pre-project-notebook-sample.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bd291df2",
   "metadata": {},
   "source": [
    "# Notebook for EE Pre-Project Research\n",
    "#### CUBEMs Data Loading and Data Analysis\n",
    "*By Patcharanat P.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4ad85ce",
   "metadata": {},
   "source": [
    "For Energy Consumption Analytics and Visualization: https://nbviewer.org/github/mpipatta/mpipatta.github.io/blob/master/CHAM5.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2418ef5",
   "metadata": {},
   "source": [
    "## Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "20877ee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set_style('dark')\n",
    "\n",
    "import json\n",
    "from datetime import datetime\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da342597",
   "metadata": {},
   "source": [
    "## Load Data and Exploratory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "46d4d84f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'figshare',\n",
       "  'value': 'https://doi.org/10.6084/m9.figshare.11726517.v6',\n",
       "  '@type': 'DataCatalog'}]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datasource\n",
    "file = open('data.json')\n",
    "data_json = json.load(file)\n",
    "data_json['repositories']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c58a7cd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "37\n",
      "30\n",
      "30\n",
      "30\n",
      "30\n",
      "30\n",
      "12\n",
      "37\n",
      "30\n",
      "30\n",
      "30\n",
      "30\n",
      "30\n"
     ]
    }
   ],
   "source": [
    "for year in [2018, 2019]:\n",
    "    for floor in [i for i in range(1, 8)]:\n",
    "        path = f'dataset/{year}Floor{floor}.csv'\n",
    "        exec(f\"df_year{year}_floor{floor} = pd.read_csv('{path}')\")\n",
    "        exec(f\"print(df_year{year}_floor{floor}.shape[1])\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "01190a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5533920"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "790560*7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "11415a70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 790560 entries, 0 to 525599\n",
      "Data columns (total 30 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   Date          790560 non-null  datetime64[ns]\n",
      " 1   z1_AC1(kW)    719633 non-null  float64       \n",
      " 2   z1_AC2(kW)    719633 non-null  float64       \n",
      " 3   z1_AC3(kW)    719633 non-null  float64       \n",
      " 4   z1_AC4(kW)    687770 non-null  float64       \n",
      " 5   z1_Light(kW)  730710 non-null  float64       \n",
      " 6   z1_Plug(kW)   730827 non-null  float64       \n",
      " 7   z1_S1(degC)   498458 non-null  float64       \n",
      " 8   z1_S1(RH%)    498456 non-null  float64       \n",
      " 9   z1_S1(lux)    498453 non-null  float64       \n",
      " 10  z2_AC1(kW)    687781 non-null  float64       \n",
      " 11  z2_Light(kW)  729811 non-null  float64       \n",
      " 12  z2_Plug(kW)   730808 non-null  float64       \n",
      " 13  z2_S1(degC)   516157 non-null  float64       \n",
      " 14  z2_S1(RH%)    516153 non-null  float64       \n",
      " 15  z2_S1(lux)    516156 non-null  float64       \n",
      " 16  z3_Light(kW)  730799 non-null  float64       \n",
      " 17  z3_Plug(kW)   730757 non-null  float64       \n",
      " 18  z4_AC1(kW)    687784 non-null  float64       \n",
      " 19  z4_Light(kW)  726489 non-null  float64       \n",
      " 20  z4_Plug(kW)   729259 non-null  float64       \n",
      " 21  z4_S1(degC)   522172 non-null  float64       \n",
      " 22  z4_S1(RH%)    522172 non-null  float64       \n",
      " 23  z4_S1(lux)    522173 non-null  float64       \n",
      " 24  z5_AC1(kW)    687788 non-null  float64       \n",
      " 25  z5_Light(kW)  726489 non-null  float64       \n",
      " 26  z5_Plug(kW)   730729 non-null  float64       \n",
      " 27  z5_S1(degC)   443156 non-null  float64       \n",
      " 28  z5_S1(RH%)    443156 non-null  float64       \n",
      " 29  z5_S1(lux)    443156 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(29)\n",
      "memory usage: 187.0 MB\n"
     ]
    }
   ],
   "source": [
    "date_format = lambda x: datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "df_floor3_2018 = pd.read_csv('dataset/2018Floor3.csv',\n",
    "                 parse_dates=['Date'],\n",
    "                date_parser=date_format)\n",
    "\n",
    "df_floor3_2019 = pd.read_csv('dataset/2019Floor3.csv',\n",
    "                 parse_dates=['Date'],\n",
    "                date_parser=date_format)\n",
    "\n",
    "df = pd.concat([df_floor3_2018, df_floor3_2019])\n",
    "# df = df_floor3_2019\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e00adb",
   "metadata": {},
   "source": [
    "## Data Wrangling (Cleaning & Manipulation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e56b01df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Columns\n",
      "z1_AC1(kW)\n",
      "z1_AC2(kW)\n",
      "z1_AC3(kW)\n",
      "z1_AC4(kW)\n",
      "z1_Light(kW)\n",
      "z1_Plug(kW)\n",
      "z1_S1(degC)\n",
      "z1_S1(RH%)\n",
      "z1_S1(lux)\n",
      "\n",
      "All:  Index(['Date', 'z1_AC1(kW)', 'z1_AC2(kW)', 'z1_AC3(kW)', 'z1_AC4(kW)',\n",
      "       'z1_Light(kW)', 'z1_Plug(kW)', 'z1_S1(degC)', 'z1_S1(RH%)',\n",
      "       'z1_S1(lux)', 'z2_AC1(kW)', 'z2_Light(kW)', 'z2_Plug(kW)',\n",
      "       'z2_S1(degC)', 'z2_S1(RH%)', 'z2_S1(lux)', 'z3_Light(kW)',\n",
      "       'z3_Plug(kW)', 'z4_AC1(kW)', 'z4_Light(kW)', 'z4_Plug(kW)',\n",
      "       'z4_S1(degC)', 'z4_S1(RH%)', 'z4_S1(lux)', 'z5_AC1(kW)', 'z5_Light(kW)',\n",
      "       'z5_Plug(kW)', 'z5_S1(degC)', 'z5_S1(RH%)', 'z5_S1(lux)'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print('Selected Columns')\n",
    "for column in df.columns:\n",
    "    if 'z1' in column:\n",
    "        print(column)\n",
    "print()\n",
    "print('All: ', df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0dfc33cc",
   "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>Date</th>\n",
       "      <th>z1_AC1(kW)</th>\n",
       "      <th>z1_AC2(kW)</th>\n",
       "      <th>z1_AC3(kW)</th>\n",
       "      <th>z1_AC4(kW)</th>\n",
       "      <th>z1_Light(kW)</th>\n",
       "      <th>z1_Plug(kW)</th>\n",
       "      <th>z1_S1(degC)</th>\n",
       "      <th>z1_S1(RH%)</th>\n",
       "      <th>z1_S1(lux)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-07-01 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>28.44</td>\n",
       "      <td>60.67</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-01 00:01:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>28.44</td>\n",
       "      <td>60.67</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-07-01 00:02:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>28.45</td>\n",
       "      <td>60.67</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-07-01 00:03:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>28.45</td>\n",
       "      <td>60.66</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-07-01 00:04:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>28.45</td>\n",
       "      <td>60.66</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525595</th>\n",
       "      <td>2019-12-31 23:55:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>26.98</td>\n",
       "      <td>63.50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525596</th>\n",
       "      <td>2019-12-31 23:56:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>26.97</td>\n",
       "      <td>63.60</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525597</th>\n",
       "      <td>2019-12-31 23:57:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>26.97</td>\n",
       "      <td>63.50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525598</th>\n",
       "      <td>2019-12-31 23:58:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>26.97</td>\n",
       "      <td>63.40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525599</th>\n",
       "      <td>2019-12-31 23:59:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>26.98</td>\n",
       "      <td>63.60</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>790560 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Date  z1_AC1(kW)  z1_AC2(kW)  z1_AC3(kW)  z1_AC4(kW)  \\\n",
       "0      2018-07-01 00:00:00         0.0         0.0         0.0        0.07   \n",
       "1      2018-07-01 00:01:00         0.0         0.0         0.0        0.07   \n",
       "2      2018-07-01 00:02:00         0.0         0.0         0.0        0.07   \n",
       "3      2018-07-01 00:03:00         0.0         0.0         0.0        0.07   \n",
       "4      2018-07-01 00:04:00         0.0         0.0         0.0        0.07   \n",
       "...                    ...         ...         ...         ...         ...   \n",
       "525595 2019-12-31 23:55:00         0.0         0.0         0.0        0.00   \n",
       "525596 2019-12-31 23:56:00         0.0         0.0         0.0        0.00   \n",
       "525597 2019-12-31 23:57:00         0.0         0.0         0.0        0.00   \n",
       "525598 2019-12-31 23:58:00         0.0         0.0         0.0        0.00   \n",
       "525599 2019-12-31 23:59:00         0.0         0.0         0.0        0.00   \n",
       "\n",
       "        z1_Light(kW)  z1_Plug(kW)  z1_S1(degC)  z1_S1(RH%)  z1_S1(lux)  \n",
       "0                0.0         0.03        28.44       60.67         0.0  \n",
       "1                0.0         0.03        28.44       60.67         0.0  \n",
       "2                0.0         0.03        28.45       60.67         0.0  \n",
       "3                0.0         0.03        28.45       60.66         0.0  \n",
       "4                0.0         0.03        28.45       60.66         0.0  \n",
       "...              ...          ...          ...         ...         ...  \n",
       "525595           0.0         0.05        26.98       63.50         0.0  \n",
       "525596           0.0         0.05        26.97       63.60         0.0  \n",
       "525597           0.0         0.05        26.97       63.50         0.0  \n",
       "525598           0.0         0.05        26.97       63.40         0.0  \n",
       "525599           0.0         0.05        26.98       63.60         0.0  \n",
       "\n",
       "[790560 rows x 10 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_select = ['Date']\n",
    "for column in df.columns:\n",
    "    if \"z1\" in column:\n",
    "        col_select.append(column)\n",
    "    else:\n",
    "        continue\n",
    "df_new = df[col_select]\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "78917f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 0\n",
       "z1_AC1(kW)       70927\n",
       "z1_AC2(kW)       70927\n",
       "z1_AC3(kW)       70927\n",
       "z1_AC4(kW)      102790\n",
       "z1_Light(kW)     59850\n",
       "z1_Plug(kW)      59733\n",
       "z1_S1(degC)     292102\n",
       "z1_S1(RH%)      292104\n",
       "z1_S1(lux)      292107\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "95cdb500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 23.52222222222222, 'Period of time')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.heatmap(df_new.isnull().transpose())\n",
    "plot.set(xticklabels=[])\n",
    "plt.xlabel('Period of time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e56023ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6R7Mme6vjedm4MsVFRVzavZ7WfYbUSmditBq7uXmRGH1LJW9JSTFJd+/gUKld2rl4UlxUSMr9GMX1alDN9XL08aXHB5NVzivNk7/8+nR/k2Zd+yjSrh/fi4VD41rbGyAx5hb2Vepj7+bNw6ga6lOpXTSoVB9DUwv6TV6Aoam50nGy/Nyqp3r+/Ae/PfcieOKYpg8++IChQ4cq/l+5ciVBQUH07NkTY2NjAOLj45/o3EeOHEFLS4sOHTpgbW3NqlWrCAkJoU2bNgDcvn0bZ2dnjIxU386qxk6VO1BVh9AALl68iJmZGS4uLir7QkND+eKLL1i0aBGdOnUCoEWLFly7dg1/f38uXLhAs2bNMDQ0pE2bNgQHB9OhQwfCwsJwc3PD3Lyi0Xfs2JFJkyZRWlqquLGeJ2t3qA4vPQ/yM9PRNTRBS7vCGdA3MaO4UIY0Nxu9Sg+m/Mx0zBoov03rmZiR8SAOWV4OsrwcSoqLObZiBunxMVg5eeA/9BMMzJR7z3bNeJ/c9GQcmvnTqFUH6kJ+Vhq6RqZKevWMq9Obhpl9Y6Xj9Y3NSH8QB0BxYSHntyyj7ZBP0KqhFzPi9H4MTC1wah1Qa036JuYUF8ooyM1C39hMkZ6XmYZFVU0m5qQlxMr3Z6RhaGapsj8nPYXczDQApf0GJvI2nJOegqGZJbeCjlBcJMOrc2/uBCu/+ZaWlnJiw1L83xqOgYkZ6sjLTENPpS5mdahLhX11DYzo99XjXwL2LP6Ch3duYNXYDe9ubz42f16GOo31b2+AJi3b0aRlu2q11GTvavW/ABvHhJxGmpdbK/tWV66BiTmpZXarjDQvl+JCmZIdNbW00DMyITc9BTQ00a9SX4Py65WThYmVHSZWdhVlZ2UQefEU/m8NVyon/Mxhjv/xM1raOvSdsqBOz+ncjDQsHKrWx4zUhLjq62NuoVKfnPQUGrh60din4oW7tKSEq8f20MizZa31PDNe8h6i+uKJrWBoaIi1tTXW1taEh4cTGBjI0qVLFQ7T07B//346dOiAvr4+Pj4+2NnZKX2ELzs7W63DVFfCw8PVOkwxMTGMGzeOqVOn0qdPH0W6n5+fIpA8ODhYESzu7++vcMoqxzOV4+LiQkZGBgkJCU+t+WWiSCZVepgBaJb9X1xUqJReLJMq9pWjpa1DSVGhIjYgZMdqmrTpRtdxsyguKuTEr3NU3p47f/w1XcfOIj0+hst/r30CvcoOTnnvT1W9RYXq61ZSlu/qwS1YOLri4NW62vJKS0uJPHeYpl37PpmmQmVNhVI1NtTRUWgvkklVerO0tHUoKSykSCZV/F95H8jrnpeVwfl//qDriIlqf1BunTlESXExXpUCbNXXRbV8dXUpUtceKtWltnQcOo43//cdJUWFHFu76LH5i2QFqhqfgb0fx+PsXb3+52/jW2cO0jTgVbQlurXXqMYuVfWV5y3fr5K/qFB+vdScC9TcszIpB3+Zh4GJOd5d+ijta+TViiGzVuDVpTf7l80hs2wItTYUqrO5jg7FRTLV+pQ9y7S0lcNFqqt/0PbfeBQXRfuBqkP3ghfDU7uO8fHxfPnll0ybNg0vL6/HH/AYkpOTuXjxIj179gTkXZ29evXi0KFD5OfnA2BmZkZWVtZTl5WWlqbUI1TOggULyMzMpEED5XH8ysHgwcHBCufI39+fGzduIJPJCAsLU/SIlVNeRlpa2lNrfplQ9wAudyqqPmA1dSocjnKKiwrRkuiiWTZ7y7XDqzi37Y5VY3cC3vuCjAexpMTeVjrGsrEbDX388R34MZFnD9bpB0BLR0JxUZGyhkL1erW0JWrrpiXRJf1BLJFBh/AfpH6YqpzUuEhy01No4tf5CTXpKaVrq7NhYaFCu5aO6oO5uEi+X51zWP63jkSXoK2/4hnQC8uGTioaczPTuLBz/WN/4OV1US0fQFu3al0kNdaltlg2csbeozld35tE3NVgslOSasyvVuMzsPfjqMneddb/DG2cn5XBw8ibuLXrXm2e0P3bWD2un2IrL6eqRm1d1XK1q3vJKrOjto5E7blA+XrJCvLZt3QWGUkJvPH5XMVMtXKMLW2wbuxK52HjMLKw5tbZo9XW5+K+rawc+5ZiU6uvsFClvYD8+sjzKztU6tpF0PbfuHJ0J6+O/hKrOraDZ4KIaQKecskBqVTKxIkT6dSpk9JQ3dNw8OBBiouLmTFjBjNmzADkb+QlJSUcPXqUvn374u3tzbp168jJyVHpcQoJCeHPP/9k8eLF6Ovr11iWhoYGxcXFKulDhw5FR0eH+fPn0759e0UQeatWrXj06BHXr1/n0aNHtG4t70Vwc3PD2NiYS5cuERUVpdLTVFLWG/IihuZeJAZmlkhzsygpLlZM9c7PSkdLRxeJvqFK3vysdKW0gqwM9E0s0DUyRVNLG5NK04B1jUzQNZR30RtlpZNy9zaNWrRX7Ddt4EhJURGFBXloGdUuPsHA1BJpTuYT683PSsfA1IK4K2eR5mUTOOtDAEVv2OZJA2j/zqc4+3cDICE8BFu3ZugaVN87q05TXlY62hJddA2UNRmaW5GXqeyY52WlY2BmUf3+TPl+o7JhzrzMNMVwRl5mepkGC3nwrkRXEWBbLJORGHWLqJAztH5tMAXZWfy98HP5ScviD7fPGkOrPkNp/br82WBoZklB1bpkltVFjX3Ly69q38chy8/l3o0QXHw7KZZLKB/6LcjJVFkGoTKG5laqGp+BvR9HTfYeNm9N9fqfk43LuX/zMiZWtlg2bFJtHq+ur+PRtovi/9CD28nLUrZLbmY6hmrK1TMyQUtHQm5mGuYNGgHyAPWCnCyFzvzq6lt2vWT5uez5eQaZjx7Q74tFmNk6KM4ff+sqhmYWinNraGhg0aBRjYHgzbu+jnubihedkAPbVeyYV0199Mvqk5eZjkVZmyyvj2GldnFy0y9cO7GPV0dPxc2vU7Vaniua/3/9flXHU7l8c+fOJT8/n7lz59aXHg4cOED79u3ZtWuXYtu9ezeOjo6KWXSdOnXC2NiYTZtUpxqvX7+exMTExzpMAJaWlmRkZKik9+rVi/Hjx5Ofn8+aNRUPKAMDAzw9Pfnrr7/w8fFRlKGhoUGbNm0IDAzEyckJCwvlmyU9XX5DWVtb19YM/ycwb+iMppY2KZWCox9Fh2PZ2E1l7R8rp6Yk372lCPgvD+i2btIUTS0tLBxdSY+/q8hfkJOJNCcLIwsbclKTOLV2AXkZKYr9afei0DUyRa+WDhOARSO53mQlvTexUqPXuklTkmPClfXGhGPVxAPPrn3pN3MNb05bwZvTVtDhXfmMojenraBR84r1v1JiI7Bxrrl3tlxTefAwwMPIG9g4uatosnVuysNoZRs+jLqJXVkQr51zUx5WmkGWnZZMTnoyds6eGJpbYmRhw8PIm0rlGFnYYGhmybsL1zFk9q8MmbWSIbNWYu3khnfXPrzx2TycW3dk2ILfFPve+HweAL0nzsWr6+uK81mW1SWpUl0So25i7aRqX1vnpiRVqUtiVDg2zk1rtBfIh2GOrVlE0t0IRVpKXBQampqYVvrBVIfVc7L346jJ3jXxvGxczqO7Edi6eNeYR8/QGDNbe8Vm5+LJw6iq5d5UG2yuoamJbRN3pXaZGH0LTS1trBo5V1yvaPXXq7SkhAMr5pGV/JABUxdj6eCkdP7Qg9uVltUoKSkm+X4MFjXMVtQzMsHM1kGxNXDx4mGU8rPgQdRN7FxU7Vhenwd3Kt1n0eGK+gBc2LWJ6yf303vsdDzadq3BsoIXwRM7TTt27ODAgQMsXLiQvLw8kpOTSU5OJjs7+4nFxMfHc+XKFYYOHYq7u7vSNmTIEM6fP09SUhKGhoZMnz6d5cuXs2TJEqKjo7l16xYzZszg5MmTfPPNN7Uqz8vLizt37qjdZ2RkxOTJk1m7dq1SQHubNm3Yv3+/Sm+Sv78/x44dUxmaA4iIiMDKygpb2+rfcP8voi3Rw7ltD4K3/UJK3B3uXz3PrX8DadpNHsOTn5mmiFlwbBWALC+XkL/XkPHwHiF/r6FIVkDj1vK3LM/u/Yk4uYe40DNkJt7j/MYlmDdsgqWTB5aN3bBo5Mr5TUvJeHiPhBuXCN35e42z1qrT69K2Bxe2riAl9g73ws5x899AxfpJlfU2LtN7acdqMh7e49KO1RRJC3Bq3RldQ2NMbOwVm0FZEKuJjT06labjpz+IUwl+r07TqQ3LSbobQUzoOcIO/0PznnJNuZU0ufoFIMvLIWjrKtIexBG0dRVF0gJcy96Km3V9nYjzxwk/c4iU+zEc+20xTs39MbGW9yw16/Y65/9eR8LtqyTcvsr5f/6gRVk5lX/0zGzt0ZZI0DM0xsTKFom+gdK+8nV6jC1t0TOs6EXT0dXDvX1PzmxawaO7Edy9co5rR/6hWY9+gLyXq7wuzr4BSPNzOPfXatIfxHHur9UUyQpwqWEosxwDUwuatO7I2S0rSbkXxcM7Nzi1YQnNuvdV6TGsio6uHk079Hwu9q6Jmuz9OP3Pw8blpCXE1no5hHLK7XZm6yrSEuI4s3UVhdIC3Pzl5RbJpIqJCQDNur3BlUN/ExN6jqS7EZzcuBzvzq+ho6snv14de3JyY8X1unL4H1r0kl+v8DOHSbh9je7vfY7EwJDczDRyM9MoyJH/Tvl0e4NbZ/8l4sIJ0h/e5+SGFRTLpHh27FX7+rQJQJqXw+ktq0hNiOP0llUUSqW4+3dRW5/m3d/k8qEdRIeeIzEmghMbltOsS290dPVIe3CPi3s349dnCPbu3gq9uZn/gdAOMTwHPMXw3J49e8jLy1MZluvfvz+LFj0+4FIdBw4cwNzcnO7dVcfHBwwYwNKlS9m9ezejR4+mb9++mJiYsHbtWjZv3oyGhgY+Pj5s3ryZ5s1rN9W8bdu2ZGVlcffuXZo0Ue1e7t+/P1u3bmX+/PmsWiVfoNDX15d169aprBju7+9Pfn6+ijMFcPnyZQICAv6/G54D8B34ERe3reTfJdPQ0Tek+evv4tiyIwD/TB9B++Gf49K+FxJ9A7qNm0Xwtl+IOnsIM3snun8yRxGH0bh1ALL8HEJ3rqMgOxNbNx+6jJmJhoYGGhpadB0zg0vbf+XwD1PQ1tWjade+eNQQYF0dbQZ9zIWtv3B46Vfo6BnS8o3hNG4l17t92nA6jpiEa5ne7p/M5sLWFdw5ewhzByd6jFeNlaiJguwMJNWsz1NVU8j2lexaPBVdfUP83xqBi698tt2fk4fR/f3JeAa8gkTfkNcnzuXkxmXcPH0Qy4ZNeOOzeQpNdq5edB3xKRd3b6QgNxtH79Z0G/W5opxWrw0iPyuDg7/MQ0NTC69Or9LiFfWroT8p7d/+mKDNK9j741dI9A3x7Tsc59Zy+27837t0fW8yHh17IdE3pPenczizaTm3yurSe2Lt7dtl1CTOb1/N/p/lS424tetB21oG03YcMppTm1Y8c3s/K56XjUEe01TdGlPVIdE35I3P5nJywzJunjqIVcMmvPl5hd0iL57i2LqfmLDuEADubbuSnZLEiQ3LKC4sxMW3Ix3e/khxvoAhozm5cQW7vp+KpMr1ir4cRGlpCfuWzlLSYO/hw4Cpi2nSqj1dR0zg4u5N5KQlY+fiSd8pC5HoPX6kohxdfUP6fj6X4+uXc/3UAawaNeGtSRX1uXPxFEd//5HP/jgMgEfbrmSlJHFs/VKKCwtx9QsgoKw+0VfOU1pSwsW9W7i4V/lLGOXHvzD+P/z9UodGaV2WPv0/yFdffUXDhg2ZMGHCMzl/aWkpPXv25LvvvlNau+lx6Ld6NnqeFdMXf/6iJdQJrZfsAWCk+3K9nRWVvFyPFe2XLF7jZbOvjtbLZd+X7fkA8EkHp2d6fv0eC+vtXPnHptfbuZ43L9eT+Bnw0UcfsWfPHgprMQ34STh79iw2NjZ1cpgEAoFAIPhPIYbngGf8wd6qLFiwgL///rva/WPGjGHs2LHPURG4urrSq1cvAgMDGTKkbjEwteHXX39l9uzZ9X5egUAgEAieGy9h79uz4Lk6TePGjWP48OHV7jc1rf1Mp/rkiy++eGbn3rx58zM7t0AgEAgEgufHc3WaLCwsVKbjCwQCgUAg+I/zkg+r1RfP1WkSCAQCgUDwEiKG5wDhNAkEAoFAIHgcoqcJELPnBAKBQCAQCGqF6GkSCAQCgUBQM2J4DhBOk0AgEAgEgschhucAMTwnEAgEAoFAUCtET5NAIBAIBIKaEcNzgHCaBAKBQCAQPA4xPAeI4TmBQCAQCASCWiGcJoFAIBAIBDXzgj7YK5VKmT59On5+fgQEBLBu3bpq8x49epTevXvTqlUr3nnnHW7evPm0tVZBOE0CgUAgEAhqRkOj/rY68P3333Pjxg3Wr1/PrFmzWLFiBYcOHVLJFxkZyZQpUxgzZgy7d+/G09OTMWPGkJ+fX18WAERM03+W6Ys/f9ES6sTCL5a8aAl1YsHSyS9aQp3Q1ny5gjAz8otftIQ6IdF6uexrqPtyve8aSrRetIQ6cS9d+qIlCIC8vDx27NjB2rVr8fb2xtvbm8jISDZv3sxrr72mlPfs2bO4urrSr18/ACZPnszmzZuJiorCx8en3jS9XHeeQCAQCASC588LGJ67ffs2RUVFtGrVSpHm6+vL1atXKSkpUcprZmZGVFQUly9fpqSkhMDAQIyMjHB0dKw3E4DoaRIIBAKBQPA46nHJAZlMhkwmU0qTSCRIJBKltOTkZMzNzZXSrayskEqlZGRkYGFhoUjv06cPx48fZ9iwYWhpaaGpqcnq1asxNTWtN90gepoEAoFAIBA8jnrsaVq9ejW+vr5K2+rVq1WKzM/PV3Gkyv+v6nSlp6eTnJzMzJkz2b59O2+99RbTpk0jNTW1Xs0gepoEAoFAIBA8N8aMGcP777+vlFbVOQLQ1dVVcY7K/9fT01NK/+GHH3B3d+fdd98FYN68efTu3Zt//vmH0aNH15t24TQJBAKBQCComXocnlM3FKcOW1tb0tPTKSoqQltb7q4kJyejp6eHiYmJUt6bN28yYsQIxf+ampo0bdqUBw8e1JtuEMNzAoFAIBAIHoOGhka9bbXF09MTbW1twsLCFGmXL1/Gx8cHTU1l98XGxobo6GiltLt379KwYcOnqndVhNMkEAgEAoHgP4e+vj79+vVj9uzZXLt2jX///Zd169YxcuRIQN7rVFBQAMDbb7/N9u3b2bVrF3Fxcfzwww88ePCA/v3716smMTwnEAgEAoGgRurSQ1SfTJs2jdmzZzNq1CiMjIz49NNPeeWVVwAICAjg22+/ZcCAAfTp04fc3FxWr15NYmIinp6erF+/HktLy3rVI5wmgUAgEAgENfOC1n/V19fnu+++47vvvlPZFxERofT/4MGDGTx48DPVI4bnBAKBQCAQCGqB6GkSCAQCgUBQIy9qeO6/hnCaBAKBQCAQ1IhwmuSI4TmBQCAQCASCWvBMe5oyMjLo06cP27dvr/NaCXl5eXTo0AEvLy+2bNmiNk9oaCirV68mLCyMkpISmjVrxsSJE5U+7hcWFsaiRYuIiIjAxsaGjz76SCVQ7KeffqJRo0YMHjwYDw8PNmzYQNu2bZXyjB49GktLS7799ltF2r59+5gyZQoTJkzg008/VaSvXLmSw4cPs3v3bkaMGMGsWbNwdXWtU/2ro7hQxsW/VnIv7BxaOhK8egzAq+cAtXnT7kcTvHUFGQ/iMG3gSNt3xmPp6KbYHxcaRNje9eRlpGLj7EXbYRMxsrQBIPvRAy5u/5Xk6HAkhsZ4dHkT714D66UOdUGio825LV8yadEOzlyOfOblFRXKCNr8C3dDz6ItkdD8lYG0eEV9vVPuRXFm0wrSEmIxb+BIpxGfYt3YTSVf6P6tZCY9oNsHUwB4EHGNvT9MVXvOYYvWY1x2DerCo7gojm9YRmp8LBb2jek+aiK2TqpayrlyJJDLB/9GVpCHW5vOdH33E3R0lVfYLSqUsW3OBLoOH0/Dpi0U6Vmpjzi+fhkJEdcwNLOgw8D3cffvUiudxYUyLm1Xbr+ePapvvxe3VbRf/6EV7XfzhNfVHtN+xGSc2/aoVF4hB7//jDaDx2Lr3rxWGqtSVCjjwraVxF05i5aOhGa9BtKsmnsu9X4057YsJz0hDnN7R9q/MwErNW3i7uUznPztW97/9YAiLS7sHMdXz1fK17hVR7qP/vqxGpPjoji5YTmpCbFY2DvSdeREbGq4/mFHdnLl0N/I8vNwbdOJzpWuf1GhjFMbVxB9WX4PtHp1EK1eq7gHslMfcXLDcsX1bzfgfdz8OwNQWlpK6MEd3Di5n4KcbGybuNN52DgMmzjXqD8pNorDfywl+f5drBwa8+oHn2HXxL3a/JcOBXJx/3Zk+Xl4tO1Cr5HjVdovwI7FX2NgYsrrY75UpN25FMSpHevITk3GprELPUeMx65J9baqSnGhjJDtv3L/qrwNN+3ev8Y2HPLXL4o23GbIeCwc5b8FWz99Q+0x7YZPoknbHty/eo6g3xYq7WvUsgMBH06vtdb6RPQ0yXlmPU2ZmZmMHTv2ib/7cvz4caytrQkNDeX+/fsq+w8fPsyoUaNo2rQpGzZsYNu2bbi7uzNy5EguX74MyNdw+Pjjj/H392fnzp1MnDiRefPmcfLkScV5YmJiOHr06GPXcvDz8+P69etKacHBwdjY2BAcHKyUHhYWhr+/PwDjx49nzpw5T2ICtYTu/J3Ue1H0nLgQ/yGfcP3gFuJCg1TyFUkLOL5yFjau3vT5agnWzp6cWDmbIql8TYvkmHCC/vgez+4D6PPVMjS1dQj6Qz47obSkhOO/zkbXyIQ+05bRduh4bhzaxt1LJ+utHrVBV6LNhm/fw9vV/rmVeWHHbyTHRfLGlEUEDBvP5b2bibl8RiVfobSAg8tmYufmzYBvlmHr6sXBZbMoLLNvOVHBJwnZs0kpzdbFkxE/bFba7Nya4dSy/RM5TIXSAnb/PAMHt2a8M2sF9q5e7Pl5hoqWciJDznBh1ya6j5rIgC++IzH6FkHbf1PKU1Qo49CqRaQmxCmllxQXs+fnGWhpaTFs9i/4vjaYw2u+JyU+tlZaq7bfawe3cO+K+vZ74tdZ2Lh403uqvP2e/LWi/Q5YuFFp8+o5EEMLGxo2b6c4R3GhjLN/fkfmwziV89eFkMDfSYmL5NXPv6X9O+MJ27+ZWDX3XKG0gKMrZmLr2oy+05Zi7ezJvytnq1wHaV4OwdtXqRyf8fAejXzaMmTRJsXWcfhnj9VXKC1g75IZNHBvxtszl2Pn6sW+JTOrvf5RIUFc3L