PPE2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PPE 31 - KYTH : Know Your worTH\n",
    "\n",
    "Authors : Hippolyte COUSIN - Sosthene DE LAMOTTE - Oscar MARZE - Paul SENARD - Camil TIRES - Tiago TEIXEIRA LOPES\n",
    "\n",
    "\n",
    "Nous avons étudié le fonctionnement des techniques récentes notamment celles proposées dans un article de recherche sur la  [Deep Distribution Regression](https://arxiv.org/pdf/1903.06023.pdf). Il nous a servi de guide pour nos expérimentations tout au long du projet.  Trois chercheurs Rui Li , Howard D. Bondell and Brian J. Reich y présentent leur solution pour le concours Global Energy Forecasting Competition  de 2014 (cette publication date du 14 mars 2019). Ils présentent aussi une solution afin d’obtenir une distribution conditionnelle complète qui contient des informations pouvant être cruciales. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation des Bibliothèques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR: unknown command \"intall\" - maybe you meant \"install\"\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip intall numpy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (1.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.3 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from pandas) (2020.5)\n",
      "Requirement already satisfied: numpy>=1.16.5 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from pandas) (1.19.2)\n",
      "Requirement already satisfied: six>=1.5 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (3.3.4)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from matplotlib) (1.3.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from matplotlib) (1.19.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from matplotlib) (8.1.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: six in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (2.4.3)\n",
      "Requirement already satisfied: scipy>=0.14 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from keras) (1.6.1)\n",
      "Requirement already satisfied: h5py in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from keras) (2.10.0)\n",
      "Requirement already satisfied: numpy>=1.9.1 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from keras) (1.19.2)\n",
      "Requirement already satisfied: pyyaml in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from keras) (5.4.1)\n",
      "Requirement already satisfied: six in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from h5py->keras) (1.15.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sklearn in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (0.0)\n",
      "Requirement already satisfied: scikit-learn in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from sklearn) (0.21.3)\n",
      "Requirement already satisfied: scipy>=0.17.0 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from scikit-learn->sklearn) (1.6.1)\n",
      "Requirement already satisfied: numpy>=1.11.0 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from scikit-learn->sklearn) (1.19.2)\n",
      "Requirement already satisfied: joblib>=0.11 in /home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages (from scikit-learn->sklearn) (1.0.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importation des Bibliothèques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdate\n",
    "from collections import Counter\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense\n",
    "from keras.layers.core import Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Visualisation des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données avec lesquelles nous travaillons sont issues de la compétition Global Energy Forecasting 2014, dont l'un des thèmes était la production d'énergie solaire, à partir de données de trois fermes solaires en Australie. \n",
    "\n",
    "Nous avons utilisé exclusivement les données d'une ferme, située dans la 'zone 1'. \n",
    "\n",
    "Cette ferme renvoyait chaque jour les valeurs horaires pour 12 variables, que nous avons choisi de toutes utiliser pour entraîner notre modèle.\n",
    "En voici la liste :\n",
    "\n",
    "* VAR78 : Teneur en eau dans les nuages en kilogramme par m2\n",
    "* VAR79 : Teneur en eau dans les nuages en kilogramme par m2\n",
    "* VAR134 : Pression en surface en Pascal\n",
    "* VAR157 : Taux d'humidité relatif à 1000 millibar en %\n",
    "* VAR164 : Couverture nuageuse totale\n",
    "* VAR165 : Vitesse verticale du vent en mètre par seconde\n",
    "* VAR166 : Vitesse horizontale du vent en mètre par seconde\n",
    "* VAR167 : Température à 2 mètres de la surface en Kelvin\n",
    "* VAR169 : Rayonnement solaire de surface en Joule par m2\n",
    "* VAR175 : Rayonnement thermique en surface en Joule par m2\n",
    "* VAR178 : Rayonnement solaire net en Joule par m2\n",
    "* VAR228 : Précipitation totale en mètre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"predictors15.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 59112 entries, 0 to 59111\n",
      "Data columns (total 15 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   ZONEID     59112 non-null  int64  \n",
      " 1   TIMESTAMP  59112 non-null  object \n",
      " 2   VAR78      59112 non-null  float64\n",
      " 3   VAR79      59112 non-null  float64\n",
      " 4   VAR134     59112 non-null  float64\n",
      " 5   VAR157     59112 non-null  float64\n",
      " 6   VAR164     59112 non-null  float64\n",
      " 7   VAR165     59112 non-null  float64\n",
      " 8   VAR166     59112 non-null  float64\n",
      " 9   VAR167     59112 non-null  float64\n",
      " 10  VAR169     59112 non-null  float64\n",
      " 11  VAR175     59112 non-null  float64\n",
      " 12  VAR178     59112 non-null  float64\n",
      " 13  VAR228     59112 non-null  float64\n",
      " 14  POWER      59112 non-null  float64\n",
      "dtypes: float64(13), int64(1), object(1)\n",
      "memory usage: 6.8+ MB\n",
      "----------------------------------------------------------------------------------------------------\n",
      "             ZONEID         VAR78         VAR79        VAR134        VAR157  \\\n",
      "count  59112.000000  59112.000000  59112.000000  59112.000000  59112.000000   \n",
      "mean       2.000000      0.041899      0.016586  93810.494829     68.094098   \n",
      "std        0.816503      0.122218      0.050877   1110.801611     20.268233   \n",
      "min        1.000000      0.000000      0.000000  90345.875000      6.348233   \n",
      "25%        1.000000      0.000000      0.000000  92904.671875     52.996803   \n",
      "50%        2.000000      0.003159      0.000127  94011.093750     71.272995   \n",
      "75%        3.000000      0.034485      0.007374  94676.125000     85.338207   \n",
      "max        3.000000      4.103422      0.820470  96317.125000    102.838364   \n",
      "\n",
      "             VAR164        VAR165        VAR166        VAR167        VAR169  \\\n",
      "count  59112.000000  59112.000000  59112.000000  59112.000000  5.911200e+04   \n",
      "mean       0.442297      0.771363     -0.266127    285.239371  1.226033e+07   \n",
      "std        0.400495      2.457004      1.997156      7.018864  6.610766e+06   \n",
      "min        0.000000     -8.908603     -8.404230    269.436279  1.147127e+05   \n",
      "25%        0.014224     -0.739636     -1.608943    279.939514  7.318222e+06   \n",
      "50%        0.367180      0.386829     -0.420534    284.494629  1.103479e+07   \n",
      "75%        0.892060      2.166183      0.950577    289.752991  1.718342e+07   \n",
      "max        1.000008     13.056718     10.710998    310.458496  3.462558e+07   \n",
      "\n",
      "             VAR175        VAR178        VAR228         POWER  \n",
      "count  5.911200e+04  5.911200e+04  59112.000000  59112.000000  \n",
      "mean   1.380132e+07  1.435861e+07      0.001013      0.183711  \n",
      "std    7.743237e+06  7.064918e+06      0.003435      0.272524  \n",
      "min    8.216176e+05  6.465230e+05      0.000000      0.000000  \n",
      "25%    7.258113e+06  9.236506e+06      0.000000      0.000000  \n",
      "50%    1.366837e+07  1.311384e+07      0.000000      0.002525  \n",
      "75%    1.999846e+07  1.976043e+07      0.000263      0.322379  \n",
      "max    3.467366e+07  3.772200e+07      0.055961      1.003550  \n",
      "----------------------------------------------------------------------------------------------------\n",
      "shape :  (59112, 15)\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "print(100*'-')\n",
    "print(df.describe())\n",
    "print(100*'-')\n",
    "print(\"shape : \", df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"POWER\"] <= 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Transformation de la date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On effectue une transformation de cosinus et sinus sur la date à l'aide de la fonction suivante afin de les rendre cycliques, car la puissance solaire dépend fortement des saisons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode(data, col, max_val):\n",
    "    data[col + '_sin'] = np.sin(2 * np.pi * data[col]/max_val)\n",
    "    data[col + '_cos'] = np.cos(2 * np.pi * data[col]/max_val)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"TIMESTAMP\"] = pd.to_datetime(df[\"TIMESTAMP\"])\n",
    "df[\"TIMESTAMP_year\"] = df[\"TIMESTAMP\"].dt.year\n",
    "\n",
    "df['month'] = df[\"TIMESTAMP\"].dt.month\n",
    "df = encode(df, 'month', 12)\n",
    "\n",
    "df['day'] = df[\"TIMESTAMP\"].dt.day\n",
    "df = encode(df, 'day', 365)\n",
    "\n",
    "df['hour'] = df[\"TIMESTAMP\"].dt.hour\n",
    "df = encode(df, 'hour', 23.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avant la transformation : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = df[0:168]\n",
    "tmp['hour'].plot()\n",
    "plt.ylabel(\"Hour\")\n",
    "plt.xlabel(\"Days\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Après la transformation : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEGCAYAAAC3lehYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABVbElEQVR4nO29e5RkV3Xf/9n16uqufr9GoxlJ05IG9BpZkLHwg+AYZJAgQYrtOJA4yASieAV+/hHHjmGRn3GcOAsn6xeykmA7ig0IB/Mw2EZeCCu8DPnZEmLAYh6SRho9kKZ7Ht09XdWv6q7X+f1x762q6emqvnUf59wenc9avaa6XrPnzr5nn/3d+5wjSiksFovFYomDlGkDLBaLxXL5YoOMxWKxWGLDBhmLxWKxxIYNMhaLxWKJDRtkLBaLxRIbGdMG6GRyclIdOHDAtBkWi8Wyq/jud7+7oJSaCvLZl1WQOXDgAEeOHDFthsVisewqROQHQT9r5TKLxWKxxIYNMhaLxWKJDRtkLBaLxRIbNshYLBaLJTZskLFYLBZLbBgNMiLyMRE5LyLHO7wuIvJfReSUiBwVkVe3vXaviDzj/tyrz2qLxWKx+MV0JvMJ4M4ur98FHHR/7gN+F0BExoEPAa8Bbgc+JCJjsVpqsVgslp4xGmSUUt8CLnR5y93AJ5XDo8CoiOwF3gR8RSl1QSm1BHyF7sEqdk6eXeGbT8+bNKEnvnHyPM+cWzFthi+q9QZ/fOQlVjaqpk3xxfzKJn/2N7PslmM0TsyV+KtTC6bN8M1XnzjH8wtrps3wxWatzueOvMR6pWbaFGOYzmR2Yh/wUtvvp93nOj1/CSJyn4gcEZEj8/PxBIG/eXGJn/ndv+afPXCE5V0wEH7ykRd458e/w6994ahpU3akVm/wvs88zq9+/ij3f+s50+bsyPmVDf7h/Y/wvs8+ziPPLZo2Z0cee/4CP/u7j/DuB45QrtRNm7Mj93/rWd79ySP8mz87ZtqUHanUGrznU3/Dv/78UT7+Vy+YNscYSQ8yoVFK3a+UOqyUOjw1FWhXhK6cOr/CO/7gMXKZFJV6g68+cS7yvyNK/uR7p/n1L55gvJDjey8WmSuWTZvUlQ/8yTG+dOwM44UcXzp2JtHZwXqlxj/6n9/mbGmDvkyKh46dMW1SV56YW+YXPv4Y/bk05Wqdb5w8b9qkrvzRt1/kPzz0FOOFHI88u8ji6qZpk7ryr/74+3z1yXOMDWT50tFk+0KcJD3IzAJXtf2+332u0/Pa+dO/maVcrfPge3+cvSP5xA8sf/joD7hx7zCf/mc/AsCXj581bFFnVjdrfP57p3nHj17DL//UK3hufo2TCZb4Hnl2kVPnV/nPP3cbb7hxmr84fo56I7lB8QvfO029ofjSL72WCTeIJ5lPPvICP3TVKA+883YaCh4+kdwJ3dJahT///hz//HXX8p6fvJ4nzizzwi6R+KIm6UHmQeAdbpfZjwAlpdQZ4GHgjSIy5hb83+g+p51js8sc3DPE/rEB7rplL996eiGxklmt3uCJuWV+7LoJXnnFEDdcMcSXEzywPDG3jFLwE6+Y4s5briAl8FCCZ4THZkuIwN8+OMmbD+1lYXWTx57vVnI0y7HZEjddOczekX7edMsVfOOp84mVzDaqdZ45v8prr5/gln3DzEwWEj2hOzZbAhzfvevQXoDEB/G4MN3C/GngEeCVInJaRN4lIr8oIr/ovuUh4DngFPA/gX8BoJS6APw74Dvuz2+6z2lFKcWx00Vu3TcCwFtuvYJKvcHXnkzmDOuZ86ts1hrcut+199BejvxgibOlDcOWbc/R00UADu0bYXKwj9fMTCRaMjt2usT1U4MU+jK8/oZp8tnkSmb1huLEbKnlu4f2sl6p882nkymZPXFmmXpDcWjfKCLCmw9dwSPPLXJhrWLatG3xgszN+0bYN9rPbVeNJtYX4sZ0d9nblVJ7lVJZpdR+pdQfKKV+Tyn1e+7rSin1HqXUdUqpQ0qpI22f/ZhS6nr35+Mm7D+9VGZpvcot7qD9qqvG2DuS5+HjyQwyx047jn/IHVjefKszw/rKE8mUzI7NlrhiOM/0cB5w7H12fo1n55MpOxydLTWv7UAuw0++cpqHTyTz2j6/sMpapc6h/aMAvGZmnIlCLrESlOe73gTpzYf2Um8ovprQCd3R00UOTAww0p8FnCB+Ym6Z00vrhi3TT9LlskRz3J2teLPBVEp41dWjPJ3QusGx2RKDfRkOTBQAuG5qkNGBLE+dTa69t7jXFuDVV48CTrt40ji3vMH8yiaH9rfs/VvXjHF+ZZPievJm295M2wuKmXSKH7pqNNG+MFHIsXfEmXDceMUw+Wwqkb4AcHx2uRnAAV59jbOML6ljQ5zYIBOCo7Mlsmnhhr1DzecOTBR48cI6tXrDoGXbc3S2xC37hkmlpPncgYkCLywmLzNY2ajy3Pxac+YKNINjEu09umWmDS17k7im4+jpEv3ZNNdNFZrPHZgo8MLCWiLlyGOnSxzaP4KI47uplDTtTRoLq5vMFsvNySfAzKTnCzaTsfTAsdMlXrFniL5MuvnczGSBWkNxeilZrcHVeoMnzyxza9vsCuDayQLPJ1B+Oj67DHBRZlDoy7BnuI/nEmjvsdNFUgI37W0bWKaSG2SOnS5x85XDZNKtIWBmqkC5WufccrJag9crNZ45v3LRoA3OvZbIa+tmie1Z+NhAlpH+LM8vrJoyyxg2yAREKcWx2dJFM1don7Eky/mfPrdCpdZoyiMeByYLzJU22Kgmq6vo+BY5x2NmMpmZ17HZEgenh+jPtSYcV40NkJLk+UK9oTgxt3xRAAeYSWjm9eSZZRqKi+QncHwhiarB8dNekBluPiciHEhoUIwbG2QC8tKFMqVy9aLZCiQ3yGwt+nt49iZt4D46W+LKkTyTg30XPZ/E2as34djqC7lMiqvGBxJn77Pzq5Sr9Ut9IaGZ19EOvnsgoarB0dkS104WGMpnL3r+2skCL1i5zOKXZ+edtPeGK4Yuen68kGMon0ncjfrs/Cr5bIprJgYuer4ZZJJm7/lVXrnl2oJj74W1CqX15KxFWi7XWFitXOIL4NQ5EucL5x3f3Xp99w7n6cukEjfheHZ+lZH+LHuGL55wXOtN6BJo73a+e2CiwGyxnDjVIG5skAnIrLsdy77RiwdtEXHqHAkbWOaKG1w52t8snHoccG/U55Jmb6nMvrH+S55vFtMTNLA0fWEbe73MK0nFdM/e/Vt81yumJ63mNVfcYF8X301STVEpxVyxzL7RbXxhKpmqQdzYIBOQuWKZTEqYGuq75LUkaq+zHRx/sC/D1FBfom7Utc0axfUqV25j77VNSSc5BVRv/7ft7J2ZLLBeqTO/kpxi+lxxg0IuzXB/5pLXDkwOJOragnN9t7u2EwlUDZbWq2xUG9v7wkQyVYO4sUEmIHPFMleM5Emn5JLXZiYLzJWSlRbPFctcOXKp40PyiulnSl6WeKm9V417xfTkaNtzJS/I5C95bSaBmaI3aG/NDABmJgd58cJ6ovZccyZIl15bTzVIku92m3AcmHQyxyT5gg5skAmIJz9tx8xkAaXgxQvJGAg3a3XOr2x2tjdhdYPZorPNzXb29mXS7BvrT5i9ZXLpFJOFS7PaJNa85krbZwYAM5MDVOuK2YQU05c3qqxs1Drae2AyWfJeS0a/1N6hfJbJwb5E+YIObJAJyGyxzP4uQQZIjPOfKzlSzXYzbXC04oXVSmI29uw2GwRntp2kG9WZcOQvWuTqceVoP7l0KlFBsZP8BM61heTUvM50mXBA8lSDuS71OSCR9dq4sUEmAPWG4uxy50zmQMLamLvNriB5s+25YpmUwJ5t6l3QulGTUkzvNminU8I1EwOJkUg2qnUWVivbyk/Q1oI/n4y6zM4TjmSpBnPFMvlsirGB7LavJ7EFP25skAnA+ZUN6g3V0fGH81nGBrKJ2Qxvpxv16nFHK07KeoPZYpkrhvMXrUZv56rxAVY3a5TKycm8Ol1bcK5vUq7tmVL3zGByMEd/Np0Ye3eaILV8Nyn32vZdnB5XTwywsFpJ7JEKcWCDTABag/b2s0GA6aE85xPSUeTZe8XI9vZOuxnD+eVkbPm/06DdtDcB17dab3CuS1YLMD3cx/xKcq4tdA4yIsL0cF8iri107+IEmjt0n0/IVjidujg9vH9HkroN48YGmQB4heluzjQ93JecQbtUZnKwj3w2ve3rYwM5MinhXEIcv1tTBbSCzLkEXN9zyxs0FB3lJ4CpoTwLqxWqCdj+ZKfMAJzrm4RrC927OAGmBj1fSIrvdu7ihDbfTcikQwc2yATAmw3u7XqjJieTmS1udB0EUylheqgvEbPBRkNxpkv3E8CeBM1e53YoTAPNleoLCTiTfq5YRqR1DbdjejifmJn2ThOOXCbFeCHH+QQM2jt1cUKyfFcXpk/GvFNETorIKRF5/zavf0REHnd/nhaRYttr9bbXHtRp91yxzEh/lsG+SxezeTgSySaNBKw32El+ApgazifiRl1Y3aRaV12D4vRwcuSyneQncCYckIyBZa5YZnqoj1ym862fpExmJ/kJHHuT4As7dXFCu9SbjOurg86jZMyISBr4KPBTwGngOyLyoFLqCe89Sql/2fb+/wt4VdtXlJVSt2ky9yL8DNp7hvqoNRQX1iuXbPKoE2+bi594xVTX9+0Z6uMHi+aLp7M+Bu2BXIahvkwiBsKmvV0kEi+TSYK9O2UG4My21yp11jZrFLpMpOKm1cXZedAGJ/NKgjTtR4ocG8iRTUti5D0dmMxkbgdOKaWeU0pVgM8Ad3d5/9uBT2uxbAd2kp8gOQXJUrnKeqW+48DiFHvN36h+5CeAKTdTNM1cscx4IXfRFv9baWYyCbF3R19ISGPFTl2cHknJZPxktamUMDWYjHtNFyaDzD7gpbbfT7vPXYKIXAPMAF9vezovIkdE5FERuafTXyIi97nvOzI/Px+B2b3eqGadqTW72iEoDuVZWq+yWTPbWunnRgVvYDF/ozq+0P3aTg7mEDE/aCulfMpPzr/HdObViy8kQZreqYvTYypBNS8d7JbC/9uAzyul2kfAa5RSh4F/BPwXEbluuw8qpe5XSh1WSh2emuouGflhzV2fsbeLPALJKfB5mcHO9iajtXK2WHY2b8x3l2n2DOcTITnMFTd2vLaZdIqJgvluwwtrFTZrDfbuMAjuSUjNq7m9kI97zZOmTeJ0ceY6dnF67ElQzUsHJoPMLHBV2+/73ee2421skcqUUrPun88Bf8nF9ZrY8Abh6Q59+x5TCclkmvYOd7e3NXs1O7DMr24yPZzvuJjNw8tkTK/6n1/d3NEXIBmSzvyq57s7Z7Vgft2U33uttc7L8PVd2WRqh2sLJGodkg5MBpnvAAdFZEZEcjiB5JIuMRG5ARgDHml7bkxE+tzHk8CPA09s/WwcLK45zjExmOv6vnzWmY2bdqZFd2AZL3S3t7VIzOzAsri6ycQOtoIzEG5UG6xs1jRYtT21eoOl9QoTPho7klDzWlx1Zvo7+e5wf4ZcJpUI302nhJH+7bdo8Wh1G5q9vgurFSZ3uLbg+G4xAdK0LowFGaVUDXgv8DDwJPA5pdQJEflNEXlr21vfBnxGXTxlvRE4IiLfB74BfLi9Ky1OFtwb1U/HmCPpGB5Y1ioM5zP0ZXZI4YeTUZxeXPXXjdccWAxe36X1KkrBlI+BZc+QeXnPW6ez0/UVEfYkYDHx4mqFiUJu241H20lKi/ji2qbPcSEZmZcuzPUnAkqph4CHtjz361t+/41tPvfXwKFYjeuA3xsVkpEWz6/6c/yJQo50ShIRFG+f8TcbBOdGvX760qNudeD5gt9MZnF1k3pDdVy9HjetCZK/62s6KC6ubfq6tkmRphdWKr6zcHAmdFeND+zw7t3Pbin8JwZPcthJfgJ31b/pG3V1c0d5BJzWysnBnFF7e5WfwGzm1ZSffPlCHw3Vki9NsLi6SSYlDOe7y0+QjO69eZ/yUz6bZqQ/a9QX1is1ytV6T0HRtDStCxtkemRxdZPhfKbrimkPb9W/yeK0Izn4Wwy6Z9jsVjgX1iv+5adh8222rfqcn6BovrFicbXCuA/5Ccz7Ajj3mt+FzHuGzXZs+a13QbvvvjzkMhtkemRhrcKkj24icDKZSr1Bcd3clvSLaxUmh3Z2fDC/nUjrRt35+g72ZRjIpY0OhJ78NOUnyCRA0vFbMwBntr2yUTO6Jb1Xk/GD6b0CPenUjy940rTpTFEXNsj0yMLK5rbH7G6H6ZXTTfnJp71TQ2YXifUiP4H5tuAFT37q37m0OZ2Axor51YqvmTaYD4q9yE+A8Q1eF3rIZJIgTevEBpkeWVzzf6OalnQ8+cmPrg2O5LC4VqFSM7MlfbOpwm+maLh7z6t37bSmB9q3pDdrr3/5yayks7Div0kBWjtHm5KmF3toAgG38/RlslbGBpke6eVGNZ3JLPbQbg2trhdTW9I3g0wPmaLpzMvvtW1tSW/aXr+DttlMZmHNfxcnOL5gUppeXAuQhb9MVv3bINMD1XqDpfWq70xm3H3f0pqZ7S56abGFVsfcBUP2Lq5VyKb9yU/g3NCmbAXn+vq9tuBcX1O+sLbZm/zk+YIpe3sppLe/z9TWMvMrmwz1ZXbcUsZjvJBjyfA2OLqwQaYHvBvO74061JchmxZjjt/rjdocWIzZu8lEoc+X/AQwVshRKlepGTpxcmG1wqTPmSvA+IC5oNhrvWtswJtwGMoMeliPBi17jQXFHmR0cHx3aa1qfFskHdgg0wPNxWw+b1QRYWwgx4VVs5mMX/nJdCaz0ENhGloD5pIBiUQp5S4W7CHIGMy8epWfsukUw/mMsQnHgs/tkDy89y0aC+K9ZbUThRyVeoNVg9si6cIGmR7otTAN7sBi7EbtTX4yHWR6vVHHDGZea5U6G9VGTwfSjRmUSBZWegsy4PiDqUF7YbXSs/wEZqVpv/UuaM+8zC1v0IUNMj3QXHzXg0QyNmBOh+9VfhrpzyJi8kb1X5gGR34CM0Gx124igPFClqX1qpFzT5qF6Z4lHXPyUy+Tuaa8Z1Ca7rU+B+bs1YkNMj3Qy2JBD5OZTK86cToljPZnjdjryU+9ZgZgJij2si7CY2wgR72hWNnQL5H43Y27HbM1JH+7cXv059L0Z9NGfKHuHbPey+TTcOalExtkemBhtULO1ar9YlKH71V+glZBUjee/NTLwGJSh1/ssd4FrYDkZcQ66VV+AsPyns8999oxJe8tuevReq3JgLkakk5skOmBhR4W33mY7IDqVX4Cc23BvXYTgdmOomYTiM8te6DNXgMD98LqZk/yE7R8wUQHVC9rkDxMtYj3sjO7h81kLNvid0fjdsYHsigFpbLe7EAp5RYje8xkDEkkQeSnXCbFUF/GiLwXSH5qNlbozxR72QfMY6yQY7PWYF3z/mWe/BQkC79goNOw16UC4CxvyKTMLW/QidEgIyJ3ishJETklIu/f5vVfEJF5EXnc/Xl322v3isgz7s+9OuxdXPO/D5iHqQ6otUqdzVpv8hOYqyEFyWTAXHF6ca3CkI/D4NoxmXn12m4N5horlnrcDsljfCBrOJPxb6+IGG2s0ImxICMiaeCjwF3ATcDbReSmbd76WaXUbe7P77ufHQc+BLwGuB34kIiMxW3zYo/rOIBmUFrUvFYmSPcTtAZt3RJJkO4ncOw1oWsHyRJbNRkTQTxYZgD6J0ithaMBMhlD1xZ6t3fCYIu4TkxmMrcDp5RSzymlKsBngLt9fvZNwFeUUheUUkvAV4A7Y7KzyYW1SnN255exgnNAlO4b1bvZxgs7H1DVzkQhR62hWNG8SMyzd6zH6zthqDi9tF5hbKC3a9ufTdOXSWm3t9FQLK337rum1k01fSGA765u1tis6ZX3ltYrpMRZAtALJpc36MRkkNkHvNT2+2n3ua38jIgcFZHPi8hVPX42MjaqdcrVenN25xdTOry3UeBor0HRkKRTXK/Qn0331P0E3o2qX4dfWqv2HBBFxEi34cpGjYaC0R6DoqlthorrwSYc3r2pe5PMpfUKI/1ZX4fBtWNyeYNOkl74/3PggFLqVpxs5YFev0BE7hORIyJyZH5+PrAhrUG799kK6L9RlwLeqKbagpfWqz1fW3AyNRMSSXG9wkgge/XPXgP7gvt+3VLvUsB7zVwNqdrzZA7MbpiqE5NBZha4qu33/e5zTZRSi0opb1HB7wN/y+9n277jfqXUYaXU4ampqcDGejfqaH9vzpTPpink0uZu1F5TeEOtlcX1SqAbdayQo1ytaz/BsVjuPZMBM2s5mr7b46A9lM+QTsmumSCNGZL3SgEnSGOFHMVylbqBHSB0YjLIfAc4KCIzIpID3gY82P4GEdnb9utbgSfdxw8DbxSRMbfg/0b3udjwMpledXgws6ittF5BBIZ7DDKmZoPF9WqgaztuYDuRzVqd9Uo9mC8M6PeFYjmYdJpKCWMDWe1Sb6lcJZ9N9SydmqohOfW5ABMOd3lD8TKXzIwFGaVUDXgvTnB4EvicUuqEiPymiLzVfdsvicgJEfk+8EvAL7ifvQD8O5xA9R3gN93nYqPYnA0Gm72aSOFH+rOke9WJB83Je0EzA9CbeQWtd4EZX2jVOHaJvLcW0he015CCZzJg7mgNXfjfHyUGlFIPAQ9tee7X2x5/APhAh89+DPhYrAa24clPvXa8gKkgE+xGLeTS5NIpI40KwWoy+mevQeUccOxd2ahRrTfIpvXM8bzGiCD2jg3oL04HrXF40vBuude8lmdTZ/boIumF/8QQamAZyGmvyQQdtJ1FYlkuaNxfSykVuMZhQodvDdohZq9aM69g0imYy7yCXNtMOsVIv95GkFDSacELiuaO5NaBDTI+Ka5XAunEYKYmE3R2Bd7WMvpmV8sbNeoNFSyTMVBDKpWdvytQd5mBGlJQ6RTM7KiwtF4J5AugPyiWXIVjJIS8ZzMZC+DcqEEH7fFCjvVKnY2qvg6ooJkMOCvTdQbFMPWukf4sKdGrazel00CZl35JJ8yEw1vsqvMMnGJAuQzcGpIRXwjWBOJ8x+Vdk7FBxidFd8FVEEwUJMNmMjpnr2FuVKcDSu/sNYx06unwOheQhplwjA3kaChY3tBjb0s6DW6vzswgjC94yxtMHQWiCxtkfFIMkcl4n9NVl/F04l7XyHjoXssRJpMB/XtWFder9GVS9OeCSKf6dfhiuRLKF0Df4tyVTUc6Da4a6K0ntnw3YFA0eN6ULmyQ8cnSeiVQZxm0HHBZ03b/nk482uMWOB6j/VmWN/QdExxmDRI49uo8SiFoiy209rfSa2/wCZJXd9Jlb3EteHu49zmt1zaEdArO2KD7GBDd2CDjkzA6sRdkipqcKYz8BE4RUym0HRMcRnIA5/rq3K8q6BY4AH2ZNAO5tFZ7g+6mAK224JIme5dCrOkBJ4hvVBva6p+hfbc/ZxdjWsLrxN5WNLoGlvCO7wVFPc6/tF4N3GILMNKvd/ZaKgfvfgLn+uqacFRqDdYCtthCK6PQ5wvh5KdRzZlXab1KLpMinw02lI4M6PMFU9gg4wOvxTasRKLrRg2rEzft1RQUi+sVhvPBWmzBsVfnbDBMpyE4wVTntYXg0ql+Xwgnl+m21zvyoZcj2dsZ6c9qyxJNYYOMD8IWpvPZFLlMStvsKgqdGPTNBpcC7lvmMTqQZa1Sp1pvRGhVZ8LIT+DYq6s+F1Y6Hc47m4Lo84Xw8hPo9t0QvuDWE3UfEqgTG2R8EPZGFRHHmXaLXKa5hhTFoA16BhalVODNPD1G+3Pa5aegvpBJpxjKZzRmBo50GnS5QNN3NWW2xRALR8Gxt9ZQrGneRVwnNsj4IKz8BJ6ko08nDtpiC06Nw/keXTdquEFbp0SyslmjFkI6Bb2+EPQcpHZ0dkCVIpBOQW+TTVhfgMt7J2YbZHwQVid2PqvvRg2zLQeY0rXD36glDdlBq8V2dwzaYaVe0FvzCiudNluuNda8wl1bvfKeCWyQ8UFYyQEcZ9ots6tcJsVALq1xIKwG2gfMw7vJddjryVyhBpaBLJs1PW22YaVecOQ9nROkIPuAeQz1OQet6ZROw044QF9QNIENMj4IqxODO3vdJTox6GuzrdQarG7WQhdPQU/mFdWgDXrsLa5XyGVS9AfY2NVDZ5ttWOlURJzMS0NWu9qUTsMHmcu5jdkGGR+EbbEFV3LYJZkM6Guz9QaD3VKTiUp+Aj0t7WFbbEFvm21Y6RT01byikNF1S9MmMBpkROROETkpIqdE5P3bvP7LIvKEiBwVka+JyDVtr9VF5HH358Gtn42SsDoxOLPt9UqdSi3+NtuwOjHoa7ON4kYdbtZkNGQya+GDok6JJIoJh84227DyE7hBUYcvRCCj6265NoGxICMiaeCjwF3ATcDbReSmLW/7G+CwUupW4PPAf2x7rayUus39eSsxEtWgDfE7UxQ6Mehrs42i+ymdEobzGU01Gff8kBDSqc4OqJJ7lkwYdLXZVuuOdOoNvEHR1VgRhe96a+h0tbSbwOTxy7cDp5RSzwGIyGeAu4EnvDcopb7R9v5HgZ/XaqHL7/zjV1MOeYONNIvTFaaG+qIwa1vWKnVqDRV4110PXfuBNeWn0AOLnj2giutVhvoyZEIcnawzkymWK8xMFkJ9R6uGVGGwL74hwwsMUdQTn5tfi8KkrniThDD3mu41dCYwKZftA15q+/20+1wn3gV8ue33vIgcEZFHReSeTh8Skfvc9x2Zn58PZOhQPsv0cD7QZz10aa9R3ai6aki7zd7lcrhOONBbkymVq6ED+PBu9F0NEw7P3ij84XKuyZjMZHwjIj8PHAZ+ou3pa5RSsyJyLfB1ETmmlHp262eVUvcD9wMcPnzY2N4No5rqBt7NFVYiGRnIUnHbbIMcOe0X73oE3RzTQ5tEUg4vPw1qbLMN2x4O+o6q8AbasL4wMpBrnksTpllnJ0oR3WuX+3b/JjOZWeCqtt/3u89dhIjcAXwQeKtSqnkakVJq1v3zOeAvgVfFaWxYWttd6JkNjoSVnzS12ZbKVVLirG8Ig64OqFI5fL3Lk0jivrYb1TqbtUYkgyDEX0NajkB+8j7vHFURv+/2Z9P0ZcJNwnSuoTOBySDzHeCgiMyISA54G3BRl5iIvAr4HzgB5nzb82Mi0uc+ngR+nLZaThLRVexdjqAwDe0DS7yyQ3G9ynB/llTIGeeoprUcYY7hbkeHvBeZL2iacHi+Fpnvxm1vBE0V4E2QbOE/cpRSNRF5L/AwkAY+ppQ6ISK/CRxRSj0I/CdgEPhjt8//RbeT7Ebgf4hIAydQflgpleggM5TPIqJDLotOJ27/vrhwagYR3ahum22YNSE7USrXQmeJ4Pz/xC4/RRRkdNWQShG0s4O+CV0UWS1c/nKZ0ZqMUuoh4KEtz/162+M7Onzur4FD8VoXLU6bbfwzllJEkoOuY4KjqHGAM9uuNxSrmzWG8uG/bzuUUpTK0WQyo/1ZFlY1+ULIgVDXURVeUPCOFwiKruUCxXI1dP0IHF9Yc9fQ5TKX3/r4y+9flGB0SDrFcpVMShgIuAOzh64221K5GmqvKo8RDRJJuVqnWlcRzV7jX4fUzGpDDoS62mxL5fDt4dCqR8bdYbYcURau+/wm3dggoxEdrYpeCh9WMtJ17G4pohqHju69qAZt7zu0tQRHIO/pWDdVWo8oM9CVyURUk2ntWHF51mVskNGIju0uorpRC7k06ZTsqpoMxJvJRCVFgmPvinusd1xE1c7ufUfsE46Iahxa64kRZbXe912O2CCjkdGB+LdMj2rQbkokMdrbaChHLotk9hr/jRplJqNj7cly2dk9fChkjQMcCapUrkVgVWeiqs9l0ykKMR9VsVmrU67WI83CL9cFmTbIaGSkPxO7TlyMqDAN8W/xvlqp0VDhC9Ogp+U6qhXeoKcDqliuMpwP3x4Oeo6qiCozAG+bofiz2kjqiTbIWKLCO/ypEaNE4tyo4R0fiL3YW4pohTfoksuik5901A0iHbQ1rOuJqsYBnjQd44QjhqzWymWW0IwOZGkoZwYfF1HsuusRtw4fZY0jn03TF3ObbaslOIrZa/wdUFFJkeD4QpxHVSilnH3hImhSgPgbK6L0XW8N3eW66t8GGY00157E5Pz1hmJ5oxZJZgDx15BaW+BEZW/MmVe5SjolFEK2h4Oe2WuUmUHc9m5UG1Tq4bfA8Yh7gWOUvqtrDZ0pbJDRSNySjrdXUxSzK4h/NhjV7gQecZ+B4w3aUewooEPeW44yk2k7qiIOotpSxiPuNWlRNoF432MzGUto4u6AisPx42yzjXIdB+iRSKIM4N53xkVU3VoQf1CMancCj2G3nhjXaZ5R23s5by2zY5ARkbSIPKXDmMuduDug4nB8iK/NNurZ64gGiSQqKTKbTjHYl4lt0Ha2wIm28A+7Z4I02p+jUm+wUY2nhuRlHVFtYXQ5nymzY5BRStWBkyJytQZ7Lmvi7oePakNEj7i3eC+Vq+QyKfLZaBLquNf1RDloQ7yNFavueSqR+0LMmUz0vhvP9V0uVxnOZyI7r0bHGjpT+F2lNQacEJHHgDXvSXdHZItPhmOeDUaeyVzUAVWI5DvbKUVY44D4tz4prldDH2XcTpxn4EQtRTZ9IS7fjTyTaQXFvSP9kXxnO8X1SmS1RNCzhs4UfoPM/xOrFS8T8tk0+Wx8bbZed0pUkk7z2N0Yg2JUNQ5wBqhytc5mrR76IKntiNreOHX4qE6Z9BjKZ5yjKmIaCKOeIOmoIUUVwOHiNXRRLJ5NEr6CjFLqm3Eb8nJhtD8X24wlLskhtppMhC220N4BVWV6KNog02goljeitXd0IMvT51Yj+752ojqwzCPltdnGWJ9Lp4TBkCekeozE3HIdZVMFXLyGbjimoypM4UsMF5EVEVl2fzZEpC4iy3EbdzkSZ4EvquNgPeKuIUVd42gWp2Owd2WjhlLRbCPiEbcvQHSZgfddcWa10Uqn8bZcO0dURHdth2P0XdP4CjJKqSGl1LBSahjoB34G+J2wf7mI3CkiJ0XklIi8f5vX+0Tks+7r3xaRA22vfcB9/qSIvCmsLbqIswMq8sxAQ5CJSs6BeBsVou6Ec74rx3I5njbbqJtAwN1aJq6mlYh9N/YJUkz2Xo7F/57bepTDnwGhBnYRSQMfBe4CbgLeLiI3bXnbu4AlpdT1wEeA33Y/exPwNuBm4E7gd9zvSzxxdkBFnRlkvDbbGGeDUeracQbFKLcR8RgdyFKpNyhX65F9p0ccmcxwjAsGo9wCB2AglyaTkljsbbaHR+oLXpPN5RdkfAmgIvLTbb+mgMPARsi/+3bglFLqOffv+AxwN/BE23vuBn7Dffx54L+Lk0/fDXxGKbUJPC8ip9zveySkTbEzOpDl6OloHemvTy3wh4/+gO/+YInrpgcj/e44zsD5xF89z7efv8DqZi3i2WD0i11r9Qa/8ecnmrWTKCWS9tn2QC6aWsRLF9b5Tw+f5MRciWxa6M9GN/caHchxeqkc2fcB/OXJ83z2Oy9xbLbED+0fjex7RSSWxor7v/UsR15YohZhezjE03K9WavzGw+eaAauD/29m7liJB/Z9/vFr2f/vbbHNeAFnIE+DPuAl9p+Pw28ptN7lFI1ESkBE+7zj2757L7t/hIRuQ+4D+Dqq80v9YljbcRnj7zE1548z4HJAe68+YpIvzuO/cA++pfPUqk1uHHvMD92/URk39s6gjm66/v8whr/69EXuXIkz9+6ZoxXTA9F9t3tmdeVo9G02X79qfM8+P05rpsqcM9t+yKrcYAnl0Xru59+7EW+cXKeAxMDvPHmPZF+dxwt4v/ta6fIpIWb9g7zmmsj9N0YsvCnz67y6cdeYt9oP4W+dGybm+6E3+6yd8ZtSFwope4H7gc4fPhwfHvs+2R0IMdGtcFGtU4+ollmqVzlhr1DPPje10byfe1EXez1pIZ3/vgBPnDXjZF9L8BQX4aURJvJeN/14Z+5lde9Yiqy74V4OqC87/qL972ObDraXaO8zCDKNttSucpt+0f53C/+aCTf187oQLR72dXqDVY2a7zvjoO8745XRPa9EM82Q953feQf3sbtM+ORfW+v+O0u2y8ifyoi592fL4jI/pB/9yxwVdvv+93ntn2PiGSAEWDR52cTiedMUbYFR100bWck4tnrRrVBpRbdbrvtpFLi1A0inA1Gvd1JOy15L7rrW1yvUsilIw8w4FyDhoKVzeiOqihGdFz4dkTdvbe8UWt+b9TEsYYujmaVIPj1xI8DDwJXuj9/7j4Xhu8AB0VkRkRyOIX8B7e850HgXvfxzwJfV04rzoPA29zusxngIPBYSHu0EMeJiFHutruVqI/djXol+laibqyIo4DuMRLDVi1RHlq3lTgmSMsRN6u0s5t8AaKf0MVtr1/8BpkppdTHlVI19+cTQCjtQClVA94LPAw8CXxOKXVCRH5TRLztav4AmHAL+78MvN/97AngczhNAn8BvMfdYy3xxHEuR9QLw9pxJJJKZG22cc+uRgZykQbwOFqBPeJoWy2VK7FlBnF0QMXpuyMR1xO9ABDbvdYf7f5lcWbhveC38L8oIj8PfNr9/e04slUolFIPAQ9tee7X2x5vAP+gw2d/C/itsDboprUfWDTOFPVuu1sZ6c9SrSvWK3UKEazG9m76OO2NcuuTUrmKSHS77bYzkEuTTUfbZht1a207rSw8mutbqTVYr9RjtXdls0at3iATgXzY2lEjpkwx4r33lstV+jKpyGq/QfF75f8p8HPAWeAMjnS1a5sBTDIacQdU1LvtbiXq2XacmQHEIJGsVxjqi2633XZEhJH+XOQ1pDizWojOF5qDdoxyGbRqKWGJetumrUTtu3H6Qi/47S77AWB3XI6AqHdijr3G0VY3iKLNNvYbNeJuuDhrHODsvhtl4T/OrDbqVfTx+0JrF/HxQvj/Qz01mah9N+FBRkT+G9BRjFdK/VLkFl3mRN1mG/Vuu1sZaW7xHs1A2NzSPeZib1RttnHWDCD6c0TitDf6CVLc9blom2zirnFEvXi0WK7sikzmSNvjfwt8KEZbXhakUhLpjCXq3Xa30uzfj3D2mk4JQxHttruV4f4sSjkbWkYRyKLe7mQro/1Zzi6H3TzDYaNap1JrxDbh8Npso5J6W5lBvN1wUfruQEzt4eBch3K1HtkaulK5xr5R/Sv8t9L1TldKPeA9FpH3tf9uCc5ohB1QxZhT+Kh1+GK5wnA+E+lK9HZG27b7jyrIRLUafztGBrI8dXYlku8qxtxUAdF2QMWeGcQgTcfVpAAXt4hHEmTWK9y0dzj094Sll5BsfLX85cJwhP3wOmocEJ3kUCrXYq1xNOsGEcp7cWYyUe4NF7cvQLSnj8ax6Wg77TWZKIhz4ShEv4Yu7izcL/HkfZaujPZnI1vQFvfstT+bJpdORTawFNfjW8cB0Z5FH8duu1sZ7c+xulmjWg+/r5Q3mMbVBALe3nu7o544nHeEmqjsjXPhKESrGlTrDdYq9UQU/rsGmfbDyoBb2w4uW7GHlgUnyg6oUrka+W677Yg4W7VE1QG1rElyiOL6rlXqke+2u5UoTx/VkclEuelkqVxlKB9Pezg4R1UM9WWimyDFXEiPcg2dDl/wS9cg035YmVIq0/Z4yD3AzBKAKAv/pXKFkf5cbDUOiLbrJe5urSg3ndSxLUeUQTHu+pz33VHKe3EPgiMD0akGUZ9/tJXWTszhJ3Q66nN+sXKZAUb7syxvOG22YXFu1Hg6tTyiPBEx7t79VkdRFDdq/BsMRhkUvcE0Xjkyup2NdazjiFI1KK5He+TyVuKYIMXpC36xQcYAIwO5ZpttWIrr8S4WhOiKvY2Gin322pdJM5BLRyw5xN+oEIUEVVyvkhJiaw8HJ+B6R1WEpbge/zqO0f5cJJnBRrXOZky7h3tEuYbOk7fjlKb9YoOMAaLsgNIhOQxH1AG1sllDqfh14qiK03GvQYK2DqiIfGG4PxvZWS/bEeW5J3HLT7C7fCHKNXS7piZjiYcoT8HTsT9RVGsjSjGvi/CIqi24GPPuBBCxL2iYcETZAeUFxTiJqiYT9557HlEFxaTswAw2yBghyhs1zrNkPEYHspG02ca9wtsjqiOj417HAa022+gyg/gnHBA+KMa9e7iHV08Me1SFrrNZRiLaZshmMi9zolrg6B0Hu1tmr7ocf7Q/muJ0sVwlkxIGcvFtlZ5JpxjKR9NmqyMziGoX8XK1TrUeb3s4OPbWGoq1Srgakq7MYDSioypK5SqDfZlIjjgIi3kLXoY0NxoM6UzeFuaxz64iknS8gV+HvVEN2qMD2VjbwyE6ea+0Xok9S4yq5brZYqtBfnL+vnD3Wty7nXtEJZfFvVNFLxgJMiIyLiJfEZFn3D/HtnnPbSLyiIicEJGjIvIP2177hIg8LyKPuz+3af0HhCSq4qmuzGC32RvVWo5SzNuIeERmr4Z29pGIFo/q893WXnZh0NHODlH7wss4yOAco/w1pdRB4Gvu71tZB96hlLoZuBP4LyIy2vb6ryqlbnN/Ho/b4CiJqs22uY1I7GsNvBs13GxQl+QwMpBlsxa+zVZHjQOiabP12sPjnml7B7iF9934myqgTeoNae9y2TshNf41ad5RFWEoJuQsGTAXZO4GvB2dHwDu2foGpdTTSqln3MdzwHlgSpeBcTMaQVqsO5MJO7DoOg42Knt1zQZHIlgwuFqp0dDQHi4iDOczoWte2n03gnttOB9vezhEt4bOZjKwRyl1xn18FtjT7c0icjuQA55te/q3XBntIyLS1+Wz94nIERE5Mj8/H9rwqBiOoG6gY7EgRLdluq7jYEejkkjK8dc4IJr9wOI+DK4d56C1sIOgPvnJ+fvC+oKezKAVFMOrBpd9JiMiXxWR49v83N3+PuX0FnbMDUVkL/CHwDuVUl4P7QeAG4AfBsaBX+v0eaXU/Uqpw0qpw1NTyUmERiPo39c1GxzeZZlBVB1QuoqnnkQSps1WZ8vqSARHVejsNIRd5LsRTOiUUixr6DT0S2wCo1Lqjk6vicg5EdmrlDrjBpHzHd43DHwJ+KBS6tG27/ayoE0R+TjwKxGaroXR/hzPLayG+o6lNT03ajrlSCRhZ4NL6xXGNGUGEE4iqdUbLG/E3x4OF7fZDgbcEmZpXd82IqMDWS6shQsyS+tOe3jQf69f8tkUuUwqdGawpGvCEcFRFeuVOpV6I/b6nF9MyWUPAve6j+8Fvrj1DSKSA/4U+KRS6vNbXtvr/ik49ZzjcRobB1HsB7a0XmGwL0MuE/9/48hA+NmrrhQ+imN3vYA6pkN+6g9/uNaS+28dK+gJ4lE0rYwOxLt7ODg1pCjkyKKmCVIUa+i8CYcO3/WDqSDzYeCnROQZ4A73d0TksIj8vvuenwNeB/zCNq3KnxKRY8AxYBL491qtj4Ao+uGdG1WPI0WxtYyuTCYKHV7noB2FHKmr0xBa8l4Yltaq2gbBaOytaLE3ijV0rW3+k5HJxJurdkAptQi8YZvnjwDvdh//L+B/dfj862M1UAMjA1kqbptt0G6rYrmqZdCG8FumK6Wc4mkh/ht10GuzDSGRtAZtfUExTI2utbhRQyYzkGN5o0q9oQIfOFYs65lwQHjVwJNOdTWBQLgJkvdvfblnMi97oihILmnsIAkrOZSrdSq1hpaBRURCn4GzpPFGjUoi0SWdjvZn3TbbcAOhPt/Nhbq2OqXTKNbQNeUyDVm4H2yQMUQUrYpFDduIeISV95aaM219QTGMvc1MRkdmEIlcpm9dRBT2LmmUekdC7gfWOnF0d9xrRY1NIH6wQcYQUaxE1qUTQ2u7i6Bttktr+uQnCL/Fe1N+0iDvRbGuZ2m9wpgGWyF8zUspxdK6Xqk3nPykr94F4feyW0pYTcYGGUOEbbPVqRODMxDWG4rVzWCL8HTrxOHlsgqZlMR6yqRHFG22ugdtCO67nnSqz3ezrFWcvzMI3lIBrUExpO8Wcmkt0qkfkmHFy5CwmYxOnRhaK8mDDty6deKwZ9F79a64W2yhVUMKM7DolU7DtVzrrHdB+Myr1RKsb0IXTkaP/0j2XrBBxhBhazKtG1WfTgzBb1QTkkPYlmCdN2pYe3VKp2F9Qbd02mwLDniv6ZROIQJf0Cid+sEGGUN4bbZBb9SSprNZPMJud6GzxRacG3Vlo0Y94G62zpoefTdqmLpBvaG0SqdhF7vqzsJbu4gHz2R0SacQRQ1Jn3TqBxtkDBG2zVa/Thyu5XppvaqtxRbCrz3RLTmEabPVPWjnMikKuXRge7VLpyG74XRKpxD+qArdWfhO2CBjkJEQi8S068TuALYUUIfXuTsBhLfXRCYTvMah1xfAmXQEv7Ze95PemsxSwHtN96DtZfthrm9SFmKCDTJGGQtxo+o69Mkj7M7GOtdFQCvzCj6w6M1kxgayoQI46PMFCLeKvrimbw0StGfhwe81nWtOvADhqRW94Eineu3dCRtkDDJeyAXezXZpvdLcHVkHfZk0g30ZLgRwfNDbYgsw4UoxSwGub7lSZ7PW0BoUxwt9bFQblCu9SyS6pVMI67tVrS22w/kMmZSEutd0TjjGC8EzGWctW3LWyIANMkYZHwh3o47269OJAcYK4WbbejMD5++6EMBeE/LTuNsNtLi22fNnTey6O14Ik4Xr9QURYSyUvXrlJy/ILAYYG1r1LpvJWKDp+EFW0ZfKeuUncIJiEMcHbzNP/TdqkCBuYtD2AloQiaSkedsTcOy9sBrCFzQPguMDORYD2uu0BGu8tiGy8KTtwAw2yBhlvJClWg+2it7ZKl2vI40VcoEcv95QlMp6axwDrhyzW27UZlAMmHnplE7BsXdlsxZoFb2uIx/aCZqFm5BOvXpKkAlS0UAWvhM2yBhkvNAHBJu96taJIbgOv+zqxDozAxFhIqC9ZuSy4LNXE9KpZ2+QYrqJFekThb5d4wuZdIrRgI0gundT8IORICMi4yLyFRF5xv1zrMP76m0Hlj3Y9vyMiHxbRE6JyGfdUzR3HWF0eN06MTiSQzDHNzO7Ctq9Z+JGDaPD624Ph/CZl27fdTKZYJM50D9oB5WmdZ6D5BdTmcz7ga8ppQ4CX3N/346yUuo29+etbc//NvARpdT1wBLwrnjNjYemDh/wRtU9sIwVcqxX6j0vElvS3G7tMV4IeKOu6W8JHs5nSUnATGZNf2bQbKzosc7RlE41t9h6E6Red4BoLhXQ1G7tEVSaXlqvkBK07U7gB1NB5m7gAffxA8A9fj8ojibweuDzQT6fJFrF6d5mWC2dWL9cBr1rxaZ04qA3arFcZSCXpi8T7MTSIKRS4hTTd0lmEDSTWTbUYjtWyKFU71vLNHcP19yoMBaw83TJlSJTAU8sjQNTQWaPUuqM+/gssKfD+/IickREHhWRe9znJoCiUsqrlp8G9sVnanwE1eG9TTV1D9rBg4wZnThMTcZE4XQ8YFDU3VQBYXzXzKAd1HdNSb0TAVuuSxpPHPVLbDmViHwVuGKblz7Y/otSSolIpxz2GqXUrIhcC3xdRI4BpR7tuA+4D+Dqq6/u5aOxM9iXIZuWniWd1uI7Mzdqr86/ZEgnHhvIsbxRo1pvkE37n0/pPBq4nbGA8p6JTMa7Pr1m4aZ8Iajv6t493GPMnSAppXpq6DA1QepGbEFGKXVHp9dE5JyI7FVKnRGRvcD5Dt8x6/75nIj8JfAq4AvAqIhk3GxmPzDbxY77gfsBDh8+HGxL3pgQcSSSnmeDBgdtCJbJ6G6xhVZjRXG9ytRQn+/PGctkBnI8O7/a02c2qnU2qvql02w6xXA+E3jQNtEEAkEyGf3SKVy8vGEo7z/ALa1X2Teaj9Gy3jEllz0I3Os+vhf44tY3iMiYiPS5jyeBHweeUM7KxW8AP9vt87uF8ULvOvyCe6NMDu4OuWxxbZPxQk5riy20FrX1bO9qhQnN1xYItCp9YdXpTNTtCxCssWLBbRTwtv3RRWDfXd004wsBF+curm4yUfA/odKBqSDzYeCnROQZ4A73d0TksIj8vvueG4EjIvJ9nKDyYaXUE+5rvwb8soicwqnR/IFW6yMkyNqTRXdgmRjU60wj/cE6oBZWK9oHFQg5sBi4UR0dvkqjhw6oxeagrd/eIDUkz95Jzb4bfIJUMeMLg15Lu//lDY2G4sKamQlSN4z0uSmlFoE3bPP8EeDd7uO/Bg51+PxzwO1x2qiLsUKOJ+eWe/rMwuom6ZRobwNNp4TRAB1QC6ubPclVURFEhy9X6qxV6kwOmclkmrvo+pSTmpmMoes7V9zo6TMLq5sUcmn6c3rlp3w2zUAu3XNQnF/ZZP/YQExWdSbI8oZSuUqtobQH8J2wK/4NMx5g0F5crTBeMNOmODaQDZDCG8pkAujwzUHbSGbQ+3Yii4bkJwi22NWRn8wMgkFaxBfXKsakSOitscLLepKWydggY5ixQs6ZgdT97wFlSn4CT4fvbYcCUwPLaIAg49UYjOrwPQyEC2teTcZMJrO41tsGr4sG5ZxepWmT8lOQTTIXDEmRO2GDjGEm3EVivRxla0p+Ak+H92+rJz+ZuFFzmRRD+UxvmcyKuUHb0/572S14YaViRH4CxxcqtQbrPZyBM79irjDdaw2pWK5SNyQ/DQVY3tBqArFBxtJGkBnL4tqm0UymF8nBtOP3eu6JScnBW6DYq73G5KcAxfTFtQpTBupd0LvvmmqwgWDLG5rSqZXLLO0EqRs4LbbmdO2lHiSSRUPt1h69bs+xYLhbC3rU4Q21W0PLd/0Gxab8ZCiTcXy3F8XA9d1dMqFbXN1EJFnb/IMNMsbpdfa6Xqmxbkh+Asfxaw3Fis8zcJqzQZMSSU83qjn5qT+bpi+T6q0mY3BdRK+ZTMmVn8z5bpbVzRqbNX/yXiurNTuh88vCWoXxgRzpBO1bBjbIGKepw/t0JlPrDDyas22fdYOFVbMdL+OF3k5wXFjdNNIODK0zcHqqyayak58megwyCwblJ+j9/KZWfc6Q7w72mIWvbCauHgM2yBjHy2R6HbSNyU89nntiuuOl1w4ok/Uu8Pas8te958hPuyeTafmCuUwG/C9wXFxzts03dTZLr2fKmOzc64YNMobpy6QZzmeYX/Xp+AZrBgBTbrCYX/Fv72BfhnxWv/wEjr2btUYP8p65ehfA1FCfb18olqs0lLkscTifIZdJ+fcFg+3WQLMj06+9C+56NFPy09RQH6Vy1b+8Z3ANUjdskEkAe4bznFv2t3K6eaMaknT2DDub751f8W+vydnV9LBznc77vL4LqxWjksOeoTznlv0GcLODtoiwZ7jPv+8aXDgKMD3k+m4P19eoLzR91/+EzlSW2A0bZBLA9HAf53uYXYG5G3XCndn5dXynMG0wyPQwsNRd+cnkjTo93Mfi6qavExznDde7wLm+/n1306j85GUyfidIC4Y2x/Ro+q6P67tRrbOyWbM1Gcv2TA/lexq0TcpPqZQwOZjzn8kYzgyamYyPG7W4XnHkJ6NBsY+GamUp3TDdBAKOvb1MkMYLfcbkp3w2zUh/1re9pjbH9GjJezvfa82dKgz6bidskEkA08N9zK9s+ipOJyElduQ9/wOLSZ3Yk/f8SDrNNT2GpEiA6aa9foKM+RXePUm9q2azRKBnec/0tYXd4wudsEEmAUwP5anUG81jiruxkIDint/ZaxLkp8G+DAO5tC97vZZVk7PX6R4knYVVt/tJ827c7UwN9bGyUWOjunNx2rT8BP7lvY1qndXNmlF7m9K0L18wL512wgaZBNAaWPxJJKZT4qmhvK8UPgnyE/gPiqYOg2tneti/Du8cBtdnZDduj6bv+pltG5afwPUFH7aaXioAbdK0L3vNS6edsEEmAfQm6ZjPZPYM97GwWqG6w87RSZCfwBm4fV1bw4sFodUi7sfehYRIpwDn/NQNDMtP4PiCH2na9FIBjz3Dec75nHyCzWSaiMi4iHxFRJ5x/xzb5j0/KSKPt/1siMg97mufEJHn2167Tfe/IUr8ZjJ1d++nqQRIDtCa7XViwfCWMh7TQ32+1kYsrlaMHAbXTi6TYryQ85nVml/hPe2zzTYJ8hM4vuBHmja9VMDDybz8TZAGcmkGckbOoeyKqUzm/cDXlFIHga+5v1+EUuobSqnblFK3Aa8H1oH/3faWX/VeV0o9rsHm2Gh1QHV3pgtrrvxkemDxKZHMG96Ww8Pp3tv5Rp1f2TR2GFw7fiWd+YTUOGBn302ML/jsNpxv1ueSIE3vDl/ohKkgczfwgPv4AeCeHd7/s8CXlVLrcRplioFchqG+zI4Dy5lSGYArR/t1mNURv/LemZLz+l7j9vaxVnFm0t2YK5WNX1twJJ2dBu1GQ3G2tGHc3rGBLNm07NgB5fmCaXv9+u5ccQMRuGIkr8OsjuwZ7mNxrUKl1l2aPlPc4MoR8767HaaCzB6l1Bn38Vlgzw7vfxvw6S3P/ZaIHBWRj4hIx6m9iNwnIkdE5Mj8/HwIk+Nlarhvx4FlrugFGbOO73c2OFcsM9KfZbDPbArvd9X/XLHMPsPXFvxlMgurm1TryvigLSJux5Zf3zVrr19peq5YZs9QnmzabNnarzQ9WyyzLwETpO2I7QqKyFdF5Pg2P3e3v085FbiOVTgR2QscAh5ue/oDwA3ADwPjwK91+rxS6n6l1GGl1OGpqakw/6RY8TOwzBadG9m0M00Ucoj4u1FNDyrgb+W0Uoq5hMwGp939yxpdVv3PuoN2EoLilI+al2ev6evrV95zslrz19ZPUKw3FGeXzWe1nYhtiqmUuqPTayJyTkT2KqXOuEHkfJev+jngT5VSzUpdWxa0KSIfB34lEqMNsmc4z/deXOr6nrlimYGcs2rZJJl0isnBnQuSs8WNRAyC3h5Q3SSS4nqVcrWeiBt1z3CeekO5p0hun6TPFZMhP4FzfZ9fWOv6nrlimfFCzsg5Pe3059IM5XeWpueKG9x85bAmqzrjR947v7JBvWE+q+2EqVzwQeBe9/G9wBe7vPftbJHK3MCEiAhOPed49CbqxctkurVWepmB8882i5+1J7NL64lw/Cl39tpttj2bEDkH/C3ITIr8BP4WOM4Wk5EZgOe7na+tUiox8pMfaTopMnonTAWZDwM/JSLPAHe4vyMih0Xk9703icgB4Crgm1s+/ykROQYcAyaBf6/D6DiZHsqzWWuwvNG5OJ0U+Ql2vlFXNqosb9QSYe9wPkNfJuXrRt0tA8tsscxQX4bhvNmsFhxfKK5335J+rlg2LpV57LRXoFdoT4LvetL0fJdMJikyeieMVGSVUovAG7Z5/gjw7rbfXwD2bfO+18dpnwnai9Od5LDZ4gY3JSCFB+dGPT633PH1pHQTgVuc3mHPqiTNBls7R3e3NwnXFi5eK3PV+MAlryulmF0q82PXTeo2bVumh/u6StNJyhIz6RQThb6u3Xuevaa7ODthV/wnBG9g6eRMG9U6C6ubiZkN7nG3pK91WPWfpMI0eOe0dBm0Sxv0uQshTePVYboOLAkpTEP7VjjbX9/ljRprlXpiZtp7hvNdpekkTTjA3dRzB+k0CV2cnbBBJiF4N+BscfulQGcTlBmAY0dDtTKWrSRpNgiOHV7g2w5Pg09CvSufTTM5mGN2qbO9c8XkdBN5vnu6g72J84URR5pe6HDkedLkpytH+3fwheRktdthg0xC2DfWTzYtPNehSydpN+rMZAGgY1fRXLFMOiXNDM00M5MFTi+VO9YNknajzkwWOl7bcqXOhbVKYuy9enwAke6+AMnJDGamBoHu9iahi9Pj2skCP1hc73iQXVK6ODthg0xCSKeEq8cHeH5+e8efTVBhGvwEmQ2uGM4bO6BqKzOTBZSCFxe3zxTnEtT9BHBgotB5wlFKli/ks2muHOnfMcgkxd6ZCc93V7d9PUldnAAHJgtU6o3mddxK0iZIW7FBJkHMTA7ywmLnQVsE9owkYyvvqaE+Crl0x4ElKS2gHt2CYqXW4PzKZqJu1JmpAgurm6xsXLqRY9KyWoBrpwq80NEXNsimxfhmnh6eavD8QrcJR3KubTffXd2sUSpXE2XvVmyQSRDXThV4YXF925Xec8UyU4N99GXMLmbzEBEOdJF0EpcZdLlRzy1voFTCBm3X3he2GQiTJj9BK/Parpg+Vyyzd6Tf+MajHk3VoEMmkzT56douvnsmgROOrdggkyAOTBSo1BpNOaSdpGze2E6nukE9IZs3tjPSn2WikNs2U0yaFAltQXFbezdISWs1eBKYmSywslFrniHUTtImHOCqBtsE8KR1cUJ31SBpXZzbYYNMguiWFidNfgJnhnV6af2SHWLnVzapJXCbi5nJAs9tU/NKovx0wKsbdLB3z7D5zRvbmZnyMq/t7U3StQVPNVi7RDVIWhcndFcNkrS9UCeS46UWru1wo25U67y4uM6ByUsXupnkwGSBhoIXL1w8I3z63ArQCppJ4cBkYdtM5ulzq2TTkqjZdj6bZt9o/7b2PnNupRmEkoJXTN/arLC8UWWutNF8PSkcmCiwWWtwZsvaKc93DyTMd2c6+u4K/dl0Yro4t8MGmQQxPdTHQC59yY361NkVag3FoX2jZgzrwMzk9kHx2GwJgFuuHNFuUzdmJgucW95kbcu5MsdnS9xwxXBi6l0eByYHLvGFSq3Bk2dXuHV/sq7t/rF+Mim5xBdOzDq7QhxKmL1N1WBLpnh8tkQ6Jdy0Nxk7a3jMTBZ46cKlqsHx2RK37BtOTBfndtggkyBEhAMTl3bpHDtdBBJ8o15ib4lrJgYYGUjGOgOPZlBsmxEqpTh6usgt+5J1bcGtec2vXlRMf/rcCpVaI3H2ZtIpt5i+dcJRBOBQwuyd6VDzOjpb4uD0oPHdorcy46oGLy21VINavcGJueXE+cJWbJBJGDNTl2qvR0+XmCjkuNLwKX1bGR3IMTaQvWS2fWy2lLhBBbYPii9eWGd5o5a4zACc4vTyRo2ltvPovSwxmfZe6rvHZpfZN9pv/MjwrewZ7qM/m74ok1FKcex0wn23zd5n59coV+uJ9IV2bJBJGDMTBV5aKlNt2xPs2GyJW/aNJGZxWDsHJi/OvC6sVZgtlhN5o3p1jHZ7vUE7ifbOuDW457fYO5zPcPU2G1Gaxqt5tRfTj50ucsu+ZElP0Cqmt2e1Z0obLK5VEqcYwPZZeJJ9tx0bZBLG9dOD1BuKJ884Wna5UueZ86uJna0cnB7kiTPLzaDYdPwE2tufS7N/rJ+jp0vN546dLpFLp3jFniGDlm3P9VOOTZ5c6jwucWh/MiccB6cH2ag2ODXvrD8plau8sLjOrftHzRrWgYPTgxyfLTWDoucXSRy0RwdyTA7mtvhukUIuzczkoEHLdsYGmYTxuldMkU4JXz5+FoAnzixTb6hEOj7A62/YQ6lc5dHnFoHWgJhUnfgNN0zzzafnm8X/o6dL3Lh3iFwmebfC1RMDXD892PSFzVqdp84uJ64BxOMnb5hGBL58zLH3RMJn2m+4cZrzK5vNbf+PzRbJpIQbE1b093j9DdN8/anzbFSd/feOzpa4ed9Ioov+YINM4hgv5Pix6yZ46NgZlFIcT3BmAPB3XjlFIZfmoWPOidjHZkvMTBYScZjWdrz50F42aw2+/tR5Gg3F8blSYgMiOPY+9sIFzq9s8PTZVar15E449gznOXzN2EW+AEkOMnvIZVJ8qWnvMgf3DJHPJqvo7/HmQ3tZ3azxf55ZoFZv8MTccmKvbTtGgoyI/AMROSEiDRE53OV9d4rISRE5JSLvb3t+RkS+7T7/WRExfwhIhLz50F5+sLjOibllHnvhApODfVyRoNXd7eSzaV5/4x4ePnGOcqXO4y8VE+34hw+MMzXUx0PHznDy3AorCS36e7zl0F6UgoePn+WxFy4AySz6e7z50F5Onlvh1PlVvvPCBfaP9TOWgDN6tmOwL8NPvGKKLx87S7lS5+jpIrcm2Hd//PpJRvqzPHTsDMdmS2zWGon2BQ9Tmcxx4KeBb3V6g4ikgY8CdwE3AW8XkZvcl38b+IhS6npgCXhXvObq5U03X0E6Jfyrz32fLx09w9+9dW8iNXiPtxy6ggtrFe756F9xbnmTNx/aa9qkjqRTwl23XME3Tp7nn//hdxnqy/Dag1OmzerIK/YMct1Ugd///57nP/7FU9y6f4T9Y8ld3X3XLc7//Xs+9T2++uR53nJrcn0BnCB+dnmDez76V5TKVe48dIVpkzqSTad44017+MoT53jPp77H6ECWH71uwrRZO2IkyCilnlRKndzhbbcDp5RSzymlKsBngLvFGW1fD3zefd8DwD2xGWsATzI7eW6Fv3vrXv7NW240bVJX/s4rpxnIpTl5boV/d/fN3HlLcm9UcGbbG9UGS2sVPvmu2xO3XU87IsJbbr2SHyyuMzNZ4BPvvD3RE44rRvL88IExTp5b4WdevZ9//aYbTJvUlTfcOE0uk+LkuRV++6dv5SdfOW3apK685VZHMlur1PnUu1+T6JX+Hsk8r9NhH/BS2++ngdcAE0BRKVVre35fpy8RkfuA+wCuvvrqeCyNgV990yv54QPj/Iu/cx2ZBO1RtR35bJrf+vu3kE6leOsPXWnanB354QPjvO+Og7z+hunEdj61844fvYbNap37XndtIo6H3on333Uj335+kX/+uusSX5Qeymf593ffwlA+w10JzsA9Xnv9JL/0hoO86eY93JywHTU6IZ3OuQ79xSJfBbab0n5QKfVF9z1/CfyKUurINp//WeBOpdS73d//CU6Q+Q3gUVcqQ0SuAr6slLplJ5sOHz6sjhy55K+yWCwWSxdE5LtKqY71827Elskope4I+RWzwFVtv+93n1sERkUk42Yz3vMWi8ViSRhJ1mG+Axx0O8lywNuAB5WTen0D+Fn3ffcCXzRko8VisVi6YKqF+e+LyGngR4EvicjD7vNXishDAG6W8l7gYeBJ4HNKqRPuV/wa8MsicgqnRvMHuv8NFovFYtmZ2GoyScTWZCwWi6V3wtRkkiyXWSwWi2WXY4OMxWKxWGLDBhmLxWKxxIYNMhaLxWKJjZdV4V9E5oEfBPz4JLAQoTm62I1270abYXfavRttht1p9260GRy7C0qpQJv8vayCTBhE5EjQ7gqT7Ea7d6PNsDvt3o02w+60ezfaDOHttnKZxWKxWGLDBhmLxWKxxIYNMv6537QBAdmNdu9Gm2F32r0bbYbdafdutBlC2m1rMhaLxWKJDZvJWCwWiyU2bJCxWCwWS2zYIOMDEblTRE6KyCkReb9pe7ZDRK4SkW+IyBMickJE/m/3+d8QkVkRedz9ebNpW7ciIi+IyDHXviPuc+Mi8hURecb9c8y0nR4i8sq26/m4iCyLyPuSeK1F5GMicl5Ejrc9t+21FYf/6vr5URF5dYJs/k8i8pRr15+KyKj7/AERKbdd898zYXMXuzv6hIh8wL3WJ0XkTWas7mj3Z9tsfkFEHnef7/16K6XsT5cfIA08C1wL5IDvAzeZtmsbO/cCr3YfDwFPAzfhnCT6K6bt28H2F4DJLc/9R+D97uP3A79t2s4u/nEWuCaJ1xp4HfBq4PhO1xZ4M/BlQIAfAb6dIJvfCGTcx7/dZvOB9vcl8Fpv6xPuvfl9oA+YcceYdFLs3vL6/wv8etDrbTOZnbkdOKWUek4pVQE+A9xt2KZLUEqdUUp9z328gnMGzz6zVoXibuAB9/EDwD3mTOnKG4BnlVJBd5KIFaXUt4ALW57udG3vBj6pHB7FOYFW+8H329mslPrfyjljCuBRnBNxE0WHa92Ju4HPKKU2lVLPA6dwxhrtdLNbRAT4OeDTQb/fBpmd2Qe81Pb7aRI+eIvIAeBVwLfdp97rygwfS5Ls1IYC/reIfFdE7nOf26OUOuM+PgvsMWPajryNi2/ApF9r6Hxtd4uv/1OcjMtjRkT+RkS+KSJ/25RRXdjOJ3bLtf7bwDml1DNtz/V0vW2QucwQkUHgC8D7lFLLwO8C1wG3AWdwUt+k8Vql1KuBu4D3iMjr2l9UTp6euF57cY4Ffyvwx+5Tu+FaX0RSr20nROSDQA34lPvUGeBqpdSrgF8G/khEhk3Ztw27zie28HYunkT1fL1tkNmZWeCqtt/3u88lDhHJ4gSYTyml/gRAKXVOKVVXSjWA/4mhlLwbSqlZ98/zwJ/i2HjOk2rcP8+bs7AjdwHfU0qdg91xrV06XdtE+7qI/ALwd4F/7AZHXLlp0X38XZzaxiuMGbmFLj6R6GsNICIZ4KeBz3rPBbneNsjszHeAgyIy485c3wY8aNimS3C10z8AnlRK/ee259s19b8PHN/6WZOISEFEhrzHOAXe4zjX+F73bfcCXzRjYVcumuUl/Vq30enaPgi8w+0y+xGg1CarGUVE7gT+NfBWpdR62/NTIpJ2H18LHASeM2PlpXTxiQeBt4lIn4jM4Nj9mG77duAO4Cml1GnviUDX20Q3w277wem6eRonan/QtD0dbHwtjuxxFHjc/Xkz8IfAMff5B4G9pm3dYve1OF023wdOeNcXmAC+BjwDfBUYN23rFrsLwCIw0vZc4q41ThA8A1RxdP93dbq2OF1lH3X9/BhwOEE2n8KpYXi+/Xvue3/G9ZvHge8Bfy9h17qjTwAfdK/1SeCuJNntPv8J4Be3vLfn6223lbFYLBZLbFi5zGKxWCyxYYOMxWKxWGLDBhmLxWKxxIYNMhaLxWKJDRtkLBaLxRIbGdMGWCyXKyJSx2lfzeKsUv8k8BHlLMyzWF4W2CBjscRHWSl1G4CITAN/BAwDHzJplMWiEyuXWSwaUM6WOffhbJYo7rkc/0dEvuf+/BiAiHxSRO7xPicinxKRu0XkZhF5zD3D46iIHDT0T7FYesIuxrRYYkJEVpVSg1ueKwKvBFaAhlJqww0Yn1ZKHRaRnwD+pVLqHhEZwVlZfRD4CPCoUupT7vZGaaVUWee/x2IJgpXLLBYzZIH/LiK3AXXcTQaVUt8Ukd8RkSmcLTy+oJSqicgjwAdFZD/wJ+rirdctlsRi5TKLRRPuhoJ1nF2P/yVwDvgh4DDOqasenwR+Hngn8DEApdQf4RwrUAYeEpHX67PcYgmOzWQsFg24mcnvAf9dKaVcKey0UqohIvfiHOPs8QmcHXnPKqWecD9/LfCcUuq/isjVwK3A17X+IyyWANggY7HER7+IPE6rhfkPAe8Yht8BviAi7wD+AljzPqSUOiciTwJ/1vZdPwf8ExGp4pxm+R9it95iiQBb+LdYEoaIDOCsr3m1Uqpk2h6LJQy2JmOxJAgRuQN4EvhvNsBYLgdsJmOxWCyW2LCZjMVisVhiwwYZi8ViscSGDTIWi8ViiQ0bZCwWi8USGzbIWCwWiyU2/n+8Bcj/0W5htQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp['hour_sin'].plot()\n",
    "plt.ylabel(\"Hour\")\n",
