EDA_ESS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b1b7cba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e223955b",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path=\"data/selected_variables.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "04c3c022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "490556"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_rows = sum(1 for _ in open(file_path))\n",
    "num_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e675dd58",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/rq/nzqjmf0x07vg4p477yrb9w800000gn/T/ipykernel_41548/2775674321.py:1: DtypeWarning: Columns (211,212,213,214,215,217,218,219,220,221,286) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(file_path)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1fbc8cd4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\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>name</th>\n",
       "      <th>essround</th>\n",
       "      <th>edition</th>\n",
       "      <th>proddate</th>\n",
       "      <th>idno</th>\n",
       "      <th>cntry</th>\n",
       "      <th>dweight</th>\n",
       "      <th>pspwght</th>\n",
       "      <th>pweight</th>\n",
       "      <th>anweight</th>\n",
       "      <th>...</th>\n",
       "      <th>regionru</th>\n",
       "      <th>regionse</th>\n",
       "      <th>regionsi</th>\n",
       "      <th>regionsk</th>\n",
       "      <th>regiontr</th>\n",
       "      <th>regionua</th>\n",
       "      <th>regunit</th>\n",
       "      <th>ccrdprs</th>\n",
       "      <th>ccnthum</th>\n",
       "      <th>wrclmch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ESS1e06_7</td>\n",
       "      <td>1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>23.11.2023</td>\n",
       "      <td>1</td>\n",
       "      <td>AT</td>\n",
       "      <td>0.9452</td>\n",
       "      <td>0.940933</td>\n",
       "      <td>0.271487</td>\n",
       "      <td>0.255451</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ESS1e06_7</td>\n",
       "      <td>1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>23.11.2023</td>\n",
       "      <td>2</td>\n",
       "      <td>AT</td>\n",
       "      <td>0.4726</td>\n",
       "      <td>0.470466</td>\n",
       "      <td>0.271487</td>\n",
       "      <td>0.127726</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ESS1e06_7</td>\n",
       "      <td>1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>23.11.2023</td>\n",
       "      <td>3</td>\n",
       "      <td>AT</td>\n",
       "      <td>0.9452</td>\n",
       "      <td>1.392155</td>\n",
       "      <td>0.271487</td>\n",
       "      <td>0.377953</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ESS1e06_7</td>\n",
       "      <td>1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>23.11.2023</td>\n",
       "      <td>4</td>\n",
       "      <td>AT</td>\n",
       "      <td>0.9452</td>\n",
       "      <td>1.382163</td>\n",
       "      <td>0.271487</td>\n",
       "      <td>0.375240</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ESS1e06_7</td>\n",
       "      <td>1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>23.11.2023</td>\n",
       "      <td>6</td>\n",
       "      <td>AT</td>\n",
       "      <td>1.8905</td>\n",
       "      <td>1.437766</td>\n",
       "      <td>0.271487</td>\n",
       "      <td>0.390336</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490550</th>\n",
       "      <td>ESS10SCe03_1</td>\n",
       "      <td>10</td>\n",
       "      <td>3.1</td>\n",
       "      <td>02.11.2023</td>\n",
       "      <td>57345</td>\n",
       "      <td>SE</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.156429</td>\n",
       "      <td>0.373480</td>\n",
       "      <td>0.431903</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490551</th>\n",
       "      <td>ESS10SCe03_1</td>\n",
       "      <td>10</td>\n",
       "      <td>3.1</td>\n",
       "      <td>02.11.2023</td>\n",
       "      <td>57354</td>\n",
       "      <td>SE</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.816802</td>\n",
       "      <td>0.373480</td>\n",
       "      <td>0.305060</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490552</th>\n",
       "      <td>ESS10SCe03_1</td>\n",
       "      <td>10</td>\n",
       "      <td>3.1</td>\n",
       "      <td>02.11.2023</td>\n",
       "      <td>57356</td>\n",
       "      <td>SE</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.606818</td>\n",
       "      <td>0.373480</td>\n",
       "      <td>0.600115</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490553</th>\n",
       "      <td>ESS10SCe03_1</td>\n",
       "      <td>10</td>\n",
       "      <td>3.1</td>\n",
       "      <td>02.11.2023</td>\n",
       "      <td>57364</td>\n",
       "      <td>SE</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.620679</td>\n",
       "      <td>0.373480</td>\n",
       "      <td>0.605292</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490554</th>\n",
       "      <td>ESS10SCe03_1</td>\n",
       "      <td>10</td>\n",
       "      <td>3.1</td>\n",
       "      <td>02.11.2023</td>\n",
       "      <td>57458</td>\n",
       "      <td>SE</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.965670</td>\n",
       "      <td>0.373480</td>\n",
       "      <td>0.360659</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>490555 rows × 333 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name  essround  edition    proddate   idno cntry  dweight  \\\n",
       "0          ESS1e06_7         1      6.7  23.11.2023      1    AT   0.9452   \n",
       "1          ESS1e06_7         1      6.7  23.11.2023      2    AT   0.4726   \n",
       "2          ESS1e06_7         1      6.7  23.11.2023      3    AT   0.9452   \n",
       "3          ESS1e06_7         1      6.7  23.11.2023      4    AT   0.9452   \n",
       "4          ESS1e06_7         1      6.7  23.11.2023      6    AT   1.8905   \n",
       "...              ...       ...      ...         ...    ...   ...      ...   \n",
       "490550  ESS10SCe03_1        10      3.1  02.11.2023  57345    SE   1.0000   \n",
       "490551  ESS10SCe03_1        10      3.1  02.11.2023  57354    SE   1.0000   \n",
       "490552  ESS10SCe03_1        10      3.1  02.11.2023  57356    SE   1.0000   \n",
       "490553  ESS10SCe03_1        10      3.1  02.11.2023  57364    SE   1.0000   \n",
       "490554  ESS10SCe03_1        10      3.1  02.11.2023  57458    SE   1.0000   \n",
       "\n",
       "         pspwght   pweight  anweight  ...  regionru  regionse  regionsi  \\\n",
       "0       0.940933  0.271487  0.255451  ...       NaN       NaN       NaN   \n",
       "1       0.470466  0.271487  0.127726  ...       NaN       NaN       NaN   \n",
       "2       1.392155  0.271487  0.377953  ...       NaN       NaN       NaN   \n",
       "3       1.382163  0.271487  0.375240  ...       NaN       NaN       NaN   \n",
       "4       1.437766  0.271487  0.390336  ...       NaN       NaN       NaN   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "490550  1.156429  0.373480  0.431903  ...       NaN       NaN       NaN   \n",
       "490551  0.816802  0.373480  0.305060  ...       NaN       NaN       NaN   \n",
       "490552  1.606818  0.373480  0.600115  ...       NaN       NaN       NaN   \n",
       "490553  1.620679  0.373480  0.605292  ...       NaN       NaN       NaN   \n",
       "490554  0.965670  0.373480  0.360659  ...       NaN       NaN       NaN   \n",
       "\n",
       "        regionsk  regiontr  regionua  regunit  ccrdprs  ccnthum  wrclmch  \n",
       "0            NaN       NaN       NaN      NaN      NaN      NaN      NaN  \n",
       "1            NaN       NaN       NaN      NaN      NaN      NaN      NaN  \n",
       "2            NaN       NaN       NaN      NaN      NaN      NaN      NaN  \n",
       "3            NaN       NaN       NaN      NaN      NaN      NaN      NaN  \n",
       "4            NaN       NaN       NaN      NaN      NaN      NaN      NaN  \n",
       "...          ...       ...       ...      ...      ...      ...      ...  \n",
       "490550       NaN       NaN       NaN      NaN      2.0      4.0      2.0  \n",
       "490551       NaN       NaN       NaN      NaN      1.0      2.0      3.0  \n",
       "490552       NaN       NaN       NaN      NaN      9.0      4.0      4.0  \n",
       "490553       NaN       NaN       NaN      NaN     10.0      5.0      3.0  \n",
       "490554       NaN       NaN       NaN      NaN      7.0      5.0      3.0  \n",
       "\n",
       "[490555 rows x 333 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "48bd7b77",
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_values_statistics(dataset):\n",
    "    # Calculate the number of missing values for each column\n",
    "    missing_values_count = dataset.isnull().sum()\n",
    "\n",
    "    # Calculate the percentage of missing values for each column\n",
    "    missing_values_percentage = (missing_values_count / len(dataset)) * 100\n",
    "\n",
    "    # Create a DataFrame to store the statistics\n",
    "    missing_values_stats = pd.DataFrame({\n",
    "        'Column': missing_values_count.index,\n",
    "        'Missing Values': missing_values_count.values,\n",
    "        'Percentage': missing_values_percentage.values\n",
    "    })\n",
    "\n",
    "    # Sort the DataFrame by the number of missing values\n",
    "    missing_values_stats = missing_values_stats.sort_values(by='Missing Values', ascending=False)\n",
    "\n",
    "    return missing_values_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "473b8e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_missing_values_histogram(missing_stats):\n",
    "    # Plot a histogram of the distribution of missing values percentages\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.hist(missing_stats['Percentage'], bins=20, color='skyblue', edgecolor='black')\n",
    "    plt.xlabel('Percentage of Missing Values')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.title('Distribution of Missing Values in Percentages')\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d8a8da7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regionat</th>\n",
       "      <th>regionbe</th>\n",
       "      <th>regionbg</th>\n",
       "      <th>regioach</th>\n",
       "      <th>regioncy</th>\n",
       "      <th>regioncz</th>\n",
       "      <th>regioacz</th>\n",
       "      <th>regionde</th>\n",
       "      <th>regiondk</th>\n",
       "      <th>regioadk</th>\n",
       "      <th>...</th>\n",
       "      <th>regionpl</th>\n",
       "      <th>regionpt</th>\n",
       "      <th>regioapt</th>\n",
       "      <th>regionro</th>\n",
       "      <th>regionru</th>\n",
       "      <th>regionse</th>\n",
       "      <th>regionsi</th>\n",
       "      <th>regionsk</th>\n",
       "      <th>regiontr</th>\n",
       "      <th>regionua</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6918.000000</td>\n",
       "      <td>7235.000000</td>\n",
       "      <td>3630.000000</td>\n",
       "      <td>7804.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>4386.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>11456.000000</td>\n",
       "      <td>4498.000000</td>\n",
       "      <td>1610.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7166.000000</td>\n",
       "      <td>5785.000000</td>\n",
       "      <td>2367.000000</td>\n",
       "      <td>2146.000000</td>\n",
       "      <td>4949.000000</td>\n",
       "      <td>7704.000000</td>\n",
       "      <td>5723.000000</td>\n",
       "      <td>5088.000000</td>\n",
       "      <td>4272.000000</td>\n",
       "      <td>5878.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.340272</td>\n",
       "      <td>1.705183</td>\n",
       "      <td>14.365840</td>\n",
       "      <td>3.361097</td>\n",
       "      <td>21.233484</td>\n",
       "      <td>8.496352</td>\n",
       "      <td>4.633300</td>\n",
       "      <td>8.888530</td>\n",
       "      <td>8.259004</td>\n",
       "      <td>2.940373</td>\n",
       "      <td>...</td>\n",
       "      <td>16.684901</td>\n",
       "      <td>2.235091</td>\n",
       "      <td>2.269962</td>\n",
       "      <td>25.807083</td>\n",
       "      <td>4.920792</td>\n",
       "      <td>4.230010</td>\n",
       "      <td>6.575223</td>\n",
       "      <td>10.079009</td>\n",
       "      <td>5.702013</td>\n",
       "      <td>12.312351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.465110</td>\n",
       "      <td>0.921299</td>\n",
       "      <td>8.074777</td>\n",
       "      <td>1.765883</td>\n",
       "      <td>11.889668</td>\n",
       "      <td>30.250676</td>\n",
       "      <td>2.195981</td>\n",
       "      <td>4.234615</td>\n",
       "      <td>4.708427</td>\n",
       "      <td>1.332828</td>\n",
       "      <td>...</td>\n",
       "      <td>8.890860</td>\n",
       "      <td>1.106241</td>\n",
       "      <td>1.080546</td>\n",
       "      <td>10.581598</td>\n",
       "      <td>2.705310</td>\n",
       "      <td>2.609622</td>\n",
       "      <td>3.069462</td>\n",
       "      <td>72.273396</td>\n",
       "      <td>3.752457</td>\n",
       "      <td>7.510126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          regionat     regionbe     regionbg     regioach     regioncy  \\\n",
       "count  6918.000000  7235.000000  3630.000000  7804.000000  2210.000000   \n",
       "mean      5.340272     1.705183    14.365840     3.361097    21.233484   \n",
       "std       2.465110     0.921299     8.074777     1.765883    11.889668   \n",
       "min       1.000000     1.000000     1.000000     1.000000    10.000000   \n",
       "25%       3.000000     1.000000     7.000000     2.000000    10.000000   \n",
       "50%       5.000000     1.000000    16.000000     3.000000    20.000000   \n",
       "75%       7.000000     3.000000    22.000000     5.000000    30.000000   \n",
       "max       9.000000     3.000000    28.000000     7.000000    51.000000   \n",
       "\n",
       "          regioncz     regioacz      regionde     regiondk     regioadk  ...  \\\n",
       "count  4386.000000  2018.000000  11456.000000  4498.000000  1610.000000  ...   \n",
       "mean      8.496352     4.633300      8.888530     8.259004     2.940373  ...   \n",
       "std      30.250676     2.195981      4.234615     4.708427     1.332828  ...   \n",
       "min       1.000000     1.000000      1.000000     1.000000     1.000000  ...   \n",
       "25%       3.000000     3.000000      5.000000     3.000000     2.000000  ...   \n",
       "50%       8.000000     5.000000      9.000000     9.000000     3.000000  ...   \n",
       "75%      11.000000     6.000000     13.000000    13.000000     4.000000  ...   \n",
       "max     999.000000     8.000000     16.000000    15.000000     5.000000  ...   \n",
       "\n",
       "          regionpl     regionpt     regioapt     regionro     regionru  \\\n",
       "count  7166.000000  5785.000000  2367.000000  2146.000000  4949.000000   \n",
       "mean     16.684901     2.235091     2.269962    25.807083     4.920792   \n",
       "std       8.890860     1.106241     1.080546    10.581598     2.705310   \n",
       "min       2.000000     1.000000     1.000000    11.000000     1.000000   \n",
       "25%      10.000000     1.000000     1.000000    21.000000     2.000000   \n",
       "50%      14.000000     2.000000     2.000000    22.000000     5.000000   \n",
       "75%      24.000000     3.000000     3.000000    32.000000     7.000000   \n",
       "max      32.000000     5.000000     5.000000    42.000000    10.000000   \n",
       "\n",
       "          regionse     regionsi     regionsk     regiontr     regionua  \n",
       "count  7704.000000  5723.000000  5088.000000  4272.000000  5878.000000  \n",
       "mean      4.230010     6.575223    10.079009     5.702013    12.312351  \n",
       "std       2.609622     3.069462    72.273396     3.752457     7.510126  \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000  \n",
       "25%       2.000000     4.000000     3.000000     3.000000     5.000000  \n",
       "50%       4.000000     7.000000     5.000000     5.000000    12.000000  \n",
       "75%       7.000000     9.000000     7.000000     8.000000    18.000000  \n",
       "max       8.000000    12.000000   999.000000    12.000000    26.000000  \n",
       "\n",
       "[8 rows x 42 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# Define the regex pattern to match column names\n",
    "pattern_region = re.compile(r'^regio.*', flags=re.IGNORECASE)\n",
    "\n",
    "# Use filter() to select columns matching the pattern\n",
    "region_columns = list(filter(pattern_region.match, df.columns))\n",
    "\n",
    "# Create a new DataFrame with only the selected columns\n",
    "region_df = df[region_columns]\n",
    "region_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7c591b29",
   "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>prtvde1</th>\n",
       "      <th>prtvade1</th>\n",
       "      <th>prtvbde1</th>\n",
       "      <th>prtvcde1</th>\n",
       "      <th>prtvdde1</th>\n",
       "      <th>prtvede1</th>\n",
       "      <th>prtvde2</th>\n",
       "      <th>prtvade2</th>\n",
       "      <th>prtvbde2</th>\n",
       "      <th>prtvcde2</th>\n",
       "      <th>...</th>\n",
       "      <th>prtvtdsk</th>\n",
       "      <th>prtvttr</th>\n",
       "      <th>prtvtatr</th>\n",
       "      <th>prtvtua</th>\n",
       "      <th>prtvtaua</th>\n",
       "      <th>prtvtbua</th>\n",
       "      <th>prtvtcua</th>\n",
       "      <th>prtvtxk</th>\n",
       "      <th>prtvtrs</th>\n",
       "      <th>prtvtme</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2870.000000</td>\n",
       "      <td>5667.000000</td>\n",
       "      <td>3031.000000</td>\n",
       "      <td>2958.000000</td>\n",
       "      <td>8255.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2870.000000</td>\n",
       "      <td>5667.000000</td>\n",
       "      <td>3031.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1083.000000</td>\n",
       "      <td>1856.000000</td>\n",
       "      <td>2416.000000</td>\n",
       "      <td>2031.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>3776.000000</td>\n",
       "      <td>2178.000000</td>\n",
       "      <td>1295.000000</td>\n",
       "      <td>2043.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.198698</td>\n",
       "      <td>29.689199</td>\n",
       "      <td>27.867831</td>\n",
       "      <td>28.696800</td>\n",
       "      <td>28.377282</td>\n",
       "      <td>25.322835</td>\n",
       "      <td>23.432682</td>\n",
       "      <td>30.028920</td>\n",
       "      <td>28.246515</td>\n",
       "      <td>28.519960</td>\n",
       "      <td>...</td>\n",
       "      <td>31.149584</td>\n",
       "      <td>30.524784</td>\n",
       "      <td>35.071606</td>\n",
       "      <td>33.074840</td>\n",
       "      <td>17.549451</td>\n",
       "      <td>23.452595</td>\n",
       "      <td>25.063820</td>\n",
       "      <td>37.843243</td>\n",
       "      <td>40.523740</td>\n",
       "      <td>37.683333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.626216</td>\n",
       "      <td>33.881083</td>\n",
       "      <td>33.524519</td>\n",
       "      <td>33.569623</td>\n",
       "      <td>33.711256</td>\n",
       "      <td>32.721191</td>\n",
       "      <td>31.578348</td>\n",
       "      <td>33.771786</td>\n",
       "      <td>33.575133</td>\n",
       "      <td>33.390047</td>\n",
       "      <td>...</td>\n",
       "      <td>32.094445</td>\n",
       "      <td>36.392476</td>\n",
       "      <td>34.374979</td>\n",
       "      <td>33.776625</td>\n",
       "      <td>28.261165</td>\n",
       "      <td>30.459351</td>\n",
       "      <td>31.239779</td>\n",
       "      <td>33.250457</td>\n",
       "      <td>34.946199</td>\n",
       "      <td>32.581478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>9.750000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 170 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           prtvde1     prtvade1     prtvbde1     prtvcde1     prtvdde1  \\\n",
       "count  2919.000000  2870.000000  5667.000000  3031.000000  2958.000000   \n",
       "mean     23.198698    29.689199    27.867831    28.696800    28.377282   \n",
       "std      31.626216    33.881083    33.524519    33.569623    33.711256   \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "25%       1.000000     1.000000     2.000000     2.000000     2.000000   \n",
       "50%       2.000000     3.000000     3.000000     4.000000     3.000000   \n",
       "75%      66.000000    66.000000    66.000000    66.000000    66.000000   \n",
       "max      88.000000    88.000000    88.000000    88.000000    99.000000   \n",
       "\n",
       "          prtvede1      prtvde2     prtvade2     prtvbde2     prtvcde2  ...  \\\n",
       "count  8255.000000  2919.000000  2870.000000  5667.000000  3031.000000  ...   \n",
       "mean     25.322835    23.432682    30.028920    28.246515    28.519960  ...   \n",
       "std      32.721191    31.578348    33.771786    33.575133    33.390047  ...   \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000  ...   \n",
       "25%       1.000000     2.000000     2.000000     2.000000     2.000000  ...   \n",
       "50%       3.000000     3.000000     4.000000     4.000000     4.000000  ...   \n",
       "75%      66.000000    66.000000    66.000000    66.000000    66.000000  ...   \n",
       "max      88.000000    88.000000    88.000000    88.000000    88.000000  ...   \n",
       "\n",
       "          prtvtdsk      prtvttr     prtvtatr      prtvtua     prtvtaua  \\\n",
       "count  1083.000000  1856.000000  2416.000000  2031.000000  2002.000000   \n",
       "mean     31.149584    30.524784    35.071606    33.074840    17.549451   \n",
       "std      32.094445    36.392476    34.374979    33.776625    28.261165   \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "25%       3.000000     1.000000     1.000000     2.000000     1.000000   \n",
       "50%       8.000000     5.000000    12.000000    14.000000     3.000000   \n",
       "75%      66.000000    66.000000    66.000000    66.000000     9.750000   \n",
       "max      88.000000    99.000000    88.000000    88.000000    88.000000   \n",
       "\n",
       "          prtvtbua     prtvtcua      prtvtxk      prtvtrs      prtvtme  \n",
       "count  3776.000000  2178.000000  1295.000000  2043.000000  1200.000000  \n",
       "mean     23.452595    25.063820    37.843243    40.523740    37.683333  \n",
       "std      30.459351    31.239779    33.250457    34.946199    32.581478  \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000  \n",
       "25%       3.000000     3.000000     2.000000     1.000000     6.000000  \n",
       "50%       5.000000     5.000000    66.000000    66.000000    14.000000  \n",
       "75%      66.000000    66.000000    66.000000    77.000000    77.000000  \n",
       "max      99.000000    99.000000    88.000000    88.000000    88.000000  \n",
       "\n",
       "[8 rows x 170 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the regex pattern to match column names\n",
    "pattern_parties = re.compile(r'^prtv.*', flags=re.IGNORECASE)\n",
    "\n",
    "# Use filter() to select columns matching the pattern\n",
    "party_votes_columns = list(filter(pattern_parties.match, df.columns))\n",
    "\n",
    "# Create a ew DataFrame with only the selected columns\n",
    "parties_df = df[party_votes_columns]\n",
    "parties_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "effddbe7",
   "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>essround</th>\n",
       "      <th>edition</th>\n",
       "      <th>idno</th>\n",
       "      <th>dweight</th>\n",
       "      <th>pspwght</th>\n",
       "      <th>pweight</th>\n",
       "      <th>anweight</th>\n",
       "      <th>prob</th>\n",
       "      <th>stratum</th>\n",
       "      <th>psu</th>\n",
       "      <th>...</th>\n",
       "      <th>iplylfr</th>\n",
       "      <th>iprspot</th>\n",
       "      <th>ipshabt</th>\n",
       "      <th>ipstrgv</th>\n",
       "      <th>ipsuces</th>\n",
       "      <th>ipudrst</th>\n",
       "      <th>regunit</th>\n",
       "      <th>ccrdprs</th>\n",
       "      <th>ccnthum</th>\n",
       "      <th>wrclmch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>490555.000000</td>\n",
       "      <td>490555.000000</td>\n",
       "      <td>4.905550e+05</td>\n",
       "      <td>490555.000000</td>\n",
       "      <td>490555.000000</td>\n",
       "      <td>490555.000000</td>\n",
       "      <td>400018.000000</td>\n",
       "      <td>109204.000000</td>\n",
       "      <td>109204.000000</td>\n",
       "      <td>109204.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>462484.000000</td>\n",
       "      <td>462490.000000</td>\n",
       "      <td>462489.000000</td>\n",
       "      <td>462493.000000</td>\n",
       "      <td>462489.000000</td>\n",
       "      <td>462495.000000</td>\n",
       "      <td>278833.000000</td>\n",
       "      <td>104072.000000</td>\n",
       "      <td>104072.000000</td>\n",
       "      <td>104072.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.631746</td>\n",
       "      <td>3.552326</td>\n",
       "      <td>9.937962e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.004424</td>\n",
       "      <td>0.960863</td>\n",
       "      <td>0.952282</td>\n",
       "      <td>0.001257</td>\n",
       "      <td>1385.398566</td>\n",
       "      <td>12191.400123</td>\n",
       "      <td>...</td>\n",
       "      <td>2.098103</td>\n",
       "      <td>3.274289</td>\n",
       "      <td>3.285525</td>\n",
       "      <td>2.477875</td>\n",
       "      <td>3.271455</td>\n",
       "      <td>2.546088</td>\n",
       "      <td>2.446945</td>\n",
       "      <td>9.047707</td>\n",
       "      <td>6.633984</td>\n",
       "      <td>3.288973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.869015</td>\n",
       "      <td>1.177988</td>\n",
       "      <td>1.818970e+10</td>\n",
       "      <td>0.418180</td>\n",
       "      <td>0.555381</td>\n",
       "      <td>1.118180</td>\n",
       "      <td>1.461442</td>\n",
       "      <td>0.002586</td>\n",
       "      <td>973.096918</td>\n",
       "      <td>7128.004376</td>\n",
       "      <td>...</td>\n",
       "      <td>1.321277</td>\n",
       "      <td>1.588870</td>\n",
       "      <td>1.583197</td>\n",
       "      <td>1.539878</td>\n",
       "      <td>1.569754</td>\n",
       "      <td>1.408285</td>\n",
       "      <td>0.818373</td>\n",
       "      <td>15.385446</td>\n",
       "      <td>15.211237</td>\n",
       "      <td>1.143798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.022428</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.773000e+03</td>\n",
       "      <td>0.858442</td>\n",
       "      <td>0.680608</td>\n",
       "      <td>0.230129</td>\n",
       "      <td>0.179632</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>358.000000</td>\n",
       "      <td>5996.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>1.057300e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920021</td>\n",
       "      <td>0.417552</td>\n",
       "      <td>0.404342</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>1530.000000</td>\n",
       "      <td>11922.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>5.146400e+04</td>\n",
       "      <td>1.051200</td>\n",
       "      <td>1.185674</td>\n",
       "      <td>1.857518</td>\n",
       "      <td>1.254741</td>\n",
       "      <td>0.001074</td>\n",
       "      <td>2359.000000</td>\n",
       "      <td>17193.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>5.101304e+11</td>\n",
       "      <td>6.206992</td>\n",
       "      <td>6.854967</td>\n",
       "      <td>5.447134</td>\n",
       "      <td>21.997766</td>\n",
       "      <td>0.045450</td>\n",
       "      <td>2914.000000</td>\n",
       "      <td>27238.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 107 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            essround        edition          idno        dweight  \\\n",
       "count  490555.000000  490555.000000  4.905550e+05  490555.000000   \n",
       "mean        5.631746       3.552326  9.937962e+08       1.000000   \n",
       "std         2.869015       1.177988  1.818970e+10       0.418180   \n",
       "min         1.000000       2.300000  1.000000e+00       0.001600   \n",
       "25%         3.000000       2.600000  1.773000e+03       0.858442   \n",
       "50%         6.000000       3.200000  1.057300e+04       1.000000   \n",
       "75%         8.000000       3.700000  5.146400e+04       1.051200   \n",
       "max        10.000000       6.700000  5.101304e+11       6.206992   \n",
       "\n",
       "             pspwght        pweight       anweight           prob  \\\n",
       "count  490555.000000  490555.000000  400018.000000  109204.000000   \n",
       "mean        1.004424       0.960863       0.952282       0.001257   \n",
       "std         0.555381       1.118180       1.461442       0.002586   \n",
       "min         0.000004       0.022428       0.000001       0.000005   \n",
       "25%         0.680608       0.230129       0.179632       0.000287   \n",
       "50%         0.920021       0.417552       0.404342       0.000543   \n",
       "75%         1.185674       1.857518       1.254741       0.001074   \n",
       "max         6.854967       5.447134      21.997766       0.045450   \n",
       "\n",
       "             stratum            psu  ...        iplylfr        iprspot  \\\n",
       "count  109204.000000  109204.000000  ...  462484.000000  462490.000000   \n",
       "mean     1385.398566   12191.400123  ...       2.098103       3.274289   \n",
       "std       973.096918    7128.004376  ...       1.321277       1.588870   \n",
       "min         1.000000       1.000000  ...       1.000000       1.000000   \n",
       "25%       358.000000    5996.000000  ...       1.000000       2.000000   \n",
       "50%      1530.000000   11922.000000  ...       2.000000       3.000000   \n",
       "75%      2359.000000   17193.000000  ...       2.000000       4.000000   \n",
       "max      2914.000000   27238.000000  ...       9.000000       9.000000   \n",
       "\n",
       "             ipshabt        ipstrgv        ipsuces        ipudrst  \\\n",
       "count  462489.000000  462493.000000  462489.000000  462495.000000   \n",
       "mean        3.285525       2.477875       3.271455       2.546088   \n",
       "std         1.583197       1.539878       1.569754       1.408285   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%         2.000000       1.000000       2.000000       2.000000   \n",
       "50%         3.000000       2.000000       3.000000       2.000000   \n",
       "75%         4.000000       3.000000       4.000000       3.000000   \n",
       "max         9.000000       9.000000       9.000000       9.000000   \n",
       "\n",
       "             regunit        ccrdprs        ccnthum        wrclmch  \n",
       "count  278833.000000  104072.000000  104072.000000  104072.000000  \n",
       "mean        2.446945       9.047707       6.633984       3.288973  \n",
       "std         0.818373      15.385446      15.211237       1.143798  \n",
       "min         1.000000       0.000000       1.000000       1.000000  \n",
       "25%         2.000000       5.000000       3.000000       3.000000  \n",
       "50%         2.000000       6.000000       4.000000       3.000000  \n",
       "75%         3.000000       8.000000       4.000000       4.000000  \n",
       "max         4.000000      99.000000      99.000000       9.000000  \n",
       "\n",
       "[8 rows x 107 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use filter() to select columns not matching the patterns\n",
    "other_variables_col = list(filter(lambda col: not(pattern_parties.match(col) or pattern_region.match(col)), df.columns))\n",
    "\n",
    "# Create a new DataFrame with only the selected columns\n",
    "other_variables_df = df[other_variables_col]\n",
    "other_variables_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7206ac1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics about missing values:\n",
      "     Column  Missing Values  Percentage\n",
      "47  ctzship          448196   91.365086\n",
      "41  cntbrth          448196   91.365086\n",
      "82  hincsrc          448196   91.365086\n",
      "72   rship4          448196   91.365086\n",
      "70   rship3          448196   91.365086\n",
      "..      ...             ...         ...\n",
      "54  hlthhmp               0    0.000000\n",
      "53   health               0    0.000000\n",
      "97  pdjobev               0    0.000000\n",
      "52    happy               0    0.000000\n",
      "0      name               0    0.000000\n",
      "\n",
      "[120 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "missing_stats = missing_values_statistics(other_variables_df)\n",
    "print(\"Statistics about missing values:\")\n",
