candy_prediction_gridsearch_lstm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib.dates as mdates\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.utils import shuffle\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
    "from tensorflow.keras.optimizers import Adam, RMSprop\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('candy_production.csv', index_col='observation_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 548 entries, 1972-01-01 to 2017-08-01\n",
      "Data columns (total 1 columns):\n",
      "IPG3113N    548 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 8.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *observation_date* was not read as Datetime. So let's convert it..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 548 entries, 1972-01-01 to 2017-08-01\n",
      "Data columns (total 1 columns):\n",
      "IPG3113N    548 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 8.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IPG3113N</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>observation_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>85.6945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-02-01</th>\n",
       "      <td>71.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-03-01</th>\n",
       "      <td>66.0229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-04-01</th>\n",
       "      <td>64.5645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-05-01</th>\n",
       "      <td>65.0100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  IPG3113N\n",
       "observation_date          \n",
       "1972-01-01         85.6945\n",
       "1972-02-01         71.8200\n",
       "1972-03-01         66.0229\n",
       "1972-04-01         64.5645\n",
       "1972-05-01         65.0100"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>IPG3113N</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>observation_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>107.4288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>101.9209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>104.2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>102.5861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>114.0613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  IPG3113N\n",
       "observation_date          \n",
       "2017-04-01        107.4288\n",
       "2017-05-01        101.9209\n",
       "2017-06-01        104.2022\n",
       "2017-07-01        102.5861\n",
       "2017-08-01        114.0613"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IPG3113N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.662524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.052931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>50.668900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>87.862475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>102.278550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>114.691900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>139.915300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         IPG3113N\n",
       "count  548.000000\n",
       "mean   100.662524\n",
       "std     18.052931\n",
       "min     50.668900\n",
       "25%     87.862475\n",
       "50%    102.278550\n",
       "75%    114.691900\n",
       "max    139.915300"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vinicius/.local/lib/python3.6/site-packages/pandas/plotting/_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = mdates.YearLocator()\n",
    "years_fmt = mdates.DateFormatter('%Y')\n",
    "\n",
    "fig = plt.figure(figsize=(15,15))\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1.plot(df)\n",
    "ax1.xaxis.set_major_locator(years)\n",
    "ax1.xaxis.set_major_formatter(years_fmt)\n",
    "plt.xticks(rotation=90)\n",
    "ax1.set_xlabel('\\nYear')\n",
    "plt.grid()\n",
    "\n",
    "ax2 = fig.add_subplot(212)\n",
    "ax2.plot(df.index.month, df, 'xg')\n",
    "plt.xticks(np.arange(1,13,1))\n",
    "ax2.set_xlabel('\\nMonth')               \n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plots, we can see clearly a **seasonality** in this data.  \n",
    "So let's build a model to predict a full year (12 months) based on previous values..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>IPG3113N</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>observation_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>85.6945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-02-01</th>\n",
       "      <td>71.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-03-01</th>\n",
       "      <td>66.0229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-04-01</th>\n",
       "      <td>64.5645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-05-01</th>\n",
       "      <td>65.0100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  IPG3113N\n",
       "observation_date          \n",
       "1972-01-01         85.6945\n",
       "1972-02-01         71.8200\n",
       "1972-03-01         66.0229\n",
       "1972-04-01         64.5645\n",
       "1972-05-01         65.0100"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set = df.head(len(df)-12)\n",
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IPG3113N</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>observation_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-01</th>\n",
       "      <td>109.3191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-01</th>\n",
       "      <td>119.0502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>116.8431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-01</th>\n",
       "      <td>116.4535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>109.4666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  IPG3113N\n",
       "observation_date          \n",
       "2016-09-01        109.3191\n",
       "2016-10-01        119.0502\n",
       "2016-11-01        116.8431\n",
       "2016-12-01        116.4535\n",
       "2017-01-01        109.4666"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set = df.tail(12)\n",
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oPh9TuaSQaAs6bSI9bWw0yI2TWf/1xdOnSFy/PHl+FT/9V8/5pdnE6wuJNoIgiB2KXx6piIs2JtbmNsUiza83vv3aMj523ws4vVDkvi1z2tjzwVsSWDctpDTxXrDmjLRAEIlAT1vQ2UkLlEdmEiokSfK/ltRkofLIL5+Yw+y6W07IIxyDvHhlA6fn+Z9LBntO946lMc/5O1IN9fdN55NY4Twct0f+8/e01QwLaU2BLEt+2So5bQQAPHVhFUfPruDffubZLStB3kmQaCMIgtih+EEksvtRIJIEzq78L2yS0wYAc97jICISClX3gM+eD16nTTdsX6w1e9o4yiMbLD3SE36CPW3BiPksZ3lkrWH5zhIjqcpC5ZHHZjcwnnFFcE0wfvI3/ucp/P6DrwrdFmCiTUU+pXK7U7XQoPHpfNKfxReXhmm3DMMWifyv1E3/Ocl5Thv1tBEAUPZc2DOLJXzj9OIW7+b6h0QbQRDEDmUQTttyyRUpvC7C9cqK5zzyHowt20FRN930SL88ku8J0Y2g08ZfHlkzLCiy5L8exCL/LaRanDaFy2mrRoo2RSim/vxyGTfP5JDWFK4EyyDzmzXhwdi27bglr+mE29vHuYdmuaorlKbzbnkkT+lt1Jw23ue02rD8YedZ708asE0AQKlu+sFFor9jRHxItBEEQexAbNtBuWFiJBBEYgn0YS0V3Sv/8wUSbQCw7Dkh/H1DbmnRWFqDxoJIOMcwuKLNvS37k7c8MqMpfmmiSHlk3Wh1drIJlXu4NpsTxxB12phoyyRUf2g3DyXdQKluYr5QE+pRLDdM2A4wmtaQ0vh6+4DmoHG/PDKXRMOyUeW4K+HI/6TAc1ptmH7JLHPaqDySAFynjYUf1U0Sba83JNoIgiB2IOWGCceBVx7pOW2cB2PHcXynjcojXViPH29pI5vnNRqc08Yp/OqmjZQadtp4yiMtXyAAopH/rc5OJqFwD9fOhp02jV+0rZXr2KgaODyd4w5DYbBh2LphY1OgX4f1+IymNaQ1xU/WjEstFAwzM5Jy163Hf06jIv95LygEnTbm+pHTRgCueJ/MuqKN972C4IdEG0EQxA6EJcK55ZHMaeNco25C907TC+S0ARB32oKiTWMhIiI9beEgEg6dEC5NFIv8t1vKIzNJvrLASkg4AkBKVbiDSM4vu0Oob57JIS1QmggAC4XmhQgRJ9l/TjOaN/pAtDyy6bQB/KKtJfJfkWHZDmwO59AV0s1wmoQio0KlcARc0TYuKNocx8E3XlmEJTiDcSdCoo0gCGIHwsrx8qlmDxWv07bslUZmEgrmC+S0AUHRxvdgMidnNKNBk8XmtNUNqxlEIhj5HyxNTKgyTM4DfrvTxhfAUWuY7T1tmswd+X9+JSDaBEoTgdYLEYsCr+9irdVpqxkWV5lleG7edN4VbZscoi0c+a+pnovL8bqo1M0WIZ1NKjvWaTMtmxylAGXdRD7lzpasc77nnbxWwP/+ty/igVNbM0NxO0KijSAIYhujGxaOz25w3y7otPnlkZx9O6w08q37RrFSqu/4ngbLdrDmRbKbnP1oRT1QHik4p00PRv77PW3xn1PdsJAOuDKsF4rnjNrmtCUU6IYdW/gFXR1GUlW4yyPPL5eR1hTsHU27TptAemSr08Yv2jYDoi2VUOA4vCLahCzBF11MtHGXRwZ62hICrnrNaC1ZzSbVHSvafudrp/Gx+57f6m0MDaW6iVxSFXLlmRP9wuX112Nr1yUk2giCIN5gvvjCVbx4ZTAfVH//7Cx+5M+f9p2zuDSdNrUZ+c9ZpcLi/t9+YAwAsFTY2QNW18p1MI3E65KxQ3A22RTRPILLsh0YluMLpoQiEkRi+j1LomtE9bQBQNwAyWpEeWRSldEQKI88PJP1ZosJlkdu6pjKJaDKEhYE0lGDJa8stp8nKd8tV23OrBtJuYfjQiPeY2HbDkzbCc1p4xfilXqrA5tNqDs2iOTyWhUXPReXYE6bmGhjQTsvXOG/6LhTIdFGEATxBvNH3ziLT33r/EDWOnltE5btcB9Km05b/+WRb9/viradniC5HJihxRsiUg3M5JIkCaoscTltzOVkTpssS0gofGWFYcGUHIjT5h726zEfj0rdRDYZkR7JqbnOLZVxeDrn76HKMSuOsVDUsW8sjV0jqRbXLS5MtI1lNP9xbXDNzWt9PiRJwnQuGdtpYymR4ch/ADA5RHC1YYacNrFy0+uBasP03zt3OqZlo2ZYyCVV98KKwCgJAHh1obhjnVteSLQRBEEIsFwU7+EyLBvHrmzAHkAD9qsLRXdNzrWK3sFjpI8gkuWSjqQq49bdeQBbE0biOA4WC7qf1NcvXz4xhytrFaHbLgVeE7w9bewAww7pqiJxHaxZIEwyFO/OlR5ptAeRAHwH/I5OW4wzmW5YqJs2RtNay9d5yyOvrlexWNTxjhvciwmiQSSLhRp2j6awZzQl9Nou1AxoioS0pvhOFb/T1uo6TueTKNTjPRh170Frddr4etps22l7XWSTO9dpq9QtlOomhWegOfbBL4/kHdru/U7aDnDi6ubA93c9QqKNIAiCk9eWSnj37z+CJ8+vCt3eMG0UdRPnlvsrs9ENCxdWXIHB2//UEkQiiw3XXi7VsWsk5ffarJUbfAsMgPuPz+E9v/8I7vy/H8Lnn5vta63lko5f+sIJfP65q4K3bzptvKKt1rAgS03Rpcky1xosTp45bYDb19bgndMWIdp4BFM/ThsL7hgJizaNT3w+d8ktPX73TZPuHkSDSDZ17BlNY89YWshpK9YM5FMaJEnyBxDzOG3VhuXfjuGKtnhr1C33Prf0tKl8F2h004LjAJmA+5nbwT1trKRvp4rWIMxxzKVUofEg1cBj+MJlKpGMA4k2giAITk5dK8BxgCdERZt3AH2xz1r+88tl/4ovbw9VWTehyhJSmuwP1+ZxVAC3PHImn/R7n3ivtA4CdpieySfxyKtLfa31xDn3+eSdp8Vg5aIA/2MZ7l/SVD7RVvcOTEHBlFQVrrJCvWG1iL6E4v6d5yxWNy1/7ADA19MW7AELktL4nLZnL61hLKPhtl2uA8yCSHiSG9lg7T2jKewddcsjeQdsl3R3eD0AX3zxOG01oz1J8/B0DvMVB09fWOt5e3aITiriPW1M7AbLIzOJnSvamDvE20N8PcKEa14wiIS9tm6ZyeHkNXLa4kCijSAIghMWJy4quthh/IU+w0hOzxf9v/OmFdYM9yq+JEl+yRR3EEm5jumAaDPMN75kiB0Ujtw2jecurfdVtvS4J9pExedySUfeO6QbnAeYmtEaq+72tPElPwLwh2sDfOWRjuOgGiqDS3KWRzqO4w34bk2PBOI5bZ1EG+tpiyuanr20jnsOTUD2HOS0QHIjK3XdPZrCrpEUGib/gO2S7jptbA8Av9OWCSVpfuJDh7ErI+EXP3fMDwLqhC/atCjRFjPNs87KdoNOm7Jj57QxsVqs7UzRGsQvj0yJlUdWvfEeh6aymBMI+tmJkGgjCILg5IJX1njy2iZ3GZxlO2Bnz2N9Om2nFwKijXemV2DoruIdbnn1TtWLe5ZlNzijYb3xB7m6F2n+npsmUdRNnF0sCa1j244v2uq8+fIeCwU3uALg7zF0o+5bAyN43FMm2oIH9CSHQ9WwbFi205oeyVke2bBsOA5CTpu7XpzsBibamDvFSKoyHMQTGosFHVfWqnj3jRPNPQi4XPObrmjbM5pGzisN5Jk3B7hOW74fpy0iSTOf0vAzb0lirdLA8z2i0pn72jKnjfW0xXxpsQCXqMh/XudxqzmzWMQP/vcnhV0y23Z8d4icNrdaA/B62hTZf73FpeKVY+8dTZFoiwmJNoIgCE7Or7gzoHTD9oNA4sJE3q6RJC6vVXHqWkF4H8GfzSse64btB0b4QSScWqVmNA+VvCJjULjBFzLe5R3Sn7vUu2wszEqpjs88cRGr3ow10Xlzl1YruGk6y538CLQPtnaDSHh62rzyyDanLd7tWVBHsIeKV7SxQ1ty4E4bCzPpvRFWZvXOg+OBPfAlWALNYdp7RlPIJN2fz9sX1yLamNPWR48hYyThCq9e5Wjs+5Fz2mLuo1JvDcgBXNFm2g737Lyt5uW5Il66uonLq1Wh2wdn/V1PCZJ/+8wVfPWlee7blerNWZ8i5ZE1z0neO5ZGSTdJCMeARBtBEAQHDdPGlbUqvvetuwHwl0gycfVj9xxAPqXi3m+LR/9fWCn7zg5vDxUTOwD8IBLe6kZWYgm4V/B5P7QHQd20kVRl7B/PYN9YGs9e4i85/fdfPIHfe+AMDk1msGc0JXQ/DMvG7HoVN05lPQErVirE4C6PNJnTFhJtMdfwRw70kR7ZdPuaazABVo5xHit2Em2eexin13B23T2Q3ziZ9b+W5uirY7BeyV0jKX/YN69oKwbKI5spmrzlke2ijWmwnqLNikiP5BzjwMR8cAwDc930bVYhyS6krFfFApOCTmvxOhEYddPCJx94FV98gT98qem0aUKR/5W6+563x/sMEwn72WmQaCMIguBgdr0Cy3bw/punsHskhZOcThk7RE9kNPzUdxzEgy8v4oLAsNbNagOr5QbetCfvrcvptHllhUDQaYt/oLRtB7ph+wf0hKpsSRBJ0DG884ZRnBEoj9yoNvCBW6bw6H84gpl8krvMBwCubdRg2Q5unMpBVSRu1zF8QOcVfqykMyVYHhkeOQDwD9euR4wdGMtoGEmpWKr0XqTg9QmF0yOZe8ic0G5cXa8in1QxlmmuweP2MRYKNUzlkkiosn/7Kmf4RtBpYwEvPE5brWEiraltX2cPb73H68PvaYua0xaztJEJlaADywQcjwAdBtjv00ZFTLRV69ef0/bMxXVUGpbQhapy3RWu4j1tFrJJFfvGUgBAJZIxINFGEATBwXmvn+3mmRzGMhp3ihq72qsqMn7s7gNwHODZi/zuEIv6v82bkSbW09acCQbwBZEwYcMOcwlF4g7fYHz+uVn8l4fOCt026BhmEirqAsmPumFjxItmT6qK0AHm0qr7umBOG3cwTCjenXdOGyvpTAqWRzIXKx1y6oD4rkx4wDfgDoS+aTqHhViizUA2ofjCgvHemyeRUoDfvP/lnkJ2dr2KGyYyfgonEOgn4/hVXSjo2OsdJll5JU/4hmU7KNfNZhCJxicco4JhGJocrzySPR/9zGljEffZUOQ/sP2cNnYhZV1QtAWdtuullO/h027irsgFt7JuQpJc51Uk8r/iVRfsZU7bJjltvSDRRhAEwQETSzdN57gP1kCgZEmR/avwIj1UzJ27bfcIAP70yLoRLI/kH67dPOR7bh1nRH2Qv3vmCv70W+fxFYG+ikbAMUwKXO0FXMHESvASqiz0fFxkr4upLDSFr7QRaHfaVOE5bWLDtZvlke1BJPHLI9udNsB9TJaq8XrawqWRALB/PIOffksSL17ZwN88faXrGrPrVRyYyLR8rdlPxue07R7xRJvf0xZf9bFkPRaqklBlL6wn3u3rphvqEg4iATjKIyOCSHh72qLKZpmA07er0yZaHhlw2orXgdPmOA4e9sakiFyoKnlBVJIk9dHTpmAmn4IiS5gnp60nJNoIgiA4uLJWwXQ+iVxS5T5YA01HTFUkv79EROxcWCkjoch+7w5vOR7rBQOaV995tsGa8tmhMqGICSbbdnwB+pv3n/LDKOISdAwTqiyU/Fg3my5XUuVPQQPcEJKxjIbxbAKqLFYqFAwiSXCWR/pBJC1OWfw5bUyQpCN62uIHkbQ7bQBw03QW67rTU/QUakZbaSTjPXtU3DCRxomrnec52baDqxs1HJxsFW3NIJKed8FnoaBjz6gr2kR62pgTM5Jq3p+0psR22qLEEkOLKdr8YJiIyP/YYr7evo8Rv09xe4o2ctpcLqxUsFDQIUuCok03kU82L0rwvudVGiayCRWKLGH3SIpEWwxItBEEQXDQMG3/gM8bFgE0Dw6aIjeHUgt8YF5YLuPGqax/IBMqj/RK6VjkP88SNaP1gK4pMhoCV97nNmvQDRsfuGUKJd3E3AbfB3ewPDKpKj37fKKoBYZKi1wxBlzRduOUK6BFnLZaOIiEc40owZTU+NMjMxE9bbHLIzs5bdM5AE03shPFDvkFT8sAACAASURBVE4b4+BE1g8aiWKppKNh2rhhIiza+EJAynUTJd30AxKY08ZTCs16nvKB8QXphBLbaWO/X1GiTZbijdiIctr8IJK4kf8RDux0PgkAKNS3m2hz98s7b49Rvc6ctqve79K+8bRweWSOOckKf1l5td5MH947lsJ8gURbL3qKNkmSPitJ0rIkSS8HvvZHkiSdkSTppCRJ90uSNBb43q9LknRekqSzkiR95PXaOEEQxFZg2o6ftsgbyw40Dw6a0hxqLSTaVio4PJNtJj/ylkeazZJAP4iEY+4SO+T7ok2wPJINKn/LvlF/XzwEHUMmuHjmRzmOAz0gxPtx2pjrydvTFtW/pCrxSxuB6NJEnvJI5nDmkuJz2vQOThsTs5dWu4u2TuWRjAOTGcyudV5