    "plt.xlabel(\"Days\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ex = tmp.plot.scatter('hour_sin', 'hour_cos').set_aspect('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Visualisation de l'allure de notre cible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On visualise l'évolution de l'énergie solaire pendant 4 jours du mois de Mars 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD+CAYAAAAqP/5ZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAA9gUlEQVR4nO29eZgcd33n//r0PdNzSHPovu3xIdmAbWEbbMxhAzYhhn0Sgp1AIJcXNnYIyR4mYb3E2c3u/ngSNtmfIZiQcCRgnCwPK2MTB4LxwWFLvoQlWfJYPjSja6QZaY7umb4++0d19bTGo+ma6arurqrv63nm8XR3TfdXX1d9+lPvzyWqisFgMBj8T6TZCzAYDAaDOxiDbjAYDAHBGHSDwWAICMagGwwGQ0AwBt1gMBgCQqxZH9zX16ebNm1q1scbDAaDL3nyySdPqGr/fK81zaBv2rSJXbt2NevjDQaDwZeIyCtne82R5CIi14vIfhEZFJHb53l9g4g8JCJPi8huEXlPPQs2GAwGw+KpadBFJArcBdwAbAVuFpGtcw77NHCvql4C3AR83u2FGgwGg2FhnHjolwODqnpQVXPAPcD75hyjQFf5927gsHtLNBgMBoMTnBj0tcChqsdD5eeq+QzwIREZAh4AbpvvjUTkFhHZJSK7RkZGlrBcg8FgMJwNt9IWbwa+oqrrgPcAXxeR17y3qt6tqttVdXt//7xBWoPBYDAsEScGfRhYX/V4Xfm5an4LuBdAVX8KpIA+NxZoMBgMBmc4Meg7gQER2SwiCayg5445x7wKXAsgIhdiGXSjqRiWTLGkPPnKKI8cMKeRweCUmnnoqloQkVuBB4Eo8LequkdE7gR2qeoO4A+BL4nIJ7ECpB9V05fXsASGT2X5X98/wA+fP87JqRwisPOPr6OvI9nspRkMLY+jwiJVfQAr2Fn93B1Vv+8FrnJ3aYYw8mf37+MH+47x7m2rOHdFB3/x/QPsfGmUGy5e3eylGQwtj+nlYmgZcoUSDx8Y4d9cspa/uvkSPvbWc2iLR3n8pdFmL63l+NH+4zy0/3izl2FoMYxBN7QMO18eZXKmwLUXrgQgEYtw6cZlxqDPQVX51Ld/zu98dRc/GTzR7OUYWghj0A0tww/2HSMZi3D1ubMJUlds7uX5o+OczuSbuLLW4sWRSY6cniYaET72908yeHyy2UsytAjGoBtaAlXlX/cd583n9NKWiFaev3xzD6qW926weOSA5ZV/9TcvJxGL8BtfeYKTkzNNXpWhFTAG3dASDB6f5NXRTEVusXnD+mUkohGeMAa9wiMvjLClL82VW3r50q9v5/j4DLd982lMYpnBGHRDS/CDfVaA79oLV5zxfCoe5Q3rjY5uM1Mo8rODJ7nmPKvS+pINy/n0L1zIT148yff3Hmvy6gzNxhh0Q0vww+ePsW1NF6u7217z2uWbe3hu+DSTM4UmrKy1ePLlMabzJd4yMBtnuOnyDZzTn+Z//PPz5IulJq7O0GyMQTc0nbGpHE++MvYaucXmii09FEvKU6+MNXhlrcfDL4wQjwpXbumtPBePRrj9hgs5ODLFPTsPLfDXhqBjDLqh6Ty0/zglhWsvWDHv65duWE40Ijz+0skGr6z1ePTACS7buJx08syawOsuXMHlm3v4yx8cMHcyIcYYdEPTeWzwBH0dCS5e2z3v6+lkjIvXdvNEyHX0kYkZ9h4Z5y0Dr+1UKiL88Xsu5MRkji8+/GITVmdoBYxBNzSdV05mOHdFB5GInPWYKzb38Oyh00zniw1cWWvx2KDVqOyaeQw6wOvXL+O6C1fw7afmNkM1hAVj0A1N59BohvXL2xc85pz+DnLFEidCnG/96IET9KQTbFvTddZjNvSkGc+aIqywYgy6oalM54scn5hhfc/CBr09aRUbZXLh9dB3vTLGlVt6FryT6UjFmMwVKJVMTnoYMQa9yUzNFLjtm09z5HS22UtpCkNj1r97fc9r0xWrSSesIOBUSAN+xZJy+FSWzX3pBY/rSsVQhalcOPcp7Dgy6CJyvYjsF5FBEbl9ntc/JyLPlH8OiMgp11caUPYdGee+Zw9z/+4jzV5KUzg0lgGoKbnYWR1TM+H00I+NT1MoKWuXLbxPHeV9Mpku4aRmP3QRiQJ3Ae/EGhC9U0R2lHugA6Cqn6w6/jbgEg/WGkjGpy2986lXw5ljPTRaNui1JJdyf5ewep72ncy65QvfyXSkrEt6YrrA6vmThgwBxomHfjkwqKoHVTUH3AO8b4Hjbwa+6cbiwsB41jJQT74yFspeHIfGsiRiEfprTCSyPfRMaA269cVXy6B3puKAZdAN4cOJQV8LVJefDZWfew0ishHYDPzwLK/fIiK7RGTXyIiZFQmzHvqx8RkOn55u8moaz6HRDOuWty0Y6ANI2x56SCWX4bKHvmZZDQ89aXvoJtMljLgdFL0J+CdVnfeqU9W7VXW7qm7v758/lzZsVKeYhbG0/dBY7ZRFqNbQw+l5Do1l6e9MkopHFzyuK2U09DDjxKAPA+urHq8rPzcfN2HklkUxPl0gEYuQikdCqaMfGs3WzHABaIvbGnpIPfRTWdbW8M7hTA3dED6cGPSdwICIbBaRBJbR3jH3IBG5AFgO/NTdJQab8WyeZW1xXr9uWeg89PHpPKezeUceeiQitCeiZELqeQ6NZWrq5zCroU8agx5Kahp0VS0AtwIPAvuAe1V1j4jcKSI3Vh16E3CPhjGyVwfj03m62uJcunE5ew6Ph6q0/ZDDDBebdDIWSg+9VFIOn5pmnYMvvvZ4FBGYCOkXX9ipmbYIoKoPAA/Mee6OOY8/496ywsN4tkBXKsZlG5bzhZKye+g0l2/uafayGsKh0XJRkQNDBVZgNIwa+sjkDLliibUOPPRIROhIxExQNKSYStEmY3vol2xYBoQrH91OxXOioQO0J2KhTFt0mrJo05mKGcklpBiD3mTGs3m6UnF6O5Js7kuHSkc/NJqhMxmjuy3u6Ph0MhrKtMVKUZGDoChYgVETFA0nxqA3mfHpAl1tlvJ1yYZlPPVqeAqMDo1lWdfTjsjCOeg26WRYPXTLoDuRXMAKjJq0xXBiDHoTUdWKhw7WZJ4Tk7mKthx0rLa5zowUWA26wmiohsay9KQTtCcchbzoSBoNPawYg95EsvkihZLS1TZr0AGePhR82UVVGRrLOs5wAaufSxjb5w6fyjrWz8HS0E2WSzgxBr2J2H1cbA99Q69l3I6GoAXAickc2XxxcR56MhbKLBenOeg2nUZDDy3GoDcRu4+LHRRMJ6Kk4pFQTOWptM1dhIeeTloeelhiDGDdyQyPOasStelImiyXxfDc8Gn2Hh5v9jJcwRj0JmL3cbGDoiJCX0eSE5O5Zi6rISy2qAistMVCSZkplLxaVstxYjLHTKHkqKjIpjMVt+S8Ynj2aamoKv/260/y3x7YW/tgH2AMehM5bRv01GzanmXQQ+Chjy4utxpmOy6GSUe3c9AX66GDadDlhGeHTjN8KhuYOxpj0JuILbl0tZ1p0EcmwmDQs/R1OM/cAGgPYcfF4VPlHHSHxVdgaehgGnQ54f7dh4HgOAnGoDeR2aDorFHr70yEQnI5Oj7N6m7nRgpmPc8wTS2q5KAvwkM3Bt0ZqsoDPz8KGINucAFbQ++cI7mMTs1QDPjU9kyuUDHQTmkP4ZCL4bEs3W3xM86RWlQ6LoboTmYpPHPoFMOnrP3NBqQpnjHoTWR8Ok9bPEoiNvu/oa8jSUlhLBNsLz2TK1YMtFPCOIZusSmLYKYWOeX+3UeIR4UbLlpFNkweuohcLyL7RWRQRG4/yzG/IiJ7RWSPiHzD3WUGk/HsbNm/TV95tmbQA6PZXJG2RRr0MHroQ4tMWYTZIRfGQz87ltxyhGsG+lnRlSKbL1IKwF1xTYMuIlHgLuAGYCtws4hsnXPMAPAp4CpV3Qb8vvtLDR7j0/kzMlwA+joSAJyYCLaHPpUrkF5EQBSqNPQQGarjEzOs6k4t6m9sDX3caOhn5elDpzh8epr3XLy64ihMF/zvKDjx0C8HBlX1oKrmgHuA98055neAu1R1DEBVj7u7zGBit86tpq8zHB56Zkkeevgkl6XEGjqTZmpRLb777BES0QjXbV1ZMehBCIw6OVPWAoeqHg8BV8w55jwAEfkxEAU+o6r/7MoKA8x4tlDxyG3CILmoKtklaejhmiuaK5TIF3XR+5SKR4hFxGjoc1BVfnZwlC89epAfPn+c67etorstXhm8HQQdfXFf/Qu/zwDwNqwh0o+IyMWqeqr6IBG5BbgFYMOGDS59tH8Zn86zpT99xnNdqRiJaISRABv0XLFEobR4Q9VWHq8Wlrmi9p3IYnL1wao47kiFszPlQvzbrz/Jv+w9Rm86wSevO4+PXrUJmI3NBCHTxcmZMgysr3q8rvxcNUPA46qaB14SkQNYBn5n9UGqejdwN8D27dv9H4Gok+rWuTZW+X8i0Bq67QktxVBZLXT9f+E5IVPZp8V98YGZWjQfjw2e4IaLVvG5D76h4pUDgZJcnGjoO4EBEdksIgmsYdA75hzzHSzvHBHpw5JgDrq3zOChqmcMt6imrzPY5f/1GCqrhW44DFXFQ1+khg7QkYyboGgVhWKJTK7IBau6zjDmAG3x4MRmahp0VS0AtwIPAvuAe1V1j4jcKSI3lg97EDgpInuBh4D/oKonvVp0EMjkihRL+hoPHYLfz8U26IsNikK5hW4APCknVL744kvw0JMxJmeMhm5jV83O50DZ5+F0SCQXVPUB4IE5z91R9bsCf1D+MThgvj4uNn0dCfYcPt3oJTUM2xNabNoilD30kGjDdr59e3JpksvR8eD31XeKbdDnq7gNm+Ri8IC5wy2q6etIcnIyF4hCh/moR3JJJ8MT7Mvml/7FZ4KiZ2I7UJ2peTz0uDHohjqZ9dDn0dA7khRKWmmvGzSy9UguIRpDV/HQlxgUNc25Zqlcbwt46EFIWzQGvUmMz9ML3SboxUV2t8T0EoJ97clYaLotVrKBlhgUNVkus8xKLmfX0IPgKBiD3iRqaehAYHPRK0HRJQT70okomZCkLdpfXEsKiqZi5IqlQAT63KASFJ3HgUrFgpOHbgx6k5ivF7rNbLVoMHPRs3Vq6GHp5VKJNSwxKAqmQZfN3HGP1UQiQls8SjYAd37GoDeJ0/P0QrepGPSATi7KLLGwCKwA4VSuEIpB0ZlcgWhESEQXf5nOttD1v5FyA3sfztYXpz0gsRlj0JvEePa1vdBtlrXFiUYksBp6JldAxOo5sljak1FKSigGRU/NWP1uRGTRf1sZcmEMOmD1hk8nosTO8uXYloiaoKhh6VidFuf3FiIRoTedCLBBL9IeX5qhClML3WyuuKSURajy0E1xEWBdbwtNfWqLR42Gblg649nCvAEaG6taNJgautU6d2mGypZpwjDkYipXWFKcAcxc0blMTBfmzXCxMZKLoS7Gp/N0z5PhYhPkfi6ZXKHSCnexpO2pRQEIYNUimysuKSAKVUFRY9CB+WcPVGMkF0Nd1DrBrI6LQTXoxSWlLMJsTnYQGinVYipXoD2+tDsZW14wPdEtanvoMTJ5/59TxqA3CUtyOfsJ1l+WXIKYzbGU4RY2HfaQixBILpk6PHT7DsikLVpYBr2Ghm48dMNSqe2hJ8kVS4FsgTqVKyypShSqNfTg7ctcMnV88SVjVgaV0dAtrNkDZz/njORiWDKqOu9wi2r6OsvDogOoo2frkFzsrI8wtNDNzBSWlKtv05WKMRGCLz4n1PLQ2xNRMmHJchGR60Vkv4gMisjt87z+UREZEZFnyj+/7f5Sg8NUrkhJ569aswlycVE9nqctQYRBQ8/ki5Ug8FLoSJoGXWD1Oc8VSwtq6G0ByXKp+fUvIlHgLuCdWKPmdorIDlXdO+fQb6nqrR6sMXAs1JjLxjboJ6eCl7pYT9ribB66/y++WmRmlr5PYAVGJ01QdMG+STZt8Si5QoliSYlGFl8f0So48dAvBwZV9aCq5oB7gPd5u6xg4+QEm+3nEkQPvbBkzzMZixCR4Gvo+WKJXLFkPHQXmG3MtXAeOvi/QZcTg74WOFT1eKj83Fx+SUR2i8g/icj6eV5HRG4RkV0ismtkZGQJyw0GCw23sLFz1McD1hNdVcnmly652IOig56HXs+YPpvOkA65+MmLJ874dy/UOtfGvhPyu5TnVlD0PmCTqr4O+D7w1fkOUtW7VXW7qm7v7+936aP9x/EJazRYTzpx1mMSsQiJaCRwwb/pfAlV6pIS2pPBb6FrZ1wsNRsIrKlFYfPQJ6bzfOhvHueeJ16tPOdE4rRbFPs908WJQR8Gqj3udeXnKqjqSVW1tYG/AS5zZ3nBZGgsC8C6nrYFj7MMV7AuyNnhFkv3PNMhGHJR6YVej4eejIWusGhiukBJZ68x+zmYv7OpTZgkl53AgIhsFpEEcBOwo/oAEVld9fBGYJ97Swwew2NZutviC3oMYHkNQfPQs3UMt7BJJ4LfEz0zs/QWwzZtiZjvDdRisSWTo6dnB2RPLDBP1CYVkKlFNc8WVS2IyK3Ag0AU+FtV3SMidwK7VHUH8HsiciNQAEaBj3q4Zt8zNJZh3fKFvXOwytz9runNpZ5e6DbtieB90c0l44KHnopHyBfV95kbi8E+v46Ozxp0J0kIQZFcHF1VqvoA8MCc5+6o+v1TwKfcXVpwGRrLsrkvXfO4dCIauPS8iqGqU3Kx4xBBZfaLrx6Dbv3tdL5YlxbvJyoG/QwPvUBEWDBjqL0SFPX39WYqRRuMqjI0lmXd8vaax7YnAuyh1yO5JGOBD4pmXAiKpsrDU8I0V9S+XkYmZyiWrD5IE9MFOpKxBfvvzw6K9vf1Zgx6gxnL5Mnmi44kl3QyGNVr1bghuaQT0cCn49lB0XpiDRUPPQTTnWzs86tY0koNx3h24b5JMGvQ/f7lZwx6gxkaywA409ATsQAa9PollyDuy1zcSFusllzCQvV5Ycsu4zX6uMDsHaPfzytj0BtMJWXRgeSSTkYDl83hhjacTkYDPyjajbRFe2ZrqAx61fVypGzQJ6bzC2a4QLXk4u+9Mga9wdge+trQeui2hl6H5JKMoWoVKQWVbK5IRKxWB0slWfY6wzBQ26a6Y+Kx8VkPvVaKsN1Swu9ZLsagN5ihsSydqdiC4+ds0ongeaJZWxuux0NPBH94w9RMkfbEwoG8WqRi4ZNcsrkiIhCPSiV1cWJ64V7oYLWUaA9A3r4x6A1m2GGGC1h56EHzRKdyReJRIVGH59kekL4bC5GpY0C0jS25zATo/KnF1EyRdCLGis5URUOfmC7UDIqCFXPw+x2xMegNxkpZrC23QDAHItcz3MKmv9PqRDl8KlvjSP+SydWfOx7GoGg2X6AtEWVVt2XQVdWRhg5WvCLr82vNGPQGYuWgO6sShSpPNEA515bnWZ+h2rqmC4C9h8fdWFJLkskV6v7im01bDM75Uwt7eMqqrhTHxqcrw2ScGnTjoRsccyqTZypXdC65BNBDr2fwsU1fR5JVXSmeGz7t0qpaD8tDd0dyCZJkVws79rCqO8XR8WlHnRZt2hJRo6EbnGNLBGuXOfTQk8HTiusZP1fNtjVd7Amwhz5Vx1Qnm1AGRfOFioeeyRU5XL7mauWhg1XEZbJcDI5ZTFERVGnoQZNc6khZtNm2tpsXRyZ9fwGejWwdU51sZjX08HjotsOwsjsFwIFjk4CRXAweYBcVrXcsuQTPQ8+6ILkAXLSmi5LCvqPB9NKnZop1pXbCbA57mDz0zIxl0FdXDPoEsHCnRZsgtBs2Br2BDI1l6UzG6Gpz5qHaGmqQPPQptySXtd0A7Amojm7NXa3vTiYSERLRSLiConkr6L6qyzLog8cX4aGHRXIRketFZL+IDIrI7Qsc90sioiKy3b0lBoehsQxrl7c5LhapeOg+9xqqsdIW65dc1nSnWN4e57nhYHrobgSPAZLxSKjy0LM5685mRZeV2mp76E4Melsi6vu74ZoGXUSiwF3ADcBW4GYR2TrPcZ3AJ4DH3V5kUFhMDjrMeuhBGkPnRsEMWJV9F63tZs+R4HnoxZIyUyi5EmtIxaOhklyswqIoyViU3nSC4xNWx0WT5TLL5cCgqh5U1RxwD/C+eY77U+B/AsGePLBEVHVRVaJgZSmIEKjpPG55nmDlo+8/OkEuYL1KMi7MXbVJxSOhMeilkpLNz2YHrSzLLolopBIgXoj2eJR8UckX/Xs+OTHoa4FDVY+Hys9VEJFLgfWqev9CbyQit4jILhHZNTIysujF+pnxbIGJmcKiPPRIRGiPB2dQtJueJ8BFa7rJF7VyWx0U7EyLeoOiYDkFYclysWMF9h3gqnJg1IncAsHouFh3UFREIsBfAH9Y61hVvVtVt6vq9v7+/no/2lccWmTKok17MhYYD92NOZnVXFQOjAatYrQyrajOoCiUJZeQBEXt5IH0HIPuJMMFZmNWfr6jcWLQh4H1VY/XlZ+z6QQuAn4kIi8DVwI7TGD0TF4dLbfNXeZccgHr5PR7oMbGziBwS3LZ2NNORzLGc4eDpaPbPfBd8dBDJLlkK3c2lmG2M12ce+iWOQy6h74TGBCRzSKSAG4CdtgvquppVe1T1U2qugn4GXCjqu7yZMU+5YmXRknFIwys7FjU37UnYoFJW7TvNNzy0CMRYevq4FWM2oE5tzz0sPRDz+TPvANctEGP+7/uo6ZBV9UCcCvwILAPuFdV94jInSJyo9cLDAqPvDDClVt6HQVnqmkPkIeeqczJdG8C/dY1Xew9PF4ZCBwEbA/dlbTFEGnotuNjG3S7WtRJhkv13/k5F92Rhq6qD6jqeap6jqr+t/Jzd6jqjnmOfZvxzs9kaCzDwZEp3jKw+LhBkDT02TmZ7njoYOno2XyRl05MuvaezcaNMX02qXiEmZBJLrYWvnqRQdGKQffxfplK0Qbw6AsnALhmoG/Rf5tOBCfLxW3JBeD8lZ0ADB6fcu09m43rQVEfG6jFMDfovrIiuTjz0FMBGBRtDHoDePSFEVZ1pTh3xeL0cwjWXNGsB5LLhl4ryPzqaJAMustB0bBo6HMchq5UjHduXcmbtvQ6+vsgSC7uXVmGeSmWlMdeOMH1F61a0nxIe8J9EMh4ILl0t8VZ1h7nlZMZ196z2bjqocfC5KGfKbmICF/6defJdrPN8Py7X8ZD95jdQ6cYny4sST+HYHnobhbMVLOxp72SFhoEMjMFRGYHVNSDLbkEadD42aj3zqbNaOiGWjxy4AQicNW5i9fPwdLQc4WSr8uRbWY1TndvDDf0pgPnobfHo0u6o5tLKh6hpJAvhsGg1xejsUf++XmuqDHoHvPoCyNcvLabnnRiSX8/O7XIv16DTcVDr3NW5lw29LQxfCobiC89KLcYrnNAtE2Y5opmckUS0Qjx6NLMWiIWIRYRX19rxqB7yPh0nqcPneKaJcotMFvGHIRc9GyuSCoeIRqp3/OsZmNPmmJJK+PG/I5bHSkBkpWpRf41Uk7J5gp1y3ltPp9aZAy6h/z0xZMUS8pblpCuaGN7akGoFp3KFVyXW2A20yUosos1Rs2dfbKnFoWhJ3omV6x7bF97wt9BZGPQPeRQOVB3wequJb9HkDz0TK7outwCsNE26AEJjLrpoadC5KFncvWP7fN7EoIx6B4yWS4I6qhDD7U9tSB46Nlc0dWURZuVnSkSsQivngxGLro96NgNUpW5omHw0Ou/A0zFjeRiOAtTMwXa4tG6NOP2oHnoHkgukYiwoac9OJLLjIsGPWRB0Xr3rT0RJZv377VmDLqHTM4USdeZrVAZFO1jr8EmkyvQ7oHkAsHKRc/k6x8QbRM2ycUNg248dMO8TM0U6KhTYqhUrwWgn4ubUsJcNvRaBj0IBTSZGffG9NnFSUZycUZvOsGh0Yxvu3cag+4hUzOF+j10W0P3sddgk8nVf8dyNjb2tJPJFRmZnPHk/RuJm1kutoc+EwLJJeuCw/Cubas4MZnj8YMnXVpVY3Fk0EXkehHZLyKDInL7PK9/TER+LiLPiMhjIrLV/aX6j0kXDHplzmEAPHTrC84jyaU3DcCrPtfRK4OOXZKmUjFbcgm+hz7lgkF/+/krSCei3Lf7sEuraiw1DbqIRIG7gBuArcDN8xjsb6jqxar6BuD/w5oxGnqmcoW6MlzAql5LRCOB8dC9yEOH4OSi243Y6j1vbGYlF/+fP7XIuhB0b0tEeefWlXzvuaPkfNil0omHfjkwqKoHVTUH3AO8r/oAVa2eAZYG/ClAucyUC0FRsCbX+Lm/BICqMpUr1F34cTbWLW9DxP+56JVBxy4Z9LBUiuaLJXLFkivn1y++fg2nMnkeGxxxYWWNxYlBXwscqno8VH7uDETkd0XkRSwP/ffmeyMRuUVEdonIrpER/23WYpl0ISgKlo7udw99Ol9CFdd6lMwlGYuyprutUszlV2wP3S1pyvbQgz5X1M1Onm8Z6Ke7Lc59zx6p+70ajWtBUVW9S1XPAf4T8OmzHHO3qm5X1e39/Uvvb+IXpmbcST8LwlzRiqHyyEMHyrno/i4umnKhGK2aRDSCSPA99Lnj5+ohEYtww0Wr+Jc9R323b04M+jCwvurxuvJzZ+Me4P11rCkQlErqWlZHezLm+0rRzIx7F9zZ2Njr/1x0u7rYrX0SkVAMuci4fGfzi69fw1SuyEPPH3fl/RqFE4O+ExgQkc0ikgBuAs4YDi0iA1UPfwF4wb0l+hM3g1vpIHnoHmW5gBUYPTGZqxhFP2J/cbvloUN5DF3As1zcbs185ZZe+jqS7HjWX9kuNQ26qhaAW4EHgX3Avaq6R0TuFJEby4fdKiJ7ROQZ4A+Aj3i1YL/gZnCrPREAD92j4RbVbOzxf+qi254mhGNQ9Nzxc/USjQhvO7+fnS+PuvJ+jcLRv15VHwAemPPcHVW/f8Lldfke20t048IMhIZe+YLzzkPfWBkYnWHrmqV3uGwms+eNmx56NPCDoisOg4vn18quJGOZPKWSEnG5h79XmEpRj3AzuGUNiva3h9UID311dwrA14Mupjww6MlYJEQeunsGvSedpFhSTmfzrr2n1xiD7hFuXpjtiZjvK0UrHrqHBr0nnSAZi3DktJ8NetkwudjELBkmySXu3vnV12GNjTw5lXPtPb3GGHSPmHDTQ09EyeSLlHzaMAi8uSWei4iwZlkbh09Pe/YZXjM1Yw23cPMWPxWLBH5iUdaD88ueAzxqDLrBVQ89GUPV3z2tbcnISw8dLNnliJ8ll1z9/X/mYmno/j13nDDlieRiG3T/NHwzBt0jplwMitrFOH7OdMnMFBCZrVz0itXdbRzxtYdedDVlEey0Rf+eO06wJRe7GZkb9KaTgJFcDFjDLQA6k/G638sOJGZ9HBidyhVJJ2KIeJstsGZZimPj0xSK/pQYbMnFTay0RX/uh1OyOfelquVp69odnTQGPfRMzRSIuOSRzk4t8m9g1MvWudWs7m6jpHB8wj+3ydW40XJ5LmGoFHWjde5ckrEoncmY8dANsxemGx5pZWqRnw162UP3mtXL/J266EbL5bmk4pHAN+eyWue67zD0dCRMUNRgj59z58KseOg+19C9zHCxWbusDcC3mS4Zl1ouVxOOSlH35rBW05M2Bt2Au9kKwfDQ65/36AS7uMivmS6TM+73jE/Go8wUSoGYt3o2Mh556L3phJFcDFZQ1C2DXpkr6mcPPVf0tHWuTWcqTmcy5ttMFzfm0M4lDD3RvRpA3pNOcNJHc2qNQfeIKZeGW0DVXFE/e+gzBc+GW8xl9bKULzX0UknJ5D2QXCpzRf3rENTCq/GGvR1JxjI539zdGIPuEW4Nt4DqLBf/XpCN8tDBv7no2XwRVfeHgKQqY+iC7KG7n+4JluSSLyrj0/5wpoxB94hJF4OiqVgUkdliJT9i5Vc3xkNfsyzly34uXjTmgnAMivZScgH/lP87Mugicr2I7BeRQRG5fZ7X/0BE9orIbhH5VxHZ6P5S/YWbWmgkIqzqSvl2Go+qPb2pcR76icmc7wyYfQfmftpi2UMPcPl/1iPJxW/l/zUNuohEgbuAG4CtwM0isnXOYU8D21X1dcA/YQ2KDjVTLqefbVvTxZ7D4669XyPJFUsUStpAD91KXTzqM9llqjJ+zm3JxfbQgym5qGo5i8oLyaVc/u+TalEnHvrlwKCqHlTVHNbM0PdVH6CqD6mq7T7+DGvuaGjJFUrkiiXXgqIAW9d0c3Bk0peB0UyldW5jPPQ1dl90n8kuky4PiLYJelDUSsnEs8IiCJbkshY4VPV4qPzc2fgt4HvzvSAit4jILhHZNTIy4nyVPsMLLfSiNV2UFPYdmXDtPRvFVKW1aaOyXCwP/cgpf3robmvoyXiwDXrGw06evWl/9UR3NSgqIh8CtgOfne91Vb1bVber6vb+/n43P7ql8GKM2La13QDsPXzatfdsFF5ecPNRKS7ymYdeaTHscqwhGQu25GJ/EXrhoafiUdoTUd946E6usGFgfdXjdeXnzkBErgP+GHirqvojguARtkfq5q3zmu4Uy9rjvtTRK9pwg4KiqXiUnnTCd+X/3mW5WPs+E9CgqD0iritVf2fT+fBT+b8TD30nMCAim0UkAdwE7Kg+QEQuAb4I3Kiqx91fpr/w4sIUES5a081zxkN3hB8HXZi0xaVxKmMZ9OXt3hj03o5kcCQXVS0AtwIPAvuAe1V1j4jcKSI3lg/7LNAB/KOIPCMiO87ydqHA7oXuZlAUrEyXA0cnyfus17dX2RsL4cfiIq/mrga9sGgsYxnb5WW922160wnfpC06OnNU9QHggTnP3VH1+3Uur8vXeOVpbVvbTa5Y4oVjk2xd0+Xqe3tJxUNvUFAUYO2yFE+8dLJhn+cGU7kCqXiEqItDGqDaoAfVQ7cM+jKPPPSedILnj/hD6jSVoh5QCYq67GltKxtxv8kudkyhUWmLYGW6jE8XKv8v/ICb1cXVpGLBbs41VpZclrV556GfmPJHPxdj0D1gyqN84s29adoTUfb6LDBq56E3Km0R/NlGN+NBp0WAWDRCLCKB9dDHMjk6kjESMW/MWU86Qa5Q8kUvJWPQPcArySUSEbau7mKPTz30tnjjPPQ1Phx0MTnjTfk6BHuu6KlM3jO5BarK/31QLWoMugdMzhRJRCOeeAzb1nSx9/A4pVLr3/7ZZHJF2uJR17XhhbAN+vCYfzx0N1suzyUVjwS2l8tYJsfydm/kFoDeDru4qPUDo8age4CXA5G3relmKlfk5ZNTnry/FzRqQHQ1q7pSJGIRXvHTPrk45WouyQAPih7L5D3LcAHoKfdz8UMuujHoHuDF1BmbbWutwKifCoy8Gj6wENGIsLk3zcETPjLoLvbQn0sqHmEmsJJLzrMcdPBX+b8x6B7gVbYCwMCKTuJR8VWmi9ULvbEeOsDmvjQv+cqge9diOMiDosemGiO5GA89pHh565yIRTinv4MDR/3TpMvqhd5YDx1gc3+aV05OUfBJIZaXd3apeDSQGnqhWGJ8uuBpULQ9ESMVjxiDHlbcHBA9H+et7OTAsUnP3t9tvOpVXYvNfWnyRWXYB6mLdk9vLyWXIGa5nMraZf/eeehg9UX3Q090Y9A9wMtsBYDzVnYwfCrrm5F0mZliQ/u42GzpSwP4QkefzpcoqXfVtKmqoOgTL43ywS/+lOeG/SPbnQ2vq0RtenxS/m8Mugd4GdwCGFjZCcDgcX946VO5QsM6LVazuWzQXxppfYM+O9zCWw1dVfmT+/bw+Euj/NIXfsK3nxry5PMaxVimMR66XzouGoPuAZMeaqEAAys6ADhwzB86eibXHA+9J52gKxXzRWDUnkTlWdpiWXJ5cM9R9hwe54/fcyFvWL+MP7j3We68b68vytrnY6xsZL2XXBImyyWMqGpZcvHOgG3sTZOIRXjBLx76THM8dBFhc3+HLwz6ZKUjpXdB0Wy+yOe+/wJb+tP85tWb+fvfvoKbL9/A3/74JZ4b9k8abDV261yvJZe+ziQjEzMtX9DnyKCLyPUisl9EBkXk9nlev0ZEnhKRgoj8svvL9A9ea6Fg5Vif09/BCz7w0AvFEjOFUlM8dLB0dD8Y9KlKy2WvCousLI39xyb4/evOIxoR4tEIv/OWzQDs98G5NB9et861Oac/zUyhxKujmdoHN5GaBl1EosBdwA3AVuBmEdk657BXgY8C33B7gX7Day3UZmBFhy8yXTLlQFwzslzA0tGHT2XJtnhjpUpHSg81dIDzV3by3otXV57f0NNOIhrhheN+Neh54lHxvJPnhautgr7nj7b2nYwTD/1yYFBVD6pqDrgHeF/1Aar6sqruBoKXF7VIvGrMNRe/ZLrYnRabkYcOsKXfCoy2eqsEr88buzHaJ985QKSqp04sGmFLf5pBHzgH83Eqk2NZewIRb/sEnbeyk4jA3hYf0u7k7FkLHKp6PARc4c1y/I8XA6LnozrT5fXrl3n6WfVge57N9NABXjoxVfGyWhGvDfovvG410Yjw7m2rXvPauSs62D3kzxTGMY/L/m1S8Sib+9Lsa/FBFw0NiorILSKyS0R2jYyMNPKjG4ZXvdDn4pdMl4xHY9Wcsql31qC3MpWxhR7t0zn9Hfzu28+d15MdWNHJobFMy8tS8zGWybPM4wwXmwtXdwVCchkG1lc9Xld+btGo6t2qul1Vt/f39y/lLVqeKY/Tz2z8kulS8dCbkOUC1v+HVV0pDrZ4Lnpmpnn7NLCyA1V4caS1z6X58LoxVzUXru7i0GiWiel8Qz5vKTgx6DuBARHZLCIJ4CYg1EOgF8KrAdFz8UumSyW/ukkeOthNulrbWE3mCiRiEeLRxmcS23d7filUq2Ysk/c8B93mwtWWzPl8C/dRqnn2qGoBuBV4ENgH3Kuqe0TkThG5EUBE3igiQ8AHgC+KyB4vF93KNCooCv7IdKlMsm+Shw5Wk65Wl1y8rl1YiI29aWIR8V2mi6panRY9Tlm0sWMwrayjOzqDVPUB4IE5z91R9ftOLCkm9DTSoJ+3soMdzx72tEtfvWRy3hbMOGFLX5qxTL6hF/9iycwUmxY4TsQibOpL80KLOwdzmZwpUChpwySXVV0plrXH2dfCmS6mUtRljpyeLufFNsBD90FPl6kmB0VhNtOllZt0edlD3wkDKzpa+jyaj9kq0cZ8SYsIF6zqbGkP3Rh0l/nR/uNcuaW3IfMz/ZDpYnvobU3yPuHM1MVWxcse+k4YWNHByyenmPFRz/RKlWiDDDpYssv+oxMUW7QFgDHoLvLyiSleHJniHResaMjn+SHTxcuB2U5Z39NOMhZhbwuP7ZvyuId+Lc5Z0UFJW/tLby6znRYbI7mAZdCz+WLLtgAwBt1Ffvj8cYCGGfRoRBhY0cHPW7goJNOk1rnVxKMRXreum6deHWvqOhbCarncvH0aWGHJd37S0Wd7oTfQQ1/V2oFRY9Bd5F+fP8a5KzrYWC5maQRv2tLLk6+MtWxRyFSThlvM5dKNy9lz+HTLztVsdmB7S3+aiNDSd3tzmW2d2zgPfWBlB9GIGIMedCam8zx+cJRrL2yMd25z9UAfuWKJJ14ebejnOiXTpPFzc7lsw3LyRW3ZKT3NDoqm4lE29LQz6KPURVty6W5rnEFPxaNs6Uu3bKaLMegu8egLJyiUlGsvWNnQz71icy+JaITHXmjNVgpTuSLtLZBSeenG5QAtKbuoKplc89IWbc5d0ek7yaUrFSPW4GKsC1Z3GQ896Pxg3zG62+JcumFZQz+3LRHlso3LefSFEw39XKdkmqwN2/R1JNnY286Tr7SeQZ8plCiUtOm1BAMrrWEg+aI/mqaOZfJNqSu4aE0Xw6eyHG7B4ePGoLtAsaT8aP8Ibz+/v+HeAliyy/NHJxiZaL0htlO5YlOLiqq5dMNynnr1VMuNW7Nb+/Z3JJu6joEVHRRKyss+yXQZK7fObTR2x8r7nj3c8M+uhTHoLvDMoVOMTuV4x4WNlVts3jLQB8CPB1vLS88XSxw9nfV8PJhTLt24nJGJGYbGWsuzun/3ESICb29QdtTZuHSDJUt9f9+xpq7DKacy+YYGRG029aV5w/plfOcZY9ADx8jEDJ/7/gGiEeGtA83pILltTTfL2uMtJ7v88PnjjGXyXD9PD+5mYMthraSjqyr3PXuYN5/TR39ncz30TX1prtzSwzefeLXlZ2eC3Qu9Oa0c3v+GNew7Ms7+FmvUZQx6HXx392He9bmHeeLlUT7zi1vpbpInGo0IV53Tx2ODIy0lJ9zzxKus7ErytvNbo1Xy+Ss7SSeiLaWjPzc8zssnM7z3datrH9wAfvWKjRwazfJoi93tzcfYVK5pd3/vff0aohHhO88sqZO4ZxiDvkT+x/ee59ZvPM2Gnnbuv+1qPvymTU1dz9UDfRwbn2mZfhyHT2V5+MAIH7hsfVPiCvMRi0Z4/fplLeWhf3f3YWIR4fqLWuMu5t3bVtKTTvCNx19p9lIWJFcoMZUrNs1D7+tIcvW5fex45nBL3c20xpXmM348eIK/fvhFPrh9Pf/n42+uNMlqJlefa+norSK7/OOuIUoKH3zj+toHN5DLNi5n35GJSo+ZZlIqKd/dfYRrzutvSnBvPpKxKB/Yvo4f7DvOsfHpZi/nrJzKNL6oaC7vv2QNw6ey7GqhOz5HBl1ErheR/SIyKCK3z/N6UkS+VX79cRHZ5PpKW4Tx6Tz/4R+fZUtfms/cuK1lvM/1Pe1s6m3nH58cqlTQNYtiSbl31yHeMtDH+p72pq5lLpduXE6xpDx7qPkFRk8fGmP4VJZffH1ryC02N79xA8WS8q2dh2of3CTsu6xmtkN+19ZVtMWjLSW71LRGIhIF7gJuALYCN4vI1jmH/RYwpqrnAp8D/qfbC20V/mTHXo6OT/Pnv/L6pnYQnI/bb7iQF0cmef/nf9zUir/HBk8wfCrbct45wKXrrUyOLzz8YtNHrt337BGSsQjXNSk76mxs6ktz9bl93PPEqy3VVbBQLPGj/cf5lb/+KR/7+6fo60hyWblgrBmkkzHetW0l9+8+wk8GT7RE/EpqLUJE3gR8RlXfXX78KQBV/e9VxzxYPuanIhIDjgL9usCbb9++XXft2rXoBe87Ms7uoVOL/rulogqK5XUeG5/mf/9wkFvffi7//t3nN2wNi+GpV8e45WtPMpMv8onrBuhMNSYHXBUKJaVQLPHd3Uc4eGKKn37qHSRjrfWlB/D5Hw3yv/91kOlCkfe+bg1XndPLPLOTPaF6n/7/hwbZvrGHv/7wZY358EXwvZ8f4eP/8BQbe9t5w/plXLy2u2HnEsxedyVVTmfz7HxplJ0vjzE5U2B1d4pbrtnCTW/c0HSn6udDp/nI3z3B6FSOc/rTfGD7ekcy0GUbl3PuiqVJtSLypKpun/c1Bwb9l4HrVfW3y48/DFyhqrdWHfNc+Zih8uMXy8ecmPNetwC3AGzYsOGyV15ZfODliw+/yH//3vOL/ju3uGTDMr51y5ua2g62FsOnsvzOV3ext4nlybe941z+8F2t+aUHcHJyhr957CW+9pOXmWpSYzMR+PJHtvOOBreLcEKppPzD46/w2OAJdg+d5sjp5urp5/SnuXJLL1ed28d1F65sqetvOl/k/t1H+NrPXuHZQ6cc/c1/ff9FfOjKjUv6vJYx6NUs1UOfmM4zMd3YgFZEhIhAJCIsb080ZHhFvdh3FI0kFhFi0QixqNCVao1iolpkcoXK5JtGEYsK8UiEZDzSMlW0tTg5OcNMobEtASIiiEAqFm1aSvBiOT4xTaFYW3rpbosvudXDQgbdyTsOA9Vi6Lryc/MdM1SWXLqBk0tYa006U3E6fWIsmkk0IqxZ1tbsZbQ87YmYb4xqM+ltclsCv7CiM9XUz3dy37ITGBCRzSKSAG4Cdsw5ZgfwkfLvvwz8cCH93GAwGAzuU9M1UdWCiNwKPAhEgb9V1T0iciewS1V3AF8Gvi4ig8AoltE3GAwGQwNxdK+pqg8AD8x57o6q36eBD7i7NIPBYDAshtYJFRsMBoOhLoxBNxgMhoBgDLrBYDAEhJp56J59sMgI0Not3dyhD2iNjlmtjdknZ5h9ckaQ92mjqs7bk7ppBj0siMiusxUBGGYx++QMs0/OCOs+GcnFYDAYAoIx6AaDwRAQjEH3nrubvQCfYPbJGWafnBHKfTIausFgMAQE46EbDAZDQDAG3WAwGAKCMeguINKoeTf+xuyTwS3MuTQ/xqC7gGkV7BhzERpcwVxz82MMeh2IyEdE5J9F5D+KyBubvZ5WRUQ+JCJPAp8tT8AyzIOI3CIifyoiZjLJWRCRD4vIQyLyWRExHV7nYAz6EhGRG4HfBv4MOA58QkSuLb9m9rWMiGwDfh/4BPAw1j59oPxa602QbgIiEhORjwN/hNWGOnQVjrUQkbSI/BXwm8B/AV4APigirTdhu4kYw7MI5hig9wBfU9VHgG8DeeA/AahqY4cvtjYrgUdU9bHyMJTPAn8OoKrNmc7cYqhqAXgKuAD4IvAbItLb3FW1Fqo6BTwDvK98ze0AxgAzG68KY9AdIiKfAf6qSjJ4BLgFQFXHgRGgS0RuKR8fSr1YRG4VkYurnsoDb7EflI36ARH5k/LxoTwH59mnZ8qDYr6ANbf3urDujc08e3SPqo6LSFRVjwLnYuIyZxDqE8YpZWN+OfB94DYR+SSWh3BMRL4hIk8BBeB/AZtFJBK2oI2IbBSRh4FPA39hP6+qjwIzIvLpqsP/PXCtiHSE7W5mgX2aKZ8308DfAb8KbGrOKpvL2fYIyIJ1Zyciq4AZYHcTltiyGINeAxGJA1cDf6iq3wHuANZj3fq9F/gT4DZV/SOgEyioaimEHvoo8A/AAFASkY9Wvfa7WNr5qvLjk5QvRLNP1j6Vvc4SgKp+ExgH3ioibxSRX2vWYpvE3D2yB9BX26uVQFZVJ0TkYhG5odGLbEWMQa9irnEpe0x5YB9wc/npnwA/w7rYzlfV/ar64/LfXg0chGCnVc2zT6KqE8DXy//9a+DW8pchqvos8FWsLJebsDyv1VgXpNknkXjZ65QqmeVrwOex4jOphi68gTjco9uq9siOY20FEiLyn7HuaExmEMagL0iVHHA/sEFELigb+J8Dp4BVACLyb4AnsPTif2jCUpuKbZRVNVt+6v8CB7DuXmw+Bfw9VjB5Bvi1sAVFa+2TWpRE5Bzgv2Lt1/mq+uVmrLcZONgj+5x5M/A2rC+7a1T1241daWtimnMBInI98HEsQ/0dVd1Vfj5a9grWY6UoJlT1U+XXvgt8RVX/SUQuxPI2X27Ov6AxLLBPEcr2qOrYy7A63r0N64svq6pDZU8r3/DFN5A69mklMIEltyxT1SMNXnrDqGOPVgMvA1cBx1R1b2NX3tqE1kMv396mROQrWBLAl4EO4LdEpLd862d7AxPAvwDbROT3yillMWASQFX3BdWYO9ynkqqqiCyrklmeBJ7F0kO/grVfBNWYu7RPXwXaVDUbRGPu4rm0WlUfMsb8tYTWoJdvb6exbuneWk6n+zbWXcvJ8kklIvJ5rEj7fuBPgUuAHwE/U9V/btLyG8Yi9uku4C+BtQBiZQK9E/gjVb0qqF94NmafauPSHr1ZVcMwi3hJhE5yEZHfA9YAT6nqvVXPfwArCPUc8BjwPaxUxH8HfFJVx8rHRYF4+cQMLC7s07XA06o62ui1NxKzT7Uxe9RAVDUUP1gFCJ8Efgz8MlbmykeBFeXX3wZcjCUNfBz4G2Bl1d9Hm/1v8Mk+xZr9bzD71Bo/Zo8a/xNb2teA/1BVFZG3A59W1YdEZBJ4F6DAV1X1R/axIvJz4BpgqpxWVa2nBxoX9qnQjHU3GrNPtTF71HhCoaFX5fbuolyGrpb+/QJwoYicN+dP3gVkKOdJa0iqGc0+OcPsU23MHjWHQBp0Eeku/zcKZ+STDwKdMtsf4mGgG6sHS0Ks1py7sUquPxV0r9zskzPMPtXG7FFrEBiDLiIREekSKz/8r2C2CEFmq8uewAq6vEtEYmqlPa0FLlPVHHAI+Liq/rqqHm/8v8J7zD45w+xTbcwetR6BMehlj2ACSABrReSDYPWatk8yVR3EugU8B7i9/KczwCvl13+kqj9u9NobidknZ5h9qo3Zo9YjMAa9zAVYbWz/Evg1Eem0AytiTYL5MvAkljdxuVhTdEaxiobChNknZ5h9qo3ZoxbCt1kuInIlMKqqB0REVFWx9LocVoOsl4CPisgOrO6I5wB3aLlwQ0R+FSst6lQz1t8ozD45w+xTbcwe+YDF5jk2+wdYhtUsawKrfDhd9dqbgL8s/34LludwH9BRdUyk2f8Gs0+t82P2yexRkH78KLmkgQeB28q/X1P12qtYEfVvAf8Ra6zXoKpOQqUdbljSocw+OcPsU23MHvkEXxh0Efl1EXmriHSp6jBW57V7gWngChFZUz50OdAPHMXqufIx4HyxuiES9BPL7JMzzD7VxuyRP2nZXi7larFVwDeAEvAilnfwCVU9UT7mKuBXgF2q+vXyc31Vr3dgtbwNbA8Is0/OMPtUG7NH/qclPXSx+pAr1ki3YVW9FqvXwyiWpwCAWulOL2N5BN0iklbVEyISLd/qTQb5xDL75AyzT7UxexQMWsqgl0+KPwP+TETeCpwP2PmsReATwJvLr9l8Caun8veBgyKyRlWLQb7VM/vkDLNPtTF7FCxaxqCXT5gnsTS5Qaze43ng7SJyOVT0uM+Uf2x+Aavd5rPAxap6uHGrbjxmn5xh9qk2Zo+CRyvloZeAP6/S5S4BNgN3AF8ALhOr4c93gHeIyCa18lungetU9ZGmrLrxmH1yhtmn2pg9Chgt46FjeQr3ymwPiB8DG1T1K0BURG4rewvrgGL5xEJV/2/ITiyzT84w+1Qbs0cBo2UMuqpmVHVGZ7utvROrSAHgN7Babn4X+CZWrqsdlQ8VZp+cYfapNmaPgkcrSS5ApUubYk1A31F+egL4I+Ai4KVyXizaqjmXDcDskzPMPtXG7FFwaBkPvYoSEAdOAK8rewj/GSip6mP2iWUw++QQs0+1MXsUEFqysEisJkA/Kf/8nap+uclLaknMPjnD7FNtzB4Fg1Y16OuADwN/oaozzV5Pq2L2yRlmn2pj9igYtKRBNxgMBsPiaUUN3WAwGAxLwBh0g8FgCAjGoBsMBkNAMAbdYDAYAoIx6AaDwRAQjEE3GAyGgGAMusFgMASE/wd9IqxMfyr6+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "march = df.loc[df['TIMESTAMP_year'] == 2014]\n",
