Generation_per_Type.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this Jupyter Notebook we importing the ENTSO-E Actual Generation per Type data (processed with OPSD time series script) and correcting the hourly data with reported yearly values from ENTSO-E statistical factsheet.\n",
    "\n",
    "The OPSD time series script converts all data to one resolution (1 hour) and interpolate gaps in the datat set with a max length of 2 hours. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Script setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Helpers\n",
    "import os\n",
    "import glob\n",
    "from datetime import datetime, date, timedelta, time\n",
    "\n",
    "\n",
    "#Ploting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn')\n",
    "plt.rcParams['figure.figsize'] = [15, 6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data directory preparention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create input, processed and output folders if they don't exist. If the paths are relative, the corresponding folders will be created inside the current working directory.\n",
    "- input -> all needed input data\n",
    "- processed -> save point and exchange with other scripts\n",
    "- output -> final data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_directory_path = os.path.join('input')\n",
    "processed_directory_path = 'processed'\n",
    "output_directory_path = os.path.join('output')\n",
    "\n",
    "sources_yaml_path = os.path.join('input', 'sources.yml')\n",
    "\n",
    "os.makedirs(input_directory_path, exist_ok=True)\n",
    "os.makedirs(processed_directory_path, exist_ok=True)\n",
    "os.makedirs(output_directory_path, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data file preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. ENTSO-E Transparency Platform, Actual Generation per Type Available online: https://transparency.entsoe.eu/generation/r2/actualGenerationPerProductionType/show (accessed on Oct 02, 2020).\n",
    " - Proccesed with OPSD time series scrips (own version INETCH)\n",
    " time_series_60min_stacked.csv\n",
    " \n",
    "2. ENTSO-E Statistical Factsheet 2018 in Comma Separated Value (CSV) format\n",
    "https://zenodo.org/record/3461691\n",
    "\n",
    "\n",
    "Original data: ENTSO-E statistics Available online:\n",
    "https://www.entsoe.eu/publications/statistics-and-data/#statistical-factsheet\n",
    "\n",
    " File - > entsoe-statistical-factsheet-2018-stacked\n",
    "\n",
    "\n",
    "\n",
    "Previously we used the Energy Balances in the MS Excel file format (2020 edition) eurostat https://ec.europa.eu/eurostat/de/web/energy/data/energy-balances (accessed on Oct 02, 2020). The Energy Balance data only provides gross electricity data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_timeseries_opsd(fn):\n",
    "    \"\"\"\n",
    "    Read data from OPSD time-series package own modification.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    years : None or slice()\n",
    "        Years for which to read load data\n",
    "        \n",
    "    fn : file name or url location (file format .csv)\n",
    "    \n",
    "    countries : Countries for which to read load data.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    load : pd.DataFrame\n",
    "        Load time-series with UTC timestamps x ISO-2 countries\n",
    "    \"\"\"\n",
    "\n",
    "     \n",
    "    generation = pd.read_csv(fn, index_col='utc_timestamp', parse_dates=True) #, header=[0, 1, 2, 3, 4, 5], parse_dates=True)\n",
    "                    #.dropna(how=\"all\", axis=0)  \n",
    "        \n",
    "    #generation.columns = generation.columns.droplevel(level=[2,3,4,5])\n",
    "    #generation = generation[~generation['region'].str.contains(\"_\")]\n",
    "\n",
    "    generation = generation.drop(columns='attribute')\n",
    "    \n",
    "    return generation\n",
    "\n",
    "def load_stats_factsheet(path, fn):\n",
    "    \"\"\"\n",
    "    Load the ENTSO-E Statistical Factsheet 2018 in Comma Separated Value (CSV).\n",
    "        \n",
    "    Parameters\n",
    "    ----------\n",
    "    path: str\n",
    "        path to data\n",
    "    fn : str\n",
    "        filename\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    generation = pd.read_csv(os.path.join(path, fn),index_col=[0], header=[0, 1], parse_dates=True)\n",
    "    \n",
    "    return generation\n",
    "\n",
    "def load_generation_per_unit(path, fn):\n",
    "    \"\"\"\n",
    "    Load the ENTSO-E gernation per unit data.\n",
    "        \n",
    "    Parameters\n",
    "    ----------\n",
    "    path: str\n",
    "        path to data\n",
    "    fn : str\n",
    "        filename\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    generation = pd.read_csv(os.path.join(path, fn),sep = ',',index_col=0,parse_dates=True)\n",
    "    \n",
    "    return generation\n",
    "\n",
    "def load_unit_info(path, fn):\n",
    "    \"\"\"\n",
    "    Load the ENTSO-E generation unit information.\n",
    "        \n",
    "    Parameters\n",
    "    ----------\n",
    "    path: str\n",
    "        path to data\n",
    "    fn : str\n",
    "        filename\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    df = pd.read_csv(os.path.join(path, fn),sep = ',',index_col=0)\n",
    "    \n",
    "    \n",
    "    # set name for the index\n",
    "    df.index.set_names('GenerationUnitEIC', inplace=True)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_ENTSOE_to_INATECH_type(ProductionTypeName):\n",
    "    \"\"\"\n",
    "    Converts ENTSO-E Generation per Type source names into INATECH technology type names.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ProductionTypeName : string\n",
    "        ENTSO-E name of production type.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    string\n",
    "        INATECH names of production type.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    return ProductionTypeName.replace({\n",
    "            'Fossil Hard coal': 'hard_coal', \n",
    "            'Fossil Brown coal/Lignite': 'lignite', \n",
    "            'Fossil Gas': 'gas',\n",
    "            'Fossil Coal-derived gas': 'other_fossil',\n",
    "            'Fossil Peat': 'other_fossil',\n",
    "            'Nuclear': 'nuclear',\n",
    "            'Biomass': 'biomass',\n",
    "            'Waste': 'waste',\n",
    "            'Geothermal': 'other_renewable',\n",
    "            'Marine': 'other_renewable',\n",
    "            'Other': 'other_fossil',\n",
    "            'Hydro Pumped Storage': 'hydro',\n",
    "            'Hydro Run-of-river and poundage': 'hydro',\n",
    "            'Hydro Water Reservoir': 'hydro',\n",
    "            'Fossil Oil': 'other_fossil',\n",
    "            'Fossil Oil shale': 'other_fossil', \n",
    "            'Solar': 'solar',\n",
    "            'Wind Onshore': 'wind_onshore',\n",
    "            'Wind Offshore': 'wind_offshore',\n",
    "            'Other renewable': 'other_renewable'}, inplace=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and filter data¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# period filter\n",
    "start = '2018-01-01 00:00:00+00:00'\n",
    "end = '2018-12-31 23:00:00+00:00'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load data timeseries_opsd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "entsoe_gen_type = load_timeseries_opsd(fn=input_directory_path + '/time_series_60min_stacked.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "entsoe_gen_type = entsoe_gen_type.loc[start:end].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>region</th>\n",
       "      <th>variable</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>utc_timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>AT</td>\n",
       "      <td>Biomass</td>\n",
       "      <td>316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 01:00:00+00:00</th>\n",
       "      <td>AT</td>\n",
       "      <td>Biomass</td>\n",
       "      <td>316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 02:00:00+00:00</th>\n",
       "      <td>AT</td>\n",
       "      <td>Biomass</td>\n",
       "      <td>316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 03:00:00+00:00</th>\n",
       "      <td>AT</td>\n",
       "      <td>Biomass</td>\n",
       "      <td>316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 04:00:00+00:00</th>\n",
       "      <td>AT</td>\n",
       "      <td>Biomass</td>\n",
       "      <td>316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31 19:00:00+00:00</th>\n",
       "      <td>SK</td>\n",
       "      <td>Solar</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31 20:00:00+00:00</th>\n",
       "      <td>SK</td>\n",
       "      <td>Solar</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31 21:00:00+00:00</th>\n",
       "      <td>SK</td>\n",
       "      <td>Solar</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31 22:00:00+00:00</th>\n",
       "      <td>SK</td>\n",
       "      <td>Solar</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31 23:00:00+00:00</th>\n",
       "      <td>SK</td>\n",
       "      <td>Solar</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4013686 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          region variable   data\n",
       "utc_timestamp                                   \n",
       "2018-01-01 00:00:00+00:00     AT  Biomass  316.0\n",
       "2018-01-01 01:00:00+00:00     AT  Biomass  316.0\n",
       "2018-01-01 02:00:00+00:00     AT  Biomass  316.0\n",
       "2018-01-01 03:00:00+00:00     AT  Biomass  316.0\n",
       "2018-01-01 04:00:00+00:00     AT  Biomass  316.0\n",
       "...                          ...      ...    ...\n",
       "2018-12-31 19:00:00+00:00     SK    Solar    0.4\n",
       "2018-12-31 20:00:00+00:00     SK    Solar    0.2\n",
       "2018-12-31 21:00:00+00:00     SK    Solar    0.2\n",
       "2018-12-31 22:00:00+00:00     SK    Solar    0.2\n",
       "2018-12-31 23:00:00+00:00     SK    Solar    0.2\n",
       "\n",
       "[4013686 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the data set\n",
    "entsoe_gen_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region      False\n",
       "variable    False\n",
       "data        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entsoe_gen_type.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load and standardize data Stats FACT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "entsoe_stats = load_stats_factsheet(input_directory_path, 'Stats_FACT_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th colspan=\"10\" halign=\"left\">AL</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">TR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th>biomass</th>\n",
       "      <th>gas</th>\n",
       "      <th>hard_coal</th>\n",
       "      <th>hydro</th>\n",
       "      <th>lignite</th>\n",
       "      <th>nuclear</th>\n",
       "      <th>other_fossil</th>\n",
       "      <th>other_renewable</th>\n",
       "      <th>solar</th>\n",
       "      <th>waste</th>\n",
       "      <th>...</th>\n",
       "      <th>hard_coal</th>\n",
       "      <th>hydro</th>\n",
       "      <th>lignite</th>\n",
       "      <th>nuclear</th>\n",
       "      <th>other_fossil</th>\n",
       "      <th>other_renewable</th>\n",
       "      <th>solar</th>\n",
       "      <th>waste</th>\n",
       "      <th>wind_offshore</th>\n",
       "      <th>wind_onshore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8100000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>67800000.0</td>\n",
       "      <td>59800000.0</td>\n",
       "      <td>44800000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1400000.0</td>\n",
       "      <td>6900000.0</td>\n",
       "      <td>7200000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19900000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 432 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "country         AL                                                         \\\n",
       "source     biomass  gas hard_coal      hydro lignite nuclear other_fossil   \n",
       "year                                                                        \n",
       "2018-01-01     0.0  0.0       0.0  8100000.0     0.0     0.0          0.0   \n",
       "\n",
       "country                                 ...          TR              \\\n",
       "source     other_renewable solar waste  ...   hard_coal       hydro   \n",
       "year                                    ...                           \n",
       "2018-01-01             0.0   0.0   0.0  ...  67800000.0  59800000.0   \n",
       "\n",
       "country                                                                       \\\n",
       "source         lignite nuclear other_fossil other_renewable      solar waste   \n",
       "year                                                                           \n",
       "2018-01-01  44800000.0     0.0    1400000.0       6900000.0  7200000.0   0.0   \n",
       "\n",
       "country                                \n",
       "source     wind_offshore wind_onshore  \n",
       "year                                   \n",
       "2018-01-01           0.0   19900000.0  \n",
       "\n",
       "[1 rows x 432 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the data set\n",
    "entsoe_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## standardize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# available countries in entsoe stats data set\n",
    "countries = entsoe_stats.columns.levels[0].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AL',\n",
       " 'AT',\n",
       " 'BA',\n",
       " 'BE',\n",
       " 'BG',\n",
       " 'CH',\n",
       " 'CY',\n",
       " 'CZ',\n",
       " 'DE',\n",
       " 'DK',\n",
       " 'EE',\n",
       " 'ES',\n",
       " 'FI',\n",
       " 'FR',\n",
       " 'GB',\n",
       " 'GR',\n",
       " 'HR',\n",
       " 'HU',\n",
       " 'IE',\n",
       " 'IS',\n",
       " 'IT',\n",
       " 'LT',\n",
       " 'LU',\n",
       " 'LV',\n",
       " 'ME',\n",
       " 'MK',\n",
       " 'NL',\n",
       " 'NO',\n",
       " 'PL',\n",
       " 'PT',\n",
       " 'RO',\n",
       " 'RS',\n",
       " 'SE',\n",
       " 'SI',\n",
       " 'SK',\n",
       " 'TR']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE', 'DE_50hertz', 'DE_LU',\n",
       "       'DE_amprion', 'DE_tennet', 'DE_transnetbw', 'DK', 'DK_1', 'DK_2',\n",
       "       'DK_energinet', 'EE', 'ES', 'FI', 'FR', 'GB_GBN', 'GB_NIR',\n",
       "       'GB_UKM', 'GR', 'HU', 'IE', 'IE_sem', 'IT', 'IT_BRNN', 'IT_CNOR',\n",
       "       'IT_CSUD', 'IT_FOGN', 'IT_NORD', 'IT_PRGP', 'IT_ROSN', 'IT_SARD',\n",
       "       'IT_SICI', 'IT_SUD', 'LT', 'LV', 'ME', 'NL', 'NO', 'NO_1', 'NO_2',\n",
       "       'NO_3', 'NO_4', 'NO_5', 'PL', 'PT', 'RO', 'RS', 'SE', 'SE_1',\n",
       "       'SE_2', 'SE_3', 'SE_4', 'SI', 'SK'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entsoe_gen_type.region.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace region GB_UKM with GB\n",
    "entsoe_gen_type.region = entsoe_gen_type.region.replace({'GB_UKM' : 'GB'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter entso e data\n",
    "entsoe_gen_type.query('region in @countries', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Biomass', 'Fossil Gas', 'Fossil Hard coal', 'Geothermal',\n",
       "       'Hydro Pumped Storage', 'Hydro Run-of-river and poundage',\n",
       "       'Hydro Water Reservoir', 'Other', 'Solar', 'Waste', 'Wind Onshore',\n",
       "       'Fossil Oil', 'Nuclear', 'Wind Offshore',\n",
       "       'Fossil Brown coal/Lignite', 'Fossil Coal-derived gas',\n",
       "       'Other renewable', 'Fossil Peat'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entsoe_gen_type.variable.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change ProductionTypeNames into INATECH technology type names\n",
    "entsoe_gen_type.variable = convert_ENTSOE_to_INATECH_type(entsoe_gen_type.variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['biomass', 'gas', 'hard_coal', 'other_renewable', 'hydro',\n",
       "       'other_fossil', 'solar', 'waste', 'wind_onshore', 'nuclear',\n",
       "       'wind_offshore', 'lignite'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entsoe_gen_type.variable.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# after changing the production type we need to group the dataset\n",
    "# reset index for groupby function\n",
    "entsoe_gen_type.reset_index(inplace=True)\n",
    "# group same production types\n",
    "entsoe_gen_type = entsoe_gen_type.groupby(['variable', 'utc_timestamp','region']).sum()\n",
    "# set the old index for the dataframe\n",
    "entsoe_gen_type = entsoe_gen_type.reset_index().set_index('utc_timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>variable</th>\n",
       "      <th>region</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>utc_timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>biomass</td>\n",
       "      <td>AT</td>\n",
       "      <td>316.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>biomass</td>\n",
       "      <td>BE</td>\n",
       "      <td>232.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>biomass</td>\n",
       "      <td>BG</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>biomass</td>\n",
       "      <td>CZ</td>\n",
       "      <td>261.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>biomass</td>\n",
       "      <td>DE</td>\n",
       "      <td>4764.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>biomass</td>\n",
       "      <td>DK</td>\n",
       "      <td>587.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>biomass</td>\n",
       "      <td>EE</td>\n",
       "      <td>71.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>biomass</td>\n",
       "      <td>ES</td>\n",
       "      <td>275.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>biomass</td>\n",
       "      <td>FI</td>\n",
       "      <td>855.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>biomass</td>\n",
       "      <td>FR</td>\n",
       "      <td>293.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          variable region     data\n",
       "utc_timestamp                                     \n",
       "2018-01-01 00:00:00+00:00  biomass     AT   316.00\n",
       "2018-01-01 00:00:00+00:00  biomass     BE   232.46\n",
       "2018-01-01 00:00:00+00:00  biomass     BG    28.00\n",
       "2018-01-01 00:00:00+00:00  biomass     CZ   261.78\n",
       "2018-01-01 00:00:00+00:00  biomass     DE  4764.00\n",
       "2018-01-01 00:00:00+00:00  biomass     DK   587.00\n",
       "2018-01-01 00:00:00+00:00  biomass     EE    71.10\n",
       "2018-01-01 00:00:00+00:00  biomass     ES   275.00\n",
       "2018-01-01 00:00:00+00:00  biomass     FI   855.71\n",
       "2018-01-01 00:00:00+00:00  biomass     FR   293.00"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the head of the data set \n",
    "entsoe_gen_type.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert entso data into table format\n",
    "entsoe_gen_type_table =  pd.pivot_table(entsoe_gen_type, values='data', index=entsoe_gen_type.index, columns=['region','variable'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th colspan=\"9\" halign=\"left\">AT</th>\n",
       "      <th>BE</th>\n",
       "      <th>...</th>\n",
       "      <th>SI</th>\n",
       "      <th colspan=\"9\" halign=\"left\">SK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</th>\n",
       "      <th>biomass</th>\n",
       "      <th>gas</th>\n",
       "      <th>hard_coal</th>\n",
       "      <th>hydro</th>\n",
       "      <th>other_fossil</th>\n",
       "      <th>other_renewable</th>\n",
       "      <th>solar</th>\n",
       "      <th>waste</th>\n",
       "      <th>wind_onshore</th>\n",
       "      <th>biomass</th>\n",
       "      <th>...</th>\n",
       "      <th>wind_onshore</th>\n",
       "      <th>biomass</th>\n",
       "      <th>gas</th>\n",
       "      <th>hard_coal</th>\n",
       "      <th>hydro</th>\n",
       "      <th>lignite</th>\n",
       "      <th>nuclear</th>\n",
       "      <th>other_fossil</th>\n",
       "      <th>other_renewable</th>\n",
       "      <th>solar</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>utc_timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>316.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3345.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>232.46</td>\n",
       "      <td>...</td>\n",
       "      <td>0.70</td>\n",
       "      <td>28.2</td>\n",
       "      <td>175.6</td>\n",
       "      <td>48.1</td>\n",
       "      <td>455.93</td>\n",
       "      <td>179.5</td>\n",
       "      <td>1819.3</td>\n",
       "      <td>478.8</td>\n",
       "      <td>41.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 01:00:00+00:00</th>\n",
       "      <td>316.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>153.58</td>\n",
       "      <td>...</td>\n",
       "      <td>0.61</td>\n",
       "      <td>28.1</td>\n",
       "      <td>172.9</td>\n",
       "      <td>48.0</td>\n",
       "      <td>461.95</td>\n",
       "      <td>177.9</td>\n",
       "      <td>1816.7</td>\n",
       "      <td>475.8</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 02:00:00+00:00</th>\n",
       "      <td>316.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>3167.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>133.70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.57</td>\n",
       "      <td>28.1</td>\n",
       "      <td>162.1</td>\n",
       "      <td>46.7</td>\n",
       "      <td>513.39</td>\n",
       "      <td>174.7</td>\n",
       "      <td>1808.7</td>\n",
       "      <td>469.5</td>\n",
       "      <td>40.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 03:00:00+00:00</th>\n",
       "      <td>316.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>3160.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>131.38</td>\n",
       "      <td>...</td>\n",
       "      <td>0.62</td>\n",
       "      <td>29.7</td>\n",
       "      <td>140.3</td>\n",
       "      <td>45.3</td>\n",
       "      <td>587.83</td>\n",
       "      <td>168.5</td>\n",
       "      <td>1803.6</td>\n",
       "      <td>455.6</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 04:00:00+00:00</th>\n",
       "      <td>316.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>3330.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>131.26</td>\n",
       "      <td>...</td>\n",
       "      <td>0.56</td>\n",
       "      <td>29.5</td>\n",
       "      <td>143.5</td>\n",
       "      <td>46.4</td>\n",
       "      <td>562.89</td>\n",
       "      <td>170.6</td>\n",
       "      <td>1809.1</td>\n",
       "      <td>461.5</td>\n",
       "      <td>40.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 214 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "region                         AT                                       \\\n",
       "variable                  biomass   gas hard_coal   hydro other_fossil   \n",
       "utc_timestamp                                                            \n",
       "2018-01-01 00:00:00+00:00   316.0  71.0     150.0  3345.0         22.0   \n",
       "2018-01-01 01:00:00+00:00   316.0  70.0     149.0  3275.0         22.0   \n",
       "2018-01-01 02:00:00+00:00   316.0  69.0     149.0  3167.0         22.0   \n",
       "2018-01-01 03:00:00+00:00   316.0  72.0     149.0  3160.0         22.0   \n",
       "2018-01-01 04:00:00+00:00   316.0  75.0     149.0  3330.0         22.0   \n",
       "\n",
       "region                                                                   BE  \\\n",
       "variable                  other_renewable solar  waste wind_onshore biomass   \n",
       "utc_timestamp                                                                 \n",
       "2018-01-01 00:00:00+00:00             0.0   0.0  100.0        832.0  232.46   \n",
       "2018-01-01 01:00:00+00:00             0.0   0.0  100.0        926.0  153.58   \n",
       "2018-01-01 02:00:00+00:00             0.0   0.0  100.0        692.0  133.70   \n",
       "2018-01-01 03:00:00+00:00             0.0   0.0  100.0        453.0  131.38   \n",
       "2018-01-01 04:00:00+00:00             0.0   0.0  100.0        321.0  131.26   \n",
       "\n",
       "region                     ...           SI      SK                           \\\n",
       "variable                   ... wind_onshore biomass    gas hard_coal   hydro   \n",
       "utc_timestamp              ...                                                 \n",
       "2018-01-01 00:00:00+00:00  ...         0.70    28.2  175.6      48.1  455.93   \n",
       "2018-01-01 01:00:00+00:00  ...         0.61    28.1  172.9      48.0  461.95   \n",
       "2018-01-01 02:00:00+00:00  ...         0.57    28.1  162.1      46.7  513.39   \n",
       "2018-01-01 03:00:00+00:00  ...         0.62    29.7  140.3      45.3  587.83   \n",
       "2018-01-01 04:00:00+00:00  ...         0.56    29.5  143.5      46.4  562.89   \n",
       "\n",
       "region                                                                        \n",
       "variable                  lignite nuclear other_fossil other_renewable solar  \n",
       "utc_timestamp                                                                 \n",
       "2018-01-01 00:00:00+00:00   179.5  1819.3        478.8            41.4   0.0  \n",
       "2018-01-01 01:00:00+00:00   177.9  1816.7        475.8            41.0   0.0  \n",
       "2018-01-01 02:00:00+00:00   174.7  1808.7        469.5            40.8   0.0  \n",
       "2018-01-01 03:00:00+00:00   168.5  1803.6        455.6            39.4   0.0  \n",
       "2018-01-01 04:00:00+00:00   170.6  1809.1        461.5            40.4   0.0  \n",
       "\n",
       "[5 rows x 214 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entsoe_gen_type_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check entsoe generation data for nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def consecutive_nans(ds):\n",
    "    return (ds.isnull().astype(int)\n",
    "            .groupby(ds.notnull().astype(int).cumsum()[ds.isnull()])\n",
    "            .transform('sum').fillna(0))\n",
    "\n",
    "def nan_statistics(df):\n",
    "    def max_consecutive_nans(ds):\n",
    "        return (ds.isnull().astype(int)\n",
    "                  .groupby(ds.notnull().astype(int).cumsum())\n",
    "                  .sum().max())\n",
    "    consecutive = df.apply(max_consecutive_nans)\n",
    "    total = df.isnull().sum()\n",
    "    max_total_per_month = df.isnull().resample('m').sum().max()\n",
    "    return pd.concat([total, consecutive, max_total_per_month],\n",
    "                 keys=['total', 'consecutive', 'max_total_per_month'], axis=1)\n",
    "\n",
    "def change_multiindex(df):\n",
    "    df = df.copy(deep=True)\n",
    "    if df.columns.nlevels > 1:\n",
    "        df.columns = [' '.join(col).strip() for col in df.columns.values]\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "    return df\n",
    "\n",
    "# plot function\n",
    "def plot_gap_map_func(data):\n",
    "\n",
    "    # Create figure and plot space\n",
    "    fig, ax = plt.subplots(figsize=(15, 10))\n",
    "\n",
    "    # plot data gaps\n",
    "    sns.heatmap(data, cmap='Blues', robust=False, cbar=True, ax=ax, yticklabels=True)\n",
    "    \n",
    "    # set x ticks\n",
    "    ax.set_xticks(list(range(0, 8760,1000)))\n",
    "    # set x labels\n",
    "    ax.set_xticklabels(range(0, 8760,1000), rotation=0)\n",
    "    \n",
    "    \n",
    "    ax.tick_params(axis='x', labelsize=16)\n",
    "    ax.tick_params(axis='y', labelsize=16, rotation=45)\n",
    "\n",
    "    # use matplotlib.colorbar.Colorbar object\n",
    "    cbar = ax.collections[0].colorbar\n",
    "    # here set the labelsize by 20\n",
    "    cbar.ax.tick_params(labelsize=16)\n",
    "    cbar.ax.set_ylabel('Gaps', fontsize=20)\n",
    "    \n",
    "\n",
    "    \n",
    "    ax.set_xlabel(\"Hour of the year\", fontsize=20)\n",
    "    ax.set_ylabel(\"Country\", fontsize=20)\n",
    "    \n",
    "    \n",
    "\n",
    "    # draw hline for seperation\n",
    "    ax.hlines(list(range(len(data.index))), colors='k', *ax.get_xlim(),  linewidth=1.5, alpha=0.5)\n",
    "\n",
    "    #draw border for figure\n",
    "    ax.patch.set_edgecolor('black')  \n",
    "    ax.patch.set_linewidth('1.5')  \n",
    "    \n",
    "    fig.tight_layout()\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nan statistic for generation data\n",
    "nan_statistic = nan_statistics(change_multiindex(entsoe_gen_type_table))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total                  3.0\n",
       "consecutive            3.0\n",
       "max_total_per_month    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# minimum \n",
    "nan_statistic[nan_statistic > 0].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total                  6457.0\n",
       "consecutive            5327.0\n",
       "max_total_per_month     744.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# maximum \n",
    "nan_statistic[nan_statistic > 0].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(entsoe_gen_type_table.columns.levels[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export statistic to csv file\n",
    "nan_statistic.to_csv(output_directory_path + '/nan_statistic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nan dataframe for nan plot\n",
    "# nan for each technology\n",
    "entsoe_gen_type_table_null = entsoe_gen_type_table.isnull()\n",
    "\n",
    "plot_table_null = entsoe_gen_type_table_null.groupby(level = 0, axis = 1).sum() / entsoe_gen_type_table_null.count(axis=1, level=0)\n",
    "\n",
    "plot_table_null = plot_table_null.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>utc_timestamp</th>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <th>2018-01-01 01:00:00+00:00</th>\n",
       "      <th>2018-01-01 02:00:00+00:00</th>\n",
       "      <th>2018-01-01 03:00:00+00:00</th>\n",
       "      <th>2018-01-01 04:00:00+00:00</th>\n",
       "      <th>2018-01-01 05:00:00+00:00</th>\n",
       "      <th>2018-01-01 06:00:00+00:00</th>\n",
       "      <th>2018-01-01 07:00:00+00:00</th>\n",
       "      <th>2018-01-01 08:00:00+00:00</th>\n",
       "      <th>2018-01-01 09:00:00+00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-12-31 14:00:00+00:00</th>\n",
       "      <th>2018-12-31 15:00:00+00:00</th>\n",
       "      <th>2018-12-31 16:00:00+00:00</th>\n",
       "      <th>2018-12-31 17:00:00+00:00</th>\n",
       "      <th>2018-12-31 18:00:00+00:00</th>\n",
       "      <th>2018-12-31 19:00:00+00:00</th>\n",
       "      <th>2018-12-31 20:00:00+00:00</th>\n",
       "      <th>2018-12-31 21:00:00+00:00</th>\n",
       "      <th>2018-12-31 22:00:00+00:00</th>\n",
       "      <th>2018-12-31 23:00:00+00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <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",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 8760 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "utc_timestamp  2018-01-01 00:00:00+00:00  2018-01-01 01:00:00+00:00  \\\n",
       "region                                                                \n",
       "AT                                   0.0                        0.0   \n",
       "BE                                   0.0                        0.0   \n",
       "BG                                   0.0                        0.0   \n",
       "CH                                   0.0                        0.0   \n",
       "CY                                   0.0                        0.0   \n",
       "\n",
       "utc_timestamp  2018-01-01 02:00:00+00:00  2018-01-01 03:00:00+00:00  \\\n",
       "region                                                                \n",
       "AT                                   0.0                        0.0   \n",
       "BE                                   0.0                        0.0   \n",
       "BG                                   0.0                        0.0   \n",
       "CH                                   0.0                        0.0   \n",
       "CY                                   0.0                        0.0   \n",
       "\n",
       "utc_timestamp  2018-01-01 04:00:00+00:00  2018-01-01 05:00:00+00:00  \\\n",
       "region                                                                \n",
       "AT                                   0.0                        0.0   \n",
       "BE                                   0.0                        0.0   \n",
       "BG                                   0.0                        0.0   \n",
       "CH                                   0.0                        0.0   \n",
       "CY                                   0.0                        0.0   \n",
       "\n",
       "utc_timestamp  2018-01-01 06:00:00+00:00  2018-01-01 07:00:00+00:00  \\\n",
       "region                                                                \n",
       "AT                                   0.0                        0.0   \n",
       "BE                                   0.0                        0.0   \n",
       "BG                                   0.0                        0.0   \n",
       "CH                                   0.0                        0.0   \n",
