pruebas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import dask.dataframe as dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "AVES_ROOT = Path(\"/home/anouk/aves\")\n",
    "DATA_PATH = Path(\"/home/anouk/aves/data/external/censo_2017/\")\n",
    "file = \"Hogares\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "censo_hogares = pd.read_csv(DATA_PATH / \"Hogares.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['REGION', 'PROVINCIA', 'COMUNA', 'DC', 'AREA', 'ZC_LOC', 'ID_ZONA_LOC',\n",
       "       'NVIV', 'NHOGAR', 'TIPO_HOGAR', 'TIPO_OPERATIVO'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censo_hogares.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "censo_hogares_nuble = nuble_shape.merge(censo_hogares[censo_hogares.PROVINCIA==84][['DC', 'AREA', 'ZC_LOC', 'ID_ZONA_LOC',\n",
    "       'NVIV', 'NHOGAR', 'TIPO_HOGAR', 'TIPO_OPERATIVO']], left_on='LOC_ZON', right_on=\"ZC_LOC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped=censo_hogares_nuble.groupby('ZC_LOC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "traduccion = pd.read_csv(DATA_PATH/\"Tablas_parametros/microdato_censo2017-geografia.csv\", sep=\";\")\n",
    "df = traduccion[traduccion.REGION==16]\n",
    "df.to_csv(DATA_PATH/\"Tablas_parametros/traduccion_nuble.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "censo_hogares.to_parquet(DATA_PATH/\"Hogares.parquet\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#censo_hogares[censo_hogares.PROVINCIA==84]['COMUNA']=census.decode_column(censo_hogares, fname=DATA_PATH/\"Tablas_parametros/traduccion_nuble.csv\", index_col=\"ID_ZONA_LOC\", col_name=\"COMUNA\", sep=',')\n",
    "cols_to_replace = [\"COMUNA\", \"REGION\", \"PROVINCIA\"]\n",
    "for col in cols_to_replace:\n",
    "    censo_hogares[col] = censo_hogares['ID_ZONA_LOC'].map(traduccion.set_index('ID_ZONA_LOC')[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "censo_hogares['NOM_COMUNA']=census.decode_column(censo_hogares, fname=DATA_PATH/\"Tablas_parametros/COMUNA.csv\", index_col=\"COMUNA\", col_name=\"COMUNA\", sep=';')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>REGION</th>\n",
       "      <th>PROVINCIA</th>\n",
       "      <th>COMUNA</th>\n",
       "      <th>DC</th>\n",
       "      <th>AREA</th>\n",
       "      <th>ZC_LOC</th>\n",
       "      <th>ID_ZONA_LOC</th>\n",
       "      <th>NVIV</th>\n",
       "      <th>NHOGAR</th>\n",
       "      <th>TIPO_HOGAR</th>\n",
       "      <th>TIPO_OPERATIVO</th>\n",
       "      <th>NOM_COMUNA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3123645</th>\n",
       "      <td>16</td>\n",
       "      <td>161</td>\n",
       "      <td>16109</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2752</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hogar extenso</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "      <td>YUNGAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3123646</th>\n",
       "      <td>16</td>\n",
       "      <td>161</td>\n",
       "      <td>16109</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2752</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Hogar unipersonal</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "      <td>YUNGAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3123647</th>\n",
       "      <td>16</td>\n",
       "      <td>161</td>\n",
       "      <td>16109</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2752</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Hogar nuclear biparental con hijos</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "      <td>YUNGAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3123648</th>\n",
       "      <td>16</td>\n",
       "      <td>161</td>\n",
       "      <td>16109</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2752</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Hogar extenso</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "      <td>YUNGAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3123649</th>\n",
       "      <td>16</td>\n",
       "      <td>161</td>\n",
       "      <td>16109</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2752</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Hogar extenso</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "      <td>YUNGAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287656</th>\n",
       "      <td>16</td>\n",
       "      <td>161</td>\n",
       "      <td>16101</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>4633</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>Hogar sin núcleo</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "      <td>CHILLÁN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287657</th>\n",
       "      <td>16</td>\n",
       "      <td>161</td>\n",
       "      <td>16101</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>4633</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>Operativo personas en tránsito (no es hogar)</td>\n",
       "      <td>CHILLÁN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287658</th>\n",
       "      <td>16</td>\n",
       "      <td>161</td>\n",
       "      <td>16101</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>4633</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>Hogar unipersonal</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "      <td>CHILLÁN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287659</th>\n",
       "      <td>16</td>\n",
       "      <td>161</td>\n",
       "      <td>16101</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>4633</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>Hogar compuesto</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "      <td>CHILLÁN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287660</th>\n",
       "      <td>16</td>\n",
       "      <td>161</td>\n",
       "      <td>16101</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>4633</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>Hogar nuclear biparental con hijos</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "      <td>CHILLÁN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164016 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         REGION  PROVINCIA  COMUNA  DC  AREA  ZC_LOC  ID_ZONA_LOC  NVIV  \\\n",
       "3123645      16        161   16109   1     1       1         2752     1   \n",
       "3123646      16        161   16109   1     1       1         2752     2   \n",
       "3123647      16        161   16109   1     1       1         2752     3   \n",
       "3123648      16        161   16109   1     1       1         2752     4   \n",
       "3123649      16        161   16109   1     1       1         2752     5   \n",
       "...         ...        ...     ...  ..   ...     ...          ...   ...   \n",
       "3287656      16        161   16101  99     1     999         4633    59   \n",
       "3287657      16        161   16101  99     1     999         4633    60   \n",
       "3287658      16        161   16101  99     1     999         4633    61   \n",
       "3287659      16        161   16101  99     1     999         4633    62   \n",
       "3287660      16        161   16101  99     1     999         4633    63   \n",
       "\n",
       "         NHOGAR                          TIPO_HOGAR  \\\n",
       "3123645       1                       Hogar extenso   \n",
       "3123646       1                   Hogar unipersonal   \n",
       "3123647       1  Hogar nuclear biparental con hijos   \n",
       "3123648       1                       Hogar extenso   \n",
       "3123649       1                       Hogar extenso   \n",
       "...         ...                                 ...   \n",
       "3287656       1                    Hogar sin núcleo   \n",
       "3287657       1                           No aplica   \n",
       "3287658       1                   Hogar unipersonal   \n",
       "3287659       1                     Hogar compuesto   \n",
       "3287660       1  Hogar nuclear biparental con hijos   \n",
       "\n",
       "                                       TIPO_OPERATIVO NOM_COMUNA  \n",
       "3123645                  Hogar en vivienda particular     YUNGAY  \n",
       "3123646                  Hogar en vivienda particular     YUNGAY  \n",
       "3123647                  Hogar en vivienda particular     YUNGAY  \n",
       "3123648                  Hogar en vivienda particular     YUNGAY  \n",
       "3123649                  Hogar en vivienda particular     YUNGAY  \n",
       "...                                               ...        ...  \n",
       "3287656                  Hogar en vivienda particular    CHILLÁN  \n",
       "3287657  Operativo personas en tránsito (no es hogar)    CHILLÁN  \n",
       "3287658                  Hogar en vivienda particular    CHILLÁN  \n",
       "3287659                  Hogar en vivienda particular    CHILLÁN  \n",
       "3287660                  Hogar en vivienda particular    CHILLÁN  \n",
       "\n",
       "[164016 rows x 12 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censo_hogares[censo_hogares.REGION ==16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>COMUNA</th>\n",
       "      <th>TIPO_OPERATIVO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15202</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15202</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15202</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15202</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15202</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5664673</th>\n",
       "      <td>1101</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5664674</th>\n",
       "      <td>1101</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5664675</th>\n",
       "      <td>1101</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5664676</th>\n",
       "      <td>1101</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5664677</th>\n",
       "      <td>1101</td>\n",
       "      <td>Hogar en vivienda particular</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5664678 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         COMUNA                TIPO_OPERATIVO\n",
       "0         15202  Hogar en vivienda particular\n",
       "1         15202  Hogar en vivienda particular\n",
       "2         15202  Hogar en vivienda particular\n",
       "3         15202  Hogar en vivienda particular\n",
       "4         15202  Hogar en vivienda particular\n",
       "...         ...                           ...\n",
       "5664673    1101  Hogar en vivienda particular\n",
       "5664674    1101  Hogar en vivienda particular\n",
       "5664675    1101  Hogar en vivienda particular\n",
       "5664676    1101  Hogar en vivienda particular\n",
       "5664677    1101  Hogar en vivienda particular\n",
       "\n",
       "[5664678 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_p = pd.read_parquet(DATA_PATH/\"Hogares.parquet\", columns=[\"COMUNA\", \"TIPO_OPERATIVO\"])\n",
    "df_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
    "    'P07': [\n",
    "        'Jefe/a de hogar',\n",
    "        'Esposo/a o cónyuge',\n",
    "        'Conviviente por unión civil',\n",
    "        'Conviviente de hecho o pareja',\n",
    "        'Hijo/a',\n",
    "        'Hijo/a del cónyuge, conviviente o pareja',\n",
    "        'Hermano/a',\n",
    "        'Padre/madre',\n",
    "        'Cuñado/a',\n",
    "        'Suegro/a',\n",
    "        'Yerno/nuera',\n",
    "        'Nieto/a',\n",
    "        'Abuelo/a',\n",
    "        'Otro pariente',\n",
    "        'No pariente',\n",
    "        'Servicio doméstico puertas adentro',\n",
    "        'Persona en vivienda colectiva',\n",
    "        'Persona en tránsito',\n",
    "        'Persona en operativo calle'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Specify the path to save the CSV file\n",
    "name = \"P07\"\n",
    "csv_path = f\"{DATA_PATH}/Tablas_parametros/{name}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file\n",
    "df.to_csv(csv_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p08 = {\n",
    "    'id': [1, 2],\n",
    "    'P08': ['Hombre', 'Mujer']\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P08\n",
    "df_p08 = pd.DataFrame(data_p08)\n",
    "\n",
    "# Specify the path to save the CSV file for P08\n",
    "name_p08 = \"P08\"\n",
    "csv_path_p08 = f\"{DATA_PATH}/Tablas_parametros/{name_p08}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P08\n",
    "df_p08.to_csv(csv_path_p08, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p10 = {\n",
    "    'id': [1, 98, 99, 2, 3, 4],\n",
    "    'P10': [\n",
    "        'En esta vivienda',\n",
    "        'No Aplica',\n",
    "        'Missing',\n",
    "        'En otra vivienda',\n",
    "        'En otra comuna',\n",
    "        'En otro país'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P10\n",
    "df_p10 = pd.DataFrame(data_p10)\n",
    "\n",
    "# Specify the path to save the CSV file for P10\n",
    "name_p10 = \"P10\"\n",
    "csv_path_p10 = f\"{DATA_PATH}/Tablas_parametros/{name_p10}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P10\n",
    "df_p10.to_csv(csv_path_p10, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pais = {\n",
    "    'id': [\n",
    "        2, 998, 999, 5, 9, 10, 12, 13, 21, 29, 32, 36, 40, 51, 56, 68, 76, 96,\n",
    "        100, 124, 136, 142, 150, 156, 158, 170, 188, 191, 192, 203, 208, 214,\n",
    "        218, 222, 246, 250, 258, 275, 276, 300, 320, 332, 340, 348, 356, 360,\n",
    "        364, 372, 376, 380, 392, 400, 410, 422, 484, 504, 524, 528, 548, 554,\n",
    "        558, 578, 586, 591, 600, 604, 608, 616, 620, 626, 630, 642, 643, 688,\n",
    "        710, 724, 752, 756, 760, 764, 792, 804, 818, 826, 840, 858, 862, 997\n",
    "    ],\n",
    "    'PAIS': [\n",
    "        'África', 'No aplica', 'Missing', 'Sudamérica', 'Oceanía', 'Antártida',\n",
    "        'Argelia', 'Centroamérica', 'Norteamérica', 'Caribe', 'Argentina',\n",
    "        'Australia', 'Austria', 'Armenia', 'Bélgica', 'Bolivia (Estado Plurinacional de)',\n",
    "        'Brasil', 'Brunei Darussalam', 'Bulgaria', 'Canadá', 'Islas Caimán',\n",
    "        'Asia', 'Europa', 'China', 'Taiwán (provincia de China)', 'Colombia',\n",
    "        'Costa Rica', 'Croacia', 'Cuba', 'República Checa', 'Dinamarca',\n",
    "        'República Dominicana', 'Ecuador', 'El Salvador', 'Finlandia', 'Francia',\n",
    "        'Polinesia Francesa', 'Palestina (Estado de)', 'Alemania', 'Grecia',\n",
    "        'Guatemala', 'Haití', 'Honduras', 'Hungría', 'India', 'Indonesia',\n",
    "        'Irán (República Islámica de)', 'Irlanda', 'Israel', 'Italia', 'Japón',\n",
    "        'Jordania', 'Corea del Sur (República de)', 'Líbano', 'México', 'Marruecos',\n",
    "        'Nepal', 'Países Bajos', 'Vanuatu', 'Nueva Zelandia', 'Nicaragua', 'Noruega',\n",
    "        'Pakistán', 'Panamá', 'Paraguay', 'Perú', 'Filipinas', 'Polonia', 'Portugal',\n",
    "        'Timor-Leste', 'Puerto Rico', 'Rumania', 'Rusia (Federación de)', 'Serbia',\n",
    "        'Sudáfrica', 'España', 'Suecia', 'Suiza', 'República Árabe Siria', 'Tailandia',\n",
    "        'Turquía', 'Ucrania', 'Egipto', 'Reino Unido de Gran Bretaña e Irlanda del Norte',\n",
    "        'Estados Unidos de América', 'Uruguay', 'Venezuela (República Bolivariana de)',\n",
    "        'País no declarado'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for PAIS\n",
    "df_pais = pd.DataFrame(data_pais)\n",
    "\n",
    "# Specify the path to save the CSV file for PAIS\n",
    "name_pais = \"PAIS\"\n",
    "csv_path_pais = f\"{DATA_PATH}/Tablas_parametros/{name_pais}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for PAIS\n",
    "df_pais.to_csv(csv_path_pais, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p11 = {\n",
    "    'id': [1, 2, 3, 98, 99, 4, 5, 6, 7, 8, 9],\n",
    "    'P11': [\n",
    "        'Aún no nacía', 'En esta comuna', 'En otra comuna', 'No aplica', 'Missing',\n",
    "        'Perú', 'Argentina', 'Bolivia', 'Ecuador', 'Colombia', 'Otro'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P11\n",
    "df_p11 = pd.DataFrame(data_p11)\n",
    "\n",
    "# Specify the path to save the CSV file for P11\n",
    "name_p11 = \"P11\"\n",
    "csv_path_p11 = f\"{DATA_PATH}/Tablas_parametros/{name_p11}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P11\n",
    "df_p11.to_csv(csv_path_p11, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p12 = {\n",
    "    'id': [1, 98, 99, 2, 3, 4, 5, 6, 7, 8],\n",
    "    'P12': [\n",
    "        'En esta comuna', 'No aplica', 'Missing', 'En otra comuna', 'Perú',\n",
    "        'Argentina', 'Bolivia', 'Ecuador', 'Colombia', 'Otro'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P12\n",
    "df_p12 = pd.DataFrame(data_p12)\n",
    "\n",
    "# Specify the path to save the CSV file for P12\n",
    "name_p12 = \"P12\"\n",
    "csv_path_p12 = f\"{DATA_PATH}/Tablas_parametros/{name_p12}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P12\n",
    "df_p12.to_csv(csv_path_p12, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p12a_tramo = {\n",
    "    'id': [1, 98, 99, 2, 3, 4],\n",
    "    'P12A_TRAMO': [\n",
    "        'Entre 2010 y 2017', 'No aplica', 'Missing', 'Entre 2000 y 2009',\n",
    "        'Entre 1990 y 1999', 'Antes de 1990'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P12A_TRAMO\n",
    "df_p12a_tramo = pd.DataFrame(data_p12a_tramo)\n",
    "\n",
    "# Specify the path to save the CSV file for P12A_TRAMO\n",
    "name_p12a_tramo = \"P12A_TRAMO\"\n",
    "csv_path_p12a_tramo = f\"{DATA_PATH}/Tablas_parametros/{name_p12a_tramo}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P12A_TRAMO\n",
    "df_p12a_tramo.to_csv(csv_path_p12a_tramo, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p12a_tramo = {\n",
    "    'id': [1, 98, 99, 2, 3, 4],\n",
    "    'P12A_TRAMO': [\n",
    "        'Entre 2010 y 2017', 'No aplica', 'Missing', 'Entre 2000 y 2009',\n",
    "        'Entre 1990 y 1999', 'Antes de 1990'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P12A_TRAMO\n",
    "df_p12a_tramo = pd.DataFrame(data_p12a_tramo)\n",
    "\n",
    "# Specify the path to save the CSV file for P12A_TRAMO\n",
    "name_p12a_tramo = \"P12A_TRAMO\"\n",
    "csv_path_p12a_tramo = f\"{DATA_PATH}/Tablas_parametros/{name_p12a_tramo}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P12A_TRAMO\n",
    "df_p12a_tramo.to_csv(csv_path_p12a_tramo, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p13 = {\n",
    "    'id': [1, 98, 99, 2, 3],\n",
    "    'P13': [\n",
    "        'Sí', 'No aplica', 'Missing', 'No asiste actualmente', 'Nunca asistió'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P13\n",
    "df_p13 = pd.DataFrame(data_p13)\n",
    "\n",
    "# Specify the path to save the CSV file for P13\n",
    "name_p13 = \"P13\"\n",
    "csv_path_p13 = f\"{DATA_PATH}/Tablas_parametros/{name_p13}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P13\n",
    "df_p13.to_csv(csv_path_p13, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p14 = {\n",
    "    'id': [0, 98, 99, 1, 2, 3, 4, 5, 6, 7, 8],\n",
    "    'P14': [\n",
    "        '0', 'No aplica', 'Missing', '1°', '2°', '3°', '4°', '5°', '6°', '7°', '8°'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P14\n",
    "df_p14 = pd.DataFrame(data_p14)\n",
    "\n",
    "# Specify the path to save the CSV file for P14\n",
    "name_p14 = \"P14\"\n",
    "csv_path_p14 = f\"{DATA_PATH}/Tablas_parametros/{name_p14}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P14\n",
    "df_p14.to_csv(csv_path_p14, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p15 = {\n",
    "    'id': [1, 98, 99, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],\n",
    "    'P15': [\n",
    "        'Sala cuna o jardín infantil', 'No aplica', 'Missing', 'Prekínder', 'Kínder',\n",
    "        'Especial o diferencial', 'Educación básica', 'Primaria o preparatorio (sistema antiguo)',\n",
    "        'Científico-humanista', 'Técnica profesional', 'Humanidades (sistema antiguo)',\n",
    "        'Técnica comercial, industrial/normalista (sistema antiguo)',\n",
    "        'Técnico superior (1-3 años)', 'Profesional (4 o más años)', 'Magíster', 'Doctorado'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P15\n",
    "df_p15 = pd.DataFrame(data_p15)\n",
    "\n",
    "# Specify the path to save the CSV file for P15\n",
    "name_p15 = \"P15\"\n",
    "csv_path_p15 = f\"{DATA_PATH}/Tablas_parametros/{name_p15}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P15\n",
    "df_p15.to_csv(csv_path_p15, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p15a = {\n",
    "    'id': [1, 98, 99, 2],\n",
    "    'P15A': [\n",
    "        'Sí', 'No aplica', 'Missing', 'No'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P15A\n",
    "df_p15a = pd.DataFrame(data_p15a)\n",
    "\n",
    "# Specify the path to save the CSV file for P15A\n",
    "name_p15a = \"P15A\"\n",
    "csv_path_p15a = f\"{DATA_PATH}/Tablas_parametros/{name_p15a}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P15A\n",
    "df_p15a.to_csv(csv_path_p15a, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p16 = {\n",
    "    'id': [1, 98, 99, 2],\n",
    "    'P16': [\n",
    "        'Sí', 'No aplica', 'Missing', 'No'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P16\n",
    "df_p16 = pd.DataFrame(data_p16)\n",
    "\n",
    "# Specify the path to save the CSV file for P16\n",
    "name_p16 = \"P16\"\n",
    "csv_path_p16 = f\"{DATA_PATH}/Tablas_parametros/{name_p16}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P16\n",
    "df_p16.to_csv(csv_path_p16, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p16a = {\n",
    "    'id': [1, 98, 99, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "    'P16A': [\n",
    "        'Mapuche', 'No aplica', 'Missing', 'Aymara', 'Rapa Nui', 'Lican Antai',\n",
    "        'Quechua', 'Colla', 'Diaguita', 'Kawésqar', 'Yagán o Yamana', 'Otro'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P16A\n",
    "df_p16a = pd.DataFrame(data_p16a)\n",
    "\n",
    "# Specify the path to save the CSV file for P16A\n",
    "name_p16a = \"P16A\"\n",
    "csv_path_p16a = f\"{DATA_PATH}/Tablas_parametros/{name_p16a}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P16A\n",
    "df_p16a.to_csv(csv_path_p16a, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p16a_otro = {\n",
    "    'id': [3, 98, 99, 4, 5, 6, 21, 22, 23, 28, 33, 34, 35, 37, 97],\n",
    "    'P16A_OTRO': [\n",
    "        'Lafquenche', 'No aplica', 'Missing', 'Pehuenche', 'Huilliche', 'Picunche',\n",
    "        'Changos', 'Chonos', 'Ona', 'Tehuelches', 'Pueblos de América Latina',\n",
    "        'Pueblos del resto del mundo', 'Afrodescendiente',\n",
    "        'Otros pueblos presentes en el territorio nacional', 'Pueblo no declarado'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P16A_OTRO\n",
    "df_p16a_otro = pd.DataFrame(data_p16a_otro)\n",
    "\n",
    "# Specify the path to save the CSV file for P16A_OTRO\n",
    "name_p16a_otro = \"P16A_OTRO\"\n",
    "csv_path_p16a_otro = f\"{DATA_PATH}/Tablas_parametros/{name_p16a_otro}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P16A_OTRO\n",
    "df_p16a_otro.to_csv(csv_path_p16a_otro, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p17 = {\n",
    "    'id': [1, 98, 99, 2, 3, 4, 5, 6, 7, 8],\n",
    "    'P17': [\n",
    "        'Por un pago en dinero o especies', 'No aplica', 'Missing',\n",
    "        'Sin pago para un familiar', 'Tenía empleo pero estuvo de vacaciones, con licencia, en descanso laboral, etc.',\n",
    "        'Se encontraba buscando empleo', 'Estaba estudiando', 'Realizó quehaceres de su hogar',\n",
    "        'Es jubilado, pensionado o rentista', 'Otra situación'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P17\n",
    "df_p17 = pd.DataFrame(data_p17)\n",
    "\n",
    "# Specify the path to save the CSV file for P17\n",
    "name_p17 = \"P17\"\n",
    "csv_path_p17 = f\"{DATA_PATH}/Tablas_parametros/{name_p17}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P17\n",
    "df_p17.to_csv(csv_path_p17, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p18 = {\n",
    "    'id': ['A', 98, 99, 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'Z'],\n",
    "    'P18': [\n",
    "        'Agricultura, ganadería, silvicultura y pesca', 'No aplica', 'Missing',\n",
    "        'Explotación de minas y canteras', 'Industrias manufactureras',\n",
    "        'Suministro de electricidad, gas, vapor y aire acondicionado',\n",
    "        'Suministro de agua; evacuación de aguas residuales, gestión de desechos y descontaminación',\n",
    "        'Construcción', 'Comercio al por mayor y al por menor; reparación de vehículos automotores y motocicletas',\n",
    "        'Transporte y almacenamiento', 'Actividades de alojamiento y de servicios de comidas',\n",
    "        'Información y comunicaciones', 'Actividades financieras y de seguros',\n",
    "        'Actividades inmobiliarias', 'Actividades profesionales, científicas y técnicas',\n",
    "        'Actividades de servicios administrativos y de apoyo',\n",
    "        'Administración pública y defensa; planes de seguridad social de afiliación obligatoria',\n",
    "        'Enseñanza', 'Actividades de atención de la salud humana y de asistencia social',\n",
    "        'Actividades artísticas, de entretenimiento y recreativas',\n",
    "        'Otras actividades de servicios',\n",
    "        'Actividades de los hogares como empleadores; actividades no diferenciadas de los hogares como productores de bienes y servicios para uso propio',\n",
    "        'Actividades de organizaciones y órganos extraterritoriales',\n",
    "        'Rama no declarada'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P18\n",
    "df_p18 = pd.DataFrame(data_p18)\n",
    "\n",
    "# Specify the path to save the CSV file for P18\n",
    "name_p18 = \"P18\"\n",
    "csv_path_p18 = f\"{DATA_PATH}/Tablas_parametros/{name_p18}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P18\n",
    "df_p18.to_csv(csv_path_p18, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p21m = {\n",
    "    'id': [1, 98, 99, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
    "    'P21M': [\n",
    "        'Enero', 'No aplica', 'Missing', 'Febrero', 'Marzo', 'Abril', 'Mayo',\n",
    "        'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P21M\n",
    "df_p21m = pd.DataFrame(data_p21m)\n",
    "\n",
    "# Specify the path to save the CSV file for P21M\n",
    "name_p21m = \"P21M\"\n",
    "csv_path_p21m = f\"{DATA_PATH}/Tablas_parametros/{name_p21m}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P21M\n",
    "df_p21m.to_csv(csv_path_p21m, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pais_grupo = {\n",
    "    'id': [2, 998, 999, 5, 9, 10, 13, 21, 29, 32, 68, 142, 150, 170, 218, 332, 604, 862, 997],\n",
    "    'PAIS_GRUPO': [\n",
    "        'África', 'No aplica', 'Missing', 'Sudamérica', 'Oceanía', 'Antártida',\n",
    "        'Centroamérica', 'Norteamérica', 'Caribe', 'Argentina', 'Bolivia (Estado Plurinacional de)',\n",
    "        'Asia', 'Europa', 'Colombia', 'Ecuador', 'Haití', 'Perú', 'Venezuela (República Bolivariana de)',\n",
    "        'País no declarado'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for PAIS_GRUPO\n",
    "df_pais_grupo = pd.DataFrame(data_pais_grupo)\n",
    "\n",
    "# Specify the path to save the CSV file for PAIS_GRUPO\n",
    "name_pais_grupo = \"PAIS_GRUPO\"\n",
    "csv_path_pais_grupo = f\"{DATA_PATH}/Tablas_parametros/{name_pais_grupo}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for PAIS_GRUPO\n",
    "df_pais_grupo.to_csv(csv_path_pais_grupo, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_p16a_grupo = {\n",
    "    'id': [1, 98, 99, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "    'P16A_GRUPO': [\n",
    "        'Mapuche', 'No aplica', 'Missing', 'Aymara', 'Rapa Nui', 'Lican Antai',\n",
    "        'Quechua', 'Colla', 'Diaguita', 'Kawésqar', 'Yagán o Yamana', 'Otro pueblo'\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Create a Pandas dataframe for P16A_GRUPO\n",
    "df_p16a_grupo = pd.DataFrame(data_p16a_grupo)\n",
    "\n",
    "# Specify the path to save the CSV file for P16A_GRUPO\n",
    "name_p16a_grupo = \"P16A_GRUPO\"\n",
    "csv_path_p16a_grupo = f\"{DATA_PATH}/Tablas_parametros/{name_p16a_grupo}.csv\"\n",
    "\n",
    "# Save the dataframe to a CSV file for P16A_GRUPO\n",
    "df_p16a_grupo.to_csv(csv_path_p16a_grupo, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "parquet_file = DATA_PATH/\"Personas_pre.parquet\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dd.read_csv(DATA_PATH/\"Personas.csv\", sep=\";\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel = [(\"REGION\", \"==\", 13)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet(\n",
    "        DATA_PATH / \"Personas_pre.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REGION</th>\n",
       "      <th>PROVINCIA</th>\n",
       "      <th>COMUNA</th>\n",
       "      <th>DC</th>\n",
       "      <th>AREA</th>\n",
       "      <th>ZC_LOC</th>\n",
       "      <th>ID_ZONA_LOC</th>\n",
       "      <th>NVIV</th>\n",
       "      <th>NHOGAR</th>\n",
       "      <th>PERSONAN</th>\n",
       "      <th>...</th>\n",
       "      <th>P18</th>\n",
       "      <th>P19</th>\n",
       "      <th>P20</th>\n",
       "      <th>P21M</th>\n",
       "      <th>P21A</th>\n",
       "      <th>P10PAIS_GRUPO</th>\n",
       "      <th>P11PAIS_GRUPO</th>\n",
       "      <th>P12PAIS_GRUPO</th>\n",
       "      <th>ESCOLARIDAD</th>\n",
       "      <th>P16A_GRUPO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>998</td>\n",
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       "      <td>1</td>\n",
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       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>9998</td>\n",
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       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <td>13225</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
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       "      <td>98</td>\n",
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       "      <td>3</td>\n",
       "      <td>1965</td>\n",
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       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>1995</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>152</td>\n",
       "      <td>15202</td>\n",
       "      <td>1</td>\n",
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       "      <td>6</td>\n",
       "      <td>13225</td>\n",
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       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>Z</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>9998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>17573998</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1101</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>12486</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>9998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17573999</th>\n",
       "      <td>1</td>\n",
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       "      <td>1101</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>12486</td>\n",
       "      <td>235</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>F</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>9998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17574000</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1101</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>12486</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2007</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>16</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17574001</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1101</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>12486</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>9998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>13</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17574002</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1101</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>12486</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>9998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>4</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17574003 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          REGION  PROVINCIA  COMUNA  DC  AREA  ZC_LOC  ID_ZONA_LOC  NVIV  \\\n",
       "0             15        152   15202   1     2       6        13225     1   \n",
       "1             15        152   15202   1     2       6        13225     3   \n",
       "2             15        152   15202   1     2       6        13225     3   \n",
       "3             15        152   15202   1     2       6        13225     3   \n",
       "4             15        152   15202   1     2       6        13225     3   \n",
       "...          ...        ...     ...  ..   ...     ...          ...   ...   \n",
       "17573998       1         11    1101  99     1     999        12486   234   \n",
       "17573999       1         11    1101  99     1     999        12486   235   \n",
       "17574000       1         11    1101  99     1     999        12486   236   \n",
       "17574001       1         11    1101  99     1     999        12486   236   \n",
       "17574002       1         11    1101  99     1     999        12486   236   \n",
       "\n",
       "          NHOGAR  PERSONAN  ...  P18  P19  P20  P21M  P21A  P10PAIS_GRUPO  \\\n",
       "0              1         1  ...   98   98   98    98  9998            998   \n",
       "1              1         1  ...   98   98   98    98  9998            998   \n",
       "2              1         2  ...   98    1    1     3  1965            998   \n",
       "3              1         3  ...   98    2    1     4  1995            998   \n",
       "4              1         4  ...   Z    98   98    98  9998            998   \n",
       "...          ...       ...  ...  ...  ...  ...   ...   ...            ...   \n",
       "17573998       1         1  ...   A    98   98    98  9998            998   \n",
       "17573999       1         1  ...   F    98   98    98  9998            998   \n",
       "17574000       1         1  ...   S     3    3     3  2007            998   \n",
       "17574001       1         2  ...   98    0   98    98  9998            998   \n",
       "17574002       1         3  ...   98   98   98    98  9998            998   \n",
       "\n",
       "          P11PAIS_GRUPO  P12PAIS_GRUPO  ESCOLARIDAD  P16A_GRUPO  \n",
       "0                   998            998            4           2  \n",
       "1                   998            998            0           2  \n",
       "2                   998            998            0           2  \n",
       "3                   998            998            2           2  \n",
       "4                   998            998            3           2  \n",
       "...                 ...            ...          ...         ...  \n",
       "17573998            998            998            3          98  \n",
       "17573999            998            998           12           1  \n",
       "17574000            998            998           16          98  \n",
       "17574001            998            998           13          98  \n",
       "17574002            998            998            4          98  \n",
       "\n",
       "[17574003 rows x 42 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aves.data.census import decode_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "P18\n",
       "98    63\n",
       "A     10\n",
       "Z      8\n",
       "I      3\n",
       "N      3\n",
       "99     3\n",
       "F      2\n",
       "P      2\n",
       "O      2\n",
       "H      1\n",
       "J      1\n",
       "Q      1\n",
       "G      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(100).P18.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     No aplica\n",
      "1     No aplica\n",
      "2     No aplica\n",
      "3     No aplica\n",
      "4           NaN\n",
      "5     No aplica\n",
      "6           NaN\n",
      "7     No aplica\n",
      "8     No aplica\n",
      "9     No aplica\n",
      "10          NaN\n",
      "11    No aplica\n",
      "12    No aplica\n",
      "13    No aplica\n",
      "14    No aplica\n",
      "Name: value, dtype: object\n"
     ]
    }
   ],
   "source": [
    "p18 = decode_column(df.head(100), fname=DATA_PATH/\"Tablas_parametros/P18.csv\", index_col=\"id\", col_name=\"P18\", sep=',', index_dtype=pd.StringDtype())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value\n",
       "No aplica    9337075\n",
       "Missing       308860\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p18.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['P07']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P07.csv\", index_col=\"id\", col_name=\"P07\", sep=',')\n",
    "df['P08']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P08.csv\", index_col=\"id\", col_name=\"P08\", sep=',')\n",
    "df['P10']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P10.csv\", index_col=\"id\", col_name=\"P10\", sep=',')\n",
    "df['P10COMUNA']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/COMUNA.csv\", index_col=\"COMUNA\", col_name=\"P10COMUNA\", sep=';')\n",
    "df['P11COMUNA']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/COMUNA.csv\", index_col=\"COMUNA\", col_name=\"P11COMUNA\", sep=';')\n",
    "df['P12COMUNA']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/COMUNA.csv\", index_col=\"COMUNA\", col_name=\"P12COMUNA\", sep=';')\n",
    "df['P10PAIS']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS.csv\", index_col=\"id\", col_name=\"P10PAIS\", sep=',')\n",
    "df['P11PAIS']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS.csv\", index_col=\"id\", col_name=\"P11PAIS\", sep=',')\n",
    "df['P12PAIS']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS.csv\", index_col=\"id\", col_name=\"P12PAIS\", sep=',')\n",
    "df['P11']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P11.csv\", index_col=\"id\", col_name=\"P11\", sep=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['P12']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P12.csv\", index_col=\"id\", col_name=\"P12\", sep=',')\n",
    "df['P12A_TRAMO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P12A_TRAMO.csv\", index_col=\"id\", col_name=\"P12A_TRAMO\", sep=',')\n",
    "df['P13']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P13.csv\", index_col=\"id\", col_name=\"P13\", sep=',')\n",
    "df['P14']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P14.csv\", index_col=\"id\", col_name=\"P14\", sep=',')\n",
    "df['P15']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P15.csv\", index_col=\"id\", col_name=\"P15\", sep=',')\n",
    "df['P15A']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P15A.csv\", index_col=\"id\", col_name=\"P15A\", sep=',')\n",
    "df['P16']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P16.csv\", index_col=\"id\", col_name=\"P16\", sep=',')\n",
    "df['P16A']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P16A.csv\", index_col=\"id\", col_name=\"P16A\", sep=',')\n",
    "df['P16A_OTRO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P16A_OTRO.csv\", index_col=\"id\", col_name=\"P16A_OTRO\", sep=',')\n",
    "df['P16A_GRUPO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P16A_GRUPO.csv\", index_col=\"id\", col_name=\"P16A_GRUPO\", sep=',')\n",
    "df['P17']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P17.csv\", index_col=\"id\", col_name=\"P17\", sep=',')\n",
    "df['P21M']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P21M.csv\", index_col=\"id\", col_name=\"P21M\", sep=',')\n",
    "df['P10PAIS_GRUPO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS_GRUPO.csv\", index_col=\"id\", col_name=\"P10PAIS_GRUPO\", sep=',')\n",
    "df['P11PAIS_GRUPO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS_GRUPO.csv\", index_col=\"id\", col_name=\"P11PAIS_GRUPO\", sep=',')\n",
    "df['P12PAIS_GRUPO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS_GRUPO.csv\", index_col=\"id\", col_name=\"P12PAIS_GRUPO\", sep=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "traduccion = pd.read_csv(DATA_PATH/\"Tablas_parametros/traduccion_nuble.csv\")\n",
    "cols_to_replace = [\"COMUNA\", \"REGION\", \"PROVINCIA\"]\n",
    "for col in cols_to_replace:\n",
    "    df[col] = df['ID_ZONA_LOC'].map(traduccion.set_index('ID_ZONA_LOC')[col])\n",
    "df['NOM_COMUNA']=decode_column(df, fname=DATA_PATH/\"Tablas_parametros/COMUNA.csv\", index_col=\"COMUNA\", col_name=\"COMUNA\", sep=';')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dask Series Structure:\n",
       "npartitions=1\n",
       "    int64[pyarrow]\n",
       "               ...\n",
       "Name: count, dtype: int64[pyarrow]\n",
       "Dask Name: value-counts-agg, 180 graph layers"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.P10PAIS_GRUPO.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_parquet(DATA_PATH/\"Personas.parquet\", write_index=False, partition_on=[\"COMUNA\"], overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "SHAPE_PATH = Path(\"/home/anouk/Documents\")\n",
    "zona777 = gpd.read_file(SHAPE_PATH / \"zona777\" / \"Zonas777-04-04-2014\" / \"Shape\" / \"Zonas777_V07_04_2014.shp\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>REGION</th>\n",
       "      <th>NOM_REGION</th>\n",
       "      <th>PROVINCIA</th>\n",
       "      <th>NOM_PROVIN</th>\n",
       "      <th>COMUNA</th>\n",
       "      <th>NOM_COMUNA</th>\n",
       "      <th>URBANO</th>\n",
       "      <th>DISTRITO</th>\n",
       "      <th>LOC_ZON</th>\n",
       "      <th>GEOCODIGO</th>\n",
       "      <th>SHAPE_Leng</th>\n",
       "      <th>SHAPE_Area</th>\n",
       "      <th>geometry</th>\n",
       "      <th>NOM_LOCALI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>REGIÓN METROPOLITANA DE SANTIAGO</td>\n",
       "      <td>134</td>\n",
       "      <td>MAIPO</td>\n",
       "      <td>13404</td>\n",
       "      <td>PAINE</td>\n",
       "      <td>PAINE</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>0.000043</td>\n",
       "      <td>POLYGON ((-70.72732 -33.80301, -70.72713 -33.8...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>REGIÓN METROPOLITANA DE SANTIAGO</td>\n",
       "      <td>134</td>\n",
       "      <td>MAIPO</td>\n",
       "      <td>13404</td>\n",
       "      <td>PAINE</td>\n",
       "      <td>PAINE</td>\n",
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       "      <td>POLYGON ((-70.70792 -33.80101, -70.70783 -33.8...</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>13</td>\n",
       "      <td>REGIÓN METROPOLITANA DE SANTIAGO</td>\n",
       "      <td>134</td>\n",
       "      <td>MAIPO</td>\n",
       "      <td>13404</td>\n",
       "      <td>PAINE</td>\n",
       "      <td>PAINE</td>\n",
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       "      <td>POLYGON ((-70.70724 -33.80454, -70.70712 -33.8...</td>\n",
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       "      <td>13</td>\n",
       "      <td>REGIÓN METROPOLITANA DE SANTIAGO</td>\n",
       "      <td>134</td>\n",
       "      <td>MAIPO</td>\n",
       "      <td>13404</td>\n",
       "      <td>PAINE</td>\n",
       "      <td>PAINE</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>13404061001</td>\n",
       "      <td>0.052736</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>POLYGON ((-70.74243 -33.81015, -70.74262 -33.8...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>REGIÓN METROPOLITANA DE SANTIAGO</td>\n",
       "      <td>134</td>\n",
       "      <td>MAIPO</td>\n",
       "      <td>13404</td>\n",
       "      <td>PAINE</td>\n",
       "      <td>PAINE</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>13404061002</td>\n",
       "      <td>0.024273</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>POLYGON ((-70.73996 -33.80852, -70.73929 -33.8...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>13</td>\n",
       "      <td>REGIÓN METROPOLITANA DE SANTIAGO</td>\n",
       "      <td>136</td>\n",
       "      <td>TALAGANTE</td>\n",
       "      <td>13605</td>\n",
       "      <td>PEÑAFLOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>13605022006</td>\n",
       "      <td>0.284534</td>\n",
       "      <td>0.001065</td>\n",
       "      <td>MULTIPOLYGON (((-70.87591 -33.62099, -70.87600...</td>\n",
       "      <td>MALLOCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>13</td>\n",
       "      <td>REGIÓN METROPOLITANA DE SANTIAGO</td>\n",
       "      <td>136</td>\n",
       "      <td>TALAGANTE</td>\n",
       "      <td>13605</td>\n",
       "      <td>PEÑAFLOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>901</td>\n",
       "      <td>13605022901</td>\n",
       "      <td>0.130868</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>MULTIPOLYGON (((-70.86011 -33.62935, -70.85790...</td>\n",
       "      <td>INDETERMINADA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>13</td>\n",
       "      <td>REGIÓN METROPOLITANA DE SANTIAGO</td>\n",
       "      <td>136</td>\n",
       "      <td>TALAGANTE</td>\n",
       "      <td>13605</td>\n",
       "      <td>PEÑAFLOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>13605032004</td>\n",
       "      <td>0.134089</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>POLYGON ((-70.85540 -33.57587, -70.85493 -33.5...</td>\n",
       "      <td>EL GUANACO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>13</td>\n",
       "      <td>REGIÓN METROPOLITANA DE SANTIAGO</td>\n",
       "      <td>136</td>\n",
       "      <td>TALAGANTE</td>\n",
       "      <td>13605</td>\n",
       "      <td>PEÑAFLOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>13605032006</td>\n",
       "      <td>0.071088</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>POLYGON ((-70.86701 -33.58194, -70.86298 -33.5...</td>\n",
       "      <td>MALLOCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>13</td>\n",
       "      <td>REGIÓN METROPOLITANA DE SANTIAGO</td>\n",
       "      <td>136</td>\n",
       "      <td>TALAGANTE</td>\n",
       "      <td>13605</td>\n",
       "      <td>PEÑAFLOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>13605032008</td>\n",
       "      <td>0.050012</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>POLYGON ((-70.87193 -33.58383, -70.87265 -33.5...</td>\n",
       "      <td>PEÑAFLOR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2376 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    REGION                        NOM_REGION PROVINCIA NOM_PROVIN COMUNA  \\\n",
       "0       13  REGIÓN METROPOLITANA DE SANTIAGO       134      MAIPO  13404   \n",
       "1       13  REGIÓN METROPOLITANA DE SANTIAGO       134      MAIPO  13404   \n",
       "2       13  REGIÓN METROPOLITANA DE SANTIAGO       134      MAIPO  13404   \n",
       "3       13  REGIÓN METROPOLITANA DE SANTIAGO       134      MAIPO  13404   \n",
       "4       13  REGIÓN METROPOLITANA DE SANTIAGO       134      MAIPO  13404   \n",
       "..     ...                               ...       ...        ...    ...   \n",
       "506     13  REGIÓN METROPOLITANA DE SANTIAGO       136  TALAGANTE  13605   \n",
       "507     13  REGIÓN METROPOLITANA DE SANTIAGO       136  TALAGANTE  13605   \n",
       "508     13  REGIÓN METROPOLITANA DE SANTIAGO       136  TALAGANTE  13605   \n",
       "509     13  REGIÓN METROPOLITANA DE SANTIAGO       136  TALAGANTE  13605   \n",
       "510     13  REGIÓN METROPOLITANA DE SANTIAGO       136  TALAGANTE  13605   \n",
       "\n",
       "    NOM_COMUNA URBANO  DISTRITO  LOC_ZON    GEOCODIGO  SHAPE_Leng  SHAPE_Area  \\\n",
       "0        PAINE  PAINE         1        3  13404011003    0.030604    0.000043   \n",
       "1        PAINE  PAINE         1        2  13404011002    0.045283    0.000071   \n",
       "2        PAINE  PAINE         6        4  13404061004    0.084715    0.000102   \n",
       "3        PAINE  PAINE         6        1  13404061001    0.052736    0.000112   \n",
       "4        PAINE  PAINE         6        2  13404061002    0.024273    0.000035   \n",
       "..         ...    ...       ...      ...          ...         ...         ...   \n",
       "506   PEÑAFLOR    NaN         2        6  13605022006    0.284534    0.001065   \n",
       "507   PEÑAFLOR    NaN         2      901  13605022901    0.130868    0.000216   \n",
       "508   PEÑAFLOR    NaN         3        4  13605032004    0.134089    0.000350   \n",
       "509   PEÑAFLOR    NaN         3        6  13605032006    0.071088    0.000225   \n",
       "510   PEÑAFLOR    NaN         3        8  13605032008    0.050012    0.000084   \n",
       "\n",
       "                                              geometry     NOM_LOCALI  \n",
       "0    POLYGON ((-70.72732 -33.80301, -70.72713 -33.8...            NaN  \n",
       "1    POLYGON ((-70.70792 -33.80101, -70.70783 -33.8...            NaN  \n",
       "2    POLYGON ((-70.70724 -33.80454, -70.70712 -33.8...            NaN  \n",
       "3    POLYGON ((-70.74243 -33.81015, -70.74262 -33.8...            NaN  \n",
       "4    POLYGON ((-70.73996 -33.80852, -70.73929 -33.8...            NaN  \n",
       "..                                                 ...            ...  \n",
       "506  MULTIPOLYGON (((-70.87591 -33.62099, -70.87600...        MALLOCO  \n",
       "507  MULTIPOLYGON (((-70.86011 -33.62935, -70.85790...  INDETERMINADA  \n",
       "508  POLYGON ((-70.85540 -33.57587, -70.85493 -33.5...     EL GUANACO  \n",
       "509  POLYGON ((-70.86701 -33.58194, -70.86298 -33.5...        MALLOCO  \n",
       "510  POLYGON ((-70.87193 -33.58383, -70.87265 -33.5...       PEÑAFLOR  \n",
       "\n",
       "[2376 rows x 14 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from aves.data.census import read_zona, read_localidad\n",
    "\n",
    "zona_censo = read_zona(region=13)\n",
    "loc_censo = read_localidad(region=13)\n",
    "censo_zoning_13 = pd.concat([zona_censo, loc_censo])\n",
    "censo_zoning_13\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((-70.75950 -33.41237, -70.76014 -33.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((-70.73929 -33.38999, -70.73667 -33.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((-70.54357 -33.54802, -70.54293 -33.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((-70.54357 -33.54802, -70.54519 -33.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((-70.78731 -33.56483, -70.79797 -33.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>POLYGON ((-70.75398 -33.36184, -70.74889 -33.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>POLYGON ((-70.74889 -33.36107, -70.74621 -33.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>POLYGON ((-70.74621 -33.36028, -70.74965 -33.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>POLYGON ((-70.73742 -33.35790, -70.73619 -33.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>POLYGON ((-70.72881 -33.35795, -70.72093 -33.3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>804 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              geometry\n",
       "0    POLYGON ((-70.75950 -33.41237, -70.76014 -33.4...\n",
       "1    POLYGON ((-70.73929 -33.38999, -70.73667 -33.3...\n",
       "2    POLYGON ((-70.54357 -33.54802, -70.54293 -33.5...\n",
       "3    POLYGON ((-70.54357 -33.54802, -70.54519 -33.5...\n",
       "4    POLYGON ((-70.78731 -33.56483, -70.79797 -33.5...\n",
       "..                                                 ...\n",
       "799  POLYGON ((-70.75398 -33.36184, -70.74889 -33.3...\n",
       "800  POLYGON ((-70.74889 -33.36107, -70.74621 -33.3...\n",
       "801  POLYGON ((-70.74621 -33.36028, -70.74965 -33.3...\n",
       "802  POLYGON ((-70.73742 -33.35790, -70.73619 -33.3...\n",
       "803  POLYGON ((-70.72881 -33.35795, -70.72093 -33.3...\n",
       "\n",
       "[804 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zona777.set_crs(zona_censo.crs, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "zona777['id'] = range(0, len(zona777))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aves.data.census import read_hogares\n",
    "hogares = pd.read_parquet(\n",
    "        DATA_PATH / \"Hogares.parquet\", filters=[(\"REGION\", \"==\", 13)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "censo_zoning_13.COMUNA=censo_zoning_13.COMUNA.astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_zoning = gpd.sjoin(censo_zoning_13, zona777, how='inner', predicate='intersects')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "parquet_file = DATA_PATH/\"Personas.parquet\"\n",
    "df = dd.read_csv(DATA_PATH/\"Personas.csv\", sep=\";\")\n",
    "df['P07']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P07.csv\", index_col=\"id\", col_name=\"P07\", sep=',')\n",
    "df['P08']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P08.csv\", index_col=\"id\", col_name=\"P08\", sep=',')\n",
    "df['P10']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P10.csv\", index_col=\"id\", col_name=\"P10\", sep=',')\n",
    "df['P10COMUNA']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/COMUNA.csv\", index_col=\"COMUNA\", col_name=\"P10COMUNA\", sep=';')\n",
    "df['P11COMUNA']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/COMUNA.csv\", index_col=\"COMUNA\", col_name=\"P11COMUNA\", sep=';')\n",
    "df['P12COMUNA']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/COMUNA.csv\", index_col=\"COMUNA\", col_name=\"P12COMUNA\", sep=';')\n",
    "df['P10PAIS']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS.csv\", index_col=\"id\", col_name=\"P10PAIS\", sep=',')\n",
    "df['P11PAIS']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS.csv\", index_col=\"id\", col_name=\"P11PAIS\", sep=',')\n",
    "df['P12PAIS']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS.csv\", index_col=\"id\", col_name=\"P12PAIS\", sep=',')\n",
    "df['P11']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P11.csv\", index_col=\"id\", col_name=\"P11\", sep=',')\n",
    "df['P12']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P12.csv\", index_col=\"id\", col_name=\"P12\", sep=',')\n",
    "df['P12A_TRAMO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P12A_TRAMO.csv\", index_col=\"id\", col_name=\"P12A_TRAMO\", sep=',')\n",
    "df['P13']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P13.csv\", index_col=\"id\", col_name=\"P13\", sep=',')\n",
    "df['P14']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P14.csv\", index_col=\"id\", col_name=\"P14\", sep=',')\n",
    "df['P15']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P15.csv\", index_col=\"id\", col_name=\"P15\", sep=',')\n",
    "df['P15A']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P15A.csv\", index_col=\"id\", col_name=\"P15A\", sep=',')\n",
    "df['P16']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P16.csv\", index_col=\"id\", col_name=\"P16\", sep=',')\n",
    "df['P16A']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P16A.csv\", index_col=\"id\", col_name=\"P16A\", sep=',')\n",
    "df['P16A_OTRO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P16A_OTRO.csv\", index_col=\"id\", col_name=\"P16A_OTRO\", sep=',')\n",
    "df['P16A_GRUPO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P16A_GRUPO.csv\", index_col=\"id\", col_name=\"P16A_GRUPO\", sep=',')\n",
    "df['P17']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P17.csv\", index_col=\"id\", col_name=\"P17\", sep=',')\n",
    "#df['P18']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P18.csv\", index_col=\"id\", col_name=\"P18\", sep=',', index_dtype=object)\n",
    "df['P21M']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/P21M.csv\", index_col=\"id\", col_name=\"P21M\", sep=',')\n",
    "df['P10PAIS_GRUPO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS_GRUPO.csv\", index_col=\"id\", col_name=\"P10PAIS_GRUPO\", sep=',')\n",
    "df['P11PAIS_GRUPO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS_GRUPO.csv\", index_col=\"id\", col_name=\"P11PAIS_GRUPO\", sep=',')\n",
    "df['P12PAIS_GRUPO']= decode_column(df, fname=DATA_PATH/\"Tablas_parametros/PAIS_GRUPO.csv\", index_col=\"id\", col_name=\"P12PAIS_GRUPO\", sep=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "personas_pd = pd.read_parquet(DATA_PATH/\"Personas.parquet\", columns=[\"ID_ZONA_LOC\",\"REGION\", \"PROVINCIA\",\"COMUNA\", \"P07\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "traduccion = pd.read_csv(DATA_PATH/\"Tablas_parametros/traduccion_nuble.csv\")\n",
    "cols_to_replace = [\"COMUNA\", \"REGION\", \"PROVINCIA\"]\n",
    "for col in cols_to_replace:\n",
    "    personas_pd.loc[personas_pd.PROVINCIA == 84, col] = personas_pd[personas_pd.PROVINCIA==84]['ID_ZONA_LOC'].map(traduccion.set_index('ID_ZONA_LOC')[col])\n",
    "personas_pd['NOM_COMUNA']=decode_column(personas_pd, fname=DATA_PATH/\"Tablas_parametros/COMUNA.csv\", index_col=\"COMUNA\", col_name=\"COMUNA\", sep=';')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 14, 13, 12, 11, 10,  9, 16,  8,  7,  6,  5,  4,  3,  2,  1])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "personas_pd.REGION.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Not all divisions are known, can't align partitions. Please use `set_index` to set the index.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[52], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m cols_to_replace \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mCOMUNA\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mREGION\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mPROVINCIA\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m      3\u001b[0m \u001b[39mfor\u001b[39;00m col \u001b[39min\u001b[39;00m cols_to_replace:\n\u001b[0;32m----> 4\u001b[0m     df[df\u001b[39m.\u001b[39;49mPROVINCIA \u001b[39m==\u001b[39;49m \u001b[39m84\u001b[39;49m][col] \u001b[39m=\u001b[39m df[df\u001b[39m.\u001b[39mPROVINCIA \u001b[39m==\u001b[39m \u001b[39m84\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mID_ZONA_LOC\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mmap(traduccion\u001b[39m.\u001b[39mset_index(\u001b[39m'\u001b[39m\u001b[39mID_ZONA_LOC\u001b[39m\u001b[39m'\u001b[39m)[col])\u001b[39m.\u001b[39mcompute()\n\u001b[1;32m      5\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mNOM_COMUNA\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m=\u001b[39mdecode_column(df, fname\u001b[39m=\u001b[39mDATA_PATH\u001b[39m/\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mTablas_parametros/COMUNA.csv\u001b[39m\u001b[39m\"\u001b[39m, index_col\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCOMUNA\u001b[39m\u001b[39m\"\u001b[39m, col_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCOMUNA\u001b[39m\u001b[39m\"\u001b[39m, sep\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m;\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/aves/lib/python3.11/site-packages/dask/dataframe/core.py:5100\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   5098\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mNotImplementedError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mItem assignment with \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(key)\u001b[39m}\u001b[39;00m\u001b[39m not supported\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   5099\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 5100\u001b[0m     df \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49massign(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m{key: value})\n\u001b[1;32m   5102\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdask \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39mdask\n\u001b[1;32m   5103\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_name \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39m_name\n",
      "File \u001b[0;32m~/miniconda3/envs/aves/lib/python3.11/site-packages/dask/dataframe/core.py:5550\u001b[0m, in \u001b[0;36mDataFrame.assign\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   5546\u001b[0m     \u001b[39m# Figure out columns of the output\u001b[39;00m\n\u001b[1;32m   5547\u001b[0m     df2 \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39m_meta_nonempty\u001b[39m.\u001b[39massign(\n\u001b[1;32m   5548\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m_extract_meta({k: kwargs[k]}, nonempty\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m   5549\u001b[0m     )\n\u001b[0;32m-> 5550\u001b[0m     data \u001b[39m=\u001b[39m elemwise(methods\u001b[39m.\u001b[39;49massign, data, \u001b[39m*\u001b[39;49mpairs, meta\u001b[39m=\u001b[39;49mdf2)\n\u001b[1;32m   5552\u001b[0m \u001b[39mreturn\u001b[39;00m data\n",
      "File \u001b[0;32m~/miniconda3/envs/aves/lib/python3.11/site-packages/dask/dataframe/core.py:6655\u001b[0m, in \u001b[0;36melemwise\u001b[0;34m(op, meta, out, transform_divisions, *args, **kwargs)\u001b[0m\n\u001b[1;32m   6651\u001b[0m args \u001b[39m=\u001b[39m _maybe_from_pandas(args)\n\u001b[1;32m   6653\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdask\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdataframe\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmulti\u001b[39;00m \u001b[39mimport\u001b[39;00m _maybe_align_partitions\n\u001b[0;32m-> 6655\u001b[0m args \u001b[39m=\u001b[39m _maybe_align_partitions(args)\n\u001b[1;32m   6656\u001b[0m dasks \u001b[39m=\u001b[39m [arg \u001b[39mfor\u001b[39;00m arg \u001b[39min\u001b[39;00m args \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(arg, (_Frame, Scalar, Array))]\n\u001b[1;32m   6657\u001b[0m dfs \u001b[39m=\u001b[39m [df \u001b[39mfor\u001b[39;00m df \u001b[39min\u001b[39;00m dasks \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(df, _Frame)]\n",
      "File \u001b[0;32m~/miniconda3/envs/aves/lib/python3.11/site-packages/dask/dataframe/multi.py:176\u001b[0m, in \u001b[0;36m_maybe_align_partitions\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m    174\u001b[0m divisions \u001b[39m=\u001b[39m dfs[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mdivisions\n\u001b[1;32m    175\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mall\u001b[39m(df\u001b[39m.\u001b[39mdivisions \u001b[39m==\u001b[39m divisions \u001b[39mfor\u001b[39;00m df \u001b[39min\u001b[39;00m dfs):\n\u001b[0;32m--> 176\u001b[0m     dfs2 \u001b[39m=\u001b[39m \u001b[39miter\u001b[39m(align_partitions(\u001b[39m*\u001b[39;49mdfs)[\u001b[39m0\u001b[39m])\n\u001b[1;32m    177\u001b[0m     \u001b[39mreturn\u001b[39;00m [a \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(a, _Frame) \u001b[39melse\u001b[39;00m \u001b[39mnext\u001b[39m(dfs2) \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m args]\n\u001b[1;32m    178\u001b[0m \u001b[39mreturn\u001b[39;00m args\n",
      "File \u001b[0;32m~/miniconda3/envs/aves/lib/python3.11/site-packages/dask/dataframe/multi.py:130\u001b[0m, in \u001b[0;36malign_partitions\u001b[0;34m(*dfs)\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mdfs contains no DataFrame and Series\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    129\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mall\u001b[39m(df\u001b[39m.\u001b[39mknown_divisions \u001b[39mfor\u001b[39;00m df \u001b[39min\u001b[39;00m dfs1):\n\u001b[0;32m--> 130\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    131\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mNot all divisions are known, can\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt align \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    132\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mpartitions. Please use `set_index` \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    133\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mto set the index.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    134\u001b[0m     )\n\u001b[1;32m    136\u001b[0m divisions \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(unique(merge_sorted(\u001b[39m*\u001b[39m[df\u001b[39m.\u001b[39mdivisions \u001b[39mfor\u001b[39;00m df \u001b[39min\u001b[39;00m dfs1])))\n\u001b[1;32m    137\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(divisions) \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:  \u001b[39m# single value for index\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: Not all divisions are known, can't align partitions. Please use `set_index` to set the index."
