Análisis de datos y planeación de estrategia.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análisis de datos y planeación de estrategia\n",
    "### Cómo Mejorar la Presencia de Mercado con Datos y Estrategia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carlos Ordoñez  \n",
    "Anibal Gamboa  \n",
    "Dennys Alburquerque  \n",
    "Edwin Veloz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_46916/2117394340.py:1: DtypeWarning: Columns (5,10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('../data/dataset_project.csv')\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('../data/dataset_project.csv')\n",
    "df = df.drop(columns=['Unnamed: 0','level_0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 708015 entries, 0 to 708014\n",
      "Data columns (total 20 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   index                 708015 non-null  int64  \n",
      " 1   CodigoVendedor        708015 non-null  int64  \n",
      " 2   CodigoSucursal        708015 non-null  object \n",
      " 3   CodigoCliente         708015 non-null  object \n",
      " 4   Longitud              708015 non-null  float64\n",
      " 5   Latitud               708015 non-null  float64\n",
      " 6   FechaCreacionCliente  708015 non-null  object \n",
      " 7   TipoDocumento         708015 non-null  object \n",
      " 8   MotivoNCR             24298 non-null   object \n",
      " 9   NumeroDocumento       708015 non-null  object \n",
      " 10  FechaDocumento        708015 non-null  object \n",
      " 11  FechaOrden            708015 non-null  object \n",
      " 12  FechaDespacho         708015 non-null  object \n",
      " 13  ClaseDeProducto       708015 non-null  object \n",
      " 14  CodigoProducto        708015 non-null  object \n",
      " 15  Cantidad              708015 non-null  float64\n",
      " 16  PrecioUnitario        708015 non-null  float64\n",
      " 17  ValorDescuento        708015 non-null  float64\n",
      " 18  TotalSinIVA           708015 non-null  float64\n",
      " 19  Sucursal              708015 non-null  object \n",
      "dtypes: float64(6), int64(2), object(12)\n",
      "memory usage: 108.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CodigoSucursal\n",
       "MA    338814\n",
       "SQ    188056\n",
       "SA     69755\n",
       "SM     62353\n",
       "MC     49037\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CodigoSucursal'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "sucursalesPrincipales = ['MA', 'SQ', 'SA', 'SM', 'MC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered = df[df['CodigoSucursal'].isin(sucursalesPrincipales)].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 708015 entries, 0 to 708014\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   level_0               708015 non-null  int64  \n",
      " 1   index                 708015 non-null  int64  \n",
      " 2   CodigoVendedor        708015 non-null  int64  \n",
      " 3   CodigoSucursal        708015 non-null  object \n",
      " 4   CodigoCliente         708015 non-null  object \n",
      " 5   Longitud              708015 non-null  float64\n",
      " 6   Latitud               708015 non-null  float64\n",
      " 7   FechaCreacionCliente  708015 non-null  object \n",
      " 8   TipoDocumento         708015 non-null  object \n",
      " 9   MotivoNCR             24298 non-null   object \n",
      " 10  NumeroDocumento       708015 non-null  object \n",
      " 11  FechaDocumento        708015 non-null  object \n",
      " 12  FechaOrden            708015 non-null  object \n",
      " 13  FechaDespacho         708015 non-null  object \n",
      " 14  ClaseDeProducto       708015 non-null  object \n",
      " 15  CodigoProducto        708015 non-null  object \n",
      " 16  Cantidad              708015 non-null  float64\n",
      " 17  PrecioUnitario        708015 non-null  float64\n",
      " 18  ValorDescuento        708015 non-null  float64\n",
      " 19  TotalSinIVA           708015 non-null  float64\n",
      " 20  Sucursal              708015 non-null  object \n",
      "dtypes: float64(6), int64(3), object(12)\n",
      "memory usage: 113.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_filtered.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered['FechaCreacionCliente'] = pd.to_datetime(df_filtered['FechaCreacionCliente'])\n",
    "df_filtered['FechaDocumento'] = pd.to_datetime(df_filtered['FechaDocumento'])\n",
    "df_filtered['FechaOrden'] = pd.to_datetime(df_filtered['FechaOrden'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "clientes_fechas = df_filtered.groupby(['CodigoCliente', 'CodigoSucursal', 'CodigoVendedor'])['FechaCreacionCliente'].min().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {\n",
    "    \"MA\" : \"Guayaquil\",\n",
    "    \"SQ\" : \"Quito\",\n",
    "    \"SM\" : \"Manta\",\n",
    "    \"SA\" : \"Ambato\",\n",
    "    \"MC\" : \"Machala\"\n",
    "}\n",
    "df_filtered['EtiquetaSucursal'] = df_filtered['CodigoSucursal'].map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "vendedores_sucursal = df_filtered.groupby(['EtiquetaSucursal'])['CodigoVendedor'].nunique().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "clientes_fechas['year'] = clientes_fechas['FechaCreacionCliente'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "totales_mes = df_filtered.groupby([pd.Grouper(key=\"FechaDocumento\", freq=\"M\"), \"EtiquetaSucursal\"])['TotalSinIVA'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eeveloza/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/eeveloza/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ventas por Sucursal en el año 2024 en Millones USD')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.lineplot(data=totales_mes, x=\"FechaDocumento\", y=\"TotalSinIVA\", hue=\"EtiquetaSucursal\")\n",
    "plt.title(\"Ventas por Sucursal en el año 2024 en Millones USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_filtered.to_csv('ventas.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered['nuevo'] = (2023 - df_filtered['FechaCreacionCliente'].dt.year) < 5\n",
    "df_filtered['aniosFidelizacion'] = 2023 - df_filtered['FechaCreacionCliente'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>aniosFidelizacion</th>\n",
       "      <th>TotalSinIVA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1561284.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2295509.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3418416.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1852769.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1580860.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2375553.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1982852.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2870930.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2881388.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1152451.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2239982.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1825302.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2334359.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>1461801.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2196300.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1725043.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>886444.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>706235.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>1366679.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>17949031.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>31</td>\n",
       "      <td>849358.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    aniosFidelizacion  TotalSinIVA\n",
       "0                   0   1561284.49\n",
       "1                   1   2295509.00\n",
       "2                   2   3418416.65\n",
       "3                   3   1852769.60\n",
       "4                   4   1580860.18\n",
       "5                   5   2375553.35\n",
       "6                   6   1982852.38\n",
       "7                   7   2870930.27\n",
       "8                   8   2881388.79\n",
       "9                   9   1152451.59\n",
       "10                 10   2239982.82\n",
       "11                 11   1825302.35\n",
       "12                 12   2334359.31\n",
       "13                 13   1461801.20\n",
       "14                 14   2196300.75\n",
       "15                 15   1725043.87\n",
       "16                 16    886444.13\n",
       "17                 17    706235.66\n",
       "18                 18   1366679.01\n",
       "19                 19  17949031.67\n",
       "20                 31    849358.13"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_por_años_cliente = df_filtered.groupby('aniosFidelizacion')['TotalSinIVA'].sum().reset_index()\n",
    "total_por_años_cliente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='aniosFidelizacion', ylabel='TotalSinIVA'>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data=total_por_años_cliente, x=\"aniosFidelizacion\", y=\"TotalSinIVA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aniosFidelizacion</th>\n",
       "      <th>TotalSinIVA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1561284.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2295509.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3418416.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1852769.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1580860.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2375553.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1982852.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2870930.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2881388.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1152451.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2239982.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1825302.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2334359.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>1461801.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2196300.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1725043.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>886444.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>706235.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    aniosFidelizacion  TotalSinIVA\n",
       "0                   0   1561284.49\n",
       "1                   1   2295509.00\n",
       "2                   2   3418416.65\n",
       "3                   3   1852769.60\n",
       "4                   4   1580860.18\n",
       "5                   5   2375553.35\n",
       "6                   6   1982852.38\n",
       "7                   7   2870930.27\n",
       "8                   8   2881388.79\n",
       "9                   9   1152451.59\n",
       "10                 10   2239982.82\n",
       "11                 11   1825302.35\n",
       "12                 12   2334359.31\n",
       "13                 13   1461801.20\n",
       "14                 14   2196300.75\n",
       "15                 15   1725043.87\n",
       "16                 16    886444.13\n",
       "17                 17    706235.66"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_por_15_años_cliente = df_filtered.groupby('aniosFidelizacion')['TotalSinIVA'].sum().reset_index().head(18)\n",
    "total_por_15_años_cliente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aniosFidelizacion</th>\n",
       "      <th>CodigoCliente</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    aniosFidelizacion  CodigoCliente\n",
       "0                   0            349\n",
       "1                   1            428\n",
       "2                   2            521\n",
       "3                   3            364\n",
       "4                   4            264\n",
       "5                   5            279\n",
       "6                   6            269\n",
       "7                   7            241\n",
       "8                   8            218\n",
       "9                   9            200\n",
       "10                 10            280\n",
       "11                 11            234\n",
       "12                 12            221\n",
       "13                 13            165\n",
       "14                 14            147\n",
       "15                 15            118\n",
       "16                 16             83\n",
       "17                 17             69\n",
       "18                 18             67\n",
       "19                 19            662\n",
       "20                 31              2"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cantidad_clientes_por_Años = df_filtered.groupby('aniosFidelizacion')['CodigoCliente'].nunique().reset_index()\n",
    "cantidad_clientes_por_Años"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>NumeroDocumento</th>\n",
       "      <th>TotalSinIVA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DAB0026481</td>\n",
       "      <td>56.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DAB0026592</td>\n",
       "      <td>9.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DAB0026593</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAB0026713</td>\n",
       "      <td>96.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAB0026716</td>\n",
       "      <td>19.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106780</th>\n",
       "      <td>DUC0003500</td>\n",
       "      <td>213.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106781</th>\n",
       "      <td>DUC0003501</td>\n",
       "      <td>121.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106782</th>\n",
       "      <td>DUC0003502</td>\n",
       "      <td>91.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106783</th>\n",
       "      <td>DUC0003516</td>\n",
       "      <td>197.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106784</th>\n",
       "      <td>DUC0003517</td>\n",