    "print(missing_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "af842756",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HD9eGDRu0ceNG/fLLL0pNTdXQoUPd+yVJN998c6H9mjhxoowx+uOPPzzWZ533v//+u/z8/Ir8uy9QsJ2rrrqq0Hbmz59fKMgHBQUpMDCw0D4fP368RPtc0jlR0jlamnGaP3++Bg4cqDfeeEMtW7ZUtWrVNGDAAB08eLBEtQM4vUreLgCA71uyZIny8/NP+/s51157ra699lrl5+fr22+/1csvv6wRI0YoMjJSt912W4m2VZrf0inqA0FBW8GHvoIPQQVfzi5Q1JmP0qhevbpSU1MLtR84cECSPM5ulFXVqlVlt9vP+nZat26tunXraty4cUpMTCx0lux0Bg8erMGDByszM1Nr1qzRU089pRtuuEE//fST4uLiFBwcrLFjx2rs2LE6dOiQ++xSt27d9OOPP55x/dWrV/e4I2OB0nxgrFGjhjp16qQ5c+bohRde0MyZM3XZZZepTZs27j7vvvuuBgwYoPHjx3u8Ni0tTVWqVCl2/YGBgYXmYMFr/y48PFw2m01r1651h+m/+3tb48aNNW/ePBljtHXrVs2aNUvjxo2T0+nUI488UpLdLlc1atRQs2bNinyuYJ6+/PLLp7y7oTU0WN8LLrroIuXn5+vgwYOn/AeVgu188MEHiouLK1X9ZVHSOVHSOVqacQoPD9eUKVM0ZcoUpaSkaPHixXrkkUd0+PBh9/eqAJwZzigBKFZKSopGjx6typUr66677irRa/z8/NS8eXO98sorkuS+DK4kZ1FKY/v27dqyZYtH25w5cxQaGqorr7xSktx399q6datHP+tdsgrqK2ltHTp00MqVK92BpcDbb7+toKCgcrnVdXBwsJo3b64FCxZ41OVyufTuu++qRo0aqlOnzhlvR5Ief/xxdevWTQ888ECZ1xEcHKzOnTtrzJgxOnHihLZv316oT2RkpAYNGqTbb79dO3fuLJfbPrdt21YrV670CB0ul0vvv/9+qdYzZMgQHT16VE8++aQ2b95c6BJEm81WKLwsWbJE+/fvP+26L7nkEh0+fNjjw/KJEycK/X7TDTfcIGOM9u/fr2bNmhV6NG7cuNC6bTabEhIS9OKLL6pKlSpn/bLTsmjdurWqVKmiH374ocj9atas2Wl/p6pz586STt5l8VSuu+46VapUSb/88sspt1Naxb1vlXROtG3bVt9//32hSyTnzZvnsVzWcapZs6buvfdeJSYm+uTxB85VnFEC4Pb999+7r4k/fPiw1q5dq5kzZ8rPz08LFy50372qKK+++qpWrlyprl27qmbNmjp+/Lj7Ns4dO3aUdPIHTuPi4vThhx+qQ4cOqlatmsLDw0t0K+uixMTE6MYbb1RSUpKio6P17rvvKjk5WRMnTlRQUJCkk5fg1K1bV6NHj1ZeXp6qVq2qhQsX6osvvii0vsaNG2vBggWaPn26mjZtKrvdfsoPVk899ZQ+/vhjtW/fXk8++aSqVaum//73v1qyZImee+65Im+tXRYTJkxQYmKi2rdvr9GjRysgIEDTpk3T999/r7lz55bqDFxx+vXrp379+pX6df/85z/ldDrVunVrRUdH6+DBg5owYYIqV66sq666SpLUvHlz3XDDDWrSpImqVq2qHTt26J133lHLli3dx+lMjBkzRh999JE6dOigMWPGyOl06tVXX3V/l8NuL9m/Cd54440KDw/XpEmT5Ofnp4EDB3o8f8MNN2jWrFmqV6+emjRpoo0bN2rSpEkl+n2t3r1768knn9Rtt92mBx98UMePH9dLL71U6K6HBb9BNHjwYH377bdq06aNgoODlZqaqi+++EKNGzfWv/71L3388ceaNm2aevToodq1a8sYowULFujPP/9UYmJiCUeu4oSEhOjll1/WwIED9ccff+jmm29WRESEfv/9d23ZskW///57sQFIOnnGun///nr66ad16NAh3XDDDXI4HNq0aZOCgoJ033336ZJLLtG4ceM0ZswY/frrr7r++utVtWpVHTp0SN9884377GZpFPe+VdI5MWLECL311lvq3Lmzxo0bp8jISM2ZM8d9RrVgjpZ0nI4dO6b27durT58+qlevnkJDQ7VhwwYtW7ZMvXr1KtX+ASiG9+4jAcBXFNwZruAREBBgIiIiTNu2bc348ePN4cOHC72mqLt19ezZ08TFxRmHw2GqV69u2rZtaxYvXuzxuk8//dRcccUVxuFwGElm4MCBHuv7+53LTrUtY07eEa1r167mgw8+MA0bNjQBAQHmkksuMZMnTy70+p9++sl06tTJhIWFmYsuusjcd999ZsmSJYXuevfHH3+Ym2++2VSpUsXYbDaPbaqIu15t27bNdOvWzVSuXNkEBASYhIQEjzudGfN/d717//33PdqLujPaqaxdu9b84x//MMHBwcbpdJoWLVqYjz76qMj1lfaud8UpyV3vZs+ebdq3b28iIyNNQECAiYmJMbfeeqvZunWru88jjzximjVrZqpWrWocDoepXbu2GTlypElLS3P3Ke4YW1lrMObkGDVv3tw4HA4TFRVlHnzwQTNx4kQjyX0HwpIYOXJkkXcpNMaYo0ePmiFDhpiIiAgTFBRkrrnmGrN27dpC9Zzq2H7yySfm8ssvN06n09SuXdtMnTq1yP02xpi33nrLNG/e3H3ML730UjNgwADz7bffGmOM+fHHH83tt99uLr30UuN0Ok3lypXN1VdfbWbNmnXafTzVXe+GDRtWqG9cXJz7b/RUSjP3Vq9ebbp27WqqVatm/P39zcUXX2y6du3q8fdR3HtBfn6+efHFF02jRo1MQECAqVy5smnZsmWhv4dFixaZ9u3bm7CwMONwOExcXJy5+eabzaeffuoxDsHBwYW2UdQxOdX7VknnhDHGfP/996Zjx44mMDDQVKtWzQwZMsTMnj3bSDJbtmwp1TgdP37c3H333aZJkyYmLCzMOJ1OU7duXfPUU0+57/gJ4MzZjDnNrawAADgHderUSXv27NFPP/3k7VKAIg0dOlRz587VkSNHTnvpIYCKx6V3AIBz3qhRo3TFFVcoNjZWf/zxh/773/8qOTlZb775prdLAyRJ48aNU0xMjGrXrq2//vpLH3/8sd544w09/vjjhCTARxGUAADnvPz8fD355JM6ePCgbDabGjRooHfeeadM37sCzgZ/f39NmjRJv/32m/Ly8hQfH6/Jkydr+PDh3i4NwClw6R0AAAAAWHB7cAAAAACwICgBAAAAgAVBCQAAAAAszvubObhcLh04cEChoaHl9sOMAAAAAM49xhhlZGQoJibmtD9Ift4HpQMHDig2NtbbZQAAAADwEfv27VONGjWK7XPeB6XQ0FBJJwcjLCzMq7Xk5uZqxYoV6tSpk/z9/b1aC85fzDNUFOYaKgLzDBWBeXbhSE9PV2xsrDsjFOe8D0oFl9uFhYX5RFAKCgpSWFgYf4Q4a5hnqCjMNVQE5hkqAvPswlOSr+RwMwcAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMDCZ4LShAkTZLPZNGLECHebMUZJSUmKiYmR0+lUu3bttH37du8VCQAAAOCC4BNBacOGDZoxY4aaNGni0f7cc89p8uTJmjp1qjZs2KCoqCglJiYqIyPDS5UCAAAAuBB4PSj99ddf6tu3r15//XVVrVrV3W6M0ZQpUzRmzBj16tVLjRo10uzZs5WVlaU5c+Z4sWIAAAAA57tK3i5g2LBh6tq1qzp27Kinn37a3b57924dPHhQnTp1crc5HA61bdtW69at01133VXk+nJycpSTk+NeTk9PlyTl5uYqNzf3LO1FyRRsf9OmTbLbvZ5RVb16ddWoUcPbZaCcFcwzb893nP+Ya6gIzDNUBObZhaM0x9irQWnevHn67rvvtGHDhkLPHTx4UJIUGRnp0R4ZGam9e/eecp0TJkzQ2LFjC7WvWLFCQUFBZ1hx+UhNTfV2CZKk/fv3a+vWrd4uA2dJcnKyt0vABYK5horAPENFYJ6d/7Kyskrc12tBad++fRo+fLhWrFihwMDAU/az2Wwey8aYQm1/9+ijj2rUqFHu5fT0dMXGxqpTp04KCws788LPwKZNm5SamqoFPx5S1djaXq3l972/aOG/R2rNmjVKSEjwai0oX7m5uUpOTlZiYqL8/f29XQ7OY8w1VATmGSoC8+zCUXC1WUl4LSht3LhRhw8fVtOmTd1t+fn5WrNmjaZOnaqdO3dKOnlmKTo62t3n8OHDhc4y/Z3D4ZDD4SjU7u/v7/WJX3C5XdXY2oqqf7lXa8mXTdnZ2bLb7V4fF5wdvjDncWFgrqEiMM9QEZhn57/SHF+vfVGmQ4cO2rZtmzZv3ux+NGvWTH379tXmzZtVu3ZtRUVFeZwCPXHihFavXq1WrVp5q2wAAAAAFwCvnVEKDQ1Vo0aNPNqCg4NVvXp1d/uIESM0fvx4xcfHKz4+XuPHj1dQUJD69OnjjZIBAAAAXCC8fte74jz00EPKzs7WPffco6NHj6p58+ZasWKFQkNDvV0aAAAAgPOYTwWlVatWeSzbbDYlJSUpKSnJK/UAAAAAuDB5/8d8AAAAAMDHEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALDwalCaPn26mjRporCwMIWFhally5ZaunSp+/lBgwbJZrN5PFq0aOHFigEAAABcCCp5c+M1atTQs88+q8suu0ySNHv2bHXv3l2bNm1Sw4YNJUnXX3+9Zs6c6X5NQECAV2oFAAAAcOHwalDq1q2bx/Izzzyj6dOn66uvvnIHJYfDoaioKG+UBwAAAOAC5dWg9Hf5+fl6//33lZmZqZYtW7rbV61apYiICFWpUkVt27bVM888o4iIiFOuJycnRzk5Oe7l9PR0SVJubq5yc3PP3g6UgMvlkiT5ycjuyvNqLX4ycjqdcrlcXh8XlK+C48lxxdnGXENFYJ6hIjDPLhylOcY2Y4w5i7Wc1rZt29SyZUsdP35cISEhmjNnjrp06SJJmj9/vkJCQhQXF6fdu3friSeeUF5enjZu3CiHw1Hk+pKSkjR27NhC7XPmzFFQUNBZ3RcAAAAAvisrK0t9+vTRsWPHFBYWVmxfrwelEydOKCUlRX/++af+97//6Y033tDq1avVoEGDQn1TU1MVFxenefPmqVevXkWur6gzSrGxsUpLSzvtYJxtmzZtUmpqqtZkBimybmOv1nJg5/eaceeNWrNmjRISErxaC8pXbm6ukpOTlZiYKH9/f2+Xg/MYcw0VgXmGisA8u3Ckp6crPDy8REHJ65feBQQEuG/m0KxZM23YsEH/+c9/9NprrxXqGx0drbi4OO3ateuU63M4HEWebfL39/f6xLfbT95kMF82uezeHfp82ZSdnS273e71ccHZ4QtzHhcG5hoqAvMMFYF5dv4rzfH1ud9RMsZ4nBH6uyNHjmjfvn2Kjo6u4KoAAAAAXEi8elrjscceU+fOnRUbG6uMjAzNmzdPq1at0rJly/TXX38pKSlJN910k6Kjo7Vnzx499thjCg8PV8+ePb1ZNgAAAIDznFeD0qFDh9S/f3+lpqaqcuXKatKkiZYtW6bExERlZ2dr27Ztevvtt/Xnn38qOjpa7du31/z58xUaGurNsgEAAACc57walN58881TPud0OrV8+fIKrAYAAAAATvK57ygBAAAAgLcRlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsPBqUJo+fbqaNGmisLAwhYWFqWXLllq6dKn7eWOMkpKSFBMTI6fTqXbt2mn79u1erBgAAADAhcCrQalGjRp69tln9e233+rbb7/VP/7xD3Xv3t0dhp577jlNnjxZU6dO1YYNGxQVFaXExERlZGR4s2wAAAAA5zmvBqVu3bqpS5cuqlOnjurUqaNnnnlGISEh+uqrr2SM0ZQpUzRmzBj16tVLjRo10uzZs5WVlaU5c+Z4s2wAAAAA57lK3i6gQH5+vt5//31lZmaqZcuW2r17tw4ePKhOnTq5+zgcDrVt21br1q3TXXfdVeR6cnJylJOT415OT0+XJOXm5io3N/fs7sRpuFwuSZKfjOyuPK/W4icjp9Mpl8vl9XFB+So4nhxXnG3MNVQE5hkqAvPswlGaY2wzxpizWMtpbdu2TS1bttTx48cVEhKiOXPmqEuXLlq3bp1at26t/fv3KyYmxt1/6NCh2rt3r5YvX17k+pKSkjR27NhC7XPmzFFQUNBZ2w8AAAAAvi0rK0t9+vTRsWPHFBYWVmxfr59Rqlu3rjZv3qw///xT//vf/zRw4ECtXr3a/bzNZvPob4wp1PZ3jz76qEaNGuVeTk9PV2xsrDp16nTawTjbNm3apNTUVK3JDFJk3cZereXAzu81484btWbNGiUkJHi1FpSv3NxcJScnKzExUf7+/t4uB+cx5hoqAvMMFYF5duEouNqsJLwelAICAnTZZZdJkpo1a6YNGzboP//5jx5++GFJ0sGDBxUdHe3uf/jwYUVGRp5yfQ6HQw6Ho1C7v7+/1ye+3X7yK2H5ssll9+7Q58um7Oxs2e12r48Lzg5fmPO4MDDXUBGYZ6gIzLPzX2mOr8/9jpIxRjk5OapVq5aioqKUnJzsfu7EiRNavXq1WrVq5cUKAQAAAJzvvHpa47HHHlPnzp0VGxurjIwMzZs3T6tWrdKyZctks9k0YsQIjR8/XvHx8YqPj9f48eMVFBSkPn36eLNsAAAAAOc5rwalQ4cOqX///kpNTVXlypXVpEkTLVu2TImJiZKkhx56SNnZ2brnnnt09OhRNW/eXCtWrFBoaKg3ywYAAABwnvNqUHrzzTeLfd5msykpKUlJSUkVUxAAAAAAyAe/owQAAAAA3kZQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAwqtBacKECbrqqqsUGhqqiIgI9ejRQzt37vToM2jQINlsNo9HixYtvFQxAAAAgAuBV4PS6tWrNWzYMH311VdKTk5WXl6eOnXqpMzMTI9+119/vVJTU92PTz75xEsVAwAAALgQVPLmxpctW+axPHPmTEVERGjjxo1q06aNu93hcCgqKqqiywMAAABwgfJqULI6duyYJKlatWoe7atWrVJERISqVKmitm3b6plnnlFERESR68jJyVFOTo57OT09XZKUm5ur3Nzcs1R5ybhcLkmSn4zsrjyv1uInI6fTKZfL5fVxQfkqOJ4cV5xtzDVUBOYZKgLz7MJRmmNsM8aYs1hLiRlj1L17dx09elRr1651t8+fP18hISGKi4vT7t279cQTTygvL08bN26Uw+EotJ6kpCSNHTu2UPucOXMUFBR0VvcBAAAAgO/KyspSnz59dOzYMYWFhRXb12eC0rBhw7RkyRJ98cUXqlGjxin7paamKi4uTvPmzVOvXr0KPV/UGaXY2FilpaWddjDOtk2bNik1NVVrMoMUWbexV2s5sPN7zbjzRq1Zs0YJCQlerQXlKzc3V8nJyUpMTJS/v7+3y8F5jLmGisA8Q0Vgnl040tPTFR4eXqKg5BOX3t13331avHix1qxZU2xIkqTo6GjFxcVp165dRT7vcDiKPNPk7+/v9Ylvt5+8d0a+bHLZvTv0+bIpOztbdrvd6+OCs8MX5jwuDMw1VATmGSoC8+z8V5rj69VP68YY3XfffVq4cKFWrVqlWrVqnfY1R44c0b59+xQdHV0BFQIAAAC4EHn19uDDhg3Tu+++qzlz5ig0NFQHDx7UwYMHlZ2dLUn666+/NHr0aK1fv1579uzRqlWr1K1bN4WHh6tnz57eLB0AAADAecyrZ5SmT58uSWrXrp1H+8yZMzVo0CD5+flp27Ztevvtt/Xnn38qOjpa7du31/z58xUaGuqFigEAAABcCLx+6V1xnE6nli9fXkHVAAAAAMBJXr30DgAAAAB8EUEJAAAAACwISgAAAABgQVACAAAAAIsyBaXdu3eXdx0AAAAA4DPKFJQuu+wytW/fXu+++66OHz9e3jUBAAAAgFeVKSht2bJFV1xxhR544AFFRUXprrvu0jfffFPetQEAAACAV5QpKDVq1EiTJ0/W/v37NXPmTB08eFDXXHONGjZsqMmTJ+v3338v7zoBAAAAoMKc0c0cKlWqpJ49e+q9997TxIkT9csvv2j06NGqUaOGBgwYoNTU1PKqEwAAAAAqzBkFpW+//Vb33HOPoqOjNXnyZI0ePVq//PKLVq5cqf3796t79+7lVScAAAAAVJhKZXnR5MmTNXPmTO3cuVNdunTR22+/rS5dushuP5m7atWqpddee0316tUr12IBAAAAoCKUKShNnz5dd9xxhwYPHqyoqKgi+9SsWVNvvvnmGRUHAAAAAN5QpqC0a9eu0/YJCAjQwIEDy7J6AAAAAPCqMn1HaebMmXr//fcLtb///vuaPXv2GRcFAAAAAN5UpqD07LPPKjw8vFB7RESExo8ff8ZFAQAAAIA3lSko7d27V7Vq1SrUHhcXp5SUlDMuCgAAAAC8qUxBKSIiQlu3bi3UvmXLFlWvXv2MiwIAAAAAbypTULrtttt0//336/PPP1d+fr7y8/O1cuVKDR8+XLfddlt51wgAAAAAFapMd717+umntXfvXnXo0EGVKp1chcvl0oABA/iOEgAAAIBzXpmCUkBAgObPn69///vf2rJli5xOpxo3bqy4uLjyrg8AAAAAKlyZglKBOnXqqE6dOuVVCwAAAAD4hDIFpfz8fM2aNUufffaZDh8+LJfL5fH8ypUry6U4AAAAAPCGMgWl4cOHa9asWeratasaNWokm81W3nUBAAAAgNeUKSjNmzdP7733nrp06VLe9QAAAACA15Xp9uABAQG67LLLyrsWAAAAAPAJZTqj9MADD+g///mPpk6dymV357gdO3Z4uwS38PBw1axZ09tlAAAAAGULSl988YU+//xzLV26VA0bNpS/v7/H8wsWLCiX4nD2ZKQdks1uV79+/bxdipszKEg/7thBWAIAAIDXlSkoValSRT179izvWlCBsjPSZVwu3fr0dEXUivd2OTq8e5fee/xfSktLIygBAADA68oUlGbOnFnedcBLImrF6+L6Cd4uAwAAAPApZbqZgyTl5eXp008/1WuvvaaMjAxJ0oEDB/TXX3+VW3EAAAAA4A1lOqO0d+9eXX/99UpJSVFOTo4SExMVGhqq5557TsePH9err75a3nUCAAAAQIUp0xml4cOHq1mzZjp69KicTqe7vWfPnvrss8/KrTgAAAAA8IYy3/Xuyy+/VEBAgEd7XFyc9u/fXy6FAQAAAIC3lOmMksvlUn5+fqH23377TaGhoWdcFAAAAAB4U5mCUmJioqZMmeJettls+uuvv/TUU0+pS5cu5VUbAAAAAHhFmS69e/HFF9W+fXs1aNBAx48fV58+fbRr1y6Fh4dr7ty55V0jAAAAAFSoMgWlmJgYbd68WXPnztV3330nl8ulIUOGqG/fvh43dwAAAACAc1GZgpIkOZ1O3XHHHbrjjjvKsx4AAAAA8LoyBaW333672OcHDBhQpmIAAAAAwBeUKSgNHz7cYzk3N1dZWVkKCAhQUFAQQQkAAADAOa1Md707evSox+Ovv/7Szp07dc0113AzBwAAAADnvDIFpaLEx8fr2WefLXS2CQAAAADONeUWlCTJz89PBw4cKM9VAgAAAECFK9N3lBYvXuyxbIxRamqqpk6dqtatW5dLYQAAAADgLWUKSj169PBYttlsuuiii/SPf/xDL7zwQonXM2HCBC1YsEA//vijnE6nWrVqpYkTJ6pu3bruPsYYjR07VjNmzNDRo0fVvHlzvfLKK2rYsGFZSgcAAACA0yrTpXcul8vjkZ+fr4MHD2rOnDmKjo4u8XpWr16tYcOG6auvvlJycrLy8vLUqVMnZWZmuvs899xzmjx5sqZOnaoNGzYoKipKiYmJysjIKEvpAAAAAHBaZf7B2fKwbNkyj+WZM2cqIiJCGzduVJs2bWSM0ZQpUzRmzBj16tVLkjR79mxFRkZqzpw5uuuuu7xRNgAAAIDzXJmC0qhRo0rcd/LkySXue+zYMUlStWrVJEm7d+/WwYMH1alTJ3cfh8Ohtm3bat26dUUGpZycHOXk5LiX09PTJZ38rafc3NwS13I2uFwuSZKfjOyuPK/WUsluk9Pp9IlapJNj4nQ65XK5vH6cznUF48c44mxjrqEiMM9QEZhnF47SHGObMcaUdgPt27fXd999p7y8PPf3iX766Sf5+fnpyiuv/L+V22xauXJlidZpjFH37t119OhRrV27VpK0bt06tW7dWvv371dMTIy779ChQ7V3714tX7680HqSkpI0duzYQu1z5sxRUFBQqfYTAAAAwPkjKytLffr00bFjxxQWFlZs3zKdUerWrZtCQ0M1e/ZsVa1aVdLJH6EdPHiwrr32Wj3wwAOlXue9996rrVu36osvvij0nM1m81g2xhRqK/Doo496nPFKT09XbGysOnXqdNrBONs2bdqk1NRUrckMUmTdxl6tZcuKD7Xw3yM19I3FiqnbyKu1SNKBnd9rxp03as2aNUpISPB2Oee03NxcJScnKzExUf7+/t4uB+cx5hoqAvMMFYF5duEouNqsJMoUlF544QWtWLHCHZIkqWrVqnr66afVqVOnUgel++67T4sXL9aaNWtUo0YNd3tUVJQk6eDBgx43iTh8+LAiIyOLXJfD4ZDD4SjU7u/v7/WJb7efvHdGvmxy2b369TDluYyys7N9ohbp5JhkZ2fLbrd7/TidL3xhzuPCwFxDRWCeoSIwz85/pTm+ZbrrXXp6ug4dOlSo/fDhw6W6G50xRvfee68WLFiglStXqlatWh7P16pVS1FRUUpOTna3nThxQqtXr1arVq3KUjoAAAAAnFaZTiX07NlTgwcP1gsvvKAWLVpIkr766is9+OCD7rvTlcSwYcM0Z84cffjhhwoNDdXBgwclSZUrV5bT6ZTNZtOIESM0fvx4xcfHKz4+XuPHj1dQUJD69OlTltIBAAAA4LTKFJReffVVjR49Wv369XPfOaJSpUoaMmSIJk2aVOL1TJ8+XZLUrl07j/aZM2dq0KBBkqSHHnpI2dnZuueee9w/OLtixQqFhoaWpXQAAAAAOK0yBaWgoCBNmzZNkyZN0i+//CJjjC677DIFBweXaj0lueGezWZTUlKSkpKSylIqAAAAAJRamb6jVCA1NVWpqamqU6eOgoODSxR8AAAAAMDXlSkoHTlyRB06dFCdOnXUpUsXpaamSpLuvPPOMt0aHAAAAAB8SZmC0siRI+Xv76+UlBSPH3Ht3bu3li1bVm7FAQAAAIA3lOk7SitWrNDy5cs9fvNIkuLj47V3795yKQwAAAAAvKVMZ5QyMzM9ziQVSEtLK/LHXgEAAADgXFKmoNSmTRu9/fbb7mWbzSaXy6VJkyapffv25VYcAAAAAHhDmS69mzRpktq1a6dvv/1WJ06c0EMPPaTt27frjz/+0JdfflneNQIAAABAhSrTGaUGDRpo69atuvrqq5WYmKjMzEz16tVLmzZt0qWXXlreNQIAAABAhSr1GaXc3Fx16tRJr732msaOHXs2agIAAAAAryr1GSV/f399//33stlsZ6MeAAAAAPC6Ml16N2DAAL355pvlXQsAAAAA+IQy3czhxIkTeuONN5ScnKxmzZopODjY4/nJkyeXS3EAAAAA4A2lCkq//vqrLrnkEn3//fe68sorJUk//fSTRx8uyQMAAABwritVUIqPj1dqaqo+//xzSVLv3r310ksvKTIy8qwUBwAAAADeUKrvKBljPJaXLl2qzMzMci0IAAAAALytTDdzKGANTgAAAABwPihVULLZbIW+g8R3kgAAAACcb0r1HSVjjAYNGiSHwyFJOn78uO6+++5Cd71bsGBB+VUIAAAAABWsVEFp4MCBHsv9+vUr12IAAAAAwBeUKijNnDnzbNUBAAAAAD7jjG7mAAAAAADnI4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwKNVd7wAAAABfkpKSorS0tDNah8vlkiRt2bJFdnvZzyOEh4erZs2aZ1QLfAdBCQAAAOeklJQU1atfX9lZWWe0HqfTqblz56pNmzbKzs4u+3qCgvTjjh2EpfMEQQkAAADnpLS0NGVnZenWp6crolZ8mdfjJyMpU0PfWKx82cq0jsO7d+m9x/+ltLQ0gtJ5gqAEAACAc1pErXhdXD+hzK+3u/Kk375WTN1Gctn5eIyTuJkDAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABg4dWgtGbNGnXr1k0xMTGy2WxatGiRx/ODBg2SzWbzeLRo0cI7xQIAAAC4YHg1KGVmZiohIUFTp049ZZ/rr79eqamp7scnn3xSgRUCAAAAuBBV8ubGO3furM6dOxfbx+FwKCoqqoIqAgAAAAAvB6WSWLVqlSIiIlSlShW1bdtWzzzzjCIiIk7ZPycnRzk5Oe7l9PR0SVJubq5yc3PPer3FcblckiQ/GdldeV6tpZLdJqfT6RO1SCfHxOl0yuVyef04nesKxo9xxNnGXENFYJ6hOC6Xq1w+zxS89kzWwWeZc0Npjo3NGGPOYi0lZrPZtHDhQvXo0cPdNn/+fIWEhCguLk67d+/WE088oby8PG3cuFEOh6PI9SQlJWns2LGF2ufMmaOgoKCzVT4AAAAAH5eVlaU+ffro2LFjCgsLK7avTwclq9TUVMXFxWnevHnq1atXkX2KOqMUGxurtLS00w7G2bZp0yalpqZqTWaQIus29motW1Z8qIX/HqmhbyxWTN1GXq1Fkg7s/F4z7rxRa9asUUJCgrfLOafl5uYqOTlZiYmJ8vf393Y5OI8x11ARmGcozpYtW9SmTZsz/jxjd+Up/sBG7YppKpe9bBdc8Vnm3JCenq7w8PASBSWfv/Tu76KjoxUXF6ddu3adso/D4SjybJO/v7/X32Dt9pP3zsiXrcx/hOUlz2WUnZ3tE7VIJ8ckOztbdrvd68fpfOELcx4XBuYaKgLzDEWx2+3l+nnGZa9U5vXwWebcUJpjc079jtKRI0e0b98+RUdHe7sUAAAAAOcxr55K+Ouvv/Tzzz+7l3fv3q3NmzerWrVqqlatmpKSknTTTTcpOjpae/bs0WOPPabw8HD17NnTi1UDAAAAON95NSh9++23at++vXt51KhRkqSBAwdq+vTp2rZtm95++239+eefio6OVvv27TV//nyFhoZ6q2QAAAAAFwCvBqV27dqpuHtJLF++vAKrAQAAAICTzqnvKAEAAABARSAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABg4dWgtGbNGnXr1k0xMTGy2WxatGiRx/PGGCUlJSkmJkZOp1Pt2rXT9u3bvVMsAAAAgAuGV4NSZmamEhISNHXq1CKff+655zR58mRNnTpVGzZsUFRUlBITE5WRkVHBlQIAAAC4kFTy5sY7d+6szp07F/mcMUZTpkzRmDFj1KtXL0nS7NmzFRkZqTlz5uiuu+6qyFIBAAAAXEC8GpSKs3v3bh08eFCdOnVytzkcDrVt21br1q07ZVDKyclRTk6Oezk9PV2SlJubq9zc3LNb9Gm4XC5Jkp+M7K48r9ZSyW6T0+n0iVqkk2PidDrlcrm8fpzOdQXjxzjibGOuoSIwz1Acl8tVLp9nCl57Juvwxc8yv/32m44cOeLtMiRJ1atXV40aNbxdRqmOjc0YY85iLSVms9m0cOFC9ejRQ5K0bt06tW7dWvv371dMTIy739ChQ7V3714tX768yPUkJSVp7NixhdrnzJmjoKCgs1I7AAAAAN+XlZWlPn366NixYwoLCyu2r8+eUSpgs9k8lo0xhdr+7tFHH9WoUaPcy+np6YqNjVWnTp1OOxhn26ZNm5Samqo1mUGKrNvYq7VsWfGhFv57pIa+sVgxdRt5tRZJOrDze82480atWbNGCQkJ3i7nnJabm6vk5GQlJibK39/f2+XgPMZcQ0VgnqE4W7ZsUZs2bc7484zdlaf4Axu1K6apXPayfTz2tc8yBWPT84kXdVHcpV6t5fe9v2jhv0f6xNgUXG1WEj4blKKioiRJBw8eVHR0tLv98OHDioyMPOXrHA6HHA5HoXZ/f3+vv8Ha7SfvnZEvW5n/CMtLnssoOzvbJ2qRTo5Jdna27Ha714/T+cIX5jwuDMw1VATmGYpit9vL9fOMy16pzOvxtc8yBWNTLe4yRdX3bjjxpbEpzfZ99neUatWqpaioKCUnJ7vbTpw4odWrV6tVq1ZerAwAAADA+c6rpxL++usv/fzzz+7l3bt3a/PmzapWrZpq1qypESNGaPz48YqPj1d8fLzGjx+voKAg9enTx4tVAwAAADjfeTUoffvtt2rfvr17ueC7RQMHDtSsWbP00EMPKTs7W/fcc4+OHj2q5s2ba8WKFQoNDfVWyQAAAAAuAF4NSu3atVNxN92z2WxKSkpSUlJSxRUFAAAA4ILns99RAgAAAABvISgBAAAAgAVBCQAAAAAsvP8DOgAAADitlJQUpaWlebsMSVJ4eLhq1qzp7TKAs4qgBAAA4ONSUlJUr359ZWdlebsUSZIzKEg/7thBWMJ5jaAEAADg49LS0pSdlaVbn56uiFrxXq3l8O5deu/xfyktLY2ghPMaQQkAAOAcEVErXhfXT/B2GcAFgZs5AAAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFhU8nYBAABciFJSUpSWlubtMiRJ4eHhqlmzprfLAACfQlACAKCCpaSkqF79+srOyvJ2KZIkZ1CQftyxg7AEAH9DUAIAoIKlpaUpOytLtz49XRG14r1ay+Hdu/Te4/9SWloaQQkA/oagBACAl0TUitfF9RO8XQYAoAjczAEAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFj4dFBKSkqSzWbzeERFRXm7LAAAAADnOZ+/613Dhg316aefupf9/Py8WA0AAACAC4HPB6VKlSpxFgkAAABAhfL5oLRr1y7FxMTI4XCoefPmGj9+vGrXrn3K/jk5OcrJyXEvp6enS5Jyc3OVm5t71ustjsvlkiT5ycjuyvNqLZXsNjmdTp+oRTo5Jk6nUy6Xy+vH6VxXMH6MI8425lrZuVwun3kP9vX3X+bZScyZopXXuBS89kzW4UvjIjFnTqU027cZY8xZrOWMLF26VFlZWapTp44OHTqkp59+Wj/++KO2b9+u6tWrF/mapKQkjR07tlD7nDlzFBQUdLZLBgAAAOCjsrKy1KdPHx07dkxhYWHF9vXpoGSVmZmpSy+9VA899JBGjRpVZJ+izijFxsYqLS3ttINxtm3atEmpqalakxmkyLqNvVrLlhUfauG/R2roG4sVU7eRV2uRpAM7v9eMO2/UmjVrlJDAr9SfidzcXCUnJysxMVH+/v7eLgfnMeZa2W3ZskVt2rTxifdgX3//ZZ6dxJwpWnmNi92Vp/gDG7Urpqlc9rJdcOVL4yIxZ04lPT1d4eHhJQpKPn/p3d8FBwercePG2rVr1yn7OBwOORyOQu3+/v5ef4O120/eZDBftjL/EZaXPJdRdna2T9QinRyT7Oxs2e12rx+n84UvzHlcGJhrpWe3233mPfhcef+90OcZc6Zo5T0uLnulMq/Hl8ZFYs6cSmm279O3B7fKycnRjh07FB0d7e1SAAAAAJzHfDoojR49WqtXr9bu3bv19ddf6+abb1Z6eroGDhzo7dIAAAAAnMe8f81VMX777TfdfvvtSktL00UXXaQWLVroq6++UlxcnLdLAwAAAHAe8+mgNG/ePG+XAAAAAOAC5NOX3gEAAACANxCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALCp5uwAAKKuUlBSlpaV5uwxJUnh4uGrWrOntMiRV3Li4XC5J0pYtW2S3F/3vbjk5OXI4HGe9lpLypeMEAPBtBCUA56SUlBTVq19f2VlZ3i5FkuQMCtKPO3Z4/UN4RY6L0+nU3Llz1aZNG2VnZxfZx2a3y/z/QOULfOU4AQB8H0EJwDkpLS1N2VlZuvXp6YqoFe/VWg7v3qX3Hv+X0tLSvP4BvCLHxU9GUqaGvrFY+bIVen7nl58pedoEnzhGkm8dJwCA7yMoATinRdSK18X1E7xdhs+piHGxu/Kk375WTN1GctkL/+/k8O5dFVYLAADljZs5AAAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABb8jhIAAPAZKSkpSktLcy+7XC5J0pYtW2S3V+y/74aHh/PjxMAFjKAEAAB8QkpKiurVr6/srCx3m9Pp1Ny5c9WmTRtlZ2dXaD3OoCD9uGMHYQm4QBGUAACAT0hLS1N2VpZufXq6ImrFS5L8ZCRlaugbi5UvW4XVcnj3Lr33+L+UlpZGUAIuUAQlAADgUyJqxevi+gmSJLsrT/rta8XUbSSXnY8tACoON3MAAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALPhBAviUHTt2eLsESVJ4eLjP/MBgSkqK0tLSStzf5XJJkrZs2SK7vXz/LcSXxgUoK194n/GFGoAz5Qvz2BdqsPKVmnyljnMZQQk+ISPtkGx2u/r16+ftUiRJzqAg/bhjh9dDQUpKiurVr6/srKwSv8bpdGru3Llq06aNsrOzy7UeXxkXoCx87X0GOFfxt1Q0xuX8Q1CCT8jOSJdxuXTr09MVUSveq7Uc3r1L7z3+L6WlpXk9EKSlpSk7K6tU4+InIylTQ99YrHzZyq0WXxoXoCx86X1m55efKXnaBK/WAJQVf0tF86VxkXxrbM5VBCX4lIha8bq4foK3y/A5pRkXuytP+u1rxdRtJJedP3HAyhfeZw7v3uXV7QPlgb+lovnCuEi+OTbnGm7mAAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALM6JoDRt2jTVqlVLgYGBatq0qdauXevtkgAAAACcx3w+KM2fP18jRozQmDFjtGnTJl177bXq3LmzUlJSvF0aAAAAgPOUzwelyZMna8iQIbrzzjtVv359TZkyRbGxsZo+fbq3SwMAAABwnvLpH1k5ceKENm7cqEceecSjvVOnTlq3bl2Rr8nJyVFOTo57+dixY5KkP/74Q7m5uWev2BJIT09XVlaWDu3ao5ysTK/WcnTfrwoMDNShnduUl/WXV2vxtXqO7NutwMBAbdy4Uenp6V6tZdeuXaUeFz8ZxQZnK2XTV+X6g7O+NC5S2cbmbPGlsanIcTndXPOlv2tfq8eXavH1+Xu23tNOx5fGRfKt9zxfmr/lVUt5zDNfGhdfq6fg7yk9PV1Hjhzxai0ZGRmSJGPM6TsbH7Z//34jyXz55Zce7c8884ypU6dOka956qmnjCQePHjw4MGDBw8ePHjwKPKxb9++02YRnz6jVMBm80z2xphCbQUeffRRjRo1yr3scrn0xx9/qHr16qd8TUVJT09XbGys9u3bp7CwMK/WgvMX8wwVhbmGisA8Q0Vgnl04jDHKyMhQTEzMafv6dFAKDw+Xn5+fDh486NF++PBhRUZGFvkah8Mhh8Ph0ValSpWzVWKZhIWF8UeIs455horCXENFYJ6hIjDPLgyVK1cuUT+fvplDQECAmjZtquTkZI/25ORktWrVyktVAQAAADjf+fQZJUkaNWqU+vfvr2bNmqlly5aaMWOGUlJSdPfdd3u7NAAAAADnKZ8PSr1799aRI0c0btw4paamqlGjRvrkk08UFxfn7dJKzeFw6Kmnnip0aSBQnphnqCjMNVQE5hkqAvMMRbEZU5J74wEAAADAhcOnv6MEAAAAAN5AUAIAAAAAC4ISAAAAAFgQlAAAAADAgqBUgaZNm6ZatWopMDBQTZs21dq1a71dEs5hEyZM0FVXXaXQ0FBFRESoR48e2rlzp0cfY4ySkpIUExMjp9Opdu3aafv27V6qGOeDCRMmyGazacSIEe425hnKw/79+9WvXz9Vr15dQUFBuvzyy7Vx40b388wznKm8vDw9/vjjqlWrlpxOp2rXrq1x48bJ5XK5+zDP8HcEpQoyf/58jRgxQmPGjNGmTZt07bXXqnPnzkpJSfF2aThHrV69WsOGDdNXX32l5ORk5eXlqVOnTsrMzHT3ee655zR58mRNnTpVGzZsUFRUlBITE5WRkeHFynGu2rBhg2bMmKEmTZp4tDPPcKaOHj2q1q1by9/fX0uXLtUPP/ygF154QVWqVHH3YZ7hTE2cOFGvvvqqpk6dqh07dui5557TpEmT9PLLL7v7MM/gwaBCXH311ebuu+/2aKtXr5555JFHvFQRzjeHDx82kszq1auNMca4XC4TFRVlnn32WXef48ePm8qVK5tXX33VW2XiHJWRkWHi4+NNcnKyadu2rRk+fLgxhnmG8vHwww+ba6655pTPM89QHrp27WruuOMOj7ZevXqZfv36GWOYZyiMM0oV4MSJE9q4caM6derk0d6pUyetW7fOS1XhfHPs2DFJUrVq1SRJu3fv1sGDBz3mncPhUNu2bZl3KLVhw4apa9eu6tixo0c78wzlYfHixWrWrJluueUWRURE6IorrtDrr7/ufp55hvJwzTXX6LPPPtNPP/0kSdqyZYu++OILdenSRRLzDIVV8nYBF4K0tDTl5+crMjLSoz0yMlIHDx70UlU4nxhjNGrUKF1zzTVq1KiRJLnnVlHzbu/evRVeI85d8+bN03fffacNGzYUeo55hvLw66+/avr06Ro1apQee+wxffPNN7r//vvlcDg0YMAA5hnKxcMPP6xjx46pXr168vPzU35+vp555hndfvvtkng/Q2EEpQpks9k8lo0xhdqAsrj33nu1detWffHFF4WeY97hTOzbt0/Dhw/XihUrFBgYeMp+zDOcCZfLpWbNmmn8+PGSpCuuuELbt2/X9OnTNWDAAHc/5hnOxPz58/Xuu+9qzpw5atiwoTZv3qwRI0YoJiZGAwcOdPdjnqEAl95VgPDwcPn5+RU6e3T48OFC/2oBlNZ9992nxYsX6/PPP1eNGjXc7VFRUZLEvMMZ2bhxow4fPqymTZuqUqVKqlSpklavXq2XXnpJlSpVcs8l5hnORHR0tBo0aODRVr9+ffcNj3g/Q3l48MEH9cgjj+i2225T48aN1b9/f40cOVITJkyQxDxDYQSlChAQEKCmTZsqOTnZoz05OVmtWrXyUlU41xljdO+992rBggVauXKlatWq5fF8rVq1FBUV5THvTpw4odWrVzPvUGIdOnTQtm3btHnzZvejWbNm6tu3rzZv3qzatWszz3DGWrduXejnDX766SfFxcVJ4v0M5SMrK0t2u+dHXz8/P/ftwZlnsOLSuwoyatQo9e/fX82aNVPLli01Y8YMpaSk6O677/Z2aThHDRs2THPmzNGHH36o0NBQ97+AVa5cWU6n0/1bN+PHj1d8fLzi4+M1fvx4BQUFqU+fPl6uHueK0NBQ9/feCgQHB6t69eruduYZztTIkSPVqlUrjR8/Xrfeequ++eYbzZgxQzNmzJAk3s9QLrp166ZnnnlGNWvWVMOGDbVp0yZNnjxZd9xxhyTmGYrgxTvuXXBeeeUVExcXZwICAsyVV17pvo0zUBaSinzMnDnT3cflcpmnnnrKREVFGYfDYdq0aWO2bdvmvaJxXvj77cGNYZ6hfHz00UemUaNGxuFwmHr16pkZM2Z4PM88w5lKT083w4cPNzVr1jSBgYGmdu3aZsyYMSYnJ8fdh3mGv7MZY4w3gxoAAAAA+Bq+owQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAA4b8yYMUOxsbGy2+2aMmXKGa9vz549stls2rx58xmvy2azadGiRWe8nrOtPPcZAM5lBCUAOIsGDRokm80mm80mf39/1a5dW6NHj1ZmZqa3SzutSy65pFzCRkVJT0/Xvffeq4cfflj79+/X0KFDi+xXcDy++uorj/acnBxVr15dNptNq1atkiTFxsYqNTVVjRo1OuP6UlNT1blz5zNez6kcOnRI/v7+evfdd4t8/q677lKTJk3O2vYB4HxDUAKAs+z6669Xamqqfv31Vz399NOaNm2aRo8eXaZ1GWOUl5dXzhWeH1JSUpSbm6uuXbsqOjpaQUFBp+wbGxurmTNnerQtXLhQISEhHm1+fn6KiopSpUqVzri+qKgoORyOM17PqURGRqpr166F9kuSsrOzNW/ePA0ZMuSsbR8AzjcEJQA4yxwOh6KiohQbG6s+ffqob9++7kuwjDF67rnnVLt2bTmdTiUkJOiDDz5wv3bVqlWy2Wxavny5mjVrJofDobVr18rlcmnixIm67LLL5HA4VLNmTT3zzDPu1+3fv1+9e/dW1apVVb16dXXv3l179uxxPz9o0CD16NFDzz//vKKjo1W9enUNGzZMubm5kqR27dpp7969GjlypPsMjCQdOXJEt99+u2rUqKGgoCA1btxYc+fO9djfjIwM9e3bV8HBwYqOjtaLL76odu3aacSIEe4+J06c0EMPPaSLL75YwcHBat68ufsszqmkpKSoe/fuCgkJUVhYmG699VYdOnRIkjRr1iw1btxYklS7dm3ZbDaP/bUaOHCg5s2bp+zsbHfbW2+9pYEDB3r0s16GdvToUfXt21cXXXSRnE6n4uPj3cHkxIkTuvfeexUdHa3AwEBdcsklmjBhgntdf7/0rmC9CxYsUPv27RUUFKSEhAStX7/eY/uvv/66YmNjFRQUpJ49e2ry5MmqUqXKKfdryJAh+vzzzwvt+wcffKDjx4+rX79+WrZsma655hpVqVJF1atX1w033KBffvnllOucNWtWoW0uWrTIPScKfPTRR2ratKkCAwNVu3ZtjR071iPUJyUlqWbNmnI4HIqJidH9999/ym0CgC8gKAFABXM6ne5A8vjjj2vmzJmaPn26tm/frpEjR6pfv35avXq1x2seeughTZgwQTt27FCTJk306KOPauLEiXriiSf0ww8/aM6cOYqMjJQkZWVlqX379goJCdGaNWv0xRdfKCQkRNdff71OnDjhXufnn3+uX375RZ9//rlmz56tWbNmadasWZKkBQsWqEaNGho3bpxSU1OVmpoqSTp+/LiaNm2qjz/+WN9//72GDh2q/v376+uvv3avd9SoUfryyy+1ePFiJScna+3atfruu+889mfw4MH68ssvNW/ePG3dulW33HKLrr/+eu3atavIMTPGqEePHvrjjz+0evVqJScn65dfflHv3r0lSb1799ann34qSfrmm2+Umpqq2NjYUx6Dpk2bqlatWvrf//4nSdq3b5/WrFmj/v37F3vsCsZ76dKl2rFjh6ZPn67w8HBJ0ksvvaTFixfrvffe086dO/Xuu+/qkksuKXZ9Y8aM0ejRo7V582bVqVNHt99+uztcfPnll7r77rs1fPhwbd68WYmJiR5huChdunRRVFSU+zgWeOutt9SjRw9Vr15dmZmZGjVqlDZs2KDPPvtMdrtdPXv2lMvlKnbdxVm+fLn69eun+++/Xz/88INee+01zZo1y13vBx98oBdffFGvvfaadu3apUWLFrmDLQD4LAMAOGsGDhxounfv7l7++uuvTfXq1c2tt95q/vrrLxMYGGjWrVvn8ZohQ4aY22+/3RhjzOeff24kmUWLFrmfT09PNw6Hw7z++utFbvPNN980devWNS6Xy92Wk5NjnE6nWb58ubuuuLg4k5eX5+5zyy23mN69e7uX4+LizIsvvnjafezSpYt54IEH3LX5+/ub999/3/38n3/+aYKCgszw4cONMcb8/PPPxmazmf3793usp0OHDubRRx8tchsrVqwwfn5+JiUlxd22fft2I8l88803xhhjNm3aZCSZ3bt3F1uvJLNw4UIzZcoU0759e2OMMWPHjjU9e/Y0R48eNZLM559/bowxZvfu3UaS2bRpkzHGmG7dupnBgwcXud777rvP/OMf//AY96K2+/f1vvHGG4X2Z8eOHcYYY3r37m26du3qsY6+ffuaypUrF7t/Dz/8sImLi3PX8euvvxqbzeY+9laHDx82ksy2bduK3OeZM2cW2ubChQvN3z9CXHvttWb8+PEefd555x0THR1tjDHmhRdeMHXq1DEnTpwotnYA8CWcUQKAs+zjjz9WSEiIAgMD1bJlS7Vp00Yvv/yyfvjhBx0/flyJiYkKCQlxP95+++1Cl0I1a9bM/d87duxQTk6OOnToUOT2Nm7cqJ9//lmhoaHudVarVk3Hjx/3WG/Dhg3l5+fnXo6Ojtbhw4eL3Zf8/Hw988wzatKkiapXr66QkBCtWLFCKSkpkqRff/1Vubm5uvrqq92vqVy5surWrete/u6772SMUZ06dTz2e/Xq1ae8BGzHjh2KjY31OEvUoEEDValSRTt27Ci25lPp16+f1q9fr19//VWzZs3SHXfccdrX/Otf/9K8efN0+eWX66GHHtK6devczw0aNEibN29W3bp1df/992vFihWnXd/fb64QHR0tSe5jsHPnTo9xlFRouShDhgzR3r17tXLlSkknzybVqFFDHTt2lCT98ssv6tOnj2rXrq2wsDDVqlVLktzHsCw2btyocePGeRzPf/7zn0pNTVVWVpZuueUWZWdnq3bt2vrnP/+phQsX8l07AD7vzL+dCgAoVvv27TV9+nT5+/srJiZG/v7+kqTdu3dLkpYsWaKLL77Y4zXWL/0HBwe7/9vpdBa7PZfLpaZNm+q///1voecuuugi938X1FHAZrOd9vKrF154QS+++KKmTJmixo0bKzg4WCNGjHBf0meMca/r7wraC+rz8/PTxo0bPYKapEI3U/j7663rLK69JAq+nzNkyBAdP35cnTt3VkZGRrGv6dy5s/bu3aslS5bo008/VYcOHTRs2DA9//zzuvLKK7V7924tXbpUn376qW699VZ17NjR4ztnVn8/BgX7UXAMitq3v4/jqcTHx+vaa6/VzJkz1b59e82ePVuDBw+W3X7y30a7deum2NhYvf7664qJiZHL5VKjRo08Lsv8O7vdXmi7BZeOFnC5XBo7dqx69epV6PWBgYGKjY3Vzp07lZycrE8//VT33HOPJk2apNWrVxeahwDgKwhKAHCWBQcH67LLLivU3qBBAzkcDqWkpKht27YlXl98fLycTqc+++wz3XnnnYWev/LKKzV//nxFREQoLCyszHUHBAQoPz/fo23t2rXq3r27+vXrJ+nkB+Rdu3apfv36kqRLL71U/v7++uabb9xnf9LT07Vr1y73Pl5xxRXKz8/X4cOHde2115aolgYNGiglJUX79u1zr/eHH37QsWPH3NsuizvuuENdunTRww8/XCi0ncpFF12kQYMGadCgQbr22mv14IMP6vnnn5ckhYWFqXfv3urdu7duvvlmXX/99frjjz9UrVq1UtdWr149ffPNNx5t3377bYleO2TIEP3rX/9S9+7d9dtvv2nw4MGSTt6MY8eOHXrttdfcY//FF18Uu66LLrpIGRkZyszMdAd2628sXXnlldq5c2eR87yA0+nUjTfeqBtvvFHDhg1TvXr1tG3bNl155ZUl2icAqGgEJQDwktDQUI0ePVojR46Uy+XSNddco/T0dK1bt04hISGF7sBWIDAwUA8//LAeeughBQQEqHXr1vr999+1fft2DRkyRH379tWkSZPUvXt3jRs3TjVq1FBKSooWLFigBx98UDVq1ChRfZdcconWrFmj2267TQ6HQ+Hh4brsssv0v//9T+vWrVPVqlU1efJkHTx40B1WQkNDNXDgQD344IOqVq2aIiIi9NRTT8lut7vPjtSpU0d9+/bVgAED9MILL+iKK65QWlqaVq5cqcaNG6tLly6FaunYsaOaNGmivn37asqUKcrLy9M999yjtm3belyWWFrXX3+9fv/99xIHyieffFJNmzZVw4YNlZOTo48//ti97y+++KKio6N1+eWXy2636/3331dUVFSxd6krzn333ac2bdpo8uTJ6tatm1auXKmlS5eW6AzaLbfcovvvv1933XWXOnTo4L6pRMFdEGfMmKHo6GilpKTokUceKXZdzZs3V1BQkB577DHdd999+uabbwrdLOLJJ5/UDTfcoNjYWN1yyy2y2+3aunWrtm3bpqefflqzZs1Sfn6+e13vvPOOnE6n4uLiyjQ2AFAR+I4SAHjRv//9bz355JOaMGGC6tevr+uuu04fffSR+3sjp/LEE0/ogQce0JNPPqn69eurd+/e7u+2BAUFac2aNapZs6Z69eql+vXr64477lB2dnapzjCNGzdOe/bs0aWXXuq+ZO+JJ57QlVdeqeuuu07t2rVTVFSUevTo4fG6yZMnq2XLlrrhhhvUsWNHtW7dWvXr11dgYKC7z8yZMzVgwAA98MADqlu3rm688UZ9/fXXp7xTXcGttatWrao2bdqoY8eOql27tubPn1/i/TnVesPDwxUQEFCi/gEBAXr00UfVpEkTtWnTRn5+fpo3b56kk5cNTpw4Uc2aNdNVV12lPXv26JNPPnFf8lZarVu31quvvqrJkycrISFBy5Yt08iRIz3G8VSCgoJ022236ejRox7fvbLb7Zo3b542btyoRo0aaeTIkZo0aVKx66pWrZreffddffLJJ+7bwSclJXn0ue666/Txxx8rOTlZV111lVq0aKHJkye7g1CVKlX0+uuvq3Xr1mrSpIk+++wzffTRR6pevXrpBwYAKojNlOSCZwAAyigzM1MXX3yxXnjhBX7w9Az985//1I8//qi1a9d6uxQAOO9x6R0AoFxt2rRJP/74o66++modO3ZM48aNkyR1797dy5Wde55//nklJiYqODhYS5cu1ezZszVt2jRvlwUAFwSCEgCg3D3//PPauXOnAgIC1LRpU61du9b9w6wouW+++UbPPfecMjIyVLt2bb300ktF3sADAFD+uPQOAAAAACy4mQMAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAIv/BxD4fUKVslc1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_missing_values_histogram(missing_stats)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "faa4570b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with more than 80% missing values:\n",
      "      Column  Missing Values  Percentage\n",
      "47   ctzship          448196   91.365086\n",
      "41   cntbrth          448196   91.365086\n",
      "82   hincsrc          448196   91.365086\n",
      "72    rship4          448196   91.365086\n",
      "70    rship3          448196   91.365086\n",
      "68    rship2          448196   91.365086\n",
      "29   ilglpst          448196   91.365086\n",
      "25    dntmny          448196   91.365086\n",
      "76       age          447555   91.234418\n",
      "51  ctzshipd          441036   89.905515\n",
      "93   marital          403968   82.349176\n",
      "86     lvghw          400659   81.674634\n",
      "88   lvgoptn          400659   81.674634\n",
      "89    lvgptn          400659   81.674634\n",
      "48  ctzshipa          400018   81.543966\n",
      "42  cntbrtha          400018   81.543966\n",
      "90   lvgptna          394869   80.494338\n",
      "87    lvghwa          392464   80.004077\n",
      "94  maritala          392464   80.004077\n"
     ]
    }
   ],
   "source": [
    "# Filter the missing_stats DataFrame to get columns with more than 90% missing values\n",
    "columns_with_more_than_80_percent_missing = missing_stats[missing_stats['Percentage'] > 80]\n",
    "\n",
    "# Print the columns\n",
    "print(\"Columns with more than 80% missing values:\")\n",
    "print(columns_with_more_than_80_percent_missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca46710",
   "metadata": {},
   "source": [
    "From this analysis, we see that most of the variables that have more than 80% missing values are not very relevant.\n",
    "\n",
    "Age is not a problem, as there is \"year of birth\" variable that has 0% missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "eb270d59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of lrscale for men and women:\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>lrscale</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gndr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.888251</td>\n",
       "      <td>2.423574</td>\n",
       "      <td>5.605819</td>\n",
       "      <td>9.655046</td>\n",
       "      <td>9.626229</td>\n",
       "      <td>30.959443</td>\n",
       "      <td>9.967557</td>\n",
       "      <td>11.533589</td>\n",
       "      <td>8.912770</td>\n",
       "      <td>2.930845</td>\n",
       "      <td>4.496877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.837842</td>\n",
       "      <td>2.612359</td>\n",
       "      <td>5.874844</td>\n",
       "      <td>9.903712</td>\n",
       "      <td>9.903712</td>\n",
       "      <td>34.976430</td>\n",
       "      <td>9.138241</td>\n",
       "      <td>9.518469</td>\n",
       "      <td>7.529794</td>\n",
       "      <td>2.634242</td>\n",
       "      <td>4.070356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "lrscale        0         1         2         3         4          5   \\\n",
       "gndr                                                                   \n",
       "1        3.888251  2.423574  5.605819  9.655046  9.626229  30.959443   \n",
       "2        3.837842  2.612359  5.874844  9.903712  9.903712  34.976430   \n",
       "\n",
       "lrscale        6          7         8         9         10  \n",
       "gndr                                                        \n",
       "1        9.967557  11.533589  8.912770  2.930845  4.496877  \n",
       "2        9.138241   9.518469  7.529794  2.634242  4.070356  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rows for gender values 1 and 2\n",
    "filtered_df = df[df['gndr'].isin([1, 2])]\n",
    "\n",
    "# Filter rows for lrscale values from 0 to 10\n",
    "filtered_df = filtered_df[(filtered_df['lrscale'] >= 0) & (filtered_df['lrscale'] <= 10)]\n",
    "\n",
    "# Group by gender and lrscale and calculate the counts\n",
    "grouped_df = filtered_df.groupby(['gndr', 'lrscale']).size().reset_index(name='counts')\n",
    "\n",
    "# Pivot the DataFrame to have gender as rows and lrscale as columns\n",
    "pivot_df = grouped_df.pivot(index='gndr', columns='lrscale', values='counts').fillna(0)\n",
    "\n",
    "# Normalize the counts to get percentages\n",
    "pivot_df = pivot_df.div(pivot_df.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Print the resulting pivot table\n",
    "print(\"Distribution of lrscale for men and women:\")\n",
    "pivot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90d18520",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter rows for essround equal to 10\n",
    "filtered_df = df[df[\"essround\"] == 10]\n",
    "\n",
    "# Filter rows for gender values 1 and 2\n",
    "filtered_df = filtered_df[filtered_df['gndr'].isin([1, 2])]\n",
    "\n",
    "# Filter rows for lrscale values from 0 to 10\n",
    "filtered_df = filtered_df[(filtered_df['lrscale'] >= 0) & (filtered_df['lrscale'] <= 10)]\n",
    "\n",
    "# Group by gender and lrscale and calculate the counts\n",
    "grouped_df = filtered_df.groupby(['gndr', 'lrscale']).size().reset_index(name='counts')\n",
    "\n",
    "# Pivot the DataFrame to have gender as rows and lrscale as columns\n",
    "pivot_df = grouped_df.pivot(index='gndr', columns='lrscale', values='counts').fillna(0)\n",
    "\n",
    "# Normalize the counts to get percentages\n",
    "pivot_df = pivot_df.div(pivot_df.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Print the resulting pivot table\n",
    "print(\"Distribution of lrscale for men and women:\")\n",
    "print(pivot_df)\n",
    "\n",
    "# Filter the DataFrame for men and women\n",
    "men_data = filtered_df[filtered_df['gndr'] == 1]['lrscale']\n",
    "women_data = filtered_df[filtered_df['gndr'] == 2]['lrscale']\n",
    "\n",
    "# Plot histograms for men and women\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist([men_data, women_data], bins=11, range=(0, 10), density=True, stacked=False, label=['Men', 'Women'], edgecolor='black')\n",
    "plt.xlabel('lrscale')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Distribution of lrscale for Men and Women (ESS Round 10)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1cb07738",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.166884635199937\n",
      "4.867882383097148\n"
     ]
    }
   ],
   "source": [
    "men_mean = men_data.mean()\n",
    "women_mean = women_data.mean()\n",
    "print(men_mean)\n",
    "print(women_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "141d9dcd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the DataFrame for men and women\n",
    "men_data = filtered_df[filtered_df['gndr'] == 1]['lrscale']\n",
    "women_data = filtered_df[filtered_df['gndr'] == 2]['lrscale']\n",
    "\n",
    "# Plot histograms for men and women\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist([men_data, women_data], bins=11, range=(0, 10), density=True, stacked=False, label=['Men', 'Women'], edgecolor='black')\n",
