main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bicycle Commuting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data used for the analysis was taken from three datasets of Mexico City's government [website](https://datos.cdmx.gob.mx/):\n",
    "\n",
    "| Dataset                         | Location    | Date of download |\n",
    "| ------------------------------- | ------------------------------------------------------------ | ---------------- |\n",
    "| Bicycle counter                 | https://datos.cdmx.gob.mx/dataset/contador-ciclistas         | 11-Dec-2020      |\n",
    "| Road accidents                  | https://datos.cdmx.gob.mx/dataset/incidentes-viales-c5       | 11-Dec-2020      |\n",
    "| Estudio de Conteo ciclista 2018 | https://datos.cdmx.gob.mx/dataset/estudio-de-conteo-ciclista-2018 | 11-Dec-2020      |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have already downloaded them in the /Data directory, so let's import them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "contador = pd.read_csv('Data/contador-ciclistas.csv')\n",
    "incidentes = pd.read_csv('Data/incidentes-viales-c5.csv')\n",
    "estudio = pd.read_csv('Data/estudio-de-conteo-ciclista-2018.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Bicycle counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>dia_de_la_semana</th>\n",
       "      <th>dia</th>\n",
       "      <th>mes</th>\n",
       "      <th>ano</th>\n",
       "      <th>contador_ruta_reforma222</th>\n",
       "      <th>contador_ruta_revolucion</th>\n",
       "      <th>contador_ruta_patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>jueves</td>\n",
       "      <td>5</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>2013</td>\n",
       "      <td>2516.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>lunes</td>\n",
       "      <td>9</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>2013</td>\n",
       "      <td>2132.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>viernes</td>\n",
       "      <td>22</td>\n",
       "      <td>junio</td>\n",
       "      <td>2018</td>\n",
       "      <td>2913.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>1163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>martes</td>\n",
       "      <td>10</td>\n",
       "      <td>julio</td>\n",
       "      <td>2018</td>\n",
       "      <td>2531.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>1013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>sábado</td>\n",
       "      <td>7</td>\n",
       "      <td>julio</td>\n",
       "      <td>2018</td>\n",
       "      <td>1534.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>584.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id dia_de_la_semana  dia        mes   ano  contador_ruta_reforma222  \\\n",
       "0   1           jueves    5  diciembre  2013                    2516.0   \n",
       "1   2            lunes    9  diciembre  2013                    2132.0   \n",
       "2   3          viernes   22      junio  2018                    2913.0   \n",
       "3   4           martes   10      julio  2018                    2531.0   \n",
       "4   5           sábado    7      julio  2018                    1534.0   \n",
       "\n",
       "   contador_ruta_revolucion  contador_ruta_patriotismo  \n",
       "0                       NaN                        NaN  \n",
       "1                       NaN                        NaN  \n",
       "2                     980.0                     1163.0  \n",
       "3                     877.0                     1013.0  \n",
       "4                     616.0                      584.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2525 entries, 0 to 2524\n",
      "Data columns (total 8 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Id                         2525 non-null   int64  \n",
      " 1   dia_de_la_semana           2525 non-null   object \n",
      " 2   dia                        2525 non-null   int64  \n",
      " 3   mes                        2525 non-null   object \n",
      " 4   ano                        2525 non-null   int64  \n",
      " 5   contador_ruta_reforma222   2150 non-null   float64\n",
      " 6   contador_ruta_revolucion   1533 non-null   float64\n",
      " 7   contador_ruta_patriotismo  1537 non-null   float64\n",
      "dtypes: float64(3), int64(3), object(2)\n",
      "memory usage: 157.9+ KB\n"
     ]
    }
   ],
   "source": [
    "contador.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                             0\n",
       "dia_de_la_semana               0\n",
       "dia                            0\n",
       "mes                            0\n",
       "ano                            0\n",
       "contador_ruta_reforma222     375\n",
       "contador_ruta_revolucion     992\n",
       "contador_ruta_patriotismo    988\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the counters didn't start recolecting data at the same time. We should see when did each start by  visualizing where exactly are the null values. However, the dataset is not ordered by date, so let's transform the dates to a datetime format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop id column\n",
    "contador.drop('Id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dia_de_la_semana', 'dia', 'mes', 'ano', 'contador_ruta_reforma222',\n",
       "       'contador_ruta_revolucion', 'contador_ruta_patriotismo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change column names\n",
    "contador.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "contador.columns = ['Día de la semana', 'Día', 'Mes', 'Año', 'Reforma', 'Revolución', 'Patriotismo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform months to categories to order them.\n",
    "meses = ['enero', 'febrero', 'marzo', 'abril', 'mayo', 'junio', 'julio', 'agosto', 'septiembre', 'octubre', 'noviembre', 'diciembre']\n",
    "contador['Mes'] = pd.Categorical(contador['Mes'], meses)\n",
    "\n",
    "# Order values in descending order.\n",
    "contador = contador.sort_values(['Año', 'Mes', 'Día'])\n",
    "\n",
    "# Change months back to object type\n",
    "contador['Mes'] = contador['Mes'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label-encode months\n",
    "dict_meses = {}\n",
    "number = 0\n",
    "for mes in meses: \n",
    "    number += 1\n",
    "    dict_meses[mes] = number\n",
    "    \n",
    "contador['Mes'] = contador['Mes'].map(dict_meses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new column with complete date\n",
    "contador.insert(4, 'Fecha', pd.to_datetime(contador.Año*10000+contador.Mes*100+contador.Día, format='%Y%m%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset indexes\n",
    "contador = contador.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sábado</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-11-16</td>\n",
       "      <td>613.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>domingo</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-11-17</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lunes</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-11-18</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>martes</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-11-19</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Día de la semana  Día  Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "0           sábado   16   11  2013 2013-11-16    613.0         NaN   \n",
       "1          domingo   17   11  2013 2013-11-17   1084.0         NaN   \n",
       "2            lunes   18   11  2013 2013-11-18   1073.0         NaN   \n",
       "3           martes   19   11  2013 2013-11-19   1344.0         NaN   \n",
       "\n",
       "   Patriotismo  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Change months back to string format\n",
    "dict_meses_inverso = {}\n",
    "for mes, numero in dict_meses.items():\n",
    "    dict_meses_inverso[numero] = mes\n",
    "    \n",
    "contador['Mes'] = contador['Mes'].map(dict_meses_inverso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace special characters\n",
    "contador['Día de la semana'] = contador['Día de la semana'].str.replace('á', 'a').str.replace('é', 'e')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a clean and ordered dataset, we can visualize the distribution of null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dictionary with column types.\n",
    "def recognize_column_type(dtf, col, max_cat=20):\n",
    "    \"\"\"Returns the type of a column\"\"\"\n",
    "    \n",
    "    if (dtf[col].dtype == \"O\") | (dtf[col].nunique() < max_cat):\n",
    "        return \"cat\"\n",
    "    else:\n",
    "        return \"num\"\n",
    "\n",
    "dic_cols = {col:recognize_column_type(contador, col, max_cat=20) for col in contador.columns}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;37;40m Categorical  \u001b[1;30;41m Numeric  \u001b[1;30;47m NaN \n"
     ]
    }
   ],
   "source": [
    "# Visualize data\n",
    "heatmap = contador.isnull()\n",
    "for k,v in dic_cols.items():\n",
    "    if v == \"num\":\n",
    "        heatmap[k] = heatmap[k].apply(lambda x: 0.5 if x is False else 1)\n",
    "    else:\n",
    "        heatmap[k] = heatmap[k].apply(lambda x: 0 if x is False else 1)\n",
    "        \n",
    "sns.heatmap(heatmap, cbar=False).set_title('Null values distribution')\n",
    "sns.set(rc={'figure.figsize':(10,5)});\n",
    "plt.show()\n",
    "print(\"\\033[1;37;40m Categorical \", \"\\033[1;30;41m Numeric \", \"\\033[1;30;47m NaN \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that the Reforma counter started much sooner than the other two. However, in recent months, the counter in Reforma stopped. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sabado</td>\n",
       "      <td>16</td>\n",
       "      <td>noviembre</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-11-16</td>\n",
       "      <td>613.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Día de la semana  Día        Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "0           sabado   16  noviembre  2013 2013-11-16    613.0         NaN   \n",
       "\n",
       "   Patriotismo  \n",
       "0          NaN  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "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>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>martes</td>\n",
       "      <td>14</td>\n",
       "      <td>junio</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016-06-14</td>\n",
       "      <td>2884.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>15</td>\n",
       "      <td>junio</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>2909.0</td>\n",
       "      <td>864.0</td>\n",
       "      <td>993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>jueves</td>\n",
       "      <td>16</td>\n",
       "      <td>junio</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>283.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>901.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>viernes</td>\n",
       "      <td>17</td>\n",
       "      <td>junio</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016-06-17</td>\n",
       "      <td>2695.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>856.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Día de la semana  Día    Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "941           martes   14  junio  2016 2016-06-14   2884.0       333.0   \n",
       "942        miercoles   15  junio  2016 2016-06-15   2909.0       864.0   \n",
       "943           jueves   16  junio  2016 2016-06-16    283.0       855.0   \n",
       "944          viernes   17  junio  2016 2016-06-17   2695.0       889.0   \n",
       "\n",
       "     Patriotismo  \n",
       "941        256.0  \n",
       "942        993.0  \n",
       "943        901.0  \n",
       "944        856.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador[contador['Revolución'].notnull()].head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Reforma counter started in 16 Nov 2013**. Afterward, **the counters for avenue Revolución and Patriotismo were installed in 14 June 2016**. Later, the Reforma counter stopped. To see exactly when, let's visualize the data with a line graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme()\n",
    "data_lineplot = pd.melt(contador[['Fecha', 'Reforma', 'Revolución', 'Patriotismo']], 'Fecha', var_name='contador', value_name='número')\n",
    "sns.lineplot(x='Fecha', y='número', hue='contador', data=data_lineplot, alpha=0.6)\n",
    "\n",
    "#Change figure size\n",
    "sns.set(rc={'figure.figsize':(17,10)});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are strange gaps in the data shown in the line chart, which seaborn fills with a line, connecting the last available data point with the next available one. They probably shown gaps in our data.\n",
    "\n",
    "Here we confirm various things:\n",
    "- The Reforma counter started much earlier than the other two. \n",
    "- There is some missing data in the Reforma counter, and to a smaller extent in the Revolución counter, between 2017-2018.\n",
    "- The Reforma counter stopped abruptly at mid 2020.\n",
    "\n",
    "Moreover, we discover some new insights:\n",
    "- There is some missing data in all counters at the end of 2019 (which the previous chart did not show).\n",
    "- The lines fluctuate, probably because there is a difference in the day-to-day usage of bicycles.\n",
    "- There was a big spike in the Reforma and Patriotismo counters just before we get a gap in our data. Then, we see a spike in the Revolución data. Was there some sort of holiday?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's dig into the missing data towards the end of 2019. We can create two line charts, from 2019 and 2020, to figure out what is going on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sErKIvo6M7735TpqcLlSRSkiWNAGwGe75ts98ae8k0kkZy3uaIAgCVvU3HzUM956hWWi6gZNXd1ivtTcloWr6vFsqDk+W0NGSgiSKDUlJA0cVyHOMRyYIAN/52Wbc9tNN876YbSAafvHlYcCPf/xj9PX14dxzz8VPfvITAICu6xDqTheAGSgIghD4Ovm/E95/s6CjIxfzW8wdXV1+1m2+cdv/PYf1j++B895qyij4l3e8Aqeu6QpsNzReQE3VsXZVJwYnStg/UsBMoYpTjulEX28LqoZ57TPZ5Jy+V1TbbCbh+8zKgRY8v2MssK2uGzgwkjf/IYjo6mpCuarCMIBkQsLYdAmtbRkoskRtr2o6Dozm8frzV6G7uxmvPKUf9z+yC9mmFDIpejDphGEYKJZVrHtFN95+6VqUqxpyaQU9Pc2QxI0YmiozfXfyXZ7YPILTjuvGmpWdlld1Ou2/LgCwY/8UJmcreMdlJ/jeb2vPIiGLGJwsR557aIe5wFh7TBe6uppw6Xkr8a2fPI+Zio5jlrbSG20z2ywfaEWbI9An6GgzF0FNzWnf+afLJuu9vL8lsG+5bAK6EX7dBidKWDXQgt6eFt97mZQCTTfm7T6sagbamlOu47e3mxIoHQL3eXk+v/3ADE45phM9Peai6pRju7Fx+2Yk0olAXfxiwWRdOnLa2l6rryvquwSGLPmuw+H8/SZmKlja02QdMyGLMETxiMzVcXC09HMh8adyTWbrc+a+0Xzkdybva5qOkckiVM3AVFnDscvaQtu9XLFYxsi8BNwPPPAARkdH8YY3vAHT09MoFos4ePAgJMkOeEZHR9Hd3Y3e3l6MjtoFLsbGxtDd3Y329nbMzs5C0zRIkmR9nhfj4/kjonnq6mrC6Oj8WbIF4Y+bBrGyrxmXnbMcubSCoYkifv3Ufnz2e0/gI285Fccvp99wm+oBV1NCwrKuLNY/uQ+GAbzqtH6Mjs5idsaUZUxMFGN/r6hrUqlqUGua7zPtuQTGp8vYd8Bk9bwYHC9Y8pHJmRJGR2cxnTetAJd25bDj4DQ2bx+1LPa82D+SR03V0dWcwOjoLI5f0oJ7NAMPPrkXZ63tifxelaqGmqpDMAxMTphMfClvBspLunPWtWW5bpv3TGBsqoS/vHAVRkdnLdnG5HSJ2v63T+6FIACrerLU95d257B1z3jkuV/cYd6DOUXE6OgsTlrWAkUWcd/DO/C3Fx9HbTNcP2a5WMFoxc9MFovmbzAyOous7F4s795vJoUKuh7YN03VUamqoX0/MDKLk1d2UD+jazqK5fD2c8HoRBHZpOw7fjYlY3Asz3VenvlibLqEwfECXrWu32rT22IGrk+9cAinHtPJfN4jgS27xtGSS6BaqmK0ZO4ESHWGYNfeCbQk7efE4ZxHDcPAwdE8VvXZx0wnZYxPFo7IXM2LI/VMWcz4U7ome+s7WNv3T2FwaJpqpwq4r8noVMmS1d3z4Ha867K1C9PZRYSFHCOiKISSvPMiKfnBD36A+++/H/feey8+/OEP4zWveQ2+973vYffu3di7dy80TcP999+PCy64AAMDA0gmk9iwYQMA4N5778UFF1wARVFw5pln4oEHHgAA3HPPPbjgggvmo7svG9RUHZMzFZy0sh2nH9uFY5e24oJT+3HN35yGztY0vnr3c9hPmGAPiENJb0cGqwdaLIb8xLqemtzccSUlP398D/7n1+EV5VRNhyz5dzH6rMRJujSEyElSCclKdiQB+Ip6Ml2YrITogJf3mJ89ZsCsdMgqKyHSClry4Kr+ZuwenIHGuOh79IUhpJMyTltjBk3kegRJIzZuH8WxS1rRlKEnH6aTMiq16N/s0HgB6aRkJTFmUgrOOK4LT2weDnQsyZdUJBNS4MRPdP80WchUfUEUruEWQiUhNVXHdL6KjhY/u262n9+kyZli1ZKQODHf1SZf2jsFAC53lBW9zRAF4aiQlewfyWNpl/uhRKRQE/PomT9dqKJS09DTbsuPMim54cPdwFGB4ckiEnWZXdBz3IuRSZMo623P4I9bhrnNABo4vFgwH+5kMombbroJV199NS677DKsWrUKl1xyCQDg5ptvxhe/+EVccsklKBaLeMc73gEAuOGGG3DXXXfhsssuw9NPP42PfOQjC9XdoxLjM2UYALpa067Xm7MJfOQtp6Ba061CHV4MjhfQnFGQSyuW9ritKWlpY0nyXlxN7DPbxvB0hAuBqhnU4I30IUjHvW84D1kSsLKv2fLeJgE3ca8IcyrZN5RHMiFZD2JRFLDumE48v3Oc6fvmQ+zxVvU1o1zVcGCYbYW9ee8ETj2mw5K/hLnDDE8WcXC0gNOODZYKsZY3PzRaQH9n1iXb+rNT+lGqqHh2B33hUSjXkAuR3IRpuKfyFQgC0JwNbx+m4SZym87AgHt+K03OFqq+KpmAWW1yljNRlgcv7ZtEU0ZBf5e9Y5NMSFjSnV3wxMlqTcP4NFtSLmDOH4fGClja7Q64mzIKJFHAxCz7sXhBgo/uNnt+zKTk2BruZ7ePWRV4G2hgPqEbBkanytZcz6rjHqknS775wtWo1nQ8sQBOQA0EY14kJU5ceeWVuPLKKwEA5557Lu677z7fZ44//njcfffdvtcHBgZw5513zncXXzYYnTIfKN6AGwBasuH2coPjRYtJbs0lsaQri+OWtlkBmGIxrfECmEK5FqnB13SdGnATFjSINdw7NIslXTlk04rFnJKEyfamJJqzidCAe8/wDJZ356xqmgBw2ppOPPLCILbun7JY/sDvVgpnuAFg+/5JnLoy/DiGYSBfrFlOMQAgicHXfWNdQ336mmAJgSyzBdwHxwo4/Vj3ccjuwGQA65gv1ZBNB08hYYuFqbzpUOItVuOEIolQNYOazwEAY9NRAff8JU3quoHZUs1yJXGiKZPA4Dx5vxuGgS17J3HcsjbXeAWAgc4ctu2Prsx5OPHjB3fidxsP4A3nr8Trz11huY4EYXC8CE03fAG3KAhoa0picmb+GG6yy9XjCLizKYXbRQgwA6Bv3/ci1i5vw4f/8pTYfdINA//x8y0498RenBgxPzTwp4up2QpUTcdxy1qxbf8Udh6awWsZ2g1PlpCQRZx+bCeWdufw8LMHLdvYoxGGYWAqX3U9I48mNCpNHiUwDANjU6VQz9iwgJtIE2iMoWEYGBwvoM+hcb7uHWfir//8GEf7YIb7nj/swg8e2BLa/0KphqoaXkzFZLj9D+wwptQwDOwbnsWyniZkkpLlUlKqM9yppIzetnSgpETXDewfzmO5p3DKCSvbkU5KuP0XL9kJmUHfrc6QeUucA9GLBScqNX/lRkEQAgPH7Qem0NueQSfl9yZQJCHS6cPpUOJqW7/u1YD2hXKNusjwtqedf6ZQC3UocbYPCpqJ3WEHJWETAKR5rDSZL9VgGKAy3E3ZxLy5lIxMljA5W3HJSazzZhTkSwu3ZawbBp7eOoJ0QsY9f9iNm/9nY2TBpP0j5k6PN+AGTFnJBKOFZRwM1y0BnRKkTFKO5cM9OlVCpaZh+4EpJheoIGzbN4XHNg3hl0/ui30MVui6gbse3GHltzRw9GC4vjvT05rG6v5mDoa7hO62NARBwHkn9WLfcJ7JtWux4sU9E/jYbY9yVUReTGgE3IschmHgv3+9DR+99VF8/FuP4zs/2xz42dGpEhRZRAulmIgZuNF9iWeKNRTKKvoc2sak4tbmhrGVuwdnQysq6nUXj6jAT9V0SBSGWxIFCKAHbmaBHhXLe5uQSsiW5zNhuFN1qcjQJN2Le3C8gKqqY3mvOwBIKhL++a/Xoabp+Nc7N+D5neOB/Q7zow4LOr0olOiBe5CfdLWmUWUsTrCwvIdGzW1xp0SBtBUFAbWAhVK+pFIXGXb7YP15uapSE2C95weAmkoPaMamSyYz2kwP3OU6Qz4fIGXIqRrutIJ8scadrD04XsCnv/8k/uvX27A74H4i99maJX5XllxaQaWmBf5ehxt7BmcxXajiqtetwd9edCxe2jeFDdtGQ9vsH8lDlkT0Umwc25uS86rhHpksorM17dpVSceUlJCqnoWyalXBNQyDe6H1aN3rfsveSa5+qJruWyhUalqo9dvwZBHr/7gPz3Banr4cYRgGPnf7U3hyy9EhsSDSkO62DFb1t2Bsmq2+w/BkEd1t5r12+nGmHIXco7PFKm74jyexbf/U/HR6HjA1W4VhAA8/d+hIdyUWGgH3IseLuyfwmw0HsKqvGT1tacyE3GSjU2V0tqR8W80EskTXxA7VtdGkjDoNoiiYwRcleKqpGrSQwKZUUWHUPxcEXTdgGKAy3IIgQJFFarBPkkeW9eTqCYIadN2wAu9UQkZvewYzhSo1YYQkXJKESSdW97fg03/3CjRlFDzw+J7Avlt+1JTglywWoth9wKkFdwdxkkgPmmuaQV2gOGFKSsIDP6JDHej0s46KLKIakHRZKDEy3JS+V1UdSYVu08jSHjAXW21NwbIUOeC6HQ4QjTZdUqLAAJDnZE73j+RxYDSP3z1zAJ+7/Wk8/dKI7zOV+s4N7bqT1xaK5X52xyhEQcApqztx4WkDaG9ORlaL3D+Sx0BnlvqbtTUnMTlbmRNjHIbhyZJLTgKY92yxonJ7FB8ctXe9th0w2cbfPXMQ19z2mKv4VhgqVQ1Pbx3Fkq4cNN3A87vYA+H7Ht2NG/7jKavfhXINH/33R0ITvcl9PFeGu6bq+NVT+5lIhMWKUkXF7sFZ7OD0tD5SGJksQZZM2dUxA+Zie1dE33XdwOhUycpZ6GxJY0VvEzZsNQPuh589hP0jeeYEzMUA8hz94+YhVCJ20xYjGgH3IsfPH9+LtqYk/vFNJ6G/MxsaPI1OlahyEgIlQM87biWfBbcFAFmmu0aomhHK5hGNc9gETY4b6Hghi9SglQQ1LdmExZiWqqqVNJlKSFYgQmOQ7MIpdMvAtqYk+juzqIT0PV+qISGLSFACSEEQzKCXheG23E7cgbsii9SgU9N0S1sfBIWF4fY4lPjOTem7bhgolMM13ErIrki1piEVwXCHtQdMDXeQQwlAxuv8BG/TdSaTmjRZf43XqYRc54//zWkAQJVXkM+QxYgTdsC9MKXKN24fw5olpqOPKAg4a20PXtw9EXh+wzBMhxKKnAQA2ptS0HRjXhJODcOwttedyCQVGIadZM2KA6N5dLem0ZJLYPv+KRiGgYeePYiqqjNf/2e2jaJS1XDVa9egOZvAM9vYA+5t+6cxPlO2znVozLRGDUtgJQFKHM26Exu2juB/frsdL+1b2HyBw4nZ+nUrLNC9MleMTJrPdlEUsLw3B0UW8cKu4F1XwMy9UTXDNebPOK4LuwdnMDJVwu+eOQCAbfd1sYAsGksVDRu2+gmJxY5GwL2IsePgNLbun8LFr1gKWRJD5QGGYUQG3LJED9zIDZegPMSdUCQRKmV7v6bq0PTgm5ZonIO0wIAj4A5IugoK/Egwpkgi0gkz4C1VVItlSiakUKZ0dLKEnvZMaLKXLIUHzIWSGlhtkfQt7LsTBElTJJGuRa4FSHCcYJGUDI4V0N+RpSYmJhT6dS9XzMJCbC4lNDlMNMMdVdZ+bLocmDAJzG/S5GzB/K2oGu7678cbOJK+koQg6r2q2ePdCyvgnucqlwAwMlUyHXIcCbtnr+2BphtUZh4wA73ZYi0k4Da/93zISspVDZWa5ku2ilttcv9oAQNdWRy3tBVb909h33AeB+vSrApj8P7YpkF0tqRw7LJWnLamEy/sGmeSA5GFC2Bre0lSeNh4J4TFdH5u42PTbtPpqhBD+75YkK8vhmcXOOAemSrFkjCNTJXQXX+2K7KEs9Z24/EXh0OPRWQoPY6Y4PS6y8n37t+Mqfo4mM9qvIcbJEekqzWFPzw3eIR7w49GwL2I8cDje5FNybhgXT+A8AAiX6qhXNXCGe6AwJGwgEHsMkFQwK5qeqjPNBvDbbYPCiCDWF4SzCmyaDHc5YqGclVDMiFBrMtRgs6vanr0QiPg3ASFci1cyyyLkclk5nHMydMrFzDP77++mmZQAy/Xueu6/fAS7Wqgj7ciS/SdhRDdun1uct397Ss18/cJQxjDrWo6pvKV0IBbEgVoevh333VoJtYDZ6ZYhSgIrrLuBE2E4eZ8mJN7IJUwj0kbr+Q1OYzhXgBf6efq0oV1joB7WU/O9PsNkJWQ5OPAgJt4cQc4leiGETs4UAMWKpn6nFHk8Ceu1jSMTBaxpCuHNUtaMTlbwb2P7LbeLzPc65OzFWzeM4lXntQLURBw+rFdqFQ1vLgnmjUenylb8jiSAEcsD8PmKUtSUoi/oDEMAy/WA25iwXo0gtyb+Xn0y/didKqEG77/JP7v9zu52tm7M7bk8zWnL0GlpuHxF4cC21mJlo7crL6OLAY6s9hxYNp6/WgKuCuqBlkS8Wen9GPr/ikMTx5dCaCNgHuRYu/QLJ7dMYbXnrnUegArMl1DDZj6bcBc+QUhKHCMknMQBAX8JsMdHNQQ2YeuG4FMuN0HOtOckKWAAESz+pZ2PDzLVRWpekAXFnAz6aAjCrDkSzWfDMQJJUA770UhwM87qIBLTdOpgZe3LRBu56iGHCcRsLNgObOEBdxy8LmrqjYnhntitgLDCHYoAezvHjQ2D4zm8fk7no7UHdMwW6yiKatQ8yXI4iUs34IGK5iWRMhSUL6EDqmeT+FFLrMwDLdhGHjqpRH0d2ZdQYAgCDj7hB5s2z9FtZIkzHXQIqnNYrjpsog71m/F1+9+PlafLVLBM85thps98Do0XoBhmAuHY5e2AgCe3TFm9b/KwHBv2DoCA8DZJ5iVbNcub0M6KWFjRNIpAJfmlgQcTAx3fSEwNQeGe/9I3pKkxHF3OZwYmy5hT0iyfhhIoL1Q8ivDMHD7+pdQqWmRrldezNQLNjmlISv7mrGyrwm/e+ZAIKFg6r5FnxsUYbkvOnMJJDE4pliMMHdGRbxirVl1fMveo0vW1Ai4FyF0w8B//mormjIKXnfmEut1KUTaEGYJSBAkjbDYHzlcDxzk6VzT9AgNt80e0SQpAKwCJUFBvxwkKXE8SFP1ktDlqopSRbMXKiFMK6sOOixgLpQjJCURBVwI8qUaEopoFb0hCAq+VE0PlODYbaMLFtXU4GuQkOlyGPKgCpOUiIJgTuhUDbcezXCHLJTG6+M9SlIS1B4ANu0ymbo4xUtmCnQPbsDU4AsAt2MFWYwqMkkS9t8rqqZT9dvmedk13DOFaqD0IwrP7hjDjoPTeFV9582JM4/vhgFQ9aWaNc/Q+9+UUSBLYqDv+/h0CZv3TMaqlkfuH9mTrEl2pqK2+TVdt4Jb4lAy0JXFQFfWYskvrF8PFoZ74/Yx9HVkrNwRWRKxtLuJybKNBNwtuQSGJsz7YNgKuIPnYXIfzxSqsRNTCbstgG9XYD7wgwdewo3fe4I74RUAZkvmvblQAfcfnh/E5j2TaM4mAismB2GYUrAJMFnuwfEiXgoIOk2HkrRvcf7q0wdw8VlLcd7JfZBlMdTwYLGhWtOQUCTrvq0xVFFeTGgE3IsQjz4/iJ2HZvBXrz4GGUdAQwqB0GAF3CGJj3IAQ04CkiimN8jTuabqoRX9nFq/oNU0eRhLAYFfUNDqZPzS9QC7VJeUEE23UmdSgzSxbE4fUQx3uLSCTVJCP07wQsmIZLijnD7M49ALDpH2NUrfLTY+hNkH6NeOSJBYJSW0vo8RD+7QBaY5loIYblJ1NY4v7UyximaKJSBguspk0wq3pMR5H4blWwQFrLIkIpWQmFxKfvL7nbjtnk2WvSErVE3HXb/bgb6ODF5FKaDRVvedL1OCsVoAy0wgCIJpDRjgxV3TDOiGEctZgswvsodUSNcZbrJjs+vQDPVe3bhtDJ/89hN4fucYDozmocgietoyEAUBxy1rRUsugTOOM1m3KA13vlTD1n1TFtNIoEhC6E4hwf4RM2FzeU8TRiaK0OuSAyA4wRiwkyY13YidLLhp9wSWdGWRyyhHVFJSLKvYtn8KY1MlrkqnBIThLlc1rqTBh549iM/d/jR2HmIfg8Wyiv/93Q4cu7QVl5y1DPlSjSvQp1VIBYCz1nYjl1bwh+fpWman7tuJ1lwSf/2aNUgokkkmHU0Mt2pKQBUGImkxohFwLzLkSzX8+KGdWLOkBa88qdf1XpiGe3SqhOZsIjSICdNwB21T+89PZ91YGe6gyc1iqgMs3oJYZifjZ7mUeCUlJHCjrIZZdNBhDLdhmA+vMA13lAacoBDgay1LInVBo6p64PWy2wrWZ4MQFribGm66rR+AaGs/yrUjAU1SiXApIZKUAP91AXaiHQ1h7H5N1Sz/2ZEAj/YwzBSqllabhqYY5d2d92GQDWZYwA2YLHe+FH7eSk3Dk1tMdnuIk2373YYDGJ4s4a9fs4a6SAtb4KkBLLMTbSFe3CRo3rpviqvPgMPdRfIy3LYMbXy6jH8NkBgRWdzt67dix8Fp9HdkrUTrd1xyPD75ttMtpjuK4X5+5xh0w8Bpa9wBt8ToG3+g7vTS3ZbG8GQJkzMV635kkZQA8ZxKylUzyD1pZQfSMQsGxUVN1fHDX2yxdhk275mwFifbD/AvwJyLYZ7g93cbDmD34Ay+cOcG/PT3u5h2Cg6M5lGqqLj07GWW/3xY5WMvDo0XIEuCTz6nyBKWdGWpC1Q9wJXHC1kSmBy0FgsIw00WzkfTYgFoBNyLDr96aj8K5RreftFxPtcIuc6A0G5y06EkeHsdqMsyAgLmKP02aR+o4Q55ULAw3FE68iAtcU21+56uS0qILaCtfQ8OAkyGO0JSItslxr0g1SFD7fFkkWnrK8/LcOu6j7GjtQUiJCWaHrjoCLvuzuMHgbbYqNSvRRTDHSYJGZ8uo7UpGXp+KaTwzo4D06iqOvo6MhieLHFvsZsMd3DAbQa+vEmTtpY+kOEO+a0AU1MfxXBv3DZq2eANjrPLaTRdx88e24OTVrbjlNUd1M+EVbRVA1hmJ9qbk5gMYLhJMLo1Rvn6oMRwsitWLNewZ2gWBmy22wkyv03OVrDr0AyWdNs2oi3ZBLrbMtbiM4rh3rjN1Huv6HN7/5sL6/B5olLVMDJZwtJuM0G1UtNc14MlaRKI51Ty0r4paLqBE1e1I5OUF5Th3jcyi98/N4h7/mAmpz63YwzZlIxsSsa2A1Pcx3MmS7Ky/WNTJRwYLeAvzluBc0/sxc8e24PfPH0gsh3Zfe5pz1jF5Xjuuz2DM1ja3RSwwJWov3m5YhaaiyqBPp9OTnPB8zvHqQu6qqojoYiQRBGC0GC4G5gDNF3Hoy8M4uRVHdRMfhI40hLoRqfKofptIJwlDkpW9LX3nFs3DGsREKSlc05oQatpzXogBktKaG4ZNQfDnVQkCCCSEtXSdIdpgbWIAMbsU3DyH4uWWZEEpsI3JlPuD9yDKoSqqsEU8AJ0az77OCGSkgBbwDA/aFd7ymKBXIsUo4abNqlGWQICjqRJynd/cc8kJFHABaf2Q9V0rpLilaqGak2nWgISJBX6zoATP/rNdjzwxF7r305NfpiEKuyaNzEE+o9uGkJHcwoJWeTSk1aqOgplFSetbA/8jFnRNnieEQQEFioCTKeSqXyVumNGxsGewVlm6z1vW+84F0UB6aSEYlm1imDRcj1IeyKjWdLln5+TCfPYYQU5qjUNL+wex7o1nb4dxaD73IkDo3kYMBM2e+oJq6QKbiKiyJVzDorjVLJ13yRkScSxS1pMhruycAw32YV6assIRqdKeH7XOE5e1YHjV7THYrjzpRqk+r3GKv16dofpzHPuib149+Vrse6YTtz90E4cGA1PghydKkGAmW/S2ZqCJArMDLeuG9g9NIuVnsUZQVCOjdO9KwxBDlhHEuWqiq/9+DmqVKZa05CQ7ef60eQhDjQC7kWFTbsmMDlbwZ+d4k9GAoJLXeu6gYnZ6AAkqPBNmEuF9/zeYN8ZTAWxhM6Ke8GSknAdeZgPNwmYBUFAKinXJSUUhjtITsMYcFPdOkrRbh1KgMOK/1ghDLd3oVNf5LA4ywD0RRpgL5iC3WHoCx1mZxsKw02YtkiXkhC2lCfgpo35F/dMYFV/M1b0mg+y4Ql2WUlYWXeCqIeBpuv4/XOHXFn2XoY7aDdpLpKSiZkyNu+ewCtP6kVPe4Yr4Cbsa2SuR+C9Gj1eW7IJaLpBrdKpajpyaQWazq/jDnNByiQVFCsq9pGAO0QO8+YLV+H1r1yOs9b2+D4jiSIUWQxdDLy4ZwLVmo7TPXIS0j7oPiXY77BW7Gk3CZZNuyaQkEV0tKRC5QHVmm2BGofh3nloBit6m6DIEjIpOZafNAD88sl9+NztT3O1GZk0g1ZBAL5//2bMFms4ZXUHTljZgUNjBe7dpNlSzbLFY2377A4z0bWnPQNBEPDOS49HJinhuz/bHHqvj06V0N5s7sZJoojutjSzlGtwoohKVcPKvmbq+1H1KZicxziD1kNjhXmt0Fmt6WalXsrv4hzDQXVBFjMaAfciwu+fO4TmjIJTjwnarqUHEFVVg2HY26NBCH6IR+uYzfaCL9h3Hi9IVlIoqZbcI1BSQhwauH243QFIJimhXDFdStIMtoAqA8MdxrTmreqQ4fZ4UQUtDMMIdDuh/W5RNorOtkB0smpQEBe0WLAlJfwOL4QBjHYpMd+nLTYmZyuhVSYBu4iSl/XLl2rYNzSLE1e0W7Z2PH6uJOAOk5QELW4JDowUUKm5E7ac92Eww62FjtdchKTk8ReHYAB45cm96OvIcG1tq6y7GmELe9Z7LWBxfNyyVoiCwC0rseUs/vOT4NEKuEPckNJJGVdesDpwqz6pSKEa7r1D5jmOW9bqey/KfhQwA+50UkZHSwrtzSnIkohiRUV3WyZyzFVrGpoyCpIJiVvDrWo69gzOYlW/Gfhl6sRGHLy0dxIHI1hhL0Ymi2hvTuEVa7ux7cA0BAE4aVUHTlxlPiu3c8pK8sWqJe9gsdEsllVs3TeFdcfYvvPN2QT+7pLjsX8kH+r4M+IpSNfbnmFmuHcfMm0PyXX3Iti9K/x5arWPISn5r19vwzfv2cTVhgekPzQXnKqqWRWdg2R3ixmNgHuRYDpfwfM7x/HKk/uCrfECNKlchWsCbs4o1oq0957bebygDPtCuYaWrPmAClpN24VvgiQlUmASmfN7p5Iy8qUaVE23JAtkRUxnag0mDTc5lxdB3tmu9pLg0k/SUK7WteDUpEn/g5j9Nw9PmiQLqDDtPG3LkgRPtOqUrvPL/r7bSZNRCZd0hrtS06AbBjLJ4EWOeW76QmnrvikYAE5Y0Y7WXAJJReJjuAvBZd3tvocz3MTlwNk3Z0Aa5B7gXWB6kUsrKFXUwIfotv3TWNKVRU+baUk3Pl1mctABbOtOKcKKMjTBeQ4LRMJwL+9twkuciZNknNMCkGxKxuBE0fKnDhrvLInlSUUKZbhn67Ix2v0WlJTuxK7BGSztMqvCioKAnnpSXE97uu5iFTzmKqqOhCKhJZvAVJ5PUrJ/JA9V07F6oAUA6pIStoD7xd0TLjZ8aKKIqqpz2fmRBMBLzloGADhmoAW5tII1S1shSwKXrETTTWkUSWBkYbg37R6Hphs41RFwA8Cx9YVT2DG8cs++jixGJkuh1ZkJdg/OIJ2UXMVrnAjMsQlZYDoRtMjbcXAan/reH32LKl03sGtwBpOzFUxzjiFWkL6XKDsoZtJk+C7gYkYj4F4keGzTEDTdwJ+d0hf4mSCGO4y9cSKMeYp6EJLz+wJuLTzgNl08VLTmEr7PO2HZdoUw3EEPQmcAkk7ImKxPBERSEiYJCfM1ts4dIk1gKQDDojULs9mTKdpMnmJF5ueD/M+jr3uN8nCMkjZY7akuJXVJCasPd8AiL+r8NsPtbk+SeNubkxDqQQsPwz1bT7iKYrhDA+76lqzzM85cinANd/B1I+OQlvhnttcsN42+jgwM2D6/UeBhuIMWC6w2lkH1AmRRxHHLWrGbs0JomGQtnZRd1pD0XI/ohTlg5iWEabhni7XAqq6SJIQGYS/tmcDeoVmXnSBxoehtz9QZvxANd13/2ppNcBdlIuN1NWG4UzKqNT3yN6hUNXzlrmfxm6f3AzB/B1Kkjef3G64H3Mt6mvCmC1bhivNWAAASioQVvc1cDDeRAbbmkkglJCYN97M7xpBLKzimvuAgCLMuBUw98kyh6mO4Nd3A2FR03siuwRms6G0OXOgFSko4dkBpfd8/ksfBsYKV8ElwaKxgLSj31HdrDjfIvU9b0FVqusVwNzTcDcTG9gPTrkIINAQlwNl6LQbmiRpwR2srzfP7fbydA56W6FSummxka46N4Q5MmpTYAr90UrYKZxCG20rkCkj4jGLswioeWiXOQ5MmpUhJiSVNoRyHxlyxTqhRPtxRQVQQu8+8SKMs8uykyXAJlBSwULILNYWPWdLea6nodVjpbs9weXGTYCVMwx20m0SwgxpwGy6GO0jDHRa0kj4FMW5VVbd86clcwyorsYLWSCtK+nfXNIPBxjJ4gajqZtDbVw9YpgLsA2mwt9gpGm7H7lQurQQnlkf0HTAXkWEMd75YDRw3UQz3Tx7agWxKxgWOgkO9deazpy0TWS+AsIPNuSR3tcmdh2bQ1pREe92aLuOwYA1DpS53JAmpY9NlK9cnKqmYoFg2favJ4uKKV67ASStt2eWapS3YMzgbOccSkAC7KaMwuwntODCN45e3WVaQBHLA/EhAgmqnPR9h1qPyJ2qqWZUySL8NENtWzfdcVEN2dNzt6Tpo8n280iOn//i8Bdz1e4CWI1BVNSRlW1LSYLgbiIWapls+0kGwHkaem7tmPUyiEtjohWt4kia953Y+IGgMN2FuScAdZQsYljTpPZ/Vd8kZcEsWA5lyXE/aajhKv0wQlrxXKNWQVKTQY8iyEPlwCUu+lKiSEk6GOyJZNThp0pzcvP2fC8NNApIoSYkoCFTnhiBPZS+CEka9166nLY2x6TLz5D1TqCKVkCymhYYw7/WZQpXK8rmSJsMY7ghbQCBYl+ps39OWhoDoBz8Bub9ZFnlB29zRW9whkpL6mCP66UmOLe2w+4UslrtaU2jJJehzZEhisRNRGu4whjtMwz04XsATmwbx6tOXuBaqRGrQ056GLIZ7KlfrkpLWbIJbw73z4LRLR0yeVVGyEtIf4uThTBaMktkRjNRZ1u5WuqyisyVtFvNhTOIk90YuraApEx1w11Qd4zNl9Hf4z0/mqKCAm1YBmiySonTc+0by0HQjIuAWYRj+Z2+N4/lAs6Ik49ArPdp5aAa5tIK+jgz2DM6EHjsuSN+9Y8swDDNpUiF5LkdXWXqgEXAvGmgaS5nuuWm4FYncnP6AnS1pMljaAPiPC9hb2y1EUhIV+AVcg0QAk+AN/JwPo7RDskArJGLpxiOYq7CkSdNZJLqAS9TWl8WUUwJuUmHUyWKw2j4FjRnvccJsAQHKdWdIgCP98+7IWEVzIiQlpF+035wcO7wtWSjRx6xS94MmW7ysFetmitVQ/TbpG21HBrC35wc6s+6kScd9GMhwR2m4U4ThDpKU2A+shCKhoyXFzHBbOwMskpIA54ToXTh6zoHh2I2yAm4uhju4yiVha5f1NIXqz1nyXCI13MVqYIK1LJrzM22n8JdP7oMiiXjtGUtcr595XBfefOEqrOpvNhnukAJkpqREREsugVJFZdbuTxeqGJsuY3W/LacguwJRDDcZw6NTZZQqqivIZLFKBWxLwJ6AIi52jg5b8GVZuaYV5NIJlyc3DaNTJRgGLBtGL8Lm9xFKwE0C/aGJ8PuOJEwGWQICDklLwC4gk4Y7xOXEKz3adWgGq/qbsbKv2fSt56xfwAJLUuJxKiLfyZk0eTQV7QEaAfeiAVOJ8SgNN4NFGwDfFlKYD7MTNA14VNIkkUpEM9wRgZ8VcLsn6ZrmZp4yDlbbGXzTkv