lab-py-AMF-timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab | Series de tiempo\n",
    "\n",
    "### Aída Moure Fernández"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Buscar los datos disponibles en la web:https://data.buenosaires.gob.ar/dataset/flujo-vehicular-por-unidades-de-peaje-ausa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2020 = pd.read_csv('Data/flujo-vehicular-2020.csv', sep =',', parse_dates=['fecha'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2533743 entries, 0 to 2533742\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Dtype  \n",
      "---  ------          -----  \n",
      " 0   periodo         float64\n",
      " 1   fecha           object \n",
      " 2   mes             object \n",
      " 3   dia             object \n",
      " 4   dia_fecha       float64\n",
      " 5   hora_inicio     float64\n",
      " 6   hora_fin        float64\n",
      " 7   estacion        object \n",
      " 8   tipo_vehiculo   object \n",
      " 9   forma_pago      object \n",
      " 10  observacion     object \n",
      " 11  sentido         object \n",
      " 12  cantidad_pasos  float64\n",
      "dtypes: float64(5), object(8)\n",
      "memory usage: 251.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data_2020.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>periodo</th>\n",
       "      <th>fecha</th>\n",
       "      <th>mes</th>\n",
       "      <th>dia</th>\n",
       "      <th>dia_fecha</th>\n",
       "      <th>hora_inicio</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>estacion</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>cantidad_pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1605209</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-08-23</td>\n",
       "      <td>agosto</td>\n",
       "      <td>Domingo</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>ILP</td>\n",
       "      <td>Pesados 5 Ejes</td>\n",
       "      <td>Tag</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Centro</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         periodo       fecha     mes      dia  dia_fecha  hora_inicio  \\\n",
       "1605209   2020.0  2020-08-23  agosto  Domingo       23.0          5.0   \n",
       "\n",
       "         hora_fin estacion   tipo_vehiculo forma_pago    observacion sentido  \\\n",
       "1605209       6.0      ILP  Pesados 5 Ejes        Tag  Tag - PosPago  Centro   \n",
       "\n",
       "         cantidad_pasos  \n",
       "1605209            41.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2020.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fecha\n",
       "2020-12-18             9076\n",
       "2020-02-21 00:00:00    9067\n",
       "2020-12-21             9052\n",
       "2020-03-05 00:00:00    9043\n",
       "2020-12-22             9041\n",
       "                       ... \n",
       "2020-03-28 00:00:00    3172\n",
       "2020-05-03             3119\n",
       "2020-04-12             2971\n",
       "2020-03-22 00:00:00    2838\n",
       "2020-03-29 00:00:00    2725\n",
       "Name: count, Length: 366, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2020.fecha.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 2533743 entries, 0 to 2533742\n",
      "Series name: fecha\n",
      "Non-Null Count    Dtype \n",
      "--------------    ----- \n",
      "2533743 non-null  object\n",
      "dtypes: object(1)\n",
      "memory usage: 19.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data_2020.fecha.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 2533743 entries, 0 to 2533742\n",
      "Series name: fecha\n",
      "Non-Null Count    Dtype         \n",
      "--------------    -----         \n",
      "2533743 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1)\n",
      "memory usage: 19.3 MB\n"
     ]
    }
   ],
   "source": [
    "data_2020.sample()\n",
    "data_2020['fecha'] = pd.to_datetime(data_2020['fecha'], format=\"mixed\")\n",
    "data_2020.fecha.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 2533743 entries, 0 to 2533742\n",
      "Series name: cantidad_pasos\n",
      "Non-Null Count    Dtype  \n",
      "--------------    -----  \n",
      "2533743 non-null  float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 19.3 MB\n"
     ]
    }
   ],
   "source": [
    "data_2020.cantidad_pasos.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2020['cantidad_pasos'] = data_2020['cantidad_pasos'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>periodo</th>\n",
       "      <th>fecha</th>\n",
       "      <th>mes</th>\n",
       "      <th>dia</th>\n",
       "      <th>dia_fecha</th>\n",
       "      <th>hora_inicio</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>estacion</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>cantidad_pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2289683</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-11-18</td>\n",
       "      <td>noviembre</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>AVELLANEDA</td>\n",
       "      <td>Pesados 6 Ejes</td>\n",
       "      <td>Tag</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         periodo      fecha        mes        dia  dia_fecha  hora_inicio  \\\n",
       "2289683   2020.0 2020-11-18  noviembre  Miércoles       18.0         22.0   \n",
       "\n",
       "         hora_fin    estacion   tipo_vehiculo forma_pago    observacion  \\\n",
       "2289683      23.0  AVELLANEDA  Pesados 6 Ejes        Tag  Tag - PosPago   \n",
       "\n",
       "           sentido  cantidad_pasos  \n",
       "2289683  Provincia              17  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2020.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  24,   80,    1, ..., 2126, 2452, 1817])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2020.cantidad_pasos.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DatetimeArray>\n",
       "['2020-01-01 00:00:00', '2020-01-02 00:00:00', '2020-01-03 00:00:00',\n",
       " '2020-01-04 00:00:00', '2020-01-05 00:00:00', '2020-01-06 00:00:00',\n",
       " '2020-01-07 00:00:00', '2020-01-08 00:00:00', '2020-01-09 00:00:00',\n",
       " '2020-01-10 00:00:00',\n",
       " ...\n",
       " '2020-12-22 00:00:00', '2020-12-23 00:00:00', '2020-12-24 00:00:00',\n",
       " '2020-12-25 00:00:00', '2020-12-26 00:00:00', '2020-12-27 00:00:00',\n",
       " '2020-12-28 00:00:00', '2020-12-29 00:00:00', '2020-12-30 00:00:00',\n",
       " '2020-12-31 00:00:00']\n",
       "Length: 366, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2020.fecha.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2020 = data_2020.rename(columns={'periodo':'año', 'cantidad_pasos':'pasos', 'dia_fecha':'dia_semana'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "floats=data_2020[['año', 'dia_semana', 'hora_inicio', 'hora_fin']]\n",
    "for i in floats:\n",
    "    data_2020[i]=data_2020[i].astype(float).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2533743 entries, 0 to 2533742\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Dtype         \n",
      "---  ------         -----         \n",
      " 0   año            int32         \n",
      " 1   fecha          datetime64[ns]\n",
      " 2   mes            object        \n",
      " 3   dia            object        \n",
      " 4   dia_semana     int32         \n",
      " 5   hora_inicio    int32         \n",
      " 6   hora_fin       int32         \n",
      " 7   estacion       object        \n",
      " 8   tipo_vehiculo  object        \n",
      " 9   forma_pago     object        \n",
      " 10  observacion    object        \n",
      " 11  sentido        object        \n",
      " 12  pasos          int32         \n",
      "dtypes: datetime64[ns](1), int32(5), object(7)\n",
      "memory usage: 203.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data_2020.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\3108720436.py:1: DtypeWarning: Columns (0,2,3,9) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_2021 = pd.read_csv('Data/flujo-vehicular-2021.csv', sep =',')\n"
     ]
    }
   ],
   "source": [
    "data_2021 = pd.read_csv('Data/flujo-vehicular-2021.csv', sep =',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
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       "\n",
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       "        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 de Fecha Operativa</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>Día de Fecha Operativa</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Mes de Fecha Operativa</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2271058</th>\n",
       "      <td>2021</td>\n",
       "      <td>Pesados 6 Ejes</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>PB1</td>\n",
       "      <td>septiembre</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Año de Fecha Operativa     Cat Cobrada Día de Fecha Operativa Hora HH  \\\n",
       "2271058                   2021  Pesados 6 Ejes                     18       8   \n",
       "\n",
       "        ID Peaje Mes de Fecha Operativa    Observacion    Sentido Tipo Cobro  \\\n",
       "2271058      PB1             septiembre  Tag - PosPago  Provincia        Tag   \n",
       "\n",
       "        Pasos  \n",
       "2271058    50  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2021.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = []\n",
    "for i in range(len(data_2021.columns)):\n",
    "    cols.append(data_2021.columns[i].lower())\n",
    "data_2021.columns = cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 4, 1, ..., 5918, 3880, 11684739], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2021.pasos.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 3029367 entries, 0 to 3029366\n",
      "Series name: pasos\n",
      "Non-Null Count    Dtype \n",
      "--------------    ----- \n",
      "3029367 non-null  object\n",
      "dtypes: object(1)\n",
      "memory usage: 23.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data_2021.pasos.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "año de fecha operativa\n",
       "2021             2228224\n",
       "2021              801130\n",
       "Total general         13\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2021['año de fecha operativa'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2021 = data_2021[data_2021['año de fecha operativa'] != \"Total general\"] #ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Index: 3029354 entries, 0 to 3029365\n",
      "Series name: pasos\n",
      "Non-Null Count    Dtype\n",
      "--------------    -----\n",
      "3029354 non-null  int32\n",
      "dtypes: int32(1)\n",
      "memory usage: 34.7 MB\n"
     ]
    }
   ],
   "source": [
    "data_2021['pasos'] = data_2021['pasos'].astype(float)\n",
    "data_2021['pasos'] = data_2021['pasos'].astype(int)\n",
    "data_2021.pasos.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2021 = data_2021.rename(columns={'año de fecha operativa':'año', 'Día de Fecha Operativa':'dia', 'mes de fecha operativa':'mes', 'tipo cobro':'forma_pago', 'cat cobrada':'tipo_vehiculo','día de fecha operativa':'dia', 'hora hh':'hora_fin', 'id peaje':'estacion'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2021 = data_2021[[\"año\", \"mes\", \"dia\", \"hora_fin\", \"estacion\", \"tipo_vehiculo\", \"forma_pago\", \"observacion\", \"sentido\", \"pasos\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "mes_num = {'enero': 1, 'febrero': 2, 'marzo': 3, 'abril': 4, 'mayo': 5, 'junio': 6, 'julio': 7, 'agosto': 8, 'septiembre': 9, 'octubre': 10, 'noviembre': 11, 'diciembre': 12}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2021['mes_num'] = data_2021['mes'].map(mes_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2021 = data_2021[[\"año\", \"mes\", \"mes_num\", \"dia\", \"hora_fin\", \"estacion\", \"tipo_vehiculo\", \"forma_pago\", \"observacion\", \"sentido\", \"pasos\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>año</th>\n",
       "      <th>mes</th>\n",
       "      <th>mes_num</th>\n",
       "      <th>dia</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>estacion</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1382076</th>\n",
       "      <td>2021</td>\n",
       "      <td>julio</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>DEL</td>\n",
       "      <td>Auto</td>\n",
       "      <td>Tarjeta Magnética</td>\n",
       "      <td>T.Magnética Disc.</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          año    mes  mes_num dia hora_fin estacion tipo_vehiculo  \\\n",
       "1382076  2021  julio        7  21       12      DEL          Auto   \n",
       "\n",
       "                forma_pago        observacion    sentido  pasos  \n",
       "1382076  Tarjeta Magnética  T.Magnética Disc.  Provincia      5  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2021.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data_2021['fecha'] = data_2021[['dia','mes_num','año']].apply(lambda row: '/'.join(row.values.astype(str)), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>mes_num</th>\n",
       "      <th>dia</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>estacion</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>pasos</th>\n",
       "      <th>fecha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>295158</th>\n",
       "      <td>2021</td>\n",
       "      <td>marzo</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Auto con trailer</td>\n",
       "      <td>Efectivo</td>\n",
       "      <td>Efectivo</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>1</td>\n",
       "      <td>19/3/2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         año    mes  mes_num dia hora_fin estacion     tipo_vehiculo  \\\n",
       "295158  2021  marzo        3  19        4      AVE  Auto con trailer   \n",
       "\n",
       "       forma_pago observacion    sentido  pasos      fecha  \n",
       "295158   Efectivo    Efectivo  Provincia      1  19/3/2021  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2021.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3029354 entries, 0 to 3029365\n",
      "Data columns (total 12 columns):\n",
      " #   Column         Dtype \n",
      "---  ------         ----- \n",
      " 0   año            object\n",
      " 1   mes            object\n",
      " 2   mes_num        int64 \n",
      " 3   dia            object\n",
      " 4   hora_fin       object\n",
      " 5   estacion       object\n",
      " 6   tipo_vehiculo  object\n",
      " 7   forma_pago     object\n",
      " 8   observacion    object\n",
      " 9   sentido        object\n",
      " 10  pasos          int32 \n",
      " 11  fecha          object\n",
      "dtypes: int32(1), int64(1), object(10)\n",
      "memory usage: 288.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data_2021.info()"
   ]
  },
  {
   "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>año</th>\n",
       "      <th>mes</th>\n",
       "      <th>mes_num</th>\n",
       "      <th>dia</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>estacion</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>pasos</th>\n",
       "      <th>fecha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1170027</th>\n",
       "      <td>2021</td>\n",
       "      <td>junio</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Auto</td>\n",
       "      <td>Tag</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Centro</td>\n",
       "      <td>2384</td>\n",
       "      <td>2021-06-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          año    mes  mes_num dia hora_fin estacion tipo_vehiculo forma_pago  \\\n",
       "1170027  2021  junio        6  25       10      AVE          Auto        Tag   \n",
       "\n",
       "           observacion sentido  pasos      fecha  \n",
       "1170027  Tag - PosPago  Centro   2384 2021-06-25  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2021.sample()\n",
    "data_2021['fecha'] = pd.to_datetime(data_2021['fecha'], format=\"mixed\")\n",
    "data_2021.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### joinear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>año</th>\n",
       "      <th>fecha</th>\n",
       "      <th>mes</th>\n",
       "      <th>dia</th>\n",
       "      <th>dia_semana</th>\n",
       "      <th>hora_inicio</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>estacion</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>pasos</th>\n",
       "      <th>mes_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>enero</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ALBERDI</td>\n",
       "      <td>Auto</td>\n",
       "      <td>Efectivo</td>\n",
       "      <td>Efectivo</td>\n",
       "      <td>Centro</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>enero</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ALBERDI</td>\n",
       "      <td>Auto</td>\n",
       "      <td>Efectivo</td>\n",
       "      <td>Efectivo</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>enero</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ALBERDI</td>\n",
       "      <td>Auto</td>\n",
       "      <td>Rec.Deuda</td>\n",
       "      <td>Paso en Infracción</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>enero</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ALBERDI</td>\n",
       "      <td>Auto</td>\n",
       "      <td>Rec.Deuda</td>\n",
       "      <td>Paso en infracción con Multa LPR</td>\n",
       "      <td>Centro</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>enero</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ALBERDI</td>\n",
       "      <td>Auto</td>\n",
       "      <td>Rec.Deuda</td>\n",
       "      <td>Paso en infracción con Multa LPR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029361</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-12-21</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>DEC</td>\n",
       "      <td>N/D</td>\n",
       "      <td>Violación</td>\n",
       "      <td>Violación - Vía Cerrada</td>\n",
       "      <td>Centro</td>\n",
       "      <td>2330</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029362</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-12-21</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>DEL</td>\n",
       "      <td>N/D</td>\n",
       "      <td>Violación</td>\n",
       "      <td>Violación - Vía Cerrada</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>1969</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029363</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-12-21</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>ILL</td>\n",
       "      <td>N/D</td>\n",
       "      <td>Violación</td>\n",
       "      <td>Violación - Normal</td>\n",
       "      <td>Centro</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029364</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-12-21</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>ILL</td>\n",
       "      <td>N/D</td>\n",
       "      <td>Violación</td>\n",
       "      <td>Violación - Vía Cerrada</td>\n",
       "      <td>Centro</td>\n",
       "      <td>2445</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029365</th>\n",
       "      <td>2021</td>\n",
       "      <td>2021-12-21</td>\n",
       "      <td>diciembre</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>ILL</td>\n",
       "      <td>N/D</td>\n",
       "      <td>Violación</td>\n",
       "      <td>Violación - Vía Cerrada</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>3880</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5563097 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          año      fecha        mes        dia  dia_semana  hora_inicio  \\\n",
       "0        2020 2020-01-01      enero  Miércoles         1.0          0.0   \n",
       "1        2020 2020-01-01      enero  Miércoles         1.0          0.0   \n",
       "2        2020 2020-01-01      enero  Miércoles         1.0          0.0   \n",
       "3        2020 2020-01-01      enero  Miércoles         1.0          0.0   \n",
       "4        2020 2020-01-01      enero  Miércoles         1.0          0.0   \n",
       "...       ...        ...        ...        ...         ...          ...   \n",
       "3029361  2021 2021-12-21  diciembre         21         NaN          NaN   \n",
       "3029362  2021 2021-12-21  diciembre         21         NaN          NaN   \n",
       "3029363  2021 2021-12-21  diciembre         21         NaN          NaN   \n",
       "3029364  2021 2021-12-21  diciembre         21         NaN          NaN   \n",
       "3029365  2021 2021-12-21  diciembre         21         NaN          NaN   \n",
       "\n",
       "        hora_fin estacion tipo_vehiculo forma_pago  \\\n",
       "0              1  ALBERDI          Auto   Efectivo   \n",
       "1              1  ALBERDI          Auto   Efectivo   \n",
       "2              1  ALBERDI          Auto  Rec.Deuda   \n",
       "3              1  ALBERDI          Auto  Rec.Deuda   \n",
       "4              1  ALBERDI          Auto  Rec.Deuda   \n",
       "...          ...      ...           ...        ...   \n",
       "3029361       15      DEC           N/D  Violación   \n",
       "3029362       15      DEL           N/D  Violación   \n",
       "3029363       15      ILL           N/D  Violación   \n",
       "3029364       15      ILL           N/D  Violación   \n",
       "3029365       15      ILL           N/D  Violación   \n",
       "\n",
       "                              observacion    sentido pasos  mes_num  \n",
       "0                                Efectivo     Centro    24      NaN  \n",
       "1                                Efectivo  Provincia    80      NaN  \n",
       "2                      Paso en Infracción  Provincia     1      NaN  \n",
       "3        Paso en infracción con Multa LPR     Centro     1      NaN  \n",
       "4        Paso en infracción con Multa LPR  Provincia     1      NaN  \n",
       "...                                   ...        ...   ...      ...  \n",
       "3029361           Violación - Vía Cerrada     Centro  2330     12.0  \n",
       "3029362           Violación - Vía Cerrada  Provincia  1969     12.0  \n",
       "3029363                Violación - Normal     Centro     3     12.0  \n",
       "3029364           Violación - Vía Cerrada     Centro  2445     12.0  \n",
       "3029365           Violación - Vía Cerrada  Provincia  3880     12.0  \n",
       "\n",
       "[5563097 rows x 14 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = data_2020.columns\n",
    "data = pd.DataFrame(columns=column_names)\n",
    "data = pd.concat([data,data_2020, data_2021], axis=0, join='outer')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\419587088.py:1: DtypeWarning: Columns (1,2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_enfeb_22=pd.read_csv(\"Data/Transito Enero-Febrero 2022.csv\", sep=';', parse_dates=True)\n",
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\419587088.py:2: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_mar_22=pd.read_csv(\"Data/Transito Marzo 2022.csv\", sep=',', parse_dates=True)\n",
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\419587088.py:3: DtypeWarning: Columns (0,1,2,3,4,5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_abril_22=pd.read_csv(\"Data/Transito Abril 2022.csv\", sep=',', parse_dates=True)\n",
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\419587088.py:4: DtypeWarning: Columns (1,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_may_22=pd.read_csv(\"Data/Transito Mayo 2022.csv\", sep=',', parse_dates=True)\n",
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\419587088.py:5: DtypeWarning: Columns (1,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_jun_22=pd.read_csv(\"Data/Transito Junio 2022.csv\", sep=',', parse_dates=True)\n",
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\419587088.py:6: DtypeWarning: Columns (1,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_jul_22=pd.read_csv(\"Data/Transito Julio 2022.csv\", sep=',', parse_dates=True)\n",
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\419587088.py:7: DtypeWarning: Columns (1,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_ago_22=pd.read_csv(\"Data/Transito Agosto 2022.csv\", sep=',', parse_dates=True)\n",
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\419587088.py:8: DtypeWarning: Columns (1,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_sep_22=pd.read_csv(\"Data/Transito Septiembre 2022.csv\", sep=',', parse_dates=True)\n",
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\419587088.py:9: DtypeWarning: Columns (1,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_oct_22=pd.read_csv(\"Data/Transito Octubre 2022.csv\", sep=';', parse_dates=True)\n",
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\419587088.py:10: DtypeWarning: Columns (1,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_nov_22=pd.read_csv(\"Data/Transito Noviembre 2022.csv\", sep=';', parse_dates=True)\n",
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\419587088.py:11: DtypeWarning: Columns (1,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data_dic_22=pd.read_csv(\"Data/Transito Diciembre 2022.csv\", sep=';', parse_dates=True)\n"
     ]
    }
   ],
   "source": [
    "data_enfeb_22=pd.read_csv(\"Data/Transito Enero-Febrero 2022.csv\", sep=';', parse_dates=True)\n",
    "data_mar_22=pd.read_csv(\"Data/Transito Marzo 2022.csv\", sep=',', parse_dates=True)\n",
    "data_abril_22=pd.read_csv(\"Data/Transito Abril 2022.csv\", sep=',', parse_dates=True)\n",
    "data_may_22=pd.read_csv(\"Data/Transito Mayo 2022.csv\", sep=',', parse_dates=True)\n",
    "data_jun_22=pd.read_csv(\"Data/Transito Junio 2022.csv\", sep=',', parse_dates=True)\n",
    "data_jul_22=pd.read_csv(\"Data/Transito Julio 2022.csv\", sep=',', parse_dates=True)\n",
    "data_ago_22=pd.read_csv(\"Data/Transito Agosto 2022.csv\", sep=',', parse_dates=True)\n",
    "data_sep_22=pd.read_csv(\"Data/Transito Septiembre 2022.csv\", sep=',', parse_dates=True)\n",
    "data_oct_22=pd.read_csv(\"Data/Transito Octubre 2022.csv\", sep=';', parse_dates=True)\n",
    "data_nov_22=pd.read_csv(\"Data/Transito Noviembre 2022.csv\", sep=';', parse_dates=True)\n",
    "data_dic_22=pd.read_csv(\"Data/Transito Diciembre 2022.csv\", sep=';', parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Año de Fecha Operativa</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>166242</th>\n",
       "      <td>2022-01-06</td>\n",
       "      <td>Pesados 3 Ejes</td>\n",
       "      <td>11</td>\n",
       "      <td>ILP</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Año de Fecha Operativa     Cat Cobrada Hora HH ID Peaje    Observacion  \\\n",
       "166242             2022-01-06  Pesados 3 Ejes      11      ILP  Tag - PosPago   \n",
       "\n",
       "          Sentido Tipo Cobro  Pasos  \n",
       "166242  Provincia        Tag     42  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_enfeb_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Año de Fecha Operativa</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74401</th>\n",
       "      <td>9/3/2022</td>\n",
       "      <td>20</td>\n",
       "      <td>Pesados 2 Ejes</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Tag - PosPago OCR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Año de Fecha Operativa Hora HH     Cat Cobrada ID Peaje  \\\n",
       "74401               9/3/2022      20  Pesados 2 Ejes      AVE   \n",
       "\n",
       "             Observacion    Sentido Tipo Cobro  Pasos  \n",
       "74401  Tag - PosPago OCR  Provincia        Tag    7.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mar_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año de Fecha Operativa</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141955</th>\n",
       "      <td>18/4/2022</td>\n",
       "      <td>9</td>\n",
       "      <td>Pesados 5 Ejes</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Tag</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Año de Fecha Operativa Hora HH     Cat Cobrada ID Peaje    Observacion  \\\n",
       "141955              18/4/2022       9  Pesados 5 Ejes      AVE  Tag - PosPago   \n",
       "\n",
       "       Sentido Tipo Cobro Pasos  \n",
       "141955  Centro        Tag  76.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_abril_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Año de Fecha Operativa</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110878</th>\n",
       "      <td>13/5/2022</td>\n",
       "      <td>20</td>\n",
       "      <td>Moto</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Exento - Reglamento</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Exento</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Año de Fecha Operativa Hora HH Cat Cobrada ID Peaje  \\\n",
       "110878              13/5/2022      20        Moto      AVE   \n",
       "\n",
       "                Observacion Sentido Tipo Cobro Pasos  \n",
       "110878  Exento - Reglamento  Centro     Exento   1.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_may_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año de Fecha Operativa</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195418</th>\n",
       "      <td>24/6/2022</td>\n",
       "      <td>13</td>\n",
       "      <td>Pesados 4 Ejes</td>\n",
       "      <td>DEL</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Año de Fecha Operativa Hora HH     Cat Cobrada ID Peaje    Observacion  \\\n",
       "195418              24/6/2022      13  Pesados 4 Ejes      DEL  Tag - PosPago   \n",
       "\n",
       "          Sentido Tipo Cobro Pasos  \n",
       "195418  Provincia        Tag   5.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jun_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Año de Fecha Operativa</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>157909</th>\n",
       "      <td>19 de julio de 2022</td>\n",
       "      <td>13</td>\n",
       "      <td>Auto</td>\n",
       "      <td>PB1</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Año de Fecha Operativa Hora HH Cat Cobrada ID Peaje    Observacion  \\\n",
       "157909    19 de julio de 2022      13        Auto      PB1  Tag - PosPago   \n",
       "\n",
       "          Sentido Tipo Cobro Pasos  \n",
       "157909  Provincia        Tag  54.0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jul_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Año de Fecha Operativa</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>239998</th>\n",
       "      <td>29/8/2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Auto</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Paso en infracción con Multa LPR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Rec.Deuda</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Año de Fecha Operativa Hora HH Cat Cobrada ID Peaje  \\\n",
       "239998              29/8/2022       3        Auto      AVE   \n",
       "\n",
       "                             Observacion    Sentido Tipo Cobro Pasos  \n",
       "239998  Paso en infracción con Multa LPR  Provincia  Rec.Deuda   1.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ago_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Año de Fecha Operativa</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64394</th>\n",
       "      <td>8 de septiembre de 2022</td>\n",
       "      <td>12</td>\n",
       "      <td>Auto</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Paso en infracción con Multa LPR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Rec.Deuda</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Año de Fecha Operativa Hora HH Cat Cobrada ID Peaje  \\\n",
       "64394  8 de septiembre de 2022      12        Auto      AVE   \n",
       "\n",
       "                            Observacion    Sentido Tipo Cobro Pasos  \n",
       "64394  Paso en infracción con Multa LPR  Provincia  Rec.Deuda  32.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sep_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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",
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       "        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>Mes, Día, Año de Fecha Operativa</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115322</th>\n",
       "      <td>14/10/2022</td>\n",
       "      <td>14</td>\n",
       "      <td>Moto</td>\n",
       "      <td>ILL</td>\n",
       "      <td>Efectivo</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Efectivo</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Mes, Día, Año de Fecha Operativa Hora HH Cat Cobrada ID Peaje  \\\n",
       "115322                       14/10/2022      14        Moto      ILL   \n",
       "\n",
       "       Observacion Sentido Tipo Cobro Pasos  Unnamed: 8  Unnamed: 9  \n",
       "115322    Efectivo  Centro   Efectivo  55.0         NaN         NaN  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_oct_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Mes, Día, Año de Fecha Operativa</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8489</th>\n",
       "      <td>1 de noviembre de 2022</td>\n",
       "      <td>21</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ILP</td>\n",
       "      <td>Tag - PosPago OCR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mes, Día, Año de Fecha Operativa Hora HH Cat Cobrada ID Peaje  \\\n",
       "8489           1 de noviembre de 2022      21        Auto      ILP   \n",
       "\n",
       "            Observacion    Sentido Tipo Cobro Pasos  Unnamed: 8  Unnamed: 9  \n",
       "8489  Tag - PosPago OCR  Provincia        Tag   1.0         NaN         NaN  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nov_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mes, Día, Año de Fecha Operativa</th>\n",
       "      <th>Hora HH</th>\n",
       "      <th>Cat Cobrada</th>\n",
       "      <th>ID Peaje</th>\n",
       "      <th>Observacion</th>\n",
       "      <th>Sentido</th>\n",
       "      <th>Tipo Cobro</th>\n",
       "      <th>Pasos</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37444</th>\n",
       "      <td>5/12/2022</td>\n",
       "      <td>9</td>\n",
       "      <td>Pesados 4 Ejes</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Tag</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Mes, Día, Año de Fecha Operativa Hora HH     Cat Cobrada ID Peaje  \\\n",
       "37444                        5/12/2022       9  Pesados 4 Ejes      AVE   \n",
       "\n",
       "         Observacion Sentido Tipo Cobro Pasos  Unnamed: 8  Unnamed: 9  \n",
       "37444  Tag - PosPago  Centro        Tag   3.0         NaN         NaN  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dic_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_oct_22.drop(['Unnamed: 8', 'Unnamed: 9'],axis=1, inplace=True)\n",
    "data_nov_22.drop(['Unnamed: 8', 'Unnamed: 9'],axis=1, inplace=True)\n",
    "data_dic_22.drop(['Unnamed: 8', 'Unnamed: 9'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_enfeb_22 = data_enfeb_22[['Año de Fecha Operativa',\t'Hora HH',\t'Cat Cobrada',\t'ID Peaje',\t'Observacion',\t'Sentido',\t'Tipo Cobro',\t'Pasos']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_enfeb_22=data_enfeb_22.rename(columns={'Año de Fecha Operativa':'fecha', 'Hora HH': 'hora_fin', 'Cat Cobrada': 'tipo_vehiculo', 'ID Peaje': 'estacion',  'cantidad_pasos':'pasos', 'Observacion': 'observacion', 'Sentido': 'sentido', 'Tipo Cobro': 'forma_pago',  'Pasos':'pasos'})\n",
    "data_mar_22=data_mar_22.rename(columns={'Año de Fecha Operativa':'fecha', 'Hora HH': 'hora_fin', 'Cat Cobrada': 'tipo_vehiculo', 'ID Peaje': 'estacion',  'cantidad_pasos':'pasos', 'Observacion': 'observacion', 'Sentido': 'sentido', 'Tipo Cobro': 'forma_pago',  'Pasos':'pasos'})\n",
    "data_abril_22=data_abril_22.rename(columns={'Año de Fecha Operativa':'fecha', 'Hora HH': 'hora_fin', 'Cat Cobrada': 'tipo_vehiculo', 'ID Peaje': 'estacion',  'cantidad_pasos':'pasos', 'Observacion': 'observacion', 'Sentido': 'sentido', 'Tipo Cobro': 'forma_pago',  'Pasos':'pasos'})\n",
    "data_may_22=data_may_22.rename(columns={'Año de Fecha Operativa':'fecha', 'Hora HH': 'hora_fin', 'Cat Cobrada': 'tipo_vehiculo', 'ID Peaje': 'estacion',  'cantidad_pasos':'pasos', 'Observacion': 'observacion', 'Sentido': 'sentido', 'Tipo Cobro': 'forma_pago',  'Pasos':'pasos'})\n",
    "data_jun_22=data_jun_22.rename(columns={'Año de Fecha Operativa':'fecha', 'Hora HH': 'hora_fin', 'Cat Cobrada': 'tipo_vehiculo', 'ID Peaje': 'estacion',  'cantidad_pasos':'pasos', 'Observacion': 'observacion', 'Sentido': 'sentido', 'Tipo Cobro': 'forma_pago',  'Pasos':'pasos'})\n",
    "data_jul_22=data_jul_22.rename(columns={'Año de Fecha Operativa':'fecha', 'Hora HH': 'hora_fin', 'Cat Cobrada': 'tipo_vehiculo', 'ID Peaje': 'estacion',  'cantidad_pasos':'pasos', 'Observacion': 'observacion', 'Sentido': 'sentido', 'Tipo Cobro': 'forma_pago',  'Pasos':'pasos'})\n",
    "data_ago_22=data_ago_22.rename(columns={'Año de Fecha Operativa':'fecha', 'Hora HH': 'hora_fin', 'Cat Cobrada': 'tipo_vehiculo', 'ID Peaje': 'estacion',  'cantidad_pasos':'pasos', 'Observacion': 'observacion', 'Sentido': 'sentido', 'Tipo Cobro': 'forma_pago',  'Pasos':'pasos'})\n",
    "data_sep_22=data_sep_22.rename(columns={'Año de Fecha Operativa':'fecha', 'Hora HH': 'hora_fin', 'Cat Cobrada': 'tipo_vehiculo', 'ID Peaje': 'estacion',  'cantidad_pasos':'pasos', 'Observacion': 'observacion', 'Sentido': 'sentido', 'Tipo Cobro': 'forma_pago',  'Pasos':'pasos'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    " data_oct_22 = data_oct_22.rename(columns={'Mes, Día, Año de Fecha Operativa':'fecha', 'Hora HH': 'hora_fin', 'Cat Cobrada': 'tipo_vehiculo', 'ID Peaje': 'estacion',  'cantidad_pasos':'pasos', 'Observacion': 'observacion', 'Sentido': 'sentido', 'Tipo Cobro': 'forma_pago',  'Pasos':'pasos'})\n",
    " data_nov_22 = data_nov_22.rename(columns={'Mes, Día, Año de Fecha Operativa':'fecha', 'Hora HH': 'hora_fin', 'Cat Cobrada': 'tipo_vehiculo', 'ID Peaje': 'estacion',  'cantidad_pasos':'pasos', 'Observacion': 'observacion', 'Sentido': 'sentido', 'Tipo Cobro': 'forma_pago',  'Pasos':'pasos'})\n",
    " data_dic_22 = data_dic_22.rename(columns={'Mes, Día, Año de Fecha Operativa':'fecha', 'Hora HH': 'hora_fin', 'Cat Cobrada': 'tipo_vehiculo', 'ID Peaje': 'estacion',  'cantidad_pasos':'pasos', 'Observacion': 'observacion', 'Sentido': 'sentido', 'Tipo Cobro': 'forma_pago',  'Pasos':'pasos'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49816</th>\n",
       "      <td>6/10/2022</td>\n",
       "      <td>17</td>\n",
       "      <td>Auto</td>\n",
       "      <td>RET</td>\n",
       "      <td>Tag - PosPago OCR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           fecha hora_fin tipo_vehiculo estacion        observacion  \\\n",
       "49816  6/10/2022       17          Auto      RET  Tag - PosPago OCR   \n",
       "\n",
       "         sentido forma_pago pasos  \n",
       "49816  Provincia        Tag  12.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_oct_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>242836</th>\n",