       "CY                                   0.0                        0.0   \n",
       "\n",
       "utc_timestamp  2018-01-01 08:00:00+00:00  2018-01-01 09:00:00+00:00  ...  \\\n",
       "region                                                               ...   \n",
       "AT                                   0.0                        0.0  ...   \n",
       "BE                                   0.0                        0.0  ...   \n",
       "BG                                   0.0                        0.0  ...   \n",
       "CH                                   0.0                        0.0  ...   \n",
       "CY                                   0.0                        0.0  ...   \n",
       "\n",
       "utc_timestamp  2018-12-31 14:00:00+00:00  2018-12-31 15:00:00+00:00  \\\n",
       "region                                                                \n",
       "AT                                   0.0                        0.0   \n",
       "BE                                   0.0                        0.0   \n",
       "BG                                   0.0                        0.0   \n",
       "CH                                   0.0                        0.0   \n",
       "CY                                   0.0                        0.0   \n",
       "\n",
       "utc_timestamp  2018-12-31 16:00:00+00:00  2018-12-31 17:00:00+00:00  \\\n",
       "region                                                                \n",
       "AT                                   0.0                        0.0   \n",
       "BE                                   0.0                        0.0   \n",
       "BG                                   0.0                        0.0   \n",
       "CH                                   0.0                        0.0   \n",
       "CY                                   0.0                        0.0   \n",
       "\n",
       "utc_timestamp  2018-12-31 18:00:00+00:00  2018-12-31 19:00:00+00:00  \\\n",
       "region                                                                \n",
       "AT                                   0.0                        0.0   \n",
       "BE                                   0.0                        0.0   \n",
       "BG                                   0.0                        0.0   \n",
       "CH                                   0.0                        0.0   \n",
       "CY                                   0.0                        0.0   \n",
       "\n",
       "utc_timestamp  2018-12-31 20:00:00+00:00  2018-12-31 21:00:00+00:00  \\\n",
       "region                                                                \n",
       "AT                                   0.0                        0.0   \n",
       "BE                                   0.0                        0.0   \n",
       "BG                                   0.0                        0.0   \n",
       "CH                                   0.0                        0.0   \n",
       "CY                                   0.0                        0.0   \n",
       "\n",
       "utc_timestamp  2018-12-31 22:00:00+00:00  2018-12-31 23:00:00+00:00  \n",
       "region                                                               \n",
       "AT                                   0.0                        0.0  \n",
       "BE                                   0.0                        0.0  \n",
       "BG                                   0.0                        0.0  \n",
       "CH                                   0.0                        0.0  \n",
       "CY                                   0.0                        0.0  \n",
       "\n",
       "[5 rows x 8760 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_table_null.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_gap_map_func(plot_table_null)\n",
    "\n",
    "fig.savefig(os.path.join(output_directory_path + '/generation_per_tech_nan_gap_map_orig.jpg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manual adjustment of entsoe generation data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We using some external sources to adjust the hourly production data for some countries.\n",
    "\n",
    "- 1) ENTSO-E production per unit\n",
    "\n",
    "Production per Unit from ENTSO-E\n",
    "Original data: ENTSO-E Transparency Platform, Actual Generation per Generation Unit Available online: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show (accessed on Apr 29, 2020).\n",
    "Processed with the following script that converts the data to hourly resolution:\n",
    "\n",
    "- https://github.com/INATECH-CIG/entso-e_GenerationOutputPerUnit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load power plant unit generation and inforamtion (capacity, name, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "generation_per_unit_2018 = load_generation_per_unit(input_directory_path, 'gen_data_2018.csv')\n",
    "generation_unit_info = load_unit_info(input_directory_path, 'unit_data_2018.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set same index for both dataframes\n",
    "generation_per_unit_2018 = generation_per_unit_2018.set_index(entsoe_gen_type_table.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sweden"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overview of Swedisch genertaion units in ENTSO-E generation per unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AreaCode</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>AreaTypeCode</th>\n",
       "      <th>InstalledGenCapacity</th>\n",
       "      <th>MapCode</th>\n",
       "      <th>PowerSystemResourceName</th>\n",
       "      <th>ProductionTypeName</th>\n",
       "      <th>ProductionUnitEIC</th>\n",
       "      <th>duplicate_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenerationUnitEIC</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46WGU00000000305</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>150.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Messaure G1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000032W</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000034Y</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>225.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Seitevare G1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000033U</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000045T</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>171.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Stornorrfors G4</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000042T</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000044V</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>136.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Stornorrfors G3</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000042T</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000137O</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>492.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Forsmark block 1 G11</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU0000000015W</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000017Y</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Karlshamn G1</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>46WPU0000000005Z</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000208</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>320.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Ritsem G1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000026R</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000026X</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>110.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Harsprånget G2</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000029L</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000027V</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>170.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Harsprånget G4</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000029L</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000224</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>162.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Vietas G2</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000027P</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000043X</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>136.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Stornorrfors G2</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000042T</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000046</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>130.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Trängslet G3</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000018Q</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000402</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>113.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Gallejaur G1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000039I</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000146N</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>531.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Ringhals block 3 G31</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU00000000121</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000048N</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>260.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Stenungsund B3</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>46WPU0000000056I</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000054</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>190.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>KVV1 Värtaverket</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>46WPU0000000023X</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000140Z</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>560.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Forsmark block 2 G22</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU0000000016U</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000321</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>152.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Messaure G3</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000032W</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000038Q</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>107.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Porsi G3</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000036O</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000144R</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>433.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Ringhals block 2 G21</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU00000000113</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000143T</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>439.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Ringhals block 1 G12</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU00000000105</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000232</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>220.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Porjus G11</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000028N</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000149H</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>469.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Ringhals block 4 G42</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU0000000014Y</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000042Z</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>136.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Stornorrfors G1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000042T</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000028T</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>440.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Harsprånget G5</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000029L</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000018W</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>335.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Karlshamn G2</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>46WPU0000000005Z</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000240</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>220.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Porjus G12</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000028N</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000138M</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>492.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Forsmark block 1 G12</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU0000000015W</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000029R</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>179.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Ligga G3</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000031Y</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000035W</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>146.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Letsi G1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000035Q</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000313</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>150.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Messaure G2</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000032W</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000039O</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>100.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Bastusel G1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000038K</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000062</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>130.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>KVV6 Värtaverket</td>\n",
       "      <td>hard_coal</td>\n",
       "      <td>46WPU0000000023X</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000046R</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>120.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Stalon G1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000043R</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000002A</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>100.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Trängslet G1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000018Q</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000139K</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>560.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Forsmark block 2 G21</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU0000000016U</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000145P</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>432.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Ringhals block 2 G22</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU00000000113</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000038</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>100.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Trängslet G2</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000018Q</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000216</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>162.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Vietas G1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000027P</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000135</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>172.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Gasturbiner Halmstad G12</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>46WPU00000000024</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000148J</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>469.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Ringhals block 4 G41</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU0000000014Y</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000047P</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>120.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Uppsala KVV</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>46WPU0000000055K</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000053U</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>1167.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Forsmark block 3 G31</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU0000000017S</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000009X</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Oskarshamn G3</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU0000000061P</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000019U</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>335.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Karlshamn G3</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>46WPU0000000005Z</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000037S</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>146.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Letsi G3</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000035Q</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000127</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>106.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Åbyverket Örebro</td>\n",
       "      <td>biomass</td>\n",
       "      <td>46WPU0000000063L</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000049L</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>260.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Stenungsund B4</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>46WPU0000000057G</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000025Z</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>110.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Harsprånget G1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000029L</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU00000000410</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>101.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Gallejaur G2</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000039I</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000142V</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>439.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Ringhals block 1 G11</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU00000000105</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000242R</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>120.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>KVV8 Värtaverket</td>\n",
       "      <td>biomass</td>\n",
       "      <td>46WPU0000000023X</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000036U</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>146.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Letsi G2</td>\n",
       "      <td>hydro</td>\n",
       "      <td>46WPU0000000035Q</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000001C</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>250.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Rya KVV</td>\n",
       "      <td>gas</td>\n",
       "      <td>46WPU0000000062N</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46WGU0000000147L</th>\n",
       "      <td>10YSE-1--------K</td>\n",
       "      <td>SvK CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>531.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>Ringhals block 3 G32</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>46WPU00000000121</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           AreaCode AreaName AreaTypeCode  \\\n",
       "GenerationUnitEIC                                           \n",
       "46WGU00000000305   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000034Y   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000045T   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000044V   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000137O   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000017Y   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000208   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000026X   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000027V   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000224   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000043X   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000046   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000402   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000146N   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000048N   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000054   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000140Z   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000321   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000038Q   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000144R   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000143T   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000232   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000149H   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000042Z   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000028T   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000018W   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000240   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000138M   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000029R   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000035W   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000313   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000039O   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000062   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000046R   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000002A   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000139K   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000145P   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000038   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000216   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000135   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000148J   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000047P   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000053U   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000009X   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000019U   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000037S   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000127   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000049L   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000025Z   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU00000000410   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000142V   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000242R   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000036U   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000001C   10YSE-1--------K   SvK CA          CTA   \n",
       "46WGU0000000147L   10YSE-1--------K   SvK CA          CTA   \n",
       "\n",
       "                   InstalledGenCapacity MapCode   PowerSystemResourceName  \\\n",
       "GenerationUnitEIC                                                           \n",
       "46WGU00000000305                  150.0      SE               Messaure G1   \n",
       "46WGU0000000034Y                  225.0      SE              Seitevare G1   \n",
       "46WGU0000000045T                  171.0      SE           Stornorrfors G4   \n",
       "46WGU0000000044V                  136.0      SE           Stornorrfors G3   \n",
       "46WGU0000000137O                  492.0      SE      Forsmark block 1 G11   \n",
       "46WGU0000000017Y                    0.0      SE              Karlshamn G1   \n",
       "46WGU00000000208                  320.0      SE                 Ritsem G1   \n",
       "46WGU0000000026X                  110.0      SE            Harsprånget G2   \n",
       "46WGU0000000027V                  170.0      SE            Harsprånget G4   \n",
       "46WGU00000000224                  162.0      SE                 Vietas G2   \n",
       "46WGU0000000043X                  136.0      SE           Stornorrfors G2   \n",
       "46WGU00000000046                  130.0      SE              Trängslet G3   \n",
       "46WGU00000000402                  113.0      SE              Gallejaur G1   \n",
       "46WGU0000000146N                  531.0      SE      Ringhals block 3 G31   \n",
       "46WGU0000000048N                  260.0      SE            Stenungsund B3   \n",
       "46WGU00000000054                  190.0      SE          KVV1 Värtaverket   \n",
       "46WGU0000000140Z                  560.0      SE      Forsmark block 2 G22   \n",
       "46WGU00000000321                  152.0      SE               Messaure G3   \n",
       "46WGU0000000038Q                  107.0      SE                  Porsi G3   \n",
       "46WGU0000000144R                  433.0      SE      Ringhals block 2 G21   \n",
       "46WGU0000000143T                  439.0      SE      Ringhals block 1 G12   \n",
       "46WGU00000000232                  220.0      SE                Porjus G11   \n",
       "46WGU0000000149H                  469.0      SE      Ringhals block 4 G42   \n",
       "46WGU0000000042Z                  136.0      SE           Stornorrfors G1   \n",
       "46WGU0000000028T                  440.0      SE            Harsprånget G5   \n",
       "46WGU0000000018W                  335.0      SE              Karlshamn G2   \n",
       "46WGU00000000240                  220.0      SE                Porjus G12   \n",
       "46WGU0000000138M                  492.0      SE      Forsmark block 1 G12   \n",
       "46WGU0000000029R                  179.0      SE                  Ligga G3   \n",
       "46WGU0000000035W                  146.0      SE                  Letsi G1   \n",
       "46WGU00000000313                  150.0      SE               Messaure G2   \n",
       "46WGU0000000039O                  100.0      SE               Bastusel G1   \n",
       "46WGU00000000062                  130.0      SE         KVV6 Värtaverket    \n",
       "46WGU0000000046R                  120.0      SE                 Stalon G1   \n",
       "46WGU0000000002A                  100.0      SE              Trängslet G1   \n",
       "46WGU0000000139K                  560.0      SE      Forsmark block 2 G21   \n",
       "46WGU0000000145P                  432.0      SE      Ringhals block 2 G22   \n",
       "46WGU00000000038                  100.0      SE              Trängslet G2   \n",
       "46WGU00000000216                  162.0      SE                 Vietas G1   \n",
       "46WGU00000000135                  172.0      SE  Gasturbiner Halmstad G12   \n",
       "46WGU0000000148J                  469.0      SE      Ringhals block 4 G41   \n",
       "46WGU0000000047P                  120.0      SE               Uppsala KVV   \n",
       "46WGU0000000053U                 1167.0      SE      Forsmark block 3 G31   \n",
       "46WGU0000000009X                 1400.0      SE             Oskarshamn G3   \n",
       "46WGU0000000019U                  335.0      SE              Karlshamn G3   \n",
       "46WGU0000000037S                  146.0      SE                  Letsi G3   \n",
       "46WGU00000000127                  106.0      SE          Åbyverket Örebro   \n",
       "46WGU0000000049L                  260.0      SE            Stenungsund B4   \n",
       "46WGU0000000025Z                  110.0      SE            Harsprånget G1   \n",
       "46WGU00000000410                  101.0      SE              Gallejaur G2   \n",
       "46WGU0000000142V                  439.0      SE      Ringhals block 1 G11   \n",
       "46WGU0000000242R                  120.0      SE          KVV8 Värtaverket   \n",
       "46WGU0000000036U                  146.0      SE                  Letsi G2   \n",
       "46WGU0000000001C                  250.0      SE                   Rya KVV   \n",
       "46WGU0000000147L                  531.0      SE      Ringhals block 3 G32   \n",
       "\n",
       "                  ProductionTypeName ProductionUnitEIC  duplicate_count  \n",
       "GenerationUnitEIC                                                        \n",
       "46WGU00000000305               hydro  46WPU0000000032W              1.0  \n",
       "46WGU0000000034Y               hydro  46WPU0000000033U              1.0  \n",
       "46WGU0000000045T               hydro  46WPU0000000042T              1.0  \n",
       "46WGU0000000044V               hydro  46WPU0000000042T              1.0  \n",
       "46WGU0000000137O             nuclear  46WPU0000000015W              2.0  \n",
       "46WGU0000000017Y        other_fossil  46WPU0000000005Z              1.0  \n",
       "46WGU00000000208               hydro  46WPU0000000026R              1.0  \n",
       "46WGU0000000026X               hydro  46WPU0000000029L              1.0  \n",
       "46WGU0000000027V               hydro  46WPU0000000029L              1.0  \n",
       "46WGU00000000224               hydro  46WPU0000000027P              1.0  \n",
       "46WGU0000000043X               hydro  46WPU0000000042T              1.0  \n",
       "46WGU00000000046               hydro  46WPU0000000018Q              1.0  \n",
       "46WGU00000000402               hydro  46WPU0000000039I              1.0  \n",
       "46WGU0000000146N             nuclear  46WPU00000000121              1.0  \n",
       "46WGU0000000048N        other_fossil  46WPU0000000056I              1.0  \n",
       "46WGU00000000054        other_fossil  46WPU0000000023X              2.0  \n",
       "46WGU0000000140Z             nuclear  46WPU0000000016U              2.0  \n",
       "46WGU00000000321               hydro  46WPU0000000032W              1.0  \n",
       "46WGU0000000038Q               hydro  46WPU0000000036O              1.0  \n",
       "46WGU0000000144R             nuclear  46WPU00000000113              1.0  \n",
       "46WGU0000000143T             nuclear  46WPU00000000105              1.0  \n",
       "46WGU00000000232               hydro  46WPU0000000028N              1.0  \n",
       "46WGU0000000149H             nuclear  46WPU0000000014Y              1.0  \n",
       "46WGU0000000042Z               hydro  46WPU0000000042T              1.0  \n",
       "46WGU0000000028T               hydro  46WPU0000000029L              1.0  \n",
       "46WGU0000000018W        other_fossil  46WPU0000000005Z              1.0  \n",
       "46WGU00000000240               hydro  46WPU0000000028N              1.0  \n",
       "46WGU0000000138M             nuclear  46WPU0000000015W              2.0  \n",
       "46WGU0000000029R               hydro  46WPU0000000031Y              1.0  \n",
       "46WGU0000000035W               hydro  46WPU0000000035Q              1.0  \n",
       "46WGU00000000313               hydro  46WPU0000000032W              1.0  \n",
       "46WGU0000000039O               hydro  46WPU0000000038K              1.0  \n",
       "46WGU00000000062           hard_coal  46WPU0000000023X              2.0  \n",
       "46WGU0000000046R               hydro  46WPU0000000043R              2.0  \n",
       "46WGU0000000002A               hydro  46WPU0000000018Q              1.0  \n",
       "46WGU0000000139K             nuclear  46WPU0000000016U              2.0  \n",
       "46WGU0000000145P             nuclear  46WPU00000000113              1.0  \n",
       "46WGU00000000038               hydro  46WPU0000000018Q              1.0  \n",
       "46WGU00000000216               hydro  46WPU0000000027P              1.0  \n",
       "46WGU00000000135        other_fossil  46WPU00000000024              1.0  \n",
       "46WGU0000000148J             nuclear  46WPU0000000014Y              1.0  \n",
       "46WGU0000000047P        other_fossil  46WPU0000000055K              1.0  \n",
       "46WGU0000000053U             nuclear  46WPU0000000017S              2.0  \n",
       "46WGU0000000009X             nuclear  46WPU0000000061P              1.0  \n",
       "46WGU0000000019U        other_fossil  46WPU0000000005Z              1.0  \n",
       "46WGU0000000037S               hydro  46WPU0000000035Q              1.0  \n",
       "46WGU00000000127             biomass  46WPU0000000063L              1.0  \n",
       "46WGU0000000049L        other_fossil  46WPU0000000057G              1.0  \n",
       "46WGU0000000025Z               hydro  46WPU0000000029L              1.0  \n",
       "46WGU00000000410               hydro  46WPU0000000039I              1.0  \n",
       "46WGU0000000142V             nuclear  46WPU00000000105              1.0  \n",
       "46WGU0000000242R             biomass  46WPU0000000023X              2.0  \n",
       "46WGU0000000036U               hydro  46WPU0000000035Q              1.0  \n",
       "46WGU0000000001C                 gas  46WPU0000000062N              2.0  \n",
       "46WGU0000000147L             nuclear  46WPU00000000121              1.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generation_unit_info[(generation_unit_info.MapCode == 'SE')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable\n",
      "biomass             2951320.0\n",
      "gas                50970105.0\n",
      "hard_coal          32347867.5\n",