jds09hHYqj4zrtC16h0fmtLE5bzxOG0vCzAedtkR8p63mO5/tQSRAPFc5agQDr6tebZhIqrJ/YQcApnP9ibY/fugsjs32N6NShEE5bVO5pP/8bmeuec7WjVM5wSAS03+/ELnYxYJIAHce4jz1tPUkjtN2H4CPhr72TQBvcRznbQBeA/DrACBJ0psB/DiAO7zb/JkkSe2XiQiCILYplu34wR0is8nYYV5TZL8XgNcdqpsWZterODyda5Y78Tptht0W+c+zDSaufLEj0IgONAeV37F3xFtXZN5cs6cN4GuqNywHlu34vWAiQSS1hoWFgo5DnjhROV3HqP4ljXPWm25YUGSpJcQjqcZPj7y0WkFCkf1QAIC/p62T08ZEWy+nradom8hgo2p0jFtnLlxYtDEx3Ij5O7JQaA7WBtznM6HKXMO1I502Lb7Ttlp2hcVEJhH5/TjlaFFz2pKK+1jEDyKxWkJI2HrjGQ2bAqJtuajjU986jwdOLnDftl/67Wlj6aG7R5PXRXrk/GYNmiJh31havKfNuyjBXo9xL5hZtoNa4ELV3rE0Fgo12Jw94juNnqLNcZzHAKyHvvaQ4zjsFfsMgP3e338QwBccx6k7jnMJwHkA7xrgfgmCILYU03agyE2xwxOTDzQPDqp3uE4oMgzOssKr61VYtuMOcmZpcAKR/32VR3rZ5f5wbVUSc9qWy5jIJnxXg1e0Nazm/WBigWeNsDskEkTCXKopz4HQOB1YvwQtnB7JM6fNsJEKiaWkKsO0EesgdWHFLbcNOiq85ZH+rLjQPlKagsmUhIur3Udb9BJtBz0xxhy1MFfXq5AltAhPoDkyIK7TthAYrM3IJhTfXY5Dqc6cttbyyLhO2wJz+8ZSkd+Pk9bHRF1L5L/KZjLGj/xPa+3X3mfyKSHRxnoSawIpr/3Sf3qku+fdI+nroqdtbqOGPaNppDSxC25l3fB72pKcZbfh8t9dI0kYloPN68DBfD2J9t35+BiAf/D+vg+uiGNc877WhiRJPwfg5wBg165dOHr0aMv3y+Vy29d46XeNYdjDINYYhj0MYo1h2MOwrDEMexjEGlu1B9N2IEtubwjvGssrOmp1B0ePHsXmho7NmoNy2Yp9+zPr7ofV6VMn4cwrkGwTl2ev4p0HjdhrnNtw15i7cBbPLbsflqfPnEVuoh57jYpex+rSPI4eXQMAKBJQqzdi3/74snvd7pWXjqNyWUFpU0fJ4HssAODFczVMJ4BTJ467/3/8Jdya1WOvUShVsaG6//7KrPuBf/TbT0A2KrHW2NTdw8rspQs4as1iebGOWt1EuRz/sVj04uwvnT+Lo7WLqJZrsGpAeTreY7FaY3s4j6OGm464saajULZRLtux1rg0W4eM1p+3cNU9nH79kaNIq1KHW7qculLFgRG57WepElCN+bo4edV9/I+/8Bxm063CbTpl4+TFxY7rmN6V97XFazh6dLnt++VyGWvF0wCABx9/Hqu7248vz7+qYzwp4aknHmv7XkIGynq8+/H0efdxO3PiWVzwRKxsm7hwZQ7vuine7+mLV9zH4tSx5zGbdNfQyzXUzXiviScvuHs4f/J5XFVan7tyuQzblDE7t4CjRzuPCznr3Y+nn3zMf69jrmk15mMxO6cDZvtrUDVrWK/z/a4DwFdec/d06eo8ylr897xO8Lx3X7nqzvlbK9fx6KOP+mMh4q7x6rkGFAlolNawWmze92H4LBVZ4/SVGjIysDQ/h5pholzmez7WilUUE+5nzuxl9/W+WeR73712+SKO2ldx9Zp7+0e+/QTSdnVbnitejzXC9CXaJEn6TQAmgL/nva3jOH8B4C8A4O6773aOHDnS8v2jR48i/DVe+l1jGPYwiDWGYQ+DWGMY9jAsawzDHgaxxlbt4V996gl8561T+NWP3M69xmcvPgepZuDIkffhi3MvorRURi7nxL69cm4FeO453H3XO3DPoQlkn3oEU7umkMttxF4jcX4VePZZ3P3Ot+P23Xng0W/ixsM3I2dcib2G9fCDuOnQARw58iZ3zUe+DkWVYt+++NI8cOw43v8d78LNM3l8bvYFNNaryOXs2Gs4joNf+vY38X1v24P3vecQ8PRjuOX2NyO38VrsNaQnH8bB/TM4cuRtWHp+Fjh9Cne9+z04d+LZWGvMrlWBo4/izjvehCN37cdz+hk8MnsRuVw29h5eXSgCjz+Od77tLTjy1j249+zTcADkcvVYa5xbKgHffgzveOsdOHLnXgDAlxaPY7GxiVwu3nPy1eWXkC+utvxbY2YJX3ztBczccifuOjjR8bZ108LKN76OH3vPjThy5LaW76Ue/QYkBbH2cOWpy8Arr+DIB96HSc91ZPzlyW/gYkXtuM5quQ489DDe/uZbceS9h9q+f/ToUXzwPe/D//nUQ8jtvhFHjhxu+zeffu0ZHErYOHLkvW3fyz3+TThyvNfmlxaPY9fIGr7nuz7kf23y+LcxMpFDLleKtcYrj54HXj2Lj3zXd/ou7t9efh7n51dj3f7hzVMYm1vAR777Q23fO3r0KEZywPjUCI4ceWfHNR7ZfBmjc/P4rg8113AcB3joAShqItY+PnvxOUwl3Pe7IF9ZPoGjp+e533v/8vwzANYwMj4V+7HsBs979wOrLwFXr8FygLu/4/1+v2HcNY4WX0FuYQ633bgfzy0132uH4bNUZI1ff/oRvPfQFPaNpfDApfPIZuO/5wGAffQbOHxwP44cuQNXEpeBs68gmYm3xuXVCnD0KN7x1jfhyDv2o3xyHp99+TjuvOsezL/6Yvx9OA5QrwOppiO9XZ+POAinR0qS9NMAvh/ATzrN2os5ADcE/tl+72sEQRBDgW5YeHm+gJPXCkK3t2y7GUQiy9xliX7kv7eGW1YoOOtNlf0yS55SOsdx0AiURwJuOR7XnLYBBJGsVxoo1Awcns75e+HuaTNtv4yPrcFT6hNOwUyqCkzbgc0TysLWYKWiCt/rIireXeON/DettgCQt+xz+wRfnit2ve2VtSpsBzg8k2v7XsIrsYy1ByM6iAQA8gkJa5VGx1JNVmLarTwyn9IwkU10TJBcrzQwkY3uAXPj9rtuH4D7u/HUhTXcc6hV5KYTKtdw7aJuIKHILY9FOqHETrBc2NT9nroo4pRHLhb1lhJPwB12nlDiP6fVuolsRILlTD6FQt3hCv2xbQcnr7rvu9UtKI8MvkduVPjL8CreY5FPadANGxdXyigIJlFuNYZlY6moY9942u955PwYckuyA2XlQPz+V9YfmtZc74i99/GE/QAAPvMZ4OBBQN8ZISZCok2SpI8C+I8AfsBxnOC751cA/LgkSUlJkm4EcAuA5/rfJkHsXB57bQVHz7aXCxFizK5X4TjANc5oeYZhOX7fj6qICy4t0NPG208QjPJm4s/g6KGKTpWTuYJI9FAQSUIglKXZC5bwkyz50yOtljltvGv4Q8ITnvDT+BITg2uw3ineoevsoJIOp0fyRP4bzceBsXskhZEEcGqu+wUKFgZzeDpCtCnxxwZEva4YIwn3ddtJ+MQRbYAbMjK7Hh1oslZpYCoXLdoyMfvJLqyUsVyq4303T7V83e1p4wsiGUm3FjOlNQWNmI/lfEHH3tHofjbAGyTfS7QVdOyOWENTJI6eNity7MBMPgnLATY4RMvF1TJKXpgHz2M5KILBLesCYSTVhoVMUvUHpv/LP30cf/zNswPb3xvJYkGH7QD7xlLcKbGA20PdsOyW93+AL+AGALLeOA0m3ngG2AMAzp0DlpeBF17gu902JU7k/+cBPA3gNkmSrkmS9O8A/L8A8gC+KUnSCUmS/hwAHMd5BcAXAZwG8HUAn3Ac542/nEIQ1xE/9dnn8NN/9TylKg0IlmA3tyGWVBVMj1Rl/sh/04/8ZzH1ilD4hntb2V+Hb6ZX++FakfmctlpIaIiIz2BMvR8iwnFyaDqGofRIEadNbT188BgBemjQOG+qaM1wDyrBWVi8YSZ10/YTMBmSJOHgiIKXe4i2855ou2k62/Y912mLd190w4IqS777GySfcH9n1svRh2Um2joN12YcmMjgSkQQiW072Kh2dtpc0dZ1aQDAk+fdHs/3HW4VbZmEigrHdO5izWiJ+wf4g0g6hZAA8SLWF4s6do+0r5HSFMwW7Vi/J4VqA6Pp9sd0ZsQtf10uxXc4Tngu24GJDL+jMgBanDYB0eYOg1b851U3bC7ROkzMeXH/+8Yy3IILAHSTBVGJXewKz/9jFwZ4wn4AAKWS++eTT/LdbpsSJz3yJxzH2eM4juY4zn7Hcf4/x3FudhznBsdx3u799/OBf/+7juMcdhznNsdxHnx9t08QO4cXt2CuzfXIZW/OU8OysVKuc9++JT1SkfsYru0eYuNEd3daI6G485MkiS89kjlRQWdG4xVtIbEjkh4Z3IdIeaRbxtgUawmR9MhQaaM/rJxD0LMkzZQ/947vsfCvOgedNpnvtaUblv9cBDk0IuPcctm/n1FcWClj31i6RTQy3Flv8fYQnJkXhom2tUr071wxptN2cDKD+c1a2+Nb1A1YtoOJbDLydilNiRX5/+T5VewfT+PAZOvYgGxS4XIBSrrZkhwJMNHm9lEWuqTkVRsmNqtG9/LIHu8bhmVjtVyPdNo+8aGb8eq6jU987ljP+7FRNTCRbX9OZvLuusvF+O+hJ65uIJdU8dZ9o/yH8wFgWLafdrghkCBZrVvIJNSW57W+BWWeg2DOqzRxyyPZGAj+OZ3tTlu8Ndj4BOa0CZdHkmgjCGKYYFe//3kL5tpcj1wODPi9thHdG9ONYE+bJvMLFaOtPFKCIVoe6R2QNVmOPYMKiJ6npSoy1/iCmmEhocqQ2WOh8IvPoNMmUtrYdAyVlj95nLZwaaPvtAmUR7IDDG+vY7XeXh7JK/x0w/YFZ5BDo+7z+upC5762K+tVHAyJFAZvT1tUPxsAjDCnrcNhmX29k1PGuGEiA9tpHjoZa97tJzvcPptUofd4aTmOg2currW5bIB7qOTpaSvpRrto0xSYNvD9n3ocn3rkXMfbsiHDe7s4bb3mCS6X6nAcRDptH3v/jfjwQRXfPL3UdY1aw0LNsDAe8ZjO5JnTFl+0vXS1gLfuG/UE8BsvdhqWjWnPIRSJ/S/XTWSTKg7P5JBPqhhNa9xVEsMCc9r2jIqVR4b7V3nXqPhjTtzfkbQv2jjLI4OijbPqZTtCoo0ghhx2mHzw5QUqkfQo1038+384wVWaw7i4WsF4xr1yLNLXZlpOM4iEU+gAzVlBzfJIfrHDDlpsDXemV389bSJBJMH5TXHKtdr30fzgdwdDS1yHIHaVm4kVsZ621nlzzH3kOcDUQqKNtzySHVSCThdvX1wnp+3giPuYvDLfWbQVagbGOwxxHklpKDZiDteO47R1KI9cKzcgS8BYL6fNm9V2JRRGwtbtJPpuGE9jqdJ9+G+5bqKomzg8014mmkmofHPadBP5ZKg80nt9FHWza0+VP6OtjyCSRW+NKKcNAKa8kQzdDsmshDDqtcFbHqkbFl5dKOLtB8aQSaj8h/MBYFoOJrMJKLLEJTYZ1YaJbFLB4ekcTv72h3Hb7nxXB3uYWS7pGM9oSGlKIEQk/u07iba4a7CexozvtLGeNkGnbX0dOLs9+wt5INFGEEMOS7FbKtZxbrn7cNqdwguX1/E/j8/h2JVN7tteXq3gvV7IgIhoa+lpUyQYnKKN9SmxNYSCSDxBwD4o3d46np42NgQ5WB4p84k2w24VbZxph2wNdx/N9EeenjYm8JrpkX30tDHh5zttfIIJaAo+7vLI0KBZwHXrLI4Uy6ieNgCYSLnls8vFzofrqFI+xu178rhWtmNdnNAjwlAYzfLIDqKtUsdENuk7t504OOkKqnCC5LpXdtlJtN2yKw/dcgM+OrHp9SeNRYiUbEJBpWHGTkssRjhtwee328WJBea09SiP7HZxYrHgPh6dRBvbWjf3kLlRUaItk1CRUuKXR74yX4RpO7hz/xjSCWWLhmvb0BQZ7zo0gfueuoynLqxy3d4NZXEfOEmSkNL4+5EB4Je/cBzv/r2H8W/+/Gmu9M1BUq1byHqloiLveeELVf5w7djpkawkvM/0yFIJOOyN//jDPwQaYoPTtwsk2ghiyDFtBzdMuB/eV9aiU9N2GqzEkdehqtRNLJfqePOeEUxmE8KijfW0aQKR/2GXTMShaoScMt54eN9p01qdNp5t1AyrRSRoigzbAVdUftBpA9z7w+OSNUL3Q6SnLdyb0U96pF8eyemS1RoWJKnV+eSN4e5UmihLEkZSWtfAhKhSPsYde0fRsIBLq70vGJV00+9RCZNU3Pu33qGnba3cOfkxyEw+iYQqYzb0XsjE4FQuuqfttt15AMBrS6WOa/uiLcLtyyRVOA5ipT82TBsrpXpb3H7w+enWCzXvuWS7RqPvC9D7fcN360aihV/Ku2hUqXd2vNjj0UkIj6Wk2E7biavuBbZ3HBhDRlNgWA7X78ggYKLtz37ynTgwkcF/+OJLXLcPjz9w36/43rsdx8GXTsxDN2w8d3k9MlSHB8t2uC+WAV4SZiIsuOLfvj2Iiq9CoVo3IUnNi2VJVYYsCQaR3HUX8Gu/Btx3H/CzP8t3+20GiTaCGHIs28GhDleXdyqXvQ86XrFzyRN7N05lsX88LdTTZtrB8kiJW6iwgwoLIuEVXECn8sgB9LRxBpGkQuWRAJ/YCe+D9xDU3tMmEEQSIRwBzgOMYUGWxJ/TasNCRlMgSU2Xib3G4i6jG1bH0sTxjIbNDsEXhmVDN+y2pEPGHXvdWW/dyisZc5s17BuLFgmSJGEql8RapYEHTy34v4uMtS4z1oLIsuTF/oecNq88cjwiNAMAbp1xRdu5bqKtxtaIdtoAoIvG8bm24c69Y64ggwnKXFL1XeYolop1TGYTLU54mF5l1YsFHSlNbhs7wPCdti53aN0vj4x+TCdTkt9/14uXrm5i90gKu0ZS/kGfI4yzuadKAxdXxCpODMuBpsgYzybw3W+a6ej6RmHbDqqGG/nPSKoydxAJK5v+njfvAgA8cZ7P7Qvyq//4Em7/rQfxvk9+i/viYc1olreLlEeG51vyvP9/+7UVHJvdbHnPkyTJK5sVEG35PPDJTwI/9EPA00/z3X6bQaKNIIYcy3br8PMpdduLNsdxcHx2o++SEHbg4xU7i15p1L6xNPaPZ9rCDOLgOm3NwznAN5TUDAeRCFytNSwbiiw158XJMuectvbySFWWuMSnblih4Az+D/42p42z3Kh5P/pIj2y0rsH+5OlJY6Wi7AAiItrSoeRGlfO1pZt2xxCQsUwCmx16qEq6e2gf6eC03TyTgyoDp3uINsdxcG2jiv3j0YEmgOvYXFmr4hOfO4b7nrzU8r21ch2THVyyMAcjYv/XKg3kk2pHoTOa0TCWlHB2sfOBv5vTxp6fOJH9bG+Hplofi++8dRr/+X1pvG3/aNdeqHK9c7kqI6F0f99wB2unWy4EBEmpzGnrvA+WsBglYgFgIiX7gRa9uLRa8d1OVmLIE57E+K0vv4yP/7XYTC7XaXPvd9IbtRL3s6hmWHCc1oRXkfJI9r5wy0wO+8bSeFJQtJ28tol/fPEaZvIpLJfq/u9xXGqN5vt3P0EkvMLPtGx87L7n8cT5Vdww0fr74ZbNCgSR5N3XFfbsAdbW+G6/zSDRRhBDDivHOzjZfnWZl7VyHd88vbRldfTPXFzHD/3ZU3j6Yn9vrH5sv+AHZlKTsW88HfvAEV6DuSAKpxsCNA8qbI2kSBCJZft9CIA306vPOW2qLPEN1zZCQSQK/2PR3tPGd+V6IOmRXi8YO9yyNXiDSFoGY8t8z0etYbYNMNY4Hk/b9ubVdRRtmi9IwpR09+udnDZNkbE/J/d02tYrDeiG3dFpA1zRdmx2A3bEUOa1cqNj8mOYGzynzXEcPHVhFb/w9y9ipVzHRI/yyn05CeeWu5VHuiJlNMJZYof1XgmUQPP9Key0KbKE/XkZKU3xHd4oKl5KYTd6DddeLETPaPNv771Uyl2cNhZE0ikcZiotYaVUj1XSvFau+04jm+3F67QZlo3Hzq4IzVgD3CoHLdz/GvMNiz1Owd8T3nJuoDU9+L2HJ/HUhTXuMCsA+KsnLyOXVPHxD9wIANyirWqYvnjmjesH2gOcmsKv+xq66fbH/tJ334IvfeJ9Ld/LJBSuWYhwHKBcboq2iQlgY+O6TpEk0UYQQ47lleNFlQTx8A/Pz+L9f/AofvZvXohV6vR6cGbR/bknr3Uf9tsNw7L9XjRu0WYzwSQjm1BRN23uRM6g0+aXsHE6bZoiCbsygHu/2aEe8ObFcQ5iBtDWkzaY8kj+AI/Wnjae9MjW0QfN8sj4H/y1iBRMgD9JrcW1VNyB1HEvjgT7S/w1ZOa09V4j6vkMMp5JdDzoFmvsMNpZJBwYkXF6odj1/rDfyf3jnUXbZDYBtkSwXLNuWijVzVg9bYA7q63asLBWaeD+Y3N44NQinjy/2rO8cl9Oxrmlcsff+abTFhG84YmouhnPacsmlI4iNKXJXcsjyzFEGyuP7PScLBb1jiEkAJD2nLau6ZGVBkbTWuSwdMANuQGawSmdcBwHq5Vmz2Jai+9aBjlxdROlutn1seuG+77Z+l4Rdy12cSOXCpZHKtx78dODVRnvv2UKhZqBV+b5Pg+Xizq+dnIeP3r3fj9htFTnG/Ldr9PWKcCp1/sme71N55NtlQFpjXMURKXiCjcm2iYnAceBWr5+A9tItBHEkGPaDhRFwg0TGVxbrwnF/l9cKeO3vvwKxrwryN2urr6eXFxxr0B3mxnVi6vrVf/KJK9DZdnsKqfkf1DxrhHsafPLIzmWcJ26QOCEUHqk7Q9EBVzxyFPO50fltwgNPqctLHZESkXrpu1F/QfSI3mCSKxQeaTCnx4ZDvBICorPoNPmu44xl4gUbd4ace7KqjckvpNIGE1rKAg6bQBwIC9jvdLoGpPOXOte5ZGM4HDp5oy2eOWRrMf3wnLZD7jYrBo9nbp9ORk1w+oYQLRRNZBNNCPQg/A4bVfWKjg4me1Smqh0LY+s1E3keok2RYbjRLsjtu1gqYdoS8YIIlmvGh372YDm2ID5HhULlYaFhmn7zz97rfO2Lx09uwzA7UMVqRZpKY/UWI9ivE0wJysfeF5SWh9Omyzh5pkcgN6PX5inLqzBsBz8yF37/YstZZHyyH562sLDtWOuoXtJPumIqoBsUuUrj2Rx/0HRBkArbs1F6TcCEm3EjuD47MaWzIUZBJZtQ5Fcp61h2VjsEt0dheM4+I37TyGpyvhP3/dmAPy9YIPiopdA149ouxxIjeMvj3Q/6BW5Kdp4IuaB1vRI1T+cxz9AGFZzZAAgnh4ZLG3UFL4Uy+jySBkxTAQf3WjtoRLpaQuHZyQ1MaeNJT7KArPeaqEyT5HyyPD4A5VTzG9UG22x6hqH8GNiaqZDOdx4JoFS3Yz8vS/qvZ22sWT3GWtAc1D9vm5OW6BnrVBtoG5a+I37T+GU57xPxnTa3n7DGGQJeOj0Es4HQikme4i+3Vn3ebncIYV3s9aIjPsHEAjPiOe0hfvZgiR7OG1xyiP9i04Rr/X1agOG5XQtj4wT+b9RaXTsZwOAybT7urjWQ3SssYsK3vOf4Xgsg3z7tRUArrnCe7ENiC6PjPv+3yyPbHXaDCv+WA4Aftm0psj++yevW3d6oYiEKuPWXXl/P/zlkc0LRSLlkeEgkrgXu/xRAYl20ZZJcDptHUSbGke0HTsGPPhg/J81JJBoI15XHnplEe/+vYe3dADlUlHHD9/7FP7pxWtbtod+YOV4B7ymXd4Syc2qgWcuruPnPnCTf6DiFQmDgjltF1Yqwq+JS6vN+88rPoMfmL5os/j2YdrNq7WazO8uGaF+tITqltLxfPC3l0fyzmlr7QUDXJHAIz7dPq72iHoeh6oeCs9we9rE0yPZ3/mcttZeMKFSoUbr+APmxMYVwVGHYx4Xl81g25WPPqQzhz2qr405bSNdnLaM5t6fot65BGtuo4Z8SsVol+HY03n30P7mPSMo1Ay8tljG556dxV89eRkAYpdHjmcTuPOGMXzu2Vk4DvCmPW7CZa+eNlbO1+nCV6Fq+I9VmKwfRNJ9b5bt4OpGta2fLYg7j7CL09awkOswOqG5RmfRxgKXujltCRmQpe5O20a1gYkOIhYAxr0ZgL2colVP7DNRLpIeqRsWXp4r+oE5IiWShmn7F8x4k2aZKAqWR7LfeZ6PMiY2VUUSKucG3FCg23bloSmy78jyVs9UA+WRSYH3PN1oHVMStzwyPN8tSFpT+CL/w6JtYgJATKftz/8c+NjH4v+sIYFEG/G68vCrS1gq1ltKYd5oTlzdhO00ryhvN/oVbUzYjGcTzSGaW+C0VRsmFgo63rRnBJbt4LzgoPCX5woYz2hIa3yHc6BZHqnIEpICpXTuGoGeNoHwDTPktIk4VIZlt5RwaTLvnDY2DDoU+c/5oZ1SgyWB/KWibU4bZ3lkOD0S6D10OGoPaS24B7H466j+vrhifiOiDI2V0JoxhPSSnPuWrQAAIABJREFUJ0JmRqKdJiZECrV2p6wUw2nLqK3/NoprG7WupZEA8L1v2Y0/+bE78V23z6BQM/x9P395HUD88kgA+OCt0/4B8BeOuMN1e5VHjjHR1mHA9ka10VG0ZTwRpfdQ4vObNRiWg4MTnR+LWEEkiV5OGysx7CLaujhtkiQhm1B7pkd2ch4Bt8RvOpfsmcLLnLapLHPa4gngIEyUsB4u3qh9oDXAiV3oifteUdajg0gAPrHDeo8Tgk6b4zg4vVDEm70LFWw/pS4XVMJYXnBRv+WRwcRcWZbcAKZeoq3R6tAFGZTTphWLwGc+Axw92vm25TKQy8X/WUMCiTbidYX1G/CWoAU5NruBF6+sC9/+5DV3D1vp9vWD5bg9VHvH0pAl4BqnaGPuiSpLSHjN57wxxYOAuWzf/7Y9ANwSD16qDRPfeGURH33L7p5ziqLwm8Bl2RcsvKKtdU6bmNOmKf2JhIbZKtpE57QFHT838j/e7R3HaUtM9MUnZ09bm9MmNKet9fHsp6dNNFAluIYfIhJjjVrDQs2wIpy2+OWRS6U6VFnq6Iqwg3fUgO0oByGM77R1ufh2baPzjDZGNqnih96xH2MZDbbTLFNkLnHc8kjAFW2AO3Pxw3fswo/ctR9HbpvuehtNljCVS2Chg2jbrBkdRUpcp43F/Xdz2lKaDMNyIl8ftu2g2rD6Ko9c8MRweLh3mExS6TmnbaLD3DvGvvG0Pwy84zqVVqfNL4/keLNgh302h0/EaWspj9T4nDbmMgd7DZlDz/NeYXj3WW0RbfHPJkvFOtYrDbx5LxNtXnkkh9PGLnZkEmHRxvGeZ1ptbllClXunR3Ypj0wnVL42lm6i7Vd/Fbj33s63rVSAbOff0WGFRBvxulHSDZzz3BRe+z/IJx84gz/4+lnh27Okwq0QKoOAOTuaIiOtKV17EKIwg31civchIzAfp18uerPVPnTbDNKaItTX9tArS6g2LPwvb9/nfkBwh4g0S1P80AqONWzbgePA72nTBNIjjcDBARC7yhmO/FeV3h+WQeqme3tZbh3mHPd+uPONECl2tqKnLRESbf30tKmyBFniK3kKjz/gCRFhqY5hwcXTF7dU1DGTT7Y8n0HGe5RHpjWl5TUZJuNd7Ol0Nd9xHMxt1romRwYZ8Uoozy013faEIreEPPTibfvHMJVL4u6D40iqCv6fH70TN3sDtLuxezSFxQ4io1A1OsbbpzUFaU3Baq37E7JWYf2FnV1D9nsT9blY8Q6tPYNIupTWLRV0KLLUc+5dNqmi3OGQXGtY0A27a08bAOwdS/d22vygGfHySPa4sL7Fbk5lFLbtiuS28kjOnrZcsr08kucj2QiEYaU4EywB4PSCe55hoi2pylBliaunjQmjdCjyn68kvH0upFvq3+N2XcojuZ02VgbJRNvYGCDLSM3PA5ubze9HUS5vS9EW/x2SIDg5da3gxzv3I5jWq42eVx074TiOL9q2q9NmBsrx3OHDfPeDXc1VFQmad/jaip62iytlSBJw03QWMyNJd3Br7zNWC/cfn8O+sTTuOTTRc7hsFOxKYjCIhOexMAOPJVsHiOeoMIxQP5rfC8Dd0xYsj5Q4g0haxRLAYurj3T48WBVoOkO8vXXJFqeN9frE+31ngjvY08Yb7BJ2ySRJ8q4Yx16iLYgkweHA+rOwwkEkHBcEVkr1jiEkAPyQk6jY/5JuYiTd/fFmJlynEvNCzUC5bsYWbUwYtYSI5BId0xajUGQJ9//Ce7v24kWxeyTth6YwSroBSZKwWTPaAmEYsizhPTdN4OWr3YchRx3uwwQP6+Efx8oVezptSmenaKGgY1c+6b8/dSKXVFHt4NCw10qnx4OxZySFb7263PXfrJbryCVV//csw0Qrx9Uu9rgw4cf7ed4US+7j1k04R1HSTWQTSstjKhJaZJjNKgdVkaHIkruHmEccNuT+dm9QuSRJyKdUt3yzu7HqwxIc2fOgKjJkif+CW3jESEKRYdjdH89w6mSQTELxhpjHfF2EnTZZBsbHkT93zv3/bqKtUgFGR+P9nCGCnDbideO4VxoJ9Oe0bVYb/hsdL1fWqn4/XT8lmltF09lxPyhE4uGbQkXe0p62y6sV7B1NI6UpSKrd09M68dSFVXzkjt2QPdHF/VhEBZFwrGEFRB9bB+ArCTTt