+snQFWDTeN4S7Tq0N62+u6EarPLIS4nVhMreP6EtY2crEQoTG0JCUhSZMA5bpzMNyqpy1r0iQQMOY42X3fAlN176b0cDqVFCuqa5wF9RugSyN2HJyGJApYPdDivn80uwhToPtAxHUncoVCQBVAr8tJf2eW2aJMi1gUEwTmiugMNpYBi1ubXRfR1mTKGiZm2ANua7FAuV9I8Lisp6mehEa3wWRhuFMhkhLdMJAvqYGSkqBCTTVVx2ObhvDqM5f6FnqZlILLz11hWhJGBCDVmo6kIlkJ7FOMLPcuot8esJlWMv6jrAGdi+0Do3lXwM1SDAywA+6gOhNhSe002JKSBHJpOZLhJvNCd3vA+UPcMkanSkgnZV9SfW97BvuG86EB6+7BWbTkEqHFaxIBhEjUvO7se9DCHoBLelQs13BorIDVdTvV6UKVW5rEAsK4exluUjCNPJMaLiUNxIaqGQyMXbCsAgiv4OZs7x2krA8TWRJ9lS6dx6IxM7akxHxQBBe+CZc2BGnlVFVzM9xJO4hLeRhuv6Qk3BnFd+4ADXdYwmRY350IczuhJX3aiwW2vgcmTUYwIUGSElbdP626KUkqC5NkWO0PA8NN2xFyOk6QB+kQo1NJVNALBDNPgMlwL+9tQiYlu/rmsgUM0XCzSEpmwyQljr4TizKWSpu1EJbYiUCWmGFhHzRHOXX36aSEpCJxMdxk0UW7X0jAvbynKSQJjW28J0OSJotlFbphhEhK6PdqpaZB1Qws6w1mOkn7sACkopoabjIXz+Sr+NFvtuNb94ZbvO08NANJFLC8xz5/mjHgds73B0byrlwJVkZ6ZLKEllwicEcsiIwJAmG0c2kZuXQiMmmSOBiFM9z0c49MldDdmva5OZ22pgt7hmbx65BKlZOzprtJmBNU0DxTi3ieEgRVn6ZpuHcPmprtVQMtWFGXucyHrIQQgt6k3KrnuRGkP1/MaATciwTEdioMQQlwzKvZIFkGa/BE2e51M3SUgLs+Ibc2RTDcxHIsiuH2sZ1uD3GnF7kr4KYFbozad3tSo32/6IBbCXiQOpEvqUgmpAC7MPO6uxnu8Otlt2XUcAeMvaDFQs2z0AkCtdJkvXiBNwGJ2j6E4Z7LAtUZNDalFaSTMkYYGW6W4CtovOq6gT1Ds1jd3+JLBHbmI9DyLQzDiEyaTCoSErJo5QR4UfW4nPR1ZFBVdUwwyGlshptBUhLTDSnoXnHqxwXBlJXwaLi9uxpOHL+sDeee2INjl7bUk9D8fdcYJVRhGu7ZiIJJ1ngN2JEJc6cB6AnKTpj3nWSx5E9uGcavn96PDVtHQ+UlB0bz6OvIuhbIrJIS5zjYP2Iy3KRgFWuAPDJZRE9IFWWLFGBkzGeLNSQTEhRZQi6joFLVQsmQ4cliXX4S8LuFFDYzLQH99QIuOmspTj+2C//7u+14cc8EtW1V1aOtUxUip3FfS3ZJCX1XhLSfcdxjOw9NQwCwsrcZS7tzEAUBu+chcdI5hp0sN7kvXYVvGgx3A3HAxXBTAmbn+0EIsrdjfZjQ2jv7Epo0GenDHb4iJ5Mqje10TirOxFNf0iTlewMMDLfFuvkfEIVSDbkQD26AkeEu1wLLw4cy3DF/c+9xghnu+oRe807oPAy3+9wVR/GCKNAWSqxV1Ehw5U2a9OYsCIKAprTCXDkvSkcNOH9z93Wr1DRUVR1tTUnIsggD9n3j9eH26vY13YCB6IVGNsR5gcZwA8AQw2KD3ds3mCWOXCAG2AJ6ZXNtTUlMzrJp7q1zB/hotzen8A9XnIhUQo5guNmSJqt1ZyYvSDJ3U0DgRuYhL3FBrnsi6rqL4YVzTJcSyZL3/WbDAUiiAE03sG84uBBNtaZZhcsIMpxJk5JoemVPF6pY2p0DYEsEojA8VbIKVNHAKynJl6rWb0D+HyYrGZ4oBurHgeAdHV03MDZVQhelrSgIePfla9HfkcW3732R2r5S0xhqFdCfi2TBGldSYiVNOhjuXYdm0N+ZRSYlI6lI6O/MYs/Q4We4nc8L54LOy3DTni2LHY2Ae5FAVXUGu62gLXI+TSuN6Y2SJgBOaQVdUkINuMumi0dCESEK4RpuAQj1GwVo0gZ38JTmkJSwul3Y3sDu7xdWHdLVXqIHX06Y0hR64E7T7lsPsoggIEgXah8nqvBN8EKHh+F2Bo7O4gVRoE2q3EmTFBtNb/AUJgXwgkUaEbybZPfdux1c09w+3ID7d2Mdr0FWZ2YegeEK3MjOE0upb02zWeYwhHkDRzvLkN8sfJHV3pTkTJrkSPKdQ9JkKiHBAF2fbAXcQZISkb44ZvU/N3X/wdIxTTeQUETkMoo1z77j4uMAALscdm9e0BaYqaQMAewM99LuHA6OFay/ATaGu1LVMJ2vumz1vEgEsLxBmC3VrF2GHEvAPVkKLDwDBO/oTM5WoOlGoPY8nZTxmtMHkC/VqDkXLPNkVK2C6JwJutMHXVIy43JMWdHXhD2Dhz9x0rnYdpIgZAfDLnwTXZl1saERcC8SqHp00BumwTbfZ5Ok0CZ0NkmJnzF03hx0DbeKbFo2vbBlKVBzpdUfaEF6NZpWjWyxuzXcdrEb53eiJaFpETIWq20AS1xVzYdYlJ1jkH+6E4VyLWTL0h80szIYkihACDl3VAlg1oVOELwsLmAnb7EgVArEuCNEswX0tk0qEsoh7hLe80e7bQQwT6p9r3oflppmWItamoyJ9XsHBdy0hUpLXV7AUgiFeVdlDraAQTsyqu4O9lubkpiarVLnHBpY81SCkiZZXKQA23mHJivJl6IkJcFJkwCDpEQSoWl0ZxxyjIQsQRQE9HZkcNbabvzZqf3oaE5h56FgptJbzRcwiZFUUmbWcK90WAou7Ta14CwabssSMIJhJv1kQb5YQy5tjvtchI1mpaZhcrYSznBTHLCcfQ8KuM229BwZ89zR86SV1F6LFxPIUpBzmTmGKlUN5aqKfKmG2WIN/Z12nZAlndn6YoFtZ5AVQZKSSv2+JCSQEiLlWaxoBNyLBKrK7lLi3XLk0WsBtICdsfANpb2b4fYP/kK5Zrl4BJWhtfsQkhxCycZ2OhcQEA13ypNgk5D9xWd4Ge64yXthLicE+VKw24nNfNm/O+uuhrnQCda6RUpKgmwBORhub/tKveIdC2gabtachcAAhjLek4rIXuKcYbER5htv9k30VVV0Sl1oibqsTGcurVATwWi/dSohI6GITL7MtgQr+rvTxhuT9SmjpKS9KQndMDATECh5wVxrIICtZM1zIQESbbfEZrjDpWNB83v0PCP4FrcEtjOQeYzr33EG/uGKEwAAq/qbsSsk4FY9eTIEmaQUKSkh13JVnRkVACzpNoM2lmCJ5FUEJSwCsAo5sUtKbHIjRwpFBQSNliVhFMNNOffEjCl5Ipp1Gqz5lTJeqiySkqC8Ls5db5pzGcF0oWq5y/Q6rkMiwXfdWeEkh0plv6QkadkCNhjuBmJC1emTmhPBNxerXovuccu63SpR2rNouInzRlgxEFUP7wMtcKMFvERS4g24wwrfsFToBMKYp3gBuxOFELcTmlUaK4NhfibYLiyqSqnNcHvtGNm36MnnCbgkJSEMd1QAJQomu+8LeilOH8mEHFqwxNuetUQ5zcaSvO9dwDp3mmiLNNbxlssoVu6EE5YG0tO+OZNgClytvrNUtFUpVWEdDH4QonfxzPeJNSCrrMTpABOGhCxRk+9Y81zIvEOzBnQm69Fgza8BSZNR90yQpSIAVKxjkPlRtiSMq/ubMT5T9hU5IQhy5UknFWYf7uU9TRAEoKMlZRELLBIuskgJ873n9eF2SkqaIhhu4qoSGvAHaLjJ/ZYOqagb9Ew3DMMkJhglJd4xy+qZH+gK5Pj3dL5qWYf2Oor/RFnOxoVzR9KVNOmRlJjXveFS0kAMOD14gxC15RitK62vSB3tSXlzlsCN5iAQreG2Nc6KErwFFPX9aQEMzY+ZyDu8ZcNpXqmsyaZE0hHb5STEVhAwfwNTUhKk4aZJSth+c7N/wUxAlKRkrhpumrMNqXjHgjDv96jrLggCJMlfSY0mbUgqYmjBEnd7hsVxUODo6LtzIUb01eR60RhuZg13SkGhXPPJLYIkKS25BKOGm43hpsmIAEbte4BLierRjxNvYlYvblYNtiLXrU+9feeoNAkEBNyOZD0aomxfiUyJtz3gTzhzYlW9oE0Qyx20uM4kpWgf7vqYzaRk9HdmMdCZ5UpyrHiYeRpsWUX0/VtTNVSqmsVwWzaaARpuy4M7QlISpoMOG/NBDiuqpsMwomsV2M8Wf1J7UJKwq72VqOufpwhRNlOoYniyCEkUXGw9b7IqK5zX0qXhrj/7yXlJaff5KL4zX2gE3IsAhmGYkorIpMmo7dao7VoS/PgLqDAFTyFBABBc+IYwGmE2PkHblgS0m5u2xW5JSpI8DHf4d5dEEaLgTy6peVijIAQlqxKUKyoMA4GSEhrbyVpdFAjP5o7yVg5iUHiS0MzzeBhuhvEGmGM6qIoaa8Dv26KnBH7JhMwlKWFnuAPuVVl0jQsix/Iy3HTv9WhJiWH4HSScOl4nWBlu5lyRgPFu7mKFtxXrQUKQjIgEzXa1STanEhZLQsARvFEYdlYfbgAo1/yBaL5YC5STALY1p89Vh8xzUQx3yDxT9RQNcWJZTw6SKAQH3AGL60xKiXYpccwvH3rTyfjbi49zzCnR91vYQoGARiQFgTDmREpCfN2Dqk0OTRTRkkuE5umE5SyQ98PaAv7fzC7ywhhwx9y1DttR6qgH11P5CobGi+hqTbuO6ZXEHS6omg5BMHcoixX7d7EZbtuH2/x8I+BugAOWFjniQUp8un2uC5x6LXfAzBG40ba5nQw3ZeAXy6rl2ZpQwjTc4QxUgjKp0hhm0w1F8G3jUV1KeGQZcnAQEFcDTkBYuuaoghiOBQ0Pw20yARGFbyKSJuNWmqSNmUpNYyrrTs4fvKPDJqehPUy8wVdSEZkkJYZhsBVwidD9uzTcmu5jw2jyAFZJCZE1lKv0gNs7z7Rk2RjummZAEBiqmwYFASqrlSRlkaW777VcRoEsCcxe3Dw2lrS+s+xAAhEMd7EW6FAC2IsJ773KniRMD9iB8MA1oUhY1pMLdCoJCt7SSTnapcQxR/a0Z9DenIIoCIHWkV5UamZ9irDfTpZM6RiLDzfNmjGXVqyKyF4MT5ZC5SRASMDNME8FOaxYeuWIeTJwB5IxSTioMFpN09GaS0IUBEvD3evRsc8Xw63W5V9pzw4KuUZJ2S7tbvb98J5/PtEIuBcBWAMIoT5R+V0XGCvAUVakfNIE/wB3suU071lngllQMhVQd2hgkpSEByCCICCdlBg13OyLDZpOz7vFFdX3oO++vV46eZUjk98JWtBqs7xsUqAoDXfQokGWzAWMUx/JJUOiMtw6e9IkTcNdD3jDKrDZ/ReoPtz+pElTuxtVcdHywmZ0BApk5z2SEu9YZh3vNJCHdJCu09u+OZtAoVSjJj07wezXT+m7Xq9Qy6Kjpu2EkTmOBL2iIKA1x24NyJo0Gfy7sQXsloabljTJLCkJ2EmLYjtDNLVkrgrSBK/qa8HuwVmq64tZ64BSodPhUrLjwDR2UgJ2muwPIEnK0YES8Q4PgyAIgUGvE6qm464Hd0ASBQx05azXc+lEIMM9EuHBDQRruAkpEVopMiBgrgTkW/jbBy1uGcd7wK55TTWLkzVnFUzNVjA8WfIH3NaOCtvOICvI/JxJyVQNN9npiZJqLkY0Au5FAJrbRhAUim8mqVIZrdfyD1Cu5DuiZaawzICfWdENA4ZhM/NhNj6qpodKamj2dEEauaXdOQx0ZV2vEW2mM6gIehjQQHP6YPeDDmcCth+YQnM2EagTtHTQLoabzdKQnD9YUqJDFITQqo+KZ2eC1S0DcE7oDltAldeH28++sJwboLP7KiV5zw5Swx8erMlIQcGPnfApuMaFd9FNdQRivO4kMPMGfXbg5me4DdjsXxB4E2VpOQcsLLFMC7gp93p7UxKTjBrumsZm62cn4Pkr90VJ/oBghtswjEiG287ViOdSEq7hNo+RDAjaezsyqNQ0n5aZLK5pC6V0Skapapar/97PN+P/Htrp+0xQUrYii0y+2WbxF7YxF3Y8wzBwx/qt2LJ3Eu+89HhX8JhK0C1BKzUNM8VaqK0fOXeQhpt199O7+LB+L1YNN2Vhz2TbGiIpkWURLbkkdg3OQNV0V8KkeW52KQ8PyLkzScXjw21WNyZxDi0/aLGjEXAvArAmPZLP+LZ/GLa4ncen6UK5JCVOe7oQDTdhS0gwlwhhuKOSkiw/aSrL6+77x686HX9x3krXa7TiNawuJUAw00o7v69txEp8+/4pHLukJZAJCWK4WRZZAF0O4zwOjb1ywmvnGJVo6QRNkjJXH27TZST6ewPmgoQWvPlcSiw7t2iWDGBJUA5mSkl7Z2DqPS6V4Wa87iRA8S4egnZkmrPu4jfj02WMU0q9R+1CEdA03DxjxpQbeJMW/fdqK0fxG42Z4Q6u3MckKQnw4a7UzPLhuRANt1UZNbC0O5uMiepSUgtnuIPs6cJyfDJJGYYBHBorYGSyhBIlaCVyFO/clmD0UGZNsE4oUqhLyRMvDuORFwbxF+etwHkn97neM3e36LZ8gN/xygsij/Em79H8/n39DpDsVRi060D9uSj4HVpYbSxpNR7Mf5uLrJZsAoPjfktAYD4lJeb8nknJnkqTuossaDDcDYRi0+5xXPudJ3zME0/gR7N4Yy3qQGeeDNd7oe0DNNyWdjAg4GZiuNVwDbcgCFAU93fn0fLSPcTZpDhm3ylMK6eGm7YSH58uY3ymgjVLWwPb01gIlVFTCgCyGKLhZrBLS3jYI9YAAHD23S5fbla849NwOx9mrPpx8/z+pMkgSQkAVKpsSWBxf/MglxLvoptuC8gmYUoEyBrs38197a3iN/XEye/evxm3r3/Jd1xWhpuaXM1xr9F2k2jyr/amFCbzFSaXAlZbwFDGkKHviQCGOx9R1h0IdqFinaPDCsCQgCzovksEFGAJI4NIbs4z20bNtpSgNaiKsaKITDZ+lWq0FzVQnydCjrd7cAaphIQ3nL/S915CES3bRCd4CZU4RFiQpaHXczoIRE5Dm2e4ZKI02Z4sojVn78h4vcjn06VElkRTsuQpfOMcv0EmEosZjYB7AfHwxkMYmij6qrqpHJISmtk7c0IQVYPNz3B7i3GQScGr/9M8DLeiRPlws/n7Wm00egBBbUuZHKyFDsNWMW2hw1vxkPbdtx2YAgAcu6Q1uH39+O4Kn2wBBGkfVviGxU4y7q6I97pbDxIOSQjgfpiZtn5sAXtQ0qTfFpCN4Wb9zYOCH+e1s8qYUwLuMEegqOse9F2CNdxmEDidr8IwDOwfmaXapGmcC3vqvcYUBPjnONqYa2tKoqbqTJXuuF11HH036rIKiUGSIgoCkorkW+yQ6xkqKaEUuDL7wsZ2BgXsgNODPSjgDhqvwcF+pu7c8czW4IA7KPAz/c4ZXEoY5WempCT43p2YraCjOUXdRUwEMNzM93rgtYtepAVVmqx4PKfDkJAlvwSKoXI14CdECEgRLrL7lU7KaPbszvBW+GSFVo9n0inZJylx3gNBFqKLGY2Ae4FQqWl4Yfc4AEqZbEamFAgJIBiDdYC+1cvDXLmcEzTd2nLzMdx15onIHsISW1gWDV6dHg/DbWdzU3y8WRIPKTo9ZgYkZGLavn8K6aSEpd0533sE1u/mlPLobL7C5PyBSZNMAbc7wSkOw03GTMVj7RQF2q5MjUNSQkuapO0IhSW7udoyBr2iKEAS6fkWgHkvkYet6VJC2Gu3hjtO4ZsgHXHQjkyzg+GeyldRqmgBAQhnRVrqIi2es4zNcNvtbS/uaGtA5l1ASt95cmwAuqf7bDG8rLt5fPpOYU0zIACRAT+bDzf9OygBbhlh451Y5e0byQOgL1aDgs5ERIBMUGGotmgfL/jenZgpo605SX0vSFLCeq8HJcWz7MQRhxWvpISV4SbnpyZNxiThSHvFwXD3tmd8i5X5knQEMdwkkdPqe0NS4sbXvvY1XHbZZbj88svxgx/8AADw2GOP4YorrsBFF12EW265xfrsli1bcOWVV+Liiy/GddddB1U1L/ShQ4fwtre9DZdccgk+8IEPoFAozGeX5w0v7p6wghbvzU30iSwSAUUSqaV/WbZqictJ3KRJWqXKmqojWbfg8z4ovAx3QpZCXEr0yAeKN+hlZSCcn6m6Fhvmg4xJB01juBkDIFEUqDsTALDtwDRWD7SEJi2SSdHNcPMFnYE7Cwxjx9Rw22OWxwPcx3BHuCV4EZR3wCwpochpaqrhax8kBfCCy9Un4EEImPeSM7jzJsHSZRmcAXdQ0qTn2qcSMpKKhJlCFYfGCtS2wNxcSrh20mhb5JTr3t5s+gSPMwfcLJIQf+DJ85sDpo7bq+H2+j/TIEUEP1GuPGE2aVU13F4vUFISor0nkhIAyKZk6pgJIlLMRGwWH242RyNFllAL2Z2amCmjvYleYj2hiIGLBfPYMXezGAJuIpX0Jk2yarjJ+WnVp3lIOOeYsV2oREtu5tVvA/Oo4a7nFWVSMipVzcpp8FYoni+GfT4xbwH3k08+iSeeeAL33Xcf/u///g933nknXnrpJVx77bW47bbb8MADD2DTpk14+OGHAQDXXHMNPv3pT+OXv/wlDMPAXXfdBQC48cYbcdVVV2H9+vU46aSTcNttt81Xl+cVG+rbbgBFH6iy6UIB4tpA0VsxBl+KLLgSB2MlTXr8oEmSljfZx6/hnhvD7U20YdVQA44AxsPuywwPMtL3uC4l5DPe754v1XBorBAqJwGcDLej7zrbhApESEoYAnfvdu2cGO4qO3PjPIdXBsUsp5EEV6VJs8iUvz0rw20/hGPuijjuN8Xh+mMx3KEabr6A2+ft6zmHE81ZBdOOgJtm2cbq7RuaK8LIkAc5dTjPT1x9hidKkcekOdMEnRvwzhNuS8IoJBWZwnATDXe0SwlLzgG1fUTSZNgi1wqevAWuQsZcxlEM5uTVHaipur9CZ8C9asogGBluBs/+RIBTCGCyxzPFGjqCGO46EeTvO1++Bu25zDLeaQmkNsPNMGYV0fe7sZIStDHjdJZpqUtKetv9Ti3zmzQpWuOrVDGvRUV1L77CxvtixbwF3GeddRbuuOMOyLKM8fFxaJqGmZkZLF++HEuXLoUsy7jiiiuwfv16HDx4EOVyGevWrQMAXHnllVi/fj1qtRqeeuopXHzxxa7Xjzaomo7ndoxZDwia5RTAbvHmZX80nuDLx3CzJ01SC9+EaLi9LiVB9klmP6If5rLPLYMj4TMo4GZdqEg0lxK2JDbzM5Lvu++o+28fG5IwCZiFRgTBPyny/ObBhW8YFjoeBwCnLCIKfoabU1ISwHCznBsgLiV+O8XgpMnDIykBAhhuR/Ig3RawHnDTLDhV08IxsvBM/SEdKCmhXLuWbNJkuMfDGO74khLeOY7G2Hnb59IKcmnFKr8dhhplkUXtu+J3KeFluGk2c7OlKiTRrBEQhDAfbrakdr9kkCCKKU4ELNLCCBkiKWlvTlqSON+uSsBiIaGIzJUmWarSemVvTkzUnWzIjoi/L0E7Qnx1FuJouEl773WfO8PNmTSpuUk08l5vRwYt2QSOW9bma0tzDjscINI1Mr6K9aJEVY+8KCjhczFjXiUliqLg61//Oi6//HKce+65GBkZQVdXl/V+d3c3hoeHfa93dXVheHgYk5OTyOVykGXZ9frRhq37plCsqDjnhB4A9DLZAKu+kZJQxMH4eTOaeQIIUnbZK0lJEUmJRpeUOBluVaUHfppuRD6MvQEMqycy4AgCPIsN9qCVUtpdYwuAaH0HgOl6lbzOFvqDwNXeEziysl6kbaj/OcMDhcq0ck3objkVa2l3GnvEM95lyV0oKih4CnroesG7I0STRhA7R6fEy5uPQJXSMAZeoiCYmlZK0iTNog0wddxOhpvKVvJKSijXnUUGRU8Mp9tg9rZnMDwRHXCbLkhzszRk8fEGTEmJN5icrZd1D9tNI/Mk7bvPRY8LRCcfWm4ZAYm21MI3dUnJ2uVtjl0VtsUCu4abzUJUkYMlKqSSb3tTkIabyIgCdqNiBtwq4/3qtV0lfYmqsOls75OpMu560yV7NpGVSyu45erzqaSQVXAohGHesHUEn/7+H5GnJGEHgYx3Mr6Ijrta011jmLbzu9ghR39kbvjwhz+Mf/iHf8D73/9+7NmzxzXhGIYBQRCg6zr1dfJ/J1i2/53o6AhORptvdHU1AQBe+sNupBISXnPWctz36B6k0gnrPQDIjpgPua7OnOt1GjLpBMq1sutzgiggk1Yi2wJAMiFDkiXrs+n9Jsva3RV9bsBkz5SEbH1WN4CmnDmRJT19qMH8rVpb0ujqakJCkaAbBtrbs77gWjcMNGWToX3IphVUa7r1mWTK1EP29bZETspT9WznjOMciiIhoUhM3zuXS0IfLbg+KysyEorI1D4hS5Ak97mUpNn/gb4W5ELcCwBz8nNed1ESkUrJTOduakpC0w36ZwUBmVT42GnKJXFwzP7umVH28Wr5+CbNc6TrY72n22wX1b6j/vlcU8r6rAEBuYixQpDLJDA8WbQ+SxY5ba1p9z1Y13fKifBrynOvppMyBEkMHTMJRYSiyNYDr7uryXpPlty/uZKQmcdrKilDkD3nTshIBozXno4sth+YxkyhCkEADANoakkjk3JojgW2eSZRl00kHeNqf1320clw3XLZJIYnS9bnurqaoCRkyLK/78v7m7Fx62joMYnLSEtTOvLcqfoWurPvZB5rb8swXfvmXBL5Us09F2oGWh1jOAiyJLjODdTv9SQhnYLbKynzunufLwAgiGLofS7X5yLvHH5o0tTHd3XQf7f3velknLqmC1v3TgKo36cddtGxoGdTc1MKqhYwJzlQUzXr+RGErq4mNOdS0PQZ6udeqPftmBUd6OryxwMd7Vlq3zNDswCA7s6m0PN3TpnXKJtzz0k6gGzG/1t4kU4pEET32BZls1oyy3jLZBIoV1TfuVnmyEzODIRTjt/IkMwFbFtr9HhPKBLkkDlpfMNBHBgt4J5H9+Cjf3O6673v37cJuYyCv37tca7XDQDZbAL9Pc3mOZLmNVR1A805+x5SBTOGSDNcY5bruBCYt4B7586dqFarWLt2LdLpNC666CKsX78ekmQHRqOjo+ju7kZvby9GR22N89jYGLq7u9He3o7Z2VlomgZJkqzP82B8PE8tVzvf6OpqwuioecNu3TuBZT1NqJbrhSUmCtZ75N8AMDtTdr1Og6HpqFRU1+dK5RqyaSWyLWBuaeQLFeuzE5P1c0+XMMqwlpFFAbOzdvtKVYWuapA8rwPAWH17ulA/H2GPBodmfLq8ak1HraqGfwfdQLFUsz4zNW0+xKcmC5GJj4XZstUn0n62UIEogOm6aTUNFU//ZmbLkCWRqb2iiMgXKwG/ewmlQngBD0kUMJu32xdLZhjAcu5aVUVN1aifLZVrkDOJ0OPoqoaSY8yNk991Nnq8AibjOj1TwujoLEbGTEeDQj3wjWpfrF+X0bE8WuuMR7lSg6bRv48XqqqhUrU/SxwtyqWqqz2ZIyYmi6HHJb9ZnuG7CwAKRfd5ZvJlSKI9ZmRRwEy+YrGSMzMljNbXooosYMZxT5njTWAbb5KIac98EjZeE5JgOWn0dWQwOF7EoaEZK2kKAMoVFbmUHHl+UqxicqpkjxmO66ap9r1G5tHZfAWy6P/uLWkFEzNl7D84ae20eWHJmSq1yHMTpnBiyh4HI6PmmC16fstA6AYKpZrnd6f33wtJFDHjuUaFYhVkdgtrTyzUpqZLvs/N5isQheDzk7be8T82YX73fJ7+u519nLkzXa2Ygdvg0AwkR85EsVRDLuN/Npnzafg9bBaEMqDVgp8LZHxomoZywPH21K1XjYDjVOuShcFhd9/HrDFbwuhoMNNcJHPZeB6jDp14uaJC1/TI31wUgLxnbE3PlCDLbM8W6AaKZXu8dXU1mcmGAXO+E2RXwDlmyI5RqRQ93iXJPUd5MT1rPqN/9/R+rFvdjpNWdgAABscLuPfhnVjZ34zXnNrvalOpqtBUzf5dRmbQ35aqX0/7O83U53LvmN1xcBq3/+IlXP93ZyJZXwwwXcfDAFEUQkneeZOUHDhwANdffz2q1Sqq1Sp++9vf4q1vfSt2796NvXv3QtM03H///bjgggswMDCAZDKJDRs2AADuvfdeXHDBBVAUBWeeeSYeeOABAMA999yDCy64YL66PC8wDAODYwX0dWQCM8H5yqvTkiYNJi9pWnueEuFmH0X/9r4sQhKFYB9uYguo+Learc8ySht8xV8Yqy3SKh7ySErMpEl/YQNWtwyapKRSY9829Do3sHoiA7ZTB61AiMpQPdDrAMC61Wq3F32SEpZkIIBuZcknKXHLE4IkJaJobo+yJ03G1HB7tnrJVjhNpuO71xh1yIApa/B+l2oteLw2OwLrFb0ms+RtPydJCY9LieQvMqVpdBtM4p4QljgZx/qUlivCKimhabhZ5wpzvMabZ5SAqoEA0b+GSEqCZBWM8jFybNqYoSZNKvRERXef2fM9aLIMgonZCpoySnDRH6syq/deZXNjCtRw80hKfJVJOarxzsEWUKJISnhsisPkiuaxDCQUEb3tGdyxfqu1GP/FH/fBgF0QytWm/lzOWBpuIinRApIm3WNo//AsDo4VUK5E+/MvNOYt4L7wwgvxqle9Cm984xvx5je/Gaeddhouv/xy3HTTTbj66qtx2WWXYdWqVbjkkksAADfffDO++MUv4pJLLkGxWMQ73vEOAMANN9yAu+66C5dddhmefvppfOQjH5mvLs8LZks1FMoq+jqyVMspgM9mjaZv1HT2JDJTC0y5uVht1jztSYKD5HGDAOiVJgF6kgVb4OfWqpFqWCygnZs38ZCaFMPYnpaJzuoxC5Cg2ZM4yOFSAtD9eVnLD1NdSnj073GTJh1e1Xaf/bZ+QfAmTYYV8kgqfjs3L5wZ/FGgJ026fzcyrmie8l5dKs8CL6n4Fw9hSVxOJntln7n9Wq3SAu7o703KTdMTD9l01LQiU7S2pPpdWOIkz7nFEOtUVlKCuthR2ZL/JE/OATk/i/adFjwRVNTwEun2b0ZPmmR2xvGNOfq9GlRox9VnTi/qqqpRSYWJmUqgJaDZl4CkScbvTlukkX+zVTf1O7Z4EwTD29PnGZZzmzlIArz5QQB7UnyYhlrVzWTdd156PCZmKrj1Jy9gZKqExzcNQRBALbBFrptTw20YBqqqzmQLyFPVdqExrxruq6++GldffbXrtXPPPRf33Xef77PHH3887r77bt/rAwMDuPPOOw9rvzRNxeTkKFS1Gv3hmBgZEaHr5oP0n980gOaMisnxg/jnNw0gk1QxNLTX+uySVg3//KYBVIvDGCqHT6wXnpDAuWu6XO3f/qp2yJLoei0Ibz7XZK/IZ4/pVvHPbxrA7NQh5Kejv9ffv67Tda5/vKwHqYSEdcv7kFDcfRDJd0/nMTJSQZMiQhL9DLeuG9CNaJae5sPNkywKeIIAhuqWzvbUZFVGplaRRZTK7smlUmWzvAL81n4ao80Z4E5c9D48zF0ChqRJTYduGBAFe8HHen5n3+1Kk6yVIukMN/NiQxRddpVhTGtSEX1BphfODP4oKJKIQsnNsngDELJzQqsi6XWX4VlgmsU8POO1pkUy3JmkjO42M4j1s5VsO0KCIPiCZi5SQaaQCgHnJs5PQyGJkyrnA1iR3TZrPAE7YF57knRKHJrmynCnGOYJWvBEEOX2IQhCvfpjPJbXTjpmY1rtugjBcyCPZ78iSzAM4tjl/p0mZsrWOKGBnN+3WGAko+bqUkJj56NsHL3taQmfLAXdAP+zhSspXg4uqgbYiaPHLm3Fuy47Ht//+RZ87odPwTCA807qwyMvDPpICPLvZEKCIJgMt11DwMFwU4wQSHvn+4sJ8540uRgxOTmKVCqDbLaXOwmTFUQCMFusQpPK6OvKmVvWwgyaswnXinumUAVmyujtzkWyKOMzZeRLNfT2OBJyxFmkkzI6W4InFQJhoghNN9DbaSaHTOUrEGYr6O1tYroWmpSHLIvoacvAMAyUjVm05JKYLVaRSbn7UK6qUMeL6GxLIyEDxuQ43nBOB8XWkDBI0dIGr1sGs6SD5lLCEbgpkghNN/wPUY6Af2ZODLefheBxWAHoW83mQyH8upNJTq2zZLwMt3PbkQRxPAsV0k+rzxy/uywLqDmcccIm42RCjmS4edgTGvvjHXOEnaL1i+bKw/q9E4qE6YKbUAgLAEjA3d+ZtV0bYkpKaH3nknVw2JwlFQntzUlLd7plzwREUXDZmIUVbwnsu3Oe4K40aTOmxNrM/O2i73VZFH07hTVNR04KLpjjai/RPfdNh4fw89OYUlZXnkBrvUBbwODdTgIez36ny4r3fBOzZRy/3G9r52sbKPWMCLgpzxbDMNilQIrfFrDKISnx2uVqmg7DYB+vXltBi1Rgls0Fz5lE9gkA553ch1JFxX//ZjvOPqEHK/ua8MgLgyiUamjJJV1tZNmUipJqk9bOqKNP1nNtDhLdhcafZMCtqtV5DbadqKp63f7LPJfpvuL+DNkGY+mPAJhpvK72gADG71J3IHC25YEgCNb5Deu18L4LggBZltHc0onl3dPB3sSctoCs22akLQA3c6Ub7LIIR+CXFO2HBU8A5A0iuAJuDwvB4yEeKilhWHQ4q3QS7aX5OvvuAAlUTT9gkUl3D/i3DTXdZNp5Kk2aDyDT8SjMno4mw/CCPFzYfbjDg1ZTUmJruF2SEp98i0PCRClXXVODvZhbMiTgzgSzlZwypprmkMPwPMQlsc5WuhnyIA11T1sGQxMl1FQN37z3RfR2ZHDt28+w26psC3oCr6WixrFYAOwiSuWqN+BmkZTE13CbfaRXtDVtAcPnmgQl8GOVHAZquENsAc1+BQfcvBpugNyfdlhTLKsoVTS0BxS9cR4/0Lc+hoabp0YEjaGu1DTk0myLrCC7XK48F0p7Vh/vsEWTd0fvtWcuxdLuHJZ2N+HFPRMATFkJCbjNwmT2czmdlFEsq7adrGMsEFtVmo2mIIDJrnehsfh6tEBYiGAbsCdLcj4R8OnMrH8xdMm07PK0N9jamu0FGI6I3TAMoO4JzNbeEaTX/y/U//MvJOz3AdS9hymFHTiYhKqqW9+fZ4tdFM3t1rkw3IC777xJk96VOJekxKu950n4DNh6Yz2OV2/JMyGT85PvXmF48LvaehYLPFVZzT4KMGDaTgLOwM/fh6QiMZR256iYGKitdOu0TUmJBlly34de5mquGu5qSPtkQsJFr1iKV57UF1ganiXPwvpekpd1c+dzhMFaIHp2JoLudeLF/cTmYeRLNRQ8ulAeGRBAS84mhAC7Dzfgvn5VVWfScHt9483zs89zQVVlyUI3DAolz4T12gVpuINIEXL/hRW/qag8Gm46Yz5Rd6fqCCh64zw+rSCdJArWjmbwucl4jbejo0j+svSsFTYBU4NOJH8AX60A8jl6YjlLnoqfSHKC9ow9blkbMinZWlA4Eye95FtTJoGpfMVRCMh9LEX218dQVXYibaGxOHv1MoJ3hU9nuOvvMRyPPJCdQbcBA6zrB29gbADMbc32dsDuYubDjlF/j/TdOylaD+OIG5zcbHbwxc74ATSGnD2AoCXG8EpK5sRwS/EXC7QKodZxGHSGTr0lYCewsS7SnM44VQ5tImkLOIJ9Xv245B4zYYU8koo/2c0LVTOLHUU9hIH6g5SqrXTIRkjSJIU99m31cgXc/sVDVPu3/vkaHLu0lRpwG4ZRLx7DM979iVhsOmaK/EvTgxnu9gyKFRU/f8zMH/EH3HySEF9xMM6kyRSFMZ2zSwmXfMtLyBj1+y6C4ZbpxZIA+v3iRNCYMUkRf9sgVxQnSD4Fm4abfjy76E1I0mSASwkroUOthsu4MwAQSYn73KwVNp3nIGOWpyAcQMkV4ZaUhATcIbtiTSTgLtVcnyd9AoCl3TnsG561fhtv7g+tuBiP3HKhsTh79TKBrhu+gIbKUMMAAmQZP/jBd/GHPzxktydtiKzDMACPpGRqagrnn38mtU+CJ+I25SjscDLcLkkJKAw37dzwM9x2JTdGrZwj+GJNDAHokhR2O0S/Xow1KQYIcymJNymqOl+1RcB/3fV6QZBohrvOHtXsCZ31ewNeDXd4iWlaW2ffeR5kgB0kEdYwLPgy3SWCHx7k/Fy7Gp7gSVXd15ssRmgLKKqGm0NH7P0uVTU4adKJBEXDrRvmMpvFLQOgBK1E1sG0UPHnHKhacCXa3nYzb2RkqoSWXAKFsuqaY3mTfInjha89qyTFw3Drhv85EARJpG2RsydIB22xG4i24kwofgkUazVdRRYhwC1D0nRzzFA13JbE7/Aw3EGuJ4ThDpWUBLmUMN5vtN1THllHQraT0gl4bQEBe7FBk6eFwcdwc+wisgTcQXNGLmMG3LPUgNtss6K3CYWyioP1+g3eRSM1T4ZjR2ihsTh79TIBLVmHxnB7A2YnNmx4CqrqcDogDDdhmd0vR8KUlDjP7a/mGX4AT7DvOLdBE5fD/m5BDDfrA82bQDd3hpvNbot2bsDUCzIHX4r/QcizbUibFFk1qURrTUvgM4/Ndt3JhM4jZwH8LiWsDxLSFvAz3OyMH/nu9q5IUHuTFQ73buXVzlMlJU6Gux6Yms4r7uPSNNw8LiVen2PWAILGVnKzxJI7eDMdgehl5b2g7ciEBazEGjCTlHHhqf3QdMPlg23tijD6aHsXx7YPNx/DTfrAY70qS4JPUsLnO0+bZ+pa6IiFbkKWUKGMVxZSQxAEX95AWPJdwpJxhDDcjP0G7CRsryxkYqYMURDQkguu5Es8+P2WhvHrLPCyxID7uciyIxHUntx37LuvnsUCxwLVuwvnRdhi0ZaUVF2fd557ea9pDrH9wHS9r+5jeYko8xjsc/RC408yaXIh8LOf3YM7//NOGIaAjo42fOr6G/HEE4/hf/73RxBFEd1dnfjoRz+OZcuW46v/379CSaYwMrgPIyPDWL16Da6//kb84hf3Y+vWLbjttq9DFCWsXLkKX/63mzCbzyM/M4k1a47FDTd8AQDw2CMP4b/u/C6SyRTWrj3B1Zcf/vB7+M1vfglJktDTuwRve+fVWNbThA996L1IpnM4sH8v3vKXb8Ff/uVbI7+XGbCbN5gthRFcyZQEXm06edZ6b1CN8WEue1iRmqYjm2BLLAHo2djMW+QB24Y8Lik+hrvKJykhEwuxUWSdUCVKAAOw6zPdCUl8Cw1yfOLQwispsa3ODg/DHaZvpLHCXvCwJ4ok1HcRbOtFb/BExiRtG3QuGm6na4QzcY8peKGwlbxWW77FrRpDvuVluAMC5s6WFLIpGReuG0B7XatbKNes761xBBCk70VH0Qw7aZLdFhCwFyy2w0L0tZckEVXVvehjZcdJH73zazVA/+qFIotWkRECvl0VkXmRFiQBccLy4WYgJRJBGu6ZClqbEpEMPU1Ow0PoBCYuMiVN2n0nC2VNN5h3P705Orw5Nl5ZBk+RqiiGu6bplp827bzppORiuL2EypKuHCRRwLb9UwD8ux00z35eQmgh0Qi45wHbt2/DN77xdXzl6/+BRLoVT/z+Z/inf/pHGIaOz950G5qaWvDsk7/Dtdd+DHfeeRcMGNi9cxu+9c3vQhRFvPe9f4cHH/wN3vzmv7L+f+GFr8Y3vvE1vO51l+LUV7wKPW0pvP9978Djjz+Cjr5j8LVbvohvfes/sHLlKtx55w+svvz85/fhiScew3e/ewfS6TS+8c3b8K1bb8Jtt94GAMjlcvjKv9+Oga7gcqROOF1SvOy6zwODkjQJUBhuRltAa2KyJha+5AhF9jIw7O29yXvm+XUoEmNiiyJB1QzLyxrgY7idE4utKeVz+vBpQxkt7hSPhdecGG5VtwIhnvbWuWNk4AP2NQuTFyQTpu7ZCNn14RlzzkQuKWE/FH2SkjrD7X04+xhunuDH4S3M65QhCAISCf+9AnBsU8uiS0vNZSloLZI8SZMB7SVRxBfeew6yKQUbt48BqFenazHfr3Em2gZ5iLMuzhMeX2ee4EsW3S4jum7KvnjclDTPfc5abCohU1xKOBaYXoY7TFZh2/gFS0qshQIHS+wN4KfzFbRkg+UkVn8o+Rs8O0pzCbgVp4Qrrdh6ZUYyxrJY9Iw3HhkSzcJzvjXcgMlyuzTcnmBfkUUMdGWxb5hISvwMN23ntpE0+SeEDRuexNlnn4vWti7Isoi3/vXbcMEFF+I1r3kdWltbYRgGLrvsCoyOjmBw8BAA4NTTzkYikYAsy1i16hjMzMz4jvuBD1yNltY23H/v/+CWr9yEsbFRFEtFbH1pE1asXI2VK1cBAN7whiutNk888Rguu+wKpNOmzvENb3wLNm96BrWaOciPP+FULkmJSWT7kyZNbbj7s0EMN02jCLDZAgJueQEX0+pzH9CZg1Yrkctb+Y/TT1pzBH6qZjBPqpIj+OJxynD1PVBSwsZwOzWC8TXc7MlArvZehjpm0qQdsNOTJvW6LVUQeBJy6HZhlKTJuobby8LNieGuf44EETy+wACQ9Gyx87BegH9Hh4+lpTHc4de9KZOAKArIpc3FhTfY5+q74um7zrfIS/rul/oWP2PCqDNg5h3vtC1+O3CN8uGmJPlyLDC9ORBhsoqwqsMElZoOIaC9/3j+ew0AyjUN6SQDQ65IvsUGd7KqR24IsI05awdRs+dI0ifWczvbc9dJkL19Z28fGXBTpHJO5NIJj0uJ/15d0WvXHPGO4SDv+MXKcC/OXh3lkCQZgiCYW++SiEqljIMHD0AQTDN3WwMNS5+dSNoaM1Pn7X/of+Yz1+EXD9yLzs4evPkv/wbHHns8DJ0cy/685GBddV1zBdSGYUDTNEvbmUpFF8txQoC7/+Zr9X/7OW7X+0TL7XcpYdNYeifVOBpuL3PFn3hoLzb4XErcDxir4iLHpOplaXkTPml2Y+TYoee2AgjNasczoTk1grwabnJ+ryUhs5xGJL8bg6SEYufmBZeVZFDA7bUFVHVqASLnb87rP27LGuxFmgG24AXwM34k6OSSlHgYapaEScB2xHBVVqVUEKQhmzIlZgWHNII36dG0H/UvNph9vD0uJSTwZvfhjhf8AHTXBpsxjVhYU9wyeBPDK4yLtCSLS0ldx8xCCAUx5uWqhlQiekctqVAkJXPQcPP5cLuT0qscyaKAkyF37wLGTZokibJsTkx2QTgaomplNGUUuqTEMRcu7222/vYz3H7feda8gyOBRsA9Dzj99DPx1FNPYnx8DJIk4N57f4LHH38Mv/3trzAzMwkDBn7+8/vQ0tKCJUuW1pMm6ZAk2QrKn3zycbz9b/8e55z3asAANm/eBE3TcPzaU7Bv725s374NAPDAA/db7c8++5X4+c/vQ6lUAgD87N67cfzaU6DUtc/m9jn7d3O5lDgibtqkaC8CBKst4Jc2sG7ZkhvXYo44s5Gdk6JRdw7gsTkj5wTsDHz2AMYdfJFAiFVS4iyIwbPlR+s7ASt7ZrWP7VJiVwhlqXjnb+9fbLB/9/piQyfXLlhGk/QESjSolMA4rN+AN9HWvcgjD6xKVaNouAX/QoM14PYsHuK0d2m4ObepTVmGM3GRfbFgSaA87D5LsJ8liVjl+Ax3UNIk64LBZ6PJoeH2uRFx7+j4AxDi9sFWadJvJcleYdOt4Q7TErP4cFc5XJyCGHPWPBmqpCSCnXWffw6SEs+OSIXDDhGgMNyc491b+IYnYA16ttjHCnfYyaUVN8NNGTMuhtvzW9J8580iWYsztG1ouOcBq1cfgw996J9w0+c+DlEU0N3VjbvuugcPP/wgPv3Jj0DVNHR1duBLX7oFoiiavHBA1Hv++Rfg29/+BlRVxXvf+0Hc+JlPQJaTaGlpwrp1p+Pgof1Yd/af4xPX3ojPfvZ6KIqCdetOt9q//vVvwMjIMP7hH/4OhqGjp3cAH/in61xOKbElJZ72QVUryeFtlxK/ThCInpwUT9DKzXBLImZVO2A2X+PUQccMgLyTYoWX4Zb9QScPgwHYdk8ErMchgYJzocNz3eU6w60bBooVlWmL193ez3CzPkzIgsrWn9eLWVDGfIqF4Z6DpIQs8rzl2wGgVFZ9wYVSD74Mh8yFxxbQ+V3IuGOV83g9yW2njngBiMZx3WxJiT1eNZ0x4E7RJCVsuQoEPrZS5/Sdl0RIomAxjtZcwRBAyZLgKu0eh+GueTXcjEmTCVmisrw8Je2L+YqrLQDqAlWWzP3OqKRJ1sW5l9AgKFc1pBjmm6QsupxtyLFSmWB3Eye8rjw8bkrepHReDXeQSwlXYbKYuSJOFylaf7k13LpfXrqkKwtJFMxcBkqei9d3XtX484QWCouzVy8DXHrp5Vhz8nloySbQVjfdf/Ob/wqvft1fYKZYc63aPvSRa12SkOuu+4z191ve8la85S22e8ill78JwxNF9LRnkE7KqFQ1DI4XcNZZ5+JVF1xgfe7DH/5/AABRFPGe97wf73nP+wGYFjxj02UYBnDrrd/BobECnw83TDcSwzBsSUlE3RurreVSEsBwR0lKPMETL8Pt3DKN47pAzunsA7stoJuBISwGu0tJfevOMOxkR245jOdhyphMRpPyZDgmNBLAjE6VUFN19HVkmduS/sVd6JAAUXP8bkHXLaFEB9w1jd0f12tpSBZ5zjFL/i6Ua8il3fIuO1FX52eoie1afZyRhCrmBDivhpuXafXqQjV2tpA2XmuMLicJRYIiiy5JCXfQWh+vJHlWC/EAD+uHL2mS4RiSR8NtX3cOeUCApCTaFtCWBxA5AZfu37srEhJ0CoJQ18qHMdwcXtTW7idFUsLIcE8X/BVKeRjusovdZ9ftk2eDxXAz7kgQBFYC5rpXPQnKvLunAQunqPs+l1ZQqWmWfIjGcCuyhP7OLIYmij6ihOrDrepQMouT4WbuVaFQwJNPPonHHnsM+Xx+Pvv0soBhwJSKeAaIUNdkuCpFBlHDFHgdQQjbzF6anbDRthaZq8o9Ob/hSJqEEFxyHu5gXIDfnk5j1IfaQa9mVb7jZbidThss5ySQvcE+50PcOynyWF6Z57cDR22OiwUC1q3HhMfjljcpRZZEGAawv55pvoTREcdq79CA80pKrODNkpQEPwCYJCUqu0OLn3nyjxnyd7Gs+hac9gLT4HqAA/ZvVlG9DDfbePMz3PWxEpPh5qqM6lhoACTvhF3+lU3JAUmTrD7c5q6j5hwzjN/bOoZi68CrKrtEQBb9bCMAZjckRXYz5IAzCY9tB9Fb9Id1F9DnUhKxSEvIEgPDHT/wMxPTdWvnKgxJT9/Jsdila24Zkr1AZi/aQxZGVU4yxv7udo4NwL57K4uiK7+Hy4kpICGfIGq+JMVvCMsddK+uHmixfLtdfacsMHkW9wsNJprq+eefxz/+4z+io6MDuq5jeHgY3/rWt3D66adHN/4ThbcoDIHoCFit9wxAYJzQrcDaCpjJ64wd8wbslEVB+PlJe8NjCxhyDMdbguD3ieUufFPT7SpmMV1KuGUZniCAp7CB83Mk0C/HYLhJ+xpv3ymV+5z/jrqGkmhukbukPJzXHQB2D80AAAY6585wx2X3wx6i5MFcjpCUxH0Y0cacJSmp1PxFHRwLJZ4HOOBguMlDvDZHDTenNR6RwxAbTJXD2s4qVlT/zrxVLrNphZo0yZuvUa3plqaat3Jd0iHP4Fmce0u7884ztACkUNezk4TSIDilY6mEff6cxFbrIClLvnL2pE/U8yn+YjNO8CRYC0K9eI3ju9ukRnSYk1BEa3FKwOWscxg03CRgrnAs0Mz2Hskf7xzpKXzDmyzqPKcTLPUinOXd25tTgeP9zReuwuvOXOLvu0TTcC/eSpNMAfeXvvQl3HzzzTjnnHMAAI8//jhuuukm3HXXXfPauaMZJGnXuwXiZpjrf4N9q8GK0b3VHhmFIT6Gm/G89vkdem2npMSRTEkQ1DefDzcj2+z04eZlmAGPnzMvSzxHDbdd3IDo9OIF3DVNZy4U5G3rKxDAcQ1dTiGcDAI5/57BWXS1pphZfau9bHs68wcgZGfAyXCHS0rCggBeb17AqR/3S0qcCzFfaXfJfhDzVti0kiarboabx6WEtWogDU4bTFE2t4rTDIEP4F8kEekT63jPphSfhjtIt0+D16bNZLh5JSWiP2GV4V43JSW2nCXMxpIGWuW9YlmFKAiRTK9dbt2j52UdMwl64ZugMUuzIXSiUtNDK0T6zi+Lrr6Tsc/CcJvj3cOUcuygem0weUgRb47M3DXcnJISSXTl97AmKDvP7X22AGxJ+YS1Jk4lQfd6NqVQF4xe/TmwuJMmmXpVKBSsYBsAzj33XMv1ogE6ghhuWpEYHlmHt4y61+s6sr11Uv5zO89PdNzmMe2FA0v7IFvASJcSx8TCG0CQz1p6Wt5S1Z7y6FVri5/PusmXNMktKTGYpSAERKrg1c7zPBSc7JHJcLMHzeShtWdolltOQtqTxBieMtmA39M5zC3DW5KbBl7nAmefaUGzHPC3q71mxF7geYM+5qRJSpEogMOpg5Ksyr+bVP/Ndbb5gSCbki1W1zo3xzxha++dNph8W9RJRfIxjqwMtwGT1QecCyX+5GqCQqmGbFqO3MkMkpSwXrukIkHTDcdCKXzM0qo7OlFV2ZMmyXlqmt33EkfATZWUcC6undedZ57ymgFw+3B75xlOMkqRROiGbe03l8RwJ1ikj7l6UipxKplrRVtyjMXKcDP1ShAEHDx40Pr3gQMHXF7PDfihB7C7ZNLTHXSwAXZZh4thBl0nHdrepwHnlZSQ4NqTNCk4j+o+h/fw3geCnQTIKClRde7AC7BXw2biIT8LQM4N8Ad+9qQ4N0kJKZLifC0KgiAEbr0BbNfAZI+cGm72MWPLJlTmiqbe9nGLOkgeOU2YlpjJh3sOkhJa0Oz823tNnWOOO1lUMhldO+DmTJr0sZXxxruT3ed29PHsDDBLSlJuSUmNU4Pt7bsWo1S0c4fArvTIEnC7Fxs2W8nWf+Lm4Hy+FMoqMhFyEsDBtHqkRDwuJc72UcRAQg5Pmqxw2AICblIA4EtMJwmj3kTd2KXdNdPLOqqkPOBP+LR3PzklULElJf4dHV73LtrvyPJcd0pKnH1gl/LQJapHtYb7gx/8IP76r/8a5557LgRBwCOPPIIbbrhhvvt2VMPQ6cmMHgm27+9IeNsb9PMENvdISsI8wENObyZNuhI2Bf/3oHwvAUIgwx21DSSJtpUUr44ZsBkDVXXIMji087IkhAZPoef2lKUnkypLBj3gloXwltk2+yn4JSUcVnNOn9qayh48mf20P7uki0+/TfrnZIklka0og/PcLJKSJIOkZC7sDzUDn4nh1rkfooIgIJmgyBo4gidVM6DpZgVM7mJLvoDbX0kzsK1PUsLJcKfdSZMaJ+OV8Hg681SktY8hWiy7lfDKwnA7XXXqvwFrW8Av5QGAYrmGXIpBx0xhK7kkJQ6Xn0xKjpQ2JBQJlfpnShUVqYS7yA2vZ39CljySEnPRxcpwk3OaSd4Gn5ZZ8ldr5K7RUCMMtw5BYL/XRfJs8gXc/K5AScWU+bBcM8DeeaElTbIQQ9l6ZdjZYtXqA8C+k0YS8sk8BfAtlBYaTAH3unXrcMcdd+CJJ56Arut43/veh9WrV893345q2Bpu9+u+gBfAyPAg/t/Vf4sVK1bV39NRKBRw6aWvx7vf/T5Pe/P/Bgx8//vfxv3334fXXXol3vvudzH1yxUw191S4khKDLgTLmmH8Pp0kw8GMa1RDzViJaW6GG5+aUNN07kZbsCtdePetrO2DeuJMfVAiLU0vHNS5N12I59VPdXAeIK4lmwC04Wq/SDicYdxXOM4DLfsYbh5fzPAEbyFsDeEFQ6XlLAvNmhBp7NPzs8A/sWbsz3vAg+gW9OxBjC2Y4uOTMoOuHkDP2cxDtaCQd4kX7vKJTvDXVV11FStrhPmewB7GUNVj8tw8y/OJWu8knmGb2fC3hUxoNSf7vmyimYGP2lvwR4AXJ77VvXImtctg95ekUUUyypmilX8yzcfx99fvhavOL7ber/CWZXWy3CXLUkJS9Ik0VFryEC2FzqcSZNEe88TcJOkdFvDzV5hk0CRJUehJQ08vvFkPnSSGqyJsmGSEpY5QxJFZFOyw6WE77rbuVUGpAS91sFiAlPA/fa3vx3r16/HqlWr5rs/LxsYAcyzaL3v/nx7eyd++MP/tv49NjaKt771TfjzP78IK1astF53SkrWr38A/3rT15Bp7o4hKTEcr/FH3MSH2y5qQ0mapNDnAoKTJllWtSQ5JQ7D7ZwcWMvJe9vHdimpS7CcLiUJRWRO5FKsctf2tif/YiFAUsIwObXmkthxcJp7QnQeX5YE9LSlIz7th5fh5gmeyJiyAhgt2NvXywp7wTuZ03TM5uuC7zMAheGmLBB5rnvSEfRVOReIQQEIK9NLxnscWYZ3N4nXwpNUmyyUVbTmJO4HsJfp5SnaYx3DYQtIktBY5lnfYoOT4aZ5mBfLNfR1ZBj67E7eA/jsHL3FlsIK3wB1DbeqYceBaVRqGvYNz1oBt26YeQusOQfkeE5pQ5kjT8ay0fQsUHl2s0ym1bDYZt78oqqDjOFZaJD2zjmSl4wh7QBwufKEBdyspJaz+I21m8XKcDsW9klIdq2Do1lSMjAwgGeeeQbr1q2DuEizPxcbguz6aAw3TVIyNjYGwzCQyWRw550/xIMP/hqapuOss87BZW96J279+pcxOjqMz934Cbz/Q9fiwM48vve9b8EwdPT3D+Caa65Fe3sH/vIvr8AJJ5yE7du34lOf+iy+9OV/RVf3Egwd2ovjjluL/uXH4vHf/wrFYh5f+MLNWLFiJX73u9/gf/7nP1GpVFCrVfHJT34aJ598qtl/R58NRLDjBsU9hcZwc1RyI4kxVtEWzkkNcCdd8gatPk9l3m1DB4vBKicBnMyXg+HmWSyEabgZJqfWpiSm8lXuBxFgX6O+jmws5sHJcMe1JCRe76pqIJsKbp9UpEAfblrhGpZz+0ouM2q4XeM1hitP0iUDqrdn3FFJegIQfobbnWQcJwhQPYmy7C4ldrXJ1lySW9Pp3ZngKXZEkPAsdlgDR69vPK/jhFOGRFAoqcgmoxlLb/CkGwa1ul8QfBruCCkQcSnZeWgaADA+U7beq3ImlQOAokgoV2ztfpkzadLVd25Cxb52cl1ewnOvJhwa8GpN41pokPM7n01c95onSdncGYpHKjjB+nzJZRSXDzfr4pR2/jhk1EKCKeDeuXMnrrrqKsiyjEQiYW2bPPPMM/Pdv3nHoy8M4pHnBw/7cTXDwIkr2nxb6FQNNwxMTIzhne+8CtVqBdPTUzj++BPxhS/cjF27dmLr1i347nfvgCAI+NznPo1H//AbfPDqa/DsM0/iM5+9GWVVwA2fuA7f/Ob30dfXj//+7zvwla98GZ///JcAAOec80p89rNfxODgIezauQPveu/HsO7kE/G+97wViXQzvvK17+Lu//0h7rvvJ/jQhz6Ke+/9P3z5y19Fa2sr7r//Xtx55w/x5S/f4uo/+Q4koHaWfLe/FygMN03DzV7JjWwb8uginW2BetIlJ2tG2jv9nJ3H5Dk3YLIvXBn4roCbL/Ajn/UVvuF4mJPAZapeupk3WRWIp98G7IeRpuuxGW7nhBzW3lvwxQnuhE1RcLny0CyvXFUnAxhuNaakhBpw87KVVXfAzezQ4ug7+T93wB1XUuJguAG+xD/Ab+GpauGLNBqcLi9c8gJHgSvSFuBhuN0LHV03UKqollY2DHYBlvr35tQCeyu1WvkWAcFTsu7DvevgDABgfNoZcNclUJySwZm4SZOe/A3eRZ5zcZ0Gv/TNaZFYqenc1qmuKsq8pIT3XuW4X7ykghOsuzNN6QQm6ostHukZQJMM8j/XFxJMAfd//dd/zXc/Xn4IZLjrbzsqPcKwJSW6ruPWW2/Bnj278YpXnI3bbvs6Nm/ehHe/+28BAJVKGalcu+1SAmDXjpewdu2J6OvrBwD8xV9ciTvv/KF1zhNOOMn6u729AytWroEgCOjs6saJJ58OCEBvbx82bjwEURTxhS/8Gx599A/Yt28vNm7c4NrVEFySEifDLVhWgXZxHr+2WxD8NydPJbdMSsFsoWpnQMdkuHknVMDPIjiPGX1ud3JJpapxTapOtw1ethEwH5q0pElWf+K2piQAYHSqVD8e/3WPYwkIAC0589wzhRr3Vq0gCJZzAxBt9ZVMBDPcvDImUoxD9bAvtEqT5nHd/aIXvuEIHF1e0HyVKpMeeQF34RtH4qFRZ0p55V/kevNaeObqjhwFJ2sWc54wz68zb3ETkADIlEawM4YkcVxzSKCcfYps7wmeihUVBsDkUqJQkkWBGIs0K2gNZ8cVWUK5plkFsZwMt2WbypM0qXg13OaCi0lSInskJTFdeZwLTC6G29F3k+HmlJT4GO4YQatut+fO1aAx3IzJzrm0gr3Ds2YbzsWxLNvPRWc/juqkyYGBAaxfvx5btmzB+9//fvz2t7/F61//+vnu24LgvJP7cN7JfYf9uLOlGsanSoGFb8jw9KpJRFHEP/7jP+Fd77oKP/rRndB1DX/1V3+Dt7717eZxZ2cxOF5yBNwGdMM92M1SyHbQkEwmrb8VRbHPawCiJLmC4mKxiH/4h7/DRRddilNPPQ2rVx+D//s/u8AR+axuabhthtuLIPMVmoab9SZb3tOEDVtH5s5wc7Jm5mftIMAq18z8IBRc+nVeSYnNcBvcbCNpT63IxXj92nLugJsn8OtsTaG/M4sTV7Yzt6Gde3K2ws2UAh62NEJeEMZwW4s0TvbIKU3wtg+yCCRtyXnjarhnCrbdlli3h2SBl61UI9hKL2i7SXy6f8GXL8ErKcmX7e/ONU/UP+tcbMRJmgRsORB3wKzbgZsAPtcG0mfA1G8D9jUJ77P7e8eVzTmTJsOum1lp0jxHd2sao9Mly22CHIO12iLpp0vDXTW10CxjlgTlpD88RcHIuQG3DIm7RkTNqeHmlJQ4vju3pMRKmjy8PtysC7ZcRsFssWbmyHD2PVBSskgDbqZefec738GPfvQjrF+/HuVyGbfeeiu+8Y1vzHffjmpEFr5xUtQeyLKMD37wI/jhD7+PY489Hr/85QMoFotQVRWf/OT/w5OPP2wXvjGANWtOwObNL2Bw8BAA4L77foLTTz8jon/OPtmd3L9/HwRBwDve8fc4/fQz8fDDD0LX7ZtJkUWIounkYMDPYLv16H6NtwC/hpsnKWllXxMKZRWHxgpWf1jh9AzlrV5HzuVdSfMkgjnZTl5JSTpZDyJKNW62kXzWW/iGR9vaWq/4NjplslA8AUw2peDz7zkby3qamNs4Qdj1ydkK94MMIMGbrYkNc7bxljR3Is7OgrMYB22L3s1wu38LL8PNE3gB7mIe1RrfdbMYboekhMcaz9l32+qLU1LiYJgB9oRNS1JSqktKVL6+ewuRxPH1dcozTA03X4ErZ5KvLLNrWr1b7ERWE1XW3eyzW0rDu9DxMtxRCw3ne2ed0A3DMO9x8xi665gsSHgqV1ZqGrO9nV3t0fPdY2qZeXfiErIEZ6XJOEmTrnPPIWmSz2FFoBohOI8X5SHfnElA1XSUqxr3veaVtMQhoxYSTFf15z//Ob773e8inU6jra0Nd911F+6///757ttRDV039RQ+lxJLkoH6/+mVa84555U46aST8dxzG3Hhha/Be9/7TrzjHX+NNWuOwwWvucThww20trXjmmuuw7XXfgxvf/tfYePGDfjYx64N7pwguJI2nac+5pg1OOaYY3HVVX+Jv/3bv0JraxuGhgYdTQWkEjJKFdU8huA/hgW6iNvPcHNsN6/sawYAbD9gJtrwBcy2rMNmuPmYACeDwZPcQc5lS0rYvU4BM+gUBGBsuszNNgKmMwaV4WacWFs8DPdCJqXYAXeZ+0EGmNdd09iCp6QiWVvRXtRi6v7DtuidD6MwDTfRZfKMN9Pn2A4geOUogJPhjmetZzLc/Oy8uSNDGDc+p45UwmQ1iQ82d989wZOms+eYEDiT8GqqzpysSs5jjVfO4ElxSM8AWNcgw8Bwk104i+Xl3JmwC0ex3WskyG3OJnDs0lYAto7bZrjnZgvIKtuzxnvVk/MQN0E6RnK3reHmI2PM9pLj3FqsyqqkKByP/IsQSWEa7qj5sjlrLgZnClVuRyGvhOploeEmyZIEzc3NkGWmpn+yMAyEBkSWhhtAV3cvbr/zJ77P3HKLvYvwzne+x/r7wGgeBoC77/4ZxqZLKFVUnH/+BTj//At8x7j77p9Zf/f19ePuu3+GvcOzMAB85ZZvYnC8AEEALrvsClx22RUAgBtv/ILrGB/5yMdc/04nJRTLNeiGvRJ3VqAkQTaNARdgs40EPDdZf2cWiixix0Ez4I7jWFGt6dwJQWZ7CZWSadAfJ/BzTqpVTusnWRLR3pTC2HQJLdlEPFmFWnO9xltJLZdWMDpdl5Qs4ISWyyiQRAGT+QpUTWfy1XXCXbAo/Du3NSXxwq5x6Ibhu39jVTd1sG60vAPiwatRvJ6dTh9xmH0nw12Ly3A7dcxxglYtXqEmlzONxXCz7yZl07IjaZLTFtDjmc/LGJrHsBlTPkkJneFmhbdqIGH5CesfBlLnwGlnaPaJUVIi+5Mmw3aTyHVe3d+MjuYUAGBihjDc/BpuU5bhTppkle15x3uNsjiOOjfgkZRwupSQxVFV5bcFTDgWG7yOQM6FvRZncSyLVIabVZZDPOKnC1XuxbFXQrXYXUqYetXX14eHHnoIgiCgWq3im9/8JgYGBua7b0c1dGfyoAOCYNrfxS3NTj5rJ11SrPcY27srRbKDyBsM3fCf2SuR8UfcAS4l7Fumy3uaLMsnnomlKVNfSRersVbCJsNtW5XFYVrjupQAQFdrCmNTZagqXwIaQHcp4Q1E2pqSsTTcc4UoCGjNJTE1W4m10JHqbCmLj/aSrhyqNd36nk7E0Qc6mdqgvAPbp5z+ek3VMVus8gfcCRGVqpm0yM9wu11KzAAixlavqnNLE8hnvcmmPGM+m1LcSZM88ivR9Me3kjb1GJUmHTsEVZXd5o1WZTNWAKLya7gBt7SBN3ghhaOc7ixhvxkZI6sHWqyAe6yeOEn6wKPhTsgidMPOcSlXVXZJicelZK4uVNwMt+JwKanXaOCBX1LCs/PqWNjHSDqUgwJuxvu+OWsG3DOFKvcCk3bdgcWbNMnUq0996lP4wQ9+gK1bt2LdunX4/e9/j0996lPz3bejGs6iMF4Igl2J0giyMwmBYHrwRZ4nrL1ZabL+b77mkCXRYi68SZPOeNuhOLEgCuZiJO8ovcz7QFzRZ2uBeSa15mwCkihgYqYSL3hyBK3xWC87GYhHX0jQ2WImFpm+5ZwT8hyTJgHTGpBsNy/0ll1bU9LUcMcIuNNJGcWyyuSjvbTbdFI5MJL3vUcrXBMFWRZc9nK085Pv4z0uKQAzOVvBxu1jOGV1J/N5AZO1MwMQ/iIiXj0uj3UnQGfN+BYqtgSKV1IC1Mu7O5ImeReoiiy6pBVxkyYtSQmHJpacE4jBcHsC9ryl4WYLuJ0sMW/Q6S0cFWXBSXaqVvc3I6FIaM4olqSEx9LP7rvbZcWUlLB/b8DpsDJHhpsz+S9RH281VUOxoiLHsCPhhCwfDh9uPZ77lxQVcEdouEnAXazGTpp0JsWznPNIgWk09vT04Pbbb0epVIKmacjl4tl7/SnBCGC4ARLwupMmeYs9ujTgnBG3zws8xthMpyTU8prv1N6kSS9I4tTwRBG5gRYAfLaAgK3jBvgewqIgoK0piYnZMrpbzYqHXMlUkqfiIWfg19GSwuhUCYZhoFrlZ7g7W1OYzldRqqgxnTrcvwev52lbky0rW0iG2zx3EvuGZ2MlsLXlkhibLjOxH/2dWQgADowWcMZx7vfiFEtyPoyCtPfkWtICWkUW8cTmYdRUHRe9YinzeQH3Fn+Vw5oOsNlKlzyA6yFeZ81UmzXjZbhrms1QA3xJwtmUgum8Kf/SOCrnETgX13HGXFK2GdOoRF0nyDXSYli0me3dGu5iuYaELDKfn1R/BPhlFQAp+GMHrWFj7pRVHXjnpcdjTV2/3dGSsqwBLRcoTltAwLxm6aQ57rsYSQ1REFyuKbGTJh1jhi9p0nQZOTRWhGHAV7+Drb0j4I5hJqBqRjy73AANN2vwbu081zXcPESUV0L1stBwj46O4qc//SmmpqZcr3/84x+fjz69LGBqQOnvuQPm+mscxxZgM+M0nXQUREGos07xJCUAkE7ImEHVVdodVo/qfxn+Y4v1izI0UcRqK+Bmr2YG2AG3JArW8VjR3pTExEwF7U0p7sRDpywjDtPa257Btv1TqNZ0GAC39VNXi7lIGJooxpKU+BhuTjahNWfbSy40g9DWlMRzO8eQVCTm4IGgtYmUpY9mP5KKhO62NJXhjldlU0LeofuntSX9oY0nWRJRqtRwyuoO9HfyFQ6yrc60evDDft0IW0kCEB7ZF2Br0+ei4fYmTfJKSoiTEe9iAbC36HXdtD+VOSss25ISXltAd8DMuyj2Bn6Fksqk37baO3IO4gRfpq2mnbOQDQmYkwkJF5zab/27vTll/Wa2hps/cCTBOk/SJGAuUCse/To/w81f7Mhsb0p5Doya8w5vkTBXcnZclxKn/Sive9ccJCWSaOYHzRRrqGk6chL7eLUtDb0M9+IMuJl69YEPfADPP/+8VeyE/NdAMMK01aKD4Y6lo3ZowGFElFenwHQv0GNLSoC6E4AoOGzK7ETJMJAgd3iyaL3GK23obksjk5Rj3VTtzSlMzJS5zwm4Ge5qjIC7rz2Dak3H0IT53XmT/zpaTJ3j8ESJm7Fz+hoT8G6VtzY5/NwXmOEmcpZCSeUOntpyCeRLNZSI7j+i70u6c9aDz4k40gavSwktaCSBMO23INeZl90GvDpi/vGakKXYDDdgb3PHSWRy3mtxJCmupEnOhSVgM7U2u86r4baTJmOVdieLDd7gSXa3L5RrTA4ldr9Fn4ab197O6fTBc907mlMYny6bO4BxKk167BzLHEmTQL3ypbeyKrOG2y1nietScnC0AFkS0d2WZm4LmMVjKjUN5apqupTESjzUYyaGu/3PCXhkOc3ZRJ3h5tuNou0sAIs3aZLpTqzVarj11lvnuy8vK+iGASlQUnIYGG6HIoU3aTKpiMgXDWtijUFwQxAE9HdkLYaZQnCbffO6AgqmNGJowk5KMwM/9k6IgoAVfU3YN+wPiqLQ1pzE5EuVWLpOJ0vMq4kFgN4Ok7UgVbV4E2O66jKYSk3jZtxMaYNfUsLzMHYy3AudlEKsAXXDiPR19YIsFMam2BxWlnTl8MzWUV810FiFb7wBN6Ut6Q9tPCYVCUu6cli7vI35nM62gK0jbs7w/WZOlxNelxKgPua0uJVRHbtJMXSZbU1JlCoqZotV7iqXgLnNTba4Af7x7vTh5ivtXpeUOL47r5sR4Kg0WVaZPLid/a7VvC4l7NcumbAlKbwVPjtaUqiqOmZLNVRqmlXzgb3v9ng3DMN0KUnySFJMIgqYC8Nt7opoOp/bRkIWoekG9g7Por8zw+VZDwA9bRkAwMhkKUbiob2rEifBOVDDrZvFtlh+w2Zyv/FquGX3eLd/t8Wp4Wb6ZieeeCK2bds23315WcHQASFgoAkQXJKQ+ovMMNlwh0tJDIYbsBNT4khKADPwsG4mStIkAnZBetszGBq3GW7TBYBvgnnVugGcH6NCaHtTCppuYHKmEovhJhMqr04OML83AOwZMgNuXoa7JWfbAfIGnaRoj5Pl5nYpyR05hrutKX6wT/o9Vk/IivrOS7pyMAAcGi+4Xo+13eoMHAMsCclvSevX31+2Fh9444mx7lFn4mNN1bkXeAnFzXDHSTx0Mty8hXOcSZMCwm1WvVhRL7K08+CMeTzOMdOSTVg2ZQCffhxwz7E8el6vLaDKYd3pbl+XlJRrzAmTgC1tAOKNd2el1qikSS86604l49PlennzeIsc4rahGwZnaXiH/pyT3XcyrXGqwhJ2fs/QDJZw6rcBWIz40EQx0h3GCzK2XVWMeWRMjjHjhKqykyPN2YSZNKnHK0vvL3xzFDPcp59+Ot74xjeiq6vL5b/929/+dt46drTDMIzA1YwgAFbxRsPA6MgQ3vk378CKFavqL+koFAq49NLX493vfh+1PW/S5Pe//20AwLvf/T5zYnIkRIW1fuSRh/HSS1vwnve833rt5ptvgizLLn/uQIabcsze9gxe2jtpeR3HSUo68/hunHl8N1cbAGhvNoOvkakS903Z0ZKCAWBiphzLpaQ1l0AyIWHvkBkE8Gq4RUFAR0sKwxNF7nP3dpjB/tB4EUvqThwq58TslJQcCZcSAt5gn/R7ZLJetCcq4O42dyL2j+SxpCuL3244iAtO7Y8njfBJSsIYbv97JM8hDkiwUa5qqKka95gxNdy2w0o2xb9ANWUZ/FIcpwRK0/iqLQLA8l4z4N5+cKp+PP6A+6W9k7FdD8i1J7IW5oC7zmySwlxzdSkplFWs4GG4Fed45St8A5jfe6Zgu8Pw6M/b6wH30HgR2/ZPczt1WCxzTUO5Pm55SI2kLPpsAVl/97kmCRN2vlTRYgXchOEenixxP5tEwZSGqjFtAYOTJnXmndjmjCkpScjinCRUL4ukye9///u4+eabsWzZMq6D33rrrfjFL34BALjwwgvx8Y9/HI899hi++MUvolKp4NJLL8VHP/pRAMCWLVtw3XXXoVAo4Mwzz8SNN94IWZZx6NAhXHPNNRgfH8fKlStx8803I5vlSyg4Egjy4QZMRlk3zAFKAueOji788If/bX1mbGwUb33rm/Dnf34RVqxY6WnvSZrkdikR6jZEdd1VSPPzz78Q559/ofVvTdMwNHQIn/3sTb5jmv3xsNqUvvW0Z1BVdUzNVtDenDIlJZxbaHHR3mRO6qNTJbQ7gjgW9BAWYbIYy6VEEAT0tWewf8RkTnmLGwBAVz3g5mXcltYn8f2jeTvg5mSgmuoFaDSd3/VhrpiLnIUE67aHePj90tWaRkIRcWA0j5/8voBfPrkfTRnFoUnkYX8YAu76w/Zw7xq0NSUhiQK+d/9mlKsqFO5iGpKnaiBf/7rb0hgcL8b2LycSqDjseialoKctjR31irS817Ylm0ChrMby+zc/L0AQgHzdmpBViyz5GG6+ecYqtR1Xwy1TCt9w+rc7XUq4NNz1HJX//s02FMsqrn7zKcxtAfs+qqq69bvxOF4kEpJVKIjoz1mfrVaSsCPgjlOUDeBPmATMBNS2piRGLIabd7zOId8ixIeb9TjN2US9tDtfXhFZLHir+R7VAXdzczMuu+wyrgM/9thjeOSRR/DTn/4UgiDgPe95D+6//37cfPPNuPPOO9HX14f3ve99ePjhh3HhhRfimmuuwec//3msW7cO1157Le666y5cddVVuPHGG3HVVVfh8ssvxze+8Q3cdtttuOaaa2J9WRpq2x5FbevvD9vxCHJVDcIx5wOnvsr3nouhDmg/NjYGwzCQyWRw550/xIMP/hqapuPss8/BX131XtzxH9/AquVLcP6fvxECgOuuuwYXXXQZTj75FNx00+cwPDwESZLw3vd+EOec80rXsc8//0zc+/NHUK1p+P2D67Fv14u4/vob8dRTf8Stt34VhqGjt7cPN9zweTz88IPYuHEDrrvuM9i06QV87Ws3o1qt4pOf/H+45pprsWTJUnzoQ+/FsceuxTMbN6JYmMFHP3oNzj33vHq1Tf9363Vsf7U3p0wGa4E0V211hnsukpDhiRJUTps16xgdGUtSwpNBT9BZ13HzBp097RnIkmC6b5xovlbjTFAxC9AkMJWvcm3vHw6QSpf5Uo37umeSMhRZxBhjlUxREDDQmcOGraOYmjUr341Pl61gKE5pd7v4jP+6ydZxD+81bW9O4ZNvPwM/e3Q3nts5juYMH2OYVCTLL583zwIwWeZfPLEPxTrLy7tVrGrmddM0g1vTCphuRk9vHQXAn/TYUl/gTdZ/f972giAgoUhW8R1eH26nhptnvAmCYMlxaqqOak3ndinxFb7hkpSIttMHZ9+zKRnJhIRCWcVfvmo11q3h853POezlCFvOE3AnZQmTqvl787LzgD9JmNcWkIDXEpCgpy1tyeDiJtWTRS63bI4qKWEvwEO8uHl1/4BXfkau/VGs4T7nnHPwpS99CRs3bsSLL75o/ReGrq4ufOITn0AikYCiKFi9ejX27NmD5cuXY+nSpZBlGVdccQXWr1+PgwcPolwuY926dQCAK6+8EuvXr0etVsNTTz2Fiy++2PX6Yodlbx3wm7tcSur/Hx8fxTvfeRWuuurNuPzyP8d3v/tNfOELN2PXrp3YunULvvvdO/CDH/wXRkdH8fBDv8L5F1yE3/zml4BhoFwqYtOmF/DKV56PW275N5x++pm4/fb/wec+9yV88YufxcTEuK8PXj1ntVrFZz/7KVx//Wdwxx3/i1WrjsEvfnG/9X6tVsNnPnMt/vmfP47bb/8R3vCGN+Mzn7nOel9VVXzmC7fi/R/4J3z3u98MvT49VuBq6rhVzqIac0FTWrEmQl5WvTmbQCohWTo5Xns6wHQqIYjDcHfWWSDeAECWRPR3ZLHf4b4Rx72hNZdccP02AWGqeSdkQRDQlktidKrM3H5pdxaTsxV0tKSQSysYmynHYvyyKQWabqBUUQOvtzUe5+EeWNXfjH96y6n48gfOxaXnLOdqm0xIjsI3/GNlRW8zdMPA7kF+HbVzq5jX09g+f1PspEcSABBf6Dg7cElZtBYsPMVjzOCHVCeNV9FWVXXuKpMASZp02wLG9+Hms3sVBAEnrWjHq9b149Kz+XbTAaCjOQlREDAyVbIL5/Aw3IpoteN1hwHsfI14DLfZz2xKRmsuEfFpOrrbMjhYt1WM4yhkJk3y7+gEMdw8Cy5yv/GeG/DmyfB79i8kmO7En/3sZwCAX/7yl9ZrgiCEarjXrFlj/b1nzx784he/wNvf/nZ0dXVZr3d3d2N4eBgjIyOu17u6ujA8PIzJyUnkcjlLN05eP5xQjj0PyrHnHdZj6rqB0eFZl+7UCRrD3dFpSkp0Xcett96CPXt24xWvOBu33fZ1bN68Ce9+998CACqVMprbOnHGOa9GtVrB4OBB7N21Beed92dQFAXPPPMU/uVfrgcADAwswQknnITNmzf5+mAFe4L5W+7atQNdXV1Ys8as9vH+938IAPDAA+Zvv3//XjQ1NWHtWpMefc1rXosvf/lfkc+bAdyZZ50DAFi+cjVmZ80HbFDxn9amJBKKaDmVqDr/5BYXQr34zchkiTvxUBAE9LRnMDxRjGWTBthOJcDcAu44517SncPmPRPWv3klJYD52w1P+sueLwTampLYP5KP9d1bcwmMTLFpuAGTHf3Dc4N49+VrcfdDOzE+XUZzJsHt3U5yBiZmK1A1g5q4aBW+4fSU50FnC5/NGFC3SavFYysBYGW9IuyOg6asI5bdl6pZBYN4scJRICuOhhuAVfkwzmLIZLhV629WSJLDDSmOh3idrSxYVSY5GG7FXfiGt9ZBNqWgXNFQqqjQDb6ETwD44JUnc33eCUkU0dGSxOhUCeWq+d1TCoeGW3EnjPIudEjgGUcHTeaFJV252CYGPe1py06Rl5BRJBGVmg5V5dftB2m4eQpONWccRdW4ySTB4cNtcM/RCwmm0fi73/0u9gm2b9+O973vffj4xz8OSZKwZ88e6z0SkOm67hpk5HVawMY7GDs6/NszIyPivGpQNZLZLtPPI0kiDMMcjGRgCCAPJBEf/vBH8Y53/A3+93//E4COt771bbjqqrcDAGZnZzFTqKFUAy655HI8/uiD2L1jM97z7ndDlslxBeu8ZnCvW5MmeT2VlCEIAjRVM4tcJBPWdiQA5POzKBaLEEUBgmDqEZ3vmzAgCOZvlE6bD3RZEqzvBkGAILrbiKKInu5mLOlqwkS+gq6uJmiagaamJLq6mrAQ6O3IYmSyhHRK4T7nir4WvLR3AjVVR0tziqt9V1cT1tbsiWmgrwW5DB+bsab+AM9mE9x9P35lBx7bNIREOoGWXBKqpqOZ8ztceMZSdHdkD9tvxXOcvq4cnt85jo52/vP3duawra7n7e5qimz/ptcciz87Yxl62jN4bPMIdhyYgpIwpSk8516VN1lGTRBhCEAm7f/dmnIpKLKI7u5m2iGOGFqa06ipOrq6mqDrQFOO7x7t7MyhNZfEYN2RqLe7mVne0FpfINRUHZIsIZmQuX/zXHMaogDoBtDRnuFrXyd5CnXGsyPGmE+nFJTqgV8nx5hNyCIS9e+rae55huUYCUWCpEhQ6oF2f08z87nbmtNQNQPtHblY433d8T2495HdGJ4xpRktLel5n9edxx/obsJEvopE2pxX+3rZv7tzvEsS/5jLpBToAHL1PKHOjhxz+6769VqzvC329VqzvAPATgBAWyvfeF810Ip9w7M444ReAEBPdxM6GBfpLc0pqPXr5oQgiUgn2a6hIdkL0pZmvjGTSsoQJQldXU1IJOljdqFiiygwBdw/+MEPqK+/613vCm23YcMGfPjDH8a1116Lyy+/HE8++SRGR0et90dHR9Hd3Y3e3l7X62NjY+ju7kZ7eztmZ2ehaRokSbI+z4Px8Tx03a2U1nWdWhnpcIGscA3doJ6HyEiqNc3S6gFwfFbEP/7jP+FTn/oE/t//+xf87//+F17/+jcikUjgmms+ite89jKcfvZrcOGrXodrrvkIdK2GE088Faqq4/TTz8Q99/wUb33r23Hw4AE8//yz+Od//oRl66iqOlpbW7Fjx3akmnvxzNOPobe7A/39SzE5OYnt23dg5cpVuP32H0IQBCxZshSGYWBgYBmmpqbwwgsvYO3aE/Hb3/4aPT19yGabYBgGdN2d2a7Wdasw3P7Fuq5jdHQWHc1J7B2axfDIDDTdQLWiYnR0dl5+Dy9y9S1WQze4z9maVTAyUYQBQK2y97mrqwmjo7NIwAAxdZydKaFUqHCdX64n22o1jbvvbXWN43MvDePYpS3QDaBaqXEd56RlrThpWeth+a3INWFFur5wKxUr3OdPJ+xF3+xMCaMMuxsigNHRWeRSEkYni5ieLUMSBa5zi7oZsO0+MIlyWYWR0X3tE6KpGV6o8c8KXdVQqmgYHp5BVdVQ4xjvBEu7c5jKm2N8eqqAYp6N6S2XzeqcqqajUKxCAGJdn76OLA6OFVDI840ZMocfGjHbFGbL3OeXBFNPDABFjjErCgJmCxUMj8xA1ey5kfV+kSURw2MFHKy7IalV9nu8Vl8gDA5OY2a2DFkSub53Z86cY/74wiEAQLXMN7/wwntNWrMJbN83iZG6dK6YL2N0lI2kMzQN5YqKwaFp5ItViALfmOtqSWHPoRmMjNXPXWAfM8X6PdKRS8S+XmnHnFYuVrmOs7qvCU9uHsKOveYO6Mx0CXp9LEShVlGh6QaGhqdduRbFUhWGwfaMVR2FcyqcYyYhiZiu358zlDma9zkzF4iiQCV5CUID7v3792Pp0qUuD+5qtYqnnnoK5557buiJBwcH8cEPfhC33HKL9dlTTz0Vu3fvxt69e7FkyRLcf//9ePOb34yBgQEkk0ls2LABZ5xxBu69915ccMEFUBQFZ555Jh544AFcccUVuOeee3DBBRfwfP8jAhJQB7uU2J8LSpo855xX4qSTTsZzz23EhRe+Bu997zuh6xrOPvuVuPzy12NwrIiWtk40NTXjpJNOsc71kY9cgy9/+V/xwAM/gyAI+Jd/uR6dne7kk/e//0P4+Mc/ipbWNhx7/MlQKwUkk0l86lOfxec/fwNUtYb+/iX41Kc+i4ceMmVDiUQCn/3sF/GVr3wZ5XIJzc0t+Oxnv2h/J+vLOy9E8DXqac/g6a0juPOXWwHYW7gLAbLNL8fYpu5pT1tfK46uNKFI6GhJYXKW3wccMCuKNWUUrkQoAuJOcmAkb21Z81Y0O5Ig9n5xrttcytJ31p10JmbK/G4XuQQEAZiYqQRavF169nK88dXHAirbA26h0N2Whm4YGJ0qxdJwA6aO+oVdZg4Jj67SlpTosRI2nec/OFbg3tFUZBHZlIyJOvMYV1JC3DLiWCLGqfoHmJap9z+2B0t7zPudx6WEnKuqatx+zoApKRnozGLLnkkAC1/xr7s1jUJZxcSsKQXiSZpsa0rCADCdr8bSzg90ZfHCrnGUKyRJmL390u4cXnvmEpx+bFf0hwPQ3ZqyyBxeueTaFWZhLXKv8toCAqZmX3I8xnl08IosIZ2UUKpo3GMmlZRRql/zOEmXC4nQO/GNb3wjPvWpT+GLX/yi6/Xh4WFcd911Aa1MfP/730elUsFNN9n2cW9961tx00034eqrr0alUsGFF16ISy65BABw88034/rrr0c+n8eJJ56Id7zjHQCAG264AZ/4xCfwzW9+E319ffjKV74S64suJEhAFhTPWRZ6hvlfV3cvfvzj+3yfu+WWb1h/v/Od77GPX5faVGoarv3MV1z+yJ2dXfjyl7/qO5bTz/v1r38jXv/6N/o+c9ppZ+D737/T9dpll12Byy67AgBw0kmn4Lvfvd3X7tZbv4OqquHQaAE9vX24+25T9x1W5r23PQ3DAB5+9hAuPXsZzj+Fv4hNXBBrwDg3Zq8j6THujd3bnrGcG3ghCAKu/dsz0JTmX6C0ZBNozijYP5rHxu2jaGtK4szj+L3MjxT66l7izZwyHGBuhXOIXdnQeJE78JJEEa25JCZnyoF+88mEhK629KJjuIkf8P6RfOygd0Vdx82rq5RdAXe8YN88fzMe3TQUS/ffkktiZNKUw/BqYgF3YjpP0SFJEqFpRqyqfwBw0SuW4tdP7cdvnj4AgE/DTbTmc7nuxyxpwcPPmgz3QrlPEZBqvPtHTJaZL+A27/PJfIW7PDpgBtyabliJ6bw66KteeyzX+fzHkNDenML4TJl7vA90ZtGcTVjyL56AnTwHa5qOJOzrXdMMZDlyF5oyCZQqJe4xk05ImKzvEAQVF1ssCA24f/zjH+Ohhx7yvd7T04ODBw+GHvj666/H9ddfT33vvvv8weXxxx+Pu+++2/f6wMAA7rzzTt/rixlRDLfo+JwBs3ANjzbdtJwSHZUi59TdwwKb4XZT3EF9W7u8HSesaMNFr1iKU1bz2T/NFRbDHePGJAUGgPi+yacd28XFOoX1gRdLunN4dvsY8qUa/urVxyxav1Ia1ixpxRfee45r0cMKF8PN+bsRLePodCnWudubk2bSZAzngyOJ/s4sBAB7h82FQDyGuzlWW3/AHW+SW3dMJ57ZNor+Tn5v45ZsAofqrg9xvrszKZq3rP1cGO5cWsGrTx/A+j/uA2DaYrLCZrh11DQ+lxGCYwbsgHuhxzvZsds/kockCly/G6nLMBmS4ByGgU5zgbq3bvt6JNycetrTGJ8pc49XQRCwdnkb/rh5GIIALhtOZ1l7J3gXbM3ZBEYmS9xjJp2UrYXCXBbnC4HQO3HVqlVYtWqVS8NtGAZeeOEFdHR0zHvnjlaQmDMo2PQy3HEeJQlFwmxdHyjEOsLhBemDK9w2zHdoaGtK4mNvPW3e+0WDxXDHeIinkzJacglM56uxHyavPm0Arz5tIFbbuWJJVw6b90winZRw4br+I9KHuSBOwAvMrUomKTltGPECr/amFPYNz5q+54v4YeBFUjGZdxJAxJPyJNCSTVgSJlYoVuU+sxhGHEcfwNyduOZv4s0zLS6rshgMt8M2lKfokCyatoC81Q6duPgVS/HbDQegSCKXywjxg67WtNgLxGOW2NVRFzroJC5Oo5MlpOvGAKwgc8Rk3QKUlxTpbc9AFASrzsKRWFz3tGewec9krN1XEnBzu7M4HIWc4JV3tNR3LnnnmVRStpKT57I4XwgwjSinhlsQBAwMDOATn/jEvHXqaIfOrOEGwFaZ3YekLIJsQC8Ghjso5l8MXfNiLgw3APS2ZcyAexFrxYKwtK7jftW6AaQ5mK+jHW05p+0U3++WTEhW0Z1YAXdzEs/tGKtrK4+uMbOkK4dt+6cAxAv8BEHAit4m7Bnmk8s4Ge64+vG5Yi7ewIBpq0jAM+YkSYCq65akJM4805JL4vJzllu7E6xwSkriWOMBpo66OaNgphjvfpkL0knZOjdvYbFsSkZCFusWnvFsAXva0xbbekQY7vruZ5xg/4TlbbHahjPc7HMGud9450hT+00C7sVNajA9cd/ylrfglltuwdTUlPXaww8/bPlzN+AGYbijNdympCROwOz0dV0MATfpglNREkJwH1GkkzKaMkpsWUdPewZb908dlQH3yas7cM6JPbj4LP7CEkczFNkMmotllYvxI+hoSZlVLmMEnW1NKcvfdzE/DGhY0pXFM9tMB6m4fb/ivJWWFpoVzoA7Tmn3w4GWOQbczjk6loY7hp+zE39x/kruNgmHpCROYSzAfL4ds6QVz2wbPSILzK62NGaKNS79NmDXaJjKV2IlTQKmFvpIBtwrepsgCGayNi86W9Poak1ZXt6sUBwabid4g9+muosWv4ZbtnaEjuqkSYIbbrgBV155JU444YTYpux/SmB1KdFBJCX811SRRdurfDFEtYS1d74WUy4z3xAEAZ942+kuBosHRNZwNAbczZkE3nvFiUe6G0cErbkEtSIaCzqbU9g7NBtrMm93JWwuxjsiGEscZabjBtyr+puxqp/PY9z5ENeOEGvlnB/mmjTJpeEW6y4lGn8RkrmCVDysqRpqmh57F+yYgRY8s230iMgqulvT2HlwhjvgBkyp48RsJVahJ8Asy/70VnOBeiSeD8cubcUdN1yCWt1Wkxdnre3BrkMzXG2CGO4apySJLHDjaLgBoFRVYxekWygw3U2yLEd6bjdgg13DbZifjfEMFgQBCVlEpaYtEobbqZOp/7lYI26Y/rxx0Vt3y0gl/nQkGS8HtDYlMTnL53tO0DGHCp/tdQ04cPRJSga67PtkIVlmwnY9/MwBs+LhkWC4HSxhnNLuLg03x+8uSyIqNdXSxC7kYqO9OQlBALYfmDY13Jl45z7juC48u2MsVrLqXEGcSuLMz21NSWzbPz0nhpvgSJUXb21KYjRmwH3lBau4SVWnhacTvAmMLfXEdp6qrIDtRFOuqFBVHelF/Fxmuhpr1qzB1q1b57svLxtEabiJ/7Oq6jBgYHRkCK961Tl45zuvwrvedRXe/va/wkc+8o8YGQkuY3/ffT/FE4+SCqDu83zsYx/G2Niov1Ed+Xwen/zkxwAAY2Oj+NjHPsz61YIRyHAv0oh7DjhlVQfe/4YTuVm7Bo4setsyaIphKQjYAXcshrs5fsLmkUZ3W9rq80L2vbMljSsvWIXfbzyIydnKEWGtWrLxvdsBO3CQJZEriJElEZqmO5ImF+67t+aSWHdMJx5+9hBKVTX2ArGrNY1PvO105GLUC5grSMAdJ9G2rSllS0piMdxmwC1Li7e8eBjiKBjIrgjxnCcw5R3sxztldQfedenxWN7LWdGVMNwVbU6e/QsBpqXA/v378eY3vxn9/f1IJu1JqKHhpsNiuAPelySxzmJoluyis7MLP/zhf1uf+fd/vwXf+MbXcOONX6Ae44UXnsPaE081z+M50c03fz20f7OzM9i+3VxAdXZ2RX6eBd7vaslq5nzkxQdRFHDW2p4j3Y0GOPGmC1bh0nOWx2pLnEriPISbswlIogBNj2ezdiQhiSL6OzPYN5xf8MXC5ecux3Spht8+tf+IsIVz13CbbRKcv7mZNGlgx8FpCDBzRhYSrzljCTbWrUOPGWiJbrDIQKwB40pKNN2ApmvxEkbb0pAl4ai7z+eCvo4MMkkZjzw/aBXuMQwDqsbniS1LIv7sVH7nrDRhuKvq0W0LSPDRj350vvvxsoJhGBDEcG/tZMKsQqZIIlUScvrpZ+Lb374Vv/vdb/A///OfqFQqqNWq+OQnP41yuYxHHvk9Nmx4CplcG/7ziQcxMzODgwf34wMf+DC++tV/w7//+7fR09OLr3/9/8PTTz8FQQAuvvgyvP3t78RXv/pvGBsbxSc/+TF8+MP/jKuvfh/uvvtn+NWv1uO///sOiKKI/v5+fOpTn8OLL76AO+74DyiKgsHBQzjvvAuQTqfxhz88DMMwcPPNX0N7ewceffQPuO2b34AoAEuXLsHHPvZJAImXZ8TdwFGJdFKOrUm1GO4Yk7lYT8Yam+YvSLEYsKQrZwbcnNXr5gpBEPDBv1wHrabh1GMW3oY2l1YgCgIMGLESbZN15k/h9HMmPtwbt4/hmCUtC1qFFzDdKvo6MhgcLx6VgWN369wCboI496okiujryGI6H0+6djQinZRx8dnL8NPf78KuQzNY1d8MTTcJt4VYKKdcDPfLIOA+66yz5rsfRwx/HNyAxwefOqzHrKk6NN3Aq/VzcHbfGdTPJBUJhVINAvwxqaqqeOih3+LEE0/Bvff+H7785a+itbUV999/L+6884f48pdvwfnnX4DTTjsDl7z2Vdjwx4fQ0tKCL3/5FgDAV7/6bwCAe+75PwwPD+P223+EWq2Gq69+L1atOgYf+cg1uPrq9+GLX7wZg4OHrPN+97vfxHe+8wO0tbXjG9/4Gvbt2wMA2Lz5Rdx55/+ipaUVV1zxOnzwgx/B979/J77whRvxm9/8Cq973cW4+eYv4rrPfg3HrFyGX/zsLtxyy7/hPR+8rhFvN/CyQOccJCWAmTj5/7N31oGWneXV/205rtd93DUjmSQTF6KEQEhCQnAtLbS0lK8USim0tEBpoYKUIsUhBBLi7j7ubtddjp+z7ftjyz1677kDGSZknn+SOfdsOVved73rWc96hidm3pDidAgnTX4SOubfNVyyyLuuXnLKjwtmJivkdzmWYzMNm+GeKXCTRYGxuClruOXSBSd17N8lBEHgsrXt/PTRg6/J5zUccBMJuqnJq52oNvIB98m+67ObQxztnarP8h9fXLGunUc3dXHXs0f5xNvO+p0ddmYSk5ISq2jyFBMDM4nTV13+Gg6D6a36bH2ZqukgCAwPD/Ge97wdAEXJsXTpcj7ykY8iSRLPP/8snZ0n2LZtC2KFSW/ZshUln23duolrr30jkiQhSRJveMM1bNnyCnPnziu7j/PPv5CPfOT9XHTRJVx88WUsXLiYrVs3M2/efJqamgGIRKKsX28uwJqamonHY+zdu4elS5fT2NiCYcCb3nQjP/7x//GBskc5E2fitRd+r4u6sIfoSdhtgV04OfGaBDC2rOBkrMZe6xEJuB1Lx5mGPcbPtAhMkkQHsKxd3HBSx/5dY+OKZn773LFTzq7/PkIQBL7wvg0nVTRZ+zsy3ABvu2zBSS/SXqvh88hce+5s7njyMAc6x5xi2VOhp853KTlZK8tTFa97wH1Oy7qKLPTJxuBYCkUzCiqWi8Ptyrf1K9VwA6RSKT7wgXdy5ZXXsHr1GubPX8Cvf31H2f3la+vt0PXiVbaBpmkl37Pj4x//aw4fvoEXX3yOf/zHz/K+932IhoZGZLnwMZGkwgnEMKwJSTAXG4aRd5zXYOHImTgT5eJz791wUmlqmGTOTmf2pVIsbI/ytY+e77gIvJ4iHHQzdpLyABtoz5jhtkBKe0PQkUec6vB5ZL74wXNes82xTrY4OpRXb3GyDHfA6yLgPfXFon/ouGxtG7997hhbDgzRaDXgORWuTLaGO51VUfXTu/HN6Xtmr+EwjMpNb+wQBMFJOVbCpF1dnQiCwLve9T7Wrl3P008/ia6b4FaSpCnBM8C6det58MH70TSNTCbDI488xJo168tuq6oqt976FqLRKO9853u5+urrOHiwOmeaZctWsHfvLoYH+8GAe+75DWvWmIuY1x68OBNnonwEfa6THsxta8DTeTKYKl6PYBtMPfDJOm04kpKT0HADrF1Uf1LH/X1FyO9+zT6vJxuiIDhZrNdivcUfMtwuyWwclMxNdkk9BdfQJYtIokAmp51huF+PoRtGVfY6HpdENqdV/O6CBQtZsGARb3/7TaYzxobz2LlzOwDr12/gf/7nmwSDwbLbAtxww1vp6urkPe+5DVVVufLKa7j44ktRVZWmpmY+9rEP8+lPfw4wvdbf//4P8/GP/xkej4eamho+85l/4Nixo9P+jtraOj75yc/w71/5LLqm0tbayif/39+R1jiDuM/EmQDqLMA9U3nBmfjDxo0XzSeTO0kNt3xyDLftOW47PpyJUxs1IS8jsexrsmD0Dx3RoJtYIoumnTpLS0EQ8LolUhnVzEy81m0Bz8TMwjBAqqKq3eOWIAlNTS3ceWepxaIkSSW2gB//uOmffcUVV3HFFVcBcOmlVxR8J39fH//4J0v2K8sy3/7290u+/4Y3XM0b3nB1wXdrampZu3Z92X2///0fdv7/ggsuYvaitfg8EvURH6qm0z2YOIO3z8SZAFbMq+U91yxhXssZ7/bXUvi9Mn7vyU2TJ2sLuGpeHZmcRkdjZTLlTLx6EbXkX6czU3q6Rjjg5nh/PM9D/tQgAJ9HJp5WgNO7A/QZwP0qhGEYVUmXHWP+PxKds8CkB7nx+irSPhNnYsqQJZGLTsJj9ky8dsPRcM8QACyeVcPiWTWvximdiSqi1qm3OH2B2+ka0aCHieQIqmYCgFO1aPG6ZRKp3Ck95snE6Xtmr+EwDBCqYLhlScTnkfHMUON32kbBT5662+aZOBNn4kz8MYfjwy2fkRG9lsIpcD6NpQmna0QCbrI5jYTFNp+KokkAn0dyGO4zgPt1FrphTFs0aUdT7cm3mz7dooDh/oOeyes3cpryhz6FGYVu6Ixmxn7n/cRzCQZSQ7+HMzoTZ+L3E3ax5Bmm9LUVNuA+VWDxjyls69CRWAY4dYWnPo9MPGUD7tN3ofS6lZQYVRY2nty+X7/MrkEh4s6/CqZ94Ol5XfaPHuKxzqd5z7LbCLor2zmeznFs4gRf2/ptPnbWB1hYM/8PfTpVxcPHn+D+Y49yy6IbuKh944y3f6V/K092PUtnvAeA1kAz57eew8XtG18372B/cgABgUZ/w+vmN78WQhQE3LI4Yw33mfjDxoq5dVy1oYM5zaE/9Km85iISMBcrIxMm4D5VbLPPI5NInf4M9+sScMuym2QyRiAQ/r1PUIZhYBgG4utw4hMEAYoZbsH25VaZmJjA7Z5596+TjbHMOCk1TVuwZdrvPnDsUY5MHOf/9v6cP139PkRh5i+tbujceegejo4f56NnffCUA/eHTzyJZmg837vplANuwzDYPLCdBn8dc8KzqtpGN3Re7NuEKIj88uDdxHIJrpv7hhm9k7898iCSIHH9vKvwSl5e6d/Krw79lnpfLSvql57sz3lNRF9ygHuPPsyOod0ARD0RLm7fyJWzL/0Dn9mZsOP2Kxcxt/lMoexrKfxembddtvAPfRqvybAbJdkM9ykrmnRL6FZ6/XTOKL0uAXdNTQNjY0MkEuO/930bBiRiGQzFRTr++9PuqbpGSk0jCxJ+1x+mGcJ0EUvkEAQwcm5UVSeRzCGqbuIuEVGUaGioA6oD3IZhsH/0EHtG9nP9/KvxSOVlN7qh05PoI6NmEQSBueFZSKJ53e84+FuOThznn8//O+ezctGfHODIxHHmhGexb/QgDx9/kqvmXEpGzeKVPVWBb03X+NG+X7J5YDsCAt/b/RM+etYHpjzu7zP6kgPsGt6LV/KyY3g3WS1X8Zr9vkPTNX5x4C5e6HsFWZR57/K3c1ZDaefT4tg/dISRzBjvWHoLh8eP8uDxx+gItbG6YXlVxx3PTjCeneCtC6/nso4LATi/7Rz+9rkvsHVw5x814B5MDfGlTf+BLMhcN/cNhN0hnul5kcc6n35NAe6EksQreZDFP86p6MJVZwplz8TrJxxJySlmuL15DZrOMNynWUiSTH399KznyUQqo/CZHz7LB25YwcZZjSe9H8Mw2Dt6gN5EP92JXrYM7MDAoDXQzGfO+aspt43nEmwZ3IGua1w266KTPoeZxn/c9WPioX2c37Geea5V/M9dPfzlLatZ2VEHQENDiKGheMXtdw3v5cDoYVySi2MTJzg0bnqANwUauLDtvIrbfGfXj5x/v33JWzm/9Rx0Q+fI+DGSaoojE8dZNAXj+0KvybJ+eNW7+fWhe7nv2MPcd+xhALySl45QKxe2nce6ptUV9/GLA3exeWA7b5p3NVFPhB/t+yW/OXwfNy+6ofIFKwrDMPjJ/l8xNzyLC9rOrXo7gMc6n8Ylunjn0pv5390/ZufQHs5uXjOjfZxM5LQc3975fxwYO8wVsy7myPgxvrvrx9y+5CbOaz17ym2fOf4SbsnN2sZVbGhaw/7RQzzf+3LVgPtErBuAOeEO5zOXKLO6YQXbB3ehaDfikv44O7490/MihmHwd+f+FTXeKABZLcdvDt9HQkkSdJ3esqitgzt5vudlDowdZlndYj6y6r1n5DBn4ky8xiPgcyGJAsMTaeAUFk3mdQA+A7hfR2H7T3p+xwYXh8aP8M0dpld2wOXn4vaNjGcnOB7rmnK7e488xCOdT6Fb7dYX1MxjVqh9RsdWdZXeZD/13rqq2fTNA9uJRXbhUqK81LeZ5/SXcM1rIaXPB+qq2sd9Rx+hN9kPQMgV4OZFN/BC7yu80PtKRcA9lp0A4IMr38WvDv6WfSMHOb/1HAZTQyTVFAA7hnZXBNyKrvJy/xZW1S8j7A5x2+K30hpoRtVVPLKHkfQo2wZ38eiJJysCbkVT2DSwjY0tZ3PVnMsA6E708kTXs6yoX8rS2kVV/f4jE8d5qW8zu4f3saF5He4qweJYZpxN/du4oO0cVjUsp8YT5ZWBrScNuA3DQDf0adl5wzD48b47ODh2hHcuvYVzW9aTtQD4HQfvZnXDcvwuf9ltc5rCi11bWdOw0mHiz21Zz8PHn2AsM+6AyKmiM9aFKIi0B9sKPl/XuJqX+jazd/QAq6tg2n/XMAyDrngPDf46fPKrn33Kajle6tvMmsaVBdepyW82ShlMDRGMnL6A++DYEb63+yfUeWvNxdHQLp7teYmL2su/42fiTJyJ10aIgkA44GYsngVOXdFkIcN9+i7czwDu33OEA25uvWwBG1e1kk1lT3o/u4b3IYsy/7jxbwm7zeKN3xy+jz0j+ytuM5Ie5eETT7KyfhlXzLqY/97xXZ7qep53LXtbVcfMaQo/2PMz9o0eQNFVzm5aw3uW3zbtdsdjnfx43x14cg3UDV3MX7xtOb/a/Sib9Jf4edd38IRv5azGldPuJ6WmWd90Fu9edutkUasBvzr0W7riPXSE2kq2SStm6mpF3RJ21exl1/Bei90+DkBLoIkdQ3u4aeGbyjJou4b3klCSbGw9BwCv7HFAsx2qrrJ7iut+ZOI4iq6wKo+ZfdP8a9g+tJt7jjzEkprq9ICPdz6DLEgklCSbB7axsXXDlN/XdI0X+zZx/7FHAbis4yJEQeTs5jU81vk08VyCkLv65hknYl081/MSB8YOM5GN8daFb5oSBD10/HG2Du7kzfOv5dwWszmSR3Jz44I38qVNX+eFvk1cMevistvuHN5DWs1wTvM657PzWtbz0PHHealvC9fMvXza8z0e66I10FyyMFlcs4CAy8+WgR2nBHBvGtjGD/f+AgGB2eEObll0A7PzWPdK0RXvwS25HaBc9fH6t5JWM1xcVGTaaO1nIDXMvMicGe1zpvF878t4RDfrT2JR92TXcwRdAf7unE/gEmW+seN7/ObwfSyuXTDja3EyMZYZJ6NlafY3nmHVz8QffRwaO0JGy7K8bslJ1SbNNCJ5gPvUabhfG5KS0/fMXqMhCAJXbphFOPC76Wf3jOxnYXSeA7YBgnIARVcrWr891f08giBwy6IbmB+dw3kt69k8sJ2JbGUZR37sHN7DzuE9bGhey+KaBewe2Y+mawAcnTjBf237XxS9tM3xw8efxC/7aIlfiK4LBN0BNtReRHbXBdS6G/jRvl8ymBqe9vhpNY3fYgjtifDs5jXIoswLvZsqbuMWXciizOLaBSTVFN2JXo5MHCfoCnB5x0WMZcfpSvSU3X7r4E4i7jBLayuD4ognTDyXcK5FcewbPYgkSCyMTrLoLlHm2rlvoDPezXarqG2qGEgOsmt4L1fMvoS2YAtPdj2HMU33oLsO38/PD/yGel8df7n2I9T7agE4u2kNuqFz79GHUcvcr+LIajl+fehe/nXzf7NtaBftoTbmR+fyy4N3cdfh+51sSX4cmzjBfcceYUPz2hJQ3RFqZX5kDs90v1h2W4DtQ7up9UVZWDPP+azeV8fimgW82PcKuqGTVFJktVzZ7Q3D4ES8uyywlUSJNQ0r2TW8t+L2v69Iqxl+c/g+OkJtXD3nMobTI/z2yIPTbjeYGuZrW7/Fbw6VdpidKgzD4OnuF2gPtjI3PLvgb3XeGiRBYnAKe8Rnul/g14funfbZmir6k4P84sBd/N/eX7BzaM+Mth1KjbBreC8XtJ2LW3IhCALvWHozbtHFz/bfedLnVE0omsJ9Rx/mcy9+mX96+d/41HNfqOpeFUdGPXki5UyciVMZuqHz3d0/4ds7/49/eeXrM35fTyaiQY/z/6fOpWQyG3s6F02evmf2Oo6h1AgDqSGW1y0p+NyWdySVZMk2aTXNC72vsLZxlZNmvqT9fHRD59meF6s67uaB7UTcYW5dfCMXtp1HWk1zdOI4AI93Ps3+sUNlPZPHs+N0hNpw40PTzYlc1w2MrJ83tt2IJEj8YM/PULXK4E83dDJqtiQlH3D5OathBZsGtpVdaKTVtCNbsGUjB0YPc3TiOPMic1hZvwwBgR0VBppYNk6jv37KlX/YHcbAIK4kyv593+hB5kfm4JU9BZ+f07yWZn8j9x59uCJYt+OJrmeRRImL2zdyaceF9Cb7OTh2hIyaYdySzRRHV6KHueHZ/NXajzA3MukM0hps5uL2jTzf+zL/tuUb0y52frT3lzzR9Sznt53DP278NB9a+S7+bPX7uajtPB7rfJrNA9tLtulO9AJww/xryrKEl3RcwEhmtGJGZjQ9xqxIa8l139i6gZHMGF/Z/F/8zbOf5wd7flZ2+6H0MGk1zexwebnUuqbV5HRlyozQ7yMeOPYoiVyS2xbfyBvnXcXF7Rs5MHaYkfRoxW00XeP/9vycrJYjlqtuMWzHsdgJepP9XNR+Xsl1l0SJel/dlH7kWwd38kTXszzX+9KMjpsf9xx5ELfooj3Uyg/2/pzueG/V2z7V/RyiIHJhXo1C1BPhqjmXcXj8GH3JgYrbjmcnGJ7iuk4VuqHzb1u/yYPHH2dt42puX3Iz7cFWHjnxZMX3q1y81LeZTz77OZ7qfv6kzuNMvP5iIhvnb5/7R17p33rKj3104gQJJckFVl3Td3b9iN3D+17VY+aTjWeKJgvj9D2z13HYIKEYcAesQqikkirZ5oXeTWS0rOPWAGaKeUX9Ep7teZHnel5i/+ihisAvpaTYO3KAdU2rEQWRJbULkQSJ3SP7SSlp5yUtB/ZjuQRhdwhJEh3Abf+3xhPl9iU30Rnv5s6991f8zRk1i4GBXy51MTm/dQNpNc2u4VLQnFLT+Kxtop4Izf5GtgxsZyg9wrzIbILuAAuicx3rtOLIB+yVIuIxbb1iZTIFE9kYPYk+ltaV6rRFQeT6eVcxkBrklZ7tFfefyCV5uX8L5zSvI+wOsb5xNSFXkP/Z9X/89TOf43MvfIlUmXueUtKEPaGygPeWRW/mgyveyVB6lDsO3j3l7+tN9LGmYSW3Lb7RuZaSKHHTwjcBMJQeKXtsgIBc/tqtrl9O1BPhqa7ywGQiF6PGFy27Xb23FkVTqPXWMFzm2IBTy1DJgnCOxf4Op8pv//uI3kQ/T3U/z8bWDQ7Tfm7LegQEXurbXHG7+449wol4FzWeKIky93Wq6I73AaVjgx1N/oYpAXdKNe/bnYfupSfRN6NjAxwZP86O4T1cMesSPrLqvfhlH/+57Tvcd/SRaRcPaTXNi32bWNu4mqgnUvC3Dc1rEQVxyut2x8Hf8m9bvnFSDHNSSdEV7+G6uW/gPctvZWPr2dy48I0A7B05UNU+nut5iR/vuwNJEPntkQcZSf/uDZvOxB9/7B09QCwX52f7f01vov+k9zOcHuUHe37GiWnquPJjx9BuZEHiLQuu42/O/nPagy38396fM5AcPOnzmC6iwXzAfWokJf7XiIb7DOA+DWP3yD4a/fU0+usLPg9YDHdKLZykDcPgqe7nWRCdW5Jiv2r25ai6ys8P/Ib/2v6/vNhXXpqxfWgPmqGxvuksAHyyl/nRuewe2c+2oZ2ohgnUE7lCwK0bOrFcnLA7hCgK6BbQNqz/ioLAWY0rWVSzgG29ldNZNhAoV3Rm61EHy4CnlJop2GZx7QK6LPZ1ftTcbkX9UvqSA2UBQVJJEZim0C3iMWU9E7lYyd/2jR4EqFgYaVvT9cYqM3cv9m1C0VUu7bgAAJfk4saFb2RJzUJWN6xANTSnOHQm535W40qW1C5kNDNe8TsASTVFsIzWWxIl/LKvQkYlgyzKFV1AJFHi/NYN7B87RDxXmBmwn5kaX6k/sUty8fmNn+Kz5/41S2oXkihzbDD15m7RRbO/vBOQW3LhFl0Vt/99xMMnnsAtunnT/Kudz2q9NSypXciLfZvLymlyWo7HOp9mQ/Na1jSuLDi/wdQw/7blGxUXhzD5nlRa6DT5GxhODVeU8qSUNMvrluCXfXxv90+nzbwUx91H7ifiDnHZrAuJeMJ87KwPMi86mwePP8bnXvjSlIBi1/A+slqOi8vUBYTcQVbULeWV/q0VzymWjRPLxXm865kZnTNMkhSNvskxtTXQTNQTqQpwHxo7ys8P/IbldUv427M/DsAvD96FoqtsGdjOruG9Mz6nM/H6iP2jBwm4/HhlD9/d/WMyambG+0irab618wdsHtjOv2/9VkWJZX4YhsHOoT0sql2AV/biltx8cOW7kQSJ/9n1Q3KvktzO9uKWJeGU1Uh4XyMuJafvmb1OI6vlODR+lBV1pR7CNsNdzIol1RSjmTFW1Zfaqc2NzOIrF/4DXzjvbxEFsSL42jKwnXpfXYGjyYq6JfQnB3i88xlngi8GMCkljW7ohNxBZFFA0woZbsnqcV/rjRLLlpdkgDmgAPjKuKLIooxX8pIoI+kwdd+TrPjimgXONh3Wb6n3mtrmclr2lJoue8z8iLjD1vblAXfIFazYXMc+93iF325Kfl5iYXQeLYEm5/MNzWv50Kp3c4lVGFcMWs1zT03LzgddgbKAOf/4KSVdEbgHXYGSRZZz7GkWKvazVMyQx3NJdEOf1onEPPdUWb3xiVgXHaG2KZ1UAq7Aqwa4x7MTbB3cycbWs0ss+M5rWc9YdpyDY0dKtovnEuiGzsLofIKuADkt50ilDo8f4+jECb6z60f8aO8vy06IKTWFa4qFTqO/HtXQykq/wBwrmvwN3LLozQykBtk9Un162ZSYneDCto2Os0xzoJE/WfVePnvOXyOL8pT6cHvBm/+c58e5LeuJ5eLsHS0PgO0x4rHOp6uuS7HDBtz574sgCCyrXcT+scqZPzvsbMA7l95CU6CR6+ddxZ6R/fztc1/g+3t+xv/u+vGU2vkz8foMwzA4MHqYpbWLeN/y2xlMDTtF7tWGpmt8f/fPGEwN8f4V72B+ZA4/3f8rnu2ZWhbWlxxgODPK6jxcUOer4V3L3sZAaohtg7sqbpvTFA6OHZ7RedoRsTTcpxL4+s5ISs7EycTu4X2oulo2ZRywJotieUHSAkSVHCkkUaLOV0PA5S8LWmO5OAfGDrO+cXXBitRmZwdSQ5zfZrp4FAMYexK1GW5Hw21NuqIFuEOuIBPZeMXJ2J5My0lKAILu8uApraQLGO6F0fkICMwKteOymmmErMLTeBHDrWgKiq7gr8AW2hF2hxAQmCja3m7Os6R20ZQa8KDLT6wMaAUznT2SGa1oe2ife6IIcOc0BUVXKzKdk8c2QWslxjOrmVKeSouOSqC1OLNQLuwizmI980TOZOtrfJGSbQqP7Uc3dNJFjJCma3Qleqd1Agm6/FMuNn6XeLbb9MEudgoBWFW/HL/sKyuPsK9lyB1wgLp9jvbzefmsi3i5f0tZzWdaSU/5vE46lZSCP1VXyWk5/LKfVfXLiLjDPNf78nQ/1YmkJSOKekozE82BRq6d+wb2jx2qqJtP5JLIgoRH8pT9+4q6JQRdAV7q21L272k1zcLoPFRd5X7LJ7/asLOCgaIF6rK6JaTVDMdinVVtby8yL2k/n7MaVrC4ZgHvX/EOXKLMrw/dN6NzAnMMeaLr2d9JavB6jYHkIN0TM5dF2TGWGa+qqDw/Do8f4+HjT9AZ766q8Lg32U9cSbCkdhGLauazqmE5Wwd3VhyPy8UTXc+yd/QAb1v0ZtY2ruLPVr+fueFZPN759JT7seuWVtYvK/h8We1i6n11U8q3tgxs5z+2fYfOeHfV52nHJMP9+4eXXfFe/nXzf5eQGfkuJWeKJs9EVbF9aDc/3ncHjb565kfnlvzdnmiLNdxxa8KertlFyBUkXgb4HRg9jIFRYt3X5G+gwWd6aJ/fugGX6CphO/MBtyQKaLo5ABQz3EF3AFVXyWjl9ZcpC1T5KoCJUAWmNV0E/PwuH1fOvtSRZ4AJbqCUJXbS89Mw3JIoEXD5SxjupJoioSQrFu7ZEXAHKjLcz/a8SNgdqtjsxV5ExYszCzYAmObcg64ABoajuS6O5DRa7KkWOtMx3LVWZqG40M3WwteW0XAXn7t5joXPe0pNo+oq9b6p/d0D1mKjUkxk4/z39u9yqAwTPVXkNIVne19iZf2ysufgklzMi8wpWwBoP4NBV4CAuzBjFVcSeCQ3b55/LQJC2WK+pJqe8p43TQG48593SZTY2Ho2+0YOVq1Fnu6Zu6jtPJr8Dfz68L1lGeOEkiTgClRMM0uixIbmtewa3ltWp51SM8wKtXNx20ae732F/97+XTpj1QEC+zkoBtyLaxYgCiL7ppGVpNQ0XsnjZFREQeSDK9/FB1e+i7WNq7h6zuXsHtnHnir14HbsGNrNrw/dyw/2/Kwqec+xiU7uPfowP9n3K+498pADGOO5BD/d9yv6pyg6NQyDLQM7ymbLZhKd8W4+/dw/VX3tfx8xkY3z+Ze+wn9t+18ePfEU3975A77w8lf5zONfKZt5nC7Sapp/fPmr/Oe271TlZGQYBo93PsPXt36be44+xJc3/Sf/8OKXebr7BZQKrmEA+0cPAbDEyrye1bCC8eyE07RrutB0jSe7nmNxzQKnGZpZXH8+Q+kRDoxWZqF3Du9mbniWU4NkhyAInNu8noPjRyoWd9vk0o7B6R22isPuNvlqAN+nup/jeKyT/9r+vzzV9byz6HG7ROxh5YyG+0xMG6/0b+W7u35MW7CFv1r3pw47mx9uyYVLdDkNXeywAVHQPTXgDrqDZQdbm1mrswBSflzWcSEXtJ5Dva/OlBdUYrg9JuDW81xKoJDhNo9VfrBPK1Mz3OWYVsMwSJUBIG+afzVrG1c5/54ErYXHdtLMVTQriXjCxIo03DbrPJ3XdcDlLwu4R9Kj7Bk5wPmtGyq2tvbJXkRBLLlu5VLk5SLogLry1z01zX4qS0rS+Fzl75UdbslF2B1iJFPEcFsTZLSMhjs/bHBUfN/tfxeDp5Jzr7BYAPPZ+cWB37Bv9CA/2PPzGUlPNg9sI6mkuLTj/IrfCVV415x31RUsw3AnCLlDiIJoPjPlZERKZSmPoeYIGAI+2VcecBfd6/NaTJ/3SnUdpdvb72j540uiWZw1mBpmy+COkr8nlOS0Y1RHqA3N0EreNVVXUXQFn+zjzQuu5S0LrqMz3s1XNv9XVcDP+e1FC0u/y8fc8Cz2jB6gPznIi32byy5OU0WZtOK4tOMCGn31/PrQPVOyjj2JPu44eDcpJY2iq9x1+H4Csp/eZD/PT5NtGEmN8Z/bv8PDx59gz8h+HjrxBN/Y8X264738+5Zv8kLfpin7BXTFe/j+np/yta3fnpEzS34YhsFdhx9gIhfjkc6nZrRtTlPYPLCdb+74Pt/d/ZMZbft870sMpoYZy05w95EHODpxgitmXYyqqdx56J5ptx9KjRQAtP2jh8lqOY5MHOc7O39Y1u42P3516B5+c/g+VjUs5wvnfYrbl9xEyB3ijoN387kXv1SxCHH/6CGa/I2OfG5F3VJEQWT7UGU5R35sG9zJRC5WYIYAZn1O0BWo6ECWVjN0xnsqFlef07IWAYGX+8tnk+wxqRpL2+KwGW6bbJtp6IbOLw/czfGirJOiKWwf3M2ahpUsr1vCrw79lp1W7YQgCA7LfUZSciamjVf6t9Lgq+Mv1nx4SgAXcPlLWDsH+LmmBn4hV6As8IorSURBdBwq8uOi9o3ctuStQHkAU8hwl7qUTDLc5rlVAjbpKYomKx07Y8shKoB0O7ySF1mUS4CjzfhNB1rB1HEXMynxKq970FWe4T4wZmYWzm5eW3FbURAJuQKl7LzN2FUhKYFS3b+zH4f1rAy4k0qyJH2aUqdnuMFcxBWzKOMWmIp6p5aU2OCsWBZi38f8jI6h5tAThVpxc5FW/ndvGtjGzuE9bGzZQEJJ8vP9v67am3rH0G4affUFvuvFEXIHiSuJkn3mL46DRQuKWDZOyJJblFvcAmUXmGACofTD/0H6vi/T5G8oawXpPO/Wfavz1bC0bhEv9m2qil2t5n1ZVrsYKO9sk6yi5XylOhVbVuRzme/yFbMu5u/O+QQGRlWsclJJISDglT3oqXH05CSrv6xuCV3xHv7x5a/yk3138MpAqZSn0nW3QxZlrpxzGQOpoSmtDZ/vfZmnu1/ga1u/xb1HHmI4M8p7V7ydhdF53HfskbJuRHb8cNudGIbOP5z3//iXCz7Lu5a+jcPjR/mXTV8nYY3h5RbHdnTFzV4EI5lRvrb12xV1/lPF/tFDHBw7TL23lu2Du6rOjmi6xr9v/SY/2PMzDoweYtvgzqo98jVd4/neV1hSs5C/P/ev+efz/45/2vgZ3rLgOm5cdg1bB3dOa3X3ZPezTgM1gL0j+/HJXt6++K3sHzs0pR/+iVgXT3c/z0Vt5/GBFe+gzlfLxtYNfGLdn/LR1R9gIhdnV5laCEVXOTx+lCW1C5zP/C4fi2sWsH1o97TjjSk3eo5Gfz3L6hYX/M0lymxs3cDO4b2MlanLillzVZ2vlEQDs7h7Uc18XurbUnaBaGOM/tTgjB1NXLJEwCufNMMdy8V5pucFvr/7pwWZrl0j+8hoGS5oO5f3Lb/dPL+8d8324j5ZoH8q4gzgPk0iqSSp99dN2867LOCuVlLiLi8pSSpJAi7/tBXFlRhul+jCK3kKNdwlDHd5WYcdKTXtTIhlz90VJFEE/NLq1IybHYIgEHIFS1xKUjNkuIsLtWyZx3QMd9AVKKvhts+n1lsz9fYWeMuPZJWLhUkAU34ino7lD7oDqIZWIgVKVwu4fTVlGe6QK4g8Tev4gFz+3JOOHtf8u6HrpB/+D5I//yTZl+/AUHMYhkFQdJNW0yVgciIb51cHf8u8yGxuW3Ij18+7iu1Du6v2yY3nktT76qZ8X0LuoFmQqhaypbaO2St5Su5NLDWMv/8oavfuiu9qytJwa8MnyDzzfbThEwCoR15G69mDPtJJoydatoCvnKzi/NZzGM9OcHB8ellNehqGG0yW23zeS4saE7npAbe9CCleZDkLcmlycR12h2gONHE0dnzac0+qaXOMQyB975dI/uwTpJ/4H7SxXs5tWcfGlrO5dbHZM2A8U8r+pqqQUM21agqm8iXvjvdR44kykhnl8a5nWFG3lKW1i7hp4ZtIKWkeOv5E2e32jBzgpe6tXD3nckfGdE7LOv509ftYWruIv1z7ESLu8