       "      <td>28 de noviembre de 2022</td>\n",
       "      <td>6</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ILL</td>\n",
       "      <td>Efectivo x4</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Efectivo</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          fecha hora_fin tipo_vehiculo estacion  observacion  \\\n",
       "242836  28 de noviembre de 2022        6          Auto      ILL  Efectivo x4   \n",
       "\n",
       "          sentido forma_pago pasos  \n",
       "242836  Provincia   Efectivo   1.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nov_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>123866</th>\n",
       "      <td>15/12/2022</td>\n",
       "      <td>10</td>\n",
       "      <td>Pesados 2 Ejes</td>\n",
       "      <td>PB3</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             fecha hora_fin   tipo_vehiculo estacion    observacion  \\\n",
       "123866  15/12/2022       10  Pesados 2 Ejes      PB3  Tag - PosPago   \n",
       "\n",
       "          sentido forma_pago pasos  \n",
       "123866  Provincia        Tag  12.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dic_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 476196 entries, 0 to 476195\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          476196 non-null  object\n",
      " 1   hora_fin       476196 non-null  object\n",
      " 2   tipo_vehiculo  476196 non-null  object\n",
      " 3   estacion       476196 non-null  object\n",
      " 4   observacion    476196 non-null  object\n",
      " 5   sentido        476196 non-null  object\n",
      " 6   forma_pago     476196 non-null  object\n",
      " 7   pasos          476196 non-null  int64 \n",
      "dtypes: int64(1), object(7)\n",
      "memory usage: 29.1+ MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 476196 entries, 0 to 476195\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          476196 non-null  object\n",
      " 1   hora_fin       476196 non-null  object\n",
      " 2   tipo_vehiculo  476196 non-null  object\n",
      " 3   estacion       476196 non-null  object\n",
      " 4   observacion    476196 non-null  object\n",
      " 5   sentido        476196 non-null  object\n",
      " 6   forma_pago     476196 non-null  object\n",
      " 7   pasos          476196 non-null  int64 \n",
      "dtypes: int64(1), object(7)\n",
      "memory usage: 29.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data_enfeb_22.info()\n",
    "data_enfeb_22 = data_enfeb_22[data_enfeb_22['fecha'] != \"Total general\"] #ok\n",
    "data_enfeb_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 263187 entries, 0 to 263186\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          263187 non-null  object\n",
      " 1   hora_fin       263187 non-null  object\n",
      " 2   tipo_vehiculo  263187 non-null  object\n",
      " 3   estacion       263187 non-null  object\n",
      " 4   observacion    263187 non-null  object\n",
      " 5   sentido        263187 non-null  object\n",
      " 6   forma_pago     263187 non-null  object\n",
      " 7   pasos          263187 non-null  int32 \n",
      "dtypes: int32(1), object(7)\n",
      "memory usage: 17.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data_mar_22 = data_mar_22[data_mar_22['fecha'] != \"Total general\"] #ok\n",
    "data_mar_22['pasos'] = data_mar_22['pasos'].astype(int)\n",
    "data_mar_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 252037 entries, 0 to 252036\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          252037 non-null  object\n",
      " 1   hora_fin       252037 non-null  object\n",
      " 2   tipo_vehiculo  252037 non-null  object\n",
      " 3   estacion       252037 non-null  object\n",
      " 4   observacion    252037 non-null  object\n",
      " 5   sentido        252037 non-null  object\n",
      " 6   forma_pago     252037 non-null  object\n",
      " 7   pasos          252037 non-null  object\n",
      "dtypes: object(8)\n",
      "memory usage: 17.3+ MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 252036 entries, 0 to 252035\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          252036 non-null  object\n",
      " 1   hora_fin       252036 non-null  object\n",
      " 2   tipo_vehiculo  252036 non-null  object\n",
      " 3   estacion       252036 non-null  object\n",
      " 4   observacion    252036 non-null  object\n",
      " 5   sentido        252036 non-null  object\n",
      " 6   forma_pago     252036 non-null  object\n",
      " 7   pasos          252036 non-null  int32 \n",
      "dtypes: int32(1), object(7)\n",
      "memory usage: 16.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data_abril_22.dropna(inplace = True)\n",
    "data_abril_22.info()\n",
    "data_abril_22.value_counts()\n",
    "data_abril_22 = data_abril_22[data_abril_22['fecha'] != \"Total general\"] #ok\n",
    "data_abril_22['pasos'] = data_abril_22['pasos'].astype(float)\n",
    "data_abril_22['pasos'] = data_abril_22['pasos'].astype(int)\n",
    "data_abril_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 258791 entries, 0 to 258790\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          258791 non-null  object\n",
      " 1   hora_fin       258791 non-null  object\n",
      " 2   tipo_vehiculo  258791 non-null  object\n",
      " 3   estacion       258791 non-null  object\n",
      " 4   observacion    258791 non-null  object\n",
      " 5   sentido        258791 non-null  object\n",
      " 6   forma_pago     258791 non-null  object\n",
      " 7   pasos          258791 non-null  int32 \n",
      "dtypes: int32(1), object(7)\n",
      "memory usage: 16.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data_may_22 = data_may_22[data_may_22['fecha'] != \"Total general\"] #ok\n",
    "data_may_22['pasos'] = data_may_22['pasos'].astype(float)\n",
    "data_may_22['pasos'] = data_may_22['pasos'].astype(int)\n",
    "data_may_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 251245 entries, 0 to 251244\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          251245 non-null  object\n",
      " 1   hora_fin       251245 non-null  object\n",
      " 2   tipo_vehiculo  251245 non-null  object\n",
      " 3   estacion       251245 non-null  object\n",
      " 4   observacion    251245 non-null  object\n",
      " 5   sentido        251245 non-null  object\n",
      " 6   forma_pago     251245 non-null  object\n",
      " 7   pasos          251245 non-null  int32 \n",
      "dtypes: int32(1), object(7)\n",
      "memory usage: 16.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data_jun_22.dropna(inplace = True)\n",
    "data_jun_22 = data_jun_22[data_jun_22['fecha'] != \"Total general\"] #ok\n",
    "data_jun_22['pasos'] = data_jun_22['pasos'].astype(float)\n",
    "data_jun_22['pasos'] = data_jun_22['pasos'].astype(int)\n",
    "data_jun_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 251245 entries, 0 to 251244\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          251245 non-null  object\n",
      " 1   hora_fin       251245 non-null  object\n",
      " 2   tipo_vehiculo  251245 non-null  object\n",
      " 3   estacion       251245 non-null  object\n",
      " 4   observacion    251245 non-null  object\n",
      " 5   sentido        251245 non-null  object\n",
      " 6   forma_pago     251245 non-null  object\n",
      " 7   pasos          251245 non-null  int32 \n",
      "dtypes: int32(1), object(7)\n",
      "memory usage: 16.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data_jul_22.dropna(inplace = True)\n",
    "data_jul_22 = data_jun_22[data_jun_22['fecha'] != \"Total general\"] #ok\n",
    "data_jul_22['pasos'] = data_jul_22['pasos'].astype(float)\n",
    "data_jul_22['pasos'] = data_jul_22['pasos'].astype(int)\n",
    "data_jul_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 266902 entries, 0 to 266901\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          266902 non-null  object\n",
      " 1   hora_fin       266902 non-null  object\n",
      " 2   tipo_vehiculo  266902 non-null  object\n",
      " 3   estacion       266902 non-null  object\n",
      " 4   observacion    266902 non-null  object\n",
      " 5   sentido        266902 non-null  object\n",
      " 6   forma_pago     266902 non-null  object\n",
      " 7   pasos          266902 non-null  int32 \n",
      "dtypes: int32(1), object(7)\n",
      "memory usage: 17.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data_ago_22.dropna(inplace = True)\n",
    "data_ago_22 = data_ago_22[data_ago_22['fecha'] != \"Total general\"] #ok\n",
    "data_ago_22['pasos'] = data_ago_22['pasos'].astype(float)\n",
    "data_ago_22['pasos'] = data_ago_22['pasos'].astype(int)\n",
    "data_ago_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 264251 entries, 0 to 264250\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          264251 non-null  object\n",
      " 1   hora_fin       264251 non-null  object\n",
      " 2   tipo_vehiculo  264251 non-null  object\n",
      " 3   estacion       264251 non-null  object\n",
      " 4   observacion    264251 non-null  object\n",
      " 5   sentido        264251 non-null  object\n",
      " 6   forma_pago     264251 non-null  object\n",
      " 7   pasos          264251 non-null  int32 \n",
      "dtypes: int32(1), object(7)\n",
      "memory usage: 17.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data_sep_22.dropna(inplace = True)\n",
    "data_sep_22 = data_sep_22[data_sep_22['fecha'] != \"Total general\"] #ok\n",
    "data_sep_22['pasos'] = data_sep_22['pasos'].astype(float)\n",
    "data_sep_22['pasos'] = data_sep_22['pasos'].astype(int)\n",
    "data_sep_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 268058 entries, 0 to 268057\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          268058 non-null  object\n",
      " 1   hora_fin       268058 non-null  object\n",
      " 2   tipo_vehiculo  268058 non-null  object\n",
      " 3   estacion       268058 non-null  object\n",
      " 4   observacion    268058 non-null  object\n",
      " 5   sentido        268058 non-null  object\n",
      " 6   forma_pago     268058 non-null  object\n",
      " 7   pasos          268058 non-null  int32 \n",
      "dtypes: int32(1), object(7)\n",
      "memory usage: 17.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data_oct_22.dropna(inplace = True)\n",
    "data_oct_22 = data_oct_22[data_oct_22['fecha'] != \"Total general\"] #ok\n",
    "data_oct_22['pasos'] = data_oct_22['pasos'].astype(float)\n",
    "data_oct_22['pasos'] = data_oct_22['pasos'].astype(int)\n",
    "data_oct_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 269350 entries, 0 to 269349\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          269350 non-null  object\n",
      " 1   hora_fin       269350 non-null  object\n",
      " 2   tipo_vehiculo  269350 non-null  object\n",
      " 3   estacion       269350 non-null  object\n",
      " 4   observacion    269350 non-null  object\n",
      " 5   sentido        269350 non-null  object\n",
      " 6   forma_pago     269350 non-null  object\n",
      " 7   pasos          269350 non-null  int32 \n",
      "dtypes: int32(1), object(7)\n",
      "memory usage: 17.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data_nov_22.dropna(inplace = True)\n",
    "data_nov_22 = data_nov_22[data_nov_22['fecha'] != \"Total general\"] #ok\n",
    "data_nov_22['pasos'] = data_nov_22['pasos'].astype(float)\n",
    "data_nov_22['pasos'] = data_nov_22['pasos'].astype(int)\n",
    "data_nov_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 264490 entries, 0 to 264489\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   fecha          264490 non-null  object\n",
      " 1   hora_fin       264490 non-null  object\n",
      " 2   tipo_vehiculo  264490 non-null  object\n",
      " 3   estacion       264490 non-null  object\n",
      " 4   observacion    264490 non-null  object\n",
      " 5   sentido        264490 non-null  object\n",
      " 6   forma_pago     264490 non-null  object\n",
      " 7   pasos          264490 non-null  int32 \n",
      "dtypes: int32(1), object(7)\n",
      "memory usage: 17.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data_dic_22.dropna(inplace = True)\n",
    "data_dic_22 = data_dic_22[data_dic_22['fecha'] != \"Total general\"] #ok\n",
    "data_dic_22['pasos'] = data_dic_22['pasos'].astype(float)\n",
    "data_dic_22['pasos'] = data_dic_22['pasos'].astype(int)\n",
    "data_dic_22.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### empezar a pasar las fechas a formato datetime y homogéneo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>282038</th>\n",
       "      <td>2022-02-16</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>ILL</td>\n",
       "      <td>Tag - PosPago OCR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fecha hora_fin tipo_vehiculo estacion        observacion  \\\n",
       "282038 2022-02-16     Auto             1      ILL  Tag - PosPago OCR   \n",
       "\n",
       "          sentido forma_pago  pasos  \n",
       "282038  Provincia        Tag     64  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_enfeb_22.sample()\n",
    "data_enfeb_22['fecha'] = pd.to_datetime(data_enfeb_22['fecha'])\n",
    "data_enfeb_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79248</th>\n",
       "      <td>2022-10-03</td>\n",
       "      <td>11</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ILP</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           fecha hora_fin tipo_vehiculo estacion   observacion sentido  \\\n",
       "79248 2022-10-03       11          Auto      ILP  Mercado Pago  Centro   \n",
       "\n",
       "         forma_pago  pasos  \n",
       "79248  Mercado Pago      5  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mar_22.sample()\n",
    "data_mar_22['fecha'] = pd.to_datetime(data_mar_22['fecha'], format=\"mixed\")\n",
    "data_mar_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43072</th>\n",
       "      <td>2022-06-04</td>\n",
       "      <td>8</td>\n",
       "      <td>Auto</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Paso en Infraccion Multa Agente</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Rec.Deuda</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           fecha hora_fin tipo_vehiculo estacion  \\\n",
       "43072 2022-06-04        8          Auto      AVE   \n",
       "\n",
       "                           observacion    sentido forma_pago  pasos  \n",
       "43072  Paso en Infraccion Multa Agente  Provincia  Rec.Deuda      1  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_abril_22.sample()\n",
    "data_abril_22['fecha'] = pd.to_datetime(data_abril_22['fecha'], format=\"mixed\")\n",
    "data_abril_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171981</th>\n",
       "      <td>2022-05-21</td>\n",
       "      <td>11</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ILL</td>\n",
       "      <td>Paso en infracción con Multa LPR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Rec.Deuda</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fecha hora_fin tipo_vehiculo estacion  \\\n",
       "171981 2022-05-21       11          Auto      ILL   \n",
       "\n",
       "                             observacion    sentido forma_pago  pasos  \n",
       "171981  Paso en infracción con Multa LPR  Provincia  Rec.Deuda      9  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_may_22.sample()\n",
    "data_may_22['fecha'] = pd.to_datetime(data_may_22['fecha'], format=\"mixed\")\n",
    "data_may_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16154</th>\n",
       "      <td>2022-02-06</td>\n",
       "      <td>18</td>\n",
       "      <td>Pesados 2 Ejes</td>\n",
       "      <td>ILP</td>\n",
       "      <td>Tag - PosPago OCR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           fecha hora_fin   tipo_vehiculo estacion        observacion  \\\n",
       "16154 2022-02-06       18  Pesados 2 Ejes      ILP  Tag - PosPago OCR   \n",
       "\n",
       "         sentido forma_pago  pasos  \n",
       "16154  Provincia        Tag      3  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jul_22.sample()\n",
    "data_jul_22['fecha'] = pd.to_datetime(data_jul_22['fecha'], format=\"mixed\")\n",
    "data_jul_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>166418</th>\n",
       "      <td>2022-08-20</td>\n",
       "      <td>10</td>\n",
       "      <td>Pesados 2 Ejes</td>\n",
       "      <td>DEL</td>\n",
       "      <td>Exento - Reglamento</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Exento</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fecha hora_fin   tipo_vehiculo estacion          observacion  \\\n",
       "166418 2022-08-20       10  Pesados 2 Ejes      DEL  Exento - Reglamento   \n",
       "\n",
       "          sentido forma_pago  pasos  \n",
       "166418  Provincia     Exento      9  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ago_22.sample()\n",
    "data_ago_22['fecha'] = pd.to_datetime(data_ago_22['fecha'], format=\"mixed\")\n",
    "data_ago_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93743</th>\n",
       "      <td>2022-12-10</td>\n",
       "      <td>8</td>\n",
       "      <td>Pesados 2 Ejes</td>\n",
       "      <td>ILP</td>\n",
       "      <td>Tag - PosPago OCR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           fecha hora_fin   tipo_vehiculo estacion        observacion  \\\n",
       "93743 2022-12-10        8  Pesados 2 Ejes      ILP  Tag - PosPago OCR   \n",
       "\n",
       "         sentido forma_pago  pasos  \n",
       "93743  Provincia        Tag     20  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_oct_22.sample()\n",
    "data_oct_22['fecha'] = pd.to_datetime(data_oct_22['fecha'], format=\"mixed\")\n",
    "data_oct_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41931</th>\n",
       "      <td>2022-05-12</td>\n",
       "      <td>19</td>\n",
       "      <td>N/D</td>\n",
       "      <td>ILL</td>\n",
       "      <td>Violación - Normal</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Violación</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           fecha hora_fin tipo_vehiculo estacion         observacion  \\\n",
       "41931 2022-05-12       19           N/D      ILL  Violación - Normal   \n",
       "\n",
       "         sentido forma_pago  pasos  \n",
       "41931  Provincia  Violación    104  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dic_22.sample()\n",
    "data_dic_22['fecha'] = pd.to_datetime(data_dic_22['fecha'], format=\"mixed\")\n",
    "data_dic_22.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>155697</th>\n",
       "      <td>18 de noviembre de 2022</td>\n",
       "      <td>2</td>\n",
       "      <td>Pesados 3 Ejes</td>\n",
       "      <td>DEC</td>\n",
       "      <td>Efectivo</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Efectivo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          fecha hora_fin   tipo_vehiculo estacion observacion  \\\n",
       "155697  18 de noviembre de 2022        2  Pesados 3 Ejes      DEC    Efectivo   \n",
       "\n",
       "       sentido forma_pago  pasos  \n",
       "155697  Centro   Efectivo      2  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nov_22.sample() #next three months have a very different format, so we need to treat them differently using Spanish conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'es_ES'"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import locale\n",
    "locale.setlocale(locale.LC_TIME, \"es_ES\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_jun_22['fecha'] = pd.to_datetime(data_sep_22['fecha'], format=\"%d de %B de %Y\")\n",
    "data_sep_22['fecha'] = pd.to_datetime(data_sep_22['fecha'], format=\"%d de %B de %Y\")\n",
    "data_nov_22['fecha'] = pd.to_datetime(data_nov_22['fecha'], format=\"%d de %B de %Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Mercado Pago OCR</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Mercado Pago OCR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Tag - PosPago OCR</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Tag</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264485</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>23</td>\n",
       "      <td>Pesados 2 Ejes</td>\n",
       "      <td>PB4</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Tag</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264486</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>23</td>\n",
       "      <td>Pesados 5 Ejes</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264487</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>23</td>\n",
       "      <td>Pesados 5 Ejes</td>\n",
       "      <td>DEC</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Tag</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264488</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>23</td>\n",
       "      <td>Pesados 5 Ejes</td>\n",
       "      <td>ILP</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264489</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>23</td>\n",
       "      <td>Pesados 5 Ejes</td>\n",
       "      <td>PB1</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3085751 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            fecha hora_fin   tipo_vehiculo estacion        observacion  \\\n",
       "0      2022-01-01        0            Auto      ALB       Mercado Pago   \n",
       "1      2022-01-01        0            Auto      ALB   Mercado Pago OCR   \n",
       "2      2022-01-01        0            Auto      ALB   Mercado Pago OCR   \n",
       "3      2022-01-01        0            Auto      ALB      Tag - PosPago   \n",
       "4      2022-01-01        0            Auto      ALB  Tag - PosPago OCR   \n",
       "...           ...      ...             ...      ...                ...   \n",
       "264485 2022-12-31       23  Pesados 2 Ejes      PB4      Tag - PosPago   \n",
       "264486 2022-12-31       23  Pesados 5 Ejes      AVE       Mercado Pago   \n",
       "264487 2022-12-31       23  Pesados 5 Ejes      DEC      Tag - PosPago   \n",
       "264488 2022-12-31       23  Pesados 5 Ejes      ILP      Tag - PosPago   \n",
       "264489 2022-12-31       23  Pesados 5 Ejes      PB1      Tag - PosPago   \n",
       "\n",
       "          sentido    forma_pago pasos  \n",
       "0       Provincia  Mercado Pago     8  \n",
       "1          Centro  Mercado Pago     4  \n",
       "2       Provincia  Mercado Pago     2  \n",
       "3       Provincia           Tag    65  \n",
       "4          Centro           Tag    32  \n",
       "...           ...           ...   ...  \n",
       "264485     Centro           Tag     2  \n",
       "264486  Provincia  Mercado Pago     1  \n",
       "264487     Centro           Tag     1  \n",
       "264488  Provincia           Tag     3  \n",
       "264489  Provincia           Tag     3  \n",
       "\n",
       "[3085751 rows x 8 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = data_enfeb_22.columns\n",
    "data_2022 = pd.DataFrame(columns=column_names)\n",
    "data_2022 = pd.concat([data_2022,data_enfeb_22, data_mar_22,data_abril_22,data_may_22,data_jun_22,data_jul_22,data_ago_22,data_sep_22,data_oct_22, data_nov_22, data_dic_22], axis=0)\n",
    "data_2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2022_clean=data_2022.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Mercado Pago OCR</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Mercado Pago OCR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Auto</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Tag - PosPago OCR</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Tag</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264485</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>23</td>\n",
       "      <td>Pesados 2 Ejes</td>\n",
       "      <td>PB4</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Tag</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264486</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>23</td>\n",
       "      <td>Pesados 5 Ejes</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Mercado Pago</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264487</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>23</td>\n",
       "      <td>Pesados 5 Ejes</td>\n",
       "      <td>DEC</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Tag</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264488</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>23</td>\n",
       "      <td>Pesados 5 Ejes</td>\n",
       "      <td>ILP</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264489</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>23</td>\n",
       "      <td>Pesados 5 Ejes</td>\n",
       "      <td>PB1</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3039632 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            fecha hora_fin   tipo_vehiculo estacion        observacion  \\\n",
       "0      2022-01-01        0            Auto      ALB       Mercado Pago   \n",
       "1      2022-01-01        0            Auto      ALB   Mercado Pago OCR   \n",
       "2      2022-01-01        0            Auto      ALB   Mercado Pago OCR   \n",
       "3      2022-01-01        0            Auto      ALB      Tag - PosPago   \n",
       "4      2022-01-01        0            Auto      ALB  Tag - PosPago OCR   \n",
       "...           ...      ...             ...      ...                ...   \n",
       "264485 2022-12-31       23  Pesados 2 Ejes      PB4      Tag - PosPago   \n",
       "264486 2022-12-31       23  Pesados 5 Ejes      AVE       Mercado Pago   \n",
       "264487 2022-12-31       23  Pesados 5 Ejes      DEC      Tag - PosPago   \n",
       "264488 2022-12-31       23  Pesados 5 Ejes      ILP      Tag - PosPago   \n",
       "264489 2022-12-31       23  Pesados 5 Ejes      PB1      Tag - PosPago   \n",
       "\n",
       "          sentido    forma_pago pasos  \n",
       "0       Provincia  Mercado Pago     8  \n",
       "1          Centro  Mercado Pago     4  \n",
       "2       Provincia  Mercado Pago     2  \n",
       "3       Provincia           Tag    65  \n",
       "4          Centro           Tag    32  \n",
       "...           ...           ...   ...  \n",
       "264485     Centro           Tag     2  \n",
       "264486  Provincia  Mercado Pago     1  \n",
       "264487     Centro           Tag     1  \n",
       "264488  Provincia           Tag     3  \n",
       "264489  Provincia           Tag     3  \n",
       "\n",
       "[3039632 rows x 8 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2022_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3039632 entries, 0 to 264489\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Dtype         \n",
      "---  ------         -----         \n",
      " 0   fecha          datetime64[ns]\n",
      " 1   hora_fin       object        \n",
      " 2   tipo_vehiculo  object        \n",
      " 3   estacion       object        \n",
      " 4   observacion    object        \n",
      " 5   sentido        object        \n",
      " 6   forma_pago     object        \n",
      " 7   pasos          object        \n",
      "dtypes: datetime64[ns](1), object(7)\n",
      "memory usage: 208.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data_2022_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### concatenate all dataframes! 2020-2021-2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>año</th>\n",
       "      <th>fecha</th>\n",
       "      <th>mes</th>\n",
       "      <th>dia</th>\n",
       "      <th>dia_semana</th>\n",
       "      <th>hora_inicio</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>estacion</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>pasos</th>\n",
       "      <th>mes_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1342485</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>julio</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20</td>\n",
       "      <td>DELLEPIANE LINIERSLEPIANE CENTRO</td>\n",
       "      <td>Pesados 3 Ejes</td>\n",
       "      <td>Tag</td>\n",
       "      <td>Tag - PosPago</td>\n",
       "      <td>Centro</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          año      fecha    mes        dia  dia_semana  hora_inicio hora_fin  \\\n",
       "1342485  2020 2020-07-01  julio  Miércoles         1.0         19.0       20   \n",
       "\n",
       "                                 estacion   tipo_vehiculo forma_pago  \\\n",
       "1342485  DELLEPIANE LINIERSLEPIANE CENTRO  Pesados 3 Ejes        Tag   \n",
       "\n",
       "           observacion sentido pasos  mes_num  \n",
       "1342485  Tag - PosPago  Centro     6      NaN  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>fecha</th>\n",
       "      <th>hora_fin</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>estacion</th>\n",
       "      <th>observacion</th>\n",
       "      <th>sentido</th>\n",
       "      <th>forma_pago</th>\n",
       "      <th>pasos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>222782</th>\n",
       "      <td>2022-03-27</td>\n",
       "      <td>11</td>\n",
       "      <td>Pesados 5 Ejes</td>\n",
       "      <td>AVE</td>\n",
       "      <td>Tag - PosPago OCR</td>\n",
       "      <td>Provincia</td>\n",
       "      <td>Tag</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fecha hora_fin   tipo_vehiculo estacion        observacion  \\\n",
       "222782 2022-03-27       11  Pesados 5 Ejes      AVE  Tag - PosPago OCR   \n",
       "\n",
       "          sentido forma_pago pasos  \n",
       "222782  Provincia        Tag     2  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2022_clean.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_20_21=data[['fecha', 'pasos', 'estacion', 'forma_pago']]\n",
    "data_2022_tojoin=data_2022_clean[['fecha', 'pasos', 'estacion', 'forma_pago']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\983447753.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_20_21['estacion']=data_20_21['estacion'].replace('ILLIA', 'Illia')\n",
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\983447753.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_20_21['estacion']=data_20_21['estacion'].replace('ILL', 'Illia')\n"
     ]
    }
   ],
   "source": [
    "data_20_21['estacion']=data_20_21['estacion'].replace('ILLIA', 'Illia')\n",
    "data_20_21['estacion']=data_20_21['estacion'].replace('ILL', 'Illia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.logical_and(data_20_21.estacion=='Illia', data_20_21.forma_pago == 'Efectivo')\n",
    "data_20_21 = data_20_21[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\poutxinha\\AppData\\Local\\Temp\\ipykernel_3964\\1866983183.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_2022_tojoin['estacion']=data_2022_tojoin['estacion'].replace('ILL', 'Illia')\n"
     ]
    }
   ],
   "source": [
    "data_2022_tojoin['estacion']=data_2022_tojoin['estacion'].replace('ILL', 'Illia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "forma_pago\n",
       "Tag                  1363272\n",
       "Mercado Pago          509139\n",
       "Violación             444086\n",
       "Rec.Deuda             300811\n",
       "Efectivo              264590\n",
       "Exento                112685\n",
       "Tarjeta Magnética      44527\n",
       "CPP                      522\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2022_tojoin.forma_pago.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.logical_and(data_2022_tojoin.estacion=='Illia', data_2022_tojoin.forma_pago == 'Efectivo')\n",
    "data_2022_tojoin = data_2022_tojoin[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>fecha</th>\n",
       "      <th>pasos</th>\n",
       "      <th>estacion</th>\n",
       "      <th>forma_pago</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1653825</th>\n",
       "      <td>2021-09-08</td>\n",
       "      <td>12</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             fecha pasos estacion forma_pago\n",
       "1653825 2021-09-08    12    Illia   Efectivo"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_20_21.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fecha</th>\n",
       "      <th>pasos</th>\n",
       "      <th>estacion</th>\n",
       "      <th>forma_pago</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32874</th>\n",
       "      <td>2022-04-03</td>\n",
       "      <td>15</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           fecha pasos estacion forma_pago\n",
       "32874 2022-04-03    15    Illia   Efectivo"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2022_tojoin.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>326</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>319</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>199</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>180</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>114</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263821</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>9</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263822</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>12</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263884</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>2</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263885</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263979</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>Illia</td>\n",
       "      <td>Efectivo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166451 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            fecha pasos estacion forma_pago\n",
       "52     2020-01-01   326    Illia   Efectivo\n",
       "53     2020-01-01   319    Illia   Efectivo\n",
       "134    2020-01-01   199    Illia   Efectivo\n",
       "135    2020-01-01   180    Illia   Efectivo\n",
       "202    2020-01-01   114    Illia   Efectivo\n",
       "...           ...   ...      ...        ...\n",
       "263821 2022-12-31     9    Illia   Efectivo\n",
       "263822 2022-12-31    12    Illia   Efectivo\n",
       "263884 2022-12-31     2    Illia   Efectivo\n",
       "263885 2022-12-31     1    Illia   Efectivo\n",
       "263979 2022-12-31     1    Illia   Efectivo\n",
       "\n",
       "[166451 rows x 4 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = data_20_21.columns\n",
    "data_2020_22 = pd.DataFrame(columns=column_names)\n",
    "data_2020_22 = pd.concat([data_20_21,data_2022_tojoin], axis=0)\n",
    "data_2020_22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 166451 entries, 52 to 263979\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count   Dtype         \n",
      "---  ------      --------------   -----         \n",
      " 0   fecha       166451 non-null  datetime64[ns]\n",
      " 1   pasos       166451 non-null  object        \n",
      " 2   estacion    166451 non-null  object        \n",
      " 3   forma_pago  166451 non-null  object        \n",
      "dtypes: datetime64[ns](1), object(3)\n",
      "memory usage: 6.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data_2020_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 166451 entries, 52 to 263979\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count   Dtype         \n",
      "---  ------      --------------   -----         \n",
      " 0   fecha       166451 non-null  datetime64[ns]\n",
      " 1   pasos       166451 non-null  int32         \n",
      " 2   estacion    166451 non-null  object        \n",
      " 3   forma_pago  166451 non-null  object        \n",
      "dtypes: datetime64[ns](1), int32(1), object(2)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data_2020_22['pasos'] = data_2020_22['pasos'].astype(float)\n",
    "data_2020_22['pasos'] = data_2020_22['pasos'].astype(int)\n",
    "data_2020_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2020_22.drop(['estacion', 'forma_pago'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 166451 entries, 52 to 263979\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count   Dtype         \n",
      "---  ------  --------------   -----         \n",
      " 0   fecha   166451 non-null  datetime64[ns]\n",
      " 1   pasos   166451 non-null  int32         \n",
      "dtypes: datetime64[ns](1), int32(1)\n",
      "memory usage: 3.2 MB\n"
     ]
    }
   ],
   "source": [
    "data_2020_22.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1096, 1)\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>pasos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fecha</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>21395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>33722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>37067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>30627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>27812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pasos\n",
       "fecha            \n",
       "2020-01-01  21395\n",
       "2020-01-02  33722\n",
       "2020-01-03  37067\n",
       "2020-01-04  30627\n",
       "2020-01-05  27812"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily = data_2020_22.resample('D', on = 'fecha').sum()#we set the data to daily frequence\n",
    "print(daily.shape)\n",
    "daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,6))\n",
    "plt.plot(daily.index, daily.pasos)\n",
    "plt.xlim(datetime.date(2020,1,1), datetime.date(2023,1,1))\n",
    "plt.xlabel('Tiempo')\n",
    "plt.ylabel('N coches/dia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#weekly frequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(157, 1)\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>pasos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fecha</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-12-04</th>\n",
       "      <td>67691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-11</th>\n",
       "      <td>54194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-18</th>\n",
       "      <td>91098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-25</th>\n",
       "      <td>80292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>75254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pasos\n",
       "fecha            \n",
       "2022-12-04  67691\n",
       "2022-12-11  54194\n",
       "2022-12-18  91098\n",
       "2022-12-25  80292\n",
       "2023-01-01  75254"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly = data_2020_22.resample('W', on = 'fecha').sum()\n",
    "print(weekly.shape)\n",
    "weekly.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,6))\n",
    "plt.plot(daily.index, daily.pasos, label = 'Daily')\n",
    "plt.plot(weekly.index, weekly.pasos, label = 'Weekly')\n",
    "plt.legend()\n",
    "plt.xlabel('Tiempo')\n",
    "plt.ylabel('N de coches')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_avg = daily.rolling(10, win_type='triang', center=True).mean()\n",