2JriMn0u/LRSTF3Obcjt8V+89u/41HsZH0+3IRXYZP4OKezUSFyO+BkuJi9i6ZiaaWFkNmraDVa4M4uvZ7Usuu/82TB7hy6B86D/uEt2cuw8TKlr1Lqm+LALKCfHYs/pqGHs0YNe8XHNy8+PuHb5AV5KvNH3HxDGcDN/DqB58zdNpiHkTd4uS2tSr5ws+fIObqRaW05PhY9q78lvZvvsP7C1dj29iFv3/4pkZ9Vbmyax1p9yLp/ukC/N4ex41DW6ttw6dWzcbaxZtXv1yCVZOmnFpV0Yb7LdiotHmWtWGHsjaclXgfh2b+Snn835lYa531zYtY3PK/yDNxmkJCQhgwYAB5eXlPfI59+/bRs2dP3N3d2bVrl9K+nJwcZs6cybhx45g0aRIeHh64uLgwbdo0unbtyuLFiwH4999/sbKyYvLkyTg5OfH666/Tr18/9u7dqzjX2rVr6d+/v2KJ9urw9fUlOjqa3NxcRVpwcDAffvghYWFhigW2AK5evapwmtq1a0dKSgohISFPbItyiqQFRJ07gt+g0Vg6uuLYsgNePQcRcXqfSt7Yy6fR1pHQuv+HmNo54jdoNDp6BsSFyh9+4f8G0sS/G+6demNq25A2g8eQn5lGQU4m+dkZWDR0pu3Q8ZjYOODQrA12Hi1Ijr751HWoLU2d7Ti14X80aWT13MoslBZwO+gwHYeOxbqxK01ad6Tlq4O5cXyvSt7oS6fQ0tGl3aCPMG/gSIchY9DR0yem0o/LmU3LObn+Z0ysGygdq6Wtg4GphWJ7cPsaaQmxdB75+B9IddwJPoW2RELAkI+xsHek87Cx6OjpE3nptNr8YUd30eqV/ji3bIedswfdR31GeNARxQ9HakIcf837jMxk1c8PxF67SHZaMq+M/hLzBo3w6fY6Ts3b8DAq/LE6i6QFRJ8/gt/A0Vg0cqVRi7L2e0q1/caFyttvq7L26zuwrP1ekdtX38RCsRXLZESc2kvbYROR6BsCkPnwHod/mEx2SmKt7aiOQmkBd84epu3bY7BydKVxyw406zWIWydV28Tdy6fR0tGlzYAPMWvgSNvBY9DW1Sc2VNnpDgn8HeMqbQIgI/E+5vaNldqGroHRYzVGXjyFto4uHd/+CAt7Rzq9I7/+UdVc/2v/7qJFr340adkW2yYedB05kVtl179QWkD46UN0GjYWm8ZuuPh2pHXvQVw7tgeAuGsXyUlLptfHX2DeoBHNur5O40rX/9bZo7R6bSBNWrbF3K4hXUZ8SkFOFvF3qn923C5rv93eGY2VQ2N6jPgEiZ4+ERfV6w85vBO/Vwfg2qodDVw8eO2Dz7l26rCS45Ofk8XJrWtp4OyhdGzs9ctYNWxMs069MLe1p/PbH5KbmUZKQu0c6yJpATHnj9B6UEUb9uwxkEg1z+C40NNo6Uho2e8DTO0a0XrgaLT19BUOlr6JuWIrlkm5c2ov/u9UasOJ9zFt0Fgpn6QW7eGZoVGP20vMEztNy5cvx8PDQ2XbuXMnQUFBDBw4kOXLlz/RuTMzMwkKCsLPz49u3bqxa9cuSktLFfuPHz9OTk6OYlXQykydOpX58+Vd3J06dVIaTisnJycHgKysLA4cOECPHj1U8oDc6WrevDlnzpzBx8cHHR0dbt6U3/yJiYk8ePCAwYMHY2xsTGhoKCBftj0zMxM/Pz/Febp3787WrVufyBaVSU+IoaS4CGtnT0WatYsXqbERlJaUKOVNib2NtYu3wqvX0NDA2tmT5Lu3AUiKvI5jyw6K/EZWdvSf9wd6RqYYmFrQ6cOv0NEzoLS0lEfR4TyKuomt25MNbzwJnXxdOX3pDl1H/fjcyky9L7evrUuFfe3cvHl0V9W+STG3sXNVtq+dixdJMbcAKJTmkxofS//pS7CtdL2qUlxUxKXd62ndZwj6xqZPpDsx5hb2bspa7N28eRh1SyVvSUkxSXfv4ODuo0hr4OJJcVEhKfdjAEiIuEZDzxa8/fUSlePjb1+jkVdLdMse7ABvTpyNT9c+j9VZ3n6tKtnDxtmL1Dg17feu+vabUtZ+K3N1/yZs3VvQoGnFsHxS1HVs3Zvz6pQfHqurJtLi5ZptKmm2dfUmWc09l3z3NrauXkqabV28eBRToTnxznUe3rlOi9eGqJSV8fAeJrYOddaYGH2bBlWufwM3LxKja7j+HhXX367S9U8puwcauHop9jdw8yYpRl7f+IhrNPRqqfhhB3j901k0K7v+Hd/+CI923RT7NDQ0KC0tRZpf8bJZlQdRt2jo0UxJv4O7NwmR6vUnxkTQqGmFfntXuf5H92IUaSe2rMG7Yw8sHRyVjtc3MiElPo74OzcoLSnh+unDSPQNMLetXW92esJdeRtuUuUZHHdHpT2kxkZg7azcHqybeJESq9qGrx/YjK17C+yatlSkZSXex9im7u1B8Gx54pimDz74gKFDhyr+X7lyJUFBQfTs2RNjY2MA4uPjn+jcR44cQUtLiw4dOmBtbc2qVasICQmhTZs2ANy+fRtnZ2eMjFS97sqxUw0bNlT6PzU1lf379yvijy5evIiZmRkuLi4q5wkNDeWLL75g0aJFdOrUCYAWLVpw7do1/P39uXDhAs2aNcPQ0JA2bdoQHBxMhw4dCAsLw83NDXNzc8W5OnbsyKRJkygtLX2qrsn8zHR0DU3Q0tZRpOmbmFFcKEOam41epR/d/Mx0zBooPzD0TMzIeBCHLC8HWV4OJcXFHFsxg/T4GKycPPAf+gkGZso9O7tmvE9uejIOzfxp1KoDz4u1O1S7u581eZlp6BmZqrVvQW4W+sZmSnkt7BsrHa9vYkb6A/kbq66BEf2+erzDFxNyGmleLt7d3nxi3bkZaVg4KGsxMDFTGVoDkOblUlwow9DcQpGmqaWFnpEJOekpADTvXr2WzOSHmFjZErTjd26fO4a+sQnt+o3EpfXj24a69qtXXfvNSse0avs1NiOjylBbbtoj4kJO8cqUxUrp7p3UxzzVlXx1bcK4unsuDbMGyteh/J4DeXzV2c3LaD/0EzSr9GyXlpaSlRRPQngo1w79RWlJCU6tO9HqzeFKZatDXVs0MDEnNSFWJa/i+ptVrJJcfv1z01NAQxP9KvU1MDGX3wM5WWQlJ2Jiacu5HeuIOH8MPSMT2vYbgXPZ9bd3b6ZUXvjpQ5SUlNCwSnplcjLSsKrSfg1NzUm+r0Z/bi5FhTKMzJX16xuZkJ2WDEDczSvcv32dDxat4cgfS5WOb9quC5Gh59k8dxIamppoaGgy6H/z0TM0rlZfZQqy0lTbsLF5NW04DVM71faQ+UB9G+45uaINl5aWkvUonoe3Qgk/sp3S0hIatQzA5/V3H9senhUv+7BaffHEPU2GhoZYW1tjbW1NeHg4gYGBLF26VOEwPQ379++nQ4cO6Ovr4+Pjg52dHTt37lTsz87OVusw1URBQQGffvopVlZWDBkif8sLDw9X6zDFxMQwbtw4pk6dSp8+FW/Qfn5+XLt2DZAPzZUHi/v7+yvimirHM5Xj4uJCRkYGCQkJddJclSKZVOWG0Sz7v7ioUCm9WCZV7CtHS1uHkqJCRTd2yI7VNGnTja7jZlFcVMiJX+eovC11/vhruo6dRXp8DJf/Vo0b+L+EOvuW/19cWKiSV8W+Ojoq1+Fx3DpzkKYBr6It0X0CxXIK1enW0aG4SKaStzyeQktb+QvjWto6KnVUW5Y0n/Cgo0hzc+j72Rw8O/Rk/y/zSLp757HHFhWq2qz8/5IiVfuqa+tV80WfP4KFoytWTk0fW/6TINes7OBoVXPPVdd+yu0admALlo6uOHi1ViknN+2R4viuH02jzcCPiL50gkuBv6vkVdEok6KlU325VfNWroNS/qJCimQFas9VXt/CgnxunT2KNC+b1z+bTdMOPTm4cr7a658YfZugv9bQ+rWBGJlZqOxXaJKqL1PdvVQoK1DsV8qvI69vkUzG4XVL6PXeBHTU3FP5OVnkZqbRa9QERs5ZTrOAnhxY8wO5menV6lPSWs1zFVTbcHE1bbhqvWLOHy1rwxVDiXnpyYrjO37wFS37fUBcyEnCdq2rlc5ngYhpkvPUs+fi4+P58ssvmTZtGl5eXo8/4DEkJydz8eJF5s2bB8gvVK9evQgMDGTGjBno6+tjZmZGVlZWrc+Zm5vLJ598QmxsLFu2bEFfXx+AtLQ0pR6hchYsWEBRURENGijHHfj5+Sniq4KDgxUa/f39WbRoETKZjLCwMMaNG6d0XHkZaWlpT/XFZXU/yuU3atUfXU0d1R+Y4qJCtCS6iq9Du3Z4Fee23QEIeO8L/p72rnxYz7niOlqWzfwpLpRxdv1iWg/48IW96TxrtHQkqs5nuX2rzMzR1pGo2rewsE7OT35WBg8jb9LxnU/qpPPivq2E7Num+N/Wuamq7sJCtCWqs4m0dOTOUlWHqriodto1NeVv9d1HfoqGpiY2Tm4k3LnBjVMHsK1hthNUOO2VKf9fq0rZ1bV1LR3lfPeunMUtoPdjdT8pWtoSSoqKlNKKq7nnqms/2hJd0hNiuRN0iH4zVqotx8jSlmE//IXEwAgNDQ0sG7lQWlLC6T9/wH/Qx2hqainyXj34F9cP/6X439a5qYqDVFxUiLau6vXUru4lq0xnaUmJ2nPJ66un6JXqOqLs+jd240HkDW6eOqh0/R9GhbP35xk09mlD237KYRTnd2/h/J6KcAV7F/X61Tk92or2q6a96+pydudG7Jq449y8jcqxAKe2/YZ1oya07vUWAK99OInfvvyQ66cP0+7NoWqPqYyWuvu+ujasrdoeStTcZ/fCVNuwoYUNAxZtVbQH84bOUFrK+Q0/0mrAR0rtQfB8eSqnSSqVMnHiRDp16qQ0VPc0HDx4kOLiYmbMmMGMGTMAeVdlSUkJR48epW/fvnh7e7Nu3TpycnJUepxCQkL4888/Wbx4Mfr6+uTk5PDRRx9x79491q9fj5OTkyKvhoYGxcXFKhqGDh2Kjo4O8+fPp3379kgk8hu1VatWPHr0iOvXr/Po0SNat5a/Mbq5uWFsbMylS5eIiopS6WkqKeu9eVoP28DMEmluFiXFxWhqyW+a/Kx0tHR0lWIMyvPmZym/PRVkZaBvYoGukSmaWtqY2FU4cLpGJugayrvojbLSSbl7m0Yt2iv2mzZwpKSoiMKCPLSMniz25r+OoZklBTmZSvbNy0xHW6KrFMMDcvvmVXk7zc9Kx8C0+jfqqty/eRkTK1ssGzapk87mXV/HvU1nxf8hB7araMnLTMdQjRZ9IxO0dCTkZaZjUTb8VVJcTEFOFoY19AaUY2BmARoaaGhWdFKb2zUkJf5uLY6tQ/s1taQgq6p9M9CvVKfc9GQyE+8pzZirbwzUtIm63HP5menom1oQe+Us0rxs/p75IYCiR3fj5wPoMOxTXPy7oVtliMisgSPFhTJkuTlKwz5NO/fBq33FEg+hB7eTl5WmdGxuNddfr+z652amYd6gEVBx/cvbbn5194CBYVkeNdf/fsX1j799lf1LZ9HIuzWvjPlKKS9Ayx5v0LRthf4L+/4iN7OK/ox0pSHEcvSNTNDWkZCbkY6lfUX7zc/JwsjMklsXTpKbkcZPH8qHmMudloiLZ5j8+14S70bi+2o/xfk0NDWxdnQmK+WRSlnq0DdVbcMF1bQHfTN1bTgdvSptOCvxHg4+ykvcACrtwcS2kdr28Lx42XuI6ounmj03d+5c8vPzmTt3bn3p4cCBA7Rv355du3Yptt27d+Po6Kjo5enUqRPGxsZs2rRJ5fj169eTmJiIvr4+JSUlTJgwgfj4eDZu3Iibm/JaHJaWlmRkZKico1evXowfP578/HzWrFmjSDcwMMDT05O//voLHx8fRY+VhoYGbdq0ITAwECcnJywslB9W6enyG8fa2vppTIN5Q2c0tbSVgmEfRYdj2dhN5cFk5dSU5Lu3FAH05QHd1k2aoqmlhYWjK+mVfugKcjKR5mRhZGFDTmoSp9YuIC8jRbE/7V4Uukam6P0fdZgALBvJ7VsezA2QGHUTaydV+9o6NyUpWtm+iVHh2DjXfpjo0d0IbF2866xTz8gEM1sHxdbAxYuHUeFKWh5E3cTORVWLhqYmtk3ceVBpNtPD6HA0tbSxalTzWjoADZw9SY2PpaSk4mUj7eF9TKxsH3usov1WCoRNrq79NmlKcoyyfZNjwpWG4VJjIzAwt8bQou7LNNSW8jaRXOmeS4q+iZWaNmHdpCmPYpSvw6OYcGyaeODVrS8DZq3hrekreGv6CsVSAm9NX4Fj87YkhF9my/+GUCSrmAGWej8aXUMTlR9IXUNjzGztFZudiycPo6q2xZtqJyCUX/+HkRXXPzH6luL6W5XVt3IQ+cPIG9g4uaOhqYmdS1PSEqpe/3uK658aH8v+ZXNw9PHjtXFfo6VmVrK+kQnmdg6KzcHNi4RIZbvF37mJvav69mvn7EH8nRuKtITIcLS0tLFxdOadr3/gg2/X8P6CVby/YBWurdvj2ro97y+QL/FgZG5JasI9pXOmJcZjam2nUpY6zBs2QVNLm9TKbTjmJhZq2rClkwcpVZ7BKTG3lIbhqmvDD29d5p+p7yi1h/SEGCRq2sPzQgzPyXlip2nHjh0cOHCAhQsXkpeXR3JyMsnJyWRnZz+xmPj4eK5cucLQoUNxd3dX2oYMGcL58+dJSkrC0NCQ6dOns3z5cpYsWUJ0dDS3bt1ixowZnDx5km+++QaAv//+m+DgYObPn4+JiYlCY7mj5OXlxZ076mMxjIyMmDx5MmvXrlUKaG/Tpg379+9X6U3y9/fn2LFjimD1ykRERGBlZYWt7eN/WGpCW6KHc9seBG/7hZS4O9y/ep5b/wbStFtfQB6IWh6z4NgqAFleLiF/ryHj4T1C/l5DkayAxq3lQe2e3fsTcXIPcaFnyEy8x/mNSzBv2ARLJw8sG7th0ciV85uWkvHwHgk3LhG683d81Mz4+b+Ejq4e7u17cmbTCh7djeDulXNcO/IPzXr0A+QBt+X2dfYNQJqfw7m/VpP+II5zf62mSFaAi1/nGkpQJi0hFnN7x8dnfAyubQKQ5uVwessqUhPiOL1lFYVSqWLBySKZVOlNvnn3N7l8aAfRoedIjIngxIblNOvSW+3igFVxb9eV0tJSTmxYQUZSAleP7yXu+iWadX78EJm2RI8m/j24uO0XUsvb77FAPLqWtd+sSu23ZQCy/Fwu/7OGzIf3uPyPcvsF5AsG2jWqk63qirZED9d2PTi3ZQXJsXeICzvHjaOBeHWTD+9UbhNOZfdc8I7VZDy8R/CO1RRJC3Dy7YyuoTEmNvaKzaCsF8XExh4dPQNsnD3R0pFwduNSMhPjib9xiZDAdfhUs7BqZVz9ApDl5XBm6yrSEuI4s3UVhdICxYKTVa9/s25vcOXQ38SEniPpbgQnNy7Hu/Nr6OjqoaOrR9OOPTm5cTlJdyOICT3HlcP/0KJsOMu9rfz6n9q4goykB1w/vpd710Pw6vIaACc2LMPYwoqAoWPIz8kkNzON3Mw0CstspA6PNp2Q5uVybONKUhLiOLZxJYXSAkVvVKFMSk5Ghf5WPd8keP927oSc5WF0BEf+XEaLbn3Q0dXD1MpWySGT6Okj0dPH3E4+C61Ftz5cPXGAG0FHSU9M4OS238hKSaJZp161bg9N/Ltz6S95G46/ep7bx3bi0aW8DaertOHQsjYcWtaGHVtVtOHMh3GYqGnDVk080ZJIuLhlOVlJ8Ty4GULYrnV4VrOoquD58cTDc3v27CEvL09lWK5///4sWrToic554MABzM3N6d69u8q+AQMGsHTpUnbv3s3o0aPp27cvJiYmrF27ls2bN6OhoYGPjw+bN2+meXP51PjDhw9TUlLCmDFjlM7l7+/Pxo0badu2LVlZWdy9e5cmTVSHSPr378/WrVuZP38+q1bJ31R8fX1Zt26dyorh/v7+5OfnqzhTAJcvXyYgIKBePGzfgR9xcdtK/l0yDR19Q5q//i6OLTsC8M/0EbQf/jku7Xsh0Teg27hZBG/7haizhzCzd6L7J3MUsTmNWwcgy88hdOc6CrIzsXXzocuYmWVvAlp0HTODS9t/5fAPU9DW1aNp176KH7f/y7R/+2OCNq9g749fIdE3xLfvcJxby+278X/v0vW9yXh07IVE35Den87hzKbl3Dp9EMuGTeg9cW6tHI9y8rMyarUOz+PQ1Tek7+dzOb5+OddPHcCqURPemjRPoeXOxVMc/f1HPvvjMAAebbuSlZLEsfVLKS4sxNUvgIC3P6p1Wf3/9y0nNi5n0zdjMLaypffY6TWuPl0ZRftdqtp+A6ePoN3wz3Fp1wsdfQO6jp3FxUrtt9u4OUqxZQXZGc9l3Rr/QR9zbssvHFryFRI9Q1q9MRynVnLNf301nICRk3Aru+d6jp/NuS0ruBN0CHMHJ3pNqF2b0NEz4JVP53Nxx2r2LPoMHT19PAJ606zXoMceK9E35I3P5nJywzJunjqIVcMmvPl5xfWPvHiKY+t+YsK6Q4Dc8clOSeLEhmUUFxbi4tuRDpWuf8CQ0ZzcuIJd309Fom+I/1sjcPENUJT11pSFnNy4gq0zxmBsacOrY6dh09iN3Mw0EsvWa1r/vxFKGvuM/h8+nV9Vq1/XwJCBU+ZxZN1Srp44gLWjM4O/WIBET96Tf/vCSQ6s+YGpm44C4NW+G1nJSRxet4TiokLc23Si69CPH2snAM92XZEV5HN+z1Zy0lKwcXRh6LTFGJqqxrZWR6sBH3Hpr5UcXzYdHX0DmvV5l0Zly7fs+noEbd/9HOd2PdHRN6DLmJlc+msl0ecOY2bvRJexs1XbsL5qG9bRM6DbJ3O5/M9aDi+ehI6uPi4dX8Ozx/P/KoOCl7uDqN7QKK28ANL/h3z11Vc0bNiQCRMmPJPzl5aW0rNnT7777jultZsex7x/o56JnmfFwi+WvGgJdWLB0skvWkKd0NN+uT4TmZJb9PhM/yEkWi/XL4Kh7svVHgwlL1fg8r306nvG/qvMfqX2n4J5Eqze2/b4TLUk5c/6iYF+Ebxcd94z4KOPPmLPnj0U1mK69ZNw9uxZbGxs6uQwCQQCgUAg+O/xTD/YW5UFCxbw999/V7t/zJgxjB079jkqAldXV8WSBuXrN9Unv/76K7Nnz6738woEAoFA8Lx42QO464vn6jSNGzeO4cOHV7vf1PTFzAr44osvntm5N2/e/MzOLRAIBALB80A4TXKeq9NkYWGhMh1fIBAIBALBfxzhMwEipkkgEAgEAoGgVjzXniaBQCAQCAQvH2J4To5wmgQCgUAgENSIcJrkiOE5gUAgEAgEglogepoEAoFAIBDUiOhpkiOcJoFAIBAIBDUinCY5YnhOIBAIBAKBoBaIniaBQCAQCAQ1IzqaAOE0CQQCgUAgeAxieE6OGJ4TCAQCgUAgqAWip0kgEAgEAkGNiJ4mOcJp+o+i9ZI10AVLJ79oCXXi689+etES6sSyVc/uo9LPAhujl+vRovmS3W+J2YUvWkKdyC4oedES6oSDqeRFS/jPIZwmOWJ4TiAQCAQCQc1o1ONWB6RSKdOnT8fPz4+AgADWrVtXbd6IiAjeeecdmjdvzptvvsmFCxfqVlgtEE6TQCAQCASC/yTff/89N27cYP369cyaNYsVK1Zw6NAhlXzZ2dl88MEHuLq6snfvXnr16sWECRNITU2tVz3CaRIIBAKBQFAjGhoa9bbVlry8PHbs2MHXX3+Nt7c3vXr14qOPPmLz5s0qeXfu3ImBgQGzZ8+mcePGTJw4kcaNG3Pjxo36NIOIaRIIBAKBQFAz9RnTJJPJkMlkSmkSiQSJRDmW7Pbt2xQVFdGqVStFmq+vL6tWraKkpARNzYp+n4sXL9KjRw+0tLQUaf/880+9aS5H9DQJBAKBQCB4bqxevRpfX1+lbfXq1Sr5kpOTMTc3V3KmrKyskEqlZGRkKOW9f/8+FhYWzJgxg44dO/L2229z+fLletcuepoEAoFAIBDUSH32NI0ZM4b3339fKa1qLxNAfn6+Snr5/1V7qvLy8lizZg0jR45k7dq17N+/nw8//JCDBw/SoEGDetMunCaBQCAQCAQ1Up9Ok7qhOHXo6uqqOEfl/+vp6Smla2lp4enpycSJEwHw8vLi7Nmz7N69m7Fjx9aTcjE8JxAIBAKB4D+Ira0t6enpFBUVKdKSk5PR09PDxMREKa+1tTXOzs5KaU5OTjx8+LBeNQmnSSAQCAQCQc28gHWaPD090dbWJiwsTJF2+fJlfHx8lILAAVq2bElERIRSWkxMDA4ODrUvsBYIp0kgEAgEAkGNvIglB/T19enXrx+zZ8/m2rVr/Pvvv6xbt46RI0cC8l6ngoICAIYOHUpERATLly8nLi6OpUuXcv/+fd566616tYNwmgQCgUAgEPwnmTZtGt7e3owaNYo5c+bw6aef8sorrwAQEBDAgQMHAHBwcOC3337jxIkTvPHGG5w4cYI1a9Zga2tbr3pEILhAIBAIBIIaeVHfntPX1+e7777ju+++U9lXdTjO19eXwMDAZ6pHOE0CgUAgEAhqRHyvV84zdZoyMjLo06cP27dvp2HDhnU6Ni8vjw4dOuDl5cWWLVvU5gkNDWX16tWEhYVRUlJCs2bNmDhxotLqoWfOnGHx4sXExsbi5OTElClT6NKli9J5fvrpJxo1asTgwYPx8PBgw4YNtG3bVinP6NGjsbS05Ntvv1Wk7du3jylTpjBhwgQ+/fRTRfrKlSs5fPgwu3fvZsSIEcyaNQtXV9c61b86igtlXNi2kriws2jrSPDuORDvngPU5k29H82FrctJT4jDrIEj7YdNwNLRTSVfbOgZTv32LaNWyrs5o84f5ezGn1VPqKHBqF/210lvUaGMoM2/cDf0LNoSCc1fGUiLVwaqzZtyL4ozm1aQlhCLeQNHOo34FOvGqnpD928lM+kB3T6YAsCDiGvs/WGq2nMOW7QeY0ubOml+GiQ62pzb8iWTFu3gzOXI51ZuUmwUR/9cSkp8LJYOjen13kTsmrhXm//yoUAuHdyBND8PD//O9BgxHh1d+RTe9KQEjq1fQULkTfQMjWnV6y38X38bgINrFnMz6KjK+Rp5tmDItMW11vsoLopj65cp9PYYNRFbJ9VrXU7o4UBCDv6NrCAP9zad6Tb8E4XenPQUTmz+lfu3wtDW0cWjbRc6Dnwf7SpTmjOSEtjwzVgmrt1ba53lJMVF8W9l+46aiG1N9j0cSMiBHUgL5PbtPrzCvtlpKZzYvJL7t66irSPBo20XAgZ9oNAbH3GdE5t/Je3hfcztHOgydDSNvVvXWmtxoYxL21dyL+wcWjoSvHoMwLOH+mdE2v1oLm5bQcaDOEwbOOI/dLzSM+LelSDC9qwnLzMVa2cv2g6biJGF/H66f/Ucp9cuUDpfo5Yd6fzR9FprrU5/fT3jtkwZTGF+rtIxw376Bx09/afSWJX6vP8q88+P32BgbErv0V/Uq94n4UX1NP3XeGYxTZmZmYwdO/aJP5Z3/PhxrK2tCQ0N5f79+yr7Dx8+zKhRo2jatCkbNmxg27ZtuLu7M3LkSMUqoHFxcUyYMIEBAwawf/9++vfvz/jx44mPj1ecJyYmhqNHj9K/f/8a9fj5+XH9+nWltODgYGxsbAgODlZKDwsLw9/fH4Dx48czZ86cJ7KBOkICfyf1XiSvfvYtbYeO5+qBzcSGBqnkK5QWcOyXmdi4NOONr5Zi4+zJvytnUygtUMony8vh4vZVSmlOvp15+9tNim3Q/PUYW9vj2a3uAXUXdvxGclwkb0xZRMCw8Vzeu5mYy2fU6j24bCZ2bt4M+GYZtq5eHFw2S0VvVPBJQvZsUkqzdfFkxA+blTY7t2Y4tWz/XB0mXYk2G759D29X++dWJoBMmk/gj9/Q0MOHEXN+wcHNi8CfZiCT5qvNf+fSGc7t2kiv9z7j7a++52H0bU799RsApSUlBP44A30TU0bO+5Ve73/GhT1buHXuOADdh3/CuGXbFNuwmUvR0tGh9Sv9aq23UFrAzp9m4ODejGGzV9DA1YtdP81QudblRF46w4Vdm+j53kQGffkdD6NvcWZ7md7SUvatmE+RTMrb03+kz7hpxIRd4FzgeqVzZKc+YtfPMykulKkr4jF689n54zc4uPswfM4v2Lt6EfjzDAprsO/5XRvp+f5nvD31ex5G3eb0XxV6966YR5FMypDpP/L6J9OJDrvA2cA/AcjLSmfXkpk0bdeVUQvW4OHfhV1LZpGdllxrvaE7fyf1XhQ9Jy7Ef8gnXDu4hXtXVJ8RRdICTvw6CxsXb3pPXYK1sycnf51NUdl1SI4JJ+iP7/HsMYA+U5ehpa3D2XUVQySZD+/h0MyfAQs3KrZ2wybWWmd11NczLjcjhcL8XAbM/V3peaatxjl5Gurz/qvM7QsnuHv1Yr1qFTw9z8RpCgkJYcCAAeTl5T3xOfbt20fPnj1xd3dn165dSvtycnKYOXMm48aNY9KkSXh4eODi4sK0adPo2rUrixfL33gTExN5++23ee+992jUqBHvv/8+BgYGXLt2TXGutWvX0r9/f7S1a+508/X1JTo6mtzcireW4OBgPvzwQ8LCwhQR/ABXr15VOE3t2rUjJSWFkJCQJ7ZFOYXSAiLPHcZ/8BgsHV1p3LID3r0GcfuU6ptz7OXTaOno4jfgQ8waONJm8Bh0dPWJC1V2WEJ2/o6xtfJqqdoSXfRNLRRbzMXjQCm+bymv4FobvbeDDtNx6FisG7vSpHVHWr46mBvHVfVGXzqFlo4u7QZ9hHkDRzoMGYOOnj4xIXK9JcXFnNm0nJPrf8akil4tbR0MTC0U24Pb10hLiKXzyM/qpPdpaOpsx6kN/6NJI6vnVmY5ERdOoS2R0GXox1g6ONLt3XFI9PS5c1HVOQUIPbKT1q/0x6VVOxo4e9Drvc+4cfowhdICcrPSsXF0odeoiZjbOeDcwp/GXq2IvyP/6KWugSGGZhaK7VzgBjzadMbNt2Pt9QbL9XYa8jGW9o50HTYWib4+dy6eVpv/ytFdtHqlP84t22Hn7EHP9z7j5pkjFEoLSH94n4fRt3jlwylYOTjR0MOH9v1HcvvCCcXxUZfPsXn2BLR0dOpg1QpuB1eyr32FfSMeZ99yve9/xo0zcvumlel99aP/YdVQrrdD/1HcPi/Xm3DnJhqaWrTp8zZmNg1o++Y7aOtIeBh9q1Zai6QFRJ8/gt/A0Vg0cqVRiw549RxExKl9KnnjQk+jrSOhVf8PMbVzxHfgaHT0DIi7Iq/XrWOBNGnTDbeA3pjYNsRv0Bjys9IoyMkEIDPpPmb2TuibWCg2iYHRk5hYQX0+4zIT76NvaoGxVQOl51l995jU5/1XTn5OFqe2rcXO2aNetT4NGhr1t73MPLHTtHz5cjw8PFS2nTt3EhQUxMCBA1m+fPkTnTszM5OgoCD8/Pzo1q0bu3btorS0VLH/+PHj5OTkKKYdVmbq1KnMnz8fgLZt2/L1118DUFhYyI4dO5DJZDRv3hyArKwsDhw4QI8ePdTq+Pfff2nevDlnzpzBx8cHHR0dbt68CcgdsgcPHjB48GCMjY0JDQ0F4O7du2RmZuLn56c4T/fu3dm6desT2aIy6fExlBQXYe3sqUizdfEmJTaC0pISpbzJd29j4+KleEBoaGhg4+JF8t3bijyJd66TeOc6Pq8NqbZMaW4214/+Teu33q/zj07qfbleW5cKvXZu3jy6q6o3KeY2dq7eSnrtXLxIipH/WBRK80mNj6X/9CXYVqp/VYqLiri0ez2t+wxB39i0Tnqfhk6+rpy+dIeuo358bmWW8zD6Fg7uzZRs5+DmzYOocJW8JSXFJMbcoVFTH0WavasnxUWFJN+LwcjMkjcnfI1E34DS0lIS7twkPuI6jTxbqJwr7uYV4iOuEzC4bs70w+hbOLgpX2t7V2+1jkFJSTGJd+/g4FGht4FLmd77MRiYWtB/ygIMTc2VjpNVGpK5ezWYDgNG0XXYuDrpVOiNuoWDm6p9H1Zj36S7d2hYSa99Jb2GphYM/N9CFb3SMr36RiYU5GQRGRJEaWkpkZfPIivIx6phk1ppTU+Q33NWle4RG2cvUuNU77mUu7exdlG+DtbOnqSUPSOSIq/TqGUHRX4jKzv6zf0DPSP5fZX58D7GNvXbq1qfz7jMh/cwsanfNXrUUZ/3Xzmntq7Fq2NPLO0dn7n+2vIilhz4L/LEMU0ffPABQ4cOVfy/cuVKgoKC6NmzJ8bGxgBKw2B14ciRI2hpadGhQwesra1ZtWoVISEhtGnTBpB/+djZ2RkjI9W3GnWxU3FxcfTu3Zvi4mKmTJmiyHPx4kXMzMxwcXFROSY0NJQvvviCRYsW0alTJwBatGjBtWvX8Pf358KFCzRr1gxDQ0PatGlDcHAwHTp0ICwsDDc3N8zNKx6KHTt2ZNKkSZSWlj5Vg8nPSkPXyBQt7QrnRc/YjOJCGdLcbPQqOQn5mWmY2TdWOl7f2Iz0B3EAFBcWcn7LMtoO+QStGnrZIk7vx8DUAqfWAXXWm5eZhl4Vvfomcr0FuVnoG5sp5bWoqtekQq+ugRH9vnq8QxITchppXi7e3d6ss96nYe0O1eGD50VORhpWDk5KaQam5qTEx6rklebmUlQow9DMUpGmqaWFvpEJ2ekpSnnXTB5BduojnFu2xb2N6vW/uG8b3p1ewaSOQ6C5GWlYOihfawNTM1Lj41T15uVSXCjDyMxCRW9OWgr2rl44+VS8oJSWlBD27x4aebVUpPX6YBIA929drZNOhd7MNCyr2tfEnJSEWLV6q7VvtXp34+glj8N08PChZY++7FkxDw0NDUpLSnj1o/9h0aBRrbTmZ6aja2ii/IwwqeYZkZWOaQPlH2U9YzMyHsYhy8tBlpdDaUkxx1fMID0hBisnD9oM+QQDMytKS0vJehTPw1uh3Dy8ndLSEhxbBdD89eFKZdeV+nzGZSTep0gm5dDPU8lKiseikQttBo3G1LZu8bWPo77vv3vh8peRUQtX8++fy+pVq+DpeeKeJkNDQ6ytrbG2tiY8PJzAwECWLl2qcJiehv3799OhQwf09fXx8fHBzs6OnTt3KvZnZ2erdZiqw8LCgr///puZM2eyfPlyDh8+DEB4eLhahykmJoZx48YxdepU+vTpo0j38/NTDO0FBwcrgsX9/f0VcU2V45nKcXFxISMjg4SEhFprVkeRTKri4JT3/hQXFSrnLZSqPLw0tXUoKct39eAWLBxdcfCqPsC0tLSUyHOHadq171PoVdZQ/n9xYaFKXs2qeXV0VOr1OG6dOUjTgFfRlug+geKXkyKZVKUXUEtbR8XGAIUy+RCA+vzK8T5vfTqT/pPmknwvmhOblePeMh495F74VVr1qnucW3V6i4pU443K42u0dCQq+dW1jTPbf+NRXBQdB9at9+uxetW1TXX2LdOrXQv7Apz6S643YNB78uML8slMfkiHfiN4d9Zy2r75Dic2rST1wb3aaS1UvY/K/y+p+oxQU6/yZ0S53UN2rMbJvxtdx86iuKiQk6vmUFpSQm56MsVlxwd8+BWt+39I7KWTXNm1rlY6q9Vfj8+4rMR4ZHnZNO89lG5jZ6KlI+HI0ukUFjx52Ei1muvp/iuSyTjyx1J6jJyAzn/sGSaG5+Q89ey5+Ph4vvzyS6ZNm4aXl9dTC0pOTubixYvMmzcPkHcJ9urVi8DAQGbMmIG+vj5mZmZkZWXV+pzGxsZ4eXnh5eVFdHQ0mzZt4tVXXyUtLU2pR6icBQsWUFRUpPJlZD8/P0V8VXBwsEKjv78/ixYtQiaTERYWxrhxysMA5WWkpaXVeRZhZbR0JBRX+gYPVDgfVZ0ELW2JykOmpKgQLYku6Q9iiQw6RN9vVtZYXmpcJLnpKTTx6/wUepU1lP9fNRhTW0ei8lAvLiysk/OTn5XBw8ibdHznkyfS+7JwYc9WgvdWDPc2cGmq8oAuLipER1fVdtplzoe6/NoS5Wti5yyf/VNUKOPAqu/o+s5oxY/UnUtnsGnsjFWVHiN1XNy7lYv7tlU6bzV6JaoBuloKvTKV/FXbxpntvxF6ZCevj5uOVUOnx+qqjuC9auxby7ZZbt8itddDuX6n//qN0COBvPHJ14rht0sHtlNaWkr7fsMBsHVy42HMba4c2UXP9x4fZK1VyWkop/x/rarPCDUvJSVFhWjp6KKhJX+fdunwKs7+3QHoOOoL/pn+Limxt7F29mLQd9uQGBihoaGBRUMXSktKObfhB1oP+AhNTa3HalWrv56ecQA9J8yjpLhIMVOu8/tfsuPrkdy/Hoxzm25PpA+e7f13btdG7Jq406S5n8qxLxpNzZfc26knnsppkkqlTJw4kU6dOikN1T0NBw8epLi4mBkzZjBjxgxA3uNRUlLC0aNH6du3L97e3qxbt46cnByVHqeQkBD+/PNPFi9eTHx8vEp8kYuLCxcvymckaGhoUFxcrKJh6NCh6OjoMH/+fNq3b6/4GnOrVq149OgR169f59GjR7RuLe+lcXNzw9jYmEuXLhEVFaXS01RSNhb/tGO5BqaWSHMyKSkuRlNL/lDKz0pHS0cXib6hcl4zS/Kz0pXS8rPSMTC1IO7KWaR52QTO+hBAESuwedIA2r/zKc7+8gdKQngItm7N0DV4st5DQzNLCqrozctMR1uii64avXmZ6vXWlvs3L2NiZYtlLeM/XlZadH8dj7YVjuzFfdvJzUxTypObkYahmart9I1M0NaRkJuZroiXKCkuJj8nCyMzC3Iz03kQFa4U2G3p0JjiokKk+XkYlA2PxF4PwbV1B5Xzq6N5t9dx96/Qe2n/dpVrnZuZXq1erTK9FlX0Vs5/YuMvXD2xj96jp+LWplOtdNVNbxX7ZqYpDRlW1qutIyFPjX0r6z228ReuHt9LnzFTca+kNyk2EmtH5d5vm8aupKoZ6lGHgZkl0tys2j0jTC0pUHlGZKBvaoGuoSmaWtqYVBrK0jUyQdfQhNz0FKwBXUPl54KpXSOKC2XIcnOUhtHqQn0940DuFFbu0dHSkWBkaUdexpPN6C7nWd5/ty+cJC8znaUfy3v3y53CO5fO8NnaPU+lW1A/PNXsublz55Kfn8/cuXPrSw8HDhygffv27Nq1S7Ht3r0bR0dHRS9Pp06dMDY2ZtOmTSrHr1+/nsTERPT19Tlx4gTffPONUhD5zZs3FV9CtrS0JCMjQ+UcvXr1Yvz48eTn57NmzRpFuoGBAZ6envz111/4+Pigry9/g9HQ0KBNmzYEBgbi5OSEhYXyzZKeLr+xra2tn8o2Fo2c0dTSVgrmfhR9E6vGbmhU+XihdZOmJMeEK+peWlrKo5hwrJp44Nm1L/1mruHNaSt4c9oKOrwrn2X25rQVNGpesT5VSmwENs5P3ntoWaa3PJgbIDHqJtZOqnptnZuSFH1LSW9iVDg2zk1rXd6juxHYung/sd6XBX0jE8xtHRSbvasnDyKVr/WDyHAauKgGzGtoamLn7E5C2Ww4gAdR4WhpaWPt6ExmciK7l80lO60ivikpNhJ9Y1OFw1RaWkpizB0c3Gtnaz0jE8xsHRRbA1cvHkRV1XuTBi6q11pDUxO7Ju48iLyppFdTSxvrRvL7+PyuTVw7uZ8+46bj0a5rrTTVRFX7NqijfW2bVGPfMr3ndm7k2ol9vPHJdJq2U+7xMDSzJDVBObYr7cF9TKztaqXdvKH8nkuJrXhGJEeHY6nmGWHVpCnJMcr3XHJMOFZOTdHU0sKikSsZCXcV+QtyMpHmZGFkacOD8Mvs+HIoRbKKGV9p8THoGpo8scME9feMKy0t5Z+ZHxB1vmJNsUJpAdmPHmBqW7v4sOp4lvffkOk/MGrhakbO/5WR83/FpVV7XFq1Z+T8X59Kc30ghufkPLHTtGPHDg4cOMDChQvJy8sjOTmZ5ORksrOzn1hMfHw8V65cYejQobi7uyttQ4YM4fz58yQlJWFoaMj06dNZvnw5S5YsITo6mlu3bjFjxgxOnjzJN998A0Dfvn1JTk7mhx9+IDY2ls2bN7Nnzx7GjBkDgJeXF3fu3FGrxcjIiMmTJ7N27VqlgPY2bdqwf/9+ld4kf39/jh07pghWr0xERARWVlZP/Q0cbYkeLm17cGHrClJi73Av7Bw3/w1UrJ+Un5lGkUwKQONWAcjycrm0YzUZD+9xacdqiqQFOLXujK6hMSY29orNoCwo0cTGHh09A0V56Q/kC8Y9KTq6eri378mZTSt4dDeCu1fOce3IPzTr0Q+QB3+X63X2DUCan8O5v1aT/iCOc3+tpkhWgEsdhgbTEmIx/w/NNnleuPt3oiAvhxObfyUlIY4Tm3+lUFqgeBsulEnJzah4E27Z400uHdhB5OWzPIyJ4Oify/Dp2hsdXT3snN2xdXLj8G8/kpIQR8zVi5zatpZ2fYcpjs9KSUJWkIel/eOH5tTh1iYAaV4OJ7esIjUhjpNbVlEoleLuL190tqiK3hbd3yTk4A6iLp8jMSaC4xuW49NFrjf1wT2C92ymTZ8hOLh5k5uRptjqC/c2FfZNra19D8rtmxgTwb/rK+yb+uAeF/Zsxv/1ITi4NVPR69OlN3evXeTyoX/IePSQy4cDib0eQsvutZvYoC3Ro4l/Dy5u+4XUuDvcv3qeW8cC8SiLS8zPqrjnHFsGIMvP5fI/a8h8eI/L/6yhSFZA49byni/PHv2JOLmHuNAzZCbe48KmJZg3bIJlYw+snT3Rkki4sHkZWUnxJNwM4cqudXj2VL9wbW2pr2echoYGDZv5E7Z/E4l3rpH+II6gP3/AwNwSh2b1O/RVn/efqZWtkkMm0dNHoqePue2znwX4OMTsOTlPPDy3Z88e8vLyVIbl+vfvz6JFi57onAcOHMDc3Jzu3bur7BswYABLly5l9+7djB49mr59+2JiYsLatWvZvHkzGhoa+Pj4sHnzZsWSAnZ2dvz+++8sXLiQTZs24eDgwNKlS/H2lr8ht23blqysLO7evUuTJqpDOv3792fr1q3Mnz+fVavkgbC+vr6sW7dOZcVwf39/8vPzVZwpgMuXLxMQEFAvjaXNoI+5sPUXDi/9Ch09Q1q+MZzGreRDKdunDafjiEm4tu+FRN+A7p/M5sLWFdw5ewhzByd6jJ+rdtXZ6ijIznjqdVfav/0xQZtXsPfHr5DoG+LbdzjOreV6N/7vXbq+NxmPjr2Q6BvS+9M5nNm0nFunD2LZsAm9J9ZNb35WBrpPqfdlRFffkAGT53H0z2VcO3EAq0ZNGDBlPhJdeU9oRPApDq39gf9tOAJA03bdyExO4ugfSykuKsTNL4AuQz4GQFNTi/6fz+HYhhVsmfsZOrp6tO7VT2nxyryyIRFdwyezta6+If0mzeXY+uVcP3kA60ZN6D95nuJaRwSf4sjvPzLpT/mEDY92XclMSeLY+qUUFxbi6hdAp7c/AiA69DylJSUE791C8F7lLweUH/+06Oob0n/SPP5dv4zrJ+X27T9lPjqV7Hv4tx+Ysr6SfVOS+PfPCvt2fltu36jQc5SWlHBhzxYu7FHWO2X9EexdPen76SzO7VzP2cD1mNs1ZMCU+XWK0fId+BEXt63k36XT0NE3pPnr7+LYUn7PBU4fQbvhn+PSrhc6+gZ0HTuLi9t+IersIczsneg2bo4i3tCxVQCyvByu7FpHQXYmtm4+dBk9Ew0NDXT0DOg+fh6X/17Dwe8/R0dXH9eA3ng9pdME9feM8+v/AZpaWpz+43sK83Oxc29Bz0/mPnG8VXXU5/0n+O+jUVp57Or/Q7766isaNmzIhAkTnsn5S0tL6dmzJ999951SbNXjWHgs+pnoeVbo6bxcbw9ff/bTi5ZQJ5atevGfUagLxSUv12NF8yV7+03Mrtus0heN9ksWRGxt9PJ9lvXjtk/W81tbfGaofj7pSbk+r1e9net588w+o/Ky8NFHH7Fnzx4K1UwPrQ/Onj2LjY1NnRwmgUAgEAj+S4jhOTnP1Z1esGABf//9d7X7x4wZw9ixY5+jInB1dVUsaTBkSPUrYz8pv/76K7Nnz6738woEAoFA8Lx42Z2d+uK5Ok3jxo1j+PDh1e43NX1+n72ozBdfPLuhj82bNz+zcwsEAoFAIHh+PFenycLCQmU6vkAgEAgEgv82oqNJzssX7SYQCAQCgeC5Iobn5Px/HwguEAgEAoFAUBtET5NAIBAIBIIaER1NcoTTJBAIBAKBoEbE8JwcMTwnEAgEAoFAUAtET5NAIBAIBIIaER1NcoTTJBAIBAKBoEbE8JwcMTwnEAgEAoFAUAtET5NAIBAIBIIaER1NcoTTJBAIBAKBoEbE8Jwc4TQJBAKBQCCoEeEzyRFO038UI92XK9xMW/PluqOWrXp2H2l+Fkwcu/hFS6gTZm26vWgJdcLQxOBFS6gTNxb1ftES6kR4fNaLllAn3vzu2IuWUGc+btv4RUv4/wLhNAkEAoFAIKgRMTwnRzhNAoFAIBAIakT4THJerjEggUAgEAgEgheEcJoEAoFAIBDUiIaGRr1tdUEqlTJ9+nT8/PwICAhg3bp1jz0mPj6eVq1aERwc/KTVrRYxPCcQCAQCgaBGXtTw3Pfff8+NGzdYv349Dx48YOrUqdjb2/Paa69Ve8zs2bPJy8t7JnqE0yQQCAQCgeA/R15eHjt27GDt2rV4e3vj7e1NZGQkmzdvrtZp2rNnD7m5uc9MkxieEwgEAoFAUCP1OTwnk8nIyclR2mQymUqZt2/fpqioiFatWinSfH19uXr1KiUlJSr509PTWbx4MXPnzn1mdhBOk0AgEAgEghqpT6dp9erV+Pr6Km2rV69WKTM5ORlzc3MkEokizcrKCqlUSkZGhkr+RYsW0b9/f9zc3J6ZHcTwnEAgEAgEgufGmDFjeP/995XSKjtG5eTn56ukl/9ftWfq3LlzXL58mX379tWzWmWE0yQQCAQCgaBG6jMQXCKRqHWSqqKrq6viHJX/r6enp0grKChg5syZzJo1Syn9WSCcJoFAIBAIBDXyIlYEt7W1JT09naKiIrS15e5KcnIyenp6mJiYKPJdu3aN+/fvM3HiRKXjP/74Y/r161evMU7CaRIIBAKBQFAjL2LJAU9PT7S1tQkLC8PPzw+Ay5cv4+Pjg6ZmRUh28+bNOXLkiNKxr7zyCvPnz6djx471qkk4TQKBQCAQCP5z6Ovr069fP2bPns3ChQt59OgR69at49tvvwXkvU7Gxsbo6enRuLHqB4ttbW2xtLSsV03P1GnKyMigT58+bN++nYYNG9bp2Ly8PDp06ICXlxdbtmxRmyc0NJTVq1cTFhZGSUkJzZo1Y+LEiUrTE8vJzs6mT58+TJo0iQEDBijtmzJlCgMHDsTR0ZEePXpw7NgxFb2vv/46vXv3ZsKECYq01atX89NPP/Htt98qnfPrr78mPT2dJUuWMGTIEH777bdaXzipVMqcOXPYe+AQ2hIJLV8dRKtXB6rNmxwXxcmNy0lLiMXC3pEuIyZi41Qxa+BO8AmCd24gLzONRt6+dBv1GfrGpgCUlpZy4Z8/CD9zmNKSYrw6v0b7gR+goak8obKkuJjt8z7FuVV7/N8aoaIhMeY2gQsn8973f2BiZVdtvR7FRXF8wzJS42OxsG9M91ETsXWqfobDlSOBXD74N7KCPNzadKbru5+go6s8Vl1UKGPbnAl0HT6ehk1bKNKzUh9xfP0yEiKuYWhmQYeB7+Pu36XastSRFBvF0T+XkhIfi6VDY3q9NxG7Ju7V5r98KJBLB3cgzc/Dw78zPUaMV+hNT0rg2PoVJETeRM/QmFa93sL/9bcBOLhmMTeDjqqcr5FnC4ZMW1wnzU+CREebc1u+ZNKiHZy5HPnMy6uKrrYmC4e15PXWDhQUFvPrkUhWH1Wvo6mDCd+92wofR3Nik3P4ZttVzkUkK/a/19WZCa95YKKvw8nwJL7cGEpGXmG96pVoazKnvxevNrejoLCY30/d5fdTsWrzrnqvNT2b2Sqlffx7CCduyTUP7+DI6G7OmOjrcCYimW/+vklmfv3qLefWrXDmz5lFVOQdXFxc+WbWHLy8m1Wbf9uWzfzx+1qys7Po0DGAGbPmYmpmBsD9e/eYP3cW166GYe/QkM8mTaFzl671qvduVAS/L/uW+7FRNGzszIcTp+Hs5qk2b0FBPht+/ZFLZ09SWlpC2049GDFmEnr6BgCkpTxi/a8/cjMsBImuLu279GLI+58gkejWi1ZdbU0WDG1Bn1b2FBSWsPpoJKuPRanN29TehG/faYmPoxmxyTnM3H6Nc3dSANCXaDFncHP6tLRHQxP2XU5gzj/XyZMW14vOp+VFfbB32rRpzJ49m1GjRmFkZMSnn37KK6+8AkBAQIDK7++z5pktOZCZmcnYsWNJTU19ouOPHz+OtbU1oaGh3L9/X2X/4cOHGTVqFE2bNmXDhg1s27YNd3d3Ro4cyeXLl1XyL168mEePHqmkX7hwgaSkJDp06FCjHl9fX65fv66UFhwcjI2NjcpS7WFhYfj7+yORSBg+fDiLF9f+x6989dN+Xyyiy/AJXNqzmaiQMyr5CqUF7Fs6A3u3ZgyesRw7Fy/2LZ1JobQAgKSYCE78uYQ2fd9l4PSfkeZlc2zdjxUajwRyJ/gEvSfM4LXxM7hz4QRhRwJVyrly+G9S78eo1VpcVMTJ9UsoLVVdL6Oq1t0/z8DBrRnvzFqBvasXe36eodBalciQM1zYtYnuoyYy4IvvSIy+RdD235TyFBXKOLRqEakJcUrpJcXF7Pl5BlpaWgyb/Qu+rw3m8JrvSYmPrVFjZWTSfAJ//IaGHj6MmPMLDm5eBP40A5k0X23+O5fOcG7XRnq99xlvf/U9D6Nvc+ovud7SkhICf5yBvokpI+f9Sq/3P+PCni3cOnccgO7DP2Hcsm2KbdjMpWjp6ND6lX611vuk6Eq02fDte3i72j/zsqpjxiAfWjQ2Z/BPZ5i2OYwpb3jyemsHlXzG+tr89Xkn7jzIpvucoxwITWDduHZYGst/+Pr6NWTmIB9mbb/Km9+dxMHCgIXDVF+enpav3vCgWSNTRqy6yKzAcD7t5cZrzdW/LLjaGjF581XazTmm2M7ekT8P+7SwY+obTVmw5xaDV5yngbk+swd41btekL+AThg7mta+fmzdHkiLVq2YMG5MtSsmHzp4gJ9//J4vpk5j/eZtPHz4kIXz5TEhUqmUMR+/j66uLhu3bue9Dz7kyymTuH7tWr3pLSjI5/sZn9G0WUsWrtiIu1dzvp/xOQUF6u+/Db/+SEzkLaZ9u5yvF60kOuImG1f/DMhfDpfM/wqZtIBZP65h4rQFhF44w471q+pN7zcDmtGisTlvLwli+tYwJr3elNdbqd5TxnrabJ3YkTsPs+g5/xgHwx7w25i2WBrLA6LnDG5Oi8ZmvLP8LEOWBNHKyZxZA33qTefToqFRf1td0NfX57vvvuPKlSucOXOG9957T7EvIiKiWocpIiKCtm3bPkWN1fNMnKaQkBAGDBjwVMuY79u3j549e+Lu7s6uXbuU9uXk5DBz5kzGjRvHpEmT8PDwwMXFhWnTptG1a1cVJyUkJIQLFy5gbW2tUs7KlSt55513HqvHz89PyWmSyWSEhoby4YcfKjlN2dnZREdH4+/vD8Cbb77J8ePHSUhIeGwZ5auffv3111g3dsO5dUdavzaI68f3qOSNvHgKbR1dOrz9ERb2jgS8MxaJnj5Rl04DcP34Hlz9OtG0Q0+sGjnT86Mvibt+iazkRACu/bsL/7dGYO/WjIZNW9B+0Acq5WQkPeDav7sxt3dUq/fKoR1I9AweW687wafQlkgIGPIxFvaOdB42Fh09fSLLtFYl7OguWr3SH+eW7bBz9qD7qM8IDzqicLJSE+L4a95nZCY/UDk29tpFstOSeWX0l5g3aIRPt9dxat6Gh1Hhj9VZTsQFud4uQz/G0sGRbu+OQ6Knz52Lqs4rQOiRnbR+pT8urdrRwNmDXu99xo3ThymUFpCblY6Nowu9Rk3E3M4B5xb+NPZqRfydGwDoGhhiaGah2M4FbsCjTWfcfOt3HL4qTZ3tOLXhfzRpZPVMy6kJfYkWwwKaMOOvq1y/l8HBsAesPHyHD7q5qOR9u31jcqVFTN0cSmxyLj/svcXdRzm0aGwOwITX3Flx6A77Qx8Q8SCLeX9fx9PBBM16fDnWl2jxdttGzN99i5sJWRy9kcSaEzGM6Kh6f0i0NGlooc+1+xmkZMsUm6xY/oIxppsza07EcPh6EpGJOXy37zYeDYzrVW85hw8dQFdPl8n/+xJnFxe+/OprDA0NOXr4kNr8f/y+lvc//Jier7yKm5s7k//3JZGRdyguLub0yRNkpKezYNFiXF3deLNvP97o25dNG/6sN73nTx1FItHl3Y8/w8GxCSPHTkFf35Dg0/+qza+to8P747/E2c2TJm5N6fpqXyJuXgXgwf04Im9dZ8yUmTRycqGpTysGjRzD2ROH60WrvkSLdzo6MXP7NW7cz+TQ1Yf8ejSS97o6q+Qd3N6RXGkR07aGEZucy4/7bnP3US4tHOVtuLC4hK+3ye+FG/cz2XY+Dn/X+h1aEjw9T+w0LV++HA8PD5Vt586dBAUFMXDgQJYvX/5E587MzCQoKAg/Pz+6devGrl27KC0tVew/fvw4OTk5jBw5UuXYqVOnMn/+fMX/MpmMGTNmMHPmTJUpjjExMYSGhtKli/qhm40bN+Ln58etW7do06YNqampCufn2rVrGBsbM3DgQB49ekRcnLzH4+rVqxgbG9O0aVNAPrWyQ4cO/PXXX4+tt7rVTxu4eZMUE0FpldVPk2Ju08DNW9FlqqGhQQNXLxJjbgHyYbMG7hVvKcYW1hhbWJMYc4vc9FRy0pKx9/BRKic79RG5GRU9gyc3LMP/reGKIb3KZCTGc/34XjoMGf3YeiXG3MK+ilZ7N28eRt1SyVtSUkzS3Ts4VNLewMWT4qJCUsp6vBIirtHQswVvf71E5fj429do5NUSXX1DRdqbE2fj07XPY3WW8zD6Fg7uzZT0Orh580CN41VSUkxizB0aNa3Qa+8q15t8LwYjM0venPA1En0DSktLSbhzk/iI6zTybKFyrribV4iPuE7A4PdV9tU3nXxdOX3pDl1H/fj4zM8I74am6GhpEBJd0eaCo1Jo1cRC5W20g7s1h8MeUFLxGKD3whMcv5GIkZ42Po7mHLhS8WJyITKFbnP+Vcr/tDRtYIy2pgahsemKtMux6bRwNFPR28TGkFLgfppq74iRrjbeDU05fD1RkXYpJp0+PwTVq95yrl+9SqvWvkrtuWWr1ly9GqaSNycnh9u3wunRs5cizdevDYG796GlpUV8/H2cmjhjbGys2O/u7sE1Ned6UqJuXcfDu6WSXnfv5kTeuq42/wcTpuLhLb+fkhMfcPbEYTybtwbAzMKSrxYsw8xc2fnIy82pF62KNhxT0YYvRqXSykm1Dbd3s+bItYdK1/j1705y/GYSAF9vu0pITBoADS0M6N+mEefLhu7+C2hqaNTb9jLzxDFNH3zwAUOHDlX8v3LlSoKCgujZs6fihoqPj3+icx85cgQtLS06dOiAtbU1q1atIiQkhDZt2gBy58LZ2RkjIyOVY6vGIq1atQovLy8CAgJU8p45c4bmzZurPc+hQ4f46aefWLt2LZ6e8rF0BwcHrl27hoODA8HBwfj7+yscpODgYBo3bkxYWBi+vr5Kkf0dO3Zky5YtTJ48ucZ6q1v9VN/EnOJCGQW5WegbmynS8zLTsLBXDnzTNzEnLSFWvj8jDUMzS5X9Oekp5GbKb8zK+w1M5G87OekpGJpZcivoCMVFMrw69+ZO8Aml85SWlnJiw1L83xqOgYkZjyM3Iw0LB2WtBiZmKkNrANK8XIoLZRiaWyjSNLW00DMyISdd/gBp3v3NasvKTH6IiZUtQTt+5/a5Y+gbm9Cu30hcWtc8/FqZnIw0rByclPWamqsd4pPm5lJUKFOypaaWFvpGJmSnKz/w1kweQXbqI5xbtsW9jWp7vLhvG96dXsHE0qbWWp+UtTuCnnkZj8PWTI+0HBmFxRW/IilZUvQlWlgYSkjNqVifxdHakCuxaSwe3ppXWjTgfmouc3Zc51J0Ko2t5A6ypZEuu7/sgqOVIafDHzHjr6tk1WOMkI2JLum5hcp6s6Xo6WhhbiAhLbdCr4uNIdkFRfzwTnPauljwMKOApUciOX07hUaW+gBYGEn4a3w7GlroczYyhXm7bpFdUFRvestJTk7GxdVVKc3C0pLoKNXYsfiyUIi0tDRGvTuUhIR42rXvyJfTvsbExARLSytSUpIpLS1VODWJiYmkp6ernOtJyUhLoWFj5Z4aUzNL7sdF13jcysWzOfPvfqxt7Rn47kcAGBoZ08KvvSJPSUkJR/Zsp1nLNvWi1cZUtQ0nZ8vbsLmhhLRKbbixlQFhsel8N6wlrzRvQHxaHnP+vq5wlMpZMsqXwe0cuZeSy88HbteLzvrgJfd16o0n7mkyNDTE2toaa2trwsPDCQwMZOnSpUpvIE/K/v376dChA/r6+vj4+GBnZ8fOnTsV+7Ozs9U6OlWJiopi27ZtTJs2Te3+8PBwXFxUhwJCQkKYNm0aP//8s2KaI8iH6K6Vjd0HBwcrxkvbtm2rGKIrj2eqjIuLC7dv36a4uOaAPnWrn2rp6ABQXKj88C+UStHU1lHJW1wkz1ckkyqOVezX1qGksJAimVTxf+V9AMVFheRlZXD+nz/oOmKi2uC/W2cOUVJcjFfn3jXWR6FVJlUqq0Kr6reGisqG4LS0q9hBW0fFBmrLkuYTHnQUaW4OfT+bg2eHnuz/ZR5Jd+/USitUbzt15RfKChT1Uc2vXL+3Pp1J/0lzSb4XzYnNyjEVGY8eci/8Kq16vVVrnS87+hJtZEXK94S0SN6jKtHWUko31NVmwmseJGXm8+6yIM7fSWHb5wHYm+tjqCd/9/t2WEt+OXyH0auD8bA3YfkH9fPDqNCro6UYXitHptCr/Ch1sTFCX0eLMxEpfLA2hFO3k1nzvi/NGppgIJHrnd3fmzUnYvh04xXcbI35cZhq72N9UFCgflVldd/6ysuTf+j02wVzef/Dj1n801KioyP5+qsvAejYqTM52dn8+styCmUybt64zs7Avymsxb1ZW6TSArR1lPXq6OhQpEZvZfq+PZK5S9ZhZWvHom8+U/ttsi2/LeNuVARD3h9XL1r1dbQUbaCc8jatW6VNGOhqM/5VNx5lFTB8xTnO30lh68SO2JvrK+X75fAd3vz+JPFpeWya0EE4K/8xnjqmKT4+ni+//JJp06bh5fX0gYzJyclcvHiRnj17AvKu2V69enHo0CHy8+Vd3WZmZmRlZdV4ntLSUr755hsmTpyIlZX6uI20tDTMzc1V0mfOnIlMJqNBgwZK6eVxTTKZTMk58vf3JzQ0lNLSUq5du6boESvHzMyMkpIStd/KqYy61U/Lf6i1Jcozx7R1dCgpKlTJq102I0RLR/VHvrhIvl/hiFU6vvxvHYkuQVt/xTOgF5YNnVQ05mamcWHn+modKoCL+7aycuxbiq1qWRVaVVdu1Sp7WFZ1qMq1Pw5NTXkvT/eRn2Lj5Ebr1wbRpEVbbpw6UO0xF/ZsZenHfRVbub6q5evoqpZf/nBXb2vl+tk5u+PSqh1dh43l2okDSja5c+kMNo2dsXJQnTb7f5WCwmIV56j8hyZfptzjUlxSyo37Gfyw9xY37meyIPAG0Uk5DGrnSFHZW/7yQxEcufqQS9GpTNlwmVdaNMDWtP5WB5YWlSDRUn5kShR6lZ2/Ff9G0XHecQJDErj9MJtlR6I4HZHC0HaNKC4bn1l9IoZj4Y8Ijc1g+o7rdPeywcbk6Wd0/bZmFe38Wik2UP3khEwmU7tycvkCgh98OJqu3XvQqrUvs+Ys4PSpEzx6lISlpSXf/fAz2//air9vC6b+bzLvDBuOkZGhyrlqy66tf/DeW50VG8gnelSmsLAQyWNWem7Y2Bk3Tx8mTl/IvbtR3L5+RWn/lt+Wc3DnNsZ/OZdGTq7VnKVuSIuKVRzm8jZdtU0Ul5Ry834mP+67zc34TBbuuknMoxwGtm2klC8yMZvQu+mM++0Sng6mtHN9cXGHlanPb8+9zDzVkgNSqZSJEyfSqVMnpaG6p+HgwYMUFxczY8YMZsyYAcgdoJKSEo4ePUrfvn3x9vZm3bp15OTkqPQ4hYSE8OeffzJt2jSuXLlCREQE3333HSDvyZk1axYHDhzgt99+Q0NDQ23vz+TJkwkNDWXu3Lls3rxZke7n58e3337LlStXMDMzw8nJSZGelJREcHAwxcXFKs5jeTzW4xpL5dVPy8nLSkdboouugfJDydDcirxM5W7dvKx0DMwsqt+fKd9vZGZV9n+aYpmAvEx597qBqYU8yFyiy7Vj8sDwYpmMxKhbRIWcofVrgynIzuLvhZ+XVw6AjV+Pps2b7+D/xjs07/o67m06K8oNObBdcf7KWgxNLaiKvpEJWjoS8jLTsWggD7AtKS6mICcLQzPV/FUxMLMADQ2lpRPM7RqSEn+32mNadH8dj7YVei/u264YwiwnNyNNbfn6RiZo60jIzUzH0r5Cb35OFkZmFuRmpvMgKlwpsNvSoTHFRYVI8/MwKIsXi70egmsdhhD/L5CYno+FkQQtTQ2FI2Ftqke+rEhl6n1SZj5RD5XjUGKSsrE3N+BRpry3LyoxW7EvOkn+t72FPkmZ6mdp1llvZgHmhjrKeo11yZcVk1WgrLe0FLLylR2/qKQc3OyMeJQt1xPzqKI+MY/kPTwNzPR4lCV9Kp2D3x7KK69W9AL/8ftaUlOUh4pTU1KwslYdBrYqmyzj5FwxPObUpAkgH4azsbGlU+cunDh9jpSUZCwtrdjx11bsHVRnPNaWnq8PoF3nnor/92xfT0a68qzrjPRUzC1UnYeiwkIuXziDT2t/DAzlvwVm5pYYG5uSnZWhyPfHL4v5d98/jJ86l7aduj+x1qokZhSotGEbE121bfhRZgFRSdlKaTFJOdib66OjpUGv5g04fesROWVDtCnZUtJzZVgYPf5zI8+DZzFJ4WXkqXqa5s6dS35+fr0uUX7gwAHat2/Prl27FNvu3btxdHRUzKLr1KkTxsbGbNq0SeX49evXk5iYiK2tLUeOHFE6j42NDRMnTmTBggUAWFpaqu396dmzJ1OnTuXGjRtKM/ecnZ3R19dn7969SkNw5XFNO3bswNfXFy0t5bfn9PR0tLW11fZqVaby6qflPIy8gY2Tu8r6SbbOTXkYfUvhkJWWlvIw6iZ2zvL4KzvnpjyMuqnIn52WTE56MnbOnhiaW2JkYcPDyJtK5RhZ2GBoZsm7C9cxZPavDJm1kiGzVmLt5IZ31z688dk8nFt3ZNiC3xT73vh8HgBvTZpP866vA6BnZIKZrYNia+DixcOocCWtD6JuYufSVMUGGpqa2DZx58GdStqiw9HU0saqkeqMlKo0cPYkNT6WkpIKZzjt4X1MrGyrPUbfyARzWwfFZu/qyYPIKnojw2ngorpOjIamJnbO7iSUzYYDeBAVjpaWNtaOzmQmJ7J72Vyy0yp+tJJiI9E3NlU4TKWlpSTG3MHB3fux9fu/xM34TAqLS/F1rnBG27paEhabTmmVgOjQmDS8GilPSHC1M+Z+ai7xaXk8TM/Hu6GZYp9bAxNKSkqJT33yGbxVufUgi6KSUlo6VpTj28Sc6/czVfR+N8SHb99Wni7uZW9CzKNcHqQXkJhZQNMGFZ+BcLU1oqSklIR09dPq64KpmRmOjRsrtuYtWxIWdkWpPYddCaV5C9XhwAYN7LG2seHO7YpYmrsx0fLJGw3siYmO5uMPRlFaWoq1tQ2ampqcOX2KNv5PPrXbyMQUO4dGis3Nqzl3wq8r6b1z8yquTVXXldLQ1ODXH2Zz5eJZRVrKo0SyszKwb+QEwN+b1nJs/z9MnL6ADl1feWKd6rhxX96GWzepaMP+LpaExWaotInLd9PwclDXhvMoKYUlI33p0axi+Qp7c30sDCVEJio7Wi8K0dMk54mdph07dnDgwAEWLlxIXl4eycnJJCcnk5395Bc4Pj6eK1euMHToUNzd3ZW2IUOGcP78eZKSkjA0NGT69OksX76cJUuWEB0dza1bt5gxYwYnT57km2++QVtbm8aNGytt2traWFpaYmsr/wH18vIiIiJCrRYHBwc++ugjFi9erFQnPz8/9u/fr7L+Q9u2bTl27JjK0BzI14vw9PR8bGOpvPpp0t0IYkLPEXb4H5r3lA9x5WamKeKRXP0CkOXlELR1FWkP4gjauooiaQGuZT08zbq+TsT544SfOUTK/RiO/bYYp+b+mFjLb8pm3V7n/N/rSLh9lYTbVzn/zx+0KCvHzNZeadOWSNAzNMbEyhaJvoHSPuOyoGUTKxv0jEyqVkmutU0A0rwcTm9ZRWpCHKe3rKJQKlUsOFkkkyr17DTv/iaXD+0gOvQciTERnNiwnGZdeqssbqkO93ZdywLVV5CRlMDV43uJu36JZrWMvwJw9+9EQV4OJzb/SkpCHCc2/0qhtEDRG1Uok5KbUaG3ZY83uXRgB5GXz/IwJoKjfy7Dp6tcr52zO7ZObhz+7UdSEuKIuXqRU9vW0q7vMMXxWSlJyArysLT//2doDuTDFzvOx/Hdu61o0dic11raM/YVd34rWxjQ2kQXPR35I2rDqbt4OZgy5U1PnKwN+aKvF42tDfkn+B4Aa/6N5Iu+XnT2tMGroSmL3m3FobAHJD9lr01lCgpLCAxJYN4gb3wamdLT24aPujThzzOxAFgZSxTDi8duPuKt1vb087WnsaUBE3q54tvEnA1B8skPf5yO5fNX3ejoZknTBsbMGejN0ZtJpGTXHLfzJPR65TWys7P4ftECoqOi+H7RAvLz8xW9UQUFBaQkyxfc1NDQYMTI91i5Yhnnz50l4vZt5s+dTbcePbGytsbewYGY6GhWrlhGfPx9Vv/6C1dCL/POMNWFb5+UtgHdycvJZsOqH4mPi2HDqh+RSvNp10U+o08mLSCj7CVES0ubHn3689cfK7l9I4yYyFssWzgd3/ZdaOTkQsK9u+zc/Dt9h4zCw7sFGWkpiq0+KCgs5u8L91g0rCUtGpvxaosGjOnlxu8n5EHrldvwxjN38XQwZfLrTXGyNuR/b3jiaGVAYPB9iktK2RR0l6/e8qKNiyU+jmas+sifw9cecufhf8NpEsh54uG5PXv2kJeXpzIs179/fxYtWvRE5zxw4ADm5uZ0767afTpgwACWLl3K7t27GT16NH379sXExIS1a9eyefNmNDQ08PHxYfPmzTRv3rxW5XXq1InvvvuO3NxcDA1Vx+Q//vhjRYD7N998A8gXuTx8+LBKsLe/vz/r1q1TSQf5t3I6d+6skq6O8tVPdy2eiq6+If5vjcDFVz7T6s/Jw+j+/mQ8A15Bom/I6xPncnLjMm6ePohlwya88dk8hWNh5+pF1xGfcnH3Rgpys3H0bk23UZ8rymn12iDyszI4+Ms8NDS18Or0Ki1eeTarqurqG9L387kcX7+c66cOYNWoCW9NqtB65+Ipjv7+I5/9IV87xaNtV7JSkji2finFhYW4+gUQ8PZHtS6r//++5cTG5Wz6ZgzGVrb0HjtdaaX02pxjwOR5HP1zGddOyPUOmDIfia48YDMi+BSH1v7A/zbIv3XUtF03MpOTOPrHUoqLCnHzC6DLkI8BeYxV/8/ncGzDCrbM/QwdXT1a9+qntHhlXpZ86FLX8PGTG/6vMXvHNRYNa8U/UzqTlV/ID3vCOXBFvv7WtR/e4LM/Qth+Po74tDzeWRrE/KEtmPCaB5EPsxmx/ByJGfKhrlVHI9HT0WL5B20w1NXm8NUHfLXlSk1FPxEL99xi7kBvNo31J7ugiKVHojhyQz5l/MKsHny57RqBIQkcuZHE7MCbjO/pir2ZHpFJOXzw2yVFT9Lvp+6iq63JD++0wEBXi2M3HzEz8GZNRT8xRkZGLP9lNfPnzuKfHdtxc/dgxao1GBjI11g7fPAAM7+ZxtWb8hfIke99gFQq5euvviQvL4+u3brz9czZgPzL8j8vW8G3C+ayccOfuLq6sXL1b/+PvfMOi+r4/vBL26X3IqggRVGwYY9i7yYaW+yaWBJLrDGxJdZorDH23nuLJnaNRlE0Yi+IoICgqCC9s0v7/bG6uO6CoGuM39+8PvM83rln7v3s5e69Z86cmcXRSXsLpBqbmPLDjN9Yv3Q2p4/+gbOrB+N+XoyhoeL794/fX6z6dQY7T1wBoEf/b9HR0WHxrAnIsrKo3aAJXw79HoCr//iRl5fLgR0bOLBjg8p5XrZ/V6btu8OcXtXZO7ohKVnZ/Hr4HsduKu7hm3PbMWbzNfZcesSThEx6Lb3Az92r8m3rCoRGp9Jv+T9Evxg+nvNnEPn5sPrrOhhL9Dh28ymT92hv0dB35SMPEGkNnfz814OI/7/o27cvXbp0oWPHju/l+BkZGTRq1Ig//vijRD8ls8S/8Byc/yL6H9mAt4Hex6V35JD3/5Mq2sSydtMPLaFEmJi/eZHW/xKBc4ofOf0vEBRV9MSd/xrt557+0BJKzJOVnd7r8T9brR0nE+DwYO3ObP03eW8/o/KxMHjwYHbt2vXejn/o0CGaNGlS4t/eEwgEAoFA8N/ivf5g7+vMmjWLffv2Fbp/8ODBDBky5F9UpPjBv3379uHv769xAcx3QS6Xs337dtavX6/V4woEAoFA8G/ykQ0mvDf+Vadp6NCh9OnTp9D9FhbqP9fxb7Bo0aL3clyJRMLBg+q/GycQCAQCwcfExz7rTVv8q06TtbU11tZvXmtHIBAIBAKB4L/Gv+o0CQQCgUAg+PgQgSYFwmkSCAQCgUBQJLrCawLE7DmBQCAQCASCYiEiTQKBQCAQCIpEBJoUCKdJIBAIBAJBkYjZcwqE0yQQCAQCgaBIhM+kQOQ0CQQCgUAgEBQDEWkSCAQCgUBQJGL2nALhNAkEAoFAICgS4TIpEMNzAoFAIBAIBMVARJoEAoFAIBAUiZg9p0A4Tf9RcvLyP7SEEpGUmfuhJZQIe9OP69a3rN30Q0soEUlXznxoCSUiy/uTDy2hRGTKP67vm7mxwYeWUCISgu58aAlvQaf3enRd4TMBYnhOIBAIBAKBoFh8XN1tgUAgEAgE/zpieE6BcJoEAoFAIBAUifCZFIjhOYFAIBAIBIJiICJNAoFAIBAIikQMzykQkSaBQCAQCARFoqujvVISZDIZkyZNolatWvj6+rJhw4ZCbc+ePcvnn3+Oj48P7du35/Tp0+/4qdURkSaBQCAQCARF8qEiTfPmzSMwMJDNmzfz9OlTxo8fj5OTE23atFGxCw4OZvjw4YwbN47GjRvj7+/PqFGj2LdvHxUrVtSaHuE0CQQCgUAg+M+RkZHB3r17Wbt2Ld7e3nh7e/PgwQO2b9+u5jQdPnyYevXq0a9fPwBcXFz4+++/OXbsmHCaBAKBQCAQ/Ht8iDhTcHAwOTk5+Pj4KOtq1qzJqlWryMvLQ1e3IMOoU6dOZGdnqx0jNTVVq5qE0yQQCAQCgaBIdLU4PCeXy5HL5Sp1EokEiUSiUhcbG4uVlZVKva2tLTKZjKSkJKytrZX17u7uKm0fPHjAP//8Q48ePbSmG0QiuEAgEAgEgn+R1atXU7NmTZWyevVqNbvMzEw1R+rl9utO16skJCQwYsQIatSoQfPmzbWqXUSaBAKBQCAQFIk288AHDx5M//79Veped44ApFKpmnP0ctvQ0FDjsePi4ujfvz/5+fksWbJEZQhPGwinSSAQCAQCQZFoc/acpqE4TTg4OJCYmEhOTg76+gp3JTY2FkNDQ8zNzdXsY2JilIngW7ZsURm+0xZadcGSkpKoX78+UVFRxW4zYcIEJkyYoHFfVFQUnp6exTpeQEAAnp6ehe7Pz89n+/btavULFy5k7969AHh6ehIQEKBm88033zBx4kSVusOHD+Pp6cnSpUtV6lesWMHnn38OQN++fQkNDX2j9pKQky3n7Kbf2DiyK1u/78Wtk78Xahv3KJQDv4xm/bcd2T9zJLGRDzTaXT+ykzMbflWpy0xJ4q9Vs9g4sgtbxvbi0r715OWW/JfVc7PlXNq+iD0/dOP3SX24d3p/obYJj8M4Pn8Mu8Z05ti80cQ/KtC7ffinGkt4gOo6HLnZ2RyeNYyY+7dLrPVVnkeGsnPGSJZ+04Ed00cQE6H52r3k+on9rBndi2VDOnJy/UKyZVnKfWmJcRxa9jMrvu3CmtG98Nu5mhwNoeWkmCcs+br9O+mW6uvya78aBC9qz8357RjcsnyhthVLm/PnuMaEL+vI31NbUN/TTmX/V03cuDqnLfcXd2DN4LpY/gd+qV5ioM/VvZNoWLPwz/U+kRrosmRQXSJWf8G9ZZ35tq3mWTmHfmxB4rbeamXp1/UAkOjrMqOnD4FLOvFwdVe2jm6Ek7XRv/Y5QoKDGNC3O40/qUH/3t0IDrpbpP2+3Tv4vG0zmvnWZtIPo0lOTnqv+sLuBzPmmz50blGP0V/34kFIULHa/b5jE/2/aKtxX25ODsP7d2P7hpXalIpUosfKce15dngc4fvHMKpbvUJtm9dyI2D9N8Qem8CRX/tQvqyNRrtxfXxZM6GDVnV+jFSqVAl9fX1u3ryprLt27RpVqlRRiyBlZGQwaNAgdHV12bZtGw4ODu9Fk9acpuTkZIYMGUJ8fLy2DomjoyP+/v44Ojq+87GuXLnCjBkzVOrCw8P566+/6NSpU5Fta9WqxZ07d1TqAgICsLe3V3Oybt68SZ06dQD49ttvmT59+jtrf5VLe9cRG/mAz8bOwbfXt1w7tJ3wa+fV7LJlWRxbMoVS5b3p/NMSHDy8OLZkqsrLHCA04CxXD25Ta3963Tzkmel0nLCQloMnEXbFj5vH95ZY7/UD64l/FEqLkb9Qp/swbh/bwaMb/mp2ObIszqycir27N23HL8LOrRJnV04j54Xezr9sVSleLbpgYm1PmaoFD6jcbDkXNs0l+VlkiXW+SrYsiwMLJ1O6QmV6TVuGo4cXfyycrHbtXvLgynku/bGNFl+NpOu4uTwLu8f5PesAhbN+eNlMcuQyuk36lXZDJxJ+8xIX929WOUZq/HP++G0KudmFj9MXh8ldq1DNxYovFp5n4vabjP2sEp/WKK1mZ2akz+7RDbn/NJVm0//i6PUnbBhaDxszKQAdapVhStcqTN1zi/Zzz1La2phfevmoHeffRCrRZ8vsr/D2cPpgGmb0rIGPqw0dZp/i+02XGde5Kh1ql1Wz67voHJ7f/q4svRb6IcvOZf2p+wBM7FKVT2uV5ZsVF2gz/SQGerpsGdXoX/kMmZkZfDdiCNV9arJp+16qVKvOdyOHkJmZodH+rxPHWLpoAaPGTmDtph1ERz9jweyZ701fVmYmU38Yjne1Gixat4NKlasxbdwIsjIzi2z37GkU2zeuKnT/77u28DD0vrblMntoS2p4OtJ2zBZG/3aUSV81plPjSmp2lcrZcWBuTw5fuE/9b9Zy80E0x3/ri4mRamekW3NvJvdvonWd74qOjvZKcTEyMqJjx45MmzaN27dvc+rUKTZs2KCMJsXGxpKVpXgur169mkePHjF37lzlvtjYWK3PntOK03T16lU6d+5MRobmL93boqenh52dHXp6eu98rPz8fLW6tWvX0qlTJ2XYrzBq1qxJWFgY6enpyrqAgAAGDhzIzZs3lX80gFu3bimdpnr16hEXF8fVq1ffWT8oXubB/ido0GMIdi4euNZoQPXWXxD49yE127ArfugZSKnXdRBWjs7U7z4YA0Mjwq8qHKy83FzOb1vK2c2/YW6n6pTmZssxMrfEt/dwrJxccKxQGdcavkSHFt0bfZ0cWRZh/5ykVpdvsC7rQdlq9fFq0ZUQv8NqtpHXz6FvIMGn00AsSjlTs8s3GBgaE3lDodfI3FpZcuVyQvwOUbfXSCRGJgAkP3vEiQXfkRoXXSKNmggJ8ENfIqFh96+xcXKmSa8hSIyMuH/5nEb7G3/9gU+rTrhVr0cpN09afDWKu+dPki3LIvHZY56F3aPVwLHYli5HGc8qfNKpH8GXzijbh167yPZpw9EzeLdIjpFEj16+rkzefYs7j5I4dvMpK07cZ0BTdzXbbp+4kC7LYfz260TEprPg0D0ePk+jmosVAMPbVGDZ8fscuf6UkKcp/LzvDpVKm5d4NV9tUdGtFH5bvse1rO2HEQAYS/Xo28SdCduucjsikSNXo1hyOIivW6lHuJPS5TxPzuJ5chZxKTImd6vGkiNB3HyYAEDPhm7M3HOLi8HPCXmawqj1l6jpboubg9l7/xynThxDKjVkxJgfcHVzZ8wPEzE2NuH0Xyc02m/dtI6+Xw2kWYtWuHuUZ8ToHwgLvU/uW0Sei8O5v08gkUoZOGwMzuXc+GbkOIyNjTl/5mSR7ZYvmIl7ec2Rv6dRjzi0byfO5dy0qtXY0ICvPvXh+6UnuPkgmoPnQ1i48yJDOtVWs/3m85pcCozi5w1nefA4nh9XnSI5XUaPFlUA0NPTYfF37Vg1vgPhTxO0qlMb6OroaK2UhIkTJ+Lt7c2XX37J9OnTGTFiBK1atQLA19eXo0ePAnDixAmysrL44osv8PX1VZZZs2Zp9zoU13Dp0qV4enqqlQMHDuDv70+XLl3UhqreldeH5xITExk+fDg+Pj40b96cnTt3qg3J7dy5k4YNG+Lj48PEiRORy+VERUUpPdOXQ3ApKSkcPXq00Mz6U6dOUbVqVc6fP0+VKlUwMDDg7l2F0xAdHc3Tp0/54osvMDMz4/r16wA8fPiQ5ORkatWqpTxOs2bN2Llzp1auR/zjcPJyc3BwL+jFlCrvzfOHIeTn5anYxoQHU8rDWzkOraOjQyl3L2LC7wGQLcskPiqCTpMW4eCm2ivSM5DQfNA4LOwVPfqEJ5FE3rqEk2fVEulNfKLQa/vK8e3dvIiPVNcb9zAYO3dVvXZulYh7GKx23FtHtuFQoRqOFQsiHzGhd3CoUJXWYxeUSKMmnoXdo3R5VS1OHt48C7unZpuXl0v0w/uU9qyirHN0r0RuTjaxj8MxtrCm09hZmFhYqbSTZxY44A9vBVC/85c06TX0nXR7l7HAQE+Hq2EF0d6A0Dh8XK3Venf1K9hx4uZT8l7pS7T95Qx/B0ZjaqhPFWcrjt54otx36UEcTaefUrH/N2lY04NzV+7T5Mtf32z8nqjsbIWBni6X78cp6y7df05Nd5sie8+9GrlhZSpl8SHFEJOODgxeeZEzgc/UbM3/hSHQwDu3qVa9hsr9XbV6DQJv31SzTU9L437wPZo0a6ms86lZix37DmqlM6uJ4Lu38a7qo6KvUuXqBN8tfMj99PFDyLKyaPVpR437l82fSa8Bg7GwtNK4/22p6u6AgZ4elwIfK+su3nlEba/SavdEOScrrtx7olJ3N/w5db3LAGBqJKGKmwONhqwn4G7xU1z+1zEyMmLu3LncuHGD8+fP89VXXyn3hYSE0LlzZwCOHz9OSEiIWpkzZ45W9RQ7EXzAgAEq6x2sWLECf39/WrRogZmZondUklymt+G7775DJpOxc+dOYmJi+PHHH9VsTpw4wfr164mNjWX48OFUrVqVbt26sXTpUkaMGIG/vz8WFhacO3cOS0tLtbUdAK5fv84PP/zAnDlzaNiwIQDVqlXj9u3b1KlTh0uXLlG5cmVMTEyoXbs2AQEB1K9fn5s3b1K+fHmsrAq+mA0aNGDMmDHk5+e/cyJdRnIChqYW6OkXPFiNzC3JzZaTlZ6CkZmliq21k4tKeyNzSxKfKoaupMamdJzw5hfQwfk/8Ox+ILYu5fFuWrJ8m8zkRKQm5ip6DV/olaWnYmhmUWCbkoiFo7NKe0MzS5JeG2pLT3hO5FU/Wo2dr1JfoeGnJdJWFOlJCdiUVr12xhaWxEepD/vJMtLJzZZjalmQcKirp4eRqTlpCXE4eXhRrkqBE52fl8fNUwcp61VdWddywBgAHt+79U66HSwNSUiTk51b4NnEpcgwkuhhbSIhPq1g6M/ZzoQbEQnM71ODVtUceRyfzvS9d7gSFo+LrSJ6Z2Mq5c9xjXG2NeFc0HMm775FSqb64nH/Bmv3qg/p/ts4WBoRnyojO7fA4Y9NzsJIoo+1qZT4VJnGdqM+82LV8WDSZTkA5OeD313ViOiQ1hWJS8ni7qOk96b/JXFxsbi5eajUWdvYEB6qnrf35InCGUhKTODrr3rz7EkUderVZ8y4iZiZqSfiaoPE+DicXVWfy5bWNkSGa84PTU5MYOPKxcxatIr799Sj4X8d+QO5XEab9l3w++uYVrWWsjElLjmD7JyCe+J5YjpGUgNszI2JS85QqXeyVY0klrE3JyFFMeyYnCaj2fCNWtWnTcTv9SoodqTJxMQEOzs77OzsCAoKYv/+/SxevFjpML1vHj58yMWLF5k7dy4VK1akcePGDB8+XM1u6tSpVKhQgQYNGlC/fn2Cg4PR09PDwkLxgrazs0MikRAUFKTRYQoPD2fo0KGMHz+edu3aKetr1arF7duKnk5AQAB169YFoE6dOsq8plfzmV7i7u5OUlIST56o9jDehhy5TMUBAZTbua+thJojl6H7uq2BAbk5JXvpNegxlPbfzyUvJ5vTa0vmsedkq2t4uZ2Xo6739c+mq2+gZhf2z0msnT2wLae9ZfFfJ0cuUxsq09M3ICdHPd/oZc6VnoFEzV7TtT6/Zx3PI0Np0KW/2r53xUiijzxHdchE9uJhLtFXjQqYSPUZ3saTmORMei/x55/7cewa7YuTlREmhoq+1Oxe1Vl+4j7frA7A08mcpQPUhxz+P2Es0UeeoxohlWUrtqUGmh+lvpUccLI2ZvOZwieEtK1RhuHtKjFjz00Vh+x9IcvKRCJRvb8lBhKN6968TLmYP2cmfb8ayKz5vxEeFsq0nzRP3tGOviwMXvv+GRhINK72DLBm6QJatO2Ai6uH2r6kxAQ2rVnK8B8mv5ffTjMyNECenaNSJ5MrtqUS1e/cvr/v0rmJF20/KY+eng69W1elZkUnJAbvJ2KnbXR0dLRWPmZKvORAVFQU48aNY+LEiXh5eb0PTRoJCQnB0tKSsmULki6rV6+uZufsXBCtMDMzK3QBrISEBJWI0EtmzZpFTk6OWvJ5rVq1+OOPPwCF0/Tzzz8DCqdpzpw5yOVybt68ydChqkMsL8+RkJBAmTJl3vxBi0DPQKL2In65rS9VXbNC30Ci5nDkZmejL5GW6Jw2ZRU5AE2+GsP+WaNIjYvBzLZ4sxL0NDg9L7f1XtOhyaHLy8lGz0DV7tGNC5T31Tw75m25fGgnlw/vUm6Xcquo5oTm5mRjIFFfF+Sls/R6Andujvq1Pr9nHddPHuDToZOwLVNOS+oLyMrOVXOOpPqKl3mmXPXBnpuXT+DjJBYcUgw5Bj5OprGXA13rOeMfHAvA0uMhnLylGEIau+Uap6a0wMHCkJhkzQnx/+sorq+qc/TSWcqQac7v+byOM6duPSUpXfNzqF3NMmwY7suakyFsPRumXcEv2LR+NZvXr1Fue1epilyuen/Ls+UYGqrP3tPXU7wi+n01iEZNmgEwaeoM+vXoQuzz59jZ27+zvt1b1rFn23rltmelKmoOUna2HKmGdXmuBVwk+O5tRo6fovHYqxfPo2Xbzynnpu5QaQOZPAeJgeprVCpRbGdkqX6Gvy6HMWuTHztnfIG+ni5+NyLYfuI2FiYleyZ/KMRK2ApK5DTJZDJGjhxJw4YNtb40+ZvQ19fXmMz9Oq+PsxfWRkdHR2MiY48ePTAwMGDmzJl88sknyrUkfHx8eP78OXfu3OH58+fUqFEDgPLly2NmZsaVK1cIDQ1VizTlvcjd0YZ3bWJpQ1ZaMnm5uei++JwZyYnoS6RIXyREv8TY0oaM5ESVusyURIwt3rxuhTwznUeBV3Gv2RCdF9M6LV8MnWWlJRfbaTK2tEGWnqKiNzMlET0DqTKBW2lrYUNWyut6kzB6RW96YizJ0Y9UZsxpg6pNP6VCnYKZS1eO7FG7dunJiZhYql87I1Nz9AwkpCcnYu2kuEZ5ublkpqWo2J/ZupxbZw7T9pvxlK/dUKv6XxKdmIm1qQQ9XR1yXyQf2VkYkinPIfm1YbWY5ExCn6Wp1IXHpOJkZczzF05RaHTBrJOwGMX/nayN/t86Tc8SM7Axk6pcX3sLIzJkOSRnaHaKmld1ZO7+Oxr3da7nwqoh9dn49wN+3H79venu1LU7zVsW/Ljp1k3riY+PU7FJiIvD1k49yd7GVlHn4uqqrHN2Ufw/JuaZVpymdh2/oGGzVsrtfds3kviavqSEOKxt1PWdO32cuOfR9GrfFIDc3FxysrPp0uoTps9fzrnTx5FKDTm0X5FXKpfJuBd4C/8zf7Fya+HLnxSXp7Gp2FoYo6enQ+6LYXEHa1MysrJJSlP/nszb5s+i3f9gYSIlNimDbdO6EBmd/M46BP8eJXIeZ8yYQWZmptrU/X8Dd3d3kpOTefy4IOEuMDCw2O1fd1psbGxISkpSs2vZsiXffvstmZmZrFlT0DszNjamUqVK7N69mypVqmBkZKQ8bu3atdm/fz/lypVTW0wrMVHx8rWzU10D522wKeuGrp6+MpkbIDr0Lnblyiudm5c4uFUkJuye0mnMz88nOjQIe7c3D2vlyGWcXjOHmIchyrq4yFB0dHWxcFCfvl4YVmUUeuMiCpK5Y8OCsHFR12vrWpHYcFW9seFBKsNw8REhGFvZYWL97g/qVzE0NcfSobSyOHp48TQ0SEXL0wd3cXRXv3Y6urqUcq3A0wcFuRRPQ4PQ1dPH7kWU7p8/tnH77BHaDZ2EZ70mWtX+KnejksnOzaemW8E9WNfDhpsRibzed7genoBXWQuVOo9SZjyOTycqIYNniZl4l7FU7ivvaE5eXj5R8dqdIfsxcScykezcPGp7FLy863nacSM8Xu36AlibSnF1MOPS/Vi1fY28HVg1pD5r/wph/BbtzK4tDAsLS8o6uyhLlarVuHPrhsr9ffvWdSpXqabWtpSjE3Z29oTeL3gWRDwMR0dHB0dH7Sz9YGZugVMZZ2WpWLka9wJvqegLunOLil5V1Nr2HzqalVv3s3TDbpZu2E2fAUOxtrVj6YbdlK/oxdqdB1m2aY9yv4enF20//4Lp85dpRfut0Giyc3Op61UwilC/SlmuBT9Vuye6Nfdm/vBWyLNziU3KwFCiTyOfcvjdiNCKlveNGJ5TUGynae/evRw9epRffvmFjIwMra6BEBMTw7lz51RKRESEio2rqyu+vr5MmjSJ4OBgLly4wJIlS4p9jpdOTmBgIDKZDC8vL+7f17xmh6mpKd999x1r165VSW6vXbs2R44cUYsm1alTh9OnT1O7tnrOR0hICLa2tlpZaMtAakiFT1pwftsynj8M4eGNi9w++TuVm3cEFMnfOXJFMqpbTV9kmWlc3L2axKeRXNy9mhx5Fu613rwWjLGFNa41GnBhxwriHoXy7H4gflsWUblZB7UIUVHoSwxxrdOcy7uWEx95n8e3/uHe6f14NlEs2paZUqDXubov8sx0rv2+huRnj7j2+xpy5Fm41CiIyiQ9jcSilPqaONqmfG1fZBlpnN2xivgnkZzdsYpsmYwKdRoDCqcyPalgSnC1Zu25emwvodcuEh0ewt9bllKlcVsMpIbEP31EwMHt1G7XndLlvUlPSlAWbZMpz2XvP5HM7e1DNRcr2lR3YkirCqw7rcinsTOXYvhiOGmL30O8Slswtn0lytmZ8EMHL1zsTPg94BEAa0494IcOXjSqZI9XGQvm9Pbh+M2nxKZoTnb+/0CmPJdd5x+ysH8dfNysaVezDCPaVWLVCYVDYW9hiOEr+SmVylqQKc8hMlY1oqenq8Oyrz/hQnAMiw8HYW9hqCwGeu9/EKRZi9akpaby2/zZPAwL5bf5s8nMzKR5K0U0Kisri/g4haOno6NDjz79WLNyKQGXLvIgJJh5v0ynUdPm2Ni+e0dQE75NWpCelsqaJfN49DCMNUvmkZWZScNmrQGQybJIeBGJsrSyVnG4LKys0dPTw6mMM1Kpoco+RZ0UM3Nz7Etpx+HLlOWw7fgtlnz3KTUrOtHe15PR3T9h+T5FnquDtQmGL4brHjxOYFCHWnzesCLupa3ZPKUzUc9TOBFQ9MK5/xV0dbRXPmaK/Q09ePAgGRkZ9OjRgwYNGmh1DYSLFy/y9ddfq5QDBw6o2c2ePRtjY2O6devGtGnT6Ny5s1rCYGF4enrSoEEDevTogZ+fH3Xr1iUlJYWHDx9qtO/UqRMVKlRg5syCRdxq1qxJRkaGMgn8JXXq1CEzM1PNmQLF6qW+vr5a864/6fY1di4eHPp1Av47VlCzQx/cajQAYOv3vQm7olhLSGJkQtsR04l+EMjvM0fyPDyYtiNnYCBVzwvQROMvx2BT1pUjv/3IyZU/41ylDnW7DCix3ppdBmFd1oNTiydyZc9Kqn7aG+fqCr37J/Ul8rpCr4GRMU2GTOV56F2OzRtF3MNgmg6drpKrlZWahMTYtMQaSorUyISOY2bw9H4g26cNJzrsHp2++1l57UIC/FgzuqfS3rNeE2p/2oPTmxezf/5ESrlVpGG3QQCEXf+H/Lw8Ag7tYM3onirlfTBt721uRybx+9hG/NKzOgsOBnH0xlMAbi/4jA61FE5nVEIGPRf706qqI2emtaRlVUf6Lr1IdJJiSGHVXw/YeCaMpQNqc3BcEyKepzF68/uNiHwM/Lj9GjcjEjg4qQXzv6zN7N9vc/iqIvodsrwLneoVzLq0NzciOUM9ednH1ZqytiY0qexIyPIuKqVOhfe/DpWJqSkLlqzg5o1rfNX7CwLv3GLh0lUYGRkDcOrkMT5t2Vhp36tvf7p27830n8bzTf/elCnjzORp2l375lWMTUyZOncJd2/dYNSgXgTfvcP0+cswfNHxPXf6BH07tnhv5y8p45ef5Mb9Zxz/rR+LRrdl5kY//jyviK5HHBhL12beANy4/4yRvx1hzrctubj2awA6j9+pMUop+O+ik1+cRKH/AJmZmVy8eJFGjRopHaVjx44xf/58/v7777c65oQJEyhTpozGWXjaID8/nxYtWjB37lyVtZuKw8Jz4e9F0/siTfb+Z/1oE3vTj+tnF6dvvPahJZSIpCtn3mz0H8LQ+5MPLaFEhK/5d3NK35VXl7v4GKjyxfw3G/3HyPTTnAyvLb47qL5m3tuysMP7m/38vvloEuKlUimTJk1i+fLlPH78mBs3brB8+XJat2791sccNGgQBw8eLHQq67ty4cIF7O3tS+wwCQQCgUDwX0LkNCl4r93tWbNmsW/fvkL3Dx48mCFDhhTrWLq6uixfvpx58+axceNGTE1N6dChA2PGjHlrfR4eHrRs2ZL9+/fTvXv3tz5OYaxcuZJp06Zp/bgCgUAgEAj+fd6r0zR06FD69OlT6P6XC04Wl1q1arFnz553laXCDz/8oNXjvcr27dvf27EFAoFAIPi3+NgTuLXFe3WarK2t1abgCwQCgUAg+Lj4yEfVtMZHk9MkEAgEAoFA8CH5uKYQCQQCgUAg+NfRFaEmQDhNAoFAIBAI3oAYllIgnCaBQCAQCARFIgJNCoTzKBAIBAKBQFAMRKRJIBAIBAJBkYicJgXCaRIIBAKBQFAkwmdSIIbnBAKBQCAQCIqBiDQJBAKBQCAoErEiuALhNAkEAoFAICgSkdOkQAzPCQQCgUAgEBQDEWn6j6L/kcVCJXofl96PrddkYm78oSWUiCzvTz60hBKRdfefDy2hRCRndvnQEkpEbIrsQ0soEZae3h9awn+Oj+yR+d4QTpNAIBAIBIIi+cj68e8NMTwnEAgEAoFAUAxEpEkgEAgEAkGR6CBCTSCcJoFAIBAIBG9ADM8pEE6TQCAQCASCIhFOkwKR0yQQCAQCgUBQDESkSSAQCAQCQZHoiDUHAOE0CQQCgUAgeANieE6BGJ4TCAQCgUDwn0QmkzFp0iRq1aqFr68vGzZsKNQ2KCiIL774gmrVqtGlSxcCAwO1rkc4TQKBQCAQCIpER0d7pSTMmzePwMBANm/ezNSpU1m2bBnHjx9Xs8vIyOCbb76hVq1a7N+/Hx8fHwYPHkxGRoaWroAC4TQJBAKBQCAoEl0dHa2V4pKRkcHevXv58ccf8fb2pmXLlgwaNIjt27er2R49ehSpVMq4ceNwd3fnxx9/xMTERKOD9S4Ip0kgEAgEAsF/juDgYHJycvDx8VHW1axZk1u3bpGXl6die+vWLWrWrKlMWNfR0aFGjRrcvHlTq5pEIrhAIBAIBIIi0WYiuFwuRy6Xq9RJJBIkEolKXWxsLFZWVir1tra2yGQykpKSsLa2VrH18PBQaW9jY8ODBw+0J5z36DQlJSXRrl079uzZQ5kyZYrVZunSpSxbtky5rauri7m5Oc2aNWPMmDHY29uzf/9+li1bxt9//601rWPHjqVLly44OzvTvHlzTp8+rab5008/pW3btgwfPlxZt3r1ahYuXMjs2bPp3Lmzsv7HH38kMTGRRYsW0b17d9atW4eNjc1bacvJlnNu2zLCrl1AXyKheuuu+LTW/AvnsZGhnN26lIQnEVg7OdO470jsy5VX7r8fcIaAA1vISE6grHdNmn45CiMzC5Vj5Ofnc2jhj5Sv24RKvq3UzpGXm8uen0fg5vMJdT7vWyz9l3atIPLGBfQMJFRu2YXKLTprtI1/HMbFHUtJfBKJlZMzn/Qcjq1LeTW7h9fOc3bdbPqvPKqsi7x5kb9Xz1Sxc/FpQLNvfnyjRk3ERIZyatNi4qIisCntQssvR+LgWqFQ+2sn9nP16F5kWRl41mlEsz7fYiA1BCA1IY4z21fw+N4t9A0keNZtjG/XAei/eBBEhdzhzPaVJDx7jFWp0jTu8Q0u3jXeSrdEX5fpnbxoXbUUWdm5rPd7yHq/CI22q76qQYvKDip1X6+/ypl7sQD0qe/MN03dMDcy4HxILD/tu0tyZvZb6SoMqYEu87+sTYfazmRm57LsSBDLjwWr2R36sQW+lRzU6rf5hTFi7SUk+rr89EU1On9SDhOpHv73njN+yxWeJmRqVW9JkRjoc3HHOMbM2cv5a9p9eJeE0JB7LJk/k4iwUFxc3Rk57ifKV/R6Y7u92zdx6PfdbNl/TFn3NOoxS+bPJPjubRxKOTFg6CjqNmikVb2RYSFsXjaXqMhQSju78eW34ylXvpJG2+xsOb9vWUWA30lkWVlUrFqDPkPGYm3roNy/c+0iLp09ib6BPo1adaBLv6Fam0Iv1dfll17V+bRGabKyc1l58gGr/9L8t65Y2py5vX2o4mxFRGwaP+26xcWQWOX+r5q4MbyNJ+ZGBpwNimHc1uskZWj3O/e2aHPFgdWrV6u86wGGDx/OiBEjVOoyMzPVHKmX2687XYXZvm73rryX4bnk5GSGDBlCfHx8idv6+Pjg7++Pv78/fn5+rFu3jjt37vD999+/B6Vw6dIlYmJiqF+/fpF2NWvW5M6dOyp1AQEB2NvbExAQoFJ/8+ZN6tSpg0QioU+fPsyfP/+t9V3cs47nEQ/o+MMcGvcZzpWD2wm9el7NLluWxeHFk3EqX5kvJi+llLsXhxdPIVuWBUBMeAhnNi2idofedJn0G7KMVE5v+FXlGPl5eZzfsZLHQdcL1XPjxD7iH4cXW//V/euJi3xA69Gz+aTnt9w8sp2I6/4a9f+1bAoOHpXpMHExdm6VOLVimlL/S2QZaQTsWaXWPunZI8pWqUv3OduUpUGfUcXWqaolkwO//kTpClXoM305Th5e7P9tMtkyzS/g+1fO888fW2nRfxTdxs/jWWgw53avA144oct+Jkcuo/ukX/l02CTCbl7iwv5NAGSkJPLHoilUrNeEL2etwbNOY/5YNJXUhFiN53oTEz7zpHJZC/quuszU/UGMaFmeNlVLabT1cDDlu+23qDf9tLJcuK/4zrarVorxn1Vk1sF7fLHsHxytjJjW+c0v2ZIyo2cNfFxt6DD7FN9vusy4zlXpULusml3fRefw/PZ3Zem10A9Zdi7rT90HYGKXqnxaqyzfrLhAm+knMdDTZcso7b7IS4pUos+W2V/h7eH0QXVkZWYw+fvhVK5Wg2Ubd+JVpRqTvx9OVmbRCbLPnkSxdf1KlTq5TMbE0YORSqUsWrOVL3p/xS9TxhEcdKeQo5QcWVYmC6eOoYJ3NaYt2oxHpSosnPYdsizN378D29Zy/R8/Bv8wgx8XrCE3J4elsyaQn58PwPbVC7l74zLf/7yYIT/8jN/xPzl7/IDW9E7uWoVqLlZ8sfA8E7ffZOxnlfi0Rmk1OzMjfXaPbsj9p6k0m/4XR68/YcPQetiYSQHoUKsMU7pWYeqeW7Sfe5bS1sb80stH7Tj/CwwePJhr166plMGDB6vZSaVSNafn5bahoWGxbF+3e1e07jRdvXqVzp07v3XGuoGBAXZ2dtjZ2WFvb0+VKlUYOnQoAQEBJCcna1ktrFixgp49e77RrlatWipOk1wu5/r16wwcOFDFaUpNTSUsLIw6deoA0L59e/7++2+ePHlSYm3ZsiyCzh+nYc8h2LmUx61GA2q06cqdvw+q2T647Ie+gZT63QZh7eSMb88hSAyNCL1yDoA7fx/Eo1ZDKtZvgW1ZN1oMGkfknSukxEYDkJYYx58LJvDw5iWkxqYa9STFPOX2qT+xcnIutv77F05Qt9tgbJ09cKlen8otu3Lv7CE124fXzqFnIKV254FYOjpT94vB6EuNiLiu6iBe3b8eMztHdW3Rj7FycsHYwlpZCvscbyI4wA99iYTGPb7GxsmZpr2HIjE0IuSyurMKcP3kAWq06oR79XqUcvOkRf9RBJ4/QbYsi4Rnj3kWdo/Wg77Htkw5ynhWoX6nLwn+5wwAT+7fRUdXj9rtumFp70jd9j3RN5DwLOxeiXUbSfToVrcsM/+8x90nKfwVGMOaM+H0baD+95Lo6VLG2ojbj5OIS5UrizxXkScwuKkba86Ec+JODA+i05h7OBhPRzOthuiNpXr0beLOhG1XuR2RyJGrUSw5HMTXrTzVbJPS5TxPzuJ5chZxKTImd6vGkiNB3HyYAEDPhm7M3HOLi8HPCXmawqj1l6jpboubg5n2BJeAim6l8NvyPa5lbT/I+V/F7/QJJFIpXw//DudybgwZPQ4jYxPO/f1Xke2WzPsZjwoVVeoCLpwjJSmJcVNmUc7NgxZt29O8zWcc2LVNa3oDzv2FRCKl+8CRODm70uub7zAyNuby+dMa7f1PHaZLvyFUrFKD0s5u9B85iYf3g4h5+pi01GTOnzxI/5GTcPP0xqt6bVp36kV4yF2taDWS6NHL15XJu29x51ESx24+ZcWJ+wxo6q5m2+0TF9JlOYzffp2I2HQWHLrHw+dpVHOxAmB4mwosO36fI9efEvI0hZ/33aFSafP/zPpIuuhorUgkEkxNTVXK61EiAAcHBxITE8nJyVHWxcbGYmhoiLm5uZptXFycSl1cXBz29vZavg5vwdKlS/H09FQrBw4cwN/fny5durB06VKtidTT00NHRwcDAwOV+oCAADw9VR+wEyZMYMKECcrtgwcP0qJFC6pVq8bYsWP57rvvlNrCw8O5fv06jRs31njerVu3UqtWLe7du0ft2rWJj49XOj+3b9/GzMyMLl268Pz5cyIjIwFFMpqZmRkVKyoeNhKJhPr167N79+4Sf+64x+Hk5eZQyqOgh+9Y3puY8BDyX0uCiwkPxrG8t0oSnKOHF9HhipdvdHgwjhWqKO3NrO0ws7ZT7o+NDMXU2o5uU5YiMTLWqOfsliXU+byP2pBeYSREKfTbuxWE1R08vImNUNcf+zAYBw8vFf0O7l48Dy8Yqom+f4dn9+9QrU13tXMlPXuEuYN67+5teBZ6j9LlK6toKV3em2ehQWq2eXm5xDy8TxnPgmvr5F6J3JxsYh+HY2JhTZfvf8HEwkqlnSwzHQAjU3Oy0lJ4cNWf/Px8Hly7gDwrE9syriXWXdHRDH1dHa5HJCrrrkUkUs3ZUi207mpvQj7wWMPwlalUH+8yFpy4E62suxKeSLsF/uTll1hWoVR2tsJAT5fL9wsedJfuP6emu02RQwG9GrlhZSpl8SHF30NHBwavvMiZwGdqtubGBmp1/wYNa3pw7sp9mnz565uN3zP3Au/gXdVH5X72rlqde4G3Cm3z17FDyGRZtP6sk0r9s6dRlHEph4lpgTPq6l6Be4G3taY3LDiQ8t7VVPR6VKpGWLB6NCsvL4/B30/H26eO2r7MjHQe3L2FkYkpFasUDHd/1u1LBo6erBWt3mUsMNDT4WpYwahKQGgcPq7Wavdw/Qp2nLj5VOU71PaXM/wdGI2poT5VnK04eqOgc33pQRxNp5/S6nfuXfgQSw5UqlQJfX19lWTua9euUaVKFXR1Vd2XatWqcePGDWWEMT8/n+vXr1OtWjVtfHwlb+U0DRgwQDmE5u/vT69evXB2dqZFixaMHj2aYcOGoaenpxWBERERrFmzhk8++QRjY80v88K4evUqkyZNYtCgQezfvx8jIyOOHi3Igzl//jxVq1bF1FQ9InH8+HEWLlzIqlWrqFSpEo6OjpQuXZrbtxUPh4CAAOrUqaN0kF5Gm27evEnNmjVV/qANGjTg/HnNUYqiyEhKwNDUAj39gge/kbkVudlystJTVG2TEzCxVM2bMjK3Ij0hTnksTfvTEhX7XavXo8WgHwp1iO75nyQ3R45Xo7bF1p+ZrEG/mSW52XJk6alqtsYWqvoMzS3JSFLoy83O5sL2JXzSYxh6EqmKXX5+PikxUTwJus7vUwexb/IArh7YSG7O2+UCpCcnYGKlqsXY3IrUxDg1W1lGOjnZcpVrq6unh5GpOakJcRiamFKuSq0CrXl53Dz1J85eirB7ac8qVG/egYPLfua3AW05uGQ6LfuPxtpRfYjqTdibS0lMzyY7t+ApG5cqw9BADytj1V6cu70JqVk5LOhZlYtTmvL7yE9oVFERFSlrYwSAtamE3d/W48LkpszrUQUzQ+2mQDpYGhGfKiM7t8CBjk3Owkiij7WptNB2oz7zYtXxYNJlit5nfj743Y0mKb0gND+kdUXiUrK4+yhJq5qLy9q9/oz7dT+ZWR8+HyUhPhYbWzuVOksra+KeP9don5SYwIYVixg5brJa3o+VtQ0J8XHKFxNA7PNokpMTXz/MW5OUGI+ltapeC0trEuLU9erq6uLtUwfTV55bJ//cjZm5JWXLefA8+gm29o5cOH2UCYO78cOATvy5c73azKu3xcHSkIQ0uep3LkWGkUQPaxPV75yznQnxaTLm96nBrfmfcnhCE2q7K54bLrYmANiYSvlzXGNuzGvH4q9qYW70YZx+TejqaK8UFyMjIzp27Mi0adO4ffs2p06dYsOGDfTr1w9QRJ2yshQpHG3atCElJYVZs2YRGhrKrFmzyMzMpG3b4r+zinUd3qaRiYmJcggtKCiI/fv3s3jxYszM3j0UfvXqVXx8fPDx8aFy5cq0adMGY2NjZs6c+ebGr7Fz507atWtHjx49cHd3Z9q0aZQqVZDfERQUhLu7ehj16tWrTJw4kd9++41atQpeeLVq1VJxmurWrQtA3bp1VZyml0NzL3F3dyc4OJjc3NwS6c+RZ6k4HAB6L6JtudmqD+NsmQxdDbYvHYccuUzZVrlf34C87Dc/1DNSkvjn94006TuyRMmTOdkydPVVX7QvP8/rDk2OXKb+WfUNlJ/z5tEd2Dh7UNpLPUE6PeG5sn2TQROp3WUQYVfOcGX/+mJrfaMWAwO1aw4oc670NVzb3Gz1BES/3et4HhmKb9evFO2zMkmOfUb9jn3pPXUpddv35My2FcQ/fVRi3UYGesrhtZfIcxTbEn3Vr7q7vSlGBnqcD4ljwNqr+AXHsqZ/TSqXMcdYovibTevkzZoz4YzYeoPyDmb82ku7PTZjib5S30tk2YptqYHmR5NvJQecrI3ZfCa00OO2rVGG4e0qMWPPTRWH7P8rsqwsDF4b+jCQSJBruD8BVi+eT8t2HSjn5qG2r3a9BmSkpbF1/Uqys7O5f+8uJw4dIKcYz5HiIpdlqY0q6BsYFOsc1//x4/j+7XT9ahj6BgbIsjKJefqYM8cOMGj0ZLoPHMmpg3s48cdOrWg1kugjz1F9rsuU3znVwIGJVJ/hbTyJSc6k9xJ//rkfx67RvjhZGWHyokMyu1d1lp+4zzerA/B0MmfpgNpa0fkxM3HiRLy9vfnyyy+ZPn06I0aMoFUrxSQlX19fZSDE1NSU1atXc+3aNTp37sytW7dYs2ZNiYMtb+Kduo5RUVGMGzeOiRMn4uWlnSTRypUrs2DBAkDRi7C2tsbExOStjhUSEkL37gVDOfr6+lSuXFm5nZCQQKVK6jMypkyZQm5uLo6OqrkztWrV4uDBg8jlcm7evMmMGTMAqFOnDjNmzCA/P5/bt28zapRqArKlpSV5eXkkJSWVaBadnoFEzbl4+eLWl6gmt+kbGJCnwVb/RVRG00s/N6dgf1H471xJJd+W2JQpV2ztAHr6EvJeGYt+eU6FftXzavysL/QlPongvv9xOk5eofE8pjYO9FqwG4mxKTo6OtiUdSc/L49zmxZQp+vX6OoWHfUMOLSTgEMFD1FH94oar7uma6VvoHgZvf5Az83JVs6ee8m53eu4fnI/nw37UTn8duXoHvLz8/mkYx8AHMqV51l4MDdO/kGLr0YWqft1ZDl5SPRUnY2XzlKmXPXBvuxUKJv9I0jJVPx9gp+lUrmMBT3qlWXfZcUQweoz4ZwOUvTuJ+29w6HvfLE3l/I8RVYiXYWRlZ2r5sy9dJYyZJo7GJ/XcebUracqUaVXaVezDBuG+7LmZAhbz4ZpRefHxs7N69i1ZZ1yu6JXFbJfS5DNLiRB9uqlC9wLvM3oiVM1HtvS2oaJM+ayYOZkdm5ai4NjaT7v2pMDe9QXGywuh3Zv4vCeTcptN09vsl/7PuVkZyORFp3Qe+0fP1bO+ZEW7b+gcevPAdDV1SMzI50h42Zga694nsfHRvP3kd9p27n3W2t+ieIeVn2+SJXfudeefXn5BD5OYsEhRUpE4ONkGns50LWeM/7BiokfS4+HcPKWYph57JZrnJrSAgcLQ2KSVSfEfAhKsiilNjEyMmLu3LnMnTtXbV9ISIjKdtWqVTlwQHtJ/pp4a6dJJpMxcuRIGjZsSI8ePbQmyNDQEBcXl2LZaop65OTkoP8iuqGnp6cSRgZUtnV0dDRGf7777juuX7/OjBkzVFYerVWrFrNnz+bGjRtYWlpSrlw5ZX1MTAwBAQHk5uaqOZAvz1nSKa4mVrZkpSWTl5uL7ovhzoyURPQlUqTGJmq2GckJKnUZKYkYW1oXvj+5YH9RPLjsh75Eyu3TigT0XLmc6NB7hF49T6+f1xTaztjSRk1/ZkoiegZSJEYmaraZKaoh/szkRIwsrIm4cQFZRir7pgwEUOZDbR3dmfq9RuBepylSE9Uop6WjM7nZcuTpaRi+IQeratNPqVCnYKbVlSN71K5VenICphqulZGpOfoGEjKSE7F5kSCfl5tLZloKJq/Yn966nFt/H6Ld4PFUqN1QWR8T8QA7Z9Vop72LB/FREUVq1kR0chZWJgbo6eqQ+yIRws5MSqY8l5TXhony81E6TC8JjUmjfClTnqcqHtDhz9OU+8KfK3KwHC0NteY0PUvMwMZMqqLX3sKIDFkOyRmanaLmVR2Zu1/zTK3O9VxYNaQ+G/9+wI/bC58B+r/Op52+oFHzguVC9mzdSGKC6kzmxIR4rG3Uk9TPnjpO7PNourVrAkBubi452dl83rweM39dQZXqNahTvyG7j5whIT4OK2sbjhzYi4Pj288QbNquE3UaNlduH9m3leREVb3JSfFYWhfe4bzkd5K1v06jSdvO9PpmjLLe0toWA4lU6TABOJZx0TjU9zZEJ2ZibSpR/c5ZGJIpz1FbniMmOZPQZ2kqdeExqThZGfP8hVMUGl2QthAWo/i/k7XRf8Jp+kA+03+Ot549N2PGDDIzM5XRlg/ByxBuWlrBjRgVFaX8v4eHB3fvFsySyM3N5d69gllJNjY2JCUlqR23RYsWjB8/nsDAQP744w9lvZubG0ZGRhw6dEhlCO5lXtPevXupWbOmWj5XYmIi+vr6WFmpJgO/Cduybujq6SuTtQGePQjEvlwFdF5LgnNwq8izsHsqSXDPQu9S6kUSdim3ijwLLbgWqQmxpCXGKvcXRe9fNtB92kq6T11B96krsCtXHu8m7fhs1M9FtrN5oT/2YUEyd0zYXWzLlVfTb+dakefhQSr6n4cHYe/qiVfTDnSeuobPJy3j80nLlEsJfD5pGc5V6/Ik6Bo7vu9OjrzgwRL/OAypifkbHSZQOD5WDqWVxdGjEk8fqGp5+iAIR3f1a6Wjq4uDawWe3C/4YcinoUHo6eljV9YNgIsHtnL7zGE+GzaJivWaqrQ3sbQh/kmkSl3C08eY22leJqAo7j1NIScvn+rOlsq6mq5W3HmczGt9B+Z2r8LsblVU6ryczAl/ns7TxCyik7Oo6FgwO8XDwZS8vHyeJGpv3aM7kYlk5+ZR26Pg5V3P044b4fFqegGsTaW4Ophx6b76cgyNvB1YNaQ+a/8KYfyWq1rT+DFibm5B6TLOyuJVpSpBd26q3M93b9+kondVtbaDho1mzY4DrNi8hxWb99Bv0DBsbO1YsXkPFSp58SginPEjviY/Px8bWzt0dXW5fPE81Wq8/TCSqZkFDk5llcWjYmVC791W0fsg6DbuFStrbB908wprf51G88++oO9Q1aVp3CtWJlsuI/pJwXD308cRKk7Uu3A3Kpns3HxquhV0kOp62HAzIlHtHr4enoBXWdXnkUcpMx7HpxOVkMGzxEy8y1gq95V3NCcvL5+oeO3+dprg3Xgrp2nv3r0cPXqUX375hYyMDGJjY4mNjSU1NfXNjbVI+fLlMTQ0ZNWqVTx+/Jh169YRFFQww6lPnz4cOXKEvXv3Eh4ezi+//MKTJ0+UER8vLy+18N5LSpcuzaBBg5g/f77K56pVqxZHjhxR5jO9pG7dupw+fZratdUfHiEhIVSqVKnEkSYDqSEV67fAb8tSYh6GEH79IjdP/E7VForQc3pyAjlyRa/fo5Yv8ow0/HeuIuFpJP47V5Ejy8KjtiKCUrnJp4T88zdB548T9zic0+vmU65qnWK9nC0dnFSKvkSCoYkZ5rbqiw2+ir7EEI96zbm4YxmxEfeJvHmRwL/249VUoT/jFf3lfHyRZ6QTsHc1Sc8eEbB3NTmyLMrVbITUxAxzeydlMX6RdG1u74SBoTH2bpXQM5BwYetikqOjiAq8wtX9G6jSSvMioG+iQu2GZGWkcWb7SuKfRHJm+0qyZVl41lVcy2y5jPSkgkhU9ebtuXJsLw+uXSA6PIRTm5dQpUlbDKSGxD99xKWD26nzaXdKl69MelKCsgBUadyWh7cvc+347yQ9f8a1E/uJuHOV6s3al1h3VnYe+68+4eeu3lQpa0ELb3sGNXZl0/kIAGzNJMqhg9N3n/N5DSc61nTCxcaY4S09qOlqxRZ/hQO38VwEo1uXp0F5Gyo6mjG9izd/3Y0hLlV7C8VlynPZdf4hC/vXwcfNmnY1yzCiXSVWnVB8J+0tDDE0KOiAVCprQaY8h8hY1d66nq4Oy77+hAvBMSw+HIS9haGyGOi9db/wfwbfpi1JS01l1aJ5RD4MY9WieciyMmn8Iholk2WREK+Y5GBpbaPicFlaWaOnp0/pMs5IpYY4ODrxKCKcretWEP00iu0bVxN4+wafd33zsi3FpbZvMzLS09ixZiFPHoWzY81CZFmZ1GnYAlDkPCW9iJzl5uawftFMPCvXoF3XfiQlxCtLTnY2jmVcqFa7AesWzuBR+H3uXLvEkb1baPap5gV2S0qmPJe9/0Qyt7cP1VysaFPdiSGtKrDutCLnzs5ciuGLIectfg/xKm3B2PaVKGdnwg8dvHCxM+H3AIVDt+bUA37o4EWjSvZ4lbFgTm8fjt98SqyWIrvvyof47bn/Im81PHfw4EEyMjLUhuU6derEnDlztCKsOJiamvLzzz/z22+/sXXrVlq2bEnv3r1JTFQM8/j4+DB16lSWL19OYmIibdq0wcfHRxmhatiwIXPnziU9PV1j3tTXX3+tTHL/6aefAMUilydOnFBL9q5Tpw4bNmxQqwfFFMlGjd5uob0G3b/Bb9sy/pg/HqmRCXU+74t7TV8ANn3Xi2b9v6OSbyskRiZ8OnIGZ7cu4e65Y9iUceWzUT8r82pKeXjRpO8ILv+5laz0VJy9a9D0y9Fvpakk1On6NRd3LOf4oglIDE3w+awP5XwaALB7Qh98+42h/CctkRgZ0+LbaVzcsYz7/sexKl2OlsNnqOUFacLA0JhWI2Zyee9qDs4ZhYGhEZ6+bancsutbaZYamdBpzM+c2ryEO2ePYlvWlU5jZ2IgVcwqCwnw48S6BYzdfBKAivWakhwXw6lNi8nNyaZ8LV8adfsagNDrF8nPy+PSwR1cOrhD5TxjN5/EyaMSHUZM5eKBzVzYvxmrUmXoPHYmtiXMH3vJLwfvMaOLN9uG1CE1K4fFJ0M5GRgDwKWpzRm36zb7rz7hZGAM0/bf5dsWHjhZGvIgJo0B664oI0nr/R4i1ddlQc9qGEv1OH33OVP2a2dtm1f5cfs1fu1fh4OTWpCSkc3s329z+OpjAEKWd2HY6n/YeV6xmKq9uRHJGlZH9nG1pqytCWVtTQhZruoofzbrLy7c085QzMeKiYkpMxYsZem8mRz983dcPcrz84JlGL5YWsTv1Al+nTWFExcLX4LgJVKpIVNm/8byhbP5fddWyrl5MGvhCuxLaSdyA2BkbMqYqb+yeflczh7/k7LlPPhu+m9IDRXfv4Bzp1i/6Gc2HQng4YN7xMdGEx8bzei+7VSOM372CipVrcngH2awbdUCfhk3GIlUSovPutKifTet6Z229zZzevnw+9hGpGRms+BgEEdvPAXg9oLPGLXxKnv+iSQqIYOei/2Z2aMaw9t48uBZKn2XXiQ6SREhX/XXAwwN9Fg6oDYmUn1O3HrKhB03tKbzXfnIfR2toZP/etLP/xC3b9/G1NQUNzc3Zd2nn37KwIEDlT970rdvX7p06ULHjh3fi4aMjAwaNWrEH3/8UeyfkwFY4v/wveh5X2TIP65ZStbGH9fPLs7bp75G1H+Z+OiENxv9h8i6+8+HllAi7p1a8KEllIhniR8+J6ckdJ6neSHN/zLP1rxdZL24bLhS8hm9hTGgdvEWSP4v8j8du75x4waDBw/m+vXrPH78mFWrVvHs2TMaNixIxB08eDC7du16bxoOHTpEkyZNSuQwCQQCgUDwX0JXi+Vj5l/rbs+aNYt9+/YVun/w4MEMGTJEq+fs3bs3UVFRjBgxgtTUVCpVqsTatWuxsytYOM3X15d9+/bh7++Pr6+vVs8vl8vZvn0769e/3XpBAoFAIBD8F9DWDxx/7Pxrw3MJCQlFJopbWFhgaWn5b0j5KBDDc+8XMTz3fhHDc+8XMTz3fhHDc+psfpFrqA2+rFXyXzz4r/CvvTmsra2xtn7zmkACgUAgEAj+W4g4k4KPq7stEAgEAoHgX+djXypAWwinSSAQCAQCQZEIl0nBx57ILhAIBAKBQPCvICJNAoFAIBAIikSMzikQTpNAIBAIBIIiEUsOKBDDcwKBQCAQCATFQESaBAKBQCAQFImIsCgQTpNAIBAIBIIiEcNzCoTzKBAIBAKBQFAMRKRJIBAIBAJBkYg4kwLhNAkEAoFAICgSMTynQDhN/1Fy8v6V31HWGibSj2ukNzo1+0NLKBGBc9p+aAklIlOe+6EllIjkzPf7Y6faplKL7z+0hBLRYfTADy2hRNxc0OFDSxD8RxFOk0AgEAgEgiL5uLrF7w/hNAkEAoFAICgSMTynQDhNAoFAIBAIikS4TApExE0gEAgEAoGgGIhIk0AgEAgEgiIRo3MKhNMkEAgEAoGgSHTFAB0ghucEAoFAIBAIioWINAkEAoFAICgSMTynQDhNAoFAIBAIikRHDM8BYnhOIBAIBAKBoFiISJNAIBAIBIIiEcNzCkSkSSAQCAQCQZHooqO1oi3y8/NZsGAB9erVo06dOsybN4+8vLxC7W/evEmPHj3w8fGhdevW7N27t8TnFJEmgUAgEAgEHx0bN27k8OHDLFu2jJycHH744QdsbGwYOFD9B6JjY2P5+uuv6dmzJ3PmzOHu3btMnDgROzs7mjRpUuxzCqfpIyMnW47/9uU8vH4BfYmEqq26UK2V5l9oj3sUyvlty0h4EoGVozMN+47AzqW8mt31IztJjnlK0wFjAXgacptDC8ZrPGavOZsxs7F/o87YyFDObllK/JMIrJ2cadJvJPbl1M/9kpsnD3Dj+D7kmRl41G5Io97DMJAaKj+z39ZlhF1TfGaf1l3xaVPwmVPjn3N2y1KehNzGxNKaep37U75OI0DRE7l+bC+BZ4+QlZaKg2sFGvUaio5VGY06crPlXNmzgkc3L6JnIMGreWcqNe+s0TbhcRiXdy0j6WkkFo7O1OnxLTbOBZ/x0Q1/bh7cTEZyPHZuXtTtNRJTa8W1e3zrIufWzlI5XtnqDWg0aNIbr21xuHcviJnTpxL64D7u7h78NHU6Xt6VC7XftWM7G9evJTU1hfoNfJk8dQYWlpYKrY8eMXPGVG7fuolT6TKMGjOWRo2baEVnYYQEBzF31nTCQh/g5ubB+B+nUtHLu1D7fbt3sHXTOlJTU6n3SQPG/zQNCwvL96YvNOQeS+bPJCIsFBdXd0aO+4nyFb3e2G7v9k0c+n03W/YfU9Y9jXrMkvkzCb57G4dSTgwYOoq6DRq9N+1FITHQ5+KOcYyZs5fz1x58EA0GujoMqFeWui4WyHPyOXz3OYeDnmu09SltTo8ajpQykxKTKmf3zadce5wCwO4vfTS2WX4+knPhCVrVfD/kHr/OnkF46ANc3dwZO3EKnpU036+pKcl82ryBSp2FhSWHTvkDsGfHFpb9Nk9lf/feX/Lt6B+0qrmk/BeH57Zs2cLIkSOpVasWAN9//z2LFy/W6DSdOnUKW1tbvvvuOwDKlStHQEAAhw4dKpHTpNXhuaSkJOrXr09UVFSJ2l24cIEePXpQrVo1atasyaBBgwgMDNRoe/XqVZo3b65x39ixY7l48aLGfUuXLqVv374l0lUUeXl5bN68mQ4dOlCtWjWaNm3KzJkzSUpKUtr89ttv7NmzR2vnBLi0dx2xkQ/4bOwcfHt9y7VD2wm/dl7NLluWxbElUyhV3pvOPy3BwcOLY0umki3LUrELDTjL1YPbVOoc3CvRd8F2lVKqfGXKVf+kWA5TtiyLQ4sm41ihMt2mLKWUhxeHF01RO7dSw1V/Lv+5jSb9RtJx3BxiwoO5uHe9cv+FPet4HvGAjuPm0LjPcC4f3E7oVcVnzsvN5dCiKejq6dF96jJ82nTlr7XziI+KAODu2aPcOP47jXoNo9uUJZjbOnBo0WRy5Jq1XD+wnvhHobQY+Qt1ug/j9rEdPLrhr2aXI8vizMqp2Lt703b8IuzcKnF25TRyXnzG2PAg/DfOo1LzzrQbvwQ9fQMubJirbJ/87BGlK9eh8y9blaVer5FvvLbFISMjg+FDvqFGzVrs3LOfaj4+DB86mIyMDI32x48d5bdf5/HD+Ils3r6LZ8+e8cvMGQDIZDIGf90fqVTK1p17+GrAQMaNHcOd27e1olUTmZkZfDdiCNV9arJp+16qVKvOdyOHkJmpWf9fJ46xdNECRo2dwNpNO4iOfsaC2TPfm76szAwmfz+cytVqsGzjTryqVGPy98PJKkTfS549iWLr+pUqdXKZjImjByOVSlm0Zitf9P6KX6aMIzjoznvTXxhSiT5bZn+Ft4fTv37uV+lTqzTuNsb8fCKU9QGP6VKtFHVdLNXsnK0MGdvUlTMPEhh3KJhT9+P4rrErLlZGAHyz+45K+fNODM/TZFx5nKRVvZmZGYwbNZSq1WuwdutuvKtWZ/zoYYXerxEPw7CwsOTAsbPKsmXPnwX7w8Po2LWHyv7+Xw/Tqua3QUdHe0UbxMTE8OzZM2rXrq2sq1mzJk+ePOH5c3Unu2HDhsyePVutPi0trUTn1ZrTlJyczJAhQ4iPjy9Ru8DAQIYNG0b79u05ePAgO3fuxMnJiX79+qk5XyEhIYwaNYr8/Hy141y6dImYmBjq16//Tp+juIwaNYrNmzczZMgQDh8+zJw5c7h+/TqDBg1CJpMBMHDgQFavXk1iYqJWzpktyyLY/wQNegzBzsUD1xoNqN76CwL/PqRmG3bFDz0DKfW6DsLK0Zn63QdjYGhE+CvOxvltSzm7+TfM7RxV2urpG2BsYa0sT4Nvk/Akgkb9RhVL54PLfugbSGnQbRDWTs407DkEA0MjQq+c02h/+9QfVGvZEdfqdXFw9aRJv5Hc8z9JtiyLbFkWQeeO07DXEOxdyuNeswE12nbl9umDAETevkxaQiwtv/4BK8eyVG7yKS5Va/MsNAiAexf+wqdNF1yr18WqVBka9x1BVloKsWH31HTkyLII++cktbp8g3VZD8pWq49Xi66E+B1Ws428fg59Awk+nQZiUcqZml2+wcDQmMgbiut77/R+XGs3pbxvW8wdylCr62AyUxLISksGIDnmMZZO5TAyt1YWibFpsa7vmzhx/ChSQynffT8ON3d3xk34ERMTE/46cVyj/cb1a+k/8GtatGpN+fIV+O77cTx4cJ/c3FzOnT1DUmIis+bMx8OjPO07dOSzDh3YtmWTVrRq4tSJY0ilhowY8wOubu6M+WEixsYmnP7rhEb7rZvW0fergTRr0Qp3j/KMGP0DYaEK/e8Dv9MnkEilfD38O5zLuTFk9DiMjE049/dfRbZbMu9nPCpUVKkLuHCOlKQkxk2ZRTk3D1q0bU/zNp9xYNe2Qo7yfqjoVgq/Ld/jWtb2Xz3v60j1dWlW3oZNl6N4mJDJlUfJHLobQ+uK6roauFoT+CyN48GxxKTKORkSx93oNOqVswQgOStHWST6urStZMeai4/JzC485+Vt+Puv40ilhgwb9T3lXN0ZOXYCRiYmnD11UqN95MNwyji7YGNrqyxW1jYF+yPC8SjvqbLf2MREq5rfBh0t/pPL5aSlpakUuVxeIj2xsbEA2NsXdORtbRX3SXR0tJp9mTJlqF69unI7Pj6eI0eO8Mknn5TovFpxmq5evUrnzp0L7ckWxaFDh2jQoAG9e/fGxcWFChUqMH36dOzs7Dh69KjSbteuXfTo0QMbGxuNx1mxYgU9e/Z8689QEg4ePMiZM2fYtGkT7dq1o2zZstStW5c1a9YQGhrKn38qeg3m5ub4+vqyY8cOrZw3/nE4ebk5OLhXUtaVKu/N84ch5L+W/BYTHkwpD290Xrj1Ojo6lHL3IiZc4SxkyzKJj4qg06RFOLhVojByc3K48udmarTrjpGZRbF0RocF41he9dyO5b2I1uCo5OXlEvPwPqU9qxR8JvdK5OZkE/c4nLgXn9nRo2Dow7G8NzHhis8cFXKbMl7VkRgVPFQ+HTGVyk3aAdCg2yA86zVV7tPR0SE/P5/srHQ1LYlPFOeyfeV62Lt5ER+pfn3jHgZj5676Ge3cKhH3MBiAmAd3KFu9wIE3tS1FxxkbMTRVXMPkZ48xs38/Pfo7t27hU6OmirbqPjW4deummm1aWhrB94Jo3qKlsq5mrdrs//Mwenp6REU9ppyrG2ZmZsr9FSp4clvDsbRF4J3bVKteQ0V/1eo1CLytfs70tDTuB9+jSbMC/T41a7Fj30H09PTei757gXfwruqjos+7anXuBd4qtM1fxw4hk2XR+rNOKvXPnkZRxqUcJqYF19fVvQL3At9fJE8TDWt6cO7KfZp8+eu/et7XcbEyQk9Xh5DYgu9ncEw65W1N1NKHz4XFs/P6U7VjGEvU/+7dqjty51kqd56lalsyQXduU7W66v1QpaoPgXc03w8RD8Mp61yu0ONFRoRT1sVF6zr/S6xevZqaNWuqlNWrV6vZZWVlERkZqbG89DckEonS/uX/3+SAZWVlMWLECGxtbenevXuJtBc7p2np0qUsW7ZMrX7OnDlERkbSpUsXPv30U1q1alUiAbq6uoSEhBAfH690iHR0dNiwYQMmr3jX586dY+7cuaSlpanpCA8P5/r166xYsUJZFxoayuTJkwkKCqJatWp4eHiotLl69Sq//PILoaGhuLi4MHz4cFq3bq3cv2nTJtavX096ejqdO3cmJCSETp060blzZw4cOEDLli1xdnZWOaatrS2bN2/G5ZUbvlmzZkyePJmhQ4eiq/tuPmpGcgKGphbo6Rso64zMLcnNlpOVnoKRmaWKrbWT6hfPyNySxKeRAEiNTek44c0PyPCr55BlpOPdtH2JdL5+bmNzK+KfRKjZyjLSyc2WY2JZ4Azr6ulhaGpOemIc6Ohi9NpnNja3UnzmtBRSYqMxt3Hg4t4NhPxzGkNTc+p27ItbDYXD4lRBNY8n6Nxx8vLysHNXzz/JTE5EamKuci7DF9dXlp6K4StOY2ZKIhaOqn9/QzNLkp5FIs9IQ56RRn5eLn8vm0zik3Bsy3lSu/swjC1tyc/PJ+V5FM/uXefuiT3k5+fh7ONL1U/7qJz7bYmNjcX9tfvd2saGsFD1HJWox48BSEhI4MvePXjyJIp6nzRg3MQfMTc3x8bGlri4WPLz85UvhejoaK1FTzURFxeLm5u6/nAN+p88UehPSkzg66968+xJFHXq1WfMuImYmZm/F30J8bG4uLqr1FlaWRMZHqbRPikxgQ0rFjF78Wru37urss/K2oaE+DiV6xv7PJrk5Pd3fTWxdq/6EPSHwNJIn1RZDrl5BaMJyVnZSPR1MZUq9r3kSbJMpW0ZS0MqO5rx1/04lXobEwN8Xa2YfOz+e9EcHxdLudfuVysbGx6Gac4Ji3wYTk5ODt982YO42OdUrV6D4d+Nx9bWjoT4OFKSkzl26E9mT/8JiVTKpx0606PPV8r740Ohq8XTDx48mP79+6vUver8vOTWrVv069dP4zF++EGR4yWXy5FKpcr/AxgZGRV67vT0dIYNG0ZERAQ7duwo0lYTxX6LDxgwAH9/f2Xp1asXzs7OtGjRgtGjRzNs2LC36tl17dqVhIQEmjZtytChQ9m6dSuPHj2idOnSWL5IRAVFJKkwh+z8+fNUrVoVU1PF8IZcLuebb76hbNmy7N+/n9atW7N7926lfWxsLIMHD6Zz584cOnSIQYMGMWHCBK5evQooIklLlixh0qRJ7N69m6ioKK5cuaJsHxwcTJUqVdBEtWrVVHTXq1ePuLg47t9/9y9sjlym9lJ9uZ2bna1mq/u6rYEBuTmqdm/i3vljVPRtjb5EWjKdBuo6X9f40vblfjX7nGxy5FkajwWQm5NNdlYm9y78hSwjlU9HTaNi/RYcWzGTmIfq1zs6LBj/3Wuo0aYLRubW6lqy1a/Zy+28HPXr+7pmXX0D8nKylXlNV/euplydpjQZMpXcnGzOrppOfl4e6Ymx5L5o7ztwAjU6DSTiyllu/LFBTdPbkJWVqfYAkkgkGntfGRmKHv3sWTPoP/Br5i9cTFjYA36cMA6ABg0bkZaaysrlS8mWy7kbeIcD+/eRreFvqS1kWZlIJKrXVmJQmH5Fb3P+nJn0/Wogs+b/RnhYKNN+mvAe9WVh8Nr1NZBIkGdr7t2uXjyflu06qL1YAWrXa0BGWhpb168kOzub+/fucuLQAXLe4/X9LyPV1yU7VzX94uW2gV7hb20zqR7fNXEl5HkaVx8lq+xr5mFDWHwGoXElHwkpDllZWRrv12y55r9hZORD0tPTGPHdOKb9soC4uFgmjPmW3NxcHkU8BBRO1+yFy+jz1SC2bljD3p1b34v2kqDN4TmJRIKpqalK0eQ01a1bl5CQEI2lfXtFR/7lMN2r/7ezs9P4GdLS0hg4cCAPHjxg8+bNlCtXrsTXodiRJhMTE2Xkx8/Pj/3797Nz506VsP3b4O7uzt69e1m1ahVnz57l77//ZubMmbRp04Y5c+YUywsMCgrC3b2g53fx4kWSkpKYNm0axsbGuLu7c/nyZRISFDMmtm/fTv369enTpw8ALi4u3Lt3j82bN1OrVi127NjBl19+Sdu2bQGYO3cujRs3Vh4/NTW12J9bKpVStmxZgoKCqFix4psbFIGegUTN6Xm5rf9iptlL9A0kai/63OzsEjk/mSlJPHtwlwY9i05CvH5kFzePFTilDm4V1Ryk3Jxs9KXq59Z/xQFSs5dIyc/L03gsAH2JoTIq1aTvCHR0dbF3Kc/TB4Hc9TuGg2sFZZtnoUEc+m0yLlVqU7djP5Ky1PPi9F44Pa/yclvvteumyQHNy8lGz0CKjp6iL+JevzVudZoB0ODLH/h9Um/iIoKxc/Oi69xdSIxN0dHRwbqMO/l5+VzcsoAanQehq1uyzse6NatYt6YgtF2lalU1B0Mul2NoaPh6U/T1FY+AAQO/oUkzxQSLqdNn0b1rR54/j8He3oG5C35j8o8TWLt6JaVLl6Fnrz5s37q5RBqLYtP61Wxev0a57V2lKvLXXjjybDmGhurPAn09hf5+Xw2iURPFtZ40dQb9enQh9vlz7OzfPHHhTezcvI5dW9Yptyt6VSH7teubXcj1vXrpAvcCbzN64lSNx7a0tmHijLksmDmZnZvW4uBYms+79uTAnu3vrPtjJDs3X805erkty9Gci2RhqM+PLT3QARaejeD1b3bdcpacConT1PSt2LpxDds2rlVuV/LWfL9KNdwPAFt2/4EOOsr9P89ZSKe2TQkKvE31mrU59Je/cuaqu0cFkhIT+eP33XTrpTni8v8VBwcHnJycuHbtGmXKKGZDX7t2DScnJ5U8p5fk5eUxfPhwoqKi2Lp1q4rPUBJKvORAVFQU48aNY+LEiXh5vXmKbXHw8PBgwYIF5OTkcOPGDY4cOcKePXuws7Pjp59+emP7hIQEKlUqyEMJDQ2lXLlyGBsbK+uqVKmCn58foBjOO3PmDD4+BVNSs7OzcXV1BRQJ5998841yn4WFhXIfgKWlJcnJqr2ZorC0tCxxgrwmTCxtyEpLJi83F90XUb2M5ET0JVKkRqqJgsaWNmS8FuLPTEnE2EI9wlIYj+9ew9zWAZsyrkXaeTX5FM+6BU7l9WN7yEhRndKbnpyIiYZzG5qao2cgIT05ASvHsoAiST0rLUWpNbOwz2xs8sJGB51Xhj6tSpUh7vFD5XZU8C2OLJ5KWe8atBo84YWtepKwsaUNsvQUlXNlpiSiZyBVyZkCMLawISvl9eubhJGFNVITC3T19DF3KFjWQGpqjtREMeRoB0hNVJ1ui1Jlyc2WI09PUxkGLA5fdOtBq9Ztldsb168lPk71JREfF4etnfqDxPZFj6ycm5uyrtyLez06Ohp7ewcaNmrMmXMXiYuLxcbGlr27d+JUunSJNBZFp67dad6yjXJ766b1xMer6k+Ii8PWTj0Z2OZF4qfLK99PZxfF/2NinmnFafq00xc0al4Q5d6zdSOJCarf58SEeKxt1PWdPXWc2OfRdGvXBIDc3FxysrP5vHk9Zv66girVa1CnfkN2HzlDQnwcVtY2HDmwFwfHDzuD7UORkJGNmVQfXR14OUJnaWSALCePDLn6d9bK2IAprRQRvBknHqgM3wHYGBtQ1tKIK4+L/7x+E5937k7TFgX3647N60l4/X6Nj8PGVnO043Xn38raBnMLS+JiFTO+LF4ZqQBwcXUjTsNssH+b/+KSAz179mTBggWUKlUKgF9//ZUBAwYo9yckJCCVSjExMWHfvn0EBASwcuVKzM3NlVEpAwMDldGhN1GiJBuZTMbIkSNp2LAhPXr0KEnTQpk7dy7BwYrkWX19fWrXrs20adMYMGAA//zzT7GOoaOjozZT5vUZdgavDPHk5OTQvn17/vjjD2U5cuQIq1atAkBPT0+t/avb3t7e3L2rmpvwkoULF7J5s2ovPC8v753zmQBsyrqhq6evTOYGiA69i1258ipOAyiiPTFh95S68/PziQ4Nwt6t+NGu5w9DcHAvfG2clxiamGHp4KQspdwr8Sz09XPf1ZhwrqOri4NrBZ49KLie0WH30NXTx7asG7YvPvOrSeTPHgRiX64COrq6lHKvSMKTCPLyCv7+Cc8eYW7rAEB8VARHlkzHuUot2gz9ET39wvsJVmUU54qLCFbWxYYFYeOifn1tXSsSG676GWPDg7AtVxFdPT2sy3qQ9KTAcctKS0aWloKpjT1Pg66xd1wPlWUPEqLCkZqYl9hhAsVD1tnFRVmqVq/OzZs3VLTdvHGdqtWqqbV1dHTCzt6e+8EFn/lheBg6Ojo4OToRHhbG1wO+JD8/Hzs7e3R1dTl/zo/adeqWWGeh+i0sKevsoixVqlbjzi1V/bdvXadyFXX9pRydsLOzJ/R+iLIu4mG4YvKBlhwPc3MLSpdxVhavKlUJunNTRd/d2zep6F1Vre2gYaNZs+MAKzbvYcXmPfQbNAwbWztWbN5DhUpePIoIZ/yIr8nPz8fG1g5dXV0uXzxPtRq11Y71/4GIhAxy8/Ipb1fQSfG0NyEsLl0tgiTV12VSC3fy82Ha8QckZubwOh52JsSlyYlP195wp7mFBWXKOiuLd9VqBL52PwTeuoF3ZfX7IT0tjXbN6nP96mVlXezzGJKTEnEu58rhP/bRu8tnKu+b0PvBOJcruuP6b6DN4TltMXDgQNq1a8fw4cMZNWoUn3/+OV999ZVyf9euXdmwQZH2cOLECfLy8hg8eDC+vr7KMmLEiBKds0Rv8hkzZpCZmcmMGTNKdJKi8Pf35/fff1erNzc3x9q6eFERGxsblfWRypcvT0REBKmpBTMl7t0reOm6uroSGRmJi4uLspw+fZpDhxRT9z08PFScorS0NCIjI5XbHTp04NSpUzx+kUT7kpiYGLZv364c8nhJYmKicirku2AgNaTCJy04v20Zzx+G8PDGRW6f/J3KzTsCigTslzlCbjV9kWWmcXH3ahKfRnJx92py5Fm41yr+onkJTyKwcnJ+s+FreNTyRZ6Rxvmdq0h4Esn5navIlmUpF5zMkctITy6IRFVu+hk3ju8j/PpFYh6GcHbrUrwbtcFAaoiB1JCKDVpwdutSYh6GEH79IjdO/E61lp8DUKFuE/Lz8/HbuoykmKfc+fsQj+5cxauxoid4ZssSzKxt8e0xmMy0ZNKTE0h/5Tq9ir7EENc6zbm8aznxkfd5fOsf7p3ej2eTDgBkphS0c67uizwznWu/ryH52SOu/b6GHHkWLjUaAlCpeSdCzh4k8vp5kqMfcWnbIqzKuGLj4omdWyX0JBIubV9CSkwUT+5e5cYfG6jUQvMipSWlZas2pKamMG/OLMJCQ5k3ZxaZmZnKaFRWVhZxL3pZOjo69O33FSuWLeGfixcICQ5m5oxpNG3eAls7O5xKlyY8LIwVy5YQFfWY1SuXc+P6NXr20t6aZ6/TrEVr0lJT+W3+bB6GhfLb/NlkZmbSvFUbpf74uAL9Pfr0Y83KpQRcusiDkGDm/TKdRk2bF9rTf1d8m7YkLTWVVYvmEfkwjFWL5iHLyqTxi2iUTJaljDxYWtuoOFyWVtbo6elTuowzUqkhDo5OPIoIZ+u6FUQ/jWL7xtUE3r7B513/nZnA/zXkufn4hSXwdb2yuNsYU6usBe29HTh2T/H3tjDUVw7XdazigIOZlOUXIpX7LAz1MTIoeK2VtTQkKlnzmmzaokmzVqSlprLk1zlEhIex5Nc5ZGZm0rSlYmKRLCtLGfk1MTWlavUaLFs4l3t37xASHMT0H3+gzie+uHtUoFbd+sTHx7J80QKiHj/i9Mmj7Ni8gV79BhQl4f8tenp6TJw4kStXrnDp0iW+//57lYT5v//+W+kUrV+/XmNu1NatJcsXK7bTtHfvXo4ePcovv/xCRkYGsbGxxMbGqjgmb8OwYcPYtm0bCxYsICQkhPDwcPbt28e6detUPMai8PLyIiSkoKdZv359HB0d+fHHHwkLC2P//v0qyxf06tWLwMBAfvvtNyIiIjh06BALFy7EyUnRM+3bty9btmzh5MmThIWFMWnSJDIyMpR/jHbt2lGnTh2+/PJLjh07xuPHj/Hz82PgwIG4u7vTtWtX5bnS0tJ48uQJ3t5vjtgUh0+6fY2diweHfp2A/44V1OzQB7caitVlt37fm7AXayFJjExoO2I60Q8C+X3mSJ6HB9N25AzlKtvFITMlCelbrB0kMTLhs1EzeHY/kN0zRhATFkz70T8rz/3gsh8bx/RS2leo24Sa7bpzZssS/lwwCQdXT+p3G6Tc79v9G+xcyvPHvPH4bVtOnc/74l7TV3muz8f+QuKzKHZOHsytv/6g9ZCJ2LuUJz05gejQIBKePmLz933ZOKaXskRe17xmVM0ug7Au68GpxRO5smclVT/tjXN1xfXdP6mvsp2BkTFNhkzleehdjs0bRdzDYJoOna7MLXP28aVml6+58ccGjs0dTX5eHo2/mYKOjg4GhsY0+/ZnZGnJHJs3moDti/Fo0AYvLTlNpqamLF2+muvXrtGzW2du37rFslVrlMPVJ44dpXkTX6V9v68G0KNXb36cMI4v+/SkbFlnZsxULAJnaGjIb0uWccH/HF06tuec31lWrF6Ho9P7Gz4yMTVlwZIV3Lxxja96f0HgnVssXLoKIyOF/lMnj/Fpy4Lh4F59+9O1e2+m/zSeb/r3pkwZZyZPm1XY4d9dn4kpMxYsJfDWdYb378m9u7f5ecEyDF/o8zt1gp7tNS/A+zpSqSFTZv/GlUsX+KZPFwIunGPWwhXYl3J8c+P/UbZciSI8PoMprT0YWK8Me28+4/KL5O413atQv5wVAHVdLJHq6/LLp56s6V5FWb6qUzAsbmGkT7qGYT1tYmJqypzflnP75nUG9etGUOBt5i1eqbxf//7rOJ3aNlHa/zhtFhUqVmLc6GGMGtKfUo5OTPl5DqCInM5btJK7d27Sv2dnVi9fzJARY2j2yvD1h0JXR3vlY0YnX9NKkRro27cvly9fVqvv1KkTc+Yo/uBRUVE0b96c06dPKxOzisPp06fZsGEDwcHBZGdn4+npyeDBg2nRooWa7f79+1m2bBl///23si4iIoLPPvuMgIAAZbL648eP+emnn7hx4waenp7UqlWLwMBApVd58eJFFixYwP3793FwcKB///7KxHBQzNbbunUrMpmM7t27c+LECb777js+++wzQDFUuWbNGg4fPsyzZ8+wtbWlRYsWfPvtt1hYFAyxnD9/nilTpnDmzJliXw+AhefCS2T/oSlqZst/kcSM9/sg1TbjmqrPvPovk/meX1TaJjnz45qtVqnF9x9aQonoMFr9Zy3+yyzpVPhPDv1XcTB/9+VKiuL8fe0tg9GwgpXWjvVvU2yn6b9O37596dKlCx07dnznY12+fJmyZcvi6Kjo7eXk5FCvXj2WL19O3boly+WYOHEiZcuWZdiwki2DL5ym94twmt4vwml6vwin6f0inCZ1hNOkQKu/PfchGTx4MLt27dLKsU6dOsXIkSMJCgoiMjKS2bNnY2pqqrIEe3FITEzkwoUL/9pK5QKBQCAQvA/+a78996Eo8ZIDJWHWrFns27ev0P2DBw9myJAhWjmXr68v+/btw9/fH19f3zc3KIKRI0cyY8YM+vfvj0wmw8fHh3Xr1ilXHS0uGzZsYOjQoVhZfbxetUAgEAgEH7mvozXeq9M0dOhQlTyh13k190cbLFq0SCvHMTU1Zd68ee98nLFjx2pBjUAgEAgEHxbdjz1EpCXeq9NkbW1d7GUDBAKBQCAQCP7LvFenSSAQCAQCwcePiDMpEE6TQCAQCASCohFeE/A/NHtOIBAIBAKB4H0iIk0CgUAgEAiKRJu/GfcxI5wmgUAgEAgERSImzykQw3MCgUAgEAgExUBEmgQCgUAgEBSJCDQpEE6TQCAQCASCohFeEyCG5wQCgUAgEAiKhYg0CQQCgUAgKBIxe06BcJr+oxjofVw3qIlE70NLKBGpWXkfWkKJCIpK+dASSoS5scGHllAiYlNkH1pCiegweuCHllAiDi5a/6EllIg5ny740BLegvf7nROz5xQIp0kgEAgEAkGRCJ9JgchpEggEAoFAICgGItIkEAgEAoGgaESoCRBOk0AgEAgEgjcgEsEViOE5gUAgEAgEgmIgIk0CgUAgEAiKRMyeUyCcJoFAIBAIBEUifCYFYnhOIBAIBAKBoBiISJNAIBAIBIKiEaEmQDhNAoFAIBAI3oCYPadADM8JBAKBQCAQFAMRaRIIBAKBQFAkYvacAuE0CQQCgUAgKBLhMykQTpNAIBAIBIKiEV4T8B6cpqSkJNq1a8eePXsoU6ZMsdtduHCBpUuXcu/ePfT19fHx8WH06NFUrlxZzfbq1auMHz+e06dPq+0bO3YsXbp0oX79+jRr1ownT54o9+no6GBubk7NmjWZMmUKjo6OAPTt25c6deowYsQIlWMFBATQr18/QkJCADh58iQzZsxAT0+PqVOn0qxZM6XtF198wfTp0/Hy8lLW7dmzhydPnjBmzJhiX4fCiI0M5eyWpcQ/icDayZkm/UZiX658ofY3Tx7gxvF9yDMz8KjdkEa9h2EgNQQgJ1uO39ZlhF27gL5Egk/rrvi06aJsGx12D/9da4iPeoiJpQ0+bbvi3aitcv+NE79z668/yEpLwbG8N437fItJWeci9cdEhHJi42JiHz/EtrQLrQeMopRrhULtrxzfz+Uje5BnZuBZtzEt+32r1P8qe+f/iLG5BZ8OHqesu3/FH7+9G0iNj8XexZ0Wfb+llGvh16oocrPlXNq1gsibF9A3kODdogveLTprtI1/HMalnUtJfBKJpaMzn/Qajo1zwXl3jP2C7Mx0lTa9Fv6OgaHRW2krioehIaxfMpvHEaGUcXFj4MiJuJWvpNE2KyuTLSt/5cqFs+Tn51G3YXP6Dh6DoZExAAlxz9m88lfu3ryKRCrlk8Yt6d5/GBKJVGt6w+4Hs2zBTCLDQ3F2dePb73+ivKfXG9v9vmMThw/sZuPeY2r7cnNyGPV1Lz5p2JTeA4ZqTStAZFgIm5fNJSoylNLObnz57XjKFXJ9s7Pl/L5lFQF+J5FlZVGxag36DBmLta2Dcv/OtYu4dPYk+gb6NGrVgS79hqKjhfEQA10dBtQrS10XC+Q5+Ry++5zDQc812vqUNqdHDUdKmUmJSZWz++ZTrj1OAWD3lz4a2yw/H8m58IR31vm2SAz0ubhjHGPm7OX8tQcfTEfo/XssnT+TiLBQnF3dGfnDT5Sv+Ob7d+/2TRzav5stvyvu363rV7J9wyo1u1JOpdm096jWdX/s5Ofn8+uvv7Jv3z7y8vLo2rUr33//Pbq6Radrp6am0q5dO8aMGUPnzpqf54Wh1UTw5ORkhgwZQnx8fInaBQYGMmzYMNq3b8/BgwfZuXMnTk5O9OvXj6ioKBXbkJAQRo0aRX5+vtpxLl26RExMDPXr11fWTZo0CX9/f/z9/fHz8+O3337jwYMHjB8/vkQac3NzmTx5MuPGjWPMmDFMnDhRqcHPzw87OzsVhwmgc+fOnDx5kocPH5boXK+TLcvi0KLJOFaoTLcpSynl4cXhRVPIlmVptA+96s/lP7fRpN9IOo6bQ0x4MBf3rlfuv7BnHc8jHtBx3Bwa9xnO5YPbCb16HoD05AQO/TaZ0hWr0n3acup07Mu57SuJuBUAQMg/f3Pl4A6a9BtBj+krMDKz4PDiqRr/Hi+RZ2Wyd/6PlPGszJc/L6d0eS/2LfgJeVamRvuQy+e5sH8LrQeMpsfE+TwNvcfZXWvV7IL+OUP4rcsqdbFRERxaMZtP2vek/y+rcXBxZ9+Cnwq9Vm/i6v71xD96QOtRs6nb41tuHd1OxHV/NbtsWRanl0/B3r0yn01YjL1bJU6tmKY8b3pSHNmZ6XSesZ5us7cpi74GR/BdycrKZN7kUVSsXJ1flm2lgldV5k0eTVYh13vLyl8Jf3CPibOX8uOcFYSF3GXr6t8AxUNp0cwJyGVZTP11DSMnzuL6pfPs3az+YH9rvZmZTP1hON7VarBo3Q4qVa7GtHEjyMrUrPclz55GsX1j4Tp+37WFh6H3tabzJbKsTBZOHUMF72pMW7QZj0pVWDjtO2SFXN8D29Zy/R8/Bv8wgx8XrCE3J4elsyYovzPbVy/k7o3LfP/zYob88DN+x//k7PEDWtHap1Zp3G2M+flEKOsDHtOlWinquliq2TlbGTK2qStnHiQw7lAwp+7H8V1jV1ysFA79N7vvqJQ/78TwPE3GlcdJWtH5Nkgl+myZ/RXeHk4fTANAVmYGk78fTuVqNVi6YSdeVaox5YfhZGVmFNnu2ZMotm1YqVLXteeX7Dh4WlnW7vgDM3MLOn7R+31+hGKho8V/2mLjxo0cPnyYZcuWsWTJEg4dOsTGjRvf2G7+/Pk8f6658/AmtOY0Xb16lc6dO5ORUfSNoolDhw7RoEEDevfujYuLCxUqVGD69OnY2dlx9GiBd71r1y569OiBjY2NxuOsWLGCnj17qtSZmZlhZ2eHnZ0dDg4ONGjQgJEjRxIQEEBqamqxNSYmJpKUlESbNm1o164dSUlJJCQoeljLly9n+PDham309fXp1KkTa9eqv/BLwoPLfugbSGnQbRDWTs407DkEA0MjQq+c02h/+9QfVGvZEdfqdXFw9aRJv5Hc8z9JtiyLbFkWQeeO07DXEOxdyuNeswE12nbl9umDADy8/g/GFlZ80qU/lg6lqVC3CRXrN+d+wFkA5Jnp1P9iIOWq1sHSoTQ12n5BUnQUGSlJheoPDvBDXyKhac9vsC3tQvO+w5AYGhFyWbP+qycOUKt1Zzx86uHo7kmbAaO57XdCxfHJTEvh7M61OLp5qrSNuHMN2zIuVG7YEisHJxp1G0h6cgJxTyJLcMUVZMuyeHDxBHW+GIyNswcu1evj3bIrwX6H1Gwjrp1Dz0BKrc4DsXR0pvYXgzGQGhF5XeGMJkc/xsjCGjNbR4wsrJVFG9GE1/nH7y8kEim9vx5FaWdX+g0Zi5GRCQHnTmm01zcwoP+343ArXwnX8hVp0roDIXdvAfD0cSQP7t1h8NgplC3nTsUqPnTtN5gLZ05oTe+5v08gkUoZOGwMzuXc+GbkOIyNjTl/5mSR7ZYvmIl7+Yoa9z2NesShfTtxLuemNZ0vCTinuL7dB47EydmVXt98h5GxMZfPq0e+AfxPHaZLvyFUrFKD0s5u9B85iYf3g4h5+pi01GTOnzxI/5GTcPP0xqt6bVp36kV4yN131inV16VZeRs2XY7iYUImVx4lc+huDK0r2qrZNnC1JvBZGseDY4lJlXMyJI670WnUK2cJQHJWjrJI9HVpW8mONRcfk5md984634aKbqXw2/I9rmXVP8u/jd/pE0ilUgZ9+x3O5dwYMmocRsYmnPv7ryLbLZ3/M+4VVO9fI2NjrG1slWX/7q24uLrz+Re93udHKBY6Otor2mLLli2MHDmSWrVqUa9ePb7//nu2b99eZJurV69y6dIl7Ozs3uqcJXKali5diqenp1o5cOAA/v7+dOnShaVLl5ZchK4uISEhKhEqHR0dNmzYQLdu3ZR1586dY+7cuXz11VdqxwgPD+f69es0btz4jeeTSCTK8xYXKysrjIyMCAoKIjAwEGNjYywtLTl37hw2NjZqUaaXNG/enCNHjpCSklLsc71OdFgwjuW9lS9YHR0dHMt7ER12T802Ly+XmIf3Ke1ZRVlXyr0SuTnZxD0OJ+5xOHm5OTh6FOh1LO9NTHgI+Xl5OFepSfMB36kdV5ahGFaq0qw9lZu0U9bd+fsQ1qVdMDa3LFT/09B7lPGsrKK/dAVvnjzQrD86PISyFQv0O3ko9D9/FK6sO7NjDd4NmmNTWnVY0MjUnLioSKLuB5Kfl8edcyeQGBlj5VDy3mhilOJa2bkVDLs4uHsTF6G4Vq8S+zAYe3cvlc9o7+5F7MNgAJKfPcLcvnSJNbwNoffu4OldXUVLBe+qPLh3R6P9gOHj8fSuBkBs9FMunDlBpao1ALC0tmHCrCVYWql2VDLS07SmN/jubbyr+qjorVS5OsF3bxfa5vTxQ8iysmj1aUeN+5fNn0mvAYOxsLTSms6XhAUHUt67mopej0rVCAtWv755eXkM/n463j511PZlZqTz4O4tjExMqVilhrL+s25fMnD05HfW6WJlhJ6uDiGxBUPCwTHplLc1UevrnwuLZ+f1p2rHMJboqdV1q+7InWep3HlW/E6ntmlY04NzV+7T5MtfP5iGlwTfvaN2/3pVqc69Fx0PTZw6dogsWRZtPutUqE3Uowj+OvInX48Y+146Vx87MTExPHv2jNq1ayvratasyZMnTwqNIsnlciZPnsyUKVOUfkBJKVFO04ABA+jRo4dye8WKFfj7+9OiRQvMzMwA1IbTikPXrl3ZtWsXTZs2pUGDBtSvX5/GjRvj7Kz6QlyxYgUA+/fvVzvG+fPnqVq1KqampkWe69GjR6xZs4aGDRtiYmJSbI16enp8//339O7dGx0dHSZPnoyenh4rVqxgypQphbZzd3fHwsKCK1eu0Lx582Kf71UykhOwdnJRqTM2tyL+SYSarSwjndxsOSaWBS85XT09DE3NSU+MAx1djEwt0NM3UDlWbracrLQUzG1LYW5bquDcKUk8uOxHnc/7qJwn6PwJ/t74G3r6BnQYO6vIL3VaUgK2pVX1m1hYEftYg/70dHKy5Zhaqeo3MjUnNSEWgMi7N3gcfIcBc9ZwcuNilfYV6zXmwfV/2D5jDDq6uujo6NL1+5kYmpgVqq8wMlMSkL52rQzNLMnNliNLT8XQzKLANjkBy9f+RkZmliQ+VUS4kqIfkyOXcfy38aTERGFd1p3aXb/BwqH4eX/FJSkhjjIuqhEWC0sbHkeGFdluxfxpnD91BDsHJ7r0HgSAiakZ1Wp9orTJy8vj5ME9VK5eu7DDlJjE+DicXd1V6iytbYgMD9Von5yYwMaVi5m1aBX376lHZP468gdyuYw27bvg95d6rtO7kpQYT2nn16+vNVEarq+urq6aw3Tyz92YmVtStpwH9+/exNbekQunj3JozyZys7PxbfkZ7bv3L1GnThOWRvqkynLIzSsYOk/Oykair4upVLHvJU+SZSpty1gaUtnRjL/ux6nU25gY4OtqxeRj2h/2LAlr96oPkX8oEuJjcXnt/rWytiYiXPP3LSkxgfUrFzF70WqN9+9L9u3YTPVadfGspJ7X+yHQptsml8uRy+UqdRKJpESOTGys4n1gb2+vrLO1VUQeo6OjVepfsmrVKry8vPD19X0b2UAJI00mJibKoa6gWVX6FwAAWEtJREFUoCD279/P4sWLlQ7T2+Lu7s7evXtp1aoVV65cYebMmbRs2ZJRo0aR+Ya8hpcEBQXh7u6uVj916lR8fHzw8fGhSpUqdOzYEXd3d+bPn69it3r1aqXdy/L111+r2PTp04eAgAACAgLo3r07/v7+WFlZUa5cOUaNGkWTJk2YPXu2Wn6Ph4cHQUFBJbwqBeTIZegZGKjU6ekbkJudrdH25X41+5xscuRZGo8FkJujerwcuYxjy3/G2NwK78btVPaV9fKh+9RleDVuy5El00l6/qxw/TLN53z9fADZ8izN+g0UnzdHLufEhkW0/Go4BhoSkTPTUkhPTqDll8PpN30plX1bcHTNAtKTEwvVV6huuQw9fdV+xcvPoXatsmVqmnX1Dch7YZcSHYU8I5WqbXvQdMgU9AwknFw8ieyskg9nvwmZLAt9A9WHj4GBATmvPaRep0O3fsxYtAFbh1LM+WkUeXnqQy871i3hYWgI3ftrL7FalpWFwWv3h4GBhGwN9zfAmqULaNG2Ay6uHmr7khIT2LRmKcN/mPzeeudymbpefQMDcgrR+yrX//Hj+P7tdP1qGPoGBsiyMol5+pgzxw4waPRkug8cyamDezjxx8531inV1yU7V/VZ9HLbQK/wa2Mm1eO7Jq6EPE/j6qNklX3NPGwIi88gNE779+3HiuL+ff37JiE7W/P3bfWS+bRs24Fybur370sy0tM5e+oYn3f98MNySnS0V1avXk3NmjVVyurVq9VOmZWVRWRkpMbyMhXoVUfr5f9fd8gAQkND2bVrFxMnTnyny/BWs+eioqIYN24cEydOLHRYqqR4eHiwYMECcnJyuHHjBkeOHGHPnj3Y2dnx008/vbF9QkIClSqpz14ZOXIkrVq1Ij09naVLl/LkyRPGjh2LlZVq2L5Hjx707dtXpe7WrVv88MMPKnWvRrKWL1/OTz/9xLZt28jNzeX48eP07t2bkydP0rp1a6WdpaVliZLjV61axeoVBQmCDm4V1Ryk3Jxs9KXqToN+IQ5Qbk42+hIp+Xl5Go8FoC8pSEqWZ2VydOl0kmKe0GXir2oz18xs7DGzsaeRsztPgm8TeP4vfLv0A+CfP3fwz8GCh76Tu2b9mpyely97Nf3Zis974cBWSrlWwK2q5kiH36512JV1pUbLzwFoM3AM68YN5M65E9Rr30Njm8LQM5CQm5OjUvfyc+i/pl1PX6KmOS8nG70Xdi2G/0xebo5yplyj/uPY+2M/Ht8JwK120xLpep0/dm7kj10FyY8eFb3Jee2BnZ2djcSw6KTzl9GpkZN+YVivdgTfuYFXtZrK/TvWLeXYgV2MnPQLZcsV/sB/E7u3rGPPtoKJCZ6Vqqg5SNnZcqQa9F4LuEjw3duMHK85urt68Txatv28yBdSSTm0exOH92xSbrt5eqvpzcnORvKGpP5r//ixcs6PtGj/BY1bK+5PXV09MjPSGTJuBrb2itm88bHR/H3kd9p2frfk3+zcfDXn6OW2LEdzLpKFoT4/tvRAB1h4NoLXp3fULWfJqZA4TU3/37Br8zp2bV2n3K7oVUXNQcrOliPVcD9cDbhAcOBtRm+bWuQ5rgZcQCo1pGbd+kXafawMHjyY/v37q9RpijLdunWLfv36aTzGy3ezXC5H+uJd+NJZMjJSnZGcn5/PTz/9xMiRI5XRqLelxE6TTCZj5MiRNGzYUGWo7l2YO3cun3/+ORUrVkRfX5/atWtTu3ZtTE1NOXPmTLGOoaOjQ25urlq9jY0NLi6KYZPFixfTtWtXhg0bxu7du1V6ixYWFkq7l0RHRxd6vosXL2JhYYG3tzdLly6lUaNGGBoaUq9ePa5du6biNOXl5ZUo1N6jRw9i7Qtyeq4f20NGiuq03vTkREwsrNXaGpqao2cgIT05ASvHsorz5+aSlZaC8Qv7zLRk8nJz0dVT5CtkJCeiL5EiNVYMV8oz0zn422SSnz+l4w9zsHQoyMWJuncLE0tr5bF1dHSwdixLZlpBj7R688+oWLcgt+zS4d2kJ7+mPylRZQjxJUam5ugbSEhPSsTGyVmpPzMtBVNLG+5dOkt6UgILB7YHCpyrkMvn+W79IaIfPqBm647K4+no6mLn7EZKXMlnShhb2CB77VplpiSiZyBFYqQ6tGtsaUNmimo0KzMlUXnN9QwMVKJtegYSTG1KkZFUspmmmmjxaWfqNWqh3D64ZzNJiarHTUqMx8pa/WGRk53NtUvnqVKjDsYmig6BpZUNZmYWpL6S3L9x+XxOHf6db8fPoG7DZmrHKQntOn5Bw2atlNv7tm8kMV71RZyUEIe1jbrec6ePE/c8ml7tFY5mbm4uOdnZdGn1CdPnL+fc6eNIpYYc2q9w2uUyGfcCb+F/5i9WblUf1i8OTdt1ok7DgqH1I/u2kvza9U1OisfSWvMEFYBLfidZ++s0mrTtTK9vCpYgsbS2xUAiVTpMAI5lXEh4i/v1dRIysjGT6qOrAy9H6CyNDJDl5JEhV39WWhkbMKWVwtmcceKByvAdgI2xAWUtjbjyOFmt7f8nPu30BY2aF9y/e7ZtJOG1TnFifDzWGl7OfqeOE/s8mu6fNgEK7t+OLeoxc8EKKldX5LZdDbhAXd/G7zxEq020OeutuENxdevWVS758zoxMTHMnz+f2NhY5fJGL4fsXk/yfvr0KTdu3CAkJIS5c+cCkJmZydSpUzl69Cjr1q2juJTYaZoxYwaZmZnMmDGjpE0Lxd/fn5ycHH788UeVenNzc6yt1R0DTdjY2JCUlFSkjUQiYebMmXTv3p1NmzapDb+VhOXLlyvDfLq6usohudzcXLXhucTERCpUKHxNotextLTE8pXE5VLulbh2dA/5+fno6OiQn59PdOhdan7aU62tjq4uDq4VePbgLmUqKpJ7o8Puoaunj21ZRSRBV0+f6LB7OFVQjJU/exCIfbkK6Ojqkp+Xx9FlP5MS+4zO4+crnaOXXD+2BzMbe5p+OQpQJG7HPg7H1bsgodHI1BwjU3PldunyXlw6tEtFf9T9u3zyuWb9pdw8ibofiLOXQv+TB0Ho6elj7+xGzx8XkPdK9OfsbsXN3qS7IgfH1MqG+CePVI6ZEB2lNsuuOFiXdUNXT5/Yh8E4eHgD8DzsLrYu5dF57WFm51qRwJOqf6Pn4UFUadOd/Px89k8dSLW2PfH4pCWgmJmX+vwpFg5l1c5bUkzNLTA1L8ivKu9VlYO7N6touX/3Fh179ldrq6Orw8oF0/h69I80aKpw9OOeR5OakoRT2XIA7Nu2ltNHfmfkpFnUbfh2eXmvYmZugdkreitWrsbebRtU9AbduUX3vgPV2vYfOpru/QYpty/6nebg7zuZs2QdNnb2rN15UMV+/oxJeHpVoXOPvq8fqtiYmllg+kr+mkfFyhzZu0VF74Og27Tv/pXG9kE3r7D212k0/+wLFYcJwL1iZbLlMqKfPKLUi0kNTx9HqDhRb0tEQga5efmUtzMh5LkiGdzT3oSwuHS1CJJUX5dJLdzJz4fpJx6QnJWjdjwPOxPi0uTEp795GPJ/mdfv30qVq7Lntfv37p2b9PxykFrbgcNG0/PLgvfOBb/T/Ll3B/OWrcfGriAHJ+TunXe6Z98H/7VcdAcHB5ycnLh27ZrSabp27RpOTk5q+UwODg6cPKk6G7dv37707duXDh06lOi8JXJj9+7dy9GjR/nll1/IyMggNjaW2NjYEk3d18SwYcPYtm0bCxYsICQkhPDwcPbt28e6des0zpTThJeXV6Ee6atUrVqVrl27smLFCmJiYt5K7z///IOZmZly4c0qVapw/PhxHjx4wJkzZ/DxUV0I7v79+3h7e7/VuQA8avkiz0jj/M5VJDyJ5PzOVWTLsihfpxGgyL15NZJTueln3Di+j/DrF4l5GMLZrUvxbtQGA6khBlJDKjZowdmtS4l5GEL49YvcOPE71V4MZwWdP8GT4Ns0+2o0EmMT0pMTSE9OICtN8Teu0vQz7l04RcilMyQ+e8zZLcvIlcuo3LCVuvAXeNZuiCwjndNbVxD3JJLTW1eQLctSRqOy5TLSkgr0+7RoT8CRPdy/eoFnYSGc3LSEak3bYSA1xMLWAatSpZVFYmiExNAIq1KKaFi1pu24deYogf5/kRj9hLO71pESF0Plhi1LfN31JYa4123OpZ3LiIu4z6ObF7l7aj+VmiquVWZygjKHzMXHF3lGOlf2ribp2SOu7F1NjiyLcjUaoaOjQ5nKdbh5ZBvR92+T+DQS/00LMLayoXTlWiXW9Sbq+jYjIy2VLat+JSoynC2rfkUmy6ReY8U1kMuySEpQRHb09PRp3q4TuzeuIDjwJuEP7rHkl0nU/KQxZcu58+TRQw5sX0+H7l/i6V2NpIQ4ZdEWvk1akJ6Wypol83j0MIw1S+aRlZlJw2YKJ04myyLhRSTK0soapzLOymJhZY2enh5OZZyRSg1V9inqpJiZm2NfSntr+dT2bUZGeho71izkyaNwdqxZiCwrkzoNFdE+xfVVRB5yc3NYv2gmnpVr0K5rP5IS4pUlJzsbxzL/196dx8Wc/3EAf033obtEjlC6KKXLugvrPpIrhB/WmWvtSnZZci1q5Yqc6yy5QnJbEYkoFAolJdGdzumY3x/DMGZKZeo7k/dzH/N4bDOfplcf03zf8/l8vp+vPjrYdsHufzzxOiEej+/fwbljB+A4sGYb7gnDLucg9GUWfunUAgZaSrBpoYbB7XRx/in307iaggxvum6YuS50VeSx7VYS7zE1BRkoyn4+RLRQV0BKbu32O2vIujr0Qf6HD9ixaT2SEl9ix6b1KC4uQvePo6n8r18t/tevuiakpGV4r1+AuylryusktGwluEaX8HNxcYGXlxdvrbG3tzffdF5WVhYKCgogIyMDfX19vpuMjAy0tLSgq6tbo59Zo5GmM2fOoLCwUGBazsnJCX///XeNfvCX+vfvDzk5Oezduxf+/v4oLS2FsbEx1qxZU+0zzrp164Z169ahoKDgm2fFLViwABcvXsSGDRvg5eVV47y+vr5YtOjzDtSurq6IiorCmDFjMGDAAPTr14/3WEJCAgoKCmBnJ3jKcXXJKSpj0DxPXD+wGbGh56HdvDUGz1/JW2f0/G4oru79B257LwAAjOx74kPGO/x3YDPKS0thYN0FnUd9/tTTdfQ0XD+4FUHr3SGnqAy7oa4wsOaeTfDyfhg4nAoEb+Kfc9czNsdw9w1obfUTerq64e7pQ8jPSkcTA1MMWbgGclXsai2vpAznhStxae8mPPwvBDot22Dk76t53/PsznWE7PSC+yHuviZmPzkgL/0dLu71QXlZKYxsu6HnmOqNCpp26gl2cRHCz/gjPysDjVsaYIzHBiir1e7Uc9sRv+CO/zZc3LQYsgrKsBw0HvpWXQAAgR7j0cV1AQx/6gM5RSU4zlqOO/5bEX/rAjSatUKv2Z68fyMbp8mQkpbGjX3rUVpUgCZGHdB7liekpARP6f5eSsqN8LvnRuzZshZXQ4LQsrUhFq3cBIWP/R0eehk7vD3hf/EeAGDM/2aDxWJh0+rFKCkuhm2Xnpg48zcAQGR4KCoqynHqyF6cOrKX7+d8+n5R5P1r3WZs81qNC2dOopVBW6zYsBUKH9cl3Lh6ET5r/8K5m9Ei+XnfS1GpERb85Y3929bh+oXTaNHKEL+u2Aj5j/0bceMK9visxL/nIpD4/Cky09OQmZ6G+a78J1O4r/WFqYU1pv/uiUM7vLBm0XTIycuj96AR6D14lLAfXWMH7qVgaqcWWNbXEIWl5TgW/RZ3Py7u3jnaHL5hSQh9mQV7fXXIy0hhzUD+EdnrLzKx/RZ35FZNUQYFQqb1fnTKyo3guWELNm9YhfOnT6C1YVus9NrK21E/9MpF/LNmGS7cqnwLgi/l5eWivLwMjVRUv924HonZQBMAYMqUKcjMzISbmxukpaUxYsQIvoGWESNGwMnJSeBqH9+DxalqK2cJ4+rqCmdnZwwbNozpKDxbt27F27dvsXr16hp935Zb37eLeH1TFrKfizhLy5OsKYZ+BrXbiI0pqkqy324kRtLzSr7dSIz4SNj7wxmfPd9uJEaeXK75h2mmtdYW/dUFvhT/TnRnTBrpKonsueqb+KwyE4Hp06cjICCA6Rg8paWlOH36NCZPnsx0FEIIIaTWxPEyKkwQ+QV7v7Z69WocP3680senT5+OGTNmiORnde3aFcePH0dYWNh3bV4lKidOnEDfvn2F7h9FCCGEEMlS50XTzJkzMX78+EofV1NTq/Sx2vDx8RHp830PUW3JQAghhDBJ3M6eY0qdF02amprV3jaAEEIIIeKHaiauBrWmiRBCCCGkrtT5SBMhhBBCJBwNNQGgookQQggh3yDpZ72JCk3PEUIIIYRUA400EUIIIaRKdPYcFxVNhBBCCKkS1UxcND1HCCGEEFINNNJECCGEkKrRUBMAKpoIIYQQ8g109hwXFU2EEEIIqRItBOeioklMSUvYK/R1dgnTEWqkmZoc0xFqZPC6q0xHqJGsJ4+ZjlAj6sbtmI5QI9FeQ5iOUCN/D/RiOkKNmPX5jekINVYUtZXpCD8EKpoIIYQQUiXJ+hhfd6hoIoQQQkiVJGzyo87QlgOEEEIIIdVAI02EEEII+QYaagKoaCKEEELIN9D0HBdNzxFCCCGEVAONNBFCCCGkSjTQxEVFEyGEEEKqRNNzXDQ9RwghhBBSDTTSRAghhJAq0bXnuKhoIoQQQkjVqGYCQEUTIYQQQr6BaiYuWtNECCGEEFINNNJECCGEkCrR2XNcYlU05eTkYMCAAQgMDETz5s2r/X23bt3Cli1b8PTpU8jIyMDKygrz589H+/btBdpGRkbC3d0dV69eFXhs4cKFcHZ2RsuWLdGrVy9cvXq1Rjm+lp2djcmTJyMgIADy8vK1fp5veZ/0AtcObEZmyito6unDceJc6LZqW2n7qEsncf/8cbCLC9HWtjt6jpsFWXkFAEB+dgZCj2xH8tNoyMjKw8iuBzqP+B9kZOVqla28lI3IwO1Ifngb0rJyMHF0gmmv4ULbZiW/ROTRbchJTYJa05awHT0bmi0NAQD+cwYJ/Z5O4xegtX0vJD+8jbDda/gea2HZGV2nLKlV7q+9e/UCl//dhIyUV9Bqpo8+k+aiSWujStvfv3AS984fQ0lRIYztuqOX62xeH3/phPefUFJRQ/9pv4skp7yMFFaP6YABVnooLq2A3+Xn8Lv6QmhbEz1VrHWxhHlLdbxKz8eywEe4HZ8BAFCUk8aKkRYYYKkHlhQQfP8NVpx4jMKScpHk5OWVk4bP/AEY1t0URexSbAoIx6bAO0Lb9rJpgzUze6ONnibuPknBfJ/zeJ6cKdBu0fiuMGyuiWl/nxFpVoDbv2vGWmJgx2YoLi3H9kvP4Xf5udC2Js1UsW6cFcxbauBVej7+DHiI23HpvMcn9WwDt37GUFWUxfUn77Do4APkFJaKPDMAxMc9hfdaTyS8eI7WbQyw0GMZjE3bCW37IS8XA3t14btPTU0dZ6+EAQACjxzA1o3r+R4fPW4iZs8XzWsYAF7EP8WWDavw6uULtGxtgLm//4m2Jmbf/L5jh//F2ZNHceDEeQDAwT3bcXjvDoF2TfSa4d9jISLLW11ysjK4fWQRFvx9DDfvC3/diCtaCM4lNtNzubm5mDFjBjIzBd8EqxITE4NZs2Zh8ODBOHPmDPz9/aGnp4cJEyYgJSWFr21cXBzmzZsHDocj8Dx37tzBu3fv0Llz5+/6Pb6koaEBBwcH7Ny5U2TP+bXSkmKc3rgUzdq2h8tfW6FnaIYzG5eitKRYaPvnkTdxJ+gQHCfOxfDf1yHt5VOEBe4GAHA4HJzbtgqlJSUY6eGN/jM9kBB9B+En99c6X1TQXmS9fg7HOathM2omYi7443VUmEC7spJihO5YDh2Ddui7yAfarU0QumM5yj7+HsNWH+S7mfZ2hrJmYzSz6AQAyEtLRrP2dnxt7Fzm1jr3l9glRTjp/SeaG5vDdcU2NGtrhpP/LAW7pEho+/h7N3E76CD6TJqHUYvX4+3LZwg9ulug3bM7/yHx4V2RZPzkz+Ht0UFfA6N8wrDEPxoLBppgoJWeQDsVBRn4z+2C+Ld56L3qKs5Hp2L3dHtoqXCL4xUjLdBBXx0uW25htE8YrFpp4C9nc5FmBYC1M/ugo3FT9F9wAPM3hmDJpB5w6mEq0M60lQ5OrXNB8K14dJ62C9HP03BhoyuUFWX52o3q1Q5L/9dT5Dk/WTrCHB30NTDyn5vwOByNhYNMMbBjM4F2KooyODq/G+JTP8BxxWWEPHiDvTM7QUuF++FpiE1zLBthjr8CH2LwuutopqmENWOt6iRzUVEhFs2bCQvLjth18CjaWVjCff4sFBUVCm3/KvEl1NTUcer8dd7tQODpz48nvMSwEWP4Hv/fL7NElre4qBBLf3ND+w4dsWWvP8zMO2DZ724oriTvJ2/fpODQ3u18941wmYgjZ67ybruOBEFFVQ3DRo4TWd7qkpeTwYG1k9DOUPDvkUgOsSiaIiMjMXz4cBQWVv1HIczZs2fRpUsXjBs3Dvr6+jAyMsKKFSugo6ODkJDPnyQCAgIwZswYaGlpCX0eX19fuLi41Pp3qIyLiwsOHDhQq9+tOuIjQiEjJ4euo3+Bpl5LdB87A7IKinh+74bQ9tGXg2D1sxPaWHZCkzbGcJw4D0/CLqG0pBjZaclIe/kUfaYshFazVmhmZI6fnCYg7s5/tcpWVlKMhPBL6DhiGjRbGKJFh84w7eWM5zeCBdomPbgBaVk5WA6bDLUmLdDReRpkFBR5BZaiqgbvVs4uQXzoWdi5zIWcojIAIDctGWpN9fnaySk1qlXur8Xd4fZxjzG/QKtZSziMmwk5BUXE370ptP2DS6fQ8WcnGFh1QtM2xugzaR5iblzkK2SL8vMQGrALTdoYiyQjwB0dcunSCssCHyEmORcXHr7F9svPMalnG4G2I39qiYKSMnj4R+NVegG8g58h8X0BOrTUAACUllfgj4CHePw6BzHJuQgIT4KdofC/ndpSUpDFpIFW+G3LRUQ/T8OZm3H4x/82ZjjZCrSdNtQad2JSsHLvdTxPzsQfO64gt6AEY3pzCzlpaRY2/ToAO9yHICE1S6Q5P1GUk8bYrq2x9Ci3X85Hp8L3YjwmOxgItB31kz4KSsrgfvgBXqUXwOvsUyS+z0cHfW7/uvUzwtYL8Tj3IBVxqXlYefwxTJupQqoOPsxfu3wB8vIKmDXvN7RqbYC5CxdDUVkZ169cEto+KTEBzVvqQ0tbm3fT0Pz8b5/0KgGGbY35HldSVhZZ3tCrFyEvL4+ps39Fy1ZtMGPeIigqKePGtctVft+WDSthYGTCd5+ikhI0tbR5t5NHD0K/tQGGjhwrsrzVYdKmCUIP/IbWLbTr9eeKFEuENwlWb0XTli1bYGxsLHA7deoUwsLC4OzsjC1bttT4eaWkpBAXF8c3QsVisbB3716MGjWKd9+NGzewbt06TJo0SeA5EhIS8ODBA/To0UPozzA2NkZERATv65MnT8LR0REA4OPjA3t7e+Tm5gIAwsPD0a5dO8TExAAAdHR00KpVK5w9e7bGv1t1pCU8hV7bdmB9nHBmsVjQa9sOb188FWhbUVGOd4nxaGb0ecSgqYEpystKkZGcAGU1TQz7dTWU1TT4vo9dVFCrbNlvElFRXgbt1p9HDnQMzJCZFA9ORQVf28xXcdBpY8b3e+i0NkPGq2cCz/s45DB0jTqgiYkl7768tGSoNBb8xC8Kb18+RTOj9nzZmrVth9QXTwTaVlSUIy0hHi1MPvexniG3j9NfJ/DuC/XfBbMuvaGl11JkOds1V4OsNAuRCZ//Fu6+yIRVK02B9Qg/tdXBpUdvUfHFoOvAdddxLfYdAOCPgIeITOAWH801leBk2wLhH6fuRMXCQBey0tK4E5PMu+/249ewNWsmkLeVngbuPX3Dd19swnvYt+NOnzdSlIN5G110n7EHEbH8I8yiwuvfl5/7N+JFBqxaC/ZvZyMdXIxO5evf/mv+w7WYNDRSkIF5Sw2ERH3+fe48z4DDiit87UXlyeNHsLC04nv9mltYIebxQ6HtXyUmoEXLVpU+X9KrBLTQ1xd90I+exT5GOwv+vGbmlngaKzwvAFw5fxbFJcXoN8ip0jYpr1/h8rnT+GXOQt5z15du1oa4cS8ePSd61+vPFSVxrJk4HA68vLzQqVMn2NnZYf369aj46tjypdTUVPzyyy/o0KED+vTpwzewUl31VjRNnjwZYWFhvNvYsWPRsmVL9O7dG/Pnz8esWbMgLS1d4+cdMWIEsrKy4ODggJkzZ+LgwYN4/fo1mjVrBnV1dV47X19f/Pzzz0Kf4+bNm7CwsECjRjUfmZg1axbU1dWxefNmlJSU4K+//sKUKVP41lN17twZN28KH5X4XgU5WVBW5x8BUFJVR3624AGupLAA5aVsKGto8u6TkpaGQiNV5GdnQF6pEfTNbXiPcSoq8PDqGbQwtaxVtuK8LMgrq0Ja5vMUioKKBspL2Sgp+MDXtigvC4pq/L+Hgqo6ir76PQqy3iMpMhTt+o35nJPDQd77FLx9+gDBntNwdsVURJ/+F+Vlolkfkp+ThUZf97GaBj5kCenjggKUlbL5/k2kpKWh2EgVHz7+Lq+fRCEl7jE6DRXtFEFjNQVk5bNRWv75yJv+oQSKctLQUOZfk6avrYTMD2ysG2uJqL/74+yiHrBpo/n1U8JnojUiVveFtoo8NoYIFrDfo4lWI2TkFqK07POb3PvsAijKy0JLVYmv7fvsAuhpq/Dd17yxKrTUuO1y80vg6LYPMQnvRZrxS7rqgv2bkcftX82v+reljjIy80uwYXxHPNwwEMGLe8LWgPua0NfmjspoNZLH6UU9ELV+ADZNsoHqV1ONopKZkQ4t7cZ892loaSH9fZrQ9kmJCUh//w7TJo7B8AGOWL7kN2RkcNdiZWVmIC83F+fPnsaoIT9j/MjB8D+4T+iSh9rKykyHlrYOf15NTWS8F/5vm5OdhT3bfTD396Wo6pB8/Mh+WNrYw9hUcK1rXdt1LAyLvE+iqLhu1qz9qPbt24fg4GBs3boVmzdvxtmzZ7Fv3z6hbcvKyjB9+nTIyMjg1KlTmDJlChYtWoT4+Pga/cx6K5qUlZWho6MDHR0dPHnyBCdPnsSmTZugoqLy7W+ugoGBAY4dO4aff/4Z9+7dw6pVq9CnTx/MmzcPRUXC15x87cmTJzAwEBxirw45OTmsXLkSR48ehbu7O2RkZODm5sbXxtDQEE+eCI5KiEIpu4SvKAEAaVlZlJexBdp+Wh8kLcP/Bi8tI4vyUsE/5rDA3Xif9AI/Of+vVtnK2CWQ+jrbx68rvipoyoX8HlIysgKFT0L4ZWi2NIR2q8/TWoXZ6bzv7zJ5MSyHTUZS5HVEB+2tVW5hv4e0rODvIazPStkf+1hoezbK2Gxc2rcJvSa4QVZOtCcHKMpKg13G/ymLXcZduC0vw/+nriQvg9l92+J9XjHGb72N8PgM+M/tAj0NRb522y7GY/D660jJKsQht84iPYNGUUEW7NIyvvtK2Nyv5eX4P0AdvxaL4T3N0P+ntpCWZmFcXwtYm+hBTrbmH7RqnVdOhtefvLwf+1tOhj+HsrwM3PoZ411uEcZtDkN4fAYC5neFnoYilBW459+sHWuJbRfjMc0vAsZ6qtgyWXBaUhSKi4shJ8f/epSTlUMpW/gBPCkpEQUF+Zjz6yIsX+OFjIx0LF4wG+Xl5Xj9KhEAt+ha+89WjJ80FQf37sQx/4Miy1tSXAzZr048kZWVQ2mp4HsaAPht3oA+/YegVRvDSp+zsKAA16+cx9AR9Tst15CwWKK7icqBAwcwd+5c2NjYoFOnTvjtt99w+PBhoW1DQ0Px9u1bbNiwAW3atMGYMWPQvXt3REVF1ehn1vvZcykpKVi0aBE8PDxgZvbtsyGqw9DQEF5eXigrK0NUVBTOnTuHwMBA6Ojo4M8///zm92dlZcHUVHDxaXXZ2dlh8ODBOHnyJA4fPgw5Of4/eHV19RovcK/M3WB/RAYH8L7WbWMiUFiUl5ZCRk7wTC3pj29EXxdU5WWlkPnqAB4WuBtRl0+h/8wl0G7eqlZZpWXlBIujj19Lf/XzpGXkBH6PCiG5XkffQtuu/fnuU9ZsjOF/+0NOqRFYLBY0mrcBOByEH/CG1fCpkJKq2YH1zhl/RJz1533d1MBEoEAqLyuFrJAzIj+dZSisvYycAm4HHUST1kZobWEj8L3fq6SsHHJfFUefDuZFbP6DfXkFB7HJufAO5o4exabkoodZYzjbt8CWC58/eT1P444Iztx9Dw/+7o9OhtoIfy6aaboSdhnkZPnfguTluF8XfvWJ/PLdl1j9byj8PUdCRloKoVGvcPjiI6gp191ZqV8rLi0XKI4+FaNFbP7ir7yCg5jkHHid5U6TxyTnooeZLkZ0aomwZ9xRmy0X4nDp4VsAwMID93FlWW/oqingXa7wkziq6+C+nTi0bxfva9N2FmB/VSCxS9mQVxB8jwCAA0eDwAKL9/jKv/+BU38HPIl5BEtrW5y9HAa1j6P4BoZGyMnORtCJoxg1dkKt8gbs342Ag59PlDAxMxcokEpL2ZAXcvZpZMQtPIt5hPmH/qryZ0RG3IK8vAKs7UV3os+PRtzOnnv37h3evn0LW9vPHzasra3x5s0bvH//Ho0b84+u3r17Fz/99BPfjJKvr2+Nf269Fk0lJSWYO3cuunXrhjFjxnz7G6ph3bp1GDp0KExMTCAjIwNbW1vY2tqiUaNG+O+/6i1gZrFYKC+v/qnUX7etqKhAfHw8pKWlcefOHdjY2Ag8LiUlmkE9i54DYWTbnfd1ZEggCnOz+doU5mZDWU1wqkWxkSqkZeVQmJsNzabctTQV5eUozs+Dsvrn9tcPbcOj/4LRd5o72tp0q3VWRTUtlBTkoaK8HFIfp16L87IhLSvPW8DNa6uuheI8/t+jKC8bCl/8HgXZ6chLe41m5vYCP0temX/EUlW3BcpL2WAX5ENBRa1GuTs4DoSx/ec+vhsciIJc/sXF3GlR4X0sIyuHgtxs3nqlivJyFOXnoZG6Jp7duY7C3Gxs+mUIgM9FZPy9m5i36/tOkU/LKYZmIzlIS7FQ/nFxTGNVeRSxy5BbxH/QfJ9bjBfv+KdIE97lQ09DEbLSLPSxaIobT98jv5hbDGR8KEF2ARuajWq39YQwqekfoK2mBGlpFso/TnnpajZCYXEpcvIFC4f1h8LgczQcasrySM8pxKHlzkhKyxVZnm9Jyy4S6F8dNQWh/fsutwgv3ubz3Zfw7gP0NJTw/mNR9CLtc/+//Phvoaep+N1F09Dho+HQux/v6yP79yArk7/QzcrMEJgC+0RBgX+0UUNTC6pq6shI506PqX2x7AEA9Fu3qXTqrDoGOo1E916fl04EHtqHrK8+ZGZnZkJTW3ARdeiVC0h/n4bRA3sC4L43l5WWYljvTljl5Yv2lh0BcIsm+649RPY+TL4Pm80Gm81fGMvJyQkMOFQlPZ374ePL4kj742skLS1NoGhKTk5Gs2bN4OXlhdOnT0NDQwNz585F7969a5S9Xl9Bnp6eKCoqgqenp8ieMywsDCdOnBC4X1VVFZqaggc1YbS0tJCTk1Pp47Kysigo+LwYOjk5me/xAwcO4P3799i4cSP8/Pzw8uVLvsezs7N5/5jfS6GRKtR1m/FuTQ3M8PbFE96aAg6Hg9QXsWhiYCLwvSwpKei2NkJqfCzvvrcvn0BKWgbaLbhnWN0JOoTH18+h/4wlMLbv+V1ZNZq3hpS0DDK/WMydnhALTf22YH315qXVyhgZiU/5fo+MhKd803CZr+KgpKEDZU3+P4a3T+/jhLsLytifDzbZbxIgp6xa44IJ4BY+GrrNeDc9Q1OkPv+qj58/QVMDwdFJlpQUmrQxwpv4GN59qS+eQFpaBjot22D0Ei9MXOOHCau2Y8Kq7TCw+gkGVj9hwqrtAs9VUzHJuSgt56Bj68+vezsDLUS/ysHXS07uJ2bBrBl/3xg2UUFyZiEqOIDPBGv0at+E95iehiI0leV4I0+i8PBFGkrLy2Fv9nkvtM7mLXD/WapA3lG92mGD289gl5YjPacQCnIy6G7VCqFRr0SW51tiU7j9a/3F2i97Qy1Ev8oWyPsgIQtmLYT1bwFSsgrxNrsI7Zqr8x5r21QVFRUcpGR+/1m2qmpqaN6iJe/WzqIDYh5H871+Yx5GoV17C4HvLcjPxwDHzngQ+XkrjPT375Cbk42WrVojOOg4xjkP4lvD9CL+GVq2al3rvCqqatBr3pJ3M21vgacx/HljH0fDtJ1g3imz5mPn4VPw/TcQvv8GYsLUWdDS1oHvv4Foa/p5JiMu9jHamVvWOiMR7fScn58frK2t+W5+fn4CP7O4uBhJSUlCb5/OSP+y0Pr0/18XZABQWFiIU6dOIS8vDzt27MCwYcMwd+5cPH78uEb9UG9F07FjxxASEoI1a9agsLAQ6enpSE9Px4cP3/cmPGvWLBw6dAheXl6Ii4tDQkICjh8/jt27dws9U04YMzMzxMXFVfq4ubk5Dh06hFevXuHq1as4efIk77HU1FRs2rQJ7u7u6Nu3L3r27Illy5bxvanExcWJbCrya4a2XVFSmI8bR3Yg800SbhzZgdKSEhjZcc8ELGOX8I2SWDgOxv0Lx/DywW2kJcThvwNb0L5Hf8jKKyAr9TXunj0MmwGjoWfUDgW5WbxbbcjIKaC1nSPuHd2GzKR4pDwMx7Orp2DcgzvKUpSXjTJ2CQCgpWVXsIsK8ODETuS+fY0HJ3aijF2MllafR7py3yZBtUkLgZ+j3doU0nJyuHtkC/LepSA1NhLRQXth2lv4Jpo1ZWTXDcWF+fjv8HZkvEnCf4e3o7SkmDcaVcouQUHO5z6y7DUY90KO4fn9W3ibEIfL/26GeU9uH6tp6/IVZHIKipBTUISG7vef+VdcWo7jd17j77GW6KCvjr4dmmJ6n7bY8x+3iNdRlYeCLPdP/uDNRJg2U8OvA03QSkcZvw0yRUttJZyMSEZ5BQeHwhKxeKgZbA20YN5SHTum2uHio7eIfyu6oqmopAyHLjzE5l8HwtpED4O7GmP+6J+w7Tj3TFVdTWUofJyue56chalDbDC0mwkMmmli/7LhSHmfh4sR9bdBYBG7HMfCk7BunBU66Gugn6UeZvxshN0fNw/9sn8PhCbCrJkaFg42RSsdZfw+xAz6Oso4EfEaALDzynP8PsQM3U0bw6y5Gv4eZ4UL0alIzysRee6ejj8j/8MHbPb+G68SXmKz998oKiqCQ5++ALhriDIzuCNRyo0awcKyI7b+sw5PYx8j7tkTrPjjd9j91BUGhkawse+MzMx0bPPxQkrya1y9FIIj+/di7ITJIsvb1aEP8j98wI5N65GU+BI7Nq1HcXERujtyR6NKSop5I2fqGlp8BZeauiakpGWg17wlbzqvvKwMKa+T0LJV7datEtGbPn067t+/z3ebPn26QLuHDx/i559/Fnp79OgRAP4C6dP/KyoqCjyXtLQ01NXVsXz5crRr1w6TJ09Gz549ERgYWKPs9TY9d+bMGRQWFgpMyzk5OeHvv/+u9fP2798fcnJy2Lt3L/z9/VFaWgpjY2OsWbMGvXr1qtZzdOvWDevWrUNBQQGUhew3snTpUvz5558YNGgQzM3NMXfuXOzYwd1l1tPTE+3bt8egQdwdqz08PHi7mo8ePRoA8ODBA77tD0RJXlEZQ+Z74tr+LXgcGgLtFq0xdMFK3u7T8XdDcXmPN+btuwgAMLbvibyMd7i6fxPKS0thaNMVXUdNBQC8jAoHp6ICd88ewd2zR/h+zqfvrymr4VNx76gvrm1eAllFJbQfMA4tLLnrCoL+cIX9uPlo06k3ZBWV0GP6Mtw76ouXty9CXa8VesxYDpkv1jEUf8iBnKLgGY6yCkpwmOWJ+yd24eKGBZCVV4RBl34w7eVcq8xfk1dUxvBfV+Lyv5vx6D9uHw9fuApy8tw/zLiIUFzY5YXfDnD3vTHp5IDc9He4vG8TystK0damK3qM/kUkWb5l+fHH+HusJY7N74a84lJ4Bz/F+ehUAED0ugFYsP8+Au+8xpusIozdcgsrR1tgdl8jvEj7gAnbwpH2cWro79NPwOEAfr/YQUlOGuejU7E08JHI87pvu4TNvw7EhY0TkFdQjFX7QnH6Jndk8tWphfhl7WkcuvAQUfFvMXfjOfw9uw80VZVw/UEihrv7C4zw1LXlxx7h77FWOLGwO/KKSuF15glCorj9+8hrEObti0RgeBJSsgrhsikMq8Z0gFs/Yzx/+wGuW24jLYfbvzsuP4eCrDS2TLaFsrwMLj5MxeIjNVuQWl3KjRrh743b4L3WE2eDjsPA0AjrN22HoiL3zMNrly9greefuHGPOzr6x/LV2OazAYvmz0JpKRtduztg3m8eAIAmTfWw3mc7tm/2xukTR6GhpYUZcxbAsU+/Sn9+jfMqN4Lnhi3YvGEVzp8+gdaGbbHSaysUPuYNvXIR/6xZhgu3Kt+C4Et5ebkoLy9DIxVVkWX8EYlyAXd1p+Ls7e0rHdB49+4dNmzYgPT0dN6VOz5N2enoCE49N27cGCwWi2+KtnXr1lUOmAjD4ojyXFEJ5urqCmdnZwwbNkykz5uSkoLhw4fjv//+E1qQVcb39iuR5qhr7/Ml61TaZmqiW5tTH5b/+4DpCDWS9aRmQ95MUzcWfkkRcRXtNYTpCDVSyBbt5Xfqmlmf35iOUGNFUVvr9PlzikT3b6iuKJqzXh0cHDB//nwMHToUABAUFIRNmzYJXc8cEBCA7du349q1a7ztjWbMmAFdXV2sWLGi2j+TVsV9NH36dAQEBHy7YQ0FBgbCxcWlRgUTIYQQIk5YIvxPVFxcXODl5YWIiAhERETA29sbEyZ8PoszKyuLtx550KBBqKiowIoVK5CUlITDhw/j5s2bNZ4FEqsL9n5t9erVOH78eKWPT58+HTNmzBDJz+ratSuOHz+OsLAwdO3aVSTPmZ2djevXr+Po0aMieT5CCCGECfW8iXq1TJkyBZmZmXBzc4O0tDRGjBjBt5Z5xIgRcHJywpw5c9CoUSPs27cPy5cvx6BBg6Cnp4eNGzeiXbuajTKL9fRcVlZWlQvF1dTU+Hb9bkhoeq5u0fRc3aLpubpF03N1i6bnBOUVV355kppSVZDcSS6xHmnS1NSs9rYBhBBCCKkbYjjQxAixLpoIIYQQIgaoagJAC8EJIYQQQqqFRpoIIYQQUiVxu/YcU6hoIoQQQkiVxPHsOSbQ9BwhhBBCSDXQSBMhhBBCqkQDTVxUNBFCCCGkalQ1AaCiiRBCCCHfQAvBuWhNEyGEEEJINdBIEyGEEEKqRGfPcYn1tecIIYQQQsQFTc8RQgghhFQDFU2EEEIIIdVARRMhhBBCSDVQ0UQIIYQQUg1UNBFCCCGEVAMVTYQQQggh1UBFEyGEEEJINVDRRAghhBBSDVQ0EUIIIYRUAxVNhBBCCCHVQEUTIYQQQkg1UNFEyA+Gw+EgKysLOTk5TEdpsKiPCWmYZJgOQOpeamoqMjMzISUlBW1tbejq6jIdqUqUV/Tevn2LgIAA3Lx5E3FxcaioqAAASEtLw8zMDN27d8fw4cOhp6fHcFJBktC/gOT2cXx8PJ48ecLXx2ZmZjAwMGA6moD8/Hzcu3cPsbGxyMrK4strb28PeXl5piPyFBcX4+zZs7h58yYvL4vFgo6ODu/10K9fPygqKjIdldQAi8PhcJgOQUTv/v378Pf3R1hYGN+nXRaLBS0tLXTr1g0jR45Ex44dmQv5BcpbN7KysrBhwwZcu3YNnTt3RufOnWFoaAh1dXVUVFQgOzsbcXFxePDgAW7cuAEHBwe4u7tDS0uL0dyS0r+AZPZxbm4uDh8+jKNHjyIjIwPNmzeHhoYGL++bN2/QpEkTjBo1Ci4uLlBTU2MsKwAkJSVh586dOHfuHNTU1AT6Nz4+HoWFhRg8eDAmT56M1q1bM5aVzWZj586dOHDgAFq1alXl6yExMRFjx47FjBkzxKrgI5WjoqmBefnyJZYvX46srCw4ODigS5cuMDAwgLq6OjgcDu8P9v79+wgJCYGuri5WrFgBQ0NDytsA8w4fPhyurq4YNGgQZGVlq2xbVFSEM2fOwN/fH0FBQfUT8CuS1r+A5PXxsWPH4Ofnh27dusHBwQGdOnWCnJwcX5uCggJERUXh3LlzCA8Px8yZMzF69GhG8m7cuBGXL1+Gk5MTHB0dKx0BS0hIQEhICM6cOYN+/frh119/reekXMOHD4ejoyPGjBkDbW3tKtu+efMGgYGBCA0NZez1QGqIQxoUV1dXzp07d6rd/ubNm5zx48fXYaKqUd66xWaz6+V7REXS+pfDkbw+/ueffzh5eXnVbp+VlcXx9vauw0RVCwwM5JSVlVW7fWlpKScgIKAOE1UtOzu7Xr6HMINGmghpwNzc3GBvbw9bW1uYmJgwHadBoj4mlXny5AnMzMyEPnbs2DGMHDmynhOR70VFUwPj5OQEOzs72NrawtbWlvG1CN9CeevWH3/8gYcPH+Lly5dQVVWFtbU1L7+ZmRlYLBbTEflIWv8CktfHAHfdzenTp9G3b1+oqqry7j948CBkZGTg7OwsMGUnbi5fvoz79++Dw+HA0tIS/fv3ZzqSgPbt22Pq1KmYPXs2b+o2MTERS5cuRWxsLKKiohhOSGqKiqYGxtfXFw8fPkR0dDQ+fPiAtm3bwtbWlvcmrqGhwXREPpS3fuTn5yM6OhrR0dF4+PAhHj16hIqKClhbW/MKFAsLC6ZjSmz/ApLTx7m5uZg6dSpevHiB/fv382XauHEjDh8+DENDQ+zatQsqKioMJq3cunXrcO/ePXTq1Anl5eW4du0aOnXqhBUrVjAdjU94eDiWL18OWVlZrFixAnfu3IGfnx969uwJDw8PNG3alOmIpIaoaGrAEhMTeQeg6OhovHjxAq1bt+YdhPr168d0RD6Ut35FREQgMDAQ165dQ3FxMZ4+fcp0JD6S3r+AePbxqlWrEBUVhZ07dwo9g+/du3eYOnUqunbtCnd3dwYS8nv27JnAtKejoyMuXLjAGw178eIFXFxccO/ePSYiVonNZsPd3R0XLlyAtLQ0Nm7ciD59+jAdi9QSFU0/CDabjcjISJw4cUKs3sArQ3lFLzk5GXfu3EFERAQePHiA9+/fw9jYGHZ2drC3t0fPnj2ZjlgpSehfQDL62NHREevWrYOtrW2lbUJDQ+Hp6YmrV6/WYzLhRo0ahcaNG8PNzY1XPM2bNw8cDgedO3dGRUUFLly4AEVFRfj5+TGclt+HDx+wadMmBAQEYPDgwXj+/DnS09Ph7u6OAQMGMB2P1AIVTQ0Um83G/fv3ERERgYiICMTGxkJFRQXW1tawt7eHnZ0d2rZty3RMHspbN4KCgngZ3717BxMTE94B3MbGBo0aNWI6olCS0r+A5PWxpaUlgoOD0bx580rbpKSkYNCgQYiOjq6/YFUIDQ2Fr68vtLW1MWfOHOjr6+PQoUN48OABpKSkYG5ujvHjx4tdX3fq1AkaGhrw9PSEra0tOBwO9u/fj82bN8Pc3Bz79+9nOiKpISqaGpitW7ciIiICDx8+RKNGjWBjYyN2B5kvUd66ZWJiAl1dXUyePBnOzs5id1D5mqT1LyB5fTx06FDMnDmzyunNCxcuwMfHBxcuXKjHZN9248YN+Pr6QktLC25ubjA1NWU6UpU2bdqEmTNnCiyqT01NhaenJ3bs2MFQMlJbVDQ1MF++gY8aNUrst+invHXLz88Pd+/exYMHD6CqqgpbW1vY29vD3t4eLVu2ZDqeAEnrX0Dy+nj//v04cOAA9u/fL3S0KSUlBZMmTcKwYcPg5ubGQELhsrKyoKGhARaLhbCwMPj6+kJdXR1z5swR++KJNBxUNDUwwcHBuHv3LiIiIpCamor27dvz3sA7duwodlv1U976UVpaiocPH/KyR0dHQ0NDA3Z2dryppBYtWjAdU2L7F5CcPuZwOJg7dy5CQ0PRp08fmJubo1GjRsjLy0NsbCyuXLkCOzs7bNu2TSy2Hfjvv//g4eGBnJwcKCkpwcPDg7e/0e3bt+Hr6wsVFRW4ubmhXbt2DKfl5+joWOWWE+KwZozUDBVNDVhaWhpvrUVERATev38PCwsL3pv4Tz/9xHREPpS3/rDZbERHR+Ps2bMIDg4Wy4XVkty/gPj38ZkzZxAUFIRnz54hLy8P6urqaN++PYYMGSJWi5QdHBywYMEC9OvXDzExMZg0aRIiIiL4RiHDw8Oxfft2HDhwgMGkgk6dOsX3dVlZGZKTk3Hy5EnMmzePNreUQFQ0/UBiYmJw9OhRsXwDF4byitbbt2/x6NEj3u3JkydQUFCAtbU1bGxsMGHCBKYjVknc+xeQ/D4WR127doWHhwevaBo3bhzu3r0LJSUlpqPV2tWrV7F3714cPnyY6SikhqhoaqCKiooQGxvL9wb+/v17mJqawsbGBjY2NujVqxfTMXkob93w9fXFo0ePEBMTg8zMTDRt2hS2tra8jExeDb4qktK/gOT2MYAqLxIrJycHHR0ddOjQgdFpukuXLmHZsmXIzc2FgoICfvvtN4wbN46xPKKQlJSEwYMH49GjR0xHITVERVMD88cff+DRo0dISEiAjIwMLCwseG/gVlZWYrewlvLWrUGDBvFGOWxtbdGkSROmI1VJ0voXkLw+/tKECRMQGRkJeXl5tG7dGhwOB0lJSSgqKoKenh7y8vKgoqKCXbt2wcDAgLGcFRUVyM7OhoaGBqSkpBjLUVPCNtssKCjAwYMHkZOTgxMnTjCQinwPGaYDENHKyMjA4MGDYWNjA3Nzc971jsQV5a1bwcHBvP9/9uyZ0AN6fn4+Nm3ahD/++KM+owklaf0LSF4ff8nIyAjKyspYt24d7xp0+fn5WLJkCZo3b46FCxdizZo1WLNmDfbs2cNYTikpKaG7l4s7V1dXgftkZWVhbm6OVatWMZCIfDcOabDCwsKE3v/mzRvOzJkz6znNt1HeumVra8u5f/8+331BQUGcLl26cBwcHBhKVTlJ618OR/L62NramvPixQuB+1+8eMGxtrbmcDgczqtXrziWlpb1HY3H2NiYY2JiUq0bIXWNRpoasFmzZmH9+vXo27cvAO4p0bt27cLOnTthbGzMcDpBlLduzZkzB1OnToWPjw+aNm2KFStWICYmBlOnTsUvv/zCdDwBkta/gOT1sZKSEl6+fCkw9ZaQkMBbx1RYWAgFBQUm4gGAwBlxHA4H06ZNw6pVq6Crq8tQqsqlpqZWu62enl4dJiF1gYqmBszb2xu///478vLy0KRJE6xcuRKFhYX466+/4OTkxHQ8AZS3brm6ukJHRwfz588Hm81G7969ERISIrZv3JLWv4Dk9fHkyZOxZMkSxMfHo3379uBwOIiNjcX+/fsxZcoUpKWl4a+//kKPHj0Yy2hnZydwn5SUFCwtLcVi36uvfbk3E0fIkmEWiwUOhwMWiyWWZ4CSqtFC8AbuwYMHmDlzJj58+ID//e9/mDlzplhf5oHyipawT7337t3DsmXL8Ouvv/JdbV0cD+zi3r+A5PfxmTNncOTIEcTFxUFGRgaGhoZwdXXFgAEDcO/ePVy5cgXz5s0Tq1P8rayscObMGbEsmt68eVPtts2aNavDJKQuUNHUwAg7W+Ply5dYvXo1xo4diz59+vA+/VR1lfP6QnnrlomJidAdiT9lFLdPvZLWv4Dk9XFDIM5FU3h4eI03Xr19+zY6d+5cR4mIKFHR1MCYmJhUq524vIFT3rolaZ96Ja1/Acnr46/dv38f+/fvR1JSEnbs2IGzZ8+iWbNmGDhwINPRKiXORdPChQvx/v17uLi4oEePHlBWVhbarqioCJcuXUJAQACaNm2Kf/75p56TktqgNU0NzLNnz5iOUCOUt269fv1aoj71Slr/ApLXx1+6dOkSPDw8MGrUKFy/fh1lZWWQkZHB4sWLkZubi7FjxzIdEVu3bhW4r7S0FAcOHICamhrf/eJwgWFvb2/cuXMH27dvx+LFi2FhYYE2bdpAQ0MDFRUVyMnJQVxcHJ49ewZLS0u4ubmhS5cuTMcm1SQ5u4SRajl+/LjQxYeVKS8vx7Fjx+owUdUob906fvw4XF1dERISgoKCgkrbFRUV4fTp03BxccHx48frMSE/SetfQPL6+Etbt27F8uXL4e7uDmlpaQDcxeFr1qzBvn37GE7H9eX1Bz/drKys8OzZM7777t69y3RUnk6dOmH//v0ICgrCzz//DDabjadPnyI+Ph7l5eUYMmQIzp49iwMHDlDBJGFopKmBSU5OxqBBgzBs2DD07t270ks4JCUl4dy5czh9+jR+/vnnek75GeWtW5L2qVfS+heQvD7+UlJSEiwtLQXut7CwwLt37+o/kBAHDx5kOkKttWnTBm3atGE6BhEhWtPUACUkJGD37t0ICQmBhoaGwBt4fHw88vLyMHDgQEydOpXRyyNQ3vqTkJCAsLAwPHnyBFlZWWCxWNDS0oKZmRm6desGfX19piMCkNz+BSSnjz9xdnaGs7Mzxo4dy7dOyMfHBzdu3MDJkyeZjsiTn58PaWlpoZfSSU9Px4YNG7B+/XoGkpEfCRVNDdiHDx9w9+5doW/g9vb2YnUKMcDNGxERgadPn0pMXknqX0lD/Vv3IiMjMWPGDHTu3BnXrl3D0KFDkZSUhJiYGGzfvr3Ga7XqQlpaGhYvXoyIiAgAQPfu3bF+/XqoqamhvLwc//77L7Zt2wZZWVleG0LqChVNhPygPu2q3LhxY6ajCHXv3j1YWVlBRoZ/FQGbzcaNGzfQu3dvhpJVTtiiZYB7tp+srCwaN26Mbt26idV11NLT03HkyBG8fPkS5eXlaN26NcaOHSs2e0rNmjULz58/x9y5cyErK4udO3fCyMgICxYswMyZM/Hs2TOMGDECCxYsgIaGBtNx0bt372qvy7t69WodpyGiRkUTYVxoaCiCg4Px4cMHdO7cGaNHj4a8vDzv8dzcXMyZM0fgcgpMSU1NxaNHj2BhYQE9PT1cvnwZBw8eRHZ2NgwMDDBjxoxqnzpf14KCgip9bNmyZZg3bx7vAD5s2LD6CVVNpqamuHXrFjQ1Nfnuj42NhYuLCx49esRQssotXLgQISEhaNKkCW+H7adPnyI1NRWWlpb48OED0tLSsHv3bqFriYgge3t7+Pj48Ea9Xr9+DScnJ7Ro0QIcDgerVq2Cubk5wyk/i4yMxPz586GtrY2JEydW2VZcd7YnlaOiiTDq2LFjWLVqFYYOHQoACAkJQePGjeHn58fbgyUjIwPdunUTi315bty4gdmzZ0NJSQlsNhuzZ8/G5s2bMXLkSBgYGCAmJgbBwcHYvHkzevbsyXRcdO/eHenp6dDW1oasrCzfY2/fvkXjxo0hLS0NFoslFp96jxw5Ak9PT74NIYXp3Lkz9uzZU8/pvm3hwoVQUlLC8uXLeWejVVRUYPXq1SgsLMTatWuxY8cOXL9+HQEBAYxkdHV1rbRfvyYOH1RMTU0RGhrKNyJqYWGBbt26wcfHR+B1LQ7i4uLg4uKC9evXi+WIKKk9KpoamA4dOoDNZlerrTgUIf3798ecOXMwYMAAAEBmZibmzJmD169fY//+/TAwMBCromnYsGEYNmwYJk2ahGPHjmHZsmVYtmwZXFxceG0OHz4Mf39/BAcHM5iUKz8/H+vXr0dERAT++usvvr2BxHWDwHv37qGiogITJ07Eli1b+PbiYbFYUFRUhJGREe+CsuLEysoKJ0+eFDjrLzExEcOHD0dUVBSSk5MxePBgREdHM5LxyynE7OxsHD16FL1794a5uTlkZWXx9OlThISEYNy4cVi0aBEjGb9kYmKCW7du8U1pWllZwd/fX2xGdIUJCAhAWFhYpVO2RDLRlgMNzJkzZzB9+nQoKChgyZIlTMf5prS0NLRv3573tZaWFvbt24dp06Zh4sSJOHTokFhdaywxMZH3ydHJyQnLly+HlZUVX5uuXbvCy8uLiXgCGjVqBE9PT0RGRmLZsmVo164dPDw8BKa8xMmny6NcvXoVenp61R4VEQfa2tqIjIwUKJru378PdXV1ANyRUyZf019uADlp0iQsWbJEYBNLW1tbHD16tL6j1UhlO22LizFjxmDMmDFMxyAiRkVTA6Ovr499+/bB2dkZSUlJGDlyJNORqmRsbIyTJ09i/vz5vPvk5eWxfft2TJ48Ga6urli1ahVzAb/SqlUrXLt2DRMmTICMjAzOnz8vUIAcP34cRkZGDCUUzsbGBkFBQdixYweGDBmCuXPnin0x0rhxYwQGBuLZs2coKSkRWFy7du1ahpJVbs6cOfjjjz9w//59mJubg8PhIDY2FufOncOyZcuQmJgId3d3sblESXR0NP766y+B+zt06ABPT08GEgl3/vx5vkKzoqICly5dElhQL27r8kjDQ9NzDdSVK1cQGhqKlStXMh2lStHR0Zg2bRp0dHSwdu1aWFhY8B7Lz8+Hm5sb7t69y1tQy7SbN29izpw5GD16NDw8PPgei4yMxNKlS5GRkYE9e/bw/S7i5MWLF1i6dCmioqJw+fJlsZue+8TDwwMXLlxAly5doKKiIvC4OBZNAPd14O/vj/j4eEhLS8PQ0BDjx4+HpaUlHj16hOjoaIwbN4635olJY8eOhb6+PpYvX847+SI/Px9//PEHcnNz8e+//zIbEICjo2O12onLujyAe7LIuXPnMHr0aKiqqqKkpATe3t4IDw+HhoYGJk+eLBZrHknNUdFEGJeRkYErV66ge/fuAqc5czgcHDt2DJcuXcLu3bsZSsjv9evXSEtLg52dHd/9L1684O11o6ury1C66ktNTUWTJk0gJSWeV1OysrKCr6+vWOwV1FC9fPkS06ZNQ25uLvT19cHhcPDq1Svo6enBz89PLC8wLO5iY2MxYcIE6OjoYN++fWjatCnmz5+PK1eu4H//+x9UVFSwZ88erF27ttoFIREfVDT9QEpKSvhO5Rd3lFc0oqKisGHDBqxcuZJv9+w5c+bg3bt3+PPPP8VyVKx79+7Yt2+fWO34/S1fjz5+TRxHx9hsNm7fvo2XL18CANq2bYvOnTsL7I/FlPDwcIm6IPIvv/wCPT09rFixAgD30kB9+vTB2LFjsWzZMgDcKfxjx46J/boxIkg8P2KS7xIVFYWxY8fy3gQ/+e233zBq1Cix29+G8tad6OhoTJw4Edra2gKXn5g4cSJ0dXXh6uqKmJgYhhJWbtasWVi9ejVev35do4v4ipOysjIkJiYiJCREbBbfh4eH830tJyeHnj17YsqUKZgyZQq6d+8uUDDdvn27PiPykbQLIkdFRcHV1ZX3dWhoKFgsFu8MYQCwtrZGXFwcE/HIdxKPjxJEZD4dJHv27Cn0ILl//364urri8OHDfGetMYXy1q3Nmzdj7NixWLx4scBjNjY2sLGxwdKlS+Hj4yMW058mJiZ8C9Q5HA769u0rtK04rHH7WmUjSbt370Z8fHw9pxHu+PHj8PX1hYuLC3r06FHpWWhFRUW4dOkSAgIC0LRpU8ZGbiTtgshlZWV8I863b9+GiooKOnbsyLuvtLRULPeXIt9G03MNzOTJk2FkZCT0IPnJ0qVL8fbtW7E4SFLeumVnZ4eAgIAqr7T+7NkzTJw4USyu23X37t1qt/16TZk4S0lJweDBgxEVFcV0FADgFSFRUVHfLEKmT5/OaBHyJUm4ILKLiwuGDh2KMWPGICsrCw4ODujbty/fxYS9vb0RHR2NgwcPMpiU1AaNNDUwMTEx+PPPP6tsM27cuG9u719fKG/dYrFYKC8vr7KNOH3ilaRCqLoKCwsRGBgoFtdF+6RTp07o1KkTXxHy9OlTXhEyZMgQbNiwQSyKkC+1adOmyg8A4sDNzQ2zZ89GWFgY4uLiICUlhenTpwPg7hR+8uRJHDp0iDa9lFBUNDUwknaQpLx1y8rKCufPn0fbtm0rbRMcHCx2+0oBglN1X5KVlYWOjg769++PefPmiU2fV5ZZXl5erPYb+0QSihBJ06VLFxw6dAjBwcFo0aIFRowYwTuZISgoCOHh4fD29oaDgwPDSUltUNHUwEjaQZLy1q1p06Zh0qRJkJeXh6urK5SUlHiPFRYW4uDBg9i1axe2bdvGYErhli9fjq1bt2LOnDmwtLQEh8NBTEwMtmzZAmdnZxgZGWHbtm3gcDj4/fffmY4LANi/fz9f0cRisSArKwtDQ0Ox2tme1K327dsLXdPo7u4utP3OnTsxZswYqKqq1nU08p1oTVMD8+DBA0yaNAmzZ8+u9CC5ZcsWbNu2DT169GAwKRflrXsXL17EsmXLUFBQgDZt2kBFRQV5eXlITEyEqqoqPDw8MHjwYKZjCujTpw+WLl2K7t27891/69YteHp64uLFi4iKisKcOXMQFhbGUEpS13r37l3tsyfFZXPLmurYsSNOnz4tthvNks+oaGqAJO0gSXnrXn5+Pq5du4a4uDh8+PAB6urqaNeuHbp168ZX+IkTKysrHD16VGDULi4uDqNHj0Z0dLRYLLB2dHSs9iVpxOGgLmlFSGRkJObPnw9tbe1vrhV0cnKqp1SiJa4XzyaCqGhqoCTtIEl5mfP+/XucPn0av/zyC9NR+Li5uSEnJwfr1q3j7Uz95s0beHh4QElJCdu2bcPWrVtx+/ZtRjcJPHXqVLXbisNBXRKLkLi4OLi4uGD9+vW8C2Y3JFQ0SQ4qmn5A4nqQrAzlFb2SkhJcvnwZp06dwp07dyAjI4OHDx8yHYtPTk4OFixYgPDwcKirq4PD4SAvLw9du3bFmjVr8PjxYyxZsgS+vr58e+Aw5cOHD5CVlYWCggLTUb5JEouQgIAAhIWFNcizzqhokhxUNP0gJOEg+SXKWzciIyMRFBSECxcuoKCgAC1atMDo0aMxfPhwsTol/kuJiYl8F79t1aoVAKC4uBjy8vLVnhqrK+np6fj9998REREBFouFn376CatWrULTpk0ZzfUtDbkIkTRUNEkOKpoaOEk7SFJe0UtJSUFQUBBOnz6N5ORkNGnSBL1794a/vz9Onz4NQ0NDpiPypKamomnTpmCxWEhNTa2y7dcXd2aKh4cHYmJiMHPmTEhJSWHXrl1QVlbGgQMHmI5GJAQVTZKDthxogIQdJIcNGwZ/f3/4+vqK1UESoLx1afz48Xjw4AGMjIwwYMAA9OrVi3dxXn9/f4bTCXJ0dMStW7egpaVV6QJrDocDFoslNpdR+e+//7Bnzx60a9cOAHevpgEDBqCoqEjgUjukdlJTU3Hu3DmMHj0aqqqqKCkpgbe3N8LDw6GhoYHJkyejZ8+eTMckPwAqmhoYSTtIUt66FRMTg+bNm6Nz586wtLSEsbEx05GqdPXqVd6FbcXhzK3qyMvLg66uLu/rVq1aQVpaGllZWbwF7OJGkoqQ2NhYTJgwATo6Ohg0aBBUVVXh7u6OK1eu4H//+x9UVFTg7u6OtWvXwtHRkem4tTJy5Ejax0tCUNHUwEjaQZLy1q3w8HBcu3YNwcHBOHjwIGRkZNClSxf06tULLBaL8fVAX/uyyKis4EhKSkJQUBDmzZtXX7GqVFFRIdCPMjIyqKioYChR1SStCPHx8cGgQYOwYsUKAEBycjIuXLiAsWPHYuHChQAATU1N+Pn5iUXe2liyZAnTEUg1UdHUwEjaQZLy1i1FRUUMHDgQAwcORF5eHi5evIiQkBD8+eefKC8vx+rVq+Hi4gIHBwfIyEjG28Hr16+xY8cOsSmahP27i9vr4EuSVoRERUXx7aQdGhoKFouFAQMG8O6ztrYWm8vUfGst3pfEZV0eqT5aCN6AfXmQvHv3LsrLy9G5c2exPUhS3vqTkZGB8+fP49y5c4iOjoaWlhZu3brFdKxquXnzJqZNmyY2a5pMTExgZWXFd/27yMhImJubQ15enq+tOCwOt7GxQUBAAG/t3aFDh7B69WocPHgQNjY2ALhnLDo5OSE6OprBpFyWlpY4e/Ysb5H0rFmzEBkZiTt37kBKSgoAEB8fj3HjxuHevXtMRgXAvfZcVlYWgM/r774mbuvySPWJ77s6+W6qqqoYOXIkRo4cyXeQnDNnjlgeJClv/dHW1oarqytcXV2RkpKCkJAQ3mN0HayacXNzE7jPzs6OgSTVU1ZWxlfM3b59GyoqKnx7XZWWlorNRZBNTU1x69YtjBkzBllZWbh16xb69u3LK5gA4OzZszAxMWEw5Wfnzp3DjBkzUFxcjM2bN0NaWprpSESUOOSHk5yczPHz8+N97efnx8nNzWUwUdUob/2ysrLivH79mukYlbpx4wbHxMSE6RjfhcnXxJgxYzj+/v4cDofDyczM5FhYWHB+//13vjZeXl6c8ePHMxFPQFhYGKdDhw6c2bNnc3r37s2xtLTkvHjxgsPhcDjPnj3jrFmzhmNmZsa5du0aw0k/y8nJ4fTq1YuzY8cOpqMQEaPpOSJxF4ukvHWLyT1jPDw8vtnm3bt3CA8Pl+ipDSZfE7du3cLs2bPRtWtXxMXFISMjA8ePH4eBgQHi4uJw8uRJHDp0CFu3boWDg0O95xMmJiYGwcHBYLFYGDFiBAwMDAAA69atw61btzBr1iz069eP4ZT87t+/j7CwMLFZe0dEg6bnSLUv3ikuKO+PTVdXF8OGDWM6xndh8jXRpUsXHDp0CMHBwWjRogVfERIUFITw8HB4e3uLTcEEAO3bt0f79u0F7v9ygfiXxGGK2draGsnJycjPzxfYTiA/Px+rV6/G2rVrGUpHaouKJkKI2KjNQUQcDpCSRhKLkJrYsWMH+vfvz3jexYsXQ19fH5s2beJbc1VcXIygoCAqmiSQ1LebEEKI+NqxYwdyc3OZjtGgSVofi9PobpcuXeDi4oJjx44xHYWIABVNhBCJJk4HyIaK+rh2WCwWZs+eDR8fH3h5ecHd3R0lJSVivY8XqRoVTYQQQkgd6tGjB06cOIHnz5/D2dkZKSkpTEcitURFEyGED10HixDR+HKErnnz5ggICECHDh0wadIk5kKR70ILwYnEHSQpb92i62DVPUl7TZDacXNzg5KSEu9rOTk5rF69GpaWljhz5gyDyUht0T5NhDRgP8J1sJjcV+pHIWl9LGl5ieSgkaYGRtIOkpS3bo0cOZKug1XHJO018SOgkTxSV6hoamAk7SBJeevWj3AdLKYPkJL2mqgNpvu4pmiKmdQVmp5rYHJycqp9kGzWrFk9JhOO8ta93NxcODs7Y+TIkZg+fTrTcRocSXxNSBIaySPihIqmBkjSDpKUt+5JynWwJPUAKUmvCUnr4y5dujT4kTwiOWh6rgFSU1PDunXrEBYWxnSUaqG8dU9SroMlqVNdkvSakLQ+/hGmmInkoJGmBiwoKAi9e/cW64Pklyhv3TIxMRF6HayMjAx069ZNLA6Qkj7VJQmvCUnsY0kaySMNG21u2YAtXrwYzs7OePbsGd/9ny4WKW4ob90T9+tgqaurw8/PD/n5+Th//jyaNWtW6U0cScJrQhL7+NNIXnFxMdNRyA+OiqYGTtwPkl+jvHVHUq6DJekHSEl4TUhiH1tbW0NfXx/5+fkCj+Xn58PDw4OBVORHQ0VTAyYpB8lPKG/9kITrYEnqAVKSXhOS2MeSMJJHGjYqmn4AknCQ/BLlrRuSdh0sST5ASsprQhL7WBJG8kjDRUVTAyZpB0nKW7cquw7WkiVLYGNjw2CyyknaAVLSXhOAZPWxJI3kkQaKQxqsLVu2cAoLCwXuDwwM5IwfP56BRFWjvORLJiYmnIyMDM7169c5dnZ2nEWLFnGKi4s5GRkZHBMTE6bjCSVprwlJ6+NPeTkcDic5OZnj5OTEGThwICc6Olos85KGh7YcIISIJVNTU4SFhUFLSwspKSmYO3cu2Gw2Vq9ejTFjxojFFgmSTtL62MTEBLdu3YKWlhYAgM1mY8WKFQgJCUFxcbHY5SUND03PEULEEkcCp7okjaT1sSROMZOGhUaaCCFiaevWrZgyZQoUFRX57j927BjOnDmDgwcPMpSs4aA+JqRmqGgihBBCCKkGmp4jhBBCCKkGKpoIIYQQQqqBiiZCCCGEkGqgookQQgghpBqoaCKEEEIIqQYqmgghhBBCqoGKJkIIIYSQavg/AOL+jiIBlUkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_new.corr(), cmap='Blues', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f1264159",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df_new.reset_index(drop=True)\n",
    "# df_new.fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2dd64fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "lower_resolution_df = []\n",
    "time = {'Date': []}\n",
    "while i < df_new.shape[0]:\n",
    "    # Date columns\n",
    "    time['Date'].append(df_new.loc[i, 'Date'])\n",
    "    # the others\n",
    "    if i+10 < df_new.shape[0]:\n",
    "        temp_series = df_new.iloc[i:i+10].mean()\n",
    "    else :\n",
    "        temp_series = df_new.iloc[i:].mean()\n",
    "    # temp_series = pd.concat(df_new.loc[i, 'Date'], temp_series)\n",
    "    lower_resolution_df.append(temp_series)\n",
    "    \n",
    "    i += 10\n",
    "\n",
    "df_lower_resolution = pd.concat(lower_resolution_df, axis=1)\n",
    "df_lower_resolution = df_lower_resolution.transpose()\n",
    "df_temp = pd.concat([pd.DataFrame(time), df_lower_resolution], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "588bc4f7",
   "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>Date</th>\n",
       "      <th>z1_AC1(kW)</th>\n",
       "      <th>z1_AC2(kW)</th>\n",