    "plt.xlabel('lrscale')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Distribution of lrscale for Men and Women')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "75d93dce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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le09eXl7UqlXrsb2hD3Lu3Dlu3rxJ//79sw2Zc3Jy4vnnn2f//v0kJSVle82T9Mb5+vrSsmVLZs+ezZ07d7J6nh8kt+/Ty8uLBg0aZNtm6u+XY8eO0bVrVzw8PLJ+jw0YMAC9Xs/58+dzfZ7g4GCqV69OrVq1su3Xp0+fx8YCUKxYsWzvcceOHeh0Opo2bQoYf59nzmv67/ymbdu2AeQYhvvCCy/g6OjI33//nW177dq18fHxyfo683PYsmXLbPPWMrdnvs8LFy5w9uxZ+vbtC5Dte9SxY0ciIiI4d+5ctnP9txJozZo1sx1TiMJIEichLJBGo2Hw4MEsXLiQ6dOnU7ly5QfOEQHjMJN79+5hY2ODtbV1tn+RkZFZ82cSEhJo3rw5Bw4cYPLkyWzfvp1Dhw6xcuVKgBzDoBwcHLCzs8u2zdbWlpSUlCd+X97e3jm2jRkzhq+++or9+/fz7LPP4uHhQZs2bbLG59+9exe9Xk/p0qUfeexvvvmG4cOH07BhQ1asWMH+/fs5dOgQHTp0yPHe/isqKoq1a9fmaL/q1asDmDQHaejQody5c4c1a9YAxmF6Tk5O9OrVCzAOQ3vmmWdYuXIlo0aN4u+//+bgwYPs378fyNn+TyoqKoqTJ0/meC/Ozs4oipL1Xlq0aMGqVavIyMhgwIABlC5dmoCAgGxz6x6kbNmygLG62sNkllEvU6ZM1rYhQ4Zw48aNrOF7S5YsITU1NdsFo6mf5UwP+jzlF3d392xf29jYPHJ75s9JVFQUiqJQsmTJHO9p//79Jn22/uvOnTvAg99/qVKlMBgMOao8PmlbDR06lLVr1/LNN99gb29Pz549H7hfbt+nh4dHjmPY2to+9ufg6tWrNG/enBs3bvD999+za9cuDh06lDWk8b+vN+U8d+7cwcvLK8d+D9r2MK1ateL8+fPcvHmT4OBg6tati5OTE2BMnI4dO0ZsbCzBwcFYWVllzV27c+cOVlZWOW4KaTQavLy8sr7XmZ7mcwgwcuTIHN+f119/Hcj5e+6/bZdZyCevflcJYY6kqp4QFmrQoEGMGzeO6dOn8+mnnz50v8yJuw+rYJZZvnzbtm3cvHmT7du3Z6vO9d8JyPkpswfi36ysrBgxYgQjRozg3r17/PXXX4wdO5b27dtz7do13N3d0el0XL9+/ZHHXrhwIS1btmTatGnZtpsywdvT05OaNWs+tJ1LlSr12GP06NGDYsWKMXv2bIKCgli3bh0DBgzIungKCQnhxIkTzJ07l4EDB2a97sKFC489NoCdnd0DC3Pcvn0bT0/PbO/F3t4+2xy5f/v3vt26daNbt26kpqayf/9+Pv/8c/r06UP58uVp3LjxA1/frl07xo4dy6pVq+jQocMD91m1alXWvpnat29PqVKlmDNnDu3bt2fOnDk0bNgQf3//bLGZ8lnO9KDPk7nx9PTMmuPyoAqST1JVMvOC9t/FCDLdvHkTrVZLsWLFsm1/0rbq0aMHb7zxBlOmTOGVV17JUbUwU368zwdZtWoViYmJrFy5Mqu3Fniqta08PDyIjIzMsf1B2x6mVatWfPPNN2zfvp3t27fTsWPHrOcyk6SdO3dmFY3I/L3g4eFBRkYGt27dypY8KYpCZGRkVs/w08r8uR8zZky2Yiz/VqVKlTw5lxCWTBInISyUj48P77//PmfPns12of1fnTt3ZunSpej1eho2bPjQ/TIvnP57ATNjxoy8CTgPuLm50bNnT27cuME777zD5cuX8ff3JygoiOXLl/Ppp59mu/D/N41Gk+O9nTx5kn379mXr+XiQzp07s2HDBvz8/HJccJrKzs6OPn36MH36dL744gvS09OzDWt62vYvX748J0+ezLbt/PnznDt3LlubdO7cmc8++wwPDw98fX1NOratrS1BQUG4ubmxefNmjh079tDEqV69ejzzzDPMmjWL/v37Zw1HyrR7925mz55Nhw4dqFu3btZ2nU5H//79+e6779i1axeHDx/O8d5N/Sxbks6dOzNlyhRu3LiR1fv4tKpUqYKPjw+LFy9m5MiRWZ+txMREVqxYkVVpLy/Y29szbtw4du7cyfDhwx+6X368zwd50M+RoijMnDnziY/ZqlUrpk6dyokTJ7IN11u8eLHJx2jRogU6nY4//viD06dPM3Xq1KznXF1dqV27NvPmzePy5cvZhgC2adOGqVOnsnDhQt59992s7StWrCAxMZE2bdo88fv6typVqlCpUiVOnDjBZ599lifHFKIwksRJCAs2ZcqUx+7z0ksvsWjRIjp27Mjbb79NgwYNsLa25vr16wQHB9OtWzeee+45mjRpQrFixXjttdcYP3481tbWLFq0KEcluILWpUsXAgICqFevHsWLF+fKlSt89913lCtXLmv9mG+++YZmzZrRsGFDPvjgAypWrEhUVBRr1qxhxowZODs707lzZz755BPGjx9PUFAQ586dY9KkSfj6+j62UtykSZPYunUrTZo04a233qJKlSqkpKRw+fJlNmzYwPTp0x87VBCMw5p+/vlnvvnmG6pWrUqTJk2ynqtatSp+fn588MEHKIqCu7s7a9euzRq69jj9+/enX79+vP766zz//PNcuXKFqVOn5hji884777BixQpatGjBu+++S82aNTEYDFy9epUtW7bw3nvv0bBhQ8aNG8f169dp06YNpUuX5t69e3z//ffZ5r09zPz582nbti3PPPMMb731VtbF3bZt2/j++++pWrXqA6t0DRkyhC+++II+ffpgb2+fo0qkqZ9lS9K0aVNeffVVBg8ezOHDh2nRogWOjo5ERESwe/duatSo8ciE5EG0Wi1Tp06lb9++dO7cmWHDhpGamsqXX37JvXv3TPq9kRuZPcKPkh/v80HatWuHjY0NvXv3ZtSoUaSkpDBt2rSHLkBtinfeeYfZs2fTqVMnJk+eTMmSJVm0aBFnz541+RguLi7UqVOHVatWodVqc9xQCAoK4rvvvgOyr9/Url072rdvz+jRo4mLi6Np06acPHmS8ePHExgYSP/+/Z/4ff3XjBkzePbZZ2nfvj2DBg3Cx8eHmJgYzpw5w9GjR1m+fHmenUsISyWJkxCFnE6nY82aNXz//fcsWLCAzz//HCsrK0qXLk1QUBA1atQAjENC1q9fz3vvvUe/fv1wdHSkW7duLFu2jDp16qgWf6tWrVixYgW//fYbcXFxeHl50a5dOz7++OOsQhK1atXi4MGDjB8/njFjxhAfH4+XlxetW7fOGsv/4YcfkpSUxKxZs5g6dSr+/v5Mnz6dP//887ET8L29vTl8+DCffPIJX375JdevX8fZ2RlfX186dOhgci9UYGAggYGBHDt2LMckemtra9auXcvbb7/NsGHDsLKyom3btvz1119Z84YepU+fPty8eZPp06czZ84cAgICmDZtWo61ixwdHdm1axdTpkzh119/JTw8HHt7e8qWLUvbtm0pX748AA0bNuTw4cOMHj2aW7du4ebmRr169di2bVvW3K6HKVmyJPv37+eHH37g999/54cffgCgYsWKjB07lnfeeQdHR8ccr6tcuTJNmjRh79699O3bF1dX12zPm/pZtjQzZsygUaNGzJgxg19++QWDwUCpUqVo2rRpjsIFpurTpw+Ojo58/vnnvPjii+h0Oho1akRwcHC2hL0g5cf7/K+qVauyYsUKPvroI3r06IGHhwd9+vRhxIgRWcVccsvLy4sdO3bw9ttvM3z4cBwcHHjuuef46aef6Natm8nHadWqFYcOHSIwMDDb4uVgTJy+/fZbbGxssn1/NBoNq1atYsKECcyZMyerV71///589tlnebpAeKtWrTh48CCffvop77zzDnfv3sXDwwN/f/987SUUwpJolMzyRkIIIYQQQgghHkiq6gkhhBBCCCHEY0jiJIQQQgghhBCPIYmTEEIIIYQQQjyGJE5CCCGEEEII8RiSOAkhhBBCCCHEY0jiJIQQQgghhBCPUeTWcTIYDNy8eRNnZ+esFcaFEEIIIYQQRY+iKMTHx1OqVCm02kf3KRW5xOnmzZuUKVNG7TCEEEIIIYQQZuLatWuULl36kfsUucTJ2dkZgPDwcNzd3VWOxrylp6ezZcsWnnnmGaytrdUOx2xJO5lG2sk00k6mkXYyjbST6aStTCPtZBppJ9OYQzvFxcVRpkyZrBzhUVRNnCZMmMDEiROzbStZsiSRkZEP3H/37t2MHj2as2fPkpSURLly5Rg2bBjvvvuuyefMHJ7n7OyMi4vLkwdfBKSnp+Pg4ICLi4v80D+CtJNppJ1MI+1kGmkn00g7mU7ayjTSTqaRdjKNObWTKVN4VO9xql69On/99VfW1zqd7qH7Ojo68uabb1KzZk0cHR3ZvXs3w4YNw9HRkVdffbUgwhVCCCGEEEIUQaonTlZWVnh5eZm0b2BgIIGBgVlfly9fnpUrV7Jr1y5JnIQQQgghhBD5RvXEKSwsjFKlSmFra0vDhg357LPPqFChgkmvPXbsGHv37mXy5MkP3Sc1NZXU1NSsr+Pi4gBj12B6evrTBV/IZbaPtNOjSTuZRtrJNNJOppF2Mo20k+mkrUwj7WQaaSfTmEM75ebcGkVRlHyM5ZE2btxIUlISlStXJioqismTJ3P27FlOnz6Nh4fHQ19XunRpbt26RUZGBhMmTODjjz9+6L4PmkcFsHjxYhwcHPLkfQghhBBCCPFvWq32seWtRcHQ6/U8LOVJSkqiT58+xMbGPrb+gaqJ038lJibi5+fHqFGjGDFixEP3Cw8PJyEhgf379/PBBx/w008/0bt37wfu+6AepzJlyhAREfHI5EwYM/CtW7fSrl071SfsmTNpJ9NIO5lG2sk00k6mkXYynbSVaaSdHi89PZ2IiAju3r2Lvb29rBv6CIqikJKSgp2dXb62k0ajwdvbG0dHxxzPxcXF4enpaVLipPpQvX9zdHSkRo0ahIWFPXI/X19fAGrUqEFUVBQTJkx4aOJka2uLra1tju3W1tbyA28iaSvTSDuZRtrJNNJOppF2Mo20k+mkrUwj7fRgBoOBS5cuodVqKVWqFK6uro8sfFbUGQwGEhIScHJyyrfeOUVRuHXrFpGRkVSqVCnH9yM3n2OzSpxSU1M5c+YMzZs3N/k1iqJk61ESQgghhBBCDWlpaRgMBnx8fMjIyMDe3l6G6z2CwWAgLS0NOzu7fG2n4sWLc/nyZdLT058qkVU1cRo5ciRdunShbNmyREdHM3nyZOLi4hg4cCAAY8aM4caNG8yfPx+An3/+mbJly1K1alXAuK7TV199xf/+9z/V3oMQQgghhBD/JsmSecmrYYCqJk7Xr1+nd+/e3L59m+LFi9OoUSP2799PuXLlAIiIiODq1atZ+xsMBsaMGUN4eDhWVlb4+fkxZcoUhg0bptZbEEIIIYQQQhQBqiZOS5cufeTzc+fOzfb1//73P+ldEkIIIYQQQhQ4s5rjJIQQQgghhAC9QeFgeAzR8SmUcLajga87Oq1U6FOTDMAUQgghhBDCjGwKiaDZF9voPXM/by89Tu+Z+2n2xTY2hUTk2zkHDRqERqPhtddey/Hc66+/jkajYdCgQfl2fksgiZMQQgghhBBmYlNIBMMXHiUiNiXb9sjYFIYvPJqvyVOZMmVYunQpycnJWdtSUlJYsmQJZcuWzbfzWgpJnIQQQohCSG9QOBAew5HbGg6Ex6A3mM1690IUKYqikJSWYdK/+JR0xq85zYN+WjO3TVgTSnxKuknHU5Tc/dzXqVOHsmXLsnLlyqxtK1eupEyZMgQGBmZ7T1OnTqVChQrY29tTq1Yt/vjjj6znt2/fjkaj4e+//6ZevXo4ODjQpEkTzp07l6t4zI3McRJCCCEKmU0hEUxcG3r/jrWO+WGH8Xa1Y3wXfzoEeKsdnhBFSnK6Hv9xm/PkWAoQGZdCjQlbTNo/dFJ7HGxyd7k/ePBg5syZQ9++fQGYPXs2Q4YMYfv27Vn7fPTRR6xcuZJp06ZRqVIldu7cSb9+/ShevDhBQUFZ+3344Yd8/fXXFC9enNdee40hQ4awZ8+eXMVjTqTHSQghhChE1BzmI4SwfP3792f37t1cvnyZK1eusGfPHvr165f1fGJiIt988w2zZ8+mffv2VKhQgUGDBtGvXz9mzJiR7ViffvopQUFB+Pv788EHH7B3715SUlL+e0qLIT1OQgghRCGhNyhMXBv60GE+GmDi2lDa+XtJdS4hCoi9tY7QSe1N2vdgeAyD5hx67H5zB9enga+7SefOLU9PTzp16sS8efNQFIVOnTrh6emZ9XxoaCgpKSm0a9cu2+vS0tKyDecDqFmzZtZjb29jb3d0dLTFzpeSxEkIIYQoJA6Gx+Toafo3BYiITeFgeAyN/TwKLjAhijCNRmPycLnmlYrj7WpHZGzKA2+AaAAvVzuaVyqerzc/hgwZwptvvgnAzz//nO05g8EAwPr16/Hx8cn2nK2tbbavra2tsx5rNJpsr7dEkjgJIYQQhUR0vGlDYEzdTwhRsHRaDeO7+DN84VE0kC15ykyTxnfxz/ce4w4dOpCWlgZA+/bZe8v8/f2xtbXl6tWr2eYzFQWSOAkhhBCFRAln28fvBJRwtsvnSIQQT6pDgDfT+tX5V4EXI68CLPCi0+k4c+ZM1uN/c3Z2ZuTIkbz77rsYDAaaNWtGXFwce/fuxcnJiYEDB+Z7fGqRxEkIIYQoBJLT9Cw+cPWx+3m72pk0N0IIoZ4OAd608/fiYHgM0fEplHA2/twW5NxEFxeXhz73ySefUKJECT7//HMuXbqEm5sbderUYezYsQUWnxokcRJCCCEs3M17yby64DAhN+LQasCgkGOYT6YPnq0qhSGEsAA6raZA5yLOnTv3kc+vWrUq67FGo+Gtt97irbfeeuC+LVu2zLGGVO3atXO9rpS5kXLkQgghhAU7fDmGrj/tJuRGHO6ONix+pRHT+9XByzX7cDzd/VzpbGS8ClEKIYTlkx4nIYQQwkItPXiVj1eHkK5XqObtwq/961LG3QGAdv5e7LsQzZZdB3imeUMS0xSGLTzCzJ2X6BHoQ6WSzipHL4QQlkV6nIQQQggLk643MG51CB+sPEW6XqFTDW9WDG+clTSBcZhPQ1936noqNPR1p32AF22rlSTDoPDRqhCLHzIjhBAFTRInIYQQwoLEJKbRf9YB5u+7AsDIZyrzU59Ak9aJmdDVH3trHQfCY/jz2I38DlUIIQoVSZyEEEIIC3EmIo6uP+1m/6UYHG10zBxQjzdbV8paWPJxShdz4K02lQD4dP0ZYpPS8zNcIYQoVCRxEkIIISzAxlMR9PhlL9fvJlPOw4E/32hKO/+SuT7O0Ga+VCrhxJ3ENKZuPpsPkQohROEkiZMQQghhxgwGhW+2nGP4oqMkp+tpXsmT1W80pfITFnewsdLySfcAABYfvMrxa/fyMFohhCi8JHESQgghzFRCagavLTzCD9suAMbeojmD6uPmYPNUx21UwYMedXxQFPjwz1Nk6A15Ea4QQhRqkjgJIYQQZujKnUR6/LKHLaFR2Oi0fPVCLT7u7I+VLm/+dI/tWA1Xe2tO34xjwf4reXJMIYQozCRxEkIIIczMngu36fbzHs5HJVDC2ZZlwxrRs27pPD2Hp5MtozpUAeDrLeeJikvJ0+MLIZ6SQQ/hu+DUH8b/DXq1IyryJHESQgghzISiKMzeHc6A2Qe5l5ROrTJurP1fMwLLFsuX8/WuX5baZdxISM3gk3Wh+XIOIcQTCF0D3wXAvM6wYqjx/+8CjNvzwfTp03F2diYjIyNrW0JCAtbW1jRv3jzbvrt27UKj0XD+/Pl8icWcSeIkhBBCmIHUDD2j/jjJpHWh6A0KPer4sOzVRpR0scu3c2q1GiZ3D0CrgXUnI9gVdivfziWEMFHoGvh9AMTdzL49LsK4PR+Sp1atWpGQkMDhw4eztu3atQsvLy8OHTpEUlJS1vbt27dTqlQpKleunOdxmDtJnIQQQgiVRcel8NKv+1l+5DpaDXzUqRpfv1ALO2tdvp87wMeVgU3KA/DxqhBS0mU4kBB5SlEgLdG0fylxsHEUoDzoQMb/No027mfK8ZQHHSenKlWqUKpUKbZv3561bfv27XTr1g0/Pz/27t2bbXurVq24e/cuAwYMoFixYjg4OPDss88SFhaWtd/cuXNxc3Nj3bp1VKlSBQcHB3r27EliYiLz5s2jfPnyeHh4MGrUKPT6f37vpKWlMWrUKHx8fHB0dKRhw4bZ4so87ubNm6lWrRpOTk506NCBiIgIk97r03j8MuNCCCGEyDcnrt1j2IIjRMal4GpvzU99AmleqXiBxjCiXWU2nIrg8p0kpu+4yDtti96dZCHyTXoSfFYqjw6mGHuippQxbfexN8HG0aRdW7ZsSXBwMB988AEAwcHBjBo1CoPBQHBwMG3btiUtLY19+/bx448/MmjQIMLCwlizZg0uLi6MHj2ajh07EhoairW1NQBJSUn88MMPLF26lPj4eHr06EGPHj1wc3Njw4YNXLhwgRdeeIFWrVrRu3dvAAYPHszly5dZunQppUqV4s8//6RDhw6cOnWKSpUqZR33q6++YsGCBWi1Wvr168fIkSNZtGhRLtszd6THSQghhFDJn8eu88KMfUTGpVCxhBOr32ha4EkTgLOdNR939gfgl+0XuXw7scBjEEKoq2XLluzZs4eMjAzi4+M5duwYLVq0ICgoKKvHZ//+/SQnJ9OsWTPWrFnDb7/9RvPmzalVqxaLFi3ixo0brFq1KuuY6enpTJs2jcDAQFq0aEHPnj3ZvXs3s2bNwt/fn86dO9O8efOs41+8eJElS5awfPlymjdvjp+fHyNHjqRZs2bMmTMn23GnT59OvXr1qFOnDm+++SZ///13vreR9DgJIYQQBUxvUPhi01l+3XkJgLbVSvDti7VxtrNWLaZONbxZVukau8JuM27NaeYNro9Go1EtHiEKDWsHY8+PKa7shUU9H79f3z+gXBPTzm2iVq1akZiYyKFDh7h79y6VK1emRIkSBAUF0b9/fxITE9m+fTtly5bl3LlzWFlZ0bBhw6zXe3h4UKVKFc6cOZO1zcHBAT8/v6yvS5YsSfny5XFycsraVrx4caKjowE4evQoiqLkmD+VmpqKh4fHQ4/r7e2ddYz8JImTEEIIUYBik9L539Jj7DxvLMTwZquKjGhXGa1W3SRFo9EwqVsA7b/byc7zt9hwKpJONb1VjUmIQkGjMXm4HH6twaWUsRDEA+c5aYzP+7UGbd7OgaxYsSKlS5cmODiYu3fvEhQUBICXlxe+vr7s2bOH4OBgWrdujfKQuVOKomS74ZI5ZC8reo3mgdsMBuMi3AaDAZ1Ox5EjR9Dpsr+/fydbDzrGw2LKSzJUTwghhCggF6Lj6f7LHnaev4W9tY6f+gQysn0V1ZOmTL6ejgwPMt7FnbTuNPEp6SpHJEQRo9VBhy/uf/Hf3wv3v+4wJc+TpkytWrVi+/btbN++nZYtW2ZtDwoKYvPmzezfv59WrVrh7+9PRkYGBw4cyNrnzp07nD9/nmrVqj3x+QMDA9Hr9URHR1OxYsVs/7y8vJ7mreUJSZyEEEKIAvD3mSi6/7yX8NuJ+LjZ88fwxnSumVcTxvPO8JZ+lPdwICoulW+3hj3+BaLI0BsUDoTHcOS2hgPhMegN+X+Hv0jy7wq95oPLf3p8XUoZt/t3zbdTt2rVit27d3P8+PGsHicwJk4zZ84kJSWFVq1aUalSJbp168Yrr7zC7t27OXHiBP369cPHx4du3bo98fkrV65M3759GTBgACtXriQ8PJxDhw7xxRdfsGHDhrx4i09FhuoJIYQQ+UhRFH7ZfpGvtpxDUaCBrzvT+tbBw8lW7dAeyM5ax6RuAQyYfZC5e8N5vq4P1Uu5qh2WUNmmkAgmrg0lIjYF0DE/7DDernaM7+JPhwAZ0pnn/LtC1U7GOU8JUeBU0jinKZ96mjK1atWK5ORkqlatSsmSJbO2BwUFER8fj5+fH2XKGCv6zZkzh7fffpvOnTuTlpZGixYt2LBhQ45hdLk1Z84cJk+ezHvvvceNGzfw8PCgcePGdOzY8amOmxc0SkEMCDQjcXFxuLq6cvv27WyTzERO6enpbNiwgY4dOz71D0FhJu1kGmkn00g7mcZS2ik5Tc/7f5xg3Unj+iJ9G5ZlfJfq2FgVzICPp2mnNxYfZf3JCALLurHitSZmM5wwv1jKZ0oNm0IiGL7waI4ZN5mfiGn96kjydF9KSgrh4eGUK1eOtLQ0XFxc0GplgNfDGAwG4uLi8r2dMr8vvr6+2NllX1Q8MzeIjY3FxcXlkceR76QQQgiRD27cS6bn9L2sOxmBlVbD5O4BfPpcjQJLmp7WuM7+ONlacezqPZYeuqZ2OEIleoPCxLWhj1qOlYlrQ2XYnigSLOO3txBCCGFBDobH0PXH3Zy+GYeHow2LX2lEv0bl1A4rV0q62DGinbEk8BebznI7IVXliIQaDobH3B+e92AKEBGbwsHwmIILSgiVSOIkhBBC5KHFB67S97f93ElMw9/bhdVvNqWBr7vaYT2RAY3L4e/tQmxyOp9vOKt2OEIF0fEPT5qeZD8hLJkkTkIIIUQeSNcb+HhVCGP/PEW6XqFTTW/+GN6Y0sVMX4DS3FjptEx+LgCNBlYcvc6BS3fUDkkUsBLOdo/fKRf7CWHJJHESQgghntKdhFT6/naABfuvoNHA++2r8FPvQBxsLL94bZ2yxXipflkAPl4dQrreoHJEoiA18HXHzeHhxTI0gLerncX2quaXIlZ7zezl1fdDEichhBDiKYTejKPrT3s4GB6Dk60VM/vX441WFdFoCk8VutEdquDhaMP5qARm7Q5XOxxRgC5EJ5CUpn/kPuO7+KMr5FUXTZVZkTEpKUnlSMS/paWlAaDTPV05d8u/FSaEEEKoZP3JCEYuP0Fyup7yHg7MHFCPSiWd1Q4rz7k52DCmYzVGLj/B93+F0bmmt0UPQRSmiU1OZ9iCw6RlGKjq5cS9pAwi47LPZerXuJyUIv8XnU6Hm5sbt27dwtnZGWtr66e+WC/MDAYDaWlppKSk5Fs5coPBwK1bt3BwcMDK6ulSH0mchBBCiFwyGBS+/es8P267AEDzSp781LsOro8Y0mTpnq/jw++Hr3EwPIaJa0OZOaCe2iGJfGQwKLyz9BiX7yTh42bP4lca42pvzb4L0WzZdYB0t3IsOXSdDScjGPlMFVztC+9nP7e8vLzQ6/VEREQQHx9fqHqf85qiKCQnJ2Nvb5+v7aTVailbtuxTn0MSJyGEECIX4lPSeXfZCf46EwXAK819Gd2hKla6wj36XaMxrkXV8ftdbA2N4q/QKNr6l1Q7LJFPvvvrPMHnbmFrpWVG/7q4O9oA0NDXnTtnFNq1r8qhK/e4EJ3At1vPM6FrdZUjNh8ajYaSJUty9OhRWrdu/dS9HIVZeno6O3fupEWLFvm68LSNjU2e9GjJd1IIIYQw0ZU7ibw87zBh0QnYWGn5/LkaPF+3tNphFZjKJZ15uXkFpu+4yPg1p2lS0aNQFMAQ2W05HckP93tTP+9RgwAf1xz7WOu0TOxanb6/HWD+vsu8WL8M1bxdCjpUs6YoCra2tvmaEFg6nU5HRkYGdnZ2FtFOhfv2mBBCCJFHdoXdoutPewiLTqCEsy2/D2tcpJKmTG+1qYiPmz037iVnDVUUhceF6ARG/H4CgEFNytOjzsM/400retKphjcGBcavPi2V5EShJ4mTEEII8QiKovDbrksMnH2Q2OR0apdxY+3/mlG7jJvaoanCwcYqa1jWzJ2XCIuKVzkikVfiU4zFIBJSM2hQ3p0PO1V77GvGdqqGvbWOg5djWHPiZgFEKYR6JHESQgghHiIlXc/I5SeZvP4MBgV61i3N0lcbUdKlaC/22c6/JG2rlSTDoPDRqhDpaSgEDAaFkctPcPFWIl4udvzctw7WJszb83Gz583WFQH4dP0Z4lPS8ztUIVQjiZMQQgjxANFxKbz0635WHL2OVgMfd/bny541sbOW0sJgXLvHzlrLgfAYVh69oXY44ilN23GRzaejsNFpmdavDsWdbU1+7cvNfSnv4UB0fKoM3xSFmiROQgghxH8cv3aPLj/t5vi1e7jaWzN/SEOGNvOVssL/UsbdgbfaVALgsw1niE2SngZLFXwumq+2nANgYrfqBJYtlqvX21rpGH9/+Obs3eEyfFMUWpI4CSGEEP+y4sh1es3YR1RcKpVKOLH6jaY0q+Spdlhm6eVmFahUwok7iWlM3XxW7XDEE7hyJ5G3lxxDUaB3g7L0blD2iY7TqkqJrOGbE9ZKoQhROEniJIQQQgAZegOT14Xy3vITpGUYaOdfkj/faEp5T0e1QzNbNlZaPukeAMDig1c5fu2eugGJXElKy2DYgiPEpWQQWNaNCV39n+p447v4Y2OlZc+FO2wMicyjKIUwH5I4CSGEKPJik9IZPPcQv+0OB+Ct1hWZ0a8uTrayRtHjNKrgQY86PigKfPjnKTL0BrVDEiZQFIVRf5zkbGQ8nk62TOtbF1urp5u/V8bdgeFBfgBMXhdKUlpGXoQqhNmQxEkIIUSRFhYVT7efd7Mr7Db21jp+7lOHEc9UQauV+UymGtuxGq721py+GceC/VfUDkeY4Ldd4aw7GYGVVsO0fnXwcs2bSpHDW/pRupg9N2NT+DlYCkWIwkUSJyGEEEXWX6FRPPfLXi7fScLHzZ4Vw5vQqaa32mFZHE8nW0Z1qALA11vOExWXonJE4lH2XrjN5xvPAMZqkfXLu+fZse2sdYzrbBzyN3NnOOG3E/Ps2EKoTRInIYQwE3qDwoHwGI7c1nAgPAa9QSZX5xdFUfg5+AKv3F/ss6GvO2vebIp/KRe1Q7NYveuXpXYZNxJSM/hkXaja4YiHuH43iTcWH8WgwPN1SjOgcbk8P0c7/5IEVS5Omt7ARCkUIQoRSZyEEMIMbAqJoNkX2+g3+zDzw3T0m32YZl9sY1NIhNqhFTpJaRm8ueQYX24+h6JA/0blWPhyQzycTF+3RuSk1WqY3D0ArQbWnYxgV9gttUMS/5GSrue1hUe4m5ROgI8Lnz4XkC8l9jUaDRO6VsdGp2X7uVv8dSY6z88hhBokcRJCCJVtColg+MKjRMRmH94UGZvC8IVHJXnKQ9fvJtFz2j7W35/b8dlzNfikewDWOvlzmBcCfFwZ0Lg8AB+vCiElXa9uQCKLoiiM/fMUITficHe0YXq/uvm6mLOvpyMvN/cFYOLa0/JZEIWC/KUQQggV6Q0KE9eG8qCBLJnbJq4NlWF7eeDApTt0+2kPoRFxeDjasPiVRvRp+GRr1oiHe++ZypRwtuXynSSm77iodjjivvn7rrDy6A20GvipdyCliznk+znfbF0Rb1c7rt9Nls+CKBQkcRJCCBUdDI/J0dP0bwoQEZvC5tORGCR5emIL91+h728HuJOYRvVSLqz5XzMa+ObdhHjxD2c7az6+Xxzgl+0XuSzFAVR3MDwma97ZmGer0aRiwSzo7GBjxUedjJ+Fadsvci0mqUDOK0R+kQUqhBBCRdHxplUfe33RUWyttJR1d6CsuwNl7v9f1t2Bch4OlC7mgL1N/g27sVRpGQYmrD3N4gNXAehc05sve9aStspnnWt68/vha+wKu824NaeZN7h+vsylEY8XGZvC64uOkmFQ6FKrVNbwuYLSsYYXTfw82HvxDpPWhTJzQL0CPb8QeUkSJyGEUFEJZ9PWTtFqIDXDQFh0AmHRCQ85lm1WMlXW45/Eqqy7A8WdbYvchevthFReX3iUg5dj0Gjg/fZVGB7kV+TaQQ0ajYZJ3QJo/91Odp6/xYZTkVLmXQWpGcZiELcTUqnq5cwXz9co8M+/RqNhYtfqPPv9LraGRhF8LppWVUoUaAxC5BVJnIQQQkUNfN3xdrV76HA9DeDlakfwyJZEx6VyNSaJqzFJXIlJ5Frm4ztJxKdkEB2fSnR8Koev3M1xHDvrnL1V5e4nV6WLOeTrJHE1nL4Zy6vzj3DjXjLOtlZ837s2rauWVDusIsXX05HhQX58/3cYk9adpkVlT5ztrNUOq0iZsCaU49fu4WJnxYz+dXGwUeeyr1JJZwY3Lc/MXeFMWhtKEz8PbK0K1+8cUTRI4iSEECrSaTUMaerLpxvO5Hgu877w+C7+2FnrjL1IHg+e0H0vKS0rqboak8S1+wnV1Zgkbt5LJiXdwPmoBM5HPbi3qqRLZm+V4/0eK/usJKu4k2X1Vq07eZORy0+Qkm7A19ORmQPqUrGEs9phFUnDW/qx+vgNLt9J4tutYYzr4q92SEXGkoNXWXLwKhoN/NA7kHIejqrG81abSqw6fpPw24n8tiucN1pVVDUeIZ6EJE5CCKEig0Fh0+lIwNgrlJJuyHrOy9WO8V386RDw+CFObg42uDnYULO0W47n0vUGbt5Lzkqkrv0rwbp6J4n41Ayi4lKJikvl0OWcvVX21roH9lSVcXegdDF7s+mtMhgUvt56jp+DjdW7WlQuzo8vBeLqIL0carGz1jGpWwADZh9k7t5wnq/rQ/VSrmqHVegdu3qX8atPAzDymSq0NIOhcc521nzYsRrvLDvOT9su8FygD6Xc7NUOS4hckcRJCCFU9MeR6xy5chdHGx1b3g0i/FYcW3Yd4JnmDWlcsQQ67dP39FjrtJTzcHzgHWdFUbiXlJ6tt+rqnX8eR8Qmk5yu51xUPOei4h94fC8XuxxzqsrcT7A8HG3yvLdKb1A4EB7DkdsaPMJjaFyxBElpGby77HjWQpvDWlRgVIeqedJ+4um0qFycTjW9WX8ygo9WhbDitSZo5fuSb27FpzJ84VHS9AbaVy/J6y391A4pS7fapVh04AqHLt/l0/Vn+LlvHbVDEiJXJHESQgiV3E1M4/ONxiF677arjE8xe0o4WXHnjEJDX/cCuejXaDQUc7ShmKMNtcq45Xg+LcPAjXvJ/xkCmMjVmGSu3kkkMU1PZFwKkXEpHAyPyfF6B5ucvVWZj0sXs8/1PIdNIRFMXBt6f06YjvlhhynuZItOqyEyLgUbKy1fPF+D5wJLP2GLiPwwrrM/O87d4tjVeyw9dE3Wz8on6XoDbyw6SmRcChVLOPF1r9pmNczWWCgigM4/7mL9qQj6XLhN0wIqjS5EXpDESQghVDJ181nuJqVT1cuZgU3Kqx3OA9lYafH1dMTX88G9VXfv91ZduZOYYwhgRFwKSWl6zkbGczYyZ2+VRgPeLnbZSqv/u+fK/T+9VZtCIhi+8GiOxYJvJaQC4GpvxfwhDR+YAAp1lXSx4912lflkXShfbDrLM9VL4ulkq3ZYhc6n689w8HIMzrbGYhBOtuZ3medfyoUBjcszd+9lxq85zca3m2Otk2VFhWUwv58oIYQoAo5evcuSg9cA+KR7gEVeOGg0GtwdbXB3tKH2A5KV1Aw9N+4m5yhWkfkvKU3PzdgUbsamcOABvVWONrqspKqMuz3LD1/PkTT9m521jgAfmT9jrgY2LseKI9cJjYjj8w1n+bpXLbVDKlRWHr3O3L2XAfjmxdr4FXdSN6BHeLddZdaeuMmF6ATm7rnMKy0qqB2SECaRxEkIIQpYht7AR3+GAPBC3dLUL++uckT5w9ZKR4XiTlR4wAWcoijcSUz7p1jFncwy68avI+NSSHxEb9WDRMWlcjA8hsZ+Hnn9VkQesNJpmfxcAM9P28uKo9fpVa80DSvI9yovhNyIZczKU4Cxel07f/Muve9qb83oDlUZteIk3/11nm61S1HCxbQ17YRQkyROQghRwBbsv0JoRByu9tZ88GxVtcNRhUajwdPJFk8nW+qULZbj+ZR0vXFu1f2EKvhcNNvP3XrscaPjH7weljAPdcoW46X6ZVly8CofrQphgwzTemoxiWkMW3CE1AwDrauW4J02ldQOySQ965Zm8cGrHL92j882nOG7lwLVDkmIx5LfVkIIUYCi41L4est5AEZ3qIqHzPN4IDtrHX7FnWhVtQQDm5RnWAvTKoOVcJa71uZudIcqeDjaEBadwKzd4WqHY9Ey9Ab+t+QoN+4lU97DgW9frG0xFQu1Wg2TulVHo4FVx29y4NIdtUMS4rEkcRJCiAI0ef0ZElIzqFXGjZfql1E7HIvRwNcdb1c7HnZJqAG8Xe1o4Fs4hz0WJm4ONozpWA2A7/8K4/rdJJUjslxfbj7Hngt3cLDRMaN/PVztLWvNspql3ejdwFhhcfya02ToDY95hRDqksRJCCEKyJ4Lt1lz4iZaDXzaPcBi7gybA51Ww/gu/gA5kqfMr8d38Zd1myzE83V8aODrTnK6nolrQ9UOxyKtPXGTGTsvAfBlz1pU8XJWOaIn8/4zVXBzsOZsZDwL919ROxwhHkkSJyGEKACpGXo+Xm0sCDGgcXmp/vYEOgR4M61fHbxcsw/H83K1Y1q/OnQI8FYpMpFbGo2Gyd0DsNJq2BoaxV+hUWqHZFHORsYx6o+TAAwLqkCnmpb72S/maMPIZ6oA8PXW89y+v7yAEOZIEichhCgAv+0K59KtRDydbBnxTGW1w7FYHQK82T26NQuH1GNAJT0Lh9Rj9+jWkjRZoMolnXm5ubEM9fg1p0lKy1A5IssQm5TOsAVHSE7X07ySJ6PaW36Bmd4NyhLg40J8SgZTN51VOxwhHkoSJyGEyGfXYpL44e8wAD7uXA0XO8uah2BudFoNDX3dqeup0NDXXYbnWbC32lTEx82eG/eS+XHbBbXDMXt6g8Lby45x5U4SpYvZ88NLgYXi86/TapjYNQCA3w9f5+jVuypHJMSDSeIkhBD5bOLa06RmGGhcwYOutUqpHY4QZsPBxipr7trMnZcIizJtza6i6ru/zrP93C1srbRM71eXYo42aoeUZ+qWK0bPuqUBGL/6NHrDo5a7FkIdkjgJIUQ+2hoaxV9norHWafike3U0Gsu/OyxEXnqmuhdtq5Ugw6Dw0aoQFEUumB9k8+nIrF65Kc/XKJTzJEd3qIqznRWnbsSy9NBVtcMRIgdJnIQQIp8kpWUwYc1pAF5pXoGKJSyz6pUQ+W18l+rYWWs5EB7DyqM31A7H7FyITuC9308AMLhpeZ4LLK1yRPmjuLMtI9oZ54B+ufkcdxPTVI5IiOwkcRJCiHzy07YL3LiXjI+bPW+2rqh2OEKYrTLuDrzVphIAn204w70kuWDOFJ+SzqsLDpOQmkFDX3fG3l8Dq7Dq36gcVb2cuZeUzldbzqkdjhDZSOIkhBD54EJ0PDN3GddYGd/FHwcbK5UjEsK8vdysApVKOHEnMY0vN8sFM4DBoDDi9xNcupWIt6sdP/Wpg7WucF+6Wem0TOxaHYDFB69y6nqsyhEJ8Y/C/dMnhBAqUBSFj1edJl2v0KZqCdr5l1Q7JCHMno2Vlk+6GyurLT54lePX7qkbkBn4OfgCW0OjsNFpmdavLsWdbdUOqUA0rOBBt9qlUBQYtyYEgxSKEGZCEichhMhja07cZN+lO9haaZnQVQpCCGGqRhU86FHHB0WBD/88RYbeoHZIqgk+G803f50HYHL3AGqXcVM3oAI2tmM1HG10HLt6jxVHr6sdjhCAJE5CCJGn4lLS+WTdGQD+17oiZdwdVI5ICMsytmM1XOysOH0zjgX7r6gdjiou307kraXHUBTo27AsveqXUTukAlfSxY632xrnvU3ZeJbY5HSVIxJCEichhMhT32w5z+2EVCp4OvJKiwpqhyOExfF0smVUh6oAfL3lPFFxKSpHVLASUzMYtuAI8SkZ1Cnrxvgu1dUOSTWDmvjiV9yRO4lpfLv1vNrhCCGJkxBC5JWQG7HM33cZgEndArC10qkbkBAWqk+DstQq40ZCagafrAtVO5wCoygKo1ac5FxUPMWdbZnWry42VkX3Us3GSsvErsZ5