       "      <td>189.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106785 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NumeroDocumento  TotalSinIVA\n",
       "0           DAB0026481        56.20\n",
       "1           DAB0026592         9.69\n",
       "2           DAB0026593         1.97\n",
       "3           DAB0026713        96.21\n",
       "4           DAB0026716        19.70\n",
       "...                ...          ...\n",
       "106780      DUC0003500       213.43\n",
       "106781      DUC0003501       121.96\n",
       "106782      DUC0003502        91.47\n",
       "106783      DUC0003516       197.56\n",
       "106784      DUC0003517       189.00\n",
       "\n",
       "[106785 rows x 2 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ventas_factura = df_filtered.groupby('NumeroDocumento')['TotalSinIVA'].sum().reset_index()\n",
    "ventas_factura"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eeveloza/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7a10752537d0>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=cantidad_clientes_por_Años, x=\"CodigoCliente\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='aniosFidelizacion', ylabel='TotalSinIVA'>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data=total_por_15_años_cliente, x=\"aniosFidelizacion\", y=\"TotalSinIVA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "clientes_nuevos_por_vendedores = df_filtered[df_filtered['nuevo']].groupby('CodigoVendedor')['CodigoCliente'].nunique().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.77272727272727"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes_nuevos_por_vendedores['CodigoCliente'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 vendedores no han traido clientes nuevos en el 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "355.84634545092047"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "promedio_factura_cliente_nuevo = df_filtered[df_filtered['nuevo']].groupby('NumeroDocumento')['TotalSinIVA'].sum().reset_index()['TotalSinIVA'].mean()\n",
    "promedio_factura_cliente_nuevo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10708839.92"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "promedio_factura_cliente_nuevo = df_filtered[df_filtered['nuevo']].groupby('NumeroDocumento')['TotalSinIVA'].sum().reset_index()['TotalSinIVA'].sum()\n",
    "promedio_factura_cliente_nuevo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='nuevo', ylabel='TotalSinIVA'>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(df_filtered, x=\"nuevo\", y=\"TotalSinIVA\", estimator=\"sum\", errorbar=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_filtered['nuevo'].value_counts().plot.pie( autopct='%1.1f%%', figsize=(8,8))\n",
    "plt.title('Porcentaje de Ingresos Por Clientes Nuevos')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>CodigoCliente</th>\n",
       "      <th>nuevo</th>\n",
       "      <th>NumeroDocumento</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000016</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000087</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000171</td>\n",
       "      <td>False</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000176</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000178</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5176</th>\n",
       "      <td>5000898</td>\n",
       "      <td>True</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5177</th>\n",
       "      <td>5009996</td>\n",
       "      <td>False</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5178</th>\n",
       "      <td>5009999</td>\n",
       "      <td>False</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5179</th>\n",
       "      <td>CU00503</td>\n",
       "      <td>True</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5180</th>\n",
       "      <td>CU02496</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5181 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CodigoCliente  nuevo  NumeroDocumento\n",
       "0          1000016  False               10\n",
       "1          1000087  False                5\n",
       "2          1000171  False               21\n",
       "3          1000176  False                2\n",
       "4          1000178  False                1\n",
       "...            ...    ...              ...\n",
       "5176       5000898   True                9\n",
       "5177       5009996  False               21\n",
       "5178       5009999  False               16\n",
       "5179       CU00503   True               47\n",
       "5180       CU02496   True                2\n",
       "\n",
       "[5181 rows x 3 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.groupby(['CodigoCliente', 'nuevo'])['NumeroDocumento'].nunique().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_clientes_viejos = df_filtered[(~df_filtered['nuevo']) & (df_filtered['TotalSinIVA']>0)].groupby(['CodigoCliente'])['TotalSinIVA'].sum().reset_index().sort_values(by='TotalSinIVA', ascending=False)\n",
    "top_30 = top_clientes_viejos.head(30)\n",
    "resto = top_clientes_viejos.loc[top_clientes_viejos.index.difference(top_30.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_clientes_nuevos = df_filtered[(df_filtered['nuevo']) & (df_filtered['TotalSinIVA']>0)].groupby(['CodigoCliente'])['TotalSinIVA'].sum().reset_index().sort_values(by='TotalSinIVA', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5949.782663526948"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_clientes_nuevos['TotalSinIVA'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9758.061257000623"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resto['TotalSinIVA'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15991772.42"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_30['TotalSinIVA'].sum() #total top 10 clientes antigues en 20023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "533059.0806666666"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_30['TotalSinIVA'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['1% clientes', '99% clientes']\n",
    "values = [top_30['TotalSinIVA'].sum(),  resto['TotalSinIVA'].sum()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'labels': labels,\n",
    "        'values': values}\n",
    "\n",
    "totales_pc= pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.pie(totales_pc['values'], labels=totales_pc['labels'], autopct='%1.1f%%')\n",
    "plt.title('Total Ganancias por clientes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "def asignar_categoria(codigo):\n",
    "    if str(codigo).startswith('1'):\n",
    "        return 'Arquitectonica'\n",
    "    elif str(codigo).startswith('2'):\n",
    "        return 'Automotriz'\n",
    "    elif str(codigo).startswith('3'):\n",
    "        return 'Madera'\n",
    "    elif str(codigo).startswith('8'):\n",
    "        return 'Industrial'\n",
    "    else:\n",
    "        return 'Otros'\n",
    "\n",
    "df_filtered['categoria'] = df_filtered['ClaseDeProducto'].apply(asignar_categoria)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CodigoVendedor</th>\n",
       "      <th>CodigoCliente</th>\n",
       "      <th>TotalSinIVA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>1000016</td>\n",
       "      <td>206.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>1000087</td>\n",
       "      <td>226.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>1000176</td>\n",
       "      <td>25.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1000178</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1000197</td>\n",
       "      <td>30.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2404</th>\n",
       "      <td>999</td>\n",
       "      <td>1007846</td>\n",
       "      <td>122.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>999</td>\n",
       "      <td>5000374</td>\n",
       "      <td>22.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2406</th>\n",
       "      <td>999</td>\n",
       "      <td>1006152</td>\n",
       "      <td>432.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2407</th>\n",
       "      <td>999</td>\n",
       "      <td>1006325</td>\n",
       "      <td>6.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2408</th>\n",
       "      <td>999</td>\n",
       "      <td>1006914</td>\n",
       "      <td>17.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2409 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CodigoVendedor CodigoCliente  TotalSinIVA\n",
       "0                  7       1000016       206.74\n",
       "1                  7       1000087       226.88\n",
       "2                  7       1000176        25.67\n",
       "3                  7       1000178        10.00\n",
       "4                  7       1000197        30.47\n",
       "...              ...           ...          ...\n",
       "2404             999       1007846       122.14\n",
       "2405             999       5000374        22.80\n",
       "2406             999       1006152       432.98\n",
       "2407             999       1006325         6.68\n",
       "2408             999       1006914        17.29\n",
       "\n",
       "[2409 rows x 3 columns]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totales_vendedor_cliente = df_filtered[df_filtered['CodigoSucursal']=='MA'].groupby(['CodigoVendedor', 'CodigoCliente'])['TotalSinIVA'].sum().reset_index()\n",
    "totales_vendedor_cliente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "cantones = pd.read_csv('../data/cantones.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 11 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Unnamed: 0             5 non-null      int64  \n",
      " 1   Sucursal               5 non-null      object \n",
      " 2   region                 5 non-null      object \n",
      " 3   superficie             5 non-null      float64\n",
      " 4   provincia              5 non-null      object \n",
      " 5   # Clientes             5 non-null      int64  \n",
      " 6   Universo Construccion  5 non-null      int64  \n",
      " 7   % Captados             5 non-null      float64\n",
      " 8   geometry               5 non-null      object \n",
      " 9   Clientes               5 non-null      int64  \n",
      " 10  % Share                5 non-null      float64\n",
      "dtypes: float64(3), int64(4), object(4)\n",
      "memory usage: 572.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "cantones.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Sucursal</th>\n",
       "      <th>region</th>\n",
       "      <th>superficie</th>\n",
       "      <th>provincia</th>\n",
       "      <th># Clientes</th>\n",
       "      <th>Universo Construccion</th>\n",
       "      <th>% Captados</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Clientes</th>\n",
       "      <th>% Share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>MACHALA</td>\n",
       "      <td>COSTA</td>\n",
       "      <td>372.403254</td>\n",
       "      <td>EL ORO</td>\n",
       "      <td>222</td>\n",
       "      <td>296</td>\n",
       "      <td>75.00</td>\n",
       "      <td>POLYGON Z ((-79.92875756702288 -3.171049806114...</td>\n",
       "      <td>65</td>\n",
       "      <td>21.959459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>GUAYAQUIL</td>\n",
       "      <td>COSTA</td>\n",
       "      <td>4827.076279</td>\n",
       "      <td>GUAYAS</td>\n",
       "      <td>1354</td>\n",
       "      <td>1779</td>\n",
       "      <td>76.11</td>\n",
       "      <td>MULTIPOLYGON Z (((-79.78230504658983 -2.947847...</td>\n",
       "      <td>365</td>\n",
       "      <td>20.517144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>MANTA</td>\n",
       "      <td>COSTA</td>\n",
       "      <td>296.084576</td>\n",
       "      <td>MANABI</td>\n",
       "      <td>239</td>\n",
       "      <td>161</td>\n",
       "      <td>100.00</td>\n",
       "      <td>MULTIPOLYGON Z (((-81.07347253555112 -1.259911...</td>\n",
       "      <td>67</td>\n",
       "      <td>41.614907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>QUITO</td>\n",
       "      <td>SIERRA</td>\n",
       "      <td>4200.905341</td>\n",
       "      <td>PICHINCHA</td>\n",
       "      <td>671</td>\n",
       "      <td>1527</td>\n",
       "      <td>43.94</td>\n",
       "      <td>POLYGON Z ((-78.49460680364508 0.2739011888077...</td>\n",
       "      <td>264</td>\n",
       "      <td>17.288802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>AMBATO</td>\n",
       "      <td>SIERRA</td>\n",
       "      <td>1022.317480</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>397</td>\n",
       "      <td>371</td>\n",
       "      <td>100.00</td>\n",
       "      <td>POLYGON Z ((-78.61245227442937 -1.110025187337...</td>\n",
       "      <td>72</td>\n",
       "      <td>19.407008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   Sucursal  region   superficie   provincia  # Clientes  \\\n",
       "0           0    MACHALA   COSTA   372.403254      EL ORO         222   \n",
       "1           1  GUAYAQUIL   COSTA  4827.076279      GUAYAS        1354   \n",
       "2           2      MANTA   COSTA   296.084576      MANABI         239   \n",
       "3           3      QUITO  SIERRA  4200.905341   PICHINCHA         671   \n",
       "4           4     AMBATO  SIERRA  1022.317480  TUNGURAHUA         397   \n",
       "\n",
       "   Universo Construccion  % Captados  \\\n",
       "0                    296       75.00   \n",
       "1                   1779       76.11   \n",
       "2                    161      100.00   \n",
       "3                   1527       43.94   \n",
       "4                    371      100.00   \n",
       "\n",
       "                                            geometry  Clientes    % Share  \n",
       "0  POLYGON Z ((-79.92875756702288 -3.171049806114...        65  21.959459  \n",
       "1  MULTIPOLYGON Z (((-79.78230504658983 -2.947847...       365  20.517144  \n",
       "2  MULTIPOLYGON Z (((-81.07347253555112 -1.259911...        67  41.614907  \n",