    "march = march.loc[march['month'] == 3]\n",
    "march = march.loc[march['ZONEID'] == 1][178:278]\n",
    "\n",
    "time_march = march[\"TIMESTAMP\"]\n",
    "power_march = march[\"POWER\"]\n",
    "\n",
    "fig, _ = plt.subplots()\n",
    "plt.plot(time_march,power_march)\n",
    "plt.gca().xaxis.set_major_locator(mdate.DayLocator())\n",
    "fig.autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Selection de données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On sélectionne dans notre large jeu de données l'année 2013 ainsi que les données concernant la ferme 1. Cette première sélection va correspondre à notre jeu d'entraînement, puis on va établir notre jeu de test sur le mois de Janvier 2014. De plus on mélange notre dataset afin de ne pas biaiser nos résultats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2013 = df.loc[df['TIMESTAMP_year'] == 2013]\n",
    "df_2013_z1 = df_2013.loc[df_2013['ZONEID'] == 1]\n",
    "df_2013_z1_shuffled = df_2013_z1.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8760, 25)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2013_z1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df.loc[df['TIMESTAMP_year'] == 2014]\n",
    "df_test = df_test.loc[df_test[\"ZONEID\"] == 1][:744]\n",
    "df_test_shuffled = df_test.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>ZONEID</th>\n",
       "      <th>TIMESTAMP</th>\n",
       "      <th>VAR78</th>\n",
       "      <th>VAR79</th>\n",
       "      <th>VAR134</th>\n",
       "      <th>VAR157</th>\n",
       "      <th>VAR164</th>\n",
       "      <th>VAR165</th>\n",
       "      <th>VAR166</th>\n",
       "      <th>VAR167</th>\n",
       "      <th>...</th>\n",
       "      <th>TIMESTAMP_year</th>\n",
       "      <th>month</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
       "      <th>day</th>\n",
       "      <th>day_sin</th>\n",
       "      <th>day_cos</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_sin</th>\n",
       "      <th>hour_cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10080</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-05-26 01:00:00</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>95509.8750</td>\n",
       "      <td>73.670883</td>\n",
       "      <td>0.003357</td>\n",
       "      <td>0.880408</td>\n",
       "      <td>-1.313563</td>\n",
       "      <td>281.783691</td>\n",
       "      <td>...</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>26</td>\n",
       "      <td>0.432776</td>\n",
       "      <td>0.901502</td>\n",
       "      <td>1</td>\n",
       "      <td>0.269797</td>\n",
       "      <td>0.962917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9792</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-05-14 01:00:00</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94333.8750</td>\n",
       "      <td>59.226471</td>\n",
       "      <td>0.018616</td>\n",
       "      <td>3.695747</td>\n",
       "      <td>-1.695083</td>\n",
       "      <td>283.197754</td>\n",
       "      <td>...</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>14</td>\n",
       "      <td>0.238673</td>\n",
       "      <td>0.971100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.269797</td>\n",
       "      <td>0.962917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13105</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-09-29 02:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>94560.5625</td>\n",
       "      <td>41.457031</td>\n",
       "      <td>0.107376</td>\n",
       "      <td>4.276614</td>\n",
       "      <td>-1.685981</td>\n",
       "      <td>289.672363</td>\n",
       "      <td>...</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.836970e-16</td>\n",
       "      <td>29</td>\n",
       "      <td>0.478734</td>\n",
       "      <td>0.877960</td>\n",
       "      <td>2</td>\n",
       "      <td>0.519584</td>\n",
       "      <td>0.854419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7044</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-19 13:00:00</td>\n",
       "      <td>0.075386</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>94675.1250</td>\n",
       "      <td>77.323059</td>\n",
       "      <td>0.832184</td>\n",
       "      <td>-4.048058</td>\n",
       "      <td>0.884130</td>\n",
       "      <td>288.686523</td>\n",
       "      <td>...</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>19</td>\n",
       "      <td>0.321270</td>\n",
       "      <td>0.946988</td>\n",
       "      <td>13</td>\n",
       "      <td>-0.398401</td>\n",
       "      <td>-0.917211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8693</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-03-29 06:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94641.4375</td>\n",
       "      <td>33.168457</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.127497</td>\n",
       "      <td>-0.781517</td>\n",
       "      <td>291.905762</td>\n",
       "      <td>...</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>29</td>\n",
       "      <td>0.478734</td>\n",
       "      <td>0.877960</td>\n",
       "      <td>6</td>\n",
       "      <td>0.997669</td>\n",
       "      <td>-0.068242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ZONEID           TIMESTAMP     VAR78     VAR79      VAR134     VAR157  \\\n",
       "10080       1 2013-05-26 01:00:00  0.000269  0.000000  95509.8750  73.670883   \n",
       "9792        1 2013-05-14 01:00:00  0.000397  0.000000  94333.8750  59.226471   \n",
       "13105       1 2013-09-29 02:00:00  0.000000  0.000115  94560.5625  41.457031   \n",
       "7044        1 2013-01-19 13:00:00  0.075386  0.000029  94675.1250  77.323059   \n",
       "8693        1 2013-03-29 06:00:00  0.000000  0.000000  94641.4375  33.168457   \n",
       "\n",
       "         VAR164    VAR165    VAR166      VAR167  ...  TIMESTAMP_year  month  \\\n",
       "10080  0.003357  0.880408 -1.313563  281.783691  ...            2013      5   \n",
       "9792   0.018616  3.695747 -1.695083  283.197754  ...            2013      5   \n",
       "13105  0.107376  4.276614 -1.685981  289.672363  ...            2013      9   \n",
       "7044   0.832184 -4.048058  0.884130  288.686523  ...            2013      1   \n",
       "8693   0.000000  0.127497 -0.781517  291.905762  ...            2013      3   \n",
       "\n",
       "       month_sin     month_cos  day   day_sin   day_cos  hour  hour_sin  \\\n",
       "10080        0.5 -8.660254e-01   26  0.432776  0.901502     1  0.269797   \n",
       "9792         0.5 -8.660254e-01   14  0.238673  0.971100     1  0.269797   \n",
       "13105       -1.0 -1.836970e-16   29  0.478734  0.877960     2  0.519584   \n",
       "7044         0.5  8.660254e-01   19  0.321270  0.946988    13 -0.398401   \n",
       "8693         1.0  6.123234e-17   29  0.478734  0.877960     6  0.997669   \n",
       "\n",
       "       hour_cos  \n",
       "10080  0.962917  \n",
       "9792   0.962917  \n",
       "13105  0.854419  \n",
       "7044  -0.917211  \n",
       "8693  -0.068242  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2013_z1_shuffled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. Undersampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans un premier temps, on étudie la fréquence de valeurs nulles à chaque heure durant l'année 2013."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = df_2013_z1_shuffled[df_2013_z1_shuffled[\"POWER\"].eq(0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class=13, n=365 (9.583%)\n",
      "Class=18, n=365 (9.583%)\n",
      "Class=14, n=365 (9.583%)\n",
      "Class=20, n=167 (4.384%)\n",
      "Class=17, n=365 (9.583%)\n",
      "Class=16, n=365 (9.583%)\n",
      "Class=10, n=266 (6.983%)\n",
      "Class=12, n=365 (9.583%)\n",
      "Class=11, n=365 (9.583%)\n",
      "Class=15, n=365 (9.583%)\n",
      "Class=19, n=274 (7.193%)\n",
      "Class=9, n=161 (4.227%)\n",
      "Class=21, n=18 (0.473%)\n",
      "Class=8, n=2 (0.053%)\n",
      "Class=3, n=1 (0.026%)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counter = Counter(tmp[\"hour\"])\n",
    "for k,v in counter.items():\n",
    "    per = v / len(tmp[\"hour\"]) * 100\n",
    "    print('Class=%d, n=%d (%.3f%%)' % (k, v, per))\n",
    "plt.bar(counter.keys(), counter.values())\n",
    "plt.xlabel(\"Heure\")\n",
    "plt.ylabel(\"Fréquence\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque qu'à partir de 11h et ce jusqu'à 18h notre cible est nulle tout au long de l'année. On va donc procéder à un sous-échantillonage afin de réequilibrer notre dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = Counter(tmp[\"hour\"])\n",
    "for k,v in counter.items():\n",
    "    if(v > 100):\n",
    "        df_tp = tmp[tmp[\"hour\"].eq(k)].index\n",
    "        drop_indices = np.random.choice(df_tp,v-100, replace=False )\n",
    "        df_2013_z1_shuffled = df_2013_z1_shuffled.drop(drop_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = df_2013_z1_shuffled[df_2013_z1_shuffled[\"POWER\"].eq(0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class=13, n=100 (8.190%)\n",
      "Class=20, n=100 (8.190%)\n",
      "Class=17, n=100 (8.190%)\n",
      "Class=16, n=100 (8.190%)\n",
      "Class=10, n=100 (8.190%)\n",
      "Class=12, n=100 (8.190%)\n",
      "Class=18, n=100 (8.190%)\n",
      "Class=11, n=100 (8.190%)\n",
      "Class=14, n=100 (8.190%)\n",
      "Class=19, n=100 (8.190%)\n",
      "Class=21, n=18 (1.474%)\n",
      "Class=9, n=100 (8.190%)\n",
      "Class=15, n=100 (8.190%)\n",
      "Class=8, n=2 (0.164%)\n",
      "Class=3, n=1 (0.082%)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counter = Counter(tmp[\"hour\"])\n",
    "for k,v in counter.items():\n",
    "    per = v / len(tmp[\"hour\"]) * 100\n",
    "    print('Class=%d, n=%d (%.3f%%)' % (k, v, per))\n",
    "plt.bar(counter.keys(), counter.values())\n",
    "plt.xlabel(\"Heure\")\n",
    "plt.ylabel(\"Fréquence\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour réaliser ce sous-échantillonage, on réduit le nombre données des heures oû l'on obtient plus de 100 jours nulle sur l'année."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E. Elimintation de features et visualisation du jeu d'entraînement et du jeu de test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size :  (6172, 19)\n",
      "Train size :  (6172,)\n",
      "Test size :  (744, 19)\n",
      "Test size :  (744,)\n"
     ]
    }
   ],
   "source": [
    "col = [\"ZONEID\",\"TIMESTAMP\", \"TIMESTAMP_year\", \"month\", \"day\"]\n",
    "\n",
    "X_train = df_2013_z1_shuffled.drop(\"POWER\", axis=1)\n",
    "X_train = X_train.drop(col, axis=1)\n",
    "y_train = df_2013_z1_shuffled[\"POWER\"]\n",
    "\n",
    "X_test = df_test_shuffled.drop(\"POWER\", axis=1)\n",
    "X_test = X_test.drop(col, axis=1)\n",
    "y_test = df_test_shuffled[\"POWER\"]\n",
    "\n",
    "print(\"Train size : \", X_train.shape)\n",
    "print(\"Train size : \", y_train.shape)\n",
    "print(\"Test size : \", X_test.shape)\n",
    "print(\"Test size : \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>VAR78</th>\n",
       "      <th>VAR79</th>\n",
       "      <th>VAR134</th>\n",
       "      <th>VAR157</th>\n",
       "      <th>VAR164</th>\n",
       "      <th>VAR165</th>\n",
       "      <th>VAR166</th>\n",
       "      <th>VAR167</th>\n",
       "      <th>VAR169</th>\n",
       "      <th>VAR175</th>\n",
       "      <th>VAR178</th>\n",
       "      <th>VAR228</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
       "      <th>day_sin</th>\n",
       "      <th>day_cos</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_sin</th>\n",
       "      <th>hour_cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10080</th>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>95509.8750</td>\n",
       "      <td>73.670883</td>\n",
       "      <td>0.003357</td>\n",
       "      <td>0.880408</td>\n",
       "      <td>-1.313563</td>\n",
       "      <td>281.783691</td>\n",
       "      <td>1506249.0</td>\n",
       "      <td>1.049106e+06</td>\n",
       "      <td>1708261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>0.432776</td>\n",
       "      <td>0.901502</td>\n",
       "      <td>1</td>\n",
       "      <td>0.269797</td>\n",
       "      <td>0.962917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9792</th>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94333.8750</td>\n",
       "      <td>59.226471</td>\n",
       "      <td>0.018616</td>\n",
       "      <td>3.695747</td>\n",
       "      <td>-1.695083</td>\n",
       "      <td>283.197754</td>\n",
       "      <td>1867722.0</td>\n",
       "      <td>9.437136e+05</td>\n",
       "      <td>2021226.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>0.238673</td>\n",
       "      <td>0.971100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.269797</td>\n",
       "      <td>0.962917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13105</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>94560.5625</td>\n",
       "      <td>41.457031</td>\n",
       "      <td>0.107376</td>\n",
       "      <td>4.276614</td>\n",
       "      <td>-1.685981</td>\n",
       "      <td>289.672363</td>\n",
       "      <td>6290867.0</td>\n",
       "      <td>2.019752e+06</td>\n",
       "      <td>6572616.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.836970e-16</td>\n",
       "      <td>0.478734</td>\n",
       "      <td>0.877960</td>\n",
       "      <td>2</td>\n",
       "      <td>0.519584</td>\n",
       "      <td>0.854419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7044</th>\n",
       "      <td>0.075386</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>94675.1250</td>\n",
       "      <td>77.323059</td>\n",
       "      <td>0.832184</td>\n",
       "      <td>-4.048058</td>\n",
       "      <td>0.884130</td>\n",
       "      <td>288.686523</td>\n",
       "      <td>15644498.0</td>\n",
       "      <td>1.736405e+07</td>\n",
       "      <td>20426496.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.321270</td>\n",
       "      <td>0.946988</td>\n",
       "      <td>13</td>\n",
       "      <td>-0.398401</td>\n",
       "      <td>-0.917211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8693</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94641.4375</td>\n",
       "      <td>33.168457</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.127497</td>\n",
       "      <td>-0.781517</td>\n",
       "      <td>291.905762</td>\n",
       "      <td>15782709.0</td>\n",
       "      <td>6.227145e+06</td>\n",
       "      <td>16785888.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0.478734</td>\n",
       "      <td>0.877960</td>\n",
       "      <td>6</td>\n",
       "      <td>0.997669</td>\n",
       "      <td>-0.068242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          VAR78     VAR79      VAR134     VAR157    VAR164    VAR165  \\\n",
       "10080  0.000269  0.000000  95509.8750  73.670883  0.003357  0.880408   \n",
       "9792   0.000397  0.000000  94333.8750  59.226471  0.018616  3.695747   \n",
       "13105  0.000000  0.000115  94560.5625  41.457031  0.107376  4.276614   \n",
       "7044   0.075386  0.000029  94675.1250  77.323059  0.832184 -4.048058   \n",
       "8693   0.000000  0.000000  94641.4375  33.168457  0.000000  0.127497   \n",
       "\n",
       "         VAR166      VAR167      VAR169        VAR175      VAR178  VAR228  \\\n",
       "10080 -1.313563  281.783691   1506249.0  1.049106e+06   1708261.0     0.0   \n",
       "9792  -1.695083  283.197754   1867722.0  9.437136e+05   2021226.0     0.0   \n",
       "13105 -1.685981  289.672363   6290867.0  2.019752e+06   6572616.0     0.0   \n",
       "7044   0.884130  288.686523  15644498.0  1.736405e+07  20426496.0     0.0   \n",
       "8693  -0.781517  291.905762  15782709.0  6.227145e+06  16785888.0     0.0   \n",
       "\n",
       "       month_sin     month_cos   day_sin   day_cos  hour  hour_sin  hour_cos  \n",
       "10080        0.5 -8.660254e-01  0.432776  0.901502     1  0.269797  0.962917  \n",
       "9792         0.5 -8.660254e-01  0.238673  0.971100     1  0.269797  0.962917  \n",
       "13105       -1.0 -1.836970e-16  0.478734  0.877960     2  0.519584  0.854419  \n",
       "7044         0.5  8.660254e-01  0.321270  0.946988    13 -0.398401 -0.917211  \n",
       "8693         1.0  6.123234e-17  0.478734  0.877960     6  0.997669 -0.068242  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>VAR78</th>\n",
       "      <th>VAR79</th>\n",
       "      <th>VAR134</th>\n",
       "      <th>VAR157</th>\n",
       "      <th>VAR164</th>\n",
       "      <th>VAR165</th>\n",
       "      <th>VAR166</th>\n",
       "      <th>VAR167</th>\n",
       "      <th>VAR169</th>\n",
       "      <th>VAR175</th>\n",
       "      <th>VAR178</th>\n",
       "      <th>VAR228</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
       "      <th>day_sin</th>\n",
       "      <th>day_cos</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_sin</th>\n",
       "      <th>hour_cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15543</th>\n",
       "      <td>0.004456</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>95264.1875</td>\n",
       "      <td>82.798676</td>\n",
       "      <td>0.197327</td>\n",
       "      <td>-1.955809</td>\n",
       "      <td>0.981257</td>\n",
       "      <td>284.140625</td>\n",
       "      <td>23502768.0</td>\n",
       "      <td>18170784.0</td>\n",
       "      <td>26044848.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.137279</td>\n",
       "      <td>0.990532</td>\n",
       "      <td>16</td>\n",
       "      <td>-0.942261</td>\n",
       "      <td>-0.334880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15841</th>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>93889.0000</td>\n",
       "      <td>43.080002</td>\n",
       "      <td>0.285556</td>\n",
       "      <td>1.442891</td>\n",
       "      <td>-0.948500</td>\n",
       "      <td>298.718506</td>\n",
       "      <td>5457367.0</td>\n",
       "      <td>2874461.0</td>\n",
       "      <td>6589267.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.353676</td>\n",
       "      <td>0.935368</td>\n",
       "      <td>2</td>\n",
       "      <td>0.519584</td>\n",
       "      <td>0.854419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15396</th>\n",
       "      <td>0.135162</td>\n",
       "      <td>0.096794</td>\n",
       "      <td>93777.9375</td>\n",
       "      <td>44.131424</td>\n",
       "      <td>0.946505</td>\n",
       "      <td>3.286168</td>\n",
       "      <td>-2.018308</td>\n",