b/P3XeZMRJzKEYmiruj+NAohRB4yGBQ+XBWCQYEutUrRrJKn2iEJYbG0Wg2fdg9Aq4F1JyPYFXZL7ZAKxK87L7H+ZARWWg3T+tahpIud2iGprlklTzrW8MKgwPjVp2WBZKEqSZyEECIPLD10jRPX7uFka8VHnQr3OitCFIQAH1cGNC4PwMerQkhJ16sbUD7bHXabLzadBYxLGNQr765yRObjw07+2FvrOHg5hjUnbqodjijCJHESQoindCchNeuCZ0S7ynKXWIg88t4zlSnhbMvlO0lM33FR7XDyzbWYJP635CgGBV6oW5p+jcqpHZJZ+fci4p+uP0N8ihSKEOqQxEkIIZ5SZsWnat4uDGgsFzxC5BVnO2s+7uwPwC/bL3L5dqLKEeW9lHQ9ry08wt2kdGqWduWT7gGyhMEDvNzcl/IeDkTHp/LjtgtqhyOKKEmchBDiKRy6HMPyI8Y1RiZ3D8BKJ79WhchLnWt607ySJ2kZBsatKVxzXBRFYezKU5y+GYe7ow3T+tXFzlqKyjyIrZWO8V2NFQZn7w7nQnS8yhGJokjVv/ATJkxAo9Fk++fl5fXQ/VeuXEm7du0oXrw4Li4uNG7cmM2bNxdgxEII8Y90vYGP/gwB4KX6ZahbrpjKEQlR+Gg0GiZ1C8DGSsvO87fYcCpS7ZDyzLy9l1l57AY6rYaf+gTi42avdkhmrVWVErStVpIMg8L4QpZEC8ug+q3R6tWrExERkfXv1KlTD913586dtGvXjg0bNnDkyBFatWpFly5dOHbsWAFGLIQQRvP2XuZcVDzFHKwZfX/dGSFE3vP1dGR4kB8Ak9adLhRzXA5cusMn642LZY95tipN/KQSpynGdfbHxkrLngt32BhSeJJoYRlUT5ysrKzw8vLK+le8ePGH7vvdd98xatQo6tevT6VKlfjss8+oVKkSa9euLcCIhRACImKTsxZk/ODZqhRztFE5IiEKt+Et/Sjn4UBUXCrfbg1TO5ynEhGbzBuLj6I3KHSrXYqhzXzVDslilPVwyEqiJ68LJSktQ+WIRFFipXYAYWFhlCpVCltbWxo2bMhnn31GhQoVTHqtwWAgPj4ed/eHl+xMTU0lNTU16+u4OOPiaenp6aSnW/4dq/yU2T7STo8m7WSawtZOk9acJjFNT2AZV7rX9Mqz91XY2im/SDuZpjC1kw4Y36kqQ+YfZe7ecLrVKom/t0ueHb+g2io1w8CwBYe5nZBGVS9nPulSjYwMy7n4N4fP1MtNy7LiyDWu30vhx7/OM6JdJdVieRhzaCdLYA7tlJtzaxQVB4hu3LiRpKQkKleuTFRUFJMnT+bs2bOcPn0aDw+Px77+yy+/ZMqUKZw5c4YSJUo8cJ8JEyYwceLEHNsXL16Mg4PDU78HIUTRc+aehulndGhQeL+mHh9HtSMSouiYc17L8TtayjspvB2gR2thBeiWXtSyL1qLg07hvZp6PGX1gidyMkbDrHM6dBqFD2rpKSHTw8QTSkpKok+fPsTGxuLi8uibMaomTv+VmJiIn58fo0aNYsSIEY/cd8mSJbz88susXr2atm3bPnS/B/U4lSlThoiICJOSs6IsPT2drVu30q5dO6ytrdUOx2xJO5mmsLRTarqeTj/t40pMEoMal+XDjnk7t6mwtFN+k3YyTWFsp8i4FDp8v4fEND2fdPXnpfql8+S4BdFWSw9d5+M1oWg18NuAOjSvaHnzmszlM6UoCi8vOMrOsDsEVfJkZv9Asyrjbi7tZO7MoZ3i4uLw9PQ0KXFSfajevzk6OlKjRg3Cwh49dnnZsmUMHTqU5cuXPzJpArC1tcXW1jbHdmtra/kgm0jayjTSTqax9Hb6ZcdlrsQkUcLZlvfaV82392Lp7VRQpJ1MU5jaqYyHNSOeqcIn60L5amsYz9YshadTzr/zTyq/2urIlbtMul8MYmT7KrSu5p3n5yhI5vCZmtA1gPbf7WRH2G12XLhLO/+SqsbzIObQTpZAzXbKzXlVLw7xb6mpqZw5cwZv74f/MlmyZAmDBg1i8eLFdOrUqQCjE0IUdVfuJPLzduPCix939sfZTv4YCqGGgY3L4e/tQmxyOp9vOKt2OI8VHZ/C64uOkK5XeDbAK6u4gXg6FYo78Upz47z4SetOk5KuVzkiUdipmjiNHDmSHTt2EB4ezoEDB+jZsydxcXEMHDgQgDFjxjBgwICs/ZcsWcKAAQP4+uuvadSoEZGRkURGRhIbG6vWWxBCFBGKojBu9WnSMgw0q+hJ55qWfbdYCEtmpdMy+bkANBpYcfQ6By7dUTukh0rLMPDGoqNExaVSqYQTX75Qy6yGlFm6N1tXxNvVjmsxyUzfcVHtcEQhp2ridP36dXr37k2VKlXo0aMHNjY27N+/n3LlygEQERHB1atXs/afMWMGGRkZvPHGG3h7e2f9e/vtt9V6C0KIImLz6Uh2nL+FjU7LpG7V5cJHCJXVKVuMl+qXBeCjVSGkZRhUjujBPl0fyqHLd3G2tWJG/7o42ZrVLAmL52BjxYedqgEwbftFrsUkqRyRKMxU/eldunTpI5+fO3dutq+3b9+ef8EIIcRDJKZmMHFtKADDgipQobiTyhEJIQBGd6jCltORhEUnMHtPOK+Z2RC4P45cZ96+KwB891Jt+d2RTzrV8Gax31X2XrzDpHWhzBxQT+2QRCFlVnOchBDCHP3wdxgRsSmUcbfnjVYV1Q5HCHGfm4MNYzoaexu+/yuM63fNp7fh1PVYxv55CoB32laiTTXzK1xQWGg0GiZ2rY6VVsPW0CiCz0WrHZIopCRxEkKIRzgXGc+s3eEATOhSHTtrncoRCSH+7fk6PjQo705yuj6rZ1htdxJSeW3hEdIyDLStVoK3WpvfAq2FTaWSzgxqUh6ASWtDSc2QQhEi70niJIQQD6EoCh+vCiHDoPCMf0m5YyyEGdJoNEx+LiCrt+Gv0ChV48nQG/jfkmPcuJeMr6cj37xYG62lrdJrod5uW4nizraE307kt13haocjCiFJnIR4CnqDwoHwGI7c1nAgPAa9wWzWkxZ5YOXRGxy8HIO9tY5xXfzVDkcI8RCVSzoztLkvAOPXnCYpLUO1WL7YdJa9F+/gaKPj1/51cZFlCwqMs501Y+8vSv7TtgvcvJesckSisJHESYgntCkkgmZfbKPf7MPMD9PRb/Zhmn2xjU0hEWqHJvJAbFI6n20wLlb5VptKlC7moHJEQohHebtNJXzc7LlxL5kft11QJYY1J24y835Px1cv1KJSSWdV4ijKutf2oX75YiSn6/n0/u9wIfKKJE5CPIFNIREMX3iUiNiUbNsjY1MYvvCoJE+FwJdbznInMY2KJZwY2sxX7XCEEI/hYGPF+Ps9wzN3XiIsKr5Az38mIo5Rf5wAYHhLP56tIWu9qcFYKCIArQbWn4xgz4XbaockChFJnITIJb1BYeLaUB40KC9z28S1oTJsz4KduHaPRQeMa8h90i0AGyv5VSmEJXimuhdtq5Ugw6Dw0aoQFKVgfg/fS0pj2IIjpKQbaF7Jk5HPVCmQ84oH8y/lQv9GxjVBx685TbrePNf4EpZHrgaEyKWD4TE5epr+TQEiYlM4GB5TcEGJPKPPuuCC5wJ9aOznoXZIQjwZgx7Nld34xOxDc2U3GIpGlbHxXapjZ63lQHgMK4/eyPfz6Q0Kby89ztWYJMq42/Nj70B0UgxCdSOeqYKHow0XohOYu+ey2uGIQkISJyFyKTr+4UnTk+wnzMviA1c4dSMWZzsrxtyfZCyExQldA98FYLWwO/WuTMNqYXf4LsC4vZAr4+7AW22M5b8/23CGe0lp+Xq+b7aeY8f5W9hZa5nRrx5uDjb5ej5hGld7a0Z3MP4O/+6v80THyd9k8fQkcRIilzwcTfujWMLZLp8jEXntVnwqUzefA+D99lXkeygsU+ga+H0AxN3Mvj0uwri9CCRPLzerQKUSTtxJTOPL+z/T+WFTSCQ/B18E4Ivna+JfyiXfziVyr2fd0tQu40Zimp7PN55VOxxRCEjiJEQupGUYWLj/ymP383SyoYGvewFEJPLS5xvOEJ+SQYCPC30bllM7HCFyz6CHTaPhUbMwN31Q6Ift2Vhp+aR7AACLD17l+LV7eX6OC9HxvPf7cQCGNvOlW22fPD+HeDparYZJ3aqj0cCfx27IEHrx1CRxEsJEKel6Xlt4hE2no7C6P379YaPYE1IyOHb1bsEFJ57a/kt3WHnsBhoNTO5eQ+YoCMt0ZW/OnqZsFIi7YdyvkGtUwYMegT4oCnz45yky8rBAQFxKOq/OP0Jimp5GFdwZ86wM6zVXNUu78VL9sgCMWx2Sp58DUfRI4iSECZLSMhg67xDbzkZjZ61l1qD6TO9XBy/X7EO5vFzsqFzSiZQMA/1nHWSvlEG1CGkZBj5eFQJAnwZlqV3GTd2AhHhSCVF5u5+FG9upGi52Vpy+GccCE0YLmMJgUBix7ASXbidSytWOn/vUwUonl1Pm7P32VXBzsOZsZHxWxVQhnoT8pAvxGHEp6QyYdZA9F4wrwc8b3ICgysXpEODN7tGtWTikHgMq6Vk4pB57PmjN6jea0aJycZLT9Qyae4jgs9FqvwXxGLP3hBMWnYCHow2j2sudY2HBnErm7X4WztPJllH3CwR8veU8UXlQIODHbRf460wUNlZapvevi4eT7VMfU+Qvd0ebrBLxX205x+2EVJUjEpZKEichHuFuYhp9Zx7g8JW7uNhZsfDlhjSs8E95ap1WQ0Nfd+p6KjT0dUen1WBvo2PmgLq08y9JWoaBVxcclgVxzdjNe8l8/1cYAGM6VsPVwVrliIR4Cq6lQaN7xA4acPGBck0KLCS19WlQllpl3EhIzeCTdaFPdaxtZ6P47u/zAHzaPYCapd3yIEJREHo3KEuAjwvxKRlM3SSFIsSTkcRJiIe4FZ/KS7/u59SNWNwdbVjyaiMCyxYz6bW2Vjp+6VuHLrVKka5XeGPxMVYfz//1RETuTVobSnK6ngbl3Xm+jkzuFhYs+izM6QhKZuGHh8zT6zAFtI9KrgoXrVbDp90D0Gpg3ckIdoXdeqLjhN9O5O2lx1EU6N+oHC/UK5PHkYr8pNNqmNjVWDDk98PXOSrzkMUTkMRJiAeIiE3mxRn7OBcVTwlnW5a92ojqpVxzdQxrnZbvXqxNz7ql0RsU3ll2nKUHZWy1OQk+G82m05HotBo+6R6ARiMFIYSFunEE5jwL8TeheFXo8j24eOfcr9m74N+14ONTWYCPKwMalwfg41UhpKTnrqpgYmoGr84/THxKBvXKFePjzv75EKXIb3XLFaNn3dIAjF99Gr3hQdUnhXg4SZyE+I9rMUn0mrGPS7cT8XGz5/dhjalU0vmJjqXTapj6fE36NyqHosAHK08xd094HkcsnkRKup7xa04DxlLCVbye7HsshOrCd8K8rpAcAz51YfBGqDsI3gkho98qDpcbjr5aN+O+t/JvTSNz994zlSnhbMvlO0lM33HR5NcpisL7f5wgLDqBEs62/NK3DjZWcvlkqUZ3qIqznRWnbsSy7NA1tcMRFkZ+8oX4l4u3Enhh+j6uxSRT3sOB319rTHlPx6c6ZuY6Eq809wVgwtpQpm03/Y+2yB+/bL/I1ZgkvFzseLtNJbXDEeLJnFkHC3tCWgL4BsGA1eBwfw05rQ6lXDNuuDfG0GK0cdv5TRAfqV68KnK2s87qKfpl+0Uu30406XUzdl5iw6lIrHUapvWrSwkXWRjbkhV3tmVEu8oATN18lruJaSpHJCyJJE5C3HcmIo4XZ+wjMi6FSiWc+H1YY3zc7PPk2BqNhrEdq/HW/Qv0Lzad5Zut51EUGSaghvDbiUy/n7yO7+KPo62VyhEJ8QSOLYLf+4M+Fap1gb7LwfYhPaeelaFMI+P8p+OLCzZOM9K5pjfNK3kalyBYHfLY38G7wm5lFRIY36U6dcuZNs9VmLf+jcpRpaQz95LS+WpL0e2FFbkniZMQwMnr93jp1/3cTkjD39uFpa82yvO7ihqNhhHtKjOqg7Ek6g9/h/H5xrOSPBUwRVEYtzqENL3hfll5L7VDEiL39v0Mq18HxQCB/aDnXLB6TFnsOgOM/x+dD0X0945Go2Fi1+rY6LTsCrvNhlMP7327FpPE/5Ycw6DAi/XK0Ldh2QKMVOQnK52WSd2qA7D44FVCbsSqHJGwFJI4iSLv8OUY+s48QGxyOoFl3VjyaqN8XZfj9ZYVGd/FOFzk152XGLf6NAaZoFpgNpyKZFfYbWystEzsWt28CkIY9Giu7MYnZh+aK7vBkLsJ7KIIUBT4+xPYPNb4dZP/QdefQGdCr2n17mDjDHfD4fKufA3TnFUo7sRrLf0AmLTuNPEp6Tn2SU7TM2zBEe4lpVOrtCsTu5nZ7wrx1BpW8KBb7VIoCny8OkT+DqtAb1A4EB7DkdsaDoTHWESxDkmcRJG258Jt+s86SHxqBo0quLNgaENc7fN/HZ/BTX2Z0qMGGg0s2H+F0StOWsQvDEuXkJrBpHXGghCvt/R76vlreSp0DXwXgNXC7tS7Mg2rhd3huwDjdiEADAZY/x7s+sr4dZvx0O4TMPWC3sYRavQ0Pj46P39itBCvt/SjnIcDUXGpfLs1LNtziqIwZuVJQiPi8HC0YVq/uthZF53y7UXJ2I7VcLTRcezqPVYcva52OEXKppAImn2xjX6zDzM/TEe/2Ydp9sU2s1/3UhInUWRtOxvF4LmHSE7X06JyceYMaoBTAc51ealBWb7tVRudVsPyI9d5Z9lx0vWGAjt/UfTd1vNExaVSzsOB14L81A7nH6Fr4PcBEHcz+/a4CON2SZ5ERhqsfBkOzwI00PlbaD7C9KQpU+ZwvdA1kBST52FaCjtrHZO6Gdf0mbs3nJPX72Xd+Z60/iyrjt9Ep9Xwc986lMqjua7C/JR0sePttsa5x1M2niU2OWfvo8h7m0IiGL7wKBGxKdm2R8amMHzhUbNOniRxEkXShlMRvDr/CGkZBp7xL8nMAXWxtyn4O4rdA334qXcg1joNa0/c5I1FR0nNkOFZ+eFMRBxz9l4GYGLX6uZzB9mgh02jgQf1ON7ftukDGbZXlKUlwdI+ELICtNbQcxbUG/JkxyoVCF41jAUlTi3P2zgtTFDl4nSq4Y1Bgeen7c26873wgLFEdY9AHxpV8FA5SpHfBjXxxa+4I3cS0/h263m1wyn09AaFiWtDH/UXj4lrQ812FI4kTqLIWXn0Om8uPkqGQaFrrVL83LcOtlbqXUQ/W8ObGf3rYmOlZUtoFK/OP0Jymlwk5yWDQeGjVSHoDQoda3jRskoJtUP6x5W9OXuaslEg7oZxP1H0JN+DBc/Bha1g7QB9lkLA809+PI0G6gw0Pj4yr8gWicjUrJInAOn6nO3wx5HrZn3nW+QN43xXY+/j/H2XORMRp3JEhdvB8JgcPU3/pgARsSkcDDfPHnFJnESRsujAFd5bfiKrStK3L9bGWqf+j0HrqiWZM6g+9tY6dpy/xeC5B0lMzVA7rELjj6PXOXLlLg42uqx1XMxGQlTe7icKj/gomNsZru0HO1fovwoqtn3649boCVZ2EH0abh59+uNZKL1B4Ye/wx65jznf+RZ5p1klTzrW8MKgwPjVp6XabT5RFIU9F26ZtG90/MOTKzWpf8UoRAH5bdclPvwzBEWBQU3K83mPGui05lMlqWlFT+YPNc6z2n8phv6zDsh46zxwNzGNzzecAeDdtpXxdjWz+QpOJfN2P1E43L0Ms9tD1ClwLAGDNkDZhnlzbPti4N/N+LgIF4mw9DvfIm992MkfO2stBy/HsObEo0YBiNxKyzCw4sh1Ov6wm5+CL5r0mhLO5rnQtCROokj4aVsYk9cbL55fC/JjfBd/tGaUNGWqX96dRS8bK/sdvXqPvr/tl1XNn9LUzee4m5ROlZLODGpaXu1wcirXBFxKPXofrRU4yFyLIiP6DMzuYCwb7lYOhm4Gr4C8PUdmkYhTf0BqQt4e20KYekfbXO98i7zl42bPm60qAvDp+jMPLFMvcudeUho/B1+g2RfbeG/5Cc5ExGFnpcXhEXPKNYC3qx0NfN0LLtBckMRJFGqKojB101m+2mKc8DmiXWVGd6hi1utx1CrjxpJXGuHhaEPIjThe+nW//OF+Qkev3mXpoasAfNI9wCyGZeag1UGHLx69jyEDZraGowuK/JyUQu/6YZjzLMRHQPFqMGQzuFfI+/OUa2o8bloCnP4z749vAUy9o22ud75F3nulRQXKezgQHZ/Kj9suqB2OxQq/ncjHq0Jo/Pk2vtx8juj4VEq62DKqQxX2j23DN71qocGYJP1b5tfju/ib1YigfzPDqwgh8oaiGCu3/LLd2C38UadqvNWmklknTZn8S7mwbFgjSrrYci4qnpdm7CciNlntsCxKht7Ax6uMQzOfr1PabO9eAeDo+eDtLj7Q9Ueo0AoykmHNm7DiZUiRycuF0sVgmNcVku9C6foweAO4eOfPuTSaf3qdiuhwvQa+7ni72uW4eMtk7ne+Rd6ztdIxvkt1AGbvDudCdLzKEVkORVE4cOkOr8w/TOuvt7Ng/xWS0/VU83bhm1612DWqNa+3rIibgw0dAryZ1q8OXq7Zb0p4udoxrV8dOgTk0++9PCCJkyiU9AaFMStPMfd++elPugfwcvN8uGubjyqWcOb3YY3xcbPn0u1Ees3Yx7WYJLXDshgL91/h9M04XOysGNOxqtrhPNqOqcb/6wwgo98qDpcbTka/VfDOKePFbb+V0HYCaHQQ8gfMaAE3iu6k/kIpdA0s7gXpicZEuf8qcMjnC/ZafYzDQK8fNA4PLGJ0Wg3juxiLxVjinW+RP1pVLUHbaiXJMCiMXyOFIh4nXW9g9fEbdP1pDy/+up+toVEoCrSuWoLFLzdkw1vN6FGnNDZW2VOODgHe7B7dmoVD6jGgkp6FQ+qxe3Rrs06aQBInUQhl6A2M+P04Sw9dQ6uBr16oRf9G5dQO64mU83Dk99caU97DgWsxybwwfR8XbxXN+Qi5ER2Xwtf3h2eO6lAVTydblSN6hGuH4FKwMSlq/h5KuWbccG+MUq6ZcRgfgFYLzd6FIZvAtaxx7susZ2DfzzJ0rzA4ugCWDwR9mrFoQ59lYOuU/+d1LgmVO/wTQxFkyXe+Rf4Z19kfGystey7cYWNIpNrhmKXY5HRm7LhIi6nBvL30OKduxGJrpaVPw7L8NSKI2YPq06Si5yNH+ei0Ghr6ulPXU6Ghr7tF3KSQxEkUKmkZBt5cfIzVx29ipdXwQ+9AetYtrXZYT8XHzZ7fhzWmUgknIuNSeHHGPs5GylCtR/l0wxniUzOoVdqV3g3Kqh3Oo+2839tUqzcUK//ofcs0gNd2QbWuYEiHzWNh8YuQeDvfwxT5ZM8PxiGYisHYu9hzDlgVYKKfuabTiSWQkVpw5zUjlnrnW+Sfsh4OvBbkB8DkdaEkpcnyIJmu3kliwprTNP78bz7feJaI2BQ8nWwY0a4y+8a04bPnalCxRAHc+FGJJE6i0EhJ1/PqgsNsOh2JjU7L9H516VzzMdXKLEQJFzuWvtoIf28Xbiek8dKv+zl1PVbtsMzS3gu3WX38JhoNTO5uXiXnc7h5DMK2gEYLzUeY9hp7N+g1Hzp9AzpbCNsM05tB+K58DVXkMUWBvybC1o+NXzd9B7r88E8vY0Gp2AacS0FyDJxdX7DnNiOWeOdb5K/XW/pRupg9N2NT+DlYCkUcuRLD8IVHaPlVMHP3XiYpTU+Vks5M7VmT3aNb81abSrg72qgdZr6TxEkUCompGQyec4jt525hZ61l1qB6tPUvXOveeDjZsuSVRtQu48a9pHT6zNzPkSuyvsi/pWUY+Gh1CAD9G5WjRmlXlSN6jB1fGv+v8QJ4+Jn+Oo0G6g+FV7aBZ2VjBbZ5XSD4M9DLnVGzZ9DDundh9zfGr9tOhHYTjd/XgqbVQWA/4+MiWiRCiAexs/5nwfSZO8MJv52ockQFL0NvYP3JCJ77ZQ/PT9vHxpBIDAq0qFyc+UMasOmd5vSqVwY76wK+4aMiSZyExYtNTqf/rAPsu3QHJ1sr5g9pSPNKxdUOK1+4Oliz8OWGNPB1Jz41g/6zDrL3ogzTyjRz1yUu3UrE08mG956ponY4jxZ5Cs6tBzTQfOSTHcMrAF7dfv/CV4EdX8D8rhB7Iw8DFXkqIw1WDIUjcwANdPkemr2jbkyB/YyxXAo2LrwrhADgGf+StKhcnDS9gYlri06hiPiUdH7bdYmgL7fzxuKjHLt6Dxudll71SrP5nRbMH9KAFpWLW0SV4rwmiZOwaDGJafT9bT9Hr97Dxc4qK6kozJxsrZg3uAHNK3mSlKa/39MWrXZYqrsWk8SP28IA+LBTNVztrVWO6DF23u9tqv4cFK/85MexcYRuP0OP38DGGa7sgelN4dzGvIlT5J20RFjyknHdJK01vDAH6g5SOyooVg4qtDQ+PrZQ1VCEMCcajYYJXfyx1mnYfu4Wf50p3H9rb9xL5tP1oTT5fBuT15/hxr1k3B1teKtNJfZ80JqpPWtRxctZ7TBVJYmTsFjR8Sm89Os+Qm7E4eFow9JXG1O7jJvaYRUIexsdMwfUo221EqRmGHhl/mE2ny7alX8mrg0lJd1AQ193utf2UTucR4s+A6GrjY9bvJ83x6z5AgzbAd61jesALXkJNn5QZCf8m53kuzC/O1z8G6wdjJXzqj+ndlT/yFzT6dgiGe4pxL9UKO6UtZzJpHWnSUnXqxxR3jt+7R5vLj5Ki6nBzNwVTnxqBn7FHfnsuRrs/aA1I9pVprizGVenLUCSOAmLdPNeMi/O2M/5qARKutiybFhj/Eu5qB1WgbKz1jGtX1061fAmXa/w+qKjrD5eNIdobQ2N4q8zUVhpNUzuHmD+wwd2fmX8v1oXKOmfd8f18IOhW6HRG8avD0yDWe3gzsW8O4fIvfhImNPJuF6SnRsMWG0symBOqnYCe3eIv2lM7oQQWd5sVRFvVzuuxSQzfUfh+H2qNyhsConkhel76f7zHtadjEBvUGha0YM5g+qz9d0g+jQsW6TmL5lCEidhca7cSeSF6fsIv52YVaq7MJe+fBRrnZbvX6pNjzo+6A0K7yw7zu+HrqkdVoFKTtMzYc1pAF5uXoFKJc18GMHtMAhZYXzcYlTeH9/KBjp8Br2XGS+EI04YF8w9+Xven0s8Xkw4zG4P0afByQsGbzSWlTc3VrZQu4/xsRSJECIbR1srPuxUDYBp2y9a9GL0iakZzN0TTquvtvPawiMcunwXa52GHnV8WP9WMxa93IhWVUuglcqSDySJk7AoF6IT6DVjHzfuJePr6cjy1xpTzsNR7bBUZaXT8lXPWvRpWBZFgVErTjJ/32W1wyowPwWHceNeMqVc7XirTUW1w3m8XV8DClR+Frxr5t95qnSA4XugXDNIS4CVr8Cq143zbETBiDoNszsYCy4UK29cwDgvexjzWmB/4//nNkJ8lLqxCGFmOtXwpomfB6kZBj5ZF6p2OLkWEZvMlI1nafz530xYG8rVmCRc7a15o5Ufu0e35ptetaleyswr0ZoBSZyExQi9GceLM/YRFZdK5ZJOLBvWiFJu9mqHZRa0Wg2fdg9gSFNfAMatPs2vOwvHcIJHuRCdwK87LwEwvmt1HGysVI7oMWIu/dPzE5RHc5sexaUUDFwDLccY14o6vghmBBkr+on8de0QzOkICZFQojoM2QzuvmpH9WglqkKZhqDo4cRitaMRwqxoNBomdq2OlVbDltAoiynKFHIjlneXHaf5F8FM33GRuJQMyns48Em36uwb05r321elpIud2mFaDEmchEU4fu0evWfu505iGgE+Lix9tTElnOUH/d80Gg0fd67Gm62MvS6fbTjL93+FFdryqYqiMG51COl6hdZVS/CMJazbtesb40VpxXbgU7dgzqnVQcsPYOBa40Knd8JgZhs4ONO4CKvIexe3GcvCp9yD0g1g8Hpw9lI7KtNkFok4Ol8+H0L8R6WSzgxqUh4wFiRKzTDPQhEGg8JfoVG89Os+Ov+4mz+P3SDDoNDA152ZA+qx7b2W9G9c3vxvNpohSZyE2TsYHkO/3w4Qm5xOnbJuLHq5UZFYnfpJaDQaRravwvvtjWsYffvXeb7YdK5QJk9rTtxk78U72FppmdCluvkXhLh7BU4sMT4Oyoe5TY9Tvhm8thsqdwB9KmwYCcv6Gau9ibxzehUs6gXpSeDXBgasAvtiakdlOv/uxrL2MZeMpe2FENm83bYSxZ1tCb+dyG+7wtUOJ5vkND0L91+h7Tc7eHn+YfZfikGn1dCtdinWvNmU34c1pp1/SZm/9BQkcRJmbVfYLQbMPkBCagaNK3iwYGhD81+fxwy80api1orn03dcZOLaUAyGwpM8xaWkM3n9GcBY7aish4PKEZlg97dgyDCul6NWcQBHD+i9FDpMMa4jdHYdTG8OVw+oE09hc2Qu/DEYDOlQvYexrW0sbA6mrRPUeN74+Mg8dWMRwgw521kztmNVAH7adoGb95JVjgii41L4avM5Gk/5m49WhXDpdiLOdlYMa1GBXaNa8f1LgdQs7aZ2mIWCJE7CbP0VGsXQuYdJSTfQskpx5gyuj6OtdCubamgzXz59LgCNBubuvczYP0+hLyTJ0zdbznMrPhVfT0deDaqgdjiPF3v9n4VF86OSXm5oNNBoOLy8FYr5Quw1mPOssWiFwaBubJZs97ew9m1QDFB3MDz/m7HCoSXKHK4Xulp6JIV4gO61fahfvhjJ6Xo+3XBGtTjORMQxcvkJmn0RzE/BF7iXlE4Zd3vGd/Fn35g2jOlYTeaC5zFJnIRZWnfyJq8tPEKa3kCH6l7M6F9X1hJ4An0bluOrnrXQamDpoWuM+P04GXrLvjgOuRGbVTVwUrfq2FpZwOdiz/fGXohyzaB8U7WjMSoVCMN2Qo0XjPOu/p4EC5+Tamq5pSiwdRz8NcH4dbMR0Plb49wyS1WqDpQMMA7pPLlc7WiEMDvGQhEBaDWw/mQEey7cLrBzK4rC9nPR9PvtAM9+v4s/jlwnTW+gbrliTOtbh+0jWzG4qS9OcqM5X0jiJMzOH0eu89aSY2QYFLrXLsVPfQIt4+LYTD1ftzQ/9q6DlVbD6uM3eXPxMdIyLDN5MhgUPloVgkGBzjW9aV6puNohPV585D9Dngqikl5u2LlAj5nQ7RewdoBL22F6U7jwl9qRWQaDHta+ZUyMAdp9Am3HG3v1LJlG868iEfOkSIQQD+BfyoX+jcoBMH7NadLz+aZkSrqepQev8sy3Oxk05xC7L9xGq4FONb1Z+XoTVgxvwrM1vNHJ/KV8JYmTMCsL9l9h5PITGBR4qX4Zvu5VGyudfEyfVqea3kzvVxcbnZZNpyMZtuAwKenmWQ3oUZYdvsbxa/dwsrXKmsNl9vb8YLxzX6Yh+AapHU1OGg0E9oVXdxh7GRJvwcLnjb0o+nS1ozNfGanG+UxH5xtLvXf9EZq+pXZUeadmL9DZQlQI3DymdjRCmKUR7arg4WjDhegE5u29nC/nuJ2Qyrdbz9N0yjY+WHmKsOgEnGytGNrMlx3vt+LnPnWoU9aCCtBYOLkiFWZj5s5LfLwqBIBBTcrzeY8acuckD7X1L8msQfWws9YSfO4WQ+YeIjE1Q+2wTHYnIZUpG88C8G67ypax7kTCLTg82/g4aJR590QUrwwv/wX1XzZ+vef7fxZvFdmlJsDiF41zgHQ28MK8f3poCgv7YuDfzfj46Hx1YxHCTLk6WDO6g7FQxHd/hREdl5Jnxw6LiueDFSdpMmUb3/8dxp3ENEq52vFhx2rsHdOajzv7U8bdAgojFTKSOAnVKYrCD3+HZU2wfL2lH+O7+Jt/eWkL1LxSceYNboCjjY69F+8wcPZB4lIso1fhi01niU1Op6qXMwMbl1M7HNPs+xEyko1zRvzaqB3N41nbQ6evodcCsHOFG4dhegtjiW1hlBQD87vBpWCwdoQ+v4N/V7Wjyh+ZyeCpPyAtUd1YhDBTPeuWplYZNxJSM/j8/s29J6UoCrvDbjNw9kHafbuTpYeukZZhoFZpV37sHcjOUa14pUUFXOykurBaJHESqlIUhS82neObrecBGPlMZUZ1qCpJUz5qWMGDhS83xMXOisNX7tLvtwPcS0pTO6xHOnw5ht8PXwfg0+cCLGP4ZuIdOPib8XHQaPPubfov/64wbJdx8dbUWFg+ENa9C+nql91VVVwEzOloTCjti8HANeDXSu2o8k/5ZuBeAdLiJXkW4iG0Wg2fdKuORgN/HrvBwfCYXB8jNUPP8sPXePb7XfSbdYAd52+h0UD76iX547XGrHqjKV1qlbKMv32FnHwHhGoMBoUJa04zfcdFAD7u7M+brSupHFXREFi2GEteNS4kfPJ6LC/9up/bCalqh/VAGXoDH90fwvlivTLULeeuckQm2v8LpCeCV02o3F7taHKvWDkYvMFYJQ6NccjhzNYQ/XR3VC3WnYsw+xm4dQacvWHwRihdT+2o8pdGA4H9jY9luJ4QD1WztBsv1S8LwLjVISZXr72bmMZP28Jo9kUw7/9xkrOR8TjY6BjUpDzbR7ZkRv961CvvLjeTzYgkTkIVeoPCBytPMm/fFTQaYy/C0Ga+aodVpFQv5cqyVxtR3NmWs5Hx9Jqxj8jYvBufnVfm7r3M2ch43BysGf1sVbXDMU3yXTgww/jY3Oc2PYrO2lglrv9KcCwB0aHwa0vjRXRRqrQWGWKc73XvqrEHZshmKFFN7agKRu0+oNHBtf1FN2kWwgTvt6+Cq701ZyPjWbD/CgfCYzhyW8OB8JgcayheupXAh3+eovGUv/nq/rqEJV1sGd2hKvs+aMOErtUp52Fhi2cXEVLkXRS4dL2B934/wZoTN9Fq4KsXatGjTmm1wyqSKpV05vdhjek7cz+XbiXSa8Y+Fr3c0GwmnEbGpvDt/WGcH3SoirujhSwoemCGcXhTCX+o0kntaJ6eX2sYvgf+HAYXt8Ga/xlLl3f+zljSvDC7egAWvwApsVCyhjGJdCqhdlQFx9kLKneAc+vh2AJo/6naEQlhltwdbRjZvgofrwph0tpQjKmSjvlhh/F2tWNcZ3/cHGyYtfsSf52Jznpd9VIuvNK8Ah1reGNjJf0Z5k6+Q6JApWboeWPRUdacuImVVsNPfepI0qQyX09Hfn+tMWXdHbgak0SvGfsIv20eE8E/WR9KYpqewLJu9KpXRu1wTJMSZxymB9DifdAWkl+zTiWg7wpoO8HYAxGyAmY0hxtH1Y4s/4T9ZSwEkRILZRrBoHVFK2nKlFkk4sQSYxl2IcQDeTgYb+79tz8+IjaF4YuO0nvm/qykqW21Eix5pRHr/teM7oE+kjRZCPkuiQKTnKbn1flH2BIahY2Vll8H1KVjDW+1wxJA6WIO/D6sMX7FHYmITaHXjH2cj4pXNaad52+x/mQEWg1M7h6A1lJK0x/81Xih7Vnln3LOhYVWC83ehSGbwLWssVT5rGdg709gsMxFlR8qZAUseclYFbFiO+j/J9i7qR2VOiq2BedSkHQHzm1QOxohzJLeoPDJ+tDH7tenYRn+fi+I3wbWp7Gfh8xfsjCSOIkCkZCawaA5B9lx/hb21jrmDKpP66ol1Q5L/IuXqx3LhjWmqpczt+JTeXHGPkJuxKoSS0q6nnGrjQUhBjYpT/VSrqrEkWup8bDvJ+PjFiNBq1M3nvxSpgG8tguqdQVDOmz5EJa8CIm31Y4sbxyeDX8MNb63gOfhpcVgYx7DV1WhszIukgxSJEKIhzgYHkOECfOEu9T0wa+4UwFEJPKDJE4i38Ump9N/1gEOhMfgZGvF/KENaFrRU+2wxAN4Otmy9NVG1Crtyt2kdHrP3M/Rq3cLPI5fd17i8p0kSjjbMqJd5QI//xM7NMtYGMLdD6r3UDua/GXvBr3mQ6dvQGcLYVtgWlMI36l2ZE9OUWDX18bS6yhQbyj0mAlWFjK3Lj8F9jP+fzEY7l5RNxYhzFB0vGnFlUzdT5gnSZxEvopJTKPPzP0cu3oPNwdrFr/SkPrlLaScdBHl5mDDwpcbUr98MeJTMuj/2wH2X7pTYOe/cieRn4IvAPBRZ3+cLWWhv7Qk2Puj8XHz94x36Qs7jQbqD4VXthmHJiZEwryuEPwZ6DPUji53FAW2fAR/TzJ+3eJ942LAhbXXMLeKlYcKLQEFji9SORghzE8JZ7s83U+YJ0mcRL6JjkvhxRn7OH0zDk8nG5a+2oiapd3UDkuYwNnOmnlDGtC0ogeJafqsYZb5TVGMa3ulZRhoWtGDLjUtaA7ckTmQdBvcykHNXmpHU7C8AuDV4Ptr/iiw4wuY1wVib6gdmWn0GbDmzX+GWbb/DFp/ZLll5PNLZpGIYwvBoFc3FiHMTANfd7xd7XjYbw0N4O1qRwNfuXlsySRxEvnixr1kes3YR1h0Al4umXNnCnnZ4kLGwcaKWQPr07pqCVLSDbwy7zBbTkfm6zk3n44i+NwtrHUaJnULsJxJs+nJsOd74+PmI4zrHxU1No7Q7Sd4fhbYOMPVvTC9KZzbqHZkj5aeAssHGpMBjRa6/QKN31A7KvNUtTPYF4O4G3Dhb7WjEcKs6LQaxnfxB8iRPGV+Pb6LPzpLKXQkHkgSJ5HnLt9OpNf0fVy+k0TpYvYsf62xTIS0UHbWOqb3q8uzAV6k6Q28vugoa0/czJdzJaZmMGntaQCGtfCzrM/M0QWQEAUupaFWH7WjUVeNnjBsB3jXNs73WvISbPzAPMtYp8Yb12g6uw50NtBrwT9FEEROVrZQq7fx8dF56sYihBnqEODNtH518HLNPhzPy9WOaf3q0CHAgkZRiAeSxEnkqbCoeHrN2MeNe8lU8HRk+WuNzWYxVfFkbKy0/