after-merging.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42ca7819",
   "metadata": {},
   "source": [
    "# This notebook has the code for After Merging part of the two datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422b5066",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e898c3d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# For better visualization\n",
    "sns.set_style('darkgrid')\n",
    "matplotlib.rcParams['font.size'] = 14\n",
    "matplotlib.rcParams['figure.figsize'] = (10, 6)\n",
    "matplotlib.rcParams['figure.facecolor'] = '#00000000'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a67124",
   "metadata": {},
   "source": [
    "## Load and process two different datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "890afa60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load datasets\n",
    "energy_df = pd.read_csv(\"data/energy_dataset.csv\")\n",
    "weather_df = pd.read_csv(\"data/weather_features.csv\")\n",
    "\n",
    "## Process Energery Dataset\n",
    "energy_df['time'] = pd.to_datetime(energy_df['time'], utc=True, infer_datetime_format=True)\n",
    "energy_df = energy_df.set_index('time')\n",
    "energy_df.interpolate(method='linear', limit_direction='forward', inplace=True, axis=0)\n",
    "\n",
    "## Process weather dataset\n",
    "def df_convert_dtypes(df, convert_from, convert_to):\n",
    "    cols = df.select_dtypes(include=[convert_from]).columns\n",
    "    for col in cols:\n",
    "        df[col] = df[col].values.astype(convert_to)\n",
    "    return df\n",
    "weather_df = df_convert_dtypes(weather_df, np.int64, np.float64)\n",
    "\n",
    "# Convert dt_iso to datetime type, rename it and set it as index\n",
    "weather_df ['time'] = pd.to_datetime(weather_df ['dt_iso'], utc=True, infer_datetime_format=True)\n",
    "weather_df  = weather_df.drop(['dt_iso'], axis=1)\n",
    "weather_df = weather_df.set_index('time')\n",
    "cities = weather_df['city_name'].unique()\n",
    "\n",
    "# Create df_weather_2 and drop duplicate rows in df_weather\n",
    "df_weather_2 = weather_df.reset_index().drop_duplicates(subset=['time', 'city_name'], \n",
    "                                                        keep='last').set_index('time')\n",
    "df_weather = weather_df.reset_index().drop_duplicates(subset=['time', 'city_name'],\n",
    "                                                      keep='first').set_index('time')\n",
    "\n",
    "# Drop columns with qualitative weather information\n",
    "df_weather = df_weather.drop(['weather_main', 'weather_id', \n",
    "                              'weather_description', 'weather_icon'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d415c572",
   "metadata": {},
   "source": [
    "## Merging Two datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75a35437",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Merging two datasets\n",
    "\n",
    "# Split the df_weather into 5 dataframes (one for each city)\n",
    "df_1, df_2, df_3, df_4, df_5 = [x for _, x in df_weather.groupby('city_name')]\n",
    "dfs = [df_1, df_2, df_3, df_4, df_5]\n",
    "\n",
    "# Merge all dataframes into the final dataframe\n",
    "df_final = energy_df\n",
    "for df in dfs:\n",
    "    city = df['city_name'].unique()\n",
    "    city_str = str(city).replace(\"'\", \"\").replace('[', '').replace(']', '').replace(' ', '')\n",
    "    df = df.add_suffix('_{}'.format(city_str))\n",
    "    df_final = df_final.merge(df, on=['time'], how='outer')\n",
    "    df_final = df_final.drop('city_name_{}'.format(city_str), axis=1)\n",
    "merged_df = df_final.copy()\n",
    "\n",
    "# Processing day, month, hour\n",
    "def split_date(df):\n",
    "    df['Date']= pd.to_datetime(df.index)\n",
    "    df[\"Year\"]= df.Date.dt.year\n",
    "    df[\"Month\"] = df.Date.dt.month\n",
    "    df[\"Day\"] = df.Date.dt.day\n",
    "    df[\"Hour\"] = df.Date.dt.hour\n",
    "    df['WeekOfYear'] = df.Date.dt.isocalendar().week\n",
    "split_date(merged_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b44a60ac",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>generation biomass</th>\n",
       "      <th>generation fossil brown coal/lignite</th>\n",
       "      <th>generation fossil coal-derived gas</th>\n",
       "      <th>generation fossil gas</th>\n",
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       "      <th>...</th>\n",
       "      <th>rain_1h_Valencia</th>\n",
       "      <th>rain_3h_Valencia</th>\n",
       "      <th>snow_3h_Valencia</th>\n",
       "      <th>clouds_all_Valencia</th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>WeekOfYear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-31 23:00:00+00:00</th>\n",
       "      <td>447.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4844.0</td>\n",
       "      <td>4821.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-12-31 23:00:00+00:00</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-01 00:00:00+00:00</th>\n",
       "      <td>449.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5196.0</td>\n",
       "      <td>4755.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-01-01 00:00:00+00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 01:00:00+00:00</th>\n",
       "      <td>448.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4857.0</td>\n",
       "      <td>4581.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-01-01 01:00:00+00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 02:00:00+00:00</th>\n",
       "      <td>438.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4314.0</td>\n",
       "      <td>4131.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-01-01 02:00:00+00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 03:00:00+00:00</th>\n",
       "      <td>428.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4130.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-01-01 03:00:00+00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 89 columns</p>\n",
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      "text/plain": [
       "                           generation biomass  \\\n",
       "time                                            \n",
       "2014-12-31 23:00:00+00:00               447.0   \n",
       "2015-01-01 00:00:00+00:00               449.0   \n",
       "2015-01-01 01:00:00+00:00               448.0   \n",
       "2015-01-01 02:00:00+00:00               438.0   \n",
       "2015-01-01 03:00:00+00:00               428.0   \n",
       "\n",
       "                           generation fossil brown coal/lignite  \\\n",
       "time                                                              \n",
       "2014-12-31 23:00:00+00:00                                 329.0   \n",
       "2015-01-01 00:00:00+00:00                                 328.0   \n",
       "2015-01-01 01:00:00+00:00                                 323.0   \n",
       "2015-01-01 02:00:00+00:00                                 254.0   \n",
       "2015-01-01 03:00:00+00:00                                 187.0   \n",
       "\n",
       "                           generation fossil coal-derived gas  \\\n",
       "time                                                            \n",
       "2014-12-31 23:00:00+00:00                                 0.0   \n",
       "2015-01-01 00:00:00+00:00                                 0.0   \n",
       "2015-01-01 01:00:00+00:00                                 0.0   \n",
       "2015-01-01 02:00:00+00:00                                 0.0   \n",
       "2015-01-01 03:00:00+00:00                                 0.0   \n",
       "\n",
       "                           generation fossil gas  generation fossil hard coal  \\\n",
       "time                                                                            \n",
       "2014-12-31 23:00:00+00:00                 4844.0                       4821.0   \n",
       "2015-01-01 00:00:00+00:00                 5196.0                       4755.0   \n",
       "2015-01-01 01:00:00+00:00                 4857.0                       4581.0   \n",
       "2015-01-01 02:00:00+00:00                 4314.0                       4131.0   \n",
       "2015-01-01 03:00:00+00:00                 4130.0                       3840.0   \n",
       "\n",
       "                           generation fossil oil  generation fossil oil shale  \\\n",
       "time                                                                            \n",
       "2014-12-31 23:00:00+00:00                  162.0                          0.0   \n",
       "2015-01-01 00:00:00+00:00                  158.0                          0.0   \n",
       "2015-01-01 01:00:00+00:00                  157.0                          0.0   \n",
       "2015-01-01 02:00:00+00:00                  160.0                          0.0   \n",
       "2015-01-01 03:00:00+00:00                  156.0                          0.0   \n",
       "\n",
       "                           generation fossil peat  generation geothermal  \\\n",
       "time                                                                       \n",
       "2014-12-31 23:00:00+00:00                     0.0                    0.0   \n",
       "2015-01-01 00:00:00+00:00                     0.0                    0.0   \n",
       "2015-01-01 01:00:00+00:00                     0.0                    0.0   \n",
       "2015-01-01 02:00:00+00:00                     0.0                    0.0   \n",
       "2015-01-01 03:00:00+00:00                     0.0                    0.0   \n",
       "\n",
       "                           generation hydro pumped storage aggregated  ...  \\\n",
       "time                                                                   ...   \n",
       "2014-12-31 23:00:00+00:00                                         NaN  ...   \n",
       "2015-01-01 00:00:00+00:00                                         NaN  ...   \n",
       "2015-01-01 01:00:00+00:00                                         NaN  ...   \n",
       "2015-01-01 02:00:00+00:00                                         NaN  ...   \n",
       "2015-01-01 03:00:00+00:00                                         NaN  ...   \n",
       "\n",
       "                           rain_1h_Valencia  rain_3h_Valencia  \\\n",
       "time                                                            \n",
       "2014-12-31 23:00:00+00:00               0.0               0.0   \n",
       "2015-01-01 00:00:00+00:00               0.0               0.0   \n",
       "2015-01-01 01:00:00+00:00               0.0               0.0   \n",
       "2015-01-01 02:00:00+00:00               0.0               0.0   \n",
       "2015-01-01 03:00:00+00:00               0.0               0.0   \n",
       "\n",
       "                           snow_3h_Valencia  clouds_all_Valencia  \\\n",
       "time                                                               \n",
       "2014-12-31 23:00:00+00:00               0.0                  0.0   \n",