       "3  POLYGON Z ((-78.49460680364508 0.2739011888077...       264  17.288802  \n",
       "4  POLYGON Z ((-78.61245227442937 -1.110025187337...        72  19.407008  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cantones.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "349"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered[df_filtered['FechaCreacionCliente'].dt.year == 2023]['CodigoCliente'].nunique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "clientes_nuevos = df_filtered[df_filtered['FechaCreacionCliente'].dt.year == 2023]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EtiquetaSucursal</th>\n",
       "      <th>CodigoCliente</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ambato</td>\n",
       "      <td>42</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>156</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Machala</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manta</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quito</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EtiquetaSucursal  CodigoCliente\n",
       "0           Ambato             42\n",
       "1        Guayaquil            156\n",
       "2          Machala             23\n",
       "3            Manta             25\n",
       "4            Quito            103"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clientes_nuevos_sucursal = clientes_nuevos.groupby('EtiquetaSucursal')['CodigoCliente'].nunique().reset_index()\n",
    "clientes_nuevos_sucursal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfw = vendedores_sucursal.merge(clientes_nuevos_sucursal, on='EtiquetaSucursal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfw['clienteNuevoPorVendedor'] = dfw['CodigoCliente'] / dfw['CodigoVendedor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>CodigoVendedor</th>\n",
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       "  <tbody>\n",
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       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>24</td>\n",
       "      <td>156</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Machala</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>3.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manta</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>3.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quito</td>\n",
       "      <td>16</td>\n",
       "      <td>103</td>\n",
       "      <td>6.437500</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "  EtiquetaSucursal  CodigoVendedor  CodigoCliente  clienteNuevoPorVendedor\n",
       "0           Ambato               7             42                 6.000000\n",
       "1        Guayaquil              24            156                 6.500000\n",
       "2          Machala               7             23                 3.285714\n",
       "3            Manta               8             25                 3.125000\n",
       "4            Quito              16            103                 6.437500"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EtiquetaSucursal</th>\n",
       "      <th>TotalSinIVA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ambato</td>\n",
       "      <td>39.897514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>63.507347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Machala</td>\n",
       "      <td>61.770419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manta</td>\n",
       "      <td>40.047938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quito</td>\n",
       "      <td>47.557067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EtiquetaSucursal  TotalSinIVA\n",
       "0           Ambato    39.897514\n",
       "1        Guayaquil    63.507347\n",
       "2          Machala    61.770419\n",
       "3            Manta    40.047938\n",
       "4            Quito    47.557067"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket_promedio = df_filtered[df_filtered['nuevo']].groupby('EtiquetaSucursal')['TotalSinIVA'].mean().reset_index()\n",
    "ticket_promedio "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfw = dfw.merge(ticket_promedio, on='EtiquetaSucursal')\n",
    "dfw = dfw.rename(columns={'TotalSinIVA':'promedio_ticket'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EtiquetaSucursal</th>\n",
       "      <th>TotalSinIVA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ambato</td>\n",
       "      <td>68.822394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>97.601810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Machala</td>\n",
       "      <td>115.587786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manta</td>\n",
       "      <td>109.374101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quito</td>\n",
       "      <td>121.904483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EtiquetaSucursal  TotalSinIVA\n",
       "0           Ambato    68.822394\n",
       "1        Guayaquil    97.601810\n",
       "2          Machala   115.587786\n",
       "3            Manta   109.374101\n",
       "4            Quito   121.904483"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_visitas = df_filtered[df_filtered['nuevo']].groupby(['EtiquetaSucursal', 'CodigoCliente'])['TotalSinIVA'].count().reset_index()\n",
    "promedio_visitas = count_visitas.groupby('EtiquetaSucursal')['TotalSinIVA'].mean().reset_index()\n",
    "promedio_visitas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfw2 = dfw.merge(promedio_visitas, on='EtiquetaSucursal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfw2 = dfw2.rename(columns={'TotalSinIVA':'count_visitas', 'TotalSinIVA_y':'ticket_promedio'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EtiquetaSucursal</th>\n",
       "      <th>CodigoVendedor</th>\n",
       "      <th>CodigoCliente</th>\n",
       "      <th>clienteNuevoPorVendedor</th>\n",
       "      <th>promedio_ticket</th>\n",
       "      <th>count_visitas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ambato</td>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>39.897514</td>\n",
       "      <td>68.822394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>24</td>\n",
       "      <td>156</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>63.507347</td>\n",
       "      <td>97.601810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Machala</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>3.285714</td>\n",
       "      <td>61.770419</td>\n",
       "      <td>115.587786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manta</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>40.047938</td>\n",
       "      <td>109.374101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quito</td>\n",
       "      <td>16</td>\n",
       "      <td>103</td>\n",
       "      <td>6.437500</td>\n",
       "      <td>47.557067</td>\n",
       "      <td>121.904483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EtiquetaSucursal  CodigoVendedor  CodigoCliente  clienteNuevoPorVendedor  \\\n",
       "0           Ambato               7             42                 6.000000   \n",
       "1        Guayaquil              24            156                 6.500000   \n",
       "2          Machala               7             23                 3.285714   \n",
       "3            Manta               8             25                 3.125000   \n",
       "4            Quito              16            103                 6.437500   \n",
       "\n",
       "   promedio_ticket  count_visitas  \n",
       "0        39.897514      68.822394  \n",
       "1        63.507347      97.601810  \n",
       "2        61.770419     115.587786  \n",
       "3        40.047938     109.374101  \n",
       "4        47.557067     121.904483  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfw2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = dfw2[['EtiquetaSucursal', 'CodigoVendedor', 'CodigoCliente',\n",
    "       'clienteNuevoPorVendedor', 'promedio_ticket',\n",
    "       'count_visitas']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EtiquetaSucursal</th>\n",
       "      <th>CodigoVendedor</th>\n",
       "      <th>CodigoCliente</th>\n",
       "      <th>clienteNuevoPorVendedor</th>\n",
       "      <th>promedio_ticket</th>\n",
       "      <th>count_visitas</th>\n",
       "      <th>TotalAño</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ambato</td>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>39.897514</td>\n",
       "      <td>68.822394</td>\n",
       "      <td>2745.842394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>24</td>\n",
       "      <td>156</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>63.507347</td>\n",
       "      <td>97.601810</td>\n",
       "      <td>6198.432036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Machala</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>3.285714</td>\n",
       "      <td>61.770419</td>\n",
       "      <td>115.587786</td>\n",
       "      <td>7139.906031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manta</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>40.047938</td>\n",
       "      <td>109.374101</td>\n",
       "      <td>4380.207194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quito</td>\n",
       "      <td>16</td>\n",
       "      <td>103</td>\n",
       "      <td>6.437500</td>\n",
       "      <td>47.557067</td>\n",
       "      <td>121.904483</td>\n",
       "      <td>5797.419747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EtiquetaSucursal  CodigoVendedor  CodigoCliente  clienteNuevoPorVendedor  \\\n",
       "0           Ambato               7             42                 6.000000   \n",
       "1        Guayaquil              24            156                 6.500000   \n",
       "2          Machala               7             23                 3.285714   \n",
       "3            Manta               8             25                 3.125000   \n",
       "4            Quito              16            103                 6.437500   \n",
       "\n",
       "   promedio_ticket  count_visitas     TotalAño  \n",
       "0        39.897514      68.822394  2745.842394  \n",
       "1        63.507347      97.601810  6198.432036  \n",
       "2        61.770419     115.587786  7139.906031  \n",
       "3        40.047938     109.374101  4380.207194  \n",
       "4        47.557067     121.904483  5797.419747  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['TotalAño'] = final['promedio_ticket'] * final['count_visitas']\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "cantones_fil = cantones[['Sucursal', 'Clientes', 'Universo Construccion']]\n",
    "final['Sucursal'] = final['EtiquetaSucursal'].str.upper()\n",
    "cantones_fil = cantones_fil.merge(final, on='Sucursal')\n",
    "cantones_fil['clienteNuevoPorVendedor'] = np.round(cantones_fil['clienteNuevoPorVendedor'])\n",
    "cantones_fil['estimadoNuevo'] = 2\n",
    "cantones_fil['Nuevos2024'] = (cantones_fil['estimadoNuevo'] * cantones_fil['CodigoVendedor']) + cantones_fil['CodigoCliente']\n",
    "cantones_fil['2024'] = cantones_fil['Clientes'] + cantones_fil['Nuevos2024']\n",
    "cantones_fil['Nuevos2025'] = (cantones_fil['estimadoNuevo'] * cantones_fil['CodigoVendedor']) + cantones_fil['Nuevos2024']\n",
    "cantones_fil['2025'] = cantones_fil['2024'] + cantones_fil['Nuevos2025']\n",
    "cantones_fil['Nuevos2026'] = (cantones_fil['estimadoNuevo'] * cantones_fil['CodigoVendedor']) + cantones_fil['Nuevos2025']\n",
    "cantones_fil['2026'] = cantones_fil['2025'] + cantones_fil['Nuevos2026']\n",
    "cantones_fil['nuevoShare'] = cantones_fil['2026'] / cantones_fil['Universo Construccion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EtiquetaSucursal</th>\n",
       "      <th>netoNuevos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Machala</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Manta</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Quito</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ambato</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EtiquetaSucursal  netoNuevos\n",
       "0          Machala         153\n",
       "1        Guayaquil         756\n",
       "2            Manta         171\n",
       "3            Quito         501\n",
       "4           Ambato         210"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cantones_fil['USDoportunidad2024'] = cantones_fil['Nuevos2024'] * cantones_fil['TotalAño']\n",
    "cantones_fil['USDoportunidad2025'] = cantones_fil['Nuevos2025'] * cantones_fil['TotalAño']\n",
    "cantones_fil['USDoportunidad2026'] = cantones_fil['Nuevos2026'] * cantones_fil['TotalAño']\n",
    "cantones_fil['acumulado2025'] = cantones_fil['USDoportunidad2024'] + cantones_fil['USDoportunidad2025']\n",
    "cantones_fil['acumulado2026'] = cantones_fil['acumulado2025'] + cantones_fil['USDoportunidad2026']\n",
    "cantones_fil['netoNuevos'] = cantones_fil['Nuevos2024'] +cantones_fil['Nuevos2025'] +cantones_fil['Nuevos2026']\n",
    "cantones_fil[['EtiquetaSucursal', 'netoNuevos']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "proy = cantones_fil[['EtiquetaSucursal','2024','2025','2026']]\n",
    "proy_monetario = cantones_fil[['EtiquetaSucursal','USDoportunidad2024','acumulado2025','acumulado2026']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EtiquetaSucursal</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2026</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Machala</td>\n",
       "      <td>102</td>\n",
       "      <td>153</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guayaquil</td>\n",
       "      <td>569</td>\n",
       "      <td>821</td>\n",
       "      <td>1121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Manta</td>\n",
       "      <td>108</td>\n",
       "      <td>165</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Quito</td>\n",
       "      <td>399</td>\n",
       "      <td>566</td>\n",
       "      <td>765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ambato</td>\n",