JRyka5EL17Jy1+s+TBJJcl/bf/fGclLdEPn7iMPUOet5c/O+gCCIPB0lX7kT3Y/R1e8h3csuZl3LbsVoOoi090j+xnPTnCh5W4T8YSdefJNS95As7+RXx68e8pF47EJky19pX8rhmGwZ2Q/S2oXcX7bOSyvW8IRq99EcZiM/v0EXQHeNP+aglodQRBYUruwLJljHvMEOV0p6TS8pmElw+kRepP9bB/azZ0VsiLHYic4Ee/i0vYLytYIXWB1Ry5XhxGrIvt6bst6RjKjTlfm/EgqSafp3o6hPeiGzkPHH+e/t3+Xf37la/x0351TZnLCAfdJM8322DGSGePuIw84n2/q30bEHWJRzXzThUpyF8wNXo+MLImndffYM4D7NImkknYAxlQRkP0lLEhCSeKR3BWdC+wIuoJktExJ+iyhpKZlac3tA06Bph2xbIKwO4ggCEjSpKREKwbcNsNdEXBn8MqeisWHAZcfVVfJ5gE/h/WqAviF3IES0Dodu5sfEXeIWC5eMMjEq5aUBMiq2RKtXywXxy/7ysqHCs7dFSy5bg7DPY2G29avV77uU+tyA65SltkwDNJqpirAXe+tZSw7UTAZTmRjJbrCchF0l5eUJB2G2/x7bvt9aD17kJoXktvxAMmffYLE/30EeYuZai6WYO0e2UtKTXPLorcgCiKXz7qIGk+06iY5thZ5qqgkoYorCYLW+2JfP/v3xJUUQc0g+/IvKzLcaTWNu/8wqd98DmX/M6Tu+zJq7z6yL/0CPOY5NWgi49mJEh20LZXIf1+W1CxAQCg76RbHVNad+RF2h4iXcRHJv256przLiJORyRUDbvNdL35O50dmc2yic9oitJSSwu/yoQ8dQ5/oR2pZjHp8K6l7vkhE9HL70pu5sO1cwtZ7XhzVLDAb/Q24RZfDohaHYRj0JPpYUb+Uj6/5E1bWL3W87ttDrSyuWcChMgsf3dD51cG7aQ01cXlRV9eltYv46FkfoDXYXHaMy4/ueA/toRbmRWbzkVXvYywzzrd2/qAqpjmn5fjtkQfpTvRy/byraPTXs6ZhJc/3vjKtzd1YZpz7jz3KyvqlnNd6Ns0B08pzKr/4/Hi250Ui7jAryzh3uSQXl3VcyGhmrKx1qnnuCt2JXgQENg1sozPezUQu7sgt6rw1ZVliMHtiHBo/yjVzryibSRUEwcxglhlfe5NmIezsol4BqxqWIyDw3V0/5n93/Ygnu54r6x72TPeL+GQfG5rXlT23Ol8tcyOzy+q47ecgv1N1caywfv+JeKmvd1JJ0RpoZlaone1Du7nj4G+59+jDxHJxArKfF/pe4cHjj1fcd13Ei889NaFSKexrOT8yh2d7XmTr4E4SSpI9I/tZ13SWgxHMmrTJ6+7zyLjk0xdswxnAfdpEUkk5AGKq8JeVlCQJVgGYK4GvRC5RFegsBwLilgc3YBVNFjLcjqTEZrinkJRMNaGVk6RMAoipJSX29pVAa7UMt4FRwDrGcwkEhOm1xDZrVwT8Ytn4lAOiHeX0wMkKmtTimLRzLJ+qTjqsZeViVaDgd2e1LLqhT2unCOakoBt6wWQYy8UIe6b/3e5kDFEQK2Z0Ai4/au8+clvuQl5wLr43fgrfdf8PqXUprkUX4LcsKksKfS15jz3xi4JIrbem6q6PSSVJ0O1H7d5D8u4vkNv9KEaukMl26gaK9pnP8to+5wklhaZrpAyFoKajj3ThT8VL5F+abmYaPCM9uFZdg/+t/4jg8ZO+78sYqQl8V/wZCAI1afP3jmfHC7Yvt8D0yl7agi0cmzgx7e9OqWlEQXQsAStFOdCq6RopNU3QHUA58grJH/05uZ2lTiOVpGeVFtfzInNIq2n6p0l7m571fpTDL4Ik47vyY/iu+SvIJlGPvjLluUN19qGiINIWbKErUR5wj2TGyGgZ2oItzAq38yer3kuDf7LotsYbLWt3GMvFGUqPcPXCS6ZcnFeq0QETtPck+ugItgFmj4L3Lr+dzlg3P933qyl/1+HxY3zhpa/yWOfTnNO8jnVNqwG4bNaFZLRMRVcZO+60rCJvXngDAA2+egSEqgD3cHqEfaMHOb91Q0UL0Kilj65UPNkZ70Y3dM5rOZuEkuROSz5iy59qPFFSarpk4WEYBr898iD1vjqHTS4XIXewrFQzkUsiIDjzbv73F0TnMpgedvo3FAN+wzA4MHaYFXVLKmZ9wVwsFNc7wCRLPBUZ5JN9uCV3+eJsJeV0eT4R7+LZnhd5w6xL+NuzP86fr/kQG5rX8uCxx5w+FMXxjisX8+5ryuvHpwu7qP7WxTfS7G/ke7t/wude+BKaoXF28xrne8UyU59bPq3123AGcJ8WYU6kmWkdJ8CSlJQpmpwuVQuViwcTSqqq7YPuABktW8CQx8oAbsMwJhluK73jklz4ZG9FpjWtTl2UVA74VSspAQi7Qk6azY5JGcv0gD1sd5vMG9ziirlQmcoSECqzdvnXbqoIlZGUpNQ0kiBNC35ckguP5K5cNKmmcImuilKmcgz3JPiZ/rrZ1oD5HSMnsjGi7kKG28gkSD/yX+ipceez7DM/IKAZJfKCpJLCLZopxeyLP0cINeK94N0IgoDctgzfFX+K9/x3VHQ5iStxfEWZhbC7VHJULhTLaSfoCqAeeQl98BjZF35K4qd/ReaFn6HHTOBXyV0m/11VT2wnILpIKkni2QQGEBJciPVz8A4cdTzu7XCuuwGeDTch1XXgf+OnEGtaca26CrltGWJtO6FxE8gUM362jrn4vs2LzOZY7ER1LLHsQx/vQ+3ZizZeXjoRKgNaHf13OkXmqe+AIJDdclfB/QZwiy5colzq96+WX1zPi5gdRY9WkATYkbR849UjLyN3rEZw+5GaFyFEmlEOPOt8L+wp/xzYv93QVZTjWzGy5YmDjlAb3fG+steyx2rI1R5sLbttuSwaTALJOv/U0jNbdlcuBlND5HSFjlCb89nqhuVc1nEh24Z2VXQxAnjw2GPohsbH13yYdy17mzPezQnPYlaojRf6Xqm4bUpJs31oF5d2XODoid2Si1pvTVXa4H2Wy0cllhcmbSrLNSYDnMXkdfPeQNAV4OjEcTpCbU5DM7uWZLwI9CaUJL3Jfi5qO69iUTtUdv9KKEn8Ll/Z+eFdy97G35z959y08Hrz2EXv6nB6lFgu7jRvqxSm1DFWoge3yaCgK4DafwhtpNT2UhAEop4w42UWKglLZrqmcRUuUebSjgu4Yf41CILZzObWxTfSHGjkh3t+UbbgtDHqo6UuYFpuVrGYLz53MOeOT67/GO9edisLovM4q2Gls2CEcgy3dAZwn4npo1iTOlXYGu78FyyRS0xb/Q84LHjx4JCw0tzTH7sUfMVycULWwGXLR3TDKPHhBnMyq5TyTCmZKQHcVMCvmoVKyGK4869bUkk7LiDTRbnmN4lcwgFWytFXUI6XZ3oq6ahjuXhVTG/IFSSr5QoaoSStFHk1erWgK0giV6FoUkk7jKeeGkc59ALKgWdRjrxi6qDLnPtk8d30WRHb+cbWcWu6RiyXKJGUqL37UI9vQeudlHXosUH8ml7CztvSBD01jj7SiWvJRQju0mcgYF3bYsAey5kyqPwIe8ozm8WRdNj1ANrQMaSOFfjf/PfIs1ej7Hmc5C/+huzm30xmdMpkk4LuAEY2Sfrxb+FPm9aS45bEIuTy49lwM4F0EgOjYLEw6QXtRbDt6UL1+G/6It5zTV2s1Dif4LDp2lGsRU6pKbzW827ougMa50Zmk9Vy03pBp5QU3lSc1K8+Tfr+r5C649Mk7/hbcvueKvheSDef7fx3zb4O7r1PIvhr8L/p06ApZF+5s2BbQRDKOhLZi2uvIKPsf4bspl9jGDoNvnoLRE09qaeUFH5FwUjHkBee5xzLtfhCtP6D6OOmn3PYHXYYNjtUXSWnK/iyGVK/+TyZR/6T5J2fRe0u7JyrHN9Kw97nyWiZEitMgO5EHwICrcHmsudYLosGk2xlTRVFxvFcaaEumP7FYEpX8uOsxpXohl6x2ZB9/DnhWSysKS0SPrflbHoSfRVlNDZB0Roo/M1mYe/0DPdENoaAQO0UTbJs0qISw31s4gT1vjqingjrrE7K+e4dNR7zuhYvUO1nsJzvfH4EK0h5ErlERalmrbeGWaF2os6xxwv+bi8g7S7LlSLiDqEaWkmtSDwXJ+gOICCQeewbpB/7BkaZRWDUHWGi6HfbvQ8CrgCN/nq+dMHnuGnhmwrmGo/k5vKOi4griZLt8+PZ7hf56pZv8NDxJ6ouSo/l4ngkN27JjVf2sKF5LR9Z/V4+uPKdBedQzHBvXNHMG9ZP3ZvhDx2vKuBOJBK88Y1vpLvbnABeeOEFrr/+eq688kq+9rWvOd/bt28fN954I1dddRWf+cxnUFVzxdTb28vtt9/O1VdfzUc+8hGSyVevTfMfMlJlipkqhd0MJL+ILV41w10qKbE7DVYjZylmajVdI6mkChhuMOUkxS4lAGFvqGIVfb6kRD2xnfST38HQJlfOjpd2GeBXiRlXO7eT2/WIee5O8V8mb/vpOzXaUa69ezxvQM2++Asyj30LbcTUwxlKFrXbrESv5LZRLcMddJfqgVNWiryamKrbpM3cqSe2k/rV35F58jtknv4emce/ida5Pc9W8OSkPFFPBFEQHTeDuJLAwCBcxHDro+YYYTuNGLqOkRrHryolWRHT7cKP1ms6A8htFTzMLZ/vcjKoYglW2B0irWam9NQ1j229q6IbfawHqWEuUuM8fJf9CYG3fxV54UZyW+/BtfV+BIRSSYmSJOQKktv3JKhZ/KpKQkk6YCHkCiK1LiVgyWHy77kj/ylaLORPQlLjfMIZ8xyLWTO7o6jau4/Ub/6exM8+gZ4aZ741qU/HEqeycXyahmvZZfje+Dd4zn8nSG6yz/3QAe96bAjvvmesTMDku2Y/f/50Et8bPorUtADXiitRDz5H5qVfkH70v8k8/2OMXLrs82ovro27v0jmme+T23YvWs9eBEFgfmTO9Ay3ksYbHwW3D7lj0jLUteh8EESH5Q67QySUZEHNgT3OyHuewMgm8Gy8HUF2k37gXwsWG+qhF2iJjQPQnShl/3vivTT46ypmpcIOU1t43+xnY7pGUSFXEEVXymqyuxI9yKJM3WAXhjr59znhDoKuwJSt6eO5yoTM+qazkAWpYhOVSsV7Tf4GBtLD04KwWC5G0BWYpqOsH0mQygJuwzA4FutkbtjMhFzQeg4hV7DANjbqiQJlALd17tXUa5TL3Jr1GgGMXJrU/V9xCpzzwyt78cneknf1yMRxfLK3YldWO2ziovi323OLPt6HkRrHmBhA6yq1Iox4IiUMd0pJY2A4eKSikUGZeak4xq1n+d6jD3HX4furAt1Vz4uyv4BEWzW/nqvPmeXIp07HeNUA944dO7jttts4fvw4AJlMhk9/+tN885vf5IEHHmD37t08/fTTAHzyk5/k7//+73n44YcxDIM77rgDgM9//vO8/e1v56GHHmLFihV885vffLVO9w8aiZkAbrmw26RhGFX520J5Scnky1UNQ14IvibBkwW4BfALWVTNKHEpgWkY7jxJiXLoBdRDL5DbclfpsfNBp816SR4MTSX95P+ij0+ydLndj5HdfBeGMXmO8aLtq5GjQHkWJa6YDLeeHMNIjoKuknniW+gTA6Tu/WfSD3wVrXt3WWu+jJolq+WqlJTYi4088KWmSxYLhqGj9u4rWKiAzQSUv+5JNYU3myb98NcRgrX43/xZArf+K4I/Sm7vk2azD0EqWChN2sP5rOMWDqKGrjqTuiRKjisDTF6/YoZbHzMZMsMG3KlxMHQCmk6yiHE09YUB1O694Akg1hUWJdkRsPSxxYu8eDmG27oPxbKj4rD35UvFwDCQGiYbVImBGryXfADX8ivQdj1MQJAKnjdbjhKQvSi7zPbO/lyWpJJkwma4PREEUSyxDIQ8DbanMvASm+bjMiAoukslJWoKn6KYmu9sCpQsyt4nqfXWEHGHqmCJk/g0HXnWKuTWpbiXX47n/NvBMFB7TMCmdu8ipJpgNZ8pdsY4XUCsNVkoz9o3IQRqUHY+hDZ8AmXvEyTv+gcCSCUZmbSaQTDALUj4rv5LBF+Y3O7HAJOhH0qPVMxQ2JI9z3g/rrnrEeRJwCv6o8izVqMcfA5DV4l4QibLXDRGAvg0Hf8Nf4d7xRvwv/XziA3zUKwFvaFrqD17ac6qiAhlGd/uRB9tFeQkMJlFK2bYbZY34p2aaXUK08u8693xXlo9NSiPfRPl4HOTv18QWVG3lL0jB8q6fOiGbi4SKwDugMvPqoblbBrYVrZro73gLB7nGv0N5LTctH7g1WQBRUGsqL0fzYwRy8WZFzHHiNZgM1+68O9pC7Y437EZ7OKMkP3MTutC5Q6QK7PQsWur1N59aD17ye18qOz2NZ4oY0XHPjpxnLnh2QXZVyOXRjnwbMGCKWKNBaWA2ySDNOu9xO0nt7u0pXzUkqTky5gms3hT4xH7ukw1ZiZySeq9tVzcfj6Pdz3DnYfucTDLf2//Lr8uY8do9yOYKpRjm3HveLiERAPTzeRLm/6jbPOsP3S8aoD7jjvu4HOf+xyNjWZh0s6dO5k9ezYdHR3Issz111/PQw89RE9PD5lMhrPOOguAG2+8kYceeghFUdi0aRNXXXVVwed/jOGwZlUwln7HNsvcJqvlUHW1KpcRr+QtAU/24ByqUsNtbmPZmBUNpu0T2/hc9DdkU0k03UBgUsMNJks8pYbbaqSij3WDIJDb/oApNeg/hPHiHciCVJBiT+d1f9PHelAPPV8g69BjQ6CkMZKjzvWJ5Qon0moBtyzKBF2Bgvbu8VySoDuINmg6C7jX34g+1kvyjk+jTwyA7EE58rJzjHzwZAOx4onIyCYxiljWckxCSkmVOJQoux8jfd+XST/wrxiZye8Gp/CjTilpfMkJpKaF+N/8WaTG+YjhBlxLLkbr2oWRGC5hHPO183o6RuJHH0XJKzzLPPE/pO79kvPvOt+kF7f9zBSnaW3A7TDc1n8DmlG28U3A5Ufr2YPcuhRBLD+MeYL1uHWdRNGkHs8lCKQTBTrcScA9tazEPhf/hOlzLeYBbjDZZs/G25FalxJUtIKMjP3s+8cHMdITSB2rCKgqiVyS8Yw5YYYtnW7QOp/CzIIlKfFV1vKKkWZw+4noYkmqN6Wk8WZTiPWzCbztS0izVqHsexJ0lbmROVUA7jQ+3UDI6xAqNc4Dlxetx5RXaF27CGnm5J1/Le1xJugOOvdLcPsI3PzPBN/7bYK3/Su+6/4GlCzegaMl40RaTeM1DFwti5FnrTafz84d6LEhR+e6a3hvWe20s0BUFKTmRSV/dy29BCMdQ9n/TNnnwMmk6QaCxTILsgfXoo3o471oY73oQ8cgl0IGmgVviTVgWk0zkhmlPQ/oFYeTRSsCTxO5OAGXf1obzUqMo2EYJuA2zIWGNnis4O8r65eSUtNlswRJJYWBMeX8cm7L2SSVFLvKeGFPxXDD9E4lE1UWltta5uKw9cNzLa1/uXBJLoKuQImsw35mpwWeFRyJbCJM6zPlOuqxzWW1/1FPpKDAOaWk6EsOMC/Y4tSEGJkEqfu/Qubp76Hsecz5rkMEFenXbdCq9e5FCDXgXnU1WvdutLHChWDUE0EztIJ51Z4rpsua28euNKfb1yDkDnHzwjdxWceFPNX9PL88eDf/vuVb7Bs96LSfz4/pGO7c3ifJPPYNAopi/dbCa9qT7EMUxGktlv8QMbUf2e8QX/ziFwv+PTg4SENDg/PvxsZGBgYGSj5vaGhgYGCAsbExgsEgsiwXfD7TqKubHoi+WtHQMP1AASDGzUmio7mRhsDU27RTD4ArYO5/MGGu4lpq66o6XsQbQpGyzneHMRnhwLGXqV92HsIUD6lDNLhVGhpCdCkmIzKrsZGG+hB1Si9eQcGtjOD1upAkgbqIC0F2IYgS4T4zXVtfHyxIg2u6RlbL0RCJUl/jJT7eT/js60gf3kr6gX8DizkJLu4oOHfjqEbA46ehIURyJEkK8GRHaWgIYega8YQJikL6GLOaLEcKn+ZsnzUytAYbqr5Ptf4oGSNFQ0MIVVNJq2mao7V4+rvISDJtl9/CmE8mufc5Gt/yCSZevpfUwVdoqv8oAbcfXVKcY40MmQNpR2Pj5O8xDLq+9Sm8HUtpuP6jAOQGOwnts+yXPJPnntEz1AZnOf9Wxgfp3vwb3I2zyA0eIXPvP+GbswplpIdwnZ+kkiz7OzN6Br+qEmifT33zZJME9fxr6dx2L67Ol4j6QuSEyesujtrPawPC0d0ks0nUV+6ged2F5AaOEz+6CQSR+lovguSiPdrIlr7dNDSE0CZMdmZei8n0NTSEMFSF+IT5bkuZMRoaQiSG0qQAv6aT0rLU1Qcctielpqh1uzGSo0SX3Ey4wv1LtLTi7zdQ9IRz7qqmmtmUE7tI7v0EodWXI4dqqUmZAF/wqVM+D+KE+cxHkkNI4XqaZpfXDA63ziXQP0BaTzn7S4yNA+Dv2Yu7cQ7R9VcQePIwqqHRP9GHrBvUN7RQ0xBiLFIPepfzrgEIwyaT09DYOuU5au2LiCr9xLVEwfeyRoaIquBvXUxjSx2p82+g/+dfwDe4g1Vti9i+fRdyUK8oXUjrWXy6Tn17K3J4cr/6nBXk+vZRX+vjeN9+Qlig1zt57sag+Q5Hw/VF5573/w1nk/Tq+J/6Jkl18twbGkLohxW8mk6wpZXahhDqBdfTuf1+5OPPsvaSt+Pb6eVn+3/NXYfv54alV3Ljsmuc3eZi1iJJ06mZNRtf0bUz6s+nb+/D5LbcRfvbPmle67xz77LWvz5BprF5crGjrr2Izud/gmdwF+g6KQTkcB3tusCBZG/BOLdvyBxnl7fNr3jvajRLUufKFnwnsz9FXaDGuRaVokOyxzi94HtDyRGSaopZmHOhON5Z8PcLomv5wd6fczh1hI2LzirYZ3bCBFNt9cX3bTIuqlvLzw/eyc6xXVy5fGPB37S+HKIgMqe1qYCtlQJzYTukxPiUvymhJphX10GNJ0fvjz6Df9HZ1F5yO6J7Us7W0BCiMVRLX3ygZF99XX14JDer5yycUpbSEKwlZRSOkfYzO7elGVkycYiha+i5DJJ3Eoy2Kw2wH1wBg4Y6c3td10kqKZqjtYgHn0MK1qAlxvAObCe87uqCYzdH69nS0+sce2vvcfO4nftIPnEHnvbF6OkExvggrro21N2P0HLJWxBlN0HFnK9VOVswj8SVBE2RKPqWZwkuOY/aC95I57Z7YftdeOabTh+BpRuZ1dgEh0DwqzTUmNufyJnvb31udMp7E9XMe6C5chW/lzEy1AdraGwM8+GG25A9Ao8cfga/y8f61lVs7t1JKOrCm9e1OKEmaYzUlN1n6sg24s/9EN/8tYRHjwLWda+f/G78QIymYD1NjZPjWLVz/KsdrxrgLg5d1wtAlmEYCIJQ8XP7v/lxMobmIyMJR95wKqOhIcTQUHU2Y/2j5mSfjekMpcxt9NQ46Qe/hu+Kj5isFZDd/BuSqjkA9gwP0yLFOREzgYqRlao6XkDyMxwbc77bPWSCUuHAZgYW7keqn1NxW93QERDoHxtlaChOlwUa9ZR5bFfMXD2PHj9CIjkXSTA48Y0/w73yKtyrryHsCaEZOp19gwVyCJvF07MiA4cPgqGTC3XguuRs9JfvQJ67juzLd+DXBYZj4865jyVieAQPQ0Nxcj2mdjo12M3QUBw9PgRWinTs+CFUT6N13YYY8pjbxzNJRN1V9X0KSAGG4ua1s1OhouIicXwfYt0shscysPRavEuvJQZobWvQdz1F/7aXCLuDDOVd985B676lZeczbawXdayfxMQwxlk3InpDpB/5EXRug/mN9I0MMxSyzj2bQNLMczcMg/SD38AAXFf8BXJilPTj3yS+93kwdGQlQjag09M/grtIPxrPJvHmcmTwFl0HD1LHKia2Popn+TJGkxPO34fGxwFIjqsoh83Wv2psmL6n70Ht3G5ubugMHDmCVNNGQAgxkYnR3T9C98igqW1+6reoWhzh3HebundDN2UCY0Pm/ew1Nd1+TUfHoKtvCL/Lb9UNpBHHTMY8HZlPtsL9U3UfAU1nND553W37raBmIM1aQ2zzg2DoGJIIc+vpGhpkjrvy89A/ah5X6DmGUDu74rOTk8MEFZXe5OTz2jVivi/esSGENTeR0P34LTa4e7yHoKaTNryoQ3HcchRyXfSNjTjbDw6aoE0Qw1M+s1rNHEKdRzieGC34XjyTZHY2S04yxyYjOBexppWRF+6l4fL3ArDp6B7WNK4s2adhGKR0BZ9mMJoWEfJkD1rDYtRDm+l78RFTgy2ZwKZ7eIghn3Xu42N4dBA8kanPnQgBTSelZukfGKe5KcrQUJyx2CheXSct2eOqG3nOWia2PY627Do+c/ZfcWj8KI+eeIonj7zIhQ0XOPvsslxb/LpBTAuQKHN8cf3b0H/zD2Q3mY1nuoYG6XCZ3+sbMcdnv+QpOnc3YtMCJnY/D7LbzHaEG2iOH+UVQeBwT49TFLe728yCBbXolL8/6ArQOzpU8J2h+KhTczDVtmrGnB97hoeYnfcM7xgynT7qrecnN9TFYN9IgbRmYXQem7p2cE3blQX7PDFmjVOZqeeXOaHZHB/rLvnOwPgIQVeAkeFCFtIwTHvJI4NdDEXL71c3dMYzMdyGl/4XH0KdGCK26QHiBzbjXnUNUsNcmhYvZXg0jRcfI6nxkuMfHjpBW7CFkYFxjMQoYrR8wWpQCjEQGy7YfmB8FJ/sZWzUzHAYukb6wX9HH+nEf/MXEX1mlk5LmwuJzsEBIropY4vnTLmlkBHI9h/FfdZ1GCe2M7rlMbKzzi84ts/wM5GN0zswhkuU2dq5D1EQaezvR4y2oiRi6Mlx08YSSN/3ZfqefxDXgnNJP/wfeAMivWOT5x6IyiiagjSRQs8kUWoXMJoSkRedT2rfU6QOmXr74Ud/iLJoPQDH+/sIqlEAeofN5z394P/SLzWZWawK4ZO99I+PVHw2xlMxWn0tzt+v77iWOqme+dG5DKSG2Ny7k92dR5ltNY1SdTPj59KK3zUz0pseRfCGkC75UwL3fQHI0Dk4SI0xSdp2j/dT56txtp8JFvtdQxSFKUneU+ZS0tzczNDQZPpoaGiIxsbGks+Hh4dpbGyktraWeDyOpmkF3/9jjEmLt8niBG3gCPrICdTOHc5nyoHncHeZmiw7vVyuzfVUESzqYGeng4KajpGaTEtlN9+FcnRTwbaiIOJ3+Rx5waTXZwhDV3HbbVbHe9B1g2Y5hpEaRxswjfntlGmxVVpasdxGZJ9TPCfWtCPVz8Z/3SdxL7sMwR8lqJdqWm0dsW6x2brFlOqxyWdKG+11ro+d4jQMw2Q60ykyL/2C3N4nUXv3mXrsIk2yHhtEnxgw27tbqTs7fRiU/WhDx5EaSyv4pbbl4AmgHHmZkCdYkLYr55Nqp+XRVdSDz6PHBlFPbMdtgFuQHV2p2QAo57iEqMe3oHXvxnP2TYjBOqTmhQTe/u8E3/XfuBZdgC9tbldsj6doCoqu4NdNsFsc7mWXYqRjBBSlRMNtu7tog0cR6+cgta8gu+lOtN59yAvNCUUfM9Pq9bZTSXqUiWzMLCTq3Uti19MYmYQpIQKkjpWmBCiXQk+MgstHQDRBgZ3mdNL7sRGEUD1CaHKgLQ7RX0NA0wvO3WlWJHvxXfZhgu/5FsH3fptI85KC+1IpEkoSn+RBjA0iTjERCcE6gppesD/7/gU0HTHSjBAwzw+gOzFIUNMdyYIcrMOn6cTzrMpSmXFcuoE7WPk3A4h1HURUjaSaImfJkwzDIKWk8Gk6gsWWCoKAa8WV6CMnaE2aRbg/P/BrpxtffmS0LAbgE90IRRZpklW0mt18FwgSwebFSAYlvz2gaQiB8q2mnXMPNzgFo/n2p+lsAp9mIAYnfatdyy+3fLQ3U+ONsqF5LavqlzGUHikqejT34zMEhArWelL9bFxLLsK7zyyeLCcpKSc/c81dhz7SiT5wBLljBWJNKy0xczGeLyvpifcRkP0OAK8UYXeoQLYGpqyimkZRvgkTKBVLG+z2101jI6akxtDRi2ziltUuYiA1VPL8O+PcNJLFem8to+mxEklPLM/JKT8EQTALJ6eQlCSVFLqhE3YHUQ48i9S6FN8b/wYQyD73Q1J3/QNd//MX6ON9RDxhUmq6pOh5JD1Gva+O3K5HSP767yraOdZ4oiV68uIGV9kXf47WswcjkyC3+TfO56Ey7l/2dfOnYmDoSM2LcC2+EH3omFNYb4ddtGlLwI5OHKcj2IorMYI8dx3+m/+Z4Lv+E7l1KVLLEsTG+eS230/q/n9F6ztAWNUL5DR2PYg/bhaqS23LAPBsfAeBW75E4B1fx3/TPyHP30Dg4EtAYYG10+dAMwqkgnboqXGnVqpcjwg7nPqyvGsoCiIXtJ1LS6DJKQjtzWvPPtlMrpSRNtQs6ontyHPXIUhyXlH85PENw2A4PeJ0Yz3d4pQB7tWrV3Ps2DFOnDiBpmncd999XHTRRbS1teHxeNiyxdTe/va3v+Wiiy7C5XKxfv16HnjAbO159913c9FFF52q0z2lkbT8OvMZfBs4akPHzX+nJjCSo3itl8gGTzZ4tQdE9fg21O7dFY8VdBWa9NtAxq/rGOnJly63+xGUIrsvc/uAc8xYLo5P9uKWXOjjAwiGOcmJ8X403aBdMtlAW58b9pbXuqXzPHb10W4QJcRoYXW26I/g17QCwJ1WM06hpRE3AbeRGsdQMo72TQg3mW4SokTA40C5FwAAVcZJREFUqmoGE0Doho574AjKzofIPvdD0vd9meRP/5LEjz5aoHVLW84dEU+YWC6OpmvObwhkUqDlygJuQZJxzVmLemIbIbe/4Nxjubjjk2qH2r0HIdyI2LQAZd9TVpGLCT5Cksc55mQDE/O3a507EDxBXMsumzy25ZcqBusJKKaMo1KnTZ9WHnBL7StAcuHPZsped0PX0YaPIzXOw7PhZtAUhGAd3vNuA3Cs1uqswW/PyH4mcqYHt5GOg6Gjdu5AH+0BQXLcRvT4CEZyFDFYW+LwYp+Hb6wPuW3ZlFkvIRDFrxsk84pqnIWOBWAElwfB5cUVbiKgGVUBbnsRIBXpt/NDDDUQ1HSyuuKAXqdWQ9MRQg0I/ggBK/uWUDMm4PabgEwMRAloOvH0uLPPVDaOT9cRglODVinaSlg1gY89kWa0LDoGPl1HDEyCTtfCjQjeEPquh/n42j+hwVfPD/f+gruO3F+wz5TTIKnUmUaMtiAEajCSo0hN85EjzeZiI79oMhs3FxqByvpzAEF2O5rZ4kWeVzcQg/WTv7NlMUKwDuXIZGvrpkAjuqEzlOf77lx3b7Si3h/Avf4tuHQNnyAXNKBxWtqX0fLKc9Zb/2cgta9EjLbSkDPvd/459KcGaAk2TZuljXgKbQl1w1y0RabRMeuJEdR7vognb2Fux0BqiIjkw2MYuJZfAUzOK3bYAKW4cNDe13SFg/W+WlRDK9FRmwXK5c+9cRrAbb+LwWQcIz6Ea/GFyK1LCbztSwRu/QreSz+Enk2R/O0/EUyb9zh/saLqKuPZCeq8tWgDh0BT0QaPOn/XxnowrMK6Gk+kpPlNIpd0aptye59A2fMYrpVX4VpxBcq+p9GGT2CoObwDx83vF8yrebUegoDUtMC0oxQlsq/cUVD4aNs9jmUmUHWVE7Eu5vmawNARw43mWG5JPQVBwLPmjRiJEfSRTsTG+YQUpaBeY8KqBwmM9SHWtjtMvCDJiNFmRH8UqbYd78XvJyh5EKDAqSSppJAMcBsG6tFNBYtvQzOLrpO//ixq184SL+z8yGhZNEOrqIFv8NUhizJ9yUmjA8c+9KVfkXnm+xh5hbhq5w5Qs8jzzSZEIb+dTSicV3O6QoNvcpw4neKUAW6Px8OXvvQlPvaxj3Httdcyb948rr7a1DJ99atf5V/+5V+4+uqrSaVSvOtd7wLgc5/7HHfccQfXXnstmzdv5uMf//ipOt1TGrbrQn4YMfMh1IePF/xXUrN4JY8ziTgMtQVMMi//ssTbNj9C7sI2tEkliRsBlwF62nzpDDUHubTDNgMYSgb1xPaC9u75XSb1UXPV3qtGcSX60XWDNsmccPTYIIamOAx3ScfHPHs/bbQbMdpawqIJvgiBXCnTarNOemIYBDOVrU8MYMQGQZSR25ejj/diGEbBatwBENkM8uw1BG77Kr5r/xrPubdCNonWZS5aDF1FHz6OHh+mJdCEbuj0JQcmGYwJc8IoB7gBc3BQMviKQGssGyfkDjq6RkNX0fr2I7ctw73kYvSJfpQ9TzjsYRA579ztduzmQKYNHkFsml8WTAihuknGsMj5welWqRtlAbcgSog1bfgzSVJq2mENU6rdAKUHlAxS43yk+tl4L/kAviv+DMEbRAjUOoB7drid5XVLuPvIAxwcO2L6DVuLO/XENvSxHnMiiJiLLCMxgp4YMVliazKyr51zztkMYkNlhhlAkFwEBJmkPjm5OcWq3kLgJ4YbCKoasTyAWy6SSgq/ReJJDXMqfk8M1TvstT0RJ3IJRDCBY6geQZQL3vugpiPaRXkW+53IA19JJYlPMxxQXvF3hxuJWPfcBtzOM6MZBSyzILtxrbgCrXMHTTmVT6z7U5bVLWb7YOGifdKZpnTyFATBeU6l9hUI3hAhVStoEpXIxvBr+rQMN0DIKgrNL9TNqFm8RYsNQRBxzT8HrXuP0yreLsbrT002VbGfmWBgatZL9EcRPEHChljCcLsNkDylWUQx3IBYPxtcPqTGeYg1bQR0A48gFzR7GkqPVAUC8rNoYIIJA8OxDKwU2tBxMAyCSCUAaDA1TIMhgyghzz4LwRdGs+YTO6KVvKir7KZrA/b83wxWn4YKYL3J38BYZtxZkBaHDd4DvQdNO8e55uJGEATEcCOuhRtpe8+/IHpDeLc/WLANmADWwKDWGzWLWsHJthrZJKnffI7M49/GMAyn+U1+x0eb4c5uu4/scz9C6liF55y34Vn3ZgRvkMzj3yL580+iP/KfuIsciZz5YbgHsW4WgtuH6A3h2Xg7WtduUvd/xSlsr8nz4u6K96LoKnNE81kTwqVZfWnWatxnvRHf1X+Ja9H5hBWNibyFkl2A7R/uRmpdVvba2tfRFWkmZBQWWCeVFH7DHP+NxIhz7QBy2+5DH+9D8EdJP/yfBFWtIuC252r35nvKZhZEQaTJ30BfHsPtLLISEyj7nyH94NcwrHlLPfIKgi+C1Gx2CHXbRfF5v91e5L5uGe4nnniC9vZ2AM477zzuueceHn74YT796U87q/0lS5Zw55138tBDD/Fv//ZvuN0mg9TW1saPf/xjHnjgAb73ve8RiUw90bxWw247nB/6hKWPHu/HyKULGAm/5HHSrUklhSxIpjWermLEhtDHejD08l3jQq5ggYVRPJckoJv3wbABt9X9zUhPOBOZsvcJ0g9/nYDoKnApmQTc3RiCyA5lFq7sOIKaoU20Bl9DR5/od+ydiiUl+TZz+mg3Ym17yXkL/gj+XJaMlnE6W6UtaQOAHh9GajJBrx4bQI8NIoTqzX0pGYzECCF30JGU2GlmbyaF4I8ihuqR21fgXnU1QqAWzRpk9LE+0FSM1AQdVtOIrniPw/z4R3oQvCGEUPnJVGpdYraSTiVJKkmHLSiuxNYHj5ngtW058vwN4PaBoeFZ/xYzTW8IzjFtP+aA7MfIJtHHeisCfjFY5wC/qRhusYLlmFjbgT9hDmj2M2c3KbLZIlvj51p0gfP/Yk0rutWJUBREPrTyXaxvOgtFVwi7AqBkQJRQu3ahDZ9ArGlDsOQCemLE1FsGagna1n72AtNmjjTDAehTRUDykkF3LMuclKW/cEAWwo2ENJ2Y5RdeKRJKkoCiIESaEdxTgBBPgKAhFRwzriTxIyH5o45+Npjn+BHUDPCY+xQDtSUOLWk1gx+xZDFaHIIoUWMzltnCe+fTJyUldriXXQ6ym9zOBxEFkfmROYxkRgtstdJ2050KFm3yLLPdtwnoQibDnSmcxANa6bHLRTBgvkv540TaUPCJrgLdMVgLWkNDteRvdeNmpiu/i2FKSSEYBr7Q9M+LEG4gWJTpMN1ZqHi/vee/E++lHzQXqJFGBEGiTnA74DOjZojnEjRY92QqL2K7AZMtzbAXLdNJSmxCJmiU2mAOpoaoz2YR62YjyG7E+jnoRQy3022xjKNP0BWYtjmYDXCG8hr+2MV7lQB3a7AFA4MtgzvK/t2+B/6ufbgWnFdy7wFcNc14zr2VcNZ8VvMXK7YVaR0uZ26zAbfasxc0FfXENtRDLzigt1ha4RvrI7fpTuQF5+K78mMIoojgCeDZcDP6RL85v0iytdBJFmwL4B3qLHDGcS+7DO8Vf4o+fJzUPf+Mnhp3Fjvj2QnHKWaOas7JYhnALQging03IbevQAw3EtI0JpTJRlO2pCSYyyE1Vs7CmftvIqxqRQx3Er+qIc/bAILkyEq0sR5y2+9DXnAugRv/AbFuFr6+QxWzgs5YnU6Q2/N42e+0BJroS+QD7kmJq3v1tWi9+0nd9QW0gcOonTuQ5613iCUxUENQM4jnjdn2O9fgm35h/4eIM50mT2EMpoZ55MSTJQOuyXAXAe7YgKXnNNCGT5gA0FqgBAQ5T1JiNiUQBMGUVRgaaApGrLyji93AwGaZk0rSSWvbrKOemhx0bJZbGzCLfgKGWBZwa6NdCJEWelTzQQ/mhmkRRpzBRh/rJewpPLYd6TzbLSM5iljbRnEIvjCBXNY5Z93QyahZU9qQTUIu7WjV9AkTcIvhRsSaNuv4PQTdk3Iax1vXAtz5ITXMRRu2ALfNBBka9XjwSG464z3EcwlkUcY1eAyxcX7FVLEgyoi1s/AmYyhWxzooTbWaPsaCaXMne3CfdR3y/HOQmhYg+CMENd25bk7HQZfPWRhUAtxCHuAu1nDb/zbtzsoDKam2HX+msF4gbXmA64NHwe1HKAN8xWiL2XTBAg6yKPPuZbdy44I3ckGtyYYGFp0Nata554IvDKKMPtGPkYkjBGsJWhKCpMXa2NmVgK4jhqsA3E6HUvN+xzJjuHUdX5H2Www3ElJ1Yso0kpJcEn8ui1RT2U8ZTPYobC1ibMCdsN41Me/Yfn8tgjUcBEUPggVshECNec/zGzXpJuisJqIR037Olgg4z7shIngL77XgDeJacjHqoZfQxntp8ZvXNZ95cpruVFiYyXPXE7j1K0h1sxC8YfNa5tsCamkCmjGtpAQgZN3XRMqcSHVDJ4NetsGVWDcLMdqCeuRl1O49GA//NxG90G4umY3h0w3kMuClZH+hBkKKQiwPgKTUtCm7qgC4paYFuOasBaz3PdJEnWo43Sbt/9b76lAOPk/yp3+JPlG+q2fEE3a8ryHPt34aSYndVCWg6gUL64SSJKmmqItNOISE1DAHfXxSTgGmVFASpFLAXabHg5FLo/YfLJjHapwGV5MMd0bLoOpqiaQku/0BUvf/KyvrlrIgOpc7Dtzl6Mzzw5bWhHJZs0FRhRBr2hwJVT7DbQPuSNLcj1g/G23wiCmF69oJbj9S00IyL/yEiGaO3zbDbRgGiVwC30gPrqWX4L30wwUOXq4lFxF813/jv+6TpmxPL5KU2Ay3kkNqWVxwvq55Z+O75q/REyOk7/sy7pxJYIxlJjgycZx6by3BZAwkd8ncVPLbQw2EVR3N0J1FtenbbtWKRCvbUAKIkSbC2VyBLWEiF8evaUj1c5Dal6Me3YTaf4jM498ClxfPeW9H8ARwr76GoKIWZD/zI5FXs6LsfrRARgOgdu+mSZcYy447ja1s3/aQbtrs+q77pNk46Lf/BJriyEkAhECtKbvLu+dD6RFERMKDpa3sT4c4A7hPUQykhvj61m/x2yMPFkxkUAq4DTWLkRxDnnc2APrwMfTh40hNCwHwIRYUTdo64PxBXLMkHkYuRXbbfY4WqriJSsJazUIpww0Wc20Yjtd0wCpcHM9OMJIeo9ZKzesjXUh1HfRrUQCaU4fwCNYLIgjoY73IkoxP9pYWTVovm9su8ijDcIu+iCONSOSSZNQsBgZ+2Ytu6bfFmjYEfzQPcDcgOYC7t0BvVsD4FaXoxYY5GBMDGNlkQepVSE/QHmwzGe5cgpDLjzExMGUVN5gFWR6roMkGrcUMt9azB7FhDoKlc/ec9UZ8l3/EPK4/QkBViVsLDUeT6vJb90WoyGQI3hBewYVAacdFJ7NgiFABTIh1HXnSiKSznc1wS43zHJBYsF20FdQcRmKS8RIFkctnXUS7aB4rsOwCsLqYiTVtCIKIEKxF6zddFcRgHb5AA5JhEE8NF5yDHwkhEC17zvkRsgCiU+ibGiVYJKsAC2hpGjE1MyUDmVSS+LPpsqne0mOb74bN2iRyCQKKVpANkQK1+K1sVChvDBBkNwEkUobisJ1pQ8WfV1g9Vfii7fg0nTELdDi6f3eo7OLQvfIqECB1x6eJPGw2GMvXVqYs4B7wlgfMdpofQPCFCGk6cTWNbujktByK1cRoOgABEIya72wiaYLmjDU++Ms09xIEAXnBuWh9B0g/+l9gaDRklAIAl0yPm3KWClmo/BDDjQQz6YLW9GaxqYbgqa6zq1jTSk0mzXBmFN3QJ1k3fx1q53aM1Djph75eNs1uN7+xNeQ2Y1vcmTU/DMNwiAGzwHkS+Nnt0xuyWaSmBQBI9XPBMNCHJ0GJKIhEPeGSBix2N10jk0A58Cyph75G4kcfI33PP6Memyyql0SJWk+U4czk++54cOcVXGrDJ8htuhOtZw9COsZ7l78dt+Tme7t/Sq6occxELoYXCbchINZV9tEWQnX4RRcSQsEibyQ9ZjbFGRsEQTRrXJQM+mgXatcu5PbleC/9IOgavh1mjw97wZHVcqiGRkAzkOeeXfadscdqIVRPQNUKrntcSRIQXEjgXPf8kFuX4Lvmr9CTY6Tv+zJRT4Sx7DhHx48zLzoHwyKMptP8C8E6Qta8aC9QxjNx/IKMBIiRKgC3phVo95PZuPm+hOtxzTsbIzFC+p4vYqRj+C75oKMJt+tUzN9bKitx6sNEF0Ym7nRzBTOLmX7kP6k/sg2YLOyN5eJ4DQF3qBFBkpFbl+C/6R+ROlYh1nUUXEvRlt3lHXs4PUJU8qA88l+OFOV0ijOA+xTEQGqI/9j6bacde37bX8MwSKqFgNsu+JOaFiAE61BPmIO0PNvyz9Qn2cn8KmB93J5kBIeZVg4+bw5wvab5frDIpN9OkQOOS4kDuCUZfaQLIznqfBbQVHRD594jD2NgcEHbOWazluQoUl0H44TQBIk56b2TvyHc6BROlmuDm1bSCAi4rMpnuxNdfgj+SAHwc1hxl99xKBFD9YiRJvSBw6BkzAHLG0TwRdDGegi7g6TUNKquThZCaYajm7XDLobTho6bzJHlEWokx5gVaqM70Wu6bQgm4zGVlSKYAN6fMUFDUjVlJfkd1PTxfrSBI8ht5fV2gi9CMGcWeabVTCHgHjiCWNNakX0TBAE5VIffEEu18/aA6A5WHNjF2vYSwJ1W0/hEF/pYd8XFhs2s2LKS/LAzKXK4HrnDtKCTasxFlhiscxwUhGAtYqgev6aTtLTVSSWFyxDwhprKAv3iCHijAI7bRzwbM7XSRYWHgttHSHSjojvvaXHktBw5XSGgqFWx66GgCUAT+YA7ly1IE9ssDWBKbfK3l33oTILllGCU1VCXCzHaQkTVGU+YgMt5ZioAXjFUj//Nf4/nvLdTF2zEpRv05rVHTlnXP1CFBlvwhglpOgamJMZxPZA8CNL0TrTuSCseTSdueaM77Ly3vKTQNf9c87guL+4NN9OQUxhIDjqAOZGN4deNsun5knMPNxBWVUt2l7WOn5qS4S4OsaaV2mQcVVeZyMYcXWmDr856X9vRY0OkH/smub1PkN1+n0MaTLbqNt8RG0RN1W3RSI2bZInsIZjNOAtzgAFroVqvaJNyL6v2oFjHHfFESpolJXIJ/MkJEj/+czJPfw99tBvX8svNDqEHJjtWakPHqJP9DpsPk3OM69kfk3ro6+jJMTJPfx8sSYA2eJSoJ8J7lt1Gb7Kf53peKjj2RC5OCBEhUDPlcyMIIlK0lZAhlDDcNZ4IjJxArG1zxldl35PmfNqxytSCLzgXuvcQcgWd5jeTTh16RStBO8RgHcFctqTJVcAQEDxBx3moOOSWxXgvfDf6eB9RXBydOE5cSTA/MschjKYLQZKJWOOG/dsnMjGChoAQrENwTb1AFyPNhFWdlJYpKO4OaAZiqBF57jqkWavxnHMLgVv/FXn2WZPbhupNGRzl27vb4164bq7prLLzQQyLCc+++HNQczSMmeOTTULGcwmCuoGUx8yLvjD+a/4K/41fKBjznSygOpkFHEqNUG/IIMrOvH06xRnA/SqHDbZ1w+Cv1v4psiDRkzeR5XQFVVcJyF4n5WI7lIiRZqT6OU6nKrFpAYIvjF/VCoom7ZSfPtFntrqOtqBb1kN2a1d91AQxTvtfi2lN5JIENJvhNgdbIzUBgoTUOB9ttLugstufM5nyl/u3cHbTGup9dWgWuJdqO3C7XSTkOgJ6DBURsaYNKTqp5w26A2U03Bn8sg9jrAdcvrKFVQWAO5cgZbNestdxKBFC9YjhJofptydYsbbNkZSAOZhOdo8rZbgdwD14BH24E7l9hXkNU+N0hNpQdIXjsU6ClkxerC/fWtzZX/1sAhaLmcylzBScoRF2h1A7d5K8+/MIbh9yhbSp6I8SyJjnG88lSCkpREHEI3rQBo9UlJM41y5YR1A3SrpNppQUogE+7xS+ob6ws0hLKkmnSZEvmzVbm1cC3JbkQh/rK/mbXZwrBSO4Vl6JPP8chzEWgrVggQUxWIcYrMWvGSQs5sqRZUSqswgNWXrghAU840qCoFq+eC/kmrrb5GSRqV6VftwXbsSt68RssJ9LmBN4HtMqBmscL+5wkWWc09k1l0TNJMiJAv5p3CKc/da0EVE1xjJm1sheYAYq2OKB+Zy6V16Jd8WVNOVUescnO08mMxOIhoG3SPteLgRfiKCV4o9l45PWpdWee7iRgG6QsGREKQt4+/P07gXfjzThvfwj+N/4KeRZq2lUNDJ6zrmPNmCuBsCIoQZCtjzB3l5N4dN1R18/7T6irdTmzDF1OD3KUNr0ovZkMxjJUVxLLsRz4bvQevaQfe5H5F6502n7HSnqdDmeixFw+XFNodvXLTmJ3L6cgKI4C3MwGW4JgRpDdmokBH8UIdRAbtt9TuYSTFlIqaQkQSA+hljbhv8t/0Dgtq/iPe82XAvPR+vehZ4ax8ilSD/wb0RH+guKJp0CuFQcrXM7yV98En3kBN6LPwCihG4de2ndIloCTSWdKmPZOCHNKHhfKl7zmlZCSqFLykh6jDpvLfrQcaT6uQjBegR/FGW/ybRK9mK/eTFkk0Rkn1M06rQ3N8Rp6w6EYB2BXJZELjG5yFOSBFTNlMpNwVLLHasAgXBOccaXeZHZ6LGhqrJoABHrnRy3siHjmRghRZtWTgIWw50nxzEMg6Sew2+NU4Lbj//qv8S9+tpS8O4JEBTM57Is4FaSyIaB1xs2nVXiw6Tu/Rdyux9DPbYZec46ahUNlyA6gHsiGyOYU5z5Iz9K+rLIboLIJAzFue7D6RHqNMNcbFRByJzqOP3O6I8o8sH2n6/5EO2hVlqCzQX+rDbL6D62g9R9XwYmCybFSKPDRiAIpj4yUEtAUUipacYy44UM98QAYqQZsbYdbbQbQ9dQ+/YDON6fTvtfJUFOU8jpOTPVG6zDyCTMTlqpcQR/GLFuFvpYj1loIskIoXr82bRz7lfNMW3obIcSsbYdr1tiXLYq1qkzrYhq2tDHBzA0tSzDnVJTpiXgWE/FAUrw5TPcKaeIyyf70OMjIHtMNiEPCNkDlljTZklKzAkznkuY1keIuAxKALfgCSCEm1APvQBazioIEzCSY3SEzHR3RssSyOUQfGHE6XR2Ne2mbANzELInokAyRvqhr5ns4o2fQ4qW1wUL/ggBy/YqnkuQtN1ZYoOQTSI2TQ24xWA9flUtSL0BJNU0XoMShr84ghb7nMglnYnckxw3911JO+4NgSdQgeG2AHcgity8CN/lH5kshMmzfRMCNZNuHXkZHZ+qVj0ZBS3P6njSXJTFtUL7vfyIWGA035YtPyYLNvXq2NKgyQDFLF/olJYhoBsF557PcIeKWrYHPZMOLYmYuXDxV2B5i8NMFeuMO4W2SWTdwBOYHrxIbctMwJ1feJiLm+4qVch4BE+woL27fd2CU7C0BdvLbqtWxKpZsOpRAlOAfdf8c0ynm2gzDYo5+Q5YTiUpLWtKkKpgqMXwZJrcfg5Sasasc6iW4a5tp06xAfcIQ5YvsA1upcb5uJdcTOD2rxG4/WtITQsdttlp1W0Bx1g27shMKoWp3xaQZq12zt0eYwdTQ9TpIq5o82R9gCDgu+rjILtJ3fsvTlGcKWuYcMCLqqtmoW4midS8CKlhjjM2uxadD4aBeugFcjsexMgmqEnESChJRwLkeHhrOt5LP4RYOwt50YW4FpyLWDerAOyvqFvK4YljTuYSzILRUC5XnRSoppVwLlvA0I9mRqmV/RiZuCnXs+z5MDRT+2+N21KzKdWM6oIjrZgsrq6ZFrjZjkSqoZHRJn97IJcxpXVThOAJINbPIpI0j+uTfTTiAS1X1aIeIGpl0mL5DHc2UxXgFjwBIqIJpMezE2S0DDoGAdGN4C6tmSjYVhAI2RnECoDbrxmI3jDy7DV4L/0Qxng/2Rd+ghBpxnvZhxBdXhoN9yTDnRknpGnTXjc7grIXDYOMliGlpEiqKWozOcTQ69Sl5PUcP9j9UwdstwbNtFRbsKWA4baBhDcxhj54xCwYi/Wbzhduv8O2itE2BJcHMVDD2kQWSZT49aF7yWhZh4HUJ/odwG3Eh9B690Mu7UhDADySG5foskBnXtqspg0wMDJxjNS46dxR2w5qFvXoJsS62YihBvxpc5t1TasdCy59pAs8AYRADR63zJhgsoeDgvl3saYVDA1lrN9iuIuLJjOmQ8l4X2XQ6Qvh17G0yJMMt0/2YSSGzdW4IBQMUnZxmlTbAWqWQNbcJpZLmJaCgoQA5T2oG+ZOMuWN8xB8IYzUGM2BRlxW4Vogk0Ksm5rdBjPtF7W05Akl6UzkgcETILvwv+kzBYV0Jdv7I0Qsnf3xWCcpJWUWTOZN4FMeP1hbou0Ec7Hn1w2ECoVwdrhrO/DoupVZMJ9Xz9iAOWl5y4MoQRCszEYpw22kY+DyIZZxHbBt3wRfGEFymdZ5gkzSSu8nMzGncUw1EbScKZLpcVP/bqiERA9CmTbPkYA1ceVZg+VHooD1ml5aYaZcdeKZCadmIFjMcAdqzPStYRDwF+7TBuDxzATJuDkhVWJ5i0OQ3UREHwkrg2YWDlbnEiL6o7RIAeJGzmGnU7mkKauYxpIQQBBFwpI5WRcA7hk4BwQk9+Q9T5qsqW+ahj/mseVJa0BLA540zAxiNSEE6ghbBXS2535OV2YsKalxBRANGM6MMpweceQkiLJpI4h578VADWLjPPThExi6hktyEZD9Drs+kYuZNpq5FNmB46g9e9GThU46+vBxy1azOc+RyLzmA6kh6nNKCYCRatvwv+XvEWtnkXn2hxi6TtQbQdEVJ/vn3LdcrkRCJUZbTJnA3ifJ7XoYwRem1vIft2Ul8VzcKt4zkOesI/CWv8d3yfvN4zfORxs67kgMVtQvRTd09o0eco4xkY0TymaqYrglq3DSBtw5TWEiF6fGYm/tedTWANtSNsDyxI8SzqQZy447DVsAgsFqFtZ1JdKKRC5OQFGcov0pz711KaEJkxCYF5kNcfO5rWZRD+AJN+PVdMYtt46JTIygqlYFuAGi1rgzkZ2YlJ5VmY2KOI5CZQB3Nm42u7IK8l0LN+K/5Z9xrboa32UfRpA9SC2Lacxk6E30oRu6WaSrTl/saYedNYvnEo50qy6VKGiQdTrFGcD9KoVhGPQlBzi3Zb0DtgHag63ElYRTFGODXr+ln1aObUafGHSYWlsfbDPdQqCGaHycK2ZdzLahXYCZejaUDEZyDDHajFRnaqBzux8BwLVgo+lFrdmFk8GCidCv6U6hopGOYaQmTL9L+7PkqFkcF6ilOTbBBa3n8KZ5poe6kUmgHH0FuWWJacrvkhjCnNSHxDzADeSGuwi5QyRyyYICGVMT7MFIx8o6XoA5kUreID4k4kpyUoNtFU3aLIgNuIVAjWMjJc0y03a1A53IoszWgR1mmhnRXCjkVZ/b4Xgsy27EiNnYQ0+OIwoi7UHz9wQSMaT6ysU8+RFtmodgGCRykwy3f+gEUvMihGm0ZoI/Qr2isSTYwSMnnmQoPUxAtgomXd5p2QCTgTEchw87kkoKn6qVXXDkh1TbQVDVGUsMTjLc4/2OK0zF49a0lAfcqYmKx7QHSiFvwAxIHpKGOZknclYDlSonI3eowVwsZE32zaCwODE/ImHzOk7EyztI2Ncv6I1M2UDFDrOYSieeSzrANaAXdjsU/FFWJzJcMpZCKmKPHXu85DBJi6EPVsFQ21HjjWAIJnBJZWIlHtxTRWvUfK574qZczGR5TUasmrCLs2O5uCMNmc4HOz+Csp+kYdl/WgAuUIVuHiAa7cCtGwykBk0JlGDgr7ITryCKTge7WC6eJz0zqi6aFAQRd/tKoppOf2KAscw4Db469MEjiPWzSsYbqWEOaIrTmTXsCU3qcbMxwq4AiZ9+gp7vfoL0/V8h+Yu/IbfjAacQXhs+gVg/27TRtAF3LmE2AEqNUJ9OlwUwojeEe9XVZt+BwSMF9nT2PsBaJJZ531yLLsCID4Gm4b3ofdRZpIBdOBnLJQggIQVqS+QIUtN8ULNObc/c8Cz8so/dlqwko2bI6TlCql6Q9aoUYk0bIU0npWVRNMWRUtWkU2YjNWsukzpWguR2DAnAIgeaF9EQM90yxrMTjv99eJqiQyj23LcK21XLlaeM41ZxyK1LiFqLlXkRs2ASqgfcYriBsKYzkRomo2bJasqMQGvU6vg4notNetZXmUnzBhuQdaNsVtAE3EaBK5LoC+M991ZnASS3LmHx+AQTuThPdT9PSs+Zz1uV5x7KywLacqbaZByhimfmDxFnAPerFPFsAtXQSlr5tgXNB6nHKpx0rNms9tvq0c3osQGHURC8QTznvA33CrNDmBCohWySN7RudPYdcgUKdN920aHWucOs7G1bBrrmpPfnRWaza3ivU8Ee1AxnJW4C7nFEf9T6zGR7pMb5iIEapNQ4ty56C3UWW5Xb8QDkMrjXvRkAr1vimNHCMXk+x2WbnW8BBJShbhZE52JgsD+PyUipGbyWMcRUzKXgi1BjiOwfOchg2gQgpqRk2BmUHd123mAl+qNITQvwntjJRW3n8XL/FjrjPWYhVQU5hWhnFupmmd6r/hoMy6ZsVti8VgFNm7J6Pj98LfPxawbxvPbJwbGBioWSBefijyIAN9SsJK1mzHN3+VF79phFqdOAP7vNeEJNFziVpJQkfk1zqs4rHr+ug/lphX2xY4xa7K9PVac9d7Gm3axOP7614HMjE694TOc+5gHDsOwnhcFENmbKabTqNNRgFtIFNBhT4nltg8uzN/5oK5JhMJEs3/3OzkYFq2BawZTVhHQY1VL86uBvzW09oYL7JchuFhherhxNlhRXhUO2Pd4IKevZ8weqOzZAjcXYj6VHSeYSJV0mp4q2llUA9PTvASCtZfEJYtW6SK83jMcwrVBHE/0IhkGgCrbQjoAnRFIU0DNJUpYEye+vbrEg13fQmFPpj/dN2khWCSAAgqEGpzW9LfmbCcMNJoNam1M5OHYIA4N6bw3a0PGy2Sip3hxr7AYjdvMbu8tkWDNASRO94CZ8134SuX052ZfvIHXn35Pb/ahJiNTPRghEC5jW0cw4qqFSP4WeV25fDoKE1rl9svlNXs0BmIC7nITLNX8DuLy4ll+G1LGKWst33gY+8VzCnFvKFB3a18G2m5VEiWV1i9k7csD53QChKt1lhGA9YaufxEQuzrAFuKPjw4i1Hc4iR6ppI/i+/ykpdJdaFtEWMxcanfFu4okhJMPAF5keMAv+wuueVFIY2AWX00sjpObFtOZ05kpB1jSsME0TLMemasKuO5jITBQukqoErf5IC25dZzw9SjxrbV/l4liyJFjxMlnBhGLWrBTbkBZs37qU1Yksiz0N3H3Y7CoeknzTylnsCFk4JJ6ecBjuWkU7Iyl5vcWIVdVvNxQwNMX0uLUM7W1ZieOprBmI0Vb04eMmU50HKNyrr5lkuq0J051JcuOCNwKmv2s+4BaCdWC1/ZbaljuyB1tWclF3L1ktxwPHH7eOrTsMgJEYNT2Q/REElxfBKjSSGuc7BW2GxVjpqXFyux9DXnCOw6p73RIxxcVD3utIS+aLJsgehFA9me4DLAjPwid72TG8x/l9aSWNzyrcnApICf4IN6RcTORiPHriKQQEPJoGuZSTdhRkD2JNW4nUQ567Dn2kkytqV+KWXIxkRqdMkUv1c8zCURt4B6IYlm65I2gOwkFNR6pCUgLgbplPRNV4fmgnDxx7FBciHt1wOvRNFTYQa9YENrZuAMBvCBgTA07DkalCDNWzMpHBwOD5nsk22Klc0vLgngZwR1tYF8+SMzSe7zW39xmC0/GrUrgWX4DYMJfMY98oAN1GujLD7UhK8iab9f52BAwePPaoaY1nCFVJI+xYqrvZo8U4PmEVDlewtpMjTQSmaH6TUBImcAxVJ2cRBIE6yUsWnZHMKJdkZOZ4S8GDzToXL/5cwXrTrSM9TmLkOAABT3WpXoCWmtkIhsFdh+5hXEmYXSqrnMRr21fj03R6R82C6ZSu4Beq8wAHUxIU1eDFvk08ObiVgGYgVXlsMCdSRRSID3WStsCXt0pLRLF2Fg05jd5EH4NjJogN+Kp/XqRQI0FNZzQzVsBwV1s0CSC1L6dW0UhZspg6RQMtVxZwC5FGcPkcHbfd3j2ppNANnWDOzAaGVl+O3L4c31V/gffKj4Eokn3hp+ZvrpttdlZ1Tdq+OpaAOa1sERqYGQupZRHqiR1O8xebXbfBW6BC4aLgCRB425fwnHsrgigSiLbjMwRG0jbDHTflKGVIFCHUgOANFRTkr6hbSlxJ0BnvdjLAIVWrSlIiiCIR672eyMYYtT24BztLiIFyNUJS82JacioiAp2xbhLpEQKaXuCWUfnYEkHPpOe+s7AX3NOOrWA5JNXN5iPjIk2BRrNpW7Bu2gZXdojhRsKqzkguxiv9W6xjyxXdUYpDirbQmNPYPbSXibiJSwKh6hbHgmUNWG7MTKhpC3BPUZRfOwvB7ecmJYRkyfwiVS6sYbIofiIxwN6Rg0RkP26jMEN6OsUZwP0qxYjFSNV4ImQ3/ZrE9z9E6o5Pw11fICp6HGtAx/lA03GteIOzfSXgaU+YenKUdU2r+efzP0t7qNV0KAGr05ngyEHk1mXmviQX2kgn2vAJGk/sZpkiOt6XASSngt92HLH9cqW6WaaeNlTvgH3bWzm39R7QNTzrb3TOz+OWyOY0dN1AEicHNnn2WaSPbiP7q8+yzNPI7uF9k/7CahqvogDClGk0wRdmdiLJ+5bfDhh4dQPV6mAl5K1o/Td8Bs+Gmwu2leesA8DXs4/LOy4y/19RKw5KgsuD743/D/ea681/B2pMfbumsLJhGWeLNcxWBHOyrCLcjbN4d3+CN7k7aA+1sgI/oieIWFdqgVhyLtaiwEhN8MZ5V+KTvUQs15JqALfgj9KsGCyUwjzd/YLTdTGlZsxFR4WmN872kos5oVYaVIN9owcBCNTNntZySnD7zcYQ9bPJPPoNpzmHkYpVBtyyG8+F78G97DLns6ZZ61kby/B8r1ncFXQHZlSBfkXz2QgY3HfEbP8cqcDeCL4IoaIug