Ng7kOcCfcgwKLy99Bh/HLme5+f5YVsYN2NTKF3MnjdaVczz4+ebjFTY853xcfN3pZAAgIcfDN0Kje733ByYBr+1hTsX1Y3r35JijPOxwneCjRP0/QOqdVY7KvOXOVzv/CaIj1I3FiHMUIcAb3aPbs3CIfUYUEnPwiH12D26tSRNhYQkTiLPnL4Zy4u/7ic6PpUqJZ1ZNqwx3q72aocl8oCVTsvXL9Sid4MyGBQYufwEC/bnXWWt81HxzNoVDsDErtWxt7GgCfnHFxmHLjl7Q+1+akdjPqxsoMNn0Od3sHeHyJMwowWcWKZ2ZMa5V7M7wM2jxtgGroEKQWpHZRlKVIPSDcCQYVwQVwiRg06roaGvO3U9FRr6usvwvEJEEieRJ45dvUvvX/cTk5hGzdKuLH21EcWdbdUOS+QhrVbDZ8/VYFCT8gB8vCqE33ZdeurjKorCR6tCyDAotPMvSZtqFrS+lz4ddn1rfNz0HbCWakk5VG4Pw/dAuWaQlgB/vgp/DofUBHXiuXPRmDTdPmdc1HXIJvCpq04sliqz1+nofGM1QiGEKCIkcRJPbf+lO/T77QBxKRnUK1eMhS83pJijDFcqjDQa4+TX4S39AJi8/gw//h32VMf889gNDobHYGetzZpYazFOLIXYq+BYAuoOVDsa8+VSytir03KssQDDicXwa0uIPFWwcUSchNntjd8zdz8YuhmKVynYGAqD6s8ZhzfGXIQre9WORgghCowkTuKp7Dh/i0FzDpKYpqeJnwfzhzbAxVLW3RFPRKPRMKp9Fd67vzDt11vPM3XTWZQnuPMcm5TOZxvOAPBWm0qULmZB8+H0GbDrK+Pjpm+BtQxLfSStDlqOhoFrjT09d8JgZhs4OLNgei2u7IO5nSHxFnjVgCGbwa1s/p+3MLJ1goDnjY+Pzlc3FiGEKECSOIkntuV0JK/MO0xKuoHWVUswe1B9HGyKwKKfAo1Gw//aVOLDjtUA+GX7RSatC8118vTVlnPcTkjDr7gjLzerkB+h5p+QP+DuZXDwgHpD1I7GcpRvBq/thsodQJ8KG0bCsn7GYg355fwWWPAcpMZC2SYwaD04Fc+/8xUFde73sIauguR7akYihBAFRhIn8UTWnLjJ8EVHSdMb6FjDi+n96mJnbUET+kWeeKVFBT7pVh2AOXsuM/bPEAwG05Knk9fvsfCAscDEJ90DsLGyoF9HBj3s/NL4uPGbxjWMhOkcPaD3UugwBbTWxnLgM1rA1f15f65Tf8DS3pCRDJXaQ78VYOea9+cpanzqQInqkJECp5arHY0QQhQIC7pSEebi98PXeHvpMfQGhR6BPvzwUqBlXfSKPNW/cXm+7FkTrQaWHLzKe8tPkKE3PPI1eoOxIISiQPfapWji51lA0eaR03/CnQvGxUAbvKJ2NJZJo4FGw+HlreBeAWKvwZyOsPMrY2KaFw7OhBUvGyvA1XgBXloENhY0HNScaTT/FIk4Mk+KRAghigS52hW5Mn/fZUb9cRJFgd4NyvLVC7Ww0snHqKh7oV4ZvnspEJ1Ww5/HbvC/JcdIy3h48rTk4FVOXo/F2daKsZ2qFWCkecBg+Ke3qdHrYOusbjyWrlQgDNsJNXqBoodtnxiH1T3NGkGKAju+NA4DRIEGr8Jzv4JO5l/mqZq9QGcLUacg4rja0QghRL6TK15hshk7LjJu9WkAhjT15bPnAtDK2gTivq61SjGtbx1sdFo2hkTy2sIjpKTn7Dm4k5DK1E1nARjZvgolnC2shPeZNXDrLNi6Gi/IxdOzdYYev0K3X8DaAcJ3wPSmcOGv3B/LYIDNYyF4svHroNHw7FTQyp+7POfgDv5djY+lSIQQogiQvyTigfQGhQPhMRy5rWH/pTt8veUcn280Xuz+r3VFPu5cDY1GkiaR3TPVvZg5sB62Vlq2nY3m5XmHSUrLyPZ5GrkihLiUDKqXcqFfo3Jqh5w7/+5tajgM7N1UDadQ0WggsC+8ugNKBhir3y18HraOM66XZQp9Bqx+A/b/Yvy6wxRoNdZ4bJE/MofrnfoD0hLVjUUIIfLZE5VAW7BgAdOnTyc8PJx9+/ZRrlw5vvvuO3x9fenWrVtexygK2KaQCCauDSUiNgXQMT/sSNZz77evwhutKqoXnDB7QZWLM3dwA4bOO8TuC7fp8uNuElIziIpLBXTAHQA61fC2vNXUz2+EqBDjGjaNhqsdTeFUvDK8/Bds+QgO/QZ7vofLe6DnLChW/uGvS0+BP4bAufWg0UG3n6F27wILu8gq1wyK+cLdcAhdDbX7qB2REELkm1z3OE2bNo0RI0bQsWNH7t27h15vHIrj5ubGd999l9fxiQK2KSSC4QuP3k+acvIrLtXDxOM19vNgwdCG2FlruXgr8X7SlN2Xm8+xKSRCheiekKLAjqnGxw1eNQ5TEvnD2h46fQ29Fhgr4N04DNObG4tyABj0aK7sxidmH5oruyHpLizqaUyadLbw4kJJmgqKVgt1+hsfy3A9IUQhl+vE6ccff2TmzJl8+OGH6HT/lJ+uV68ep04V8CrwIk/pDQoT14bysNpIGmDi2lD0JpabLhL+ewGXV9XACoHaZdxwsn10p7ZFfZ7CthonwFs7QOM31I6maPDvalzzqXQDSI2D5YNgfnf4tjpWC7tT78o0rBZ2h68qweVdYONsLDdetaPKgRcxtfoYe/mu7oNb59WORggh8k2uE6fw8HACAwNzbLe1tSUxUcY3W7KD4TEP7WkCUICI2BQOhufjQpWWJHQNfBeQ/QLuuwDjdsHB8BhuJ6Q99HmL+jwpCuz4wvi4/lBwtLDy6ZbMrSwM3gDNRhi/vhQM8f/pqTTcnwMV9D74Ni/Y+AS4eEPl9sbHR+epG4sQQuSjXCdOvr6+HD9+PMf2jRs34u/vnxcxCZVExz88aXqS/Qq10DXw+wCIu5l9e1yEcbskT4Xr83Qp2DhczMoOGv9P7WiKHp01tP4IHDwevd+BGdLrq5bMIhEnlkDGw2+YCCGEJct1cYj333+fN954g5SUFBRF4eDBgyxZsoTPP/+c3377LT9iFAXE1LLQFlc+Oq8Z9LBpNDxwUKMCaGDTB1C1E2h1D9inaCg0n6d/z22qOxicS6obT1F1ZS8k3Xn0PnE3jPtJr1PBq9gOnLwgIRLObYDq3dWOSAgh8lyuE6fBgweTkZHBqFGjSEpKok+fPvj4+PD999/z0ksv5UeMooA08HXH29WOyNiUB6YEGsDL1Y4GvkV8UvyVvTl7mrJR5AKOQvR5urzbOHdDZwNN31I7mqIrwcQFcU3dT+QtnZWxnPyur41FIiRxEkIUQk+0jtMrr7zClStXiI6OJjIykmvXrjF06NC8jk0UMJ1Ww/guxuGW/y0Snfn1+C7+lldCOq/JBZxJCs3nKXNuU50B4FJK3ViKMicTe/pM3U/kvcD71fUuboN7V9WNRQgh8sFTLYDr6elJiRIl8ioWYQY6BHgzrV8dvFyzD5/ycrVjWr86dAjwVikyMyIXcCaz+M/TlX3Gam1aa2j6jtrRFG3lmtxPXB+WaGvAxce4n1CHuy/4BgEKHFukdjRCCJHnTBqqFxgYiMbEldePHj36VAEJ9XUI8Kadvxf7LkSzZdcBnmnekMYVS5h/z0BBcSkFGi0ohkfsIxdwmSz687Tz/tym2n3ArYy6sRR1Wh10+MJYfAUN2ecY3v8sdZhSpOcVmoU6AyB8BxxbCEGj5PshhChUTEqcunfvns9hCHOj02po6OvOnTMKDX3dLeMityCkxMLSPv9Kmv57AXdf0Gi5YPgXi/w8XT9sHHKk0UHzEWpHI8C4rlOv+cbiLP+eZ+hSypg0+XdVLzZhVLUz2BeDuOtwMRgqtVU7IiGEyDMmJU7jx4/P7ziEMH/6DOMCnLfOgrM3tPzAOP/l3xdwWmvjmjLHFxt7KXTWqoUrnlJmJb1aL0Gx8qqGIv7FvytU7UTGpZ0c37WZ2s3bY1WhhdyoMBfWdlDzJTgwzbimkyROQohC5KnmOAlRZCiK8S73xW1g7QC9l0LdQfBOCBn9VnG43HAy+q2C4XvB1gWu7Yet49SOWjypm8chbLNxSGbz99SORvyXVodSrhk33BujlGsmSZO5qXO/SMS5DZAQrW4sQgiRh3KdOOn1er766isaNGiAl5cX7u7u2f4JUSgdmAGHfgM00GMmlKpt3P7fC7jileG56cbn9v8CISvVilg8jZ1fGv8P6AkefurGIoSlKVkdfOqBIcO4IK4QQhQSuU6cJk6cyDfffEOvXr2IjY1lxIgR9OjRA61Wy4QJE3J1rAkTJqDRaLL98/Lyeuj+ERER9OnThypVqqDVannnnXdyG74QuXd+M2weY3zcbhJU6/zo/at2gmbvGh+v+R/cOpe/8Ym8FRkCZ9cBGmgxUu1ohLBMdQYY/z8639hjL4QQhUCuE6dFixYxc+ZMRo4ciZWVFb179+a3335j3Lhx7N+/P9cBVK9enYiIiKx/p06deui+qampFC9enA8//JBatWrl+lxC5FpkCPwxxFgMos4AaPI/017X6iMo3xzSEmBZf0hNyN84Rd7J7G2q/hwUr6JuLEJYqoDnwcYJ7lwwLiAthBCFQK4Tp8jISGrUqAGAk5MTsbGxAHTu3Jn169fnOgArKyu8vLyy/hUvXvyh+5YvX57vv/+eAQMG4OrqmutzCZEr8ZGw+EVj8uPbAjp9AyaW5UdnBT3nGItI3D5n7HmSu67mL/oshK42PpbeJiGenK0TBPQwPj46X91YhBAij5hUVe/fSpcuTUREBGXLlqVixYps2bKFOnXqcOjQIWxtbXMdQFhYGKVKlcLW1paGDRvy2WefUaFChVwf52FSU1NJTU3N+jouLg6A9PR00tPT8+w8hVFm+xTJdkpPQrf4JbRx11E8KpLx3GwwYKyY999dH9ZOtm5oesxCt6ArmtMr0Zeqi6HBsAII3jxZwudJt2MqWhQMVTqjd68MKsRqCe1kDqSdTKNmO2lq9sXq6HyU06vIaDsZ7Mz7hqd8pkwj7WQaaSfTmEM75ebcGkXJ3W3wDz74ABcXF8aOHcsff/xB7969KV++PFevXuXdd99lypQpJh9r48aNJCUlUblyZaKiopg8eTJnz57l9OnTeHh4PPK1LVu2pHbt2nz33XeP3G/ChAlMnDgxx/bFixfj4OBgcqyiCFEM1Lv8Mz73DpGmc2RnlQkk2pZ84sNViN5CjRsLMaBjT6UxxDhVzsNgRV5xTImgzZkP0KCwvcokYh3Kqx2SEJZNUWh19kNcUq5zovRALhdvo3ZEQgiRQ1JSEn369CE2NhYXF5dH7pvrxOm/Dhw4wJ49e6hYsSJduz7d4oOJiYn4+fkxatQoRox49IKTpiZOD+pxKlOmDBEREY9Nzoq69PR0tm7dSrt27bC2LjrrEWmDP0W391sUrTX6vitRyjZ+5P6PbSdFQbfqVbShf6I4eZExdBs4lcin6M2XuX+edGvfRHtyKYZK7dH3WqRaHObeTuZC2sk0areT9uAMdFs/RPGqafzdZ8bUbitLIe1kGmkn05hDO8XFxeHp6WlS4pTroXr/1bBhQxo2bPi0hwHA0dGRGjVqEBYWlifHA7C1tX3gEEJra2v5IJuoSLXVsUWw91sANF1/xMqvhckvfWQ7dfsJbp1Bc+ss1quHQf9VxnlQRZBZfp5iLsGp5QBoW45GawbxmWU7mSFpJ9Oo1k6BfWDbRDSRJ7G+dfqfpRzMmHymTCPtZBppJ9Oo2U65OW+ui0N8/vnnzJ49O8f22bNn88UXX+T2cNmkpqZy5swZvL29n+o4QjyRy7th7dvGx81HQu3eeXdsWyfotcBYZeryLtj2Sd4dWzy9Xd+AooeKbcGnrtrRCFF4OLhDtS7Gx1IkQghh4XKdOM2YMYOqVavm2F69enWmT5+eq2ONHDmSHTt2EB4ezoEDB+jZsydxcXEMHDgQgDFjxjBgwIBsrzl+/DjHjx8nISGBW7ducfz4cUJDQ3P7NoTI7s5FWNbPWPzBvzu0+jDvz1G8srHnCf7f3n1HV1XlbRz/3hSSUAISWkIJBOm9iIaO9CqogHRBHHnFEWQYEXEEZgDb2B1AVKrSFERB6SJFpIPSRHqT3gIEQsp5/9gQjARyA0n2TfJ81srKvifn3vNkE5L7O/ucveGn92DnvJQ/hiTf+UM3F+msN8huFpGM6MaaTlu/hGuRdrOIiNyDZF8rdPz48URHhPLmzcuxY8eS9VpHjhyhU6dOnD59mrx58/LQQw+xZs0aQkNDAbPg7aFDhxI8p0qVKvHtjRs3MnXqVEJDQzlw4EByvxURI/IsTO0AV86Z0YZ2Y8Er2ecU3FOuHRxeD2v+B3P+D/KVgaDiqXMscc+qdyEuBorVg8I1bKcRyXiK1oX7isK5A2a6/5QczRcRSUPJLpwKFy7MTz/9RLFixRJs/+mnnwgJCUnWa02fPv2OX584ceIt2+5xLguRhGKuwczuZpHGnIXhiWngG5C6x2w8HP7YZBaFnNENei+GLNlS95iSuAtHYfPnpq3RJpHU4eUFVbqZS5Q3TVbhJCLpVrJPq/fu3Zv+/fszYcIEDh48yMGDBxk/fjwvvPACTz/9dGpkFEkdjgPfvWDuOcqSAzrPgBx3P+2427x9of1EyJYPTm6HeS9ocVxbfnofYq9BaC0oWst2GpGMq3IXcHnBodVwOuUmgBIRSUvJHnF68cUXOXv2LM8++yzXrl0DwN/fn0GDBjF48OAUDyiSan5634w2uLyg/QTIXy7tjp2jgCmeJrWGX2dA4QfhgafS7vgCF4/DpkmmXe9Fu1lEMrrAYCjRFH6fb0admmiCHBFJf5I94uRyuXjjjTc4deoUa9as4ZdffuHs2bO8+uqrqZFPJHXsnAtLhpl2s9ehROO0z1C0FjS6nmHBS3BkY9pnyMxWfwgxV6FQDXN/k4ikrhuTRPwyzVwmLSKSztz1HfDZs2fngQceoEiRIsyfP5+dO3emZC6R1PPHZpj1NODAA0/Dg8/Yy1Lz72aq3tjr91pdPmMvS2Zy6RSs/8y06w0Cl8tuHpHMoEQTyF4ALp8yI08iIulMsgunDh068NFHZkrlK1euUL16dTp06EDFihWZNWtWigcUSVEXjsLUJyDmilmzp9nrdvO4XPDIaMhdHCKOwKynIC7WbqbM4OePzM9ASFW4v6HtNCKZg7cPVO5s2lrTSUTSoWQXTitWrKBOnToAfP311ziOw/nz5/nggw8YMWJEigcUSTFRl2BaR7h0HPKVhccnmD/ktvkHQsfPwTcr7FsGP1ou5jK6yLOw7hPTrveiRptE0lKVrubznqVw/rDdLCIiyZTswunChQvkzp0bgAULFvDYY4+RNWtWWrZsye7dmilHPFRcLMzqDce3Qra8ZgY9/0DbqW7KXxZav2/aK96E3xfazZORrRkN0ZehQAUo2cx2GpHMJag4FK0DOLDlC9tpRESSJdmFU+HChfn555+5fPkyCxYsoEmTJgCcO3cOf3//FA8okiIWv2quqff2M2s15SpiO9GtKnYw91wBzH7aLBYpKevKeVj7sWnX1WiTiBXVnjSfN3+uS5NFJF1JduHUv39/unTpQqFChQgJCaF+/fqAuYSvQoUKKZ1P5N5tGG/uaQFoNwYKP2A3z500HQkFq8PVC2Zx3OirthNlLGs/hqgIc6lm6Va204hkTqVbgX8uuHDYXJ4sIpJOJLtwevbZZ1mzZg3jx49n1apVeHmZlwgLC9M9TuJ59v4A3w007QavQPnH7OZJio8fdJgEWYPg+K/w/UDbiTKOqxGw5n+mXfef4HXXk4qKyL3w9YdKT5i2JokQkXQkWe8coqOjCQsLIyAggHbt2pE9e/b4r7Vs2ZJatWqleECRu3ZqF8x8EpxYqPgE1E0nRUjOQvDYZ2Zh3s1T9MYipawbZ0by8pSEso/YTiOSuVXpZj7/9r1ZHkBEJB1IVuHk6+tLVFQULt0XIJ7u8mn4oj1EXYAi4dDmg/R1P0vxBtBgiGl/NxD+2GI1TroXdQl+/vNok7fdPCKZXYHyULAaxEWbBXFFRNKBZF+r8ve//5033niDmJiY1Mgjcu9iomB6Fzh/EO4rCh2/MJfApTe1B0DJ5hAbBTO7mWm05e5s+AyunIXcYVDuUdtpRASganfzedNkcBy7WURE3JDsRWzWrl3L0qVLWbRoERUqVCBbtmwJvj579uwUCyeSbI4D3zwHh9eAX07o/CVkC7Kd6u54eZnJLMbVNzPsfd0HOk3XvTnJdS0SVn9o2nUGesbaXSJi7jld8DKc2Q2H1kBouO1EIiJ3lOx3YLly5eKxxx6jadOmhISEkDNnzgQfIlYtfxO2zgQvH+g4GfKWtJ3o3gTcBx2mgI8/7F4IK9+2nSj92TgRLp8yU9BX7GA7jYjc4JcDyrczbd3LKSLpQLJPvU6YMCE1cojcu61fwY+jTLvl2xBW32qcFBNc0Xw/3/SFZSOhUDUo/rDtVOlD9FX46frCwnX+Ad6+dvOISEJVnzTrOW3/Gpq/Dv46ASsinkvX/EjGcHgdzHnWtMOfu7nAYkZRpStU7QE48NVTcP6w7UTpw+YpcOk4BBaCSp1tpxGRvypUHfKWgZgr5uSXiIgHc2vEqUqVKm7PpLdp06Z7CiSSbOcOwLROZhKFUi2g8b9tJ0odzd+EY7/AsS3wZQ/oOT99TnqRVmKiYNW7pl27P/hksRpHRBLhcplJIhYONpfrPfCU7UQiIrflVuHUtm3bVI4hcpeuXoCpHSHyNBSoCI9+knGnmvb1hw6T4eO6cHQjLBgMrd6xncpzbZkKEUchR/DNNWNExPNU7AhLhpqTQsd+geBKthOJiCTKrcJp6NChqZ1DJPliY+DLJ+HUb+bNcecZ4Jc9yaela/eFwmOfmjWqNnwGhWtApSdsp/I8sdGw6npRWaufKTpFxDNlC4LSrWD7bDPq1FKT4IiIZ9I9TpI+OQ4sGAR7fwDfrGaa7sAQ26nSRonGUO9F057bH05stxrHI/06A84fgmz5rt8bJiIe7caaTr9+aZYQEBHxQCqcJH1a+zGs/xRwmcvzQirbTpS26g2C4g3NDdUzuppLFsWIjYEV/zXtmn+HLFnt5hGRpBWrZ5YMiLoAO7+1nUZEJFEqnCT9+X2huZEYoPFwKNPKbh4bvLzNJXs5C8PZfWZGQcexncozbJsF5/ZD1iCo3st2GhFxh5cXVLk+6qQ1nUTEQ6lwkvTl+Db4qhc4cebSjprP205kT9bc0GESeGeB3+bdXK8oM4uLhRVvmXb4cxn/njeRjKRKF3B5wcGf4PQe22lERG6hwknSj4vHzQx61y5BsbrQ8h0zlW1mVrAaNH/DtJcOh/0r7eaxbcccOLMb/HNBjadtpxGR5AgMgRJNTHuzRp1ExPO4Naven8XGxjJx4kSWLl3KyZMniYuLS/D1H374IcXCicS7FmnWaoo4AkElzLTc3r62U3mGaj3NAsC/TIOvesIzKyEw2HaqtBcXB8tvjDb1Bb8cdvOISPJV7Q6/LzDLCTz8L/2eFxGPkuzCqV+/fkycOJGWLVtSvnx5txfGFblrcXEwpw/8sQkC7jPTjgfcZzuV53C5zOjb8a1wYpuZov3JeZnvDcdvc+HUTvALhBp/s51GRO5GiSaQPT9cOmEKqDKtbScSEYmX7MJp+vTpzJw5kxYtWqRGHpFbLRsBO74BL1/o+AUEFbedyPNkyWpG4cY1gMNrYPGr0Ow126nSjuPcHG16sA8E5LIaR0TukrcvVO4Mq941k0SocPIscbG4Dq6i4NmfcR0MhLC6GXfReZFEJPsepyxZsnD//fenRhaRW23+AlZeXwyxzYdQtJbdPJ4sqDi0G2Paa0bDttl286SlXfPhxFbIkh0e+j/baUTkXlTpZj7vWQIXjtjNIjft+BbeK4/P522pfnAMPp+3hffKm+0imUSyC6d//OMfvP/++zia+lhS24FVMLefadcZCJU72c2THpRuCbVfMO1vnoNTu+zmSQuOAyveNO0aT5vZBkUk/QoqDkXrmNlTN39hO42AKY5mdoeIPxJujzhmtqt4kkwi2ZfqrVq1imXLljF//nzKlSuHr2/C+yhmz85EZ7kl9ZzZaxZ2jYuGsm2hwRDbidKPBq/AkQ1wYKXpw6d/yNgTJexZAn9sBt+sZgpyEUn/qnY3v8M2T4G6A3U5mE1xsbBgEJDYCXMHcMGCl8yJO/07SQaX7BGnXLly0a5dO+rVq0eePHnImTNngg+RexZ5FqZ2gCvnzHTb7caaxRHFPd4+8PgEyBEMp3+Hb/+ecRfHdRxYfn069uq9IFseu3lEJGWUaWOWFbhwGPb9aDtN5nZw9a0jTQk4EHHU7CeSwSV7xGnChAmpkUPEiLlmhv3P7IGcheGJaeAbYDtV+pM9L7SfBBNbwPavofCDGfPen30/wpH14OOfuRdDFslofP2hYkdY97GZJOL+hrYTZV6XTqTsfiLpmE7ji+dwHPjuBXN5RpYcZtrxHPltp0q/ijwITUeZ9qJX4NAau3lSw/Lr9zZVe1I/KyIZTdXu5vNv38Hl03azZGbZ87m5n34HS8aX7BEngK+++oqZM2dy6NAhrl27luBrmzZtSpFgkgn99D5s/hxcXtB+AuQvZztR+lfjb3B4LWybBTN7QJ+V7v8R9HQHVsGh1eCdBWr1s51GRFJagfIQUtWs4ffLdKipexjTnOOYWUvvyAWBIRBaM00iidiU7BGnDz74gJ49e5IvXz42b95MjRo1CAoKYt++fTRv3jw1MkpmsHMuLBlm2s1ehxKNrcbJMFwuaP0B5C0Nl47DV70gNsZ2qpRx496mKt3MH20RyXhujDptmpxx79X0VHGx5h7ZNaP/tNGV+L7NXtfEEJIpJLtwGj16NOPGjeOjjz4iS5YsvPjiiyxevJjnn3+eCxcupEZGyej+2AyzngYceOBpePAZ24kyFr/s0GGKWePowEr44d+2E927Q2tg/wqzKPKN6ddFJOMp/5iZMfP0LjN6Lmkj5po50bZ5irkK5JH/mb8jgcG37luxPZRtk/YZRSxIduF06NAhatY0w7EBAQFcvHgRgG7dujFt2rSUTScZ34WjMPUJiLkC9zcyZ60k5eUtaf7wgbkkcudcu3nu1Y17myp3hlyF7WYRkdTjHwjlHjXtTZPtZsksrkXC9E6wY445OdV+IlTpaoqj/tuI6TqHDaH/R2zN/mb/3+bDpVMWA4uknWQXTgUKFODMmTMAhIaGsmaNueF8//79WhRXkifqEkzraC4hy1fWTKHtfVe33Yk7yrW9uc7RnGfNWlnp0ZGNsHcpuLw12iSSGdy4XG/713BVV7akqqsX4PNHzfp4PgHQeTqUfeTm1728cUJrczR3OHH1X4bgynDtIvw4ylpkkbSU7MLp4YcfZu5cc7b6qaee4oUXXqBx48Z07NiRdu3apXhAyaDiYmFWbzi+FbLlNTPo+QfaTpXxNRoGRcIhKsIsjnvtsu1Eybfi+mhTpScgdzG7WUQk9RWuAXlKQXSkmehGUsfl0zCpNRz6GfwCodvX5kqQ23F53Zy5deNEOLkzTWKK2JTswmncuHEMGTIEgD59+jBx4kTKlCnD8OHDGTNmTIoHlAxq8avw+3zw9jNrNeUqYjtR5uB9/bKLbPng5A6Y90L6uuH6jy3w+wLzB7vOP2ynEZG04HJBtR6mrcv1UseFozChORz7BbLmgSfnQWh40s8rWgvKtAYnzix7IZLBJbtw8vLywsfn5uVUHTp04IMPPuD5558nS5YsKRpOMqgN4+Hnj0y73Rgo/IDdPJlNjgKmeHJ5w68zYMNnthO5b8Vb5nP5xyGouN0sIpJ2Kj5h7rf5YzMc+9V2mozlzF4Y3wxO/w6BBaHXAgiu5P7zGw03/zZ7lsDuJamXU8QD3NUCuCtXrqRr166Eh4dz9OhRAKZMmcKqVatSNJxkQHt/gO8GmnaDIWbGJEl7RWuZy/YA5r8ERzZYjeOW49vgt3mAC+oOtJ1GRNJStiAo08q0N0+xmyUjObHdFE0XDkHu4qZoylMiea8RVPzmbLiLXsk4S16IJCLZhdOsWbNo2rQpAQEBbN68maioKAAuXrzIqFG6OVDu4NQumPkkOLFQsSPU/aftRJlbzb+bSyziomFmd3N9uydb+V/zuVxbyFvKahQRseDGJBG/zoDoK3azZASH18OEFnD5JOQvb4qmu71svu5ACLgPTu2EzbqcUjKuZBdOI0aMYOzYsXzyySf4+vrGb69ZsyabNm1K0XCSgVw+DV+0h6gLZnKCNh+a69bFHpcLHhltzjJGHDWTdcTF2k6VuFO7YPsc01bBLZI5FasPOYuYmd92fGs7Tfq270eY/AhcPQ+Faph7mrLnu/vXC7gP6g827R9GwtWIlEgp4nGSXTjt2rWLunXr3rI9MDCQ8+fPp0QmyWhiomB6Fzh/EO4rCh2/AB8/26kEzEyGHT83C0zuWwY/vmY7UeJW/BdwoHQryF/OdhoRscHLC6p2M21NEnH3fvvOnMiMvgxh9c3seQH33fvrVu8FQfdD5GlY9c69v56IB0p24RQcHMyePXtu2b5q1SrCwsJSJJRkII4D3zwHh9eAX07o/KW5Vl08R/6y0Pp9017xFvy+0G6evzq9B7Z9Zdr1XrSbRUTsqtzZzKp5cJX53SDJ88t0mNENYq+ZE1GdZ4Jf9pR5bW9faDLCtH8eDecOpszriniQZBdOzzzzDP369WPt2rW4XC7++OMPvvjiCwYOHMizzz6bGhklPVv+JmydaWZw6zAJ8pa0nUgSU7EDPPC0ac9+Gs7ut5vnz1a+baa6LdkseTM9iUjGk7PQzbWFNElE8qz7BL5+xtxnXKkTtJ+U8ld/lGwGxepCbBQsHZ6yry3iAZJdOL344ou0bduWBg0acOnSJerWrUvv3r155plneO6551Ijo6RXW7+6uZp4q3egeAO7eeTOmo6CgtXN/QMzu3vGzddn95sbwQHqarRJRICq19d02jIVYqPtZkkPHMdc7vz99dlIazxj7m/19rnz8+6GywVNRgIus1jx4XUpfwwRi+5qOvKRI0dy+vRp1q1bx5o1azh16hT/+c9/UjqbpGeH18Gc6yOQ4c9BtSetxhE3+GQxo4JZg+D4r/C9B0zCsOodc3a0eEMoVM12GhHxBCWbmkW8L5/0vEuLPY3jwJKh8MP192h1X4Tmb5j7xVJLcEWo0sW0F76cvhZZF0nCXf/PyZo1K9WrV6dGjRpkz55C18dKxnDuAEzrZIbqS7WAxv+2nUjclbMQPPaZuYdg8xS7N2CfPwRbppl2vUH2coiIZ/H2Nfc6gSaJuJO4WJjXH366fg9rkxHw8JC0mdG2wSvgmw2OrIfts1P/eCJpxO1x2l69erm13/jx4+86jGQAVy/A1I5mVp0CFeDRT8DL23YqSY7iDczixD/8xyxWXKAihFRO+xyr3jNrTBWrB0UeTPvji4jnqtodfnoP9iyGC0chZ0HbiTxLbLS5n2nbLMBlJgCq1iPtjh8YDLX7w7KRsHgYlGoJvv5pd3yRVOL2iNPEiRNZtmwZ58+f59y5c7f9kEwsNga+fBJO/QY5gqHTjJSbrUfSVu0BULK5GTWc2Q0iz6bt8SP+uHnjt2bSE5G/CioOobXNxDFbvrCdxrNEXzFLgGybBV6+8Pj4tC2abgh/DnKEwIVDsHZM2h9fJBW4PeLUp08fpk+fzr59++jVqxddu3Yld+7cqZlN0hPHgQWDYO8PZk2gTtN0BjA98/KCdmNgXH1z6eXXz5hCODWvi/+zn9430+WG1oKitdPmmCKSvlTtbqYl3zQF6gxMu99PnuxqBEx7Ag7+BD4B0HEKlGhsJ0uWrNBoqPn7seJtqNwVsue1k0Ukhbj9W2b06NEcO3aMQYMGMXfuXAoXLkyHDh1YuHAhjm78k7Ufw/pPAZe5PC+kiu1Ecq8C7oMOU8DHH3YvgpX/TZvjXjwBGyeadl0PmKBCRDxT2TZmfcALh2D/j7bT2Hf5DExqbYomv0DoNtte0XRDhQ4QXBmuXbw5y65IOpas0zN+fn506tSJxYsXs2PHDsqVK8ezzz5LaGgoly5dSq2M4ul+XwgLB5t24+FQppXdPJJygitCy7dNe9ko2LM09Y+5+gOIuQqFaphV7UVEEuMbYNagA00SEfEHTGgOx7aYmVF7zIXQmrZTmVHAptcLpo0T4eROq3FE7tVdj2u7XC5cLheO4xAXF5eSmSQ9Ob4NvuplrjOv0g1qPm87kaS0Kl2vr5viwKzecP5w6h3r8mnYcH2CmXqD0mb2JxFJv27cu7NznhlxyYzO7ofxzeD0LnNPUc/5dib0uZ2itaBMa/M+YdErttOI3JNkFU5RUVFMmzaNxo0bU6pUKbZu3cpHH33EoUOHNCV5ZnTxuJlB79olKFoHWr6jN7oZVfM3zeUWV86axXFjolLnOD9/BNGR5lLP+xumzjFEJOMoUMH8voiLhl+n206T9k7sMEXT+YNwXzHotQDylrKd6laNhpuJKvYsgd1LbKcRuWtuF07PPvsswcHBvPHGG7Rq1YojR47w5Zdf0qJFC7x0Q2bmcy3SrNUUcQSCSpgbUH2y2E4lqcXXHzpMBv9c8McmWDA45Y8ReRbWfWLaGm0SEXdV7W4+b5qcuRZbPbIRJraAS8chX1lTNN0XajtV4oKKw4PPmPaiV8wsvCLpkNuz6o0dO5YiRYpQrFgxli9fzvLlyxPdb/ZsLXSW4cXFwZw+5g10wH3QeYb5LBnbfaHw2KfwRXvY8BkUrgGVnki5118zxoxeFqgAJZul3OuKSMZW/nFYOMQshXFkvfndlNHtX2FOXl67BAWrQ5cvIauHz3Rcd6CZOv7UTtg8Gaq7tz6oiCdxe6ioe/fuNGjQgFy5cpEzZ87bfkgmsGwE7PjGDLt3/MKcSZLMoUTjm+sqze1v7nFLCVfOw9qxpl33RY02iYj7/AOhXDvT3jTJbpa0sGs+fP64KZqK1YXu33h+0QTmBGv961cr/DDSTJ0uks64PeI0ceLEVIwh6cbmL2Dl9VnW2nxobvqUzKXeIDiyAfYuNYvj/u1H8L/HkyZrP4aoCHO5SWnNyigiyVS1uxnN2DYbmr5miqmM6NcvzbpITiyUamkWt/X1t53KfdV7wbpxcGYPrHoHGg2znUgkWXRzkrjvwCqY28+06/wDKneym0fs8PI2l+zlLAxn98GcZ+/tvoKrEbBmtGnX1SKWInIXCj8IeUqayWW2zbKdJnWs/xRmP22KpoodocOk9FU0AXj7QpMRpv3zaDh30G4ekWTSOxRxz5m9MKOrmbmobFtooClFM7Wsuc0fbe8s8Ns8+On9u3+t9Z/A1fPmTU/ZtimVUEQyE5cr4SQRGc3Kd+C7fwAOPNAb2o41RUh6VLIZFKsHsVGwdLjtNCLJosJJEhcXi+vgKgqe/RnX7wvMhABXzkHBatBurEYFxPwsNH/DtJcONzcrJ1fUJfj5f6ZdZ6AZzRIRuRuVOpl7b//YBMe32k6TMhwHFg+9WWDU+Qe0+G/6/hvsckHTkYDLjA4eXmc7kYjb0vH/PEk1O76F98rj83lbqh8cg8+XXeHsXrMa+RPTzGrtIgDVepo3K06cWQg54o/kPX/DeIg8A7nDoPxjqZNRRDKHbHmgdEvT3jTFbpaUEBcH3w2An94zjxsNh4avZozJcwpUMIurAyx8OXNNIy/pmgonSWjHt2aB08TeAEeegcNr0z6TeC6Xyyx8nL88XD4FXz4JMdfce+61SFj9oWnX+Qd4uz1XjYhI4m5crvfrdIi+YjfLvYiNhq//Zk4u4YJW70Ht/pZDpbCHXwHfbGYK+e1aykbSBxVOclNcLCwYBNzuzI8LFrxk9hO5IUtWsziuX05TWC9+1b3nbZoEl09CriLmRmcRkXsV1gByFoGrF2DnPNtp7k70VZjRDbZ+CV4+ZjKe6j1tp0p5OQpA7RdMe/Ew832LeDgVTnLTwdVJXGrlQMRRs5/InwUVh3ZjTHvtmKRntYq+CqveM+3aA9LvTc4i4lm8vG5eApYe13SKughfPA6/zwcff3hiKlR43Haq1BPeFwILwoVD5m+HiIdT4SQ3XTqRsvtJ5lK65c2zh9/8HU7+dvt9N0+BS8chsBBU7pw2+UQkc6jSBXDBgZVmRtj0IvIsTGpjcmfJAV1nQcmmtlOlrixZoeFQ017xNlw6ZTePSBJUOMlN2fOn7H6S+TR4BYrWgejLZnHcqIu37hN77U+jTf3Bxy8tE4pIRpezENzfyLQ3p5NJIiKOwYQWZkbAgNzQ41soWtt2qrRRoT2EVIFrF2HZSNtpRO5IhZPcFFoTAkOA283Y4zJD6qE10zKVpCfePvD4BMgRDKd/h2+eu2W2JNev0yHiCGQvAFW6WQoqIhnajUkitkw1Ey14snMHYEIzOLXT/F7sOR8KVrWdKu14eUHTUaa9aRKc2GE3j8gdqHCSm7y8odn1dXluKZ6uP272utbakTvLnhfaTzI3Ne+YA2vGxK8LVujsKryXv272q90//a16LyLpQ8lmkC2vubR89yLbaW7v5G8wvpkpnu4rCr0WQL7StlOlvdCaUKaNWdpi0Su204jclgonSahsGzNDWmBwwu2BIWZ72TZ2ckn6UuTBm2cQFw2B/5bE5/O2VDs4Dtflk+Dygqx57GYUkYzLJ8vN+yc3Tbab5XaOboIJzeHiMchbBnougNzFbKeyp/Fws4Dx3qWwe4ntNCKJUuEktyrbBvpvI6brHDaE/h8xXedA/60qmiR5avwNCoebM4iRpxN+zYmD2U+bdcNERFJDleuX6+1elPzFuVPbgVVmIogrZyGkKvT8/tYTlplN7jB48BnTXjQEYmPs5hFJhAonSZyXN05obY7mDscJra3L8