       "      <td>128</td>\n",
       "      <td>198</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EtiquetaSucursal  2024  2025  2026\n",
       "0          Machala   102   153   218\n",
       "1        Guayaquil   569   821  1121\n",
       "2            Manta   108   165   238\n",
       "3            Quito   399   566   765\n",
       "4           Ambato   128   198   282"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>EtiquetaSucursal</th>\n",
       "      <th>Machala</th>\n",
       "      <th>Guayaquil</th>\n",
       "      <th>Manta</th>\n",
       "      <th>Quito</th>\n",
       "      <th>Ambato</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>102</td>\n",
       "      <td>569</td>\n",
       "      <td>108</td>\n",
       "      <td>399</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>153</td>\n",
       "      <td>821</td>\n",
       "      <td>165</td>\n",
       "      <td>566</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>218</td>\n",
       "      <td>1121</td>\n",
       "      <td>238</td>\n",
       "      <td>765</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "EtiquetaSucursal  Machala  Guayaquil  Manta  Quito  Ambato\n",
       "2024                  102        569    108    399     128\n",
       "2025                  153        821    165    566     198\n",
       "2026                  218       1121    238    765     282"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transposed = proy.set_index('EtiquetaSucursal').T\n",
    "df_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>EtiquetaSucursal</th>\n",
       "      <th>Machala</th>\n",
       "      <th>Guayaquil</th>\n",
       "      <th>Manta</th>\n",
       "      <th>Quito</th>\n",
       "      <th>Ambato</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USDoportunidad2024</th>\n",
       "      <td>2.641765e+05</td>\n",
       "      <td>1.264480e+06</td>\n",
       "      <td>179588.494964</td>\n",
       "      <td>7.826517e+05</td>\n",
       "      <td>153767.174054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acumulado2025</th>\n",
       "      <td>6.283117e+05</td>\n",
       "      <td>2.826485e+06</td>\n",
       "      <td>429260.305036</td>\n",
       "      <td>1.750821e+06</td>\n",
       "      <td>345976.141622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acumulado2026</th>\n",
       "      <td>1.092406e+06</td>\n",
       "      <td>4.686015e+06</td>\n",
       "      <td>749015.430216</td>\n",
       "      <td>2.904507e+06</td>\n",
       "      <td>576626.902703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "EtiquetaSucursal         Machala     Guayaquil          Manta         Quito  \\\n",
       "USDoportunidad2024  2.641765e+05  1.264480e+06  179588.494964  7.826517e+05   \n",
       "acumulado2025       6.283117e+05  2.826485e+06  429260.305036  1.750821e+06   \n",
       "acumulado2026       1.092406e+06  4.686015e+06  749015.430216  2.904507e+06   \n",
       "\n",
       "EtiquetaSucursal           Ambato  \n",
       "USDoportunidad2024  153767.174054  \n",
       "acumulado2025       345976.141622  \n",
       "acumulado2026       576626.902703  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transposed_mon = proy_monetario.set_index('EtiquetaSucursal').T\n",
    "df_transposed_mon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transposed_mon['EtiquetaSucursal'] = ['2024', '2025', '2026']\n",
    "df_transposed_mon.set_index('EtiquetaSucursal').reset_index()\n",
    "df_transposed_mon.index = df_transposed_mon['EtiquetaSucursal']\n",
    "df_transposed_mon.drop(columns=['EtiquetaSucursal'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eeveloza/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/eeveloza/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Proyeccion de Ventas por Millones USD de clientes nuevos hasta el 2026')"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IUMaPH2+23f79+5WAgABl2rRpprIuXbooAQEByv79+822bdasmdKrV68HHlNRFOXvv/9WAgIClEWLFpmVz549WwkICFC+/PJLU1mvXr2UOnXqKHfu3DHbdsKECUpwcLASFxf30GPt2LFDCQgIUBYsWGAqi42NVYKCgkzv4aVLl5Ty5csrH374odlz4+PjlZo1aypvvfWWqSz983D/eX/99deVxo0bm5Zv3LiR6bU8SFpampKSkqI0atRI+fjjj03l/fr1U1q2bPnI598v/TW3bt3a7PxfuHBBqVChgjJu3DhFUdTPZq1atZTmzZsrBoPBtF18fLxSvXp1pX379qay9M/KF198ka0Y1q9fr5w7d04JDAxU/vrrL0VRFGXw4MFKly5dFEVRlL59+yr169c3e279+vWVUaNGmZbPnz+vBAQEKMuXLzeVpX9/z58/ryiKoty6dUsJCQlRXn/9dbN9Xbp0SQkKClKGDh1qKrP0HFr6ndywYYMSEBCgHDlyxKL35v7X2rdv3yzXpR/n3td99uxZpXnz5kpAQIASEBCghISEKN27d1e+++47JSUlxeJ9K4qifP/990pAQICydu3aB26Tfk2493OpKIryyy+/KAEBAWbn6d1331VCQ0OVixcvmm07f/58JSAgQDl+/PgDj2PJdU1R1HP3qM/LnDlzlLJlyyoHDhww2y79PG3evNlUFhAQoNSoUcPs+nLt2jWlbNmyypw5c0xl8+bNM/u8pbP02nHz5k0lICBAWbhw4UNf34NesyWf1/Tv3I4dO8y2y+r706RJE6VVq1ZKamqq2bb9+vVTatasaboeLF68WAkICFBOnTpl2iYuLk4JCgpSJk+ebCp77bXXlOrVqyvx8fGmsrS0NKV58+ZKnTp1TNeh5s2bK2+++Wa234P07/v9f5u++eYbJSAgQLl69WqWzzMajUpqaqoSFRVl9h219Hy8/PLLpmvVw6SlpSmpqanK2LFjlVatWj10W71er1StWlXp16+fWbnBYFBatGihtGvXzlR2/3UuO4YNG6aUL19eiY6ONpXt3r1bCQgIUH799ddM23/99ddKQECAcuXKlQfu83//+1+Wf7uDgoKUsLAwpUqVKsp3332nbN++XZk2bZpSrlw5s2vv43puqvJfe+01KlSoQKVKlejXrx+FChXim2++oVChQmbbNW7c2Gx5x44dODk50aRJE7Py9EbS6bfktVotXbp0YfPmzaZf/ufOnWPr1q106tTJ9Mv9r7/+ws3Njfr165OWlmb6V65cOby8vIiKigLU2/8Gg4HOnTs/8DXt3buX+Ph4s/1bIr0KqHXr1mblISEhlCpVKlM1g5eXFyEhIWZlgYGBj7zDkX6c9LuV6Zo3b262nJyczI4dO3jppZdwcHAwe1/q1KlDcnIy+/bte+ixqlatip+fn1lV8po1a0hJSTFV42/bto20tDRatmxpdgx7e3uqVKlieu/TaTSaTD2DLXnd6dLS0vj6669p1qwZQUFBlC9fnqCgIM6cOWNW7RwcHMzRo0cZP348W7duJT4+3qL9p2vevLnZ+ffx8SEsLMz0/p8+fZqrV6/SsmVLs2pgZ2dnGjVqxP79+zNVRTdq1ChbMQD4+vpStWpVli9fTmxsLH/++adZE4rcsHfvXpKSkjJ9dr29valWrRo7duwwK7fkHFr6nSxXrhy2tra8++67rFy5MlPv1tzk5+fH6tWr+e677xg0aBDVq1cnOjqaCRMm0L59+0dWy99Lua95UVYe9F1t2rQpNjbmFWubN28mIiKCwoULZ/quApm+R/ey5Lpmqb/++osyZcpQrlw5szhq1aqFRqPJFEdERIRZtWahQoUoWLAgFy9efOSxLL12uLu74+fnx/z581mwYAGHDx+2qMlTuse95tzr7NmznDp1ynRO7z9X165dMzWreuWVV7Czs2PlypWm5//666+kpKSY/tbp9Xr2799P48aNcXZ2Nm2n0+lo0aIFly9f5tSpU4B6TduyZQufffYZkZGRJCUlZSv2rN4DwOx9OH/+PMOGDaNmzZqUK1eOChUq0KVLFwBTHI97PgDWr19Phw4dCAsLo3z58lSoUIFly5Zlajp0v7179xIXF0fr1q3N3nuj0Ujt2rWJjo62qDbqYT7//HPWrFnDmDFjCAoKyrT+YXnBg9b9/ffffPDBBzRu3DhTlb2iKKZmFp07d6ZatWq8/fbbdOnShV9//dVUE/24npuq/E8++YRSpUphY2NDwYIFKVy4cKZtHB0dM/X6jIuLo1ChQplOYsGCBbGxsTGrmmjbti1ffPEFP/74I0OHDmXp0qU4ODiY/XG+ceMGt2/fzvJDBBAbGwtgatv2sE5LlmyTlfSYs3oPChcunOki6O7unmk7Ozu7R/5xjIuLw8bGBg8PD7Py+9vOxcXFkZaWxpIlS1iyZEmW+0p/Xx5Eo9HQtm1bpk+fTnR0NMHBwaxYsYLixYubqjiuX78OPLh3/v1t9xwdHTNVdVjyutNNnjyZpUuX8vrrr1OlShUKFCiARqPhnXfeMdtHv379cHJy4pdffuHHH39Ep9MRHh7O8OHDTR32Hub+H1fpZUePHgUy3rus2iwWLlwYo9HI7du3zaqis/psWKJdu3aMHTuWhQsX4uDgkOkH3eNK/+w+6LX8+++/ZmWWnENLv5N+fn4sXLiQefPmMWHCBPR6Pb6+vnTt2pXu3bs/NG6dTofBYMhyXXr5/QmgVqulSpUqVKlSBVATg3HjxrFu3TqWLVtmcXKX/n1+2Dl90PtqY2OT6ft/48YN/vrrLypUqJDlvh72Xc3pNSsrN27c4OzZsxbHkdPrGFh+7dBoNCxcuJCvvvqKefPmMXnyZNzd3XnllVcYMmTII0cVeNxrTlYxf/LJJ3zyySdZbpP+Hrm7u9OgQQNWrVrFW2+9hU6nY+XKlYSEhFCmTBkAbt++jaIoD/zuQcbn6J133qFo0aKsW7eOb775Bnt7e2rVqsXIkSPNmto8yP3nKr2KOD3BTUhIoFOnTtjb2zNkyBBeeOEFHBwcuHz5MgMHDjRt97jnY+PGjQwZMoQmTZrQp08fChUqhE6n44cffjC7CZKV9Pd/8ODBD9zm1q1bOR4VaObMmcyePduUGN4r/f3L6rsYFxeHRqPJctSJrVu3MmjQIGrWrMlnn32WKe9xd3fn2rVr1KpVy6y8Tp06LFq0iEOHDlGiRIkcvZ57PTeJaalSpR75Rz6rXxDu7u7s378fRVHM1t+4cYO0tDSzpMvV1ZXWrVvz888/06tXL1asWEHz5s3NPgAeHh64u7szb968LGNI/yWa3k7x8uXLeHt7Z7ntvdtkR/qH9urVq5n+QFy9ejVTIplT7u7upKWlERsba7bPa9eumW3n5uaGTqejZcuWdOrUKct9FS9e/JHHa926NV9++SXLly/H1taWw4cP89Zbb5nOW3oMX375JcWKFcvpy7LYL7/8QqtWrRg6dKhZeWxsrNlnwsbGhp49e9KzZ09u377Nv//+y/Tp0+nTpw+bN29+ZNvF9Avg/WXp5zn9dd//voN6vrVaba4NjdOoUSMmTJjA3LlzefXVV03tpXNL+mt60GvJyWfX0u8kQHh4OOHh4RgMBg4ePMiSJUv4+OOPKVSoEC+//PIDj1GwYMEHjpucXp7VD4x7OTk50a9fP9atW/fINuTpFEXhr7/+wsnJ6aHXv3vf1yJFipjK09LSMrUL9PDwIDAwkCFDhmS5r4clwJZc1yzl4eGBvb09H3/88QPX55bsXDt8fHxMMZ0+fZr169czc+ZMUlJSmDBhwmPHkp643t+B7/4kJD3mfv36PbADo7+/v+lxmzZt2LBhA//88w/FihUjOjqa8ePHm9a7ubmh1Wof+N2795hOTk4MHjyYwYMHc/36dbZs2cLUqVN54403Hjo+pqV27NjB1atXWbJkCVWrVjWVZ9Uu/3HOxy+//ELx4sX5/PPPzf7+P6pjKmS8F++++y4VK1bMcpusOjNZYubMmcyYMYNBgwbxxhtvZFrv5+eHg4MDx44dy7Tu2LFjlChRItMPoK1btzJgwACqVKnCjBkzsmwvGhgYmOX5T6+VyU7HzId5bqryc6p69ero9fpMvfBWrVplWn+vrl27Ehsby+DBg7l9+3amXzL16tUjLi4Oo9FIcHBwpn/pHWJq1qxp+mX2IGFhYbi6uvLjjz9aVF2XLv0O4i+//GJWfuDAAU6ePGnWiPpxpI/ruWbNGrPy+3tmOjo6EhERweHDhwkMDMzyfbHkj0yRIkWoXbs2a9euZenSpaYOOelq1aqFjY0N586dy/IYltydvN/9v+TvpdFoMo0duXnz5odO7ODm5kaTJk3o1KkTcXFxFlUz/vrrr2bn/+LFi+zdu9d0wfb396dIkSKZttPr9WzcuJHQ0NBc67jj4ODAgAEDqF+//gN/ZDyOsLAwHBwcMn12L1++zI4dO3L02bX0O3kvnU5HxYoVTZ0lDh069NBj1KhRg+PHj3PixIlM69avX4+Tk5NZc5n0P/T3S68+tPSO9syZMzlx4gTdunV7aEeHB31X169fn6mnfb169Th27Bh+fn5Zvl/3Jrb3s+S6Zql69epx/vx53N3ds4zDkh+z90v/Pt9/hzKn1w5/f3/efPNNAgICshwpIyd8fHwAMo3usWnTJrPlkiVL8sILL3D06NEHxnzvHcNatWpRpEgRVqxYwfLly7G3tzdrduXk5ETFihX5/fffza53RqORX375haJFi5oluukKFSpEmzZtePnllzl9+nSujGCRniTenzz9+OOPD33eg86HnZ3dQ6/h9yal165dMxul4EEqVaqEm5sbJ06ceOD7n5POQl999RUzZsygf//+D+wQZ2NjQ/369fn999/NmoZdunSJyMjITD9Utm3bxoABA6hcuTKzZs16YFzpTby2bNliVv7333+j1Wpz9Dc0y/hzZS/PsFatWrF06VJGjRrFxYsXCQgIYPfu3cyZM4e6deua9cAH9YNfu3ZttmzZQuXKlTONnfnyyy+zZs0a+vbtS9euXQkJCcHW1pbLly8TGRlJw4YNeemllyhevDj9+vVj1qxZJCUl0bx5c1xdXTlx4oQp8XV2dmbUqFG888479OjRg9dee42CBQty7tw5jh49ynvvvZflaypZsiTt27fnu+++Q6vVUqdOHVOvfG9vb9MwWY+rVq1aVKlShU8//ZTExESCgoLYs2cPq1evzrTtuHHj6NSpE507d6Zjx474+PiQkJDAuXPn2LRpk1kvy4dp164dmzdv5ueff6ZWrVpmd2WKFy/O4MGD+fzzzzl//jx16tTBzc2N69evEx0djaOj40OrXbLi4uKCj48Pf/75J9WrV6dAgQJ4eHhQvHhx6tWrZ+p9nz4E0/z58zPdpX7jjTcoU6YMQUFBeHp6cvHiRRYtWoSPj49F1SI3b95kwIABvPbaa9y5c8f0azd9piGtVsuIESMYPnw4/fr1o3379qSkpDB//nxu377NsGHDsvWaHyX97m9ecHNz480332TatGmMHDmSl19+mbi4OL766ivs7e1z1HPZ0u/kDz/8wI4dO6hXrx7e3t4kJyebqvPuvw7cr1u3bqxatYquXbvSr18/AgMDuXXrFuvWreO3335jzJgxZklC8+bNqVatGnXq1MHPz4/k5GT279/PggULKFSoUKYq5du3b5vaYScmJnLq1CnWrVvHrl27aNq0KYMGDXpofKVKlaJFixYsWrQIGxsbUyI9f/78TNWdgwcP5t9//6VDhw507doVf39/UlJSuHDhAlu2bOGDDz54YFW9Jdc1S3Xv3p2NGzfSpUsXevToQWBgIEajkZiYGLZt20avXr0eeKfqQQICAgD1jljr1q2xsbHB39/f4mvH0aNH+fDDD2nSpAklSpTA1taWHTt28N9//2WaFCGnvLy8qFGjBnPnzqVAgQIUK1aM7du38/vvv2fa9oMPPuD111+nd+/etG7dmiJFinDr1i1OnjzJoUOHzMbJ1el0tGrVigULFuDi4sJLL72Eq6ur2f6GDh1Kr1696NatG7169cLW1pbvv/+e48ePM23aNFMC9+qrr1KvXj0CAwMpUKAAJ0+eZPXq1YSFheXKj+CwsDAKFCjA+++/z8CBA7GxsWHNmjWZknVLz0dAQABr165l3bp1FC9eHHt7ewIDA6lXrx4bN240TXZx+fJlZs2aReHChTlz5sxDY3R2duadd95h9OjR3Lp1i8aNG1OwYEFu3rzJ0aNHuXnzJh988EG2Xve3337Ll19+Se3atalXr16mvhf3jmU8aNAg2rVrxxtvvMHrr79OSkoKX375JR4eHvTq1cu03a5duxgwYACFChWiX79+mYY1K126tOka0KZNG3766Sc++OADYmNjKV26NP/++y/ff/89nTp1Mv1oelySmD6Cvb09ixcvZvr06cybN4/Y2FiKFClCr169HvhHsFmzZmzZsiXT3VJQv/yzZ89m8eLFrF69mrlz56LT6ShatChVqlQxXRgB3nrrLUqUKMF3333H8OHD0el0vPDCC2YNkl999VXTsEXvvPMOiqLg4+PzyGEsxo8fj6+vL8uWLeP777/HxcWF2rVrM2zYsFyrAtNqtcyePZtJkyYxb948UlNTqVSpEnPnzqVp06Zm25YuXZoVK1Ywa9YsPv/8c27evImrqyslSpQwDVViiXr16lGoUCGuX7+eZcebfv36UapUKRYvXszatWtJSUnBy8uLoKAgOnbsmKPX+dFHHzFlyhT69+9PSkoKrVu3ZvLkyYwbNw4bGxvmzp2LXq+nfPnyzJgxgy+++MLs+REREfz222/8/PPPxMfHm/7wvPnmmxbN1vP2228THR3NmDFjiI+PJyQkhGnTpuHn52fa5pVXXsHR0ZG5c+eaZsiqWLEiixcvplKlSjl63dbSr18/PD09WbJkCevWrcPBwYGqVasydOhQi9qv3c/S72S5cuX4559/mDFjBteuXcPJyYmAgABmz56dqc3V/dzd3fnf//7HzJkzWbRoEVevXsXe3p6yZcvyxRdfZGqLO2zYMLZt28bXX3/N9evXSUtLw9vbm+bNm9O/f/9Md0z37NlD+/bt0Wg0ODo6UqRIEUJCQujfv/8jY0v30UcfUahQIVauXMmSJUsoV64cM2bMyNQUpXDhwixbtoxZs2Yxf/58rly5grOzMz4+PtSuXfuRzUIsua5ZwsnJiaVLlzJ37lx++uknLly4gIODA97e3tSoUSNHfyQjIiLo168fK1eu5Oeff8ZoNLJ48WJT+aOuHV5eXvj5+fH999+bmlj5+voyatSoXB37ccqUKXz44Yd89tlnGAwG6tevz9SpUzNd86pVq8bPP//M119/zccff8zt27dxd3enVKlSma7BoCYec+bM4ebNm1leP6tWrcrChQuZMWMGY8aMwWg0UrZsWWbPnm02uUm1atXYtGkTixYtIjExkSJFitCqVassq51zwsPDgzlz5vDJJ58wYsQIHB0dTeOL31tLZun5GDRoENeuXeOdd94hISEBHx8fNm3aRNu2bblx4wY//vgjy5cvx9fXl759+3L58mVmzpz5yDhbtmxJsWLFmDdvHu+//z4JCQl4enpSrly5TB04LZE+7u7WrVvZunVrpvX3JualSpViyZIlfPbZZ6Z2w9WqVeOrr74yG1Jx+/btJCUlcfHixSzbyqd//gFsbW359ttvmTZtGnPmzOHWrVv4+PgwbNiwXL0ZoVGyUwcsLDJo0CD27dvHpk2bnvppAEX+FRkZSbdu3bJMbIQQQoinkdwxzSUpKSkcOnSIAwcO8PvvvzN69GhJSoUQQgghskES01xy9epVOnTogIuLC+3bt8/zKbuEEEIIIZ41UpUvhBBCCCHyBRkuSgghhBBC5AuSmAohhBBCiHxBElMhhBBCCJEvSGIqhBBCCCHyBUlMhRBCCCFEvvDMJKY7d+7kjTfeoFatWgQGBmaa294SiqIwf/58GjduTFBQEHXr1uXrr7/Og2iFEEIIIcT9nplxTPV6PYGBgbRp0+aR80I/yEcffcS2bdsYOXIkAQEBxMfHExsbm8uRCiGEEEKIrDwziWndunUfOqd6SkoKn3/+OWvWrOHOnTuUKVOG4cOHm+aAPXnyJD/88ANr1qyhZMmSTypsIYQQQghx1zNTlf8oY8aMYc+ePUyfPp1ffvmFJk2a0KdPH86cOQPApk2bKF68OJs3b6ZBgwY0aNCAcePGERcXZ9W4hRBCCCGeF89FYnru3DnWrl3LF198QXh4OH5+fvTu3ZvKlSuzYsUKAM6fP8+lS5fYsGEDU6ZMYdKkSRw6dIjBgwdbOXohhBBCiOfDM1OV/zCHDh1CURSaNGliVp6SkoK7uzugdnxKSUnhk08+wd/fH1DbnLZp04ZTp05J9b4QQgghRB57LhJTRVHQ6XQsX74cnU5nts7JyQkALy8vbGxsTEkpQKlSpQCIiYmRxFQIIYQQIo89F4lpuXLlMBgM3Lx5k/Dw8Cy3qVSpEmlpaZw7dw4/Pz8AU/vTYsWKPalQhRBCCCGeW89MG9OEhASOHDnCkSNHALhw4QJHjhzh0qVL+Pv788orrzBy5Eg2btzI+fPnOXDgAHPnzuXvv/8GoEaNGlSoUIGxY8dy+PBhDh48yHvvvUfNmjXN7qIKIYQQQoi8oVEURbF2ELkhMjKSbt26ZSpv3bo1kydPJjU1ldmzZ7Nq1SquXr2Ku7s7oaGhDBo0iMDAQACuXLnCxIkT2bZtG05OTtSpU4dRo0aZ2qEKIYQQQoi888wkpkIIIYQQ4un2zFTlCyGEEEKIp5skpkIIIYQQIl94qnvlG41G0tLS0Gq1aDQaa4cjhBBCCCHuoygKRqMRGxsbtNqH3xN9qhPTtLQ0oqOjrR2GEEIIIYR4hODgYOzs7B66zVOdmKZn3cHBwZkGzs8LBoOB6OjoJ3Y8IcTzR64zQoi89qSvM+nHe9TdUnjKE9P06nudTvdEL+BP+nhCiOePXGeEEHntSV9nLGl2KZ2fhBBCCCFEviCJqRBCCCGEyBckMRVCCCGEEPnCU93G1BKKopCWlobBYHjsfaXvIykpSdp+WZmtra2cAyGEEOIZ80wnpikpKcTExKDX63Nlf4qiYGNjw9mzZ2XcVCvTaDQUL14cFxcXa4cihBBCiFzyzCamRqOR06dPo9PpKFasGHZ2do+dTCqKQmJiIo6OjpKYWpGiKFy7do0LFy5QpkwZuXMqhBBCPCOe2cQ0JSUFo9GIr68vTk5OubLP9JkLHBwcJDG1Mi8vL86cOUNqaqokpkIIIcQz4pnv/GTJYK7i6SM/DIQQQohnj2RtQgghhBAiX5DEVAghhBDieaEokHDN2lE8kCSmIkdWrFhBeHi4tcMQQgghhCWS42HXAphTB+23L4Hy+MNo5oVntvNTfnHjxg2++OILtmzZwvXr1ylQoABly5Zl4MCBhIWFWTs8IYQQQjzLrhyCXd/C/p8g5Y5aprPH8c4ZoLI1I8uSJKZ5bNCgQaSlpTF58mR8fX25ceMG27dv59atW1aLKSUlBTs7O6sdXwghhBBPwLX/YHaNjGXPUhDeC2NwexKPnbNeXA8hVfl56Pbt2+zevZvhw4dTrVo1fHx8CAkJoV+/ftSrV48LFy4QGBjIkSNHzJ4TGBhIZGSkqez48eP07duXSpUqERYWRqdOnTh3Tv1Ade3alY8++sjsuG+++SajR482LTdo0IBZs2YxevRoKleuzLvvvktKSgoTJkygVq1aBAcH06BBA+bMmWN6zoIFC3jllVcIDQ2lbt26jB8/noSEhLx6q4QQQgjxuG6chK3T1HakAF6BULwqlG8J3VbDwF1QYyA4eVo3zoeQO6Z5yMnJCScnJ/744w9CQ0NzdJfyypUrdOnShapVq7Jo0SJcXFzYs2cPaWlp2drP/PnzefPNN+nfvz8AS5YsYdOmTXz++ed4e3sTExPD5cuXTdtrNBrGjRuHj48PFy5c4IMPPuDTTz9l/Pjx2X4NQgghhMgjhlT4bz3smg+nNqtlvhHwQk31cc/1oHt60r2nJ9KnkI2NDZMnT+bdd9/lxx9/pHz58lStWpVmzZpRtmxZi/axdOlSXFxcmDZtGra2tgD4+/tnO5Zq1arRu3dv03