1sh/kZ62htVneaRpt/SzufuIH0TCnruBp0dyz2lzm+GDItjti+MLIglfMU6qCpdzWQsdYHh6HTcq7vtTeMYYr9O2q4uzw4ZFR8X+l+pG17h/wHUfMwmlY3kkKzfc26M8ksEE6rmlEvaNpd3SzpiDtYPcMJGJHITdjT2jqbYgkl/4+2P4+F8/D8t2Ova0AcAHbpnGUtXB1S4l6qzcMDzCIUi3sjgm+rL9BJEUa9jVozSS7bFTTxsraewl2tyYdqvrKJq1cqOl9FVV3M8iHqeNuUNN0SZWGt/saeN7/y/rZlsJsf8ccLxXNC/8eeKR87Pw4koFe0dTLb+zuZTKFURSNZjT1nqxi3dOW7jEkcdpSyXa5UcmobrJoHH3ERZtADAxgdz58+7fe4m2bei0kWgjXjdOtIg2MZHgOA42q4awyDg557psksRfTjEMsEO0IjGnjd9d8p02WYIm4C4NimrD8uvvezXiR2HZDgyreahKCAylZh+YsiQ204tdrW32tPEHkTQsx38eALG0wnDkv1seyVdWGHSnALcPK+79aMY299ebp3foaYt7pbXuPQ5Bh0bMaWv9KEzGOHy0rZEIlljGd0/XfUcjFETCMU5iqVjHri5OWz6pQpGljk5btxASxkhK61iCFSetMAhLmCzqJm6YSOMdB8ZwcLJ7iMmg2D2awmbVaEmpm9+s4ZmL6y17i+I7vT46Fj3PsGwHf/D1M5jfrDWdsi5BIt1EWyWGUwe097QF3f7Fgt6zn439jEqH8kgm0Ls9HkCg1LHLL8x6pdEWEpNOKFxOGyubE3bazGZpPBAMIolfHhm+uMGeR5H0SHahifez0P0cbd1HPqlxBZGwx7J1tqTMF/nfiO5p67VGVJUGI8NbNstEW1B8TU5C0b2LMoUuQ8spiIQgWnnNu4oKiIu2ct2EaTtcbyZB2BXRAxOZbem0+c5OoA6f936YgcREkT6uQRFsAk+pCvf9MKzWD10xp80tTWQlcACfaLOsVqdNFYj8Ny3bL30DxJrAo8sjeZy29vJI12kDPv7XL+DvnrnS9fZRJS5CQSRtPW0K1xquaAu7ZHzpkeGeNoDvqrNtO26gSkuJZvzSxk0m2kKH2rgllrphoVAzuoo2SZIwltawGeGUFWu9nTbADTzoFPnPnLY4QgFAi5s1nU/hL3/qbnzyh98W67b9whIVg25b0G3q5iwdns5iMiXhn45dwz+9eM2/uHBhpYx7j17Aw68uoVI3kUkoHfsLgWYvVJTDUvGdtpiizbKxWW3gbb/9DX/49GJBx+6R3q5nJqF2DCJhvdO9xCMr9ax1US6r5XpbMmgmoXD1grU7bf2VRzZ72uKtU9SNtsdCJD2SicdgmSbPZ2HY1Qdcp42np429f2eCJd2q0ldiLsDKI+P97Kj0yDRvQE2p5AqvQOUKCyMBQE4bQcTFtGzMbdRwm1d3LSqYWOO8qDO0WW0gk1Awmta2p9MWcMkACCUm+sJPkoTcpUFhWHagN0/mfj6YaGP3QVMk7sfCTX4MRz4L9LT55ZHunzwz1sLpkXHn24TXaIn8FxiunQiJNnfWG/CtM0t44XL3tFYW1Z5LNg/fmoD4jEqPBOJfuY4Snzxi3nGctp42to+45ZHsdZyOcNriPCWsDC0cgBE3zGS56AZfsBlonRjLaL5ABNw+uOOzG/GdtnQ3p60GWQKmewRfMILuzXQuibFMgrs3TRQmLBcCYSRB4dKtPFKSJLxjRsHx2U38yj++hFe8/iJ2cbCkm6g0eg/GZhcnot4D4/TEAa2VApdWK6g0LLx0tYCSbqDSsLB7tPdzkU2qKHewNfwyzx5lmux13020rVUabTP4eJ02JqyZY6dzfob5ycFK8zOIZx3XaQuLNv70SPb+GBSPPJ+FumG19AEDwIigaAu+ZyW5+3jbL3Yltd7Cr2ZY0BQpMvhIyGnLhxrjw6Kt02fzNg0iIdFGvC7Mb+owbQe37HLtZ9GeNibaRMsj1ytuY7mIQzUMsNIoWWqWdIgGkSiK5M9S4X08DcvG2cUS122i9qEFSlNEexKCAtbgsXXgzUgL3B7gcx2tUD+CKiS4Wue0CaVHmv2nR4aFiirLcADYDiIdmSBfOjGHkZSKuw+N+18TEZ9tThvnleso8cnTF9ewbNhOe/w0z1Vn9joWTY/crBoYSan+a8lfQ2avz+7P63LJG6zdxWkDXIdirdwUbZ/61jn8+F88g9Vy3R9Y3I1eTttMPtV2HzrB+lqB3mJz0LASTlbS6ThOS4lgt7lkAPDjtydw70++E4ArfAF33AHgHuzLdQvZLv1sQPN1Hlke2eB02kzbv4gyv1kLlKr2dtqyCaVjxHpc8cjeRzoNRbZtxy2PDIm2TEIR62nL9Vce2RRLnkCIuU7UxQ2R4drhipGk1n3QehjdtNvEUi7J29PWGvkPxCttbNlH1MWuGCWWUQ4doynaOJy2sGjzBmwDcAVbOWIerG0DtRqVRxIE48q6G+9+qxfBLFoeuekNgxUtj9yoNjCe1YR6qIYBKyxUBIJIIt06zjUeOLWA7/1vj/mHAxFcwcRKQvhK2Nzbex+63mEloSqoC0T+hyOf+dIjm6WmQPMx5SqPtFsFl1BPm2W39MVpCmd6pBGRHhko54qKhmcUaga+/vIifuDte1sj/0WCSEy7pdSH98o162kLklDjD11ngiu8RlKVYx/E/MZ6Lfh8xC+bXa80IkM49o+nMZ1P4rFrZtcevyXPaesWROJ+P9Xy+3txpYK6aaOot/fqRDGS0joGkSwW9dj9bAzmtm2VaGMlnbrhCvcZL20x7AiFUWUJd+x1U+dYlD1LoyzrJqr13k6bn1wY8Utf9tMjYwaRWLZ/X+Y2a37ZZ7fB2oxsUkW1ER0iUo0pHtNd+vMAt6zQsh0/qp+R0VShnjZWvso7XNvsVB7JEUSST7b+nog4beFAlJTGF0SiNzqVR8bvaauFIv/ZfniugYqWlUc5dIy0xjl0vZvTxly0qBLJarX132wjSLQRrwtswGjTaeuvPFI0PXK90tjWTlswoh4QCyIJr6Ep/CWWhZoB22kOyBbBaBFM/D1tzQZuT7QpEvfrwnW5mOjrPOeoE2EBzHM49/dghpw2X+zw9bS1DsaWBdIjQ05bYL1CF6ftayfnUTdt/OhdN7R8nY2T4BldUDesll6wJOeV66hAFR6nrdOgV67ySF+0tff3xQkRcS8stQuFlKbgFz90M85u2Hj8XOeYeSbEZvLdD+l7RlNYKOi+AGQX1gDEmo82ku5cgrUQM/giCCtDfKNFWyahIp9UfZeMORQ//8HD+Oovvr+n0wY0h0Svld01rq67TltJd0cf9BRtfmm2+9p59uIaTl5zg7t4g0jqpu0LtbnNmv/ZG2f8AvsZ1YhfOCYeMx0O2Ixe5ZF+CmVoSHea22lzD/vs5/FWajTMUHkkp2gr6UZ7eqTInLaQ05birJ6JGmqdT2ko17tf3AlSjexp40yPbLSnRyZVGbUeyi/qdgx2oaIvp42Jtre+1f0zSrQx942cNoJwmV2vIqHKODjhXsngvSrGYD0YosEZG1X3KnZymzptrFdKkZtXB8WdtqZY4U5d9N5Eu0Vdx1lDa7m6KDYkXFP66e+zW1xLQGxOGxPAzTlt8ffgitf+I/+TIaeNKz0ywqEKxuZvRqQMMp69uI69oym8bX/rjBtRpy3Z4rQx1zGu02a1jS5IcIi2TvHsPAeYqMZ69hqLO1y7U/jFj7/rBkymJPzFYxc73p4JqV4ljrtH06ibtp/GO7/ZdN3i9LTlUxqKuhF5MFws8DttY2n3PsftgxskU/kkVjzBxRyl0bTmDyzuRSbhlnf6Ttum57TVvZ62HuWR4SCS3/7qafzuP78KwH1NylJ0ul6QlvLIgNN2bqmEbEKJJaJZv1pUGEmcQBWgt9PGRHHYpXKDSHh62kxkk4oveLnLI0MX/VgYVRzBZNkOKg2rTUinBOa0maF9JDmdtlqjvbwwl1RhWE7896yIi1X85ZHtJfbfcXgSc2Wna090lEPHGGhP2513un9GJUhWvAtW5LQRg+Djf/08/uKxC1u9jb6YXavihvG0P4uD1x168NQCPnbf89jos6dt4zpx2pqljXwzqIJrBGe98a/h/vurG+KiLRhEktL470f4Q1doZl2gn0woPTIkgDWOWHaGYUWXR/L2tAUbufnntHUvjyzUjI5zl0q6gclcsm0QsiJLkDgGtBqWDct2Wp02zkNQlPjkSY9k7y9hd8Wd0xZvD9GDxuM7sBuVzgOdk6qCN08qOLPYOQWtaphIqHLPfrK9gbLAuY0aLNvB4Wn30DLSI9YdcMsjDctp+71l7hKv0zayReWRgCsUV0NOWy93LIgkSZjKJf0eQdbTVtJNVOpW7PJI9trZrDZwabXi7yebUHsOGk8q7hqNgNPWMG08c3EdN+/KxxpUzkRIJ9EW5zHp1dNW9i4qhF0qEactk1ChKRJkgRE+4fJIIH4aM+szbAsi0fguMgHNHlWNtQqoCpcAjeolYxdsajHb2moNd75l8L3TjfyPd3vLdtCw2nvr/td3H0A+Afzpt853/tkRYo/ByjX7ctre9S4Ub78d+PCH3f/v5rSRaCMGwVMX1vDUhbWt3kZfXFmv4uBkVjit8PjVTXzrzDLOL7u/XLbDl9AHuIfCom5iPJPwRMI2dNrYXDG5WdLB61paodlirtPG+1gOwGlrCSLhd9qYq9YUTGJz2tSAawlwOm3hyH+OAcjBNYICidddsmwHtoOWAA5Vdq+Sxp5vFjWnLXCYsR2g1KGxvVw3I0u3JMlNBIv7cLLDf7TTFm+Nhmkjoba7ZHGf0w3PKZloE23xrzr7QSSJoGiL78C6JdydRdOerITVcqNjyWqtYXUd5MzwAziKNVxecwXCz7zvRiiyhAMTvWeksQNruK+NlWfGCb4IMpbRIEvtQ8XfCKbyCax6TlslZg9ZmIlsAmuVOkq64Zfxu6It+vcjSFi0FWoGlkt1VOpmbLEULI9cKtZ9d+/sUgm3zMQr+8okmGiLCkRpd5ai6FUeWepQ7pnWeOe0uc6fJEluj7pgeWSwND0Z8yIoc7PDoq05Qif+Ppq92ewCJmd6ZESIVM4XbfHLIzOa0jbfkvtCVWhAdiah4qOHNDz22gpeW4oOLtMj5rv5t2e9nnEzVT79aeCXf7n1a4cO4di99wK33OL+f5RoY04blUcS/aIbFqoNy79ytx1xHAezaxUcmMgEShB432Ddf//cpabNztvWxj5IJ7LadeS08ZcEhoWGpkhoCCZQXu3jdWnZwSASBabttAyE7QW7H2wGltCctkB5pKrIkCW+0lszJIA1gSASoy1EhK8vjt3n1sh/vn1EzfphazABUOgQRlLu4iTESQ9jRPWCNXva4q3hDngNO21K7AHdG/5g63bRxltqFHQMtZh9irphoWZYkT1tjF1Zd63Lq9H9pNWG1XWQM2OPJ6rmN3XMehdfvufNu3Dst74Hdx+a6HZTAE1nLJgg+e3XVvx+O16n7Z5D4/jgrdP++9IbyXQu6fe0xU1rDDOZc9M42Wd1SpPd8shYQSTNuHnDsv0eo8trFVTqVs+YfaC1UmChUMPbD4z534sr2phQjQqxYOWRvehZHtlB8ORSKmoGuPqw2H5c0SbmtAWrHOIGYjXvQ+vFFVmWuHvBjNDFR545bZbtoBEKbwKapadxRVvNaO8rc8sjY908EL7U/vq4Z7f7PB+7shH7ZzPY6/61DSveRfZ/8S+Ae+6J/t6IV+pM5ZHE6wlr2p3bqMV+Mxs21ioNVBoWDk66V295h90CzTfY4ABUTnPIP5CNeU7bduxpYy5Zq9PGJ1SafXHiThu7Ojjbh9NmWM0gEj8quY8POxHRFgwiEVnDLzVVQk4bl2hzIodr87hLAELlkfEdP9t2sBnRR8Ue1zu8vh6W3BqmXDc69kBpavwEMt9pa4n85yuPXCnXMRXqiWLrxSpNrEaHJPAcYKKuOqsxRXQn0RhktyfaLnUUbWbHQ1CQaS8dcbGg48paFSlNxkw+2TIzrRusBKvoHWA/+8Ql/G+ffQ7/11dPu/uMkVYY5MfuOYC/+pl3cd1mUEzlkijqJuqmFTv4I8xENoH1SlO03bYrj6I3I61nT1ugLyvoXF5arXR0ssMosgRFlrBarkM3bNx1oDl+49Zd+S63bHLzTA4pTcbfPnOl7XtxAlWApmjrWB7Z4fEdzyRgOt3nuwUJiuGUytcHBgQDQELlkbGcNvc5inpeeEKLgEB5pEDVCTtHdXba4u0hKgwkwXE/ug3Ink5LyKdUvDwfIZbQvactqSr4+Q8exgtLFv7lf3scn3t2VvwcPOr1XFMQCfF6wkRbzfj/2XvzKEuuu0zwi/XFW3PfalWVapNUWmytxpaUlm0wcMBmBrsZ08Bh8NgGTwPdTbN003OmgZ7TM00z03QDg6E5QJvlYBbjMWBhyyrLsi3JWqxdqk21Z+X+Mt/+Yps/btx48SJuRNwbmVKWrPjO0anSy4xbsb2I+93v+30/2//7mw1X6uQltnuUrOzyWhCCYNXniJa1+dansu5FeLvCFsudBj1m1SdtWWLut942gI6x3OgJr3D6Y9iu7+Onk/O+EGkbbkqqZ0jBJGrfcHKjyL0ZTo9UBWxwFJHm2oI1bfSYh+2RNLkxfXuaBBq2pdFjuXk3WbGPi/0nNTvsl67mNejmAVNpEyCwfcvBWqsfSU0UGWOtZUJTpMhkrKDK3CEJdNJZYChtaUNsdgYhGHGYLpFawbMJShuPIqLIEmaqBSx4pI06IXhBVYbNjomvn1nBr3z+JTxwbBqzNQOyBEyntBy4ljDp1dGtNPuxYTSpY1QKWGn2fMv4DXM1/zuTRnZkWYKuEOIRtL2eW2kRcsKhnALk+UUX0g7PVP0U0EOcStt01cA/e+Aw/uGFq3j41aWhn7X7fORxYI9kf+F80maESRu5n9YT2osM709IaRNchA2nRwL8KcbU4slarCqoilCfNst2oCmS/90jxyLWoiSaHilqj7RQ0kLPPAFrO6uOl0KSJNw4V/Mbz4fBClIJ4he/+xh+5u0FFFQF//pvnsfTF9iKXSporVuSPTJX2nJsFUGidrn+5rRIUkJAv5hZ1CGWEiTaqy24ik33JSPf2DGE+4JRZUhk9clvrr2FyP/gub+UMYwk2CPNELTBkd+lZGXYKipyLoJqHxlDbEGBLib4dXUyf7R7cIwhtU8w8p9euwIjgZLndNLEu3CzWzpBe5tns4prsN3sWqgU2CRDRKGizwS20pZ+ILQmKUwWRJRLqjiGyUtRV9Gz+axbPV9pC5A2mU/5HIRgxE9iNFnCnrFigtIWv3IdxuyIgaubHVxYa2HfuNiEZaQ4UNr+v2evoKwr+O0ffjs+84l34Ld/+O2RGslrGTSxcqXR8+u5RJW2ibKOnuXghcsbqBZU7A3UBfIoVAUvQXcz0EbhtZU2t8IFkHv9vFefODtiYNdoEUVN8RdMefDRew9g92gRfxJS23gCVYDB9zdOMWv2LOiKHLk/Rrz00HXOxWkaRAKIN6QG2PbIAqc9Mq6mDSDvMlHHCH1/kH0gfSV5FpRZPSEBcXtkO0ZpE7VHxj13ju8ewcsLm8zeoV3TjtTChfG2aRW/5TWwP7eS0d2jKERJO3WKxP8///zgZ3kQSQ4A+MyTF2NfrLwIkrY3a11bWNnhfTAOjzH4svtKhKBIttYik84xr6YNELPjbSfq7T7+1Weexa94ViJe0NOgSMO9ZUTORZhoZKoFC7CBrBZJMxDA4ZNokQAPv55soLS5rpg10XYGah8g3kLBV9qUkNLGuQ+uS1K39ABxpCu/3EobtUeqgzEGil/6jvh9k0KWvPuPTOFf3WHg7oOkvmmDEfvfs2z0bSfeHilS0+Y9EwrMmrb07Qf9ydj2SJ4FAdrHMYyqocJ2+VJv/X5WgUkQvR5pPeuaCSv4QRyYrOC1lSbzZ+0+/yR/bqSIM0stnF9t47qJ9PCRIOgk+7XlFr740iLmj03D0BTsHS/h/cfnhMbaaQyUtl5AaRO3RwKkru+m3bWhlgs8oSYFrz8XVdoMTSY1bX0LFc5QlAOTZZzz+rLN1gwcm6vi1r0jqTH94f04NF3xm7RTkBTL9P2QJAnFhBqzZteKqGzAQGmLU/TDoEEkgHhDaiDBHskxjp+AyVisErVHmrYbUvv4nRYsdwIQsEdy9tdmNbjOYo+Ms2Uf311D13SY7oAke2QQtEZ2S+JFrQZ87nPACy8Ajz02+DwPIskBAL/018/7vVayYjWotL1JSdtgYjuwwm3FHklXRUWDSFhKW19QrQOAX/7s8/jZP39GeDsK23HxA7/9dXzmqUt48MWrQtv6SluoIajIIqMdqsPSM6UuOqBiBG0kKwrLdgL3BE3dEmkoPWyPpGEeosmNSii5MUsQiZ8eKdinLfzdALx+QQr/aq2vOCoMZYfjdK4FbMNBqIqMmyYV36rHmkz5SXsxL2uR2Gi20sa/uLLkBUmE7ZEidlPS2Do6EWOFbsRhcbOLakEdmvTz2mZbnHHzByfLeG25xVT+WKvmcSBKWxc9y8EH37abaxuKyYqOew9P4r98+RRWmn18102zQttfS6BtBpYbPTT7RAkK2o15QGspV1t93LJndCikgsfeSIkHJW037x7BaystboULAD71I7fjxrkaCqqM6VoB/+F/uAW//2MxwQwpx0IbhVO0BRS/oq4k1rSxVEwavrOe0BMyiOB5EY3JBwAzzh7JE0TS82raYuyRos21g/Z4f27CMUYcaRstalAyz/oSAAAgAElEQVRkCRt98VAXCpFndzshiAQAbtpF6sleDNW1ua7LTdoMTcFkpeCX22RCrUbaAgDA4uLg89wemcNxXFiOi4dfXcJSo5u+QQzWW33IErFqvFntkeG+YFkaQgdJBX3BCgeRtPoo6QoMTclEdii++do6PvutK7ERtmlYa5EePIos+UllvPAJV0hpE1OoGAmUGQI8xks6SrqCs8vsFX+e/aCT2YJPokX2gQZwDDfHFrKmOGF7pCyUpBmuaZMkEgbAe2+G6/KC+8Ftj2SkR4rU1q3F2CMpCqqCkq4w7ZGDnksJ9kjOc9FlFNWL2CN90lYLK238Ku5aq8+MnPdDNzgq+5ca3cg+8JJo3x6ZMsnfM1ZEq28zY/87Xnw3D+jq9ffeMofju0dSfnsYkiThVz5w3K/HevfRKaHtryVMeNd8pdlDO6FGMwnB++b47pEhYsJjtaRtaOg1vWXPKNZafdTbfW6r5nTNwF/95Hfg7376XSioCoq6ImzzBAghX2n2/UUBx2smzUvaDFWOj/zvsknbKFXaOOQhxyGT/SGlTdC9Y7LskZxBJM0e6WvG+p4Re6SYPX6YtPG3fIkjbbIsYbpaQL0nkLq7BXsktYvWYhwCByfL0BQJpxaH5wok1RcwOBeZdo8aW5sHjwSecUHS1mwChkEslG8y5KRtm0BrWmzHxb/97Av47v/8VZxeEp/kr3qTiD1jxcy1QzsNVl8wcXuki33jJUxXC7ht76j/mQjWAgl5RgaSQEFrZ37/q2fFNwaw2iLb7xkroi3STRRRkkAnpCLnwg6pQ1lq2myHrA4mFRinwQpYE2l6mlABd6g56sA2K1bfNxREkjU9MjCGKvOHb5jOMPEM7odo6mJwDJEUyzXvfkxKLBwtakylbZAEFxdEInO3cej5AR7RUBYewrW82YUsDSbg/hgCNYL1thlprA2QRtIAOwo9jKsbXb8HGoXsJful3RdxcehhUPWz0Y2SSN4gEoCoOaMlDf/ifUe4fj+MA5Nl/NoHj+Nn33c4En/+ZoKhKagaKpa93mii1khgeNHjlt0jQyoMX1NqorTR9Mgffcd+3LZ3FI4L7B7jr0kr6goOTfOlRcZhslJA3+trCgyUFB57JEAm4fE1bSZToRr17LZ1jpo2OvZw5L/gwqPFsEdqfC6gVs9CSVOYttOCqojXtAWe3fRdyKe0sYNIAGITr3f5w5MiSpsqw3YHvWGTENcCgUJVZEwG2moM9j+5Fi6MXaPFrSttFGGl7U2osgE5ads2BCcqD764iJcXNnFyUVyRWGv1AqRt55S2zz17BR/+3W9kilsN9wXjXc0KwrQdjJd1PPFv3ov7j5AV3SzpkdT6JNrAmMKyHay1+9AVGZ995gqXXSqMlQZ5Ke0bL6FvO5lqqIJBJEA2pY2qdboq+y8w7jE8W+Hx3SN4aWFTqL8aQGwR9pDSJn49zFBz1MHknH8/IpH/mdMjh0NAeINIrBilTSR10WSkR4r0i1trmSh7CnQcRko6NhiR/wPSxn5Za4rEfW/S8x7cD0WWyBgcE5ilRg8TlcLQ9QSC91by9o7jYr3djzTWBgKNpBkkKYzFzR5mqtG4e1WWcHbDxi9/9vnYiRCvPTLJrtnuWyhxKiJ3H5zAM//2fbh+Knstx4fv2Iufmj+UeftrBVPVAkmP5ExJDGOiTNTVqqFi/0RpiHjzKHfU4rfZMaGrMvZPlPHZT74TT/yb9+Ajd+0T3p+tYLJKvgPUItnmvC8pipqCboI9ssoYR1dlGApfeiR1qFByna1PG8seKXMFmiT13itostDio+m4kTAU8nn6toOo/ejUfbpmoN7je/iywov8vn8cEy2/BULCYtNUtYDlkOU2LcAkjN2jRVyub6H9FVXaDCNK2t6E9WxATtq2DfSB8P237sL//M4DAOKbTSZhvWVirKSTm3UHSdszF9bxxGtrWGmKtx0IhzXwxuoGYQWKdUWbD1Ostc2I0iZ6SdZafbgucPOeEfRtB2sZzgdV2mjfuraARdJ2owQYECNt4Zq2rOmRmkJIW7tvx4YixMH0w1C8IBKB1UV/jJC9RTQqHxjELVOIKm2UMA0pbYoEXu45KIaPKm28BJbub2HIHknr+3jCN3oYj7FGUsQpba2Y+G4KXVW2FPkP0JXr9ONY3OxGQkjo9gCQJmpvdknrA1Zj61qRT2lzHBdLjS5mGI2ldUXGS6sOPv3YBZyPCe9p9i3oqhwh8WFUY+yapu3AtF1ueyQAoZj/b2dMVsikMqmFRRKKOrER37x7BJIk+Ql+AK/SRojHRsccavkwXTUiCxGvNygBpe/7uN5qcShqCUpbTBAJAJQ1CXWOmjbqUKHXKUsQSd+312exR8YTe4PzeUVhWjFKG489MqZPGwDM1PjskbbjotE1/WccBX2v8vWtsyBLyUrsFENpSwswCWPXaBFd0+FuCxFBrUYskA88ELVH5krbWxu2Nym9be8oPn7/QQAQfqgAZII/UdExVtbR6FlcUvXrASp/n1sVT8PcrvRIqmaogul6FJuBl2FWpY2uFNEIZVGyAwxehPu8SOiWAFMJ2/H0DMcRtVhm6dNGQkSO7yZ2gxcui1kk/eRHGkSSpbl2yN6ShbTZjgsloJLRtgEi2wMYsliqssytAvt1efLwo1ekCJzdXJu/T9tqi60uBTFa0pgBAY0Ue6Su8Nf3sZprk7FVNDne0UuNHpO00RrYjZRVZzoRGGcEkcSRpDDW2n2YtosZxn4EJ2YvL7C/L80uW4UIg9o1w0pbW3ASlGOAqUoBK42eUMR+GB++Yy/+yZ17ASCktAnYI7smd4Pz1ws0VIWWAviBQyJBJAmR/3GEp6JLXEEkYaWNLARnDSIZDgHhISntvo1SzDOvkCHynxVEwnM4SfbImaqBponUudZqswfHJcpcEH44GGez8UpBTVwAmqoySFtKgEkYu7x5V9giWW/38cffOBcJz4ngYx8D/ut/BQ4cyO2ROYYxmJRK/spJ3EMsCbQw3m8+nIEkbAfoSluWFgYDS98gKVDcHjmw0mkCNSpB9C3Hn9iLJDQFQQkXfXiIkh2APCRVWcLsCBmDqhU8sCNW0yy1YOE+bZLwfUXj+g9NVVBQZbxweSN9I8Y+hCP/RdI8ByEe2e8L03F8KyEgTmBZNW2aAFHxj0EdftlpAqSNbY/kT9KMC98IomZozPqppOhrgBwH74JCnNJ2ZLaKi430AyGkLapw0bCN1ZT6DhrIspWaNtp2IFzTBgwWFyQpnrQl2a7Y+zN8TejKdZaarLc6ZmoGFja6XrR9tvP3v3//TfjAbSSFM6gm8SifBa9B9EbHjA10eKNA7ZE+aevTgBzOmjYtPj2ykaC0VTQ+e+TgPs/eXNtyHMgSIunBfOmR8feIIZoe6Qxb9EWCSOicssCwR854JGxpM5nIDFJ3hxea6HP4DEfQWKNrpda0TlULWG31h9wfojVtdLE8HEbylZPL+N/+9kVcTHOj3X038IlPALOzQL0O9Lxz02zm9si3OoK1R/QLJWqPtB0X9Y6J8ZLur3qI2gq3C3RycC4DafMtYAGikaWmzQ+coJNzQdHRtB1/26x92uhK0e5R8kAUPQ6AvAjHy7q/oi5E2sL2SC2bugQM92kTaWpNx1AVCaoi44a5Gp4XJW2hWi4jQ5qnGRNEImQVDUX+66qSqcYwrHLxhqFY/ncjVIclkNxFCbfOUNp4Lut6q4/xclQZCqJqqEzS1kppBl01VGz2XC6bZpcR+Q8AN8xVcaWZXPtpOy5Wm71IaiNAJh/jZR3rKaSNNvVlqY4lXYEspUf+U9I2U4uStoIq4+CIjOunKnh5gR1K1eSMd6/RxtahpD1/cp3B3vdWx+GZCjqmjfOrrcxKWxCaIsPQyH889kbDKxsI2yN3AuMlHZI0WKTkrbWkiOvT1rcc9CwHlRjCU+G0R7ZCixOGRt5hIrXV/UDLGYqCqsC03dTG1q0EtbAgmB5pWsN9OgsCpQK9BKWKPgvT0svj+lu+94YZTBgSfubPn/F/Jw6bXSs1PGmqWoDt1Q1TdLwkOF5nAA3kCZcKffmVJUyUddzCm4A7M0P+/NM/BfbsAc6fz5W2tzqCSkJBlSFJEJbv621SPzVe1v0JqaitcLtAbVDnV8UTLMPhGbzFvkEEa9pE4syHxnAG0boDpU2MrNCVx60pbX1MVgr+C7AtsCwXIVwK/6ocBb03KVfRFQW2k/6iCsK0B3bVozNVZtPMxH0I1XLRyH8xe+QwYcoUROJsLYiEnR7Jb49k1VUAYpH/PYY9UvO/I8ljuK6L1VY/Nu6fompo6Jh2pJ9fIyWifv7oNJom8Phrq8kHATIRKjDquW6cq8F2k1d84yw+FLM1A2tppK3N7lcHeA2DVXZaYxBXN8jzgUXafu2Dx/Hjxwu4Ya4Wb4/smVyNlOmEMUwi/RoRgZq2HARHZ0niomm720Z6KwWNuw6sUlBQb/ex3tp50qYqMsZKuv++a2YgbSxnUVoNbFmXuCL/26HFIvo+F1kYN63hABCA36afGESiitkjLSdsj/RsiRxjJClV9BkUbpIeBlXaws+ssbKOn73dwHrLTE3KbnRNX/2PA+2vG7RIigaRjJU0GJo8ZI+0HRdfObmM+49O8TeRp6Ttd38XuHwZuHgxV9re6ghaAiWJELeu4ATfb3pbKQyUtgwkYTvQ9CYHWeyRrJo2YTueM1gV07IqbYGC36xK20qjh6Km+BaqTDVt3iSZWjuElDb/viL/n6UWzHYcqLLk+8+pNU9UraPXs2qovk2OF2bonqBKmwiJDttbdP84RHrkRINIhNIjQyoy/TvvYVghi2dwP0TtkUNBJJx9wdp9Gz3LSYz7Bwb1OeHrTFec416W7z46DV0B/u65heQdAYgtjDFZvWGO1E3GER0g3uJDsWuUn7TRflFhlFQplbQtbnYhSYM6uiDmj05jb1XGDXNVXK53mD3WWj2ba5KvKjLKuhLZn3Zuj8yMIzODmPztUNoA8r3hHevOA+No9W1crneY34M3GpMVHSve94reV7xktqizI/jTAk0qmoSNjpm62ESVNrpYRN8fIqSNkKXh5xZv/1bSs459LgyN2CN5Ew77dtgeSUse+OyRJGE33h6ZppJR+yStYwxib1XGkdkKXrma3K6qwam0AWzSxlvTJkkSrpso46XAu+CZC+uot008cGyaawwAA9L2+OODz3Kl7a2NMFGJswskYS1g16FKxE4pbfRhe261JRy3GlXaFK+pomADSjo5F0jGC6IftEdmUHYAorRNVnWhIt0wVpu9IaVNpMF2uD4wS0Npy3GHJtmZxgjUGJYLKjqmLXQ9wrZAVZGhyvzx8EDU3qJ7jTFFyLzluJGaBpHm2r7SFuqRJhxEEon851+tTWyunXIu/MbaKTVt9IUcJgnNrpU4kSvqCm6bUvCFF6761zwOcQEMByfLUOVk0uY/K2OOY3bEwFo3+d9fa5nQFCl2QllUpYgdMYzFzS4myoXE9McbZgkJfZUxEeKtaQNIomV4f2gSbR5EIo5KQcUez37FW7uVhqqhchPo+45M+fOFnVbaADKJX21ls0caMUpbI6UPYUWT4LpR228YHe8+p5N9X2kTTP5l2SOBdOdKK6GmraDKcAFc2UgmSxSWHbJHCizEdk0nVqUaK2lQpHSlbbHRxUTAzRXGoakKziwl17U1e9lIWzdDcNIDx6bx+Gtrvp39Sy8vQZEl3Ht4insMn7QBgO69M3LS9tZGuIFxlj4idCV2pKj5JCFLAuV2oNm1YGgy2n07kgCUBp/ABurJXFcsst8KPGB9ZShDTZsW2AfymXh65GSlMOhhIkjaXNfFSrOHibLuTwxaAg22wz3WKPkUUZccRkNpQMxuagbSPCsZyKcf+R/qkSNyPSx72N6SLfLfHZpgb0d6ZJYgknDk/6GpCs5vOnjwxaupY7DSI/22GCmHkkZ2KKqhtMLnLtXxk59+CmvtfqoydOesitVWH9+6WE/8vbgABlWRsbsix9aB0W2B+Mnu3EgRLROx4QgAsaOPlfTYBLSSlm6PXNzsYnYkuT7w0DSx4bDqgxsJtTJhVA01Nj0yr2nLhqOe2rZdStvu0aJf/5yGmqHh7oPjAK4N0jZRKUTtkZwEtKiR2uDwQl5aX8eKTr57aQmSdB5ErYSZ7JE2wx7J4cBxHBftfnzt6ftunIWhAB/5vcewlKJykf1whmqaRWrauqbN7NEGEFVqtCDhqfNr+NXPvxS7aLa02WM6AygOTVdwZaOb6AhqdM3UIBKq5AV7tdHrHFfjyMJ3H5+D7bj44suL+NrpFfy3R8/igWPTYt+ZIGn75CfJn7k98q0NVjqeKOGiEzpdlXfUHmk7Llp927cpnROsa6MENhgxDwjWLzmBmjaBZLzBPrhwXETskTy+8SBWGqQejT7s+7YYEW/3bXRNB5PVYE0bP9lxQspOlnMZVpey1MUFG2OXswSqMAI8DE0Ruh5kpXSYLAGi58IZIlzbkR6pyhJ3fWDHHF4xpvi57zqKAyMyfvbPv4WrKSu2rPRIekxpCxuUtLF6kwVRCyltj55ewT+8cBVPn19PJRmzZbJfaYs9mx0r9sW7ryonKm1ppG2XN3Fe2IhPF1tr9RNtokVVSg0iubrZw2xMXR0FtV+yxkoKOAiDlejp2yO13B6ZBbSubbtI23/80K34v//Jbdy//55jZDJ5Ldoji5oy9JxLQlFn2xWbveQmzBXvsNMSJMNJs0aGsLfw+wMIqFwJw7TN5IWRo7NV/NydBs6vtvEXT15M3Q/LdqGpwfcgnZvw2SMpyWNhzJDwzXPr+G+PvoYzy+zSluVGN7YWGBgsMsXVFLuuy2WPLBdUlHXFv6cA4Mnz69g3XsJIjCWdheO7a9g9WsRvPXwaH/2jJ3FwsoJf/8FbubcHABSLQNWzQ//CLwDveAdw551iY1wjyEnbNoHWqgTDN0Qj/y0/HU8aNIjdAXskVVCOebaecI+MNIQntry+8aExAqtRmQInQjY0WmeYyR65BaVttTmwo1FbQxalTfXruMTPpR1S2rI0KyeR/2Q7+vISOY5woigwqAUQGSOskgFitlkrrDoqMhyXfwz6PQ+ulKoCcf0LHiELT/QNTcGHjujomHZq5HLfciBJYbWP71zQVc8pRj1DENVQ5D3tV7ba6sdOwChK3o/TVKq4mjYAmC5JWG31YydlaaRttkZsbwsJBHi93ccYo0cbBU9N28JGhxn3H0RZV0kSZcgCZqes4IdRK2qMIJLcHrkV+KRtm2oCKwU1VYEI4v3HZzFbM3wL7U5islJAq2/jA7/1NTz08qKQekvfbeE5T6ObXNNW1sgzLC1BsmsN13INykfE7JFhGzOPPZLHKnpoVMFUtYALa+kL3H17uO2MrpDgOp53es90Er/ro4XBuKwaWoDYJ1l9JSkoaTu1yH4PdU0HluNy3edT1YL/znEcF0+8toZ7PHWZF5Ik4XtunsX51TbuPjiO//4TdwmRPh8zM8DRo+TPr38d+OAHxce4BpAvz20TfNuUkt0eGbQ90RWgnuWAMx9n20DDB2iRf8e0MSqwvW0PEw1e33gQwRqqgT1SLO0QGI5FJyRBrA5rrd3HVDU7aVtpDYp+ZVlCSVfIS4DzmUMj/2VpmAALhW8wGkoD2cJMgIA9UkBpsxhKGyHRYn3aWPZI3uOwHReui+G6OEGLJVWRgwvQIvbIhXoXiiwxAzToe6iZcl573uQjaOvjrWmj6leSPQaI1rQFJwBpylDR+76mqVRJTYVL3mSu0bWYReubHRO6KscWtA+UtnjSttbq+5N29j4k19o0uibqbRN7x0qxvwMAsiyhamjYDIe69JMntGFUDRWnl7zr0Tbx7z7/oh8+UMpJWybcdWAce8aKiffB64ldo0U89q/fsyP/dhjfd8sunFtp4dxqC2eWWzgyw28foyQqbEemz7I4Vabq2SOpAyAOXdPxw0cA+L1wuwKrfmbIGg/wOVfSwlQo9o2XcHEtfYE7TB4lifT25XkXJtkjAeADh3Tcd8te/MYXTzKJsOO4WI5plUKxf6IMVZZwOmbxkC7kpSltwHCD7VeuNrDRMXHPwYnU7cL45+87gvcfn8Xb940lNvROxMc/DoyKzGSvTeSkbZsw6KdFPdeycI+1QfPggD3SdMDnkB/AcVx89luX8b23zCVK6XGgDyk6sRMlnxGlLUPioRmIxc1ij2Sl9BGSwH8say3SgmGqEmzBIEjavAcWjVgv6aqfhMWDcFphlhAR22YrbWKpi1uzR1J//VAIiKaI9WkL2VtEFVjLie6DKPGjSt0QYZJlbtJ2pd7BTLXA7ONkeMeWdl5Ny0UhtD39jjyzZOM3HzqFn37PYea2y40eqgU1VZkZkDaqtA3IS5oyZKikoXSYpAThOC42E/pTlQLEj0Uw03pbUTKzkOASqLfNRHtkSZXQ7JtwQkE+FJe83kF7UkgbQPqsRXqspcShR8YwNP96PPbaKv766cu4fooU0+eR/9kwN1LEo7/wwE7vxjWBfRMl/McPEdvZycUGOJ2RAAb3X8QemaK0UWVoKcVKTcjK4B6nixTtvs09iTXthPTIhOc//Z6mBczsHSviyfPrqfsRfJf6+6HJ3PZII2FOt7cqY/6W3YS0MRacaLNrVosSCk2Rcd1kGadjwkg2U8JlgpiqFnzFjraBuTsDaSvpKm7fL6bQRfBzP7e17a8RpNojJUn6A0mSliRJeiHw2bgkSV+UJOmU9+eY97kkSdJvSpJ0WpKk5yRJevvrufPXEsIBBYamoCtobRxY+qRAcap4TduJk0v4F3/xLL52ekV4W2AwUaOFpKK1ebZXQyWF1CHhtELvXNIHbRZ7ZDhaVyT4gj70Rkt6oKZN0B7prSDSc1kpKEI1bX6PNe9cSJJEmmNvoaatIKguATSIZFhpS1OEhrZnBHAYmiyofEZDRAB+BZZF5AekjW+MYG0fhSpL3AsKVzY6mPN6/oVheEQl7bx2TNtfCKGgx/Tsso1PPRLfY2ep0cVUwiorxcAeGVXaqimkTZZIImOSStXqW3BcxPb6oapj3BhppM3QFFR1YCEmGMDxmr6m1bS57qA3XRgXPSsUTSBMQs3QInYl0YQ+EkRiwXVdv9ns2ZUWiprC368oRw4OHJmp4tA0v/oYZ49s9ixIUrwSrCsSRopaah1v13TYpE1g1a9nOpEgEp7+rbQMIM0uune8hCv1TqS3ZRh9hk3TUPkWMLumnbrgRmtoNxh1grTxdlyrFIoDk2VciMkyEFLaKgW/BcFjZ1exd7yI3THvvxx84Klp+0MA7w999osAHnJd9zCAh7z/B4DvBnDY++9jAH5ne3bz2ke4ps1Qxe2RQaIxUNrEa9oeOUnImkjNURB0ojbpqUOitXmm44RIgpg90nVdomgE6tFELGjAgFzpETuewBjWoB+WT9oElbZmaFWqpKtC14UGXITDM8RCRBymuiTUpy3QWyZL64JBveYWroftROrRyNic+8CoRxNX69yh7QFCRG1O4riw0fUbtYdR9N6BaaRtcbOL6erwSmlwcaLZs2Lv0+VGL7WeDRiEIVHCstExQcVFHpJRM6L1V0Gk1aQNlDb2uUgjbQAwWZSZiY1kXBOOmxzIQodvxBwHVdr2jnMobaHz8aWXFvH85Q0A4GquDZCaNloHR/9t182tkTl2HpRIhO2RjS4J2kmytM3WDFxNSV3sWcMLVYN/j/8d1GEQHh4XUIvTHrl3rATHJRb4JJDFx+HzwbuA2TGdVPdUpaBCkSXUO1F75JJvj0/2b42VootMFIM2Duk1HnOjRWx2LTR7Fl692sAte9789sSdRippc133EQBroY8/AOCPvL//EYAPBj7/Y5fgMQCjkiTNbdfOXssIK21xzSaT4KdHhmraRPGop7CJkkYKOmmsFTXSfFhwnLAdTzSIxK9/Gkro4w97AAKqihoKvhAkCQCZwMoyIY6ipK0fCkQpF5RMtWBhpUyIcLns0AoxpW3woqGtC5pbCFQBqPLJvw+W7QwlJhI1V8TaSBdFhi2agFhdXDhRjbdPm+O4WKh3sSsmuEKTyTGl3R9X6p0I8VND+xT3wl1q9BKTw4KoBtIKNzomju8aAcBJ2orRpMMgaLBJrcgeyydtGZU2ANhfk/H85Q0/gTUImlY3nhJEAsQHqlxcb6OkKxjjKIon9kgyju24+OSfPo1f+/zLAOLj0CNjBNTPy/XBKngeQpJjp2EkKG1pyvx0rZDaEJrUtAWVNprEzP8C6fTtyALHuKe0L7TiH+B0cTLNHrlnnDyTL64nh5GwAlEMTeF6B/U4lDZJkjBa1FBnKG20vixNaUtadEvrvRfEnPeuu7zeweV6J7X+N0c6sta0zbiuu+D9/SoA2gRhN4Bg5ukl77MFhCBJ0sdA1DjMzMzgxIkTQz9vNpuRz0Sx1TFEtn9umdzIz37rGTTPKVhb7mGzZaPZdLjHOHmarIx8/dFH0PWeRS++chKjkz3uMda6Dk4vkVXY5196BVPNM8Ln4amL5Mv6/NPfhAoHp89dwM37TO4xzl/swXVs//dPrZOD2Wx1uMboebnlF86/hhMnLgMAJNdGt2dx78PlJnkCnnzlFZzYOA0A6LY6kFybe4xX1sh+v/TC88CCAgUuzpw7jxv38J+L02cG11SWJHSbXTRNF80m337Q7R/96iP+xNy1TLR7/PfVwtUuut3B75+tk+NqtPmuBwB0uz0sLizgxIk1dL3r8/xLr2Jsus81xre8AIVnn3kaG2fJS6dR76Jr8V+PpZUOejaGfl+VgE6Xbx/WvWbLZ0+fwoneOQDAqUWyX5vNNtcYFy724NjD9+HachemnX49Nnou+raD1vIlnDixGPl5q9VCQZbw6pnzOHEivl/bhZUWduvD185xXVQ1YER3cakl4R+/8jXsrkTX5K7W2zhWiT9fwWeF6vRx5sJlnDixitVGG4cqKj5wvYaJ9j2TcL0AACAASURBVAWcOHEpdv+azSacroILC/HPnZdXyT342qsv4cTKq5Gfu/02AAlPPfciqusnIz9fXG+jhlbiOd9VMHGiK+Ev/uFhvw0BxWnvmXTx9OD5EIZkdQFIeOQb38TieHSi9K1TXYzrDr7yla/E7gM9n+16D8sb5F5fajvoWQ56Fvluv/z8M2ifj5+I0TEuLpB79aFHvo5XLgZqgMxu4nm4Ft6l2zHGtbAP2zHGtbAP2z3GhU3yfXrymeeAhcG08rVLXUgJz8Zmswm0e7iwkvweWFjqoB949lNV6sVXTmFqhu/5v7bZxrgSfc4fHZPx6KU+Hn74YaYi+PQFOh96ApcMts7RbDbRefU5AMCXvvEMzEvshRjqIrp88cLQM97tdXCxY+OhLz+c2Gah3myjvhI/H6TXQ3NNnDpPnt1BPHPOO5anHscZLfrv0O1Xr/bR7tv40pcfjiwIPnmJjPHiM0/iajF6PoL31ZL3nP3zLz0G03bRWb6Y+G5jjZEF19r3Yzux5SAS13VdSZIE2x4Drut+CsCnAOCOO+5w5+fnh35+4sQJhD8TxVbHENnefnkReOpJ3HXHHbh5zwhObL6Ip1cuoVIpcY/xdP9V4PRpPPDueaKwPfQF7L3uACq4xD3GZ568CIA8PPYduB7z9x4UPg+nv3oWePFlvO/d9+L/euYrmJyeRqWyxj3GQ/UXYCxf8X9/4tIG8PijUAsG1xgbHRP40j/i6OFDmL/3IACg9OgXIakO9z68eGUDePRR3HrzccwfnwUA/P7px3F1hf84lFPLwBNP4M7b34Y7rxtH8ZF/xMzcLlQqK9xjPNl7FfKZ03jg3e8GAHzm8tN4dbGBSsXlGuNb1kng1Ck8MD/v163UnjwByF3uffjTC0+iKbUxP38fAGDqygbw2KPQOK8HAODEg9i3dw/m52+C67qQHvp7zOzZj4q+wDVG5/kF4Omncfddd/r9//7m6jO42ODbHgB++9VvoAJgfv4d/mfGiQchqeAa43K9A5z4Mm684Rjm79gLAHBfWQKe+SZ0o8g1xoNrz6FYXxr63c8vP4tX1y+nbv/sxTrw8Ndw3x03Y/6m2cjPT5w4gdGyjZHJSczPs/vQNHsW2l94EHfceAjz89cP/ezxd1r4g8+dwK8/2cOR47fhzuvGI9v2vvAg3nbjIczfP7xtcB/occy88CiKJR333XcnOg/+PW46dB3+5XceTTxGOsbe2Qqu1DuYn7+X+Tu9F68C33wK995zB47vHon8/MEvPQygjdl9B5n72jvxII5eR+7HOFzYfAg43UVx91HM37Z76GfWS4vA40/i/nvuwK172Zad1/72IQBdXH/sOOZvnIn8/P989qs4usfA/Hx8vx96Ph9tvoSnly9gfn4eD7+6BDzyTf935t95D/ZPlFPHkE4u43eefQLHbrkNm888CV0hSv7k2Ajm59+Zuv1WcC2McS3sw3aMcS3sw3aP8dpKC/j6CVx3+Bjmb9/j//z3Tz+OmYIVe3+eOHECtx2dw9eunMa77r2PGdAEAL/1ytdRk2XMz98DgJAf6Ut/j9k9+1Ep8L1D3Ee/iAN7ZzE/f/PQ50uVi/j5v3wOtYO34fb9Y5HtTj1yFnjpZbx3/t5YS+CJEyfw/nvvwy8++gWUpvdifv4Y8/f6lgM8+A84fP0BzM8PwqK6kwv4xKefxmL5enzk7n3xx/DIP+K6vbswP388dj/m5+cx99LXoOsq5ufvHvr50//4KqRXT+P975ln1sHS7c/r5/A3p1/E2+76DkyE7PSnv3oWeIHMD1k1ycH76ki9g3//+JexoU8BuIwH7rkN9x+Zij0+1hhZcK19P7YTPDVtLCxS26P355L3+WUAewO/t8f77NseYRuboSnC6ZF9L9Kc9hQDIDzG0xfqvh0hqz2Syt9lXc0UqBKOmBdNj7RClkIguz1SV4dthUIJlqH90AUbMdMxgsdR0hW0BZtSSxKGHrAFVU5tohweQ1UYdlXBZuXUHilJEsqCtXm0Bi/o5TdUsT5tYXskQKzE/DVt0V5xwumRIesvwB/5Txs9x9W0ASRJMMkeSdMQaaR9ECVdRcVbPV1nRGjzWmMoql5aYdMLDUmzIwZRM9Qt1bTpCrlOLHuk7ZDmrmkNiXdVSF3e85c2Ij9b9+KwxxNq2mgPqTVGdLbruri01uYKIQGIXbTVt2HZDs6Gmt6KRP4DpC5yvW3izgNkgpnXtOXYaewaNVAzVHw9FH7W6FmopNQ+zdQMOC6w0oyP/SdBJMMx+SVNEbJH0obhYXzPzXPQFeAvn2K7B5qc6ZGqImPXqJEY+8+q7QaA77ppFkfGZPzGF08mztu6KX3aKEaLGrOmbbNroWZoqcFF1LbOqine7JJwmQpHb8PpagGyBDzxGqmw4n1e5ohHVtL2OQA/5v39xwD8beDzH/VSJO8BsBGwUX5bI9qnTUbfdvwgCR4EI2lpSqBoTVvXtDFa1iBL4qmPFM2ehbKuQJFJ/5BwcXEabMeJRO0D4E5utELnEiC1aWLNoOkEfTg9UiStsG8NagyBrKRtOPGwXFCFUhfDjbEBj3yKkJ0QieZtxDw0hj08hnBtHvN6iPdpC58LXaC+b5BgGU2gFIn8VyLpkTLXubziFagnkbZyQU0MeLmykTxGRafNahnJYV7dSFqPNoqqoaLRtfwUsjSSFEStqCWmR9KfxY0pSRKzmTQwCAZJI5GqLOGmXTU8l0DakoJIxg0Jqizh/Go0zGSzY6HRs7hCSABCYgEy4Tm73ETNUH2yxt1c25v8vrLQAADce5isWOekLcdOo6Aq+N5b5vCFF68OpSM3u2ZqTdusV2ObFEYSjvwHgKKuomPyvYNc10XHjNa0AWTR5MiYgucv15nbtnoWipqSaFukODpTw5Pn1ph1tABp1wIgoihKkoT37NOw0uxFFnWCx9C17KF+dXEYLenMd8BGx4ytIw6CPmtYz/BG10RFV7kSa1VFxmzN8IOT8uTIrYMn8v/PAHwDwFFJki5JkvQTAP4DgPdJknQKwHu9/weAvwdwFsBpAL8H4Kdel72+BsFS2gD+8A2ATGzVULpeL4PKpclypubeFM2u5dsADE1GV5CoRCPmxcIefIUrRDRE+oqFA0DIfshCys4giGTQI60nGPkf7g1T0snqoMtJ5lnBF7pweqS7JXWJ/O7wcZQLKpoi6ZGMyP8sfdrCK5SEtPGfSwAxCZT8Cwrs9Mj0ba/UOyiocmJwRSWF1A+UNvbLj6pDSclh4eTJOPikLUUVi9u22bNiJy+bXhpl0oSuZgzCO4IQ2Z/ju0fw8sJm5PO1lglNkfxQHRZUWcK+8RLOLA1Poh49tYJf+htiQRdR2gBy3GeXW7h+uoK37RuFKg9cFeljkHNFj+eO/WMwNDlVAciR443AD7xtD9p9Gw++OKhZavasVCWZ9gxLCiPpWlHSRt+lPOhZDlw3PrSnooH5rAGAVt/mXlj5vlvncGWjG9uvbZBqHSU84wbtWcc+D/QYChw9GUeKGjPynyfAiW5Pfz+MRtfiCiGhoC1upquFyDXMIY7UM++67v8U86P3MH7XBfDJre7UmxG2M2y9oqshYmmFboRkiCptlteAOIutkaLZs/yGr1nIX5gkiNrxWBN8XeFvYBwcI2iPNHRFTNmxwvZIRVhps5xhIl4uqLAcl9veaDsuFCmstClYF+rT5gyNIZoe6TguXHc4wbJSSLbxRfdh0DiewlBl9B2vPiEhEpqCRdo0hd/yajLskaLW3XD7BDoeD5df2CRx/0nHWtbVxJ5FV+odyBIwE6OWGQqxa64nJIfxKm2VArFHbmYgbTVDg+OS5DVWHchml6TKJa3WVmMSzERI20S5gEbPitw7da9HW9p9d3CqjLMrw01m/+Brr+HhV5dQ0hXctCtaj8eCv3LdNXF2pYl3HZrCe26YxlS1wHXvA+R4dUUOWI1K+Ph91+OGOf5+WjlyvF64Y/8Ydo8W8flnF/ADbyN1bc2uldo8fmaEPI8SSVvIHgmIkTb6e6UY0lDWJGzU2c6AVs/ibsvx3htmUNQU/O23LuOuA9Fm0IME4+hCzUhKo3F6DEkLTRSjJQ2NnhURAjY7ZmxvzCD8RaYYp0PaNQ1i12gRT51fz62R24Ss9sgcIUT6tHE0bQzDtJ2hFZiCKl4XR61whioLb0ux2TX91TFSm5elpi06MebvhRV9sKmKlK25dtCOJ1hDxappEyXRfcsd6hVHH7i8fCd8Lul+iNajbaVPm8nw4ZOaNvE+bcP3BT0XfDtiOdH+NiJknkUcs/R6C1s0Vc59WGv2MZFgxwPSa9qubJAebXEF+5IkYaTItsYsNXrQFIkroh4galmrb/s1XTwv++C2QHxc/kbHxEjKftSKKnN7EdJG9yN8Ttda/cR6NoqDUxWcW20P2V8XNrp44Og0Xvx338Vtj6THeqXexeJmDwenyviem+fwGx++jWt7gLwPPn7/QTR6FjRFwnS1gH/+viN4//G3RFedHNc4ZFnCXQfG8ZKnBNuOi1bfTlXaJssFqLKUuFjVNe1If7KSzl+60U6J7S9pEundyFjUbfUsbjW7XFDx3htn8HfPLzAXeKk9Mrz4CACjHmlbjiFtg6bW6c+9UZ90DT/3eJW2gT0y+vxt9dKvaRC0xQ3vszJHMnLStk0YWK/IKaUyvMjk2rIdaGpYaRNXuTRFQkFThG2NFM3eQP42NFm4No70aQsehxiBpbVHwT5tGmcvrMEYUaJR1GUh5dP07ZFkjIIioy9sV3WGFMOS97DrcEptLHtkQcASSMcIq5Zk37LbCkltnkAQiX9Nh0NZgGhD1tgxLCdCVgxNRo/zvrIZxHFgFeU/F2qIOGoysUemWV43OiZGU4hKqj1yo8MMIQlirKShzgjPoI21eZUd+gy44lky00hWEEkrtQDfim/NYNfFiZC2Sgx5XG/3U68FAFw/VUbfcnB5fRAucHWjg9kRg/s8AoNJ0LOX6v64WfDJdx/Cwcky9o6XuGpKcuR4I3FouoKFjS4aXdOvzU2z0skyWYBIqmnrmQ7DHqkO1c8lgb5j4uyRJVWC65LglDB4LJ5BvOfYNOptkyRqhjBYAI1+d3VFQtVQ/drjMET6o416/efC74HNLq/SRmtwo8/fZs/itosCAyt/rrRtD3LStk2gE2A6JxUlKkA0aCGLsmPaxL5VUOUt1bQFlbZs6ZGD41BkiVgZOImKxQiM0BQZllCoC8MeqSpw3AEZS0MvYo/cenokPa+8fMd2hwNAAPEUzIjSRkkb5+k0QyoyAFSyBpEE6+K81cuk4I0gwvZhAJgbKWK1I7YYENwHOvHf7PPXtIWvh8pJgskqZ7K6Uy4oaCXUPF6pd/0agTiMljQ/aCOIpUaX2xoJDIgGLSIXrWkD4utEeFZ84xq8CiltBTZpE1HaAOCMZ5HsmjbW26bfNJYXdBL0lFfrcr03rigMTcGnP3o3fueHb8+0fY4crycOT3vfl+UWmt53jofwTNcMLG2yFSbbIf0tw/bIYgZ7JCs9EgDK3qOEtUjU7tsoc9ojAdIsHABWW9HjYS0mD21bLcTaIzcFlDa6wFYPHQ+PwwEg50lT2Om9rZ5YTduAtOVK23YgJ23bhLDSRh8wosEXQzVtmiJM2mg92ZaCSAIrS0VNPD0yrC4BRK5nOLaYMJ3oBF/bBnskXWXrcJ4Xn/gFSZtwEMkwEafqUpdXHdpiYiJAwzMGY8gyScUTjcofal2QsaZNDYWZAOBuHWA5TqSAe99ECSsd19/HJNgMe2TV0LBrxMClBt/JYKV50mNKS5Csc6g75YIK23GZ33vXdXGl3vHtJnGISw67Uu9gboR/tZO+mC+vd6DIyaEdYVDC14hT2jhWfGvFrQeR0AlOWL2st02MlThI2yRRxGiiG627mRU4j8DgfDx9fh1lXfHJYBbsGi3i6Gxex5bj2sPhGXJfnlps+O8Hnvqn6Woh1hZI3UasIBLedzn9vbikVRrgxAreaPUs3yHDA7oYtN6KjuUvSMeo5DM1I5a0NUWUNhokEngP9CwbXdPxk2yTIEkSaobGPB8kXZz/fNy0q4bJio637WP3w8whhpy0bRPi0iNF7HjhPlQFNYMdz7MmGlr2mrag/J01iCRs6Rsp6WiaYkpbOD0yS+T/kN3Uuya8xzNYFRukR2ZR2oLXlJLhDue5IEpbNIiEt30CwL4eImmctsNS2sRaF7CuaclbveRV2lj2yP3jJdguqTNK3d6O2iMB4NhcjZu0WYwgEnpMSQpu33LQ6tv+yzQOlRhlCCApZj3LSVXLRotahLS5rovL9Q52C1hUqgGlbaSoidkBU+yRvEpbx7Qj37mNjgldkSMr7yzQCWOzN9iPVs/CWrvvp9YlYbysY6So4cwyUdrofSaqtJW8FiqW4+LWvaNc8eE5crzZsHesCF2RcXqp6VsNeZS2qWoBy80Y0ubNY8IpqyJBJDz2SICttNHQJF5Q0rbGUNr6jHlJEERp20Z7ZCBFmC6A8TomSMsVtl1U1B755C+/D8dma9zb5IhHTtq2CZH0yExBJNHUReH0SGfr6ZGW7Q7quLJE/jPUodGihhY3aWMpbWLqkl9DNdTMmRxTl5NJm7YDWRrY37LYI8Pngj7QG5yqI4twlXQFHRuxceqRfWDE1IuEmbAaY5d1FT3L4e71ZjnkXMoh4gdEQyKS9iNsK9k3QSwXF9ba6fvAII4AcHS2ioWWy3Vtk5Q21soqBV2x5KlpA9jnhDbMHk1RiMbKesQeudbqo2s6Qn1y5rzauTPLTSFrJJAcRGLaDpYbPcykEJ/BGMPndbNjosZJIlkk+JWrDbgucONc+iRCkiTcsmcET50jtkYaljArSNrIyjXZl7fvGxPaNkeONwtURcbBqTJOLzXF7JFVA2utPvMZ3I1R2oqaKhBEQpU29r5Qe2RYWXJdFxudfuozNwiq4K8lKG3hdxDFdM3A4maPaY8XCSKhz+vg4h1dQOPtt0larkTPR0uwxi/H9iInbduEqNIm3gsrYo/MkB5JJ/mGmt0eGSQKhhdzL9IknKm0CZC2fkjhon8XaQbN6vVGV9l4yWzfGr4eWUhb+JpOVIhKstkTqaEaPpcHJsuwnEGtURpsx42EFogEu9j2sPUXgO/x73LeYqYdJY7UqsJrjwz3vAOA/RPEvnZ+lYO0MRRDADg2W4XtIhLtzt6H6PV4x/UT0BXgx//wiViLz4a34jmS8vKnK5gsFdNvCJ0yxmhJQ89yhr7/l70wERGl7fqpCu68bgyW4wo11gaCNW3RictCvQvHTS9Mp/9mkHB1TRuPnFzBgUm++ggWeaTpdjfs4lv5feehSby62MBSo+srbbMcKl0Y9Hhym1COb2ccmq7g1FLTf4bx2COpe4BVB0aD0NiR/xZXz9NBemSM0ubZI8POgHbfhmm7XKFFFJoio2aoTKUt7N4JY7paQN9ymLZwEaWNLhAFSRslpNykrRitKe6aDhwXQkpbju1FTtq2CXbIq2xkCCKJ2CM18fRIM2CPFE199PfDcSKKoVAKpuNEJuijJQ0JQsTw9gySsB32SHpNeMls33aG4vqz1LSFr2nNUKErMnfwhcMgbUe8epaTiw2+fQhcTwqRMBNWjSFdaetyh8tE6xypL54nAcz2esWFlbbZmgFVAs6vRZO6IvsQk9xF64NevZp+PllK27HZGv7l7QbOrrTwJ4+fZ25HX5689kim0uaNkRbZP+qFnQTVNpp+KKK0AcBPvOsAALEQEoAsOBVUmWmvubhOCHYqaTOiFss//sY5XK538DPvOcK1H1UjSoJfXtjESFFLrQ2keNehSQDA10+v4upGBzVDzTRpoefwtr05acvx7YtD0xVcXG/7C1h8ShshbawwEvq+NkKR/0XdCxbjeI/RmrY4e2RcTRsN8kh7bocxXtax2oqGQfV9FxF76k3JK8si2ehZMDQ5NsQkCFWRMVrShiyndAGNt3VLrRitafOJuEAwS47tRU7atglxNW0iZGc77JFbDSJxHBdOoJFyMUOgCisWfaSkoWm6XKtiFqMv2HbYI4uiEfOhFgy6kiXNc/iaSpKEiYoukFYYJVw0oetVTtLmOFF1qVJQudsOsEg0nbTyKm3hMJTgGDz2SJNhmQXIcU2WJFzgUNr8sKDQS+/gZAWKRGxzaWClRwLA0XEFE+VCbINYn7RxBJEAbKWNxjen2iO9fyNo16RKm2js8vtunMXBqTL2jYvHNY+VdKww6lQueaRtb0qaGF0RphMH23HxWw+fwf1HpvCuw5Nc+1DUFMjSoIgfAF66sokb52rcNXo3ztUwWtLw6OkVLGx0hcJcghgt6bhuouSr7TlyfDvi0HQFrgs8d2kDAFAtpJMESlZYTgWftDGCSAC+JOZOSnqkoZB3SYS0cT5zwxhnWNSBwbtUj02PJAtJrDCSRtfkskZS7B8v4WKgbEAkwAmgLVeG30P0XZ0rbTuH/MxvE6glkE4Ein5NG/8Y4Xj4gkrTI/m5teU4UJTspM1P+dtCbR5rgj5a1GE5RF6PW+2iYBEuEvnPvQtMeyS1V/DW6JmhxtiFbYj8B+CRNj7Z0XYAOTS5rBoaJgxpS0pbraiisclPHIFoEAnAr7SxzkPZDyJJv0/pCiXrZTddkrnskWZMcpeuypgrS3jFs80lwWacS4rJio6VZvRFDQRXbFOaayfZI72V27So+rFyVGm7tN5BWVeEFTNFlvC5//VdsZOMJByeqTDv0UteGmVamMdEhRwHJX71dh8bHRMPHJvm3gdJklApqH49iO24eOXqJj5y137uMWRZwjuvn8TXTq9gslIQrmej+MX3HxN2TuTI8WbDdZ5l/cUrhLTxxOXTmHwWWaGOoQLDHgmAq09nWuQ/rTmNkja+xbYwxss6s3xh4ACKsUfW4pW2za5Y1P6+iTK+dXF9aHtg0H4kDbWiGrFHNnPStuPIlbZtQrj2qOArVGJ1WJHm2oLEy3JcaLKUKUAEiKoRWRRDVk0bfeixImTDsJyohYBE/oudS0WWhmq56LHwKm192xl6uFJ7JI9aGNyPMFmZrBS4a9psRvsEANhdlbnsfGQMdo0hbwsGi0GifaWNM0CSpb4WNQUSgDaH0jbYBwZpK0q4sNZOvS6sgBuKmbLMVSNoMY6DYrJSYCpLwGDFNq1HTiWhDcJa24Qkpa+UDtLLAvZILzlSJAEyuE96TNpZEo7NVnFqsRlpx3BxrY25ESPWIkQRtkzR4xnj6K8WRNXQ/CS7c6stdE0HN3LWs1F8500zWNjo4vnLG8LJkRQ37qrhbXkISY5vc+z3wqFOLTVR1JTU7zkATJQTlLa4IBKdv+dpx7RhaHJiQ/qRYlRZ2gppYyltrFZEQSTZRBtdS0hpu26ihMvrHX+hWdQeOVLU0A/VRlOlTSRNM8f2Iidt24TwCnxBlSFJYpH/pk0Ilz+GliE90ib2rYIXICJCMIAAYQoFqogrbaGaNppm1GErEUGwFBGR4Aw6Rrh2iT70eVe7wyEiuiLDdSFYWxfdj4lygdseabtRpQ0A9lRknF1ucfUnYymfNUMgzZNhK/RbF3ArbdF7QpIkFBSgyfHWjbNHAkRpa/asIZLCQlwQCQAYisQVH82qaaMgSltMY9SOCVlKf9n56mOMPbJmaKlx8eMMpe3yeke4nm2rODpbQ89ycC6kgl5a73DZNCsFFUVN8Vff6fWdECZtqm+PPOktdBwT7HP2/bfuwntvmAEgnhyZI8dbCVVDw0RZh+24XCEkAFkQHS/rTIWpF1PTVtJElDYrNjmSglXDRecraQ6JMMbLBay1+pH5F02fjSOBlQKpeV9jEL5G1+TqsUaxb7wExx1Y4zc7JgqqHCG/cWDVFOdK284jJ23bhLDSJkkSsdNtgz1ShHhRKxwlW1lIH7C1fnPsPm3RCNq0fYjUtAnaI8OqjKjSZlrRIBIAYo2t7WhvsUmvpo3nusbZ8XZXJPTt6ISYOYYdrcOqFTW0BUJEgGESTS0W3GPEKIaGKnEFkQxSt6KPrJpOxq2nqLgWwzJLUVD4mq5bjHNJMVEpYDXBHlkraokrvQAJZ5Ekdn+z9baZGkICDBZIgvsi2qNtO3AsJuDl4nobe1Lq2QDyDJ2pFSKkjacpdhDEHknusRVvDGpD4oUkSfj1D92C+aNTuJezni5HjrcqqNomoshMVQqJ9khWeiQA8HSMafftWGskxQiLtGVW2jSYtusr/BSPnV3FwakyJmPqWiVJwlhZ863wQTQE7ZHXTRKb6rlVEtLF0xszCL/XZicnbdcSctK2TWCtwBua4ve44oFpuxF7JAAhskJtaKJJiRTxNW0iY0SJBqtvSNL2QLhPmySotEVJWzFDc+2gLSwLaevb0d5ik5UCLAeRBzoLFiNiHgB2V8iYpzjq2siCwvBntaKGjgWuNgrhewIYXE+RRFAW+TQUdv1WGKw6Rwr6Lk5rjzFQDKNj6AofeUxW2gpo923mOPW2yZVAJssSmbww7DH1Nl+/IJocRpW2Zs/CRsfE7lG+mPztwqHpChRZwitXSa3gV08t4y++eRGLm73UEBKK6aqBJS/cha4+01o3XlSNQSN4v9ed4Mo5QMII/vDH78Lt+8eFt82R460EWtfGq7QBZCElKYikELFH8ittXdOOjfunYEXcb3RMGBq/OkUx7tk9g+TLsh1889w63nFwInVbVo+3RtfkCnWhoMSZhnRtdk2h1i0jxWhJC7Xt533adg45adsmsFLlDFURV9pCFkvyudh+0PRIAMKx/3E1bWKtC1g1bWSStCFgj9RCkf8u+EgGQEJEovZIsSCSiD2SXg8BIm450d5idNIZp8oE4bhs0jZS8Bo6c5Bgm3FvUptFk6MoLc4eqcgS2pwWSxaJBoCCymdLtBKUNroAm2Z7HZBPttLWNZ3UhuWW40KJrWmLv671jpnao41ibsTAAiOFcr3d51LaAGC8NIic9uP+32ClzdAUGz/oiQAAIABJREFUHJgs+6mc/8+XTuHn/+o5APwpllOBidxakya5ia16VwxtQNra/cw1ejly5OAD7Z8pMrmfqiSTNkMNK21kbJ45VrtvpwagkZq2aHpklgWeca9bdzD2//nLG2j2LLzj+jTSpjF7vIkqbVOVAkq6kllpm6pE6+sG6ZF55P9OIX9zbRNshpJgaLKg0ha2R4o36LZsF6oiDwiKsNI2nBSYpUk4K6xhVERpY9Qv0fNicsptpsOwR6qi9shh4ketkkKtByxWeiR5GMbVPwXBaq4NAAVvv7jUITd6b9KHN8uGF9kHhj2Spm21uO2R7AAPQ+GL/O8nFHDThY40K3BSEAmdW6RZJFkqMgW1vCwzrutGu8/d62d2xMDVjWgoynrL5LYGjpV1f5X3cp2stL7RNW0A6YH3ytVNuK47pArvHedV2gZtFFZbfVQLKgqq2IQhmB5Zb5sYK4uRvhw5cojhukny/RYibd4CTbhsgC6yxkf+86VH8tojg//+etsUXiQC2ErbN86uAgDuSVHaxkp6ZDHWsh20+7ZQEIkkSdg3XsKF1TZeurKJp8/XsY/zuQsManevBhYQfXtkSn1gjtcPOWnbJrAm14YmprRZEXuk4o/Nvx/OsNImGDFth+2RGZqEs2raSroCRUqvOwLYNjZKnnibW5sMW6IsS1Bl/nMSr7Rxbe79bpSsDBSZdNLGaq4NEGUIYKcMBuG6LvN6hHtgJSHOVkgSKPlJG6sWzFAltLhq2rz+Noyo5IHSxmmPTCTByefTjrGrAgPStsJYLa53+F/+cyNFLGzEKG2cIRzjZd2vAaNKm2iPtu3AjXM1XFzr4MxyC5tdCz/6jv34wdv34ObdI1zbT1cNtPo2Wj1L6PiDqBmDmjaiVoqPkSNHDn5QciBij5yqFNC3ncg7Ka5PW1GwT1uaPXK0SOrQNgPuk422mDpFMe49Y4JK22Nn13BkphJbz0YxEXh2U1CyJKK0AcQi+eT5dfz4Hz6BkaKGX/qeY9zbjpU06Krsh6cAZIG1pCuptdk5Xj/kpG2bwIpmFyFtrusSksBoG5Albn+gtIk3gwYCzbV18ch/y3aYSYFlLdq8kr0P0cAIX2nj7rEWtSUCng1OoLl2gVVjKKiehntc+ZN7DnskK/kRINenoMpom8mEJ0zCKUYYRcax+8Bork3H4K9pG763KQoK0OZ46ybaI/2athSVzB7upRjeDyBdhY27HkDA9sooIuetaQPICmejaw3V+vUsG+2+LWSPpC/+S/UOdEX27S5vJG7ZQ8jZXz19CQDwXTfN4tc/dGuqVYnCj8Bu9LDW6qf2qGOhUlDRsxz0LQfrLb66wBw5cmQHrWkTCSKhRO+1ldbQ5z3vXc5aCAa2Lz3yiBec9NKVQb/OeifbIs94Zbjtiu24eOb8Ou46kF4PO1bWsdExh5Kh6aKTKGl799FpGJqMXaNFfOpHb/ebd/NAkiTM1owhpa3Vt/J6th1Gfva3CWylTcYa5wSfEoGt2CNd1yXR6oqcOYjEDtX90HF4VrOCY7DUiIpGVq7SYNoOZAlDqzmaIkaYLIY9EiBWOl4i2w9ZGzVBe6TtuHDdKNGgE08ee2TcuQRIglMa4fFj7kMElsb5ivXNi6p1lxu8CZQx9khVQqv9xtgjzQRrI1Xa0lQ/Vn0ghd8MOqS02Y6Lza5YTRtA4qEPTVcABFPMOJW2CukT5LouLq93MDdq7Mjq6C17RgEAf+2RtsPe8fDCbza72cVqs58pbp+u9jd7FtbbJg54qWo5cuR4fTBa0vCuQ5N4+37+voRHZghpOrnYGOpn2DXtSNw/IDY36XDUtN3qPaueu1T3687qGe2RZV3BSFHDqcUmAODUUgONnoW3c/RpHLRsMTHlLVoNSJvYvvzQXfvwQ3ftE9omiNkRY8j10ezZOWnbYeRK2zYhNj2Sk+z4MfcMe6TJaU2kfEaVJT9pKWtNG51g+2qfaJ82xgS9rElcfdpoXV4QdLw+d4hIdAwA0Dmj3YFojaGoPTKut5imyChrfEEkSaStpCtcJAOIKm00sp+vpi0aDAOI2SNZNYYArWnji9oHtmaPZNWdUlClLcke6bpuYk1bQVVQNdSI0tbomnBd8CtttQFpA0jD2bPLZPWZd9V3vKT7kdOX6298jzaKkaKG66fKWNzsoWao/iSEFzPeuVhq9LDezqa00YlOs0sslrnSliPH6wtJkvDpj96ND9y2m3ubveMlFFQZJz2iQ9E1nUhyJEAWdYuawqW0dcz0mrbxso49Y0U8d2kDAHnekwApcdImSRLmj07h4VeXYDsunjq/DgC4nYPEsvps0ppckT5t24HZmjFkj2x2zTzuf4eRk7ZtQmx6JKcy1GeEPYgqbcEQkaz2yPAkP0uT8LhY9JImcQWRhJuMA4MQEO4gEsuBzop2l0Ui/4fr4kSDSOg1DdsjAaCqS1hlJESFkaTslHV+pS3coJsV5xs/hndfMWrauINI4iL/vZq2tJ51ZqLSRv7kSY9kEXlgoLQl2SO7pgPHBUoJyVlTlUIkiGTRS9/iJSxzI4RgLXhhJB/770/iJ/7omwDAbY+ktV/rrf6ONNYO4ra9ZKJyeKbKtKYmIWiPXN2CPRIgk6BG18pr2nLkuAahyBIOTVdwMtTGpmfakR5tFCVd4VLa2hw1bQBR2569VAdAnvd9y8mUHgkA771hBmutPp6+sI6nzq9jsqJzBYHQerhgXRtV2kRqBLcDcyPEHknfz62enSdH7jBy0rZNYCtt/M216aQ0GEU9qGnjnxgDJLSDqnRpE9nofgzXtEmSJNS6gKgRbKJR4SRtluMMKY5AMD1ya/ZIXZG4lbZ+TJ820evBIitFVeJSmGzXRUzCPEqF7EpbWVchAdjs8Ef+h4l0raihbYKrSXgcYTIUwHXT1c+k5tq0djGtTxtRTtOUtvjzQQlu0kt8slKI2CP99EbOIBBqCby60UXfcvDC5Q1fAeRViSY8cnN1o4ulRu8Nj/sP4ra9pK5N1BoJkIUBXZVxbqWFvuVkVNrIROeSF8gynqdH5shxTeLoTNW3FFJ0LTu2T1qpkK602Y6LnuVw1dHevGcEl9Y7+ItvXsTvnDgNQLzFCMX9R6egKRK+9NIinj6/jrfvG+NatAouuFE0euTdI2qP3CpmagapBfbmbc1eXtO208hJ2zYhLj1S1Eo3XNMmFgLi1y/Jg8j/tIlsGOGaNkCsdUHQohmGofI3Ug4rKnSVjCfiHki2R/Kek35IrRNtru1fU0ZPKF7Fj4RnJChtaWmHfk1bNEmzpInZI8P390hRg+2mJy6SMdi2woJnd0wjsInNtXntkQlWU19pS7gm1Nqb9BIfZyR/+emNnGqXoSmYKOtY2Ozi5GIDpu36/yYvaaEv/he9ovo9nM2sXw9Qpe1QBtImSaTZOG3QPZ5BJaOTjIvrhDzn9sgcOa5NHJ6p4upmd8gB0jWHA8GCmK0ZOFN3Essm6DOdR2mjwUk//1fP4Te/TEgbr7shjJqh4Z6DE/jTJy7g3GqbyxoJDBbcVhlKm2gQyVYRrK8GSM13bo/cWeSkbZtgM2pdSHpkdlVGtLl2sJ9W1sh/VuiESApmuM9bELoicRKVqCISDBPgQbw9UuI+J3F984RJG4N08Z6LOKsp4NW0pZyPOKUNAEoqX5onq7k2IGqxjFfagHQyTgkZqymy6pO2dOLHslcCgz5tSQTUDwNJqE2rGmrkHqXpjWlRz0GQXm1dvHiF1Fd86kfuwP/xAzdzB3HQF//zl8n2O2mPPL67hl/94HH84O17Mm1/bLaKpy8Qy9JWlLbzq4S05fbIHDmuTRyZIQs7p5cGFsmuGa+0/dS7D2Gp7eLTj52PHZO+W4ocvcVu3j2CakHFfUem8PH7DwLY2oLXP3vgMO7YP4bb9o7iO2+a5dqGLioFlbaVZh+SNAgQe6Mw471v/t+vnMH3/ZdHsd7q56Rth5Gf/W0CjRMPoiBgj+yz7JG+0sZH/PwJuhIgbZnTI8OkjS/bPYkk6DKZfDuOm5hkxwoyoc0cm10+0hZvjwQ2BCL/gyqZrohdD18dYoRn6ArQ5FD8bDf+XJV0JVXlSiLRJU3ijPynKnB8XdyuFFJgxkT+G965SSPjlJyyrBmyJEFXZA6lLckemd6njZK2pML0qqH5q6IUl9c72CWY3jg3YuByvYsXr2yiUlBxx/4xrrhoirEQaduJHm0UkiThR+7Zn3n7j9y9Dw+9sgRgEKUtgl2jRSiyhCfPrQFA3lw7R45rFDRB8if+6Enced04fu9H70DXjE9+nD8yhZsmZPzml0/hI3fvY5I7WqdcSgkiAcjz+5GffzdGihpkWcLH77s+00IRxV0HxnHXgbuEttFVGdWCirVAEMnppQb2j5eYi5avJ6jS9rlnr/if5fbInUWutG0TbAbRKGoKLHdAZJLAtEdSiyO3yjUgTIaaLYhkYLEcVvx4g0jCNXFB0OduajS77UTUqaqo0hZjj9QUPvWRtk/QGemRvEqblRCeoct8KZaJSltBTVWokkh0WRNrrs2yRwKcY8RF/nM2tabXPW6Vr6DK6TVtifZI8mcnsaaN2iPjX+IVT2lzAt/5y/WOcE3ZnrESXltp4utnVnHjrppwXH9ZV6ArMs4sN6HKUqao/GsF80en/QL+LPZIQ1NwbLaKU0ukViZX2nLkuDaxe7SI//HtezBe1nHCS17smg4z8h8gC0L37dFQb5uR/m4U9P3E2yR7rKz7z9utELatYLyiDyltJxebOOwR2jcSU5UC6KuHnr+ctO0sctK2TWCmR2r8YSAseySdXDT6YhZLRZahKjJUmc+Cx96PwbEUdf7WBclKG/ksbZ9YE3z6oOAlbf2Y5tq6LKHDwUBZyqcoaeunhGfwkrbYPm26kloLFke4AGKP3ORQLpMi/wH+BEoWiabP/7Tr2uxZXsBOjL1Rk1O/Z+G+e0GosgRNkdDaoj2SRjI3A+TvUob0xn96z37YjovTS03ctKsmtC1AJjPXT1cwVSng1z90a+xxvxmgyBI+dt9BVAuqH9Iiitv2jvp/z0lbjhzXJmRZwn/68K346LsOwrRdLG52sdzs+c4BFqZL5N1G7c9h0BrjpDGuNYyVdL+mrWfZOLfS8q2jbyRURcZ1k2W8/6ZZ/C/3HgAQv3Ca441Bfva3Ccz0yIDalTZP6DNCK3RVxkRZR73HxxKoFY6SFUNTiNImsMhuM+x0hqqgJdDUGogGXwADpS1N6SK9sIa3pw+KtBqu4BisqH0SRJJOlljBF356JEdaIjCc5hlGQeGzrib3aVPRMe3E37GTSJsm4cIGH+EKNzsHMtS0seyRVGlLIZ/NLimAjkvfKqhKqoLb7FqJNQFFTUmM/K93TGiKlFjQ7i8ueP9W17Sx3Ohh96hYXcSh6Qo+eu9B/M6JM7hp14jQthR/9ZPvgCrLb7il5vXAP71nP37w9j2xtS1puG3vKP7k8QswNJkrRS5Hjhw7B2rnfuXqJpYbPRycKsf+7nSJPN8urrFJG11syxooshMYL+tYapDwj9dWWrAc17eOvtH4y098B0q6gnrbxO8/+lritcjx+iMnbduEuPRIgG9ybtKghRDRmKoWUO+xH0ZhhCfohiZnCCKJEo2izh9EkqS00TGTJsYA7Y8W6tOmylBloCFkj4xR2kyOBEsrqpKJ9mlLjKmXJXQ59iOpmTPtl9Ix7VjLQlLbAZEgEpbFs+aRNr66OPYYNAwrrXVBKyVquKCm17Q1eiamq/ErGCU92W5ab5sYKeqJsc00kpnWtS14qVtZIvd/+oHDGClq+O7jfAXsYZQ4Cu/fTMhK2ADgbfuI0parbDlyXPvY69mhHzm5AgA4OBlPFMqahJGihvNrbHskVdp2yuqYBVOVAp69WIfrun6z8Z0ibfS8zY4oePqX3yds1c+xvXjzL8FeI4hLjwQ4o919sjR8SWZqBupdseALOjkuqErmIJIgAS1mSMFk1rR5h5ZWZxdnpSsq/EqbGWOF0xVyrtOadLP65lEiyd/GgX1NAaK0mbbr173FwXGiChcFnZS3E87J4HpG96GkkfrCdLuqwyTA1QLp9cZD/OJ6pFGlLe26NlJIm67KqQpqo2slRianBbtsdPoYKSYToUHKKTknNO4/S3pjUVfwifuvz+0o24CDkxVUC2oe958jx5sAu0YNSBLwyKllAMCByWRr4L7xEi6sdZg/q7f7kHcgeXEruGl3DautPhY2uji12IAiS9eEwpUTtp1HTtq2CWyljT8MhNojw5PjmVoB6z3B9EhvPwpaejhDGGHiB5DJI28Yis1Q6ih47ZEkmp2dNMibHtm34+yRfHV1PYbSJknEGte1eEk0O3VxaD9S1CEepS2pDst2kxt8A+mEyWQkowKDXm/8kf/s3n1AeipoqtKmpdsj00hbUU+xR7bN1Ek/HZ/WCtLG2juZ3piD3KvvPjaNG+fE6wNz5MjxxqKgKpipGji73IIkAfsnku3l+yZKuLAao7S1+xgt6W8qwnHrHuIMePZiHScXG9g/UfLTxHO8tZGTtm0Cq6atoA3sa2mIs0fO1Axs9Fy+BEq/nswjbRx1PmHYoTEAQaUtQdnhJUzh/mgUhipxB5HEkQSfOKYlDdrs63F4pooLm3znNNkeSfcj/ly4rgvHJZH2LFClLYl0sWoUKWiPtLQwE9txY4MseCyWrut63w9WjzUSLsITRFJJIFzEHpleF1dNWG1NV9rMxBASgKiP9N8CiNImS3hTpzd+u+A//9Bt+E8fvnWndyNHjhwc2DtOFrp2jRRTrdH7xku4tN5hzpPW2yZG30T1bABwbK4KXZHx1Pl1fOtiHcdmd8YamePaQ07atgmkT1soPdJbGeEJvoizR05XC3ABrLZ6qWP4Kpe3H7oipdoAY/cjaI/UFXBmoaT2aQOypUcCQFHlS4+0HTeWaPDuQ5y18ZbdIzi/6QxFuqeNwTqWQcR8gkqWcC6BQe+6JKKRVNPG2yMtUe3T0kmbmbAPAKkDS0uxbHa3VtPWNW30bSdxjKKuop1wX9TbZmKPNiBa03ap3sFMzXhTpzd+uyCpFjFHjhzXFmhTax5b4P7xEizHxZV61CK53uq/6WpZC6qCG3bV8GdPXMDiZg/ff+vund6lHNcI8plEDP7siQv41W+wPdIssJQ2mlLGEwZixtgjp2tkhX5pM520hevJdFVGX1hp+//be/P4uK7y/v9zZh+Ndsn7IjsOWZ3NDoGEnYQQlhIohBYKAbrQPbwobU3pwrfl920LLbR8Cy2lBAptIS3QEkhCEkJsUpbsjmM7sR3vlm1ZstYZSbOf3x/n3tFodJczmmvNkfV5v17z0sydOc885947R/e5zzY3J00111YeE18dPDw70Yqnzd/L5eSVSWp62jw9XHV4+wDMqbx35doOZEvA4bMZbT2cwjRtPby8QxWvpUtD6JZKeKROTpuzEew3HrCLiLgZbf7hkfb5b/cdrKU9EUE66y0j41uIJOwZCmwbUe1eOW3RsE+ftgI6k97//J1y2uaTz0YIIUuZdVZIuVcREhu7j6NTBcnRqcKiM9oA4Oq1HZjMl7CqI4GbLl3ebHWIIdBoc2EoncOh8bK2p6pYlnMuruvKafMIjwSAMxNZDR1mG37RcKh+T5tDTptd4lxnHl7eoXglNNG/t1gs4hzO5xfKZ48HnI2luGZ45ExO22w9rrL6PT3bP66hh3083PXw6hlXtozksIuHoOJp89gnXn3a7CIg/p4250blgGob4Ge0nbEqKNrnci1tiUjFqHLD12iLhip5oW7j1Xd5hEfG3fveFUplZHJF3zCblmgYQswYiSfHppnPRgghdWJ72jZqGG12tcn+UTdP2+IKjwRmrjXefd161/+/ZOnBM8GFGYNLrwJHqVyec3Fth0c2Uj1yeZtqJntGx9NWYzDFIt4Xss4ynHPaAL3cPC8jwXa0+Mkpenja/C7ugZn8QKewRDvs008HNy/ZpmWtiIf1jLZC0b0oi62HlxfWa18CM8b0fD1tdnikX4805fl0MRwjwrfk/ym77L2Lx6ktEfX0tJXKElP5kmcVRb+cNlu+l+HXEgu7nhe2YepntIVCAq1xZYSWyhID49l5lfsnhJClzKblqmLkxSv9iwf1tqrrpLM1aSRSSoxO5RdVuX+bmy5bgfffsAG3X7+h2aoQg2AtaReS0RnvkkdrpwpODY4TUX0PlVt45LKK0ebvaSvVeMli4frDI2sNP6BOo83BU2cT0w6PdK80qFPy3ys8cqb4hl/FxLnNzgFl/PS1h/Bs/5i/HmV3PXRy2so+RpttxHiV/J85nu46+O2LkktRF2DG0yaldM0ZOm3lGaxyNdoinue3bZR6VX7UDY/0Lvnv3qfNbtDa4VOIBFDFSNLZIs5MZFEsy7obaxNCyFJna18Xvvc7L8fmNf5GWzIWRioWxtl0ftb26UIJuWJ5Ubb6aE9E8X/ecnmz1SCGQU+bC/E6eqwBzjlt9Xjr8g4l5u3X7TFgMK3jaZudTxadh6et5FA0ImF5dLzyfdx0qEanYqItI+riaZsulHx7mxU8wiO7k0qvkw4Jy7NkeOSj9bWHsG8g7TkecG7QbRPTCBV1MqCrmfG0+Rczcc5p0wuPLLg0xgZUTluhJD0N+lNjqoLiCusGRC2t8YinDnYlxkYKkdieNq/wyGQ0jGzBucjM+LS6GND559+WiCKTK1TOMXraCCGkfq5Y26FdQKinNT6nYNuodbOtO7X4wiMJcYJGmwvJOo0255w2/UIkXnlYnfEQBrVy2maH48Ub8LRVX+S32J42j/wrm0pOm2NIICCE/z5NZ53LuycqfcV8wvk8wiM74wLRsMCJ0bkJy9W4GdGAqpg4lS/5tmEoeu4L/zBNr8bYgDJUwiGhV4HSo0eav6fNubk2oPYF4F2M5NR4FsvbEq5x+So80l0HWz/P8MioX3ikjqfN3aNse9r8Sv4DqhhJOltsqLE2IYQQfXpbYzibqTHaJvVvthGyGKDR5kI9oY2AS582K7Qu63FRbeNnaJxJa4RH1hhc8ypEUi4jHBKz7m4lPS5m54539+wIIZCIhD2Ntul8CVP5Enpa5y6ylUbMPh4/r/DIkBBY05l0TFiuxu5L51QQJW4dI7dQuhkZ7t46OzTRDukbSucwMD77GM8cT2f5Qgi0RMOeOW1ens9ISCAWDvnuz6LDuW3TomG0nR6fxqpO9xjjtoTytLkZwWnLaPPu06Z6ErpVONUy2iyj0Gl/1hUeac2n4mmj0UYIIeeUntY4hjOzwyNHp9TrxZjTRogTNNpcqCePS0qpPG01HhEhBKIhIKvh7arktDlcHLfFBEYnvYs9KBk1OW3zKPlf9MjN09kXTuGVs2WFPA1hO7yhx2GRrYTz+RQjceuxZrOuuwX9DqWBZ8nw8LRVSuX7ePyKHnrY3lB7n/7+N3fhlZ/ajs8+9ELF8LANDa8eXy3xsGchEd9ebz7j7Xm4hkdax2R8ysNoG8tidYe74dJWKZPvfFx1wyOlnDn2taQ1ZNheNKe5jGkWIrG/I50ton90Gj2pWOWmByGEkHNDb2scZ+cYbWrdXozVIwlxgkabC/Xko9kOAseG0mE9GYWyRCwccozfbo0CY1N5h1GzKdWU/I9FQq4Xsa4ySnJWY22gyoDVymlzL3xhy/Iy/uw7ZT2puflPdhER/xws53L9Nmu7WnDCx9Pm1qcNmPG0+fU3K5TKEMLZyxWvye87bhmRf/fQATx/WuXLPbxvEABw3cZu1+9I+eSD+VegjPiGR+ZLXuGR6q+bp01KiVPj01jt4WlrrzSkdpZh6+dntAHufe/S2QJaYmHP0sn23djhybm/tfGpPITwzomzscM9T45NM5+NEEIWgN7WGEYmc7MiNhgeSc43aLS5kKgjp80rBC0WEnpGW9HjwjgmMJkv+XrNCjVerug8c9pq5+GV61OLVw4VoPar1/4YsRbZbofwSN3CGVNWOKptbNayrjuJkcm8p7GS96pAadkOfh6qfKns6iWzVbP36VA6hy19qi/L0eFJAMC9u0/hmvWdlX41TvgV8fAqRKIzHgD6R6ew2iXEzy88cnSqgGyhjFUanja3vLa0jtFm7VC3YiR+fd6AGaNtxMlomy6gPRF13Y/VtFnNwvtHpxgaSQghC0BPKoaynH2D2w6P1MlFJmQxQKPNhbpCAj1C0JSnTS880u0Cv9W6MB6b9va2zRhMVeGRJfc8HyeK5fIcb0Qlp02jEImXAQuoi2uv/WEnEvc6edoqhUi8jQx70Xa7u2YbQV55bV6FSHQ9bUUHr6VNSAjEIipUdDpfQiZXxJb1XQCU1+3o2UnsOTmBN12xyvM7/IwuP09bKu6dE5fOFnBmIodNy1qdx/sYbaesvC4vT5vtvXKbh5anLWx72pzPrXS26JnPBsyE5Dp52samC1qhkYAq+Z8rlnF4aBKb13RojSGEEDJ/eq3qxNUhkgPjWXSnYmxOTc4beCa7YIdHevV+svFrKK0bHulmtNkXxmMeeUNOeth90eqJkPTqN9eoAatkhbQ8bU6FSOxcMr+ctkocu0uZ33VWyNoJj7w222vpVEREt+pioVSe0+dtthy1L2xDdWNvCl0tURwfmcL39wwAAN6gYbR56VH2CVdV4ZXux+PwkPL6bVqWcnw/GVEVQd0abJ+2iqt4edpaK542Zxn28farHgkAOZdzayJb8A1t7LI9bRkHo22qoH23trpgyk2XrtAaQwghZP7YKRXDVRUk9w2kcdEK5xuOhCxGaLS5UE8hEq/iG7Gw0CtEUiy75mC16hptNcVM7HwszQKYloy5lQLnk9Pm5tlJ+oRHDk/mEY+EKiGZ1SSs/ZP2MZbskIguF0/bum7lafMq+++V05aoeNp8Wg94FPAAlAczWyhVevAta4tjfXcLToxM4aljI7hweatveJ1dXt4Nv+PRGo94Nuc+NJQBAGxa7vyPLyQE2uIRV0/xZx4GAAAgAElEQVTb6XG7sbZ39UjAPTwykysiHgk5HgubmZy2+XvaouEQ2hMRjEzO7Yk4Nl1Ah2ZehO0RXNed5AUDIYQsAMva1Pp81rrxWy5L7B9I45KV/s25CVksNGS0CSE+LITYK4TYI4T4hhAiIYTYKIR4TAhxUAjxn0KIRZkBWl9Om3Vh7FQlMKRZ8t8jPNJ2GI36FCOp7Qtme4nqqfpfKss5+WixSAhhUa+nzXkuiWjYs2/dcCaP3ta4Y0EWXQ/X2FQeyWi4cgxr6UnFkIyGZ4VH/uG3duEPvrmr8jpfLCPkVkTEEutl7ADqmMZcDHFgpijLkGW09bbGsa67BcdHprCrfxxXrvUPrWvzCY8cm8ojJNzz+1I+nrpDQxlEQgLru93z6jpaoh7hkVlEw8Ix3NXGNqYmPIw2v3y0eMQ/p83PaAPsBq3OhUh0PW22R++mS1doN4YlhBAyf2xP21nr/+nxkSlMF0q4dFVbM9UiJFDmbbQJIdYAuAPAtVLKzQDCAH4RwCcB/J2U8kIAowB+JQhFF5rgctqEVnNtFR7p4g2J+ZdVn62HOqzRiqetnpw2Z+9QLKyZ01byzmlLREOezaCHJ3OuPVUiIYFENORbOGNksuBZ4lcIgbVdyVnhkQ/vG8Kz/eOV11P5UsUQmDMHzYIoxdLc/MBZciyv45AVzrG8bcZoG0rncNXaTk/5wEz1SLe8xSNnJ7G2q8XVS5WKhT3ncWhwEut7WjzbDnQkvYy2aazqSCLkUcDDr3pkJufcbL2aiqfN5feazhbQFvc3urpTMcebI/XktK21wm/f6BPaSgghJBg6klFEQqLSNmjfwAQA0NNGzisaDY+MAEgKISIAWgCcBvBaAN+y3v8qgLc2+B1NIRwSiAi9IiKe1SM1S/5P50uuniE7p83X02YZTLYatqetngKSdnPtWmJhUVdzbfecNu9CJCOTecd8Npu2RNTXeB2byvuW+F3XPVP2fzCdxdlMDiNV+7d/dKpy8V1LxdOmER7pZogDal9MF8o4m85BCGUwrO9ugW1/XaHhaWtNRFAqS1cP09HhSWzodc5HAyxPW77kavQdGsq4FiGx8TLaTo9PY1WHe2gkoAyuaFi4hkdO5opIxXyMtmjj4ZGACqmtbdBaLkuMT+vntG1e04HH//hGvHiDe6sGQgghwREKCXSnYnjiyCj+5Du78ejhEYQEcNEKetrI+cO8jTYp5UkAfwvgOJSxNg7gKQBjUkr76qsfwJpGlWwWugaXp6ctpGf4KUPD+aIwEVY9x8ZcLoxtipa3zg7Jsr0rdRltDjltgJqHTk5bpcS8R8l/t15agAqPdPO0AcCqjgROT2Q9dRidyrsWIbFZ15VE/8gUpJR47pS6Izc6ma8YL8eGp9DX4xwSGAmpyo86fdq8PFSJaAjZvPK09VgVruwwxEhI4LJV/ncI2+Lu+WBSShw9O4WNLvMAlNHmZvQVS2UcHZ5syGg7NZZ1bRdgI4RAWyLqWmAmndXxtLmHRxZLZUzlS74yABU6W1vyP50tQkqgvY6y0cvbvA1VQgghwdLTGsfjR0fw748ex7/+9Cg29KYq1a8JOR/wv4pxQQjRBeBWABsBjAH4JoBb6hj/QQAfBIAVK1Zgx44ds97PZDJzttVLozIiIYnDx/uxY8eQ5+dOZ9SF4oF9+7Bj4uCs90S5iInJsq8ep4ansK4t5Pi5yclJJMMhPHfwGHbsUFUFJwsSn9uZxS9vjmNZizIMjhzLQ0hZkXFgQF0ET0xOae+HM0NZTOfknM9HRBknTp/xlXPgsLrg/dmPf4x4ZLbhlslkcHYgh8x00VGOlBJDE9OYGhl0fD+TySBaiODgKe/9eersFNa3O+9L+5zIjRSQzhVx30M7sP2EMjiKZYnvP7QDyQhwZGgKGxLTrjLiQuCFw8exY8cZVz0GhrLIOuxLW8ZUOot0TqIwNY6kUJ8bnFLn0uqUwKM/+V9X2fY8TpxSx/iHj/wEK1OzDcTxnEQmV0Rx9DR27DjrKOP08BEAwIMPP4L2+OzjNTBZRqEkURg+UTnvnGRMjuYwNF6aM8+ylBgYn0Zh3Pl4Vs8jXC7g4LF+Rz1PDE5hVavz8bRlnNz5FABg567diA4+P+v9yYIyxM/0H8OOHac89cgM5zGcKWD79u2Vmx/2MRk4fgg7dhz3HN8IJsgwQQdTZJigQxAyTNAhCBkm6BCEDBN0MEVG0DokSlmkosANqyP4wbEiesJZLfmmzaNZMkzQIQgZJugQlIw5SCnn9QBwG4A7q17fDuCfAJwFELG2XQ/gAT9ZW7dulbVs3759zrZ6aVTGi//8Xvmhbzzt+7n9AxOyb9s98p5dp+a89yufv19e8fH7fWVs/cSD8o/++1nH97Zv3y5v+vQO+Rv/9mRl2/8eGJJ92+6R9z47851//t298vI/m/muh54bkH3b7pFf/s5Dvt9vc/udj8m3fO7Hc7a/5i/vk++98zHf8Z97+AXZt+0emS0UHefxye8/Ly/4o3sdx2ayBdm37R75TzsOOr6/fft2+X++u0de9qffl+Vy2VWHq//8Afkn/7PbVYaUUn5/9ynZt+0eubt/TP72fzwl+7bdI/u23SOPns3IgfFp2bftHvnVnx5xlfGyv/6h/PBdO111kFLK93zpUfm2z8/dl7aM3/i3J+VNn94hb/3cj+V7vvSolFLKfLEkL/zYvfKj33Y+F2rn8cCe07Jv2z3y2RNjcz7z+JFh2bftHvnwvjOuMr755AnZt+0eeezs5Jz3f7BXnT9PHRvx1OMv73tOvuhj9805JvZ+/NrPjvrO442ffUT+8lced/zM5X92v/z43Xs8ZRw7Oyn7tt0jv/XkiTnvHxnKuL5Xq8e/PHJI9m27R45N5eXYVF5u+9auyu/oB3sHfMc3ggkyTNDBFBkm6BCEDBN0CEKGCToEIcMEHUyREbQOgxNZeWpsSpbLZfnpB/bJnx48uyB6mLAvg5Bhgg5ByDBBh0ZkAHhSuthL8/a0QYVFvlQI0QJgGsCNAJ4EsB3AOwDcBeB9AO5u4DuaSiykV4ikWHIvq65TiERKibGpAjo8wq86W6KzctrOWCGC1eGbpXJ5VuXH6Dxz2pzCI+NhzZL/1r6IulSPTEbDKJWlY+hgpUebR3jkms4kJvMlTEwX0eEQTmrnH3kVIgFmGmyfGJnCc6cm0BILYypfwshkvtKjra/HIxcsFvENj8wXvQuRJK1KmlP5Ei6w8s6i4RC+ePu1uFgzDt8O+XMqJnLkrOqxttFjHq1Wgp7T+MNnrXL/vf7hkflSGdOFElqqcs9O2o21fXLaAFWE5dT43LDXdLaATK7onxdn5bQ5/dbsapDdHrmSNnZo7shkHvc+ewp3PXGi0mJCtxAJIYSQhWdZ20yV4t+7+eImakLIuaGRnLbHoAqOPA1gtyXriwC2Afg9IcRBAD0A7gxAz6YQDQutfDSvnLZoSF28lzwqOE7mSyiWpWehg86W2Kw+bQOW0VZtVBbKs/PR5tunrZFCJKVyGULAtVpgbSsFKSV2nRhDsVSuNJnubXUvD283aT41Pu34/kS2gLKEViESANh/Jo0jw5N46QU9AFQ+3LFhZez0eZS5b4mHMenRlBqwS/575LTFwpjOq3lX/7N5zcXLffPAbOyKiLbR9ZkfHMAHv/YkAODo2UlEQsK1oAqAipHlZIAeGpxEb2vc0TiuxjaAjw3P7nt3ekydozpzuWhlGw4NZir98WzsmxMrfYw2uwKlU26dfTPAq+2AjW20nR6fxld/dgwA8OTREQA02gghhBDSPBqqHiml/LiU8hIp5WYp5XullDkp5WEp5XVSygullLdJKed2ql0kaHva7OqRDsU37BxYr+Ib9oWm10VhV0t0ltE2WPG0zVzklmoMrhlPm37J/1LZpRBJGJ6l+m2KLuNtEpZHZLpQQjpbwG9//Wnc+vmf4K4nTuCsVbXPqxDJaqtJ86mxGaNt+/5B3PL3jyBbKFUu0P0KkXQko2hPRPDNJ/shJXDjpcsBqEIox4anEA4JrPEwdlrj/p62okPPu2oSkTBGJnPIFcuzjLZ6mPG0qXPju8+cxEPPn8Fkroijw5NY193i6e1Lxd09dapypLuXzsb+zKGhDPLFcqXvm91Ye3WHv9F28Yo25EvlisFsc9ryvq1s9zbakrEw2uIRDE7MXW6GrZsBOp42u9fPl398FEPpHJLRMM5YMjuSi7LlJCGEEELOAxot+X9eEwsL175P1XhXj1TbvDx2Y1bYo3d4ZAxj09XhkTlL7ox+tT3W4vOpHlmWjhf58Tq8jm492oAZT1uuUMY/PHwQD+w9g0Q0hKePj2LPyXEIAVzgYSjYXptqo+0rPzmKfQNpDIxnMTplG8D+F9hru1pwcmwaazqTePMVqwFYnraRKazpTHpWfmyJhTHl4Wk7MTKFw0OT6PbQ48ZLl+PSVe1Y0R7HNev9e7I5YTedzmSLODORxdHhKZQlsKt/DIeHJrHBo3IkAHQkrcbWDh6qQ0MZbFruHRoJABf0tkII4OBgBn929x5c84kf4A++uQsHzqTREgujPekfhX3xShUOum8gPWu7bbSt0jD8lrfHMZieG2I5rBF2a2Mb+w89fwZXre3A6y5bUXnP6/dJCCGEEHIuaSSn7bwnFgYyWn3a3HPa7NZrXq0D7L5jXnfyO1uiyBbKyBZUP7cBh5y2oltOm76jzdPTNqWT0+bSnNumOjzykQNDuP6CHsQjIezuH8dQOoeLV7ShLeF+cbysNY5oWODxo6O4f+8Afu0VF+AnB1XFweHJXMUb6WUs2azrTuK50xN490vWoz0ZQTQsMDJZwLHhSddy/zZeOW2FUhm/842dEAL48OsucpXxsgt7ce8dr/DV04uK0ZYr4fEjI5Xt9zx7GvsG0njzld4Nnm1j6OTY7HDTkck8RqcKvuX+AeXlWtOZxKGhSTx1dATLWuP49tP9KEvlhbOrMHqxaVkrwiGB/QNpDIwfxg2benHZ6nYMWEbb8nZ/T+TytoSjp21kMo9ULOzaB7Ea29OWiIbw6Xdejfv3nAagmpC7NSgnhBBCCDnX0GjzIBYCpn3yloBqT5uzhwrwbsSsEx5p3+UfmypgZUe4Kjyy1tM2N6etHk9boeTSXDukm9Pm7Wmzc5N27B/CvoE0/vCW1cgXy3h4/yBOjU3jLVd7t/ULhQRWdiTwvV2qdPvjR0Yq+/9sJl/pV9alYbRdsKwV0fAgbrt2LYQQ6GqJYWQyh6NnJ/GWq1d7jk3FI5UwQEDl5n32hy/gls0rkc4WsevEGD71jisruXPnikQ0hHBIIJMr4PEj00jFwljWFsddj6vS9Lf67M9UPILOlihOjs422g4NWUVINMIjAeDC5a149PAwhtI5/NmbL8OZdBb//KPD2rl5iWgYG3pa8J1nTuLEyDRuunQFvvS+azEwkUV3KqZlcC1vj+Pp46OV19LquTecyWmFRgLKAH3TFatw8+UrcOHyVlxoeRp1PLeEEEIIIecKGm0exMJCq7m2l6fNjgxLZ90bY9tNs73Cr1qrco/KZYnBtB0eOTunrdpwnClE0nhOWzysvqtclq5FRgCV9xb38EhsXd+FDT0t+NsH9wMAXrapF8OTOUipCrLohAmu6kjixMg0XnnRMjxyYAi9rXGczeQwnMlXvIGdPjltAPAbr9qEn7tydaURcncqhmdOjGEiW8Tlqzs8x7bEw5isMsRPj2fx9w+9gFyxjCvWqLH233OJEAKt8Qgy2SIePzKCrRu6sbwtjqPDU9iyvlPLaFzTmZzjaTs0aBtt/p42ALhwWSt27Ff9DLf0deGSlW340f4hXLNOP+zzkpXtuHe38mz96MAgxqcKGBjP+uaz2Sxvi2NwIodSWeJzDx/El358GLdf34fhyTy6NYqQ2Hz+l7bMzGu5CttkaCQhhBBCmgnjfTyIhfUKkZSsQiROxk4qqrbZHiAnxqb8PW220TaZK2J4Ml8xFKdrwiNnFyIRln6+U6hQcs1pU39zPm67oZpKiLWEQgLveWkfcsUy2hMRbF7Tgc1Vxs2W9V2+Om5alkJnSxT/8K5r8OnbrsJn3nkVAOBsJofRqTwiIYG2uP/9iI5kFJetbq+87k7FcOCMMlb8jMdULIJ8sVypdrjn5DgAYCSTryuHKgha4xGcHMti/5k0rtvQVdmHfl42mzWdyTmethcGM4hHQtqeMjv3LR4J4bJV7UhEw7jvjlfUVXb5IqvNwfUX9KBQknjguQGcHs/6lvu3WdGeQK5Yxref7sffPXQAuUIZTx4dxchkHr3zPBZ9PS2IhgWNNkIIIYQ0FRptHsRCAjmdnDaPPm3JiNo24eFpG58uIBYOIekRAlZd5c8ugw7MDY+MVuW0xcNKXl0l/11z2uwwT++8tsF01rcS4ju2rkU8EsINm3oRDgksb0tgVUcCHclopV+ZFx99w6W4745XoCMZxdu3rsUrL1qG9kQEw5kcRiYL6GyJauVR1dJlXdi3xiN40XLvPmktVllQuxjJnlMTAFTRi5GMXcFy4Yw2Oyzw8jUdeMPmlXjvS/vwti2aRluX8rTZ4YQA8MiBIVyzvtMz1LUaO4zwijUdFQ+vl0fWiVdc1Iu1XUn8zW1XYl13Et/bdQoD49O+5f5t7PNux/5BhATw+s0rceTsJIYzec+KpF5EwyFsXtPhm+NICCGEEHIuYXikB7EwkC+VffO0KjltDuXdbeeZl6dtfDqP9qS3oVEdHmkbakLUGG21Jf8jtqdNPzyy6JbTZtmTfp7HwYkcLlvV7vmZzpYY/uNXX4IVVWFvb9+yFvlSWetCvyMZneP56G2L4+xkHlO5YiXcsV7s4iVXrevwNVYqns+8avK91/a0TeYwPJlDeyLiWX0ySFoTEew/o6ouXryiDV2pGD7x1s3a49d0JjGVL+HJY6P4xuPH8YEbNuKFwQze/ZLLtGXYYZRb+/w9pW5sWd+FH297LQDgHVvW4e8eOgDAv9y/jX3cf3JwGBt6U7hkZRu+t+sUwiGhndPmxFd/+TrXZvGEEEIIIQsBjTYPqis/pjzC7YoeJf8rnjaHkuo2Y1MF38a91eGRdh7bqvbErJy2Yrk8O6ctPI/m2j6eNq8cv1JZYngyr2U0Xbuhe9br33+9fhidE72pOIYzOQyMZ2eFW9aD7RnTCdFssY6H7Xncc8o22lR4ZI9Hg/Cgsc+NtnhEO5SwGrv59mcePICfHR7Gj19Q1ThvvnyltozuVAz/cvu12DLP1gW1/MarL8D2/YN45sSYtqdthVVhcny6gBs29VS8tqWy1Gqs7Ua7RzVTQgghhJCFgLePPZjpsebtXbLfj4XnhjcmwkBIzPW0febB/bj3WVV0YXy6gE6fnJlUldF2ZiILIYC13S2zdCvVNHOOhEMIifqqR7p5Fe2cNq8qmCOTeZTKct6NohuhpzWG0+NZnBid1gqxdKLbMpx1iqGkLNdjJlfCYDqLMxM5xCIhDGdUeOR8w/Hmg91g+6KVbfMKC13TqUL/fnZ4GCEBDKZzuGJNB9Zo5rPZvO6yFYEZq/FIGP/4S1tw82Ur8NILerTGLK/yyF20og0bqs6DhTwehBBCCCFBQ6PNA92QwNMevaSEEGhLRGdVjyyVJb7wyGF8wyrLPjZV8C10YHtT0rkiBtNZ9KRiaItHZulWKM01uKLhUF192lRe3NzTIqrRJHzIqmi5vElG27HhKZTKEhs1y9TXcu2Gbrx4QxdeXOMFdKLaiN5r5bNdt6EbacuoXkgjwS66YhfyqJc1XTPG2W+9+kJcsaYD737J+kB0a4TVnUl88fZrtdsmtMYjlVzDi1e2YUNPldHWQHgkIYQQQkizodHmwUxIoLer6uToNJa1xV17SbUnI5io8rT1j04hXyxj34DKQxqfLqDDJzzS7sc1mStidLKArhbVu2qOp63GaItFQpXwTR38PG1eXsfBtDJem+Fp663y8Gzs1StTX8vmNR345m/c4Nnc26Y6x9Auj3/9JuUROjYytWCVI4EZA/LiFfObd1dLtFIE5/WXr8T3fvfleNd1zTfa5oOdJ3nRijYkY+FKuGgj4ZGEEEIIIc2GRpsHMWvv+IVHnhyb9gwla4vP9rQdtC7yVW+xnBUe6X2RL4RAKhbGZK6kjLxkFPFoqCanbW65/lg4VHdzbaectqjGvpjxtM2vEEgjVIflbeyZn6etHipGW7ZYCX3dWJVDtaDhkfGZ8Mj5IITAmq4kulqiuHy1dxEZ01nWFkcsHMIGq9qjfUzoaSOEEELIYoaFSDzQKb4BKM/Z5R7FL9oSEUxMz3jaXrCMNkCVis/kilp9oFrjEaSzRYxPF7C6M4Fkjaet6GBwKU+bvtXm5mmr7AsPC9Bu+N0UT5tlJPWkYr5eyyBos/LI0tkCMrkiUrHwrHkvpNG2rC2OSEjgkpXzN7hu27oWEvWX6TeNa9Z3oiUWrty82NCbwk8PDS+o55MQQgghJGhotHmgk9NWLkucGsvi9R6V9tqTUZwYmaq8PjiYQTIaxnShhIefPwMA6E75GxqpeASTOWW0XbKqzTE8stbgikVCKGiGR0opHb11wMy+yHoUIhlK59AWjyAZc+83d66wPW0b5lmEpF7sEMp0toh0toDWRGSWodazgJ6dd2xdi+s2djdkKP76qzYFqFHz+KM3XDrr9du3rEVrPOIaukwIIYQQshig0ebBTHiku3fpbCaHfKk8q5hDLW2JyKzqkQcHM7h6XSf2n0nj3x87jkhI4MZLV/jq05qIYDJfrIRT2oaflBJCCMciItE6wiNt286x5L9diKTobbQtcyjGshD0WkbSxgUy2mKREOKREDK5IjK5ItoS0VnenO4FzKFKRMPzLkJyvrO1r6uh3nGEEEIIISbAnDYPdMIj+8emAcAzp609EcWEldMmpcShwQwuXN6Ki1e0oVSW+LmrVmO1Rnn11ngEY1OFSjhlIhpCWaqqkYBzY+x6ctoKJfVBp/DIqGYhkmUL2J+sGjtE8OIFNF7aEqrATDpbRGs8gvZEtGLwMhyPEEIIIYQEBY02D+ziG17hkSdHLaPNw9PWnoggkyuiXJYYTOeQzhVx4fJWXLJKGRi/+oqNWvqkYhGcHlff15GcCfmyvV/K01ZT8j+iX/K/5NEk3PY6Tue9S/5X98paSNoSUXznt1+G917ft6Dfmc4WkM4W0ZaIIBQSlQbdCxkeSQghhBBCzm8YHulBIqKMl6lc0fUzJ3U8bckopAQy+SKeP616el24vBU3XbYCW/u6cPlq9yIm1aTiEZzN5AEAnS0xZCy9svkS2hNRFB1y2uLhECY0c9rs1gBOOW3hkEA0LFzDI6VUBmkzerTZbPYoBnMuaLOM8UyuiNWdyljtScUwlM6xmTMhhBBCCAkMGm0epKKAEMDIVMH1M/2jU2hPRDx7e81UGizi/j0DaImFrSp3EU9jz00OAHQkoxUj68jZSXzzqX7ki2VEQjUl/yP64ZFenjYASETCruGR/aPTmMqXFqwQiAnY1TzT2UKl7H53KobWeATxCAtfEEIIIYSQYKDR5kFICHS1xDCcybl+5uToNNZ0tXjKabcMurPpHO7dfRq3XL4SLbH6d30qPmMIdLREMZlXnrZvP92P/3qyH8BcgysaFtpGm90awCmnDQDiUXej7enjowCALes79b7sPKAtEcHZs5PIZIsVo31dVwuGLW8oIYQQQgghQcCcNh+6UzGMTDpfhJfLEs/2j+OiFa2eMuwL+rufOYV0tohbr1kzL11S8dmetqSV03Z0eKadQDjs1KdNMzyy5O1pS8ZmmnmPTxfwvi8/Xmll8PSxUaRi4QUtBNJs2hJRjE8XMJkvVTxtf/TGS/CVD7y4yZoRQgghhJDzCRptPnSnYhh2Mdr2nBrH8GQer7pomaeM9qS6oP+vJ0+gtzWOl23qmZcurTVGm12I5PjwFGw7a66nrf7wSDdPW3V45N5T4/jRgSE8sHcAAPDU8VFcta7TMR/ufKU1HsGQ1VDcDl3tbIlpVQIlhBBCCCFEl6VzhT1Pejw8bTv2DwEAXuljtNmetkyuiD9986XzNmxSsVqjTckZmMhiy/ou/O5rL8QbNq+aNUY119aTb3vkanu92SSsvnAAKvtk54kxTOWLeP50esn1w2pPRCq97arzDQkhhBBCCAkSXmn64BUeuWP/IK5c24Fen95kPa0xREIC79i6FrdePb/QSEA11waAVCyMaDhU8bQBwIr2BD5y88VzxsQjIZS0S/5757Qlq3La7H3yzPEx7DoxjlJZYsv6pWW0VRefaY27F6IhhBBCCCGkEWi0+dCTimF0Ko9STTn9sak8njkxht95zYW+MtoTUfzwI6/CWp+CJX7Y4ZEdSWUgVBtty9udDcdoOISCZk5bwSenLR4NIZ1VxU/sYhsnx6bx748eQyQklpzR1lrlXaOnjRBCCCGEnCsYHulDdyoGKZWRVs0DewdQlsCNl67QktPXk3L1YOliFyLpaFE9wJI1njYnYkHmtDl42gDg3t2n8ZarVqOjZWl5m6oNtVYabYQQQggh5BxBo82Hbiv0sbYYyXd2nsLG3hSuXLtwDZ1brZL/HVZhk1meNpem1tE6+rT55bTVhkeu7UoiZn32V19xgd6XnEdUh0e2xWm0EUIIIYSQcwOvNH3oTSmv1nAmD1hOtYHxLB49Mow7XvsiCNGY96we7LypzqTSyS5EAnh72kpStScI+Xj6/HLaEtGZkv/Dkzms6khgY28K0XAIl61ur28y5wFts8Ijl5aXkRBCCCGELBw02nzoblUGUnU44D3PnoKUwFvn2W9tvqQqnjYrpy3i72mLRZRhVyiXEQ+FHT9j49enLRENI1uc8bRt7E3hH961pY4ZnF9Ue9cYHkkIIYQQQs4VDI/0oTtlG225yrbHj4xgY28KG3tTC6pLKhaBEECnlTsWComKUbbcw9MGAHmNGMmiT05bMhrGdN422groTvxgguAAAB5tSURBVMURi4QqOiw1bO+aEKqiJyGEEEIIIecCugd86LKKflTntO09NYEtTehJFgoJfOrtV+LaDd2VbYlICCGheoY5YRtUfkbbfz5xHPfvUY2yI2G36pFh5IpllMoSo1N5dKeWdkigHR7ZGo8saJgsIYQQQghZWtBo8yEaDqEjGa2ER45O5nFybBq3X9/XFH1uu3bdrNfJWBidkZir0RC3jLacj9H29cdPYNeJMQBAJOTWXFttH0rnUCpLdKe8+9Od77TEwggJFiEhhBBCCCHnlqUZ11YnPalYxdO299QEAODy1QtXNdKLRDSMFS492uz3AWDaqvroxumxaUQtD1uLS6if3WLg5Ng0ALVfljJCCLTGIyxCQgghhBBCzil0EWiwsiOBQ4MZAMCeU+MAgMsNqZZ4QW8KfT3uuXW20Zb1MNryxTKGMjn85qs24eUX9uLC5a2esk5ZRlv3EjfaAJXXxiIkhBBCCCHkXMKrTQ1uvHQFPnHPczg4mMHeUxNY05lElyEGy1c+cB2klK7v2yGNdql+JwbTWUgJrO9uwQ0X9vrKOkmjrUJXKoquJdZUnBBCCCGELCwMj9Tg565cBSGA7z5zErv7x4zxstl4FcGwvWM5D0/b6fEsAGBVZ9Lze5I1nraeVhptf/W2K/HRN1zSbDUIIYQQQsh5DD1tGixvT+CGTT34xx2HUCxL/MorLmi2StpUwiOLGkZbh3PbAJt4jdFmV9Zcylyx1ozcRkIIIYQQcv5CT5smt21dh2JZ4sM3XYT3vGR9s9XRRic88rRlhPkZbXYz7/7RaaRi4YpBSAghhBBCCDl30NOmyVuvWYPrN/VghUsTa1OxDS27KbYTp8ezWlUQk1ZVyQNn0rhqXWdwShJCCCGEEEJcoaetDhabwQbohkdO+3rZlCx1upQlcNVaGm2EEEIIIYQsBPS0neckKyX/54ZH3v3MSXz1p0cxlS9hpY7RFpkJh7ySuVyEEEIIIYQsCDTaznPilZy2uZ62+/cM4OnjYwD0PGfJWLXRRk8bIYQQQgghCwHDI89z4pEQBJxL/j/bP155Xo+nrS0ewQW97g29CSGEEEIIIcFBo+08RwiBaAjIFmeHR45M5nFybBpb1iuP2Zou7x5twIzXbvOaDoRC7r3hCCGEEEIIIcHB8MglQDQ8Nzxy90nlZfvIzRdjeDKPGy9Z7isnHgmhNR7Bizd0nRM9CSGEEEIIIXOh0bYEiIXEnJL/u/tVLtsVazvQ7lPq30YIge/97su1Kk0SQgghhBBCgoFG2xIgFp4bHvls/zg29qa0DTabjcxlI4QQQgghZEFpKKdNCNEphPiWEGKfEOJ5IcT1QohuIcQPhBAvWH8ZS9dkYmExKzyyVJZ45sQYNq9h2X5CCCGEEEJMp9FCJJ8FcL+U8hIAVwF4HsBHAfxQSvkiAD+0XpMmEg3Nzml7cO8ABtM53HL5yiZqRQghhBBCCNFh3kabEKIDwCsB3AkAUsq8lHIMwK0Avmp97KsA3tqokqQxYmEgZzXXllLiCz86hA09LbhlM402QgghhBBCTEdIKec3UIirAXwRwHNQXranAHwIwEkpZaf1GQFg1H5dM/6DAD4IACtWrNh61113zXo/k8mgtbV1XroFJcMEHYKQ8anHMpguhfHxG5I4NFbCJx7N4vbLYnjtev18NhPmYYIOpsgwQYcgZJigQxAyTNAhCBkm6GCKDBN0CEKGCToEIcMEHYKQYYIOpsgwQYcgZJigQxAyTNAhCBkm6NCIjNe85jVPSSmvdXxTSjmvB4BrARQBvMR6/VkAnwAwVvO5UT9ZW7dulbVs3759zrZ6aVSGCToEIePnP/N9+brP7JBSSvmdnf2yb9s98oUzEwuqQxAyTNDBFBkm6BCEDBN0CEKGCToEIcMEHUyRYYIOQcgwQYcgZJigQxAyTNDBFBkm6BCEDBN0CEKGCToEIcMEHRqRAeBJ6WIvNZLT1g+gX0r5mPX6WwC2ADgjhFgFANbfwQa+gwRALAxMWzltmVwRANBWZ9VIQgghhBBCSHOYt9EmpRwAcEIIcbG16UaoUMnvAnifte19AO5uSEPSMLGQQNbKaUtnldHWGme3B0IIIYQQQhYDjV65/y6A/xBCxAAcBvABKEPwv4QQvwLgGIB3NvgdpEFi4ZnqkZlsESEBtMTCTdaKEEIIIYQQokNDRpuU8hmo3LZabmxELgmWaEggVxUe2RqPQNWIIYQQQgghhJhOo33ayCIgFgbypTJKZYmJbIH5bIQQQgghhCwiaLQtAWLWUc4VS8hki2hLMJ+NEEIIIYSQxQKNtiVANKxCIafzJaSzRRYhIYQQQgghZBFBo20JYNccyRbLKqeNnjZCCCGEEEIWDTTalgCxkPK0ZQslZHJF5rQRQgghhBCyiKDRtgSoeNoKJaSzBYZHEkIIIYQQsoig0bYEiFpHOVsoI50top3hkYQQQgghhCwaaLQtAWJWIZJ0toBcsUxPGyGEEEIIIYsIGm1LALvk/9lMHgBY8p8QQgghhJBFBI22JYDtaRtK5wAArSxEQgghhBBCyKKBRtsSwC5EcjZjGW0MjySEEEIIIWTRQKNtCWAXIrE9bSxEQgghhBBCyOKBRtsSwA6PrHjaaLQRQgghhBCyaKDRtgSo9bSxuTYhhBBCCCGLBxptS4BoCEhEQ+gfnQbAnDZCCCGEEEIWEzTalgBCCFy1thPThRIAlvwnhBBCCCFkMUGjbYmwta8LABANC8QjPOyEEEIIIYQsFnj1vkSwjbbWeARCiCZrQwghhBBCCNGFRtsSYct6ZbSxCAkhhBBCCCGLCyY3LRG6UjFsWpZCPBJutiqEEEIIIYSQOqDRtoT4yM0XI1csNVsNQgghhBBCSB3QaFtCvPGKVc1WgRBCCCGEEFInzGkjhBBCCCGEEIOh0UYIIYQQQgghBkOjjRBCCCGEEEIMhkYbIYQQQgghhBgMjTZCCCGEEEIIMRgabYQQQgghhBBiMDTaCCGEEEIIIcRgaLQRQgghhBBCiMHQaCOEEEIIIYQQg6HRRgghhBBCCCEGI6SUzdYBQoghAMdqNvcCONug6EZlmKBDEDJM0CEIGSboYIoME3QIQoYJOgQhwwQdgpBhgg6myDBBhyBkmKBDEDJM0CEIGSboYIoME3QIQoYJOgQhwwQdgpBhgg6NyOiTUi5zfEdKaeQDwJPNlmGCDpyHWTqYIsMEHTgPs3TgPLgvTNWB8zBLB1NkmKAD52GWDpyH94PhkYQQQgghhBBiMDTaCCGEEEIIIcRgTDbavmiADBN0CEKGCToEIcMEHUyRYYIOQcgwQYcgZJigQxAyTNDBFBkm6BCEDBN0CEKGCToEIcMEHUyRYYIOQcgwQYcgZJigQxAyTNAhKBmzMKIQCSGEEEIIIYQQZ0z2tBFCCCGEEELIkodGGyGEEEIIIYQYDI02QgghhBBCCDEYGm2EEEIIIYQQYjCRZitACCGEkPMHIUQHgFsArLE2nQTwgJRyLADZr5NS/kDzs+0AlkkpD9Vsv1JK+azG+JUAIKUcEEIsA/AKAPullHvnobot8y+llB9rYPxGANcAeE5KuU9zzHoAg1LKrBBCAHg/gC0AngPwL1LKos/4twB4UEqZna/elpxXAjgjpdwvhHgZgOsBPC+lvLcOGa1Q59Y6ACUAByzdyprjLwFwK2afm9+VUj6vPxNX2R+QUn6lDj3WAHhMSpmp2n6LlPJ+jfHXAZBSyieEEJdB7ZN9Usr75qk+hBBfk1Le3sD4lwO4DsAeKeWDGp9/CdTxnxBCJAF8FDPn5V9KKcc1ZNwB4H+klCca0DsG4BcBnJJSPiSEeDeAGwA8D+CLUsqCppwLAPw8Zp+bX5dSTsxXtznfYUr1SCHE6wG8FbN/SHfrnLymYC2GtwGQAL4F4LVQi8M+AF/QXVQc5D4spXxtYIouEoQQ3QB+B8ApAHcC+BisRR7qBz26QHo0fZFvdIG3PmvUIl/vAm+Nafoib9ICz3XTU+6SWzdNWDOFELcD+DiAB6HORwBYC+B1AP5cSvm1BuUfl1Ku1/jcOwH8PYBBAFEA75dSPmG997SUcovP+F+HWl8EgE9CGTp7ALwcwKeklHdq6PD/ajcBeC+ArwGAlPIODRnfkVK+1Xp+qzWnHVBrzl9JKf9VQ8YeANdJKaeEEJ8EsAnAd6B+a5BS/rLP+GkAkwC+D+AbUAZ4ye97a2T8PdR6HwHwAIAbLXmvArBTSvkHGjLeCeD3ATwL4DUAfgoVMXYFgF+SUu72Gb8NwLsA3AWg39q8Fmo9v0tK+df1zMlBvu65eQeA34b6XV4N4ENSyrut93TOzY8DeAPUvvwBgJcA2A71G3tASvl/NXT4bu0mqH36MABIKd+iIeNxKeV11vNfs+b0PwBuBvA9v/0phNgL4CopZVEI8UUAU1D/A260tv+8hg7jUOfmIahz85tSyiG/cTUy/gNqX7YAGAPQCuC/LT2ElPJ9GjLuAPBmAI8AeCOAnZastwH4LSnljnp0cv0eE4w268d8EdRCVv1Duh3AC1LKDzUo/8+klH+h8bnXW9/7Qynl0artvyyl/LLG+H8EsBxADMAEgDiA7wJ4E9TdJd95CCFq7/4JqH2zHwCklFf6jO+VUp6tev0eWBfGUHfUfA+4EOJtAH4kpRyx7i5+GtadPQAfkVL2ewpQMj4D4NtSyp/4fdZDxn0AdgNoB3Cp9fy/oBamq6SUt2rIeA2At2P2hfGXpJQHNXVo+iLf6AJvfa7pi3yjC7w1rumLvCkLPNfNWTK4biKYNdOSM+91UwixH8BLar1qQoguqJtOF2nIqF1rKm8BeK2UMqUh4xkAb5BSnrZuWH0NwB9JKf9HCLFTSnmNz/jdUOtkEsAxABdaHrcuANullFdr6HACwI+gDFhhbf5bKMMDUsqvasio6CqE+CmUcXJECNEL9Zu7SkPGc1LKy6znTwF4sX0zRAixy0+GEGInlIH3Dqj/fZuh1u5vSCl/5Pf9loy91rgklDG/xjIio1BG22YNGc8CeKk1rhfAf0gpXy+EuBLqBs8NPuMPALi89saadSNur5TyRZo6OL4F4CIpZVxDxm4A10spM0KIDVD/w/5NSvnZOs7Nq6HWygEAa6tuZD7mt95ZMp6GWpu+BHXDTED9P/xFANA5rjXn5hMA3iilHBJCpAA8KqW8wmf881LKS219qq9lhBDPaP7GdgLYCuAmAL8A4C0AnrLm8t9SyrSGjGellFcKISJQ5+ZqKWXJuqG4S3N/7gZwtTWuBcB9UspXC+XlvtvvmGojpWz6A8ABl+0C6uKjUfnHNT7zl1AXUH8PdTH3u1XvPa35Pbutv1EAwwBi1usIgGc1ZXwXwL8DuARAH4ANAE5Yz/s0xj9d9fxPoO5ovQ/ANwH8naYOz1U9/08AH4a6KHs/gB9oyhgC8CTUP7tPAbhmHsftmarz4KTTez7j/wrAVwC8B2pR/BsAvwZ1gXyb7rkJIOqwPaZ7bkLdFXR67AaQ0zmvALRazzdY+/VD1uuduucmgDCUoTEBoN3anqzj3HzaOjdfDXV39NUATlvPX6UxfmfV8yegwpYAIGX/djRkPO90ruueE7YeUHdnb4byRgwBuN/6nbTpHE/rbwTAGQDhqvNUd1/urhrXAmCH9Xx9HceU6+aMDK6bsvE10/pcQ+sm1JrZ4bC9Q/e8BDAKZbS/qubxaihDXvu8qnq9Cupi7g6dc7PmnNhV857ub7TN+m18HepCEAAO13lMq/V4fJ56PABl7ALAt+3fBICe2rn56WC9Xmntx58BOKGpwx7rb8I6vknrdbj6t+N3TDHjbEhi9v+UPRrj9zmtB9Y6sV9ThzNQBlNfzWMDVPSFjoy9Na9bof4HfUbnd1oz75017+n+zkNQa9QPoIyN+ZybuwB0WefRk/Wem1Dr6wes518BcK31/CIAT2jqUHtuRqEMt28AGNI9N6Gu6boApAF0V52rz2vK2A0gbj3vqt4fOuem9j4PSlBDSqgL2Bc7bL8O+hdzEy6PNICi5g6PWM87AdwH65+1zslX+zkA99e8p/VDsj77NqgLobdYr7V/SDU6PA0gZT2P1rEv91c9f2o+87D1sH58fwpgL9SC+XGou1G650UX1IXsOIAN1vYeaCzy1fOFugD8ifW8S/dHBAMWeTS4wDucF01Z5NHgAm99rumLPAxZ4MF1s1bOkl830eCaaR/Tqud1r5tQxu4hAP8EFZ75MQBfsLa9X1OH7wN4jct7j2jK+CmATTXb2gD8EHo3y56CdcMOypNhb09Aw9CpkbUVKrLh9wEcrXNsqeo3mQewytoeg/5NjXXW9z8C4HtQRtN2KEP8Rt3z0uW9Pk0dPgngf6Fu2P2NpccfQ3khv1CHjAescf8L4GPW9m7U/J90GX8LgIPW+fVF63G/te0WTR3uBPByl/e+rinjYVj/Q6u2RaC8wSWN8Y8BaLGeh6q2d0DzZlnVmLVQ/1c/B40bdTVjjwI4DOCI9dc+N1uhZ3x2APhXa214DEDBkvMjqMgAHR28zs0WTRkftr73GNTNiB8C+Beo/28f15TxIaj191+g1mz7OmUZNNcsre8JSlBDSqgF7TEoV+2D1uN5AI8C2Kop4ziAFS7v+d4JQs3FFtTdnzutk9l3MbDGfB+WR6Rm+0rU3CHTkJWCuii/G0B/HeP2QYXkbMXcu4O6Fw7/DOAvoO5kfRrA26ztr4EK/9GRMWfhAHAl1F3cg5oy3gVl8JyBCtV5CMpgOAnggxrjd2Hmgno9lLvefk/3mDZ9kUeDC7z1+aYv8mhwga/S91/RxEUehizwULl8XDdnj1nS6yYaXDMtGUGsm11QYVYfsR6/CKCrnmPZ6APAVQBe5LA9ChVi6Dd+PZyjLNYAuGke+giokPB/D2h+nVAhdvWMuRQqX/TtUKGfIc1xrw5I5+uhwhsBlVf3+wDeqauHNe6N1rjXVW0LwboJpjE+BOCl1j54u/U8fK7Pxxod1gJY6fLeyzTGO84VQC+AK+ap05ug8l6DmF8LgI11fL7d+r1uhcv/I4+xWk4ADTmrMeMN74QKBb6uThmXW+MuOVfnjhE5bTZCVWqqJNRLKQfqGPv/QRWHeNzhvU9KKbf5jL8HwN/ImjheS+7HpJTzbo9gxfempJSD8xh7FdTC/AXNz++Aik+2ebdUMf09ULlL12rIiELdybKTk9dC5QB9D8BHpZTHNWT4xmXrIIQIQ4VDFK1446uhzo3TGmN/ASrE6ACAiwH8ppTyXivf5LNSyndr6hCC8l5UF3t4QtaZiD1fhBBrobwec34PQoiXSY38FyFEXEqZc9jeC2U8eSZwu8h8E9Q/mHlXQrPktEAt1EfqGNMOYCOU8dovpTxTx9iLpJQH6td0lozVACClPCWE6ISKpz/utP54yLgc6iJqj9SsAucih+vm3LH1rpvbazYt2nWzkTXTGh/UurkCs89L7d+oSTJM0MEUGSboEJQMB5mtsqrI12KVYYIOQcgwQQeTZFRkmWC0Cc3yu+dYhyQASCmnHd5bI6U8OXfUnM81PI9ztS+sf+RxKeVUneM6oMKfhuscF8SJHsT+7AZwAdRd6nmVm7YSSSeklGNW0vC1UB4G7bLPLjL2SSn3LMR4U2SYoMP5NA9LzrWoKhjRiAFYL0Gsm1Wfb3geQe+LxbhuWnIa2g+NrJtCiKuhwiE7oArkCCgDdgyqyM7TGjKugQqv7MDsCpT1yKjWo1bGb0opd57L8RoygtgXC6JHQPMwQoaHbK3Kj6bLMEGHIGSYoINJMiqyDDHaSlDhRndBVSN6bh4yGrrAD8hACGIeDckwxXA0YX8GpMNHAfw6gBxmqn79BCqk4k4p5WfOtQwTdOA8zNLBkvEqqDC8Maiwkp9AhaUVALxX1tHSIICL/HmPD2IeQe0LUwzH+cow4ZwQqmrjr0spH6vZ/lIA/yz1qh02XYYJOpgiwwQdAprH77m9BeCPpZTdGjo0XYYJOgQhwwQdTJKhgylG206o3iXvgirZOQlVFOAuWVVC2kdGoxf4QRhcQcyjIRkmGI4ByjBhX+yF8oC0QOVkXSBnSto+JvXKFDckwwQdOA+zdLBk7ARwszVuI4DPSCnfJoR4HYA/kFLerCGjoYv8gAyuIObRkAxTDMcAjocJ58QL0qV0uhDioJTyQg0dmi7DBB1MkWGCDkHIEEJkoYqgODUT/7CUslNDh6bLMEGHIGSYoINJMrSQ5yhZrp4H5lZzuw4qmbwfwE81ZeyE6gHyf6GKROyCaoy5YSHGBziPhmQENA9TZJiwL+zy7mGoJq3VRTy0q/w1IsMEHTgPs3SollElp7o0uG7BiJ2Yab2wEarpOKD6ej14rscHOI+GZAQ0j6bLMOSc+H8A7oW60XaD9fgFa9vnNHVougwTdDBFhgk6BDSPn8KlSBP0Wxc0XYYJOnAewcvQeRjjaZMOyddCCAHglVKvyV9tY77roCpWvROqQIBf08WGxgc4j4ZkBDQPU2SYsC/+Faq0cgqqkXMRqnrka6F6er3zXMswQQfOwywdLBlfhio69DBUy4KTUsrfE6qwy9NSyks0ZDwrrcahQuVvPWH/ZoQQe6WUl5/L8QHOoyEZAc2j6TJMOCesz70BqkphdfGm70op7/Mba5IME3QwRYYJOjQqQwhxMYARKeWQw3srpEZBExNkmKBDEDJM0MEkGTqYYrS9W0r59QZlNHqBH4TBFcQ8GpJhguEYoAwT9kUEwG1QF0LfgiqT/C6oUumfl1JOnmsZJujAeZilgyUjCtX0+DIoL/KXpZQloYqDLJdSHtOQ0aixE4SREMQ8GpJhguEYhAwTzglCCCHnCBmQy67ZD6gSzU0bb8ojiHmYIuN80IEPPkx+QPWs+i2onnu/BqtfEVSvsb5zPd6URxDzMEVGs/cFVGW/v4bqGTgCYNh6/tcAOjV1aLoME3QwRYYJOgQ8j30B6NA0GSbowHkEL0Pre4IS1JASqrnuXwDYC2AcwBBUg9j3N1u3hZ7H+bIvTNmf51CH9y2UDBN04DzM0qFGxp7FvFYEMY/zZV+YsC8D0OEBANtQ1TwYqlH6R6Gf29d0GSboYIoME3Q4x/PYFoAOCybDBB04j+BlaH1PUIIaUgK4G8D7ofpt/B6APwXwIgBfhWaHdjR4gd/o+ADn0ZCMgOZhigwT9oUJx7TpOnAeZukQoIyGLvIbHW/KvghoHk2XYcg5sX8+75kmwwQdTJFhgg6ch1k6cB7By9D6nqAENaQEsKvm9RPW3xBUs1kdGSZciAUxj4ZkBDQPU2SYsC9MOKZN14HzMEuHAGVw3QxuHk2XYcg58SCAPwSwomrbCqg7zg9p6tB0GSboYIoME3TgPMzSgfMIXobW9wQlqCElVKnMl1vP3wLggar3dK1cEy7EgphHQzICmocpMkzYFyYc06brwHmYpUOAMrhuBjePpssw5JzoAvBJqNyOUaj8juetbd2aOjRdhgk6mCLDBB04D7N04DyCl6H1PUEJakgJ4EoAj1sT/TGAi6ztywDcoSnDhAuxIObRkIyA5mGKDBP2hQnHtOk6cB5m6RCgDK6bwc2j6TJMOCesz10C4CYArTXbb9EZb4oME3QwRYYJOnAeZunAeQQvw/c7ghJ0rh4APqD5uaZfiAUxj0ZlBDEPU2SYsC9MOKam68B5mKVDPTK4bgY3D1NkGHBO3AFgP4DvADgK4Naq957W1KHpMkzQwRQZJujAeZilA+cRvAyt7wlK0Ll6QDVBblSGCRdiQcyjIRkBzcMUGSbsCxOOadN14DzM0iFAGVw3g5tH02Us1DkBYDesO80ANgB4EsCHrNc7Nb+n6TJM0MEUGSbowHmYpQPnEbwMnUcEBiCEeNbtLahEvkb5cwBfOdfjg5jHOd4Xje6HBZVhwr4w4ZiaoEMQMkzQIQgZJugQlAwfuG4qFs26acg5EZJSZgBASnlUCPFqAN8SQvRZeuhgggwTdDBFhgk6cB5m6cB5BC/DFyOMNqh/Jq+HCseoRkDF1/tiwoUYAphHozJMuQAyYX+aoENAMkzQIQgZJugQhAwTdAhEBtdN60OGrHkByGj6OQHgjBDiainlMwAgpcwIId4M4MsArtDRwRAZJuhgigwTdOA8zNKB8whehj9BuewaeQC4E1bis8N7X9eUcQbA1QD6ah4bAJw61+MDnEdDMgKahykyTNgXJhzTpuvAeZilQ4AyuG4GN4+myzDknFiLqgazNe+9TFOHpsswQQdTZJigA+dhlg6cR/AydB7CErjoEULcCeArUsofO7z3dSnlu8/leFMIYh6myGgUE3QgxGS4bipMWfNM2J8m6EAIIWQu543RRgghhBBCCCHnI6FmK0AIIYQQQgghxB0abYQQQgghhBBiMDTaCCGEEEIIIcRgaLQRQgghhBBCiMH8/wIdto4+QKcgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(train_set)\n",
    "ax.plot(test_set, 'r')\n",
    "ax.xaxis.set_major_locator(years) #locator and formatter previously defined\n",
    "ax.xaxis.set_major_formatter(years_fmt)\n",
    "plt.xticks(rotation=90)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be using the *blue* data to predict the *red* data..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "train_scaled = scaler.fit_transform(train_set)\n",
    "test_scaled = scaler.transform(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(536, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.39245953],\n",