      "hydro              36130459.0\n",
      "lignite             3091189.0\n",
      "nuclear            53278071.5\n",
      "other_fossil        2940224.0\n",
      "other_renewable      857555.5\n",
      "solar              12024589.5\n",
      "waste               2616967.5\n",
      "wind_onshore       48917756.0\n",
      "dtype: float64\n",
      "total: 246126104.5\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E per type data for 2018\n",
    "print(entsoe_gen_type_table['ES'].sum())\n",
    "print('total: ' + str(entsoe_gen_type_table['ES'].sum().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "biomass             9800000.0\n",
      "gas                  600000.0\n",
      "hard_coal            500000.0\n",
      "hydro              61000000.0\n",
      "lignite                   0.0\n",
      "nuclear            65800000.0\n",
      "other_fossil        1700000.0\n",
      "other_renewable     1300000.0\n",
      "solar                     0.0\n",
      "waste                900000.0\n",
      "wind_offshore             0.0\n",
      "wind_onshore       16600000.0\n",
      "dtype: float64\n",
      "total: 158200000.0\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E fact sheet for 2018\n",
    "print(entsoe_stats['SE'].sum())\n",
    "print('total: ' + str(entsoe_stats['SE'].sum().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we compare both data sets, we can see that per type generation has less generation technologies than the ENTSO-E fact sheet. But the total generation of both data set is similar. We assume that the generation from missing technologies are incorrectly assigned in the per type generation data.\n",
    "To corrode this we assume:\n",
    "1. That the generation of biomass (9800 GWh from fact sheet) is assigned to the tech type \"other_fossil\" in the generation per type\n",
    "2. That gas generation (600 GWh from fact sheet) is missing in the generation per type data\n",
    "3. That hard coal generation (500 GWh from fact sheet) is missing in the generation per type data\n",
    "4. That other_renewable (1300 GWH from fact sheet) is missing in the generation per type data\n",
    "5. That other_fossil (1700 GWH from fact sheet) is not correctly represented because about wrong assignment with biomass\n",
    "6. That waste (900 GWh from fact sheet) is missing in the generation per type data\n",
    "\n",
    "Correction steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. With the use of two biomass power plant generation series from ENTSO-E per unit generation and 90% of the other_fossil generation from ENTSO-E per type we build biomass generation. \n",
    "index = generation_unit_info[(generation_unit_info.MapCode == 'SE') & (generation_unit_info.ProductionTypeName == 'biomass')].index\n",
    "\n",
    "entsoe_gen_type_table['SE', 'biomass'] = (generation_per_unit_2018[index].sum(axis=1) + (entsoe_gen_type_table['SE', 'other_fossil'] * 0.9)).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. With the use of one gas power plant generation series from ENTSO-E per unit generation we build gas generation.\n",
    "index = generation_unit_info[(generation_unit_info.MapCode == 'SE') & (generation_unit_info.ProductionTypeName == 'gas')].index\n",
    "\n",
    "entsoe_gen_type_table['SE', 'gas'] = generation_per_unit_2018[index].sum(axis=1).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. With the use of one hard_coal power plant generation series from ENTSO-E per unit generation we build hard_coal generation.\n",
    "index = generation_unit_info[(generation_unit_info.MapCode == 'SE') & (generation_unit_info.ProductionTypeName == 'hard_coal')].index\n",
    "\n",
    "entsoe_gen_type_table['SE', 'hard_coal'] = generation_per_unit_2018[index].sum(axis=1).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Other_renewable is missing in all hourly dataset. By using the same generation profile as other_fossil (10%) and later scaling to the value of the fact sheet.\n",
    "entsoe_gen_type_table['SE', 'other_renewable'] = (entsoe_gen_type_table['SE', 'other_fossil'] * 0.1).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. With the use of 8 other_fossil power plant generation series from ENTSO-E per unit generation and 10% of the other_fossil generation from ENTSO-E per type we build other_foosil generation.\n",
    "index = generation_unit_info[(generation_unit_info.MapCode == 'SE') & (generation_unit_info.ProductionTypeName == 'other_fossil')].index\n",
    "\n",
    "entsoe_gen_type_table['SE', 'other_fossil'] = (generation_per_unit_2018[index].sum(axis=1) + (entsoe_gen_type_table['SE', 'other_fossil'] * 0.1)).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. With the use of 10% of the other_fossil generation from ENTSO-E per type we build waste generation.\n",
    "entsoe_gen_type_table['SE', 'waste'] = (entsoe_gen_type_table['SE', 'other_fossil'] * 0.1).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable\n",
      "hydro              6.211010e+07\n",
      "nuclear            6.578728e+07\n",
      "other_fossil       1.022170e+06\n",
      "wind_onshore       1.630541e+07\n",
      "biomass            8.116121e+06\n",
      "gas                4.082863e+05\n",
      "hard_coal          3.784609e+05\n",
      "other_renewable    8.334513e+05\n",
      "waste              1.022170e+05\n",
      "dtype: float64\n",
      "total: 155063494.205\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E per type data for 2018\n",
    "print(entsoe_gen_type_table['SE'].sum())\n",
    "print('total: ' + str(entsoe_gen_type_table['SE'].sum().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "biomass             9800000.0\n",
      "gas                  600000.0\n",
      "hard_coal            500000.0\n",
      "hydro              61000000.0\n",
      "lignite                   0.0\n",
      "nuclear            65800000.0\n",
      "other_fossil        1700000.0\n",
      "other_renewable     1300000.0\n",
      "solar                     0.0\n",
      "waste                900000.0\n",
      "wind_offshore             0.0\n",
      "wind_onshore       16600000.0\n",
      "dtype: float64\n",
      "total: 158200000.0\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E fact sheet for 2018\n",
    "print(entsoe_stats['SE'].sum())\n",
    "print('total: ' + str(entsoe_stats['SE'].sum().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Italy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overview of Italy generation units in ENTSO-E generation per unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>AreaCode</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>AreaTypeCode</th>\n",
       "      <th>InstalledGenCapacity</th>\n",
       "      <th>MapCode</th>\n",
       "      <th>PowerSystemResourceName</th>\n",
       "      <th>ProductionTypeName</th>\n",
       "      <th>ProductionUnitEIC</th>\n",
       "      <th>duplicate_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenerationUnitEIC</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26WUUUFUSINA-T42</th>\n",
       "      <td>10YIT-GRTN-----B</td>\n",
       "      <td>Italy CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>280.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>UP_FUSINA_T_4</td>\n",
       "      <td>hard_coal</td>\n",
       "      <td>26WIMPI-S05FTSNK</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26WUUUUUUUNOCE1E</th>\n",
       "      <td>10YIT-GRTN-----B</td>\n",
       "      <td>Italy CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>161.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>UP_NOCE_1</td>\n",
       "      <td>hydro</td>\n",
       "      <td>26WIMPII-NOCE-1C</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26WUUUUUUGISSI19</th>\n",
       "      <td>10YIT-GRTN-----B</td>\n",
       "      <td>Italy CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>420.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>UP_GISSI_1</td>\n",
       "      <td>gas</td>\n",
       "      <td>26WIMPI-S13CDGNT</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26WUTRRVLDLIGA63</th>\n",
       "      <td>10YIT-GRTN-----B</td>\n",
       "      <td>Italy CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>390.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>UP_TRRVLDLIGA_6</td>\n",
       "      <td>gas</td>\n",
       "      <td>26WIMPI-S12TRRVU</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26WUUUS-F--DEL16</th>\n",
       "      <td>10YIT-GRTN-----B</td>\n",
       "      <td>Italy CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>145.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>UP_S.F._DEL_1</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>26WIMPI-S19SDMCS</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26WUCTNUCENORD1T</th>\n",
       "      <td>10YIT-GRTN-----B</td>\n",
       "      <td>Italy CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>410.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>UP_CTNUCENORD_1</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26WUTRMVLRZZTR3M</th>\n",
       "      <td>10YIT-GRTN-----B</td>\n",
       "      <td>Italy CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>99.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>UP_TRMVLRZZTR_3</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>26WIMPI-S15TDARG</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26WUNPWRRVENNA3F</th>\n",
       "      <td>10YIT-GRTN-----B</td>\n",
       "      <td>Italy CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>114.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>UP_NPWRRVENNA_3</td>\n",
       "      <td>gas</td>\n",
       "      <td>26WIMPI-S08ERNPQ</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26WUSTTNNRGSRL1T</th>\n",
       "      <td>10YIT-GRTN-----B</td>\n",
       "      <td>Italy CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>80.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>UP_STTNNRGSRL_1</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>26WIMPI-S20EONCO</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26WUUUUUUNOVEL1U</th>\n",
       "      <td>10YIT-GRTN-----B</td>\n",
       "      <td>Italy CA</td>\n",
       "      <td>CTA</td>\n",
       "      <td>97.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>UP_NOVEL_1</td>\n",
       "      <td>other_fossil</td>\n",
       "      <td>26WIMPI-S01NNVLN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>175 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           AreaCode  AreaName AreaTypeCode  \\\n",
       "GenerationUnitEIC                                            \n",
       "26WUUUFUSINA-T42   10YIT-GRTN-----B  Italy CA          CTA   \n",
       "26WUUUUUUUNOCE1E   10YIT-GRTN-----B  Italy CA          CTA   \n",
       "26WUUUUUUGISSI19   10YIT-GRTN-----B  Italy CA          CTA   \n",
       "26WUTRRVLDLIGA63   10YIT-GRTN-----B  Italy CA          CTA   \n",
       "26WUUUS-F--DEL16   10YIT-GRTN-----B  Italy CA          CTA   \n",
       "...                             ...       ...          ...   \n",
       "26WUCTNUCENORD1T   10YIT-GRTN-----B  Italy CA          CTA   \n",
       "26WUTRMVLRZZTR3M   10YIT-GRTN-----B  Italy CA          CTA   \n",
       "26WUNPWRRVENNA3F   10YIT-GRTN-----B  Italy CA          CTA   \n",
       "26WUSTTNNRGSRL1T   10YIT-GRTN-----B  Italy CA          CTA   \n",
       "26WUUUUUUNOVEL1U   10YIT-GRTN-----B  Italy CA          CTA   \n",
       "\n",
       "                   InstalledGenCapacity MapCode PowerSystemResourceName  \\\n",
       "GenerationUnitEIC                                                         \n",
       "26WUUUFUSINA-T42                  280.0      IT           UP_FUSINA_T_4   \n",
       "26WUUUUUUUNOCE1E                  161.0      IT               UP_NOCE_1   \n",
       "26WUUUUUUGISSI19                  420.0      IT              UP_GISSI_1   \n",
       "26WUTRRVLDLIGA63                  390.0      IT         UP_TRRVLDLIGA_6   \n",
       "26WUUUS-F--DEL16                  145.0      IT           UP_S.F._DEL_1   \n",
       "...                                 ...     ...                     ...   \n",
       "26WUCTNUCENORD1T                  410.0      IT         UP_CTNUCENORD_1   \n",
       "26WUTRMVLRZZTR3M                   99.0      IT         UP_TRMVLRZZTR_3   \n",
       "26WUNPWRRVENNA3F                  114.0      IT         UP_NPWRRVENNA_3   \n",
       "26WUSTTNNRGSRL1T                   80.0      IT         UP_STTNNRGSRL_1   \n",
       "26WUUUUUUNOVEL1U                   97.0      IT              UP_NOVEL_1   \n",
       "\n",
       "                  ProductionTypeName ProductionUnitEIC  duplicate_count  \n",
       "GenerationUnitEIC                                                        \n",
       "26WUUUFUSINA-T42           hard_coal  26WIMPI-S05FTSNK              1.0  \n",
       "26WUUUUUUUNOCE1E               hydro  26WIMPII-NOCE-1C              1.0  \n",
       "26WUUUUUUGISSI19                 gas  26WIMPI-S13CDGNT              1.0  \n",
       "26WUTRRVLDLIGA63                 gas  26WIMPI-S12TRRVU              1.0  \n",
       "26WUUUS-F--DEL16        other_fossil  26WIMPI-S19SDMCS              1.0  \n",
       "...                              ...               ...              ...  \n",
       "26WUCTNUCENORD1T                 gas               NaN              1.0  \n",
       "26WUTRMVLRZZTR3M        other_fossil  26WIMPI-S15TDARG              1.0  \n",
       "26WUNPWRRVENNA3F                 gas  26WIMPI-S08ERNPQ              1.0  \n",
       "26WUSTTNNRGSRL1T        other_fossil  26WIMPI-S20EONCO              1.0  \n",
       "26WUUUUUUNOVEL1U        other_fossil  26WIMPI-S01NNVLN              1.0  \n",
       "\n",
       "[175 rows x 9 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generation_unit_info[(generation_unit_info.MapCode == 'IT')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable\n",
      "biomass             3490127.5\n",
      "gas                74489448.5\n",
      "hard_coal          25835965.5\n",
      "hydro              47619798.0\n",
      "other_fossil       62413766.5\n",
      "other_renewable     5700344.5\n",
      "solar              17793923.0\n",
      "waste                331551.5\n",
      "wind_onshore       17314414.5\n",
      "dtype: float64\n",
      "total: 254989339.5\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E per type data for 2018\n",
    "print(entsoe_gen_type_table['IT'].sum())\n",
    "print('total: ' + str(entsoe_gen_type_table['IT'].sum().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "biomass             14900000.0\n",
      "gas                124000000.0\n",
      "hard_coal           28800000.0\n",
      "hydro               49500000.0\n",
      "lignite                    0.0\n",
      "nuclear                    0.0\n",
      "other_fossil        13000000.0\n",
      "other_renewable      7800000.0\n",
      "solar               22900000.0\n",
      "waste                2100000.0\n",
      "wind_offshore              0.0\n",
      "wind_onshore        17300000.0\n",
      "dtype: float64\n",
      "total: 280300000.0\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E fact sheet for 2018\n",
    "print(entsoe_stats['IT'].sum())\n",
    "print('total: ' + str(entsoe_stats['IT'].sum().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we compare both data sets, we can see that some per type generation and their order of magnitude does not match the type generation from the factsheet. But the total generation of both data set is similar. We assume that the generation from some technologies are incorrectly assigned in the per type generation data.\n",
    "\n",
    "To corrode this we assume:\n",
    "1. That the generation of other_fossil (62413 GWh from per type) is incorrectly assigned and most of the power plants are gas power plants instead.\n",
    "\n",
    "Correction steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 20% of the other_fossil generation from ENTSO-E per type we build the real other_fossil generation. \n",
    "entsoe_gen_type_table['IT', 'other_fossil'] = ((entsoe_gen_type_table['IT', 'other_fossil'] * 0.2)).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 80% of the other_fossil generation from ENTSO-E per type we build the real gas generation. \n",
    "entsoe_gen_type_table['IT', 'gas'] = ((entsoe_gen_type_table['IT', 'other_fossil'] * 0.8) + (entsoe_gen_type_table['IT', 'gas'] * 1)).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable\n",
      "biomass             3490127.50\n",
      "gas                84475651.14\n",
      "hard_coal          25835965.50\n",
      "hydro              47619798.00\n",
      "other_fossil       12482753.30\n",
      "other_renewable     5700344.50\n",
      "solar              17793923.00\n",
      "waste                331551.50\n",
      "wind_onshore       17314414.50\n",
      "dtype: float64\n",
      "total: 215044528.93999958\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E per type data for 2018\n",
    "print(entsoe_gen_type_table['IT'].sum())\n",
    "print('total: ' + str(entsoe_gen_type_table['IT'].sum().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "biomass             14900000.0\n",
      "gas                124000000.0\n",
      "hard_coal           28800000.0\n",
      "hydro               49500000.0\n",
      "lignite                    0.0\n",
      "nuclear                    0.0\n",
      "other_fossil        13000000.0\n",
      "other_renewable      7800000.0\n",
      "solar               22900000.0\n",
      "waste                2100000.0\n",
      "wind_offshore              0.0\n",
      "wind_onshore        17300000.0\n",
      "dtype: float64\n",
      "total: 280300000.0\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E fact sheet for 2018\n",
    "print(entsoe_stats['IT'].sum())\n",
    "print('total: ' + str(entsoe_stats['IT'].sum().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estonia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable\n",
      "biomass            562051.40\n",
      "gas                 30592.70\n",
      "hydro               27273.50\n",
      "other_fossil       500249.65\n",
      "other_renewable     53702.80\n",
      "solar                6117.30\n",
      "waste              125657.55\n",
      "wind_onshore       627136.00\n",
      "dtype: float64\n",
      "total: 1932780.8999999997\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E per type data for 2018\n",
    "print(entsoe_gen_type_table['EE'].sum())\n",
    "print('total: ' + str(entsoe_gen_type_table['EE'].sum().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "biomass             700000.0\n",
      "gas                      0.0\n",
      "hard_coal                0.0\n",
      "hydro               100000.0\n",
      "lignite                  0.0\n",
      "nuclear                  0.0\n",
      "other_fossil       8800000.0\n",
      "other_renewable          0.0\n",
      "solar                    0.0\n",
      "waste                    0.0\n",
      "wind_offshore            0.0\n",
      "wind_onshore        600000.0\n",
      "dtype: float64\n",
      "total: 10200000.0\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E fact sheet for 2018\n",
    "print(entsoe_stats['EE'].sum())\n",
    "print('total: ' + str(entsoe_stats['EE'].sum().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we compare both data sets, we can see that some per type generation and their order of magnitude does not match the type generation from the factsheet. Also the total generation of both data set are different in one order of magnitude. We assume that the generation from some technologies are incorrectly assigned in the per type generation data.\n",
    "\n",
    "To corrode this we assume:\n",
    "1. That other_fossil generation (8800 GWh from fact sheet) is missing in the generation per type data\n",
    "\n",
    "Correction steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. With the use of 13 other_fossil power plant generation series from ENTSO-E per unit generation we build other_fossil generation.\n",
    "index = generation_unit_info[(generation_unit_info.MapCode == 'EE') & (generation_unit_info.ProductionTypeName == 'other_fossil')].index\n",
    "\n",
    "entsoe_gen_type_table['EE', 'other_fossil'] = (generation_per_unit_2018[index].sum(axis=1)).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable\n",
      "biomass             562051.40\n",
      "gas                  30592.70\n",
      "hydro                27273.50\n",
      "other_fossil       9592431.50\n",
      "other_renewable      53702.80\n",
      "solar                 6117.30\n",
      "waste               125657.55\n",
      "wind_onshore        627136.00\n",
      "dtype: float64\n",
      "total: 11024962.750000067\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E per type data for 2018\n",
    "print(entsoe_gen_type_table['EE'].sum())\n",
    "print('total: ' + str(entsoe_gen_type_table['EE'].sum().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "biomass             700000.0\n",
      "gas                      0.0\n",
      "hard_coal                0.0\n",
      "hydro               100000.0\n",
      "lignite                  0.0\n",
      "nuclear                  0.0\n",
      "other_fossil       8800000.0\n",
      "other_renewable          0.0\n",
      "solar                    0.0\n",
      "waste                    0.0\n",
      "wind_offshore            0.0\n",
      "wind_onshore        600000.0\n",
      "dtype: float64\n",
      "total: 10200000.0\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E fact sheet for 2018\n",
    "print(entsoe_stats['EE'].sum())\n",
    "print('total: ' + str(entsoe_stats['EE'].sum().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Netherlands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable\n",
      "biomass            239753.0\n",
      "gas              36625468.0\n",
      "hard_coal        10887558.0\n",
      "nuclear           3571693.0\n",
      "other_fossil      7770123.0\n",
      "solar             2130133.0\n",
      "waste              329325.0\n",
      "wind_offshore     3574250.0\n",
      "wind_onshore      5644886.0\n",
      "dtype: float64\n",
      "total: 70773189.0\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E per type data for 2018\n",
    "print(entsoe_gen_type_table['NL'].sum())\n",
    "print('total: ' + str(entsoe_gen_type_table['NL'].sum().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "biomass             3500000.0\n",
      "gas                71400000.0\n",
      "hard_coal          16700000.0\n",
      "hydro                100000.0\n",
      "lignite                   0.0\n",
      "nuclear             2800000.0\n",
      "other_fossil              0.0\n",
      "other_renewable           0.0\n",
      "solar               3100000.0\n",
      "waste                     0.0\n",
      "wind_offshore       3500000.0\n",
      "wind_onshore        7400000.0\n",
      "dtype: float64\n",
      "total: 108500000.0\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E fact sheet for 2018\n",
    "print(entsoe_stats['NL'].sum())\n",
    "print('total: ' + str(entsoe_stats['NL'].sum().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we compare both data sets, we can see that some per type generation and their order of magnitude does not match the type generation from the factsheet. Also the total generation of both data set are different in one order of magnitude. We assume that the generation from some technologies are incorrectly assigned in the per type generation data.\n",
    "\n",
    "To corrode this we assume:\n",
    "1. That other_fossil generation (8800 GWh from fact sheet) is missing in the generation per type data\n",
    "\n",
    "Correction steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. With the use of some hard_coal power plant generation series from ENTSO-E per unit generation we build hard_coal generation.\n",
    "index = generation_unit_info[(generation_unit_info.MapCode == 'NL') & (generation_unit_info.ProductionTypeName == 'hard_coal')].index\n",
    "\n",
    "entsoe_gen_type_table['NL', 'hard_coal'] = (generation_per_unit_2018[index].sum(axis=1)).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. With the use of some gas power plant generation series from ENTSO-E per unit generation we build gas generation.\n",
    "index = generation_unit_info[(generation_unit_info.MapCode == 'NL') & (generation_unit_info.ProductionTypeName == 'gas')].index\n",
    "\n",
    "entsoe_gen_type_table['NL', 'gas'] = (generation_per_unit_2018[index].sum(axis=1)).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable\n",
      "biomass            239753.00\n",
      "gas              38298322.26\n",
      "hard_coal        27130254.52\n",
      "nuclear           3571693.00\n",
      "other_fossil      7770123.00\n",
      "solar             2130133.00\n",
      "waste              329325.00\n",
      "wind_offshore     3574250.00\n",
      "wind_onshore      5644886.00\n",
      "dtype: float64\n",
      "total: 88688739.77999997\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E per type data for 2018\n",
    "print(entsoe_gen_type_table['NL'].sum())\n",
    "print('total: ' + str(entsoe_gen_type_table['NL'].sum().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "biomass             3500000.0\n",
      "gas                71400000.0\n",
      "hard_coal          16700000.0\n",
      "hydro                100000.0\n",
      "lignite                   0.0\n",
      "nuclear             2800000.0\n",
      "other_fossil              0.0\n",
      "other_renewable           0.0\n",
      "solar               3100000.0\n",
      "waste                     0.0\n",
      "wind_offshore       3500000.0\n",
      "wind_onshore        7400000.0\n",
      "dtype: float64\n",
      "total: 108500000.0\n"
     ]
    }
   ],
   "source": [
    "# sum of production per type in ENTSO-E fact sheet for 2018\n",
    "print(entsoe_stats['NL'].sum())\n",
    "print('total: ' + str(entsoe_stats['NL'].sum().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling entsoe generation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# zwei Möglichkeiten zu skalieren\n",
    "#1 skaliere nur, wenn der Wert in Quelle größer 0 ist und größer als im zu skalierenden Datensatz ist. Sonst überspringe und skaliere nicht.\n",
    "#2 skaliere alles nach FACT Sheet\n",
    "scaling_option = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace all nan with 0\n",
    "entsoe_gen_type_table.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = entsoe_gen_type_table.columns.levels[0].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AT biomass stats = 0\n",
      "AT other_renewable entso-e = 0; scaling not possible\n",
      "AT solar stats = 0\n",
      "AT waste stats = 0\n",
      "AT wind_onshore entso-e is >\n",
      "BE gas entso-e is >\n",
      "BE nuclear entso-e is >\n",
      "BE other_fossil entso-e is >\n",
      "BE solar entso-e is >\n",
      "BE waste entso-e is >\n",
      "BG gas entso-e is >\n",
      "BG hard_coal entso-e is >\n",
      "BG lignite entso-e is >\n",
      "BG nuclear entso-e is >\n",
      "BG waste stats = 0\n",
      "CH nuclear entso-e is >\n",
      "CY wind_onshore entso-e is >\n",
      "CZ hard_coal entso-e is >\n",
      "CZ hydro entso-e is >\n",
      "CZ other_fossil entso-e is >\n",
      "CZ other_renewable stats = 0\n",
      "CZ solar entso-e is >\n",
      "CZ waste stats = 0\n",
      "CZ wind_onshore entso-e is >\n",
      "DE biomass entso-e is >\n",
      "DE hydro entso-e is >\n",
      "DE other_fossil entso-e is >\n",
      "DE solar entso-e is >\n",
      "DE wind_offshore entso-e is >\n",
      "DE wind_onshore entso-e is >\n",
      "DK biomass entso-e is >\n",
      "DK gas entso-e is >\n",
      "DK other_fossil entso-e is >\n",
      "DK waste stats = 0\n",
      "DK wind_onshore entso-e is >\n",
      "EE gas stats = 0\n",
      "EE other_fossil entso-e is >\n",
      "EE other_renewable stats = 0\n",
      "EE solar stats = 0\n",
      "EE waste stats = 0\n",
      "EE wind_onshore entso-e is >\n",
      "ES hard_coal entso-e is >\n",
      "ES hydro entso-e is >\n",
      "ES nuclear entso-e is >\n",
      "ES waste entso-e is >\n",
      "FI gas entso-e is >\n",
      "FI hard_coal entso-e is >\n",
      "FI other_fossil entso-e is >\n",
      "FI other_renewable stats = 0\n",
      "FI waste stats = 0\n",
      "FR hard_coal entso-e is >\n",
      "GB other_fossil stats = 0\n",
      "GB wind_onshore entso-e is >\n",
      "GR gas entso-e is >\n",
      "GR lignite entso-e is >\n",
      "HU gas entso-e is >\n",
      "HU lignite entso-e is >\n",
      "HU nuclear entso-e is >\n",
      "HU other_fossil entso-e is >\n",
      "HU waste stats = 0\n",
      "IE hard_coal entso-e is >\n",
      "IE hydro entso-e is >\n",
      "IE other_fossil entso-e is >\n",
      "IE wind_onshore entso-e is >\n",
      "IT wind_onshore entso-e is >\n",
      "LT biomass entso-e is >\n",
      "LT hydro entso-e is >\n",
      "LT waste entso-e is >\n",
      "LT wind_onshore entso-e is >\n",
      "LV gas entso-e is >\n",
      "LV other_fossil entso-e is >\n",
      "LV wind_onshore entso-e is >\n",
      "ME lignite entso-e is >\n",
      "NL hard_coal entso-e is >\n",
      "NL nuclear entso-e is >\n",
      "NL other_fossil stats = 0\n",
      "NL waste stats = 0\n",
      "NL wind_offshore entso-e is >\n",
      "NO hydro entso-e is >\n",
      "NO other_fossil stats = 0\n",
      "PL hard_coal entso-e is >\n",
      "PT gas entso-e is >\n",
      "PT hard_coal entso-e is >\n",
      "PT hydro entso-e is >\n",
      "PT other_fossil entso-e is >\n",
      "PT solar entso-e is >\n",
      "RO biomass entso-e is >\n",
      "RO gas entso-e is >\n",
      "RO hard_coal entso-e is >\n",
      "RO hydro entso-e is >\n",
      "RO lignite entso-e is >\n",
      "RO nuclear entso-e is >\n",
      "RS biomass stats = 0\n",
      "RS hydro entso-e is >\n",
      "RS other_fossil stats = 0\n",
      "SE hydro entso-e is >\n",
      "SI gas stats = 0\n",
      "SI lignite entso-e is >\n",
      "SI solar entso-e is >\n",
      "SI waste stats = 0\n",
      "SI wind_onshore stats = 0\n",
      "SK lignite entso-e is >\n",
      "SK nuclear entso-e is >\n",
      "SK other_fossil entso-e is >\n",
      "SK other_renewable stats = 0\n"
     ]
    }
   ],
   "source": [
    "def normed(x): return x.divide(x.sum())\n",
    "\n",
    "if scaling_option == 1:  \n",
    "\n",
    "    entso_scaled = entsoe_gen_type_table.copy()\n",
    "\n",
    "    for country in countries:\n",
    "        for i in entso_scaled[country]:\n",
    "            try:\n",
    "                if entsoe_stats[country].at['2018',i] == 0:\n",
    "                    pass\n",
    "                    print(country + ' ' + i + ' stats = 0')\n",
    "                elif entso_scaled[(country,i)].sum() == 0:\n",
    "                    pass\n",
    "                    print(country + ' ' + i + ' entso-e = 0; scaling not possible')\n",
    "                elif entsoe_stats[country].at['2018',i] < entso_scaled[(country,i)].sum(): \n",
    "                    pass\n",
    "                    print(country + ' ' + i + ' entso-e is >')\n",
    "                else:\n",
    "                    entso_scaled[(country,i)] = normed(entso_scaled[(country,i)]) * entsoe_stats[country].at['2018',i]\n",
    "            except KeyError:\n",
    "                pass\n",
    "                print(country + ' ' + i + ' KeyError')\n",
    "\n",
    "elif scaling_option == 2:\n",
    "\n",
    "    entso_scaled = entsoe_gen_type_table.copy()\n",
    "    \n",
    "    for country in countries:\n",
    "        for i in entso_scaled[country]:\n",
    "            if entsoe_stats[country].at['2018',i] == 0:\n",
    "                pass\n",
    "                print(country + ' ' +i)\n",
    "            else:\n",
    "                try:\n",
    "                    entso_scaled[(country,i)] = normed(entso_scaled[(country,i)]) * entsoe_stats[country].at['2018',i]\n",
    "                except KeyError:\n",
    "                    pass\n",
    "                    print(country + ' ' +i)\n",
    "\n",
    "else:                    \n",
    "    pass\n",
    "    print('Select one of the available scaling options')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "country = 'DE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "variable\n",
       "biomass             40184808.0\n",
       "gas                 42959069.0\n",
       "hard_coal           71546375.0\n",
       "hydro               25316891.0\n",
       "lignite            128361330.0\n",
       "nuclear             71844721.0\n",
       "other_fossil         6665863.0\n",
       "other_renewable      1394941.0\n",
       "solar               41231973.0\n",
       "waste                3372002.0\n",
       "wind_offshore       19075448.0\n",
       "wind_onshore        89488871.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entsoe_gen_type_table[country].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "541442292.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entsoe_gen_type_table[country].sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "variable\n",
       "biomass            4.018481e+07\n",
       "gas                8.730000e+07\n",
       "hard_coal          7.290000e+07\n",
       "hydro              2.531689e+07\n",
       "lignite            1.348000e+08\n",
       "nuclear            7.190000e+07\n",