     ]
    }
   ],
   "source": [
    "\n",
    "traduccion = pd.read_csv(DATA_PATH/\"Tablas_parametros/traduccion_nuble.csv\")\n",
    "cols_to_replace = [\"COMUNA\", \"REGION\", \"PROVINCIA\"]\n",
    "for col in cols_to_replace:\n",
    "    df[df.PROVINCIA == 84][col] = df[df.PROVINCIA == 84]['ID_ZONA_LOC'].map(traduccion.set_index('ID_ZONA_LOC')[col]).compute()\n",
    "df['NOM_COMUNA']=decode_column(df, fname=DATA_PATH/\"Tablas_parametros/COMUNA.csv\", index_col=\"COMUNA\", col_name=\"COMUNA\", sep=';')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>REGION</th>\n",
       "      <th>PROVINCIA</th>\n",
       "      <th>COMUNA</th>\n",
       "      <th>DC</th>\n",
       "      <th>AREA</th>\n",
       "      <th>ZC_LOC</th>\n",
       "      <th>ID_ZONA_LOC</th>\n",
       "      <th>NVIV</th>\n",
       "      <th>NHOGAR</th>\n",
       "      <th>PERSONAN</th>\n",
       "      <th>...</th>\n",
       "      <th>P19</th>\n",
       "      <th>P20</th>\n",
       "      <th>P21M</th>\n",
       "      <th>P21A</th>\n",
       "      <th>P10PAIS_GRUPO</th>\n",
       "      <th>P11PAIS_GRUPO</th>\n",
       "      <th>P12PAIS_GRUPO</th>\n",
       "      <th>ESCOLARIDAD</th>\n",
       "      <th>P16A_GRUPO</th>\n",
       "      <th>NOM_COMUNA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>352765</th>\n",
       "      <td>8</td>\n",
       "      <td>84</td>\n",
       "      <td>8421</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2752</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Septiembre</td>\n",
       "      <td>1980</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>2</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352766</th>\n",
       "      <td>8</td>\n",
       "      <td>84</td>\n",
       "      <td>8421</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2752</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Febrero</td>\n",
       "      <td>1999</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>Missing</td>\n",
       "      <td>8</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352767</th>\n",
       "      <td>8</td>\n",
       "      <td>84</td>\n",
       "      <td>8421</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2752</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>9998</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>Missing</td>\n",
       "      <td>11</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352768</th>\n",
       "      <td>8</td>\n",
       "      <td>84</td>\n",
       "      <td>8421</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2752</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Febrero</td>\n",
       "      <td>1991</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>4</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352769</th>\n",
       "      <td>8</td>\n",
       "      <td>84</td>\n",
       "      <td>8421</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2752</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>9998</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>12</td>\n",
       "      <td>No aplica</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        REGION  PROVINCIA  COMUNA  DC  AREA  ZC_LOC  ID_ZONA_LOC  NVIV  \\\n",
       "352765       8         84    8421   1     1       1         2752     1   \n",
       "352766       8         84    8421   1     1       1         2752     1   \n",
       "352767       8         84    8421   1     1       1         2752     1   \n",
       "352768       8         84    8421   1     1       1         2752     2   \n",
       "352769       8         84    8421   1     1       1         2752     3   \n",
       "\n",
       "        NHOGAR  PERSONAN  ... P19 P20        P21M  P21A P10PAIS_GRUPO  \\\n",
       "352765       1         1  ...   6   6  Septiembre  1980     No aplica   \n",
       "352766       1         2  ...   3   3     Febrero  1999     No aplica   \n",
       "352767       1         3  ...   0  98   No aplica  9998     No aplica   \n",
       "352768       1         1  ...   3   3     Febrero  1991     No aplica   \n",
       "352769       1         1  ...  98  98   No aplica  9998     No aplica   \n",
       "\n",
       "       P11PAIS_GRUPO P12PAIS_GRUPO ESCOLARIDAD P16A_GRUPO NOM_COMUNA  \n",
       "352765     No aplica     No aplica           2  No aplica       <NA>  \n",
       "352766     No aplica       Missing           8  No aplica       <NA>  \n",
       "352767     No aplica       Missing          11  No aplica       <NA>  \n",
       "352768     No aplica     No aplica           4  No aplica       <NA>  \n",
       "352769     No aplica     No aplica          12  No aplica       <NA>  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.PROVINCIA==84].compute().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aves.data.census import load_personas\n",
    "load_personas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "read_table() got an unexpected keyword argument 'filter'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39maves\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdata\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcensus\u001b[39;00m \u001b[39mimport\u001b[39;00m read_personas\n\u001b[0;32m----> 2\u001b[0m personas_13 \u001b[39m=\u001b[39m read_personas(path \u001b[39m=\u001b[39;49m DATA_PATH, filters\u001b[39m=\u001b[39;49m[(\u001b[39m\"\u001b[39;49m\u001b[39mREGION\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m13\u001b[39;49m)], columns\u001b[39m=\u001b[39;49m[\u001b[39m'\u001b[39;49m\u001b[39mCOMUNA\u001b[39;49m\u001b[39m'\u001b[39;49m,\u001b[39m'\u001b[39;49m\u001b[39mDC\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mAREA\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mZC_LOC\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mID_ZONA_LOC\u001b[39;49m\u001b[39m'\u001b[39;49m,\u001b[39m\"\u001b[39;49m\u001b[39mP08\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mP15\u001b[39;49m\u001b[39m\"\u001b[39;49m])\n",
      "File \u001b[0;32m~/aves/src/aves/data/census.py:510\u001b[0m, in \u001b[0;36mread_personas\u001b[0;34m(path, filters, columns)\u001b[0m\n\u001b[1;32m    508\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    509\u001b[0m     DATA_PATH \u001b[39m=\u001b[39m Path(path)\n\u001b[0;32m--> 510\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_parquet(DATA_PATH\u001b[39m/\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mPersonas.parquet\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mfilter\u001b[39;49m\u001b[39m=\u001b[39;49mfilters, columns\u001b[39m=\u001b[39;49mcolumns)\n\u001b[1;32m    511\u001b[0m \u001b[39mreturn\u001b[39;00m df\n",
      "File \u001b[0;32m~/miniconda3/envs/aves/lib/python3.11/site-packages/pandas/io/parquet.py:670\u001b[0m, in \u001b[0;36mread_parquet\u001b[0;34m(path, engine, columns, storage_options, use_nullable_dtypes, dtype_backend, filesystem, filters, **kwargs)\u001b[0m\n\u001b[1;32m    667\u001b[0m     use_nullable_dtypes \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m    668\u001b[0m check_dtype_backend(dtype_backend)\n\u001b[0;32m--> 670\u001b[0m \u001b[39mreturn\u001b[39;00m impl\u001b[39m.\u001b[39;49mread(\n\u001b[1;32m    671\u001b[0m     path,\n\u001b[1;32m    672\u001b[0m     columns\u001b[39m=\u001b[39;49mcolumns,\n\u001b[1;32m    673\u001b[0m     filters\u001b[39m=\u001b[39;49mfilters,\n\u001b[1;32m    674\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m    675\u001b[0m     use_nullable_dtypes\u001b[39m=\u001b[39;49muse_nullable_dtypes,\n\u001b[1;32m    676\u001b[0m     dtype_backend\u001b[39m=\u001b[39;49mdtype_backend,\n\u001b[1;32m    677\u001b[0m     filesystem\u001b[39m=\u001b[39;49mfilesystem,\n\u001b[1;32m    678\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m    679\u001b[0m )\n",
      "File \u001b[0;32m~/miniconda3/envs/aves/lib/python3.11/site-packages/pandas/io/parquet.py:272\u001b[0m, in \u001b[0;36mPyArrowImpl.read\u001b[0;34m(self, path, columns, filters, use_nullable_dtypes, dtype_backend, storage_options, filesystem, **kwargs)\u001b[0m\n\u001b[1;32m    265\u001b[0m path_or_handle, handles, filesystem \u001b[39m=\u001b[39m _get_path_or_handle(\n\u001b[1;32m    266\u001b[0m     path,\n\u001b[1;32m    267\u001b[0m     filesystem,\n\u001b[1;32m    268\u001b[0m     storage_options\u001b[39m=\u001b[39mstorage_options,\n\u001b[1;32m    269\u001b[0m     mode\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mrb\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    270\u001b[0m )\n\u001b[1;32m    271\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 272\u001b[0m     pa_table \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapi\u001b[39m.\u001b[39;49mparquet\u001b[39m.\u001b[39;49mread_table(\n\u001b[1;32m    273\u001b[0m         path_or_handle,\n\u001b[1;32m    274\u001b[0m         columns\u001b[39m=\u001b[39;49mcolumns,\n\u001b[1;32m    275\u001b[0m         filesystem\u001b[39m=\u001b[39;49mfilesystem,\n\u001b[1;32m    276\u001b[0m         filters\u001b[39m=\u001b[39;49mfilters,\n\u001b[1;32m    277\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m    278\u001b[0m     )\n\u001b[1;32m    279\u001b[0m     result \u001b[39m=\u001b[39m pa_table\u001b[39m.\u001b[39mto_pandas(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mto_pandas_kwargs)\n\u001b[1;32m    281\u001b[0m     \u001b[39mif\u001b[39;00m manager \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39marray\u001b[39m\u001b[39m\"\u001b[39m:\n",
      "\u001b[0;31mTypeError\u001b[0m: read_table() got an unexpected keyword argument 'filter'"
     ]
    }
   ],
   "source": [
    "from aves.data.census import read_personas\n",
    "personas_13 = read_personas(path = DATA_PATH, filters=[(\"REGION\", \"=\", 13)], columns=['COMUNA','DC', 'AREA', 'ZC_LOC', 'ID_ZONA_LOC',\"P08\", \"P15\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mCanceled future for execute_request message before replies were done"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "personas_13 = pd.read_parquet(DATA_PATH/\"Personas.parquet\", columns=['COMUNA', 'DC', 'AREA', 'ZC_LOC', 'ID_ZONA_LOC',\"P08\", \"P15\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>REGION</th>\n",
       "      <th>COMUNA</th>\n",
       "      <th>DC</th>\n",
       "      <th>AREA</th>\n",
       "      <th>ZC_LOC</th>\n",
       "      <th>ID_ZONA_LOC</th>\n",
       "      <th>P08</th>\n",
       "      <th>P15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [REGION, COMUNA, DC, AREA, ZC_LOC, ID_ZONA_LOC, P08, P15]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "personas_13.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'hogares' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m census_data \u001b[39m=\u001b[39m censo_zoning_13\u001b[39m.\u001b[39mmerge(hogares[[\u001b[39m'\u001b[39m\u001b[39mCOMUNA\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mDC\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mAREA\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mZC_LOC\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mID_ZONA_LOC\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m      2\u001b[0m        \u001b[39m'\u001b[39m\u001b[39mNVIV\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mNHOGAR\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mTIPO_HOGAR\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mTIPO_OPERATIVO\u001b[39m\u001b[39m'\u001b[39m]], left_on\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mCOMUNA\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mDISTRITO\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mLOC_ZON\u001b[39m\u001b[39m'\u001b[39m], right_on\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mCOMUNA\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mDC\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mZC_LOC\u001b[39m\u001b[39m\"\u001b[39m])\n",
      "\u001b[0;31mNameError\u001b[0m: name 'hogares' is not defined"
     ]
    }
   ],
   "source": [
    "census_data = censo_zoning_13.merge(df[['COMUNA','DC', 'AREA', 'ZC_LOC', 'ID_ZONA_LOC',\n",
    "       'NVIV', 'NHOGAR', 'TIPO_HOGAR', 'TIPO_OPERATIVO']], left_on=['COMUNA', 'DISTRITO','LOC_ZON'], right_on=['COMUNA', 'DC', \"ZC_LOC\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fkUwmg2QyiUwmoxMZtzxiWD04L7RglUqlZYLR7BjTDJxHVIi5c4E77wRuugmYNQvYYw/gnHOAVArYf39g0CDg2WeBZZft+dlyGXjvvWqK4qWXgI8/rv59rbW6oy2bbAL0giHWAoVDDgG+/756bRuAPRkY5qfehLeDb/x4RI48Eqn334e60kquHiYFce3qC9Hb4EornU439XlqNdxK/0iShESTKVZN01o2m3KCjo3+ggmjpk1RlBqSwnRKqVRyTFZ5f9O/o1XQCdbu82enNNlORCWRSPRKitUMTNs0O+b0djS0eaJCFIvA2LHAdddVtSsAsNtuVdMxu2rg33+vRl5eeqn68/vv1c9us02VuOy4IzB4cEuHuVBgpZWqJO/mmwHUDhZ2xbA68nnE11oLWiSC7JgxUFdf3dVD5YPCY5rfwWqHZpqI8aF3a+AFgH79+jX1ud4Q0YrozfBxB/MOxg7I4v+LxaLj56Zfv36uPS/USrmZ7mxEVAKBgC5m7Y3opRHUsDVLVNrZK8gMrcfgAgHgyCOr3h3PPFONsIwfb5+kAMASS3SngGbNAj75BDj//Grq6NRTgSFDqpPwp5+2fLgLLPL5ajRljTUAdIfweSOxOVg6nUYqlUI+n69fDhcKIfPSS0AkgtgOO8D36KOuHm6xWNQFdwtC+J/GUc34onDF2RuW+vXgpKOtW5gXg3QHvQ9j1Y34f07qzWzTDfCed6M9hl0EAgEoijLP7n/ODc2Uekej0V4lKYAbEZV2I5UCXn+9Wj20yy7A/ffP6yPqm7juumrqbcoUaEOG6CFFr9erK9ybQj6PyAEHwPvhh0jOnFn1ZXERsiwjGo263sRqXoCPUjMRCbvlgnaRSCSaup69vcKjmLiDDuqV+xrhdtokHo+jUqm4Vq1UL6LCCE47y7jtoJmKo96s9BHR90tu4nFgzz2Bv/8deOqpaol0B93QtGp/n/POA848Exg6VA+nAt1izaYRCkHZYYdqxVYbbk5qOxYEkS0f3mZKbt221Hd6LXtTRCuiE1HpgHCqVXJzsqxUKr0WUQmHw1AUZZ6SFMD5GOFmB3mn6PtEhdhvPyCdrmpYOqiiVKpa4R99dPXnmmvaM+FkMtDauOpl34h5/eC6AZrcNfNAl8tl1wbLZshpbxpOER0hbQdAdQxwkoZwe1HDvj/tbhRKg8NmtGxuQpZleL1e2/OEJEl65HNeRL7nH6Ky6qpV/cXDD8/rI+k7OPlk4JFHqumwO++E1jXou/0QSJkMtFjM1W2aIZfL9bpGAujZrtyNn0Ag4FhzUiqVIEmSK/1/mmldMK+IyvweSeugdbCHVjQahd/v143XzIi7z+dznVAUi8Ve0an4fD7d96U3QGfwYDCIUCiEUCiEcDisV+zYTXXN68KH3lMPuYHdd6+mOTqoRpb+8x9g9GhoBx9cTQEBuleJm5DS6bZGVES4GVUgzCoOjE23xMnS+DdN0+D3++HxeBqSQLFvSCgUcpR643sDgUDL0SUnk/+8ENESvSlg7KBvg9FIo6MyTdZ4f8qyXPMMuwUSlXYSdre7Lptt3+v16mZ1vFaEaEth105BkiT4/f55qiGcv0aJtdeuGsP9/nu1Umhhhapi6um34pDoJFy41GrYqatqpBl7ezvobaLSTAUAYSQlrKihwp4DYSaTcbRdsdtoPbDZWSaTQSQS0cse7aJUKrnSIXZ+iVL0dvVAB30bZvcCoyf1evW4AVrqtxOaprkeDaKLrs/nq+nsrKoqCoVCD+LFCIvdBdS8rkYE5jeiwt5CkycvOESlVKq6yTp4QIpjHsZ+31yKLzzDsNfewL33VrDrru3LeUqZjLNy8xbAlZJd+3ejV4xITEQDO/H9LMtrp6mZqqrI5XKIRCIIhUK203GlUklvetaKR0QzJlq9jQWxnUIH8y/K5bLuMt0u+3/uw+v1Nt0tmelhRk7EsY/EpN7xO33mOkTFKSZNqv7bZB+bPodisdrv6Ouvq340oRAQiQDLLw+ssEL1Z4klqpVPpVLVV+a++3DmlFPwpTwcL7+Sx513BnHYYT7ceWcE++7bnkZgUiYDdZll2rJtM5TLZcuognECZqSExKQRaNvu9XrbQlTEQaBcLqNYLMLv99c31zMcHz1VeouozLO8s9e7wLgTdzD/o1gsIhgMtjX9Q6Li9/sdERV6NDE9Rg2aoih1Oz2bwZgOqgePx9MnBO/zF1H56qtqH5vFFpvXR+IObryxSlLOPx9YcsmqaVs6Xe2L9NNP1SaDf/xR/bssA7EYHktuh1E4ATevfjvWXvtAjBpVRjgcw7HH+pDLRXHYYc5SGrbQ5qofI8rlsi7eMrPipli12UZ+iqK07eEzTrr5fF7vHm3XBbJUKrW8smMVg13MC7LQ0ad00AraVaHTTqLCcUfTNITD4Zq0FnsXqaqqi2CZGhWfT7a4aDb15YSo+Hy+PrGYmL9Gim+/XXCiKTNmAFdcAZxxBnDVVfXfqyiAx4Op/5uOo7dbBCPxCE7+8m9QN74Z6RdewL//DUQiUZx6qhfZbAwnnthczxkrSL1MVNgJVZZlPRJhFKV6vV4Eg8GmiUpv5KKJXC6HaDSqmzw1AlddoVCoaQMq+tLYHWDmFVGZ1wNgB/Mv2nHvuO2nwogEdSQ8ZvY6E6vz/H6/nmZhOodjX7lc1s0xzVLaTuBkAdQXoinA/EhUdthhXh9F6/j1V+Ckk6opnUsuafx+rxefjfsGRxxcxmKo4MZ7fCg/vTd8Tz6JxLBhSL/8Mi6/fHVEo1Gcf74XmUwMZ52Vds2fTSoUILnk2GgX5XIZfr/f0nmyUCggEonA5/M5rpBhtMHJZ1tZVbATK5sxNko5sQqn1TLlUqlkW63v8Xh0nwTmuzmIttqd1gy94VnRwYKLdjlZK4qiO686JQP0JvF4PHpaMxaL1USF2brErHO4JEl6WsgsWqw3jPX5mta32C0KEI+pLywm5p+Rolyu9rJZZZV5fSStIZ0Ghg2rdpa++eYqWamDqW/OxN7x17D2Qasio4Xx4EWfIbLX1sjdfTdy//kPUCwitvXW8P3vNZx7bgZXXFHGP//pwWWXxdEC6a5B6dBD4b/vPng+/9ydDdpAuVzWS+2sXq9UKk05uTLa4IZfiRFWDzXb0QeDQVsTtBueKlbRJhIRGgOyUoziVq/XC7/fr3svxONxJBIJRKNR15wp+8pKrYP5F+0gunxmGkVVZFmGz+dDKBRCNBpFIpFAPB7XiQZQJT3ZbBbJZFInBnyezUgQO5cD5oaNLDRoJeLDVI4doiLLcp95TuefiMoPP1RTIPM7URk3riqKnTQJWG21hm8/Yfup+Ka0Mv678jXYc9yu8K64rf5aed99kV5uOcR23x2RkSORu+UWnHzywYhEwjjjDD8ymTj+9a8UWr3XCmecAe/zzyN8wglIv/FGVfjbZohaFKvVA6MqzSjoWarcLpgNRLlcDrFYDNFotGF/DTc8Vdh4jF2d660QI5EIPB5PTSk1V1NcITLdZgxbi34z7CLdscbvoN1ox0pfFNvzueOEzR8xZSlWGpbLZcvFQaFQ0NM/9Rxhxf2bjWmtEhW/32/72exLVXnzD1GZPLn678UXA/37A8FgdcIMBnv+zp9FF62KVFdYoVpJM68vuqYBd9wB7LqrLZKSmjobb5Q2xr/XuBv7vHmC6XvUDTZA+r33ENt8c4RPPhmF337DkWecgXBYw9/+5kcuF8dNN6XQUvDA70futtsQ22YbBP/1LxQuvriFjdlHI/M3piTC4bD+u92OpIqiuOJX4gSapul6lXA43LChmhueKlyZNQpj81qL+WsSD1VVawZthrcZRjZ2xSWRYdSmVCo1HaruoIPehqqquvst73GglpSw2sZJekhRFFvpZk3TLBdR3EYzcJr26UuLjfmHqKy9NnDEEdXUSaEAzJlT/bdQqJb5FgrV6hjxd3FwjMeBNdcENtoI2HRTYJNNgMUX791z+Ogj4NNPqyJaG7h+t/+hgpHY7tTBdd+nrrgikp9/jvhGGyF4xRWQfv4Z+99wA8JhDUcdFUAul8CddyZbCoSoq6+OwjnnIHj11Sjvsgsq663X/MZsgjqVeuIvljKLE7pxhU/zI5Yxc7AJBoPweDyuPpCNViBU9tMmvN4ETk+VVjxf6g16xn3ZEfCqqmq5avR4PIjFYigUCvogTwM8njcjZfOLIV0HtTAaKtZ7XztX4xSatgN87jweD1RV1dO2req0GM1otJ16URNGSf1+v+NCAr/fbzvtI0kSYr3QNsUuJG1BHTE0rUpqfvutqm35/PMqSXj3XeDnn6vvufJK4IILeu+Yjjuuan3/ww9olI95/uAHsdvYA3HpYrfi798dZm/7uRxim28Ozw8/oLz99sg+9BBe/V8IhxwSxBZbAPfem0QLpq+AoiC6ww6Qslmk33oLaLMRkCRJiMfjDdu5s+U7J0irH0K85e22iqcQ1qxtu/F9fr+/YSlyLBaDLMu23gfAkbutiHA4DJ/PZ6s02s57xfJKI+FgrxZ2wyaMreE52HbEtH0fRkNFVuGJk61xCpFlWbfBbxdZoQV8O4zZJElCIpFAoVBoycvICKapGz2LgUAAoVAIqVTK9PwSiYSuf3ECJ5/jWNBJ/bQbklSNosTjVUfbnXfufu2nn4DbbwcuvBCIxYC//713jimbraakGnz5342ZiIPH7oLd/C/j5Pf3sL/9cBjpDz9EdNdd4XvlFUS32grb/e9/ePxxYP/9gzjggAQeeCDZSL9rDa8XuVtuQWyLLeC/5x6Ujj++yQ3ZA1dNjXKyrJApFAp1V1lG4sL+PW7C7oOdzWZ1vUo9O/9isdiSpwrN4+xEjlglZBXp8fl8iEQipp8VV9DGiatYLKJYLNZYffPYgO5rZiRBnOj6go/DwgIjMRFdnu1GHhm55GTXju+PUdJ2gFFYty0MisWifv/XS//QnoARHSPo3O0EFPjadcj2eDx96plbOJc0yy0H/POfwFlnAaeeCowZ0zv7PeEEYMqUasWPBaY++QW2OXI5LIlfcct7K0NetJ+zfcgyMi+8gNI++8AzaRJia62FTdeYjfHjc/jiC2DffROYO7f5r11dbTWU990XgdGj9UaI7QSrf+qBEZFGFSlM+ZRKJRQKBd2bYF6Adtder7euDoUDWrPVNsyj2yFkXClb7YsTValUQi6XQz6f182nxInMKkjLMu10Oo1UKoVMJoNMJoN0Oq1HYSqVCpLJJFKplP4vq7Q6cB9GMXSlUkGxWEQ6nUYymUQ2m0WxWHScHtU0DdlsVp8Y57fvrx2mkEx7NtKYUFBbT6fidNwKBAK2yV1fjHb2raPpTUgScO21wNFHA0cdBTz1VPv3ueaaVYv8PfcEttyyWyDchR9veQZbjVgEYTWDFw69F4kVF2l6V7n//hfF006DPHMm4sOHY/0lpuLZZ3OYNg3YY48Y/vij+YewdMgh8PzwAzzvv9/0NuyCnieNJmpGVZw8YOzKGolEEA6He/xQs8HVDQBdMxIIBOD3+3UVv5mwtBHslCxzVdtKmbKT9vXlctlygORAJ8sySqUSisUiCoUC8vk8stlsy26ekiT1mBBVVUU2m+3oWlyCkZhQg5HJZJBMJpHJZFAoFFzTbXHbZlVizYLH3U60S/9i91muF9Fxkjrl+ObxeGw/n81YPrQbC65GxS4qFeCAA4CnnwbuuQc48MD2VAdls8Duu1cFtcsvD3z5ZfXva68NPP88sOSSOGvQExgzbTO8NSGJAcPdEfr6//tfhM8+G5osI/3SS/g2vhH22iuCaBQYPz6DpZduYkBSVcTXWgvS3LkojRgBZZttoGy5JbREouXjZUpGJB20z29U0kutSj3Nibh9cUI2ewyaDX0aQ91mjRGNx1SpVCx1KEy5ZDKZpgbQaDSq9wcxg9V5ksgZ36tpmul3wdx6MplsajJKJBI6+TGC2p++FI6eXyDej2JfrFYdTp2AAlCmXcVJuJnvNJvNNl22bweyLCMejyOfz7vaE4yC80bHz8akZjoVJxoaPjeMfFqBi0HqUvrac9YhKkC1Uujww4GHH646395+OzBokHvb/+Yb4NBDqxGU558HNt+8+vvxxwMTJgA+H3DMMdjp0aMR+ONn3PfFoGpPo1bx22+IHnQQvJ98Ag0AZBnZe+7B98P3xl57VV1Ix4/PYoUVnE9+3pdfhv/RR+GZNAmeyZOheTyorL8+yjvuiNJBB0Gz2d2aHUBZGmscUJmPDQQCDUVozIkbRXbGNuhiyS0raho98BS1Uk/C4zT+SxJULBZ7vGb1O71K6g2K8XgcqqrW1bNYwe/3IxwOWzpiGsHyYuOqmp9jBY/Z51olKvl83nTl126iYqxm4eQ+P+pjjP2xxDYUfWW4p5khn32715qCXjsC+FbRyjPXaLuVSqWhqLUecbd7bIlEApVKpeH7+vpCoENURDz3HHDyydVOxXfcAay/PjBggLMIy5gxwNix3f+vVKpkZMUVq39fd93a948aheknX4trcQ7+g2NxAa7C2dfHUTr66ObPQ1URvPRSBEaNAlQVylZboXDuuYjuvTdQKKBwxRX4Ye+/Y8SIGNJp4OmncxgypPnVifTTT/D973/wvvoqfK+9BlQqKO++O4onn4zK2mvXvJflqhRrcYCiPsFsQHWyumFUhcI1ruK4D75G0OLazgNP47R6YGrIbgNCghUB2WzWNGpit5rICvUGPSPsXhMjWiEqXGlaRY14Xds1kPLeYNm61+tFoVBo6z7dhEisxHPpS14Y9UDSwhW9GWnpTZICVBc+Xq+3YSTXKexW/0SjUciybLp/O8fm9/sRCoVsVUf1tSofIzpExYhZs4Ctt+7Wjyy+eJVcrLsucNhhwEorWX/28ceBkSOrERN6tEhSNb1z+umAmPtTVXwzaFf8a/r+eBAHI4EkTpNvwhnqvxFCHvmrr0bpBHOTt7r47TfEN98c8uzZUBdfHNl770Vlk030c4tvthmkP/9E8ZRTMOOUK7HPPjH88gvw1FMFrLZa6yFO6a+/4B83Dv6774b800/I3XEHyiNG6B2ExWoCkZg0gp0yXVpaiwZNZuREhN3y3XqrfRGtEIp4PA5JkkwHFk7kzYainZQ5M6ftdIBuhag0Ksk0lji7BfpKiAROTCHKsqxfu744iPM6l8tlnZzM70M6o6xigz4AttK/boITfbOLAyuwjL9RKpfvy2azujaMWjhGoeodm5Nnnm0zGNnua5HEDlExQ6VS9Tr58kvgs8+Ajz8G3nuvaiQ3ejRw0EE9P/Pee1WCs+ee1chJHaFTZdbvuHTdp3DVrKOxVOBPnHZCBged3Q+RCIBUCvE114Q0dy5yo0ejPHKk7cMOXHcdgv/6F6SuVVThpJNQuPLK2jflcohtsAHkmTNRGjkSs679L0aOjGHKFOCJJwpYd12X8rHlMsInnwzfo48if+ONUI48ErFYrOkVkVmkgmFjMW1kdI5sBOo/jN4fTt9DhEIh+Hy+pgZU+saoqmo6uLTiqcKctx0S0SzZakQ2WtknV6GtDp5mviDi/ShODtQQiILEvjR4k/Dncrn5npyYQZZlRCIRfeFhJ6Lp9v7j8bhpg8BWUS9Cy35bJA9GvRt/6B1lBrP72C58Pp8u8DemQvk7j7O30CEqdpHNVsuLH3ig+u8pp1Q9UcJh4M8/ga22qkZbXn21+ncTVO69Hz+ffxuO/fVyvKZtg0uGPoDj39qzp2PsrFlIrLcekM8j+8gjULbbrv6xzZmD2DbbwPPTT1AXWQTZe+9F+MQTIf/yC7IPPABl111r318qIbb11vB8/TWUjTfGL2OfxSGHLYJPPgEefbSEjTd2aTBQVYTOPBP+sWORfvtthNdeW+8n42giS6fh++orRFZeGeryy9doO8SHplmxKYVpVpEKJxN3q+FiRp7MjJkYVUilUo4nJkYG7ESFuB+nRIVkSHQEZmVTI9QLc7uV9uFxsWqiUqn0mCjqHQdFyfOarHDSKBaLvTpxzyvQbNFtYasdNGuu1gjG+4zERBT5816lzsgJ4ah3H9sFxzIxMs1jo6VCbz0LHaLiBJoG/Oc/VYM44wMzdCgwcWK1D5GA3IX/xJ13argpewx+yi8ODTIWl//AvedNwiZn1eo3RMhTpyK2ySaAoqB47LEonHNOtXeRCWIbbAD5u+9QOuww5G+8sRrNmTEDifXXB4pFFP7xDxTPPbf2Q6qKyH77wffaa6isuCJ+e+UdHPm3AZgwQcKDD5aw7bYuDYD5PGKbbgp12WWRfeopJPr1qx9VURR4vvkGno8/hufjj+H96CN4vv0WAKDtvTekJ55AoVCo6f4bj8f1lWUzaPR5J5EMN/LaJArGlIRdp14r2BXxcTXmNDLCiEo+n9cFzCSTjHQVi0XTbVrpYujgC7Q+KJqleYyot9JNJBJ9gqQAaFjFsaCBJDGTyfSq7qbZNGgj8FkxRvgaPScEBfjGqIeqqvB4PPpz2K57hGNEb6WIOkSlGfz0EzBjRjUVlMtVf7beGjBUujy61lU45fOj8QcWw6G4HxvFvkTispOx/h790b9/4wlA/vRTxPbYA1ImA02SUNlwQ2TGjKnpURS84AIEb7sNxQMPRP6222o3MHMm4ltvDWn2bCg77IDsuHE9UlLBs85C4K67oC22GGZPeA/Hn78inntOwj33lLHbbu6I1ryvv47oiBHI3nYbvEcdhUAgUE2jJJPwfPEFvJ9/Ds9HH8EzZQrkH36AlMtB83igrroqKsOGwf/IIwCA8rffwrvSSj0iCk6EokbYiTQ4IUJuDWzhcBh+v79H6LaV7dsV8TUb8maKTCQ47DbLlaIo+BSjLWbXmMfhJupF3Th5mKX46rny9jbmRWShL4D3QiaTabuPCtFsdNEOeD7sumxFwMRUEJuBWrlAW/1dhFVq2Sm8Xm+NJtB4DG6iQ1TahPz/JmKZbVfGBtGv8K8nl8Hg9OdQtt66qW15X38dwcsvh+ezzwCPB8WTT0bh4oshf/ABYjvvDHWZZZD+4gtzXUyphOiuu8L70UeorLAC0q+/DvTrV/OWwI03Inj55UAkgj8nvINT/r06Hn5Yxu23K9hvP3shT/nzz+F//HF4J05E8eCDUT7iiJrXw4cfDv/TTyN3+eUIff898MEHwOTJkDQNWjCIyhproLLyylBXXhnKOuugsuaakOfMQfj44+H58ktknn0W2tprm5IKu0JXM3DyrpdOcbJ9N1dgZqvIVjxVnGht+vXr5zi10Gj7HHCN1R0UChqjBCQqbubE62l06kXO+lLaB6i6MbfTR6QvgosKtyZaO7Dre9IMKORnewmjdYH4A3RHTMQGq8YxQCQyxrYb3A5dap1cQz4bsiz3SIUFAgGdrBgF0G6hQ1TahPuWvQBHzLwKn4z/GituubQr2/S8+SYiRx8N+Y8/oC66KKRUCtA0pD75BNpyy9X9bPDMMxG4+24gEkH6xRehrr56zeu+sWMRPvlkwOdD8qlncNZj2+Kuuzy48UYFRxxhKFPNZOB78kn4XnoJns8+g/zrr7qAFwA0rxeZxx6D75VX4H33XXi+/x5S10OhyTK0tdaCvNFGKK6xBoqrrw515ZWrXjICvM8/j+jBB1dTRqNHo7LxxgCqD4wkSTUrkFbM0BqZxFk12rMCxX9uDaRcdaXTaX2SsuuNYIZ+/frZGnSb8ZBwQoQA1JAWWZZNj0vswNyqEZWmaXVXxlZaJU5WfQUMty+MkRVquHqzTLkdOhU+K3ymzSJEJCbUh9SLItG/iakfoLslhqZpesUQ00VAlbRTyyJWVpk9Z9R00THXauHWLhuBBbcp4bzE++/j9pm7Y/vI21hxy9Ubv98mKltsgdTkyQhcdRWCN94ISdOQveGGhiQFAAr//jcq666L8CmnILbllqgMH47yPvug+Le/AbKM8kEHIbvooogceigSu+6M/7t1FCKRY3D66V5kfyzgDM918E2YAM/kyUAmAwmomshFo6issQaUTTdFaZ99IP/yC6KHHILYXnsBQDWFM2AA1E03RXnbbVE68EAgEkE8HodX05C3mNA9339fPe7TT9dJCqt7APR4GILBoL7KsLvy4aq+XqSETdXs5sXdfkAzmYzevJDkh80DmwEHrUYwc6V1G1wR5vN5y6aL1CEVCoWWohqskLECbcPN7gW/39+nyjV5HOxQ7GaH374ORVFQKBQQCoX09GFv7NOtvj9+v1+vqKFItlVBNNPEvEf5HBnbjpD4cPxIdDmJi347Xq9XJ0fcDsdIkqpYLKa3GCHafR92IiptwJQBm2Pl39/Cg/+cgl1OtOfQ6hShE09E4KGHMPenn6odoG1C/vxzRPfeG1IyCUlVoUUiKPztbyj+4x+ALEP+9lvEttsOyGahxuK4PHsWLlcvwgW4EpdLl0BbaklU1l4b5R13RHnvvYFotMc+AjfdBKRSKO+9d4/IDcGVUQ0zV1UEL7gA0DSUjjwSgZtvhv+RR5B56inIW26JcDhcE7KUZbkmjWBcGYg/fEjF8KkdZbzTkuBmSoj9fr9OPIxkiz2FZFnWV5Fc4VtZ85v9y3MPBAL64Fjv0W9GFGymUXETrazWNE2rW95qJTSmgLmvkBQj2M5gYRvGI5EIfD5f05FUJ3BDpyLLsv5MsdrMjeP2+XwIh8OmGj0xdWR0pJZlGcFg0LaNgxFcNLKEGugmPO1wuO0QlTbgRu/ZOE+9ElNn5NEu/V3wwgsRHDUKyc8+g7b88s43oKoIXH89grfcAimTgZpIIDVlCuD3A3/+ifi660KeOxcAcO2Gj+Lc9/fFKSdXcNnladdaIZEopNNpaH/9VS2xnjZNf10LBiHx4VtnHainnYbMRhtBXdy8DxIHFHZFNuZ4zZwuG4V06wlpKSQTyU8wGLSVMgkEAjUCU2PHVFGXwdWNmM/m53K5XI98ttUPtyv+bkbguOJyOji3k6h4PB5Eu0hxM4OgqIPh9lgFBMBSh9Auozk30e6+N30Vdt2iW4VdgzYr8B4C3K/WSiQSvarZEeHxeHQTT6AqCq5UKm15XjqpH7cxbRqer+yALRf9HJFIHRfbFqF1aRekP/5ojqjIMornnIPiWWchePHFCI4aheD556Nw/fXAoosiNXUqIiNGwDdhAs6cdSYC/9oRp50bQzYXx7XXpuBGJDSXyyEWiyH41Vfw77ADkM2ictxxwDHHQL7zTkivvgpMn14932nTIB92GOIAtLXWQmWbbVDeemsU118fNKJhSqNe7porAfYPasTTSRSM2wiHwzrBMJIhSZIQjUaRy+VqJmw2/SIZEStfxH2IuWTxtXA4XNOHx+PxoFwu215NS5KkG9IVCoWa8kaxUVyzA027JnNjaLsZsPGjMSLD1B4NtIxwK+TfTiysa01Rj9FO0O3X7/c7IioejwfhcNhUgOoUNGEzE9y67fFiB4xu8jjEhU47xoEOUXEZ6fOvxgTciquP+KWt+2GnYvmvv9DS2lWWUbjySvgfeQSBMWNQuPDCalWQLCM7fjyC552HwB134JR/rYDw9d/ihHMWQy4Xx803p4z6V8dQVRWVm29G4IwzAEmCds89kA47rLqqHzWqmgLJ5yHPmAF1xRXh/eMPBN56C57XXoNn7Fh4b7gBwXAY2vrrQ914Y0hbbgl1003r7lMcaBoRFTqhkihIkqQ/oIyaGEkMuzMHAgHEYjG9qSIHFb5fURTLqAt1GUbkcjmd3LACwufz2Q7dcjABUHdVR2IAOFu12imNbAZuWHprmqavOklWGQHzeDyW17AvR1KI3irV7WvoLaICQF8Y2AVNIjVNs+zfZQckOx6PR/dYIURL/d66B0TyZXw2+By1Q8/VSf24jCcSR2Kf1D347LM0ll++fcZE9CYpHnII8rfc0vL2fE88gfDRRwOSBGWLLZB94gm93Dlw660IXnQREI1i7D8n48gzl8LOOwN33pm0MuFtDFVF+Igj4HvmGSCRQPbll6HU66NkhKbB/803CL/zDrR33gHeeQfSH38AAMrbb1/1jGkwsDTyX6EjZjKZ1B9QCssaTdySJOn6EtFZUlXVhpVGdsHSXSdhXyf5dq/Xq6eoGMUxRolEtCsMze7PdmE2UJZKJdPrTe8Uq/RJu5shuoFmu1XP73DSFsKNfdlpNspoqqgpawaSJNU4w+bzedP7Mx6P65HkdqcAxRSWWRodgC7ENTY4FL+fpqK1HaLiIqZNw1aDpiPfbym8NG1A23cXHzYM0u+/I/n99z28UZqB99VXEbrkEt1aP/P88/prvvvvR/jvfwfCYTxxzRc45JxB2Gwz4N57k851OH/+WdWjTJ8OZfhwZF54odqKoAno5balEnzffYfQPvtAmjULyV9/Bbz1A4aNrPM54CiKohOOVs2mJEnSfVlazVXXMyizAr1JCoWCbZW+GCUCoOtYjMff6Hq2ApJGo8bGbNCjcLZYLOppLatmfWLXWJaBiuk2pySpt9Go5HpBBjVR7ejFU29fjGoYxwGRpLQSReE9CTTWtIjRz3YJi8UoCtCYaGQyGXi9Xp3UMK1KAsMUs5PIS+M6xQ5s4519bsAEbIUzz+ud/eVuuQWoVBA57jhXtqdstx3S77yD8nbbwTtxIsKHHaa/Vj70UORGjwbyeYw4c1U8cckHmDgROOCABJyMk56JE5FYbTXI06ejcPTRyEyY0DRJEbskQ5JQXmklVAYNglSpwPvOO1Xn4AYwMv1IJIJwOKynBFhZVCwWXRGJ8pjdCNUWi0U9d24XDB/b/QxXdYVCAclkUo8khUIhJBKJXpvE8/k80uk0CoUCMpmM5UqV3yejVyQfVusxVmGQtDFlF4/HEQwG9fB9X0VfPrZ2g99xOBzWo51ugs9+KBTSXYnD4TDi8Tji8TgSiYT+d7GBolOSQr1cNBpFIpHQ07mpVKouSWHFjaIoSCaTbSEpwWCwxmul0TVmZWGhUEA6nUY2m9VT1gBq/u5kDOxEVNzCnXditxOWwY/BVfHmzP71mie7itgmm0D+5huk33rLshTYMVQV0e22g/fTT5E//XQUL75Yf8kzYQKi++4LqCrePOcJ7HXHHhg8GHjssQz69au/qg/cfDOCl14KeDzIjh4NZe+9WzpMlumKK4nA9dcjcNttkP/6C1ooBGXTTaFstRVKe+wBbemlq+msrgcukUjUhEvNyn7Zf8atHHCzfXSsQG8RJ6tqtq9v1BxS7EdiFAMyLcTVkaIoOqHpLRMyngdQu8qzu7JkdMkY3TJWZPVlLMwRFUIUdhrtCAhOshTS873GyjdW3okltowIMELHv4uluRSU1iMpHo8HgUBAv6+M1YhMEdez0xfRbJdzOxALBpw8A2IvKn5WXBRJUnczzWg0qo8fjdAhKq1AVZG+/Ea8854Hr70u4/rS33HX6BxGjGxvGFKE/O23iG26KeD3I/Xee81VAJlBURAfPhzSrFnI3ncflD32qN3nttsCuRw+OPxG7P78qRgwAHjiiQwWX9zkAVNVRHfYAd6PP4a6yCLIvPIK1MGDWz5ES9dYTYP85Zfwvf46vK+/Du977wHFom7Xry6xBKRcDnKlgsyXX0Lpigpw4mpnrpeTqxuDC11bg8Gg4/C3HbdNamBKpVJNGFdVVV2rYkwLaZpWEx5vN2RZRigU0oXPTiZuO3qdfi6kVNuNhVWjIoL3gZieMNNIkIwY32OcjOkvIhqfGcH2G4SVuD4YDOoCbmq9jD5P/HECu7oZJ+B1pC7NKVHnZ6x6EPE9fJ/d7XeISrNQVXwyeCS2+vEepBHHkpiFPVb8Ald9sGEjaYTr8D73HCKHHgqEw0h++CGw1FLubHj2bCSGDwfKZRTOOQeV1VaDMnw4sMwywNy5iG+6KaRff8WkrU7AzlNuQzgMjB+fwTLLGMp5X3utGoUBUBk2DKV990Xx6KOBFhvO2fXtkLNZhB9/HJ5gEFI+D/z2GzBtGvDQQ1BvvRWV447TP8+wZalUaouSvt7kyNWe2KvDGHK1GlhVVXVkzsY8uNUgR20GoxMsX6arpljSyyqlqGD+RzLTG51uacrG3+1O3NFoFJIk1RX/0uytL0ZWeI4Le0Slt8FIBgB9rKDRI59Bpg1535D4uLkA4n3fbDsN47Z4zPx/O+G0f1eHqDSJWbsfhw2evQhL9S/htsciGLyGH5Jn3kl+fA8/jPAJJ0DZbDNkn3nGte3K772H2K67QhImbQ0A/H5o4bBuCvfdCttgR+1lVDQZTz2Vwwor1D6QvnvvRfA//4H87bdVR1wA2uKLQ9l8cxSOPRbqRhs5PjZOpvWICp0bgW7nRD4c/j33BObOhfrWW5CEML/48FitfMRQsROYhWtZamympDcashnD1Oy9EQ6HHYlqGY2yih6ZVRRxYGQLer/f3yN0S8dbej40qhRyC2KVBPdr/DHqkeLxeMMKrr5s+MZqkHYLSTuoQhRe877hPcW0Me32JUmqISftmmY5BjJSwxYDTkCXcKDvluO7RlTmvj8ZT4yahQP2KiC82zZVh9MFFakUdk28hc+86+G1SSEsuWTf8DGIr7YapGQSyZ9/dnfDuRzkL76A96uvIH/3HTzTp0P65RfIf/wBKZkEymWgVMLPnuWx/WKfIin1w6OPFrD66iZaBVWF99lnERgzBt7XXgMfi/Qzz6Cy2WaODosaCqsVNEmKVclsYNw4hP72Nyj//S8y++yj/93j8eihWjGfbEZkRKMjKzIjwmhNz0GCk45Zft0OmmlUyMaD+Xxe13pQqxGJRHqQGFYYGAmRGPkRq2sCgYBe2mtVKeQmRGdis++N4tpcLldD7uzodADnq8B2gvfIvHAkXdggpnjK5TIKhUKPe8a4ICoUCr0STQS6tS8cs6xK8a0gErC+CleIyo/f5LHLsGn4BsMwBN/hOs952PPB/SDtv58bx9jn8M0tr2LY37fD3Qe/iL1vdR4JaBdCxx0H/6OPIvnFF8DAgb26b+8LLyBy7LH4PRvGbv3exRe5wbj22goOOyyDmvtfVRG48UYEbr0V8ty50AAo226L7NixjsltPR+FRiQFAFAsIr7zzpC++gr5L79EycKa3wixE6no7mpFZLja0TStRkshTqCZTKalVVc9YZ3oPivm8Dk4iTllMa1jTCWJgzFD3o2Omek56llUVdU/6zas+ix5vd6an3w+r5PRRukyRroymYzexZn2+8C8JS290edmYYZIUEiyjQTF4/HoGim6srrdadkuJElCLBaz1cJDRCKR6NMkBWixPFnTgGeeATbcCCgigKe2vBYDB3mxd+UxrHPAUIw/9DEscKaJqopbzp2BAfgVux3arNtZe1A64ghIAIJ3393r+1Z23hnJb79F/9WWwNtzV8Nxnrtw2mleHHtsAsmkBKRSCJ16KhJLL43QlVdCKhZROO44JGfNQvaxx1qKwFmRlEqlUn/FGQggc+edgKIg5CBdRnV+oVBANptFOp1GKpVCMplEMpnE3LlzkclkdJMmTtIkKZIk6VUELLdtdb1QKpX0Sibxp1+/fojH44jFYohEIgiFQgiFQrrWhMQllUohlUohk8lYGlWVy2WkUim9n0c8Hm/o1snzymaz+jGytNnNlApJoxkB4gqX15mNLEVdkln1gdfrhcfj0VfQxWJR/67FFbOYAmwmHegU1AYtbCQlGAwiFovp93UikUA8Hne9RD4QCCCRSMDr9aJUKiGdTuvRToJNBmOxGDweD/L5vH5feL1e3QelHWAfLuOzw747TqIp80NlG+AwovLXX8BnnwHZLJDJAPfdreDFV7zYES9hjOcI+L+fCPTrh4kv5nHdYd/j9fLmiHgLGDLMj5WWmIuVFpmNwcsUsegSXsQHJhAfOgCJ/l706wf07w/MB9cLyXHPY5mDtsDpw1/BmRO2nteH0wOJxReHOmQI0hMnzrNjCF54IQK3347H1b1wjOdehDwl3F4+Bntp46EuthiKp56K4t/+pjvfNguagM3t0skA3VU1TtIgscMPh/zzz0i++mrbb8JGItZWwNU/Ix3iDwDTvzES0ygFYgRTIo1W9YyopNNpnRzlcjmEQiGd5JC0tRIqt1uqKa6SxcgWUymFQkFPd9nprs3yS5Yzi72f2jkBLAzRFDGlIbagEMk/I2OAOw3/2JWZHcaNzwSreEhEzDROsVgMsiy35RlnZSIAfSFG0kTC5CRaOT+kfQAnROXiizHy+SPx2Mcr6n9aET/gBpyBPSKvIfPUeKjrrtv9/kwGX210Ot6buRy+w1BMwUqYgpUwE8uabj6IPAZ6fsHAJcsYuP5S2GbvBA49tO+Rlxs3fwLnvL07vnxjGgasucS8PpweiG28MeTvv0dy9ux5eyCzZyN6wAH49ZNfcQLuxHPYDftv/jP+eW8Ciy7qTu7WOOk3Q1IAIDJ+PHxHHonkpEnQljW/P90CJ8p2DGLUaKRSKUeNCpupHDBO4izBpA6Ewj6xMotaHHE/4kCpKIrepNEpqLexcw4kdPl8Xp/8RI8LRsxYAeZUnMiokWgW5+ZEML95p4ik1AjjfappWo02jB4+iqLoBMUIsaSW1WZOSRzTJpIkmRIen8+nkyZGtKwiF+z03Y4UEEXwhUIBwWAQhUJBtwbI5XKOnh1ua36AfaIiSdgKr6Mf5uI2nIhIoILQsOVQ/tsJKI8YYbk69t1zD0JXX43K2mujtNNOKOVVpP5Skf0tj8zsIpJZL/5M+TAzGcPPM2X8XB6AH7ECPsQG2HngJIx+Y2UsO6iJtMDVVwPrrAPsuKPzz1ogO/0PDB6kYif1efzfX3u5tl03EbzsMgT/7/+Qfu45VDbZZF4fDgJXX43A9f/Gg+oBONV7G7yJKEbdXsb227femj0SicDj8SCVSunq93rN/qwQ/eQTeLfdFplnnoHiUNDrFOIxuw2SDqc9RkhwaFYVCoX0icEKxmaFLOPlcRCMWKRSKb0qwmzwZmkkuxwXi0XbK0MOuHajDPWs/o0Tkt2GjFbHFQwGdSExV76twmkp+rwA9TyiXboZRE2U0WCt0T1ohNfr1b1UnBBvserFaNhmbApYKpVsEVd6nLRipW8GpncLhYIexWuWuPbl0nsj7BOVjz7C+vuviOGr5PHve/xtq+rxTJgA31NP4ZUnijg2+W+kEMeZe3yPcx4Yji6tnD28/z6w8squ9MBRSio+Pu5O3D4mgrHaAZi030UYcGcv+eQ7xYwZSAwfDmXnnasC1b6AuXMRPeggzJ74A07AaDyNPXDRBSWcdkaupewPV/WFQqFpkgIAiYsuAu6/H6lPP4W2yCLNH5AN2EkntIJmiZDoQyJOGJVKBaVSqYY0MEoiEoN4PA5FUZDL5WqEu9TAlEolfYCtN9CLHhVAtQqJrQKs4CSdRgF2I+8dj8ejh/jFVb2YfmgEOpyKZl9udIJuqL3qZbD5pjHlRaJnJkIVPxsMBh2nHhsdDw0KeRxW0TaxG7rR1p1aN1bkOY30MULj9rPOpqYc94zPpx0w+jw/kBTAoUZl2DAN225bwRVXtGYuYxe5Ox/CzRemcZNyMuLePC67RMXR5y6GNuqUekCZm8Fa/X/CV+owxJDCeYfPwIn/t0zvHUATiK23HuQffkDqs8+gLbfcvD4cHZ6330bomONwzW9H4SJciT3XnYFbnow5I6ACaB/PtEEzJEX+7jvEtt0W6hFHIH355c0diAPYMRlrBWYkwg7EiFQul9P7nPh8vhpHzXK5rP9NHIBFomIGu66/JFrZbLYmZWA14TmNIjlJEfG4xYkYgK1IC7UOAGrSYLyWPHbj73Ymjr5AVEjiWFFGMiD2WLKjOXLTtMy4XX5ndHCmdTvQXTpvVc7L56GVa92OFFCzUVMj+rI/kBkcEZXll9dw4IEV/OMfvUNUAAC5HObs9Xdc9uEeeACHYEn/nzjuJD+OOyuOpZdu/+5f3+U6bPPC2Ri314PYdvSuvUqSmoX8xReIbbklKmuuicwbb8zrw+mBwO2345WLP8Qhyr0YuHgBD74cxgorOA+PUnFfLpebGgikZBKxzTeHHI8j/8orKHb5ZbQT7CXk5qBshJmnCle6Yqmy8W9er9fUip8DPVMzZjn6RkTFrC+TEQxrixOK0ciNwltuw27fIqDbWK8ZkzRxQOcq3Co6xP0wTSAOsSR/PBeKQRltEcvFrSaReUFUSNjEknamZsrlsj4hNzN5MorGKrh2gCW44nVlM0ojcSYZcMOnxk4KyBh5E6N2FMeT9LE/jhNzRzNwQTO/wFHgPZsFwuFeNrINh9H/5f/i1neG4JOh+2L30mO4/kYZyy1bwREHlfDLL+3d/eOv98NAaQZ2vHv+ICkAoA4fDmXLLeH5/HN4X399Xh9ODxRPPBFbTL4e7y26M5TZf2HrDSVMmOCcJDDk2exqRZoxA/KMGcDxx6PSbFinD6JUKsHj8dSUKLOUMxqN6mXKHETF1IRZV2UO6KlUSk/tmA2SYgWGFeqt4JgmEgd0ravRWzKZRLFY1PUosVhMbxlv1yCPosNmxLpitRSJjlVZs+g1Y1wHkuCxXJtdoTOZjF7iXm/t2M7SZ0bRuNoOh8OIRqN6ebvf79cNApPJJDKZjB7lqlQqTVePFAoFKIqCYDCIRCKhG+y5CUYE8/k8crkcUqkU0um06b3A/buxmGBpu5k2iaLraDSqP3ck4pqm6VGfaDSqe7UQsVisJSFsb5nRuQVHEZVQSMMVV5RxzDHNh5xahfzxx1COPwdjpm6Gq3Ah8sFFcOElXpx+OtA1DrmKIb4fsaP6Aq6ac6D7G28n/vwTicGDoWyxBbJPPTWvj8YcpRIqm+2Gw767BC9iJ1xxFXDCCele6zwNALF99oGsKEj20jWiIVM7DaEYvaAg0ViWbCxRJuzoW8ys55nmEEt9xZJo8bjYxkBMExBMDzVqsCiG7QH74lKmGNy69mZlqGKbATvRAdE8EKhOII1Wum5X/Yh9aazMChk5sZou7ETMGoGVV6yWajW9AUCvBiIRbxR5Y8pHjOq1CqaAxPORZVlPAzc6T1YKmrXYAJovy54fjN4I21NCpQIUChIikXnbGkhdd13IH72GY+5eC1O8w3BM4VZcdKGK1VbT8MQTcN1gLqXFsDTaHLZpA+Q5cyABqKy00rw+FGv4/fC89yIe3+w6/APX4IILPNhvZByzZtU3EXMT6i67AO+9B+nXX3tlf70xMJAISJKkV8+QtPA1swlH7INkBU5cDB0nEgn4fD4UCgV9lU2PC+O+VFXVw9yhUAjxeLwm0kMBZKNjKBaLujkdJ51GBnIMqbvZFI6EJyZE40gy7Ewcfr8f8XgckUhEJ1+NSAq3S/FzsxDN0+hvUy6XkU6nddPCZDKJVCqlR3/qrWnpvCy3sMpge4NUKoVyuawbmzkFo26JRAKBQKCGnNbbnqjTcoukANAF6ex0HolEdKFtOp1uSMYYUaSJZKFQQD6fRz6frzFfDIfDenqOZoX1vg87ztJ9Bbb7/PJa9nrqxwLK3nvDs+qquHHLLXFcaTROmzsG++yzHtZYrYLzz5cwcn8ZDUwz66Ly/TSM+sdP+LOyKaJIVRlQby71GyGTgeeLL+CZNKnaf+fHHyHPmgXpjz8gZbNA14Om7LDDPD7QBpBlFJ4Zj0v33x/bvPw/HP7WWGyyyRK46aYy9tij/ZE7ec4cSKUS5B9+QGXJJdu+P6C94Xug26GykWusESQqtLq3gqIoetqD6TeGksVeP7IsW+b4jW0IxLSRncmOaSsRnOzNhJkM57tp3c/IGAkbiVgul7P1HYvfjyjUFf9PMMLA6pNIJKKXZNuFaAxGjQk1GtFoVBectnJ/tkJUCHqhyLIMv99vW7cilrjTSVjsgi4Kwa169TSrd7MDSZJq0o9Oo0WsPBNRKpX0Mng64pqRdaZHxVLwRmnavgTbdxW/u75CVABAXWUVpN9+GysPLuGlOevjTWyOpb96BQceLGOY/3vcs9tjKGedDUzlKdMwdrUrsd7QuTj18S1xvHQnDr5o6T5BUqJbbIHEMssg0b8/+g0ciNiuuyL8j38geNdd8L72GuSpU4FKBeqAAdC6Vnnyjz/O24O2iezDD2PzPaL4orwKdiw+hcMP9+P00+NoN+GXZs+GFgigss467d1RL4K6DaeDEUlGI40Ay3RzuZy+qhORyWRQKBRqUhpGGNsQUJ9hF0w1MezNSUnU54g25hRduw1WmzFt0ihtJYL6BZEYmPlaMIrFiY0rftH/ox6YlqH4XIxIUWNCk8BIJNI02Wg1omIEWy7U26YYPQkGg3r0hHb2IiHh9TNGVeiV0g6DNhrJ+f1+XX8CdPv1uAHeg3yG5s6dq0fCGIHhsy2K5xVFsYyu9jXY1qhMnQoMGQI891wem2zSvg6oTSOVgu+FF+AfOxafvZXHP7XzMR57YXn8iHM2eQdHvXIgguE6D9H06Xh7pytx0LcXYQaWw3ax93D+KXOw9klrAy73kmgWiS6PD2WzzaAutxzUwYNRGTYMypprAkstVftmRUFiwIAqmXvnnXlwtM2BhnX3hE7EUfnbcM01FRx/fPsqHOKPPQb52GOR/PzzXinlZmmhm6FlADopYXVOsw3SKKRs5HJpNHyzeo/TCht6RDRabdarMpJlWZ9wy+UySqUSotFow9LoZkEi4ISkiJ+lKLLeStisHxQ9Yep50/D7JKmrd3zU17AqrZkO3q0Y5JkdD1MkRhJrjJ6QpDY6ZrbdYNUM9wHAdb8T8T4Uq34Y2aKTc7uadNrB/OJOa3u5xQhjKNRHuwzG4yjvvz/K+++PoQDuAXD+9Q/hhhuCOOXdA3BFbDaOO8mPI05fBCuuaPL5ESNw3rf/h0XDBTxy5xdYZbdVevkEbECSquLY8eMbv9frhTp4MOQffmj7YbmJwiWXAIqCI269Fd+GVsEFF5yCDTcMYa213J3YCblr1S2pKnpjXdGsRkVM5ZCUiAZbZs3FGOoW/ToaoVgs6rn6ehEOu54fTtNPgL1rZFV5BEAvK+VEzuhLO0gKAN0dtJnJhpESthcgRJdfq1RSoVDQy4Y5WYtgeoyErtE9QLEpIwBOSoUpxnVzwmXFVywWQygUQrlcRjAYrEldOTViy+fzuvYlm822zddINJ3LZDI19yqJJ1M2NF5rtU9RM2DbClYd9VVx7Xyd+mmEoWfthNt/2QpfHHktdlefwo23eDBoELD11houu6za+XnmzGoX6I9W2AdvY3OcecJcrLJb3zFJq4HHU9Wf2IQ6cCDQJl+CdqJwxRUonHcelsr/gEpFwvcn3orQn3+6vh//s88CJ5yAyhprQO0NU54uOA21+nw+XXDK8mL6LnDQLpVKegdkdnJltMFpiJmfa9VnQVVVx3lwOwRI9JqoB3ZMBtAwhdAKmGprFowgUVdhDMfXO+5cLodKpdKjnw6FldRc2D0+VVWhqqrj7r+M4rld9krjOL/frxs88l5np2+nKBaL8Pl8SCQSurmgW6649NAJBoNQFAXJZNLymjBlQwLWrvuzEfL5vH6P9NU0kO1RZH4kKsQyN5yIf4+ciBtGDsUT2R0x7q1DcMs7G2FOuaqcX3wxFeU/T8Ka+Aw7HLWo5XYCN98M+YcfkL/hBvc1K6oK+YMPIOfz1RIrRakyqK7fpWy2Kuh18EBVhg6F73//g/TTT33KobYhVBX/vTeC0/FPnOm7CUd9exakIWfDt+WWyN18M5SBA1vehTRjBkJ/+xuwyy7I3n5721pCtAqGiSkwtOMZIq7MOMg7WR2z1w4Fs82WmyqKYurLUg92BkpOonYmF5H4xGIxV63aCYbwW4Fog14sFvWqHk3T4PV6607IjAxEo1Gk02m9JLfZElt+b41E1SKofXETxs6+Zg6yzYCN/CRJ0gXQbvTjqdczqB6oN6I42u370w4YWWmmyqo3sFAQFQCobLwx8ONX2O+yy3DIw4cAs2djJpbFx/J6+OSPtfEnFsUFm72GwDL3mG9AVRG84gpIigLvhAlIv/YasKg1qanBnDlALGY+Gc6di9CFF8L/+OOQGkwmGoDi4Yfb2yeAyhprAAC8H3yA8vxCVFQVY9cYhb/PuginrfICLnj3cGQ+G47IKadAfuMNRIcPh7rSSsiffTbK++7b9G7kn36ClE5DW2cdqL1MUpysWjjwNRuaLhaLug+Dk8G4UCjA7/cjEonoficUddodSNmB2OPxOFppN4qoODV5Y0qDRnHGUHwrYD8jNydpppFI8hpFpZgi4flJUrUzdLPHxPSI3YmTk36zqTVGEpjGFI30KEBltVKrEFMyFJjy/81eLxq3MarkVOvC749lywBcbylgB3017QM4ENM++CBwyCHAzJlz3deWFgqI7rsvlDXXROG884BeEPdI33yDRFd3YWW11VC49FIo221n+X7/6NEIn3sulOHD4fniCyAcRvqpp6Cut16P9/ruvx/h007TS5qlrgddC4WgDhiA8k47obTvvgiffz48H34ISdOgLrYYSgccAHWZZarRGlmG5vEAkgR4PNCiUSgbbdRTNFsH8uTJiG+0EQqnnIJCL/SxaRmqioeG34oTZ16Mv6/8Ai6duDHEZ0f++mtELrgA8ltvQapUoMViyF1+OcpHHNHU7mJnngl53DikvvkGWiLhzjk0QL3OvUZw8Gu1A2s9ozO/328a9TDTvBhB4lJvQHVyvoC9XkhO2hCI5y5Jkt4U0i2yQk8YNw3YCLGXlR0jNRrQUXzaapkxzQnNhLzG/QL2yLTX69V/xJYBIjRN69GMkgSD1UlOwe+dKUOm2oBuQ7VmWivw+wfgipCYHZGZrm0kancTVsZyfQG2icro0cAJJ2iYMycJt88jdOqpCIwZA6AaNVB22gnZcePc3YmImTOR2GgjIJNB7q67UB4xouFHYuutB3n6dCRnzYLvyScRPvFEQFWRHTsWyk476e+TP/4YsR12AIJBlLffHvIPP8A7aRLUeBza4ovDM3UqAOjCzcrqq6Nw+eVQtt7a/fNUVST692//9XQDqopHB/8Tx829HicNeRFXfLCR5X3mq1QQueYa4PrrURkyBOkPP3S8O+8HHyByxhmQvvoK6RdfRGXDDVs8AXuwUwkjrtDccMjktsRBnhUJFIKaWb3TdMvn8yGVStWIdyngDQaDNd4TnOCMMBq/WTnlctCvN+k16iskwni9RbLSKgEEoFdvtKMbNiuCmKKotw+SFEZiuEpvhYxx/4163rA/k1V60efz6YJRToKqqpoaEJL8mm2DPid20z/sEyWmCq0iRE7JCj17zIiPG5AkSX8+20GCzcDy9b4IR2LacBiukxQA8L73HrRAAPkrr4QEwPvaa+5bzBIzZyKx4YZVknLrrbZICmbPhjx1ajWi4fWiPHIk0m+/DcgyQhdc0P2+OXMQ2313QJKQfuUV5O69F8VjjgEA5G+8EemPPoLGkObuuyP55ZfIvPVWe0gKUI3M+P2Qf/qpPdt3C6qKJ9a8EcfPvRYn4jbclD0anklfWL697PFAu/ZaaB4PtP79He/O99RTiOyyC1AuI3/LLahssEErR+8IdlYrrWoMjBA7xgKoccYUdS8U41KQKzp0RiIRXSzJZnQsi2VZtHiOzHnTDE7TtBpjN7GnTCgU0g3M+B72J6JzLXub0MzOzqRAp1pxRSqW3nJyagVu6FOsUKlUdFE0q3vMEI/HdeLF3kEkZK2cHyNRtHs3A6+x2eTOY2BFE8XNdLzN5XIoFou6azKJiwjRvVeSJFsiXx5vPB6vOf96+iSSVj579bYdj8cRDAZ14tMOXQmvl91zdgPzSsxrB46ISluaLRYKkKdNgxaPI3jFFdBkGZnHHmuPwZqqIr755kA2i9yoUSgffLCtj4X++U9IQDUtxU2tsgqUzTarlv/OmAGoKmJbbgnk88iNHg112DAA3YZr4aOPRmL55SEpCsqbb47cmDHAMsu4fYY9EQh0C4z6IlQV49e9Ccf8fBmO6fcYbtzpOci//YrYVlshcMUVlh/TNA0Ih5sqv/bfdx+w3nqofPYZiocc0h72XQf1gphsDOe21wpFtYlEAl6vF8ViEel0GqVSCcVi0dKcjWF4KztuNt5jnp+rY/pD5PN5fTCnwRTJUDKZ1H9oUsXPZ7NZPWXEz4ll2YFAwLTRmwhGFriaF8/JDbLCSiI3hJhWaFRWHY/HIUlSTXRIVVU99cMKsWbByJXH4zGtAvP5fKa6JZ/Pp6/QqRHid2kniC/awmuapjdyrGdiSIIiGtvxGTJrEGkEI1BiFMZs+0xNuql1MgOfBbeM4RrB7nczLzDPiUrkgAOAchnSX38BpRKyDzyAyhZbuL8jAL4HHoD8118onH02ygcdZP9zTz4JNZFApUvTQuQvuQQAEL7sMkT23huemTNRPOWUmihN8R//QP6cc6BsuSXUaBRaMIiSnSjOQoKnN7gZR/14CY5a/jVcM3UH5Mc9iPR770FbfHGEbrgBsY03BkxKk1VVhbbuupBnz65W7ziADAChEPJtnGBM92uDfDP14UZ1g4hisaj330mn0zUDN7UAVo60fN2KGNCplmAlhRHsAWQFRmzoWst0Aicp9qFJJpN67xQ6klpB0zR90hQnOCNZaWbVyu2126xLbL4owoykiEilUvrE20o1B6NnXq+3xz3AdBPBdIsYRXHSziEUCunfKSMWJNRsimg8F6Yxjc67rGJh9287IPkwkhXeQyQp7azMoVMuU6jtJMIi6ATcF9E2oiJPm4bghRciuvnm8D32mOl7fI89Bt+ECaistx7UJZcE/H4oO+5ofycOEXjwwWrlzBln2P6MZ+JEyMkkynvu2eM1de21oS2+OPyPPw7fm2+ivMUWPUWrfj+K552H7PjxSH/1FZKzZjUt/mwKmoa2+9A3iWc2uRVHTr0Ihy/7Gq79ZH09iKYOHYrUN9+gNGIE5G+/RWLYMHhffLHms5VKBfKTT6Ky7LLwjxsH30MPdb+oqvC++irkTKYqTHv6acS23RaRc85B4P77oS2xBPDXX/Os1bnVqoV6ByelxHbBQZ4+GWavW00mYlTFDBzgWFJbqVRM3+vW9dY0TU9xKIqCYDCoN6ATwbQP9RVi40NuR5yYnE7mTqqPWgEnD14/Xms7IlvRp6NRBKoeKG6l3gSAHqkiUeFkziaVjKKQLFqBRCORSOhW84xYmPW24b0lEhRGCWmdL0bSnNriG8kKGwkyUtXO75vNIn0+H0qlEpLJZFvGAzOI1X19DY7Kk+sSlVQKgQcfhG/8eHi+/BLI5UBupo0ZY1pK6vnySwBA/rLL4J0wAaFrr4XvgQdQPuwwJ+fQENL06YiOGAHPDz9A2WADwMGAFLz5ZmgA8uefb/p68dBDEbrhBmh+P7JPPunSEbuDwKhRQCYDOZeD/O23UFfpO267z28xCkd8cx4OWeo1/PvzDXpm+mQZubvugnfPPRE59lhEDjwQpZEjkb/jDqCroZ2WSCDz3nuIDx2K8EknofzCC5D/+gueSZMgzZ3bY5+eTz6BOFzKkyZB7Srh7g1wZWw2EIi6lHY5VCqKYulUy1Jipp2MKJVKCIVCCIVCPVJSiqKgVCrVNFzz+/09ypLF9I0bq0Q2BfR4PPqxBQIB5HI5ffui/oZdimnyxdczmYxOVMTXGqGd+hQRYhSKUQ2WINshf9lsVnepjUQitpsmGsHoCJ1wfT6ffp3D4TD8fr9eBSZel3rGf9QlAdDTkPWuKe8ziocB9KgQAqrXiWL0Zr4jlnszMtTOZoUE71+3/GKaQS6X65OW+rYjKpkMEInU3tze119H+JBDEB86FInll0eoq9xWXXRRlA46CKmXXqpWt1iElAvHHw8NQPDqq1E880xofj/CZ54J36OPtnBKtQhcey3i66wD+YcfUDzwQGReeMHR5+Uvv6yWSw8YYPq6usIKAIDCaaf1icaFAABVRfiIIxC88EK9OWFsq60gT548jw+sihe3vR2HTToXBy/xCm6YZEJSBCh77IHk119DXWklBB59FPHVVkN41ix9NRvo3x+Fl14CIhH4nn4anrffBjIZKAcdBGXnnaFssw2KJ5+MudOnY+6XX6LSJW4GgPgWW/QJoTErCEqlUltXTyQYZqtb6kCs8uGapulExwwkN+yvQ6MyEZxU66UBmgk9c3LkREJhLo+boO7F5/MhGo0iFArpkx0bLHq9XtOqJbPjbFejQyOYBqGvDY85HA4jHo/b0qCIwmda5DcDWtbz2nm9XsTj8ZooipEYKIrSo9ydjSM9Hg8KhYIu4K5HKhgVYvNDYwRFBE0S3dB5sXFmOxEIBHRiNa9ICtBt/NbX4CCiotX6pxQKiIwYAQmAusgiULbdFqUDDkB5r70AwwClmYVi5sxB4D//AWQZ8p9/An4/Mk8+ieg++yB83HHISVJLhl4AED72WPgfewzqoosi8/DDpp4njSD/+Wc1LWUB/5NPVtNJp57awpG6iEwGsa23huf776EMH47MSy/BO2ECIgcdhNiWWyI9YQLUlVeeZ4f38g534JBPzsb+/V/DjV9uAFup6/79kX7/fQQvvhiBW2+Fb/hwKOPGQdpzz+rEutlm0ObOhaooyBk6ptYgHkf6uuvg3XFHRA48EJKiwPP++1B7yQyPA7VxUA0EAqhUKq6KZzmZiWZasixD0zT4/X5TXQUjIVZgCsEMvOaMuLDKRwSjG8FgsC2DIUuqRVMv47WmVocrbp/Pp4soufomAahXzdFb+hQxvRIMBmtW20xLMBrUqO8NK2wikYhusU8LficgoWNkjsJvqyiN6HJMsbjH49EjYvXICaN8JDrUm5RKJcv98Zo1a5hmLGFvNxmVJEnX0rjdrLQZ5PN5/RnpK5oVR6mfAQOEGyMYBGQZyhprIPPGG+YfKhQgwYSoFApIrLEGpHwems+H3HXXAQAqm2yC5OTJSCy/PPzjxrVEVHz33APfY4+hMmwY0m+91Xy0I5+v9syxgOfTT6Etvnif6LAsf/01YjvuCGQyKB58MPK33goAUHbcEdkHH9TJSur996Etv3yvH9+rO96Ogz88CyMXeQ03fb0+PD5n30nh8svh3WsveHbaCd6RI6EuuSSSH37o2CBQ2W47lO68E4Gjj4Zs2qGyCg6qFHcS4qqWOV2mPxptC4A+KNE7hIMBSz3FH6A2ymD1uwg7vXLM7NE5yVulZiiaNXtd7BHD8mGzcD9Dy1YpJjfACZmdgI3gZMAqDnZWZtl1JpPRtQ9Whlu9oU/xer16pI3aDXG1TWIm6ig42Vml1uiJQq1JLBbTI3l2nX5FsmpHWMp7gykrNlC0qsRhsz6RnLDUvdG+PB6Pfq2cEjBZlnUDxHoiZbfB6zkvIylGUI/UV+CIqPSYi73eai8aK8yZA6AnUQledRWkfB65a65B6dhja0iEPGsWJACVQYPsHlpPzJ2L8LnnApFI1eq+WZIyYwYkTUNl6FDTl30PPghp7lyUd9+9+WN1Cb6HH0a4q/old+ONPQS7yk47VcnKwQcjvvnmSH72mf0WAC7g9V3vwEEfnIm9+72BW75dH16/8+/E6/XCs/XWKEyeDM/ZZ8P3+OOIr7ceUp984pgo5nfYAf5FFkHwH/9A+ZFHoIZC8D/xBGRFge/XXyEdfDCk1VbrUdZKVCoVfYXIKAUdNwHU+EJQQwFAn8w5CIhkRIwCiAZYZn8ze93uZ+kDYRwYG+kWOPCL2gQRqVSqxqiOxMt43QDUdI0VJzu3mgdaRa9EULtCnw5WJVFLwwoWMzOzdutTWInESV6SJMu0oGjOR8LSyBiNn+H3RfFmvTJeHhM/S+JhB5VKBT6fr674l2JyPlO8N+wSDoprAeg+PYygkbwYxaKSJOnkhKXUJHu9IbYXidW86PFjhb50LIBjMa3hBlYUoI71uPzXXwAAzbDi9U6cCE2WUTr++J6f+fnn6mdaWPFHR4wAymVkx4xxJJw1wvf++wCAylpr9XjN+/TTCJ9yChCNIvfvfze9DzcQPPtsBP7736qt/7PPQl17bdP3KTvvjNz//R/Cp56K+MYbI/X55y1dH7uYsMed2P/dM7BH4k2M+nbdpkgK0J13Li6yCPDf/yIweDCC116L+PDhSL/+OjQnzQqjUeQefhjhkSMR22ADQJIg/fGH/nIlm0Xhmmv0KILcJeAFust12Ugsm83qOWZGSmhqxoGcnVLNEAgEEAqFek1ExxW6MapCQhMIBHpMJrIs62Srnu+Ioih6KguoupZyouWEz/8z6kIRMdCtX4nH4y25vdohKgTJCsu3SULESJfP59MjFbwf2pX2IZGlriSRSNiKELCxYTAYtL1Kz+fzyOfzut8KSaZIiowC3mKxqN8DdokK00XGaByfG5JTmgwyNWeXLDBdw5QPiTCFv4zSiBDvDfr+9FaFDdEXoylA3+v703xEJZMBVBWVOvl9iQONKE6bPRueTz6BahGlkLss5j2ffGL30GoQOvlkeD/9FOWdd66xtm8Gns8+A4AezqXe119H5MgjgUAAqXfeARZfvKX9NI1SCdFddoH3449RWX55pN94A+jXr+5HyocdhsKsWQhecw1im2+O9Pvvt1UE/Naed2C/t07Hbom3cPvkdeALNLcvtkEXJ/vieefB+9578L35JmKbbYbyfvtBnjwZ8l9/oXjMMSgdemjdcyuvvz7Ko0fDf+qp0EaORGmHHeAbORLQNGQPPRSqMBFVKpUeKQCxqVkmkzFNp1DzUW9S46qPE3y7xWxsOmeMqtDWnHblfM1oilZvEOMk5PP5aizzuXJkybDorWGMWimKoos0myUr9SqsrMAoBCNG9LMAau3kOXm2S0/A5naFQkHfv5N9NVOeSzM1alfYcZvfG/UkPHenK27eW4xyGC3ojboTEgy7EI3YeKxcSJAkVyqVHqlVer3Mi5JcEtK+oEsxYj4nKt1fpmfSpKqQdsgQy89IXT0KxIiK57vvIGkaSvvtZ/oZ3zPPAOiqonGKVAr+Bx9EZdAgZB94wPnniRkz4HvjDfgffhgaAHXwYP0l+YMPEBk5EvB6kX7jDWjzqivxzJmIb7UVpD/+QLkrrWOXcBTPPRfyrFkI3HcfojvvjMxLL7XlEN/Z4d8Y+eEF2NnzMu74ZpOmSQoAPVcvrsaCl14K35tvojRiBLRgEN4334Q6aBAqiy6K8GmnwT9uHHLXXVe3BDm3ww7wzpxZnSBvuQX+QgHpV16xJNJGiF1uzQZWu6tuCthCoVAPTUw7IEZVgKoAURRIcpAXS0fZUbmRwRpTYUB3RQ7LY+kWy/2YpVVoMEYb9HQ67XgicRJRoesurzn/VRQF6XRa99PgtpiSMSOmrYLpD5JVplqc7qdYLOrpRJF01gMdgZlCIdExa+UgpursIp/PIx6P6x4w/J7NoiZ2K6rEFJlRmEvdWF8lA6zm6u0qG0YE+QyaXf9WW0u4jaaJijxtGoDu8lwzMKIidqb1fP119XMWXYCVbbaB7+23Ic+ZA6dDgP+RR6pW9//4R3NRglQKkeOPh/fFF7s9YOLx7m3NnInYbrsBANLPPz9Pq2di++0H6Y8/ULjwQhTPPNPx5/P/93+QZ82C7+WXET7ssKqlv4t498C7sc+HF2B7vIJHKvvAN8SH4pFHonDxxYDD0kjmnWvC0c