notebook-ph-do.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting in Aquaponics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Libraries and setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "import horovod.tensorflow as hvd\n",
    "import os\n",
    "import sys\n",
    "\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "\n",
    "# Initialize Horovod\n",
    "hvd.init()\n",
    "\n",
    "# Parameters for figures\n",
    "mpl.rcParams['figure.figsize'] = (14, 8)\n",
    "mpl.rcParams['axes.grid'] = True\n",
    "\n",
    "# Set seed\n",
    "np.random.seed(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check how many GPUs we are using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  4\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)\n",
    "if gpus:\n",
    "    tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')\n",
    "    \n",
    "print(\"Num GPUs Available: \", len(gpus))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data adquisition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data adquisition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'dataset_aquaponics_03012021_05222021.txt.zip'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-107-77c7d5dbcff3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mtextfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"dataset_aquaponics_03012021_05222021.txt.zip\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtextfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-107-77c7d5dbcff3>\u001b[0m in \u001b[0;36mread_file\u001b[0;34m(textfile)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtextfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtextfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'zip'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\\t\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Dataset ready'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    686\u001b[0m     )\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1181\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   2008\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2010\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2011\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/zipfile.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, file, mode, compression, allowZip64)\u001b[0m\n\u001b[1;32m   1111\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1112\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1113\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilemode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1114\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1115\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mfilemode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodeDict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'dataset_aquaponics_03012021_05222021.txt.zip'"
     ]
    }
   ],
   "source": [
    "def read_file(textfile):\n",
    "    df = pd.read_csv(textfile, compression='zip', sep=\"\\t\")\n",
    "    df.drop(df.columns[0], axis=1, inplace=True)\n",
    "    print('Dataset ready')\n",
    "    return df\n",
    "\n",
    "textfile = \"dataset_aquaponics_03012021_05222021.txt.zip\"\n",
    "df = read_file(textfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Organize data in columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label                      1          2         3       4       5    \\\n",
      "DateTime                                                              \n",
      "2021-03-17 00:00:14  16.409998  60.726665      0.00  6.7044  6.6820   \n",
      "2021-03-17 00:00:42  16.406000  60.923004      0.00  6.7047  6.6820   \n",
      "2021-03-17 00:01:09  16.412000  60.940002      0.00  6.7050  6.6819   \n",
      "2021-03-17 00:06:50  16.371998  59.305553 -99999.99  6.7035  6.6792   \n",
      "2021-03-17 00:07:16  16.367500  60.477780  -2806.00  6.7036  6.6810   \n",
      "\n",
      "Label                      6          7       8       9       10   ...  \\\n",
      "DateTime                                                           ...   \n",
      "2021-03-17 00:00:14  47.336426  40.645813  100.83  97.179  13.060  ...   \n",
      "2021-03-17 00:00:42  47.335526  40.644165  100.85  97.139  13.058  ...   \n",
      "2021-03-17 00:01:09  47.337580  40.642605  100.86  97.126  13.059  ...   \n",
      "2021-03-17 00:06:50  45.667645  44.009052  100.87  96.994  13.063  ...   \n",
      "2021-03-17 00:07:16  45.667736  44.007435  100.89  97.000  13.068  ...   \n",
      "\n",
      "Label                      107       108        109        110   111   112  \\\n",
      "DateTime                                                                     \n",
      "2021-03-17 00:00:14  45.937490  77.43749  46.593754  104.34376  10.0  10.0   \n",
      "2021-03-17 00:00:42  45.937490  77.43749  46.593754  104.34376  10.0  10.0   \n",
      "2021-03-17 00:01:09  47.249985  77.43749  46.593754  104.34376  10.0  10.0   \n",
      "2021-03-17 00:06:50  53.156246  77.43749  46.593754  104.34376  10.0  10.0   \n",
      "2021-03-17 00:07:16  45.281254  76.78126  46.593754  104.34376  10.0  10.0   \n",
      "\n",
      "Label                 113   114  115  116  \n",
      "DateTime                                   \n",
      "2021-03-17 00:00:14  10.0  10.0  0.0  0.0  \n",
      "2021-03-17 00:00:42  10.0  10.0  0.0  0.0  \n",
      "2021-03-17 00:01:09  10.0  10.0  0.0  0.0  \n",
      "2021-03-17 00:06:50  10.0  10.0  0.0  0.0  \n",
      "2021-03-17 00:07:16  10.0  10.0  0.0  0.0  \n",
      "\n",
      "[5 rows x 114 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df_grouped = pd.pivot_table(df, index = 'DateTime', columns = 'Label', values = 'Value')\n",
    "df_grouped.index = pd.to_datetime(df_grouped.index)\n",
    "\n",
    "start_date = pd.to_datetime('2021-03-17 00:00:00')\n",
    "end_date = pd.to_datetime('2021-05-22 23:59:59')\n",
    "\n",
    "df_grouped = df_grouped.loc[start_date:end_date]\n",
    "print(df_grouped.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_sensor = 5\n",
    "\n",
    "df_grouped[sample_sensor].plot(x = 'DateTime', y = 'Value', kind = 'line')\n",
    "plt.xticks(rotation=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting target "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target variables:\n",
    "\n",
    "* 4     = pH sump B\n",
    "* 5     = pH sump A\n",
    "* 8     = % oxygen B\n",
    "* 9     = % oxygen A\n",
    "* 93*   = Weight cell 1\n",
    "* 99*   = Nitrate B \n",
    "* 100*  = Ammonia B\n",
    "* 103*  = Nitrate A \n",
    "* 104*  = Ammonia A\n",
    "\n",
    "These sensors are not online yet*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_target = {\n",
    "# 4: 'pH sump B',\n",
    "5: 'pH sump A',}\n",
    "# 8: '% oxygen B',}\n",
    "# 9: '% oxygen A'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select features - Human knowledge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label                      1          2       4       5          6    \\\n",
      "DateTime                                                               \n",
      "2021-03-17 00:00:14  16.409998  60.726665  6.7044  6.6820  47.336426   \n",
      "2021-03-17 00:00:42  16.406000  60.923004  6.7047  6.6820  47.335526   \n",
      "2021-03-17 00:01:09  16.412000  60.940002  6.7050  6.6819  47.337580   \n",
      "2021-03-17 00:06:50  16.371998  59.305553  6.7035  6.6792  45.667645   \n",
      "2021-03-17 00:07:16  16.367500  60.477780  6.7036  6.6810  45.667736   \n",
      "\n",
      "Label                      7       8       9       10      11   ...    91   \\\n",
      "DateTime                                                        ...          \n",
      "2021-03-17 00:00:14  40.645813  100.83  97.179  13.060  13.340  ...  391.0   \n",
      "2021-03-17 00:00:42  40.644165  100.85  97.139  13.058  13.341  ...  391.0   \n",
      "2021-03-17 00:01:09  40.642605  100.86  97.126  13.059  13.344  ...  391.0   \n",
      "2021-03-17 00:06:50  44.009052  100.87  96.994  13.063  13.408  ...  391.0   \n",
      "2021-03-17 00:07:16  44.007435  100.89  97.000  13.068  13.404  ...  391.0   \n",
      "\n",
      "Label                  92    102     103        107       108        109  \\\n",
      "DateTime                                                                   \n",
      "2021-03-17 00:00:14  391.0  4.49  1467.1  45.937490  77.43749  46.593754   \n",
      "2021-03-17 00:00:42  391.0  4.54  1456.8  45.937490  77.43749  46.593754   \n",
      "2021-03-17 00:01:09  391.0  4.47  1469.7  47.249985  77.43749  46.593754   \n",
      "2021-03-17 00:06:50  391.0  4.29  1499.7  53.156246  77.43749  46.593754   \n",
      "2021-03-17 00:07:16  391.0  4.28  1501.1  45.281254  76.78126  46.593754   \n",
      "\n",
      "Label                      110  115  116  \n",
      "DateTime                                  \n",
      "2021-03-17 00:00:14  104.34376  0.0  0.0  \n",
      "2021-03-17 00:00:42  104.34376  0.0  0.0  \n",
      "2021-03-17 00:01:09  104.34376  0.0  0.0  \n",
      "2021-03-17 00:06:50  104.34376  0.0  0.0  \n",
      "2021-03-17 00:07:16  104.34376  0.0  0.0  \n",
      "\n",
      "[5 rows x 46 columns]\n"
     ]
    }
   ],
   "source": [
    "drop_signal = [3, 12, 13, 14, 15, 22, 23, 24, 25, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 56, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 93, 94, 95, 97, 98, 99, 100, 101, 104, 105, 106, 111, 112, 113, 114]\n",
    "df_grouped  = df_grouped.drop(columns = drop_signal)\n",
    "print(df_grouped.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Denoising dataframe - FFT filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check sampling frecuency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 days 00:00:25\n"
     ]
    }
   ],
   "source": [
    "sampling_time = df_grouped.index.to_series().diff().median()\n",
    "print(sampling_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Design sample filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5aAkAICyCpzxTn4OgoLWyGfD95sXpun3MTL05MzclseMnp//i38XZU+yc09XPTNFHC9al3G5zdV2T2/p2pZBGoXNOCh7p789ZQ8dOBnSsaBqUjp/LKB8A5DOCpzxT34oLlEVrt+qa56ZmfS5OTX320tc2VXkX67kq1uCn7Z20dz9J0urNxTnKsrm6Tk9PWqzzHhyfcrsr/vlJk9v6d++QqWYhS5waR1l9G6uaBspIT03c91anuOBpxB599f6c1dlsEgAgTQRPeaY1I083vzhd/5y4UJPmpx4taGtVeTr3p66+QWtaEdwkSg30Lyh7diqXJJWXFudHqCGyS5vbt8G1as4cuoMkaadenTLWLmRHQ4OilSVvPmsfScXbUZBN8SO1JYH4dOSpe2r3fl21aN02Rvkkrd9ao/vGzlFtFjvnACCM4rzyK2AtDZ6Wbdim16avkJT9BS5XbqzK7guGdM1zU3Xwb15v0tsb1mszVjS5zb/Y2b1fV0lSz04VLW5fPgs7mhg8Xv3rwAYu/Aqek4umqFaWeaeJZz+iElxrbY4bvevbpXGU9pJjd1W/bh1UU9eg9Vtrs920vPPguHm6fcxMvTEjNynaQHuxraZeA0eO1pMTFua6KQWD4CnPNLSwk+0Hj0yK/uyU3YvXk/74jhatTW+18lEvz9Q5972f9mv5Ix1h1sUcPdWbeN3StMIFa7Y0uc2vMHjhkYMkSSfutV2Lnjvfhe3traptTBH1U04LcNoe4jQE5jwduHNPSdLEeWtz16Aiccern8f8vnPv2FHa7bt5wdTyPO2Qyia/8Ex7WUsQyJXPIwt23/3GrBy3pHAQPOWZoTe/2qKTxfRA2ej4uQrZkG7VufvfnqNJzRQjSGTxum2SFGpewNYa78I+OBJSV9+g+8bO0baa8PPCDhvcK/rzvNVeQFVaYt5aTxlIKTnjnnH675SlWrulRvvcMEYfLcj+RWtdfbgIaEtNfTS90UWDJ6KnghdY02z3fl3Vp0uFhkRGW9EyNXUN+t+UpZKk8lLT0UP6NNlm+8h8weUb8it4eurDhRo4crQ2ZHFErLLcuzzZVst6emi0dP02XfnUZNZZbEN+J+hOPUm5D4vgKQ+1duHZbHypxM8Hevj9+Rl/zaB/jG9+eLlzZDL2h4Ee8xcmL9XtY2bqj68338OyerMXxP7tgmHR24IjbJVl4dd6ev6TxaHmXznn9NmSjfrpPz/RRwvWaUtNvf781pxQr9GWqtI4hvxeq5q62CAqHcs3VLXbsu/5qME5lQTODqs31+ifE0npaI3g/MBHLzxUj/9geJNtosFTno08/fLZqZKkjxZmryOnS2WZpKapjmjfrnt+qp77ZAnpnG3I70zuFPnM5cLJf3xb3/zrBzl7/XQRPBWhsAUcpi7eoIEjR2vZhm2htt9WUx/todgUN/G5pSk9mVyvyU8j+8GjXkrjq9OW673IiNWmquZ7UB94Z64kqWuH8uhtVYH5U5XlpaGq/i3fUKWfPzVFFz/+UbPbBuOOslKv578uB3lwv31pRvTnurjRtQ3bYt87f9/7QXtLUk+P+d1bOuOed9N/YIFYualKt46eXjBLETQ4J1PTEewwn5tcyfcBz1mRToZff3kfHbFb01EnSdqua6XM8m/kyVdWkv4lw5bquhaNWPnfe/HfN4VgW019wrTv9u7xD+Zr4Zr0UvzjbamOXIPk8XdRodkYeS875XA9v1krNmtCAaWGEzzlqXQrDA3q01n7DvBWq68Kmebw+Pj5kqRxs1ZrW029rnxqcsqKWnvdMEZH3f6mJOlfbdQLnU5KxpszmxZwSCU+iLz48Y/03MdLJCntCdkTrj1BkvTST4+K3lZZVtJklM85p6cnLYopUuHvyzAXRMHrv7JIdYr6FkQjj74/X1+s3Jz243xfrGp8bHyJ6vjCATf+d5okRUfhWpK2l81y97lw3fOf6W/j5unHT3xUEAGUU+y8wm8furMkac3m/Jt/kv0k5ZZ5NDI6f97wnZNuU15aoj5dKvM2eGpJRvg+N76ioTe/mvZFsx9sfrwwu9Vj28J5D47XsXeMpWpiQFVtva5/YZqOueOtVj1Pz85eZ2awUxOtsyzyfdO9I+9pWARPeWpjmr1t67bWqH/3jpLCjzxFv9dNev6TJXrukyX6/aup09n8VLZXpi2XJHXtkP4wbzD1bUuCRVaT+X6gKIYkvTR1mT6PFHBIJNUXQUVZuEN/yHZdJEn9unXQ/FGna7ftGud9eMFT7Hv9yrQVuvqZT/WH1xrfx5JIEBQmqAiebEsjj6sNOf/IV1PXoBv/O01fa0FBDl8w9/kPr32uO19pnOh+W2BUKmjifK/X6NXpK7RkfbjRzHiZHInMJT9gemXaiuhFdD5rcLELQvsdM89/siRXTSp4nSMpMWXNLG/QtUOZnpq0KC9HXNKdT7t4XeN3fboXzdOWevN4P164Pq3H5QO/zVvTmFtb7FrTQVbf4PT4+AWqqWvgPc2ABZGODX+eIZrHO5Wn7n5jduht6+obtGFbrXaI5MuHmfPknIt+YEzBCn3hLl79k8Ozlx4Rup2+o3/XeBIdN7vlC0L++ImPdcpd7yS9vzV1M2ZHej1TXcBUlpU2mfPkv/fBiwa/vHmo4Cnwsz/gFJ8215ynJi2S1Lp0l/fnrIn+/I/xC3XvW19EA5tEaYRzVsWOcrV0ZLJYJ4cH1/OJX+snk+au2qyBI0enPwrpXEybz9jfW8Nraw3zT9L1t3fmauznK7V43VYds3vfZrefu8pL93pw3NxMNy1t6Y6a/vLZT2N+D9s5snJjVbTanqS01+u7b+wc/eTJj9N6jG9jVa0efX9+i0eNgt/9a7fk30htrrR0yRBJeu7jxbr+P5/p/rcbiz0Vwgh+ofCP2dbso9YI7stC2a8ET3livwHddeRuvbX/jt0lSY99sCD0Y5dtqJJzUrfISEuYCbYX/H1idKTAn9vjaRpxNDS4mAu+aUsbJz4HeyL9gCMdV/17StqPCSvYtvgvheY+oCf90QvKuqSYQFlZ3jRtr1OFt/2Lny7T78bMjHmt5r4Tlm+o0lVPN74ftZHoKZ05T845Xf+fzyRJR+zaO/Tjlq7fpvvGzpFzLmna56J1W5Ped+If3o6mGUrpjZbNX904NyCdkchcq29wenPmilAXWcFRnGymmY3+1CvX/9zH6a3RFCxVLjWO4v5t3Ly2alreyMQFw5bqOr08dZm++pf3dOtLM/S9hz/Ums01Ki8Jv/dzMdcxkeDx/d2HJ2rC3DUpto41Z2XsvJ8whXok6c2ZXjGArxzgBe3pBiG3j5mpFyPHfrpGvTxTN/53mt5pYcfeRY82ZkjEdyols3JjVasWdC8Erfmc+XONl2+sio5gUdW17fjZQLkKntYFKkzn44h7IgRPeeCWF6dr6pINeu+LNbrm1L3Sfrw/krNjTy9t7/evNX+CCo74zF65OeVk69temqF9b3wl+vvpf2qc2L9jz47Rkrt+Kl+8+8bO0fkPTWi2TW0teMI9YtSbMfeF/eKduzr5pN9EaXvdAmmMfxk7R8ffOTY6gtRcr+sNL3ym/0ZKGUvShQ9/KCl82XBJej1Qgai59KCgn/7zE90+ZqbmrNqs2Su8E/4Fh+8Ss82MZRuTBrvOxV7spTPqN+LOsdGfszUq88q05Xp5assurnwPvDNX339kkv4ydk6z8wyD18zpjohW1danvY6az0/DSPek6ORysuRBti1et1W7/+pl/TsyWtsWquvqtc+Nr+jSJz6OSTmra3Apv098/vd4W6ewVtXW67cvz0jrM7ZhW60GXfNS9HfnpB/9o/nCN77j9vTWwTt4F2+tsHve/CLU4/zU4GEDvWUi/HmVYQQLCbRk9GhpJOU4OPKVjlmBTsSwKWaH3vaGDv7N6y16vdb43ZiZOu/B8aG3n7Nqc/T9SVfYudjxNlXValqkUuWitVuj32WT5hfeXLh8VN/gomn26VTZbUvL1jfO8SyU7BOCpxxraHB66N3G3tw+XSrSfryvNTX6fxUZrUh0vfRIijkaHcpL1bdrpSTpziTzpW4fMzMarN34wmcx9x06qFeihzQR7JV7+MJDmt0+vvpffCGMVBPfV25q/CBffvxuSberLCvVxHlr9UlgQrM/8uSbu3pLqJ4y55xenZ64IEbYi53pSzfqh4819nq+M2tV6Mf6222prteZ93rB8W7bddGe2zfO8Zq/Zmt0JMP30yTvTzojHcH5Zx+k0avdGj96/CNd+kTL0np8iyKpDne88rmufW5qym2DgUiYCo1BV/17io7+3VstWoKgsqy0Ra/Z0ND0u+CQgT0jz1UYJ7cw/AvdloxSrN5crYEjR2vMZ7GPXZCiMMKIPZpP23v6R4dL8r5b29JTHy7SX9+eq7+8FS6AkWJHhX1hl2fw9e1aGZPe/fr0FTrjnnEpAxu/SI0/4hlMI27O6zMav0fTPe7nrd6isZ+vktSyxXmrautjMgzW5fkCv38ZO0fvfRH+vT3h92836YgMa9G6xqBrU1Vt6KJY+930qv71ode5MW726uhC9Y+PX6D7xmZ/GY9iM3vlpmimSLqf7bZsgyS9fuUxGtCjY07akC6Cpxw68Q9va/C1L8XctlukQIGUfK7LpqpaDRw5Wv8Yv0A3vzg9evt23Spb3aaqBD1l23VN/bx9u4R/