \n",
       "161        Cinton      Alford  nh_black  0.008053  0.271638  0.324622   \n",
       "169         Ernst      Wilson  nh_black  0.004945  0.007094  0.400638   \n",
       "189             J    Brantley  nh_black  0.007167  0.015779  0.190786   \n",
       "190        Justin        King  nh_black  0.462633  0.010932  0.154281   \n",
       "195          Leon     Wheeler  nh_black  0.010030  0.073444  0.134283   \n",
       "202        Romana     Newbold  nh_white  0.050604  0.165724  0.435759   \n",
       "203        Walter      Reaves  nh_black  0.064367  0.159891  0.148826   \n",
       "204         Tracy      Snyder  nh_black  0.005756  0.040685  0.032133   \n",
       "209     Stephanie     Koffler  hispanic  0.006414  0.028487  0.124926   \n",
       "...           ...         ...       ...       ...       ...       ...   \n",
       "149826     Breyon    Jennings  nh_black  0.013907  0.040884  0.348087   \n",
       "149830     Janice  Singletary  nh_white  0.019588  0.004278  0.746299   \n",
       "149833     Joshua       Allen  nh_black  0.008846  0.031768  0.178895   \n",
       "149842      Nazli   Khodayari  nh_white  0.446764  0.060626  0.098077   \n",
       "149846   Sunshine        Moss  nh_white  0.052204  0.056220  0.554738   \n",
       "149863    Abraham        Hunt  nh_black  0.030136  0.021549  0.407746   \n",
       "149866     Thomas     Pascual  hispanic  0.024577  0.135112  0.084182   \n",
       "149874      Tammy        King  nh_white  0.461504  0.005805  0.145937   \n",
       "149877      Diana     Delcher  hispanic  0.005577  0.032688  0.522226   \n",
       "149880      Darin  Siepulveda  nh_white  0.024851  0.471789  0.100555   \n",
       "149882      Sarah       Smith  nh_black  0.008667  0.026777  0.365516   \n",
       "149901    Allison       Berry  nh_black  0.005735  0.018290  0.198833   \n",
       "149907    Georgie      Murphy  nh_white  0.015914  0.048747  0.488451   \n",
       "149911      Larry         Roe     asian  0.068267  0.025236  0.154806   \n",
       "149914      Annie       Rentz  nh_black  0.003510  0.018384  0.420418   \n",
       "149926  Kimberley       Snook  nh_white  0.492619  0.006185  0.036634   \n",
       "149949     Jamaal       Craig  nh_black  0.006509  0.007635  0.448903   \n",
       "149953      Jonay  Whitehurst  nh_black  0.021470  0.097062  0.148701   \n",
       "149954     Danmay       Abreu  hispanic  0.022513  0.437588  0.063272   \n",
       "149956     Hector      Duenas  nh_white  0.003735  0.489240  0.155488   \n",
       "149961    Tiffney        Hall  nh_black  0.002789  0.007570  0.361504   \n",
       "149970     Cheryl       Lucas  nh_black  0.009917  0.073624  0.153512   \n",
       "149971   Fernando   Ritzinger  nh_white  0.400779  0.223310  0.029582   \n",
       "149972     Angela      Miller  nh_black  0.004869  0.034061  0.166609   \n",
       "149975     Jahala    Stirling  nh_white  0.023756  0.027080  0.654816   \n",
       "149983      Betty      Wilson  nh_black  0.004455  0.007805  0.214573   \n",
       "149995       Faye    Harrison  nh_black  0.006507  0.016057  0.171779   \n",
       "149996      James        Hall  nh_black  0.003098  0.006102  0.133579   \n",
       "149998      Jesse      Murphy  nh_black  0.016585  0.049813  0.388527   \n",
       "149999        Lee       Brown  nh_black  0.005050  0.024608  0.279732   \n",
       "\n",
       "        nh_white      race  \n",
       "5       0.881118  nh_white  \n",
       "8       0.631140  nh_white  \n",
       "17      0.277843  hispanic  \n",
       "23      0.566306  nh_white  \n",
       "38      0.408475  nh_black  \n",
       "47      0.743821  nh_white  \n",
       "58      0.525118  nh_white  \n",
       "69      0.825892  nh_white  \n",
       "80      0.777191  nh_white  \n",
       "81      0.546311  nh_white  \n",
       "86      0.283612  nh_black  \n",
       "90      0.765135  nh_white  \n",
       "91      0.472773  hispanic  \n",
       "100     0.144514  hispanic  \n",
       "109     0.765904  nh_white  \n",
       "117     0.451721  nh_white  \n",
       "119     0.291225  nh_black  \n",
       "126     0.195315  hispanic  \n",
       "127     0.371933  nh_black  \n",
       "136     0.489476  nh_black  \n",
       "137     0.594454  nh_white  \n",
       "161     0.395688  nh_white  \n",
       "169     0.587323  nh_white  \n",
       "189     0.786268  nh_white  \n",
       "190     0.372154     asian  \n",
       "195     0.782243  nh_white  \n",
       "202     0.347913  nh_black  \n",
       "203     0.626916  nh_white  \n",
       "204     0.921426  nh_white  \n",
       "209     0.840172  nh_white  \n",
       "...          ...       ...  \n",
       "149826  0.597123  nh_white  \n",
       "149830  0.229835  nh_black  \n",
       "149833  0.780491  nh_white  \n",
       "149842  0.394533     asian  \n",
       "149846  0.336837  nh_black  \n",
       "149863  0.540569  nh_white  \n",
       "149866  0.756129  nh_white  \n",
       "149874  0.386754     asian  \n",
       "149877  0.439509  nh_black  \n",
       "149880  0.402805  hispanic  \n",
       "149882  0.599041  nh_white  \n",
       "149901  0.777141  nh_white  \n",
       "149907  0.446888  nh_black  \n",
       "149911  0.751691  nh_white  \n",
       "149914  0.557688  nh_white  \n",
       "149926  0.464563     asian  \n",
       "149949  0.536953  nh_white  \n",
       "149953  0.732766  nh_white  \n",
       "149954  0.476627  nh_white  \n",
       "149956  0.351537  hispanic  \n",
       "149961  0.628137  nh_white  \n",
       "149970  0.762947  nh_white  \n",
       "149971  0.346329     asian  \n",
       "149972  0.794462  nh_white  \n",
       "149975  0.294348  nh_black  \n",
       "149983  0.773166  nh_white  \n",
       "149995  0.805656  nh_white  \n",
       "149996  0.857222  nh_white  \n",
       "149998  0.545075  nh_white  \n",
       "149999  0.690609  nh_white  \n",
       "\n",
       "[19304 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edf3[edf3.race_orig!=edf3.race]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:39: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(edf3, 'ROC Curve -- Ethnicolr+Soundnames LSTM Model')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}