8/j+BNNyF/6aUo/v3vPWzwS2+9hdDZZyO21VYo/u1vKFxwgWWai6FvaeONAQBSOl2tajv0UJS33x6l446zPDb6OrghDs1ms4jFYgiHw2235xajKiQmiqLoJaxM8xg9Kpj6MPNTAbojSCQXnDiMYfVEIlHTS4gQB89sNotwOIxE1/ghkg4z/Y3Y7JADrV27diurek7e7JLL45MkqUaI6saKXPTSKRQKpve9E/BzFG87mRBJQOpVvYhC8EYQnV4ZQbHSqlBEa7VfWZYRDodrqsrMWhmwT05vObs6AQX57S579nq9+vVkJY9RA8Z7n2XfJO5Ovt92w/Zsns9LNRoVecYMAEClTtWEHlHpcqwEgMBtt0HzeCybAUrcrhDFsAv5++8BAOWNNnL2QVVF8KKLkBg8GN4XX4S6yirI3nUX0s8/j+SkSd3bnzMHKJehLboo1DXXdHx8rmHmTMhff43KJps0RVKI7MMPQ1l7bfieeQbBc8917fDeO/Re7PPiidg2/B7++9OGqJx3FjSfD8FRo5BYZhmEjz5aF1o3ApvYGY2bgv/6FwBA69cPMFktKptvjvSbb6Jw4YUI/Oc/1VYNFhqRcrmMSiaD4C23VPf58ceIjhgB36uvInzuufC8957l8dHy3a1+HJzg2YStnWDFCCtYxDLddDpt6VFBTw8zP5RAIKATjXpRAKPNvizLPUqKjZ4OJE2MrFEPxEFYbHTIc7IDO6t36oc4gSaTSV3ASnFyK6CRGqMMQHWSsVPpUg8kBHbvT7/fr/vI0NW20XFbgemyRCKhE3krx1nj5xiVCwQCNRMrreVFq3wrAzuS5r6m/+C9yt5H7QBTp3Rj5mLKTKhOQseCANHSoK/AUdghFBKISpfoVa1DKLxvvgkNgLLaatU/pFKQp02Dsummpqtb7/jxCNx7L7RIpKmUivTnn1WDOYtojSlyOcQ23hjBW2+F1q8fsmPHIj1xIsojRqCy8caAQLLU4cNROP10yLNnI7rXXo6Pzy2Euzo25668suVtZV5+GeryyyMwejQCN97Y8vbeP3IMRjx7HLYKfYC7vhmGQMyP4jnnIPXjj8jefjvUAQPge+IJJIYORXSXXaqGenUg9gwRkb/uOpR33BHh005DfP31zdsE+P0onn46cjfdBN+ECfCPG2e9o403hvTggwCA0FVXQfrlFwDVtKVWx0cHqLX1dgOZTEYXBxJ044xEIggGg3qlRivgAM6JQYRZF9tYLKb3O2EbehFsbGhn5V6pVCDLMmKxmN4hmZO9OEAWi0Vks1k9SsI0BPv/sBcMmxzOnTtXJxHNpH3qgWkrXrdcLodkMqkTAbvma3a2Tf2GG2kBtjioV03FUu1QKKT3g2oU0bMi6LwW9CIRCYWdKCGP0+Px6JbybFJIDyCRRFudF6MCfc0SniS6XcJdn89Xs9Ax2h2Ywc575iUcEZWa1M9vv1VJgTCRG+GbMKHqMdLVJTh0wQWQABQtQunB0aMBAOnXX3dyWN3H1NXTw2g4Z4kZMxBffXXIU6ageNBBSH33HZSdd677keLFF6O85ZbwvvMOghdd1NRxtgRVhe+FF6AttZRldY8jeL1Iv/sutP79Ebz88tqeOQ7x4TH3Y8T4o7FZ8CPc8+2qCMZrB+3yAQcg/eWXyDzzDCqrrQbPxImIbb45YuutB+9rr/XYHldTTA943nkHvocfhvT776hssAGyDz0EZf31q6lFqwdM0+AfNw7q0kujvM8+lsde3moraLEYtBNOQOaVVyBlMlCXWgqZRx+tm94EoItD3YqqMN8utqtnKoaDNxu/cZJvtrEeJ2izwZwaB+6fVR/JZBLpdLoHWeFka2dyFTv9koyItvbGY6RVPsWR9cDJiymqehN0vbSPCFYnmU0ubJioadXGjVbNHeuBglWzc2kV1EmZHZdZQ79MJmNrv4VCocYfiA0FQ6EQ2I/JKipXD/x+ST7FiFAmk6kxl+NzN6/6djUDN8rvzcDnkc9kXyUdzcCRYkZcQMnTptXVGnhefRVSJoMSTdtUFf5HH4W6xBJQdt215r3BCy+E5/PPq1U20ahtIWMPFIvWE5YRioLEppsC6TQKV16J4kkn2d5N9oknEBs+HIFbb4WyzjpQ9t67ueNtAv7//AdSqYSCw47BdREOIzVxIhJrr43w3/6G7IABULbe2tEmPj52DPZ+/ChsHPgU9329Ug+SIqKy2WbIvPUWpOnTET7rLHj/9z9E9t0X2tJLI3/BBXpn62Aw2O0RcffdCHeluTRZRmWjjVDeemt4P/xQ72JtBv9dd8E3YQIyjz8Orcs3hnlYcXIqXHEFSlddVW0INmUK5DlzkB09GhUbacRKpYJKpaKXdroBrgSZXxbD5eLqhyWfnEj5uUqlYssOm1UuIkTXVp6f0VeCOgOWwrJqg6mQRtU6LKU17hcwFyUrioJsNqvb49udTFldQr1MuVyuSad4vV5b0RSKnK2uJ43U6Esiy7KjnjpmXYjdhCj6BtAjrWLWL6cRKPINhUJ6+TJTiM3otZi6szIcNHMKJoGxSu+IZNrJNW3m+tv9jFmvLTdAIbaTY5lf4DD103WTzJoF+fvvTf1FiPBZZ0GTZeQvvhgAELjmGkjFIoqGah7vyy8jOGoUfG+/DeTzyJ93nqMTqIHPZ7tTcPjooyGl08hfc40jkgIAkGWkJ0wAgkFEjj0W8rffNnGwzcH/wAPQPB4UTzjB3Q0vvnj1nHw+REaOhPzFF7Y/+umJD2Cvx47EBoHPMebrIQgtYi9fry2/PLKPPork1KkojRwJ6fffETnpJCSWXx6xa68FKLbUNARuuw2lESOQnDIF+RtvhBYKIXTVVQCAkoXeSZ4+HaFLL0XxqKOgbLON/ndaiBsnaJZQ+ldaCcrGG8PflQqyg1Kp1NJKSYyeJBIJPcVCgahIEjjpUk+SzWb1KAerKCiIZcSlXqMxluIyzC46xyaTSUthLx1gObmwwkeSujspOwEHVytNBr1o2Cem0fWuVCpIJpPIZrM6CWCESLzGjSZVTuh2tA70JWHVlJ17gmTUbHJ2a0Jj+oeaEZY9s8+O06iHz+fTvWdYYszrSFGwHVCbxKigLMsoFAq6DQFJuFlzQUZT2KfKCN5PTEnSkVn8MZYrG9MfJP5WPzTk4/No9kPzR/60A1xYLGgEhZA0m3enJAHvvpvBqqsqCB9zDHyPP470Sy9BNXiMAFWtSfTII1HpEqWqw4YhPnQopFwOyRkzaipyEostBqgq0i++CHXYMMd26CLChx8O39NPIzlnTt2qH8/EiYjusgvUVVdF+t13m96f/NFHiO2wA7T+/ZGaPLlXmhImllkG6oABSDfpM9MI8gcfILbzzoDPh+SnnzbU+3x+yoPY/YFDsY7/Kzz4zSBEFm0h/VEqIXj55Qjcey+kbBaaz4fi3/+O8o47IrbDDsg8+iiU7bbrfn8yCfnXX2uqr/SSUU1DZMQIeL7/Hql3363x8kkIVWhmFuCJRALq6acDL7xQ9ZmxAVmWEY/HmzaNikajeu6aIjuKRVkh47RCgCp/Rkho1mUGXhNGDeyUVdPinaSGx8cUjZmbrliZY/w/V/nJLhG+GZh/B+pXpMRiMWia1sNkj1Eo0R2Yx8eIi3iNSLpIwuzC6/Xq+irRf8QMFACL502zsrkmXcCbBdM7jGY1M2kyQkVzvEKhUPMdMJXEiKVVo0Hx3qF3jpm4lG0Q0kzrC6BI1MrGn8TH7LPzEryf3Kr4ERcGCypRcTSzsurH99RT1X8//BC+sWPhefddYPZs/X3BUaMAAJ5vv0V8000RPOOMqo1+uVxrlz9tGqRKBaUDDqgSnhbbS/tef72an6pHGFQVkYMPBjwepFvs0qyutx5KRx8N+Y8/4H355Za2ZRv5fEPNRCtQN9ig6uhbLCL8j3/Ufe+kMx7CHg8cgrV8X+PBr1ZojaQAgN+PwpVXQk0mof7tb5DKZQRuuKFKBn0+KFtsUfv+RKJHiTjFmaGHHoLvjTeQ+7//q5KUSgWBp55CYqutIMXjUA44ANp77+kRhIDfD1nw0ZBzOUe2/KJZWjNgKoPlrjSTA7r7btTzNjATS7Knih0PCepsxOiD1X4o7CW5AqB35eXKmO+lUJe/i5U54XAY0WhU/xz1IvXAc0ylUk2lF6hHyeVyOkmi4yx9bETdDyMPTitH6MECoOY6mYE+IGbHSiGpGxMQCWEul3NMUiiQpf6Euhzjd8AUmNiJmukZ3juM8FETw6iX8ZioL7K6f6lNsYrA9dVJuxX/HRJtulmL5ft99XzdgCONSjisAaoKqCokAKELL6x5nUOMOmwYsnfdheAll8Dz88/wvf8+5J9+gqRp8Lz9drX0eMAA+J5/HgCgmERlnEKaPh1SOo3i4Yebv2H2bHjffx/B//s/yH/9VU0xdYl8W0H+jDPg/+9/4Rs/vmUn3IZo0HvILSi77gptySXhe+ml6vdtQvy+OucR7H7PAVjD9y3GThqIyGLOBYRmYNmrMmMGtBVXRHbcOPjvvx/qoEG2/FcqlQq8APxdJKuy+uoIPfUU/NdeC+nrr6HtuCPKu+8O+YEHIG+yCdR114W8554IffMNQg8/DG3YMKiHHgqEQtWu3hbnbwZ2F24lB2228qbRWjgcNtV+cOIXTc44Idstb6VWxawHEFCNknAFDHQLftnzhUJaMUIiCi2tVrViGD4cDtu+bpFIpKWVMokTtTUkZyyHZ9QF6NZiOIWxl1A+n+9BAnkcxr+T3FHQLJZ9N1vWWi6X9fJTu+fDfTM1ZZfkUPPCKjUSHKBb81SPaDIKaBX5aaRNAfrmxE2ibUf8y5Sgpmk13aeBnj5CffFc3YQjohIKVQft5NSpVeLx11+QZ8+GPGMGpJkz4Zk+Hb5XX4U0Zw7KI0bA99RT8Pz8s27yBgCxrgZ+4nAUHD26RxM9p/C9+ioAoCxoEbzPPYfQBRdAnjULKJV0E7fK0KEonnNOS/vTsdRSQCgEbx2vDbfg+/BDANXJt90oHnkkQldfDd+4cSgffHDNa19f9CR2+89IDPNOwbjPlkZ0QMRiK84RCoWqmok33gBUFd6PPkLxrLOqfil2jrtYhDcchnrWWZCvvhqJVVYBAGg77IDCqFEoUFd12mnwvvQS/OPGwXfddUA6DWXkSEiyDM+550I97TTIP/2E8DvvQN1++5pSP+O/xt+DwaBjJ0y6wlpNINlsVvdEEH02uOqkRoATnyRJeiqFg2KjwcysB5AocGSI3hjOLxQKevrHmFqiUVo8HjcVQzK/z/Np5IhZLBYRCoVslYPXIz1MORgJAnUFxWIRsVjMFVOuVCqFaDSqC07Fa0QyZLxu/M4YaWOqShRN8zu3K+Bmo0j6s9QDNVyNHGQbgVb24XBYv6/qaTWYOmJpcbPRlL4KkajQqVmSpBqyzIiJeI/z+y6VSjV9n/h8LehwSFS6funXD2rXxCHeupGDDoIGIHfzzQCA3H33oXLTTQhdeinyp5yCyjrrwDN9OqRZsyD/+iukOXMg//47Si5Uzei+LiutBAAI3HADgldcAXi9UIcMgbL22qisuy6UNdaAut56Le9PRGXIEHi++cbVbZrB02U+V1l33bbvq3jaaQj+618IjhpVQ1S+vfwZ7HbrnljZMxUPfTIAsaVjdbbiDGJfE/n66xEYMwbhk0+G+s9/IvXVV7a2US6XoQGQL7wQ6nHHQXrxRRRXXhnFLsKiw+OBsssuUHbZBSiXIc+cCbWrEWasUoHcZf7mmzoV6HIjNk58RqdURhHEihnRvKxcLlsO0JygrSYDivIoHmR0g5ENq+3ScwWAPvjxOIyDPN1qSRapRVBVta6+hUJC9nERUSqVUKlU9BLYevoTrgyDwWDDSBBTNc1OVJzsrT5PnYVb4sdMJqPb0ovpDBJA43HwPqDjqnEiI3EJhUK6FTt1HvWuCf1orIiK2NeJJKjV0l9FUZBKpXRizShNKpXq8UyJzQ+tjpERnkbpOEVRmva1aRd4vhS3k6gzKkqoqqqnJ+t5E/W182sXbBOVcFirHwEvleB96aUqKdhhB/3PvhdfhCZJKF56KSDLaJeZscYvee5chI89Fr7HHoO22GJIvfmmMwO4JqBsuSW8kyZB/vRTd7xNLCBPmVLtPWScdNsBvx+VYcOqBKwr/TH5ymew6427YIhnGh7+uD/iA607ZzsF0wSc2Cr77YfyfvshuuWWjrQiAHStiLzEEpAOPxxBTUPAUJ5aM0D6fDW6n/QttyDWrx/kuXMhHXEESl2aBjugDoPRFf7OpoOiiJQTEAcjYwt6I7LZLBKJhC5UrUceCIp7qQlgakOs1BBXuGwJwCoeu6H+XC6n6ymMYmIKUalrsRKlsgqDJc9idZOYAmA3XAotudoUV/2NokdWJbAAatx6Wb3jREhrhWw2q0/SHo8H2WzW0uKd94JZ1RC/L/aHImlhBE28ZmYaEmPUitoc/r1UKjUkPE7B6h6SS7NSaNoGMB1nBbvRlL7mrcL0JtBNRMRj5Hdn1vzUant9rSdPu2D7LCMNovv++++HpKooGTQi0owZVUOuNlfEKF19WmIjR0LKZFAZNgzp116z7O/iJkoHHIDgrbfC//DDKLSTqPz0U1Wn0QvVRQBQ3ndfeC+9FL7x4/HVlCB2/ffOWNHzEx79cFEkll/E1X2J5ZI6Uil4vvkG+csvd7QtCvkokhS9RriiA7qjFD1W78Eg0tddB6CaivJ3WU/bGbiZOiDi8bguaCVJ4L/ihAzYy6mz86zTXkDlchkej0fXuDAFwJUc0zpiObSTyZnEKxgMmob2OTmJURqzbQBAMpmsuU6chEVSQrLJslNO+HauCYmaFckLh8P6+TPCFIvFXKkeIZmiFTzD/lZodE/wurIJoRhtMUsRlcvlGhM8sWeOm32LCCMJKhQKltdd7C9lBUZT7KRW2ZfKLNJnhWAwWGM1b1a5JmqxAOhdmRnhNBJ1lv7z+8jlcqbfuVMPJrHB54IO14iK3BWaD150EQI33IDKuuuitN9+kH//XQ+ptwxVRXS77eD59NOqPfoxx6BwxRWAqiJ8+ukAUDWZ231315vs1T2s1VaD5vPB99ZbaI8pchXyb79Bi7mXammE4tFHI3jppfj+/LHY9bdxWF7+GY9PjCGxorskxefzwePx1DhOAoDv5ZchlcsoGwwCG4GDCMV4YticqxBGPkSyYEZcRCFrM6tqcRAxkhjqSSgcZJkhy4PNJoxCoaAr/p0cT6lU0kmasYSaRIDHAnQPrk5KrbPZLOLxOMLhsGnHY15LKyGsGA2h0R8hEhejuZ04YdQDo1xiZIukha8b+8PwXohEIk13cjaiXC4jk8nokbF6olIn++K2uD3xWvG753mL5mAkD24SFBqtkQQ1MpQjyWo0WTOaYoeQMq3Cvjr1IDadpEbJzF9F9F4xg6bVWuPTHwmAaRfqVrCwpH0AF4lK4frroWyxBQKPPALPBx/A++qrusC1tN9+LR2kfgy77grvp59CGT4c8i+/IHjbbfA/8ACkTAZS18SSP/NMFA3VSL0BdeBAyD/80NZ9SKlUtfqltxCN4uvwetj+t7FYVv4Fj78bQWLoYq7vhq6fxlWG79lnoay9NrSBAx1vs1Qq1YSaCXE/+Xy+h7urMe1A4mLWIdnMSMpMXGsFRjEA6AJckTxZheCNLqN2wH2Z2cAbSQFQXWmz666dMmBGNZjGCoVCpumyQqGgVwkZv29eC7OIC6M0nHDENBYnXPqWcCXt8/nQz0KEzdJfs78XCoWa/dNoTiQrraZFmFYz9ksimDJsJX3Ba1YoFPTvRCSiXI37fD7b37MdiDoXu463vA71CAXJvBPyzGe3XiUeo4pmQvF6EMkL03SRSESvSOI9CqCuNqsZmHVBXpDhQKPS4A2yDGWvvaCwWV8qBf/YsdU+Oi10+CWCp58OX1dljfeLL/SqIVko18z+5z8o07K/l6FstBH8Y8cCM2YATUysDaGqQLEItR3btkByehI75MZjKfyKJ1/3ot/Ki7u+D4ahe6yw83n4XnkFhSbvHbvdc42rUBIXY6SDq1DCSEDE1T3z55qmIRgMNqxQ4QqfgzrD5X6/H36/v4eokSkUpxEPlk/bQS6X0yc3swlMvE6MiIlhcU60xu+AJIuCXSOJZDqnkS6Gq2oxUiZWxfA93I4YeeE+RRGrqqq65sVssmQqiL4v9QTMdiFWVhmvsZi2aQUej0e/j7hPEsF0Oq1HWyKRiKlmyQlEt2e7BIURBwqM671fFA3bBZ+VcDhsWr1F7Uwzhori/Q5AN2gUu4Az1eM2Fqa0D+CAqJRKVXd629clHkfJRZv3wGOPQQ2HUTrySCCXq0ZRSiVowSCUTTdFeffdAZslrO1AeZ99EBg7FoGHHkLx7LNd3748bRokoO0eKiJu2O8rpLA5Xr/jM/Qbvmlb9sGVtXFA9r7+OqRcrvq9OgRXjizXdQIjcQG6qywodmOUw+gbYnV+jWCcmFnJw3MRCQs1DqxocEpU6HhqJx8u+sIYI09iDp95eR4LU1hWvY9yuRyi0Sii0ailLwy1G3ZXt6JWA+h2/7Qbamf0rV46jZN7NBq19EVxgnoEtlVTMN4zJJBitICEDOiOpvH+NmqWrCrEzMDIDLtdNwLvbU3TanpZmYELBqeTPkvQrZ5Dpt/cIhPcl9sRFBGiSH9hgW2i8vnnElZcMYGVVgKGDlUxZIiKwYMrGDxYwYorKk4LM5xBUYBsFpXNN0fhyivbuKPmoWy1FTRJgu/VV90jKn/+icB998H37LPwfP45ALQ9vSTis2n9sLvvJSyx/3aN39wEmLs1m0z8Tz+NyiqrNNWgkoMznTGppLcqD2a1gVUlAVeYTPOIpMqN8Gu9ED97x7BaJBaL6RMHIxp2V92sBrHbPJHpM6ajGCFh+syKSPA9VvthxQM7vYor2Ww2q5MpMfVkFemoByeaC0a1xKZ4Vr13jKXGzbRMkCRJjyqZRazMTOAawdhM0qysnPew8VqK5NyoWbKqEDOiUqnoaZB6EKModnUbzURTCEZVQqFQzb4oZrZLrOygN3xdmnW/np9hm6g89piGyZOBKVM0TJki46WXZMyZ033BBg7UsNJKGoYM0TBkSEUnMcssU4ENbyZrlEqIbb01JE1DiWmlvghZhrbkkvC00qBQUeB7+mn4H3oIno8+gvTXX5BQ7RasLrsspGQS/hdegLzLLsg8/TTQ5tK0SCWFQsxd4SzBSda0ykBV4X3lFZQOO6ypbdNFM5vN6toSlm5ypUgfDlHhX921qlePiISA6Q0AdRv8mcENLw5GLLjqpQbAqaiWTQTtgNEipry4OhUNp6xQLBZ1wabZ+VM7IQp8+XemAfndMZTOf8Uy83r26XYnH36fbOYoeuCwhFTcD8lKOBx21LBQBCtc6n3ODgGlbw+N+hgRNOpsCEYw6pE+Y0TRrELM2GwPgF4Gb5bKIsRtNIqiiMfcTDSF4P3i9/t1fyA+y820E2i0L0Yg3RQni7D7/C5IsD3T7bOP1DXIdzdTmjvXgx9+8GLqVC++/96D776T8PbbEu69V0axSJMpDUOGAEOHahgyRO0iMQoGD65gkUUas8/gRRfB8/XXKO2+O8pHHtnkafYOlHXWge+554BUCrDZPVb++GME7rkH3jffhPzzz5A0DRoArX9/lHfZBaUDD6yakslytU/RvvvC9/rriK+xRtUjZnH3dSNEFBnMVVtvM2CGcDish+uN8Hz4IeQ//0RZ8ONxAnGQENMBtIEXBZgczJnOEXUOxi6qoumapmmmKzGzcKybfg48H7/fr+tfotEo8vm8rf0wneOkZJN9h3j9uLqtVwHDKiNODlbnwiiAWVSC59qvXz994jUr7WakyDjB2pkoGE0rFos1qSu60lLkaVaiyn3xHO1GQEgsrLxKGL0yu24k+OIP781GtvRAd3rGCcTvht+XeC+Izwar7cTjYCqKZJ/PCEX0xpJffp+85mbRFLECR5ZlPepmhUqloje0ZETQba8YoHs8YerZbYgC3YUJjpbkzFMzXNuvXwXrrFPBOusY7Z+BX37xYto0H77/3oOpU2VMnizh4Yc9mDHDC6DKCBddVDNNJa2wgqLbnwTGjYOaSPRquXGzKO+2G/zPPQff449bk6rffkPgrrvge+EFeKZMqepsACAcRmW99VDec08UDz3UnOjIMrJPPIHA5ZcjeOONSAwfjvSTT0LdaKO2nE9EyuFnxX0fmkZ6AN8rr0BdZBFUmuwBZWVFz0oLERxsGRI22pszBG+sAmHDula8NUgYxN8pkBMnRu5TDFuXSiXdHE2WZcRiMVthdEYH7JRscv8kZQTLtRsNxqwyqre6ZP+ZRitQY1WKWNpt5rViF6wUEidjTdOQSqX0SVCcEPk7hcZimssOUWEKwvh9iq8HAgF94hV1QWLvF/447dbNSb1ZGFM/RuIiiqwp5KWwndFM3uNmlXJAN9knceE5kzyaLQaszA9J6BltIsFuFzhO0AHabSxsIlrCce6AN2I4HLYMxXk8wMCBCgYOVGBseJvLAdOn+/HDD9UozPffS5g8WcbTT8tIJn1d+9Cw/PLASovMxsrpKzFkiyWxwpsBDBpUxtJLq73ld+YY5b32gnbiifA//3w3USmV4Hv4YfgffxzeTz8FUqlqOsfjgbriiijvsANKRx0FdfBg2/spXnwxKmuthchRRyG2yy7IX3stSscc4/r5ROUcshX3iQoHYktn0Oefh7LjjmglZ2h3peT3+y3fa1a2C0Bvzsbuv834q4iW2Zys6fvAgZ05f3FFms1m9RUxw8zpdFrvG0LyUE9LwDC4HbDsUgSjJY2ISj6fRywW0x2HrY6FKRSzCdfYhE08LuM5ioZ6TIXUA4mo1TjG78AqAsGIkd3rySgQfTqMrqJMr3ASZ1dckbTmcrmWJkBW/bgF8TsgefN4PIjFYjo5ceofIhJQsXqP3zmfE5Ju3mPlcrnGJ0mMmmYyGZ3MthM8Rqv7uVUwDbawoSmRA1XHRnGSHYTDwKqrlrDqqsZuocCff3qEKIwHU8ZNxyvYHndMXAXlPSUAIYRCGoYOZRSmqocZNEjB4MEKEon25ARtIxiEtsgi8H7wAcLHHgvv229D+vXXKjEBoA0YAGXbbVE69FAoW27ZksOsssceSL/zDmI77IDQ2WfD8+mnyI8a5dqpAEBUziJTcVclXU9ACwDy9OnwfPMNCuee29T2ObHbISqiZbdTlMtl5PN5hMPhHoLQRqDuQoxWxONxvdqH/h7iBMrFQTQa1dMU4uqNqR9j5QZJixgap06lno08wXSL2d8b2XeLfUysiArJgBtW4CKp4MqzHkKhUE2Js1PYvZ4UzlL74vF4TP1TxOgBJ3iSQlrut5pGNCOerYDVQsZoD4Cm/WbExpDRaFQn41agWDbeFYUWozfGTtN27/tWwHYSbrVeIBa2Sh8RTY8OosOjG8xRkoD+/Svo378C9gyMfHg2vL+8iTm//Imff65NJU2ZIuOBB4CZM7tTSYsvXk0lDRmiYujQ7lTS8ssr6C0Tv8oaa8D35pvwPfYYEI1C2WwzlEaMQPnAA12381dXXhnJr75CbJttEBg7Fp6vvkLmxRdd209EyiOjuktUOEhSQGkcyLwvvgjN50N5662b2j4nVTvhf3pVNHv/Mv1CHwo7ZMXn89X4sbDfC8PgTDWxooTXh43daMTm9Xp1jUwwGNTFpZx0uXpkWoS28CQurHCxQ1TMqgzE0uV611qcaK0m2XoC32YGZn6m3nFZ+vfY3H48HtfPXdRmGK8nSQnLrel4agZWlInPBCdWY1+YZuFGRIUiZ5GcMPrI6BL1J63CTpm2KDRXFAXRaLSmxJ9wct+3Aop3myHf9HURBd18rhfWtA/QAlEhuHJzWjpoCVVF5MADoUWjkAoFSJoG3y8/YYUVlsMKKygwzl+ZDPDTT4GuNFI1lfTllzKeeEJGOl39smVZw4orAiutpGHw4G4SM2hQGUsuqdr3hrGB7B13VL1U9t23PcZvRkSjSL/3HsJHHgnf008jvtpqSL/+OrTllmt90548MloEQPNeEUZwsuVEamwh73vxRSibbWZbjAygZpInUaHVthmMVuoicRAnN7E01gzMy2uappOBXC6np0e5L4avGY7m9njebKgXCAQQjUZ1IaGZyJRVCuw/w8oZYy8ao4kdJzwx5cTQer1Bm5OEccJg6XKj9E+xWEQwGEQ4HNbPW3TuFf81VovwO3UKO0TFyr9H3AZ/xL4uxn1QeEs9iQgxsmUnumB2LJxUW/FrMe7DjibICNGXhdeE0Qrj/dNMjx0r2I2OAt36MvrCmKFZAuEUTMs6QSQS0RdP4v1l7Ky8MKLlb0zTNF1U5sbD5Jk4Eb6XX675m/TLL5YTbzQKDBtWxLBhxuMCZs82pJKmSHjpJQ9uv92DSsUPIIRIRBT0ahgyRMGgQRWsuGLZyVzZjaWWQrGr71CvQZaRu+8+BG66CcHLLkN8vfWQHTcOyrbbtrTZiCeHnBZGpVJqrcTcAKY0mOOnN0hx9mx433kHecErRyQg9IAAau3pzVxi2ZTNChTWATAduLhNRiK4GhPBKEA6ndZFeyIhIcSBVlz5iYM4O8bGYjGdzFhVw3CCjUQi+gDWyKrf2NuHAkOuPq36C1l1I+bkbae6gROHscSX+2IaqZ7zbyQSsW2iJYoxzcD0I68JO1uLXa+Nxy+uzjVNw9y5c/XydrE0WDRTk2W5KcdTgsZ6bvaHaeTUagRTViQ2hUIB5XK5Lnlw0mOnHkSi6ASM6phF8fhaK4Z67YDY3sB4/y2MERQjWiYqvIFJVlpVOvsfeQSV5ZZD+tNPEd1+e8i//Qa1ieoPSQKWWKKCJZaoYMMNa18rl4EZM3xCabWEKVNkvPkm8Ouv3ZdkySWNVUnVsuqBAxX0Rc+d4qmnojJ8OCIHHIDIvvuicPHFPUmToiB40UXwPfss5LlzoYXDUIcMgbLBBijtvDPU9dbTtTNRX5V45nJAO3oh1piZ/forItdcA5TLCO6zD0JdQkKzCZIRBw6IxtJGI1ilkkqlbA9OiURCH2StRHhih+FCoaCLXiVJsqxC4ERmJuDkBCdW5lhFPFjBRGdNJykM+qDEYjGUSiW9AaGYQuI+xf47xkHfjk4F6G5GyNSHGUThpEhiSIbM+jZZoV5EhYSXKRjR9p3XXbyXRM8aI0Q3XJG0iB4drSzeRJ2RW+CCsp6oNBKJ1FTkUKQqSdYdp41gxVcr4PfoNOXFKGggEOjxnInpn1YIoFgeTZ0VwUoou2MNI50dWMOVGJjYC6WlHhiFAvzjx6N43HHwfPghvJ98guwDD7QkOjWDzwcMGlTGoEFlbGcwXU0mu1NJU6d68d13wMcfyxg3TkYuV33wvF4NgwZ1e8MMHVrBoEFVPczii7ubSnIKZeutkfzoI8S33hrByy+H5+OPUTz+eARGj4b8++/wTJoEKZ+vEpQlloCUSsEzcSK8776L4P/9HzRZRumgg5C/6SZEA9UBslDwIBZzzwsEAOTvvkNkzBjIr70G6YcfAA6AG28MrLii7nEgThzxeFyPPNjaR9dgy4mGFRciyWm0MiyVSrY1LPl8Xl8FM9Igbp+6BisnTFZ8ZLNZqKqKRCJR14dE3DaFe3bD+WI6I51O1wg+/X6/LjJllMVM10Abfzv7auSIa6e/jN2VZT2iEg6H9WIAEoFSqWQt7u76LhuRJPp8OEn1NAIJldugD4+xGEIsl6eYlaX5Xq+3oZmbCDciF7znmtHmWBElkgqfz9cUUZEkqabporhdUatkppGxgjHd00FPuJasI1mJRCLIZDJN3Vy+F1+ElEqhtN9+8D35JNREAuWdd3brEG0hkQDWWKOINdao/bumAb/+6sGPP/q7tDAypkyR8MwzHvz4oweqWr3J4vFqVdJKK6kYPFjVXXpXXLHcsAO1axg4EMVjj0Xommvgf+45+J97rurV4vFASySQv+QSlI4/vvv9qgr588/he+45+B95BIEHHoD/8ccRy1cjWdop5yHumQZ4vdCiUWilEjzffQfMmQOEQqgMGQJ1q61QOuIIKFYT12+/IfLoo/A+/TTw5ZeQugYJze+HOnAg1AED4H33XajLLWcpcOXq2oqo0E/ELBUE9Oy7U29CadZZkgMz7fs5CTIiAFRJBTvyivB6vTViykarUp4nV8hOyQoHbKC79BWA7uLLKAvPywgn1UPUtDQDMZXiZFzh4E+dEMtWAdSNeolgfySPx9NwwmXqyo0+L6IA122ITq3UODG9w2o247myLJjWAo3A+8FpPyoRvOeaITrFYlGvSDJrF9DsvRgOh/VKQerL2JhT1P446VO1MFfz2IWrqiJ+SdFotKkVhf/hh6Gsuy7UoUOhrrgi5GQS0s8/uyIMbRWSBCy1VAVLLZXHxhvXvlYsMpXk0wW9U6bIePVVGbNnd080yyxTbTNQjcRUugS9CgYObLHNgAHe115D8JproPbrh8oaa0BZd12UjjgC2vLLm39AlqGuvTaKa6+N4oUXInDDDQiMHo1IvroiyL70NiR09Rrq+ojm9wORCPDnn/B+9x3wwgvw/+Mf0NZYA+UNN4Q3EAB+/BHS9OnADz8A2azeDkAbOBCVbbaBsvLK8N19N+Rp0+CdOrW64R9+sHxoqf+wAoV+4kCrqqreGTaTyegTF8vrzdDsoMGBKp/P66ZwXFWytJR6CLOJ39jfhT4kLGXlsRtNr7h6i0QiemdfOxN6qVTSqzfE9zOSwi7OPC8juIK0Q1ScNkQUYRThNgLHIfqQiNsgCbZ7DIw8NTLwCofDDZsa2gUnPSsb/FZBMi1JEhKJhJ5eEjt0m4Fk005/KTfKzptdLADQ22BYERWn96IkSTX9iUTyReIHdFftUGheLBYbEjs3y8UXVLgufxbJSiaTsf2gSX/8Ae+rryJ/1VUAgPJOO0GLxRC45x4ULrnE7cN0FYEAMGRIGUOGlGF0fZ87V8aPP/pq2gxMnChhzBgZhUJ1he/3axg82NhmoJpKWnRR56mk0JlnQgKgJhLIjh/vOHVWPOMMFM84A/4dzwA+AOa88g6S63U9bIoCWVWhCtEJr6pCfvJJBEaNgvzFF/B/8QWAqncMAgFgiSVQ2WUXlPbeG6Udd9SPJ3j11fBMmYLcdddBHTIElUGDEBo6FD6LE6aRkhX4wBtTLuKAadQ/WEVT+LoTiJ1qgWrpMUPs0WhUnyDNKpJIssRBTRT7itoNrm5Zmsy0Azv70lK/0WqcuXwrW3XR18VqMKWWhqtLq+fdTvrHCs0QFaBbLMsUnizLuteG0/3XI8isiBGr15qFLMu6gNstwzCjgZrR7ySVStm61ykE9/v9ttImTvpKmaHVCbxYLJqWxvNetCv2FUlKI8M9VVVrKvO4SGqnG+7CgLbUaXGgYGSl7kNQKMD33HMI3HwzAKA8YkT177EYiocdBv+996J46qnQ+vVrx6G2Hf36qVhrrSLWWqv2gVBV4JdfeqaSHn/cg59+8kDTqtdwkUV6ppIGDap2rK4JCCgKwkcdBXnGDMi//QYA8EyfXlXCOmyiRwQj1WMoFoUBw+uFcSpSZBnYZx+U9tkHkiTBM2cOlGwWWGaZuiRJk2WoSy9d46qrdb3fzJLcjrEW0JNgmEUOuKI0y6Eb3VBZbSSSEDOIpcfivgHoZEWsdBHBAV38O31K6jl78j38bCqVQjQa1Vf4jSY7Eo1676tnEsZVtllPHOM2mu1R0ipRIcRKJSdkycr0TtT1sMliq+A90mylENCtqxF7VpGYsOSeqREKZ+0SLOqSTJuJmryXkYVmdIt2OjHXAz18gsFgj+vphESJ0TK714lRSbZMqKfVER2oOzBH2wrKRbJiljeXv/4a/gcegP+hhyD/9ReUjTdG9v77oS22mP6e4kknITBmDCIHHAAtkYA8eTK0AQNQ2n9/lPbfH70n+nAfsgwsu2wFyy6bx2ab1b6WzwM//VTbZmDKFBnPPSfjr7+62wwMHAg9lbTSB/dj2McFDPVmsJxSggdAZeWVmyYpABCKVW+PXMr+IKNpGpRFFwUWXdTuB3p8HqhO7EaiIjbHqzfwGe81sW8KB5p6OXTeu9QJiAMIB3uxe6xIaMwGI5Iiim1p8c4IDFfimqbpPXs4kbDZnN1zBYBMJoNIJGKrs6+oU6n3HiuSIVa9BINBXfxrRi7EfL7TShY7YkNGI9wOpXPCNaYixH5HzXb2FUHSkMvlHE3QrGQiOeEET2LNjtdG8Llw0oGb6Ug7kTHeB6FQSE8rOYk4tZL6oa0B9UnUD/EeYoQ1Go0il8vpzy11LUbiaazsaXTcjFxxf/WIili91yEr5mir8w1XrBTYIpOB/8kn4b//fng//BDqYouhdPDBKB16KNSVVurxeW2ppZC95x6ELroIWiCA8h57wDN1KkJnn43g5ZejdMghKJ5wArRll23nafQ6QiFg5ZVLWHnlnqH7v/7q2bF6wgQJd31zEEo4AlCAYFDDkOVKGLqqF0P+qdV0rO7Xz37OO7BoNWSjXnAlgq/+gcL55wP9+7t1mlXhj+HhFQcmMwvqeqt7/t04KPDhFydbUf1vJdxliJgW9GIDNgpOge6KD1mWTdMtJCRiJ2d6yFAvw/QES4V5HnZD0yLEqgROKFaDLFfI9QZSEq16EwejFZqm1Z30EolEU0QFsE4FsHy5UcSG0QWnqSdRm0GCS98VO4JcO2CkkNWTwWBQ11nwX4Jl8EZ3WN6z4n1m59yc9L9hpZzojmwGPiuS1N3AkcfJ50VMF5rdV3YiPdy+0W/J+EwYtTKMklH0LgqvuW+OC060NmLjRJF4sMKqntUAG2TyvDroRtst+iQAno8/Rnz0aEiPPAJks1C22QbZ++5Deaed0MjbXtl2W6QNxmXyTz/Bf9dd8N9/PwL33ovMs8+istZa7TuJPoRFFqlg3XUrWHfd2oHIe/W1+P3ah/D1AZdg8noHY+pUH6ZMkfDQQ6jpWL3YYmKvpO5U0gorKDBGQiOFPwEA+Wm/IjhtDAJjxiD9+utQV1/dnZOR5WqrbQF8UNlHxyh4Y2SkHuhVYhx8jJ+zKq/lQGMsIxZLaCnGExuhUe1vBImKiHw+r5MIam/ocksYNSt2wRRXJpNBIpEw9ZMguP16VS2cLOpNHPSCaKSLqRedIVgize9OrNyhxoQTuGi2Rg0Q+8OYHaNd8a8IUVCrKIpOjJhCaRXi9nhfUFBLclWpVJDNZnWNCI9LtK5vBkxPOBGW8tmkqSL3TXIippto2Z9KpfTzIdEHatO1omUA0yHUNfG9oqBcNIAUU6p8TqlVYx8tI4GOx+N6KozRSwptjVFUO2BFkBnZoLmiVRq3XC4jlUrV9ITqoBvtIyp//gk88ADw3/9CmjQJGDgQ6umnIzNyJLQWreXV5ZZD4bLLUDjzTET33BOR/fdH5oknoK62mksHP/9BOfcsrHjHKKz49N+w2e171ryWzVa9YaZO7W4z8M03MsaPl5FKdbcZWH55ppKqepiVPdV+BenTL0Bm0z0Q2XdfhC67DNlHH3XnoD0eU6LCskx6XYgDaL1UBaMChUJBHyw58BlXTyKJacbrQQwVM4pgNYmbERUOhvSLKZfL8Hg8egiY27UTbjZOyuIKkCtmht+N58lrUa+qRbTRNzseGqdRSFgP4sRjBaYwxCgNBcSyLNdEopgaEwmS1fdpTFHR3M7ONSbBkSRJ9+NpRUdC0JdDvHbGiAgjZCTgiqKYlhA3AxI+J0SFfafo80PCQnIiNgOkgJkdtI33GFOhYlREXFCQ3IjPj0hKaE5Yj3hadbfmc5LNZm3dP40glr4bwb8xIiummwhex04KqCfcJSqqCrzxBvDf/wJPPFGdhPbaC7juOkjbbVctd3QpnwsAiMeRfeQRRPbZB9Fdd0V27FhUNtnEnW3Pb5BlVFZYAZ6uihsRkQiw6qpFrLpq7d81DZgzp2ebgVde8eCOOzxQlCMBABV/BGpXVEuaNav6PbuhA5Bly9QPrcONXUgbpSoY1TCu7DmgAt0pSa7GjDoVO4MEyw6Z/673GVmWewzQXIkaq5NyuVyN+K6ZPD1JntfrRT6f1yd3sbeS0c6+nq6DhMHqPTx/O5UNdkgB32O2PZ4Lm/RZVW1ZaYWoEwK6/WI44dGx1wyiU7DRUr8VkDgbt2csDee94JYehqCOSZLs9+ZhBAjoJupmnYqBxkJoK5JBgsNzbuV6M2pkTDnSp8gsVSz+XyTq9QiMHW0UU1WM8uTzeWiaph9fh6SYwz5R2WYbdNXQAsOHA2uuCSy5ZFVn8NtvwN13A3fdBUydCqy8MnDllcBhhwFLLKFvQgJ0/wq3+ldoiy+OzDPPIHrwwYjtuitK++2H3I03AkLfkIUB3ueeg+eLL1BZZx3bn5EkYLHFKlhssQrWX7/2NUUBfj/qCnz9zHRsecK9iCUiwJJLwvvVV0issALSzz/vKAUkf/01YjvuWK1CCgQATYNUKEAVvC5q3t/VJ8WYAiqVSnqI1Tho1JvUWQHAbRKJRKJHSogro0Qi0SOfzkGVqYhGplScBIyDMcP5RjCMTw1LPB5vWGpsHNhE8bDYa8aqGaSdihyWQgOomcyorbDr+WGnYV2lUtEjJkaCJ7qVWpEUq2sriipTqRSA7tJiGtwx/E/SwuMQhc9uucVyIhZtHJja4f5EfxM6x0ajUUt340bg90UdB+9NsdTdKrLGSBRTbSTrbnR1NoLfc6FQQDAYbGnO4LU1Rg3FhpmMcPCaiPcWPxMOhy31V2a6GCvwfYxEiuNWh6SYwz5RWWwx4JNPgIcfBrjSWWIJYLXVgLffBrxeYORI4N57gU03hZX5BwcpPvSuIJFA5skn4R87FqHzz0dk7lxk77+/of5lQYL3rbcgAcjec4872/MCK/8yAcO8nyO1aHXw8v30E7TrrwcuvBDRffdF6ttv7W9QkiBlMihvvz3K1BxJEtRVVql5m/GBNUsBNUpVWMHMOltRlB6TNNMNxWKxR5knt2P8TCQS0VeHxkognocR9SZ2ijTD4bBO1BjmFieGeuJWIwEzawZJjU0jzUYqldKbIPp8Pr1LNEWfTg3U6pUIkzyZNTzkJFmPGALd3iEiEWMDRnGyEaNvXNWadatVFMU0XN8sRJJCPQa9OngNjNWSJJbhcFgnK/WOR9SEiGkJRkD4w/spFoshFAqZPlc0MSPRd/rsOZ2ARd0RxcMUCjsFn2eziEcul9Ovp3hfGK8rU6iRSMQ0ukP9k5PzFN/bISj1YZ+oPPJI9V9VBX78Efj88+rPZ58B228P/Oc/wNJL294c2btrFtE+H0qHHw514EBEDjwQ4VNOQe7669vTTa8PwvvJJ9AkydUKKHnaNGiLLVaTP4+dcw6kl16C9Oabjralrrwy1EUXRWXttWvt+w0QUz9ELpdDPB6vMRG06nhcb+AuFouIRCI1fioURxq3A/T04eDAbxyUxFJnYyWQWZSHq69Gkx4nVXZmFktky+Wyvn2zQY4pMrP9i80gmbtntKFeWiGbzcLn8yEcDtdofZyG5aldAKA38xOrNUTxrBGNwuuMRIiNJLlSBlC3AkosC6dupFFKyCkYURZJCokRtU7U+5iR0HK5jEwmg2g0ilgsppMZq2tIUieSXKv7rlAoIBKJmEayKBx3+l23Ul5MMH3ZKOLT6Dis2kCk02k9xSxW+ojg4sbv9+tuvozKddB+ONeoyDIwaFD1Z++9m94xy/AA89Vms1C22Qa5W29F+KSTkHjqqWqDvWuvrYYIFlSUSvB8/HG1xNstDwlVBf76C9pWW9X8OZ/PI7r22sCECZAnT4a68sr2tifLqGy4ITzvv1/3bWYDm6ZpNSkgK6Ji9XmCKRHRWZPhfWMliNl2jPl0Ttpi9QcnXrH3h3Fi4LHbXZ2Lq34OkiRN3J7RII8OuGZRCYINHinS9Pl8SCQSekM6K++NZDKpl2HaDcfzeoiVGolEokfVBydT0chORCPNDklkKpWq6cHCFByra6yuPYXHbrqJiuXTvB9IUqLRaI0XCwXU9SrbKpUKUqmUPrlaXUMxVWkHvC68RgSbZbYSAW82okJks1ndyLCZXnL17hlVVXWywjnJbPuMcjKqKILjSAftwTxrMsABpx3lWOWRI5H69FMUzjkH/jFjED7mGEgzZ7q6j76EwB13QFJVFE45xbVtyh98AEnToBoaGymKAm3//QEA/gcfdLRNZYMN4P3oo6oAxgJWuVqubKkjoCDW7PNcfTFtQndKv99fs/qkloHkhWB1ST3QjdVMeFcsFpHJZPQVl3HC56q/2TRCsVhEKpVCMpnUJ49QKIR4PK4bnokplnoQy8FTqRQKhYLufRSPxy37IdFx2o67J7dH4kQhc6lUQjab1c8lnU4jl8vpvVTMBv5GbqWsFgKg29Dzu0gmk9C0qqmeGegi2kyUyOw4IpEIEokEotGo3seJx1KpVBCPx/UKEFFzUiqVdNGlFUjG+JlCodDjGjbjBsvvX7xvaIDWDi2KFcwq5Zjqsio/r4dGZfEkK0TYQuPIiI4x4teb12ZhxDzthsSbjQ6PbkJbZhkUzzgDudGj4X3nHUT32AOSC11N+xx++w3Bf/4TmteL8oEHurZZ//PPAwDKu+7a88UNN4Tm8cD3v/852qay4YaQsll4vvqq7vvq5ZMB6MTWOPAwlx2Px3W9AaMNopU1uywnEgl9Vc+Jhe/hfqwIC0tjk8mkZcSCq3jjINasNTgjNOL2SFqYAqD2hH4jYumoGbiCZ08UcXtMS3CyNT6jDMk3Iitc5RP8HpnCMiNsVmS03rXj+60maJGARExcrRnFkSSpR7TCLlhGLEZKMpkMkslkjeEXNSrG0ligtgdTI6RSKd0514lxmxXY9ZcpUj4PrURTmqlkMauw4fdXj2xawW6qNZVK6eXM9CAygvcsnYR5bG7plzroiXnetpE3MB8Mt1EeMQKZl1