/mRSI/j0w1cVerHAS6S6vlk3Mfnz/sUVw9N4Ckji7D9xEv84AM1hDWdI2OHiSfMNLPfVR3zA1DXsJDb+2N0JwcY1qrT6zvH9gZpNmT6LNYojYavSsALJrv/rsEU7112GzdElvPWwVjV7DpAKGwCnWN3/xup9BgeQ3Am5GnPva6DJaksMS3N1/eZgDQ4gxSzGG5gXirL7uF9Tgc+n65XzbqBKdeoc0/ez5qYOWZKZQqcBUFEapjjNLFq9Nczlh3nGzu+B0AoFTdbm0cmsxuuOevwv/Uf8V37Sdzr34LUYjYYkwNR/IZAd7yXI+Pm/urVqZtEybNWo491E8xmERAcpw5blxvyRMp2eQQr+2bdF6l+NrU5dZLhzlpa4jJzmSAIpnY9r3ByWd1iBAS2De5yOmyGZ7BIbLAWwtuHdjGSHkNCJKQojhvJVCHWtuFy+WjGzYl4N3G7VmQaS0A7wtaYEs9N2mCGAnXT+mjbIbcuRR88ZnYOjg1WncEDwBOgVheIGzkeOP4YbkOgyVdf9bHFSBNXjiYZyk3wcM/z5rmHqytadOpUisZMVVfJ6IoJuKeYXwRRRG5ZTLjnIG/quBSA+mD142vIuucP9j7PkYljXB0w369qZEh/iDgDuF+lGLVWfKH4GLlt9yHPOgvvJR9Enn8uzfEY3YP7TQseJYVbkJABqWWxI1GoNEA6oNcqnInkeTkLgVoEu5FI/SwQZaSWRWbL4dp29NEulP1PA3DpoDmoCpg+zLh8ILkcD2S7gttz7q34rvkrBEFwmDg9OYahqSgHnsO1aGMBSPa6JDKKhm6AmAe4Pee9naabP4Ugu1l8ZA8JJcnRiRP0JvrJ6Qr+XAYhWFtxYAcTkBipCVbWL+O2hMz5Y0lyW+4yzzff3cLtL9mPGG5ArJuNcmwLl826iIg7TG02M2URmNyy2JE+2AsQIzVO0BXglpiBv7ajauAnym5q6+Zw/rFD/MWS27i1bxypdUlV2wuSCzwBjNQEYXeIz57zSS4bSZiFS9W4ZYgSQqCGCzU/E7kYWwd3MpIeI61nTRBWBRPiPe921o1PylGCzUun3QbMe+G76uNm++2uXRi6hpFNTMn8uJdeUpAOldqWcQURsNwTgp7qmBs76hdfyvpYhpiVTQoFKyxmBYGQ4GJESzuLwfxIpMcsOUv10gh3qIG6sUHzvcomy7J1YrAewe0t9Y31BLh8PEOPS+CJBvM3+8t0W6wUYv1sVgXaed9QBq8hUCu4q9Ke29Hqq6dPVMiOdpMWDAIzOLbgC1OXSrCucRUXi3UsTeVmlJWwu01mQjWkdQWvUB3bB6ZO9CzNyyfSEdYkFcI6tLWtqf7cQw2cHcuQ0bKTGR3BVTXjaEdj7RwAvKIb77FtJnFRAQiI9XPMngeaymUdF3HDvGscXWztxKhJfBTbogkCcvtyPGtvcM5NCNTSogrsHN7DQyeewGsIhAL1U/pYy7POAsDo3k3YHXI03IlcEtmgpCtrpRDrZlGbUxhKDplt3XOmBWel5itS43yztshqABRw+VnTuJLHO5/hOSsTF/ZEqr7ujTWzOWcizZNdz7FrZB81yIgur9MwbqoQBBGpaSFt6Syd8W7T1s8Qq5YRuYL1eHSDhJLHcIeqB81S61IwNBI//DP0oWNV67fNcxe41Ajzp0odnz/vU3x+IEd9pLR5XKUQw40sSuVY6apjRImbWbwqpDQwCbiLLWcnfcyNKSUlAK6ll2KkY6x/7m7+3/FhWmunbiSXH+5wPR5dJ6amubDtPM4xAiAICMHqx5pTGWcA96sUo+lxRERcL92B4I/gvfRDuBadj/eyD9EeamdQT5M5tsU0mbcmEzFYa5rwS+7KDLc1aenJMQw1R273o2hjPaYlYN5q3L32BvzXf8opyJNqO9CGT6AcegFcXjrSaRaHZhMyRCSv2X1O8IXR7KYjFhAVQ/WOnEVwwP6o2dlMyyEVMawet0Qmp6LpRgHgFgSBwKKzca97M4uTGSRBZOvgTn609xcEXQHWxzLTOk+I/ghoOYzYAKv6e7l2/lXmdV16SVVuIfLcdegDh/HkMnxu/Z9z2XCs6rTb5EJnHMPQ0UY6karUb9vhufDdGLk06Qf/3dRdVqHfdo7vjzjNh8KiC7HvYMm1n3L7YB2LE2ma/A3cf/QR/mXT13ELMkuT2arSnnLzQja0n4dgGKa2sW1F9cf2hc3mR8PHMTImiKjG7cIOQRBoXnkd62OWl/QMiu/AXDxe7p2FaJ37VMV3K8UIQ4LKLw/cVdJxMpG1WK8qi/cA5DlrQVVI/eZz5m8pA17cZ11H8y2fLguo1ko1XJKCFDpu0YU8A9AnCCLe89/BvIkJ/vboIJczs4XKmgVvICuKPLT1h+iCgG8GGmZ7kjUycbOwzhNwyIBqwpZGeK58N0rr4qrdWewQ6zpo6DnELaM5vrjhr6mdgX5cDDcyN6PQIAedjI6/SpeT/GhsMd+RmnQKfeAI7jXXV2QdpYa5oKvoYz24JRdXzrmUL2z8FJ8+++M0D/eVbQZW9tz9Nbx/IMnfrP9zbl50A7fEBeTpiqqjzaYNaucOot5IQdFkQNORqmRbpfrZNCgaw9kx9o4eJGeohKZgiU0PaqsOyIp3Lr2FBdG5HBw/gmRAYAbSALGmjWtGEoRlH/3JAWqyWeTWpVU1WwIzC9sWNwsH+4wMwRncczFYR62isWt4L32Wf31oBqBXaluG54J34z77rbjXv8UsZp1BeEONzBkfo070ICTGqtZvgylpEfxRruvsMRdYuoFUpQZacPsJIhE3cgUkheN2ousIZTrE5oc8axX+N38W0ROgVtXLdpquFKK/hrqcxlw5zE0Lr0ePjyD4a2a8OD5VcQZwv0oxkhonLLpgpBvPxtuddKQgiMxefDm6INAztNd0ijAEcPsRXF5cK68i8LYvVUxfCrLHZDuTo+S230/2hZ+S+tVnzFVxHmAVfeHCNqh1HZBNgpJxZBLvql/H+xMeJ+Uj+CJgtUMt14JZ8IZAlDGSY2j95kRUzB54XBI5RUfVdKQyk4tY04rHMFjkaeCZ7hfoSvTy9sVvxT8+fRrNBsfKoRcBc8B2LdyI98L3VDWoynPWAqB27sCVTSNigfgqwnaW0FNjGLEhs5PlNA1vikOqbcez8XYni1BNwaRzfF8E3WpMpPbsAV2tSk7ibB+sg/gIl3ZcwHBmlHpfLZ8IrqI9q07LQNjRuOFtLFZEU4/bVD0LAaYmXhs+bjZVgqqPaYc872zekHWzPpZmbmTOjLYFaFx0MedOpGnJqlNaRp3XeBaXjCZ5rvdl7jv2SMHfEraN4gwKcuSOVQRu+1dcK69CjLaU1fCKwVp8s8tr+X1X/QVvuezTrKhbQoN/5mlSqWGu2WjFMJBnuFBZ2rKaxVKEJxgHzLbw1Ya9iDNSE6idO5CbF83o2M3+Rtyii8ePPU9GdpVt6z5VmDUYIr4r/mxGbCGY2TABONc9ud1MMgt2NLaaUoZ6wY3/LZ/DnScZLDlfu/ujJSsBkEWZFl0yx5oqpGdgkiJiJk5HoImLW89h2fBQVeBLnrUarXcvUVdwUlKSnSCoalV73os1bZw/kaVe8PC/u34EQFA3KupppYY5yIsuILfzITSroN8tufmTVe9hTngW9aqBWGWBMoDUOA+vIPPmtLmwq8lkqpKT2OFafBEdLnM+yAkQcFVfLyGE6rlxMEYsF+chW5YxTQfigu0FEfeyS/GsuR7P2htm/sxGW9An+kn87BPOv2cS3ks+QH3jIm4YSXO24i5rm1opQq4AOjgOVpBXcCt6q8qqSfWz8d/4eXzXfAIxD7dMF4Lbx4eGMvyJ0IEsyqZF8Gmq34YzgPtVi9H0GOFMBqltOfLc9QV/m1szD9GAuxOHGM9OmEUpllxDEMWSttPFIQZq0IZPkNv5kNN2VWyY46QGy25jMcBitBX3kosB8MdGaU6lHPAzyXQKCP5S1tOUldSgJ8fQ+g8hRJpKAKvXbQLfdFYtYLid8wg3gSCywvBhYHBu83pWhmaZhY/TMNz2IkA59AKCJzhjwCvWtCH4o2g9+xzwWi3Tms9wq12mHaPUWP3AYIdrycXICzea5zIDplTwRx2wqnXuAJfPadhQTYg1rRjJUdb39PCRle/mE+v+jLqcYi70qmSABNnNOzZ8hA8tvmnGDILUMAcjPuzYBM6E4QbTFrJ++VXcNBjHXyXblx/y7LN4Y0znT3umzmrI7cu5ajTJuYHZPHT8cbYMbAdAN3QSWpaA6JnxbxcDNXjPu43ALf8y7btdsm24Edkf5cOr3sNfr/vojLa1w332TQjeUMXCuanizStvQ7Xe42qb7sDkgko9ttlso73g3BkdN+gOcNWcy3mlezvH4134Zsgwu8+6Dv9bvzCjRa0dgtsPngDrcjKiYDbHcs0Q8AP43QFmh9pYsvz6ae1DhVADuP0oux8ldc8/k3rgqxi65mQcqy3OtusBjNS4SQzolQsm88O18DzQVMKpxGTzl8zEjDI6guwmFGnhvdkgbqtXgSkJqQzePOfcArKH7PM/cTJKXtnLX67+IB/pGqmqYNIO0R/Fs+Fmlp44wI0ZL+dPpJDbqwfcgiQzZ/2tSNZ5hKrMfoLJcHdkVd7ZchFgmDVZNdUztb9ruM+6Ds9F78U1bwO+eatnvMCV21fge8NHufSt/87NV31+RtvazZryddxOj48KFqzlQnB5kDtWVq09tyMQbUcYPAyAnhiZESFyquP05N3/CGIkOUZdLos0a2nJA1TjjfJ2o46fCiMYiV5WqRLCDFbyQqAWrWsnCCLec29DjDbjXn3tlNtIdbPA5cO18koEbxDBF0Eb6zUdSazJUfSH0TCtCCuBCjFQg5EYQR/rRZpdqov0us3BNZ1VC4omnXOXZMRIE2clc2QWXcXF7eejD1udKqdluC3WLD6EPG/DjFu3CoKA1LoUrWcP8ty11j6rHFQ9AbOBUHIMrXs3Yv0cpCo8Vsudg/eSD4Khz2hgESxJiaFkUE9sR25fPiPg515xJfpEP+r2+5jXdwD56o+jpuNVyUnyo6Z2DjWWNnUmIVpsj9q10/z3DI8L4Fp+GUIgitQytTtKuRBkN96ll6D27p+ScRHrOhB9Yd6SlBiMzOGn+++kLdjCY53PEBc05sozY+Z/XyEKIm7p5PgR0RcmcOuXYQaSDjtm1c5jraeZrdl+AjPwALezZsq+p0D2IM8+a8bHvnzWRWwa3EJ/YgjfDBlmwe2bUWq6OMRIM4HYCGvmrKSzb+eMHEry4/+d/RdVfU8QBOTZZ6Ee34ooiuj9B1GPbjKtXAWxqgYqMGmnqSfHHPlWVdZ0DXMRmxYQHOoiEzR9sOO5BPUz8LwHs3aornMnH7zuo/x6y/do80+9regL4zn7rWSf/zGJH/wJGBruFVfiWnoJXl2fEeAGcK24AvX4FjZ0HzAtcsPVz6sA3tlraDl0J92CQmgGGSX7PFdqbm5Oe00t9EmMcScbgttnEmlLLqahIcTQUPnC72n3U6YnxXQR9NdCdpSB1DDNlqe33XgpdBIL1ZmGPG892Rd/buKZxBji/NOzYBLOMNyvShiGwUhqjLBauSPe2ugibhmIISAQymYLfIenC5ttdS25uOoqasHtI/jO/8BlsdtiTSv68AlQMoWSEsrLSZz9BGrRho5iZBPIZRhWjwW4UxUYbjAnANdYP1fPuRyf7MWwOzpOy3BPgmOpfXo7vXIhty7FSMfQevaW7HPKYwtmwxKtcwf6aBeuJRed1PGdfc0gZQe2fl0h++LPMdIxXCuvmtkxXR58l3wQ76UfQhs8THbTrzHSsZMCvicTNsOnWdmBk5mMBFHGdRILLTvcG27G/+bPTn0MQTStGnv28t5lt+ESXXx503/yYt8mLh/PcE5gzkkd+w8dgts/42fOjresfQ/rIvOZ3bGu+uPZi+NMHHnOmhnpt+1wiTLvXXsLwIwB9+8aUk0r+lgPb19yEx8anZkH98mG79IPEXzPt/Df+HnESDO5HQ+iDXeaGuspisnzQ8grqrezSdXOEe4VVxJJmOz293b/lDEtRWNOmxHgdi3YiJFJ0L7lEf6sc5RIFcV3rqWX4jn3VlxLL0FqXUZuxwOObLAaD+78EAQR78UfAJfXkRDONGY3mVmRkOWPXtVxrfk7++LPWNvbxfXLbpoxU/tajfZQO35N53u7f8w9Rx4ip012qPV7Xv35RZ5rNjFSdj8KhnbaWgLCGcD9qkRGy5DVFSKqVhFEirXtrElk+Pic67lkeLxq6yN7W9w+3OtumNF5CbLbGQTEmlZ02wKwSFIyFQgVAjVgdayU/n979xodVXnuAfy/99xyv4CTG0RQAkklBxSCIFUiggQIcTBAiQcFZCFXFalAEasggodbhSVU2oVVbMW2q42IgHK65IAHhOWFo6INUCyGmzEJJIFAMslc3vNhMgMhmWTPZPbMDvn/vmgS9p79PCuZPHn3s5+3mdtWYQbXL3Vxw5SSRtcfnwLn5TLPzpfOS6WApGv1zVUyRQKS6/x6BfOrm+N+UNF26gtAkiH5MNdYjohzrTjpjDD0GOjX6/vL079+/BMYMoY0+8eOEoaeg2HoPRy2on1wXDwdtFUYyRQJKSbRteqm03vmxAeTJEmKfgnqu2ZC1F5G7NXLmHrHIxAQGIF4PFhxFcbMYUG4Um3pFN4J0/rPRIQvt4dNrmkBAGDo4Vs7yfXuSs7EL3qN9cyfDxY5PgWi9jJMdhuirTVBKbgB9/eoDEPfUXBePA3HuW8hd1LeOudejHH8eAy2Y/td864VXrv+tn6IbXhY9VjFv/Cg1BlDrDqfxiHqu/aGaVAB7MVHAKddUbEvyTKMfUYi7J5HED58LqSozqj/v/dd8fhYcAOuHvzIgjUwZj3s87EA0K2zq1XQvamKEpLe6FqocgqEDZsNQ6+f+/Xa7VFMXArmn7mIvvV6/Pfp/8HqQ2twuvosIhwCeoVjSNtCjuoEOTENtn8ddH2s4ZYSFtwqqGzogYu1C6+rA+4tX1MrylxPcvswMsvQexii/vNVz+g+f7huUbp61ZqucHu/FncPqhQW3ew27O4VbgDNPjQJNDzQIZyezXqcl36CFGNudQVOkmRIETGQYpP8eiMGXG/gUrQZqLvqGtPlw5g09+qR/vYBire4DhT3XQcpLBqmu3/RpnOZsvJdf1TV1wb1tqd7lVsKj9X06o/77on93D/xs869sKrHI3jg+xMw9h0NnYLb89TwsxoWDZgioeuqfKJNc7K7DkZqdHDz7m7hcFSeB+prFG/rHiiGtHtcP5tOh+IHJgEAxghAZ3S18tjrET5stuJDJVmP29KycfelWsyJz8KIagf0Pj68BwCG/xgBQ8McfZ3CVhjPNRhMCPv5o64VG0n2rBz7Sg6P8as9AgD6mjMxpMtgdI/x7VkR0+BJCM9dCMPtwf3jMNTkW7ojJiwOBZccmFZag+r6Kyi6eAIRDkerezwEiuH2uwGHDQA0u607wIJbFe6dumINkd43DIhJBGS95wE8X26DSJLs06pDs69/XeHg3gzDXXy1NLnDXXTqkno1WzS5H5oEWlrhdr0JO6vOu/57qVTxoH9D+hCfRybdyP0gleL+7Qbuorct7ST+kuOSAZ0BpsH/2eLOXUpIxnCYBhW4/j+YBXfDrp3BfE1/yBFxkDt1hePsUdjPF8F+6B1IMQme6T6kjK5rJoyZIxQ/lKslnveoslOAEEFb4XaT9EYYMkcAUP7AJOBaIZfjkiHHJiHC8gJ0Cb5NE4q6YzjGy4lI/WI3HD/9y+dpGe5rMA1+FOEPPQ+5mak8rdF3uwv62we4Hiz3sw2qLaIMkZiYPhZGnbI2HjfD7QOgT8lQ6aq0S46IQ9Sk9Yj8xX+hd0o/PFVyBb1iu6NHrc3naVT+un4whZanlLS/d8J2wD1WKT7S+wMbkqyDHJ8MZ3kxAPjUwx0I1z+5LrtbSiIab/LS7HEN1+ltQkajFW6vPdxJACQ4K0sgnHY4L5XCoHCagMnP24TX03W5A7bjn/g8KcOQNhCQpGZbadQmR8YjaurmgBUv+h4DYbJZofdhbFZbyQ2jz7RecAOuYtF2dA9qdx8HdEaEj3xGcR8tuYQPnRHqS/CbFNkJMITBUXrS9Ykgr3ADgLHhAXdf5vUDQMSYRYDO4Nf3q2SKRMRDz8Nx/p+w/XOv6z3PD5Is+932BgBhD8wCGnbEpfZDl9QTcSf+F3M6D4T1yOeQMtu2OKSUHNUJusSerrvlreyAHEosuFVQZa0CAMRFtzwLU47vCudF14SOYO+MJIfHQAqLdk0pabjtI8cmwZj1MPS3Z3k/7pZuMN6VB72XHjV3DzfgfYVb0psgRd8CZ9WPsB0/ADjq/Rrf5S9dimuXRKUzuD3HJfRodo5ysARypVCSJBh/dn/AzqeEu6UkWA9qtoXxjmGAENAl94K+S6am38Qp8FwrxSlwlLrGjQV7hRtwrXL78zPa1nY31w6WmdC3sRWoTdcg64AQrG5T27j35bAXfwUguIsrpp8/CnG1Imiv5w8W3CqorLmAKLsTxoRWdvhyj5XzcRe2QJHjU+D46XtX3x9crSqmfi0/iCnJOpgGjPP69etXuL0V3ICrRcJx4TQcJSegS+wJXcOuY8Egh8fAmJUfkpXqjkwyRsB45xjo2sFtVznGjLB7Hgn1ZVAIyfEpsJefAhCagpuovZFiEyGFRcNx+mvXx21sf/SF7pZuQCvz7kONBbcKqmouNEwoabkvWdcwGN/XzTACRZeYBmG9EtAH2MIUtJQADcV+w0xm4/A5QX+IztTvoaC+HrmY7h4f6ksgUkQXnwJ3U0OwH5okao8kSYIuMQ320w0r3EHq4W4v+NCkCqqsVYhxeJ/B7eZe4fb3Sey2MmY9jIixLwb0nHqdDL2uYfRgCwW3e9qD7lbfd8UiIlLb9ZvNcIWbSBk58VrvPgvuxlhwq6DKXoNYu2h1PI0U1RkwRSreOjfQJFmvSm+qqaGPu6UVbl1KBuT4rm0ecUdEpIZGW6JzhZtIEc9ABb2JD5rfgC0lAVbvsKEGDsQbI1ud8SxJMiIsz0P2cTyd1oUZdbhqtUNuoU1EjklA5IQVQbwqIiLlpKjOgN4I2OvbPIaVqKPQ3dINkPVB7d9uL1hwB5xAtBPoEaFsFuTNuJGGyagHUNfiCjcRkZZJkgw5vguclT9CkvmrkkgJSW+ELrEHhNMR6kvRHL6LBJhB0uP54grE3n0PnKG+mBBxPzjZUg83EZHW6W7pBtTXhvoyiNqVsPufAFhwN8GCO9Ac9QAETMk90FHfpq/1cPMRASJqv0wDJ0LYrKG+DKJ2RY7W7vbqocSCO8AkQxgiH1mHyG6pqL1wJdSXExJc4Saim4FkDGf/NhEFBJcgVSBHxgd9rrSWuDe/Yb1NRERExIKbVBCmYCwgERERUUfBgpsCLszo6lRiSwkRERERC25SgYk93EREREQeLLgp4JTsNElERETUUbDgpoDjlBIiIiKia9pFwb1z506MHj0aI0aMwLZt20J9OdQKd0sJ53ATERERtYM53KWlpVi/fj3ee+89GI1GFBQUYODAgUhLSwv1pZEXnhXuDjwakYiIiMhN80uQhw4dwqBBgxAXF4eIiAjk5ORgz549ob4sagHHAhIRERFdo/kV7rKyMpjNZs/HCQkJOHr0qOLjO3eOUuOyFDGbo0P22qFkl2TIsoTbUuOb5KCj5gTo2LF7w5xcw1w0j3m5hrloijlpijlpTCv50HzB7XQ6G+3aKITwaRfHixevwOkUalxai8zmaJSXVwf9dbVAD2DjvPsQbtI1ykFHzklHjt0b5uQa5qJ5zMs1zEVTzElTzEljwcyHLEstLvJqvqUkKSkJ5eXlno/Ly8uRkJAQwisiJcJNmv9bjoiIiCgoNF9wDx48GIcPH0ZFRQVqa2vxj3/8A0OGDAn1ZRERERERKaL5ZcjExETMnz8fkydPhs1mw/jx49GnT59QXxYRERERkSKaL7gBIC8vD3l5eaG+DCIiIiIin2m+pYSIiIiIqD1jwU1EREREpCIW3EREREREKmoXPdxtIYdwt8NQvrZWdeScdOTYvWFOrmEumse8XMNcNMWcNMWcNBasfLT2OpIQIvi7whARERERdRBsKSEiIiIiUhELbiIiIiIiFbHgJiIiIiJSEQtuIiIiIiIVseAmIiIiIlIRC24iIiIiIhWx4CYiIiIiUhELbiIiIiIiFbHgJiIiIiJSEQvu62zatAm5ubnIzc3FmjVrAACHDh1CXl4eRowYgfXr1zc5ZtGiRXjvvfc8Hx89ehTjxo1DXl4eZs6cifLy8mZf69ixY8jPz0dOTg6ef/552O32Rl/fsGEDNm7cGMDo/KOFnBw5cgTjx4+HxWLBlClTcP78eRUibUoLsX/55ZfIz89HXl4eZs2ahUuXLqkQqXJayIlbUVERMjMzAxidb7SQi+3bt+Pee++FxWKBxWJp9jWDTQt5KSsrw4wZMzB27FgUFBTg3LlzKkTaulDn4uLFi57vDYvFggceeAB33XWXOsEqFOqcAMC5c+cwadIkWCwWPPbYY0H7ndISLeRF6fFqC2YuvB3/448/YtKkSRg5ciRmz56Nq1evtj0wQUIIIT799FMxceJEUVdXJ+rr68XkyZPFzp07RXZ2tjhz5oyw2Wxi2rRpYv/+/UIIIX766Scxc+ZM0adPH1FYWCiEEMLpdIrs7Gxx+PBhIYQQu3fvFjNnzmz29XJzc8VXX30lhBDiueeeE9u2bRNCCHH58mXx3HPPiT59+ojXXntN5ahbppWcDB06VBw7dkwIIcTf/vY3MWvWLDXDFkJoJ/bhw4eLkydPCiGEWLt2rfjNb36jZtgt0kpOhBCipqZGFBQUiF69eqkYsXdaycXy5cvFzp07VY5WOa3kZcqUKeLdd98VQgjx7rvvinnz5qkYdfO0kgs3h8MhHn30UfHBBx+oFHHrtJKTBQsWeP7/j3/8o3j22WfVDLtVWsiLL8erKdi5aO54IYSYMWOG2LVrlxBCiE2bNok1a9a0OTaucDcwm81YvHgxjEYjDAYDevTogeLiYnTr1g2pqanQ6/XIy8vDnj17AAA7d+7EsGHDMGrUKM85KisrYbVaMWjQIADA0KFDcfDgQdTX1zd6rfPnz8NqteLOO+8EAOTn53vOu3fvXnTv3h2PP/54EKJumRZyUl9fj3nz5iEjIwMAkJ6ejpKSkg4ROwB8+OGHSEtLg81mQ2lpKWJiYlSP3Rut5AQAVq1ahSlTpqgcsXdaycW3336L7du3Iy8vDwsWLAj5HRAt5KWiogLHjx9HQUEBAGDcuHF45pln1A/+BlrIxfUKCwsRHh6OvLw8FaNumVZy4nQ6ceXKFQBAbW0twsLC1A69RVrIi9Lj1RbMXHg73maz4YsvvkBOTg6A5n+e/MGCu0HPnj0934DFxcX46KOPIEkSzGaz598kJCSgtLQUADB9+nRMmDCh0Tni4+MRERGBgwcPAgB2794Nm82GysrKRv+urKys0XnNZrPnvGPHjsWMGTOg0+kCHqOvtJATo9EIi8UCwPUmuWnTJgwfPjzgsd5IC7EDgMFgwIkTJ5CdnY3PPvsMubm5AY9VKa3kZO/evbBarRg5cmTAY1RKK7kwm82YM2cOPvjgAyQnJ2P58uUBj9UXWsjL2bNnkZKSglWrVmHcuHF4+umnYTAY1Ai3RVrIhZvD4cDvfvc7PPvsswGN0Vdaycm8efOwdetW3HfffXjzzTfxxBNPBDxWX2ghL0qPV1swc+Ht+MrKSkRFRUGv1wNo+vPkLxbcNzh58iSmTZuGRYsWITU1FZIkeb4mhGj08Y0kScJrr72G3//+9xg7diyqq6sRFxfX5M3e6XT6dN5Q00JO6uvrsWDBAtjtdsycOTOA0bVMC7Gnp6fj0KFDmDNnDubPnx/A6PwTypyUl5dj8+bNeOGFFwIfmB9C/f3x29/+Fv3794ckSZg+fToOHDgQ4Aj9E8q82O12FBUVYdCgQSgsLMSwYcOwePHiwAepUKi/RwDgwIED6N69O9LT0wMYmf9CnZNf/epXWL58OQ4cOICXXnoJTz75JIQQAY7Sd6HMi9LjgyUYufCmufMHokbTt/kMN5EjR47g6aefxpIlS5Cbm4vPP/+8UaN9eXk5EhISWjyHXq/Hn/70JwDAxYsX8frrr8Nms3lWaRMSErBs2bJG571w4UKr5w0VLeTk6tWrmD17NuLi4rB58+agvQGEOva6ujocOHDAs6L/0EMPYfXq1YEO0yehzsn+/ftRVVWFSZMmeb5msViwbds2REVFBTLUVoU6F9XV1SgsLMTUqVMBuH5JaOHOWKjzYjabERkZiaFDhwIAxowZgxUrVgQ6TEVCnQu3jz/+GKNHjw5kaH4LdU4qKipw6tQpz/tqTk4Oli5disrKSnTq1CnQ4SoW6rx4Oz4uLi6QYSoSrFxs2bKl2WM7deqE6upqOBwO6HQ6Ra+nBFe4G5SUlGDu3LlYt26d57Z937598cMPP+D06dNwOBzYtWsXhgwZ0uJ5lixZgqNHjwIA3nrrLYwcORKJiYnYsWMHduzYgS1btqBLly4wmUw4cuQIAGDHjh2tnjcUtJKThQsXolu3btiwYQOMRqOKEV+jhdj1ej1eeuklfPfddwCAjz76CP369VMx6pZpIScTJkzAxx9/7Pm37q8Fu9jWQi4iIiLwxhtv4JtvvgEAvPPOO3jwwQdVjLp1WsjLrbfeiqSkJHzyyScAgH379qF3794qRt08LeTC7euvv0ZWVpZKkSqnhZzEx8fDZDLhyy+/BOAq7iIjI0NabGshL96Ol+XglonBzIU3BoMBWVlZ+PDDDwEA77//fkBqNK5wN/jDH/6Auro6rFq1yvO5goICrFq1Ck899RTq6uqQnZ3dat/osmXLsHTpUtTW1iI9PR0rV65s9t+tW7cOv/71r3HlyhX07t0bkydPDmg8gaCFnBQVFWHv3r1IS0vDww8/DKDlv0wDRQux63Q6rF+/Hi+++CIcDgcSExO9Hh8MWsiJVmghFzqdDhs2bMCyZctgtVrRvXt3zwitUNFCXgBg48aNWLp0KdauXYuoqKhG1xMsWskFAJw9exZJSUmBCawNtJATSZKwadMmvPzyy7BarYiMjAz5CF4t5MWX49UU7Fx4s3TpUixevBibN29GcnIyXn31Vb/iuZ4ktNC4RERERER0k2JLCRERERGRilhwExERERGpiAU3EREREZGKWHATEREREamIBTcRERERkYo4FpCI6CaTnp6OXr16NZqhm5mZ6deYr88++wwvv/wydu3aFchLJCLqUFhwExHdhN5+++2QbuZBRETXsOAmIupA/v3vf2PlypWoqqqCw+HAY489hvHjxwMA/v73v+Ott96CLMuIj4/H6tWrAQA1NTWYP38+Tp06hbq6OqxYsQJZWVn44YcfsHz5cly9ehXl5eXIyMjAhg0bYDKZQhkiEZHmcOMbIqKbTHMtJW+++SZiY2NhsViwZs0a9O7dG9XV1Zg4cSJeeeUVhIWFYerUqdi+fTuSk5OxdetWnDp1Crm5uXj88cfx5z//GX379sXWrVuxb98+vP3221i9ejUyMjJgsVhgs9mQn5+PJ598Ejk5OSGMnohIe7jCTUR0E2qupeT777/HmTNnsGTJEs/nrFYrioqKUFdXh3vvvRfJyckAgKlTpwJw9XCnpqaib9++AICMjAwUFhYCABYuXIhPP/0UW7ZsQXFxMcrKylBTUxOE6IiI2hcW3EREHYTD4UB0dDR27Njh+dyFCxcQHR2Nv/71r5AkyfN5q9WK8+fPAwAMBoPn85IkwX1j9Je//CUcDgdGjRqF+++/HyUlJeBNUyKipjgWkIiog7jtttsQFhbmKbhLSkowZswYfPfddxg4cCAOHz6MsrIyAMBf/vIXrF27tsXzHTx4EHPnzsXo0aMBAN988w0cDoe6QRARtUNc4SYi6iCMRiNef/11rFy5Em+88QbsdjvmzZuH/v37A3C1iEyfPh0AYDab8corr6C4uNjr+ebPn4+5c+ciIiICUVFRGDBgAM6cOROMUIiI2hU+NElEREREpCK2lBARERERqYgFNxERERGRilhwExERERGpiAU3EREREZGKWHATEREREamIBTcRERERkYpYcBMRERERqYgFNxERERGRiv4f3/e/F2SGAq8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_lineplot2 = pd.melt(contador.loc[contador['Año'] == 2019,['Fecha', 'Reforma', 'Revolución', 'Patriotismo']], 'Fecha', var_name='contador', value_name='número')\n",
    "sns.lineplot(x='Fecha', y='número', hue='contador', data=data_lineplot2)\n",
    "\n",
    "#Change figure size\n",
    "sns.set(rc={'figure.figsize':(12,4)});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_lineplot2 = pd.melt(contador.loc[contador['Año'] == 2020,['Fecha', 'Reforma', 'Revolución', 'Patriotismo']], 'Fecha', var_name='contador', value_name='número')\n",
    "sns.lineplot(x='Fecha', y='número', hue='contador', data=data_lineplot2)\n",
    "\n",
    "#Change figure size\n",
    "sns.set(rc={'figure.figsize':(12,4)});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both of the previous chart do not display data for all months. The one from 2019 stops at October, and the one from 2020 begins in March. Arguably, this is because we have missing data. Let's see the raw data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>domingo</td>\n",
       "      <td>15</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-15</td>\n",
       "      <td>1662.0</td>\n",
       "      <td>498.0</td>\n",
       "      <td>506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>lunes</td>\n",
       "      <td>16</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-16</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2117</th>\n",
       "      <td>martes</td>\n",
       "      <td>17</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-17</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>901.0</td>\n",
       "      <td>1084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2118</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>18</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-18</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>1038.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2119</th>\n",
       "      <td>jueves</td>\n",
       "      <td>19</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>2870.0</td>\n",
       "      <td>763.0</td>\n",
       "      <td>987.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2120</th>\n",
       "      <td>viernes</td>\n",
       "      <td>20</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-20</td>\n",
       "      <td>2976.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>1032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2121</th>\n",
       "      <td>sabado</td>\n",
       "      <td>21</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-21</td>\n",
       "      <td>2341.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2122</th>\n",
       "      <td>domingo</td>\n",
       "      <td>22</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-22</td>\n",
       "      <td>1784.0</td>\n",
       "      <td>614.0</td>\n",
       "      <td>662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2123</th>\n",
       "      <td>lunes</td>\n",
       "      <td>23</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-23</td>\n",
       "      <td>3630.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>1203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2124</th>\n",
       "      <td>martes</td>\n",
       "      <td>24</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>2833.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>1129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>25</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-25</td>\n",
       "      <td>2584.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>1112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2126</th>\n",
       "      <td>jueves</td>\n",
       "      <td>26</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-26</td>\n",
       "      <td>3868.0</td>\n",
       "      <td>989.0</td>\n",
       "      <td>1251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127</th>\n",
       "      <td>viernes</td>\n",
       "      <td>27</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>3740.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>1211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>sabado</td>\n",
       "      <td>28</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>619.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2129</th>\n",
       "      <td>domingo</td>\n",
       "      <td>29</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>1093.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2130</th>\n",
       "      <td>lunes</td>\n",
       "      <td>30</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>1039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2131</th>\n",
       "      <td>sabado</td>\n",
       "      <td>1</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2132</th>\n",
       "      <td>domingo</td>\n",
       "      <td>2</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>1472.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>498.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2133</th>\n",
       "      <td>lunes</td>\n",
       "      <td>3</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>1603.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>586.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2134</th>\n",
       "      <td>martes</td>\n",
       "      <td>4</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>3518.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2135</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>5</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>3618.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>1299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2136</th>\n",
       "      <td>jueves</td>\n",
       "      <td>6</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>3699.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>1446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2137</th>\n",
       "      <td>viernes</td>\n",
       "      <td>7</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>3778.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>1347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138</th>\n",
       "      <td>sabado</td>\n",
       "      <td>8</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>733.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2139</th>\n",
       "      <td>domingo</td>\n",
       "      <td>9</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>1869.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2140</th>\n",
       "      <td>lunes</td>\n",
       "      <td>10</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>3527.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2141</th>\n",
       "      <td>martes</td>\n",
       "      <td>11</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>3771.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>1401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2142</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>12</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>3884.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1416.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2143</th>\n",
       "      <td>jueves</td>\n",
       "      <td>13</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>3942.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>1336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2144</th>\n",
       "      <td>viernes</td>\n",
       "      <td>14</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>3366.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>1201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2145</th>\n",
       "      <td>sabado</td>\n",
       "      <td>15</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>677.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2146</th>\n",
       "      <td>domingo</td>\n",
       "      <td>16</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>663.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2147</th>\n",
       "      <td>lunes</td>\n",
       "      <td>17</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>3575.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>1284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2148</th>\n",
       "      <td>martes</td>\n",
       "      <td>18</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>3880.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>1430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2149</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>19</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>4058.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>1447.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150</th>\n",
       "      <td>jueves</td>\n",
       "      <td>20</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>4003.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>1403.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2151</th>\n",
       "      <td>viernes</td>\n",
       "      <td>21</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>3882.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>1386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2152</th>\n",
       "      <td>sabado</td>\n",
       "      <td>22</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>2235.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2153</th>\n",
       "      <td>domingo</td>\n",
       "      <td>23</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>1893.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2154</th>\n",
       "      <td>lunes</td>\n",
       "      <td>24</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>3691.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>martes</td>\n",
       "      <td>25</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>3893.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>1437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>26</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>3829.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>jueves</td>\n",
       "      <td>27</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>3860.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>viernes</td>\n",
       "      <td>28</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>3886.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>1363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>sabado</td>\n",
       "      <td>29</td>\n",
       "      <td>febrero</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>2274.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2160</th>\n",
       "      <td>domingo</td>\n",
       "      <td>1</td>\n",
       "      <td>marzo</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1813.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>634.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Día de la semana  Día         Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "2115          domingo   15  septiembre  2019 2019-09-15   1662.0       498.0   \n",
       "2116            lunes   16  septiembre  2019 2019-09-16   1145.0       283.0   \n",
       "2117           martes   17  septiembre  2019 2019-09-17   3000.0       901.0   \n",
       "2118        miercoles   18  septiembre  2019 2019-09-18   2923.0       795.0   \n",
       "2119           jueves   19  septiembre  2019 2019-09-19   2870.0       763.0   \n",
       "2120          viernes   20  septiembre  2019 2019-09-20   2976.0       845.0   \n",
       "2121           sabado   21  septiembre  2019 2019-09-21   2341.0       650.0   \n",
       "2122          domingo   22  septiembre  2019 2019-09-22   1784.0       614.0   \n",
       "2123            lunes   23  septiembre  2019 2019-09-23   3630.0       923.0   \n",
       "2124           martes   24  septiembre  2019 2019-09-24   2833.0       880.0   \n",
       "2125        miercoles   25  septiembre  2019 2019-09-25   2584.0       843.0   \n",
       "2126           jueves   26  septiembre  2019 2019-09-26   3868.0       989.0   \n",
       "2127          viernes   27  septiembre  2019 2019-09-27   3740.0      1034.0   \n",
       "2128           sabado   28  septiembre  2019 2019-09-28   1990.0       582.0   \n",
       "2129          domingo   29  septiembre  2019 2019-09-29   1093.0       434.0   \n",
       "2130            lunes   30  septiembre  2019 2019-09-30   2956.0       843.0   \n",
       "2131           sabado    1     febrero  2020 2020-02-01   1890.0       124.0   \n",
       "2132          domingo    2     febrero  2020 2020-02-02   1472.0        98.0   \n",
       "2133            lunes    3     febrero  2020 2020-02-03   1603.0       103.0   \n",
       "2134           martes    4     febrero  2020 2020-02-04   3518.0       290.0   \n",
       "2135        miercoles    5     febrero  2020 2020-02-05   3618.0       326.0   \n",
       "2136           jueves    6     febrero  2020 2020-02-06   3699.0       246.0   \n",
       "2137          viernes    7     febrero  2020 2020-02-07   3778.0       226.0   \n",
       "2138           sabado    8     febrero  2020 2020-02-08   2286.0       120.0   \n",
       "2139          domingo    9     febrero  2020 2020-02-09   1869.0       114.0   \n",
       "2140            lunes   10     febrero  2020 2020-02-10   3527.0       270.0   \n",
       "2141           martes   11     febrero  2020 2020-02-11   3771.0       275.0   \n",
       "2142        miercoles   12     febrero  2020 2020-02-12   3884.0       270.0   \n",
       "2143           jueves   13     febrero  2020 2020-02-13   3942.0       261.0   \n",
       "2144          viernes   14     febrero  2020 2020-02-14   3366.0       202.0   \n",