3Ufj0hEnzlsbPUnV1TfEBIMLNtbrsic/Vl19g26PpMBJ0nF7bNfs60xqZmHZCfPWJl1X6LS7x0V/vvKk3ZM+R2Xkov/sv6QuzHDiH96WJK2PDEc3NLiYvHipcfHfRMJerJ72p3FNbtv3xldigrtk/It7/yQlSecN30VjfnaMJl7nVRoc9fLMJo/72Yne+9M1Lr1xxcZwvbb1DS5mVOS65z9LsXXbaIse/VWbqvXkhMZ5XX4KbDLlgVHAsL3vPj9gTWdhZ1+0EmKaAY9XbS82evIr7i1JcawWGv+iobw0vVG2t2et0jGREf/Hx8d+pz0xPnnK9Ul79Wv2uXfo0VGdK0qjFxRtZUtkvtrsNOa/LVrXNBAM2zO8cM1W/XPiQnWIjH76GQoXPTZJny3ZqFPvbvp9JTV+Pvt1q4xJr97vplcSbh/PHzmX0k8B+kVgZH1ZGhUPpy7eoKcnLdLfAinwnSpKY9oSxo8en9TiEa98N/bzxqyI/W56Naajr6VuHzMzprMT6Zu5rPF7Zm2awf60pRu0pbpOj30wv1VzlWYs26jKshLt0rtzi58j2wiecuTpSYsSTuIOXrAkW5TSL2P78HvzYkaFgpXg0p3jEH1cgmpa8RdR8S46enD057sCOe3rt9ZES5tLSlqq1l8UeLfrXtbI5xonGN8/pVqjP12m+Wu26ulJyf+e4Pu4raZeA0eO1u/GfJ50e5+/rtBdr8/SwJGjtS6S5rc6MCqV6m+vDPQM+3N1XIqCG855I0x3vTFbR93+VkwqVqrskhUbq/XC5PBVzm47ez99Y9iO0d+/+dfm0zL8yn5fBC7Y/Nt6dUo+GlpSYhq6Uw+Vh6xeGC/RRX1zBTK2VNdpzqrNenz8Ak1dnP7Cum/PXhX9uaVrhcyPW8OluQy3jnGjCG/MCF92v1dn7/0/+Devt7hgQ6rgPBEXVzBCknbu5Y1sL2xhCmHY1/3r23OydkHkj0xU1XodN2Hf3+/+fWI0JSu+5z6+gyiobzMdUb7aBqeKJGm3e17/si58eGKo5wl6M5LS+9r0FfpfID04lUVrvePms1+fkvbr3fjfz2Ke495zD4q5f+byTQlTufzS5N8+dOeYzsRNVXWhRtKDa8Wkmwbsf+fttl2XtDoJzrz3XV39zKcxKfMVZSUpszYSeWXaCh1y6+uau2qzfvavTzRp/tqkI3SL123VwJGjNXDkaE2PVCZsaHB6cNzctOeOrstCYYs+cZ2s/ghfKmFSBA+99Y0WtwmxaaafLFwfOtV10dqtOv1P72qfG1/RDS9M0zMftTz1ecGarerTpTKmkzHfFU5Li8zVz3ya9L5z/XVAzPTcx4t1zn3vxwxx+xf0wWP8+D1jR2OufDrx3JQNW2s16JrRzbavps67mNj9Vy83W3Y6eEFw1+uzoxe/B9z8WsxF21cDpbOvP2PvaE+2JA0c6bUpGCT5F291zaxz5I/sSN5aVOlwzumu173Khgfe8lpavfvBC5Az7nlXA0eO1pfvfS/lY0764zv6U6SS4qpAKmFtgr/xkmN3jZZKv+Jfk0O361uH7KQrTmwcMQtTTt6/+P8wQR55WWlJTNn3UV/dL+b+ytKShBO6w1yIJpqLc/Ifk1dQlKTvP/KhTvj927r+P5/pzHvfjV5AXPzYJL01M3bV+RUbq/T05zXR3uza+oboXDJJGnHH2GbbKHkdFqmOjUQLygZtjbtI/MGjk0LNAahvcNH3tr7Bae8bXgk9dyA4BzGdtCcp8SK5O2UheJq1YrN++/JM/fSfn2TsNYLuj6T9vPvFag2+9iXtfcMrLZo0Hex17dOlQmUlpn4JMgG269Yh1PPttX1XrU2yFl1VbYPeCnHhGW/3QArup4vXh3rMwrVb1bNTecrCOcnEt7F7x/KYOaFSY2XBIL8cfueKMu07oHt05FtqusZcIsFlPsIUT/JV1dZr4ry1OmnvfhrQo6NmLt8Y+rHx+nfvEF1LMNmaXc45XfPcVI1PkKp8/O/f1n8mL9U5938QMw82KDiqdeerXmfhq9NX6DejZ0SLFYV1eYjPW2vWrJq1YpPueKX5Ds14YTuBWU+r5TZFPiP7DfCKlYWt9Lwm7py/KY3PWtD7X6xu1fImuULwlAPJLn6GRirtnRsJKq7/z2e68ukpmrRgncZ85l0ILVm/LXphsThwsN169r4pX/PgW17TwJGjNfTmV2OCrviTmeRd0O7+q5c1+NqXUl5InH/YLglv/9p97yc8yQXnHf3gqEG66ct7J3z8dc9P1cCRo7Vks9fQRPOTXrz8qCa3NSe+AIIkTV8We4IMBl83n7VP6OeeF2IiuBQ7SnbBQ17v8bIN2/R0ggnrA3p2TJpis6mqVof/9o0mc7skbzQomDcc/yUXzzmnT+NGcD6+/qSY3wf2aRxO/1Yg6JUa04Ek6S/nNfYuh5lH8vB78yVJt3yl8fhtblJ9slXIX52+Qhc+8mHMbVc+PVkvzavVR5He7MN/G5uvv2ZLTZMUykQO++0b0aInL0xeoq/f/0HM/c2NPA3q3XQ+4mUh5lwlKre85/VjNHnR+pSPa2hw+tHjsRP7nXOqb3A6455xei3J/LrGbaWSuLND3y6VKi0xzV+dueDJD0LSXcS6JWrrG5p8/iU1my6XKGMgGKiu3VKjLx+wg+77zsHR2/yUtbCLUE5ZvEHvzFqVMhUm3QvGdCr9+Rav2xoNmoNakkIqNa7148+lPO1P4/TtB2JHxv2/yu9E3K5rB31608mSFJ3vksrWmnoNinxfnfXn1J1ZQf6agXtu31W9O1do/pqtoVJ8E13gf3DNCdFzcjBdLWhBJK3xWw+kzgxYnWR+bnBu15szV2rivLVaFRmxTRZ4J/PuF81XFmzNYsV/DFHEKpH4edTBOdKVgWyHsGtboqlF67Zq7/7dNDzy3vqdyelqaYEhv8hV/+7hOpbyBcFTDlzxr6a9PL86fS89fYk/UbjpbvF7ho4c9Wb0hB8MbPwFcoNWbPS+SBsaXNIL6E9vOiXmgldKni4Y7+R9GvP3g+kVUxZvSFmC/Jdf2lOSN5k90eftiQmxr3/eg42V+r5+sJeOtu+A7nrg/MaLE3/kKpn/O2UP/er0psFasHJgvHRKfbfE5uo6Oed0+G/f1F/fbrqmy3mH7qxOFY3pXs656CjLFf+arGUbqnTbSzP0xowVmhK5mP72oTul1YYJc9ckXPjUTxXz+c+f6D0JBlbBi71EParx/F6u6tr6aJl+qe0qjdXWuZjniy8cIklH3R5u8c5JC9bpwXFz0xoF9D37sfceB69f35i5Um/OTB3EvPxZ4gqWX2nmovDeBEUBjhz1pjZX1emzJRt15dOTUz4+0chTSYmpvsHp7+8lL1f+9IeLoj3l9Q1Of37rixalRmajIzlY2CE4OpvqO0GSzrin6VydBWu8Mv4DR45Wg5N6dqrQQTv3jN7/wPnDNH/U6Wm3Mf54DWYg/C/NIhe1LfhMLVy7NVqI6LT9to/e3lxaZbCD8Hfn7B+43Wt/cD6RXySmvsHprZkro3MJOwdGu7p1KNdRu/XRPycu9N7jJH/LorVbtWT9tpjCS/vf9EqToh6JPPrBfEnSKftsH5170Vxv+Maq2qRZHmcO9cqsP5Bkva6HU3yOgoLnAN+H89c2OU/e8uL0aJGRlozENLcW3OOBdNQFa8J1FvreCxGcxUv0NwQ7C2/6cmPn5sYWpl8358FxczVw5OiY5VmKzdjPV+mLlZt15cletoq/IHpz4gtgVbQwdb+yvERlJab3fnl8ix6fKwRPOfDKtMYLpi9uPVVPXjRcPzhqULQyVrK8z9+/2vyw90l7NwY0w297Q2u31OjSJ1KXlj1tv/4xv4ctC1saiHxuOCPxKFIiftlaSZr+6y+Ffpwk3RZIGTt5n+1j7kt1QnfOpf3hDs4hS+Slnx6d9L5DB/XSP394WLOvMfTXrya+fcfuKikx3XvugdHbLv1H4yiEvw7K4nXb9INHJ0V7WAf3aQxibzzT2ycDE4x6SN6FxjcfGN/k5J+q2s3vvzG0yW2dAvN59uofnHcXfp7W/6YsjenhHnztSwnnyF351ORmnytYJcsv5NDgvAsO3069Yv/G5oJv329Gz0h4e6oKay9MXhK9CJtw7Ykx933/keSTpuNTIS87btdQbZSkPyTo6V26oUoPv+9dsDXXR+gVjEh+f7K5JFc/+2l0ZPy16ct1xyufR8tOh+GPTMSXzF20dmubz4Py1+B54bIjNelXJ+rfkc4rKXnwvnjd1phebj/1+J8TF8bMHfAvnP0OgY4JLoBT8b9P41O+gilpv3zm07RGgILbhhkBq29wWrxumwb28T6Xf/rWgbo/MpqWLBXN51+I33vugfrGsKYdOkdFRuKCHhw3t8nIcVC3jo3B1Nf/+kHCbfxlO4Lpxxur6nTJP1KP8i5dvy36fTWwT2cNH+z1wv8jRfEPSTo7rhPjjauOjWZFdOvgvceJRlEbGlzKuXFSY1XZROXO40e+Ja/n3r92SDUKkGw08/NmRvX+M7npMhpBG7bV6v635zR5/uq6+mj1RMnryAzj3ynmOe+5fVd9+9Cd9aNjB0dfOxP87/v306hIWEj8DvgDdu6hThVlOnpIn7SKPQX5FZvT9eq05erTpVIlLRgZzyWCpxx64qLhKist0RG79YkpTJCs0yhMla5gaWlJOuiW12KCtaDgyM28354WosWNunYo02GDG0ch9h3QPcXWsYJzmNK9qEg1oTA4cfTZSw9PuM295x6ot34xIq3XTGbvHbol7U3eu3837bF96uBLUsxJJcg/BHYOBBRjEqyjFd8z/cmixouGC48cJMkrMR7sxfN7yP0LjXj3B9KN4vXr2nRo3d+HpSWmXft20aPfPzR634YU6SPBXvRBfTrr1q/Epp4mSqlKVNAk3v9F5hPG9xoHFyC+/LghTQKoZL2XYXpx458rKFhON53zw+i4daguO243XXjkwOjvKzemH0z4KRnNnaicS10s5aUQa2T5Vdm21tSruq4+Omqail+tzA9Gpy3dED1Wh9+WeGJ4S8tB+8HT4L6dVVlWqkMGNqYEJRqhlBpHKf/+vWF6f+Txui2SmrVw7Vbd/L/Gyqd79/d6b/918WH6JC4FNgz/4j3+GA7ON9tWW6+9bhiTtGpo0C/+PSVmhHnHyGjSui01SS88V2+uVn2D0/aRrIay0hLt2tcbkVmxqVqPj1+ggSNHJyzp7X92/fch3h3n7K+TAx19Uuz8p+d+fET8Q7QycFH30YLEKWT+GlmSNO7q42LuS/U5Ds6x61JZpr0i7f4ksE5XInPi5mzt2rdLk3Ph2i01MZ/VuvqGJlV2j929aQl7/7s/PuXtmCTf269OXxH9XKdK97zg77FrLvrp96mKmZz9l9ggce7qLdG5zQ0NTsf87i2d8PuxGvXyTL09KzZNMZie/NUDB+jHI7xOoN37dVEqVz/rfY8HRxH//r1hkhoD06N284LwjRleWLU1C/I65/Tu7NUZX4D22Dve0p8jGQe3vTQjYYZTPD9l3c/o2aV3J63a5C3B0FzHTNgU5FRem75Cc1Zt0fIWnMtyjeApBw7cuYck6fDBidPCdkkyUhDGzwKFApIZtktPPXD+wTEjN81V1PPNue00zfvtaZp60ykxF2DxaV7xzj5wQPTnLdWxH8qWXFw0J37UyB/VO2P/HaL58JnWq3OFbjlrH5WlccXsz43wv6sTjX4dFDl+EtkuQXAjxY5iJOo1DNpvx6aB8ODIe5bootsPnrpUlsnMottK0kPvNk1ZGTfbm8sRnBd38j7bq0dcVb/4QiHJLn4qSktiglh/bZoZSxuDLycXc+H5jUN20ms/Pzbmef4Ul+s9b/UWrdpUHbP4bzJ+RbFEauKqB8ZXLktWXfD6QE/egB4d1amiTDee2Ziqcuhtb4SuNhgcVZFSjzw99eFCzVu9JWGg99dIh8sOCdKE4/m7zyT9b0pjsLVuS41q6ho0fU3Tk3Owg2BbTb2+FagUmez6xU8BS3ddJL8iZ9cOjRcB/pyKb/z1Az0aVynND7Yk6fg9+2mHHh1lZtGedL8z4Ocn7h4d5e5UUaaezXw3JuKPwsYvjvyzyMjrj45prHA6tZngqb7B6Zm4Oagdykv12ZINOvCW1zT0169qw7Za7XX9mJj0Kn/0aJdAB06/yLyERWu3Ro/PA25+rclI1EPjvBHOgXGlh+/+1gEa3KezOlWU6TuBObN19Q0xF6jBlEdf8P2XEo8O+oUtfnj0IO3Uq1PM3NjgeoqSdO3zU6PpfF+JnJ/G/MzLJujesVxHD+nT6kW7/TTnBwIlzI/7/dgm2yW6OPf/Fr8Nzjn94JEPo99jPzx6UDSY9flz7+LXUHp71iqd+7fxamhwMdUhfzxiV52+v5d5kux7aODI0dEg8sjdekcD1MmL1quuvkE/f3qyFq7dGp2bVRtY1P25jxdH57RMufFk/eGbB3jniL6dNaRf47mtocHp6memJAwMR566V/Rnf3qCH+j5QVSmRp58Yc4ByUxasE7feWiC/pYkfbMt1NY3aMGarbrjlc+1enO1Hnhnrl6YvLTZdMYFkWPJT1P109ylpp138fwA/b7AtI9/hZzyUVVbr4YG1+bLMWQTwVMOPPr9Q/XfnxyZtPfXzNIqDRuch1Ia4kL9mUuPaJLylsjPEwRipSWWNNBKtcBvcKg+fk2Vllxc+JItOum/D2beQrfnxxWL+NpBOyZ6WJs7//CB+vw3p+qsA3Zodtv7v3OQrj7Fmw+WquT5cz8+MmbUMOgnSRat/eWzU6Nz4BItRLtfMyOHz/34CL1x1bEJ7/PLcPujgsHe3z+9+UXMuiWvT1+h8x+aqHvenK11gVGp+NRRSXokUkzCN+ial5psI0nPREYZz4hcBPhFNi4KrCMSzOf/QyT1sEN5qV79+THR2x8MXFy9MHmJjrtzrA659XV99+/hykL75X6XbdimgSNH60t3eVUDgz3qvbtUxqQ5St5JKv5CML7n+NHvJ14Y2l/QOGhMgnlSB8ddjCb7DC9au1W/fNZb8DdRBUV/Um+oSoGRC8IvVm2OKYgybelG3fPmbP3uw6qYVEpJMYH3XjeM0aa4i9fht70es3BrMKBONFc0XX+NjLrOX7NVN/53WsxImV/B7KHvDot5jD/fc0okkL3ixCGtbke3DuXqUF6iNVtq5JzTa9NXaNaKTdERuctPGBKtQtdcCl2w3LM/Kby6rj66VIPkpQ9vq62PWWrihsiC5rsHLnL9i9X4ymnB1MyFa7bq80gKY/w57qwDBujNyMj/Mbv3jY643P3G7GgQOGS7xCMSfs/9HpH2fLpkg/a78ZWYDoQl67fp24fupGtP8y64g6NA8Sm3T05YGE3nW725WmaK6VjbtW8XTV+2UQNHjm52nbQBPTrqW4c0TU/0R+GD3y1+R4sftEjSmfs3PT9061CuLw/dIVrU6aMV9Xojkq593B59dd3pe2vDNu/zcWJk/TB/TbjggutTFq3Xd/8+Ue/PWdNklPbqL+0ZPR8nmhMX/z30+PeHRztizrn/A51xz7t6IZDOJzUej9V19TEp4cGRilKzmO+8wde+pKcnLU5YyTMYWO7Vv5vu/tYB0fR9/zkzHTw9FVj/MF1++nlzc8paI9jRM+w3r0d/npegmmWQ3zY/tf83Z+8bTff/RYp561LjsVFaYtHPZLIR4aC6+gbtef0Y/eKZKdq4rXWdE7lE8JQD3TqUa/8de6TcJp3SsE/Gza1JNTKRjktH7KrZt56qJ384XFLsXKVE3v6/45rc9vH1J2n+qNPVL1Ci9+ghTQOe4YEqOoP7dG4yn2jIdl0S5kr/OW7tEJ9/zu5QVqqrTt6jSa/07V/bL8GjGvXs1LIh6ZPiUlEk78vl8uObv6D60r6NJ9Nkvex+xcBkwW/8WhrnRIbjX5+xQsNve0MT4gKnZy45XM9eeoSe+/ERevHyo5KmNPboVKFd+ya+qPEnNNdHhhriL8yDaUV+VcIVG6v1wDteOluyBUonLVgXvTh+MEmv3WXH7Rr9LPl/q9R0DtMNLzTO4/Pno0jehWGwys+EuWvknIspChG2zPdznyzRio1V0Yp+M5dv0vtzGnvy/QqGJSUWPUFJXhn6ByITk78TKY4SPBk+/oNDU86/+/ETH8VM4L7kH03nOJaUWPRzLHkFOpxzennqsphe6mAq57jZTSd5+5+jj0JU3vKrYX22ZKMmzlsbHY35zkMToinI8QuCJqou+dPjd4t25KzYWK1xgXW6VrZwQdFkJa8TpaL4++LtWd7rnhC30G0wVbq5VKR0dKks1wPvzNWga17SDx+bFFPCv0tlmbbr2kEDe3cKXR3w4mMGR6taTlvaNCU2np+Slqjkerz/TlkaHSX7v2e8i667v3VAs4/zA4573vwiWknvkMC5IOi6SMGfrx3sjRJ95c/vaVN1XXR0e/3WGm2qqtPgPl1Sdg7Ep4/W1Tdo8qL12q5rZTRDQYpdSmKPX42JPmbtlhpd8vhHMamd7408XqO+1lgYwxdMM99SXRdzAR08d1UmCPw7VpSqxLzU7vFz1+ileY0BwsMXekHZPy46VGfs3z869yeoqrZeP3jkw5iKg+8FvssGR0atyiJlNeNHnpxz2jWQXjjvt6eppMRiilMlqn54ZySwDnY6xae3l5ZYNChKlCIbnN9YVx97MjzrgAHRz6n/f6bT9lq6PMM37v8gWjyrhcXoQklW2KS5KopL129TRWlJdCSvvLQkmu6f6nklaUpkuYPSEouO2IaZV+5nCjz38ZLodVaqqQL5iuCpADwWmEMieakz5/lrQSXwSNz2YU36VeNk9s9+fYoqykpUXloSeuGyzpVlMReFUmM6X3BELNHo2PWBghP/uGi49t6hMVf+/u8crNeuPFaXHdd0VKVzZZl6x41cnbJPv2ibk71PZc38TTukKJoQb+Spe0Z/TpaKGaxG6E8ETsSfnJ0s/TIYhB4YIkgeGpeC981AWdwHzj9Ywwb20sG79FR5aYn2HdC9RSmN/shTcCTph0c3fgEHJ4HfGumhnjhvTXT7YFD29YNjRwRHT10m51xMr3EwsD37wMbtR+wRu9ZZMvHHc7AK46eLNzRb2v2rgRTUoFtenN5kXs65f2ucXxBMbY0Pfke97I1q+Ce7m19snD+TqLMh6KWpy3Xjf6dpyfptCReVfOIiL2gaPqjx2NxSU69dr31Jlz7xcTSQSTU/zdchcnF539g5GjhytFZtqk44QiU1fZ97JAhM/HSYzdV10YvTjuWlejMwytmholQDAqOZ/qXUms3VCSfOh+Ff0MSPIiXKBrjxv9PU0OBUVdugbyYofrBL787Rjp1Eyyq0VLJ5V8Hjb/6arXpp6nL9JzKfyQ8OrgksNj566jJ1qijVFScMiXYqBUergucXv8JisEJifCAy97bG+bHBjgi/d95fSuCsAxJ/ToKO36vpZzb+HOI7d/jOmj/qdH0l7nn9AQx/VC5V2nuieZ67Xfeyxn6+qslE+USL5G6tqdNBt7ymMdOWx/Twp+J3Lu1z4ys6MzLa9+RFw2O2if+s+Gvp7bOD9/39rQfGa+4GL7gJzk/ec/tuuvfcg3TIwF568ILYY3nP68dER6p8wbldz//4SElSmT/yFBekTAqMInQoLwmd2u+PFo+f6x0HL1/RtLCSmcmP1eJTj51z0ff+kmN3jY5yJ1pqxK+SuSFPRzAmBkbW00nfl7zKpbtf93KoBYwfjsvSuC4y8trc2lqrNlerb9fKJvvWn3M4M8G8Y98fX/PS3DtVeOn6B+zUI9SSLcGlTfxA6sQE3wP5juCpAByze9+YkZgT9+qnW8/eTzeftY+uOqnpRXa3DuWaP+r0mLVzfMnSvSRv1GLubadp6k0nx4x8+RfH26WYUOr77uEDoz+nM3oWvGj3A5dHvtRZ80edri/tmzrF0C997jtgJy8YmPWbU6PpG4kkGiXyL4ISlX5Pxr8o/MawcKmAxyW4yD80Mlm9a2TfJWqblP5aCsG88nhhUjfDSNTbFP83rtxUFdPbG5xoHex1uj2u9/YnT37SJF3vvZHHB9K7Wl/TOpg+c+tLM1JeFA3dsbvu/PpQvfKzY/T6lYnTGMMY0KNjTAnnoPMeTL3uS/xEeMmbH3TkqDd1xKjGdawmXHuC5o86XUdGJlXHd1r4F50rNlRpc3WdnvskdjQm0ahup8rYEdxDbn1dB93yWsKiBfHpZFW19U1Gj/3RipcCZbcvOGIXDe7bJVoco6K0RDsERgf9dJGv3/9Bi3qEP4j0vnfvWN5kFCkomBLsp5Tt2T/x58kvnlOWZBS1tf74zaE6JbI0xB1fb1rx8mdPTY4pAvPPiV4g8/B78/TMR4s1Yo++6hyZkyjFjvgdEyhW4H+9+Bdj1yX4/gwGmH8KjC7d+9YX0SUNwl4MVZaVNlmzLzj6k0h8YQN/XoffEx4MtFtjSyQICI6g7X3DK02289egSmbidY2dkn4RlfjFkuODJ/876YfHNB1RShbEnLh3v4QBRiJfO2jH6KiN/9rxc0z9jomBvTvp3z+KLeARf85NZa8ERUNKSxrT8eLX9Rt0zUs6+y/vS/KOIz8lL9GocFlpibpUlmWsVHlQuuXf4zs/Oqe50PTVz36qmvoGHRhZozOdAkG7BUbA4+f6Ba3aVJ1wuoXfIR2fGRB0emTpgsMj00YG9OgYKksjOK/qkffnq1NFabOd2fmo8Frcjky87gSNv8bLa997h24ad/Vx+ucPD4teBF1w+EBdfkLydLBz4xYznXvbac1eMJeUWMwEasnLG7/z60N1e5KLvfjH3/Ntr7x2OpcR6X6xBH0p8iG+65sH6PT9+kcXV6woK0lZVWzUV2NT97649dRoqe10quv4F6Hx6+Kk464UKS4/CYy41QdOcGFe7bAkI2GJqju1VKJ5xofHrQf15XuSr0sUHOUrKbFmF3zuVBHuWNk5weKex++Z+KLu6R8lrs7ou/msfTR/1Ol64SdHqaTEtMf2XZOWgE/kb3G9wpKajJj6ghO6//TtA5vcv1OvTtGqU75ECweH6eyQvNGffW98Rb8OVIuTYoNKX58ulXr9ymOa3H7GPe/GpNq8MHlJzOiZ5M1tjB899tN+/MpakrcYr9Q4x6W8tET7BkZQ/R7c5hZTTubbf/OC03/8YHjK7U4PzMPzU5+SHT+dI0FlWfzKwm1g/qjTdfaBO+ov5x2s2beeGhMEz/rNqSkf6+9TvziDfwG4dIPXs++nuvrzp/yefH8E8xsJ5vEExV/I3/Q/Lz325wk69ZLZd0D3pKNNYV5zYO/Oqm9w0dTc7eMCk0TeH9n8mjL+/NGT9u6X8vuhW4emF/VBiToR+8alV8enLgfPwcGqhMmqF6bytwuarjEWDAT8z1N8epzv9SuPbVJE6GsHpR5VbK6qZqk1pu09Hllf66YEx8D23TtEK08myyzo1qEs43OepNRBSCJ+J5zfObg5SWXdRBIV73gzbhTR55xrMqp15K599KXItd4tL05PGlyu2lSdsMqiX43zz2OTV3hevaUmZo6qP5f6P81UxI0voJTpKoSZQvCUx7br2kHbB3pcd+rVqclFaSqlJaaxgTksramjf87BOzZ7kvAd46cZxb3cXd88QF9t5ku3JfyRtq8cOEB/Pu+g0CU0e3epjK7TInm9WH7VmXQWyPXnBCSqUpfMQ98dFnPxFlykM94vTtlDE689Qafs008XHDEw9Gv45o86vUkp+t2STMpuifgeS8m7wAmma6QqRRo/cnV6guIRvisinQWHRfZPt7h9PfmGxsqN71x9XJMS4vEBsy/RPEG/HTNv+ZIuCIyotkSiz22Yz/KZCQIYKfHoZdCdXx+asIc60WhX/JpKUtM5CkG7bddVM25uuj7b4kCaUzCY80eV/TlQ464+LjpKO3nROq2J66H1v2f8dKeuHcpivnuuj1wk+5/RixP0zicTrF7X3Oc1UYWtZOm8fjpjZQsXikzkXxcfFvMdUVpiTUYoKspKkgbhKwKfuSN29UYf/cf7KW6/i4z0bte1g3bt21mvz1ipmz/YJjMvUE72XXrFCUP0vQTfRZ8sXK9deneKppuFlW4l6KmB0Z5H3p+vZRsaj73mKr/+9fyDtUOPjk3mZMUHXWcdMEDzR52uThVlSdOZE2V3JDLzli/FdOYE16ySvNHVZH50bOP6bsftmX6nlz9Xed5vT9O1p3kjRsElQvxj4tmPF0fng/nz+6TEKe7x37vJxFcD9Hlpe95Onx85FvffqUeT7Qb06Kgjdu2j2beemnTOda8uFUlTh79YuVlXPT0laSXBVOJHmpKt8Rf0+vQVuueN2TEBxD6RKQjNjUrX1jdE19pKlF6abMH0Gcs2xXxXfXL9SaooK9E9gTUi97/pVf3kyaZrna3eXNNknrTkfdfsuX3XJh3Cr05bHi2esmpTtfoEAq/fR0bEm5tnFV8CPThFo5AQPBW5gVkqyx3kfzFfEvjSl7xysH/4xgFJH3fLV/aNfgCz5bdf3U+v/vwYTbjW63ndtW8XTbz2BP3gqEHNPLLR8Xtup2cvPSLlPLR4J+zVL2ahyOaqJG7XrYP+ev6w0AFsPDPTC5cdGf39tP3aJmVPSj4Ctlf/bi0qux8/8hnk92jfdOY+ev3KY5uUZo8fwRz7i+O0Y5fGFiZLCUp0gfDn8w7S/FGnhyqBHT+SFz8BtmOC5wgzgpYsRcfMYoqsxEsW/CdasHR0IGVOaqxamErHitImKV3BuQvBC/pbzvIuMP30nZ16ddLvzvE+54vWbmuyfpmfGvjjEbvp8uN30xmRSmT+Z9TXpbJMu/frklZHwJORUrqPppgXeubQHfStQ3ZK2KGRbP7nTr066ZvDdtJfvpO4gE1LHDa4d8LFZOP5o9bd4trrr2uWaE6TryTBXNS5Gxr0z4mLUhaK+PlJu+umL++T8L5k1fLaUvx3hL/+ltT8shv+RfsZcRXuUnVgBXvnT913++iobHMjML4O5aU6LpAG2qSNKZrcKRDohP3+D+47f46omen7Rw7S5cfvpqsDaXf+RX2wQIxf7CFZel6H8lJ9etPJuuWsxMeAz/+cxystMS1cu1VzA+XnD0wQPPnvU6p51/26dkhacfJnT32iZz9erBnL0i+J3ZLq5Bc9Nkm/f21WdEkByQvmB/XprE3NjDz94bVZOuWud3Tu38ZH5wIFvT1rVcIRJL9Y0B+/OVRvXHVstHpxeWlJzGf/xU+9+cP+c9Q3OK3eXJ202vHwQb20cO3WaOA5Y9lGXfz4R7rpv95o9uq4USv/HPjcx8kXN5YaR719bbFeVC4QPKHNVZR56+4kKvCQyvmH7aKvHRxu3lBb2r1f15hCDNt16xB6cqzkfcEfvEtPmVnKGTi9O1fo1CTztxJdXLe1oTv1iF7ANjevIB2HRgoRJKpQmCgoPCDBSTL+MfNHna53f9l0bo+voqwk4UVzfPpCaYnpN0c1BnCJqlr5Zt96apP5F6kEj5HgCObc207Tl/bdXvcHLqTDLCEQr7mqmalKYqdT8CReqnmCQd88NDYQu/G/jRUN/RPij44drKOG9NFD3x2m38aN+h25g3exet3zn6lrZVn04t9/bM/OFbrq5D2iI5PB3vJNVbV6dfoKzVqRXvnflyNrlxyTIii559sHatTX9tf6EAU0fKUlptvP2V97bp/9XlS/w6BX54qYQgR+D3AwkE71tfbIhbEBZdhCQfH8hUtbItFoVjKpRqgT8Y8v/yKvtMRi1hhsbt7F9yNVyOoanK4/fW89fOEhoVOIJamqtunox89P3D3lAttS7H44JmS6dfDCP/g9VVZaoqtO3iPmgrVDinNBsjRVyQvk4gPQeMmq1paYN/p5/O/fluQVGDIzzfrNqc3OIYu3XbcOMSnDQf7g0SvTluu9L1anlXrnryvWklTJoE4VperRqbzZ75OPIwU6Us0bOukPbze5za+Id9we2zWpiDskrvrnna9+rv1velWL1m6NjtYl2//+94r//KfePU6S9EWkuueqTdUx6ad+R0yDazq6FBS/6HM2rn0ygeAJyIBEFykfXX+S7osbkZg/6nTNH3V6WsFaa4z66n46crfeTb5UW2OP7btq/qjT9ckNTU96iebdBHt4kwWTkpd3/cwlqecixTMznb5//6SlT1Olx/gVB1siWNTEP4kES883J9E8jebakuziNj5ISVfYwKtbh/KE8xSkxkVB/+9kr0DECXv1a3KheeSAxt+HDewZXUg7WXpOMCXu4se8cuzBQhJhbK6u19cP3jHU5+20/frryN16R6tptvYiKlPKI/OsBvbprMN37a1rTo0dLQgGBanS43bo0VEfXNM4FyjsvM+bz9pHt3xl3+ho4y4tyHY4Z9iOOnnvfvrxiF2b3zjiz+cdFFPVszmTbzhZX9x6aszodLAzpbl5oH5J+E8Xr9f23Ts0mzob77tHDFTXDmUxI6hXnDhE464+PuV82d2266I/fGOo7juxU8LiC4mkc6ym6vlvLgWyuTUak6X3fTg/dqmDiZHfK8pKoqNr+w4I9zf061ap1ZtrEs6d8Q/he9/6Quc9OEG3vDg9Wkq9Of6aRfeee2B09G9rTfpV/cxMvTolTy30VcW1/65vHhD92a9GHF8RUvLKqHftUJZwP8aP0P75LW95kHmrt0Tbk2zk3s9aenrSopg5bPUNTpuqarWpuq5JAasvR6pvxi9oHRQ/qrYtxJqB+YjgCWhHhu7UQ09cdFibjjylEl+x7YQ9t9PtX9tfk284SQ9eMKxJMBlv2MBeaQdQfz73oKQVGlsz7y8T/HWBzk6Q/tPcPKtk17bNpUFce9qeodbgCSPRvKABPTpG119K1Zu/d+/GY3C/Ad11/Rl7a8qNJyftzQ8Gvv7k5BvSKDSwubpOqzdXa1CSeRjxenWu0BMXHabdIr256VQPzaa9+nfVj44drJu/vK/MLGaOTLxE8xOD+nfvqJ8f7HV4hE0Ru+DwgTr/sF2iJYj7pxnQ+q/1wAXDmlSha841p+6lsb8YEV1DLZWSEmtyPPrfg985bOeE6wgG+SlKV5wQvhhG0N47dNPUm06JyXLwpVoUXZK+etCO6lgW/rvLHzE6NEVqry/4nTi4b2d9/pvG+YzNBU/JvHzF0br7WwcknE+TyB1xczE/uf4kPXPJEUm2juV3PL0WWBg4lXHNzMnxfThvrfp08VLu/HTnR99fEOqxPn+Obo9OFVq/NXXwVB0IIo7ZvW80EJEUsxZfvEVrt2qnnp0Sdgj5C7L7lUt9b85cqX996KUw905QbU9qnLvuB1y+lZuq9cnC9ZKkz5bGVln104wTrcv41syVmrWiafpkS7Iy8kF+ng3Qpv7vlD1CrQmEtpPuBOiWyNZoVWv0Dpw8Lx2xa0wO/YlJyrHHa+loUNB/LjtSy+NyrZP54dGDms1PD2vKjSenTJUac0Vj5bqfHj9EH85fqwsi8w3iJ5XH83PRDx/cW/eee6C+89BEzVi2sdmT0cXH7Jp0jkCipQ9SqU8QPKVTcO6Cw3fRYx8sUI9OFSorLVH3jskfnOh479+9ozZWhZvP4KfFDOzdsnmg8WXa80VZaYmuOTU21XLkqXtq1Mszm8xfS7S/4vlL5qQ7F8FPS+vZqWUX3C1RUmLRHvKbztxb26exxITUmCIchh+8F8q13qc3nRy6gMngPp01d/UWzV21JaZjrSUXtucftov26t8t9CiZ1DRDobkRraAekdTAy578WKfvn3pfHjqolybOW6sN22qbPb4nzFurQwb2kplFy9bfPmamLk0yOhocnbnypN317UN3jgbcvTqXa+mGKjnnkp63gwsOP3jBMJWUmJ66+DBJ0lOTFiVt58K1WzUkySLqJSWmObedphKLXQvqkcAi7Mk+r13i5gDuN6C7pi7ZoMXrtkWL0VwStzjz2QcO0NXPfKr/TF6qu74VWyk2uN5jUKq00XzGyFM7cNlxu0WrLSGzCuS8mhPpzlHwtXTuRdABO/UInUZ33el7a9TXmlala4nuHctT9uCXlFj0X8eKUh2ze9/oRVpzo4N+SsyBO/dQ7y6V0Uni8WWPE9mua2X0oiPoyBAFCoK+dvCOTTpmVm/yeljPP6z5NWf8VJswRTkkr2pZsHJk8MSfrMNixcYqLVq7NRqUphs8+SMqndOY35JrtZH3Nb5cf32IXp29epWqZ6fypBeJyfgV6RIdV9nwvSMHNbsmYGscFEknbat1pIJas8xFMt06lIfOMHj2Um+Upy0WKw3Tp/fqz4/R6J82zi9taSEkKXWwHjzaJ99wUnTtth8+Ninlcy5dv01L1m/TIZH1F28MFEd54J05TbZ/8dOl0Z8vOHwX/fSEITHFFPx0u7Dr0vnzPIcP7q3hg3tr1Fe981H8SGBNXYPmrNoSs/BsvNISk5klXLNNalxmIV6nwHfyH785VP8LzAf+v2e8pSXiA5/guXrN5mo1NDg9MWFBynTHDinmIeezwmw1gILhp6a1NAjyez+vTHNUJNvu/tYB+vmJrW+jn1rVXK/xvgO665lLDtcvIvOK/HVawqw1VFJimpxgjtoBO/ZIq619ulTq+R8fGXMh5OewJ5u7FFQV2TZsD3mH8tKYntuencubvewcftsbMaV/060A6c8fOXNo6snx+WRr5H2Nn4wdXMvn30nSYbtXmj654eS0R3yfuGi47v/OwVlLCc62cw7aUaN/epSOHhKuaEMh6dm5Qi9efpTuTbAwdrrChIG79+saU86+NenUfmpiohTFGcs2Rn/u0akimgo9cd7alKOwH85fG/OcXSrLotWDb3tpZsy2zjn95MlPor//OkEVyqMjnVL+EgHxmluAt6KsRJ0rSpvMm5oXWesuzNpmJyXJ9EjWKRTcJ0Mj54VgxUi/XfG+GUlx/GLlZj0wbq6ue/6zhItL+8J2nOWbwulKAwpAFrL1Co5/kmpNukvY1JpcOuuAtlnD7KcnDNFdr88OFVAMG9h4weAHXfEVB9PR0ouYHRKkSw1tpqqi1Jjq1dITaEvmIaW7IPeQfl0157bTCio3f6fIIpfxa1n5n8X9BnSP9qq3lR16dGxVlcd8V1Jiaa9fVUiCwfKZQ3eIWZA9Hemkkx+xa29NDwQ4LX29M/bvrymL1ze577DBvTR+buOac8Hvi/vfnhOtCDxz+UaVmmlIPy/9beK8tepSWRaTenje8J11/9veqNPAkaM1f9TpTRYDnvfb0xL+/f73tD8XNF6wEMRjSZZROG2//vr3R4tVU9cQDVq+8dcPJEnHpaiK6BvYp7N+duIQHT2kr/p0qdDbs1ZpzsrNoUaK/bmIvzx1T731+aqU2/7k+N301KRFmrt6i0a9PDPltpJ08j7h0vfzDcETkAEFMB0pa/wOvkK6+Myln524u644YUjac9p27dtF4+euTXvSfVsIlhH3xa87lIifApVqTaFUmnuPNmyLLQ8cJqBLpNCO3W8fupP22L5rk9G/vfp300l799NVJ+f3KC5y655vH9j8Rm3gyR8e1ibPs+f2XfXip8u0ubouJkA6YKeeMcFT0B2vfB4Nnr50l1eG2++kmzhvrQ7epWfM536nuBTYN2Y0LVCR7PtoQI+OMkuetjd+bmN58mTl6CdGRsPuGztHV5w4RA3ORb/fTgiZbvmzQGbEBYeHT1/uHPl+j++s2j5BcRi/A+Wa56amfM4+XSr13sjjCnakmrQ9ABnlVxzqn+Zk7vasJcVAfnX63nrq4sPSWjS2rXQoL9WLlx8VLTduFq7gwP+dsoce+u6wmBG0MC44fBftFyKtbOivX435/T8/DlfBq9D5a8/Fqygr0d8uGJaT9aiQedkoVJSPdo+MGMVXc/NH73cMzFO78+uNC/cOHDk6WnjHt25LjWav3JwwDfCB8xurw/7g0dh5U1d/KXm1xoqyEvXv1kGLkwRPiRbFjffHSOnyNz9fKUm6ZlxjAaRMBSB3fn2oLjh8l+j5KJhhMPG6E7Rjz6Yp0Ik6mpJVwyzUwEli5AlAhn3lwAH6yoFtk9KG5DpWlGr44N7Nbxjwv58cpXMfHN8m1QX3HdA9WuzCueYXHZW8oOuEvdJP27j5rH3T2v535+yvwwf3LogKlUBr5fIwz8Vr+xUXF63dqoN2buw08Ncqe+GyI6O3nXPwjvrFv6dEf9/tupdjnsuf75QorTXRPMAHzj9YZaWmEbunHv3ZqVcnzY3MUYo3O0EJ73j+fNQpi9arocFpxVbvb7vvvNbPU0vmnIN31DkH7xj9vSSN6pS+0T89Sr06V+itX4zQcXeObeMW5g4jT0CB4jIwt4rh/d9vx+6aetMpbfZ8a5pZCDKb3prp9dDe8pV99Y1hOzVJuwHQ9jJRObA5A3t3Vol5RQqCJkRS9nqHXG9K8oKnitIS7b9j00Bphx4d9fSPYgutnLR3Px2/Z79m54vu0KNjwiUi6hucnvtkSbPtilmP69qXoj+fsk/mKky2VHDdLn+e4KAWLJ6dzwieAABt4vUE8wBy5bUZK9S1Q1m0+hOAzMvFyFNFWYl26NGxyZwif55QvD36JV4X6V8TF2rivLU6YKceSYvYHDqol07bzwtYdu3bOfRodo9O5dH1onz1DU6vTlse6vGSt/hw0O/O2T/vFn6XGkcCixnBEwC0c3/69oG699zWTxL3y3kPTzBfINsWr9umwX06JyynCxSzXM59ytWl/I49O2rJunALoT9/2REJ50yOfG6qpizeoGEDUy+zMGuFN8KVTpp0l8oybaqui0nR+9Jd7+jSJz4O/RzxCw8PyNPqll2TFAv6xrAdE95eiDirAG2oufUagHz05aE76Iz9W7+O0Z1f318PfXeYHvreIW3QqtZZsaEqJ5UHgXS11WhNPkzpy1UbOleUqaquPvp7Q4p1nDpVlDVZcDZo176pi+74lUTPTmMur7/O4bceGC9Jevi9eZodl2YYxrRfn6L+kSp3iaqc5oNkxYLaYsH7fFE8fwmQR3KR9w3kWmWZVwCiJesvtbUVm6pCLR4J5Fox9bnlqihLRVmJauoaK+c9+O7clNs3pHjT+ycowR3kz+1MJ2PurUiVvDVbalRdV69f/2969L6yEosuxt2czpVl0fmbrVnTL5P84GlwEafvETwBAIpKVW291m+tbfH6UQBaJleX85ur6zRrxWYNHDlany/fpPvf9oKng3bukXD7VMFTovWLgk7Y06sQul3X8J0zlx67a/TnPX41Jua+MT87Rs/9+Ah9etPJoZ7rD98YqhN3LgsdcGVbp4oy/f7rQ/XED4fH3B6Mq/NhlLQ1ct89CABAG1q5sVqS1I+RJxSAQr+QjJGjv2Xc7NXRn0+56x1JUp8uFfrXxYcn3L4+RVpfc8HTNaftqW8fml4FzyEJilS8edWxev6TJdG1+ZIVqYi3Y89O+s7elaGWg8iVrx3cdH5TMWXk5O87DwAFoKgufApQojSh5Ru9ksAET0B25eoC+afH79bktlvP3i9pwZiGhoQ3S/JGTlIpLy1JGAyla3DfLrrq5OSL6xabYjpXMvIEAC1QRNMUis6qTd7I03ak7QFZlasL5CtP3kM/P2l3baut13cenKCPF67XcXskX7g2UdreM5ccHu14Qds764ABeuyDBZKki48enOPWtA7BE5ABLguX1vG96h3KGUjOJv8a4ajd+uS0HWhq/TZvQnePjskragFoe7kcXDAzdaoo03M/PrLZbe/4+lDd9fos9epcoa6VZRqx53Y6aOfUJcrb0q1n75u118oXB+/SU/NHnZ7rZrQJgiegDZ07fGd9vnyTrjhhSMZf67df20/H7tFXPTqWq2uHcg3sEz7/Gq1XUmJ6/cpjtUMPUsPyzeLIei/JSuYC+SS4aOuhA3O/Rlo6+nWr1IrIHENJOnmf7XPYmvAG9emsu7/V+rXtWmpEilEx5D+CJ6ANdaoo0x1fH5qV1+rWoVzfGLZTVl4LifkTfZFf7hs7RxKjsSgMPTtXaP6o07Wtpl5lpYU1MWTc1cerwTmVllhRreOTaX26MCpeyPLySDezHmb2jJnNNLMZZtakXIqZjTCzyWY2zczezkU7AQD5K1drzgAt0bGitOACkIqyEnUoL7x251plWX4ucItw8nXk6W5JY5xz55hZhaSYfCQz6yHpL5K+5JxbaGaMfwIAJEkDenTU8MGFlf4EACgMeddVYGbdJR0j6SFJcs7VOOfWx212rqTnnHMLI9uszGojAQB5a93WGvXsRFoMgPwysHenoima0J7lXfAkaZCkVZIeNrNPzOxBM+sct83uknqa2Vgz+8jMLsh+MwEA+aa6rl5ba+rVg2IRAPKES1AaHYUrH9P2yiQdJOly59wEM7tb0khJ18dtc7CkEyR1lPSBmY13zs2KfzIzu1jSxZLUr18/jR07tskLbt68OeHtyC32S/4rhP3DcZSf2mq/zFxSK0kaP3685nQq0foqb/XLVUvma+zYJa1+/mzJl2O0WD8vhf43hdkvS5Z4Ve9mz56tsTXzM98ohLZ8i/e9tG3btjY5Fov1c1oo8jF4WixpsXNuQuT3Z+QFT/HbrHHObZG0xczekTRUUpPgyTn3gKQHJGnYsGFuxIgRTV5w7NixSnQ7cov9ksfGjJakgtg/HEf5qa32y+qPFktTp+iwww7TTr06aebyjdLYcRp+wL4asX//1jc00/Lss1R0n5c8e39bKsx+eWP9Z9LCBRoyZIhGHDEwK+1COHNXbZbGva1OnTq1ybFYdJ/TApN3aXvOueWSFpnZHpGbTpA0PW6zFyQdZWZlZtZJ0nBJM7LYTABAHlqz2VsgtzelgNFOUWQSyKx8HHmSpMslPRGptDdX0oVmdokkOefud87NMLMxkj6V1CDpQefcZ7lrLgAgl/wpBWu3eMFTr84ETwCAtpeXwZNzbrKkYXE33x+3zR2S7shWmwAA+Se+k31bTb0kqWM566igfaI2AZBZeZe2BwBAS1XVecFTB4IntDOk6wHZQfAEACgayzdUSZI6VhA8AQDaHsETAKBoLFi7VRJpewCAzCB4AgAUjU7lperZqVylJeQwAQDaHsETAKBobKmpU+8ulbluBgCgSBE8AQCKxqaqOnXtkJeFZAEARYDgCQBQNDZV1alLJcETgPxB9fjiQvAEACgam6sZeQKQn5iJWRwIngAARWNzVZ26VpbnuhkAgCJF8AQAKBqbqmrVhZEnAECGEDwBAIpCfYPTlpp6ba2pz3VTAABFiuAJAFDwnJxWbaqWJE1dsj63jQEAFC2CJwBAwbLADOy6hgZJ0gWHD8xNYwAARY/gCQBQFKpqvXS9DuWlOW4JAKBYETwBAIpCVa038tShjFMbACAzOMMAAIqCP/LUsYKRJwD5h8VyiwPBEwCgKERHnkjbA5BHWBy3uBA8AQCKQnTOUxnBEwAgMwieAABFoarOLxjBqQ1A/iBdr7hwhgEAFIVtNVTbA5C/SN8rDgRPAICiUFXnzXmqZOQJAJAhnGEAAEWhmnWeAAAZRvAEACgKFIwAAGQawRMAoChU1TaotMRUXsrMAgBAZhA8AQAKnnPeyFOHshKZETwBADKD4AkAULCCcdK22nrmOwEAMorgCQBQFKpqGwieAAAZRfAEACgKVXX1lCkHAGQUZxkAQFGorq2n0h4AIKMIngAARcFL2+O0BgDIHM4yAICiUEXBCAB5yLlctwBtieAJAFAUqurq1ZHgCUC+YhWFokDwBAAoCttqGHkCAGQWwRMAoChU1TZQbQ8AkFGcZQAARaG6jpEnwDHBBsgogicAQFGoqm2gVDkAIKMIngAARcGrtsdpDe2bGVUJgEziLAMAKHh1DQ2qa3Ck7QEAMorgCQBQsCxS+3dbTYMkUaocAJBRBE8AgIJXVVcvSaTtAQAyirMMAKDgVdV6wVMlI08AgAwieAIAFLyqWi9tjzlPAIBMIngCABQ8f+SpQxmnNQD5hrW3iglnGQBAwYsGT4w8AchTFJEvDgRPAICCV1VH2h4AIPMIngAABa/aLxhB2h4AIIM4ywAACl5NvTfyVEHwBADIIM4yAICCV1NH8AQgv1E2ojhwlgEAFLzqSPBE2h6A/EOpiGLCWQYAUPAYeQIAZANnGQBAwauuixSMKKXaHgAgcwieAAAFyyLZMNW1jDwBkuQcM2uATOIsAwAoeFTbQ3vHrBogOzjLAAAKXk1dg8pKTKUlXEICADKH4AkAUPBq6hoYdQIAZBxnGgBAwauua6BMOdo1ZjrlM/ZOMeFMAwAoeIw8AR4zUlfzFXumOHCmAQAUvOp6gicAQOZxpgEAFLyaugZVlHJKAwBkFmcaAEDBW7R2K+lKAICMI3gCABS8zdV1+mLl5lw3AwBQ5AieAAAAACAEgicAQFG46KhBuW4CAKDIETwBAIoC1fYAAJnGmQYAUBTKqbYHAMgwzjQAgKJQXkq1PQBAZhE8AQCKAiNPAIBM40wDACgKZQRPAPKQc7luAdoSZxoAQFGoIG0PQB5jIe/iQPAEACgKjDwBkmOYA8gozjQAgKJQVkKvLtovjn4gOwieAABFgXWeAACZxpkGAFAUyko4pQEAMoszDQCgKLDOEwAg0wieAABFgXWe0J5RJgLIDs40AICiUMbIE0A5bCDD8jZ4MrMeZvaMmc00sxlmdniS7Q4xszozOyfbbQQA5A9GngAAmVaW6wakcLekMc65c8ysQlKn+A3MrFTS7ZJezXbjAAC5F+xlZ84TACDT8rKbzsy6SzpG0kOS5Jyrcc6tT7Dp5ZKelbQye60DAOQjRp4AAJmWr2eaQZJWSXrYzD4xswfNrHNwAzMbIOlsSfflooEAgPxCqXIAQKbla9pemaSDJF3unJtgZndLGinp+sA2d0n6pXOuIdXkSDO7WNLFktSvXz+NHTu2yTabN29OeDtyi/2S/wph/3Ac5ae22i/Tl9VFf5788SStnFWYAVS+HKPF+nkp9L8pzH5ZsqRakjR79myNrZmf+UYhtCWbGyRJW7dsaZNjsVg/p4UiX4OnxZIWO+cmRH5/Rl7wFDRM0r8igVMfSaeZWZ1z7j/BjZxzD0h6QJKGDRvmRowY0eTFxo4dq0S3I7fYL3lszGhJKoj9w3GUn9pqv2ycslSa8okk6YjDhmtgn87NPCLP5Nlnqeg+L3n2/rZUmP3y5obPpIULNGTIEI04YmBW2oVwZq/YJL37jjp17qwRI45t9fMV3ee0wORl8OScW25mi8xsD+fc55JOkDQ9bptB/s9m9oikF+MDJwBA+0GpcgBApuVl8BRxuaQnIpX25kq60MwukSTn3P05bRkAIO9UUDACAJBheRs8Oecmy0vNC0oYNDnnvpfp9gAA8lsZwRMAIMM40wAAigLrPAEAMo3gCQBQFFjnCZCcc7luAlDUONMAAIoCwRPaM8ZdgezgTAMAKAqlJVw+AgAyi+AJAAAAAEIgeAIAFCzGmgAA2UTwBAAAAGQIJTyKC8ETAAAAkGGMlBcHgicAAAAACIHgCQBQ8IwuXbRzpIYB2UHwBAAoeKzxBHiMngQgozjbAAAKXjlrPAEAsoDgCQBQ8MoYeQIAZAFnGwBAwSsvZeQJAJB5BE8AgIJXVsLpDACQeZxtAAAFr5Q5TwCALCB4AgAUPIInAEA2EDwBAAoewRMAIBsIngAABctf0oalbQDkK8cKxkWF4AkAUPBKiJ4ASZLjSj1v8TVVHAieAAAFr5SrErRzfAKA7CB4AgAUPGInAEA2EDwBAAoeBSMAANlA8AQAKHjMeQIAZAPBEwCg4JUw8gQAyAKCJwBAwSN2AgBkA8ETAKDgUW0PAJANBE8AgILHnCcA+Y4luIoDwRMAoOAROwHIV3w/FReCJwBAwePiBO0dgxpAdhA8AQAK1uaqOklSQ0OOGwLkCaMnIe+QrldcCJ4AAAWrZ+cKSVJFGaczAPmNuLY4cLYBABS8UmqVAwCygOAJAFDwCJ4AANlA8AQAKFj+XAKCJwBANhA8AQAKVkMkeiojeAIAZAHBEwCgYNU1eMFTCcETIElylHYDMorgCQBQsBoaGHkCJIlPAJAdBE8AgILljzyVUgMYAJAFBE8AgILljzxRMAIAkA0ETwCAglVH8AQAyCKCJwBAwapraJAklZUSPAEAMo/gCQBQsGrqvOCporQ0xy0BgMScqIBYTAieAAAFq6beC57Kyxh5ApDfjJqIRYHgCQBQsGrrvB7dylJOZwCAzONsAwAoWLX1/pwnTmcAgMzjbAMAKFgNzht5otgeACAbCJ4AAAXLn4ZtLJILAMgCgicAQMHyR56IndDeUc8NyA6CJwBA4YpcMZYQPQGSGIUFMo3gCQBQsKIjTzluBwCgfSB4AgAULMfIEwAgiwieAAAFqyESPBE7AQCygeAJAFCwnPyCEURPAIDMI3gCABSsxrS93LYDyBfOUXcPyCSCJwBAwaJgBODhM5C/iGeLC8ETAKBgRUeeGHoCkOfILi4OBE8AgILFyBMAIJsIngAABcvPhqFgBAAgGwieAAAFy58czzpPAPIdc5+KA8ETAKBgNTR4/xM7AchXfD8VF4InAEDB6lRZKknqUM7pDACQeWW5bgAAAC31i5P3UJ8ulfry0AG5bgoAoB0geAIAFKzOlWW67Ljdct0MAEA7QZ4DAAAAAIRA8AQAAAAAIRA8AQAAAEAIBE8AAABAhrC+U3EheAIAAChwXJ/nP9Z7Kg4ETwAAAEXCuEIHMorgCQAAAABCIHgCAAAAgBAIngAAAIqEozoBkFEETwAAAAWOmU5AdhA8AQAAAEAIBE8AAAAAEEJeBk9m1sPMnjGzmWY2w8wOj7v/PDP71Mymmtn7ZjY0V20FAAAA0D6U5boBSdwtaYxz7hwzq5DUKe7+eZKOdc6tM7NTJT0gaXi2GwkAAACg/ci74MnMuks6RtL3JMk5VyOpJriNc+79wK/jJe2YrfYBAAAAaJ/yLniSNEjSKkkPR9LxPpJ0hXNuS5LtfyDp5WRPZmYXS7pYkvr166exY8c22Wbz5s0Jb0dusV/yXyHsH46j/MR+iZUv70Wx7pdC/5vC7JfFS6olSbO/+EJjaxdkoVUIa+HGeklt9/kq1s9pocjH4KlM0kGSLnfOTTCzuyWNlHR9/IZmdpy84OmoZE/mnHtAXlqfhg0b5kaMGNFkm7FjxyrR7cgt9kseGzNakgpi/3Ac5Sf2S0SefZaKbr/k2fvbUmH2y1sbPpMWLtCQ3XbTiCMHZadhCGX60o3S++PUpUsXjRhxTKufr+g+pwUmHwtGLJa02Dk3IfL7M/KCqRhmtr+kByWd5Zxbk8X2AQAAAGiH8i54cs4tl7TIzPaI3HSCpOnBbcxsZ0nPSTrfOTcry00EAAAA0A7lY9qeJF0u6YlIpb25ki40s0skyTl3v6QbJPWW9Bczk6Q659ywXDUWAAAAQPHLy+DJOTdZUnwwdH/g/oskXZTNNgEAAABo3/IubQ8AAAAA8hHBEwAAAACEQPAEAABQ4FyuGwC0EwRPAAAARSJSSAtAhhA8AQAAFAnnGIMCMongCQAAoMAx3gRkB8ETAAAAkCE9O5dLkoYP6pXjlqAt5OU6TwAAAEAx6N+9o9646ljt3KtTrpuCNkDwBAAAAGTQrn275LoJaCOk7QEAAABACARPAAAAABACwRMAAAAAhEDwBAAAAAAhEDwBAAAAQAgETwAAAAAQAsETgBY5ca/tct0EAACArGKdJwBpm/brU1RZRt8LAABoXwieAKStcyVfHQAAoP2h6xgAAAAAQiB4AgAAAIAQCJ4AAAAAIASCJwAAAAAIgeAJAACgwH3zkJ0lSSfs1S/HLQGKGyWzAABAUfrFyburf/eOuW5GVuy9QzfNH3V6rpsBFD2CJwAAUJR+cvyQXDcBQJEhbQ8AAAAAQiB4AgAAAIAQCJ4AAAAAIASCJwAAAAAIgeAJAAAAAEKg2h4AADny5A+Hq3MFp2IAKBR8YwMAkCNH7Non100AAKSBtD0AAAAACIHgCQAAAABCIHgCAAAAgBAIngAAAAAgBIInAAAAAAiB4AkAAAAAQiB4AgAAAIAQCJ4AAAAAIASCJwAAAAAIgeAJAAAAAEIgeAIAAACAEAieAAAAACAEgicAAAAACIHgCQAAAABCIHgCAAAAgBAIngAAAAAgBHPO5boNWWNmqyQtSHBXH0mrs9wcNI/9grbAcZSf2C/5if2Sn9gvCOJ4yLxdnHN9E93RroKnZMxsknNuWK7bgVjsF7QFjqP8xH7JT+yX/MR+QRDHQ26RtgcAAAAAIRA8AQAAAEAIBE+eB3LdACTEfkFb4DjKT+yX/MR+yU/sFwRxPOQQc54AAAAAIARGngAAAAAghHYVPJnZPmY2MNftQCwz65PrNgDIDDMbkOs2oCnOh/mJ8yF8ZlaW6zYgsaIPnsyst5ndYmbvSvqDpJ65bhMkM9vOzH5jZu9LetjMzs91m1B4zKyXmd1uZu+b2eBctwceM+toZr8ysw8kPWZm1+a6TeB8mK84H8JnZt3M7DYz+0jSsbluDxJrD1HtmZK+Leki59zYHLelXTOzUudcvZmZpO9I6i7p+/IWe7vZzJY6594wM3NMxkMSZlbmnKuL/HqcvOOos6QDzWwex05uxO2XoZL2knSZpJWSnjKzcc65cTlrICTOh3mD8yF8ZlbhnKuJ/Lq/vE6NVZJ2N7MJzrnNuWsdEimqkScz+4qZvRTXAz1d0jOSlke22S8njWvHzOwsM3tO0t/M7LDIieBlSb9wzs2UNEHSh5J2kyROFEjEzE42s5ck/cPMhkdufs85d4mk+yWdJC+IQhYl2S9nSFrtnPtYUkdJH0v6KFdtbI84H+YnzofwmdmxZvY/SS+b2UlmViFpsnPuUkl/k7SvJNKe81DRBE9mdqa83pu9JV0VuGuOpDpJb0SGQW80s9+bWcccNLPdMbNfSrpS0mOSPpV0lZkd7pyb4ZyrjmxWKekQSa/lqJnIc2bWWdIF8i78XpN0uZmd7ZxbHtlktKQ9JO2Soya2Swn2y8/M7FhJj0qqNLMPJb0laZikX/K9mx2cD/MT50PEOVfSm5LulPRNSZcGRpneldRN0pActQ0pFGzanj+UHRjSnijpHUm9Jb0vL11Ezrk1ZvampM8l/UNSL0nPS7pE0h/NrMQ515CTP6IIJdgvz0p60jm3KHKhdZCk8uC2kvaUtFXS2pw1HHklQarKUEm7Oee+Y2blktZJOs/MXnTO1TrnFprZUklHm9kXzrlq0l3aXoj9sl7S5fJSw26RdJOkE51zG8xsbOR+vnfbGOfD/MT5EMmY2b6SBjvnfhT5fY2kv5jZ/c65aufcCjNbKGk/M5vonFvJ5zN/FMzIk5l1NbPvmdm3pcah7MD/K5xzG5xzcyVtM7OTAg9/1zn3uPOskfSkpKMjj+NAbIUQ++WLyImig3Nui6TBajzuLPL/eZLGOufWZ7f1yBfmOcfMfhe5Kf676TNJHc1se+dcrbwUsFp5qXq+MZKG+z24BE6t14L9MklSvaThko6RtNk5tyGy7WOSTpD43m2tZPuF82FuhdgvnA/bCTPrYmY/NrM/mFmv+Pudc5/JC4wGRH6fKG9+6DmBzd6VtGPgMXw+80TeB09m1inyRfSupJMlnWJmv4702vjbWOR/fyTtcXm9nzKz8sBEPJnZrpKOkjdHAi2Uzn6RJOdclZkdLmmjpCmRXrYGM+srL6XnVTO70cz+Z2ZUgGp/KiX9SNIvzKyzc67evyNyHNXJmwtwRuTmdfIu1P05NnLOPS7pADN7ysxe5ThqEy3ZLx/JSzv6WNKF5lUSGyxphKS/Z7Htxay5/cL5MDea3S8S58NiZmaVZnaTvLTMwyS96pxbG7dNeeTHMZK+F7jrPwp0CDrnXpaXiv4PM5sWOT6QB/IyeDKzQ8zsiMgXylZJr0o62jl3rqSn5EXi3f3tAz3M/hfVA/J6PRXpDZWZnWBmYyT9W9J8SeOz8bcUk1bsF98ZkiY459YF7jtZ0pGSHpI3sXykc25d8ESD4uIfR4HfS+SlF70gLyC6JHJ7ueQdR5HjbayksyO3bZTUSdLySG/vjmb2jKSdJC2VN/ma4ygNbbhfKiXVOOc+l1cO+xF56UrL5eX3Iw0t2S+RTTkfZlAr9ouP82GRiLs2qpbUV9L9zrkLnHNjAtuVSo2fQ0lPyJv35NskaYWZVZhXsvyfkg6XNFXS+c65VRwL+cHyKbPFzDrJO5h2l7RY3sX5vX4ajnk5or+VNM8599PA446TV5nmscC2k+SllKySd/KolrSDc46TRJraYL/8Q16K1eOR7baX19v5x8hzdnDOvZ21Pwg5keA4ekXSfc65bWZ2gaSdJb0XuW3PyInIBY6jZ+VdfF/rnHvJzP4l6d/OuWfNrLekHZ1zU3LxtxWyDO2XZ5xzz0Sefwfn3NIc/GkFrQ32C+fDDGiD/cL5sEgkOBZed87dYWZnSDpF3qj8HpLmSno20rnkXxsd7py7zcw+lnS3c+5RM/urpPHOuYfNrFLSIOdVYESeybeRp6Mk1Tvn9pFXIWiIvHUPZF41oMskTZHUybxFxHqZ2Z3yKpWsk1Rn3iKA18lbY2SQvIj9C+fcQk4ULdaa/bLBObdN0jfkVZN5SF61ocmS1jvnJnCiaDfij6PdJV0Yua+DpI+dc29J6mtmZ8mr1naHpN9L2hRJfbhN0llmtkzeHIGPJG8iPIFTi2Viv3zoPzmBU4u1dL9wPsys1uwXzofFJf5Y2M3MLpT0urz5bJdI2iZvPcK7zayvmY2UdLO8+U2KPG5vM1sgqb+8Yi9yXtEIAqc8lbNqexaoGuL3zMjL/90+ssk0SW9IOsnM+jvnlkm6NLJ9H0m/kfRl59wv4p53m6QZkoZw0k5fpvaLvH1yi6R7nHOrs/CnIIfSOI5ONrNu8qq2bTGv8Ei9vApgh0r6tXPu//zndc49bWavSLrBObcie39RcWC/5KdM7RfOh62Tqf0izocFJ+Sx8Lq81MvnJF3mnJsf2X57STdI+pJzbpSkUf7zOufeMrMPJN3pnFuVrb8HrZO1kScz29/MHjGz+yI3RfMFA/m+1ZLmmtl+kdtmyYvaD408h0W2Xy2pQtIy//ZALulW59xznCjCyeJ++cQ5dyMniuLUiuNoq7wJslsk3SvpREnXS1rhnJvknNscPI4iz7eBC/Rw2C/5KVv7hfNherK4Xzgf5rlWHgsjnHPzA9dGy+Ut4N7k2ihyfxWBU2HJSvBkXu7mVZIaJB1vZjs55619ELnfrzyySt5k72Mivy+XN4HOHyEbZGZnm5dTv50ik1ydJ1rVBuGwX9AWWnEcrZC0QVKpvJzvns65Hzjn/iqp2sz2kTiOWor9kp/YL/mJ/QJfG14bbRe4NuqlxpQ8joUC1+bBk5l1NrOLzOzfZnaGmfVw3qTVUc6578tbuM+vLuKvgeBXHqmRN8HupMiw6ApJR8j7YpKkH8rLIX1J0tmucf0QNIP9grbQxsfRcnk542ucc0siz18Z2Xawc25atv6uQsd+yU/sl/zEfoEvw9dGl0q6WN610VddpGAECl+bBk+RHpZn5E2Oe0neYm83Ru6eFfn/RUlnSpJzrt7MSszsUjMbL2/S5SvyShDfbma3yIv8l0Uee4Nz7hTn3GOBgxfNYL+gLWTwOFriv4ZrXOCWxQBDYr/kJ/ZLfmK/wJeFa6NbnXOncm1UhJxzaf9TY4nzQ+SV1zwn8ns/SUcFtjtMXuWZ4GO7yxu6PDTye4m8ym3HB7YZIOkieSVV921JG9vjP/YL/ziOivcf+yU//7Ff8vMf+4V/HAv8y9S/tNd5igxNOjM7RtKf5UXlPSRtdc5dZd4kuIbINmdLOlrSL10g6javjHW1c+46C1QwCT5/Wo0C+wVtguMoP7Ff8hP7JT+xX+DjWEAmhCpVbt5CYOdLOlbSW2b2iKSD5K2g/Gcz6xm5fYRzbqx5k+lq5S0SttA1Ha58XNJoM+svabyZPeQik+c4CMNjv6AtcBzlJ/ZLfmK/5Cf2C3wcC8i0Zuc8mVef/kVJI+QdQBdL+qqkwyXVSZJzbp2kFyRdHvm91sx2lnSkc+62uOf7hqQn5U2oWyxvJXqqjqSJ/YK2wHGUn9gv+Yn9kp/YL/BxLCAbmk3bM7OOkvZ3zk2I/P49SXvLWxDsMuecv9ZPf0mfOOe2j/x+vqQ+8nJAvy/pU+fc22Y2XFKVc25KZv6k9oH9grbAcZSf2C/5if2Sn9gv8HEsICtcM5OiJJn/L/L7QZLejvy8RtL2gW1fkXRs5OcJ8tY/eF/SvyUd1Nxr8S/8P/YL/9riH8dRfv5jv+TnP/ZLfv5jv/CPY4F/2fzXbNqeiwjcdIWk5yI/vyTp55JkZr0kLZI0w8x2lPShpJ9KOs4593Xn3MfNvRbCY7+gLXAc5Sf2S35iv+Qn9gt8HAvIhrSq7UUOsIckXe6cm2Vmu8rLJ91HXqnGT5y3qBiyiP2CtsBxlJ/YL/mJ/ZKf2C/wcSwgU0JV2ws4UNJUSfPM7CJ5k+eulfRNSTOJ1HOG/YK2wHGUn9gv+Yn9kp/YL/BxLCAj0h15ek/SYEnzJS2V9Gvn3KeZaRrCYr+gLXAc5Sf2S35iv+Qn9gt8HAvIlNDBU6QO/o2S5kn6h3OuOpMNQzjsF7QFjqP8xH7JT+yX/MR+gY9jAZmU1sgTAAAAALRXzVbbAwAAAAAQPAEAAABAKARPAAAAABACwRMAAAAAhEDwBAAAAAAhEDwBAPKamdWb2WQzm2ZmU8zsKjNLef4ys4Fmdm4z2+wXed7JZrbWzOZFfn7dzL5sZiPb9i8BABQ6SpUDAPKamW12znWJ/LydpCclveecuzHFY0ZI+oVz7oyQr/GIpBedc8+0usEAgKLFyBMAoGA451ZKuljST8wz0MzGmdnHkX9HRDYdJenoyEjSz82s1MzuMLMPzexTM/tRqtcxs++Z2b2Rnx8xs/vMbLyZzTWzEWb2dzObEQm6/MecbGYfRNrxbzPrkqG3AQCQIwRPAICC4pybK6lU0naSVko6yTl3kKRvSvpTZLORksY55w5wzv1R0g8kbXDOHSLpEEk/NLNBabxsT0mHS/q5pP9K+qOkfSTtZ2YHmFkfSb+SdGKkLZMkXdnKPxUAkGfKct0AAABaoVzSvWZ2gKR6Sbsn2e5kSfub2TmR37tLGiJpXsjX+Z9zzpnZVEkrnHNTJcnMpkkaKGlHSXtLes/MJKlC0gdp/zUAgLxG8AQAKChmNlheoLRS0o2SVkgaKi+boirZwyRd7px7pYUvWx35vyHws/97WaQ9rznnvt3C5wcAFADS9gAABcPM+kq6X9K9zqt41F3SMudcg6Tz5aXzSdImSV0DD31F0qVmVh55nt3NrHMbNm28pCPNbLfI83c2s2SjYACAAsXIEwAg33U0s8nyUvTqJD0u6Q+R+/4i6Vkzu0DSGElbIrd/KqnezKZIekTS3fLS6z42L69ulaSvtFUDnXOrzOx7kv5pZpWRm38laVZbvQYAIPcoVQ4AAAAAIZC2BwAAAAAhEDwBAAAAQAgETwAAAAAQAsETAAAAAIRA8AQAAAAAIRA8AQAAAEAIBE8AAAAAEALBEwAAAACE8P+OTY/SMfk87gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f_filtering(df, cutt_fc):\n",