       "      <td>292.993652</td>\n",
       "      <td>15447972.0</td>\n",
       "      <td>17594112.0</td>\n",
       "      <td>20695024.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.034422</td>\n",
       "      <td>0.999407</td>\n",
       "      <td>13</td>\n",
       "      <td>-0.398401</td>\n",
       "      <td>-0.917211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15915</th>\n",
       "      <td>0.669870</td>\n",
       "      <td>0.236113</td>\n",
       "      <td>93955.3125</td>\n",
       "      <td>92.488327</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.936502</td>\n",
       "      <td>-4.393728</td>\n",
       "      <td>291.027832</td>\n",
       "      <td>4291167.0</td>\n",
       "      <td>5518969.0</td>\n",
       "      <td>8345551.0</td>\n",
       "      <td>0.004534</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.401488</td>\n",
       "      <td>0.915864</td>\n",
       "      <td>4</td>\n",
       "      <td>0.887885</td>\n",
       "      <td>0.460065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15620</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94816.3750</td>\n",
       "      <td>65.258621</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.032138</td>\n",
       "      <td>1.028440</td>\n",
       "      <td>292.670166</td>\n",
       "      <td>18147920.0</td>\n",
       "      <td>27508528.0</td>\n",
       "      <td>23037472.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.188227</td>\n",
       "      <td>0.982126</td>\n",
       "      <td>21</td>\n",
       "      <td>-0.519584</td>\n",
       "      <td>0.854419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          VAR78     VAR79      VAR134     VAR157    VAR164    VAR165  \\\n",
       "15543  0.004456  0.000269  95264.1875  82.798676  0.197327 -1.955809   \n",
       "15841  0.004791  0.000151  93889.0000  43.080002  0.285556  1.442891   \n",
       "15396  0.135162  0.096794  93777.9375  44.131424  0.946505  3.286168   \n",
       "15915  0.669870  0.236113  93955.3125  92.488327  1.000000  2.936502   \n",
       "15620  0.000000  0.000000  94816.3750  65.258621  0.000000 -1.032138   \n",
       "\n",
       "         VAR166      VAR167      VAR169      VAR175      VAR178    VAR228  \\\n",
       "15543  0.981257  284.140625  23502768.0  18170784.0  26044848.0  0.000000   \n",
       "15841 -0.948500  298.718506   5457367.0   2874461.0   6589267.0  0.000000   \n",
       "15396 -2.018308  292.993652  15447972.0  17594112.0  20695024.0  0.000000   \n",
       "15915 -4.393728  291.027832   4291167.0   5518969.0   8345551.0  0.004534   \n",
       "15620  1.028440  292.670166  18147920.0  27508528.0  23037472.0  0.000000   \n",
       "\n",
       "       month_sin  month_cos   day_sin   day_cos  hour  hour_sin  hour_cos  \n",
       "15543        0.5   0.866025  0.137279  0.990532    16 -0.942261 -0.334880  \n",
       "15841        0.5   0.866025  0.353676  0.935368     2  0.519584  0.854419  \n",
       "15396        0.5   0.866025  0.034422  0.999407    13 -0.398401 -0.917211  \n",
       "15915        0.5   0.866025  0.401488  0.915864     4  0.887885  0.460065  \n",
       "15620        0.5   0.866025  0.188227  0.982126    21 -0.519584  0.854419  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Modèles de Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Régression Linéaire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous avons d'abord travaillé avec un modèle de Regression Linéaire sur notre jeu de 12 données. Il ne s'agit pas vraiment de précision, mais nous obtenons un score de prédicition de 75% par rapport à la valeur produite effective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score :  0.7222219789342638\n",
      "Test score : 0.7604717960568754\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(X_train, y_train)\n",
    "\n",
    "train_score = reg.score(X_train, y_train)\n",
    "test_score = reg.score(X_test, y_test)\n",
    "\n",
    "print(\"Train score : \", train_score)\n",
    "print(\"Test score :\", test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Random Forest Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous avons ensuite travaillé avec un second algorithme, le Random Forest Regression. Un Random Forest est constitué d'un certain nombre n_estimators d'arbres décisionnels, dans notre cas 30. Chaque arbre dispose d'une vision partielle des données et à partir d'une étude des possibilités, établit une prédiction. Finalement, la valeur finale prédite est l'estimation majoritaire de tous les arbres.\n",
    "Parmi les paramètres de ce modèle :\n",
    "\n",
    "* random_state : la graine utilisée par le générateur de nombres aléatoires, pour que le résultat de l'algorithme soit le même à chaque fois\n",
    "* max_depth : le nombre de variables que l'on va diviser (split) pour créer les branches de l'arbre\n",
    "* min_samples_leaf : \n",
    "* min_samples_split : \n",
    "* n_estimators : le nombre d'arbres que l'on souhaite générer\n",
    "\n",
    "Pour que cet algorithme soit le plus précis possible, il faut que les variables que l'on divise soient les plus optimales. Pour cela, on utilise l'algorithme de Grid Search qui va déterminer les paramètres idéaux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score :  0.8221474088376193\n",
      "Best params : {'max_depth': 3, 'min_samples_leaf': 7, 'min_samples_split': 2, 'n_estimators': 40}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params = {'min_samples_leaf':list(range(1,8,3)),'min_samples_split':list(range(2,11,2)),\n",
    "          'n_estimators':list(range(20,41,10)), 'max_depth':[2,3]}\n",
    "\n",
    "clf_rf2 = RandomForestRegressor(random_state=1)\n",
    "clf_gs = GridSearchCV(clf_rf2, params,cv=5)\n",
    "clf_gs.fit(X_train, y_train)\n",
    "print(\"Best score : \", clf_gs.best_score_)\n",
    "print(\"Best params :\", clf_gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83195283668053\n",
      "0.8690109229163321\n"
     ]
    }
   ],
   "source": [
    "\n",
    "clf_r3 = RandomForestRegressor(random_state = 1, max_depth = 3, min_samples_leaf = 1, \n",
    "                               min_samples_split = 2, n_estimators = 30)\n",
    "clf_r3.fit(X_train, y_train)\n",
    "print(clf_r3.score(X_train, y_train))\n",
    "print(clf_r3.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prédictions sur 4 jours du mois de Mars 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "march = march.drop(col, axis=1)\n",
    "march_pred = march.drop(\"POWER\", axis=1)\n",
    "power_march_pred_reg = reg.predict(march_pred)\n",
    "power_march_pred_randomforest = clf_r3.predict(march_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEcCAYAAADZQfNOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAACB30lEQVR4nO2dZXhU19aA3z0zcXcI7hTXAi3aUqhdqNvtBdpSd7uVe+v61d1dLoXSFqvQFilQSotLcSdCnGQik4zs78eeCSFEJmQsyX6fZx4y5+xzzprNzFln7WVCSolGo9FoNLVh8LcAGo1GowlstKLQaDQaTZ1oRaHRaDSaOtGKQqPRaDR1ohWFRqPRaOpEKwqNRqPR1IlWFJqARgjRUQghhRAmf8vSHHDOZVfn3+8IIR46wfMUCyE6e1Y6TaCiFYXGJwghRgohVgohCoUQ+UKI34UQQ/0gxzQhhN15oysSQmwQQpzrazlqQwgxVgjhcMpnFkLsEEJc5Y1rSSlvkFI+4YZMS4UQ06sdGyml3OsNuTSBh1YUGq8jhIgGFgCvA/FAG+AxoNzL163NCvlDShkJxAIfArOEEHHelKUm6pAvwylfNHAf8L4QolcDjtdoPIpWFBpf0B1ASjlDSmmXUpZJKX+WUm4CEEIYhBD/FUIcEEJkCyE+E0LE1HQiIcRVQohtzqftvUKI66vsGyuESBNC3CeEOAx8XJdQUkoH8BEQBnQRQsQ4r53jlOW/QgiD89wHhBCDnX//07mE09v5/hohxJwqn+V+IcQeIUSeEGKWECLeuc+1jHaNEOIgsLge+aSUcg5QAPRyWkO/CyFeFkLkAY8KIUKEEC8IIQ4KIbKcy0lhVebkXiFEphAiQwhxdbW5/EQI8WSV95OdFlaRU/4zhRBPAaOAN5xWzhvOsVWXsOqat2lCiBVOGQuEEPuEEGfV9bk1gYdWFBpfsBOwCyE+FUKcVcPT+zTnaxzQGYgE3qjlXNnAuain7auAl4UQg6rsb4WyWjoA19UllPOJfDpQDOxCWTwxThnGAFOc1wD4DRjr/HsMsBcYXeX9b86/bwXOc25LRd3k36x26THAScDEeuQzCCHOR1k+m52bhzmvnQI8BTyLUsQDgK4oa+1h5/FnAvcAZwDdgPF1XOtk4DPgXuf1RgP7pZT/AZYDtziXm26p4fC65s0l8w4gEXgO+FAIIer67JoAQ0qpX/rl9RfqxvgJkAbYgHlAinPfIuCmKmN7AFbABHQEJGCq5bxzgNudf48FKoDQOuSY5rz+ESAXWIW6gRqdx/aqMvZ6YKnz72uAec6/t6EUzFfO9weAQVX2nV7lHK1r+Cyd65BvLOBwypcPbAAuqyL7wSpjBVACdKmybQSwz/n3R8CzVfZ1d16/q/P9J8CTzr/fBV6uRaalwPRq2yRKMdU3b9OA3VX2hTuPbeXv76R+uf/Sa5wanyCl3Ia6aSCE6Al8AbwCXI568j5QZfgB1I01pfp5nMsWj6BuegbUjWdzlSE5UkpLPeKsklKOrHbeFCCoBjnaOP/+DXhBCNEadXOcBTwihOiIepre4BzXAfhOCOGoch57tc9yqB75MqSUbWvZV/XYJNTnX1vlAV045QM1r2urfZ7aaAf8UI9cNZFI3fMGcNj1h5Sy1Clr5AlcS+Mn9NKTxudIKbejnmb7ODdloG6wLtqjnvqzqh4nhAgBvgFeQFkjsaibW9VljBMth5yLevKvLke6U+bdQClqaWmZlLIIdQO8Dlghlb8D1I38LCllbJVXqJQy3QMyVj82FygDele5VoxUjnCATJQCqPp5auMQ0MWNa1anznnTNA+0otB4HSFETyHE3UKIts737VCWxCrnkBnAnUKITkKISOBpYKaU0lbtVMFACJAD2JzWxQRPyCiltKOshKeEEFFCiA7AXSjLx8VvwC0c9UcsrfYe4B3nOTo4P2uSEGKyJ2SsQWYH8D7KT5PsvF4bIYTL9zELmCaE6CWECEdZYrXxIXCVEOJ0p2+kjdPyA6Wwa8yZcHPeNE0crSg0vsCMcmj+KYQoQSmILcDdzv0fAZ8Dy4B9gAX15H4MUkozcBvqxlQAXIHydXiKW1Fr/nuBFcD/nLK5+A2IcspZ03uAV50y/SyEMKM+6zAPylid+4DdwCohRBHwK8rHg5TyR9Ty3mLnmFqjrKSUf+EMDgAKUZ/NZSW8ClzkjFp6rYbD65s3TRNHSKkbF2k0Go2mdrRFodFoNJo60YpCo9FoNHWiFYVGo9Fo6kQrCo1Go9HUSbNLuEtMTJQdO3b0txgajUbTpFi7dm2ulDKppn3NTlF07NiRNWvW+FsMjUajaVIIIWrN3NdLTxqNRqOpE78qCmcZ4x1CiN1CiPtr2N9eCLFECLFeCLFJCHG2P+TUaDSalozfFIUQwogqv3wW0Au4XBzfnOW/wCwp5UDgMuAt30qp0Wg0Gn9aFCejyg/vlVJWAF8B1WviSFTfAVAVOjN8KJ9Go9Fo8K+iaMOxJZPTOLY0McCjwJVCiDRUldDj6v8ACCGuE0KsEUKsycnJ8YasGo1G02IJdGf25cAnztr8ZwOfu1osVkVK+Z6UcoiUckhSUo3RXRqNRqM5QfypKNI5tlZ+W46vYX8NqlIoUso/gFBUoxSN5oSwOyTbMov4bn0axeXVq5hrNJqa8GcexWqgmxCiE0pBXIYqG12Vg8DpwCdCiJNQikKvLWkaTHG5jftmb2LZrhzMFqUg7j6jjFtP7+ZnyTSawMdvFoWzKc0twEJUn+FZUsq/hRCPCyEmOYfdDVwrhNiIam4zTeq66JoT4Nt1aXy/OZOz+rTipUv607dNDD/9fbj+A1sYUkoyjpT5WwxNgOHXzGwp5Q9U69MrpXy4yt9bgVN9LZemeSGlZMZfh+idGs1zF/UHILe4nKd/2M6h/FLaxYf7WcLA4YtVB3ho7t/8+8we3DimC1V6cWtaMIHuzNZoGs3m9EK2ZRZx2clHW0ZP7N0KgIXaqjiGWWvSCDIKnvtpB4/O+xu7QxvwGq0oNC2Ar1YfIjTIwKT+qZXbOiRE0LNVlFYUVdidbWZzeiH3ndmTa0d14tM/DnDrjHVY7Q5/i6bxM82uKKBGU5XSChvzNmRwdt/WxIQFHbPvzD6teHXRLnLM5SRFhfhJwsBhzvoMDAImDUglOSqU5KhQnvphG50Sd3LvxJ7+Fk/jR7RFoWnWLNiUSXG5jcurLDu5mNi7FVLCL1uz/CBZYCGlZM6GdEZ2SyI5KhSAa0d35tIh7Xhr6R7+2JPnZwk1/kQrCk2zZubqQ3RJimBIh7jj9vVsFUWHhHC9/ASsPVBAWkEZ5w9MPWb7I5N60SkhgjtnbqCgpMJP0mn8jVYUmmbLnpxi1h4o4LKh7WuM3hFCMLF3K1buyaXIYvWDhIHDd+vTCQsyMqFXq2O2hwebeO3ygeSVlHP/t5vQ0ektE60oNM2WdQcKABjXM7nWMRN7t8JqlyzZnu0rsQKOCpuDBZsymdg7hYiQ492WfdrEcPeEHiz8O4v1h474XkCN39GKQtNs2ZVdTLDRQMeE2vMkBrSLRQjYm1PiQ8kCi6U7siksszJ5YPWanEcZ3U3VUMsqtPhKLE0AoRVFM2ZzWiF7c4r9LYbf2JllpnNSBCZj7V9zo0EQGWJq0UtPf+3LJ8RkYGTX2suoxUWoiLGC0pY7Ty0ZrSiaMfd8vZF7Z2/ytxh+Y1dWMd1TouodFx0aRFFZyy0QuDunmC5JkQTVoVDjwoMBKCjVDu2WiFYUzZhss4X1BwtaZLSK2WIl/UgZPVq5oSjCglq0RbE7u5iuyZF1jgkNMhIaZOCIVhQtEq0omil2h+RImRWHhN92tryCu7uy1ZJbt3pugADRoSaKylqmoiitsJFWUFavogBlVeilp5aJVhTNlILSClyRjItbYETPriwzgHtLT2FBFFla5tKTy4nvjqKIDQ/miFYULRKtKJop+c7lpuhQE0t3ZGNrYfV6dmYVE2IyuFUZVvkoWuYNcHcDLK/YsCC99NRC0YqimZJXrH7QkwakUmSxse7gEf8K5GN2ZpnpmhyJ0eBMtCsuhmnT4NCh48ZGh7XcqKdd2WaMBkGHhIh6x8ZFBGlndgtFK4pmisuimDygDSaDaHHLT8dFPM2bB59+CnfffdzY6NAgisttOFpgSe3d2cV0SAgn2FT/rUAvPbVctKJopuSXlAPQIT6cIR3jWlTmcWGZlcNFFrqlVFlO2bJF/TtlynHjo8OCkBLMLbCH9u7sYrom1bLs5HDAmjWVb+PCgzhSZtVlPFogWlE0U/KcFkVcRDCn9UxmR5aZ9BbS4tLlyO5R1aL4808YNAjOPfe48dGhqmxFS/NTVNgcHMgrPVahVuXpp2HoUFi7FlBRT3aHbLGO/5aMVhRNmDJrGR+u+5BhHwyj06udOFh4EIC3V7/NY6tPIyP0Gt7461VOc9Y6ainLTzuzlIPWtfS0LmMtnQYvp/fZ+1i05CPYseOY8dHOPhUtzU9xIK8Em0NSKjZw9pdnU2Gv4n/Yt49N7z3BkOvgzh/vYOWhlUSHKYWqHdotD60omiivrHqF9q+0Z/r86ZRZyxjdYTShJtVHoF1MO1JCBiAMNubsmEOXpEjaxYfx246WkU+xM8tMWJCRxEgDdoed6NAYRp9yOdakBCb/eg2rX7rnmPHRoU5F0cKys10RT9/teZkfd//I/B3zj+7MyuKV0yPYnAJvWX9n9MejMZnUeO2naHloRdEEyS/L586Fd5IQlsDiKYvZ+I8f+PTMd0mOUJbDud3PZWDkf0kOHkCGOQMhBH3bxLAvt2XUfdqVbaZbSiQfrH+f4CeDiQ2N5dPzPuW3q5aRTARnxX3P9pxtleNdT8otzaJwKYonT3scgI83fFy5r3hQH2Z1q2BKqzPJmfArP/7zRzrEtQZ0GY+WiFYUTZD4sHjMD5hZc90axrUegWjXDm699Zgx+SUVxIakkF2SjZSS1JgwMo5YWoQjcmdWMd2So8gwZ2AURuKffRWuv57WUa35ueNDRFkk+9Ytqhx/1KJoYYoip5g2sWGc22MiD458kB93/0hG9h54/nlKi/KYNmAa105+jOhTT+P0zqez68hfWEW6tihqweaw8eOuH/li0xcALNuZwxkv/UZpRdO3VHXP7CZKZLDTAblsmfr3nHOO2Z9XUsHEHnfw3EWfI4QgNTaMMqudI6VW4iKCfSyt7ygoqSDHXE73lEhWFmTQOqo1ho/nQSvVkKfruVPYceP9BKeYYSJc+e2VJIW3xs6gFuek3ZWlajxtzdnKeT3PY0/BHsr+70l46ROShw3jjbPfAClh/nxERARTV04i1HguBaVn+Fv0gCLTnMlzvz/HjC0zyCpRbXXP6noWry7axa7sYg4XWuhcW2RZE0FbFE2QxfsWc+/P91JSUQJLl4IQMGYMHDwIl12Go+AIBaUVJEdFYRDqvzg1Ngyg2Uc+7XSV7milLIrU8BQVGjt8uBrQujXBfQfAwoXYHDaKyot45c8XKDb92KIsCodDsjdXKYrhHwzny81f8lXIP+ny0iccvOMqVnQ0KOtTCPj3vxEvvkjb6LbYDXm63lM1nl/5PK/99Rqntj+VFye8yDeXfMOurArWOhtnFTeDsGutKJogv+3/jRf/eJFgYzD89hv07w9xcbB1K3z7LdY77sTukGDM5dp517I2Yy1tnIoio5krikxnY512ceFKUVjDVD7AsGFHB335Jcydi8lgYt7l82gV2QphymlRPor0I2VYrA7axIO5wkyqIwKuuoqto3ty73AzYz8ZS3aJM0pu7FhYsYK2UW3AmKejnqoxqv0oHh3zKN9c8g13jbiLC066gBl/Hu3DXtwMLFWtKJog6eZ0UiJTCLJL+OMP9UMGOPNMmDKFoNlfE1ZhITrMwAfrP2Bj1kZSY1VEVHNXFGbnzT46zMT0gdO5tLCt2nHyyUcH9eoFMTGVb9vHtMdhyGlRUU+7spXlFROhHNqpMxZgsZYxfMIhZm2fzTndzyElMkUNHjMGiopoawvHLrRFUZ3zTzqfh8Y8BIDdYefLjXP4dvMfDO8cDzSPRE6/KgohxJlCiB1CiN1CiPtrGXOJEGKrEOJvIcT/fC1jIJJuTqdtdFswGOD77+G6647unDIFQ2kJE3b9QeeEdgBkmDOIjwgmxGRo9ktPLj9DdGgQd464k0uSxsKkSZBYrXvb66/DK68ASlHYRMuyKFwRTyEhhQCkXnUboZ/9j8v6XQHANQOvOTp4zBgA2uaUUyFzKXBm/WtASsn+I/uxOdT3ziAMXDXvcooMv3LLuG4AmJuBReE3Z7YQwgi8CZwBpAGrhRDzpJRbq4zpBjwAnCqlLBBCJPtH2sAivSidznGdwWSCceOO3TlyJGWpbbng7yUkRD9EQlgC6UXpCCFoE6sin5ozxeU2gowCSQVpRXm0uuYqTNdee/zARYtgwwa4/XaeGPcExdnrW5SPIuOIhagQE0VWlVuT2u9USOzJg0f6kxyRzNndzj46uHVr6NGDa7aHs3nE63rpqQp5ZXl0erUTL098mTuG30G5zYHBkUhCdBG9UqMBKG4GDyD+tChOBnZLKfdKKSuAr4DJ1cZcC7wppSwAkFK2jNTieiixltAmqg18+CGsXHnsToOBzf+6id86DSY+Ipg20W1IN6cDyqHd3C0Ks8VKVGgQazLW0O7ldizZ82vNAydOhAMHYNcueib2pG1UtxYV9VRksRIdFsTwtsP5oP0ttP9CJdt1jO3Ik6c9iclQ7Rly6VK6fzKfHvHDONKClujqY1/BPgBSI9szd0M613y6GmFPIiysgIgQI9A8nNn+DI9tA1St+ZwGDKs2pjuAEOJ3wAg8KqX8qfqJhBDXAdcBtG/f3ivCBhJ7btuDvdwC8Qlw9dVwyinH7F898WI+Ygf/jgimc1xnrHb1RNMmNowlO5q3rjVbbESGmMgwZwCQesYF8N3vMHDgsQMnTlT/LlzI4dRo9pZ+S2FpPx9L6z/MFhtRoSY6x3Wm85JCWP4W3HFv7Qe0aoW53MxBy89klSb5TtAAZ98RpSjum5mFtG6gVXQoA1K7sK94OSEmI8Emg/ZR+AAT0A0YC1wOvC+EiK0+SEr5npRyiJRySFJSy/gSG9eth9LSo47sKuQVV5BEBaHfz+e7S79jwRULAGVRZJvLKbfZfSyt73DdACsVRVaZWqKrTufO0KUL/PwzGeYMFmc9Q3b53z6W1n8oy8vE2oy1bCk/dIxzv0asVsy3Xc+3++8j37aGClvLaoRVG/uP7Acg1NCKmdcNZ+X9p3HWSX3JKsnCYlPLezrqqXGkA+2qvG/r3FaVNGCelNIqpdwH7EQpjhbLhsMbuPyby9m15Bu1YfTo48bkl5RzxfYlcP75sHlz5XZX5FNWYfN1RhZXURShBBFrofab4OTJYDTSIVpZocW2rBbTk8JssSmH/8I7uaX1eoiOrvuAoCBSfluD0SGwiVyOlGk/Bailp1BDDG1i4hnWOQGDQXDVgKvYcP0GggxBRIaamsXSkz8VxWqgmxCikxAiGLgMmFdtzByUNYEQIhG1FLXXhzIGHFtztvLVlq+wrV4FvXtDDRZUXkkF64ZPAJOJ5V8+w1lfnkWmObMyl6I5+ymKnD6KjOIMUkU0Amq/Cb74IsyZQ3x4AsGGMGwiu1ksE7hDVcsrtVjUb1EAxjHjSC0GB7m6jIeTy/pcRr/IG4kPP1rtoF1MO/q36o/RYCRSWxSNQ0ppA24BFgLbgFlSyr+FEI8LISY5hy0E8oQQW4ElwL1Syjz/SBwYpBcpo6vtis2VYYvVyS+pIKhVCowZg3n9Kn7a/RMHCg9UZmc351wKs8VGVIiJKf2m8IhjlNoYFVXnMcJuJymsDXaR02Iin4os1kpfTmqRdEtRMHYsbQslIbY0rSicjInqw5cvzuaUvWsrt1lsFt5d8y5rM9YSGWJqFg8ffq31JKX8Afih2raHq/wtgbucLw2QVpRGVHAUUdlHVMZxDeSXVHBS62gYOZI27y2GU5SCGZAyFGjuikKtvU/sOhEGlcONrcForP2Am2+G1atJuboNOeaMFpFLIaXEbLERFFRGma2M1FsegUE313/g2LG0/Q52JWTpCrKAw2bl9xvOptuR/QQlxqvfo8GAQRi48fsbeWTMI0SFntMsfm+B7szWVKMy2U6IGm+AUkrySipIiAiGU06hTaGsPC40yEhiZAgZhU3/i1sTUkqKy21EhQbxZ9qf5I0/Fd56q+6DUlNh9Wqe6PU4KeVPtojs7DKrHbtDYhf5AKTGd4BIN4rWtW7N48Wn8M8tZ+tcCiDzyfsY3ecvpl10KimWIujYEbKyCDYG0zqqNQcLDxKlfRQafxBqCqVXREe45hrYuPG4/SUVdipsDuIjgmHUKBI27iLYGFy5ZNUmNpT0Zpp0V1phxyEhyGRh+IfD+WjNe6r6aV04w2T7bdqBgYgWYVG4MoXbxbTjp0vmM+aDX49WIa6Hdt/9xpy+/9RlPH7+mX0fvwzA+tbjkJ27wKFD8PbbgMr2P1B4QPkotKLQ+JovLviC2d3+Ax99BFlZx+3PL1ZPevERwRAWhujaleFthxMRHAGoENnmYArXhOsGWIFyY6W+OwNOPbXugwYNgqQkcv+YQ4HpE/YXHKp7fDPAVQ8rOTKWiXFDaP3qh6rCrhvklKZRHPQNh3L2eFPEwGfBAvZ3SQDARCsMJ52k+rG/+SaUldE+pj0HCw8SGWrCbLE2+T4wWlE0RcyqoFtNTto8Zx2ehEhnFMaKFfy2rAsPn/ogcFRRNPUvbk24boDlzniH1CO2eh3ZGAxw5pnkb1lBUdBsduZv97aYfseVgX64dBtztn6LBPec2UB61i7yTB8T8ss73hOwKfDaa+x7WDULM8lk9WB2992QmwtffEGHmA4cLDxIRLARq11S3sTzTrSiaEJkl2Qz8qOR/JyxXG2o4SaYX+KyKELUhkOH4OOPK/MpUmPDKK2wU9gMo3tcN8Ayu7N+UW55/fkBANdcQ4eptwOQXtQSLAo1T0vSZjHtj/vqDiGuRttW3QGQhzfXM7L5s6/wAAlhrRAEq2ZgY8YoC/Wll7jvlH+TcXcGUaEqXqipLz9pRdGEOFh4kN8P/Y6ltEhtqOHHnedUFAmuLnannMK7g2Ho9+chpaSNM+muOeZSuH6MxTanosguc+8GOGYM7W7/L0jB4ZKWoCjUQ8KR8mxSg1QpbHctitZRrRESrCUHwNa0b34nzIoVMH48d6ZewPV9nwNQeRRCwBtvwBtvkBAWT3xYPFHONrtNPZdCK4omRKVDWkSrKJU6LQqnomjfnuKkaNbYD1JYXlgll6L5ObRdN8Czu/6DL87/gqhcs9s3wOD8QiLtkeSWZXhTxIDAZVHklh0m1RirbnBuWhTBxmCiHJFkhVth3TovShnA/PknLFpE307D6RihfGBxEUohMGIEnH46+eVH+O/i/5Jeonw/2qLQ+Iy0ojQA2ky9Vfkp4uKOG5NfUkGIyUB4sDN0VghS2/YCVF+K5px057oB9k05iX/2+yfceSec4WZ/5w8/pE+mmdKSTC9KGBi4kgpzSg6T2rk/2O3Qz/2CiFGhrUmLBv76y0sSBjjr12Nt14b/Zf7MnoJ9BBkFkSFVUtI2bkS8/TZPLX+KXUdWA02/J4VWFE2IdHM6JoOJ5Ija23LkFascCiFE5bY2vVRR3vTMnSREBBNsMjRTRaFugOuzVrAlews8/jicdZZ7B599Nos/hXt3nO5FCQMDs8WGEA4yizNUuXohlFPfTab1/pgzD1yMHDzYi1IGMOvXc2hod/757T/ZlLOCuPBjf2/89BOxd/+XyKAI8suVhaotCo3PSAhL4LROp2F462248cYax+SXlBMfGXzMtjZTbgEg3XGksoFRs/RRWGwIAbctvIFnlj0F2dlgddNp37cvZdHJ9N24sv6xTRyzs3zH+uvXc1NOR7j22vrzTarQLrYVL586lZJBQ70nZKBSWgrbt7O/dxsAhD356DKvi0mTEEB7RzQ5pWoVoLi8aQePaEXRhLj7lLtZeOVC+P13+OWXGsfkl1QcjXhykhrdhlHtRxETotbrU2NDm6WiKLLYiAgxqiW2ihBISYE5c9w7WAi+Gd2LN7v/Tu6R5u2nUJVjQ+id3Jt2a3bC//6nrAo3ySnfQqHhLXKWLHZfETcX8vPh9NPZ11W11pW2JOLCqymKnj2ha1c65Nk4XKL8itqZrfE9ZnOtzsfK8h1VCAsKY9nuUZx/h4p9T4oMIa+4+ZVgMFtshAdbsNgspOKcHzedtAA7e3fjqz529q783ksSBgZFFhuGoAze+OsN8s3Zbjv8K4+3HeRIyA9Yr5wIW7fWf0Az4bedOczJESoru20ERmGktCzueItCCPjHP2i/N4+CMhWB19QLA2pF0YTo81YfXln1ilIUNUQ8ldvsZBZaaBsXdvzBZWXI5cvAbic6LKhyPb85YbZYCQouBCDVHq42NuAmaOg3AYD97WM9LVpAYbZYsRi2ceuPt1JUkt9gRdEpTvXvOBQNrF/vBQkDk49W7OPFH5ViPFR0iNSoVApL7UcjnqoyaRKvLo9g/4hZBBmFdmZrfENReRF/5/yt2prWoij255Zid0i6Jh9f4O2Bdjvocp2Fsh1/ExVqoshia3bZ2cXlNgxBBQCkWlW+SEMsirZJqifWrvz9nhYtoDBbbDgMKnu9dV5Fg+YIoF+r3iANLOxugg0bvCBhYFJcbuPlt27HNnUaT532FHMunceRMusxvSgqGTWKkMO5GEaMICLYqJeeNL7hcPFhAFpFtoL4eGjX7rgxu7JVaY9uyccrkTN7n8e+OJjx+ztEhwZhd0jKrM2rJarZYqNdZB+WTF3CgIqGJZIBpETGEWKLYuncF+HIEe8IGQCYy63YOUJsaCwhphBo1apBx3eITSXMMYwPB0nKNq7xkpSBR0lJGX2y9lAcFknb6LZ0jOmFlKis7OoYjRAczJPLnuSQ4Qkd9aTxDXml6gkwITxBObKdVSqrsju7GCGgc1LEcftGj7qSPlnwevq3lTHfza2kttliJTE8gbEdxxI1bBQ89ZRSqm4SEx5Mj4JTkblZlC771YuS+hezxYaNIhLCEmDBApg7t0HHRwSbiLb9gzgZwYF9GxoUMdWUiU/bT6itgvROPflg3Qf8tGux2l6TogDYuBHTK6+R7/idg0U7fCip59GKoomQV+ZUFGEJtY7ZlV1M+/hwQoOO71MhwsK4xTSCDSKL9LINAM3OT2G22Ch0bGXmlpkwcCA8+CCE1eCvqYXo0CDKQqfz81chhP/2hxcl9R+upkUVslA9dJwA4SFGQhx9ebzLl/T8eH6LURTtD6ib/Y7WXbnn53uYt/M7oA5F0aoV0xfmEOQwsvHITF+J6RW0omgixIXGcW73c0kNT4HTToOvvjpuzJ7sYrom1d6A5soXfiEmJIaF+z8FaHa9F8zlNnYUfc9NP9wEmZmqIGIDiA4zUWEK5Ui/QaSt+pkduU37KbAmSitU06Ib+rzBd5d+BxdfDF980aBzBBsNmAwG0tt1pfjUoWSUHPaStIGD3SHpkr4LiymYDREJFJYXEizUsuZx4bEuUlJI7NiLsw/Gs7/0JwothT6U2LNoRdFEOLX9qcy/fD7tDLGwZIm6EVbBZnewN6eErim1K4qI4AhmTPqMfw99FDhabbU5UG5TDZus0rmk8sADMHJkg84R7SzgljboZE49eQs3z7veG6L6FVf0TUpUvHromD0b9jSst4QQgvBgI8XlFfR5sQv3fP4vb4gaUJRU2NiS0oVXT72c7fm5AJicIdi1WhQAp53G/YuLsFPGJxs+8YGk3kEriqZGLb0oDhWUUWF31GlRsHgxZ/W7gN4HsoGjNX+aA64bYLn9iFpSKSpqcDRPdJhSFIf6juD2g61ZdOg3lh9Y7nFZ/YlruXH+vldZstXZrr6B8wTKT2GpgPP/tvN11hIyzc27RpbZYmNu73G8Pfxi9uWrzyqkmrdaLQqAceMYfrCcwYVnc3rnplseRiuKJsIdP91Bv7f7HVUU1X7cu7KcEU8pdTTq6doV7HZ+/vsLjpj+16wsCpeiKLUVKouiqKjB+QFRISaEgB09BnLDV7uJDY3l042fekNcv1FksSGxMnPHy6zYt1RtbOA8AYQHGympsHFdyKnYDJIFOxd4VtAAw2yxEm0ppntMEBlFKolO2iMJCzISFny8T7CSMWP4e8w5tC06iz7JfXwkrefRiqKJkGHOoMJeoW6AcJxFsTunGIAuNUQ8VdKuHcTE8EfuX5hNPzQrZ7YrTr3EVqAsisLCBj8pGwyC1Jgw9uQUEx4UToeYDuSU5nhDXL9htlhxoL4rCQ5nrsmJKIoQI6UVdjr3UmW2c7L3eUzGQMRssTHns7t4Zv5LmOw9WDl1O5GiV93LTgAJCSx+6GXWJabw3bZ5Tdby0oqiiZBXlqdugEajKgmdlHTM/t1ZxbSOCa1slFIjQkC/fiRmF+GgmMLS5qMoXErvjTO+4/Gxj5+QRQEwrFM8f+7NQ37xBfGrt5BflO1pUf2K2WLDLtTDRoIpCjp3hsTEBp8nPNhEaYWNkEFDCa+A/EM7PS1qQGG2WImsKCM8IRZBENaKeIotxpqzsqsRGWIksXgbF8yazIqDK3wgreepU1EIIYxCiDt9JYymdvJK89SSytChsHEjDBlyzP5d2cU1ZmQfR79+xB/MBWEj39UprxngWkbrldydDrEd4LHH4JprGnye4V0SyC2uIC0qic+/tvNdwk2eFtWvmC02HEItUyb0GqIc2WPGNPg84cHKoqBvX2bOhquLunha1IDCbLERUVFGTHI8ZYYNvL7mefKKy+v2TzjpumElCz57BID8snxvi+oV6lQUUko7cLmPZNHUQaVFUQMOh2RPjpuK4sILiZ8wCYAcZxJfc0AtqZQwY+tbKqz1sstg/PgGn2dEZzXHS2M60sYaSuLytZ4W1a+oeXIqijpycuojIthESbkNEhI494fd9Lr9SU+JGJCYSyuIsFqITorHGrSGeXveVOU76lt6AsoHn0y0RVXnbZaKwsnvQog3hBCjhBCDXC+vS6Y5hgt6XsC4juNUSejhw4/6KoCMwjJKK+w1lu44jnHjiJ98OQITBWVHvCewjzFbbNhEDk/9/iCbMjeodpU5DfcvtIsPp21cGL8fKmbFWb155PCMZlUTq8hiJYpTKH6gmL4rd8Ppp59QuZLwYCNlFXYQgrVhR1ic1ryiw6pjKVA5EMFxMYSGlBIsYsgvqXDLoghLimd3YlfC7IZmrSgGAL2Bx4EXna8XPHFxIcSZQogdQojdQoj76xh3oRBCCiGG1