       "      <th>z1_AC3(kW)</th>\n",
       "      <th>z1_AC4(kW)</th>\n",
       "      <th>z1_Light(kW)</th>\n",
       "      <th>z1_Plug(kW)</th>\n",
       "      <th>z1_S1(degC)</th>\n",
       "      <th>z1_S1(RH%)</th>\n",
       "      <th>z1_S1(lux)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-07-01 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>28.452000</td>\n",
       "      <td>60.655000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-01 00:10:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>28.471667</td>\n",
       "      <td>60.628333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-07-01 00:20:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>28.645000</td>\n",
       "      <td>60.331000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-07-01 00:30:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>28.831667</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-07-01 00:40:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>28.794000</td>\n",
       "      <td>59.944000</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79051</th>\n",
       "      <td>2019-12-31 23:10:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>26.978000</td>\n",
       "      <td>62.985000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79052</th>\n",
       "      <td>2019-12-31 23:20:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.051</td>\n",
       "      <td>26.972000</td>\n",
       "      <td>63.079000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79053</th>\n",
       "      <td>2019-12-31 23:30:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>26.970000</td>\n",
       "      <td>63.310000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79054</th>\n",
       "      <td>2019-12-31 23:40:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>26.979000</td>\n",
       "      <td>63.230000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79055</th>\n",
       "      <td>2019-12-31 23:50:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>26.976000</td>\n",
       "      <td>63.440000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79056 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Date  z1_AC1(kW)  z1_AC2(kW)  z1_AC3(kW)  z1_AC4(kW)  \\\n",
       "0     2018-07-01 00:00:00         0.0         0.0         0.0        0.07   \n",
       "1     2018-07-01 00:10:00         0.0         0.0         0.0        0.07   \n",
       "2     2018-07-01 00:20:00         0.0         0.0         0.0        0.07   \n",
       "3     2018-07-01 00:30:00         0.0         0.0         0.0        0.07   \n",
       "4     2018-07-01 00:40:00         0.0         0.0         0.0        0.07   \n",
       "...                   ...         ...         ...         ...         ...   \n",
       "79051 2019-12-31 23:10:00         0.0         0.0         0.0        0.00   \n",
       "79052 2019-12-31 23:20:00         0.0         0.0         0.0        0.00   \n",
       "79053 2019-12-31 23:30:00         0.0         0.0         0.0        0.00   \n",
       "79054 2019-12-31 23:40:00         0.0         0.0         0.0        0.00   \n",
       "79055 2019-12-31 23:50:00         0.0         0.0         0.0        0.00   \n",
       "\n",
       "       z1_Light(kW)  z1_Plug(kW)  z1_S1(degC)  z1_S1(RH%)  z1_S1(lux)  \n",
       "0               0.0        0.030    28.452000   60.655000         0.0  \n",
       "1               0.0        0.030    28.471667   60.628333         0.0  \n",
       "2               0.0        0.030    28.645000   60.331000         0.0  \n",
       "3               0.0        0.030    28.831667   59.800000         0.0  \n",
       "4               0.0        0.030    28.794000   59.944000         0.0  \n",
       "...             ...          ...          ...         ...         ...  \n",
       "79051           0.0        0.050    26.978000   62.985000         0.0  \n",
       "79052           0.0        0.051    26.972000   63.079000         0.0  \n",
       "79053           0.0        0.050    26.970000   63.310000         0.0  \n",
       "79054           0.0        0.050    26.979000   63.230000         0.0  \n",
       "79055           0.0        0.050    26.976000   63.440000         0.0  \n",
       "\n",
       "[79056 rows x 10 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample = df_temp\n",
    "df_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d2414d9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 79056 entries, 0 to 79055\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   Date          79056 non-null  datetime64[ns]\n",
      " 1   z1_AC1(kW)    72336 non-null  float64       \n",
      " 2   z1_AC2(kW)    72336 non-null  float64       \n",
      " 3   z1_AC3(kW)    72336 non-null  float64       \n",
      " 4   z1_AC4(kW)    68818 non-null  float64       \n",
      " 5   z1_Light(kW)  73125 non-null  float64       \n",
      " 6   z1_Plug(kW)   73126 non-null  float64       \n",
      " 7   z1_S1(degC)   50437 non-null  float64       \n",
      " 8   z1_S1(RH%)    50437 non-null  float64       \n",
      " 9   z1_S1(lux)    50437 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(9)\n",
      "memory usage: 6.0 MB\n"
     ]
    }
   ],
   "source": [
    "df_sample.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d611ebc",
   "metadata": {},
   "source": [
    "### Set Date as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6b7a677a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>z1_AC1(kW)</th>\n",
       "      <th>z1_AC2(kW)</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "<p>79056 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "                     z1_AC1(kW)  z1_AC2(kW)  z1_AC3(kW)  z1_AC4(kW)  \\\n",
       "Date                                                                  \n",
       "2018-07-01 00:00:00         0.0         0.0         0.0        0.07   \n",
       "2018-07-01 00:10:00         0.0         0.0         0.0        0.07   \n",
       "2018-07-01 00:20:00         0.0         0.0         0.0        0.07   \n",
       "2018-07-01 00:30:00         0.0         0.0         0.0        0.07   \n",
       "2018-07-01 00:40:00         0.0         0.0         0.0        0.07   \n",
       "...                         ...         ...         ...         ...   \n",
       "2019-12-31 23:10:00         0.0         0.0         0.0        0.00   \n",
       "2019-12-31 23:20:00         0.0         0.0         0.0        0.00   \n",
       "2019-12-31 23:30:00         0.0         0.0         0.0        0.00   \n",
       "2019-12-31 23:40:00         0.0         0.0         0.0        0.00   \n",
       "2019-12-31 23:50:00         0.0         0.0         0.0        0.00   \n",
       "\n",
       "                     z1_Light(kW)  z1_Plug(kW)  z1_S1(degC)  z1_S1(RH%)  \\\n",
       "Date                                                                      \n",
       "2018-07-01 00:00:00           0.0        0.030    28.452000   60.655000   \n",
       "2018-07-01 00:10:00           0.0        0.030    28.471667   60.628333   \n",
       "2018-07-01 00:20:00           0.0        0.030    28.645000   60.331000   \n",
       "2018-07-01 00:30:00           0.0        0.030    28.831667   59.800000   \n",
       "2018-07-01 00:40:00           0.0        0.030    28.794000   59.944000   \n",
       "...                           ...          ...          ...         ...   \n",
       "2019-12-31 23:10:00           0.0        0.050    26.978000   62.985000   \n",
       "2019-12-31 23:20:00           0.0        0.051    26.972000   63.079000   \n",
       "2019-12-31 23:30:00           0.0        0.050    26.970000   63.310000   \n",
       "2019-12-31 23:40:00           0.0        0.050    26.979000   63.230000   \n",
       "2019-12-31 23:50:00           0.0        0.050    26.976000   63.440000   \n",
       "\n",
       "                     z1_S1(lux)  \n",
       "Date                             \n",
       "2018-07-01 00:00:00         0.0  \n",
       "2018-07-01 00:10:00         0.0  \n",
       "2018-07-01 00:20:00         0.0  \n",
       "2018-07-01 00:30:00         0.0  \n",
       "2018-07-01 00:40:00         0.0  \n",
       "...                         ...  \n",
       "2019-12-31 23:10:00         0.0  \n",
       "2019-12-31 23:20:00         0.0  \n",
       "2019-12-31 23:30:00         0.0  \n",
       "2019-12-31 23:40:00         0.0  \n",
       "2019-12-31 23:50:00         0.0  \n",
       "\n",
       "[79056 rows x 9 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample = df_sample.set_index('Date')\n",
    "df_sample.index = pd.to_datetime(df_sample.index)\n",
    "df_sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da37100",
   "metadata": {},
   "source": [
    "## Model Development"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dc6c451",
   "metadata": {},
   "source": [
    "### Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa2f82c",
   "metadata": {},
   "source": [
    "#### Feature Engineering\n",
    "*Feature Creation*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "50a6c2c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_features(df):\n",
    "    \"\"\"\n",
    "    Create time series features based on time series index.\n",
    "    \"\"\"\n",
    "    df = df.copy()\n",
    "    df['hour'] = df.index.hour\n",
    "    df['dayofweek'] = df.index.dayofweek\n",
    "    df['quarter'] = df.index.quarter\n",
    "    df['month'] = df.index.month\n",
    "    df['year'] = df.index.year\n",
    "    df['dayofyear'] = df.index.dayofyear\n",
    "    df['dayofmonth'] = df.index.day\n",
    "    df['weekofyear'] = df.index.isocalendar().week\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae05ff36",
   "metadata": {},
   "source": [
    "Fill null value groupby created features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d6ec677e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>28.471667</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01 00:20:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>28.645000</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01 00:30:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>28.831667</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01 00:40:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>28.794000</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:10:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>26.978000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>365</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:20:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.051</td>\n",
       "      <td>26.972000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>365</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:30:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>26.970000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>365</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:40:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>26.979000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>365</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:50:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>26.976000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>365</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79056 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     z1_AC1(kW)  z1_AC2(kW)  z1_AC3(kW)  z1_AC4(kW)  \\\n",
       "Date                                                                  \n",
       "2018-07-01 00:00:00         0.0         0.0         0.0        0.07   \n",
       "2018-07-01 00:10:00         0.0         0.0         0.0        0.07   \n",
       "2018-07-01 00:20:00         0.0         0.0         0.0        0.07   \n",
       "2018-07-01 00:30:00         0.0         0.0         0.0        0.07   \n",
       "2018-07-01 00:40:00         0.0         0.0         0.0        0.07   \n",
       "...                         ...         ...         ...         ...   \n",
       "2019-12-31 23:10:00         0.0         0.0         0.0        0.00   \n",
       "2019-12-31 23:20:00         0.0         0.0         0.0        0.00   \n",
       "2019-12-31 23:30:00         0.0         0.0         0.0        0.00   \n",
       "2019-12-31 23:40:00         0.0         0.0         0.0        0.00   \n",
       "2019-12-31 23:50:00         0.0         0.0         0.0        0.00   \n",
       "\n",
       "                     z1_Light(kW)  z1_Plug(kW)  z1_S1(degC)  ...  dayofweek  \\\n",
       "Date                                                         ...              \n",
       "2018-07-01 00:00:00           0.0        0.030    28.452000  ...          6   \n",
       "2018-07-01 00:10:00           0.0        0.030    28.471667  ...          6   \n",
       "2018-07-01 00:20:00           0.0        0.030    28.645000  ...          6   \n",
       "2018-07-01 00:30:00           0.0        0.030    28.831667  ...          6   \n",
       "2018-07-01 00:40:00           0.0        0.030    28.794000  ...          6   \n",
       "...                           ...          ...          ...  ...        ...   \n",
       "2019-12-31 23:10:00           0.0        0.050    26.978000  ...          1   \n",
       "2019-12-31 23:20:00           0.0        0.051    26.972000  ...          1   \n",
       "2019-12-31 23:30:00           0.0        0.050    26.970000  ...          1   \n",
       "2019-12-31 23:40:00           0.0        0.050    26.979000  ...          1   \n",
       "2019-12-31 23:50:00           0.0        0.050    26.976000  ...          1   \n",
       "\n",
       "                     quarter  month  year  dayofyear  dayofmonth  weekofyear  \n",
       "Date                                                                          \n",
       "2018-07-01 00:00:00        3      7  2018        182           1          26  \n",
       "2018-07-01 00:10:00        3      7  2018        182           1          26  \n",
       "2018-07-01 00:20:00        3      7  2018        182           1          26  \n",
       "2018-07-01 00:30:00        3      7  2018        182           1          26  \n",
       "2018-07-01 00:40:00        3      7  2018        182           1          26  \n",
       "...                      ...    ...   ...        ...         ...         ...  \n",
       "2019-12-31 23:10:00        4     12  2019        365          31           1  \n",
       "2019-12-31 23:20:00        4     12  2019        365          31           1  \n",
       "2019-12-31 23:30:00        4     12  2019        365          31           1  \n",
       "2019-12-31 23:40:00        4     12  2019        365          31           1  \n",
       "2019-12-31 23:50:00        4     12  2019        365          31           1  \n",
       "\n",
       "[79056 rows x 17 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample = create_features(df_sample)\n",
    "df_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d88e59db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "z1_AC1(kW)       6720\n",
       "z1_AC2(kW)       6720\n",
       "z1_AC3(kW)       6720\n",
       "z1_AC4(kW)      10238\n",
       "z1_Light(kW)     5931\n",
       "z1_Plug(kW)      5930\n",
       "z1_S1(degC)     28619\n",
       "z1_S1(RH%)      28619\n",
       "z1_S1(lux)      28619\n",
       "hour                0\n",
       "dayofweek           0\n",
       "quarter             0\n",
       "month               0\n",
       "year                0\n",
       "dayofyear           0\n",
       "dayofmonth          0\n",
       "weekofyear          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4c24ef59",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>z1_AC1(kW)</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2018</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">7</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
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       "      <td>0.030200</td>\n",
       "      <td>28.966023</td>\n",
       "      <td>62.466622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>29.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.030267</td>\n",
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       "      <td>0.030333</td>\n",
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       "      <td>62.540958</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>29.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.069667</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.030333</td>\n",
       "      <td>29.020219</td>\n",
       "      <td>62.316379</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>29.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.069200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030333</td>\n",
       "      <td>29.187317</td>\n",
       "      <td>62.061375</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>29.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2019</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">12</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">6</th>\n",
       "      <th>19</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.074433</td>\n",
       "      <td>26.199333</td>\n",
       "      <td>64.338167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.074567</td>\n",
       "      <td>26.193750</td>\n",
       "      <td>64.375833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.074967</td>\n",
       "      <td>26.192000</td>\n",
       "      <td>64.293583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076167</td>\n",
       "      <td>26.198583</td>\n",
       "      <td>64.228417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.075233</td>\n",
       "      <td>26.213083</td>\n",
       "      <td>64.208667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3024 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           z1_AC1(kW)  z1_AC2(kW)  z1_AC3(kW)  z1_AC4(kW)  \\\n",
       "year month dayofweek hour                                                   \n",
       "2018 7     0         0            0.0         0.0         0.0    0.069933   \n",
       "                     1            0.0         0.0         0.0    0.070000   \n",
       "                     2            0.0         0.0         0.0    0.069933   \n",
       "                     3            0.0         0.0         0.0    0.069667   \n",
       "                     4            0.0         0.0         0.0    0.069200   \n",
       "...                               ...         ...         ...         ...   \n",
       "2019 12    6         19           0.0         0.0         0.0    0.000000   \n",
       "                     20           0.0         0.0         0.0    0.000000   \n",
       "                     21           0.0         0.0         0.0    0.000000   \n",
       "                     22           0.0         0.0         0.0    0.000000   \n",
       "                     23           0.0         0.0         0.0    0.000000   \n",
       "\n",
       "                           z1_Light(kW)  z1_Plug(kW)  z1_S1(degC)  z1_S1(RH%)  \\\n",
       "year month dayofweek hour                                                       \n",
       "2018 7     0         0         0.000000     0.030200    28.966023   62.466622   \n",
       "                     1         0.000033     0.030267    28.975000   62.480433   \n",
       "                     2         0.000000     0.030333    28.969408   62.540958   \n",
       "                     3         0.000067     0.030333    29.020219   62.316379   \n",
       "                     4         0.000000     0.030333    29.187317   62.061375   \n",
       "...                                 ...          ...          ...         ...   \n",
       "2019 12    6         19        0.000000     0.074433    26.199333   64.338167   \n",
       "                     20        0.000067     0.074567    26.193750   64.375833   \n",
       "                     21        0.000033     0.074967    26.192000   64.293583   \n",
       "                     22        0.000000     0.076167    26.198583   64.228417   \n",
       "                     23        0.000033     0.075233    26.213083   64.208667   \n",
       "\n",
       "                           z1_S1(lux)  quarter  dayofyear  dayofmonth  \\\n",
       "year month dayofweek hour                                               \n",
       "2018 7     0         0            0.0      3.0      197.0        16.0   \n",
       "                     1            0.0      3.0      197.0        16.0   \n",
       "                     2            0.0      3.0      197.0        16.0   \n",
       "                     3            0.0      3.0      197.0        16.0   \n",
       "                     4            0.0      3.0      197.0        16.0   \n",