    "    \n",
    "    new_df = df.copy() \n",
    "    index = df.index\n",
    "    \n",
    "    dt = 25 # seconds\n",
    "    n_samples = new_df.shape[0]\n",
    "    \n",
    "    # Fast Fourier transform\n",
    "    f_hat = np.fft.fft(df.to_numpy())\n",
    "    f_mag_2 = np.real(f_hat * np.conj(f_hat)) / n_samples\n",
    "    fc_df = (1 / (dt * n_samples)) * np.arange(n_samples)\n",
    "    \n",
    "    # Cutting frecuency\n",
    "    ind_filter = f_mag_2 > cutt_fc\n",
    "    \n",
    "    # Filter signal\n",
    "    f_hat_clean = f_hat * ind_filter\n",
    "    filter_signal = np.fft.ifft(f_hat_clean)\n",
    "    \n",
    "    \n",
    "    filter_signal = np.real(filter_signal)\n",
    "    \n",
    "    return filter_signal\n",
    "\n",
    "df_grouped[sample_sensor].plot(x = 'DateTime', y = 'Value', kind = 'line')\n",
    "plt.xticks(rotation=20)\n",
    "plt.title('Raw data')\n",
    "plt.show()\n",
    "\n",
    "# Cutting frecuency\n",
    "cutt_fc = {\n",
    "1: 5,   \n",
    "2: 150,     \n",
    "4: 0.001,   \n",
    "5: 0.005,   \n",
    "6: 25,   \n",
    "7: 25,   \n",
    "8: 1,   \n",
    "9: 1,  \n",
    "10: 0.1,  \n",
    "11: 0.1,  \n",
    "16: 0.1,  \n",
    "17: 0.1,\n",
    "18: 500,  \n",
    "19: 5,  \n",
    "20: 20,  \n",
    "21: 20, \n",
    "102: 10, \n",
    "103: 50}\n",
    "\n",
    "df_clean = df_grouped.copy()\n",
    "\n",
    "df_clean[sample_sensor].plot(x = 'DateTime', y = 'Value', kind = 'line')\n",
    "plt.xticks(rotation=20)\n",
    "plt.title('Filtered data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selection via correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_mat = np.abs(df_clean.corr(method='pearson'))\n",
    "sns.heatmap(corr_mat)\n",
    "plt.show()\n",
    "\n",
    "corr_thl = 0.15\n",
    "corr_label = np.zeros(df_grouped.shape[1])\n",
    "\n",
    "for i in label_target:\n",
    "    corr_label = corr_label | (corr_mat[i] > corr_thl)\n",
    "\n",
    "df_clean = df_clean.loc[:, corr_label]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe  dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         count         mean        std          min          25%          50%  \\\n",
      "Label                                                                           \n",
      "4      83751.0     6.661477   0.042329     6.210000     6.637200     6.658400   \n",
      "5      83751.0     6.675937   0.027618     6.116000     6.665900     6.677000   \n",
      "8      83751.0   102.547391   3.100177    94.335000   100.340000   102.050000   \n",
      "11     83751.0    13.455714   0.589912    12.027000    13.052000    13.468000   \n",
      "16     83751.0    10.888024   0.363838     9.751400    10.639000    10.858000   \n",
      "17     83751.0    10.398421   0.554129     9.000000     9.958750    10.247000   \n",
      "18     83751.0   432.042805  30.027955   287.000000   403.000000   434.000000   \n",
      "20     83751.0    10.770612   7.895316     0.000000     4.524946    10.399938   \n",
      "21     83751.0     6.346685   5.469337     0.000000     2.949953     5.749941   \n",
      "59     83753.0    28.205664  93.949043     0.000000     0.000000     0.000000   \n",
      "87     83753.0  1355.731138  22.163138  1312.000000  1337.000000  1359.000000   \n",
      "88     83753.0   350.214559  12.318614   327.000000   341.000000   352.000000   \n",
      "89     83753.0   388.198071  14.008798   363.000000   377.000000   387.000000   \n",
      "90     83753.0   349.059437  16.988944   321.000000   335.000000   347.000000   \n",
      "91     83754.0   416.076259  14.060818   391.000000   404.000000   416.000000   \n",
      "92     83754.0   416.076259  14.060818   391.000000   404.000000   416.000000   \n",
      "102    83754.0    10.593582  11.037496     0.000000     3.690000     6.845000   \n",
      "108    83754.0    80.447001   6.014080    24.281242    76.781260    77.437490   \n",
      "109    83754.0    40.402275   4.693247    30.187502    37.406260    38.718760   \n",
      "110    83754.0    94.436540   5.186919    67.593765    89.906240    93.843740   \n",
      "\n",
      "               75%          max  \n",
      "Label                            \n",
      "4         6.681900     7.116800  \n",
      "5         6.688200     6.958000  \n",
      "8       104.720000   116.270000  \n",
      "11       13.883000    15.521000  \n",
      "16       11.141500    12.417000  \n",
      "17       10.922000    11.700000  \n",
      "18      457.000000   655.000000  \n",
      "20       15.724897    60.000000  \n",
      "21        8.524895    60.000000  \n",
      "59        0.000000   721.000000  \n",
      "87     1379.000000  1381.000000  \n",
      "88      363.000000   364.000000  \n",
      "89      402.000000   409.000000  \n",
      "90      363.000000   380.000000  \n",
      "91      428.000000   440.000000  \n",
      "92      428.000000   440.000000  \n",
      "102      12.880000    89.370000  \n",
      "108      84.656250    95.812500  \n",
      "109      45.281254    56.437508  \n",
      "110      98.437500   121.406240  \n"
     ]
    }
   ],
   "source": [
    "print(df_clean.describe().transpose())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split dataset: train, validation and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_data = len(df_clean)\n",
    "\n",
    "train_perc, val_perc, test_perc = 0.7, 0.2, 0.1\n",
    "\n",
    "train_df    = df_clean[0:int(total_data*train_perc)]\n",
    "val_df      = df_clean[int(total_data*train_perc):int(total_data*(train_perc + val_perc))]\n",
    "test_df     = df_clean[int(total_data*(train_perc + val_perc)):total_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Min-max normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_normalize_minmax(df, train_df):\n",
    "    norm_df = (df - train_df.min()) / (train_df.max() - train_df.min())\n",
    "    return norm_df\n",
    "\n",
    "def f_inv_normalize_minmax(norm_df, train_df):\n",
    "    df = norm_df * (train_df.max() - train_df.min()) + train_df.min()\n",
    "    return df\n",
    "\n",
    "norm_train_df   = f_normalize_minmax(train_df, train_df)\n",
    "norm_val_df     = f_normalize_minmax(val_df, train_df)\n",
    "norm_test_df    = f_normalize_minmax(test_df, train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show tails with box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mCyuh4XDvTlmTfCCRTPJBheGMip5S8RPe0rZVToIwdtzHUa698sor8cevvfqahZXQcBiAqCDxIJcdLjcqduZYIbkHiAEoO9L2cXZZj3ap02qhUAiPPfoYzqhbiLXzrsXWt7aisbHR6rIoBbl7dxLPDlNO0+iwV48IgLBGcipskGbOLkPgJOIlQKPz0ksvoaOzA5dOvxQXTl0Jt8ONRx55xOqyKAUGIMqatDNpdiF5uUmujSgvTMHC6XRaWIh9SQ5nkgOQtOXGIZej9+gjj2JiWQMW1Z2BKk8Vzpl0Dp568ikMDAxYXRolYQCirLGxPDrG8pJ8IJHcOCDKBwXEe4A4BK7wyDxuSaxpkOzq5Ghra8Pb29/GBZMvgENF9x0XTlkBn9+H117jtUDSyN27k3iSh8DJPMjJx+VGxc4cLBiAMie5t0D8fk14eYC8dSrRCy+8AK01zpl4dvy1+TXzUeOtwXPPPWdhZZSK3L070RiIP+AJxeVGBLE9QJJDhpnk2tjLTbny3LPPYWblDEwpnxJ/zaEcOLt+GTa9vgl9fX0WVkfJZO3dyVZ4ICk8knv1iPJB8RqgMZMWHM0kn+SRFhyl1SOZ1hp79u7BguoFQ947ufZkBENBvPvuu/kvjNKSu5ci8SQfSCRqb2+3uoQRMdRSseMQuOzYpXeKRovH+UwopTB3zlw09R4a8l5jTyMcyoHZs2fnvzBKS+7encRjY3l0AoGA1SWkxTBLNBQDUHbYczZaMifIYagdncVLFuPAiQMIhBOP9bu79uCkk05CeXm5RZVRKnL37iSe5AAkuUEvsTbj4CaxNqJ84hC4sWNjeXSMva7c5cbjQibOOOMMhCIh7OveH3/NH/bjwIkDWLR4kYWVUSoMQJQ1yY1lybXJPcjJDrVE+WCXIXDS2GW5STs2SD4esAdodBYtWoRSbyleOPZC/LVXml9FKBLCueeea2FllIrcvRSJJ7mxLO0gZxeS1ylRvkluyEsmebmxIU+5UlZWhtXvWY3Xj29Ch68DER3BxkNPYt7ceTjjjDOsLo+SyN1LkXhsLBceBsfCw3U6OnbpyZCGQwfHILaJSgtn0uqxgzVr1kBD42+Hn8bb7dtxrO8Y1n5oLZelQC6rCyD7YgAqPGwsFw4ecMeOAShzDI6FjfuTzEyePBkXrLgAz776HA73HkHdhDpceOGFVpdFKXAvRVljACo8DEBU7NiQHzvJy01mQ56zwBWS5cuXoz/Yj23t23D2OWfD5WJfg0Ry91IknuQAxIZ8dngjVKJBbPRlzi7Bkes0O1xumVuwYPBmqPPnz7ewEhqO3L0UiSc5AEkmOZxxnRYOyd8zu2CjLzvSAhB7Miifpk2bFn9sDkMki6y9FNmK5AaW5Nokhwz2ABUeNviyx2WXOfYAjZ202qTVYxfmSUBmzZplYSU0HLl7KaICJTkASa6NKB/YWzB2DECZk/x9k1ybXXg8HqtLoDTk7qVIPMm9BZJ7gCTXJnmdEpE9sLFcGLgex47LUC4GIKI8k9jLYtTEAEQ0iI2XzHEI3NixNqL8kbuXIipQEgNQMBhM+H8iomyxsUxE0jEAEeWZxCFwoVAIAHuAiCg7dgk9dqlTAl4DRIWMAYiyJrEhb5Bcm8weoEDs/9kDVGgkbwtE+WCXhrzk2ogKDQMQFSTJjT6JASgQYAAqNGxMFR67NOSpMPA7NnbGsZXkYQAiyjOJ4cwIPtxZExFZg4GjMAwMDMQf79ixw8JKaDgMQER5JjMAsQeIyE4kz7RGmbNLr57k2qR566234o83bdpkYSU0HO5BKWsSG/J2IHG5BQKcBa7QSPyeEREVMq01nn/+eSgolDpL8eorr8Lv91tdFqXAAEQFSXLjT+JMaxwCV7h45rYwSVuv0uoxMx8PJB8bpLFL75QUHR0duOUbt+Dpp59GlacKNSU1OHL0CP7pc/+EPXv2WF0eJWEAoqxJPpBIrk0iBiAi+dggJZLphRdewGc/81m8seUNfHT+9ZhcNglVnip8ackX0dvWi5tvvhm/+c1v4recIOvlNAAppVYrpXYrpfYppb6a4v2ZSqm/K6XeVEptU0pdlct6aHxJDhmSa5M4CxwDENFQkkOG5Nokk3xskLZOpdUjUU9PD773ve/hO9/5Duocdfivs7+JVl8rmnoOoamnCY+8+xiW1C3GuRPPxW9/+1v8y83/gsbGRqvLJgCuXP1hpZQTwE8AXA7gMIBNSqmHtdY7TT/2dQB/1lrfo5Q6DcDjAGbnqiYaX5IPJJS5SCSCcDgayoIhXgNEREQ0nJaWFjzxxBN4/LHH0XOiBx+YuwbvnXU1nA4nmnoOYSAcnQlud9duAMDXln4FSxvOwq93/y/+6XP/hEsvuxRXX301FixYYOV/RlHLWQACcA6AfVrrAwCglPojgPcDMAcgDaAq9rgawNEc1kPjTOK1LAbJ4UzacjN3yYc4CQIREYFDLpOFw2Fs2bIFjz76KDa9vglaa5xRdwY+uOwDmF01a8TfXzZxKeZXn4QHDvwVf3/qGWzYsAHzT5qP977vvVi1ahW8Xm8e/ivIkMsANA3AIdPzwwDOTfqZbwJ4Uin1BQDlAC7LYT00ziQO5TJIDkDSlltCAGIPEJFYbIQS5V9nZyc2btyIxx97HMdbjqOqpApXzXoPLpp6ERpK60f1t6pLqvHJUz+Bj8z/MF4+9gr+fvRZ3HHHHfjp//wUl11+Ga6++mrMnj07N/8hlCCXASgT1wP4ldb6dqXUeQB+o5RaqLVOaCEqpW4CcBMATJo0Cc8+++y4fPjatWsBYNz+3niSXFt9fXSDb2lpEVdfXV0dtNZ45513sH//fqvLSVBVVYWKigr09PSIWm6RSCT+fXO5XKJqA4CGhgax24PL5cLatWtRW1srrrbe3l6sXbsWFRUV4mrz+XxYu3YtvF6vuNpKS0vh8XiwatUqbNmyBU6n0+qS4pRSqK+vx6pVq8Qtt1AoJLY283JrampCS0uL1SXFuVyueG1aa1HLzuPxxI/3ErfVXB8X/H4/Ojs70dvbC601Lr/4clR5KlHmKgOUwnG04DiGfpf6dvQlPvf2YfusnUN+bvK8KbheXw9/2IeeYA/6gv146qmnUFpaipqaGlRUVOTkv8tYp9LWJ5Dftm8uA9ARADNMz6fHXjP7NIDVAKC1fkUp5QVQDyR+o7TW9wK4FwCWLVumV61aNS4F3nrrrQCAjRs3jsvfG092qK22thbnn3++xdUkuu222xCJRHDKKadg5syZVpeT4I477oDP50NZWRnG6zs8Hrq6unDbbbcBAOrqavH73//R4ooS3X333XjooYewbt06UcsNAO6//36sX78e1157bXzHLcULL7yAu+66CytWrMD1119vdTkJdu3ahTvuuAMLFizAjTfeaHU5Ce6++250d3dj/fr1uP/++1FVVTXyL+XJr371K7S1tWH9+vXijg2///3vceTIEZG1/fKXv4wvtzvuuAOnnXaa1SXF3XffffHaPv/5z4vax/3pT39CW1sbgGgYkFQbkPt20sc++jH0dvfioikX4qJpF2Fq+ZSMfs/R64DX68Xq1auxYcMGOHodWNg48neuJ9CDF4+9hCeOPI62gXb88pe/xNSpU8f6nzGEsdykrU8gv23fXM4CtwnAfKXUHKWUB8B1AB5O+pkmAJcCgFLqVABeAK05rInGkbRrWcwkD4GTttzMQ/KkDc8jIiKywuw5szHBOwHXL7gu4/ADAP2hfqxevRrr1q3D6tWr0R/qz+j3Kj2VeM+s1Vg0YRFKvaWYNGlStqVTBnLWA6S1DimlPg9gIwAngF9orXcopb4FYLPW+mEA/wbgZ0qpf0V0QoRPaMktV0ogeT57yV8jaSGDAYgoNWnX3Eirx0zyPtdM8jIkWRYtWoTNmzfjROAEqjyZ9wSXucqwYcMGAMCGDRsw0TVxVJ+7q3s3Tl94uqjht4Uop9cAaa0fR3Rqa/Nr3zA93gngglzWQLnDAJQdaT1ABrYLCovkbcAuHA7eK5yKV7HPArdo0SIAwFOH/ob3zX4fPE53Rr9X6iqFr9eHBx98MPq8pjSj39Na4632bTjaexSrF63OqmbKnNWTIJCNSe4tkNz4kxqAqDAVY8OFyKzYG/KUnfnz5+OMhWfgke2P4rljz+PSaZfgkmkXo9JTOa6fE4wE8Wrzq9h46Ekc7j2Cugl1uOiii8b1M2goBiDKmuSGvOQAJDk4EhU7cwOZPUBExcvlcuH7P/g+3nrrLaxfvx5/3fQgHmt8HCsmX4ArZl6ByWVju0anN9iLvx95Fn87/DS6/d2YM3sOvvSPX8KqVavgdmfW20TZYwASTmst9owVA1B2pC43uUuMyBpS972UPa5TGg2lFJYsWYIlS5agsbERf/nLX/D000/j70eexZkNS7B65pWYXz1/VN+rlv4WbDz0FF489gL84QCWLl2Ka6+9FmeddRa/n3nEACRcJBIReyEcrwHKjrQAFN/hakApnvEmosIlqYF5zz33oKurK/78hRdewPve9z7rCkry6quvxh8//vjj6OjowLp16yysyFqzZs3CF7/4RXziE5/AI488gkcefgRvbHkTa+ddi/fOvjqjv7G7czf++80fQDkULr7kYlx77bWYM2dOjiunVBiAhJPWkDfXw6Fc2ZEWHM3DfCQ1Doisxu2h8EhapwcOHEAwGIw/N+65I0V7e3v8cXNzMw4cOGBhNXJMmDABH//4x/GRj3wE3/ve9/DQ6w/j3EnnoKG0YdjfC0VC+N89v0V9Qz3u+NEdqKury1PFlApP9wonrbFslwAksTZj2YntAQLgcMhpHBBZgRfMFx6uR8oFr9eLf/qnf4LT5cTv9/xhxJ//2+GncaT3CNZ9bh3DjwAMQML5/X6rS0hgl3vGSK5NWqhN7AHiLoGIChfDEI2nhoYG3HDjDXizbSu2tr0FAJhZOQOlzlKUOktxcs3JmFk5A13+Ljx48CGcc845WL58ucVVy5eP0U9s7QjHAJQdibVpHa1JcgByOLlLIDKwsUxEI1mzZg1KS0vxRusbAICPLfgoZlbOxMzKmfja0q/gYws+ip0d78AX8uFjH/sY9ytCsLUjnOQAJO36JDOJtRk1SQtACUPguGMmIiLKWCAQwMDAACaWTkz7M8b1QeZJL8haDEDCSQtAZhJ7WQwSa5MagMyzDHIIHNEgnqklopE0NTUBAKaVT0v7M9MqpgIAGhsb81ITjYytHeF8Pp/VJSSwSw+QzAAU/X9pAShhCBxv/EgkluR9LlGxMkLNtPKpaX+mzFWGCaUTGIAEYWtHOGk9QHaZBU7aTGvA4LIzT3sqQeIscNwlUPG655570NPTE39+7733WlgN5QJDJI238vJyAECnvyvtzwTCAfQGeuM/S9Zja0c4aQGIPUDZkzoELrEHiEN+siF5W6DMHThwIGH73L9/v4XVDMXvWXbMy43LkMbbWWedBZfTha1tW9P+zM7OdxAIB3DuuefmrzAaFgOQcNIa8uwByp7U+wAlBiDnMD9JyexwjQgbfIWJ6zU7ko9bZE/l5eU444wz8FbHtrQ/81bbW/CWeLFo0aI8VkbDYQASSPLZKk6DnR1zPZKHwMEGDXoaHTuENMqMXU5ASSbtmEqFYfl5y3G09ygae5qGvBcIB/BG25s4a+lZ8Hg8FlRHqTAACWQ+sEnrLbDLQVfacjMPq5E2BM7MyWuAiMQyN96l7eMkY3CkXLvssstQXlaOhw4+NOS9vx95Ft3+bnzgAx+woDJKh60dgcwX4ZofSyA5nJlJq81cj7TazDgEjsge2JDPDpcb5UJFRQU+eO0H8Ubrm3j3xLvx1/1hPx5rehxLFi/h8DdhGIAEam9vT/lYArsMgZPWy2IOPdJqM5M4CQKHrBBFmbcFyfsRadgDRPnwgQ98ABXlFfjrwYcws3IGZlbOwDOH/44T/hO48R9utLo8SsIAJJA59HR0dFhYyVB2CUDSelmk9wAZ14lI7gHitSxU7BiAsmOXoYM82WNv5eXl+MAHP4C32t7C5TMuw/Xzr8PTR57G4kWLsXDhQqvLs5V8HO8ZgATq7OwEAJS5XfHHUpgPHpIDkLTGgX16gLhLIJLKLg15ySQft3iSx/6uuOIKKKXw0rGXsbtzN9oG2vGeq95jdVmUAls7AvX19QEAGspK0Nvba3E1iezSkJdWW2JwlNdwMY67DECUT9IbfJLOyN9zzz3o7u6OP//Vr35lXTE2wyFwlC8TJ07EmUvOxEvNL+P5Yy+irLQM559/vtVlUQps7QhkBKAJpSXo75MbgCSfgZQWgOQPHTSGwMndJUhqjNoJl1thSL5Ja2Njo4XV2Jfk4xYVhsuvuBxtA214pfkVrLp4FUpKSqwuiVJwWV0ADdXX1wenQ+F4nw8BWbeMSTgASz6QSAtACT1AApebcSLe6eQ1QIVG8nJjOMsel13meO0U5dOSJUvijxcvXmxdITamtc75sUvu6d4idvDgQbiUQiAcRlt7B06cOGF1SXF2GQInLZzJnz5cfg8QEQ1iAMoch8BRPtXW1sYfT5482cJK7Csf+ze2doQJBoN4Z+dOlLldKHNFO+h27NhhcVWDxPcAxTaaYFBW11nC0EGBB+DBWeC4SyAySA4ZkmuTjAGIcs3cczFlyhQLK7EvBqAitG/fPvgDAZS5XfC6nXA7HXj77betLivOHIAk9gAZm4y0AJR4DZC84GiHSRDY4CMaxO0hc+wBIqtUVVVZXYItMQAVoc2bNwMASt1OOKAwr7YSm15/XczBTvoQOGM5SeudMg66ClIPwNEE5HLxskAiO5ByTLADTh9OVpF8DaZk+dhOGYAECYfD2PDEE1g4sRYuFV01y6c3oOnQIbzzzjsWVxcleTYz8wYjtgdIyVtuwOBOWuIkCDyAEA3FAJQdLjci+fLRTmIAEmTTpk1oa2/HxbMHL5pbPq0BXrcLjz32mIWVDTJChoK8M2mSp+g2L7dImAEoG2y4UL7xO2d/99xzD3p6euLPn3nmGQurIaJM5OMkNgOQEIFAAA888ACqvSVYMnlC/PVStwvnTavH8889h6NHj1pYYVS8Ia/khQzzBiO5Bygs8hog+QGIPUFEgxjOMpN8/6TW1lYLqyGiTPj9/px/BgOQAAcOHMAXPv95vPXWW3jv/GlwJV2IfsW8aXBC43Pr1mHjxo2WHvjM17KEw7KuAZI8QYOxzpTYIXDR/5d4DRAbekRDcbvIDpcbkXw+ny/nn8EAZKFIJIL7778fX/jCF9B5vBlfXH46rpg3bcjPTa0sw3cuPhOzKkrwwx/+EN/61rfQ3d1tQcWmAKQAHZF1IJEcgMyhR+Lxlz1ARDQWDBZENF4YgApYS0sLvvKVf8d9992HRQ3V+M7FS7DYNPQtWX2ZF1+54Ax85PQ5eO3VV/HZm27Cpk2b8lhxVMJ0olpWT4bka4DMPWda2HIDABWbdENyAGIDKztcbtmTvOxYGxEVKvN1e7nCAJRnkUgEjz76KD57003Y8847+PSZ8/HP55yKqhJP/Gd+t20/Grt70djdi1tf2IbfbdsPAHAohavmT8c3L1qMskgQX//613H77bfjxIkTea0fkHkxv+QpuuMNAgVEhPWcmUkOQOwByg6XG+XaPffcg5aWloTnRETZ6urqyvlnyBvwX8AaGxvxozvuwM533sFpDTX4xJLTMam8dMjPNXX3YSAUbczvah861G1mdQW+edESPLirEU889RRef+01rPvc53DRRRcVdWPHHICkXWeT2AMkLwAZ3xuJ1wARkWwHDhxIuGj5wIEDFlZDRHZkbht1dnbm/PPY2smDQCCAP/7xj/jTH/8Ir8uBz5y1ABfMmDimsOJxOvDh0+dg+fQG/GLrPtx6663421NP4fNf+AImT5488h/IkvkLKq0hL/kaIMnLDbDHNUBEZA8S93FEJNvAwED8cT4CEIfA5djevXux7h//Eb/73e9wzpQ63HrJWVgxc9K49dTMrK7ANy5cjI+dMRfbtr6Jmz7zGTzyyCPj8rdTMQ/lAmQd5CRfA2QXkgMQG1VERESF6fjx4ykf5wp7gHJIa43bf/AD9Ha04UvnLcQZk2pz8jkOpXDFvGlYOqUOP39zL3784x9jyZIlmDFjxrh/Vnw6Z8hrkJqHvUmbotsuHA6550SKeXgnkZ1IOzYQkXzHjh0DEG3THsvDfS/ltnYKwFtvvYWD776La0+ZmbPwY1ZX5sVnl54Ml8OBhx56KOefJ01iD5Csa4ASZs/jJAhENEY8IUBEhcQIQOVuL44ePZbzz2MAyqG//vWvqCzxYPn0iaP6vYFgCF6vF2vWrIHX68VAMPPejGqvB8unN+DJjRvzMo2gJImTIMgaAme+Eaq0oYNmknuAeFaZ8k3yd05yAJK83IhIpiNHjsChFEpdHvT19+X8fpdyWzs2197ejtdeew0Xz5oEj3N0i7k/FMLq1auxbt06rF69Gv2jvKD/ynlT4Q8E8Mwzz4zq9+zOPNMaJ0HIjsQeoO3btwOQ3eAjInvgfoRIpnfffRclTjdKnO7481ziNUA54nQ6obWG1z36BmWZy4UNGzYAADZs2IBJntGtJq/LGa9hvNnhRqhKye0BSn4sjcQeoFyfBSKyI8kNecn7OCKKCofDYk56aq3x7sGD0QDkGgxAixcvztlnymvtFIiamhpMnzYNe9pHf5PSUrcLPp8PDz74IHw+H0rdowtAxmcuXLhw1J89EvMkCNJGciUMgRN2DVC8d0r4jVAlN6qIaJDkbZUBiEi+YDBodQlxra2t6OvvR4nLDZdyosJTioMHD+b0MxmAcuj0hQuxr7MH4Tw3eHe3d6O8vAwzZ84c979tngZb2kEuMWTICkDmcKa1FlefgY0qIhoryduq5H0cUT5JCkDGzZNLnG5AATMq63EwxzdUZgDKoaVLl6LXH8R//P0NvNh0POdBaH9nD+54dQeebzyOpUuX5WQ4k/k6G2mNePMQOGn3ATLXBshbdgbJjQPJtRHlm+TtQXIAIqIoiQHIG7v+Z2ZVAw4ePJjTthyvAcqhCy+8EE6nE7/73W/xszf24KHdh/C+BdNx/oyJcI1jONnXcQIP7m7C28c7UVlRgY9//ONYs2bNuP19s8TpnGU14s3hLCystuQAFAqF4HLJ2/wkN6qIaJDkbVVyAJK83IjyyefzWV1C3L59+zCpojZ+4n529UT4DwZw5MiRnIxmAhiAckophRUrVuCCCy7Aq6++it/99rf4+Zt78dCew3jf/GlYMXNSyiA0s7ocjd29AIBZ1RWYWV2e8u/vae/Gg7uasKO1C1WVlfjUpz6F973vfSgrK8vZf1PiRANyQ0ZEWA+QcabFETv2BgIBeL1eCytKTeIkCAbJjSrJJC83NkazJ3nZSf7OEVHUwMCA1SXEHdi/HzMrG9AT6AcAzKqO3j5m//79DEB2ppTCeeedh+XLl2PTpk347W9+g19u3YOH9xzGdafPxjnTGhJ+/mOL5qGpuw8A8LWVi4b8vWM9/fjfbfuxs7ULNdXV+MxnPoP3vve9eWlQGyHDAcHDzCCvNr/fDwBQsXwhqevZLiQ3+CTi8ipsXL/Z4XIjiurv77e6BADRk+ktra1YOmdGPAA1lNUAAFpaWnL2uQxAeaSUwjnnnIOzzz4bb7zxBn7x85/jJ5t24bUjrfiHRSeh2usZ9vfDEY0N+w/jr7uaUOItxWc/+1lcddVVee1JSLzORta9duI9UgKvAQoEAgASe4AkYuOAyB7YW0tEYyElAHV3dyMcDqPWWxF/zetyo8xdgra2tpx9LgOQBZRSWLp0KZYsWYL169fjN//7v/iPv7+Bjy2ci/OmN6RshB4+0Yf73tyLg509OP+88/D5L3wBdXV1ea/duMGoUkAoJCtkSB6eZ/QAGW0W47k0DECUT2woFybJ65X7OCpm5pPDJ06M/jYtuWCEnFpvZcLrtaWVaG9vz9nnMgBZyOl04iMf+QjOO+883P6DH+CnW3bjtSOt+MTik+I/E4pE8Njew3ho9yGUl5fja1/7Gi666CLLduLma1kCQZk9QBInQTDG2hqrTcqZFzuR3Kgiyjf2AGVHcgCSvNyoMPT09MQfd3V1WVeIiVFThTtxNFOFqySnIU3uHjSPrN7pzJw5Ez+84w589rOfxc6OXtz+6k7o2F1GH91zCA+804gVK1fiZ/fdh1WrVlm6AzcCkAIQDMm6jiVxeJ6sANTf3w+HGhwCJ+niQzPJjQPJtRHRIKuPqUSUmjn0dHd3W1eISXl5dKKvgVDipQED4UD8vVxgAIKMC9KdTic++MEP4nOf+xwOdffCFwpDQ+P5phYsXboU//Ef/4Gamhqry0QwGITC4BA4SQc6yZMgDAwMQCn2ABHR+OAJgexwuVEx6+zsjD+W0gNUVVUFAOgLJk7L3Rv0obKyMtWvjAsGIMi6HuPCCy+Ex+NBty+A/mAI7f0+XHnllVaXFRcMBhMa8hLCoyH5GiBJ4ay/vx/KMbjcpPYAEeUTG6PZk7zsJO17k0lebkS5Zlxv41COnE4wMBpGAOoNJLaLev2++Hu5wAAEWTNylZeXY8WKFTgRCKHTF0B5eRnOO+88q8uKCwQC0QAUey4pAJknaABkTYRg9AAZw/bZAzR6khtVRPkmuSHPbZVIJmNSgVJXCdpaZQSgsrIyeEtK0DYweL3PCX8/AuEg6uvrc/a5DECQFYAAYMWKFYhojd5ACOeccy48nuGnx84nYwicxOmczT1A5ucS9PX1wsEhcGMiucFHlG/cHrLD5UbFrK2tDQ7lgMfhFtMDpJTCzJmzcKRncMY34/GsWbNy9rkMQJA1BA4AJk+eHH88ZcoUCysZKrkHSGQAij03eoQk6O/riy83l1Ohr6/P6pJS4plbyid+34iI8qetrQ0uhxMuhxMDvgExbZFZs2fhSG9H/Pnhnmg4szQAqagblFLfiD2fqZQ6J2cV5Yn5wCupEQ8ADQ0NKR9LEAqFogEjdhZNUsiIT4MtcAhcf2wIHAC43UrsNUCSG6SSayOiQZK3VfYAUTFrb2+HW7ngckTvgtPR0THCb+THrFmz0OXrjd/C5GhvB8rLynJ6v8tMeoDuBnAegOtjz3sA/CRnFeWJOfRI6wEyz3ohLQCFw+HoLHCm51Ik9wBJCkA+n88UgKLPJWLDhYiIANnHA8pOR3tHvAcIQE5vNDoaRtAJR6LtuG5/PyZMqMvpcT+TAHSu1vqfAPgAQGvdCUDORSlZMnf7SekCNJhXuISpr83C4TCgZIaMeBiLFSeldyoSicDvD8Svm3I55QYgIiIiKjyRSASdXZ0JAUhKD5Bx4j8cC919QR8qq3I3BTaQWQAKKqWcQPTOnEqpBgByWr1Z6u3tTflYmlxOAZiNSCQSGwIXfS6pByg5jEk5exUIBKC1jvcAuZyaQ+CIiIgob/r6+hAOh+FUTrhUNABJuRnqYACKtuP6cnwPICCzAHQngL8CmKiU+i6AFwH830z+uFJqtVJqt1Jqn1Lqq2l+5sNKqZ1KqR1Kqd9nXPkYSe4BMpMWgCQzwpm04XnGcEsjADmdGoGArGGXBskBSHJtREREkhltXafDAYdyJLxmNbfbDWDwOB+MhOFyuXL6mSP+da3175RSWwBcimjbco3W+p2Rfi/Wa/QTAJcDOAxgk1LqYa31TtPPzAfwNQAXaK07lVITs/zvGDXz3XCldAGm4vV6rS7BNiKRyGD6gZwGc/IkGy6nvIk3DFKWGREREY0fY7STQzmglEKpu0TMCKjm5mYAgNsZ7Zmq91ai+VhzTj8zk1ngZgLoB/AIgIcB9MVeG8k5APZprQ9orQMA/gjg/Uk/8xkAP4ldVwStdctoih+LlpboRzmUQmtra74+dtR44XfmkofASbk+ybhZ7GAPEBAQNvGGgQGIiMaKxy0ieYyw44z1/pS5S8UEoMOHDwMAPLHZ6SZX1OLIkcM5bZNk0r/0GKLX/ygAXgBzAOwGcPoIvzcNwCHT88MAzk36mQUAoJR6CYATwDe11huS/5BS6iYANwHApEmT8Oyzz2ZQ9vBCoRAaGhqgAEydOnVc/uZ4Wrt2LQCIq2vRokU4euQwlENh7ZUXYd++fTh06NDIv5gHDQ0NqK+vh9MBrF29Ctu3b8fu3butLguBQAAnnXQSGhvfxYA/OrFFTY1X1LotKytDSUkJtNai6gKiXePG3aCl1eZ0OrF27VrU1taKq623txdr165FRUWFuNp8Ph/Wrl0Lr1fWdtDV1ZXwvK+vT0x9ybV1dXWJrS0cDoutzefzia0NkLWPczgSz5FL+s4BcttJgMza+vr6sHbtWrzywsvoUwFccflqeMtKRdSolMJHPvxhvPz8iwAimLNiCaoG5uGZZ56BM9YrNN4yGQJ3RlKRZwH43Dh+/nwAqwBMB/C8UuoMrXVXUg33ArgXAJYtW6ZXrVo15g/+7ne/i66ODpQ6XXhq45P49W/+d8x/czzdeuutAICNGzdaXEmiG2+8Ea2tbXA5gPXr1+PSSy/Fv//7v1tdFgDgrrvuQntbG2ororX9/Oc/x/Tp060uCwcOHMC+ffviz1ta2tDZ2Y3x+B6Plx//+Mfo6ekBAFF1AcC9996L9vZ2aK3F1bZ+/XqsX78eH/zgB+MHPCmef/553HXXXVixYgWuv/76kX8hj3bt2oU77rgDCxYswI033mh1OXGPPfYYmpqa4s/Ly8vFfOeSa6upqRFbm9PpFFub1+sVWxsga//7i1/8IuG5pO8cILedBMis7YUXXsCdd96J2VXT4HV58NITz2HqabNw3XXXWV0a1q1bh66jrRgIRUfHPPHQo2g60Yr/+q//wvLly3PymZlMgpBAa/0GhvbkpHIEwAzT8+mx18wOA3hYax3UWh8EsAfRQJRzfX19cCoFp8OBvn4ZF4HZwcDAADSAYGx0mdFtKUHyNUBShsClmoyBQ81GL/lsJGWGw5Eo3yR/5yTveyXXRvZnDMd3xLZPt8MVf81Kvb29OHjwIBxKYSAUwEAogKYT0UtTtm/fnrPPHbEHSCn1RdNTB4CzABzN4G9vAjBfKTUH0eBzHYCPJv3Mg4jeYPWXSql6RIfEHcjgb4+Zz+eDAwpKKfiEXo9Bo5N88JAcgGKzyosj+QAsuTYiIiLJ4tcjx84Uu5RTxIRMO3bsgNYaZe4SwHSHkFKXB2+//XbOPjeTU6qVpn8liF4TlDyZwRBa6xCAzwPYCOAdAH/WWu9QSn1LKXVN7Mc2AmhXSu0E8HcAX9Za5+W2tAMDA3AoBQcUgsGgmCmTpUs+syfpTJ/WOmEabCkN5lRBTEhpNEZSvmPDkVijpP0GjT/J61fi9kCUD4O35JDVA7Rjxw44HU6UujwJr5e6SrB3796chbRMrgH6r2z/uNb6cQCPJ732DdNjDeCLsX951dvTA4dywOGIfhF6enpQU1OT7zJsR/KBLR40VNJzi9npgCu5VsnfPdZGRESShUIhAIMniZ0OJ3zBkHUFxRw7dgwNZVVDjlUlThfCvjBaW1sxbdq0cf/ctAFIKfUIhhmno7W+Jt170oXDYbS1t6O2xAu3Izq7RGtrKwNQFiQ1rpIb71Ia86nrkFEbEdF4k3RcSCbluECUb4O35DD3APmsLAkA0NbahtqSCuikdpHbGY0oeQ9AAH4w7p8mRHt7OyKRCNwOZzwAtbS0YP78vMy/YGuSh8BJvQ9QqgOu1GMwGwdENFaSjgtEFBUfAofBACThGqDWlhacXDoRbQMnEl53mToociFtANJaP5eTTxTAWJhupzN+11njxqhkX1prKCXvGiAaH5LXp+TaiGgQt1UqVgMDA3A73fETFF6XBwPd/ZbWpLVGR1cnamrnpg1AHR0dOfnsTGaBmw/gVgCnIXojVACA1npuTirKA3MKdsSayxJSsB1IPrNnpx4gGj3J3z3JtRHlm+TtgftjKlb9/f0odZfEn3tdXgz4Bob5jdwzJiFLngABQHym5oGB3NSYySxwvwRwD4AQgIsB/C+A3+akmjwx31XWGHPIe4xkR9KBTmoASl0HD8JE0rGxTMWM3//CMjAwAK9zMACVOj3wBwKWzoLs80WvQSpxuoe+qQCvyx3/mfGWSau/VGv9NACltW7UWn8TwNU5qSZPjLBjvuDKHIrInuw0C5zU4woPeNnhciMaJOnEWDJuq1Ssenp6UOaKD+RCmbs0/rpVBgNQ6gFpJU6PpQHIr5RyANirlPq8UuoDACpyUk2epEq7vA+Q/Um9D5CdvltSllkqkmtjg4/IHrg9ULHqaO9AbUll/HlN7HGurrHJxPHjx6O1eFPHiuqSMhxvPp6Tz84kAN0MoAzAPwNYCuAGAB/PSTV50tzcDADwOJ1wKIVyT0n8NbKv5KAhpQcoVQCSehCWWhcgO2RIxOVFVuF3LzuS979kf+3t7agpqYo/r/VWxV+3yt69ewEAc2ompXx/TvUk7NmzJyfbRiYBKKy17tVaH9Zaf1Jrfa3W+tVxrySPDh8+DKfDEZsCW2FyeSUOHzpkdVk0RpFIZLD7B3J6XtLVIfFgJ7EmIhpK8rYqOQBJXm5EuRIMBtF9ojupB0hGAKotrUR1SXnK92dXT0JvX2+8p2g8ZRKAbldKvaOU+rZSauG4V2CBw4cPY1JFFYzW8pSKKhxiAMqKpINJJBKJDoETdg1QugAkpT4zSeszmeTaJOLyKmxcv0SUKSNA1JXWxl+r9VbBoRyWjoDau2cv5lRNTPu+0TNk9BSNpxEDkNb6YkRnf2sF8FOl1NtKqa+PeyV5orXGnt27MaOyOv7azKpatHd0WJqC7ULyQVfqLHDpApCUHioziTUZjLtYSyT5jLfk2ih7kvfFknG5UTE6fPgwAGBKeX38NZfDiYnlE+Lv5Vs4HMbRY0cxrbIu7c9MrZgAADmpMaO5n7XWzVrrOwH8I4CtAL4x7pXkybFjx9DW3o5T6gbHG55cH02fb7/9tlVl2Zakg0lTUxOCIaAjNqHJY489Zm1BMaFQaFSvW0lKaEwlVzPBENmRpH1vMsmhW/JyI8oVY5TTlIqGhNcnl9bjUJM1I6Cam5sRDocxuaI27c94XR7UllbiyJEj4/75IwYgpdSpSqlvKqXeBnAXgJcBTB/3SvLECDmn1Q8GoNnVE1DqdmPbtm1WlWUbkg8eAwMD0ACCsU4MKRNbpOtVkRiAJNZkhLJc3QyNiIiokB0+fBhVJRUoj019bZhcXo+jR49acvLTCDWTy9MHIACYXFZjWQ/QLwB0ArhSa71Ka32P1rpl3CvJk23btqHKW4qppiFwTocD82sbsO0tBqCRJAcgSYFIam12uAbIWFYMQNmR8l2j8SV5vUrafxCRbIcOHcKUsvohr0+paEAgGEBLS/6b9d3d3QCAcnf03kQDQT+8Xi/WrFkDr9eLgaAfAFDh8aK7q2vcPz+Ta4DO01r/P6310XH/9DzTWuOtrVtxyoSGIV30pzVMxqHDhyydD90OpIaMKEm12IvRmJJ4nY0RIPv7+y2uJD3JQ36oMMna99oHlxsVo0ONTZiaNPwNAKaWR1+zYiKwuXPnAgCaTrQCAPpDfqxevRrr1q3D6tWr0R+KBqCmnlbMnTdv3D8/o2uACsWxY8fQ2taGU+snD3nv1NiQOA6DG57kAJRcipTa0tUhpT5gsBbJAaivr8/iSojkkLT/ICK5uru7caK3B1PKUwSgiug18FYEoFmzZqHE48H+zmMAgDJXCTZs2IB77rkHGzZsQJmrBD3+fhzv7cLJJ5887p9fVAHorbfeAhDt7UkWvQ7IE/8ZSi35oCtpGIbUcJauDonLLhAIWFzJUEYA6u3rtbiS9KR816h48DuXHS43KjaDM8ANDUCVnjJUlJRbMhOcy+XCvJNOwv6u6PXape4S+Hw+PPjgg/D5fCh1l8TfYwAaozfffBM13jJMraga8p7T4cApdRPx5htvWFCZfSQ32mUdTCTVMjJJw6aM9So5APWcOGFxJelJWpfJZG2jNF4kr1fJtREVG+Nam+qSipTvV3sq0JWDa2wysWTJEuzvOoYuX+oTnG8070eJpyQnAciV7g2l1CMYpkWptb5m3KvJoXA4jDe2bMHihslQSuE32zahsTt6vc93XngSs6prccbEKXhz2yYcOXIE06ZNs7himaTeaweQOwQuXeNYUqPZWI9+v9/iShJprQcDUE+PxdXYk6TvGY0fKfs3IpLtROzkYYWnDL/d+SgaT0SHnP3f136GmZVTUOEqjf9Mvl188cX4/e9/j1eO7B7yntYarzXvwfkXnA+v1zvun502AAH4Qez/PwhgMoDfxp5fD+D4uFeSY3v37kVPby8WnbIEANDY3YmBUPR6h13t0f+cy+dGE+aWLVsYgNKQHICGJCAh7BSApN1rxzzxQW9vL7TWopabgY1Ryjd+54goE/EA5C5DU88xDISix/ldHQejr3vK0N5tTQCaOXMm5s+fj5eO7ESpy5PwXm/Ah/6AD5dddllOPjvtEDit9XNa6+cAXKC1/ojW+pHYv48CWJmTanJo8+bNUADOaJiS9mcmlVeiobwSr7/+ev4KsxktOAAlNwfYQMic1KmmjV4fhxMIhyNiZ4Ljd210uLzGTtK+NxnXL5EcxnG9xOlJ+b7XVYJ+C4/9l112GRq7WxAIJ96G40SgH7XVNTjzzDNz8rmZXANUrpSaazxRSs0BUJ6TanIkEongqSefxCn1k1BZkr4bTSmFc6bMwJYtW9De3p7HCu0jLDkACT3oSu8BMl/3098va6Y1Y+yyI9ZXbVU3fTqvvPIKADnrkoqH1P0dILs2omJTVlYGAPCHU1/j6wv5UVZamvK9fFi4cCEAwB9KnIXWHw7itIWnw+l05uRzMwlA/wrgWaXUs0qp5wD8HcC/5KSaHNm6dSuajx/HxbPnj/izq2bPjwamp57KQ2X2orUWPQtc8hA4HoQzY+716R+Q1cNiXJjpcCc+l4InSsgq3L9lh8uNik15ebTPoj+Ueoj7QMiH8srUEyTkw/Tp0wFEA49ZIBzCjBkzcva5mdwIdQOA+QBuBvDPAE7WWm/MWUU58Pjjj6OipATLpswc8WenVFTh1PpJeOLxx2U17gVIdYG8pGXEIXDZiQcgBQz0yxoCZwQepyvxOVGxk7x/k1ybJH19ffB6B+98L20WzkAgkFAf78VmT0YPUH8wdQDqD/njIckKXq8XDfUNQ4bAAbA2ACmlygB8GcDntdZvAZiplHpvzioaZ4cPH8bLL7+MFdPnwpNhN9ols+ej+fhxvPDCCzmuzl5SXSAvKgDZrAdISn3GQc3pAAYGfKLWabwHKBaAOjs7rStmGFLWZSqSa6PsSV6vkmuTpK+vD6tXD975XloACgaDCfUxANlTbW0tAKDLn3oIeZe/B9XV1fksaYgpU6cgGBkagKZMSX/d/lgNNwuc4ZcAtgA4L/b8CID7ATyaq6LG07333guPw4H3zT894985d9osPLJ3B+772c9w3nnnweNJfeFYsZEegKTOAie9MdDbG51/PzrRgMbAwIClZ4PMOjs7ARWtDZDbAyT5GiDJtVH2JO9XJNcmSXl5OTZs2AAA2LBhQ/xMvRRutzuhPs6Oa0/GELNjfW1D3otEIujynchpT0smSkpKUu43ctn+zuQaoHla6/8GEAQArXU/AFscUbds2YLXXnsN719wBqq9mV/g5VAO3LBwGVpaW/HAAw/ksEJ7kR6ApA6BS7eMpNQX7wFyJj6XoLOzEw4HoBTgLnGgo6PD6pKIRJCy/0hFcm2SlJeXw+cbvPO9tJOtbrc7oT4pJ8ZodCZMmIBSbymO9bUOec8fiV53Y3UAcjqdKW88mqsJEIDMAlBAKVWKWPtSKTUPgKy7Jaagtca9P70XE8srceXcU0b9+6c1TMbSKTPwxz/8QexZ53wTH4CEDoFLV4eUZWcEHqOXRdINRzs7O6Fieyl3KYfAERkkf+ek7NuIKDoKYMaMGTjaOzQABWITDxi9RFZxuVzQKSKQy5XJQLXsZBKAvglgA4AZSqnfAXgawL/nrKJx4vP58G7ju1g1ax7cKRLkQDDx4r6B4NCxt5fPORkDPh/efffdPFQsn90CkBTGMjJ/38yvW80Y4uCPzX/w+9//3sJqErW0HoeKbb7usgha24buwImKkdT9HSC7NqJiNGv2LBzta8FA0JfQFukN9sPlcmHq1KmW1ud2u1NexZDLADTiX9ZaP6mU2gJgOaJD327WWg8dSCiM0bh0O1J3n/WHBi/uA4CXnvzbkJ8xgpOUhqrVpM8Cl0zKQTgcDgNAwvftwQcfjL9utZaWFgCAUc6hQ4csrGZQJBJBa2sbHLFRIZ4K4PjxZmuLSoPX2RANkrLvJaKouXPn4qmnnoIDjoS2yGMPP4qZs2bmNGhkwuPxpOwBKikpydlnjvhfrJR6GsDtWuvHTK/dq7W+KWdVjQOjcZmuYVLmSry4b5Jn6DVCxm9KbuTnU6oZaqQsm1R1SDkIG99F8/fN/LrVkuuQUldnZydCwRCMy/e8FUDrnm4EAgFxY+WJ8k3K/i0VybURFaM5c+YAAFwOZ0JbJKIjmDtvrpWlAYgFoBT7DbfbnbPPzGQI3BwAX1FK3WJ6bVmO6hl3kTQ74lK3J+HivlL30AaV8ZvcmUelGgKntYwAFAoNnT5RynozajN/38yvW01qAGpujvb2GEPgSioSX6fMSNkOaHxJXq+Sa5OMy41yxQhA0IltkbCOYPbs2ZbWBqS/BsjqANQF4FIAk5RSjyilrJ0sPEOVlZWorqrCoe7sL5puiv3uzJkj30C1GASDwSGvRSIydtiSe4BSLbfhXs83qQHIuPbOuAlqafRWBmhsbLSmIJvi8DzKNykjA4goqqamBtVV1fCHh47kkRCA+vv74VBDI0l/f3/OPjOTAKS01iGt9ecA/AXAiwAm5qyicaKUwqmnnYZ9Xe1Z/429Ha2YUFuLiRPF/+fmRaohcFJ6gKQ02lNhAMrOgQMH4PKoeA9QWU10OuwDBw5YWhcRDU/KyadUJNdGlEszZ82EPzy03TFr1iwLqknU3d0NZ4oAlMtZmDMJQP9jPNBa/wrAJwA8maN6xtVpp52GYz3d6AlkN2v3vq42nHraaTyDGpP6GiAZB5NUjXYpB7pAIIBUXyEpd/0Oh0NJz2UEoP3796OsVsevxXO4gNIaB/bv329pXVQ8pOxDUmFtRDQas2bNQiCSGIAcSqGhocGiigZ1dXWlDEDd3d05+8y0AUgpVRV7eL9SaoLxD8BBAF/KWUXj6OSTTwYAvJtFL1BfwI+W3p743yDZPUCSh8DJD0DyeoAikQgOHjyA0gmJr5fWRrBv/z5rirIZKd9/Gh92Wp92qpWoWMycORORpDZbidcr4iR/d1cXXA4hAQiAcTOQLQA2x/5/i+m5eMa4xsMnukb9u4d7ogs9fuEYpZkGW8aBTkKjPZ1AIIBUuxc5Q+AiSc+tX5aHDx+Gz+dHRX3i6xX1QHtbO29OTDnR19eXeH+4gQGrS7IlBiAieaZMmTLkNSkzqnZ1dcGZ4rY1lgyB01q/N/b/c7TWc2P/b/yzfs68DNTU1KCqsjIeZkbDCE0SxkZKEQgE4EjRkpfQYJbeA5QqAaUKlPkWDoeHLCcJ63PfvmgvT3lSACqvS3yfaDz19fXF75GxevVqBqAsSdn3EtGgyZMnD3lNQgDy+XwY8PngSjEErrMz+4nMRpL2PkBKqbOG+0Wt9RvjX874mzV7Nt5tOgKt9ai6+d7takep18sJEEziQ7n00NdLS4feRymfJB9wJQ+B6+vrG/KahAC0d+9eOFwKZTWJ69UIRHv37sWyZbaZjZ9sory8POEeGXV1dRZXNMjonVq9ejU2bNggpgeZiOxh0qRJQ16TEICMXp7kHiCnw5HTHqDhboR6+zDvaQCXjHMtObFy5Urcfffd2NZyFIsnTYu/Pqu6Fo3dHbHHEzCrujb+Xlt/H148fBAXXXyxiLGRUvj9/pRDufx+v+UBSHIPUDAYTLncJASgVFNMSpjCtrGxMTrrW9IJIZcHKK10iJsKW8p3jcamvLw8fo8MAPB6vdYWZGLunQKAp556yuKK0uP2QCRPSUkJnE5nwklOCQHIqnZ22gCktb44n4XkylVXXYW/PvAA/rDzTZwxcUp8nvEbF52Nxth9fr6+8oqE31n/zlZAOfDxj3883+WK5vP5Ug6BS3WD1HyTfMBN16MiIWikGuITiURG3WM63pqaGuGtTr1OPVURNDXJCkAST5RIrCmZHWqUIrl3SlI4SyZ5f0xUzNxud0KbxOUarh8kP2prox0QoUjShEyRSE574TOZBhtKqYVKqQ8rpf7B+JezisaZ2+3Gpz79aRzu7sQLTSPfP6SxqwMvHTqANR9Yw+FvSXw+HzSQcJGw8brVJISJdCKRSMprgCTUnO4mY1YOr/H7/Whra4c3zS2XS6uBI0eOsJFFOSfpO2bunfL5fDm9QzoRFabkwCNhP+LxeFBZUTEkAAHAhAkTUvzG+BgxACmlbgFwV+zfxQD+G8A1OasoB1auXIlTTj4Ff9m1DYERrm/40ztvory8HNddd12eqrOPgYEBaI2Ei4QBGQFIskgkknIInIQAlO4i71zefXkkx44dg9YapVWp3y+tBnw+Pzo6OvJbGBFlRFJwpMLH71vmkgOP0zl05jUr1NXVweVwotTlQanLg1lVE+Ov50omPUBrAVwKoFlr/UkAiwGkOTcrk1IKn/r0p9Ax0Ie/Hdyd9ud2tR3HtuNHcd3116OioiKPFdqDb6AfThUdfnHPPffEh2NInSlJyk5RKZU8bwQAwJFizvt8S7furAy1xmc7PcDBV4G+9ui/HY9Fnzs91tdI40PKNpqO9Ppo9LhOC4+Ek4l2kdwDJGUY8oKTT8ZAOIBZVRMxq3oizp9+KgDk9F6cmbTABnT0bpeh2M1RWwDMyFlFObJ48WKcdeaZeHjvDvQHU93QU+PP72xF3YQJuOYaW3Vw5U1fXx88TiQMwwDkBiApG7bH4xkycx4go+s5XYiwMlwYk0M4nEB/OxAORv+daI4+NyaK4SxY9idlG6WxSQ4VDBlEMknp8Ul25plnoi/ggy8cPf7vaGvCjOnTLe8B2qyUqgHwM0RvgvoGgFdyVlEOfeKTn0Sv34eN+3cNeW9by1HsaW/BRz/2MZSUlFhQnXz9/f0pp3NONZUyDXK73Sl7gBiAUjOCTYp7ogEAVOx1CbPoUWFjQ55IPvYAZU5qAFq8eDEAoC/oh9YauzsOY8mZZ+b0M0cMQFrrz2mtu7TW/wPgcgAfjw2Fs52TTz4ZZy9bhmca9yKiEzeYvx3cg7oJE3DllVdaVJ18A2lmgbPyehE7cLvdtusBktCrl7btKbBNKrmhzNqomBn3TzIm7mHPceHhfiRzUgNQXV0dZkyfgf6gH75QEP5QMB6KciXTWeAWKaWuAXAWgJOUUh/MaVU5dMWVV6JzoB87Wpvjr3X7B7Ct5SguufRSEY1SiQKBAEKhMANQFkpLSxFJsX8uKyvLfzFJJPYAGVNiBtNkMOP1XM4OU0gkDzOTXBvAhlWmkkNGKBSyuqQ48/2TVq9ezQBUgLidZk5qAAKAMxadgYGQH/0hPwBg4cKFOf28EScAV0r9AsAiADsAGN0mGsADOawrZ5YvX47y8nK8aJoS+9XD7yIcieCyyy6zsDLZjB6BVAFIQm+B5IZURUVFyt6M8vLy/BeTxO/3j+r1fDCCTSBNrg70R9e3EZSIcoUNq8wk36T1iSeesLiiQXa6fxJlh9tp5iQHoNNPPx2PP/44uny9mDZlas6P8ZncAWm51vq0nFaRRx6PBxdddBGefvJJzK6eAAWFV482Yt7ceZg9e7bV5YmVLgA5lIweIAkzqqVTXl4uNgD5fD4oNXS4mZU9QFVVVXA6nQj0p56yPtAPVFZViN6RE+WapEZfcsiQcHNFg/n+SQA4wysVNcnHTaPHJxgJ4/Qzctv7A2Q2BO4VpVTBBCAgupD9oRCC4TAAjabuTpyx6AyryxLNCDmOpJ4Wh5LRAyRZuqFukgJQqtet4nA4MGFCbfoeoD6gob4hv0VRTkhqxKcivT4pkm/SKikAUeHjdpo5ySeLJ02aFH88Z86cnH9eJnup/0U0BDUD8CN6T3uttV6U08pyaNasWQAAfzgEpQB/KBR/jVIzxk0nN5aVgojx3qmGwEkZFldZWZnydQlnItMFICuHwAHAxEmTcKitLeV7wX4HJs2cnOeKhiflu2Y30pebpIaVpFqIJOG2kTnJPUDm40F9fX3OPy+TAPRzADcCeBuD1wDZ2owZM+BQCv5QCArRBc4ANDwj5CQ3VxTkBiAp0gUgCZMgBAIBmQGoYSIONL0Dd9nQXY6/V6OhgT1AhYANF8o3fucKD9dp5iS3lczyMclRJn1hrVrrh7XWB7XWjca/nFeWQyUlJZg4cSL84RAC4WjjfcYM293bNa/iISfFtiMhABkkzkSUqqfH6XSI2BH5/f6U69TqANTQ0ABfb2TIjNdaA6GAnABkHHglH4Al1yaNnW7oKak2SbUQUXoS2h2ZyMckR5n0AL2plPo9gEcQHQIHANBa23IWOMPUadOws70dzogD5WXlqKqqsrok0YwbjaXqAQqHU1+snk/GRi1xJqLUAUhGN7Tf7xu6TpX1Aai2thY6giF9zsb97mpqavJdku3Y5UAnmaSGvaRaRiK5Vsm1UXa4TgtPSUlJzj8jkwBUimjwucL0mm2nwTZMmTIFW998E07lwNTp06wuRzzjotYhZ+RN71nJaOyZZyKSEjJSDXVzOGTUFggEhqRapWD5vTKqq6sBDAYeg45lbWkBiGGjMLAHiMh+uC0UnnwcU4dtuSqlnADatdZfynkleTZlyhREtIYvFMLkKVOsLkc84waxyfsZbXpPAvN0p1J69VLN9iYlnKW8BkhAADICjk4OQOwBohySHIAk1WJnXI6Fh+u08OSjjTTsNUBa6zCAC3JehQXq6uoAAGEdyctsE3YXD0BJr2stIwBJngUu1Y33pExFGQwGUg5rDAQCVpQTN1IAMnqIrCb5GiCJNSWTso3akaT1Kzk4UuHj963w5OMa7kzGLm1VSj0M4H4AfcaLdr8GyNyAktKYkswYjzmkB0hHby5L6TmdTiilEnbScgJQMOWFXVb3ABkzwCQHIGNIXD4ukCwUkkOGtIaL5Ia8pFrsjMuRSL7u7u6cd05kEoC8ANoBXGJ6zfbXAJmH0DAAjcy4kD+cdPAIazlDzSSTGoDC4aHTTCsMTnphlerqajgcDkSS6tNhoKKyXESvo5nVy8uuJIcz6SQ15CUHx2SSa6PscJ0Wnq6urpx/xogBSGv9yZxXYQFzo50BaGTGvWxStfPS3eeGBiU39KQ0/MLhMHQkOkxv9erV2LBhA4JBn+Uz+zkcDlTXVKF3oCvhdR3Jz/0BMiV5CByNnuSGvKRakklebskk10ZEUfkIQCOehlZKTVdK/VUp1RL79xel1PRM/rhSarVSardSap9S6qvD/Ny1SimtlFo2muLHorS0NP441TUalMjpdKKs1ItwimNHqmmeKZHkABSJDE4fvnr16ui9dgTcQ6m+vgGRpBwWiQAN9ROtKWgY7AHKDhujhcFO69FOtVJmuE4Lw4kTJ+KP9+/fn/PPy2QI3C8B/B7Ah2LPb4i9dvlwvxSbQe4nsZ87DGCTUuphrfXOpJ+rBHAzgNdGV/rYmEMPA1Bmqqqq0NvpG/I6e9BG5vG4EQ6H4HYBwaCca1gikQgcjsTpw5WS0aCf2DAR+w/sTXhNhyHmJqjA4IHX6h4zu5JyIsBgp4aUpFqTa5Gw/6DiIWlboOxt3749/vitrVtz/nmZXIjQoLX+pdY6FPv3KwCZtEDOAbBPa31Aax0A8EcA70/xc98GcBuAoS3rHDLfu8bcG0Tp1U6YgBSXjHBK4gzUVFejxAPU1QBOl8KZZ55pdUkAogcOh2tw+nCfzwflkHFAaWgY2gOkIzIDEBt8hUnCdmCQVEsyDoEjK3GdFoatW7dCQaHOW4n9Bw4k9AjlQiY9QO1KqRsA/CH2/HpEJ0UYyTQAh0zPDwM41/wDSqmzAMzQWj+mlPpyuj+klLoJwE0AMGnSJDz77LMZfPzI1q5dCyDa1dbU1DQuf3O8GLWN13/reDj33OVob2vHQGtr/LX6+nocOXIELS0tFlYWPQNfX1+Ptra2+Gter1fM8lt18cX429+eApzAB9asQk1NjYjarrnm/Xjuub/D1z+43Gpr63HmmWdaXt/kyZNRXVUNv98fvz6ppKQEVVVVltdmcLvdqK+vx9SpU8XUZOjt7cXatWtRUVEhrjafz4e1a9eK2kYBoK+vL+G5y+USU19ybaWlpWJq6+/vT3guaRtNvpagvLxcbG0Oh0NMbcDQm5x3dXWJqs9oJ23dulXMvfUMEttwwNDvnKR1Wl5ejokNDXA5nLh25ZV4/vnnc3qJRSYB6FMA7gJwB6Kzv70MYMwTIyilHAB+COATI/2s1vpeAPcCwLJly/SqVavG+vEAgFtvvRUAcP/994ubycyobePGjRZXMuj//b//h7a21oTX2trasGLFCst70To6OhLCDxBtZI3Xd2WsPv3pT+FEdxtqq4C/PLAeH/rQR+I7SCvdfvsP4HQlTnnd1dmGLVu24IYbbrCoqqinnnoK3d3dWLNmDdatWwcAePDBBzFp0iSce+65I/x2fvz0pz9FR0cHGhsb8ZGPfMTqchI8//zzuOuuu7By5Upcf/31VpeTYNeuXbjjjjuwYMEC3HjjjVaXE/fXv/414XkoFBKzD3nggcSJVwcGBsTUdv/99yc87+rqElPbo48+mvC8p6dHTG2PPfZYwsnXSCQipjYAuO+++xKe19TUiKrPaCf99re/FTUyAJDZhgOGfuekrNP9+/fj1ltvxaTyGlSUVODJR5/AGUuX4Bvf+EbOPjOTWeAaAVyTxd8+AmCG6fn02GuGSgALATwbGwc+GcDDSqlrtNabs/i8rJWXl+fz42yrpqYGkaSeZqWU5eHHqEMyp9MRv4eS1vm5y7HdGbO9ma9PMr8uCYfAZUf6diuJ5GXFIXBkJa5T+3vmmWfgdDhQ5SmDUsDyKQvwzGuvobe3N2e9QGkDkFJquNiltdbfHuFvbwIwXyk1B9Hgcx2Aj5r+QDeA+F2OlFLPAvhSvsMPwMZoplJd6+NyyVh2khsHABDt8IzSWk69DsdgMDNoyNgmjO+bcX1S8usS8BogItkBaGhtFhVCOSPp+0ajFw6H8fdnnsHihjnoD/kBABdMPw1PHnwTL774IlavXp2Tzx1uEoS+FP8A4NMAvjLSH9ZahwB8HsBGAO8A+LPWeodS6ltKqWx6lMhiqWZ7c7lk3JAyVaCQEjKAwRuf6qTnVktXh4T60g1LlXjfKckBiI2DwiRp/5ZM9ndOcm2UKfN3TPb3jUbS1NSE9o4OnD1lQfy1OdWTUF9Wjc2bc9cnkrYHSGt9u/HYNFX1JxGdze32dL+X9DceB/B40mspe5a01qsy+ZtkHSMAeZyAQ0XPpEm5dkpyYwAYGiik1KuUStkekFBfSUnJqF63gh16gCSsy3TYcMmcndajpPXKHqDCZF6vkve/NLKjR48CAKZV1sVfU0phanktjh45mrPPHfY0r1JqglLqOwC2IRqWztJaf0Vrbe2UX2QJYxzmhDKF6dUKlaVOnHbaaRZXFSWhx2I4Tqcr4cCbPLuOVVwu19AGgZZRn8fjGfKaUkpkQ1BSg4/Gj6TvmtSbKQMpQoZFdWRGdnWUGfYAFQ4jAE0sTxxlNKm8BseOHc3Z+k3balRKfR/R63h6AJyhtf6m1rozJ1WQLRgByJgIYSCoczpF4WikCkCSGgjGNTXGdiwlsLndQ4cw6jSv51u6ACSR5DOQbBxkTur3KxVJtQ75jgn6zrEHqDCZ97ncx9nbsWPHUOEpRbnbm/D6xPIa9A8MoLu7OyefO1wr7N8ATAXwdQBHlVInYv96lFK5vTsRiWTMlheJaGgAA4GImBn0pASKdJxOZ8J5RwmTDADRoJOqB0hCAEq5TuW0+QDIHgInqYFMYye5B4go3zgErnCUlJTAHw4ilHTn8/6gH0qplCdDx0PaVqPW2qG1LtVaV2qtq0z/KrXWMi78oLyKByAdPYsW0UBZWZnFVUXZogdIJz0XwOOR2wOU8qye0BN9PANZmCTtQxiAiKgQnXrqqQiGQ2g6kXifyX2dxzBr5qyctTNlnzYnUZxOJ1wuZzwAAYDX6x3+l/LEDj1AwOBykxKAvN6yodNgaxnr1Q5n9ST3ANmBtEa8tHrM7BSAeDqAco0nnQrHqaeeCgDY2zE44UFEa+zvOobTTs/ddeayW40kTomnBBqD1wFJaCgDNukBGua5VUpLS4cGoIiM9RoOh4e8JvWgl6pWq0ldVpQdOwUg2RfaSK6NqPg0NDSgob4euzsOx187dKIV/UF/PBzlAgMQjUpJiQdaDx5CpExJLD0AORwOaMibBMHr9aZsq5SWlua/mCT9/f1DXtNaiwobdugBkrQdGIzlxpCWPYnr1SCpNlsFR8oK9yP2d/Y552Bb67vxdbmleR+UUli2bFnOPlNGK4xsw+PxJAyBy9XFaYVG6jVA6QKQhB6gvr6+Ub1uBWNnHQqFLK7EnqQ3RiXXJ6k2e4UMybVRNmR/3ygTK1asgD8URF/QBwDY3LwPp592OiZMmJCzz2QAolFxu90JPRkSLpY3SD4IJ/f4SOkBKisbeg0QABGz+6ULOql6hqzCAFRYJO0zRiKp1iG1CK5NUGk0BpK+/zR2ixcvRkV5BU74BxAIh3DoRCsuWHFBTj9TRiuMbMOYNlmbnkslaQcZHwJnei5BeXk5dIrRWxJm9+vq6hrV61YwAlAwLDcAcXhIYZKyDwFShAzRvSySayMqTi6XC+cuPxd9QR/6AtFeoPPPPz+nnylnD0q24HZ7bNMDJEl8yJuwWeDSBR0JAaitrW1Ur1tBcg+Q5O2B1wCNnaT1m1yLwyG3NkGLjcbAvF4lbQuUvVNPPRVhHcGJQD8m1NZi0qRJOf08BiAaFbfHDbAHaNSkXgOUbqibhADU3t4+qtetEO8BCgYsrsSeJG2jgOxhtMkk1ZbcG6UE9U4lk7TcKHsMQIXnlFNOAQAMhAI4+ZRTcr5e5e6lSCSXK/EaIJfLZWk9ZpJ3gk6nM2EInJQAlC7oSLgGqK2tDSrFHqq1tXXoixaJxMYPBgNBiysZSnLviuTa7ELS/m5ID5Dg2iQtNyIaNGfOnPhjIwzlEgMQjUryNUCyAlDyczkHuvgZUg6By9jx48ehkhaTwxl9XQoduyFWMCQvABkkbQc0fiRdAyR1kpdUuD0UHq7TwmBuT06fPj3nnyd3L0UiDc4Cp+PP5ZC7E3S5XCKDo+QAdPTYUTiSApByAs3NzdYUlMLgEDh51wDZgfSeIMkNK0m1MQCND8m1EeVTdXV1zj9D7l6KRIoHoNhzSfcBktwD5HZHr52CsKGD6W54avWNUMPhMNpa24YEIIcTaG4+Zk1RKRhD4EICh8BJZgQfSdsoIK+e4Uiq1U4BiAqD+eSJ9BMpNHoMQCROfAicwFngknuAJDUQXC5XQnCUstzS3fDU6oDW2tqKcDicMgCdONEj5mao8SFwwaDYg7DEuiTWZDeS9m9DZ4GT27SQtNwoe+Z9SCSS4l4OZGtVVVU5/wy5eykSyePxJAzlktwDJIkReKRNHpEqAElovDQ2NgIAHEmLyXje1NSU54pSMx+Eg0FZvUBs6BU2SeuXAYjyjSdRCltJSUnOP0PuXopEGrwGaPC5HHIPbEZQlLbcpAcgZ1IAcroT37dSOByOPoh97aQFIDuQ1oix04xhkmuTsA9JR/Jyo8yxB6iw5WOiKLl7KRLJPAucQykxs5kBshsvRuCJjZgS03OWqg4l4CaGTU1NKCl3DJkGWzkBh0uJCEDxwONIek4jknoNEI0PBiDKNXPokXYihcaOAYjEiV/LouUM40pH0oEuPgQuts+WEoBS9URJuIfH/gP74a0eelZPASirAQ4ePJD3mpIFArGbn6qk50JIbhRIrs1M0j4kmeTaJAcgypzk7xh7gApbPvYh3EvRqJivZXG7ZQUgyTtrI/BEhA2BM0KswwG43IDbk35ihHwJBoNoamxEeV3q98smaOzdt8/yRnRyD5Df77eumGFI3C6MdWf1OrQzievVIDkASV5ulLn4EGQwABWifGyncvdSJFJ8KBfk3MzTIHkIXPwaoNh+Oh8X+GVCKQW324USL1BTC9TWAVOmTLG0pkOHDiEUCqcNQOV1QG9PL9ra2vJbWJJ44OEQuKxJ2kbtRvKyYwCiXDOHHnMYIsqU3L0UiWQc2KJD4GQFoGSSDnRG4JHWAwQAzthNWgEgErG+tgMHosPbytL2AEX/f//+/XmqKDXpQ+Bo7CTtQ+yEy41yzRx6GIAoGwxANCpGr4/WgCP5Ji0Wk3zQjfecxQKGpFqdprO1OgI4k6dey7OjR49CKcBbmfr90tj90Y4ds/aGqPHAE5s0QuoQOMk4BC57kvYhydgDRLnGIXA0VnL3UiSScc0Ih8CNjtEDpDXg8ci6dsq8HrW2fma/5uZmlFQ4htwE1eDyAk63QnNzc34LS5I8BI49QJmzS/CRtA+xEy637HC5ZS4UCsUfc/hx5uyy780HBiAalfiZPS0vACWTdDAxT4PtdsuYAc7gdDriQ+C0gPV69OhReCqiZ/TK6qL3/nG6garJ0edG75C0AOTz+awrxmZ4EB47Sfu3ZKyNcs0cgDgEjrIh61Q0iRe/BgjyhjlIPrCZb4Rq9TU2yRzCeoC6u7vgjk1EN2c50N8efXz61YM/4/JqdHV15r84k+QAJHUIHMNG9iTvUyTjcisMktejudeHPUCZ43DBQbJasCSeeRIE6QFI0s47cfpwWQEoYQhcxPoeoKqqKoRGyBLhgEJVVXV+Ckoj3uMTuwZIag+QpO3AYJdQJnHZ2QGXG+WaudfH3BtEw7PLvjcfZLVgSTxzD5ByyDrI2SEARR/L6nh1OpwJQ+CsDrY1NbUI+YevITSgUFNTk5+C0ogHHmfScyF4oCMaStJxgbJn7vXh9ZeZs0sPUD6OXwxANCrmAOR0yGrIV1RUAADcsQbptGnTLKwmkblXxWXxLGvJzLVFBFwDVFNTg9BA+ve1BgI+jepqKT1ASc9pRMbBTXpjVHJ9rI2KmTn0cAhc5uwSgPKBAYhGxTwNtlPYfYDmz58PlxOoKY8+v/baa60tyMQcKpwuWUPgXC5XNNEiOgTOmOnPKrNnz4a/PwLfidTv97YBkbDG7Nmz81pXsoGBWEpTgHIqBqBRsEvvFBvyRDKZAxB7gDJnlwkj2ANE4iQEIGGzwBkNeWOzkVRfQg+QsODodDrjyywi4Bqg8847DwDQ0Zj6/c7GaE/kOeeck8eqhopPeqAA5WYAKkQMQJRP/L5ljgEoO3YJQPnAAESjEg9AkHcjVIfDAfM5A6sb8mbm62qsvtFoMpepR0pCD9DkyZMxe/YsdDalfr+zSeH0haejqqoqv4Ul8fl8QKy9IjEA2aExZZeeICocdtguaGTmWTcZgDJnlwDEHiASx9wDJG02M5fLBQ3EL+iXGoCkHYDdbnd8mUUEBCAAOP/8C9BzHAgmXQs00A30d2qcf9751hRm4vf74wFIuxSnwR4FXgM0dhLXK1G+mEOP1H2vRLxeahADEI1KfDpnyGgom5mvZYk/F8IcgKyeZS2ZeTlFIlpEsL3ooougNdB2IPH1tv3RRumFF15oTWEmfr9/cA/q0uJ6gAwSG/F2uRBX4rKzA4YzyjX2AGUnOQAV87YqqyVG4iVeyyInYACD17JI7wGSFoA8Hk88OIbDMgLQ7NmzMWfObLQfGGyAag20H1A444wzUF9fb2F1UebAE3HJOwjb4cAmvUYGICKZjP2tUg72AI1C8nHKLkPickFWS4zEMzeOJQYgcw8QA1BmjKGDQDRkSFmvl156GXpaNIz9c18bMNCtcemll1pbWEwgEIgPgYMT8Pll9gBJJHUI3Ny5cxO2zxkzZlhYjX1JD7Zkf0bocSqHuJNPkiUvq2JedrJaYiRe4g09re8pMItfA2R6LkXCNNiCghkw2ANktFk8Ho+1BcVcdNFFAIBQLFd0xGZ/W7FihYVVDTJPggAX7wM0GlIbyOvWrUNpaWn8+cc+9jELqxme1GVIhUPaCQqzQCAABcUeoFEIBAJDllUxH7cYgGhUJAcg8wQNgKwAlDgLnKwAZEyCYDSnpASgiRMnYvKUyQjHTlD1NCvMn39S/Ia3VvP5zQFIsQeoQJgbfdJ6a+1CcjiT3KinzPl8PiiloJTcCWikOXTo0JDXGICIMiQ5ACUHHklBQ3KjyuPxRMNPrM0iab0uWbwE4UA01Pa2AosWLba6pLjkHiC/jwfhTBkNZMkNZUDetmomedlJro0KQyAQgFIOKHAIXKYOHjw45DUGIKIMmXsHpPQUGIwAFBHYAwQMhiBJAQMYOgROUn2LFi2C1kCgPzpD3eLFggKQuQfILe8spOQz3VKvAQJkn6wwY8igYhYNQNEeIAagzBw4cADJe9yBgQGR+5J8HBvk7t1JJMk9QJKHwAGyA5AWeA0QAMyfPx/A4P2AjOcS+H1+wNhJuwC/X+ZBWOLBTWJNqUgOQJLZZf2SfQ1eA6QQELrvlWb//v0ocSYe38PhMDo6OiyqyFrcu9Oo2KEHyDj2Smu8SA5AwOByKykpsbCaRJMnTwYAREKAt9SL6upqiysaFPAPzgKnXArhUEjklKISe1kkYw/Q2EmujQpDMBiMD4GT1vsukdYaB/YNDUBAtGdIGvYAkTiSe4DMAcjlcopr+NklAEmqz+PxxNfrlClTxKzTcDgcvaGcsQeNLbJiHk89Gna5BkjK942IEpl7gJJv7klDtbW14URvD7yuoSc49+/fb0FF1mMAolExDyuT1FAGEq8BkjQBgsE4myxtuRn16Ej0ubSePaNHauqUqRZXMigedEzXAAHR8dTSSAwZkq8BMpNcn8T1apBcGxWGaA+QgoJCKBSyuhzxmpqaAAAlzsT2h8vhjL8nCXuASBzzkBBpDXnzNUAugQHI2KClBYx4ABLYAwQMBltJw9/iQcfYR3uSXhdEYiNecgPZvLwkLjuD5GVImZk7d27C/rahocHCaoaqq6uLP548eTLmzp1rYTWJgsEge4BGoaWlBQDgdiQe390OF1qOt1hRkuUYgChr0hrK8QAEmT1A8c4CYctNegAy1mVlZaXFlQwa0gPkUomvC/Dkk08CYEOZ8k/yd05SqF23bh1qamriz1euXGldMSksX748/viqq67CunXrLKwmUTAQjM8CxwA0spaWFjiUAy5HYtvI7XCjubnZoqqsxQBEWZMWMszXADldsmoDEJ8xTFrAkB6AjAaLpAAUv+i2VwNtEejXo88lTcd6/Phxq0sYkeSGMiCrsZxM+rIjyqVgMBTtAeIQuIwcP34ctaVVQ/ZpbocL7e3tRbkMGYAoa9KmmU4IQA6BAShG2nIzAk9EaAAySFpu8QAUAhAA0B5deJJ6gCRj453yZe7cuQkn68zDusi+QqHBHqBibLyPVk9PDyrcZZhZOQWlLi9KXV6cMmEO6ktrENERkcO3c40BiLImrQfIuD5JQ1ZjOZm02uL1CL1/kkHS2fh0065Kmo7VLhMNUHYYIjOzbt06lJeXA87ofu3888+3uKL0pG2r0uoxi4aeaA9QREdE3oJAklAoBJdy4obT3otZVVMwq2oK/uPcz2D5lEXx94sNAxBlTVpD2TwJgrRwZiZtuSXfP0lafQZJB+N0B1sehDNjl8a7XeokKjbBUCh6TIgdF4qxAT8awWAQbsfQY7sr9loxXkfFAERZk3aTQHMAklabmbRwZl5u5ufSSGqMpqvFDjVKIinUEpF9RMLh+DVAAE8+jSQYDMKphh7bjUkRGICIRkFayDAPgZPaiAfk1ZbcAyStPoOkxnIkEhnV61ayQxCi0ZO0XufOnZtwPKivr7ewmjRii0vSfoSyFwqFBmfhBHuARuL1euEPD52kx3jN6/XmuyTLyWrBkq1IC0DxhrsGHEIb8YC8gGFebgnPKS079AAZJDf4JC4vc00S65No3bp1KC0tBWL7josvvtjiiuxD8vYpWTgSid8HCJB58kmSyspK9IWGTnTQGxyIv19sZLVgyVak7bjNPUDSwpmZtNrM908yP6f00n33JW0Tkhvvkmszs0udRMUmEhvyZgyBYw/Q8CorK9EXHBqA+oL9KPGUiLtBez7IaomRrUhq7AGJwcIheBpsaQEj+RogaQHNIKkxmm4ZSVu3NDaSvnNENCgSiSS0QdgDNLyqqir0BvoRjiReK3Ui0FeUvT8AAxCNgeQA5HTK/WpLCxjxenR0nUptxEv6vqVbRpKWneTGu+TazCTXKbI2gSUNEl0cjVJEG4GHQ+AyMXnyZER0BO2+7oTXW/o7MGXKFIuqspaslhjRGNilB0haADIPgZNWm1TplhOXn/1JvQYoeaIByY0WSScrqPAMhh1lzILNADSC6dOnAwCa+9oSXj8+0I5p06dZUZLleLSmrEk7yCUGILlfbWnLzTwJguSeM0nS9fRI+t5Jarwnk1ybVPGJBmKuvvpqC6shso4RdlSK1yi1adOiIcccgPqDPpzw9cbfKzY5PVorpVYrpXYrpfYppb6a4v0vKqV2KqW2KaWeVkrNymU9NL7ENuQhayhSMkmNZCDp/kmCl5ukRrMdeoCM5SVpudkNlx2RPPF7/qjB+wAxAA2vtrYWZaVlONbXGn/NeGz0DkmSj31vzo7WSikngJ8AeA+A0wBcr5Q6LenH3gSwTGu9CMB6AP+dq3qo8NmlB0habeb7ALldQ+8ULYWkwM0eoOLAZVgYzPsOSfsRyk7YPAOc4o1QM6GUwuw5s3G493j8tUM9zQCAOXPmWFWWpXJ5tD4HwD6t9QGtdQDAHwG83/wDWuu/a637Y09fBSAvhpJt2OUgJ602t9sdf+wSHIAkSbcOJQUg44wop4fNnuQAJG0/QpQv5rBjbAXcz41s7ty50dAT260d6mlGqdeLSZMmWVuYRXJ5tJ4G4JDp+eHYa+l8GsATOayHChx7gLJjDj1ut9wAJLkxKpERgAKBoXf/tprkdSm5NiIa3KedGOhER+yalj//+c9WlmQLs2fPRn/Qh2AkGhYP9TRj9uw54tok+SKitaOUugHAMgAXpXn/JgA3AcCkSZPw7LPPjsvnrl27FgDG7e+NJ8m11dfXAwCam5tF1RcMBuO1nXbaaaJqA6Lz8FdUVODIkSPo6Oiwupw4rXV8uV166eXilpvb7UZ9fT2UUmJq8/v9qK+vR1vb4AWl9fX1OHbsGLq6uqwrzMRYbqedLm9b8Pl8WLt2Lbxer7jaysvL4fV6sWrVKmzbtk1Ur2hFRQVKS0uxatUqtLS0iFp2FRUVKC0rw6qLLoLf7xdVm9frhdvjwaqLLhK1HwGiw2nr6+uxatUqhMNhUbW5XK74scHj8YipzTjWm/e/+/fvF1MfILMN53a7sXbtWrz8wksIqAjOW3kBKqsqRdVoLLfnnnsu55+Vyz37EQAzTM+nx15LoJS6DMB/ArhIa+1P9Ye01vcCuBcAli1bpletWjUuBd56660AgI0bN47L3xtPdqhtypQpWLJkibXFmDQ3N8d3iLt378ZHP/pRiytKdMcdd8Dn82HGjBlYuHCh1eXEhcNhfO973wMAvPzyS/jEJz5hbUFJ7r33XrS3twMAxmvbH6tdu3YlHHwBoK2tTdQ2cffdd6O7uxvPPfcc7vvZfVaXk2Djxo1Yv349FixYgBtvvNHqchLcdddd6O3txfr16/HrX/8akydPtrqkuDvvvBN9fX1Yv349vvvd72LZsmVWlxR35513om/Ah/Xr1+Nf//VfxWyrQGxb6Imu03Xr1omq7ec//zna2tqwfv16fOELXxBV2/333x/fzwUCATG1vfvuu0P2v263W0x9gMw23IkTJ/ChD30IE0snoKykAn++/8/47Gc/K3K5PfHEEznvmcrlX98EYL5Sao5SygPgOgAPm39AKXUmgJ8CuEZr3ZLDWqgI2GUInLSx++YL+j0lJRZWYh/pLriVdCGuUUtfX5/FlVDRiO3aOIyQcsnvH3qunLPAjayqqgrVVdXwhwMIhIMAgJkzZ1pclXVy1krUWocAfB7ARgDvAPiz1nqHUupbSqlrYj/2fQAVAO5XSm1VSj2c5s8RjcgukyBIZCyvEo/H4krSk9SoSnfBraQLcQcDUP8IP2kd6duppO+cPUTXJ5cb5VJ//9B9GgNQZmbOnAl/OAh/OBB/LlE+9iE5HdystX4cwONJr33D9PiyXH4+5Za0xou510dabWYSa3M4FMJhDY9Hbg+QpOVmhwBk1OL3+eD3+1EiqHePDeTCxsYo5VKqXm1Jve+STZs+DTt37EQwEkq4xkuafBzv5Y4TIhol8wYjeQicREpFl5dHcA+QJMFgMOXrUmZc01pHA1Bsk5A06YadMKhlTmttiyFwkk6kUHZS9QAxAGWmoaEBYR1GIBzExPqGom4rFe9/ORUcXgOUPYcjNgROUC+BZD6fb1Sv51tvb2+0EeqOXt8lLQAZDWTJDWXpZC47wUPgJNZEWWEPUPYmTpwIABgI+TBxcnHe/8cgt5VINEoSg0UqEuu0Qw+QpEaV9ABkzJoHT3SUc2trq4XVpCdxW5BM0jaQLFqb4ABEBSNVAOKwy8wYASisI2hoaLC4GmsxAFHB4BC47BnLiz1AmZEegI4ePRp9UBINQM3NzRZWkx4bytkQHDJiu2Cejadc6u3tjZ+0M/A7l5na2tqUj6XhNUBEo8BZ4LJnBCDJPUCS2CYAuR1wlJcMPqdRkRwypNVmlx4gHhvsr7e3Fw4GoKxUVVWlfFyMGIAoa9IOcgxA2TOWFwNQZlLdhwJKziQIR48eBZQClAO6sgRHjgy5B7WlpO077EjaMtRA9DsHqcORostL8rFBcm2S9Pf3MwBlqbKyMv64urrawkqsxwBEBcMuAUhibQxAo+P3++Nn4uOUnB6gw0cOA67Y7r26FIcOH7a2IBoX5tAjLQDBNAuctAAkcZ9L2RsYGBgSgKR956RyuQbvfmMOQ8WIAYgKBgPQ2Jl3jpReIBBIGYCk9AA1NR0aDEA1peju6kp54TDZkJI5zCxaTrQ2kWfjY4tL8v6XMpMyAIUZgEarrKzM6hLS4jVARKNgl2mwJXO73VaXYAvpGngSGn79/f3o7OiIByBVUwoAOHTokJVlpcTG6OiYI4+8M95abDiLitbEY4P9DfQPDJ0EIWL9vtduSktLrS7BUtwTEFGc5AAkqVGVrhYJNcav93FF7wGEmuhZvsOChsHZ5T5A4gKa4IkGZIczGitJ24I/EBgSgKRtD3bg9XqtLsFSDEBUMOwyBE4yp9NpdQlpSVqnWmtARw8ga9asiR9IJByE40HHGAJX5QWUEhWADJLWqR2YRpnJCxnxcKbE1cZjQ2EJBoJQQ8Ygy+iBt5Niv+0FAxBlTdqBhAe5sZPcAySJEXRWr16NdevWYfXq1YCW0SiNBx1nbAic0wFHVamoACQhKNqSqQdIwnfNLF6aQ4luiPLYYH/BUDDlegwGgxZUY1/FPukRr3gmyjPJjT+Oj8+MsZw2bNiQ8P8SetAOHz4MR1UpIqYGQqTaiyZB1wDZZQicNKJ7gEzTTEterwxA9hcKBuHE0MZ7KBSyoBr7KvZJj9jaoaxJO5CwB2jsJDTg7cDpdManvX7wwQej018rGcuv6VATItWJY7tVTSmOHjki7sy89O1UXH2Ce4AGyQ5APMljf5FIZOgsnJC8TchU7CM+uCegrEk7kNglALFxYH9OpzPxqm/z6xYKhUJobGyCmlCe+EZdOYLBoLgbotLoSO4Bim8OAne9ko8HNHoRHUl5DZC0bUI69gARZUnaQcUuAUgyBqDMpFxO2vrld+jQIYRDIaAuMQCp