DamufPqWa8ypf8U2LdP3QRDjvac2J2tnJNuWRTA5IhB3G6agdHewSuy+oPichsO19q7I1gp0xkzTuhcwzsnsGpfHr8PTOTxk7IpzTzgSVH9itliIzLERERwBKbde2HxYgiu/2ZXnYgQEyUVqlbY01/fym2fNe+FB7PVwfNjphI0djRGUzFCRmG22NyyKCJDTKxq34/4Eocq694EMdU3QEo5zhsXFkIYgTeBM4A0YLUQYp6Ucmu1cVHA7cCf3pCjKeCQDgAMwqDCY4OCalQU3dxUFIZxp3FlUhfuv+Q/nhfWT5gtVoymEgASrM4f7wnkB4Bafpq9Ng1734GweyH5lgIi6FjnMVarlbS0NCwWywld01dMbGNnTEoUK9etJG7UKPjxRzjQcEV4Zls7I5MS2bZtG3ee/ARl0sq2rVsb1ACpKTG8nZGyZ25je3QYH415HIdDYJCxxIZb2LZtW53HCruD3s/dzYLy6RjiE+od721CQ0Np27YtQUH1O+Jd1KsohBApwNNAqpTyLCFEL2CElPLDExcVgJOB3VLKvc7rfAVMBqp3QnkC+D/g3kZer8myJXsLA98dyLeXfMvkGpoW7c4uJiEiuOYqljVQNOAkXgj5jYOlK4GGOzIDEbPFRlBQCTggwer8Wp9A1BPAiC5q7T4tTPVdyA83cHyt3mNJS0sjKiqKjh07Hts/OcDYn1tCkTUDKUo5qSJOdWw76aQGnye7yMLhIgs92sSQkWUky15Ez6SuiBOwTpoCabnFlFvK6ZIaR3m2FYc9BJNMoX18OLH1LD9Z7Q52ZhyhvdFGVHI8mOq97XoNKSV5eXmkpaXRqVMnt49zZ+npE2AhkOp8vxO4o6EC1kAboGoxnjTntkqcvpB2Uso6W44JIa4TQqwRQqzJOYF16UAnrzQPh3QQHRKtfBPVcijcjnhyEtSnH1/1KKbI4t8nG09ittgwmpz5AWXOG/UJWhSpsWF0SAjnQLb6eeT/uaTeYywWCwkJCQGtJADsUiKxYzKYwGZT4dYngMH5OR0OidEUjBTgKA9sa6oxmCyldDm8D0pK6BzbE5NU4ekmQ/3/3wYhsAsDheEm8q3+jTQUQpCQkNBgy9cdRZEopZwFOACklDbA640MhBAG4CXg7vrGSinfk1IOkVIOSaqWX9AcqKwcG54AXbocU8PI7pDszDI3SFGE9R1EqBXCS9Kx2h0el9cfmMttnBR1OQfuOEBYSbnaeIIWBajlp52H1dzkPXgnpKXVe0ygKwlQ3xdwKEURHAzh4Sd0HoPzzuGQYApST9T28ubXh70Su/OWZzAQajLiunUajfXfQg0CBFBsyeVQwQG/V9s9ke+pO4qiRAiRAEjnRYYDniiDmA7HWPRtndtcRAF9gKVCiP3AcGBeS3RoV/aiCEuARx+Fzz+v3LdyTy5mi42RXRuQNNWvH/FlEF6R1eRbNLowW6zEhUXQPqY9DBqkHNldTjy2f0SXBCosbVg35Ecu3IoqxNgMcDiURWE0GKFt2xOeo0qLQkpiwhPomQsmW/N46KgR5wOVzQCZxWlIoR5G3LEohBAYDAKTzYFN2iHA/Vg14Y6iuBuYB3QRQvwOfAbc6oFrrwa6CSE6CSGCgcuc1wFASlkopUyUUnaUUnYEVgGTpJQtp6WWk2Msimp8tz6dqFAT43omu3/CLl0Ii25NenRosynjYbbYSKv4ng/XfQht2qg8iri4Ez7f8M4JCIysjuiCiI9X0UFNgDlz5iCEYPv27TXut0sJSIIMQbzyyiuUlpae0HUMQjB31v+447ZbCQ4KIbLPIAytWgPwySefkJSUxIABA+jVqxfvv//+iX6cwMGhLAorDnJKczAa1HujG4oCwCgEDmOIWqIrNntNTG9Rr6KQUq5FeTxPAa4HekspNzX2ws4lrFtQ/o9twCwp5d9CiMeFEJMae/7mxJDUIdw5/E5CTaEqieyeewAoq7CzcMthzunbusZmRbViNBLf6iTKTWHNpsud2WJlV/F8vtz8JezapSyARpj4KdGhdEgI552NzzJ7cjelKJpAPsWMGTMYOXIkM2oIDZZS4nBIWoV3p31Me1554QVK9+49oeu47o9Sgt1hJ9eSj8V29En50ksvZcOGDSxdupQHH3yQrKysE7qOu9hs3v0eC4fTolAr8AQbTRiFqLSs6sNgEDiMKsrIVtIMFYUQYgXwGGqZ6ICU0mOPoFLKH6SU3aWUXaSUTzm3PSylnFfD2LEt0ZoAmNBlAi9NfEm92batMkHql21ZlFTYmTygTe0H18J73Z7lxnVDmo1FUWyxUe4oUlbXxx/DxImNPudJraL5u3AuC3sY4eBBOMGbqq8oLi5mxYoVfPjhh3xVpbGV3W7nnnvuoW/fvlx4xql8/N5bvP7662RkZTHuoosYN05FwEdGHvVzzZ49m2nTpgEwf/58hg0bxsCBAxk/fjxZWVkYnJpCohTF/iP7MWfsP06m5ORkunTpwoEDB1i0aBEDBw6kb9++XH311ZSXl7N69WouuOACAObOnUtYWBgVFRVYLBY6d+4MwJ49ezjzzDMZPHgwo0aNqrSWpk2bxg033MCwYcP497//7enprERKiTkojKKkVlid7tno0FBiwt0PLzUIAVI9zNnKSrwipzdxJ07rX8Ao4ELgeSFEObBcSqlrQPmIgrICIoIjCDYGqzwKZ9TTnPXptI4JZVgn9/tCu2i1YjEv/PAKi++9Chri3whA7A5JSYUdS+gR5cdxVY5tpHO5Z+so5O4IclrFwurV0LGjW8c9Nv9vtmZ4NrqlV2o0j/yjd51j5s6dy5lnnkn37t1JSEhg7dq1DB48mPfee4/9+/fz15p17Mo1U1q6l74dTuKlZ59lyaxZJPbrV+d5R44cyapVqxBC8MEHH/Dcc8/x1LPPASCRyt8B2EuLwXGsn2Lv3r3s3buXtm3bMmzYMBYtWkT37t2ZMmUKb7/9NrfccgsbNmwAYPny5fTp04fVq1djs9kYNmwYANdddx3vvPMO3bp1488//+Smm25isXMpMC0tjZUrV2I8wegtd3BIKA0KwRITg3AoayA+IlT9Ht3EaBA47OH0FsmElGariDM/hsk2FHcS7vYJISxAhfM1Dmh44LXmhDnnf+cQFhTGoit/qVQUecXlLNuZw/RRnSuf7hrCd+1y+fFyuHvLZhhb940i0Cm22JDIKr0oDjYq4slFz1bRGGQU6Zai44owBiIzZszg9ttvB+Cyyy5jxowZDB48mF9//ZUbbrgBg9EI2AiKFNjsFeogQ/1uyrS0NC699FIyMzOpqKigU6dOlUsuUqpEUIHAZpBQoc47c+ZMVqxYQUhICO+++y45OTl06tSJ7t27AzB16lTefPNN7rjjDrp06cK2bdv466+/uOuuu1i2bBl2u51Ro0ZRXFzMypUrufjiiyvlKS8vr/z74osv9qqSAOWwD7ZbCbYasAQri8JkaNhN3iigQhoIS0qF5DYnHJbsL9xJuNsD5AL/Az4EbpVSNuPwhsAjryyP/tH9ocRpskZH8/3mTGwOyXkDU+s+uBYOJ5mY3wPu2r4R+KfnhPUDRRYrklIc0u7sRbH5hHMoqtKzVRQGosguyYPffoOVK1WL1Xqo78nfG+Tn57N48WI2b96MEAK73Y4Qgueff75yjN0Z8QRgcq06V7lhVQ2brBpnf+utt3LXXXcxadIkli5dyqOPPnqMj0IIgUkYsQkbOG/il156KW+88UblOTZu3Fir7KNHj+bHH38kKCiI8ePHM23aNOx2O88//zwOh4PY2NhKq6M6ERF19F/xEHaHpJU5j6hCK7H9+pISkdLgEFODQWB32Mmy5BEZHEmE0ftyexJ3op5eAw6iqsjeBkwVQjTvmsIBRmWJcbsdJk+Gnj35bn06PVtF0bPVid0QW6WoOk9y/2ZPiuoXisttGIhg3nkHuHnozSfUBrUm2seHEyyiKK4ohaVL4T//OaqsA4zZs2fzr3/9iwMHDrB//34OHTpEp06dWL58OWeccQbvvvsu5VYrCDuFBYUYhZGoyEjMTgsAICUlhW3btuFwOPjuu+8qtxcWFtKmjfKDffqp6vh31IpVDn6jwYTdQKWiqE6PHj3Yv38/u3fvBuDzzz9njLO8+ahRo3jllVcYMWIESUlJ5OXlsWPHDvr06UN0dDSdOnXi66+/VleTsk6l4w0cDolROiqV6onkIRiFwC4lh4oOYS44DIcO1X9QAOFO1NOrUsqLgfHAWuBRVHa2xgc4pIMCi7NrW2wszJlDyRlnsv7gEc7q0/qEz5voCrXN2OEZQf2IKxckLjycEFMIvPACPPdco89rMAjGJD3EhPivYMAA9fi8OTAV64wZMzj//POP2XbhhRcyY8YMpk+fTvv27Tll6CAuOWMCC+csxBQcynW33sqZl1xS6cx+9tlnOffccznllFNo3frod+vRRx/l4osvZvDgwSQ6mxwZhEDgUhPQNb4L7cqCao0MCw0N5eOPP+biiy+mb9++GAwGbrjhBgCGDRtGVlYWo0ePBqBfv3707du38ob85Zdf8uGHH9K/f3969+7N3LlzPTVtbmGXEoPDAQYDGeaME+pSZzAIpBRqic5ariobN4EoukqklHW+UNVi/wT+Bj4ApgKd6zvOX6/BgwfL5kR+ab7kUeRLK1+q3HYov0R2uG+BnPnXwRM+7697fpU8ivzXm695Qky/8uvWw7L1/a/JS2dOlwePnPic1MR9szfKAY8tlI59+6QEKd9+u8ZxW7du9eh1vUGu2SLXpe2X6zM3SIfD0ejzbUk/ItMLSj0gWWBTUFIuS9dvlLYdO+XW7K1yR+6OBp8ju8giNx4qkOszN8j9h7dLuXq1lMXFXpDWPWr6vgJrZC33VXeWnv5AJbr1llJOl1J+Kp2F/DTex2Qw8czpzzC6w2hYtgySk6n4/Q8AosPcD8+rTnJEMuGGdhQYQ+ofHOCYLTYqDAeYue0DSq2lMHs2bNnikXMbQrezy/oMO0JNyqKrZa28KWCXEqOMoW9yP0RhobKOGpElbBACh0M9FZvLzWSXNM0S2vXhkBKDlGA0YnPYGuzIhqOJeUZhxOZatmtCGdruLD3NBoYJIV5wvv7hA7k0TqJCorh/5P0MTh0MBQWQk4PZWXYmphGKom9KXy6M+oipC/6E33/3kLT+4diubfFwxRXw5ZceOXd4mJkS01JWHdqnlp/27/fIef2BwyERCOWItjkdz40IITY4190BjliOkFZ4CLZvb1pLKm5gd0BmVCIkJWF1WE9QUah/DcKITTjnpxZ/TiDiTsLdM6h+EFudr9uEEE97WzCNotBSyL6CfdgctsqudoWmUKBxigIgPDKM83/4FH74odFy+pMiZx9ogSCOMLBaPeLMBujdSkWVbclIhwULVP+GJordAXZDLunm9KNF7hoRpmkQKscAlOXrQOIoLj567maCXUqKQiMQkRE4pOOEFEVEsAmDEEQa29AlvssJF2P0F+4sPZ0DnCGl/EhK+RFwJnCud8XSuJi3Yx6dX+vM/iP7VQ4FUGBQy0UNyQytiR8L7uGJcTEB66B1lyKLFQxmYkNjMRYfDSH2BO1jVQ2tXTmHISKiSTfmsUuJgzK1POcJRWEQOKQr6smZdFdH5FNTxWF3EG21YLeWE2wMJsjQ8N+dyWggLiKYIotESgP06gWpJxba7g/cURQAsVX+bnwmk8ZtKgsChiVUKopco2csityKnaxODYVNjS7d5VfMFhtBJgcpkSkqKxs8knAHEB+mst735mdDXp5a1vrpJ4+c29eoEuPOXhRms8oMbuTSk8tH4XrKtjVDRSHtdjrmpRF0pIh+Kf1IijixVgaJkcE4KGVvftMKjQX3FMUzwHohxCdCiE9RIbJPeVcsjYu80jwMwkBMaAz07AlXXEG+w4jRIIgIblx2Z4QphvRIk2qFeYJVRAOBojIrfcPvZetNWyuX5zxlUcSHxRNuiiXbXII1PEI5ypcu9ci5fY0r4c5kMClF2qbhNcKqcszSk6iiKJqQk9YdZJVeFI0hxGQk2FSB2ZqDPT8Ptm5tMst07nzyX4ARwLfAN6g2qDO9KpWmkryyPOJC41S/7MmT4csvKbTYiAkLanSjnKjgWPLCDMjwcFX0rolSZLER5ZqPk06CVauOae7UGGJCY/jy7C2EWSewr8gKvXsHbOST0WhkwIAB9OnTh4svvvi4EuJ2hwPpalrUqhXU0eRr2rRpzJ49G4Dp06ezdWv1DsXO0tlSsnTpUjat2US/lH5EhsW4XcOoY8eO5Obm1ri9b9++9OvXjwkTJnD48GG3zudJxo4dy5o1zhqkzpv50o2rmXLdFCrsFXUcWTdRoWrZuMhSoR7O6rG+Hn30Udq0aVNZsr2mqsC+oFZFIYT4hxAiB9gM/AXkSSnnSSl9/7/Wgskryzvah8K5HlxYZmv0shNATEg82REGDqdlK2uliVJUZmVr2Qt8tP4j5SQcNgziG14osTZ6tFJFGLdlFqnIpwBVFGFhYWzYsIEtW7YQHBzMO++8c8z+8opyQu0GQsobVpL7gw8+oFevXsdtd/koli5dyqpVqwg2BiO6dYPkBvRGqYUlS5awadMmhgwZwtNPezd2RkqJw1F7VSKXRdFrQB9ue/S2Rl0r3NlTPM9ec+STvQYL484772TDhg3MnTuX66+/HqvV9xWf67IongJGSSlboyrH6kgnPzB94HQeG/uYenPxxTB0KIVl1kblULjoHt8Xk2xLUXnTLt1ltlg5UPozm7cthaFDYadnCwe8uf6/FAXNYMdhs1IUWVngh6fchjBq1Ch2797N0qVLGTVqFJMmTeK8McM4KcPKs48+x9ChQ+nXrx/vvvsuoG6Wt9xyCz169GD8+PFkZx/Niaj6dP3TTz8xaNAg+vfvz6WTz+bQgQO88847vPzyy/Tu15ufFv1ETnY2F154IUOHDmXo0KH87gy/zsvLY8KECfTu3Zvp06e7EnrrZPTo0ezevRuLxcJVV11F3759GThwIEucHQfPOeccNjl9bAMHDuTxxx8H4OGHH65smPT8889Xft5HHnkEgP3799OjRw+mTJlCnz59OFRXSQ1nd7tlv6/kzil3YjKYePTRR7n66qsZO3YsnTt35rXXXqsc/sUXX3DyySczYMAArr/++sqb/4033sjpI0/nknGX8MKLL6jB5eV07NiR++67j0GDBlWWKqmJbt26ER4eTkFBQa2fC+CJJ56gR48ejBw5kssvv5wXXnih3nmuj7psRJuUcjuAlPJPIURUo6+maTBndDnj6JuiIjAaKSyzesSiuHnwg6za8BfRTzwCSdGqzWoT5EhZGVZZSsLX30NaEISFefT8azNXI0KsSlEMHgz9+6sSDK1a1X7Q2LHHb7vkErjpJrXkcPbZx++fNk29cnPhoouO3dcAv4jNZuPHH3/kzDPPBGDdunVs2rSZNuZi3vvuW2JSUli9ejXl5eWceuqpTJgwgfXr17Njxw62bt1KVlYWvXr14uqrrz7mvDk5OVx77bUsW7aMTp06sWN/BuWmcK6//noiIyMZ989xtA5N4vZ/TuXO225j5FlncfDgQSZOnMi2bdt47LHHGDlyJA8//DDff/89H374Yb2fZcGCBfTt25c333wTIQSbN29m+/btTJgwgZ07dzJq1CiWL19Ohw4dMJlMlUpp+fLlvPPOO/z888/s2rWLv/76CyklkyZNYtmyZbRv355du3bx6aefMnz48DplKDMFk9uqHfbM3SBUtVyA7du3s2TJEsxmMz169ODGG29k9+7dzJw5k99//52goCBuuukmvvzyS6ZMmcJTTz1FSGQIW7K2cPMld7BhxHAGOEuiJCQksG7dujrlWLduHd26dSM5ObnWzxUWFsY333zDxo0bsVqtDBo0iMGDB9c7z/VRl6JIFkLcVdt7KeVLjb66pl7WZKwhNSqV1KhUFakSHU1RmZX28Y2Pw44KVcomaPNGMOc3WUVhLsmGUEg4XARzlkO7dvUf1ADiw+IxmPayL7cEpo0N2KWnsrIyBgwYACiL4pprrmHlypWcfPLJdEhJobQ0n+/W/M6+XfuZ/c03gCr4t2vXLpYtW8bll1+O0WgkNTWV00477bjzr1q1itGjR9OpUycAEhITyDhSdrSCrMGEzQC//vknW++5p7LSblFREcXFxSxbtoxvv/0WUJZAXB2taseNG4fRaKRfv348+eSTXHXVVdx6q+rA3LNnTzp06FCpKF577TU6derEOeecwy+//EJpaSn79u2jR48evP/++/z8888MHDgQUM2ddu3aRfv27enQoUO9SgKgXBixRoZhk3YMVRZhzjnnHEJCQggJCSE5OZmsrCwWLVrE2rVrGTp0aOX/SbJzKW7WrFm89957WG020tMz2JSRyQDnUtSll15a6/VffvllPv74Y3bu3Mn8+fMB+Pnnn2v8XGazmcmTJxMaGkpoaCj/+Idn8qPrUhTvA1F1vNf4gDGfjOGGwTfw4sQXlaJITXVaFI1verIibT7pIQ+wp9M4Er/+QzWdaWRkh68pt9kZt+0Hdg2EhCnXgxs//IYSHxaPnY0czC/FancQZHRjjuqyAMLD696fmHhCkVUuH0V1IiIioCCfciPYsPPyKy9z7tnHpkL9cAJJl5Wlxp3vjcKIXdpxSMmqGTMIrachUl0sWbKksgBhXQwdOpQ1a9bQuXNnzjjjDHJzc3n//fcrn6KllDzwwANcf/31xxy3f/9+t0qUO6Qk2FpBWGkFwYagynwRgJCQo+VvjEYjNpsNKSVTp07lmWeeOeY8+/bt44UXXmD16tXExcVx3iVXkBccWplLUZcsd955J/fccw/z5s3jmmuuYc+ePbV+rldeeaXez3Qi1PqNl1I+VtfLK9JojsFis1BqLT3qzDabkVFRHlt6Mhkd2Azp7GudCGVlTa70Magcih45u0gtMZFy1sX1H3ACxIfGY7EXYXNI0grK4KGH4JRTvHItb2ELi+BIaDjDxwzn/Xffr3SI7ty5k5KSEkaPHs3MmTOx2+1kZmZW+gCqMnz4cJYtW8a+ffsAKHSulUdERmI2m5VF4bAxYfRoXv/oo8rgC5fyGj16NP/73/8A+PHHHyvX2t1h1KhRfOksy7Jz504OHjxIjx49CA4Opl27dnz99deMGDGCUaNG8cILL1RWop04cSIfffQRxcXFAKSnpx/jf6kPh0MSbSkmNv0ASeGJhAXVvax5+umnM3v27Mpr5Ofnc+DAAYqKioiIiCAmJoZ1u9axfMkvLteH20yaNIkhQ4bw6aef1vq5Tj31VObPn4/FYqG4uJgFCxY07CK10HR68bVA8kqrJNsB/OtflHfrgX2b9IiiSI1WT2z7Ep05Bzt2QIcOjT6vLykqs/LayMd5f2IHxnTs65VrdIrrRNuo9pSbHezLLaaT0ahCcEtKVLZ2E6AiIori8gLOv+J8ZIFk0KBBSClJSkpizpw5nH/++SxevJhevXrRvn17RowYcdw5kpKSeO+997jgggtwOBzEJybx6qezOevsc/jXFZcx69tZPPj0g7z23HPcfMst9OvXD5vdzujRo3nnnXd45JFHuPzyy+nduzennHIK7du3d1v+m266iRtvvJG+fftiMpn45JNPKp/oR40axaJFiwgLC2PUqFGkpaUxatQoACZMmMC2bdsqP09kZCRffPFFnV3xXE72c845B1NQEDa7lVP69OX2B/+DKq5eO7169eLJJ59kwoQJOBwOgoKCePPNNxk+fDgDBw6kZ8+exKXEMXDoIIzlFrWM2YDaWA8//DBXXHEF27Ztq/FzDR06lEmTJtGvXz9SUlLo27cvMZ5IPq2trGxTfTWnMuMbD2+UPIr8+u+vK7elFZTKDvctkF/9daDR5/8z7U/Jo8hr331Wym7dpJw7t9Hn9DXrDxbIDvctkIu2HfbqdfKLy2WH+xbI95ftkfK771TJ8VWrKvcHdJnxwkJZWGCWa9N2yPWZGzx2WnOZVW48VCDNZRVSSiltdpsqX26xSHnokPq3CbIzd6dcnb668rUmbbU0b17rsfNvOrxJbj68Q+7dd1iVGy8o8Ni5pZTSbDZLKaUsKSmRgwcPlmvXHi97Q8uM12lRCCEMwEVSylmNV0mahuKyKBLDE5X/oKyMwhIVA+8Ji8JVniLDaPN4SKmvaHv1FZwSW8BDKyIZ1e0HgoyNn5eaiIsIJjY8SDm0B/RXGzduVDkbgYyUsH8/ocEhiKhgohqZzV8VlzvLlZ1duX4fEgJt23rsOr4mMjgSu7QTExJDhc1OcFYR4faG5Z7Uhclgwu5wUOYqLujhkifXXXcdW7duxWKxMHXqVAYNGtToc9apKKSUDiHEvwGtKPxAr6RefHXhV/RJ7lMZjhn66HNAL4/kUSSGJxJvPBnh8Ey5C5+TmUnCop/InNqD9VmbvKYk1mas5dYfbyUu9jr25UZDxz6qREiARj8dQ0kJVFRQFp+MUYbSKc5zpdoMzsoArsKAReVFFJQV0D6mPUJKVcqjiVVJBWgd1ZrWUarDX2GZFWzmo3XCPYDRYMTmsGIzGJEGA8LDisLlB/Ik7nz6X4UQ9wgh2gkh4l0vj0uiOY6UyBQu7XOpsiic5RRy2qt25Z6wKGJDYxkZ9xJxxmGqfagXIoa8ynffIaRkR2IM8S4/jhewOWz8kfYHsZFmZVEIAdddp/IpAp28PBCCsvAoDEJU3tw9QXVFUWYtI6c0B7vDDhkZsG2bsoSbEFJKbA5bpZ/C7pAcjkzA3t5zvruqZcodwSFNooiiO85sV4DvzVW2SaCz58XRVGVrzlayirMY13oEPP00jBzJgd5DYMdmjygKgOhQE2aLVTWy+fNPFYIb1USioL/+moIOXTgS5qBHeP2hlCeKa4kuOryc9YUWSitshD//vNeu5zHKy1XyXnw8NgQV4iCZ5sTKp+XG4gqPdUXvVFaQlTZMERFq2au0FCIjPXI9X1Bhr2Bz9mY6xnYkMTwRh5SUm4IRHgxa6BTbiXKbg51ZZiqiYwnz4HKgt6hXUUgpO/lCEM3xvLvmXT7d+ClHwh9XT2hffEGhxXM+CoDFuTchba2g97Vqw44dMGSIR87tVbKyYNkytl92Aw6xiOQI70VrucKTQ0PKANifW0qv1Gi1rGMweDwT3GMUFyv52rTBVmTFQTkO6bknfINTU7ievisVhcN2NBqspKRJKQqbQ/2+XJ/F7pDEWEowlho99gClkhPV3BXHJRIWFfjtiN1aeBNC9BFCXCKEmOJ6eeLiQogzhRA7hBC7hRD317D/LiHEViHEJiHEIiFE04rdbCS5Zblq2en992HMGBg3jsIyK0aDIDLEM5HNUlRgtmUhu3dXG7Zv98h5vY7NBrfcwpoREwg1JNE7ubfXLhUTEoNAYDSppkj7cktUs6eoKPj+e69dt9EkJEDfvhAcfNwN0BMYhEBUaYda2bzIYYfgYAgKUvk5TYjq8+RwSFqbcxA1VLk9UYrKizhQuA8hJFa7Qy3PBfgSnTutUB8BXne+xgHPAZMae2EhhBF4EzgL6AVcLoSoXqJyPTBEStkPmO28dosht9SpKFauhI8/BpRzLTrU1OgS4y5SIhOpcJjZE52iup01FUXRpg28+ip7EtvTL+xxXprovYoyRoORsR3H0i1RZdHuyy2Gbt3U03oAObSPKTN+/vmqzLipylM+7isKd8qMg1p+WrF8GStXrsRkMCEQ2KWzAmpISJ29KQKxzLjVoRIR/1z5J71792b8qGFUlJY2qhPgcdewWymwFPDhGy9iKCmGdeuU9RfAuGNRXAScDhyWUl4F9MczXe5OBnZLKfdKKSuAr4DJVQdIKZdIKV1F9VcBTTfmrqHYbOQe3kuiNUg9uTrr63gqK9tFh7hkHJj545BZdW/r2NFj5/YaOTmwfDk4HJgtNqJCvZ83unjqYu4ccRutY0LZm1sCoaGqNPvGjV6/trtUlhlftozgsjLeqVLOwS5t2Gy2E7IoaiszDsqqWOlUFCHGEAa1HlTp06FNmxMOk/VXmXGXQp01YxYPPPAAPyz9g4iQ4HpL29hs7ofPuiyv9197mXLhVEAVJ97jwhe4oyjKpJQOwCaEiAayAU9UXWsDVK0ZkebcVhvXADV2thdCXCeEWCOEWJOTk+MB0fyIxQK33QatW5OTsZvEPZnH7Pa0omgXm4w0FLFqbz589hlMn+6xc3uNL7+E0aNh926yS7JYab6GeTvm+eTSnZMi1NITqJLjAaQoALWEkZ7OqGHD2H3oUGWZ8ZunTuGycZdjwsS9997rkTLjp59+OhlpB/nikw94+eWXGThwICtWrCAnJ0eVGT/tNIaOG9ekyoxHBkey5JslfP311zz00EPcft1VICX3PvUUffr0oW/fvsycqfq2VS3h3qtXL+x2e41zm5mZyejRoyutvT9X/snrT7+OxWLhzDPG8M///hf80GOiIbjzeLFGCBGLKgq4FigG/vCmUNURQlwJDAHG1LRfSvke8B7AkCFD3M+HD0S+/BJefx0uuohZg4cQOXr8Mbs91YvCxej2o1m6I40/9mQjpURYrcrM9qCp7XFmzlQ36e7dyZv3O0dsuyi3eTfE8N6f72Vj1kZOTnyR+Rsz1Vz176/+v/Lzjxs/9pOxx227pPcl3DT0JkqtpZz95fFlxqcNmMa0AdPILc3lolnHlhlfOm2pe4Lm5WErKeHH1as501k5dN26dXz9y0oG9enBl599RkxMjEfKjOfn55NnC+KKqdfQLjmee+65h0OFh7j+2uu56867GDl8OAe3bWPixRezbfv2JlFmPDI4kntvvZe/1/7Nueeey+BTx/Pt5++xYetWNm7cSG5uLkOHDq2sJbVu3Tq2bNlCp06deO+992qc22+//ZaJEyfyn//8B7vdTkFRAQk9E5j98WxmLVxBn+w9yucWwLgT9XST8893hBA/AdFSyk0euHY6x1ombZ3bjkEIMR74DzBGShn4AceN5fvvlbk+axbDa/BDFJVZaRvnuSibi3tfjCwbzr9nbyJ95hzaTr1M1TFyli8OOPbtU/I9+ywAheXqJp3oxfBYgBJrCesy13Fpp0gKy6wUlFqJP+sstQ4fIBV3y8rKGOCscTRq/PjKMuNDh55M2/YdMArBzz//zKZNmyr9D40pMx4fH09BTvExpYrMFWZ+W/Ibe3fuVdaNxUKR2dxkyoxX2CqO8f+VS8HC3fu4/MorMRqNpKSkMGbMGFavXk10dDQnn3xy5XzUNrdDhw7l6quvxmq1ct5559Gvfz/SytNAgEQqP1JTtSiEELXmfQshBkkp6+6yUT+rgW5CiE4oBXEZcEW16wwE3gXOlFK6X/KxKTNlCkyaRGF5EfN3zmdMhzG0izmqTz299AQwrFMcEjt/GeJpW1EBq1cHrqKY5SwS4KzfX1SRDwbvK4rUqFTyyvJoG6d+Mvtyi4nv0wf69FEDMo9dIqzLAggPCq9zf2J4ovsWRBXCwsLY8NVXyp9VpRBceEQ4dpFHekkxUkpef/11Jk6ceMyxJ1JmHFTf7KpLSEGGIBwOB6tWrSI0OFg5alNTGxwi668y43uP7K0s/CelxOpA3chrsbCrnqO2uQVYtmwZ33//PdOmTeOuu+5iypQpldexJiYRHBrYIbJ1PQq9WMer0b31pJQ24BZgIbANmCWl/FsI8bgQwhVV9TwQCXwthNgghPDNQrQ/Oe88mDaNPQV7+Nd3/2Jt5trKXVJKjyuK7bnb6fpWHKFRq/mlPEL1ml692mPn9zg//aQyyDt2xGZ3UGo7AnhfUbSOVElqEWEqOmVvjtNPceAArF1b22G+p29fVV6kClKCREUiTZw4kbffftsjZcbz8/MxCAiPjMJsNgMQZAxi+JjhvP7668rSCg5mg7NzW1MoM25zHHX4O6TEZLcyZkB/Zs6Ygd1uJycnh2XLlnHyyScfd2xtc3vgwAFSUlK49tprmT59emUnu6CgIKxWK5a4RI/2ePcGtVoUUspx3r64lPIH4Idq2x6u8vf44w5qzixerJ6+evYkt1SFDVa9AZZW2LE5PFNi3EVyRDI2h43UpDJW7ctHDh6CCGRFsXBh5dN7cbkNA1F0ixlytGeHl0iNUqGx0piPySCOOrRvuEElQ371lVevXy+uMhA1PPk6i1FgEiamT5/O/v37PVJmPDk5mY9nzmXs+DO576ZpzJ07l0f+7xHufvxu3nviPVVmvLSU0YMH88655zaJMuNVFYXdAaG2Cq4Y2J8Nf2+mf//+CCF47rnnaNWqFdurhZLXNrdLly7l+eefJygoiMjISD777DMyzBlc8q9LuHjCSAYNHMjszz9Vy5iBSm1lZV0vIAi4DZXHMBtlBQTVd5y/Xk22zLjDIWWXLlKedZaUUsovN30peRS5LWdb5ZB0Z4nxGX82vsT40cs6ZMgTIXLSZzfIDvctkDm33yOl0ShlSYnHruExHI5j3h7MK5Ed7lsgZ60+6PVL78jdISfNmCTXZayT415YIm/4fI3acf/9UgYFya1//+11GWrF4ZBy0yYp9+ypcfeRknK5Jm2z3Ja93eOXTi8olVvSj1S+zynJkZsOb5IVNlV6XB44IOW6dcf93wUiDodDrk5fLdMK06SUUhaVVcj9uw6pUuClpR691r6CfXJD5ga56dARad6zX13Dh3PU0DLj7njh3gYGA285X4Od2zSeZOdO2LMHzlUtKl0WRVJ4UuWQwjJl0nrSohBC0DqqNcYgtQzwR+9T4cknAy8K48gR6N0bqnTscs2HJ6PAaqN7QnfmXjaXga0H0jUpkh1ZaqmFAQOUI9KfzsgjR5RFUYtzWGVO2zEZPZ9vYhDKZy2dforE8ET6pvQ9Wsk3JQVOOsnj1/UGdodannNZFFa7xOjKmPZwwEKQIQirw4rJCFZXLkWg/eaq4M43Z6iUsmqZzMVCiAALHm8GuEpBnHMOoBSFQRiIDY2tHOINRQFqWaWwIod28WEsCE3hH/ef59Hze4TXXlPVSNscTbUpsljJCfo/nl8dz8Tec3wihpSS3qkx/LIti5JyGxEDBqgd/kyYyspSJTNiY2vcbXdIDEQSE+K5EuMuDEIgkUipiuoeRyAvp1RDCEGHmA5EBivHu83hwOCqjeXhcHGXIjUZJRUuJWSzqbInAYg7isIuhOgipdwDIIToDE7PmMZzLFignpidrUhvPflWzut53jHN3L31BD2l3xTs0s6+sAR+2ZaFIysbw+HMwCmjXVgIL78MkycfE41lttiwGTKwOnzT82Do+0Ppl9yPK7o/jZSwLbOIIV27qp4LFRUqt8KDZbzdoqRElX9o27aWO7VSFCaZSGK45/uOuAoDOqTEgMDmsLG3YK/qdRIWr8yNnBxVJDDAiwMaDUaSIo5a8Da7xOj08HjDogAwCDsVOH/jVqtPCky6rL+G4I6iuBdYIoTYCwigA3BVg6+kqZ2yMpUbcNttlZuSIpKO+dKC9yyK64eo0MFZHOLrtWmUXnUNkbt3BE7Xu9deU8srzqxaF0VlVuwUkRzh3YgnFwZhIM2cRp826sl8S3ohQzrGw9y5hMbFkZeXR0JCgm+VRV6euonVEUpqc0gMBu8oseo9KYzCSFF5ERFBERCGUl5paWoJKsAVhdVupcJeQVhQGAZhwGp3UBIVT0rHVI8rihBjCOFB4ZiMAovLA+CD5UspJXl5eYSGhjboOHcS7hYJIboBPZybdsiWkPjmS8LCVORMlS/KjM0ziA6J5pzu51RuK3IpinDPKgqHdJBdkk3ftuqHvL9TL/r8uEDdnGtZzvAZZjO89NJx1gRAkcWGQxTRKjLZJ6K0jmzNnoI9JEeFkBgZzJaMIrVj/HjaWq2kpaXh8xIyUqplkV27ah2SW1xCiS0XS24S4UGetb7KKuzklVTAkRBMBkFhmQ2zNR9LkIWicOf8HDmiLB9nCG2gYq4wk1+aT5voNpgMJnLM6jZnK/TO8plAkGVJx1xagdVoR6SnQx2hu54iNDSUtg2swVVXwt1Q4JCU8rCUslwIMQC4EDgghHhUSnl83QJNwykvVwk91W7Iz6x4hi7xXY5RFIVlVgwCIoM965T8fOPnTJs7jZ037yIqxMRfIV3oA7BmDYz3c4RyZCR8/TW0Pr7ZTl5JEVKU0zo6qYYDPU9qVCorDq5ACEHv1Bj+dimKzEyCvv2WThddVFm80SfU6hg4lolvvc3POTexZOoSBncc7FERlmzP5toZq5lz86n8siOHl3/dR2bI7cSHJrP0qoX0aBUF998Pe/eq0uwBzLMrnuWBRQ9Q8mAJ4UHh3PD8Eqakr2F0rxjV0dALzNuYwW3z1/PLnaPplhK4DcPqsqfeBSoAhBCjgWeBz4BCnHWVNB7giSfg5JNVJ7Aq5Jbmkhh27HKCq86Ta13YU7g6nh0uyaR/u1gWhjmfNgIhn0IIpax6H99voqC0jGg5hoGtBvhElNaRrckry6PcVk6fNtHsyjJjsdrh0CG45Rb4w4cl0MrKVHLd11/XO7SgPA/wTlKiqzvbmv35vL54F//on0qfVh05UpHNOa8t58fNmdC9u7J4Aqnnwl9/qWW7KuSW5hJmCiM8KBwpJdlF5Yxc9SO89ZZXRDjry7P4bo/KXc7ftDVwlnproC5FYaxiNVwKvCel/EZK+RDQ1fuitQAOHFC9qnv2PKYJvZTyaC+KKnijfAccTSbLLM5kYPtY1hSBo0tX/yuKJUvgrrvU0kUNWG1h9A55iLO6neUTcU5pdwo3DbmJcns5fVJjsDkkO7PMqoyHr3tTfPUV/P13nb4JF96shxXhtG6fX7iD2PBgHp/Um3N7nsr4bn1JiQ7lm3VpSlGUl0P6caXc/IPFAhddpOZuwoTKzVV/c8XlNsqsdiLLS73WGjjDnMHhUrVk2OWmq9R3PUCpU1EIIVxrHKcDi6vs834DgJbAv/+tbjDOAncuzBVmrA6rzxVFhjmDAe1isTsk2594UfXp9idvvgmff36MEq1KYWmFT3IoXJze+XTePOdNokOi6Z3qcmgXKfl69PCdopBSVRju3RvGjq13eHGFUhQJYZ7PXg8PURZFuc3B0+f3IS4imMfGPcb8K75jWKd4NhwqRF5+ufJRtPNEdwIP8NZbygo8/3z45Rc4eBBQisIVQJLt9E/EZR6EBmSQN4TUqFRyyw7TMSGcw6ExPvFPnCh1KYoZwG9CiLlAGbAcQAjRFbX8pGkMK1eqAnf//vdxP6CayneA9xRFXGgcIcYQMs2ZDGgXC8Cy1N7K0vEX2dkwd64qkhgcXOOQLUd+YFHR2ezJ3+Mzsax2KxabhXbxYUSFmtiS4fwpDBjgO0WxciWsX6+Wu+rxUUgpERXdGdv6uqNJcB7E9X08b0AqE3q3OmbfgPax5BaXk2411KrsfU5hITz1lLIk/vtftW3lSgDuH3k/T5+mHo6yi8qJKy0kPCvTawUyW0e2JsOcwdCO8ewzRiCzsrxyHU9Qq6KQUj4F3A18AoyUR4NvDcCt3hetmfP++8r0vffe43Z1iOlA+l3pXHDSBcds93QvChdCCJ474znO7nY2CZEhdEgIZ9PeXPjkE9VJzh98/rlKQLrmmlqHFJXnY5MlxIR6PpGsJnJKcgh5MoQP1n2AEII+qTH8nV5FURw8qG5E3uaNN1R12CuvrHeoxerAZOvPxd2P/555gsTIEP537TCeuaBf5bY/Dv3BSW+eREiYelLfcOgIPPoouNF/wuu88ILqH/LMM9Cvn8rvcPawGNl+JBO7qsqv2WYLnfIzkAaD1/KJUqNSOVx8mMEdY8gIiVGK4gRyHHxBncHBUspVUsrvpJQlVbbtlI0vMa557z1VBLCGUsdGg5HUqFSiQo5dGy3ykkUBcNuw2xjTUfWFGtgulrVphch771UKzddICR98ACNGQC0tOAHM1nxAEBdae18DT5IQnoDRYCTDnAFA79Roth02Y7U7VFRMYeEx5b29xg03wKuvupWXUGSxYqeAkCDvxeif0iWx0qkNEGwMZnvudoxBeQSbDGw4eAR++AGclWP9hpTKrzNtGgwapKINhw2rtCh+3PUj+wpUZdwccznr2p5EUVY+jKmxX1qjGdBqAOd2P5c+bULJDY/FUF4esCHEgdFxpaUhpUrV79u3xt1/pf/FE789QVF5UZVDPF9ivCq5pblszlLhiwPaxZJVYsUyagz8+qvvn3JKS9UP+Oab6x5mO0KYMeaY7HVvYhAGWkW2IrNYVa/t0yaGCpuDPTnFKrzZS07P4xgzBqZOdWtoYZmV7JDHeGez7xYBXFF0uWVZ9E6NZmPakaNtY/35xCwEfPstfPzx0W2TJ0OfPlhtFZz9v7P5YtMXgFIUwSYD0QnRtS59NpaLel3E3Mvm0rt1Cuv7j+SjO57z2rUai1YUvqa4WJm8c+fWOmT5geU8vPThY1Lty6x2rHbPlhivymNLH2PkxyMBGNhePaHv7jtMlfSuVk7Z60REqGWvf/6z1iFSSiz2I0QG+caacJEalVppUfRpo0pibEl3KvQXX4RXXvHexcvK4IEHVJc/Nykss2Kn0Ov9OqqSHJGMQRgqgyM2pxdi79tPhaP6M/Kppnpct90Gn35KnuXYyLBscznP/fIW4tNPvS6WEIKUof35IHkQNDBj2ldoReFrPv8ctmyB5NqziXNLczEZTESHHK3Nk1loASDWS4oiNSqVovIiSipKOKl1NMEmA8vaO9edFy3yyjVrpLTULadwSYWdEHs/RrQ+z+siVaWqouiUGElYkJEtLj/FokXHPq16mjffVBFyzigddygqs+IQRSRH+CYpEVT11ZSIlEpFYbE6ONDWGVHvyxDi6gwZArfWYFlJSW6umlOXoijIL+TcNT/WmfHeWLKKs0h9MZUP133I8NZhdNq4iszNgZlLoRWFL5FS1S0aMkR1aasFVzx31do8czdkIASM6u6dH3zVXIpgk4E+qdEsqYiCjh1V1VZfMWeOijKpJ3mtqMxKlP1sLut5h0/EcnF5n8u5bpDK0jUaBCe1jjqqKIYOVQ8BteR9NIrCQuWAnTixQWvm2WaVvZ4S6TtFATCpxyR6JvZkYDtl8a2Obqu6uDWgq51HychQmeE1hbqefjq5990CHFUUYbt2YnLY1ZKZl4gLiyOzOJO0ojROjjPw5cz/kjVjtteu1xh0PoQv+fVXtYzz2Wd1hjXmlh2bbGd3SL5ec4hR3ZJoE+ud6pKVisKcSdf4rgxsH8cXqw5Qvm49IXGxXrlmjXz2mfoxDxtW5zCzxYYDC1Ehvv0KX9L7kmPeD+0Yz0e/76O43Ebk+PHw+OMqUfD88z174RdfVNE6DcxtyTCr2Pw20b6ph+XinXPfAdQSYXxEMKvz7Vyam+tWyRGv4LKKzzjj+H1du5K7/kvodVRRJO9xLrd6UVEEG4NJCk8isziTrqd0BiB3b5rXrtcYtEXhS+bOVZEql1xS57C80rxjGhYt35VDZqGFy4Z6L2HJ5YB0LauM6pZIuc3BbxkWr13zODIyVALUv/5Vb7XOwrIKDoVexqxdz/tIOIXVbuVg4UEsNjUv43omY7VLVuzKUcotIkI9EHiS7GxVGPHii1W0TgOosIYQVzGdsZ1O9axMbiKEYEC7WDYeOuI/JQHqe5WYqPyD1Tn1VMZsLeXnUe/ROa4zFqudjum7qAgNhy5dvCpW6yiVS2EMDsIcGUPpoQyvXu9E0YrCl0yapJ4I62nmsmTqEuZdPq/y/czVh4iPCGb8SSleE61TbCc+nvwxw9qqJ/lTuyaSEBHM/HUH4cILVSawt/nf/1Q9oClT6h162FwAwkaSl3tlV+fXvb/S4ZUOrM9cD8DgDnFEhZpYvD1bRaycfbbnO5U5HOrh4oknGnyozRZOa+OF9G9Vc4Sdt3jtz9eIeTYGq91K/7ax7M4ppmzWbPWEXlzsU1mQUinv00+v+QHklFNIKoUz9kBEcAQ55nIcwkDu4OEeLy9endSo1MooOmtCEqbcHPJL/NgEqxa0ovAlEybU7EyrhtFgrOyylVtczi9bs7hgYBuCTd7774oIjmDagGl0jO0IQJDRwDn9WvPzjjzsu3Yp34G3+eYb5bvp3r3eoRlFakmlVZRv196rW15BRgOjuyexZEcODoeEmTPh3Xc9e9FWreCjj1SZkAaSac4mOPQwNodv22yGmkIpKi8iqySLAe1jkRL2F1hUiKyvq8jabCoLe/r0mvd37crvAxOY//3LICXZ5nKeOP1adnwww+uindvtXM7sciYAptRWJJYe4a99efUc5Xu0ovAVGRmwbl29T5sO6eCGBTfw6161fPHdunRsDsmlXlx2crHx8EbWZqytfD95QCrlNgd7+w1T2atlZd4V4NdflY/CDTKLnWvvMd6zsmqiqtPfxWk9kskxl6uy467lFbuHmkB+/DH8+ecJH746az4brFdxxHLEM/K4SetIpVAzzZkMaBsLwF8xTkeyryOfgoLgpptqL5kvBG9N7c0dIwpBCHLMalkxKcr7bVxvPvlmnjhNWYphL73Iq/+4lf/9dcjr120oWlH4ihkzYPBg5ZCsg4KyAt5d+y5bc7YipeSr1QcZ3CHOJ7Xqb/rhJv79678r3w9qH0fbuDDmJPVR1T+XLvWuABER0K2bW0N35agn+vY+VhSJ4YmYDKZKiwJgbI8khEAtPwGce67yszSWnBxVz+kEy1zvyy1hV14mwofZ6y6qFpqMCQ+iV+tovkh3IGNjlVXhSxYtUvlAdZCbEEpigiqvH/L9fH786BZa5dd9jKew2q3YHXaChp/MKRePZ9nOHP7O8EEpmAagFYWvWLVKNbWpI38CjhYELCwO5bL3VrEnp8SrTuyquIqUuRBCMHlAKh8FdUBGRMD8+d65sNUKp52m+oa7QXaRhZU7jAxNuJpuCZ29I1MtVM/OBkiIDGFAu1gW73Aqivh45TxtbP+FV19VVtz995/Q4R//vg9EEXGh8T7LXnfhWqJzzdMtp3VlV04JOV1O8q1FYbXCeefV69+pLDH+wAOc+tiddM89SFxn71SNrcr3O78n5MkQNmVtgt27uWr3b8SaJO/+ttfr124IOjy2Cr/s+YX7Fx3/o/xk8if0TenL3O1zeXzZ48ftn3XRLLrEd2HG5hm88McLx+1fcPkCWq9axYfnpvLWe8d3GFsydQnRIdG8/ufrvPbnGwC8sSiL9hGdeWxSby4c1LC2hSdKalQqc3fMZfB7g7m8z+Xcc8o9TOwTz39X3kOfW0MJDZ8P76llkOkDp3Pj0BvJL8vnjM+PDzm87eTbmDpgKocKD3HezPOO23//qfdzce+L2ZG7gys+Ohu67YVtByFD9cV+fOzjnNP9HNZnrmf6/GPXljOPWEBeydeXv0ZK5PG1srzNU6c9RZuoNmSaMzl3xrkAZJeWk2220P/taO4fMZzLP89l9+/zuXTb8d+XR8Y8wqQek9iUtYmr5h7ffv7/xv8f4xOG8sc3r3DLv2Ng+ZXO2s2KN856gxHtRrB432Lu/eX4Yn8fTfqIDtEn8dHa2ZQHLadDxPHdAb1NckQy/+r3LzrHdebAkQM8tPICCiKLGXJaPklBEt4bzIMjH+TCXheyLWcbV353fIHDJ8c9yVndzmJtxlquW3B8h7mXJ77M6A6jWX5gOXcsvOO4/e+c8w5D/0rjp1bF/Kfjz1Dtt/f5+Z/TK6kX3277li3ZW+iT3AfCwgguK2Vvcgc6R3i/4m1ieCISyaWzLyXKXA4HDjJpyCd8sUnQs/MWPtz4ynHHzL1sLm2j2/Lx+o95Y/Ubx+xbefVKQkyeXzLzq6IQQpwJvAoYgQ+klM9W2x+C6qo3GMgDLpVS7veWPGFBYZUmc1Vc5ZnDg8Jr3G8yqGmMCI6oef/hbEhLI7LzaaRGHb/0ZBDKsAsyhFNUnEi0I5UHx0/iupF9jim25m2u6HsFBwoP4JAOYkJUcbseKTHEBCeTH5rCkI7xlWNdBQsNwlDjZ3Y5400GU437Xb2bg4xBpB4ugYoQSO0Jzu59YUEqXyTYGHzM8RarnR0leZzfsx0dEnyvJACm9FdRWdkl2ZWyRZms5BXlYyKayAEnA28T9PsqUjvU/tmrfzYXYaYwePttQo+UkNphFEQdW2jQdSMINYXWeHywMZgv/zyI1RbE8E6ncGnfyY36vCeCyWDis/OVvym9KJ3U6FRCDBVsqIghPiWKdlHhx34H6viO1DZPoSZV7iLEFFLj/hBTCLz5JmFxSaS2OfrdchFkOPq7nthlIlP6TYGzRnLwgy/4u2MffGGr9m/Vnyn9p5Bflg+2LDAfZHLbcGbsFyzfYa7zfhMVEnXcfuGtEGQppV9eKOWwB+gMBAMbgV7VxtwEvOP8+zJgZn3nHTx4sAw4Zs+WEqRctarWIeVWu7zyg1Wy8wPfy8Xbs3woXP28vXS37HDfAvnMd+tl6fadnj354cNSmkxS3nuvW8Pv/2aT7PbgDzKtoNSzcjQSh8MhT37qF3ntp6ulw+GQsndvKc8448RP+NJLUl522QkdarHa5JAnf5FXflD7980fOBwOeck7K+Xgx3+WJcU++P/btk397p56yq3h+cXl8tOV++SQR36U0z70w9ytWqXkXbBA3vv1Btn9Pz/IHLPFZ5cH1sha7qv+tChOBnZLKfcCCCG+AiYDW6uMmQw86vx7NvCGEEI4P5RHsdkdHMgvrX/gCSAGnULorLmUtekKOUdjyNW3QuKQ8M5ve1i+K5fnLurHuB6+zaKtj2mndORgfiljbv8n+xwVZCz8jU5Jnnmaj3nnQxJtNg5OvgRrTs3x9VJKbA5JfnEFX685xBXD2nstQ/1EEUJwVp/WfLJyP+NeWMp/z/4XvVpFUlbLZ6qXK69VLzePd32XbA7Jsp055JjLeeFi7/RROFGEEPz7jG606d+DJX9NJv8/j9A1KZKkqBCv5OJFz/mBhJAQDk6+DLtzHtWdQyIl2KVkf24JW9KL2JReyB97crHaJT1bRTFtpG99XwCkOAMzsrK47twxfL02jQkvL+OMk1KY2CfFLQs6xGSgbZznl8z8qSjaAFXjwNKA6nUbKsdIKW1CiEIgAcj1tDCFZVZOf/E3T5+2CkZYu6rOEXef0Z1LhgRIu8gqhAYZefr8vhxc8Q/av/Q0U9/6mZzI+PoPdIOxe6yMGfwPHpufBdTf4Ss0yMDN4wKzZfsDZ/ekT5sYZq89xPS8PpANNPA7lVqUzYCMnfzQ49RGZTL3SIlidDffVYx1l8GdE8lMSqbdupXc/N1mL2dr9yDh2g/J+3wbUHu9MqNB0DUpkikjOnLhoLb0So2udaxXae30JR06RNfkSD6/ehhfrz3ED5szmbnGvZDZAe1imXOz57Pwm4UzWwhxHXAdQPsT7G8bEWLi1csGeFAqhbDZ6PbRG2SMP4fizseHfhoNAoMQxIUHM7yzZ26+3qL91Evhpaf5OCmLPZNP89BZBwDTebWOEUIIggwCo0HQo1UUKdGBWYo5xGTkosFtuWhwWw7ll7JpexqdZnzEoX9cjCW5Vf0nAAbffzOtl/7E5JsucvsYF0aDwOT8PvVtG+O99epG0vqeW2l9882sGR3M1m4DKSj1fCaysFqRQTVXWhZCIFA6qm1cOD1bRREa5NuosBoJCYE1a1R0JDCyWyIjuyVSbrPz1758tzK248K91M+itjUpb7+AEcDCKu8fAB6oNmYhMML5twllSYi6zhtwPop169SqwIwZ/pak8TgcUrZrJ+XkyZ45388/S5mR4ZlzBSJ79ij/y623ujd+xQr1XXnoIe/K5W9KS6VMSZFywgTvnN/hkHLAACkfeMA752+mUIePwp95FKuBbkKITkKIYJSzel61MfMAVyuvi4DFzg/UdFjlXG6qo6x4k0EI+Mc/VI6ApZHFAktLVZG7GnqGNxs6d4arrlIlPQ7Vs3TgcMDtt0ObNnDffb6Rz1+EhcHdd8PPP8OmTZ4//5o1Klejsx/8DI1l2TLViiDA8JuikFLagFtQVsM2YJaU8m8hxONCiEnOYR8CCUKI3cBdwIllHvmT335Ta48dOvhbEs9w220q07WxLRtnzlQ9Fq47Pj6+WfGf/ygP6lNP1T3unXdg7VrVmKiGPurNjhtugJ9+qrUdcKP45Rf172TfhwU3mu+/Vw9PjU3W9DB+9VFIKX8Afqi27eEqf1uAi30tl8ew2dRT0+TJ/i2x7ElOoDBdjbz7Lpx0Eowa5ZnzBSodOsC116rPO3Fi7X0q2rZVZT/qaP/arIiKUvPhDRYtUuXEk3xbMNIjdOyoWrYePgypx+dQ+AtdwsOb7NmjSgicdZa/JfEsmzfDnXeqz3YirF+vCt1df33zUaB18cwzcOWVdTdjmjSp3oZWzZJHH1X1rDxFWZkqYHn66Z47py9xrTwcOOBfOaqhFYU36dFDNZQ/7zx/S+JZdu+GV15Ry2onwu+/q+UVN/pONAuio+GTT9QTot2ufBCzZ6u+DB99pBSJp6rNNjXS01Uf+RN96KiO1QqPPQaXXuqZ8/karShaKMHBjV/PDzQmToTwcNU/4kS45RZIS4M431Y0DQjWr4cPP1SO/MREuPlmVV7dyw1yApZzzoGiIlixwjPni45WirieVroBi1YULYysLOjfHxYv9rcknic8XHVy++67hj8Jl5erf2NjPS5Wk2DIELX+vHSp6pEwYoRSHC1tycnF+PHqQer77z1zvsWLoaDAM+fyB5GRqndNgEUDakXhLRYuVKF/zfWGeMEFShn+8Yf7x0gJp5yi/BstGZMJxoxRfbAXL1YOzJZKZKSaC08oisJCOOMMePnlxp/Ln7RuHXAWZmBJ05z48UfVe2LAAH9L4h3OOUdF6qSnu3/MokWqy1+fPt6TS9P0uOIKGDlSRfs0ht9+U2Glp3mqaoCfmD0bHn64/nE+RDS1/LX6GDJkiFyzZo1/hbDblZI491z49FP/yuJNpGzYksn48bBtG+zdq8oVaDSe5I474L331NJTU/5+3XEHfPABmM0+XZIUQqyVUg6paZ+2KLzBX3+plqfNLSy2OkKoXJGs+ov5sWaNsijuvLNp/4g13kFK9QDRGBYtUpZJU/9+degAJSX1tk32JVpReIOQELjoIpgwwd+SeJ8JE1QET32W6UsvQUxM88/E1pwY//0v9OqlSrucCFlZsGVL082fqEoARj5pRVGVoiLYtavx5xk0CL7+WvVObu5ceiksXw7fflv3uFdfVWuv0X4q4awJbMaOVRFx9UUJZmfDvHnHP5gkJ8PWrc0jN0crigBGShg3TmXQNsZvs3s37NvnObkCnWuuUfV67r239kKBUqpyCuPH+1Y2TdNh9GiVhPnDD3WPO3xYlcRZtuzY7UKokjCung5NmQ4dVGRcXp6/JalEKwoXQqiSEn/9pUJbT5RHHlGx8o2N4GgqmExqWWnfvpqrXn7yCZx6qko002hqIyQEzjwTvvxSlYipTlmZ+k1166ash2efPbpv1Sq45BKVxNkcSEhQD13Tp/tbkkq0oqjKtGnQvr0qAXAiVkV+vspWvuKK5peNXRfjx6sIr7lzj86blPD006rMdkQEdA3MrnSaAOKll1RexavV2ljZbGqJc/JkpVBuv11Vnt24Ue1/+20Vjh4T43uZvYEQYAyARkpVqa1RRVN9Nbpx0TvvqOYxCxc2/NhXX1XHbtjQOBmaIrm5Ulqt6u+vvpJy/Hg1F//8p5Tl5f6VTdN02LdPyooK9XdenpQvvyxlz57qu/Tmm2p7fr6UkZFSXnGFGhMSIuWNN/pLYu/w+utS3nSTTy9JgDYuCkyuugratTta095dpIT331fLTv0Dq6m9T0hIUMtQoAIC1q9XNXc++6xlWVeaxtGxIwQFQWamsu7vvFNVN5g5U5U8AVUj7PrrYc4cVZyyvFz1t2hO/P03fPWVv6WoRCfc1URenrrxNYQ9e5Qz7Y03dAioRtNYFi+GBQtg6tSaH7xycpTPYtw4FSjx++++l9GbPPssPPCASrqLjPTJJetKuPNr46KAxaUkLBYIDXXvmC5dVDmL8HDvyaXRtBROO63uUhxJSer3ecklMHiw7+TyFVVDZHv39q8saGd27bz1luofUFJS/1iL5WgIaEtoY6nRBAKhofDkk7V3DWzKBFguhVYUtdGrl6oZ405Vy5tuUqF9zWwZT6PR+ImOHZWyCJAwe60oamPUKGjVSjnR6mLnTlX4r1evlttTQKPReJbUVNi/P2C6Y2pFURtGo6rX9MMPyqFUG489pkzg++/3nWwajaZl4HCoPBI/oxVFXVx6qfI/zJ9f8/6//4YZM+DWWyElxbeyaTSa5s3evSpEuL46aj5AK4q6OOUUeO459W9NfPKJCl0LsLaFGo2mGdChg7IoAiCfQofH1oXBULcSePRRVcysoTkXGo1GUx9Gowr/fecd1ebVjyVKtEVRH3Y7fPedqluUlqY0/HPPKb9FRAT84x/+llCj0TRXLrtMZZ7PmeNXMbSiqA+DQZUJ+M9/VGmP9u1VaYpZs/wtmUajae4MG6ZCZf28/OSXpSchRDwwE+gI7AcukVIWVBszAHgbiAbswFNSynpiVb2AELB0qXJc//STKi1w551w9dU+F0Wj0bQwhFArGH5u+OWXWk9CiOeAfCnls0KI+4E4KeV91cZ0B6SUcpcQIhVYC5wkpTxS17k9UutJo9FoWhh11Xry19LTZOBT59+fAudVHyCl3Cml3OX8OwPIBpJ8JaBGo9EEDBkZcNddqoGTH/CXokiRUmY6/z4M1JmEIIQ4GQgG9tSy/zohxBohxJqcnBzPSqrRaDT+ZtcuePll9fIDXlt6EkL8CrSqYdd/gE+llLFVxhZIKeNqOU9rYCkwVUq5qr7r6qUnjUbTLLngAtUnZ9cuVV7Iw/hl6UlKOV5K2aeG11wgy6kAXIoguxbBo4Hvgf+4oyQ0Go2m2fJ//6cqRTz8sM8v7a+lp3nAVOffU4G51QcIIYKB74DPpJSzfSibRqPRBB7dusEtt8CHH8KmTT69tL8ys58FZgkhrgEOAJcACCGGADdIKac7t40GEoQQ05zHTZNSbvC9uBqNRhMAPPSQcmgnJvr0sroVqkaj0TRF8vPh0KGaW8WeALoVqkaj0TQ3br5Z9RW/915VByokBK65BoKCPH4prSg0Go2mKfLCCyoC6pFHjm676iqvXEorCo1Go2mKtGkDq1crn0V5uYqICg72yqW0otBoNJqmihAQHq5eXkRXj9VoNBpNnWhFodFoNJo60YpCo9FoNHWiFYVGo9Fo6kQrCo1Go9HUiVYUGo1Go6mTZlfCQwiRg6of1RJIBHL9LUSAo+fIPfQ8uUdznqcOUsoam8M1O0XRkhBCrKmtNotGoefIPfQ8uUdLnSe99KTRaDSaOtGKQqPRaDR1ohVF0+Y9fwvQBNBz5B56ntyjRc6T9lFoNBqNpk60RaHRaDSaOtGKQqPRaDR1ohVFgCOEEP6WIdDRc6TxJPr7dDxaUQQ4UjuR3EH/sDUeQ//mjkcrigBFCDFVCPGTEOLfQoih/pYnEBFCXCmEWAs8L4S4yN/yBCpCiOuEEE8IIcL8LUsgI4T4lxBiiRDieSHExf6WJ5DQiiIAEUJMAqYDTwPZwO1CiNOd+/T/GSCE6A3cAdwO/Iaao4ud+4x+FC1gEEKYhBA3Ag8CFwMtLqPYHYQQEUKI14CrgUeAXcClQojB/pUscNA3nQCh2s3tbOAzKeUy4FvACtwHIKV0+EG8QCQFWCalXCGlnAc8D7wIIKW0+1WyAEFKaQPWAT2Bd4GrhBAJ/pUq8JBSlgAbgMnO39w8oAAI8adcgYRWFAGAEOJR4LUqyyfLgOsApJRFQA4QLYS4zjm+xa3JCyFuEUL0rbLJCoxyvXEqi51CiMec41vkd7uGedogpbQAbwNtgfEtdW6qUsM8fSWlLBJCGKWUh4GuaN9XJS3+C+NvnEriZOAX4FYhxJ2oJ5osIcT/hBDrABvwCtBJCGFoSc42IUQHIcRvwH+Bl1zbpZTLgXIhxH+rDL8HOF0IEdnSLK865qnc+Z2xAB8DVwAd/SOl/6ltnoAyUNaoEKIVUA5s8oOIAYlWFH5ECBEEjATullLOAR4G2qFM4HOBx4BbpZQPAlGATUrpaGEWRT7wJdANcAghplXZdzPKN9HK+T4P54+7hc0R1DJPzidkB4CUcgZQBIwRQgwVQvzTX8L6kerzNNW5veq9MAUok1KahRB9hRBn+VrIQEMrCh9R/cblfMqzAtuAy52bVwKrUD/kHlLKHVLK353HjgT2QvMN36thjoSU0gx87vz3HeAWp4JFSrkR+BQV9XQZ6imxNepH3iznCBo2T84nZFFluekz4C2U7yvUp4L7GDfn6dYq8+TyE/YCgoUQD6GssBYfLaYVhZ+osjTyPdBeCNHTqTg2A0eAVgBCiPOBv1Br8l/6QVS/4brZSynLnJvmAjtRlpaLB4AvUAEA5cA/W5ozu755kgqHEKIL8CRqvnpIKT/0h7z+wo15cn1vTgHGohTpaCnlt76VNPDQRQG9jBDiTOBGlAKYI6Vc49xudD7FtEOFwgZLKR9w7lsAfCKlnC2EOAn1hLzfP5/A+9QxRwac97kqYwejKniORSnTMillmvOp0Opz4X1II+YpBTCjlp1ipZSZPhbdpzRinloD+4FTgSwp5VbfSh64aIvCCzhN/VAhxCeo5ZAPgUjgGiFEgtMEdj29mIGfgd5CiNuc4YsmoBhASrmtOSoJN+fIIaWUQojYKstNa4GNqLXmT1BzRXNVEh6ap0+BMCllWXNVEh78PrWWUi7RSuJYtKLwAk5T34Iybcc4Qze/RVlwec4vqxBCvIWKvNgBPAEMBJYCq6SUP/lJfJ/QgDl6E3gVaAMgVFTYGcCDUspTm6MSrYqeJ/fw0DydIqU84K/PEMjopScPIoS4DUgF1kkpZ1XZfjHKgbgFWAH8iAp5vQm4U0pZ4BxnBIKcX/hmiQfm6HRgvZQy39ey+xI9T+6h58lHSCn1q5EvVGLOncDvwEWoSKZpQLJz/1igL2qZ5EbgAyClyvFGf3+GJjBHJn9/Bj1PgfPS8+Tbl+nE1IumKlJKKYQYB/xXSrlECFEMTAAk8KmUcqlrrBBiMzAaKHGG71X1VzRbPDBHNn/I7Wv0PLmHniffon0UjaRKfPoanCUlpPIv7AJOEkJ0r3bIBKAUZ6y/bAEZxHqO3EPPk3voefI9WlE0ECFEjPNfIxyTD7EbiBJH68f8BsSgajQFC1XCeBOqfMIDzdmK0HPkHnqe3EPPk//RisINhBAGIUS0UPkNr8HR5BxxNJvzL5SzbIIQwiRVeF0bYLCUsgI4BNwopZwipcz2/afwLnqO3EPPk3voeQostKJwA+cTjBkIBtoIIS4FVe/f9eWVUu5GmcJdgPudh5YDB5z7l0opf/e17L5Cz5F76HlyDz1PgYVWFO7TE1Xu+1Xgn0KIKJdDTKjuYR8Ca1FPPycL1XktH5VM11LQc+Qeep7cQ89TgKCjnmpACDEcyJdS7hRCCCmlRK2HVqAK8+0Dpgkh5qGqvXYBHpbOpCYhxBWo8Lsj/pDfF+g5cg89T+6h5ymw0RZFFYRK7f8e1RviEiFEhPMLC6qNZJGU8m/gb1RJ8DdQjWGukFLudkVjSCmLm+sXVs+Re+h5cg89T00DrSiOJQJYCNzq/Ht0lX0HUREWM4F/o1pM7pZSFkNl2fCWEHan58g99Dy5h56nJkCLVxRCiClCiDFCiGgpZTqqkuQswAIME0KkOofGAUnAYVRNphuAHkJVd6U5f2H1HLmHnif30PPU9GiRtZ6EEAJVovp/gAPYg3qauV1KmesccypwCbBGSvm5c1tilf2RqNLgzbJGjJ4j99Dz5B56npo2Lc6iEKoPhES1Fk2XUp6OqgWTj3qyAcAZVrcf9QQT41w7zRVCGJ0mb3Fz/cLqOXIPPU/uoeep6dNiFIXzy/Y08LQQYgzQA3DFY9uB24FTnPtcvI+qaf8LsFcIkSqltDdXk1fPkXvoeXIPPU/NhxahKJxfxLWoNc/dqN4PVmCcEOJkqFzvfNT5cnEOqizxRqCvlDLDd1L7Fj1H7qHnyT30PDUvWkoehQN4scq650CgEyrc7m1gsDPMbg5wmhCiozM+2wKMl1Iu84vUvkXPkXvoeXIPPU/NiBZhUaCebGaJozVifgfaSyk/AYxCiFudTzdtAbvzC4uUcm4L+sLqOXIPPU/uoeepGdEiFIWUslRKWS6PVo88A1UaAOAqVGniBcAMVKy2K0qjxaDnyD30PLmHnqfmRUtZegIqq05KIAWY59xsBh4E+gD7nHHdyJYYN4yeI3fR8+Qeep6aBy3CoqiCAwgCcoF+zieahwCHlHKF6wvbwtFz5B56ntxDz1MzoMUl3AlVfGyl8/WxlPJDP4sUcOg5cg89T+6h56np0xIVRVvgX8BLUspyf8sTiOg5cg89T+6h56np0+IUhUaj0WgaRkvzUWg0Go2mgWhFodFoNJo60YpCo9FoNHWiFYVGo9Fo6kQrCo1Go9HUiVYUGk0NCCHsQogNQoi/hRAbhRB3O4vY1XVMRyHEFW6c+5hxQoghQojXPCG3RuMNtKLQaGqmTEo5QErZG1Wn6CzgkXqO6QjUqyiqj5NSrpFS3naCcmo0XkfnUWg0NSCEKJZSRlZ53xlYDSQCHYDPUa08AW6RUq4UQqwCTgL2AZ8CrwHPAmOBEOBNKeW7NYxbD9wjpTxXCPEoqhx3Z6A9cCcwHKWo0oF/SCmtQojBwEuoJj+5wDQpZaaXpkPTwtEWhUbjBlLKvYARSAaygTOklIOAS1EKAeB+YLnTEnkZuAYolFIOBYYC1wohOtUwrjpdgNOAScAXwBIpZV+gDDhHCBEEvA5cJKUcDHwEPOWVD67R0MKqx2o0HiIIeEMIMQDV2rN7LeMmoArhXeR8HwN0AyrqOf+PTqthM0o5/eTcvhm1bNUDVXn1F2dlbiOgrQmN19CKQqNxA+fSkx1lTTwCZAH9UVa5pbbDgFullAurnWtsPZcrB9UqVAhhrVJ+24H6zQrgbynliIZ/Eo2m4eilJ42mHoQQScA7wBvOm3YMkOns0PYv1BM9qD4LUVUOXQjc6FwqQgjRXQgRUcO4hrIDSBJCjHCeN0gI0bsR59No6kRbFBpNzYQJITaglplsKOf1S859bwHfCCGmoJaFSpzbNwF2IcRG4BPgVdRS0Tpn97Yc4Lwaxq1viGBSygrnctZrQogY1O/4FeDvhn9MjaZ+dNSTRqPRaOpELz1pNBqNpk60otBoNBpNnWhFodFoNJo60YpCo9FoNHWiFYVGo9Fo6kQrCo1Go9HUiVYUGo1Go6mT/weo5j4HWuPVqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "plt.plot(time_march,power_march, label=\"Actual Power\")\n",
    "plt.plot(time_march,power_march_pred_reg, \"r--\", label=\"Predicted Power Linear Reg\")\n",
    "plt.plot(time_march,power_march_pred_randomforest, \"g--\", label=\"Predicted Power forest\")\n",
    "plt.title(\"Solar Power Prediction\")\n",
    "plt.xlabel(\"Datetime\")\n",
    "plt.ylabel(\"Solar Power\")\n",
    "plt.gca().xaxis.set_major_locator(mdate.DayLocator())\n",
    "plt.legend()\n",
    "fig.autofmt_xdate()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Random Forest Quantile Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tiago/anaconda3/envs/ppe/lib/python3.7/site-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestQuantileRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                              max_features='auto', max_leaf_nodes=None,\n",
       "                              min_samples_leaf=1, min_samples_split=10,\n",
       "                              min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                              n_jobs=1, oob_score=False, random_state=0,\n",
       "                              verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skgarden import RandomForestQuantileRegressor\n",
    "\n",
    "rfqr = RandomForestQuantileRegressor(\n",
    "    random_state=0, min_samples_split=10, n_estimators=1000)\n",
    "\n",
    "rfqr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score :  0.9735483389603301\n",
      "Test score : 0.9564222482480492\n"
     ]
    }
   ],
   "source": [
    "train_score = rfqr.score(X_train, y_train)\n",
    "test_score = rfqr.score(X_test, y_test)\n",
    "\n",
    "print(\"Train score : \", train_score)\n",
    "print(\"Test score :\", test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prédictions sur 4 jours du mois de Mars 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "march_pred = march.drop(\"POWER\", axis=1)\n",
    "power_march_pred_quantrandomforest = rfqr.predict(march_pred)\n",
    "power_march_pred_randomforest = clf_r3.predict(march_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "plt.plot(time_march,power_march, label=\"Actual Power\")\n",
    "plt.plot(time_march,power_march_pred_quantrandomforest, \"r--\", label=\"Predicted Power Linear Reg\")\n",
    "plt.gca().xaxis.set_major_locator(mdate.DayLocator())\n",
    "plt.title(\"Solar Power Prediction\")\n",
    "plt.xlabel(\"Datetime\")\n",
    "plt.ylabel(\"Solar Power\")\n",
    "plt.legend()\n",
    "fig.autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Validation croisée du Random Forest Quantile Regressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([X_train, X_test])\n",
    "y = pd.concat([y_train, y_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.90684175 0.91654037 0.90322519 0.89087883 0.94195493]\n",
      "scores mean = 0.9118882150131642 scores std = 0.01713218538535058\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(rfqr, X, y, cv=5)\n",
    "\n",
    "print (scores)\n",
    "print ('scores mean =', scores.mean(), 'scores std =', scores.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Transformation du problème"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L’objectif était de transformer un algorithme de régression en un algorithme de classification multi-classes selon le modèle présenté dans le papier scientifique. Pour réaliser cette transformation, nous avons partitionné nos données afin d’obtenir **m** classes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans la fonction ci-dessous, on va utilisé un pas(step) afin de partitionné les données en  500 intervalles équivalents. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_cut_points = 500\n",
    "def split_by_bins(m, df_po):\n",
    "    step = 1/m\n",
    "    bins = list(np.arange(0,df_po.max(),step))\n",
    "    tmp = pd.cut(df_po,bins,include_lowest=True)\n",
    "    categories = tmp.cat.categories\n",
    "    tmp = tmp.cat.codes\n",
    "    tmp = np.where(tmp==-1, m, tmp) \n",
    "    return tmp, categories\n",
    "y_train, categ_train = split_by_bins(m_cut_points,df_2013_z1_shuffled[\"POWER\"])\n",
    "y_test, categ_test = split_by_bins(m_cut_points,df_test_shuffled[\"POWER\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualisation des catégories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IntervalIndex([(-0.001, 0.002], (0.002, 0.004], (0.004, 0.006], (0.006, 0.008], (0.008, 0.01] ... (0.898, 0.9], (0.9, 0.902], (0.902, 0.904], (0.904, 0.906], (0.906, 0.908]],\n",
       "              closed='right',\n",
       "              dtype='interval[float64]')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categ_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualisation de la répartition des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class=60, n=6 (0.097%)\n",
      "Class=383, n=10 (0.162%)\n",
      "Class=422, n=6 (0.097%)\n",
      "Class=0, n=1674 (27.122%)\n",
      "Class=132, n=10 (0.162%)\n",
      "Class=315, n=10 (0.162%)\n",
      "Class=114, n=11 (0.178%)\n",
      "Class=323, n=11 (0.178%)\n",
      "Class=307, n=11 (0.178%)\n",
      "Class=71, n=10 (0.162%)\n",
      "Class=12, n=38 (0.616%)\n",
      "Class=61, n=19 (0.308%)\n",
      "Class=213, n=6 (0.097%)\n",
      "Class=301, n=7 (0.113%)\n",
      "Class=251, n=4 (0.065%)\n",
      "Class=289, n=11 (0.178%)\n",
      "Class=9, n=53 (0.859%)\n",
      "Class=72, n=10 (0.162%)\n",
      "Class=397, n=6 (0.097%)\n",
      "Class=217, n=4 (0.065%)\n",
      "Class=1, n=110 (1.782%)\n",
      "Class=414, n=7 (0.113%)\n",
      "Class=2, n=64 (1.037%)\n",
      "Class=360, n=11 (0.178%)\n",
      "Class=373, n=4 (0.065%)\n",
      "Class=260, n=4 (0.065%)\n",
      "Class=91, n=14 (0.227%)\n",
      "Class=359, n=15 (0.243%)\n",
      "Class=37, n=19 (0.308%)\n",
      "Class=262, n=9 (0.146%)\n",
      "Class=70, n=13 (0.211%)\n",
      "Class=130, n=9 (0.146%)\n",
      "Class=186, n=11 (0.178%)\n",
      "Class=6, n=47 (0.762%)\n",
      "Class=264, n=9 (0.146%)\n",
      "Class=112, n=11 (0.178%)\n",
      "Class=349, n=8 (0.130%)\n",
      "Class=238, n=2 (0.032%)\n",
      "Class=5, n=38 (0.616%)\n",
      "Class=255, n=9 (0.146%)\n",
      "Class=269, n=8 (0.130%)\n",
      "Class=356, n=12 (0.194%)\n",
      "Class=401, n=13 (0.211%)\n",
      "Class=86, n=15 (0.243%)\n",
      "Class=73, n=6 (0.097%)\n",
      "Class=24, n=27 (0.437%)\n",
      "Class=111, n=7 (0.113%)\n",
      "Class=319, n=13 (0.211%)\n",
      "Class=150, n=1 (0.016%)\n",
      "Class=361, n=12 (0.194%)\n",
      "Class=221, n=6 (0.097%)\n",
      "Class=193, n=10 (0.162%)\n",
      "Class=14, n=38 (0.616%)\n",
      "Class=96, n=6 (0.097%)\n",
      "Class=16, n=29 (0.470%)\n",
      "Class=117, n=12 (0.194%)\n",
      "Class=232, n=15 (0.243%)\n",
      "Class=49, n=17 (0.275%)\n",
      "Class=285, n=6 (0.097%)\n",
      "Class=137, n=7 (0.113%)\n",
      "Class=57, n=16 (0.259%)\n",
      "Class=377, n=14 (0.227%)\n",
      "Class=400, n=9 (0.146%)\n",
      "Class=131, n=4 (0.065%)\n",
      "Class=151, n=5 (0.081%)\n",
      "Class=153, n=8 (0.130%)\n",
      "Class=105, n=9 (0.146%)\n",
      "Class=336, n=7 (0.113%)\n",
      "Class=245, n=10 (0.162%)\n",
      "Class=219, n=5 (0.081%)\n",
      "Class=128, n=9 (0.146%)\n",
      "Class=275, n=8 (0.130%)\n",
      "Class=121, n=10 (0.162%)\n",
      "Class=256, n=6 (0.097%)\n",
      "Class=169, n=7 (0.113%)\n",
      "Class=322, n=17 (0.275%)\n",
      "Class=189, n=6 (0.097%)\n",
      "Class=17, n=44 (0.713%)\n",
      "Class=358, n=13 (0.211%)\n",
      "Class=328, n=7 (0.113%)\n",
      "Class=177, n=4 (0.065%)\n",
      "Class=173, n=5 (0.081%)\n",
      "Class=274, n=4 (0.065%)\n",
      "Class=282, n=8 (0.130%)\n",
      "Class=59, n=9 (0.146%)\n",
      "Class=21, n=32 (0.518%)\n",
      "Class=8, n=34 (0.551%)\n",
      "Class=204, n=7 (0.113%)\n",
      "Class=118, n=11 (0.178%)\n",
      "Class=195, n=4 (0.065%)\n",
      "Class=351, n=10 (0.162%)\n",
      "Class=156, n=6 (0.097%)\n",
      "Class=297, n=12 (0.194%)\n",
      "Class=369, n=13 (0.211%)\n",
      "Class=222, n=7 (0.113%)\n",
      "Class=408, n=11 (0.178%)\n",
      "Class=396, n=12 (0.194%)\n",
      "Class=418, n=6 (0.097%)\n",
      "Class=203, n=3 (0.049%)\n",
      "Class=56, n=7 (0.113%)\n",
      "Class=233, n=9 (0.146%)\n",
      "Class=52, n=13 (0.211%)\n",
      "Class=63, n=7 (0.113%)\n",
      "Class=229, n=9 (0.146%)\n",
      "Class=35, n=17 (0.275%)\n",
      "Class=25, n=23 (0.373%)\n",
      "Class=350, n=9 (0.146%)\n",
      "Class=354, n=9 (0.146%)\n",
      "Class=4, n=42 (0.680%)\n",