    "weekly_avg = weekly.rolling(2, win_type='triang', center=True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,6))\n",
    "plt.plot(daily_avg.index, daily_avg.pasos, label = 'Daily Average')\n",
    "plt.xlabel('Tiempo')\n",
    "plt.ylabel('N Coches')\n",
    "plt.show()\n",
    "#we can see that a period in 2020 has no data, Covid-19 maybe? registrations did not took place or no vehicles passed due to confination\n",
    "#also, for some reason there is no data registered around July-August 2022\n",
    "#there are also some peaks in september 2021-2022 (2020 was Covid-19 age). Are they maybe related to outliers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prophet import Prophet\n",
    "import cmdstanpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>pasos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fecha</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>21395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>33722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>37067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>30627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>27812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>13496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>13512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>14426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>13888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1096 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            pasos\n",
       "fecha            \n",
       "2020-01-01  21395\n",
       "2020-01-02  33722\n",
       "2020-01-03  37067\n",
       "2020-01-04  30627\n",
       "2020-01-05  27812\n",
       "...           ...\n",
       "2022-12-27  13496\n",
       "2022-12-28  13512\n",
       "2022-12-29  14426\n",
       "2022-12-30  13888\n",
       "2022-12-31   6889\n",
       "\n",
       "[1096 rows x 1 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>21395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>33722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>37067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>30627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>27812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>2022-12-27</td>\n",
       "      <td>13496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>2022-12-28</td>\n",
       "      <td>13512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>14426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>13888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1096 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds      y\n",
       "0    2020-01-01  21395\n",
       "1    2020-01-02  33722\n",
       "2    2020-01-03  37067\n",
       "3    2020-01-04  30627\n",
       "4    2020-01-05  27812\n",
       "...         ...    ...\n",
       "1091 2022-12-27  13496\n",
       "1092 2022-12-28  13512\n",
       "1093 2022-12-29  14426\n",
       "1094 2022-12-30  13888\n",
       "1095 2022-12-31   6889\n",
       "\n",
       "[1096 rows x 2 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dailydf_2020_22 = daily.reset_index()\n",
    "dailydf_2020_22.rename(columns = {'fecha':'ds', 'pasos':'y'}, inplace = True)\n",
    "dailydf_2020_22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16:17:24 - cmdstanpy - INFO - Chain [1] start processing\n",
      "16:17:24 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<prophet.forecaster.Prophet at 0x2e3943a7ed0>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = Prophet()\n",
    "m.fit(dailydf_2020_22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2023-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2023-12-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2023-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>2023-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>2023-12-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds\n",
       "1456 2023-12-27\n",
       "1457 2023-12-28\n",
       "1458 2023-12-29\n",
       "1459 2023-12-30\n",
       "1460 2023-12-31"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future = m.make_future_dataframe(periods=365) #we create the predictions based on our data (2020-2022) -->2023\n",
    "future.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SmNB+KTxon2TS0tISGjNceeWVuOyyy6ZcfNO+KTxonxQutG9GZ6y/zbQZqb3jHe/ACy+8gGeffdb7d9JJJ+FDH/oQnn32WaxcuRJLlizBb37zG+89lmXhD3/4gyeoTzzxRESj0dAynZ2dePHFF71lmpqa0N/fj7/+9a/eMk888QT6+/tDy7z44ovo7Oz0lnn00UdRXl4emhQoBoRUEXwhJaSUODBswXIkjFGyB/z3S3Ah0dXeHqrjllIZs/385z9Ha2srfvSjH+VsR5bNGd0wDE9wA+G+4ARBEARBzFxisVjGmOH2228no1WCIEqGaRPds2fPxpo1a0L/qqursXDhQqxZs8br2b1582Y8+OCDePHFF/HRj34UVVVVuPjiiwEAc+fORXNzM6688kr87ne/wzPPPIMPf/jDOPbYY3HmmWcCAI4++mi8613vwiWXXIK//OUv+Mtf/oJLLrkE55xzDo466igAwFlnnYVjjjkGGzZswDPPPIPf/e53uOqqq3DJJZeE0sSLASFcwQ0gbnG83DWIuOUgavqi+8BQCnHLyfl+LiX27H49I1othMCiRYu8Wetc7ciyOaN/7nOfyxr93rVr1+F+ZYIgCIIgppGdO3dmLUGjCXaCIEqFgq6Kv/rqq5FIJPCpT30Kvb29OOWUU/Doo49i9uzZ3jJ33HEHIpEILrzwQiQSCbzjHe/Aj370I0/QAcB9992HSy+91HM5P++883D33Xd7r5umiV/96lf41Kc+hbe//e2orKzExRdfjNtvv33qvuwUkbA5LC5QFjHQm7BxIG4jYTmIGEp0CyGx88AwZpdHsKYuc8JBusssX3lkRh23aZpYtWrVmLajubkZ69at82q7AIQi3eNdH0EQBEEQhYnOcMsmvPUE+3TUeBMEQUwVBSW6f//734ceM8Zw44034sYbb8z5noqKCtx111246667ci6zYMECbNu2bcTPPuKII/DLX/5yPJs7I+lL2Bi2OKrLIrC5hMmAuRVROEKlhw9ZDgZTHAury7K+X0iAC4klS5diy5Yt2LTpExDCr9tuaGiAzQWi5uhJFA0NDaGb7NatW7Fp06ZQHTjdhAmCIAhiZqMz3IIp5hqaYCcIohQoKNFNTD5xm8NkDFICthBgYDCYqtUGgGGLY9hyUJZDNAspwaEi3h/92Mcx66i34rXXduKctx+PE49ehd64hdcPDuPYujkoj5hZ15GL9Og3CW6CIAiCKA70Pf5b3/oW7rjjDppgJwiipCDRXYJokW1zCQkJMAPcjXQnbI6kwz0Rno6Q0qsLFxJYuLgex8xehCX1yj3+UNxGwhbe+sZLevSbIAiCIIjioKGhAV//+tdx2WWX0QQ7QRAlBYnuEkT3Hk/ZHAbTkW4gaXP0DFrgXOYUzUKo9HIpAYsLV3yrxwCQdAQStkAOzU4QBEEQRIlDE+wEQZQa0+ZeTkwfDHBFs6rpZgC4lOgcSKF7KAkAsHl21cylhIAEFwIvdQ3C4m6LL1dlDyRtOEKANDdBEARBEARBEASJ7pKEMSjhLCVMg4ExBiEkUg6H5QjMq4zCyeIwCqhemy/+9c/Ys7cNA0kHwymOsojh9f+2uIpyZ0tPj8Vi2LFjB7UGIQiCIAiCIAiiZCDRXYIwKCM1LiQMxlSkWwCDloNZ5REYjIXSy/ceiiNuOWhpacHpJxyNzZ9Yj/eceiz+62c/wZE1VWBQIvuptj5VJx5IN9e0tLSgsbERa9euRWNjI1paWqb0OxMEQRAEQRAEQUwHJLpLEMOAV4ttGqqm25FCRa1Nw32sVHPS5oj1J/H0q7tDrT6EEPjeTddgf1eHeiyBuMWRsLnq5R1Q3bFYLOO9mzZtoog3QRAEQRAEQRBFD4nuEsQAU62/pITBlLGalIAjJCJuurnDdY22g8Gkg869uzN6awrB0da6B4wpcW4LiYGkrYzVAsvt3Lkz472cc+zatWuyvypBEARBEARBEMS0QqK7BNFu5UIAhutkLt0WYIyp123XIC1hcyQcjhVHHgnDCB8uhmFi2fIVYGBI2AI2F1g+vwqQgAikp69evTrjvaZpYtWqVZP8TQmCIAiCIAiCIKYXEt0linDNznR6uYQS3ge6OvH8E39Gd0cHHC4wlHJgOQJ19Q3YsmULDMMEABimiU9+6WuorVvq9f12hETENNS64BunAcDWrVthmuq9pmliy5YtoXYhZLJGEARBEARBEEQxQn26SxQhVE23MlJT6eb/85/34a4broIQAoZh4Iu3fQtn/vNFXo32ORd+GPe94a147e87cezRR8GYUwNApacLqXp7RwyGA10duOEHt2HL3d/21rV161a0trZi165dWLVqVUhwt7S0eDXfetnm5uZp+mUIgiAIgiAIgiDyB0W6SxTPSI2plPKerg582xXcgDI7+8o1l+Pvu/dCQqWe7+1NIDp3EY4/5e1YVFfvrUunqwPAQ9vvxSXr3orvffvODOM0ADjjjDMyItxkskYQBEEQBEEQRLFCortEEZDgAjAMBsaAtj27IdON0jjH66/vghCuSHej45VRE0JKHOjqwFOP/REHuzshpcTB7k5svvZyZPQLQ3bjtFgshp///OdkskYQBEEQBEEQRNFC6eUlSjC93GAM9Y0rwAwjJLwNw8Rb3nQ0zDITjpBIOhwLqsoAAL/6+Tbc/m9XQrop4Vd99ZuYtXhphoDWpBunBVPKR1uWIAiCIAiCIAhipkKR7hJFSoC7OeEGY1hYW4dP3XAbDNfszDBMfPamr2PJ0nowBiQsDlsAUZPhQHcHvuEKbkClhN/+b1eivLIqw6UcUKZrQeO09JTyINlM1giCIAiCIAiCIGYqFOkuYWxXdJuu+/g7//livOdd78Lru3Zhfn0jFtTWwQCDAYYUVy3BZpdF0d6avWd3KhnH9bfeic3XXgEhOBgz8KGNn8YHP7YR73nrm7xls/XtBoA77rgD73//+0lwEwRBEARBEARRNJDoLlEqoyaSDgeg3Me5AASA2rqlmLtoCQaSDoSUXt9uGWgxtmy56tkt0lLRlyxbjre880w0Hv82GANdMOfX45hVyzFscXApYUD1BNd9u4PvN02TBDdBEARBEARBEEUHpZeXIAe6OtDzylOYY/cDAEyDuW7m6nUG5vbtVoJctwRTNeDA4rp6XPblb/g9uw0Tl970dSysVY7mNUvq8ZZTT8PC2jokbAGbq/dqGhoaRu3bTRAEQRBE8RCLxbBjxw7qTkIQRElCke4S4xc//Qm+fPXlngHa9bfeifPXbwCXgJS6xltHtoGezna89OprOOoNbwCbXQPmGq+te9/FWH3S22H0d6OmvhEVCxbBcnxhzYUS8QNJG1HT8AS9prm5GevWrcvat5sgCIIgiOIhaJ5qGAa2bt2K5ubm6d4sgiCIKYMi3SVELBbDV1zBDSgDtM3XXYHuznYIIb1OXwxKgD/6i/tw/tvfjOs+9n68/x+Ox29/cT8A3ZdbYmFtPU5qOg1L6uvhBFQ1A9Dd2Y7nnvgT9uxrUwI+XXVDRbzT+3YTBEEQBFE8pJunCiGwadMmingTBFFSkOguIbIZmAnO0da6x0sfBwAwhv1dHfjuTVeHbpLf+/I16O5sB1zRrWGMwQms9jcP3o9zm47Dl/71A7j8nFPxPw/cByFHTy2j1DOCIAiCKC6yjT0459i1a9c0bRFBEMTUQ6K7hNAGZkEM08Sy5SsgIKFltMGA9r17Qj27AeVQ3ta6BwaU8ZrW3Qbz2491d7ZniPW7b/g8vnXH7WhsbMTatWvR2NiIlpaW0LpbWlpGfJ0gCIIgiJlHtrGHaZpYtWrVNG0RQRDE1EOiu4RoaGjAF2+70zdAM01cf8sdqK1bCiH8SDcDULdsOVi6QDeUQGdZIt3dne144a9/xnNP/TWrWP/SF76QM7WMUs8IgiAIojgh81SCIAgyUis5LrjoI1hx/NuRPNiBZctXoLZuKQDAYMyLVjPGsHBJPT7xxduw5avXQHAOwzCx8YtfQ23dUjhCIiirf/mze3HzdVdACuG5ncuQKDcgZfbUsoaGhhFTz+imTBAEQRAzm6B5anV1NYaGhhCLxegeTxBEyUCiuwSpWVKPJW9YGXpuVU116LEE8E/nX4Rz3vMuvL5rF+bXNyI6dxEAYH9XO55/4RUsbliO2XYVNruCG1AGbIwxrw+3YZrYcOl1uPdbm9P6ehvo6elBLBbL2bebUs8IgiAIojhoaGjAr3/9a3IxJwiiJCHRTQBQke4gQirhXVu3FPMX1aE3aaMvYeOh7fdi87WXQwgBZhj40L9+KiNKLaXEjd/+AYzquXjLm94Iq3IB3nzkUnz+ss+Ac+5Fwj/4wQ96N92tW7di06ZN4JxT6hlBEARBFBm5SsnWrVtH93uCIIoeEt1EdqT0+3YbDA6XONDZ4QluAJBC4L4ffBfMMEJ13IZhYs0Jb4U5twa1cysR60/iog0fxQfeew4ef/xxrF+/PuOm29raitbWVurbTRAEQRBFCJWSEQRRypCRWonT3dmOpx77o2oFFkACEK6fuckAW0h0te3JjGoLgQ9f8mkYpm/OdtlNX0fNkvrQ2oRUqWU1NTUj3nSpbzdBEARBFB/kYk4QRClDke4SJpgqbhgGrr/1Tpy/fgMAN73ca9vN4HCBJctWZNReG6aJ9R/fhPUf34Tnn34SUko0vukEcCHAoFLWpfTdzql+myAIgiBKD+1iTqVkBEGUIhTpLlG6O9txcyBVXAiBzddd4UW8ZSC9HAAcIbC4bimuv/VOL6rNDAMXN38SAPD4H/4XX/hMM67/9MexYe2J+NXP74MRKBPXonv2wlp8//tbqHUIQRAEQZQYzc3NaG1txY4dO9Da2komagRBlAwU6S5Rftry/cx+2pyjrXWP10YsoLnROL8KUYNh9foNaDp9Lbb/+xZsu+c72Lb1btx3z3fc5dUbhBC440tX4dhT/xF1c5RLuiMknmrrheVIvOOCi9D67ndR/TZBEARBlBgNDQ103ycIouSgSHcJcqCrwxPKQRhjOHRgP7o728GDihtAZdRExPQPl233fCfUJkymLS8ER8e+Vu+x5QgMJh28trsVf9ixAwBwxhlnAAB27NiBWCyWj69GEARBEARBEARRUJDoLkE69u3JEMmAEs/Xf/rjOLfpOPzvgz/N+f5sUfJ0DMNEfeMK77EjJP7fz7ah+ay34uMfOBeNjY346Ec/isbGRqxduxaNjY1oaWmZ+JciCIIgCIIgCIIoQEh0lyD1R6zIcBANIoTAD2+5DnbHaxnO5t2d7di29e6M9zDGvHUaholP3XBbyMG8PRbDt264yhPrQgj8+Mc/zmgdRhFvgiAIgiAIgiCKCarpLkFqltTj+lvvxObrroDgPOsygnM0n38WpJQwDAOfufYGHHPcCWjd9fesy687//04/cx3AwxYdeyJMOcugvZRYwD27N41anSc+nUSBEEQBEEQRG50tipjbJQliUKCRHeJcr5riPbbX/4X7vjKF7IuEzRG+/bmG0Zc368f+k888uB/wDAMXPXVb+Lk93wAUAblYIyhYflKMMMYUXhT6zCCIAiCIAiCyM2LXYNwuMAJDfOme1OIcUDp5SVMbd1SnHnOe0dMNR8rQYF++79diZ6uDu81BmBx3VJ85LLrc76fWocRBEEQBEEQxMjELQ5bZHozEYUNie4SpzZL7+2PfeZzYxfiWVJblHP5Hu8lxlT7sSPf9Oasq7jjjjuoXydBEARBEARBjIIjBLL4IRMFDqWXE16qeVvrHixbvgK1dUux9IjlI9Z8A8AVX9qM4086GR87/yzPEA1QRmqLljbCcFU3gxLdda6BW3BZ0zTx/ve/nyLcBEEQBEEQBDECUkrYXMKkcu4ZB0W6CQAq4n1S02morVsKQAnxhx97Dt//2cP42Gc/l7G8YZo48+zz8KbjTwxFyg3TxPW33oE3rVqBhVVRAKqmW0KiZkk9rr75DhiGWpZSygmCIAiCIAhibFhcwOYSEhTqnmlQpJvISndnO9r27MayFStxUtNpmD1nLu6+5SYIIZSwvuWOkEBPj5QH0enlAPDe9R/GG08+DTXOIbz5mDeS4CYIgiAIgiCIMWBz6aaXm9O9KcQ4IdFdwgSFdW3dUu/xyy884wtsw8D1t96Jj2y6FOvOe19OYV1btzTjOQ0DICHBGGAwhgW19XjbijWYX1WG1kNxHBhO4aRl86fgGxMEQRAEQRDEzERICSEp0j0TIdFdojy0/V5svvZyT1i/+4IP4n9+8bNQvTWg3Mg3X3cFmk5fO6KwTico6M05iyClinYbTF8w1HIHhy3YnC4cBEEQBEEQBDESXLhjaBo6zzioprsEOdDV4QluQAnrX/3nTzMEt0ZwjueffhJPPfZHdHe2j7r+h7bfi3ObjsMn1p+Hc5uOw6//8z5wKWEwpuq7pRLeAOAICU5tDwiCIAiCIAhiRIQEhJAqjZSYUZDoLkE69u3JKbCzwRjD9Z9p9kT0Q9vvzblsd2d7hqD/1g2fR1dHO4xACzGts20uQJqbIAiCIAiCIEaGCwkB0Nh5BkKiuwSpd1t3jQXmLicDInrzdVfkjHi37dmdJUWdo23PHq+FGKS6aHQNJGFxQXUpBEEQBFGCxGIx7NixA7FYbLo3hSBmBEJKCKH+Dacc7D0Un+5NIsYIie4SpGZJPT5z3Q2e8DZME2e//6JQ269Lr78J3//Zw7j5rh9AyrAoFpyjrXVP1nUvW7EyQ9AzZiBaUelFugGgN2Fjb28CliPR09FBN12CIAiCKCFaWlrQ2NiItWvXorGxES0tLdO9SQRR8HDXF0kCeP1gHN1DqeneJGKMkOguMbo72vHvt38Zd7nu5IwxfOaaL+Gmb37X68v98GPP4SOfuBQnNZ2GN590coaINkwTy5avyLr+2rqlob7dACClwBUXvQe/fuB+9Rhqpi5uOfjVf2zD+jNOoJsuQRAEQZQIsVgMGzduDJWibdq0iSbfCWIUhPAdzB3h9uyWlDE6EyDRXUK0tLRg3clr8Isffc9LF5dS4u6vfRndne2orVuKk5pOCzmUp4vo9B7d2Th//Qb88MFfgzE/tC2FwLdv+LyXlm5zgX1tMXznps/TTZcgCIIgSoidO3dmlKJxzrFr165p2iKCmBkIKaELM6UEHPJGmjFQy7ASQc8qZ5sN0+niuYT0+es3oOn0tTl7dGcjEY9npqUL93OOXgApgdY9r3viX6Nvug0NDeP4dgRBEARBzBRWr14NwzBCwts0TaxatWoat4ogCh8uJYQAIAEJCUdIxPoSqKkuQ3U5ybpChiLdJUK2WWVNerp4d2d7RnuwbFHwkchW220Y4c9ZmsXQjW66BEEQBFHcNDQ0YOvWrTDdLDrTNHH3d7+H2rr6ad4ygihsdJtdIQEu1H9f7RlC5yDVdhc6JLpLBD2rnI5hGKF08fQe2yO1BxuJjLR0w8QVX74dtXVL3ZZhEjVL6nHVV78Bw9DLGLjlllsoyk0QBEEQRU5zczNaW1uxY8cOtLa24qR3vx+v9gxN92YRREFjcYmIwSABOEJAAkg5gtp2zwAoD6FE0LPKGzdtguAczDDwoUs+jYs+vskT3Nl6bG++7go0nb52zBHuIDot/eVX/46ymgasWb0cAGCAefUn51y4AQcP9eKH3/wqhBC49tprsWDBAjQ3N+flexMEQRAEUZg0NDR4E+379hwCQ/aMPIIgAMsR6B5MoipqQkqVWi6lBCcjtRkBie4Sorm5GUe8uQlPvfAq3vymo0Jiu23Pbhw6eCCzx/Yo9d6jUVu3FDW19egcTGJWmTrcDKbSYwzGcKCrA//+za+G+oBv2rQJ69ato4g3QRAzHillyFSSIIhMhJCwhUA5JWASRE6GLAdDKY55lRHELaH6dbstxHiOElKicCDRXWLU1i/FcZULUDunAoBKJ9fRbcYYGGMhA7SR2oONFdNgaJhb6T1mjMEWAqbBsHcvmakRBFG8/C3Wj6VzK7DEveYSBJGJ7bY+ipg0QUUQueBCuZabBoOEhHAzR5X4nu6tI0aDphRLmPR0ci22de33WNqDTQSDQd1cDYZly48EIzM1giCKEIcrIdGfdKZ7UwiioLG5hM0FIu54IBaLYceOHdRClCACaBM1A8xrGQb40W6isCHRXcK07dmdkU4upcTNd7fg+z97GA8/9hzOX78h75/LGIPFBSIGw5L6pfjsjbeHHEy3bNlCUW6CIGY8tpsyGzEoekcQI2FzAUdIGAxoaWlBY2Mj1q5di8bGRrS0tEz35hFEQcCldEuW1HiduxFuCWVQTBQ2JLpLmKxtvUwTx534VpzUdBoAZLQOywdSqhltkzEwAO+84CI8/8pOz8GUTNQIgigGbC7c6B2JboIYCYtL2EKiu6MdGzduDBm6btq0iSLeBIFApJuptHIpldjmbm03UdiQ6C5hMtp6BdLJ89U6LBtCqlQy03BryAHULV2KM844gyLcBEEUDSpllupUCWI0Ug6H7Qjs2f16Rgae9nkhiFKHu6eGaTAltCEhpQpmkegufMhIrcRo60vgW3/cg01NjVi+oMpr69XWugfLlq9Abd3ScbcO0+7ny1asHFP9t5Aq5dIw4KbIqNQYgiCIYkKlzJKjLEGMRspR50pD4woYhhES3uTzQpQ6DhfgacKauwZq3H2KaroLH4p0lxgb/+N5/GbnAVz9y1e852rrluKkptM8wZyt1lu3DktnIhHxhVVlqIqYKDcNMKgZOpqgIwii2LC4gMXp+kYQoxG3OKQEFtctxdatW8nnhSACvNIzhBc6B2BxAcNtQanruXXKOfXqLnxIdJcYHQMpAMDuQ3EAwM79w9j8u53YP5TylslV653eOixXRHy0GvCyiIGGeZVeizIwRmkxBEEUFQ4X6E84sBxBopsgRqA/YaNnyILBGLiUaG5uRmtrK/m8EISL5Qhwof6rq5W4FKqeW+oxON1oCh0S3SVKdZmaRb7ovr/hFy904Zv/t9t7baRa7yDjiYiPBEW6CYIoNl7tGcKhhKXaulABDUHkZMhyMJhyML8q6kXrGhoayOeFIAI4wnX4d405hZAQALibX07j6MKHarpLlHmV0dDj9v5k6HG2Wu90lq1YCXbCuZDl1cBftgPIHhEfC3StIAiiGOgaSGJWeQQpR2D/kAUJmlQkiJHgbqswg7EpidZ1dHTgpZdewtFHH02inpgRSEhwoTKodHq5lIDDJd64eBYOxm2q6Z4BkOguIVIO9/6eVxENpXTXzi7PWL62bumIxmhzFy2B/KdL1IMXHoWR7M8aER8NBuovSBBEcbC3N4GIwVAZNZG0OSrLTJpUJIgRCIoFPsm+gz/84Q/xyU9+EkIIGIaBrVu3Uvo6UfBw4fblluFOGDYXYIzBYDSOngmQ6C4hugf9uu3KqIGewOOFVWVjWsezHf1o603i3DfVonPAf/9Xv/sjnPDGsbmXpyNBaTEEQRQHjhCIGCYcITC/MgpbUKSbILJhOQKMqUg3Y5h04RCLxTzBDfg9wNetW0cRb6KgkVIZpTE3KwQAVi6shu3OUjGQkdpMgER3CXEobnt/pxyBroB5Gh9jXsq//vx5AMCqmiocDKzvDce9BbULqye8bVTzSBBEMeBwCW5KMAFlFAnqn5oPYrEYdu7cidWrV5NAKhKe7+wHA/M8ZiY7623nzp05e4DTMUUUMlz6/bh1rLs8YqA8oqy5GGM0uTsDICO1EuLgsOX9nXIE+gKiOemoG9HO/cO45y97kbR5xvstx79ZHYzboTrwlDPxnDAGQAh4M3YEQRAzEen2UXW4hM0FTDckQWOhw6OlpQWNjY1Yu3YtGhsb0dLSMt2bROQBXfFmCwnT7WYiJPDkvl70JeyR3zwBVq9endGZhXqAEzMBdW9R9xLGWM5lJCnvgoZEdwlxKBEW2cHHut77ovv+hi1/2YefPtOR8f79AdFeHjHQNZgf0S0B9Ayl8Ex7P3buH8LfewYnvC6CIIjpQkhAAHCEMr1h0JOKNKE4UWKxGDZu3JiREhyLxaZ5y4jDhXuTVMoc6kB3B/72+B+xc88+7Nw/nPfPa2howPe+9z2vB7hhGLjlllsoyk0UPPpcEVKVYqTDGJVqzgRIdJcQh+K+aB62HGz+3S7vcbpo3teXAAD84fWD2Py7nbAcgZ5AOnrKEejNEin/W6wfL3VNTDQPJh0cGLbQm3Am9H6CIIjpRLgpgI4QsLmAweBF74iJMVJKMDGz4UJF7ywu8Jtf3I+Lz3gLrvzIBfjw2rfggft/PCmf+bGPfQzXXXcdDMOAEALXXnstZU4QBY2UEsL1BpEAssW5DTBIkJlaoUOiu4Q4OOyL5GA9NqBuesOWL3aXuG7mVz78Mn7xQhd+9Up3yIgt6XD0Jf3lU45A92AKG//zefzL9mfHleLCmHp/0uGIWxzdHe3YsWMHRTIIgphRCJ0CKNXfhsHcOtXp3rKZC6UEFydCSHAhICXQHmvHt2+4yptckULg61+4Evv2teX9c2OxGDZv3kyZE8SMgbv9uIXbftLIEup27UOolKnAIdFdQgQj3emkHIF9vQnvcZlphITzkMXRM+S/P2mLUM1VyhF4vnPAe+yMY5SpZu9UHeT/+9m9+OfT3ky1ewRBzDiEa3Qj3b8NMDBq5XJYNDQ0YOvWrV5KsGma2LJlC6UEz3C4lODu5NS+Pa9nZDMIwfH8K6/l/XN37dpFmRPEjMKbyB2lEwallxc+5F5eQqRHt4MkHYGuQCQ7xTl6A6K6N7YH8fK5/utOWHS/+uqr4FXzQ69HzbHP6QgJdHe2464bPw9J7TwIgpiBCKFq7gypUv2ipu/I3NGfxP6udhzq2EcO3ONk3bp1uP/++8EYQ1NTE/12RYDyPVATVHWNK7x0b41hmOBzapVbcw7jqImwatWqjM+izAmikBFSeqUYErkj3UJSJ6BChyLdJcRIZmeWI0KiPOWIUB/un2y5C//5H//hPX5iXy9iAffyn9zzPdz3nw+O6bPS0X06Y627PcGtoRlogiBmCkLqf674ZgxgDEJKfHfLVrzlmDdQFs840c7lH/zgB7F+/Xr8+te/nu5NIvKAw6XbBkmiprYen/vKN2B4BmcmPn79rahcsMTzi8kXDQ0NuO222yhzgpgx6Ewp7U6e1UgNzH19ijeOGBckukuI+z/8Fjx52Wmh595/XB0AFen+v9cPes8nHYFX9gUczKPlQLnfh/t/d/nLAgAiZUD1Au+hNc72X1xK1DQsB6PaPYIgZigi0LJFu8wyAJ0d7bjluiuojnSckHN58cJdcyg9UXXOhR/Gg398Bpt/+AC+/8gTuORfm5GwHcStzPalh8tFF12EnTt3YseOHWhtbUVzc3PeP4Mg8oWeyOVQ6eO53MsBqukudEh0lxhlEQNG4IRdOrcCANA1mMJje3u951OOwJ72Hn/BSBlQMTv3ihc2AEcc5z18+skn0d3ZPraNkgAXwJtWrcBlN90Ow6AZaIIgZh48YKQm4dd0t+7OrFmlLJ7RIefy4kVIP9Kts0KW1DdgzVvfhgW19TANBinHP4E/VhoaGnDGGWfQ+IIoeLRBpxASAhIsi385Y37dN1G4UE13ifGLn/4EIlULlFUCAPY+/1cAizKWSzkCs2fNB+AK8Ug5UDEr94qPXRd6eOPVl8E4uBfX33onzl+/YcRtklAXivKogfd84EM47Z/OxEL7IFatWkU3RIIgZgzK6Ea6LrN+pHtplppVyuLJTSwWw86dOzFr1iz63YqUYAskADCY+qfPIQ2lyxKljjbo5O45k83hgIGp+86Ubx0xHijSXULEYjF85erLAduv1f6vH38v67KWI2BHKvwnalcBi1dmLHd0+XD2D4uUQQiBzdddMWrEW0qASwHGlEFETW0dzUATBDHjEBIQcCPdEtjf1YHn//pnOBy4/Mt+zSpl8eRG13CvXbsWp556KjZs2ED1t0UId88V7X/AGANjDBzhFnskI4hSx4t0K3/yULaqxmDwskaIwoUi3SWEl6rn+KJbxvuzLqvcyf0+3Fj51qzLnbTmDXjl6Syi2owCAATnaGvdg9q6pSNuGxeAyRhsSK8uMp+OpQRBEJMNd6MRMCV+/cD9+O5Nn4cQAoZh4NKbbscv/vgM5qYoiycX2Wq4t23bhscffxzDw8P0uxURwq3plm5Nt77bByPdDICYnOxygpgxeBlUrgdCtqJuxph3LhGFy7RGur/3ve/huOOOw5w5czBnzhw0NTXhf/7nf7zXpZS48cYbUV9fj8rKSpxxxhl46aWXQutIpVL47Gc/i5qaGlRXV+O8887LMFnp7e3Fhg0bMHfuXMydOxcbNmxAX19faJl9+/bh3HPPRXV1NWpqanDppZfCsnL3tZ6JrF69GoZhAI7/vZgVDy1TXaYiCjsPDOPRv+8fcX2VUQNzK6LZX4yUAQAM08Sy5StGXI+Equ1SM93Ua5AgiJmJw1VK+f7ODnzHFdyAEo/fvuHzkBJ488lvI+GYg1w13MPDw5T9VGTwgImalH70jrt1q4A7FqBI97h5vmMArx/IkYVIzDgk/HGxELkj3dpwjShcplV0NzQ04NZbb8VTTz2Fp556CmvXrsV73/teT1jfdttt+OY3v4m7774bTz75JJYsWYJ3vvOdGBwc9NZx+eWX48EHH8T27dvxpz/9CUNDQzjnnHPAue94efHFF+PZZ5/FI488gkceeQTPPvssNmzw64w55zj77LMxPDyMP/3pT9i+fTseeOABXHnllVP3Y0wBDQ0N+OJtd4YU7eeuvi60zMfeugwAsH84x4SD7bcJW1BZhvJI+BAy9A0yUgbDNHH9LXeMGuUOXkgY/AsMQRDETMIWApAMsb17MtofCsHRtnc3XuuhwXAuvInhAFTDXZzotHIJN9LNGLo72/HCk3/G/s5ObznSEOMnbnH0JezRFyRmBDp6zRiDk7OmG9QybAYwraL73HPPxXve8x684Q1vwBve8AbcfPPNmDVrFv7yl79ASok777wTX/jCF3DBBRdgzZo1+PGPf4x4PI77778fANDf34+WlhZ84xvfwJlnnokTTjgB27ZtwwsvvIDf/va3AIBXXnkFjzzyCH7wgx+gqakJTU1NuOeee/DLX/4Sr732GgDg0Ucfxcsvv4xt27bhhBNOwJlnnolvfOMbuOeeezAwMDBtv89kcMFFH8HS5X5t9vs/eBEigWmzmuqykVfw0u+8PyuiRkh0r1kyGycumwcAaL7iejz82HOjmqhpuHvTNdxQN83WEUR2YrEYduzYQW2TChDLETANoO6IzPaHhmGi/oiVGLYcWHnuPVwsNDQ0YOvWrVTDXQJoIaF7Dz/8s3txbtNxuHnTB3H5uafioe33qtencRtnKime/zZrxPQRHA9zkb30kjHmTmDRGVPIFIyRGucc27dvx/DwMJqamrBnzx50dXXhrLPO8pYpLy/H6aefjsceewwA8PTTT8O27dAy9fX1WLNmjbfM448/jrlz5+KUU07xljn11FMxd+7c0DJr1qxBfX29t8y6deuQSqXw9NNPT+r3ng7MiJ8SHjEYzIDonlsxcpk/G/RTzk3JQ6K7usxEeUQNlpauXI3auqWIWxzOKC0/JJSRmt4M3XKHIIgwQZOpxsZGtLS0TPcmEQFSjkDUYKiprccnvnibZ5xmGCY2ffFrWFhbh6TDEbdpUJyL5uZmtLa2Ug/lIkeJB+WDsL+rA5sDfeyla8J6oKuDxgLjxOYCDpeUll9ECCnBoFLInRFMDhijmu5CZ9qN1F544QU0NTUhmUxi1qxZePDBB3HMMcd4gri2tja0fG1tLfbu3QsA6OrqQllZGebPn5+xTFdXl7fM4sWLMz538eLFoWXSP2f+/PkoKyvzlslGKpVCKuWbkumouG3bsG0/tce2bTiOE3puunAcG8G5Y8GdkMKdHQ3PoB1XPoDnU3O8xycctRJ/c//++/NP4zlnL4AlAIByk8F0NXjCstHVN4wP3PcsjlsyG99+79E5t8mUHAnbgeQOBHfgOBKWbYFJ87C+62gU0n4hwtC+ySSbydSmTZuwdu3aKYkE0j4ZneFkEkxycEfgjHM/gHefdSZ2734d82qXoWzeYliWBdviSFkW7DzdfYtxv9TW1nr35Jn4vYpxn+SbZDIFU3DYlkBsz66MWn7BOTr3vg5rzZGw7RzeMROg2PfNcMpBMmVhdnTmnDvFvk8OFytlQzgOFpQb6Bm2wZ3sv5Nj2dh7YACGrMZAwsYRC6oO+7Np34yNsf4+0y66jzrqKDz77LPo6+vDAw88gH/5l3/BH/7wB+/19DSKsbhapy+TbfmJLJPOLbfcgptuuinj+QMHDoTEuOM46O1V/a4jken9yQd7hyACqUeDfYdCM2MsNRRa/uRjVuL5Zw4AAEwm8czv/we48B/Vi46FB39yD3D+F9XrwoHhrmtwcBj3PTmAhC3wRFs/BvsO5dymWQIoA8dgn4WUI5CwBQ7sFyiLTG4iRiHtFyIM7ZtMnnrqqawmU08//TTKy8sn/fNpn4wMFxKHDvbD4RIJm4MLiYVzK3D00cegL2ljaLAXg3YUFuc4dECAV+ZHSNB+KTxon4wMFxLt3UPgcQspKbFg/vyMfuyGYWD+vPno6z2I/TI+wtrGR7Hvm8GUg6H+flSLcuzfPzOEUrHvk8Pl0EASycFhzJldhvooMNiXyrrc8EASfTKB591xfYUz67C7ANG+GRtBr7GRmPZfsKyszDNJOemkk/Dkk0/iW9/6Fq655hoAKgpdV1fnLd/T0+PNgC9ZsgSWZaG3tzcU7e7p6cHb3vY2b5nu7u6Mz92/f39oPU888UTo9d7eXti2nREBD3Ldddfhc5/7nPd4YGAAy5YtQ01NDebM8aPDegakpqYG0Wj+ZmwnQpdTBmbsBaC2afa8BaEkpPQZtJW18wEo0V1hAMNWwn/RSUEGen7PqqpwOxkMgpVVYFeP345s9rwFY9q+qM0Bi2P+wgWoKpvcw7OQ9gsRhvZNJieddFLGwNQ0TZx44olYtGjRpH8+7ZOR6exPwooKLFlYjvb+FBwuMHteFYSUGO5PIRJxEK2MgnGJeQvmo2ZWfiZKaL8UHrRPRqZrIAlWxWAyBxBA7Yp5uO7mb+CWf7sKgnMYponrvno7lq56I2bNqcaiRbPz9tnp+yYWi2HXrl1F046ODaVQ1scwe14lFi2aO92bMybofBmZYSOOimQUs+dWjLhc1IkjOqsc1WUmUo7A/IXzEDUPL3hF+2ZsjDXwMe2iOx0pJVKpFFasWIElS5bgN7/5DU444QQAgGVZ+MMf/oCvfe1rAIATTzwR0WgUv/nNb3DhhRcCADo7O/Hiiy/itttuAwA0NTWhv78ff/3rX3HyyScDAJ544gn09/d7wrypqQk333wzOjs7PYH/6KOPory8HCeeeGLObS0vL8/6Q0ej0YyDMxKJZH1+qolEoqEWf2Yk6g7i1ZObLjgL2Pgj7/XFsyu9vyvLIohzxxfpjgUm/ceVAZH8fNcQnoz5JnTBOvKRMGGAcYZIJIpodPIPz0LZL0QmtG/CrFixAlu3bsWmTZvAOfdMplasGLklXz6hfTICpg0jEkF5WRkM04HBDJiRKEwADnNgmACMCBiTiOT5N6T9UnjQPslNeZnAkMOweG4V2geSMMDwzx/6KN629p1oa92DZctXoLZuKdr7kzAm4TfU++YnP/mJV7JjGAa2bt06Iz0EeuMWJIAFVWUwIgIyEoFhRmbUsUfnS26MSARGJDLqONqIRMCZCTMShRTcPQYOv0yT9s3ojPW3mVbRff311+Pd7343li1bhsHBQWzfvh2///3v8cgjj4AxhssvvxybN2/G6tWrsXr1amzevBlVVVW4+OKLAQBz585Fc3MzrrzySixcuBALFizAVVddhWOPPRZnnnkmAODoo4/Gu971LlxyySXYsmULAGDjxo0455xzcNRRRwEAzjrrLBxzzDHYsGEDvv71r+PQoUO46qqrcMkll4Qi1sVId2e7Et2GOjGDkWsAqA4I6aryMqy/6hrcrb3UuI3mT16KHxxUDysipmfe8efW3tB6HCFDLum5YGDUMowgctDc3Ix169YVVWSmWFBtj9TfEvB6DQOA5ZpJOq5/BplDjZ+hlIOKiIHIYUZuiOlHQk3zV5dFwLkETDUWaduzG8tWrPTajDKodqKTQS6PjHXr1s246+rrB4chJbDgiDLV51xI71pDzHyGUg4iY0gTF5BIuZ0xbCHgkKtawTGtd6/u7m5s2LABRx11FN7xjnfgiSeewCOPPIJ3vvOdAICrr74al19+OT71qU/hpJNOQnt7Ox599FHMnu2nGt1xxx04//zzceGFF+Ltb387qqqq8PDDD3stRwDgvvvuw7HHHouzzjoLZ511Fo477jjce++93uumaeJXv/oVKioq8Pa3vx0XXnghzj//fNx+++1T92NMIcHTsG3P7vCLTlh0B93JyyMGzvvn93uPz37vBXjX2eeGXi/LMSAaa4scg7l9u+mGQRBZaWhowBlnnDHjBobFjgx0XRASCJbfr1hQhbo5FeBc0vVtjOw+OIz9Q+p+JKXES12DaO1NjPIuYiag3ZgBoG5OBZ741X/g3Kbj8In15+HcpuPw0PZ70d3Zjhef/DM62ienNeKuXZnmbZxz7Nq1a1I+bzIR0h/XcSGV8ObqmbjlYF9vHJKuOTMSKSX6knZoLJ4LIZR7vYSEw9VxQBQW0xrpHq3dDWMMN954I2688cacy1RUVOCuu+7CXXfdlXOZBQsWYNu2bSN+1hFHHIFf/vKXIy5TjCxbsRJ4InCT4U7o9YpoWHRXRPzJjKrq6tCFoCJq5DzJLS5QhdHTXNSNWFIkiCCIGYWQ0o90y3C+TnnEQMrh4FLCkCNn8sRiMezcuROrV68u6YmVA8MWeuM2Fs0qR8LmiFsO5lepFD4pJQ4OW5hXGaXI9wxE6lA3gFRfD+664apQxPnmay4DGIOcxLTvVatWZfXI0B5DMwkh/CwbLqX7Tz3eP2Rh14FhVJdFsLC6bPo2kpgQKUcg5UhUjMlYWLqTLiq7lCLdhQfdrUqc2rqlAPMPA8MMC+OgqI4YLGO2LRQJN42cjuOpMUa6wVho1pYgCGImIKR/4ZLITCE3GAMXQl3fclzgqA+7j8P9H2nYUr3NdSZVz5CFF7sG0ZeYGe7MRBgZmFhv27M7I+IspYRMS/uOxfIf8f7c5z7nZUVqj4yZONElpPSyZ7iQkFKG+jn3pxwkbJ7r7UQBY3MJmwtEjdHlmpS+2HYovbwgIdFdgnzkRHVTed+xSzJee/ix57y/GcKiWgIwg3XZMvw6YwzludLL+dhEN4NO06SLBUEQMwdH+C0ms6WPzyqPYNm8SvTv78Sf/u/3GSIiV43pZIiNQkeJBv83tLlE0vGFWsLmGEg6NDk7Qwlq7Mqq0XsJ5zvt+6c//SlWrVqF22+/HUIIXHXVVWhtbZ2RJmqAut7oc8Piwi1hUY9TDoftiNA1affBYQylnCxrIgoNtW8lxmCJBMYYHKH2NaWXFyYkukuQpsb5eOSSU3Dt2sw0qsf/8L/e31Jw/M9/3D/iusoD6eaMIRTpXhRIZRprpNtgahacrhUEQcwkuPAHRkLIrFk/v/nFT7HxXSfjg+e9OyOSvXPnzqKpMdXEYjHs2LFj3BMHjpDgQniTvEK6aZOBaB6XVIY0U5HwSzES8dF7cOcz7TsWi+Hqq6/2zjUpJe644468rHu64FJ6JraWI2AwBimVoVrCFhCBMZUQEvuHLOwjf4QZgcpiwJj6ba9aWIUFVWXKSE9I8g4pQEh0lyg11WVZT+LN114eenzL9Vd4f6efvxIIOZIzIBTpPv3IhVgyW7VUe+5vf0N3Z/uo28UYo0g3QRAzDi58c6ijFs/CygXhCF53Zzs2X3t5zrTZ1atXw0hLIZypNabA4aXKcyHhCJWSrx8LKT1DTpurNH0aVM5MgqaDy1aszDjuGWPec0ae076LwUDtUNxCR38SgJuKH4hsW0IgYjIIqPNj2OIQwj9XbCFUurI5htApMe0Idzw8lkh3xDRQHjG8LCG6PBYeJLqJEOk3I8Fz1wFlE8YhY7WIASs+BAC45UvXeK6ko8EAinQTBDGjUEZqamRkMJYxqZmtdjU42G9oaMDWrVuLosb0cFPldU2iwVQq7IFhC0Kq2kZAp9BKSi+foQT3W23dUlx/652enwwzDHzokk/jhw89iuvv2Iob79iCdevW5e2ztYFakJk2ubXnYBzt/SpSLSS8rA8pJeIWR7lpQEpg/7CFQ3ELphvMAPwa4cgYaoSJ6YcLdZ0bS6QbAEzG4LjvoStk4UFnHYHr3DTzjxw7P+NmFDZWG/kEZkCoZRhPDeNQlzvIipQpV9JrLx814k19ugmCmGk4AffybGSL6KUP9pubm9Ha2oodO3bM6BrTw02V122PGIDeuK0GkVLCdmdjbWq9NqNRLcP8k6Xp9LW4+dv34IIPfRQAsG3r3fjoe9+JWz63CV+69F/zairY0NCA2267bcZPbulopurLrQR3yhFI2gLlESW6B5IObCFRWWaAC3/CyuIylKVIFC7jvcYZnuim8ptChEQ3gfcdV4dHN56CS9+xBtffeqf/gpS4/pbMWiedlXTyEfMBAHPKVee5UxrnhyLdyf4+wLHcN7mtXoTA9n/fMuo20WCKIIiZhODSS4fOhhfRM0Ye7BdDH/bDTZV33Jpt/XvarjmUw4Pp5TSonKkoo3+18x7afi/ObToO13364/jFfT/yyi+kayAF5N9U8KKLLsLOnTtn7OSWrvMFVJRbu5cnbA6LC1RETAgpMZiyEWEMBhi0rY7N3SwSGv3PCMab9WkY2sGegleFCJ12BABgQZUyPTt//QbvuUg0EnqsBzj/9fGT8c3zjsHaVQsBAP/v42/FQx87CcvmVYZEd+3ihQB3W7pEVG03opXYds93R49209WCIIgZBJdANsnd3dmOpx77I7o723H++g3Y8sgTuO+h/56Rg/2xcrip8sooTQ0a09PKkzZHyn1Mk7MzExXp9n0O0rMispHvuuuZPLmlhFgg0u2WWiRsAS4koqZKJ0/YAmURA4zBayGWcjhSnCasZgrjvcaZjMHxyg0maaOICROZ7g0gCpdcNSRLZpd7BmmAaoUzy412B9PLFy9cgLolS9AJAJEosKAB2PAtyFf/iLbWPapHeA6oFoUgiJkEz5Je/tD2ez1RYRgGrr/1Tpz4rvdjTd0b0VBTPT0bOkU0Nzdj3bp12LVrF1atWjUuccNd52Ud0XOEuh9xIfFi1wAcLiEofXLGwt32etl8DnIx0+quJxMdyQRUZxiLC5SZhjc5xRiDhHSfZ3AE89pHxS3VQozOnZnBhNLLua7opp1caJDoJvJK2EjNxBFHHIHOjjgQKQNOb1Zp5m9ai2XLV+RcB0O4jydBEEShI0Q4vTw9iieEwObrrsCtDUdiYKdExclvnpFRtvHQ0NAwoe/IhURPZzv6XuvC4mUrEJ23CAwqtscFMJRyQo7NQkjs7U2gYV4FoiYl8BU6ws0K0T4H2YS3nvSXUs7YuuvJIphenrA5Uo5EdRk8zwNtRisEEDEZDKYycQCgP2l7GSRE4TPu9HIGcCmKJtItpcRLXYNYsaAK1eUzX7LO/G9ATBpHzKsMPW6cX5ljSZ+g6K6MGphTXQUgDhYth6w7yntt5Cg3RboJgpg5SLemMhjpzhbFE5zjmg+do1rAGAa2bt1atCnmh8P9P/kRrr3iM5BuhsCnbvg6ms6+0DNPG7I4gpGcIctBz1AK5RED9XMrpnfjiVHRWSHa52DzdVdAcA7DNPGZa76EY978FixbvgLDKY59rbvxljVvxD+++ajRV1wi8ICailsOuFAt9HSkW3mkqWuSwQwYjIELlXo+bHGKgc4Q9H1lPDDGwN0ZlmLYxxYXiNscPUMWVpDoJmYq3Z3taNuzG8tWrMwQwD/64JvR8mQbLv8HFY3+wQeOw3+91I3L/iF3dFoTTC8vjxgoc0V41Ts3YdjOHr5O3xbGqGUYQRAzByEBgbBJSq4oXro51Lp16yiCB+C1niGUmQaiiUO4zhXcgPqdvnvT1Tjqrf+ARbOWgwuJOeUR10BKvdcRqu7bJEfmGYEI9LQ/f/0GNJ2+Fm2te7Bs+YrQeGQo5WDe4jpUVUTQn7Q975lSRgjpZdUMpRzs7U1idnkE0u1jzxgAxtz+zkqAM6gMEYsL2K5ZQjFEQYuZtt4EeoZSmFsZHfd7j66djVh/YsylG4WMMtAcuTPITILysEqQRx+4H+c2HYdPrD8va+/sNXVzcMd5b0Lj/CoAwPFL5+KGs96AeWM4+YOR7ohheCI8l+DWzqXBbSmWtBiCIEoD4VrFBtPLs/UfTiff5lAzmb6EjUNxK2u7MSE4etpaVXq5lKiIKnMo7kb2HK/3cJGMzIqcYE97b9I9TXAD6nySUCZgnGbiAcCbbFLGaRxxy8H8yiikBJKOgMlUOrkyV1O/szZSsxwBRwhEDYNi3QXOwbgFLiUcLiYkOBlYUQSvuJBwhESxXNpJdJcY3R3tuOumz2fUGY7mJj5WgqLbMMKPNRGoz85V83igq4PqjQiCmDEE64uDnL9+Ax5+7Dl8/2cP40cPPXpYbbSKHe2unK3dmGGYWHLEcpVu6Ub5DDCvTtUREhb3W0wRhY2QAGPZJ92DGAxeBLcYBEQ+0M7+QkpvIsJgDALqscHcDujuNclg7uvSj3RHXHdzorDxBGfWvhgjo+v6ZzqOUC3u2AR+g0KERHeJsXfP617ankZwjrbWPXlZfzDSYIDBTg5nLOMI6c1uZ6t57Gpr9XpOvtI9SDPcBEEUNLplT7Y+3bV1S3FS02l40/EnjqlPd6niuG7lDQ0Nod/JMEw0X38rjjlyhTe5ETFU9I5LiZTDkbC56uU9zd+BGBuOEDjQ1ZF10j0UAHAjtrYAjQNchNu/XGUAqP8yd3KCSwnTYGCMQUA952abQ0iJrsGU165N/5oJm6M3bk3X1yFyoPeZwyce5S2G4BWX0u1eMd1bkh+oprvEaFxxJJhhhIS3YZojuomPB8YYmhrno2swiaNrZ+GBgf7MhQwTrXt2Y3mWmkfDNFF3xHIcGLZwYNgCg6pbmkhdC0EQxFQg4Q5wRxkYnL9+A95w0mmI72/H2W87ngS3i47oaF114tvPwKdvvgsLq8tx1JtPxJK6pSiLGIg7HFwylDFXWAiJ5zsGYDAGO/B+orCREujctyfrpHuwnagBlV5ucz4pojsWi2Hnzp1YvXr1jDkXhVRCJCL9NmDMjWQLIWG61yAJnVGgs0IkDg1bsLhEZZR5v32sL4G9vQmcfMQ8zKmgcVYh4QiJFBcwJqC6tWif6XAd6S4S0U2R7hKjtn4pPnvD1706Q8M0cf0td4zoJj5evn3+m/CzDSciahqoqVmQfTsalmfUPOptqVlSj7jFEbc5Ug6HQyMpgiAKGClV39yxjAsWLanHW5rePmMG+fkgFothx44diMViWV9XNbvq7y1b78H7/uF43HXdp3DTZf+K3/3yQRiGTjUGuBAwDNccSqpexBYXEMJPL7ccgZ7BFKWbFyhCAksbV2aWEaQFAHQE1+YSdp5NoX74wx+isbERa9euRWNjI1paWvK6/nzSM5jCi50DAHSPbt2/XqeX6zZi0hNo0l3OYPDMaYdtjrrZ5WBuaUbC5uhL2Bi2HGWwRhQMDGpSxeYiawbV6O8vDm8kLlS0vwg84QCQ6C5JznrfxV6d4cOPPYfz12/I6/oZY95FYuG8ef4Lr/ze+3PeoiUAwjWPelu4UAMpbQyi22AQBEEUGrZbJykxuuru7mzH83/9MzpziM9ipKWlZVRxY3MJLgS6OmL41Cc/4UXhpJT4wddvwvYffAeMMa+FjjaLUqmHEnGbe5E9AHi5exAvdA0gYfMp/KbEWNGRqw/966c84R0MAHR3tuOpx/6I/V0d7kSLRNzK377s6OjAJz/5yVBq+6ZNm3JOCk03bX0JDLvfX9VywzMVBNyyFl3D7V6EtNmaHo/ptPTyiFpCCIkXOwfUuSOLIypabAjp97QfN+4+n+lwKeFMoHVaoUKiu0TRdYb5jHBnoyxgpHb+O8+AfphyfCGdvi2q36QyBUk6PLQsQRBEIfG3WD92HRhWNd2B4ZEWDrpGVZtGXfPR9+GfTzu+oCNr+SIWi2Hjxo2jihsu1PW+rTXT5wMAvn/bl9HT2a5Sa4USGYwx2I5ytU5Y3BUZamCWsDmZbxUwD/98GzasPRH3br0bUkp8eNNnvUn3h7bfi3NOPRafWH8e3vu24/DrB+6HhGofli/27MlMbS/0TgI6+KAj3dKt99VBUAHfoA5w08vd9+osEZ1urv0QLC4xkHTc+u8p/kLEqOh9PZFWiEVjpMbdSHcRfBeARDcxyZQH+navOnIlyiMqlfzpJ/+a0zFdp9QIN62M0ssLCy4kdh0YpgwEouRJORyDKUcZGgUGvOmuzD/5/rczTKMKObJ2OARTybO1/8ombrhrArW0caXXSiqIFAIde/dAwo10G8xNL1ciPG4L19lcLS/c9NtiiY4UE/v2teGb/3ZlKJvh/h98F4CaqLr5msu8sgApJb5z41XY39WBuJ2/uu4VK1YUVCcBISQODKVyvx5wKhfSF9M8cHzr411nGUopIYM13/rcYX7qscMFBpIORboLErW/+Bi8QrLBGJAva8m4pSYxpwPlYSCKpsUdiW5iUimL+FeLiqgJ6SiXzC9d+ZmsLUIAVadku062XBRPWkmxcHDYQvdgCv0Je7o3hSCmlWGLI26pbBztFJytFeJdt9404yJrEyE9lfypp54ak7jh7qRFzZJ6/OtVX8pYr2GaWLZipUo1dm8HnruvELAcjojBvIEZF/DqXonC4u/Z+rC7BmrPP/XXjDp8KSVee+4p2FyVneWD+vp6fO9734NpFkYngVh/AnsOxZFysqfQa5dyXV4h3HFRcOJb+0oYwUi3+1Pq1mvSjXSDMaS4gC0k6uaUw2TFERUtJnRquZSYeMuwPO3U3QfjeKlrIC/rGi/+8T4tH593SHQTk0ow0m0N9yPed1A9iJTl7BG+ZHa5qtcTaqYuXxcOIj8MWxxDKQcRky4fRGmjzbsst11VT1cHfvvLhzJEhRQiI4JbbD26s6WSX3fddfja1742qrgRrpgWUuIDzZ/GJ665AUzX+homrtv8TdTWLVVCItCbGG697+qaapgGcwep0ot0k5FaYWFzgSOW5zZQy7W3VHsskddo28c+9jG0trZix44daG1tRXNzc97WPV50Rl+uXsQ6+CBd53Jdr21zGarhFpDedUYbqwEBd3N3fQwqbdfmAuVuzV+xRBKLBV2DHywZGA/KAyM/2+IIkbcJr3F/tuvv5BRJZiW1DCMmlWBN99CBHsCNdMNUrSnSW4QAwLzKKOZWRNDen4QQfmSDKAyGLQdJh1MGAlHySPcfFxKPPnA/vnvT57PWJBumic9c+yXcfeuXITiHYZj43ve+X1QO5rlSyU866SS0trZi165dWLVqVdbvrGsX4Qrviy/5LNaefQH2te7GvCXLcPwbjwTgp9ACfqSbBx2bXRHOAwNWonDYdWAYh4w5uOzL38C3vnQVhOAhA7U3n3SyZ5inYYzhyDUnqkn4PN9zGhoaCuIc5G4pXbbvJ12zQBW5dkso3BrtUGTbPebNwGOt1kz3XJHehJV/3Yow5kZFJ/1rEuOAgXnXO9OYWIAjX2eLdJ3uuZhYffnhoMtLi6XMlEQ3MamUBaKhS5fWAXyPehApA5C7RzhjDE4gakEUDkMptwasSC6CBDFRpBs96u5sx3du+jxkDsF9/S134Pz1G7DuvPfhtb/vRF3jCnzg7cdN/QZPIqtXr4ZhGCHhraP52cRNX8KGkBILqsoySokMBtQtXYo5i5ZgOOhA7opswE2fhBYefnSvN2FDCHgihSgcDsUtHBy2se59F+Ptp78DBzr2evf/px77I5atWIkvfO1bXnkGYwznbNgESL/cbLKYzp7dXKiWaPrb7R9KYV9vAicumxeYRPIdy1VNtvpNvMg2dFTUn4DSa9RRT/3zqWXU8obB6FwpQPxI9wRrupG/iRTpOuZr0d2XsNE9mMJRi2fl5wNGQIvtyTz3pxLKDyUmlfJApLu+djHq6+vVAzM6Yo/w7s52PPeXP+JQdyfdDAoIhwsM29xzSiWIUkanfLa37s4quK/40uZQW8bauqV4y6mnYVFtPfa1tY3Yu3qm0dDQgK1bt465Tva1niHsOjAMwI10Qzswqx45JmNKbAQuNCIw2ee1SXJfUzICeP1AXEUEQdeoQiLlcMQt4UWxl9TX46Sm0/D4H/43ZDoIAA8//jw2bPoswBge/sn3cfl5Tfjtg/dPWtbbWNraTSZ+L2L1BXuGLNecUQluIbT7uCvCwNwsGz/yKIQ6N4KByODEuHduuUg3qOEZr036tyTGg4B/DZtQTXeejNSkDGRZuMfPrgND6BlKTUn5jp5k5UWiA0h0E5NKMNJdGTWxZPEiAMAln/8SHvzTs0i98R3Y15sIvUc7/35l0wdx+XlN+MX9P5nSbSbCSCmxc/8QEjZH66E44hb19SQIwB2oSoklR6zwapA1hmnizLPPyzqp+N//cR9WrVwxbYP8yaK5udmrk/2/P/0Zi5cekXVSwXKElzEDuDXdbqGqhBpkmgYDFxL7uzq81muOTDPgkvBFOVPrjduOMrYLRM6TNser3YNU4z2NOFyZ3nHAi9BmMx3cfN0VONDdhfvu+Y43kSWFwPduugZtbW15366xtrWbTHRquT46tUGgjk5rwSOlOsZNQ5dS+D2cpTvRpCPdq2uqceTCau8zpPDXr9tJ+Y/9ns42F2jvT1Am2zQjuO9LYaSldAupOsiMFP1lyM8YTXsF6NIdAEjYArZbaz3ZeJHuIrl2k+gmJpVgpLsiangivOHIN+AHL8Xx9d+/jn975FVvmfSbsBQCX7n2iqKJBs1EBlMOugZT2D+UwsG4pW765HY6Ifb1xpGwszvUEjMP3aJnQW0dPvGl22C4Ud6Rsnj2d3Xgji9+LjTI37hxY9Fc4xoaGvDUU0/htLc14bx3n+VNKqQcjsGk6rWccgRSXHiCQQ+o9CCTMcA0GH79n/fhU+85xYuC/u4XP0VVmfsbM+YZpgFKqCdsDptLDNuOuz71YudACv1JZ9rMgIiAG7PrRMwAtO3J7MsuOMezTz6R+bzg2LP79bxv11jb2k0mFueeUzUQiHynlV0IKTFoOSgzDa+GO6jHgiIsYhqIBoIe2qgQUFHQoHGahH8e7TowjFe6hzBs0X1qOnGEyg5J2ALpZdT//tc2rN/2N9z959ac78+XkZpw6xq0gZ8QUpmbYWoCL1yoeywvEnMnEt3EpBK86FdGTM9Y7YZf/x0Pv9wNAHi5e8hbJtdNuNha68wkBlMOBpIOoqaBhC0wr9I1wSuSmcepQkqJ7sEU9h6KT/emEHlCexVJCbzj/Ivw8GPP4fs/eziUUp7OS888mRFxFULg8ccfn4pNnnS+/vWv4/Of/7z3HXXk8Ld/ewUvdw8CcAdwQonrA0MpHBy2YLoiWrp1qd2d7fjul6/2op1CCPzwlutQnjgEwDeQCgoJRyhH5oWVZTANf9DpCDGiOzQx+aj9JN0UcSUWl63I7mR+/FtPyXzeMLGscWXet0t7EQSZ6s4CKpoXFtch8zSvph0YTnGURwxwIGQiCIxc96pFOuBGxwOLMgZwd0IqaXOkyCh12uFSoKaqDPVzyr0SAM33H98LALj36dEnag83u8fzAnCPwYGUA+5OEElvmfz6LfQMptQxKFRnENNgRWOkRqKbmFSCs6mVUTPUQkxTN7vc+zvXTbiYWuvMNGwukXSEF0WK6BoyuimPC6HTASfiikIUJN4gGWoAUlu3FCc1nZY1wq1hRXzexGIxXHPNNRnPc86xa+frcFwBzYX06rj3D1voS9jea8EoaHqdvOAcsb2t6oF2ZHZf05EdR0gsrC5z1+Wb8KjoTPH+9oVOsI2bkAAYQ23dUlx/651ehgirmoPrb7kDbzr+xNDzhmnik1/6GqLzFuU9U+jXv/51SJgYhjHlPbu9LAD3sSOkF/lW54p6PelwWFyqSLfb2z5Y7zuSMNFmbIAyT+OBGl0DDFwqk9SELUKCipgabC6wfyjlPRZSZftURM0JrY8FerMfDvq8VcefwM79Q263Cf/6uudQHM+09x3eB+nPExJ7DsWxtzeB5zr6Id3foVjGm+ReTkwqNe7gB1Cp5sEWYprZFf5hqG/Cm6+7wmutc+3mbxZEW49SRUg1YE3YArYQqC7Tke5p3rAZhnahJc1dPOhxgB4Aj4VjTzrFD4+7MMbQ1NQ0CVs4tezcuTNrZMUwDNQ0LIfO7uZCgsMXYL0JG0wbpwWioMwwQsI72O3CCLTUAbLXMOprlCMkbEEtxKYTva+02NMB2vPXb0DT6Wtx5//txm86gfq3rQk939a6B8uWr8Bw2TykHIGO/iSOrKke4ZPGjq7nTj9m161bl5f1jxWRJmL0hJQjBHYfjHvbl7A5LIdjbrnpmWxpTdYwtxJJJ/eERLlpIFqmxl8mY7C49M5HlSUi8GLnoN8HvEhEzkyh9VAc/UkbNdVl3rky2lhhpO5d2ljycPfiK252kpB+ursjJCKGfwvrjduw85T+nXQ4Uo6AyRiGLTXJZLp95qWUMz5oQZFuYlKpiJh45JJT8OjGU2AaLGSspnHSTtbz12/w0jR//LuncO6FH56qzSWyINzoUdLmsLlA1PRbkhBjR9fneW6xUmJfbxw21ZnOWLSIC4q/0VhStxSXfvkbYExdCw3DwD333FMUE4vZUnUB4Mtf3YwFi5d4A3lHqPRyLoCBhI1y00Dj/EpPfDA3CvrJL94Gw8heJ2+4ObJ6nUZanWrQHMrRkXW6aE0bOuqm+7EHU2Zr65biN53q77v+1Bp6XmeOWFxi2OKonGDkLxvZ6rmFEFNazqbbQkn4E3f6WB1IOrC5dM3n4Ase04AQevJC/Y6zKyJYNKs85+csX1CFpXMrAAAR16RQpwQzxhC3hEodFjJU401MDTqjQUo1uZJyRMgTKRuzynLHTT2H+wlc81IOxzOxfliO8Fz0BeBlPOrjRl9fJWQeRbdA0hHe3ymHe5HuYrh8U6SbmHTSo93ppLKIjtq6paitW4qDwxZIk0wvnAu3bYTM6IlLjB11U/XNo3oTNtr6EiiPmKidnXuwRBQuWuSN51xgDDjrgotw6j+uxXB3G97VdHxRCG4gM1UXADbf+jV86tIr8MfdB8HdiSf9T09EzamIqDpVGU4A/3hzM85+z7u8aGcobZ8xb5CqHoaNg4LCIalTZumSNW3ofSEl4IywHxZURbM+HzUZ4pbjtcjKByP1lp8quJBev3npeh04gcmnpMNVJg1TRoEy+L6A6B4PpsHApfAMDA23VCNhOxDCVF0E6GSZUiT8fZqwVYQ3WzlmcL/MLh9BdDMAYmKR7r6EMp0cSNqwuFCu+EIi5QgkvZaxvgh2+MjHy6tutPyNtbNH/eyUK7QtLty/hWsc6BqXznBfDop0E1NKtki35eRW1fnqNUhMHD2BGZz8YEz17CbGjq6L0mOkwaSDvoQDN3EAliPwt1jfiOcDUVh4QgJjH9wwqAH23EVLcMrb/6FoBHe2VF3GDJx9/vuw88CQl7bKhUR/0vYEuF7aYMw7R4LkqpPXQsH7LITfG7xGcanquelOMn0E91VPZ7vXBg4IG4AtqCrLeC8ANM6r9KJu+WK8veUnA127LaGj2VxNEkGZAiZt4RpXqcd6zkEtL0dMMc6FaTA4PGyspiepUg73PCqIqUV7VCRsnjOVejDleH9Xl42c9aEi3RPbDkeokbflBl0ANUaxuITliNC12hbCFeHZP2ww5WDIcrK+lo7DVSmQbqOXcpSRmj4/ZjoU6SamlGCke2FVFAfj9ohtXPLVa5CYOEHzI402XiHGDpcqgtHd0YG9z8cQXbgUFpvt/Y59CRuWIzGYcrAwkn3gSRQWQgg1MZij/u6ptj48FevDv57S6BkQMqYGEMVWVpAtVVdKgZdf24llx57stiwCeoZSGEopZ1qd/qh/u/H0BjYYC1+TWDjaomu+ATVhONEBKJEf9L74/X9tx79vvhZSCBiGgetvvRNNZ1/oLZcrehcxDW/wnU+am5uxbt067Nq1C6tWrZrySTC/LRg8wZVyOCKmAUdIpDgHlwJgEbeW1h9DOXxikW6DMdiBc0dniUiolN5oxCgKgTOTkNI3uxup08Jgyq/bHykgxZg25xv/jpQSbss6eK3r9OSOIwVml0Wg3fZ1CzHtMWAyYO+hOIYtjmOWqMg2F0CWeFtWuFTt8nTU/4h5lZCQSDqyKLQARbqJKSUY6f7Am+sBYGTRnadeg8TESa/fAdQA1xHFJRomGyEl/vs/78Npx78Ra9euxeknHI1HH7jf+30dIdVgK4/pk8TkwqVbOyclsp0On3jgBfzgiTb8784D3nNapNu8uOoms9VzG4aJ5SuORNLm4FzXcUscGFYuvcKtFfT7dY8vmsGl3w1AT2bot+vfWbWz8UtkiOlBSIkDXR2e4AbUpNXm667AK61+6yM7y4nU7UbGD3R1TMp4oKGhAWeccca0ZJ14abpubffBYQuWULFm7WJeP7vCMx00A7cHR0ws0g2Ef2dDCzTP+4DSy6caCddQDyqSDJb99x8KRLpTI2WJ6vvSBCPdKtosPAd9PblTFVHeAXqSyBYCjpsaoceIB4YtDCT97eRSjHk7HCHd5dVxWFVmwmDMvX7PfEh0E1NK0L18rutabmUJmXZ3tuM3Dz+I3//3f6Gns51uANOI46Y5hS6aaVEmYnTa2tpw1w1XedFAIQS2fuUatMfaAKjIZ4oXx42lVOBCggGIGJmVZrGYLySCKYG6R67FRdH0HgUyU3UNw8Snb7gNNXX1SDpcpZdDDeZSXKJuToU61t2JCyDcAmwscJkuHPxoCIPbKkxHEamme1ppj8Xw50cfztoGbk9bh/c4vbzmoe334pym4/CJ9edh47tOxv0/+eGUbO9UwYWKWOrsl/aBJOaWR91oo/IAmeVG/4MpxwyALSbu5jynPOoZqzF33abBcNTiWTAZpZfnGyGUcepI2Tw6ZTvlCERy7NdgmvZIotsrQ5jARU9KdSw6bscVR/gTo7q7hK6xtrlKRQ+aier3acaT1GU5ImMCW0+gjicTqlCh9HJiSikPTNPOrVCGKXpgpCN8D22/Fzdfc5lfb8QY+rdsxSWX/OvUbzAB7rZskJDehTe9fpIYnV27dmUOOAXH66+/jre+aTUcIWE5vChSqEoF7kaajlxYFXr+oe334uavfxPY8G0AwJ/++wG8UTQhEY9j2YqVcMrne2l5xYRO1X3u5VdxILIA8xfXq7pUR0WbhRs9MZlyUNYprnp8yYUEG0coQAiJiojb41m7lQdC3RLSu7+Mp+6eyC8tLS3YuHFjRvkBoFzpK+YvArAfQFhIdHe2h8YCUghcfdlnsP6fz53xXghDKQezyiMhMZFyBGwuURExkHSdo4PSS0hfTM0qj2AwZU/485cEzDvVpHp4wovuQ/nlUNxC92AKVVETNVlc5qWUnmdB0nXszkYwvTxpj3D/YIEsinGiS+Fs16lSB1gkVCmQnqQJinODBYzVhPDbaXqZRmO7sNuBGnKNgeKp6aZINzGlBCPdcwL9uXV9Y/pNFlAn7Sc/+YlQ5IiYOnQ9jgykfhpkcDduGlccCZYl/XbZipVwuMBgyvFaKQFKgHQNJKdjU4kxIpTFMJj7D1DXsM3XXg45zzf++uOO3+FfzjsTn1h/Hs5tOg6/fuB+AECKS+w5GC8K8d3Wm8Bg0kFDQwOaTvtHzF9c75o/qYHkigXV4FJ65US6Vbn7EwJw3ZvHcV1ZsaAay+ZVeusLJk2pSDcwbHFv8EkZU1OPNtjLJbivv+UOsArf1TjYzeS5p/6asc+kFHj88ccPe7s6+hN4qq33sNczEfYPpfBS1yDiluNmZ6h7qsWV2ImaBiT8ySKNDLiV188px6qaWXnZHoP5xmwAlfVNBlo05spM0L+/ErIyp+geV3o5JjbRyIXKSFIRa6BxfqW6vgq1XsYY4E7U2O4xC/jlPE6gdMrLNApsSV/CxnAqu7Ga5V4nQu0fXb+OYsgDJNFNTCnBmu55lX5rED0Qa9uzO+vAiHM+pb0zCR8uhessLANBJFYUqT5TyZL6pdj0b7fBMP2+wx+7/hYsqq3DM+396E/anhkJALT3J7C3N4GkzXOvlJg2pOusmj42atuzWwmMWfP9JwPGeEIItNx8LTB0EFJKtPUlEC+CfRzrT+Dv+4cA+OY7OmU2YjCURwzPoAfwI9MSfnp5VZk5rlFiecTwBqdG2jWJMWUK9ff9Q14fZLpkTT3ZDPYA4IovbcbDjz2H89dvwEAOIZFPdwspJeKB1NzuwRSskXqXTSIHhi30Jx3PzV+njau+yAJRk3m1tBrhGldpzcYYy5v/h6FFdiA6SaI7v+j9masaQBtLculmA+US3cH08ixRYU0wBXy8pBydlSS9SVFtahxMW5cSSDrcu94LqUR2sMtAyuv17a9/5/4hvNozlPWzLVsLeP85Xd5YDNdvSi8nppSge3l1mQnTjU7oOq5lK1a6s6xp6SVT3DuTUHjmLUbmrGkxXACniv6EjUNxG2ecvx4fet+5cA51YL+5AKnK+bC4Em9DKdWPVd8kE7YgwV3A7B+yELd5xuB02YqVqvdv5Rz/STPce1gIjp5YK2rrlsIWPKdT7UxBCImk7adEphzhimlF+vdjLFxzqAeijfPDafrjQUW6gzXeqhFS56AFA35KJDG1ZOuFbZgmzjz7PK8N3GDAdClY033cSSdnjAcYY2hqahr3dsT6kugeSuK42moArriR05NhErc5bM5Vv3q3IkKLGikRciT3skCEdoeenGtFuqih9PL8oiPZufaedi63uTomojkj3eExwU2P/h1nrKrBGUcuDD0fTAEfLymHQ8hwhw3tmRHcqoTNsa8v4Rnx2VygYyDpGfxJKbHrwDASNvd8CQD1Hcsj/poOxS1URU2URwzP4C+43YczgVBoUKSbmFKigUh3ZdT00s21mVpt3VJ84WvfCqXgMMbwne9+b8bXcM1EtDmGqVuKBEyKyLx87Ow6MIyUayZVW1ePE089DQuX1CFiGLC5AJcSfQlVn6cHPkmbI+mIorjR5CIWi2HHjh0zsnTE4gIHh+1QmQygrmHX33onUDnXfzKtBRwzDBw6sB979u0rirQ53YPbcgRsLrCvL+FFtCWk58Sra0cZmFdzCOQnoqnTH4NICdiOwLDFqaZ7mmhoaMD3v78FhuFn+Fx/yx2hvuu5It3eeMAty2HMwOY7vjOhsUDc5sq0zD0ILEdMW8mBcvFX29QzlMq9XLDMDjo9Of/bozPZ/PRyOlfyjZTKXCxXerkQfi92LsaWXg4Av3ylB1c9/HLmgm43h5Qz/sn7hJ0lgs6Y8hgIbH9/0sGhYQtL5pRDSonOgRR647ZrmukL8aGUg0C8LaNDwesHhvFK95A7CaXa4gXLjvQEQjFkV1Kkm5g2KiIGykwDCVuE2oadv34Dmk5fi+effhIpR2DVcSfifW9bM41bWrromXfTCKf3MKrpHhc69Uq6piQvdQ2CCyBiMlhuK6XaWWUYsnwjtbjNvfcVI0FzJcMwsHXrVjQ3N0/3Zo0ZISWiJkNl1Mx47fz1G/D76N/wp7Zh9YSZ1nddStx8xUYww8Bnb/w6Trvqs3nZptZDcaQcgaMW56fWc6zoiF2KCyRtgcGkgwVVUQymHOUJ4Y6egm2PfHMcmRfVbWgjNRdt9lgeMbBiTgU6BpKUMjtNrN/wUZSvOhH9Xftw5JGrQoIbAHYfjHt/p9epNp2+Fpvv+gHAgJpVx2HtW9444e1whPTEpcUFTCZDg/upQvcg7uhPwub+xFP65JuEOq41jhCoiOR/2O5nnfj/pXMlv0hgFOdyP718pGOyO8ckzYb7n8GtZx+d5kgP7OuLQ0jg5CPmZ31fxnZK1bpUj+/chCE30BJOj7fd/txRw/C23eIqNV1HpoU70aCPJydL1w5txJZ0BCxHoCyiTTGzt4OcyVCkm0Bfws6YPZssgoOiyqjp1Xin32hr65bineecj3ec/V7U1NYVxck2E9HmaabBQhE5fQEmxobOGJBSzSIPphzELQcGc29cUrWdUstK77+iSAc/6eZKQghs2rRpRkW8uRh5sJ6UgcFxJJxeHnRjvvvGqyf8vXvjFmK9Ce/x/qGUlzExlXAhveidbg9W5U5GqAEkCy1rGCxgjpOZfj4RVKRbhh7r65WpP++wP4WYCI4QmLeoDqe87R8zBPerPUN4JVDfGTRSe2j7vTi36Thc9+mP4wuf+Vc8+9jvD+t6qMUud4/V6RrIO0JFAgE1/hKuQRoX4fmn9Gif7paQdxhCLZ6A4kjlLSSkVKWUuX5XLba5UGaSmU0o1fV+x66DWd//Ss8Qfv1aj/fYcK9/SVuMq72rTm/XwWi9uTqFXJc+MHebTcb8ll5S9RjnrnmaOtfUpGvQWM01RQcAr3bcYAwJm8PiqiOFOjfD1/NiGHKS6CbQM5xCW19i9AWzYDkCr3QPIjHG9JWgUDMN5oluOz0v0MXrDUg3gGmBuxdSU6eCepFuNfMYrMUjcqMHeoBKS046HMNuDayaIQa0sbk+RbjQkcHiO/azmSvNNLNEHjgf0nmxaxBPxfr9J9Ij3QGE4Nj9+sS+9+6DcXQO+g732vhmquFS93NVkW4hlbAGMusAtcDQ5jh5CnSDMZbhXh7sAT5dqcQEvCyfbIJRm+9p9AS87gIQnJj73pevQUf7xCfmuNtPWA38/WjcVKJbQ+mxUH/SRnVZBKbBYHMREjXp50Z6em++MMDCE1agCarJQB9z2dBmj9qELNu5EutX9dK1s8uxoCqa8XoyaELoRpfj9vhK1Gwu4HCZYdKXfn2VgOs/4BvxcaEi3Rx+VpMjBAwj3MNbPcfSHgNxi3v94tNN54rl0k2iu8SRUqLMzEyPHCtJh8MwGOLW2ER3g9veRVMW8Y13snIYvQaJwydYYxPcDzpK9UrPIA1kRyDlcAh31jfkRCsljphXieqo6fVp1Sm4QkqvlYyXfltkaHOlIOYMM0u0HBEyPALUgGVfbwI/f64jvLCOdOcYMFdUT8xATLhCF/DrASXklNfKc1fEOFz1mFXHs3pNIjyAFN5ATR/b+Yne+etT+CmJ/nPFdybNDIKt9dLR9dyra5TBWbCTSfrEnBAce15/fcLboSJ4brRbimkR3UK6k1Tu/SBiMCysisI0mEp5d08G79gN/GbaoCrfGCwtc40hq+M8MXGk6z6fa7ykJyB1Fka2c0WbqM0tj6DczJRv6cErCTXRNJ6OlKpNmECZaYT6xOvsRiNNCAdfl1LdF/U55nB1nJvML/3RNet6PWqyVgn3lMPB9GfJ7NH+mQ6J7hLHERIVkew3w7EgAS+9ZCysqqnGre95I374wTcDQCDSnf1CdDi9BonDR8/IR00WunAzAALKvTI5Qq/IUueFzgG8fnBYRTd0SrF7QFdEDG82muvoH1TN04tdg16WgT72X+4axIERTHdmEg0NDdi6dStMd8LPNE1s2bJlRpglWm7dmc392XrN5/7fy7jgx0/h8T1pKYCRMjDDwLVf+XrWdcaH4lmfHw070A/V5qq36v/bvg2NjY1Yu3YtGhsb0dLSMqF1jwcvbVequjwJuL3LM9PLdaRbR0ekWviwt0Gl34pAS6XMlNkinL+aEehIdzZ0tlRNtcoGSQU6maRPzBmGiYblKye8HTrtdTilTdX8ieSBpI3uwcm/vupop/41IqYaf6lSI99Ayzt30t47GZFuxhiCYRMGBrqt5xehj7ecr6vXRop063Zhs8pNVESzie606x38tO/RgiPabC3lqEh3WcRwMy/U6wbz28f66/ePRz1JZLlCW0e5BXR5onqPLkXSa3G4G+lmDMMWR8Q0vGv3VHstTAUkukuMpCMwZHH0xi0AalY5apqImhMU3VLN1I7n3We+YRGOrVPtdLya7hHSy72BGTHlOO6sZMRINylSBZIJS4w5y6EUsblUZlJubRMQjvrojAGdjssYw5D7m+rjXkcg+hI29vZOrAykEGlubkZrayt27NiB1tbWGWOi9kLnAF7oHIAlBNIvm4/v7QUA9Cbdc6KvU/3XjEIKgbkLFmYVEiuPPHJC22ILP3XQ4gKd7THc9oUrprxWXo/1DIRbPqlIoi8c9ESSH+Xw2yUdLowxLKouR/2csJFQqEaWpm+nhZEG/IOpTNHdG7eweEk9rr/1Thim73r+6Rtuw+K6+glvhyMk4jbH/mHLO2b1+fNK9xB2Hxye8LrHio5yK+Htq2+DqUh30NRMwjc5Mw02aZFuxgAeEGzpWSPE4aPrm7P9rHoMoCLT0pu0TEd7L1WXRVAe8TNUj3AzSK30SLfOspMjG7ImbY6XugZxcNhCwladHiJu5gULHHChwAvLdNPnwi810mUUKgWdecKfS4DDP768rA8pEbcEooYyJ+2N23nrQ19IkHt5iSElUDOrDH1xB/OrlCiIGr6J0/jXJ12TmomdHF7LsBzTqoZbmEeDpenBCdwAuPQHrXoWPu5wDFsOFlbnrlktVbRBSHlEp425acBIa73mLmu6Kbc2Fxi2HNU2A/BS05MOn/DkWDqxWAw7d+7E6tWrpzW63NDQMCOi20G4W6NsOTIjvTyDwQPAvDogUgbDNHHciW/F9bfeic3XXQHBOQzTxCe/+DXU1S8deT1Z0LV3bjcuOEJiX2tmSq6ulZ/M31lfv/VV+kBXB7pf7kBkQT3mr2j0hYIrwnUPWl33l6+IRvA6xMC8c05DOiL/KLfhkccP2Qb8f9nbi9t+/zocdySvRbcjJN659QlsPPUIbHQ7mbS17sGy5SsgqxdOWAxqYeNwgbjtQESkF+m2HNXWKFv0MN/o1FvDEy3+wW9xicrQqNyPJNbNqcBQyhn9mjMBtCjSaNM2In94Zqo5XlPjgszsnCBDboBjVrmJgZR/rJ7aOA/7+hLZI91CQgiM2IbM5kogJx2OwZQDkzGYbuaFfgtjLFyCIP2sJY1KjRfeZKsj/G4V+vvpcjq9qY7rdu4IiRTniLgdQZbNq5wc08BphkR3CRJhDKZn2iQRMY0JH9wSyHkijwUd6U6fofNwUxDp+j896NYOGTVfUBfQlMPRn7CB+dOxdVPPeMQqlxK2EJDSDBmpBYslDPdGxuGm40L9rklboCLKvJvxkNt6ycpRhjEeZnqrrulGR6BsIUa/9sVV5BuRMq838flpQsKqXDChKUWbC9hcIgLp1XMvblgOwzBCwnsqauWTDvcm4h7afi9uvvZySCHADAOf+8o38O73fwiA+t0cLr16ROGWWkzG2Eo76gZXTtG7/NI5kER7fwJvWTovo9QiiG4bFOQzD74YerywOmwMtfUv+7Dx1EbU1i31HM+7BlMTnjjRosdy29pZTGCW+7zFVdvS8lEmD/KBjjpGXLOo4M+manL9CGbwlhsxGOZVZppn5XO7vPpcV4QnbI4y0zisMR6hEG6UV0+4pxyO9v4kViyoUl0c3NecEe7xOtI9qyyCiGF5z88uV1IuI9IdiCqPJOZ1vTkDw1DKQVlE7fNkWhZjyKjSHbukZxLNKougproMB+IWHLcdqjaM1ZF8Lc4B10hNqgwXm/tdL4oxyg1QenlJEryA6ov/RBEyP6L7Fy904rHWQxmvM4xcD0ZMLloosrSZcMM1KRICOJSwS2L/tLS0jKtWVhui6NZf+rcMmY+40Q4Z+J0l1M3ZdnPRHCHw9/1D4MI1yjqM37oYWnVNN7pGToyh5uzdZ64FALz5lNNw/voN3vO1dUtxUtNpnpiYyC613Vo4PZhxuMT8xXW46qvfmPJa+YGkg6hh4GB3Bza7ghtQLdHu+OJVONCtjOVUeqGfQqsjP5NRp6rXH6QELlNTykDS8VpvZYML1UJIG+aNRM0o2VLdne14/ok/oaNjYtcq3ZJJGz0dMa/Ca4NpuRNYfucIid0Hh0Pt97RYOFx0r/BomlFVVdSEwwWqy/xYWHp5xGQihPT2gcEAW0g83zGA1kMT85sgwjjuvVsfQXt7E+gcSKq+1tI3UhsJP9IdCS07u0IdM9mM1KT22xjl2FVp7cqTI2IwZewnwibGwcCLkaXuWkjVCrgiakLqSDfcaLg7jtct0fR6HaGyT7zSoyJXpUX+9YhsBEWyqhGa+GVdyrAz4XjR7uXPdw7i0odeyhAUutcqjZWmlp7BFJI2d1uUqOf0oAXw3csBCcuRuTMVioSJiFU9m6tvqI772wWHnwzMmwEHAiJcSi+qbXOJlOPflA5n3FcMrbqmG2+AhPCAOFvbw0e2bQEAyBFahk20f7TFhVczJ6SqjXWExLs/8GG0trZi24P/jSdeeHXSsxiklBiyHJSZDB1792R1nO7c2+o9Dt5zRjIWOlz8vsdB4x8in3DhTvzkuP+/0j2I5zr6xyQo5lVEMybwteDT/bqv//j7cc6px03IHJC7pk62q2SjpuGdO8MpDlv4ZlNxi+O1niHsDQjOZ9r78FLX4Lg/N53hlLqvKnNSPwOgLGLg6NrZXqq+ztSYqoDf0bWzsaCqzP1sBssR6E86RX9vnypSPOyWP5h0MGzp8YEMjwvS9nmsL4GPbn8Wv3y5GwBQXRbuODSrTIvu9ElG6fUGH0l0SxmMQCsPH4Mx2K4ppiYYLdcBsaB+SHc317XdoUi31Onu8LZZZ3+oe2pxRrg1JLpLkFCke4Q6DwA4FLew60BucxHuRsonGkEoS2t7EE/r961nyNJTm4nJZW9vHK8fjCNpc69tg5QSVe7FXrd0ULOZInfLtyJhrGL1UNzCsJsCpkUydwdzDhdZMgbUf/Vz6gambsA25+rGxwWSjvDNdw4jXFcMrbqmG88JNm3AoVP/gsjBAwCA4WQy4zUfNqF9quvwdPnBobjl1tNJ1NcvxbJjT0ayYsGkOzJztx4vYhioO2JFVqO4pY0rAOhBl3/cByM/+cZLLw88pkh3ftFtgfTP2jWQxNNtfd7rwxaH5YqN0ZhVHskYbr//J0+jvT2W0a97Itk5jlBRP9sRXgRZSODgsIW2voSqLZXa7El6hlbe+3n2ibXxIKXEgbiFiOven96LOIiekJ2sLJCR0L+Nqu+d8o8vSoYt7tU1A3Dv6cJzLZdS5rwPfOW3O/Fi1yCGA5Hu4BRirjJNCf8c3XVgGAeHLWRDSNVdRd9TTIMhYjA4QqAy4HMQEu4s3NtdTygEj1fVHkx1ONKv64g/l2pbLbfFpE6xL/bDjUR3CaJbfHHXydNMu6gnHe7V8h4YthBx06CyEZzF0iTssbtZp4vuV7qHMj8DFKGYSrSpBaBuDHpSZsWCaiydq5yBdf907aj69/1DSDnF62I+VrG6++AwXu1Rx3A4vVx6NXO6JhhAoA+9f4QLCZRHDBy5sBqGocSVrjnUUdaJMpNbdRUKOtsgfT8MprIc/0l1LMST2Qc7AIAJCk+bC9e9XP19YMhCdVkEEuoannIEWg/FvU4Vk4FuDSOlErWL6upxzeY7YBiu47ShHKdr65XjtJTKJVnX/8pJnFDVJoWeh5sk9/J8o3rs+hlQ3UMpLxomhKoJ1pG80SiPGFmX++1zmdkTE8nOcdzrp8Zws7UOxi3s7Yt77Y10KRBPq1fVImEi2Fzg6bY+7O1NINaXwIKqKAw2cqahFr7Tgf5thDtGJA4P7p4LWlgC4fuINtfzkOFj4kCaWJ5VZoauZNpgNTO9XK03xQWSDsf+HC1H1Ta4qd5upDtqGli1sBpzK5SPQFXUVIEXt+Ya0jcE9L5nYEIVUOUnjlAp43rCQWfwBScf1Laq/y/SUm4PEt0lhta4UiJrBERIiba+JLoGVGSmImJiVpmJRI62UFyLbvcMUm2N4l7Pv9FIdz39xAMv4KnATLmGIhRThx5IMShzC13zXx4xvAGCcgb225gkbTGiAchMZ6xiNXjPC/Yu1jfW2eURL0MAAAywkIAz3GicAealP+pe6SmHezPXh8NMbdVVCAQNbyTCaYCD6ZFubgO2uo5yyfDUY39Ed2d75konGIEdtlQWiteaBSpFV0qVHmtxrnrBj3/VY6IvYeOZ9j4kHe4aQjEwMJx94Yfx7795Et//2cPY+sgTeMcFF4WEhZPW63XSXJJzmD8S+cPhMtTVQkp49wFHSLdOWob6p+eizDSyisyKBYvzkp1jpwkb5vqScCExpyyChrmVXjSOCxlqa6S/z0SvvQmbw+bSzUaRqIiaMBjLGczQjBQJn0z0byOkxGDKNUslxk3c7autPQNUAMmdlApkVejAkr+vR75ulUXC50rUHdinp5cLoa6Byp2fe5mK6ehj3uYCTsAgNBIIiqWXPwDu8akzISG9+4DG4gKzytSxru+ZNlfr198/YXNEDMMvnSvG5twBSHSXGMfXz8UR8yvBmDoh9CwWoAZRew7FMb8y6osAA6iIGjnreqT0TbUA1e9PtzoaC+mRbgD40e9fyBic0lhp6tDGFoBy2MxWfqDTyxnUICtuF3d6OTA2sZo+SONeJE9idU015lREQhE3nQLrGatBCzp/PbZrMqIGhfmJ1TU0NOCMM86gCPc4EdIvG5AyPMDIEN2ODSbV5GPP/v34xPrzcG7TcXho+72hxSYage1P2KhwB0Ba6Oj68JQ7yCszDUzWXNhg0kHKUU7Q0o1w6P6+i5bU46Sm01CzpB5C+uOoqqiZIYTFJF3d1cRgQGTBb9tH5AcnkBYK+C7Juj2XcNN6HAGMNv2Tyzm8ava8cL9uY/zZOcIVFFr0BMf1UgKGwbzyA2+iVISN1dLTzceDkGq8FaxXNRkbsR5evU9OS7qtPo8FJDr6k3ht/1BJmKXmk5TD8XL3IHoGU7AcJWZ1KaYQ/vElQvcTbU4W3uvpR16ZOyHvP1bLp4/TF1RFsWhWORbNKlelAjnCyPq+ZnO/r/ZYSDdSS8/c0JFwP9tI+dPo7FibK9EdNZk7mU3p5USRETENRE0VsYwYDHNc10MJVRdoGgZmlZshB8GIMbJRmo7OAYAjJcrGEV0pj2Qu+Zc//19ocKoFHjE1qBpNdUHVNfvpqJuDmhG3hbqhlELZ/WhiNfgbeAM3uOeHLuuQfkScudEOfZ/SNd+eCPdqX4GIqV4jf4P8E4vFsGPHjlHrRHVdvd6nwTMjPQ1zdnUVNt/xXfWgrAo44ngIMGy+7orsEe9xYHOBuM1REVXt6HTETBvx2QER7kyS0JRQGS4WFxBuP2GGLGY6gWhI3ZxyHLmw2nutIosIz+f2CaSfW5PyUSWLPu70zyqlX3LgnStStU5kacdEOtG04uHTVy4EoFrznb9+Ax5+7Dnc/uNf4Gd/+Nu4s3NsIbxSH542ASChrre6V7XDJQ4MW55JnN5et5nEhNC/QRB9L8i1Sn0uT0Zf7tFgUCIoajAsmV2OvoRT9L4t+cbh6phRrbBUJqAWmzqqq1P4vRIdMAiBjBTr9NNFiWwZeJw90r14VjlqqstQU102oveSjnSPd5ydfnymt8CT7hhRZ3PFLY7+pIPqqEqPH0jaGLY4ZukMQEovJ4oRHZGwhcTscrcJPZQgr4gwlEf8WTQ96xU86QeTjjfQMw3mXqDVAlxIRI2xD26iWSLdiEQhhPAGpxKUFjiV6MGGvhBnbSnn9mgEgJW6z2QJ5CP0DKYQ60vkfD1409I3MTXwhDewCxqmGCwsSvRAK4j+XQ3oG3Zev1LJM55WcP5gyd0vgVMj3cuiqrIc3HIN1MqrgPd/GVjzTgjO0da6J7TseK5vOl3Q4gLlpsoq0uJAH1++CJ94Z4nRkFLV46Uc4XaxgDLXSYvUBAdijLHQNf+IeZUhEZ5vgjqHQYnEfb1xWCQg8oInrD3xHYx0qywGATVJFDwm4lnK1crTxgLp0bvauqU4/lSVPTFelACSbjlGOINVuMJA+23s7U2oMU6orZFvZDkRHC5hO+F7pMEYOB95ndMVbNATAoAKzmTUHBNjQk+o6/IffYzpgIYqYQD2D6Ug3AAHlyIz0p3226dniEZ0TfcoE6y59uFofbxzri+t/CF47ddRfKW5VSvUQwkbCZsr7xEJDLgdN/R9zBPpRQyJ7hLECDhnziqPqhOWqQttdVkklALFmD8DDKj08a6hFA7FbViOQJlpeCYJgCu6TWPMAix0o3VNh2CqlPfg4LRzIFnURl2FhK7p1q7Eo6WXM8+Zcso3dcpp61O9NXOhZ2sBFc3Wbdb0DK5OJ9fnk0p5DUTjgFA6sK75Bvw2baUwuTFVjLcVnM400KmmwYhZuu9FuWmgsfGI8AqOfw8M08Sy5StCT491vBPrS+CZ9n7PJ0D7adg8nManDXXUpI56Lm45+Htamuhg0pmwI7MtVE9XbfDHGHPTUsMZAAK5U2RNg2WfeM0D5aaByqiBhV4bJDVJ0DmQQqw/98QZMTZ0CnnwiuRPSPk1q9LNvAjeRoasTKf/9OMgkiN6N5LLc67t3HVwGFwK1aYrzVPAS4FlDAISg24ngKChqCoVEhO+9tpCZLi4M6b8DUZaY7qomSrU+FBdF/TvUgr393wi4Wd0KPGtxajf0UQKiaTDcShue+VKjpAZjvEZke6IgYqIX5/tu5ePvJNyRrqFP3E25sONMZXtGFh3cIJVupNWhqsoJIADQxaiBlOaQaosAAa/tKMUDjES3SWIwfyUENPwI3AMmf3/VKRbnfS9cQux/iRml0dgMJVOObs8guqyCAZTDvb2xiGhB4Jj25bgjZY98XP1hyu69eCUS4lhi+P5joHD/ObEWHC470grc9SUMeb3bFQC/PBaWc0kRhx8yECphRB+aYQrSkx3sksvo9PL9cSGEcggUI8RWlaUyOTGVDHevuU6euedG4GTI93XoCzCsLQ+LSp3sA3X33IHauuWhp4e62D+UNx2DZ387gGQAddYd1LHcbMnDLduFAC6By30DKZCUcZXewZHbAk5EnHLgcNVpFunlOtrgf5dvON5OlJkGUPj/KpA32MGh0vP+I2YGLo1HhcBEyj35xTCr4sOuTPLcK1nNkfs9MndsiyOzNqzYDy3GkdIDLkRtahpwOFpNbNSByLgTmAJ1d5J+pFBnTI/0Sicw2XG5JaOIFfkqGVn0O77U3/u+BlY/j2oFCLdKYd75meHiypJUPtcex9o8TmU4t7EacIWGLYcNYlrhHu3a7JFuq/+pyPRMLcC/3bm6kB6+WiR7lzPy4xSkbGQfkwEjdSE8M3h9LmVcjgqy0w38g0MW46nRwSJbqJYMRhzoxIMEcPwBLfBGI5aPAv1ui0UfEEupETPsIWyiIHZ5RGvPmPxrDKY7gW6PGKifJxuucHB5jnnnqf+MKMwTNMbnAq3LiZRAmZdhYBvIuPunSwDDV27WV1mqgtqicyE69nbrK/JwG8GwAq0W9NHLtOiO/C+JbPLsSxwznHpTx/r5YHSGvxMFdlawRmmCcxdknV5LnSdfmZP0fT08qhhZHRnOP4fz8L56zdkrHesu1Q7xOqewqbBfFFjMK8EwXIEwNRK9XlZHjEwmHIw5IpuLpSpzWgOyrnQPV318czc+4oIDLxqZ5djyeyK7CUqU4yeAEvapeE/MRnsH0rh1Z5BJGxXNIiwEaBvMui3QtKp5kHGci/P5sisj+/x7D6bK+dmy3FbXAYmkkXgesoCA3/GgNkVEW+7ve8ywQPHERJ22nujpmoLOa8ymuNd0xfpDt53mFuyUgrnzAudA3ipazAv6xJuCjljbnkFADBl5rjnUNzbtwmbY9jiEIDbRi6zpjs9gB01GZYvqMJDH3srzl+zxPNDGK1sJtfkrp5MHi/ByUvT7evtR7r9CVgvYAB1DvsiXCJq+o8nen7NJEh0lyCmwWAywDSZqslm/gxu1DRCA6TgKWAyhvo5FTANlR5WETVQM6vcExbzKqOonVXhRVsAdUF5/eBwzihocGLujNP/EQDQ+IZj8PBjz3mDU3/WfHxpZcTE0DcLnSqYvaSbYXVNNWqqy7z08lLYN8EBZjp6wCYlMJxyMJByPBNC/Q6DqTquIPMqo14qZbCUQy+vjX5UPdjE6wqJTLK1grviptsRmbso6/I6OuG3DAukl6eJbiFlRu1dXGTecnX0bizoekCdiWJ6kT+3ZZc7eFEpisyLWAHqv3FbeO0cUw5HiosxC2KHC7zUNehFU2ye3Xk5WBtoGmxEUTGV6N/Z5oJquidI0lau+LrEQh1bYfdy/V/lfg3PlTh4mI2lpWi23sPMDRWOZ+JR9R4WqIwaStTwcAqsFjvBSc35lVE3uOBPQAtMLBKnWzFlc84frbQimL47lTD4Jnk6e6UUJnvzOSEnpH/tTboT8AwMQymOvoTtRYW9yU8uvQ4L6eZ56SZ26feVqDtxnD6xk07O9HL3OB9P+cTCqihmlZle14G6ORVYMrvCuyfqMhCdRaIn41gwI8u9/2jvkeI/wkh0lySmOzgzoVKc9GBN1+Tp53R6kT7/yyMG6udUoCpqImELVEVNzKuMeu+viho4cdlcL4UGAA4MW140JBtBZ1198pZVVofSL/UNnEuZ2ZaHyDtevZHMnV4OqJo7fYHVN+ZiZ6T07qAgfrVnCAmbe26l+nltnpMLxsKzxzolVmenqNnjEvihx8FYncdzEWwF99qu17Hu/R/KuY+93uvCN4nRpItunpZSCwB/3z+MR17tyVjvmCPdUhsdSi+yHBQ1vr+CHuz4ESsudYRcrcviarA3VtHdn3QQtxz0uT17/XTE8MbrGtlCw3AVGxcSB+MWDsWt6d6kGYduK6TNoXzPChVl8zKkpH+tVFkhcsSskGxoIZFepxq8no4FnZGxZHa565khwfTIV8J1i2YBk0u/3E4Lbf1dRuoE4OSYhHqmvQ/9SWfECdtcTNe13mC+CZgXhSzy207K4Ug6Im+/eTBlO+lwJS6hfteEzTFsc+86aQsBm/sZSGEHcJmR8p4uunVG1Ujp5VrQZyNhc+8+MlaipoGGeZXeGDCSNsGqJ4hZ6L7knmuuV40jpJtt6wd5ih0S3SWIYajaUtNwI93wzQ50HaB/0fWNbhiUS2LUNJRbrXtlYG7ejG5DZjC/pltFY3KPwIKpjb4ZRPjCoGdZhQBe6xmilkmTjDdQQmY0LxfjTfmbqajj2v+mbb0JdA+mAIQj3RYXGNQDrbQMjZEyNlStvP9Yn4fBmzAd/j7jcR4fCd0Krn5pQ8gMLx0/3VR6taCa9JruXBON//bIa+EnJBvzYNxLLxfhY8iLdMOPsHilDe7xZnPd2k+9z3JUxHesLYmkO9mgl9bfL/1rCuG78RcaQqpMk/1D1piEHxFGCQnhHWM6OpVyBJ7r6PdcmvU9W1/79H2ku7MdTz32R3TtPzDqZ5W57USDjszMHZy/0Dkw5nGA7RqY6fsY5zJkgBg0slSRed9xWUfVg6ZwuXi2ox/7ejMN+uyAQ/m4PAunMfrHGPM9W0pkstd26+7zdX/VmSAOlxhKOm6rXt8wbPn8Km/ZioiB1TWzvPcFr8kWlxnp5RmRblNf65GzXCjYMlEIiafb+nBgSI1dEq5Jcb7HFkL455o+t/ysEvW9DAMU6SaKH8Ngnvg23BRz7a6sZ6ZsLhAxmReBllACWhucBM2fGIObqq5bb/hRkIgb7cuGE7iaZDNOAfzBnpDSvQCVwqk5fegJjvFcgHW9ZLETrOnmQmL3oWGvhZiua1f9i9VMdtRgGTPII9XQqj6d/uvazdQIqG4hJfYPpeBwUdLCYbzO42NBCOntr/SJESEk+pO2NzGSPn7OiHSPMMA+MOxHWUfyCcjcPvXZjgjWpfr9qPVgRkg/ZVa1bJLoS9iuk7Rv7mNzmTP6kY4EvHY2PYMprz1M+rZzTItv2qio+5Ia9ERNFrr3EGODu8eMdmDWwttyPVcsN9qrJ4d880eJ//nP+3Bu03H4xPrzcNtNX8y6/g8cV6f+++Y6RIzMmm7dntR2DfHGQvr1Nj1lm0vhTRLpulOd4aejb74pXPYJUz1GGQhk4h0ctuBw4WUEqOXGtMkAgHJT3QtGClpMFtoUUTv/A8U/2RvsnZ2f9alIb8LmsLhUfkeMeeVi5RHlp8SlEqZ6PO2kfXy24zwaCR8TQRGeK9qtIt1q5Y47cauP1+Qk+CXp9oH68JVCevcMzz9B+mVQpVI+SqK7RDEY3Gi3n5YIFnAyZypaV2YaqHRbEzD3fSqt2K/R0u8JRr71qSMlPLOfbJx7TC0AoKlxvhdRz9b2gLvp5ao2qvhPzOnEq1kdxwVQR3iLHR3NBpTz5lCKe+m5vW7bDyEBWwCLqstQM6s89B4AqKkuwxHzK7OuP33yQkW6wzfEuMXx9/1DeK5jAC90lq6j/3idx8eCzvLQKbKAigg7XKC9P4l+txZPiMxZ+UzRnfuEODg8sdRm3QPZ4SJ0jVViMlyCoFNmIYHdB4c913Jd96cFU3pLphE/3/259/bGIaVKKUw/7ws1E8nzR3DvWxM1kCtldIq1jqjpxB9bqAlAHdWV0s/QEVKip6sDd3zxSu98lZGyrOv/3Okr8YMLj8OV/7gyqyOz3ncpzsd83Kbv5+Bki06T9+Y0AxksehwjoQ3j/Cg+oK4LQb8EJ1CLq9vzdbuTo/oaPp6J6flVZVi5sDrDjHE0/vD6QVx479N4tWdoXO8LwhjDGxbNwqzyiHqM4p9U9zIq8/Q19cRU3OZIue119USOxvcYcOugkXnfyGY6mFnT7YvwnKKb+X4bjhCwufC6qdhcIGoaed/HIpBhoseV4Ui39EyddQZAsUOiu0TRA7KIYbgp5eHWYQaYe6FgqIj6LVdMN33cMJg3E63fo3sLmsxQN113YKjqWrOfTvVzK/D7TzbhW+e/acS6FD3QTTkkuicbh+sBwtjfo9ODih9fjKlaQeXWmXI4dh+Me+nnUkpUREzX6Co8gVFTXYbqskjOT+Ah0c1CUU1AmbL0JWwkbD4pM9QzhWzO46ZpYtWqVeNaz/6hFJ5u64OUEp2DSU9Q6wjeE/t68WrPECy3PZYtwqJcky66nbQFVi6sQv2ccgBhw5vxDGj15+qWYOo53yUW8Fs5+X3hVT12l1sGoc9vPZEZFCUJm+cUzVL6NYEq2ukL2SCFen0O17+zDHMiYnQsLsBdAWq5jswSSoAmXXEhoVqCdQ+mkHRUrWhsz+7wBFk0+6Rj1DRwfP1cREzDy3yz0kS3lIDlyIwytFxkZGKkPcGDwgD+fU/XeB8cttEzlAq1CgSA5zv78ZorbJVfgi/e+xIOBpKOylySvrAabwJt+TgFNwBc+fDL2H0wjq/+due435sLfT0sZvJt1qszQZSYlN7YOdhq0stkC9zgVTaR/zib6WB6SZAuEwVy9+oOZiXpSLdpMLfsSGX/CJnbw2ciiMB30xMaXqQbvggHtH9CcR9jAInukkUJZ7gXAjelHG5PPTfdPOUIzC6PeA7nKirupqRDRckBVZOh1qeeMA11kd51cBhJbSY1wrk0qzwCgzG/7UEO0a0vFJRePrno339cke5R6t2KBZ0+Dugbl3quN27jwLAVShUOGluN9afRM8DlgZnsdG2QclSUp2MgVfQDoZHI5jy+ZcsWNDQ0jGs9sb6kN5HRl7C9lFIJldo3kHQwZDkwGEPSjXoLZA7O0nsPp4vPCGOeS31oYpGNPTqsI/Hq/dJ9TgtgP1qihaUa9KsUx1llJuZXlXmTAY47kalFNxcSL3YOoHMwmfWzdZRPSF376Lcs8wOFckKtZ6YKfb5EDIbUGNOTCR81ka6Ot7jFPXFguWZlZRF1vMUtjp6hFGaXRSAA1DWuDE+QRctH/Sx9rjjp6eWQsDgfk+geTjmha2RZxACXAqY71mBQxpaem7kbzdaBBCmBzoGk24vePwcANfGkJ9oc9/vrczDpcCRt7goaX+AEz5XJJniNyYeQHGMVyoxFC2Tt5H246PTyoJDUk1TBMYIjhFc+Vl1mgvNw14uxlJAxxkbt1c3gZ32o+nUtulXwoMw0PB+nfCClmtDy+na7GWTpLcW8+5aQRX+MASS6SxbTgGempm8wegZK/20LibkV0UDKuW++pt8HaMHN/HRzN2Wr3E1LN7PU/WWjbIT0cseNcuuUHWLy0EYewZvDaOjBfbEjAgraESI0GaSOz8BNNXBzGesMbsQ00Di/CjXVfvqlw0XoRqh/5qqoMjQs1HTefJKwuRehDRJ0Hm9tbUVzc/O41+0IAcvhqiyA+32FhZQYSqnP1eaSSdflVdd+B9k/FE4ZT79OmQbz0gBDKbPI7XmRjo5024HBkVQHmHf91REWvc36uVnlEUQMv5ZZt83Sv+uw5SBu85wDH92KTP/zRbevJHSf7nzTeiiOh17sOqxrv+6Bq/1HijXSPZC0MZC0J2XdjvSFyaDlhAb6psHQMLcSkCobhwuJujkVEEKiprYOn7/5mzDcCTKUZ490B8ka6Ya6nqa4hJVe/JpG92AKL3cPIukIT+jOrYhixfyqkKjRKa6A7+WgfW30dTvhcO+6oDOXguntOmNEn2+WI+C456l6TXjtI6dKdS+oUk7S2/4Ww7vueQJ7DsUPa33FHIVMORz7ehOeV4eQEgNJG8/E+id8zQmZ77mrYEwbTSpUpNsXntVlERxdOzvUSm4sPe0BZXIMZA9aqc+CNyGq2+gxqHur5aa/B7ctH3Dpb4sq0UgX2cFIOFAKCea5cxyJooaBebWoqj6bhVJeGIA5FREsdAf/Kn1cDeK0YVowvTzC4KU7moYBLiQqIgYsd8A6lgu2HpDqWkN9ci5fUAXbrakUwiDRPck4UiJiMneWdmyMr8PjDMYT1a4JlRSe+NYtmXQ6WSj9dxzj+8qoGXocTP9Vg071eFZZBFzPHheoW3S+eKlrABURE2vq5mS81tDQMO7odhCLC2+gZbt/SylxKG4j1p9Q6eTu/tUO5Vp4aPoSNvqT4bYu2UR3GbKZQ429lCM4kAtNckoRSJF1Xzf8yTAuGaJMlQDp/tpadOpU92GLI27xnDWkOnLjuKnlNpeIuDWB+vibXxkFFxLVZWbWdUyU9//kaQBqYvY9Ry+e0DoYGBzBYbj3vmIV3a/1DMFgDCcum5f3dTtc9at2hHJkLjONjN9RIpwSqyOI5174Yfzj2jPR1roHD+6fhV/vHhzxs7TwCJViuJlADldtlJ5u68NRi/3a4yDDloOELWAyHhK6ZREDKfdvwwC47a9fn9c6809PvunvqIW3IyRsIWAw9blaxOgxi2rB5HvQ6EmwyXb2D5pu6fZNd/7fHgDAD59sw5fXHTXhdRfjsGsw6WBWuYmO/pQ34Sol0NaXxMG4em4o5WBuoBXWWNGu+XoiHnBFdmCSx2CAJQQiI0SXx2qWWmYaGEZurwPGGATXkW6hvi/8Y8Y0mFtqMbbvNxbCE7AyZD4q3AkOv+a7NLIlKdJdQqT3s/VagbmCW98MtElaxDA8h0UdBdfp6CbzB32Gm5KuH5tM3WgjhuGZR4zlZArO7gVn6yqjJuZURHF07exQehcxOXAuveyEsUavSyHSLQM1X9IdeHGuXGu5m4FhC+mlXzL/jZjoDG51mYkFVVEvamEwBocHjH5kKcwNKwOvyRJJDvf3p8W1g636vIGkA1uoFFHbURHxRbNUamzQ4G6v2yqodna5JzjX1M0OfY5p+CU06eZQYzl3vBpBqO1jaa9pTw3hHo8Gwq6w+jrOhcTrB+MYthxEDH8S03J892n9uD9he4912iWX0pugMAzm1Y/r71g7uzxvKYrp7DowPOH3MubW1rrlUvlKIy00dKePycDmAlJIDKYcxG3hTRBqR2ZN8LFuP8kYQ23dUpzUdBpYtGLUz8p6rsCPRg+kHDhCen3j09H9kJ0RJiWXzK7A4lllXgBCty3S5w6gJldtx59ssxw3ZZxL6Ix53RpKH/YJW3jXFN2vWXcSmMzpUd2+EgDK3daumlmHMRFWjPd3mwu83D2Itr4EJCQStuP5Y/QmLK98YKTe1yOhJ8uDP5uKdAcfs9Bxk43EGMtgcnX/CaL1uCMkHPc6PpziXtciPUGWDxoXVGLxLL+MRGeIadJN6wq5LCmfkOguEdL72T76wP3ejQZQF9VgpBuuqNYi2xfaYTM1AF66uUalzKgBWWXU9G6UoxFM+RqpnQvVdE8ujpBuKtzYJzjG0/ZopqJnZrWE1tEcRwg3wq2iH9JNk9LnkxZxE4ExhsWzyr0IiunWokY84VD8BjdAZtTYcgTyUaeoJ1KEVNccHQXWddP9ScedWJFIcYH5lVHUVJdhxcIqlUrr0ua2jWucX4kfrz8eH3rLUtzwzjcAAD7z9uWojBq4+owjs0fvsny/bGjDG91HPChswymybvaDe53WIkynmwsA/QkbAynHc7TVYpoHJnHa+hJ4KtaHQbetjC7t0ULCFmpyTghMmsgGwtG7BdXjjzgFcdwJAgPMy24oNrgMD197BlPozyFMx4OfXq0ihBYXntFXsDaTucec1ydepE1CAt4xpctoTs4SlY9maRkGXfYEP7ocHHsESXmTSLmjdxGDYUFV2End69vN1D3QFsIreZOQ2HlgCC93DSpRFRQxwm85ZXG/FE6Lb0zSJOlf9vbiXfc8gT/tOYTeuL+fU45AT6DkJViyNH7y5+o93QQd5weSDjoHUpBSlUQEJ9UtLpB0RE5jstHQ4jdojGaAha71+vo7EmM1Sx2p+w+gy2skOgeS6l7iTjYP2SpjxWT57ZVdETGznlsanTGoKcYJ0GxQenkJkK2f7RevuhQfft+5AOYB8KPWgOtiDkAE0s71gE2lmCsTNX2+aHM1fcqoGTO1zsWzyjCYcsaWXm76GzGSSQqll08eum5TtQIau5On2ufFma6p0fXc0k1xHEg6bpsNdaOriJpuza8MRSLzObA3DYYUDzqhiqIZDI1E+m/4fGc/FlWXo3FB1WGuF146ue22UQlGuk32/9n77ji7qmr/797n3DZ9JmUyaSQQRBAQUMCABRHBAor6ExGIT42ICihYkGLBAohPRZHHExF5ShELgoI+BRQRBKT3lp7MZGaSTKbfcsrevz/22fvs026bO0ngzddPZM69556621rru75LVFhgnKPo+II3WdMIzJ6SWt6VS2FJVxPOfvPu6ruPHrwIp7xuIUwtp1sf3yglVZWvkoI3krIoF3L6eCvhMlmahWhCa/CMcI6UQTFatNW4zSFyu/Vc9ZLDMFlytUWqqCHrMumkEAaPw8pHauqBpPfPak5jYCwYvQNERG9wvIT950fTDcrBcTloymOJsFcma8pxOfQMgbVDeaSMqdPNZXqBfGZ69Jhp7S88/sGLkurts29UiPV99ag9AQAHLIi+R1/jResrxKeAS62LpDrWsqSXMLqra6Aq59Zb73BvLLBVyS9hBMn+Ig8rhdRcj7arixPKfbNe6kWjccYtzwAAzvrDs/j+cfuoz4suw0bPGSivvW4QRMozvhyxfnseWydKOHhxp2IOifQwpjSD5FxjOWLMr5dh5XiOzqLDVBuNM7LLBZiAePXyOMQxQ3SI4BdH70hBOYc4BwqW78R32fQmCurznHBK+d8t6Wp6xafJATOR7v8TSKpnu2bNGrUtKeaAVC8nKoc7LJhmePnc0iCTEXEJg/iUwwMXtFeds0iIrmBeJtL9Slwp7SKQkVMpkFQbvXyaL24noGi7ir4ojR1woVQ9XnLQkja8nD8XuZSB+e1ZZcTIDtVIZgYB/Ei3vKZX4oPXwFhQPNFxGSyHR9TC6zo294WhpKKrjHbIxb6MipYcP2czDJl3F87Hl5CsIhXp1sY3kTJQeWG3bdIS0QkA0Oi7OZPCZUGxPV1J2eV+JJJ60TuZhz2nOS0cSVxQ64VfiXvXyFR0Uz4rqXbOOcees5uVaGajl0oX/201jrn633hwwzA2j/lq6nIB/O5rHsLHf/Mk1gxVTzcXue1MCYO+ElkijssCpepcxgNMgalAlT/iftRXgmlMC+J9p2+7zN92GUef906XdjXh8KVdsSUU5VogqF7uC5k5TPSjJHta5lPXWgbJ8RwG0khhnKM5ZWBxZw7cc76NlxwRTODARMnB1smSYopYrnDeyZNSAuw1twVZkwb613RgtBiMdA9NWoHtevFKmd8Hx0tKmEw6kVwm5nM5rslxQaYM1Dt/2w5HxqRKYBXwKdw6HF7+vRS897b33BYAwNGvmhO7X9zcooN4DlbJyuAQziTbC7JQb90+Xc1zSVcTetp0unmwJ2RNo+aa9C9HvPLvcAZV1bMVk4zmmybyH1GLNZ9OLmtvC4hItx/5NrwoqUFFiZxaqIeVyh4Ar8zoxK4C4f2FiuBWb3S/Mt/LswPjeGmrqMWq6nhCiE4VHRe5tKGoaQYJlgTRIz8NK8MBEcExiKx1+coXsHM5V1EDQCxqLZcFNCDqhW90a/mXXrTbdrmKAEjDPIHJirwlDJumCnmTviERzFMtuRwbh5PVhS2HYXC8JER4gED0rrstgwXtQTVoPaIgFe5lmpAUUWtJG6pKAfOMBdl2ASHwo1OwXca8/FTmRdpJTTXGa8EtzwwAAH764EZs0wyHcORpzbbqFZlzKVGOpyVjKkGuV5zR7Y3f8r5k7exGLGYVXTrmO1mHWPwdZGLIWsRye+tESfWt7tbk0mGpmEi3Xmu95LhaOk8UQjkcXgmwGu6TyRr3PgukPZtC1jSUw2q86KCrKQ2Hcby4ZQKWI/rYpO1420ytZTiH5gCbvvZGCTCmiTmWHIbtGt18Ks6XV8L8zjlH0Xb98YxzNQaUHKbakmT7Se2KeiP8DmPImlQp+QM+80g2xyZvTIpzOklIh+4+3S34x6eX46J3xovhxTFDdFDiq+zLiLblMI8ZQ1W62nQhlzLQnvXTgzj/vxlAmzG6/w+gmnq2uno5IAxwP8Lt/5P76ka2FFKT07HcL6XUzYO5G+VQrla3xCttobQrQUYHZHmTah91NblJL1dIz7E0yjj3lUll7rvL/EgieFBIaPeuZiybNTUatAQlQMlloNTvVzurP4SFGacLYuHjL+ItV0RlzVpW0onH9oXGbC9Hda+5LeDQy24RxXBIOqNcGDUlRLol4vLupJOyf6wk6O0xCxHGhXiVLPPF4V8L1RhCgDD89VQPl3FfJZb4ziNKdTE+IRbFtT5v2VwZUIPjJWzPO+CQEU+Nvs4a51QKoyVjBFThSw7DxmGfMtsao1qdhJRBsXd3KzKmXGC+8nQoZJ6zShFwmZcm0Yi+4tPGw8JpWmDXg/+9ZEPI9cRmL11gXmsmMR8bSKbLyq4zaQmRq1gngOc8czmryelJQAL6CCL1xBeXldolnbmUlyMrRNXytmijriuippYbja6LdKDpiyQ2p81AXylYLu54aavalmPHP9YM4aK7VtUU+Zb95eUMGdmWTkiZduN6LCfOhBCmHOPkOFkPQYB7DioR6XbV2jgc6dbHpCRIR0pLxvQchvFtWQqpSb2EMKinvyECK8zLW/fTCWW++Y56y4s6cljQVllQ8ZWGGaP7/wgq1bMV6zG/M+u0ckk918WcDEoDkW59ASyj6inTp6VX69BKV6TIlI+Cz2BqEB5ev2ZjtSB4ZdKcpVcYEFEjy2GKogXoObPBSI8eiUybFGYDorKAWOx1t2TQnDZV1GdnPPawMOM111wzbecKMy4sT0W8ATa3MjLlQguQi2M/ekeJV5cbybmhk3aVkW5Zp5vp0Tux8CvaLp7cPIrnt0xErxPiGczxxJBkWaM4LOrIYY9ZzWqbIZiHK48lSkOK9rt1wkJRRro9+q7NhFI14xxrhyZRcl1ldOjPokK55CmhJW0GRMBKDgvUGy7VGb2jqE0o8uUC6TSVrLSp0GPDcJloS9JRo/cF6YSRYNr3GS/9QUKmhbRlyztM0jGig4A/z3TkUl75uuj9SUdTnIhbJQTEnrhvdKvxFhytnvHjMvHMi46kLAs1aMtlkXPKfj5dTS5rUlz78Ca1/dK2STwz4Jdlk0b2F297Drc8M4D/fWFL1ceeLkbLdMN2GQa8VAbFaoII6qwbyiuHLuNCaK4jl/LSUDzDnNXXf2T7MynFrFwa8z3jUjh1ajuWTMXoqWCgyjXGhXe8pDQTdMiUQUcyQLgWZDHEPNdI9fJKaMmYaK0wBrwSMWN0/x/CwoULccQRR8TWtKUkqPApPbvqO2hGt2dk+zndRC1U5feEEKQ949vUqOiVIKPjVoJ7kaI60aEZ1AepapkyCGpazpLqarG/3OAwn8BddBhKrswn9D3XOvVYUrSm60kQQlT9VUn5a4SKdy2IE2Y87bTTpi3iLaMO8qGOl2xVkmeqYFwapVwZovp3BFA0/nKGbsEqn9MtEZvTDaJSFCyHxwrnSOdAVkZENMpqGCLy7U/tcvEI+HWOXS5KjMlD9I8Vkbd8o1qKDEmHh8M4JkquKnfjK6U33tmm16VtyRgY0aJ3RccN5njX6YDd2SyR6YJ0EsoZV247DRDBkjn9hudE99NnvBJ1VDfCfUN3flsWu3X6TJ981X3FY72F1gLync1uTnsskbhrFeJuBpF6B9Xd46LObEDlW7YRGZCQootyfSSUzZnKE+YQny3qyCpWiYScKxoJ+SwBYKuWhhGHUig1o6bgBYl/zrs6NgwXsGmkoEWuAXBguGAL/Q7oJRU1hyv3nT31jG+uxs6a1ZwOjMduhRzuMKQBvbC9vNGtV/+55G+rIt9LtoLjVeOQuysHrEpXexm+6JcRZozuGQDwc7jVtkYv9/O6xXfUKxHmR7qDOd4y59uvA944ejmlwZILM2gsHC8Zj3i00WpBALzSCAjcm6A4ByZLDjYMF8C8CVMaTUJki6u8PUkh3BEt1DccdsDJNKxevTpWmHH16tXTcj65WJKU797RImzWGNV2vUSMbkjIyI6eB82BRHXVfNX08ihlllJZI5wpkZvodQbFqWq5dWkUAVq0zouGEvk9EzRG6dvwSyB5kSAm6O0+jd2/gkYbrnoOt0FIJNI9HNquB+SVKqTG/NJVgO88acScKZXLTalY731OqRgP9b7BEazdrhvYVfcVGh/pDkcefc0BwchwtXzclEHgsGQHVRhZ0wgZ3d49ag4GGcWXBprtcsW4kOucrGkoVomEjHI2ssnpwmmVUHIZ1msskZYaUjNerpFux1OT1wXSZHMqOa6imevMHQ4/rxtAXUGeZ/rHVXqSDsma0tOByoFzrozuBRWMbh1D+Wi7kCXBZEqE6fUvP01JXFvGrL+e+wwqY8bongEAaWT727qRDfiTjIRJCdo9aojhKZp3NaW03xIl3kJriHRLb13SQCdrDc5geiAVPIHaRC4IERHDVxJ0wyNvu9g2WcKc5gyE0rVUQfYNX+m42lG5olLoZ0cvhpYtW1ZRmLGRUMYRIRgt2hgrOkgbjWFWMG8R7OduhnM5iTLQJM00Dn5Od/kpNS59RgpNOV6uXdz4phb78J9HtdE7nd5Lify950Dw8lRdLs6bMYQwYMEW+bLy2lzOURgaxEuPPoDB/r5A+270eKwrLj++eRR3rxlS2yWHoV+LdCcxoiphZzmsGoGRgq1E9xyX4en+McWOUIrdzCurNlFUdFIASrugrvPmbTiMwfQc37KvGISg5PjRZOnISUrFqKT0LyHXD+HrZYwHcvllWxyatLB1wsJIwVaRTeEgqN8brCp4KKNbMmKCY/2spjTmtGS01KJoZLsWbZtqkZS/G4eSw7B+OB/YrhaSwfVyA+NQTihZDlHOmSVXN8T98ZR584EUu6yHXl5yGIaLVkR3JGVQLO5oqrpm+mjRUe9pXhnRQXFOn/UQtyagXjqEZL8o3R7ve0IIlnY1o7tlKvXcZ1AJM0b3DAAAr5rTgj1nt6htPYcbQGTwOGBBOxZ15Lx9RQ3QribRWYVquV8/06yBmhSnWKqj2vI6M6gPuspsLZPNKzGnW5XW4J6yMxcLRQ6xTTWDTFK0JPVyR3LxdvRjr0aYcapYOzSJvlEhmKUWRRwYLzpwGEeK0oZFuuX70tXJFWWWSEOVlzW6J6tWL48aEoT4ERmxSPS/Kzku+seK3iKK+2JOSI66h7Gkq0mN7cIgEkYYpX6Ot+NydLekYVBx/NGiA5vJSCHDn39zAz5xzMH46ic+iOOW74+7fn8jACCXEqUjaxE0q4QxzZBYHVInv2vVNvx9tW+EF73nePm963Dun56v2gG1sxxWjcCabZMYGBdiZAWbYaxoY8uE2FbOG86wdaIkakpzUT8aADaNFPDk5tGazicN+v7xIprTpldXngWEVaWDRsIt4xTKV6l/INccjAcdwK+a0xKI+sl36DChQi2NCamzIUTcarrlAPS+JunJlBCVFsK9HG81TngiciS07lHpQA10TscZ3e98dbCk1Fv3mAVAGIJb6ywhRrDrOtX1sZQxjkGvbwAyNUaQpmUqkXA8CXaCrBXNuTaeemP9oo4c5rRkalrXDIwVlSOwM5uKrRrQlDaqFvabsMT7zaVoxWodRdt/DpNWNEVJOn0kE0RWrtAhRCan0FlmUBE71ei+5JJLcPDBB6O1tRVz587F8ccfjxdffDGwD+ccF154IebPn49cLocjjjgCzz77bGCfUqmEM888E7Nnz0ZzczPe8573RPILh4eHsWLFCrS3t6O9vR0rVqzAyMhIYJ+NGzfiuOOOQ3NzM2bPno3PfvazsKzyeTKvFLRmTbTnfDn/cP8Oq4waXi3vOBiEIEWJOoZS7awCabM8vZwgSjebQeMgcme9tAII4a5qEF5gvBLguD5V0/LUonw6rqDnKpEtFd0Ieo93BHZ0TjdQWZhxqtg2aWHAUzlW9YEhFkoU4jnX63yzXYZNwwVwLkqmcC5yNnVFZkk51/Uq9NzOMKpVL5eGhD6GGYTAZiICKaMQ0sh4om8ML22d8CnDhAAcATX3SqCEqPFbGZtebq6MxjlcEwLkwFjRRooKobVNm3px2de+EMjh/+9vfhmD/X0wq1DfrRV62aNKKDkMBdvFLx/txV2rtgVE1irh5Rq9A4K1qyctX8hLtmfOhYNnoiTapbSvJi0XRZtVzRCYLDl4dmAcI14OrKrny/xItkGJKC+njUMsombuw9c/qI4VAgQNq/DaQ57V9YxuDqB3pAiX+VT4eiGNNKLsMZEHDKiuqBgosnIFJEMG4Zxu0nAnz3hMX5nXGqQhL5stRBUtl+GFQV+ksVyFmDBkulnBdnfKfJOEvOXgqc1jGPNo9v3jRWwYziPvGavCiRmsUiFSAkQfkIwIObTK36j5HNUzeVzGsWG4gHXb8w0raVmwxDuqNK8AQSdKnDOGEAKH+5FuOg0aAzOojJ1qdN9zzz04/fTT8eCDD+LOO++E4zg4+uijMTk5qfb57ne/ix/84Ae44oor8PDDD2PevHl4+9vfjvFxX5XxrLPOwi233IKbbroJ9913HyYmJnDsscfCdX1vz0knnYQnnngCf/nLX/CXv/wFTzzxBFasWKG+d10X7373uzE5OYn77rsPN910E26++WZ84Qtf2DEPYxfDPt2t2LenVW2blFRUG5WgBGpyBmqjl8cJDYWPPZPTPX0Qj1Y831fNacG8Kks6SC/+KwkOY6qEhlygiCbtRVGkkQ2xOKNeisaOLkW0s557OWHGqUI3KlReKheRPdPLRa53HNg4XMDmsSLGSw42jhS8clpBxWUZAVcLMe+3iXW6JWW2QvQuHRvpJl6NWGl4+9FuyxM2lI4HqhaGUzEkoCIdKvLNhNKuNBQsl3s6HRxr10Rz+BlzsWn9urqvoRwmaqDMWiE185piNPzlGekW6QDiul0uHFF5r3zW9ryNlEFVlFdGyqR4U8qgkVrn5VByGVzOkbfcAPPD1XK6O3MptGdSaNbafjkHVa36B0DyekCeC5C6CC5cxrFt0lJRb8etP42AKwPMz/eVx/KrsvBAv5SnoiSovdCo1KOi7eLmp/qxZaKE8VI0ohmmIcta6M8NTuAvL/olxGrVQ3AZ8OzAGHpHosrYOwtFRzgqZZTXcoQjVb4vWYJRpgXIaLdkhCxsz8LlCIz1kgEjUyyrZfw5jKHoCOV6Sd+eKvJVpmIAUI43QDjX4uZHx2Vg4IH+OxPX3rHYqXrtf/nLXwLb1157LebOnYtHH30Ub37zm8E5xw9/+ENccMEFeP/73w8A+MUvfoHu7m7ceOONOO200zA6OoprrrkG1113HY466igAwPXXX49FixbhrrvuwjHHHIPnn38ef/nLX/Dggw/i0EMPBQBcffXVWL58OV588UXstddeuOOOO/Dcc89h06ZNmD9/PgDg+9//Pj760Y/ioosuQltb2w58MjsfTelg0zhwQXvVkRVdRA0QkZxqJ5p0JXq5pI6xoFrqDBoDJ1pwtSrIBcUrCTKXlXOhKE2gR7r99AvhGQ+qn+6oZzEd0ZNdASqXEmLR43LAZUwYhwb1avDWd9/CUGHYNmlh26QlohzwhdMAWUKMgRIxDkrBnaS+IdWLcxVEaHyhSD3SDbjedslhntEkvtNryYqIfLTuca3gjINRf/yU92pQA/AMKsur7UwIMG/xEhBKwTXDm1IDi5Ysrev8SXBcBtOgAXq5xLF7z8Xtz0dLHJUchrVD9eWpAi/PMctlQfEw2VdGCza2TpQwqyml3mnRdtGaNdUYZVKCou2i6LhorXL5p87BoEoLlZir2A0GJcqwU78pQy9XfaVKVghQhvmmRetKjgvbFY4rmzHkUtSL7rG637PM+w8Y1d6xpECsZKBogW7ve6L6LODXtM9mpyZUdcW/1uOmJzbjpidyeN++PZHvw++iPSFYIvvKi1sm0DdWxJHLZieeU0V8HaBQZ5m+6YBkncm25nhq8vId2Tq13JuXKRdOnIxJ0ZIxsWXC8uZv6h3Tr0evC08yzsuW/hSUda4Yco1g/1TLoAJEZQcdk5aDtmyQqSij9kzQMVSO+wx2HHapnO7RUZFr1NXVBQBYt24dBgYGcPTRR6t9MpkM3vKWt+D+++8HADz66KOwbTuwz/z587HvvvuqfR544AG0t7crgxsA3vCGN6C9vT2wz7777qsMbgA45phjUCqV8Oijj07THb98QMvQycMwqKCXy90pqT4fSKn7likZpoSVZtBwOIxXnSuqI0yleyVA0rAY5yg6TNBvPXquXpfWF1ITH9Rah3MqeCWWPQJ8sS8AqhYv58KbLxkG9Ua6XSaUwvOWKxS75XG470iRi8wA5RDRPGrbZXhhy4RawMbl8OkwvfHNCUW69+5uRWdTGkWHBctwcb/MC4ekrZKahNTCkJEcg/hOJCUO5Y2vlivaOwXBnHkLcMbX/xPUy+Gn1MBnvv5ddPcsqO8CYnDbs4N485X3475122Mps0k1aksOw4bhQmC7Wgiafc2XutPhMH+lLMu6uZzD9lg5JqXgEDme7dkUWjOm6j/McziVixzrkL/Rc6RJJbaZl3Iz1ZxuQoi/Hkg6n7YWKDkMrtd2GRN1wJvTBlyXozldX3yJe84KEtiW1+dHrinx9RH0SLcruecedp81daEqWV973fZCLI24LWMGRqn2bHyKmOwrJ9/4OM65/Xms3jYZux8gqfQ8kru/syHLH8orsl0Oywmq90u6uHxXkl4uEdbrkGWziOdIYZxj7fY8nqighWC7DCXHVaKURgOeU7UMKiA69sWxIOa3ZdCWMdHdkn5FrtleDthlKpNzzvH5z38eb3zjG7HvvvsCAAYGBgAA3d3dgX27u7uxYcMGtU86nUZnZ2dkH/n7gYEBzJ07N3LOuXPnBvYJn6ezsxPpdFrtE0apVEKp5Is2jI2NAQBs24Zt+5L9tm3DcZzAZ69kOLYDwh04tg3bNsFdB47lYM2WMcxvy5RdmHop3RiaLMGxrYihz1xHeBQtG3yKnsT/a++lGli2Be7acJ3avPHMdeBw0rBnuSu8m2LREm2YMFgWAOaAu1R8RoUbyXUIHNuBwQm4S+EyAtcW6rmuM/3XzhwHtmXvkOe0o94J5xy2bYFyinyxhOHJAmzbQqFkwHZFBJa5Liyb1nUthVIJhVIJxQyBZVlwKUHWMGCZXvTQ8Z4rZ+BpAhcMrm3DoS5YCnA1X/WFd6zCX1/yhb0od8u+d9NbkltudL/ZWYIMoSg4FizbRoow2LYFx+EoWRaY48B1bDDHgeNCXAthcB0brvddNXBsB4QRMNcRQkKODU4A5lJwx0a+VIJlWYKh5DpwbIq3vueDePvbjsTzL63CwiVL0dLV3dD2/Y07XwIAnP/n5/HWPboi389tDi5VPnLQfPzysc0o2o7K3wSAgmVXfV3McWDbFmy78cug6ewrlmWJFADLQsmy4Fg2rBJFsWiAOQ6YK4yNQomDMwfcEYZJybJQKlmwbQeWZcG2K4/xlmXBsmxYlgnLttBkpMAcF5blIE0YXCdqWDDHEer/KcB1oov6fEk8kyxFxXeVohS266JYsuDmovM9dx3vnmxMFIpwbRv5ogXLspChBig18KpZWQAuXC8SWEt/Ya4D2xH/lb91GBV9xmGwbRuuy8EcCua6wlFre32bOSi5AHP8vm4CYFMMFOsiWXe+tDXyfYoEQxzNCc27aNvYMuYb2tvGC1jaEe8QYI4Nl1NYLodrmw1v1/X2F8u2YVuW91uKiWIRli36RZow0W4cB4VSCeu35+E4DkAAy6aAK9qAbduwTQ7mpLyx3oFtiPbLHQYXHNvHRZ62ZUXXpBKi3dmwLArHtoAMie0ftWCyKDSlciap2F5nN6UwqjksRyaLmNcc7OPNJtDssbG2TFhwvLmu3LFrnV+mC8xrH7a9S8WKFaptuzXPNh/96Efx8Y9/HG9+85trvqhyOOOMM/DUU0/hvvvui3wXbuRC2KJ8Yw7vE7d/PfvouOSSS/CNb3wj8vm2bdsCxrjjOBgeHgYAmOYu4+eYNuQtF8WxPEZ4ASkrg/GRAkaHx2FQgvUTQMY0VHmxMIgrGu7VD/Vi3dZRnHt4kD5VtIVozNatrGJUqRJ29ntxGY8I1O1sjG4fR2nSxjirXpAIAAolB4QAW7awqhkR5bCz3w0AbB0pwJmcQLFEMV4yUbIZJh0ThfESaEbUYaVFA6XxIphBMMEFra8wLtSmx83pz30rjpewbZuFkWGCea2ZaVUe3VHvhHGOidERWIaBxwvjmLRcWON5TKKAksORNSlKDsOoZWBrpvaFwNC2ceTzFkadNHi+iJacicmCi4mSAYMCtGjCmizCApBnWZQoUBgvgJYMTNrpgMikbnADQGliBOPF5HHJLYl+VSiWMD6yPfo94ygUbWzbxjCZMjA5NgLb4dhuFFEcn8Q4MihNlMA4kHPTsA1hPBcmxkTFCaPyeymNF+FQYBxZgAPFiRJAgIKbQSlvYYTnMVF0kDEoSraLCcdEMW9jbksOe+29D1wOFMdHMM4aX1omRQm2TxQin7cRfz41KdBExHvfNp7HI5v9sWpkdBTjrdVZNcXxEoa328g6yRG+ejFdfYVzjnFPAHbrVobtkxby42MYYWmk7TSK45PIWyZsxuHmKeySi4JJYTOOLVs5to8UUBqfxPbtDtJ2Zb2O4YKNiZFJbHczyI/mkbFSKDkM+bwNmjGRjnFYFL32mbVTcGLm6PGCJ05r52P7gA6TCvNxdGQYHYiWTCpNWBihRWylRQwPjaBQsDG0zcLkWAGmlYIdM7/W0l/siTwKJhV9xdt2CMG4WYTLOIpFG47LMYEMCpYLm3GULAfjJIuS5aLkitSkcV6+3FMt0IW91g9H+4qdHw9s8+J4ZB8AmMwX8fSGQbVdnBzH+Eh83ymWHFiEoOC4GKdFbOW1rQ8qod7+Mpy3MT46geGUBZZPYWjbKApFB0PbGCZMirHhUVguQ/+gi6GxAoqTFgxKMO5mUCpYGOdplMYLKNgm0pYJixKUxvMoWCYm7RQshwmRyaKJgu1iMOskrtm2TZQwOTqGUTeNfMFG3s1MOWF6+6h4dym4FfvK+YfNxRWPbMHjA6JNfOfvq/DjdyxO3L+UtzFZMlDM2xiP6VsStc4v04XieAkj2xlosTpx3x0NXWesHGp+guPj4zj66KOxaNEifOxjH8N//Md/YMGCqdHMzjzzTPzxj3/EP//5z4Aoz7x58wCIKHRPj298bdmyRUWl582bB8uyMDw8HIh2b9myBYcddpjaZ3DQH1wktm7dGjjOv//978D3w8PDsG07EgGXOO+88/D5z39ebY+NjWHRokWYPXt2IAdcekBmz56NVGrXbDCNhMs4RsgYdp/TgmzKwASdRGrCQGvawITlAgZFq1duLIym3AgAwRi4a904Lnr3awLfp2wXKLnonNUVyTuvFTvzvXDO8UTfKHraslWLle0IbCyZaDIstLbUtkhgRf9ZNiLXflfoM33WCFra0yAEyGZNUIehtSmN7byITMZA2iBoyZgwnUmkTQOtHeI9ppw8GOdo7Wie9mscI0WQpixGSw72aO9Ac42lm3p7e7F69WosW7asoiDajnonlsOQHSXImhSptAFSsJFy0mhqy4LYDK1ZA7TkIpcxMWdONCpaCblJA2lqobktgzZSQk97Fn0jRaRMgqaUgZaMiQwrwGEcbZ05UEKQsieQSZtobc2UdZR1dXaVzftrbWUABsGpidaO6LU7jKM0UUJH1yw0pw3kRgmIw9DS0YxcKY3WjizGUITlcrS0C9aQ69jgHGhp74RhVn4vKScPSogag0eJcA61dmQxaZaQa0qjaFpozZrgRQe5bAoZaqG1IwtetFFwGEzG0ToN49ZoycUDvVEjeP6cLgB9AEQucHtrC4CtkX2NbHPsc409F4po62jDnFlNU73sCKarr9iu6BsgwKzZs1FKFZAapci2ppFtzqCplEZzzkTRc06RnIusaSBvu7DTLUAuBbPFRHtHJ+Z0VXHfEyVkSim0duSQttJoa8sgb7kYJSXkmlJojak3PEaKcFyO5rY0sjEaBzZEZZmu9na0dnSUPX3GNIASQ6q5LXY8naRFNHXk0NnVivQIkKY2Mm3NSLkZtLdlYqnQtfSXlJWGSSlaO8SzanKysBlDa0eLcJBNlECZ6DuG5aLgMNhFB60dOZi2C1awwRyeuN6ZDnR1dgJY7293dQJYp7Y/fvAC/PzhPrhGCv0lf74wcs1o7QgyRiXcgg1KAKvooqWjFanmNDqqrGxSDertL2y8hHQphY6uNsxuySAzTpAxHXR0dcGkFOYIAJejtaMN3ErBhIu0QdDUmkYxZaO1NYO0PYl02kBrSxqmQZGy0shkxFhfdFwUHY7W5hTcvA2zWZwnDgUjD3OUItuaQcaw1XpgKuApMTa3NWUrjmv7dgA/WdKDH923ATc+0Y8Nozaa2zsT0wFKKQvZlIGcaaO1Pflaa51fpgujKKKjqxNzKtQr31nIZKq7rpqtlptvvhlDQ0O4/vrr8T//8z/4+te/jqOOOgorV67Ee9/73po6DOccZ555Jm655Rb84x//wNKlQWGWpUuXYt68ebjzzjtx4IEHAhB0p3vuuQeXXnopAOB1r3sdUqkU7rzzTpxwwgkAgP7+fjzzzDP47ne/CwBYvnw5RkdH8dBDD+GQQw4BAPz73//G6OioMsyXL1+Oiy66CP39/crAv+OOO5DJZPC6170u9vozmUzsg06lUpHnYJpm7OevRKQAvH43X5QjnU7DME005TKgpig5QQ0zNiqXSQWbZLiTmzBAXRuGmUIqNXWv2856Ly7j4NREgZFdq00QE2Yq+twrwTSFKrKZSjUser8z+wznHHmXoCmbQcFxQY0UUoTDMFMghg1imEilDBimAVAThFL/mRkGCK/9GdYDajhgxETedQHDrOlZXXPNNfjkJz8JxhgopfjpT39asfTXjngnLlzxTA0DuUwKg5MuqGGCmikQ7sI0UzCZAxgGbC60JqpRd5WgZgrEYKBmCobJYJgp0JQDTgjMlAnDNNGcdTFStJFKCaOCUNN756lENgGBGOvKsQ0y3nOzWXz7IJwDBoNhmjBNE8RIgXIXxDBBTFNdK8BEX/MMfMP7rqo2ZxgAIWpfYjpgXns1TQZqmiAGQyaVRsqlMEwDRop7Yy5B3nVAzR3TviU6m32jJWsayKXjz20xcV9FxwU4kC3TLqjpgJq19Zla0Ki+sm2ihA3DBbxuUQcYcUWbIFDtA4YBYpiwQJBKp5FKmbC4C2JQpIiBlElBGcWGMQucE4CKvlTNdRkmA4wUuGr/abQYHJkiQ1dLTrU/HdR0wMGRMtMwtEh3yWH41/rt2J4XFNhsunJ7Naj4/Zl/eB53nvaGiAFhpBgMIwVimODUBDcYCDVBvWtNvq/q+gs1TMDw+8qy7jZw7pUt4xwwXBDi9R1Ovb4qttMwwEqyP09fX3ndwnb0jhQwOCEYBE3Z4H03hdaoc1q8qL0LDBV8OrLDaeJ1yo8ZAcZtjtHhEl7fkmsoU6+e/mKYLkANMS9TQ7RTb2ziABgxQA2AUxOMGHCJGFupkULKW+cYhglmGDBTaaFjYZhqLnWJAYu7SJlpuIRh45iNOW1NsY5VYprg1PDmKt6Qd170tBeaMlWO7QA+++Y98LunB5C3XQxMuliU4PDJpMX7NKoYy2uaX6YJ1HR2aRuq2uuqi587a9YsfO5zn8Pjjz+Ohx56CMuWLcOKFSswf/58nH322Vi1alVVxzn99NNx/fXX48Ybb0RraysGBgYwMDCAQkHQIwghOOuss3DxxRfjlltuwTPPPIOPfvSjaGpqwkknnQQAaG9vx8qVK/GFL3wBf/vb3/D444/jlFNOwX777afUzPfee2+84x3vwKmnnooHH3wQDz74IE499VQce+yx2GuvvQAARx99NPbZZx+sWLECjz/+OP72t7/hi1/8Ik499dT/c8rljYZU3M2lKFIGUbVi45A2goN40Q7SnYT4Q331BUuOGznezoIsg7Rrkcs98agK+6wdmsQz/WOBzwiEdOuuVMNzKig5TFCZU1QopHJfGCVcvkkp2MLf3lGaoIQQOEwIt9RSt7q3t1cZ3ICovXzaaaeht7d3ui61aqi63JyDQqSrAJrgDXwl4GcGxvHcQHW0LgmHCcEl/R0SEK92qfiguzWDJaFIoMPjU5Ak0gatSO9XwlAJQlZC2MwXvRKiZ1HhqqnUHg6r6zOt30o1aJdBMVb07ykhdavGJyGu3fZo0QyDBGs6m5QkKgOXHFF/990/ewgfvuGxskJBBFK5fdceszaPlZQiMwu3C869kkjBcnoAtBJfRJRJcrmnhly9+KKsFKK3N4MS7D6ruWwd4jihv//613qcc/vz2DopjMNq6hiPFEQEdLTo4P71w5HvpeCh7Yq8dal43sg3yrR712vey0oVqu8AATVow6sR3sjmFTe/tmeDAYxw3whvSwG7obyF6x/tU5+XEyFUfcVrg5OWs0uIcDHOVVnPoiPKK8p3UrBdMCau2/bKeO05u8UTuwuqyruuL4wKCDFUWWtdiqxZDsP2vKXEzcJwVB/lDWt/+RrqdEuYlKja7C9unSi7n+XUr+w/g/owpaTY/v5+3HHHHbjjjjtgGAbe9a534dlnn8U+++yDyy67rOLv//u//xujo6M44ogj0NPTo/79+te/Vvucc845OOuss/CZz3wGr3/969HX14c77rgDra1+DenLLrsMxx9/PE444QQcfvjhaGpqwm233QbD8BvqDTfcgP322w9HH300jj76aOy///647rrr1PeGYeBPf/oTstksDj/8cJxwwgk4/vjj8b3vfW8qj2gGEAMWhaCLpk2qauLGwaTBJjkaUrKlpHrFZs45ekcKSqly9dZJPLRxBFaNZWWmA1INdjpzcOuBXKglwXYZTrjuMXz0109irOjn04bLpTQSY0U7IJa0I2C7HA5jSFEq6niyUB1PBN+dbogs6shhYfuOoRNKhVyX8WSF3xisWrUqUnvZdV2sXr260ZdYM5QxwTkcLhRzO3Ipr20Fa+IKxeLq71sZLsxfKEvopWcIIRFqbCXjLGVU7suqTneCbLZ0SMrxgXOhpKsbPbOahPJsvVEmuZBU0JxGYnHNVZ1aue+2gc145P57sW1ws1rYNgrbC9G8/MOX+lRKjqDhQIj/HMOwXIbnBscxWnSwaaSoHDaxIMBw3sKjvSOqNM+uCIeJutqy3SoVZnCMFW1kTKrU/imBUA/nosSXrDUsf5e33JpKGjJPHbpWx0Tc/rc80x/YDjvY46DXHn5wQ9TolpVRHObXRm6cyQPs1tmEuWVSrRh0NXNxbmkY+0Z3465nMqY9t2dTgXEsE+ob4XrR8vtnBsYDT6pUxmkrneqMc0xYDlxWX+Cj0ZBzH+Mczw2Oi+cN0Re2TVhekAdK2d/wnFJ6tRFCAIcHtWjU/KA5rCYsF3nLjX0HgCgDKcfvRgUfCjXU6dbR7bXZkULyusk0iKopPoMdh5r5ubZt449//COuvfZa3HHHHdh///1x9tln4+STT1aG8E033YRPf/rTOPvss8seq5qGSQjBhRdeiAsvvDBxn2w2ix//+Mf48Y9/nLhPV1cXrr/++rLnWrx4MW6//faK1zSD2kCJiG5TbyFbtF3v3cfkW4UGgLGiE6g7KQc1Wc5pYLyI9mwKLTG5rFsmLKwdyiNrUsxuyaDoMExYDkrO1EXYpgrpod2VTG5Vn7KMI+DZQT+qOFoM1oGsZTFXC17cMgFKCF63qKPhx06CXMQZlKhJXVIdpTFkaAaaqxlR9ZanqRWD/X14+tkXsHSPZSCtswNlUCphzz33BKU0YHgbhoFly5ZNx6VWxHMD42CcY9+eNthMeN8ZhHBiV1MazWnDjxp5hgSHV9eaUlXjuRJkG1V1wEMlwpJACIHLyz/faiJ3cgFc7l1Jp6Re5kYfF3MpA3vNbal4riTsOSeYG6svEmVdWkC0r+eefxFrnn8KV/3nt8C9NIRPfOVSvPuDp9R9/jBGYxaGc0NllXRDgqB8pHvddl/kqdxzluUnh/M2LIfVvLDdUbC9ur/SAJbRbtvlGC7YaE6bakyiXkk56bihhIJScZ/EiwQTQqteaDPm1+muFh3ZFMaLyYJTEtX0Fx1x9dsJIWDe8xFGt3BANGpebUobZUub6XOejAbrcDyH4VSwebSI0373FD504HwcFVNLW0S6/e3wOBiez5P6TrlghGTAMMYxXnTQ5LW5nQ3GARfiHRRsYRQblKBgu9iWt9CSNjFStCPjgM5yasuaGPLYF/5xJbMHiunWkjEi5cZ02K5gmnDeOIdEvk6j2zQqzzMmpYIhMmN071DUvDrs6ekBYwwf/vCH8dBDD+GAAw6I7HPMMcego4JAxgz+74AQKAMlY5LAwi4MKzRIrB/OBxaJsm7iqq2TaM4YyFsutpoWDljQHjnWRMnBWNH2hb1qiJJPN1wGODEUvOmC9NwmiZwxxrWFWvC75wfHsWG4gHe8ei4e7/Vp5fqAriLd0/BoXQbQHbwelgaeMLpZgC7JGIfL/MXMsllNO7Q2NwDcetN1uPjcs8AYA6EUK8//Dg4+6zNV/37hwoX46U9/itNOOw2u68IwDFx11VUVxdQahbzlwHK5WpBOWo5asI4VHUxaQgDK9upF64aoqicNEenOpTge2TSKvbtb0F5hgavqFcOnqgN+VIokLNczJkXJCUY4wuNINZE7aWiUq5Ms6ZGyHHOjeTnhvFj5PNQ2gLt+fyP++5vnRNgQjDH87Nvn4ogjjwLalzTkesZK0Ui3bqjEOQJ1w2FWUwpvXTYbv3uqHyWHYfWQb3SXpcwSv2b7rjAnSEyWHPSOFvGqOc0qfUSmGPhUf4JJy0XRZmhJC6E0eQ+GN89xEFAqadAcjBMs6shhcLxUvdFdx3zZkjGxd3drxf2qYYbomIgxugFBBZYOM2kk7ai3yTUqMSHBbQDYa27LlB0AP/jnWvSPl/DDf67DITGO5/Zcss5EHBIdVhWctrLNTFoOciljhz3jcmCci5JtTMzLsk2NFG1MWi56WjMYLdmBdYl0MsonNrclg9khQUDZnojXdzgHTELgIDldwPKMbsanPmZvz1s46w/P4rlBQQ/P1NhX0lXMMyYVke5G1BOfQfWo2ei+7LLL8MEPfhDZbLLaXWdnJ9atWzelC5vBKwcEIg9qdnMas5vT2DoxBJYwZIcXSef9+QUcuWx2JI9qtGgrCmprgmKzjCarnCyPgjddC6znB8eRNij2mB2vWt07UkBHTkTlxbUhcZHfaDw9MAaXcRy0sCP2+8f6RtCWTXlGTfCaVvzqCQBAT1sWfWN+GSxLG9DFe0l6q1ODyxlS2DFWd/9YEX2jBSxoz6noH/cWdfrkpNPwTYPWPpBOAYP9fcrgBgDOGH5+8Xn48PuOw+6zXl31cVauXIljjjmmavXyRuL5wQm4nOOQxZ0A4DkxxHdbJ0tIUaIie4bMS+VMLZYIATgTiyeXcQwVLOwxu7IaszJkvVw/+Q7ntKSRNpIjqIs6cpHc4zB1uZrIXbqKCIR/nVwt+KYT4nn40brBzZtx5TfPAU+gwDPmYsNzT2BsYBMWLd0d3T21Vy+ZKDn40/NbcNSeszFeiqfM6tcXhv6emjOm2i45DIPjfnmxspRZLz9dzhO7CtZuz2OkYGP3WU1IGQSOyxU9XPYJkwuNEsdlSBspTFguGER7pp4DCZSDevOLCwBM5nij6pQMkeoxPfeZlCKQhDijmxIE3h8hJDaffLog+ymg53QH58Va4TKO//rXehy0sB1vXNqFLRN+ex6PeQbtWbOmVUQSy6+ig4pzNKUNzG3JYKLkBnLddxYcz2EmaeUd2RRGvTJunAumWripy+CC7qjQ39OclgyG88IRKKnocn9ehvVhuzK9YeprzKsf3KgMbiDKXqiEVIU0Jonulswuy/B5paJmju2KFSvKGtwzmEEYUsyFEgJKiTKc4xCOdAPA0yHRLpGzIwY/y2GJHnM5EMtTyVzF6ZorxoqOEn6JQ/9YEau3iRI3Ik+OTdviwGU8QBezHFaWPmu7gjbGfMZtBBuG8wEaln58+ZNGC6lxLvKpdhQ2jxZRsFngnJQQWC4PTMwi/3fneIg3rVsbE4F0sWZN7fnYCxcuxBFHHLFDDW5A9F/9GTuMqQhv3nKRMamiM1JKtAWtJ3Aj6Y5cLCycKo1T2f/F8fx3SAlBZ1Oy2rFJSUQJOyyoU03kzl8Mlb/YScvBcN4WY9U0W926YW9Qgk3r1yQa3AAAQnD+6SvxqRPfg+OW749bb7oued8EfPPOl/Cf/1iD8/78fKwx1Z4t78bSje6sSZUBt2G4gH+s8WunW07ysyPwckKn0bCsBxMlB0WbKREzl3PF6rAcpgy7kiOcnJT60Tg5z8qIuGQ2ccbhwmMMEFLWyaA7hOQ1AMnzQr2olV4+EeOcke8wEuneQe+TcxFlBfzUt6me+w/PDuCXj/birD88C0CsKyTGi9G+0poxa3o3cWXcgAr0cgjhStW+Gpo5Xz9sl6ucfpktFL4uEvqvnEeSsh9mN6cVu1JqCHFNSyTu/XLP8De9vjjVhxNOpajVQZXyLrbS2qkjl0p0NM9gejDztGcw7SCEwKAApVLJvIzRHTPwP7xpJLAt86gY5yi5LHHytl2mVF4Bn/48XQrblaIluj+BsenN6X5ucBxP9Y8Gzp1027qBLgWUJPQF2MB4Cfeu2662Aw4Sb7Hz3GBtStKVwLig4e8o2K6gm+oRMkoBy3UDk/RU1KOnikVLdwcNCQ5SamDOgt12qYhdOdguDyzbZN6oUGHnSBkUzKPyURAlaCPfCgFReawlhwmjoop2IqmFhkcDnArCgjrVGBGV1MslxooOBidKilod7peNhP7cDELQs3gpCC1zL5yDc1/1/uLzzsZgf1/y/jH4+2phGD/WN5YQvSuTJkCCOd5Z01ALx3vWDgV2LRfpliKEki2xq0Dmb8sIm3Q+rRmaRO9oQTmSJdNIRq6l0QFIZ4JvKLhcGN7+/gnndhme2jyGrV50VabWAFM3JsOsrlrp5XHtRBq6luOKtQWwQ81BpsmHytS3ep7T6m2TKqL9/GBQcVoXk41jhWTMylUTdOhpMFmTYsXrBFMlLuAhIVMxCHzHxq6QkuEwrphoevUJwG9v4ci20q6pYvXlC1v6zl5dv0WOG4IRIueVxqcwVpO6pEPOReXe6Qx2DmaM7hlMOwgAg1DlJRWUHTEoDYwV8bxmqM3Scms+8joRfZsILW65R81mjMN2WOLQ6TCu6ECOy8BcX9lyOuCWGWxFxMKPbLscqtTFdMByWMDILycCZbsMpUBk1/9O97L/9MGNwXPoOd0QVM2C3dhB3mEMFRhSDUXJEWVGirar2tWC9izaM37eXDZlBCbeHY3ungU4/zs/BPWqM1Bq4IwL/xMdc3vweN/ITruuWmB7eaqAF6nyogm2KxgZaUPmocLLSw1GZHUleaXsXMVCR9J0Za7+VMzYsNEdVgmOQ4pWvxiyvRI0Mp+9gSVxA0gbFJ1eDrVBCTrn9uDUr1waaF/v+8SZiQt75rrYtH5d2XMwzvHE5tFYlfCxmOidXiIsDoFId4qWEVZLViUX9HJfAXlXgW5o+8JpwnFasIWDiXMuxiiVehF0mPplDX1DQw7xNCT8qMPxosaybZfc6Zujao3exRvd4r6HC44SbG2kkFolMM2ZTQDP/q7tiW0eLeLE6x/Du372EABgez7IltPve83QZOT3aYPWdL8ZLdLdlPYdVsWykW6/ZKJkCe0CNjcsxlSUmXtzhT836M4QRJzm1fopZBujRDiA5WMqOS4e2TSCwfGSl+ohnMV6oKdehJ9t7fTy6py7M9jx2JGpiDP4PwqZZybLl0hPKQAMF+zAgHLqGxZj07ZRvDY3ie0F4fEN5z/KBQaDL3gVBxnp7h8ritJhmhHeCGzYngfjwNJZTSpaknRkGcWT3lXGuVLdnC7oC0mRkxy/nyyPJfMF9cV1nGKshJ7TTSCiSrIERb3ljMLYkfXMXbXI5Sg6TC1Ys6aBbKu/UFnckdupkW4AOP7EFVj+liPx2LMvoKNnMV69dDcRpS9Dp91VYLvMW6B4FDgmHESM+22xJe3lqXKhwCwX0+F60gyALVka3jAha8jv29MWObfM6Tbk4nwKDasYMiBlTuRgfx82rVsbm++s7rkKo9tyGBh8Cm2jI92B6+wS1ykE5RiOeO+JeO+734lVq1Zj9sIleOmlF3HLz+Krg1DDwKIlS8ue6+an+nHp3Wtw+JJO/Oj4fdXnJiWx9PJy1SUIgka3SQmsybHYfSv1B5cxj1Gxa/QbVaMdfi1uPXKWt1wlKOgwDpPI+QQBYUAGHohmM3BQbVtvflvGS+jIpZA2qR8V9L4TFHavzzX4XlNVzBG7dzVhradGP6nGAz2aKcaAkYLtl4PagVZ3V1NKXU+99PLH+kYD28MFK2FP4PrHooySjEkjDrnTD1uC/7p/PU48YH7s/upvw0/NkGy3F7dMoOgwvHa+Nn4SL9XHEOkJfnx/50KWL3SZoJlTEJV+oYxvHox0y/z0asdTma5AvUi340002/M2tk2WML8tiyGHYdJyMb8tg2J+6uUHwyvJ2iPdntG9E4MDM4jHjNE9g2mHpJdLajkhQMFx0belACAYIbr71pvwwLln4V+MgbzhQ8BhJ0e8dcJrT8CYrL8YHf6l91MuRiYtV4sINOa++kaLMCjB0llNyjhMmnAZDxrBUnVzutZ6+kJNUtm5EX8ymzHYLvPzALXxfbSYnKMeTgUQ3l4DBduNLeFWD1zmTZDTFeYLnEu0F5+qFr+fQUnDnApTQXfPAhzeORd9o0WkTYrhgo2m9K5PXpLRNNOzAixHOn2IZpBT1YYJ8QWT/KgSUQrkUrxG0qQLtlvW+SXp5S7HlJRbw1Hbos0CqvKUUpz/nR/i+BNXqH0k7U9GWOPakbwky4sySspsI9tc0nUalAjqPxftq3XWPKECXLQiJeYk3vm+EyLOhZLDcMW/1uEtu8/C6xd14H8e3gQA+Nf64cB+rRkTv3pic+SY5fIMCQka5Vv6+/CTn14DHH1mZN9qFJllRHlXgKSpSifuhuGCcjAzzpVKOYfniPHaBPOMDmlYuC4H16xsxji413x0ITXOOTYM5zFcSGGvuS1+2pO3ryjv6TmHp9j8wl2tmvZ85Qf2w12rtuJ7/1gLDmBgrIT57b6mECUi13ii5GBRRw5jnsBqbbHf+hGu4V1PYFFnenDOA5HuahgYcfTyjx68EG/ZowtLuqLiknpqRsqgqi9ZLsOk5eDkGx8HAPzj08v9edxrl5JePh0U6nogAzKCEefP2Uyjj0tqvF/EhgTaeCVIx5eQQ/D1EKQuCaXwSuTJ7xtwY6FHWysrxPQYVTtSD2cG1WHXX6HN4BUBwzO2RbkfMdFkTUPU7/ZGw4gqsyMmn7GJYI6T8CZL1VkWMFy3TpRguwyP940qBUvAK0nEeWSyqGXiGBwv4aWt4lpKjhsQUnIYLzvhKgEnb6CfKDqqHvF0QEb01LWxZGeD44pr969Ri3THUD8ldIps2qRoy6TgMIZnB+rP6+acY2jSUu9NRuB3xBJKqd1L426ahdIGtPZUL9qyKezd3erVJmWJ73hXguPV1ZX9eLRoY7LkAp7DgwNKdVbmdEvxRXl7vmASx5KuJqQo9Z1MHIkpCbKNG8RX7K4XYSG1yZIdGL/i8p31PNZyUQjm0YdLjnAW2m7j+kB4nNWv05RGt7evNNBmdc/H+d/5YWyu9//e8ptITvcvHtmEXz2+GZ+6+WkAwHZNYLKoUb6HE4QnMxUWmfr3q59/Ftwpxe5XThwKkEYNqbjfjoJ03DIOTFgOJkqO157F5wXbVc4mURRMwK/T7R9Lb1+6eKk0VAHBLLFdrRyiMq7EPiLFwROemmILDDPWqslDnt2cxokHLECzVyv7h/eujewjHemitODUGSxTQT16MXrJvJLLAmkrSf1j77kt6u84ejkhBLvPao5Ec2nIYQUCZA2fXv6vdb5TLOAMgGCFSFFL8B0nVpcEXYtBtgEZ1GHMp5MbnqEcFUKt7jyulwpleExNOW0w5tUIZ6JfmpQqh8SU7y20bdZZMmwmp3vXw4zRPYNpB/eoPDLSLRaRDE1pAyb15SwiqsyumHBGx4OGiTSO5IJADnIu41g7lMfG4YIXPfMXj463kJT0OUDUQnxxy0TF8j0SvSMFpU4uFisscG43ZEQ/OzCOvtFC4JrFbxn6xooqqiCxbaLUsDIcelTQ9ijfSYQwlwdL0iTldEvs3yNqsIYH9O6WNMZKYqFYDX02DuMlBy9sGcfAWAmTloP12/ORCfLFLRPYNhG/yJ4KpMEtn8V0r9uOveYhnHTD44GSMPUiaxpK4XtXh6CT+86NwfGi6ks6bZ8hyLyQ24AMuumpJf7fMk0gDrL/y3JRU1mch/ULCrYbVZUP5TvrEYvV2/KIA4WIloyXHCxuz8GgQj2/UZHuWPV77Totl/ljAPGf+fEnrsBFP/5Z5HhxOd3PhBxvOltptJDsyJMoTy8nQYONc8DRKLkDq/CadvFnuTJInHM4HlNi81gRq6foAGsEdAE1x+UoOq4qk8fBkTEoulsyvhiUjGyHtmc3pwOOiWWzm/GqOc2B8wg2GIPNmGKbyXlKUnBdrglPTaH5OS4LpCPVihNeK2jSL20N5jRTRXX2xgVSXXR4ulDPqXWhtJLDAgyauLlh/562QJmnjElx0ALR4CuNEQRBqjKF39fuXj2E8//3BfWdPr/nUgZmN2cwqymtFPB3dqRbMtOkg1bOFRRE6Od4+4la91zblqJw1TVoyUoyvRRJSS93PbYi40LMU65lZZ+ZCsL59TWrl8/kdO+ymDG6ZzDtkJErCeGRFgO5oRndEVVmz+hO54IUKc4FlU6W3ZGTbNF2lbiMyxmKWj6aoyK5/uJ8OG+j5LCaJmmdWhSNJgcNtYmSg4ExqQLrUfq4MNYthyNjUnUtkyUHa7fnMdggY1IuqgAhRFdw3MC1rdo6gU3DBXVPgv7u5y5JhI1uSsSCDoga3YQQLGzLwXZ52cVuOXAu8vNGChY2j5YUg0Cfw4bzNjaNFJMPUidUzV6ULynSCOg1nnsbdC+zmlK7SCGXKAq2qxSRHc8o5l40b9ukjfasqTQaJDgLMi+45kRDJMebKOdZubrLMmIoIxJTecWRnG6GqKq8YaB57kIVSdVTaT560xOx1ymjyx25FLIp4UyxdUN4iohVv9fysue3ZbCkU4y51BNIlHjt6w8p+1uJcoZ1XMrKkctmBY8ZY+GdeuhiAMA5b90j9E3I6HZKaGsR11+OXi6idxxzW9IYmrSwdSI5l3ZHQTr9OBfXXrSZ0tngHGjJmGjPppQDWT4lJTrofTCnJYPdOv15U4qYir+hBEVtVxj3Ms1CZ2RJZ61M9ZhKicQwK6RWnHyQSF/oHS0GNAAICYp6UexcY7AeY0svwzlZcgMG15bx6HqgM5dCVs/LNinOeOMSfGr5bvj1KQeVPZdkA0gQkpzKETbYZjen1TpuVxBSkyKDei1u4kWjhVCa396dAL08uF0JHMIZLO9dDilSA4dxYSSb3rNthNMnbzWmZNhMTveuhxmjewY7BAbxB3uRE0thermxcuIMqzITT3GbpoJ14blHt/PLXInPC7aLgs2QohQOE6Wf5JgjRc4kxRwQeeWy1mm10OlMXDNs5QKFapOSbpRKI1gaFi5nMClVXlPHi9w3Ci7zafgjecsz8vxZZrhgKy+6H3WMPonwYkkaAkC8SFHapLAZq5vWJAwmqNJQtitytaS5ZTkMJdetudxMNZDOB/EcprcOd/+4b2g36l7kwny6SuJNBc8OjGGVF6WSDioOYMuEaJvNaVOJQ0nI9qvqo8JnqSh6ubcvJT59VTpP4p6DyPXzc/OqjXbEIU6p/4zzvq6MUmoYOP+Sy0BaZ6HXY7yE29QjvSORY/jXJrel0d2YdhJRv/euU+Zlt2VTKvollX+3DW7GI/ffCwBlfyuhG9b6e8iYNJY905GLlgiTxsC8VpE3e9ry3fDPzxyGN+zWGdjvVfvsB8L8Y+6x++5oaxFR3XK0ccmSMihBeza1S+R1Mw5VGq/kMDE/eca0bmSLfYNiUTLnthIoEQdbvW0Sq7ZNhAwKrvqFr70wdUXwvDU1o7sjl0J3i3D2rtvuM0Qku8c3uoIsrx2NetrQiEYh3zIZNLLj9A46cmYgiJE2KJrTJj5x6OLYHG4A+NY79kJz2sBl731N4HNCSGIqRzmHlSxjuDMh9Q4M71rUXEAlnVxsi+izNp+T6vsK4NHIvT4iDHhvnvH6ihSP1dkiU0U9lTEC+xszOd27KmaE1GYw7eAcnsiE2BYK5sLg9j3sIs9SqjJvWr8OqzEb3/v31ogxuufsZnD45TPkRFdyGQqOq+jSItrKvWvgnodSDMh5y8FkyVFCOtXA1QZ26eWUsL0FiqyFKo3+lrSp9pcMb7mYSRnCSHp68ygsVzgRpiLspINzn747XLQDZdoAbxIxfGqirtytI7xYakmbZfOFTG/Cq5dKKOvmygVlyaMdy1dkuQwlhwdqIssJdqpGsmwLUtm60bBdhtGig9nNaWwe9Y1uyQq4/blBWC7D+/frqev4+oJzZ+U0xoFzHlhEOC5XDo7B8aJQ36UAc4J5n8IA8d8r0xfX3nFlj6Seocq9vs/gUQJDDo2i44usTXU9UogpR3XFJd8AYwyEEJzx5a/hPR86BWuG8oltc+1QHocuDhqRon400yI1pK7+VE5FXR9nFy1ZGvleggC48+Yb8ZNvnQOuia7ddv+TZX+rU2b1d59L0cB3EnF1ua854bW46sENOOPwJeqzprQR2W/+wkU47Sc/xxfuEgbKbosXVZXTaHpl4+RiemcbEYA//nHPqVxyRB6tQYJGNhF885g81codXzpStuctlSYlwbic5zgmSi5sl2mR7vrva6qRbkBE+QcnrEAkmBBPUDFAL99xJcPCqC+n2+8Pj2waDXz3aO9oeHd05FKBXO9yooMS73z1XByz15yI444gOZWjvMNq53Oqtk5YsFymhDQlpGCur1ZO4DgMTabY7simMF50qhZn5VzWvCfq2AA8wUmxznMYV++hEZHusNFda6R7Jqd718VMpHsG0452zzPb6ilhpgwKwzO8sylDefElunsW4PXL34jZswTlMGx0mwZFyqDYrbMJHblUQBTGcryyVVwMfrmUoaZklzEvIsbxzMC4EgyqdohkzF+YSQogVwOwGHjllVoOg+34i36Zp8e5X+aCerlGE5YLjvoN1TCkAc0hFUldZFM04CRwteixzL8W1xg8VnixlDaJyglLGtAJKgsYJUEaWjLSZ7u+Cr3tikWo5QQj3Y/3jaB/bOq0fD1dQEaXGomP//pJvOPqf2PTSAGbtestOgzjRQcX3vESLv7b6tgSStWAggSiwbsKXObXUQW0SLfnTJG5ctxLvQhTZqFt63RyDl8wjRKgt68Xf/vb3zE4sFlEJ2Kew2jBFqwbIMCgqAeRmtPFCV8EknNccek3MbC5T+lZxCG+9jDBYP9mPPXQfRjs74NBomNgJdx603U4bvn++NSJ78Fxy/fHrTddF9lHjrNJBjcAbBnYrAxuwBddA1D2t/p96ZE8zoGxGHp5XKT71XNbcNl7XoM9ZjVHvtNBCNDT3a22sym/9rB0aI0U7EjKS1dTCgvbc0rQczocbdXA8dg8gM/0kbAZ8/UxNCNb6k4EjlPlDRCPgl2wXYyXbBVN98/PVZqPFD8VytBToJdPMdIN+Mr/el+Q/VgpVe9kenl3SwaLOnI1/WZcc0L95IENFfdvz6YC76LaKGjc+6OEJBrtlQy2Rj1nORdUA8Y4Ht00gvGiCJiMFR2YBg38nsBfP6hzaE6jloyJvbtba2rPkmovWZmMcRXxthwWKEGWMali59SL6Lpr5+Z070z23EtbJ/CPNUM77fyNxozRPYNpR8Y0cNDCDjVppg0KQglMStCUop7XPtqpU8pbF9/hcykjQHMShi8TgyDj2K0zB5NSpfQqKUJ528VESaiH8xpqtLoan1UZh+rcXAlEATLy7RsQSiAHvliUyK3jHrXco8M3YHBT+eZcDN4lhyFrGoFHrEcNLZcjbRAVgdQRHvwzhqG8qEmGAEf9uUS+kJCsRcvAOUfJcfHk5lFsHivC0hZZliPy4yes+gxVHbqRJsWLGonntwihprtXD2Gblse3ZetWbBgpqO2wiEq1ICHK9a4Cxn2HFWMck5ajWB5+KRZx7SXHX7wwBFkoYSOaa///15tvxPGHvRZvf/tROPmIg/Dn314facuccwwXbGS9MUenJNaDokcvP2avOXhtB4DffiV4366LTevXolz2wEQpaoz89eYbcNJbD8J5H/t/OG75/rjnDzdhXmum6jJ85dTJa0Xv+rXK4JaIE04rBz0qV7QZrnpwY2Sf9mz9pDuCYLQva9KA0d03WsS7f/YQPn3zU8HfEaKeqb6YrgePbhrBxuF4YbxKeHLzGB7rHQXnHKu3TSrhUUCWvvTTJiS452CT9lYuZcCtcsyVqRmWw1XlCtkTGPcV1CdtB2mDijJyU/RITDYg0i3zVPX1gGCFhLZ34gDY2ZSuqp/e+dJW/H31NgCIZX6UQ0vGCLAOpsLwIkiuFFDJ6K53qeKXV+VepZkRrN42WfmHEMw3h3Fsm7TgMobZzWmkaNBh5ud0+59NlWYt+yABVPk+uY6T+iSyLy7takJnU3pK55sqvVz2FafOdZiOou1i5W0b8LU7Vk35WPXgpBsexxdvey6QVvJyxozRPYMdjrRBYECUQRB0wfgog++tSx44ZAmHgu1ie95WZVWk51HQqr2cN4867LjCy285Uv28uuuWi3RJN+Le4qRou9g2aSkjwmUc40XHE+vQI91igFYDOCFwHD8vyHIaU/JJrxleckQN7rRBVKTQL50m9ncYQ8Y0RHmukBkSjlBkTKK8rkliaQYhdUc2xLvicF3hEJFrNWmQCQaB75wQeY9uzfSrOEyUHO/9evmTDeRo61GoloyBJ555Tm1f8rXz8Pv/vVNt1ytCJw3XXS3SLQUMhROFYyhvoSlliLbmRe9k3W09F09nlgAikq+r9kq2yWB/Hy7/+hcDRublX/8SNm7sDVyHdG6lqKyVPbXnJB1Se3e34OJjdgcdWh/4nhoGehYvVboVcQhHugf7+/Cjr30xGFk+//MojWyp+roqqZPXgsVLdwchlYXTAOCeNUNYtz0fqVzQp6dSuCzgcJKIi3RXi1lNQaXuXMp3DP599Ta899qHUXIZnupPLmXosbXrdsI4jNctxGa7omxe3nIxVnJQ0kp4NaVM7Dm7Rc1jlIg28vRD92HLwGblfFzUkcPSrvKMAAlJyXY5R3drxpsbgOG8heGCrcpH2o6YL1OUwNIYKPWg0MBId6B9EQIXWjqNF5XflTFSsHHen1/AObc/j7zlVnSy7j23Bft0B0uENUrfASQYRV3alfOrk1S4rnrnmcf7RvDU5jGs257Hk5tHUbRZ1ewu6YSS65eMQT2mpBbpjhEzm8pYv6gjh26vHrssvTeUtxXTSRy6cU56h0WFaGtXLy8fsKoFd6/djo1jFv760o6PNuttbKABbMZdATNG9wx2OCilMA2KxR05vGpOS4ReLuFHVJMHDgJhND61eUwNVCKy5gtfAH55GBnhKrkcBYsFhKfGinZZA5/JfDseLO0ylLcwOF5SauSrt01iYLzo5W6L4xVtF47L4Dg8QCdzPI/pSMH2aNRTHyTlcTh8KvvQln489uB92LipVxn/gFBNn7SEUW4zHqlvHIl0m0bF92IaJDbftRrI5+p4Bpec+Dn8+r5y0gVEvXTL5XWLkZUcFxu251FyXGwcLqAtY4p0BD61HMYwtuf9Bbk1MYrHn3pGbXMjhT/e+Y/ANdUDSa3cxWzuQO3hSctB0WbIpY2Ag4DKSCMPihHqt7LnnOaAUJBsfrFGJnOxes3qwGcyBcQ0iDD6tTy8eiAXXTnTSBQnmzNvvnIqxCHM0GiEwVxJnbwWPPjPuyML2jjhtMd6R/GF257DB3/5aCBHFQC+8pcXK56nyVNprwUXvXMvvGFxB05bvlvgPaYNP9LdO1pdZQCCqYkQhiNrtYBx4Qwq2C4cl6M5Ja6dg4NSBETO/vzbG3Dc8v1x4aknCEbH764HIPapti3rfU1VBoAox1W0XSVyaLsi3z1l0ICjsx5UE+meKDl4YctE4jtIqbQmrT0CcAPbjaOXD/b34ZH7762LIVIOAZZTQrUSvbxbV1MKWdN3NmZN2rCFe5he3p5NKcdmJYOt3sdsOUJIdrTgwHGFIKVZpVHpMrHuEJo6vhq7fi2ieo0/D6YMX7uhHrRkTOUUlHoIvSMFTFoylcgL3DRowRDnoErXuL6pJmBVLYYm42vF7wjoTulaKfa7Kl4ZdzGDlxUIvLxuzygWC57oCC7zoctRlUWpIGG0TnoLWCnSRMQOyhCRkW4OMVEXXTeQN/rcwASe2jwWex7m5UkzCGXegbGSMmwtR0wCXU1pcM6Rt1wlriEXACMFYdDbjGPTSBHtWVOI43hR6YmS07ByEzp9y2Ecd/3+Rpx0xEH44kfej92XLsHPfvYzZfD0jRUxWhTRDceN5laFI9Zpg1YU6UhRUndkQ9WP5UxQ9rk/sTkeBV++S0BEzuqNDANCyKpvtIiCLbQAcinDW7g1VoxHX2gNbtkKZP3IBcw00OyXTKr3flSEeAda3b29vbj77rvR29ubuI9Mq2BcCKoVHRc501BGtRRWdD1HS1AcqowTzLvPWCOTGliy++4AgM2jRfx7wzDylgvHq0mcTRnYu7s1sJitFZJeLhepx5+4Arfd/yR+8uvbcNv9T+L4E1cIdV2avB4LR3ji7oVQilyu+jzRSurk1ULS1MMr2uVvOTKy770vbFJ/x6mTV0LGpBU1FMJG0DF7zcUV798PHblUYEFGSXJkKCnv2RP0rotpJMfbeqOPHMJIKHqsJFmSUYxB3jG9CPcPvvqFAKPj+1/5Ys1GocxfF8rP3jVwv5Sfy5n6mxKoOvGDm+s3QqthPg3lLSXEGQfl7NXGBFX6T3v01UQ1Oef42+ohDIzFO2Wq0USoF7oh8UTMemPZ7KZAukVXUzpSIqxRLKwwvTyXMlRfqkgvr+N8nHOR1uf92GYMRccN3F85OIx5Rren0UOjivUyp1uiPZvCbp1NAZZUvZDaI0KLR7xHJV465aMLTMakysU5DIbzVqDcnA6lf9CA9eSIpsFRrW5EozCc98+9qzH46sWM0T2DHQ5CgBT1y4jJBU8Y6SooMpIW6HJfIVkYw9xbzPud1eVC1pRzERnda06LoosDQMkVud5xDgAp5gUObPfqe0sqsoxMyoi9QQmKDoPLGBwmDO4tE5YSdXO5MO4oJR7dnGP3rmakjfrFfKTx89Ka9Vi9bVJ5ent7N+HKb54TWKid/plPY2BzHzgXuULDeRsdOTN2ko1GuinSpp9PHQeTUpHTXoeXVRloHhV4VlMac1syXvQZGPdyYFXpNibYAvWOxxMlB3mvvrssjwOIKOpUykmFoRvdTR2zgFyb/6WZBpo71KY0uh9YP4xn+uOdQLEILT6mG9dccw122203HHnkkdhtt91wzTXXxO4nHSmci/bEvQiF7D8ERIu++c6D3Wc1q3rRcZBtoLtnAT7xxa+CyFJd1MCnv3YpeuYvBABsHC5gOG8j70USa82PS8KAF6XqavKp0WFxMs4Bg9Ay9PJg/+ruWYAvfPv7oNRfIHLG8LHjj65p4R/nAKgVcVF3zlgk6n7rTdfhumt/prZvu/32isd+x15zAoZFJUMibAT98ieXBwzAjOY8KScOlRT5kRGserL8XY/RVLfR7fWPosNU+S6ZKqIfsXd9PKOj1rQBOScyrlcGEI4D2/VEolzm9UWhvXLHzTfiY0cfXLcRKueRQxd34MAFbbjonXvFXBdBNkUTDQUzRhyKeGKk8jkZlIAxHqtyr+Ova8dw/l9W4eQbH49810hNhDhs1wyJb98VzZOd1ZQOOAO7mlLIpDQmRxUOqmqxd3dLwGGVMakywis5f+sxgoRavpba5gUnqr0d2ddkmVMliqn7Bb0KNfrVNcLgBvwULstlKFiuEvTkaBwzLo4VEjc2Dk6UsDXJ6JY53Q2IdG/P+06AkuOCcY5fPtKLJ/pGp3zsyucO6oG8EjBjdM9gpyBtUlBKVM3cuPFb5l6WGzh0L2fRdtGWTakcbuKVeHCUgcaFsFroGDKPmHNhwMXN+ZOWg4LtQrKIio6rIuqTlusN/r64TcFyvYgsw9aJEsaKNjpyKaV4TghRpan00kb1TGS68bP3q/bAzTf+UuTlgWPN6tURISTXddG3Ya1QTHcYWjIGsqaBpV1NWDY7mBcYzemmFZ0htQp6CVVy32HCmC9WYsoUAe9+hj2atoxmuJzDdeun5csam0WHqZQEUXaGNZRernukH97iALN3879MZTFvmV8/teQybBjO48xbn8FHf/1k1efw1csbccXl0dvbi09+8pOBhelpp50WG/H2a69LRoffb8XCX+aZBiPdGZOqmvBx4JyjLZvCrTddh59971uinROCT53zNRz1/pNUm3AYU5FE0X+n/mIdl6lc5aUJtXHlvYtxzj/nD96zD3b3fhOXy/juD56Cb//iD4HrrGfhH3YA1EqXrYamrqLhqaz67Je3/63isbua0oF3Wy7SHWcEXX7x1wMGYNiRkmR0JxkSMoJVT99xPOeqfgl/e/wF3Hn3PWUZIDpczlH0FtvE80JzzQFFQNC9cEkso6PWtAFZp1veK4GvNVJyxLxlM194dPuWfvz84i9HFOxraYtyHlncmcPVH3wtjtlrbmQfQoCsaSSKXsn1QNhx0pYx0dOWxWB/H5548D50uKOxGgF++9+Mu9eL/P44EbNGaiLEYThfPve/qykVaL9NaSNghDci0n3DSQfiI69biM++cWngXJTEq8THoZ4p13IFo0yO/w7z2X7VwOVQorMug8eWbGwOdzlQT3vEdhnmNKcxu1mwG9HASHe1ejiUkkTKfCNzunUhzJLDcPtzg7j8vnX4xG+fKvOrxiBw7ldI+bMZo3sGOxwEBCalIJAiSvHGZlwOV/RY8BYoHHvObkZTKlg+wiDCqG3PprBURZODx9Pzs8Hjr2Wi5KJg+0JnRYcFIhQpw49OUEJU1N1lIp+3OW0EFJPlvcvyXpT4xl4tiDN+Lj3/89jaLyLZC5fsriKAEtQwMG/RUiW0ZnrfZ0waqH8NRMsiBY3u5KgRT3iOcXi6f0zR+uXz5Z6RLQ0yGRXtyKU8x4rnSOG+SFc9cFlwwSsx1bq0Yeh5ro+FPMR0+YkYcPwSIyWH4WlN9KnaPFOpwLwjKqiuWrUqsjB1XRerV6+O7Os7tTgsh8MkskQYlBML8Bdf1daq37u7FWZ+KGCQgXP85D+/iW0Dm31RPibP3bhJu3e06JUkpJjbkqxUy7gYg/Q7evPus/Cdd79aHecFT9VeRyE/GXnvcQv/wf4+PPLAfdg6OFD2euuhy3b3LMAZ531dYxDQCE1dGShpn/7ODzslcqy37jELszRGgMhTrY4yG2cEScQZgOXo5YmRbqDunG6XyXKU4vp/evXVePfyA/DRU07CsmXLEhkg6h68+UcfTwkRLCh9EOqcNx/nXPQDP22AGjjnou/XnDYAeE5Nzeou2S5cJlKlAPGc5LzUCCNUGhLN5SKOXOQrJ6WTpRPKIM1vz+KvN9+o2vd7D3ttpH3r7f/4Nx2EDYPJolCN1ESIw1A+miN70IJ29XdL2gwYwhmTBvpK1jRwzF5zAAC7ddZWnkxir7kt+OyblqIlYwbo5TpLpJyRU89aBfDLRUoKuFQB143k4bwVmY/93zPlEOLe9cocbtlVcikKxjhaKrAd6oFkZDnMq54DX4CxUZT/sHJ5EgxCEw39RuZ0646pksPwwPrhKR+zWugOqnq1bnY1zBjdM9jh6GnLoCOX8hZayfWQK5WmAjSDTGwEtyG8gXKtLaIp8Vx2abjJKHkYlivqpcp8HpmzLHLhWIASaLkiN681a8JmDBMlF1nTCNRRJgSg1PP2eosICv9aq0Wc8cOYi74N68A5x+x58/GZr31X0VWpYeDz3/weOubOA/PYAUl0W65R9iVMSiob3Z6YTbVRIyE450UlXaYYAODimchrATg6sqYnjAK1vxs6V/9YUbUZzjn6x4qJirbco7KXXBawjFwWX9e0XsSVhpIIX1rJYdis5RpW662WjJEdEenec889IwtTwzCwbNmyyL6Mcy+KLdu+T+HXyyO5XPyjNcxKSQbBwKb1ijIrF3VTEbuKnNcr8ba4I1d2sSUi99EhZ16rHxn+3VP9kd/NXrg01lmmL/ylIXH6ye/Hye8+Er+76Ubc+sxAQLQPqJ8ue+tN1+HHF18YiHCGoQyUVPnF/6zmIGW2sykVED9MGxSvnS9SLsKL5TgjSEfYACRl6OVJhoSYN6YS6RZR4d7eXnz6U5+qigEiETfvCCdAkHbLGMexHzoFt93/JL7609/g6r8+hOM+VHvawGB/H57697+wdaDPOxdBySsXWXKlWKUYFw3SGCN0i8f06WhKVqnnEBG6JIHOVExOt7yfcu077vttCQJmQH2aCLWwSPToncRizXjOaCXvACBrBI3utEFw+JJOXPfhA/CLEw+oeL5K0KOllBDl3CjnpCQgdZVmc712RQiUgr78r8SqbZNYlVBCzA2krXF1Lfr8bhpU6HU0iFKuQ+khMCFyqKcvNgpVG92eiFwclNJ/AxYDYyGjuy9BB2E6MKKde4ZePoMZ1IlsysBec1sU/RsJA1c13joV3fPoPWFRDYMI5Uq9FFZYdENGSxmHMorDsF0WyCkrOgyduZTKNZYKxRzCGO3MpdCSNpVAjalFwiUVyfBE3uSEQ0nwXpO8vTrijB9KDcxZuETkujOOo95/En73z8dx6S9+j9vufxJvfu+JKNgMAEfRZYl1hLdNWpFBm3HuC60kUjVrixrZXv6gNPIN6itXSyqyeC/iAwK//qTtqbPL6O5kycGG7QUMjotF1fa8LXJ6YxY6vhJ9sI6nbBMNDHRjrFi9AmjJYejXymNU6+GVuW31KjDXgoULF+KnP/0pDG9hahgGrrrqKixcuDCyr8q/575wGqD1W21bUvyrRZJBMG/REqEK7RndItWjcfnuMhe7vUKpK8YBg9II+6ApbeC8I4WD4pmBYN5+LmVgwYIFOPfiyxIX/nGGxH/+cz2+fdcqnPfnFwLHqyZSGTYaBvv7cNGXPxdpSxefe1bAsJAGSkAYUH7X6rM3wpTZ5rQBPU6TNSm+ecxe+PAB83FtyJAIG0FhxBmA+rnet+88lT+eZNCpiNUUDAmA45Gnnq2aAQL4KuLSaJB9gXrRO59eDi9CSNDdswD7HnwYurp7as7tlY6aC0/9IM549xtw603XqWO7nKlIN+fe+SlpiDCfrMO8bFb5smZGGSE1uyjq9A6PBJlCldp3LFOiqaPsddSiiVAri2Q0NBdkTIqOkL5BkE5uhFIxDBBCsHd3a1U1wStBN9x0lkg5Z2+4Pnq1cBmHK//r9RvG/PQwoeXCEvuhdNBzr9zkVK6lHsj1pcs5KKLK6Y2AZIW8fmE7lu/WidMPWxLZh3PhEEvq/9WmCMSBc46L/7YK379nDYBgpLvosEAJyOmGLio3Qy+fwQwaAAJ/wROGHDhcnjyoEgiqnBJOg6QiS0OWBBbbYjGjLfThRxqU4FPMeSwVgeWwHBctGQNdTemAkSMdAI7LfYG4kEPAp9QKh4PI6dbqdnv3OVKw8XT/GCYrLuhDKAABAABJREFU1K+MM34+/bVLMXfefEXxpoRg7vz52P/gw9ExpwfjJUcJ5ThljJwXt4qF0u5azirjPs0vmV5efdSIMUn75dietzEwXlL0cZXvC/89UUk3947tMF+kCxCTQt52VNsp2C7ylhMbzZfvXDlZ5PV6baSRVndYMAsA3rV3NK8REEb3Vi0SU32kG3VH6+rBypUrsX79etx9991Yv349Vq5cqb7jnKvUBMbloxR5syrSzUSUQ+Y7S+XkaunlgGaQUZ9ye/4ll2H2vPlCZM+ViswNuWWFvLcYiKPLjhVtlTvMuVAvjxtU3rKHUKxfO5QPONgMSrCkqwkfOOkjiQv/WENiH6Eq/mhv0CipFKmMMxo2rVsb67xhMUJqx5+4Akv3fV3gs57WDBa0+UZ3ayZImc2aRqB/pU2K2c1pfOGIPWJz5Je/5UhcdPnVuOTKn+OzF3yjrAH4qjnNgRI72ZSfEiPfS9jJUG1qRpxav8NEOUrOgfTshVUzQABEHL3qkRPiRQT9+wjnqbq8tiEq7KjhXkR4aHCzOB4TY7qprp8oFspUhPmKtquYIWHNEAnH09YwPXHRMG696Trc/mthzN70i2sChm2l9p1rahKaAyf/ADg8mvoACEP4ortW4ZkBP60nrIkQh3pYJJOhuaAjG6WTB/pKika+ny4QQqpSL5eOmlrheg4m4QSFcojKocxmDJaTXN6raDMvZSh4Mc4OmvNkWpTS6lDjRuMgVdHbcyn8+H374mOHLIrso5dLi4MUUrNiKtJUwuaxEn7/9AB+9fhmFG03oLZfdNxYHYTpgs4QnEqVml0JM0b3DHYqBL08QUhNWzgl5XkpujiHZujq9HJ/YJf7hycLpk0EnPu5boxxtbiWytYiki3yTsW45kfvVF4gPEEu+A4BEI12ra5dCNboDgEZkR8t2CjYrKrBXDd+HnnmRRz1/pOwx6ymgBo0kQIgjKErl0JPW1YZq0k06pe2ilxTvWYo47wKenkyTT8MmzHYTOxf8krmtGVM9XtV49h7rtJJI50TUulWOi7ylouC7arFaNFhKDj+c/SjUj7jwfUm0SC9nCM5Y6o6OuFEycGzA+PgnAcmLokeLRIIAJ1e1LTkMGzXIvPFKiPdKk96B0S6JRYuXIgjjjgiEuHeMFzAM/1jKpJHNKEq+VR1RwoAtZiqtZ7q8SeuUEyO//rzg8ogYJx7aSHcc8U1TpFeKszGqSRvHiti/fY8Ji0HIwUHBomKNwLA7Oa0R08EJhIohUkL/0VLdwdJZYGDPwB0RdkF4WMkRSqTjIZcU1Psgo5SGksrLrLgvh25aG3hbCpoOOi/KJfKIZ0C553+cVxwxifQ1t4ZawBe9+ED8JWj9sSblnYFFJlzKSOQqhTnZKgmNSNJrV/mqbqMY868+fjiRT9QTiDDMPDf//0TDPIWjJZj24RaiBxDJZrShqJ7y+/dkFFeCUkR4f5N69W8tbgjh9asGTuOVGOExmHzWBGMi7SBWQn08pGig7mtmdjIrRLrc73nR42AYVspEl/I54F9jwa6lwGHngCQ6LL3W3euwi3PDOATv3mypnurJ999IlQSqj2XikSyw0a27jTOJOgVNAKBSHc5ejmprx667CfSWS6ZJ9LRYjkcVkKFCcthijEWWON59PJGMtPKQQp+Sq2OgD5CA/CSF+xY3JGcsiNFDpO6v752rtU5oq9Vtk5agZEpbPhO91pDFxp9pRjdU+emzGAGUwDx6MJxnVcXw7EdjmxCa2UcypANG91GgpGtQ0W6vV/Jb5/fMoGJkoNDd+uE7Qrvq6SpGiLkGjQOvQivTA9WUXZ4uckkGM0ghMDWBhJCCFxvEsrbbkTErBwWLlyIhQsXYmCsiPUbhlVuu4x0S4cAYxwpg3oGPlQ0OQ5bJkQe3sIOP/+Uc/+9bBgu4M6XtuLIZbNDeWHiKVYzKUuRK8a5GlSp773waq1LurpkCIj7Gpr0BVfkKy46LoqOf27LM+Tl9mO9I2hKm3jNvFb/vTMpbifjsQSMJw/wt950nTJUKKU4/zs/jI38fOK3T2L1tjx+8J59Yo3ueZrR3ZEz8calXbjtuUEMTpTUxAvUPtnsqEh3OYyXnEBNddm/9dIq8nNlhHNRPaCeXPq58+cj19UdoG4yDowULCUIJc+fhMH+PmxatxaLlu5e0bCQFMA4o9ugFI7LsHG4AMMgKFcKPEUpSp4GRC3o7lmAN5x7NR6YbAcOOxn0x/9PjYHtMQPl8SeuwPK3HIlN69dh0ZKl6v6SjIZCoYALLv1RgGJOCMH53/lh7LMJt2+p2SGRMY2AsZAxgsJpSc8+ySlw2/1P4vXL3xg4597drehiY3j0gftQavePkTMNVeZwYMuW2OMd8qa3grfMTlxEJqn1H3PMMXCauuByX1vi6A+chAWvOQSZ7etw9JuXY8Gi3fB43yiG8lYkHUFFuiW93PtcOmPkdkcuhZaMqYwR4s0ttXQVGRHW3zc1DCxYvBRyBGxKGxgvOQ3NU5U5qm1ZM9FJ4DIhfNUc059UG5VGt5FShq1sK3HtW7apXFMT0NTmHzATjLa7jOOeNUJYrdYc2KRnWi7fPazv0ZEN95WgGGw4sj2dkW6R011FpFuywapA3nLgMFFpQrKbXI+ibav+JJ3oYiyMM7qf3DyKkssiBr9MxWgoNa0MOONgXpRZX+M1CjLdaP+e1sR9ZLm0pJagC+LajJedg8LQx/K/vrg18N2vHt8c2C65LOBcbTR0Z/QrxeieiXTPYKdD1uwNQx94y0W6Za4ooEWTvUFclgzj2v5RI1yjlnPfKB/OWyrKaLuShi1rgPvGH4dPg5Z0c88mV9vEC9EyaT3K+3IZZjenvWv3DabxouPVBq88nOuURw5g28BmPHL/vZ6Cs38tgIiyU+IzBHgZI0culppS/iLe5X5ON+PAeX9+Af/eOBz8oXRGVLxy+M+bCyq4dF4wGdmWxpJ2PPn9C1smUPQGYulBHy86Xo4YvGvkytkAiEWV9Jb7ufwy4u9fV5J3eLC/DxdVSSdcvU3kIN750taIUdKaMdGc9p9rV1NaieXc8FjwWJanlP+bJzfjsRB1OA67gM0NxxVOFJeLPH0/b9uPaqrIt9b+7DonVsnk0O+dcY6tExZy3nOVTIk41JqXWU6NWbZhQgiyplE2up5KUGSuBtty88Ufhonv//wm9fms5ng19bhIZTlq7vEnrsDtDz6NS668Fpdc+XPc/uDTsc4lxqOCi1GjmwZqaYcj3UnPPskp8NSjD0euQ3+HZ614n/o8m/KrMmzu64893uYN68qmZpRT63eYKFvIuRhfiraLzu55OPDgQ7Fw4UIl6BfXDuQ5KZFq5eJzaVjoY1LYEJEq0NUiEhH2UjHm9MyPpklVecxqGD+ybejjXRiMc5gGDThvRSoX89uo642hhhlr2OrtW28LHzv+aCzde3+1H2nuCPyu5FTHKItDPfnu4bmgOW2E1MlpjLK/v38qSYSlASAEykFVLtJNPa2cavD84ARe2DIBl3EMF2yfVQjhfBPGONQ5rYROyLgYd+Mq0OyonG55HfJ0co3XqNO7jGP9dpGKsdfcqE6Gvp9Ubo9zFAaM7hodusOauv5PHtgQ+O6+ddsD2yWHYbzo4Lw/PR/5rhF4JUa6Z4zuGex0yNzmMAghFcuGUW0xH9j2vjcoCXiviecV1aetgJCapBwzriJ1UrFcXqeMQOj1TgVNXpxZ5vvI2q8qQhtTR3nv7lbMacmoa3OYMLAKjgugsvBTmPJ46oqT8MljDsanTnwPTj3mYNzw0ysChiw0o0dG6ZOm8EIMhVZEuoO/2DwWVIKVQmrVOAzk82ScY6xkI20Q//eAchh4l66eE+ei5rM0flzO8UTfKMYtR73PtUOTGC85gWfOuGAROFr0W5pq8q5mNYtyRnGLm19d85NI3fNKdMINwwVsGC4EPmsK5ek1pYxkxWWH4R9rhvDdu9fgk7+rXBtzR9LLkyDrqDLOsT1vIWsaQQeUB73Ui0mpEsirFeE2R4hYbIhyfp7RHTLwJerKy0ygl4scbnEOgxK1eE6mAVaOKiVhy/CI+vvznz1d/Z2tIRJWyWjo7lmAtx97PN5+7PsSDYnf/OqGiNHSkTUjdbgj9HLtmSQ9+yTl8vPPWBkwziP5yrY/JuVSfpS9fW53pA0QQrBo6e7KCRmHcmr9PttGjDt9vb147pEHMNgvVOklnTYua8J30hIw5ldrIJB9I77h9LRl0FEmcpyEQG72A4KaLzkgEnLeqnTsah1VvtEddVAVbBdFxwXjIg9VdyxsHi1i7fZJ1UYJ9xw7ZrqsYRvXn9dt3KS+v+Tnvw3srwtV1lNmqtZ89zC9PB0RTgs6qMKOu0aVporDgvas6iuVhKuqNTSFEr6oKlKwXbWm4lyk2DSlKFwu1j2Ww2A7LLB2eKx3BMN5C4xzjJdc30HlwaAErRkTc1sycadvOMJBHVkOc6rYOlHCcMFWPbEjmyzSKdaYyWtnvUxlrbW640Rnk2A5DD/451rcuWobzvrDszWdpxrofaXaNLtdHTNG9wx2Oso5blPeQsdJmgDC0WMVffYHmjnNaSxoExRp6ZnUfq6MMhm1lpEJ+ZmMOJvUF9GQ8x6DZ0jAp5szLhZPitLNdSG1ZCqSHEAf3jQCy/HV1OPQ29uL3/zmNxHK45/+eIs/AXCO//7Ohfjdz/8LBpGLQi2PHvBqYce/gHzMYmmvuc2BfEkgqswt0wWqmYfE5Coi8JbDlYPAhXyuIorJOQfXKJgMYlEqHQOci/ztsaIwsl0mopy6UQ/4Qne29n4VXc17DlnTwJKuaE7rYH8fbrj6vyL3EJfnqnvenxuM1mFOm1FxnPBzlSg6DP+qoTbmrkAvl5T+gs1QsBmyplDwZvAXA5IiK51AS7tyWBIjolUNFHnce88UBEWHqbrfkkUS19Jryct8ZNMI/t8vHsG9a4VXP2x0u9xf8FDiG93g8X1MOrBqpbUO9vdhdMJ35PCMHxWRBvyqrZP48p+ex/rt+cDvwpHJqYhkDfb34fuXfCvyObPyIeE0GtlOYgDoz14ZXCGDl4eM88g7dPyyacKBJueR5OesO/bCCAtWUsPAeZdchoULF6oIjMM5/vjr6/Cpdx6K75x+Mo4/+i249tprvXJ1LNbxIsdJISDGQo6I5GvNmgbmtWUTvy+HMOMhrMCsM7aSUI2jaqLk4Pv3rMHDm0YAxBvdG4bzWDeUV5Fu03NWOy7DeMmBSQXV+vgTV+CTn/0CAODNRx9bto3GigyavkF27j1bAl/p+hmdZUqalUO1+e4O417lEB9h4bS47Wm0swEAP3rva/C+fefh5IMWaMrXye+fEtGXBsaKyWszD5Y3F8iUMCCY2iYDAptGitg6WQqUELVdJoRFJy1QQmA5LtqzZkAclxCCBe3ZxPmz0ZBrRHHu6rRrKmHjcAHv/NlDOOGXjwIQ80K5+xFrTH+tFYYesKr0fsIYiTG6Z+XinVElh+Gxvsrsu3ox/goUUpvJ6Z7BTgeNoQtJpAwC2MmRIKVWHtrW51ydbkngLfS1WiyuNymI6GrQ8CYG8YxuwPRytnWKrJ/TrUfbAJN6k4lHRxf08/JCV9IoH87bGC85SBs0dt9rrrkmYGxXwtXf+xaWLlmCOXvuj0WLFmHbwAB6169B14Il6JybXHbGp5cb+NXJB+Hfm4bx//brCSxSgGAdR3EfJDAxlYOq582D+b7KwCZ+5Fu9Y5lH5R2/NWOKidoVRrdJxSTuK5v7jg75vlSpHm/yKhfxl0hSdD751NMji61wWRgAWNSRxaYRUW4jE6IQho0SHZbLAoZTORAA2/MWNo8VcOCCjppFyRoByQzhXEyUDmNoTqeUVUO1a9KdE6ZB65+QiKjdrjNexLk50imqHCtxi9da8jI/dfPTge3mVPCKA5FuAhXtTXK1mVWIFumQearbh7YB6Xb/i5yfsyrLPp1y42NwuYig/PxDB5TVIujuWVCzQBbg9YmYGt2FsRF0zvMdARmTBjQ6sikj0ZAIP/vjT1yBpuYWnH/6xwP76Xm9zz31ePAgmtFtGn7d7oHBLZE+zDnHpvXrMG+frrIOq5UrV+KYY47B6tWrMZKehUWLFkGq9BNC0Nfbix985QuBuuaf+cxncNhbj4LLWmPTeGQaDfXaLwl9V6uxVYsugQ4OP4IqtEvKj93lHFXyvD+6dx1ueWZAfR+nf0CpX2PZIAQGFf9WD+VBKUHKpHA5YBKgq7MTwBCMdHlnQ1x/RjpZlGqTxkKyikUM9vfV1ReqwaQV1fbIGEEWSNakAUMua5aT9GwMDl/ahcOXdonrMYNK/3EQjhmOp/vH8eq5LVjUGf98HVeokWdTBlwGjJUcdORSWmobBSFiLp4oOQB1PEFN8QCKNkPRdpHynC9zWjJIGRQlt3KlgemCfmYak9ZUD/76onAEjXl06kql4KSTSq614pAyKCzXVWKz1SIu0v2aOTn8c2M0eFByGTY3uITY84PjeGLzGD50wPxAKsYrxeieiXTPYKfDIATb8xb6x4qRyb6S11UaqnqODefJMYtwTUcK4qlXe7/gIsLgMj/X1/bKmRgxkW5VZoZoEV5A5XzLS5FiZuWi1zKCO2m7fg3w0D5hQZ+qwDnOP/3j+OQ7DsH3zjsTH3zzAfjcKe/DR972Ovz1dzckTuh52yuLlDaw55xmnHLQQphGcPEMILGERDUeYGmcx1Hf5XOWUSAeeMfi35KuJmRMiqLDYLkcC9uzyKaocprojAVxTULIhXFRw1tSO2XJuXKIo7oSSnHix0+L7Ls9H524Xr+wQ/2dMaIKtWGje26LcBaVHIbeKic2DqkCz6eU58Y5x6ObRrA9b1XeOQT53F2PLij7DuPBnFkCP8JbTW5oOUh9BWk4yCi66zFUZL+PY3VMpQ5xcyZoSMj8XEKEQZE2KFqTFCDhR7qTNCt06HTeC85YCRhaVK5llvpT0mXlkDk4XqqLQh8H/T2NFGzMXrgEJBsV/JkzqytaIkxDRL3ce/aEUpy08tOR47329Yck5p4P9vfhiu98I/gD1+9/hhb1ae2aHXucXC6Hp/79L/T1bkI5SLX+2fPmg4NjcLyEobyF5rSBjWvXxOZ9r1m9ukykW7QZ2T/89uu157JXE0StugQSap6SaTYEShQuCZXKdAHA06H68y0xOd0URDnhpNYIJUITwKQUKa1ud6pCqUqJ2LrumlMqjC/e/rz6e3DLVvXs9PGz1vEpvP9VD2zAhX99MbbGcTSyHZ0b9i0jqtVoyPn9sb5RvOfnD2FwvBTZRzoxB8eL2DRSwBMJ0U7LFXORdE61pE20atVJBJNNrHtsxpH3mGvy2Zcc10sREsEPySASaXjTcPNVgDFN+JBoa8AKuP6xXlz3aG/sd/mQaG4cKyRwDRyqgk5SP/X1QmozVsNBlNaMgWbNKXTIog50eyKwU9FDSMKKXz2B79+zFt++a1XA0C7OGN0zmEFjYBCCtGEINe+QcZ2uMHAo0S1F9/EVxZPAlCw2BB0dHMPSuPA8h3LR4UdMRfkxUQLMz72TRjSFlzcNXUjNK5PknYoS6SWNn8xVjjjjyhscphjGCfpUC84Y7rr1N4E6rf/1jXOwZWBz7P5JCs1RoztqYFZbUkSKqgTqZcMXV6Ne9EMXlZPieT5tXyQJ2Iypa3O93HgZ7WbMd4hIR8rgeElb8FbOlYszzi5IUHOOM1a7NbXyaC1WA1ntub52fhv2mCVUdqVYiUQ1+WNWKC+uVtiuKOciRVUY43hxcLyqCVzSAznnXm63V14lRCdf1JFDR9as21jQQRBU0xV9TVAgZT3TsJaDDp1ife0tf8XCxUuqWmA3hYTUOPfTUCgBls5qiuyjQ6bPVBJSi+Qsh9/tO85SfxZD735uE62rtFEY+ns69ojlOOqqB/GJ/+3FgSd9LrLvrK7OSJ6qjjBl9rb7n8SK084E5xzX//QKHPuG/QLtoJxjJJZO7PhjEiVE5almWtoix3nn+07Ax44/Gl/7xAdx8L574ZprrvHGjPh30tvbi0fuvxf9m/tQdMTivyllYMGSPSI0eMMwsGjJ7oGylTpKnnaHoZxC/nduGQdtGFNxqujaI4DHFuPlx5lqHFVhGnWcIUE9rRNAjMPS8DYIgUFE/5BtWZV9C72XcikTl1x5rXgnIcVyAJgdRyU3U2CM4aKfXIc3XXEfrvzjvfjlVZfXND7detN1OFb2k+X745ZfXYer/70Rtz+/Bd++c1Vk/7icbr0dZEyKA+a340fvfQ1+/x+vL3vuRkCf30XN5v7IPoQA8vUWHTcipChhuyK1ggOYtB2kDKJS26RQqlz3WK6LvCXerczZHi+5ouQnh1eFQqzDyqWJTDc6cyl0NXnit1WsNwEhyvvDf67Dj+5dp9gO2yYtJRQWVrSPc1DpkA462XcBYM3QZMCpI+eWWnO6IyXtsqkA1b27NaPG06LWxxvNxvjjs4MAgC6vn1bLBtvVMWN0z2Cng3qTrRkSPQN8+mUSRcb30gtI2lPiuYgoy5XzJjkC0Zk3jxXRljFV1NyPdAvjw+UMBiHKSAxEuvXItzw/QSSyLXPnOBcLhR99+6tqcj5u+f7402+vD0bCY6hDcYI+UxFWYcxF34b4hbdOL9cRzjV6ZNNo5JmXy10PnF+7Xw4En6P3uTCY/N/4kW4/Oi3fmUHFIs5yucrH16Pp8p0WbIYJy9FEjKqbmKrNf42L/ndrQi/ZsJqzEczpbsuYylD547ODAVGbcgI3Mk+25FanfJ8E22WwGVNU6QnLwVhJ1J2Ow0jBxqObRuC4gtItnVaWywLUVV04rSltgEwONSQCKw16fVumEGwb3IynH7oPA5v7ytJ1u3sWoHfDOnzs+KOrXmCH+4Z03nCI8cwgybVUAZ3JU35BETUskw9achh+eeOv1PbT9/wFzz31eCxLI5dLpt3qiBj9c5cBAEYKDh4bjhpTsXmp2veUEBy6uFN85z2D6356hRrMOee46Mufqyr3PD7q6m9TAqRM37kRdrD87+9/HSkFds8TL+KxvpHIfUnhyrNXvA/vXb4/vv3V8zA0sBmEALPn9eDMb3xPUwenuPLKK9E9f4GaT8JYM5RXjj+XB99qLbV/p+JUoUTko8smKCLdlQ3+SmNhsYronaqkARnl9iP9BiUwDb9fmzE5quUcdlIE8ILv/BDIBLUiXtWViWegmMKY4sd/FRYDfr4WuPyir1c9Pg3294kye5pj+6ILv6K+X+ulCe03z49cp41oqpEOOU8cvrQLixNo3I2EVC+XSBlRM4GCwHYZmtIGJi0XluPGtlfbmw845yjZTI2HqnKFtx/jIoJsM4bmtCnKibkM67ZPouS4nhibMDQNQuBUub6YDnS3ZpRom6pgU+Fitk76TviSw7A9b+EdV/8bH/RyuMNOi5ZM5Ui3FB1ULE+QgL5OvTndkUoUWRMZTXipOe0zMTaO+KkZMjjTP1bE2X94Fo94Wg61IG6MfNuy2QBmIt0zmEHDYBAC4k2wYfXidAVKmU7pBlCWbgOIiXzZ7GaV500IgeUyOExMfv5i3Y/C2p4Ss4yI6rl2MnKqSlsxmScs6eW+ABggDMm7b/kVjn3Dfrjup1cE8v++/5UvYsvAZjV5xYmRRQR9KMVHP/6JSISlWlBqYPHS3SOfc+5TvcKLpXDpmrzt4i8vBOs5VnJ+SASMYfgMAjd0744bKgsHrow5Xbla5tYL8RahBi/PMVq0FZ2zYLvIWy5ciNzbclHQMKoRzYmry93d6msLpIyosFQS3TwsVFLO48vBvUgzqyioppeaC8NmIkot37XtBrfDGCnYKsLtePRyGemWIIhG8xoRgVW/096h4D4Ad/3+Rhy3fH+c97H/h5OOOAh33Hxj4u/riRaGHVB6iT7J0ihXdzypOkM4ehcxLDPJgnMlh+G/fvxj/wNq4IpLv4kzzvt6gHLLGcPHjj+6KmZB5D2ltLzaVrEowqoH/Mszgur/cXoFHz14Ec556x749UcOws9+9L1IuIhzHikNFtf3kqKuEku6mnxFZq/vyOMU8vlYSviLq1YjLJYbTu3hnOOaKy/Hqe84BH/53Q0AB45+/4dx672P45Kf/wa/uO1vePNxJ2BgTND7//XPe9Db2wvLYXh004hQ7rZdjJccMbeE0h9qcZpVQ/dOgkFFPrnLmbbNKjJqOOe4ewBgC14TOxZGI91RI5d4Dnd5Xml4U0I8p5WfghKO3FXbX48/cQUybbMCn7Vng3Xj/YcRKrfHohHccuPTk488FH1uuXb1p7yXfTWjO65EmI6kMXe6EH4uP3lgA377ZJANR4hYF+VSBsaKjmIAhmGruSA4Nsp0Bul0EY5xYHFHFk1pA8wVRrftchAu1oV6uVPHbYxi+FQho/SVXFRbJ4JG90MbR8TnnjEepZdXiHQzrlgDE5aDNUOTAV0AQHPo1phmNlEKR7qDRreuz/Htu3zmhhyvzv3TC7h33faI/kk1CFPbASgHx0xO9wxm0CAYRDREk0ZraKcr5nQHRbtIFVFL3XMrjHZhnEkBmZLDULCFQcYgIt0cwfIQfu5x0GMrjHCNXo7gcLxtYDOuufjc2AlDRJ3XBj6Lu5OVK1di/fr1+OIXvwgAuPaaqyNlrJJwwGFHBOq0fuprl8YumEqub7TFCeCEcfeabZHrrmZOlM9L5XhpDAL97l3uU2alM0NFwuE7KGR95JIjrt9W9XMZVm2dVKkIBduF5TBB7/QoYo1c24TpYkCQXg4EIxqUklCZmGjuvEQlupii1Jd5AeFSc9dcc03ge9tlsB1fFMzxjPCkZyTTAwgRnnXXYxiEaYCMc+gxz6kYC2G4Wt4sIcDA5j5c+c1zAovyy7/+pUQjuh4HQLh8nnwGutFdrlmlYyLdcdG7sGFJcsk5nhwAX3aY/4GZBnNd7LP/Qbj2lr8GDTvGcNG5Z1VkFkTeUyqmPM/ooPozHNmOK2OWMSlOeO18pPLbccuN/1PmbiojLup60ykH4b8/sB8WdeQS8xvj2p9hGJi/eEmk/ySl9nDG8MOvfQlb+jeDcaB7/kIccMhh6JrTje15C7+74Rf49DsPxUnHvwu77bYbLv7Opbj/3n/ghTXr4XKOOc1prwxSqO3VEMurRPcul5Ms0neYEh8lpLq6x/evH8b37lmLz/z+mdjvCyFDIlZIDQjldPv/TEphxNHLvXdYbX91XIZSaBzqyJqxjiAYpvgnobVpdc1lxqfYvt4UzSefq80F6VDlipRB8WqvRnOsY2CaERfZvvTuNZH0L9sVlSHmtWaUUR2GjHT7lVOIcpozj9qmdHi4n+PtMD9NyfTyuUXKjnDSSMr6rgBRaaX8PoMTfl580WHYohnhDuMRgb1KpesYODIGBSXA0KQFzoVuRibQjkRrTKL+JyG8fzYVEsFMEH0tOcJR9+zgeE3n0xEn4jbXC1ZYLquJ/bOrYsbonsFOB6XEE8yiUXo5jS5KwxB51P52LSksIiImvLGmt0DeNmlhYLwILiPdmtdfGvgBejmCNGc9Pzh8bX3r1yYayIQQ9CwKLvbKeXN/8IMf1JzffdLpX8JFl1+NS678Of77L//Gke87KXa/vDbw5srkpEqs2joZ2JZCQJXg7xON6uuvXLIOxLG9PCqvrVBKAsI/kvIl6eUGIRgrOhgp2OJziAnC5RxzmzNlRbbqRVykW68jysEDhghB0DDJpozEestlVWWJ7whKWjiHI3aSUqtHvGVkW74fhwkjPOmNci5TMoB12/OJzzMc6Z6KiFkYc1syin5JCUFvTF9jzMVTjz4ca4BUcgDEjUFhx4hguQg2hmRhlGtXpjIGK0fvlr/lSNV3L7/pT5FjHfMqLZr3+uP9v400qGGgrWcx7uvNg5NQHjpjuOby7ydeIxB9T6SpPbgDc4H8iNoML8rSJk1kxG9atzb2c0II9n/dIYnXFDYkw1HwZbObcfCijsD1hBlT4fsyDANXXXUVurp7qkrtkWDMRe+GtWDMc7hCjFn9m/tw6QWfDzCavvHV8/HZk9+H17/mVbj9NzdgZMsAnvz3fRjc7EcT5e8r3bOOJLp3EgVbHmv7wGZV4g/wxEVZZfruczGL6zueWI2/3yOuL/z78Hgm51EKn04uRQgJEX3DoD5zLRXqK+X6q/6cJmKMjvZM0ED5yOsWqr8/+Jkv+TuOBxlclcan/V9/SLS/5zoi+0mRTEBWsgg6XNuzKdzxyUNxx2mHxp5nOpFURUMPfDBvXWRQIVKYbHT7Od0yDc8gom2HI99+jrdwskvGVor6TnTijacZgwbStXYmqjEEt2hGd8lh2BrYdiMR3krq5ZbDkNZy/7MmRdokAYeJXDuf9Ydnq65+AkQj3RmDIqOlHCStTcqtOapFrBaO9p7DjryXI2ZKhs1gl4Dh0cm2F+yAcEM6NNHGQRhfQS9s1fDyrJU6OcTgL88nc7olpGEh4zgyR1vRy0NGOA/l8nUvWgJCaaLhzTjH9sF+PLJqAOas+WBe3mMY9QqqfXnFceBeyaBPfvW7OOp9H47sMzBWxN1rhgCICbgcPfbDB8zHr57YjN5RUa9T5uDL51IJjElDEYpNoL7T3unC9pwv9KMZloBoO1GhNSijWxqik7aLJtNQR00bFB25FLZOlLzfTs3oHivaeLxvDIcv6YxMXJQEF50cvl6BRNibnIRSmPeqg/sLmqT5L67tuK6L1atXY+FCsfh0GBN5c94xpBGe9E6lWrztlW0T71EurPzn6sQY48efuALL33IkNq1fh0VLltZdrkeK2wCiTS3YbWmkrxFCcP4ZK1UfCJfNOv87P8RF538B3LEjC+y4PP0wvZxzDkqpp7LrVy0Qv7dhEBJYUPniUOWjdzf9/CrccPV/qXJfJ3/zvwH0BPbrbskoWn0AqQzOv+QyfOWfW7F2O4CDjgMeuSWwy+9vuBYLlyzBR077bOQeJeR7eurRh/HXbTn8Y0T7sjgO4lqBvqW/53JjSGyJJwBnnn8hAOCR+++NlMAqV/4sDnIxGscSkff12DMv4qiD98Oh+y7DP9cMISS4rlJ74qtHEKxf8xJauhdhYcceiuW0cV2yk5Uxhsu+8nn8kBJwxkAoxee+8T2c8h8fQ3suBZtxtHltZbC/Dzdd8xNc/7MrY9uuRLj0W5wT56Jzz8LY6DCuuOQb6vmd9tXv4vDjPgRA5ngnPkqFcOnI7/7P7/CbkW5gdBD0P44Hzro18H04gsq4GLsZfGaI/C8gjDPAF4pSDirmpwic/50f4uLzzgZzXdVfH7jn74G28elvXwFgYfDcpUkUxoqQcSfdCP7drbcDH1wuNrxzn3Lml9C575vwtv2WqjEyrjxbd88CXHDpj3DRuWeJ904I0NwReXa6IZExKJrSBr5xzKvAmG9w6ePZjkQSw8p2mTLI27ImiraLtqzpGcQ+M04y1wzPWIYU5iM+s8HVItuy7JbY03eqS5aaSQksL4pKiTj/kq7k9JodjWrsTJ1eXnRcbAnleIed9OUCHRMlB2mTImX4bKKMaUTem+5gvPbhTfjGMXtVvE7OeSTSnTYp9MB7JsTM0KHnXbeXqdqRhOGYqi+ztZK/YRr+yxEzke4Z7HRwiIG4qymFRR25gBq2v1gql8caNPBqMboJgoOm9LrKiLsY/KN5qTrNVl/HyWtRke7QpXR29+Dj538nNgebc45br/sZzjz2DfjUie/Bqcccght/eW3sde+5556JEbRy+d16xOWqb56DrTHK5e+59mF8/x4RfUqagCUWtPu5nXrukDSkK0Hk4RIlcOZ7bo2AB7klYyrhGyoF6bzvqCe0Jr2sVHnSBbUwZYhJ3HWFwBePuTA9T79enHHLM/jCbc/h+sf6IgqgYSMkbBkREowwZFPBic2kRNM3KP9gpbGc1A/iInaGYWDZsmVq22XiecljWI4LhyWTfRmXYkziuc9qTgEI0vYJEY6U8GMe7O/DU488hO3btoYPWzcIgFnz5uNTX/2uH531Fmx6HwjngO7/9vcj+/nf47gf/DEiDhV2pAAJkW5PLV03JABgYLyETZrwDACkqHyn4pqSytJd7xnc8rqv/5/ouNDVlIpdDC3b9wAcf+IKJeI06w3HRvYBgCsu+UZFmvkD9/wdF5yxEv/45z8Dn3d3deBz51ygtuOcRq9b2B75DIiJolOKz57/DbS1d/pq6W/YD7/8yeUA6sy9r1APvbtnAfY7ZDnmLVigqe9H91u5ciWefmE13v/RT4feE8flF34Zpx1zMG696TrV1nsWL0mMjsvf6aJbMv2BEoK5LRkQQoQadowGSDWCg3FOHM4YLr/4wsDzu+pbX8aQNxdIUcJK0+iItkAe2NyL3zzsMRbau8FIfDpB4Dq4J4xFfUq57Dcy0q07keJSzcLR/eVvOTLSNv7rR1EWxy3XXYOn/32f2s6lDTW+8t0O9Hc0hXH81Jw34ccvAn/aJBb95QTcjj9xBW5/4Cmce9H3xVgXKleWMQjaNINEPpd3792N417THbnWHY0kg0pfg1FCMK8tq5yKnPsR3zVDeTzuiRDang4IByCrkSinOPz3LdPJZOSbca76qqw8oTMMdyVUs97UI9lFmwUiuiWHRSLdSWwDQBi2XU1pLGzPqtSMbEpoaOjJTLqQYVyudBzythuZ4zMmDaQ5JNHLgSA9XKqOT5Qc3PrMQEDkLQlxju2OXEqdb8bonsEMGgSRG5TFgvZswFsmKWUX3vESHk+oBRmmtDDGKwpRSAgdDP/3wlgRA6OYLHhIFdkrZxHIi9R+Dz/XWGwHDRXGOI5474n4wc9uiBjNhFL84ZdXBRZh5559Jnp7eyOiVwsXLsSll14auR9KDfzwpj+LUikxCx8dnDP8+cZrIp/rjzOcsypxwduW4ehXzcHx+85Tn+nOiXJGX+Aa4CvQ6491cWcWizuSvdmSrgZ4pdw0OqT0pHPOscesZmQMqq7HceMFxmqtiRuH5wYnAAB/fXErxkM53ZEFZ8zvA0JqoTrex+w1pypBEQ7PYOaCihVHxwqL8UlKrYzgAF7kWot0W654QUnvlHOhfiwZB3oZItnOm1IGXDfosPrlVZfj3Yfui/NO/zjOP/3jkVJR9UI6Zt72vg/jtvufxGXX34qzL70ykq4RzgG9+G+rUXA4butFJOIeR1NNxeR0S3qxoJn70W6p3Ksb3lJVW+a+x9HtT/7EZyLRUp6Kqhi3Zcx42h818ftf+c90aHW8wA1jDHfd/oeyyszKoNHEoQBgbkcr5s726e1pk2JeSMPgbctm49J3740/fOxgdTxJAdaNp9sfeArHvPcDkRJpl1/8dfzyqsvrzL0Xz2VgvISNw4XYfbjnYB0rOrGLT4nu+QvwkS98FVf85n9jjsFx8blnYZtnwHbN7cHnv/V9UFo5RQcQNHX9PuQzj9UAqUJwcNHS3eOds+F+wFwM928EAEXvrQR9gb1u7bpgRDcdLdEV7Su6oe0bDMT7n0lpwHmbSkg109MKYsvHxfQVbpUAR4s+jo/6DrRD/p+/oylSM54aEZu3PjNQldPngXv+jku/8kXx3kLlyrKUB+jklRzbOxr6nP+R1y1UQm5Jzl5RKtWff0cLNkoeVcJiTJVQ1OddPX1Br+oCIqqPcPhGvkH8tD065Vm68aiGUj2mOWxLDgtEdCctN6LMHe4rOhyXoT1rwjSoqNVNiagEAhJYy+rHjDNm4xCnRROml+tCaoBoI9Io7x/zS5bJceerf3kR375rFS75++rK57ei19mSMZHzROLC4owvR+xavX0G/yfBuV+j06TBEjs6Je1rf3kx9vdhSY0957RgUUc2dt8waCwfE5i0HE9YLZjDTSCUM4ORbv8AjCEQSZWCVnKBmSkM49m7bsEXPnFyYCFFDQMnxSyuXdfFj370o1jRqy996Uu46JJLVWSbUgOf//b3sM/+B+Ltxx6Pcy+5TC32kqLff7r+p2WjJXGiKgDwvv16cPG7Xh0wDPVJOeGxRiCNOJfLKKh4sJQQlAsQMe6/d0PzhMttOcmbBvGonuI7m+nv0vsvIV4uZmMm9NaMEaGLxUV5giCRSLe+nUsZiXmpweP6NcoHx0t4pn8sdj8pxnf33Xdj/fr1WLlyZeB7meMpm7JUg0/SGGDeeWVpJPkopREKCJrYstnNajL+5U8ux+UXfT3yXC6uQtirEqQYFIdYlB946OHYc//XRfpBWBRJL+0CCAP5M79/Gg9vGomI3QDRBbOrojmyDcvInadkTgkKWnpAONINRKN3H175qZg80ZbItYTbjES+aOGSSy7xP5gcjuwjcdm3LkgslRYwaEKGRFvWDKjnZkyKQxd34IzDl+CH730NAGDLwGa0b30eZn4oUSyurPEEEY3PNTVVfI9hyDJID2wYxvt/8QiGJqO5g4AwJNcMTWLCchKX97ZX7SKfn4z9njGG/k0bAI8x9Z4PrcDP73gIP/vtbVjxsZWxv1H3QQ3kcjnljEh6DkB1goPdPQtw8qmnl91HHuugfV8NwB8Pw2rIYehj3JrVq4HmLv/LmLrYYVEw6ZwzPENLjhO+kJpniHtjTjinOw6xTgaprq/DCRrd40Nb4qN3XmqGRE9bpqLTJ+IoCT2LjlxKGRH6fe0qCIq6kVixRx1K2FTpf/iBCtsRWjmqOon3blzmM87C1Uck27DkuGrslEy4HSzkXhU6cyn0tJVfb+qR5pLDAg6rbTFjUTkBPVsLKsmylG0ZM5KOUAhEuitHmQEEcs3VtZg0oF6eNYM53a0ZU7WZBzaMqM9lcODeddsBAHe+FBTbjUPY6M+lKExKVInffI2icLsiZozuGewSMIis100CTnjd67o9Jt8DiApZmB69s1pEa0wL76PMq5G5Z4Af6dYNtKCnM5hLy8Bx9y03qQXmCW8+AJeef1Zg0iaU4tpb/ooPf/y06GKS0oBgmhS92rBxE8aKNj579ufxP3c9gh/f+Adc/deH8I4PnKzu/QMnfQS3PeBHjw5501uj985YorAUUNkLT4hPew7UgyTV1YfU88A4rz6rWi+npk/KYtsz4r33JKMlDFzlFLPQyRo5lA8XbDy5OWjshp9jpUh3tgylq1ykW+ZWMy4mKMaTS4MtXLgQRxxxRCDCLSEV0KWzwnKEET7Q349//OMfkWPJEmVS0E5pHsDvK4T4Qi+D/X348SUXxt8DY3WVDNMhUxbUNgE65/bgs3od5RhRpLCQy5f/9Dwe2jiCT9/8dGwUIFzOR1U28AxuwKeYixqzNBCtSarOULEs3T5vi3yUDpWh++QbFgMACpYD3jrH35GKBdv7T/5Y7DiZRF0OUN9DJcus8WEUx3xjPmNQbBnYjH3t9dgzWwgY2ce+YT9c9OXPlY0UJkVoGWO4609/CERqCSEVxffC/W/1ULzB7Lgc64byyJlGLKsjbzno8/Qr5u+2e2xOCqUUCxYvEawTLrRCZvcswOuWvxGfOfvLiQ5QSg0c+Z4PBOrEx9VXl+eoVnDwwys/FTknIUQdV/aD+Qv8cWBpVzPmVzAk9NzPH37320Bzp/9lTEm7+//+l8A28+jlKhWD+uMEIQQHLmhHS8bX4PBLICWPf7FOhte+K7qjaweM7oXz58Ua3d2LluDo9/tio92tmdi2SQhRDpCIUR52UDVlka1CnHRnQe8rpieUBpQp20qCkWyh/eEZ3S5TFTD09DE9Mi7p6Uq93DO6J0ouTM+Bqauf72robs2gI5cqu48e6b79ucFA5FnP95ZIovgDnpCc971hCGduey4Vqa9etGuPdP/2qf7otRgkuBYJpb7pzt7rHvXXBeF1SndLvEbBtkkLd63aCofxSLBCMkKyKtI9Y3TPYAZThhAfIsrLSQLf+X+7MYsgAJjXmsWijiiFrCp4Bpp+TsY5mlIGOnIpZaBJwyGbonC1CXVOSybgqQ7Tqrf29+Oai78coEmGo4WcMRQKBcybvxArz/+Oik5TamDlZ86MFb361+PP4KWtE3AZx6zuHrx++Rsxa16PUusGNKEXbzHw8H3/iLl/ISwlF3k6DRWozgsfVw+Sevm8gHBKjMaUggDEItYvFVe9J1uPuhoyqumdz/AiNY7ypPtUZ8dlMAgCNcGB6hRIy0GfDNZtj9JXI5OodzpJwT1y2axQuQ8azfHWag2XUzGW1PrxkoPbfnNd2dJgSXAYCyykSi7D3X/4Nd515Jtw9NFHR47leO9P1ueWfTnJ77Jp3drEqDmltK6SYYFjeG1CsRkgxPaOfv9JsQrPEmH62kuaKn8c9S28CHS1ezeV0S3+GQSqtqqEr16ebEgEntWr3wL0vBqYu7vYtnxV2vzocKDNTAwIurBLKEiLZhB5lNnc4SeA/8cVQEs0EhhHXQ5Q30OGxMN3/xUXfPojavtvt9+caGTHjYHh83X3LMCZ510YuS6Z3x74PSFY/pYjI/vqCBvd40VH9aFnn3gUj9x/L4YGNoODozVjojVjBuael7ZO4MUtE9g4XIDlMjiMYVb3fHzum98PtAFCCM7/zg8xt2e+GqMMItsj0D5nHk7/+n8GxvjPnv8N/OTXt+HyX/8Zf//j7wLOiHB9dUIpTjntTNz2wFNlheN0dPcswAWhlIULLv0RbnvgqcR+UElAEwCGJ30qKcwMYGqL6kyUiXHt5d8NjFeciz5heA6pcKQ77IQP17TXx0D9b8EMoYBMr5q1CADw+h5/jUBcG3B9Y2de99xYo9vhBJs1ymw1JbwiugwhB8TY9mBUfYpTT8Ohv3eDkkTHoL+/pH+L8V9WvXBcUSFERbq576MSeiHib0FP13K8PYdp0WFIGdTXcNlFI91JcFymIrN6pPn+DUGm0d/XRCPA5YIdnPulPE2PQWWQYD43EHTsDxdsvLBlouI1b/BSb96991z1maCTB+nlkQBBTN+xHBYIvHS3xjvxTr7hMZz7pxfwh2cGIropct0kA2CvhJzuGfXyGewSML0JlhISGCxKWqeVC/rwQreSl7EcZHkUmf8sc7ylZ00qccrBfn5bNjD5zG5OB9QVAbHwlgbvqrW9FWto6zTBlStX4vhj34neDethdPZgn3ltuObKHwcMb0opBga3oPjYo+hNMQxnZmPOsqWKzpg1o+q+J33iM4lGTkCc52vnAaf/Sn03OZpMRZWQxkVAcI4Iww0A1gxNYrRg4+AYJfaxooO0QTEBEZWtVilFTuLiXCTwTqiXYyaNbgIxATWlDCzqyGHTSCGwAABQlXBQGBuG87j+0T78x8ELY3972JJO3L9ePD8jdF9zPK/vTacchMGJEvaY1Yyik5wHnjUNNQHdf98/cd43To1VbhbRNdEO1m7YhEvOPTvCkjjmmGNio9s6pAPF9WpuD2zuw88uOS/QVvRjyei6w/yIBIWo/xu3UEpSrJZGS70K5vpxdCddxhQ0tY5cCpnWoMLzaNFGS9pUC5kkTMZEusPgkl4OqOPp5cPkAlVCCXxp7VdXRQaA4aFtQoV9z8OBd33B/3EpD/S/ACw5SNzH1n6kjXb19Y1XXAoc+2UULBdzjjsTW+R6JpXF+Zdchm+vtoGuRcBB7wH++fPAfeiROx1S6ftDN6/FhL4+ci1wV4t8fv0cX5uiio4VR5X+yKc+CxD4KtuGgZNWfhrX//SKwH7cY0bUEun+7l+fxvAPTwK3fToloRRf+vYPcKRX0YHDF/saztsgBJjTnMFYsYChgX70bevFQYcfgat//1c8+I+7gOYO7Hf423D4fq/CeL4YqGxBiegLeRt45/87GQcedgSsoT6YXfNx4Kv3ACBU2uNoy/vsfxBuu//JKan7J1UIqOVYjHN8+fbnMbsljT0HHsCkvdg3bM1Q+aamaB15XioG3pOrRT4pfGNPN/oo8dOI5Dt0Gcctv7oOl5wn5je9cgilFO98/4fADRP4yI9FnW3PQbRHdyce6RdGxRcu+Bpe3DKJ2zwd0cnhITjFqLO0GKICl1wmnGAHHAscegLwu68C29aDc67urbtnAb548Y/wn5d8C3x0SyS/fcOLz+Ku2ycBCGdXpXFnZ0I3upNECEWkmoN51SscV9TWtr25wKQEFhOsKd0wdLS2ro8Q8vXbjClHi8s5XOyake4knHTD4+gdLeB/Tz20bC7yvWu3Rz6rxDBU7DEvLcM0SCwzR8cpNz6O2z9+MObFMFhufqof2yYtFWnu1uvIGxR6BbNwPfBsyoh1RpUcFqDOt+fEQe56aSuu+Nd6XPyuV2Of7lYMeQzWBzcMR9gU8riHLu7ErKboWvvliBmjewY7HTLXRwoO6QNryQ4OzJOWW7GGYS0g8MoYUblN4LLgxK8b+pSQgKhEGLmUgf/93Q249uJz1YKAEBJYeMrP5EJSpwmmDIp58xdi3vyF2DRSwLz5bTjvkstwyfmfB3NddawvnPZR/3iU4oJLLsPr3/lBOIxhaHBLROjlhp9dGS1VJnlcOkKiMwMb12Gwv6/s4iwV4wmXlDNA0JwtN+owsV2GksOQNqgyequdUsPiJY4m/CPVyyUjgZD4FAL9WuoJNpz626ewPW9j9bZJfO7NUQPl1XNblNEtDcDL3rMPbn1mEGccvgSAEAmR7VmfaAkRZUAkdG/yH3/3a/DmLuC4c8GeuxsXn3c2lr/lSHT3LBCCZi6wW2cO657cWLE0WBKksJfLgecHx7FxbbT0kTwWADx43yNonrsY7txXqYiEjO7FRczC5X5ACN5/0n9g5We/OGWDe7C/D2tXr0ZmzkJwzrHthX4sWro7lsUcd8NwHh/4xaM4fEkn3lJ4HMAS9Z1Qyz4QgIjGxUW6k0DgO1qksJpBAYNQ2EzL6Q6VQdKdZboxQQgBFu8fPElpAtCMxoULFoBuH/G/L4g6yi4Itjj+YuVNxxyL4953IL59uafe7GgRyyowd958FNg6BHqNYwNaH+RW+WOWGwN1p8NHTvssjnnPB5SxCAA3eGWz1LEoxbpVL2L7tq147esPiW0/YabJdjcFHHMWcLsvRskZw/e+8gXsv/wt6FmwQBMCFAaCSYVC8B9uug4/+dY5qiSUNDoIpfj4+SYO3+9VytHEOLBloA/PvfASZne0YnIyj875u6G7Zz5m77lUCQ8N9vf5zhXdweo5I8KlwOrBVI/x/OCEKiVJfvJV4FMaK6qlK7Dv/zv9fPxuXZDdRMACjhXOfYct16oa6MOFQYlqZnoqx8Vf+WKsU4cxhj/97lfCEdXRI/55mKMt2OfM6oLVPAfYLNggZ51yPPhRZwAL9gYAfOaw3XDl/RtguUGl6bGJPBYduDvw1lPFB4edBPzx4ojT6BeFV4Gv/Bk+5D6MX4+H8tsdG+ef/nHgradi6aFH4aAF7dhVIds8UIFeDvGaRF1uEYm1HOGwTRsE3BUFweQrnNOSCTiZOQumh4lIOdCcNuB6OeJxVUcaCcdleLRvFK/taZsy/d9lXFWL+NJtz9X8+3L0csBfJ3FwldddjUPi+S0TEaObcx4ROZvbHKwjnzH8Z18u9U1HyWXKoAYA23PanPvnFwAA3/n7avzywweq71uzpoq0S0jK/OmHL8GmkQIO3IX7SrWYMbpnsEtACqnRkIEaVnV8dnAchybUrq4XjHFVk1XmD+mQlNFqkC1uVwY34C+YZVRPLjCrrUvsMo53n3AKDjvibXjhyUfxpU99LLYEzMXnfx5XHXg4uubNR9+GNbH7rDjtTNzwsytVTdMzvvw1XPGdb4Tyz0I0fdeqIooUE+mGX3ZN1m8OR5dLDoPlMrRlTUU5rfY5z21JBxZhrssV44F6nnU5NVGPbq6iKoTA5UyjHlenQBqG1Bh4adskxmNypmZpwiayTb9p91l40+6zIvvK65IgCOdR+d5kfsQngJQ3cc57FdgTf1LviEO036xJsVtMNDlcGkzHRMlB1qQwDarepcs5xoo2OucvjjUIHvj3Q3jb294mjERK8c3vXY59j3q/WgC4jMNIcFI1qj63Dt1o1Q2ipLrGNz81AAD41/ph3H/V14DTfim+cG1cfsk3gLNvBQB0NqVjc7rjsG1gM55++jlk99sHWNQh8lW955EyCEqOPy7oDquwKrI+DnLOASPk5S9NCkEoDz3zusEeHfC/LybQCVMZ9I9rRrFnnOvQI3cSq7dNgnGOxR25aH9xLWBiSG2GI/pxRrb+7gER6X3u6ccD9aPlO5PXcetN10UchZwxXPqVL6rzXHDpjyLvObYKg14aygNjLgY2rsP8BQvAIAwJxkT5Ipoi6Ovt9Q1u8aCU64EzhmsvPg/vP+5daOmcBc457rzlJlz17XMRZiqdceH3cOKK/wABiThapGM1TnNgZ0J3OvGm0Bx82MmBzbDBDQCf+/JXA/fiMo6MSQWTTEvhEo53sY/OfNNFPTmpYBSZob5iFbDm6UcBiLE3rH/ArUKAbm7aIq3Edjku+F9fwPXfD9yP+90OKOdcKR94T4P9fXjy4YcwOC6i2Pa8VwNWTF8BgLuvxoZ//hzb3vdkw99xwXa9AMHUMkhzWlpTOQE7wCuvykTqRYpTJcZpUgOcu4DmWAlHLBn3XXjEUy93uUiV40SIdE43Df/K+zfgl4/24t17z62qpnU56LnJj/XFi5m2Z81ArvX+Pa14ql+MxUlVYyT8pY8IVgnRwWDQ4pBFHXho00jgd3F6MHHz2my9jrxJkTb834WN7HDke25LGlu8PPWBcX9+KoWcNgQk4Hhpz5qRnO5dTdm/EXjl3dEMXnZoz6aUp04KDkmEB4nTf/9MbL3ceiFoqP45CTzam7aPpIxWg97166IGL+e46IprAvlzFYWSIBYeQimXY27PfCDbmqhky1wXA5vWw3E5dlu6R0R8hxoGTvz4aYF81o986rOR+rgR1VnXqZhfa8bldGu0QIfxgLr4WNFG32hBGd1yYHVC0edy6GpKoy3rpxXs3d2qFEQpIbAdn9ZMQgaAQcQ1ye9bvUhzroJa77MD4zj+2ofx99XBHKyOXCpWqKRdq8Va64KBECA/6hsxGVMTL0kFPdV6lMUgBK63eJ/bswDnXnxZ2dJgOp4fHMfa7XnYHqWcEqHUbzkM2c65OO2CS1S7MgwDn/rS1/CV88/zjUTG8PUvfQ5Dg/3qJlzOypZ5qaYfVIuw0QotdzhJHEzPxec6DdRIAVmfIksdC3e+OFjxGu76/Y141xv2w1dXfhDvesN+uOaaazwhNW9x5Knpx5VBKqdULa4p6CN/9Z57YN/9/Oh3xiRob9HyR4tRYxoQY6peNuvET3wm0u/CkTvLYTjx+sdw0g2PY1s+RvnbsUGLYzilcwC//PABkbJncTnE8t0/cM/fVf735Rd9PVFgrVz5LIkk9fuH7vlbdGcrKqZGqYEFS3b3VLPF8SyXwWaiD69ds7psupAs+UVAsG3LAH4SMrjlfV1x4ZcwsLkPLz79WCTfHYTgkit/HptrraOcrsN04J6/a8/wg98Ofjlvz4q/X/7GNwa2OYQTStTp9oUHu5rS6MwJo0xvlgF9EVohXhQ2ukuT+N/f+JH5tEHw3KMP+d87VkBYrTSSoLRspHDxt76uNt/x9iNx2/1P4r0fOgW//dX1OPYN++H8z/tCbsWRrdE51fEdEtWUfasHG4YLWJsgFlgNPn7IIuw7rxXvevVc5ewoVzWDQDh7izaD6/Ub26tkIXK6xftOmt/1Shl+CVGPMUWjWjnTgV96ImB/en7LlI81kqBhI7GoI4sD5vtR2/asib27/fkmzti0vRxxfY1MCGBQCsOrkqE/oYvf9Wp85ag9cevHXq+q+cQ5TkZilM3naqJnGYMG+l7KiAqp6dsf3H+++vvLf3pe/V3yxFj1c+gicmu25bF2yNcoARBbAvPljlfeHc3gZYf57Vkc4NFGqEd5lgurkhv1wt21qnLpgVoQEFzyaNE+PZDXFOmOiKhALDr3f93BNRsXUvzJ8WjYcxctjVWylefoXrQELmfonr8wsuiVnviwkXP8iStw7S1/xdlfvQj/c+sd+PCnPx847qv2enXFa5Zlj4I53QQlm2FwvCRytzRP9qqtk+gdKaLksIBDo55ocxI4h6prLRwp/neEEKViDggq16vntijnQRLO/uOz6B0t4pzbnw983hHjoQWEM0m/nmogDfWtj96Fcz/pR4+efPDeeBGf/GggGrawPYsFbT5b4d0nnFK2NJiEbGMlh+Hp/jE4TLBPCrZIDditswlHv//DuPmv/8Cdd96Jex9/HrvvvV9sDmr/Rn8R6WgMg+lGJaM1boEbUENtagv+oMVnJGwpAVvz5Z19g/19uPKb50Ry6Df39Xn0clF72KB+FDutUTfjxo4AQnmzLzzyLzzz0H1qO2NSZHMaU8XKByjfEhP5Ah558lm13TF3Pi649Ecgi/cHDv4AiGEGIneP3H8vXly/0b/P8WhZmf93kihzdtZ/fBD7dLdGyp4lORojjpIQ9HdW0SkhfxNSvx/s78MNP7ksuqMR1AKh1MBnv/Gf6O6ZD0JEibO7774ba9dvhO264Jxj0dI9EtXHAd9ZQQBs7t2YaKAz5uI3/3MVvnzKsbHCmp2z5pQdd8Nq8D/89len1fge7O/Db3/za/+DXFvyzjo2PqX+fPHJRwNfuYyLWtwIUmP3mN2MPWYLQ1V8Jz7fOrAZkKkZ7zgLaOpIPm+4NndpArB9dsdT/74Pf7rlN/73/5+9746Tosq+P6+qOk3OkSEHAUEEFFFZI0ZEDKsIYlZUzLpKUEEl6ZpFjJgD7hpQMKc1LKCCICiKIGkYZoaByam7K/z+qHpV71VVp5lB+e13zn5cprqruyu8qnr33nPPCfMWYj27FMIVkhdaWoG5WFhcgvzCYsxc9hPu3ZkPLb2As0774PtfALtHO/M78di+tQXtrXBffXh3vDB+CPwe0aT4RhN71KAHmttrmiErerAdkvWCgUckkDXeSjLil4DpEWe0QRRNc1jDdgTK6lo7dN5B4ZaEP7qX9TzJSfZy5ygryYMkhtLudv521LagrL7V7OMGYCQ1LE0keuwAvRgw7sACdEkPoFe2fj25JU7sIrcCAWc/5pUEBJjtSfKKUenlmUkeuBXqW2XVrIDT7WOfJXaBOaCz0t2JTuwzmKJDxOoRAtzpMHVxeg7GA03TuKonIUYQzghVROpLdQOn8Asr4AXgqrQaDQQwlHL14CUnvwh3z57rDOoF/Td6dusKRdHVct0mvXZUlpfhkdl34KJxJ+Che2bg4nEnIKjx3921a9eY++za0w29MrStutkU12LnliFFr3Kzj7uOzGT3yU226OYGvZz+mCjo5zTRWJC1rGPZFhkBD+fDSUGFQ4D4923pJYfixdO6YsGMazmRp7eefwJKsNmxfm5OtnluK8vLsPa7/6K5Rq/I0laJaNZgFCEj6BaNhATN1Ot9eiq8oi4OlpNXgKOOOgo5BYXILu7mmmAqKOluLita/NdOexEraHWb4LJqqL3Pm8p/IC0XDqiRKealW9373rf8sZlTL6fsGsBiiciKZt47IgV1eV268S+0NnJBdf2e3VypY+JFl3ITfNTp42LDL7/gpZdeMF8OyirGjZ8E7ezZwKgLceebyzFu/CQusLvk/PHm+ss2OCtBvXr1cgSJkYJs9t4XTyAdMBIJMZMSBuzq96Vbt0ALMYmCb4x9FyScP/lavPjeZ3jyjaV45uPvMfqsCRAIwfv/fgXnHzMUJ4w+HoMO6I03nn0ca1Z8i6Vv/Stqpf2aqXciv7AYAgEKu5REPJeECHhj0ROu2bhYgZhbG8IrTy/AGBd/9cryMnz/3/ZXw0u3boHGMD/iQtVWIGwxKuxikpqmGZVuEjEYo1VPug1mANvzEGCgrlhPCMHU2ffzH7Rva2sTF3Q/9c97+GNvq3QXFxY47AABAJIXJNUKqmkQ88EfDToDafBJXLIOB5/m/A5Vf1YQQcA1t93Z4dRyvY+646b2VqU7+jOsKSRje02LQSun1mFGklvRTEswN3AWYsb8j/p2CwIxhNo6bJcAAF9u3oPTn/8Bd336O/c6y1BrK9wq3Wz1OMUn2YJuLxd0e1zOn6rqrDyBqXTrughWpZudO7PwRmEr1NrmLileCUkM608kemD9xBkD8NTZgxDw8HTygEeE3xGEO9s/grKK3UyQLauaa5X9+D6Wm4ans9LdiU7sW7C+ygBw2oB8AHq/y7G99YdZrN6iRMH6blOaFAtZVROq1tkD3vq6Gow5bJBZlaB/n+YySWJBhahkVaf7fvjWq7hjhkXnPeeK6/Hsv5fhqY++w7jxk5AR8KB/fqp5Q45G3V2y+GWMOWwQXn56AadI/c6/3+DWi8cyzLWn2wj4QooKVdXMhyhAFa41NIcUcyLm94icFVtHghBAsQmtKQmeUztYVU6RENdKdxrz8Habp9sDkMryMmxYtRxlG37UzzEzCdRCLWhtdPaG0c50NkCi48ptLEdCWNEtWvRqLEFQVsxee+o1rB9HDdXNIeyqb0VGXiH++cjjnP3R9HkPIafAopfJyp9T6abiW5y9EiMuE6k/lvUy3aza+lRHXej8oR3ruD5q9vot6tbDEWSJoog+ffoAxuQx2StBAHGtdAP6vWPOY8+67uPu3bZg19azXbVzO9fG8PozC/ige+37+r8ePzDwePPl2rp6LrlJAmnOwC6QYb7/0UZn0B1L+AdwH6MlPXpGrRwDQEuLHrjZE5pucFO/L+nRE0Rjrs9m4zqSPHjt2YXIyS8wLRdVVcPu8l144PabOcbCyw/NxnXnj8N9d98RlbYyYPBQuiHIyi3A5dPnObZXEEWces75rlVwN//teBMVmkHHL9+l02SXLH4ZYy6/BVd/04gxZ54T9VkTCyU9egJJThGjoQVRrDqb6wDFOu5Dhh/Cva1oenChax64fwV776Bq/iaMvnJN05Celc0nZAK2oDvYyF23LMVbh6Z7dxvYvH4NvG7DTPLhpPMus75WVlFWZnkTo6XOISoHAMf0tgLx4YceqgdHqooF8+9q13lxQ9ho2eooVfRoARsLRdUdQnpmJRltGdZ1omiqaQnmBmo5BlhzB0v537LB7Eg8sWI7AOCDX3dz1lYdoZDtVhjKY/qk/aJgOuQAQGbAgyRv9Eq3z0OQ7BX14Jp5nSZyqRaC22GyP2dY2BMEqX7J9McGrDzu0OI0DOuS4dg+v2Snm4sOz3BAb1HinABk1bVY0TvHasf4k0hyfyo6hdQ6sV+BwHgAa7oQ1gXDSzAgPxUHFqRi4fJtAGLf/BNBwCNC9HqQmWRRDVkaFCF64JBotY5SuV968lE8OtfqAbMrrbLK0w4YVHdVA3aVleHRO2/m7qj/fuZRXHTZ5Qj6nQ/5aIjWG6nZxJo8osCpCbttp0T7UrmebmL2ctPeSPouFVVrlRWzmlCSEYhKX2sPKFuB3TZFVU1afOLfx9PHWmUF32512n4kM5lru8e83dLt5DPPxYdvv8GLKTGTRKKGkZ2VAZTXct8TchHgouNq4Ii/oaAoviqKyTzQdNG7nGSvKWJkKr0bFYsNFfXY26pBEAjOnngBCgaNxJYtm5FX3A3D+vdGaW2LeZwUTU3Yii1R2I/lNVNnYsBBQ81qoV2ojR3PLdF8P7NcmAGtDfqE3aB6s9dvZl4hrr/rfjw26x9QDLHCp556Cl1LumDL5j2QBIJe2UnYWdti9XS76CEcNPxQVzs1eHm/XwSbOD/kbj16AGusPk574gYt9a77tbe2zjxnAJDiFVG69Xeo+X30AGLTCo4yS5OeZw0qwFvrdeG2WP7FleVlmDP1Bt6ecNqNuGbqnVGDWLbqu2VvE44dNx4jjzoWny17Fw/dM8Ox/tzHn8PoMePM36Tn+eqbpuJx2pVEe91FD0dfX//zryjs2gONFaVx0dijbSt9hB099lycftqp+H3TZnh8AWzbtg1b1q7A26++4Pg8EQQ8v+QTDBwyzHzNPranz38YI4861n18AFB7HIoz/7UZVw0sw4KpN0C7YQkAQPvbxdGfNTGQX1iMg0aNxk+MuLCPqMhKTQYqjBf/+B5ILwByDHZUSz1Y6sWKr77gGFd6pVvQ2TURptesxZS+3VusNw2KO23f4twQ7K0iwWaAbVWTg1xEL4giho04DD8YZKJZ114CTHwQSM4AAJzZLx1vb6xDal4Rvg0KAPT7f1BWsW4j07LS2uRKe++Xm4IvN+saHau/+cKhNdHW8+KGsKIzCDqq2u1xSaq7QdP0qqhuX8VbjCmKBjWq9p1l2SkYzckKsxzBraxdYNlqbLU32TXbkhhqW/hg0iMSbo7p94hcv3KqT7JVup3XgwDBmIcSq0hELFV/nl7Of96NjWhuqy1BkOIVud93Y9+zlXi/rfJtVbr5YxB0CbrrWpxBdwFjV7a/edh3BDor3Z3Yr0BvKvRikwSCw7plIsUnMSqaHXgHJkC2EWBQqBo48Sc5Vi9SBFSWl+GxebOirhNNSIVWKjVNw/rV3ztVezUNP636PuFsYDRKJ/HylYuyLZsc1Sk7rIeyjSGgaJAVzRBW4b08VU1DmKmCSgJBoJ02HZFgV6QnRK+2J3I/ZxMU6QGJy2T/WFaPnXVOiyQ2+8t+3i1Ifv/N1x2q1YShMl9+zU3Izshw/EZIdhfgUhUFu3ZsNY95LIQV3ZYlpKgIK5rpJsAet+rKcqz6biU2bytFSUYAAvSgPzu/EH0PHslVuAFdmE5RNHeaZgfB7VguuPduzmaJZXu89NSjHNNk647SxH6wpZ6riLHXb1hRcdq55+OPLVvxwcefYeOmP3DppZeaImr0P1GwPFXdJrQRaeZ2QaZwK9igJr+wCOyoJoS4B902VO3dy1m1BBUVXbr3AM77J3DaNN12KSXT8bkUYn13rEr364uedFR2VUXBY3Nn8ck/QsyKJctOoEJLVU0h5BcWY8ghI1x/p6hLCQBnVV0kzG/Q40AEQPLg+2/+g9NGDsYdl/4dl51wCH5c8XVcNHYWRBAw4dKrAOhjcs3Kb7FndwU06Ofz4BFHYMvGDbj/1itdA24AuHbaTC7gjpRMA4Dp8x+G60Np7DSEIeLRX0L8PSHY3G7Rrsyi7txybloSN9k++YTj0K0b047UUs9VlOfYBO40WJTzSFVZVjTKQZHvNwroe4RJ6acMs3kLn0f/4bxom17lttHJ66vMxaXLf0KXkhJ+fSbp2btIp7w2hFSO1dQqK0Cm1eMNyQv4rUQYBRtIaGFeiLCjxdQoM4k9pvWtYddWvXjgjZNerkEz73UaI5xKEW3eFpBEzopVBf/MlPdBQp49j2y1N9Z+JvrdgK7vwiYmfbY+aJ8k8JVul4SJBsuKk176A/JT0SdXH2/R6OX0t1zbNW2Bb8AjcoyEknSnrzd7bmJZiF00XE/y1gdlzP5sk/l6q6ygPuhkBGTZCmD/a+gMujuxX4FSzdwutXh7i9oL1l4K0LO00RSYAXcl2dKtW2JSoqL179HjoBh95274/r9fx634TRGpN5IQgiNOHMu9tvr7FRHVhCmsap2Nwq2peoCtatCgT5plVYOs6kG4rKp/Sr8v20MLWBZhidzP2d7fFK/kSouyg+1rYk9fPH2smqZh1n0Pm8vHnXIaV7m4+BB9ghgyAiS33uou3XqY/qmxQL1Q9aBbNRW3abJkyeKXcclJh2HqVRdh8ikjsexfr+i/L6tQNA0hWeFaNAAgze9Bn9yUuKjHbUWkhIPbJPalJx/Fo3NmclWmRjsNsHIzUB9FvTbYyAXd7PUbUjQEJBHdupbg5BOOQ+8eTA82sWwRWQqgpV4ex1ny2SrdShj2s8suTbx8itk/CiCimvmvGzZgU5mlzB6UVTSGmGMaSAOSnUH3S/febv4drapWWV6GV5953PU9x/3RxekB0MeZXxLMBE5jo7syc0tLi2uw+thsqypOmD5jEBHPLXiAU7x/aeFD+NuJY8y2iUgQBAFnXHodRo85A9Cg91Ub7UNTzj8L151zEr58dzEAXQTsYRtTyQ6Tmm4g2tgeN34S5i5YFHnjVIVX8G6oardol901JCPg4ap1eZnpyMpgKswtdZwGgqaqWPzcU+YyFYRK8UkRE3P6taIfs9KtW/g3RQkYcxuKDjjYfGnFV19gxjWX4teNG7lVR446Clf9g2FGKGGQnetwTEoNHjl9IPILi9FQV2e9L/PCapkBXnSPIiRrCHsZKrvkdRWZy2eCbvb+AXS8mBr1yWZR0RjkHAsSQbzFDl34zFpm2/aykrxRA+eumQHTfQQwrPrYHMk+KHm2MK1FtVwFNj5ryGhg7fUAvdWM9f7WA1WeTs4GqpGemXo12xJS83tEU7uGig663WI8Uc6hfVvp+X7zgmFYdM5BKEzzOT7D/oTXbiFm6/EuTHMG7YBR6XZ1fWGC7v/BUndn0N2J/QpuXt0UZl/KvuAaMWBvuQSArFn2U25w61UEjF5Cl6AyVp8pu55m9Db1HjzMdZ13X38Jeyt2xbVfFPmFxbhm6kznG4Qgt7gb/5qNbu4W0LhVugl0KrJlF2YF2oohrPZn3U8J+Js3VYVnfz6WuB0bZBPibrNhBzuRZCdB8QhCCaKIocOHm8vpfg/3IKb94hqA7PwiV/E+AoI1K75FaWnsai49Ty1hXQRHNLUVNOyp3KW3IzDWYHOn3Yg9FbsQVvWkCs22q5rGMdv2ZZUbiOwWYJ/ERmSd2APZ5jq+OmzDiaNHA3+s1Bfqd3PXr6xqXLWChUgIRGIpzFIJQbd+SbP9w0Evt/XPymFnRoVZPu/SK/lZsIsft75xXpRWWu0R33+3EuP/fha3Su+DDuU/oyrQGq3P0EmXm07BZ8uWxN2PKQiCq9NDSFHhk0QzSVHS3dk/T8/7T6u+d9o2MkJqDzz+NLPv7sHUVx8vw4OvL8NDryzBqee623b1G3Io3nnuMXy67B1omsVSofuqqSqenTMNb760CL+s+SFqos0u/gbEHtu0DcEVrY061ZtCDrbb87spxAcjmQEPl1j02SpeCLXySR8Arz7zuDk2VOjBYd/cZAzt4uwXB3gRRkdPt4G0fL2SxiVbbK0YvXv3wYVnnIqumQEMyvXh/qdfxLtfr8Y/LxuLI3rorRMpacw2yCEuOI4krhWUFY4ye+r4C/G3U89yrCe11FoLTCKCENIhXuzNIQXNxvlRNY0TrZNVzVXYKl54ovQDs9CYIFuDLvxFn3v5qT7OEisWNJXv4c5L8aJ7VlKUT7QPtTbac3thv1YCEk8nd6t0s8/KSElMqnnkNq8UjF5vN5X3aD3d9m2l84zuWUk4qMjdpYC9zu1+8PYe74wICauyula894vThpMXoHX96P/X6Ay6O7Ffgb1p2xEtW9eRYAO0dL9Hr3RHCB4iUQCjqcW+8O6nUVXFKQhopRtIyynA6Rde6bKtKipKt8W1XywGDD7Y8ZqmqthTW8e/6I3sCU3Beg2b3wU94A4rlGam/8t6dut2YQlvesKgNGl6VqkgGH2oR0qa1DSH8I9lG7B8WzWXkW0Iynj4a2cldcLBzonTzNF9ke6XMOfkA8zX3BTuTz37PEfQXFjUBQ+OHYA5J/fTLUaYBx07CQwpqkO8DwDGH30wbrnwTPTq2QOLFkWpikE/Hqqq/6doRlUWeqV713an9zzrC69oumK+3g6BmKyQRLGnKYTPN+1xrXZEcguwT2LdWSfEGci28EH3pGHF3GRp1BGH4+3Z12FCd+DFicO561dVNVfVWYB3ZxAMBgFgTWhlJmHlyoQQPc4AUQmBvVOuWvEtWlqtalZ+YTEyshkF9qCtOrztR2MjvAh5LUrsR0vfBdLyrPUkHzYzlmHmdzEVY68oOK6jmTddjdNGDsZD99wOByIwXCZePsU8dzXNITOQCCkafJJV4ckpKMbk2++zRPyM804rnY6fU4I4PF/ECX1zEVBarcAnQtCtqSpaWpoxZMQROP/qm12399cfV8bhBahh/u23YNZ1l0VkJLmJvwGxx3ZUYblgE0iqJd510lkToj5r4oF9cp7is/dyiqiuZBLActAhWEbt3GRF19TQhaFI5GPD/B0pME3N1Knf3HVjC7r3lu/E3t3l+PekYXjmnCEYMnwE8gv5dhhHwxFDL0/zRwgcyiu4gC2roBhBYltXU3H5yYcxO8Xv68ijjnX97kSwq74VO2r1hnRF1ThF+JCsOryWE4E3Sj8wBSt+S0GfC22BBj54zEry7rP2MwB4+jvr/tYhQXeQv1Z8kj0wdV47bNBtf46w55SKy9lBny1uk2caBLuxRO0MFm8cJ21wURpO6JuLy0d05b5f3xc+oRApYVXV5J7cTvdZ149dC+d/AZ1Cap3Yr0BvHG69HPH2FrUX7MMj1S+hfxSrlEgUwHdeewka3BU3W1paMHzkkY7X7aCVRkBDSYYfYyZehqUvP839niCKKO6WODWNVuHZ7SOEQAqkALBUkXsOGIJt74tQDWEot4DGrdKtwVAphwZVJSCC3mtEacySYQPyJ8TcACHczdvvEUGgT6QiJU1GHnUsFvzUgC8378WXm/di4ZkHmp+3i6RQ5KU6VU9PG5iPMQPyHJPKkUcdizmPPgMQYPCwQ5FfWIyr/zHDIfr1t57WxJl9kKX5mKBbVpHstcT73PZp8uTJOPHEEyPahgXDCohx3SmGaJOq6dWK/K49HMJNgiiisKQ7Ksp3Yt363+AZ1B9I0itGHZ1IuXDxWlQ2BPGPo3vh3CFFWPJzBT77vQrzT+2PFJ+EceMnYeRRxzqOHQtaNeSuVW9A7+tlcPCgA1EXyMMW4xLISfbBJwloNSZiyV4RXUtKcBPb/2mAeqa6wfRRJZQCSHu6nZVut20lgVTnXEoOA79/CxxyFlBdiikTpwDj7tDtlOguev0AjR3sFfzSdUD3oUC3g/HldkYJXfIC6fn8sr2fvLWBs2BqrNnjqlMQCRdfcyNefPxhfh8FAeMvmWwu724KQVU19M9PhaJq8HhFqJpVzRt99gQcMuoYhKt3mYnA00YOdheIVFWsnHYarpk6E7XFJYCSqttPRQi6AeD39T9hxOGjkFtQhPMuvRqvP+tOkU8E9LwSQcBxZ0zAkMOOxDF/c3eYAHQ1+z4HDMDaH77DkENGcD3f9H069tf/tAaP01MSbMQJ516Mj414UPRFURmPE1W19QCs41W+fQu6DbHui1t/W4/f1/0I9Pub/oIcAn58Fxg0Gvjta33/jaRtc1hBwCsi1Rd9ChpP61TQuHa468YWdH/wr5fx0a1v4ZqpM9Fv4CBkZWUhNYMXIK2vs2keMAmDHb+s4d/75Qtg4LEo3VWOV8JWYrrVTZE52KT7gJs7xWt9lG7b2q5Kd2tYd5qgFXc9QANoyiKkqPB7hDb3RcdT7CAgpsUXhaZpcZ0/N2jGs2dfwb4vW/Zadpw06F5VWosF/92G247plVCVHtDt01j44qh0swJudoaYomqGJgitaEdmULrNjqMp0NuTafF4vAuEYO4pViHBG0VYLcUnQSRAvNP2AHMcOlqxfn9AZ6W7E/sVBBK5L8XNmqqjQSIE/JEQiSr87CP3YdEj/3S8Hql/y43eLBCC3bt2YcXHS/Hfj5cCAOY9tMCqfAgibp39IHJtGfv2oNWW5U3PzY/p9+3W060Z/duKokE2qOSappmVUVp5butDOREIRK9CUnVQSSDom5sCnyRE7Zv8Y4/1IK53sQSzIyvgbjVi30daEZw25RLMuOYyrPjqCwDRLd4APpsc8FqZcXsSym2fFEXB5s2bI257SFEhCQQqrGo1TX5l5Rn0dZs12NoVX2HsyINw52V/x2kjB+PTt14zlP879pxWGt6e/92m05lnf7YJK3fU4l2Gmhbr2NmrgkQQcPHNzgrsiEMPQXamRTNN9YlcL15zfU3ENgQNlu+2HQfkpWJggT5xExmxGzNhxWT63CqcV02f7fhOosm47tILQJ6bDLxyo/7i188DwSacf6Degy15mKCGtUwCgKZa122F5DXtmMxluzhUayMIQ7/dW1GWkOJ33/6DMPGyq817JxEEXDt1Jnf+JOa+qic0BLP6pWqACCC3oMg877G0ElRVxaNzZ2LalEss6rAYOeh74cHZeOWJh/Dio/fi4BGHx7Q2iwVN0zBl7uN48o2lePe/P+HSGfNx+AljOVV9+9hasvhlXDzuBDx0zwxcPO4EVyFLOvZPmXiF9WI4iI8/+dRcbG/17qUnH0WL7TvWrvga4WarZWHpq8/xwaUcBBr3gjx1AfD5E1zSVja8pCNdLxQEsW2DaLsZd93YkwxK2Dz/UyaeiYmnHov33niVWyUlzUalZWwmp19xHkcLx55t+r+5PbggZ3VpHX7dzVv5IdjMLzNBt1tbQaKQVf3aoIwzDXpQRgMWWdViugtEQzRqMgW19WLPldoOJhsVkO1ItIQVzPjwN3y+aU9UTRaaxLnyrfX4uaIBD3y1JeK6keCgbIvOINsehB+Ql4KT+uXigmHOxLh+/yPm/NjtuFL/brf5qydKa6Z9WyOxtaKB/Ul7pdtON6fCam6glqUU+7i+9pegs9Ldif0KBJG9Bj1S5GxdR0JJoJGEPuhNq5IoiFQpZm1hCCGYePkUnHfplfji00/x4B03WWrWhGDOgwvw6her8fvmzSgq6Y7+vbujMZi48Icb3VbTNGzbvgOQcszXKiqrkF94UNRMPLXeYimyaX4JQVlFdXMIIUWBJIimkJrV0/3neDgLRA+y3RRyXSugADas+xFN3pHmcjzCaZHEdlj8snY15tx2fZssY9iJU0AS4RUFyKriSEK57ZMoiujdu7e5vHPnTmzatAl9+vRBly5dEFJ0j25q7yYwD3ZV0zBu/CQMHH44Nv+6DjndB6JrTjLGjBzMWUA9cfdtOG70CfBn5mFfIM0n6WrBBuLpF2dto+wV8QZPBp5/5UdufcvuhC7zWfvbr7oAWtmvpn2TPQkVSRgwlaHY6ckM/W9aIahsCOKRb7bi6sO7wSMKjm2tEdOx8DW+2jbnoYXIai6HVltuvVi9E1g4EUcufhcAQEQJpnWLZrtvRlAzH3HMifhu1WrrBY8fvow8BJnbxYAD+uH2Kz/GhHe3AwCSA/6INlZ2EEIw/ZpLrZ51YnkWp2VkYtz4SfokUyQIK/rxVDQNkmg9F1TNKRYV6Vp2hRl0R75mNU3Fcw/NM5cPOGg4fl+/Bqrqfr8detjhWPP9Slf/bUAPsHoPGorhg/pC1TT8XtUIyUjo2J8B106bhRNPPysiC8ftXrF1J5MIkrxA/6PNxe07dgDoH3Ffo6GyvAyP3jsbuP4t/g05iKpdOwAYCRk5xLMp5BAEUcTzb3+IlpYWjoWiavFdv3ZR1WSviKaQgpv+1hMfbdyNDZWNXNJx3PhJGH7kMRj3pq39x8byUFUV826/BYcfO9rcJn9SMgDmmmBZMOFW/TtoO0pzrev2bqludr5oT3bRRFOEtoK2gN6vw4qKoKxyz1VZUeERPQm5dbCIZjfFQlX5+x/b450o3PqS24vnfyjFxxur8PHGKrx5gbtGDqDvJ/tMbcs+OPukCf9c8TgDU0IIZjNtaCxk1TqWUoR2DCqw5jZ39tkq3e+sL8dPu+px++i+Dnp5WxI07PnyRlAzp8J1xTY19AuHd8GLq3a6/nZnpbsTndjHsAsNsYint6i9SPaK0DQtLooNBe2nvfGOORHXufHOua6VYjsVWNM0vPL0Apw6YhAeuP1G7qajaRpuv/lahBUNvQ46FLmFRW0WmnCr0BNBQHmlodxcuRlQwqh4dWbU/nTAnSIrEIL8VB+6ZSYhO+A1VbBDimrSa/WM/J+DSJY0kUTlFsy/G42t1kTtkW+cPdz46UNukRUAccOSxS/jotNHOx4k8VrGeBlLlYBHNJeDtqDbXikVDb9oSi1ftGgRunXrhmOPPRbdunXDokWLEDKsvVTNmOQaD3aVsX7JKyzCgUMPRVZ+gZ60sTMEVAU7t23pUEV6lhKZ6pc4Bd5YfqqRevXprMSu2go4lVjtFQkqyOWm3UAATsAoEtiWOTbweHn1TvyX8XunFUwA+PFHG70VQH5enrvYlkCs6hkz1Bz3J3uPt4GS3gdg+HGnmsuTp92DoMb/Rm52Nn5a+bW5PPXqS3DymedG1CmgoNVibuzYElCV5WV6CwqxSJS6Pp/+v8ryMqxe8Q1qdpdzk8+oPc520KBbip0oo/jtp1WY+fiLuHr6Pa7vr/luRcSAmxCCq2f+E1l5hQD0+2NxWgD5qT7XZ8Cjc2fisfmzYirz1zSH8OBXW7BlbxPWrv/FWrGwH1DQx1zctOn3mPfxSCjdugXwuCgQy2FwoZzNZouoMqbPewgDhwxDSfceKN26xdwGzSVp4gZCWO9h4I3zh+LuE/vhnCFFrs8dAEjJzrd/jUMxHHAey91/bOB+Nye/gF05Lvs9V8hhniXRVEN/pEP6uVnxyrpWGTkpXiR5RPM5Kxt0c3qp6Had8RcuolGT7dthJWrbl1TXHWQ6dnawlaGQuyXRj+husXu+31Fr/t09K/HWDAe9XLRVtkVn0B0NepJR/1sQ3MjllpBadPVy/c05n2/Gsl93479bqyMKqbUVAiEuPt7WPTkziWcEskE4PSa02n/dke1jgeyP6Ay6O7FfgUS9ccSmObUXAY+I/vmpCVNs8guLcfyY091VJQUBx5861jWjHZkS6R5Nq6qKndu3oFd2st73ZOujSmR77RTWiZddbU2u/vsKsOBcaGUbYgaEkXy6ASDJKyI7Wb/JbqtpxsbdjfBLetUbWtuyyB0NN1E5VVHQzFiK2B9MADifVwBRhV5MRWqXgR2vZQz74Ap4hKhJqHHjJ+Gtr9dg/gtv4ZeNmzFu/CSsKq3BN+t+xxVXXOHo9y4v26n32cMaU6YAnfH1hAD00ivp0dOpHi2IKOjaI2YiJZZSPIs9zdZkOSCJnJ90tPtAZXkZ5tiqhHNuux5jmCD8s08/dXzO3mvn9/CTJV28TId90q4hcnKHhSBa1E+7Su0ty37Fbe//ai7TxMGD852Bnk8SkF9YjGlzHnD1tgZ4n9Pjx5zOf0HYVoEzUNcSxqpy6zg/tWavYx1NbsV90663lhv24MN3/oXn3/nYbEW568GFpm/ybbPvx7yFz+G2u++LWr2gx1RRNXhE4jien779Kk4bORg3X3AmLjxuGBb98y5uHMWTAAVgBmFE0u1w4p3g79q+FceefLprwtJtvyZcdSPmLXwO7y5fh+POOI/7nVS/BFEgEZ8BH73zpuM1+73irk834bU1Zbjg5e/x7BNRes4FD9at/iHu645FSY+eIC7e0yOPHIVevXtZL9hstq64Rq/KuyW/4q10E4NfTo9sQZofp/TPgyQQq3pnUGYbgzK2bC/FypUrnV8UwZEgENADqsryMnw9/0pg47fAMn2M7tltV1Zmzm+rjUJuw0CmB7hnr16Y89izwHvzgB/fM/vbNUNUrr3Q24GsSndeshf5qT4QENS1ymiWFb0HWNOP987aVvyx16UiHwHx0MsBSi83+oo1DSraLqrZKzsZvXOSY6+YAFjvbLd2Mdba6vp3rQRWW+zKGl36pNnniiDwle9Y6vJUSE0UDBcMl2vH1AtxmTuyPt3NzLZpcCYIotk/RoTtJ9nbqX3fM2zCakXMcae7dd2oHvj66sMxpDgd/2voDLo7sV+BGLY6bog347qvECtYyC8sxilnjedei0Uhi8c+ioUgCMjr0k1XsgQMBfC2PdjsitfnXXol4DE8GcOtgCJDEEUEAoGo+231pUYRWiH65KC2JYy8FK/p270/IJI1TzjWMGuu4So70R5WkSbWgiDEbRnDqor6PaL5e5H6NfMLi3HgIUegsLgYv+1uQHNIxYZfN7r2e2/Z8gcCHp3lAc3qpWQvNVbgML+wGNffdT+nHj1l5n3Iyi9sk71eJFQ1Wsc3pKjYyyie0sm2m03Vw/fc7qg6aprG0eHfeJ3v6QSMCgRzHptrq+FnJ0TM5J0IAqr3VNl0GKLuDgC9Gk7Hvpui8Oeb9kDTtKgWSIA1HsaeOxGvvv8FFr72DpYu/wkjjzrWPB7sJWYfY0TjJ1uXjdDF4T7dtCfmPoSbm/Rj+eQFwNMXA+FWqIpiikTS36Jq4vfefgumT7kU82+/Jer30qBSMXq42VtbZXkZFt59K1cRfueFJzBm5GC88/pL2FbdjLCiIr+wGEMOGRE9kDaC7tkLn8e8hc9HVFO3Y8DQQ1BQ5LTou3bqTBfGgYDx51+I0WPOQGFRccS2pUjWkna43StWbNerpq2QnH33LDw+TJ9ySdzXHYv8wmJc8g8nG6i4qAg+dvyGg1yf/1P3zcKYwwZhzm3XOyjyVeVlcbFCBEK4dgwWtCIXVFTIiopjnliOc/61Ebf/4wbHuqeecbbr97e06Mml0q1boIVagPfv08UJXcFsr83z/vyh/LV1RA+rapqdma7bu235DvjPs2abR0f6cwtGIKaoQLJPMhWudzcFTa0RGow1hxUkecW4g8n46eV8ZVtR476sXH+zI+wmt+xtMtXl2UC7wSXozkn2uv5moha1skHxZ+G1MaYAOBhV0aBo+rOJkMiJXdMyLGqlW0V5gyWACU1zaMLEo15uh/032fyj3UrQbiFWkOr0AQcQ0X7z/3d0Bt2d2O/Aqvuy+DPo5ZEQK1ioLC/Dp0vfwQdvv8F/0EYhswcItOIc79Pp5DPPQXZ+kR5ok8T6z93AClDlFxYjlVLzwq0QRBEnn3EOLh53QtTJGhU8kqOcF51erlOCqWWShr++0k17fq+ZNtNhzeNA416+wtFcxwXa0R6ckej8zy/5hGs5iJTYqSwvw4afLIqxX2Ir3ZEorQCgoTGooKFVwZ7mELr2dE/y/PrTGvglQWdPgGbNedV3S01fx/Ajj8EdDz2JW/75JJYu/wknnT0RshKZFtgWez3WA7dVVlFtW3azqRpz2CB8umxJxO+k0AYc53jNJwko2/K7ufyPS85B2dZN1gpsxUzTMH3KJdx1EU+lmxVS+/Q9ZzUT0MXxSrdugdpzBJDTHcjv7ViHpQHm5hdg2GFHYMVXX3DHo6nJnUIOAIv+9a75NwGQm+w++XFDZnqqPo6aa/XrAs4gwo02bQdrF8VW6akCsygQVDQEoQKuLQ2AXjGcN+0mVJSXoa5VjtjGwUHRJ93J6VnIzMqOSAtncfRpf0efA4eAEOJIWF5w5XWOQPzG2+82ban0a8n9e/MLi3HttFkxf3/OgkWOewV3//dFCbpFj348fMlQ8/tgTozrzg7Se4TjtTefewKzb7JsLKfNvhcTLrzEWkEOcb7lFKqioGzHtvgq3TASWa7zASPZK6vYsHUHNBA9aVzYz7Hu8EOdnubseHVNfjM93U++sdTo+TbeUniWSBdbnypbvfOKQtzWhm0BpZdrRr+WVxRMUa0Ur+QIciSRIMkjxh1MxlvsUDR70K3+pc/3rdXNOOflH/H3l3R9CrZ32a1dLCPgcX2G29u3YsGtr9/nIhpoF1KLBkXV4JUEpHglRMrtRy9YWWyF8npr7H67rca5bhvo5Yd1ywAAU6yWveZ1urm1XWm2SjfrYLC/FGL2JTqD7k7sd4iU2f4z6OVuiBUssGrUjuoaQyFjA4Qxhw3CS08+CkCvOL/4rpPq6oYP3/k39lToXqgExBDY6JDdRFBW0aTpN8Dpt9+BB555BR+8tThmkBQr+KNgWs+gglqKdMy2twXs+Vgw7y5cc9ud5iTadb5e+QcviqOE4PdbgUq0viy3SdeM+Q9zFkCREjv09dm3WXTegEc0K5WVVTzNnYIYyY3GkIwWWUFzUEb3kq6YP3++Y91XHpmHqopdus2d8RpVfacQQMxJ/pLFL+OC44bhrusvxwO3XY0VX30BgQCyqkY8p9GU4iOB7b0LygpqGLp5TW2dq01V5GDLtmFFurDUyG5WVaq5vgbff/WJtU44iLJtjHotE3TbxfD2Vu6Ks08VZiX7oZm3uq4TklU0phYBY6cBFzwKDDhGf2P7WnMdU4StfBfW/rASv/y0xnE8GupqI25HMeN64JME+D3RpwNsNS89LTVmEBFLTRwA5j7+HJatXO9wR1BUDSLRqZQ1zSGk+yXXlgYKVVVQvWsHanaXc0KFLLhkkGEHVbVnT0S20ZmXXYcpd96Li6/7B1587zNMuedhqMw9zK6YzwbiS75ejZPH8dVVRVO5Ecgm2C648jqcMfGiiMdJEEUMHmZZwb301KMYc9ggfiWXSveQFON+JRl9lOPvBc77J7TiQQlRm1dsqwUADC9k+luVELSQVTUbOnQY78sdgdItiCLyS7rFKaSmc25cK92U6aNo+G1LqfXGsHH6v4zAYF5uLj9eBQHTZt8f0fNcEEWU9LCo88NHHgmf3wqk//Xhf7htyUnhE1Zc0G08F+yJmvb6plNQthvtfdeDbqpyTdArOwkFadb2eY3+4qAcnwCrJ07XGNXGumtr61tH4T+b9WQgTdw2MIKze1w8olO8omvwm2il+8PfdF2cXtkWO8ktkE2o0m2o/ffISsKBBWkR1xOEWHa7KnbVW9fskp8rAABdMwOOdRNB39wULD5/KJZeqt+josXOSbY2PHbf3ajx/2voVC/vxH4HUSCul168wV1HI1awwE507aDZ9EhiOSDABZOvw8Ahw3D7fY9iztQb9MA9goS7qiioKN0GHNDLoBJpIB2UO3vx9X9B1YqB1kbMvf6SiL9v9xW1kiGRb5gExg3VeCgrigZNajv9rL1wS6QsuPduLF3+EwBg7sxpwNW8z/CAvr2wYa/1sL7oiilY0iJATyHEfnBG85N22545U29AOBTCfXfeqo+JvTt0derWBrz6zBr88UcAKDoAs/5xPcKXn+eYxFGqWWtYxd6KXags3YZCMgDDhw93bJuqKijbvhXdM/KgGTNcQghHQaQtAnsry/mxbASdLx4yCqnZ+SARtKnc1KXdKJbbqpvx9voKXDC8C0cD/G5bNapbmYlTdU1CNlXGIAQkn64gbAQiJRl+rNiur1K7u8Jpe8SJRbkHEqqiYHfpdghkcMzNkASdUVC6dQu0sPv3BRUVO0PMRD6gT7SOHtgN/zHIFl5RcKheO4LNqq1Aag7cwI5XUSAxxy87cfe5KKzbq3bxqIkve/M1ZGZlo6RHT+7zigb4jBm73yMizSchuWtXXHnHfXjy7luh2ZTYBUFESnIyvvrw3YhJF+51VZ+IV1RWIv+QUbhm6kz9fsxgyXOP4/nPfkDv7t3gkwT8sbfZVPaPigi/r6qa2arAnjdBEHT/6P4Hup5DO6380YVP4qX77nb+zmHnOn6zMM2HtY3Qx7rkBbK76m90PzghajO9DjM1q9ffrlbuEwXk5+UC26qs92GxGei+Tp/3ELLzi2LahQGWKrersCp1M5FV+DPyABgioBm6WB02fgOMOMfcNjpet/+xCZmZmejZbwD3ffbxPP2bPSjdZQmmsYe7S3ExgG3mMtunKhIgN8USi2KTsZRR1pEwCtxGUkkzW88EEGhEg9+jU8lp0olAr3bHYcgBgDnO8dDLmWX2N/8MKKqG65f8jLxUH+4c3RfVLfy9NVaSwW+jQVMkWuShleTDu2eZvfN07npMr2z8ursRI7tloiVsbU+se69qiPsKAoFfiEy7Fo1Eux1W0K2hutn5zDmhbw6e/a6UWzdRROvBZ7fJnoDghEr/92Puzkp3J/Y/RJrUeMT4bv4djUjU4Oo9Vfhp1fdRA246WYpU9VkwzxICGjd+EpatWIcn31iKuQsWuX+nIKCgpLu+DQa9vCOea5XlZXj6pdf0herSiHc/N19RahkWradbA1/pVjQNKizhlT8b0RIppVu3QJOciqVd8rLRrVtXc/mEk0/htt9euWErWfRvAFx1jL7uNo40VcW9t99isSdUBXjxGmDxVDw2d5Y5qdUEiWMg0O+sqiiDpmn496sv4oqTDsXtl56NEw49EKtWrXLpP6XBL59rZie7lG5eXrrV9dhV7NiKsBo5Vx0vxfLCxWvx2poyzP18E75fZVl6sQE3APhT0hLQQzCSWP4UYMrrwPkP01dNoT8ASE3y8UrHYVvQrbrPVAVBQFG3HnFVdmgQUdKjJwjcr5mQrLqqq484xEqY1O2piE3f/nwhTuqZilcnHOx4z9FjaJtsTWQq26JAOPo5nSRG80ePR038v1986tq6ojJ2Q5JAUJDqR3ayF8edOQEvfL4K1914Myced/SYs3DLhFPx5Hxn7zFgBH7sWDHo5Tu269kWVzFFIxFFT6mm6ToUkQIJWn2+cvxYjBs1FB8u4VsHumcmoSQj4Jpge3TuTMy//RbuHBJBwPmTr8XSFevMhNrS1b/jpdBA4JTovfEUgbDRe+wJAEdeYL1RV4EVX30R8/PfbNmLz36vMoPuHoXZ1ptyiBMWfGnBP/nxb1iGnXLWeHO/uP2L41qh1Vq3mwodr9u2bcOO3U6a7CmjrbYuOnfILyzGsMOOQC6rTG6Hua22l5m/7ff6rRvWmX/7PSInqtkW3+NYaJUVky6tq4QTo6dX/1cQiCFKayzbkvgiEeLu6aZB2PryeizbUBkxqaXQ6B/6sVK0tvt0twXryuuxckct3vtF38ZaphVJc+ldLsnwY0C+xQ7x2ewh6bYH5fiOk6ZpaA0r5rWSxyRe6Hi5b0x/vHvxIQh4+N+KdS1oiG8cie6XihnohhUV1QxbjKIglW+HaC/s28Au2ynwbPLt/0LQ3Vnp7sR+B0FwqdiA6eH6kyvddi9uYjzApk+5xMzi2ydLcxc8i8HDDjUno1Qsx9HfZtDPWZpbfmExKsvLXKtEV916J3IKLFpoRwXdry96Ekgz/JVZ318bJl4+BQCwavk3ZnWK3jSj9XTTeRN9kCmqatDi2r/tbUHMqmsg1fGZLnlZKKsIA3X6Q8sn8UJP9sn4aSMHmxVIwPAtZfyd7VVK10qlHRoNsMB5DdOEwYqvvuAqaJOun46XH5nLCYhNmzYNt981G7Nn3gFVVSCIIq6b9U/kF3VBVWOQe/CpqmZOIKmaeWFJD9dj16V7T8iqrnbO+mOzAVms6ihgKcX/Ul6Hvcu/BQ4e43ooRH+S47qMfPyM13sMB0TJrIal+CQkMxNkNWRT9JZD0blyBsZeMBm5hUVx9TBSP/T8wmLMmP8wZlc419lVUY6mIH/tJ3tFbuJVsWNbRHE+VVX1pMYdMzFu7JDI28GAnQRKAkE+Q5nNDHg4WmC8fX/s+f5h+ddY9Mg/Xdeze1CrBr2c/T1a9Swo6oJ7770XZ190Bb5atR5dczNw0bgTotp1zbj3EdTX1ljVbIMG/dlH7+OGM49xvx8IIoq6drdskIzmC7d71ktPPspVylVVxUOz78RRJ45BUUk3AHowBsRHuyeE4IUln2DgkGEIyqrRikPw0o+GonbfIyzP6Ch4+5lHgHG3A74kYOhY6w3BE9XzG9AFoW58bwP3Ws+iXICq2duujTefewL4+kfg5JsAAAteXIzU5CRcPO4ELuieO+1GPDXkcAhxKBMLpnWh870dm38DkI73l7yp918fcib3/snHHo0PlvwMIHYlEXCyD3rd9hoAiyLMJwz4QTDnlquAi58AoCez/Fzw1vEPua17myEIBP1yU8znKCEEApgA3Kh802UNQGtYgSQQCEL8ejCUyaZqwKxPfkfvnGQckOdsZbDTmpUo+h77AnuZCm5Y0bhWpDqXsn6G38PdT70S4e5rFx9SgkXfl8Zd6b5p6QZ8t73GtMRi2Q5UG4UQYlpGsorlGf7Y1oVuopt2sM4YLOjceXdjCG+vdz5wChkWU0d4pNu3IV6/bTdq/P8aOivdndjvIEZ4yP6V6uW0H4sq3bKTCA2W/ywRBEy87Gou4AYii+VEUjDNLyzGbXMeMtWhiSDg0lvuxPjLrzEDPQJA1tr/UK8sL8OrzzxuKSRHsEMhhCAzJ8fRdxwP7V8/XCzFTe8P/qtavqJVXVd89YXrhLYoN9vWh+Ws4Jnvy2GuAmnv//1l7eqYVUo38NU6Y5IheiAIAgKBgKOC9tLDc13VygcNGYaFH67EI68sweIvf8SYc8436ejsg69nVjJKMvRjIRACVdVQRNWbqXK5oB+7kpIukBUVn739WlTRwWjVURYeTQF8kSlrQVl1XJeO42V/TeQnN6k+0QyGAKBr1y7ger9tdPKTzvi7y5YQHHz0yabieyxQEUQAXEvA4VKZKUp21fl/xy8bN9q2VeIC0e4uoniCKOL5JZ/E1TfKHhsCfjyn+yUEmB5v+7IbDTMS6Pn+23EnRl2PbdlRNL0DQDU6UiSTMguzcldcXIwDDzkcjY1NrgH3ZTfchnkL9Z7xceMn8dVsI2GlEdFMetrvB5dMn4+s/CJzXKmqwdaxjanK8jI8Nm+Wc39UFTu3O/um43Gs0DQNLS0tKK9vxfFPrcDsz3Qxvxa2TuImnLZ5Jd8eYVPZNiF5Yuop1LoEKpzYnsLTyyEHzaQgAHTv0QMtzc3urJjSbXGyQojZRsWisrwM3/3H0EGRvEBShuOz9j7maHBjH2x+4gb0zfJh7skHAIiRe2O0PiSi2SizHR9IiCIxBUzDimpWtAkBV/FmA3BC9F7mLhkBZAY85jFVNQ1lda0Re5ftx66C6QlmoT/PrZOq/Mk93az+R6uscGrlVY1OSnVGwMPpWPgkkTtvaUYgHO9885st1QgpGiob9LFAVeMB/d5hhyQQvDFpKF6ZcDBSfLHrn1IcrC7RaAlrtVHpY41/NsHaEULF6bYkQrzf+L8fcncG3Z3YD0Gzsnb8VfRyivzCYnelW8aKCJqGV55e4BpsXHDldbhuxl0RPXXtOH38+Xho6XLc++LbWLZiHf5+6RRdOI0+2Ahpt3o5YPSWapoVaIZaXNc7Y8JFWDDvLoewWnO9Tu2L2tNNeGVKUSAI0xn1PkQ0mzc3YRs6+XKzaAo21vG2XRLvQlpZXoYb+qro5g8D/5oWcZtURcHaH75z0sk1DceePDbCp4B5C5/T/V4pqGiR5MHEy6e4TnA1TXWtNqz9cRUycgsx6NDDkZlfqFc/iFPIRK8wWp8vTPMjP82HceMn4V9frcE9i97Esx9/j3HjJ8EvichS63HfjJsSUiiPhPQkH9BjmOP1vln6BIFOEiNelwAuve4f/AuSl1sU5SCCjXXmclFhEY7hzoEG9m70ybtv4bhTxtqOqYZZl4zDJ2+9Ftdwpur9dqz47AOuZWDd+l+49yUliMZ6i0YbKXE0cMiwuJIa9m3i/clFmy88n5xoi8LtwCHDcOrZ50XeBqZ1RdP0SreiapAEXf1WMDxxCKzAAgCQWej0jBdFnHHeJIwecwbHNjKDXerT7fGZv2m/Hxx1+rlcSwz1HrbDvH/aQAQBXbr1iOhYEY12T4/FS6t2oiWs4t1fKrFq+TeoY8Sg3ITThg8dgiQfM+ENRvCTlrwxLavqW3kaqkCArCTru4kqA/WVwOolwPJXuYAbAMYefhA2rFvjmhgqKOkeV7KYshvsh7d06xYr0BU9DmaSh/B91rGqhG7sA622Ajf1CeGEfrkxt5NLPigyR5ndF4rMXkF/9uxpCqExJJs0fEJIxABcIASypiHd7+GCsOrmMDRoru0sgJPW7JaMyUn2ItUnmc9Hj0D+dPVyljYdlFWTMQUAuxuDjvUzAh4uyPbb/KTTjfETj5Ca2/XPqXJHCCd7ZbuzBliohuBsPJVukRDUtoSxzaagHouans5cK3IiOikRcNmIrhjeJR2zTuib0Of+DxS6O4PuTux/EAhBbUuI8+QFLIqMomp/GQ0lVpXCXtG0BxsXTL4OS42+7ViVKEkUMKx/Lxx3zDHILyyGQPR9FwRaLdYVcdt7KMx9okF32Bl0E0HA8MNHuVYtavfoAjbRbtbUMow+NgjR6Yv7MhMejye0vepqTr5cKqz3z7gRuyss6r2dsnjayMG497Kx2DHvbJDKTfaPmxBEEUMOGeE6jr748D3XzxBC0NTYiIoyRqXX6EuF5MX4Sya7+40LIiZceYPj++bddQeqKndB0YwxZdLbo9PLfEwQXlBUhP7DRnLtDm1RKGfBsiU2VgdNATEWB5VkAQAqq/aY11dRtx6uk/tRx53AB8i2SnfpxnW4f8ZN5rJfEjDwIKsiOnXO/fy+qCq++HCps99TVfHozH+grCx2ciHSPFRrqmMSKV7AzwcSOzeux4O3XsW9Nm78JCz5ejXuf/pFLPl6dZsVkQlB1Amoz0aZjYeu64a7HlyIqbPvd33vlPOvgJaSA03TQOe5NFgQDTscqspMzH8tmj7rGe+WzOSCXSPoPurk0x2MJPZ+YK/WKS6JxUjPhMuvuxnfff2l6z2IDfCvm3EXzzogBNPnP4z8wmKuL/XK8WPRwvaX+p0tMAP79kaADbojsJbg8cW0rLJTclN9EterfNmUG/Rkx1fPASsNq8ymWvN9Kk7pZseYUxBfKwY9z4rtgivp0ROE3v9Ej+M+4RcUNFZXmsuxnpGu905RRCAQsDzvme9wJBEZdoHHY6vydfBcRVE1eAxbsJawAp8kmmOUJqQoQ4BdFozEt1cikJgArjWsIM0nxbSbomDHJIVHFFCc7jfHcWGaH13SY7c/dCRohRnQ7STrmKTRmrI6x/oZAYlLJPokgUs0UmureCrdbIBPkeJj2pbakXmRFQ2SEJ9vOT2F9qp4rPt1KlOZjrfXPxoyAh48efZgjBlgWNDG+ZX/F9TLO4PuTux3EAhBwCtxnrwAn637K2zDqBBWrCoFRaRgI156LQBT7RYwMtWMRZhPEvTex3ZGrqZXuE+v7hI5iFPPPs9hb3XQcHev04ICXZQmGi1J7y6z+s99oi7ksq9i7rZ4QgPM5Msl6NbCQWz5/Vdz2f4gs1PF6bFy8yIeOGQYps9/OKIFkuO3NQ2zb7sej827y3rRoJeXHHUmdiPVtfI5+Y57cfDIUY7vU1UVH732HFSNWs4Yv4MEss0EDu/hSJPXeJWS97qIvNhRvUWvAK9buwanjRyM85/4CBe/vxM3zX2U2/drbrsTLc3NuHb6LCuo8dkYDK1N0FRrwuQVBW7/u/fq6zgg+vl1HiRVVbD1j80xt98+5m8+qieOLEkG2bycaxlwBFXBJmjb1wKr3sFNh1rVt/zCIgwZPsL0hG4LNFXlql9+m6iQbinGTFDbIbYzavRJTmFKIuC8S65EY1BGRUPQSuDRYJvSy7lKtx5ESCLBDVdPxpLla3HvC29FTWbSYPeo404AAPQeGF1tXrEpMNuDP8BZuSaCgGunzsQxJ56KeTNujngPos+BCyZfh2Ur12Pewuc5OjwA7K6P7LMOv/MelRHwIBRkqnrBZl2A0YbTJ1wUM0FTZ3v+pvgkLvFyzEljsGzFOkyafK11PkvXASteB5beq++zomDA4KGuVlnxig4SABUNrRxlNr+wGKNPMbQeJK8j6G6oLMNZRw7BkEADju+Tw/WsusHt3nnyGefg4nEnmAmTIHNcTxtpGzdMpVuSeKpwR4cRsqpBEgkIVfc3tEX4pBS4BBVNqkoCgUcQuKBMQ2QdHcBJTf7096qYiQRRIHFRpjsSbE93S1jh6OYvrNrpWD/N5+HuYz5J4Np3aNAdjKPS7dYz3lH+04pG2T6x77n0vNqdAaJVyZO9IiSBmMJvR3TPavvGRkC8feL/FyrdnUJqndjvQMUmUn0SgrJqTv7Ym39Y1uCPMXq/3VqNnXUtGD9ED25XldYirGgY2T0zoe2xC6xMn/8wli7/Cd989jHmz7g54ucSCTbihaJpZqaM0qPaWnViMW78JLyvrcKayhbcNPV2nDfyAFz9jxkOwStWuIoGkHm5OQAqoqqXg+jZXoHJhDeHlH0mtBKp4rpu9Q/IzHIKfAFAY1DGHd/uxYl3LsKHG/c4v1QOck8FgQmm7dA0DXMffw6Z2TnmGLAfy3HjJyEpOQXTp1wS305pGp8JNqp1pWoaLn7jJ6y87kiHUFmTNwNy/R5XkbEPX30a4y+ZjKz8QkvsDYlVZhRNBSG8JY7bGImWYGpolfHx71U4vk8OalzsTLKSPBx18It/vwicdCMgeaH60/BbMBlAGL1OPhVLlx+N0m1bsWHdj2YrhCAIuHbaLBR26Yp3q5Kwki16BPmgxisJyEq2sv7fffsfQIv/Mbl69WqccPxxUdexV5TOO7gY5x1cjCXhBzH7R51lQrx+pOQVoYEdwrRq+fXz6DuFF42KFycfkIsPf6vCif1yjaprdwBAfV0tvlj2FgBdnd+tss0ux1N1iQS3MXLj3fejoKgIda1hNIUUs0pkVrVBj5sVQAiEIKyoSPFJ8IoCioq6QErLRV5K7ACruDgI7C5DKIoyMbG17+jL7vc4+3WXk5uHbz79ICLrw60KP3oMbyFYunULdtfIsKZptmPuUukmwUbdm53axMkhnRFjsxmS7MknF9hpxPZKt2qIAV4/426Mv2Qy1q3+AdOvuRTaCstqkVaL7aKK1vmMDnqeA14JzSGFS0IPO2QEPvliM4QD/uak/Qcboaoq1s2/AEuX/xTXc4Y9h4FAABePO4FLmEBRAEGylhk8+cZ7uPI797HU0fRy1Wi9oHvk94gmhZx6ZZtMEM2i6AsgZquGXU+FRPBCB5zU5I1VTXj+h1JccmhX9w/8RWCD7D1NoZjHPeARuIq/TxK452tGAj3d9uo/AZDkFfH3wYX4bNMenD24MI49cAdloolxjGHaViARmOKLgDNxctqAfCzdoDNBaHLkzQuGo6YljOJ0P/4q/B+IuTuD7k7sfyDQbzIpPhGNQRk+ow+TzdbFcyO84V29InZwUTq6ZgZw5VvrAQBfX304kryxK9VAZP/kOY89i7SMyMF7rGCjtiWMPU2hqN6Gju806OUiW5HyxLcf8UAm+u2gMFefsLl5irqpT1PqVvRKN3H0dKfGypq0A25qxIQQfVLIJE/Yas+Lq3bix7I6ANkAtcYJBwGPMYmX+QdrZXlZxEq9IIoYPOwQB3XVjuKSrvGplrvBtj21LWHkJHu58/bH3iZk5RXinEuuxhuLHufWV1UVu7ZvRWZeIfZUlKF8xzaQzEIkZeXH9fMSIVAUjZuIA/EplLOY+clGfL2lGl9v2cvZVFH8rWc2lvxsKa5qLYY4lC+J8yb+bOlbGD2kLwJJSQ7tAerB/uWaBqCuyvrycCvXiyoJBKcekId1u+rRN1XD/ec+BPQ/Buh3pCNAd8MdM6Zj0sQJ6NKlS8R17HMnGmCNPOpYHKSV46fKFtwy+0E8+sMegA0nQnqfXnuSedOP64Pj+uSih7cZfx91A3DDEv0NTcUjd08FrtJtAwUXujkrOtTeyZF9jPgy8gxKLIFHBFINirQAK0AjxGD5UCqt0evrEQR4JSvooGBtx+yIxwlDb9+xvrBXdlLUexx73SlyGMVdu8flS28Hm+TF5YuAVIPVYA+UR1/j+GxrTZWtXMRv7+UjuuKZ73bEpYtSZ+vpTvaKEAWCwYVpqGkJoXe2tT35hcUYPAw4/7Kr8eqzC031fFotVm33XA3xO1cQQpDsER3HniabXc+goUsSKckRCfQcrlr+jVNhfsXrwN8uAn7+zPG54SOPBL7TmXCtLS0co6oj6OVhRUVrWEWqXzLHtQD97NLr1KSVm1RzRkzNsBCTBAKvKKBVUwFCvbQj6+gATno5ACxcvn2/CLqXbahEa1jB2QcVccJpi9fscl1fZNhZdr0Sn8SznJKNOWI8Pd1u14pACG47tjduObpXu9iImiEqGc9XSKJgnGt9vkVPnT1J2i8vGUsNYwI6fpK8Ytzz4kQR6xLon5eCX3c34vg+Ofvk9/cndAbdndgPoWe3/ZKI+hbrRkoIgVckCCkawoqK2pYwNu9pwrAu6Y5MdjPTY9MYkrGjxupTbg4rcd9cXAVWVNWsTsZjF+aG05//AU0hBf++YBh6ZMWuOgD6Q1RWNXT0fXFDZQM++q3KFBuJdmzcrKBMn+4YiRBVA/aBZakrHDZvgsAJ3tktigA9O84i4BGQ1rwblZ4S/QU5BHZqorcOWFVRQRTjqu7SY7hh/RosmHdX2ydlNs/o6uYQcpJ5oTBV07C7fBcGDB4CQgRoTIApCCIKunbHx2++igV3/QOaqletL5kxH/2vuDzmz0uigD65Ka5VT7eEDQXd/0BSElqam/H1Fv315dtqMHaAM+AvSLUqlyIBFErlzO2h/2fgrddfwVvz17pb8xmT7/rWdP7LlRBISx3vwSsKuGN0X6xa/o3+PRu+1APuish9+ubXKQqWLVuGK6+8MuI67ETPzqLpadgUCYFUBJXd/AcN3+NYzIFoCHhEHN0rG6uWb+DvaxqgBa17JCG8mrlPEjkqZkdU79gxUt0cgmQcF5EQJHkFZCV5sbO2xUUUipjbKBACSdADb9aLuDmkYEdtM7pnJXHVUQpavQtH2ZHsZC+qGAEmjyggkRxnbn4Bps15APNuvyVu1kdleRnmzJkNTfLpgaOH6YtNjk377FJUAKy2jRtmvFHKa1yU2Rb+/kKD3GfPGazT+gX3cUwIwfmTr8XoU093VIvnTrsRhxx5NEhKTtwsJ4HoQczeZrvGS5SHiXGPaGuCyi1pi1XvANt+BPbuiPrZirJSnDZynJnQ6ohrZVt1CxRNwwH+FKiqZRFG+7sBNjFltDIRgM6CTK9u6Alvei01tIbhFQUjgeW+oW605pR9FJwlglZZwaxPfgcAHNcnh6t0L99e41i/MNWHntlJ+O82/T2fTQjVJwrcc4CKRSqanpzYXtOMj36rwgXDujgKBnZ6OZuIbm/7nwrNcG2IgxkC3YNdFOj5pPdK67N6QpXvZd/XuPywrpj6/m84zeX5DgCPjBuIr/7YG59o4f/n6Ay6O7HfQYM+8Qp4REcPnUcUEFIUhBQNl/17DSoagnh03EAcbutDYdUqRUKwnQm645lwULg+fNltNbyXTV/ceQ9h9Jgzou+fppnCGz/tqkePrKSIvsZ2dIRauR0XvL6WW072ut8W3Gj248ZPMvuHYllNqJoG6U80CWOradV7qhw0blVR8Nmyd3H8mNMBAOUVldz7KR6Cyh1bgAPYoNtCIBBAOGSphC5d/lPM6i5XxYoBIgh48NlXcdOlE1wTOxsCffDyemty8cSK7bh/zADuIf/F24vx7NzbjIDa8gIXBBGXzZgPTdOwYNYtjAWeiufm3IYzTzuF24fK8jJs2/w7NEUGESV0791X92hPcEJh7r8vBWip11+8yRKPawg6e+NYOxNomuM8mBC9xirOcUgn303bq7jXTxl7JqaMugUflakoTuNpdYGkJCuA/+M77j0iCLjtnvtw7+23cokMALjqqquwcuVKvPDCC66bSYw+VVebop/XAn0OdySAAODUM87G1Q/c2uaAm4V5XzNf0avIdJmA2Ox0BG7i1tHiUJQWq28JkJXkRXpAAlWD4vpVmW00e1kFAlEg5qS5ISjDK4qQFc11lkNZU9EYUznJXkcSK1GMPXciDj92dNysj59+2wLt0md1QbKnLuDU9i+ePhfPuxfwTLgGorZqHhAfU8x+LXqZ4I697O3jWNM0vPbsQgwYfLArvf67db9ixBF/i9u4gp7j5rCCXXWtKDLor24VWBNK+xJU+YXFOPnMc/H+m68zr2rAnm2u6/+ydjW3rKoqsPEboN8onD+s/derrKpm8Ksa1yoNlCWBoFtmABsqG0GIRRsnRjUcsCrgItGvE0o/r2oOoSjVr2tZRPhtdkyl+yXUtcodYivVXtTY1Mrtavt2ZCZ5HLoUHMXelqxlk4whWcW5L/8IQA9arzq8O/fd9kp3RwayKhXOi+NZKxBdJI8Qggi3PogG28Hc1j+hEnJ8n1wsuzTN7Bu3IyvJizMGtZ2C//8TOoXUOrHfQdN0cTB20kdBbxZnvrgKFYZa5eqddY71qphJa6usYluNFRwlIsJGH77RcO5FkzFv4XMx1cgpapj+nwy/FJfKNqALkf0Z3tbJLllsV5r9bTegsrzMnMBWNYVcVUIplCh0z0hoDMn4dms15DYK51GxIjcROAB46J4ZGHPYIIw5bBBWfc8HVj4oQIjxJJVDHE/q4nEnoHblEgBArhSKKZBnP4bRQMWYRh1/Embc+4hD1G70mDOQlZHBfeabLdV4+POfud+jATdgZb5vvvufuHr2ozjymOOwYc0PjgBK0zSsW/29uUzH55SJZ+KaC87BlIlnRh2nkWDuf49DgKteAY44n3s/zStgxXffOz6Xl2o9qBVFscTG7JB41WA3az67ymy37t2QX1iMC4eX4Pi+VpZ9yeKXcfG4EyIG8DPmPwzJ40UkUuaLL76IH374wfU9gejBoRuLhiYUFn1f6vhcj+7dI46tqsoKrFrxLX5ZuxqfLn0Hnyx9J6pooCmeaELD9HkPWYuEnzjaJ2ZFHdD3F1ZUMwGqMfcGAmL2rFIarVXtZkQJjQCcTkYFYrWwhFUVfo8QMbCjz5F1u+qxblc99140m8G2IBHhzHKPQa9MztD7sJmge72dpQEAe7YDtZajQk3FLieXk/DJEyA+yqz9WolUWY6kn6HTmp1uCl179DTPZTwgRi+yAIJWWTWvSbtt3YEFVo/74aOOivt57IbK8jJ8+PYbca9/0emjnS++/088MAwYkO/svY+FVlnh1LgBwEPp9EaPr95qQdAlI4CcFB9Ege+VZzoxzKCbVTYXiN63HDCEtCLl89nkxvWjdNZASFE7PPGWKFjRzdpWOWbLRFaS1+nAYBuC7C6xFf7msHUtlDc47ccag/FdK22BzhCM71rxSQICkgABkVvWRMLPrdti/9gWFKT6/lQLuf0VnUF3J/Y7aNBpyD5RgKLxEwS37HZhmnMCyFa6W2XVDNCBxCrd8Tx8X3/uCcy45jKs+OqLiOv8UFqLf/7nD7SGFeyqt7alunpvQirbtAdrX8KNXu5Ks9dULH7uKe4Bc/m/17l+J0HbbDP+8f7vuOHdX/DKj+2bAEfzxtU0TX9AeXgBpswUvylWBkAP9piDr6oq8N9XgA8fxJ6FV8ScpLsGWRGgqSoWzL8LSxa/jJFHHYs5jz7jSOz8tnaV43OvL/0UD8++w2RO2L2rNU3FgzNvxYLpU3DR8cPx03f/jbAF+n5GShS0xX/b3P/jr9ZfGHEO975XC+OLL53XkNJoowrGqHQDemD8/JJPHIrJTSH36h0Lt30mgoCHnnvd/L6RRx2LuVNviDrx/O9/3Y8tnQy7Wk0pkas1kSZy773xKiaeeiymTDwTF449HtOmXILpUy7BmMMGRU2MsAHJwT2LHQEKG3RTwaFn/j4Ys07oG1cgEbLZ9tixeU8TtlbrffIqp6CvGRRYK/C2FJjBVfKIYAkMSYJlOSMrGnySGLGXkE6ot9W04Kq31pvjIt4E6L6CmMQcVx/v37uq2uUDtbssv2oAKckuyRDmnkXHUDAuGyT7teL+4InkWDB42CEORfBLps9HgWF/Ge9jjI4FUSBI9onmtrPXQ2GqD4d2zTCXiwsL28UISeReDdiYHzQpJIo4oHfPNv3+ztpW1AWta0cQ9OSTomqoaAiafbt25gfb021PUFGKMk1gAQQ+ScDA/FQ9qIsUpDEBH2XBadg3rLtEUM20G3y+ySl8mu6XMKqnxYDMtPly6/RyfhSyeyQKxGRynfSMlZAvcUk4OhJUHRjIarBa+GIhySsi2StBiJJEEQXSIfaP7cFA4/nRXibR/4/oDLo7sd9B0/RsnE8SEPAI2FptVand+otoL3FtSxi/V+kKv1WNTKU7rDioSPEi3odvrCDkqrfW4421u/Cvn8pRUW9VT8srdsfta5zqk5AZ8CAj4HG815H47cfvHftR0qOnq8Hwq888bvp0U2xjzhcLVYvsUUyxZW8Tbl22AX/s1b/jxzK9CvXBr7ujfSwuULugG++Y476CxD8ActKScegRfzOXiaag9wED+M/IIeDX/0BrrI7pRR3L490OnU1wPcaMHIxpUy7hEjuV5WX45J3Fzg+JHrzy9AKMGTkYG9at4ZTFKVgv+U/efNVxUgghGDzsEADRx38i/tsAs/+s6jLzd0BQXftW6yuZsUiIQ0DOfMurJ01YWzZ7hdE+OQo28VVOILKOQyA5xfy+eO4LRxxxhOvrVLTIzaZo+KEjIn6f20SusryMs6XitlnTMHfqDfhl7eqIlduXzxuC0wbkY+5pg7jXG2prUVdV4Vj/4OJ0y3s1BnbUtqC83lkVohAEYlapNRsLxgy6majCKOxxPasCLJ0IQRAAo/+SCASioLe0uIENIIOKij1NoTbbDHYkWC0SkhKHdU9zPecRrYZakZ5lfU4QRa7S7W1PpTvC5NxtHFNmCetHvnT5TzjuzPOMii2Ju+qVm+I1A6CAJJjPbzZYyLAFVO0NeuK6V9Pe7tL1jrcSpbYHZRXbqpvN8aprt4jmsiToVOialjBEkVgsD4APsmH179oTVFaLBmEq3wRJhugXDdJaZYWbI7FtJb0Y8bx4xPj2JVhHi+d/cDKDspI88Nuo8Sy93G2M2JOobgGpmw5EY4RWjI6AqmkOC7BISPaKSPIKhqik+/mRBML1nP8Z9HI77h3TH+cOKcLTZ0e3bPxfRGfQ3Yn9DqpBL0/2SshJ9oJ99rn5DdIHxFkvrsKEV9fgt92NnLBFUFG5G3RQVhzfEQlxB0pEgJpZgu1bt0RdraopiN1MQiAlKyduX2NCCPI7mKLjlq2+6cKzHFWeFV994ZoJV1UVKz7/mHutrK7VsR6gn1d7ZtmOyW+uxxeb9+LmZRu51yP1AiWK/MJiHD/mdPdzKvGV7jSfhN79+pvL7365Ejedeqi+YOvxpecsGjXVzc83FjQX8Tezih12CWhEPSGjqSoemzcrJgVQ01SMveBKc1uIIGDGvY+Yk8Vo4z9RkSKT0iwynWaMv24dfMCAYx2f69bNppIruwdyV9xyh6OyzYLVUqB48t678NKTj3KvxeM1Huu+cOGFF+KQQw5xfY9WqDRNcwQlBxzQn1u3C1NVcZscxQr+VVXFRaePjli57Z+fipkn9EWuzWbr13U/4qxRQyJ+bzwIK2rUyadOIdfvB/aEnGhU5ERCmCCbmIE3AKM3lafTagAaWsPICnggILKIlT15W9MSjkiTTiSx1F6wQfeUh1+N4wO13PXQrVtXpCRbiayly3/iVvclVOnmr5Vok3P7OGavP5ZeLxBiiN7FTsBS9MxORpd0PzyiPhYoY4o9hwGPwAUS7Q168guLcc3UmY7XKeNl3sLngSV364rm7/+T/2xRl4Sp7XubQggqKtf65hGI+cilfdiALoJlBtBgA2s+QLZT+AnTi0/Y/wivh7CtuplrxwOAxecPxbPnDEaXDEvYL5E2vX2Bahd7SRZZSV6+RcZme+iXRARsLYz2PXIbR24JK2crRsfN0eyihdHgEQUcXJwBkkDQ/WfRy1kUpPrwj6N7oWtmIPbK/2PoDLo7sd9B0yxLqewkL6e06DaJokE3DbRX7azlRGCqm8L4pbLBXE4kQ+uWxb9u+l2Y/ugLwFGXABmG+MMxlwMXPIofQ1nGPmgmtZLtR073ezjKpS85DdPnPwySmgWkF7RbnThRuAlXQZG5AG/XzlLMnXpDxO94+oF7Yn4nIboKaKyJFu13L28IojlsHbe81OjeuxTx9GM6gl9aUbNZ8qT5PZxgUFFxFxzaNRMfXHYoTvHwE/GTzzgHK776wqSmjjlsEOZMvdHsraXbNfKoY83J6ZzHno1rn1jQIKCkR0+eivwf47uYgFYPuHnaoz3QFwQRI0efirmPPYt5C5/DshXrHBNmN1o+EQRMuPSqhLffMRENWAFCfdB9EueYwLD73VQD/PI5AP26jtY72xJWnR3YcgiPzp2Jx/9pjeFolTvHOob/sSCImHTVjXjooYfw/fffRxRRAywVbnovo0EJAFRXWOM2P8XLUWa9kvPiiScpyDIbolVu7a+zrQltad8khE+SNrTKnBq4xAgZUXEowFDgNYTRBIFwQYWdPmtfBoCmsIK8lOjJSftkurZFjivZsq/B2h4tWO2kzA7IT0FXJvAhwUbdh9tAl6IijrdtvxboGKLssC8378G8zze5Ok/Y6eVuLDMW8fSuSwKM3mNejC0W9Eo334PrcwRQHavIPGDwwY7XKONl9JhxuH3G7RC+/xfQXMvdH/2BQJue36wPs0cUTEaMpmmQmCSFz7C6oiwQGmiLAv98tSc2WAVzuz6CSKz7BCEESR6RC9p65yRjSFG63ltvfGcst5J9DZa9SDGkyEriZgVswmmSwM0lvSLBhKHF6J2ThCmGMNqJhnp2dyMYdAtI6XyzsiGI/2zeA1XT0GhvxehgIbVEFNBpsjJSwlF3BmJYIX9Bpfv/MjrVyzux30GDxlU3WMguAXNIUbkKQYbfg7pWizb6+PJt3PqtCVS6AXff4Sv+/RMwLAvodRjw3BXAkFMBAK9vqMOVRwMPfr0Fr6/ZhSfPGsRlhwMekVMmbpVVnHvu+Zhd0R0A8NoZ3dDbXtnrAGia5mrRUm+zutADGmOSbgR4ZXXN0am0NrpvY8j9+Gpa/CJwHoGgojHMLcdCJHV1il/WrsbaH1ZiyCGHYeRRx+LWu+7F9i1/oFvPXug/eAiu/rYZTczh0H3infuiNezBR2/z1O4P3n4DH7z9Bida9s5rL+Cd114AYCmjsttVWV7mqoxP1zVtzmzK5YGAMZ6YyTaod7UYpfVA03DGRVdiyYtPQ1V1C6O/nXImpl8w1uFdzqrp0/G//Y9NUOUwVnzzFV5b9AReeXoBXnt2IabPf1i/PuJQ3+cCO0V29K26YfyxhwLX/pseHL6nW1PN5T3Vbk2vFuxBBABz7D7/2INITUvHBZOvAxCf1/i48ZMwcMTfUL5jK3KKu6N7txKM6pkdc39oxYm9k5ljd/hZwJH6mM1O9iIpBg0wv7BYt6WKQDG3I5pvcSnL0ukAkSTRoMTSe8/OuhaIIkFuis8MJNgzQuvegjGxFwUCkQkqiK2ypwcRNjqt0ZqaEfCgujkUsRpnT+S8v6ESR43pz9kM/tkJUICnzLohJ9mLsGIlLq6/ZSo++b0KG+r05ViTfTqGQrJ+fv+x7FcAwLAuGQ67HkeluwMCCStR4v48igRdcVkfIXRkssGC3yNwVcuOCCTcnEvYJIz9HvHedhlPrdyB247p1abfk5ibgke02DAKiHlNyKqGgEcw6eVs8oK106PLhDDPDqOiTf/W/9XfEwVLvZw6xwRllUtkUHhEneL/V9PL612S+z2ykrDWEEYMeEVbpVvkEj0+SUS634PF5w8zXzttQD6K0vzol5usr+MyjihL5KwXV6FVVjH35AMSYoUkChVaQi4hhOitGJFaSERbpdv/F1S6/y+jM+juxH6HjIDHzGLa7zVhl4llUFZRyVqECQQN9mCSAZ1wJAK77zDtNUZGAZ58Yymu/E7/zmxDGOL1Nbq3y8Ll23DDKEtMJSSrqGthqe4qdtRadmZaUuxJeyyU17di9mebcP7QLhjZPRPLt1Vj+ge/4fbRfXB8H35i5RA6YgJoOsGo2hiDXmnzi7b3N1Eoqha3ek6KT0QN2yJgPEBkVcOephDn3Qy4q6uzPtwzb7raZv9iAyEQblwCdgP9kojd1bWOVSP1/EaDvdpIt8vuJX7t1Jk48fSzzInciq++MN+nv3PxuBMw4bKrwfVdtMYOugVRxJiJl+H0SZejvqIUaSnJuGjcCQ76en1dDRbMu8uRvMjJzcOWjRvw+qInuM/Mue16gBBH4E6hahq+2LQHBxakYssfTGAXbuH7uynWfQQMPsk6dnYaPVvp1jRzOZCaEXHfAWcQoX+XFcAvmHcXThx7lnmdR/Map8jJL0RhUTGCihZ35Y61+wEIP3aZfUsV1bhogGPPnYgDhxyMmpoaJKekYtfOUgAairp05XySgeiV25IePYHvjfMTbIpvZ6LAY1BiqVssIbqfNqDfCySRQFB4OiztNxUNgTQaWFvr2OjmDEWdakERQuAR9e+Il17+5R978fqaXZgQR7JlXyKW7VF2kper8OXmZCFjtwzUGd7DooARXTPw9voKpPmc0zs6hoIKLy5qh6ZpjuSpW2tXItCTL+x5ix8CIfAYtlY0H8QGVAGPyCWo3FghicJ+f47EeKHLlxcCk4Z14aqriYCOV1XTzGSSpvF6B7KqwidJlte2jVLOVstTfBJXjWYr3wKxkn+ArSJu6ClEEkrzGkH3X13ptjPqRIFwwlx2OrnOEAC3bIdACA4pyTCX3cYRDWZbjX/XlNUlzApJBJqGuHu6KdL8kisTAKD0cuv7OrBbsRNxoDPF0Yn9Dj2yknCQQROy3xBYf8gzBxUA0AOyCkawpzWsuGZBKRLp6Y4Hww6zBJNybWqMWUlem32ZgtpWPuheX25R3yP14SSCWZ/8ju921OLaJbp91HVLfkFjSMHU939zrOte6eYptVrIvUfb2mj+eLpS1qFnbCOagdqQ7JVQ12p9L80uT/vgV4xZ9L1pTUZp2z+t+j5iP+Y3n30UPeAGANED1TYNXLPiK7z18iJzmfbDJiqIZgfbJ8r2Qi5bsQ4XXHkdR9McN34Snn/nY25ipaoqXn12IR9gG0FSWjafVKEQBAHT5z6IrLxCZOcXYvjII9HS3OxIFqiKgsfmzoooJlW2Y5uLir173znFe79UYuoHv2Hia2uQXsiwOBQFSMl0bmxzXbTDx0PTzKSPJ5AcdVXXoJtJMqmqmnD/rmoEedWVu/Djim+xc+fOmJ/RK1BWvyaXxGGq+CTUzDkJRKve5eYXYNhhR2DgkGEYPWYcRo85AwOHDItJk2eRX1iMMWl7gN1/AF+/4Kr0Hy+of7DeW6i/JgmW17esahCJbum1vaYZLWEVktHra/ZymwrmTFDBHAJdSI2v9ClGwEJpsJE0DdwSGB9vrDKPQ7wWXx0N+70zIyBxLQaZSR5H9Y59RhJCcP2oHrh+VA+8NGGI4/vpGGoJK1i8xrpG6TG865Pfcc7Lq1EflB1BV3sps4pm9eoTxG8ZRuEx2hE0c5mnY+8LcahovepuaGvADcCw7dJ0OrGRlFCh6Ro3xnFTVA3JHgmDC9NMCzD2KLK73S8vBQcWWnRrgVhK/9ZV5WQU6ucm8nZ64/C4/zNgZ6Fl+CVby4HgWGZp2vGMEfaeS/U1gorKXRsZAY9jWzpaEVxM8FpJ93vQFFbQEnY+8wRbpfsvdn77P4e/NOj++uuvcdppp6GoqAiEECxZsoR7X9M0zJo1C0VFRQgEAjj66KPxyy+/cOsEg0Fce+21yMnJQXJyMsaOHeuY+NTU1GDSpElIT09Heno6Jk2ahNraWm6dHTt24LTTTkNycjJycnJw3XXXIRSKLtTQiX0DQoiZ2bPnw2XmZtclXafZhhQVu5t4SzBHMMkgaHhMLlyxA+/8VhNxvXggCYSjBGYFPFwGODPA93Dvqg/ih1IrqAgqqqnUTfelvfhjr3uVyq0vyB6IZGdnOSYYRV0TCzIjVbpjWYaxLQIpXhF1zIOMVrq/3LwXAPDmunLO3mfGNZc66IqCKGLDuh9x4yXnxd5ob5Ljpf8sextgAkwaTEazH4sH9mpjrEl+S3Oz00tbVXHEcVY1mGhW4Gk/V0QQ8PySTzDuvAugwgoUS3r0dPR4E0Fw/BabJCju2j3mWFAVBZ8te9cMvL8w7FzqWmX40nOsFZMzgFEXueywU1E8EtLS0zHhwksARFZk3tsUQm1L2P2eYLPo2rDux7h/G9ADgI/efBUTjxmK6yaOQ7du3bBo0aKon6ETZdphXtKjpzV2me3JSk9td59qokHDrEvOwPvXjsaTi57H0uU/Id9glBzVKzEGjqrB6semCsyi5ZstG32KBICsWn3cbKUbML6DrcIxC4IRmNOXCAEU1er/lQQhcqXb5V6oxZsR3IdosE3eMwN8kJ3kER2BhP2+l+yVMGlYF/P5yIIGEU0hhbNhpP7XSzdUYsveZnz9h7NVo72BrKpqptVVIkJqgL6fkm0ssNsjEOwzcag/KwkjGsJpinnt6HRzRdPMZUXV4DWCRzN8No5J75xklGREFqZiTx8N2FkdBQrBuEFFKgDQKu5fTS+3zzMyAh7Of9rew61bhIFbjgX2EIw/uAiAPhfZy4i4+STBwRrpSCE1IHoSxA1pfgmFaT6OVUnRLyeZq8QrnVH3n4q/NOhuamrCQQcdhAULFri+f9999+HBBx/EggUL8MMPP6CgoACjR49GQ4NVGbzhhhvwzjvvYPHixfj222/R2NiIMWPGQFGsh9eECROwdu1afPTRR/joo4+wdu1aTJpkTT4URcGpp56KpqYmfPvtt1i8eDHeeust3Hzzzftu5zsRF+z0KTagpTfNoKxyVJqvtux1pc6N7pNjrr9qZx1eXL0Lj6+qatf2pfslrkdb0TRUMr+d7BW5yf6Hv/HWVw1BGV9stgRzErEziwT7xI3CTskGnH2ufq/HMcHILijErXMeYkSjXG4b1Vaiy+33swxBvGiTITY5IRDgoz+s4MtOZ0sXwhydnAaKdNsEUcQ1t92JBfPuivh7HHwuVVI5DPz8qf73lh9cK9TzFj4f1+zR7KETBFxz250JTeAiCTyddeoJ5vKCF3QveQXEoZB+7dSZGDhE71tTNX0iIRB9Mnn57fda51UUce3UmVHFpHLzCzBtzgPc97v1Zj50zwxTLZulqUbzbTYRLei2WaAFkpKQlZEBwGJD7G4MYu2mbVi1/Bts21GKE5/5Dsc/tRKvfPOz8/sUPrG6YP7dCVlE7S4vw73Tb+SYAZMnT45a8aYUW9cYj6l0+0QByUylu62UxUSDBnb9f00ain9NGooDC2L7crPgKLLGsiRYEw7doQIgAoFPJPB79KoUIZZdGMALp9Fl9m9RsCbGVM1ZNOjlkkAiVrrZY0knyOG/OIgAnJXuzABve+T3CA5xqFi3n9F99efe4d0yI95/Q4rKOX58tWWvY532UmZpxVY0BMASqXRLooBhJRnwSlZSkN0ekRCOFfJXeA+3F6IgcJVteotQVRjaBoaVmJFosXzr9eOYHvCgIM3Fp93AoMI0DKYMQuP/iEulWzD+i3Q10MTNX00vt4uXpfslPsgW7T3dvC93PO0SbEU73a8zy0I2ZuXitbscn+sITQFF1dBqVKoTZYVkJnmR7uNbzV4cPwRnHFiAW22aA3+x3fr/OfylPd0nn3wyTj75ZNf3NE3Dww8/jBkzZuDMM88EALz44ovIz8/Ha6+9hsmTJ6Ourg6LFi3Cyy+/jOOPPx4A8Morr6CkpASfffYZTjzxRPz666/46KOPsHLlSowYoXugPvPMMxg5ciQ2btyIfv364ZNPPsGGDRtQWlqKoiI9m/XAAw/goosuwpw5c5CWlua6jZ3Y9yAEXLqR9Ug0g27bhIGtJFOkeEWk+vXhHpJVrN5Za76nqBoSqVmylJ00v+Sgi7P95fZts8PuP90RlW72QcEG1fmGLVBdaxjLt9XgmF7Zjr69SBnacedNQt/hR6Jsx1aM6JWPk449iqcZv3QdcNDJwDGXo9Ylu+qTBHTPclaTWbATzg27+Wq9vfddbqx1pTnPffw5ZGbnoKR7D1fKeUT4XLZNCQF1lcCCc4FQKxd8mrZdmhYXP+uMCRfhnddegKaqWDD/LqRlZDqqjrKqYUdNM7pmBLgerki9hX87sBfme9NQnO5HslcCsBPBsIIuXbvjoqtuwPOPP+j4PU3TjKBbP89HnT4exb36oXbzegw5ZAQGDhmGtIxM1z5GxaBijz13Ig4/drTZ9/rxe2+ZPeAsKNW85M5l5mvRGCgmEqh0h1pbzAlOSFahahpOefZ7/c3HzgXJ7gJMeAAA8N32vUBKNg5JqscPzcY93SYCGE1ozA07tzn7+xVFwebNm9GlSxfXz9BjTy9TcxwBXBIg2FiHgMfajr8ikEj2SuiZnfg0QTH6UPWAG9AUDZIgQFb1c9QQVJAekMzJf0GqH7nJXmyuskTUAIuOTGGnl0ssLdYITCTD05m1QWpolbGzrgV9clMcFdN5p/THzUs3/OV02bCiOpKu6TYFZr/kFkhEx+3H98GoHtkY1SMr4jpBWcVu5rm1YpvOABvTPw/LjGdUe8cfHROEGP7SbfgOgRFSY5lbguHhTRFPVT4oq2gNhZCV1DF2lO2BBpgJJEW1rp2woqKsrhXF6X4zgWVSxI0kU7xVUB8TkNrt9+i/jUHZ7LmPxEzz7Cf0cnty3+8RHT3cjmvF1ooRC2wVmI6vkMJfK7TocvrAfLz7SyWAuLvoomJXfSuawwqSPGJCrBAKUSTcdgwsSMVAl+RpR7Q0diJ+7LdCalu3bkVFRQVOOMGq5vh8Phx11FFYvnw5Jk+ejNWrVyMcDnPrFBUV4cADD8Ty5ctx4oknYsWKFUhPTzcDbgA47LDDkJ6ejuXLl6Nfv35YsWIFDjzwQDPgBoATTzwRwWAQq1evxjHHHPPn7HQnHGDVSgE+oGQn226BHousZC9XGf+j2qJ0hxUViTx2WcqOQAhXZQ/KKmpbrMCiNaxGpL26IZF148HeJkYB3HhY3vjuL1hX3oCJQ4sdPpWRqhkCAVJy8jGsuBgje2Zj1j8fxcxbrrWCBVUGGnTWwPeltfhtdyMOyIutTM0iUi84oD/oWP/vpPRMV2XZwcMOQX5hMZYsflkX+LKDEDy06DX0HTgI61b/gLqavUjPzIJSfCBu/6qCX5dWHUMtXPDJqqRTu7FofthEEPDO6y9GFFMz9781DNkQMMoI8Ochkpr28X31Hu6Kev3YtLSGcOX4s7jPsr+nejM5L9Iv33kdi+ZOdYigsb8FAJ8ufQeKoqBXnz5IzcgyBYSWLH7ZNeA2f1tR0MBcLz/udCbEeuckYfMexhc21OxYB7t+BYr6A799zb1cs6cKwSbdti+oqFi7aZv1ZnIGNA9Dt0zRKdKrlr0GHHslAICoMnd/SdQiqrBrD8c4FEURvXv3jvgZWunWGHq5+R1MEiA3OxNJHazIHAmsWn1H0GhpYkfTdNq2otKeVYI9TSE0BGVkJXnM3t7uWUkIeESzym0G3QLfYtQvN9W8j7HrAZbvMA24JUZIrS4YhiQKeiAhEC5gyAjoFaG/vHLncv/L8Hv4PlRXcajoM/Jkr4RT+ucBiMyk0oNuJuFjHIs+uSmAEXS3d/wpqgZBsES82hJIEOKe4xSIrlZtLcf+8h01LYAo7R9Bt6GBoCcO9XGvqEBzWIVXEnV6udHrTU8DtR5s63F0s9srq29Fmk9ihB6dsCrdf36wFpRVtMoKUn2Sg6XnFmTb6eaJHis2IPUy80e3+WbPbIstJ3dA+VhWNSR7RIRVLeFKN2CwFeLYjFhtf53oWOy3QXdFhT4Bzs/P517Pz8/H9u3bzXW8Xi8yMzMd69DPV1RUIC8vz/H9eXl53Dr238nMzITX6zXXcUMwGEQwaGW86uv1Ck04HEY4bF2U4XAYsixzr3UiPihyGKosm1U2Fh5iqEiGFdS2OPvvj++Tjc826TS5JEmAx7hvvfxjGRe8twRDMQVQFFXDih21GFSQippmtn9cQU0TL+JW3dTKLMuuk7njemfh883OvrmWUNh1X9uKvY1Wdbg1rECRw1hnCLd99nsVju7FVz88AnH8virL0BQZraEw0n0EoqYAquJ8KFdsMv/85LdK9MmKz1uboq45sppua1hBVYO1L6IvgGlzHsDcF96E1vVgkK+exbS75iEnNw+7Srdj7tQbHNsnCAKmzXkAhx99HCrLdyEtPR1bk3ujS/cCI+Dnr3WiKbhm6kz0HzwEXbr1QH5hkfnddlo7iyOOHo0VX3+uq3+LIs67eLIufMZAVRRs2rgRgfQso0oNhMJhiKoKKDLnBkaRk5uHnFz9XmY/R3srDYqb5K5erioKtv+xCem9DwZRBShyGJXlu/CcEXADVnCekZmFDT/9iOy8fPy8ZhUev/duzsN16ux/Ytx5F6CyfBd3LNwgCALCEAAj1H/dhYqX3lgOn5iBIJ3EyS5aGktmA31GAhu/se8Y6vfuBiAgGFaw8tftAOWtCCIQcLKUtDqLXTLlptuwcOYNZlV/2uz7kZObF/c1mJGdi5n3PoS7brsJqqpAFEUsXLgQ+fn5Ee/3iqJCVcKobVVAkrzIyc3Tbb9uvwUqs++Z6WnwCtb4kqC4bpcih6EY98jK8l0o3bYFJd17Ir+wyLGuG95741XTcowQggmXXoVzL74i7s9TyKqGppCMdL8HoZAMqLoolBJWEVRUfRIoKwhqgBe6AJ4my9AEGPcUAkGToSnEPHZdUj0QBGvZLwKAhnDYuP4UGeGwZj5jNVWBoCn634oMJRyGIouQQzJETdOPFQQUJltTH6/xHAnJaofde9lzEgtbqpvhEQTXnnK/yHeveIgGLxP7eqDqtnnM70aDGGEWHgzL2F3f4ng9L8l6LhLNffzFi3A4rLMPFEBVZMhyHKwXG1QlDFkOQ5FtWhSaBh+xjoMc5djTcyMrIjzE+bz7K6DIdNwr0IyEkaZqUMIKfASAIkNTVGiKAtWYS6qKbB7HMEk8cFLlMFRBRTgcNudZHk2DoClQZSAcVqDIzmCPxrHBNsxV1lc04NFvt+PGUd0xID8F22pa8P6vVThvcC6EOK6Xc15ag7L6IN65YIiDFu0VAC9zHLyCBg+z7IGGkSVpEAhwYH5KXNvOWtRKxjMsKCuodpmrpPuYNki5fdcKoN8T/aKIUEg/LuFwYhoymiIbYz3652Q1+n0vkXvZvgQd9+Hw/tk6Em98t98G3RR2Ckgkv+Fo67it35Z17Jg3bx7uusvZM7pnzx4uGJdlGTU1Ol1Lkvb7Q75foa45jNaGOjTAGcAprToF+SdG/ZtFlsd6CEtQQWT9nNhVWWtqaiDJzgkHizd/rcGTq6vQN8uHK4ZaCtF7m0J44Jtt5nJLKIzdNRY9dtveRmyqdt6gszzuD8n6hgY01Lb9psJmWEUC7NpjCcXtranBlo0bzOV0L0FtA19VJKqChlo+GdDaEEQDfFCaWhHUPPjl512YdduNzh9v3Avy2UJox1+N1aXVaKhNrNK9p8ZZBaVoCcn4ZrPVf9/Y1IxjTjwZc6r0/qTLzx2LY4Z2RUNtNX7/ea1rIDh93gM4evTJ+NcLT+Oh2XdCLT4Q+Pts4KcanDsw07H+rPkP4Ih+FkU42nezuOa2GbjmthkoK92O4pJuAIDXn3uSr8oLAmRvMsoqqpCX4oUoEDQ1h6GoKlpkCWhN7AG7Y+PPAIx9ECXew9v4vczMTDQ31EARCRqI33VfVEXBLZefH/F3NE3DvXfcisFDh7sqmdsxdMThWBuDvfHjl+8j+cjzEKQVTVvQff/TL+L3DT/jmUfud0lyaBA1GYAXLcEgqgMBAMbnJS+Q7KTUkqZqM7w56phjcMSwz81zlZtf4Bj/0dDcEMSJp5yKvgcehLId23HU0AEoKipCVVVkrQhF1dDaUIvaZhk11UCXDD+OOfFkHDjkYPx3404s2GGsGG6B3Gztb6ixDg0uEy9VkdHSWI+P3nsLj8y1rN5uvP1unDzu7KjbX1VZwXl8a5qGV59diFeffQI333lPzM+z2N0YRHNIQbfMJDSHdQaBrACapFsM+SRB71HUNIAALcSLYFMIggDUVGsIeSWEGvV7QFUVE0gCqIrgYNZS3wBZJKjyhlDXHEawoQ4tqhdVVQoaalvQ0tCEBvjQ0tAKkRA0al54RAIfgMdP7ooUr4BQs36/DsrOe19bQc8JIYAgRn7mN4YUnPevPwAAC04qcbxPlBBHw1Zbm0CY6yPcVM9Nhtu6/fVNTQi3Op+BacR6djU2NKChtk1fr3++RUbAI6KhRWcaVFUlHig21TbqzyOFr07LoVa0MhvX2NiAhlr376fnJhT2gHg9aJBiuHP8CWhtCKJF8aAlqCCkqEgLeCCrKpqCCpK8IlrCIoKKglCLjLoaFd6wFy319WgNKajeo7VJOK65vhaaR0SVX0a9Mc/SAPhlCc0CQWtYhTfsHLuCptO66xvqEx4Pl735OwDgpqW/4t9n98L4V3+HBmBvfQMuP8AX9XpRNQ1lRi/17R86nVgEJQyltdFaP9gMtnFQbqmH4PXh3XN7wyuQuK4VmdGGot/dGpJRVdvoWNfDzCGbW1vbfS9pbQzC5/cg1BJGXY0GMRjZDtQNjXXNaGloRoMWvfjRL12Iuq3x3sv2NVobgqitViG0JnYc/iywWmPRsN9GgAUFuh1URUUFCgsLzdd3795tVqULCgoQCoVQU1PDVbt3796Nww8/3FynsrLS8f1VVVXc93z33Xfc+zU1NQiHw44KOItp06bhpptuMpfr6+tRUlKCnJwcrg+cZkBycnLg8eyfA2Z/hdYQhL9BQGqGLhBySr8cfLBxD47tlYWM9HQAzsoZRdecdAB60Jnk9yI1JRmAUyBGSk5Dakb0APGjLaUAgN+rgwhLFmW1xeb5HdYI3v/DuvjcAm4A6JabDvzqVE5fv/5nnD3stKjbEg0s7UkSCGZ+ZR2fnTvLMGHMOcD1bwMA8tICCNq0FDVBRGoGH6jUk1akZwZQpTQjNc2Lmu1lrsGWIAi45O+n4dkaoLRednxPLIQl57E6tV823t+4F+WNYZQ3WvumSV74GV/m9NwC8/f6HjjElXp+6JHHoLmlVQ+4VRXI6Wa+/8EmfbLvE4lZce3RsxdSM3iBNbfvtmPZ2//GddNmome/AWbl8epb78DC+2ZDVRUQImDKrXegpHd/ZCRJEEGQ6pfQJLTCLxAEJEt/IF4ccOAgYLMh3iV6uKCbVnB79huAjVWN8AgiUjMCce2LG1RVRU1NTVyf//G75VBHRv8+rbEGRA0DtMlD5sfBqONPxqjjT8aYv0/EGy88g9cXPWFR5DUVLy54ADhtGtZUtGAN+0HJB6TaxqAcxNXX3YjHjTlGZlYWUgvz0LPfgBh77Y46tCIzKxXdigqgjRiGnJTY7A5V1ZDUQOBJ1lDdHIY/NQCPKCA1IwuhzG7AjvUAgIy0VKSlJwPQo/Cs7GyOWkyhyGHsrqgwA279N1Q8NGcmjjpxTNSK9cZfN0Q4fxoevOeOmJ9nUaO1IODXkJQegBqUIQoEYUWDTyJQggrSAh5orWEoioaAV0R6wINmsRWEEGRnZyPVLyG9RYKqacjNje/ekRH0ICvJi9ysJJDGIAINAtIyfMjNzUST0Ax/0ItqVYPq9yIt4EGSXzJZTcMz9O/Q21a2Q9aQ8D0L0DU+tla3oH9espmkV+QwNA1ISc+EGIF9AgA7K63J+4LVzolvSnKS0cOrv5eRnoYkZr6fnZ2F60al4sd//4xzBhckvP0XDC3CSz/uAiQf3NLOJXnZoONPCiS36fhQtEpBZKZ4IUAfF7m5zkRnLFTIXtShBak2wTC/P8BtW1ZGOlIz0l2/g54bT9iLpIDfnFv8lahDK1JSvGhpDMEHIDvDh4ZWGcFmGekpXvg9ApqCMkKSjKzsTOSm+ZHaIkFrlZGTm8X1a8eL1EbdZi03NwNCUwiBRl0fICvFB0VTQYIKUpOd1PuArxJAC0Rf28dDXVBBakaWmfwsbVQRSEmLer2w2jgb9zrnCilJAUNUU38OZqSlIT0gmctZmZlITfMjIUlIss38MyMjA0ApKppkvLOx1rFqfnYm6HyUiJ52XSuAPiZSU7xokUL6OY/j2cKinjTBF2qIOL7/NfEgrNlVjzH98yBFEQaI9162r1GHVmRkZSLXRRB4f4DPF9927bdBd48ePVBQUIBPP/0UBx98MAAgFArhq6++wr333gsAGDZsGDweDz799FOcc845AIDy8nL8/PPPuO+++wAAI0eORF1dHb7//nsceuihAIDvvvsOdXV1ZmA+cuRIzJkzB+Xl5WaA/8knn8Dn82HYsGERt9Hn87keaI/H4wiuJUlyfb0T0eH1qhA9HvNin358XxzTJxcjumZi054I5Q8DxRmWOJbfI8LvdR/uMoSYN5NmRjytKQorbkdtfFnzImbb0FIPbF8DHHAUPn7/PVx76og291U2M8FW0N5zJXmhJWWYi6l+L2d9AehCdeyx0DQNgiQh4PPB4w3D5/Wid//+jmCL2lKldx+AZ19YhbCqJnyDbgo7KxPFESxQQgpQ0WSdk/SAz/y9opJuuvCYQX0WBAHT5z2EopJuWLX8G2u7PdbDqCGkv3bmoEKTAh3weR37YH63ITTmhteeXYiBQ4ahYlep2e8sCAKOGH0qvv1kGTRNxeP33QPFn4qzJ1yg9/BJHmiCjCSjn06UPAjKKsrqWtA9KylmT9faFd8C6K4viB4Q0oprp83CgIOGcj3gRJAAUR/vjn2J1DBpgyAI6Narj0PgzQ2u7WI7fwHS8oA0nTFCQs1I9vvQ0ELp5TxNiz2vN94xGyecdgYu/I/B0NA0aGH3xNYNs+7F+tYUfL7NilIyU5Lw93PH4/EnVgAAknw+iO0QiBIkGV6vFwUZ0UUCWWiaBo/HiyQ/QU1Qw9baEPrn61PBAKM4m+TzIj/d+t6AzxtxHFTs2unKWti1sxRFJd1cPwMA3Xv3jZg40TQNv/y0JurnWQhSGB5Br4YQEfBIIjRBRVjVEFQ1JPl9aFYIZChIDniRmuTF7mYFokDg9Xrg8Ujweb1QNC3u5+Swbjlmj7DXq0GUJHgkLzweD7xeDyCJUGUNokTg83ohSCJEW4Di9xn0ckVr06Tyhrc3YO2uesw75QCM7muxoERJgih5on5nq2qdzy3VTi2DgNfDefQm+bym2wAABHw+ZCQH8PmVI6NOnCMhQFtbFN02zI6MZOv+q8bxnIwKQUHA69PZWGL855iF1+OFIMmO7RhYmA5R8uCWo3tiU1UTRnTPjnrPFCUJgirB45H08dqWxuh2QFZUlNa1oiQjoGsQeBR4vV40KyF0z0rC0JJMLN9WDYWo8Hq9GFCQijVldRA8BF6vPr4ljweSoo/rtijLezxeiJIAj8cDvw+QPB4IhMDv8+qtInLI9Xx7DbamDNLm8ZDqkwDBmo8VpvljXi91Ieu5QBl9p/bPw/uG5oDfKyHgt5IEAZ8HSdz91Jfw9rLPr4A3eu9/ZrI1n5A1tDtAFSQZHo8XkqTq5zjB68Xj8UAwjqkbeuZ60DM3PpHoeO5l+xr68dh/Y6h4t+svJcc3NjZi7dq1WLt2LQBdPG3t2rXYsWMHCCG44YYbMHfuXLzzzjv4+eefcdFFFyEpKQkTJkwAAKSnp+PSSy/FzTffjM8//xxr1qzB+eefj0GDBplq5v3798dJJ52Eyy+/HCtXrsTKlStx+eWXY8yYMejXrx8A4IQTTsCAAQMwadIkrFmzBp9//jluueUWXH755Z3K5X8xCMAFAn6PiGN65yDJK0ZVU/WJgimQAzg9G1mE5NiBButnvbM2OhXdDaxgmUckSPMxCYBgsxloaIJk2lK1BdHEyOx025awgvXlvFK0vf9cNQSRPKKlKtylSxfMe2gBZzU1Y/7DGDhkWLs8PN22vTBCVjOo8GrmVPjn2e924Mb3fjGssfRtYCnJnDe1lw/oCYCcFOvBGkk4iPU+PmPiRY73NU3D9CmX4NE5M7nK4zcfL+XE1J68+zbsqSi3thMwbI7076luDkHReJXYyvIyrFr+DWdrVVlehnnTbrCq26IHIAQnnn6Wq1WUwgRY7L48tOg11/3ljhEhmHLrHSjdugWV5WXc5y++9ibn+gEXBklLPVfNPmfiBfD5mWy8W083+/HmZuDH9/SFb150eG1TdOs7ACGBnyilB7xI8Ul44qxBePKsQR3i55torEPtfqgolsB8Abs9fklAut+DRecchFcmHBw1iHDzT49HFI4mTiIrDCV2HQvG+DX0ykCgX9f5qT70yNYTCIqqIckjGorMuhgUPQR9c1PQJ8fFvi8CqLc3YNlLCsyyqurHVDKs7dxySvQ6V1StTUq+a3fp99D3bU4U8YCt3tF75on9rMDdLg5l1x6hbhNtCbgBa7zplmG2ZJdAEPAIOKlfLvJSvDg6Qa92OxRNF+rslhngns2JQLCdw9cmHozbj++DY3vr2zZ+SDHuGN03LuEpXcGbdIjSdKKobZWhqBpajHmF3sqov5eT7NVtwaCPyTS/hPSAx/TVpntGmP/aAt5+zxAmJAT981PgFd2vFcAac+1RL0/1SZyta1Yc46G62flcyGOqv05fbpHbx7ao77PTmFifp5ZiAKL6pScCek9rS06oLeJrndj3+Esr3atWreKUwSlV+8ILL8QLL7yAW2+9FS0tLbj66qtRU1ODESNG4JNPPkFqqkUQeeihhyBJEs455xy0tLTguOOOwwsvvABRtC6+V199Fdddd52pcj527FjOG1wURbz//vu4+uqrccQRRyAQCGDChAm4//779/Uh6EQMEMPT062/3m5vNWlYF7y8WqcSpfhE7gbsl8SIQVQ8Dw826H5hldOD96R+ufhoY+Q+znEDC8wKqk/kJ1KQg1ag4fElpJ5sR8ygmxGW+s8fTqq9PQFB/XYlgUCAZVcy4YKLkNX/UCg15VwllZ4TOoGN98YfklUsXuNsFciM8DAOyir30KZ2UU+u0EUWv3nrOTNZo2maqd694qsvrCSOLehO9UlItnngRgJV7y7p3gNLXn/JFCNLBKqqYNeOrUjPLTBfI7CSBLKqwSMKqCrfhcrSbdiwfg1XOadK46VbDdsqJaz3c4seaKrKWV9Rdeqirt2RU8AH4XRfAOC4U8fi8/ffc93ek04/G6IkYsF99zjUzgHg6vNOd3zmguun4UV7IbqlDpCttp2jjzse332x2Vx+/OU3MOX7yMetpEdPkK+fg7bidSDYBBT2c10vKKsOi7I0g7Z/SElG5B9IEGIbAh6/R/eM9UoCp8PAWh3R+9dBRbETv9Q/fd6MW6CqvNVbLIwbPwl9DhiAC08fzSU4CSEYPOzQRHYLEgHCqoo9TSGk+pJACNAqK8hL8SLFK0EgBLKqQRIFQ3mZmBNLAAm3VbAgMHIHjCKzompI8opQNePacgmxWL/ekKJGTM7GAh1bL6/eiY9/2405RxfEpLLWxAgkvKLABdT2e1J7K7T0+0Iu10q6X68Azz75AN1Ws42BPYUGDT5JQE6KL642DDcIhE8D9c1NQd/cxLRDKKgXNk0Q/ZmQVf1Y0DwZtf8CrG2hx5u2lFCbL4EZ3+y1kyjYHB31TxcN5X8hiiOHR2qbejmrpZPqk1DJPL/DrpQoHqxLDEW+LUluVy9n4daaEwtsEi5W0J3ik/DM3wfjs017cOmhXRP+LTuIYZdIleYT/3y7N6ET+wB/adB99NFHR7faIQSzZs3CrFmzIq7j9/vx2GOP4bHHHou4TlZWFl555ZWo29K1a1csW7Ys6jqd+PNBbTFUTRcGY2HvYyphelfc7CLYm+