2PMDBw5YURaNFz041bTMxp4ClLxJEMx4bLC/dN8vaT3c0ll9vLJacf/X05hYfbabxl+x7xAz5XK5UvYAWT2kwAg4qj6pB6i2DMrhEBeAJPeGShS7ygaA1ACE2O2A5IYMybVRZsLhMGeBGwfFfrwv7v96GpNi33ioeKUcOqCtPymwb98+KJczfu8fg3I6oCaUYe/evRZVlpr0xqi4+kx5UVp4jN2dCICWt9xMJNdGI9Nax64BYgCisWELlrJmdWOPyCrpApDVY6p37d4N1JVDOYY2DnR9BXbv2SOi4SyhBnvS8WtsxC5DLTsAkb0ZM72l6gHiLHA0GgxAlDUGoNEx5tyX3DiQXJsk6Ya6WXlfhXA4jH379gINFal/YGIF+np70dzcnN/CaNzIHgJnJDN5+xFeH1o44gEoxWpkDxCNBgMQZU3aEDjzGVGJZ0fr6uqsLoHGSbqgY+U1QIcOHULAHwAmViDy0n6gvRdo70X4oW2IvLQfqqESALB7927LajTwPkBZ0lrsLHBKAdCAjkREnxxjALI3v98PAFBqaPvD5/PluxyyMVktWLIVBqDsSFtuNHrpApCVPUBHjx4FAKjaMui2PiAQjv471h19Xlua8HMSsDFaOJSRgKBFByCyt/7+fgCAI0UAMt4jygRbYpQ1HuSyI7nRJzk4SiKxByg+tK3Sm/J95XLCUVaC48eP57EqKhpKxW7UymMD5Y4RclL1AA0MDOS7HLIxBiDKGg9yoyM5+NiBpHDm9aYOGaWlpXmuZNDx48eh3E6gJP1EDLrCwwBEOaEYgCgPhusB6uvry3c5ZGMMQJQ1DuXKDoNQdiQtt3RBx8oA1N7eDlXhHXY56XIPWtpa81hVmjp4DdCYSVt2DqUAHb0uyer7YVHh6uzsBAA4HUNP9BjvEWWCLVjKmuQAJK1xQNmrrq62uoQhJAagjKjU08fmm7F9Sgq1diNt/6uUAmITM1g9HTwVro6ODgBAOByE1+vFmjVrYj3yKv4eUSZk7UHJVqQdgM3YsCocZ5xxhtUlDCExADmdTqiRgn9ERuOUJyiyEx1mFn0sbf9rHgJn5WQgI+F3z946OjrgdLjgD/mxevVqrFu3DqtXr4ZDORiAaFSsPxKSbUk7ANsFD8D2V15ePqrX88HpdAKRkQKQhtMj5/oMbgvZkNl75nA4OASugEnZVltaWlBdVosSVxk2bNgAANiwYQNcDhdaW60f3kv2wQBElGfSGi40ehIDkMvlgo7o4Qe4RSJwCbpAndvCKJl6gKRNNGDuAZIWgOxyiwQaWVNjExoqpiAQDsDX5cODDz4IAKj01qCxsQlaa+5XKCM8hU9ZYw8QFat0Q92sDEButxsIj3BzzHBExPAkNkKzowCxM62ZG53SAhAVhnA4jEOHD2FS1fQh73lcJejv70N7e7sFldlTse+H2YKlrEk7y2KuR1ptVFicTmfK71i66bHzIZMApCKaAcjGJE81bT4hJuE7lg6/e/bV3NyMYDCIiVXThrzncZYAABobG/Ndlm0V+7bAAEQFwy4BqNh3OoUiuQdUKWVpr6jb7YYeKQCFtahJELgtjI45AElYj2bsAaJc2717NwBgSs3MIe95XCUJP0Mji0RGGDFQ4BiAKGvSQoZdApDk2ihzyWHH6iGhWuvoNSLDUUpU6OC2MDrKdK8daT1AdglAkr7/diJhW92+fTu87tKUAcjhcGJS9XRs377dgsrsiQGIKEsSdohmdglAVBikBaBgMAjlHL4G7VAIBoN5qojGm3I4xE40YP7+S6vNjAHIvt7e9jZm1s2HQ6Xez82uW4CdO3ciHA7nuTJ7KvblxABEBcMceqxujFLhkxaAAoEAMFIAcsoIQGyEZsfBIXBUpE6cOIGmQ02YU39y2p+Z03AKBgYGcODAgTxWZl/sASLKkuReFqsbo8Nh468wJH//rd4eoj1AI9TgdESDksV4DVB2zN8xyQFIWm1kf2+//TaAaMhJZ07DyQk/S8NjDxBRATGOwZIDkNUNZRofqSZBsFIwGBzsAQqE4PV6sWbNmujMdIFQ9HWnQkBAD5DB6mVmN5J7WRiAxk7y9mB1bdu2bYPb5cH0CXPT/kx16QTUV07CW2+9lcfK7IsBiKigRHfSkgMQFQaJPUDaEashEMLq1auxbt06rF69Oh6AlMOBQFBODxCNjl0CkLQJGsj+tr21DbMmzIfLMXy4nl1/Cra/vb3oh3dlggGIqIAYh2AegEfHyht42pW0ABQKhQZ7gDwubNiwAffccw82bNgAeGKNBqdDxDVAlB279LJIro0yZ/U+zdDf34+D7x7E7Ib01/8Y5tSfgt6+XjQ1NeWhMnsLhUJWl2ApBiAqLLEdNgPQ6MyePdvqEmxHWgCKRCKDZwA8Lvh8Pjz44IPw+XyDAUgBkRHuFZQPvAYoO5IDEHuAKFdaWlqgtUZD5ZQRf7ahcjKA6E1TaXjsASIqQBwClx2rG/F2ZvWyczqdQGSEQBHRcLmsb5zGg4/w75vV6zSZXQKQ5P2v5Nokf9+s1NLSAgCoKasb8WdryuoBAK2trTmtqRAU+2gAuXsCojHgGcjs8Ix85qT1AGUagGRtG/y+jYbkAGTGkJEdabVJubeeEWYyCUAV3io4HU4GoAxwCBxRAZLVyEvEkFEYpAUgl8sFNUIA0hENp4CG8+AQOIsLSUFKoy8VycPMJPcASVuP6Uiu08ra+vr6AABeV+mIP+tQDpS4S9HT05PrsmyPPUBEWZLckJd4dlTywY1GT9o02KWlpdDBEcZ0B8OoEDDhxeDYc7n7EInYAzR20mqzer8xHCknAyZNmgQA6OxvG/FnfcEB9Pt7MXny5FyXZXvsASIqQNLOjlLhkdYDVFlZCe0LDntiwhEIo6qyKo9VpWYceIv9ADxaknuAzKzeFpLZZblJC2fmeqxcp1OnTgUAtPUeH3ytZha87lJ43aWY03AKptbMAgC0x37G+B1KT8JNsa0k9xQS0RhIPshRYUhuEFjdeKmsrIQOR6BCw8zy5g+hsrIyf0WlYfQAhUKyZyGyep0OR1ptUnoLRiJtuZlJW25ShjUaYaa9Z3Bmt/ctuQFHuxoBAJ9d9Z/x140ANG3atDxWaE/FHoDk7gmIxkDy8BAqDNJ6gKqrq6MPfKnHdWutoQcCIgKQ0fMTED4G3ep1mkxKg3QkXG6Zs0ttVq7T8vJyTJ06DbuPbxvxZ3c3b0NpaSmmT5+eh8rsze/3W12CpWRtbWQrvAaIipm0ANTQ0BB90JvmoOYPQQfDmDhxYv6KSqO9vR0A0NnRIW4/YvV6zJS0xrKZtGVol2unpJEyBA4ALrvsUhxoeQcdfelndwuEfNh+5HVceOGF8Hg8eazOPiKRwRECnASBqABxCBzlmrQAZFworHt8qX8g9rqEi4Obj0eHqQT8ftGzNUnbj0g5I29n0tapmbRQK+naqcsuuwwA8GbjS2l/ZvuRzfAHfbj88svzVZbtmIe9sQeIKEvSztya8SxfdtioylxdXeyeFLETjbW1tdYVAwz27PSkOajFXjeCkpWOHz8OeD2Dj4WSvD1IayxLJrkHSFLISGaux+raJk2ahMWLl2BL4/OI6NTXOW5+93lMnjQZCxcuzHN19mHu9WEPEFEBknaQA2QHRhq98847L/qgLrobXbp0qYXVAB6PBzUTaoHugZTv6xPRHiCrA1BzczNOdHdDTY8Gtl27dllaTzLJ12RIJjksSg4Zkmszf/8lbAtXXfUedPS2Yk/z0GuBmrsP4UDLO3jPVe8R/V20mrkHiJMgEBUgiQHIwJ0z5cr0qdOAE2mGwHUPoKKyEhUVFfktKsn9998P5XTAsfx0OCbW4v716033BZJFQqMvHe5HMie5B8hM2vfNHMgk1LZixQpMmFCHl/ZuHPLey/uegsftwVVXXWVBZfbBHqBB1n+jiXJA2pk0u2AvVeYSDh4OJeKeNtOnT4c6kWYIXPcApk+3dmrY9vZ2bNi4AVgwE6q8FFgyH8ebm/H8889bWpeZ5DPyZgxAmZMcgCR/38yhR0JtLpcL73vfe7H3+HYcP3Ek/np/oBdvNr2ESy69BFVV1t/nTDLzySYJxywrMQBRQZJ2kDNjyCgM7e3t0T2oAhwVjvjMZlaaOnUqIv1+IMV3TPX4MW2qtQFo/fr1CIXCcCyeH61p9hQ4aqvw+z/8QczBmEPgCo95nbrdbgsrGUpyADIfR6XUdtVVV8HtcuP1A3+Pv/ZG40sIhgJ4//vfb2Fl9mCeBa7Y2yLcu1NBkhyAeOa2MLS1tcX3oLo89txiNTU10QfVpYDHGf03pRqqvhx6IGjZRA1aa/z2t7/FAw88ALVgBlR1OYDYtrDsZDQ1NuLrX/86+vr6LKkvHSmNPhobySFDcu+UxABUU1ODpcuWYvuRTUCs/b798OuYM3sO5s6da21xNmAOPQxARAVIys6aCtfx1hYg9jXT5QrHW6yfzay8PBosHGdMBeoqgLoKON+/CGr5HOhQOP5+PoXDYfzoRz/Cb37zG6gFM+BYuSThfcfcaXBcdCbe3LoV//alL1nek8YeoMLDHqDsSJoFzmzFihXo7u+APzSAcCSExra9WLFyhdVl2YK5B8j8uBhx704FiQ0XyqWenh40vvsu4I42XtQEB443H7e88V5WVhZ9EEyaVCD2PP5+ngwMDOCWW27Bhg0boM5aAMeqs6CcQ7dNxymz4HjPcrx7qAn/fPM/o6mpKa91mhkNUofTyd7aAiE5AJlJ7gGSVNvy5cvhdDrR5+9Bn78HGhorVjAAZULazH5WKu7/ehoTyY0DSTvrZMXe7VwIXn75ZUTCEcAb2wbmuqC1xgsvvGBpXfFtMpx0Zi+c/+/ckSNH8G9f+hI2bd4Mx8rFcJ592rD7DMeMSXC8bwU6+npx87/8C1555ZU8VjvIqFHSGW8Db4SaHXNDT9qxQXJt5nokBcfKykqcsfAMDAT7MBDow+RJkzFr1iyry7IFj8eT8nExYgCigiSx8WJgw8X+nnvuOTiqnfGboKoJDqg6J5597llL63rnnXeiD+qTprouc8NRXpKXe+5EIhE8+OCD+Ow//iMOHm6C48pz4ThtTka/qxpqoNashM/rwje/+U18//vfR29vb44rTqpBcACi7Ei+zkZybebQI622eSfNQyAUQCDkx0nzT+JxNUPmdSop1FqBAYiyJnmHU+xdu9mSvE6l6OrqwptvvonIvKTv2Dwn3tn5DlpaWqwpDMD27dvhmFAO5U08sCmloCdX4q1t23LaA9nc3Iwv//u/45577kFoci3U2ovhmDV5VH9DVZZDfeAiqLMW4G9PP43P3HQTNm/enKOKU3y+EYCENfikk7zvkDwETnIAkjoEDgDmzJkDQCMUCWL27NlWl2MbktdpvrGVSFmTfMDj2dvscHjeyJ599llEIhGo+UkHj9jzp59+2oKqopMNbN+xA3pymvtgTKlGR3s7jh8f/8katNZ47LHH8JmbbsKO3bvguOhMOFYvj97rJwvK6YDz7NPgXHMhuiJB/Od//id+9KMfob+/f5wrT/HZRgDiSZSCIXmiAcnhTOoQOAAJoYcBKHMlJSXxx16v18JKrJfTPbxSarVSardSap9S6qsp3i9RSv0p9v5rSqnZuayHxpfkACS5B4ghw942bNwANdEJVZfYkFLVDqipTmx8cqMl63jfvn3w+3zA1NQBSE2pBgC8/fbb4/7Z99xzD+68804E66vgWHsxHKfMGpf9g5pYC/XBi6AWnYQnnngCN//LvyAQCIxDxcN8Zqxuyfs3iSQvL8nXTknuAZI8BG7KlCnxx5Mnj66XuZgxAA3KWStRKeUE8BMA7wFwGoDrlVKnJf3YpwF0aq1PAnAHgNtyVQ+NP2kHEjPJAUjycqPh7d+/HwcPHAROTnMW+RQXjh09hp07d+a3MABbt24FAKipNal/YEIZHKWe+M+Nl7fffhsPPfQQ1Glz4Lj6fKjK8Z1pTrmccJ63EI4rz0VTYyN+97vfjevfH/J53D4LjuR1KjkASe4BMs9oWVWVptebhjB/3xiAcuccAPu01ge01gEAfwSQfJve9wP4dezxegCXKsl7KrINiQGIPT/2t2nTpuiDeWkaKvNcgAJef/31/BUVs3Xr1uj1P2WpZ/ZRSkFPrcIbb745bt9Fv9+P23/4QziqyuFYfnrGDU3d3IHIm3ugmzsy/izH7ClQC2biz3/+M/bv359tySOSfAiSXJtkkpeb5CFwknuAzMf4yspKCyuxLwag3JkG4JDp+eHYayl/RmsdAtANoC6HNdE4khgyDJJrI/sa8T46Ovov3/fbAYCd7+yEbqgY/ocmVqKjvR2tra3j8pm/+93vcOzoUWDlYih3Zg0k3dwB99NbcM30k+F+esvoQtB5CwGvBz+4/XaEw+GRfyEL8SFwOfnrhcsuIUMayQFIcg+QWWlpdtcaFrtiD0AqV2ellVJrAazWWv9/sec3AjhXa/15089sj/3M4djz/bGfaUv6WzcBuAkAJk2atPSPf/zjuNS4d+9eAMD8+fPH5e+NJ9aWnfXr10NrjWuvvVZcCGpqaoLf78fMmTMTxuFKcPToUfT19WHq1KkoLy+3upwEra2t6OrqQkNDA2pqaiytpa+vD0ePHgVqHYAb2PrimwCAJSvOjP5ACEBHBJMnT877WckjR46g3zcA1JZBORS2vvharLZzAUSzGTr74YIDs2fPHpdG4f79+xFRgKqtyjgxvPPGVsyfNhP/+NnP4n9++lPsPdKEU89akvFn6t4BYMCP6dOn56Th89hjj6G/vx9Lly7F3Llzx/3vj4VR25IlS8Ttf5944gn09vaKrO3JJ59Ed3c3axslrTXWr18PACKPqffffz8A4EMf+pDFlQwluZ1kLLdrrrlGXFtkvJfbxRdfvEVrvSzVe7kMQOcB+KbW+srY868BgNb6VtPPbIz9zCtKKReAZgANepiili1bpsdrStQrr7wSALBx48Zx+XvjyQ61Pf744+Jm1Ln22mvR29uL9evXi+sW/8d//EccPHgQd999N+bNm2d1OQluueUWvPrqq7jllltw/vnnW11OgrvvvhsPPfQQ1q1bhzVr1lhay65du3DzzTdDXeWFmj20x0MfCUE/5MNtt92GJUuW5LW2gwcPYt26dcDpU+BYMfT7FdlxDPqFffj617+OlStXjstnPvzww/jJT34CdcY8OM8/I6PfMXqAVl9xBTY8+SSCly6Fmjwho9+NNDUjsuE1nHvOObjllltysv955plncNttt6G2thbjdbJtvDz66KO46667AMg7Nrz99tv40pe+BEBebe3t7fjoRz8KQF5tXV1d+MhHPgJAXm3BYBDvfe97AQCPPPKIuBtn2qGdJLm2++67DzNmzLC4mkTjvdyUUmkDUC7j/CYA85VSc5RSHgDXAXg46WceBvDx2OO1AJ4ZLvwUEyuG0IyWtLNBZvwaUS5MnToVTpcT+t1Qyvf1u2EopTB9+vQ8Vxa9L8bVV18NveMYdEdfYl3+ENTmJixcuBArVqwYt8+85ppr8IEPfAD67f2IbD+Q0e+oyRMQvHQpHj68e1ThR7d1Qf9tC+bOnYuvfe1rOTv5Ygz1kbgPkbzPlVybtOtXzOxSm7STnTR20np/8i1ne6zYNT2fB7ARwDsA/qy13qGU+pZS6prYj/0cQJ1Sah+ALwIYMlV2sfrOd74TPZsrmF3GVdPIuLwyU1VVhauvuhp4JwTdHUl4T/dGoLaHcNlll6G+vt6S+v7hH/4BZWWl0C8fSGjA602NiPiCWLdu3biv68985jNYvnw5Ii+/jciBIxn9jpo8AY4zF2Qefrr7oDe+htqqKnzn29/O6Zh/o9EncZuQ3AiVXJtdQoY0ku+fRGMn+bqufMjpKRut9eNa6wVa63la6+/GXvuG1vrh2GOf1vpDWuuTtNbnaK0zO4U4Tm655RZ8+tOfzudHZuz000+3fLgPjS+JDSoaveuuuw5utxt6U+L9aPSWABxw4IYbbrCoMqC6uhqf/MQnoQ93Qe+PXkqp23qhdxzDe6++GieddNK4f6bT6cTXvvY1zJ8/H5GnNiH87BvQ/vG5V4/WGpHt+xH5y99RElH47ne+g7q63M6TI7lBKrk2yQ1k1kY0lOT9ST7I7bPOg/PPPx8f/vCHrS6DiGykrq4Oa96/Btgbgu6M9gLpngjwThjvec97LL8p39VXX4258+ZBvXIQOhCCfmE/Kisr8YlPfCJnn+n1evHD22/HRz7yEai9h6Hv/zsijc1j+pu6qxeRh19E5KW3sXTxEtz705/mZVICyY0CyY1l1pYdyd83yt7y5cvFTSiUjD1ARAVI4vh9ys7ZZ58NADj11FMtrmTQ2rVr4XQ4oXcEAQB6ZxBKI34xs5WcTidu/ud/RqQ/gMhft0IfP4GbPvOZnE8K4vF48KlPfQp33nknZjRMRGTDqwg/vRnaN7reIB3RiLy1F5G//B2lPT586Utfwne+8x1MnDgxR5UnMhqkEvchkhvLkmuTHIA4MqAwfe1rX8N9991ndRnDkrhd5PO6JAYgKkiSDyoSG1aSnX322XjggQdw8sknW11KXE1NDc477zw49oahQxqO3RGctfSsvDXSR3LKKafgEx//OCaVVOHyyy/HZZddlrfPnj9/Pn7y45/gxhtvhDp4DPr+ZxA5mtl9h3S/D/rhFxB5dQfOPfsc3Pezn+Hyyy/P6/ZsNAok7kMYMrIjeYIGKkxerxcTJmR2jaNVJG6zX/7yl7Fw4cK8fJbcvSlRgZLYsJJO4lCC1atX48UXXwSe90P3hvGe1e+xuqQE119/Pa6//npLPtvtduOGG27A+eefj+/+3/+LI4+/isjlZ8MxK/3wQN3TD/3Yy3D5gvjiV7+KVatWWbKtMGRkR3JtRDSUxBMDK1euHLfbNIxE3n89EZENnHXWWairrwN2hVBZVYnly5dbXZI4c+fOxQ9vvx3z5s6FfvJ1RPYdTvlzuqsH+uEX4Q1p3Pa97+Hiiy+27ESB5Ia85DH7EhtTRETpyD3VRTQGEoeZseensDidTnz7W9/Gzp07sWDBAtGNUytVV1fjv2+7Df/nG9/Ajqc3A4EQHKfNjr+v27qgH38FFR4vvnfrrZbfJNgIQBL3IZLDmeSeMyKiZNxjUUGSGDauv/56/OhHP8KkSZOsLoXGybx58yxvsNtBeXk5/u93v4tvf/vb2PzCZkRe2jb4ZiSCuvp6/Pdtt1lyA9lkRk+GxH2I5JAtOZxRYfrKV76Cjo4Oq8sgm2IAolFbunQptmzZYnUZtrNy5UpccMEFHCpCRcnr9eKb3/wmHnnkEXR3d8dfd7vduOKKK8RMICG5Ic/aiAZdcsklVpdANsYARKP25S9/GY2NjVaXkdLMmTOxc+dOkWdvAY6Tp+LmdrvxwQ9+0OoyhiW5IS95mJnk5UZEg3gSO0ru3pTEqq2tRW1trdVlpPTFL34RL774IioqKqwuxVZWrVqFV155BbNmzbK6FCJLGScpeA3Q6PDkDpE9fOUrX0FLS4vVZViOAYgKyowZMyyb+tfOVq1ahXPOOQdlZWVWl0JkKaP3WGIvsuQeIAYgInuorq5GdXW11WVYjnssIgIAhh8iyJ4EgT1ARETjg3ssIiIiG2AAIiIaH9xjERERJeE1QKMjsceMiCgdBiAiIqIkEhv0kgMQEZGdMAARERElkdgDxGFmRETjQ+6UMkRERHkmsefHILk26S677DIsWLDA6jKISAgGICIiopiSkhIAwMqVKy2uZCjpQ+AaJk7EJRdfbHUZKX35y1+2uoS0PB4PJk6caHUZKdXW1orsDSUaKwYgIiKimPLyctx9992YPn261aUMYQSgVatWWVtIGr/+1a/EhzSJ7rnnHng8HqvLSOlHP/oRAxAVJGW3L/ayZcv05s2brS6DiIgo7959911MmjQJpaWlVpdCRCSaUmqL1npZqvfYA0RERGQTs2fPtroEIiLb45QyRERERERUNBiAiIiIiIioaDAAERERERFR0WAAIiIiIiKiosEARERERERERYMBiIiIiIiIigYDEBERERERFQ0GICIiIiIiKhoMQEREREREVDQYgIiIiIiIqGgwABERERERUdFgACIiIiIioqLBAEREREREREWDAYiIiIiIiIoGAxARERERERUNBiAiIiIiIioaDEBERERERFQ0lNba6hpGRSnVCqBxHP9kPYC2cfx744m1ZYe1ZYe1ZYe1ZYe1ZYe1ZYe1ZU9yfawtO8VS2yytdUOqN2wXgMabUmqz1nqZ1XWkwtqyw9qyw9qyw9qyw9qyw9qyw9qyJ7k+1pYd1sYhcEREREREVEQYgIiIiIiIqGgwAAH3Wl3AMFhbdlhbdlhbdlhbdlhbdlhbdlhb9iTXx9qyU/S1Ff01QEREREREVDzYA0REREREREWjqAOQUsqplHpTKfWo1bWYKaXeVUq9rZTaqpTabHU9Zkqpf1VK7VBKbVdK/UEp5bW4nl8opVqUUttNr30oVmNEKWXZLCepaou9/gWl1K5Yjf8tpTal1J9i37mtse/gVkG1LVFKvWpsE0qpcyyoa4ZS6u9KqZ2xdXdz7PUJSqmnlFJ7Y/9fK6g2EdtCrJYh+zWl1GKl1Cux1x9RSlVZVNuQ/ZpS6gXT9nBUKfWgoNouVUq9EavtRaXUSYJquyRW23al1K+VUi6Lars5VsMOpdS/xF6zfFsdpjYR22qa2r4fO2ZtU0r9VSlVk6daUh0LUq5DpdTHYvW9rZR6WSm1WFBt1bH921ux5fpJQbXVxtbpNqXU60qphRbUlva7r5T6mlJqn1Jqt1LqynEtRmtdtP8AfBHA7wE8anUtSXW9C6De6jpS1DUNwEEApbHnfwbwCYtruhDAWQC2m147FcDJAJ4FsExYbRcD+BuAktjziVJqS3r/dgDfkFIbgCcBvCf2+CoAz1pQ1xQAZ8UeVwLYA+A0AP8N4Kux178K4DZBtYnYFmJ1DdmvAdgE4KLY408B+LYFdY24XwPwFwD/IKW22Po9Nfba5wD8SkhtnwJwCMCC2GvfAvBpC2pbCGA7gDIArtg+9yQh22q62izfVoep7QoArtjP3Jav5ZbmWJByHQI4H0Bt7PF7ALwmqLb/MD1uANABwCOktu8DuCX2+BQAT1uw3FJ+9xE9hr0FoATAHAD7ATjHq5ai7QFSSk0HcDWA+6yuxWZcAEpjZ/XKABy1shit9fOI7kzMr72jtd5tUUnmOobUBmAdgO9prf2xn2nJe2FIWxsAQCmlAHwYwB/yWlRMmto0AKN3oBoWfO+01se01m/EHvcAeAfRRuD7Afw69mO/BrBGSm1StoVhLADwfOzxUwCutaiOtPu1WK/UJQAetKa0lLVZvj2kqa0PQEBrvSf2vlXr9FREG8D9WusQgOcAfBACttV0tQnZVtPV9mTsOQC8CmB6PopJcyxIuQ611i9rrTvzVeNoakN0e62MHVsrYr8XQo6MsrbTADwT+71dAGYrpSbls7ZhvvvvB/BHrbVfa30QwD4A4zb6o2gDEIAfAfh3ABGL60hFA3hSKbVFKXWT1cUYtNZHAPwAQBOAYwC6tdZPWluV7SwAsFIp9ZpS6jml1NlWF5TCSgDHtdZ7rS7E5F8AfF8pdQjR7+DXrCxGKTUbwJkAXgMwSWt9LPZWM4CcHTwykVSbJKn2azsQPcgBwIcAzMh7USPv19Ygelb0hKDa/j8AjyulDgO4EcD3JNSGaC+QyzSMZS0sWKeI9mKsVErVKaXKEO01ngEZ22q62iTIpLZPAXgi75UNymQdfhrW1Jiuth8jGi6PAngbwM1a63y3PdPV9haiJwegokPLZyFPATcD0xDtUTYcjr02LooyACml3gugRWu9xepa0lihtT4L0W7cf1JKXWh1QUB0rCiijZU5AKYCKFdK3WBtVbbjAjABwHIAXwbw59hZIUmuh0W9P8NYB+BftdYzAPwrgJ9bVYhSqgLRIVH/ktwo1tF+e8um1hyuNgFS7dc+BeBzSqktiA7dC+S7qAz2a5ZtD8PU9q8ArtJaTwfwSwA/lFAbgI8BuA7AHUqp1wH0AAjnuzat9TuIDtV6EsAGAFuT67BqW82kNquMVJtS6j8R7bn4nRX1JUu1DpVSFyMagL5iSVExSbVdieiynApgCYAfK4uudwSG1PY9ADUqes3vFwC8CSHfx1wrygAE4AIA1yil3gXwRwCXKKV+a21Jg2Jn1ozhUX/FOHb5jdFlAA5qrVu11kEADyA67pYydxjAAzrqdUR7IOstrikuNpTlgwD+ZHUtST6O6PcNAO6HRduEUsqNaMD4ndbaqOe4UmpK7P0pACwZ1pimNjFS7de01ru01ldorZciGjL2W1Ba2v2aUqoe0e/aYxbUla62CwAs1lobPXx/gjX74ZTLTWv9itZ6pdb6HESHN+4Z9q/kiNb651rrpVrrCwF0xuoQsa2mqU2EdLUppT4B4L0APhZrQFsl7TpUSi1C9LKG92ut2wXV9kkMHvf3IXrt3CkSatNan9Baf1JrvQTAPyB6jdKBPNeWzhEk9kBOj702LooyAGmtv6a1nq61no3o2apntNYiejKUUuVKqUrjMaIXH24f/rfypgnAcqVUWazX4lJErzWgzD2I6EQIUEotAOAB0GZlQUkuA7BLa33Y6kKSHAVwUezxJQDyPjwv9p3/OYB3tNbmM+4PIxrQEPv/hwTVJkK6/ZpSamLsNQeArwP4HwvKG26/thbRSXJ8FtSVrradAKpj+w8AuBzW7IdTLjfTOi1B9Cy8FesUpjpmInpS5/cQsK0OU5sIqWpTSq1G9JKBa7TW/VbWhzTrMFbvAwBuNF2DJqI2RLeVSwEgdn3Nych/yEi33GqUUp7Y6/8fgOcFjR54GMB1SqkSpdQcAPMBvD5uf13ncLYHO/wDsAqCZoEDMBfRMZlvITo+/j+trimpvv8CsAvRUPYbxGYzs7CePyA6/jyIaO/KpwF8IPbYD+A4gI2CavMA+G1s+b0B4BIptcVe/xWAfxS4TlcA2BLbLl4DsNSCulYgOmxgG6LDGbYiOka+DsDTiIayvwGYIKg2KdtCyv0agJsRPcO8B9GhGMqi+lLu1xCdlWi1FTUNV1tsvb4dW57PApgrqLbvIxrIdiM6FNOq5fYComHxLQCXxl6zfFsdpjYp22qq2vYhei2GsW/5nzzVkupYkHIdItrz02mqcbOg2qYiOqzw7di2coOg2s6L7X93Ixogay2oLe13H8B/IjoyYDdiM8GO1z8V+wAiIiIiIqKCV5RD4IiIiIiIqDgxABERERERUdFgACIiIiIioqLBAEREREREREWDAYiIiIiIiIoGAxAREYmilOodxc9+Uyn1pVz9fSIiKjwMQEREREREVDQYgIiISDyl1PuUUq8ppd5USv0tdkd1w2Kl1CtKqb1Kqc+YfufLSqlNSqltSqn/sqBsIiISiAGIiIjs4EUAy7XWZwL4I4B/N723CMAliN7V/BtKqalKqSsAzAdwDoAlAJYqpS7Mb8lERCSRy+oCiIiIMjAdwJ+UUlMAeAAcNL33kNZ6AMCAUurviIaeFQCuAPBm7GcqEA1Ez+evZCIikogBiIiI7OAuAD/UWj+slFoF4Jum93TSz2oACsCtWuuf5qU6IiKyDQ6BIyIiO6gGcCT2+ONJ771fKeVVStUBWAVgE4CNAD6llKoAAKXUNKXUxHwVS0REcrEHiIiIpClTSh02Pf8hoj0+9yulOgE8A2CO6f1tAP4OoB7At7XWRwEcVUqdCuAVpRQA9AK4AUBL7ssnIiLJlNbJIweIiIiIiIgKE4fAERERERFR0WAAIiIiIiKiosEARERERERERYMBiIiIiIiIigYDEBERERERFQ0GICIiIiIiKhoMQEREREREVDQYgIiIiIiIqGj8/4/dNdPLtbWDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "melt_train_df = norm_train_df.melt(var_name = 'Label', value_name = 'Normalized value')\n",
    "plt.figure()\n",