       "other_fossil       6.665863e+06\n",
       "other_renewable    6.100000e+06\n",
       "solar              4.123197e+07\n",
       "waste              5.200000e+06\n",
       "wind_offshore      1.907545e+07\n",
       "wind_onshore       8.948887e+07\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entso_scaled[country].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "600163853.9999993"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entso_scaled[country].sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "source\n",
       "biomass             40100000.0\n",
       "gas                 87300000.0\n",
       "hard_coal           72900000.0\n",
       "hydro               25100000.0\n",
       "lignite            134800000.0\n",
       "nuclear             71900000.0\n",
       "other_fossil         5800000.0\n",
       "other_renewable      6100000.0\n",
       "solar               41200000.0\n",
       "waste                5200000.0\n",
       "wind_offshore       19000000.0\n",
       "wind_onshore        88200000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entsoe_stats[country].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "597600000.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entsoe_stats[country].sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2563853.9999992847"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entsoe_stats[country].sum().sum() - entso_scaled[country].sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "GE_compar = pd.DataFrame(index=entsoe_gen_type_table.sum().reset_index().groupby('region').sum().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GE_compar.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "GE_compar['ENTSO_E_Generation'] = entsoe_gen_type_table.sum().reset_index().groupby('region').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "GE_compar['ENTSO_E_Generation_scaled'] = entso_scaled.sum().reset_index().groupby('region').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "GE_compar['diff'] = GE_compar['ENTSO_E_Generation_scaled'] - GE_compar['ENTSO_E_Generation']\n",
    "GE_compar['diff_pro'] = (GE_compar['ENTSO_E_Generation_scaled'] - GE_compar['ENTSO_E_Generation']) / GE_compar['ENTSO_E_Generation']\n",
    "\n",
    "GE_compar['diff_pro_gen_all'] = (GE_compar['ENTSO_E_Generation_scaled'] - GE_compar['ENTSO_E_Generation']) / (GE_compar['ENTSO_E_Generation_scaled'].sum())\n",
    "\n",
    "#CI_compar['diff_bottom_up_vs_CI_1_top_down'] = CI_compar['CI_bottom_up'] - CI_compar['CI_1_top_down']\n",
    "#CI_compar['diff_pro_1'] = round((100 / CI_compar['CI_bottom_up']) * CI_compar['diff_bottom_up_vs_CI_1_top_down'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "ax = round(GE_compar, 4).sort_values(by='diff_pro')['diff_pro'].multiply(100).plot(kind='bar',\n",
    "                                                                     ylabel='Generation scaling (%)',\n",
    "                                                                     linewidth=0.5,\n",
    "                                                                     edgecolor='black',\n",
    "                                                                     alpha=0.7,\n",
    "                                                                     width=0.35,)\n",
    "ax.set_xlabel(\"Country\" , fontsize=24)\n",
    "ax.set_ylabel(\"Generation scaling\\nof country generation (%)\", fontsize=24)\n",
    "\n",
    "ax.tick_params(axis='y',labelsize=22)\n",
    "ax.tick_params(axis='x',labelsize=22)\n",
    "\n",
    "\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(os.path.join(output_directory_path + '/_generation_scaling.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "ax = round(GE_compar, 4).sort_values(by='diff_pro')['diff_pro'].multiply(100).plot(kind='bar',\n",
    "                                                                     ylabel='Generation scaling (%)',\n",
    "                                                                     linewidth=0.5,\n",
    "                                                                     edgecolor='black',\n",
    "                                                                     alpha=0.7,\n",
    "                                                                     width=0.35,)\n",
    "\n",
    "ax.set_xlabel(\"Country\" , fontsize=24)\n",
    "ax.set_ylabel(\"Generation scaling\\nof country generation (%)\", fontsize=24)\n",
    "\n",
    "ax.tick_params(axis='y',labelsize=22)\n",
    "ax.tick_params(axis='x',labelsize=22)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "# zoom-in / limit the view to different portions of the data\n",
    "ax.set_ylim(0, 60)  # most of the data\n",
    "\n",
    "fig.tight_layout()\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(os.path.join(output_directory_path + '/_generation_scaling_zoom.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "ax = round(GE_compar, 4).sort_values(by='diff_pro')['diff_pro_gen_all'].multiply(100).plot(kind='bar',\n",
    "                                                                     ylabel='Generation scaling (%)',\n",
    "                                                                     linewidth=0.5,\n",
    "                                                                     edgecolor='black',\n",
    "                                                                     alpha=0.7,\n",
    "                                                                     width=0.35)\n",
    "\n",
    "ax.set_xlabel(\"Country\" , fontsize=24)\n",
    "ax.set_ylabel(\"Generation scaling of overall\\nEuropean generation (%)\", fontsize=24)\n",
    "\n",
    "ax.tick_params(axis='y',labelsize=22)\n",
    "ax.tick_params(axis='x',labelsize=22)\n",
    "\n",
    "\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "fig.tight_layout()\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(os.path.join(output_directory_path + '/_generation_scaling_realtion_all_gen.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check data coverage in different data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_n_compare(compare, unit='Generation (TWh)'):\n",
    "    \n",
    "    # make it eays to plot\n",
    "    compare = (compare.unstack('Country').swaplevel(axis=1)\n",
    "               .sort_index(axis=1).reindex(columns=None, level=1))\n",
    "    \n",
    "    sns.set_theme()\n",
    "        \n",
    "    fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "    \n",
    "    colors = {'gas': '#b20101',\n",
    "              'biomass': '#0c6013',\n",
    "              'hard_coal': '#707070',\n",
    "              'geothermal': '#ba91b1',\n",
    "              'lignite': '#9e5a01',\n",
    "              'nuclear': '#ff9000',\n",
    "              'oil': '#262626',\n",
    "              'other_fossil': '#d35050',\n",
    "              'other': '#d35050',\n",
    "              'waste': '#68896b',\n",
    "              'wind_onshore': '#235ebc',\n",
    "              'wind_offshore': '#6895dd',\n",
    "              'solar': '#f9d002',\n",
    "              'PHS': '#08ad97',\n",
    "              'hydro': '#08ad97',\n",
    "              'ror': '#4adbc8',\n",
    "              'load': '#ea048a',\n",
    "              'other_renewable':'#08ad97'}\n",
    "    \n",
    "    compare.T.loc['ENTSO-E generation per unit'].plot(ax=ax,\n",
    "                                kind='bar',\n",
    "                                stacked=True,\n",
    "                                legend=None,\n",
    "                                position=1.5,\n",
    "                                linewidth=0.5,\n",
    "                                edgecolor='black',\n",
    "                                alpha=0.7,\n",
    "                                width=0.3,\n",
    "                                color=[colors[col] for col in compare.T.loc['ENTSO-E generation per unit'].columns])    \n",
    "\n",
    "    \n",
    "    l1 = ax.legend(title=\"Technologies\", loc='upper left', bbox_to_anchor=(0,1), frameon=True, framealpha=0.6, title_fontsize= 18, fontsize=16)\n",
    "    \n",
    "    plt.setp(l1.get_title())\n",
    "    ax.add_artist(l1)\n",
    "    \n",
    "    compare.T.loc['ENTSO-E generation per type'].plot(ax=ax,\n",
    "                                  hatch='/',\n",
    "                                  kind='bar',\n",
    "                                  stacked=True,\n",
    "                                  position=0.5,\n",
    "                                  linewidth=0.5,\n",
    "                                  edgecolor='black',\n",
    "                                  alpha=0.7,\n",
    "                                  width=0.3,\n",
    "                                  color=[colors[col] for col in compare.T.loc['ENTSO-E generation per type'].columns],\n",
    "                                  legend=None)\n",
    "\n",
    "    compare.T.loc['ENTSO-E generation statistical factsheet'].plot(ax=ax,\n",
    "                                  hatch='//',\n",
    "                                  kind='bar',\n",
    "                                  stacked=True,\n",
    "                                  position=-0.5,\n",
    "                                  linewidth=0.5,\n",
    "                                  edgecolor='black',\n",
    "                                  alpha=0.7,\n",
    "                                  width=0.30,\n",
    "                                  color=[colors[col] for col in compare.T.loc['ENTSO-E generation statistical factsheet'].columns],\n",
    "                                  legend=None)\n",
    "    \n",
    "    ax.set_xticklabels(compare.columns.levels[1].values, rotation=45, fontsize=16)\n",
    "    ax.tick_params(axis='y', labelsize=16)\n",
    "\n",
    "    \n",
    "    \n",
    "    import matplotlib.patches as mpatches\n",
    "    \n",
    "    entsoe_unit_gen = mpatches.Patch(facecolor='none', label=' ENTSO-E generation per unit',\n",
    "                               hatch='', alpha=0.7, edgecolor='black')\n",
    "    entsoe_type_gen = mpatches.Patch(facecolor='none', label=' ENTSO-E generation per type',\n",
    "                                hatch='/', alpha=0.7, edgecolor='black')\n",
    "    entsoe_stats_gen = mpatches.Patch(facecolor='none', label=' ENTSO-E generation statistical factsheet',\n",
    "                                hatch='/', alpha=0.7, edgecolor='black')\n",
    "    \n",
    "    \n",
    "    l2 = ax.legend(handles=[entsoe_unit_gen, entsoe_type_gen, entsoe_stats_gen],\n",
    "                    handletextpad=0., columnspacing=0.5, ncol=1,\n",
    "                    title='Datasets', title_fontsize= 18, fontsize=16, frameon=True, framealpha=0.6, loc=\"upper right\")\n",
    "    \n",
    "    plt.setp(l2.get_title(), fontsize=18)\n",
    "    ax.add_artist(l2)\n",
    "    \n",
    "    ax.set_xlabel('Country', fontsize=18)\n",
    "    ax.set_ylabel(unit, fontsize=18)\n",
    "    \n",
    "    ax.set_xlim(-0.5, len(compare.columns.levels[1].values)-0.5)\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "generation_unit_info['generation'] = generation_per_unit_2018.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
      "  alpha: float or None\n",
      "  animated: bool\n",
      "  backgroundcolor: color\n",
      "  bbox: dict with properties for `.patches.FancyBboxPatch`\n",
      "  clip_box: `.Bbox`\n",
      "  clip_on: bool\n",
      "  clip_path: Patch or (Path, Transform) or None\n",
      "  color or c: color\n",
      "  contains: unknown\n",
      "  figure: `.Figure`\n",
      "  fontfamily or family: {FONTNAME, 'serif', 'sans-serif', 'cursive', 'fantasy', 'monospace'}\n",
      "  fontproperties or font or font_properties: `.font_manager.FontProperties` or `str` or `pathlib.Path`\n",
      "  fontsize or size: float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}\n",
      "  fontstretch or stretch: {a numeric value in range 0-1000, 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded', 'ultra-expanded'}\n",
      "  fontstyle or style: {'normal', 'italic', 'oblique'}\n",
      "  fontvariant or variant: {'normal', 'small-caps'}\n",
      "  fontweight or weight: {a numeric value in range 0-1000, 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black'}\n",
      "  gid: str\n",
      "  horizontalalignment or ha: {'center', 'right', 'left'}\n",
      "  in_layout: bool\n",
      "  label: object\n",
      "  linespacing: float (multiple of font size)\n",
      "  multialignment or ma: {'left', 'right', 'center'}\n",
      "  path_effects: `.AbstractPathEffect`\n",
      "  picker: None or bool or callable\n",
      "  position: (float, float)\n",
      "  rasterized: bool or None\n",
      "  rotation: float or {'vertical', 'horizontal'}\n",
      "  rotation_mode: {None, 'default', 'anchor'}\n",
      "  sketch_params: (scale: float, length: float, randomness: float)\n",
      "  snap: bool or None\n",
      "  text: object\n",
      "  transform: `.Transform`\n",
      "  url: str\n",
      "  usetex: bool or None\n",
      "  verticalalignment or va: {'center', 'top', 'bottom', 'baseline', 'center_baseline'}\n",
      "  visible: bool\n",
      "  wrap: bool\n",
      "  x: float\n",
      "  y: float\n",
      "  zorder: float\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filter to countries in the model\n",
    "#filter_countries = generators.index.levels[0].to_list()\n",
    "\n",
    "entsoe_unit_gen = generation_unit_info.groupby(['MapCode','ProductionTypeName']).sum()['generation']\n",
    "\n",
    "entsoe_type_gen = entsoe_gen_type_table.sum()\n",
    "\n",
    "entsoe_stats_gen = entsoe_stats.T\n",
    "\n",
    "#entsoe_stats_cap.index = entsoe_stats_cap.index.remove_unused_levels()\n",
    "\n",
    "# merge data \n",
    "compare = pd.concat([entsoe_unit_gen, entsoe_type_gen, entsoe_stats_gen], keys=['ENTSO-E generation per unit','ENTSO-E generation per type','ENTSO-E generation statistical factsheet'], axis=1)\n",
    "# convert to GW\n",
    "compare = compare / 1000000\n",
    "compare.index.set_names(['Country','Technology'], level=[0,1], inplace=True)\n",
    "\n",
    "# # filter all data with nan and 0\n",
    "compare = compare.fillna(0)\n",
    "compare[compare.sum(1) < 0.5] = np.nan\n",
    "\n",
    "# # plot and save figure\n",
    "fig = plot_n_compare(compare = compare, unit='Generation (TWh)')\n",
    "fig.savefig(os.path.join(output_directory_path + '/_generation_compare_plot.png'), dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_n_compare_scaled(compare, unit='Generation (TWh)'):\n",
    "    \n",
    "    # make it eays to plot\n",
    "    compare = (compare.unstack('Country').swaplevel(axis=1)\n",
    "               .sort_index(axis=1).reindex(columns=None, level=1))\n",
    "    \n",
    "    sns.set_theme()\n",
    "        \n",
    "    fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "    \n",
    "    colors = {'gas': '#b20101',\n",
    "              'biomass': '#0c6013',\n",
    "              'hard_coal': '#707070',\n",
    "              'geothermal': '#ba91b1',\n",
    "              'lignite': '#9e5a01',\n",
    "              'nuclear': '#ff9000',\n",
    "              'oil': '#262626',\n",
    "              'other_fossil': '#d35050',\n",
    "              'other': '#d35050',\n",
    "              'waste': '#68896b',\n",
    "              'wind_onshore': '#235ebc',\n",
    "              'wind_offshore': '#6895dd',\n",
    "              'solar': '#f9d002',\n",
    "              'PHS': '#08ad97',\n",
    "              'hydro': '#08ad97',\n",
    "              'ror': '#4adbc8',\n",
    "              'load': '#ea048a',\n",
    "              'other_renewable':'#08ad97'}\n",
    "    \n",
    "    compare.T.loc['ENTSO-E scaled'].plot(ax=ax,\n",
    "                                kind='bar',\n",
    "                                stacked=True,\n",
    "                                legend=None,\n",
    "                                position=1.5,\n",
    "                                linewidth=0.5,\n",
    "                                edgecolor='black',\n",
    "                                alpha=0.7,\n",
    "                                width=0.3,\n",
    "                                color=[colors[col] for col in compare.T.loc['ENTSO-E scaled'].columns])    \n",
    "\n",
    "    \n",
    "    l1 = ax.legend(title=\"Technologies\", loc='upper left', bbox_to_anchor=(0,1), frameon=True, framealpha=0.6, title_fontsize= 18, fontsize=16)\n",
    "    \n",
    "    plt.setp(l1.get_title())\n",
    "    ax.add_artist(l1)\n",
    "    \n",
    "    compare.T.loc['ENTSO-E generation per type'].plot(ax=ax,\n",
    "                                  hatch='/',\n",
    "                                  kind='bar',\n",
    "                                  stacked=True,\n",
    "                                  position=0.5,\n",
    "                                  linewidth=0.5,\n",
    "                                  edgecolor='black',\n",
    "                                  alpha=0.7,\n",
    "                                  width=0.3,\n",
    "                                  color=[colors[col] for col in compare.T.loc['ENTSO-E generation per type'].columns],\n",
    "                                  legend=None)\n",
    "\n",
    "    compare.T.loc['ENTSO-E generation statistical factsheet'].plot(ax=ax,\n",
    "                                  hatch='//',\n",
    "                                  kind='bar',\n",
    "                                  stacked=True,\n",
    "                                  position=-0.5,\n",
    "                                  linewidth=0.5,\n",
    "                                  edgecolor='black',\n",
    "                                  alpha=0.7,\n",
    "                                  width=0.30,\n",
    "                                  color=[colors[col] for col in compare.T.loc['ENTSO-E generation statistical factsheet'].columns],\n",
    "                                  legend=None)\n",
    "    \n",
    "    ax.set_xticklabels(compare.columns.levels[1].values, rotation=45, fontsize=16)\n",
    "    ax.tick_params(axis='y', labelsize=16)\n",
    "    \n",
    "    \n",
    "    import matplotlib.patches as mpatches\n",
    "    \n",
    "    entsoe_unit_gen = mpatches.Patch(facecolor='none', label=' ENTSO-E scaled',\n",
    "                               hatch='', alpha=0.7, edgecolor='black')\n",
    "    entsoe_type_gen = mpatches.Patch(facecolor='none', label=' ENTSO-E generation per type',\n",
    "                                hatch='/', alpha=0.7, edgecolor='black')\n",
    "    entsoe_stats_gen = mpatches.Patch(facecolor='none', label=' ENTSO-E generation statistical factsheet',\n",
    "                                hatch='/', alpha=0.7, edgecolor='black')\n",
    "    \n",
    "    \n",
    "    l2 = ax.legend(handles=[entsoe_unit_gen, entsoe_type_gen, entsoe_stats_gen],\n",
    "                    handletextpad=0., columnspacing=0.5, ncol=1,\n",
    "                    title='Datasets', title_fontsize= 18, fontsize=16, frameon=True, framealpha=0.6, loc=\"upper right\")\n",
    "    \n",
    "    plt.setp(l2.get_title(), fontsize=18)\n",
    "    ax.add_artist(l2)\n",
    "    \n",
    "    ax.set_xlabel('Country', fontsize=18)\n",
    "    ax.set_ylabel(unit, fontsize=18)\n",
    "    \n",
    "    ax.set_xlim(-0.5, len(compare.columns.levels[1].values)-0.5)\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
      "  alpha: float or None\n",
      "  animated: bool\n",
      "  backgroundcolor: color\n",
      "  bbox: dict with properties for `.patches.FancyBboxPatch`\n",
      "  clip_box: `.Bbox`\n",
      "  clip_on: bool\n",
      "  clip_path: Patch or (Path, Transform) or None\n",
      "  color or c: color\n",
      "  contains: unknown\n",
      "  figure: `.Figure`\n",
      "  fontfamily or family: {FONTNAME, 'serif', 'sans-serif', 'cursive', 'fantasy', 'monospace'}\n",
      "  fontproperties or font or font_properties: `.font_manager.FontProperties` or `str` or `pathlib.Path`\n",
      "  fontsize or size: float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}\n",
      "  fontstretch or stretch: {a numeric value in range 0-1000, 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded', 'ultra-expanded'}\n",
      "  fontstyle or style: {'normal', 'italic', 'oblique'}\n",
      "  fontvariant or variant: {'normal', 'small-caps'}\n",
      "  fontweight or weight: {a numeric value in range 0-1000, 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black'}\n",
      "  gid: str\n",
      "  horizontalalignment or ha: {'center', 'right', 'left'}\n",
      "  in_layout: bool\n",
      "  label: object\n",
      "  linespacing: float (multiple of font size)\n",
      "  multialignment or ma: {'left', 'right', 'center'}\n",
      "  path_effects: `.AbstractPathEffect`\n",
      "  picker: None or bool or callable\n",
      "  position: (float, float)\n",
      "  rasterized: bool or None\n",
      "  rotation: float or {'vertical', 'horizontal'}\n",
      "  rotation_mode: {None, 'default', 'anchor'}\n",
      "  sketch_params: (scale: float, length: float, randomness: float)\n",
      "  snap: bool or None\n",
      "  text: object\n",
      "  transform: `.Transform`\n",
      "  url: str\n",
      "  usetex: bool or None\n",
      "  verticalalignment or va: {'center', 'top', 'bottom', 'baseline', 'center_baseline'}\n",
      "  visible: bool\n",
      "  wrap: bool\n",
      "  x: float\n",
      "  y: float\n",
      "  zorder: float\n"
     ]
    },
    {
     "data": {
      "image/png": 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jtbFkyUL09Azp1q1nmvbN2jq2by9fvqBmTfWyuORJ7tzxE08pvS8uqfH69UsqV66qVi+hccYxNTVFV1eXqKjIeM/Mw8ODJ08eoaenR1RUFM+ePcXAwABHx+9VU+FOnz7J1KnjOXRoP4MHD0/qEQghhBBCiBzi4MF9QOx+bufP/8nNmzdo2rQF48ZNVtUJDw8nJCQ4yXaeP3fj2LEjVKpUhYULl6mV+fv7qb4OCQnh9u2b2Njkp337zrRv35moqCiWLVvM7t3b2b9/Lz/91BctLS0iIyP54YdGam29ePGcf/65pbas+nP//HMLhUJBvXoNqFevAfBxo/tdu7ZLskKkm0zds2LBggXUqFGDOXPmUK1aNXr37k2PHj24evUqgYGBrFu3jkGDBtG9e3fq1avHunXrCAoKYu/evQC8evWKAwcOMHXqVFq3bk2jRo1YvXo1jx8/5vTp05k5tCylePESWFhYsmfPLoKDP/5gDAoKZMqU8cya5YRSqcTSMhdFinzHqVPHCQoKVNV7+/Y/du7cjo+PV0LNf2Xf7LGwsGT//r1qMYOCAtm/fw8WFpYUK2af6vuKFy9B7txW/P77QcLDw1X17t27y5MnjxLtl6amJlWr1uDSpQs8ffpErczV1YUJE0bh6+tLdHQ0Q4b0Y8mSBWp1SpaMPQ5WqZRtYYQQQgghvgX//feanTu3YWRkTPv2HVWzHwoVKqxWb9Om9URERKhmPwOqD1ljYmJn93p7x95ra2undu/Ro4fx9vYiOjq2npeXJ6NHD2XlyqVqbZUuXeb/XyvQ1tamRAkHHj68z61bf6u15+w8k3nzZuHp+UGtH3HtQ+xpI05Ok9SWe5ctWwGlUpmiGdlCpFamzazw9vbm5s2bLFumnikcNSr2RIWLFy8SHBxMvXofM3UmJiZUrlyZ8+fP06tXL65ciT2PuHbt2qo6dnZ2FC1alPPnz9OgQYP0H0g2oKmpyfDho5kyZTw//dSVZs1aoK2tw+HD+/HwcGfKlBmqvR2GDBnBiBGD6N27O82atURDQ8G+fbswMjJS22AzPfr288/dadasxf+PLv0NLy9PZsyYm+APwYTuAzh8+GC8+wYPHs6UKePp3/8nGjVqgq+vD3v27ERLSzvJZSq//DKYmzdvMGhQH1q3bo+VlTWXLl3g0qXztGjRWvUfT9u2Hdm0aR3jx4+iSpVqhIWFcujQfnR1dWnSpEWaPzMhhBBCCJG5jh8/gqlp7B5uYWGhvHz5nEuXLhAREYGT02z09Q2pUqU6Ojo6rF+/Gnf3txgZGXPr1k1u3bqBpqamau8K+LjP29q1q6he3fH/G96bcPjwfsLDw7CysubBg/tcuXIRTU1NwsNjN9i0sclPxYqVOXfuLCNHDsbBoTS+vr6cOPEHBgaGtGgRu+xj2LBRDBjQm1GjhlC37g9YW+fhypVLPHx4H0fHWpQqFZvcsLTMBcCZMyeBGDp16k6rVm1Zs2YlP/3UlZo1vycmJoazZ08RGRlJ27YdM+qRi29QpiUrHj9+TExMDPr6+vTv35+LFy9iaGhI586dGThwIC9evAAgf371zR5tbGw4c+YMAM+fP8fS0jLe8ZM2Njaq+0Ws2rXr4eLiyubN69m4cR0KhQaFChVmzhwXatT4uI6ifPmKLFmyknXrVrFhwxp0dHQoU6Ycw4ePTLfNIuP6tnHjWjZsWIOmphYlSpRk/PjJlClT7gvu06RECYd499WpUx8nJ9i0aR0rVizB0jI3gwcP59ixI/j6+iTafr58NqxevZG1a1dy+PB+goNDyJs3H4MHD1f7wfzzz/0wNjbmyJFDLFt2FaVSSalSZZg8eYbsVyGEEEIIkQNt375F9bW2tjYmJqaUKVOOn37qh719CSB2KbaT0yxWrFjKwYOxS9Rz5crNsGGjuHfvDqdPn8Td/S158uSldet2XLz4F5cvX+DRowc0b96KOXNcWLjQmZMnjxETE4OFhSXduv1EZGQE27Zt4vr1q1SqVIVZs5xZunQRly5d4O+/r6OlpUWxYvYMGTJCtZy6aNFiLFu2BlfXRZw7d4bw8HDMzMzp1KkbffsOUI2lRImS1K5dj0uXzrNp03qqV69Jjx690dLS5sCBfezatY3o6GhsbGyZPHkGderIEhCRfjRiktqpMB0dPXqU4cOHkzt3bpo2bcr333/P9evXWbFiBcOGDSMmJgZXV1fu3r2rdt/ChQvZsWMH165dY8qUKVy7do1jx46p1Rk1ahRubm7s378/1f3z8gpMdIdeb+93mJvH30dBV1dL7cSM9JSRsTI6XlrGioqKwt/fHzOz+KeSdO/eASMjY9at25gtx/Y18RJ7DX+NXLmM+PAhIE3bzCrxZGwSL6vFyuh4MrbsGU/GljoeHq+wtlY/RUypVKg2305vGRkro+Pl5LFldDwZW/aM9y2OLaGfqXEUCg0sLBLfLyXTZlbErXlydHRk7NixAFStWhUfHx9WrFhB3759E52iH3c9JiYmwTqJXf8SST20gABPdHXjn1ABJHo9PWRkrIyOl1axIiKgVasfad68JRMmfNzY6Nmzpzx//i9dunRL03gpkRX+3rS0lOTKZZTmsVLb5uKVW3n1zj/JOrZWxgzt3zVN4qVGRsbK6Hg5eWwZHU/Glj3jydiyZ7z0ivX+vUaCe05l5D5UGb3nlYwte8aTsWXPeN/a2BQKjVT/vM60ZIWBQezZwzU/O8qhevXqbNu2DWNjY8LDw4mIiFA7ujIoKEh1EoihoaHqrOFPBQcHq+qkVlIzKyIiohL8tDq7zj7IavHSOlbduj9w8OB+oqNjKFbMHi8vT/bv34OJiSlt23YGyLZjS228iIioNP9E6ms+5Xr8wpMg05pJ1gl5cV6t/ZzyCV5mx8vJY8voeDK27BlPxpY946VnrOjomHifDmaFTydzQrycPLaMjidjy57xvsWxRUfHJPrzOsvOrLC1jZ0K8umusoDqHGBNTU1iYmJ48+YNBQsWVJV/+r2dnR2enp6Ehoaiq6urVqdChQrpPQSRTYwdOxFb2wIcP36Uo0d/x9DQkIoVK9Onzy9YWqbPPhwi47mu2czV6zewtimEvkH8ZGW+XIb07d4hE3omhBBCCCGE+FKZdtZMkSJFsLKyirffxLlz58idOzdNmjRBR0eHU6dOqcr8/Py4du0a1apVA6BatWpERUWpNtwEePHiBU+fPlXVEUJHR5eePXuzY8dvnDlzkUOHjjNlygzy5Mmb2V0Taejq9RtoF2hETL7GBJnWjPfnvw+ByTcihBBCCCGEyBIybWaFQqFgxIgRjB07lqlTp9KoUSMuXbrE/v37mTZtGoaGhnTt2pXFixejUCiws7Nj5cqVGBoa0q5dOyB2dkajRo2YPHkygYGBGBsb4+LiQrFixahfv35mDU0IkQmsbQoRY10ks7shhBBCCCGESAOZlqwAaNmyJZqamqxatYrffvuNPHny4OTkRIcOsVO1R4wYgUKhYP369QQHB1OuXDnmzJmjth/F7NmzmT17NvPnzyc6Oprq1aszceJElEplZg1LCJEJ9A2MiL+DjRBCCCGEECI7ytRkBUDTpk1p2rRpgmWampqMGjWKUaNGJXq/vr4+M2bMYMaMGenVRSFEDhAclHGbzAkhhBBCCCG+TqYnK4QQIr35eDwj/M2/md0NIYQQQgghRApl2gabQgiREXw8nnHvz41Y2xTK7K4IIYQQQgghUkiSFUKIHCsuUeFQu2eCx5kKIYQQQgghsiZJVgghcqRPExVmckqIEEIIIYQQ2YrsWZGGXNcv5V/35xkSS6mpICoyWu2abS5b+ncf+MVtOTpWZMCAoXTu3C3Bcnf3t7Rr15wZM+ZQp44cCSuyPklUCCGESGudOrXFx8crAyJpADHxrpqZWbBjx161a2fPnmby5LGJtqRUKjl37ioAI0YM4tq1K8yc6cz339dVq+ft7UXz5g3p128g3br1okmT+vj5+SbZyxkz5lKnTj1CQkJYudKV8+f/xNvbC6VSSd68NrRq1ZbWrdvFu+/+/busXbuSR48eEBwcjJGRESVKlKJXrz7Y25dIMuan40iMo2Mt5sxxSbad9NKtWwd0dXVZs2bTV7Xj6FiR9u07M2TIiDTqmRDZjyQr0tDLDy95b+6ZIbEUCgXR0erJCj6kTywLC0s2bNiClVXe9AkgRBqSRIUQQoj04OPjxbFjZ9M9jlKpICoqOt71Ro3qJHpPv34DKVy4aIJtfW7hQmcqV66Gnp5eou1Nnz6bsLAwAIKCAnFymkSTJs3VkhwlSjgAMGbMMB4/fkiLFm0oXtye4OAQzp07jYvLXLy8vOjTp7/qngMH9rFgwVysrKzp0qUHNjb5efnyBYcPH2DgwN6MHDmOJk2aJ9qvOGZmZowfPzXBMisr62TvF0JkD5KsEMnS1tamVKnShIZGZHZXhEiSJCqEEEJ8i4oVs6dy5arJ1tPW1sbLy4uFC52ZMCHhN/sAFSpUUn3t7R07m8TGJj/Vqzuq1XNze8atW38zdOhI2rXrpLrerFkLBg7sw54921XJCje3Z7i4OFOihAOLFi1HW1tbVb99+84MGNAbF5e5lCpVGltbuyTHoaWlHa8vQoicR5IVAgA/P1/Gjx/J1atXsLCwoF27jrRv3xlIeBnI7ds3WbNmBU+fPkFHR4c6derRv/9g9PX1ARg0qC9FixZDS0uLo0cPER4eQYMGjRg4cBirVrly7NgRdHR0aNu2I9269VL14+rVy2zbtpGHDx8SFRWJra0dvXr1VmXyo6KiWLXKlVOnTuDj402+fDa0bduBli3bpqhc5FzBQQHcO39cEhVCCCFEIkxNzbC3L8GxY0do2rQFpUuX/ar2PD1jp/VGR8dfttK9+09cvXqZ8PBwtLW12bRpHVFRUUyePF0tUQGgp6fHpElO9OjRkU2b1jN58vSv6ldi3r59w8yZTjx58oiIiAhsbPLTu3d/ateup6qzf/9eduzYwvv37zA0NKRu3R8YNmy0albzqlWunDx5HC8vT5RKJQUK2DFs2GjKlCmXYMzw8HBcXOby119/EhQUiJWVNf36DaRevQaqOg8fPmDevFk8f+6GoaERAwcOS5fxC5HdSLJCALBz51bq12/I7NnzuX79KkuWuKCvr0/Tpi3j1b18+SJjxw6ndu16dO3ak3fv3Fm9egX//uvGkiUrUShipxweOXKIsmXLM3XqTO7e/Yf161dz48Z1HBxK4eQ0mxMnjrJq1TLKlauAg0NpHjy4x+jRQ2nTph09evQmODiIbds24+Q0iX37jmBmZsaOHVv4/fdDDB48HCsray5e/Iv58+eQJ08+qlSplmy5yLk83vwriQohhBDfpKioKMLDw+NdVyqVKJVKtWtjxkzk1q2/mT17Olu37klwqUhKlS5dFiMjY1avXsajRw/4/vs6VK5cDX19fapUqab2u9fdu/+QN28+8uRJeFlx4cJFyJs3H7du/Z2i2AmNF4iXCPnU5Mnj8ff3Y8iQUejo6LBz5xacnCbh4FAaS8tc/PbbHlxc5lKlSjV++qkfbm5P2bVrGxoaCoYPH42r6yL27dtFx45dsLcvyevXr9i+fTMzZ05j9+6DCcYcP34kN2/eoHXr9hQt+h1//HEEJ6dJ6Ojo4uhYC19fX4YPH4iRkRHDh4/Gx8ebhQudU/QMhMjpJFkhAHBwKK3KYlepUo3379+xefOGBJMVa9aswN6+JNOnz1Zdy5MnHyNHDubSpQs4OtYCYvfVmDFjNjo6ulSqVIWDB38jJiaaCROmolAoKFeuAqdOneDBg3s4OJTm+fN/qVWrDmPHTlAtObGysuann7ry4ME9atSoyT//3KJ4cXt+/LEpAOXLV0RHRxddXV2AZMtFzmVtU4gYSVQIIYT4Bo0ePTTB6w0bNo43S8HExJT+/Qfj7DyT1auXM2hQwvemhJ6eHrNmzWPGjCmcPHmMkyePoVQqKVSoMI0bN1NbGuLr64ODQ6kk27O0zMWjRw+Sjfv+/Tvq1q2eYNnq1ZsoUaJkgmX//vuM5s1b0axZCwBKlCjJunWrCAwMxNIyFzt2bKFw4aIsWLBUdY+XlycPH94H4MOHdzRv3ppffhmiKvf392fHji34+/thZmamFu/Bg/tcvXqZ3r3707NnbwAaNWpCjx6dWLNmOY6OtdixYwvBwUGsXLkeO7uCABgbm7JgwZxkn4MQOZ0kKwQA33+vvmlTjRo1OXPmZLydoIODg3n69DEDB6r/x1alSjWMjIy5ffumKllRpEhRdHQ+JgnMzMzJn99WNfNCW1sbPT09AgICAGjSpDlNmjQnJCSYx4+f8urVK27evA5ARERs9tzBoTRr1qxg8OB+1KxZmxo1atK37wBVjOTKRc6lb2BEUGZ3QgghhMgEAwYMpkiRYvGuJzaLoXnzVhw/fpQ9e3bQpEkzDA2NUh27XLkK7N17mEuXLvDXX2e5e/cOz549ZfHiBZw/f44lS1YCEBMTg0KhTLKtT2eBREREEBMTo1YWV25mZp7oUpG4N/yJle3fv5d///2XatVq0KBBI6ZO/RWA4OBA3N3fxjsdb8qUGaqvZ8yYC8C7dx48fvwIN7enXLt2GYDQ0LB48S5dOg9AvXoN1WaCVKxYmT17dhAaGsr9+3exsrJW63fTpi1wcZmb6DiE+FZIskIAsT/0P2VqGpsZ9vT0VO1DARAYGEBMTAzm5hYJtGFGUNDHt4uf3hcnqRkOISEhzJs3i9OnTwIx2NoWoGjR2P944/6z6tq1J7q6uvz++0GWLFnAkiULKFeuAlOn/oqlZa5ky8W3KzgoILO7IIQQQqSLIkWKpWiDzU9NnDiV7t07MmPGFObOXfhV8RUKBY6OtVQfWHl4uPPrr1O5efMGZ86com7d+piZmePllfSpeZ6eHzAxMQXgp5+68vy5m6qscuWquLi4AqClpfXF4wWYN28xixcv4Pr1q9y6dYOVK5dSvboj06bNwsvLGwBzc8tE73/w4D6zZk3jxYvnaGlpkSdPXnR1405Vib9vh5+fHwCdOrVKsL0PHz4QFBQUL1mkpaWFgYHBF49PiJxGkhUCQDW7IU7czs8mJiZERHw8BcTQ0AgNDQ1V+ef3mJiYpLoPCxc6c+3aFRYvdqVEidJoa2vz/Pm/nDjxh6qOUqmkQ4cudOjQBQ8PD86f/5N161Yxe/YMFixYkmy5+Db5eDwj/M2/md0NIYQQIsvIm9eGzp27s379avbt25OqNqZNm8CDB/fj7ddgbZ2HGTPm0qzZD7i5PaFu3fr/X/57nDdvXmNjkz9eW69eveC//95Qv35DACZMmKL2+6mFReJJhJSytMzFjBmxyytu3rzB4cMHOHnyGCtXutKrV+wyDR8f9d9xX716gZubG46OtRg3bgQGBgYsX76WkiVLoVQqWbNmOU+ePEownqGhIQDOzgvR1NSKV54rVy4MDY1wd/9P7Xp0dDQhISFfPV4hsrvU76gjcpSrVy+pfX/u3BlsbGzjzUbQ19enaNHvOHv29Gf3XyYwMJBSpcqkug/379+lSpVqVK1aTbU5Uly/4mYBDh8+kKVLXQCwtramXbuO1KpVm3fvPFJULr49cceZWtsUyuyuCCGEEFlKz569KViwEDt3bknV/fny2fL27X9qHyzFefr0MQDffVf8/7F+RktLCyenSfE2xwwPD2fGjKloamrSpUsPAOztS1K5clXVn8KFv25fqvfv39G4cT0OHNgLxO5rNnXqr5iamvL+/TtMTEyxsrLm2rUravdt3LgOZ+df8fT8gLe3F40aNaF06bKqJSnXr18DUFuyEqdSpSoABAYGqo3l4sXzbNu2CS0tLcqVK///ZSUPVfedPn2CqKiorxqvEDmBzKwQANy4cQ1X10VUrVqdv/46y4ULf+HkNCvBuj/91I/x40cyZcp4Gjduxrt3HqxevQwHh9JUrZrwZkcpUbx4CS5e/Ivffz+EuXku/v77Ojt2xP7nGRYWCkCZMuXYtGkdFhaWFC9egpcvX3D27CnVMavJlYtvS1yiwqF2T/R13TO7O0IIIUS6ePz4IZGRkQmWlSzpoFpa8TmFQsGkSU707dszVXG7dOnOmTMnmDXLicuXL1KlSjV0dXW5d+8uhw8foGTJUqrj521t7Rg1ahzOzrPo1q09LVq0wsamAG/evOTgwQO8f+/B8OGjU5SUiIgI59KlCwmW6erqUr58xXjXc+e2Ileu3KxevZzg4CDy5LHhr7/O4uvrS506sUeXdu7cnYULnRkzZjj16zfkyZNHnD59gk6dupEnT15MTEw4dGg/lpa5UCqVHDlymAcP7gEQHBx/56zy5StSunRZ5s+fzcuXzylc+DuuX7/C778fpGHDxiiVSjp27MqBA/sYO3Y43bv/TGhoKFu3blTt8SbEt0ySFWmoQK4CRLlHZ0gspaaCqEj1WLa5bFPdXt++A7h69TK//bab3LmtmDLlV7Xznz/l6FiL2bPns379GsaPH4mxsTH16zekX7+B8Y7H+hKDBg0nLCyMBQuciYmJ3QRp5kxnlixx4d69O/z4Y1O6detFVFQU+/fvw9NzBWZm5rRv35levfoAJFsuvh2fJirMrIuAryQrhBBCpJ6ZmQWNGtVJvuJX0yCh/Q/MzOLvFxZn1apliZbNnj2fmjVrJ1peokRJmjVryYED+76kk0DsjNs1azazYsUSrl69zLlzZ4iOjsbc3ILGjZuqnZoBsRtH2tkVYsOGNWzbtoXAwAAMDY0oWbIU06b9ir19wqd4fM7Hx4cxY4YlWGZmZs7hwycSLJs71wVn55ls3ryBkJAQLC1zMXjwCH74oREAbdq0JyYmhh07tnD16iVMTEzp3Lk7/foNBGDatFksXOjM/Pmz0dHRoUCBgjg5zWLq1Alcv36VIkWKxou5YMFS5s2bxf79ewkKCsLExJR27ToyaNBw1TN0dV3FrFnTcXVdiK6uLp0792DLlg0pehZC5GQaMQnNWRJ4eQUSHZ3wo/H2foe5uVW867q6WqojN9NbRsbK6HgytvSPl9hr+GvkymXEhw+p28Ry6oJ1BJnWTLKOge95nEb+nGi8T9uIl6hI4P4v8TVjy+rxcvLYMjqejC17xpOxZc946RnLw+MV1tbqHwAplQqiojLoA6kMjJXR8XLy2DI6nowte8b7FseW0M/UOAqFBhYWhom2KfOLhBA5SkKJCiGEEEIIIUT2IskKIUSOIYkKIYQQQgghcgbZs0IIkSMEBwVw7/xxSVQIIYQQQgiRA8jMCiFEjuDx5l9JVAghhBBCCJFDyMwKIUSOYG1TiBhJVAghhBBCCJEjyMwKIUSOoG9glNldEEIIIYQQQqQRSVYIIb4JwUEZd3yfEEIIIYQQ4uvIMhAhRI7n4/GM8Df/ZnY3hBBCCCGEECkkMyuEEDla3HGm1jaFMrsrQgghhBBCiBSSZIUQIseKS1Q41O4pe1oIIYQQIseIjo7O7C5kiG9lnCJhsgwkDe1cuhjfZ24ZEkupqSAqUv0fr2EBO9oOGJwh8YXI6j5NVJhZFwFf98zukhBCiGysU6e2+Ph4ER4eQUCAP0ZGxmhra6V5nIiICPz947dvZmbBjh171eqePXuayZPHJtqWUqnk3LmrAIwYMYhr164wc6Yz339fV62et7cXzZs3pF+/gXTr1osmTerj5+ebZD9nzJhLnTr1CAkJYeVKV86f/xNvby+USiV589rQqlVbWrduF++++/fvsnbtSh49ekBwcDBGRkaUKFGKXr36YG9fIumH88k4EuPoWIs5c1ySbSc7u3v3H+bPn82mTTuBj68DFxdXKleumsm9Szvnz//J9u1bWLFiXWZ3RWQSSVakoYAXz6n635sMiaVUahAVFaN2LfEf20J8W+IlKoQQQoiv5OPjxbRpM3F2ns2sWc5UrVo9zWNcuXKJefNmM3Nm/PYbNaqT6H39+g2kcOGi8a4rlfEnUS9c6EzlytXQ09NLtL3p02cTFhYGQFBQIE5Ok2jSpLlakqNECQcAxowZxuPHD2nRog3Fi9sTHBzCuXOncXGZi5eXF3369Ffdc+DAPhYsmIuVlTVduvTAxiY/L1++4PDhAwwc2JuRI8fRpEnzRPsVx8zMjPHjpyZYZmVlnez92d3Ro4d59eql6vty5Srg7LwIe/uSmdirtLd//17evfPI7G6ITCTJCiFEjiKJCiGEEOkhPDwCZ+fZjBkzPt0SFc7Osxk3buIXfzperJh9iu7R1tbGy8uLhQudmTAh4Tf7ABUqVFJ97e3tBYCNTX6qV3dUq+fm9oxbt/5m6NCRtGvXSXW9WbMWDBzYhz17tquSFW5uz3BxcaZECQcWLVqOtra2qn779p0ZMKA3Li5zKVWqNLa2dkmOQ0tLO15fvmWmpqbyPESOJMkKAUBYWBgrVizh1KkThIeHU7dufczMzDl58hh79x4mMDAQV9elnD//J15enhgaGlK1ag2GDh2FkVHsXgD3799j+fLFPHnyGE1NTSpUqMSgQcOwts6TqWMT3w5JVAghhEgvAQH+6TqjIi4RUq1adaKi0medvqmpGfb2JTh27AhNm7agdOmyX9Wep+cHAKKjY+KVde/+E1evXiY8PBxtbW02bVpHVFQUkydPV0tUAOjp6TFpkhM9enRk06b1TJ48/av6lZQ1a5Zz6NABAgL8sbMrSP36DVm50pVNm3ZSuHDs7w4nThxj/fpVeHi4o6enz/ff12HkyHFoacUuy2nSpD4NGjTC3d2dGzeuEh0dTdmy5Zk+fRaGhsaqWCtWLOWPP37Hz88Xc3MLOnfupkrquLk9o0ePjnTp0p3ffz9EZGQkkydPp3p1R1atcuXkyeN4eXmiVCopUMCOYcNGU6ZMOebO/ZXDhw8A4OhYkX79BmJjYxtvGciBA3vZsWMr79+/Q09Pj5o1azNy5DjVs09sDFOn/oqJiWmCz27u3F+5evUybdt2ZNu2jYSEhFC4cFHGjZusenYA169fxdV1ES9fPkdbW5tKlaoyfvwUDA0NAejWrQN58uTF0/M9L148p1atOkybNlMt1qfLfRwdKzJ16q/Mnz+HihUrMXPmPFW9169f0blzG8aOnYSPjzfr169m/PgpLF++GD8/P2xs8jN8+BjKl6+ouufJk0csWDCXp08fo6GhQalSZZkwYQq5c1t92YtJpDvZYFMAMHv2dI4e/Z1evfowdeqvvHnzml27tqnKJ00ax4UL5+jffxAuLq507NiVkyePsXHjWgBCQ0MZPXoolpa5mDNnAWPHTuTJk0dMnTohs4YkvjHBQQGSqBBCCJFujIyM0z1Rkdr2o6KiCA8Pj/cnKioqXt0xYyZiaGjI7NnTEyz/EqVLl8XIyJjVq5fh5DSJP/88TXBwMABVqlRjyJARqjfHd+/+Q968+ciTJ2+CbRUuXIS8efNx69bfKYqd0HjDw8OTvGf9+tVs2rSeihUrM27cFKysrFmzZoVandOnTzBjxmRy5crNuHGTad68JcePH433O+2hQ/sJCgpkzJiJtGvXiRs3rrFw4XxVubPzTHbs2EK1ajWYOHEaDg6lWbx4AXv37lJrZ8+enfTs+TPduvWiQoVKuLouYufObfzwQ0OcnGbRq1cfPDzcmTlzGgCdOnWlWrUaaGpq4uy8iAYNfkxwnPPnz6FQoSKMHz+Fhg0bc/z4UUaPHpbsGBYvXpDkM/T19WHTpnV06NCFgQOH4eHhzrBhvxASEgLAvXt3GDNmGBoaMGrUeLp06cH161cYPnygWjtXrlwkd25rxo2bzI8/No0X55dfBlO8eAnMzMxwdl5EpUpVqVKlGjduXCMiIkJVb//+Pejp6ameQ1RUFPPnz6ZhwyaMGDGO8PAIxoyJ7SeAu/tbBg/uj7e3F0OGjKBfv4G4uT1l4MA+hIaGJjl2kfFkZoXg1auXnDp1nAkTptK4cTMgdvpfu3axawbDwsKIiIhg1KiP/4mWL1+Re/fucPv2TQCeP3fD39+Pdu064