       "2145           sabado   15     febrero  2020 2020-02-15   1939.0       153.0   \n",
       "2146          domingo   16     febrero  2020 2020-02-16   1695.0        98.0   \n",
       "2147            lunes   17     febrero  2020 2020-02-17   3575.0       289.0   \n",
       "2148           martes   18     febrero  2020 2020-02-18   3880.0       316.0   \n",
       "2149        miercoles   19     febrero  2020 2020-02-19   4058.0       287.0   \n",
       "2150           jueves   20     febrero  2020 2020-02-20   4003.0       307.0   \n",
       "2151          viernes   21     febrero  2020 2020-02-21   3882.0       209.0   \n",
       "2152           sabado   22     febrero  2020 2020-02-22   2235.0       187.0   \n",
       "2153          domingo   23     febrero  2020 2020-02-23   1893.0       112.0   \n",
       "2154            lunes   24     febrero  2020 2020-02-24   3691.0       272.0   \n",
       "2155           martes   25     febrero  2020 2020-02-25   3893.0       310.0   \n",
       "2156        miercoles   26     febrero  2020 2020-02-26   3829.0       297.0   \n",
       "2157           jueves   27     febrero  2020 2020-02-27   3860.0       276.0   \n",
       "2158          viernes   28     febrero  2020 2020-02-28   3886.0       259.0   \n",
       "2159           sabado   29     febrero  2020 2020-02-29   2274.0       131.0   \n",
       "2160          domingo    1       marzo  2020 2020-03-01   1813.0       107.0   \n",
       "\n",
       "      Patriotismo  \n",
       "2115        506.0  \n",
       "2116        468.0  \n",
       "2117       1084.0  \n",
       "2118       1038.0  \n",
       "2119        987.0  \n",
       "2120       1032.0  \n",
       "2121        738.0  \n",
       "2122        662.0  \n",
       "2123       1203.0  \n",
       "2124       1129.0  \n",
       "2125       1112.0  \n",
       "2126       1251.0  \n",
       "2127       1211.0  \n",
       "2128        619.0  \n",
       "2129        681.0  \n",
       "2130       1039.0  \n",
       "2131        648.0  \n",
       "2132        498.0  \n",
       "2133        586.0  \n",
       "2134       1306.0  \n",
       "2135       1299.0  \n",
       "2136       1446.0  \n",
       "2137       1347.0  \n",
       "2138        733.0  \n",
       "2139        664.0  \n",
       "2140       1328.0  \n",
       "2141       1401.0  \n",
       "2142       1416.0  \n",
       "2143       1336.0  \n",
       "2144       1201.0  \n",
       "2145        677.0  \n",
       "2146        663.0  \n",
       "2147       1284.0  \n",
       "2148       1430.0  \n",
       "2149       1447.0  \n",
       "2150       1403.0  \n",
       "2151       1386.0  \n",
       "2152        910.0  \n",
       "2153        666.0  \n",
       "2154       1335.0  \n",
       "2155       1437.0  \n",
       "2156       1409.0  \n",
       "2157       1477.0  \n",
       "2158       1363.0  \n",
       "2159        698.0  \n",
       "2160        634.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date = '2019-09-15'\n",
    "end_date = '2020-03-01'\n",
    "contador.loc[(contador.Fecha >= start_date) & (contador.Fecha <= end_date)].head(60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, **we do not have data on any counter from October 2020 to 31 January 2021**. It did not show on one of our earlier graphs because the date rows are not even present on the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at when the Reforma counter stopped working:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2297</th>\n",
       "      <td>jueves</td>\n",
       "      <td>16</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>749.0</td>\n",
       "      <td>693.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>viernes</td>\n",
       "      <td>17</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>858.0</td>\n",
       "      <td>697.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>sabado</td>\n",
       "      <td>18</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>706.0</td>\n",
       "      <td>686.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>domingo</td>\n",
       "      <td>19</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>634.0</td>\n",
       "      <td>878.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Día de la semana  Día    Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "2297           jueves   16  julio  2020 2020-07-16      NaN       749.0   \n",
       "2298          viernes   17  julio  2020 2020-07-17      NaN       858.0   \n",
       "2299           sabado   18  julio  2020 2020-07-18      NaN       706.0   \n",
       "2300          domingo   19  julio  2020 2020-07-19      NaN       634.0   \n",
       "\n",
       "      Patriotismo  \n",
       "2297        693.0  \n",
       "2298        697.0  \n",
       "2299        686.0  \n",
       "2300        878.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador.loc[(contador.Año >= 2020) & (contador['Reforma'].isnull())].head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Reforma counter stopped on July 2020**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's investigate the spikes in the graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_lineplot2 = pd.melt(contador.loc[contador['Año'] == 2017,['Fecha', 'Reforma', 'Revolución', 'Patriotismo']], 'Fecha', var_name='contador', value_name='número')\n",
    "sns.lineplot(x='Fecha', y='número', hue='contador', data=data_lineplot2)\n",
    "\n",
    "#Change figure size\n",
    "sns.set(rc={'figure.figsize':(12,4)});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>sabado</td>\n",
       "      <td>1</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1327.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>domingo</td>\n",
       "      <td>2</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-02</td>\n",
       "      <td>971.0</td>\n",
       "      <td>579.0</td>\n",
       "      <td>2794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>lunes</td>\n",
       "      <td>3</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>235.0</td>\n",
       "      <td>863.0</td>\n",
       "      <td>3384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>martes</td>\n",
       "      <td>4</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>2442.0</td>\n",
       "      <td>893.0</td>\n",
       "      <td>3184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>5</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-05</td>\n",
       "      <td>2642.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>1027.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>jueves</td>\n",
       "      <td>6</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-06</td>\n",
       "      <td>4899.0</td>\n",
       "      <td>991.0</td>\n",
       "      <td>1107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>viernes</td>\n",
       "      <td>7</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>5854.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>sabado</td>\n",
       "      <td>8</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-08</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>domingo</td>\n",
       "      <td>9</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-09</td>\n",
       "      <td>924.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>595.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>lunes</td>\n",
       "      <td>10</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-10</td>\n",
       "      <td>13676.0</td>\n",
       "      <td>757.0</td>\n",
       "      <td>884.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>martes</td>\n",
       "      <td>11</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>10391.0</td>\n",
       "      <td>819.0</td>\n",
       "      <td>839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>12</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-12</td>\n",
       "      <td>7191.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>jueves</td>\n",
       "      <td>13</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-13</td>\n",
       "      <td>15012.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>viernes</td>\n",
       "      <td>14</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-14</td>\n",
       "      <td>7291.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>1041.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>sabado</td>\n",
       "      <td>15</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>1674.0</td>\n",
       "      <td>629.0</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>domingo</td>\n",
       "      <td>16</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>3013.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>lunes</td>\n",
       "      <td>17</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>1077.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>martes</td>\n",
       "      <td>18</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>17327.0</td>\n",
       "      <td>914.0</td>\n",
       "      <td>1111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>19</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>1061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>jueves</td>\n",
       "      <td>20</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>959.0</td>\n",
       "      <td>1081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>viernes</td>\n",
       "      <td>21</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>878.0</td>\n",
       "      <td>963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>sabado</td>\n",
       "      <td>22</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>627.0</td>\n",
       "      <td>558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>domingo</td>\n",
       "      <td>23</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>423.0</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>lunes</td>\n",
       "      <td>24</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>693.0</td>\n",
       "      <td>814.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>martes</td>\n",
       "      <td>25</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>773.0</td>\n",
       "      <td>917.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>26</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>849.0</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>jueves</td>\n",
       "      <td>27</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>922.0</td>\n",
       "      <td>967.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>viernes</td>\n",
       "      <td>28</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>932.0</td>\n",
       "      <td>971.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>sabado</td>\n",
       "      <td>29</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>626.0</td>\n",
       "      <td>504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>domingo</td>\n",
       "      <td>30</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>590.0</td>\n",
       "      <td>899.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Día de la semana  Día    Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "1323           sabado    1  julio  2017 2017-07-01   1327.0       556.0   \n",
       "1324          domingo    2  julio  2017 2017-07-02    971.0       579.0   \n",
       "1325            lunes    3  julio  2017 2017-07-03    235.0       863.0   \n",
       "1326           martes    4  julio  2017 2017-07-04   2442.0       893.0   \n",
       "1327        miercoles    5  julio  2017 2017-07-05   2642.0       928.0   \n",
       "1328           jueves    6  julio  2017 2017-07-06   4899.0       991.0   \n",
       "1329          viernes    7  julio  2017 2017-07-07   5854.0       881.0   \n",
       "1330           sabado    8  julio  2017 2017-07-08   1199.0       475.0   \n",
       "1331          domingo    9  julio  2017 2017-07-09    924.0       493.0   \n",
       "1332            lunes   10  julio  2017 2017-07-10  13676.0       757.0   \n",
       "1333           martes   11  julio  2017 2017-07-11  10391.0       819.0   \n",
       "1334        miercoles   12  julio  2017 2017-07-12   7191.0       886.0   \n",
       "1335           jueves   13  julio  2017 2017-07-13  15012.0       900.0   \n",
       "1336          viernes   14  julio  2017 2017-07-14   7291.0      1007.0   \n",
       "1337           sabado   15  julio  2017 2017-07-15   1674.0       629.0   \n",
       "1338          domingo   16  julio  2017 2017-07-16   3013.0       600.0   \n",
       "1339            lunes   17  julio  2017 2017-07-17   1061.0       926.0   \n",
       "1340           martes   18  julio  2017 2017-07-18  17327.0       914.0   \n",
       "1341        miercoles   19  julio  2017 2017-07-19      NaN      1017.0   \n",
       "1342           jueves   20  julio  2017 2017-07-20      NaN       959.0   \n",
       "1343          viernes   21  julio  2017 2017-07-21      NaN       878.0   \n",
       "1344           sabado   22  julio  2017 2017-07-22      NaN       627.0   \n",
       "1345          domingo   23  julio  2017 2017-07-23      NaN       423.0   \n",
       "1346            lunes   24  julio  2017 2017-07-24      NaN       693.0   \n",
       "1347           martes   25  julio  2017 2017-07-25      NaN       773.0   \n",
       "1348        miercoles   26  julio  2017 2017-07-26      NaN       849.0   \n",
       "1349           jueves   27  julio  2017 2017-07-27      NaN       922.0   \n",
       "1350          viernes   28  julio  2017 2017-07-28      NaN       932.0   \n",
       "1351           sabado   29  julio  2017 2017-07-29      NaN       626.0   \n",
       "1352          domingo   30  julio  2017 2017-07-30      NaN       590.0   \n",
       "\n",
       "      Patriotismo  \n",
       "1323        913.0  \n",
       "1324       2794.0  \n",
       "1325       3384.0  \n",
       "1326       3184.0  \n",
       "1327       1027.0  \n",
       "1328       1107.0  \n",
       "1329        996.0  \n",
       "1330        508.0  \n",
       "1331        595.0  \n",
       "1332        884.0  \n",
       "1333        839.0  \n",
       "1334        910.0  \n",
       "1335       1187.0  \n",
       "1336       1041.0  \n",
       "1337        571.0  \n",
       "1338        679.0  \n",
       "1339       1077.0  \n",
       "1340       1111.0  \n",
       "1341       1061.0  \n",
       "1342       1081.0  \n",
       "1343        963.0  \n",
       "1344        558.0  \n",
       "1345        580.0  \n",
       "1346        814.0  \n",
       "1347        917.0  \n",
       "1348        990.0  \n",
       "1349        967.0  \n",
       "1350        971.0  \n",
       "1351        504.0  \n",
       "1352        899.0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date = '2017-07-01'\n",
    "end_date = '2017-11-01'\n",
    "contador.loc[(contador.Fecha >= start_date) & (contador.Fecha <= end_date)].head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the **Reforma counter**, the clear outliers are the values for July 10 (Monday), 11 (Tuesday), 13 (Thursday), and 18 (Tuesday), all of which had more than 10,000 records. In contrast, by looking at the graph, the average for that year seems to be around 2,400.\n",
    "\n",
    "It's strange that there were so many records on those weekdays. There does not seem to be a pattern, such as a weekend plus holidays. Also, I searched on the internet for any cicling news on those days, and I couldn't find any. I also checked is an account on Twitter that posts about cycling in Mexico City (@arelibiciteka), and there doesn't seem to be any uncommon events, except for a protest, but it was on July 17th. (To search for tweets in those specific dates, use *from:arelibiciteka since:2017-07-09 until:2017-07-18*).\n",
    "\n",
    "My guess is that the counter was not working properly and maybe that's why it abruptly stopped in a few days later. In any case, we should handle these outlier values, even if they are correct, because are not representative of the average records on the counter.\n",
    "\n",
    "In the **Patriotismo counter**, the outliers were in July 2 (Sunday), 3 (Monday), and 4 (Tuesday), all of which had more than 2,500 records. In contrast, the average for that year seems to be around 1,000.\n",
    "\n",
    "In contrast to the Reforma counter outliers, there seems to be a pattern in these values. The most likely explanation for these values is that there was a weekend plus holidays. For that reason, we can keep these values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outlier Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start, then, by replacing the outliers in the Reforma counter. I will use the mean of the past month's corresponding day. So, for example, if a value is missing on Tuesday of July 2020 , I will replace it with the average of all Tuesdays from June 2017. I believe this gives us the best approximation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mes    Día de la semana\n",
       "junio  domingo             1157.50\n",
       "       jueves              2947.00\n",
       "       lunes               2129.75\n",
       "       martes              2104.50\n",
       "       miercoles           2644.00\n",
       "       sabado              1099.25\n",
       "       viernes             2929.60\n",
       "Name: Reforma, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate mean of the past month's corresponding day\n",
    "contador[(contador.Año == 2017) & (contador.Mes == 'junio')].groupby(['Mes', 'Día de la semana'])['Reforma'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>lunes</td>\n",
       "      <td>10</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>757.0</td>\n",
       "      <td>884.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>martes</td>\n",
       "      <td>11</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>10391.0</td>\n",
       "      <td>819.0</td>\n",
       "      <td>839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>jueves</td>\n",
       "      <td>13</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-13</td>\n",
       "      <td>15012.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>martes</td>\n",
       "      <td>18</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>17327.0</td>\n",
       "      <td>914.0</td>\n",
       "      <td>1111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Día de la semana  Día    Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "1332            lunes   10  julio  2017 2017-07-10      1.0       757.0   \n",
       "1333           martes   11  julio  2017 2017-07-11  10391.0       819.0   \n",
       "1335           jueves   13  julio  2017 2017-07-13  15012.0       900.0   \n",
       "1340           martes   18  julio  2017 2017-07-18  17327.0       914.0   \n",
       "\n",
       "      Patriotismo  \n",
       "1332        884.0  \n",
       "1333        839.0  \n",
       "1335       1187.0  \n",
       "1340       1111.0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show outlier values\n",
    "contador.loc[contador.Fecha.isin(['2017-07-10', '2017-07-11', '2017-07-13', '2017-07-18'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace outlier values\n",
    "contador.loc[contador.Fecha == '2017-07-10', 'Reforma'] = 2129.75\n",
    "contador.loc[contador.Fecha == '2017-07-11', 'Reforma'] = 2104.50\n",
    "contador.loc[contador.Fecha == '2017-07-13', 'Reforma'] = 2947.00\n",
    "contador.loc[contador.Fecha == '2017-07-18', 'Reforma'] = 2104.50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>lunes</td>\n",
       "      <td>10</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-10</td>\n",
       "      <td>2129.75</td>\n",
       "      <td>757.0</td>\n",
       "      <td>884.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>martes</td>\n",
       "      <td>11</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>2104.50</td>\n",
       "      <td>819.0</td>\n",
       "      <td>839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>jueves</td>\n",
       "      <td>13</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-13</td>\n",
       "      <td>2947.00</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>martes</td>\n",
       "      <td>18</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>2104.50</td>\n",
       "      <td>914.0</td>\n",
       "      <td>1111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Día de la semana  Día    Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "1332            lunes   10  julio  2017 2017-07-10  2129.75       757.0   \n",
       "1333           martes   11  julio  2017 2017-07-11  2104.50       819.0   \n",
       "1335           jueves   13  julio  2017 2017-07-13  2947.00       900.0   \n",
       "1340           martes   18  julio  2017 2017-07-18  2104.50       914.0   \n",
       "\n",
       "      Patriotismo  \n",
       "1332        884.0  \n",
       "1333        839.0  \n",
       "1335       1187.0  \n",
       "1340       1111.0  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show outlier values\n",
    "contador.loc[contador.Fecha.isin(['2017-07-10', '2017-07-11', '2017-07-13', '2017-07-18'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "INSERT MISSING ROWS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have to deal with the **missing values**. The values for Revolución and Patriotismo up to 14 June 2016 are simple: we could replace them with a cero, since cero bicycles were, in a way, counted by the counters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values up to the installation of the counters.\n",
    "contador.loc[0:940, ['Revolución', 'Patriotismo']] = contador.loc[0:940, ['Revolución', 'Patriotismo']].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the other ones, let's look at them more closely:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>19</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>1061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>jueves</td>\n",
       "      <td>20</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>959.0</td>\n",
       "      <td>1081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>viernes</td>\n",
       "      <td>21</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>878.0</td>\n",
       "      <td>963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>sabado</td>\n",
       "      <td>22</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>627.0</td>\n",
       "      <td>558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>domingo</td>\n",
       "      <td>23</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>423.0</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Día de la semana  Día    Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "1341        miercoles   19  julio  2017 2017-07-19      NaN      1017.0   \n",
       "1342           jueves   20  julio  2017 2017-07-20      NaN       959.0   \n",
       "1343          viernes   21  julio  2017 2017-07-21      NaN       878.0   \n",
       "1344           sabado   22  julio  2017 2017-07-22      NaN       627.0   \n",
       "1345          domingo   23  julio  2017 2017-07-23      NaN       423.0   \n",
       "\n",
       "      Patriotismo  \n",
       "1341       1061.0  \n",
       "1342       1081.0  \n",
       "1343        963.0  \n",
       "1344        558.0  \n",
       "1345        580.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador[contador['Reforma'].isnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>viernes</td>\n",
       "      <td>14</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-14</td>\n",
       "      <td>7291.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>1041.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>sabado</td>\n",
       "      <td>15</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>1674.0</td>\n",
       "      <td>629.0</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>domingo</td>\n",
       "      <td>16</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>3013.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>lunes</td>\n",
       "      <td>17</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>1077.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>martes</td>\n",
       "      <td>18</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>17327.0</td>\n",
       "      <td>914.0</td>\n",
       "      <td>1111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>19</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>1061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>jueves</td>\n",
       "      <td>20</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>959.0</td>\n",
       "      <td>1081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>viernes</td>\n",
       "      <td>21</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>878.0</td>\n",
       "      <td>963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>sabado</td>\n",
       "      <td>22</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>627.0</td>\n",
       "      <td>558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>domingo</td>\n",
       "      <td>23</td>\n",
       "      <td>julio</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>423.0</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Día de la semana  Día    Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "1336          viernes   14  julio  2017 2017-07-14   7291.0      1007.0   \n",
       "1337           sabado   15  julio  2017 2017-07-15   1674.0       629.0   \n",
       "1338          domingo   16  julio  2017 2017-07-16   3013.0       600.0   \n",
       "1339            lunes   17  julio  2017 2017-07-17   1061.0       926.0   \n",
       "1340           martes   18  julio  2017 2017-07-18  17327.0       914.0   \n",
       "1341        miercoles   19  julio  2017 2017-07-19      NaN      1017.0   \n",
       "1342           jueves   20  julio  2017 2017-07-20      NaN       959.0   \n",
       "1343          viernes   21  julio  2017 2017-07-21      NaN       878.0   \n",
       "1344           sabado   22  julio  2017 2017-07-22      NaN       627.0   \n",
       "1345          domingo   23  julio  2017 2017-07-23      NaN       423.0   \n",
       "\n",
       "      Patriotismo  \n",
       "1336       1041.0  \n",
       "1337        571.0  \n",
       "1338        679.0  \n",
       "1339       1077.0  \n",
       "1340       1111.0  \n",
       "1341       1061.0  \n",
       "1342       1081.0  \n",
       "1343        963.0  \n",
       "1344        558.0  \n",
       "1345        580.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador.iloc[1336:1346, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is unlikely that cyclists simply stoped passing through Reforma, since they were passing through the other counters. Also, the period is too long for it to overlap with, say, a holiday. My hypothesis is that the counter simply stoped working for some reason.\n",
    "\n",
    "The same is probably happening for the final values of the Reforma counter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
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       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>sabado</td>\n",
       "      <td>11</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-11</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>domingo</td>\n",
       "      <td>12</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-12</td>\n",
       "      <td>2550.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>814.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2294</th>\n",