       "       [0.23699667],\n",
       "       [0.17204055],\n",
       "       [0.15569928],\n",
       "       [0.16069108],\n",
       "       [0.19023512],\n",
       "       [0.20587945],\n",
       "       [0.22598222],\n",
       "       [0.27314603],\n",
       "       [0.63038957],\n",
       "       [0.61545676],\n",
       "       [0.61961491],\n",
       "       [0.45526542]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scaled[:13] # Shows the first 12 months + 1 (this last one will be used as the target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.39245953, 0.23699667, 0.17204055, 0.15569928, 0.16069108,\n",
       "        0.19023512, 0.20587945, 0.22598222, 0.27314603, 0.63038957,\n",
       "        0.61545676, 0.61961491]), 0.45526542247082236]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence = []\n",
    "sequential_data = []\n",
    "\n",
    "for item in train_scaled:\n",
    "    sequence.append(item[0])\n",
    "    if len(sequence) == 13: # 12 + 1 months\n",
    "        sequential_data.append([np.array(sequence[:-1]), sequence[-1]])\n",
    "        sequence.pop(0)\n",
    "\n",
    "sequential_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequential_data = shuffle(sequential_data, random_state=25)  # for better measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.63336   , 0.66718994, 0.71880434, 0.77377127, 0.96923349,\n",
       "       0.96050149, 1.        , 0.75759582, 0.75238441, 0.64709053,\n",
       "       0.63766606, 0.5750428 ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = []\n",
    "y_train = []\n",
    "\n",
    "for sequence, target in sequential_data:\n",
    "    X_train.append(sequence)\n",
    "    y_train.append(target)\n",
    "\n",
    "X_train = np.array(X_train)\n",
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5745172914537728"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*X_test* will be determined with the model predictions..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.65717161],\n",
       "       [0.76620794],\n",
       "       [0.74147753],\n",
       "       [0.73711209],\n",
       "       [0.65882433],\n",
       "       [0.70363847],\n",
       "       [0.61129188],\n",
       "       [0.63599092],\n",
       "       [0.57427526],\n",
       "       [0.59983708],\n",
       "       [0.58172879],\n",
       "       [0.71030764]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_scaled = test_scaled\n",
    "y_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Keras Model (w/ GridSearchCV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524, 12)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples, timesteps = X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524, 12, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train.reshape(samples, timesteps, 1) # (samples, timesteps, features)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(optimizer):\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(LSTM(128, input_shape=X_train.shape[1:], return_sequences=True))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(LSTM(128, return_sequences=True))\n",
    "    model.add(Dropout(0.1))\n",
    "\n",
    "    model.add(LSTM(32))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(Dense(1, activation='relu'))\n",
    "    \n",
    "    model.compile(optimizer=optimizer,\n",
    "                  loss='mean_squared_error',\n",
    "                  metrics=['mse'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KerasRegressor(build_fn=create_model, epochs=30)\n",
    "\n",
    "param_grid = {'optimizer':['RMSprop', 'Adam'],\n",
    "              'batch_size':[32, 64]}\n",
    "\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0805 17:13:29.386117 139629960554304 deprecation.py:506] From /home/vinicius/.local/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "W0805 17:13:30.675256 139629960554304 deprecation.py:323] From /home/vinicius/.local/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "524/524 [==============================] - 2s 4ms/sample - loss: 0.0657 - mean_squared_error: 0.0657\n",
      "Epoch 2/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0362 - mean_squared_error: 0.0362\n",
      "Epoch 3/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0349 - mean_squared_error: 0.0349\n",
      "Epoch 4/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0333 - mean_squared_error: 0.0333\n",
      "Epoch 5/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0347 - mean_squared_error: 0.0347\n",
      "Epoch 6/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0340 - mean_squared_error: 0.0340\n",
      "Epoch 7/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0293 - mean_squared_error: 0.0293\n",
      "Epoch 8/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0275 - mean_squared_error: 0.0275\n",
      "Epoch 9/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0295 - mean_squared_error: 0.0295\n",
      "Epoch 10/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0226 - mean_squared_error: 0.0226\n",
      "Epoch 11/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0189 - mean_squared_error: 0.0189\n",
      "Epoch 12/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0252 - mean_squared_error: 0.0252\n",
      "Epoch 13/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0201 - mean_squared_error: 0.0201\n",
      "Epoch 14/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0189 - mean_squared_error: 0.0189\n",
      "Epoch 15/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0160 - mean_squared_error: 0.0160\n",
      "Epoch 16/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0182 - mean_squared_error: 0.0182\n",
      "Epoch 17/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0146 - mean_squared_error: 0.0146\n",
      "Epoch 18/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0153 - mean_squared_error: 0.0153\n",
      "Epoch 19/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0128 - mean_squared_error: 0.0128\n",
      "Epoch 20/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0146 - mean_squared_error: 0.0146\n",
      "Epoch 21/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0130 - mean_squared_error: 0.0130\n",
      "Epoch 22/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0141 - mean_squared_error: 0.0141\n",
      "Epoch 23/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0105 - mean_squared_error: 0.0105\n",
      "Epoch 24/30\n",
      "524/524 [==============================] - 1s 1ms/sample - loss: 0.0140 - mean_squared_error: 0.0140\n",
      "Epoch 25/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0115 - mean_squared_error: 0.0115\n",
      "Epoch 26/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0093 - mean_squared_error: 0.0093\n",
      "Epoch 27/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 28/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0128 - mean_squared_error: 0.0128\n",
      "Epoch 29/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0124 - mean_squared_error: 0.0124\n",
      "Epoch 30/30\n",
      "524/524 [==============================] - 1s 2ms/sample - loss: 0.0111 - mean_squared_error: 0.0111\n"
     ]
    }
   ],
   "source": [
    "grid_result = grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'batch_size': 32, 'optimizer': 'RMSprop'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0073923540354052285"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the next 12 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.73086982]),\n",
       " array([0.85241646]),\n",
       " array([0.82746979]),\n",
       " array([0.77951043]),\n",
       " array([0.64803958]),\n",
       " array([0.64385678]),\n",
       " array([0.64173793]),\n",
       " array([0.59329004]),\n",
       " array([0.5760759]),\n",
       " array([0.57969285]),\n",
       " array([0.58581971]),\n",
       " array([0.60538912]),\n",
       " array(0.74413186),\n",
       " array(0.80423933),\n",
       " array(0.79023814),\n",
       " array(0.74885499),\n",
       " array(0.69151086),\n",
       " array(0.67288023),\n",
       " array(0.63516796),\n",
       " array(0.58941323),\n",
       " array(0.56714135),\n",
       " array(0.56545913),\n",
       " array(0.58742714),\n",
       " array(0.64239031)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_points = list(train_scaled[-12:])\n",
    "test_seq = []\n",
    "\n",
    "for _ in range(12):\n",
    "    test_seq = np.array(test_points[-12:]).reshape(1, 12, 1)\n",
    "    test_points.append(grid_result.predict(test_seq).astype(np.float64))\n",
    "    \n",
    "test_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(0.74413186),\n",
       " array(0.80423933),\n",
       " array(0.79023814),\n",
       " array(0.74885499),\n",
       " array(0.69151086),\n",
       " array(0.67288023),\n",
       " array(0.63516796),\n",
       " array(0.58941323),\n",
       " array(0.56714135),\n",
       " array(0.56545913),\n",
       " array(0.58742714),\n",
       " array(0.64239031)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_predicted = test_points[-12:]\n",
    "y_test_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_real = scaler.inverse_transform(y_test_scaled)\n",
    "y_test_predicted = scaler.inverse_transform(np.array(y_test_predicted).reshape(12,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[109.3191],\n",
       "       [119.0502],\n",
       "       [116.8431],\n",
       "       [116.4535],\n",
       "       [109.4666],\n",
       "       [113.4661],\n",
       "       [105.2245],\n",
       "       [107.4288],\n",
       "       [101.9209],\n",
       "       [104.2022],\n",
       "       [102.5861],\n",
       "       [114.0613]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[117.07998991],\n",
       "       [122.44436518],\n",
       "       [121.19480932],\n",
       "       [117.50151241],\n",
       "       [112.38375447],\n",
       "       [110.72103837],\n",
       "       [107.3553535 ],\n",
       "       [103.2719085 ],\n",
       "       [101.28422414],\n",
       "       [101.1340919 ],\n",
       "       [103.09465744],\n",
       "       [107.99992263]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.DataFrame(index=test_set.index)\n",
    "df_final['real'] = y_test_real\n",
    "df_final['predicted'] = y_test_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>real</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>observation_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-01</th>\n",
       "      <td>109.3191</td>\n",
       "      <td>117.079990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-01</th>\n",
       "      <td>119.0502</td>\n",
       "      <td>122.444365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>116.8431</td>\n",
       "      <td>121.194809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-01</th>\n",
       "      <td>116.4535</td>\n",
       "      <td>117.501512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>109.4666</td>\n",
       "      <td>112.383754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>113.4661</td>\n",
       "      <td>110.721038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>105.2245</td>\n",
       "      <td>107.355353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>107.4288</td>\n",
       "      <td>103.271909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>101.9209</td>\n",
       "      <td>101.284224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>104.2022</td>\n",
       "      <td>101.134092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>102.5861</td>\n",
       "      <td>103.094657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>114.0613</td>\n",
       "      <td>107.999923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      real   predicted\n",
       "observation_date                      \n",
       "2016-09-01        109.3191  117.079990\n",
       "2016-10-01        119.0502  122.444365\n",
       "2016-11-01        116.8431  121.194809\n",
       "2016-12-01        116.4535  117.501512\n",
       "2017-01-01        109.4666  112.383754\n",
       "2017-02-01        113.4661  110.721038\n",
       "2017-03-01        105.2245  107.355353\n",
       "2017-04-01        107.4288  103.271909\n",
       "2017-05-01        101.9209  101.284224\n",
       "2017-06-01        104.2022  101.134092\n",
       "2017-07-01        102.5861  103.094657\n",
       "2017-08-01        114.0613  107.999923"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efdb02ef630>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}