      "Class=23, n=26 (0.421%)\n",
      "Class=22, n=25 (0.405%)\n",
      "Class=387, n=15 (0.243%)\n",
      "Class=420, n=8 (0.130%)\n",
      "Class=140, n=5 (0.081%)\n",
      "Class=318, n=15 (0.243%)\n",
      "Class=263, n=10 (0.162%)\n",
      "Class=145, n=3 (0.049%)\n",
      "Class=99, n=10 (0.162%)\n",
      "Class=13, n=34 (0.551%)\n",
      "Class=120, n=7 (0.113%)\n",
      "Class=157, n=6 (0.097%)\n",
      "Class=28, n=16 (0.259%)\n",
      "Class=89, n=17 (0.275%)\n",
      "Class=3, n=50 (0.810%)\n",
      "Class=144, n=16 (0.259%)\n",
      "Class=288, n=11 (0.178%)\n",
      "Class=209, n=12 (0.194%)\n",
      "Class=18, n=36 (0.583%)\n",
      "Class=15, n=41 (0.664%)\n",
      "Class=19, n=27 (0.437%)\n",
      "Class=404, n=9 (0.146%)\n",
      "Class=110, n=13 (0.211%)\n",
      "Class=398, n=5 (0.081%)\n",
      "Class=309, n=11 (0.178%)\n",
      "Class=167, n=4 (0.065%)\n",
      "Class=95, n=10 (0.162%)\n",
      "Class=257, n=6 (0.097%)\n",
      "Class=304, n=6 (0.097%)\n",
      "Class=135, n=8 (0.130%)\n",
      "Class=237, n=7 (0.113%)\n",
      "Class=206, n=4 (0.065%)\n",
      "Class=119, n=12 (0.194%)\n",
      "Class=278, n=3 (0.049%)\n",
      "Class=378, n=13 (0.211%)\n",
      "Class=344, n=8 (0.130%)\n",
      "Class=65, n=11 (0.178%)\n",
      "Class=97, n=12 (0.194%)\n",
      "Class=292, n=15 (0.243%)\n",
      "Class=82, n=9 (0.146%)\n",
      "Class=308, n=9 (0.146%)\n",
      "Class=39, n=13 (0.211%)\n",
      "Class=423, n=4 (0.065%)\n",
      "Class=211, n=9 (0.146%)\n",
      "Class=50, n=12 (0.194%)\n",
      "Class=148, n=6 (0.097%)\n",
      "Class=313, n=5 (0.081%)\n",
      "Class=10, n=36 (0.583%)\n",
      "Class=370, n=17 (0.275%)\n",
      "Class=106, n=8 (0.130%)\n",
      "Class=7, n=48 (0.778%)\n",
      "Class=88, n=14 (0.227%)\n",
      "Class=155, n=9 (0.146%)\n",
      "Class=40, n=14 (0.227%)\n",
      "Class=116, n=12 (0.194%)\n",
      "Class=374, n=9 (0.146%)\n",
      "Class=124, n=16 (0.259%)\n",
      "Class=81, n=15 (0.243%)\n",
      "Class=45, n=9 (0.146%)\n",
      "Class=395, n=8 (0.130%)\n",
      "Class=164, n=5 (0.081%)\n",
      "Class=380, n=9 (0.146%)\n",
      "Class=347, n=12 (0.194%)\n",
      "Class=261, n=5 (0.081%)\n",
      "Class=393, n=14 (0.227%)\n",
      "Class=107, n=10 (0.162%)\n",
      "Class=154, n=5 (0.081%)\n",
      "Class=252, n=4 (0.065%)\n",
      "Class=160, n=8 (0.130%)\n",
      "Class=366, n=11 (0.178%)\n",
      "Class=102, n=17 (0.275%)\n",
      "Class=44, n=25 (0.405%)\n",
      "Class=390, n=14 (0.227%)\n",
      "Class=165, n=9 (0.146%)\n",
      "Class=75, n=12 (0.194%)\n",
      "Class=332, n=8 (0.130%)\n",
      "Class=27, n=23 (0.373%)\n",
      "Class=48, n=12 (0.194%)\n",
      "Class=248, n=8 (0.130%)\n",
      "Class=277, n=6 (0.097%)\n",
      "Class=294, n=10 (0.162%)\n",
      "Class=38, n=17 (0.275%)\n",
      "Class=67, n=8 (0.130%)\n",
      "Class=451, n=1 (0.016%)\n",
      "Class=30, n=8 (0.130%)\n",
      "Class=41, n=14 (0.227%)\n",
      "Class=77, n=7 (0.113%)\n",
      "Class=170, n=4 (0.065%)\n",
      "Class=55, n=11 (0.178%)\n",
      "Class=363, n=8 (0.130%)\n",
      "Class=338, n=9 (0.146%)\n",
      "Class=345, n=12 (0.194%)\n",
      "Class=379, n=14 (0.227%)\n",
      "Class=391, n=10 (0.162%)\n",
      "Class=196, n=6 (0.097%)\n",
      "Class=192, n=4 (0.065%)\n",
      "Class=208, n=6 (0.097%)\n",
      "Class=329, n=8 (0.130%)\n",
      "Class=47, n=11 (0.178%)\n",
      "Class=104, n=11 (0.178%)\n",
      "Class=290, n=5 (0.081%)\n",
      "Class=29, n=18 (0.292%)\n",
      "Class=417, n=5 (0.081%)\n",
      "Class=241, n=6 (0.097%)\n",
      "Class=101, n=9 (0.146%)\n",
      "Class=134, n=5 (0.081%)\n",
      "Class=405, n=8 (0.130%)\n",
      "Class=243, n=8 (0.130%)\n",
      "Class=314, n=9 (0.146%)\n",
      "Class=334, n=12 (0.194%)\n",
      "Class=246, n=9 (0.146%)\n",
      "Class=310, n=13 (0.211%)\n",
      "Class=392, n=12 (0.194%)\n",
      "Class=53, n=10 (0.162%)\n",
      "Class=293, n=9 (0.146%)\n",
      "Class=352, n=7 (0.113%)\n",
      "Class=331, n=8 (0.130%)\n",
      "Class=425, n=4 (0.065%)\n",
      "Class=133, n=6 (0.097%)\n",
      "Class=216, n=7 (0.113%)\n",
      "Class=320, n=15 (0.243%)\n",
      "Class=385, n=14 (0.227%)\n",
      "Class=362, n=12 (0.194%)\n",
      "Class=224, n=2 (0.032%)\n",
      "Class=324, n=7 (0.113%)\n",
      "Class=303, n=12 (0.194%)\n",
      "Class=305, n=7 (0.113%)\n",
      "Class=32, n=14 (0.227%)\n",
      "Class=136, n=7 (0.113%)\n",
      "Class=201, n=5 (0.081%)\n",
      "Class=68, n=11 (0.178%)\n",
      "Class=372, n=16 (0.259%)\n",
      "Class=281, n=5 (0.081%)\n",
      "Class=230, n=8 (0.130%)\n",
      "Class=125, n=5 (0.081%)\n",
      "Class=205, n=5 (0.081%)\n",
      "Class=26, n=28 (0.454%)\n",
      "Class=46, n=14 (0.227%)\n",
      "Class=33, n=22 (0.356%)\n",
      "Class=365, n=12 (0.194%)\n",
      "Class=100, n=11 (0.178%)\n",
      "Class=236, n=6 (0.097%)\n",
      "Class=225, n=9 (0.146%)\n",
      "Class=36, n=16 (0.259%)\n",
      "Class=115, n=12 (0.194%)\n",
      "Class=343, n=6 (0.097%)\n",
      "Class=163, n=8 (0.130%)\n",
      "Class=271, n=7 (0.113%)\n",
      "Class=161, n=3 (0.049%)\n",
      "Class=218, n=10 (0.162%)\n",
      "Class=58, n=13 (0.211%)\n",
      "Class=188, n=8 (0.130%)\n",
      "Class=348, n=9 (0.146%)\n",
      "Class=227, n=10 (0.162%)\n",
      "Class=311, n=9 (0.146%)\n",
      "Class=187, n=8 (0.130%)\n",
      "Class=357, n=9 (0.146%)\n",
      "Class=407, n=4 (0.065%)\n",
      "Class=74, n=11 (0.178%)\n",
      "Class=34, n=15 (0.243%)\n",
      "Class=93, n=10 (0.162%)\n",
      "Class=51, n=8 (0.130%)\n",
      "Class=317, n=8 (0.130%)\n",
      "Class=333, n=6 (0.097%)\n",
      "Class=367, n=9 (0.146%)\n",
      "Class=141, n=6 (0.097%)\n",
      "Class=382, n=8 (0.130%)\n",
      "Class=42, n=13 (0.211%)\n",
      "Class=371, n=16 (0.259%)\n",
      "Class=321, n=12 (0.194%)\n",
      "Class=11, n=43 (0.697%)\n",
      "Class=103, n=9 (0.146%)\n",
      "Class=384, n=9 (0.146%)\n",
      "Class=272, n=8 (0.130%)\n",
      "Class=268, n=3 (0.049%)\n",
      "Class=296, n=10 (0.162%)\n",
      "Class=231, n=6 (0.097%)\n",
      "Class=394, n=9 (0.146%)\n",
      "Class=199, n=8 (0.130%)\n",
      "Class=181, n=5 (0.081%)\n",
      "Class=207, n=7 (0.113%)\n",
      "Class=368, n=10 (0.162%)\n",
      "Class=109, n=13 (0.211%)\n",
      "Class=291, n=5 (0.081%)\n",
      "Class=389, n=10 (0.162%)\n",
      "Class=172, n=1 (0.016%)\n",
      "Class=399, n=12 (0.194%)\n",
      "Class=386, n=9 (0.146%)\n",
      "Class=267, n=10 (0.162%)\n",
      "Class=376, n=11 (0.178%)\n",
      "Class=76, n=12 (0.194%)\n",
      "Class=228, n=11 (0.178%)\n",
      "Class=226, n=9 (0.146%)\n",
      "Class=171, n=3 (0.049%)\n",
      "Class=54, n=12 (0.194%)\n",
      "Class=43, n=18 (0.292%)\n",
      "Class=299, n=5 (0.081%)\n",
      "Class=247, n=6 (0.097%)\n",
      "Class=113, n=6 (0.097%)\n",
      "Class=298, n=10 (0.162%)\n",
      "Class=249, n=7 (0.113%)\n",
      "Class=178, n=6 (0.097%)\n",
      "Class=175, n=3 (0.049%)\n",
      "Class=79, n=10 (0.162%)\n",
      "Class=214, n=7 (0.113%)\n",
      "Class=416, n=5 (0.081%)\n",
      "Class=108, n=9 (0.146%)\n",
      "Class=388, n=15 (0.243%)\n",
      "Class=66, n=8 (0.130%)\n",
      "Class=353, n=5 (0.081%)\n",
      "Class=259, n=8 (0.130%)\n",
      "Class=139, n=7 (0.113%)\n",
      "Class=286, n=3 (0.049%)\n",
      "Class=430, n=2 (0.032%)\n",
      "Class=287, n=5 (0.081%)\n",
      "Class=406, n=8 (0.130%)\n",
      "Class=254, n=12 (0.194%)\n",
      "Class=375, n=10 (0.162%)\n",
      "Class=179, n=6 (0.097%)\n",
      "Class=403, n=7 (0.113%)\n",
      "Class=284, n=7 (0.113%)\n",
      "Class=87, n=9 (0.146%)\n",
      "Class=152, n=4 (0.065%)\n",
      "Class=158, n=5 (0.081%)\n",
      "Class=180, n=8 (0.130%)\n",
      "Class=402, n=5 (0.081%)\n",
      "Class=85, n=8 (0.130%)\n",
      "Class=235, n=11 (0.178%)\n",
      "Class=84, n=9 (0.146%)\n",
      "Class=149, n=13 (0.211%)\n",
      "Class=98, n=3 (0.049%)\n",
      "Class=197, n=3 (0.049%)\n",
      "Class=31, n=18 (0.292%)\n",
      "Class=90, n=8 (0.130%)\n",
      "Class=312, n=16 (0.259%)\n",
      "Class=159, n=2 (0.032%)\n",
      "Class=78, n=3 (0.049%)\n",
      "Class=442, n=1 (0.016%)\n",
      "Class=129, n=13 (0.211%)\n",
      "Class=244, n=9 (0.146%)\n",
      "Class=185, n=3 (0.049%)\n",
      "Class=92, n=10 (0.162%)\n",
      "Class=215, n=5 (0.081%)\n",
      "Class=20, n=25 (0.405%)\n",
      "Class=346, n=6 (0.097%)\n",
      "Class=127, n=8 (0.130%)\n",
      "Class=212, n=11 (0.178%)\n",
      "Class=413, n=5 (0.081%)\n",
      "Class=220, n=5 (0.081%)\n",
      "Class=123, n=7 (0.113%)\n",
      "Class=412, n=1 (0.016%)\n",
      "Class=198, n=7 (0.113%)\n",
      "Class=341, n=6 (0.097%)\n",
      "Class=445, n=3 (0.049%)\n",
      "Class=194, n=8 (0.130%)\n",
      "Class=202, n=5 (0.081%)\n",
      "Class=62, n=6 (0.097%)\n",
      "Class=138, n=6 (0.097%)\n",
      "Class=300, n=9 (0.146%)\n",
      "Class=355, n=9 (0.146%)\n",
      "Class=184, n=8 (0.130%)\n",
      "Class=330, n=7 (0.113%)\n",
      "Class=210, n=10 (0.162%)\n",
      "Class=364, n=12 (0.194%)\n",
      "Class=191, n=5 (0.081%)\n",
      "Class=410, n=10 (0.162%)\n",
      "Class=295, n=8 (0.130%)\n",
      "Class=240, n=6 (0.097%)\n",
      "Class=126, n=8 (0.130%)\n",
      "Class=325, n=6 (0.097%)\n",
      "Class=279, n=9 (0.146%)\n",
      "Class=182, n=5 (0.081%)\n",
      "Class=411, n=3 (0.049%)\n",
      "Class=276, n=6 (0.097%)\n",
      "Class=266, n=7 (0.113%)\n",
      "Class=340, n=7 (0.113%)\n",
      "Class=239, n=5 (0.081%)\n",
      "Class=426, n=5 (0.081%)\n",
      "Class=339, n=3 (0.049%)\n",
      "Class=94, n=5 (0.081%)\n",
      "Class=143, n=8 (0.130%)\n",
      "Class=250, n=7 (0.113%)\n",
      "Class=302, n=11 (0.178%)\n",
      "Class=176, n=5 (0.081%)\n",
      "Class=190, n=6 (0.097%)\n",
      "Class=122, n=8 (0.130%)\n",
      "Class=342, n=5 (0.081%)\n",
      "Class=381, n=9 (0.146%)\n",
      "Class=432, n=2 (0.032%)\n",
      "Class=265, n=9 (0.146%)\n",
      "Class=253, n=5 (0.081%)\n",
      "Class=327, n=10 (0.162%)\n",
      "Class=335, n=7 (0.113%)\n",
      "Class=409, n=5 (0.081%)\n",
      "Class=168, n=4 (0.065%)\n",
      "Class=83, n=8 (0.130%)\n",
      "Class=174, n=6 (0.097%)\n",
      "Class=147, n=4 (0.065%)\n",
      "Class=306, n=4 (0.065%)\n",
      "Class=415, n=4 (0.065%)\n",
      "Class=64, n=11 (0.178%)\n",
      "Class=316, n=7 (0.113%)\n",
      "Class=69, n=5 (0.081%)\n",
      "Class=436, n=1 (0.016%)\n",
      "Class=200, n=5 (0.081%)\n",
      "Class=437, n=2 (0.032%)\n",
      "Class=146, n=6 (0.097%)\n",
      "Class=223, n=6 (0.097%)\n",
      "Class=429, n=2 (0.032%)\n",
      "Class=431, n=2 (0.032%)\n",
      "Class=435, n=2 (0.032%)\n",
      "Class=273, n=4 (0.065%)\n",
      "Class=80, n=7 (0.113%)\n",
      "Class=280, n=4 (0.065%)\n",
      "Class=337, n=7 (0.113%)\n",
      "Class=438, n=1 (0.016%)\n",
      "Class=283, n=8 (0.130%)\n",
      "Class=166, n=2 (0.032%)\n",
      "Class=234, n=6 (0.097%)\n",
      "Class=183, n=4 (0.065%)\n",
      "Class=433, n=3 (0.049%)\n",
      "Class=142, n=8 (0.130%)\n",
      "Class=258, n=2 (0.032%)\n",
      "Class=427, n=3 (0.049%)\n",
      "Class=421, n=3 (0.049%)\n",
      "Class=424, n=4 (0.065%)\n",
      "Class=162, n=3 (0.049%)\n",
      "Class=326, n=6 (0.097%)\n",
      "Class=242, n=4 (0.065%)\n",
      "Class=419, n=2 (0.032%)\n",
      "Class=500, n=1 (0.016%)\n",
      "Class=270, n=1 (0.016%)\n",
      "Class=441, n=1 (0.016%)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counter = Counter(y_train)\n",
    "for k,v in counter.items():\n",
    "    per = v / len(y_train) * 100\n",
    "    print('Class=%d, n=%d (%.3f%%)' % (k, v, per))\n",
    "plt.bar(counter.keys(), counter.values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Malgrè notre sous-échantillonage, on remarque que la catégorie 0 correspondant à nos valeurs nulles **27%** du jeu de données."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Transformation de nos labels en catégories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vecteur de classes convertit en matrice (entiers de 0 à num_classes). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size X :  (6172, 19)\n",
      "Train size y :  (6172,)\n",
      "Test size X :  (744, 19)\n",
      "Test size y :  (744,)\n"
     ]
    }
   ],
   "source": [
    "y_train_cat = keras.utils.to_categorical(y_train, num_classes=m_cut_points+1)\n",
    "y_test_cat = keras.utils.to_categorical(y_test, num_classes=m_cut_points+1)\n",
    "print(\"Train size X : \", X_train.shape)\n",
    "print(\"Train size y : \", y_train.shape)\n",
    "print(\"Test size X : \", X_test.shape)\n",
    "print(\"Test size y : \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60, 383, 422,   0, 132], dtype=int16)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_cat[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Modèles de classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Régression Logistique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score :  0.2712248865845755\n",
      "Test score : 0.396505376344086\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "\n",
    "regl = LogisticRegression(C=50. / X_train.shape[0], penalty='l1', solver='saga', tol=0.1, multi_class='multinomial')\n",
    "\n",
    "regl.fit(X_train, y_train)\n",
    "train_score = regl.score(X_train, y_train)\n",
    "test_score = regl.score(X_test, y_test)\n",
    "\n",
    "print(\"Train score : \", train_score)\n",
    "print(\"Test score :\", test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. K-nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_classifier = KNeighborsClassifier()\n",
    "knn_classifier.fit(X_train, y_train)\n",
    "knn_predictions = knn_classifier.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score :  0.411049902786779\n",
      "Test score : 0.3951612903225806\n"
     ]
    }
   ],
   "source": [
    "train_score = knn_classifier.score(X_train, y_train)\n",
    "test_score = knn_classifier.score(X_test, y_test)\n",
    "\n",
    "print(\"Train score : \", train_score)\n",
    "print(\"Test score :\", test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "march_pred = march.drop(\"POWER\", axis=1)\n",
    "power_march_pred_regl = regl.predict(march_pred)\n",
    "power_march_pred_randomforest = clf_r3.predict(march_pred)\n",
    "power_march_pred_knn = knn_classifier.predict(march_pred)/m_cut_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "plt.plot(time_march,power_march, label=\"Actual Power\")\n",
    "plt.plot(time_march,power_march_pred_regl, \"r--\", label=\"Predicted Power Linear Reg\")\n",
    "plt.plot(time_march,power_march_pred_knn, \"g--\", label=\"Predicted Power KNN\")\n",
    "plt.gca().xaxis.set_major_locator(mdate.DayLocator())\n",
    "plt.title(\"Solar Power Prediction\")\n",
    "plt.xlabel(\"Datetime\")\n",
    "plt.ylabel(\"Solar Power\")\n",
    "plt.legend()\n",
    "fig.autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Graphique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "plt.plot(time_march,power_march, label=\"Actual Power\")\n",
    "plt.plot(time_march,power_march_pred_reg, \"r--\", label=\"Predicted Power Linear Regression\")\n",
    "plt.plot(time_march,power_march_pred_randomforest, \"g--\", label=\"Predicted Power Random Forest\")\n",
    "plt.plot(time_march,power_march_pred_quantrandomforest, \"m--\", label=\"Predicted Power Quantile Random Forest\")\n",
    "plt.plot(time_march,power_march_pred_regl, \"c--\", label=\"Predicted Power Logistic Regression\")\n",
    "plt.title(\"Solar Power Prediction\")\n",
    "plt.xlabel(\"Datetime\")\n",
    "plt.ylabel(\"Solar Power\")\n",
    "plt.gca().xaxis.set_major_locator(mdate.DayLocator())\n",
    "plt.legend(title='Legend', bbox_to_anchor=(0.5, -0.5),fancybox=True, shadow=True, ncol=2, loc='upper center')\n",
    "fig.autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Modèle de Deep Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Architecture du réseau de neurones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(keras.layers.Dense(100, activation=\"elu\",input_dim=X_train.shape[1]))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(keras.layers.Dense(100, activation=\"elu\", name=\"layer3\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(keras.layers.Dense(100, activation=\"elu\", name=\"layer4\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(keras.layers.Dense(m_cut_points+1, activation='softmax', name=\"output_layer\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 100)               2000      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "layer3 (Dense)               (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "layer4 (Dense)               (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "output_layer (Dense)         (None, 501)               50601     \n",
      "=================================================================\n",
      "Total params: 72,801\n",
      "Trainable params: 72,801\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Entrainement du modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1235/1235 - 4s - loss: 8269.4570 - accuracy: 0.1351 - val_loss: 0.0123 - val_accuracy: 0.2648\n",
      "Epoch 2/10\n",
      "1235/1235 - 2s - loss: 21.6574 - accuracy: 0.2714 - val_loss: 0.0119 - val_accuracy: 0.2648\n",
      "Epoch 3/10\n",
      "1235/1235 - 2s - loss: 41.2368 - accuracy: 0.2698 - val_loss: 0.0119 - val_accuracy: 0.2648\n",
      "Epoch 4/10\n",
      "1235/1235 - 1s - loss: 63.5042 - accuracy: 0.2696 - val_loss: 0.0129 - val_accuracy: 0.2648\n",
      "Epoch 5/10\n",
      "1235/1235 - 1s - loss: 59.3148 - accuracy: 0.2690 - val_loss: 0.0134 - val_accuracy: 0.2648\n",
      "Epoch 6/10\n",
      "1235/1235 - 1s - loss: 40.1947 - accuracy: 0.2710 - val_loss: 0.0136 - val_accuracy: 0.2648\n",
      "Epoch 7/10\n",
      "1235/1235 - 1s - loss: 30.4830 - accuracy: 0.2706 - val_loss: 0.0123 - val_accuracy: 0.2648\n",
      "Epoch 8/10\n",
      "1235/1235 - 2s - loss: 28.0074 - accuracy: 0.2704 - val_loss: 0.0123 - val_accuracy: 0.2648\n",
      "Epoch 9/10\n",
      "1235/1235 - 1s - loss: 27.7906 - accuracy: 0.2708 - val_loss: 0.0123 - val_accuracy: 0.2648\n",
      "Epoch 10/10\n",
      "1235/1235 - 1s - loss: 33.4628 - accuracy: 0.2694 - val_loss: 0.0122 - val_accuracy: 0.2648\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(X_train, y_train_cat, batch_size = 4,verbose=2, validation_split=0.2, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Visualisaton de la Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history[\"loss\"])\n",
    "plt.title(\"model loss\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend([\"train\", \"val\"], loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.9001470e-01 2.5112037e-02 6.0564443e-03 ... 5.4911000e-04\n",
      "  4.8783509e-04 5.4167438e-04]\n",
      " [4.9001470e-01 2.5112037e-02 6.0564443e-03 ... 5.4911000e-04\n",
      "  4.8783509e-04 5.4167438e-04]\n",
      " [4.9001470e-01 2.5112037e-02 6.0564443e-03 ... 5.4911000e-04\n",
      "  4.8783509e-04 5.4167438e-04]\n",
      " ...\n",
      " [4.9001470e-01 2.5112037e-02 6.0564443e-03 ... 5.4911000e-04\n",
      "  4.8783509e-04 5.4167438e-04]\n",
      " [4.9001470e-01 2.5112046e-02 6.0564391e-03 ... 5.4911000e-04\n",
      "  4.8783509e-04 5.4167386e-04]\n",
      " [4.9001470e-01 2.5112046e-02 6.0564391e-03 ... 5.4911000e-04\n",
      "  4.8783503e-04 5.4167386e-04]]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_train)\n",
    "print(predictions[150:160])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ppe",
   "language": "python",
   "name": "ppe"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}