yT5nDg4f+DO+2hdMBFJLXnuh9Ba5nfRli9sp7np94Xw+WNmMoSidcxEEFlz207lGer+00yOceq39DmdvGR4KpxEJHUcXG0uQbktrQsmIqnsxiQRm6ZAXJzdLABbv4LpnSHmKpRsDl2+BL8ctlN5joBcUH+waS8bZRYyFvEgKpxEJHVoXTARsa1MG/DLCecPwv7ldrNsmACzekNcjJmCu+MU8A2wm8kTVe8JQSXMJd4r37GdRiQBFU4ikjq0LpiI2JYlK1Rsb9o2J4n46X2Y1x9woHovaDcOvH3t5fFk3r7QZIRprxltZhwU8RAqnEQkdWhdMBHxBDcu1/ttHlw+k7bHdhxY+m9Y/Kp5XKs/tHzHrF0kt1eyKRSrZxZMXzLcdhqRePqfKyKpQ+uCiYgnCK5kPmKvwa8z0u64cXHw/UBY+bZ53HComXLbdbuTSRLP5YKmIwEXbJ8Nh9fZTiQCqHASkdSkdcFExBNU7WE+b5psRoFSW2w0zOkD6z8FXNDybagzIPWPm5EUqABVupr2gsFp8+8mkgQVTiKSurQumIjYVuFx8AmAUzvhyIbUPVb0VZjZw4xuubzh0XHwQO/UPWZG9fAr4JsNjm6AbbNspxFR4SQiaUDrgomITf45oVw7007N9YGiLsHU9rDrO/D2gye+gIodUu94GV2OAlD7BdNeMgyir1iNI6LCSURERDK+G5NEbJsNURdT/vUjz8LkR2D/CsiSHbp+BaWap/xxMpvwvmYioQuHYc0Y22kkk1PhJCIiIhlfkYfM+kDRl1P+sq+LJ2BiK3NJWcB90P1bKFY3ZY+RWWXJaibWALOu06WTdvNIpqbCSURERDI+l+vmqFNKrul07iCMbwont0P2AvDk91CoWsq9vpgFg0OqwLWLsGyU7TSSialwEhERkcyhUifw8oGjG+H4tnt/vVO7YHwzOLcfcoVCr/mQv+y9v64k5OUFTa8XTJsmwYkddvNIpqXCSURERDKH7HmhVAvT3jzl3l7rjy0woTlc/APylIJeCyB32D1HlNsIrQll2oATB4tesZ1GMikVTiIiIpJ53FjT6ZfpZurwu3FwNUxqDZFnILgy9Jxv1qeT1NV4OHj5wt6lsHuJ7TSSCVktnIYNG4bL5UrwUaBAgTs+Z/ny5VSrVg1/f3/CwsIYO3ZsGqUVERGRdK94A8hZGK6eh9/mJf/5uxfDlHYQFQGhtaDHXMgWlOIxJRG5w+DBZ0x70RCIjbGbRzId6yNO5cqV49ixY/EfW7duve2++/fvp0WLFtSpU4fNmzfz8ssv8/zzzzNrlhZFExERETd4eUOVrqad3DWdts2GaU9AzFUo0QS6zgL/wJTPKLdX958QkBtO/Za6a3KJJMJ64eTj40OBAgXiP/LmzXvbfceOHUuRIkV47733KFOmDL1796ZXr17897//TcPEIiIikq5V7gK4zJpLZ/e595xNk2HWUxAXA+UehY5fgG9AqsaURATkgvqDTXvZKLh6wWocyVx8bAfYvXs3ISEh+Pn58eCDDzJq1CjCwhK/ufLnn3+mSZMmCbY1bdqUzz77jOjoaHx9fW95TlRUFFFRUfGPIyIiAIiOjiY6OjoFv5OM50b/qJ/uTP3kHvWTe9RP7lE/uUf9dBvZCuAd9jBe+5YSu2EycQ2G3LGvvNaOxnvJqwDEVulOXLO3wHFBJuxXj/iZqtQVn3Uf4zqzh9jl/yXu4VftZbkNj+indMAT+ik5x3Y5juOkYpY7mj9/PpGRkZQsWZITJ04wYsQIfvvtN7Zv305Q0K3XC5csWZInn3ySl19+OX7b6tWrqVWrFn/88QfBwcG3PGfYsGEMHz78lu1Tp04la9asKfsNiYiISLoQfH49NfZ/yFWfXCwq/y6Oy/vWnRyH0sdnU+r4NwDszteCHSEdzZpQYlX+C5t5aN+7xLp8+KHMG0T63f6KJZE7iYyMpHPnzly4cIHAwDtfemt1xKl58+bx7QoVKhAeHk7x4sWZNGkSAwYMSPQ5rr/8srpR9/11+w2DBw9O8FoREREULlyYBg0aJFqcyU3R0dEsXryYxo0bJzqaJ4b6yT3qJ/eon9yjfnKP+ukOYhvhfDAN/8jTtCjhw7VijRL2lROH16IheF8vmmLrv0LRmv0omsmLJo/5mXKaEzd1I94HVtCQlcS2+NRelkR4TD95OE/opxtXo7nD+qV6f5YtWzYqVKjA7t27E/16gQIFOH78eIJtJ0+exMfH57ZFkJ+fH35+frds9/X11Q+ym9RX7lE/uUf95B71k3vUT+5RPyXC1xcqd4LVH+Lzy1Scks2vb/bF18sF3/aDX6aZfVv8F+8aT5PImFSm5RE/U81Gwdg6eO2Yg1d4Xyhcw26eRHhEP6UDNvspOce1PjnEn0VFRbFz585EL7kDCA8PZ/HixQm2LVq0iOrVq+uHUkRERJKnSnfz+fcFuHZ8S8GzP+PatwxmdjdFk8sb2n0MNZ62m1MSV6DCzRkSFwwGe3efSCZhtXAaOHAgy5cvZ//+/axdu5bHH3+ciIgIevQwi9MNHjyY7t27x+/fp08fDh48yIABA9i5cyfjx4/ns88+Y+DAgba+BREREUmv8paEPKUAB5+ve1H94Bh8prWHXd+Blw90mAyVnrCdUu7k4VfANxsc3QDbtDyNpC6rhdORI0fo1KkTpUqV4tFHHyVLliysWbOG0NBQAI4dO8ahQ4fi9y9WrBjff/89P/74I5UrV+Y///kPH3zwAY899pitb0FERETSqx3fwuldiX8tLgacuLTNI8mXowDUfsG0lwyD6CtW40jGZvUep+nTp9/x6xMnTrxlW7169di0aVMqJRIREZFMIS4WFgy6ww4uWPASlG5pFs0VzxXeFzZOgAuHYc0YqJP4BGMi98qj7nESERERSRMHV0PEH3fYwYGIo2Y/8WxZskLDoaa98h24dNJuHsmwVDiJiIhI5nPpRMruJ3ZVaA8hVeHaRVg2ynYayaBUOImIiEjmkz1/yu4ndnl5QdPrBdOmSXBih908kiGpcBIREZHMJ7QmBIYAt1vQ1gWBBc1+kj6EhkPZR8ykHotesZ1GMiAVTiIiIpL5eHlDszeuP/hr8XT9cbPXNTFEetNoGHhngb1LYfcS22kkg1HhJCIiIplT2TZmrabA4ITbA0PM9rJt7OSSu5c7DB58xrQXDYHYGLt5JEOxOh25iIiIiFVl20DplsTsW8GWlQupXKcpPmF1NdKUntUZCJu/gFO/mfudHnjKdiLJIDTiJCIiIpmblzdOaG2O5g7HCa2toim9C8gFDV427WWj4OoFq3Ek41DhJCIiIiIZS7UnIU9JiDxt1nYSSQEqnEREREQkY/H2hSYjTHvNaDh3wGocyRhUOImIiIhIxlOiCYTVh9hrsGS47TSSAahwEhEREZGMx+WCJiMBF2yfDYfX2U4k6ZwKJxERERHJmAqUh6rdTHvBYHAcu3kkXVPhJCIiIiIZV4NXwDcbHN0A22bZTiPpmAonEREREcm4cuSHOi+Y9pJhEH3FahxJv1Q4iYiIiEjGFv4cBBaCC4fNLHsid0GFk4iIiIhkbL4B0Gioaa98By6dtJtH0iUVTiIiIiKS8ZV/HEKqwrVLsGyk7TSSDqlwEhEREZGMz8sLmo4y7U2T4cR2u3kk3VHhJCIiIiKZQ2g4lH0EnDhYOETTk0uyqHASERERkcyj0TDwzgL7lsGeJbbTSDqiwklEREREMo/cYfDgM6a9cAjExtjNI+mGCicRERERyVzqDISA3HB6F2yaaDuNpBMqnEREREQkcwnIBQ1eNu1lo+DqBatxJH1Q4SQiIiIimU+1JyFPSYg8Ayvftp1G0gEVTiIiIiKS+Xj7QpMRpr1mDJw7YDWOeD4VTiIiIiKSOZVoAmH1IfYaLBlmO414OBVOIiIiIpI5uVzQZCTggu1fw6G1thOJB1PhJCIiIiKZV4HyULWbaS8cDHFxdvOIx1LhJCIiIiKZW4NXwDcbHN0I22fbTiMeSoWTiIiIiGRuOfJDnRdMe8kwiL5iNY54JhVOIiIiIiLhz0FgIbhwGNaMtp1GPJAKJxERERER3wBoNNS0V74Dl07azSMeR4WTiIiIiAhA+cchpCpcuwTLRtpOIx5GhZOIiIiICICXFzQdZdqbJsOJ7XbziEdR4SQiIiIickNoOJR9BJw4WDgEHMd2IvEQKpxERERERP6s0TDwzgL7lsGeJbbTiIdQ4SQiIiIi8me5w+DBZ0x74RCIjbGbRzyCCicRERERkb+qMxACcsPpXbBpou004gFUOImIiIiI/FVALmjwsmkvGwVXL1iNI/apcBIRERERSUy1npCnFESegZVv204jlqlwEhERERFJjLcPNBlh2mvGwLkDVuOIXSqcRERERERup0RjCGsAsddgyTDbacQiFU4iIiIiIrfjckHTkeDygu1fw6G1thOJJSqcRERERETuJH85qNLNtBcOhrg4u3nEChVOIiIiIiJJaTAEsmSHoxth+2zbacQCFU4iIiIiIknJkR9qv2DaS4ZB9BWrcSTtqXASEREREXFHeF8ILAQXDsOa0bbTSBpT4SQiIiIi4g7fAGg0zLRXvgOXTlqNI2lLhZOIiIiIiLvKPwYFq8G1S7BspO00koZUOImIiIiIuMvLC5qOMu1Nk+HEdrt5JM2ocBIRERERSY4iD0HZtuDEwcIh4Di2E0kaUOEkIiIiIpJcjYaBdxbYtwz2LLGdRtKACicRERERkeTKXQwe7GPaC4dAbIzdPJLqVDiJiIiIiNyNOv+ArEFwehdsmmg7jaQyFU4iIiIiIncjIBfUH2zay0bB1QtW40jqUuEkIiIiInK3qvWEPKUg8gysfNt2GklFKpxERERERO6Wtw80GWHaa8bAuQNW40jqUeEkIiIiInIvSjSGsAYQew0WD8V1cBUFz/6M6+AqiIu1nU5SiAonEREREZF74XJB05GAC3bMwefztlQ/OAafz9vCe+Vhx7e2E0oKUOEkIiIiInKvzuwFElkIN+IYzOyu4ikDUOEkIiIiInIv4mJhwaDbfPF6MbXgJV22l86pcBIRERERuRcHV0PEH3fYwYGIo2Y/SbdUOImIiIiI3ItLJ1J2P/FIKpxERERERO5F9vwpu594JBVOIiIiIiL3IrQmBIYArtvv4xMAwZXTKpGkAhVOIiIiIiL3wssbmr1x/cFtiqeYKzC5DVzU5XrplQonEREREZF7VbYNdJgMgcEJtwcWhAYvQ8B98Mcm+LQhHN9mJ6PcEx/bAUREREREMoSybaB0S2L2rWDLyoVUrtMUn7C6ZkSq/OMwtQOc2QPjm8LjE6BkE9uJJRk04iQiIiIiklK8vHFCa3M0dzhOaG1TNAEEFYenFkPROnDtEkzrCGs/tptVkkWFk4iIiIhIWsiaG7rOhipdwYmD+S/CdwMhNsZ2MnGDCicRERERkbTikwXafASNhpvH6z8xo09XI+zmkiSpcBIRERERSUsuF9TuDx2mmGnK9ywx9z2dP2Q7mdyBCicRERERERvKtoGe30P2AnByB3zyMBxebzuV3IYKJxERERERWwpWhaeXQv4KcPkUTGwJ22bZTiWJUOEkIiIiImJTzkLQawGUbAaxUfBVL1j+FjiO7WTyJyqcRERERERs88sOT0yFh/qax8tGwNd9ICbKbi6Jp8JJRERERMQTeHlDs1HQ8h1wecOv02HyI3D5jO1kggonERERERHP8sBT0OVL8AuEQz/Dpw3h1O+2U2V6KpxERERERDzN/Q3hqcWQKxTO7YfPGsG+H22nytRUOImIiIiIeKJ8paH3UihUA65egM8fg42TbKfKtFQ4iYiIiIh4qux5ocdcKP84xMXA3Odh0b8gLs52skxHhZOIiIiIiCfz9YfHPoX6g83j1R/AzG5w7bLdXJmMCicREREREU/nckH9l+DRT8E7C/w2DyY0h4g/bCfLNFQ4iYiIiIikFxXbm0v3sgbBsV/gk4fNZ0l1KpxERERERNKTIg+ZSSPylIKLx2B8M/jte9upMjwVTiIiIiIi6U3uYvDUIghrANGRML0zrP4QHMd2sgxLhZOIiIiISHoUkMsslFutJ+DAoldgXn+IjbYcLGNS4SQiIiIikl55+0Krd6HpKMAFGyea9Z6unLccLONR4SQiIiIikp65XBDeFzpNA99ssH85fNYYzu6znSxD8ZjC6bXXXsPlctG/f/877ve///2PMmXKEBAQQKlSpZg8eXLaBBQRERER8WSlmkOvBZAjBE7/Dp82gkNrbKfKMDyicFq/fj3jxo2jYsWKd9xvzJgxDB48mGHDhrF9+3aGDx9O3759mTt3bholFRERERHxYMEV4ekfILgyRJ6BSa3h15m2U2UIPrYDXLp0iS5duvDJJ58wYsSIO+47ZcoUnnnmGTp27AhAWFgYa9as4Y033qB169aJPicqKoqoqKj4xxEREQBER0cTHa0b5+7kRv+on+5M/eQe9ZN71E/uUT+5R/3kPvWVe9RP7rHeTwF5oOs3eH/7LF67voPZTxN7chdxdQeZy/o8hPV+SuaxXY5jd87CHj16kDt3bt59913q169P5cqVee+99xLdt1q1arRo0YL//Oc/8dsGDx7M22+/zeXLl/H19b3lOcOGDWP48OG3bJ86dSpZs2ZNse9DRERERMSjOHGU/eNLSpz8DoAjuR5ic2hv4ryyWA7mOSIjI+ncuTMXLlwgMDDwjvtaHXGaPn06mzZtYv369W7t37RpUz799FPatm1L1apV2bhxI+PHjyc6OprTp08THBx8y3MGDx7MgAED4h9HRERQuHBhGjRoQFBQUIp9LxlRdHQ0ixcvpnHjxokWpWKon9yjfnKP+sk96if3qJ/cp75yj/rJPZ7VT62I2fI53vMHUuj8GkKyxRD7+GTIns9yLs/opxtXo7nDWuF0+PBh+vXrx6JFi/D393frOf/61784fvw4Dz30EI7jkD9/fp588knefPNNvL29E32On58ffn5+t2z39fX1gB/k9EF95R71k3vUT+5RP7lH/eQe9ZP71FfuUT+5x2P66YGekKc4zOiK19ENeE1sBl1mQr4ytpMBdvspOce1NjnExo0bOXnyJNWqVcPHxwcfHx+WL1/OBx98gI+PD7Gxsbc8JyAggPHjxxMZGcmBAwc4dOgQRYsWJUeOHOTJk8fCdyEiIiIikg4Uqwu9l8J9xeDCIfisCexZYjtVumKtcGrYsCFbt25ly5Yt8R/Vq1enS5cubNmy5bYjSGAqw0KFCuHt7c306dNp1aoVXl4eMUGgiIiIiIhnylPCzLgXWguiIuCLDrD+U9up0g1rl+rlyJGD8uXLJ9iWLVs2goKC4rcPHjyYo0ePxq/V9Pvvv7Nu3ToefPBBzp07xzvvvMO2bduYNGlSmucXEREREUl3suaGbl/D3P7wy1T47h9weg80HQletx+4EA9Zx+l2jh07xqFDh+Ifx8bG8vbbb1OpUiUaN27M1atXWb16NUWLFrUXUkREREQkPfHxg7aj4eF/mcdrx8C0ThB10W4uD2d9Hac/+/HHHxM8njhxYoLHZcqUYfPmzWkXSEREREQkI3K5oO5ACCoOX/eB3QthfDPoPANyFrKdziN59IiTiIiIiIikonLt4MnvIFs+OLENPnkYjm60ncojqXASEREREcnMClWHp5dCvnJw6QRMaAk7vrGdyuOocBIRERERyexyFYFeC+D+xhBzBWZ2h5Vvg+PYTuYxVDiJiIiIiAj4B0Kn6VDjGfN46b/hm74Qc81uLg+hwklERERERAxvH2jxJjR/C1xesOULmNIOIs/aTmadCicREREREUnowb9B55mQJQccXAWfNoIze22nskqFk4iIiIiI3KpEY3hqEeQsDGf3mhn39q+0ncoaFU4iIiIiIpK4/GWh91IoWB2unjeX7W3+3HYqK1Q4iYiIiIjI7eXID0/OM2s+xUWbCSOWDIO4ONvJ0pQKJxERERERuTPfAHhsPNT9p3m86l34sgdci7SbKw2pcBIRERERkaR5ecHDr0DbseDlCzu/hYkt4eJx28nShAonERERERFxX+VO0ONbCMgNf2yCTxrC8a22U6U6FU4iIiIiIpI8oTWh9xIIKgERR2B8M/h9oe1UqUqFk4iIiIiIJF9Qcei9GIrVhWuXYNoTsGYMOI7tZKlChZOIiIiIiNydgPug62yo2h2cOFjwEnw/EGJjbCdLcSqcRERERETk7nn7QusPoPF/ABes/xSmdoCrF2wnS1EqnERERERE5N64XFDreej4Ofhmhb1L4bMmcO6g7WQpRoWTiIiIiIikjDKtoOd8yBEMp36DTxvC4XW2U6UIFU4iIiIiIpJyQipD76VQoAJcPgUTW8HWr2ynumcqnEREREREJGXlLAg9F0CpFhAbBbOegh/fSNcz7qlwEhERERGRlOeX3dzzFP6cefzjKPj6GYiJgrhYXAdXUfDsz7gOroK4WLtZ3eBjO4CIiIiIiGRQXt7QdCQE3Q/f/QN+nQFHN0NUBD6XjlMd4OAYCAyBZm9A2Ta2E9+WRpxERERERCR1Ve8JXWeBT1Y48ztcOp7w6xHHYGZ32PGtnXxuUOEkIiIiIiKpr1hdc/leoq7f+7TgJY+9bE+Fk4iIiIiIpL6Dq+HyyTvs4EDEUbOfB1LhJCIiIiIiqe/SiZTdL42pcBIRERERkdSXPX/K7pfGVDiJiIiIiEjqC61pZs/DdZsdXBBY0OzngVQ4iYiIiIhI6vPyNlOOA7cWT9cfN3vd7OeBVDiJiIiIiEjaKNsGOkyGwOCE2wNDzHYPXsdJC+CKiIiIiEjaKdsGSrckZt8KtqxcSOU6TfEJq+uxI003aMRJRERERETSlpc3TmhtjuYOxwmt7fFFE6hwEhERERERSZIKJxERERERkSSocBIREREREUmCCicREREREZEkqHASERERERFJggonERERERGRJKhwEhERERERSYIKJxERERERkSSocBIREREREUmCCicREREREZEkqHASERERERFJggonERERERGRJKhwEhERERERSYKP7QBpzXEcAC5evIivr6/lNJ4tOjqayMhIIiIi1Fd3oH5yj/rJPeon96if3KN+cp/6yj3qJ/eon9zjCf0UEREB3KwR7iTTFU5nzpwBoFixYpaTiIiIiIiIJ7h48SI5c+a84z6ZrnDKnTs3AIcOHUqyczK7iIgIChcuzOHDhwkMDLQdx2Opn9yjfnKP+sk96if3qJ/cp75yj/rJPeon93hCPzmOw8WLFwkJCUly30xXOHl5mdu6cubMqR9kNwUGBqqv3KB+co/6yT3qJ/eon9yjfnKf+so96if3qJ/cY7uf3B1M0eQQIiIiIiIiSVDhJCIiIiIikoRMVzj5+fkxdOhQ/Pz8bEfxeOor96if3KN+co/6yT3qJ/eon9ynvnKP+sk96if3pLd+cjnuzL0nIiIiIiKSiWW6EScREREREZHkUuEkIiIiIiKSBBVOIiIiIiIiSVDhJCIiIiIikoRMVziNHj2aYsWK4e/vT7Vq1Vi5cqXtSB5nxYoVtG7dmpCQEFwuF3PmzLEdyeO89tprPPDAA+TIkYN8+fLRtm1bdu3aZTuWRxozZgwVK1aMX9wuPDyc+fPn247l0V577TVcLhf9+/e3HcXjDBs2DJfLleCjQIECtmN5pKNHj9K1a1eCgoLImjUrlStXZuPGjbZjeZSiRYve8vPkcrno27ev7WgeJSYmhldeeYVixYoREBBAWFgY//73v4mLi7MdzeNcvHiR/v37ExoaSkBAADVr1mT9+vW2Y1mX1HtLx3EYNmwYISEhBAQEUL9+fbZv324n7B1kqsJpxowZ9O/fnyFDhrB582bq1KlD8+bNOXTokO1oHuXy5ctUqlSJjz76yHYUj7V8+XL69u3LmjVrWLx4MTExMTRp0oTLly/bjuZxChUqxOuvv86GDRvYsGEDDz/8MI888ohH/kL0BOvXr2fcuHFUrFjRdhSPVa5cOY4dOxb/sXXrVtuRPM65c+eoVasWvr6+zJ8/nx07dvD222+TK1cu29E8yvr16xP8LC1evBiA9u3bW07mWd544w3Gjh3LRx99xM6dO3nzzTd56623+PDDD21H8zi9e/dm8eLFTJkyha1bt9KkSRMaNWrE0aNHbUezKqn3lm+++SbvvPMOH330EevXr6dAgQI0btyYixcvpnHSJDiZSI0aNZw+ffok2Fa6dGnnpZdespTI8wHO119/bTuGxzt58qQDOMuXL7cdJV247777nE8//dR2DI9z8eJFp0SJEs7ixYudevXqOf369bMdyeMMHTrUqVSpku0YHm/QoEFO7dq1bcdId/r16+cUL17ciYuLsx3Fo7Rs2dLp1atXgm2PPvqo07VrV0uJPFNkZKTj7e3tzJs3L8H2SpUqOUOGDLGUyvP89b1lXFycU6BAAef111+P33b16lUnZ86cztixYy0kvL1MM+J07do1Nm7cSJMmTRJsb9KkCatXr7aUSjKKCxcuAJA7d27LSTxbbGws06dP5/Lly4SHh9uO43H69u1Ly5YtadSoke0oHm337t2EhIRQrFgxnnjiCfbt22c7ksf59ttvqV69Ou3btydfvnxUqVKFTz75xHYsj3bt2jU+//xzevXqhcvlsh3Ho9SuXZulS5fy+++/A/DLL7+watUqWrRoYTmZZ4mJiSE2NhZ/f/8E2wMCAli1apWlVJ5v//79HD9+PMF7dD8/P+rVq+dx79F9bAdIK6dPnyY2Npb8+fMn2J4/f36OHz9uKZVkBI7jMGDAAGrXrk358uVtx/FIW7duJTw8nKtXr5I9e3a+/vprypYtazuWR5k+fTqbNm3StfBJePDBB5k8eTIlS5bkxIkTjBgxgpo1a7J9+3aCgoJsx/MY+/btY8yYMQwYMICXX36ZdevW8fzzz+Pn50f37t1tx/NIc+bM4fz58zz55JO2o3icQYMGceHCBUqXLo23tzexsbGMHDmSTp062Y7mUXLkyEF4eDj/+c9/KFOmDPnz52fatGmsXbuWEiVK2I7nsW68D0/sPfrBgwdtRLqtTFM43fDXs0iO4+jMktyT5557jl9//VVnk+6gVKlSbNmyhfPnzzNr1ix69OjB8uXLVTxdd/jwYfr168eiRYtuOVMpCTVv3jy+XaFCBcLDwylevDiTJk1iwIABFpN5lri4OKpXr86oUaMAqFKlCtu3b2fMmDEqnG7js88+o3nz5oSEhNiO4nFmzJjB559/ztSpUylXrhxbtmyhf//+hISE0KNHD9vxPMqUKVPo1asXBQsWxNvbm6pVq9K5c2c2bdpkO5rHSw/v0TNN4ZQnTx68vb1vGV06efLkLRWuiLv+/ve/8+2337JixQoKFSpkO47HypIlC/fffz8A1atXZ/369bz//vt8/PHHlpN5ho0bN3Ly5EmqVasWvy02NpYVK1bw0UcfERUVhbe3t8WEnitbtmxUqFCB3bt3247iUYKDg285MVGmTBlmzZplKZFnO3jwIEuWLGH27Nm2o3ikf/7zn7z00ks88cQTgDlpcfDgQV577TUVTn9RvHhxli9fzuXLl4mIiCA4OJiOHTtSrFgx29E81o2ZUY8fP05wcHD8dk98j55p7nHKkiUL1apVi58x54bFixdTs2ZNS6kkvXIch+eee47Zs2fzww8/6BdiMjmOQ1RUlO0YHqNhw4Zs3bqVLVu2xH9Ur16dLl26sGXLFhVNdxAVFcXOnTsT/LEVqFWr1i1LJPz++++EhoZaSuTZJkyYQL58+WjZsqXtKB4pMjISL6+Ebxm9vb01HfkdZMuWjeDgYM6dO8fChQt55JFHbEfyWMWKFaNAgQIJ3qNfu3aN5cuXe9x79Ewz4gQwYMAAunXrRvXq1QkPD2fcuHEcOnSIPn362I7mUS5dusSePXviH+/fv58tW7aQO3duihQpYjGZ5+jbty9Tp07lm2++IUeOHPEjmTlz5iQgIMByOs/y8ssv07x5cwoXLszFixeZPn06P/74IwsWLLAdzWPkyJHjlvvjsmXLRlBQkO6b+4uBAwfSunVrihQpwsmTJxkxYgQRERE66/0XL7zwAjVr1mTUqFF06NCBdevWMW7cOMaNG2c7mseJi4tjwoQJ9OjRAx+fTPW2yG2tW7dm5MiRFClShHLlyrF582beeecdevXqZTuax1m4cCGO41CqVCn27NnDP//5T0qVKkXPnj1tR7MqqfeW/fv3Z9SoUZQoUYISJUowatQosmbNSufOnS2mToTNKf1s+N///ueEhoY6WbJkcapWrarpoxOxbNkyB7jlo0ePHrajeYzE+gdwJkyYYDuax+nVq1f8/7m8efM6DRs2dBYtWmQ7lsfTdOSJ69ixoxMcHOz4+vo6ISEhzqOPPups377ddiyPNHfuXKd8+fKOn5+fU7p0aWfcuHG2I3mkhQsXOoCza9cu21E8VkREhNOvXz+nSJEijr+/vxMWFuYMGTLEiYqKsh3N48yYMcMJCwtzsmTJ4hQoUMDp27evc/78eduxrEvqvWVcXJwzdOhQp0CBAo6fn59Tt25dZ+vWrXZDJ8LlOI6T5tWaiIiIiIhIOpJp7nESERERERG5WyqcREREREREkqDCSUREREREJAkqnERERERERJKgwklERERERCQJKpxERERERESSoMJJREREREQkCSqcREREREREkqDCSURE0p0ffviB0qVLExcXZztKmvvxxx9xuVycP38egHnz5lGlSpVM2RciImlJhZOIiCTJcRwaNWpE06ZNb/na6NGjyZkzJ4cOHUqzPC+++CJDhgzBy0t/xlq1aoXL5WLq1Km2o4iIZGj6iyMiIklyuVxMmDCBtWvX8vHHH8dv379/P4MGDeL999+nSJEiKXrM6OjoRLevXr2a3bt30759+xQ9nruuXbtm5bh30rNnTz788EPbMUREMjQVTiIi4pbChQvz/vvvM3DgQPbv34/jODz11FM0bNiQGjVq0KJFC7Jnz07+/Pnp1q0bp0+fjn/uggULqF27Nrly5SIoKIhWrVqxd+/e+K8fOHAAl8vFzJkzqV+/Pv7+/nz++eeJ5pg+fTpNmjTB398/wfa5c+dSrVo1/P39CQsLY/jw4cTExMR/fdiwYRQpUgQ/Pz9CQkJ4/vnn4782evRoSpQogb+/P/nz5+fxxx+P/1r9+vV57rnnGDBgAHny5KFx48YALF++nBo1auDn50dwcDAvvfRSguMVLVqU9957L0HGypUrM2zYsPjHLpeLTz/9lHbt2pE1a1ZKlCjBt99+m+A533//PSVLliQgIIAGDRpw4MCBW/qkTZs2rFu3jn379iXaZyIicu9UOImIiNt69OhBw4YN6dmzJx999BHbtm3j/fffp169elSuXJkNGzawYMECTpw4QYcOHeKfd/nyZQYMGMD69etZunQpXl5etGvX7pb7cgYNGsTzzz/Pzp07E70sEGDFihVUr149wbaFCxfStWtXnn/+eXbs2MHHH3/MxIkTGTlyJABfffUV7777Lh9//DG7d+9mzpw5VKhQAYANGzbw/PPP8+9//5tdu3axYMEC6tatm+D1J02ahI+PDz/99BMff/wxR48epUWLFjzwwAP88ssvjBkzhs8++4wRI0Yku0+HDx9Ohw4d+PXXX2nRogVdunTh7NmzABw+fJhHH32UFi1asGXLFnr37s1LL710y2uEhoaSL18+Vq5cmezji4iImxwREZFkOHHihJM3b17Hy8vLmT17tvOvf/3LadKkSYJ9Dh8+7ADOrl27En2NkydPOoCzdetWx3EcZ//+/Q7gvPfee0keP2fOnM7kyZMTbKtTp44zatSoBNumTJniBAcHO47jOG+//bZTsmRJ59q1a7e83qxZs5zAwEAnIiIi0ePVq1fPqVy5coJtL7/8slOqVCknLi4uftv//vc/J3v27E5sbKzjOI4TGhrqvPvuuwmeV6lSJWfo0KHxjwHnlVdeiX986dIlx+VyOfPnz3ccx3EGDx7slClTJsFxBg0a5ADOuXPnErx2lSpVnGHDhiX6PYiIyL3TiJOIiCRLvnz5+Nvf/kaZMmVo164dGzduZNmyZWTPnj3+o3Tp0gDxl+Pt3buXzp07ExYWRmBgIMWKFQO4ZUKJv44kJebKlSu3XKa3ceNG/v3vfyfI8PTTT3Ps2DEiIyNp3749V65cISwsjKeffpqvv/46/rK6xo0bExoaSlhYGN26deOLL74gMjLyjrl27txJeHg4LpcrflutWrW4dOkSR44ccacb41WsWDG+nS1bNnLkyMHJkyfjj/PQQw8lOE54eHiirxMQEHBLbhERSTk+tgOIiEj64+Pjg4+P+RMSFxdH69ateeONN27ZLzg4GIDWrVtTuHBhPvnkE0JCQoiLi6N8+fK3TLSQLVu2JI+dJ08ezp07l2BbXFwcw4cP59FHH71lf39/fwoXLsyuXbtYvHgxS5Ys4dlnn+Wtt95i+fLl5MiRg02bNvHjjz+yaNEiXn31VYYNG8b69evJlStXorkcx0lQzNzYBsRv9/Lyit92Q2ITXvj6+iZ47HK54i9h/Ovz7+Ts2bPkzZvX7f1FRCR5VDiJiMg9qVq1KrNmzaJo0aLxxdSfnTlzhp07d/Lxxx9Tp04dAFatWnXXx6tSpQo7duy4JcOuXbu4//77b/u8gIAA2rRpQ5s2bejbty+lS5dm69atVK1aFR8fHxo1akSjRo0YOnQouXLl4ocffki0EAMoW7Yss2bNSlBArV69mhw5clCwYEEA8ubNy7Fjx+KfExERwf79+5P1vZYtW5Y5c+Yk2LZmzZpb9rt69Sp79+6lSpUqyXp9ERFxny7VExGRe9K3b1/Onj1Lp06d4md2W7RoEb169SI2Npb77ruPoKAgxo0bx549e/jhhx8YMGDAXR+vadOmtxRer776KpMnT2bYsGFs376dnTt3MmPGDF555RUAJk6cyGeffca2bdvYt28fU6ZMISAggNDQUObNm8cHH3zAli1bOHjwIJMnTyYuLo5SpUrdNsOzzz7L4cOH+fvf/85vv/3GN998w9ChQxkwYED82lIPP/wwU6ZMYeXKlWzbto0ePXrg7e2drO+1T58+7N27lwEDBrBr1y6mTp3KxIkTb9lvzZo1+Pn53fYyPhERuXcqnERE5J6EhITw008/ERsbS9OmTSlfvjz9+vUjZ86ceHl54eXlxfTp09m4cSPly5fnhRde4K233rrr43Xt2pUdO3awa9eu+G1NmzZl3rx5LF68mAceeICHHnqId955h9DQUABy5crFJ598Qq1atahYsSJLly5l7ty5BAUFkStXLmbPns3DDz9MmTJlGDt2LNOmTaNcuXK3zVCwYEG+//571q1bR6VKlejTpw9PPfVUfKEGMHjwYOrWrUurVq1o0aIFbdu2pXjx4sn6XosUKcKsWbOYO3culSpVYuzYsYwaNeqW/aZNm0aXLl3ImjVrsl5fRETc53KScwG1iIiIB3jxxRe5cOFCgsV4M6tTp05RunRpNmzYED/phoiIpDyNOImISLozZMgQQkNDiY2NtR3Fuv379zN69GgVTSIiqUwjTiIiIiIiIknQiJOIiIiIiEgSVDiJiIiIiIgkQYWTiIiIiIhIElQ4iYiIiIiIJEGFk4iIiIiISBJUOImIiIiIiCRBhZOIiIiIiEgSVDiJiIiIiIgkQYWTiIiIiIhIEv4fqmTsBa43cQ8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter rows for gender values 1 and 2\n",
    "filtered_df = df[df['gndr'].isin([1, 2])]\n",
    "\n",
    "# Filter rows for lrscale values from 0 to 10\n",
    "filtered_df = filtered_df[(filtered_df['lrscale'] >= 0) & (filtered_df['lrscale'] <= 10)]\n",
    "\n",
    "# Group by essround and gender, and calculate the mean lrscale\n",
    "grouped_df = filtered_df.groupby(['essround', 'gndr'])['lrscale'].mean().reset_index()\n",
    "\n",
    "# Pivot the DataFrame to have gender as columns\n",
    "pivot_df = grouped_df.pivot(index='essround', columns='gndr', values='lrscale')\n",
    "\n",
    "# Plot line plot for mean lrscale values for men and women\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(pivot_df.index, pivot_df[1], label='Men', marker='o')\n",
    "plt.plot(pivot_df.index, pivot_df[2], label='Women', marker='o')\n",
    "plt.xlabel('Year (essround)')\n",
    "plt.ylabel('Mean lrscale')\n",
    "plt.title('Mean lrscale Values Over Time for Men and Women')\n",
    "plt.xticks(range(11))  # Assuming essround values range from 0 to 10\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}