JMTAwlSpSgcuXKaDQafHx8zLbv0aOH6bGvry9vvfUW48ePl8RUCCGEyA9uXYA9i2H3IohPv7GkgTIvga1jxnZPUVIKkpjmucaNG1OvXj127drF3r172bZtG/PmzWPixIlUrVr1kc8/cuQI4eHhpqQ0p4KCgsyWW7duTa9evWjSpAm1a9emXr161KpVy7R+x44dzJkzhxMnThAfH4/BYCA5ORm9Xp9rM2kJIYQQIgdWD4R9S0ExqsvOXhDWFSr3AI8SVg3tcUkb0yfA3t6emjVrMnDgQH788Udat27NjBkzTLNSKeltQSBTFb2Dg8ND963RaMyen9U+ABwdHc2WK1SowJ9//slbb71FUlISQ4YMYfDgwQBcvHiRvn37UqZMGWbMmMGKFSt47733HrhvIYQQQuSh+Gugv5mx7OSpJqUv1IZ2C+Dtw/Di+099UgqSmFpF6dKl0ev1eHqqjY+vXcsY6PbejlAAgYGB7Nq1i9TU1Cz35enpafZ8g8HA8ePHLYrDxcWFZs2aMXHiRKZPn85vv/1GXFwcBw8exGAwMHr0aEJDQ/H39+fq1avZfZlCCCGEyClFgTPbYFkvmFYOIjM6KFPtTRiwE3r8CkFtwObZGWlHqvLzUGxsLG+99RZt27YlMDAQZ2dnDh48yLx582jYsCEODg6EhoYyd+5cfHx8iI2N5fPPPzfbR+fOnVmyZAlDhw6lb9++uLq6sm/fPkJCQihZsiTVqlVj8uTJbN68GV9fXxYtWsTt27cfGdvChQvx8vKibNmyaLVaNmzYgJeXF25ubvj5+ZGWlsaSJUto0KABu3fv5scff8yjd0kIIYQQJolxsP9HdezR6/9llF85mPHYtaj67xkkiWkecnZ2pmLFiixatIhz586RlpZG0aJFefXVV3njjTcA+Pjjjxk7dixt27bF39+fESNG0KtXL9M+PDw8WLRoEZ9++ildu3ZFq9VSrlw5KldWB8Vt27YtR48eZdSoUeh0Onr06EFERMQjY3NycuKbb77h7NmzaLVagoODmTt3rmn/Y8aM4ZtvvmHatGmEh4czdOhQRo0alTdvlBBCCPG8u/Yf/PMlHFwOaYlqma0zhLwK4b3Au6J143tCNMr9DRSfIgaDgX379hEaGopOpzNbl5SUxOnTp/H3939kO01LKYpi6vyj0WhyZZ8iZ/Li/AqRHzzsuiaEeMYoCqTnEyf+gO/aqo8Ll1eT0ZD24OCW64d90teZ7BxP7pgKIYQQQjxJVw6rVfW3zkOnn9Sykg0goj9UaKWOQ/qc3gCTxFQIIYQQIq+lJcPh1WpCem57Rvm1/9QZmrRaaDrZevHlE5KYCiGEEELklZunYNcCddxR/Q21TKODsi+r1fUFy1g3vnxGElMhhBBCiLyQoofZtSA1QV1281EHwQ/rCm7eVg0tv5LEVAghhBAiN9y+BHuWQLU3wKEA2DlBcFu4HaPeHS3T6KmbIvRJk3dHCCGEECKnjEY4tUmtrv9vPSgGdWamqq+r65t/obYfFRaRxFQIIYQQIrsSrsPe72D3Aog9k1FeohZ4+mcsS1KaLZKYCiGEEEJkx5bP4O9PwJCiLtsXgNCOanW9V6B1Y3vKSWIqhBBCCPEwSbfUf+5+6rK7n5qUFqukJqNBbdX2pOKxyf3l59yMGTNo2bLlY+3jwoULBAYGcuTIkVyKSgghhMgHLu2F1QNhaln4bWxGebkW0Hcz9P0LKnWVpDQXSWKaD40ePZrAwEDee++9TOvGjx9PYGAgo0ePtkJkQgghxDMuJQH2LIa59dR/e5dAqh5ungFDqrqNrQMUC7NikM8uqcrPp7y9vVm3bh1jx441zQWfnJzM2rVrKVasmJWjE0IIIZ4xty/Bts9h/4+QfEst09lB+VZqdb1fted2mtAn6blLTBVFITHVkOPn6lMMYJOGxsIPp6OtzuJt71W+fHnOnz/Pxo0badGiBQAbN26kaNGi+Pr6mrbbsmULs2fP5vjx4+h0OkJDQxk3bhx+fn6mbS5fvswnn3zCP//8Q0pKCiVLluT999+nYsWKpm1WrVrFl19+ya1bt6hTpw4ffvghLi4uFh/jXgaDgXfffZcdO3Zw/fp1vL296dSpE927d8/2+yCEEELkGUXJSDYVI+z8Rv3fwx/Ce0JoZ3AuZN0YnzPPVWKqKArtvt7O7rOxT+yY4SU8+PmN6jlKTtu2bcuKFStMieny5ctp27YtUVFRpm0SExPp2bMnAQEBJCYm8sUXXzBgwABWr16NVqslISGBLl26UKRIEWbNmoWXlxeHDh3CaDSa9nHu3Dn+/PNPvv76a27fvs2QIUP45ptvePvtty06xv2MRiNFixbl888/x8PDg7179/Lee+/h5eVFs2bNsv0+CCGEELnq5ml1mKdjG6HfFrCxgwLFocG74F0RStaXYZ6s5LlKTAGeppvwLVq0YOrUqVy4cAGNRsOePXuYNm2aWWLauHFjs+d8/PHHVK9enRMnThAQEMCvv/7KzZs3WbZsGe7u7gCUKFHC7DmKojBp0iTTHdIWLVqwfft2U2L6qGPcz9bWlsGDB5uWfX192bt3Lxs2bJDEVAghhHUY0uDYBtj1LZz8M6P8v7VQobX6uPZQ68QmTJ6rxFSj0fDzG9Ufrypfn4iTk2OeV+UDeHp6Uq9ePVatWoWiKNSrVw9PT0+zbc6dO8cXX3zBvn37iI2NRVEUAGJiYggICODIkSOUL1/elJRmxcfHx5SUAhQuXJgbN25YfIys/PDDD/z8889cunSJ5ORkUlNTKVu2bI7eByGEECLHbl9SOzPtXgR3LmWUl2qoth0NaGK92EQmz1ViCmpy6mSXs5etKAqk6XCys8lxspldbdu2ZcKECQC8//77mda/8cYbeHt7M3HiRAoXLozRaKR58+akpqo9B9M7Tj2MjU3m9yM9+bTkGPdbt24dkyZNYtSoUYSFheHs7Mz8+fPZv3+/Ra9ZCCGEyDXftYWrh9XHTgUhrCtU7g6eJa0bl8jSc5eYPm1q165tSgBr1aplti42NpaTJ08yYcIEwsPDAdi1a5fZNoGBgfz888/ExcU99K7pg1hyjPvt3r2bsLAwOnfubCo7d+5cto8thBBCZEvCddi3FEq/CEUqqGWVusGRNerd0XKvgI29dWMUDyWJaT6n0+lYv3696fG9ChQogLu7Oz/99BNeXl5cunSJqVOnmm3z8ssv8/XXXzNgwACGDh1K4cKFOXz4MIULFyYs7NFjsFlyjPv5+fmxatUqtm7dSvHixVm9ejXR0dEUL148m69eCCGEeARFgXM71Lajh1epMzLdPAWvfKGuj3gDqvW3aojCctLl7Cng4uJi1gY0nVarZfr06Rw6dIjmzZszadIkRo4cabaNnZ0d3377LQULFqRv37688sorzJ07N1OS+yCWHON+HTt2pFGjRrz99tu89tprxMXF0alTJ8tfsBBCCPEoSbch6huYXQMWNIHo/92dJjQMStTM2E7GHn2qaJR7GxM+ZQwGA/v27SM0NDRTopWUlMTp06fx9/e3qJ2lJdTOT3qcnJyeWBtTkbW8OL9C5AcPu64JIe468DOseQtSE9RlG0cIbqdW1/tUsm5sT4EnfZ3JzvGkKl8IIYQQ+VuKHuLOQuFy6nLhsmpSWigQqvSGkPbg6G7VEEXukMRUCCGEEPnTtf9g1wLY/z04FYKBu9SB74sGQ9/N4B0qVfXPGElMhRBCCJF/pKXAkV/UhPTstoxyB3d1HNICdzvSFnt0B17x9JHEVAghhBDWl3wHtk6Fvd9BwjW1TKOFgKZq29FSDWSa0OeAJKZCCCGEsA5FyaiKt3GAfd+rSalLUXUQ/ErdMu6QiueCJKZCCCGEeLJux8DeJbBnCfT4FTxKgM4WXvwA7JwhsKm6LJ47+eae+Jw5cwgMDOSjjz6ydihCCCGEyG1GI5z8C37qCtMrwF8fwa1z6l3SdKEdoXwLSUqfY/nijumBAwf46aefCAwMtHYoQgghhMhN+pvqNKG7FsDNkxnlftXvThPawnqxiXzH6olpQkICI0aMYOLEicyePdva4QghhBAiN60bDgeXq4/tXKFiBwjvmTGXvRD3sHpiOmHCBOrWrUuNGjVynJgaDIYsyxRFMf3LDen7eYony8q3Lly4wIsvvsjKlSspV64ckZGRdO/enaioKNzc3DJtn35eDQZDludfiKdV+udZPtfiqZR8G030zyhuxSGgsVoW2hXt9eMolXuiBLUFu7tTbMtn3Gqe9HUmO8examK6du1aDh8+zLJlyx5rP9HR0VmW29jYkJiYiNFofKz93y8xMTFX95eV69evs2DBArZu3crVq1dxcXHBz8+PZs2a8fLLL+Po6JjnMTxJBQoUYOPGjbi7u6PX60lOTgbU99rGJvPHNDk5mdTUVI4ePfqkQxXiiXjQdU2I/Mjx1gm8zvyC58U/0BqSiPcoz3/6IupKxRXCp6u97w+fsG6gwkx+vM5YLTGNiYnho48+4ttvv8Xe3v6x9hUcHJxp7tWkpCTOnj2Lo6Njrs2lrigKiYmJODo6osnDmSbOnz9P586dcXV1ZdiwYQQEBJCWlsaZM2dYsWIFxYsXp0GDBnl2fGtxdXU1PU7/TDg6OuLk5JRpW61Wi62tLaVLl8618ytEfmAwGIiOjs7yuiZEvpKaiObwKjS7v0VzcbepWClYBqfKXQitGKKOQyrynSd9nUk/niWslpgeOnSIGzdu0KZNG1OZwWBg586dLF26lOjoaIvfLJ1Ol2lbnU6HRqMx/TOTkvCIHdqD7u5bk5YCxlT1saJASiIaGyVjnxot2DpmrE/Vm+/Lztmi13CvDz74AJ1Ox/Lly82SsrJly9KkSRMUReHixYs0bNiQVatWUa6cOnfw7du3qVKlCosXLyYiIgKDwcC7777Ljh07uH79Ot7e3nTq1Inu3bsDsHPnTnr06MHmzZvx8vIyHWfy5MlER0ezdOlSYmNj+fDDD9m1axe3bt3Cz8+Pfv360bx5c9P2er2e8ePH8/vvv+Ps7EyvXr3466+/KFu2LOPGjQMgMDCQr776ihdffNH0vPDwcMaOHUubNm24cOGC2etJf3+zPH/3lGd17oV4FshnW+Rr5yLh+9cgKU5d1tqqvenDe6EpUTNPb96I3JMfrzNWS0yrVavGmjVrzMrGjBlDyZIlef311/P2jfq42MPXv7oQKrRWH2+aAP/OAEADZEozi4Wp8/UC6G/Ap6XM14+/la3QYmNj+eeffxg6dGiWdwoBi7/wRqORokWL8vnnn+Ph4cHevXt577338PLyolmzZlSpUoXixYuzevVq+vTpA0BaWhq//PILw4YNAyAlJYUKFSrw+uuv4+LiwubNmxk5ciS+vr5UrFgRgClTphAZGcnMmTMpVKgQ06dP5+DBg5QtWzZbr10IIUQ+lZYCl6OheGV1uUh5MKaBux9U7glhXcClsHVjFM8EqyWmLi4uBAQEmJU5OTnh7u6eqfx5cu7cORRFwd/f36w8IiKClJQUADp16kTHjh0fuS9bW1sGDx5sWvb19WXv3r1s2LCBZs2aAdCuXTtWrFhhSkw3b95MYmIiTZs2BaBIkSL07t3btI+uXbuydetWNmzYQMWKFUlISGDZsmVMmTKFmjVrAuod17p16z7GuyCEECJfiDsHuxeqA+En34ahR8DJE+xdoc+fUChApgkVuSpHiWlMTAwXL14kMTERT09PypQpg52dXW7HlnfGXnr4et09bV4bvAf1xgBqG1O9PhEnJ0fzqvx0TgUfvW8L3X9XdNmyZRiNRoYPH25KUC3xww8/8PPPP3Pp0iVTh6F772S2adOGL774gn379hEaGsry5ctp2rSp6W6twWBg7ty5rFu3jqtXr5KSkkJKSoqp89X58+dJTU0lNDTUtE93d/dMibUQQoinhNEAx3+HXd/C8Y3A3ZFoXIrC9ePgF6EuF5ZaMZH7LE5ML168yI8//sjatWuJiYkxGzLJ1taW8PBwXnvtNRo3bow2h7+elixZkqPnZVt22n3a2AF3k25FgTQN2DllzO17L40mR21K7+Xn54dGo+HUqVNm5b6+vgCmjj7p7/G95yEtLc3sOevWrWPSpEmMGjWKsLAwnJ2dmT9/Pvv37zdtU7BgQerXr8+KFSvw9fVly5YtLF682LT+22+/ZeHChYwdO5bAwEAcHR35+OOPSU1NzXT8h9FoNJm2vT9eIYQQVqQosPUz2L0Ibp3PKC9ZTx0IP7CZzMgk8pxFienEiRNZsWIFtWrVYvDgwQQHB1OkSBEcHBy4desWx44dY/fu3XzxxRd89dVXfPzxx4SEhOR17M8kDw8PatasyXfffUeXLl0e2M7U09MTgGvXrpnKjhw5YrbN7t27CQsLo3Pnzqayc+fOZdpXu3btGDp0KEWKFMHX15fKlSub7aNhw4a0bNkSUNutnjlzhlKl1La0fn5+2Nrasm/fPooVU9vu3rp1izNnzlClShWzeK9evWpaPnPmzBMZdksIIcRDKIr6T6tVb66c+ltNSh09ILSz2n60UGlrRymeIxYlpra2tvzxxx+mZOheBQsWpHr16lSvXp2BAwfy999/c+nSJUlMH8P7779Px44dadu2LYMGDSIwMBCNRkN0dDSnTp2iQoUKODg4EBoayty5c/Hx8SE2NpbPP//cbD9+fn6sWrWKrVu3mjo5RUdHU7x4cbPtateujaurK7NnzzZrk5q+j40bN7Jnzx4KFCjAggULuH79uikxdXZ2pm3btnz66ad4eHhQsGBBpk+fnqkpQrVq1Vi6dCmhoaEYjUY+++wzbG3ll7cQQliF/ibs/0Gtrm88CQIaqeV1Rqgdmcq3AlsZik88eRYlpqNGjbJ4h9Lp5fH5+fmxcuVK5syZw9SpU7ly5YppzM5evXrRqVMnAD7++GPGjh1L27Zt8ff3Z8SIEfTq1cu0n44dO3L06FHefvttNBoNL7/8Mp06dWLLli1mx9NqtbRu3Zo5c+bQqlUrs3VvvvkmFy5coHfv3jg6OvLaa6/x4osvcufOHdM2I0eORK/X079/f5ydnenZsyfx8fFm+xk1ahRjx46lS5cuFC5cmLFjx3Lo0KFcfueEEEI8kKLAhZ1qMnpwBRjUiUzY911GYlpS/oYL69IoOZhf8+bNm1y8eBGNRoOPjw8eHh55EdsjGQwGU6edrAbYP336NP7+/rk6wL5er8fJyemZG6PtnXfe4fr163z99de5sr+uXbuajWOa2/Li/AqRHzzsuiZEjiTfgQP/g10L4Mo9g5wXCYYqvSD4VbWXvXhuPOnrTHaOl61e+cePH2f8+PHs2bPHrLxKlSqMHz+ekiVLZj9aYVV37twhOjqaNWvWMGvWLGuHI4QQIrdFfg2bJqqPbRwgqK3amcmnctYdeYWwIosT02vXrtGlSxc8PT0ZPXo0JUuWRFEUTp48yf/+9z86d+7Mr7/+SsGCBfMyXpHL3nzzTQ4cOED79u1N45AKIYR4SqUmwqFVkJoAVdTxqQntolbdh3WFih3UcUiFyKcsTkwXLlyIj48PP/zwg9nc9nXq1KFjx4506tSJhQsXmmYMEk+HvBqi64kN/SWEEAKun1Dbju5bqk4Tmt6r3tYR3Lzhze3WjlAIi1g84Oi///5Lnz59zJLSdA4ODvTu3Ztt27blanBCCCGEeABDKhxaCYtegZmVYcdXalJawA+qD1QHyhfiKWPxHdPz589ToUKFB64PCgri/PnzD1wvhBBCiFxy+xLMrQfxV+4WaCCgsdp2tPSLoJWOc+LpZHFimpCQgIuLywPXOzs7o9frcyUoIYQQQtzDaICz/8ALtdUOS67e4FRIXVepm/rP3c+6MQqRC7LVKz8hISHLqnyA+Ph4i6enFEIIIYQF7lyBvUvuThN6DvpsguJ3e9N3WAoFiss0oeKZYnFiqigKjRs3fuj6Z21sTyGEEOKJUxQ4s1XtzHRkDRjT1HIHd4g7oyamAJ7+1opQiDxjcWK6ePHivIxDCCGEELsXwb8z4MbxjLLiVSC8N1RopfayF+IZZnFiWrVq1byMQwghhHj+KIraflR398/x1cNqUmrrDCGvqZ2ZvEOsG6MQT5DFw0UZjUbS0tLMyq5fv87MmTOZMmUKu3btyvXgnlejR48mMDCQ9957L9O68ePHExgYyOjRo3PteCtWrCA8PDzX9ieEEOIRkuPVKULn1IF/v8wor/I6vDwVhh2FVz6XpFQ8dyy+Yzpu3DhsbGz48MMPAbWzU7t27UhOTsbLy4tFixYxa9Ys6tatm2fBPk+8vb1Zt24dY8eONc0Fn5yczNq1aylWrJiVoxNCCJEjVw6pbUf3/wQpd9SytGSo9bbaoalQafWfEM8pi++Y7tmzx6zz0+rVq0lLS2Pjxo388ssv9OjRg3nz5uVJkLlNn6pHn6o3jSKQmJaIPlWP4e5gxMmGZPSpelKNqQCkGlLRp+pJMaQAkGZMQ5+qJyktCQCjYjTt8/5j5FT58uXx9vZm48aNprKNGzdStGhRypUrZyrbsmULHTt2JDw8nIiICPr168e5c+dM6y9cuEBgYCAbN26ka9euVKxYkRYtWrB3714AIiMjGTNmDHfu3CEwMJDAwEBmzJgBqOe4TZs2hIWFUbNmTYYNG8aNGzdy/JqEEOK5lJqkJqLzG8HsGrBznpqUepaCRh9Brw0yZ70Qd1mcmF65coUSJUqYlrdv307jxo1xdXUFoHXr1pw4cSL3I8wDEd9HEPF9BLHJsQB0/LUjEd9HsOfqHgDGbB1DxPcRLDu2DIBvor8h4vsIPt31KQCbzm0i4vsI+v/RH4BTcaeI+D6CJsubZDrG42jbti0rVqwwLS9fvpy2bduabZOYmEjPnj1ZtmwZCxcuRKPRMGDAAIxGo9l206dPp3fv3qxatYoXXniBYcOGkZaWRlhYGGPHjsXFxYVt27axbds2evXqBUBqaipvvfUWv/zyC1999RUXLlzI1SYEQgjxXDj5J6zsC+cjQWsD5VtCt9UwcBfUGChz1wtxD4ur8u3t7UlOTjYt79u3j5EjR5qtlwH2c1eLFi2YOnUqFy5cQKPRsGfPHqZNm0ZUVJRpm/uH8Pr444+pXr06J06cICAgwFTeq1cv6tWrB8DgwYN5+eWXOXv2LKVKlcLV1RWNRoOXl5fZvtq1a2d67Ovry7hx43j11VdJSEjA2dk5D16xEEI85Qyp8N86uLQPXnxfLSvTGHwjoPRLUKkruBa1aohC5GcWJ6aBgYGsXr2aYcOGsWvXLm7cuEG1atVM68+dO0fhwoXzJMjcFtkpEgBHG3XYjR+a/4CiKNjr1MkDJtWexMSaE7G9O2jx68Gv06NCD3QaHWnJaTTwa0Bkp0i0GvWGc0n3kqZ93n+Mx+Hp6Um9evVYtWoViqJQr149PD3Nf1mfO3eOL774gn379hEbG2tqnhATE2OWmAYGBpoepyegN2/epFSpUg88/uHDh5kxYwZHjx4lLi7ObN+lS0sbKCGEMLl1QR3qac9iiL+sloV1gYKl1B73vTc+/PlCCCAbiembb75J3759Wb9+PdeuXaN169Zmiejvv/9OpUqV8iTI3OZk62S2nJ6gprPX2cM90wzb6myx1dmiKApppGGjtTElrQBajTbTPu9fzqm2bdsyYcIEAN5///1M69944w28vb2ZOHEihQsXxmg00rx5c1JTU822s7XNiDd9IoT7q/vvpdfr6dWrFzVr1uTTTz/Fw8ODmJgYevfunWnfQgjxXDIa1Wr6Xd/CsQ2g3L2mOntBWFewe/A03kKIrFmcmFarVo3ly5fz77//4uXlRZMmTczWlytXjpAQGdYit9WuXduUCNaqVctsXWxsLCdPnmTChAmm4Z5yMmyXra0tBoPBrOzUqVPExsYyfPhwvL29ATh48GBOXoIQQjx7DGkwuzpcP5ZR9kJtddzRss3Bxs56sQnxFLM4MQUoU6YMZcqUyXJd+/btcyUgYU6n07F+/XrT43sVKFAAd3d3fvrpJ7y8vLh06RJTp07N9jF8fHzQ6/Vs376dwMBAHB0dKVasGLa2tixZsoSOHTty7NgxZs2alSuvSQghnjqKAmf/gWJhYOesVs97V4T4KxDaGSr3BK+AR+9HCPFQFiemq1atyrLcxcUFf3//h7ZVFI/HxSXr6iCtVsv06dOZOHEizZs3x9/fn3feeYeuXbtma/+VKlWiQ4cODBkyhLi4OAYOHMigQYOYPHky06ZNY8mSJVSoUIFRo0bRv3//3HhJQgjxdEiMg/0/qtX11/+DFjOgUjd1XaOP4JUvwS53mm4JIUCjpPdoeYQqVapkWa7X6zEajdStW5fPPvvsgUlUXjAYDOzbt4/Q0NBMdxOTkpI4ffo0/v7+pgHqH5eiKOj1epycnEztNIV15MX5FSI/eNh1TTwhigKX9sDOb+HgckhLVMttnaHuSKg1xKrhCfG4nvR1JjvHs/iO6c6dO7MsNxqNHDx4kHfeeYevvvqKUaNGZS9aIYQQIr84ug7+ngwx+zPKCleAKr0g+DVwcLNebEI8ByweYP+BO9BqCQkJYfTo0WzatCk3YhJCCCGenLSUjMcJ19SkVGcPIR2g10bo/w9U6SNJqRBPQLY6Pz2Mn58fV65cya3dCSGEEHknLRkO/wK75oObD7Sbr5YHt4PkO1CxIzgXtG6MQjyHci0xPX/+PEWKFMmt3QkhhBC57+Yp2LUA9i0F/Q21zNYJkuPB3kXtcV9joHVjFOI59tiJqaIoHD58mMmTJ9OgQYPciEkIIYTIPYY0OLZe7Vl/8p4mZ24+ULmHOhi+vQyGL0R+YHFiWqVKlSx7ouv1egwGA7Vr12bgQPmVKYQQIp+5dQ5+6nJ3QQOlX1QHwi/TSB2PVAiRb1j8jRw7dmyW5S4uLpQsWVLGMRVCCGF9RiOc2gQHV8IrX6iJp2dJtUe9WzH1Dqmnv7WjFEI8gMWJaevWrfMyDiGEECLnEq7D3u9g9wKIPaOWBTaFcs3Vx22/sVpoQgjLWZSYpg8qb6nsbi+EEEJkm6LAue2wcz4c+QUMd4d9si8AoR2hcDnrxieEyDaLxjFt1KgRX3/99UOHg1IUhX/++Yc+ffqwZMmSXAtQ5NyFCxcIDAzkyJEj1g5FCCFy3/ftYUFTOLhMTUqLVYIWM2HYUWj6CRSUJmZCPG0sumO6ePFiPv/8c2bOnEm5cuUICgqicOHC2Nvbc+vWLU6ePMm+ffuwsbGhb9++tG/fPq/jfubFxMQwY8YMtmzZQlxcHF5eXjRs2JABAwbg4eFh0T68vb3Ztm2bafvIyEi6devGzp07cXOTgaKFEE+Zi3vAuRC4+6nLftXgzFZ17NHwXlAszLrxCSEem0WJacmSJfnyyy+5fPkyGzZsYOfOnezdu5ekpCQ8PDwoX748H374IXXr1kWrfezJpJ5758+fp3379rzwwgtMmzaN4sWLc/z4cT799FO2bt3KTz/9hLu7+yP3o9Pp8PLyyvuAhRAir6QkqPPV7/oWLu2FiDfUu6GgzsZUpTc4FLBujEKIXJOtcTKKFi1Kjx496NGjRx6F82QY9fosyzX29mh0OhSjESUpyVSuvdteVklNxZiYiBHMh87SaNA6Oqr7TkmBtDS13MYGrZ1dtuP74IMPsLW15dtvv8XBwQGAYsWKUb58eV566SWmT5/OBx98QGBgIF999RUvvvii6bnh4eGMHTuWNm3acOHCBRo2bMiqVatwdXWlW7dugDr0F6gd2iZPnkxKSgpTpkxh7dq1xMfHExQUxJgxYwgJCcl27EIIkSuuHlEHwt//IyTfUst0dmBMy9hGpggV4pnzXA7g9l+lylmW+y1ahHNEVVLPneNkk6ZqoY0N5Q5GAxD3v5+5MnFipufZlS5FqV9/BeDqZ58Ru1htY+vRrStFHzDM1oPExcWxbds23n77bVNSms7Ly4tXXnmF9evXM378+Gzt19vbmxkzZjBo0CA2bNiAi4uLaf9Tpkzht99+Y/Lkyfj4+DBv3jz69OnDxo0bLbozK4QQueb8Tvj9PTj3b0aZhz+E94TQLjJNqBDPOKl3z2fOnj2LoigPHBe2VKlS3Lp1i5s3b2ZrvzqdjgIF1OquggUL4uXlhaurK3q9nh9//JGRI0dSt25dSpcuzYcffoi9vT3Lli177NcjhBCPlJqY8VirVZNSjQ7KNoeuK2HQHqj5liSlQjwHnss7poF7dmdZrrG3B8DWzy/LbdxfexXbJo1xcnTMVJWfrvDw4RQeMkRdsMn9t1dRFDVGW9tc2d+5c+dITU2lUqVKpjJbW1tCQkI4efJkrhxDCCEyMaTBsQ1q29H4K/DGNvVaWqwSNPsMyr6sDogvhHiuPJeJqfYRY6xqtFo0WWyjsbVF6+iI1skpy+lZAbVNaQ7alabz8/NDo9Fw4sQJs7aj6U6dOoWnpydubm5oNBpTopouLS0t03Mscf/rURTlga9RCCFy7PYl2LMYdi+CO5cyyq8egSLl1eS06uvWi08IYVVSlZ/PeHh4ULNmTb7//nuS7umABXDt2jXWrFljmoXL09OTq1evmtafOXOGxMREHiT9LqvBYDCV+fn5YWtry+7dGXeIU1NTOXjwoEwzK4TIHUYjnNwEP3aG6UGweZKalDoVVKvoB+9Tk1IhxHMvR3dMb9++zYEDB7hx40amO3atWrXKjbiea++++y4dOnSgd+/eDBkyxGy4qBdeeIEBAwYAUK1aNZYuXUpoaChGo5HPPvvsoVX8Pj4+aDQaNm/eTN26dbG3t8fZ2ZmOHTsyZcoUChQoQLFixZg3bx5JSUm0a9fuSb1kIcSzzJACy3pBYqy67FdDHXe0fAuwsbdubEKIfCXbiemmTZsYPnw4iYmJODs7m1X3ajQaSUxzwQsvvMCyZcuYOXMmQ4YMMf0AaNSoEVOmTMHx7tBUo0aNYuzYsXTp0oXChQszduxYDh069MD9FilShEGDBjF16lTGjBlDq1atmDx5MsOHD0dRFEaOHElCQgJBQUHMmzfP1FlKCCEspihwboc6Z/2LH4CbN9g6QER/0F+Hyj3l7qgQ4oE0yv23PB+hcePG1KlTh6FDh5oSJGsxGAzs27eP0NBQdDqd2bqkpCROnz6Nv79/pmGXckpRFPR6PU4PaWOaV7788ksWLFjAt99+S1iYzG6SF+dXiPzgYde1fC3pFhz4n9qZ6ephtaz+OKg70rpxCSEyedLXmewcL9t3TK9cuUK3bt2snpQ+bwYPHoyPjw/79++nYsWKMsOWECJ/uLRPTUajl0Fqglpm4wjBbSGgsVVDE0I8fbKdmNaqVYvo6Gh8fX3zIh7xEG3btrV2CEIIkeG3cbB9ZsayV1m17WhIe3B0t1pYQoinV7YT07p16/Lpp59y8uRJAgICsLlvrM6GDRvmWnBCCCHykWv/qYPhFwtVl/3rQuQcKN9STUhL1DAb11kIIbIr24npu+++C8BXX32VaZ1Go+HIkSOPH5UQQoj8IS0Fjvyizlt/dhu8UBt6qFMwU7ohDD0CLl7WjVEI8czIdmJ69OjRvIhDCCFEfhJ7BnYvhL3fQcI1tUyjBXs3NVm1sQOtTpJSIUSusurMT99//z0//PADFy9eBKBMmTK8+eab1K1b15phCSHE8+vmKVg3Ek78AdwdtMXVGyp1h0rdoICPVcMTQjzbcpSYRkVF8e2333Ly5Ek0Gg0lS5akT58+hIeHZ2s/RYsWZfjw4fj5+QGwatUqBgwYwMqVKylTpkxOQhNCCJFdKQlg56w+dnCHM1sBBUrWhyq9IaAJ6B48eYcQQuSWbI85tHr1anr27ImDgwNdu3alc+fOODg40KNHD9asWZOtfTVo0IC6devi7++Pv78/b7/9Nk5OTuzbty+7YQkhhMgOoxFO/gU/dYVp5SDptlru5AmtZsGgPdBtFZR7RZJSIcQTk+07pl9//TUjRoygR48eprLu3buzYMECZs2axSuvvJKjQAwGAxs2bECv12d7APl7536/t0xRFNO/3JC+n9zan8i59PNqMBiyPP9CPK3SP8959rnW30Sz/3s0exahuXky47jH/1B71wOUa5UeTN7EIISwqjy/zjzgeJbIdmJ6/vx56tevn6m8QYMGTJs2Lbu747///qNDhw4kJyfj5OTEV199RenSpbO1j+jo6