uBQGgATE1Nu3rxBdHQ0CoXkxUT68njzryQqhBBCpJv02EwzLRIVAKNHD03wesOGjePNUjAxMaV//8E4O89k9erlDBqU8L0poaenx6xZ85gxYwonTx7j5MljKJVKChUqTOPGzdSWhvj6+uDgUCrJ9iwtc/Ho0YNk475//466dRN+XqtXb6JEiYT3btizZyeVK1dl6tRfAWjUqDF9+vTg4cP7qjpr167C1rYAS5euUl0zMTFhxQpX3NyeqWYQmJiYsnjxCtXvuHfu3FYlWry9vTh69DCNGjVh/PgpADRo8COjRg1l06a1tG3bQdV2tWo11J7Thw/vaN68Nb/8MkR1zd/fnx07tuDv74etrR2WlrnQ0NBQLf148OBj/6Oioti5cyvlylVk9uz5qtiGhkZs2LCG27dvUrZs+UTHEPe7fWLCw8MZM2YijRo1AaBo0e8YMKA3+/btomvXnqxatRwDA0NWrFiv2helYMHCTJgwinPnzqj2PdHW1mb69NnxZtnEKVq0GMbGxnh7e6nG2bJlG86cOcnp0ydp1KgxAH/99SeVK1dTtRMdHU379p3p3Tt2+VHlylXo2LEVmzevZ8yYiaxbt4rIyAiWLVujmklRqVJlunfvxN69u+jatUeS4xcZS5IVQvVDqWbN2qprurq6VKtWg5s3b6Cjo8OyZasIDY3A3f0tr1+/4t9/n/HixXPVD4YCBewwNjZh7Njh1KvXgGrVHKlQoRLlylXIjCGJb5C1TSFiJFEhhBAim0irRAXAgAGDKVKkWLzric1iaN68FcePH2XPnh00adIMQ8PUH+9drlwF9u49zKVLF/jrr7PcvXuHZ8+esnjxAs6fP8eSJSsBiImJQaFQJtmWUvmxPCIigpiYGLWyuHIzM/NEl4rY2RVM8Prjxw8JCPCPdxLK99/XVSUrAgMDefPmFa1bt1ebpVGvXkOWL1/KxYt/qZIVhQoVVvswzsLCktevXwFw7doVIiMjqV27rlo71arV4MqVi7i5PUu0vzNmzAXg3TsPHj9+hJvbU65duwxAaGgYxsYk6cGDewQHB1O3bn21623adGDDhjVcvXpZlaxIagyJ0dHRUSUqIHZ2jZmZOf/8c4uuXXvy5MkjypUrj1KpVI29atXq6OjocOXKJdXzt7DIlWiiIjHly1fEysqaEyeO0qhRY+7e/QcPD3dGj1af9fLpKTRWVtYUKGDHgwf3gNjTaOzsCmFqaqbqn62tHblzW3Hz5nVJVmQxkqwQ+Pn5oqmpqdp7Io65uYXq63Pn/mTBAmfevv0PU1NTihUrgY6OLtHRsdMH9fUNcHVdzcaNa/njjyP89tseDA2N6NdvIK1atc3Q8Yhvk76BEUGZ3QkhhBAiBdIyUQFQpEixL96Uc+LEqXTv3pEZM6Ywd+7Cr4qvUChwdKyFo2MtADw83Pn116ncvHmDM2dOqfZC8/LyTLIdT88Pqv0SfvqpK8+fu6nKKleuiouLKwBaWlpfPN642BYWFmrXLS1zqb728fEmJiaGfft2sW+f+nKNuP7F0dHRUSvT0NBQJVd8fLwBGDNmeIJ9cXd/q0okmZtbqpU9eHCfWbOm8eLFc7S0tMiTJy+6unEnt8TfG+Rzvr4+AOTKlUvtuqmpKZqamgQFBaZoDIkxMoqfLTE0NCIwMLbdkJBgLlz4K8GZL15eXqqvjZPLuiSidu16HDiwl+DgYA4e/A1LS0uqVKmmKtfU1FR7DxPbZxM8PN4CEBQUxOvXrxLsn7GxSar6JNKPJCsElpa5iIyMJCAgQC1hEffD7vXrV4wbN4pGjZri6tpbNWVq8uRxvHjxr6p+oUKFmT59NhEREfzzzy327NnBggVz+O674pQs6ZCxgxLiM8FBAZndBSGEECLNExWplTevDZ07d2f9+tXs27cnVW1MmzaBBw/us3v3QbXr1tZ5mDFjLs2a/YCb2xPq1q1PuXIVOHXqOG/evMbGJn+8tl69esF//72hfv2GAEyYMIWAgI//d1tYWMa750vEJQc+fcMMH087gY9voJs0aU69eg0SaCNfimLFvaEfPXpCgrNbihUrjqdn/MRNVFQU48aNwMDAgOXL11KyZCmUSiVr1iznyZNHKYptZmYOwIcPH9Sue3t7ERkZmejmmSkVHBz/o6HAQH8KFCgAxCZAypQpT/v2neLV+9q/Q4A2bdqxe/d2Tp8+ybVrV+LNlImMjCQwMFC1mSeAv7+f6u9EV1eX4sXt6dv34x4aCoUG0dEx8T64FZlPNhIQlCpVBoVCwYUL51TXIiIiuHo1dsrZkyePiIiIoGvXHqpERUhICHfu3CYu+XrlyiWaNv0BHx8ftLS0qFixMsOHjwGQ85FFpvPxeIbHm3+TryiEEEKko6ySqIjTs2dvChYsxM6dW1J1f758trx9+x8nTvwRr+zp08cAfPdd8f/H+hktLS2cnCbF2wgzPDycGTOmoqmpSZcusdPw7e1LUrlyVdWfT0+bSA1bWztMTU05f/5PtesXL/6l+trExBRr6zy4u79Vi62trc2KFUt48ybpJRJxKlWqgkKhwMvLU62dx48fsnbtShKbvPD+/Tu8vb1o1KgJpUuXVS17uX79GoBq1sOny2U+V6yYPfr6+pw5c0rt+v79e1V9+xrBwcFcvXpJ9f0//9zCx8dHddxtoUJFcHf/j4oVK6vGnT+/LcuXL1bbGyQlElo2lDevDd99V5zdu7fh7e1Fy5Zt4tU5dux31dceHu68fPlcdfJNsWL2eHh4UKpUGVX/SpUqw5o1K1TLbUTWITMrBDY2+fnhh0YsXjyf0NBQrK2t2bNnF15enlhZ5aFo0WIolUpWrFhKq1Zt8fX1ZefOLXh7e6nWmsVuZBTDxImj6dKlB1paWuzevR1DQyO1o4KEyGhxx5nWqpn5vxQKIYT4dqVnouLx44dERkYmWFaypEOin6YrFAomTXKib9+eqYrbpUt3zpw5waxZTly+fJEqVaqhq6vLvXt3OXz4ACVLllJ98m1ra8eoUeNwdp5Ft27tadGiFTY2BXjz5iUHDx7g/XsPhg8fnaKkREREOJcuXUiwTFdXN8HfPZVKJe3adWLt2pXMmDGFKlWqc+HCOe7cua0qh9gjV52dZzJu3Ejq1q2Pt7c3W7du/P8Rl2VS9FysrKypW/cHtm7diL+/P6VLl+Xhw/vs2bODUqXKYmpqmuCSmDx58mJiYsKhQ/uxtMyFUqnkyJHDqv0W4mY1GBoaERERwaFDv1G+fCW1NrS0tGjTpgNbtmxgwoTR1K5dl0ePHrJ//x7KlCn31cfVAsyYMYUePX5GQ0ODTZvWkydPXlq0iE0a9O7dn1GjhjB4cD+aNm1BWFgo27Ztxs/PlzJlyn1RHENDA/z8fDlx4hgVKlRUzcxo2PBHlixxoWDBQgm+XlavXk5QUCBmZhZs3boRPT19evT4GYCff+5H797d6N+/F23atEdTM/Y9y4sXz/nll8Ff+WREWpNkRRoysivIlcj0ORf7c0pNBVGfxTIsYJfq9kaNGo+uri6rVy8nKiqK+vUbUKdOPV68eI6tbQGcnH5l1aoVjB49FHNzC6pWrUGTJs1xcXHG0/MDlpa5mD9/KStXLmXGjClERkZQooQDixYtx9TU9OsGK0QqxSUqHGr3RF/XPbO7I4QQIhszM7OgUaM6qbo3PDyCgAB/jIyMmTZtYjK1NUhobwIzM4v4Vf9v1apliZbNnj1fbRP1z5UoUZJmzVpy4MC+ZPoVn76+PmvWbGbFiiVcvXqZc+fOEB0djbm5BY0bN1U70QKgadMW2NkVYsOGNWzbtoXAwAAMDY0oWbIU06b9ir19wqd4fM7Hx4cxY4YlWGZmZs7hwycSLOvR42cCAwM5evQQp04dp1ChIjRt2oLDhw+olg00b94KDQ0FW7duYObMC+jo6GBvX5Lhw8eoLS1IzuTJ03F1XcipUyfYvz92L7cffmjEiBFjk7xv2rRZLFzozPz5s9HR0aFAgYI4Oc1i6tQJXL9+lYIFC9OqVVvOnj3F/PlzaNOmPaVLqycB+vUbiK6uLgcO7OPixb8wNDSiYcPGycZOCS0tLTp06MK6dauIiIigVKkyTJgwTfUBZqVKVZg505lVq5Yzd+6vaGpqUrBgYaZMmUH+/LZfFKtDh67cunWTmTOnMmjQMNWpKfXrN2TJEpcEl+pA7Cye7du3EhQUyHffFWf27AWYmZkBsRuaLlq0nMWLF7BoUexpKTY2+fn1V2f5gDUL0ohJbheVb5SXVyDR0Qk/Gm/vd5ibW8W7rqurRWhoRAJ3pL20jOXr68u1a1dwdKyJvr6B6nr//j9hbm7BrFnzsu3Yslq8rDK2xF7DXyNXLiM+fEjdvhBTF6wjyLRmknUMfM/jNPLnRON93saniQoz6yLx7v8SXzO2rB4vJ48to+PJ2LJnPBlb9oyXnrE8PF5hba3+pkqpVBAVlUEfSGVgrIyOl9lj27lzG5UqVVH7NH7mzGn8+edpTp48n+bx0lNmPMtZs6Zz7NgRzp5N3+USyY1t796duLou4sCBY2ofim7ZsoFVq5bx229HVEvX0yJeWsrsfwOZESuhn6lxFAoNLCwSTwLKzAqBjo4OLi5zOHv2FC1btkGpVHL27Cnu37/LwoWJZ+qFyKo+T1QIIYQQQhw79ju7d2+na9eemJubc+fOP5w8eYwff2ya2V0TKXDy5DFu3LjG2bOnqFGjpsze/gZIskKgp6eHi4srq1cvZ+rUCURGRlC4cFHmzl1IxYqVM7t7QnwRSVQIIYQQIiGzZs1j3rzZrFq1jNDQEMzMzGjTpgMDBw7N7K6JFPD0/MCJE39gZ1eQUaMmZHZ3RAaQZIUAUO0vIUR2JokKIYQQQiQmb14bmTX8FcaOncTYsZMyLX6nTt3o1KlbouXduvWiW7deGdgjkd4kWSGEyBGCgwK4d/64JCqEEEIIIYTIASRZIYTIETze/CuJCpGs1Zt38d+HQLVrwUEBeLz5F2ubQhS1y0Pf7h0yqXdCCCGEECKOJCuEEDmCtU0hYiRRIZLx34fA+KfG/H9GTox1Ef778PW7wQshhBBCiK+nyOwOCCFEWtA3MMrsLohsRvY4EUIIIYTIumRmhRDimxAcFJDZXRBfKaElHJ/Kl8swxUs4JFEhhBBCCJG1SbJCCJHj+Xg8I/zNv5ndDfGVPl/CEb88ZUs4JFEhhBBCCJH1yTIQIUSOFvfG1NqmUGZ3RWQBwUEBkqgQQgiR7UVHR2d2FxKVmr597Xiy8vP4Wll5bOndN5lZkYY2blzPu3fvMiSWUqkgKkr9xWFhYUnXrj2/uC1Hx4oMGDCUzp0TP7c4La1bt4qdO7dy8mTW28guo5+FSF+ffoKur+ue2d0RWYCcGiOESK1Ondri4+MFQHh4BAEB/hgZGaOtrZXGkTQIDw+P176ZmQU7duxVq3n27GkmTx6baEtKpZJz564CMGLEIK5du8LMmc58/31dtXre3l40b96Qfv0G0q1bL5o0qY+fn2+SvZwxYy516tQjJCSElStdOX/+T7y9vVAqleTNa0OrVm1p3bpdvPvu37/L2rUrefToAcHBwRgZGVGiRCl69eqDvX2JZJ9O3DgS4+hYizlzXJJtJzu7e/cf5s+fzaZNO4GPrwMXF1cqV66aaf3y9vbCyWkSXbv2pFKlKim+79Ch37h48Txz5y4EYPbsGfzxx++cPXs5VffPnfsrx44dSfH9KeHoWJH27TszZMiIBMu9vb0YM2YYz549RUdHl4MHj6Grq/tVMUNCQpg1axoVK1ahRYvWKbqnSZP6lC1bjpkz531V7JSYN28WJiam9O07IN1iSLIiDXl6fiBPHusMiaVQKOJlstzdPTIkthDZQbyp/r6SrBByaowQIvV8fLw4duwsV65cwtl5NrNmOVO1avU0j3Pt2hXmzJkZr/1Gjeokek+/fgMpXLhovOtKZfxJ1AsXOlO5cjX09PQSbW/69NmEhYUBEBQUiJPTJJo0aa6W5ChRwgGAMWOG8fjxQ1q0aEPx4vYEB4dw7txpXFzm4uXlRZ8+/VX3HDiwjwUL5mJlZU2XLj2wscnPy5cvOHz4AAMH9mbkyHE0adI8iacTy8zMjPHjpyZYZmWVMb+LZ6ajRw/z6tVL1fflylXA2XkR9vYlM7FX8M8/t/n77+t06dLji+7bs2eX2hv7zp27xUuofcn9nTp1pXbtel/Uh6+1e/cOHj16SP/+g7CzK/TViQqAt2//4+zZ05QtWyENepj2Dh78jfbtO6drDElWCCGyhXv37zN1wTrV93p62oSEhKu+v//oGXZVY/czkD0JRGL0DYwIyuxOCCGyrbhExZgx49MlUXHlyiXmzfvy9osVs0/RJ+ra2tp4eXmxcKEzEyYk/GYfoEKFSqqvvb1jZ5PY2OSnenVHtXpubs+4detvhg4dSbt2nVTXmzVrwcCBfdizZ7sqWeHm9gwXF2dKlHBg0aLlaGtrq+q3b9+ZAQN64+Iyl1KlSmNra5fkOLS0tOP15Vtmamqao55HgQJ22NjYpvp+W1u7ZF9Dac3f3w8tLa1UzXIXiZNkhQBi/4FNnTqBS5cuoK2tRcOGjRkwYCiamrEvEU/PDyxZspirVy/j6+uDqakZdev+wC+/DEZbWxt397e0a9ecIUNGsnv3dsLDw5k505lSpcqwffsW9u3bhZ+fL99/XwcLi1yp6uPBg7+xZ89O3r79D2trazp27Erz5q0AiImJ4fDhA+zdu5M3b96QO3duWrdup5btCwoKZM2alZw//ydeXp4YGhpStWoNxo4dh5ZW4p8uiKwhOEKhtrlihI4m4TqRqu+DQh8CkqgQX0dOjRFCJCY8PCLdExXOzrMZN25iuk3lNzU1w96+BMeOHaFp0xaULl32q9rz9PwAQHR0TLyy7t1/4urVy4SHh6Otrc2mTeuIiopi8uTpaokKAD09PSZNcqJHj45s2rSeyZOnf1W/krJmzXIOHTpAQIA/dnYFqV+/IStXurJp004KF479veHEiWOsX78KDw939PT0+f77OowcOQ4trdglOU2a1KdBg0a4u7tz48ZVoqOjKVu2PNOnz8LQ0FgVa8WKpfzxx+/4+flibm5B587dVEkdN7dn9OjRkS5duvP774eIjIxk8uTpVK/uyKpVrpw8eRwvL0+USiUFCtgxbNhoypQpx9y5v3L48AEgdmlCv34DsbGxjbcM5MCBvezYsZX379+hp6dHzZq1GTlynOrZJzaGqVN/xcTENMFnFxERwezZ07ly5RJBQYGYm1vQpElzevfur7YkacSIQVSuXBUXF1d8fX1ZsGA2N2/eICAgAF1dXRwcSjNpkhPm5hZ069aB58/dVOPZtGkn+/btUlsGcvnyRZYtW8zbt29QKBQUK2bPqFHjKFiwcIL37927U20ZSEREBEuXunD27CkCAgKwtMxFt269VEsrfH19cHaelWgfk/N5H5o1a8nYsZO4evUSa9as4Pnzf4mIiMDMzJwWLVrz0099Vfc+f+7G/PlzePz4IRoaGhQp8h1jx04iKiqKHj06ArGzoQ4c2MeWLbt4+PA+8+fP4cWLf4mJicHOrhBDhoygbNnyqjbDwsIZP34U169fISYmhjJlysX7e03qtQkQHh6Oi8tc/vrrT4KCArGysqZfv4HUq9dANU6A3bu3c/z4UY4cOZXsc0oN2WBTALB9+2ZMTU2ZM2cBLVq0YffuHRw4ELsuMjo6msGDB/DkyWNGjIj9QdiwYWP27NnBoUO/qbWzdu1KfvllML/8MphixezZvn0Lq1a50rhxM3791ZmIiEh2797+xf3buXMr8+fPpkqVasyd60KdOvVxdp7JyZPHAFi1ahkLFszB0fF75sxZQJ069Vm2bDGrVy9XteHkNIkLF87Rv/8gXFxc6dixKydPHmPt2tVf8eREViKJCvE1fDye4SGnxgghEhEQ4J/uiYoxY8ZTrdqXtx8VFUV4eHi8P1FRUfHqjhkzEUNDQ2bPnp5g+ZcoXbosRkbGrF69DCenSfz552mCg4MBqFKlGkOGjFC9Ob579x/y5s1Hnjx5E2yrcOEi5M2bj1u3/k5R7ITGGx4enuQ969evZtOm9VSsWJlx46ZgZWXNmjUr1OqcPn2CGTMmkytXbsaNm0zz5i05fvwoU6dOUKt36NB+goICGTNmIu3adeLGjWssXDhfVe7sPJMdO7ZQrVoNJk6choNDaRYvXsDevbvU2tmzZyc9e/5Mt269qFChEq6ui9i5cxs//NAQJ6dZ9OrVBw8Pd2bOnAbELnGoVq0GmpqaODsvokGDHxMc5/z5cyhUqAjjx0+hYcPGHD9+lNGjhyU7hsWLFyT6/JYtW8z583/SuXN3pk2bRenSZdm4cS3Hjh2lXLkK9OjxExC7LOmXXwYDMHr0UG7e/Jvu3X/GyWk2jRs358aNayxZEhtn7NiJ5MmTlwIF7HB2XkTevPnUYgYGBjJlyjjMzMyZOHEaAwYM5dWrF0yYMCZF90Pse4CDB3/j++/rMnnydAoXLsL8+bM5f/5PAEaMGJJkH5MzduxEtb+TTp268uLFc8aNG4mOji5jx05m/Pgp5M5txfr1q7l9+yYAvr6+DBzYh//+e03fvgMYMmQUHh7uDB8+kLx58zFq1DgA2rRpz9ixEwEYP34U0dFRjB49kVGjxhMYGMCECaOIiIhQ9efq1UsEBgYwduwk2rRpz/XrV1m40FlVnpLX5vjxIzl+/Cg//tiU8eOnkCdPvv+/l/rr/20sAqB27XpMnz47Rc8pNWRmhQCgYsUqDB8e+4++QoVKXLz4Fzdv/k3bth358OE9RkbGDBkykiJFiqrqXL16mdu3b9K2bUdVO40aNVZl3KKjo9m+fRPNmrXk55/7AbH/cfXs2Zm3b9+kuG/R0dFs2bKBxo2bMXjwcAAqVarC27f/cefOP1SuXJVdu7bRqVM31QYvlStXJSYmhh07ttC+fWf09PSIiIhg1KiPv2SUL1+Re/fucPNmyv5TFFlbSJC/JCpEqsUlumrVTPs3IUKInMHIyDjdExWpbX/06KEJXm/YsHG8WQomJqb07z8YZ+eZrF69nEGDEr43JfT09Jg1ax4zZkzh5MljnDx5DKVSSaFChWncuJnaJ7W+vj44OJRKsj1Ly1w8evQg2bjv37+jbt2En9Xq1ZsoUSLhvRv27NlJ5cpVmTr1VyD299Y+fXrw8OF9VZ21a1dha1uApUtXqa6ZmJiwYoUrbm7PVLMvTExMWbx4BQpF7Ge/d+7cViVavL29OHr0MI0aNWH8+CkANGjwI6NGDWXTprW0bdtB1Xa1ajXUntOHD+9o3rw1v/wyRHXN39+fHTu24O/vh62tHZaWudDQ0FAt/Xjw4GP/o6Ki2LlzK+XKVWT27Pmq2IaGRmzYsIbbt2+qPoVPaAxxb6QTcu/eHfLnt6Vr19g9KerUqYeOjg76+vqYmppSpEgxIHZZUtGixQgODiYyMpLBg4fTqFET1T0PH95X9dnBoTS6unro6uomuJTl/v27hISE0KlTF6pXj51ha25uztWrl4mKikr2fk/PD5w7d4YWLVozatR4AOrVa0C3bh24fv0qFSpUTraPyXFwKB3v7+SPP36nQAE7Fi5cpkrYVapUhZYtf+Tq1cuULVueHTs2ExgYyIYN21Wvq3z58jFlyjiePXtCqVJlgdhlLQ4OpXn3zgNPzw906tSNRo0aA7HLsw4fPkBgYCBmZmZA7L+juL/XRo0ac/v2Le7fvwek7LX54MF9rl69TO/e/enZszcAjRo1oUePTqxZsxxHx1qqcebObaW2bCytSbJCAFCqVGm1762t8xAYGDsd2srKmtWr1xEcHMbr1694/foVz549wcfHO94mRnZ2H4+HfPXqJb6+vmr/8WpoaPD993XYsWNLivv26tVL/Pz8qFGjltr1uP9oLl26QEREBHXq1Fcrr1evAVu3buT+/bvUqFGThQuXAeDu/pbXr1/x77/PePHiOTo6Oinui8iafDye4ePxnIpNhkui4hv2+b4mkPTeJnHk1BghREqk/akfabcHxoABg1VvFD+V2CyG5s1bcfz4Ufbs2UGTJs0wNDRKdexy5Sqwd+9hLl26wF9/neXu3Ts8e/aUxYsXcP78OZYsWQnELtlVKJRJtqVUfiyPiIggJiZGrSyu3MzMPNGlInZ2BRO8/vjxQwIC/ONt3Pj993VVyYrAwEDevHlF69bt1WZp1KvXkOXLl3Lx4l+qN5WFChVWvcmH2FP5Xr9+BcRukhoZGUnt2nXV2qlWrQZXrlzEze1Zov2dMWMuAO/eefD48SPc3J5y7VrscobQ0DCMjUnSgwf3CA4Opm5d9d+L27TpwIYNa1RvlJMbQ0Ls7Uuyf/8eunfvQLVqNfjhh0ZJ7n2ir6/Phg3bgNhlL25uT3ny5DHu7m9VS82TU7y4PXp6ekydOoHKlavi6Pg9tWvXS/EGmtevXyUmJoa6dX9Qu75ly8dZBJs37yAqKjrVfUzIjz825ccfmxIcHMz9+3f591837t+/C6B6Tdy/f4/cua1UrymI/TD1999jl1R8+jqB2CSEhYUlK1cu5fr1q1Sv7kj9+g2YOHGaWr3ChYuo/b3mzm3F27f/ASl7bV66FHtiY716DdXqVKxYmT17dhAaGpomG4imhCQrBAA6OuovuM9PGzlw4DeWL3fF29sLCwtLSpRw+P+bfPU1inEZPYidLgnEW/eWkrVfn/L394vX9qfi4pibm38WJ/b7oKDY7fQuXDjHkiUuvH37H6amphQrVgIdHV1iYrLu2cUieXFvNM2sC0qi4hv3+b4mkPjeJnHk1BghRGZJy806ixQp9sX7XEycOJXu3TsyY8YU1ZGPqaVQKHB0rIWjY+wHSx4e7vz661Ru3rzBmTOnqFu3PmZm5nh5eSbZjqfnB9XvjT/91FW1DwCg2gMBQEtL64vHGxfbwkL991BLy497qfn4eBMTE8O+fbvYt099uUZc/+J8/mGXhoaGKrni4+MNwJgxwxPsi7v7W1UiydzcUq3swYP7zJo1jRcvnqOlpUWePHnR1Y3bWy3+3iCf8/X1ASBXLvU94kxNTdHU1CQoKDBFY0jIkCEj0NXV5fTpE2zbtplt2zZTqFBhZs6cT/78+RO859Ch/axZsxwfHx/09fXJn78AWlpaScb5lImJKS4urqxatYzLly9y7txZFiyYQ4cOXdVOmknMx+dhlWidgwf3s2qVa6r7mJCIiAimT5/M+fN/EhUVhZmZOcWKFQdQtRsUFIiRUTLZp08olUoWL16Bq+si/vnnJleuXGTJkgX8+GNTRo+eoEpQfO1r088v9r1Xp06tEqzz4cOHRP++05okK0Sybt36m5kzp9OzZ29at26vShr06dM9yftMTEwA8PHxUbsel3xIKUNDQ+DjD5s4sTMufDH+f4rZ29ubXLlyq8rjdq82MTHh9etXTJ48jkaNmuLq2pvcuWN/YE2ePI6XL59/UX9E1vHpG823j85kdndENiN7nAghMkt6nyqSEnnz2tC5c3fWr1/Nvn17UtXGtGkTePDgPrt3H1S7bm2dhxkz5tKs2Q+4uT2hbt36lCtXgVOnjvPmzWtsbOK/0Xn16gX//feG+vUbAjBhwhQCAj5uemxhYRnvni8Rlxzw8vJSux73+yKg+p2ySZPmqmXN6m3E3w8hIXFvQEePnpDg7JZixYrj6Rk/cRMVFcW4cSMwMDBg+fK1lCxZCqVSyZo1y3ny5FGKYpuZxX5Y9+HDB7Xr3t5eREZGJrp5ZkpoaWkxcOBQBg4cyosXz/njj9/Zt28X06ZNYN26+LOmHz9+yIIFc6hSpTqDBg1VndAxcGAf3N3fpjhuqVJlcHVdTWhoKBcv/sWuXdvZtGktDg6lqFatRpL3xv1deHq+V3uDffPmDWJiYjA0NGTevFlf3cfPLVo0j/Pn/2TQoOE0bNgYIyMjAgMDadSotqqOnp4+Hz68j3fvH3/8rrZh5qfs7Aoyf/5ioqKiuHz5IgcO7OXw4QMUKlRYbTlRYlLy2oybAeLsvBBNzfgzyj5PhKUn2WBTJOv+/btoaGjQo8fPqkSFp+cH3Nzcksw45s9fAEvLXJw7p/4m8sqVS18U39bWDmNjEy5duqB2fe3alSxbthh7ewc0NTU5e1Z9F9rTp0+iVCqxty/JkyePiIiIoGvXHqpERUhICHfu3CYlWWqR9Xi/ffpFbzTllAfxKUlUCCEyS1ZIVMTp2bM3BQsWYufOlC/P/VS+fLa8ffsfJ078Ea/s6dPHAHz3XfH/x/oZLS0tnJwmxdsIMzw8nBkzpqKpqUmXLrH7Idjbl6Ry5aqqP59OlU8NW1s7TE1NVZsqxrl48S/V1yYmplhb58Hd/a1abG1tbVasWMKbN4kvkfhUpUpVUCgUeHl5qrXz+PFD1q5dSWK/Pr9//w5vby8aNWpC6dJlVcterl+/Bnz8RP7T5TKfK1bMHn19fc6cUf+9eP/+vaq+pVavXl1UG43a2RXkl18GU6ZMOVXC5/N+3blzm6ioKPr2/UWVBAgICMDN7Znae4hPlyx87tixIzRoUIt37zzQ1dWlXr0GjB8/GYD//nuT7P0VK1ZGoVDEex7OzjPZtGldivv4pR49eoiNjS1t23bAyCh2mVXcwQBx7z1KlHDg3TsPtRlE9+/fZebMady9+0+853n79k0aNarNjRvXUCqVODrWYvbsBSgUihQnVlLy2ox7jQQGBqrVuXjxPNu2bVKdiqOhoZHq55NSMrNCJMveviTR0dEsXryAOnXq8e6dB5s3ryciIpzQ0NBE79PQ0KB3737MnTsTc3MLKlWqwpkzJ3n06CFKZcrzZJqamnTr1osVK5ZgYmJKhQqV+OefW5w9e4pZs+ZhampK27Yd2bFjC0qlkrJly3H79i127NhChw5dMDY2pmjRYiiVSlasWEqrVm3x9fVl584teHt7oaOjnXwnRJbi4/GMB+c3pfiNpo/HM8LllAfxf5KoEEJklvRKVDx+/JDIyMgEy0qWdEj003SFQsGkSU707dszVXG7dOnOmTMnmDXLicuXL1KlSjV0dXW5d+8uhw8foGTJUqo9Imxt7Rg1ahzOzrPo1q09LVq0wsamAG/evOTgwQO8f+/B8OGjU5SUiIgIj/chVhxdXV3Kl68Y77pSqaRdu06sXbuSGTOmUKVKdS5cOPf/D64+vtHu3v0nnJ1nMm7cSOrWrY+3tzdbt25EQ0ODUqXKpOi5WFlZU7fuD2zduhF/f39Kly7Lw4f32bNnB6VKlcXU1DTBJTF58uTFxMSEQ4f2Y2mZC6VSyZEjh3nwIHZzxODg2KXNhoZGREREcOjQb5Qvr765oZaWFm3adGDLlg1MmDCa2rXr8ujRQ/bv30OZMuW+6rjakiUdOHz4AEuWLKBUqbI8e/aUmzdvqE4kiZtV/eefpzE0NMLBoQwaGhosXDiP1q3b4ePjzZ49uwgMDEBT8+MbcX19fd68ecXZs6epUkU9mVKxYmUARo4cTMeOXdHW1mb37u3o6upSq1btZO+3ts5DzZq1OXx4P5qaShwcSnPy5DHc3d8yYsRYjIyMU9THL1W06HccPXqYVauWUaxYce7c+Ud10mLc+6fOnbtx9OghRo4cQteuPVEqlWzatA4bm/zUrl1PdbLO9etXKFSoMCVLlkJHR5dZs5zo0qUHpqamHDlyCA0NjXh7ciQmJa/N8uUrUrp0WebPn83Ll88pXPg7rl+/wu+/H6Rhw8aqfyu6urrcv3+Xy5cvJjvDJbUkWZGGLC1z4e7ukSGxlEoFUVHqey187fS4xFSoUInhw0exfftWjhw5RO7cualTpz6amprs2bMjyWOimjZtSUwMbN26kX37dlOxYmW6d+/Ftm2bvqgPnTp1RUdHh927t7N793ZsbPIzbdosatasDcCAAUMwNTXl4MH9bN++GWvrPAwcOFQ1HcrWtgATJzqxYcNqRo8eirm5BVWr1qBJk+a4uDjj6flBbc2iyLri3miW++FnDC0S3kQrofpyyoMAOTVGCJF6ZmYWNGpUJ9X3h4dHEBDgj5GRMdOmTUyipgYJzfo0M0t8z69Vq5YlWjZ79nzV70sJKVGiJM2ateTAgX1J9Clh+vr6rFmzmRUrlnD16mXOnTtDdHQ05uYWNG7cVO1EC4CmTVtgZ1eIDRvWsG3bFgIDAzA0NKJkyVJMm/Yr9vYJn+LxOR8fH8aMGZZgmZmZOYcPn0iwrEePnwkMDOTo0UOcOnWcQoWK0LRpCw4fPqBadty8eSs0NBRs3bqBmTMvoKOjg719SYYPH6OqkxKTJ0/H1XUhp06dYP/+PRgaGvHDD40YMWJskvdNmzaLhQudmT9/Njo6OhQoUBAnp1lMnTqB69evUrBgYVq1asvZs6eYP38Obdq0p3Tpcmpt9Os3EF1dXQ4c2MfFi39haGhEw4aNk42dnKFDRxEVFcWJE8fYt283BgaGNGjwIyNHxh6x6eBQmkqVqnDkyCGePn3CmjWbGDhwGDt2bGH69MkYGhpRunRZfvihIZs2reP169fkz5+fLl26M3OmE9OmTWDmzHlqMS0tczFz5jyWL1/MokXziIqKwta2ALNnL1DNlk7q/thnOpOFC505fvwP9u/fi5VVHqZMmaHa92Tw4GFs25Z0H7/UoEHD8fb2Yt++XURGRmJhYclPP/VVfXALsfv4LV26mnnzZuHquhClUknJkqUYN24y2traaGtr07BhY86cOcnTp0/Yt+935s9fzIIFc1m1ypXw8HCsrfMwcaITDg6lk+nRRyl5bS5YsJR582axf/9egoKCMDExpV27jgwa9HGvizZtOrBnzw4mTx7L0aNn0NNL+003NWK+Zn5LDublFUh0dMKPxtv7Hebm8Tdp0dXVIjQ0IoE70l5GxsroeDK29I+X2Gv4a+TKZcSHD6lbajF1wbp4GyN+7v6fq8lbvK7qjaZVgeKEh0WqlZes3Vftnk8/QbfRdcdp5M+p6t/XjC2rx8tOY0vudZLQa0BbR1P1OvHxeMaNIwuTPDXGwPe8vE4yOVZGx5OxZc946RnLw+MV1ta2atcS+pAovWRkrIyOl9lj27lzG5UqVVGbvTFz5jT+/PM0J0+eT/N46Smzn2VOiZXR8b7FsSX0MzWOQqGBhUXiSUCZWSEyVUJTFiMjNdSuK5XKDFkTJbK2iLDgL/pEXE55EJ+SU2OEEEIcO/Y7u3dvp2vXnpibm3Pnzj+cPHmMH39smtldE0IkQJIVItO4u7+lXbvmydZbsmRlgmsPxbcl0McDh9q9U5eoEN80OTVGCCEEwKxZ85g3bzarVi0jNDQEMzMz2rTpwMCBQzO7a0KIBEiyQmQaS8tcrF27Od51bW1NwsM/zqywtS2Qkd0SWZShmbUkKsQX+/zUmOSSFXJqjBBC5Fx589qwcGHi+3sIIbIWSVaITKOlpUXx4iXiXc/ofR1E9qClo59sHUlUiE/JqTFCCCGEENlXys+PFEKILOxL97QQOVtc4qpM3Z++aEaOtU2hDOidECKri47OuA3whBAip/ran6WSrBBC5Aixe1pIokKoz7Axz1v0i+rrGxhlQA+FEFmZhoaCmJiozO6GEEJkezExUV91UIIkK4QQOUJK97QQOduXLgWSpUNCiM8plQpCQ0MyuxtCCJHthYaGoFQqU32/JCuEEDlCSva0EDnbVx9vK4QQgJGRGUFB/oSGhshyECGESIXo6GhCQ0MICvLHyMgs1e3IBptCiG+CnPKQ88nxtkKItKCjo4e+viF+fl7ExMQmKxQKDaKjYzIkfkbGyuh4OXlsGR1PxpY9431LY9PQUGBgYIiOjl6q25RkhRAix5NTHr4NKVkKFOL7ijvHZvLB/RW58tji9ewPvJ59LLcy0UnnXgohsgNDQ1MMDU1V3+fKZcSHDxmT9M7IWBkdLyePLaPjydiyZzwZ25eRZEUact2xBTdfrwyJpVQqiIpSn5pYwNCE/u26fnFbjo4VGTBgKJ07d0ur7gHQtm0zqld3ZMSIsWnarhBfIu4T9Fo1q2d2V0Q6S8lSIHNzTYL9XtPppwbktc0br/zDk/fp0TUhhBBCCPGFJFmRhl4E+OBRuWSGxFIqNIj6fErPtfsZEluI7ELtlAdd98zujsgCfN4+o0ABU97cvcqbu/HLDfQsMr5TQgghhBAiHklWCCFypHh7EvhKsiK783j9kHf3bidaHhwQnGwbZe2NWTU18Y2etp8wTUXPhBBCCCFEWpNkhQDA39+PqVMncOnSBbS1tWjYsDEDBgxFU1OTiRNH8/r1KzZv3qV2T6dOralRoxaDBg3Dy8uThQvncf36FfT09OnXb6Ba3Zs3bzBkSH9GjRrPhg2rUSo1WbZsDdbWeTh8+AB79+7kzZs35M6dm/btO9K6dYeMHL7IYWTzxJyp+HdW1Pwud6Lle7fdSLYNPT3ttOySEEIIIYRIJ5KsEABs376ZVq3aMmfOAv7++zqbN68nb958tG3bkUaNmjB+/Cjc3J5RuHDsG7+HD+/z+vUrGjZsTFRUFCNGDCY4OIgxYyYSExPDqlXL+PAh/trvTZvWMXr0RAIDA8iTJy8rV7qyY8cWunTpQdmy5bl1628WL3bB09OLvn0HZPRjEDmAJCpyrvdvHvH86cVEy0MCDL86hr9/8rMzhBBCCCFE+pNkhQCgYsUqDB8+BoAKFSpx8eJf3Lz5N23bdqRaNUfMzMw4efIYhQsPAuDEiWMUKlSYokW/48KFc7i5PWXlyg04OJQCIH/+Avz8c/zNPtu164SjYy0A/Px82bVrG506dVMlJipXropSqcG2bVto374zpqamGTB6kVNIoiJnq1LKlM4NAhMtbzvx62ZN3HsSwLN/w7+qDSGEEEIIkTYUmd0BkTWUKlVa7Xtr6zwEBsYePaOpqUmDBo04ffoEAFFRUZw+fYJGjZoAcOfOPxgZGasSFQDFihUnT574O+0XLFhQ9fX9+/eIiIigTp36anUaNGhEREQE9+8nsPudEIkICfKXRIVItXtPAnBZ40aRQtaZ3RUhhBBCCIEkK8T/6ejoqn2vUCiIjv54NGrTps1xd3/LvXt3uHHjGr6+PvzwQyMAAgICMDExjdemuXn8XfXNzMxVXwcE+P+/nrlanbj7goKCUjcY8c3x8XiGj8dzSVSIVIlLVIzoUxhj4+SPPxVCCCGEEOlPloGIFLG3L0HBgoU4e/Y0wcHBlC9fkVy5Yje6MzExwdfXO949fn5+SbZpbGwMgLe3t6otAC8vT1W7QiQnbumHmXVBSVSIL/ZposLhOyPuvMjsHgkhhBBCCJBkhfgCDRs25vDhA4SFhamd9lG+fEW2bt3I339fp0KFSgC8evWSt2/fAFUSbc/e3gFNTU3Onj1FsWLFVddPnjyOUqnE3r5kuo1FZC0eb57z7v7TJOsEB/rHu/bpHhVvH51Jr+6JbMLPyx3/C0vVrmkqFURGfZwlZqfnDRgB8RMVQgghhBAi65BkhUixhg0bs3r1crS1tfn++7qq65UqVaFs2fJMnz6JX34Zgp6eHmvWrEBTUyvJ9kxNTWnbtiM7dmxBqVRStmw5bt++xdatm+jQoYtq5oXI+Yrb21OzRNMk6+xdP1vte++3T9X2qEguWREcFPDV/RRZWwEbHSo3qah2TUdbk7DwSNX3bmfvA5KoEEIIIYTI6iRZkYbsjMyIunY/Q2IplQqiPvm0EKCAYfoum8iVKzeFCxehYMHC6Ot/XNetoaHBnDkuLFmygEWL5qOpqUmnTl05d+5ssm0OGDAEU1NTDh7cz/btm7G2zsPQoSNo1ap9eg5FZHM+Hs94cH6T2h4VIX7veHZxfYL1gwN90dOQPVByugBvD64d2a92TampICry489KvWgf7j3RlUSFEEIIIUQWJ8mKNDSoUzdCQyMyJJaurlaaxbpw4Ua8a7NnL4h3zdPTk3//dWPAgKHxygwNDZkwYSoTJkxVXevSpYfq6/LlKyYYR6FQ0LVrT7p27am6lpZjEzlP3NKPcj/8jKHFx9Nl8uQrQJMuo+PVf/vyIWd+W06l6tUzspsiE5QuDBMH6qhd09bRJDzs48yK5kOiGTbHjfwFc7Hqj0j4w0etvlKhR8uM6KwQQgghhEiSJCtEsv777w2nTx/n7NnTFChgR8WKlTO7S+Ib9ekeFeZ5i6q9CU1IXKKibusBaAW5ZVAvRVZ170kAz3x1KdB/OMGF7AhOoI51Bs2OE0IIIYQQSZNkhUhWTEwMO3ZsxczMHCen2WhoaGR2l8Q3KDwsWG2PiuR8mqjIW8CeDw8kWZHdPbz7DqebHomW+4eHJFoWt0dFLu1caJz4iwD+SrCevp7BV/dTCCGEEEJ8vUxNVvj4+FC1atV41xs2bMiSJUuIiYlh5cqV7Nq1Cx8fH8qXL8+kSZMoXLiwqm54eDjz58/nyJEjBAcHU7NmTSZOnIiVlVVGDiVHs7HJz9mzF2RphshU/t7uONTulapEhcgZ7EzsaWRQPtHytS8PJnj90800d66HX4o5JNrGsaDEEx5CCCGEECLjZGqy4tGjRwCsW7cOQ0ND1XVTU1MAli1bxurVqxk1ahT58uVjxYoV9OzZk6NHj2JkFLsp2tSpUzlz5gxjx45FX18fFxcX+vbty2+//YZSqczwMQkh0oexeR5JVHzjnr99ykqf94mWBxv6AdZq1+4+8lfbTNPd519WXjuVeBt66btRsRBCCCGESJlMTVY8fvwYS0tLHB0d45UFBgaybt06Bg0aRPfu3QGoWLEiderUYe/evfTq1YtXr15x4MABFixYQOPGjQEoXrw4jRo14vTp0zRo0CBDxyOESD/aOvrJ1pFERc7mYG9B1+IJ7TQRy+mkrtr3954EsGjdc7VTPwoXUDKlmWFCt3PvVQBrb4SmXYeFEEIIIUSqKTIz+OPHjylWrFiCZf/88w/BwcHUq1dPdc3ExITKlStz/vx5AK5cuQJA7dq1VXXs7OwoWrSoqo4Q4tsQFhokiQqhErf0Y3T/Iik6nvTeqwDmHfiXorbWydYVQgghhBDpL9OTFSEhIXTs2JFSpUpRq1Yt1qxZQ0xMDC9evAAgf/78avfY2Nioyp4/f46lpSX6+vqJ1hFCfBv8vN5KokIA6ntUlCpunHz9/ycqRrcshIlh8jN4hBBCCCFE+su0ZSDR0dG4ubmhp6fH2LFjyZMnD+fOncPFxYWwsDC0tLTQ1tZGW1tb7T4DAwMCAwMBCAoKwsAg/s7tBgYGeHgkvmN8SlhYJDxNGCAgwBNdXa0EyxK7nh4yMlZGx5OxpW88LS0luXIl/2nzl0ptm3q62uhoJ/3jSFOpQFtHvc6n31tY5adg0VJJxviaMafH88oq8bLL2PT0tNDRSfx1oqmp4PHzYBate87YAd+pEhWfvk40NRVqbdx94Y/LoedMbFeUUnbGPH6iJa+TLBAro+PJ2LJnPBlb9oyXk8eW0fFkbNkznowt5TItWRF30kfevHkpUKAAAFWrViU4OJi1a9fSv3//RI/IjLseExOTYJ3Ern8JL69AoqNjEiyLiIhK8GQMXV2tDDsxIyNjZXQ8GVv6x4uIiOLDh4A0jZUrl1Gq2wwJDScsPDLJOpFR0YSHfayjraOp9r1SUzfJNkJCw1Pdv68ZW1aPl53GFhISQVhY4n/HDx4EcOHGe4rkN2fnLn924o+mppLIyChVncAPgao2Pp1R8V0efcLCIgkJiZDXSSbHyuh4MrbsGU/Glj3j5eSxZXQ8GVv2jCdjU6dQaCQ5SSDTkhVKpZJq1arFu16zZk127tyJnp4e4eHhREREoKX18ZPhoKAg1UkghoaGBAUFxWsjODhYVUdkXWmRVBIipYIC/DO7CyKdhfnpMbxKY4p9cnS1jramWhJrnV/s8aafJiocbOX/CyGEEEKIrCbTkhXv3r3jzz//5IcffsDc3Fx1PSwsDIjdTDMmJoY3b95QsGBBVfmn39vZ2eHp6UloaCi6urpqdSpUqJBBI/lo+/rFeL91y5BYSqWCqKhotWtGuezo0GPwF7fl6FiRAQOG0rlzN44ePcysWU78/vsp1RGyaeHTGAEBAbi4zKVDhy4ULy77C4j09/blQ16/fJbZ3RDpTEsrkrMv73L25V3VNU1NBZGRH39WhkT5ce+VviQqhBBCCCGyuExLVoSHhzNlyhRCQkLo2bOn6vrx48exs7Pjhx9+YMqUKZw6dYo+ffoA4Ofnx7Vr1xg0aBAA1apVIyoqijNnzqiOLn3x4gVPnz5V1clIfu+eU9rkTYbEUio0iPpsmcqdD1/fbrVqjqxcuQFDw8Sn46TGypUbsLbOA8DTp485efIYHTp0TtMYQiQk7jhTx+rxZ3KJnKVEQW0mt1X/2aWjo6m2dGTsVk1JVAghhBBCZAOZlqzInz8/TZs2ZfHixWhoaFC4cGGOHTvGiRMnWLZsGQYGBnTt2pXFixejUCiws7Nj5cqVGBoa0q5dOwBsbW1p1KgRkydPJjAwEGNjY1xcXChWrBj169fPrKFla2ZmZpiZmaV5uw4OiW98KER6iUtU1G09AK2gjJn1JLK2155BTGgtiQohhBBCiKwu05IVADNnzmT58uVs2rSJDx8+ULhwYZYuXUq9evUAGDFiBAqFgvXr1xMcHEy5cuWYM2eO2n4Us2fPZvbs2cyfP5/o6GiqV6/OxIkTUSqVmTWsbO3zZSDR0dFs3LiW338/gJ+fH1WrVqd06bIsXbqQCxduANC2bTNatWqLu/tbTp8+SVRUFLVq1WbEiDHo68ee1hK3DKR4cXuGDOkPQO/e3fnxx6ZMnDgNgD17drJv3y7evfMgX7789OrVm3r1GmTKcxDZ36eJirwF7PnwQJIVAvJbGkiiQgghhBAiG8jUZIWuri4jRoxgxIgRCZZramoyatQoRo0alWgb+vr6zJgxgxkzZqRXN79pa9asYMeOLfTt+wsFCxbh998PsGrVsnj1tmzZQJUq1XBymsXLly9YtmwR5uYWDBgwRK1esWLFGTFiLC4uc5kwYSplypQDYP361WzatI6uXXtSqVJFzp37i2nTJqKhoaBuXZklI77M54kKIeIY6mbqf3tCCCGEECKF5Lc2kajg4CB27dpGt269+Omn3oSGRlC1anV69uyMm9tTtbq5cuVm2rRZaGhoULlyVW7d+psrVy7GS1YYGBhiZxe7QWqhQoXJl8+GgIAAtm7dRJcuPejT5xd0dbUoW7YSwcFBrFy5VJIV4otIokJ8Db/A4MzughBCCCGEQJIVIgn3798lPDycWrVqq65paGhQu3bdeMkKe/uSaseQ5s6dm6dPn3xBnDCqV3ckMjKSyEgNIiMjqVq1OkeOHOLt2//ImzdfmoxJZE0vnt3ln7u3kqzj7+ebbDuSqBBf496rAJ6+isjsbgghhBBCCCRZIZLg6+sLgKmp+oabZmbm8ep+enQsgIaGgpiY6Hj1EuLv7wdA//4/JVju5eUpyYoczs7Oglzf5U6yzt5tN5Is93h9j0MbrmOdvwAPruzlwRX1chM9+XEnEnfvVQDzDvxL5ep1M7srQgghhBACSVaIJFha5gLA19eH/Pk/Jgt8fX3SNI6BQexRg7NmzSd37txoa2sSHv7xqEFb2wJpGk/kPD4ezwgN8qBVtxbktc2bYJ0PT95ncK9EdhGXqBjdshC3g/UzuztCCCGEEAJQZHYHRNZVtGgx9PUNuHDhL7XrFy/+lcgdKfP5SS0lSjigqamJj483xYuXoESJkhQvXoJ//3Vjw4Y1xMTEfFU8kbP5eDzj3p8byZ03f6KJCiES82miQk4JEUIIIYTIOmRmhUiUoaEh7dt3YsuWDejp6VKwYBGOHz/K48eP1Pan+PJ2Y98QXLp0AT09fQoUsKNt2464ui4iIMCfsmXLcP/+A1avXo6j4/eqmRdCfC4uUeFQuyfBL/dmdndENiOJCiGEEEKIrEuSFWnIxKogd96mbJ+Gr6VUKoiKUo9llMsuzeP06tWHqKgoduzYSmBgINWr16RVq7b88ceRVLdZsGAhGjZszNatG3n06CHOzgsZMGAIZmZmHDq0n3XrVmFhYUn79p3p1atPGo5GZFXv3zzi+dOLSdYJCVBPWnm/fapKVJhZFyH4ZdIxgoICv7abIgeRRIUQQgghRNYmyYo01PmnoYSGZsxO8rq6WmkW68KFjxsXNm7cjMaNmwEQERHB6dMnaNeuI0OHDlPFmzZtIjY2Nqp79u49HK/NoUNHMnToyARjKBQKJk+ezuTJ09WudenSgy5deqTp2ET2UKWUKZ0bJJ1MaDtRW/W1j8czHpzfpEpUAIT6vebakf0J3usfEExUuHaCZeLbI4kKIYQQQoisT5IVIlFaWlps3LiWw4cP0LNnL5RKba5fv8qZMycZO3ZSZndPfKPiln6U++FnDC0Kqq7b22mxaIxOvPr3ngTgsuYllWvIKQ8CfAJDmHfARxIVQgghhBBZnCQrRJKcnRexYsVSnJymEhwcjK1tAcaPn8KPPzbN7K6Jb9Cne1SY5y1KeFhkkvVjExVujOhTmDsv5JSHb929VwE8fuPH3O72kqgQQgghhMjiJFkhkmRrW4DZs+fL0gyR6cJCw3j8yR4Vyfk0UeHwnRF3XqR/H0XWFbf0o5iNiSQqhBBCCCGyATm6VAiRLfh4fUh1okJ82z7do8LMUC+zuyOEEEIIIVJAkhVCiGzBzCKXJCrEF7v7wv+LNtP0CwzOgF4JIYQQQojkyDIQIUS2oKMbf/PMz0miQnzq3qsAXA49T3Gi4t6rAJ6+kuVuQgghhBBZgcysEELkCCEh4ZKoECpxSz/GtSmS4kTFvAP/UtTWOgN6J4QQQgghkiPJCiFEjuDxzl8SFQJQ36OilJ3xF9U3MZRTY4QQQgghsgJJVgghcgRrK2NJVAi1xMOXzKhIaX0hhBBCCJExJFkhhMgR9PS0M7sLIpMFhkZKokIIIYQQIoeQDTbT0MGtiwnzdMuQWEpNDaIiY9SvmdrRpOPgDIn/pW7evMGQIf1Zu3YzxYuXyOzuiG+Qv7+c8pDTvfYMYkJrSVQIIYQQQuQEkqxIQ5G+/9K1+OsMiaVUKoiKila7tvVRhoQWItu59ySAZ/+GZ3Y3RDrLb2kgiQohhBBCiBxCloEIIXK0uONMixSSUx5yOkPd5PPvX7pURAghhBBCZA6ZWSFwdKzIhAlTuXr1EpcuXURbW4sGDRozcOBQNDU1cXd/S7t2zZkxYw516tRX3dezZ2eKFv2OiROnAeDv78fy5Uu4ePE84eFhlCjhwKBBwylcuEiCcR89esiKFUu4d+8Oenp61KvXgF9+GYKurhYAMTEx7Nmzk99/P8CbN69RKjUpWdKBwYNHqNocNKgv+fMX4N07d+7du0vz5q0YNGhYuj4vkX3EJSpG9CnMnRdyyoP4sqUiQgghhBAi88jMCgHAkiULMDU1Y/bs+bRu3Z49e3Zw+PCBFN8fGRnJsGEDuHz5Iv36DWT69DmEhYUxYsRA/P3949V//vxfBg3qA2gwffoc+vcfzOnTJ5kyZZyqzo4dW1m5cilNm7ZkwYKlDB8+mhcvnjNz5jS1to4ePUTevPn49de51Kv3QyqfgMhpPk1UyCkhIk5Kl4oIIYQQQojMJTMrBAAODqUZPnwMABUrVubixb+4fPkirVq1TdH9ly9f4MmTxyxbtoYyZcoB8N13xejbtyePHz9AqVR/qW3cuBYzMwvmzVuEtnbsKQ7589sycGAfbt78mxIlSvP+/Tt69PiZ9u07AVCuXAUCAvxZunQhwcHB6OvHflKur2/AsGGj0dSUl7OIJYkKkZiULBURQgghhBCZT35rEwCULFlK7ftcuXITEhKS4vvv3r2DoaGhKlEBYGZmzp49h4DY00A+devW39Ss+T0KhYLIyEhVHwwMDLh+/SolSpRm2LBRAPj4+PDq1QtevnzBxYvnAYiICAdikxU2NjaSqBAqkqgQX8MvUE6NEUIIIYTICuQdngBAR0dX7XuFQkF0dHQiteMLCPDH1NQ8xfX9/Hw5ePA3Dh78LV6Zp6cnAC9fvmDu3F+5c+c2urq6FCnyHfr6BgDEfHJqq5lZyuOKnE0SFeJr3HsVwNNXEZndDSGEEEIIgSQrRApoaGgAEB0do3Y9JOTjJ5AGBob4+vrEu/fmzRvkyZM33nVDQ0McHb9PcJlJ7tyWREdHM3bscIyNTdi8eSd2doVQKBT89tserl27/LVDEjlQQEAQLms8JFEhUiXuONPK1etmdleEEEIIIQSywaZIgbjZDF5eH1TXPnx4j7v7W9X3pUqVJjAwgDt3bquu+fv7M2rUEK5duxKvzdKly/Lq1QuKFbOnePESFC9egty5rVixwhU3t2f4+vrw5s1rmjdvRaFCRVAoYl+qV69eAmJPChEijo/HM169eieJCpEqcYmK0S0LYWIop8YIIYQQQmQFMrNCJMvY2BgHh1Ls2LGV3LmtUCqVrF+/GiOjj28Ka9SoxXffFWPatIn07TsAExNTtm3bhIVFLurVa8CTJ4/U2uzRoze//PITkyePo0mT5oSHh7Np01rev39HsWLFMTY2x8rKmj17dmBuboFCoeCPP37n0qULAISFhWboMxBZl4/HM+79uZHKRa0kUSG+2KeJCgdbI24/Sv4eIYQQQgiR/iRZkYY0TQux9VHGfOKv1NQgKlI9ltLULt3iTZs2g5kzZzB9+mTMzS3o2rUnN25cVZVramri4rKMZcsWsXjxAmJioilTphyLFi3D0NAwXnvFi9uzePEKVq9ezqRJY9DW1qFUqTJMmjSd3LmtCA2NYObMeSxaNI8pU8ZhYGCAvX1JFi1aztChv3Dv3h2srfOk23hF9hCXqHCo3RMjf9fM7o7IZj5PVAghhBBCiKxDkhVpqEXXoYSGZszmbLq6WmkW68KFG/GuzZ69QO17O7uCLF26Su1ay5Zt1L43NTVl4sRpCcYoX75ivDilS5fF1XV1ov0qXtyelSvXJ9nfpO4XOZv326eqRIWZdRHwT7q+v7+c8iA+kkSFEEIIIUTWJskKIUS24+PxjAfnN31MVCTj3pMAnv0bngE9E9mBJCqEEEIIIbI+2WBTCJGtxC39KFP3pxQnKlzWuFGkkHUG9E5kdT6BIZKoEEIIIYTIBiRZIYTINj7do8I8b9Fk68clKkb0KYyxsZzy8K279yqAx2/8JFEhhBBCCJENSLJCCJEthIWGqe9RkYxPExVySoiIW/pRzMZEEhVCCCGEENmAJCuEENmCj9cHSVSIVPl0jwozQ73M7o4QQgghhEgBSVYIIbIFM4tckqgQX+zuC/8v2qPCL1BOjRFCCCGEyArkNBAhRLago6uTbB1JVIhP3XsVgMuh5ylOVNx7FcDTVxlz/LQQQgghhEiazKwQQuQIISHhkqgQKnFLP8a1KZLiRMW8A/9S1FZOjRFCCCGEyAokWSGEyBE83vlLokIA6ntUlLIz/qL6JoZyaowQQgghRFYgy0BEisXExKChoZHZ3RDfKD9vb/wvrld9r6lUEBkVrfre2lBDEhVCLfHwJTMq4urffpQBnRRCCCGEEMmSZEUa2rt2NQH/vc2QWJqaCiIjo9Wu6VpZ0apn73SJt3//Pl6/fkPfvgMAmDlzGo8ePWDLlt3pEi8ltm/fwvbtmwgNDWXcuMnUr98wXeO5u7+lXbvmzJgxhzp16meJZ/AtKWCbl8otx6u+19HWJCw8UvW927H7mdEtkYUEhkZ+VaJCCCGEEEJkHZKsSEMh7zxoZJAxx+IpFQqiotWTFcfevUu3eOvXr6VatRrp1v6XCgwMZMWKJdSr14BWrdpSoEDBdI9pYWHJypUbsLW1TfdYIu35+8spDznda88gJrSWRIUQQgghRE4gyQqRLQUGBhATE0OtWnUoU6ZchsTU1tbGwaFUhsQSaevekwCe/Rue2d0Q6Sy/pYEkKoQQQgghcgjZYFMAEBkZyZYtG+nUqTV161ane/cOnDhxDIC2bZvh7v6W337bg6NjRbX79uzZSZs2TalbtwaDBvXl5csXauXXr1+hT58e1K1bg1atGrN27UqioqJU5W3bNmPFiqX07duTRo1qs3v39mT7evToYdq2bQbAlCnjVF8nNYY4ly9f4Oefu1GvXg2aNv2BWbOc8PPzS7Lc3z+23N39LY6OFTl79lQKn6rICuKOMy1SSE55yOkMdZPPv3/pUhEhhBBCCJE5ZGaFAGDGjClcvPgXP/3UjyJFinLu3BmmT59EWFgos2bNY/ToYZQqVYaOHbuq7nn58gV//PE7w4aNIjw8nCVLXHBymsT69VsBuHHjGqNGDaV27br8/HM/Xr16yerVy/Dz82PkyLGqdnbu3EqPHj/To8fP5M+fP9m+VqvmyMyZ85g4cTT9+g2kSpVqyY6hWbOWeHi4M3HiGJo3b83AgUN5//4drq4LiYyMYMqUXxMtDwsLw8lpVho/cZER4hIVI/oU5s4LOeVBfNlSESGEEEIIkXkkWSFwc3vG6dMnGDVqPC1btgGgcuWqBAYGsmrVMg4ePIa2tjbm5ubxlkE4Oy/E0jIXAB8+vMfVdRFBQYEYGBiyZs0KSpRwwMlpNgBVq1bH2NiYWbOc6Ny5G3ny5AXA1rYAP/3UN8X9NTMz47vvigFgY5Of774rnuwYGjduxoMH9wkPD6dr155YWloCoKenj5fXe4BEy9+9c0/VcxWZ69NEhcN3Rtx5kdk9EllBSpeKCCGEEEKIzCXJCsHt2zcBqFu3vtr1+vUbcPr0CV6+fJ7gfVZWeVSJCkCVfAgICESp1OThw/v06TOAyMiPJzZUqVKd6Ohobt68QZMmzQGwsyuUIWMoVqw42tra9O3bg3r1GlCtWg0cHWthYKBLaGhEouVKpfKr+yeSdu2xF5cf+iRZ552vQYrb+zxRIUSclCwVEUIIIYQQmU9+axMEBPijVCoxNjZRu25mZgFAUFBQgvfp6uqofa+hEbsFSkxMNAEB/kRHR7NqlSurVrnGu9fLy/OTOGZf1X9I2RgKFSrCokXL2bp1I/v27WLHji1YWFgwbtxEqlWrRb58NgmWjxgxju+/r/PVfRSJiylQifeVSyZdZ+2xJMsf/hfK4MVhBPoH8/r5B/IXzMWqPyLhj9gkiFKhR8u06rDIsfwC5dQYIYQQQoisQJIVAmNjE6KiovD391N7s+/t7QWAiYlJYrcmysAg9lPwHj1+pmbN7+OVfzojIy2kdAylS5fF2XkRoaGh3Lhxje3bNzNu3Gj27fsdS8tcCZZPmTKOfft+T9P+irTnb1GCZ5XK4bZ9H4V79ya4kB2fvu20vnY/0/omsod7rwJ4+iois7shhBBCCCGQ00AEsW/gAc6cUT/l4vTpE5iZmWNjY4tC8WUvFX19A4oU+Y7//ntD8eIlVH80NbVYudKVd+/epVX3gZSN4fDhA7Rr14LIyEh0dXVxdKxFnz6/EBUVxYcP75MtF1lbiL9fbKKicxuMC9lldndENhN3nGlRWzk1RgghhBAiK5CZFYIiRYpSu3ZdXF0XEhwcTJEiRTl//hynT59gxIixKBQKjIyMePz4Ebdv36RMmXIpard3736MHz8KQ0NDatWqg6+vL2vWrECh0KBw4SIZPoYyZcrh5TWPyZPH0qpVOyIiIti0aR358uWjaNFiGBgYJlieJ09suSQssq63Lx/i+eol3w3sIYkK8cXiEhWjWxbidrCcGiOEEEIIkRVIsiIN6VlZc+y/txkSS1NTQWRktNo1XSurVLc3ZcqvrF27kt27t+Pv74etrR1TpsygQYMfAejVqzezZ89g5MjB7NjxW4radHT8ntmzF7Bx41qOHj2Mvr4BlSpVoX//Qejq6qa6r6kdg61tAebOdWHdulVMmhR7dGr58hWYNWsOmpqaiZZPnformpryTyWrevvyIWd+W46lbQFJVIgv9mmiwsHWiNuPMrtHQgghhBACJFmRptr27ktoaMasd9bV1UrTWNra2gwYMIQBA4YkWF6vXn1q1Pi498TEidPi1alVqzYXLtxQu+boWAtHx1qJxt2793Cq+psnT954sZIbA0ClSlWoVKmK2rVPn2VC5YnFTOgZiNTxf/iIgJt/J1kn0l873rW4REXd1gP489aO9OqeyKE+T1QIIYQQQoisQ5IVIkuJiooiMlJD7bjTzykUii/eQ0NkbY4W1jQy0EuyzsS7r9W+/+/Fx0RF3gL2RB96TcDKzYner1DIjzvxkSQqhBBCCCGyNvntXWQpQ4f+wu3bN5Os8+OPTWVWwzfu7cuH/HlghSpRAVBW15CxxRwSrP/43Tv2v3XPyC6KLEwSFUIIIYQQWZ8kK0SWMmbMBCIjwwkPT3xmhYmJacZ1SGQ5cUs/GnUYgmXeosnWf/zuHasvXqCqY/wjdMW3xycwhHkHfCRRIYQQQgiRxUmyQmQptrZ2ab4fh8g5Pt2jIp+dPWFJJLXgY6Kibw1HnhsYZFAvRVZ171UAj9/4Mbe7vSQqhBBCCCGyOFn4L4TIFkJDQ9T2qEjOp4mKYl9xUo7IGeKWfhSzMZFEhRBCCCFENiAzK4QQ2cKL148xL1CUB1f28uAKKDUVRH1yfK++n7fqa0lUiE99ukfF/ms+md0dIYQQQgiRApKsEEJkuudvn7LS532SdSzNNGjepYbqex1tTbVlIGeXXmHltVP4BobwzOMDRaxzcfblXc6+vAtAjFnu9Om8yNLuvvBX20wzuWSFX2BwBvVMCCGEEEIkRZIVQohM52BvQdfiSb9J7LlHO8nyAvm16FI5hnkH3rKwf/zNE7c+svjqfors5d6rAFwOPU/xZpr3XgXw9JXslyOEEEIIkRXInhVCiBwhODRCjqMUKnFLP8a1KZLiRMW8A/9S1NY6A3onhBBCCCGSIzMrRIrFxMSgoaHxP/buPCyqev8D+BsY9k0EBGUR3EDFNXNFJTVTyzLLzDKXLCuzxa7dSiuvWWq5lWVuadlqWabWtbK0m0vuiormBgwIOMo+K8sAvz/4zTgDzMbsM+/X8/Q8zZzvnPM5MDLnvOe72LsMclOSUhGO/fdH9eOGc1bcvHYD7z1q3I0puTbNOSq6JYSgslL/qjGa7dPlATaqkoiIiIj0YVhBRvnxxx9w7VoeZs6cBQB4553/4OLFC/jii+/sXJnrSE3tg1mzXsAjjzzW5Pbr1wswYcK9WLRoKe64Y4SNq7O/7onA/Jd81Y99fAWo0rgJnfcGV3kg7eDBlB4VqvbpF21QJBEREREZxLDCgj765gtklhXb5FheXp6oqanVeq5tUCienjDZKsfbvPkTDBgwyHBDIjsJ8uefM3cnrVCaFVQQERERkePg1b0FCSWlEPXtapNjeXl6oKa2TvvJY+dtcmwiZ8RVHlzftSIZ5o1nUEFERETkCjjBJgEAlEolvvjiM0yaNB7Dhg3ElCkTsWfPrwCABx8ci+vXC7B9+zakpvbRet22bVvxwAP3YNiwQZg9eyZycoRa248fP4Inn5yKYcMG4f77x+CTT9ahpqZGvf3BB8di7doPMXPmNIwalYbvvvvaqHo3bVqPGTMew+rVKzBqVBqeeeZx9Xl88sk6jB9/N4YNG4gZMx7DiRPH1K87deoEUlP74MyZ03jmmccxbNhA3HffGPz00w6t/ZeWlmDRojcxevQw3HnnYLzyyhwUFOQDAPbv/x9SU/vg+vUCdfsPPliB1NQ+6jYAsHLlu3jyySkAAJlMivffX4577hmFtLT+uOeeEXj77QWQSCRaxy0vL8Nrr/0Lw4YNwoQJ9xr8eeTlXcNrr/0Ld945BKNGpWHRojdQVlZm1M/QndSv8iCydxlkZXERgQwqiIiIiFwEwwoCACxa9Ca2bPkEY8fej6VLV6Jbtx54663X8dNPO7B48TKEh0cgLW041q37VP2anBwhfvnlZ7z44lzMn78A167lYuHC19XbT5w4hrlzX0CbNm2wePEyTJr0GLZu/RLvv79c69hbt36J/v0H4o03FqF//4FG13z16mX8888FvP32e3jssfqw4r333sHWrV9iwoRJWLx4Odq2TcDcuc/j3LkzWq/9z3/mY+jQYVi27AN06pSMd999G9nZWQCAysoKPPfc0zh79gzmzHkZr7/+FkpKijF79kyIxWL06XM7BAIBTp48rt7f6dMnAQBnzpxWP3fs2FH0718/dGbhwtdx8OBfmD37Baxc+REefngyfv/9V3z22SeNfhYBAYFYsmQ5hg4dhtWrV+Lnn3c0ef4lJcWYNesJiETX8frrCzF37mvIyDiHl156FtXVXH5Rhas8uI8gP8OdBU0dKkJERERE9sFhIITMzKvYu3cP5s59DePGPQAA6Nu3P6RSKdavX4OdO3+Fj48PWrZsiZSUblqvfe+9VYiIiAQAFBbexEcfvQ+ZTIrAwCBs3LgWXbqkYOHCJQCA/v0HIiQkBIsXL8QjjzyG1q3bAADi49vi8cdnmlx3TU0NXnjhX0hO7gKgPjzZvfsnvPLK6xg7dpz6mMXFRdi4cS1Wr16nfu2DD07Eww/Xz+/RvXsK/ve/fThy5G8kJrbDL7/8F9eu5eDzz79F27YJAIA+fW7HAw+MxQ8/fIvp059Et249cPr0Cdxzz30Qi8XIyrqKTp2ScPZsOkaPvgci0XXk5eWif/9BqKysRHV1NebOfQ1paUNRUVGN3r37ICPjLNLTT2mdU0pKd7zxxlsAgH79BuDmzRv4/PNPcc894xqd/3fffYOqqkqsWvUxWrRoAQDo0iUFkyaNxx9//IbRo+8x+WfqarjKAzVkylARIiIiIrIf9qwg9Q3zsGHaK0yMGDESZWWlyMnJbvJ1UVGt1UEFAHX4IJFIUVFRgX/+OY+BAwdDqVSq/+vXbyBqa2tx6tQJ9esSEto1u/a2bRPV/3/6dP0+BwwYpHXM/v0H4ezZdK3eBl273gpdgoND4O8fgIoKhXo/sbFxiImJVe/D19cPPXr0VPem6NdvAE6dqu9NkZ5+ChERkRgx4i6cPZsOADh69DBatGiBzp27wNfXF6tWrUH//gNRUJCPY8eOYOvWLyEUZqO6ukrrfIYOvUPr8aBBg1FQkI/y8rJG537q1Al07dodQUFB6jpbtYpCQkKiVq8Pd8Wu/tQUY4eKEBEREZF9sWcFQSIRw8vLCyEhoVrPh4WFAwBkMlmTr/Pz89V67OFRn33V1dVCIhGjtrYW69d/hPXrP2r02uLiIo3jhDWrbn9/f/j7+6sfl5eXAwDGjRvdZHvNG34/Pz+tbZ6eHqitrVXvJydHiLS0/o32ERsbDwDo338Q1q37CLm5OTh9+iS6d++J7t174uOPV6OsrAzHjx9Bv34D4OlZ/zM5ePAvrF69EgUF+WjRogWSkrrA19cPtbU1WvsPC2up9bhFi/qfTVFREQICtHsGiMXluHAho8k6W7YMb/Jn4C4YVJAuxgwVISIiIiL741UbISQkFDU1NRCLy7UCi5KS+mVYQ0NDdb1Up8DAQADA1KkzMHjw0EbbNXtkWEpQUBA8PDywdu0mCASN39qhoS0A5Bi1nw4dOuHVV19vtM3b2wcA0KFDR0REROLUqRM4c+YU7r33fiQnd4Gfnx9Onz6BkydPYM6cfwMArl3LxRtvvIpRo+7Bxo3PIDS0PpB4441XIRRmae2/4YSbmr+DhvNQBAYGoX//gXjiiacb1dkw2HAnDCrIHFw1hoiIiMgxcBgIoXv3ngCAffv+0Hp+7949CAtridjYeHUPAWMFBASiQ4dOyM/PQ3JyF/V/AoE31q37CDdu3LBU+Wrdu/dEXV0d5HK51jGPHz+Gb7/9Gl5eXkbv5/r1fERHt1HvIympM7799mv8/fcBdbt+/Qbg778PIDPzKnr06A2BQICuXbvj22+/hkwmRb9+9T0eLl++iOrqakyePBVRUVEAAIVCgbNn01HXYPXZo0f/1nr811/7EBsb32S40717T+Tk5KBduw7qOhMT22Pz5g3q4SjuhkEFmYOrxhARERE5DvasIHTo0BFpacPw0UerIJfL0aFDRxw48Bf27t2Dl156BZ6enggODsalSxeRnn4KPXr0Mmq/TzzxFF57bS6CgoIwZMgdKCsrw8aNa+Hp6YH27TtY/Dw6dkxCWtowvPXWG3j88ZlISEjE6dMnsWXLJjzyyBSjA5e7774X27Z9izlzZmHy5OkICQnBrl0/4q+/9mHkyJXqdv36DcSbb76KFi1aICGhfu6Mnj17YdOm9UhJ6f7/PTnq6/Ly8sLatR9i4sSJuHmzGFu3foGSkmL4+PhoHfvEiWP46KP30b//QOzf/ycOHtyPhQsXN1nnxImP4tdf/4u5c5/HhAkPQyAQYOvWr5CRcRZPPvlMM36Czq1UrMCyHaUMKqhZVEFX34HD7F0KEREREYFhBf2/N998G598sg7fffc1xOJyxMcn4M03F2HkyPr5H6ZPfwJLlizCv/71HL75ZrtR+0xNHYolS1bgs88+we7dPyEgIBC3394PTz89u9GcEZY+jy+//AylpSWIimqNp5+ejUmTHjN6H4GBQVizZgPWrPkAy5cvQXV1FRIT22PJkhUYMCBV3e722/vBy8sL3bv3goeHBwCgZ8/eAKC1BGt8fFvMn78Qn366AS+8MBstW4ajf/9BuPvue7Fy5XsoKipU95yYOXMWjh49jO3bv0OrVlF48823MXz4yCbrjI6Oxscff4K1a1fjrbfehIeHB5KSkvH++x+jY8ckk392zizjsgSXc8vx7qOdGVSQybhqDBEREZHjYVhhQQnBYag5dt4mx/Ly8kRNTa3Wc22DTJ9bQsXHxwezZj2PWbOeb3L78OEjMGjQrbkn5s//T6M2Q4ak4eDBE1rPpaYOQWrqEJ3H/f77n5pV74wZT2HGjKcaPW/oPHr37tOoRgD49df/aT1u1SpKZ48GleDgYPz111Gt53r1uq3J/Y8cOQojR46Cn583KipuzT0xbtyD6v9XvW7ixEebPF7r1m0a7TsxsR3ee+99vXW6uozLEqzcmIlO8aEMKshkDYcOpV+0d0VEREREBDCssKjZkx7TuhG1poY3va6ipqYGSqUHlEqlzjaenp4mz6FBrkkVVLz0ZHvs+r7E3uWQk+EcJ0RERESOi2EFOZQXXngG6emn9LYZPfqeJnt2kHs5d1GsDipSOgVjF/SHFVzlgTQxqCAiIiJybAwryKH8+9/zoFRWoapKd88K1cSV5L4yLkvw/qZsdVBhsH2uBFdyXa8nEjUPgwoiIiIix8ewghxKfHyCyw5xIctQDf14ZVYnJCUangyRqzyQplIpV40hIiIicgYc+E9ETkNzjopuySGG22t8gx4axFUe3F1GrgSX8soZVBARERE5AYYVROQUZHKl1hwVhrCrP2lSvR+SYrlqDBEREZEzYFhBRE7hmkjOoIKaRfP9EBbkb+9yiIiIiMgIDCuIyCnERQcwqCCTnROKTXo/cNUYIiIiIsfACTaJyCkEBhj+c8WggjRl5Eqwcle20e8HrhpDRERE5DjYs4KIXIJUoWRQQWqq4OrVBzoYHVQs25GFjvHRNqiOiIiIiAxxiLCiqqoKo0ePxquvvqp+rq6uDmvXrkVaWhp69OiB6dOnIzMzs9HrFi9ejEGDBqFXr154/vnncePGDVuX7/auXy9Aamof/PnnH/YuhdxY3k0ZgwoCoN3DplsCV40hIiIickYOMQzko48+QlZWFnr06KF+bs2aNdiwYQPmzp2LmJgYrF27FtOmTcPu3bsRHFx/M7JgwQLs27cPr7zyCgICArBy5UrMnDkT27dvh5eXl83PY+f3H0AhyTTc0AIEXp5Q1tRqP+eXgHvuf84mxydyNLGtAhlUkMlDgRq2T79ogyKJiIiIyCC7hxUXLlzAF198gbCwMPVzUqkUmzZtwuzZszFlyhQAQJ8+fXDHHXfg+++/x/Tp05Gbm4sdO3ZgxYoVGDNmDAAgOTkZo0aNwt69ezFy5Eibn0u1PBuPjMyzybE8vTxR2yCs+HqPTQ5N5JCC/O3+54zsTFph2lAgznFCRERE5LjsOgxEqVRi3rx5mDFjBqKiotTPnzlzBnK5HMOHD1c/Fxoair59++LAgQMAgCNHjgAA0tLS1G0SEhLQsWNHdRsy3vnzGXj22Sdx551DMHr0MLz++isQia4DqP89ffbZJkyaNB7Dhg3ElCkTsWfPr3r3d/ToYcyePRN33jkEw4YNxLRpj+Cvv/apt2/atB4zZjyG1atXYNSoNDzzzONWPT8irvLg+q4VGT8UiEEFERERkWOza1ixceNGVFdXY+bMmVrPC4VCAEBcXJzW87Gxsept2dnZiIiIQEBAgM42ZJyKigq8/PILiIiIxNKlK/DKK/Nx+fJFLFgwDwCwaNGb+OSTDRg79n4sXboS3br1wFtvvY6fftrR5P4uXMjAyy+/gMTE9li6dAUWLlwMPz8/LFz4OkpLS9Xtrl69jH/+uYC3334Pjz3GsIKsp36VB5G9yyAri4swbigQgwoiIiIix2e3ftOZmZlYt24dPvvsM/j4+Ghtk0ql8PHxafR8YGAgpFIpAEAmkyEwMLDRfgMDAyESmX9TEh4epHObRFIEPz/vJrd5etku/2l4LIGXp8669MnMvASxuByPPjoZ3bvXzxsSGRmBEyeOITc3C3v37sFrr72OBx6YAAAYMmQwFAo5NmxYg/Hjx8PXt/5t5O0tgJ+fN/LycnDHHcMxf/7r6mPExcVi8uSHcfXqPxg8eCgEAi/U1NTg5ZdfQZcuXRvV1JzzMIctj+cI5+bt7YXISMvfpDV3n/7+3ur3kS4CgSd8GrTRfCwQeDa5j3NCMVbuykZq2l1mnbM1fl6OcjxnOTdD75PQIJ8mt2s+V6Gswcpd2Zg/oWOTk2/6+3vzfeIAx7L18Xhuznk8nptzHs+Vz83Wx+O5OefxeG7Gs0tYUVtbi/nz5+PBBx9Er169Gm2vq6uDh4dHk69VPa+rjb7XmqK4WIra2romt1VX16CiorrJbQ3nkbCWpuasUNbU6qxLn9atYxESEoo5c57D8OEjMWBAKm677XZ07doDP/zwHQBgxIiRWvu+444R2LPnV1y6dBn+/vW9W6qrlaioqMbIkXdj5Mi7UVoqRk5ONnJzc3Hq1HEAgEymQEVFNZTKmv8/dlyjmv38vJt1Hs1ly+M5yrlVV9egsFBi0WNFRgY3e58KRTUqK5V62yiVtajSaOPjK9B6rFTWNtqH5jfo6XJBs+sz59wc/XjOdG6G3ic1TbwHfH0FWs9li6SYNz4RnVoHNLkvhaKa7xM7H8vWx+O5OefxeG7OeTxXPjdbH4/n5pzH47lp8/T00NtJwC5hxRdffIGCggKsX78eSuWti8W6ujoolUoEBwejqqoK1dXV8Pa+9a2wTCZTrwQSFBQEmUzWaN9yuVzdhowTEBCIjz7agM8++wS//PJfbN++DUFBwXjqqWchkYjh5eWF0NBQrZvesLBwAPW/E1VYoaJQKLBs2WLs3Vs/42d8fFt07JgEoP53rOLv7w9/f39rnx65Ka7yQE0xdqgIEREREdmXXcKKP/74Azdu3EDfvn21nr948SJ27NiBt956C3V1dcjLy0NiYqJ6u+bjhIQEFBUVoaKiAn5+flptbrvtNtuciAtp16493nprCaqrq3HmzGls2/YNVqxYiscfn4mamhqUl5fD1/dWKFFSUgygfuLThlateg/Hjh3B8uUfoEeP3vDx8UF2dhb27PnFZudD7o1zEpAuQX5cNYaIiIjIGdhlgs2FCxfi+++/1/ovISFBvTTp3XffDV9fX/zxxx/q15SXl+PYsWMYMGAAAGDAgAGoqanBvn23VpgQCoW4cuWKug0Z58iRv3HPPXeitLQU3t7e6NOnL+bM+bdWmz/+0F4Xde/ePQgLa4nY2PhG+zt//hz69RuA22/vr5535OjRvwEAdU2PrCGyGAYVZA6uGkNERETkGOzyFVO7du0aPefn54cWLVqgW7duAIDJkyfjgw8+gKenJxISErBu3ToEBQVhwoT6SR7j4+MxatQovPHGG5BKpQgJCcHKlSuRlJSEESNG2PR8nF39BJd1mD//ZTz66FR4e3vju+++RlBQMMaPfwhZWVexatVylJdL0KFDRxw48Bf27t2Dl156BZ6ejfOu5OQuOHRoP3755WdERUXj5Mnj+OabLwAAlZUVNj47cicMKsgc9avG2G5OGSIiIiLSzWH7w7700kvw9PTE5s2bIZfL0atXLyxdulRrPoolS5ZgyZIlWL58OWprazFw4EDMnz8fXl5edqzc+YSEhGL58g+xbt2HWLToTSiV1ejSJQXvv/8xWrRogTfffBuffbYB3333NcTicsTHJ+DNNxdh5MjRTe5v9uw5qKysxAcfrAAAJCQk4p133sPq1SuRkXEWo0ffY8vTIzdRKlZg2Y5SBhXULKqgq+/AYfYuhYiIiIjgQGHFzp07tR4LBALMnTsXc+fO1fmagIAALFq0CIsWLbJ2eUbxDkjE13tssxqIwMsTygargQj8Epq9v+Tkznj//Y+b3Obj44Pnn5+DmTNnN7m9des2OHjwhPpxWFgY3n773UbtBgxIVf//jBlPYcaMp5pdL5GmjMsSXM4tx7uPdmZQQSbTXjUmwPALiIiIiMjqHCascAX3PfiCyy6BSeSoMi5LsHJjJjrFhzKoIJNx1RgiIiIix2SXCTaJiCxBFVS89GR7hIVwGVwyDec4ISIiInJcDCuIyCmduyhWBxUpnQzfaHKVB9LEoIKIiIjIsXEYCBE5nYzLEry/KdvooIKrPJAmBhVEREREjo9hBRE5hXP5tXjug1JIxXJcyy5EQvsorP9FCfxSCgDwLGt6WVyu8kCaSqVcNYaIiIjIGTCsICKnUK4IxvVroRDlyRAd2x0360KhLL21Ik7b6tJGr+EqD6QpI1eCS3nleHcKV40hIiIicnQMK4jIKXSGBwpyCrBgUCqSoqLg6yNAZZVSvX3T2Uyt9lzlgTSp3g9JsVw1hoiIiMgZmBRWXLt2DSdOnEBeXh5KS0vh6emJiIgItG7dGgMGDECrVq2sVScRubmCUjFmDroDSVFRBttyTgLSpPl++PFY4x44REREROR4DIYVcrkc27dvx1dffQWhUIi6urom23l4eKBTp0546KGH8MADD8DPz8/ixRKR+2oTFsKggkx2TijWej8YCiu4agwRERGRY9AbVnzzzTdYtWoVqqqqMGDAADz++ONISkpCbGwsgoKCUFdXh7KyMohEIqSnp+PkyZNYtmwZVq9ejVmzZmHKlCnw8PCw1bmQE6mrq+N7g0wS6OtjsA2DCtKUkSvByl3ZRr8fuGoMERERkePQGVY8+OCDqKysxOuvv46RI0fq7CkRFRWFqKgo9OjRA1OnToVUKsUvv/yCzZs3Y9euXfjhhx+sVjw5H4lEgpUr38XEiY8iObmzvcshFyKrVDKoIDVVcDV/Qkd0am14clWuGkNERETkWHSGFVOmTMHYsWNN/vY7KCgIEyZMwAMPPIBdu3aZXaAz+fSLTRAV3rDJsbwEnqhR1mo9F9EiEo89Ms0mx2+uK1cu4ffff8XEiY/YuxRyMXnFUrzxEIMK0u5h0y0hBJWVSqPbc9UYIiIiIsegM6y49957zdqxp6cnxo0bZ9Y+nE1haSEiO9lmklEvTw/U1GrPH1J4+aZNjk1kD9fLC7Hu2B/qxwKBJ5QagV0L/1oGFWTyUCCuGkNERETkmLh0qZubNu0RdOzYCfPn/wcAIBaLcffdw3HXXWPw+usLAQClpaW4996ReO+9VfD09MIXX3yKS5cuoqZGifj4BEyf/gSGDq3vOl1TU4P16z/CH3/sQWlpCWJiYvHggxMxbtyDOHXqBJ5//mkAwBNPTMHo0feoj7tt21b88MO3uHFDhLi4OEyd+gSGDx9p858HOa72cV5448Eg9WNfX4HWN+aLvg+0R1nkQKQVpg0F4hwnRERERI7L6LCiqqoKK1aswO+//47CwkIolY271Xp4eODChQsWLZCsq1+/Afjjj9/Uj9PTT6Gurg5nzqSrnzt+/Ch8fHzg5+ePF154Bvfd9wCmT38ScrkMX331ORYufB0//PBfhIWF4ZtvvsDPP+/Cc8/NQVRUNA4d2o/ly5eidesYpKR0w0svvYKVK9/FvHkL0KNHLwDA5s0bsGXLJkyePA09evTC8eOH8Z//zIeHhyeGDRth6x8JuSiu8uD6rhXJMG88gwoiIiIiV2B0WLFixQps2bIFbdu2xYgRI7g0qYvo338gvvpqC/Lz8xATE4vTp0+iU6ckXL58CYWFNxEZ2QrHjx/Bbbf1QUFBPoYMuQP/+tcr6tdHRUXj8ccn48KFDAwaNBhnzpxGcnJnjB59DwCgd+8+8PX1g5+fHwIDg5CQkAgAaNeuPWJiYiGRSPDll1vw6KNT8eSTzwAAhgwZDLFYgnXrPmRYQRbBVR7cQ1xEIIMKIiIiIhdhdFjx3//+F8OGDcOaNWu45KQL6datBwIDA3Hy5HF1WHH//Q9izZoPcPZsOoYPH4njx49i2rTHcffd9+Luu++FQqFATk42cnNzcerUcQBAdXUVACAlpTs2blyL5557CoMHp2HQoMGYOXOWzuOfP38OVVWVGDgwVd1bR6n0QP/+A/Hf/+5CQUE+2rSJsf4PglwWV3lwH0F+hj/STB0qQkRERET2YXRYIZFIkJaWxqDCxQgEAtx2W1+cOnUCaWnDkZV1Fb163YaUlG44c+Y0EhPboaioEKmpg6FQKLBs2WLs3bsHABAf3xYdOyYBAOrq6if7nDx5Gvz8/PDzzzuxevUKrF69Ar163YYFC95GRERko+OLxeUAgKeffrzJ+oqLixhWULNxlQdqyJShIkRERERkP0aHFd27d8fFi5wm3RX16zcAmzevx9mzpxEWFob4+Lbo2bM39u79HTExsYiNjUdsbBzefPN1HDt2BMuXf4AePXrDx8cH2dlZ2LPnF/W+vLy8MHHio5g48VGIRCIcOPA/bNq0HkuWLMKKFasbHTswsH7CxMWLl6NVq/qVVHx8BKiqqu9lER/f1urnT66JqzxQU4wdKkJERERE9uVpbMNXXnkFu3btwueff47i4mJr1kQ21r//QBQXF+Pnn3eie/f6SS979OiNrKyr2Lv3dwwYMAhA/ZCNfv0G4Pbb+8PHxwcAcPTo3wCA/+9YgTlznsWHH64EAERHR2PChIcxZEgabtwQAagPMzR16ZICgUCA0tISJCd3QXJyF3Tp0hVZWZn49NON6h4bRKbgnASkizFDRYiIiIjI/nRetSUnJzca8lFXV4clS5ZgyZIlTb6Gq4E4p6ioaCQktMPBg/sxZ87LAIAuXbrC29sHFy5k4Ikn6pcbTU7ugkOH9uOXX35GVFQ0Tp48jm+++QIAUFlZAQDo0aMXtmzZhPDwCCQnd0FOjhB//vkHHnroEQBAUFD9jePffx+Ev38A2rZNwIMPPoyPPnofEokYXbqkIDv7Kj7++EOkpg5V97wgMhaDCjIHV40hIiIicgw6w4px48Zxfgo30r//QAiFWejRozcAwNvbG127puCff86jZ8/652bPnoPKykp88MEKAEBCQiLeeec9rF69EhkZZzF69D147LHpqKmpwY8//oCiorUIC2uJhx56BNOnPwkASExsh7vuGoMvv/wMFy/+g/feW4VZs55HWFgYdu36EZs2rUdERKTWa4iMxaCCzMFVY4iIiIgch86wYunSpbaswyVEhkVCdPmGTY7lJfBEjbJW67mIFo0nsDTW7NkvYvbsF7We+/DD9VqPw8LC8Pbb7zZ67YABqbfq8vLCjBlPYcaMp5o8jqenJ9544y288cZbWs89+uhUPProVACAn583Kip4w0CmKZUqsGxHKYMKahauGkNERETkWHSGFYMHD0b//v3Rt29f9OvXD/Hx8basyylNf2yGzW6yeUNPdEtGrgSX8srx7pTODCrIZFw1hoiIiMjx6AwroqOj8csvv+Cnn36Ch4cHWrVqhX79+qFfv37o27cv4uLibFknEVGTVDeaSbGhDCrIZFw1hoiIiMgx6Qwrtm3bhqqqKpw9exanT59Geno6Dh06hF27dsHDwwOtW7dW97ro27cvYmJibFk3EZHWjeaPx0rtXQ45Gc5xQkREROS49K7h5uPjgz59+qBPnz7q53Jzc3Hq1Cl1gPHTTz+htrYWbdq0Qb9+/bB48WKrF01EdE4o1rrRNBRWcJUH0sSggoiIiMixmbzgfHx8POLj43HPPffgwoULOH36NHbu3IkLFy7gxx9/ZFhBRFaXkSvByl3ZRt9ocpUH0sSggoiIiMjxGR1WKJVKpKen48iRIzh69CgyMjJQUVEBLy8vpKSk4KmnnkK/fv2sWSsRkfpGc/6EjujU2vBkiFzlgTRx1RgiIiIi56A3rLhw4QIOHz6Mw4cP4+TJk6ioqICHhwc6d+6MSZMmoV+/frj99tsREMDZ04nI+jS/Ee+WEILKSqXR7bnKA3HVGCIiIiLnoTOs6NevH8RiMTw8PNCxY0dMmDAB/fv3x+23347gYF7kEZFtSSuUJnXd5yoPpImrxhARERE5F51hRXl5Oby9vXH33XfjzjvvRN++fRlSEJHdXCuSYd745gUV5N64agwRERGR89EZVixevBhHjhzB4cOHsWPHDnh5eSE5ORn9+/dHv3790KdPHw7/ICKLOHclD6+l5+ptE+QDvcHD1etSvLY1F+VSBbIKytGuTSi++rsU+Lv+5rQ20AP3WrRqcgZcNYaIiIjIOekMK8aPH4/x48cDALKzs3H48GEcPXoU27dvx6ZNmyAQCNCtWzf069cP/fv3R+/eveHj42OzwonIdUS1jkX3UP1t9p7M0bs9JDgI7WNaYtv+TEy4owsSorWDjbPlseaWSU6Gq8YQEREROS+jVgNJTExEYmIiHnnkEQDAxYsX1auCfPPNN1i/fj18fHzQs2dPbNmyxaoFO7JPv9wGUYnMJsfy8vJETU2t1nMRIX547OEHrHbMTZvWY+vWL/H77wfM2s+DD47FwIGpeOmlVyxUWb3fftuNdes+Qnl5OWbMmIm+ffvj7bcXIDc3B3379se7767S+/p33vkPLl68gC+++M6idZFtyCuV9UHFkPaNggpyP1w1hoiIiMi5Gb10qabk5GTEx8cjJSUFAwYMwO7du5Geno5jx45Zuj6nUliuQGSXe2xyLC9PD9TU1mkf/8LPVj3m2LHjMHBgqlWPYY4PPliBDh06YsGCmWjTJgYffLAcZWVlePfdVYiIiLR3eWRlohIZgwoCwFVjiIiIiFyBUWFFXV0drly5gjNnzuDs2bM4c+YMMjMzUVtbi4CAAPTs2RNz5szB7bffbu16yY5atYpCq1ZR9i5DJ7G4HP37D0TPnr3//7EYnToloW/f/naujGwhumUggwoyeXJVrhpDRERE5Jh0hhV//PEHzpw5gzNnziAjIwMKhQJ1dXUICQlB7969MW7cOPTp0wddu3aFl5eXLWsmC5o27RF07NgJ8+f/B0D9Df7ddw/HXXeNweuvLwQAlJaW4t57RyI5uTOEwmz1MJDU1D6YN28Bjh79G3//fQg+Pt4YOXIMnn32BQgE9W+t4uIirFq1DMePH4G/fwCeeurZZtVZVlaG9es/wpEjf0MsLkfXrt0wa9bzSE7uglOnTuD5558GAHz88Wp8/PFqrdempvbB6tXr0LlzV3zwwXIcPnwQEokUCQkJmDp1Bu666y6t9tu2bcXWrV+itLQUXbp0xcsvz0Pbtgnq7X/99Se++OJTCIVZCAkJxejR92D69CfV5/zgg2MxfPhInD59Erm5Qjz++Ew89NAjyMu7hrVrV+PYsaPw8vLEoEGD8dxz/0KLFi2a9TMhbQG+zeooRi7E3OVtiYiIiMhx6Ly6nz17NgCgZcuWSE1NRZ8+fdC3b18kJSXBw8PDZgWSdfXrNwB//PGb+nF6+inU1dXhzJl09XPHjx+Fj48PevXqA6EwW+v1q1evwF13jcGSJctx5sxpfPrpRsTHt8X99z+ImpoavPTSc5DLZfj3v+ejrq4O69evQWHhTZNqlMvlePrpx1FTo8TTT89GYGAQvv32Kzz77JPYsGELkpKSsW7dp3j66el48MGJGDFiFABg5cql8PX1w7PPvojExESsWfM+Tp48gRdeeBmhoaH473934Y03XkWnTh3RunUcACAnR4hffvkZL744F1VVVVi9eiUWLnwdmzd/CQDYuXM7li1bjHHjHsTMmbNw5colbNq0AdevF+DNNxepa9669UtMnToDU6fOQFxcHEpKijFr1hOIiIjA668vRHV1FTZuXIuXXnoW69d/Bm9vb5N+JmQ6iYyrPLg6Lm9LRERE5Dp0hhULFixA37590b59e1vWQzbWv/9AfPXVFuTn5yEmJhanT59Ep05JuHz5EgoLbyIyshWOHz+C227rA19f30avT0npjjlz/g0A6NOnLw4d2o/Dhw/h/vsfxOHDB5GZeQXr1n2KlJRuAIC4uLaYMWOySTXu3r0LBQV52LJlKxIT2wGoD1kmTRqPzZvX4513lqn336pVtPr/AwICERAQoH6cnn4at9/eF8OGjQAAdO/eE2FhLVFTU6N1vPfeuzXHRWHhTXz00fuQyaTw9w/Axo1rMXz4SMyd+yoAoG/f/ggMDMLy5UvwyCNT0KFDRwBAfHxbPP74TPU+1637CFVVlfj44/Xw8wsCAHTpkoJJk8bjjz9+w+jRtpnrxF0JRRLk5FXZuwyysriIQAYVRERERC7CU9eGTz75BEKh0IalkD1069YDgYGBOHnyOADg9OmTuO++BxAQEIizZ9MB1Pes0DWxZteu3bQeR0a2QkWFAgBw9uwZBAeHqMMCAEhKSkbr1m1MqjE9/TQSE9upgwoA8Pb2xpAhaUhPP2X0frp1646fftqBV16Zg507t6O8vAzPPTdHHTAAQFRUa63JOFW1SiRSCIXZKCsrVYcdKnfeWT+M5MyZW7UkJLTTanPq1Al07dodQUHBUCqVUCqVaNUqCgkJieqfPVmHUCTBtv2ZaBsbbe9SyMqC/AwPBTJ1qAgRERER2YfOK7v8/HzI5ew27eoEAgFuu60vTp06gbS04cjKuopevW5DSko3nDlTHxIUFRUiNXUwdu7c2ej1vr5+Wo89PT1RW1u/pKpEIkFoaItGr2nZMtykGiUSCcLCWjZ6PiwsHDKZ8UvFvvjiXEREROLXX3fj0KEDWLHCE0OGpOE//1kEgaC+14ifn3bvEQ+P+jyvrq4WEonk/4+rXUtgYBB8fHy1agkLC9NqIxaX48KFDPTvf1ujukz9eZDxVEHFhCHtIfbnKg9k2lARIiIiIrIfzkhH6NdvADZvXo+zZ08jLCwM8fFt0bNnb+zd+ztiYmIRGxuP2Ng4k/cbGhqKsrKSRs+Xl5ebtJ+QkBDk5gobPV9SUozQ0FCj9+Pr64cZM57CjBlPITdXiD//3IstWzbhww/fx5w5rxhVBwCUlmqfk0QiQVVVJUJCdNcSGBiE/v0HYtas2aiq0l5GMSCAN9HWoBlUJEQH46xpbztyUcYOFSEiIiIi+9I5DITcR//+A1FcXIyff96J7t17AQB69OiNrKyr2Lv3dwwYMKhZ++3duw+kUqnWMIfc3BwUFOSZtJ/u3XsiOztLa3LP6upq7N//J7p162HUPmpqavDYYw/hu+++BgDEx9evBNK1azeIRCKj9hEf3xYtWrTAvn1/aD2/d++e/69Tdy3du/dETk4OOnToiOTkLkhO7oLExPbYvHmDergNWU7DoIJIxZihIkRERERkf3qv2r777jv8/fffRu/Mw8MDixcvNrsosq2oqGgkJLTDwYP7MWfOywCALl26wtvbBxcuZOCJJ55u1n5vv70fevbsjbfeeh3PPPM8/P39sXHjWggEpq18cffdY/Hdd9/g5ZdfwJNPPoPAwCB8993XKC0twZQpjxu1Dy8vL3TpkoLNmzfCx8cH8fEJOH8+A2fPpuO11143eh/Tpz+JVauWISQkFIMHD8XVq1ewefN63HHHCLRr10HnaydOfBS//vpfPP/8LDzwwEQIBAJs3foVMjLO4sknnzHq+GQcBhVkjnIphz8SEREROQK9YcXx48dx/Ljxk/+5e1gRGeoP0YWfbXIsLy9P1NTUaj0XEeKno7Vh/fsPhFCYhR49egOon8Cya9cU/PPPefTs2btZ+/Tw8MDSpSuxevUKvP/+cggEAkyaNBl//fWnSfsJCAjEmjUbsGbN+1i58l3U1NSga9du+OijDejUKdno/bz44svw9/fH559/itLSEkRFtcazz76IcePGo6Ki2qh9PPDARPj6+mHr1i/x8887EB4egYcfnoxp057Q+7ro6Gh8/PEnWL/+Q7z11pvw8PBAUlIy3n//Y3TsmGT0OZB+DCrIHBm5ElzJNe5vARERERFZl0ddXV1dUxuSk5Mxb948DB8+3KQdxsTEWKQweysulqK2tskfDUpKbqBly6hGz/v5eRt902suWx7L1sfjuVn/eLrew+aIjAxGYaGkWa/9ZPm/0D1U//CgvSdzsOThePVjX18BKitvzf/x2JrLuFmm0BlUnC2PxRNzVzSrPnPOzdGP50zntmvdvzA5+ZrO7Yu+z8UbD8ZrPdfwfdJUG+DWcqZ9Bw7DY3PXNqs+Z/pZOvKxbH08nptzHo/n5pzHc+Vzs/XxeG7OeTyemzZPTw+Ehwfp3K63Z0VYWJjLhA/keGpra9Urh2hSKj2gVNbfXHh4eMDLy8vWpZGTyciVQHi9DFPv6sIeFWQyVVDx8rh2SJdzwlsiIiIiR8CZxshuPv10Iz79dKPeNtHRrfH99z/ZqCJyRqobzYTWLRhUkMk0g4qU+GCkX7R3RUREREQEMKwgO7rvvvEYNGhwo+d9fATq5T29vX1sXRY5Ec0bzfX7Su1dDjmZhkEFERERETkOnWHFkiVL0LNnTxuWQu4mIiISERGRjZ639bwO5JzOCcUNbjT1hxUSGVd5oFsYVBARERE5Nk9dG3x8fBAXF9fsHdfV1WHnzp3Nfj0RkS4ZuRIs/eGq0TeaQpEEOXkiG1RGzoBBBREREZHj0xlWbN68Gffddx92796Nqqoqo3cok8nwww8/YMyYMfj8888tUiQRkYrqRvPVBzoYHVRs25+JtrHRNqiOHF2pVMGggoiIiMgJ6BwG8v333+Prr7/GggULMH/+fAwePBhDhw5FUlISYmNjERQUhNraWpSVlUEkEuHMmTM4ceIE9u/fDx8fHzzzzDOYMmWKLc+FiFyc5jfi3RJCtJakbIoqqJgwpD3E/lzlwd1l5EpwKa8c707pzKCCiIiIyMHpDCs8PDzw6KOP4r777sMPP/yAr7/+Gnv27IGHh0eT7evq6tCxY0fMmTMHDz74IAICeGNARJYjr1Ca9I24ZlCREB2Ms+U2KJIcliroSooNZVBBRERE5AQMrgYSFBSEqVOnYurUqcjJycHJkydx7do1lJWVwdPTE+Hh4WjTpg0GDBiAqKgoW9RMRG4ov1iG/0xoXlBB7k2zR86Px7hqDBEREZEzMGnp0rZt26Jt27bWqoWISKeY8EAGFWSyhqvGGAoryqVcNYaIiIjIEZgUVpB+azd/A+F12/Q1F3h5QVlTo/VcTGQQZk6ZaLVjbtq0Hlu3fonffz9g1n4efHAsBg5MxUsvvWKhyizDUudH1hHgZ/jPFYMK0pSRK8HKXdlGDx3KyJXgSi6XTSYiIiJyBAwrLCjvphSyFoNtciwvLw/U1NRpPZdfaN2b7LFjx2HgwFSrHoOoueSVSgYVpKYa+jF/Qkd0am14DiVV+74Dh9mgOiIiIiIyhGEFGa1Vqyi0asV5ScgxiUpkDCoIgOmrxmi2T5dzcmgiIiIiR8Cwws1Nm/YIOnbshPnz/wMAEIvFuPvu4bjrrjF4/fWFAIDS0lLce+9IJCd3hlCYrR4mkZraB/PmLcDRo3/j778PwcfHGyNHjsGzz74AgaD+rVVcXIRVq5bh+PEj8PcPwFNPPdusOsvKyrB+/Uc4cuRviMXl6Nq1G2bNeh7JyV0AALt3/4Q1a97HwoVL8NFH7yMnJxsxMbF4+unZSE0dCgCoqanB+vUf4Y8/9qC0tAQxMbF48MGJePjhSVrH2rt3Dz75ZB1u3BAhMbE9XnxxLrp166Henp5+Chs3rsWVK5fh6+uLO+4Yjqeffk69As7s2TMRF9cWN25cR0bGOdx77/2YPftFlJaWYO3a1ThwYD+Uymr07t0HL7wwF23axDTrZ0LaolsGMqggreDB2KEfmu3TL9qgSCIiIiIyyNPeBZB99es3AKdOnVA/Tk8/hbq6Opw5k65+7vjxo/Dx8UGvXn0avX716hVo0SIMS5Ysx/jxD2Hbtm/w0087ANSHAy+99BwuXfoH//73fMye/SI2b96AwsKbJtUol8vx9NOP48SJY3j66dlYuHAJ6urq8OyzTyIz86pWuyVL3sL48RPw3nurEBraAm++OQ9icf08It988wV+/nkXnnzyGaxY8SH69RuA5cuX4vDhQ+p9VFRUYMOGjzFjxlN4662lqKhQYP78f0OprP9m9vDhQ3j++acRHh6BhQsXY8aMmfjjjz34979fRG1trXo/u3fvQps2MXj77XcxfPidqKyswHPPPY0zZ05jzpyX8frrb6GkpBizZ8+EWCw26edBTQvwZfbq7qQmLm9rarBBRERERLbDq3s317//QHz11Rbk5+chJiYWp0+fRKdOSbh8+RIKC28iMrIVjh8/gttu6wNfX99Gr09J6Y45c/4NAOjTpy8OHdqPw4cP4f77H8ThwweRmXkF69Z9ipSUbgCAuLi2mDFjskk17t69CwUFediyZSsSE9sBqA9ZJk0aj82b1+Odd5YBAKqrqzFr1gsYPvxOAEBYWDimTZuEU6dOIC1tOM6cOY3k5M4YPfoeAEDv3n3g6+sHPz9/9bHq6urwxhuL1PXW1Cgxf/6/IRRmo0OHjti4cS06d+6Kt95aon5N69Yx+Ne/nsPffx9EauoQAEBAQCBefPFldQ+THTt+wLVrOfj22x8QHR37/z+v2/HAA2Pxww/fYvr0J036mZDpJDKu8uDqrhXJMG88gwoiIiIiV2B0z4qqqiosX74cQ4cORUpKCjp37tzovy5dulizVrKCbt16IDAwECdPHgcAnD59Evfd9wACAgJx9mw6gPqeFbom1uzatZvW48jIVqioUAAAzp49g+DgEPWNPwAkJSWjdes2JtWYnn4aiYnt1EEFAHh7e2PIkDSkp5/SWU+rVq0AAApFfT0pKd1x7NgRPPfcU/juu2+Qn5+HmTNnoVev3urXeHl5oUuXrurH0dH1tUqlEsjlcly5cgl33DFc65j9+g1AcHCIVi2xsbHqoAIATp8+gdjYOMTGxkGpVEKpVMLX1w89evRU/+zJeoQiCXLyRPYug6wsLsK45W0ZVBARERE5PqN7Vrz33nv48ssv0b59e/Tp0wc+Pj7WrItsRCAQ4Lbb+qp7H2RlXUWvXrchJaUbzpypDwmKigqRmjoYO3fubPR6X18/rceenp7q4RASiQShoS0avaZly3CTapRIJAgLa9no+bCwcMhkMq3n/Pxu1ePhUZ/F1dXVr5oyefI0+Pn54eefd2L16hVYvXoFevW6DYsXL0VwcBgAwMfHB56etzI8T08PAEBtbS2kUgnq6uqarD8sLEyrlob1lpeXIydHiP79b2v02tjYeP0/ADKLajnTgalc5cHVBRmxvK2pQ0WIiIiIyD6MDit++eUXjBw5EqtXr7ZmPWQH/foNwObN63H27GmEhYUhPr4tevbsjb17f0dMTCxiY+MRGxtn8n5DQ0NRVlbS6Pny8nKT9hMSEoLcXGGj50tKihEaGmr0fry8vDBx4qOYOPFRiEQiHDjwP2zatB6LFv0H7733gcHXBwUFw8PDAyUlxSbXEhQUhA4dOuHNN/+DqirtlQm8vRn8WYsqqJgwpD3E/lzlgUwbKkJERERE9mP0MBCZTIbBgwdbsxayk/79B6K4uBg//7wT3bv3AgD06NEbWVlXsXfv7xgwYFCz9tu7dx9IpVKtYQ65uTkoKMgzaT/du/dEdnYWhMJs9XPV1dXYv/9PrVU6DJkz51l8+OFKAEB0dDQmTHgYQ4akQSS6btTrAwIC0LFjJ/z5516t548ePQypVKq3lu7de+L69Xy0adMGycldkJzcBUlJnfHtt1/j778PGH0OZDzNoIKrhJCKsUNFiIiIiMi+jA4rUlJSkJGRYc1ayE6ioqKRkNAOBw/uR8+e9WFFly5d4e3tgwsXMpodVtx+ez/07Nkbb731On799b/46699mDdvLgQCb5P2c/fdYxEV1Rovv/wC9uz5BYcOHcDcuc+jtLQEU6Y8bvR+evTohe3bt+Hrrz/HqVMn8OOP3+PPP/9oNAeFPo8//hQuXMjAm2++hiNH/sbOndvx1luvIyWlO/r3H6jnHO5FSEgLzJr1FPbu/R3Hjx/Fm2++hr1796BDh45GH5+Mw6CCdDFmqAgRERER2Z/RV22vvPIKnnjiCXTq1AmjR49Gy5aN5xBwd7GtgqC8bptvyQVeXlDW1Gg9FxMZ1Oz99e8/EEJhFnr0qJ9s0tvbG127puCff86jZ8/eBl7dNA8PDyxduhKrV6/A++8vh0AgwKRJk/HXX3+atJ+AgECsWbMBa9a8j5Ur30VNTQ26du2Gjz7agE6dko3ez2OPTUdNTQ1+/PEHFBWtRVhYSzz00CN48smnoVTWGbWP1NQhWLJkOTZv3ojXXvsXQkJCMGLEXXjqqWfh5eWl83WBgUFYs2YD1q37EMuXL0F1dRUSE9tjyZIVGDCg6clLqXkYVJA5yqVcNYaIiIjIEXjUqWYfNGD06NEoKSmBWCzWvTMPD1y4cMFixdlTcbEUtbVN/2hKSm6gZcuoRs/7+XmjoqLa2qXZ/Fi2Ph7PzfrH0/UeNkdkZDAKCyXNeu0ny/+F7qH6hwftPZmDJQ/fmozU11eAyspb838891kuerRvqTOoOFseiyfmrmhWfeacm6Mfz5nObde6f2Fy8jWd2xd9n4s3HtSesLbh+6SpNioZuRJ8csIPb63+sVn1OdPP0pGPZevj8dyc83g8N+c8niufm62Px3NzzuPx3LR5enogPFz3F+5G96zo0aMHPDw8TDo4kT61tbXqlUM0KZUeUCrrby48PDz09lggUimTVbBHBTWbajnTvgO5agwRERGRIzA6rFi6dKk16yA39OmnG/Hppxv1tomObo3vv//JRhWRs8rIlUB4vQxT7+rCoIJMpgoqXh7XDulyrhpDRERE5AhMnmksMzMTe/fuRUFBAby9vdGmTRsMHToU7dq1s0Z95MLuu288Bg1qvMKMj49Avbwnl/UkQ1Q3mgmtWzCoIJNpBhUp8cFIv2jvioiIiIgIMDGsWL58OTZv3tyo6/6yZcswbdo0/Pvf/7ZoceTaIiIiERER2eh5W8/rQM5L80Zz/b5Se5dDTqZhUEFEREREjsPosGLbtm345JNPkJaWhmeeeQbt27dHbW0tsrKysHHjRnz66afo2LEj7r//fmvWS0QEADgnFDe40dQfVkhkXOWBbmFQQUREROTYPI1t+OWXX6Jfv35Yt24devTogaCgIISEhKBnz55Ys2YN+vbtiy+//NKatToUIxdRIXI4rvDezciVYOkPV42+0RSKJMjJE9mgMnIGDCqIiIiIHJ/RYUV2djZGjhypc/vIkSORlZVlkaIcnaenF5RKDlMg56RUVsPT03lXWFHdaL76QAejg4pt+zPRNjbaBtWRoyuVKhhUEBERETkBo8OKwMBAFBYW6tx+8+ZN+Pr6WqQoR+fvHwSptBzV1VUu8S01uYe6ujpUV1dBKi2Hv7/u9YwdmeY34t0SQgy2VwUVE4a0R3AgV3lwdxm5ElzKK2dQQUREROQEjJ6zIjU1FV9++SVGjRqF5ORkrW3//PMPvvzyS6SlpVm6Pofk6+sHAJDJxKitrVE/7+3therqGl0vsyhbHsvWx+O5We94np5eCAgIVr+HnYm8QmnSN+KaQUVCdDDOltugSHJYqqArKTaUQQURERGREzA6rJgzZw4OHjyIBx54AKmpqUhMTAQAZGVl4dChQwgODsaLL75orTodjq+vX6MbvsjIYBQWSmxyfFsey9bH47k57/GsKb9Yhv9MaF5QQe5Ns0fOj8e4agwRERGRMzA6rGjTpg22bduGFStW4K+//sJff/0FAPD398eIESMwd+5cxMXFWa1QInJvMeGBDCrIZA1XjTEUVpRLuWoMERERkSMwOqwAgNjYWKxatQq1tbUoLS1FXV0dWrZsCU9Po6e+ICJqlgA/w3+uGFSQpoxcCVbuyjZ66FBGrgRXcjl5MhEREZEjaFbK4OnpifDwcERERDCoICKHIK9UMqggNVNXjVG17xjPVWOIiIiIHIHOryo7d+6M9957D2PHjgUAJCcnw8PDQ+/OPDw8cOHCBctWSERkBFGJjEEFAWi8akxlpdLo9ulyrhpDRERE5Ah0hhXjxo1DfHy81mNDYQURkbVki6R4bWuu+rHAyxPKmlr1Y18BGFSQVvBgSo8KVfv0izYokoiIiIgM0hlWLFmyROvx0qVLrV4MEZEugYFBGH5bW/VjXx8BKqtufWN+U5Jjj7LIgUhNXN7W1GCDiIiIiGzH6AknpkyZgsOHD+vcvm/fPtx9990WKYqIyNIkMq7y4OquFckYVBARERG5CJ1hhUKhQEFBgfq/Y8eO4erVq1rPqf7Ly8vD/v37kZeXZ9LBq6qqsGrVKtxxxx3o2bMnpkyZgvPnz6u319XVYe3atUhLS0OPHj0wffp0ZGZmNtrH4sWLMWjQIPTq1QvPP/88bty4YeKPgYhcmVAkQU6eyN5lkJXFRRi3vC2DCiIiIiLHp3MYiEKhwLhx4yCRSADUT565ePFiLF68uMn2dXV1GDRokEkHX7JkCXbu3Im5c+ciPj4eX3zxBaZMmYJdu3YhJiYGa9aswYYNGzB37lzExMRg7dq1mDZtGnbv3o3g4PoLzAULFmDfvn145ZVXEBAQgJUrV2LmzJnYvn07vLy8TKqHiFyPajnTganD7F0KWVmQEcvbmjpUhIiIiIjsQ+eVXcuWLbFs2TKcO3cOdXV1WLNmDe68804kJSU1auvp6YmWLVuaNAxEIpFg27Zt+Ne//oVHHnkEANCnTx/069cPO3fuxJQpU7Bp0ybMnj0bU6ZMUW+/44478P3332P69OnIzc3Fjh07sGLFCowZMwZA/aolo0aNwt69ezFy5EiTfhhE5FpUQcWEIe0h9ucqD1Q/VGTeeAYVRERERI5O79dQQ4cOxdChQwEABQUFePjhh9GjRw+LHNjf3x/fffcdYmJibhUjEMDDwwNVVVU4c+YM5HI5hg8frt4eGhqKvn374sCBA5g+fTqOHDkCAEhLS1O3SUhIQMeOHXHgwAGGFURuTDOoSIgOxtlye1dEjsDYoSJEREREZF9GT7C5ZMkSg0FFSUmJ0QcWCATo0qULQkNDUVtbi2vXrmHevHnw8PDAvffeC6FQCACIi4vTel1sbKx6W3Z2NiIiIhAQEKCzDRG5n4ZBBZGKMUNFiIiIiMj+TLpq27FjB/bs2QO5XI7a2lr18zU1NZDJZLh69SoyMjJMLuLjjz/Ghx9+CAB4/vnn0a5dO/z+++/w8fGBj4+PVtvAwEBIpVIAgEwmQ2BgYKP9BQYGQiTiZHpE7ohBBZmjXMpVY4iIiIjMteHzb5FfWH/fLpdJIMrLQnRsOwQE3ro+79I+GrOffFjnPowOKzZu3IiVK1fC29sbQUFBKC0tRXR0NMrKyqBQKODn54fHHnusWScyYsQI9O3bF0ePHsXHH3+M6upq+Pn5wcPDo8n2qufr6uqabKPreVOEhwc163WRkba7ObLlsWx9PJ6bcx6vucfy8/OGr4/+P0cCgWejNpqPBQJPFBTLsf1gNh4d3gmJrUMaHcOcnwV/b/Y/nr+/N3x9db9PvASeTW7XfE5XGwA4JxQj93od3ycOcCxbH4/n5pzH47k55/Fc+dxsfTyem3Mez13OrURaheqoO1BScAVn/t6MHsMeh3ebjqjWaF8mO6t3f0aHFdu3b0dycjK++OILlJaW4s4778Tnn3+ONm3a4Ntvv8WiRYuaPZ9FcnIyAKBv376QyWTYtGkT5s6di6qqKlRXV8Pb21vdViaTqVcCCQoKgkwma7Q/uVyubtNcxcVS1NbWmfSayMhgFBZKzDquIx7L1sfjuTnn8cw5VkVFNSp9lXrbKJW1qKy61cbXR6D1uLRcjq/2FmPCkPZoEx6gtU11jObWx9+bYxxPoahGZaXu90mNsrbRdl9fgdZzTbUBbi1n2nfgML5P7HwsWx+P5+acx+O5OefxXPncbH08nptzHs+dzk2hqMKN0ovI+N9nSEmbhqDwRFQ1uAar8KhuuBstRs9ZkZ+fj/vuuw9BQUGIi4tDaGgoTpw4AS8vLzzyyCMYM2YMtmzZYvTJFBYW4ocfflAP6VDp3LkzqqqqEBoairq6OuTl5Wltz8vLQ2JiIoD6yTSLiopQUVGhsw0RuT6hSIJrN8o49IOaRRVUvDyuHUKDuGoMERERkbnkMok6qAiL7tCsfRgdVggEAq35Idq2bYtLly6pH/fr18+kSS3FYjHmzZuH3377Tev5Q4cOITw8HCNGjICvry/++OMP9bby8nIcO3YMAwYMAAAMGDAANTU12Ldvn7qNUCjElStX1G2IyLWp5qiIi2rBoIJMphlUcJUQIiIiIssQ5WWZFVQAJgwDad++PU6fPo0JEyYAABITE7Um0xSLxaiqqjL6wO3bt8ddd92Fd999F9XV1YiLi8OePXuwc+dOLF68GEFBQZg8eTI++OADeHp6IiEhAevWrUNQUJC6hvj4eIwaNQpvvPEGpFIpQkJCsHLlSiQlJWHEiBFG10JEzklzMs3DF41fjYgIYFBBREREZC3Rse1QZ0ZQAZgQVowfPx4LFy5EVVUV3nrrLQwbNgwvvPACPvroI7Rr1w6fffaZeu4JY7377rv46KOPsGHDBty8eRMdOnTABx98gFGjRgEAXnrpJXh6emLz5s2Qy+Xo1asXli5dqjUfxZIlS7BkyRIsX74ctbW1GDhwIObPnw8vLy+TaiEi55J9Xay16oehsEIi4yoPdAuDCiIiIiLrCQgMRuPZJbVVyKV6txsdVkyaNAkikQhfffUVBAIBRo4cibvvvhsfffQRgPrJLufOnWvs7gAA/v7+ePnll/Hyyy83XZxAgLlz5+rdb0BAABYtWoRFixaZdGwicl5CkQTbD2YbPUeFUCRBTp7xPb/ItTGoICIiIrKvUtFVQJ6rt43RYUVpaSnmzJmD5557DgJB/ctWrFiBSZMmoaysDL169UJ4eLh5FRMRGaAa+vHo8E5oE254MkRV+4Gpw2xQHTm6UqkCy3aUMqggIiIispNS0VVk/O8zjL9vtN52Rk+wef/992PNmjXqoEKlT58+GDFiBIMKIrI6zTkqEluHmNQ+OJCrPLi7jFwJLuWVM6ggIiIishNVUJGSNg1+AUF62xodVpSUlCAyMtLs4oiImqOiSqk1R4UhmkEFVwkh1dCPpNhQBhVEREREdqAZVBizSojRw0DGjh2Lb7/9FgMHDkRsbKxZRRIRmepmqQwThzKoINNpzlHx47FSo1+34fNvkV+oe+KnmMggzJwy0RIlEhEREbk0U4MKwISwwtPTE1lZWbjrrrsQHx+P8PBweHpqd8zw8PDAli1bTKuaiMgIrcICGVSQyc4JxVqTaRoKK8qlt1aNyS+UQtZisM62+YUHLFYnERERkatqTlABmBBWHDp0CGFhYQCAyspKFBQUmF4lEVEz+fkY/nPFoII0ZeRKsHJXttFzVGTkSnAlt9oGlRERERG5h+YGFYAJYcW+fftMLoyIyFZMndOCXJtq6Mf8CR3RqbXhyVVV7fsO5KoxRERERJZgTlABmBBWaLpx4wZEIhHatWsHX19fCASCRkNCiIhsyZQ5Lci1ac5R0S0hBJWVSqPbp8stt2qMas4LuUwCUV4WomPbISDw1vuTc14QERGRq5LLJMg48FuzgwrAhNVAAODkyZMYP3480tLS8PDDDyMjIwPHjh1DWloadu/e3awCiIgswdg5Lci1aQYPxg79MKW9KfILpciraI39B/6GT9tRqIsZA1mLwer/9E3eSUREROTMRHlZZgUVgAlhxdmzZzF9+nTIZDJMnTpV/XxoaCgEAgHmzp2Lv/76q9mFEBGZw5g5Lci1SSuUDhNUAP//jYIZXR+JiIiInFV0bDuzr3+MDis++OADxMbGYufOnZg5cybq6uoAAN26dcOuXbvQvn17rF+/3qxiiIisRSKTG25ETu1akcxhggrAMt8oEBERETkjzaGvulTI9fcyNfqryNOnT2PWrFnw8/ODQqHQ2hYUFISHHnoIq1evNnZ3REQ2IxRJkJNXZe8ybEI1T4IurjxPQlxEoMMEFUD9Nwp1DCqIiIiIGikVXQXkuXrbmNRv2sfHR+e2yspK1NbWmrI7IiKrUy1nOjDVPVZ5yC+UQtZisJ7tB2xYjW0F+Rn+SDN1qIg5AgKDIbPqEcjdmBtGfrTxcxw9fqLRZK+m7IOIiMgYYlE6iq8canKbRCzD9fwC3Dlc9zUrYEJY0aNHD/z888+YMmVKo21yuRzbtm1Dt27djN0dEZHVqYKKCUPaQ+xvuVUeyHldK5Jh3njrBxXGkMsk9i6BnIy5YeTR4yfqJ3uN7qAzSHPlQJOIiGxnaG8fPDLyZqPnMy5LsHJjJlYsbY+s4iC9+zB6zornn38eFy5cwOTJk7Fjxw54eHjg7Nmz+Pzzz3HfffchLy8PTz/9tOlnQURkBZpBBVcJIRVjh4pYW6noKkR5WfYug9yMJSY7IyIiai5VUPHSk+2R0snw9ZjRYUWvXr2wfv16iEQivPvuu6irq8OqVauwePFiVFRUYNWqVejfv79ZxRMRWQKDCtLFmKEi1lYquoqM/32G6Nh29i6F3Iwxk52xxw8REVmDqUEFYOKcFYMGDcLvv/+O8+fP49q1a6itrUVMTAxSUlIgENj/ApCIiEEFmaNcat1VY1RBRUraNAT4XbfqsYhMVSq6iir2+CEiIgtrTlABmBhWAICHhwdSUlKQkpJi6kuJiKyKQQWZIyNXgiu51Vbbv2ZQERbdAShjWEGOQ/X+HDJ4oL1LISIiF9LcoAIwMazYv38/fvrpJxQVFaGmpqbRdg8PD2zZssWkAoiILEEiq2BQQc2mWs6070DrrBrTKKggciDs8UNERNZgTlABmBBWfPXVV3j77bcBAOHh4XqXMSUisiWhSIJrN8owY0wXBhVkMlVQ8fK4dkiXW37VGAYV5MjY44eIiKzhQHoBNm/NRFxiJNb/ogR+KW3UplVEKSbp2YfRYcXnn3+O5ORkbNy4EREREc2pl4jI4lRDP+KiWjCoIJNpBhUp8cFIv2jZ/TOoIEfG9ycREVlLfnULeD/xGuTtEqBrRrDIi3l692F0WHH9+nXMmzePQQUROQzNOSoOXyyxdznkZBoGFSa//vx5LFixSf3Y398HCkWV+vHJU+kovPkbbwTJITGoICIia4pOSEB5uwSz9mF0WBEfH4+ioiKzDkZEZCnZ18Vac1QYCiskMuuu8kDOxdygAgDk1Z6QtRisflztK0CVrxJA/Y3gteyt6HP3HN4IksNhUEFERNbmHxyEcgNtKmQyvds9jT3YzJkz8cUXX+DKlSvGvoSIyCqEIgm2/nnV6Mk0hSIJcvJENqiMnIElggp9VDeCYdGJvBEkh8OggoiIHIE4S4jCPAsNAzl58iQCAwNx3333ITExES1btoSHh4dWG64GQkTWphr68ejwTmgTbngyRFX7ganWWeWBnEupVIFlO0qtHlSkpE1DwcV9Ft8/kTnkMgkyDnBoEhER2Zc4S4jMr39Ar9H36G1ndFhx4MABAEB0dDQUCgXy8/PNq5CIyESac1Qktg5BZZXS6PZif8uv8kDOJSNXgkt55Xh3SmerBBUlBVe0vrE2FFbIZRKL10Ckjygvi0EFERHZlSqoaP/IA/Cr0h9HGB1W7NvHb4iIyH4qqpRac1QYohlUJEQH46yhQXPk0lRDP5JiQ63Wo+LCgS1G3wiWiq6iKi/L4nUQ6RMd2w51DCqIiMgGxP9chOTUSa3nZFIJRHkFaB3bBh579kMa21bvPowOKzTduHEDIpEI7dq1g6+vLwQCATw9jZ7+gojIZDdLZZg4tHlBBbk3zTkqfjzWeI1vc6mGfvS6cwaCwhONbj9k8ECL10KkT0BgMPRPZcYeP0REZBmp4dEYFeivfnzpxg1sOH8JCwalIikqCgBwyMdP7z5MCitOnjyJd955B//88w8AYPPmzaipqcG8efPw6quvYsyYMaaeAxGRUVqFBTKoIJOdE4q1JtM0FFaUS01bNUZzjoqWbTqiqlL/0CTN9gF+1006FpG1sccPERFZyqXcK/in5CYAoFymgPBmIRJaRWL31XPYffUcACAiqavefRgdVpw9exbTp09H69atMXXqVPVEmqGhoRAIBJg7dy4CAwMxdOjQ5p4PEZFOfj6G/1wxqCBNGbkSrNyVbfRkmhm5ElzJrTZ6/9WVcpNWVWi0CkMZwwpyHOzxQ0RElhSbGI7uPRX/f31egEfvbnx9nuMZpncfRocVH3zwAWJjY7F9+3bI5XJ89tlnAIBu3bph165dmDRpEtavX8+wgojswtQ5Lci1qYZ+zJ/QEZ1aG55cVdW+70DjV42RloqQkvZE84IKomYQXfsHNzLSdW6PCvVt1uvlUjEKr+cisnU8xCX6l5EjIiIylrlfJBodVpw+fRqzZs2Cn58fFAqF1ragoCA89NBDWL16tckFEBFZgilzWpBr05yjoltCCCoNDM3QbJ8uN37VmKCwaAYVZFPJnaIwuFMrndsLL980+fUFuQXYt+s8Jj0+Em3i2xjcBxERkTEkMjl+O2jeF4kmzVnh4+Ojc1tlZSVqa2ubVQQRkbmMndOCDHtn2fsoU9RPxteUmMggzJwy0cZVGUczeDB26Idm+/SLt7aJ8rJx4/wVna+trtYfggAMKsiybuZdRPaVQzq3B/qHm/R6sUSOazk3Ede2FfLOHUXeOcP7ICIiMkZOnsjsHs9GhxU9evTAzz//jClTpjTaJpfLsW3bNnTr1q3ZhRARmcOYOS3IOGUKoC5mjM5VA/ILD9i0HmNJK5RmBRUNJXfujMFd7tH5+u83L9G7f0nRNZz65X1EtmmH4sz9KM7cr7U9KpTvWTJNv24t8MhIqc7tX+9pYfTrMy5LsHJjDja+1R4pnW69/w3tg4iIyBhtY6OREF1i1j6MvlJ6/vnn8dhjj2Hy5MkYPnw4PDw8cPbsWVy5cgVffPEFCgoKsHDhQrOKISKyFonMtFUe3Jkxyxs6omtFMswbb5mgwhJ8vYF7n3kLbdp2bnJ74YWfrXJcIkPqg4pMvPSkdlBBRERkKcGBAQD0hxVSuULvdqPDil69emH9+vVYsGAB3n33XQDAqlWrAACRkZFYtWoV+vfvb+zuiIhsRiiSICevyt5luAy5TGLvEpoUFxHoMEEFAISGt9EZVBDZC4MKIiJyBEKRBPkVhXrbmNQHddCgQfj9999x/vx5XLt2DbW1tYiJiUFKSgoEAnZnJSLHo5qFeGCq8as8kG6loquoysuydxlNCvIz/Dlk6lARc/j6BVp1/0SmEovlDCqIiMjuVNfnY+69TW87kxMGDw8PpKSkICUlpdnFERHZguZySWJ/41d5oKapJoscMnigvUtpNlOGilibTCK2dwnkZq5miRhUEBGRXWlen3sE+OttazCsyMnJwfbt2/HMM8/Az88PYrEY999/f6N2r732GkaMGNH8qomILKjhus5ny+1dkXPTXNUiwO+6vctpNmOHilhbQc4/uJZz1d5lkJvp0C4aKZ3Mm+yMiIiouRpen+cYaO+pb+NXX32Fe+65Bxs2bMCZM2cAADU1NcjPz0dQUBDatGmDNm3aoKSkBAsXLkRlZaWlzoOIqNka/iEk87jS8pvGDBWxtoKcf7Bv+8eIa+vcP0tyPiEhhnuYicWcjJiIiCyvOdfnOsOK9PR0LFq0CLfffjt+++039OvXT2v7q6++ii+++AJffPEFFi5ciMLCQvz444/mnQERkZkYVFiWKwUVxiiXWvdGTRVUDBs/C4HBIVY9FpGpMi5LcDVLZO8yiIjIxTT3+lxnWLFlyxbExsZi3bp1iI+P17uTe++9F0lJSfj999+Nr5iIyMIYVFiWuwUVGbkSXMm13o2aZlDBVULI0ahWCenQLtrepRARkQsx5/pcZ1hx4sQJ3HffffDx8TFqR3feeSf++ecfkw5ORGQpElkFgwoLcsegYtmOLHSMt86NGoMKcmSay5kaM1SEiIjIGOZ+kagzrCgrK0NMTEyj5/39/TF9+vRG26KjoyGVSk0ugIjIXEKRBNdulDGosBC5TOKWQcXL49ohNMjyN2oMKsiRaQYVXCWEiIgsRSKTm/1Fos6ZxsLDw1FWVtboeT8/P7zyyiuNni8sLERERESziiAiai5VYhsX1YJBhYWI8rLcMqhIiQ9G+kXL7p9BBTkyBhVERGQtOXkis79I1NmzokOHDvjf//5n9I7++OMPpKSkNLsQIiJTaXYtCw70s3c5LiM6tp1bBhWWJpeWM6ggh8WggoiIrKltbLTZXyTqDCvGjx+PY8eOYefOnQZ38vXXX+PChQt44IEHzCqGiMhY2dfFJnUtk8i4HJ+xAgJd/8bF2kFFqegqbhZkMqggh8SggoiIrC040PDQWqlcoXe7zmEgo0ePxvbt2/Haa6/hyJEjeOqpp5CQkKDV5tq1a/jss8/wzTffYOTIkRg6dKhxlRMRmUEokmD7wWyjgwqhSIKcvCobVOb4Ms6fx4IVm9SP/f19oFBo/2zOX7yKhP6Dde5DLpNYrT5bsEVQkfG/zxDdpj2DCnI4DCqIiMgRCEUS5FcU6m2jM6zw8PDA+++/jzfffBM//vgjduzYgcjISERHR6Ourg5FRUUQiUSoq6vDmDFjsGjRIoufABFRQ6qhH48O74Q24YYTW1X7ganDbFCd45NXe0LW4lYQUe0rQJWvUquNrEL3yk6loquoysuyWn3WVipVYNmOUqsHFSlp0yDP+9vi+ycyh1gsZ1BBRER2p7o+H3PvbXrb6QwrACAoKAgrV67Eo48+ip9++gnHjx9HZmYmamtr0apVK4wbNw733nsvBgwYYNHiiYiaojlHRWLrEFRWKY1uL/bncnzmUt2IDxk80N6lNEtGrgSX8srx7pTORgUVN3NOIPvyIZ3bFZIKrcclBVe0VlExFFbIJGLjCieykKtZIgYVRERkV5rX5x4B/nrb6g0rVG677Tbcdpv+1IOIyJoqqpQmzVHRcF3ns+U2KNKFafYYCPC7bu9yTKYa+pEUG2p0j4p+KZ54ZGSxzu13PlOJq4c2AwDk0jIUi4QIj05AceZ+FGfuR1XJGRzb8VqTrxWLJaiprALwjMnnQtRcHdpFI6VTib3LICIiN9Xw+jzHQHujwgoiInu7WSrDxKHNCyrIPJpBRVh0B6DMucIKzTkqfjxWarH9to1vg77jXlMvT/rY7LcR0aajenvmr5Px7pzGYYdqzoC+Azk0iWwrJCQAgP6wQizmZMRERGR5zbk+17kaCBGRI2kVFsigwg4aBRVO5pxQbNJkmuVS027UVEHFsPGzEJNgeDJNzckN628ciRxHxmUJrmaJ7F0GERG5mOZenzOsICKn4OdjuCMYgwrLcvagIiNXgqU/XDU6qMjIleBKrvE3agpFhTqoMGbVD67CQI5M9f7s0C7a3qUQEZELMef6nGEFEbkEU+e0IP1cIahYtiMLrz7QweigYtmOLHSMN/5GrfDmTQYV5BLY44eIiKzB3C8SOWcFEbkEU+a0IP0UMrFLBBUvj2uHbgkhqKzUv2qMZvt0ufE3apGtWjGoIJs6dqkYh//RPe+Kl6c/xpn4eqlYjmvZhYhLjMT6X5Tw8izWuw8iIiJjSGRy/HbQvC8SGVYQkUswdk4L0q9UdBWlomz0uXuO0wcVpvSoULVPv2j8sfz9/Qzvn0EFWVBd29txs29Xndujj5036fXiLCEyv/4B7Z94AvJ2CZAbsQ8iIiJj5OSJzO7xzGEgROQSjJnTgvRTDf0Ii050yqBCWqE0K6iwNLm8ikEFOSx1UPHIAwhpl2DvcoiIyMW0jY02+4tEhhVE5BYkMi7Hp4/mHBX+gSH2LqdZrhXJHCaoAIDronIGFeSQGFQQEZG1BQcaHlorlSv0bmdYQUQuTyiSICePy/HpYupkmnKZxAZVmS4uItBhggoAaB0dyqCCHA6DCiIicgRCkQT5okK9bdhvmohcmmoW4oGpw+xdik2Irv2DGxnpOrfLJdo9TEoKrpgUVJSKrqIqL8vcMq0iyM/wR5qpQ0XMERDgY9X9E5lKIZEic8+vDCqIiMiuVNfnY+69TW87hhVE5LI0l0sS+7vHcnzJnaIwuFMrndu//+qE+v9LRVdx4cAWk4KKjP99hiGDB1qkVnu4ViTDvPHGBRXHjv6Do39l69x+Q9ZT7+sLrpXgnZW6vzEQV1di3CSDZRBZjEgoZFBBRER2pXl97hHgr7ctwwoickkN13U+W27vihyLKnjodecMBIUnGt0+JW0aAvyu26BC6zB2qAgA9E/ujMnJQTq3T9sepvf1MaFhWHBn0yFZRq4En5zwMKoOIhXxPxchOXVS5/YA/0C9rw+sqUH5nv2QYH+z90FERNRcDa/Pcwy0Z1hBRC6n4R9Cd3Iz7yKyrxzSuV0hCdIKHlq26YiqSqXefTaa06LMecMKY4aKWJtqzoy+A91jaBJZTmp4NEYF6v4W6leZ/onKRrRpq/f1ALDtZlGzaiMiItKnOdfn9r9qIyKyIHcOKgCgX7cWeGSkVOf2sf+qM3mOClPaO7tyqXVXjdGc3DNd7h5Dk8h5XLpxA5kFzhtGEhGRY2ru9TnDCiJyGe4eVBijtLgQKWkvMqhoQkauBFdyq41uLym+imM7XlM/9hJ4okZZq34cKCkE0FZr/5qTe6ZftEjZRBZx6cYNbDh0EP1Th9q7FCIiciHmXJ8zrCAilyCRVTCoMEJYeCSDiiY0Z2hG9wQx5r9UrH7s4yvQGlKzePGtOSlstVwqUXOogoqZg1KRHcg5K4iIyDLM/SKRYQUR2d25gmIc/qdUbxulrELnNqFIgowCMRIS2+A3oRIQNt5XUKj+cdruwtfP12Cb6kq5WwYV1hqawaCCHJlmUJEUFYVsA/NeEBERGUMik+O3g+Z9kciwgojsLqDb7RD37aq3jWDl4iafVyW2wSnd4P+vOahtshUQcOy8mVW6D2mpCClpT7hdUGGNoRkMKsiRNQwqiIiILCUnT2R2j2dPC9ZDRGRTml3LAoLYddlSgsKi3TKosLRSsYJBBTksBhVERGRNbWOjzR6azbCCiJxS9nWxSWPgFBLdK2SQNm9f11+lwtpBRcZlCS7nljOoIIfEoIKIiKwtONDw9aRUrn/oIYeBEJHTEYok2H4w2+igQpwlhEIotH5hbkIuk9i7BLPYIqhYuTETneJDGVSQw2FQQUREjkAokiC/olBvG/asICKnohr68fAdHYwOKjK//gHRCQnWL84NlIquQpSXZe8ymq1Uat2hGaqg4qUn2yMshJO6kmORyGQMKoiIyO5U1/Mx0ZF627FnBRE5Dc05KhJbh6CySqm3vSqoaP/IA/AvktmoStelWs50yOCB9i6lWTJyJbiUV453p3S2SlBx7qJYHVSkdArGLpTobV8ulVu8BiJ9MvPzGFQQEZFN6FrtTyaVQ1RQiOg2kciT6F7tD2BYQUROoqKyGtv255g09EMVVIS0SwCKuBqIOVRBRUraNAT4Xbd3OSZTDf1IirXO0IyMyxK8vylbHVQYU8+V3GqL10GkT/uYWCS1irB3GURE5AaaWu1PfX3+7yfg3y4B/hfz9O6DYQUROYWSYjGmNzeocCPHLjWdYqvcKBWglYn71AwqwqI7AGXOFVZozlHx4zHdP5tm7///h368MqsTkhINTyalqqfvwGEWr4VIn+BAw6smSWTshUZERJbXnOtzhhVE5BRahocwqDBCuTwYNxSxOrdXKWtN2l+joMLJnBOKteaoMBRWmDo0Q3OOim7JIaiq1D80STM4SZe7/qorZFmnRXk4Uqb7PezbIgyTzHi9VCpFpacXZppRIxERUUPNvT5nWEFETsHP19tgG3cPKgAgNTwaowJ1T+z4bMlVo/fl7EFFRq4EK3dlGz2ZpqlDM85eKMKfx3IQFx2IjVuy4ekpRG1t3a0G0hoA8Vr71wxO0i+acjZEgFf3lEZdajVFH9M/3E3f61V/P9MGOOecNERE5JjMuT5nWEFELqGqohI5bh5UWJIrBBXLdmRh/oSO6NTaOkMzfOu88dFjPdVBiK+vAJUaPSsWfZ/baP/WWoWECAD+OXkJC0o2QS6TQJSXhcQOSfDw8lNvP3/zKsKbCCs4GTEREVmDuV8kMqwgIpcgKSxB+8enMqiwAIVM7BJBxcvj2qFbQohWgGCovSlDM+IiAo3uscGggmxBUeGFvIrWyDjwG1LSpkHZJllraJIs9xTCG7yGkxETEZE1KCRSZO751awvEj0tWxIRkX0ER7ZkUGEBpaKrKBVlu0RQYe0gIcjPcN7PoIJsqbpSYVLQyKFzRERkLSKh0OzPF/asICKX4OPna+8SnJ5q6EdAoC+Kr/6CYh3TW0SFOubPWlqhtFlQYY16iMwlKxYhJW0mgwoiIrK76IQElJv5+cKwgojcgkIitXcJDk1zjgp5zve4+6E+OtsWXr5pw8qMd61IhnnjHSOoMLUeIksIDI9mUEFERA7BPzgI5QbaVBhYLpvDQIjI5YmzhBAJhfYuw2GZOpmmTOaYwY+jzSFhbD1EluLtq3slIBUGFURE5AjEWUIU5uXpbcOeFUTk0m4tx5dq71IcUknBlUZBRUX5NRz7749NthdL5Kip8rFliUYzZg4JWw7NMKYeIluqVigYVBARkd2prs97jb5HbzteSRGRy+JyfPqViq7iwoEtjXpUdE7wxvv/bjwvRcZlCVZuzEHfQcYv7+loHGloRrlUbu8SyM3Ii26i/fQHGVQQEZHdaF6f+1XpjyM4DISIXBK7OuunGvrRY9jjRg39qA8qMvHSk+0REmL88p6OxlGGZmTkSnAlV2TvMsjNBES04t9DIiKyG1Ovz+0aVtTU1ODTTz/F6NGj0bNnT4wZMwZffvkl6urqAAB1dXVYu3Yt0tLS0KNHD0yfPh2ZmZla+6iqqsLixYsxaNAg9OrVC88//zxu3Lhhj9MhIgfBoEI/zTkqWrbpaLC9ZlCR0sn+N/rmcIShGao5MzrGR9u7FHIz3v6G57TgZMRERGQNzbk+t+tV28cff4wNGzZg1qxZ6NmzJ06cOIHFixdDoVDgySefxJo1a7BhwwbMnTsXMTExWLt2LaZNm4bdu3cjOLj+gnnBggXYt28fXnnlFQQEBGDlypWYOXMmtm/fDi8vL3ueHhHZgbsHFdkFV7CuVPdqHcWl/igzYTJNVwoqjGHtoRmak3umy523hwq5JnGWEApORkxERBbW3Otzu4UVtbW1+PTTTzFjxgw888wzAIABAwagpKQEmzdvxqRJk7Bp0ybMnj0bU6ZMAQD06dMHd9xxB77//ntMnz4dubm52LFjB1asWIExY8YAAJKTkzFq1Cjs3bsXI0eOtNfpEZEduHtQAQApncMxOVn3DfeApWVISXuRQUUT6odmVFt1/5qTe6ZftNqhiEzGyYiJiMgazLk+t9swEIlEgnHjxjUKFBITE1FSUoIjR45ALpdj+PDh6m2hoaHo27cvDhw4AAA4cuQIACAtLU3dJiEhAR07dlS3ISL3IJfK3D6oMEZYy0gGFU2w9tAMWy2XStQcWpMRBwfZuxwiInIR5n6RaLewIjQ0FG+++Sa6dOmi9fyff/6J6Oho9bwTcXFxWttjY2Mh/P8uitnZ2YiIiEBAQIDONkTk+oQiCQrzRAwqjODr23iVj4YUiiq3DCpeHtcOoUGWH5rBoIIcGXukERGRNSgkUrM/X+w/05iGbdu24e+//8brr78OqVQKHx8f+Pj4aLUJDAyEVFo/+ZNMJkNgYGCj/QQGBkIk4izrRM6i6NJl3DxzWm+b0LIyAGGNnheKJNi2PxMhfq1QsfcgKvYebPL1Ar/GfyuoaaIbYrzzovsFFdYYmsGgghwZgwoiIrIWkVBo9ueLw4QVu3btwoIFC3DXXXdh8uTJWL9+PTw8PJpsq3q+rq6uyTa6njdFeHjzukFGRtruYtSWx7L18Xhuznm85h7r9nbt0apVK71t/tqZA18f7T9ZBcVybD+YjUeHd8Keqz4YmTZC5+tv3rxp1s/CWX5v/v7e8PXV/addIPCET4PtDR/HtGmB3t0aB0Oax7DHz9LQuXkJPJvcrvmcZptzQjFW7srG/Akd0S0hRH0MVX3NOZ7mY4miqtH+G56Pue8rZ/j37QzHc5Zz8/f3afR3UJOhf98CLy/168szsyHcuh2dpz6E0PaJWsdwlr+VzvJ7c/Rj2fp4rnxutj4ez805j+cu59Y2uSPKk/UPPfbz89a73SHCis8++wxLly7FsGHDsHz5cnh4eCA4OBhVVVWorq6Gt/etk5DJZOqVQIKCgiCTyRrtTy6Xq9s0V3GxFLW1dSa9JjIyGIWFErOO64jHsvXxeG7OeTxzjqVQVKGqSqm3jVJZg0qNNgXFcny19zImDGmPNuEBqLmofx9FRaXNrs+Zfm8KRTUqK3X/HJTKWlRpbPfxFWg9BgAfn8bPNTyGPX6Whs6tRlnbaLuvr0DrOVUbzR4PnVoHqNtonpupx9M8VkauBOdzSvDulM5a+294Pua8r5zl37ejH8+Zzk2hqNL6O9iQoX/fypr6v6OaPSr84uK09qlQVDnF30pn+r058rFsfTxXPjdbH4/n5pzHc6dz8/T21fuZBQBlxWV6t9s9rFi5ciXWr1+PcePG4Z133oFAUF9S27ZtUVdXh7y8PCQm3kr8NR8nJCSgqKgIFRUV8PPz02pz22232fZEiMhmhCIJth/MxoQh7ZEQbTiYzM/PR25urg0qcw9isXWX97Q2aw/NUO0/KTaUQz/IpipuZODcf19TP/b09ERtba36sTSolkM/iIjIIYizhCjMy9Pbxq5hxZYtW7B+/XpMmTIF8+bN0xq60atXL/j6+uKPP/7Ak08+CQAoLy/HsWPHMHv2bAD1S53W1NRg37596qVLhUIhrly5om5DRK5FNUfFo8M7oU244ckQ8/Pz8fvve5CaOsQG1Tm+srLrKD/4ofqxwMsTypparTbVkiI890HTSbhULIdUUo4pT1m1TKsplSqwbEep1YOKl8e1w4/HSi2+fyJ9+vfywIcv3Lq0a9izYtgbFQwqiIjI7lTBea/R9+htZ7ew4ubNm1i+fDk6deqEu+++G2fOnNHanpKSgsmTJ+ODDz6Ap6cnEhISsG7dOgQFBWHChAkAgPj4eIwaNQpvvPEGpFIpQkJCsHLlSiQlJWHECN1j14nIOamCiglD2iOxdYjBrmWqoOLOO0fC09PLRlU6trZtfNH3/j7qx74+gkY/xy/XZyKztPESnjKpBKI8GXp3ibd6ndaQkSvBpbxyvDuls1WCinNCsVaPDUNhRbnUuXuokHPJuCyBKKcY7Z9+hkEFERHZjdZQxCr9cYTdwoqDBw+iqqoKly9fxsSJExttP3z4MF566SV4enpi8+bNkMvl6NWrF5YuXao1H8WSJUuwZMkSLF++HLW1tRg4cCDmz58PLy/emBC5korKamzbn2/S0A9VUBETE4Pr17lCEABISkU49t8f1Y+9BJ6oUWr3rEhSVuI/SSlaz126cQMbzl/CgkGpyA5qPFmko7P20IyMXAlW7so2usdGRq4EV3KrLV4HUVMyLkuwcmMmotsmM6ggIiK7aTQU8aKDDgMZP348xo8fb7Dd3LlzMXfuXJ3bAwICsGjRIixatMiS5RGRgyksleCRZgYVdEv3RGD+S77qx01NsPn6q1Vajy/duIENhw5i5qBUJEVFIVumsEmtlmLtoRmq/c+f0BGdWhsemqRq33fgMIvXQtSQKqh46cn2mLfL8BLOConUBlUREZG7ac6cSZ7WLYmIyDIiw4IZVNhBw6DC2TQcmmGIqUMzNIOQppYn1dc+NMhwsEFkjnMXxeqgIqWT4fe/OEsIkVBo/cKIiMitNHdyZ4YVROQU/Hz1r8MMMKiwNGcPKjJyJVj6w1UTh2YYP1xIWqE0KQix9iokRJoyLkuwbN1Vk4KKzK9/QHRCgvWLIyIit2HOKlQMK4jIJVRUVjKosCBXCCqW7cjCqw90MClI6BjfeGJRXa4VyRhUkENSDf14+ekOJgUV7R95AP7BQTaokIiI3IG5y2UzrCAil1BSUs6gwkLKJQqXCCqsPTQjLiKQQQU5HM05KrolG37/m3shSURE1BSFRGr25wvDCiJyCS1bhjKosICMyxJczS90iaDC2kFCkJ/hOaoZVJAtKRTVJs9RwaCCiIisQSQUmv35wrCCiFyCn6+v4Uakl+ob2Q4xkU4ZVDjaHBKm1kNkrhvXJQwqiIjIIUQnJJj9+cKwgojcgkwms3cJDk2z63hosL+9y2kWR5tDwpR6iCwhqnUwgwoiInIIxsyBVGHg+pxhBRG5vPz8fOTm5tq7DIelGVQYc6MjcdDgx9HmkDC2HiJL8fc3vGoSgwoiInIE4iwhCvPy9LZhWEFELk21nGl8fLy9S3FI5y6KTQoqLt24gcx8/R8s9mLMHBK2HJphTD1EtlSpqGRQQUREdqcKziNjY/W245UUEbksVVBx550j4enpZe9yHE7GZQne35TdKKi4XlqIdcf+aNS+TKrAVVEhunTrZssyLepakQzzxjvG0IxyqdzeJZCbKReVoP2jUxlUEBGR3Wj28POr0h9HMKwgIpekGVTExMTg+nWRvUtyCOfya/HcB6WQiuW4ll2IhPZRWP+LEvilVN0mKLQaSx7THmdYP3SiAKuebod0eRtbl20xjjI0IyNXgiu51fYug9xMaHRLBhVERGQ3jYYiXtTfW5dhBRG5nIZBBd1SrgjG9WuhEOXJEB3bHTfrQqEsrdVq07a6VOtxwzke0i/asmLLcoShGaqfZ9+Bw+xdCrkZX3/DqyYpJFIbVEJERO6mOXMm2f+qjYjIghhU6NcZHijIKcCCQalIioqCr48AlVVKrTabzmaq/99Wk1E6CmsPzdD8eabLA6x6LCJTibOEUAiF9i6DiIhcTHMnd2ZYQUROQSipxvojt77x9xJ4okZ5q0dAudyLQYURCkrFmDnoDiRFRRls625BhbWHZrhSDxVyTJfzqvDcB7f+TgoEXlAqa9SPb5QFIUHHa1UXkmkDUq1bJBERuRVzVqFiWEFETqE2IQm1T05WP/b2EaBao0dA1eIPGVQYwdtbiT9zzuHPnHMAAIHAE0ql9jAQRU05MnID3C6osObQDHcLfsg+SiOScXPiw+rHDXtO1W3Z2uTrNC8k/Yscc2liIiJyPuYul82wgoicnjhLiML8fDx03/0MKgzokuiDNx68NXmmr68AlZXaw0Be+VLgVjfW1h6awaCCHFmjC8mi8/YuiYiIXIBCIkXmnl/NWi7b07IlERHZlnqd5pgYBhUWcq1I5jY31tYOEhhUkCMz9xsvIiIiXURCodmfLwwriMhpaV5oBwQHGX4BGcVRlve0NmsHCaVSBYMKclgMKoiIyJqiExLM/nxhWEFETqk8M9ukC22ZjOOwjeUIy3tamy16VFzKK2dQQQ6JQQUREVmbvxFfJFYYuD53/StSInI54iwhhFu3G32hnZ+fj9zcXOsX5iasvbyntdlq6EdSbCiDCrIpz9x8SNZ9rn6saDCBblWFjEEFERE5BHGWEIV5eXrbMKwgIqeiutDuPPUh+MXFGWyvWs40NXWIDapzfdZe3tPa6odmlNpkjoofj5UafgGRBXXzD8R/klLUjxuuBvLIycMMKoiIyO5U1/O9Rt+jtx2HgRCR09D8RjC0faLB9qqg4s47RyIwMNAGFbo21Y14x/hoe5fSLNYemnFOKDapx4az91Ah53Lpxg2I8goYVBARkV1pXs/7Gbg+Z88KInIKVRUVyDfhG0HNoCImJgbXr4usX6QLs/byntbWnKEZR3OKcfhimc7tHpIKrf2v3JWtFVScLa7Fszuafr1EIoO0qhyPGX0GRM136cYNbDh0ENFt2zCoICIiu2k0FPEih4EQkQuQ3ixC++kPNyuoIPM0nOMh/aK9KzJNc4dm1Ha6HaK+XXVuj964WGv/8yd0RKfWt4IcWZsOED0+s9HrVB/UaQNSTTgLouZRBRUzB6VieVmhwfYKidQGVRERkbtpzpxJHAZCRE4hqFUEgwo7sPZklNZm7aEZmj+fbgkhBttrflAbM0s2kTkuikTqoCIpKspge3GWECKh0PqFERGRW2nu5M7sWUFETsHHz89gGwYVluXsQUVTQzMMtTdl8lCFohrLduQYvf9GH9RF540+FhEAFF26jJtnTuvc7l1apv7/SzduYNPhQ1pBhfy6CIqNXzb5WoVUisJreeiZrLs3ERERkanMWYWKYQURuYSqygoGFRbkCkFFU0MzDLXvO3CY0ccQFYrxXnODCqJmSGkdhztb657g9nThDgC3hn7MThuKxJYR6u3tAkMxZGBao9epgt7HR42Fp6eXpcsmIiI3Ze71D4eBEJFLKCsqYVBhIfXLezp/UGHs0AzN9qFBxk8eGh0ZwqCCHI7mHBXJ0YZX7mGPNCIisgaFRGr29Q97VhCRS2gR0ZIX2hagWt7z3SmdnT6oMHboh67JQw11uW9RIQMQqXO7LL8QN1etQ+G1PETGxaJi70FU7D2o3i7w43K6ZFmKyiqT5qhgUEFERNYiEgrN/qKGYQURuQQfX8NzWpB+zVne05FIK5RmBRUNGepyf+l/WXr3H+PtjdyCQjw+amyTN4JcTpcsTVRWihcZVBARkQOITkhAuZk9SjkMhIjcgkwms3cJDk3zxj0syN/e5TTLtSKZxYIKS7hZUs4bQbKp6BZhDCqIiMghGLPqWYWB63P2rCAil5efn4/c3Fx7l+GwGt64/3isVG97U5f3tJW4iECHCSoAoFXLUN4Ikk3lVVfiPyePqR8LBF5QKmvUjwvlMgYVRETkEMRZQhTm5eltw7CCiFya6sI8NXWIvUtxSOeEYpOHTpiyvKctBfkZ/kgzdaiIOfz8fK26f6KGFIlxED85Wf3Y10eAyiql+nH18rUMKoiMtOHzb5FfKNW5PSYyCDOnTLRhRUTORd/cX6rlshNTh+rdB8MKInJZmt8gcjm+xjJyJVi5K9vkoROmLO/paK4VyTBvvGOscsKhSWRrZUUlGMuggsgo+YVSyFoM1rP9gA2rIXI+uub+0lwuu2XLcL37YFhBRC6pYVdnTmaoTRU8zJ/QEZ1aG16uU3PoRLrc+OU9HY2xQ0WsjUOTyB64ahIRuTpDPWIA9oqxp4bX5wpFhd72DCuIyOVwTLZ+msFDt4QQVFYqjW7fcHlPZ2PMUBFr49AkshdjVk1ijx8icmaGesTUt9HfK0YVeMhlEojyshAd2w4Bgbe+6GDY0TzNuT63/1UbEZEFMajQz9LLe7oaa08eyqFJ5MjY44fIsjS/5W/qxpc3vY4pv1CKvIrWyDjwG1LSpqEuugNkWts5BMhUzb0+59KlROQyGFQY5mjLezqS+slDrTdciO9PcmSq92d8fLy9SyFyGapv+fMqWmP/gb/h03YU6mLGQNZiMGQtBhscrkD2IZdJkPG/z5CSNg1h0R3sXY7TM+f6hz0riMgpVFy/gZsbv1Q/9hJ4oUZjST5lwXX8npvHG0EDHG15T0fRnMlDLxRX49kdZerHXl6eqKmpVT++IbnVc4JBBTky9vghsp5S0VXe+DqYjPPnsWDFJvVjf38fKBRV6sfHj/7N35eFmHv9w7CCiJxCQlAo7hqYpn7s4yNA1f8vyZefn48fT53F/fc/wBtBAxxteU9H0NzJQ8WtkyDSs0xk3YefAmBQQY6NkxETWY9cJlEPJeCNr+OQV3tqzWtR7StAle+tz29B4En+vixAJpPh4MH9Zl3/cBgIETk11YV269ZteCNoIaYMFXF21u5BwqCCHBnfn0TWJcrLYlDhhLx9nXfVM0eSm5tr9ucLe1YQkdPSvNDOyDhn73IcwrkreXgtXfcEeQqZ4fGxjrK8p7VZO6iQS6S8ESSHxaCCyDwNhxIAt4YTqCbTLJXVIZlBhcuRyyT2LsEpxMfHm/35wrCCiJxSXl6e1oW2obDCXZbji2odi+6hurfvPZljcB+OsLyntVk7qBBnCVGYn4+H7rufN4LkcBhUEJmv4VACoH44wY3Si+qhHzUX99mpOrKWUtFVVOVl2bsMpxAYGGiwjVyufxU2DgMhIqeTn5+PX3/91egLbS7HZ1nWXt7T2mwRVGR+/QMiY2J4I0gOh0EFkfWUFFwxaTJNfkPvXFSTpUbHtrN3KS4hPz8fBQUFetu4/tdnRORSVBfaY8aMRqtW0Ua3T00dYoPqXF/98p7V9i6j2UqlCizbUWqRoKKpFWqkZeUovJaHyLhYeEorzC2XyKI4NInIeKXC8ygqOalze2WDv/Gloqu4cGCL0UEFv6F3LpqrugT4Xbd3OU5PdX0+dux9etsxrCAip6H5jWBsbKx6NRBj2nM5PvM1Z3lPwPBfiwAAtOpJREFUR5KRK8GlvHK8O6WzUUFF3sWL+OfwIZ3bw6tr8aDGCjU3b4qwe/cveHzUWMTExODbTZ9g3/YfdL7eOyDIpPqJzMGhSUSmGRAbjlFJ/jq3P3v4qvr/VTeyve6cgaDwRIP7VrUfMnigRWol62q0/GwZwwpzaF6fBwTon8yUYQUROYWKikqTvhHkcnyW1dzlPR2Fqv6k2FCje1R0DQlFf4nuLrrva/x/fn4+9u79Xev9Ge/piacEPk2+NlMiwb46Y6snMo9qaFI8hyYRWZzmjWzLNh1RVan/ixR+Q+9cGgUVZJaG1+cKhf5eqAwriMgplJQUY+TIu5oVVJB5Gs7xkH7R3hWZRrP+H4+VWnz/pg5NypRI8FV2JgakOWcPFXIuqqCi/SMPwPu/fxps7y6TEZNz2vD5t8gv1L2q1TXhVcQl3LqhrKupQPbVS4iObYeAwGDERAZh5pSJFqvH1BtZfkPvXBhUWFZzrs8ZVhCRU2jZMpxBhR1YezJKazsnFGvVbyisMHXyUFOHJqmCikcT26PQ3/l6qJBzKc/MVgcVIe0SoDDQnpMRk6PLL5Q2WoFD0/Wyf9Dy/7er5pDoMngq6qI7QAbgt/+t0xl2yGUStPAH5r/8IgDgUu4V/FNyU+exbpb5Q2JOUEEOjb8vy2ru9TnDCiJyCn5+vgbbMKiwLGcPKjJyJVi5K9vo+k2dPLSy0rShSZpBRfvgYBQafSQi04mzhBBu3a4OKgzhZMTkSnTNIdHUcqPq9gd+05pDIjYxHN176o749n9XipS0F/XeyNYUZ6Ng3/uQyiQQ3chDdFQsFBd+huJC/faIln7NODuyNoVMzKDCgsy5PmdYQUQuwdQ5LUg/Vwgqlu3IwvwJHdGpteEeDM2ZPLSovBx3jr23WUEFkTWphn50nvoQ/OLiDLbnZMTkLIxZocNWc0i0CIs0eCPbNdQPE9sFY8OhM5g3IhVJUVFa23+VGervRKYSXfsHNzLS9baRS3T3oiwVXUWpKBt97p7DoMICzP0ikWEFEbkEU+a0IP0subynPWgGLd0SQlBp4EK1uZOHRoSGMqggh6M5R0Vo+0RUmrBqEicjJkdnaIWOKTv24NzPy9Q9GPIu74ZSWave3tRyo82dQ8LHx3CPz4qqSmw4dBAzBzUOKsg6PH0r4RNcrreNh9izyedV74eAQF8UX/0FxVebbIaoUMO/e6qfA+ngwf1mfZHIsIKIXIKxc1qQfqYu7+loTO0RYs7kob6+hi9WGFSQLVVVVCBfY44KQzh0jpzNaVEejpTpnnuoXHod3eLiEBRUDaAEAoEXlJ416u1XK8px7vBaAIBCWo7SglyEtYlHXvbvyMv+HW1CvS1ab9b1bCRHhGD31XPYffVco+2eLVtZ9HgEeNYBtXX6b3E9UNvoOc3gKjt9vd7Aw9M31Ow63UFubq7Zny8MK4jIJRgzpwXp15zlPR2JtEJpVlBhaRXV1fgqP59BBdmM9GYR2k9/mEEFuSyv7ikQ9+2qc3vtus9Q+/Q0iP//sa+PQKt3UcCWrWgz9T6Is4S48fUP6PTyZK1/L9HHzlu03lYxLfDoyAid28+Wh1v0eASkhkdjVKDu3jcA8GyJdpeJkoIrWj1shLX6A4+qikqL1Orq4uPjzf58YVhBRG6By/HpZ+3lPW3hWpEM88Y7RlABADelEjzOoIIsKO/iRfxz+JDO7Z4+3nqDCmleHvZt/wFSuRx5N28itlUrXDp6BJc02gSH8eaJnJfA3/CElZpDpYwJ9nSpLM5Hwb73tY8v8NQadiKolAHQHVaQ5WUXXMG6Ut2ruADA1Tw5sn5aBACoUsggL7mBgJZROH/8GwBAcoUcq5KGNvnaSzdu4McCLjlrjMDAQINt5HL9q7AxrCAil8fl+PRreONu6eU9bSUuItBhggoAaBUUzKCCLKprSCj6SyQ6t7/mr//bxBgvL4xVVOIroRAv6wjSjoSwezO5LqWiwiJBBQD0aOGFOQO0J2Bs2JNjyd/6e2pIZI75eerMUjqHY3Ky/p/ribWV8GjlDZlUApHsBjr0iIWf/60b64p/SrDu2B+NXlcmVeCqqBBdunWzeN3uKD8/HyUl+q85GVYQkUvjcnz6nROKTR46YcrynrYU5Gf4I83UoSLm8PO27NhnInNVVFXjq/wcDk0ityW/cRPtH3vI7KACAK6VFmLVIe0bWi8vT9TU3OpZUSYtBxDd5OuFIgly8qrMroNMF1Mhhn9+Nq6KCjEyOhIRtTIoy28FwYqoGix5PEjrNfVfdBRg1dPtkC5vY+uSXY7q+nzs2Pv0tmNYQUQui8vx6ZeRK8HKXdkmz/FgyvKejsaUoSLWJlHwGzWyrRKJGE8zqCA3FhDVyiJBBQC0CPfCPWna/5Ya9qz45n9ND0sRiiTYtj8TA1Od9/PUmUVF1kFUUh88pMQHw9dXoLVy2KLvtX9v5kzGTY1pXp8HBOhfha3pdVuIiJwcJ4/TT/XB++oDHUweOhEaZPzyno7G2KEi1pYpkUB4g0tEkm21DA5hUEFuzZg5LaxNFVRMGNIewYHO+3nqzK4VyRxmMm53Y+r1OcMKInI5DCr00/zg7ZYQYlJ7Z/+gNmaoiLWpljNNiGq6azCRtfj5GB6axB4/5M4UEqlV968ZVCREO/fnqTNztDmu3EVzrs/tf9VGRGRBDCr0kzvY8p6OxpTJQ8tEIuzb/oP6scDLE0qNscq1pWVAeKTWa1RBxaOJ7VHoz2/UyLFkSiQQVnMMPbkncZYQCqHQavtnUOE4jPniwt2uf6ytudfnDCuIyGUwqDAsv1iG/0xgUNEUUycPTaitwzSBj/pxwzGvn/n4aLXXDCraBwej0PySiSxG9f4ckMYx9OR+VMuZpg1Itcr+GVQ4F1tOxu0OzLk+Z1hBRC5BKpUyqDBCTLjhro9Xr0sx65NLyCooR7s2ofjq71Lg71tLS9UGeuBeaxdqY9aePLRhUEHkSNjjh9yZKqho/8gD8C+SWXz/DCqcjyNNxu3szP0ikWEFETm9/Px8XL9egPvvf4BBhQEBRnR9FPj4IfOGBBPu6NLkhdXZ8lhrlGY3mj1I0uWWv1FjUEGOjD1+yJ1pBhUh7RKAovMW3b9EVsGgwgk5ymTczk4mk+Hgwf1mfZHICTaJyKmpEtvWrdswqLAQUYnMbS6srD3UhUEFOTK+P8mdNQoqLEwokuDajTK3+Tx1JY4wGbcryM3NNbvHM38TRGR3eRcv4p/Dh/S2kVQ1nvRNs2vZwV9/0ZrssKHgsHCz63QX0S0D3eLCytpBhbiygjeC5LAYVJAlbPj8W+QX6l5BIyYyCDOnTLRhRcaxRVCxbX8m4qJauMXnqbsxZTJudxYfH2/2F4kMK4jI7rqGhKK/RKK3zfsNHufl5WmNgYv39MRTAp+mXgoA+N3b8JJ9VC/A1/U/GmzRoyKnvAyzkrrwRpAcDoMKspT8QilkLQbr2X7AhtUYx1ZBxYQh7XH4YonF90/2Zepk3O4sMDDQYBu5XH/w4/pXpETkcvLz87F37+9Gdy1zp+X4zhUU4/A/pTq3y/NL8NrWW48bLrcJAMVl+teZl8ic+xsFWw39aBvagjeC5HAYVJA7qxRLrBpUZF8Xa81RYSiscPbPU3dj7cm43U1+fj5KSnRfswIMK4jIyaiGfowZMxqtWkUbbO9uy/EFdLsd4r5ddW9fuRjDbwtTP/b1EaCySqnV5pv/5eh8vVAkQU6e8wY/pVIFlu0otckcFQdK+Y0aORa5TI6vbhYyqCC3JM4SoiznGrrMetxqPSq2H8w2eo4KZ/88dTfWnozb3aiu58eOvU9vO06wSUROQ3OOithYwytSaN44BnM5PrOpura2jTUcEjmijFwJLuWVWy2ouCoWm/SNtUTBb9TIdsRZQhQUMqgg96Qa+tGibZxVh348fEcHo4MKZ/48dTfW7pHpbjSv5wMC9F+fs2cFETmFyspKk9Zp5nJ8TbsmrsL6I7e63HkJPFGj1B4GopRVNHqd5hhcsRMGP6oLjaTYUKv1qNiam230jaA7DU0i+1PdqHWLjGRQQW5Hc46K4gNHLL5/zc/HxNYhjXor6mvvjJ+n7oZBhWVpBhUxMTFQKBpfc2piWEFETqGovBx3jr23WUEF3VKVmIzaqQ+rH3v7CFDd4MJKsHKx1mPNC6uE6GCcLbdJqRajeaHx4zH9YyObQ/V+m57UCXG+hi883W1oEtmX5o1awCdfG2zPHj/kSsozs7XmqDAUVigk+udsaqjh56Op7Z3t89TdMKiwrIZBhTE4DISInEJEaCiDCjsw9ULM0ZwTik260DB1OTLN91uHkBCT2nNoEllbwxs1QzIlEghviKxfGJENiLOEuPzFNqPf/+IsIURCodH7r6hSmhVUkGNjUGFZzQkqAPasICIn4evra7ANgwrLcvYLq4xcCVbuyjb6QsPU5cgUSqVJ7zcOTSJbEmcJIdy63aSggj1+yFSivGzcOH9F5/aoUPvcaqh6FHWe+hD84uKMbp82INXoY9wslWHiUAYVrsjak3G7m+YGFQDDCiJyERXV1fgqP59BhYU4+4WV6huR+RM6olNrwz0YmrMc2Q2FDI81M6ggsiZTb9Q035+F7PFDJkju3BmDu9yjc3vhhZ9tWE09zaFPoe0TDc4hodnev0hm9HFahQUyqHBBqsm4353SmUGFBZgTVAAcBkJELuKmVMIbQQuRSeVOfWGl2XWzW4LhoRma7UODjL9Ri/IPZFBBDqfhjZohfH+SK1EqKkwa+qT578XUVUL8fAx/52vqUBGyL2tPxu1uZDKZWUEFwJ4VROQiWgUF80LbAsRZQhQXFGLWSOe8sDJ1jGnD9ukXjT+Wv8DwRyhvBMmWqioqkW/iHBV8f5I5Lp/bh8MHd+vcHh7qB+BRm9Ujv3ET7R97yOpBhbFMGSpC9mXtybjdUW5urllBBcCwgohchJ+3t71LcHqqC7fkNpFOeWElrVCaFVRYmqlzWhCZS1JSgvZPT2VQQTZz18AWeGRkns7tX+9pYbtiAAREtXKYoAIwfqgI2VfDybgNhRWmTsbtruLj480KKgAOAyEiN8Hl+PTTvHALNGEohCO5ViRzmKACqJ/TgjeCZEvBLVsyqCC3JvD3M9jGVkEFYNxQEbKvjFwJlv5w1cTJuLlqkjECAwMNtpHL9V+f818QEbm8TIkEwuoqe5fhsEy9cJPIHDP4iYsIdJigAjB+TgsiS1GWi3Fz45fqx14CL9Qoa9SPI0rLkAkBgwpyW6bOaWFtjvp56i5sMRk36Zafn4+SEv29WBhWEJFL43J8+pVnZpt04SYUSZCT55jBT5Cf4Y80U4eKmMOYOS2ILCkxIAjDBqapH/v4CFClsRLCnxmXGFSQRf1z7gYWntL9LbPS2xvjJtmwIANMmdPC2hz589QdNJyMu7JS/6oxmu3T5c7ZA9WRqFYJGTv2Pr3teCVFRC6Ly/HpJ84SQrh1u0lBxbb9mRiY6rzBz7UiGeaNd4x10zk0iWztplSCxxlUkAUlhHbGqMDeOrf/KlPYsBrDjJ3Twtpc4fPUmZk7x5Upk3FTY5rLmQYE6L8+Z1hBRC6p4ZjsQnsX5GBUQz86T30IfnFxBttrrhMvtlPwczSnGIcvlunc7iGpMLgPmZcf1p6qAU41vR9P/wDc28z6TMGhSWQPXDWJ3J0xc1pYmyN8nro7U764sNXQUXehGVTExMRAodB/7cawgohcDieP009zjorQ9omorNLf9VHzwiohOhhny21UaAO1nW6HqG9XndujNy42uI/qhM4QPT5T9z6OnW9Wbabg0CSyF2NWTWKPH3JnConUqvt3lM9Td+doc1y5i4ZBhTEYVhCRS2FQoZ+pk4s1vLBydda+UOXQJHJk7PFD7kycJYRCKLTa/t3t89SRGTPHFYMKy2pOUAEwrCAiF8KgwjBTJhdztwsra1+ocmgSOTL2+CF72fD5t8gv1B0Ux0QGYeaUiVatQdXjMG1AqlX2726fp87OlpNxu4PmBhUAwwoichFShQJf5eczqDDA2MnF3O3CytoXqgzSyJGxxw/ZU36hFLIWg3VuvyLcbdXjaw6N9C+SWXz/7vZ56gocaTJuZ2dOUAE4UFixd+9ezJ07F6dPn1Y/V1dXh3Xr1uHbb79FaWkpevfujddffx3t27dXt6mqqsLy5cvx3//+F3K5HIMHD8b8+fMRFRVlj9MgIjvIz89HblERnm/fkTeCBhgzuVhFZTW27c9xmwsra1+oMqggR8YeP2SO7IIrWFd6U+f2urBWZu2/VHQVVXlZZu1DH82//yHtEoAiy85bJJFVMKhwQsbOaUH6yWQyHDy4v9lBBeAgYcWpU6fw8ssvN3p+zZo12LBhA+bOnYuYmBisXbsW06ZNw+7duxH8/xd8CxYswL59+/DKK68gICAAK1euxMyZM7F9+3Z4eXnZ+lSIyMZUiW18RARvBC0kq6AUbSMj8ZtQCQhLG20PCvW3Q1WGXSqsxLM7yrSe8/LyRE1NrfrxTXkQ2mpst/aFKoMKcmR8f5K5UjqHY3Ky7klZv7wY3ux9l4quIuN/n2HI4IHN3oc+jf7+W5hQJMG1G2WYMaYLgwonY8ycFmRYbm6uWUEFYOewoqqqClu2bMEHH3yAgIAAVFdXq7dJpVJs2rQJs2fPxpQpUwAAffr0wR133IHvv/8e06dPR25uLnbs2IEVK1ZgzJgxAIDk5GSMGjUKe/fuxciRI+1yXkRkG5pdy3L3/GbvclyGV9ee8H96Gmp1bA+wwYoZzVES0xmiqQ9rPefrI9Ba7aR2y1b1/1v7QlVcWcEbQXJYDCrIkamCipS0aQjwu27x/dsiqNi2PxNxUS0YVLigcilXTTJGfHy8WUEFAHhaqJZm2b9/PzZs2IB///vfmDx5sta2M2fOQC6XY/jw4ernQkND0bdvXxw4cAAAcOTIEQBAWlqauk1CQgI6duyobkNErikvL8+kMXBcjs94jrAOvbVZ+0I1UyJBTnkZbwTJITGoIEemGVSERXew+P5tFVRMGNIewYGu/3nqbjJyJbiSK7J3GU4hMDDQYBu5XP/1uV17VnTr1g179+5FSEgIPvzwQ61twv+fkT0uLk7r+djYWOzbtw8AkJ2djYiICAQEBDRqI7TijO5EZF/5+fnYu/d3o4MKLsdnWdZe3tPabBFUfJWdibahLXgjSA6HQQVZUnpmIc6cLdK5vS7QD/easD9rBxWVYolV//5nXxdrzVFx+GKJ3vYSGb9IcSaq5Uz7DuSqSZaQn5+PkpLGw4012TWs0DcJplQqhY+PD3x8fLSeDwwMhFRaf6Esk8maTGwCAwMhEjHxInJFqqEfY8aMRqtW0Qbbczk+y7L28p7WZu0LVc0bwQOl+i9SiWyNqyaRpYXGdkfr1rqHXV+/bvh6XJSXjRvnr0AuLUNhQRYi27RDceZ+FGfuBwBEhVrmdkWcJURZzjV0mfW41XpUbD+YbfRkmkKRBDl5/CLFWaiCipfHtUO6nKsmmUt1PT927H162zns7CF1dXXw8PBocpvqeV1t9L3WWOHhQc16XWSk7T78bXksWx+P5+acx2vusfz8veHrq//PkaBOiZs3Rdi793eMGTMasbGx2tsFXo32cVUsxtbcbExP6oTilqFm/Syc5ffm7+8DXx/dP0uBwKvR9oaPm2oDAOWZ2RBu3Y4RQ9Ps8rNszrkBt86vPDMb4mt56P7cEwhtn6jzGKr6DL0vvQSeWts1328dQkLwt6RM7+uroTT7feUM/76d4XjOcm6G3pOCOiV8GvwbUD3Oy8tDXkkxXkpKQoeQEL3HcJa/lc7ye3P0Y5lzPH9/n0bvuYbbG+674eNevXqgwi8Re7Z9iKnPL0ZMQmet7cUXd6tf09zPONXnV3i7tohM1t1jQ7PeS8ViHLtSrrNtdUWV+ljZ18XYfjAbD9/RAYmtb/37Egg8m6xX1X7osJFO8+/N1sdr/nvS8DVlw89vAFqPG24/JxRj5a5szJ/QEd0SQnDpsvP8nbT18TSPpevvQ15envp6vmXLUL37c9iwIjg4GFVVVaiuroa3t7f6eZlMpl4JJCgoCDJZ42Xm5HK5uk1zFRdLUVtbZ9JrIiODUVgoMeu4jngsWx+P5+acxzPnWBWKalRWKvW2kckV2L37F9x550h1j4oqjYkTlcoarX1ofsMd5xuAfEV1s+tzpt+bQlGlNaFkQ0pljdb2hhNQNtUG0B464Vkks8vP0tRzA26dn6r+kLhY+MXF6dyPQlGlrs/Q+7JGWavenimRYGtuNh6OT0ScbwAqK5Va2xvKlEhwUSo3633lLP++Hf14znRuht6Typparb+LPj4CVFUp1d9gxbYMV78/9R3DGf5WOtPvzZGPZe7xFIoqrfdcU9s1993UsYoKi3Dw758wbPwsRLTp2Ojvs6Li1j6a8xlXePGq+vOr+MARva/XrFfQqSeq+z6qs63nysWorFJqzVGR2DpEa/9KZW2j42m2F3sJnOLfm62PZ9570vA1ZcPPZ19fgdZjze2aPSo6ta7/+6lwkr+T/9feeYdHUbVt/CYJaZTQa+joQhIgtNA7hGJAkRJKqFIERQFFEcEC36uiAnbBXhBRUSyvSBHkpYn0EgglIT3Z9LI92eT5/ogz2d1smdmd2Wzg/K6L62Izs3PO7s6c85z7PMXd7Vm2ZW18ME2O36xZC+h0ervX9Fixol27diAipKWloUOHyh0w09ft27dHbm4u9Ho9/P39zc7p3bu32/vMYDDkI7eoCGMmThKco4LFZEuH3OU95ca0/3nHT0t+fe5+W6C4H238HLuGstAkhjsRWzWJJSNmuJubV46iRWNfpF38CmkXqx6v4+cHwLZoYI+ihESz+cvRHGCakyn35i1kX7bSoX8JKixEktLHLEeFI0yFivYt6uGKbccNhgdgKlSEtWX2pKuYzkdCq4R4rFjRs2dP+Pn54c8//8TixYsBAEVFRThz5gwef/xxAMCAAQNQVlaGI0eO8KVLk5KScPv2bf4cBoNxd9AkKIgJFdWA3Mko5cYVQ1UIpvdb5/r1He7mmJ6fE8BiXhnyYlk1KcXB+SwZMaM6GDOwER6dmg8gz+rxXQeDrf7dEcV3kpC0+yfB85dlTqawlm0wpqXt3FiH9tx0SahgeDZMqJAWZ4QKwIPFijp16iAmJgZvv/02vLy80L59e2zfvh1169bFtGnTAFTUbh03bhw2bNgAtVqN+vXrY+vWrVAoFBg9enQ1fwIGgyElfn5+Ds9hQoW01HShwlVD1RE6o1HU/WZ5f+YIbonBEI8zVZOYxw+jOqhfPxCAtAmJufmr67zp8LeoLGjv/OEDBgtuI6dAhVkOhIckdTl2nC6ARq2FMiMHLVo1xYEkI5BUUQGhblCA4PYY7qNArcMbPxcwoUIinBUqAA8WKwBg9erV8PLywmeffQatVouePXvitddeM8tH8eqrr+LVV1/Fm2++ifLycgwcOBDPP/88vL29q7HnDAbD3ehLS1mWewm5G4QKuQ3VLJ0Gc5wUKhgMOXG2ahLz+GF4IsXF4kKTTOevoE4d7OaosDw/ILdqLjxbNG1Yz6GHRHm7zigcE1lx/WcWIaBje5SbHA88U7PCKu8FYlNUuJlWhM1zuzKhQgJcESoADxIrVqxYgRUrVpj9zcfHB08//TSefvppm+8LDAzEpk2bsGnTJrm7yGAwPJhstQoL2UJQEuQu7yk37jJUmwfUYUIFw+MwNQyDg4PtJj8EmMcPo/q5FZeG/8QmWz1WUKxHnqYh5i4Vdi2jTi9q/nIlJ5O/X22H55ToDUiuwfPpvQYX+qEIDmJChQRoNBqcOHHMaaEC8CCxgsFgMFyhWd16bCEoAXLXoZcbsR4hrhiqAT6Op1AmVDDcicFgELWDxe5PhifQpVUwYrpUrcDHLRz7Dxwg+FrarGx0mjPdufFfBgrupKJR+47QHz4B/eETVY77+NeRpV2GeExzVOw9U1Dd3bkruHbuHBr5+ODmP6dx08Y5He7vYvcaTKxgMBh3Bf61He9wMOzDGW4N2rWpkUKFyztqEiM2pwWD4SqsahKjJnIpIQeXr+Sa/a1YpcOd9Fx0bN0EiVnCPd4Cmzdz2/ifpCrFjtPmi1pvHy+UGSsDPZrVrYeYyCib18jMVDrVNkNariYVmyXTdCRWFKlZ1SQh9A8Oxph8+/lorte1P/8wsYLBYNwTsHJ89pG7vKc78LQdNTE5LRgMKWBVkxg1kaDg7mjZMpJ/nZ6ejr8PHcSYiYvQunVrUQt6nwB/h+dINf6Xt1egfHGM2d9q+/qg1CT0KvDdz52+PsM9xKaosPXXRMHJNGNTVLidUuqGntV86gU4Tp6r0evsHmdiBYPBuOu5l8rxOaoLr9dW3aGyNNykLu/pLty5oyYEoTktGAypYFWTGDUdV5PxOUKsB5499JlZyP54p9nfvH28UWYs41/XLiyyew2NRrjXCEN6uNCP56fdh/tbOk4uzJ0fMZBVTZKCBJUKabn2MyUxsYLBYNzV3Gvl+BzVhf/y1FGz10UJiaJDJ8SU93QnQnbUpDRUHSEkpwWD4U5Y1SSGuzl66gLSit5CYJ2K+y0gwBc6nfnmQWZCPFq2bCG7UAGI88BzRPu6QRg7cLjZ33x9fcyS2h44sN/m+9PT05GSkuJyPxjOYZqjolv7+jAY7CcjNj3/kpZVTXIVzj4f37u33fOYJcVgMO5aWDk++xTfSULS7p9E53gQU97T05DSUHUVFprEkJpCpRJHfvqRf+3j7QVjWWX8fGqWEss73seECobbKEEgqPUEcP4DpX4+KPEzXxRqdKfcIlQAwj3w5Ib7vIMHD63urtyTqPVGsxwVjjAVKsLa1sOlG27o5F2MqX1u8A+wey4TKxgMxl0JK8dnH0546DpvOvzbtBF8vtjynp6Gpxiq91JoEsN9tC8nzPfx5V/7+fmY7RZ+WC+ICRUMt1Lbz/5CBAAMer1bhApAmAee3JgKM15e3tXdnXuS1FwN1j3snFDBcA1L+/y6g/OZWMFgMKzy0VffIT3Hdm6C1k3rYsncaDf2SDgsJts2+swsJG/bjpzUNDRtEwzNnydQbBJfC1TNa+FKeU9PwxMM1XstNInhOQgJTWIePwx3U1iQiwfGyi9UCEHunEyWHiSsGkj10KZJHSZUVAPO2OdMrGAwGFaJu3oCeSrbO+jFyjoAPE+sYEKFfVr4+KMgIwcLx01E69atq8TXAuZ5LdyVjNJTkNtQZaFJDE+GefwwqoMGDZt4hFAhd04md4W6MBxT19/xEpgJFdLirH3OxAoGg2GVYb18MSsy2+bxXQcburE3wmBChWPy8/MQGTlWkKF0rwkVchuqLDSJ4ckwjx9GdeHnX/0eb3LnZGJCRc1CbE4Lhn1csc+ZWMFgMKxy5mYe/o4rsHnc2ysAD7mvOw4Tx6mysvCN0ciECgc0atSYCRVWkNtQZUIaw5NhHj8MudAU5SD+5Gf8a8u5GwCgLrZ/DZnLe8qdk4kJFTUPMTktGPZx1f5hYgWDwbAKteuL7IhQm8cLPvwJL275lH9tWY4sNSkebdp3BgBoNSoo0+6gRXBHvnwZIC7vhb3EcQkqFT7QarBcEcIWgg7w9/dzeE6JXo/0e1CokMtQZUIFw5NhHj8MOQkODsaomc/xr/18fWCwCD08/NUam++Xu7yn3DmZ1Go1EypqIEJzWjDso9JpcdBF+4eJFQwGwyl0OYlINFbG93t7e6HMZLckM/UOjKVZUBVrkJmegZatWyErwzwfgBR5LzhDu11QA7YQlAh1di46LZhxzwkVchiqTKhgeDLs/mR4MnKX95TbgzA9PR2ZmRmYPHkKEypqGEJyWjAck5SldHl+Yb8Eg8GwSnHcDagunLd5PKRZFna8VOku7OvngxKTEnn/eV+PB8cosfXjBGx5rRPC7gcA811rV/NemBraxwvyXboWo5K6zZrcm0KF1Nc36NlCkOGxMKGC4cnIXd7THULFoUMH0bJlKyZU3IUUqVnVJCG0b94CnfJds8+ZWMFgMKwyuHELjKtjuz76ytM3seLtypwWPj7eMJqUwEy6kI/T/+Sic5tG+PEnDX5EVfd6Y+3aeGimc/2LLy42M7QdiRWsHJ9wfD0g2ZncyG2oJqhUSC4qZKFJDI+ECRUMd5GbfhW/frScf+3tXQtlZWR2jlFjnh9L7vKe7hIqxoyJRGzsVcmvz6heYlNUuJ1SWt3dqBHUCwgEYN8+1+h1do8zsYLBYFglMeM2thfYrgaS6dscXtFP8K9N41CL7yRBc/hdvDRyNBTNm9u8xn6N/QHKFgkqFXanJAo2tFk5PmmRu7yn3LhDqGChSQxPhQkVDHfSt4sRr62p9Lq09MIEgCUvVuZSkjsZpaFYJev4n5aWZtZ/R2KF3MlDGdLClTONGMiqJklBgkqFtFz7mZKYWMFgMKwS1rUxYrrY9kYY82EeVNu/4l/rfLxgNJZDo1ZBmZaB9j6l+Cv5Kv5Ktj1RU8NmovvFGdoLFPejjZ/jrPWsHJ+0yF3eU27kNlRZaBLDk2GhSQx3k5qswcuvVYoTll6YAJCfV+HNJ7dQUXwnCYXJqQhZvlA2j4rDhw8J7r/cyUMZ0sIJFWse6ohLWlY1yVU4e2l87952z2NiBYPBcIo+fn54VBHGv/bz9cGV1HR8dO0mXhw0GCeyTmPjrLp2r/H+OdthJtYwXQh2rl+frwYi5HxWjs915C7vKTdyG6osNInhybDQJEZ10MQrGIvaDeRfW6sGsiH7rFuEioRdP6JBuzayhn5MmDAezZq1EHy+XMlDGdJiKlSEta2HSzequ0c1G1P73OBvfy3AxAoGgyEJN5RKfHTyBJYMGgxF8+Y4kWX/fLExfzqjUdSOICvHJy1yl/eUG7kNVRaaxPBkWGgSQy7SbtxA3N8nbR6vXVzs8BrpiYmIvXkDwc2a4eY/p3HT4ni9ho1d6qPp/JV3/LRL17KGqdASHByMkhL7GylyJw9lSIulUMFwDUv7/LqD85lYwWAwnCKzKAfbz/wJAChU63AnOxcdmzXhQz90ZUUArOercCbmL0unwRwnhQqGa8hd3lNu5DZUWWgSw5MRG5rEPH4YYgitH4T+KpXN49/6+tk8BgA3s7JQmpeLNZ3vtzlfn64f5HT/ihISzeYvR3OA2JxMYj1C5E4eypAWJlRIizP2ORMrGLLz0VffIT3H9uDfumldLJkb7cYeMaSgUxtvbJha99+BPAPvPnYf7m9ZuVDbtMd6RQlnY/6aB9RhQkU1IHcySrmR21BloUkMT0ZsaBLz+GG4k5tZWfjo5Am0b9hIlvm6+E4Sknb/JHj+EpuTSa83uCRUMOThn+Q8/H2j0O45tVR6h9dhQoW0OGufM7GCITvpOWpoGgyxc/y4G3vDkBLTgbxbe8cLNVdi/gJ8HA9XTKiQlpouVMhtqLLQJIYn40xoEvP4YbgLTqhYMmgwfjstvccbN391nTcd/m3aCD5fTE6m/Pw8REaOZUKFh1F+f18oI0LtntPi41fsHj+drsWRrwvQpm0TfHihDLhQaHbcKyAQk1zt6D2EK/Y5EysY1U7stWt4ccun/OuAAF/odJU7O8zzwjNR642iFGe5FWqxC0eGfe4GoUJuQ5WFJjE8FWdDk5jHD8MdmAoViubN8ZuD88WGJpnOX0GdOlRJ6GnvfDE5mRo1asyEihrKzRwDHvu5kH/t7e2FsrJyAIBKpcGVYl90Wbkamo7tYe2OaHGmZoXDVieu2j9MrGBUO9pSLzPPi1I/H5T4VU4szPPCM0nN1WDdw54hVADiFo4M++jUaqQLFCo8MczLXYYqC01ieCKuhCYxjx+G3Fgm43aE2NAko04vSmh3JSeTv7/9fByA+FARhnvIb90Vynkz+NdclRrufqh/X/0auVHjaah0Whx00f5hYgVDdpRpici6dtvmca3afqZoS88LrUYFZdodtAjuiMA6FTc+875wP22a1PEYoQIQvnBk2Cc9PR3ZN26hWZf7oT98AvrDJ6qc4+Nfp/J8DwvzEusR4oqhykKTGJ6Gq6FJDIbUZGvsJ+MGgGJNEYCq5T6dCU3SZmWj05zpzo3/MiAmVIRRvcidjPteJClL6fL8wsQKhux06doVQ0KibB7/cssTiD/5Gf/ax9sLxn9dsQCgoKByMVSgjEfs8QMIGz4f1KIz75rFvC/cT11/x8OHO5MTCVk4MuzDuaqGtm6LKZG2n1lPzV7u8o6axLDQJIa7YaFJDE+jfStv/GeB7WTcALDqzaoJuZ0NTQps3swjxn8OoaEijOpF7mTc9yrtm7dAp3zHVajswax7RrXTuk17jJ6+mn/NuWJx7PnsVQD/ChVHv0DY8Plo2KKz2/spB1/v+gK5hdadbjUaNVKTU9G3Tz8smr/UzT1zHbE5LeSGleOzj2lMbWzsVbe1ayuMhPOg6te3Dx5fPFfQtTxtR42FJjHcDQtNYngiYpNxuxKa5BNgvRKZKe7MySQkVIRRvcidjPtepl5AIAD7YoVGr7N7nIkVjBrB3ShUAMD12OPQ6PKq/L1YpUVqcjbatGuGhFu1ANQ8sUJMTgu5YeX47GOZ/MuRWKHRCM/r4LBtK2Ekph5UOcWZgq/laTtqLDSJ4W5YaBLD07ieqcOqL/LQsXUTfHnagG/O5cBoLDM7J7dIBzSo+L/c96dYDzy5kXI+ZYjHHcm4GbZJUKmQlmtfjmRiBUN2kuKv4vLVizaPpybdxp/fbeJfe/t4ocxYGQaSnngNybcuoGmrjshLOIa8hGNVrtE8qGbeyrV8yxDgY77jry7WIj8nB11Cm6JufaCWV5mNd3s2QnNayA0rx2eftLQ00XXiU1JSZOtPFWGyULhYIWRHzZ2GKgtNYngaLDSJ4W7yjXUxcuIsfn7x9fVBiUXS4/zEilBgdwhpYjzw5Ebu+ZRhH3cl42ZYh3vex/fubfc8Zkl5CJ6YUV8q2rdvjKb3N7N5/M+fMjD6wW78a9MwkIyUDMTHlWPaoo1o1a6rzWvkXP+vdB12I9SuL7JNakHzA+GiRdB2bA8tam55JCE5LYrU8oZmsHJ89klPT8fhw4dECRWHDh3E4MFD+b8lXTmF9Jw/bb6nddNAAI8I6o87PKg8yVBloUkMd8NCkxjupklQkKD5xV0eP0I98OTG2nzqThytO1KT4tGmvfk8HBDgC52uwktVq1GhQQDw/JqVcnZTNtxZNYZRFdPn3eAfYPdcJlZ4CJ6WUV9KstNuIPH2SZvHtRqD1b9npGTgyK+H0KJNZ7tCRXXy373vwqhPsnncx789oiavsHlcczsWutiK70aj0iAzRYk2bVug9tGD0B399xy/IAl77DnEpqhwO6VUtuuzcnz24QylCRPGo1mzqlnYbZ0/ZkwkvLy8+b+PC+mIcXVsTzTP/HPVrJqPqbEDANduxKN9/yFuC/XyFEOVhSYxqgMWmsRwN35+Asp7lpbim/R0t3j8CPHAkxtb86lb++Bg3ZFZGIdGFsdL/XxQ4mfkQzWHDhkodzdlw9NyXN1LWNrn1x2cz8SKuwRLhdSTynv269YAsyJtq7fRq5U48/te/rW3jxcKCtR8zoaM7BSzMBFr+HmVApgtVZcFc/3iCdQuS7Z5vNQ7za5YMbptIGK65PHJp96f1RFhbf0AVLqi7bxx93kEcJ83YqA8oRksJts+poZScHBwFZdce+e3bt1aVDUQTXmAmUHEGTv8cX2cW3PSeIKhykKTGNWFkNAk5vHDcDfZahUWesh8LXeVB1fmU0/AdL4O9BcepulpeFqOq3sFZ+xzJlbcJZgqpDWtvGf3TsDzj1Uq7zcTtdj8QTI+3tgJYffXQ/TqZNTzibf5/mKVFtoSX3d0tQpdWgUjpgvZPL7zRrDDa7izvKcnYPp5L2mlF2KYUGEfvd4gOkeFmPPFYlDewtX482jRPBi66/+FzkJib9LIveKC3IYqC01ieDLM44dRHTSrW88j5mu5qzzIPZ/KTX7GbbONhdiTf5p5TlrStH5twdW83I2nVY25F3DWPmdixV2GJ1bNiLuahZcv2FaOi0sqS9bE3lLhrU8TsXpxhVABVBUzTIm9pcLWj5MRMahm7lAWqbV44+C9KVSEta2HSzekvT4TKhyTn5+HyMixHiFUAEDDchVeGR0BRfPmVo/v19gvaSUlchuqLDSJ4ckwjx9GdeFfu3Z1d0H2Kg81XagoUMbj+vEvzdYX2lIvm6EkBcp43Lm532PFCkd4WtWYmo4r9jkTK+4iPFGoAID2QV0xrk4vm8c3/O87vPyaEoUqHeJT86Fo3wQ//qTBj//6hJiKGaZUCBUJWL24E64k1cwdytspyntWqJCaYoOeCRUCaNSosccIFQDQrEFjm0KFO5HbUGVCGsOTYR4/DE9G7tAkuas83A1CRezRL9BzzCOo27iD4PPvlZwWDPu4av8wseIuQatR8aEfniRUCKGZdysM8b8fH50/gVWDpqB7m9Zm5YNWnv4RK94uMHuPuliL1MQctOnQFDv+MMLbKw8PubnfUnBf2xYIa5tX3d2QHbmFigSVCslFhViuCGELQQf4+wtIdiYiVCQx4za2F2TbPJ6SU4a8vz/kX/v4eMFoUpq4ban7PCdsIbehyoQKhifDPH4YnozcoUlyV3lQq9UeKVQUKy8hz07y+xJNxTxtuhHaqNV9KDHYz3F1r+W0YNhHpdPioIv2DxMrPISUm8eQkbvP5vFWTerCXvk/ZdqdGilUAIDWUIKPTp7AkkGDre6wahoqkB1d6UZ2N5X3DKobCMC+WCF3eU+5cYdQ8U1iAtoFNWALQQBpN24g7m/bBoiqxLHRl5mYgFZNmuLmP6dx08rxeg0b8/8P69oYMV1s36PnPsxDrYAi/rWlWKHSKrH9jO3Sp9oAeavhyG2oMqGC4cmw+5PhycgdmiR3ToL09HRkZmZg8uQpHiVUAMCwXr6YFWl7o+HBFTlIOLwBmekZaNW6FUpT9yAzwwtlZZXzNydocFTx8C6suWKFJyTjvhtIylK6PL8wscJDeHBEE8yK1Ns8vvj/jFbL/3FVPxKzMlCn/BDSEg/ZvEaroOqPCbRGZlEhltkQKgDAmJYJ1favAAAatQrKtAy0DG6FWgePQYVjAIDAgDpu6687kbu8p9y4S6iY3aETjhfkS379mkho/SD0V6lsHn9LwDUUgXXwZP0GNo+fri9cQOjj54dHFWH8az9fHzPPqU8NCdg4q67V98amqPDJOdvjoqvIbaiy0CSGJ8OECoYnI3dokjuEikOHDqJly1aChQrLyn6WSFnV71ZcGv4Ta7uanZ8+CzDeQWT3BmhYv+I8y82Gwvx2/P89NRRdLuROxn230L55C3TKd80+Z2KFh+AoCeWd1FZQhJmX/8squMGHfvgF/I5WSx6020bQ4X8k66+UtAxqYDdmvZefP1YownAzKwsfXbuJF60IG+5Mwucu5C7vKTcFah3e+LlANqEivrjYzNB2JFawcnzC8YRkZ3Lf/3Ibqiw0ieHJMKGC4cnIHZrkLqFizJhIxMZeFf4+k8p+1jhwdDvSc9T8RmWHzgrU8q70AEhNikeb9lWFAu78fn378AkvHVWzm3WtCBuntjez3/z8fGAwCQO5lFGxUXivCRVyJ+O+m6gXEAjAvn2u0dtfwzGxwkNwlITyYoZ56U7L8kFJtj25AFTvg+Uopt1otL9zmqXKwmtHfkO8MgedWzTFX8lX8Vey+eBPDZtJ0ldPQe7ynnITm6LCzbQibJ7bVTaPit0piYINbVaOr5JCpRJHfvqRf+3j7QVjmbkrZ3lBIdC4qc1ryC38yH3/u0OoYKFJDE+FCRUMT0bu+9NQrJJ1/E9LSzPLUeFIrNBohOdJ0pZ6IU3fkt+oNLbqYpZDIrMwDo0sxI4CZTx/fk6x8LCMNk3qOLTfStRJVUJFslMrjzeud/d5PcudjPteI0GlQlqufTmSiRU1EGvlg5CWxodKWMKFTvQKCXVjLytxFNM+f1uOWcy6pZtZYKAK2rI8bHvU9g79zhuNrf69JiJ3eU+54fqvCA6SNfRjgeJ+tPFzvJBl5fjMaV9OmO/jy7+23CkBgC98fS3fxiO38CP3/S+3ocpCkxieDAtNYngycgsVxXeSUJicipDlC2XzqDh8+JDgZJrp6elISUkRfP1Sg1aUB4MrOSTq+jteIjZADvKLUjCqayM0qFfVO9xYu43g9moCcifjvtfgnvfxvXvbPY+JFR7CRWUaThcW2Dyeo/VFK9guH9Tdty5eMIkL5zANnUisW1+OrrtMpzbe2DC1MmbdcvE06y0j1j3MynvWBEz7v/eM7fuZQ2zyUFNDpnP9+lUW2fbOZ+X4XMcZ4SezyL4YqSsrAlAR1iV7eVuZDVUWmsTwZFhoEsOTcYdQkbDrRzRo10bW0I8JE8ajWbMWgs8fPHio4DbUBUqEDV/knFAhA4aiAKzqN8FmKPfdFKItdzLuew3T593gH2D3XCZWeAje3cNQHGHb88Fr2y+iywfdzMoyq7KRWEMHDSGuaHcDNV2ouJpUbNZ/R2KF2OShOqNRlCHDyvFJi7PCjyMxctOeinhbdwgVchqqLDSJ4cmw0CSGJ6PW6fBNerrsQkWnWVOQd/y05Nc3zVERHByMkhL79rnp+V5e3oLbqduwhccIFQDQqmF9uznn7hbkDh2917C0z687ON/LLb1iuEypQSdq4LEUKmoyQlzRWHnP6iU2RYXXfowX3H/u897X1vHuA0eWTuO0UMFwDbm/T3cJFZ1mTYFffflCk+Z27izq/mzfXPj9z2A4i+nzW9/PcTk+5vHDcCfp6elIyc2VbX4pSkgUtdAUW+XBVHgQGvoh5nxTagsIfXVnsss6frZDRu8WmFAhLc7Yk8yzooZQmH4LTRq3QV78H8iLr5oUT5eThpfOlwEA1Go10jMy0bpVS3yblgykVZQc8mvQEDOrpffywsp7ysPV22l47pLtWEqdpmJC5/r//LT7cH/Lyok0PlON53ZXfX+RWoc7GUXo2CoIKZnCY/qbB9RhQkU1IPf3KXfVGEtD1dGumlhDlYUmMTwZsaFJzOOH4U64hXvbJk1k86hI2v2T4IWm2GT0er3BbUKFEMTmtJAblYjkoZ4IEyqkxVl7kokVNYSWTY3o1KoIQBEAwNvHC2Umcd9XajdB8ZOPVj5Ya1egvGN7FJtco8WZuy++qqaX9yxSa/HGQc8TKgCgectgdA+yffzw+WQzoaVbe/OFWv16dTGqdzuz9yQpVfjhWAKmjQhB+xb1cKUoWHB/AnwcD1dMqJAWd3hUyFk1Rm5DlYUmMTwZZ0KTWDJihrswXbinHDwg+fU5e7jrvOnwb+M40aMzVR7y8/MQGTnWI4QKQFxOC7m5mZWFhAzhCT09DbmTcd9ruGJPMrGipuClR6mhcpe6rNQL5eWVYoWxrNU9pwBaK2/40VffIT3H/s5o66Z1sWRutDu66JDbKUqPFCqEoNUbRXmE8ELF0E5o30L6zyt24ciwj7tCP+SqGuMOQzVLp8EcFprE8ECcrZrEPH4Y7sBy4e6oHobY0CRTezioUwcYHOSQcLbKQ6NGjT1GqACE57SQCssE2hyFah3ilTkI6dbNbX2RErmTcd9ruGr/MLGihqBp0wv6eTP4136+PuaD7/Yv7lmhwrS8YXqOGhqLGtOWHDi63UzQCAjwhU5X6fbqTjHjvrYtENY2zy1tSc21pHzcH1wP35wqAE4VVAlNyius/I7lFioAcQtHhn3kLm8otmqMWNxlqLLQJIYn4kpoEvP4YchNWlqaqIW72NAko04vyh52pcqDv7+fw3PEhoq4gpCcFlJimUAb4Ob3DGx7tCMuaVu5tT9SIHcy7nsNlU6Lgy7aP0ysuEvQZmWj05zp98SDZS/HQ7HyEvJun7T7/uKsO0j0LeRfe3t7ocxkkZ2V5A24SawIqhsIwL5Y4anJQ5s3bYSHh3XiX1sKaN8erciV4g6hAhC+cGTYR+7yhmKrxoi9/8V6mLliqHpaaJIjzzJP8ipjyIOroUkMhpykp6fj8OFDooQKsaFJYuxhd3gkiwkVkRutRsX/31FeshtJOXhut/nfquTL05jPN7Y2EmsKcleNuRdJylK6PL8wseIuIbB5s3teqACAlgHZaOubbPca/gW56NpYz7/28fGC8d/8HwXFeuRpGkrW37iEKyj/9in+dUmJEbHXU9C5YwvUrx+IlDtXgC622/Pk5KH+vo6HD3cJFYCwhSPDPnKXN4xNUWHrr4miqsaIuf9d3lGTGHeHJjnyLLudtE/2PjCqFxaaxPBUuFCICRPGo1kzx5WQnA1NEmoPuyt0WmioiNwUKONRknaHf+0oL1m2ClXyjlluSh0+X2lve2qyeKHInYz7XqV98xbolC88mb41mHV/l+AT4LgcWU1/sIQMhF1aBSOmC9m9zqYiYMOYtvxrPz8fGAxG/vr9Bw6QrM/Z2Znwja1QsguK9UhIKUantvWRl5KJPABFuWoA1sWKmp48VF9idJtQIQRWjs8+poaho4oBzmCraoyj88Xc/562o+ZJoUmWhirj7oSFJjE8EdOcDcHBwShxEJrnSmiSEHvYnTnehISKcDjyDjbm30H22cr52dIz2PI4h6pYg8z0DIR37yK4L2Ko6UKF3Mm472XqBQQCsG9TavQ6u8eZWHGPUNMfLLkHQtPrbzlVjPgtn9o8V4wrddt6dbFhTFv++ltmhJgt1Dbtse6CZy15aE0ju0CD6GGeIVSwcnz2sTQMHYkVYoUfe1VjHJ1vev/n3ryF7MsXbb7P29/PoaFRmpaJ5G3bkZOahqZtgqE/fAL6wyf44z7+dYR9KAF4SmhSgTIesUe/wNAhA6u7KwyZ8bTQJAbDYBCXs0Hu+1OsB57caEzKe5YZ01DHy7Z3cDMfDbrVq9yQ8/HxhtFYxr8u8slCHa9is/doNQZoc1RQtK8HX59cCXtewd0gVMidjJthmwSVCmm59uVIJlbcA9T0B6tArcMbPxfINhBaxtBn/5aJAthWtsXmtHBloVYTY/44mjWs4zFCBSvHZ5v44mLRMe5ihB+1yKox9u7/sJZtMKalbffhL08ddXj95n7+0GfkYOG4iVYN58xMpcNrCMUTQpM4oSJs+HwE+tfcMnIMaWBVkxjuJreoCGMmTvIIoQLwrBxv6enpSEmp3LSq0300lBGhNs8vfmEz0nIrYzcsxYrCJs3R6LHlledzHiRPTIFXx/aoc0Z4TiYhFMlsn8uNu5JxM6zDPe/je/e2e171W1IMWanpD1Zsigo304qweW5X2TwqLGPoWwVmI7ix9Z1jsTktXF2o1WSE5LRQaSq+5yu56Tidk2r33DvqXBTmePGvfXxqwWgkm8cBQKPTISO/CK0aBSFRI33ViZpOgkqF3SmJopPxiRF+UnM1WPew59z/hbn5mOiGrOxCkDs0yVSoaNiiM1DIxIp7HU8KTWLcGzQJCvIYoQLwnBxvXGjM4MFDBb9H1akdiu1UBqz1ZWVGTLlDXZKUKtzJKMKb84XZ53u/+AT6rCybx/2bN8fk+Yuk7KJd3Fk1hlEV0+fd4B9g91wmVtzF1PQHi1u4KIKDZA39sIyh50I3bJ0vJqeFo4XalbxyPPZzIQBApdIgNSUXbdo2wYcXyoALFX/3CgjEJOEfq8aQpFQhOa1ih94/pB2KB9t3vzce8YZx5P38ay9fHxhNJmnL48WZRbh99Abum9ULfi2D4J/fROJPULPhJooFivvRRkC5M2eTnbVpUsdtQoU+MwvZH+80+5u3jzfKTHae/H18PEKosPRQcVTJIzUpHm3adzb7m2XZ5Stn96FegwqvMC5GuVXrVihN3YPsVKBxPelCXBg1E08JTWLcO/j5CSjvWVqKb9LT3eLxIySnhdyY5vDw8vKW/PruECp+OJaATq2E2+eXz55AeX62zeNejZrZFSu++/JdqHKSrB5TabRITlOiV8RgzF38lNVzLPG0HFf3EpbC5HUH5zOx4i6lpj9YpgsXR6UNAfHlDaWKoXeEo4WaplVnKBcuMXHVWwxNx/Yw9YFpIbHbnifATXQDB8sTmsELFcO7oH5LO+mu71FMJ4rO9R3f/64kO6vr73iakcqjon3dIIwdONzsb76+PmbJ3A6o9jt9famw5qHiqJJHZmEcGlkcL/XzQYlf5Wfr2u5HvPWMD2JvqbD14wRsea0Twu4H8O+IsuugdJWOGDUTIaFJLBkxQ0oKlUoc+elH/rVl+UsASM1SYnnH+zxCSJM7Gb2pUNG6dWtJQw8B9wkV04Z2QkK68GTcwR0ao3u47USKV4oa232/KicJ3YPSrPbnwImK/hQXZQjuj6dVjblXcMaDiokVdyE1/cGyzCHhSKwQW95Qyhj6f5Lz8PeNQpvvrVVqfxEI1PzfSyymE12xiB16oTChwj5iY9YdTSwOnwGV3uYxQNzzmHbjBuL+tp1PRlXiOJdGYVqameFsSe3Aug6v4QrOeqgIhRMqVi/uhLD7q9/wZ9QsWDJihtS0LyfM9/HlX3MV2Ez5sF6QRwgVciejtxQqpMZQrJLVnkzMLDar8vbn+RQ8t9t6ongA0HiXyOoZbGpPtm9RD1eKhL/X06rG3As4G+rFxIq7jJr+YFnLIeHofLHlDaWMoS+/v6/dZEgtPn7F7vVLdAbcqcG/l1hcmViEwIQKx4iJWRcysbj6DIh5HkPrB6G/SmXz+FsOrwC09fLCUhPD2ZQElQpH7Fc+dglXPFSEcCWhECtfy0ObDk2x4w8j8Ie50OvtFYCHJG6TcffAkhEzqgtPSEYsdzJ6uYWK4jtJKExORcjyhbJ5VPx0ItGsHH2dOnUxqnc7m+efSqkleT9Mr29qT0qNp1WNqem4kpOm+kcHhmTcDUKFtRwSjs6XOjTD2vVlS/aXnY9O8+bVyN9LLHJPLHqNngkVAhAas+6uZGcab3+zHC2WuCtni9wLNbm/zwJlPJIytGjZpQvSyusAhVXPaeEgiRXj3kVujx8GwxXkDk2SOxm9O4SKhF0/okG7NrKGfswedT9aNXY8Psgd6iu3PQl4VtWYmo6r9g8TK+4S5Hb9kht3lvd0Zwy9Ixo0a1Qjfy+xyD2xFGcWoVBZgLCocCZUOEDIDpY7yxuWtu8K5cIlNo+7I2eL3As1IRN1sfIS8m7bDnEx5t9B9lnz+GBvby+UlZXzyTS7BpVjzQB/AGVWr3GliC1CGVWR2+OHwXAFuUOT5E5Gr1ar3SJUdJo1BXnHT0t+fVP7rUPL+g7Lezob6nvs5Clcuv4g/7p2bW+UlprMZbpcdB8Z7BahAvCcqjE1HZVOi4Mu2pNMrLgLkNv1S248rbyn2P64gm+A4yzZcid7kht3CBW3j95AgxYNmVAhEZ5U3lDM/W+ZyA2omsytvKAQaNyUfy33Qk3ojsKwXr6YFWk7U/p/3tfj+cfMd/x8/Xxw4WoBn0zzk8+F5+5hMAD3eVAxGM4gt8eb3B7J6enpyMzMwOTJU2QRKooSEs3670isEGtPirXfXAn1bRxImDmgMo+EZVnWb4+WuU2oADyjaszdQFKW0uX5hYkVNRy5Xb/cgZQ5JNzdH7mRO9mT3LhLqLhveBfkxtmu380Qh6eUNxR7/1smcgOqJnP7wrfyuNwLtWKDXtbrX71RbJFM035mdpWGVXlgVMKECoYnI7fHmzuEikOHDqJly1ayeVQk7f5JcP/Fzqf6EqNLQoXUiO2P3NT0jUR30b55C3TKF141xhpMrKjBiHX98tQHy5NySIjpj9zInexJblQavawTS1FGoVmOCkdihY4t1ARzLyQ7k3uhlqBSIbmoEMsVIbJcP/aWCm99mii46keSUoXktHujysNHX32H9Bzb813rpnWxZG60G3vkeTChguHJyO3x5i6hYsyYSMTGXpX8+lz/u86bDv82bQSfL2Y+zS7QIHqYZwgVYvsjNzV9I9Gd1AsIhKONFI3edklbgIkVNRaxrl+e/GB5Ug4Jof2RG7mTPclNklKF1KxCPDIhRDaPioRjNwUn0yzOLIImrZh/zRYzriEm2dnNHAMe+7mQf83lWeDI1taFZS5xue9/dwgV3yQmoF1QA8HXj41Nw/qLyTaP30jIxfrNFf8vUulxJ6UQ93VohN1788EZAnn51u9puZOdOcLR8wZI+8yl56ihaTDEzvHjkrRTU2FCBcOTkfv+lDvHW1pamlmOCkdihUYjbo4znR+DOnVwmEPC1nx6NSMPf8cV2Hxfbrk3DiQZgaTKc7x9vFBmrJy/jRq920IzmjWs4zFCRU3eSPQ0ElQqpOXalyOrf1XGEI0zrl81+cFyZw4JIRSp3ZeVWo5kT3LDTVxtmjeQNfQjZEwoAprUFXz+8MEj+L85Wszs/e1V3E7KRGCdiv4HBPhCpzPflU5Nikeb9p2h1aigTLuDFsEd+fOBu1fwEJvsLL91VyjnzeBfW8ahln+52+x8ue9/uUMzTA3t4wXCXR9bNAxGdzu6W046MPq+dhXP18UETBsaAkXbhuYxvelVxQ5nk51JycnDB5GeY3/cbN000GOeF3viCve89+vbB48vnuvmnrmO3Pc/g+EKsofmyZzjLT09HYcPHxKcTDM9PR0pKSmCry+2nKa9+TSwW18U2yk7XuvL3Sg3mbsBoLavD0pN5hzjqy+7LTTD39fxklXuUEdP2Ej87953YdQn2T3Hx789oiavcE+HXIB73sf37m33PCZW1DCcdf2qqTv0gLgcEldvp+G5S/YH/htJOXjOZH1kmYBPp7G9AxibosLtFPmS2NX08rOmC6O/bzheqImdWExzVAS1aoASRzsKJucH+AlfqJXWCgC1ngDuiSn180GJn3lbmYVxqKVvidjjBxA2fD6oRWeYPmF34+5tTU92JndoRnxxsZmh7UisEFuOz53JzqRE0aAMXch+46cTbuLhMcP41z4+XjCa7ODVCmqJsAFjbL7fHZ4ZBcp4/nnPKc6UpC13Ivf9z2C4gjuECjlzvHGhHxMmjEezZi0Enz948FD+b7k3byH78kWb7ym4cg3N7+sE/eET0B8+AW8fb5QZKytmaBITkf3xTgCATq1GTmoamrYJ5s/38a/jwiesSn5eMRZ4SA4JuUMdPWUj8cThgyjT2/bCBABv/3YeL1aYPu8GB2XVmVhRg3DF9as6H6x/kvPw941Cm8drqfR236/x9seHF8qAC7av4RUQiEkAmre0vzsJANkqYFTvSsdzy53ew+etDwJcKErEwJq5UJObxMxis4WRI7FC7MRSajCa5ahwhKlQUb9lkKOQOTNqCxA2Sg1axB79AmHD56Nhi87CL15DqenJzpwJzRB7/d0piYINbbEeKp6W7EwMYV0bI6aLfWFm/jZvtAyqNKQtxYrfbsUj/fdia28FADQPqi2rZ0aBMt78eS+sWWKF3Pc/g+EKcnv8yF3e0zRHRXBwsMONFNPzvby8+b+HtWyDMS1tCx2/5BThwcgo/rWvr49ZWwdUeowdOJy//sJxE808PDIzlc58PJs0aly/2ucXQP5QR0+yz3u06Y9xdUbYPee9m//Dz98+xb8uKTEi9noKOndsgfr1A6vd88JSmLzu4HwmVtQQXHb9qkbydQFQaprYPG7Mz7Ub056q9oXazvsBoIGhlsv9tIdpzoxL2pq3UJObJKUKP51IFL2QEjOxaAvUuG+Ek0KFDKgLlAgbvuieEypqYrIzZ0MzxF5/geJ+tBEgdDnjoeJpyc6kplMbb2yYWhnWZVnFJXBPvtlxU2JTVPjknJfgtmKvXcOLWz7lX1uGeV27EY/2/Ss9K6oIFTUMsfe/WI8fBsMV5Pb4kbu8p6nwIDT0w/R8MQKCv7/jcvdi++MK/n61Zb2+EOQOdayJ9vmdlCQU6xIBAGqNAek5GrRuWge6vAQA1et54YwHFRMragjarGx0mjO9xgkVANDPLxD9c20bSE+X1MENEzHCMoFPB3U+ntWVWXsrgIob/4iAAdxZLJN7Xroh7fU97fcSCzdRzB51P1o1djxRODuxBDas6zFCBQDUbdiiRi5cxFLTk53JHZph+v10rl/fbIHt6HwxHipCk4vVRKHCFZzxeNOWepmFeViGeWn0cfz/a7pQIfb+F+vxw2C4gtweP7KX99QbXBIqpEZsf+RG7hwScoc61lT7vENAR6wIH4KbWVn46OQJvDR8Cjo0qlxnWXpecBQXaxF/R4nuPQdj6oyqx13FWXuSiRU1hMDmzWqkUCGEMF0p5puIEX5+tWAwVL7+wtv2bSpHDH2iUo3ndlfkvShS63AnowgdWwXhm1MFwKkClNephUkStaVRaZBaw34vU0wnig4t6zsMTXJlYqnt53i4cpdQUdGf6klW6E5qerIzuUMzdEajqO/HFQ8VIcnFPK0OvdzI7fFW04UKZ+5/OXPSMBimyO3x5o7ynvn5eYiMHOsRQoXY/khBanEJdpy2XlFEo9aCahXK1rbcwrzcGynOkphxG9sLsu2eU6zR80LFkkGD0aVFCzP7PEuphL9FaH2hSof41Hx0btMIt69cBGZAUlyxJ5lYUUPwCfB3eE5NFCpcQa4Y+jp16mJU73aVA+EI8/KbV4qCJWknNkWFzJR8dHpsWY38vTwthl5sTgu50WpU1d0FlxA6sThKCKbXWk/sK3eyM3eEZmTpNJjjpFAhB55Uh15u7Hm8FSRdQ27+eZvvNajt50kC7g6hwpn7X66cNAyGKZbjodQeb1KV93REo0aNPUaoENMfqSjp0KVKxRBA/iqEctuTcm+kuIKQHFBPfFjCCxWK5s2rHCetN4xZlTmiCtU6xCu16NyiDepqA0ACNgfF4Kr9w8SKuwSxOS1qOnLH0Ms9EHKGdsu2XWrs7+VpMfRiclrITYEyHiVpd6q7G04jJtmZo4RgX546WvX6Joah8tu9fPZyaziTvdxdoRnNA+p4jFABeE4dermxFCosGRDcGOMUtrOLP/Z3vN3r6zTFd4VQ4cz9L8d8ymCYYhma5AixHm9Slvd0hJAcEu4MzRDSH7mRuwqhO4QKOTdS3MFtZQbaN26Ov5Kv4q/kq1USVjduoMWrCys2Xivm0wxse7RyPt15o7FkfVHptDjoov3DxIq7BDE5LWo6chv+Ko3eLULFmoc64pnLjhdiYpM9uQtPi6EXmtMCAJRpici6dtvmca3adtUBR3A7skOHDHT6GtWJu5Od1a7fAA8PHG7z/Pj4BFHXFzs+uLJQC/BxPIW6S6gAhIWK1HQcCRWuUqCMR2Z6LJp36IK0xENISzxk9bxWQdWfWM4aroYmMRhy4o7QJE/L8ebu0Ax7aDTSiwemyF2F0F1ChVxVY9xF9/sCsTmmKf/aMmH1pj0V3vpyz6cAkJSldHl+ufstm3sEoTktajpyG1ZJShVSswrxyIQQWQbCq0nF5gPDZfvni0325E6ELIzcmexPSE4Lji5du2JISJTN419ueQLxJz/jX/t4e8FoUqEGAAqViWbnAIBWXYicjDto2qojigtyBffHU/C0ZGfp6elISUkRfH1PW6iJ7Y+rJKnLbcYPA0BtPy8skr0X8uEOoSL26Bdo2qQemjUFANtJdQK9xXv8uANPC01iMDjcFZrkaTne3B2aYQux86lY5P4+5d5IlLtqjDup6+/YHnaHUAEA7Zu3QKd813LSMLHiLkFITgtPfrCE4A6h4odjCWjTvIFkA+HNHANfllWl0iAtLQ/BwU3w4YUy4EIhsrV10c7Ge+WO+ZMbT0v2pxORlbp1m/YYPX01/9rP16dKzOuf3281OycjOQ5HfvoAM5dtRKt2XZFz/b+ud9qNeFqyMy7Gd/DgoYLb8LSFmpj+SEF5u84oX7rE6rHiO0mo/dc/bumHHBSodXjj5wLZhYqw4fNRP/YbvKAIs3v+D9nSiZHbf9iJZLW5MKJTqaFMSkKL9u3RpWVrPDotRtC1PC00icEA3Bua5Gk53jwhNMOZ+VQMcn+fcm8kyl01xtNQ641uESoAoF5AIAD7NqVGr7N7nIkV9wg1/cFyl1AxbWgn/H1DuoVafuuuUM6bUblQW/UoNG3agHPEK/9yt9X3yR3z5w48KdlfcWYRNGnOh3Y4ghMqRj68HK3adZWtHbnwtGRnpsnIvLy8Bbcj9ULtSm46Tuek2jxeprf/bOp9auE3fSGgL7R6PNCvlsO+SkFNFz5jU1S4mVaEzXO7CjKsLirTcLrQtodJjtYXrUxe52fcNstRceufIrx0/ozN96vVahi8vGFdFhJPsroIyohQ/nXxnSQkHNyPTrOmoKhjeySfEe5K7WmhSQyGp3m8iclpkXbjBuL+PmnzuKrEcS6NwrQ0HPnpR5vHawfWdXgNV3B2PhWKO4QKqTcSTXFH1RhPIzVXg3UPyy9UCCFBpUJarn05kokV9wByPFjfffkuVDlJACrqKCenKdEuuAXq1alw1avXtD2i562QpC0xyf6cITGz2MwDwJFYIbZutCtZqeWI+XMXnpLsjytnOnzwCFmuX9OFCleTnTky5nLyc6CLu4GmbYKhP3wCGf/7G2XGytLEtQvNd5Qts6ZnZioFfxYhCzUxhrN/SDsUD7bt7u+zO87u+73aNYLxAYXt6+c3sXlMKmq68Mm5qiqCgwQbVgV+vsgIsr0AKElJRcaRtwAAao0K2TlpaN40GLrr/4XuOpBdZkBZkPWksTq1GjnFhQjvEmr1uKvIbfi7OzSJwfA0jzcxOS1C6wehv8p2Za+3BLTX1ssLS318rR5LUKlwhCpfO5pPlampOGKSd8IyRNXyuFqrRVp2NoKbNcPNf06jXkPpEicC8pf3FLuR6E77vCbOpxxtmtTxGKHim8QEjO/d2+55TKy4y5HrwVLlJKF7UBqSlCocOMEt9PPBufpckSiduNzJ/pKUKvx0IlFU+c3kNDdmpa7BCMlpIXZiEQsnVNw3vAsCTGJkk+Kv4vJV2+U2i4sdlzYEar5QIUWyM0fG3BojYeTESXzMrq+vD0pMDILvPv2E33WyNKxuApIbV1KGZiTr1XjLxPPCx6cWjMZKyzPXR4tGdt4vJjTJGWq68GkaU7v3jG1PCUscVai5mPMznhnQ+d869Jfx0thh6NCoUjjacLYAQ60kfeWEtIXjJtbIHUrA/aFJDIanhSZ5So43Z+bTL7x9MN9E+LBMnGh6PEGlwjdJSVhj8n2eri9dtTS5y3uK3Uh0u31ew+ZTU4TktJAb0+fd4G+7ehfAxIq7mpqelVfuZH9c/2ePuh+tGjtO3sSdP3Bwzc1KLYarGXn4O872AsGosb+gd5TsT6PWgmoVOts9h5gKFfVbBpmFzAX65IB88my+N6NAjz+/28S/9vbxQpnRPMFm+p1zSL15HG3atUbaxa+QZqF91PHzAzBbio8iC+5KdtYkKMhucjFu18maYQVIa1wBwg1nIRjaBsE48n7+tZevD4wmQozPkVs232sZmpR4Ow1xOck2z0/OyMV3FmGdluXIVPWa8eKIp40nYrFMRuxIrChSVwo/jnYnaxcX/ytUVNSh79KihdmOWn5GehW3bbl3KIX+XnKHJgHuC09i3BtI7fHmKkJyWsiNs/OpM9eXK1m2nOU9ndlIrE77/OW1S3E91nbG/FKDFrVN7CxLm7JxEx80ad4UnTu2QP361u8HH//2iJosjce6I0znUzmwvD+vOzifiRUeQu7NW8i+bHunV68V5xUhf1ZerYlHhXxChdBkf2Jj6E2Flg4t6zt0/TI9v7gGZ6UWQ2C3viiOsO3q7LP1Fbvvd5TsT86YvypChQX1YUAjH9uLnwzvYtT3qTRovH28UIbKiaVYpYVOk4Qe4cGoWz8LQFaVa9TyalXlb56CO5Od+fk5Ti7mzhh6IYaz3FgLTbqvQTD62xnmv/AF5geap+O13FV7WpmBwrc+gkajQY5SiWYtWqD814Mo/Pe4fx1phR+5iE1RYeuviYKTf8WmqHA7pZR/7Wh38tNatXihQtG8eZXjHVALc0x2L+XeobwTew3Xr8eiQbs2yDt+ukoW+jLfyjlH7tAkAKBU6RdPDIY9PMnjR+5k9K7Mp85cX2rkLu/p7EZiddrnPdvXxZZxbW1eY9OeFGyYWnnccu4e/cpp1CpTorTohs1r1PLv4BaxwnI+lRpn7s/qt9oYABy7rX556qjga7lj4ZucppRNqLCMoXckVljG0DtCbJUKSw+SK7ar2VXB07JSewJyx/w5EioAQKuqA2N+U6vHAKBcn4NamqTKP3jVQq3yCvd+jcaAnCw1itEE5/Ob2kxy3LauZ+5OelqyM31pKb5JT/eYGHqxwqdYbIUmSUGYrhRDsgvxTWICnuzQCZ28/AFdZX6Q042qZxF69XYanrtkv2SeTlOxQOBCP56fdh/ub+m4v9z5EQOF76hdyc9DcHBrfJuWDKQlw8fHG0aTPCo6XeW45A4hrRaRXVfqFiISbLqK3MmIGQxrSOnx5gpyJ6OXXUiQOceb3OU9XdlIrMn2ee+2zbA5pqPdc3becJz801WcmU/F4Oz9z8QKD8GR22qBqgD4eCf/2tvH2yxJHed54a4Hq11wi39zVEiLFDH0jhBTpULuUBexMXPuxJG3jyY9HztM5ilLt7asbAOMJvcs8G9yutQ0Ptlirr/tHUJnECJUAECTzvehZcshNo9nH/DHoNHj+NdcngU++eMDkYiNvYqxY8fZvIaYxJDuROpkZ47c0vP97Wc6v1GYg+AGgTZd093pki5W+BSLvdAkSa4vs6FqykdffYf0HPuGaNP6tfH44rlo3jIY3R04Inzy+zUs/+Qm7mQUoWOrIHx9Ih/GssryoDeScvCcRfGkIrWOPz8lU/iX6dO5I8rXrgC3JLcsTey7rsJrzF0ePy3at0eRB4z/cicjZjBs4REebzJ7fMo9nojN8ebQxktMRLaJDadTq5GXlo7Gwa2hP3yi4p8dr2+