ZRPbOxs7YZf1S3RLRRSQRF6dH7wgrT+G3jg+6Q6c97+HEnt8uyp8HFB1mCChkCIHlBUjId085jemfjy816AG5PQKiafh16BL0iZ2bZCZBTUISSA3px67M0N9rPGQ27G4N47Nut6J+X6lpxSIswCW9oakFNszXZCsoqqhjLG3u1VFUUfPPZx7jvjn9Y9xw/P1lL9UucP3Ykz3IW+YXFuHbazKi2WZEgCCK6du/J7zczqZRVDV+/uxiP3/WPiB7YI4861rKcUsIAAoDo4aqcrMWZIAi4ZupMDBh8sMMeasnil/Hlh5HvgR+9+2bEbTADfxuK+wwEfra92FzHVbp9Im/lMuLwUei79Uf8XtXk6rebX1iMGZTWDl1V2W3sBBXVcT2k+Dr+kdeWCdGA/FTUtoSxpymEkKiZSZGMIovK7Ylj/LEYe+5E5A06HLvLtuPwIQMSuo8MHDIMt9/7CN+SMf/hqN/hZjNG+7izkjzwSQKCsgJJEJDkFY3Joz759gjEUbVrL2hQYvkY669JgoCAJ3L3BJuMDcsaEon7WWZQuvHBR77RmUof/1GHS/NzXT9HUW2zHhII0LLbSur6JQESMw7aYnsUDfT6WrOr3vQbpmCvlfYG3IB+7Nv7PXrQ3TG1aTPQ1TR0zAiMH7KqCwtavtzWfUQUmCDbGL/6sjGmmWMoEtLmLRcJMZkTouFdLwiAJAicx70dplVrgpXuGmasJ3lFVDOOM/HMwezXCgDkpbKVbt6nWyBAt8wAcpO9SLM92+PFFYd1w/wvNmPswHxH0cYvCWZLhlfUFecPLk7HwcXuWgJtgWAwd9py2XTG3Psn9tue7k50ArCoNW4PAHvmsTjdFnRLkYNuv0ewfBc7wJ+1yCbscuHwLniRqYgXpVkPB78kcgmDAwcNQpehffDRLqBbn/7t2g63SvcZg4vx73XlgOSDNvpax/v5TNXB/iCVVRWSIGBv5S78/P1aeA48ACjJiBho8EG3GjM7POuT3/H9jlp8+JvOEuiZlYTqlpDpdZ5mC5LI8tegHT4Ba1avglBfAkCvArbKKkpZ2r/Afk6vcc2//RZ+1h3gH45pPonrn2SpvpF8jO0+1dE8u/sdNBybfl6j21MJIqbOexAlXUtQ1WgFoAKxvIV3l5dhwV3/iFhFVxUFpdu2YvjIIzF9/sOYvVk/ZsTrM6ucleVlnK2Xqqp4dO5M/beYSrV9vXhBt4Hzmqb7IopIySkEUM5/qLUBYPqwfZIA+9Txn2MG4MkV2zBpWBe4gfUS17KKcdWHTvZJUFZRb7sekmJYAyaCyvIy/Pzzb+jhHYLCtF6xP8AgxSehvlWGVyT48p3X8MTdt+qWXR4/cO2/ALStgpVTWIS8omLkJ0DRpojkBe8GeyJn+vyHMfTEsyEKeiVbIAQDClLx3XZdLFKnkNPKolHpJvzr7QUxghKrYqh/rygAPlGErGquQTdbSQ4qakJCS2yizy+JaGYYUUlxVNZqbIGE2lyPN597GRg9BQANsttHkY0G+hyyB9xA2yqDsdBWGjyFIAiJdjxEhGiMl7+CXq6oGrw+AYqmYdOeRmQEvGarBD3dghEEm8uCYCaWKASh7dfOoMI0MwlCK9weI+Bmn0N20GRgor72bPtCWFFRy1Su4wm63byxOXq5SLgxqxntDO9dckibqdZnDSrA8C7pKMkIOFpPrjy8Gx7+Wk+wtdfD3g5V08zzLBptEImC3g87sX9h/zQ860QnDJhsQZfZkr0SmcoEaHaqkWj0UFIEPBbdPCSr0DQNL63aiZXbnYrissvTZ2S3TC4gzEvhe0eLbZXvAiYo94j8tqQmBdCtq05HamsCgPb6bqtwUsbt22LHrs2/mH87g24Nn7z1KoYN7Ivpl5yNUw8bjEWLFkV8BLDVuXgeyht389YbeSlerrqZ5BU4hoPWaOxfl4FY3WLRbrdt246ftzPBnWScj7/PASY9DBBBH0PFA4A8I0Dy89PrNL+Exnrr/NPxsWTxyzht5GBcOX4sThs5GEsWvwxAP+ZzXAJVe28txab1a/D8Ox/jnkVv4umPv8MFF14MSdDZBDRQYY9rRenWqLR1tpo9bvwk5OfrHt73L1psUr5/WvV9xECaVqppQiHRgJvdBtobLBhtPSSQiqlzH4InJcP5oXCQ68P2SYIjLV+c7sc9Jx0QlTqaX1iM4SOPRFGB5W7RNycZo/vmAKD0cn6i1hE93IA1Ju649GwMHdAXixYtSvg7NGiorarAQiPgBsCJwrVlvtSeyhdgHdNYFW57ImfutBuxp3IXREGArKj6JN6o0AiEQBCsflSLNkmYQLn9s0NKV6eVQPpbEiEIGJV2tyopIcR8liQaSOxuss5XUFaxrcYSQ4snQHYEEq0NQNgS42yu559H0bRJ2gJvFCaSG8ukvWhv0K2nTzsGghHktqGNv/2/bdC5w4oGv0eCJBB4RT3gpdeCKBJO70Ay6eU8k6Ot9HKvJJhBN62m0jErRmEUWPOmxA4cyzpqDat44yernYzOO74vrcNdn/yORpfigb2gkOQRkeLje7jZanYXg/noEYU2MywIIeielQTR8E5nUZDK95N3JNiWB1Foa6X7rxnbnYiOzqC7E/s1aM+Se6XbuuESwFbZFh2TEy/XL2kF5SFFw+eb9+DRb7fimnd0Lqysanhm5XasL69HU8j5AMgIePjA2Sdx08bCVD7QzUrie5PtNChT0KYNVPc3XnsFp85chCtnzMGrvziTBjkuth8svln6b/NvuwBaeVkZHrrjZm6CPXnyZFTsKoMbBKMvzO273GDPcKf6JUdlh6XToZUP0ik+WrYECxY8br0gefVKdskgILcHkJKti8idOx84/yF9HT9fDWzYXYYHZ00zl99745WIAUZleRleX/SkIyjWNA1zFizCjXfMcWyjqipoaWnBwENGIiuvEKl+DyRBgKZp2Lynyax00Ix6UdeeEQN4t55dv1cfYymZuqjQksUvY8Y1l7p+3twmW6WahR7ARH5EUFV4ug1UVG32S+9Bu+pVfOEb4qrIfNHkKRh11NHWdhuiQW0Fex0mea3rfmt1s2MMdkQg4TYmJk+ejJ07ndX2aPBLIiodiRXNZAH0yIpPEb2yfBfW/rASleW72h3UxAO3BI2qKKjYsQ0iAWRDFIpWzGhQTQNiQbCSA8QMJNq/XYQA7Eiiv+0RCfrnpYIggpo+wAhAJnb/5at1CnbUOMUdo6ExaFP9b23kWi9qqyq4a8MnCjhzUAH65SZj8mHt6xsVSfTroSMSVLKqoVVWdE0WdEDQ3QHjJKyo2FbdDAF8EF9e34rfq9yfL/sCAiFoDStI8YqQBCpWagVYlP9Dg2y96m1VtgkIx8xqD0SBwMPoB4hiZEYBnUclmqBiWUe/72nC1mrmWjHmAde++yuWbqjEa2uc8wv7tZLql7g5Hk34fzb5MHx0+QgkR3CraSvsyY0MPy/U25FgNYxEoW3jvrPKvX+iM+juxH4NfbIWqRePnWDBJpwmOHoiOXq5JHAqnKtK67h1F68tw1Mrd+DiN35ypWxnBKSoPeMZAf6Gzypb2reFEGJOfhKtdFeWl+GfH/wIDDgGOH2G6zopPinqpFZrrscAg2k9dkC++b2rln+Dn1c7K6WKomDb1j8ifl8iVSP7Omm25IVfFPiJciuvtm5C8nEicZC8QIZVAYUgAtnWJHXB6+8hJZf3H97w/TfQGMu1udNudK0Uq4qCdat/wKvPPA47BEHA4GGH4PgxpzvVpAW9KtwSVpHklZDu1yscLWEFmUkeM/ikYz23sBg33v0ApxI/ZepdePKNpVi6/Cezmk1hTYY0MzCMpplBv9OtUi2IImbc+wiWrliHJ99Yiutm3GVVsQUB50++FktXrHNsQ35hMbb79OO8ckctnli+3fGbB/Q/AKkpVsLDbcJiV2l3U2239ptpCWAYLm+vr3Cu2wGTI7egU1EUbN68OcIn3JGd7MWooYMsNX0D5OkLsXhcNyR5Y1c0lyx+GeNGDcUtky/CuFFD8eWS1/e5/ZFbgkYQRRR27a7TyxXNrHLTRJJVrRMggAYXRk2PtF8QDKB0XF53QoCu+G1P5tnBXjuJgA0kgrKKPYwwWjxJR0dFr7WRa73oUszfowghSPFJeHXiUFx+WHxWbpEgCsRx7Z03xPq9jggktlU3Y1t1MxSjn7stfbUs2suIUFQNIUWFKAh6MAOrHai2JfynKj4LRG+L0vVnRL0iy1CCJRvdnAq/carjHTiD90uCqaFiF6xj4Wmjenk0kdegwTakoMK1L/xQigtfX4vGoOz4fLJX5Fxh6GHJCHhiFho6AqwCf8fTy+2V7sTHZad6+f6Jzp7uTuzXoD16bg8AdhJFbPRxj8jTrjTYgm6PZTkWlFWHDdianVZw5yZOlhHwQNSshw4Nuqmwhj3IZgMDag1lbjvYSre+py1hBZ44JimlW7cAmTYqaEMVinIysSuoX960qt8Sdn9IklAjZh3bHX8E/RjVI4vr1wShFDzrDIiiiF69emNL0F1V3iMIaIEalyKpfV6a5vdwGVpJFHhFZTnoPhmQvLwauT3olrxAcoa5OGDYYWhetZL/jlALULbB/FtVFLPaa+9V1jTNNaCdePkUs/LLelgTQcCkG6Yhv7AY9Xua4BF1ERtJFBCUVeSliAh4RYjMsRYIcPI5E3Ho345BTXkpknKLIaTloF9uimsAwfbaxUsXv2bqnVyl2q2nl1KOjz/ldPz+y0/oO/CgqN7NrbLTszvdL5n2SH5J5Kt3NmsXe7/wyWeeiw/ffsOh2k7htbFGogULHTE5cutfF0URvXv3Tvi7evfoihvufgCP3Mmojt8zH727x65iulXcn7j7Nhx42FHolZ34tsQLuz87ZV3kFBTpNkeaZgYMdvVdUbDo5vrrMCuO7QUb3AOWsBq91wpGFd6OyvIys90h0UCCVfxulVVUN1mV73iCbkcgEm7lKt2F+flI8+8bX1pRII4kVBfG6qgjWCFhRYUkCqb6+V9Z6d7TFMKepiCKUzx6wC3wejGCQNpMQ44HrC4IAlkQCEFYUeERdf0Z0WCCmPRywWrPoMts+0huipdTAG8vBhWmmfdHWuxwgyWkFh+T7este3Fo1wzXeZS5nqJhd7P1Pu3VXvDfbQCAZb9WOhJU+rzGSkz+2VTqQJxOJ22BZji/0GumU738fwedle5O7Neg6uUxK3YEXOXZLSjh7Hg8AqfCuauef3jVB63Jk1uGdtvPq7Htt/Xm8n8+WMI9sO2q2/YEgNP+TN+WqqYg/vPHXpz90mqc+8qPnM2GG0p69LT6lylaG7Fry+/m4sovP+EeCsek8BT0Sy67Aj27lWB031zU7angBbWM4y4yFdCnnnoKxV26RKT9m5nwNlDl0/ySo5rB/sbCl//l/sHcHkCfw61lyQtkMkG3x8cJp1U3h51UUzkENFUDT18CPH0RiFG1tleAp897CAcNP9RJxxYEjL9ksrk8bvwkXDP1Tv04qSpeengulix+2dQpMH1Xia5Qe1BRGrySAA2WVRuBrhI/fOSRSMrKhygIcajKaq7VSDcMGDyUW47W05tfWIQhw0cgv7DI8R5biW61JXd8koDhjEWXVyS2oJunl9sDyffffN2V3m/fb0CfOEcLrGOp6ccDkxVgMBBE45ro0sVd9C0adpWVoaikGx7/14cRGQyR4ErzVhVUlm5LeDsSxbjxk/D8Ox/jxjvm4Pl3Pja3mRC90k2DK0kQ9KCaVm1EowJOrJ7vtvrQ2mGnqRPm2gJ4kUIK2ptfU6n3l37+8Ycxf6euNYxPNlahVVa4SveephBeWm21GFDKrKZpXBvNLxUN+KG0FgDQGOITVMedcCKm3TPfXPZJAgpSfVh8/lB8cFli9m2xkOzl6bk+SeAqhB2lfyASYgTdwp/S/hAJ1c0hBLwSVE0zxx9gBWvUy31fwK4L8tk7r5mBVIpXxLCSDFP3gN62zUq38R3ECMJou1WXjACGdaD1od8jmt8tRGAXAlYrhr067YaFy7dh2ge/4R9Lf3WIWrIIyip21FksEUXVuLmPRIhjHma30/uz25fZKrtnH1S6zfPdxvtjZ8y9f6Iz6O7Efo1o6uXceiAOCzEeGjeJYH27g7KKvQwtUFY11LVYN/gGe98dgI/eeBFgqMgLZs+AoljrJZT5ZKpzGyobccvSDahsCGJ7TQtaws7fZpFfWIwuvQ/gX7RRFF987D5IzBEcPWokt/qY08aaf7tN5DVNw2uvvYbHXnsXH65ch0svvZRRInYG1yZVM45Ktx2pPsnxtGCf68WMaBYAYOVi/d+8nvzrklfv46YQvUCaZd/z+w6nJ7gp7tW4R696axpWfPWF2avMBkWudGybxVJleRkWzLvLnJhoRsBYW1VujGtiVv1EQa/4ioawj04vo36pxuYZFcRI8xy2184hbOYyoWSF2NoD+4Ry4++buPfT/RICNr0FFvbqUqwKPe1Dp2CrAHbBRAAYz1BmO4oGOG78JDz/6Q948o2l2LZtGy69NHrvvBsWLVqE3j174B8Xnokp55yEndu3JmTz5UrzFnSa977GksUv4+JxJ+Che2bg4nEnmOKCAtETRnQSKgl8X6pECIhAxz2BKAhmb3d7QYhFwQUscShWoZm9dDimgHHtv/TME64tDCxufHcDpn/4G55cvp2rdK/dxbe+hIz7343vbcApz36HhlYZqqbhwsVrcdVb61HTHHJU79JSUjB06DBzmV4rvXOSOaXm9mD+qQegMNWH+8f05xhYKV6RU/fvqGtFEAhkVYUodIyQWps/Swh8IvWJtyqIVDBMEjsm+WOHKyPlrttQVaGPM0v4j7ZcGNeKKHCVbsBQXO/4TXQg2nGgY+aT36sw9rkf8NUfe1FR3+q67lvrdHHTH8vqsKvOuc6p/XXxz5CiYm8L26qhcIr6yT7J0dNtv8//lZXujv5tql4uMIF3ouioZGYnOhadQXcn9mvQCVnMmxrhH+huGVhWvExi+tme+m6n2UME6EEka2/hVunWQq1gUwFasBkqQ02MOrmwbRpB5AlOPHTHlMwc/oXWRs6nWgu2QNCs/bOLurEPL7eJvCiKOPzww3HI4aOQZwQFtKq0vaYZO2pbODsNDxP8RYPbOUp18VFm16qvruTfrK9y/3K3Hm8mCN9UWu78jM3bW9M0s6rqVgF2C8ZZRBScKt1u9WsRove40sCAACo0qIyaOT1Oeyp2YcOq5RGDAsnWa8du37IV6zDj3kccFftYQV60Xmr6vn1CuW79Om6dNL9k+sECQFPtHucXsbTgGBX6aMkCgRDHdZ7PqMx2VPUOADLzCnHEqL+1qcK9c+dOXHHFFVEr+LHgSPwIIm68535kF8QfuLcFkcQFf1+/BmtWfovqynKO0s31pQoEIvhAuKOiCBrIUxBY/sN0W1hw16cRdGtE4BI6blhXrgfXH/62mwu67QjJGmRFxbdbq1HXKmN1WR2qGeG16paw2Y5E4bNpkfgS9GqPB8f3ycXSSw/FgYVpXALM7xER8PI6Je2FYFCkZVWDTxTa39NN2k6b9Uq65ZasahCJYI4N6pUtEcJNiEOy6toqkygiMVLKDUaK3VdeYMYr/Q+g41n4U3p1o/0GW9Utbwji5qUbMP3Dja7rsuN72a+7He9Tq9WWsIJPt1hJq5CicdT5kKKi0SZoS+/l9Ho5qCgNfybY68NuJ9ZeaEZPd0Gqz2QEJQqCjksGNIcU/La7MSbzshOx0Rl0d2K/htXTHZtezoLebAqNCfcxvXK4wJYQp+UYRVBWuEB79mebHOsQVeZ7jTUVhPv+yHdJ+77oVfoIQXccFG3HOsEmri+QaDKSfRZtMJqom9tE/t6HF6BLly5cAKM/lHUqb4pP5G7G8QqtuInGxVKyrirdYfuSCGqzkhclA4ZyywhYFmHPu+nAqc4JtL2qakc0OnZJj54OkSxBFFHUtbtDkZZOvKhqrArNDFg06JXFa049DDMv+zvGHXGQWVlkQcf33uoaM1Bmty9WksAOewX7vTdedazj2jsu8RW5NJ+EnZt/M5evn3gGdmzdwq1z9eHdAQBnDipwMAhOPfu8uJMFgsAH1tlJHiQzgmQdaYOkalqbA5NNmza5JmRiBXx2jBs/CUu+Xo37n34RL3y8AmPPnYQOYNBHRaRk0tTzx+C6ieNww9iReO+NVwBYCuKWzZFNWK2NVRw3sBV1+tu0kg7oASCb6OMSjEbQTTy+uNkfkkDwye8Rkn7QA4edTNtSildEVaO1XMME4BReUeAUqTuatmqHT+KD7IDUsZVu2oMcVjSuMthWEMRBe4u0LdDHhKJqkASr5x8Awqpq6MBYSc4/9jZhm4v7QqKIxEjp2bMXCAHoKaA2YnTuQEBMijkAxhGg3ZsUE9Er3c5xQRNRiaIgTX9WVDWF8VOlpavTKiuoZK6VtWX1piYIBb2Xf3z5CCy5aHhMa9SOBnuv6eigW4XuAJEZ8GBYSUabmEAdOU6aQgpEgXQG3R2AzqC7E/s16KQt1j0tkqrp6+cPxRuThmJwURpXQSCIXPUKympMkZDzL72CezLNmPdgXD20kRBpghOPmrl9ncMOPQRQrQz9jdPuRJLfCoSSbarI9kCEDc6e/ug7TLrwYgDAsJIMFBqZ6V1lO7H++2/RsKcSIhGgMCcoXiXgJhfavk8Soj7we/S0TYht1WmKrMPGojRoJReI1w/4o2fCL558jasqc1sp2Nl5hbjyzvss9XFBxKz7HkFuQRFTCaQUWOP3jDlleX3QHPu7y3fpSuQxqqJ0fC98YL7DU5wiHh9mwL2aOe/2W1BVySuCu/aOB3j/cx9krPziI3NZC7di3Y/fc+sc2SMLn14xAtOO7e1IDtz14MK4kwWCjV6emeTlKbMRrvlYFX0WO+taUFbXCg1tD4r69OnTIWNN0zRk5xdiyPARyMgr1MfVPq6ERdILYNsoZt16PXbu3GkGCWbgC0tMDcbfHbW1diE1QqjdjnGtge/p5hKMRtA97vxL46b4Vza633sogoqK7YyFWEhRsZv5jFug4pME5KX40C0zgN45SY57dUeDvfdLhHCK+R1R6SZm0K12yL7oud7ER4yiamY7j6pqppUciB4wyYreEkGYMSIIPHOirXBLZN88+3507VpitlgARtLIrr7PjGkCcEH4vkQ0lfhIxYp4UJLh51iAmQF3kUB7y9/SDZWOdei9PM3v4QQA/wp0dCyq0faydow/eu3F6ruPb3s0SKQzXOwIdB7FTuzXiDvotle6jX9TfBJ6ZScb6/BVkEhVr6YIfdRsJvW40Sdh0NDh5vK48ZPipvLY1yMkcjBA1cyjWSjZK91DhwzGcaecZi6f+ffxnICUfTLl9tv5hcUYdtgRyCkscvTcLlq0CAP69sK0i8/GRccPwydvv+Za6Y5FL292Oc5eUeAe+JXlfO/1hh9/4D8g26pFq98FAFQr/MP8ujvnIb2ou+P3BhdaAWKPHj1cRdMS6bNlEVRUjDnnfDz2/grMfe4tvPaf1fj7xAuMiaO+jsD0dgOGaKDxedGYgJVtd68s2quiakjvmdN8yQBImyjLFJGqmWWl27nX3Hrbs4r5wJEEmzmNAcghwKVvOzPJa16j9uRAvMkCgfCBhG4rE73Sba/ou7EIWDSFLCZMW3tUu3TpgqefftoUKCREwIRLr0r4e7bXtOCPvXo1TjF6Vfd1c6ObXoAdKmOhJhKGEmuMdXpPIeg4xWha6TYDfKIHkpb1jrNIShM8Aw4cDAA4aMSRUX8jngms1aeqYUet1ccalFWu0r3QxU7PJ+m05zcmDcOrE4bu8wCLvfcTwveptie4ohANto6saVyLSVtB2siZ1TRNT/YIumOGAN0XW2fR6QkRjyHyZ95/mf7q9oJNJD710Xc45/wLDVaDlWTWBQb5CqXIaHF4RMFIWHXIJkUF+xsNrTKnLeOWaIw3odInJ5lr/0lzaScD9GuF1dWh6JvLWE3uYxZIIlA6utKtae2+L9J5RkdsmarpIpidde72Y/8ZtZ3ohAsI9EGqxqSX8zeoWHQfnV7uPvyrXCoYvbKTUMIE3T5JhMfTNmEbumXFRtX4+D45ER8gIUV1BAUzb7qaW65v5gVKkrwiAoEkc1mfWFnHh1V5B/Rj51bpUzRDZZi5+bv1oj7y/9h77zhJrvJc+DnnVFXHyTM7OzsbtKucw0rACgGyjJDB5GAEGIMtksE24dom2XDBIMAGDA4EYczlYmxwIHyAuTK2MZYRNiAbsAlCaSXtavPu5E5V53x/nPOeOlUdpnumZ6Zn1c/vB9ru6a6qrjrhDc/7vG/5TRx5KHaOaVNejl6+WG3gdHvJ1NfTH5NU137Tr/1K8guh68zVgIUTDc+VKQ5hMarfxFxV7YzHO6Zgt0ItUsh6HGOT23DFvmuwbXo7FGJqPhDX7BGllDNdLlAIBDzTMm5q5+62sqKVRUO13/c84FlvA7AyyjIAzBanwPY+re6c0w1ahaXvGcvkE3+fHBtCYrsOq2uWjU0LqWU8nsjepQNMP/zeHXjn61/dUW21bu2j/70aYaibbroJ+/fvx/Ne+iqAAX9xy5+05fS7iJTuZQ8Yw8jU43Qju9EK7jP/s8/9vzrHm1qoMZOtYSlHwr1t3VK0psx2/JqZwFXMKmnksE1OTWN8bBTA8oHCRm0Xr9w+lHi93ewT1UglargrocSxxfq9hfYBIN6TvDVuXxWfz2F/MZbqe7z8+RcqIe48utB0vFFbLigkmGYrBcPKDP/IqEFz6PpyxoDLp4fBAISmftg3Di79Fgp6dgsUyJ6Y2oZdI3n7jF3bJX3LXSf7jNE8zhwrrE+m2znH4YVKgmbfyG5qVCLSaEwMZf3E+pzu8kKohhIz5fryi8fsHo2vo8ttulaDbq63SikcW6iuOriirYw29JAaoBJK/PjIvLMeKnjOvrJUjfBgqs1uH+2hd0ZtH300AGPtCam1uz5dMT0IwRmu2T2azP4KDrIJGjndwzk/EakPVlE4SZvO+66bxGvPAy4brCaO5y62h48eWbaFUiVlB2Y9UZfVd+vI0+rRzTJ9UipNf3OO1bAWVUZ4YH9co+tzynS3fmhp5V59bckladle0y69vJqsZXfx+1+/B2GKA5b3RWLTp3O3m1VdDrVIIuNxY+Qx+LZ1UfyMuTEEbY23+W8gOHK+gMeBscltiRZVnDfOwA8POfT5XZfpz66QHv/6fz4E9bibwHZfYY/zxne8FxOTWxt+3r1naZXZ8eEhXOcwL5iK8GuPOxfFQOCX9nYuQtYKvuCJMZT1eCp7F//tC5/5FF78tOvrDCYZRfiPH/yo6TkE1+J39O/V4rMf//CypQONoCm7HMxcg1K6btZ1TI4vVjFTqjdeuwF65udecgV+423vjdsKcoGb3//HVmAunZ3zeHzfLtw6gLMnil25HsYYpody2D2mgz4MaSG1+pZhBNvJYhmn+2Spfm9IU1snjMr4fDXC3/8krvkuhxKnGjyLx58TC2GutyOR3CeQaCHWTv3m0YUKBG98X5VSpo7anKsLLuxK/U1qE8Z53L6MGEanSjVsHcwi8OKOHEB35nb9dehxSFoGdZlt1wE3r90+3SP5VHvQNQJdk1I6cOyOk0bBk0baM42YbMM5PxFka9aDvhw1znRvdUUxeyrT3b1jVULZlbIGKrdZyaUtVEJwzizDQUFnumluHJ4vY7EarXlw93RE4zBTH330CGjhCNvIXLto9umPPPsSVEOJrC8Si/ZTL5zEHQdmce/JJXzme/UG73DOt84koB3XTg2At91wDj70zf14+w3n4guf+ZR1pj/IOV71zj8GsAMAcMO5E7j7+BLuOr6IgwcPtXY8/RyQqrVptBm5ty+dWWqkRLzvcddhaHyryXTHn6VaVPea0m2KfK899fJm9PJH7RzGA6dKWoW9FZTEi17+KnySgvC1ClhUa3uTGc55KeGg7hpZminATW0e4Ami4cU7IQWVrGNgalB9wXHuliL2n1yEAvCUX/hF7L780Tj+0P0468yzsGvnjvrfMzgAIK4TZbki3vS2m9uvUT10EA/edy+27Yqd9Ne+609xDjuOHWfsxvjEFszPnGx5jFok6xyXjMdx8WV78U+36Yz7l277LrZu245flKprWc5X7NuFv/3BIbz8UTtx0Glfk/EE8k72jhxyqllvZDRwLjA+fUbTcwnObM3dag3zVoJqyz23WqTgC4EKaromNdA1uZTQrUZafXm+HGK4Se1kN6AU8MRnvwDPum4f/t8//wvyuy7CjU++DoDbxisODLjZvawvukI7Jkw5WWOFZLYy3TLMRdxxQeHoQgXv+8a9uPGybbh8OpnFbuQIbCkmHSHqwPDDY0kGUjWSdWJQABLq+htJmU1T/duhzFYjhWJGmHmUZptROyz9fjcStCs9hFRxTXeolGWqMKZZdGeM5jFZzOD4wkkopXtEuw5vt6CUsm3ARINMd9rZ6na2vV3QU6tGutWqq3HTyL6oRLpnt+ucp4OvgG4f6SLdreRXH7UDH/73B7FYCXHbvfV7zdaBeH5neyjTLbtZ1M10QmC1T54BjjBgZ8dScAM+ClLpPvaUvFHQdmTnR+6jd0ZtH300QasMBYFqis41NT9PPn+y6bHIyEv07fa5fX3nscW67w1nvcTnV9LK5efPn8RXXvJIjESzdY7un7zzd+NrcXqID09MthZoy9QLiDQSwGl1+5oZ/ZHZRF1DLF2LyoXAG971foxuiftnB5ZevoyQWgN6ecbj+IUzM7g2ewT49Gtbfh9K4drrHh+/DqtQidrhCq7KN1dVLXpJqn23s0yR1MYd1ZlePDWI0XwAxw41xpcj9mSCTPlAYDjnW0rs0YUKpqancdkjr8HE1m0Nz5eutfvYl29rmx7vsh2e9oRr7fvTkxO4ct81+MdDwPv+df+yke1G7IUgpZa1dZvOgHbL4QaAlzxyJ776kkdg62A24bhkPd6w93BD1XXodmW//KZ3NXV4rTHOk6rcK8VqBNVqUsLn2rAqRxGynkAhELZOdbEaYijrJ+j13UQYSRyZryCSCv/wd3+Jax/zaNz8u2/E7774afjrv/gkgDjbnKaTr0fbo4zgKAQethinVrSg3VPA7YO33Ycn/dm38U93Hcdvf/nHdZ9Lty0Ckk4zZ80FyCqhxFwDyuxk0W1pt3EmbPqRtJPpFpwZY7weUlEHBn3wbvyylVKrpdTP3zMK9rZlHePgYKadmdGPgZnnqbKIbkASzZ3FPeTdc6Sp5m7f+fUEN+qHD82VEZh7Q3D3yXc96Tz771qkoJTCXccWUQllw1araTp5IBhcU2o4p//+nwcb79ukdg6suV5kR+imerlS9a0DVwJ9f1ZGL3fniD6KEXlV8TV6TRgufbRG3+nuo+fhtchQvOtJ52HbYAbv/vnzAQC3POcS/PkvXIonnjex7HGTxrloqdY6nPMT1K+MJ1ZsRDQy+FU1ro8RnFnqe35opGULJZZNKkXra6tXAG/lLDUz+qlXZNpAvummm3DPvffhj/7yi/iHb/8PnvOCX0rQqyy9fBlq+GKDTflrX/wb/MJjL8O/3PxSYNYolv7Lx/V///2z6R+Fb37tK/HrqJakmy+cwne+8bWm5//pHbfjv//j3+zrbmeZLKXRGE6eqWXlTgaFXtPz0fYps/eQM4ZqpLBUk/BM+6Nm7fPSmfqXfOVBfO/g7LLXmVYqV4W4n3lNStQiiT/813vx1z84jAdmWys2LzSp02/dCK47IIM80XvY48g5TifdokYK3IxzfOIL/4Brn3Zj0ww2GeNUIrDaX7V9+/Zk6UAH4n01o7gsmBZczBn2DmU3yqFE1hNr5uAemC2jVIvw4IED+MBb/lc8fqTEG1776zhw4ACApCCVzpAka7rXCpwzXLh1wJYXCN5cKr1R9q4RFbyRI+E6zUDzDFwlrM90e5xhLB+zEDY2051EO46Ez5OUbBeRUqZ8hllGT7evsV1QC0ba4215D4+ZRtxco1QKkdIsnG4N0zDSNbKL1RAczCr4p8XaROoerUddfyNoJ0sHF4uBB5/H7aJcZ9BVH69GEv9093E879P/id/+8o8aBmAHs37CW6aWmIR0O9M03Lm2Eczm1z52DwDgN6/dk3ifxHq7Afpd525ZXdkNQxxE6hRSJQNCjFHWPP6M4GxjHsImR59e3kfPI91f1cX150zg+nNiB7sQeLhkW+vWUAQ3q5D1eUuneyDjJURxAo/jnIkivntgeacmDTL4XcebqVgqTnAWi5GFCk+/8YXY97jr8OD++7DjjN2YnJrGK3/rzXhw/31YLE7hf/1TUuE7EPXGQqu18U3v/gBufuNrIaMoYfTPl0NLM0tj547teNSjixjK+QgjlVIvN1TNsPFJicZ8pDxa97f3/+5v2vpWi//8InDX7cD8sdQmrfBn738n8KrPmJcyVeOdVs2uAf/zD8BlP69fVxbxd5/4MPCMtwJYu3pKcs4SAQxqS8PiljH0mrM4Cyw4QzWSGM75CARDTaqmz7KRquxXfnwUl6UosmnUBYEGYqe7XJN4wBFMaRRGUUrh9//lHuwczuGyBnMv6/F1rf1K1HT7IpHppuAQKXCnx/2Fl+3FT47Oo1mSQRpjPJRxxmq1eNbzX4iz9l6D8vGDdn63g1ok4QttxFbCCANZz4r0KQXUpOqKeFUzhFJhKOvjp/fdUxdEjIx6+faLrkpkuvO+QCWM1kUMKg3OGMJI4SdH5zE9mMOAk3VrNPddpWRCI8rseCGmlzO0aP/YwOkeynoJen1ab2M9UZ/pbuM7Rt+gUSCQSjCAeP3r9jW2Cyl1gMpt1Qi4a3OcfdZzR+rMd5ec3lJNIvAEFqoRcr6w9HKdUIjvnRsHJLXyjZorDNrJ3TqYwclSFaEJOLrj23WSK6HEJ7+jA23f3H8Kz7m0npGV9erDr+7WMJBpPv7zvkgI/XVbMbwdvOCKaTzpvAlbW/+p512Gv/3BIfzq1Wes+tg1U36S83ld68uVgMZ2syB9KxC9nG4xhYbISu1nuleOvtPdR89DsLVpVeBuHjk/meneM5bHQMbD9x/SNKd09tjjDC/ftxOCM1xvhHCedP4W/NV/PYRLpga1GNmDc8COS4B7v4MvfGa/pfo2Mvjf+I4/wDtNYpez2AgkBfDJqemEMU6v//3+UwBSTrfHE5RHoHW0s5FTr7+jmmYoGGPYNZLHSN7HgZlyIitCGddGmW63lh2P/kXgkb+QvM5aYyE0zGtBooTzpgBUk3WTCac7rNr+uwCApVN1ratchkG3s0yGVWmoyHFrMJ1B0J/RyuXx8+FM04+IzidYLMiWDwTmGtSEEhoZ+8spyAMNgkDF2OmuhBL3Ocq1jUoG7jgwi7/5/iEAwIefdXHD6/qZs8bwV997CFfvGln2elaLRBmIxxPBCDdz1GzcU6/cdI0ioB1NeoZdcyTAMD45ha1n7+7oe5FUyHg6axxJoBh4cUZi9Ze1LCgTsmX7GXVBRFIvr6U0IXaP5jEVrqzrw2rBmV6TAiGwUA2TTneLNkiH5yv49B0H8NzLtzXM3rlBHYXm9PJ/uPMYjswn17ehrJ8qcdk4ziyN9eddtg1f/vFRvOCK5YM/HPUZMAKxQiKpmgZvO77GFX4vUkBecJtJtv81rAvG3BpY03nCFwkq6FI10mJzK9AgiJTSJWks6eCTPgSgnWyt/aHPunUgg0xKCHK9QPu+7hsf4M6jceLDXU+LGYGM4KhEEtUoKRTYiBXSiAXiDp30Hjyc8zBjdBRyflLQrat11B3AFbM7f3IAv3t9PduwESqhxL0nFrFnrNBwjXhoroxSTWJ6MNuV0iU9ple4GZgWe+6xuOOEK+i5vVxXoT7q0Xe6++h5CN6YvrZauAt8zhOJ15PFTMKR1MZtchEsBB5e/ZjYUP61R+/GpVODOCNTwvMf9xogKABnXw389N9w85fK2Pe466xh38jgf+cHbtO/l8U9xCsNVEFdNKoxzAiOF+7djgOzZfzsWTogUNcbHMm1OO3UK6WM0mpzx4JqJdPZNFeUyEWaxoyxXXXH5Jx1sJmqZI5FKSBKOt03POnJuHXevF6aSbUYSwqvdTPTXQ0lQqUzV1qdXL+vnfD4c2kxLnLQuWMUAkAhI3Dp1BBuu+9E00x3ow2wkapsGukgEHJxtvruu+9GdjB2lBs58Uec3sM/PDxf9/fA07W1f/dLe9cla+MadjQ2b3rEDhyYLePCySRlLz3uARg6bCwSI5VmF2iVZmWpoVQ2sFq0EvhqBXLw6Dp0j3t9zaGU8Hl9toQcoW6A9B6Gt0zhDe/6Q7znTa9DFEXgXOAPPvgn2L59O8JIopgR1pHgnCEfbIzZQUZo1q9fyxsxAmjtfd0Xf4ifHl/Edw/M4nFn1rNz0vc40bJOMLzoqh245d8fSAj8EYqZpJij30q/Y41Bw+J/XXsmXv3YPctqLmhxspiS3ezvFKDqCjRxmT8AALRsSURBVL2cNQ+ILXetgemBDsSCZcJ42tYRhhb4DKWqU9Z+YKYEpRTOn2zPyXIRSoW540ew/967tY7DWWN1a/9Qzsc5E0V7XsYYRtdJrTwNWxJl1zlmM8vusMj5AoGnne5KuLzTHTTIdLtIz6XnXT6ND5t+9nWdTTaZv7dUDSGEVgRv5HRHho0Rqe4EqahsYqX0cp9zSKVQDaUV9Aulwk+PLVgV/vXyuU8nlfS+091Hz4MzhrlyDeVahDNG88t/oU0kaahJevl4IUhkFRvVSTc63uPPmcB3b79NO5bleeC/bwWgablpReJGBj+gfy85gM0UwE8uVTGQ8Zr2us75Am+/4Vz7Xppi1Cw7Qbjz2AI8rjPmyxk3aVpzsz7dCRpzkAf2XAkAGBQSc5H+zpve/QG88w2vqaeYN0TyB+w682xcs+sl+DT5gGEFHos/c94552B0ewG302ONanjZr78OHzWtvVfSgqSZ8XdssYrxvG+pYrSFUh0f3Xu3vpteu6I1ZPDkPGE3vmZ0MZfOn/M5SjW5rJgdwQ0CfeXkAL50l2Z4/M1ffgoYGAcufSKAxplul3L7Nz84VPd3CiB1anRTGcKO3Xs6at/mzmO6t53Q/yiDS0Gne08sIZQS520ZsIYR1X92g17ejlBkMwSC2zF0+NBB/PA/D+C4P47axNa6LNlMqYYjCxWcOZq3TvDqrluvI6FUeNbzfwm/+vxn4t+/9yOUChP4uasuAKCzdhPFjclsp0FzKesJhKn1paEis3G6f3pcC2vedXwRV6R6cgPJ8cZSr/NBa62QdEu7jYQ7ktsROST6eLPSTqoLVUwBrEvq5WYp7VQ1uRpJFAPhCKhRUJPFFHMT8DxVqkEphW1DmYSD7wuOali/37aDL//1X+BDb/stSCnBOcf8Rz6Kx133eHzvP76PzCUXYPvw2QCat9BabzDo4KMQzN4jesZFJ2hWCIRltlWN401o6HQvs+6k5wr1vG/0t5XQpjcSUgEea25HSgX4HgV22aqDVPTtFQmpIQ4G33dqCXmmWQa1UNq/0TWvNSLj6Oc2KFjbbfSF1ProeQjGjBJ03DewG3Azm2l6+XDOS2Us6rMjzdBIpKmTfslu3VSjvrEPzpTwhFv+A6/4u//GnUfr22o1okkPplpztPol1VAiF3ioRbIt+qwvtNGfFlpJBwx27N4Dxjmw9RxgdBrgAijPY+7e7yc+984//rPlTwoAKhkBDYIM/vIjH4z/Xqvg7//6U/bllvFRXH3NY+zr577wxbj+559qX6+kBcndx5dwz4lY7X6+HKJciyCVwtRQ1qqVW+GeFG1MUwzhOOHJFkvcZMZJ1CfNUHDhBjle/zNn6VvQTmEm9H0sjE7iyn3XYKHqfMcLEpnvSqgVat/4lR/jzV/9CQCtkk0g+uwTz9ti31sJZbZZ7/h2sNr+rZxqkM1DqUXSMg4iRX8nevnqPQldXrAy60XPPeAbf/857LvsQjzphuvxosfvxVf/5tMoBCJBXa5FEmP5oKFAWKeg1keCMYSm9dv27dtx9WMeiy1T0+uiTt4p6JllfV7Hpmmkh5AOGgrWQp2/yeuM11orJP33jaBr7jMlH8+9rP3AFhCPAWeqJBCZUgxiGHRjROg1sHNFZqVga6ndFo0iVSbCWFyS4gue0BAhdfNOcOTQQXztS5/HnxqHG9DdQl7+8pfh/HPOxG+84OnYd+l5+PjHP97ZgdcYnMftzCiwQmyGfCDwyRsvw188/3L4Ih6/aVYVZahdLJe8SM+VKadFWHqepQUMex0Kegy1yvV7JgDbnUw3CdWtoKbbtNVTCsj7HjwBMwbiVmJ8hcfuFKEJdJ8uOH1+SR+nLQTXKpqDWQ8HZ8ttqaq2A1ftOZsyfrJe0mDtRK2c6Lqu4vib3vWHAIDv3n4bjhyq7wPuQvD42mgj+5vvP4R/ufs4AODLP9LF399/aA5f/B/977PGYwZAI5r0G647C+dvKeKdTzTZ7xY7XymMbDS7HeOZWoqExpANUvTyI4cO4ru3a+r8M956C/D89wLPf5/+cmURkHEg5eY3vhbTO3a2F+U9vt/eYwCoVitQbo13mHyd8TiyDpVzdHgo0b94JQu7Yirh2B5aKNsa6MGMl2gvBRAlN/6+znwnM7NulokxnXHwjCo1M1ny44vVupY+ofOaAh+NgjaN8JZbf4prP/wt3HNiEcdmnEBObhA459H2ZSVSeHC2jK/ddRy33nkMi9WwoRO3c9jJUHR4X9NlCNQ7frl5Q0i3OlkOpVqEh2b1OFFKwfQFsFF8crIBaoMUG6LdYGoLwVdM0wsEx4kjD+Hj7/29hHr4Le94PcTiSXgmWFCqRTi+WEXOF4lxEkm1IupepGIKqlLKnsfSiXvP57bPK+NxhKnf7O4FJKCWLu3xBcdXfny07rhpanqaaZFNiaP9yiN2xOd1KM8AsKWw/o7E+596Af72l/biZ88e7+h7ehgRhVXfz0gqG4RT0PfGMLi7Ri/nK2S1BqYUw+2/bYMClPk2Tnkx4+leySbDG0kFz2EstQMKHL7xVb9Sx9xSSqWc8Jdbtf9eAAPg8aQAqLvdXLh1AOcZdW3a7xfbSIgsJw7mOtaCMxQy9SKDH3jahXjOJVN45sVTdd/vdaSF81xIpeAJo4Fg7IbVgAJdKzKXmQ4A6IAqkPM9+IJZIVFaS9eD9R1JBb+HerKvFqfPL+njtIXgusZ5KOuhmBFdE9BItAzzkzXd2kFLvnZ7RC6Hp9/4Qnzp9u/jI5/9Er50u87ktpu5u2ByII4eRxLff2gO7/n6PfhN0zfWpZSTU/WY3bH4VaNM3/bhHD71/Mtxw7k6A3nRVl2XVmjQw7caSuRNwKEdp5sbxzAWWonpZumM5R2lVD1cOel0yyjCj/87mfmuw1+8FvjJN8C++j4bzACATCYDJh0HUIZgMs5MZT1R90yHcz7+9JkX4ZZnX1JX60rBglbOHndi0kopaxh4XPeDF9zUCDvGWiLTzbWomiuk5raqobo/ojMz6Dr+E0vVOmfXzcxlbOCjPaf7qz/RzsRn/ushhJ4zzg2tnFCLJA7Px3XzlVDi+GKyjZgvWCKY0WmtfKOWetQ7vmO0YbccnC1joabHibSZ7PiZUDYM0OI9+UCYVkNdbIPU4WGoXtYXHA89sL/OqJcywvGD+21Q4MGZEjzBEQieMJ7vOr6IIwut28A1gpvpBuLgg1Kwvel7DWTICsawUIkSmTk34PYrV2mnuBLKRECi3EQfIT0Gkk53vaOxbbCeMvvxX7gU73vKBdjm0GnXC77gKyrbojZcDHreEA300Jxmu5DQH62QXct0d+hJ0Fyhdnpu72sh9NocM5H03wYyHgqBZ+nltlWg8yjvPr6IYwuNhT/r9EuWAan99wqoqwatcW6mOw1a3481WEeGsh4u3hrv+Z3QyzlDImBFe/c1u0fx+uvOWrNuI2sFclhp/b3/1FJi72QmIETjdbXQz25l31WmfoOuZaIYwOPcspqoj3s3+5M3A+mTnC7YXKO2j4clpDJO70AWnLG6LMVK4S7aXkp0KOsns6KBx3HtFuCaCeB3r5ls6/iTU9O4ct81ANBW5u4zv3gF3nbDOXjM7lG7OVUjhf9K9VpOUxzTFMV2Wk3c/MTz8MyLt+LPn3tp3d9IJEMI1rR1kou0YAeJAc0vLtb97vsfeDD55eqibvXl4N2/85uts29H7wH+/n24+eZ3WUV4AAiCAK//3zc7F8bxgl9+qX1Zx2Yw9/iRO0fqajXbpTe7hlg1UtbZ5ZyZiDCr2/zSDonHGbYaUbo6YTXjKNjMDGeQsrHx8pxLdeT/CedMOHX1nc2VrM9x30xz+nElUjg4G7MHKqHEbCk5Hot+sk613dYnFOTI5fOrKs9w0Q6pVSoFn3NrXGsachxEosg+AIRKIedxKzDUDVvAVYVdDovVULN9pO4B73GGnVS24R6TC5x55lmJ4E3O4/BEXJJRCSXygUDZyVBJpdoKNtEaQQ6nWyPuBil6CRTQArTqsisA6M4nChhVo/oWXwCwY7i1Y5ysh07uK8M5HTgm0Hpx6bZBPO7MMfQ6yrUIPz4yj9C0LrQ0U6VwZL6CiWIGWZ8bIU7SP6hfA1cKZiJUnaxqkXEUqBTDXU8BJNpy6QAykA90twjGGMqhxENzZb0OOCNbAXUBR8L+e+rb6NnfwHldoIbU/nsFjMFqiACAx3nT+l0aw0caBCB2jeQSGduMx1s63q30ETabk52GVPo+0vobSSSCNnSXIqXq+rWvBBSgWqm5zKBFbRnTLFPOYevNBfQ8WY9imEiqVZeM9RJOn1/Sx2mLcqh7Ww5kPAjO62i1K4U7kXVGwo2qJmu8b//aV/DMay7Fv73xqXjns/d1VGPabuburPECfv78SSvaAuis8/2n4rZWoVR14mmDGS9h/LdDk54cyOBNP3s2zhyr70WrAKNcysHb8LopC0tRT6Jqzs0v6t+981KgaKiLXtJgPfesM+t3hTZ2CS4ELtl7VeI9BuBZN77Avr7hqc/E459wg32dppene+KSs/HD793RNr1Zt5fSv5dae1FGWlPzmDFekkae23bmiu3DGDO9fu3nbeZF15LaTAyYMWbrn8v2oRxue9XVeOcTz41bzrWhXu7Op1vvPNaSkl6JFA6kne5y0kk/dfA+/Pe3/82+bode7gY5fvnpT8CFl1+Z+PsTn/ELHYmpEUbzywsTaaVWnYGgbIQbRBKkGAY9NAOP27Zi3aHMtv/Zg7NlLNZChGT8cIatU9N4+W+/FYLKWbjAr73tD7Bz5w5b2sAZkA88PQ7N+Uq1CHlHy0IphVs+9mdtBZuIZk9ZQ/cn9C69PC7d2DqQTf0t/veIGTPVUGKmQenERVtbq1e7Y4Kz5D4zmPFTa9DmMsFmyyF8wVEJIyhFAVftBNekwngh0BosSgf8coajzFh3qroZ0dnbMAGqpt1hKBUE4yYYxBLrKYBE8IzG8hkjeTu2D89XTF1pLHJJAQX3WbuB4m27dtcFwhjn+LWbP4Sv/PsP8LGPfczOVyEEPvrRj2L79u0ruylrAJor5DB7gjUNhNNe06iGeyjrJ+51IDje/nPnYjTv43evP7vu84nSKrAEM60Hl5SOIA1VW0GPlUAwpNtZcqaD6t1gClml/xW6xozpIDNHrF8SSdh9h0rdgPbsjJWC5t7pgs214vfxsERoemYWAk3XbZOxtSzcOr7RfJAQfMp6PFHT/TGnZrLTGtOVCKuRMXZkoYJ/+Okx+34ljOrahA1kvcQi3Y7ybCsoMqiXEf0gED3P9hsl5XWRBbvo8cCzfw+48d3mj0mK/jln7sGTH3VJy+NzIfDzz35eXY18IyfMNYLy+UIykOLzpj1xXafvRU99fFtBEqr/paMQ9Urw2DgkB9rdQy+YHMC5E/XBDkI6000Zc0BTd8MWkd+cL0zQRn++nT7dc47TfHKptchWNVIJOnI5lJhZSmU4SvP43Cc/al8ul6FoVMP933d8O/GZr37+r9uebwDwez93Lp5x0VY84ZyJtj6v++Uq24ebgkiWVso0wyRSMIb76tVlCTpA096xpAICIWxGXhhGxeOf/Cx8/4c/xj/+4z/h0/9yB5707BfEqswmG0mlJDRPa5FE1mnhc+jgAXz85je0tc5R3aGeQ/WZw16EznRzbClmsCUlwuTWuY+YTHekgBNLjSiz7StMMyQFGrOpsqXNlsFRiPcmSUJqTBvdGY9hciCjBU+rEbK+Vq6PW4at/vzkFLfjRpws1VCNJBarkWYeCVqT04GROCDAzAmIUs2g14ZiRiDvezYDGUbaaXK32juPLdjM5ejkFH71Lb8PzuNA2Jvf/QE86glPwbZt23HTTTdh//79+PrXv479+/fjpptuWv3N6SL074570FNQshFaBVWHsl4q4MxwweQAbn3pI/G0C7cuexHuXFkPKvNa4dBcGbPlGjxT3hNKXb+d8eLMN+0zUqm2GIbLgQL/K71rDNrW1kFbBo/pdmYe5zaYK82eee/Jpaasj9Wilb2zGXF6aLD3cVpjciCDuXKoI2yMdS3TzRjDe352GixTwHghqMtAuK9VLelYkBPmOn7NWhyl+yC3choJtMjcvv9U4v1KKBMtmgAj2NWGo91uCyba20SblMC0gi3Ry394vAw84TfMRRo16yCX/G5YwvChO4DbfgQ8+N/1B2cMn/j8rRif3IprfuZ6gAGX7H1Ew+u/ZNtg3XtJBXqRouHr59tO/V2jIImrZA2Y7Afj1uEmw82tIQTQsk+xL3RvVqr71DWIsXI+Z/EmtNSgXVz8W9uv6T7Zjpr1Pf8BnPlIVCOJGYdOXgllorUeAKC8ABXGx1xuw2zEBElDRhH+8ctfxOOf/LS2Mt5PPG9LQkG9FTxOTjbR5zT1TxnjSHC95tQiifGCj8mBAEcXKl0zAnVWvb1jUYaEggOCmewbA3bu2IELzjkL37jnBKqRspkTmpsZjycCIJFU1ggEgP333lNfG+6sc0opzJRqGM75ODBbwtaBLLIet/XthF6s5wb0fPS4Nm5zfnJMuvMk7+hcHG1QpzqYbd9sYqy+NWV2BaUXvQJl+l1HUqESSts+rxxKDOV8FI14ZCWSCISmaOv9obvq5ZWwca/jNKhGNhCxgJrb6o8BltniHp+CCXTdwzk/kW2jNZj2Xd1KUGC+EmKimEEoFZ70nBfgUY+9Dvfcczd27zkTl593Fn54ZM5+Z/v27T2V3XbBOcOFWwet0yt4417sQOug6nDOx+H5etp5OwFLhmTZyuZ1uYH5ahhrhRhHVYtPSrOWx+MwUqothuFyIBtkpRpIFHimxAFdu+CAVFRWomuuc4FIdBZaqkaJdXQ16KuX99HHOmP7cA4XbB3QmRXBE1mJ1WLvVAGP2zMKIGUceSKxybqCXEC9E7ZcDXBaWM2tRW6EZgZFNZRYSqmEDmS8ZWuAOmnBRAI53OHYHjhwAF//+tetwqr7Oq7p1h9emj/V7NCAn6R1Lhw7jP/7p+8Hvv9V4GQD9Val8OH33WxVYN/8ay/Bt77xz4mPfPaFV+AV+3bhFft2Jd5vaPCmsk7A8k5fsyCJlCpBBae+zrYtDeJ693bJB4wxnDVesIY51XRburnJyPqCY6EW4tBcueFxOqnpPtUou31nTA9HrQKcesge74Tz+VItQint+1cWE07YcsZxIyZII/zh77254/Zh7YBqthW08Swcumk1lMgYAaZI6WBGPvCsUU5Iz49O0ImT6pka2TBSRrDM6AeIeNzp4GSc4WaMQUJTf4cchzEkYR8zb7fubFAb7qxzR+YrmCmHKNUk8oEO9PmCW/rhSn7PesMzTqKbqQGSmW43SHS0QZ1qo0z3sy7WWbuX161BrE5vIy3muNZ44FSpqeBXpyB9lWqkcKpc02VInKMcSuR9gZyvx2c1lBAUcLRByNWfn47z0FwZ4XIBRaWDSjSnaa1OB0G5sz67aua0dodSoRB4CSHV44tVeCLW36hFEhnBrIp/GGknfMvUNM7fezV27tihM+Ngq2airReINQW0ruluFVTV9PLWv/eKaa2nctZIkn1S97VN7HX7PC5vkEqPD2KwRcYJpxp6KdEWw3A5kA2y4u9DBwDIfrE97oleDv1Iwkih4AubdCnXIjwwU2pYmrNSbJIp0xb6TncfmwoZj68ZzShtHLmbxW+/9R1N6c3ttjgiYbV2MnWl+ZmG75dDiflKPb28VbS54xZMCjh+5CH8z7e/iSOHD+LjH/84du3aheuuuw67du3Ci1/84sTrT/z5x62C7Rc+8ym85w2vaXhYzrnu++wgw2XTWjHCv3/jn1te+5ljBbzkkTsT4l32+M598TlL0M3pbzt272kaeX/tW262QZJIKhyYLdkaZmkET+i7WukzpiXSIWmzXQk4S9J1aTMOBMdQ1sdSrbHhGQvxLZ/pbti3eeFE/O/yHBDpz/z1j07h3pOxxkBDSllUwQtueqV9uZyR2ajFnltO4KLT0o52QDX3SilEylyvccgqVKcPhjCSNvPitnpLz49Oe+524oxQgCBSCty5do9xa5hQxoQcCCr9sJlvOqHSDgBNv5EtW/Hqt703psSm1rnFWoTBrG+FqQqB0FRDJ3MonPKKXoOy9fpxn1lyJLYPxQwczpgtPfrjf9tfd5yhBpnu3/6Zs/CZX7wCv3zVjsT7HOl9JdmKstu0SakU9p9cSmQXS2HUNeqnUvqaFyohBjO+vVdKKQxmqUWiXnd8wW2/Z6A7+gfccQLbEVT1uG5d6fNYa0NwljB8OXfmBK3d9D/jFA1kPOtQK6UAFreEBPTvzXjCrnWhVPA5ZQYZBrIecr7Q5SCbMGvnt6jpToufuSySway37Pr2riedh1c8agfe8TOt7aLN6nMrpeCb5657W+uuM54wTrdUuO/kol1LI5NN7gqYprYvG6Bq9FWmg9AcbjJB/9c3AVelVFymRNsK9BpRbqOF3HKgMqpeDuR2is03+/t4WMMXK6fLLAfXAMqm6IfPuvEFTTPVXW1xBO24vv8tv9Xwb1q4Kul0D2Y8POm8LThzLI/nX16/cXV6fV/73F/iyY+6BG/6lWfj5x95MV760pcmnN5PfvKTideveMUrcPzwIRw7/BBufsNroMLGBt7PPfO5de+dtfuMtrKc7V67C4ZkTRhYUpiFssGTU9P49Tf+77rvcyFw2ZWPwIP33Ysjhw6iVIuQ8z2cWNQOaFz/a66LsqRUx20cj9VsGLqWijmvjcid0OO1mcCIFVJrY7NNq+EDwPXXPiZ+Uavg2p+9vuF3/78fHql77xnPvhEveNZT7et2jO00E+Rt7/8QvnT79/Ha331n3WdXM7fSUCaSz5g2zo8vVrRTBh1EWqyGpr+vzgy7rUs8oTPcL3vZy1bVc7cTB5WyIzrTHWfjyKF2P2NbzYHZ7IT+jF4/05T2UCo85bm/iE987TsN1zmiqkdG8+HM8YIOCol4DmwpBtg1ksdABxTs9YKml2sWAIPJnhhH4lG7hvFb156JP3uO1pdYLnuXhuCaoZIe6zrTnWx7lO6K0U0sVSMMZP3EnO6GEjJBQT/7Ui3CkNESIaeanErBGGqRQsbsoZQ57ka2io7le7wtbRePM4RSIvCdNTRVDuEyW5LspLgW3VXyrka6FZob5NXvxcdxSzc8zrBzOG9aSHbnPqw3BG+uGO9q47z4qh0JvYSsz5cdf2OFAL985TTG88k1I70uLhec71VIIzgYCBIB1L3sbTceqZD1Pd0XnengRreClgwMgSdwfBmtluT1Kmu/uF0q9GPU41e3n2SJ+nQXVAK1WtSkLlOhPfh0QN/p7mNTwc1OdBtp2p+7V/zntzTdtlGmeiVCac1AWel0DTlhplyrq2nPBwL5QOCzL9yL1z1uT913ml1fLpezrYFc5e4Pvf23rROhlFp2s4uiCIcevA8P3mfapET1CzwD8Pdf+mLd+4ceuA+/9sa3NsxqNsNy95bW/0ftGkkYtQzJYIq7iP/SK34Dv/Hmt9n7xIXAE5/xC/jlpz/BUvL/v89+SlMIRZzN8LheRE+ZvtmM6dfMOX479fat4Bp8MXVd01Y5GhsjZAjVouWf33ylPiI9KGKjfc+ePbjsiisafvc/U+3sAGB4aBATxQz+7/Muw9+9aG/Lc7tIM0Emp6bx+Cc/rWtzqxHIqOCMYaEaYryQMYJLDAuVEIXAw2QxY6P+FKi5aGoA520ZwF133VUX0Oq05267PhEphpOhRtoJWcETSsOuSrd1IHjc4iw9HOj8FZOtG9u6reE6R2M6NMGljMncCMSBJcYYJlIiZb2CoayHM0bzGCsEjlie/htjDM+9bBsuM1TXVrTvTmoVGUNdoG8tFZlJk8A1grudWaWx5QvdgtEzWWS6p+To2vaJNnu8+l9LnCHfnGP5a+WoRQoBZamBOhHEhJAaUeERs0Q4WMIxr5l6dbc2P5S61p3Gk4SeI9qBgi358Hi3qtvXF62u2d1jBzJeSzuqo3Omvrd2+tgxFiphXTeO1YLmZGBKWyqhZk+N5X1ww8TImlZqtG+v1mYgMKbbI1bC9rPOsaMdMzUAp/QCsbgrZe3dmmvq8NENp5uE3PwUO2Uz43T5HX08TOC1iLiuFumabnfZa1UL3Ygeu5xQWjPYrHTYeOFvRBNcri6w0fW5DuWTH3Uxnvyoi/GKG5+KFz/t+joxpeUghMDOM/Zg2y7j3Ef1mVMFANn6Vjt/8/EP4U/e9Tb82uvfghe+/Ncb9BvmbSuXE/6/X3kEPvC0C3HtmWN1WWY365R2Pn7p5b+BL33rB/jIZ7+ET3z+Vnz1c59NZDA/8Jbfwqmjh+y4CE02gzHgyEIVnuCxMi6LM7yC601oJXW/CskNmDJJAIyIVeMglJupW66u+8jJ+hr8z33kvfbfxVzW9jRvBzQeL5gcwK6RfNvfa4Ruzq1GUI4C81ItQjHj4ZKpQSsONZDxMJoPNM3UGNaAHkfFjIezzz67LijQac/ddpkQWkOAFOyldazOGM1j50hSoNB3HB5yjKnOG9BBovSokNKsr06d84+PzNusKdOek2Z4UOacI6E50MuggIBbAiKb7Cbt9hJeDpyl6LcpJ3xudmbZnuidQCpYwTDAUOq7/GzcMoYzRvM68Mjj94l9Q7+Tgj3dGCPESgkEx8HZMubLYdOgIq2d1N6L4AYxSQMhkelGrNgMk+WmQBwzTlMgWEK1OzSOhzueBGeJuUKdBjYjWj079z5kfZ4s4RK87fVtWayB4RdJlWh3dWC21FD4bTUgJzbjaeG9Ui1C1hO66wxiHZHBrJ8InHYDDPoZdOL/0p5IsAFcxAHq8UKgbQ+pII2oIH1FGoXzbiTHSMjNE/W97Tcr+k53H5sKnK287+BySNDLPQ5enk/8vVU9aadCaY1w5NBBnDpx3BTTOE73f/8DcERnzo41UNNtpw/y0298IT7x+Vvx2t99J973sb9IOJRuNruRAcMYS/QUfdGLXlTXY5Qz4OD99+I5v/QSQNY73QCA/Ej9e1EVUkr80bv+N/7iY39ar56sFC5/xL6O7u3kQAbX7B5tQPVM1hdPDtRn5CjbWlpaqqfkywg/+f537WtqEUa5C6r9jTM7GpdtG8K/f/mvV1z3627AgakDGysE2DmSB2eNW6m4RuZyCuZHjs8k34hqUEtxBjubUr0G6sfco8+In22361S7Mbeagah/ml4uMVkMNA2UcdQiicGsFztoqp6qu337dtxyyy2r6rnbrikRmWvloJpR/U1PcBQzMTXzvC1FnDGqgx2uMJQr+neyVMPUQNbU5cUq7czJhNdMDfuCUcmPz21Eshwa+2Yzhyjz34wE4jrKz7t8W6KMI9uG003f3zs9lJgPDMnM84f+4J1tiVu2C6KGWpqzStUsdwE0pgYyApMDGQiuxfRonQqMgeybOeGuiasFraueYBjJ+zi8UKnTOCEQtRvQtd2AHttph8Z1MCiTR9fLoe8fZboZGJZqEYZzfiIYShl0Mk10D/O4BImE3DzO18x+WUu0enbu3pBLaRZkPI6pwZUxX9LnXIv7tv/UEu49uWhfe3x5OnwnOLlURSil0T7gZm4AhUAg44lEYOaciYI9d7faUlOQqpNYD+2JdC2ujgmDzpyfOa6vtRzqkrvhnG+fjiJB2S6YATSPSMvkdEDvFV710UcLcM6bGkqrhbvWZjyOyfJh4LufB47ea99v1CqMMDk1veIM3Bc+86lk26rIca5rZcDUSf/JN/fXfbedukD3+FQ31Aqcc0gpwYXALR/9KG644QbcfffdOOuss7B9+3a84x3vsK9vvfVWPOOay+JrH48VfB+9vYBvHjCbWrGB000Z/WY0dqVw8xtfiy/d/n1cue+aZX9nK1At5V88/3IsVEKMF4KmnyVKftrxftOvvQSveusf4IUv+mWdbXQ2AzK0OU/2pT186CG84hUvr6v7veGGG5Z1zjR9k1lKqy8EPKFr0wuBaFpu4dbZLVfXzXNFAAvxG2EVTIZ2E834POGETA9msHfHsK3n5kxntb9p2tsF3U6tYXVzqxUklHYYAAjObbDC93Sf+mLGs1kvhcbGy0033VQ3PzoBOcXLXquKNQOof3gjFDIeCsYJd8XU3IyjLzh2juSgoHDvCWC2VMNQzkPeFzZbp5T+HIPJfgA2489ZnFF1M6ubBXp+ttcGSbdGE6gZBk8g9NxbbNGy79M3XoJ//NFBPO+q7S2porTmUUB33+OuW9U4p8AQ/SyZ0p1YDSLnWDqIExvjnuDWyPZMuQPFFhjrnuNPLKKM4CgGHnzRJMCLJDVWOB6Aq16+cziHnM+tY8Gc30fXrMsn4rr0aqQwVgga3lN3NDHEpRiCA0rRf1d5EzYArR5dWgsnSS8XeMW+XTi1VMOTzm+vhWN8Tn3SqYEMDs1X8DNnjXd20W2gGsbUaKWUVebvBkq1CMcWq8gIjsGsj8BjCDwKSOlzepwbp1wkmBCii0pqwqzRygi9Lge91sdldO48pvIRAHYu+B5D3hdgJgGgoLrGfIpM1t0X3Q0cbiT6me4+NhXWK9qV9Th27tkD/m+fBH7yjfj8XawnJTTsE+3Sy2uVpBOewnKZxfTxl3e4Bb74tX/BH336C/jS7d/HTTfdhO3bt+Paa6+1DgW9BpAQkgKQoJd/8w9fq68fAAqj9SdrUP+dxmqFs3716l141M5hXH+O3rTP21LElTuGW36HaM1puruSEh9622/j0MEDpvexsIaapVMiqRK9mrrfnC9w6bZBDBrxJsFNfROLhbEaOQ56Y9fnd+lzjRCKVPAhquF1b3qrfenxJJVyOOclnPDhnI9C4IpF1de8kmZAN1XHuwGKpFNNKhnpGcHhMW7q8GLKXzMHKj0/OgEzF7LcvHRruoEkm6EZdE1q/PsoW5fxOAYynj1WKYywbTCr61TNZUQmm66NqZhGTllx6jvbLaduPdGsvp3gjvfxgl/X2eKPnn4Rdgxn8YGnXdjw+zuGs3j2BSOJut+GcG5cNwQCaYww57Vbj7zaYwuux50baBEc8FicQXbXJ8AIkbHusCEo0+0LjnMmCvB4c9axDpIl+3LT9RICj2N6KFma4d4/1wGngJUOeHqJutdG10DK1El6+dp1X1lLtHp67tzI+aJurgxmfbzr58/HY/aMdXhOjU8+7zK8/6kX4FkXT3X0/bbOweJMbi1Stu98NxAZ2rVUur3jhVsHbftJGoO+pzPdFNiNO0905xoouOh1IGwmnewy4Op1JJk6wpQi5TxhhM50AkAqYoms/EdUQqkdeBM4C0wgb7OK6bnoO919bCqsJZGR6iILgYAn+JrXkxIa9ol2FMBZVMXZZ5/T9PvL1Rg260NNtajUkxTQv/FX3/Ie7L3yKlx59WOW/a2NHMqEI11ZAELjdE+dW3+AFsEEe52rDHTc9Iid+JNnXtyWk+Li6Te+EP/r3R+qe1/KCA/s1+wHUiSlmi0grn0lrLbu16Wj6o2Z2zrEZk63vjb9vUaR+3+66zhe/Jnv4cBMqU69fGJ8HM/8hecl3ksqMIuEYz2S8xPZjXQQ6P9+9I+sZkC3aLT3n1pqqLreKajNGxk65Cj4gkMI1whiib93E9xwWZczJ6TUlD1XyGo52GACi8eLpc46DrMOIHFTvqMRKaUzDCBnTq+/ZEwCMCJR3RcEW2vQPWhmw7mZ7oLpSU7IeByXbhvE5198Fa7Z3SCQ2AJ0lAlSar7vjviauhDQpcy2gs60HZ6vWCr9akGq9T5Pliq4AUAgnjtET2U2CLnqS7CGP2M62NfqGRJN3FUrH85pZ7lRYJDg1p9zZ95ocTVmqbcUtDm+WMVw1msosKegbL9yymRuRreh7Ux3Sp1/JX3oX/fYPQCAt/+cthdG8wEeu2dszdZewbSTd8+JRcvsISxUQiy1YLS0QqTidVIwZttq0XwAAI/pThQ0nnzBujZfAVgHX2fUlxFUNfoIFLijvc9jjjaDw+yja9SMO1P6aSajO99DqfDQbLltpz+SCvfPLOHoQtWUiGgGWieBg15Gn17ex6bCWmZUsp7AN165L7G4P/3GF2Lf467Dg/vvw44zdq8JxbURlZk5kiwvfdVv4N4FhrvuOg5AZ2GuPWsMt955DEC86R05dBAP3ncvduzek7jORsfnQuATn78VpVLJGnr0G2v5Mau4uhzOPvvs+jddIbVqKQ4gnH9t/WcbCMZd/5Rn4p++8gVLb1+LQEe72HHB5WCcJ2rNOReY2rkbjGm6mNs7mzKRbnCI6n5f/vKXI4qiFdX9uvBFbOS2ytYFgmMREaqRxH0nl/BX/3UQv3LVDmwdzOL1X/kxAOB9/3ovFlLq5fmMn6CnA2mRwSSFcDTvJzJ6Lr38/37kj/BHN8dZ827QaKVSqEUKRxcqiVrmleDEYk0L2hiDghwFX+hWbdRLlTF01RhyoWM0DCeXahjL+1isRg1/F1HtqGa2XRvUVTBntpe8a0AxANpAjJxxHhq19lokHZX3uB0SHVuwzedI0DNtJqSWHu/urV6NZgEFNz//y4/AX//1Z/EnC8cg0b2Arq7h1v8+OFsGAOv0rRZS6vvmizjoB7hOd/zaS2e6u0gvj8skmjN9AFM6wphhHun38oGHy7cP49ixY02OTwEZyvLFtHIK1DD339CCi3vG8sj5Xl3wKf6ucbJEcx2BXkarR5eYK/7q+9A//4ppPPOSrS0DI92CYNoRPr5YNeMaYM7CenCuDCkVzp+sF4FdDqFpLVeqRXYckWXgBqhILIxKNkgtvxugwJMvGI4tVDE1mGkYrC3XIhycK2GikMHxpSq2DmTM/GWJ+nLPWUtoTylmPBuQ0plulRgw5VoEBeDwfBnbU6ySRliohhjO+qiGEoJpZ9s3Aatal6j/G4l+pruPTYW1ppcXAq9usU+3MuoE7dBqG2XUX/3637F/nxgdSWxsWwYC5B0nZ2nuFL7wmU/hKfsuSWQT6dwAGmbsL7xsr/1d7m9UKhZ/Gc03r3sGtEP5pje9KfmmK6RWq7SmkKd6enMh8JrfeTu+cNt/4r23fBJf+Nc7uiqc1SnGp7bhFW/5fXvvhBB47e+9F2OTU1BKt21iJjsYeJry6GZKCDfddBP279+Pr3/969i/fz9uuummFV2PpsMxW1PLmHbGGiFuGybxgk//Jz7334fxB/9yb+Iz+08u4afHFxPvZRoohQYJwypZ410IvKR4jhME+uN3/e+662qHRluLpI1q1yKJmVI8hqqRRGBaFa0GlVCiHEXaeAC0wrdZYBRMcIPF9fkca7P+kGF+qlTDiaUaDsyWUW7Q4kUqZetlOW/fkXIz3K7DYo0/I76VplZKk+mWMs5+0CnJECOHarM5EsReaIdenk31iPW7oHKU9QV+6QXP77pAINHJAf3cc77WgOBdGLjkHNC8cGmnHueJ1xQYBGBrmrsFcl7oXC7llLKSJ5eqDgW8/WAZCa3FH3d6dtv5YwJWPHY0Mp4Ab0B1t98xnx/NB9g+lF3lHVh/UAu1RqjPdCfp5SvBejjcQCwGqaDbdnk82ZpqNQE2HbRkRnA1ZsGRqCXgsJZorvCkPsJqQeNvIONBAjjRoPsNAJtRDqVCzhcYyvo2uEb15WlGiysQqMuY9H1MixVGSqGYEXWtbpuhXNMCpnRdPtfJDZ9z1DrsrNOL6Ge6+9hU2Ew6ua54Geccb3r3B5oaVm5GPZfL4eTcEmCC8dQPlTCaD/DQvXcC0D1l3/bql4I9+IOEKM87X/9qgDEo59xfuv37bWXsKTO2d5m6Z8I73/lO3PHfP8KtX/qCfqNWjv/o0ssNzuCz2C/1tbsOuZvticIa8rksBoY7o292Gx7neMIzn48nPuEJ+J+f3IWnXHMZjmHA1rWSwAdnDGN5rXzNWGO67fbt21ec3XZB/ZkpM9uMckUR7UokLcX8wGwp8ZkHZ8p130sL8zEkFZuzHq+r23ONJPr+g/fd27AGi3O+LI32nhOLABjO21LEQ3NlVKySOEMt1BmEaJVRb6mUNpYBaxS77I6AjDDH8V4TiiOLa0XnyrWmdcBkgAjOEmrRy8HNtnEW1/y6Dng8lvV3QqlQjSSGuA+pdNeEolMD7p57s4moAfE9CdsQUtNzOv6N3RT0WSuBQECP1ZwvkPMFOFt97+FY1IhaI7rnSgakqO4bAM4aLySCZquF6we5QpILlRAHZkuYHsxhoRrh0m2DkAq4/1RnY9R10smZiEsxjLMPtzQj2ZKMFOSPH34IP/qv/0HmwvMBIwJ21nhhdT9+g8AYmta/pAOy7r1up7PKRuGh2bLpBgJE0gSoTBCd2BM+Z6iscL6HUu9Tbgsuu/aaz1hBNafFo9dBQHU5kC1y1ngBlVC2FH/0PW6DdrvH8lisRHaPgIoz2xQQYEDscPO4TEkhriUHYFttth/4UhCMQ8FpGcY162w5fZrNgL7T3cemwmax79LiZe3QaienpvGtb/yz/h7jwKs/ByBZLwwABR7i9n/+f8CjnqvfCKt1zo1SyvKO6dztKIDT9zo1pP/kz/8Cn/+Hb+DQnd9HJgjw7j/9LXttiWz28fux/+g9wAXXAQCe9PRn4rnPeY6luW8UjbwZyBEZ3boNlw5vwa4dYzh+YEa3TeLM1oCls4lrNU51fZNTE8Y5KmHj2maK0n/zvlP2vT1j+YZO+tMv2oov/M/hhsdRSGcz6sVyEtkN89kdu/egkVL+r73xrcs+Z86ZdapJsC6MFAKPoRpJZDyOslxZrR0hjJTJYuoaT2tgmHMGpo4sbhm0VvRyTePLehwLlRAjea+hgUv9hj2TYWs3G0JOvXv99B45n54Zy/T3/SeXTPmEpu9GSmE459VlaMjw2mSJbgB67pRVYyOuFb18Jbhs2yC+99AcnnbR5CqPBCxWQ5Rq0nZfINGhnAnWuEGiwayXUDNfDco1ifFioJXJmZPZBhIZMGVaddFYCTyOLQ3aM64USVG0eOzVIgnOGSqRhG+yyjOlWoL63g5cjQI3wy1M/S8JtBHlnAJWlMmTCvinz/0V/vRtv2UD7rfccsuK2U29AAY0Nb7cUqScWUcJK810rwfmKjWcOVbEyVIVkZRxptsEcqRU8D0OVlvZPkP9qgFAiJiZwRkS2WPOWTLTzbvX25yCQyP5ABmfN9VBkUqLZlJJkWVTcYdVAtQxXKjkiuweEj9jxgPXNeKdBRGkUyIjqYMGY9g2mMVo3l/N7egJ9O6M6KOPBuilrEor6ngj8bLlaLUJR92pi16YO5WIGFdnT+paaUK4vBhZu8q4CnGmuxMwAGdffDle8JJfxWOu/znwI3cBh+401+dkOZZmE6///m8+jfHJrSum768lqA83Z7HYCVG76bUvktnC+N6tzTjdUgywezRva3490TQBYTMQn/jOg/a9QuDh1FL9eNlSjMsIGtVIpp3s9Ouc3754zg1PfVbLvwOxeAsQq6dT67NqJJH1RMNWaZ0gNIYWYwxZXyuVC+s4KKOYGtcuu055N8GMA5zzY/G2Rj9NGrqiZ2jg7a6FnCUNI27SH3GmO6YK0iEZY5geytqMg8cZdo7k45Y25HQztimF1AAj/NPkb8k2SGLV+85HnnUxvvwrV+GybUOrOg4APHCqhBNmDkdS4eBsOcFgIfaNUgyDWQ9Tg9muBAErkcRkMWMdBfeQ6THr0su7jQTLwnlfQdfoRobO65tgGQWp2kWaSk1zhuYHOU70fjpg9cADD+JPjMMNxC0iDxw4sJqfvaFo1WbUfdstMwK6U4qxGiilUG7iNBcD3ZWAg5mstIgZG0qhJiV8vroyJhpH3HFcmTN3aBzFQUyWCFitFtNDOWwfztlraLbeRaZULjI2D41nwWIGCzHB3ICUcOYBZbpJvZwxfT5iBbYLEu0E9F5PbIDtwznkg82fJ+473X1sKvSKz92ohtoFiZe5SKvTpp32Zirjp44dxb0/+W/7+rv/+rUkhTuqmsyXcRB4fU1uu8q4um8l67iWyTWEJqem8cRnPjf+o0svX5pJ1XyX8Zk//2hH51ovkBIwZ3rjsDVMjGOhGtpMNxiSmxNbuyIIxhjGi3HWiPPmxlBaDA0AKmGE4w3qukZycQQ5fTiGpCMdCJbKBKbEc5ahl7cT/HFrUBnTWeBQavG0hWqEQKw8uxpJhVNLVURGsZueY9bn1jiYGswmXpMxtlZCahy6bi0wc7dR4CNSVDubzCwue/xUVltTZl2KbNL5BrSxPDmQwYiTWXAFuei+DGU97BjOY6LYWvuhFyFEcxEu99ZmvdVHFTzBsXWwe7W85AjUTNmFu+IEpEKvFAaznmm1s/pzMsBQ1ZMUb/d63Ndr5HNrNWaztnERr39K6XGphf70fBKcwe/gWmgO2G4ezPkfMV6QrG8lB5/O8aOf3pkQ3gTabxHZq2h1+9yhlfVEYu5sdG/lIwtV7D9VsvP86EIFPzk6D0Aryw9mPbvX+IJhIOPbOn3tLMasnk5BIpNA0jag8UOvGeI9U0GP3W453duGspg0LBPO0DRQrZSeU7UopnOna7h1ptv9fZT5doQpTfu8+LW2ozoRUtT0dGa1IVZ6/3sVfae7j02FXph+zajjbsZ7uXZjjZz2Ro46AAzlM7jtq1+M36hVgDB2upmSePN7PogvfesH+Mhnv4Qvf+sHePN7PriiVmdLtQiFQCSE2toBGfZ0f776uc/Gf3Qz8aVZwKUFRyE+/bE/7bn+zUAsSsQYMxFXU1vE9abiG1oYR9JhWSvnrBEEY0030oYqpaHETKmeYkbCJcDymW7tBLstxFJtYhx6+XKBp2YQznhSStfG1SKJE4tVTBQD8BaZl+VwbKGCIwtVlGoRBOMIBEMxEMh6sZM9lPNxqZOVdLPF3QZlEHQWu7kivVJxbVwnvZfJuQZiFgv1HiaH3zobIPqsDsSQoUbZTWoV5R5vciCz4cb1SuCxFs6om73zeE8ZSozFQY/IUaOPjHFLvYCZparG3y3VIhyaK3c8d2wbIZNF91gc9NKZuTggM17QFHR/FRnCVjhrvIjzthQBJLN3ClpdOTT95SljSB0I2oV72S69XP8tFlKzJSc8yXI6Y/dZq2oR2Ytotda4+4WbCd0oLFbDxL8zHrd75MmlGnImW8pYzFTjjGFqMIvtQ1lw0zuG5tNK1ny3vM8VTouDOvpzgtdntr0VnnM5kGhmI0SS6OXS2DjMBncTrA/HeWZgSXo59F6hjKYBAwBTmiSW2a9nyzVUnHptvRdpZfTNKDzYCr20l/TRx7LoBXp5u9Txp9/4wobqtM2cdiCpMs6+9Zc4P7OIPZkKVM3JFoeVBL38//zdV/D0G1+YUCBvdu7lUK5JjOaDjtVuGeL1/Pvf/Xby/jhO9yUXnJ90uqF/fzvZz/WGri3SolXVUCLnc/3aZHqHcr41tpIO2drVdKfRql1TI7ZCNZSYq9SLGg1m6jPdF5o2KU+9cGvK6a6nm+ecTDeJwqy0z30kSSlb30RNb9bUt3zgYTjrI+eLFWe6Q0MdD6VCPuA4b8sAtg5mceHWgaa9r+l61oIyy0B1pKyu7dBCJcRPjy1AmQxCnGnunF6u/500+kgAhzl/A6jGW/9eLWYT9xpGByJuvYxWgRv3XU90r8ayG3B75SplWDacWRpnYEomBCh4FQt+nViqwuO8Y2GzSMZjgCHJRBkrBDhzrIABE7gbyHrYu2O4K4rpjTCc85HxqH49zt4pI7pUkxJZX98DBth+w+0ifdlu2QVncfZWcOoiEJ+DgWF6+3Z86MMfgXDWvdW0iOwF0L1sNF92DudSn12ni2qAk0tVPDhTspTyMDK9sk19cT4QENC/g0qIGHN0L8xzPLFYw9GFyooFzYht4RslcjdbzMkhNf/2eLJFmMf5mggGt9Le0J0xTF9ux6Zxxz6VGdGVuZlu+nwl1J1GKLDbTgmYVArHFqo4MFvCQiVEJZQ2+OwGjE8XbH6CfB8PK/TC/GvW97pRBs9Vp6U+2idPHG/qtDfqC37k0EGwqBYvmGEVTEb29fS2bQ2vcyXKuBKqIS15OdDC+IXPfEorp7twnO4rH/EI/OD730t+t83s53qDaosU0/WMBYqQQ4/DnSM5a4y4Dst6sqHImG6ERk53JZSYK7fOdFMLso8+52IcnC3jzLGk4i5DUpU24/Gm5Qgr6XOvjJPnznXBtbPMGTBRDFZVz62Upq1VQomcL6yz0KpeLKaZroHT7WQu8z5HJZI2e3R4vgJfcJBQO2dx7Won48zWDNrxGhuBZDDZLDi04acNrPh7RDVcafan17CcEZpAD/1ewWJht8hklTiDyVJpvQX7XDnJ9FEPXT3XZ8sdOt2U6Sbn3pkHjGmhpo2Am71T0M9UypjuTk6NbHPBOHu8gNnU+uhSYzljUEzZYwszYWhu0Pz5lZtuwtU/87P4//7te9h36QW47orzuvODNwhMDyLcf6qEyYFMQsPjjNE8PvC0C62w30bmuquRruenp82MGJhUgJLatokkEgEqDgBO4JwxhrlyDYEnVhxk1XOE25pxOg51ySAQeySd6V4LuEr/abhsJjo/ZboJ9e30KPAUB6FOlaoYKwQ2iCHNk3CPs1SNcGi+gqGsh/FCgFItQjHjoVyL8OBMCYWMZ4NYaylKu1HoO919bCqYtT9WSFwGS9UIOb++xnk1oAzezW98LWQUtZXBc9uHMUZCN/EK6DqeaWd5cmoaT332c/HFWf2aRTXc+OKX4K+MKHW3FULFCmiBhx86gG/e+nW87w2vqXcCnbZgYyMjeMzPPB63mdbQnVDf1xux061rnfKBYSAg3ac22Qt2PTeKVsrEjWu6JeYaKJgOOU43HS/riTqHG9DzL9GX2+MoBAL7do2gGklMpup7Ow3+RLS5Qxs/1cio6SsGQJl+oitvHaKUrqFbqkYdZYtJNKbboPEE6Ptai6Q1GiOlUPCE6aWtjbPQ0h7bvxY3JkKONxBTyl2ngoEUmbnNNsT9WNlpYwi1MkLTc2ojgwzlMMJcOcQWo+VAFF6drYPtLVyLFDyug236GSm4gcCYQdK8trMZdKabJ8ZfL4A7gRNdl8o1nZXaGjGWcMKWQ9YXdS37EsJtjEGy+NiJPQCk8q0/t2vHDlz2yDy2jm7ONmEuKHATSoljC1XsHElmt6/ZHbf2/Nmzx/Gx/3gAZ6Q+sx5QSiWysYEpr1CAFdhTSlqNFt8ohbtZVRraGW/lzCbr1DuCZEC8lhJ0UChm0gxmfHi8UidI1w3o+9B4JlD5CJUa6c8btX7nK/VOeJodJTCSCzT1HDqwHXgcA5nYvqhGEmMFH4sVzUYo1yQKge7jXQ51ZxLKtlPG/HRC3+nuY1OBm9VRiy20Ri2SeGBmCeOFjI3CdgudZPDSdHIKGFC2vB3H89GPeSy++OUfAwB+47ffhPN2TuGv/k6LqzWjxK4EFM3sBB//+Mfxspe9rKEIHAA8+rHX4psmYJDxOC685FLc9q37AQBfuv37PelwA9owFZxBcb2R22wui0WpACToue7r9YDgDPOVEKValMg+ALrvJuEV+3bhI9+6H5VQYr5BpruYaV3T7YIxVkcvZ4zhj59xUdvBsFYgA4ABODJfxWBOX5tyouZS6XPcdWwRggN7GgQHmkFBt0VRUB05j2KNxKEoozCQ9XDWWAH/eu8Jm5nLCBJWi+mzJPDT7jy9YOsAQqenuUuRpQw+c8euGdukZ8C4mwXqTMStlyGWMUKTn9243/vgqTLAgC3FjOlhC7sHRqbFD2MMtUiiaLJEQJLGyhgwU65hKOvZ3sHtYqZUM+NBG9YRZ6sSmOom3KAjlUEopWy2NetxeKIx66ftcyQyk8kAjD4sOd5m7iB2rjpx+HsZ5ABRi8VWOGu8gC//ylUbwn6QRhCMgkrMsHWUUqhFmrYcSj1XrBYBT7LV6OflfG/F814qBc6BwDN1z04XDHd/pMwyvUWlGWsBQdGHBohMgkEHdpPtzAg5X3f3KJjkg0Ky5p2CtCN5HyM5H8cXqyhVI+QCkbAvIupfbi6mJhWKgtuASYaChthY1sRaoe9097GpQNFI1YbXTdH5MFp5VqwV2s3gNaoBV0rhVTf/Kc6YnsL555697HFco2FibCxB7+2m8cPQGXX0wIEDLR1uLgSmt00BszMA9Ka9d3ssUNWrDjegx09gNoOkiidLRHhpTMYR4vW7Rs40he7+UyUrLkRwx8WesTwATZNP0yeBJFtiudmyuDCPO78Xt79xRdS6EZUmARvKFAxldb05RdY9zhCZdJNiCtVIq5qP5Py2A1CeMZbb3dQZi2vd1gLW8ONx/S0Q1+9S7pvGXCcU75wvAKe9qZvpJmOa27GbpPXZDBAnJyIWEtzsEII3ZYmk3/7d68/BK/72B3jFvl1rfl1phFIin/GglEKkYidQKiN8mdGK4lXDxolr+N3gCMNCJcSe0TwiVZ/Jb4b5cogjCxVkPY7RfADBGQoZD+dMFDGY3fieua4fIZUWxwPie1TIeLh4atDWgHeKrC9QqsXrZVpMkaGe4URzikSyVir42EswcR5kfbFsUBZAV5X6O4FSCp7gtl+0ACyzI5TKKvvbbiTcbQUXC4IButQHWNmeJg0Dxec81dkkqQBOJRvrsZryFtmASDosJ+daXRsiH3i4Yns2cT8Ei/dQz1DNh3O+7qjAGMqhxHgxMG0MY3VzNwAQSi1SS+8HHo8z75t/m6lD3+nuY1MhKejRekZKFWdrugWqy96xe09Dh9H9O6Ad7lw+37AG/JxLr8T09DQmR/LLnjfrtmjyBS7cOoBH7RzGVNc3N9WRQX3XXXe1dLjf9K4/xPHhIQAzAPTvuHx6CH/yjIuwY3j96WfLYa5cw9GFKs4aL0BBR6plLW6rBAAM8YYN1NPLzx4vNjl69yE417VjAavLMkcOh5Ragt1/qoT7T5XqjuM6q8sZiX/19l+HOnof8MpPA2hMY18NpKN+yqBpftuGsrh/pmSdcXoWgnEopgMJkVRtzQer/p2qG18OXpd/J4EMvwmiDzOGKvRzoPYySsEaRJZSvEKLRDvSyWyord1232Okbq7/R06Eb8SHNjta3b40a+S8LUX886/u25BggxCxWji1MVRKYaZUw1ItAqkIl6ua7ULBEpfBwBnAOcdgzsdcqQbZZv61EmndA+rfS+tLLzjcgHG6zXolVUx7d