       "2015-01-01 00:00:00+00:00               0.0                  0.0   \n",
       "2015-01-01 01:00:00+00:00               0.0                  0.0   \n",
       "2015-01-01 02:00:00+00:00               0.0                  0.0   \n",
       "2015-01-01 03:00:00+00:00               0.0                  0.0   \n",
       "\n",
       "                                               Date  Year  Month  Day  Hour  \\\n",
       "time                                                                          \n",
       "2014-12-31 23:00:00+00:00 2014-12-31 23:00:00+00:00  2014     12   31    23   \n",
       "2015-01-01 00:00:00+00:00 2015-01-01 00:00:00+00:00  2015      1    1     0   \n",
       "2015-01-01 01:00:00+00:00 2015-01-01 01:00:00+00:00  2015      1    1     1   \n",
       "2015-01-01 02:00:00+00:00 2015-01-01 02:00:00+00:00  2015      1    1     2   \n",
       "2015-01-01 03:00:00+00:00 2015-01-01 03:00:00+00:00  2015      1    1     3   \n",
       "\n",
       "                           WeekOfYear  \n",
       "time                                   \n",
       "2014-12-31 23:00:00+00:00           1  \n",
       "2015-01-01 00:00:00+00:00           1  \n",
       "2015-01-01 01:00:00+00:00           1  \n",
       "2015-01-01 02:00:00+00:00           1  \n",
       "2015-01-01 03:00:00+00:00           1  \n",
       "\n",
       "[5 rows x 89 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e632a5c6",
   "metadata": {},
   "source": [
    "## Features Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "740db956",
   "metadata": {},
   "outputs": [
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       "      <td>1035.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.375</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.475</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>281.286</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>269.251688</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>274.086</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>269.686</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22734.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>281.286</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>269.203344</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>274.086</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>269.686</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>281.286</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>269.485500</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>274.086</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>269.686</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20264.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day  Month  Year  Hour  temp_Barcelona  pressure_Barcelona  \\\n",
       "0   31     12  2014    23         281.625              1035.0   \n",
       "1    1      1  2015     0         281.625              1035.0   \n",
       "2    1      1  2015     1         281.286              1036.0   \n",
       "3    1      1  2015     2         281.286              1036.0   \n",
       "4    1      1  2015     3         281.286              1036.0   \n",
       "\n",
       "   humidity_Barcelona  wind_speed_Barcelona  temp_Bilbao  pressure_Bilbao  \\\n",
       "0               100.0                   7.0   269.657312           1036.0   \n",
       "1               100.0                   7.0   269.763500           1035.0   \n",
       "2               100.0                   7.0   269.251688           1036.0   \n",
       "3               100.0                   7.0   269.203344           1035.0   \n",
       "4               100.0                   7.0   269.485500           1035.0   \n",
       "\n",
       "   ...  wind_speed_Madrid  temp_Seville  pressure_Seville  humidity_Seville  \\\n",
       "0  ...                1.0       273.375            1039.0              75.0   \n",
       "1  ...                1.0       273.375            1039.0              75.0   \n",
       "2  ...                1.0       274.086            1039.0              71.0   \n",
       "3  ...                1.0       274.086            1039.0              71.0   \n",
       "4  ...                1.0       274.086            1039.0              71.0   \n",
       "\n",
       "   wind_speed_Seville  temp_Valencia  pressure_Valencia  humidity_Valencia  \\\n",
       "0                 1.0        270.475             1001.0               77.0   \n",
       "1                 1.0        270.475             1001.0               77.0   \n",
       "2                 3.0        269.686             1002.0               78.0   \n",
       "3                 3.0        269.686             1002.0               78.0   \n",
       "4                 3.0        269.686             1002.0               78.0   \n",
       "\n",
       "   wind_speed_Valencia  total load actual  \n",
       "0                  1.0            25385.0  \n",
       "1                  1.0            24382.0  \n",
       "2                  0.0            22734.0  \n",
       "3                  0.0            21286.0  \n",
       "4                  0.0            20264.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = ['Day','Month','Year','Hour',\n",
    "          'temp_Barcelona', 'pressure_Barcelona','humidity_Barcelona', 'wind_speed_Barcelona', \n",
    "          'temp_Bilbao', 'pressure_Bilbao', 'humidity_Bilbao', 'wind_speed_Bilbao',\n",
    "          'temp_Madrid','pressure_Madrid','humidity_Madrid', 'wind_speed_Madrid',\n",
    "          'temp_Seville','pressure_Seville', 'humidity_Seville', 'wind_speed_Seville',\n",
    "          'temp_Valencia','pressure_Valencia', 'humidity_Valencia', 'wind_speed_Valencia',\n",
    "        ]\n",
    "\n",
    "target = 'total load actual'\n",
    "\n",
    "selected_df = merged_df[inputs+[target]].copy()\n",
    "selected_df.reset_index(drop=True,inplace=True)\n",
    "selected_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62eb92eb",
   "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>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Hour</th>\n",
       "      <th>temp_Barcelona</th>\n",
       "      <th>pressure_Barcelona</th>\n",
       "      <th>humidity_Barcelona</th>\n",
       "      <th>wind_speed_Barcelona</th>\n",
       "      <th>temp_Bilbao</th>\n",
       "      <th>pressure_Bilbao</th>\n",
       "      <th>...</th>\n",
       "      <th>wind_speed_Madrid</th>\n",
       "      <th>temp_Seville</th>\n",
       "      <th>pressure_Seville</th>\n",
       "      <th>humidity_Seville</th>\n",
       "      <th>wind_speed_Seville</th>\n",
       "      <th>temp_Valencia</th>\n",
       "      <th>pressure_Valencia</th>\n",
       "      <th>humidity_Valencia</th>\n",
       "      <th>wind_speed_Valencia</th>\n",
       "      <th>total load actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>3.506400e+04</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "      <td>35064.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.729637</td>\n",
       "      <td>6.522930</td>\n",
       "      <td>2016.499544</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>289.867178</td>\n",
       "      <td>1.287211e+03</td>\n",
       "      <td>73.830710</td>\n",
       "      <td>2.782740</td>\n",
       "      <td>286.443451</td>\n",
       "      <td>1017.483858</td>\n",
       "      <td>...</td>\n",
       "      <td>2.433807</td>\n",
       "      <td>293.166541</td>\n",
       "      <td>1018.536990</td>\n",
       "      <td>63.780516</td>\n",
       "      <td>2.482717</td>\n",
       "      <td>290.783954</td>\n",
       "      <td>1015.979381</td>\n",
       "      <td>65.093971</td>\n",
       "      <td>2.692477</td>\n",
       "      <td>28698.281385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.800218</td>\n",
       "      <td>3.448752</td>\n",
       "      <td>1.117795</td>\n",
       "      <td>6.922285</td>\n",
       "      <td>6.723623</td>\n",
       "      <td>1.346303e+04</td>\n",
       "      <td>17.720809</td>\n",
       "      <td>1.996081</td>\n",
       "      <td>6.803527</td>\n",
       "      <td>9.820662</td>\n",
       "      <td>...</td>\n",
       "      <td>1.973041</td>\n",
       "      <td>8.082475</td>\n",
       "      <td>6.867486</td>\n",
       "      <td>22.881475</td>\n",
       "      <td>1.868503</td>\n",
       "      <td>7.234569</td>\n",
       "      <td>11.937650</td>\n",
       "      <td>19.678281</td>\n",
       "      <td>2.582959</td>\n",
       "      <td>4575.828854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262.240000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>266.850000</td>\n",
       "      <td>961.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>271.050000</td>\n",
       "      <td>986.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>268.830656</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18041.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>284.707375</td>\n",
       "      <td>1.014000e+03</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>281.452594</td>\n",
       "      <td>1013.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>287.330000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>285.150000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>289.411250</td>\n",
       "      <td>1.017000e+03</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>286.170000</td>\n",
       "      <td>1019.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>292.440000</td>\n",
       "      <td>1018.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>290.190000</td>\n",
       "      <td>1017.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>295.150000</td>\n",
       "      <td>1.021000e+03</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>291.040000</td>\n",
       "      <td>1024.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>298.880000</td>\n",
       "      <td>1023.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>296.150000</td>\n",
       "      <td>1021.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>32194.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>309.150000</td>\n",
       "      <td>1.008371e+06</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>312.470000</td>\n",
       "      <td>1042.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>315.600000</td>\n",
       "      <td>1090.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>311.150000</td>\n",
       "      <td>1087.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>41015.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Day         Month          Year          Hour  temp_Barcelona  \\\n",
       "count  35064.000000  35064.000000  35064.000000  35064.000000    35064.000000   \n",
       "mean      15.729637      6.522930   2016.499544     11.500000      289.867178   \n",
       "std        8.800218      3.448752      1.117795      6.922285        6.723623   \n",
       "min        1.000000      1.000000   2014.000000      0.000000      262.240000   \n",