       "      <td>lunes</td>\n",
       "      <td>13</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-13</td>\n",
       "      <td>1835.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2295</th>\n",
       "      <td>martes</td>\n",
       "      <td>14</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>1782.0</td>\n",
       "      <td>774.0</td>\n",
       "      <td>627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2296</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>15</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>643.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Día de la semana  Día    Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "2292           sabado   11  julio  2020 2020-07-11   1913.0       710.0   \n",
       "2293          domingo   12  julio  2020 2020-07-12   2550.0       605.0   \n",
       "2294            lunes   13  julio  2020 2020-07-13   1835.0       711.0   \n",
       "2295           martes   14  julio  2020 2020-07-14   1782.0       774.0   \n",
       "2296        miercoles   15  julio  2020 2020-07-15   2008.0       738.0   \n",
       "\n",
       "      Patriotismo  \n",
       "2292        674.0  \n",
       "2293        814.0  \n",
       "2294        565.0  \n",
       "2295        627.0  \n",
       "2296        643.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador[contador['Reforma'].notnull()].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>domingo</td>\n",
       "      <td>12</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-12</td>\n",
       "      <td>2550.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>814.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2294</th>\n",
       "      <td>lunes</td>\n",
       "      <td>13</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-13</td>\n",
       "      <td>1835.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2295</th>\n",
       "      <td>martes</td>\n",
       "      <td>14</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>1782.0</td>\n",
       "      <td>774.0</td>\n",
       "      <td>627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2296</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>15</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>643.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2297</th>\n",
       "      <td>jueves</td>\n",
       "      <td>16</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>749.0</td>\n",
       "      <td>693.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>viernes</td>\n",
       "      <td>17</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>858.0</td>\n",
       "      <td>697.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>sabado</td>\n",
       "      <td>18</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>706.0</td>\n",
       "      <td>686.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>domingo</td>\n",
       "      <td>19</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>634.0</td>\n",
       "      <td>878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>lunes</td>\n",
       "      <td>20</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>750.0</td>\n",
       "      <td>581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>martes</td>\n",
       "      <td>21</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>802.0</td>\n",
       "      <td>676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>miercoles</td>\n",
       "      <td>22</td>\n",
       "      <td>julio</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>832.0</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Día de la semana  Día    Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "2293          domingo   12  julio  2020 2020-07-12   2550.0       605.0   \n",
       "2294            lunes   13  julio  2020 2020-07-13   1835.0       711.0   \n",
       "2295           martes   14  julio  2020 2020-07-14   1782.0       774.0   \n",
       "2296        miercoles   15  julio  2020 2020-07-15   2008.0       738.0   \n",
       "2297           jueves   16  julio  2020 2020-07-16      NaN       749.0   \n",
       "2298          viernes   17  julio  2020 2020-07-17      NaN       858.0   \n",
       "2299           sabado   18  julio  2020 2020-07-18      NaN       706.0   \n",
       "2300          domingo   19  julio  2020 2020-07-19      NaN       634.0   \n",
       "2301            lunes   20  julio  2020 2020-07-20      NaN       750.0   \n",
       "2302           martes   21  julio  2020 2020-07-21      NaN       802.0   \n",
       "2303        miercoles   22  julio  2020 2020-07-22      NaN       832.0   \n",
       "\n",
       "      Patriotismo  \n",
       "2293        814.0  \n",
       "2294        565.0  \n",
       "2295        627.0  \n",
       "2296        643.0  \n",
       "2297        693.0  \n",
       "2298        697.0  \n",
       "2299        686.0  \n",
       "2300        878.0  \n",
       "2301        581.0  \n",
       "2302        676.0  \n",
       "2303        710.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador.iloc[2293:2304, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, the counter stops abruptly, while the other counters keep going without any problem. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "INSERT THE MISSING DATE ROWS. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To handle these missing value, I will use the **mean of the past month's corresponding day**. So, for example, if a value is missing on Sunday of July 2020 , I will replace it with the average of all Sundays from June 2020. I believe this gives us the best approximation. \n",
    "\n",
    "I discarded other alternatives because:\n",
    "- Using the _global average or the mode_ would be misleading, since the number of cyclists over time will probably fluctuate.\n",
    "- _Dropping_ the missing values would alter the total cyclists in a given year since the nulls are clustered in certain areas.\n",
    "- Using a _forward fill_ (that is, populating with the last valid observation) would be innaccurate, since each day will probably have different number of cyclists (see graph below). Also, in the previous linechart, we saw a spike in the counter just before the missing values, so it would populate with this outlier values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(contador.groupby('Día de la semana')[['Reforma', 'Revolución', 'Patriotismo']].mean(), cmap='Blues');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, weekends tend to be much less crowded.\n",
    "Therefore, let's use the mean of the past month's corresponding day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-7d520ddacd05>:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  means = pd.DataFrame(contador.groupby([ 'Año', 'Mes', 'Día de la semana'])['Reforma' ,'Revolución', 'Patriotismo'].mean()).sort_index().reset_index()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Día de la semana</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>domingo</td>\n",
       "      <td>1004.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>jueves</td>\n",
       "      <td>2044.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>lunes</td>\n",
       "      <td>1916.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>martes</td>\n",
       "      <td>1530.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>miercoles</td>\n",
       "      <td>1984.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>2021</td>\n",
       "      <td>febrero</td>\n",
       "      <td>lunes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>482.25</td>\n",
       "      <td>627.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>2021</td>\n",
       "      <td>febrero</td>\n",
       "      <td>martes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>481.50</td>\n",
       "      <td>708.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>2021</td>\n",
       "      <td>febrero</td>\n",
       "      <td>miercoles</td>\n",
       "      <td>NaN</td>\n",
       "      <td>446.75</td>\n",
       "      <td>716.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>2021</td>\n",
       "      <td>febrero</td>\n",
       "      <td>sabado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>460.00</td>\n",
       "      <td>772.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>2021</td>\n",
       "      <td>febrero</td>\n",
       "      <td>viernes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>567.25</td>\n",
       "      <td>745.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>588 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Año        Mes Día de la semana  Reforma  Revolución  Patriotismo\n",
       "0    2013  diciembre          domingo  1004.00        0.00         0.00\n",
       "1    2013  diciembre           jueves  2044.00        0.00         0.00\n",
       "2    2013  diciembre            lunes  1916.40        0.00         0.00\n",
       "3    2013  diciembre           martes  1530.20        0.00         0.00\n",
       "4    2013  diciembre        miercoles  1984.25        0.00         0.00\n",
       "..    ...        ...              ...      ...         ...          ...\n",
       "583  2021    febrero            lunes      NaN      482.25       627.75\n",
       "584  2021    febrero           martes      NaN      481.50       708.50\n",
       "585  2021    febrero        miercoles      NaN      446.75       716.00\n",
       "586  2021    febrero           sabado      NaN      460.00       772.25\n",
       "587  2021    febrero          viernes      NaN      567.25       745.50\n",
       "\n",
       "[588 rows x 6 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate mean of each day of each month and year. \n",
    "means = pd.DataFrame(contador.groupby([ 'Año', 'Mes', 'Día de la semana'])['Reforma' ,'Revolución', 'Patriotismo'].mean()).sort_index().reset_index()\n",
    "means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Día de la semana</th>\n",
       "      <th>Día</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Año</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Reforma</th>\n",
       "      <th>Revolución</th>\n",
       "      <th>Patriotismo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sabado</td>\n",
       "      <td>16</td>\n",
       "      <td>noviembre</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-11-16</td>\n",
       "      <td>613.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>domingo</td>\n",
       "      <td>17</td>\n",
       "      <td>noviembre</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-11-17</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lunes</td>\n",
       "      <td>18</td>\n",
       "      <td>noviembre</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-11-18</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Día de la semana  Día        Mes   Año      Fecha  Reforma  Revolución  \\\n",
       "0           sabado   16  noviembre  2013 2013-11-16    613.0         0.0   \n",
       "1          domingo   17  noviembre  2013 2013-11-17   1084.0         0.0   \n",
       "2            lunes   18  noviembre  2013 2013-11-18   1073.0         0.0   \n",
       "\n",
       "   Patriotismo  \n",
       "0          0.0  \n",
       "1          0.0  \n",
       "2          0.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contador.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define list of months and counters\n",
    "months = ['enero','febrero','marzo','abril','mayo','junio','julio','agosto','septiembre','octubre','noviembre',\n",
    " 'diciembre']\n",
    "counters = ['Reforma', 'Revolución', 'Patriotismo']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block of code substitutes the missing values. However, we should be wary of using any means in the month of July 2017, since we got outlier values in that month. For those cases, we could use the values for June 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Value: 2644.0 | Reforma | 1341 | Year: 2017 | Av. Month: junio | Month: julio | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1342 | Year: 2017 | Av. Month: junio | Month: julio | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1343 | Year: 2017 | Av. Month: junio | Month: julio | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1344 | Year: 2017 | Av. Month: junio | Month: julio | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1345 | Year: 2017 | Av. Month: junio | Month: julio | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1346 | Year: 2017 | Av. Month: junio | Month: julio | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1347 | Year: 2017 | Av. Month: junio | Month: julio | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1348 | Year: 2017 | Av. Month: junio | Month: julio | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1349 | Year: 2017 | Av. Month: junio | Month: julio | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1350 | Year: 2017 | Av. Month: junio | Month: julio | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1351 | Year: 2017 | Av. Month: junio | Month: julio | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1352 | Year: 2017 | Av. Month: junio | Month: julio | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1353 | Year: 2017 | Av. Month: junio | Month: julio | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1354 | Year: 2017 | Av. Month: junio | Month: agosto | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1355 | Year: 2017 | Av. Month: junio | Month: agosto | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1356 | Year: 2017 | Av. Month: junio | Month: agosto | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1357 | Year: 2017 | Av. Month: junio | Month: agosto | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1358 | Year: 2017 | Av. Month: junio | Month: agosto | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1359 | Year: 2017 | Av. Month: junio | Month: agosto | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1360 | Year: 2017 | Av. Month: junio | Month: agosto | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1361 | Year: 2017 | Av. Month: junio | Month: agosto | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1362 | Year: 2017 | Av. Month: junio | Month: agosto | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1363 | Year: 2017 | Av. Month: junio | Month: agosto | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1364 | Year: 2017 | Av. Month: junio | Month: agosto | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1365 | Year: 2017 | Av. Month: junio | Month: agosto | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1366 | Year: 2017 | Av. Month: junio | Month: agosto | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1367 | Year: 2017 | Av. Month: junio | Month: agosto | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1368 | Year: 2017 | Av. Month: junio | Month: agosto | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1369 | Year: 2017 | Av. Month: junio | Month: agosto | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1370 | Year: 2017 | Av. Month: junio | Month: agosto | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1371 | Year: 2017 | Av. Month: junio | Month: agosto | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1372 | Year: 2017 | Av. Month: junio | Month: agosto | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1373 | Year: 2017 | Av. Month: junio | Month: agosto | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1374 | Year: 2017 | Av. Month: junio | Month: agosto | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1375 | Year: 2017 | Av. Month: junio | Month: agosto | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1376 | Year: 2017 | Av. Month: junio | Month: agosto | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1377 | Year: 2017 | Av. Month: junio | Month: agosto | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1378 | Year: 2017 | Av. Month: junio | Month: agosto | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1379 | Year: 2017 | Av. Month: junio | Month: agosto | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1380 | Year: 2017 | Av. Month: junio | Month: agosto | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1381 | Year: 2017 | Av. Month: junio | Month: agosto | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1382 | Year: 2017 | Av. Month: junio | Month: agosto | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1383 | Year: 2017 | Av. Month: junio | Month: agosto | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1384 | Year: 2017 | Av. Month: junio | Month: agosto | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1385 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1386 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1387 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1388 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1389 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1390 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1391 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1392 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1393 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1394 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1395 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1396 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1397 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1398 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1399 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1400 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1401 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1402 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1403 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1404 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1405 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1406 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1407 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1408 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1409 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1410 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1411 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1412 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1413 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1414 | Year: 2017 | Av. Month: junio | Month: septiembre | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1415 | Year: 2017 | Av. Month: junio | Month: octubre | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1416 | Year: 2017 | Av. Month: junio | Month: octubre | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1417 | Year: 2017 | Av. Month: junio | Month: octubre | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1418 | Year: 2017 | Av. Month: junio | Month: octubre | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1419 | Year: 2017 | Av. Month: junio | Month: octubre | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1420 | Year: 2017 | Av. Month: junio | Month: octubre | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1421 | Year: 2017 | Av. Month: junio | Month: octubre | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1422 | Year: 2017 | Av. Month: junio | Month: octubre | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1423 | Year: 2017 | Av. Month: junio | Month: octubre | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1424 | Year: 2017 | Av. Month: junio | Month: octubre | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1425 | Year: 2017 | Av. Month: junio | Month: octubre | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1426 | Year: 2017 | Av. Month: junio | Month: octubre | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1427 | Year: 2017 | Av. Month: junio | Month: octubre | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1428 | Year: 2017 | Av. Month: junio | Month: octubre | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1429 | Year: 2017 | Av. Month: junio | Month: octubre | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1430 | Year: 2017 | Av. Month: junio | Month: octubre | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1431 | Year: 2017 | Av. Month: junio | Month: octubre | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1432 | Year: 2017 | Av. Month: junio | Month: octubre | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1433 | Year: 2017 | Av. Month: junio | Month: octubre | Day: jueves\n",
      "- Value: 2929.6 | Reforma | 1434 | Year: 2017 | Av. Month: junio | Month: octubre | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1435 | Year: 2017 | Av. Month: junio | Month: octubre | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1436 | Year: 2017 | Av. Month: junio | Month: octubre | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1437 | Year: 2017 | Av. Month: junio | Month: octubre | Day: lunes\n",
      "- Value: 2104.5 | Reforma | 1438 | Year: 2017 | Av. Month: junio | Month: octubre | Day: martes\n",
      "- Value: 2644.0 | Reforma | 1439 | Year: 2017 | Av. Month: junio | Month: octubre | Day: miercoles\n",
      "- Value: 2947.0 | Reforma | 1440 | Year: 2017 | Av. Month: junio | Month: octubre | Day: jueves\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Value: 2929.6 | Reforma | 1441 | Year: 2017 | Av. Month: junio | Month: octubre | Day: viernes\n",
      "- Value: 1099.25 | Reforma | 1442 | Year: 2017 | Av. Month: junio | Month: octubre | Day: sabado\n",
      "- Value: 1157.5 | Reforma | 1443 | Year: 2017 | Av. Month: junio | Month: octubre | Day: domingo\n",
      "- Value: 2129.75 | Reforma | 1444 | Year: 2017 | Av. Month: junio | Month: octubre | Day: lunes\n",
      "- Value: 3426.4 | Reforma | 1502 | Year: 2017 | Av. Month: noviembre | Month: diciembre | Day: miercoles\n",
      "- Value: 2639.4 | Reforma | 1503 | Year: 2017 | Av. Month: noviembre | Month: diciembre | Day: jueves\n",
      "- Value: 2564.75 | Reforma | 1504 | Year: 2017 | Av. Month: noviembre | Month: diciembre | Day: viernes\n",
      "- Value: 2454.0 | Reforma | 1505 | Year: 2017 | Av. Month: noviembre | Month: diciembre | Day: sabado\n",
      "- Value: 1345.0 | Reforma | 1506 | Year: 2017 | Av. Month: noviembre | Month: diciembre | Day: domingo\n",
      "- Value: 2398.0 | Reforma | 1507 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: lunes\n",
      "- Value: 2260.75 | Reforma | 1508 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: martes\n",
      "- Value: 2163.3333333333335 | Reforma | 1509 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: miercoles\n",
      "- Value: 3055.0 | Reforma | 1510 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: jueves\n",
      "- Value: 2904.5 | Reforma | 1511 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: viernes\n",
      "- Value: 1376.5 | Reforma | 1512 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: sabado\n",
      "- Value: 970.0 | Reforma | 1513 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: domingo\n",
      "- Value: 2398.0 | Reforma | 1514 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: lunes\n",
      "- Value: 2260.75 | Reforma | 1515 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: martes\n",
      "- Value: 2163.3333333333335 | Reforma | 1516 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: miercoles\n",
      "- Value: 3055.0 | Reforma | 1517 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: jueves\n",
      "- Value: 2904.5 | Reforma | 1518 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: viernes\n",
      "- Value: 1376.5 | Reforma | 1519 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: sabado\n",
      "- Value: 970.0 | Reforma | 1520 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: domingo\n",
      "- Value: 2398.0 | Reforma | 1521 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: lunes\n",
      "- Value: 2260.75 | Reforma | 1522 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: martes\n",
      "- Value: 2163.3333333333335 | Reforma | 1523 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: miercoles\n",
      "- Value: 3055.0 | Reforma | 1524 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: jueves\n",
      "- Value: 2904.5 | Reforma | 1525 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: viernes\n",
      "- Value: 1376.5 | Reforma | 1526 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: sabado\n",
      "- Value: 970.0 | Reforma | 1527 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: domingo\n",
      "- Value: 2398.0 | Reforma | 1528 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: lunes\n",
      "- Value: 2260.75 | Reforma | 1529 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: martes\n",
      "- Value: 2163.3333333333335 | Reforma | 1530 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: miercoles\n",
      "- Value: 3055.0 | Reforma | 1531 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: jueves\n",
      "- Value: 2904.5 | Reforma | 1532 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: viernes\n",
      "- Value: 1376.5 | Reforma | 1533 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: sabado\n",
      "- Value: 970.0 | Reforma | 1534 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: domingo\n",
      "- Value: 2398.0 | Reforma | 1535 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: lunes\n",
      "- Value: 2260.75 | Reforma | 1536 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: martes\n",
      "- Value: 2163.3333333333335 | Reforma | 1537 | Year: 2017 | Av. Month: diciembre | Month: enero | Day: miercoles\n",
      "- Value: 2724.0 | Reforma | 1725 | Year: 2018 | Av. Month: junio | Month: agosto | Day: martes\n",
      "- Value: 2819.5 | Reforma | 1726 | Year: 2018 | Av. Month: junio | Month: agosto | Day: miercoles\n",
      "- Value: 2586.5 | Reforma | 1727 | Year: 2018 | Av. Month: junio | Month: agosto | Day: jueves\n",
      "- Value: 2748.4 | Reforma | 1728 | Year: 2018 | Av. Month: junio | Month: agosto | Day: viernes\n",
      "- Value: 1375.4 | Reforma | 1729 | Year: 2018 | Av. Month: junio | Month: agosto | Day: sabado\n",
      "- Value: 1535.5 | Reforma | 1730 | Year: 2018 | Av. Month: junio | Month: agosto | Day: domingo\n",
      "- Value: 2029.75 | Reforma | 1731 | Year: 2018 | Av. Month: junio | Month: agosto | Day: lunes\n",