CzLbWxsSExMxGg0Zjuuh0lMTMzV/T0pr7/+OgEBAYwYMcLaoTy25ORkUlNTpTOeeGY96LqWU843D+J1dg0elzajNaYCYLBx4kbxl7hWogVJKSVAaquEeK7k9nUmN2Q7MfX29mb79u2UKFHCrHz79u14e3tnOwB/f39WrVrF7du32bhxI6NGjeK7777LVnIaHByc5ZSkZ8+exdHRMVenJE1MTMTR0fGJTEm6Z88eunTpQo0aNZg3b95j70+r1WJra4uTk1OO9xEZGUn37t2JiorCzc3tsWPKqfTXUrp0aZmSVDxTDAYD0dHRWV7XHof225FoLu4CQClaESW8J1RoQ0E7Fwrm2lGEEE+DvLrOPOp4lsh2YtqzZ08mTpzIkSNHCAsLQ6PRsHv3blauXMm4ceOyHaydnZ0pyQ0ODiY6OprFixczYcIEi/eh0+kyvbE6nQ6NRmP6l5vyYp9ZWbFiBV26dGHZsmXExMRQrFixx9pfesyPE/u9+3gS78HD4tBoNFmeeyGeBY/12Y45oE4TGvwqvFBTLav+JpzYBFV6oSlWyarfXyFE/pAf/4Zmu/NTp06dmD59OseOHWPSpEl8/PHHHD9+nOnTp9OhQ4fHDkhRFFJSUh57Pw+Tmmyw6J/BoDYBMKQZ1eVUddloMFq8j5zS6/WsX7+ejh07Uq9ePVasWGFaFxkZSWBgIFu3bqVVq1aEhITQrVs3bty4wd9//03Tpk2pVKkSQ4cOzdTswGAwMGHCBMLDw4mIiGD69OlmbWZXr15NmzZtCAsLo2bNmgwbNowbN24AcOHCBbp16wZAlSpVCAwMZPTo0QCkpKQwceJEqlevTnBwMB07duTAgQM5fv1CiGxKTYR938M3DWFObdi9AKLmZKwPagutvgKfyjI7kxAi38rRcFEvvfQSL7300mMffNq0adSpU4eiRYuSkJDAunXriIqKypVq64eZ+9bfFm1Xp0MAwfWKs3v9GXauPUNQHR/CWxbn1L7rbJx3yKJ9DPi6QY5iXLduHf7+/pQsWZIWLVrw4YcfMmDAALO7HDNnzuTdd9/F0dGRIUOGMGTIEOzs7Jg6dSp6vZ4BAwawZMkS+vbta3rOypUradeuHf/73/84ePAg7733Hj4+Prz22msApKam8tZbb1GyZElu3LjBpEmTGD16NN988w3e3t7MmDGDQYMGsWHDBlxcXEzV6FOmTOG3335j8uTJ+Pj4MG/ePPr06cPGjRtxd3fP0XsghLDAtWNqErpvKSTdUsu0tlC+BYT3tm5sQoh8RzEaSb1yxdphPJBVB9i/fv06I0eO5OrVq7i6uhIYGMi8efOoWbOmNcPKF5YtW0aLFi0AqF27Nnq9nu3bt1OjRg3TNkOGDKFy5coAtGvXjqlTp/LHH3/g6+sLQOPGjYmMjDRLTL29vRk7dqxp/Nljx46xcOFCU2Larl0707a+vr6MGzeOV199lYSEBJydnSlQoAAABQsWNLUx1ev1/Pjjj0yaNMk0OcKHH37IP//8w7Jly+jTp09evU1CPN92L4Q1b2Usu/tB5Z4Q1gVcClstLCFE/qEoCiknT2Lr54fWzo74TZuI+WACTM9+h/UnwaLEtEqVKha3R4qKirL44B9//LHF2+amvl9YNrOU1kZ9zZWbvkBYoxJoNJCcmkTJ0EIW7yMnTp06RXR0NDNnzgTU0QWaNWvG8uXLzRLTwMBA0+OCBQvi6OhoSkoBChUqlKmxccWKFc3OZWhoKAsWLMBgMKDT6Th8+DAzZszg6NGjxMXFmar5Y2JiHtgh7dy5c6SmplKpUiVTma2tLSEhIZw8eTLL5wghciD2LNw8CaXu1sSUrA9aGyjTCMJ7qeXa/NVeTAjxZCmKQsrpM+ijItFHRZEQtRPD9ev4LV6Ec9WqOFaujDEhAU1srLVDzZJFienYsWPzOo4nytY+exdunY0Wnc3d8UtTQavTorPJuzZay5YtIy0tjTp16pjKFEXBxsaGW7dumcpsbDJOn0ajMVtOL8vOUFl6vZ5evXpRs2ZNPv30Uzw8PIiJiaF3796kpqY+8vn3/3hRFEU6WAjxuIwGOP672pnp+EZw9oKhh9VB7z1KwLBj4Cz96oV4XimKgpKUhNbREUVRONX8FVLuuymksbcn9cJFqAo2Hh6U3v4v+w8etFLED2dRYtq6deu8jkPclZaWxurVqxk9enSmJg2DBg1izZo1jzVd6/79+zMtlyhRAp1Ox6lTp4iNjWX48OGmob8O3vfBtbVVZ4C5d7BcPz8/bG1t2b17t2nkgNTUVA4ePEj37t1zHKsQz7X4K7B/KexeBLfOZ5QXKQ8J18Dt7igdkpQK8VzS795N7E8/oY/aiXP16hSb9DEajQY7X19Sz5/HMTQUp4iqOFetikPFimjt7EzP1dhYtSXnQ1kUWXx8vMU7dHFxyXEwAjZv3sytW7do164drq6uZuuaNGnCsmXLGDNmTI73HxMTw6RJk2jfvj2HDx/mu+++Y9SoUQAUK1YMW1tblixZQseOHTl27BizZs0ye76Pjw8ajYbNmzdTt25d7O3tcXZ2pmPHjkyZMoUCBQpQrFgx5s2bR1JSklmbVSGEBZJuU3LXeLRr/wVjmlrm6AGhndX2o4WyNwGJEOLpl3rpEgmRUSTu2U3R999HY2ND6qVL3P5FnQo+cc8e07ZFPxiPzt0drb29tcJ9LBYlpuHh4Y+skk2vtj1y5EiuBPa8WrZsGTVq1MiUlAI0atSIr7/+msOHD+d4/61atSIpKYlXX30VnU5Hly5daN++PQCenp5MnjyZadOmsWTJEipUqMCoUaPo37+/6flFihRh0KBBTJ06lTFjxtCqVSsmT57M8OHDURSFkSNHkpCQQFBQEPPmzTN1lhJCPIDRAOejwK+aOoyTvSuOt0+hMaaBb4TadrR8K7CViSSEeF4Yk5O589tvJERGoo/aSer5jFoT93btcKxYEedq1SjYrx/OEVVxvGcqd9siRawRcq7RKBZM/J6dDk1Vq1Z9rICyw2AwsG/fPkJDQ7Oc+en06dP4+/vn6sxPer0eJycnaTtpZXlxfoV4YoxGuLATDq2AQyvVavt+W8C7IgaDgZO/f0upkAh0xUKsHakQ4glIvXoVfdRO7EqUwDE4CCUlhf8iqqGkj0Wu0+EQVAHnqhG4v9oOOz+/xzrew/KnvJCd41l0x/RJJptCCPFMUhSI2QcH7yaj97YbdSgAN0+Bd0UA7nhVgiIVrBOnECLPGRMS0Do7A3Blyqfc/PZbANw7dsAxOAiNnR3u7dqhsbPFOSICx0qV0bk4WzPkJ8aixPTo0aMEBASg1Wo5evToQ7ctW7ZsrgQmhBDPlG8bw/nIjGU7Fyj7MlRoow7zZGP34OcKIZ5qabGx6KN2oo+MJCEqEsP1G5T59x80Wi32Jf1Bo8G+XFnsfDPuhBYd92yNiGQpixLTVq1a8c8//1CwYEFatWqFRqMhqxYA0sZUCCGAGyfVO6OhnaCAj1pWpALE7IeAJhDURh171NbRunEKIfJUypkzXBj8FsnHjmW5zr5kSVybNMX1xRfRySyJgIWJ6Z9//omnp6fpsRBCiPvEnVOr6A+uUKvsQe2wVGOQ+rjeGHhpAthn7tgohHj6Jfz7L/F/byEhKgqvwYNwrV8fmyJFSD59GgD7MqVxqhqBU0RVnKpUwcbDA+C5qaK3lEWJqY+PT5aPnwYW9O0STyE5ryJfuHMZDq1SOzHdW02v0UHJelAoIKNMpggV4plhiE8gcfcuDLfvUOCV5gDE/vgTdzZuBEC/Yweu9eujdXTEb+4c7AMCsCkoYw5bwuIRVnfu3GnRdlWqVMlxMLkpfSB4vV6Po6NUlz1rUlJSAJ5Ib0IhHmjdCDjyy90FDbxQS62mL9cCnAtZNTQhRO4x6vXod+9BHxVJQlQUSQcPgcGAjZcXbs1fRqPR4Nq4ETp3d3VQ+4gI03Odq1e3YuRPH4sT065du5qGSHrQ3ar81MZUp9Ph7u7O1atXAXJliCdFUUhOTkar1cpwUVZkNBq5du0aTk5OmaZhFSJPJMbB0bXqndGQDhDyqloe3E69axrUFsq3BDdvq4YphMgdxsREEvfuxTEsDK2jIwmRkVzo/6bZNra+vjhFVEVJTETj5ESBl1+mwMsvWyniZ4fFf9ULFCiAs7MzrVu3pmXLlnjcbRuRnxUtWhTAlJw+LkVRSE1NxdbWVhJTK9Nqtfj5+cl5EHknOR6ObYCDy+HEH2BIubtCk5GYlm+p/hNCPDPOv9Gf+H/+gdRUfOfNw6VWTZzCw9VENDzcNM2n7d0puEXusjgx3bp1K3/88QfLly9n3rx51K1bl7Zt21KnTp18mxxoNBq8vb0pXLgwqampj70/g8HA0aNHKV26tFQhW5mdnR1ardbaYYhn0Zl/YOc38N8GSEvMKPcqp94ZDWpjvdiEELnCmJJC0v79JERGoY+MxK50Kbzffz9jg9RUbIoWxXjnNgA6V1dK/77RStE+XyxOTO3s7GjWrBnNmjUjJiaG5cuX8+GHH5KSkkLr1q0ZNGhQvq1W1el0uZJIGgwGABwcHCQxFeJZkZYCybcz2oRePaz2rgfwLKkmoxXaQJHy1otRCJEr4rdu5cb8b0ncuxclOdlUnnrpkumx19tvU2TMaGylVs4qcpRJent7M3DgQFq2bMm4ceOYO3cuPXv2xF3G4BJCPA0MaXBmq1pNf2QNBDaD1rPVdeVbQtxZNSH1DlXnrxdCPFUUg4Gk6Gj1juie3RSfMQOtnR3GBD36HTsA0BUqhHPVKqYhnBRFQaPR4BAY8Ii9i7yU7cQ0JSWF3377jeXLl7Nv3z7q1q3LnDlzJCkVQuRvRiOc36Emo4dXQ8K1jHUXdqpThmo06rBOjSZaL04hRLYpaWkkHT6M1sUF+5IlUQwGzvboiZKUBEDS/v04VamCU0RVirz3Ls4REdiVLCl3RPMhixPTAwcOsHz5ctatW0fx4sVp3bo1n3/+uSSkQoj878QfsHoQ3MmorsPRE8q3UO+Mlqgpd0aFeIooBgNJR4+iv9tGVL97N8b4eNw7dsD7/ffR2tnh2qA+SmoqTlUjsPUrAYCNhweenTpZOXrxMBYnpq+99hrFihWja9euVKhQAYDdu3dn2q5hw4a5F50QQmSXosCVg+owTmVeUssK+KpJqb0blG2uJqMl64LO1rqxCiEsohiNpF66hF3x4gBcnjiRuB9+NNtG6+aGxibjO+0zbdoTjVHkjmxV5V+6dIlZs2Y9cH1+GsdUCPGcuXZMraY/uBxuHFeT0SHR6p1Qr0DouhL8aqjThAohngqply9z5aOP0EftRElLIyByBxobG5zCwrj9y5q7wzepbUQdypZFIx2Tn3oWJ6ZHjx7NyziEECL7bp5WB70/uBKuRGeU6+yhWCgk3QJHd7WsVANrRCiEsICiKKScOkVCZCT6yCg82r+Gc40a6AoU4M7mvyE1FY2TEynnzmNf0h+3Jk1wa9YMTT4dDUjknJxRIcTT6eIe+KZ+xrLWBko1VKvpA5uCg5v1YhNCWOTW2rXE//knCVE7MVy/biq3LVoU5xo10Do64v3hBOxfeAGHChXQ3J1uXGNnZ62QRR6TxFQIkf/FX4PDq+ByNLT4Ui3zDlWr6wuWUpPRss3BydOaUQohHkBRFFLPnychMhIlMRHPbt0AiN/8N7fXrQdAY2+PY1gYzhFVcalb1/Rc91atrBGysBJJTIUQ+ZP+pjrG6KEVcHoLKEa1vOZbajKq1cLAXdJmVIh8KuXCRWy8CqG1tydh2zbOv94XAJ27Ox5duqDRainwSnPsSpTAOaIqDhUropU7oc89SUyFEPlHapJ6Z/TgCjj5JxjTMtb5VFbvjDp6ZJRJUipEvpF66RIJUVGmIZxSL13Cd/48XGrWxDE0FI29PQ5BQThHVEVJTkbj6IhLnTq41Klj7dBFPpKtxNRgMLB7924CAwMpUKBAXsUkhHieGFIzhm1SjLB2GKTEq8tFgtW56Su0Bk9/68UohMhESUkxtfU83b49SfsPmG9gY0PquXNQsyY6V1cCdkbJHVHxSNlKTHU6Hb1792bdunWSmAohci4tWR30/uBy9f9Be8G5INg5QUQ/0Nmp89N7ydSAQuQnidEHifv5Z/SRkTgEB+Pz2acA2BYpSpL2oOmOqFPVCJwqhaF1djY9V5JSYYlsV+UHBARw4cIFfH198yIeIcSzypAKp/5Wk9GjayH5Vsa6479B6N3ZWBq+Z534hBBm0m7eRB8VhX7nLgqPGonWzo7UixeJ+9//ADAmJ5vmly8yehTeH3+EzsXFylGLp122E9O3336bTz75hLfeeosKFSrg5ORktt5FPpRCiHspCqwbrrYbTbyZUe7qrd4VDWoLPpWsF58QAgAlNZU7f/2lthGNiiT5+AnTOremTdTB7KtWwbN7d3VQ+/DKprnmbYsVs1bY4hmT7cS0T58+APTv39/0gQRMv5pk5ichnnNGI1zYCd4V1c5JGg3cOKkmpU6FoEIrNRn1rab2rBdCWIXh1i30O3diU6QIjsHBoNEQM2YsxoQE0zb2AQE4RUSg81A7Hdp4elJkzGhrhSyeA9lOTBcvXpwXcQghnmaKAjH77k4JuhJuX4D2S6Fcc3V93ZHqME8v1AadDAYihDUYk5PR2tsDcG3GTK7PmgWKQoG2bXAMDkZjY0OB1q3BaFTviFYJx8ZTxgYWT1a2/0JUrVo1L+IQQjyNrhzOmJ8+9nRGuZ0r3InJWC5R48nHJsRzzhCfQOKe3aZpPlPOnyfgn21obGywK+EHioKdvz+23hnV8EXfGWfFiIXI4Timt2/fZtmyZZw8eRKNRkPp0qVp27Ytrq6uuR2fECK/+rkHHFqZsWzjCAGN1Wr6Mi+BraPVQhPieZZ66RIX3n6bpIOHwGAwW5d87BgO5cvj0qAhpf/+G9siha0UpRBZy3ZiGh0dTZ8+fbC3tyckJARFUViwYAGzZ8/m22+/pUKFCnkRpxDCmmLPqkloyXpQLFQt866o9q4v/aKajAY0AXvp/CjEk6Tfs5f4LX+jj4yiYO9euL74IrpChUj+7xgYDNj6+uJUtQrOERE4Va2KbdGiAOhcnNG5OD9i70I8edlOTCdNmkSDBg348MMPsbFRn56WlsY777zDxx9/zNKlS3M9SCGEFdyOuTsL03K1MxNA1b4ZiWnlnuo/R3crBSjE88WYnEzivv2kXb9GgZdfBiD2+++5/euvACT8+y+uL76I1s6O4jNmYF/SH1sfH2uGLES2ZTsxPXjwoFlSCmBjY0OfPn1o27ZtrgYnhHjCEm7AkdXq0E5ntgHK3RUaeKEW+EZkbCsJqRB5SklJIfHAAVMb0cR9+1BSUtC5u+PWtCkarRbXFxuCVoNzRATO1aqZnutSu5YVIxci57KdmLq4uBATE0OpUqXMymNiYnB2lmoBIZ5q26bB9pkZy74R6lijFVqBa1GrhSXE80BJSSHx4CEcAgPQOjuTELWT83eHaExn4+WFU0QExoQEdK6uuDVpgluTJlaKWIjcl+3EtFmzZowbN45Ro0YRFhaGRqNh9+7dTJkyhZfvVi0IIfK55Hj4bz0cWgG+VaHW22p5UBv1Tmn6/PTuftaNU4jnxMVhw7mzaRNKYiLFZ8/CtX59nCqFYVO0KI5hoXfbiEZg5/+C2RjiQjxrsp2Yjhw50vS/4W5vPxsbGzp27Mjw4cNzNzohRO5JTYTjG9Vq+mO/QVqiWn7jJNQcog6E71MZ+v1t1TCFeFYpBgNJh4+gj4okITIS22LF8B4/Xl2XkoKSmIjO3R1DbBwAWicnSv+1SRJR8VzJdmJqZ2fHO++8w7Bhwzh37hyKolCiRAkcHWVoGCHypUv7YMcstQd9SnxGuWcptTd9UBs1KRVC5ImEqChufrsA/a5dGOMzvoM2hQujvP8+Go2GQgMHUGjgQOzLlEZzz4xokpSK502Op2BxdHTEzc0NjUYjSakQ+YkhDRKugZu3uhx/BQ78pD4u4Hu3mr6NOtyT/NETItcoikLysWPoIyPR79xJsU8+QevkhDE+gfjNmwHQurqqc85HVMU5IqMzoUNgoJWiFiJ/yXZimpaWxsyZM1myZAl6vR4AJycnunTpwsCBA7G1tc31IIUQj2A0wrnt6tBOh1dD4XLQQx1ChpL1ocYgKNcCileRZFSIXKIoCiknToDOBvuS/mAwcLZTZ9Nc84kd9+FcowZOVcIpPGIEThEROJQri0ans3LkQuRf2U5MJ0yYwB9//MGIESMIDQ0FYN++fcycOZPY2FgmTJiQ2zEKIbKiKHBxt5qMHloFdy5lrLt6RO3gZO8CNnbQaKLVwhTiWaEoCimnTqGPiiIhMgp9VBSGmzcp0LYNxT76CI2NDc51amO8E49TRFXsSpQAQOfqSsHevawcvRBPh2wnpmvXrmXatGnUrVvXVFa2bFm8vb0ZOnSoJKZCPAmX9sL/ukHcuYwy+wJQrrlaVe9fF3RSeyHE41AUhbSr10zTdl799DNufvut2TYaB4eM4X6B4tOnP8kQhXjmZDsxtbe3p3jx4pnKixcvLtX4QuSVa//BlYNqZyUAD391ZiZbZwhsqpaXbgg29taNU4hnQNqNG1yZ/Il6R/TWLQKiItHa2eEYEozG3h7HsDCcI6riVLUqjsHBaOzsrB2yEM+MbCemnTp1YtasWUyaNAm7u1/GlJQUZs+eTZcuXXI9QCGeWzdPqUM7HVqpJqU2jlCmsVo97+gO3X8B71Cwc7J2pEI8tVIuXFQ7K0VF4tb8FVxq10Lr6sqdjRtRkpPR2NqScuoUDmXL4tKggZqk2ssPQCHySrYT0yNHjrB9+3bq1KlD2bJlATh69CipqalUr16dgQMHmradOXPmg3YjhMjKrQtqInpwuVpdn05rC/51QH9DTUwBStSwToxCPOXubNrEnT/+RB8ZSerFi6ZyrbOLmpja2VH0vXex9fHBMTQUrYODul7ujAqR57KdmLq5udG4cWOzMm9v71wLSIjn1p0rMD0IU4M1jVZtKxrUBso2BydPq4YnxNMo9cpV9FGRGOJu4dlVrdW788ef3FqxQt3AxgbHoCCcIiJwqZfRd8K9bVtrhCvEcy/biemkSZPyIg4hni/6m3BkDZz4A9otAJ0NuBZRpwfV2qjTgZZvCS6FrR2pEE+VtOvX0bq4oHVwQL9zJ2e7dgNA6+KCR8cOaGxscGvaBBtPD5wiInCqVAmts7OVoxZCpMvxAPtCiGxKug3/rVOr6U9uAmOaWn52G5Sspz7uvkY6MAmRDWk3b6KP2nl3ms8oUk6eNM0171ChAho7O+zLlMEpIgJjUhI6FxdcatfGpXZta4cuhMiCJKZC5CWjQR3w/uByOP47GJIz1hUNVmdgKnTPjC+SlArxUIrBYBqg/lyv3iT8+2+mbVJOnYL69dE6ORGwYztaJ+kgKMTTwqqJ6Zw5c9i4cSOnTp3CwcGBsLAwhg8fTsmSJa0ZlhCPJy0ZdHbqDEsaLfz5AcSeUdcVCoCgdmq70UJlrBqmEE+LpP+OcWvFchIio7Dzf8E0VqjOwwMA+4AAnKpWxSmiKk7h4djcLQckKRXiKWPVxDQqKorOnTsTHByMwWBg+vTp9O7dm7Vr1+IkFxPxNDGkwqnN6p3Ro2uh1wYoUkFNTiP6q/PVB7XNKBNCZMlw5w76XbvQR+3Ea/AgtI6OpF68wM1FiwFIu3oVRVHQaDR4vf02RcaNxcZTOgYK8azIlcT09u3buLm5Zft58+fPN1ueNGkS1atX59ChQ1SpUiU3QhMi7xgNcGYbHFqhVtcnxmasO7pOTUIBqr1hnfiEeAooBgMJ//xDQmQk+qidJB06BEYjAC61a6lzzYeH496+vWlQe83dH3d2xX2sGboQIg9kOzGdO3cuxYsXp1mzZgC89dZbbNy4kUKFCvHNN9+YxjbNiTt37gBQoECBbD3PYDDk+Jg5Oc6TOp7IvzSbP0azdwma+CumMsXZC6V8K5TyrdXe9fI5ETnwrF9njHo9iXv3oitQAIegIBSjkYsjRmK8dcu0ja2fH05Vq4JbAfV9cHam8HvvmtY/q++NEE/Kk77OZOc4GkVRlEdvlqFhw4Z8+umnVKpUiX/++YchQ4Ywffp01q9fT0xMDN/eN4+wpRRFoX///ty+fZvvv//eoucYDAb27duXo+MJYTFFwenWMVIci5Bm7w6A3/6peJ1bS5qtG7HetYn1qc+dghVBo7NurELkNwYD3O2sZLN2LbY//Q+NwUBazZqkvNkfANtFi9AkJ2MoVx5j+XIoBQtaM2IhRB4JDQ1Fp3v438ls3zG9du2aaUD9v/76i6ZNm1KrVi18fHx47bXXchYpMGHCBI4dO2ZxUnqv4ODgR77Q3GAwGIiOjn5ixxNWpChw9QiaQyvQHF6JJvY0xkYfoYSqf0jxHoPhTjc0/vXw1NkiLdxEbnnarzPG5GSSDhxAHxVFYtROko8do9Tff6Oxs+XO5cvEfP8DNt7eeAQE4BUaqj4p/X8hxBPxpK8z6cezRI5mfoqJicHb25utW7cyZMgQQL3jmdNbwh9++CGbNm3iu+++o2jRotl+vk6ne6IX8Cd9PPEEXT+hdmA6tAKuHc0ot3FEm3zbdOeHYsFAsFVCFM+Hp+06k3r1KpdGjCRx3z6U5GSzdSlHjuBUKQzXuvVw+n0jtsWLm9qJCiGsJz9eZ7KdmDZq1Ijhw4dTokQJ4uLiqFOnDgBHjx6lRIkS2dqXoih8+OGH/P777yxZsgRfX9/shiNE7tkwFnZ8lbGss4PSL6lDOwU0yZijXojnXOKhQyRs3Yo+Kgr319rj1qQxNh4eJEVHoyQno/MqhHOVqjhFROAcURXbu38bdC7O6FxkliUhxINlOzEdM2YMPj4+xMTEMGLECJzvTuV29epVOnXqlK19ffDBB/z666/MmjULZ2dnrl27BoCrqysODg7ZDU0Iy92+BIdWgVcglG6olvlUUtuIlqqvDu0U2Awc3a0ZpRBWp6SlkXToEKkxMbg1aQJA7HdLubVyJQA2xYrh1qQxGltbik39DLsSJbDz95c7okKIHMl2Ympra0vv3r0zlffo0SPbB//hhx8A6Nq1q1n5pEmTaNOmTbb3J8RDxV+DI6vh4Ao4+y+gQEDTjMS07Msw/Dg4S8cL8fxSDAaSDh9Rp/iMiiJx126MCQloXVxwffFFNDY2uNSrhzEhAaeIqjhXr2F6rmv9+laMXAjxLMjxOKYnTpzg0qVLpKammpU3bNjQ4n38999/OT28EJZJjIUjv6rtRk9vAeWedtC+EVDmpYxlW0f1nxDPEcVoJPm//7D19UPn4kzinj2c7drNbButmxtOVapguHMHGw8P3Bo3wq1xIytFLIR4lmU7MT1//jwDBgzg2LFjaDQa0kebSq+2OXLkSO5GKMTjOPA/WD8yY9k7VK2mr9Aa3KVNs3i+xbw/ntsbNmC8dQufL7/ArVEjHCpWROdVCMegYJyqVsU5oir2gYGm+emFECIvZTsx/eijjyhevDgLFy6kYcOGLFu2jNjYWD755BNGjRqVFzEK8WipiXB8o3pn1KUINPtULS/fCvYshgqtoEIbKFjKmlEK8cQpikLKiRMkREWhj4xC5+6O94QPADDGx2O8dQutkxNp168DoLWzo8yWLdJGVAhhFdlOTPfu3cuiRYvw9PREq9Wi0WgIDw9n6NChTJw4kVWrVuVBmEJkIS0FTm5Sk9H/1kFKvFruUAAafQQ2duBaBPr/Y904hbCCxP37ubFwIfqonRhu3DCV6zw8KPrBeDQaDQX79MazW1ccKlRAY5Px50CSUiGEtWQ7MTUajaae+B4eHly9epWSJUvi4+PD6dOncz1AITK5fgL+mQ5H1kBSxjSGFPCDoNZqVb3O1nrxCfEEKYpC6tmzJERGoY+KougH49G5uGC4fYc76zcAoHFwwKlSGE5VI9SpPhUFNBocypWzcvRCCGEu24lpmTJl+O+///D19aVixYrMmzcPW1tb/ve//8k4pCJvGI1w+wK4+6nLhhTY+5362KWoOs5ohTZQPBzkTo94ximKQuqFCyhpadj7+4OicLp9B9Nc826vNMe1Xj2cKoVRaNBAnCMicAgJQWtnZ+XIhRDi0bKdmPbv35/ExEQAhgwZQr9+/ejcuTPu7u5Mnz491wMUzylFgQu71BmYDq1U74C+dUBNPIuUh7qjwb82+FUHrXTKEM+21IsXSYjaiT4ykoSoSNIuxeDW4hV8pkxBo9XiUrMGaVev4RQRgf0LLwCgdXbGa8AA6wYuhBDZlO3EtHbt2qbHvr6+rFu3jri4OAoUKCDtksTjURS4fODulKArIe5cxjqHAhB3FjxeUJfrj7FKiEI8EXfumB5emzGT6199Zb7exgYlOcW0WGzqVLn+CiGeCTkex/Re7u7uubEb8TyLOwdLWsONExllts7qoPdBbaBUA7Cxt158QuQxw61bxIwfT+KBaByvXMG4Yzs6FxccypcDnQ7HoCCcqqrTfDpVCkPr5GR6riSlQohnhUWJ6cCBA5k8eTIuLi4MHDjwodvOnDkzVwITz7ibp+D0VqjcXV1284Gk22DjAGUaqR2YyjQCO6eH70eIp4hiMJB84iRJ0QdIPBBNYnQ0BXt0p0DLlmhdXIj/ewuKXg8aDcnHj2MbFoZz7doEREbKHPNCiOeCRYmpq6trlo+FyJa482oV/aEVcGmvWuZfBzz91XainX6CQmXAXj5j4umnKAqKXo/27igmMe+P59aaNWrieQ/93r0UaNkSjU6H9/j30RYsxDGjAceQEEAdVxTpuCSEeE5YlJhOmjQpy8dCPNKdK3B4lTo//fkdGeUanZqUpo89CuBT6YmHJ0RuSzxwgOtz5pJ4YD/2L/hTYslidYUGNVF1csIhKAjHkGAcQkJwrBhqem6BFi0wGAywb59VYhdCCGvL0ZSkBoOBF+72/Ex35swZbGxsKF68eG7FJp52ackwozKkpHfk0ECJGmqb0XItwcXLquEJkVNKSgpJ/x0jMfoASfsPkBgdjd+Cb7EtUgQlNZX4P/8EICk5BcVoRKPVUrBHDzw6dcK+VCmZ3lMIIR4g24npmDFjaNu2babEdP/+/SxbtowlS5bkVmziaZJ0C46uhUOroPXX4OSpdlYKaASxZ+/OT98K3IpZO1IhskVRFJTUVLR2dhhTUjjXtRtJR46gpKSYbZd44AC2L72EQ/nyFB49CseQEBzKlUOj1QJgd981UwghRGbZTkwPHz7MRx99lKk8NDSUDz/8MFeCEk+JlAT4b71aTX/id3Xge4Cjv0Klburj1nNkFibxVLr1yy/cWvMrSQcO4NmzB4XeeAOtnR1psbEoKSloCxTAMThYTUBDgnGqpDZF0To6UrBHD+sGL4QQT6lsJ6YajYaEhIRM5Xfu3FHbRoln39G1EP0z/LcB0hIzygsFqndG/etklElSKvIxY2IiSUeOkLj/AEnRBzDcicfvm7kApJw5Q8LWrQAkHjxoek6xyZOwKVgQWz8/GaZJCCFyWbYT0/DwcObMmcO0adPQ3W0nZTAYmDt3LpUrV871AEU+YEgFNKC7+3GJ/BpOb1Efe/irbUaD2kLh8jIlqMi3FIMBNBo0Wi2J0QeJee89ko8dg3t/UGs0GOLj0bm44PrSS+gKFcIxpCIOAWVMm6TfGRVCCJH7sp2Yjhgxgs6dO9OkSRPCw8MB2LVrF/Hx8SxatCjXAxRWYjTAmW3qLExHfoFWsyGwqbquck8oGqImo8XCJBkV+dq1GTPR79xJ0sGD+C1aiGNwMDr3AiQfOQKAzktNPtVq+WDTnPIO5crhUK6cNUMXQojnTrYT09KlS/PLL7+wdOlSjh49ioODAy1btqRLly4yA9TTzmiE85F356dfBQlXM9Yd25CRmAa1Uf8JkU8Y7twh6eDBu4PWH8ChfHm83nwTgIRt20jcvx9QOyg5BgdjW7w4PjO+xDEoCJuiRaVKXggh8okcTUlapEgRhg4dmtuxCGvaMRv+nQG3L2aUOXpA+ZZQoQ28UMt6sQlxj/ThlwDili/nxrcLSDl1ChTFtI3h+g1TYurZoztGvR6H4GDsS5UC1Lbybi+99OSDF0II8VA5Skxv377NgQMHuHHjBso9fwwAWrVqlRtxibykKHDlENi7gMcLallKvJqU2rtB2ebqHdGS9aTzksgX0m7c4PqcOSQdiCb14kVK/70ZjVaLkppGysmTANj6+OAQEqxWy4dWND3XrWlTa4UthBAim7KdmG7atInhw4eTmJiIs7OzWRWYRqORxDQ/u35cHdrp4HK4/h9UexOa3J3Jq2JH8CoHpV8EWwfrximeW2k3b5IUHU3i3UHrC/bqiXP16mjs7Ihd8p3prmjKmbPYl/THpV5dis+ehWNICDYFC1o5eiGEEI8r24npJ598Qtu2bRk6dCiOjo55EZPITbFn1PnpDy6Hy9EZ5Tp7SEvKWC5QXP0nxBOiKIrph+3ljz8mftNfpF64YLaNY3AwztWro3N1pdCggdgVL45jSAi2JUoAYFu0KLZFiz7x2IUQQuSNbCemV65coVu3bpKUPg3+nQEb38lY1tpAqQZqm9GyzcChgPViE8+lxOiDxC1fRuKBA9iXLIXPZ58CkBZz2ZSU2vn7mwatd65e3fTc9DajQgghnl3ZTkxr1apFdHQ0vr6+eRGPyKn4a3B4FTi4Q8iraplvBGi0aseloLZQroU6VagQeSz1yhUSDxwg6cABEqMP4jt7FlpHR1JjLhH3408AGOLiTNt79uqJR8cOOAQFoXNzs1LUQgghrC3biWndunX59NNPOXnyJAEBAdjYmO+iYcOGuRaceITEWDiyRm03evpvUIxQuEJGYlq8Cgw9Cq5FrBuneC4Ybt8mZtw4Eg9Ek3blitm6pCNHcKpUCcfQUDx79cIxJATH4CDTeqewsCcdrhBCiHwo24npu+++C8BXX32VaZ1Go+HI3UGrRR5JvgNH16ljjZ74E4ypGeuKhal3Ro0G0OrUge8lKRV54PbGjcT/tZnE6AMU7NED93bt0Lq4kLB9B8b4eNBqsQ8IwDE4GIeQYOz8/ACwLVyYIiNHWDl6IYQQ+VW2E9OjR4/mRRziYRQlY3al01tgZd+MdUWCoEJrdXgnz5LWiU88kxRFIfXCBdM88kqagaLvqm2WE/75l1srVwKQuH8/7u3aodFq8f5wAjZeXjiUL4/Wycma4QshhHgK5WgcU/EEpCWrd0QPrYAUPXT8Xi0v/SIUqwRlXlI7MRUua904xTMncf9+rs2aRdKBaAyxsaZyjZMTRcaOQaPTqfPIe3rgGByCY8UQ0zYyZqgQQojHkaPEVK/Xs3PnTi5dukRqaqrZum7duuVKYM8lQ5raVvTgCrXtaPIttVyjhfir4FIYbOyh71/WjVM8ExRFIXbJEtOYoSUWfIutjw8oCgl/bwFAY2uLfblyapvQkGAwGECnw6VWTVxq1bTyKxBCCPGsyXZievjwYfr27UtiYiKJiYkUKFCA2NhYHB0d8fT0lMQ0J27HwJYpcHg16G9klLt6362mbwvOXtaLTzzVFKORlNOn7yagB3AMqYh761ZoNBpuLvmO1PPnAXUeeVsfH+zLlaPIO+/gWDEE+8BAtHZ2Vn4FQgghnhfZTkwnTZpE/fr1GT9+POHh4fzvf//DxsaGESNGSFJqKUWBm6egoDpvNzb2sGcxGNPAqZA6P31QW/CrDnfnBBciu26tXk3cylUkHTyodki6K+3qNdxbtwLAo0MHlJRkHIJDTNN4au3t8ezS2RohCyGEeM5lOzE9cuQIH3zwATqdDp1OR0pKCr6+vowYMYJRo0bRqFGjvIjz6acoELNfbTN6cKV6Z3TECbBzUscWfelDtb3oC3VAJ01/heXSrl8nbuVKtU1oXBwlliwGIOXiRfQ7dgCgcXTEsUIFHEJCcI6oanpuwd69rBKzEEIIkZVsZ0A2NjamaQQLFizIpUuXKFWqFC4uLsTExOR6gE+9q0cy5qe/eTKj3M4Frh6G4uHqcnWZ1UY8nJKaSvLx4yQeiCYx+gCe3bvjEBCAUa/n2tRppu0McXHo3N1xffFFbLy8cAwJwb5UKTQ28oNHCCFE/pbtv1Tly5fn4MGD+Pv7ExERwZdffklsbCyrV68mICAgL2J8OiXHw/yX1OQznY0jBDRWh3Yq0whsZVpX8WhXP/8cfdROkg4fRklKMpU7lC+PQ0AAtr6+/L+9+w6PqkwbP/490yczmUnvISGhBUhClV4UFZQmooivWFCsu7q6P1dZXFnLKi6WLZZ9dZdlXV87Kop1sbsogkoJTRCkBEII6WX6nN8fJzlkKEKQFOD+XFeuzDnTzhmSw537ee77cV9wAdauXbEX5OttmmzdumGT30khhBAnkRYHprfddhv19fUA3Hrrrdx5553cc889ZGVlMW/evBN+gCeNql2w6R0441qtub3VCSYbGMxaa6feU6HbOG2/EIfh/f57apd+iKdoLba8PJJuvRWAhpXf4PnuOwAM0dF603p7nz6AtrBF2kOn8e+eEEKIU0aLA9P8/Hz9dlxcHH//+99P6AGdVGr3wvrF2rzRXV9r+5J7QecR2u0LnoLoFLDHttshio4n7PPh27gRz9oivJu/J/X++1EUBc+qVex/4gkAQuUV0BiYxl11JeGLL8JeUIglOwtFCuKEEEKcomTSWQsZfdUo3/4LNrwB2/8LqI33KJA1TOs52iQprx2OUHREwfJy9j/5pLaK0vffQzCo35cwaxaW7Gyi+vfHNWmi1rS+sUIewHXOOe1xyEIIIUSba3Fgun//fv74xz/y1VdfUVFRgaqqEfdv3LjxhB1ch6Oq9Fh2M4b64gP7MgZqw/Q9LwBXarsdmug46pYto+HrFdo68ldfjXPECAw2G5UvvQzhMADGuDitaX1hAYpdmxNq7dqV9Pnz2/PQhRBCiHbV4sB09uzZlJSUcNNNN5GUlNQax9RxKQpVqcNJrtuA0nuq1vw+Nqu9j0q0k3B9PZ516/EWrQWjifiZVwFQ9dJL1C79EAB7QaEWmDocJP36Nszp6djyCzCnp+ndLYQQQgihaXFg+u233/LCCy+Ql3d6DlPv7n4Nif36YzQa2/tQRDvwFBVR+dJLeNcW4du6Vc+AmtPS9MDUedYYDG439vyDeobOmtUehyyEEEKcNFocmKamph4yfH9aMUhAejpQAwFqP/xQX0c+8+mnMTodBMvKqH7tdf1xptRU7Pn52AvyUcNhFIOBmCkX6CsrCSGEEOLYtTgwnTNnDo8++ij33nsvGRkZrXFMQrSpUFUVnqJ1+jryzuHDwGik5Hd3E25sjeZdvx7HoDOw9+lD/PXXYy/Ix5afj/l0m84ihBBCtKLj6mPq8Xg455xzsNlsmM3miPtXrFhxwg5OiNZS8/771H70Md61a/Hv2KHvj7n4YpzDh6EYDLgnTwbAVpCPtUsuAKa4OJJuu7U9DlkIIYQ45R1XxlSIk0WwspL6zz/Hs2YtaiBA6v33AVrT+polS/THmbM6Yc8vIGrwIH1fyty72/x4hRBCiNNZiwPTKVOmtMZxCPGzBcvK8BQV4Vmzltj/+R/MyUn4t29nz52zAVBsNlLm3o1iNhN9zjkY42KxFxRg690bU6wsgiCEEEK0t5/VYN/r9RJs1igcwOmUJTdF21BVlYp/LsSzZg2eoiKCJSX6fba8HpjHjcPWsydRAwZg690be0E+NBbuOQYPwtEsOyqEEEKI9tfiwLShoYFHHnmE9957j6qqqkPuP6Ub7It249+5k/rly/EWFWHLzyd22jQURaHq1Vfxb9+uPUhRsHbJxVZQgCk5GQCD1UrW/z3XfgcuhBBCiGPW4sD04Ycf5uuvv+b3v/89d955J3PnzqW0tJSXX36Z//f//l9rHKM4jaiqSmD3HrxFa/Hv2EnCDdcDUL1kCfsf19aRd+zbR+y0aQDEXnYZqs+LLb8AW69eGJ2Odjt2IYQQoqNSVZUab5B9NV5Kqj14G0LtfUiH1eLA9JNPPuGPf/wjgwYNYs6cOQwYMICsrCzS0tJYsmQJkyZNao3jFKewYFkZla++indtEZ6iIkLl5fp9sZdOx+h2E9V/AFFDBmsFSgMH6vfHXT6jPQ5ZCCGE6DDqfUFKa7yU1vjYV+vVb5fWeNlX46O0cZ83ENafk2A3sHxIx+tL3+LAtLq6Wu9f6nQ6qa6uBqB///7ce++9J/boxCnHs349nlWr8axdQ/xVV2Hr2RM1EGD/Xx8/8CCTCVv37tgK8gn7fBiROaFCCCFOP95AiLJaX0SgWVrbGGzWePXAs9YXPPqLNXLbzSRFW+mXSIdcGrvFgWlGRga7d+8mPT2dLl268N5771FQUMAnn3xCdHR0axyjOAmp4TD+7TvwFq1FsVpxjRsHQOmD8/B8+y0A9t69sfXsiSk1lZhp07T5ofn52Hr2xGC1tufhCyGEEK0mEApHBJxHynJWNQSO+TWdVhNJLivJ0TaSXVaSXTaSXAduJ0fbSHJZsZmNhEIhVq9e3Xon+DO0ODCdOnUqmzZt4owzzuC6667j+uuv57nnniMUCjF79uwWvdbKlStZsGAB69ato6ysjCeffJKzzz67pYckOgjvxo3UfPCBNiS/bh3hmhoAbPn5emDqGDYUg9MRMSSvKAqp90m2XQghxMktFFYpr/c1y2g2Bpq1XvZWHwhCy+v9HOvq7laTgRT3gcAyuVmwmdQYhCa5bDitP6vRUofR4rO46qqr9NuDBw/mvffeY926dXTq1IkePXq06LUaGhro3r07F154ITfffHNLD0W0EzUQ0Fo0rVmLd9Mm0v74EIrBgGfdOsr/92n9cYrV2tiuqb++L/Gmm9rjkIUQQojjpqoqlQ2BiOHzpmH10hof+xqD0LI6H6HwsUWcZqNCUlOw2SzATD4oy+mymzrkkHtr+dnhdVpaGmlpacf13FGjRjFq1KifewiEQm1TWdb0Pm31fh2BGgzi37oVz9oibL17YcvLI+zxsuOKKyGsTaKOnTVLG4bv2xfXlAu04fiCAqxduqA0Lll7On1mQvwcp+N1Roj2oqoqdb6gltmsjRxG39e4b1+Nl7JaH/7QsQWcBgUSnVY94ExyWUmKbspwWvUANNZuxmA4esAZDoeP+piWauvrTEve55gDU6/Xy1dffcWZZ54JwKOPPorf79fvNxgM3HrrrVjbYW5gUVHRKf1+bc2wZg3G9esx/LAVw/btKD4fAIHJkwlMuxgAa69eqFYr4dwcNu3aiVpXqz35oou07z4frF/fHocvxCnhVL/OCNHavMEwFZ4wld4wFZ4QlZ4wFd6Qts8TosIbptITxneMASeAy2ogzmYg1m4gzm4k1qZ9j2vcjrMZcNkMGCMynIHGrzrwgM8Du0ph14k+4ePQEa8zxxyYLl68mE8//VQPTP/v//6Prl276oHojz/+SHJycsRQf1vJz8/HaDS2+vuEQiGKiora7P1aW7i+Hs+aNXiLilAMRuKunQXAnueeo+79D/THGRwOrL17kzygP+4+fbSdL73YDkcsxKnvVLvOCHGi+QIh9tU2ZTMPZDf3NWU8a32U1vioO45K9eTDZTcbh9kTnFYsJkMrnlnbaevrTNP7HYtjDkyXLFlySND56KOPkpmZCcCbb77JCy+80C6BqdFobNMLeFu/34kQ9vvxbdqEZ20RMVMuwOBw0PDNN+y+6RcAmJKTSWxsZu8aczam2Fjs+QXYC/KxdO6McpKdrxAnu5PxOiPEzxEIhdlf52tWmd68RVLTPE4vlS2oVI+yGElxacPpKY3zN49UqX466ojXmWMOTLdv3052dra+bbVaIybjFhQUcN99953QgxPHTw0GqXn3XTxri/CsXYtv40bUgPbLbO3WFccZZ2DPz8eckYG9oAB7YQFqKIRiNOKeOAH3xAntfAZCCCFOBT9VqV7abF95va9FlepNRUJJjQHmgRZJTZXrp06l+unkmP/FamtrI6Lq5cuXR9wfDocj5pyKthOsqNCq5NeuxV5YSPTo0WA0sveBBwk3LoAAYIyJwVaQj2LS/tlNiYl0+XBpOx21EEKIk5mqqlQ1BPTK9KYs596a469UNxmUA8GlVKqflo45ME1JSWHLli3k5OQc9v7vv/+elJSUFr15fX09O3fu1LeLi4vZuHEjbrf7uCv9T3Xhhga8GzYQKNmrZzX3P/kUlc8/D4B7yhSiR49GURRipkwBNYwtX8uImjMy5BdZCCHET1JVlVpfMHIo/TBZzn01PvyhY6sYNyiQ4LQeNcsZF2U5pkp1ceo65sB05MiR/PWvf2X06NGHVN57vV6efPLJFrd+WrduHVdccYW+PW/ePACmTJnCQw891KLXOlUFKyqo+/hjPGvW4ikqwrdlC4RCKDYbrvPGoZhM2PsUUr98OfaCApwjR+jPTZ59ZzseuRBCiI6mwR9sFmw278cZOa/TEzj29j7xDsuBeZtHyHLGOyyYjKdG4ZBoXcccmN5www28//77jBs3jssuu4zs7GwURWHbtm08//zzBINBbrjhhha9+aBBg/j+++9bfNCnqkBJSWMAupa4GTMwp6YS2L2Hkt/dHfE4U1IS9sICQrW1mGJjcU+ciHvixHY6aiGEEO3NFww1VqZHZjn3NWsCX1rjpdZ77JXqLptJn6upz9tsrFhPdmv7E0+hSnXRMRxzYJqQkMCLL77IPffcw6OPPoraOENZURSGDh3KPffcQ0JCQqsd6KkmVFurtWmyWonqr62MtHPm1fi3bwfAltcT94Tx2Lp3wzF0CLZevbAVFGAvKMCcnNyORy6EEKKttHaluh54Rh+43dQiyW7pWNXa4vTQonK1zMxMFixYQFVVlT43tFOnTsTExLTGsZ1SfD/+SP1XX+FtHJL3b9sGgHPUKKKe1gJTe9++KFF27PkFWDLSAVAsFjr985/tdtxCCCFOvHBYpbzef9jq9OYFRC2pVLeYDAeG091SqS5OTsf10xkTEyPB6BGoqkpgxw4t+PxxO4m33AxA7Qf/oezPf454rDkzE3P6gSKv1AcfkOIkIYQ4iR2pUv3gLGdZrY9gCyrVk6Ktkf03D5PldNvN8n+IOOnJn00/U7CiAs/atdi6dsWcnk6wpISt487T74+dcRmmuDiiBg7AMWKE1jO0IB9bfj6muLiI15ILihBCdEzN11RvntH8OZXqSuOa6lKpLsQBEpi2kOGHH6hcvQbf+nV41hYRKC4GIHnOHOKuuBxTairmtDRMiYnYCgtQA9pE86j+/en092fa89CFEEIchscf0qvUm8/bPBB4at8b/MdeqR7nsDTLaDYFms2Kh1w2EpxSqS7EwSQwbSHzSy9TtnFjxD5Lbi6KxQJoWc/cD5eiGORiI4QQ7amtK9WbhtoTo61YTVI4JMTxkMC0hUKFBbhSU4lqXMbT1rs3xujoiMdIUCqEEK0nGAqzv85/1CxnRf2xr0ZoNxtJcdsOm+VMkUp1cQoJq2E8QU97H8YRSWDaQsGJE0nv0ydieVYhhBA/39Eq1ZuynPvrjrNSPaI6XdvXlOV0WmWJS3HyqfJWUeGrIMmehNPiZOXelXxf8T2FiYXkJ+bz4Y4PWbRlEQOTB3JN/jV8uutTfvXJr8hPyOe25Nva+/APSwJTIYQQrUpVVao9gcgVh2p97K32HnelurF5pXq0lRS3VKqLk1MoHMJoMFLrr2VTxSYABqYMpNZfyz/X/ZP6QD1zBs0B4Ir3rqC0vpR/jvsn6c50fvHxL1hbtpY/n/lnxnQawzvb3uG1La/xiz6/ID8xn9KGUpbtXobD5ADAYXYQVsNU+6rb7XyPRgJTIYQQx+XgSvXSg6rTS5vt8wePvVJdW1M9MqPZvPF70xKXUqkuOpL6QD3Vvmri7fFYjVZWlKxge812BqYMpLO7M0u2LuHDHR9yZqczuaDLBbyx5Q0e/PpBRmSM4LHRj7G5cjNXf3A12a5slkxZgorKP4r+AcDtA27HYrSwp24PpQ2lVHorSXemE2ONIdoSTSCsLbBQmFhIfaCezu7OAAxJHcL9w+4n25UNQEFiAZ9M+wSH0cGGog3t8jkdjQSmQgghDiGV6uJ0VuOvYUf1DkwGE3nxeez37GfR5kUoKFxfeD2+kI9rPriGGn8NL094GbvJzoVvXsie+j28cP4L5Cfm8+8N/+az4s+4Z8g9dHZ3Zlv1Nj7e9TGpzlQArEYr3pBXz17G2mLJdmWTEZ0BgNPs5LK8y3Bb3IRV7Q+7P478I2aDmRx3DgBPnPVExIjAlK5TmNJ1ir6dE5NDTkyOvm01WrHarYRCx/5729YkMBVCiNOILxiirNZ3IMvZGHhGZDlrvNS0oFI9Wq9UP3KWM8klleqi7QTCAWp8NcRYYzAajHxd8jV76/cyJG0ISVFJvLr5VVbuXcmEnAmMzBjJs+uf5Zm1zzAhZwK/HfRbvtzzJb/57Df0T+7Pv8b9ixpfDU+ufhKXxcX1hddjMVhYt38dITVEja8Gu8mOy+qizFNGfbAe0LKXBsVAUlQSAKMzR5PqSKVHXA8ARmaM5P2p7xNjjQEgx53DkilL9HMwKAZmnzE74rz6J/eP2D4Vp6lIYCqEEKeAtq5UP9AiyUqURf4rEa2n1l9LSX0JVqOVLFcWxbXFLN2xFIfZwbTu0yhrKOO3X/yWQDjAs+c9i6qqDHp+EIFwgKUXLSXFkcJj3z7GhvINPDnmSZKiklizbw3v/fge3WK7MTJjJAoKNf4aqnxVAMTb4kl1pBJn0xbCibfHc1G3i/QgUlEU/nLmX4gyRxFj0/Y9f/7zmA0H5jRfW3BtxHkUJhZSmFiobzstTpwWZ+t+eCchuZoIIUQHFg6rVDT4D8po+iit9UasQNSiSnWjIaI6vWneZrLLSopLKtXFiaWqKvWBej0IW1GyggpfBcPThuO0OHl+4/NsqtjEJd0voXdCbx5f9ThvbHmDy3tezszeM1n8w2Lmr5zPednnMX/UfHbX7eaxbx8j153LtO7TtIzo3q8BCIaDmAwmXBYXFd4Kqn3VpDhSKEwsJNYai8OsFQGNzR5Lt9huegZyfM54hqcPJ94eD2jFR/+56D/6Obitbn4/5PcR5zUqc1TEtsVoaZ0P8DQjgakQQrQxbyBERb1f/6psOHC7vM7H1t2V+JZ/1dgc/vgr1SOWumw21B4TJZXq4vjUB+rZ79mP3WQnKSqJH6t/5Ms9X5JgT2Bs9li2VW/j0W8exWFyMH/UfKp91Yx6eRQhNcR3l3+H2WBm9hezKfOU8cqEV8iLz+OL4i9YtmcZA5IH0DuhN/6QnzJPGeWecgDibHHE2eKwmWwApDnTmJgzkfTodABcFhcPjXgIt9WtH+eSKUuIMkVhNGjTR5qq2puMyBjBiIwR+na8PV4PSkX7ksBUCCF+hnBYa4VUflCAqQed9f5D7ju2giGffktRIN5hjZi32bwlUlPGM85hwSiV6uIoAuEAZoOZsBpmTdkaqrxVDM8Yjtlg5tn1z7KrdhdX9rySTFcmD614iM92fcYv+/6S8TnjeXrt0yxct5DLe17OHQPvYH35eh5a8RCDUwczNnssgVCAz4s/14fAnWYnIVX7ea/x1RBvj6cgsYBqXzUGRStym5A7gQEpA/S5l5f2uJTzO59PsiMZ0LKZ43PG68efGZ3JgyMe1LdNBlPE/QDRlsiFb8TJQwJTIYRoxhsIaYFkU0DZ7HtFg5+KusbvjfsqG/wcY0IzgtmoEBtlIc6hfcU6LMQ7LMTYTTRU7qN/zy6kxkSR7LKS4LRilkp1cRBv0EuVrwq7yY7b6mZL5RbWlK0hIzqDwamDKSorYuH6haQ4Urhj4B38WP0jl7x9CTajjc+nfw7AVe9fRVgN8/HFH5MYlchbW99ic+Vmzso8i0xXJlW+Korritnv2Q+A2+LWh8MBsqKzODfrXD2oTHOmcd/Q+4i1xQJgNBj56OKPcFlcesbzz2f+OeI8JuRMiNhOc6aR5kxrlc9MdHwSmAohTlmhxmxmRb2PivoD3ysb/JTXaUGlHnQ2fnkCx9dGJdpmIr4xwIxrFnA2Dzqbf48+wvzNUCjE6tX19OmZLCvMnQZUVUVRFAKhAJurNlPnr2NQ6iAAFhQtoMJbwXUF1+G2url72d0UlRVx1+C7GJgykPuX389bW9/itv63cXXvq1m2exmPfvsoE3ImMDh1MDX+GpbuWEr32O6A1lzdE/TgC/kIq2EMioG8uDwMioFgWOvCMKXLFKp8VXpLo1m9ZzG9+3QyozMBuLr31VyTf41+/PmJ+Tw6+lF9O9oSHdGuCNCr0kXbCYXCBLwh/N4gAW+IgC9EYmY0RrOBbavLaKjxgePor9MeJDAVQpw0GvzBxkxlgPJ6X0SAeWDovOm+AFU/I5sZ57AQG2Uh3tn4/aDAMi7KQpxT+x4TZcFikozm6SwYDlLrr8VqtBJljuL7iu/ZXLmZHHcOvRJ6saJkBW/88AbdYrsxs/dMviv9jl998itSHCm8OvFVqv3VTH97OgoKq69YjUEx8O8N/6bCW8HkLpNxW92U1JWwtXor+xr2AdrcSpNiwh/SOi3kxOQwOmO0nr3sEtOFOYPmkBKVAmiV5u9OeReX1YWC9kfRSxNeijiPGT1nRGx3ie0SsS1zk1tHOKwS8IUIeIP4vSEtqPQ1BpXeIPEZThIyotm9uZJtq8pIyHSSNzSNvT9W8+WiH7Tn+A48N3SYBS1m3D8Ed6Kdb9/fQfnuOgZe1TGnO0hgKoRoF6GwStXBczIbDjOE3mz43Bs4ttWDDuaymYh3WomNMhPnsBLnMB8IMiOCTyuxDrNUo5/mfCEfu2p24Qv76BXfC3/Iz4ubXqTaV81NfW7CZDDxm89+w46aHTw04iFyYnK48cMbWV6ynAeHP8jE3Im8ufVNntvwHDN7zaRXQi/21O/h7W1vMyx9GDN7z8RqslLlq9Irud0WN0lRScRYY/AEPTjMDqZ2nUowHCTarAUQv+z7S64NXUuXGC1Y/PWAX3PHwDv0n9WRGSMZmTFSP49kRzKX9rhU3zYajGS6MtvqYzzlBf0hfJ5gRGbSf5jg0mIz0fecTnjrA3y4cANBf4jJt/VFURT+b+5X1Ff6CB7l2jZwQmcSMqIp313H2k+Kye2XRN7QNEL+MCVbj7y8qNFkwGwzYrEZCYe098joEYsz1oqq+o74vPYkgakQ4mdTVZUGf0ivMD94eLx59XnTfVWewDG3N2rOYjQcfni8WQYz1mHWg8zYKIvMzzzNqKqKJ+jBbDBjNpr5vuJ7dtTsoFtsN7Ld2Xxe/Dkf7/yYvkl9mdxlMp/s/IQHvn6AnvE9+etZf2Vb1TamvT2NRHsiH0/7GINi4JFvHgHgip5XEGOLYXPlZrZVb2O/Zz85MTl6RXh9QGuu3jWmK4NTB+ur+BQkFHD7gNvJcmUBkOvO5Y1Jb+jPMxvNfHTxRxHncUu/WyK2+yT1idg2G8wn9oM7hamqSjAQRg2pWOwm/N4g+3fVNmYaQweCS1+oMagM6veNmNYVV4KdDxduoHhTBSOmdyO3bxJfvvYDRZ/tPup7uxPt9D2nE4oCO9ZpnQZCwTAms5GgPxwRlBoMCmabsTGYNGG2akGlK16bn5uU7aLfuCwSMrTWW3HpDsZd1zvi8fptmxHjYa59Qy7IbZwytPoEfLInngSmQohDBENhqjyBQwLLw7U3avryHeNa6Adz280HhscdTYHloUPnTd8dFqNkM08jvpCPfQ37CIVDZLuzqfXX8s62d/CFfFzZ60pUVeWWT26h2lfNX878C7G2WKa9PY1NFZv4+7l/Z3DqYBasW8B7P77HHQPvINudzebKzby25TVCaojJXSZjUAyUNpSSYE8AIMYaQ4w1Rg8aTQYTU7pMwW6y6z97vxn4G8JqmG6x3QC4b+h9PDTiIUwG7b/Voy0NaTPZDhkmF5HCoTAGowE1rFJRUo/fGyK5swuDQWHLN6U01Pgb5082y1AeFFz2GJJKv7FZbPyyhE+e20RWfjwTflFIRUk9bzy66piOo/+4LFwJdnwNAeqr/fjqtfm4ZpsJFLBYjZhtJiw2Y2Ng2HjbZsRiNeGIseqPP/PyHlhsJpTG7hmTb+1zIKtpNWEwKT95fUvp7Cal84G2WHanhdx+p9YcXglMhTjFqapKvT8UWV3ebOj84CrzigY/VQ2B43ovi8lwyPB48yKgiIKgKAuxUWZZF/00EAwHMSgGDIqB7yu+p7ShlB5xPUiKSmLpjqWs3LuS4enDGZkxkre2vsXTa55mWPow5gyaw4qSFdz00U3kxeXxysRXqA/U88DXD2BSTFzR8woUReHb0m+p9ddS6ask1hZLlCkKQF+DvEtMF/ol9dMrxQckD+CXfX5Jz/ieAPRN7stLE14izqq1OEp1pvLF9C8izuG+YfdFbA9PHx6xHWWOOvEf3EkmHArrWUa/N0h0nA2LzUTJ1mqq9zWQlO0iLtXBzvXlbF9XTuAIw98BnzYEbneYueqPwwmHVF66fwUA1zw6ApvDzNdvbaN6n+eox1Rb4QXAbNUKCQNerbjRFmXGnWSPyEqaG7OMBwea0Y3ZyqFTu3DGxBx9e9Ckzgy+IOeY/1A2GBR6DovsNhCb0kErkNqRBKZCnGSCoTCVDYFD5mZWHKHKvKLBj/84s5kxUWY9i3m4wLJp6LxpX5RkM09pgVCASl8lqqqS7Eim3FPO58WfY1AMTO4ymTp/Hb//8vfU+mt5+pynURSFsYvGsqd+D4snLyY3JpeHVjzEN6Xf8PDIhxnXeRzf7P2GFze9iNPsZGTGSPwhPztrd5JTp2UXXVYXdpNdn4sZY43hnKxzcFlcBNUgZsXM7wb9DrPRTKI9EdDaEVmNVuwmOwDXFVzHdQXX6efRJ6lPxLC4y+KiV3yvNvoUOw61seCmqXCmeabREWMlKctFRUk9W1aWEuWykD86g9oKL58+/32zLOWB54UOmic5+dY+ZPSIY81Hu9j63T6GT+tKXKqD0u01FH1SfNTj8/u0INJoNuCMtWIwGfSinqxe8TR08mtBpdV02OFvs81EdJwWROYUJnLtn0bqAWpMchQz7hvSos/r4CDSIH9UtwoJTIVoR6qqUucLHqHKPLLNUWVDgPI6HzXe4HG9l7Uxmxl3cCbzMAFmrMNCjF2ymacitXFir6IobKncQrm3nJ7xPXFZXLy77V02VmzknKxzKEgs4PmNz/PaltcY33k81+Rfw5JtS/j9l79nZMZInhzzJCX1Jcz9ci6pjlQmd5mMyWDiPzu0ZRzrAnVEW6L1oe2m7GVuTC4NwQa9p+Ww9GE4zA4GpgwEtAKeZ8c9q7cYKkgoYMVlK/Tjt5lsPDb6sYhzOj/n/IjtpszoqUZVVYL+cEQLoENu+0KEQyr9xmpzWT9cuAFfQ4CzrszD7rTwzlNr2be9Br8vRNB35NZoPYamMuYKF9VlHr55dztJWdHkj85ADavsXF/+k8dpMCmYrUbCjS0xEjs58XuDOBuHtNO6xNB/XFbEXMiI4LLxtsV2oF3alfOGRbzHiEu6teizM5oNGM1yPTsZSGAqxAkUCIUPNGI/eOWfZlXmTcFnZX0Af6jl2UxFgRi7+YhD5IfbH2WRX/dTSSgcoi5QB2jreO+t38u3pd/iMDsYnTmavfV7eXzV4wA8MPwBAuEAZ796NjW+Gj695FPcVje//vTXbK/ZzsKxCxmQMoAPd37I0h1LSXOmUZBYQI2/hi2VW9idqBV4uC1uDIqBUFgLaBLsCYxIH6EHkTaTjTmD5uA0O/XCnAVjF2Az2vSVeH43+HcR53FwJXlSVFJE38uTOQOvqirhsIrRaCAUClPZOE+y+VzIw1V0F47JJCnLxcp3fmTLylIKzsyg96gMVn+4iy9f++Go72swKPQ9txOKorC9aD++hiDeugB2pwVffYCGGn/E4xWDcsj8yKZim5gkO/mj0nElatlnu8vCWVfkRWQlm56rF9wc1Dqt/7hs+o87sJ3ePZb07qfmHw/i55P/qYQ4AlVVqfUFD78C0GGqzMvr/dQeZzbTbjZGZCvjmrU1Otx3t90sS0+e5FRVxR/2YzVqWaSN5Rup9dfSK04bUn5729vsqtvFhJwJZLuz+fvav/Pxzo+5NO9SJuVO4h9F/+CJ1U8wtetU7hl6DxvKNzD7i9kUJBQwOnM0wXCQt7a+hd1k54HhD2A2mPEEPQTVIDW+GtxWN9mubD2jCTAqYxRpjjS9Ifv5nc+nMLGQTKfWYmh05mhWXb5KX0oyxZHCU2c/FXFezdsTNT3mZFRb4cXvCeJOtGOyGNn9faW276Bim4A3GDFHMrVLDMOmdmH35kre+vNqYlKiuHTuIHz1QV7+w8pjeu/s/ASSslx46gJU7m2grkpr69M0DI1C4xB2ZKGN2XpgGFtVtT9gh0zJRVEU7NHaVIhR/9OdcFhtDCa15xrNhiP+ARCb4mDkpd31bbPFSN7Q1J/xyQrx0yQwFacNfzB8SDX5T60AVNngJxBqeT8jReFA1jLq0JV/9CCz2RC63SIr/JxMGgIN1AXqcJqdRJmj+KHyB7bXbCfLlUXX2K6s3reaj3Z+RI47hyldp7B+/3rmr5xPUlQSD496mNL6Usa9rqWQvpvxHYqicM0H11AbqOXNSW8CsGjLIlaXraZHXA+y3dnsrd/LuvJ1FNdqc/OaKsYbgg0ApDpSGZQyiNyYXADi7fHc1v823Ba3vrrQS+NfIsocpVefPz7m8YjzmtxlcsR2litLb28EWh/MjqZ5Y3KL3YTFZqJybz2VextwJdhIyIimbGctW1fti8hKapnKg4JMX4hr/zISo9HAoj9+Q0O1n2lzBpLYKZpv3ttO8abKox6PzaFlik1mbSjb3/jHqsVmxO6yNAaTzYewG4turAeGseMbWwHlj0ont0+inq3sMSSFbmckY7YY9aruo+k1Ij1iOz7decyfrRDtQQJTcVJSVZUab/DQLOZh2hg1NWiv9R1fNjPKYjzsCkDNh86b3+eSbGaH1BSc1fpr2Vu/F7PBTLY7m/2e/Xy26zNMBhOTu0ymPlDP/JXzqfPX8cioR1AUhSvfu5Lddbt55txnyHHn8MuPf8nKvSv1Ap5XNr/Ci5te5LqC6+ga25XNlZv51/p/cVbmWUzpOgV/2M93+76jU3QnQFsasmkJyKasabY7m/pAPSFVGyY/M/NMusd1J9WhZaemdpvKiIwR5Lq1wPPCrhcytetUzEYtEMqLz+MfY/+hn6/dZOfq3ldHfAbN2xW1FzWsEvCHDhnGNlmNJGe7aKjx8/3yvRiMCoVjMgkFwnz47IbDtgIK+EIE/Qemwpx1RQ/yhqax8csSVv1nJ4VnZzL8omgq9tTx7Xs7jun4At4QRocBu9NCOKQSapxqk9zZhcGo6FnGiL6RzYa1nbFaBjwhw8mV84ZqLYUAk8XI1fOHH/F9Dyc2xRFRcGMyG0Fal4pTnASmokPwBUNU1gcOqjL3UdEQOLRRe+PqQMHjWGvS0CybebgG7YdrcWQzd7ws0ekkEArgCXlwWVyEwiFW7VtFfaCe4enDMRqMvLTpJfY17GNa92mkOFL42+q/8e2+b7mq11UMTx/OE6ue4N8b/s2lPS7ltv63sXTH0sgCnroS7vnqHtIcaXpPy9e3vA6AJ+ghyhzFvoZ9lDaUUuuvBbTA0qgY8YW0IdYsVxZ9k/qSHJUMQF5cHlf2vJLucdoQaI47h0dHPaoX5TjMDj686EOcFicWgzbE+sL4FwC0xtes5sqeV2I0HvjZ6xnfU29vBOhV6q2tqTH5kfpFNt3uPTIdk8WotfEp8zDg/GziUh0sW7SF7UXlB4p0/CE4zK9uevcYLritH55aP1++/gP2aDOFYzIxGBV++GbfUY/TYFQIBbUXdifaSe7sIjpWmycZm+ogf1T6gXZAzYaxD2lMbtf+W5x+9xkRrz94cm6LPjetktzWoucIISQwFa0gHFap9Qb1KvODq8v17w2N1eb1AeqOM5vpsBgPGib/6QbtLpsZg2Qz20yFt4Jafy2J9kSizFGs3reanbU76R3fm5yYHD4v/pzPiz+nb1JfxueMZ9nuZfzlu7/QPa479w+7n43lG5n29jSSopL46OKPCKkhZn4wE4D/Tv8vbqub5zc+z/aa7QxLH0aKI4XNlZv5uuRrzul0DqAVz3iCHn1FHrfFTZwtTu91GW+PZ3TGaBKjtFZDNqONW/regsPs0OdSzh85H4NioLO7MwCPjXoMk+HAsqWX5V3GZXmX6eedn5hPfmK+vu22ujk3+1x9W1EUkh3JrfKZN1ez33OYlWyaD11rt3P6JJKZF8em5SUUfbqb7Px4Bo7vzLbVZbz/zDrUY/gjsEv/JJwWIz+u3U95cR09BqcQl+qgodZPVWnDoU84qDF5UxBpj7bQbVCyPiSuGBRGTu+G0Ww4ZPi7eRPz5hXXvUakRwxhJ2W5SMpy/cxPUwjRFiQwFUflDYQOu9LPwdXn2leAygY/oePIZhoNSmO20nzMQ+eSzTyxwmqYsBrGZDCx37Of3XW7cVlcdHZ3ZlftLj7b9Rluq5uJuRMprS/lkW8eQUFh/qj5qKrKea+fR62/liVTlhBni+Pq969ma/VWFpy7gDNSz+D5jc/z/vb3mX3GbHJicthYvpGXv3+ZkBpifM54vCEvGys26tlAh1kbxqzza9XnFqOFrrFdsRqsBMLaIgDndT6Pal81cTatOfqlPS7l7KyzyU/I17cn5U4ixhoDwJisMYzJGqOfc5ozLWKupaIoXFtwbcTn0jzIBPTh8xPJWx/A7wlisWs/0+W766gt9x2xcrvpdnScjTNn9KC2wssrD6xERWXWo1qV+wv3fn1Ib8nDccZaycyLw1MbYN/2GmKStDmNJrMhIiiNWO7woCHspjmPhWdl4GsI4k7SAv9+52bRc1jaIcslmiyHL7iJclk4Z2ZkT9H80RnH8YkKIU5GEpieZsJhlRpv4NAq88OsANR0X73/yL3uforTaiJWL/Q5UFV+uKHzeIeVaJtJspnHqal1UCAcIMGegCfoYeXelfhCPs7J0jKHf1vzN2p8NdxQeANuq5v7v7qfTRWbuH3g7fRN6svvv/w9r295ndsH3M6Vva5k8Q+L+ct3f+GCLhdw/7D72Va1jT+u/CO943szMXciQTXI+9vf16vKFUWh0ltJQ7CBen89cbY4oi3RRJuj9SCyR1wPav21ejug/sn9uaHwBnrH9wagMLGQv539N+Jt8QCkO9NZdukyPbsJ8Pqk1yPO/aY+N0Vsn5EaOQQbZ4vTg9bjoaoqoUCYoD+sz40M+rUvR4wNd6Kd8j117NlcRXS8jez8BKpKG1j78a7Gx4cJNnte07zIgF/rI3nJ784gJjmKd55cy95t1Zx7rTZcv+6zPWxcVnLU44tN1YJ3o8mAt177nNWwimJQsEebCQXCkcskWiMzjWabkdTcGAA6FyYQk2QnOl4LTNO6xnDVQ8O0iu9jLLjJGxq5so0U2wghWkIC05OcNxBqUZV5ZUPguLKZJoPS2MbIQqzDTLzDGhl0Oq0RDdpjosySzWyBXbW7qPPXkROTg9Vo5b+7/8vu2t0MTRtKpiuTJVuXsLxkOWdlnsWYrDEs2bqEZ9Y+w9C0ofx20G9ZXrKcGz68gR5xPXh14qvU+Gr4xUe/wKgYWXX5KhRF4bkNz1Hrr+Xi7hfjtrr5oeoH1u5fS1lDGQAmRbscNA15x9viSXem47JoQ6BpzjTO63yeXqUdZ4vjzoF34jA79MKiheMWYjPa9BZB/z7v3xFZsWvyr+Ga/Gv07QEpAxiQMkDfTrAnRCz1aDQY9ff/KaFgGBQwGg001Pipr/JpQaA/RNDXGARGBIdhLHYj/cdl4/cEWbpwA0F/iEm/6oOiKLw6byW1lT6CjY9Xj/ArM+D8bAZNyqFkSxWfv7SZnD6JZOcn0FDjp+iz3Uc9bkCbcwmYbUZMZgPhkApGrX9kSo5LCygbM43NbzcNa0c1tgGyOc1cOncQZpsRGj/yKx8cdqS3PayYpChikg78EWCyGDFJxwghRBuSwLQDCYdVqj2BQ5qwH9Kovdl9DceZzYy2miKGxyO+oiyH3OeymU7qRtcnkqqqeIIeTAYTFqOF4tpiiuuKSY5KprO7M5sqNvF58eekO9MZnzOeHyp/4M/f/Rm31c0Dwx+gylvFpMWTaAg2sOKyFRgUAzPenUGFt4LXJ71O19iuLFy3kBV7VzB/5HwyXZms27+Ot7a+RXJUMmOyxuAL+dhes51sdzZwYMi7qRgn2hJNXlweTouTYDiI2WhmevfpqKhEm7VG5zf1uYn6QD29E7Rs5c19b+bGPjfqgeCUrlOY0nWKft5dY7syf+R8fdtusjOj54yIz6Z5cQ4caI4eDqsRgaHZaiLKZaGu0kvZzlqsDjNpXWKor/Kx8auSxoDw0AxlwHcg03jejfnEpzlZ8vhqdq6v4OyZPek+KIXv3t/Bmo93HfXfMTreRv9x2aDA9rX7AQgGwpgtRhpq/XgOakIOjSvaWLQhbJPFiDVKu4S6k6PI6ZtIcmfts3Ml2BgwPhtzY2Bnthq07xYjJmvTdwNmixFH42o4E28uRFEUrfhp9R4Kz86k39jso56HfmwGhbg0WXdbCHFyk8C0FXn8oYgh8iM2am+8r7LBz3EkMzEZlMiVf46wtnnzbKbVdHpmQXwhH5VerRdhiiOFal81y0uWAzA2eyy+kI/Hv3ucukAddw2+C7PBzM0f30xxbTEPj3yYLrFduPHDG1m2ZxkPDn+QibkTeWHTCzy34Tlm9p7Jr/v/mo3lG3l81eOMSB/B+Jzx+EI+Piv+TG/7YzfbqfRpx+AJenCYHSTYEzApJn3Iu19yP1wWl95vcnTmaFIcKRQmFgLaajn/GvcvfW3w/IR8vpvxnT73McocxSsTX4k491v63RKxPSh1UMR2tNGF3xvEWx+i1l93INPoCzXLOIYxGBV6j0wnFAzzxStbCPpCjJ7RHZPZyPvPrNMLbppnLJvWt27S5+xMhl3UleJNlXz07EY69Ywj7ZY+1Ff7+PrNbcf0b+lv0ArmmlaZCTb+kWZzmolyW5oFgY1BodWofzdbjNhd2mdlthg5c0YPTFaDPpXk/BsLUBQFk8WgP95kMRxxbezMHnFk9jgwXcAZa2PQxJa1ZpI//IQQQgLTFlFVlT21QQLbK6jyBCOqzA83dO4JHGc202aKXMf8cEGn88D3aOupnc0MhUMYDUbCapgtlVuoC9RRmFiIyWDinW3vsK9hH+d1Po8URwrPbXiOVftWMbXrVIalD+PZ9c/y7/X/ZlKXSfyq36/4z/b/MOe/cxiSOoRnzn2GPXV7uP2z20m0JzI2eywGxcCzG54F0JqTW91sr97O9prtVPmqAC3oA/TlINMcaXSJ6UKsVWsFlBuTy9SuU+kWq63lnBGdwb1D79WLbywGC29MegOnxYndpM3le23SaxHn/Is+v4jYHpw6mDMSB+nrnLuJBa8NY9AALgh4wmwv2q8Fkc2GrpuCyoAvTDCgbQ+f1pWEjGg++b9N/LimjCFTcskbmsbKd37k2/eP3uvRHm2m98h0FIPC+s+14ephF3XBZDZSsaeOyr2HqcBuomjDwzT+vDpirCR3dhGTrH2mUS4reUNTD2QVGwPDA4GlQQ84m+YunnVFnva5Nq6rPeD8bAacn33U89APyaDQc3jkvMjEzOhjfr4QQogTRwLTFvjzRz/wxCf7gf3H/ByzUTmkkvxwQ+dNgWZMlAWL6fBZmZONL+Sjzl+H0+LEarSyuXIzu2t3kxOTQ5Yri5V7V7Js9zLy4vMYmz2Wb0u/5anVT5HlymLukLlsq97GJUsuIcocxWeXfAbAxUsuRkXl02mfEm+P5x9F/+CHqh/Ii88jxZFCUVkRS3cspV9SP4alD8MX8rHPs48KbwWgDXk3zaUEiLXF0i+pn56ZNBvMzMqfhd1k15dqvHvw3YQJ0zW2KwD3DL2HPwz7gx5Uzug5gxk9ZxAKhfF5guSau3Nr7h0E/CFKfqgi4A/RxzeCcI0KWn91qr4yUearJ+68MBabgWWLtlC1zxM5D9IfbpatDKOGVXqNTGf0/3Rn18ZKPvj7OlK7uLnw9v401Pj56F8bj+nfxVOjZWUDvhCe2gC+xsyjqXG5Q6PZEDHU3BQYNg1JN7XxMRgUzpjYGaPZoLfqGXFJN0LBsF6xbTI3vk5joHnw0oeZeXFk5jXPNFr1QPNYNR2PEEKIk58Epi2QGWsnxmbA7bARf1AWs6nK/OCg03mSZjNr/bWUecqwGq2kO9MprS/lq5KvsJvsjM0eS4W3gidWPUEwHOS+YfcBcMnbl1Dtq+bf5/2bpKgkZrw7g00Vm/jfs/+XYenDWLhuIW9ve1uv+i7aX8SCdQuYlDuJsdljqQ/Us2LvCj0TaTfa8Ya8hFVtGNigGMiMzkRRFH3Ie2TGSPLi8vRs5aQuk+if3J++yX0BmJw7mRHpI/TAc2TaKP47ebl+ngnmRP7Q+U8EfSG2rCwl4A8x2nchQX+IddtLtVY9fpcWIJ5rgAzY9J9yflxTRuFZmfQYksqqpTtZvnirVrTyE8xWI136J6EoCt+8u51QMEyvkVobnd2bqyjbWXvUf5em4Wqrw4Qjxord2Vj44jDTqWecFkBaDZFzGZtnHS1G4tK1eYiDJ+fQf1yWPsex77md6Dc2q0WdEQaO7xyx3TzIFEIIIVpKAtMWuKh/Bl2M++nTp0/EiiwdQSgcwhfyEWWOIhAKsL58PQ2BBoamDwXg+Y3PU+2r5n96/A8xthj+8t1f2Fi+kRsKb6BPUh8eXvkwb2x5g1kFs7i699W8vuV1HvnmEcbnjOehEQ+xtWordy+7m26x3RibPZZgOMirm1/FoBi4d+i9KIpCcW0xNf4a6vx1JEUl4TA7UNCamwNku7IpSCjQh7R7x/dmRt4Mvd9kj9gePDRkPrHGOGr2ezD5onhp4GIsWPXzfCx9AX5PkFiDFmieVX0RVXsb2LU1xI++IgJ+JxZ/V1b7qvnGv7wxsAyR2wfOvDyR4o2VvPPkWhI7RTNtzkA8dQHe+vPqY/qMc/slkZARTX2ll/276qir1AqNDAYlIihVDIo2r7FZMGi2GvSlCQF6jUgDRZvfCNBvbBa+hkBEAGmyNiuaaRZkgjan8aqHDlRcR7ksTLylzzH+tGhcCfaIbeMR5k8KIYQQbUUC0w6g2let93a0GC2s2reKPXV7KEwsJCM6g493fszXJV8zOHUwZ3Y6k492fMQ/iv5B3+S+3DHwDlbuXcnVH1xNrjuXxRcspjZQy+XvXQ7A6stXYzQYeWbtM1R4KxjTaQwxthiKyor4eu/XTMydSJ+kPoTVMLWBWn3JRZfFRbQlGrNBGyZNikpieNpwOjm1VkHRJhc3Zv2KKJzs3lJBKABzkx/GEDJRvUphVXAn1wfuQo2Gbkat6GdY9QTStw+ma0IydAHXjkxSl4xgrz/E075PG9e8trKdelbxlf75KAp0eSodRVH44uXN+BqCpHePxWo3sfW7fZT8UH3Uz9jn0YarzVatlU7TXE2z1UhcmuPIldPNso1xjf0i88/MJLdfkj4vssfQVHL7JerPNZiUo2bJR1zSLWK7S/+ko56DEEIIcaqTwPQ4qKpKWA1jUAzs9+yntKEUt8VNRnQGP1b/yPKS5STaEzk762x21uzkqTVPEWWKYu6QudQH6rl4ycXUB+pZetFSLEYLF755Ifs8+3h5wsv0jO/J02ueZtmeZTww/AEyojNYtW8VL2x6AavRypmdzqQuUMe68nXE2GKAyGKccCiMJWiji7kH0QYX3qAXh8XBRPslhABrSMuSTTbMYLRxKuZvUvnim83k15/Lfb7RGL408+bnqwj5MrnF/2fSDI3vsT+B/DemaSvCDAH8BtSXcqgH3mRNxOezmbqI7YSMaBIynFSW1LOjqJyExqKVUCBMbbn3sJ+xPs+xMTAMh1SMJoWs/HhC/jCmxjmNPQankt49NmIu5MFFMmaLEZtTC7DTusRw01Nn6oGjzaH1fmyJhAwnZBxoGm61m7Da5VdJCCGE+Lnkf9MWemrXU3y7/lvmDpnLhV0v5LkNz/HPdf9kRt4M7jzjTtaXr+fBrx9kcOpgzs46G0/Qwzvb3iHBnsDcIXOxGCyUVJViDlkp3VtBFE7SG3Jx1CdRsqEWs72UvLIhxNTnYKtxA9CjeiDXVWWRU6ll1TpV9eTXu/6CcYeZ579YTsAX5GbfE4QCYf720acAnM2NAFinautPJ3xZSH2Vj6jBMdqJbIyhekOYaiqAiiOeb9P8Q2Pj0oQBnzbHsamHo8lsaBxyPpBh1INDqxGz2ahnFrsMSCY+w6lXPGcXJDA13XlIQGmyGI84z/HgpQoPrqY+mmNZuUYIIYQQ7UMC0xaKrkimS3l/Kr5R+e7HHTiLOzOm9FKiqnP4aMMG6mpjuLziLqK9WobOWu3mxnWPYrIrMA0IGZi14mEA3vp2AwCDmA7ApqJ6NrEeE5lkkklGgVYFHleTzvaNQdyp2pB4VCiahmKAQOPXoRRFq7IO+sOYzEYSO0XjjLXqfRg79YrHGWttLJYxNss4Rs5pjHJrxTUJGU6unKctTQja3MhZj41s0WeXmusmNdetbzvcVhxu6088QwghhBCnEwlMW6jv3jF03ang2wpfsRVw0ZXBAGxiLwAOkkhxxgAQbXWi1powhrVA1WQ2aMsFqkTMX9TmJxoOZBotRqLjtGxnZl4sJotBX1UmrWsM467rffjm4Y1V2UZTZFue8TcVRJxH4ZjMFp230WTAGStBpBBCCCFajwSmLRSbZCPWGYXFampWdW04JOvojNWCSneCnYvuHKBnGhVF4bo/j8JkNhzzsHJGjzgymq0qEx1n04NWIYQQQohThQSmLZTe10afPgXH3C7KZDHqmc4mZmvHajUlhBBCCNERSONCIYQQQgjRIUhgKoQQQgghOgQJTIUQQgghRIfQ7oHp888/z1lnnUV+fj4XXngh33zzTXsfkhBCCCGEaAftGpi+++67zJs3jxtvvJHFixfTv39/rr32Wvbs2dOehyWEEEIIIdpBuwamCxcuZOrUqVx88cXk5uZy1113kZKSwosvvtiehyWEEEIIIdpBu7WL8vv9rF+/nuuuuy5i/7Bhw1i1alWLXisUCp3IQzvq+7TV+wkhTj9ynRFCtLa2vs605H3aLTCtrKwkFAoRHx8fsT8hIYGysrIWvVZRUdGJPLQO935CiNOPXGeEEK2tI15n2r3BfvNlMwFUVT1k39Hk5+cfc8P7nyMUClFUVNRm7yeEOP3IdUYI0dra+jrT9H7Hot0C09jYWIxGI/v374/YX15eTkJCQotey2g0tukFvK3fTwhx+pHrjBCitXXE60y7FT9ZLBZ69erFsmXLIvZ/+eWX9O3bt52OSgghhBBCtJd2HcqfOXMmd9xxB71796Zv3768/PLLlJSUMH369GN6vqqqgBQ/CSFOHXKdEUK0tvYqfmqK236Koh7Lo1rR888/z4IFC9i3bx/dunXjt7/9LQMHDjym5/r9/g45cVcIIYQQQkTKz8/HYrH85GPaPTD9OcLhMMFgEIPB0OKCKSGEEEII0fpUVSUcDmMymTAYfnoW6UkdmAohhBBCiFNHu678JIQQQgghRBMJTIUQQgghRIcggakQQgghhOgQJDAVQgghhBAdggSmQgghhBCiQ5DAVAghhBBCdAgSmAohhBBCiA5BAlMhhBBCCNEhnHaB6dNPP83UqVPp27cvQ4YM4aabbmLbtm0Rj1FVlccff5zhw4dTUFDA5ZdfzpYtW/T7q6qquP/++xk7diyFhYWMHj2aP/zhD9TW1h72Pf1+P5MnT6Z79+5s3LixVc9PCNG+2vIac9ZZZ9G9e/eIr0ceeaRNzlMI0X7aOpb59NNPufjiiykoKGDQoEH88pe/bLVzO+0C0xUrVnDZZZfxyiuvsHDhQkKhENdccw0NDQ36Y/7+97+zcOFC5s6dy6JFi0hISGDmzJnU1dUBsG/fPvbt28edd97JkiVLmDdvHl988QV33XXXYd9z/vz5JCUltcn5CSHaV1tfY2655Rb++9//6l833nhjm52rEKJ9tOV15oMPPuCOO+7gwgsv5M033+TFF19kwoQJrXdy6mmuvLxc7datm7pixQpVVVU1HA6rw4YNU59++mn9MT6fT+3fv7/64osvHvF13n33XbVXr15qIBCI2P/pp5+q48aNU7ds2aJ269ZN3bBhQ+uciBCiQ2rNa8yZZ56pLly4sNWOXQhxcmit60wgEFBHjBihvvLKK617As2cdhnTgzWlrN1uNwDFxcWUlZUxfPhw/TEWi4WBAweyatWqI75OXV0dTqcTk8mk79u/fz9333038+fPx2aztdIZCCE6sta8xgD84x//YNCgQUyePJm//e1v+P3+VjgLIURH1lrXmQ0bNlBaWorBYOCCCy5g+PDhzJo1K2JKwIlmOvpDTl2qqjJv3jz69+9Pt27dACgrKwMgPj4+4rEJCQns2bPnsK9TWVnJU089xSWXXBLx2rNnz2b69Onk5+dTXFzcSmchhOioWvMaA3DFFVfQs2dPXC4XRUVFPProoxQXF/PAAw+0wtkIITqi1rzO7Nq1C4AnnniC2bNnk56ezsKFC5kxYwYffPABMTExJ/x8TuvA9L777mPz5s288MILh9ynKErEtqqqh32Nuro6rr/+enJzcyMmAz/33HP6fUKI01NrXmMArrrqKv12jx49cLlc3HLLLdx+++3Exsb+/BMQQnR4rXmdCYfDANxwww2MHTsWgHnz5jFy5Ejef/99pk+ffqJOQ3faDuXff//9fPzxxzz77LOkpKTo+xMTEwFtGL658vJyEhISIvbV1dUxa9YsoqKiePLJJzGbzfp9y5cvZ82aNeTn59OzZ0/OPfdcAKZOncqdd97ZWqclhOggWvsaczh9+vQBYOfOnSfgDIQQHV1rX2eaXic3N1ffZ7FYyMzMpKSk5ISfD5yGgamqqtx333385z//4dlnnyUzMzPi/oyMDBITE1m2bJm+z+/3s3LlSvr27avvq6ur45prrsFsNvO3v/0Nq9Ua8Tq/+93vePPNN1m8eDGLFy/mmWeeAeBPf/oTt912WyueoRCiPbXVNeZwNmzYABz4z0QIcWpqq+tM7969sVgs/Pjjj/q+QCDA7t27SUtLa5VzO+2G8u+9917efvttnnrqKRwOhz4PIzo6GpvNhqIoXHHFFTz99NNkZ2eTlZXF008/jc1m09sj1NXVcfXVV+PxeHj44Yepq6vT2y/ExcVhNBoP+QeLiooCoFOnThF/1QghTi1tdY1ZtWoVa9asYdCgQTidToqKipg3bx5nnXVWq/2HIYToGNrqOuN0Opk+fTqPP/44qamppKWlsWDBAgDGjRvXKuemqEeacHCK6t69+2H3z5s3jwsvvBDQ/hJ54oknePnll6murqawsJC5c+fqk4q//vprrrjiisO+zkcffURGRsYh+4uLixkzZgyLFy8mLy/vBJ2NEKKjaatrzPr167n33nvZtm0bfr+ftLQ0xo8fz6xZs7Db7a1zckKIDqEtY5lAIMBjjz3Gm2++idfrpbCwkDlz5tC1a9dWOLPTMDAVQgghhBAd02k3x1QIIYQQQnRMEpgKIYQQQogOQQJTIYQQQgjRIUhgKoQQQgghOgQJTIUQQgghRIcggakQQgghhOgQJDAVQgghhBAdggSmQgghhBCiQ5DAVAghOgBVVbnqqqs455xz2LRpE5dffjnFxcXtfVhCCNGmJDAVQog28t1335GXl8c111xzyH3FxcUYjUbmzp3Lb37zG2JjYw+7vLEQQpzKZElSIYRoI3fddRdRUVEsWrSId955h7S0tPY+JCGE6FAkYyqEEG2goaGB9957j0svvZTRo0fz+uuv6/d9/fXXdO/ena+++ooLL7yQwsJCpk+fzrZt2yJe44UXXuDss8+md+/ejB07lsWLF7fxWQghROuSwFQIIdrAu+++S+fOncnJyWHSpEm8/vrrHDxg9ac//YnZs2fz2muvYTQamTNnjn7f0qVLefDBB5k5cyZLlixh+vTpzJkzh+XLl7f1qQghRKuRwFQIIdrAokWLmDRpEgAjRoygoaGBr776KuIxt912G2eccQZdunThuuuuY9WqVfh8PgAWLFjAlClTuOyyy+jcuTMzZ87knHPO4Z///Gebn4sQQrQWCUyFEKKVbdu2jaKiIsaPHw+AyWTi/PPP57XXXot4XPfu3fXbiYmJAJSXl+uv0a9fv4jH9+vXj61bt7bmoQshRJsytfcBCCHEqW7RokUEg0FGjhyp71NVFZPJRHV1tb7PZDpwSVYUBYBwOHzIvuavcfA+IYQ4mUlgKoQQrSgYDPLmm28ye/Zshg0bFnHfzTffzJIlS+jatetRXycnJ4dvv/2WCy64QN+3atUqcnNzT/QhCyFEu5HAVAghWtGnn35KdXU1F110EdHR0RH3jRs3jkWLFvHb3/72qK8za9Ysbr31Vnr27MmQIUP45JNPWLp0KQsXLmytQxdCiDYnc0yFEKIVLVq0iKFDhx4SlAKce+65bNy4kQ0bNhz1dc4++2zmzJnDggULmDBhAi+99BIPPvgggwYNao3DFkKIdiEN9oUQQgghRIcgGVMhhBBCCNEhSGAqhBBCCCE6BAlMhRBCCCFEhyCBqRBCCCGE6BAkMBVCCCGEEB2CBKZCCCGEEKJDkMBUCCGEEEJ0CBKYCiGEEEKIDkECUyGEEEII0SFIYCqEEEIIIToECUyFEEIIIUSH8P8B4UVOpm6VhR0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "g = sns.lineplot(data=df_transposed_mon)\n",
    "g.legend_.set_title('Sucursal')\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Ganancias por Millon (USD)')\n",
    "plt.title('Proyeccion de Ventas por Millones USD de clientes nuevos hasta el 2026')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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>EtiquetaSucursal</th>\n",
       "      <th>Machala</th>\n",
       "      <th>Guayaquil</th>\n",
       "      <th>Manta</th>\n",
       "      <th>Quito</th>\n",
       "      <th>Ambato</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>102</td>\n",
       "      <td>569</td>\n",
       "      <td>108</td>\n",
       "      <td>399</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>153</td>\n",
       "      <td>821</td>\n",
       "      <td>165</td>\n",
       "      <td>566</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>218</td>\n",
       "      <td>1121</td>\n",
       "      <td>238</td>\n",
       "      <td>765</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "EtiquetaSucursal  Machala  Guayaquil  Manta  Quito  Ambato\n",
       "2024                  102        569    108    399     128\n",
       "2025                  153        821    165    566     198\n",
       "2026                  218       1121    238    765     282"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eeveloza/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/eeveloza/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df_transposed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}