       "25%        8.000000      4.000000   2016.000000      5.750000      284.707375   \n",
       "50%       16.000000      7.000000   2016.000000     11.500000      289.411250   \n",
       "75%       23.000000     10.000000   2017.000000     17.250000      295.150000   \n",
       "max       31.000000     12.000000   2018.000000     23.000000      309.150000   \n",
       "\n",
       "       pressure_Barcelona  humidity_Barcelona  wind_speed_Barcelona  \\\n",
       "count        3.506400e+04        35064.000000          35064.000000   \n",
       "mean         1.287211e+03           73.830710              2.782740   \n",
       "std          1.346303e+04           17.720809              1.996081   \n",
       "min          0.000000e+00            0.000000              0.000000   \n",
       "25%          1.014000e+03           62.000000              1.000000   \n",
       "50%          1.017000e+03           76.000000              2.000000   \n",
       "75%          1.021000e+03           87.000000              4.000000   \n",
       "max          1.008371e+06          100.000000             15.000000   \n",
       "\n",
       "        temp_Bilbao  pressure_Bilbao  ...  wind_speed_Madrid  temp_Seville  \\\n",
       "count  35064.000000     35064.000000  ...       35064.000000  35064.000000   \n",
       "mean     286.443451      1017.483858  ...           2.433807    293.166541   \n",
       "std        6.803527         9.820662  ...           1.973041      8.082475   \n",
       "min      266.850000       961.000000  ...           0.000000    271.050000   \n",
       "25%      281.452594      1013.000000  ...           1.000000    287.330000   \n",
       "50%      286.170000      1019.000000  ...           2.000000    292.440000   \n",
       "75%      291.040000      1024.000000  ...           3.000000    298.880000   \n",
       "max      312.470000      1042.000000  ...          18.000000    315.600000   \n",
       "\n",
       "       pressure_Seville  humidity_Seville  wind_speed_Seville  temp_Valencia  \\\n",
       "count      35064.000000      35064.000000        35064.000000   35064.000000   \n",
       "mean        1018.536990         63.780516            2.482717     290.783954   \n",
       "std            6.867486         22.881475            1.868503       7.234569   \n",
       "min          986.000000          7.000000            0.000000     268.830656   \n",
       "25%         1014.000000         46.000000            1.000000     285.150000   \n",
       "50%         1018.000000         67.000000            2.000000     290.190000   \n",
       "75%         1023.000000         83.000000            3.000000     296.150000   \n",
       "max         1090.000000        100.000000           15.000000     311.150000   \n",
       "\n",
       "       pressure_Valencia  humidity_Valencia  wind_speed_Valencia  \\\n",
       "count       35064.000000       35064.000000         35064.000000   \n",
       "mean         1015.979381          65.093971             2.692477   \n",
       "std            11.937650          19.678281             2.582959   \n",
       "min           969.000000           8.000000             0.000000   \n",
       "25%          1012.000000          51.000000             1.000000   \n",
       "50%          1017.000000          67.000000             2.000000   \n",
       "75%          1021.000000          82.000000             4.000000   \n",
       "max          1087.000000         100.000000           133.000000   \n",
       "\n",
       "       total load actual  \n",
       "count       35064.000000  \n",
       "mean        28698.281385  \n",
       "std          4575.828854  \n",
       "min         18041.000000  \n",
       "25%         24807.000000  \n",
       "50%         28902.000000  \n",
       "75%         32194.250000  \n",
       "max         41015.000000  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fab4f78e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_df['temp_Barcelona'].plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38d501e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total load actual       1.000000\n",
      "Hour                    0.395586\n",
      "temp_Valencia           0.220790\n",
      "temp_Seville            0.204571\n",
      "temp_Bilbao             0.196535\n",
      "temp_Madrid             0.185388\n",
      "temp_Barcelona          0.167338\n",
      "wind_speed_Bilbao       0.155898\n",
      "wind_speed_Valencia     0.147982\n",
      "wind_speed_Seville      0.132973\n",
      "wind_speed_Barcelona    0.120719\n",
      "wind_speed_Madrid       0.083669\n",
      "Year                    0.060122\n",
      "pressure_Valencia       0.010565\n",
      "pressure_Madrid         0.006952\n",
      "pressure_Bilbao        -0.000613\n",
      "pressure_Barcelona     -0.002199\n",
      "Day                    -0.005919\n",
      "pressure_Seville       -0.024875\n",
      "Month                  -0.040919\n",
      "humidity_Madrid        -0.201517\n",
      "humidity_Bilbao        -0.268101\n",
      "humidity_Seville       -0.283103\n",
      "humidity_Barcelona     -0.300901\n",
      "humidity_Valencia      -0.314687\n"
     ]
    }
   ],
   "source": [
    "correlations = selected_df.corr(method='pearson')\n",
    "print(correlations['total load actual'].sort_values(ascending=False).to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fdd16a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlations['total load actual'].plot(legend=True,kind='bar',rot=90) #plot usa column\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fab3908d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the dataset to a csv file\n",
    "selected_df.to_csv('data/merged.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d049f1",
   "metadata": {},
   "source": [
    "## Splitting datasets into train, validation, and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f45d913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((26305, 24), (4415, 24), (4344, 24))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting datasets into train, validation and test sets\n",
    "train_set = selected_df[selected_df[\"Year\"]<2018].copy()\n",
    "test_set = selected_df[(selected_df[\"Year\"]==2018) & (selected_df[\"Month\"]<=6)].copy()\n",
    "val_set = selected_df[(selected_df[\"Year\"]==2018) & (selected_df[\"Month\"]>6)].copy()\n",
    "\n",
    "# Scaling the columns from 0 to 1 limit\n",
    "scaler = MinMaxScaler().fit(train_set[inputs])\n",
    "train_set[inputs] = scaler.transform(train_set[inputs])\n",
    "val_set[inputs] = scaler.transform(val_set[inputs])\n",
    "test_set[inputs] = scaler.transform(test_set[inputs])\n",
    "\n",
    "# Setting X and y for train, test, validation\n",
    "[X_train, y_train] = [train_set[inputs].copy(), train_set[target].copy()]\n",
    "[X_val, y_val] = [val_set[inputs].copy(), val_set[target].copy()]\n",
    "[X_test, y_test] = [test_set[inputs].copy(), test_set[target].copy()]\n",
    "\n",
    "# Checking shape of the datasets\n",
    "X_train.shape, X_val.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e3bde92",
   "metadata": {},
   "source": [
    "## Training and Validating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7455484",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for calculating average Deviation\n",
    "def error_in_parcent(actual, predicted):\n",
    "    percents = [] \n",
    "    actual = actual.to_numpy()\n",
    "    for i in range(len(actual)):\n",
    "        p = abs((actual[i]-predicted[i]))/actual[i]\n",
    "        percents.append(p)\n",
    "    average =  (sum(percents)/len(percents)) *100\n",
    "    return float(\"{:.2f}\".format(average))\n",
    "\n",
    "\n",
    "# Function for checking the model\n",
    "def checking_error(model):\n",
    "    train_preds = model.predict(X_train)\n",
    "    train_deviation = error_in_parcent(y_train, train_preds)\n",
    "    \n",
    "    val_preds = model.predict(X_val)\n",
    "    val_deviation = error_in_parcent(y_val, val_preds)\n",
    "    \n",
    "    print(f'Average Deviation on the Traning set: {train_deviation} %')\n",
    "    print(f'Average Deviation on the Validation set: {val_deviation} %')\n",
    "    \n",
    "    # Plot for training\n",
    "    plt.plot(range(1000,1200),y_train[1000:1200],label = \"Actual Value\")\n",
    "    plt.plot(range(1000,1200),train_preds[1000:1200],label = \"Predicted Value\")\n",
    "    plt.legend()\n",
    "    plt.title(\"Training Results\")\n",
    "    plt.ylim([0, 45000])\n",
    "    plt.show()\n",
    "    \n",
    "    # Plot for validation\n",
    "    plt.plot(range(1000,1200),y_val[1000:1200],label = \"Actual Value\")\n",
    "    plt.plot(range(1000,1200),val_preds[1000:1200],label = \"Predicted Value\")\n",
    "    plt.legend()\n",
    "    plt.title(\"Validation Results\")\n",
    "    plt.ylim([0, 45000])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eab5b8c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Complete\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "model = XGBRegressor(random_state=42, n_jobs=-1, n_estimators=11, max_depth=5, subsample=0.8, \n",
    "                         colsample_bytree=0.7, seed =1301)\n",
    "model.fit(X_train, y_train);\n",
    "print(\"Training Complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8caeb04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Deviation on the Traning set: 7.52 %\n",
      "Average Deviation on the Validation set: 8.29 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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17GtkwluJRCKRSLzk6z2FfL6roFfnPH90HOeOiu30eUuXLmHatBmYTCZmzTqTb7/9ijvuuAuTycQbb7zK119/wUMPPUxCQhLPPfckjz76Bx5//BkyMtLZuXM7f//70x3OUV9fx/3338Vll13Jww//mdLSEv7617/w1ltvcN99Dx33XJPJxOzZc1i5cgXnnXch0GT2XMHNN9+Kpmk8+OA9TJw4iXvueZfa2lqeeeZxXnzxHzz11POdei3Kykq5//7fcPPNt/LHP85EVffz5JN/JSgomJkzz+jUWL2N1KhJJBKJRHKSUVpaws6d25k160wAzjhjDpWVlaxfvxZN0/jii0+5+eZbmT59JikpA7nvvt8yYsQozGYL/v7+mEwmIiOjOpynoaGBa665gRtuuIWEhETGjh3P7NlzyMrK8GqdCxaczdatm6ipqQFg166dVFZWcuaZZ9HY2MjixRdw++13kZiYhBCpnH32eWRmejd2Sz799CNOO20il19+FUlJycydu4DLL/8ZH330XqfH6m2kRk0ikUgkEi85b3QcZ4/ofBmg3mbZsu8xGAxMnz4TgFGjRhMVFc13333D6NHjqKysQIjU5v6JiUn88pe3d3qeyMgozjlnMR988A6HDh0kKyuTtLSDjBw52qvzx42bQEREJGvXrubss89jxYofOP30WQQGBgFw0UWX8f3333LgwD5ycrJQVZXQ0NBOrzM7O5MNG9Yxf/6s5mMul4uwsPBOj9XbSEFNIpFIJJKTjGXLfsDpdHLuuXObj7ndbtat+xGbrXOljFqXz3K5XM3/l5QUc/PN1zBsmGDKlOmcf/5FrFu3ll27dng99rx5C1m1ajkLF57DqlUrmk2m9fX13HLLtYSEhDBr1mzmzVtITk4Wb7/9VodrbL1Ol8vFvHkLuf76m4/qYzD0f8OiFNQkEolEIjmJOHw4hwMH9vHrX9/DpElTmo8XFRVy//13sXbtasLCwjl4UGX48NTmc371q5t5552PjhF6TCYTdXW1zc/z8/Oa/1+zZiUBAYFH+Yx9/PEHdKZc0vz5i7j11hvYunUzDoeDadNOB2D79q0UFxfx1lvvYzLp4srmzRvajEo1mcwA1NXVtbnO5OQUdu7cTlJScvOxTz75kNLSki5pEnuT/i9KSiQSiUQi8Zply74nKCiYCy64hMGDhzY/pk+fyZgxY/nuu2+47LIreeONV9m8eQNZWZk888zjDB+eSkhIKP7+AZSVlTYLOiNGjOLzzz8lPT2N7du38sEH7zTPFRISSmlpCZs3byAvL5e3336T1atXYLc7vF7vsGHDSUhI5MUX/8FZZ83FbNaFrtDQUBobG1m9egUFBfl89dXnfPLJhzgc9mPGiIiIICYmlv/97w3y8nJZsuQb1q9f29x+8cWXcejQQV5++Z8cPpzDypXLeOml54mJ6XyQRm8jBTWJRCKRSE4ili37nvnzF2G1Wo9pu/DCS1HV/cyadSZz5y7gz39+mF/+8nqCgoJ5+OE/ATB79hwMBgPXXHM5FRXl3HLLbURFRXHDDT/nueee5JZbbmseb86c+SxceA6///1vuemma9i6dTN33nkPOTnZnTKxLlhwNocOHWTBgrObj40ePZYbbriFZ599kuuuu4pvv/2Se+99iKqqKgoLC48632Aw8Nvf/p6MjHSuueZyli79nuuuO2LmjIuL54knnmXLlk1ce+0VnsoHv+Siiy71eo19hdJbie16E4fDpVVW1vt0jrCwAHw9R39G7v/U3f+pvHeQ+z+V9l9YmE1cXMpRx4xGAy6Xu49W1LecynuH7u+/rc9TE9HRwVuBSW21SY2aRCKRSCQSST9FCmoSiUQikUgk/RQpqEkkEolEIpH0U6SgJpFIJBKJRNJPkYKaRCKRSCQSST9FCmoSiUQikUgk/ZROVSYQQrwGDFVVdbbneQrwb+B0IAe4R1XV71r0Pwv4BzAU2ATcrKpqWov2O4EHgVDgY+AOVVXrPG1W4AXgMsAGPKOq6hNd26ZEIpFIJBLJiYfXGjUhxFzgphbPFeALoAyYDLwFfCKEGORpTwa+BN5Gzw1SCHwhhDB42i8GHgV+BZzlGePpFlM+CcwA5gG/BB4WQlzZpV1KJBKJRCKRnIB4pVETQgQCrwI/tTh8FiCAWaqq1gD7hBDz0IW5h4FbgJ1NWjAhxI3owtocYBlwF/CCqqpfetpvBZYJIe5DLxJ2C7BYVdWtwFYhxBPAHcD73dqxRCKRSCQSyQmCtxq1x4BVnkcT04DtHiGtibXA9Bbta5oaVFWtB7YB04UQRnQN2poW525AFxwnAOMAq2e8lmNPFkLIQvISiUQikbTDpZcuZubMSc2PM8+cymWXXcD//vdmj85z22038frrrwDw2GN/4i9/+X2H52iaxueff4zb3bUM/5s3b2TmzGMT+GuaxiWXnMeLLz7fxlnwyScfsHjxApxOZ7tjFxTkM3PmJHJzD3dpbb6iQ6FHCDEd3U9sNHBvi6Z4IL9V9yIgyYv2MMCvZbuqqk4hRJmn3Q6Uq6ra2OpcCxANFHS0bolEIpFITlXuuOMu5s9fBIDT6WTbti38/e+PEB0dzaJF5/b4fL/5zX1e9duxYxtPPfV3zjvvQgyGnotnVBSFefMWsmrVcn71q18f075ixTLmzp2PyXTi6XqOu2KPQ//rwF2qqlYIIVo2B6A7+bfEhq4J66g9oMXzttpN7bTRYvx2MRoVwsICOurWLYxGg8/n6M/I/Z+6+z+V9w5y/6fS/ouKFIzGY4WJto71N4KDg4mJiWl+npBwPsuWfc+aNSs599zFXR635d4VRcFg0F+j0NAQr843GJTmcbryOjYJd22du2jR2bzzzlukpx9i+PAj8kpJSQm7d+/kzjvvOu6cTW1Nezpen66gKF2TTToSLf8AHFJV9aM22hrRozVbYgXqW7S3Fqqs6MEHjS2et3W+1k4bLcZvF5dL83nR4FOpMHFbyP2fuvs/lfcOcv+n0v41TTumCPeJUpjc7W5r7UZMJjMul5s77vgFgwcPYePG9dhsNt588z3sdhvPPvsEmzdvJCQklPnzF3HzzbdiNpsB+PHHVfzrX89TWlrMeeddiNvtbp7nscf+hMvl4g9/eASAZcu+5803X6egII/Bg4fwm9/cR2RkFLff/gsAZs2awvPPv8xpp03iiy8+5e2336KyspyhQ4fz61/fw4gRowCoq6vliSf+yrp1a4mKiuK88y4AaPM9GDRoKIMHD2HFimUMGTKs+fiyZT+QkJBIauooioqKee65J9myZRM2WyMDBw7iN7+5j/HjT2ses2lPM2dO4tln/8XkyVMBWLLka1555UU+++xbADIy0nnuuSfZs2c30dHRXHjhpVx55c9RFKXN90TT2pdNoqOD230vOxLUfgbECyFqPc8tgNHz/K/ovmQtieOIWTLP87x1+x6OCGtNz/H4nkV6zncB4UIIi6qq9hbn2oDyDtYskUgkEolPsOz/CMve93p1zsYRV2JLvbTL5zudTn76aQ2bNm3g//7vj83Hv/32K55++p9YrRZCQ0O55ZbrGDx4CK+//jaVlRU89dTfcDqd3Hnn3WRmZvDwww/xq1/dybRpp/PBB++wZ8+uZiGmJVu2bOKRR/7Ab35zH1OmTOPLLz/j/vvv4qOPvuSxx57gd797gM8++5awsHDWrl3Da6+9zAMP/B8DBw5mxYql/PrXt/Hee58SFRXFk0/+jZycLP75z1eprKzgscf+dNy9zp+/iCVLvuGWW25rPrZixdJmM/Ajj/yBgAB/Xn75DTRN4+WXX+Cpp/7G22+3pY9qH5utkfvu+zULF57D/ff/H7m5h3nyyb9iNpu49NKeTVDRkQ5vNrpv2njP49/AFs//G4DxnojQJmZ6juP5O7OpQQgRgB4osEFVVTewuWU7ehCCE9gO7ED3U5vRauytqqq27wkokUgkEomEZ599gvnzZzF//izmzJnBo4/+icsv/xkLFpzd3GfatBmMGzee1NSRbN26mfz8PB588GFSUgYybtwE7rnnQT799EOcTifffvsVY8eO44orfk5KykDuuedBIiMj25z7888/Ye7cBVx88WUkJSVz6613sHjxBdTW1hIcrJtIw8MjMJvNvPvuf7n66uuYNWs2yckDuO66m0hNHcHXX39ObW0tK1cu484770GIVKZOnc5119143H3Pn7+InJxsMjMzACgqKmTv3t3Ngtrpp8/irrvuZ+DAQQwaNJiLL76crKxMNE3r1Ou7dOkSgoND+OUvbyc5eQDTp5/OLbfcxocf9rwQf1yNmqqq2S2fCyEqgAZVVdOEEJlANvCmEOJPwHnokZ5NudbeAO4XQvwO+Az4PXpS3OWe9heBfwshdnmOvwi8oapqrWeut4AXhRDXo2vT7gN+0a3dSiQSiUTSDewjLqNh+CV9vYwOueGGWzjrrHkAWCwWIiOjMBqNR/WJi0to/j87O5Pa2hoWLZrdfEzTNBwOB0VFhWRlZTBs2PDmNpPJxJAhw2mLrKxMFi++oPm5wWDgV7/6DQD5+blH9c3OzuSVV/bz2msvNx+z2+3ExMRw+HA2LpeLoUOPzCPEyOPuOy4untGjx7Jq1XIGDRrMihXLSE0dwYABKQBcdNGlLFv2PXv27CI7OwtVPQCAy+U67rjH7jGLrKwM5s+f1XzM7XbjcDhwOBzN5uKeoMvhD6qquoQQF6AHG2wF0oGLVFXN8rRneZLaPgv8Dl3DdoFHm4aqqu97Khu8hO5/9hlHR5Xe42lbAVQDf1FV9YOurlcikUgkklOFsLBwkpKSj9vHYrE0/+9yuUhKSuaJJ547pl9MTCzAMVqn9iIozWbvRQuXy8Udd9zFlCnTjjru7+9PcXGR59mReb2J2lyw4Gw+//xjbrjhFo/ZU9ciut1u7r77dqqrq5g7dwGnn34GDoeD3/3ufq/X2vL/8eMncv/9vz2mX2uBuLt0SlBTVfXhVs/TgDOP0/874LvjtD8OPN5OWz1wnechkUgkEonERyQnp1BcXERISCghIbp5cufOHXz00Xv8/vd/YdCgIezatb25v8vlIj39EEKkHjNWUtIADh5Um59rmsY111zOHXfcjdV6dJxg07wthcqnnvob48efxvTpp2Mymdi/fx9Tp+opWg8dUumIOXPm8Y9/PMXOnTs4ePAAjz/+DABZWRns2LGNzz9fQlRUFACfftq+b5rZbKa+vq75eV7eEW3ggAEprF69gri4+GbhceXKZWzatIEHH3z4mLG6Q/+PMZZIJBKJROJTpkyZRkJCIn/5y8McOnSQPXt28fjjj2AwGLBarSxefCEHD6r85z//JicnixdeeJaSkuI2x7rssitZsWIpX331Obm5h3nppReorq5m9Oix+Pvr6SkOHjyAzWbjyit/zscfv893331NXl4ub7zxKt988yUpKQMJDAxi4cJz+Mc/nmLPnt1s27aFN998rcO9hIaGMWXKNJ577gkmTpxCRITuSxcUFIzBYGDFih8oLCxg5cplvPGGnrDXbrcfM05q6kjef/8dDh/O4aeffuTbb79qblu48GwcDgePP/4oWVmZbN68gWeeeaLZB68nkYKaRCKRSCSnOEajkb///RkMBiO33XYjDz54N+PGTeChh3TtUHLyAJ588jlWrFjK9df/nKqqymYtV2vGjh3PAw/8jv/97z9ce+2V7N69gyeeeI6goCCGDBnKlCnTuf32W9iwYR1z5y7g1lvv4I03/s0111zOmjWr+Otfn2LYMD0P2j33PMDYseO55547+Otf/8wll1zh1X7mz1/EoUMHWbBgUfOxmJhY7r33Id5//x2uvvoy/ve///Cb39yHyWRqU1N39933Y7PZuPbaK/jf//7DLbf8qrktICCQp59+noKCfG688WoeffRPnH32efziF786ZpzuonQ20uFEwOFwaTKPmm+R+z91938q7x3k/k+l/RcWZhMXl3LUsRMlj5ovOJX3Dt3ff1ufpyaio4O3AsfWxkJq1CQSiUQikUj6LVJQk0gkEolEIumnSEFNIpFIJBKJpJ8iBTWJRCKRSCSSfooU1CQSiUQiaYeTMeBO0vt053MkBTWJRCKRSNrAaDThcBybX0si6SwOhx2jsWvFoKSgJpFIJBJJGwQFhVFZWYLdbpOaNUmX0DQNu91GZWUJQUFhXRqjy7U+JRKJRCI5mfH3DwSgqqoUl8sJgKIop6zQdirvHbq+f6PRRHBwePPnqbNIQU0ikUgkknbw9w886gf2VEr425pTee/Qd/uXpk+JRCKRSCSSfooU1CQSiUQikUj6KVJQk0gkEolEIumnSEFNIpFIJBKJpJ8iBTWJRCKRSCSSfooU1CQSiUQikUj6KVJQk0gkEolEIumnSEFNIpFIJBKJpJ8iBTWJRCKRSCSSfooU1CQSiUQikUj6KVJQk0gkEolEIumnSEFNIpFIJBKJpJ/iVVF2IUQq8E9gGlAG/FNV1Sc9ba8Ct7Q65W5VVZ/ztJ8F/AMYCmwCblZVNa3F2HcCDwKhwMfAHaqq1nnarMALwGWADXhGVdUnurRTiUQikUgkkhOMDjVqQggz8B2QA4wHbgd+L4T4uafLSOB+IL7F41XPucnAl8DbwCSgEPhCCGHwtF8MPAr8CjgLmAw83WL6J4EZwDzgl8DDQogru7xbH5JVVs/XewvRNK2vlyKRSCQSieQkwRuNWiK6Jux2VVUbgDQhxDLgTOAdYATwsKqqhW2cewuws0kLJoS4EV1YmwMsA+4CXlBV9UtP+63AMiHEfYDmOX+xqqpbga1CiCeAO4D3u7jfHmd/UQ3/XpfNjxnlAMQF+zFpQFjfLkoikUgkEslJQYcaNVVVs1RVvUJV1QYhhCKEOB04A1guhIgDIgC1ndOnAWtajFUPbAOmCyGM6Bq0NS36b0AXHicA4wArsLZF+1pgshDCK5Otr6m1Obntw13syq/mxqnJGBXYmF3R18uSSCQSiURyktDZYIJcdGFpPbo/2UjACTwihMgTQuwUQlzfon88kN9qjCIgCQgD/Fq2q6rqRPeBS/KcW66qamOrcy1AdCfX7RO+3FNInd3F85eM4baZgxgdHyIFNYlEIpFIJD1GZzVTFwAJwEvAsxzRpO0EngdmA68IIepUVf0ICEAPAmiJDV1TFtDieVvtpnba8LS3i9GoEBYWcLwu3UYDPtyRz6SUcGakxgJwZmoML6xMQ7OYCA+w+HT+vsZoNPj8Ne7PnMr7P5X3DnL/cv+n7v5P5b1D3+2/U4KaqqpbAIQQAcBbQAjwnqqq5Z4uu4QQw4DbgI+ARo4VqqzoWrPGFs9bt9ejy0JtteFpbxeXS6Oy8rhdus363GryKhu564zBzXONiw1C02D57gLmiX6h9PMZYWEBPn+N+zOn8v5P5b2D3L/c/6m7/1N57+Db/UdHB7fb5k3UZ6IQ4vxWh/ehmyCDWwhpTexHD0AAyAPiWrXHAQUcEdaa2z2+Z5Ge9jwgXAhhaXWuDWg9Z6/zxrpMksL8mDUksvnYyLhggqxGNkjzp0QikUgkkh7AGx+1EcCnQoiYFscmAiXAb4UQX7fqPwE44Pl/AzCzqcGjiZsAbFBV1Q1sbtkOTEf3edsO7ADs6Ok5mpgJbPX4svUZewqq2XG4iqtOS8RoUJqPmwwKk5LD2JRdIdN09EMKqhvJr2rsuKNEIpFIJP0Eb0yfq9E1aG8KIe4FhgB/Bx5DF6ZWCCF+DXwDLAKuBeZ6zn0DuF8I8TvgM+D36PnYlnvaXwT+LYTY5Tn+IvCGqqq1AEKIt4AXPQEKccB9wC+6sd8eQQFOHxLJeaNaKwthako4q9LKOFzZyIBw/95fnKRNHC43N7+3g5JaOzMGRXDlaQlMGxjR18uSSCQSieS4eJOewwGch67p2gi8AjwHPK+q6mrgKnThaS96MtyrVFVd6zk3C7gYuAbYAsQCF3i0aaiq+j56wtuX0POqbQHubTH9PehatxXAy8BfVFX9oDsb7glGxYfw5vWTCbAYj2mbmhIOyDQd/Y0VB0sprrWzaEQMB4prufOTPaxJL+vrZUkkEolEclyUk9FE53C4NF87PLbnVKhpGhe+tonU2GAeP3+kT9fQl5xoTqU3vLud6kYnH90wCadL46r/bsVqMvD2NadhUJSOB2jFibb/nuRU3jvI/cv9n7r7P5X3Dj4PJtiKXsHpGGRR9h5GURSGxwSRWXbqfpj7G3sKqtlTUMPl4xMwKAoWk4FbpqdwqKSO5QdL+3p5EolEIpG0ixTUfEBymD+5VQ243CeftvJE5P1teQRajJw3Orb52HwRzeDIAF5dlyXfJ4lEIpH0W6Sg5gOSw/1xuDSKalrn65X0NiW1NpYdLGXx6DgCLUdiZ4wGhV/OSCGrvIEl+4v7cIW+5XBFA8sPllBaZ+/rpUgkEomkC/SLmpknG03RnocrG0gI9evj1ZzafLuvGJdb4/LxCce0zR4WxYjYIP669CAltTaunpSEyXjy3Lu43Br3fbGXDI8ZflBkAH8+WzAitv3EihKJRCLpX5w8v0r9iOQwj6BW0dDHK5EsP1jCqLhgkttIlWJQFJ65aDQzB0fyr7VZXPfOdnbmVfXBKn3Dkv3FZJTVc+esQfz6jEFUNzr56w+HcJ+EAUQSiURysiIFNR8QHWTBajJwuFIKan1JbmUD+4tqmTs8qt0+UYEWHj9/JE+cP5KKBgc3v7+TB7/cR+4J/t7ZnW5eWZfFiNggrp6cxDWTk7nrzMEcKK7l231Ffb08SQ+TVVbPA1/uI0feHEokJx1SUPMBiqKQHOYvL5p9zApPROfc4R3XXT1rWBSf3DiZX8xIYX1WOTe9t4Oaxj4tgNEtPt1VQEG1jV/NHNicfmRBajSj4oJ5cW0WDQ5XH69Q0lM4XW7+8N0BVh4q5c5PdlMm/RElkpMKKaj5iORwf2n67GOWHSxhZFyw136C/mYjt0xP4dUrxlHZ4OCln7J8u0AfUW938caGHCYlhzYnYAbd1Hv37MGU1Nr53+bDfbhCSU/y1ubD7C+q5cZpAyivs3PXp3uos5+4NxkSieRopKDmI5LD/MmrasQpUz/0CXlVutlz3nHMnu2RGhvMZeMT+GRnPgeKanywOt/y8Y58Khoc/GrmIJRWyXzHJYYyb3g0/9l4mBUHS/pohZKe4mBxLa+tz2FhajS3nT6Qvy8eyaGSWh75/mBfL00ikfQQUlDzEQPC/XC6NQqrZRHwvqDJ7DmnC4IawC9nDCTM38zjy9NOKOf7BoeLt7fkMi0lnDEJIW32+b/5wxgRG8xvv97P13sLe3mFkp5C0zQe/eEgIX4m7pszFIDTB0dw07QUlh8sRS2u7eMVSiSSnkAKaj4iuUWKDknvs+xgKSNig0gMPTba0xuC/Uz85szB7Cmo4aPt+T28Ot/x2a4CPShi+oB2+wT7mfjnpWOYlBzGn5cc5NlV6VQ1OHpxlZKeYE16OfuLarlj1iDC/M3Nx688LZEgq5E3NuT04eokEklPIQU1H3EkRYfUqPU2mWX17CusYb7oOIjgeJw9IoYZg8J5fk3GCaGdaHS4+O/mXCYlhzIuMfS4fQMsRp65aDQXjInjva15XPT6Zt7dmsvJWPv3ZETTNF5bn01iqB9nj4w9qi3Yz8QVExJZcaiUtNK6PlqhRCLpKaSg5iOiAi34m2WKjr7g672FGBWO+QFrxu3EnL0SOhBKFEXhT4sEIX5mfvf1furt/TtS8ss9hZTV2bl5eopX/a0mAw8vGM67105kdHwwz67K4PPd0hR6IrA2o5wDxbXcOHUAJoNyTPtVpyUSYDbyn5NYq+Z0a3yzt0hqgyUnPVJQ8xGKopAUJiM/exunW+ObfcXMGBRBVKClzT7+u94g7OtrMOf+2OF44QEWHjknlZyKBp5ckXZU25acSu76dA92p7tH1t4d3JrGe9vyGJsQwmlJx9emtWZodCDPXTyaqSlhPL0ynbQSqYXpz2iaxmsbckgI9eOckTFt9gn1N3PZhASWqiUn5ftpc7p58Mt9/GmJymNLD/X1ciQSnyIFNR8yINxfatR6mQ1Z5ZTV2Vk8Oq7NdsVeQ8DWFwAw52/0asxJA8K4fmoyX+8taq5c4HZrPLUyjZ8yy9l8uLJH1t4dtudWkVvZyCXj4o+J9PQGg6Lw57NTCbQY+e3X+2SetX7MxuwK9hXWcMOU5OOWPLt6YhJh/mZ+983+k+r9rLU5+c2nu/kxvYypKWGsPFTK6rTSvl6WROIzpKDmQ2SKjt7nqz1FhPmbmTk4os12/52vYWiswO0fiblgi9fj3jB1AJGBFv75YyaaprFkbyHppXoNzTVpZT2y9u7w1d4iAi1G5gzrWpQrQGSgrj3MLm/gke8PnlDRrqcSn+8uJNzfzLmj2jHtewgLMPPIualkltXz+LJDJ4X/oaZpPPzNAXbkVfOXc1J57qLRDI0K5InladTaZO64UwZ7Hf7bXsRvz3/7eiW9ghTUfEhyuD8ut0ZBlQwo6A0q6x2sSS/j7BExmNvQNCiNFfhvfwXb4LOxDV2MuWg7uL27uPubjfxi+gB25FWzOq2M51emMSgigNlDI/kxo6xPhZpam5PlagkLUqPxMxu7NdaUlHBumzmQpWoJr6zL7qEVSnqKygb9M76onc94a6amhHPz9AF8s6+YL/ec+P6HS9USNmYWc8+sRBaNiMFkNPC7BcMoqbXz0tqsvl6exEf473yN4CW3ErDhcQI2P0fk26cTtP6vBP30CLhO/kocUlDzIc2Rn9L82SssO1iC062xePQRTYPSUE7ApqcJ2PA4IUvvRHHUUTf1fhzxk1Gc9ZhK93k9/vlj4hkQ7s8fv1NJL6njlhkpnDUsipJaO/uL+i4qdJlaQqPTzeJRbZt7O8v1U5I5f3Qsb2zI4atWP+4V9Xb+8s2+E7q81onMDwdKcLg0zutAm9aSm6alMGVAGH9beuiErvNa3ejg6ZXpvBj8Fr/IuL35+Oj4EC4aG88nuwooleWzTjrMeesIWvsnLPnrCdj2IoGbnsIZMZy6SXehOBswFe3o6yX6HCmo+ZD4ECtA/0t663YRtOoh/Pa932Hk44nE9twqYoOtDIsOaj7mv+t1Ajc/S8D2lzDnrqVxzHW4IobjiJsMgLlgs9fjmwwKt88aRL3DxfCYIOYOj2LGoAiMCqzpQx+Zr/YWMSgigNHxwe32UepLCPvoPMx56zscT1EUfjtvGJMHhPHY0kNHVWf4149Z/G9DDstPgqoGhpo8wj4+n/D35xP+/nwCNj3T10vqkG/2FTEsOpDhMUEdd/ZgNCg8ccFIJiSH8cfvVN7flufDFfqOf/6YSUNDHXO09ZhLdmOsOBLcc9VpibjcGt/uPXEFUcmxKPZagpffizN0IGXXrKf0l2mUXr+Nqgs/pGHcTWgoWPLW9fUyfY4U1HxIdJAVo0GhoNrW10s5CkvmEvz3vk3wyvsIXnoHir3/5wjzht0F1YxpKaxoGtZDX2BPmkXpbVmU3pZF7RmPAuAOTsAVlICp0Hs/NYCzhkZy85R4npnhIHDHKyTsfoHxCcGsSS/vya14TVZ5Pbvyq1k8Ova4QQRBa/+EuXgHfnvf8Wpck9HA384bQZi/mb98fxCHy41aXNtsPtuYXdEj6+9LLNkrMBdtwxWUAC4b/jv/7bUpvC/IKKtjX2FNp7RpTQRaTDx30WhmD43k6ZXpfLLzxEniDKAW1/LZrkJ+O6wQk1P3DbVkLGluHxgZwITEEL7YU3hS+OJJdAJ/egRDbR41c58DcwAYzWiBeqSz5heOM2oU5ryf+naRvYAU1HyI0aAQG2ShoD9p1DSNgG0v4gpJoW7qA1jTviLs48Xg6l/CZGcprbVRUG07qmySqWQ3pqosbMPOb/McR/xkXaPWiQu74rLx0OFbGL3kEoLWPUrg5me4JKGMtNI68qp638T9Y7oeyLAgtUWaBkc9gWv/jKl4J6ALJH6HvsBtDcWSvdxrn45QfzO/nTeUQyV1vLnpMM+sTCfEz8T8EbFsyqnEdYIHyZiKduD2i6D63Depn3IfBnsNpuJdfb2sdvlmbxFGg8KiEW2n5GgiaPXvCFr9u2OOW00G/rZ4JDMGhfP0ynTUDsz1P6aXkVHWP1J7fLg9Dz+TgQv9tuG2BOOIGo0147uj+lwwJp6ciga2eyKzJSc2psKt+O97h4bxv8AZP6nNPo7EGZgLt4Hz5HYvkoKaj4kP9etXGjVz3jrMxTupn3Ab9ZN+Tc38f2GqOIQlc2lfL61b7CnQzXOj448IatZDX6AZzNgGL2rzHEfcJIx1hRhqvDcFWdO/wVSRhmveo5Rf8QMAZxr3ALC6D6I/N2VXMigigNhg65E1Zv5AwM5/E/bJBQRsfo6g1f+HM3wYNWc9icFegznX+zvQM4dGsTA1mn+vy2ZbbhW3nj6Qc8fEUd3oZF/hiVewviXm4h04YsaBomBPnKEf66dmFE3T+G5/MTMGhhMR0HZ+QNDTz/jtew9LKyGmCZNB4c+LUgn3N/PQ1/vajZSsbnTwwJf7eH51Zo+svztUNjj4/kAJ542IIihnKfaUudiGLcZcvBNDzRHN4NzhUQRajHwhkzafFPipn6CZ/KmbfE+7fRxJp6O4bLqw1hGam8C1fzohf+ukoOZj4kL8+pWPWsD2F3H7R9OYeikAtiHn4AqKx+/Ah328su6xu6AGk0FBNPnuaG6saV9hH3Amml94m+c44z1+aoXe+6n5734LZ9hg3FNuxRU1EmdkKtGlG0gO82NHXnW399EZbE432/OqmJISdtRxy+HVuK2h2AYtInDTUxhrcqmZ/Tj2lDm4zYFYW5iMvOG+s4YSHmBmSFQAF46NZ8aQSBRgw4ls/rTXYaw4hDN2PABaQBTOyFQsuWv7dl3tkFFWT0mtndlDj59+xZL5A4rbjrG+GKW+bb/JsAAzfz1vBAVVjTz2w8E2TYUrD5XidGtsPVzZ5wmdv9pTiM3p5obEfAyNFdgGL8I++GxAd+Nows9sZNGIGJYfLJXBLic6bifW9G+xDZyvmzzbwZEwBU0xenWD5bf7TQJ2vob/9pd7cqW9ghTUfExCiJWSWjsOV99nrzeW7MWSs5r6cTeByU8/aDDSKC7FkrMKQ92Jeye6p6AaEROE1aR/pE2FWzHW5mMb2rbZE8AZmaoLLoe+xJL2NZasZceYBU2F28Chq9VNJbsxF22jcfS1oOjz2JPOwFywmdRIExm9XFdxd341NqebKSktBFFNw5yzBnvyGdQsfInqBf+i5qwncCZMAZMf9pQ5WDN/ALf3CVDDAsy8c81pvHL5OEwGhfAACyPigtmQdeIKaubS3SiaG2fM+OZj9qSZuinc2X9urJrYnFMJwORWQnlrrGlfo3k+m6ay/e32G5cYyu2zBrHsYCn/3Zx7TPuSAyUYDQqNTje78nv3BqQlLrfGxzsLOC0plEHlq9CMVuwDzsIVNhhn+PBjzJ8XjonD5nTz/JqMfnHNlXQNc/5GDA2l2Iaed9x+miUYZ8zYDgMKjBVpBK17DM1oxVy0FcV+YlkDvBLUhBCpQohlQohaIUS2EOL+Fm0pQogfhBB1Qoj9QoizW517lhBilxCiXgixSggxtFX7nUKIXCFEjRDiP0KIwBZtViHEq0KICiFEoRDige5uuLeJC/FDA4pq+t78GbjpSdzmIBpHX3PUcVvqZSiaG6v6CQCG6sMEbHkBHPXNfZT6Uvy3/atfBh443Rr7CmuOinr0O/SFflEftKD9Ew0mHIkzsGYtJfT7Wwn95npCvrtZ/6HWNAI2PU34J+cT9sUVKLYq/Pb8F83kR2PqZc1DOJJnorhszLamkVvZgK0XtQ8bsyswKjAx+UjJKGPZfoz1RTiSzwRFwTbsAhpH/qy53T54EYaGEkxF21DqSwla+SDWQ190OFdUkJVQf3Pz82kDw9lbUN2/NRfOBiyZP2CoLTimyVSk++85YsY1H3MkzfSYUbb22hK9ZXNOJYmhfsSH+LXbR7FVYclZjW34RcDxBTWAqyclsTA1mn/9mMnaDN1sb87fgHnJnezLKeSKCQkYDUqfak7XZZaTX9XIZePisWZ8jz35TLDoPxG2IWdjzt+I0nAkkCc1NpifTUzk892F3PTeDnI6WcLv5Z+yeHZVeo/uQdJ5rGlfoZkCsKec1WFfR+IMTEXbwd7OjbLLQfDSX6OZA6iZ+wyK24k5t3+6OLSHqaMOQggz8B2wErgVSAXeFULkA+8CXwD7gcnA+cAnQohRqqpmCiGSgS+BR4CvgT8AXwghxqiq6hZCXAw8ClwD5ANvAk975gF4EpgBzAOSgP8JIXJUVX2/JzbfGzSl6CiobiTJk1etL7BkLcOatYzaGQ+jWY+uBekKG4wjfjJ+Bz7CPvhsQr+4AmNtAZaclVSd9xaKo47QL67CVHEIQ10xdbP+3Ee7aJv00joanW7GePzTDHVFWA9+jn3gPDTL8dMYVC94EWO1XrjakruWwLV/IvSbG3BGjSRgxyvYk8/AnLee0C+uxFRxiMbhFx31+tkTpqEZLExw7sClLSK7vL5TqRO6w6acSkbHhxBoOfI1tuSs1tc14Iw2z7GnzEEzWAjc/Cym0v0YGkrw3/cOjdkrqD3jUTRL+yk+WjItJZw3NuSw+XBlt6ohdBdjZQbWQ18CR5vvjNU5WNK/w+CoxT7gTKoWHx3tairegSs4CS3gyNodCVN1M0ruWhxJp/fG8r2iyQQ5X0Qft58lcymK207DmOsxH/6xQ0FNURQeXjCc7PIGHv7mAG9ePICJ39+GoaGE+031jB77AvsLa9iYVcEdswb15Ja85tNdBUQHWVho2oaxNo+6Kfc2t9kHn03gln8QuPFJas98rFnLfffsIUxIDOWRHw7y8/9u5eeTkrhmctJR35O2aHC4eGdLLgZF4c4zBrdZ7F7SCzSZPQfNB1PHv5n2xBkEbPsXYV9fg7vl9UtzY6zNx1iVheKyUbXoFewD56OZArAcXo198EIfbqJn6VBQAxKBTcDtqqo2AGlCiGXAmUABIIBZqqrWAPuEEPOAm4CHgVuAnaqqPgEghLgRKATmAMuAu4AXVFX90tN+K7BMCHEf+pX3FmCxqqpbga1CiCeAO4ATSFDT74D7NKDA2UjQj3/EGT6MhrE3tdmlMfVyglfeT9hH54HRTO303xK48UlCv7gSxVaFob4Ee/IZ+O9+k8aRV0LYxF7eRPvs9phmxiSEgNtJ8A+3o7hsR13U28XsjytSANAQKXBbQwlecQ+W3B9pGHUNtWc+hiV7JSFLfoHisulmz6POD8ARP5GU6s3AIjLKekdQq250sL+whpunDzjquOXwapwRAndQQpvnaZZg7MkzsWavwBk+nMrFb2PN+oGAzc9hLthC9fwXcMad1uH8Y+KDCbQY2ZBV3qeCWuC6x7Bmfn/McbclGNuQcwHwP/ABxvKDuCKGN7ebi3fibKFNA48ZJXY8lty11POgbxfeCdSiGursLiYPCDtuP2vaV7iCk3DGjMcVOQJj6fEFNdD9up68YCTXv7Odys/vAqWCdeZp3MD3VNZvZ9rAZF76KYvyevtxgxh8gcPlZnNOJdenKoStug9H1KijIrid0aOpH/9LAna8Am47tbOfAINemWP2sChSY4N4fk0mr2/I4bNdBfxuwXDOGBLZ7nw/ppfR6NGIHyqpZUSsdzctkp7FnLceQ2M5tqGLvervSJiKLWUOhvpSDK3cFlwhA7APmI0jflKzX6M9aUbzDe2JQoeCmqqqWcAVAEIIBV3DdQZwOzAN2O4R0ppYC8zy/D8NWNNirHohxDZguhBiJboW7tEW527wrGkC4AKsnvFajv17IYRJVdV+bHM5QmywFQX6tIxUwI5XMFZnU3n++2A0t9nHNnQxQT/+Ec3kT9UF7+GKGI4rfBghS25FM1mpOv9dXGGDiXh7FkFrHobB3/byLtpnT0E1EQFm4kOsBGx8Ekv+BqrnPnfUD7O32FIvRfMLx1iZQcO4m/WIwIFzqTz/Pcwlu3FGjznmHEfSGQRufJwYQ3WvpTPYcrgKDb1E0JGF1GPO30TDmOuPe2791Adwxk2mftzNYPanPnoU9qRZhCy9k7BPL6J+yj2eMRQ0o+WIP2MLTEYDE5JC2Z7bh6kQHPVYclbRMPo6as94pFWjAoqC0lCG36HP8d/1BrWz/663NJRjrM6hYdQ1xwxpT5pJwNYXMNQV4baGgGICgwm6UOi+p9jk8U+b1IagZizdh7l4F2guLIfX0DD2RlAUnJGp+O/6j54XznD8y3xciB+fTssiZd1GnnBexRsNC1gXlk/o8nuYMedzXkKPLu4oLUhPs7ugGrfTzi9KngK3k+qFLx/zWayb8TCaOYDAzc+iaBo1c48kLY4L8eOv543gZxMT+fuyNH771T5evGws4xJDW08FwJL9xYT4mahudLI9t6rfCmqmkt2YSvYC4ApOxJE0s08/nz2NNe0r3OZA7APO9O4Ekx/V53lf89OefCbWrGUYqrJwhw7U0zP189fPG41aS3KBBHQz5sfAc+gmy5YUoZspAeKP0x4G+LVsV1XVKYQo87TbgXJVVRtbnWsBotG1ef0es9FAdJCFgl72UTNUZuK//wNMBZsxF22lcch5OJJntttfswRRccnnaP6RuAP1hJr2QQuouOxrNJM/7jDd9FE3/SGCVz2E+9u7CC3LwVh+gPrJd9M46urmsYyl+3CFD2tXKOxpdhfUMCY+RC8xsvUFGkZcic0T1doV7APnAnOPOuZMmKI75LfVP3kWgRsfZ3HQQdJKe8dEtCm7gkCLkVFxR35MLHnrUdz2Di9wzujROKNHH30sfhIVV3xP0Gpdkxq48UkANJM/1Qtfwj5w3jHjDI8OZH1mOXanG4up9+OSLDmrUFw2bEPOaTZ7tUbzj6RRXIyf+jF10x5E8wvHXLwDAGfsuGP6O5JOR9nyDyLfPFpjrBmt1E2+m4aJd/T4PjpiS04lQ6MCj9FomYp2EPbZJSieHIgaCo3DLwbAGTVCj/6szMAVMRylrvhIdLNiwp40s9nXy1S4leTNf6IhdjIbHFfiLqqn4qxnCF9yOTO/msY2v0CqfkpEGfKp16bxnmBzdiV3mz4msmoXVQtfbr4GHYWiUD/lXhRbNQG7Xqd2xu/Q/I/Wmo2OD+Gfl4zhpvd3cO/ne3n9qvGkRBwdSVjZ4GBdVgVXTkhkZVop23Or+NnEJPobhspMwj4+H8XtaD5Wde5bnmvWSYCjAWv6N7pvsRdmzy5N4bk+WnJWYxsSROjX1+CIn0zdrL/4ZL6eoLOC2gXogtpLwLNAANBaArGha8LooD2gxfO22k3ttNFi/DYxGhXCwtoP6e0JjEaD13MkRwRQUmf3+ZpaYlzyZ5SMlWjx43BPuRXj9LsIC+hg/rA2kgq2Pjb9JtwHP8Sw43+Yo1Op84sjeNVDKJoDy8SfY/r+AQx7P8Y19y+4p/n+R62m0UlORQOXTkwiRH0JAqMwLX6aMLNv/QGPev9DpqL5hTHbrLKs4oxeeZ935FczeWAEUZFHzKyG4nVoJj8CR8yGLu0/AC57A2faUpRyvTyPsudjQr67Bdclb6EN1/PRNe199IBwXBsPU+HUEFG999luwpi7FM0/nMCRZx1fa3T67Sj73iM84yPcM+7CsHsfGgqBQ6eCtdW6Q+fgdL6gO6i7XbpGyu1CKdhB0Ia/4290oMx5uNe+yzaHi5351Vw5OfnoOWsKMS25BYJicFzxAViCwOxPcIBHSBmom69DGtLRwsZj/OpODDlH8udpYQNxLf4XAMavfg7BcZgu/w9vB8ZRWmsnPtQPV+gHKDnrydq5hdPq1uCo3QuD53Tq2tcdduRX87LlJ9xDzyVg0uUcb0bltCtg1+uEVWxGi7/4mPawMHjj+slc/uoG7vp8L69ePZFhLVwUlhw6jMutcdmUZOrdblYfLCU01L/Nah+9tf+2MC5/EoxmHDeuAGswpvcuIWTjX3GOPbtDzWmPzO/jvSs7P8Ngq8I05UbfzRM6Ci0shcDMrwna91+UUhWTuxGzF/P11XvfqXdWVdUtAEKIAOAt4A2gtR7ZCjSFCzZyrFBlBco8bbTTXo/uo9ZWGy3GbxOXS6Oy8rhduk1YWIDXc0QFmNmdX+3zNTXjshOV/RONo69pLpmEHbD30PznvkdYgJtvDjq4/7MdPG9+gbNX/56qNU8SQh1ucxDO9B+pTr2xZ+Y7DnsKdP+0JD83pC2lcdiF1NZpdPAR6Tat3//QiFQGVWWTU15PYUkNfmajz+a2O91kldZx5pDII2tw1BO55xPsSbOo7u7+o2fqD0AZeBGhX/4M0yfXUX32a9gHzm3ee7wnCnRHZhmxfr7bb5u47EQe/B7b4EXUVNvRP+DtYBlIaPIZmDa8iLM4E3Puj7jCh1HZYISGNl6nlIuOPTbGTZDlQfx/ehqX20nlafcf28cHbMmpxOZ0MzY26Mh77Wwg7POrobGSiku+wGUeoF8tW37HTUlEGUzYc3ZgM8YQnvMTdZPvxjbkHIw1+QT9+AeM/zsPjBZcwUlUnf8BbncY1DTiD/pckdMhcjp7tf2ctn4+Bfs34x8xrVPXvq5Sb3dRlJtGlKWMmthpNHY0n/9wIi3BONQV1Ca0neA61ADPXDCSuz/by0UvrePes4Zw4Zg4FEXh8+25pIT7k+BvYlR0IJ9tz2dnRhkDI4/9Ue6N/beFqXAr4Qe+pG7y3dRbde2iZcoDhC75JY0b3jwquttX+HrvYZtewxk+nIqQ8eDDeYISZ+G/9200UwC2lDlYclZRWVLW4Q2uL/cfHd2+trpDe4UQIlEI0ToZ1T50E2QBENeqLY4jZsm847Q3CWvN7UIIExDpac8DwoUQllbn2oC+KazYCsVeQ/APtxP65c8I2PwslqzlmA//iDlnFX673iBkyS8Jf38+I/wqKKq14+ylkjumoh0ozgbdvOELLIG4AmJ4fk0G8eHB1C58iY0hCylwhbL1zHewDTkXc+GWXin4nlmmf2nGOHZhcNS1W4XA17jChxFjy0ZDI6vctxfxnMoGXBoMafEj4r/nfxgaSqk/7Vc9OpdmDaXq/HdxRgwneMV9R6VnSYnwx2hQ+qTMkDlvHQZ7NbbBZ3fcGaif9Gs0gwlrxncY7LXNKSy8RjFQO/txGlKvwLj+H0dlxPcl23OrUIDTkkL1aLj9HxDxzpmYi7ZRPe85XFEj2z7RaMEVPhRj2X4Ctr2I2xpG/YTbcEWOwD5wLuVX/EDjmOtwxIyn8sKPcAe2vkwfYXBSMoVaOM7Cvb7ZZBtsz6tiAipwJDH1cfGk2rF0UHVjVHwI7157GuMSQvjr0kNc9Ppmbv1wJ9sOV7FwRAyKojDe48O2rY9LUSm2akK+uZHAtX/GWJlB0LpHcQXEUD/+1uY+9sHn4IibSMDGp49Kp3QiYirehbl4Jw2jr/a5z1ijuBRn6CCqFv+PxpFXoWhuTOUHfDpnd/BGozYC+FQIkaCqarHn2ESgBN25/wEhRKCqqk1X65noQQF4/jbnCfBo4iYAj3rSc2z29F/m6TIdcALbATf6PeIMYFWLsbf2i0CChko9ZUXpHlzhQwnY9AxKqxQBrsA4jHWFTAzbiss9jtJaG3HHyYPUU1hy16IpBhwJ03w2x6fb88goq+fxxSOYPTya2oGvsOjlDZybH8ufEyfqkXZVmbjCBvtsDaAXJTcbFRKLV+A2B+FImuHT+drDFT4Uf2cN0VSRUVZPqg8dkZsS6w5qEtQcDQRsfwl70kzvftQ6iWYNpfbMvxH+yfl6Vu+FfwB0/8sBYf6kl/b+D4Q1Y4meZyl5VsedAUfCNMqv39K9SRUDDRNvx//AB1gzvtWDTXzModI6ksP9CbKaCP38Mix563HEjKNm7rMdphBxRo7AkrkUg6OWukl3HZ3h3RJ4RNveAQMjAlC1AQyvVLuxk86xObuSKcaDuM2BOCNTvTrHnnQ61szvMVTn4A4Z0G6/qCArL1w6hk92FrDtcBUltTaGxwSx2FPsfkC4PxEBZnbkVnHx2Pge2U+n0dwEL/sNluwVoCgE7Pw3ADWzH2/2LQRAUaid8XvCP72Q8E8uwO0XcdQwjrjTqJ98Nxh7N2K3K+h5Kv2xia77F3uLM34SFVf/CIChKhsAU+lenLETfD53V/BGUFuNrkF7UwhxLzAE+DvwmKct29P2J+A89EjPphwQbwD3CyF+B3wG/B7IAZZ72l8E/i2E2OU5/iLwhqqqtQBCiLeAF4UQ16Nr0+4DftGN/fYIiq0K08dXQel+qhe+gn3wQhRbNcZytTmCxB2UgDsogcg3xpJiU4FxFFT3jqBmzv0JZ/QYNL8wn4zf6HDxjxWHGBMfzFme1AxBVhNzh0fxg1rM/eMmEAyYCrb4XFDLKKtnYJgFv6ylukOt8bjuiz7DGTEMAGHM97ngkllWj0GBAeH6D6//3rd1bdrku302pzPuNBqHLiZgxys4Z9xCk8fDkKgADhT3ThJkQ3UOlpw1KM56rBnfYUuZ02ZEqi9xhQ1GixmFNe3rXhHU0kpqGRYdhKH6MJa89dRN/DX1U+/3SuPgjByB38HP0Ex+ejRoF7GYDBRYhzCj8SsqXY6OT+gBthyu5GZLGs7Y07z2vXJ4LAiW3LUdmgENisJl4xO4bHyLNDZuF/47X8dUuIWX/aopywZD3dPH1Tb6ioDNz2HNWkrtzD/TOHQx/vvexVBXSOOIK47p64yfRN3UB7HkrDwqyACXncCtL2A5vIbqBS/iDk05diLNjf+uN7Bkr8AZMRxn9GjsgxZ1mH+yp1FsVfgd+tyTpzKk4xN6EHdIMm5LMCYv0tn0FR2aPlVVdaALYE5gI/AKerTn86qqutADDGKArcC1wEWelB5NqT0uRk9ouwWIBS5QVdXtaX8fPT3HS+hatS1Ay+RX9wCbgRXAy8BfVFX9oBv77RFMRTugPJ3qc15vTpqnWUNwxk/WowPjJ+MOTtTD5GPGEVWjfwAKeqPmp6Mec9E2HIm+0yy9vy2Pomobd54x+Chn2/NGxVJrc7G8NBy3NVQ3f/qYrPJ65gblYGgoxT6o7xIYusL0ghuTg4p9bgrMLK8nKcxfL5flbMB/+0vYE0/HkTDVp/PWTXsI3E6Mq//WfGxwZCB5lY00OrwvSdVVgtb8nuDVDxH0019QGiuxtagQ4UtsTje//mQ372zJRdM03CMuwFy4BUOtb82fDQ4XuZWNDI0KxJy3Xl/LsPO9Ngs5I0cA0DjiSjT/iA56H5/6MIEZB8bKjG6N4w2VDQ7yi4tJcWXjiPM+X6MrfBiugFjMHZg/28JYkU7YpxcRtPaPmIt3Mcydxdnu1dh3f9LpsbqLJWs5gZufoVFcSsPYG9ECY6iffJeeXqYdobV+0p1UXvzp0Y/Lvqbq7NcwVmUR8f58wt+dTfi7swn94kqs+z/AUH2YkG9uIGjtnzBUH8Z/79uELLsL/20v9vKOwXrwcxRn47F5KnsDxYArcgSmsn29P7eXeHWroqpqDnrVgbba0tCT37Z37nfolQ3aa38ceLydtnrgOs+j3+AYcCbOezOw13R8d+mIGUfA4TX409grgpq5YBOK2+Ez/7RGh4u3t+Ry5vBoJiQdHUcyMTmM+BArX+8r4fLYCT4vxdPocJFf1cjskI1oBotX5UZ8hTsoHrc5kDGWIj7wcc3PjLJ6BkUEgLORkCW3YqwvpmbhSz6dE8AdmkLDmOvw3/k6EWlLAbg8/Axe5SIyy+t9m3dK0zAXbqVx+MV6BQWTf6+lf9lfWMP6rArWZ1WwPbeK5+afS/jqv2JN9635M6OsHg0YEh2IJWc9br+ITuUGdCROo37cL6ifcGvHnTvAFDsSSsBWsIfgIb41D+3Mq2K8IQ0DbhydMeUrCo6k07EcXg2au92ULa0xFWwh7Isr0Ex+VM//J7ZhF6AW1zHgw1mQsxHTtNu7uJPOo9iqCFr5AM7IkdTM/lu3fbXsgxdRETWKgK3P6/6lmoapdA8hK3R9iGawUDPrERrHXA+am/APF2Iu2dUDO+kcfoc+xxkh2sxT2Rs4o0ZgPfBJpz43vUn/W9GJgpc/Es6Y8Siam+n+ub1SncCS+xOawdy5C1wn+GpvEVWNTn7ZRkkZg6Jw7shYNmZXUBE+HmP5QRSb7xxycyoacGsao2rWYk86vVdzPB2DouAKH8pg8sivtlFv942Gyelyk1PRwPBwhdBvbsCSvYKa2X/3uTatifrJ9+Cefif2lDm4g5MZkvcpUVSR4WNzr6EqC4OtEkfCFN000ktCGuiJVwFumT6AtZnlXPFpGc6IVKzp3/h03vQSXeBv0qg5Eqd17kfE5E/dzD+gBXY/UW1E8kjsmpH6vN3dHqsjDpbUMclwEE0xeFUloyX2pJkYGsowlnnpGO5sIHjFPbgDoqm4agW24ReCojAkKoBtCMIrdvRKUFQTgev/jqGhhJo5T/VYHjF3SDK1Zz1JzcKXqFn0MhU//5GKS76gbtJdVF76JY1jb9AFQoMRZ/QYrypa9CSGmjzMBZuxDbugV+dtiTNyJAZHLQZPOcH+hhTUfExT0edp1mwKe0OjlvsTjrjTjnYc7iFcbo13tuQyJj6YSS0z4rfg3FGxaMBP9iEoaHqxXB+RVV5PilJEcEMu9kHzfTaPt7jChxJr1x1TfWX+PFzZiMutcU3hY5jzfqJm7rNHJRv2NZo1BPecP+kX/rOeRNFcXGheT7qPtYhmz+fIEev9D7emaby/LY+d3Yze21tYQ0KoH7+YMZB7zxrCweJaChMWYi7Y3GbB957iUGkdfiYDA5QSjDW52BOm+2yujhgaG066loihF37E00vrON2ShisitdM3X01+agFb/wnOjguyB256GlNlBjVnPdmc6Bv0yhs5AWMIdpb32o+3KX8T/nv/R8PYm3DGjPXdRIqCM24i9VPvOzbxdeRIjPVFKPWlvpu/FXqtXmgc1qbRrldweqKnTaX90/wpBTUfowXG4AqKZ5whw+caNaWxElPJbhyJvikovfJQKXlVjVw9ObnNRJAASWH+RAdZWFOfgqYYMBf4zk8ts6ye0w16ygBf7bkzOMOH4W8rIYh6Dpb4RnDJLKsjjjIGlK6mfuKd3arA0F1cEcNwRI/lcvNPZJT5VqNmKtqOZvLvlOnvzU2HeXplOvd8vrdbbge786sZE68LDE0lu36yehzXM9r16ug2aaV1DI4KxFqgB9E7EvtOUIsMtJBhSCG09pDP58osrma0drBLVgF3cAJ1k+/GL+1Lwj9ejKlkD0pDWZsPc+5P+O94lYaRP8PRRvRwfbRu4jUXbO72njrE5SB41UO4ghKpm3Kf7+drQXm9nR2eUnDNAksv+mtZD32BI2a8Xs6pj3BGpKIpBimonco4Y8YxxHmQwupG3D5Uo5vzfkJBO8Y/rSfm1DSN/24+THKYH2cep7AxgIgJYlepG2fkCJ/6qWWV1zPHquIKiPV5dKk3uML1gILR1iIO+igSMqOsnrONmwCwDT82A3tvYxMXM9ydjqvEt6kbzEXbccSM9ToCcKlawotrs5g5OAKXW+O3X+3H4XJ3et