7PTK3W4AeDciQJCZx31BEuwI9y1iLn/M/tDun/5ZgUzYyljynJ6FeTs0r9pX1ZQCKVCgXNwJo0Dbhxukw2nTC8pdFuKt3HgO7EjI6mQ84Xdr+L1tb40Z632lTQYaxxsK4cRsj5HMeMlkgmA2V+Ve4z4b9Sij94SXLM8qAyLQVPJBzJ+IllGQXUy391acc6ZZc7RNZ9u6DvdfWwqkCHRzkamoCBE9+oP3bpszjne9O4PJFTB03XbgDGcOccTn/lcfPXzf21rwN/wzvdjy9ZtLTNtrgOf8Qbs+9Ta6E+eeXF3fpgDBaCTku6zzz67LqDAGMfNf/pnuGTvIzA5NY1P3RFnRckAetSukW5dcldxdKFqhcSIulmLVDKzzZJq5eSo0N+HcutnkDITOc95WoDLzTq7xqJbU5UGGYcv3DuNT91xEK997J6Gn/vYz23Dy172MqgHfgB4ca/wmVMnAIx1fO3NDBmi9VO9ZjHwEAgOgZiOZzO+TNfcB6YOrF1wYwi1+42zx4sdKz63C8ZYglJIxgkZjYAei656eqdCaonzOYZfPJbjbB3gCEcZA5T+qzPdvO371stoZAATbnrEDnzv4CyeeuFW+95GZfcDIWy2rhJKCMYQQaESSuSNYc/BEEYKWU/YjBaNb2rrkzXaC4vVqO1MdygV8r7ATKkGf52cg04gHPq2ghNk6NLxPaHp6YSLtg4mgpLUdQDm3G42UTBmqM6b3+0msq8vOKSqZ0r1CqjPtc1089jZrEZ6vixUI4ShRM5n1lEkhxsAPKbtq7F8BoNZDwdmS22VM7qIlILvxW23KFCj92tXh0DXma9H3XKz9SuMFDJCrw1gyTWxlS1onWXz2oqf0X9NBIrYN5Qsoyx2elroJJne77cNZnDfiaU+vbyPPjYaDCRCtvxnpdR0s25M22ZtvvY97jqrMJ6u246vQ+Krn/9rfOLzt6JUKmHHGbsxMjGFA7PlpkZf2sH/ld/7EACtUt5t4bQ0OjEut2/fjltuuQUvf/nLEUUROBd41Vt/H9c/+Rn2M+71Ls2eANCbDjdAWURNKa+5PSsRZ8aUQrINCEvWdK8nKMslOEeacOA63a3GDJUq/MY1u/Hiq3ZYOnca1WMPQO3/T3PwWJH+2JEjAM7p6LofOFXCYjXE+ZMDdX8jZ5w2acoWUEY4kenmDJFCosd2O7C06TbH+kDWsyrnaw1hgopUo8pZfR33amQcKGsVv3ad8GTdqks3B7QT4vPOhbh6Ec0yPwAwmg/w6Rdcsb4X1ABKKXjmeSxVQzw0V8ZEMQspGRSkzWyTsWv7DiN+jxwKwZwWP23uimEkUQx8hEaEqtdA4/bQXBmFwLO/S3RR58QFtcwjXDw16DjdbuCKAoTNSxg2E2hd8EV7baA2ClRTL5UW5Izb5en/FTICbFE7xW5rLOaseZ4ppQm8uIysnXJGF1IqI+LGEroD3MxJQs4X8DhDMVg5G6NdNLt8TbWPa7JjUU+BQHCMFnwsztZ/T6uux7+HgrI2iMu0kJ3OXOu/nyzVMFOqYXIgDtrTPiylQuBzTA9lLZvqdChjSqP3VtE++mgBMpaJHldz2uukERmDvRtRxOV6czf6e/qzh07O4oK9+zA5NQ1JWXjnM0cOHcR3b78NP/zeHXUO/sff/077uWwPOd0AcNNNN+E/f3gnfu/jf4tP/tN38cTnvCDx9x/d8R/2369/2Qvwhc98qivXuRYgcZBKKBPOHGX7CF4qO0ibynqDNknBgAdnSqg5+gVRC6f7CedM2H+7Wc9mDjcA7DhjjxWEcbkmE5OTHV/3YjW0/bzTsOrlLG4DZEW9WOxEMBbTO7U4VHvnTqoOd3zpaw4umMkIxHWG1ILG4wxTgxkM5/wVi0M5zD4ASSecHDJL70Ms9AbA0stPh+xdr9YLRlJZVkXMdmC2HZIwzy+MFAqBsI4BKc4nWsAhViGO50uSIt0SlKlzFMF7CQwMlUiCMeD8LUXTqaSzrORqkPEEAi9uT+bqJQA6ODa4TsG6tQQDzNjr7Rp1Gu/Uh5v6ypO9mPOFXU+JucHsc4sDixQwcTO0nSBSek+iYDF92y1TA7Rju3fHMAotmGjdgrvOL1RCHF/UgXNi9NG+4JZoXDY91LSV5taBDM4cKyRKcQTj9p5zJJlpjAHlUBoauz6HNAH2eA4RHb8+OHy6oAeX0T76aA4b/TKr2D0nlvDQbLnhZ6WJZnYDO3bvqXOqGGO2zdeO3XvAWmQCuBDITezAgzO6nlYpwI1tfuEzn8JT9l2CV9z4VLz4adfXC69V4zrctch0h1JZQ3olt2x6+w5c/IirMT09nXB+jhw6iC9/5pPxG9US3vn6V+PIoYOrvOK1g8d1LeNA4GHHSN5SyWmjSKt2AvUCO+sFbUjrZ5YPBA44c8HNdKcdtIun4gxzuw7U5NQ2vPZ33g4uklH5i3Zv7/i6ifLaCIpatZECrRWui2n85BySwqpWI18e+req2CnpwU2dQxtp1mhksMq7vuDI+gJnTxRXHORx6eT6dTzn3V7d9Fk44oqcaeM76uV0V5vQLZAUDs6WE3Nlo3F0oYLD8xWEkbRzge6/T1kjMNSkznSP5TOGeeOs3QyOw80SDndH65Ryegz34GRhTM/pgYyH7cNZm4neiGslt8099WXbhrB7dHnNll4HYyQ+qVvT9XLQjTG9fkbUJYbplp+CM8sMCSNl90RyrC17ijF4DDZQRcdrF5VQYr5Ssw621uEw9HJs3DzSKu7634fmKijVIpNcUMj4jmJ4m5fHGKtTqPeIaWPKwziLA+Mcei0iJ54xqu/WujDUJcP2vO/gWjYT+k53H5sKcYRMOxUZj6McNs4w25ZPa7hBHD9yGN+9/TYAwEt+5z3g3PRyZk5dpG0Jts25tlhMohU1ncBULF4SdNnpVkrhnhOLeMjQ3VeyKZAT5ItkO5rvf/fbQFiJPxhWoZTCD+74djcuvesgkaowMi0/KAKcMlZdBgV3NueNgN6oOAqBlzCGXMcozY6YcHppd+JvPPHpz8YX/vUOfOSzX8IHrp/G+55ywYqMSoqGN4JUSfqr3cSZq6zNjCHILOugnXkupXIotu3TbNcTwtC3D86VbeasFinbsmm1uHTbIC7dNmhfu9Xt8ViPs0BuwCkwNa6FdaBDrjU4j8WAyrXeEYcKpbJBJKli/QGlFHzuqC2DYUsxg10jOSt25wZLaF1Kzh04T7sdxMKavUgvp99DdbE6CLcxxrp7fwmeWJ963bUGY8wykySAAzONEx29AB2IibtdMAZUQ4lAcOSMc6kVs/XvEc780AcwGVrb776zPbJUizBeyJhSD5bQHQA2zul2p2/G4xjKeijVIkipTGvKpN26ErgCn8IE/Nz9mjHdegzQzydSmsFFmi3MXbda2AibGZuf99LHwwo0GRUUqqFExuOJHrQuqB8g2VPlWoT9p5Zw5lih497WD953b51Rr5TCi592vRVLe8nvvAd/+KXbkVk4ajPg1Md7Yus23H+qZJ0iZYwpGTWnprt9vP/X77wdf3DU3IM2lqJyGKEWqrbqUBerEYZzPmZLNRQzXke1sQSX7uveJQYk6n/jf/fmcmoj42bsuGrZNtOtkuq4jG2c86avhZu+uYDv7KyhM6bSgZpxJ0LdaVBqcmobtu3YtcIr1vBE817BVMfNoA08l2lgRW8QO4O+KdNoxzCKFGxmnDb3XgNnzGQ5499YjSQynlixYrmLNK0/MZbBEveFnAiq8/MEx2XbhpqWBmwmMGihq6yvA7ehrGF4HUUQmyGSyvYa1gr2zGbnfM5jcTvjYFrWCIvrK03CN1ECA5BAYXtCpAR61D2ooxaLxZlr83hnAondvRb9f724pnQDFPAbyAjMNWg7udGgrhfcZJYfnClh21AWDHr9zGeEDaIDya4bLvuHwSlpAjGPOtsjMx7HrpG80zs+/ttGlbUkAtlm3QhDiVAZp7sL1+bxeK8gFpoNcpv/ugmASCpwrunl1uFGsjTmdMPm3zn7eFjB1noaNcqMJ5qqqpLxTsb4kYUKhrI+Kk0y462wY7dbz5o8B6Drrv/sHW8AwHDlvmtsD2/6t0w5atKpN2p0bC4EPvGFf8BHPvslfOn27+PZN8Z10mOF5Q3D+04s4eBce9HoaiRRCISpvVnZwksLtseTQiuXXPmI5I4TVsEYwyV7r2rruEopnFyqorROmShyctxIOG3IdF8mioHdWIDYUdkoaIEkbrIq8fuP26MVxbcUA+ucEtw6w/Vg1pJewZFDB6041Mmjh+x7LnQdKwAzzynQRs8mzsTq93yTkWgr023mnceZaRXTe+CMmdo3YY0VYvWsBVz/2TIAzI2xjAJn7JwODjdgSjNMNubYQgVHFirLf2kdoAD43K3rN2NAKZs9o2dkjVKWdhxYKvMdH6dTQ5aM4LUSJ1sN7G9ygg0bZazb59KTq8rqoJxSvazHkQ9ET5SYhFLh3hOLCCMZt7UzZSO6fpshECZoKfQeSawIWsc0G8QJlrBYeZ6ys7S1HJ6vYK7cuouFUso67rq8Iw4w0141tAF1/vT7lqoRyjXdBYEYAb7HrZO8Grhld+SAk8I/N1Gp3aN5TA5kNINAwtDLuTN39fe7EWDuRfQz3X1sKsR0H4VSTWdoa2G9Q3Z0oYLZSojRQmBXTKm0cMVKMDk1jTe9+wO4+Y2vhYwiMM6h0sJqMsLRA/uBi8+u+z7V5jEwHJmv4JRRcGRMH/s33/E+vPd3fhNSRpaOfuFlexPH+H8vfSQiqRJtoVqhXSqqNMJVbl/LTsEZUfz0b3XbnT3vJb+KvzplPhjV8Ob3fLBhj/NGmK+ECCFwcqmG6aH1o7SGkUpkh902VTtH8tiZ6q2+URFZBaJfA0Ilr+HZl27DloEMLpnSVOKM4KhFeq64Y2ithXHSSvyvv/kPsVgN8cf/+7egGrTfkypu0RYqCbcu1e3fSY4EtSppt6ZbByC47SfaaxCcoRZJ5P3AzCvtgKV7R3cLLvOHnGy3hnslzJfNAG4Ma/p9KxWm6zak0hntWMFeX5+URjHYmQuxUx2XTACOOJQTPKHR08nTpDnl8Y0NLDaDSfDH2TXjUG3EtKZr6cElZdXIBx7OGM3jyHwFWV9gqRaZfWNjf+xCJUSktJ2g13Rtg4RSIesL7eRyhkgq2zZTK24jETgnGjQQl5gJxiBharoN62SxGuHUUrVln3oS/wRo/YznkeDJ9pDrCfp9D8yUkDVONjEvbbuvVTxPSrokWDUsZgrSvpL1BUKpYno5N2y11B7fi3tzN9B3uvvYVGDQ9Iz5SoRSLcJEMdPQ2J4th9g5nLPO5AOnSjZrsFIf4+k3vhD7HncdHtx/H3K5HF789CckHG/OBbbuPKPhd6WKneBTpaql0tC1POk5v4g9e6/B7KEHcNH55zZ0SscLQd17zcBSxvJSNUK+RR1mTN9dWZyezsfA8LXP/SU+/Pbftk7Wc9/2Efu5L//bHdi6rV54y3XS3d9ejRQKOYHZ0vrQ2WijCKVK0M8Ebx2M2CjHxNLLGQNLPV6PM1x31rh9nfE4FqrG6fYditca+tyNWu29+02v1dfutt97w2txzpXXYM+unViohol6Lh1UoA08fo+E1HwT7Gkn8SKlNrh8x3npNdCwCzwyIpV9vRZwxXAsmwBxxlSw0zN7xwCrUEzCP70BZhhaylJmYesfGTwRO3cxfVx/L9FRAa4BC0gk/0aohtIavY1AgZ9eNIIp2OCuCxtVNuK2nzodMVHMIBAcdx9fBGO90TpMKWWpyQfnyhgvZOAJvX9nPYZiJs7IUy2xxzk4kuPZnRNS6fIOj+syM2beK4cShYCjWXhXKYX7T5XgCYaCFQtjGMr5mBrIrtUtaBv0+zzOkCGnW5LTrS2/1ayBZ4zmcXKp5rQQ0/PS1nXDLV/Sn4kcRkI60y1O04nUG6HdPvpoE1TPFiqFsyeKCfo4AMyXQ7PgckwNZjGY9aCgKdSCmx6OHdbnuCDK+IWX7cVL3hwLp3Eh8KtveQ8mJrc1/J4yghHMOBAe53X1zxNT07j0kde0nQVuBTcLUqpFODRfsS0i0pBK2fpGur6OzwftLBw5dBAfMg43oB2qz3z4/fZzjRxuV7n9KfsuSbQUCyMtgLJebUpIcVM73XH2b7lMz0YmyQQH9ozlMVHMLP9hg7Xu9U5opFegpGzIErnzrrtxcK6MrB+rmzLOEpFv6wzaqDhsb+J26u4ipcCBuDa2C7+x26BSE49zjBV8S4dcDyMkzXTRDg0/bR0JMgp9vpocT3dBTmNklNWp5zZplFy6bchkpZwyACRLg9yMkT6mW2uZdBvuPbmIw/P11HqiyfrGAemV++OCm0UgQavfyACBwzY4HTGU83HeliI42KrsqG4hUkDGONkDGQ8ZwZARwgihMpy3pWiDq0Qnt/25zRbIkAyq60w3TwgQSqVwYrGGoazfdO9cqkVgnGG+HCb0D86ZKLalrbPWoDVBO90CnMeB6li3ZuXH9wVP9d82jDLOEveSbFMGvR/7Tj90Kh8DksJvpxM2fiT00UeHYAzIeRznThRwYKZkjW2pFB6aL1tl3WLGw2ypCgVlskZxFpM+v5p942ef8Txc/uhrwWcPY3rXGYgKYyjXYmfigVMlFAKBsUJgVWjBAK6YpQnStadbiK0WgumaVUAba6N5HzNLtYbZcmIAUC/klSy8nGuVzgfvu7fOoVIP/QTXTwFXnn9W3fcaZUJvfuNr8YhHPxb5XBaR0kbfemzvSimjoAkASXp5q0z3ORNFVKPOdQK6AQUdzCkEXkdtjzJtliisFqRX4DrejHFdr51iiew8Yzc4YxjIeIYBwsChElRnV+yGequePVHE8cVqS0VdZdrHhNL0NubxeO81kNNVDHRrsO8dnLEtodYDbgCJMarJO/2gQGq7aKoLshGgYFI1UlY8T0qFyAQChWO0upltN8tKmXCCznTT8UkXRe8+nLOGHUCo3Mgazj06V9x1gboZbNSVMpy+mW4Cp3Kejfe5TSBKYKkaIeNxFDICgWCGFWI0GzizewXgOpjxg0qznmwPb+h5dMLYTq1KfBarEYazHpYqIXgPrScEbdbouvesr2uoIxNYs/TyLl924Kj3JxxuE/gLI4WcJ2Jn2zm/YAyj+fbZnZsFp2ksoY/TGUQrpQgaeWTVSCLrC0gZZ14F55pSynQtiaZF6S/cdXwRh+dX3vqCM4aJrdtw5b5rMLF1us6IL4URjhpxHqpTJcc/bnOkPyuhupp5c+lStAE1y2ArU+dK/Z5XehUeZw37lXMh8JrH7sGzLpmq+06jTKiMItx/3z3m2uqvZ74crkgMbznonpFx7+dEzVcL+ulA1sNYB9T/boNEYJZ7aq6N1K3+9cuBtBBcRsjrfu99+F/veF/8Hhd43Tveiy1Teg4VTdAsppsls9tAPL45A4Zzfstxu1gNceexBVRCiYVKiB3DufgYvWcbWQPE7Y+cpkOuJQSL569es05PM0EaA1Qw078Xa69v0A5IpT+MJPIBtw411XcT2ytZsx1/1x6DJY+ZpGBrhphuodc4y086JDrT3ZtsBwZtxNJc0b3sNy6Ytpr9c7OA6MG90KubGGnlMIIvGC6fHradMTRLJ+77TPPBZrpdJgiPmSHnbikm9wgAlTDC1EAGwzm/abCBAgDA6mqj1wqU9PGEppdrwTlp7xGxqbr1WCeKQcLOiPfsmG4eKYV8IBJ/I+zdMYzdY5u/x30a/Ux3H5sO6cWU1ohaqGnltVDaPouMAZGSCLgw4hExpSbgAgurcN64k06gTJq71PqcoyJ1DS3V5unP6I2Coqn7Ty6hVIu06FuXwA2VXikFKZVRp2z8WVchlxyZTkFK8ZNT03jlW/9A13RHkdOjvDFlvlEmlHMBMbLNKlencXCuDM50hrmbCKWyxiVH7PTsHM6tKjizlhjMeDi1VLMBH8pgNTI6N8pGevqNL8TEhY/CkQf349GXXwA+MI584OGCRzwW5WMHMbB1B87YuQMnFqsA09T3LcUMji3E2gdAnNUCXCG12MloRi+fr0QIhEAtkvAEw0DWQ7kmtaG1HjegQ5DxQXTIQFDd8fqcnzfIdPdilnO10E63/rHU1WKjpaFIgZkzXU86kPHgcQZStCAjlrYet6bbVS/X78X/vnDrQOJ9CjBQV41GXCKb6TalUL3gZKVBjgT9Ut+Ubm3UMzwd50kaVMrQC8NBGkX/aiiR9YQj2sVsj3QqjbBsCCuiqY8xUczgwEzJHrOY8VDMxCVOWnhToJDxWjLaInMtwPoFtTsBrQ8e55gayCJSCnefWDTljsla625g+3AO203wQp8/ZnExBoBpjRVSLuesN/fjbqPvdPex6UAGt60RMVO1EklkPY5KTcLn1GeR6d68TFNqapGy2QxhIvgrhZv5jEid3LxFYhwVYy2dWKphLO9bR30o59lNoGocTsG6R6MWZlFT0HVP2RaLqTJBAOoHu5KF1xfc0JYErn3ajXjczzweJw89gB1n7G5Zo55Whedc4NVvfy+GJ6ZQri0iVxQoZjz7jJVS8AVHbQ3o3KFUpuczS9ApB7IeBrLddfC7hfFiBqP5wFL+yHze6M3ryKGD+MF3v42ZUycxNDKKwq4LcckjrkZhIIMDsyUMZD1MbN2GgV078eBMKaFvsGM4h8GspwNHMulYu/Ry19huZeOEUiLrc6tcTutG79LLk5kZUuBdv0y3Fv8hbB/OYaQH+ld3G4HQ/e3PGi9goRri2GIFGy3IHBq2DWNawV5whm2DWRyc1UE/V2XeDUJZIaJUdpvgdu2gNZ7U0T0GSFX/o6Up7dCiUr3hZKWRrgP1TDB7owIEDwenm8ZZr4wHuue+5+wTPO7DbRM0Tg23u5dMDmQStcguKBGR8TQbpuaojp5aquLYYtUG/5UyTEbemwwqSqhwxjA5kMGR+QqgHAo+YjbMml0Bi4N+nDFIpmxQeaW252ZD3+nuY9PBpZdrJ0MvhOUwwlA2i2MLVeQDAc8ILGlaHsf0UBZH5yuYLWnKqUCy/2wnIPoSbTxKIbH5SydjsVAJE+qvnAFjBU29OTBbgmAckklLIVwtIqnsIiZV3Cap2S+lFhmMJdUjO4EvOC6fHsLB2TKKgYeT+VFcuW9nW991VeFHpnZgbOs2nFooo1JSGMkI5HxunzHVeK9FCfXRhQq2FDMIRJLe2+uwaqGAfeaNLj2dCR7Kepgth9gx3F1l1S985lN45+tfnaDqMsbwi695My659HIMbdsFPrYbjAFHFioYzPqJDJ4wkXcqiSCaf7KfdDJ70YrSGUYKg1nPzIs4ot6rz1fXm8e/Sd+P9VNzvXTbYOLeTA1uvPLuWmCimEEhEMgHng1UbbQfERm2DWdALdIG6a7RPA7PlxOUWD0XWGLc19WpNjGgqe748HwFtUgi5wuoBnoQs6VQt/PhWq9jo+9NI7hrAGBE8UxN70bgdBV/ckHlD70gpAbE49wVCuRgVgiVysViEc5kP+nlQLTrtJjqiaWatdnSY7AXlbe5SS/THui29Ir337W7bv1c4jnLoJkpWn0eLW3U0wl9p7uPTQc3SukugtI4ZJQtIHEMDm3AbylmcGKxCqkUDsyWURDa2QwjCdHhTKDsuYTCQiXEgdkSJopZG+EMVex0z1dCbDV0HqI0bilmMFuqmegoILmOMkbh6jcyHWQwL5SytcqtjkzX6gYOOgXnOgAykvexWOusxdfk1DQmp6YxU6oZNWGgLKWhxcfXE0rNYqh2eVOrRdJQeuMezj2ohdISdtNqkq5L26G3POcSfOLbD+Klj2ovONIOjhw6WOdw63MrfOoP3wEAYJzjTe/6Q+x78nOhoANQ6bptMoqEeSYAcPHUoPO5ZE12S2OXacOsGirruNB/e6GGN42YaaFfa0eieXlIt+Gtbbqjp5A3rX1slniDU926xhJGWVhZpWRuMnXpzLY7JrgzZi7cOtBUWJGCTpGM6eWNnNT5SojxQmBZTD04VeCqsgN6nnuc2zZR6389G3LadQVpCsgNusdpUBDJnRtAvI5xs5e7z6aTFniUgU1rgHjGWa1Gmtruvt+LAV2X3m3XDxPcJpblWl633XcpwcPigAbt+w8LpshGX0AffXQKxuLoJs3RcqjVK31Tx02LsOuce6beK5TKiqpxxtruU1yuRZgp1QDoTDY5qTOlGgazPjKeI14mdXZdcE099wSQ9TigXIPJOBAmQNANenkkFQ7PV2xUU6qY+t4KVP++WntzeiiH8UKw4kgvZek9zlGNpH2ehP0nl6yabjcwU6rhx0fmEUot6DFRzJgWVL3Ut7c90LhvdwydOVbAO554HnaNdE+s5MH77l3WkVVS4l1veh1OHHkIHtNq5SN535m3xDTgiSh8xhMJeq3LymilGkzihZFU5t/x+70IWhvcAAO1sOljbUB64Cv1I5aqUVdKXqimOyO0EU9Ot8eSYnq2923KCaCAadYXti41DZpjhcCD73HbozeNwOMYyHq6pz1vrpmwkaCMHa05GU/A4wxD2Y0phzh/ywAunhrckHOvJ4hBuJGglnYAEuU3tHb6PH6ddrI7UeOn4G89k0S33qqFurVfLdIlhZ1k0dcTzNlX3BZeMRNgbYNGtL8DtF8zo52j3+tVYdNuo+9097Hp4EbEyFgoVSMUAoHBrGdbqriLCtXJeVxnNTMmes/RfrbrvpNLtp+phEosxGP5AFOD2ZTTDXDGoRQwmg+wfTgH5mTwbJYOlMlYudFHmCnVsFSLrGOv4FDfnc8pFde2M+d6VhtpFJxhrBBAcNZRCysCZekFZ6iFChlnc4ykQj7wTN11dzBfCSEEs87+juGcrfXsVaesGchx2EhbaMfuPW0xJWQU4fAD+8E4w9aBDEZyvh2z9G1haNbNnkNSzVyPc+mMaxec6fFoMxbQ2cTecyNovXIMFAb4p6mYWa+g04BVGg/MlHD/qdLyH2wCcmCqkUIx8GygiepS/ZSYHrE/mgmntYJ1Tix1vD7WGhrnf/doHlnjyI71YPsexpIsF8EZ9u4YTmgSrCcKGS/RavJ0hcf5qm2V1SIybVh9o0eTYEGZ9wFiQySZQunMd2swe0x3ilmdICkxX6khELHoYC96j+790a9j25Pe54xhaI16iuu9nTn/TgZLGHrytnUdp//q0MdpB3KogXiSlmoSY/nAZilpApMhT/U9gnPUpBbjCnThZEebBxlDYaRMtpWhJiWKGYGxfADGgEooMVMK4bE4imcddDiRRUb1twweORer9JgoK+aZGibXAaF/RVLhrhOLWkgDcU031dusFjZiuoLfEqlYzVLBZOCd6854uie1m+leLUXYY9xxyIxirxCbzskhdkezu7Eezvjk1DTe/J4PLut4CyGwbdduPeaceQokMxaiRYbX3cTpfD89toAHG/Tr5kw7EhQkywcCgjHNPukx6KAcd4wRrcTbi9mT0wUMVNPd+SRRStder5TSfHiujHtPLCGMJGqRxEBGmCCx3q8A2HrSWN+A2c8AcZ1qO3DnGzkJdOVH5iu458QiQinhcS0exTfYkW2FNL28j/VBL1SgSFM25zuCaUBsT9Vltrn7un3mENmb6Uy3MmrlkdTtXjOeQOAZpf+Njkg0QCzQGe+ZZDPQ68unh7B1jTQ8SPUeiJ1t7qxb6e4/pyv6Nd19bDroDHacpQV05jnni0T22daLcAaRygx4HPC4gOBxlkEphVJNIh+I+pMaWKfbKF2HUv+b2pExMNx/ask4jIEVevOcYECc0WO2R7Zv2l2sdqnmjJnAQ5y5UYro9Pq3LlZDTBQyOLlUtd8julU31COZcZ5W8luk6bsJsxgLZ5PQ/cZjujHhzmMLmChkVtQrWynEmW5nIwjEWit5dh/acWhOA10vM4CE8W77x1tx/z13Y9eZZ+Lg8Rl8+oPvgpS6jdwff+jDmNi6DeUwsoYAbbpxJJ5B8OZX7RpA9K+MJ7BYrdcT0GUkyhoZxYyHy6aHerL2jure4tfo2Zra0wWMAjgruMe1SCHwGNQKTcaFaoThnI+a1DM353uQKkyIOaYV7BmSlNnJgQwOz1XaOh+VYjAGHXziceDy5FIVI3kfC5UIHo/bWvYqaO3oT431hc50r+yua/FYtmqBRmnsAcsCMe+TI+eWIKbbQ3ZCAbfOod2f6It67EmldYMyHrMljD3oczsll/o12aCJ7P0a74ex/R0HDd0S0M2W6FgJ+k53H5sO526J2zfRJFUqdhxdGhBn0CrlqYntcY4MV4nWF0fmK5gp13DORLHh5GfGgS/XIhyaK2P3WB6VkEFK2J6QjOmMbM4X8Hisuk3BgLjVS7zoM6Zr0LqQ6AZgsiKcgUmY3sQceV9nbjUF16hGOl6le22rBS3kK/ktJB5EzhctzIwxhJGmPA7nfDxo+mpWQ4lACCxWI4wVOj+fgoLHuHXobT1YF+vG1wtkSPeCBfqtb/wzfv93fwtSSnDO8atv+X188p++izt/ehd+bt+luPL8M/GNe07a6LdmgcRzFIBxBppHPtznQ//0BUM1Ypgra52F+O96/HDne736fLWBF7/WvYcfHvVuGwXb434F361GclWZbm4MUPp+xuOoRjyhP0AZ3XjPoP7aGtNDOUwP5eoP3uh8zGTvAFw0NYD7TuggcbkWwRM6uz1bCjGc8+D3uBy3vg8bfRUPP3h85aVw5ZpEKKV1upuVBDWCUgoH58qYLGbw0FwFE8UAPrFAXDo5Tyr7e07wnq6/3bwq7asUGKZrVVDG6dYZ98ATYNAJnY2ud28EriOLDsWbJfbd9YDdexvYwQ8Hhxvo08v72IQoZjwrEpOIXtoMcjyBqZbbpawyALmA46KtQ2CIW1+UQ6kd5SbrpTAU7IVqhHzGsz2+KRPFEJ874/G4DQOL+z7z1AJOImpTgxkMmNY1q4Vv6OWc6d8UCI6cr5dWpZSm2ToZe4nY6OwGwYeewUpaiihoAR9tZDqGpVLaMXboZICm8ud8vqo6OjJ4ubl2XzDdNmyTkZ1ojDczhm68bBsA4LF7Rtf0Oo4cOoib3/AaSNN/XkqJD7/99WCM4cKrrsaO7dtBd9duvDa4EmcS8r6HwGv+DFxjm8avxzkyguNkKUQklRXbIZZHt9gcawkyGgljhQBnjRcSQYQ+ugvKYK3EVg6lsrRSwkyp1ramBQWdyqFE4HFN6Xb2DIInYgZEI3GoduEqBY/mAy1iCIYHZktawZgxo6Le+2KSLl22j/UD5yvr/CCVMiU98TP76bFFPDTbHkujGiksViMcW6hCcIbxQqCD9E4Aipt9wB0W7twkzZZ2hw1z9qj0dND7Cgy93JQtrSIgsZagQJtbmunqoqwHrGiak+mm/Xil69lmQz/T3cemhmtAk4NtM36IRYlcJ5yULQOPGyE1/Vlp2nw1Wy9jejgwlPESbbZokScHnyjqUkHXazv07bg+KBaPmBzI4IFTS6tWiJUqSWWvhBIjeR85o9bu9u0GNJ1wPB+gEAgbEFgtrPO+wp9iAxhkeJobdWiujF2j+cQGWokksr6wWaKDs2VUI4ndo+0pclM9e7kmbSuU7UM5FILNJ4hD46/ZGLrpkTuxd/sQLto6sKbX8eB991qHmyBlhMMP3IftF40n6K1uXRnRROnxnj9ZrD+4gzNG8sj6ep4RVY5aa3mco1SLkA+EGUf6O16PZ+6ApFCkfs0w0oMiVqcTTBJoResvaU0QlFI4ulAFShWMtBHfos4Vc7UQAxkPWZ9bMUnXQNb9u81rxCygThHvO7EDL6GQ9YRmg3CGSG6OzJNdN3rQyTmdIVZ4z6uhRMYTKIURACCMJIqBwFypgsE2YooKCr6xtcjp5oxBwKUp15eGuTbD9FAWg1nPCq0tBzeJQ4mVAzMl1CLNjpNKB+y3DerOLauh3q8lXMVywClzXKd5ft6WIipOh4e0FsMl2wY3WZpjZeh9C6SPPlrAnaSxIxtHPV3Kuf2MqbsBYBTD9QIpFdVONl4whVmgIpPZOG9L0dCaYjoTYwDjOkunj6lM79C0o80SkUd9jc2z7O2CqOO0uJZqEQYznq1hUtB105SFr4YSWwcCFDNeXX3PSrFSqqZUWsws5wvAccRo0xvK+UZkLb7IMNJ9u+kZzldqHUeZPa6VrT0Wj4mV1IdvNGjcNxtDHme4csewdVTXCjt27wFPObecC0yfsdvUK8fBJnrGLkvFFUdrZRCMFzMx48VmumFKKWBLKeyaYP6+GdCnzK4vVlMSY/vbmu/WIoWM19kc45yhVIuQ84WtUaU9g+B2VDhjNIctxcyKDOZ0z2EGcoa4FSIlVlGvgwJqmy1AutnBV7C/A0YVX8T212ItQjHrISs45sshHpqtF8F0ERktHb3PKTuO3ZJCcsjdqZEOYnbCGkpQoM0mtWgCuvqa9Gdyvi4x9Bx7pOfA6mu61yu4Vsh4GHWCx2nWmS+47a1+OuP0/4V9nNZIU4hsXbKzsAjHAaAFlJxOlwpEWeJm6yXRiyKpkPW5dl4YLC2ZnAYoTVHWtD3XqU6KlVFmlfOkwMVqoBT1ijQUbwUMZn27wCqlEDp9uxXgZAu7VdNN19LZ98o1iazHTdbdZQQwUyvFcPZEIbFJRAqJZ9ap+A+Nl0ipTeOQNYPL8NhITE5N49ff9l5wbsaVEPiNt/0BJrducxxgZh1sSzFnK58Hep5ppyRnWBsShl7O9BhhjNm6v16HYJvjOk8X2LV7BSDmEC131Ugi47WnGUDlD9wEcwtBvBbX0csdJzwfeDijTTZPQzjzjE6REXrt1FlM1ZW9YD1wzkSxbWZTH92Bdro7dyyrkUys8dVQ7/mCM5xYqmK+GrX8vpSw+zut+Y3sFpfRRC34MqvY4N1Mty4fFMj6+ngS0jJT9Ll7s70ekCzHsAGLDZrnJJT7cEOfXt7HpgY5ZKR2TRk0ypYJzhLOFC34FFETnCG06uVIGE8uyAhhTLcL83mcqSYf3z3vRDFAxhOm1sjJcrtRU1r0AJspJ1RCiXtPLOKs8ULbNCjAtNniHJdtG8I3959EIDiyPrfnCqXCUjVKGHMkqOZe/2pA9NhON+VKGCHr65rGOBMa1/1wkwUPo1idWiuYcnumwNTSh042vxVo046k2vRRVrpnvRBkv/rJv4BzrrwGfPYwxqd3YWB8q8NaSM4ZG5DiutXKSrJ3ejxrx/r8LQP4zwMzWq9AwWQf+KbKdG8CFvxpBaYXvxVlqKjtI62dtUhTaMttjGMan2R8uowsTSePP0sCmd0Ac/hgaQo7XbbYJIOwr3Ww/lgJK+/4YhXHFyvYPpSzY64SSYyJeMx5y8wZHRznqIbSlp8B9aUQrlDaQNbD5dNDq5o77nkYgKzHkDVsFimTzuwV24d6VjckmfFPrlvrDcE1S+Dhhs2xqvbRRxMYW9vWxcXGt/471VsTtBPu0M+ZzpbS3ygbnEakTMsIRg5dXMMNFmet6T2ic1tn3Mnc0oLsRmrTghaz5RoCT6ASdrYqSQVkPU3T8U0vSrpWzhiOLFSsEx8ZY5Goed3yOTulaiqjXlqTCsXAs7Rioj4lHe/YaSvVIlQiac6lLNVcO92N79tiNcR8OXba6VxRm056L4Oyx6vVBegGIqkwuW07LrrqalRyI5aFAkbsDta4xgwryzhSxJ4zbWQJQ/FTSoFDi/JxsE3hSGQ8rVxdzKxtGUAfMSgItNI2h8zJ/JG4YzvrHwVzaViS8020addR6KYeAe07+lyx0+A7rRJ7vFtYHxsId6ucKdVQa8N7WqrqNnQuFdwmOhwbrBWIXh7RvKH9xDGehJk7gTOAVxusctuP6SAZR8Zz2pU5AYBedbgBJGycRrXv64m871m2wMMJ/Ux3H5saRPdxDXiisAKo6+ucdsK54x1SHVAjW2muXLOLaqRMpttm7vR/yekXLH5PO93O55hbU8Os00e/ha67agy3TuttlIqVyXUNoOvY601ry2DGnD9ZJ3jR1sEuCqm1jwdmSijVJIoZYemVdO8s5dilIRuv+/hi1UbGldJRc2r1FUaq4ep2aK6CrMeR8bTiuZTSyez07mbZDuie9YJyqlTa6KmYjgCuU0x32eMMNRaL+lkDagXPwaWt07Glokxi3NJvMwRWMp5YdWamj85Aa9ZK5o5EkopdjRQCIdrq3hAzMWi/iv/mBocLpp92t8aviX/pf5MWCdOdL2gebQYhtT42BjQylVI4slBBRvBlyx1oHSYnOzKspkTrR85w59EFjBUCjDfQVZFKwRfCdgwgRp3gseNeyHg4d2IAA9nuuDcXTw0iMgePEwDABZMDmCuHuOt4nFDpdYgE63Nj6d0XbB1YkQL+Zkff6e5jU4Pq3ii6CSSF1DhnpuY3XjRdw8UTHDKMP+u2jZkp1XByqYqJQgZHF6qYHMggkkpHWz2iZMdK29apMLUqcRsup2YVcJyM2LHUx4qzJdVIYigI2s5almsRFqoRpIqdaNogYuV0beQVAoGtgxkcmCkBjpBGtwS2uPnR7VI1SzXtLCsFLaIGhx3AYwVzNyPDGUPGFwilNPVl+jiFQKAaSbtJpsGYrseSUDg8X0HWiG4B3TNoNwp0z3qBX05OtjJ91zlgBXDi+83BmLTjTzCGcIWPwFVBB7S4YS2SOLBUxXghY4XaNssz7jvc6wsb/FzB3KFsHaCDpYHHEtm7ViCH3TdBIdIcYECik8ZEMYOhrN9RqVErUMkT4O5DzJYacd4fg300B/lqWrlfIGrCLEt/x2dkG2nWWTEQGMlReYBmOlFAfbwQYLZcw7GFKrYOZPDQXBmRVBjOBTrTbcawVPUOb7ccbiAp0kcJAAoWWEajk+jpZZw1XrQMAM42PtHQy6yAtcLDL7ffx2kFMqaJ3tOo72DWE9g2lAVQv9B4xjhSSml1csTO4qG5MqTSjnDWFza7DZak5sVZPH0tAkgsxPbfLKUa6ziR+tri6yInvl0bcP+pEk4sVaEQC4K5Dio5/AAwXsjYFmJ0bd0EZVzbNV8DEfe4pQ3OZQUIYg8gGahgAHIet8GKchhhSzFoafDS742kQiWSGMr6VmBl09d02zGz8U63dqh1KYYvkmUOtNEKHj9TAEbVfGWDMQ586e/7nGGmVMPWgQy4yRD2s3d9tAJb5dxhAOYqIYayPoayfmJdjZwUulIKxxYqpvwhDgSTAU9I08m7qdCdyHQzBsbjoNRA1jMlGf250kdj0MiIFJBpIT6b/I4uX6DYViWUGMh6yPpC7/U81hUglKoRxosBqpG0+wbncUkHiduu17ru2iWubeUGk3sZwzkf+SDu+NHfD9cfm9vK7ONhD6KVcx474GnH+5Jtg7ZVAdV0x9/XzmEklY3AuiBDR9dJk3habJCQsc+QVCd3s8tg8SZVL6SW7MUKGAVyTm0x2rsPygiM6N8YO65uHTkZd9QWhii53d4sKLvfVqZHKQQes7RKdxNIb2zccZzoPuZ8z9Th6yx+MeOb6LeyNb3Ja4truAPOtFCdcRA3w6a5HDhjPUEvJ1HD0PQwtrX5jt4CA7O0byCevyuBLeegTLeZA4HHUfB1jbe3jsZZH5sPgjUW0WwXCppxtKUYmNaT+v1IKtx1fAEzpRoAYL4SYqYcYrZUw/2nliy93BU1CjzeVTp5Gu65yGnIegJ7dwyjGHhGWXhNTt3HaQA30+07QmitQIwO6qwSSoWsx21ygZsR6SZFIhUnRgRjuj2rk0wgm20lwq0rgasx49pQbjB5M2GjM90PR/Sd7hQ+9KEPYffu3chms9i7dy9uu+22jb6kPlpAEM2HMt1OZrkRqKab/k5GeKScmm6zdvtC02KlArI+t2qy5OQDLr08fk20Izcjaym0TjYv7SjQ4h1SL0rWmSiWbyLOLn2dMsX63NrJ0eIfcXCi25vFcv2VXVQjaTPdCkmqvd3YeBzQsL/L3Kuxgo9CoGnmPmfIBwKeUVb96bFF3HV8EQ+cKiE0Qi9U4071ZLYvLj89HDKXkrpRUEpZBzeUyvZW584zBeIaUhcrNQLi7IN+7XMa8xxnjud1oImfHoGVPtYG7VLCW0FB1+S7a0mV1MxrkXmtkDNtkrIe1WrzRKZ7OOfjvC0Da6bM7ZJ66LzuPsH55mmv18f6g0I25TDSLI029k6t82Fca6b72VO7MEpOaLZFnKSQpl+8gg66+4mEQhyYX6+has8Jl10Vs/s2GwTHqlqp9dE5+nfbwWc/+1m85jWvwZvf/Gb813/9Fx7zmMfgiU98Ih544IGNvrQ+moAWbFcBXC+AjZdAxpLiYW7ElpzgI4cP4ru334a544ctRdZjDOdMFCylibLKdC63Po7E20hJ3aWUu/13044COfz3nyrpdmNoX9iHMV0PpZAOAPBYWI3pbB9dW5wBb+8cnUD/luUvPpJKXyNLKlenKVtuFtSt7z5rvICMJ1CNdG1ZzteRdAXNTgg8gZwvUImk7dlMgY24zndtMv4bASEaq++vJ+IAFrMBpDg7ET/jLcVMXU/1FTvdSNbVUalARjBMFDOIy09W+qv6ON0hWFwSc3i+gntPLLb1vfSQoiAeoRpJ21ECML2JfWEdiYLvGcclGSwuZtZOcufs8SLO21IEEO9XFOAVTJdI9adKH83AGFCJFObKYV0/+WYgfQ/AMLKgEJgAlbWDzFh0D8eY7kThsTiY6jrcbiB3rZHIcvNU6eAmNCDOnRjAOROFjb6MhxX6QmoO3v/+9+Omm27CS17yEgDABz7wAdx666348Ic/jHe9610bfHUPPxw4cAC333477r77btxzzz1YXFxEoVDA+Pg4jh8/jsVFbRTNRQLDo6P4ZLiEhcVFHJ2vYGigiHN3bUt8jr578PBR1ColKClRiwAUx1BemkN5cQnHjx7C/9zxH/YazrzgMly09yo8+ZnPwWPPfLymP7FYZZY5iy4Q15ZrNU0VR0Hp7yn1SHfDIGpw1uMYzfuYK4dt88upZlXJpJKuG2AAc2ugkvSobkMwhmobn9NON6AUQyjj+8TArGAJ0Jh2nog0M5jaXQ7BuY2QTw1msVAJIaXbTxemLzc9K+60m9vcEIyhGmmRuMlisCGUt0hqBX0G3dOeSjq0Qx0bVNuHc5g2WgsAsHUwg5lS2PCYy8GlGurXyZo1N5PXx+kL2jMA4OqrrwYA3H777Thx4gTGxsZw9dVXY/v27Q2/ywWzeh53fOfbuOsH38H11z4O45Nb8YPvfhszp05iaGQUl175CBRGJyG4Zspohg4FcXX5zoEHHsK3/vn/4b6xUey8aC98wfGDO74NuTSHMCjikY/ah5EtW8EBFDPCtOqqX4O+853v4LbbbsNjHvMYTE1Ntf1b2rkfAHDq1Cnc+dO7sf/ISYwOD+Ccndtw8PBRPHD0FAayHraODtn9dmBgAC94wQtWdB3dwmrvx+l2HRsFSm5sG8oiTNkcaZRrEUKpQzoeZ5gcyODQXBlQlABwSvSYTkyQTUKJiTBSVlSVNELI8WVy/fZut4QQSCZ7NiO6KTjXR3vo33GDarWKO+64A294wxsS7z/hCU+wm1Yf64ePf/zjeOlLX7rhWbt7fvQ93POj7+GLn/oY/u1FL8Iv/857rVMNECXcUUtnzDp/UhllZmdBHssHti0WZ8nMHmXrMp7AuRNF3HFgBmGbv58ogtrRj49JmxqBggJ2s8DaCIC0S9WkmjDJ6rPNzHnhtgRxs/RAnOF0M9e1SNfF7xrJ4UdH5iGh4p64jCFUChke99cU4vRxuufKNXiCYyzv12WSO8FMqYZixuu4rjRyWAShlBjNBzhrvIAfHZ6vC264QYHpoRymh1Z2rSw1l2geJlkfbE0CTH30BtrZMxhj+NjHPoabbrqp7m9UO/rW170SX/nbvwIA/MX7f6/hMV7yO7+Pxz71uTh/ckB/VzBIAAEDPvOpT+LVr3rFstfx6re/Dz/z9OdheiiLWqQSjCgAePGLX4xPfvKTK/otwNrsoR/84Ac7vo5uYbX343S7jo0E7dW6NEyPL80kq19f7zu5pBkdgcBYIcBg1tOfcxiBNmNM2jhmLVdKv6fbtHL4nCMQZp8Xrn2wfk07KNECxLTy/r7SR7voO90Gx48fRxRFmJycTLw/OTmJw4cPN/xOpVJBpVKxr+fm5gAAtVoNtVrNvl+r1RCGYeK9PprjwIEDPeFwp/HJT34S+57yXEydfTFkFKJWY5BRDTIMEYUhapCQUQ0siiCjEFEYQUUhFOf22e8c0oJudixEIaJQmTFSgwpDeJ6CBwlEEWq1EFEbfZSYjCCjCFGoEIU11GoeamEVTOprU5LpazHXK8H1vxkQmr93EyrSY365a6/WagiYAKT+vXqeMERhDUpG9j6pqAap9H2LpEIU1eBB39eoFkKFIVTEEYYhZBSiUqkiF3BkuAKiEKFUOLJUBuMMMlKoVWuQQkFGIWRYA4tCc9+60zZto6Ck/v2+8HDo1CI4A6acbHK7iKTCQzNLGAhi5f8orCEK9X1KQyqFeaPcXKuFgFSIFKBkBGXWPv1MQ4S1Gmq8+3NbOXNJhjUgjMwc0udGVNPnrp1e215/f2l/z1BK4eUvfzmuu+66umykjEL86L++bR3uVsf4+Dtfjwuuuhon8mcgy4EAEioMceL44WUdbjrGH731N3HxIx6NaOcgokiCyRDSPMfvfOc7LR275X7Leu6hra6jW1jt/XCxmvnSzevYzIjCGiAjFD2gkPHx0MkF1Gq1huVBQkkwyRDVFGRYQxRqu4lHCpDaZpHGVpJRCETM2C3GVolqqFZrCDICgnOoKASLQjApUKvVwACMZwUmCt66rIEqCqGYsUUium512q6//f2lPbR7f04v66MLSEfqmkXvAOBd73oX3va2t9W9f/z48YQzHoYhTp06BQDwvP4tXw7f/e53e87hJvzXN/8V41t3YvakQsUXWJydR6Ua4sQJnVktzc2jHEocO6b7eVfmZyAFx7FjjftYlufnEHKGY5kaZks1VOZnUYt8nDgBlObnsFSJECzjJEQSCEtVSMZQCSPMnJTwqwEWTi2gEiqcOKHPvTAzj0olxKmTWnStPD8DBuDE8eZjfKVYnJ1DqRxivtZaCGhxqYZswUe5EqEcRjh1QqGW8bAwewrVhXkcP34cnudhcXYeAHDsWAilFMpzM4iEwLFjErPzFVQX51BhGRw7plCaXcLS3CJE1sPcKaA8P4+FaoRqGCHneeABx+JiFZmajxPHFeYWqqgtLmDulISorI1w0XphaXYJtYV5qKyPk+UaGGMoqlzHxwkjBbVUQanMMK8yALRTUlqY05Q/kRyTp5ZqmC3XMD2UQyWMbCYgXKphbkbimFzE3MwSqgsVnDwRYalLPeFdlObmEXkMx4Iq5syYWJit4Zgoo1SLUJ6fxwyvIBNmun7ujUR/f+lsz4iiCHfccQcymeQ4KM0u4gff+te2jiGlxMGf/ghTIwPYOpBBtKTX7v0/+Z+2r0NKifvv/CFmzplCKCUq8ws4lalBVALceuutq/ot672HNruObmG198PFauZLN69jM2N2qYbqwiwWZmtgGQ+VhVnMR369060AVaogUgqSMczPhFAlD9W5OYRKYvakRNkTqCzMoLY0j6ovIH0P1XKEeVZCeb6CRZXB0mwZfs4D8wUWVQmVhSWUpI9jx3Q5UgBg9tTCuvz20twsGBiOHZOohlK/DprbeJsd/f2lPczPz7f1uf4dNBgfH4cQoi6rffTo0brsN+GNb3wjXve619nXc3Nz2LFjB8bHxzE4OGjfpwjI+Pg4fH9zG/XrgSuvvFLXJ/eg4/3Ix/0MCsMj2LJlFBlPYKDsQVVCTIyPwRMcByo+MpHExMQopFTIzzNkPIGJidGGxxssexCMYWJiGGKpitw8x0AxwMTEKAZKAlEpxEAhaHlNC5UQQ1mFUEqgKjE6NoKJwSzmWR4z5SomJkbsuVQ5xJaJMfiCoTCnqV1btox3/T4NVjxUF6oYKLY2OhZYGeNjBWCpAlWJMD4+hoGsh+KiTn7TnCkMjUApoGDEhYrzHIHHMTExijAoIzsvMDiSwcTEKGbUPNiSwMBgBpNbxnGg4mPmZBmToz5qkUIhEJhRZQwOZ7FlyxiiTBnFWoDRsSGML3O9vY45toh8yUch76OyVAUDw8BwvuPjLFUjDIgaOAMGnEy3UkBxaATCS65jC6yMbBChMJQDqiEyHodUwCKvYnR0BBMjOZyU8yiJMiYmRpFdA6d7qOIh73uYmBhAGJSRWxAYHiliYmIAi5UQg1Ufw0N5TIx0HoToZfT3l872DCEE9u7di4mJicT7J2UWFz3qscCfvnfZY3DOsfPci+AXhrFlooiRfIDcPMeeCy5u+zo459h93kUYHx9HNZIoVHwMjwxgYiiLG264AW9/+9tX/FvWew9tdh3dwmrvh4vVzJduXsdmhpqvoLDIMTo6hKGcj/wCRz7r1/WSr4YSRa+GaqTAAYyODWMo5yM3zxFKibHxMWR9jtwpiVBK5AdHkAl8REGIwlAGOVbB4FAGh8MlFAYCDGY8jA9kcTSax9BABhMTK6xHWgWKi1r8bWJiBNVQojDPkQ+a23ibHf39pT20G2DrO90GQRBg7969+NrXvoZnPOMZ9v2vfe1reNrTntbwO5lMpuGN9n2/bnB6ntfw/T7qsXv3bnzsYx/rOYr5i170Ilxx1SMxs1RDNgh0+y0/gBcxZIJAC314PrKess858OlzjZ97PpvBZDED3/cR+ArC9+H5gR4rQQBWRZ2Dk0a1LDGQ97BQiVBFhCDQ3z97cgg1KeF72sGha81mAi1IEgTm/e6PycAPwIVa9tqVCFHIZTBbU/BliCDw4fseztwyhEOqYufMUHo+OffVDyII30Mm0Pcxk8mACw+BHyCbCZDJZCB5FflsBuVQwvMEFK8hEwT2+0EQIJMJNv389H0fmWwA3/fg+5qps9wzaAQVApmsFuYTno+TS1Ucn6thGBzC8+uOybwIPgSE50OFQDEfoFSNIHyFIKOfoef7CAKln7HXfaf7gqlRZH3dEi8IIggzFnzfhy8ZAj8D7zRdgx/u+0ujPYPYO+4ewjnHRz/6UezevbvuGJlMgHMvvRJPeuaN+PvPfabpuRhj+I23vRdbpneAexz5bAZB4EF4Prbt2IWPfPSjeMXLX75sTffLfuc9mNqxC5lMABZKZPzAjs+rr74aL3rRi1pSmVv9lnbvRzfQ6jq6hdXejzRWOl+6fR2bFUEg4QUBgoyPTODD9wIwT0B4AuUwQiWUGMr6UDJCNsOhqqHWPAgCBL4Hzw8AKfX+7HH4vgfheXofzwQQNYBxD74v4XkBGK/A83zsGh/AaN7HPTMVBP7G7Ne+H+h2l74PcKn3Nk+c1mvvw31/aQft3pu+0+3gda97HV74whfiyiuvxL59+3DLLbfggQcewCte8YqNvrSHHW666SbccMMN+Na3voW7774b9957r1UvHxsbw4kTJxKq5O57JxbLyOULOG/XdMvPRVEEyTiQH4GolcCjCpaUQGZsGoMZgXDmMPYfPoXM0Bhe/ssvxDOufyx+8NAcAtNbkuAK4Fw8NYjIMWqWU7a8dFscqSVlbdIHEW2KkVUjhZzvYbEqE8JonDNkeNK58Zjb3mztxD84Q9v9orM+tyrqdG0j+QBhi6wzCa8AsTo8CX6RSByJsfj2WTFkhBZiAXRrH4Jgp0efbs6YfcaCAeEK7Wvqr11VmjI3Vw4xVvBRmW98QAXqp6pQDRWGsz6qNZW4r1rMbO3us6vEqtXMky2YXGG1Pk4/uHsGAOzbtw8A8K1vfcsqS+/bt69pna3gDAvVCM9/0+/jhhtfjO99599x7WOu0erld3wHs6dOYGhkFGdetBfDW6YwU9IZIE/EbSEZA2666SV41DXX4s//5osoDgzgvMsfidG8j6/f9k2I8jyibBGPvebRWPRHbPcFUmJWzqr5f/7P/8GrXvUqfPOb38SjH/1oTE1Ntf1b2rkfgFYvv/fee3Fidg5DxSImt0zg2LHjuP/YSfhcYPvEsN0zBwYG8LznPa/j6+gWVns/Trfr2EhoO8VREGexkNn+kyV4nGEo69vuJJwzSKNyTqKvHE6Pa7P/ZwRH1gRky6E0bUCF7fKydSBj+31v2G9nSNpQp4nt0Mf6oO90O3juc5+LEydO4O1vfzsOHTqEiy66CH//93+PXbt2bfSlPSyxfft2POc5z+n4e/9zaA7jhQBbB1sLSNVqNew/eAj3lTyct3UYO0fy+K8DM/jh4XnsGSvgqp3D+MY9J3BssYLLd2nq0ORABgdmZKIG2m0hkaZX8Q4WZDLa6FiCsbYcV6WAvM+1w43W/VU94ba7WDvFT86WP3hoeqPnfGFU4Os1FZrB7S9OBq/g8e/iTqs0930h4n655KQzAIJv3CbeTSjovtikyh6t8PlGUiHnc1TsM2RgTFmnoFSLwBmzfVeVMsEipa9hIOPhxGIVLNUGiQJLaw0y7uhsGU+3hRvO9SP1pzMa7Rnt7iG0BmWEwAWXXoFd512C7cO6FOH6J0/bzz0wU8Jg1sNiVdeT+pzbYCcpMU9vn8bVP/tzGB0dBfd8DGV9PPoJT8FiVYsNTg1m8ZOjC87+oBruE1dddRWuuuqqjn/Lau/Htx84hbwvcNHUYMO/r2Rf7gZWez9Ot+vYKFA7RnfcR1Lr2AxkPNSk3idCKXWLL8bAzRinzhbMzhdzHDCM5gMUcwEenCmhHEoUAnK6nRah0AHhjfJzBY/Vyq3N1fe5+2gTfac7hVe+8pV45StfudGX0ccq0MxQaAQGBp/zRK9sQDvPFIHlTgZ2ciCDyYFkBrZV0LUTgTJqP0bOovZbl/eaFBQCT2eLqRd3MwgWXywDA9jaeN28jYBBNZTIeBw5XyRamLV3fDeDqjc+60Snnhm9HwiO7cM5HF/QIocuW6HTtli9ikgqeGY8C87A5cqebzmUGMkHulc8yNCI4yj3nypBKWVbJimlAzrUqi3nC936BXFWgIys9egdzlPOvic49u4YXvPz9rF5QY5APhA6gMS0Kn/aGda9tT0o6MCe22ObHAMK93DGEIh4fwmEgMe5bXfEnHWsl9Ygj+usYh99NAJjxByKnWil9HwRPGZYRQrIOr24ubFPBGOIWBww54yBceCM0TxKkZ4HlVBi+3AudtDt/s42dHyeOVaIExd8ffazPk4f9J3uPh728HhMAefWQYuNKKB1trqVq+jSoJcDUa3I9mqXXq6vl1uaU7PTeSJJXxScIVojwU3OGJbz97SDyOCb+09GarvHJ/iCJwxWcuxEikbuC4bJgQxOLVX161Qf8NMBUsFkuinT2zlqkTS96LURRXB7r3sU4TFQUOBMi+NQH3s3mwEAOU9gjtfWKdMdB6/66KMd6DHjBAFhxrszjCKpOz1kTJBTcDjjPckiYtDf9U1WjgEQgiHn637DnMMGpYiy2isSJldsH97oS+ijxyFYHFhiDJBQkCrORkulcGqphsGsZzLacQBLl38p64zT+Lc9u6H3npzPbRDdXc83MoA6kk8K2xJDsY8+2kHf6e7jYQ9fMHiGXkwLe9bjCfpTK2ewlcNGm0g7sOczX9A1shpKKSxWIxSNerdSCqFUxqAz2RS0dlzP3zKAmuNlr+VmoWu6W1uQEsq5x50FKDzObLYoEAy+4AiNl2+Ph5i94DIIPGMEE808HwgIDlvrvZkhlQ5kcKYZHCXWeVSlEkrkfIFC4NknSNR/es2doAZgMt2Gzs5NJkKzDeLxv304i7GC35KJ0S1QRmW5MdhHHwTGgIzHTBCWNdSliKSCJ3TtaczOYXF9q8PqoHz31GAGHue47+QSBAOyvtBOOrSzzhhDIRDYOpDtZ5f72BRQyslSm8x1KBWUUuCmTGOuHOp91gSlQhnbUYLrVqfWCYfjeJs5EUll5xWAtu2o9YarRdNHH8vh9Chk7KOPFSIfCOwYzmNLUUcvPSO2JDh3RG5aO4OtFlwSCGsHjCWde5350Gbf0YUqDs6VrdN8dKGKe04smrpo2DreVsJtgcdtyy06/loJgHiCN1VSmy3XcGCmZOqAWUwF7uB6Lt02iIu26jKCQHBkPNfppjpt2NeUjQL0OYVTUjCY9XHptiHkg80fg9SZbv3b8gFHu3EEpXQ9HqCd7qwXC9rov+s5QOwFzoCAM4ROEEeY12RkUTbQzW6s1z3WjlDvZA776H0QFVwYJ4Ix1A2g0DgCemy7Na0x3RYw/zXv7xzJwzMBVMEYxgsBCoGeWxTsZYxhx0jOBgL76KPX4Q5VwRmkokw3saIUxgoB8oFXZ0fZZIajt8IAhzGi9yStQVMf5O0ldGLj9dFHf4Xv42GPLQOZWLzMOttxlhQtMt1jhaClo7h9OIfpodaCbgRruJnXgnPrty5WQ+R8YW3ApVoEwbl1cnxDL3fpvMuBostrAYbm6uUPzZaxWIuMsml8AURXaweeiBXkPcERCG6dRl0flnyGZBwD2ljweDI67Z8mxu5o3ofHGQKPJZzm5XBkvoK7ji8AAKqRxGDWq9MqcO8X40l2AaCfOQWBBGPwBLf15esNCrT00Ue7kErZUhU99utLZKgkxg0oMQrquZluJJlEll7OdYkLOR096kf00UdLKKN1QMPX43qukAYCZ7qe2+MM528p1jFB0toeRCG3gmnmTfoe/b0X0Vcu76MTbP7UTh99dBGMIWE8kXPWzH4/YzSPM0bzTY83tYyCeuLcqboldy2vSYVCJnbCiUashat4nOlGa/VyF1sHM6itVN56GSy3EXkmoFBfi72y8wUew6BpGaWp5fE1UAaKUwkBZ5aCfbphajCLqcEspFQ4sVQ1jAK1rNjLYi1C1hMII4lQKgxkfHt/9PcBgFnmhVZsZXF7PDPwqOSBm3sciI2h3hF7op/o7qNdRFIr/28dyEJB4dhitZ5ebtZdWm+B2IFwA57xWkz02bhLAmmIcAbwvtfdxyaEQtJxFkJnujXtXO8NoVTIc8NksyUbcWY7EcSFDpQDcSmTx/RcC42OQq/GxfvB3T46Qd/p7qMPB1b12dlMlmvD1S1QxsRSFJ2zZjwBjrhGVSl9bdVIwhOxAnsnjuv0UK7rv4Gw3DUQHc2lU7q/vVNctm3IcbKTBjBRjd0oO1GgT1dwHteLprSgGiIQHL7gKIeaLh6XK8DWaXMGSOiAjw5OxexbDj1PQuO4APp5Bh5flxruNHSme91P28cmBvWmnxzIoBZJu97WIokDs2XkfAGP6/prmluU0QYQU9Lhtn+k18zOGRvwYwzeaRj46+P0hy84BIdd6z2mRQetTgsYqlJasdJYt8V9nQy4J7Pgeg/zBIdUsiMG33qjRy+rjx5F3yzpow8HREults2CavPWRfypvqYb0K21sh7VGOr3SLiqJhUyQtjMIh1no+HerrlyDXcdW7SvmaFVShUriBPDYKXwBHfqw5LBByvOYlxP3eaHn/biJ+QQtFPX7HGGrM9RjSQYYNoc6aNoKqEZV6ZujxwMynRTdiIyjgvMMYIN8nxpDLTTcq+PPgDdBkyLajIbuCtVI9x9fBGjeR/lUJfEUD031agmHQl9LJp7tOZQ3aer7N8vgehjs2I45+O8LQMYzPoA4hpuV2AtlNLWYXs8WeIFwAqhAsm5ErNC4pINgd6waxpBsJhl10cfy6E/UvrowwEpUdpMN4tp22t/7mRdM+0xlVAi4wlEUtkaQwVNvaqEElk/dnIEX7s67U5Av6Jci3B4vpLYMKmVlUzVdPMu+Wdp6pprINP5SaH4dAaNW5Xue9QAFMRZqElwQ5+tRbBOdkw1h6Wrc+NkExjiulgA2DqQsX3Y1xvEWOmjj3YxOZDBeCHQDnek3ztVqiHrC2Q8rnsLS6VZR4mgXszssI4DsWxSmW5G/0ZMN++jj82IoiPKSp1WpFLwWKw+Hme6kwGqPWN5lGrpTirxnLG964VRPe/htfyK7UOnvS3RR/fQX/H76MMB50jU+8Y13Wu/qGpKlfPaGHCVSJoWZjG9nCLDtUhap6aXapUZA6qRwslSzWaHbD0wbcqmPhLQtcjd6qtM/XYt9Zklgxl0n073LCjRW9v5lRKkESDhc24z3Qq61pUbdoCEQuRkvqVSmC3XQOrzQFynzznDWCFoftI1BD3zfLD+Dn8fmxcUqCHDPxAchUBYxoaEikXUEGfl9HdjQUZuHWz9N/qc7eNNJTDr9cP66GMNIayQWhzkjmQcVNJ7cuw8j+QDbHMEZt15lOjlTczDjTdpmqLvcPfRCfqZ7j76cBD35Tav+fJ9ursF6iU9kjeULWORlcMI2wazKIcVxLpVROHSRiAAK/DTC1sA3cOpwYzJgMLQkmGz8VEU13STAFh3zp0sB2AsGSln0BH009vlpjHC2m6bxZkWrRFc90DnkX5O85UQwzkfeR+Ainu0cqaf6bH5CoZzfoJKu9FgjOGiqYENo7f3sblB62ukFAqBZ51pYoQQ00Mhdqwvnx52WErUpZtex06EPr6ZQ70wWfroY5UQDKiEERiAgaywc8d3xrfXQjRQM5P0v925Qs66YP11vI/TA/2R3EcfDojyx9Kv18k2unx6yDqfZLIpBQxkvYQz7YpkufTGjWrRlAYzju9Axjdt1eKMqzBZIOrT3W24G7h+jTpVeI/ztp3RzQrKtsk2wgtK6fEWSWWzfOQYLNUibB/OWsX5uC2MdtJzvkgo/vcKZTbjiX4Woo8Vwc4dBWQ8lggIxtlwlsheC54K7jkMKXLiXUp6L6zTffTRDQjOUZMKS7XIimqqlFCq12JfOGu8iJ0jWtiVSvyIPEU6COn2fX30sRnRG9ZRH330EASPM6XUqmK9EhJpo436whYDATDt8FCtVFwnpT+/cySHiWLQE9kTzhgEgKzHsX0oZwS9lKUqM7OjrsWlMuNk0x5tRbXM66ynVYiLmdObekwlCO2m9JkZX0T1o2x2MfAwkov70S9VI3AGZDzdJ97num8xZSj6vkQfmx20Pun1Nym6GAtWtqaWun+zdHPLBmFrEnDso4+NAAWyx4sBAGb3eBrjE8Ug0R40jYGsh0IQt/xkiNt+aqcbtrSujz42M/r08j76SEE4WQi9mWxMVoLayhQCgcGsr7PDAEq1CPnAMxtT7OTkfIE9Y4V1v85GoAg1ZwzDOR9aBVtnXYleThnTbqNhOxIeq3gHHn9YiJ+QA9yuqcIMXTym92nDKetzFDPCsBUUyqHC2ZNZVCOlW4QZsTTdDz3Z6q6PPjYjyOinoCcJpxG9HDBzpMXs+v/bu/sYqar7j+Ofe+887TMsyD4AIgKl1aKtplEI5UGB0tha/9A0YlAbWqupUtOGtNGk0DaNFhHTYIjSP2htDakJEE1riCYgtClQq2ugNGBLpRUXxB8PuwsIy+6c3x937p07+ziwe2fu7LxfyUZn5nLn7H7nzMz3nnO+J/ge5PWL4OdKVIpeAkPlJcfVmcTZ2wEmOyBQqatHV+Z1ruwFeXfmiEln1oynB/+3QNQx0g304FXXljLrf4v05ciduijVpuL+vsnGSGc7u1WbivWqnhslfgEUOzsa7xXlcizLvVgga9gqluc+t/sFwCuU5o18B430hFvKFm5K5zmP3s7MRrCUrQDvXdTxEviubqky4Wji6EolnMwacMvS1aMr/G3EIjDRAhgSO7N8J21MYJsvK2cddnBHhD7PEXjMr15uZR+LyCoMYMjcJVzuZ8Pk+kq3v+jKBiu8C17elzBv2Rwj3RgJeNsHAoJrpCWvuFlx1klblttBvcI9ltyktbM7rebalD8FOIr5ozstP7CfbWa/525jspV7rXAuGHhruE3gtju9vLw+tLNbhuV3vJWTYGf3KvYuWHgVyivj7sh2zLHVlXa3D6uvjOck6UCp817HwR0sgu9Zbv8aYHp54DHvAph3zuDUW6DUeXvQj6tOalxNMttfriDD8C54Bb8buLuNDGODgSIh6QZ6CK49ituWYs5guxyHw0uavMI9tm3p5PlOja1KaFxNwk+4ozjS7U6fzP2y6Y9029mDwmi71ePDvmdhtXIRHHXOf3u0QOX+wDRz73zezA/3cS8p77Ekg+nlGAG8pDjmBC4eWoEK5Jcx0p0tTJh9jH3kMVLYdu7WXrFM8dkr/XwP9hXJ/S6Rio/sGiwoD6zpBgJ6vtlPGFWhmlQspwpnwdpi5Y6I2Jnq0mOrEkrGHL+CeRS/ulmSYla2fd7WVWmTWZ/lj3SH8/yOZemqzB7RfiG1MrtS7o1Mf3KuUx+e6dZnx1UPOq3eXYeXTaqDyYF/kScwvTZ4jLcGnGmzGAkcf8Qt+/qWTKB/SGaA/jS6Mq7xmXWsPWf2UL0cI03wwmtwW70r4W2557muge0fMTKQdAMBV1Un1ZU2qk1lC4LUVyaK0hY3Wc0mOd5njrfdZc/pjlFiFEzOrMz2O2718kTMnZbsVScNw80TR/n/H9XZAGHz1sZduNQtx7F06JNzSsZsTa7PLWjT1Z32X2veNEHv3wdHy70vQsHp5t5MDMuy/JGOaF4GAi6PrexFqNyE2X3cXY7R/5W8ptqUxmQu/Hl9yROcMQKUOq/AYHY7vaFdVO/5mV2ZIFXByMClIyDAsS1NGFURiUJbXkGw7DpA97/ePsje9O0ofnfz9uD2k7lMG7uN8QvTBadqhslNDAeeCjoS2YGq+zHLVsy2dOFSd6/jPjnXqVEVcaViTo8psFbOhZHsRaDA45aleOZqkLf/ehRfj8Dlsh2vGFRmFM+Wf3FJcneLyHf0zZuxFNwyjH6CkSQ4vdyx7SFdVO95kQoYKbh8BESUV8TK++xxbLtXMR4veY0aY7Jbg3kfoMa4a7pTMUefXuou2Ch9zLE1cVSlxlTGQ3+uqPFmG8Qcd8uVrsC2Kxe6uhWzbXWnjRpqkqpJxfwCat6/DS6r8PdPDYz42ZZ7bvc5bL8IDlDqHCs7Rdax3aTZWNnp5dc11uRdK8FSti8q8//1lQk11SbDaDpQUMYEl2AE64Fc2fmC33OAkYSkG4gob6qvlfNBlpv0WIrmiEkq7ihmW+7+zZkLBxe70+o20tiqhM582jmkNV+Xq6GmPL/c+sWgbEtuvu3eThujD89cUCKzFV0y5vivpWBV5eD/e1+EHMtNxG3LkmVbSngzL/zRPKD0OYFp5dl9603Oe1a+F5gs/zzZ+6aMrRruJgNFYTK1DrJLL7KJ95VgxhRGKqaXAxHVc02UV6jEuytm20P6YAtTdTKmG5prVZ2M+V84L1zqVnXCUWNtMrBOstgtHdm8geqYbSnmZIucnbvYrVEpd+TfstzjvJHsYKLtjWJL2Ys8VuC2LfnTy70RQWKKkaDnEhh37+Ere89iizCMZN6a7uB3kaF8Fgy2HR9QqhjpBiLKTYayHz3eVG1vpCXh2AVbF30lvGTMq2B+oSuthhrHvd/y9vGMaONHiFgmy47ZttLGyM68mi6l00rGLF3qttWdWQrgjVwHc4MxlQnVV3rJeXZ/dSm7ZtxLzG279xcvoFQFR7qzt6/89U1Vf4xUccf2L9x6hlKhn76CkYqkG4gov/iON9KS+cJnBUZeYnZpXBG2bUuXutOqzYyu+r9Lkds10mX3TZXSsvyKyWnjrsFOqyvzeGZfVeWOdAenwPZcg+8X+svczi6HCPVXAgrCKyToFU5z7KHNKrItqTLBXsMYeZpqk6pOOkrGsq/v2BBGuhOOrSr6CkYgkm4gorz9Yb20xktw/JEX21Lcie5Id5CTGdX2vnTGHVt2V7ost/IqJC8JdmxbCTubQHSnjVsYynhTyrMzEvqLSc+RC9vKHdl214OLCykYEXpOL/cuMl2p6xpqcpISYKSwLMu/oO4JFla7XF8YXzcczQIih6QbiChjcovvxBxvDbd727bc5LUU8lZ/251MWxOOPaQr4chPLFPx3rEtJWPZP3Y6M6W82xglHDtnr22jvisy9yxu400tzy5/sPzt7ICRwLFzL0INZW9t9hpGuTAa2vRyYKTiUwCIKHdkRUrGAtWhA1ePHcsa0pfAQrIDSZ0kxR1LyZjNh3LInMyU8FQs9+KMu45bSqclO2b5Rflitq3ufnZBsm0rZ61ddqTbvW3JHd1I57eLEhBpPROH8XUptV/sKm6jgBJBUU2gN5JuIKISMVufHVfjr21y7Nx1UhVxJzO9PPqfbO52UtlRo6balC6eOk8htZA5jntho6EmqYRj63+nP5WkzLRyyx3xzsxAiDu24o6l7n6yZkvyC7FJmZFu2/aXP3hJeL57FwNR5u49bPvvt421KTUWt0lAyXAY6QZ6IekGIqw6me2ijp1buGpcTVJVCUepePTXCcYdtzK2V6RrTFVCY6oSRW7VyOfVBfCWKViZrds+7er2j7EsdxTb21asv6S7r+mCwZFur4LtUNa9AlFhJF7PwBVyCw8WuxVAtJB0AyXCG1kMjg5XJUujC6dithyxL3eh2f4Mg8wsA2N05PSnqk1lXzd+ATXbUnyANdlucp69Hc+syx+buXhSVxHXZ66q7lVQByhFxp8FwpsWcDkmja7QibOdbB8J9FAa39gByJK79VMpfozZtq2YM7Tqv7gyMTu7lZdluWvp6zKJsd2jYnnc6X/Lr54j3Y5t6eaJo3KOIeHGSOGOdLMuFbhclYmYrqknvQB6olcAJcLOVIcuxRWz7gUDCqcV2tmLXapJxlSdjOliV1qW5Rbmq0zYmUJ2yqmI/7mGGsWdvmM00HZiwEhEBWYAwHBhfxegRFh+oapit+TyWZY7ispId2GNrUqoJhVTRdzJJtiSpo2t1lXVycxmxNlkujoZ63cvYTsz6ldRAjUEgKEyJlO4kvcsAMAwYKQbKBF2ZsutdAlm3bZlKe7YTNUssMbalBprU5KUqTLuTTPP7rttK/8lC1PGVKl5dEVIrQWiw7KoQQEAGD4k3UCJqEo4itmWv4VYKbEkxW2bwipF5I10G7kJuDfqbVn5j+YlYsQQ5WHa2Cp1sC83AGCYkHQDJSIVd3oVrioVtamY/u9cp2IMHRWNlZlGnjbGTcDt7Kg3gFypeGlsxwgAKA0k3QBCN7oyoZsq4oySFpG3btvyppjLG/EmJgAAAGGikBqAgiDhLi5vpNvbOsytzFyaW9ABAACUEpJuACgDXsItZbf/sjI/AAAACA9JNwCUAbdoWjb5jjmWYrbFSDcAAEDIWNMNAGXAtiw/8bZkKWZn9iFmpBsAACBUJN0AUAayxdPcrcMc2933nZwbAAAgXEwvB4Ay4BVPU6aQmmWRdAMAABQCSTcAlIvAaLckOZkK5gAAAAgP08sBoEzYliVjyR/ddmxLlFIDAAAIF0k3AJQJb5Tb2yasuTYlm/lOAAAAoSLpBoAyYVmWZBn/9sTRFUVsDQAAQHlgjAMAyoQ70s10cgAAgEIi6QaAMmFbVCsHAAAoNJJuACgT3l7dAAAAKBySbgAoE5aVLaIGAACAwiDpBoAyEdyjGwAAAIVB0g0AZYI13QAAAIVH0g0AZcQm6wYAACgokm4AKCM2OTcAAEBBxYrdAABAYYytSujTS93FbgYAAEBZIekGgDIxqb6y2E0AAAAoO0wvBwAAAAAgJCTdAAAAAACEhKQbAAAAAICQkHQDAAAAABASkm4AAAAAAEJC0g0AAAAAQEhIugEAAAAACAlJNwAAAAAAISHpBgAAAAAgJCTdAAAAAACEhKQbAAAAAICQkHQDAAAAABASkm4AAAAAAEJC0g0AAAAAQEhIugEAAAAACAlJNwAAAAAAISHpBgAAAAAgJCTdAAAAAACEhKQbAAAAAICQkHQDAAAAABASkm4AAAAAAEISK3YDRhJjjCSpvb095/5Lly6po6NDyWRS8Xi8GE1DH4hLdBGb6CEm0URcooeYRBexiR5iEl3EJj9e3uflgf0h6R5GHR0dkqSJEycWuSUAAAAAgELo6OhQXV1dv49bZrC0HHlLp9NqbW1VTU2NLMvy729vb9fEiRP14Ycfqra2togtRBBxiS5iEz3EJJqIS/QQk+giNtFDTKKL2OTHGKOOjg41NzfLtvtfuc1I9zCybVsTJkzo9/Ha2lpetBFEXKKL2EQPMYkm4hI9xCS6iE30EJPoIjaDG2iE20MhNQAAAAAAQkLSDQAAAABASEi6CyCZTGrlypVKJpPFbgoCiEt0EZvoISbRRFyih5hEF7GJHmISXcRmeFFIDQAAAACAkDDSDQAAAABASEi6AQAAAAAICUk3AAAAAAAhKduk+6mnntKXvvQl1dTUaNy4cbrrrrt06NChnGOMMVq1apWam5tVUVGhefPm6cCBAznHbNiwQfPmzVNtba0sy9KZM2d6Pde7776rhQsXatSoURozZoweeughnT17dsD2XbhwQQ8++KBmzJihWCymu+66q9cxx44d05IlSzR9+nTZtq3HH3/8cv8MkTMccTl16pQee+wxTZ8+XZWVlbr66qu1fPlytbW15Zzn9OnTWrp0qerq6lRXV6elS5f2Gb+e9u/fr7lz56qiokLjx4/Xz372MwVLI7z11luyLKvXz8GDB4f2xymyqMemHPtMIWPyi1/8QrNmzVJlZaVGjRqVdxvLsb9EPS70lfBicuTIES1btkyTJ09WRUWFpkyZopUrV6qzs3PQNpZjX5GiHxv6S7jvYXfeeaeuvvpqpVIpNTU1aenSpWptbR20jfSXaMamHPtLvso26d65c6e+973vac+ePXrzzTfV1dWlRYsW6dy5c/4xq1ev1tq1a/X888/r7bffVmNjoxYuXKiOjg7/mPPnz2vx4sV64okn+nye1tZWLViwQFOnTtXevXu1bds2HThwQA8++OCA7evu7lZFRYWWL1+uBQsW9HnMxYsXddVVV+nJJ5/UjTfeePl/hAgajri0traqtbVVa9as0f79+/Wb3/xG27Zt07Jly3Kea8mSJXrvvfe0bds2bdu2Te+9956WLl06YPva29u1cOFCNTc36+2339a6deu0Zs0arV27ttexhw4d0rFjx/yfadOmDcNfqHiiHpty7DOFjElnZ6fuuecePfLII3m3r1z7S9TjQl8JLyYHDx5UOp3Wiy++qAMHDui5557TCy+80O93BE+59hUp+rGhv4T7HjZ//ny98sorOnTokDZv3qzDhw/r7rvvHrB99JfoxqYc+0veDIwxxpw4ccJIMjt37jTGGJNOp01jY6N5+umn/WMuXLhg6urqzAsvvNDr3+/YscNIMqdPn865/8UXXzTjxo0z3d3d/n0tLS1GkvnXv/6VV9seeOAB841vfGPAY+bOnWu+//3v53W+UjLUuHheeeUVk0gkzKVLl4wxxvzzn/80ksyePXv8Y3bv3m0kmYMHD/Z7nvXr15u6ujpz4cIF/76nnnrKNDc3m3Q6bYzp/7Uw0kQtNkHl2mfCiknQxo0bTV1dXV7tob+4ohaXIPpKeDHxrF692kyePHnA9tBXsqIWmyD6S/gxefXVV41lWaazs7PfY+gvWVGLTVC59pf+lO1Id0/elIr6+npJ0gcffKDjx49r0aJF/jHJZFJz587VX//617zPe/HiRSUSCdl29k9dUVEhSfrLX/4yHE0f0YYrLm1tbaqtrVUsFpMk7d69W3V1dbrlllv8Y2699VbV1dUNeJ7du3dr7ty5OXsWfuUrX1Fra6uOHDmSc+wXv/hFNTU16fbbb9eOHTvy/6VLRNRig/BicqXoL66oxQWFjUlbW5v/PP2hr2RFLTYoXExOnTqll19+WbNmzVI8Hu/3PPSXrKjFBv0j6Za79uEHP/iBZs+erc9//vOSpOPHj0uSGhoaco5taGjwH8vHbbfdpuPHj+uZZ55RZ2enTp8+7U9lOnbs2DD9BiPTcMXl5MmT+vnPf67vfve7/n3Hjx/XuHHjeh07bty4AeN7/PjxPp872LampiZt2LBBmzdv1pYtWzR9+nTdfvvt2rVr12C/csmIYmzKXZgxuVL0l2jGpdwVMiaHDx/WunXr9PDDDw/YJvqKK4qxKXeFiMmPfvQjVVVVacyYMfrf//6nV199dcA20V9cUYwN+kfSLenRRx/Vvn37tGnTpl6PWZaVc9sY0+u+gVx//fX67W9/q2effVaVlZVqbGzUtddeq4aGBjmO4x9TXV2t6upqffWrXx3aLzOCDEdc2tvbdccdd+i6667TypUrBzxHz/P0F5e+njt4//Tp0/Wd73xHN910k2bOnKn169frjjvu0Jo1a/L5tUtCVGNTzsKOyWDoL32LalzKWaFi0traqsWLF+uee+7Rt7/9bf9++kr/ohqbclaImKxYsUItLS1644035DiO7r//fv/1T3/pX1Rjg76V/Ry1xx57TK+99pp27dqlCRMm+Pc3NjZKcq8YNTU1+fefOHGi19WjwSxZskRLlizRxx9/rKqqKlmWpbVr12ry5MmSpNdff12XLl2SlJ16Xu6GIy4dHR1avHixqqurtXXr1pzpMI2Njfr44497Pe8nn3zin6evuDQ2Nva6UnjixAlJva8qBt166636/e9/P/gvXgKiGptyFnZM8kF/6S2qcSlnhYpJa2ur5s+fr5kzZ2rDhg05j9FX+hbV2JSzQsVk7NixGjt2rD7zmc/oc5/7nCZOnKg9e/Zo5syZ9Jd+RDU26F/ZjnQbY/Too49qy5Yt2r59u58AeyZPnqzGxka9+eab/n2dnZ3auXOnZs2adUXP2dDQoOrqav3hD39QKpXSwoULJUmTJk3S1KlTNXXqVI0fP/7Kf6kRYLji0t7erkWLFimRSOi1115TKpXKOc/MmTPV1tamv/3tb/59e/fuVVtbm3+evuIyc+ZM7dq1K2eLkTfeeEPNzc265ppr+v29Wlpact78SlHUY1OOChWTfNBfsqIel3JUyJh89NFHmjdvnm666SZt3Lgxp6aLRF/pKeqxKUfFfA/zRlEvXrwoif7SU9RjgwGEWqYtwh555BFTV1dn3nrrLXPs2DH/5/z58/4xTz/9tKmrqzNbtmwx+/fvN/fee69pamoy7e3t/jHHjh0zLS0t5te//rWRZHbt2mVaWlrMyZMn/WPWrVtn3nnnHXPo0CHz/PPPm4qKCvOrX/1q0DYeOHDAtLS0mK9//etm3rx5pqWlxbS0tOQc49138803myVLlpiWlhZz4MCBof+BimQ44tLe3m5uueUWM2PGDPPvf/875zxdXV3+eRYvXmxuuOEGs3v3brN7924zY8YM87WvfW3A9p05c8Y0NDSYe++91+zfv99s2bLF1NbWmjVr1vjHPPfcc2br1q3m/fffN//4xz/Mj3/8YyPJbN68eZj/WoUV9dgYU359ppAx+e9//2taWlrMT3/6U1NdXe3/HTs6OvptX7n2l6jHxRj6Slgx+eijj8zUqVPNbbfdZo4ePZpzzEDKta8YE/3YGEN/CSsme/fuNevWrTMtLS3myJEjZvv27Wb27NlmypQpOZXJe6K/RDc2xpRff8lX2Sbdkvr82bhxo39MOp02K1euNI2NjSaZTJo5c+aY/fv355xn5cqVg55n6dKlpr6+3iQSCXPDDTeYl156Ka82Tpo0qc9zD/Z7TJo06Ur/LEU3HHHxtono6+eDDz7wjzt58qS57777TE1NjampqTH33XdfXltL7Nu3z3z5y182yWTSNDY2mlWrVvlbVBhjzC9/+UszZcoUk0qlzOjRo83s2bPNn/70p+H48xRVKcSm3PpMIWPywAMP9HnMjh07BmxjOfaXUogLfSWcmGzcuLHfYwZTjn3FmNKIDf0lnJjs27fPzJ8/39TX15tkMmmuueYa8/DDD5ujR48O2kb6S3RjU279JV+WMZm5AgAAAAAAYFiV7ZpuAAAAAADCRtINAAAAAEBISLoBAAAAAAgJSTcAAAAAACEh6QYAAAAAICQk3QAAAAAAhISkGwAAAACAkJB0AwAAAAAQEpJuAAAwJPPmzdPjjz9e7GYAABBJJN0AAAAAAISEpBsAAAAAgJCQdAMAgLydO3dO999/v6qrq9XU1KRnn3025/H169dr2rRpSqVSamho0N13312klgIAEA2xYjcAAACUjhUrVmjHjh3aunWrGhsb9cQTT+idd97RF77wBf3973/X8uXL9bvf/U6zZs3SqVOn9Oc//7nYTQYAoKgsY4wpdiMAAED0nT17VmPGjNFLL72kb37zm5KkU6dOacKECXrooYc0Z84cfetb39LRo0dVU1NT5NYCABANTC8HAAB5OXz4sDo7OzVz5kz/vvr6ek2fPl2StHDhQk2aNEnXXnutli5dqpdfflnnz58vVnMBAIgEkm4AAJCXwSbH1dTU6N1339WmTZvU1NSkn/zkJ7rxxht15syZwjQQAIAIIukGAAB5mTp1quLxuPbs2ePfd/r0ab3//vv+7VgspgULFmj16tXat2+fjhw5ou3btxejuQAARAKF1AAAQF6qq6u1bNkyrVixQmPGjFFDQ4OefPJJ2bZ7Df+Pf/yj/vOf/2jOnDkaPXq0Xn/9daXTaX/6OQAA5YikGwAA5O2ZZ57R2bNndeedd6qmpkY//OEP1dbWJkkaNWqUtmzZolWrVunChQuaNm2aNm3apOuvv77IrQYAoHioXg4AAAAAQEhY0w0AAAAAQEhIugEAAAAACAlJNwAAAAAAISHpBgAAAAAgJCTdAAAAAACEhKQbAAAAAICQkHQDAAAAABASkm4AAAAAAEJC0g0AAAAAQEhIugEAAAAACAlJNwAAAAAAISHpBgAAAAAgJP8PvMZEOQ7bP10AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast = m.predict(future)\n",
    "fig1 = m.plot(forecast)\n",
    "#we can see many outliers and 0 values as predicted in the previous figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "#weekdays: Saturday and Sunday usually the number ofvehicless passing decreases (makes sense as many people uses car for work\n",
    "#, specially paying tolls. If car is for personal use on weekends and tolls can be avoided, this makes sense)\n",
    "#usually the vehicless passing increases in september.\n",
    "#the trend also makes sense because there is a huge peak at the beginning of the analysis (2019). Then it decreases because of \n",
    "#Covid-19 and then it increases again but being stabilised (slightly increasing) with a confidence interval similar in both\n",
    "#positive and negative signs."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}