       "...                               ...      ...        ...         ...   \n",
       "2019 12    6         19           0.0      4.0      349.0        15.0   \n",
       "                     20           0.0      4.0      349.0        15.0   \n",
       "                     21           0.0      4.0      349.0        15.0   \n",
       "                     22           0.0      4.0      349.0        15.0   \n",
       "                     23           0.0      4.0      349.0        15.0   \n",
       "\n",
       "                           weekofyear  \n",
       "year month dayofweek hour              \n",
       "2018 7     0         0           29.0  \n",
       "                     1           29.0  \n",
       "                     2           29.0  \n",
       "                     3           29.0  \n",
       "                     4           29.0  \n",
       "...                               ...  \n",
       "2019 12    6         19          50.0  \n",
       "                     20          50.0  \n",
       "                     21          50.0  \n",
       "                     22          50.0  \n",
       "                     23          50.0  \n",
       "\n",
       "[3024 rows x 13 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.groupby(['year', 'month', 'dayofweek', 'hour']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0f7f513b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fillna with groupby\n",
    "for col in df_sample.columns:\n",
    "    df_sample[col].fillna(df_sample.groupby(['year', 'month', 'dayofweek', 'hour'])[col].transform('mean'), inplace=True)\n",
    "for col in df_sample.columns:\n",
    "    df_sample[col].fillna(df_sample.groupby(['month', 'dayofweek', 'hour'])[col].transform('mean'), inplace=True)\n",
    "for col in df_sample.columns:\n",
    "    df_sample[col].fillna(df_sample.groupby(['dayofweek', 'hour'])[col].transform('mean'), inplace=True)\n",
    "\n",
    "# https://towardsdatascience.com/6-tips-for-dealing-with-null-values-e16d1d1a1b33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4c57b95c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "z1_AC1(kW)      0\n",
       "z1_AC2(kW)      0\n",
       "z1_AC3(kW)      0\n",
       "z1_AC4(kW)      0\n",
       "z1_Light(kW)    0\n",
       "z1_Plug(kW)     0\n",
       "z1_S1(degC)     0\n",
       "z1_S1(RH%)      0\n",
       "z1_S1(lux)      0\n",
       "hour            0\n",
       "dayofweek       0\n",
       "quarter         0\n",
       "month           0\n",
       "year            0\n",
       "dayofyear       0\n",
       "dayofmonth      0\n",
       "weekofyear      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a453a9b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 23.52222222222222, 'Period of time')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.heatmap(df_sample.notnull().transpose(), cmap='mako')\n",
    "plot.set(xticklabels=[])\n",
    "plt.xlabel('Period of time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "113f43d6",
   "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>z1_S1(degC)</th>\n",
       "      <th>z1_S1(RH%)</th>\n",
       "      <th>z1_S1(lux)</th>\n",
       "      <th>hour</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>quarter</th>\n",
       "      <th>month</th>\n",
       "      <th>...</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>sum_ac(kW)</th>\n",
       "      <th>sum_light(kW)</th>\n",
       "      <th>sum_plug(kW)</th>\n",
       "      <th>sum_power_consumption(kW)</th>\n",
       "      <th>SMA30</th>\n",
       "      <th>SMA15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2018-07-01 00:00:00</th>\n",
       "      <td>28.452000</td>\n",
       "      <td>60.655000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01 00:10:00</th>\n",
       "      <td>28.471667</td>\n",
       "      <td>60.628333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01 00:20:00</th>\n",
       "      <td>28.645000</td>\n",
       "      <td>60.331000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01 00:30:00</th>\n",
       "      <td>28.831667</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01 00:40:00</th>\n",
       "      <td>28.794000</td>\n",
       "      <td>59.944000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:10:00</th>\n",
       "      <td>26.978000</td>\n",
       "      <td>62.985000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.050267</td>\n",
       "      <td>0.050143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:20:00</th>\n",
       "      <td>26.972000</td>\n",
       "      <td>63.079000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.050233</td>\n",
       "      <td>0.050286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:30:00</th>\n",
       "      <td>26.970000</td>\n",
       "      <td>63.310000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.050233</td>\n",
       "      <td>0.050143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:40:00</th>\n",
       "      <td>26.979000</td>\n",
       "      <td>63.230000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.050233</td>\n",
       "      <td>0.050143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:50:00</th>\n",
       "      <td>26.976000</td>\n",
       "      <td>63.440000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.050200</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79056 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     z1_S1(degC)  z1_S1(RH%)  z1_S1(lux)  hour  dayofweek  \\\n",
       "Date                                                                        \n",
       "2018-07-01 00:00:00    28.452000   60.655000         0.0     0          6   \n",
       "2018-07-01 00:10:00    28.471667   60.628333         0.0     0          6   \n",
       "2018-07-01 00:20:00    28.645000   60.331000         0.0     0          6   \n",
       "2018-07-01 00:30:00    28.831667   59.800000         0.0     0          6   \n",
       "2018-07-01 00:40:00    28.794000   59.944000         0.0     0          6   \n",
       "...                          ...         ...         ...   ...        ...   \n",
       "2019-12-31 23:10:00    26.978000   62.985000         0.0    23          1   \n",
       "2019-12-31 23:20:00    26.972000   63.079000         0.0    23          1   \n",
       "2019-12-31 23:30:00    26.970000   63.310000         0.0    23          1   \n",
       "2019-12-31 23:40:00    26.979000   63.230000         0.0    23          1   \n",
       "2019-12-31 23:50:00    26.976000   63.440000         0.0    23          1   \n",
       "\n",
       "                     quarter  month  ...  weekofyear  sum_ac(kW)  \\\n",
       "Date                                 ...                           \n",
       "2018-07-01 00:00:00        3      7  ...          26        0.07   \n",
       "2018-07-01 00:10:00        3      7  ...          26        0.07   \n",
       "2018-07-01 00:20:00        3      7  ...          26        0.07   \n",
       "2018-07-01 00:30:00        3      7  ...          26        0.07   \n",
       "2018-07-01 00:40:00        3      7  ...          26        0.07   \n",
       "...                      ...    ...  ...         ...         ...   \n",
       "2019-12-31 23:10:00        4     12  ...           1        0.00   \n",
       "2019-12-31 23:20:00        4     12  ...           1        0.00   \n",
       "2019-12-31 23:30:00        4     12  ...           1        0.00   \n",
       "2019-12-31 23:40:00        4     12  ...           1        0.00   \n",
       "2019-12-31 23:50:00        4     12  ...           1        0.00   \n",
       "\n",
       "                     sum_light(kW)  sum_plug(kW)  sum_power_consumption(kW)  \\\n",
       "Date                                                                          \n",
       "2018-07-01 00:00:00            0.0         0.030                      0.100   \n",
       "2018-07-01 00:10:00            0.0         0.030                      0.100   \n",
       "2018-07-01 00:20:00            0.0         0.030                      0.100   \n",
       "2018-07-01 00:30:00            0.0         0.030                      0.100   \n",
       "2018-07-01 00:40:00            0.0         0.030                      0.100   \n",
       "...                            ...           ...                        ...   \n",
       "2019-12-31 23:10:00            0.0         0.050                      0.050   \n",
       "2019-12-31 23:20:00            0.0         0.051                      0.051   \n",
       "2019-12-31 23:30:00            0.0         0.050                      0.050   \n",
       "2019-12-31 23:40:00            0.0         0.050                      0.050   \n",
       "2019-12-31 23:50:00            0.0         0.050                      0.050   \n",
       "\n",
       "                        SMA30     SMA15  \n",
       "Date                                     \n",
       "2018-07-01 00:00:00  0.000000  0.000000  \n",
       "2018-07-01 00:10:00  0.000000  0.000000  \n",
       "2018-07-01 00:20:00  0.000000  0.000000  \n",
       "2018-07-01 00:30:00  0.000000  0.000000  \n",
       "2018-07-01 00:40:00  0.000000  0.000000  \n",
       "...                       ...       ...  \n",
       "2019-12-31 23:10:00  0.050267  0.050143  \n",
       "2019-12-31 23:20:00  0.050233  0.050286  \n",
       "2019-12-31 23:30:00  0.050233  0.050143  \n",
       "2019-12-31 23:40:00  0.050233  0.050143  \n",
       "2019-12-31 23:50:00  0.050200  0.050000  \n",
       "\n",
       "[79056 rows x 17 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AC = [col for col in df_sample.columns if 'AC' in col]\n",
    "Light = [col for col in df_sample.columns if 'Light' in col]\n",
    "Plug = [col for col in df_sample.columns if 'Plug' in col]\n",
    "\n",
    "df_sample['sum_ac(kW)'] = df_sample[AC].sum(axis=1)\n",
    "df_sample['sum_light(kW)'] = df_sample[Light].sum(axis=1)\n",
    "df_sample['sum_plug(kW)'] = df_sample[Plug].sum(axis=1)\n",
    "df_sample['sum_power_consumption(kW)'] = df_sample[AC+Light+Plug].sum(axis=1)\n",
    "\n",
    "df_sample = df_sample.drop(columns=AC+Light+Plug)\n",
    "\n",
    "# Calculating moving average\n",
    "df_sample['SMA30'] = df_sample['sum_power_consumption(kW)'].rolling(30).mean()\n",
    "df_sample['SMA15'] = df_sample['sum_power_consumption(kW)'].rolling(15).mean()\n",
    "df_sample['SMA15'] = df_sample['sum_power_consumption(kW)'].rolling(7).mean()\n",
    "df_sample = df_sample.fillna(0)\n",
    "\n",
    "df_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cd68eb4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 79056 entries, 2018-07-01 00:00:00 to 2019-12-31 23:50:00\n",
      "Data columns (total 17 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   z1_S1(degC)                79056 non-null  float64\n",
      " 1   z1_S1(RH%)                 79056 non-null  float64\n",
      " 2   z1_S1(lux)                 79056 non-null  float64\n",
      " 3   hour                       79056 non-null  int64  \n",
      " 4   dayofweek                  79056 non-null  int64  \n",
      " 5   quarter                    79056 non-null  int64  \n",
      " 6   month                      79056 non-null  int64  \n",
      " 7   year                       79056 non-null  int64  \n",
      " 8   dayofyear                  79056 non-null  int64  \n",
      " 9   dayofmonth                 79056 non-null  int64  \n",
      " 10  weekofyear                 79056 non-null  UInt32 \n",
      " 11  sum_ac(kW)                 79056 non-null  float64\n",
      " 12  sum_light(kW)              79056 non-null  float64\n",
      " 13  sum_plug(kW)               79056 non-null  float64\n",
      " 14  sum_power_consumption(kW)  79056 non-null  float64\n",
      " 15  SMA30                      79056 non-null  float64\n",
      " 16  SMA15                      79056 non-null  float64\n",
      "dtypes: UInt32(1), float64(9), int64(7)\n",
      "memory usage: 12.6 MB\n"
     ]
    }
   ],
   "source": [
    "df_sample.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6fbb951b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sample['weekofyear']= df_sample['weekofyear'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0f4f8e04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 79056 entries, 2018-07-01 00:00:00 to 2019-12-31 23:50:00\n",
      "Data columns (total 17 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   z1_S1(degC)                79056 non-null  float64\n",
      " 1   z1_S1(RH%)                 79056 non-null  float64\n",
      " 2   z1_S1(lux)                 79056 non-null  float64\n",
      " 3   hour                       79056 non-null  int64  \n",
      " 4   dayofweek                  79056 non-null  int64  \n",
      " 5   quarter                    79056 non-null  int64  \n",
      " 6   month                      79056 non-null  int64  \n",
      " 7   year                       79056 non-null  int64  \n",
      " 8   dayofyear                  79056 non-null  int64  \n",
      " 9   dayofmonth                 79056 non-null  int64  \n",
      " 10  weekofyear                 79056 non-null  int64  \n",
      " 11  sum_ac(kW)                 79056 non-null  float64\n",
      " 12  sum_light(kW)              79056 non-null  float64\n",
      " 13  sum_plug(kW)               79056 non-null  float64\n",
      " 14  sum_power_consumption(kW)  79056 non-null  float64\n",
      " 15  SMA30                      79056 non-null  float64\n",
      " 16  SMA15                      79056 non-null  float64\n",
      "dtypes: float64(9), int64(8)\n",
      "memory usage: 12.9 MB\n"
     ]
    }
   ],
   "source": [
    "df_sample.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d23733cb",
   "metadata": {},
   "source": [
    "#### Time Series Train Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44164265",
   "metadata": {},
   "source": [
    "Due to time series prediction, *it's important to preserve a time sequence*. So, train_test_split method which randomly select training set, and test set will be not used at this point. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8567ca7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 80% train set, 20% test set\n",
    "df_sample['Date'] = df_sample.index\n",
    "df_sample = df_sample.reset_index(drop=True)\n",
    "num_temp = int(df_sample.shape[0]*0.8) # number of row split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1623bdc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_sample.loc[:num_temp, df_sample.drop(columns=['z1_S1(degC)', 'sum_power_consumption(kW)', 'sum_ac(kW)', 'sum_light(kW)', 'sum_plug(kW)']).columns]\n",
    "y_train = df_sample.loc[:num_temp, ['Date', 'sum_power_consumption(kW)']]\n",
    "\n",
    "X_test = df_sample.loc[num_temp+1:, df_sample.drop(columns=['z1_S1(degC)', 'sum_power_consumption(kW)', 'sum_ac(kW)', 'sum_light(kW)', 'sum_plug(kW)']).columns]\n",
    "y_test = df_sample.loc[num_temp+1:, ['Date', 'sum_power_consumption(kW)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2f504645",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.set_index('Date')\n",
    "y_train = y_train.set_index('Date')\n",
    "X_test = X_test.set_index('Date')\n",
    "y_test = y_test.set_index('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdf579c1",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50368695",
   "metadata": {},
   "source": [
    "In this case, I'm not sure about the best model to predict energy comsumption. So, I will test several models and select the best model considered by a proper accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "35c91993",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[09:18:45] WARNING: C:/buildkite-agent/builds/buildkite-windows-cpu-autoscaling-group-i-03de431ba26204c4d-1/xgboost/xgboost-ci-windows/src/objective/regression_obj.cu:213: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[0]\tvalidation_0-rmse:12.89150\tvalidation_1-rmse:11.45593\n",
      "[100]\tvalidation_0-rmse:1.73691\tvalidation_1-rmse:2.30511\n",
      "[200]\tvalidation_0-rmse:1.42322\tvalidation_1-rmse:2.28679\n",
      "[210]\tvalidation_0-rmse:1.41633\tvalidation_1-rmse:2.28733\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=50, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.03, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints=&#x27;()&#x27;, n_estimators=1500, n_jobs=0,\n",
       "             num_parallel_tree=1, objective=&#x27;reg:linear&#x27;, predictor=&#x27;auto&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=50, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.03, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints=&#x27;()&#x27;, n_estimators=1500, n_jobs=0,\n",
       "             num_parallel_tree=1, objective=&#x27;reg:linear&#x27;, predictor=&#x27;auto&#x27;, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=50, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy='depthwise', importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.03, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=1500, n_jobs=0,\n",
       "             num_parallel_tree=1, objective='reg:linear', predictor='auto', ...)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gradient Boosting\n",
    "import xgboost as xgb\n",
    "# from sklearn.metrics import mean_squared_error\n",
    "\n",
    "reg = xgb.XGBRegressor() # prepare for Hyperparameters Tuning\n",
    "\n",
    "# finding the best parameters by GridSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# param_grid = [{\n",
    "#     'base_score':[0.5],\n",
    "#     'booster':['gbtree'],\n",
    "#     'n_estimators':[500*i for i in range(5)],\n",
    "#     'objective':['reg:linear'],\n",
    "#     'max_depth':[i for i in range(10)],\n",
    "#     'learning_rate':[0.01*i for i in range(10)],\n",
    "#     'random_state':[1]\n",
    "# }]\n",
    "\n",
    "# grid = GridSearchCV(reg, param_grid=param_grid)\n",
    "# grid.fit(X_train, y_train)\n",
    "\n",
    "# best_estimator = grid.best_estimator_\n",
    "\n",
    "# best_estimator.fit(\n",
    "#     X_train, y_train,\n",
    "#     eval_set=[(X_train, y_train), (X_test, y_test)],\n",
    "#     verbose=100\n",
    "# )\n",
    "\n",
    "reg = xgb.XGBRegressor(base_score=0.5, booster='gbtree',    \n",
    "                       n_estimators=1500,\n",
    "                       early_stopping_rounds=50,\n",
    "                       objective='reg:linear',\n",
    "                       max_depth=6,\n",
    "                       learning_rate=0.03, \n",
    "                       random_state = 48)\n",
    "reg.fit(X_train, y_train,         \n",
    "        eval_set=[(X_train, y_train), (X_test, y_test)],\n",
    "        verbose=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76bc61c5",
   "metadata": {},
   "source": [
    "#### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "566ec445",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('dark')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a58e0151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_important = pd.DataFrame(\n",
    "    data=reg.feature_importances_,\n",
    "    index=reg.feature_names_in_,\n",
    "    columns=['importance']\n",
    ")\n",
    "ax = feature_important.sort_values('importance').plot(kind='barh', title='Feature Importance', color='royalblue')\n",
    "ax.bar_label(ax.containers[0])\n",
    "ax.legend(loc='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "5ab54d1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sum_ac(kW)': 380491.4563858987,\n",
       " 'sum_light(kW)': 139243.76006545784,\n",
       " 'sum_plug(kW)': 19841.437518434846}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec_usage = df_sample[['sum_ac(kW)', 'sum_light(kW)', 'sum_plug(kW)']].sum().to_dict()\n",
    "elec_usage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61829528",
   "metadata": {},
   "source": [
    "### Testing Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "790382fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "cada6c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d5abe366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_score =  0.9516\n",
      "mean_absolute_error_score =  0.8373\n",
      "mean_squared_error_score =  5.1534\n"
     ]
    }
   ],
   "source": [
    "print('R2_score = ', r2_score(y_test, y_predict).round(4))\n",
    "print('mean_absolute_error_score = ', mean_absolute_error(y_test, y_predict).round(4))\n",
    "print('mean_squared_error_score = ', mean_squared_error(y_test, y_predict).round(4))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "a9cff5a362bc38ef45d817ae74b1af54d6a076e3d773891282bce078b815ba34"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}