6iGhvFtXZGx+uRaMlhfkQFWVhVHo4rOBlz/qZOj+kNmqaRVlLH0KgALPmd90/zBRVBw4hwFkNDpc/msDndBFar+GmNOOLbqJTiBfVT7qXyvP9hqCsm/MNFRL0xrs1H2BdX4A6MpW7Gw22OE5I0mmrNH+fhjd3ZkleYCzZhqjhI3Yz/Ozr9Rht8vquAj3bkk1lWj9YD1/ZX12Vz60e7KKuz97pmyViRjrl0j25y7kvM/rjCBmMq65+Rn76vOSHBETOeqIwl+LlqKK93EBXom5w2lpzVuC3Bzblg3JrGB9vzeeWnLG6aNoBrJid3eex9RbXsL6rloXlDMRqO7+A6PCaI9ZnlNE44jcBDn/rMQTOjtI5J7MWRNLtfFNV1hespOmYEl7K02FcatXrusGzCGTECV/gQn8zRGRqHXUDA2keY2biCWtsFBFl9cElx2TCV7qNh7A1edd9fVMOfl6iMSwjh74tH8lNGGQ9+tZ/n12Ry71mde832evzTmjRqiqIwcUA4O3MrcSSPw1y8s3N78ZKyegeVDQ6GRAVi3tsF/zQf4IwcCbXgLtwD4Z3zHfOWrLJ6pikeLXl81/0uHSlnUXHlUiyZP4DWvr+ofcBZ7aaDGB4bynb3MMbnb8HXMc2WnJVoBjP2lOPX7Fx1qJTHlh7RaqaE+/PaleMJC+i6z+aOvCpcbo0l+4v5+aQkXIFxvSaoWQ99gYaCbeh5vTLf8XBGjmx2sehvSI1aL+D0+KmNNWRSUOUjPzVNw5KzWk+CaTRTUN3IrR/s5JmV6QRajDy/JpPv9xd3PE47qB4N0fSBHYf5i5ggXBrk+QkMjjqMVVldnrc9nG4NS2UaYe4Kn6Yi6Qyu0BQ0g5mx1iLSSmtx+aASRU1pDmM1FdvQxT0+dlfQAqIpjprGhcafyCz1jRbRVLoPxW3H4WUyypd/yiLQYuTJC0ZiNRmYMzyaKyYk8P62vE5/B3YX1GAxKgxvURdy0sBw8qttVIaOwlhzGKWhrFNjekNTIMHYwMo+909rIiBR92cqz9rhsznSSus4w7CLhpChuIMTOj7hOLgDY2kcfQ2NY65v99FmbjEPw6KD2OoeTkhtmk+DogAsOatwxE8+bv6y8no7f116CBETxEc3TOKBuUPJqWjgvW25XZ63ptHZHAj01d5CNE3DGTWq10yf1rQvcSRO6/VcdW5NY29hDcsPlvDBtjwOldTijBqpf599/F53BSmo9QJNjqFjlQyfpegwVqZjrM3DnqxnSnluVQYHimv5w8LhfHrTFCYkhfLn71W+3lvIfzcd5rdf7eP9bXlem4OyyurxMxmIC+k4oayI0VX3e9z6RdBU0vOlZ/IqG5jsufO29xNBDYMJV+ggBpFLg8NNbmXHzsydwenWGFW9GqBf3IE2YU+9hCSllMq0zuev8oamgBSnF4EEmWX1rMus4NLxCYQHHNFc/+bMwYxPDOGRHw6yv6jG67n3FlQjYoIwG49cKid7/NR2a7p2zhdatTRPcMYIm54qoS/905pISkyhXAuiMdc3WkSA7OJSphoO4BrUd6l2mgiwGMkJHKMHRRVu89k8hrpCTGUHsA+Y3W4fTdN47IdD1Nmd/PlswcCIAC4bn8Cc4VF8sD2fmsauFevZU1iNBswZFkV6aT1qsUdgqUgDl299qg2VmZgq0rANPsen87TF8oOlXP/Odh76aj9PrUznieVpOKNGAXopq/6GFNR6Ac0aiiNkEGMNGRT6KKDAkrMKAPuAM9E0jZ351Zw1LIrFo+Owmgw8ef5IEkL8+POSg7zwYyY78qp5emU6l/1nC6vTOo7wySyvJyUiAIMXJsaEED+CrSY21sagGUyYSnteUMssq2e6YR+N/vHHLRfT27gihhJj0yM/ezqgIL+qkUXKBsoDh/ULn7wm/Eecgx0TgTnLOu7cBcxF23EFxOIO6ricz/vb8rAYFS4Zd3Rfs9HA4+ePJMzfzH2f76Ws7jhF3D04XW72FdU2+6c1kRoXQqDFyMraRDQUPQF2D5NWWkdEgJnwvOW6D2Yf+6cBDIwMZI82mKAS35mHrAUbsChOnMcRWnoTZ+wEnBh8mrzbnKPffB1PUPtqTxFr0sv41cxBDIk64sN2w5QB1NldfLSja8mXd+VVY1Dg7tmDMRsVvt5bhCtyJIrbianct4EjlsMd79tX7C/UNeVvX3Mal46LZ29hDXUxk9GMVixZS3t9PR0hBbVewhU7jvHGDPJ9pFEz56zGGToId8gAimvtlNXZGRV3JLQ91N/My1eM46kLRrHk1ml8+8up/OPi0fiZDTzw5b4OU4dkl9czMMK7vD6KojA8JpB9pXZc4cMxle7p1t7aIrOslumGfTiST+8X/mlNOMOH4Vd3mACDs8cDCgpyM5hkOEjlwN6/Az0uliDS/cYytHpDjzg3t8ZUtB1n7PgO3+fKBgff7Cvi7BGxRAQc6wcaEWDhqQtGUtXo5LYPdzVrrdojrbQOm9PN6PijU0QYDQpjEkLYWODAFT4Mky80aiV1jI1UsOSswjb03D73TwNd2FX9JhDdkIFSX+KTOQZWbsSuWHstL2BHpMRGsd89ACXPd5GflpxVujDuqSTRmryqBp5emc7E5FCumph4VJuIDeL0QRG8uzWXhi5UB9mZX82w6CDiQvw4c0gUS/YX0xCu11Y1+thPzZKzCldICu6wY3Ny+pq00joGRgQgYoKYPigCh0tjT5kTe/IsrJk/9GruPG/o+2//KYIzZhxxlNFQ4YO8Sy4blvz1OAboZs+9hbppp6WgBhAVaOHMoZFEBlpQFIUZgyL423kjcWvwU2Z5u8M3OFwUVNsYGOF9bjYRE0RaaR32qJE++cLbCvYSrtSiJfumAkNXcYUPQ9HcnBlWysGSnhXUjFl6iVzryL7LN9QeFfFnMITDFOel9+i4SmMFpqosr/zTPttVgM3p5spWP2YtSY0N5rmLRlPV6OD6d7bz6c78dqOid+Xr36MxCcc6m49PDCG9tJ76yDG66bMHL+wut0ZmeT1nW3aguGz9xh8RoDJGF6DMuWt7fOyqBgeTXdvJC5nQ6zVc20PEBrHNPQxLyU49KKqncbuwHF6jX7vbuBFxuTX+/J2KosAfF4k2LRo3TE2mqtHJJzs799vidGvsLahhrOfzfd7oWKoanawuD0Ez+fnWT81lw5K7DnvKbN/NcRzSS+sYGq1rJsd59r8jtxr7oAUYa3Ix9rPoTymo9RJNiW/Dqnpeu2TO34zibGxWIe8tqMFkUBgW3XFh3YER/iSF+fFjevuCWk55g6dv5wQ1m9NNScAwjPXFKHVdD2Roi8hSPS1CfwkkaKJJE7DA7wBqD0d+Wsv2UUMAlui+N4O1JniEXme1au+SHh23yazo7EBQa3To5p+pKWEMjTp+eoNJA8J499qJjE8M4W/L0rjo9c28tekwm3Mq+GJ3Ac+vzuDat7fx1Io0YoIsxAUf65c5PlEvn5ZuHo6hobRH635mV9Rjc7qZ3vgjrqB4nJ2od+lrQlImUKUF4MxY0+Nj52WrDDEUUJtwRo+P3VVETBB7tYGYnHUYqrJ7fHxT8Q4Mtqp2zX/vbs1le1419541hPiQtoXXcYmhTBsYzqvrsjrlF5teWke9w9UsqE1NCSfEz8RPmVU4I0f4xGWlCXPBFhRnPfYBve+LWN3ooLjWzpBI/ToR6m9maFQg23OrsA2cj4aCNfP7Xl/X8ZCCWi/hjB6NGwNJDQd63DxkObxKD+32RIbtK6xmWHQgFlPHb6+iKMwcHMnmnIp2VeeZ5XpU0MBI7wW1pii5A+hq7Z780jtcbgY37qbcEt/tyLCexh0UjzN8GKe5dlBWZ6fUC18ob4mqP0SBdXC/MvU2EZsyijyiCc7v2R9wc9F2NJQOM7W/vSWXklo7N0z1zl8xMtDC85eM4bFzU4kNtvLPHzP51Ue7efSHQ7y7LQ+rycAt01N44dIxbSZ3HhUXjNmosN42ENB/cHsKtbiWEOpIqliHbch5/cLs2cSI+DA2uEdizV/X42NrmSsB8Bs+r8fH7ioRARby/fS0O74QXCw5q9AUA/Y2Eu6W19t5+acsZg+N5LxRsW2cfYTfzR+GyWDgD98ewOlltPmufD31TJOgZjIojIkPYVd+Nc7IkXqKDh+ZAJvTkfRBNHO6J8p1SPSRG7rxifq+HX5ROONO09O69CP6zxXgZMccQHnAIEZq6VR1MUKn3aFz1+lloyyBuNwa+4tqjzF7Ho9ZgyOwuzQ2ZVe02Z5VXo9BgQFh3teeGxgRgMWosKlBN0P15EUuu7yBcUoaVeHje2zMnsSePIukmp1YsXOoh8yftY12BrlzqAlN7ZHxehrFYEANmsaw+q09Gi1mKtqOK2L4cUsJFVY38uamw8wdHsXE5DCvxzYoCgtSY3j1inF8dP0k/nXpGD6/eTJrfzOTf185nltmpDA4sm3tnJ/ZyLiEEL4ujkQzmDH3YECBWlTHItM2DG5Hv4ruBb3u6AZGE9SQ1+OllSKLfiKPaEIT+tdn3BSTihOjTwQ1c/4GnNFj0PyOLcn3zd4i7C6NX80c1G4lmCbiQvx4aN5QdhfU8J+N3r0vu/KriQq0EN8ikn9sQgiZ5fXUho3AYKvyWfksPR3JlA6T+/qCJt/UIS0UDxOSQql3uDhYXItt0ALMJbsx1PSclry7SEGtF6kOH8NYQzoFVT2YtsFRj6l0b3NyyOyKeursLkbFey+oTUgKJdBi5MeMts2fWeX1JIb6eaWha8JkUBgSFcjOcgVXyIAevcjl5aYRr5RDQv8xCbXEkXwmRreNSQaVgz1k/szNPkSw0oAhZmSPjOcL6pJm44+N2kM/9syAmoa5eAeO2PHH7fb8mkxAT8HRVQZGBjAlJZzEUH9MHSR0bmLawAj2ldppDE/t0YACtaSWS/w24wpK9ColSW9iNhoojtSvNZbcHkzH4mxgaN0WdlsnoRj6189SakIkh9yJUNTDbiuaG1PJHpwx449t0jQ+313IuIQQBnlpyViQGsPC1GheX5/NqkMdR/LvyqtibELIUULgmAT9d2O3QReWzYU9H0ThTToSX5JeWkeQ1UhsC5eGCUm6K8P23Crsg3Q3DktW/9Gq9a9vxEmOK2YcEUot1UWZPTamuXgHiuZq9mPZW9AUSNB2tu02xzAamD4wnLUZ5W06VmeV13fKP60JERPEweJaHJEje1RQc+XqofLBg/pHZFhr7AnT0AxmFvrtb04U3F1qDuuCQGjyuB4ZzxdEpp6FTTPTcLBnwtsNVVkYGiuO65+2PbeKpWoJ105OateHx1dM8+RTy7YKPfdSDziba5pGXnEJE507dG1aPzRzhw8YTYkWhulwzwUUmLNX4U8jOTHHz8zfF0xKDmOfloKheHePjmusysJgr8EZPeaYtm25VeRUNHDh2M4lgn1o3jBSY4N56Kt9fLO3qN1+n+0qIL/axniPgNLEqLgQDAr8VBOD2xKCOb/nBbXmdCRdCCRwuNx8u6+IenvXa0Wkl9YxNCrwKAE1OshKUpifXqUhfCjOsMFY+1GaDimo9SLWZF2YUnrQTNKUiNERp9957y2sIdBiJMXLVBpNzBoSSVmdnf1FRwsWTrdGTkVDlwS1aQPDqW50kmUejLEyE+w9o10KKtuJHTNazKgeGa/HsQTiiDuNM4x7OpVc9biU7MONQlDS6J4ZzwcMjo9mCyOILlzZI0KL2eP35TiOVundrblEBlq4thvl0brKsJhAwv3NrHUIDI5azD2gYSqqsTHcvg8Tzj7TOHTEmKQwfnKPxJj7U4/5MLkPfE2FFoRhwOk9Ml5PMioumEPKQPztpT2alqQpsaqjDf/Lz3cXEmQ1Mm94dKfGDLKa+NdlY5iQHMaflqi8ui6LWtvRrjZvbTrMX5ceYsagcC4ac7QgGGAxMiw6iF0FtTjiJvqkIL0ldy1u/2hcEZ03cf/zx0z++J3K777Z36XKL5qmkVZad1QuuibGJ4ayPbcKt6bhSDwdU+F230T6dgEpqPUifvGjsGsmAst67s7MXLgVZ9iQZh+HfYU1jIgL9ioxbUtmDIrAoMCaVslvC6oacbi0TgUSNDFrcCQhfiaWV8bp2b17KOQ5qW4vh63Dweibmqk9gSP5DFIc6dRVFlNZ7+j2eCHVByk0xqP0gU+HtxgNCjvCFxHtyG9OwNwdTEXb0Uz+7SZ7rbU5WZdZzrzhUfiZjd2er7MYFIUpKWG8VjoSt184/nvf7vaYanEtUw37cSsmHP0o2rMlYxJD+ck9GktjKcbyA90f0GUjKHc5S10TGRYX1v3xehiT0YA9Ur9B6knLgKl4F5rR2lwjuImqBgcrDpZw9ojYLn2uAy0mnrtoNAtENP9en8P5/97Es6vSeXJ5Gre8v4N//pjJwtRonrpgVJvjj4kPZm9BDfa4yZgqDqI0tu273CU0DXP+BuyJ0zutLV6bUca7W/MYERvE2oxy/vlj5y1TxbV2am2uNgW1CUmhVDU6ySqvxxkzFoO92iflD7uCFNR6EcVkJd04iNjaHspPo2m6oOa5oNucbg6W1HUqkKCJMH8zU1LC+XpvEc4WZaWaIj4HdUGjZjEZWJgaw4f5en1QU0n3BdTK2jpStQwqwo8fBdjX2JNmoaBxumFvc167ruJ0ayQ5MigP7H9pOVrTMPhcCrVwzNte6fZY5qLtOKLHgqHtQu9rM8qxuzTmi85pHXqSaQPDKWxQKBhwAZbM77udhkYtrmWaYZ+ezsfc+e9cbzA4KpCNhom4MOJ34ONuj2c5vBaLs5bvtSkM7sINYW8QPnA8APb8nisvZCrZhTNqJBiPLqj+3f5i7C6NC8d0vf6l1WTgsfNG8ObPJzB5QBjvbc3jm31FON0av5iRwl/OST2qLFpLxiaGUO9wkRWgC6fmwq1dXkdrDNXZGGsLcCRO69R5JbU2/rzkIMOiA/n3leO5bHwCb2/J5as9hZ0apymQoK0UPmM9FUj2FtQ0p9PqL+WkpKDWyxz2S2WA/VCPqFSNVZkYGsub77zTSvRC4CO7IKgBXDY+geJaO6vTjxSZzm5KzdEFQQ3g3FGxZDvDqfQfQMD2l1Aa2s/X5g0lGduwKg60fhpI0IQzZhwuSwhnGHezp6C6W2MdLiphAEU4IvtXNFxbjEqM5C3nQgLyf+peomOXDVPJXr0iQTssP1hCTJClzYS0vcVUj5/aD9ZFKG4nfgc+7NZ4WYWljDNk4EzqX/kBW2I0KETFJrHePBW/Ax91O8rXkvEt9UoAuaFT+kQz6g1jBiVz2B1N/eEdPTNgcyDBsTecG7MrGBjh35ziqDuMigvm8fNHsvrXp7Pyjhn852cTuGV6ynEtLmM8AstG20A9orlgU7fX0YQlbz0Ajk6m5fjr0kM0Olw8du4IrCYD98wezKQBYTy+PI38Ku+r/aR7yvoNiTr292xAhD8BZiP7i2pxhQ9DM1p9UnWkK0hBrZcpDxlJIA0YK7qfwd3kudNpEtSa8sMM6yDhZ3ucPiiC+BArH24/EpacWVZPZKCFYL+2tRodMTI2iEGRgfzZfA+GhjJClt4B7q47gjoP64EEYYM7d0fW6xiMOAbMZr5pO3sLKrs1VGn2LgyKhn/isU7H/Y2RccG8756D3eBHwM7XujyOqXQfitvebkWCJrPnWcOiOm3m70mig6wMiQrgu6IQ7InT8d/3brduwvyLt2DEjSOhf3++R8YF81rDmRgay7Gmf9f1gVwOrBnfs4pJDIo9NkVFf0HEBqMqA/Gv6Bn3DWNlJgZHra4xboGmaewpqGkWlnoKf7OxwxQfTSSG+hERYGZbkR1n9Jge9VMz52/A7R+FK3yo1+ccKKphbUY5N0wd0BwBazIa+OPC4SjAkyvSvM5NmlZaR0yQhRA/8zFtBkVBxAZxoKgGjGacUaOkRu1UpSFqPADu7O5HTJkLt+K2BDf78KSX1WE1GUgI7Vr0m9GgcOm4BLblVpFWWkdprY2thyu9rvHZFoqisHhULJ8Vx5A98fdYDq8hYPMzXR4voGwHRUQQGpPS5TF6C/vgswnXqvAr2tKtJMf2Aj0tQMSA8T20Mt8RYDESHRXLCus8rAc/w1DXfuTZ8ThSkaDtQIL+YPZsYmpKODvyqqgWP8NYnYP5cNeS/lY2OBC2XbgUI464ST28yp5lZFwwq52jaAhMxq8bvnnm/A0YbJV8bpvI8Oj+639pMihUhqQS7cgFe/cjuZs0Na0jPvOqGqlscBxTX7Y3URSFsQkh7M6vxhE/GVPRTnD2QI1qTcOctx57wrRO+ae9uekwgRYjl084Orl5XIgfv5iRwtqMclanlbVzdsvpNQ6W1Lbpn9bEiNggDpbU4XRrOGLGYS7Z3S3FQk8hBbVexhIznP3uAfjtf7/bY5kLt+g/ZJ7M5RmlehoNo5d5oNri/NFxWIwK//oxkxve3UFFg4Mbvcz23h5nj4jBoMA79jNpTL2cgC3PY6jO7dJYiXV7ybCM8PrusC+xpczBabByhnM9ORVdz53nV3GAevxRwrr3PvQWYxJC+EfdPBS3A6v6SZfGMBdtxxUQizsovs32/mD2bGJichh2l8aOgJm4/cLx6+Kedf+0/VSFje6TRKCdYXR8MBoGdkSdjyV/A8aKtC6NY8lahstgZY17LMO9KHnXl1jix2BAozxzR7fHMpXs1gMJWgXKNKdX6mGNWmcZmxDC4cpGysMnoLjtPeJfbKjOwVibjyPRe7NnVlk9Kw6WcvmEBIKsx1p1rjotkWHRgTy5Io06e/uJ5DVN4x+rM0kvrWfawPY1tyNig7E53WSW1eGMGYfirMdY2bP1i7uCFNR6mYQwf95znUVQxd5uffgVew3GMvWoyLD0sro2be+dISzAzILUGNZmlON0a7x6xTimpHTPJBEVZGVYdBA782uon3gHClqXkglqtcXEuwupCOv/JkAALIFUx89koXEzewuqujSEpmnENKRR4DekX+bUaosx8SHst8dQHzIUS14XUlZ4IsOccae1uec6e/8wezbRpP3YVdyIPWkW5rx1XUpbkVFQwlglA5L7r39aE3HBViIDLXzObDSDGb+973RpHEvOSg4HT6ARK8Ni+rdwGjd0MgB5B7rvs2Uq3oUzatQxgTK7C6rxMxmOq/XpDZoSwG526xGpPeGnZs7fANAps/5bmw9jMRm48rTENttNRgMPzRtGSa2d69/Zzu78Y/2BNU3jX2uzeGdrLpePT+CqdsYCXaMGsL+wttl/sD+YP6Wg1sskh/nzuet0HIoFv73vdnkcU9F2FLRmQa2m0UlJrb3dkjed4aZpA7hgdBz/+dl4UmN7RgU/Ki6YvYU1OEIH4QwfijWz88kEa9XlANgT+1+upfYwiPNIUMqpytzSpfMzSmsZqmVjD+//gQRNNGm5MgPH6wkzXZ1LT2KsOISxNr/dPGJbD1dhd2mcOTSyu0vtESICLCSG+rG7oAZH4gyMdUUYqzqfOsCZsxGz4sKQ0v8/34qiMCY+mPXFJuyD5uN38PNO++YZqrIxVWawyTSR6CALEQH9N90OwIABQ6jUgnDld9PB3O3CVLoHZ8yxN5x7C2sYERvkdXUMX5EaE4S/2cD6IiPO0IGYi7Z3e0xL3nrcfhHtpttpTUF1I9/tL+bCMXHH/WyMTQjhhUvG0OBwc/P7O3hyeRpZniC47PJ6HvxqP29tOszFY+O5b86Q41pjksP9CbQY2V9UgytsCJopoF8EFEhBrZcJ9TcTGBLJ5oAzsR78DBz1XRrHXLhVL1btcbbOKNOjWXoivD0pzJ+HFw4nrgczvY+KD6bO7iK7vAH7wPmY89ej2DoXDenOWEGpFkLcsP7tv9MS56D5ODESX9C1LNdph/YSojQQOKD97Pz9jeQwP0L9TGxwj0Rx1nf6QteUg609QW1jVgV+JgPjEkLbbO8Lxnh8euwes445t3NFyzVNI650LS4MOOIm+2KJPc6ouGByKhqoSFqAoaGk2a/QWyyH9Qz13zSM6vdmTwCj0cBhyxBCqrqXO85YmYHBUYcj+ugqI3anG7W4ts/NnqBrqsYmhLA9rwpn9BhMJd3PH2fOW6+n5fBSC/76hhwMClw9KanDvlMHhvP+dRO5cEw8n+zM57L/bOHn/93KFW9uYUNWObeensKD84Z26DJjUBRSY4P0xO8GI47oMZhLThCNmhBiiBDiKyFEhRAiVwjxtBDCz9P2qhBCa/W4q8W5Zwkhdgkh6oUQq4QQQ1uNfadnzBohxH+EEIEt2qye8SuEEIVCiAd6aN99iogJ4h3HbAyOWqxpX3VpDHPhVr1YtVX/Uqc3FZrtY5V5ezSZh/YUVGMbtBDF7excUlTNTWzZejYwlkFR/f+i3oTmF0Zm0ESm2H7C5ui8U2pNjl55Ijilf6cjaYmiKIxJCOHLav2rbsnrnNBiyVmNM3wY7uC2TRQbsyuYkBTaqdqzvmZMfDCldXbyDQm4AmN182cnKMvayaXuJaRHL+z3/mlNNGlOt1omoSlGLJ0suWPJXokzOJm1lWEM7+dmzyZqwkaQ4syi0db1lCSmMj1tjTPq6Moqh0pqcbg0xvRhIEFLJiSFklZSR23oCIw1h1EaK7s8lqGuEGNtnl6I3Quyy+v5ek8hl4xL8FphEGQ18dv5w/j6l9O4c9YgzEYDF49L4LObpnDTtOOnJGlJakwwh0pqcbrcOGPGYirZA+72/d96gw6vdEIIC/AVYANmAD8HLgQe83QZCdwPxLd4vOo5Nxn4EngbmAQUAl8IIQye9ouBR4FfAWcBk4GnW0z/pGfOecAvgYeFEFd2dbP9heExQXxbPRB72FA9nL+zaG5MhduOigzLKKvH32wgLsR6nBP7joERAQRajOwtrMEZOwG3fySWzO+9Pt9UsodgVyVZodP7hV9SZ6gasJAUpYjcQ50zf7o1Db+yPTgw4/TSXNBfGBMfws4KE7ZwgdmTO8krHA165vJ2tGmF1Y1kVzQ05y/rL4z2aEF2F9biSJyh54vy1k/N7ST6xweoJpCq0//gw1X2LCNigzEosKMUHAlTsWZ2wu/UZcOS+xPFMTNxuTkhNGoApvgx+CkODqd3vUC7sewAmmLEFXF0ioo9/SSQoInTksLQgP0MBLpXlcFYqqc1aS2ctser67KxmAxcP6XzpeGiAi1cOyWZN38+gQfmDiUysHMm9ZFxQdhdGulleoUCxWXDWH6w0+voSby5JZ0CDAWuV1V1v6qqq4HfowtsACOALaqqFrZ4NNnzbgF2qqr6hKqq+4AbgWRgjqf9LuAFVVW/VFV1C3ArcL0QIsijWbsFuEtV1a2qqn4BPAHc0e1d9zEiJggNhcNxCzEVbuv0nYqxIg2DvbpVIEE9gyID+60QY1AURsYF6xcjgxFbyjws2Su99l/SMlYA4Bhwhi+X6RMixi7GrSk41CWdOi+9tI5hrnSqgob263JZbdGkQc0NnYi5cLPXSVHN+RtQXLZ2BbVN2ZWAburoTwyPDsRqMrCnoBpH4gwMDSUYKw55da7/jn8TVbOPv3MjAxI6NvP0FwIsRoZEBbK7oAb7oAWYylUMXpbcMedvRnHWs9uq32wO68epOVoSPVi/5lZlb+vyGKayA7jChoDx6Jvq3QXVRAdZiA3uHzfbI+OCsRgV1tTqaTG6I6g1lQ90Ro7osO/B4lp+UEu46rTETgtZPUGTX/aBohocCVNx+4Wj9HeNGqAC56iq2jJ5jAZYhRBxQISnT1tMA5qTCnkEuG3AdCGEEV2D1jLp0AbABEwAxgFWoGXCsbXAZCFE17Kv9hOEJ+P0LuNoFDTMhZ3TtDT1d8a30KiV1jGkn5ZfaWJ0fDBpJbU0OlzYBy3AYK/2OppIy1zBbvdAhqb0//xprQmOTOCASRBfvLJT523JqWS0IQslflzHnfsZo+KDMSqwSRuF4mzE7KX/kiVnFZrRiiOhbRPJxuwKogIt/e6zbjIaGBEbdLSfmheaRENdIYGbnmKtcQqFCQv77Y1We4z21IVsTJkHgDVrmVfnWXJWohksfFs3nIgAMwPCu56rsTcJik/FhgWlGz5bprIDONuoMrK3sKZL5f98hdVkYHR8CGsLDbgC43QTYBcxle7DFZSA5hfWYd9X1mUTbDVx9aTOa9N6gqQwP4KseoUCd1ACZTfuarOCRG/SoaCmqmqJqqrN3z6P2fIO4Ed0s6cTeEQIkSeE2CmEuL7F6fFAPkdTBCQBYYBfy3ZVVZ1Amac9HihXVbWx1bkWoO+zXHaDmCAL4f5mfqxPQTNYOmcaQq9I4PYLxxU6CIDKegfl9Q4G91P/tCZGxYXg0vR8UfbkWWhGK4Ebn+zwAqDYqgkv38ka97hmE9OJRm7UGQx1pmGv8D5/XHamSphShyVhvO8W5iMCLSbGJoTwSflANBSvfbYsOav0PEumY3+43ZrGppxKpqSE9cs8eqPjQzhQXEtjQDKuoASvfPP89n+E4rLxcP0VjEvsP8ER3jI6PoQam5NMVwzOCOGdO4PbhSV7OY6EKazNtTEpuX++n21iMFFgHUxkbdcCChR7Lcaaw7haCWqVDQ5yKxv73fVtQlIoanEttohR3daoeaNNq2xw8FNGGRePi+9yNZzuogcUBLOvqUZzP/hsduWVeAZd4zUZmO05thN43vP8FSFEnaqqHwEB6L5tLbGha8oCWjxvq93UThue9nYxGhXCwnx7x200Gro1x6jEEPZX2NEST8OveDPmToxlKt6GljSFsHBdMFMr9PqZY1PCfb7vJrqy/9NTjfDFXtIrGzlzVDyuhY9jWvFHwj9chDtlFjgbUCqy0BIn47rwFbDomkflwHIMuEgLmUZSbP+4kHV2/6GnXQjfvUrNgSUMOfvXHfZ3uzUo0qMl/QdPxq+X3ldv8HbvZ42I5Zll1TiTRuFftAlLR+dU5mCqTMc1+aY2x9+bX0Vlg4M5I+J67XPeFu3tf9rQKN7ekkt+o4v4QWdgSV9KWKhfc0LqY9DcmA5+SFnUFLJy45mVGtun+/KWlvufMTwGvj9IelUjp6Wei3n9Pwiz2sC/hWm6vgz8wsCg1/E0LP0dxoo0csffSVmanTNTY06IfTdRHjWSobk/UKsoxIV2ThOo5Oo3pdYB4476Puwv1zPrTxwc2a9ei1mpMby+IYeiYMHA3JVo7sbOr89pw1iZjpJ6bofnrsjMw6XBBacl9enrMGVQJK/8mIHZ30Jgi0S73f3d7ypeC2pCCAV4Dt3x/1JVVfcKIfYB76mq2lRpe5cQYhhwG/AR0MixQpUVXWvW2OJ56/Z6PObVNtrwtLeLy6VRWdm1tBfeEhYW0K05BocHsCGjnLpBkwja8RKVxSVeRXspjRVElR2ifujF1Hvm35mlv/yxfiaf77uJruzfBMSHWNmcUc7Fo2Jh0KUoVy/Af+drWNO+wh0QgztpFtZDX8L/LqLqvP9iqC0gaO1z1OIPCRN7bX8d0dn9JySNJEuLQzn4HZXTb+6wv1pUy2BnGm6zkQpLCvSTfYP3e58Qpwvaqt94Rh1+n7qd3+BIOavd/v5b38cMVMXMxNXG+Mv2FAIwKrp7373u0t7+B4fql6d1B4sRMVMJ2f0+9Tu/wT5wbpvjmHN/IqwikxVJP8diVEgONPebz/fxaLn/CLNCkNXI5owyFo6dS7j2DK5vHqDmrKfAaMZ64COCV96PMyKV2jMexVR+gOBNL1E/9kY+b5wGpPf5+9lZrInjCM/7lLU7dzFtbOeSb/tl7SAYqPIbhLvFnpuu4XH+vXcN94bBIVaMBoUNtYkM0ty4CvZQGTiyU2MYS/YS4XZSFzQUWwd7+3ZnPjFBFpIC+vZ1SI3yx+XWWHug6KjApe7+7h+P6Oj2zd7epucwAG+gC2BXeBz7UVVVayGkNbEfaIqrzwPiWrXHAQUcEdaa2z2+Z5Ge9jwg3BN12vJcG9B6zhMOEROI062RHTgeRXNhLtrq1XlNiQcd8UcCCTLK6gi0GIkJ6v8O56PiQthbeCR/mmYNoX7KPVT8bCVVF35AzfwXqF74EqbiXYS/N5fw9+djKD/Enx3XMDohog9X3j38LCZ2BUxnUO1WFC9qBW45XMkYJRNbmABTz+Wz602GxwQREWDmDeVCnBGphH57I5aMdgp4u5347/kv9sTTcYUNbrPLlsOVDIkKIKoPHIy9ITrISnyIle25VdiGLsYZPoygVQ+0Gyzkt+893NZQ3q8drztu96N0I95iUBRGx4WwPbcKZ8w46qbci5/6CaHfXEfAxqcIWX43zpjxGBpKCf/0QoJW/x/2AbOpO/0PbM6pJCHUj8ROaqX6mpjhuv9kdXbnk8CayvfjNgfiDj46aCSttI5QPxORAccWC+9L/M1GRscFs6QiBgClsPPVdLwNJGhwuNiQXcHsoVF9bgofEx+CQYGdeV2rKNPTeHtleBr4GXCxqqqfNh305FP7ulXfCUCTAX8DMLNF/wBP+wZVVd3A5pbtwHR0n7ftwA7Ajp6eo4mZwFaPL9sJTVNAwTb3MDTFiDl/o1fnmQq3oilGHDHjm48dKKplaFRgn3+4vWFMQjAF1TYKq9sv8msfcg5V57wBBhMNE37J+5M/5yPX7H5R17E71A6YhwUntkPLO+y7MaucccYsiOtbJ9buYFAUpg0MZ/lhN+Xnv48zZiwhS24l7NOLCPvwHEK+ugalQb/nsmT+gLE2n4axN7Q5llvT2J1fzfh+7sc1Y1AEG7MrsGOhZt4/MDSUEfTj74/ppzRWYs34jrqhF7Kz2HFC+qc1MX1QOBll9eRVNVA/+W6q5zyNOW8dgVueo1FcSuWFH1L+s9XUn3YH9gFnUb3gRZwY2ZpbyeQBYX29/E5jjh+FGwVDF5zrjWUHdP+0VubwjNI6hkb3z2v41JRw1pQE4LKEQlHnk7+ayvbrdU3DBh233/qsCmxON7OH9X3FkSCriaFRgezI61xSdl/RoelTCDENPY3Gb4EtnkjPJr4EVgghfg18AywCrgWadP1vAPcLIX4HfIae1iMHaPqlehH4txBil+f4i8AbTRGmQoi3gBc9AQpxwH3AL7q62f5Ecrg/AWYju8s1nNGjm+ugHRdNw5y3Qb8zMet28qIaG3sLa7jt9IG+XXAPMWtwJM+uymDFoVJ+NrH9VASOlLMov1Z/TTb/cJBgq4mUiBPrzrs1iSPPoOJAENatL+JfcwAMZjCY0QwmnNGjcCTrqUfsTjcFeRmEmmqoiT5B6pq2w/SBEXy7r5j9VUZGLX6XoLV/wFidixYQjOXwWoKX/Ybq897Cf/d/cAUnYR84v81xMsrqqbO7GNvPhfWZgyP4ZGcBW3MrmT5wLPUTf03g5mdwByXiCh2AZrDoRa4Lt6K4bOyKWozTbWd8Yv/e1/Fo+k7/mF7OlaclYhtxBe6QARgr0mkc9XPdGdtooW76Q83nqIU11NpcTDkBBTUsQZRakomqO4TD5cZs9FLfoWmYyg5gG3LeUYfdmkZ6aT2LR8f6YLHdZ0pKGK+uVygJFMR0UaPmjBDH1DVtzapDpYT4mZiQFNbFlfYsE5JC+XJPIU6XG5O377GP8MZH7VLP3795Hi0xA1cBf0DPcZYBXKWq6loAVVWzPEltnwV+h65hu8CjTUNV1feFECnAS+j+Z58B97YY/x5P2wqgGviLqqofdHaT/RGDojA8JhC1uBbHgGn4734TnI3HNXMFbHoaS8FGaqcdueCtOlQKwJzhUb5eco+QHO6PiAlimVpyXEGtJTtyqxibEHLCpS5ojYgL41NlLtfUfod52wEU7UilAs1opeyG7WjWEHbmVyHc6QA4T3BBbWpKGAqwPrOcUXEp1M45ks/ab89/CV79fwSvuBdL3npqp/9fs8N5a3Z5TBD9XVCblByG1WTgx/Rypg+MoH7inZjzfiJg2z+P6WuPn8ra2gQgq9/v63gkh/szKCKAH9PLmotnOxKn69G77bA5uwKASSeioAY0RoxgRMFW9hTUNBcw7whDXSEGW9UxqTkKqhupd7j6bdT+qPgQAi1G9mkpxJZ8pWfp70DoaompdB+2lLb9NJtwutyszSjnjCERfV7ntIlxiaF8sD2fgyV1jOzjtCkdvtqqqt6Hrslqjw89j/bO/w5oxzEFVFV9HHi8nbZ64DrP46RjRGwwn+4qoGHyZAJ2vILfvnf1RIgt0PwjcYWm4Lf/AwK3PEfDiCtoOO325vblh0oZHBnAwIj+EynUEfOGR/GvtVkUVjd2WB6ktM5OdkUD549u7ep44mE0KGwaeAdPp/+MD2+YRGyQGdwOzEXbCfvsUiwZ32EbcQUbsys53bgXzWDBGdVxSHt/JjzAwoi4YNZnVXDz9KNz4DWOugZz/kb8DnyEZrTSOPKqdsfZlV9NRICZxND+7a/nZzYy9f/bu/P4quo7/+OvmxuSEAKEJUAg7MhHWYoIKJvWbVw6uOvU2p/+HJe6VFtrtc6M2lbbsTNanVGnqOU3tnY6VcftB7VTfxZRER0qRBYX/FKWAGErEEIgkO3m/v4459JDyHIhxJx77/v5eORBcr7nnHs+nHxzP/d7vsvQXry3dhd3nzmSSLQLey5+iUhNJZFYDcTqIJpDPJpHPLcny1/7jJF98+mRF66+SUfq1JF9+M/ScvbVNlCQ2/ab+JKNlYzq2y30C7G3JH/ISfTd9v94Ze06JpYktw5vdJfXI6jp1Bxrd3qd00eFNFHLzoowZUgh72wdxJmxGrJ3fHxwjem2RPbvIOvATmJt/B0rLd/D3toGTh8VngaHCf6Hp+Wb93R6opZ6vVfTyKTBPaltaGRF1gnEs3Lo/t73Kfzt1w/56vVf59F3zgkULPohtSO/wr7THz44r8uu6jqWl+/hzOPC88udjLPNmwbvrdU729x3WbnXknLS4NTtwxN084xhxOJx/nn+n4gTgWgu9cWnEOsxlDy/++fK9Zu5PPs9akfNanY+sVQzc3hvPtlaxZY9TfolRiLsO/2fqe93IgcmXE88r+XVBlZuqeJLA3uEsg9PU6eO6M22vbUH34CJZBHv2pvGgoE09hxGY8FA4l17EyOLlSnQ7y4Zp43sTawxzgfr2x7nVVMfY8WWqpTsn5YQHTwVgJqy5OfAzPYTtaYtaol1mkeEbBLnoJOH9mJu9RjiWdnkrv1d0sclO5Dg/XUV5ES9Pq1h0a97LgN75oWin1pKz/Cf6k4qKSQrAou3wYSrFpC1v2niEierejvRqg0AHJhwwyGPht5Zs5M4cNbo1Jr/t6SwK8f3K2D+6h18fXLrjz+Xle+ha5csju+XGmsBtqWksCs3TR/KEwvX89bqnV7SGolQM/oS8pc+zt4dmxhf8Qb5XQ6wu4WO9anmwvED+PfFG3hx2Wa+c3qTFuOcAiov/22rx1fsr2NTZQ2XfKm4Iy/zmJk5whud/N66XYxqZWmkNTurqa6LpUWiNq64B4Vdu/DeugrOOb5fq/uWlu+htqGRacPD86Z8pBqKxlEfyaHvnuUcqI/RtUvzj+yDsnetItat/2EfSNburKa4R25SLZGd5ZShvXiYAsp7TWXgmtepnnZvUhPBZu/0F6BvI1FbXLabiSU9yUvi//GLdOKgHiwu2008Hu/UD4lqUetE3fOysX4FLNlUSWPPYTQUT27yNYW6UbM4cNI3vcedTdaGW7B6J0N6dWVk3/B+EmvJ2VbEJ1v3srWV0Z8AH5VXMmFgz07vzHksfW1SCcf3K+CRBWuoqvHWOq21S4kQp/KjF7km+iZVvcYn/Xgh7Pp3z+Ws0UXM/Xgb1XXNDNiORFr9o//xFu8Tbar04+pbkMsJ/Qt4b+2uVvdLDP1P5YEECdGsCDNH9OaD9RU0NLa+GP3/rK8gNzuLk0LSafyoRHOp6jWeSRHH8iSncMj+84pmFyVfs7OakSF97JkwuDCPgT1y+UNkOtG95WRvT25qkuzty4kVFBPv2vLUStuqalhfsZ+pw8I3/dKEQT2p2F/PpsrW36c6Wvq8+6WoyYML+XhLFTX1sbZ3Dqg8UE/ppkrOPK7z55w5Gmf5gx/mux0t7lO5v561O/enzWPPhOysCPedM5o9NQ08+MZqGuNxYoUjqO83gePXzWFU1hYaJ17f2Zd5TF01aRDVdTFe/2T7ER+7YnMVXaKRg4slp4JTR/bhk617+fPelhejX765iv7dc9vsp5kqThvZh6qaBko3Vra63wfrKw4Oukhl2UOmMjZSxvL1W9vcN6tqE9mVaw+O7E6ojzVSVnEg9IlaJBLh5KG9mLPD66aTu6b1VnAAGhvIKX+PupLTWt1tcZk3sCRMjz0TTh7i/Z4mPlB3ltSuKWlg8pBCGhrjrDjC5+AL1+wiFk+d0Z5NlRR2ZWJJT361pJzd++ua3WeZ/0n1pCRHVaUS61/At788gnfX7uK5DzcBUDn8QvIbq6nK6kn96As6+QqPrbHFPRhf3IMXlm0m1kaLS1Mrt1RxfL/uKfXGfv4J/YhmRXj6/bJmy+PxOMs370mL1rSEacN6UVSQw88WrW/xHm/afYBNlTVMC+Gb8hEbdDJdIjGqNyxpc9ecje8AUDfk0JU5Nuw+QKwxHtqBBEGnDO3F1tpcdvabTu7a1yHe2Or+2duXkVW7h7pWViMBWLxhN/0KchgZwj56JYVdeeub0zt9DdbU+cuXpk4c1JNoVoQlmyqP6LgFf9rJwB65Kd13656zRrGvtoGfLljbbPmy8j3kZmdxQgq1pByJKycO5Bwr4un3y3j07bVcsXgwNfEulA//2mGPudPBVZMGUV5Z0+YjwaC6hkZWbd+bMo89E0oKu3LlSYN4/dPtf1ncOWBrVS079tWl9ES3TeV1ifLt00awavs+5vrLfTWVGGwwfXj4HnMdqfoBk4gToX/lcvYcaL3FJWfD28S6Dz5s1Y21O7yBBKnQfWXykEIiEfgg9zSi+7aSva311XRyNr5DPJJFfcnMFvdpaIzz4YZKThnaK7RPhsLwAbHzryDD5ed4S3QsbeNxQdDemgb+uGE3ZxxXFNpf7mSM7NuN66cO4U23g3fXHP7m/VH5HsYXp+bSOsmIRCLce85ohvXO54WPNlNcPJhPL/wDRef8XdsHp6DTj+tLSWEeTyxcl/Sj/v8pq6AuFmdCCrY8XT91CL3yu/DY22uJxw9tYVqeRv3Tgs45voiJJT2Z/d56KptJXj4oq2BwYR6De6X+aOZ4XiH7ehzHpKzVfFTeSj+1WB055Yu8lqUmf6/X7qommhVJiemVCrt2YfzAnjxfNY54NJe8VS+0un/OhrdpGDCJeF5hi/t8tm0ve2sbQvnYM0zS8x0wxUweUsiq7XvZV5vcyljvrdtFQ2P8YD+vVHbtyYM5rqgb/zT/T+zY95f+PHsO1LP6z/tSu8NxEvJzosy+4ks8+7UTeeKy8QwZMuqIJpNMJdlZEf7hr45jU2UNz3ywoc39G2KNPLFwPUN7deXUEanXAlOQm80tM4axYksVb3z+54PbG+Nx3v7TTgpyo6Hvm3SkIpEI3zvTayl/cuG6QxLUmvoYpZv2pEVrWkJWySlMyvoTr3+8ucV9umxdQqRhP3VDTj+sbPnmKob3zk9+dYNONmNkH5Zsa6DKrqTrqhfpuuyZZveL7N9Jlx0rm405aHFZBRG86T+kZanx25HmpgwppDFO65/KAhas3km/ghzGFqf+I8HsaBY/ONfYXxfjxhdWUF55gJ37arn9FW+pkhkp+AZ9pPp0y0n5dUyTNWVILy750gB+U1rOp1tb75f50oqtbNx9gDtOH5Gyo34vGDeAE/oX8MAbq/nFHzdyoD7GP7y+infW7OJvJg5K+dU2mjOqqBtfn1zCvE+2c/9/f36w9fSD9RX+tBzpU6djg06mIHKAnWUr+NBfbaGpnI1vE8/qQv2g6Yds37T7AMvK93DO8akzvdKMUX2IxWH+oNupGTmLgg9+RN7Hzx22X86md4DD++Q1tbhsN2MGdKewa2pP+NzR0vOje4oZV9yDgtwob37+Z04b2fqCtPvrYizesJuLxw9Imz/y1r+A2VeM59uvfsKNL6wgmhWhqqaen148ttNnhJZj71unjeD9dRU88MZqHr9sHMX+qMfqugaWl1cxqqgbudlZzPlgA6cMLWRGCr+xR7Mi/Nvl4/nJH9Ywe1EZv1qyieraGHd8eQRXTRrU2ZfXYW47dTgFudk8taiMNTuryYlmsWr7Pnp17cKkNBocVD9gCgDXdV3Iz94ZzaSrTyHaZAmknI3vUF98MvGcQ/sTz/tkG1kRmDU2nGt8NufEwb3Iy85i8aZ9nPZXTxCJ1dJ94b1E95RRfcr3oIv3SDtnwzs0du1LQ9G4Fs91oD7GZ9v28r9PHvxFXX7KUqIWArnZWVw4bgAvLtvCt06rpV/3ljuSv+9/Kk3V0Z4tGVvcg2e+OoHbXv6YxnicOV89EeufugMlpGUFudl8/1zjrrmf8je/WMqN04YSi8f5z6Xl7Klp8PeJsr8uxh2nj0zpfpgAPfK68NCs45nxWS+e+3AT950zLOUmqT5SkUiEvz1lCNavgH98czV9C3K548sjOOf4otBNatoejd0HUTNyFpevfZ2pe5ey7v9dzbgRf1kqLdJQQ/auz9k37d5DjmtojPPbT7czY3hvigpSZ+BQbnYWkwYX8scNuyE6iqpzn6Lg/QfJXzGHnLL5HDjpFuLRHHI2vUvd0DMh0nJL+Gfb9hKLkzFPE9pDiVpIXHHiQJ4v3cwrK7dyy4xhLe731uod9M7vwoSB6fOpNGFk32689LeTiUSgW45+NdPZKcN68dLfTuaRBWt58r31gDej/2UTitnoPxIaX9wjJaYtSEYkEmHW2AHMGpv6a9YeienDe/O7m6Z29mV0nEiEvec9zYGNC6n57+8zfd2/wLpDd4lHsqgbfs4h295fV8Gu6jouGp8aq20EnTy0kPfXV7C1qobiHnns+/JD1I78a7ov+C7d3/7ewf1qR5zX6nk+3eqNhh43QIlaW/RuGBIlhV05dWQfXluxletOGdLskOCVW6pYsHonX5s06LDm9XQR5mVU5Nga0COPRy8ey7LyPeR3iR7SgnrVpNaXFhMJk4Yhp7Hjste55YU/0L8gm4f+egzdcr2Ww3iXbsTzD21BnffJNvp0y0nJPrin+B3//1i2m4v9Zd3qS2ZQ8fV3ydrnT/6blUNj94GtnufjrVWUFOZRmK/+aW1JzR66aeqrEwey+0A9bwZGiCXU1Md44A1H/+653DhtaDNHi6SmiSU99ZhbUt6oogK+feHpLK4s5Ntv7+NAtyE09hx2MElbXFbBTxes4R9eX8X763Yxa2x/slPwA/eIPvkUFeTwvj8n3kHRXBp7DvO+2kjS4vE4n2zd2+kTyaYKJWohMmVIISP65PMfS8vZtPvAIWVPvV/Gxt0HuO/c0Wp1EhEJoVOG9uKB841l5Xu46/9+enBt2wWrd/DtVz/ht59sx/15HxNLenL5hNR77AneY/zzT+jPwrW72NjkfSpZ2/fWsrO6jnEaLJYUveOHSCQS4ZYZw7j3d6u4/BdL+CsrYmDPPMora5jvdnDZhOKDzc4iIhI+5xzfj5r6Rh76w2pufGEFV00axD+++SfGFffg3y4fT9c0GExx1aRBvLhsM79ason7zhl9xMd/6q/WMU4DCZKiFrWQOf24vsy94WS+PqmE99ZW8Ksl5Xy2bS9njS7i9tOGd/bliYhIGy4cP4B/uXQcmytreOCN1Yzok8+/XjIuLZI08OZ+vGBsf3736Xa2761t+4AmPt6yl5xohNFF6TFYqKOpRS2E+hbk8q0vj+DmGcPIyoqkZD8GEZFMNm1Yb+ZcOYFXV27lG9OH0j0vvd5ur54ymNdWbuU3peV85/SRR3Tsp9uqsH7dU2ZFhs6m/6UQy8nOUpImIpKiRvcr4O/OPo7e+TmdfSnH3MCeeZx3Qj9eXbGV3fvrkj6uIdbIqu37GD9Q/dOSpURNREREjti1pwyhoTHOw2+tTfqYP+2sprahkbEaSJA0JWoiIiJyxIb1zucb04cyf/WOZqeVas4yf01rrUiQPCVqIiIiclSunjKY8cXd+ee31rBj3+EDCxrj8YPfb6uq4f/8z0bGF3dnQCtLJcqhlKiJiIjIUcnOivDD84+ntqGR78377ODcalurarjlv1Zwwc//yNKNlcQa4/zg945YY5wHzj8+5dfw/SKl1zAUERER+UIN6dWVB843fvzmar723FL+emx/3vx8B/E49O7WhVtfWslJg3vyUfkevn/uaAb36trZl5xSkkrUzGwk8K/ATKAaeBG41zlXY2ZDgTnADGAjcKdz7veBY88AHgdGAR8CNzjn1gTKbwfuAXoCLwO3Oeeq/bJc4EngCqAWeMw593B7AhYREZFj66zRRUwY2INH317Layu3MbGkJz84bzS983N4dMFa5n6yjbNH92XW2P6dfakpp81EzcxygN8CnwHTgX7As37ZXcBcYBUwBbgQeMXMxjrn1pvZYGAe8CPgdeD7wFwzG++cazSzS4EfA1cDW4BfAo8CN/sv/4j/mmcDJcB/mNlG59wLxyB2EREROUb6FuTykwvG8M3KAxT3yCPqTy9137mjueLEgQzvk69HnkchmRa1k/Faw052zu0DVpnZ/cBjwO8AA051zu0FPjOzs4HrgfuAG4EViVYwM7sO2AacCcwH7gCedM7N88tvBub7CWDcP/4C51wpUGpmDwO3AUrUREREQqik8PBHm9a/oBOuJD0kM5jAAV/xk7SEOJALTAWW+UlawiJgmv/9VGDhwRM5tx/4CJhmZlG8VriFgWMX4yWPE4EJ/mssanLuKWamvnUiIiKS9tpM1JxzO5xz8xM/m1kWXqvWe0Ax3iPLoO14jylpo7wQyAuWO+cagF1+eTFQ4ZyraXJsDlDU1nWLiIiIpLqjaZl6DK/FawpwJ14n/6BavJYwgPxWyvMDPzdXnt1CGYHzNysajVBYmN/aLu0WjWZ1+GuEmeLP3PgzOXZQ/Io/c+PP5Nih8+JPOlEzswjeyM9bgcudc5+aWQ3eaM2gXGC//30NhydVuXitZjWBn5s7Pt5CGYHzNysWi1NZ2eou7VZYmN/hrxFmij9z48/k2EHxK/7MjT+TY4eOjb+oqOUltZKa8NZ/3PkscAvwVefcXL9oMzCgye4DgK1JlCeStYPlft+zPn75ZqCXP+o0eGwtUJHMdYuIiIiksmRXJngUuAq41Dn3amD7YuBEM+sW2DbT354on5koMLN8vMemi51zjcCSYDneIIQGYBmwHKjDm54jeO5Svy+biIiISFpLZh61qXjTaPw9sNTMgi1k7wIbgF+a2Q+BWXgjPa/3y58F7jaze4HXgPvxJsV9yy+fDcwxs5X+9tnAs4kRpmb2HDDbzK7Fa027C/jGUcYqIiIiklKSaVG73P/3J3iPJINfEeAivElwS4FrgEucc2UA/r+X4k1ouxToD1zkt6bhT1z7Y+ApvHnVlgLfDbz2nXitbguAp4EHnXMvHlWkIiIiIikmEg+sbJ8u6utjcQ0m6FiKP3Pjz+TYQfEr/syNP5Njhw4fTFAKTG6uLNk+aiIiIiLyBVOiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiIRU9pHsbGa5QClwh3Nuvr/t58CNTXb9jnPuX/3yM4DHgVHAh8ANzrk1gXPeDtwD9AReBm5zzlUHXu9J4AqgFnjMOffwEcYoIiIikpKSblEzszzgeWBsk6IxwN1AceDr5/4xg4F5wK+BycA2YK6ZZfnllwI/Bm4FzgCmAI8Gzv0IMB04G7gJuM/MrjyiCEVERERSVFItamY2BvgNEGmm+ATgPufctmbKbgRWJFrBzOw6vGTtTGA+cAfwpHNunl9+MzDfzO4C4v7xFzjnSoFSM3sYuA14IekIRURERFJUsi1qpwJvAtOCG81sANAbcC0cNxVYmPjBObcf+AiYZmZRvBa0hYH9F+MljxOBCUAusChQvgiYYmZH9MhWREREJBUllfA4555JfG9mwaIxQAPwIzM7H9gJ/Itz7pd+eTGwpcnptgMlQCGQFyx3zjWY2S6/vA6ocM7VNDk2BygCtiZz7SIiIiKpqr0tUyf4/64AngBOB54xs2rn3EtAPt4ggKBavJay/MDPzZVnt1CGX96iaDRCYWF+a7u0WzSa1eGvEWaKP3Pjz+TYQfEr/syNP5Njh86Lv72J2mzgeedchf/zSjM7DrgFeAmo4fCkKhfY5ZfRQvl+vD5qzZXhl7coFotTWdnqLu1WWJjf4a8RZoo/c+PP5NhB8Sv+zI0/k2OHjo2/qKh7i2XtmkfNORcPJGkJq4BB/vebgQFNygfgPbZMJGsHy/2+Z3388s1ALzPLaXJsLdD0NUVERETSTrsSNTN71Mxeb7J5IvC5//1iYGZg/3y/fLFzrhFYEizHG6zQACwDluP1U5seKJ8JlDrnGtpz3SIiIiKpoL2PPucBC8zsW8DvgPOAa4Cz/PJngbvN7F7gNeB+YCPwll8+G5hjZiv97bOBZ51z+wDM7Dlgtpldi9eadhfwjXZes4iIiEhKaO+jz3eBr+ElT58C3wS+5pxb5JeXAZcCVwNLgf7ARX5rGs65F/AmvH0Kb161pcB3Ay9xJ16r2wLgaeBB59yL7blmERERkVQRicfjnX0Nx1x9fSyuwQQdS/FnbvyZHDsofsWfufFncuzQ4YMJSvFWcDqMFmUXERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIZR/JzmaWC5QCdzjn5vvbhgJzgBnARuBO59zvA8ecATwOjAI+BG5wzq0JlN8O3AP0BF4GbnPOVQde70ngCqAWeMw59/DRhSoiIiKSWpJuUTOzPOB5YGxgWwSYC+wCpgDPAa+Y2XC/fDAwD/g1MBnYBsw1syy//FLgx8CtwBn+OR4NvOwjwHTgbOAm4D4zu/JoAhURERFJNUm1qJnZGOA3QKRJ0RmAAac65/YCn5nZ2cD1wH3AjcCKRCuYmV2Hl6ydCcwH7gCedM7N88tvBuab2V1A3D/+AudcKVBqZg8DtwEvHHXEIiIiIiki2Ra1U4E3gWlNtk8FlvlJWsKiwH5TgYWJAufcfuAjYJqZRfFa0BYGjl2MlzxOBCYAuf75gueeYmZH9MhWREREJBUllfA4555JfG9mwaJiYEuT3bcDJUmUFwJ5wXLnXIOZ7fLL64AK51xNk2NzgCJgazLXLiIiIpKq2tsylY/XyT+oFq8lrK3y/MDPzZVnt1BG4PzNikYjFBbmt7ZLu0WjWR3+GmGm+DM3/kyOHRS/4s/c+DM5dui8+NubqNXgjdYMygX2B8qbJlW5eIMPagI/N3d8vIUyAudvViwWp7Ky1V3arbAwv8NfI8wUf+bGn8mxg+JX/JkbfybHDh0bf1FR9xbL2juP2mZgQJNtA/jLY8nWyhPJ2sFyv+9ZH798M9DLzHKaHFsLVLTzukVERERCr72J2mLgRDPrFtg209+eKJ+ZKDCzfLyBAoudc43AkmA53iCEBmAZsByvn9r0Jucudc41tPO6RUREREKvvY8+3wU2AL80sx8Cs/BGel7vlz8L3G1m9wKvAffjTYr7ll8+G5hjZiv97bOBZ51z+wDM7Dlgtpldi9eadhfwjXZes4iIiEhKaFeLmnMuBlwE9MNbseAa4BLnXJlfXgZcClwNLAX6Axf5rWk4517Am/D2Kbx51ZYC3w28xJ14rW4LgKeBB51zL7bnmkVERERSRSQej3f2NRxz9fWxuAYTdCzFn7nxZ3LsoPgVf+bGn8mxQ4cPJijFW8HpMFqUXURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSkstt7AjO7CvjPJpvnOucuNrOhwBxgBrARuNM59/vAsWcAjwOjgA+BG5xzawLltwP3AD2Bl4HbnHPV7b1mERERkVRwLFrUxgCvAcWBr2vNLALMBXYBU4DngFfMbDiAmQ0G5gG/BiYD24C5Zpbll18K/Bi4FTjDP8ejx+B6RURERFJCu1vU8BK1lc65bcGNZnYmYMCpzrm9wGdmdjZwPXAfcCOwwjn3sL//dXjJ2pnAfOAO4Enn3Dy//GZgvpnd5ZzbdwyuW0RERCTUjlWLmmtm+1RgmZ+kJSwCpgXKFyYKnHP7gY+AaWYWxWtBWxg4djFeYjnxGFyziIiISOi1q0XNzHKAkcAsM3sQL/F7CfgB3iPQLU0O2Q6U+N+3Vl4I5AXLnXMNZrYrcLyIiIhIWmvvo8/j/HNUA5fjJW2PA93xEq3aJvvXArn+9/mtlOcHfm7p+BZFoxEKC/Pb2q1dotGsDn+NMFP8mRt/JscOil/xZ278mRw7dF787UrUnHOfmllf59wuf9MKfxDB83ijPXs2OSQX2O9/X8PhSVcu3uCDmsDPLR3folgsTmVlm7u1S2Fhfoe/Rpgp/syNP5NjB8Wv+DM3/kyOHTo2/qKi7i2WtbuPWiBJS1gFdMF7bDmgSdkAYKv//eZWyhPJ2sFyM8sG+gSOFxEREUlr7UrUzOxSM9vu91VLmAhU4nX+P9HMugXKZvrb8f+dGThXvn/sYudcI7AkWI43CKEBWNaeaxYRERFJFe3to/YuEAF+bmYP4fVZe8T/egfYAPzSzH4IzMIb6Xm9f+yzwN1mdi/ePGz3402K+5ZfPhuYY2Yr/e2zgWc1NYeIiIhkina1qPmPPc8FhuJNrfFz4GngJ865GHAR0A8oBa4BLnHOlfnHlgGXAlcDS4H+wEV+axrOuRfwJrx9Cm9etaXAd9tzvSIiIiKpJBKPxzv7Go65+vpYXIMJOpbiz9z4Mzl2UPyKP3Pjz+TYocMHE5TirdJ0GC3KLiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkMru7Atoi5nlAk8CVwC1wGPOuYc796pEREREOl4qtKg9AkwHzgZuAu4zsys795JEREREOl6oEzUz6wbcCNzhnCt1zs0FHgZu69wrExEREel4oU7UgAlALrAosG0RMMXMQv/YVkRERKQ9wp6oFQMVzrmawLbtQA5Q1DmXJCIiIvLFCHurVD7eAIKgxM+5LR3UpUt0Z1FR9w0ddlW+oqLuHf0Soab4Mzf+TI4dFL/iz9z4Mzl26ND4h7ZUEPZErYbDE7LEz/tbOU6tbSIiIpLywv7oczPQy8xyAtsG4LWqVXTOJYmIiIh8McKeqC0H6vCm50iYCZQ65xo65YpEREREviCReDze2dfQKjN7GjgNuBavNe0/gG84517szOsSERER6Whh76MGcCfwFLAAqAIeVJImIiIimSD0LWoiIiIimSoVWtS+EP6aoqV4qyDM97cNBeYAM4CNwJ3Oud8HjjkDeBwYBXwI3OCcWxMovx24B+gJvAzc5pyr/mIiOjJHGf+3gNvx5rv7GPiuc+4Dv2wg3mCQoD3OucIODuWoHGX8q4HjmpxqonNuuV+etvffzMpofjj5L5xz16XD/fe3j8L73e4e7BebTnUfjir+tK77/vaWYk+beg9HFn8m1HszOwv4J+AEvFgeds79e+CYL7zuh30wwRfCzPKA54GxgW0RYC6wC5gCPAe8YmbD/fLBwDzg18BkYBsw18yy/PJLgR8DtwJn+Od49AsK6YgcZfz/C/gR8PfAicA7wBtmNsg/xRi8yYmLA1+jOz6aI3eU8ecCI/CSmGCMn/jlaX3//W3BuK/GG/jzM788pe+/v30w8DqQ18z2tKj7cFTxp3Xd97e3FHva1Hs48vhJ83pvZsfhxf0a3u/2g8DPzOwCv7xT6n7Gt6iZ2RjgN0CkSdEZgAGnOuf2Ap+Z2dnA9cB9eGuQrnDOPeyf5zq8m3YmMB+4A3jSOTfPL78ZmG9mdznn9nV4YElqR/zXArOdcy/7+/+9mV0GXAA8jVdhP3fObev4KI5eO+I3IA4scc7VN3PqO0jj+++c2xE4Rz7wEPAj51ypvzml77+ZXQz8HNjazGFpUffhqOO/ljSu+23Enhb1Ho4u/nSv98BXgeXOuYf8n9eY2ZeBrwO/pZPqvlrU4FTgTWBak+1TgWX+m1TCosB+U4GFiQLn3H7gI2CamUXxMumFgWMX4yXGE4/p1bff0cZ/H15lDorzl09gYwB3bC+1Qxxt/GOAdc39sc6Q+x/0bbx7/9PAtlS//+cC9+LF1lS61H04uvjTve63Fnu61Hs4uviD0rHe/xdwW5Ntwd/tTqn7Gd+i5px7JvG9mQWLioEtTXbfDpQkUV6Id2MPljvnGsxsV+D4UDja+J1zi4MFZnYeXhN34pd0DHDAzJb653oPr49T03N2qnbc/zFAzMx+j1cJHfA959wfyYD7HzgmF/gucH+TNXlT+v47527xt53ezGFpUffh6OJP97rfxr1Pi3oPRx1/Yv90rferg/uZWX/gSuABf1On1H21qLWspXVGc5Mozw/83NLxYddW/AeZ2WjgV8CvnHMf+ZtPALoB38L7RR8E/N7MUuXDQVvxnwD0AmYDXwE+AxaY2TAy6/5fgff44Lkm21P9/rcm3et+0tK07rcm3et9stK+3ptZN+BVvMTraX9zp9T9lPvP+wLV4I3aCMrlL2uMtrQO6S6/jBbKW1ujNEzaih8AMxuH14T8OXBToGg4UO+cq/X3uwyvz8N0Dm0aDqu24r8K6OqcqwIws1vxOhhfgzfvX2L/lo4Pu6TuP16fjpf9RwBBqX7/W5PudT8paVz3W5Pu9T5ZaV3vzawn3qCCEcDMQJydUvfVotayzXgrIQQN4C8dLFsrT9y0g+X+J4o+NN9BNYzaih8zmwy8C6wGvhJsAnfO7UtUVv/nP+P9vwwiNbQav3OuPvHH2v85jveGNYjMuf+5eJ1oX216cBrc/9ake91vU5rX/RZlQL1vU7rXezPrC7yNl6Sd7pxbGyjulLqvRK1li4ET/ebPhJn+9kT5zESBPwJmIrDYOdcILAmW43VabACWdeRFH0Otxm9mI4A38DpSfiU4osXM+pvZHjObGthWAvTF+6OWCtqK/0MzuydR4A/P/hLeiKe0v/++8Xh9MhYFD0yT+9+adK/7rcqAut+iDKj3yUjbem9mOXgtaX2B05xzTQdGdErd16PPlr0LbAB+aWY/BGbhjfi43i9/FrjbzO7Fm3PlfrxJQd/yy2cDc8xspb99NvBs2IZot6Kt+H8GVOM98uhhZj387fucc9vNrBR40sxuwvtA8AQw3zmXKn+w2or/dbz7/zGwFm+ps954vxeQ/vcfYBxQ1nQyxzS5/61J97rflnSv+61J93qfjHSu998BJgHnAdVmlmgdq3POVdBJdV8tai1wzsWAi4B+eDMXXwNc4pwr88vLgEvxJvxbCvQHLvKzapxzL+BNfPcU3vwqS/FGyaSE1uI3s+54v8hD8P5YbQ18/Z1/iq/ijYh6E++XeI2/LSW0df+Bf8R7w3oaWI436u0s59we//i0vf+B3foDFS2cIqXvf2vSve63JhPqfhvSut4nKZ3r/RV4DVjzOfR3ex50Xt3XWp8iIiIiIaUWNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkPr/6AFHTp6AUVYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "checking_error(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc34c44d",
   "metadata": {},
   "source": [
    "## Testing with test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eaf584a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Deviation on the Test set: 8.37 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_preds = model.predict(X_test)\n",
    "plt.plot(range(1000,1200),y_test[1000:1200],label = \"Actual Value\")\n",
    "plt.plot(range(1000,1200),test_preds[1000:1200],label = \"Predicted Value\")\n",
    "plt.legend()\n",
    "plt.title(\"Testing Result\")\n",
    "plt.ylim([0, 45000])\n",
    "plt.show\n",
    "error_in_parcent(y_test, test_preds)\n",
    "print(f'Average Deviation on the Test set: {error_in_parcent(y_test, test_preds)} %')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e567b47",
   "metadata": {},
   "source": [
    "## Test with signle Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fba0d91e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_from_input(single_input):\n",
    "    input_df = pd.DataFrame([single_input])\n",
    "    input_df[inputs] = scaler.transform(input_df[inputs])\n",
    "    predicted_result = model.predict(input_df)\n",
    "    return predicted_result[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5104e40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"Day\":29.0,\"Month\":3.0,\"Year\":2015.0,\"Hour\":22.0,\"temp_Barcelona\":284.92,\"pressure_Barcelona\":1023.0,\"humidity_Barcelona\":100.0,\"wind_speed_Barcelona\":1.0,\"temp_Bilbao\":284.542,\"pressure_Bilbao\":1025.0,\"humidity_Bilbao\":72.0,\"wind_speed_Bilbao\":1.0,\"temp_Madrid\":282.38,\"pressure_Madrid\":945.0,\"humidity_Madrid\":71.0,\"wind_speed_Madrid\":1.0,\"temp_Seville\":288.97,\"pressure_Seville\":1033.0,\"humidity_Seville\":54.0,\"wind_speed_Seville\":3.0,\"temp_Valencia\":283.97,\"pressure_Valencia\":995.0,\"humidity_Valencia\":75.0,\"wind_speed_Valencia\":1.0,\"total load actual\":23928.0}'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_df.iloc[2111].to_json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dc013afe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26331.207"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_input = {\"Day\":29.0,\"Month\":3.0,\"Year\":2015.0,\"Hour\":22.0,\n",
    "                \"temp_Barcelona\":284.92,\"pressure_Barcelona\":1023.0,\"humidity_Barcelona\":100.0,\"wind_speed_Barcelona\":1.0,\n",
    "                \"temp_Bilbao\":284.542,\"pressure_Bilbao\":1025.0,\"humidity_Bilbao\":72.0,\"wind_speed_Bilbao\":1.0,\n",
    "                \"temp_Madrid\":282.38,\"pressure_Madrid\":945.0,\"humidity_Madrid\":71.0,\"wind_speed_Madrid\":1.0,\n",
    "                \"temp_Seville\":288.97,\"pressure_Seville\":1033.0,\"humidity_Seville\":54.0,\"wind_speed_Seville\":3.0,\n",
    "                \"temp_Valencia\":283.97,\"pressure_Valencia\":995.0,\"humidity_Valencia\":75.0,\"wind_speed_Valencia\":1.0}\n",
    "\n",
    "\n",
    "# Prediction \n",
    "predticted_result = predict_from_input(single_input)\n",
    "predticted_result "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b45fd7fb",
   "metadata": {},
   "source": [
    "## Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9f414b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loadForecasting.joblib']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "loadForecasting = {\n",
    "    'model':model,\n",
    "    \"scaler\": scaler,\n",
    "    \"inputs\": inputs,\n",
    "    \"target\": target\n",
    "}\n",
    "\n",
    "joblib.dump(loadForecasting, 'loadForecasting.joblib')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}