xwo+rG4lS4077XEgOCblDM0xzRF3SelZoUvWPDgwAjt1WX9IY0EBZGWNvmQG4QFWA5G3bzRaC+sMnzK7hI+HCsKLqh7SWtrti6IVWqZB7IATExcy5G4fePgDKF8fwr2v7+qDUxOBv/+7neNgkSZ1pcjouh4GUC3q9Ri9IqHAWyyoVsbFX7Z6vMcnI7Whhl5oUj8ZNm0OZdgctgjuicZPG0OkqF82tm9bFkrnRrn8ISJ/szJFbuvbbOLP4f8uxq7RpIPym94St/RN3VMwAnBM+xSBUSHMWuZMRW/L35fPIKLLtKqpTF6Ee9Hh88VxB1/Ou7Y+ELBWmjQhB+xb1qggI2SpgVO/KMBh+fP73/CtFwYL7biwqNjPCLcX/JgWFSICP+0KT6tWFiA1BWZDT44fBcBXZPd5k9vh0h1AhNsdbgyINvAts5+5CbhHaBFQcV2u10GRnI7xlCwSoDYDaUPF3Py+rb7Um/DjKg3YtrRjt2zbDgSQjkFRQZQMsKVeP4n/zoGnUWigzctCiVVP+fACoG1SRqNGREAM4Dq9X376DRsGtrK6nAGnXVI6QOzTDVKgIa1sPl2xHoziFq/c/EytqCO3LyW4G4Gfzi1GUV4QezZqhrslAYkq9hm7pKgDxVR7cGUMvpEqFO4QKwHOyUsuN5UJfaoozi1CoLEBYVLgsC8G0tDRR/U9PT0dKSqXbe+zZf5CbbzshaVryTcSWlaNF82CUaW4hK8E8ceJZbZGZ2KHVqHhhI7BOPVFihruTnQXXAlbaGbvW1an+hZHcyc7cIVTImYzYGi3C2gE2ctoU30lC1q4f0UvEDqU7Pd46BNbFSBMh1bJCzV+xNz2qvKfcMfRye/wwGK4gu8ebC8noC5XKKsl4LQV5VVYWvjEaZRcqhOZ44xBaecQ0Z09Io4Zm8/dzvlWrItkSfhzmQdv+BQIenc9nB7PcACv7cjfK582ovP4zixDQsT1MU58H/hsyp8/OQnme/ZVBWX37Xp/+RiP8cnJQnmP9OvrGtkOJpcQyNENMWKUt4jPVeG53xTVMPRS/OVUAnCpAeZ1amCRN96HSaXHQxfmUiRV3AQkqFYwF+VjjYEdN6qz6thBbPsjjYuhFuqK5gpCYObkNVVu4GppUeuMmjhQWWy1HySFFRn3O0G7QoqFsHhWHDx8SJVQcOnQQgwcP5f82ILgxxilsl2ZamXkZy0yS/VnuKi/671+4feE8AECtUUGZlcYLGwBQ0MgfkMjzAvCwZGcyl/eUO9mZu4QKsYaqXDi7Q+lOjzfLBYbl4iI1S4nlHe/ziPtf7hh6ue9PBsMVZPd4czF02nIjETAX5BNUKnyg1cjm8SY2x5tYDxWx9rYrwo+UOSTahLaDXyP7ng+qfWl2PexqNQBaP2C7jWYmXp+OBATLMEbLOcdamCNgPdRRSFjl4fO2K4sBQP16dTGqd7sqHoocYjwVHZGUpXR5vcbEChnZ/sNO3MhMhzIpCS3at0dAPXMVr13dIDw6LcbGu4VRHTtq9nCmfJCUMfSO3MwAc1cyoGquhTRlIR6JvF92oUIIchuq9nCkur9Vr6Hd3cm/b97BaJ3BajlKDlcFNFNDOzeuakUOV+GEhwkTxqNZM9shMZbnjxkTCS+vqjsOtmgZ1MBqVQKOHg18sGJA53/LLV7GutHmVQz2a3Q23+sMHpPsTOZkf7ILnzKHJsltqIrFlqF66cJ1HM9Ksvvecu8yu8cB6TzeHHkqflgvyDPu/2qumsRgVCeye7zJnONNbvvcmRxvYjxUxG4kyv19Sp5DoiAf5SY2ay0vL5SXV64Fyoz250vTjRRHAoJlGKOjMEfAdqijkHWONj3frjhyMVODhD/TrYbSAJXhNFLQvnkLdMp3bSOFiRUyciMzHUf/PoFOs6agqGP7qjGpLmb4lru8p1icLR8kZQy9IzczoNKVjMPS1ax03boqD64pUj7E9pDbUJUbtU4na5WHooxCM0PbkVhhOrE48hpRpqbi15wc3iPkxt9/I7as3OwcVYn5pG8Z6iImH0egn6/DcyqECtvlFqXEI5KdyZzsT/as7DKHJsltqIrFnqHap00I+nvVt/v+5+Mv4v3dV/jXXt61UF5G/OvUnCKcj01Gs3qB+P1oYpX312sunagl5P6v6TH097pQsfeLT6DPsj9n+DdvjsnzF7mpRwxTZPd4c5NQIZfHm7M53sR4qIjZSHRHcn+pc7z5tvRH65GV17LccDMcsT1fyr2RYk+YF7LOCdz6Ckb1roz9txRHjpTocUNXbjWUBqgMp5GCegGOcxxq9PY33KrfIr2LUSYlyfbgyl3e0xGWyp5lspu6QXaS9lggxDB0V1Z2AChXdDcTMyyR8iG2hdyGqtykp6cjJTcXT3SSx5W6OLMICcduCja0LScWR14jHxKQY+IRYrnzCgAvmbiSWwt1kSLEhUNrKHGbUCEEMQu1ZL0ab5lUC/HxqQWjsXIRmuujRSOL98id7M8dQoWcoUnuMFTFIIWhep9vI8xvabLzZPHMvZR/BXPadrD5e51uLZ3bqiM8OYZe0PXvcaECqEgQayi0vzt56cRhfHvhH/615cZGYUEBGjRsiBK9HoU5OWjQtCl8/Std2UM7dsaLK5+VvvN3OXKPz4ZileDxylG1qzJ9VfvM0j7/IivVbA60xJtKMF9o5+FajjcxHipCNhK1WTkospPcX8oklJ6S403ujRQpPAhTi0uww4YHuUatRWqOHqGrHveI7zNBpUJarn058q4RK77//nt88sknUCqV6Nq1K9auXYuePXtWa5/kKkfmjvKejjBV9qwlu5FyQS9lsj9J+iN3sjMbhup7r25E4s2bdt+bS/5o330g/zogwNesqkTGxSPwLdMjKz8fzRs1gr+feYxgB4UCjz/3gqB+2or75hburX19ZV0IhowJRUAT+wmSTM8XM7EI2VHgXMlNk0+Zni9ljpjMokKznBbVidiFmqFtEIwj7+dfe/n6wGhi8PscuWV2vr1kf84YjpZc0xTgbH42WjUKslou1dVSqXKHJrnLUBWKu8ple0poUk0Qfuyh0eqRfY8LFQDg3T3M4e5krS93o47JxkUV1+0vd6NsSH8k7foRnRbOqPJ76d2wsSEX3335LlQ5SVX+rtJokZymRK+IwZi7+CnJ25VdSL6ThMLkVIQsXyjo+XJU7cpnd5zZa2v2eVlwEIwPKKz3J7MI3knC5xxXc7yJ8VARspFYJ78I+dm5NpP7S5nY3xNyvNnaSHEUmuEo/Jw7bqvKCSDOq7ukQxerHuTc/NLw/voeI1R8k5iA8b172z3vrhArfv75Z7z44ot47LHH0K1bN3z99dd45JFH8Msvv6BNmzbV1i85ypG5q7ynUNxhqHpSsj/Zk53Z+T4be3ljdh/7D7RpIkYA8PExrypRW5mBMirB0zYWvqa5DxwtDOtp1VWqPFzPLeAX7o52EwDxOwqmE0VQqwZmLnuOzjedWBx9tmwymC1kLb0BgIqFsbs8fhzltHAX1V3eU6zhaEmCSoU0byPum9ULfi2DrJZLdaVUqiuhSUIQe7/J6Uqde/MWkk8ed1u5bE8ITarp82mCSoXs3ALc91DPe1qokArJY+g9CFVOEroHpZn9LUmpwoET/4b6FmVI3qY7hIqEXT+iQbs2soZ+CLXP5dpIseyPnPaJr0aFNfY83tyU2B+ohmTEJhspjkIzHIWfl325G4VD+tuscgJUbgI7W5bVdH5RfrvXLLmoJe4oy2p6fxr87Qsx1T/7uwgR4Z133sH06dPx+OOPAwAGDhyIcePG4csvv8T69euruYe2EasAurO8pxCEGlau7obqfWpZ3QE1hdsNFfIQaxIT7WYAtlV7WfZkZxIYqlwiRg7TnaCbWVnYHF+AZyPHClr4il0YWro+ltW2vZsAiN9RKDUYRbku25tYHH02HPG26w0AAMmfnsbWhBuy7dCbX8txTgsOR8/bHXUuCnMqa6NbE2IszwEAjU6HjPwitGoUhESNffdpZ5DCNd0y5ASo/Hxc//2aB8iyUHM1NMkRnlY1qUP9Rki/EouF4ybarJIjJm+Lq8idQ8JT5lNH2Aq7cub+/+ir78xKJlsjNSkebdp35sspd+isQC1v/yrHLeHO79e3Dx5fPFfgp5OOwsuxKDxlO28RAJQkJqGwoPIZtdwNVcVeQ/MO7VH+60EUWnm/f53K79mWpwJHQnIaOrWrDGXy968Nvb7U7jmmqDRaeAU0xZPPbLT7mZzF0jX9islOnLs93hz9dtZ+t+IiFXKUSjRr0QLq+EQUvvWRzfeb/m5CEWufO7uRUtsLdsfzhBIN3spJNZuvTb9PKe0SwHM83mp6MmIxoUkNijTwLrAfal+gKjBb5+i1WmQnp/AbC3UystCm3Mvm+2sHGmweE4u1e9ry/mxbYN9WqPFiRXJyMtLT0zFyZOVOX+3atTF8+HAcP368GntmH0sF0NEAdas422wg/0NZZLbAsJwM5DZUdSo1Eg7uF/RgOVoYpn9x1m5Me34zXzSZ2tVuG5RaMdgLeYiRW4Q2AZXnWGbJzc1Ixe2Nb5i9xaDToTg/H/UbNUL25SuQjIQkFL71ETQaDT+RWho+zkyclnDJGTs2ay7ZDr2pUazR6aAsKEKLhpUTo7VcBBzO7ChoC9S4b4STQoUM5Ad4odMEeXboxaLSVD7/jp43owAhxvIc/vv81yNB6s8m1e9lGXICVHy+/OQ8vv8kQ2iGp4QmcbhjR+3auXNo5ONTpRyxKVzeFkfzG+B4QcMZ4dbQ6HQwFjTECoe9dg5Pmk8dYS3sSuz9z3n8HDp2BKm59kUgr7wM5Kam8eWUkwxxZt58yvQE6PPNfUxNyy/funIJgPvFin5+geifaz/X1xcl5Zivq9zI8POrBYOh8vWHtWpjmZc/YHKOKacbVS5CT10+D7UdbwRjXjYmd6987efrA4Of+bj8e0oisrVVhWLOdbxLt152P48YTN3arbmmm7qku9vjzdFvZ/m7pRpU+PzmLTzZoRM6eflXOW7JIRE51JP1avwn5ZbZwsvSPre0hVzZSMl1YC9nNfZGrUGtzOZr0+9T6rnbEzzeanoyYrGhSY5yrgHASxoDGigr1jlqrRYZuTno0aQpH6pTri8x84w2JUGlwhGyesgpLO9pS3vSCMCPrG94cFT/XeYiSf8u+Nu1My/50qZNG6SkpKCsrAze3sJLCHJ4eTmnPpq+r3H9IJRbcW1Rp6Qh5/BJ9OzVhz+/9aCeyAtqb/O69X69jLb9O6JeswpDqbavN0pLKgdb79/i4de4GQAgRa3Bn/m5WDpgENrWrbxB6jdu7PTnsvxspFZj0JL5qNvWusrfuH4Qf37jho2A+rbb1fftikYDOvCvLT9b278T0ax+U5vvV2WrQAaCl1ctDOjQET0D7c80e1ALU012Ai0zAFseT1Fr8LMqGQ+F9kLbunVwsXnLys9m4zfmMDRugmYmx31r+6DEy2TqqBuAxqpiZBWp0L1ZUwT41gYs8gEEBNSGl1ct1GvYED6BfnY/W6PmzeHToHIw9a7tg5RMJX5OvIMnJ03C8cREs+OW1PP1F/27qbJVKDydgG5zB8O/YeX9Zut3U2WrkHUzHYMnD0OwXzC8vGo5bAsA7utyPzoqKnfoLO8TfXM1mtVvanZ97nkBgMbUUPhn+/dattqy1h9LTNtL0Rfh/cyqFQx46gZW+d18Sk0SuQXUxvvx162+VavTwuDjJ+lnMz3H2vcp5XdZVC8PupvqKr8XhyttAYC+QGN2fd98b7vjSR1NgKj2AvT+/PfTKLiB1XvSFGv3P1AxPvsZbecCCTXcj5C2bc3+5uvrA5iMXY2NpciqU8fq+M+1IdUcMC48HIOLCu2ez42VjuY3AFDuuYxvfCq/O59a5TD6VFpM3mFt0Wh8tyrv48af/n0HCP4uGxtL4de8Gf/a2vfIHa9x86nFPSf2/jedT7u2b4Ymze0bxfqrWaCSdCja1Eegrx7eZEBZrcrfLbBFANoFV5qaWq0WytIihPbphMDAQPjWryPZ9/jNvp+RkK1ETloamgYHw79O1UVf6zr1MHvCQ8jwKjW736yR2SDA7j1Z2KSe3WsEeJXy/WszaDRywu+zea7yrbew907tys/l7YXyMnPb1TekN5osmm/2N3VKGrJ+2Y/QR1ehQ75Osu8yqFEblAQ0gFarhcaQg3Z9QhAYWCm+BAXUE3xPercs4u1TwPx5456vHt3C0ErgfCrm+U5Ra3CgKN/s+bV8/k1JUWugBgn+bOru96GwWIOQmQNt2uemtpDL86kDe7nV0dvQCJxPxY6TgP2x0hredQIls5ctjwOV6ylu/Gx86bZgm9KRbWI5v1t+n2a/m5Ofjet/aJ/e6BRif1OWmweEjF2dGjbAotbBFeuX4mIs790bLX0rPd62luqtXkOn1yOrRINOndpKNpaY/ha27POG1MDu9WoRkYT6ifv573//i6eeegonTpxA06aVN90PP/yA9evX4/z586hb1/FOF4PBYDAYDAaDwWAwGAzPwHbASg2B01pq1aol6O8MBoPBYDAYDAaDwWAwPJsaL1bU+zd+VKMxj1PSarXw8vIyc1tjMBgMBoPBYDAYDAaD4fnUeLGCy1WRmmqeeCs1NRUdOnRgnhUMBoPBYDAYDAaDwWDUMGq8WNG+fXu0bNkSf/75J/+30tJSHD16FAMGDKjGnjEYDAaDwWAwGAwGg8FwhhpfDaRWrVpYvHgxNm3ahKCgIPTq1Qs7d+5EQUEB5s+fX93dYzAYDAaDwWAwGAwGgyGSGl8NhOOzzz7DV199hYKCAnTt2hXPPvssevbsWd3dYjAYDAaDwWAwGAwGgyGSu0asYDAYDAaDwWAwGAwGg3F3UONzVjAYDAaDwWAwGAwGg8G4u2BiBYPBYDAYDAaDwWAwGAyPgokVDAaDwWAwGAwGg8FgMDwKJlYwGAwGg8FgMBgMBoPB8CiYWMFgMBgMBoPBYDAYDAbDo2BiBYPBkBxWZIjBYDAYDAaDwfAMaqptzsSKGohKpcIff/xR3d1gOAk3WJSXl7ulPXe1AwAlJSUAgFq1atXYQdERd+vnYjA8Efa8Mezh7vuD3Y8MBqOmUVZWBqDCNq+JMLGihqFWqzF+/Hh8/vnn0Ov11d0dWbibjQG9Xo+VK1fiwoUL8PLykv2zajQarFixAkqlUtZ2AECr1WLKlClYu3YtgJo7KNrCnYP93foMVMfnulu/S2u467O6o53S0lIAd7fwKSdqtRr//e9/3daeSqXCs88+i+LiYre0525hXKvV4tq1a269H9193xuNRvas1TDKysr4Z4HhPGq1Gj/88IPs7RiNRt6WdBcajQZr165FamqqW9uVEiZW1CDUajUmTZqE+++/H++++y78/f2ru0uSo1KpEBMTg7S0tGrrg5yT9blz53D16lVs2rQJV69eld3w+fnnn3H48GHZ7xW1Wo3Jkyfj9u3buH37tqxtce25Y2LhcOdgX53PgByTqE6nw+7du1FYWFgtC09OXJLbQNDr9fjll1/w/vvv4+jRo8jMzJS1vZKSEvz55584dOgQzp8/D0C+hVtZWRmKioqgVqv5duT8PktKSrBixQq89NJLfHtyeIip1eq70ktRrVZj4sSJ+OWXX9yyqcGN/7du3XKLJ19paSmio6Px7LPPApBfsCgvL8eLL76IJ554AkVFRW4RrNVqNdatWyf7OMKh0WgwY8YMXL161S3tuRvu/ribxBiVSoWZM2fiypUr1dL+3eLVpFarMW7cOPz444/Q6XSytAEABoMBK1aswOeffw6j0ShbO5Z88MEHOHbsGJo1ayZrO3q9Hj/88AM++OAD7Nu3T9K5wEeyKzFkRaPRYNasWWjWrBm2bNmChg0buq3t8vJyeHnJr2up1WpMmTIFdevWRUBAgOztGQwGHD9+HHFxcfDx8cF9992H0aNHy2qIDB48GGvWrMGnn36KdevW4T//+Q+6d+8OIpKl3T59+iAgIAB///03xo8fL0s7nIjWoUMHjBo1Ct9++y1ycnLQuHFjWe4bbmIJDg5GVFSUW+4VbrD/v//7P1nbceczYDAYcPr0aSQlJcHX1xfjx49HgwYNJG/nq6++wrZt25Cbm4u5c+eifv36st3vHDqdDl9++SXy8vJQt25dzJs3Dw0aNEBZWRm8vb0lb0+tViMmJgYlJSUoLCyE0WhEv3798MILL6Bp06aytLd06VIkJCSgsLAQDRo0wIIFC7B06VLJv1etVotNmzbh2rVr8PPzw4ABA7B69Wp4e3vL9jtqtVr4+vri9OnTePPNN/H000/Dy8tL0rmIG7cUCgVGjhwJPz8/Sa5rjbKyMuj1eqhUKtSpUwf16tWTrS3uc7Vp0wb/+c9/3CJUP/jgg2jXrh1ee+01WcYQS3Q6HQYPHozPP/8c9erVw/r163nBQo770cvLC/fddx+OHTuGxMREhIeHyzaWABXfaVRUFDp37ozAwEBZ2rBk3759iI2NRceOHd3Snrvg7omMjAzebg4MDJR9DpIbtVqNhx9+GEFBQejQoYNb287Pz0e9evXg5eUl2zzACeQajQb+/v5o2rSpLL+X6TzwyiuvyGp3lZaWIiUlBUlJSQgICEB0dDR8fORfhteqVQv+/v4wGo2yzXNqtRrz58+HUqnkbaALFy5g/fr1klyfiRVOotPpkJCQgLCwMNnb4hYwycnJCAsLM1OI5RpstVotfvvtNzz44IPw8fGR3FC0hBsw2rZti9dffx2NGzc2Oy71Z1Wr1Vi8eDGKioqg1+uh0+nQuXNn1K9fHxEREZK1Ywr3/Y0fPx5lZWXYuXMnnn/+ebz22msIDQ2V5fds0qQJAgICcOvWLYwfP14WoeLBBx9EmzZtsGXLFvz555/Q6XTQ6XSyCRXumlhMcddg765nQK1WY8mSJVCr1UhNTQUR4fLly3jppZckW9yoVCp8+OGHeOaZZ5CQkIDvv/8eRIR58+bJKlio1WrMmTMH+fn50Ov1KCoqwqFDh7Br1y7Ur19flva4Z2DNmjUIDQ3FV199hY8//hiZmZm8WCHV5+Xaa9WqFdatW4egoCBs27YN3333HUaMGIH777/f5TZM25o2bRq8vLzQuXNn3LlzBx9//DHq1KkjizACVHxPDRo0wIsvvoitW7fi4MGDICKsWbNGsnmI+w7btm2Ll19+WVahQqPRYOPGjYiPj0dcXBx69OiBp556Cn369JG8La1Wi1mzZqFNmzbYsWMHateuLXkbpmg0GsyZMwdlZWXYuHEjv3Mn90Kwfv36WLBgAerUqYO33noLAGQTLLjrLVmyBL/99hu2b9+O7du3yypUPPjgg2jfvj1eeeUVBAUFydKOJf7+/vDx8YHBYEDdunXd0qbcaLVafPjhh7hw4QLi4+Ph7++Pjh07Yu7cuRg8eLDsz4dcaDQaTJ48Ga1bt8aWLVuq2ApywG0AnD9/HsnJyfD390fPnj0xadIk9O7dW9K2NBoNXnzxRdy5cwc3b95Eu3bt8NJLL0lum5vO3a+++iqaNWsm6zqnbt262LlzJ5544gl88cUXACCrYMEJqrVr15ZtvAIqvseHHnoILVu2xJtvvgl/f38cPHgQn332GYYOHYqhQ4e63AYLA3GSV199FVOnTsXZs2dlbYdTT5s3b45t27YhPz8fy5YtQ15enmzGQHl5OebPn48XX3wRs2fPxltvvYVbt26ZPcBSumOZ7gS9/vrr/IDB9UWv10v6WbVaLWbOnAk/Pz9s3LgRhw8fxoEDB/DEE0/wgyEXAyjl5+QMbQCIiorCrFmzkJycjOeeew5XrlyRzJWVc882Go1o3Lgxevbsibi4OLNjUmC5uG7QoAFatWoFAEhISAAgbXJPy4mlefPmsrscc9+XOwZ7dz0DWq0W0dHR8PX1xfr163Hs2DF88sknmDVrlplQ4cp3y7mFnzlzBuXl5Xj99dfRt29ffP/99/jyyy9RXFwsi+s29z0GBQXxC93Vq1cjKSkJv/32m6Rtce1NnjwZwcHBeO2119ClSxcA4MeXq1ev4tatW0hNTZVUqAgODsaWLVsQFRWFYcOGYeHChVAqlZK6/HPfZfPmzfHhhx/i7bffxs8//4y2bdvi3Llzsj17XMhH48aNsXr1avTt2xeHDh3CG2+8AQAu5/rhPldwcHCVZ01quI2G9PR0jBkzBs899xzuv/9+2YRcLhRDpVLB398f3t7efO4POdqbNGkSUlJSUFBQgBMnTpjlGZGbRo0aYfr06XjyySexc+dO3utN6nHFNAQpOjoaN27cwD///CPZ9U0xfb4t700iku23BICAgAAYjUZZ3eDdiVqtxsyZM3H27Fl06tQJa9aswYgRI5CZmYnly5dj27ZtkuTyKi0t5cPj3AHnxZeRkYHly5fzQoWctpBarcasWbNw+PBhBAYGIioqCq1bt8b+/fsRExODnTt3SpY3gxszlUolJk6ciHXr1mHYsGFm9pBU7XCCtemz5uXlhZKSEmzYsAG3bt1yuZ2SkhKcO3cOe/fuxV9//YWGDRvigw8+QIsWLfDll1/iu+++ky0khBuHAwIC+HlTLpurVatWePPNN9G/f3+Eh4djyJAhqF27tnT5VIjhFHfu3KEFCxbQwIED6Z9//rF5Xnl5udNtGAwGGjBgAM2cOZPy8vLIaDTSqVOnaPjw4TRt2jTKy8tz+tqOePvtt2n+/Pm0bNkymjx5MnXt2pU2bNhA+/fvNzvPaDS61I5KpaLIyEiaP38+ZWVlERFRWVkZEVV8/piYGNq5c6dLbZhiNBppw4YNFBMTQ8nJyVbPeffdd2ny5MlUUFBARM7/hhqNhg4fPkwajYb/W2lpKX/NqKgoioyMpEmTJlFUVBRdvnzZpfYMBgP/3Zmybds2GjhwIBUXFzt1XWsYjUZ66qmnKDo6mv/diIhSU1MpPDycPv74Y8naIqq4T0aOHEnz588npVJJROb3yfr16+nmzZuStmnaxo4dO2jUqFGkUqlceqat4c5noLy8nDZv3kwxMTGUlJTE348cpaWldOPGDSooKLB6LwmB+63mzZtHWVlZZtdZvXo1DRo0iN555x0qKiri+yQFKpWKRowYQXPnzjVrNzU1lSIiIiQdR4iISkpKaNq0adSvXz9Sq9VEVDkeZmZmUv/+/SkyMpJCQ0OpV69e9Pnnn1NJSYnT7XH3Cff5iCrHkwMHDtCYMWP4Z6C8vNyl75X7DefOncs/b1xbq1evphUrVlBpaSn/HUv9TBBVPgO5ubn03HPP0ZgxY+jNN9/kjzvTJve55syZY/VZ+/zzz/nP6yoGg4EWLlxI8+bNo9TUVL6/Wq2WP8eV+8EUlUpFo0ePppiYGNq9ezcNGjSIpkyZwn82y+fcVYqLi/l7MSUlhTZt2kShoaG0a9cuMhgMkrZFZP5bW/7ueXl59MEHH5BCoaBNmzbZPM8ZLO2btLQ0GjBgAG3evNnla1ti7ZkzvTeXL1/O2whSYPnsHjx4kEJCQszmc6nQaDS0efNmWW1WUwwGA82cOZPmzZtHiYmJZsdu3LhBmzZtoi5dutCrr75q9jyKpaSkhCZPnkxvvPEGFRYWuthrx3Bz3Pjx4yk8PJwee+wxio+Pl7VNvV5P06ZNo3nz5lVp69SpU7R8+XIKCQmhL774gohcX/MsXryYHzNN/27t/86i0+loyJAhNGXKlCrzm8FgoMjISJo1axbl5+e71I5KpaK5c+fSuHHjqEePHqRQKGj79u1ERFRQUECzZ8+mMWPG0M6dOyUbozUaDb3++utmz9qHH35Iw4cP59cA3G9UVlbmtJ1HRKRWq2n06NG0cOFCys7OJqLK7/Hq1as0bNgwszHLFXuBiRUukJKSQnPnzqUBAwZYFSxMfxBnB7JffvnFzHgqKSmhU6dO0YgRI2QVLA4ePEiRkZF04sQJysnJoU8++YRGjx5NERERtGTJEtqzZw+pVCqX2igtLaWxY8dSnz59+EGQ+84MBgNNmDCBHn74YUknz+LiYnrooYfoww8/5P9m+rC+//771KVLFwoNDaWpU6e6JFi8+eabpFAoaO/evWYTIvfZoqOjKS8vj/bt20dTpkxxSbDQarUUGRlJI0aMoGeffZZ+/PFHOnfuHJWXl9Px48cpKiqK/45dGZw4SkpKKC4ursr9l52dTYMHD6aNGzfa/Rxi+qDVat0ysXC4c7AvLS2lyMhItz4DCxYsoBdeeMHsb0ajkd577z2aOXMmhYeH04gRI2jHjh2ixxdLwYCo6m+9atUqGjx4sKSChUqlorFjx9LcuXP5PnPt3rx5k0aMGEGHDx+u8j5X2s3MzKQNGzZQSEgIff311/zf9Xo9RUVF0cSJE2nv3r20Z88eWr58OSkUCvr999+daqu0tJSmT59O3bp1o5SUFLO+6/V6mj59Ok2ePJlSUlIoOzvbJcNZq9XS0KFDafbs2fxvyC3aCgoKKDIykj744AOr73VGvLa3kLEULMaNG+e0EKrVamnEiBG0YMGCKveIwWCgkSNHUkxMjMvzGsetW7fogQceoCNHjlR5Bv73v//R1q1bad26dfTVV1+5NIaUlJTQ0KFDafr06VRQUEAGg4F+//13XrDg7hMpBYtp06bRQw89xBuoBoOBNm7cKItgodFoaOPGjXTp0iX+b7YEi7CwMNq6datL7el0Ojp37lyVv3P39hdffEERERGSCgcGg4EefvhhGjZsGP830zkuKiqKJkyYQLm5uZK0p9fr6dFHH6U///yT/9u+ffuob9++Nttw5R49efIkKRQKWrVqFW9TycmRI0fogQceoNOnT/N/MxUG8/Pz6bXXXqMuXbo4PSZzbNq0iUJCQuj999+X9bOZCq16vZ5OnDhBYWFhtHz5clkFi19//ZWioqLMnj/T5zs+Pp5WrlxJCoWC/v77b5faio+Pp6ioKDp06BARmT/nhw8fps2bN9Pjjz9On3/+uUvPQnx8PIWGhlJ0dDTFxcXxz7Zer6cHHniAoqOjKTMz06XPolarady4cTRnzhw6fvw4paSk0MmTJ+l///sf/yzl5+fT7NmzaeTIkZIJFidOnCCFQkGrV6/m57mdO3dS37596bPPPqOLFy/a/WxCbSKDwUDz5s2jrl270pEjR8zey4mFAwYMoFOnTtH//vc/ysjIMNu4FdMWERMrBFNSUmJ1Ak5OTrYqWJj+COfPn6dly5bxP6iQtvR6Pf+au7FNd0nkEixMJ6RHH32UoqKi+L4UFBTQtm3bSKFQUEREBI0ePZp27txJt27dcqqt8vJyeu211yg0NJQ+/PBD/nOYLuZdHTAsOX/+PIWEhPCGhqlx/cEHH1CXLl3o119/pWPHjtHQoUNp6tSppNPpnGqrrKyMVq9eTT169KDvv/+eiCo+8wMPPEDTp0+n9PR0/tzff/+dpkyZQpMnT6bz58+Lbis1NZVeeOEFeuKJJ2jo0KGkUCgoNDSUJk2aREuXLiWFQkF79uzhz3d2kVZWVkYGg4GysrKopKTEzFOEIyYmhmJiYqisrMzs+1WpVLz6Lob4+HgKCwuTdWIxxZ2DfV5eHr399tsUEhIi6zNgMBgoJyeH9Ho9TZw4kd8ZLCgooBMnTtCUKVNIoVDQoEGDaO3atfw9wxlyQj4Tp7IvXrzY6s7gt99+y5+7evVqSQWL//u//yOFQkEHDhwwG8O4STMkJIT27dtHv//+O508eZJ0Op0kC6q8vDx66aWXSKFQ8M/4hAkTaPr06ZSWlsafd+vWLYqKiqKpU6c65Z2jVqtpx44dFBYWRm+88QY/6RsMBnrooYdIoVDQgAED+N2bYcOG0csvv0x79uyhuLi4KkaCPf7++29SKBT06KOPUmFhIf996vV6io6OJoVCQStWrKAFCxbQunXraM+ePXT9+nUicm5Bwz1vthYy3DWzs7Ppscceo4kTJ1JsbKzodg4cOEAKhYIef/xxs78bDAZ64IEHaMaMGZSRkSH6urbYv38/de/enb+/DQYDpaSk8M+WQqGgnj17kkKhoGeeecapNjQaDe3du5dOnz5NSqXSbGyUU7DIycmpsljQ6/WyeFhcvHiRFAoFLV261Ox3t3yGcnJy6I033qAhQ4YItrUsKSsro+joaAoJCaHHHnuMbt++zYtX3H0YGxtLUVFRtGPHDiJy3bu0vLycUlJSaMGCBRQeHm62eNbpdJLPceXl5XTt2jUaO3YsjR8/no4fP05EFbvkCoWC/vOf/9BPP/1EN27coJSUFJfGZY1GQzt37qS8vDzav38/9erVi5544gmbi3rLtpxte9u2bTRq1Ci792BSUhJNmzbNzItWKCUlJXTr1i26ffs25ebm0hdffEEKhYLee+89q9ey9jnEfratW7dSTEwMKZVK/l48fvy47ILFpk2baOLEiXb7e/78eRo+fDitWLHCbO0ilqNHj1J4eDglJSURUcWYmZycTIsWLeLHzIiICH6+cIXLly/TwIEDaebMmZSamsrbk9OnT7f6rBkMBsHzQ1lZGW3YsIFmzZrFby5Ywtn/Go2GFi5cSCNGjKCdO3e6PJ4QVawnevXqRY8//jhpNBr68ccfSaFQ0JgxY0ihUFB4eDhFRUXRsmXL6OOPP6ajR4/S33//LXj+VqlU9Oabb9Irr7xCQ4YMoUcffZTOnj1LRBXPRlRUFG9PDho0iBQKBYWFhdGkSZPogw8+oN27d4uei5hYIQBu1/qxxx6jd955x2wALC8vp+TkZJozZ45VD4sLFy7QzJkzqUePHnT79m3BbS1fvtxqW6ZGh1yCBXcTHTt2jEaMGEHfffcdEVXsUPbt25eefPJJ+vzzz80GEFs7bULYsmULKRQK+uyzzyg7O5sfMKwNDBqNhk6dOuX0501ISKCuXbvyCwuiiu9VqVTS3LlzzQwFbgI6deqU4OuXlZVRaWkppaenU1lZGWm1Wlq7di2Fh4fT999/T1FRUWaGh+kE8Mcff9CYMWNoxowZpNfrBU1mnHiQn5/PD3I6nY5u3bpFv/zyC61evZqWLl1KXbp0odmzZ9sU1ISgVqtp48aNFB0dTREREbwCbjnorFq1isaPH2826KrVanrhhRdIoVDwE5EYLl++TP3795dlYrGktLSUfv/9dwoPD5d1sJ82bRr99ttvRFRhiCgUCvr8888lfwZUKhVNnz6db+vdd9+lLl260KpVq2j27NkUHh7Oe8OYXnP16tU0f/58InJ8r5SXl9Ojjz5KCoWC363jfn+DwUBjxoyhqKgosx1/TrB47733nPYEUKlUtGnTJkpJSaHJkydTZGQk/7yWlZVRVFQUhYeH815H3HgVGRnJ/3ZXrlwR3J7BYKALFy7Qzp07ae/evZSWlkaFhYW0adMmUigU1KdPH5o9ezb/u5l+b4sWLaLo6GinPid3rY8++oi6dOlC77zzDhUXF/MLmSNHjtCtW7fo0KFD9Prrr1NMTAx17dqV/6xCdqE0Gg398MMPpFKpaP/+/dSzZ09asWIFGY1GXjwbM2YMvfDCC7RmzRqaPHkyhYaGkkKhoJCQEBoxYgS98847go0Qrj2NRkN//PEH9ezZ0+ZChnuu0tPTqVevXqK8KzQaDZ04cYKIiHbv3k1hYWG8oVtWVsaLS7bGETFoNBo6evQoEVWEDISHh9P//d//0YULF2jr1q38PbhgwQK6efMmJSUl0W+//UbdunUTNc9wvPHGG7z3HofpjrwcgoXl+03vcakFC+7aJ0+epJCQEFq8eLFdwSIxMZH3CnOW2NhY+s9//kOjRo2i8PBwWrVqVRW7btOmTTRgwAA+/MtZVCoVzZ8/n2JjYykpKYmWL19OYWFh9NdffxER2b03NRpNlRAHIe0tWLCA4uLi6Ny5czRz5kyKjIykM2fOUHx8PD+3ceNk3759adq0abRixQp+kREXFye4vb1795JCoaC3336bCgoK7D7npr9lfHy8S9/tSy+9RFFRUQ7Pe+edd6hPnz68h5AQVCoVLV26lEaOHElhYWE0ceJEOn36NH3zzTekUCiqeFiY2gRnz541sz2FUFJSQmlpaRQfH2/mfck9h3J7WKxcuZJiYmL4dm3x/PPP07Bhw1zyTFMqldS3b1968cUX6ejRo/TGG2/QsGHDqEuXLrR48WKKj4+n7OxsOnr0KHXt2pUfa4VgMBjo7Nmz9NNPP9HRo0dJr9fT1atXebsyMjKySlgzh1qtprVr19LatWsFbV7qdDqaOXNmlXGorKyMduzYQY888giFhYXRtGnT6KeffiKDwUDz58+nsWPH0qeffipasOA+248//sh7w+/fv5/Cw8PpmWeeoc8//5xGjBhBBw8epF9//ZW2bt1K8+bNo8jISP5Z79Gjh6AQSJVKRYMGDaI5c+YQEdF///tfGjhwID3++OP0zz//8Guc2NhYSk1NpcTERPr+++9p48aNvP2sUChE2+dMrBDAmTNnSKFQ0NSpU2nKlCnUv39/2rZtm5kbYEZGBs2ePZv69+/PK0wXL16kmTNnUs+ePQUP8LbasmZQl5aW0smTJ2nEiBE0Y8YMysnJEf3ZNBoNvfPOO7R+/XrasGGD2Q2k0Who6tSptHLlSrpz5w717duXli1bZraw+O2332jVqlWC8wWYtrd+/Xq+Pc7o6tOnD02fPt3q5KFWq2nNmjU0bNgwp8WKlJQUGjRoEK1cubJKDgfLBdOzzz5LkyZNEhxXbLqY79u3L+8CrNVqadWqVaRQKGjUqFFVDAzTyezQoUM2lVhH7Y0fP57+/PNPq8r2kSNHaPjw4TRv3jwzVz2hgoVaraYHHniAZs6cSa+99hrt3r2bnn32Wdq3b1+Vz/Hee+/RgAED+IFPpVLRhg0bKDw8nK5duyaovbKyMiouLqbMzEx+oXXlyhUaNGgQRUdHSzqxWLbHGRp//PEH9e7dm5566inJB/tRo0ZRdHS02TPLCRZSPgOmbXHX02g09MYbb1CfPn1o6NChtHbtWrp69Sr/XXET5Zo1a2jp0qUO2+Dgdhznzp3Lj4HcInfmzJn8s246Ea9Zs4bCwsJox44dosUzzi02OjqadDodZWZm8u7SJ06coKioKJo5cybdvHmTF/TOnTtHn376KcXExFBYWBiFh4fbzF1jrb25c+fS+PHjqVu3btS3b1+Kioqi5ORkyszMpNdff51CQ0PpnXfeMXtfWVkZ5efn0/z58+mFF16gkpISQZ/VUhjhxoVPP/2UunbtSt27d6fo6Gib91x8fDz9+uuvgj/frl27SKFQ0EcffURarZb27dvHC3acl4/lHHP9+nX666+/aPXq1TRz5kxBgryj9uztvBIRTZ48mQ8zE8LmzZtJoVDQH3/8wbcbGhpKK1eu5A0rUy83DrVaTU899ZSosILXX3+dFAoF/fzzz0RUkfuJE3QUCgXFxMTQ3r17zQz6rKws6t+/P98/MSiVSlq9ejV169aNb5PIXCiUQrDgvEKEILVgwc0rJ06coK5du1oVLEyfpylTptCzzz7rVFvc91ZSUkKFhYW0ZcsWmjx5MoWEhNC6dev4zYzc3FyaPHkyvfvuuy55HnAC/pAhQ+j69euUkpJCy5Yto7CwMBowYADFxMRYNeq5eaBnz56k0WgE94Frb+jQoXTz5k06f/48RUdH05gxY+j//u//+HDRGzdu0A8//ECvvfYaTZ06lYYPH87PTaYeY44oLy+nzz//nLp27Upbt251KFgQVWzwzZgxg1asWCFY/NdoNPTVV1/xNtzbb79Nffv2pRs3bvD9MIWz6U6fPk3h4eGCPbW4+XTWrFn0xRdf0JYtW2jo0KEUHh5OV69epR07dvCbd5af7eLFi7xnWk5OjqDfjBNGhg8fTj169KCJEyeaifGOPCykyNuydu1aGj58OO8hZvmbcM/3Tz/9RAMHDhQ8ThBVLOq///572rZtG/3xxx+Ul5dHO3fu5D0EFQoFzZs3j3799VezMZMLN/3xxx8FtWMtdwQneF++fJkXkTlR2xRuoy0kJISuXr0qqL2ioiKKjIykLVu28Ncw/f0HDRpEK1asoGnTptGQIUPo5MmTVFxcTBMnTqTJkyeL2rwx/Wzdu3fnN72Ki4t5D4sePXpQZGQk/xsSEb8Jce3aNTpx4oSgTUS1Wk0jR46kRYsWmY0Dv/32Gw0cOJDfxDNtx5ScnBy6deuWqHuEg4kVAlm/fj1NmzaN4uLiaNOmTRQZGUndu3enl19+md9JTElJocWLF/PJ3LhdSzFKtL22Nm7cSAcPHjQ712Aw0KlTpyg8PJzmz58vyg1XpVLRhAkTaPLkyRQdHU0jRoygYcOGmRmkp06doq5du1KXLl1oxYoVVhdQQo0Ra+0NHTqUb49LkrV58+YqN7tareYXvEJ3Qm0JMdu2baPQ0NAqsYqmi6j4+HiKjo6mrVu3UllZmcNB39pi/plnnuHb4JJAduvWjfbu3VtlES3WfVqMeMD1/ciRIzRkyBBatGgR7/opBIPBQEuWLKH58+fbXPiYfncHDhygLl260OXLl6mkpIQ2bNhA3bt3FyxUqNVqev7552ny5MnUs2dPGjRoEG3ZsoWuX79OcXFxvEAg1cRirb233nqLzpw5Q3/++Sf169ePQkNDJRnsuUmWSz5JZP7bv/vuu5I9A9baMiU7O9tmno+srCxasmQJvf3220Qk3OiJi4ujCRMm0IIFC+j06dM0adIkqx4ipvfL888/79QOoWkSTw5OsFAoFBQVFWWWpMtytyIjI0Nw3Ktarabx48fT/Pnz6ejRo6TRaCgpKclsNzw2NpYXXU3DnVQqFb344osUEREheOfLUhiJiIigBx54gO7cuUM6nY6+/vpr6tatG23YsMFs/LUMGRSDSqXivW527NjBCwiDBw+m0NBQ3vAnqrrYNRgMot1/bbXHLWSs3Zs3b96kiRMnitqdVCqVtGrVKgoLC+PHx127dtGgQYOoe/fu/P1jeo9z40hoaKio3ASceBAWFkYHDhwgogqR9aeffuLjsDm43+jixYtm+YrEkp2dTU8++SSFhYU5FCyio6NFL2DKysooJiaGHn30UcH3LydY9OjRgz7//HPZBQuO27dv06RJk2jXrl2Cr20pxFg+O6mpqbRnzx6aMGECDR48mBYtWkSnTp2iVatW0apVq1zyVDl9+jSfhLdPnz50+fJlSklJoVWrVlFoaKhZUmDud+PmgV69eonyCrNsr2/fvhQbG0vnz5+nmJgY3l3bUowwGo2k1+vp7NmzToWilJeX02effUZdunSxKliYiu7nz58XvcFHRPTzzz+TQqHghcX4+HgKDw+ndevW8edYGxM//PBDioyMFOTFwc2nc+bMMbOD//jjD+rVqxef3PWNN96gLl260Pvvv88vsDkP6z59+gj+XNaEkWHDhlF4eLjV/C1ShISYigf79++nsrIyOnv2LC82cVj7Ljdv3kxRUVGi1gMPP/wwDRgwgEJCQvgEuYWFhRQfH09//PEH/e9//zN7D9fulStXKCoqyiwniS2s5Y44ceKE2bWvXLnCh4SY/j7cRpsY+5WoYkxZtWoVjRo1itauXUuPPvoo9ezZkyIiImjdunX8nJOTk0OTJ0/mw3ILCwtFiYGOPpvBYKA//viDhg8fTr17966SF00MarWaJk2aRBEREby9ZmpT7d+/nyIiImj+/Pl08eJF/u+mopor4hkTKxzAfbm//vorRUZG0rFjx4ioYvD5/vvveSVr6dKl9Msvv9D58+dpzZo1fDyqmAFXaFuLFi2iX375xUxUOHPmjCiDnzP2uaoYWq2WkpKSaNSoUfTuu+/y5ymVSoqOjqaoqChKSkpy+maz1d7IkSPNdiK5nBimrnQajYbWr18vasCwJowMHjyYcnJyKCsri2JiYig8PJwOHDhQRTjIycmhdevW0YgRIwQpgEIW85zb3pNPPkndu3enH3/80eks1GLFA9P/Hz16lLp160aPPfaY4PavX79ODz74IB08eJAfdEyvaXpPqFQq3p10//79vMEq5ncbN24cTZ8+nTZv3kw7duygFStWUNeuXSkyMpJ++eUXio+Pp2HDhkkysdhrb/z48bRr1y46evQojRkzhnr16uXSYG8v+aRpTpytW7dSly5dzHZnxD4D9toyGAxVDApTLyMuceSgQYOcCtnhBIvw8HCaMGGCzXwJzhr51j6b6f2YmZlJ06dPpxEjRtCZM2eqTJRiXSxLS0vpmWeeoblz51JqaqrZb8axc+dOevHFF+nkyZP8Tj4nWIgdu4QII+np6fTNN99Q165dafPmzU7n1bHWNjcG79ixg4qKiujAgQPUq1cvevLJJyXPeG/Znkql4gWLxx57zExsysvLo7Vr19Lo0aOtekLYIzs7m5544gkKDQ3lBYvdu3dT9+7dafXq1fx55eXl/GJQrIFqra1ffvnF7rmFhYW0du1amjx5skuhnEIEi3379lFoaCjNnTtX9PVPnz5NgwYNomeffdbuQsgyJGTt2rXUv39/mzttltjz4LAULB555BGzxUpeXh6tW7eORo4caXbf2MOeEGM5xmdmZtLRo0dp8uTJNHPmTHr44YdJoVDQf//7X0FtWSM7O5vmzp1LS5cupVWrVlHPnj3p8uXLlJSURI899hiFhYWZbao4u3iy1961a9f4kJCuXbuabYiVlpaKnuusjbNGo9GmYLFy5UoqLCzkF/Ri7WauTc6D44033qC8vDzey+mtt96y+p68vDx6/PHHae3atQ4X2NbyMZl+L9OmTaOFCxfyr7k5/MMPP6Tjx4+L3ri0J4z07t2bNm3aREajscrcdvz4cQoPD6e5c+dSQkKCoLZM27QUD1588UVKSUmhFStWkEKhsCqeEVXcV0uXLuX7JaQtbj1w+vRpunbtGr3zzjs2N6FMKSwspOeee44efPBBh97kQnJHcL9nXFwcDR06lGbMmEFxcXGkUqnohRdecPpZS0xMpAULFtDgwYOpb9++tHr1ajp79ixvD3Hf06JFi2jlypVOiciOPltOTg4VFxfTkSNHaMCAAbRy5UqnksByv1e3bt1o1KhRtG3bNv5zmNpxXEjI0qVLzXLvSeHhw8QKETz44IP0yCOP8K8zMzMpPDycFi9ezKvV48aNozVr1tCUKVOcTjzpqK2xY8dSSEgIjRs3jj788EPRg5JaraYxY8bQI488Ypash4ub2rZtG506dYquXbtGBoOBDh48SAqFgnftFmvsO2pv69atfHt5eXn06aef8oJFRkaG6AHDnjDy/vvvE1GFuDNz5kwKCwujl19+mQ4ePEjp6en01Vdf0RNPPEGDBg3ik8Y5wtFi3lSF5hLyde/enfbu3Ssq8Z3Q9izFA8vjx48fFyVs7d69m8LDw+2GOHA7aatWraL8/HwaNGgQ9evXj7p37y7KxZLLdm05+J46dYr69+9PgwYNol9//ZXi4uJo0KBBNGvWLKcnFnvtnTx5kvr160dDhw6lr7/+mv766y+KiIig1atXOzXYO0o+2a9fP7MKHaaChdhnwFFb/fv3N2vr66+/pjFjxtDu3bvplVdeoUceeYQGDhwo+P63xo0bN/hQnQsXLvB/d3XScvTZuB13zsMiMjKSTp8+7VLSquzsbHrooYfo66+/5q9jzRuG2xk6deoUHxLywAMPiAp/EiqMbNiwga5fv047d+6kkJAQ2rx5s9OJzUx/E649LiTpiy++II1GIzhEQ4r2tFotHT58mPr27UuTJ0+mjz/+mD755BNasWIF9evXT/RChsOaYLFr1y4KCwvjBQvOA85ZA9W0LU48MM0nsWfPHn7x+ddff9GaNWtE7bYKbdOWYHHw4EG6c+eOU9c/f/489evXj5555hmrgoXp72raptB8AEI8OLj75dSpUzRw4EB66KGH6PXXX6f333+fli9f7tT9IUSIsRy39u7dS//3f/9H3bt3F72LbXmtv/76i8LDw+mbb76hxx9/nPeYSElJ4XNYcPeMM/emvfYee+wx6t27N127do0uX75M06dPp3HjxonyvOTQaDS0f/9+unLlSpUk8Vw/duzYwQsW+fn5vGAxZ84cio6OdkqoML0+J4i89957dOnSJX6RvXHjRrNkoXFxcbR+/XoaOHCgw9/PWj4mIuLD+crKyujBBx+kFStWmIkeW7dupZCQEOrduzf17dtX8OcSK4yY9pOo4vcdOHCgKC8Ye+LB5cuXKTY2lqZMmULdunWjDz74wOxz3rlzh9avX0+DBg0SNLaYbjaY2pTnzp2jbt260f79+83O3717N5/X4dChQ/TUU08JHjOF5o6Ijo6mnTt30u3bt2nIkCE0a9Ysevzxx0VttFmjsLCQsrKybAqhKSkpNHPmTPrqq69EX1voZ5s9ezZt376djh49ShEREbRq1SpRwrhKpaKhQ4fSokWLKD09nZ555hkaNWoUbd26lfdIMhUsuJCQpUuXmnlYuAoTKwTATbyHDh2iiIgIOn36NN26dYv69u1Ly5cv5+PPPvnkE5oxYwb16tVLtFuz2LY+/fRTio6Opt69e4vaZSorK+MHHS5pFNemWq2mIUOGmMWLLVmyhC5dukRz586lOXPmCN4hcaW9hIQE2rFjB+9636tXL1G7ko6EkbNnz9L169fp2LFj9P777/MJpXr06EGjRo2ip556SpQAJGQxr9PpaNOmTbR69WpSqVS0evVqUigUfNJDMYgRD5588kl+YnF2ofjVV19R//79q5TttGTx4sU0atQoKiwspHHjxpFCoRAs2FmboLm2uN/w/PnzNHDgQIqMjKTY2Fi6cuUK9evXj2JiYkRPLGLaGz9+PP3999/0xx9/UHh4uFmVECE4Sj4ZFRVFM2bMqOL+t3XrVtHPgDNt/fTTTzRnzhzq3r07jRo1ip555hnRAqg1OA+LOXPmmJUCdPY+FJrEkxPoTAWLM2fOOC1YHD9+nBQKhVXjiPuN/v77bz7B2ssvv0xxcXH04osvihI9icQJI1w7O3fupG7dutHLL78s2AVXr9dX+TycIMJl8+ayrm/fvt0sp4TY+19se1xZ6du3b9OsWbNoxIgRFBkZSU888YTLCeTsCRarVq2iF1980WUD1bQtU8HCYDDQiBEjKCQkhLp160aDBg2ihx9+2Cy8Rso2TQULqaqA2BIsLJMIfvnll06VkhYiHJi6gj/77LM0YsQImjBhAj311FNO3x9ChRhL93fLvFf2MH0v92yXl5fzAtnrr79Oly9fpjlz5lDv3r3NBIuePXvStGnTROd9EtJeTEwM9enTh65du0bnz5+nGTNm0Pjx40VVVCkvL6eFCxeaVSFbuHAhffTRR7Rv3z7Kzc3ln/dvv/2WTxKcm5tLBw4c4PO6iHkWbHlwcJtd27dvpwsXLtCmTZsoLCyMBg0aRNOmTeMrgIwbN06wgGCaj8lUfCeqyI/Ro0cPq16XW7ZsobCwMMHtOCuMWH4nYrx27YkHoaGhfC6ds2fP8pWMHnjgAVq6dCktWLCApk+fTqNHjxb0Ga3ZXdzYdPXqVRoyZIhZaFNiYiJfqSw0NJQGDRpE06dPF3yfiMkdMXz4cDp06BBdvXqV92KXQkQ2xXTTITs7m9avX0/Dhg1zKoeDkM/2+OOP07Rp02jUqFG0d+9eviLW2rVrBYWKlpeX0+rVq2nBggVmOceefvpph4IFV/5cqhLPTKwQQWpqKk2cOJEWLVpEffr0oeXLl5uVCyOqUJelqMwhpC2tVutUW2fPnqUBAwbQs88+yyfG5BLhTZ06lQ4fPkwZGRn02muvkUKhoBdeeIHf+XLkoiVFexs2bKCysjJ+d1LogCFGGAkNDaUlS5ZQTk4OZWZm0qVLl+jo0aOUm5srOjxD7GKec61ct26dU8aV0PaWLFlCo0ePdjpemLvuqVOnKDQ0lD7//PMqx4gqB2Au+3BeXh7l5eUJDiGwNUGbDqaWWeFfe+01IqpIjiR2YnGmvTfeeIOIKkpCiRnsOewln7SV7Z2oIllpSEiIqEnTmbZKS0spJyeHdDqdZCEFROaChaWR5wxCk3hyZGZm0kMPPUT9+vUzE0zEcPz4ceratSsfJ8z97qmpqTR37lzau3cv/7c333yTwsPDKTExkVJTU0WPz2KFkRdeeIGUSiW9//77FBERISgHR3l5OT3++OM0ceJE/jfhDA3TcsAJCQn87hqXU2L//v2i739n2uNCEbn7Mi8vz6WSeKZYEyy+++47XgSSQqgwbevJJ5+k0NBQOnnyJB9Ws337dvr777+dSoottM2wsDD69ddfJb++vYX9xYsXafr06aRQKAQlGxZ7fSLzcVqn05FWqyW9Xu9yXgwxQgz3nAkVXjUaDb3wwgv00Ucfmf2de/8333xD48ePp9zcXEpNTaWYmBgzwWLhwoU0YMAAwcKnM+316dOHrl+/ThcvXqQHHniAHn74YVHJO69fv07jxo2j3r1704QJE2jZsmXUo0cP6tq1K0VERNC4cePoxRdfpF27dtFTTz3FJwJUKpX0119/Cd7gE+rBwY0jWVlZdOvWLXr11Vdp9erVtHbtWvrll19E59/g5rKYmBh+HHv33XcpNDSU9wawNiaKFe3ECCOuIkQ8MF1scqFQK1asoIULF9Ljjz9Ou3btErRham+zQa/X08yZMykiIoKOHTtGBw4c4K+Zl5dHp06doq+//ppOnz4tOM8UkfjcEW+++SYRVfzWzoTA2uPdd9+llStX0k8//UQ7duygJUuWUEREhNMerEI/W3Z2Nk2ePJm2bdtGRBUb4WLuH6VSyXtTcvd3aWkprVmzxq5g8eOPP1JkZKRk5cCZWCGSH374gc8bYerC70xCs+psi5uY169fT9evX6eoqCiaPn16lVjPFStW0OjRoykhIYEefvhhwZnlnW3viSeeMItxFTvQOyOMCK32YYkzi/levXoJrpwiRXu9e/emW7duueR+n5ubS2PGjKEHH3zQbOKyvOa7775LY8aMccpN3N4Ebdnec889R7179+aNDWcmFmfay87OJqPRKHqw5xCafJKowoj47LPP6MaNG6J278S2xcXof/bZZ5LvIpj2ZeLEifTQQw9JorKL+R6JKkpIzpgxw+mxKysriyIiIsxCZ4gqJmZLMeKJJ56gBx980Kl2iMQJI1u2bKHw8HCKj48nnU4n6rm7evUqf/+fOXOGiMwFLc5w1Wg09Pbbb/NVOzQajVP3vzPtvf/++06PzY4wFSy4hcZPP/0kuYHKtbVixQqrSZ3lIjs7m/fek6NNbj5fs2YNv8jkkiNyi14pru9IOPjiiy+c8uAQ2y6R80JMWVkZTZ48mRfDoqOj6bfffquywJs9ezY9/fTTRER069YtmjdvHvXr148uXrxIycnJgtt0tr25c+dS//796cqVK3T58mXBeT9MuXHjBk2aNImWLFlCV65coaKiIrpw4QK9/fbbtGrVKr50Yc+ePfn+iSnV6IwHx9atWyXzLOLmnoULF9Jzzz1XJaeIVDgrjIhBjHjwxx9/UHp6uktlSYlsbzZwSbFHjBjBe9h1796dxo4dS++99x59//33Tv+GQnNHPPLII7Ry5UqXPp8tjEYjfffddxQREUHh4eE0YsQIWrFihcuiU3V8Nu6aQgQLV+8XU5hYIZKUlBS+rJKUP0R1tHXu3Dnq168f9e7dmx588MEqlQ6IKnYKR4wYQQUFBS7vuAptjwslcBahwsiTTz4pKvmXLcQu5l39Ld3dHhHRsWPHKDQ0lJYuXWp1wcklWFq1apXT94npLrxpch4O7vP9+OOPFBYW5nKogpj2QkNDJQ2NsEw+aVmJYN26ddSrVy+XFk9C23r++eepd+/esizUOK5evUrTp08XlenaHkKTeHK4YqxqNBpaunQpDRgwoEo8rSnXrl2jadOm8TGkzgiEYoWRhx56SHQbHEJFH7VazYefmAqkcrb33nvv8QsZuTBd0FtW6pCzLXcJFkqlktauXSvZLqwl3Dz77LPP0m+//UZz5sxxqvqZo+vbEg6mTZvmkgeHo3alFGLOnTtHvXv3pqlTp9LYsWPp4YcfpqioKNq3bx//XB87dozmzp3Lu7hfu3aNHn74YRo+fLhoryJX2hsxYoRLXirXr1+n8ePHmy20OXJzcykpKYl+++032rx5M82dO1f0/SnGg+Ppp5/my1Sajp+ubN7ExcXR2LFjqWvXrmbJ6KXGHcKIGPGgR48eNHbsWHrnnXfMKvyI/S5tzQPXr1+nrKwsunPnDv3222/0wgsv8P1QKBQu7dDLmTtCDNnZ2RQXF0dKpdLpBPuWVMdnEypYSJFYk4OJFU6wefNm6tOnD59MRg6vCne1denSJerRo4dVlU+lUtHy5ctp2bJlkrngOmrvscceo2XLlrlNGBk9erQkGe7dsZivzvaIiL7//nsKCwujyZMn065du/gKFpcuXaJ169ZR//79XTaMbQkIpvf9F198QZGRkZLsqLm7PSLbySeJKsvS9ejRQ3BiUk9pyxGuumlbIlcST2vcvn2bevbsSZMmTbK6sM3Ly6PnnnuOIiMjXRJk3CmMENkXfSwT9e7YsUOy59td7TlC7gV9dbXFIdWOsi3Onz9PAwYMoG7dulGvXr0k986S24PDUbtSCjEXLlygYcOG0csvv0yffPIJvf766xQSEkJLly6l77//ngwGA02bNo0vY0hUMcaJrXxTXe2ZEhcXR+PHj6c5c+bwi2CiqsnZnbUrnfHg+OijjySbG27cuMGXjJRz7nGHMOKseOBMGVvLNrl5wNbCvbi4mK5fv+5UTgd7SJk7wtNw12ezFCwiIyPplVdeEVQG2BmYWCECbiDiEtIsX778rmjL2g6GSqWi9evXU69evVyqalKd7blLGOFwx2K+OtsjqhBJRowYQd26daNhw4bRiBEjaNy4cRQVFSVZojh7Hg+5ubm0YsUKWrlypWS/m7vbs9Um9wy4WomgOttyN1In8bTHqVOnKDw8nIYOHUpbtmyhjIwMys7Opr1799Lq1aspIiJCksWau4QRDqGij5SGvjvbc4TcC/rqastdnD17lgYOHOh0eKMj5PbgsNeu1ELM2bNn+QSiGRkZdOXKFVqzZg317duXVq5cSa+++ipFRERIkt+nOtozxda8Wl5eLsmzLbcHhyOkzsdkC3cII9UhHlibB7h7w7Ikq5RInTvCk3D3ZzMVLJYtW0YPPvigJDkbrcHECicoLS2lVatW0ciRI10u4+YpbZkKCFevXqVNmzbJupBxV3vuFmLcsZivzvaIKlzVDxw4QK+88gpt2rSJfvnlF8ldca0ZAiqVip5//nlB5cY8vT3LNk+dOsWXwpPjmXNnW+7GXUYj19aMGTOoR48eFBISwldrWb58Od2+fVuydtwljHC4U/SpjvYY8iKlkGsNuT04bCGHEHPu3DmKiIigp59+mrKysqi8vJyuX79Os2bNokmTJvGJIV0pt1yd7Zki99gstweHkPalzMdkrx2557jqEA/cPQ/IlTvCE6iuz2YqWEi9DjClFhERGKJJTU1FeXk52rVrd9e0deHCBTzxxBMAAI1Gg507dyI0NLTGt3fhwgUsX74cw4YNw5w5c/Dzzz/jhx9+wLfffouQkBDJ28vOzsalS5dw/vx5lJWVoXv37ujXrx+aN28ueVvV0Z67uHHjBp566ik0btwYy5Ytw5EjR/D999/L9ru5uz2uzWeeeYZ/xnft2iXbM+fOttwN99m8vb3x8ssvo3v37rK1VVxcjLy8PNy4cQNEhJ49e6JevXqoW7eupO3cuHEDL7/8MuLi4lBaWopatWqhdevW6Ny5M1atWoXOnTtL3h53/69atQo9e/aU9PrV3R6jZnPu3Dk8+eST+Pzzz3H//fe7rV29Xg9/f39Jr3nhwgUsW7YMw4YNw5IlS9C5c2cQEY4dO4bDhw9j3rx56NSpU41tzxS5x2bTcWTlypXo1asXAIBb2tSqVUvS9iyJjY3Fpk2bsHXrVrRu3Vq2dtwxx5l+l08++SR69+4NoOK7lOt7rI55ICcnB3l5eWjYsCHq16+PgIAA2dt0F9Xx2crKyuDt7S1vI7LJIIwaCbeTINeufHW1d/78eRo0aBANGjSIwsPD3RKjfzdjqnzLuRvK7VyEh4e7JbeCu9sjqsg/MHnyZMm9fKq7LXcjdRJPT6CoqIju3LlD+/bto99//50yMjJkTezsrp3C6mqPUbOR24PDnZw/f54iIiLomWeeMfPckGs+dXd7psg9NrvTu84aUudjsoU75rjq+C7ZPMBwBPOsYFRBjp0ET2iP25n57LPPoFAoZG2LTJRoklGVrq723Mn169exfv16bN68Gffdd99d1x4AlJSUwNfX965ry93czZ/NXbhrp7C62mMwPAXOuzQ8PByrVq2SzbuhutozRe6x2Z3eddWJO+a46vgu2TzAsAcTKxj3FO4WYhjS4O5FKFv0Mu5l2PPGYLiHM2fO4Pnnn8fOnTvdErrp7vbcCVvwSkd1fJdsHmDYgokVDAaDwWAwGAxGNXC3erNWB2zBKx3su2R4CkysYDAYDAaDwWAwGAwGg+FReFV3BxgMBoPBYDAYDAaDwWAwTGFiBYPBYDAYDAaDwWAwGAyPgokVDAaDwWAwGAwGg8FgMDwKJlYwGAwGg8FgMBgMBoPB8Ch8qrsDDAaDwWAw7k7UajW+//57/Pe//0VycjLKysrQuXNnTJs2DdOmTYOXV/XvmeTl5SEgIACBgYHV3RUGg8FgMBgmsGogDAaDwWAwJOfOnTtYtmwZ0tPTMXHiRISFhcFgMODw4cM4d+4cJk6ciDfeeAO1atWqtj7+73//w9NPP429e/ciODi42vrBYDAYDAajKsyzgsFgMBgMhqQYDAYsX74chYWF2LNnD7p06cIfW7hwIV5++WXs2rUL3bt3x9y5c6utn1euXEFxcXG1tc9gMBgMBsM21e9/yWAwGAwG465i165dSExMxHPPPWcmVHA8++yzCAoKwu7du6uhdwwGg8FgMGoCLAyEwWAwGAyGpEydOhUJCQk4c+YMateubfWcpKQktGrVCr6+vgCAc+fO4b333sPly5cBAN26dcOKFSvQt29f/j0jR45E69at8fXXX5tdy/LvI0eOxODBg9G7d2989NFHSElJQcuWLTFv3jzMnj0bALB27Vrs3buXv0ZERAS+/vprzJkzB76+vggLC8NXX30Ff39/PProo3jllVfw0UcfYdiwYWZtT5s2DUSEPXv2uPitMRgMBoPBMIV5VjAYDAaDwZAMIkJcXBzCwsJsChUA0L59e16oOHz4MObMmYPMzEwsW7YMy5YtQ2ZmJubPn4/Dhw871Y/jx4/jP//5D8aOHYvnnnsOAQEB2LhxI/73v/8BAKKjozFmzBgAwHPPPYdHH32Uf++FCxfw+++/Y82aNZg8eTImTZqE2rVr448//jBrIzU1FVeuXEFUVJRTfWQwGAwGg2EblrOCwWAwGAyGZBQUFMBoNKJp06aCzjcajdi4cSOaN2+OH3/8EXXr1gUAzJgxA1FRUXj55ZcxdOhQu8KHNTIzM/Hzzz/zYShjxozBkCFD8Ouvv2LYsGHo2bMnFAoFDh06hNGjR5sl2NRqtdi+fTv69evH/23w4ME4fPgwSkpKeJFl37598PLywvjx40X1jcFgMBgMhmOYZwWDwWAwGAzJ4MqRlpWVCTr/+vXrUCqVmD17Ni9UAED9+vURExODrKwsxMbGiu5Hhw4dzPJlNG3aFE2aNEFubq7D9/r7+5uFnwDAxIkTUVxcjJMnT/J/+/3339G3b180b95cdP8YDAaDwWDYh4kVDAaDwWAwJCMoKAi1a9dGfn6+oPPT0tIAVIgLlnTs2BEAkJGRIbofjRo1qvI3X19flJeXO3xvgwYNeNGFY+TIkQgMDMT+/fsBAAkJCbh58yYmTpwoum8MBoPBYDAcw8JAGAwGg8FgSEatWrXQs2dPxMbGwmg0wsfHuqmxbds2pKamYtSoUTavxeUAdxQCYs2Lw1JsEIO3t3eVvwUEBGD06NF8KMi+fftQu3ZtREZGOt0Og8FgMBgM2zDPCgaDwWAwGJIyZswYaLVa7Nu3z+pxvV6PPXv24NSpU3wIxZ07d6qcl5iYCABo0aIFgAoBoqSkxOwco9GIgoICKbtvk6ioKKhUKpw9exaHDx/GkCFDEBQU5Ja2GQwGg8G412BiBYPBYDAYDEmJjo5G69atsXnzZty6dcvsWFlZGV566SXk5uZi8eLF6NGjB5o2bYpvv/0WarWaP0+tVmPXrl1o2rQpwsLCAABNmjRBYmIi9Ho9f96RI0dgMBic6ifnfSG0ivugQYPQqFEj/PDDD7hx4warAsJgMBgMhoywMBAGg8FgMBiS4ufnh/feew8LFy7E1KlTMXHiRHTr1g2FhYXYv38/4uLiMG7cOCxYsABeXl7YsGEDVq5ciSlTpmDq1KkAgD179iA7OxvvvPMOLypERUVh06ZNWLRoESZNmoTk5GR8//33aN26tVP95PJafPLJJxg6dKjdkBQA8PHxwfjx4/HNN98gMDAQI0eOdKpdBoPBYDAYjmGeFQwGg8FgMCQnJCQEv/zyC2bPno1Lly5h8+bN2L59O/z8/PDKK6/grbfe4kWIsWPH4rPPPkOzZs3w/vvvY8eOHQgODsZXX32F0aNH89ecNWsWVqxYgbS0NGzatAlnzpzBe++9h/vuu8+pPj7wwAMYOHAgfvrpJ7z55puC3sMl1Bw5ciQCAgKcapfBYDAYDIZjapFQ30cGg8FgMBiMe5zLly9j+vTp+OijjzBs2LDq7g6DwWAwGHctzLOCwWAwGAwGQyC7d+9Gs2bNMHjw4OruCoPBYDAYdzUsZwWDwWAwGAyGA9avX4/U1FScPn0aa9eutVrelMFgMBgMhnQwzwoGg8FgMBgMB+Tl5eHKlSuIjo7G3Llzq7s7DAaDwWDc9bCcFQwGg8FgMBgMBoPBYDA8CuZZwWAwGAwGg8FgMBgMBsOjYGIFg8FgMBgMBoPBYDAYDI+CiRUMBoPBYDAYDAaDwWAwPAomVjAYDAaDwWAwGAwGg8HwKJhYwWAwGAwGg8FgMBgMBsOjYGIFg8FgMBgMBoPBYDAYDI/i/wH3Tq5wlj5MhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filter to countries in the model\n",
    "#filter_countries = generators.index.levels[0].to_list()\n",
    "\n",
    "entsoe_type_gen = entsoe_gen_type_table.sum()\n",
    "\n",
    "entsoe_stats_gen = entsoe_stats.T\n",
    "\n",
    "entsoe_scaled_gen = entso_scaled.sum()\n",
    "\n",
    "# merge data \n",
    "compare = pd.concat([entsoe_type_gen, entsoe_stats_gen, entsoe_scaled_gen], keys=['ENTSO-E generation per type','ENTSO-E generation statistical factsheet', 'ENTSO-E scaled'], axis=1)\n",
    "# convert to GW\n",
    "compare = compare / 1000000\n",
    "compare.index.set_names(['Country','Technology'], level=[0,1], inplace=True)\n",
    "\n",
    "# # filter all data with nan and 0\n",
    "compare = compare.fillna(0)\n",
    "compare[compare.sum(1) < 0.5] = np.nan\n",
    "\n",
    "# # plot and save figure\n",
    "fig = plot_n_compare_scaled(compare, unit='Generation (TWh)')\n",
    "fig.savefig(os.path.join(output_directory_path + '/_generation_compare_plot_scaled.png'), dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "entsoe_gen_type.to_csv(output_directory_path + '/entsoe_gen_type_hourly.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "entsoe_gen_type_table.to_csv(output_directory_path + '/entsoe_gen_type_hourly_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "entso_scaled.to_csv(output_directory_path + '/entsoe_gen_type_hourly_table_scaled.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export tech Dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic = {\n",
    "            'Fossil Hard coal': 'hard_coal', \n",
    "            'Fossil Brown coal/Lignite': 'lignite', \n",
    "            'Fossil Gas': 'gas',\n",
    "            'Fossil Coal-derived gas': 'other_fossil',\n",
    "            'Fossil Peat': 'other_fossil',\n",
    "            'Nuclear': 'nuclear',\n",
    "            'Biomass': 'biomass',\n",
    "            'Waste': 'waste',\n",
    "            'Geothermal': 'other_renewable',\n",
    "            'Marine': 'other_renewable',\n",
    "            'Other': 'other_fossil',\n",
    "            'Hydro Pumped Storage': 'hydro',\n",
    "            'Hydro Run-of-river and poundage': 'hydro',\n",
    "            'Hydro Water Reservoir': 'hydro',\n",
    "            'Fossil Oil': 'other_fossil',\n",
    "            'Fossil Oil shale': 'other_fossil', \n",
    "            'Solar': 'solar',\n",
    "            'Wind Onshore': 'wind_onshore',\n",
    "            'Wind Offshore': 'wind_offshore',\n",
    "            'Other renewable': 'other_renewable'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame.from_dict(dic, columns=['B'], orient='index').to_csv(output_directory_path + '/entso_gen_type_dic.csv')"
   ]
  },
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