      "- Value: 1735.0 | Reforma | 2297 | Year: 2020 | Av. Month: junio | Month: julio | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2298 | Year: 2020 | Av. Month: junio | Month: julio | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2299 | Year: 2020 | Av. Month: junio | Month: julio | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2300 | Year: 2020 | Av. Month: junio | Month: julio | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2301 | Year: 2020 | Av. Month: junio | Month: julio | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2302 | Year: 2020 | Av. Month: junio | Month: julio | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2303 | Year: 2020 | Av. Month: junio | Month: julio | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2304 | Year: 2020 | Av. Month: junio | Month: julio | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2305 | Year: 2020 | Av. Month: junio | Month: julio | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2306 | Year: 2020 | Av. Month: junio | Month: julio | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2307 | Year: 2020 | Av. Month: junio | Month: julio | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2308 | Year: 2020 | Av. Month: junio | Month: julio | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2309 | Year: 2020 | Av. Month: junio | Month: julio | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2310 | Year: 2020 | Av. Month: junio | Month: julio | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2311 | Year: 2020 | Av. Month: junio | Month: julio | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2312 | Year: 2020 | Av. Month: junio | Month: julio | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2313 | Year: 2020 | Av. Month: junio | Month: agosto | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2314 | Year: 2020 | Av. Month: junio | Month: agosto | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2315 | Year: 2020 | Av. Month: junio | Month: agosto | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2316 | Year: 2020 | Av. Month: junio | Month: agosto | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2317 | Year: 2020 | Av. Month: junio | Month: agosto | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2318 | Year: 2020 | Av. Month: junio | Month: agosto | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2319 | Year: 2020 | Av. Month: junio | Month: agosto | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2320 | Year: 2020 | Av. Month: junio | Month: agosto | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2321 | Year: 2020 | Av. Month: junio | Month: agosto | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2322 | Year: 2020 | Av. Month: junio | Month: agosto | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2323 | Year: 2020 | Av. Month: junio | Month: agosto | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2324 | Year: 2020 | Av. Month: junio | Month: agosto | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2325 | Year: 2020 | Av. Month: junio | Month: agosto | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2326 | Year: 2020 | Av. Month: junio | Month: agosto | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2327 | Year: 2020 | Av. Month: junio | Month: agosto | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2328 | Year: 2020 | Av. Month: junio | Month: agosto | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2329 | Year: 2020 | Av. Month: junio | Month: agosto | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2330 | Year: 2020 | Av. Month: junio | Month: agosto | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2331 | Year: 2020 | Av. Month: junio | Month: agosto | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2332 | Year: 2020 | Av. Month: junio | Month: agosto | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2333 | Year: 2020 | Av. Month: junio | Month: agosto | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2334 | Year: 2020 | Av. Month: junio | Month: agosto | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2335 | Year: 2020 | Av. Month: junio | Month: agosto | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2336 | Year: 2020 | Av. Month: junio | Month: agosto | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2337 | Year: 2020 | Av. Month: junio | Month: agosto | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2338 | Year: 2020 | Av. Month: junio | Month: agosto | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2339 | Year: 2020 | Av. Month: junio | Month: agosto | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2340 | Year: 2020 | Av. Month: junio | Month: agosto | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2341 | Year: 2020 | Av. Month: junio | Month: agosto | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2342 | Year: 2020 | Av. Month: junio | Month: agosto | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2343 | Year: 2020 | Av. Month: junio | Month: agosto | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2344 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2345 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2346 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2347 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2348 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2349 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2350 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2351 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2352 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2353 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2354 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2355 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2356 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2357 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2358 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2359 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2360 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2361 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2362 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2363 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2364 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2365 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2366 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2367 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2368 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2369 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2370 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2371 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2372 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2373 | Year: 2020 | Av. Month: junio | Month: septiembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2374 | Year: 2020 | Av. Month: junio | Month: octubre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2375 | Year: 2020 | Av. Month: junio | Month: octubre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2376 | Year: 2020 | Av. Month: junio | Month: octubre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2377 | Year: 2020 | Av. Month: junio | Month: octubre | Day: domingo\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Value: 1396.2 | Reforma | 2378 | Year: 2020 | Av. Month: junio | Month: octubre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2379 | Year: 2020 | Av. Month: junio | Month: octubre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2380 | Year: 2020 | Av. Month: junio | Month: octubre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2381 | Year: 2020 | Av. Month: junio | Month: octubre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2382 | Year: 2020 | Av. Month: junio | Month: octubre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2383 | Year: 2020 | Av. Month: junio | Month: octubre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2384 | Year: 2020 | Av. Month: junio | Month: octubre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2385 | Year: 2020 | Av. Month: junio | Month: octubre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2386 | Year: 2020 | Av. Month: junio | Month: octubre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2387 | Year: 2020 | Av. Month: junio | Month: octubre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2388 | Year: 2020 | Av. Month: junio | Month: octubre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2389 | Year: 2020 | Av. Month: junio | Month: octubre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2390 | Year: 2020 | Av. Month: junio | Month: octubre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2391 | Year: 2020 | Av. Month: junio | Month: octubre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2392 | Year: 2020 | Av. Month: junio | Month: octubre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2393 | Year: 2020 | Av. Month: junio | Month: octubre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2394 | Year: 2020 | Av. Month: junio | Month: octubre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2395 | Year: 2020 | Av. Month: junio | Month: octubre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2396 | Year: 2020 | Av. Month: junio | Month: octubre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2397 | Year: 2020 | Av. Month: junio | Month: octubre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2398 | Year: 2020 | Av. Month: junio | Month: octubre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2399 | Year: 2020 | Av. Month: junio | Month: octubre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2400 | Year: 2020 | Av. Month: junio | Month: octubre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2401 | Year: 2020 | Av. Month: junio | Month: octubre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2402 | Year: 2020 | Av. Month: junio | Month: octubre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2403 | Year: 2020 | Av. Month: junio | Month: octubre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2404 | Year: 2020 | Av. Month: junio | Month: octubre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2405 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2406 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2407 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2408 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2409 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2410 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2411 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2412 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2413 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2414 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2415 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2416 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2417 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2418 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2419 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2420 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2421 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2422 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2423 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2424 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2425 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2426 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2427 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2428 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2429 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2430 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2431 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2432 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2433 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2434 | Year: 2020 | Av. Month: junio | Month: noviembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2435 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2436 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2437 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2438 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2439 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2440 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2441 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2442 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2443 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2444 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2445 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2446 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2447 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2448 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2449 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2450 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2451 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2452 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2453 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2454 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2455 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2456 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2457 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2458 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: jueves\n",
      "- Value: 1804.0 | Reforma | 2459 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: viernes\n",
      "- Value: 1801.75 | Reforma | 2460 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: sabado\n",
      "- Value: 2312.75 | Reforma | 2461 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: domingo\n",
      "- Value: 1396.2 | Reforma | 2462 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: lunes\n",
      "- Value: 1644.6 | Reforma | 2463 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: martes\n",
      "- Value: 1715.75 | Reforma | 2464 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: miercoles\n",
      "- Value: 1735.0 | Reforma | 2465 | Year: 2020 | Av. Month: junio | Month: diciembre | Day: jueves\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "single positional indexer is out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-fdcea410c5d5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m                 \u001b[1;31m# Get mean of past month\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m                 \u001b[0mmean_last_available_month\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmeans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAño\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0myear_last_available_month\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mmeans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMes\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlast_available_month\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mmeans\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Día de la semana'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mday\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     29\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m             \u001b[1;31m# Assign value to cell\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 895\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1499\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1500\u001b[0m             \u001b[1;31m# validate the location\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1501\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1502\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1503\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1442\u001b[0m         \u001b[0mlen_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1443\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0mlen_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1444\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"single positional indexer is out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1445\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1446\u001b[0m     \u001b[1;31m# -------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
     ]
    }
   ],
   "source": [
    "# Fill missing values with the mean of the past month's corresponding day.\n",
    "for counter in counters:\n",
    "    for i in contador.index.tolist():\n",
    "        if pd.isnull(contador.loc[i, counter]):\n",
    "            # Get current date\n",
    "            day = contador['Día de la semana'][i]\n",
    "            month = contador['Mes'][i]\n",
    "            year = contador['Año'][i]\n",
    "\n",
    "            # Get mean of last available month.\n",
    "            mean_last_available_month = np.NaN\n",
    "            index_adjuster = 0\n",
    "            while pd.isnull(mean_last_available_month):\n",
    "                # Get past month and its year\n",
    "                index_adjuster -= 1\n",
    "                last_available_month = months[months.index(month)+index_adjuster]\n",
    "                year_last_available_month = year\n",
    "\n",
    "                if last_available_month == 'diciembre':\n",
    "                    year_last_available_month -= 1\n",
    "\n",
    "                ### Avoid outlier values from July 2017\n",
    "                if last_available_month == 'julio':\n",
    "                    index_adjuster -= 1\n",
    "                    last_available_month = months[months.index(month)+index_adjuster]\n",
    "\n",
    "                # Get mean of past month\n",
    "                mean_last_available_month = means.loc[(means.Año == year_last_available_month) & (means.Mes == last_available_month) & (means['Día de la semana'] == day), counter].iloc[0]         \n",
    "\n",
    "            # Assign value to cell \n",
    "            contador.loc[i, counter] = mean_last_available_month\n",
    "\n",
    "            # Print value assigned to cell\n",
    "            print(f'- Value: {mean_last_available_month} | {counter} | {i} | Year: {year_last_available_month} | Av. Month: {last_available_month} | Month: {month} | Day: {day}')\n",
    "            \n",
    "            \n",
    "except:\n",
    "    I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By doing a quick scan of the values we used for replacement, there don't appear to be any outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contador.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, we do not have any missing values. Let's see how our new dataset looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme()\n",
    "data_lineplot = pd.melt(contador[['Fecha', 'Reforma', 'Revolución', 'Patriotismo']], 'Fecha', var_name='contador', value_name='número')\n",
    "sns.lineplot(x='Fecha', y='número', hue='contador', data=data_lineplot)\n",
    "\n",
    "#Change figure size\n",
    "sns.set(rc={'figure.figsize':(16,9)});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create column with total cyclists of a given day\n",
    "contador['total'] = contador['Patriotismo'] + contador['Revolución'] + contador['Reforma']\n",
    "contador['total'] = contador['total'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change column type to integer.\n",
    "for counter in counters:\n",
    "    contador[counter] = contador[counter].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contador.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contador_final = contador.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contador_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contador_final.to_csv('Data/contador_final.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Road accidents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's work only with the columns that seem relevant for our purposes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes = incidentes[['dia_semana', 'fecha_creacion', 'hora_creacion', 'mes', 'delegacion_inicio', 'latitud', 'longitud', 'geopoint']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should transform the date and hour columns to a single datetime column. Also, let's see the distribution of hours of the reports, to check whether there are any mistakes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes.hora_creacion.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the hours seem to be properly registered. Now let's check the format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes.loc[incidentes['hora_creacion'].str.match(r'\\d\\d:\\d\\d:\\d\\d') == False, 'hora_creacion']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have multiple values that do not have the proper format. Let's see if they represent time expressed as number of seconds, in decimal form. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = \"11:12:21\"\n",
    "(h, m, s) = t.split(':')\n",
    "result = int(h) * 3600 + int(m) * 60 + int(s)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No, the numbers we had before are too large to represent seconds. Also, they do not seem to have a distinguishable pattern. Therefore, let's just drop them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(incidentes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes = incidentes[incidentes['hora_creacion'].str.match(r'\\d\\d:\\d\\d:\\d\\d') == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(incidentes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's proceed to transform the time columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean day column.\n",
    "incidentes['dia_semana'] = incidentes['dia_semana'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine date and time columns\n",
    "fecha_hora = pd.Series(incidentes['fecha_creacion'] + ' ' + incidentes['hora_creacion'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert new column\n",
    "incidentes.insert(1, 'fecha_hora', fecha_hora)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change new column to datetime format\n",
    "incidentes['fecha_hora'] = pd.to_datetime(incidentes['fecha_hora'], format='%d/%m/%Y %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change type of time columns\n",
    "incidentes['fecha_creacion'] = pd.to_datetime(incidentes['fecha_creacion'], format='%d/%m/%Y')\n",
    "incidentes['hora_creacion'] = pd.to_datetime(incidentes['hora_creacion'], format='%H:%M:%S').dt.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop extra columns\n",
    "incidentes.drop(['fecha_creacion', 'mes'], axis=1, inplace=True)\n",
    "\n",
    "# Create new columns with year, month, and hour. \n",
    "incidentes.insert(2, 'hora', incidentes['fecha_hora'].dt.hour)\n",
    "incidentes.insert(2, 'mes', incidentes['fecha_hora'].dt.month)\n",
    "incidentes.insert(2, 'año', incidentes['fecha_hora'].dt.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove special characters\n",
    "incidentes['dia_semana'] = incidentes['dia_semana'].str.replace('á', 'a').str.replace('é', 'e')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform months back to text\n",
    "incidentes['mes'] = incidentes['mes'].map(dict_meses_inverso)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes_final = incidentes.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidentes_final.to_csv('Data/incidentes_final.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Estudio 2018 Ciclismo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estudio.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have too many columns, so let's expand the default display configuration to analyze them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)\n",
    "estudio.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is based on a large questionaire and, unfortunately, it is label encoded and abreviated. Therefore, one has to read the [accompanying file](https://github.com/FranciscoGalan/Bicycle_Commuting_Mexico_City/blob/main/Data/Diccionario%20Estudio%20de%20Conteo%20Ciclista%202018.csv) of the dataset, which explains what each label means. \n",
    "\n",
    "I already did a preselection of the columns that interest me, so I'll proceed to select them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "columnas = ['CALLE_LEV', 'F1', 'P1', 'P3', 'P6', 'P15', 'P18', 'P19', 'P20', 'P21_NIVEL', 'P23_1']\n",
    "estudio1 = estudio.copy()\n",
    "estudio1 = estudio1[columnas]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's change the names of the columns so they are easier to understand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dictionary to rename columns\n",
    "nombres = ['calle_lev', 'edad', 'origen', 'destino', 'motivo_viaje', 'motivo_dejar_bici', 'robado_bici', 'robo_forma', 'sexo', 'educacion', 'ingreso_mensual']\n",
    "ch_columnas = estudio1.columns.tolist()\n",
    "res = {ch_columnas[i]: nombres[i] for i in range(len(ch_columnas))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Rename columns\n",
    "estudio1 = estudio1.rename(columns=res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should also remove the label encoding and insert, instead, the textual information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dictionaries with number-text answer\n",
    "dict_origen = {1:'Hogar',\n",
    "              2:'Trabajo',\n",
    "              3:'Escuela',\n",
    "              4:'Centro comercial, tienda, mercado',\n",
    "              5:'Otra vivienda',\n",
    "              6:'Restaurante, bar, cafetería',\n",
    "              7:'Otro',\n",
    "              98:'No sabe',\n",
    "              99:'No sabe'}\n",
    "dict_destino = {1:'Hogar',\n",
    "              2:'Trabajo',\n",
    "              3:'Escuela',\n",
    "              4:'Centro comercial, tienda, mercado',\n",
    "              5:'Otra vivienda',\n",
    "              6:'Restaurante, bar, cafetería',\n",
    "              7:'Otro',\n",
    "              98:'No sabe',\n",
    "              99:'No sabe'}\n",
    "dict_motivo_viaje = {1:'Ir al trabajo',\n",
    "                    2:'Regreso a casa',\n",
    "                    3:'Ir a estudiar',\n",
    "                    4:'Hacer compras',\n",
    "                    5:'Llevar o recoger a alguien',\n",
    "                    6:'Recreación',\n",
    "                    7:'Ir a comer',\n",
    "                    8:'Relacionado con el trabajo',\n",
    "                    9:'Trámite',\n",
    "                    10:'Ir de visita',\n",
    "                    98:'No sabe',\n",
    "                    99:'No contestó'}\n",
    "dict_motivo_dejar_bici = {1:'Lesión o incapacidad',\n",
    "                         2:'Fala mecánica de bici',\n",
    "                         3: 'Cambio de trabajo',\n",
    "                         4: 'Llevar prisa',\n",
    "                         5: 'Recorrido muy largo',\n",
    "                         6: 'Falta de infraestructura',\n",
    "                         7: 'Clima',\n",
    "                         8: 'Inseguridad',\n",
    "                         9: 'No haya bici disponible',\n",
    "                         10: 'Ningún motivo',\n",
    "                         11: 'No sé',\n",
    "                         12: 'No contestó'}\n",
    "dict_robado_bici = {1:'Sí', 2:'No', 8:'No sabe', 9:'No contestó'}\n",
    "dict_robo_forma = {1:'Estacionada', 2:'En movimiento', 8:'No sabe', 9:'No contestó'}\n",
    "dict_sexo = {1: 'Hombre', 2:'Mujer'}\n",
    "dict_educacion = {1:'Ninguno',\n",
    "                 2:'Preescolar',\n",
    "                 3:'Primaria',\n",
    "                 4:'Secundaria',\n",
    "                 5:'Preparatoria o Bachillerato',\n",
    "                 6:'Normal',\n",
    "                 7:'Carrera Técnica',\n",
    "                 8:'Licenciatura',\n",
    "                 9:'Maestría',\n",
    "                 10:'Doctorado',\n",
    "                 98:'No sabe',\n",
    "                  99:'No contestó'}\n",
    "dict_ingreso_mensual = {1:'>1,000',\n",
    "                       2:'1,001-3,000',\n",
    "                       3:'3,001-6,000',\n",
    "                       4:'6,001-10,000',\n",
    "                       5:'10,001-15000',\n",
    "                       6:'<15,001',\n",
    "                       9:'No contestó'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change values in columns\n",
    "columns = estudio1.columns[2:].to_list()\n",
    "dictionaries = [dict_origen, dict_destino, dict_motivo_viaje, dict_motivo_dejar_bici, dict_robado_bici,\n",
    "              dict_robo_forma, dict_sexo, dict_educacion, dict_ingreso_mensual]\n",
    "\n",
    "for i in range(len(columns)):\n",
    "    estudio1[columns[i]] = estudio1[columns[i]].map(dictionaries[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estudio1.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estudio_final = estudio1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estudio_final.to_csv('Data/estudio_final.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save dataset of motivo_dejar bici"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = pd.Series(estudio_final['motivo_dejar_bici'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.to_csv('Data/motivo_dejar_final.csv')"
   ]
  }
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