+GNGcOghdf7Pr2ewtSMgnvSy9B+vXXmr9Hjj8eKBaRvesu99I+AHwffgjIMpQ11ujxWj6fB9ZYA56vv0Z4wgTbluWVtdeG5vc3TP8A5qFiTdOQy+X01RbvFwoRge4S1GKxqJfMJpPJmh8AuvFSoVDQBx6WvjJ9wKhCv379kEgk9AmIA5SVjkC/hl0TjZGoWFWlANXISCKRQCwW6zFIkiiYfZaRIabJmKaiT0c8HtcJh3ECZHREdLRltVAqldL9R6LRKOLxuC40pXbDKk0jglVBJKH10nfiORqPtR5REb1j6m2XImnxvmV6gxGPYrGol5GKItd6iEQiNQSoVCrpDerE4yc5zmQydUmV3fEwk8mgXC7rqdFmQW0Ov8toNKpHU5qJzjSLepVbqlptlEkyaRf1DA3Ntp9Op3UCH4vFenwXFN6KEccOUWkf+oRwg2kgNjF0+wtXV1wRxVNPRfBf/0Lh4ouhuWkD3xvQNET23hveTz8FAKjLLlv1CznySHjffBPqKqtA2WMPV3cpT50K9O9v+l2USiUoo0cjtvnm8O21F3xbbIHyXnuhuPnmqAwZYr3RYBCVNdeE9733UDruOMu31VuBsXRU7JnDwULsbGqs2hC3zdVPvQoCht6ZImIkRlzls0LEajtWKZ56q37a6UuSpKesuA2uCq0mavq1GH1kPB6PXuHj8XhqhKJcKadSKT11JIoEaaBHEz0KP9kBOp/PIxqNIhQK9RDi0q1W7NjL42P0ywo8Z2N1UD2SR4LZaPwwu7eME7LY6ToQCCAWi1karZHESZKkp63C4bAuZuZ14fsomq13nDQptItsNqunOe3qSfj9iAJc0VyvWQGtGYzXnFouMRUogpE+M9BvKBwOW1bhGPdttx2FuI9MJqOLn9kTiOMLo4Tid8T0Uker4j76BFEBaslKo4e4GZQOOwzBf/0L/rFjUXRRy9EbkCdPhvfTT5EbNQqa14vAnXfC+8knCF15JTRJQvbOO93doaoCc+ZA22gj6/LXoUOR/PBDRA89FJ4334T/zTfhA4CNNkLp5puhLrIItOnTUV59dWiyDM+XX8IzaZKpIZ0RIlERCYLocUB/C3auNesJVG/7dnxD6q0kaUhV7z41tpcXYfY5nidFrtQKiOXRjfxUrMyzgO7yVq6cOQlz4G+UxydJZLrH7/frkSyKecXUBqMfJCfisdENtZ6JltjEUHQsFe36xe9ZbDpZD0zbcb/0BjESLdE3RSQsvF78PKMo2WxWP8dcLqcTukgkovv/cLuNxjerRoj1wOPn58yEziSbYo8gs1JlghHGViIqYnm4+CzztUqloqecAOgLkHrXiD5AoVCoYbUa7+lmqoV4z1PIKy6kjRWDTjoyd+AMfYaoALUdlxmOdgta//4ob789fM880+eJiv+ee6q6k0gE2jLLQPrjD2ihEEp77AFEoyjvuy8iI0bAN2ECsuPGQR0+3NX9y999B0lVUVlvvfpvHDgQmTfeAObMge/DDxG+7TZIb72FwAYbdL/H54MWi0H6809okoTKJpsgN2pU3c1SBNqvX7+Gx2rVO6PR9ltNM9ZLwxCMytCbgiswq6gABzlx1SZGR+qFr5m6MJtQeK78LKuUSC6oaxHfUw9Mr7EvDQkHvWTy+Twkqb6jqFX3auqFRFMyEgGxdF309aANPgkZe6qYTUxcXYuEj5NivTRMPcLC1brx84VCQTdSIzmiN06jyZ/C8Uadwo1g5ZAYzaGpnUhOKE6vt226+EYiET1l2ghi1ZsZKTE2IzS731ha3oh0MiXJe9BpZNMJstmsrltixNFoXshz43l34B76FFEBasmK0fCoVWhLLAH5rbdc257rSKeBSAS+J56AFouhvMcekGfOBJZcslpi3bV6hSwjd889iG69NYL//CcyW24JWKQ6moGvS0hb2W47ex/o3x/lnXaCss8+8A4dCmn6dP2lyu67Qxo6FOruu0Nec014IhEEulIOVt9tLpfTw/h8D38XJyuG4+3AOGiKXYob3WN8XWxTbyfNIFr0GxsOMtpiFhGwOp56AzdTRlaRGvHaiX8zEg7qMsS0kPgZMyOsUqmki0eN3WitIKZ2CGpkOJmKLrHiqjybzer6AUaHxOoXRjFYoSESWZI28b5hRU4jiISF3jaxWEwnZWYoFos68RYN9xgJtIJoVOc0mmH0/GAU0g45MYJlwf369au5f8TnkhAnaGOpubGqph6cRJFIiut5rPBZbQXUxzGywggkBefUufE9zQiIO7BGnyMqQPeqJxKJ1G0Z7wS+ceMQuPtuFE4/veeLmgZ5+nRoiywCzcLSvd3wvvwyIgceWI2gzJkDZcstUbj0Usv3a4ssgtyYMYjuuCNCZ5+N/M03W7oBOz6WiRMBAOUttqg2BLL7Oa8XWiyG0rHHVomVEYqCfl3aBK7mzQgLV3z1wAnTqoyQ20nU+T6tdCz1wAnUbt8PCiaZumAqIxQK6X4dDLu3Ul1RT/didqxGUSoJB9MqTFuIFSj8DMt+jSLRUqlUQ3q4GmfKyQhjsziufK10auLKHOg2rmPUig65FMsy+sHVeSaT0c9BjOg0SqkZoWmavh8SlXok0qgZYmPGRpHAZiN/PGfxejVrTsbJnylCluYafwDUNAg0EnC7gnvAXs8cESKBMPNYsSukbQRFUXQiTFO8XC6HSCSCWCyGZDKpR9lInjtkxR30SaICdIfN7Yil7MD/9NMob7YZSvvvD3nqVKiDBwPZLHyvv47AtdfCO2kStEAA2TFjoOywgwtnYA9SMgnvCy8g+O9/A5EIyltvDe8nn1TTPA1QGT4c+X//G+GTTkJlnXVQOvJIV47J8803QCKBiiTZJipMPWiZDDST0k+gewBiRQUJizFSIg6wnBiNoXXxM2YhXxICcZLkZ0iCRU2BGcRBhvl1toRvNLkZTd3EaAzPkxEM0dGW5aBcpdvNq1ulffiaWV8ks8mVE43H49EHZB5LqVTS0xFcNZK0iJ40nMRJdijkNRIW/i5qJUQfFCsYXycJMTsPMTVAPY0Ipn2a9cEwazjZCIVCQa++qvdZp0SFBJjHxGgDq7Wa1f4xBeTxeJrqMeRU49LM5M7Ij7EgwyzV1wooKg+HwzoxyWQyuiUCU44kNB24gz5LVIDufHUjsZQdaMEg/C++CN9GG0GTJKgrrQR56lRIigJlvfWQefBBBO66C+Hjj0fhvPNQ3n13aEst5dKZCMjnIRUK0BZZBJ4vvkB0990hpVJQ1lgDuZtuQtmh22/poIPg+eQThM49F5XVV0fF2F2wCUizZ0MbPNjRoEa9gJxOQzOZEADopIRRA1a2iIJZUdDHnLrYp4W5bdF/wywKwW7JokiP4ITaKMRsfI3lpjy2RkJawFrzQZ0HQeJC3xdWxYhVSoqimNq519OncNvG1xpNsFw9s1MxK3WCwaCug6CWgKTF2BGa1UIiYeGqVCyf9vv9OhGt5x3iJCVA8F4hweJxEXbTPmYQCaUTMOLQiKhQ+NzoGPg9MJ1oLHvOZDL6qr/ZqspsNot4PF7X6djq+Jq5Rk6/Z3qskKwwgmSW6msV1KuIzQyNpLLjVOsu+jRRAaBXd7RKVvJXXgl1pZVQGTasajr20UeoHHMMlK22gtpVUlvZeGOETj0VoQsuQOgf/0Dp0ENRuOQSaIsu6s65/PEHotttB/nnn6Fssgk8330HdZllkJk4EZqDPkk9zu2f/4Tnyy8ROfxwpF9/HdqAAc0fZC4HFIvQVl3V9kcodsxls4hlMtAs/A2MAjnmfQHo5Z1W0TOxWoFhfTFC0szgKw4sxm2IPWg42Pp8Pl1/QcJkZVPuNNwsEpdEIqHnwLmSpQ07y5VFDUc9fYq4ffHcjH8zA12AOZFzv5x4KJwluRKvj9i4joSFKaVoNKoTFlVV4fP5dKIiihONaGbgZ+8aphNFcthM2kdEOBzWq5oYtWnUKJBoRMr4HlYkiRVKTMMZNSEklEaQvIj9gZxGkMTJ2AnpoD7EyTVuZYIvFou6SzPHBjHV5wb4rInjh9HNuuOp4i76PFEBuslKK62+tWWWQeG887r/cNBBPd/TpfuQ/voLvocfRvCaa+B75RXk7rwTyub/3967xdr3vWdd7zqvNedca33RtlairVAJAeLfqEESDyRNFEUEQcUUqGm1kbZqOOWPp1ZrIW1tCASNAY1igIaoAUnoBUSMFyQELgRBL2qMCV6YqAWT/vdhnQ/Ti/37jPWusceYc8zDOu09nmTne9hrzfMc4xnv+7zP+4/XPXwREen9lb8i6fd8j8h6Lev/4D+Q/l/+y7L/lb9SVj/yI41IioiIDIey+KN/VKbf+Z2Sfu/3yutP/7RIzbYE/b/wF6Qjb3b3IcALYb1ey/H5WTqbjZPYMeH7NBhlK1s7DcKk1+l0zKBE5EEPwnihMLBrnwNST3aqSefdAd4Sru3wPS1ArbtS1+6zdrqI6JImL0zCXBPfBK8HTo67bLLyeU+4ro3WfECo7AmNlBDaESIxmjjh31GnS67r+ImquRY5TdI+mIDRyI/7hk6iDKvVSmazWWFUhePSDrlaaEyDRVsT4gJiViIOZWlPF+poZuoIWV2RM+1lpP2M9I/9eZ6nqpqXENj9mCCVIBKVdvEQREVEzEqrzKirDeQ/7+fJ9gd+QHa/7tdJ8oM/KOlv+A2y+Kmfkv2v/tX1NrjZSPobf6Mc/96/VxZ/5I9I/m3fJpvf9tvaPeZv/VZZ/NE/Ktmv/bUy+aEfcotZA9D/H/9HERHZBZ4rnV43m41pUZA7yoqJBDBw6cGZQb5sYtd+IgxmRGEgI1SeAJdVOoM7AxmDCsZxrPQRzWL25BKkQh6YoHlG64S7RYpTRi5vFwgLxncQBM7B1QCRgTtkonJNMkUD/3a7LWw9IHJOWMbjsXFr9ZFEUHWlzbX0LW7qkEmqzUROjR9FxOh0yrxhAM/dcDg8e05cPidoLLj3dSfBPM9NZCVN0zMDsxBUNaETcffscYlxNRHj3GkzwP/r8+BPHVnUlUYgSZKLpGBsoSy+QPYxRrSDhyEqgKZhbXZc9iH/+T9fFn/6T0v6W36LJL/tt8nLX/pLkn/zN1ff0G4nnedn2f4L/4Lk3/Zt7R/oVzj8il8hqx//cUl+9++W/T/4D8ruu76r8jYGf/2vi/R6cvh7/h6RkpWmSfl8tVrtfOMbIiLOyikICvl0HQEROXeiRK+hNSnH49HYe+uUD6QHIzLtIMrvCVtTFuork6TE0XZC5ZjsCUILZTFoI0rD9cG9NXTVzsAeOtARdaHShdSAnQ6yiUro9l0VWSEDf8hnmOiZaCCZVGvxTFBWW4eo+K4l5Liq3kKXXttmefzfZDIJKgBgFT6ZTM46POtnWuTtOaoirHYdN4SW55uGnSH+KEQseOd4vjWJ8P2de0D1XVnJOtCRLrx2tP9KKFg0XAL6eDlWHQ2MVT/t4eGISp7nZ7nhi6PXk+V/8p/I9B/9R2Xywz8syzousFkmu3/2n5XRf/FfyPb7vq92WiYE2+/7Pun9tb8mye/8nfL6S36JHP7+v7/S97v/5/8p8s3fXFp6i4mVHlA6XwnYbKKC2BMhH+kYJiabBLiaVPLS04ND5NSbBBEhqQQqE1w9dqpCVyOV4Xg8mqoAyDRmai5PDxfKtC1MBHb4W69M9TVmoiAFwjbqou3Bl0jadruVzWZjqqsgXjq9xSQeWkVSdC11VUgINEmhzJmUJ6DiqSyqosWveZ4bwkPFiP3cVtWGUISAq6xd1q2jkVRC2QRDb8s+FsZfG/r/+DtkWS8YOOdOp2O8snzXivNwieJD0EYKRqd7GQfs8ckW1BY1Ao2ojocjKrxkiGuvQVbyb/kW2fyO3yHjH/1R6fzYj0n+Td9UeRu7X/NrZPjTPy29/+V/KXd8bYJOR1a///dL72d+RtJ/+V9+E9dW6W30cz8n+T/2j3lD/hAUkVOXY8LIna9WkXZ5Mis5u1SXSpL5fP7uPmrfCy0atPUnEJLpdGpC5FqkqM+DbTA42yFkV6SBf0PKQiZI7SmyWq3MxMQP23FNljzfTHYuMuI6PvtPbRxHddVkMpHtdusMx7tgawUYnPEtYVC2w/f6OKqCCZ70kMjbiprJMc9zQ0i5jjjT2igiNEwsIQQUP5jj8WjEsr5nAaKioyq6oktHTSifhoD7YBvjuYCOD82YyHmqULcbgPyRYrKJhCYUvp+mYCKvWxYeCiIqZeSW98zWs7nsEnTqOUmSs7GMa0OqO5KVdvBwREXknKzUEYXVwfY3/SYZ/57fI8Of+inZuEzjCjD4b/9bSX7n75T9174mh1/2yy50hArjsSz++B+X6Xd+pyTf932y+FN/SqRkoBMRkZ/9Wenk+btjtAkKRkciJ1HrbDaT7VfRArvqp2h1yaBq6460aNA3mGFLzuf5P47TN0joycleRdoTF8ent8tgjreI69z0MRO273Q6pjrE57HA5Kldc9mfLTatYg7X7XZlMpkY8XDIBME7JvL+WhL+1ytmfspWk0wAuvu13qfru3memzQFkRZW21SAkUKic3an4y/ZZptlmgtNUkKcsnVUBfJsC2B11ARdUxF8RMVHTrRGyXdOw+HwzLemDa+qKqjjuFsHLAjG4/HZwkc/g7YYV79z3Ctddi9y8lViEQIx4V40bdERcY6HJCoip4kM99pLM/P8b/vbZPvd3y3jP/AHZPfP//Ny/PZvD/pe/8//eUl/4Adk+13fJcvf//tFVFvwSyL/u/9uWf5X/5Wkv+E3yPj3/l5Z/+iPln6n+7M/+/aXv/1vN6FpfkTOCQpg5ZtlmQy/Si0MvnyRnVVl0rTjrQ3y3xCcl5cXU/3jM7ZicPG5dPpSTiJiVtI6baXLhbW3i+98mcS0U6oLZZMhYfMq0JPRbDYLmiTQcNjC4zzPzTZc1TQM4Li2si39pwbGfBynTRbtf2tBMY0hKffVHadFiiubyogKJDyUpACd/vG5tepzGY/HhSt+StCJfujUEedB6qzsPYIk8y5zjvP5/GqLPkhkyJjdRkSCkmXaCYicp6aIiBQ1IbWh310iZqTwdGotoh08LFEROe+4XNfIqApWP/IjMvjv/3sZ/97fK8v/8r8M+s7wT/5JOXzbt8nyD/2h1izuQ7H/lb9S1j/6ozL59/99OfwD/4Dsfv2vL/7CVy9v76vVt8ipMV6ZtuL19VVmLy8ik4mkXxEGDMrQTbjg6ngbMiHYHVHz/M16fTqdmlRUG+TVFvva5cJ6da+jIGXNCkNKSouOaTAY1C7V5xjKwD30VdkVRax4N30aBf6eJElpRKXoOvE7baePz46IGCtzzsN2xnV5mZDO1ESgyr3K8zcfGdICRelpbf7mmyS3261MJhNJ09SkJ6uQEzAYDIwxIwSTezuZTEzZ8qXT6VWqztoAxLHT6Rh/nzbnCojyaDQy1YFZlp1ZGEQ0w0MTFZFzslLXIjoYs5lsvvd7ZfwH/6DIalUcHTkeZfSf/qcy/FN/Sta/63ddnaSAzb/xb7yJa//Nf1NefvEvlmOBkdvx279dchGR/+v/kufn58oTab5aSWc4lJeXF5OTJ0KBHbuu4mFF60phlL3g6Cz0YMfKF2Or5+fnd89D0Tm59sn3XVoGwuwM7J1OxxmVce2nConqdN4cYSllpVfMeDyu3RsoZJ/6Txtlzwb3ogx2yaurSqNodWqnRSCTEBDEzbraDNKCUZtOTZIeE3mrZiOSURUIqdEFFcH25CgCTRHrkFyIk07zkGbknUySJGhh0gR1iAqEs27vt/V6be5FGxobG3qcOB7fzPdCmnNGhOHhiYrIaTAlDXSJBxHsfv2vl8mP/ZgM/vyfl90/9885PzP8z/9zGf/kT0r3535O1r/9t8v6h3/4YsdTik7nrWrpV/0qmf4T/4QcftkvO/380l8q+Wwm3b/5N6XzN/+mdP/f/1c6IiJ//a9Lt9utHJHo5LnIVxESVmzdbtekCYhA6EkQ4SiTCN4lIm/3k3CsfSw+IyltXW0PEHUHjNDBRoeTfSDaUOXaEloWOWlD8jxvRFQoMy2qICo77yLyEDq52/tw3VN7EtCAlNjCX55f3RAQwsIPn0eoq/18VquVIdKhJMLGZrMxUZCiZ8L24HCB79e936zu7e+jQXp5eTERLt5HSFHbCBVzi5yehyrfcWG325lzuwS55/6gvTke3wz2sETQwvaI6vgQREXkpNomstI2WSEicPza1+T4y3+5jP/EnxD5F/9FZ13/+Cd/Uo7f8R2y+ImfuGyFTyjSVF7/9J+W4X/z30jvZ35G+v/T/yTDP/EnpGOFeI/zueRJIvJzP2dKfivh5UXEIaQVkXfddkXeBkn0CWg/tBCTSUKLESEupIwwF9NluwALeLsqhUnBHjQYzHVXZbZHLt/X8Vlvo2hSYqVf9JkkSZwDM9EUSB+OqHVC6NqzRMQt2tWuvS4UERVNdorginj5Uj+ua6u9d+y0nH1ddPSLyhdKeHm+bM+bJulDXUZfNOGjUyl65yAzIdfUBZ3KBeyT3k0ib1EkNB2TycSkM+o8Y6GtGoqw3+/l+fnZVHrVBfdX66HaBO+OvtfowrTWjx+tlYnkpRwfhqiInMgKkZWmgJy8e7C+//tF/rV/TdKf/VnJf8EvkNVqdRbe3X/nd0r/L/5FOfyiX9T4GNpC/nf8HbL57b/99B/brXT/j/9DOouF5N/6rXL8lm8RGY9l8Cf/pKS/9bdK/2/8Ddn+XX9XtZ28vopYDQmLem3wO3sQT5JEhsOhEb2yAqajL0TCdqEVOdcT2JU6QBMRF3yrW1IuDHZ2SF8TJczetGkdq/Yi8TCVQfr3aHwwPtPnWoeocD/K3pGi6p2ydAzH7ZtcENxCRLULqX1eerVKlEOXjvJ5TVrKolaseklH+q4FRLfOddb3vIioUEo+HA69KZfdbmfKiaseB9VHug8RehX7meJ4Xl9fjTiZZ9m12PAB3xn9DOhngXvOuXOtfNep2+02TkfZNvdtgmikr9eS/SxSccR4EglLMT4UURE5VYOkaVqr5I4yXD3YUe44Go3etBu/+lfL7MsX2f/4j0v3P/vPZDKZyG63Own7fs/vkdmv+BUy+YmfkNV/9B+1fYrtYDiUo6NUevdrf63k3/It0v+P/2OR3/f7qm2zIKLigk9ka090tisnkQSM3fSPiJhqHFujUlb146pkmc/nZ6XAEOEkSWQymcjx+NZMkCgPx69TBrYPA+kuBjA9gJMyCVnFhvhrANvLIzRSUHT/MD2DyOsUksgpTaVJhS8tB6EjpUX6QZeOaoLJdSRVCKnSZc8+Moi+h+0WTRBso27kSqcyfWCiLrqXiG6rEhUIoO05RcqnKNrBe8f1IqqBxqfoOCCgOgWsnxEmZvsdcRESyH3TNBRRqVrR4hLYneHLQNSSii4iXpGsuPHhiIrIqRojpOMyA7juIcPqQavDtW10niSy+frXZfJDPyTLX/7LZfJbf6upPBL5qgHi178u4x/7Mdl8//fL8Rf8gsuecJsYj+Xw9a9L79/5d6T/r/wrsv+lvzT4q52XF8k9ERXn5z1EpSzEy0TrGyhtgzm93aYDAbln7V+RJIk8Pz8bUzh7dU7kR5d601CRbepzy/O3svuySjbfClHvz2WYh5C0DEQDJpPJOyLC9jJ1v12Ekf/X9uf6p9PpyHQ6NcdE6o3JiUkcnQdRAf2MTKfTs1QWP1xDDdKNpHm0Z43vGkAQ6oAoXBmImBQhJDoDuK6dTudd2TGtKEg5DQaDwq7P6HzsFg0ckz4+fng+qrQ60eXDGlrj0QSQvaLIVR0wh9QlP1gAQNBjdOU9PiRRESnuuMzgpIWdlBH6Stf0Kl9EZPODPyjd/+1/k8m//q/LdrmU0e/4HWfEaPP93y+jP/yHZfSH/pCsqkYmbozX7/kemf+RPyLpv/1vy9Of+TMiAe6dIvIWUZnNzv6riKiIVM9fs2IuWrn4IgZ19iVyntJhG3Z/odlsJt1u10meXP4MvV5PptOpcbTUOhy90iw6NggDTfLsCIEmA1ogGgoIgZ6UWFFzHxBh2iW8ZdEroFfS7ENE3lWe0EHYNxmQrtBpvzRNzwTHWZYZ23vd5ZqxwkfeXJVIALLqmvjKmjPax89z5ntOQ6uDeCZF5J3HFM/WZrMxrsmTyUSm02lp3x+eY7QrIqdmjFojxp91Ju5er2fuNYaKdaNZNni/2iYqvB9N5AZ5npvSaVcj1c+OD0tURE75ftIHiMe0boIJJ1T0ZwasTkdWf/APimSZjH7X75L9//1/y+Anf/L0Uk0mcvja16Tz//w/Fz7LC2A4lM0f+AMy/jW/Rib/3X8nq9/4G8O+9/Ii8vN//tl/FQ28ZcJA/XsmBCbPIqJSFKkpE3nye9T6IieTLLZBNIc/tW1/aHia58jlIAqJdp0Dx2KnUfQxaU2MiBgSVXVVygTtmsB4l+zohkbI/lzVEHn+3tre53nC5+17yjsNoSKKsFwuz1b5TKb9fr+QqGgXXdsOX0SMrsgWboMiImQfhy8Kgald0TsFSclzt/EhKR/eH1IPvmtbdKxc3zYmfSKM+pkihcv1p2cWz3jVBoUib347WZYFRdtvAcaTMiH7Z8OHJioiYrQluFXSWbeq4dY7oiIi0uvJ6id+Qo7f9m0y+ff+PTn+wl8o6Q/8gBnYO4uF5F++tHk6V8P6H/lHZPgv/Usy/KEfkvWv+lXOjsjvsFg4NSpFA3RZ6od0idYUhAxOPmdYkXKChBcKK1gGDwZSRL06GqejDSEoOhe74gf7eyKA7K/b7QZ1v0VnUBVF16tMMOsiG0X70Z91RTCKohqIYl3QK/TX11fn/Skrp4YIkZqArJKuQnOkS1O1V9BsNiskQhxDWYppt9uZHkuu5wa7fx9JIT2mq9bQ91QxeeMaHg4Hk/Ko280ezRfjhK5A0tecKB7VWkALdUkTQmB0Py3Asdr+OU3As9nW9rbbbWka8LPhwxIVvcoiatLUcdGXO9z84A9K93//32X49a+L/Obf/FYi/bf+lvT+1/9Vdv/kP9lon7fE63/4H8r0z/5ZyX78x+XlJ3+y/AuLxVt581dg8nGRBi2qs2FX6JCjJlQdIuILSd+5oFNL6/Xa2PK7BmK8SLT4mkqGojQix1BUEXM8Hs8EpSInrcDhcDCDewjqlrRq/xL7HpZdy9A0BdEHfZ1c71nRZFBUDq0jSaPRyPnsHA6HM4KgO3vr8njuqWuRUxRVsLfvQxlR0VVEjGU8dzgjo5Fy3W/8YnTUjwmxSpqG4+AYJpNJYU8uH3QFn6vBrEmzW32teA5s/x8qA8uIDOTSpWGqChbCduqzCYiORmfbEz4UUdEPCqvgtu2gfQ/O7p/+p2X0x/6YrP7W33qzo/53/12R5VJ2v+7Xtbr/a+L4d/6dsv+RH5HBv/VvyfC3/BbZfu1rxV+wypOLHCj5Hak4l2MkEYz9fm9KI8tSKwz0RRoV34TF93VqqawiBELz5csXcw6UflIVpJvQQW58OXzdSp4JgNx13YGwKVFxXYMiEqN/74ItLraJoEuAqkuUQ7xUCJ1Thk1H4+Fw+I50UnGD0JbrxWS83++D7PB9CCVtZYRGV1O5UgO2TkeD3kN2JaTdI8k2yStCt9t9a50xm5lJn+iSToG5fkTk7P64QBTcTvNqcbYGqaHFYmHIi/2jLQ263W7jFBCeR0XeSnVA24SIN3wYogJb1uHYS8A3ceXf9E0iInL4q39VDn/mz0j/j/0xWf3Yj8nxO77jIsdxLSy+93tl/lM/JZOvf122f+7PiRStDBcLESUEYzWrJxkmYiYiBiN6kGh7cO1NEJpS0ZUfNrRfha/Dsi96UgTdd2i325nVNRERPano64EYF/2NXr0fj8fSfkWhz3ho5MUG+/YRlaLqBH5H5Mel3dDHr8mH65yLtCT8Tusc7Elws9mYCVn319HN/XSaz94Hkb06JbJF+hqNshJlRJZMivo5Z/L2gffLPi+eWSIRw+HQlN4XVYhRiSXyJiKlx5bW7ehj408dESpaSCJwriKi1c+Wq+pPXwvd7Zhu5FXBMdKYtU1EonKOD0NURMQ8/JeEN9T9lSdJ9l3fJSIi+b/6r0r/d/9u2Vy5fXrr6Pdl+ft+n6T/1D8l6X/9X8viu7/b/bnjUTrLpeRf6QF6vZ550XS7e5FzU7bVamWcMAeDwZkDJYN2lVWsTzTKMYnIO0ElIG1TVRxIVMA+TnsAI62kdS4MmOhbGKDItftQJSTsS4WFoIiMiJxKwUXETHY6ZM3v0QvYvZ508z+A864d/dBaEhoPsi8mQCJYLnL7+voq8/ncaFY4PzQBdorBPqa6VvohFT18ToP+TiKnFJnv2S0C5eVl6SlsGbQRGZoX/RwQOdF+Nev12jzLZWX18/ncSZo0Qkz7XAgh7yxmnp6eTGVYnUaFXI9LNVcs0059JnwoonJpFA3avf/5fzZ/X/zhPyy97/5uGX0V3m7bXOja2P3D/7Acv+d7pP9DPySj7/gOOXzrt4okieTjsXREpPv//X/S/2t/TUREetOpzJXwlnN36YTm87lZldH9lhUOEzkREO3yWjQYsXqdzWay3W5NuLduVEGkfPCzBxMIiSYmLqJGKsFejWmPlTZQhajYpaYcjw6X29UuOk1zPL456B6PR5lMJqX9Ylylp7osWk/KiG61qJWSWUSdZSaPTKiHw8F0uuU4iuzVSZHUAfsoi8igteF5sk0BubZVAeELHYcg66vVylwXrZtiTNPErUpUoUyLwzFwX6tY3pe9q3Y5+XK5lNlsJpPJpJZB6CVB1CrqVD4YUbnGDfXug7DsD/6g7L7ru2S338vwqxVjmZfEI+Dlh39YZj/90zL5Z/4Z72fyb/92Of59f5/svtKThDirDgaDs5WeJjNU3+hVpch7AzEGcfLZ2+3WNCDTA/xutzOlm87j/2oApYST/UGaNPRzMBqN5Hg8eiNHRFsgJmWkoSyKoT8XAral3Vs1GXHpB/Q+SE/xbz2J5HluBnid1up0OmYl7wP79nnPQPJ0+gwxqP18hXY53mw2RkOh70OZ4RpRnjppAi2CLbPSFzlFEomgNKkkIV1Ut4RYX2fOn/eD96VO1CPkXq3Xa8myLPiah2ixEByzPd7NsrJv3/4uiTYqiD4KPhRRuSUOX/uavPwP/4Mc/qF/yPzfarV6Zzr1qMi/6Zvk+DM/I/I3/oZsn56ks15L/lUp4eHn/Tw5/qJfJPm3fMvbhwPPlTSHb4CwRW66GkPbprsGDDs/ri3tiwYAPSmLnCoMytxLdWVIKCEpQluDoB350AO5JlI6HaPLa7Msk/1+71xt6o7XNppoxNDqdDodQywhpb6oB6W79rNERMBl46/LcvFZ8pXqcj2a6FTKonrcCxyORd7Iui1A1Z8vimS4ypGbQAtb8zw3JfNViUqoZkdHy9rQgPgExev12uiXqkRVip7/NhCJygmRqFSE94Xvdt91SiY6cKnW4lfHN3+z9L71W0VWK9l9ZS7WBJvNRsbjsUyn06AScqIoDFoI/4hYaXW/K3IAikiAXYUwn8+Nv4MP9B5ipagjMJowaTKgtRo2QiIqoUSGz2k31iogolL1d/b+XdBpF0pstebEPm4tgrYB6UUbxXbYv23Sp7UJ/J6KFd8z2CT9Y1cy6QoUfaxcB5HT9bV9Wbg2TK7D4dBpgZ8kyVn0oA3oZ4iUYNXtcy1CIhhVoyq+8bnT6ZiUnyvVSFQlFBgvbjabixGKIu+gz4YPRVTu8aauViujiL+3HGhVbLdbo5hnMuHFZxKounJjUqbiAN1BiGOwLkt1mTu5MJ/PZTKZGGtuPSn4tBxl56RLde3JRBMl2xfBJzwMHaCqXOu6g2mRoK+s7NZFuNBg6GuvXYcRwh6PR5nNZmfHrTUcNknmmkNmeIa0xkMDAbfWyOx2u8LzwTjPnmB19VCn03ESW46XJoF2FRjvDykIBKtF0JVM9EuCSGCQ2EYXeR905U8V+KJfNrQjdFOiov1aXCCqMpvNnGZ5GhhQ6gq/S+BSlauPiA9FVO4RWhHfVs+KW2Gz2RjNACTCrl5h0OWn7GVjsnt6ejLbYhJLkuSMtPj8M6pc18ViIWmaymQyMaFzkVM0xp4AQwgDxxWqRWI1nSSJc7DWJKetwarudkh3uBDSjRaSZpcC86yQEnE57KLrQECqq4t83i0QjdVqVRjxQ6syHo/NZO4jIvp8RcRUs+lICAQb0u26DhBhhK0uC3h0IKH3C58XfFCIroxGo0pl/XUQ4g/Du0w0CRLA2KGPj6oxHWXi9yHXw/euYodQtJjBKC9N03ekz3VOInLxhWdM/ZzwIYgKD3Hb5m5F+6qCzWbzLk3xqFgul2YwXi6XZoWitQDD4dCshGziYr989Mlh8mF7DNgu8zQGZ7aPQC6kIoLvMRC1QQRY6YeK8cpKj0OJCtdIT/72n02qnThWEXfPJq6li+Cx336/b9x9+Y4WiFIC6yIfdjRHExUXMEx7eXkJSkvaqRzOZzQaOVfKRBBxStbPNfvjfGZfNee0tSW73a4wBUcpcxXkeW6cXSeTielofel0M88944Fd4WafB4JzncpDJK0/z3u+2WzkcDgUCuCB7d2iAbEsIxaHw0FeXl6MEP/W6fpIVE54aKLCQ2mXGl4KTVJLNMNydXN+NCwWC3MuCEjtKgxWMayotOOnHtyJzNiiPIR7DBaQFkgQ+yUdVeXea/FrCCBfvhWZnkDbeAZ15YzW3eiSZ106rM/DlWoJrSJyQafXXJEfVsbaNIxJi6gJkzOExa62QRviSpfYK3Y73aCfM5uwlYEICiSJZ9PeJxEBrvvxePQuOLTOyhYnuzxjbGgCV3XhRdQRt1ScWn2o6sXjA3YEOqIKGdfHQxQL4sC11M00XWm6kHfVR1TKys5tEGUlsuPrDYWW6NIR8rrptY+GhyUqWmzXtivgJaC7Yl5SgHUt4EaZJMm7VvIip5SXBvoBXYEDKBlFn2BDVzfgtQL5EXkTtLpWuC5Unbi1nsJl+a2JStXJxXUcbG+qGjzyvDPpkT4o21/IhFUEXZrqAroQUiZMVi7xrm+y8ZmpubQ6ECO7FBxCVKXZnE5f8ryQzoJU6NQOQu6yyJ1v5R5S+RP6LGkhrsv5V+uk+Lf+u9b6QKhccP2/rqTDXqCoyu3l5UWm06mMx2Pjttz2+Md1tY+XSGyV6AgpaxYnNtA+0TbgkmN51Km84eGIyrWjKL791wGGZkzuj46XlxeZzWZvTRgdZMWG3UOEFTYrZ21tzepKp3mAXrVmWXa2otdC3yLiUjRhsIImLcBATDWJDR1ZqAOdl9fNGum7IiLmmbmFxsmVQrLJiYjf8VfDdd0RsdorZ9Ig+v+1toHUgNZCQYRDr5Otg9nv92d6Ml0p1BQhZbkQPZ4l17Mh4vYU0qaIZeNiv9+XLMuc1UKhQHvkq17TgKwkSVJrn6ERFY5LNymsoyUp67u0Xq9Ntd8l3dCJqNxjocg18VBE5dGiKDby/K0DMM3PrqGpuTSen59NU7IytbwLti5Fd63VaR4dSdDXDX2EThto4aaeAHV6Cm8UQs+kEHSKCgKlQ9k+8WCVEK3u8Iohlw6bs5oTETMIQpLaIipVhboM/twThKGr1cpUa5UN2D7NjLbM573WUTe7vw/Cxyr7KDomPSFx/S8xxlSp5ur3+2cOvDyPPIcIz+uu6HUkqi6IxIRebxY2VECGPsuuawYRsVOhdnuEMssDH8qicq5mmpfAo0fe28LDEBVe1FtEUdoE6Z8kSZyVDo8I3ZTM1701BK6BRYe4yTczcEMO7MHC3oaPuCD41QMbK3VSAPijiJxW+PP5/CwVAJFxrcB0Oa69f0LwrkiJ9tPg2Op4VnBMkAxIkg6V6+up/61TZJSKct66qgar9zL4ngtIKGZqrPZ1ZRAg4uEjWlUjW9xnHaloEh0rQlXRNR3DXdeW9GfdlXZbFWVViaG9sCl7boio6XQf/8/+dfUYZL9KywAXykglROXSeOS5rk08DFEp0i9cE208nAhrP4K3isjby0RolzRQWy+xLdK1J32RU/M2HabXg5RNXJIkkeFwKE9PT4awhIT3d7udPD8/G8JkE6dOp2NWdC7NgB5ESVVNp1NnpMSuSAkpBdWAnHF8SZKcEa7tdus0xXNVa0DQfGXEImHCzCJtEPoSPicizlV3UaWRSHkHYqBTKiInfxNQtwFhEXRlUBHh3O/3Z9ElF7Tgug7aSiXUIXWQFTtl7BKN620TwdDpLT0ffPny5cxwUacL9Xd1hLYuqr6PdREahfvoeBii8pFERUwUVYR/947j8Wg8SlgthaDqfSUXL/JGUDCCclUX6YoCXbHDRMDnfBOC69hIPxHFYb9oa7if6BG4Lq5JlXPRpdfA7i0DcSl6Xoia6OeK0nhMyKpc7+FwKJPJRF5fX8+6WruuU6jfjO9zRDb0+bmqKkjN+LxUuE7cW51KhGyJnKc+dMUP5e5Mmm2+m9o/pAi4WRdVlXAvbk1UfJHEIrCo0N3SdVRGC8chBMvlsnAhAalfLBbmedQVc9gcuMTZkBZ+eG6K9ke14aWrOD/C3NAGHoaoiNyn82xdIKxFOf4RgEdGkiRXiRZRDmwr+imRZeIWOYW6dfljG0I4LbhM0/Qs4kB0pSgE/fLyIvP53KnD0OkQbRFvD179ft9UQekUKQMtq8yqpNAmIE0XC3pytbfFsQ6HQ1mv16UToOt3EJI8z9/5mDDpUZ2iCa+N7XZr7kmd56Pf70uapt5JsCxVosmYb7JsI6LSxuLPJRDWbSyIfNrROls0rIXR+vnGJ6gs2olOyqdHweX49fX1zO/Fjt7oYyx6b1mwDIfDSFSugIciKveANiM7NC1sq+nWPQDPDN9qw3YoRScS6mSr4Vv1uAR0pEEYlEiHTCYTb3VHlWNxRQFCIg1EdgaDwZmZGNEPu9xUExpWiZCXzWbjHDR1xVLdvH3RxBYaUSkiKlq8LFJcyssEZ/ub6O0StSwrvy1CXZ0K39N+G1oD09bk02QsqkNwqHKx71+e58ZLxd62FgLb1Ukip+ojJn4bWiNWBF8qUB8T+ywiqXxWn48PIVVcTWFrxT4rHoqo3Ppmtb1/JtTxePxhiIrI2+qo0+mYSVREzlZUhNmZmLSTLWShjLiwvdDKAbs0mioTl8iVY6haPSIi71Z/Zc+M1lxghCUiTsEo+guboLjKgu3+M3meGzIUAt0WwedRoc895FqVRQFIyWltDS6rgJA+v7NFzfv9XubzudEC1UWo1sUFzs+V/gxJJ/FsQy7tyAz/1vuqc4yu+8n1tUui+ZNolxa5EvXTYmyuH6lD33NHiwzfGBhqqtbtdgvH0CoEkWevbEy+1nwUiUokKjfHR2paqLFer89SL9q91jVoaYfREOICAapb4s1ApAcjvUJn/yJvKZyiNgAi7kk4ZCVIJETk1GQO7Y3eBrqLbrcraZoa7Qvn3+12ja5BRxc4ZohLmfEV1T2aeIQI+kI1KkWfRZsBYfORJCZJn69M1UoU37EUVRcVQXvL2AiZLFmlQ+Rt7xSAEJyGjjaRKdoXzxLpSa3dYWLU24FQ2NcbzxlXJO94PJrGj0UE2TcGhi5G+JxvH2VE2wa2BSGE8iNpJ+8ZD0VU7gFtP5hMVGXiuUfEcrk0qzPb/8SGrTXRviZ276DdbnfmgtoW7Aqj+XxuNC1FbQB0qseuUgiZwPUgr8kTfjvaT+NwOJiyVVaspLRc0QVwPB5N6N71jGmCcjgcTC6frrOcjwuh70SZAJTr2el0Ckv3dfmybz9Nyos1ySGiAzEMQdMFFffSpV3TkQ7ShroCrkgsyu91hAQ9iY4kjsdjWa1WQVFeSJILOl1ZVsHkKi4g7VN2HCyGfGNniObE/nzIZ4vOvU1EMvRgROWWEZVL7psW4x+haaENKkZwpAwlYhAXyIsdcWHins1mZyXJbRI9BlodwodQutJFHKf+fhMcDgdTpgrJo8JI97WBTHW7XW+1FXbxg8Hg7BppgoIYmt/bqY+iKEFVjYoPOH4W6baYHEK6KJdBN9PUEziAuGHAxrUuimDoqpM6E1mR8FkTUFJ5NoGyxaL6vHim8QuCjNKkEyPE0Em9zOrfl0LVhm3dbtfp2h1KNiEMRWlikbBoltaElaHb7X4I085HwEMRlVuiygNcB3irjMfjm3ftbBu2IVwdQacdcUGHoFeUZWXJbaAoXSTyNrjqY2sCjns0Gp2RM6Iq2leIlElRqgLis1qtpN/vS5IkToIC9LbKxLRFn+H+sEIuui66SWUZUfGld4pEjj4BLscEKaRMud/vy+vrq9EQ2ZUhrusAEInqKqMQIh2qp/Bd7zKxqMZoNDp7t6qkSXjnirRAkLwsy7wtAIhgrNdrEyGkrULIdeAZ9kGPC2Xg3obcp2tFOmJE5YGIim9w+ChA/MdK8qM9nLov0PPzc6PzY7IgxQH06tguS9apGl2iXISQz9gpFiZCrS2oEvVhQuT72vtlu916v6tLkX2fWa/XkmWZcfikXNP3+ao6D1bydqmnPSmFOOyWXfui9I5tIOYiJnaKrNPpmJJmroc23XOlfuw0iv6BmJFWsc3stOjUroYJTT3U0c/YsMvdqyzIIJ5Fz7N2/rV7EfHz5csXQ3gg3KR3QwTRZcSiyryho3UhaKqFigjDwxAVkY+b+gHaW+UjNC208fz8LPP5XCaTiSyXSxkMBjKZTM70JrvdrtSBWIsNNWzSIOJO1fBdCItdagnq9NaxjwF/k6Koz/F4NOSGa0FFWJIkQX1nOBffMSO2JUUUsk17IkRcqiuVtEcLeg6td9ATMak6omD25G6nKMrO1zVJUIWS57lMp1MvMbHBPdETFHqZ4XDonDA12XCB/T8/P5tz1iZkXD8d/eG8y1b/bUV47fRF1YhK2XHu9/tKrUK0a3en0ylMreDsLCImfck90T9V9UrHY1jfrhhRuR4eiqjcEpdO/bBtPEg+StNCG5vNxoTUyf+vVisjurU7KENc9HUvy0nb+7NTNdina+Ligk1i6p6va/9EfXTaYb/fm07NGlVIsj3AdrtdSZLkTNMSIlDku3oiglDpiiR+Op2OOfaiyYu0DhMM/6f/HhrJYnLSZeaa5KDDCCWblEcDyNVgMKhV6rzZbAwRh5j6oEnMZDIJTt00eT55Vlzl9CEknWhME38eEXn3Dmy3W0M2y9xoddpRV+q5RMXaG0U/Z5pw8n/cjzbGgDYQKsz/qHgoovIZbhSW54Q/PxrW67VR9+vyWqDD5kRcIC664qeuaNa3onZhPp+3rupn/5AHIhRF9vahz72OCPR6PaOpEBGjaaEvUghsjcpyuTQ9iuxIwpcvX4JKOkXE6BG08ZcGVU4aEBE9oYuISddg8Lbb7eRwOJyl20KhyY9OudWtIMJNWAtEfSDqhAg4NKLS5PnkvDTJgJyhryGyZ19H3ZJgOp3Kfr831WhNQXFBGViw+Jy99bOin2NXqo5roZ87ni2gibnIiehBum3Co0XWoRoZF+6BKN0akajc4b6Xy6VMp1NJkkSWy+XV9nstsFJyETGd9tCRFn4mk4lZ5WZZ9s7Ire3j7HTejLcwtGoK7ZFCSWZRdKjKSup4PJrrApmwUzx4lYSsgtHI2OkA1/dIKZWB8yki4dxf3ShQRzS5Xph8rddr5/Wr+s7aNv78X5Pmc9zjUFQlKk0jKvYEutvt5OnpyRjvEUE7Ho9nFYlEM56enkzlGH5DukotFPpeacsG33MKuSgT8laN9GAJsFwunalJ/YOvk0ur5IMrzeyL7PD3z7BAL8NDEZV7wDXYLfn0j9S0UCN0UhN53wQQDQ8rXWzwSRWV+bWEghUj3in9fr9RTyatx4GEQSzKBqKQgUprRYo0KAyuZURFr9hDB8q2BlQtgNaiSy2C7nQ6JuLleifrhMqZGDQxQafC/yVJcvZ5/WOXKusJN7R5HfewbJxpK6Li249+56jY0dAux6Q3ISxpmpqoWci76DoGIq9Jknj9ZBDUtwnOq+kY4iI2+EKt1+tCfZbruf3sUZWHIiqfJaIicgp/VmlaqMWMNiv3hdlvAVu0WAV8jy6puvSVdJE+Z5qdhWI4HJruuUw0pB3qHi9pHozU7H5CRaStbMLVGpQ8z2W1WpVGmNCJFH1Oe0+ECCyrRFRCPgeZK4Pv2oTuR6eU0E1pQOxodMmx8T3bSM11HOwnBOi2ytBmRKXqMZFOsUkChAVSjoNyyL10XbuiXmhUHLU5prFoaMMewkU2eQZCtq8JDJq+z4zPffYVcE2iAvvGD4IIQhHhYKJmBVw0gNt/Lwo96lLCtqCPsepAawtpCe8ykDHhMLEgWEWv4Ksosp1ZtX6Gib0KOp2OJEliBlSXHodtl02orntpExQRMcLjMkDwfJU3rO5E5Ew30QZRCUFINKTsuXFtg2cC7YLIKXrDc7/dbt9NiryHGOpRzjyfz2W73Z5NPC4NBH41IeD7ZWL6NohKaFVbp3NeLl3mGKt7mJHqpHKqSgqVqjhXH6AqfatCMRgMSkXPTVDFcl8TnXtZYN4SD0VUPkuuTodamSRdJYy2uEtEzoy7ynKs9sRUlmNti+QwwNQhKmVCWpu46CofBk0tzKVaQIta7e0T/g+pxOp0OpKm6ZmItcw+vChaYxMAu4qH7c9ms2AyhWi0qPKGz+nUTxlRCXk/2w5hFz2rvV7vzGiM541npNfreQmkaz/62XClifh/ewLVvj6h56P7Ofkm5CbXkutRNtlz7fQ7oattisCiYrFYGLJi97ACGCXq49PAFZgIlyuiw7Hpdhto3kIJ2SVTLHW3/1nmvSI8FFERufzD5IM2kboUw0W42e12TYWF/p3dn0P/3VVKF5LrLjqWS5McyjCrVOKEDK4aetuQDVuYS8WCb7u6TNU3qdkEgjB42fUv04DwfW0EJ/KeAOn7EOJtMRqNzCTYhnATYlCGkOexCukpet64JrbZn8jJgyNkPwhq7XsP2SlD6JjF+4Ox3GQyMQJVl9lcE7hKk11ggYRejghbyPuK2PVwOJi+X3a0yT4XPWbxdxyX9aIiz3Oz8OAZ1uk4Fnh8R2vdfM/7pQnBreauj4CHIyq3gmbylwAlySLiVM27VmqXxKVJjtaU6P1xnoSPNUhV1L0OeZ6fiQRJzYQ0m/NNSnUJCtB28K4BlGeO6Idv+9iPh0CHlEMtysvOp8i23kbIhBA6afg+xzPy8vLiPPYqHimUddtkpWlFkA1d8cM7wGRMRIF7xvOm358qKQtShmXf2e12xqdGC7/Lrpl2owV1U8hUqlF1RGpMlx/r6I09bhBlGY1GMh6PTQqYdg3gGoULMY1TDw9HVHS9u44qdDrvLdUvuf82MZlMZDQamdTDoz/MoSSHbr6LxeLMel1rS0TeD3BtEbYqRIw+OcCOcFQlKKCMqPB/aCF826+iWdBRmirCzZBtlq0arxVR0SJX3z5DPVL0Cl1PgqQEQyuoyuDyNcH/Bk8cyLrI2/mnaWo+a0citDW/PS5WqbrTYv7pdFraiFDk1NGY4w4RRvsAYSOiici9yvc5XnyEJpOJTCYTY5OAaLztKiKNGFGpj4cjKlS2iLwf9KoaPBUB1q5twkXeVgqEM5ui1+udCc0+omdKEagQ4O8adt8Yu3cO5EUPNHUQOomQLrG7F9clKHq7RcfBdotIiv5+VaISgpABtg1xp2t7RQghREXb0ULhMri0RIwBLmGnbt3Q6YQ1GfRZ0hMp0IDkQyLsNLAm/CLnJIZJuc69ovQ95HO808Ph0OubFAKiX8PhsHHZMClTIlR2awvGI9sNuy1EolIPD0dUNOzcZtNtaeGl/SLrgTjLMnl5eQnK7eucrJ54tVh2s9k0WnE8KnTpp0vAag/+mjTq6h60JgzCaBLajK4xWLKCbUpQNEJSimVkoez3ugyXsH/VnLw+RltfEBppbDOiUgQdqfItKvb7vUnplD0rrtQWzxspEb2g4X7Q2DCEFFZdcZMiKtumTpHYzr7T6dSkQ8v2jVYkNO1DhQ8l+rburgooCW8D3MfFYmFIG+ZyRErH47GIiBHdl/UfC0GMqNTHQxMVG1UfAi2s1L1QXCIvGzQOdO3TldNl2wxuDAyXDDU+ApgAQogaURRbZMwEgRBZl9PyA3GpM1D2+/2LEBSNkBRGEZh4uAYu0aLI+fPNNbP1Q+zP/q5tKW6D9EhTghh6bctSP2WER+tUyo4Zh1n7/BARk7oj2krFlMjbtZzNZkFO06HnHkrkfDoUIgqdzptPB2kQl3YD6NRLEbBW4HOLxUKm06mkaWpSUlXda6tooMqgSRq9xvT4A6FjbtD9x7R+qM5YEolKPXwoohIKBJD4AWiUDQD8vtvtSpZl5mF15Y958e/JbO3esN1uzwzRqkKTkMlkIovFwmgP9A+lkVr3EoLJZGJs7lmBtQkIsm+1GBqp0FUR2slV/8nkjajQfv7t9AAkD38JV8hffz9JEmNQ6LtOIQN1m7bhZRVVrhJjF7Sjr01UbHt5e/+QmLYmWrZbd9JjnNJkwaXd0C7PRJlDFhR2eijPc3l+fj7zN6pCWrrdrozH49bePRxzaRGQpunZ/bMXRCyGdAEAGjrOgdYBZYhEpR4+HVHRq2OR+iVpOmQochqwEHpds0LnkYFQsKmBk90d2JX6sclLGQlAOHiJ1JyuFmIgTNPUGwkJeU4Ph0OQizEryqenp6BjDTXCWiwWkmWZZFnmnbhFys+lijA3dGEhcvIE0VoOrvN8Pi+9HkSM9ITk88Hp9/vvjN7anKSaEpU8z8/IwXq9lvV6bUgB4yRRIp6BsijCeDyWTsfdy4l3crVaeUmLq3cXbQuatLCwgcU/+pkiuMYTlyO2jrrY5pI8Z5Go1MOHIiqhlSYi7fkQYA8dH8D6IKzbxLo6pJmbrXvp9/uSZdm7cP5gMDCDY6ghWBUghBQ5tUpgwNOVGhDfLMskSRIz+PkG1tAV56We1ePxKMvlUtI0lTRNnWXfVfZdhahoAsLfRU6RAlcKjD8R1ZYZ+rnEtz6XZY6BSsS2n6FQga4LRQSdeygiZyaJIMuywggIE3+Ilw+khYgn7x3+Stvt1qRhLlFowHnV2bZtLEnK2Zcu4nrEeaIePhRRKYOuHGkKtsOq4FLahc8Ayg9pMifiT0PoF59/i5z3pgkF4Xy9qtIkoq0Gh4A0EtumkofS9NfX18LvE3K2DcyYLC/57IVuG/M8BIk+8lnkWeHT5TD5ownhfZ7P515hvSsN5tNslBEVfFM0CdNGkLZnCN8Jdb6tgqYRlZD3BMLB+7Db7UzkD4K32+3OJusQsa0NrRPR1UpJkpylH316m7oIiaaEQhvKiZyKM/RPnBvq48MQlZAXnYe9LbKi94uhUJllesR7bDYbY1amV6i2uNMHfU+n0+kZybH/tEvLKVFEc6Qt+ikd3+12jVZ0rBQZxNfr9dlk3O12g0zH1uv1mb5Enz/bugSqTIqcY7/fNwTM1nSUgc+gS9DkxLUNBJ6auIm8iX/xJiqDz9BPh/hJmfT7fZnNZvL8/HzWDsLeHhOsbmLIufie7xAC0bQUPKTHD5U6EDCiGuh0WKBBSFk4iEgjcs89XK/X8uXLFxERo+9BF8K9LooulqGK5qYOSK1pjU+WZZGs1MSHISoi5S+uXYXTJhjMCZWuVqvWw70fGev12qzAfVoVm8DoH0qU+Zz+vP7+cDg8K5PUfW/yPD/rbozPzWAwMFUSVQ35iNAQUnf1geE4fNDly0TuRORsAkcbcGvMZrOz4/CJa4uOlUiXTufpihruHYL4NtICGPqhE9JNC0VOZJjVf5qmJmWny9YBz+JgMDATLv+vJ1sdIdQdmovQhKjwThQ9b5Bh3ofJZHJGRDSZwG+Kiqg8z2U6nTp1GlXAddALP1sTUpW4QDohjrZO55LgntXR4d3De31rfCiiUgS9IrsU7AH6cDh4S5gjzkG0YDKZeFMgOtVjgxVrmeCOMknEg5RoEjXR24eYUE0DoaECpmiQ0zqXoigbfj1lRMU1WOncd5tVEb5jCAGT4H6/N+kZLa4Njf4wAZWlw9qCNvRj0hORd5oX/YyQAtF+Jq7IClEePutDp9OpRFTq3G+IsevZ1QJv3V+oqBEoxQNERUmnuHQaVfxIeJ71e2NH53zERfsoaWKAuy2LmmsWPEQhbTN8GqJyibSPD7qEmXRQRDnQNtTx4gh12nx5eZE0TWU0GpmIhMv5UyPPc0NsKHPWFRH2/SWKEhKBCW3kV/a5KuLKuivxULDaFjlV2dni2rLtbTYb4wB8qQnFJgakAfr9vhHg6giIrubj/9qsRCHtWdZzpilRcREm3WvM1RC16B50u13TMVyLcW2dhk1ctH2DfSzdbre0B5eLuOhIF/ePfTGu7Ha7xuL9qmg673z2qMqHIipFA3CbHgahYCXehqvhZ8BmszFRlaoTQNlgqkHbedwnQ5oSAgRzeuBFi7FeryVNU+NRETIQhggby7xmfNoN176YhKvgcDiYCgn0CQz+dsWbvRjgM0mSyHg8PhMbFoFnIUmSVsmAhn4G7HNgQvNph0L7BFUBx1D2TDRJ/UBURE5tQnQDQTuCxYRf9G5pYqEJjq3TsImL9jbSRmrj8bhWOxTuWb/fP/NtoaqI8+H6sni9RmTlsxONpvhQRMUHVs63QpIkVwthPzrW67UkSVJagWGj2+1W+jyDYN3+UFTp9Ho9E1LW1RGhq7UQYWOZ1wyDIAJWOgbrkkn0WUQI0OuEVGi8vr7KdDo1kwrHC/nXGhFX1FKLa23tRxGIqlyqqy3X8vn52fihTKdTYxpYhLY6J9tl1SJi9BP8ztZjQTboqO0iLi4hObofXSnFRL1er53nzDkWTeY8l2Xvn4u4FBmphZY662PVlTx2xEXvj31pouSL8LQBriMi7Ihq+BREhZzkLVgtL0dRqWbECdvtVsbjsaRpaqy8fSXK/L1OaTJh7iYEUptjiZyiOpg/ld1vJqEQosQK0LVNzh/ioDvtco1sp9HpdHoW4SiDK6pB129bxOh6z5bLpXz58qVSmSnPAo7DbYNjGA6HRq/EZB5CVEgd+SZon8GcbTZnAz2UyHvyof9NebvW/fh+tEAY3UyIDTxCcF/0hkVgneoZrScBX758MalOnb7x6U40uGY+PZjeznq9vhpxYT+6vN3Ww0UU40MRFdeND1XSXxKdzls/jbYb5X1UEFXRDd6Aa2DnvuvKAx/BYaXW1ERKG2FtNhuT5qNKggGvqFRdmwaWgZC4LcpDAImGgsgLgl8fWaJyJTSq4tsGzqLaz6VoQbDdboMnNZuItg2eFU0AQ/RAIqfyY6JELjJinwt/6kofrhsROUrXtVDaBnqQqjYIWZZJp9OplEori2ZBPNuwZODZxkRTREzVXQhxoaInNLJ6LeLC/IMj72Qykel0KsvlMs4HgfhQREWDfOgtoykaeZ5LkiQx7BcAVtKHw8G5ktYdYElvMLm4Vqque1/XLVT7rTDpcm+ZhHBnpXLBtx+fsNGF7XZrBm2t88iyTETEpHtCJwwG+CpRFRdeX19N6ku3MWBwtn1CKPkOSblxn9v0uiDlBcGwexi53Gd9YIVst28gWlFkMGeDSTjkXtStIKGyaTQaVSIW/X5fptOps7EnpLgNoCPRx6bL8fmMi7hQvdckSlFEXFyamlDiAplDfL/f702rCcaH3W4XSUsBPhRRYdLiQebBvTVJEYliqqqArLhWdLYVPt4XRStFSA3dheukfCi/xF9CDyx21RF9b5Ik8WoetLCxDESG+v2+mcwQ7kJSqmK5XBrtSd0VMVUes9nMRBEY2IGOJjDw2/+vIw3cXyIebazWtSeOyKn03L4v+/1eBoNBkC6GYwztmVSEKvev6YSMSVtZQ0Cuw3a7fdfYUwtXi1KcCHUhxr7jJupc1sDV7gVke7hAZOyS6DrXy05N1SUuRHrA8fjWxJLn0bUd/R5EfDCigq+ByPuV3K3R1oD7WYCvCuZ5RQiZVLACr5pPp7pHd5xFG6DhKg+GrKRpKq+vr06ztyoDqG51T/i/qgGdHmwhVyFmcfo4XQJQvGi4NkXHZKdK9I+u0GC/s9nsXRqPNFPReRI90XqdMpfh3W5nND4h/WraKqGuGpWoM/GiN1ksFmedg5mQ7eglpFxfL0TRPEM6emantlkY8IzpiZgfzgPNWFUtEtEIhMWLxeKssghSJiKVvVxs1CEuLJRdZE5Hi/g+VXU6Khy1LB+MqIjcDzGxERrWjThhv98boWPRyxpSmtzpdEyzsxDCiNaDyZyVXJFrrv07Ii+EeW2H1qqVSpvNRgaDgUynU+l2u6Wmc+xDW8DbaQqgG8+FQEdBNEIm7CL9hcjJ3yRJkjMjNTutR2RhNpuZf+s/NZlZrVZB911HrsquLcfWtPO3SFgnaFA3otLr9UxKCv0SEyS9dTQpcVWk6YkaMTnRKsgIn4EcPj09GQKqIzMip+jocDis3SuN5xrSrqMuOjVse7k0bY0RQly0l0sRXPpFezufGR+OqNwjtHguIhyr1Uqm06kZxHwIMTtDy1EUptbaBZGTB0hoUznXIJvnuYmsTKdTeXl5Mfqp0IofoK397dw90KRE5/BF5MxRV0+s8/n8ne8LItmnp6fCyaPb7RofDV1eqs/f/tNFcDRIEUH0fMDci2tjb3s0Gslut6u8SqdUuQhEgGwxbl1wzKGl2FUndJ8GRlc+2b8rez5tczf6A/EMEmUTOTUeBDw3pOrzPK+tRSKK4VoIQlz0OUGCXWmiJlFvF3Gh31QdsC2in58ZkahcCbHvTzWQ2hA514bYEx5gVeeaGFnJEYLW94LwtDa2Wq/XtfLavgmGcPt0OjWrf46/apRttVqZCVhjOBwaTQ/bLjMtA67KGu25UgSEtHyOtgFlsLdtrxhDSNxisZD5fC6r1cp5jnV7e+kUm+6nxMpdp7za0sBVISp1Iio8/65r6ioZ59r57gHHqcXlRLBIIxWVbmsRMxqzuhgMBsFkgPsFCdYRENJEbehbROQsXRzRDJGoRNwVGPhYjUE0dFjfVdGj0xr2xEGYd7vdmmocETnbLqupuiH8sqiOPqblclk7DbjZbEyY/vn52ZQnoz84HA6m421oJMGVtgqZfFmduvQNIqfya+3nIXJetSXyPupCuD5khV00absIWAh2u50Mh0Njisa2NAHUVuw8T02gzz0EdPbWx2VHlPS/NZm04donOiGfwSCaEK3B4L4yMYcuzkL0QD5wnFWiMcPh8EysaqeJfPoW3UhS5NSNvWg/PnIYUQ2RqFwJnz3HGAK7a6v2R6jq/yBy6kHS7XaNwdJutzurRLCrCOqAwayI5CA85LhCoHUZWp+B74buVKzJD3n42Wwmr6+vhZOAzyI95HnV/hAu+PZbNoHRR6mMNNrVQ67913Gk1n4eutTYhRDjtxDYRQAhn9e+NbZ2xz4fjtUFtE+u6q/ZbGbeEa6B1pf0er0gJ98ihGjMfNAdlEPAgsb1zBbpW/ixPbnoLO0S6bahXRKJc4dIJCpXQ4go9DMDvYGri3FdczIGGb299XotWZbJZrNpzZ+DAck3WDKRIdRkQrAjCHZqQQ9QrudGh9k1cFlN07S0fYNti6+3XfasMmgPBoOz6gXuY53JC/1OyKRftmJlIq9alUP0IURoyQq7LaISQmLRmoQ+v91ut1ArQYQIYex2uzVROZ5XSoe3260Mh0NnX6A64FmvM6Hz3lTRB/HMhAqrXeONJoYYimqRriaRsYCiHUSicgUw6E8mk0Yq848InerxVWbgK1DVnGwymZioCbCrDJoInIl0MLj7fkf572q1kv1+L9PpVNI0FZFTHlsLXm3XUv6kK7OIeMukgau01AVfr6GQVRyOvITI6elD13C9Qg29zpCf0IaORRMcxHA6nZpJtig6InIiSlWeMyILTcD9Co22VVnwlEVURN4WA1S6aQIICSbVpNNcw+HQpGfrQgvXq4LjrEpU2qjQ4pzRtPHc6MhLnr8ZQdK0lAhdXKxWRyQqVwKeDnZflM8MX6rHhapRFYiAa6J+fX2V+Xwe3EPGLnG0Q+0iYko8XSH84/F4VpqMtoGQsU2mXOcCQdlut6WROfoPrVarUj8TXZmhAXkqiqzYjRnRC2ADjw8OAmbda8iHKqF8rYdwAbdSDMvoG1REWqoQJb2fqh2pbUAAysaGOt4aIWRAR0jQQOlro6vMIDSTyUQmk4nxMqkTUarizmyjKulAINym0zEgVcg1YPFFmbbtscJ7H+eCMESickUQVblU2/p7AuWHvrJUXBldqR4XqkRVEMMVRUzW67UZcPUAq70QfCW+egWtfTr4Lsfrs08PjarZBCUkAkQYOkR7g5jYNcFAYubzuREN+gZ4hLt0vbbFlFxnzMV8njRVQvkh5d1sX/tlUKUEaWGlC2mxq8tCgDV9XX8TkZPmpCwlUZeoVPm8JtQurFYrWa1WxkdFR2HK/IZs1NVfELmrEqEmyncNt1cilfqdgbBgmscijXEiEhc/IlG5IkgHNGkE9wjQDfuAa0DyVYv4sFqtJMuy0uvHarBowqN6ZjKZvAvbipyIFYMHEwgeDHobIRNMFTDhi4QTFJFTg0Ls4ctAxY5P/6LB6tV33XUFkA0mtl6vZ0rN6ffDxEZ1V2goP7QLs51O0Y0abdJCuW3Vd5OJr8zvpwjsuwzazC4UVR2QITZl11b7qGCmpu9tiAtsVRIFfNqqov1U1bPUBelcm+jZXjKhxCWKaSNRuQkIl360XGW/35ckSbwiT5HTSp3uyP1+P3iwCWmkx/5d+7ajJUwM2Hf7DNFAlmVGF4ELpvZuaYq6BAWQ7gqN2HH+RE30j/ZRWSwWJsw/mUycq+ayCiCR8/SCTgvRWZx0GDn9siqq0JWxjwDYpAXSyeRBc7qQ9gwIausSFZ8fix1BuUZEpY7ehjSgyClayv0lteka8+oQlSqCa1BHz1IXEMMywmsTF4ToNnGJRCUSlauDCWA8Hl8kV3oLIKhkoC/SmzDov7y8yGw2kzRN31nLF4GoiqvKAhGbnkTRBmH6JnIKs4caojFosH/7802JSlOCInIyzQqJUPX7fePUyzV0+dQwSO73e3l5eTHmeHrVvNlsTGfnKu7L6EcIxetuy4iPWTG7UoNl+hQQOtDjA0K5Lc8M/jQ6PeQCWgQ+7/qcXW6u+yWxCp/P5+az9nno6JAuKy97/kLdbut+3obWs0AAIbmatIjUM6/TWqgq37lWg7+6wmo79YN9fl3jwo+ESFRuACbPplUn9wAGIRGpXPL7/Pws8/m8UpWFjqrYRAUNBGkG3S1XREzTvCoD1nQ6NT1SXOJUmh3WgSYoHFud54HzpKqoDNrEyhf1ms1m71IG+vNERJiAqogUO52O8Up5fX2VPM+NHodOxKzI6YSuz4tJPeRcq1TSEEWBkBDhQYtRJMTF6hwCaEenfOXmupxV5ETkbeM2KsjYhm4kyfftKjEiPVV7SjUxYLNB1Ipr2O/3z7RKvFtVwIIo9BjRs1xrYQhZbQqIC+T1MyMSlRsitOrkHqEdUVn11n05q64W1uv1me+KiBj7eDqpasvwzWZjvFOqkBQdpfANpsvlUrIsk9lsJs/Pz8HbxvWUKp661w5tQJU+JZRT+3Qs9orQtcJm1Ux5Of8XAlxNISn0CdLfJ02APbsGK2rOuyhNFEpUOGfOUxvp8X+2pkXrMOi6TDQPYoHRH+lETSTqQPdgsv13dEWa63t4o3DcPtTR6ZTBp2fh32maBlWFFRm2+UDa51ptTNq+fp89miISicrNoM2CHq0PkBbLhnalLULVF5HVLBMVOV2RkwOsPi5+V2VyIFdc1qEY3QVk5eXlJSiUfTgc3lUUVQVkER1JCHxCP3u7Iqd0xmw2M0TIjmLo9FAZ6Prc6XQMSRE5CSPtyYeIQJIkZ8QAXQOrdO0kyypURyjKQvGTycQcg+12q0ma1rS4dBgiYhoUahfX8XjciIy6QNrHR9B0c0o+r51VMT2z0yd8/pJpEvQsRO64R2VVYSLVDNtETuPstcZYuxljRDuIROWGoFyZ8Oe1cqh1Qf8LVmZtOFPWBVb4X758EZFT51JXZKeOVwPVMyEDItciTVNDVsr29fr6aiax6XQqy+WycmlimqbS6VRrLaBb3JdhsVgYPdVwOJQsy850KSLhvVbQCNn+EaRBXed+PB7l6enJ5OqpGhI5F29qPQn5fDRIuoGgC1ThaTt6kXJLe5cOg8/PZjP5xje+ISJvmqPxeFzqEByKUFLrK3WFsGBENhwOz549SOo1Jlpd2g8pLqoKE6nnnSJSTc/SBEXNH+sgRlPeEInKHUArvIkW2HnnW4JwNy8hq7mmojuRet1nSe2Qpy5Le1StLCCVUcWjQUdWptNpEFnZbDay2+0ky7LKtv4QxqopN58TrYY9WRNJ0GmPyWQim83GTChlx4CQ0o78hBAdPelSeaXPwfaMIdoBacnzXObzufG10McK8Xl+fjarYX3uIWkjrcMgXUhKlNV/U5+VtkD5q36H0zQ1pKBqKXMT2EZ0dlUYTrhErCCTVdI+VUXeTdH29YtE5Q2RqNwYNI0jjKwHS/70hd0vDVY2kILVamWErPyujcZ+VV5G0gciYgSuIdsPXYVxD+pobhCHViErx+NRnp+fJUkSExVYLpeFx8u1h+iEosiJVsN3P6h8ofoHUW5Z1El71YzH47M+R6FEB2y3W0OSfNDRDjQdpNpINZBGIlUjcu7cWoWoAByIv3z5cuYpA1GZTqeVdEyXAqlQTQqI+oiI8bS5tNdT0QLC5YSLdw73/xJ6lqZoS0gLIlF5QyQqNwK5WO0C6XrxCIXS+E6H3S8FKjpI8dhiUgY4u/SwqitlVdAtmPLR0WgUtL9utxtM8rTdex0w+BJiDx20lsulERVmWSbr9do5GUMSbNfLEDBBhRCVolUh1T9EN8oqN+gJ0+l0zLNF5LBqNQYapNBVK8eF1ogJT098vrRAXQ8Lu/cPGqI0TSVN01YE9HUnMO6BfmcgBZA6fI7Q1lxqvAk9B20YiAsuehZXlAxU1bM0BWXnH9nM81aIROUG0CH1MvCSEgrVYfe2865MIuT4X19fCyd4HfIuyy37EBoOn81mhjiRCsBgLkSjEUIYSPnUWYFxDShpXS6XlfP8+/1enp6eZDqdnrWP12DVW0fvUOREqxFyT6gyETmJq7VVPcCETk8muiw7z3NDikMmlDorVl35oxcEVB+FfK8KiN5oYB6HcFefq/ZT0Z2zXV207WhrVUBWXWlNSODT05NJ80FYdISjLVRNySIeXq/XZx4j0+nULPz0mHhN7xQROWt30RZiROUNkahcGbyYq9Wq0kuqy0EZPNoU4NJkjBVKle3a/ho6t0x0oojwFF2HXq93Zk7Gca5WK1kul5KmqVn5aTCIsbLleOz96kmZv1dxGGVAZ0IrKvsNAZOWq4SU1FDdUHZoNULI4Iim6unp6V1TQs4frYbtF0T0KMsyozsglYROy+UxxLWpM2G6zqksYlJXa6AN7DQ2m425VjyLZfvXP3WPjftAJZsr1WaLmiF0WpcEwWqLsDTR7Gh7eaIsEGYWM51O52oiWpFmDRZ9iETlDZGoXBkh3W+LQKRjPp/LaDRqNClqMECVWZeXQRMq3bDseDyaKIvLNM0FcucQoePxaLQcVArohnB2Z2NCw7h36pfetYpnQNeTJtUj9nHr9Bj6HU1Y6oIWAPaqTKd86kwUlPGGEpWy51Ob9LHStXup6J5IruNBnyMixkuF50U3kMNfA9JZ9fyLIiPdbte0ECDNqb9HeXYV8Jy5ymKXy6Ux8KJk2UVIfNcfghc6fuA3xLZ9rS203YAGhIX3AsJiRy/qoG6fHw1dzkwFl+5UfE0xbdv6lIgTIlG5Epj02hJ20RulrUoCBmotLmwCbfCEUJgf3D8JbTO4uGzcbQv19XptUmCr1UoWi4UhbZAK20vDhdFodOaLoQFh4YcVMPeQ47b1O3bEpg4wkJtMJiZ6AkETqZfyERGjcQp9Voo+x73SZEqbsBEx6fV6XlLe6/XOCMfxeDQkF9Jpa0mY1KvCR1Q4Tp5BO13DRFrlugFdjm0DvcpwODzzkwlBlRW2bilRtqApEzXTRkG/D3xnvV7XEvpX0Y6FgF5d6/Va5vO5cY32pYbaRkhFXVUwvn/2yEokKldC22HI1Wol0+lUsiyr5KNRBLxJ2garMgY3JiFAyFRPRoR07dUfkRmiKiJvE06SJMFhaU00XNDRH5GTAJESbfQGrkGpKWkkyqA9VtDjNE0pVXn+yoiKbTa2WCxMWbC2/PZFU1xRI71vrSWBMEKG5vO5+X1IhMg30HOfRU5GeDbsQTEBUAAAimxJREFUKEsoOGYXiGpocW3V5yZk5Q7BK3tu7JRdEbRZGwsMUnhViQBauLZBhJXr6koNVa2YK0NoRV1VfHaCAiJRuSC09qHM4bQqWD3gp9HGCw+ZaKs0EcdYJgEGJnLfIuIt12Ql7QKeHkmSGNHqfr83mpiya8F2Q1Jc6Cd091UfGWrTJwPSlWWZ0RXUvScM3KGrvbLzIFKiQUdpPYGhp7InQLQQoc8sETIdWWHyIZ0BaXEdNykcG7ocFyFpW7Arf1znBMFO0zQ4UlZl4goVk5KGqzKG6OgFujGilCEgwnUJooKgnW27UkNUDTXpsaXB89N2xU8kKm+IRCUQ9qqszLmyKB/cFugv0lYTMUK/dYkKAwFhf60TgUxwnL1er7aegxSPXrGSAgpxAGVQCSE0WmdRlra7xAoRe/EmUTNWzFmWyXa7bZR+5Hr4SI+ewGazmYl88b7wXGCZrpsAloHST45fN7pDQ4EIWW/TFrfa5BO0Ocm4Kn9sUCWVJIlkWRZMVkIIla4asquMNOgB1GScwkCuStqDcfMSVTn0srKhn01dNQSxoXN53X1GXA6RqARAV+rwYlGe6XKzFPF3pm0TpEn6/X5r0ZrdbmcmthCgP9GllIRWfTbeIs1TJAj87F4sTEBFkw5VNUUYDoemjw4lvYiCfdbkIs3PS4PBtA3RItoPqrKKCEtRRAVdFCnHIoKxXq8lSZKz640TLPcPgkGkDeJiw9VDReugut2uST3ZnY71dYD4QVKKrkPd8mSR0+RdFu0MISuakKGb6fV6hc9wnufy+vpaWrFDNKXJ+ME9rTLeXSqiwjhURprsqiE6gY/H41q6m0sJaWNE5Q2RqJQA7cRisTgbGHT1BZ1StR/CtdwQqXpxlXM22Z5vIGRCYNBkUtOTzDUsuClr1RMBplWTyaTwOMpKXFlxQwS1Z0in05Gnp6d329aVHm2Vb3IMTYgKkwii1vV6bXL1RYTFde1IIS2XS6OfKSIr9LnJssyUqbPa1fuEYOhqDTsyQhSsKJKD55DIeadj7k2WZWffp5u2jnzolMFmsznrEF4FvDu+54FSYf0O9Xo9U4GkATmBMBONKnO5RU+CABaHYJ4nIp9Nxir0ZlWfUc657cmd5yf0fvmqhqisCk0NXcroLRKVN0SiEgCbpNi4Zi8JG6Q82urQygSvS5+ZSHQ6R6866oZvm7yEvuZfi8VCZrOZJEniXOGVCWlFTpM77Q0ARmWuSZyqleFwKPP5vFDLEgKiVE2jcrbJG1GI9XptKk9chAW7eZFzO3miFPv9PqhVwPPzsymRJVJnD+h6pd/r9QwRZnKl0irPc9PJGWLsg93pmIos3bEa4uWCLhNGz2D/Xn/G/jsRA94d/o//1xE4xg+0N3bkiHMlqocuzeUf5ALRAzslwqTe5DmtS6ablvH7gJ6rzmKpbmoopCN5XUSi8oZIVAoA2773rsZUwVCK2DSaQ04fy3pdIrdYLILcTS8NX/Mvoga4xNqDR5mQlgHKFaEqC7evVivTi4YUFP4vVcAkVJQ6CwErdtfzwOSPVwkRFlIWXF+dwtCr8Sp9jSBbus+OD4hyARO9XoHryEtI9c9ms5HxeOytSqHclv5O2otH/4icSte5LrZTrCYgvDNcOyY7KqZc1wGDw26369WsUDFFJLXsXSTlp8uPKV1uEq1LksQ8J2mantkIlKFN4blGXUNAG1VSQ2XRvqaIZCUSlUJciiW3DULerJD7/X6lCVJ7htgiWH4I37d1PZoOUkU5YSIb2Ovrz5UJaVkhuib3kPAuHWBJQVFeXCUyUifn74J2erVBdIPnhMkcIgcpYdCfzWbv9CM2WXl9fS0kgFX7+oic0jNZlhmSzPZ0yqis+sdX+SNyIq0hlUgYrhU1juT9gaxWaUQYGg1YrVamN0+ZCFdHCAFErwlRYSEAeeQZ8fXesb/bdhTaFY1qCp0aQts0GAxMSTZk5lILt0hS3hCJSgEuyZIvgcViYRrbTadTWa/XZuWmXyQfMWHF5wqrNxEWtg1WqUWkgVLrNE3PKmaKhLSkt4oG/tDomu0dM5/PTcSlCHVz/i74qh9E5J1jJ34h9FTSRnakoVw+G8fjW8fgLMvekQmNJqJNUid6uzrapNNFdvUP/aYQ0bpQVsGnwf0veo7YXpOUqG8S1ymm1WolSZKY3l+u7xA50dcdF+A2BLSQkvV6bQwdp9OpiZzpH469LmktA4T1UhHww+FgFoUQZN2z6hKIROUNkah4cMkH/pKgsV2WZWdCQh2K1isAogdlhOye3BGZcIqOmc8wiWnhsy80XFTVw4CkUw4hoPssupnBYFC44kySpLGAluP0maoViShdnj9laSgqTLIskzRNnfoc24m2CnDV9UGni3zVP5DbIoQ83/o9KgOr7Srl/tpvqAykfO2WA9yrw+FgtDl2Os3+v6qgIzbHSXQGwsIkrisI7arItqPVl3CG9YGFSKfTkdlsdrG54l7G3FsjEpUCPFI0xQaliVowyKoUMeSXL19KRYkAoV9baEJ8fEJa+zMMJHR/1eXTNrBt9w2euvw6TVOTpw4ZbKke0pbeCEj14E1VRl2bfA2Xe6z+nU9E6bs+dKr2CbY1WSGXTxqAybIOUanaiNBX/QNJcemM6qQgQqKL3He0YyGo6i+yWq0kyzITsaBCTevKNCGFODWZWO3qIRtaHA1B1D8sCNrGrVL1VT1kIqojEhUPHv3hI4RP+Nt+gav6fmiR4TWFtLpcU1efiMhZszgiITQPZMLdbrcynU6NuZiIexKAhPkGX67X8/Oz0Z9AWMoiUpCczWYj2+1W0jQ1q04iLuS/2+iIzYTgOhfEga4BnWtgTyJPT09Gb8Gk6+rhk+e56QfDZ6fTqdlmnWeHtE/daAyr/G63a8ThNuxKnBCEfvZwOFRKmZb5i9gicp7x4XB4psfS0Qx97arY5ftAhCiEbBCZ1s80/b7aRFX35bZA5DZGVC6LSFQ8eDR9igYD8uvrq/ccqr4ADEqXFo7p6gqRUymrqwKDyZYqFd08EKKCUJSBzCekJcrgG3x1p1fCvvQPwZ9DVwIw6eOgiyhP5E1L1O12JU1TSZLE+NbkeXlflhAQwXCt4ovC/gy6pLe0eZ8t2KYyykWGdJqIe9Lr9UxzOK5VyETXlrU90Q2bNNCNWySctBO5IMWkjeds4Eukn6+i56wsouISkWthLXosn/CddDD3ghRMKBEkmlKXOF7K6M22zb8WQqK7TRCJyhsiUXHgEkZE18J0OjUkJYTlh56nFhFWvTakVXq93rvQ9Hw+L/SycOkoKHO1Jwf2g7CSY6bjcpGJXb/fL9SFuFrSUw2AiFATliRJCqNWiFCx6Wfl2UbfJiImrv0Whf0xPrN1BXpSWiwW0ul0DMGiv4tv4kLTQSSKH8L/2sXYd0xthvPtZ41r9PLyErwqzvPcPM8ixfbp6GOY4O3taOICkSoiTL6OzJDIIrt8bdgHuUd8DLlF4+KLmDSN+kFU2o5AlHkjXQo+m4Q2EEnKCZGoePCI0RSMoMoM6kSqp37Ynv3ykGbghWVw08SEwYlJC5Fhv983fWDs9A6GX75j93moEOmwyeZyuTSpGhu64aAPRZEkcvKasIi8TeqsvH3fRSPE9ZpOp97USggo0XRFU9Bq+Fb/hM7RyEAIbXAPEbmihXJZtLPS1WWe/L/+gajZUakmq3cXNFHmXEROkSbSdCLi7a+0WCyM4yyRDB8J5v9I+7lcrHkXyt5JPuO6t7rKrEgs7mpuCVGnQkhHJkmp0vKgSJsSgqoanDrbviYuZZ0fcY5IVD4Q6EobQrKqEhU+i/aAwVZvRw8UkA9SL3bjOaIelI/6js9FEMqqIlzqf7ZBZEXbs4eIC1lF4yfjcuTVhIVIwG63M2ZbLjBJYdQXklopAtUfrmeg6HccC8c5HA7fCTHtbUFq6JJsExZW7UUTq8g5acG6nONou/qOawt4TiFLPMfa2t4GzzTnQSrIdZykI4jYFV3/MpQZi61WK6PHsj14OCfX84QtAdvgmYSAUUnF9RmNRrU9lT6S50iITULT7Ue8IRIVD64pGG0DrHaqrj6rnieDJQOm3f32ku3bRU4vb9H2XWFg0hrb7fbMnh2UTR6QI6qIWNnqzsBARxaY8HyDOpMmhMBOrWjBbQh8ZcBEEsqiRlw33XPHhu2rgvGbTViouip7JvWkh48M0Z88zyVJklb6SBHZ4Z7ZzxBaIZ6VKvsqEszanb7roizNoPVYNnHiWQ95jjQZWq1WstlszryXtMkeP4j2y977S9kcuPRHl4YeUy6BSFROiETlg4BQbehqLWTCt8GAVGTUVEe/UmVCKDtuXy8fBnm7GR7pJSJFiAvtCZ1tslJlMuY72thKi/psIa0NV6lmnr81wcR3he63ZdVFpJxcxKAsvaVt3kXeQvO4ja5WqyBfFQgLpcy2qVwI2O5mszGpJybHJEmMhiK0rF5DC4RtTKfTs/RlFcJPRMUHWlI0RUhzRPRYdtNCnYILgV3NYt9vrTdzpYt0U0kX2tBiadxiYQmZj6mfyyMSlQ+CqmKyuiHYtlh+6MttH2dZxMbXy8eVS9arRiYTBl3dhZU0hv4+UQ4qWhiwdSrMTnXZkx+9UlzEjxJfdB1Jkpi0E47DGugMXMA7I8Qgj2NeLpembxHaHrQZPp2Evq6vr6/GL6YOOB4s/judjkmpuboshzTHLFp1E/Wq07agyPVW5GT81sQNFgfpkNX7crk0ho802CNNMZ1Og9pr6I7NLmhiTk8rjtMVdSHlpasH28SlIjU+0P26bXddjRhROSESFQ8eLfUTAqyzCeGKvPmU2CLGa4DrW/VlDCnftEmCzzTMDodzHZhUdIUKGhW9EnRVHnGNWXFCtGhUyLEhonRVzJD60dt/fn42EaAsy94Jbvk8fiFUbiAuFpHCQZXnQV/X/X4vLy8vpgx7Op0aYzF6IvmemSRJRKSeyZuInEU3RORdNMjusgyR1OlI+x0mOtM29vu9OQbXuME1a0JUeK5Dvs/5U5FF+wIs9ylN1tE/OxIYmkbRz43tNgtp4U+ilxDGNrVHPOdljUPbAo7HbQq9bUSickIkKh8crpw/gxSCTzq2MtAUTWhtR1TqEJWy8k178vSlX3x6DrtChdQLJOB4PHobzfnSBlR7kGLK8zfPFNfEQymrXXWC9gXvD/Qr3EPEt3SOTtP0bEVbJkD2/V5HdSBtIfcB4aVPMF2EsmoKu8sy16MoTVRkWthkYcLzVjT5Hg6HwqhLGar6yWhhbbfbNdcB8z5EshAN/R5qcqihReK8G5wT3ac1bKM3FgC6dL8tQEy1LcElwCKi0+k0bhoasq+IN0Si8sHA4EPkRJucIYwTEVMKi4cFkQMEnK4oS5vh1bpEJaRfi0ufInI+8LLiDwmls1p/fn6W4XAok8lEZrNZpa64pAeYgF9fX70+FRAQ30SNDwbCVSI13FvM2VjFMimlaep0KeYciwZ4KkF0FQuW+vb29HOHdkFHeELg8wvxQRNLzhW7dltLgG+NLoknNYJmqIq2Zr/fF1b+iJw0P3URok/ROB6PJppiv7e+zuDaUsAlACdqhG5Ko+yZFZGziNClBLX9fv9iUY5erydpmppI6DUiN9dOad0rIlH5IGCgnc1m75T4rgHOFqUifGOwHg6HwVGWOsC3oo741gefkNa1Otct6sugTbi2260cDgfJsiyYrKCvGI/HcjgcvP4c3W5XJpOJMY0rAoJbomGuqIX2RMECnxX0brc7WwGz6rbBtcMsDJt2iEqapu90M0SMuDZ2hAdS4RvofRNlKCAZnU5Hnp+fja6Fc3dFEQAE1hU10sSGPzXh0e6oLj0UUbmqzzzPddXU7GazkeFwaFKfRdAlyj5gFkd0COJdVMptg89cokLnUqk9kdP7k+d5sJlmU0SCckIkKh48mkaF4y1yCdXwVc+wEluv1ybKor0n2roueItUfeGLBkSXkBYvD31NijoLu2BPLghL0zSVLMu8TQQhKJCyMrEmk2SVpoT4nZTd89VqZSIrRNDQJ+kyYw1IENDlp/v93qmbYXLU98CO8BDlwHzMJllV9Bg+6JWorliZz+defxrSQqycN5uNcZb1/WjTNlKs7J8/9bllWWaeYf3j04ro61EnUvDy8iJZljVKO4mcyuhJobnE3KFjw6WiBJjStVVRhN4MsmeT+0sjEpUTIlH5QKhSVhkysLiiLN1u10xwukcILzVaDF4yVtt6QGfCxNME6BfTN7AWERVdcWNvR5MSJsnQFSqDlMZ+vzdut7bBVrfbNV17RU56iqLBE++LqgNhlciQ1rHotAhtF+zJR6eUfMdm62ZIs7g+qyM8pBh1aog0pO45FHpuNnx6lKJSYk0adCqnyjtFhRWGfZAZ3fhQR2t4H+xJyfWM6/Jf+1iLYFes1YFLpK5R1ebgEpPwZrOR8XhsDBTrAHdeBPGc82KxuLpbeSQqJ0Si4sBneECqnCNRFk0wyFOj5NckAbEu5mg+K3zbJdSGawAuChm7Ps8AyyTMpBDq+FrUm4QVFikQNAGkPkJXYDRURPAYClaPIWk5NAVMujpyBsmwV/GUX4ZM1Mvl8sy5t2ziguBoUkfqhGcJ/Y2u5tElrkXQGij9XJSVEgPE5lWMFNknpmsi53oQLVqHwGhBtY7Q6D+5z2zT9e5q0qL/hPgRIQ0RVrvQZjXNJXUX6FSqEBVXwQFd0S9Z1RMRjkhUPHi01E9V1B0o8jw3oXyiKHrwswdAjLQWi8VZqNsV/eDflHKmaeo87pA+RmwXogJBIb0SOuhCVHyrKUib7llDt+FQIO6sWkVA2WkIuRkMBkb0aYN7oUPmbLvKQF0UTfFBl2Br8e9qtTINHxGG4+FSVobMdvW5gd1uZ6qqit5xUmX46lS5DhwfAlOAQH0ymRh/kyRJzLlqQq3Bu/D8/HxWPqx7Bvn6BomIqb6y/0//XaeqeI91CXObVvGXJiohRBTdEs8U51rFBfrS+AwL5lBEovJJESp+07A/HzKRIYqsqjdgX9Pp9N32Op2OzOdzp9BR5KQD4PN6OwyS/FuvPl0/tp+HBqkOPsPKu0p/Ht1Isip85dU2WCkWkac8z81qH1FiFbLFpJ7nbx4rdcTXOjUE7EaHNnHBP4YSZMiYntD15A9RKevKjH4K0lxV94BZnv097OhHo5E8PT1JmqaGOPnuJeekS4BDoiIYvImceiaRwnPpbHSDUdckaWvVbH2NJjb62EhZ6WjPpSZhCKILpOUQNXO89CK7N0SickIkKp8UdYlKnZenSX6cSAbHqnP7wPaA0ORD/x8TBP109KpU93hx6QWYEBnsEbGSv97tdsY2PlT4yPGgzagCyiSZXMr0L0WRF76/Xq+NVwYTdKhGRHdAZkJjn0267drQxAWxsnarFZEz/ZGdKtT6k1DzNBF5Fx0pA4TI5S9CFA6CSpm5LyWZ53mtfkFc/36/L8/Pz4a4aCfZIugoDX/X11i/Q750lIh/wp3P52efswXI9rscQs6ortKpKlLQEBjSOvdITjS49hGRqHxa1CkPrJMOq0OI9L7qTOI+4CYZMkBpTwl0Alqc6fKZQVwbkpIhDYW/TVVgbEVVAsJm17Uqi7wgFtaRL6qVcKOlzYAPVPq8vr6eCUppR2CLr9tAnp+71c7nczOxUUpKWanWuPjSA3Z0gbQS1zmEqGgfIxF5Ry70+4DJHjonu9mjRt3Ghnp/2lslxCXXF0WEnNtwERsiqlrcTEQLF2g7uqO/60JR2oq/UzbPOyzydg1Xq1UrFUER10UkKh48okalCvtuUglQlXzcw8CgV7Ah0CWt+Jsg9lwsFt5Je7FYSJZlkiRJYaUA7pY+T5UiUDL8+vp6NhkPBgNDoJiEyqzsfR4dRC4gLFmWeVstQObYBxElxLKQFiIMl2zZQIk0x4Wtuo64ILjEFI4J0gZao6J0AmDFrqvpdJrS9W5qsrBcLo0Dcqfz3vVUR2iqRHbs/VI+niSJPD09BW9Hb8/37pe953xvvV6b5ymka7n9w7MOdAk51x/xsSZpItWq5G6NGE05IRKVDwTSF1qfoX/s/6PaJBR1O8DWeeHKwsZVUZb+KPoe1yi0CgCykqap0xyKgbbO6o5Q+2azMdcIcTOkIk1Tox9iYPZNLqw2fdfFJixsW3dy1iJiG7q6SLcQyLLMpDTaXOXq8yQSBDRp4vknYsLkRXovSRITVbPTjjbSND2zuNdCcTQb9qqfVI8G14FGkJqscFwQL1tL5SLOPCv272haaO8jBHUjpBpEtULcdnWURGvCeL5dz1yapkaLY6d2qVbU29X74nm8B0SickIkKh8M2qDL9SICBhxWmVVQZbA6HA4yHo9rr2LaeFmpJKmyEu31epIkiTFIqzKg5/mbe2WWZWbliEhUu8/WSYMURUgYuKmeYTKkOstFRpiIy+4n2yadowmLbfDmA8QB8zdIC/2TmCTqTIREIoqOA9JEFOr5+dm7LxoNajG3T4CrFwGQ4c1mU/i8Q5ZsQEDplq3FxRBGe0EC7ModokSuHjxsq6o1v95PE6AZCwEkGS0Vnb2Ltk3PL/vcIC0QGJHz+4e/zy18U2xEonJCJCoePFrqh9UbE2rRIInVfqiLLfCVDBfh9fVV5vO5JElSyXEVNH1ZmVir6CPQVYiIt3FgGfI8l5eXF7Ot+Xx+ZnBX51owwOquyS5QPYORG06reZ6bfjsMwjpiEAKcZDVhYZ9VoCcRdDbaS0VX8YSA74UQJp/Higb6CU3k0RRBeLSglG1BQMtIuSYSNihVptMx6UHdz4jjt/UgaDzQktDywHV+VE1V6Vkl0s7YSDrNdw+49lq8u1wug56HItE/98/3TrNASdP0zIQw4raIROWDISRqwUtcNcJRNx2jxYKhZKGNwbDf75voRUhEhM/XiaL4ABlEfxBaEeQCfish32fCwqPDZ19ft9sskyZpDxxB62hPEPHOZjPzf6TcIOB4phT1Bwr1vwh9jrl2On2AWDZJEtntdmftEUTkzE/H13iSYyjaP+0W0jT19pTS1TEa2myu6B1frVZGT1XF+6aNd5OIzmAwOLtvaEu0ZX/V1GCT46MXl9Z98c41rV5zVSoW/V+MqJwQiconBC9A1QlKD/BanW//6dLFiEitFFDdl5UOukwYZfsg8oFYts1VFAMdGh9tahZ6LZgkQ/Pndndo7VECWSE11Ol0gqpAXIDosEIm1aU1LCHQxAoiRnqo1+udERddwUNFDtUlIfCZwbng6kcF6YTQLpfLs3eDSQ4the8YyvYPWS4iKzaquBwTMUAEHLrIaSv1czgcTCWeyMkjh2PT17UK2pjgdYqSZ49KMvZhp9LL/l4Vl/SbeTREovIJ0SSigjFY0e9tcyf+HI/HwSmgJoNhp9MxKYmywR1xXafz1qTwUk3HIE3Pz8+GFE2nU9lut6WpHJGTBX5o1KCoJJloCKk8rhepoaLOxhqseild1ukEmhRCJsq25xLk2qkfUkRao6EH89BnpgpRscFq/3g8OiMmlD6XCbdDjxWykmVZUESSqFtoapF7lmVZ6btCqkqnwPT7zQ8CYh86nY7R+yDQplpnu902FrNWaZBYBp5B3ZxQ5LRYuyQiSTkhEhUPHk2jUgV1V0UYOzEx2cSkDMfjsXIKqM7LysBXNFjrlW9RLr8NIPTkeKiCSdP0rIGdruSxEepCKxJOahjQn5+fnamh3W531jHZBitM2/l1uVwaoaZtUQ5xse38ddWMD7bDMaF5ok3T6fRssuT4XaiyWmX72rPGlxbkeMrShtpXpOy8mSzL3pvxeFzZ5TjP31pi4P9TlN5Am8M44PI70aRR/+iJXX+e3/FOtIFLTPD2s0drkIjrIBKVD4TQF9ReCSC41Ep4F5iY6mosEEiGpoDqhD6TJDGlib5VPJ4eItLqAOkCaSUmaI3FYiHdbteQN7oFbzabs89WjaZgcFZ2fbWQ1u5sPBgMTMh7v98bES6fJ6rhunb2oK5Xo7rnDJU+mJlVTT3Z4tLRaGRSCXYpKhEOhKoi4kxd2j96Is7zvPR5Ce2/xL2hOqUMeI/4UnSkKKoIkAHXZTQaFRJTjsOH+XxuxKda00M1FM+k/sHPpi2XWKI1lxbAxmjHdRGJyicDHWFFTn4DkBNd3qhhr4CaYLFYNKoCKgJheW14pqGN26iQurTxEx4svpQSKQSRN5LFpK6N20KJh8ip7DXUwt+1Te1PQZRlMpnIZDIxJIRUTQjJs4kLVSk8i+gSmrrW8pzSABPyzX50t2/OTf/b9jphMmXSLTNIC42m6H2FEhWILvfARt3GlgA9BlVGda0EQp2fAeS3re7M3M82hPBFiETluohExYNHT/0QqqZFvB12zfPcCOhYLZcNFFmWtZKXrVIFFDogkLrw9ZbRxm0MjJPJ5KLlh0zIRYZrGpAZba4GQqMppG1CHD9DOuISQeh2u6ayB21K3VUwKQTAc9V0okLYy7VGG8M5auMvyCDPfZGugnL+stRIlW7WdXQym83GGNHp+4vGqgnxp5x+Op3KdDp1GhUWgXGhLvFvI42Ca+01yokjUbkuIlH5QGBAtcPVDNYMyAxsdezb2yBwOgXkSolU3Zeu8LFXUqRWWLExmCNoJbqy2Wxab+9OyqaqQFcbt2E6lyRJUM8dJoyyDsa6/0kIMNpi28PhsDUHzyqlxWXbwX+EZ10TF+2hQTTJRTxc6aA8zwv7/VSJpgAIcyi2262Mx2OZTCbm3dWNLZsSPcgKRoVFhng27KhsFVQxfysCZdkxmvLxEInKB4EWEeof1wqn7otW5Xt2mbJdssxxpWl61uXU/rNsn0UVPkXGbUQKiF7QwRbC0pSQkdJoon/Z7/emGulwOJieO5RQu+4t5ayQMF+XWCb1OqtPJvk20ma+XkN1t5Pn+ZmIV+RUCqt/RMRE1exye1+qs+hZrBJNASElyjY2m40RzULK6ja2dAGyP5vNZDQaBT+/EK46781utzPn1KT/GNHESyMSlesjEhUPHjH1E5qjRtxWB/g0FBGRohdZX1ctavR9pmzgIeKgw97a/l5HUVxw2c6Px2PT3K7udZpMJt4VeygowaXMV1vY+8pJEY8uFguZTCYmurBcLs/OpaojLcDCPc/z0qhN6PZEqjvb2iAd9fLyYs4LbQo9e2zb+Tw/dVnWmhQIvib6lHK7tBR4qlRdyRe50/oAUYGg1I2Mlh0XguRQ1LU8EBGzYEBfVgfXiqaIRKJyC0Si8oEQ+gI1ISoip0HBFh/a/in8PU3T4IZ+GvP5vHAypV28TocQRWGyDp0AqXpBcEvZ8H6/N3bpocDro+mgySSqz4HoCuF5TcJIP2A1/vLyYoSw0+n0zG3XJ6QtA6tWIjxocOr6z3AcTRcGpPD0dny9XnQVUmgHYXRG7AdQ1RWiC7JR19BL9+nhnarqJVMERLE8T3rx4eqPQ9WgyEnUS3sB/Vn7e/Zx1yUqOppyjQVmJCrXRyQqHwCIZkNf0rpEpYkpWtsDCCkbUjXoOUgj1CUJpFVETloMJvnNZhMU2mfialpy6SuzRKuSJImJmCAk1OJRETFCaap35vO5+V3ViRVh9mKxMGXUpJjm87nR+lQ5b9IufN8uIQ5FiDBYREzXXQhGFRyPx3fpPJ1aDIGOOkICeG6LUp6uyZ79lzUWbeJwqpsyFm2fP7VvTtnnbej0nQaVbD5Lg6r3oCkiUbk+IlF5cCRJcha+LkObK69bgSgBFT5YmlPq21aeGhttXYVT1F5eRExarI3S6yLtBj1oMD3Tpbk2MPXCxp9URVUiZeswSClBkvQ12u12pWkhdCFEK3QJsc/7xLedqjoXJrwquggInyYUXBPSSvy42km4JjjIj4g4o5LYB2AG6LMQ8KHT6Zg0pOt+F21H+/rozxYdQ7/fN5G+Ku8hqTVX9Rc6ItKaImKuD0Z4oeX7bSASlesjEhUHHmESpyNup/Pm6ojDahmalBGWrZSuAUgJJmR0CN5sNq0JCm1oHQsTqRbe6lLTtgZNjNGKyMRqtTJN8ohGFH1eRxOqCg8p7XWF5jEB5BqREtJ+MC4SwQStex5xPtr7hGthT+SkoHR/mFBw7lWIChEkHZUSOZEBfT340yYe2noeMe83vvEN7z4RijfROpHmqvp+kPqp8pwwiVddLOj74fouz5DdIBJSe6l334cmUaqI6ohE5QGhG7SxciGiUIYmoreq5ZRtQAtuWXER4mf1fkn7ew0apWVZJnmev3OTbVPQRyqv7LwWi4V5HkKdSevoU0Ldcdk/HjI0KsSvR0+4rnPkc9r7ZDabmdUzURiiSRq6B01ZBY5daRYCjlVXYkGY6kDrPnz3o6ixYSiIBFXFdruVyWRSiazU7bOj+4Gx6NKRKjRA9rONG/G1oiki78X/EZdHJCoPBJ8nCKhCVOoMrvg4tOUiGQoa3XEMTFCXtr93QTcXFDl3kxWpd119ILVQ1ANIREx1UpIkhrxRteRCr9ernPbp9/uVtCNUPNH0jmolyBTtA0JIlYg4UwmkWCBCRGP4ne2fog3eRE6mh1XAO9DGc8fxFZGlpsJ3ETEapqrW8lQYVfHMaTqJ6zJxkVPa0ve8hqa820QkKtdHJCoPAj1Zr1ars5UtL3bIJNkkfaN9R6oK1+ruk5U8kw0pi2vY39tA9KgJom2C1uv1ZDqdtmIgZ0dtKFN2gWgOJcykqOxnBQFnFaKCRqBJj6fdbmd6/WhRb9lxYNXverb1/3O9icCsVqszjUev15PBYGAikb7S+DLgbVOlsaYP+hhc51flvS4C36/TA+dwOJQSFa2VauIwzP0kIhmqp7t2CkYTqYjrIBIVB26tw7CBYPZwODg9E0jHVImo1AV9XkIJDxMNZYtVSqgB4fZer1erzLkNEBXwOYAivGUypDdOWVdkH7i+z8/P73oAkRrR26S7MlEM9A+0KoDYkW6pQlQoQW5KDDEmW6/XZ2Z8RSjrhYN+hc9ox11XBIb+P3pyraJvIN0zGAxaJSou8P9NPWZIWdVxf/U1Q7TJCSSjKPpRBttX5t7GYRC7Jl8fkajcMWzBrC/czABkrxZd6nxSFLqPjEaIE6yIlLpWsnL2TRwu4zf775rclBm3XRKUkDIQF0FHDxCC+roih4KoDZU1iHnZJvoDPXFyvbSPChGHKhMApaGDwcDbubcqdLVGWdSprJpnMBicueQWkXb2y/bw7ggBqRPetTYmq7L7oAlAUxwOh0rHjOYNgopuRHvQtEFONHQF1D2DaxmjKtdDJCp3BspHCblqwawPDBq286YNHUptmmd1ERUd2hc5eZKkaVrZX4NIjMj7VNe1AamrQpS0H0uapmaAh1zUWSUTSdFdoHVpumviw0cF0lR14sMTptPpGH2OXe1UByFiYSJ3RdfK1rngmhtCxlz7xsGWqItdWqyjgk0s30XKhe0QC8Tb7N8m9kTfbC8W/SfpCqrk7GPw/RtiR/SE52e9Xrf+TjLmVUlP3yLqstvtYlTlyohE5Y6gJxNehOVyWTqxMGCWWWmTvydFUBfdblem06nJ01OSymrTZdleBUQMfKmuawLLeDQPdQBhQZiYZVmjRoiaBGVZZgjqfD53usTmeW56HaVpGkwYCetj8EabAaI6pKHqpIVCKo+K9CnA1nBUEZ+SUqSRJ98XOaVLKDXXFT58ZzQaNSqLLdNgaGGw1kXYZEKTJ1d0EnLD4gQHVxf50eXUpA8hfyFjTBOUlSgXoSlprALGvLKFYUR7iETFgVuwdML6IucDWJZlxjzL9yKGCu4Y+JtWLDBgawFpnuey3W4b+xmkaSr9fl92u91V+nYUgb5GeLY0Bek77jWCVyp07Ocu5DnEMA2hs3aJ9fmXhBIV22GXNgMip9Qeepz9fl/JVTbESbasuswVcakyyemqIN3Es6zhIuSpacffEKuAEHLAO1PW7Zgy79D3ipLg4XBookyXRB2iwvlybnbz0UshVv5cF5Go3AEwtrLFpvy93+/LdDr1voShq0jy+W1gvV4bX5Plctl4Itc+Kev1+iaCWRtoZNomTKRwWLGiY6naCJGKFnod4RILibD9S/TKuAxMTL7qLu3OS6SI6qAyIsxKtGxCwZHVB9JedSMq+/3eEOyqz29dbxKNstV4qPh8tVrJdDqVwWBQeB6683KVqBMl5hCiJuJenVZzufdWTUvv93t5fn42er46pfdVMZlMYuXPlRGJyo3BBC3iH7h4gYk0uMBqmlC8q+Kh3++35jtCsz5e2ibo9/vmGlRpJHhJcF6XjOogeNWNEBHehoheXdboRMyYXLQZXChJ0dsOmcCJ3JCGKhPdok8pus/oQ6roU0BVMl7n+aVMeTgc1ibpIRGVkMkQs7SyY8EDZjKZVH6uaXMwHo8LtVplRASQYiKKxbMJkaoCHQG7BnmI+pTrIxIVB66Z+ikjKRq+zzw9PRmiwsTEBLbb7Uz4loGhrXzuZrORXq9nJldXuLbsvLTLLiZqtwbXsa7otSpcjRC5br7Jh1LboogD9597RDfoMuA7UmUCpmkjK28dqbCfixC3VVe0xHWcmhAxGVZ5trUerAqYYJsQFfZf53c2ICFF7zZl6XUra/gu+4GI2B2V2ZdOo9k/vnOjD9U9I4ppr49IVG4IUh2hJKXoBdaheCZaVui6MkCXJZcNJKEDJeW7rlw623GFZPGHuQc9CqBvS0gp8iWAH4vWf0wmk3eTPuZ/ISky9CqhkyopLwhTKGHj2Elp8fzZ3i8h6QPITJHQ1I64VPETAohGfcfAhKwnZv3TZFJtK/UjcjJjTJJE1uu1V1SLs3OZYZ02ytMREqIqmoggOA4hImWoSxyvCUTYEddDJCoOXCOiMh6PK6vGQz+rm9Ohf+l2u/L8/GxsxvXgq48jdFUk8jahIYrEOdeladjv9ybKQ3mt1qNc2wa/CC732VtAlyJD6HQzxqqW9qRbQogK0ZQ8z02kjlRfCGHRnjdadIv3C71yQo7BB4ThLqJChA8NStmx9no9mc1mIvK+rFeD90GLaXmHQsmRNp1r2zNkt9sZbVIZIMEuQmOXY3PebP+SDUCrkDONa0VheB/rHmdEPUSicgMQPq2Kurn0xWIhWZbJfD4vJQauVRTeEiLviQwCXyYiV4UM+4fYsFK/Fz0KYFIuqrC6No7H49mkT7WQnlBDyEroKnU8HpsoCkJK9lunrJpIHwM8BKNIw0Lkosg11pU+IvWk/WVEzvUQVDHxXcqNiQroiTlNU1PJ5rrGdpmyfndcERi+o1GW+qkyGdI+QXsWufQhlNbqe6iPj2of1z3SY8ElUGeROJ1OKwnFm8A1DkZcHpGoXBm9Xq82I6/7cuhQLMJKXykxERPfRMKgAAFiu+v12kRvmBA0Xl9fZTabmcjKtToeh4JSZAzZ7hFM+jq1QjqnrFootAsvOhb9WfariRL3ObRiBsKFUV3ZZ8uqP3wRF0gd1VTr9fqs1w/VSfaEiAjdPo4iIgj54T64xKLag0VHKEmhuCZ99ll10ua96/f7hRGP7XZrvGPqiGqrdlSugjppFcjV6+vrxaPh956W+qiIRMUBqlBIobT18OPuyd/rbqPO8VBOvN1uz0pYQ71PEL2KiLccGTKSpqm8vLycHSfpp3swcXMBzce9aGWKwKCMNoiJssimP6Rsk0obH/EgYkG6bzgcSpIkhrSEuA+z8g05R987QqSirGoIbY79XYgL2yeKZMPuPeMCwsr1en1GSKrou/DSOR6PhgjqKE6Vdx5RbRGaiGopcR4MBhchKnXOmfTdNVL29y70/aiIRMUBSnmJEBAGbUpa6NtTlaSwzyqaBA1WkQhEEayxQi4jLJSd7vf7UsfZ19dXmU6nkqapLJdLI+ClQuUWAtUyXKMU+RLQHi86wmLrSUI7JtOpuihCQiRDNz/EXp1qnyKzubImg8BXeixy6ldVt3QZASjwNUcMaeQHMej3+7UjcSwC9vu9iXxxL0SqTdpEPMbjcWGKF6JZp3/T4XAwZKhJ1ZLr9xABRNg+u3/992t6mkT/lNsgEhUPXDldndbQorqQlQXh0ioPuSYoVYzANEj10D6dbWIQpjvZsipHEItAVkSCuxYfj0dZLpeSJIkRKF7TMbIqsP/fbrd3pZepCipubJv+9XptJrqQaEcRSaFTsf4M5He1WhkPFfaP2JVoAyQ95DoXuS1jv180EYa6NRchJA3RtOSXsnzuG2lR9FJV9SCQsOFwWEhUSO8hPK4CbUtQFSHjH2kx/e/QbV86qhIjKrdBJCoB0C8XYjn9OwYHfmxCoe3xQ6AJil1qWBV0QfVVsUAsICxYsBP2xuOjCknCSp28vSv/fw8gdcI1+AjQNv3j8dikGnWJugukP4qICpPmZDJxRtZ4/iEtrNin06nRavC5IrhKjzWKoi0iYc0MQxD63iFiLSv5LYItcmdhUqefzHa7NVHCovd2u93WIhtElovGlbrgnez1esG+Sr1eT7IskyzLLq5TidGU2yASlYpwPajdbtdUG0BcdHmkyx7fBV4wxIttvHCUFJYNokzW6HNCGx26QLdgqj2oRLgn8azIqRT5HjUzTYHIlUggFSq+6BwTVtE9ohM2OpWiz0JaRMREWSiRxtqfChybxJY1IywjIXUqM3walZBtEFXlOQ9FndRICCAqZQ60pK3K0kQuMK60DSJUELRQPRNRXFqNPHJ0NOI9IlFpCXbOVEdRikiKfhHbJCiA/i/ardYHtCwibxMNZdShg1inc2ojj+CW/8uy7G6cZ0XErPbvqRT5Etjv9/Ly8vLOph8nWez6AdESH3QEMRSQEcqT6XOky4hZoe/3+8LOyqSfQvZP+olyW/4Mfb84hhCDut1uVys6UQQtLK2KkHRUaJrIt33SU21HS3lWqNoK/c7Ly4tkWWaKBppGoyPuB5GoXAg+4ZcLdEe+1Eu1WCxkOp1KkiTy+vrq7APEJKI75KZpalwoy1aK3W5XsiwzTfJIISD41KHZW4NJm8n6M0Db9KOHSNP0XWVLWbquzLa/CAhFtaAaXw7+5HkTeYt4QTB4ZonKlJVio88ZDAZGT8F7qDVmIic3YkiNXbkTKgCucz2KoPev/+37u/4/Uq9lJCs0TWSjicalDNyHwWBQiUAdj0d5fn4+q4TbbDZ3ZSgZUQ+RqNwYnU7nKsz/5eXFlA6/vr6aQalIMEuon0Hcp1/QTQURwvV6PUO+EFwmSSJZlpVWDl0aHOs9kKZbAD0EaSHIChEwOtEShkdozGRfd2Kye/OIyDsxOkZvTLKkUxHmhohLKYO300pEOiFGgBQP74GInJEhuo43tYfXQIdRNIlClnQFUCg4Rv1eun6PwJn77SI9vv+j1PsSaBKhIiKYZZnZRhtkJepTbodIVG4IbQB1DejS4cViYVbWVAS5joNoCGTFnmh0E0S0HkRnSK2glyAFNZ1Og/1b2sajliJfAqRBIBCQAsgJ4mqqUUTeE4tQhHifiJz60WhSTJoOr4xut2sE32hhiITQf8dFqnHatYF2BxEwRIbeNhA3fYzayC3Eb0WD9269XhdGpyARy+XyrFRX/+lyvOX/OQd9zYv8Yrh+rs8Upa5DUmNVgdt1k9TS6+urZFlWK7XlQqz4uR0iUbkxrlkNg0o/yzKZTqci8r7awAW+Qy8fjtnXVHC5XEq325XpdHpm1Y31Oq6hEJlrXYOPUorcJnQjv6enJ6NjQd9AVGw0GpmJbzqdGjITSrLZT51Ow5q44EWEzkVrwTTph7CEHh+TkBYB24AscR3sHll21AJjOdtan8+WET62rSumqqDX68l0Oi1NcfI5hNZVMJ/PTcq4TRwOB8nz3IwvddFWp3i2FXEbRKJyIxBmbdIivg7I3WMXH7rSIBoDWaHVu4/oMCCjPWAgw1Nls9lIkiSSpmmQkVxTfMRS5KagGghBJYZ8+LHQjfd4PMpms5HFYmFIDJE0UixlpKUt51CeJR2N01V3REHwBQrpJxXazsJVoQQg7aR0tDGYFgtzndAI2b5MOr3ExFj3uoVWIyEyrlNezfYvER3FAK8uiA61tRDi2YopoOsjEpUbQHsQ3EKVTtmw7sYbgpeXF5nP5yafXTQJQD7G47FkWWYEwzqF8PLyYiqNWKVfikR85FLkuiCsvlgsZD6fG7G1iBgvFlIOlLFut1tDKiEDmrQgwrVJSxtExZc+gkjpSRanXB8g2tqILhSagJMmIkqCnmW3271rgKgBUSfd5krhgCbXLcRSn+NJ07RyGme9XkuWZRep/gnVJPnAdataNu5DjKjcDpGo3AD0arklVquVaW4XqjugIgSSVRYBIWTP6pzwshYPM7GRt5/P52dVQ23gs5QiVwGrflbCh8PBDMS6ggtPCrof232FmAAgLePx+KxhIb1wSJPMZrOzSh5C/CGg4idk0tECXBeIvkAURN60Kvq4SLnoDuJaiMuCA8+kJEmCUqmASItdtq+roOo63gLtqVIU9YBQjcfjSmMT1T+XIiqIq+tue71eN26iyH1oei8i6iMSlRuAge8STb2qoKiJoA10Jbp8ORQo+Mk522WDeZ4bcjKZTIyHQlVHXBc+YylyCLQ4mggB5DBJkjPNhshJ38R3dV8hCAvXFxEkpIXtsELW1TxsW3ud+MgLRCUEIatfFgw82whnfW6wEJP1em0iJvY1rbLq9kVxtMN108lRW/yXpWfqOtVSStw22tKpiIRFpejxxudxV44pn9sjEpUboNPpSJZlrUcO6gDtSZZlzrQIpYt1mwp2u12TKmLlmKapWYGv12tzDZg4WJkjBGxSofPZS5F90MQEh1HuLb4hvnuNjoWmlvixIJbW5c+k/Vz3ENM3HUGwyYv+KTKCs8HkEgIm6NVqZYiZbkxK1KRs31UnszLn1bYmRpxey4Slm83G6JKqpGBXq5XMZrOLRFWaEoQQ3x2RU080vS99byJJuS0iUbkBGKAQ3t2yo7BuImgPUJRldjqdWqQKkmNrQxaLhUklsJpllSpySgeRw6+r3EeHEMWz57CbC6Kr4O94+5RF/IiiYNSmdSybzcZ4tPiIpivlaHud2NGN0KhYFaLCyl1HQ4hEVJl47W2UIbSlBr2SSBW5fvi86yfUUl9HX6oAAXAbkQ8bpJrrYDKZBEWSiBDa9yOSk/tBJCo3Ai/BcDiUXq931tn42kBvgCBys9mcGcG9vLzUGizoGeRK4ZBK6Pf7Z9U/q9XKTBykx+rsm8mz6mTzGYBIFv1Fv9+X9XptolxVLfJJB9o2/VR4VYHL64Tt0j8qBAhlfXBFM5pUmIiIaSJI5+rtdlt4DGUEnN/pkusi7xQfOM8QYSqOtlUrgOj902YHY861LlEhwlN0HlgWRNw3IlG5MdAH3LqZ1nq9ll6vZ1xlmczqVsnoaFHROe33e3l+fjaVI1T/YD5VJxqiS5Gjsdt7UFYuIqbXzna7NX2a6ppjcb15nkXengN8a+qSTl26Gwqf6VvRPupUdWiCgKkhKSM9ASK4FTkRhxAyJSLG8K0MVEXhIaP/DiEtS89wn6pWyqxWqzP9WRtoUmWj23kUIUmSqD95AESicgfgJUnTtNStsgp40UMH+OVyeZZrrjvJa+fN0MmCNILOFReZbxUBf5BYivweiFt12kdEDElhQiQdVEfwTb+e5+dnmU6n7xp0uvQnZavwqvoUEal07ETw+D7pJ+2HolF1YiONVff7ISi7PrPZLKg3D2m7qilX0j9tjF+6jQPi7BBx/WAwMO+/Nqe0QZSuaml6xG0QicqdgJAppXRNdRU6deNqRGgD0auINGrkRbVHFTM5DSI7/X6/FlFiVbder2MpsgO4iEIM9PXGD0SLCrUJmV2669s+ugD2gT8Ov9Olt3o/NnnhmSUiUCXtI1KNqGB4Np/PzwS9VN9oIkUVmR0h0XoRTfJ4F5+fn8+2gwbLB92Ppw2QnimDrr6rIkLnexjZAV+fId/fiYjmeS4vLy/S6/WMhq7oePBkQnfnW+SQnmRfEfePSFTuCLw0g8FAptNp7fLc0WhkvCwQtBb5nuimgiFOnj6gN2lSqUOIerPZVM5161Lk2DH1PWzvFJGT22qWZWf9mkTkrCLHFrbakRH+zkQBqdBRGq0XouKHsDvHZk8g2qkVsWrZO4G5XJV3h3cFIbcmczaqNrpj23a0AeLgOydNdNpICVPdFFKdg1alCqgaIvVVhiKSQCdk/q4F//YiBAJMFNAXSYXw+KJkEfeLSFTuEOSV0a1UEYNiyoWDKNuh/NgefNGGsHqpK4TTZchNSoFZSdUhGjqCFPEevsaSIm+DuP3/LiEyBly2KZke+Ln+/L9rErbTQbi4opHQ5mqkZLi/RdEXkXraht1uZwziyt63w+FQSXgL2bLFwJAhSqPb2FcRuEaue20D0jEejyu9izQxrbpYsVMw9jNDmwZtOIiWRld4kTLSppI8pzGK8riIROVOQSooTdMg/xK8Wez+O6wwICvatt/XVLDOsWZZJiLyzmWzCpiY6pAURMCxFNkP7Z1i/39oTxRXd2ORkyGgFk8j1IXAQkLo4XI4HGS5XBo9iLbif3l5MceDMHexWBiS5Iu+YD6mOwL7zsOFUKO4qtbuLjt4CFdRCS2/n81mJkqkS49dZctFkSStxSkC3jFVOw9TNUhkJTQSpNNmPug+VDwzPEP0q0qSRPr9vnlm9PYjQXlcRKJyxwgtYdZ+J67UDdUYaZpKmqYmjAopaJomcXml1AEr/qpivF6vZ1xzYymyG7Z3igYTKGSv6vPAatXuvktVC4ZjrGzpPQNx0ZM0E7IGFTyunj7sX3c2JkU0m83O0lPszzchhk7ipHK4bnaVDUJwfa197q20jyjaF8TL3g/wuei6CA1pttCSXNKpVVPQLF6+8Y1vVPpeCMrGrDbEzxH3hUhUHgC6hHmxWJyFuRGQkbrxDSg0CUySxKw2qqaVXIDwNO1+zERTNbKDBifP81iKXADtnWJjt9uZvjcYtlWpsMGZ1o5mIYC1yQX6FBdp4llH68T/lZXUanJOvygRcRIkJn37WoSWKPM9oogi54JaSIw+P1/fmt1uZ6JRLoIOKcJfyAfackBkbOKkreE7nU5Qo0I+X1Wror+bpunV3ku0UtHy/uMhEpUHAS9dlmWmhLlq/x0GL3rrNCUplA1qV9m6KNJPlH0vliKXQ3unMJDrChUaNo7H40qEM8sypy6JdJIr9F/UBHO5XJq0ARoX0kYh4DtET/R78vLycpY6sp81NA9lIDqBQaF9vUhBkN7SDe36/f7ZfnW6ykdUOK8iooIOpgy0wyhL0RKRWq1WtcuNSeVVNY+ri8PhIM/Pz6bKyiYruqooEpnHQiQqDwRdwqxD+SG6DL7DapLGf1Q5VI2G0I/HDvfXBfnw2WwWbHyn/VpiKbIftncKWibSEwhJSZuEIssyk260v8eEX9WHRQt4O52OsY8PBZEDvd9er3eWAvKB6EVIZVGRBkaX9+rFgT4+DZp2ukDapqomxofQ+8v1auKLgsM148S1nLcph0d8LXLe+JKIXsTjIBKVB4O2z3bZjbs+j8BMkxrs0ulwy6QVYtDW7/fNd9oSr9JLKE1TybJMttvtmRcH+9XVIKycYylyMXQZMDbn2+3W3EfuZRWiMplMpN/ve0kl0YQmkxPHXCXKBinR4F0pIyCch96GdnYltcKfRUCXQ3fp/X5voguu/RKddJ1rm5U/VSIJ2+02OEXkw2KxkNls5m16einsdjt5fn4291w/h8PhMBKVB0MkKg8KXcLs8z7pdrtm1Ws73mrdAJEJJi1KAX0TEJqEtsuACd0yoKNjQCTJylQPtrEUuRjaMwKhKpUS/H48Hpt0SMjqGcKj/+3qmxPqSjwajYxgVkc88G2pEpVx2dKje5nNZiJyilLoCjiRU0qH549nDtji1CKy4iLwEBK7GoYyZV9TvzpVRj5UIY7oZ6qWKNv7Wy6XkqapTCaTqzZg5Vm3QeQs4nEQicoDQ5cw20SkijMtKnrd/I3mdERZSBkhXL3k6ojjybLMHIcmT1QZfXSSgm18k7QWKcKnpydD/vQkqkWwoQ3lNGmE3NKEb7fbnVW+hGA8HhtHXG0kJyKV9Cmcg/2sPz8/mygchoL6HDS0qZwup7aPgeaDRI5CQITJZeBGBMb3vdCUVBmqEBWuATq0uqAxIJVhddoytImYJn48RKLy4HBZ74/H4zM/ilDoJn6DwUBGo9GZtwUryGuFcF1EhBXper2++YB3CVCdolfztkU7K0Wddy/aHiv2srLO0EmMCZXnAAKELoP7EkIwIDVUjZHa0yZy2kME0uC692zL9TtNNnhXXBMW17fsGWcfeHmEgHNwpXGKXGq1oLapHsxXfeQD5dOhJNaH1WplHLC1kFen0vi7Lr/Wz3hb1gPch9jn53EQicoHgLbe//Lli4hIYxM3LWrUjQKJ4NyiykYL865RRXBtZFkm/X7fmKpx/RnE9Y/LPMxFZHS1TxvodrumsR0TKkZcIqdnxdYF+IBWgJ46dpkxGhXtgmufL9/jPSgjDj6SwndD9Avsm3YPofBtv8illmtZdV++/RelmWygUxmPx43TNsvlUrIsk/l8bv7PlVrj3uhnnWgbn9MRrzoEhnRUrP55DESi8oGg7crbdGhlBfj6+iq9Xs+E+6+Zb8ZbQ+srPhLQUVQx4LPJC+kTETmLyLRpgkc5su8erNdrGQ6HwekaiJmPOCyXyzOtC9dJC1ttAjOdTs8mPbuZYpHuRduyl6UIdJTRB+6JnW6ytw+5LEr/hHi8QCR1vyUbCKdDEOKeGwpIBSSJajPdzwmgmSP6Yt/zXq9nhL51CAzFA3U9YiKui0hUPhjwyLDt8uuCiYDKBazAqRryeUm0iWtpY24JIlZVVsy2foVJUWuO+Fwb0NqgoucqVJ+SpqnR4dgg9WMTCjQr9vZxQoXsuUgM3xc5GdUxYfH/bDeUqLgmOvrRaNM1NDyHw8F0TbbvdZFLbajHy3Q6NWSoiKhUEedSbh2qkcE8UBvQ2WmWsu7oEEpSl77S8hACo31v9LOjm2xG3DciUfmAQHhH1+QmgCDoVf5isTArt+FwaLwumhi/YXe+2WzebaMti/57BgLLJsQSYsKA3iZ5JCpQpg2CcJURFSYzn8kfkZYq+g/Krl0RKR15QmQOicFskOgLnjJl56Cb/FElpH1TaLJI5ABQiuwiKj6X2jJXWq3vWK/XMh6PTUdsmyxwfFV0KlT/6Ega95CKMZuQ2FEtHTlJksQspsqubdF9cBEYxr8yAsPfI1m5f0Si8kFBiLxoMCjDeDyWbrfr/D5upoT6h8OhITV4oISCVaDuwEykhgng0lGbSwEBoch5aNqOCrShJbGJSlughBlPkCJAPsruFVEOX9sHX6SlCDr1aUNHnzgXuosTecEIDLFp0blCNkTEpJoOh4Os12uv2Bf4Ihoco89kjQmVyjztqQQgSJyL1izpNAtRnRCiwnUdDAbmXbUJid3JuqxabbVaSZIk3tLnuhog+3js89DEhfsecf+Id+kDA7JSp98GEROfnwogxLzZbM5SQvSWKetRQgXGYrEwJZoYMtGTKNSI7h7BZLbZbJwrO23r3VRLQtqvbULH5BQyYeB4W4ayVWyV8mY+H+q5ogkNE6q+9hgkalCyr6tSKBsObWEBKPl1AQ2HNja03W31M0WEQv8p8hb1LKrUYQGgwf60b5FNSDh+yBTHUBUYDjLGuLYRqssJhYvATKfTM6+hiPtEJCofHOR5q5IVogBVIiNECbrdrgyHQxmNRkZ86et6y4DHRME2iAYRuXlUUGGiV41aEMqqrg2C0eagDpIkMYM499KuttERhFCCsV6vDal1rairEhWXdX4Ryhxq8SHypXOoUprP58HpOlIlTP40NdRkwP6druDq9/umqo/oTRGKjguB7Gw2exeZsUWprggJZKnJM0vJcpZlzt5DbbYO8KEN992IyyMSlU8AyEqSJEEVM6y2lstlLc0EEzNpodFoZHLcu93OWOPjcOsiIvSneXRdiivCYAtCaRTXxr6aiqc10HBAMm0LebvaRkRM2oQUYNGx+iIgPiFt2bGGkL0Qt1u2QSPAonROlVU/DtAQPVoWQAJ4BkjT2NHI+Xwuu92uFQ8hiAqES+tIQp6hNtxyqR7Lssw5Nl1DPxKJymMgEpVPAiaQELJCB902ylpJ2yA41J1kRcQZ5dG6lEdHyITbhuNom9sBSZKcEUnXtokOkSokSpQkiSGbTILam4cKFlfUpKqQVqTYG8X+nO9c7PN6fX0tjepUWfVDQJ6enoI+79qXq0VA3W0hvK3zzNAOoKkRnC5/tl1+2yTdEY+NSFQ+ESArRR4opHw6nY5p896GPgT/k06nYzQsrkESDQdlo48MogZlE0G3222FFLa1HZFTpVVZulBb3I9GI6PX0Fb1CDuJJmj0+/13x1xHSBs6gUOCiibBKvtmwg9BUyv8tlxiRU7nGErwbGy327N+XCHQomWX9iZJkrMI6jWIihYlR9wvIlH5ZIAoiLzXn2jPlO12a1bFuGVW0Qz44NOrAEpHm7jq3guIHJWVllZNc7iAtqGNiApCzs1mU7khIPeVPlEa6I70hK0rokiD1JnIQ69hyLb5fcgEVpXUiNSzwidlRVlxUxKviUodcqvdcl3HovVXNinhB/dlqqDSND2L+LZFVBCDa32R9liJuH9EovIJgTGY7Y9ie6a8vr4abUuWZUZfcqmXG1Opj0BSRMJ0E+gb2qj4EQnrr1MGyAP+GKEmX2VkgcmWvj58TwuLSc30ej1jta69OHw9X6pU/IRco1B9hG1MVvZZX8mtK9LgMkrDZr8pUdE6mbog7WULw21Swt99Xd5FxJS+0yIDMtE02qGN99DLcew00IxeKvePSFQ+MRCRYRDlIgn7/V6en59ND5fpdGpMtdoWboaUQz8SQtIRWlDZdF8izYkKJAV7ccz8yvorFWlOwGAweFc9YpcHI7xmVa2JDM+IyHkEpsrquErUKWTyCjUm09Vd/X7f6UcicjovXWmjf4g8TCYTU2HHT1WRLdGrKkBjpg3mdMsCIiWaUOJAXPZsavG9jno00V5pPZy+zlQmtpFGi7g8IlH55ICs0GjOt7KniidNU6MxaUu/0ul0jHDzI/XxCXVobQMMyE16MOEyutlsZL1ey2q1ele1hdbInjjQlRQN+r1er/R5gcz4Pme7jeqeRq4eP3aoXyQ83B9CVLQxGREKlwdJERmxXVuLsNvtDIkkKkpEoEonZxE5qzRyAVLCNec8uL4cR5nB43a7dYplXXCdf5Noh34+fNuN0ZT7RyQqnxyEkjudTtAEh31+m/qVyWQinc7Hs8gPWb3XFTPagKg0CeVDFnU6UFdtsTJ2tUwoM3oj/B5C3EIs021CbRMY7eUjImcTut2x2Qc9gaGpsX+YCHU1m13uq39wtG3S2mK5XBpTOvxHdEfiUOiKJUgq6Tl+NCnRzrMgRIxKC4EQIqWjkDqiUlfDFUnIx0AkKp8cTKasyEIiJMfjUV5fX00FURP9CtUgdcsk7xUhfh0i7ZQUUwkiUl/rQsrHpw9CAIsT6Wg0Mpomcv1FBIPVfxlRqWr0po/PRWBs8uLqtqwjHHob9DeynUtdpne73c4Qk7LjDO3OXASXriKkk7NNskTeSI4mJZyLrxGgRmh5dhU7fK6L9uYpgu4erv+MjrMfB5GoRBijMJqzhU52pIq0foWKnpC8LymfEJfNe4RrgORHm3eVbaNp+gx9CBVVdb5Pw0Hf5AkZgqjolglM+hiauXoZsZouei5CGxpWgcsiX+REYDSRgTRPJpOzyVJP3E2PjQZ/TSNp9nVEb8KzyLnpv9tkS0SMlwrnWBWhfipFbQM0DoeD2R7Hy/jiStX4iEgUyH4sRKISISKn3DPlgVVW5lq/om3RyyZgSpGbdni+BdI09Q68rLbtrrk2QslMEVjxs986mEwmcjgcSsWydqoEMsK1oFeT7mWE3kWXLvvgEtteCr79MLGPRiNvQ86m+0XL0qTSyz52ypZ1pRR/EsmyI0bdbtd0La/7DOLs6vLDsT8HIS4iezxDROC4Xvbfi/6PcydSFPH4iEQlwgCykqapmWCqTH5V9Cs0HsRO/9FA3pw2A2UmYr5tiDQrTR6Px2a/dSYbjN101ZV9HqzGiwSuu93OCKGJKGVZZqIsdnWLC6ENDS8JXV3TRlsDF6qWBXN/tC8J/w/oNF4l8qNJU13okuui5xhBcxlRoUR5vV7Xig6CkOct4nEQiUrEGXi5qewh/14WHQAh+pVOp2MqSJoMRrcE+pMm0RBKk5uAsti6DptMMNwT+jHpMnGs8H1ExdbisIJfLBZnjrRE20TeSJUdrWg77dMElyTPZdU2mpDYETPSWOhCZrOZSbfWqZhrSlREwnUqVdoNNCUZWjwd8fiIRCXCCV5wKgGSJDkjLWXh+SL9CjnoR0z5aDSdzJo2EeQ6ou/p9/syn89Nf56ySZ/UmyaRSZIYcnE4HGS73RaulhENu/aly937/b68vr4aLYg9YV1Cn9IElyQquqEfwl5NSLRZGnog1ztHmo3ScaKgoUDQ27REfr/fGwIa8rlreZfsdrtokf9BEIlKRCH0S47oEE0DE1FRVMGlX0FA+shVPm3Y1TclKsPh0Fz/xWJh3DeJjJQRQUiGPg9W5do/ReR07+17TafkMrMzXVWD54i9nWvpU0JwyYmUCVtrKEjXkA4NiTTSV+nLly+S57lZFGhfEx2Vsf1d2Hedc+12u8YbhYhMmYcP/YHKzPFE2rG2xwco4vERiUpEMGxnx9FodNZpmV4aLiwWC6Nb+CjGbm2lbeqAyUdPDAh4J5NJqe4lyzKzkp7P52caE5GTf8p0OjVVI9Pp1DSLJA0UUr1iVza5mg42FZe2jUumDThPbOOphup0OrX8UETE9M1hMcAErc9B+7tQSo1w2AW7sSQVRXq7RH3Q0RSB8xwMBt7nPrSRZ8TnQiQqEbXgsqNGqa9Ji57MWZ0/esqnjUaCrGjrEhVNEDWIVhStyPEHWa1WJj03GAxkOp2+M92DDG02GxmNRibKMplMZLPZlLrNMsFxnLqUG9KqJ+57QRv27WXAg0Xv83g81mpayPO4XC5NtIPtFUWqMKFDVO0jI0TEqB6yt4sBXRl02suFphVsGjHl83EQiUpEK9AeB1pAia6FELTLfv3RoB1I64IBuS7Z6fV6zsmMMHyWZbJer99N/t1u1whm+f56vTapFw1bRIvGiPQfK3HKTiEddvREp4by/K05Hdon/azcW0RF5HJExSeoPRwOrYhbQ4kO56ZTb6RltbtuGQ6HQ5BPiu27YpvQaU+gpohE5eMgEpWI1qFJCxOSyGnF+OgI6YpcBnQbdaIIuMi6JiOEsaPRyEQsIBgibwQmz/OzNA+ExHalJURvTxpU7KCFgbxAPNgebrY27J5SWZa1UgHVJkJdUevCVwFTFnFwoUmaSkddmkQ6uddax2S7AmvTOd2UUR8LaGOcuFR5ecT1EYlKxEWhV055nkuWZbLdbh9ao9LGKttXaWF3CxY5pYl0/xWRt0iGi6xATAaDwVlVyOFwMB2y2QZVOHZkx6WBsUEURX9G6xq058d8Pj8zH9N6pqZptEtAR1QuAYSz9rPEhH/JlJONNqI46E+yLDP/Z5MQCBHPsW47wJ9UnbUByG+MrDw+IlGJuDg6nY4sFgsj9KSNexudl2+BNspoISH2ytI3uPODiBmr8dFo5HUBJnLR6/WMDsWOZmhfjvl8Lvv9XlarlTGSK0rHuAza7M7H8/n8rMwZ8abu/Mt2yszAboFLCmpdVvqcfx2dSl0QxWly/SF2PE+6G7TdFXoymchwOHQuVtokFpRCRzw+IlGJuCi0uFbkLTWBxgGzKe0h8fr6evfpoTYiAKxgESlqMoI7MGXHGqRuXl5epNfrGRdg3DxdhEV3o7W3x/91u13JsuzM46RpA0E8LFarlSErfE/31yEKw7mJnPfoCTUbbBPXSP1QASNyKv/muaga4WhynLxvXP88z2u9hzzLZV4u+/3etCiw90HlEqS6LhBxR3wMRKIScTEw6Nnpg9fXV5nNZqb7LpNSr9eTNE3l9fX1rvQKGkmStFY+WWQT7iMBusrmcDjIy8uL9Pt9mUwmkiSJibDoQV63K/AB/QCfYbKgnYJ9LCEGbaPRyNlHhvTPfr83pOX5+fks7QWBxWxQ+7DodMEln5NLpw3yPDelxLqyRmuKQrfTBKSbIBjj8bhW08RQl1ueGXqC2b+jEumexNURt0UkKhEXAykf10CKzb4ekPFZSZLk3cr/1uh2u5Kmqam2aTqIFpEdjNjsCZ5Qth012e/38vLyYoSsRGOIZEwmE+PZ4QOpHj6z2WxMiB4Ny2azMfsOMWgrK13mXIku2C0JuN7r9dpE3Wx/EK1vsFMNdsqhKkhLXQpcu1tHEbnm+pnDTLDqdkL0Jdxrn2iYZ89lMBgK3XsrRlYeH5GoRFwEeZ6f9Yux4SqhJGyMALQoAnAtkF5hwkJr03SbIv6VsM/tdTgcFna6JU2CO61OpRQRPyIZ9up2tVqZZnej0UiSJDFVPjRl9MFHqmwUGb2RGnBFGIi2uITHNrQdvetP++/6e5cEpbr3UIpLRJP0IZ2YeSdDtCuHw8E4T5cRryLbfdpsjEaj2qL74/Eo6/XaVKBFPDYiUYloHb6UTwgodUVwe+vwL6XAmKO1MalAVHyDuS/s7vNOsYFzbJqmJjpD8zqXfoDB3LdtiAK9ZcbjsTkemk7avWjKSJXIKXLkIzPdbtd7/0kdsa/JZCJPT0/me7oklrQW1TRav2BP8Dy7pDEuWY3E8TdxKK4DO8XGtRIRU7pOOwZd5r7dbgv1Jz6BsAtFOhW25aqKqgKOJ+LxEYlKxEXQJHWzXC5NJOP5+fnmehXbxKwpytw3mbh0KTImWVWOgyoOXGVZpWq7fLxuqvSWGQ6HkiSJKXfGqZbIBOWuIdEUV+RIl2KHTFJ276CqPYNIK2lSQx+mSxMVCFETouJ7jjRh04TEdpzVXc31NnWZO1E6FhCu95uoVEi6rEinIvI2BszncxmNRrUjq7ceNyLaQyQqEa2CAa7pAI/g9tZ6lUvkt5kEXQMpg3y/35fZbGaiI8Ph0AgNQ4CfBQZw+/3eRKoI6+vJsYp4E2KAQRgTIakYQvq6AR326/qcXc0Jif7oZn0hx9NE4+EjCZSOXxKhAtQicIxEKHRvHvYBgcD3xo4OIpx2gYgZpfGj0UjSNHW+l6HnAwkcDAbeKA3pofV6HUnHJ0ckKhGtgdVZlVbzRdsiV31N8ysX2l5VF3VNpqT39fXVNJjDcyQ0msJEtdls3pmJ0QuGbYucqltCJwM7JaJLiUXERFy2260hR1r8CnFxNadjguVcy1J/RD8u5clzSTGtSHWioku7IYYcI3oXrjGpU56ByWQig8HAea20lb7vmkNy8jz36sh8rQFcKNKpiLxFVWazmYzH41pRlaoOvxH3i3gnI1pDp9Np1XF2vV6bBnq3MIdDEHoJokIVkV1ySzWN1mBMp9NCvYYNGsz5TLyYcLi+o9FIptOp+f+yNES32y2MwJDS0c8CjfLwaaE6BKM5kfNyYMgP0ZKiCikRucjzcemqH5HiSpkiUmKLgDudjjw/P5fuy0cMKPXu9/ulz9l6vTYaFlsUX7QPwPPPcVOebTswcw10Cbc+d9sQ0e5LRDoyVv08PiJRiWgFGLu1PaljinULokJq4hI6BVxmEZSKnHLq9kSBX0lIxAOSstlsZDgcSpZl8vT05PxunueyXq+NdTnkqYiwoHEoIjOuVIxdvZMkiQyHQ3l9fTUTFxNynuem4aGrg6+enIg2XSLido1JjqgChEtXMrlIyXa7ffeeoT8qA34pPgfaKtGQ1WplGlxCtDTBoGWCff1c1zNJEnOeGpyzttwXOT2DPlO3mCb6eIhEJaIxGBguUU5MT5RboN/vX4SkQCRIkRFtIAKi98mEFXJt2Qbb5rtlAzd9gF5eXs5Km7WeAbBa9l0XJpCy9B/aFXvCTNNUut2uvLy8iMj7iVtX7WgSw/G6SpDrkhgmyUtiu93KZDIxmqIyUlJ0nGUpUqImrpQb26mShlosFqahJJFHXeatdVi+/4MYFzVE7Pf7xhzS9R5w7rqyy47O6P/jOxGPg0hUIlrBZrO5yEpmvV4bnYbWQVwaCBMv5eViV6mgJ7EnkbKeO3p1TCUOJCGknBnfC4SRiHdpaMhEoitAihxhQ1MxPl2Jq/eNL3rT6XTOVu46OuUqO2byDxUOa8Owa3iqLJfLWqQKIhPSH6iIjNTp3EwXbXRIIalDDXo8lX1mu92aHlf2c1M36gmBybIsEpc7RyQqEY3ACqkNAa0LrIjxQ9jtdlepAsLC+xLEyFd2i9GbHnSLNAOj0cjk4QG6EPQ1odEUex+c+2AwMPthMioiIaRuiiZcVrauCa2KJojUyHK59LYb0OW5+MBw/XXExZca45guSVT0sTT5fkg0pIiMkBoKPV8iXJCH4XAoaZp6y5d9+wyJmNo9wtp4Ly9dfh7RHiJRiWiEtgW0Lry8vBg9x3g8liRJLrpPJuRLkC8mCddEYFfTUAFUFp3YbrcmQsFkh6aFSg+X7b8dTXEBwsK1p/xYp240SQhJl/nSR4TnQycPSJFvBW9b8tNiwNa/iLgFmhChS5Wok6ojXdYEeNeEfM5HDLhW6JxExBuh8l0TUkvT6dSk74qAX1BIE8LX11fTYmO1WrWmW9OENOI+EYlKRG0wSVw6HcPKhxXfJaMdIu2XI2sw+blWz3Y1DemuojYEIm9ExT7m4/EoT09PMh6PzUrX7tfji6a4sN/vZb1eS5ZlZnJBxKlLYkPSTb6oCxNt6H11+bAUwX5mtOZFG75pS36R5hMY2iOM5Wx9TRvVRVRIlaFIUEtkBrKptT6hPyJvhDBJEpnNZkGVSJCbkPuuyYpIO9Vey+XSaGAiWblPRKISUQuXFNAWYb1em4Hw5eXlItUeTPpl/WzqwEdUXNGEskk/ZCW4Xq/NNcP7giaFZdEUG5Qda+EjpcZEWqjYYSLkRxMKnxMr26/i59Lk/hM5KdJUfPnyJZhEUEqrnW75f/any9ERn/b7fcmyzFuNE4LQFArPV1Hlj32Pq2K32xlCEUpWqkSUXl9fJU1T08m8ilmhCyy2biXajyhHJCoRtUFp67Xx8vIi8/lcJpPJRfQqba1yXWAytkE6hMmD9IR2d7UHZJf1uQ/aXTRJErP6Rhxbx6qe49UTHh2OsV3nPLQ3jE+fUsVhFiLQdJIqg2+VXdYvB92GJiQ+AsY5N3neiJSU9SciSlKkU2nDKO1wOMhisZA0TWU2m8nLy8u7ShxdjVP13Nl2WZ+qEJB6itGU+0UkKhGVwWB36UmiCIj3rlGR0SZ8Qlomac6F6AQTBxERkfNJvyqOx6MsFguZz+emymg4HMput5PNZlPqj1K2T5yJ7dJr9BgQMlJS2helSs8bVuDX8NfBXRedVFG/nDzPg7QZGtzzpkRFJEwjZEcwSHVRCt7WhI2oFrLiqsSyj78KFouFJEnSmKxwzjH1c7+IRCWiMq4hoC3DarUyjpVtEyZWV5fyUPHpU5ikiRZowSCTJJOKjlZgumWbofkIHJUThOQnk4mJghC5sfUCDOZVdUG20RsVRLvdzpyLFmzSh4g0ie0+qnUUVdJETQBp5Ge32xmiqO9lmqa1RLFtRPDYRlGDSTsdhSOwJl2IiNtqW6HJCpo2+k+1AVI2TcTIHFN0sr1fRKISUQkMNvdQ1nc4HEw31zaPh1x10662LvgiQDodQuREkwI8QDQwwmKFb4evmVTpzyJyinDoba9WK1mtVmcN52zhLSmrphEMdCUuoqtX9bZrr31eEL7JZPKOxLSZjqyi2QgVtLrQRqqRbtZaFFzUNZk+QKSnOIbZbNZq2wq6bkOotSlhE6BVo4dVE+BjRGVbrAS6L0SiElEJlyrbrYPlcilZlkmWZa2WKy6XS+l0OmYSbLO6CMGl7jBNOTREBSJRFi1gRW9PpKwwe72eSbssl0vZ7XYmTO4a2LWpG8Lb8Xgsm82mcoWND0R+fOdTZO5mu9RyLfm9hu2T0oTMhE5WEKg66cg6LriQU7sP0Gw2M8ejSQkVXkXnriNWbabV0Kz0ej0Zj8cm9bfdbmuNJ5qkNBH+amgdF9HamBK6D0SiEhGMe4qmiLwNqs/Pz6ZckQm5jQl1sVjIbDYz+o22wKA3nU6NBkVPbKHeKUD7fQBdhkv/njRNTZi8bGLg+xhsaZO04XD4LuURCrszchUw2RbdCz1hu0qNXWTGJi+uf4emFbS4uQ5RKYvGEHFiArVTNmhPaHvQxECuSSpFa110uwN9zCJiegUNh0NZr9fBz8UlSIqG1lnxDkDUI2G5DSJRiQjGPUVTNF5fX42nR5qmrQ1e9BlqK18vIqYkOM9zY5xGdGA2m52taEP61JRNKsfjUV5eXozNeZUJlImg1+vJdDo1USZIiy4/rlI1dCkBbIjA2EVmiqz3RcKb3OnqnarPiz0JMsnbIlc+t16vzyqKRMTcp9AqLh98lT8hBMSOQNjuv/bfIStJkhiSVYRLkxQb7IcoUMRtEIlKRBDuLZpiAwEhq582tCXL5VLm87kkSdLKoMjgz+rXVdbLYKiJBaWurmhC6CpvvV7LZrMxEZLBYCCLxSJoQiNig2/NaDQyKQciQKTIXL4p+vxvXaFVlczYJKEIvBt1Vt3ch+l0+k5Pgj5Jp+5cCwa20fT5x5OFYxHx907SpIM/kySRzWYT5LFEOgpBd57723Fcm6Ro0NwzRlRug0hUIoJwr9EUjc1mI6PRSJIkqRRKLtvmeDxuJapC5MM14eltPz8/G7dOyAB9gIhIYJdf5ZjyPDeN78bjsUyn0yBRI+JC9qWreETEEBddiQQR08SlLQJ5aWgyMxgMjKV8GVHh/tQlKtoYjsibyz3WlyIq80ipAqIk+jkL1ffUEQavVivjdpzn+bvKpVuTlOizcltEohJRClb19xpN0dDeCpPJxEyqdVfybVaQFNnni4jRw/B7CInIG1lgwtTRFibGqkSKCNloNJLhcGh0DS6UGbFp4uIzfEOLcw3fkzZRJ51TZ0Lb7XbyjW98Q0TengPcg22QjvQRp6ourxo61STib/ZYhrqmcVjZE1XkmbolSRF5KzuPuC0iUYkoRRs21dfC4XCQl5cXI4Kj5Ha73ZoSxFuB8LELpBp84XImel2Zg7Ayz/NKdvIYrVGFocW2Lo+LKmXaLsM3KigQxD4S6qRzmpYZ615K9nvH8fjuSRlJ0F48dt+htvoP1dV2oX0REdNDKs/zm5KUJEnOUnERt0EkKhGF0OWNjwQGtk6nI0mSmNX9brczQsQQtKmp8Jm9iUhp+3pKmLfb7Vn6Z7VayXw+D55UmKAgRIfDQZ6fn40Y2U4H1TV6AxAXyoofEVUm7SY6Bsgn26HrtQZpNN/x6MadWqhtV9yQwkEIbf/M5/Pa92u73Rod1G63eycSdv3J+Xc6HXl9fTURUc7pFiQFgh1xe0SiElGKRwvXa+R5bvoBIdibTqfGgdUn/AS+0tY6KCIqvV6v8Dr7evJUtV/3RTaI1KRpepYOIsXQ9Bl4FH2KD1W9VOqAiiq9LR98JILnYzweG9LBc6Odi8sIeN0SZUwIReSMbBRBHwv9kV5fX41D8S1IisgpqhOjKbdHJCoRpXhkoqLhcmBFf4Mhli1QpXdKG/ocn65AR0t8KOqk7PJS8e0fm3wfFovFmfeKSPOoUhP/lHtA1TRInZQJOiOdNvOh6H7jMLvb7Rq5tZaJcm3nWyI2pHsg5ZwLzxDvVmj7g1ulnPEOiiTlPhCJSoQXCC5vqeu4BIgedLtdGY1G0u/3zcAkct5ortvttpb28hEVNCM+MgSR8R1H6KqP1gBlZaN4r5AOoi/Mfr83pmtVyAtlyY+WPgRVVtVVIhFM7HiJAMix7xqjSWrjGHxAZ4KA2+WXoo8Hgk+kMk3TRq7OtlvzNTEYDIwIPOI+EIlKhBePJKKtg+PxeDZpa5Gh7iLbZrUTpbzaKI2VtA+kfXzHERpRIe0TSjy3261pIChyuj6IGyEtZdfnHvxTmqKuRkWTETQjdu8dvodHDSmHp6cn5z7KBLN1q27sbYicKr58Dr5Fz1ITUTHl+NdeJPV6PUmSRERin597QiQqEU4QVXhkXUFV2GRgOByaSbkpmJgQF2qzNJHThOBagRalfUTCVvyYl1XxwmFVjpZHREw1CqtOJlVIi0vz8+j6lLIIBWREp0Bs4zaR8947mCe6DOhwQfVF4Cgr7/V6zmcTw7Ym4D5iFFgVTaqGRKTVSGYV+HpHRdwWkahEePGRoykhYLBOkkQWi0UjwsKgbVcc4TcCcRE5OddqO/KiQTuEqJS5frqA0ZueSNkG29HHzz6Itmhy86hpH5HT9dUNEYsiI5CLIjJSBD7b7Xadz1zZ79GINDUpLEsxlX03ZLLnOF1/3oLcNi0tj7gMIlGJ8OJRxY9tAa1GlmWmh1Ddgd/nSqsb7dHtWJtuMdhnWWYIjP4JDY+jg6mCkJSN3QDRpfnh3Eaj0bs0wiOAiX86nYpIeGSkLsqISJnPCd9xlTdXQZOoCEQFPxUXEXGJVXkmbkVUdMo34n4QiUrEO1CO+igTySVBeeR0OpXpdCqr1aoWgStzpRU5Tz3N5/N35m5oRHTKiAm/aHC1vVNCUTVlY2t+SJ0dj0dnSF2Llm3tg/73rcE9WS6XVyPvZSShKNqhG1s2ISp25Y+OIukfETkjHfoeU1nHMfN3XWEHAdf6kNA+VG1Cn88l0MZ4+llJVCQqEe/w0UW0VXE8HuXp6UmyLJMkSaTf71cu/awyAA6HQ5Mu0eZuGkRdICFMbK7BvY4rbBvhd8S4+/3eXC9XCkV3MeYzGi4Bp/6/0FLXuuCaXnPiLEu7lP2e1GFVDAaDM1G5yBtpdt0TTTTt5oQibyRltVoFjyX4yLy8vNykXQdtKXT1Hwi5lpqQ2X+G/F+ozkw3SAz97qMjEpWIM3zUkuQ28Pr6KmmaGjO0KpMjA8lsNjtrKujCcDg0q0wfdKqh1+vJdDp12uiHeKe4wCTYZMIg8qMnKp6vKh2MtSZEh+Z9JbK+6ExdMtOkK3JdlEVUynxOigS1iLn1NbavKdeu3+/Ldrs1Y0KVa1lV47Jarcxx3YKosEDwkWJNzkTeE5JrgfeAaj+iUB8ZkahEnCFGU4pRdwWvB0CqZXa7nWy323eRjl6vVyn6oRvn2Qj1TrGBkDbLMjkcDoZc1fFPqTPphJAZl+mYr3+NyHmqqYjUuNC0iqUqyvZX1pwQXQ3VQboiyfYLIuWoNVBgPp+b+18VVf1c2L+rx9Gl0e/3pdPpXDW9Vxc8G3R1r+Lz86iIRCXCgEH7kUtJL42ixoJl0Hbg4/FYhsOhyeHTNFGkepUMx+Oa2Kp6pwDKpbfbrQyHQ1Myi+lbyGCO/8ulALkoulaazBCZIaIQQmb0tbvmZFDmhaKjaaystU8LxIQeUrzXtqV+GZoQNNc5lFUirVYrybJMhsPhVQkDvi33TFKorLMjZR+dpIhEohJhIUZTisHqxbeSDQUlvlTI0DQRVCWLrjA7WoM8z2U2m8lqtQomQHTnRdAr8qYhQNiL9oQyZHvyKesGfS2Ekhl7orfJjMj1iQrHpiNm+hjzPDfOsSLnuhFIQtMIQd0SZa6d9pQRebuGi8XCez8gUUQcr5FWocP3LdJNIRgMBpIkyZldwWdDJCoRZ7jnFcU9YL1ey2AwkPF4XHkS9pl3EWWZz+ci8jaYay1L6MoXrQoVE5ixLRYLSZJE0jSV3W4nq9WqcFXr681Dr6RutyuTycQQIdJapGtIS1QV8N4K2v7dh9lsdrUJAu2IiMh0OjUREx8hgTDqe9rtdmU2mzWe6EPM7nRVmp1e0tf2cDiYxqBFz8VyuZQsyyTLsqtU/9C+4FbND4vQ6XTONCifkaSIRKIS8RXyPI/RlAAw6CJ4Db1mZQMMFQ/Pz88mZE+U5XA4yGazKV1hUuI5nU5NWfN6vZb9fi/Pz89GGzOdTmW73cp6vXZuj9W6bzI5Ho+mI7XIqS2AXTp9ryvUOrjESrbb7ZrrZpf6ipxXGdGqwCYkPrSVrkKUS7WWrjSz9S6akGifHw2qisr2+fr6ashKE/+iMkC07pVQ24aCnxWRqEQYxGhKGBaLhWRZJuPxWEajkazX62DNxmg0ck425OT5f1Z3o9FIhsOhTCYT03dns9l4jcAooyaaop1oSeNQuQSRcZU+V1mJ6/SQiJzpWT4S6k4YEJIiUSuCVS1sbRoNaYNc4c6sI0ou48FQIrHf74P6EB0OB3l5eTH+Ra+vrxchvvj7NOk0HXF5RKISYVbPsSQ5HK+vr9Lr9QyBIBVUtDIjHaP1Hdvt1nSKdUVndKdnNCLk032VOK+vryYS48JisZButytpmkqSJDIajWS5XJrPu8qcq4C0xK31KbeAToPUjTq0CR9R0eSJvjqu5w+yybPa9Hh3u51MJpOzyh59vXTacbPZyNPTk8xmM5MGapv8EhmNuG9EohIhIlFEWweEqBGXpmkqh8PhbNLXQB+CeJbIAygaMHW6hSgL36fRoZ6Uyu4n7QEGg4FMJhOTDtpsNtLr9Sr1BLLxEbol27CjE7bhnq4gclXZVIk6tAmeTV2+zXFyXiJvxAVCDHGwPWvqkARX1VWe52fPvq296XQ6MhwOzTP8/Pws0+lU0jSt3STRBY7rnqMppH3xePms6OQfbUSJqAS0KU0mpog3MOl3Op13Vvvz+Vy22+1ZlKHT6ch4PDb9UDBDc3mruICXAhoCwuV1gCaG4yqqzCgCQuDdbnfXE0BVZFl2pq1wpUFcPiTXgta76PSSyDnJ2mw2ZyXK2tdHn5c+N4jY8/Nz8PGQFi3zsbF/RE7VZfb+uAfH41HW63VjXUmSJDIYDOTp6anydzU5vcb95tw/K1mJEZVPDHLjkaS0Axr0YbXvcmXVID1CJc14PJZ+v2+8VUjH+e7P8Xg0ZKBphQfHQSlpmqZnlTyhpaJUAX3UZ4r0B5P4tdd5Pr2LyCmaY6eWjsejzGYzOR6PznQcdvvaMVlHfyAdIuIU1OZ5/o4g8zuqdqpobjCrs0H0kgq2/X4vq9WqNlEIdWymfNklIhY5pfQYSy8ROePd/KyIROWTgkHjI6167wVY7WO2FSK01aQDokCEg67D2hTuUmDSWq1WZnDGx0GXwvoG+MFg8G6i+wjAl+TauhtK4W0BLscEKRmPx7LZbLzHVyRiZaL3lWhzL3XPHxY5lMWTXrKrVBDjhkD72ZCmsp8jXcE2Go0kyzJ5eXmp/LzxbtnXy5XS84mIOS8+j36Mz202m9Y0Nezzs1YBRaLyiXEp9h9xqgyCrIiE9wThnhwOB1ksFmYARLSrhbRtg9JkvX2s2HHGpHkb5bJMliLhq9RHg27+ds0oCjbplCdjMd/r9c6iGMPhsLRJoa/3DxEB3/dJHVEirydpOmTrCbpOxElrVnR6yAdE5vP5XCaTyVm5fMi+iBDZUQpNSkJFzzzvmAQOBgPzgxt1G+PsZ1ZpRKLyCcEDHwW0l8Xr66tMp9Mzw6Y0TY1wsSid0u12DZFkUO71eoa0JEkik8nErNzaApOihl3m3Ov1zGTJal9P5EQf6vZFukdoX5Jrng9mbzrySfrF/lxZk0JflILf+6zyeR5c/ZfYVtOKHJ1CqjKpbzYbGY/H0uv1SlNApI14RrfbrSFndt+jOveYdK12Ne52uzKdTmtFfTTQH31WfN4z/+SIJOU6eHl5MY6zRFfyPD/zRaE7LWDytwkDg+h6vZZer2e8UNC0NA0JE1Yum3CobAKQJ8iJrmQSOXdSdXWjtf/vHnGLfj8i7uaDLtLB51xERKcvBoOB890vIzqkeOxnUkcTmoBzqzqZr9drGY1GMplMCp1liUjWIUOhoJqv2+2aaOjhcJAsyxqTFXyRPmPaRyQSlU+LSFSuh/V6LZPJRETEVP4U+aIMh0Nn/xyNw+FgBLCsFJtO8qwuq66MERHb27L1BropoKshoIg4yYvdIPAW6cpbERXtbcN90Wk23mP8SWhCaTcnFCluMGhHI0hdcB9F3E0vy7Ybiib3lPfL5zDL+6HbVbQF3mMiKPv9XpbL5dk7hMtumqbOqjzdkkLknNjzTtfpt/SREInKJwP6g3tduX5EbLdbsyJCvKd9UdB96Dx9lUqGtjQh6FPaIAJEf0LLrLXPhv5hle8iND4Sw/G3SWh0Y8BrAjGq3i8TWL/fN0SFa8Bzxg/pmsPhYNIeLlBpo/tNcU23222h9X0bRIVoQVl3ZRc2m415f1zPm9Z8tQWaiXJNivpykb6FTNnPehFp13//rNEUkUhUPh18DqgRl0OZJohBTvui0D9ns9kEEcs2iCdeLtdGaJREN0HUg71tTMagrkXMtodHHRO2W04WNklwaUr2+730ej2v30lRekenXmjRoCddTRpd361KVIgk6ghbnueSZZn5jH2tXQJXsFqtjKjXFpkfj2/dsweDQeOqrU7n1A0a64CQ95NjTdNURE7PZNF1+8zExEYkKp8IrLBipc/1ETKY6xJlyIrum9NmueMjIsRMDfEiqTQ77WSbZrnIDKJKl4FbyOTRtuDW9ewcDgdTYqv/r0xQ6/q9XU7sqibTzQldfkBlzzbERHu/MB5xzSnDt8cnnf7QpoTcJ8gLESXX8W+3W0nTtHEDQp6n7XZbydqBtJNOa/b7/TNiFuFHJCqfCDGacjtUXXWi+5jNZiIipuyRULxuYNgWikpYHwVcEyJRLjDZaBKj/051lYaeTCltdW1Xf76O46nvnFwRFRtllT273c54j2hfFn3cPiIGQUYoqoHQVm8Dh1wEvpqY6HSUxmAwkDzPg0qNEZJ3u90zB9w8z43Vvt4+5ok0Ba2LJpVs9iKDVhMxclKOSFQ+CWDyn3lFfkvUCY8TGn99fTVVHUyUo9HIabff6/Uky7JaNuPoFOroBO4JZRENVuJlsHUEVGNtNpt33jj6T8q227qOh8PBSVQ6nc5ZhMAlstXQehefhb3ruujr6eqKDdlJ0/RMfKsn9JeXl9JrXlQibcP2EcKxlzLe6XR6tn8iZG2IUiH0TdNIn7ncuCrilfok+Mi25o+A4/FYOVpBB2TdwZZJCWEeVvd4Q1D9w8ShfVjKwETS7/cvYiZ3DejKiaaw0xCsxssmqP1+b0pV23jnXJESfa94JnRKoeh+2+eA5ofUik6R2VEiyIAL9OHBI4hnHl2GCzr60qSXjf2c4/MD8dF/n81mhuTXWbhRYdWEiHJ8MZoShkhUPgFYWTRt4hVRH2gKQvULDKy+SVGXJlN9wISzXC5NpQYeE+TuiyZZPdE9KlFhMrplRIiITVsrZiZTiABwRQiwyp/P57JcLp3mfUQf7MlSC2pdFVWTycSU34qcdz2ez+dOEoep4WAwcEZUaLanK4zagIuo6XYEpI7wBKpjMueKXEG4fHqqbrcrSZJ8yA7jl0QkKp8EUZtyW+jy1pC0Q2ifIER68/ncbJvvEIEhFTEajYxHy2q1cg7OOKE+KnTa4ZbY7/fvxK51AVlwVfnYZIj3HO8QIjq6/BeiQiqYHyZyRJ82RqORqQzTOh+iJ760SlG1EdHAKp2Z68L2++GdIE2EZUDIs+OKXJF2FRHjqaL1NnRXBzGaEo5IVD44ykpjI64DyEkoUen1epXuGYOerzvuYrEwwsPhcGgqY+zVZJuRgFvgnogKE3tbfV40EeA8O52OJEnyrqJJa0r4nC7RXq1WZ2mPfr/vFQkDooJUz4CnpyenjgZA2lzRxKJmiZfGer2WzWZjdC34GYWk6yB2GpAQ7kGv1zO+NCCKZ+vhcUekiGBEknJ7+FbFLpDTr3LfdKlm0TGsVitjOw5h0RMXK9xHxa28YGwgXB2NRo1El1pbQXmvNgljZW+bu1EOnOe5iVbMZjNDWm2wzeVy6b1+EA5SG9vt1mg1igguhmxaTwPQ3/T7/ZsI/Yla4gitS6eLwOf1cduaE9d79Mjv1i0RiconQCQq9wM90fgiKwzooRMu5Zmhws08f2syCGGhZBVjrEceTDudzl1ULBG5CIkWaAM7+097e2jNcP21z3U0GkmaptLpdGS73Z49E5rg2IBAFD1z+/3eVD2xXfRRRQRXa59cvYLQsNyqIpGKLhEx7wPRRt87qo9bRIzHTMRlEInKBwaD2j2sMCPe7sdwODTaBe6Prj7QJbChoFS5zkBPpQRh79FoZAbgRxRfX7q7cZXJCN8SoDvq2r2QRN6LWbFkp09Nmb9IlmWGDNg+Imzf94yQFnI1HtR9gyAcGr1ez+ii0L7g+7Pb7YzTra+k95bpxuFwaN45DYwDOX7uDREXInf6nYlpncshEpUPjFiSfF9gVbzZbOR4PJrKg+FwaCYmxK6hfUm0lfxkMjlzU60yYRP+HgwGkiTJQxOVNnu61IG+J67+OSInYqDTNUVEs+xesg8qvnzbKPOXYdLFMt9OM3EeABEt15x+QvqzlCv7SnpdLruXhl19YxMM/g3hsmFfx0hQLotIVD4ool3+/YGVpu54K3Kyy9cOm6PRKKiHCE3oID4ip0ET0qLJS9kkTlrhUbu1XjqiovcTmq7hmtLL6fn5udJ7GZLOQl9SVCWmBbn6mPnh2HVjQzvNxOQOtJsx58hxpGlqPktJry591x3ErzlOUe0DikiG73eRmFwXkah8UES7/PuDr1+KLpvEn0H3+LHdZwEW5ehNQL/fNz92B2ImIE1c7OZ8VKw8Ki5JVAaDgczn89J0je9+aU1HCOz9+BAi1CZyYR8/hIoUTJH1v91mYbfbnQls0cboz0LqRN4iLlRD8Tzaz28IXNVdZdeIkm3byC7i/hGJygdEtMu/T4SUKOtBW7vPuvQshOntQd6nV6EMkxU0mgkROZusRE4Rg1unUergkqtznT7BdyN0f9pLJxRtEhVIhY/8kuYoKqm2RbNo4BDwAqI1Iqe0FASZaO/hcAgiKL7eQb5zdFUuDYfDM9fmiMdCJCofEFGbcp+o6qWC+ywrca1noW19FR2JbXjFsQwGAyd5GQ6HjfuZXBudTkdGo5FpcFf0IyLv/l227dVqJdvt1qTqptNpoYGeBvu4FFGxfVYgHnTdZlv7/d753HD8RREf/l8/e/v93ohLj8ejzGYz82/Sz3Xs6qnGsT1YiogGfX5Wq5XsdjtDmDBQjCTlMRGJygdDtMu/X1TxUtHQ3hdazyIilVrN+47JlSLE6fbRwITs66NSNFEVkRq7pJxqqdFoJOPx2Bjovb6+Fh5fVedfrTcq2642LaNCR+Tk78Fk77sG+vO+8UOTGZ3a0R2oD4dDYX8pnXrxnZfWkfDZEJLBZ+z+QpGkPDYiUfmAiNqU+0YTAkBUZDqdmhJKrTPQXXCbpECqdLK9J2iiMJ/Pjfuohq7I4c+iH5GTvseOhG02m7MIy3w+LywlrjphhkZUcErFHfV4PMrz87N5Tkiz2Cka+9jsyIwNfqedbImYUM1mgxSmj3DoZ5fy5yrkxEY0Wvt4iETlAyISlftFnYiKbzv2at81GOsUB+RFm4fxp53+CDUru3e4Jni7K3IIIIS+fUCISFXQpM+OeNXtpVR2vJvNxghZSROKiLy8vFTav/691jPxAyjrLRLe8rkyYbauoNL/FxEBHn8kijDI8zySlDtHW03/er2eM1pgRwm0VkFEzsiHa2Wrjas6nY7J7duEhn/fM9osVe52u6XvVp7nslqtTEoIwqK7VteJqISeA7o0uxLHPkY7YgJR0KZuX758MZ/X/j+73c70FgrRwVXRhkRyEuFDJCofDJGo3DeOx6PTQKoKcNJ06Qhc6QlWv3aHWr2StdMhQBtx2RMJokyXlfutwWTcBlHhuoSKQemdRIQF0kJqpMibwzaLqxPVohLHRVQoQUbHQgm7yImUULK+2+3enTPPnk/bpIFIOyKiKSJR+SDI8zzIICzitsDLoslqnxLTEHKgvVZsUJXhQrfbldls9s5EjCoKfFrG47FMJhMzsWEQdmu02UUZslb1vGwXYozURN5SSXa6zhXhagIXScAjRxsFUpFTRsR0H5wy0bDIyTguRkoimiISlQ8ABpxHKyX9jKhaomyDlXaRA6kGk0XVcnVSO4PB4Gxf6Dt0NAdNBBMgv3etyK8FiEobkZ5+v2+uhx2BssW4IsVVKrbpnp1Ks/8+mUxq2cv7Uow8c660YRnG43EwSRFxE6WIiDqIROXBwaqraZlqxHXAxF2XqFDVEVJ+XhRNCQFeLWXQJnV4mFAmeyvSElotEwIICboNDe3tQUSziHiQCgqd7OtG3rQbrAbErWqUg0qc0GcpkpSINhGJyoODHh/3EG6PCEOTyh+644ZMXj7n2lDorsqhq2/tn0GUhW3g7wNpuWSaEsOxtrZFea9LTDybzeRwOAQtFqpGeOqmTXxRN4gPJBbxbNn9RecUQjZp+BfTPhFtIRKVBwYDf2gaIOI+gFdEVU0Rq9qQ+93tdo2Asy4QVdbdjnbCRcCJKRmTHp9pm7S0RVSIKBUtBooEsjaYvIts6l3fqQKiGUS3XKXrg8HAuPeG9AXT9v/6uDkXnRLjmkWSEtEWIlF5ULCqiymfxwNEZTabFTYdtIH+I2RVizalqW5ps9nIZDKpNLG6oMWadBFGkJskSesVRG2VJjOZF0Usq0QOfBO+D1Uib0SwiH7wTNkuuyJvJHS/38tkMjFRryICvN1uZTKZSJqmpru23XMnxOI+IqIOIlF5QDAg+NwvI+4bus0BTQepvthut94JrNfrBUdTqvYB8oEJKkmSYF1FGTTB7na7ZnJtu4KoDcIT0pixDlHp9XrBmp0iwtXpdExqDfJDaXQZsiyTfr8fVPEj8kY2KWeO7q8R10QkKg+ITqdTqWtrxH3heDxKr9czTQeZpKmaIZ2nicZoNApO+7TVBwhst9talSchsKvV2qogaiui0u12311zX6rDBhEjHX1gMu/3+0FkAs8SG71ez6R2RMRoZKpcI4hv6HNCSikSkohrIxKVBwN+KbHp4OMCTQOT6X6/N9EK+qLYUZbhcGg0I0Vghd1mhY1uvnfprtxtVRC1QVS4RxwD/6dBNCXPc8my7CzaoCuPuHeHw8GQztBj0Odhp3f2+70sl8ta51rFJXkymXjbNEREXBqRqDwYmIhYXd/KpyKiPoq8VHxRFr5XNgGzwm7TUwddzHA4vDhR0WhaQVR18rabEepohW6aZ/85HA7NRA4Z0Y0h7ePQDfpCjknkZLZWNb1TBCJ7ZeDaR0TcCpGoPCD0Kk9X/kTS8hiAnBRNVjrKkqap6cNCd16f8BTxZ9tpwc1mI2maGk3DtRFaQaR9Qvh9WXfkokhBnuelWjCiKnYDQB/KGj7q3jsiYs6xTnqnCIfDwZAmH6mLpcYR94BIVB4UOrwcSctjgVV2aNide/38/FwoPGUCvERakO2Px+PWRLV1UVRBpNMt6EM0dOWLz5iN6EmapkHHw/ZChLd8nrJerXeBnOhzgIStVqvWyWeI+WCSJCISdSkRt0UkKh8AkbQ8JkKICn111uv1O+EpGg7du6WKe2hVbLfbu0sB6Aoi3VCxjahSaCNCiIpvwteEpNfrmfdzNpuZ72tSws+lTRxJmfkI1ng8PiNOERG3QiQqHwyRtDwGQiMqk8nE6y5raziSJDG6iEsAUW2SJHfp31Pmd1IVvoobG7rkWETOSIkW1+oeP+h+7qHztOs5pGw8kpSIe0AkKh8YRaRF5/wjro8QojIYDKTf7wcJY7mXl3Qp1t4vnwE+omITEQgKomcdIcEL5h4IiQtEVGzcW+Qs4nMjEpVPgkha7gshRAUfkZDqjvF4LJ1O5+L3sU7/n0cEYlc0MNo3xY6Q2GXT90hIfHBV/ujKwoiIe0AkKp8QkbTcHtiQ+8CKPbTMuN/vXzTtA7QXyEcmKkSN0P5ASEifXrqp4rVwOBzeVSDFaErEvSESlU+OSFpuA9v0zQb3IZQMhNrrtwH6/4RWuTwiaGr49PR060O5KPb7vakkYwz4LKm9iMdBJCoRBpG0XA9Fpm8iYpxoQ0A64lpCafr/jMfjD9tvqmkTxnsGz4vW12RZJiLV+hZFRFwLkahEOBFJy2VR5GGBCVdomTHpiWveEyqAPiquSfyugU6nI5PJ5F3PJtJatu1/RMQ9IRKViFJE0nIZ+AS1uMu20V33UsBTZTKZtGrXfy8ILU1+BOg2DDYiMYl4BESiElEJkbS0Cx9RqbKaR+9yzXSF7v+z2Ww+zKQOPgJR6fV6kiSJecYiKYl4VESiElEbkbQ0gyuighNoFWGs1rtcc3JdrVaSZZlkWSbPz89X2++lQRXMowqF6XGEKDYSlIhHRyQqEa0gkpbqcBGVfr9fWW+irdCvqas4Ho+yWCwMWbl1D6C2AFF5lIgKDRip2KGSLBKUiI+CSFQiWkckLWFwddHt9Xq1NCe38jY5HA6yWq0kSZK7tdavCu7BPfuk6FJiSoujKDbioyISlYiLIpIWPw6HwzvPin6/X2slTxpmOBxezU8FbLdb0xvmIxjB3WNpMgaAdITWhDaSk4iPjkhUIq6GSFrOwWTIxIhNe52J/tZdbtfrtfR6PRmPx3I4HB66tPfWpcm6lxDERBsDRmIS8dkQiUrETeAjLfv9Xrbb7acgLdpL5Xg8Vnaj1RiNRsbi/VZYLBYynU4lTVN5eXm5u6hEKK5Z8YO+RBMT3UuIz+g/IyI+GyJRibg59ECMKPAzkJbj8XgmqB0MBo0qTe5BU6HFtY9YCYQY9RLPXKfTeRcp4d7bpMT+e0TEZ0YkKhF3hc9IWrrdrvT7fel2u7XN07C1v3X/nePxKMvlUtI0lTRNH85in6hWG9dQExLur0gkJRERVRGJSsTdwkdajsej7Ha7YIv5ewYRFc6tLhHbbDYyHo9lMpncvEx4v9/Ler02/YAe5T7xnGE3rwG5KItaadLp0pXYf4+IiChHJCoRDwE9uHe7XRmPx2e6jEetNIGotBEJWS6XkiTJXVTebDYb6fV6MhqNZL/f37W4tt/vmw7CEAu7J04VRFISEdEuOvk9JLYjIgKR57ms12vZ7XZGhEup5uFwkO12e1NBaVVMp1PTwfb19bXxhJ6mqfT7/bsRs06nU+l2u/L8/HwXGhrQ6XRkNBrJcDiUbrcbDdIiIu4YkahEPBxeXl7Oog/dbleGw+HZpHM4HGSz2dy9pgWicjweWxOfzudzORwOslwub05Wut2uZFkmInIX4lrIrTbaiwQlIuK+EYlKxEMhz3N5enry/r7X65k+J5CW/X4vm83mLtMPs9lMut2ubLfb1lxdh8OhTCYTyfP8LshBv9+XNE1lv9/fRFzrIrKRnEREPA6iRiXiYQDpKAKW7qvV6qw522AwMB1/1+v1zSMNNtq0nif1lSTJzauARN7EtdjsX1NcS/QEoTKIJCUi4rEQiUrEQ6HKpKtFnJAVfqiw2Ww2NyUtnU7nIhM35cr9fv/mRIXjQVx7OBwulpJzRU9EIjmJiHhkRKIS8TBoYm2ORb9u5saElue5bLfbq5fRjsdj6XQ6FxP/0lH5XrBaraTX60mSJK2LfWP0JCLi4yISlYiHQUjqJ2QbVAZp+/5blDsPh0PZ7XYXi+i4ujPfGtjst+FcG6MnERGfA/c1ikVEeNCWW6i9TUgLpmsIUWmud6lyZ+zT6zrRhmC328lkMrmrbsB5nstyuZQsy2r7vcToSUTE50IkKhEPg0tW7RyPR9lsNrLZbM5W6kmSyGQyab3cmaqcS5ZP41bb7/fvylumDuHknoxGI6/ja0RExMdEJCoRD4Em+pSqOB6Psl6vZb1en5U7p2naWt+hXq93NfIwHA7viqgQHQuJqGBnj5YodhKOiPh8iD4qEQ+BMv+Ua0CXO+tV/eFwMKXPIYRgNBrJZDJ5Z1x3CQwGA0mSRHa7Xasl0E3R7/clyzJZrVZesoJuSCQSk4iIz4wYUYm4e1xCn1IHuty51+ud/Wh9i8hbVAbyYgtmh8OhHA6Hq5wTJdjj8dhEiu4BXJfxePyOqCBujtGTiIgIkRhRiXgA5Hkum83mbiZZH2gu2O/3DYFhks3z3HR+7vf7hZGESyBNUxkMBrJcLu8mDdTtdmU6nRrH2n6/L5PJxPRuigQlIiJCJBKViAdBGw37bgHICz+UC9uTsH4Ny/4e+n/238fjsYjI3TQsFBGjP9nv96aKJxKUiIgIjZj6ibh73Evqpw5IASG8zbLMacKmJ+fQibrqGoPt3ku5cqfTMT8+AhcRERERiUrEXYN0yUcI/HW73VYN2OpO6vdwLUejkYnwiESCEhER4UckKhF3j0dM+bhgl9jeCrckKlEoGxERURWRqETcNa7pn3JpDAaDWx+CiNyGqEShbERERF1EohJx9/gIRAUx7T1gNBqZKiTfT1vodrsymUzO7O4jSYmIiKiCSFQi7hYfSZ9yLxUteZ4bEzURP2ng2ts/+vdlfx+NRjIcDkv3FREREVGESFQi7hofIZoi8qbNuIeJOvQYOp2OaZxY9bvgHohZRETE4yMSlYi7xUfRp/T7/bMJ/5HQhGhEkhIREdEGHnP0jPg0+AhEReszIiIiIiKqIRKViLvF4XD4EBP8vaR9IiIiIh4RkahE3CXyPP8w0ZRIUiIiIiLqIxKViLvER9GnxLRPRERERDNEohJxt/goRCVGVCIiIiLqIxKViLvER/BP0R4iERERERH1EIlKxN3hI+lTIiIiIiKaIRKViLvEoxOVTqcj/X4/pn0iIiIiGiISlYi7w0cQ0sZoSkREREQ7iEQl4u5g95V5RER9SkREREQ7iBb6EXeFj6BP6Xa70u/HVysiIiKiDcSISsTd4dGJSvROiYiIiGgPkahE3BU+gj4lpn0iIiIi2kOMT0fcFR5dn9Lr9aTX6936MCIiIiI+DGJEJeJu8BH0KTHtExEREdEuIlGJuCs8OlGJaZ+IiIiIdhFTPxF3g0fXp/T7fel2I/ePiIiIaBNxVI24G+R5/tD6lJj2iYiIiGgfMaIScRfI81zyPJfxeGwEtRCXR5n8h8NhtMyPiIiIaBmRqETcDTqdjoxGI/N3AImBwGgSo/9+S8S+PhERERGXQSQqEXeBokm+0+lIp9ORbrd7Fl2xv2MTGBehuVR0ZjgcSp7nkaxEREREtIxIVCIeCkVEACGrLWgti864CE1VDAaDSFIiIiIiLoBIVCI+HJpGZzSZcaWYbFO6SFIiIiIiLodO/ihKxYiIG6Ao1QRx6fV6Me0TERERcSFEohIRERERERFxt4g+KhERERERERF3i0hUIiIiIiIiIu4WkahERERERERE3C0iUYmIiIiIiIi4W0SiEhEREREREXG3iEQlIiIiIiIi4m4RiUpERERERETE3SISlYiIiIiIiIi7RSQqEREREREREXeL/x8Bn1HlRt7ohAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 830.82x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from aves.visualization.figures import figure_from_geodataframe\n",
    "\n",
    "fig, ax = figure_from_geodataframe(zona777, height=7)\n",
    "\n",
    "# contexto\n",
    "zona777.plot(ax=ax, facecolor='#efefef', edgecolor='white', zorder=0)\n",
    "#comunas_urbanas.plot(ax=ax, facecolor='none', edgecolor='#abacab', zorder=1)\n",
    "zona777.loc[[562], \"geometry\"].plot(ax=ax, facecolor='none', edgecolor='blue', zorder=2)\n",
    "merged_zoning[merged_zoning.index_right==562].plot(ax=ax, facecolor='none', edgecolor='red', zorder=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viviendas = merged_zoning.groupby('id').agg({'NVIV': 'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 830.82x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_data_geo = zona777.merge(viviendas, left_on='id', right_index=True)\n",
    "\n",
    "\n",
    "fig, ax = figure_from_geodataframe(zona777, height=7)\n",
    "\n",
    "# contexto\n",
    "#comunas_urbanas.plot(ax=ax, facecolor='none', edgecolor='#abacab', zorder=1)\n",
    "grouped_data_geo.plot(ax=ax,column='NVIV', cmap='viridis', legend=True)\n",
    "censo_zoning_13.plot(ax=ax, facecolor='none', edgecolor='pink', zorder=2, linewidth=0.2)\n",
    "zona777.plot(ax=ax, facecolor='none', edgecolor='black', zorder=1, linewidth=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aves.data.census import read_region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((-70.75950 -33.41237, -70.76014 -33.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((-70.73929 -33.38999, -70.73667 -33.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((-70.54357 -33.54802, -70.54293 -33.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((-70.54357 -33.54802, -70.54519 -33.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((-70.78731 -33.56483, -70.79797 -33.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>POLYGON ((-70.75398 -33.36184, -70.74889 -33.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>POLYGON ((-70.74889 -33.36107, -70.74621 -33.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>POLYGON ((-70.74621 -33.36028, -70.74965 -33.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>POLYGON ((-70.73742 -33.35790, -70.73619 -33.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>POLYGON ((-70.72881 -33.35795, -70.72093 -33.3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>804 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              geometry\n",
       "0    POLYGON ((-70.75950 -33.41237, -70.76014 -33.4...\n",
       "1    POLYGON ((-70.73929 -33.38999, -70.73667 -33.3...\n",
       "2    POLYGON ((-70.54357 -33.54802, -70.54293 -33.5...\n",
       "3    POLYGON ((-70.54357 -33.54802, -70.54519 -33.5...\n",
       "4    POLYGON ((-70.78731 -33.56483, -70.79797 -33.5...\n",
       "..                                                 ...\n",
       "799  POLYGON ((-70.75398 -33.36184, -70.74889 -33.3...\n",
       "800  POLYGON ((-70.74889 -33.36107, -70.74621 -33.3...\n",
       "801  POLYGON ((-70.74621 -33.36028, -70.74965 -33.3...\n",
       "802  POLYGON ((-70.73742 -33.35790, -70.73619 -33.3...\n",
       "803  POLYGON ((-70.72881 -33.35795, -70.72093 -33.3...\n",
       "\n",
       "[804 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zona777.set_crs(epsg=4674, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1, 17):\n",
    "    df_region = read_region(i)\n",
    "    spatial_index2 = df_region.sindex\n",
    "# Check for overlapping geometries using the spatial index\n",
    "    overlap = zona777.geometry.apply(lambda x: any(spatial_index2.intersection(x.bounds)))\n",
    "\n",
    "    #overlap = zona777.geometry.intersects(df_region.geometry)\n",
    "    if overlap.any():\n",
    "        print(i)\n",
    "#37.2"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aves",
   "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": "3.11.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}