    "ax = sns.violinplot(x = 'Label', y = 'Normalized value', data = melt_train_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot normalized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yZMSZD03VCX/9INPNQI7y1l6Oc1NNJWnmis0Zag3SofSrtZluAtDsEMjUw5vyZ5QMibDuhYq1ts4+MhcdOYDyy03E6i0suEX6eeeHC48coBH9O0mS1pJq1Kx9vmyTCvKMfnhg79BtM5dvzlyDgGaAK6l6eGtkcnFGpqbGau1WLvAS4V2g1Fhpy47KUAqJv/xyUUGeKqtZ7N8UHHLbO5luAnJQjfv2zzNG9509TBJBdXP0+//NUcn4SdpVVaO/lX6jbm1bqMg3iLWaz1kgKQQy9cjlGZmHP1ysg299R4vWbst0U7KOF598u3GHht70ppasL5NUu+ZKkgrz81Rj6+ZMA8gNXmqZMVLn1kWSpBtenpvJJiEFHp+6TJK0x/WvSZK+iwhWL3r8cw24blLa2wU0FwQy9fDWMuTiYv+35zu5vCxGbLzIfYe8PSL8qWXeiNyuKmZlUqWmxuqJactUXlld73EDu7VJU4uAWt4YRp4xYecGzgnN2+TLj6hzW42VPv5mfQZaA2Q/Apl6eB8uuZgB1LZFgSTpqU+/zXBLYlu1Zaf+/cnSTDejjshZlilfrZMUnlq2xA1u9p7wevoalmOmfLVWv3txjv70+oJ6jxvYlUAG6eeloOa5n8ITThosSVq0tixTTULAIoPSloX5GtyzXdRjz/7HtAYHXQDURSBTD+/Cs7om9yKZQd3bSpJKOrfOcEtiO/+x6Zrw0lytaWI5xpEzMl4w6K9W1rNDy7S2qTlZu7Vc5z/2WYNruLx48slp32rnrtgXCK2K8oNsHhAX/4yMJA3o6pxrz3lkWqaahIBt3rkr7PvW7gBhpL12cz5v311AVTOgsQhk6pGf4hmZ8spqXfXsTC3fuCM1vyAJ3sVdmxgn3qZg43bnQ6KprTOJ1Zw834zM5ccMTFNrmp/nPl+hdxas1X+/WFnvca1bOK/hiqoaXfffWTGPiww8gXTwUpa98Y1+7qCRd15D9tuyozLs+54diqMeN+nyI9S1bQtNnr0qHc0CmhUCmXrUBjKpiWQ+XbJRL8xYodtem5+Sx09GQb7z3Cub8GyUlVcdLLEL0VRVZdsU40LEv32MMewlk6jWbpC9ctPOeo8zqu3jWSu21LnfW6dUTRyDDAillrnngv5dWmtY3w7qxWxts7F5Z3ggE+08dPeZQ5WfZzS0d3t9vYa0wnQr/WqtprC/T1YjkKlH7WL/4B975vLN+smjn0pSvWkvmbakCS/29+KXqgT+QG/OXa2Db31HH34d/ALLnz32WdTbY22EmYtV8ZLRotAJZBpaFF3lC8K376qq+zhuIEP/IxNqIgIZ7+uVm3dqweqtmWoWAuQNau3bq33U+2/+3hCddoCzp8yArm20ZMN2zkdpdt4/P9PP/hn9MxvZgUCmHt4HTCpOLKfe/1Ho66Y4IlzqLlB/c94alYwPpjTka7NXad8b3ghsQaO3eVxVArNGM77dLCm9O2lHzsKcPqyXpOgX2YjNK4u+q4GcT3+Au6Oi7mvOe1+TWoZM8O8j4xk7ZDdJSskAC9Lvwsc/lyTNXunMxPxsZEn4Ab5zT/8urbWrqkbfbal/phlAOAKZeninmEmzV+m4u97T5h2pyV1e3wR3c/50ycaw720AF3u3v/GVtlVUacWmYNcEeUFXY3iTI7GC1EmzVmned8mNiv7vkpF69Lzhoe+rIi68DypxdvMuq0h/IHPb5Pn6zfMzQ99f/dxMnfngJ2lvRyK8DWobCmT8G45uq6iqk6/urVFoamuskBv8+8h4zjusRHlG2hqRkoTs8z3fYOUrlx4uSfr1MYMkhf/NPf27OGuklqxvulkQjfXUp9+qZPwkrS9retc4kSI/n5E9CGTq4b/I/Xptmfa/6a1AHjfyDdO/a9OtDOZ5eeZ3ST9GcWHt4utk+fvwlkmNX2PkpXm9t3Bd1AD1kidn6MR7Pki8gZL279NBR+/VPfT99ogUQq+CTVl54wMZa60e/2RpwmmJD76/WM9OXxH6/vnPV2haRPDaVG1wPxTfmLO63uMig/E73qwtw2ytDb0Og5yRmbVis9Zua1pV9NA0eS87/x4yBe5Gufe8uyiQwSNkzpfLN4e+3rd3ey2dOE4dWhXFPH5AMwxkrvvvbEnSl24GRFO2gSIbWYtAph53vbUwJY+7OmKR+dIkTlyfL9uokvGTtHJz4tPRkVVyoo1Q//rpLxN+fE+Qm0DOTXK2xEvnmL5sk37x7+mSnOd9/5RF2lqe+Gjo2/PWSJIO7Nexzn0LVm8L+77Y7Y/yysb3xzvz1+r3L83Vb16IXY2rufrzm877sqrG1juK9vCHS8K+/8/U2j2RyitrQheSQc7InHLfRzrpng8De7xcsWVnpf7wylxVVDXd9YJBq4moWhZpIQu/s9Y29zPknEP66qVLRsb1M13btlDrovzQBsrNiffZH4TNO3YFlsXgT3Nvjv0er+oam9UBNIFMPb6NUhZ5pm+UJVFrtjojyv/82UHq0b5YLQsT38fiz284F3WTZ9WWbbzzza80dfGGuH7+42/Wa9jNb6nUV7Xj6c9qL/juPeuAhNsWqUW+83LbmeQambVby8PWGCXCv/D+s6WbJElvzVujO974Src3sIFifbzndubwPqHbvLSCP5yyT9ixoUXr1dH7Y2t5pZ6Ytixq+puXNrWriV74PTnt26RT8+Kxviz6KJp/luWCw/uHvl7npnHu8K1LCiqO8S7C1zbBVNGm7i9vL9Q/P1qqG1+ep/mrcmOhe+Q+Mp6LR+0uSU2yLD/i89KXTgbDAX06amifDnH9jDFG/bu21uIsvqD0888oelVQg7D/TW9pyA1vaNmG5PvJvxbt429yd13awx8s1ug/l2rR2m0NH9wEEcg00r8+Xpr0Y6ze4szI7NauWHt0b6vKJK6kenV0SnW2a1m738u97y7Sjx6aGtfPz1jmXMT703D+8vbXoa+PHNQ19HUyqQ4L12zTp0ud3/HUp8sTfhxJoWpvkZ6c9m3cm2P6A5m2xU7feXnpO3clPmPk7co9clCX0G1eWkGn1uFpBUVuYBcr1e7hD5body/O0QeL6p5gvWufoLNPgkhnsdbqty/OTjo1L5otEWsHXp31nR7+YLGe/Sz8NbV+Z+3zOHT3zrW3u2lpX/t2T39v4TpNmpX8/g3s/5E472X31Kff6oS/Bv+6aYqirZGRpO/t7xQB+f1Lc9LdJATEmzE4Zu9ujfq5/l3a6P2F68IGFrPVDl/acyKVRaN56cvavcOOuqM0bEAqEa/O+k7GOCX9n/98RcM/0Ex96F5jrGhgS4OmikCmHqcf0Cv09U8O7SdJDW7CFw8vtaxH+2IV5uepMsFUq23llaH1Ap4g0mS8UesPrx2t9q0KQ7cnkgLlGXP3+6GvX0lyvU1kipbknOB+++JsXeimiTUk2mJLb0S9RWFib4ut5ZX66ztOENi5dexcaI/3e2IFMmvcgDf6finOEwg6iz6I9UsTXpobQEtiPbZzcecFgbdMmq9bJs2vk2L3m/ejn5DveedrbdlZWWevn0uenJF021ZtYW1MogoDHLFNpZWbd2r3304OZLYxWvllSRrUrY0kxT0og6bh0yUb9ec3vpIkfbO2TF3atKh3TUw03oL/85pBOWB/inYQnytS3RT3aYsTX9dprdW7C9bq+8N6q6rGatWW8qQDI8kZqB5z93tJLRmI5q15azRnZd19iJI149tN+sCdmdoepbpnNiCQqUdvd7Zj4S0n6Ipj9wjkMa21esZN3WrfslD5edKmBKuh7Xvjm5riVuy6991FkhR3jnlVdY22R+SZVtdYPfZR7bqC9i2dIMb7YH186tKE2hlNENPCft4JLnL9USz5vosHbzTYSwv7KkqgFI/9bnwz9HVxHOmCXhnhWBtzekHka3NWaexf3g9bRJ7ofprvLVwX+rCNJpn1QZKz3uvxqcskSYf5ZkISsW5bhe595+uw1LptbmGEf/7soDrHe2uvos0q/Xykk1722pzVuumVeb60nqSaGOb0v30c3INloTVbyxNOvS3Iz46Ponfnr1F1jdV/pi1L+rG813V+xJs5L89oWN8OqrGxN9dFLWttIOsuG2t7RZWqa6z+OGme/vH+Yp3x4Ce6b8oilYyfpEXrykKfm43Rq0NxClqafjU1Vpu2136WvLtgrX700CdJzfhH+9nLnvoirDplY3y9tkxby6u0X+/2uvDIAZKkDTFSlRvjwsena+GaMj3qu5YKwi/+PV0n3Rv8+kv/51a2bgyaHZ8eGfC30kW6xw0Oigry1ME3M/FxlFSfeP3l7a9DiziNMXpj7hqt2lKedOk/b0qwIs5Zk6ufm6l9bngj9L2V9OIXK3XjK/NCt7Vxq2r96OC+kqRbJze8dmTa4g0acevbocWOUvSUm20JVOqKJrIufzxT2NbasDz8sooqPfvZ8tDz+9xNt0u1ju5oXeSIrMcLdD74er0WrN6mg//4jl6d5cxmeT/R2M+Fnz76qe6bsij0/fqyirAyzIlUUPNUVtdo1J9Lwx47mQ+uG16eozvfWhiqplZd44yg9WxfrME92tU53ivrPTHKGqdfHFm7TmZbeaU273RekwV5wZwCF60NX5jdFCtO3T9lkWZH2Vk8KCff+6FOvf8jLd+4o9EX4IVBRpSunbuqtXhdsAvmC92AK9FZdL9Ya2Sk2teTP5UG0d337iLtcf1rdQbmUslaq31ueEO7/3ay/vHBEv1xcnjlzC++3azPv23850jXti2CamKj1dRY3fXmV1oVwD42A347OSy1+KlPv9XUxRuTSr/1r6194oIRatOiQGUVVfrHB4sTejyv2mnP9i21T0/n8+S4u99LuH2S04ez3HNs9EyKxCQarDXV3xM0ApkINTVW1lrd/nr4qLV/M8NJsxPLp7fWhlKPIjW2Cke0EaiS8ZPiflP/z12M+JybF7q9okq3RpyMved8pG+9R0OLwW5/4yut2Vqh+auc4zbv2KVhN9eWrT55aE/39sRG/v1TvycP7akJJw0Ou7+hvUUk6ZVZq0LP3xOr+le8U82JTPl6a3NemLFCX0T50PPSDPwuffIL1dRYlbt//8Zc/0WWI5ak4be8HVaGOdEA8+s12zTod6+F3bZwTZlemJH4hViZO81dXlmt1VvKdflTX0iSvttSro6ti/TQuQeGHf/pko36eNF6Pfhe3fdAt7a1I501Vvpu807l5xm1a1k7QJFoKWtJddbYNLVS1pt37NIdb3ylk+9LXUU1r8jBEbdP0RG3T2nUzxZGzMgEsQnxTa/O09F3vhdoUOlVXwriAz+0RibKp/BLboGQrQEN+DRnd7/tFLwJcq+Sxz5aopLxk2L+naNVeBq9Z9ew7xOZJRq1R+PW1ARp3qqtuufdRbrq2ZkNH1yPyHWMfsmUOPYG2W753hCNHNhFky533iO3v/6Vamqszn1kWijbJR5eMY1j9u4WKrxTXlmjud8lPtjzhW9Guk+nVgk/jt+Gsoqwz9ZkPqcivbtgTejrnu2LA0+HSxcCGZe1Vv+dsUIDfjtZ/a+bXO+xT0yL782yYtMOlYyfpHfmOy8WL/1Lqk1bO26ws89IZBDRkLfmrYl6+99Kv2nU4yzb4LyZ//3JsrDRkptOra2w5R8wvH9K/Y/vXTR4H9KRe+9ccayzIdg5j0wLu72quibqxcuUr9bqBt+i11d9F4ynDO0pY0zYepSGLsR37qpu1Ilq8IQ3Gj5ICpvynfOH4+P6GW8fmamLN+q0KGlJkRd3nu/97aPQRX1+RCSzekt5zA/fi//zeYNtSjTN8arnaj/87vFVurv6uZm64F/T66zlioc3I1VZXaMnpi0LDSA8cI4TwIzZZzctnThO718zWpI0/r+zdfbD06I+Vn6e0SfXHS1JGtC1tVZu2qke7YvDLn6SGf32LqZ+5VacirfYRrq8PT+1KQORVQQbOzATmVr2pzcSrxzoeepT5zydbJVEz45dVbrSvcjblOBAjJ+tZ0amf5fW6te5VcJprrmkdZFzHq3vAjpeC9ds048fnhrKTIg1yxM50ClJyzft1B7da9PJLhm9e6N/f16e0dDe7SWFlwZOB29x/srNO5Pa/Nu/4fXJQ3vqyD26Rr2vMTaUVegP7t/EGwDs17l2oO9XT8zQB1+v17UvzI4rY2bdtgotWb9d1xy/p4wxYZkc4+75ULNWbE6one/MXyNjnM+uoAKOB98PH5j7JslZ5oc/WKwl67drV1WNfv5Y7ZrisUN6aP6qbVlZAp9AxvXkgl2hD6lYnvvloY16zJnLnQvmZ6c7FZX8+9KUXj1KUu2H2bPTVzRqROl+X3pQLMmsQzlm79qNHEt8J4wXv1gZtaDA85+v0N2fl2uGu/HVba/VvRA5/YBeYY814aU5ocBn4O9e04WP173Q/tk/P9O/PqnNR7/NF/B5QWDkxfy0KKWnK6qq9eOHp2rvCa9HHbGvj38z0NfnrFbJ+En6bGnsEXcvJa8hkYHKewvXhX0fax3MLF960GtzVoc+8Morq3XIbe9o/Auzo/5crNGwFgV5uvI4Zw3YYx8vTWjzU68S35jB3XXK0J6afPkRofvenr9G//ig8fnC3p915orNYel+x0ZUAtqtffS88lERI6Q92rdUh1aFKq+s1rbyqtAaME9VALMA5/lSHRv7oV1RVa2vVm/Trqoa3fHGgkAuzCQnELzaF2gGtQeDX7Jl6SMX+z/43uKkKzd5BSEGT3hDny7ZmPTMjH/vqmRGbT0N7SOzR/e2+mpN0whkFq0t0yMfBpvzHxRvQ9FH3fZVVFUnfDE25u739dGi2s+PD76OflH8+ty6m/EuWlsWtvfPmcP7Nvj7or0if3pYiSRn1rg+N748VyXjJzX4O+L12hxnoGjZhh1Jbf7tDY6+etnhuvesAzTct6daonu13DJpfmggy//5+vaVR0kK/3uc/fC0BoPAv7gDT/v2coLGks7hsyen3PdRQueL/0xdppaF+eratoWWBrQO+Bm3IudbVxwpyQm2E1VeWa1bJs3X6D+Xao/rwzMohpd01K7qGr02u/6NppsiAhk5swFvLWv4w/2gkk6hr6212lpeqb+8vVA3vTIv6oveu8COHCDv17lVaASyU+vai6kbX66/2tOOXVXaVVWjC/41XfPi2GthhpuuZK0NvbEfn7osrpOf/2QROVp6/5RFYWt63lu4Tlc/N1Mz19WePGYu31ynss8fTt0nbBfrf3+yTP2vmxyapn97fvRZJu85SNIhA+ouIH/0vPCF32c+NLXO7upnPTQ17AOqMbzZD0l68H1nRuqHD3yiN+eulrU2sLzSnz76qZZt2K7zXt+ukvGT4t6E9N53v9YJf/1Aa939id6M8iFbn69uOUHnu3utlH61Luz5xsNaG0oruvvM/SVJg3uGr2FJZA2YVzDh/inf6ONvav92ka/HooK8Oml4dxzZMupId3FBvhas3qadldVqWZivvXZrG7rvcV/AnKjOrWtz3A//05RG5e3f+PJcHf+X97XH9a/p/inf6Ig/vat9JryukvGTdPAf39a/Pl4aNjsZ6YMVlfooYjTSy+X3+9UTiVVoq6iqTmrj3foURLmaP++fnyVcpWdbeWVYmukZD36i/tdNTvi9+tXqbfrAN9AQa/+ixqhvjYwk7dm9rZas3x5oKkmixtz9nm5+dV6Ty6H/fNmmUMD/vy+/08n3fqiDbnlbh9z6TqMfK97F5N657JABnfTirw6L+Xhe1kU0RjGiV0k9Ozg/993m+gvXPOZuBRHUzE2LgsT3s/P7xD1X797VmZ3q6Ftf/J+piZ1j/YOy/kI6A7q0Vt8oKVxffLu53kDEy6rxPjcGdW+rT393jJbcdmLomBkJrHHyHrNdcWHCBXn85n63RVt2Vmr0nl1V0qW1igryktoMPHItp+e8w0q0v7vf0f8982XCj58pBDKSfvjgJ3Ef641c//Ojpdrvxjf1l7e/1qMfLQlLNaiusbrzza905bNfSnJG3rw0B0l65KfDQ19fc/xeoa9XxyjfWjJ+kkrGT9LgCW9oj+tfC7vgL8rP05++v2/Un/MqcBx39/va6/fOBdHv/xff3gSRswof/GZ06Ou73lqoMX9xyimv3LxTP42xr4t/sd+/fn6w2hY7J7TInY5H+xaIV1RV65ZX56lk/KSwEYPKaquXvlyp1+bUvUgf0qu9zjo4fPTrJ4+Et8mbKarPB78ZHXZh6/fZ0o2qqq4Ju+i48PHP1f+6yWGzBZ/97tgGf099jrqjtM5tR/jWKEVz/5RvNH/VVh15h7MuYVtFlaYsqB3NXrR2W4OLr1vHOYt0/mOf6W7fzKIUntLhf5x3rjoq9HVjZzvWbC0PSyP0fPH746Ie76VjSM5JuWur6Ke2XdU1atuiQEvXb1dentEzFx4aeu0kOvrtVYG7/OiBdWYHX4/yeo1l+tLwD86t5VXavqt2k80bXp6rf32yTK/FWKP3yJxd+nFEat2ZD02tk6v//sJ1+uLbTY2+ALr2+VkaOfHdsMf769tfBzIq3Koo+usvkSo9i9eVaV9fBUG/Qb97LWxmPF7H/+X9UPEXj//v8NXqbRp+y9uNKnIQax8ZzwF9O6i6xmp2CkquNpb39g0qTS8ol0aUTZ+9cou2lldp047KRgeA/4mRMv5CxP4ii9zUntOH9dYBfTvqnENqP3t+edTuOm5wd3Vp0yJs0K4xermBzPn/iq8EcxB720mJremJZt22CrVtUaCWRW7A4XuBL92QWGrZl74Z326+ggh5eUZDetUOmnlZM2f9Y6r6Xzc5ZopYx1aFGtClddg6lm5ti2WM0X/d4PSa56Ovm61PQX6eDujbQRu279LUxRv1vyS363hjrnOtd9OpQ1SYn6ehvdsnXIxoQ1lF1PPp3388TDeesk8ogJaCXYeTDjkfyFhr9UWMi9wFN4+ts97BS0e56dV5Ybef7HuBPPDeN7r33UWhnNN3F6zVdf+tTfcZ2K32Ytm/SeL0ZZvq5D82lO/54iWH6cyD+uq9a0bVKUnrFRaIFYXXJ/KCLHLhmjdFfFYcawGG9e2go3x5skP7dNAxe0Vf1Ljn9a/rYTdFwH9i/fObX4XNUESmBd3yvSF69qLa1L8Fq7fpxS+cD6AtceSzz7vpePXp1ErPxkgf/OEDn2jg716LOo7mXw/h35g0KBccMaDRP/Ozx5wPQWutjr3rfR1wc/RUgZcvHRn1du9nj7x9ikrGT9IHX6/Tvz5eqncWrA29rqYt3qBPvtkQGjn8fUThhSLfzEm8I7mfLtmo5Rt3hAYBInWMsT/PZl8wdcVxsUulD+zWRlt2Vuq7LeX6dMlGtW9VqBtOrm13Irupe1Xgdrl51n/78bDQfVO+WhvX3k7VNTZ0YdsQbz1OPPwFHvzvwdP+9nGdgLQhXvrGyD+9q8mzV2l7RVWj2lIfrw8P271znUXTD73/TaPSzN5pYD3QPe983aj9tmJVerv4iRl6352luf31BVpfVqE358UfuNpQaln0C15vDcAZcQ60/fuTpXojYjb23QVrQiPkQUj3uo2GeHs3/fG0Ibp+3N5h910Ux5pAz7byypiDfP6KZL97cbbG/sUZoBvR38nQmHBS7XrSHxzYW//4yXB99rtj4v7dkbq3c1Jl49175bbXFgRSHCPZ0vuehWu2aeTA2sE3b5CpZ/titSzMb3TK1oayilAgPWrPrhrUvW3E/bWDB/40Nik8NbC6xuqDr9fp4Q8Wa9OOSo3aM/o1yLC+HdWhVaEWr3MyI+ItSfzC5yu0cfsutWlRqLNHOMHt/z3zpS55YoYe/mBxo/t3fVmF7nE/a73rrwP7ddKclVsaHWh8t3mnfhnxfvD2RvSnZv/75wdLyr4yzDkfyETmoS+57UTNnDBGr152uIoL8+vMTMRaaLVy807NWblF97zzte6oZ5+OSJEBwzF3vqf7pyzS5h27VF5ZHXPxsmc396TXr3NrjY54Y24rr4prtNRLK0rEt3Fc+P3k0JI6tz3sm5WKx0MRC94+vHZ02Pf5eUYH9+8UdtsVz8zUxu27tC6OtUfeRXe74toAKdrszNf1BIUDurRu9PS8v6hCLIX5zkL1GTFmI2I59f6PQuuzYtmvd4fQ1/4L3crqGh1z53uhv++5j3yqGyJSH898aKrO+sfUUL7xPhHpZL06tAyVE41nX525323RGQ9+oiNunxI1DfCxKHvHeLwPs2uO37NOkOtXlJ8XVnpbctYIeaKt06qPP9j2KuYU+zZUfXXWKu3+28kNBkiXPjlD38SZP+7Pw/dErq+SnEWdfpEpmA++vzhsAKYh3mt73bYK/eqJGXVS1k7cd7e4H8vv6DtLQ6+zO88YqnvPHhZ2/62TFzRqg8DIUrhH7dE1bEZZir8aoaQ6wYHfTx79VNOXblQr93OiMe9/L7aPFcj48/bjuXCZ8NJcXRTx+v35Y9N11j+CKzxRviv6xfWXyzcnvYVAY3lrvf7v2EH68Yh+uuCIAXrriiP16mVONav3F66Lu9zv8e6GzV5wYoy0+NYTNaBL69BClpoaG1boxws0iwryQp/j3t/MJJFXVFSQp37u49R30d/DdwH6/IwV2rmrOqkNsbcFEMiUV1Zr6YbtYUUP9uvdQTNvGKOLR+2unZXVoTTkeHmDMS9cfJge+9nBde5f7Ku0ZYzRxaNqiyz431sPvb9Y5z7yaajs8rj9Yp+vjvZdS/3sn5/FFXx5BW++3bg9lLkjOVVub5k0P2x9bzyG3/J2ndsO7NdRVTU2rjXSfodNfFef+Wb8f3RQH113wt566NwDdUDf2uDvgL4dJNWuIcoWOR/I+NeafDnhOBlj1L5VoYb0ah/1+Ppezyfd+2FCaQuR7njjK+1/01va6/ev13vcIQM6qXOb8Lrz/hzPeH1v/16hr3t3bKnbv79f1ON+eGDvsO9vj7Jfh1Q3AIhcryI5J5zI0smN0bY4+sWq/yJSkkZOfFf3vltb8vrpCw/Rx+OPrvNz/nUX95x1gN656ijd/+NhdY6rbwH2qb5+jNdPDi3R0onj9Pcov8uvR/uWYbN38Zi5fLOujVj4f9oBsdt4jG8R/aDfvRb2ARHpJ750wjfdCnodI3axzsszoVS7Rz5cEnXUsKKqWpc8OUMl4ydp3D2xL6qLC/NijqBJtTM+kQv8IxXkG1W7b2IviDTGhBZcz1+1tVEjZ94CWak2J/yoKCVU65sV3bGrKmrKpGfxrSfWCRIjL1iipXd6H9iSdMKQ3ZSfZ/TCxeEzjrNXbol7oX6rovov0v2jsI3hXwBcXWPVpkVB1HTKeC4mIsuHDurWRvf/eJj6dGqlu84YGrr9HxEDI/W5L8pFw9Trakfcf/DAJ9rlLi5vzPVrQ4v9/eekRPP1Iz03fblKxk/SvTG2AWjI9b41Wmu3lofSnr93/0eNrpiZLC9boWf72pSYQd3bhn12PxZH2tWVz3yp79yZnSd/cYi+nHCc5tx4vPLyjI4d3F3bKqq0cvNO/dP3WAeVhI/8z/3D8Zrx++MC29jVG1zcXBH9Nb9zV7VWbSkP7aP2m+dnae8Jr+vmiEyReHy0aL1Kxk8KpTFFs3ZbeVzr/b5ZV6YaK+0RcQ3QvmVhaKZphpsa9c78NSoZP6nBQZ6pizeoZWG+9usd/ZrMSzXzZsJ/fcyg0H2rfXvi+M8NLQvzdWC/8IFPv5YR57poVUX9/IVdvHOzV9DJ89Sn9Q8q+vlnPp+58JDQ1we6M07RzkmxRDtv9u3cSi2L8jVmn/BgzruuWrimTAtWJ74WJ91yPpDxqmgd1rNAHVo1fKF49fF7hn1/xbGx01ji9Yq7Z0A8bjp1Hx0xqIv+c/4IPfWLQ+rcn8hI0L692+ubW0/U0onj9OG1R+uMg/pEPe5PEQFOrA+u23+wn7q0qe3LWJVKfn54f53i7isTlMi+3FlZrZd8e8YcMqBzqFpY6xgXZqcM7andu7YJq7AWj8ggqjEiT/ySE6hGuuesA/TEBSMS/j13nTFUvzgi+gzcuYf0i3q7VxnG7/0oMwCtW9R/obs8ShWvG1+eV2cPFo//tfH59fXPRnkLFbtEBPaRSjq3Vrm7aWxr37oMLxVAko67K/amaN9u2BG2yaI/3fFUN0iMnGWVFLOKkrW2wRLfeXlGky4/Qp9fX7v+yl95LNpFrj+Peszg7vq7W7L6wH6d6sxYnf+v6Ypm0/ZdYR9mDaUVtY5Y5xJP5ajIx/TOX4+fX/c13tAeUdbasA1ZJWfNgjerfvqw3qH0o3veXaR/f7JUb81bU+8odqzgabf2xWHV87yLwGh/+2ienb5cf33naxlT/zn79h8459xoxRAS4eX93/nWwtAMSnWN1UeL1sfsB//fyP++96dLSw3vpRVU6pLHC9SPHdy9zn2Xuxez97zztc544BM9GWP9y3ebd+q/7jqGqdcdo/w8ow6tikJr/c5218+9u2Bt6H1/3ODuuv0HQ8Mep7gwv9EDTfXxZntmrY/+HrplkhOw9O/SWnefWduWeAK3SJF7zw3o2jqseMrE1xbo4D++o/1vctadrd1arsETXo+6/sRbLziwW5s69+21mzMYM3/1Nn22dGPovHPE7VP02EfRK+Jtr6jSvz5ZpiG92sXcjuCmU/fR0D4ddLSbru6f/fdXPPWfj7xS/LF4s0beZ+WXDQz2+FN4vQHNki6ttXTiuLDjohU7qKyu0S/+PT2s2upWd8D0+H26a4SvwJH/NbZma/3FIDw7oszmRp6r/Q53B6RO+OsHMY9panI+kOnZoaVeuPgw/XSf+E5C7VsWat5NtetmRg6sW0WrPqcPqzsivm+MkYZohvXtqMfPH6HDB3VpVNDStrggrN3+dBopvg/geBcv7te7g648rjbgqy+t6K8/2j/q7dedsFfU26XYAYikOvmzfkPdi12v1KsxRpMuP1wTT49eLCE/z8RsXzSRlboaI1rPRruGOmVoT40c2EULbzlBTzYyoLnzh0NljNHvxkWfCYv2ehq3Xw8N7NZGSyeOC1uDFI1/ZDSao+4oVXWNDZUVraiqDiuCEemW04aop5s+0VAxghtP2UevXnZ4aNQvFv8GmP7H/M3Y2tfbmq21ucmRjrxjio6+s26gk2ekc3zBkD9NT3LSo6KNaA6M2EQ0krdHjqSw2dcXfBvZnh4xWlheWR1Wyv3Xxw4Ku//LCceF0m8kJxc7ciO5j79ZrwNufktj//KBvtu8U39+46sG906JfPkcGxEQbtlRqePuek8l4yeFNoCNnHXu5VtwGrnmYc7KLfXuoRC5EL2kcyuN269H2G3+SooTXpqrX/x7er2pY1c/V3fBrzfrd9/ZdWdR66tG5fnfFyv1GzegaGiSaZB7Qbi9Ealw0SzfuKPOCOtMd+3PpNmr9OOHp8Xc6HlGxOLikvGTdPK9H+qdBWvD0t/WlVXotL99pOdipLPWN0CQCC8dMVoA4U/t+XTpRv32xdlRR/8fdsvCP3DOsKhl3L0Ur9//b04oreyhcw+MullxkLy/+6Zy5wVirdUbc1drzdZyrdy8M9SWIwd11WkH9NbiWxufieEp/ao2OH3sZwdpv17tQ0HtP95frAfecwYsK6utSsZP0hkPfqIdu6qjltSf4j7WoG51P4f7dHLe2/e887V++ED4ui9v355IXvpqn46xN5c8sF8nvXTJyLDrjGm/rZ0xfXveGm0qrwlbR9nQoLU3gHFgv06htPMdu6o0dfEGHXjzW3UyMxav2678PKNZN47RifuGn3Mu8KXuXx9lHdbqLeV6a94aXeqrFrrMfZ2eMbzuoLKXyTLi1ndUMn5Sg+mE/kGvB84ZpuLCPI3Zp27w7/mPe11hbXyz4E1BzgcykjNd1yI//qDAX2EnP8/opIgPS8/N3xsS9n2vDi111xn7Rz22ocpUnm7t6h9xlqTnoyxYf/S8g9SqqEBLJ47T/JvGqvSaUc7jtW348fxiVY36ePzRenhMq9AIhP8C4pLRA2M+njGmzij6rBvH6MIj6y5wP++wEo3o30l//uHQOvf5+Tdk9DvUHdnwX7Dv07O9fhRR8cyvMeliRwyqP62pPpGpcv/4Se0aomgXR0UFedrfzWeN1/cjUgPj8WffyOPB/WOXG/2/YwfFDHR/5ttbZc/rX9NhE9/V+rKKOhW9LjpygD6//lh9eO1ofXjtaLUrLtT/LhkZ1/5NxYX5MdNB/doV1753/evf2kX0fzwpov7A5JmLDg17XT147oFhx367cYfueTc8OLpt8vx6P4TuP3uY+naO/gF+k1vZL3KdiuSkYvgXCu/TM7xfjDEa0qt92KCKPwWxvLJaZ/+jdm3eYRPfjZnK4B9x9D7zvGBk+cbatI6H3v9GQ296M7TGLDJVo1eHlvpyQvi5JXLt3vf//omOcYPIf360pM4mnD/zraPZu0c7lV4zus4gynUnhgdHkrNP0UWPT6+zCeDOXdV6YcaKOsePcy9Uigvz66y9qaiqDpW7jxYgWWsbVd7Ue42+MWdNUhcVR9w+JbRI3asa+f2/f6znpi/XQ25J+YUxNt/0Fs37U4G9SmrjT9hLSyeOU99OrTTvu6364tvNMas9rXHLww9r5Hkrapsejz6L6Hd6lDTaI26fEgrorLV6d8EaHTqgs8YOif4ZbowJW+8xcmDnpNa/xKt7u2IZI72/okp3vbVQ/a+brIse/1wjbn1HIye+GzrOK/Gcl2d0jZst0piCJf61Yq9edrhG7dlNeXkmdF6KXG8m1VYei5wl9O+tFm1gNLLfBvdoF5buecp9H4atBfPPckReSzWke7vi0JrZC/49XVeU7gwFbD8eEfvz3nOOm50wtE97XX+Sc864/fWv9KOHpmrD9l119pG6b8oiVdfYOp8jUt3tAlZs2qFv1pWF0jKPuN2pNuqttyyvrNaz7t4xkeduSerfNTyI3v23k1UyflJYpsAHX6/TrZPnq6bGhtKOfzN2T40d0kMLbj5BPRoYdPT8NcEU1HQjkEnQ4B7O6Ht+ntENJ+8TdoEkSb89cS+de0i/sPUm7epZhPz3cw5U2+ICHbt37EhZckoENmR4SSdd5RuRWnzriWF74LQsyg9dHDf2o7Fj66Kw0VxPzw4tw05s/vSVhqbcHz+/dgHf0N7t3RrsRm+6G0Dt17u9bjp1H914yj565qJDdcK+0T90PLHS1X7uXlC3dC9u4jmhpUtXX0C5T+e80Gaf9WlVVBAaLdq9a2u9dcWROrgket6vv0So5Ez9e1VL6hOZK+xfGOgXOQPh5+9nrwzzGQ9+EjY6/pND++m6E/dW5zYt1LtjK/V2R+C6tSsOe+0my/9BE5kKd2dEgBxZStdfOWvnruqw1JrIoiDRZiHfX7hec1ZukbVW1to6OzZPve6YsLTByNkEyQn2GnLePz8L7QVUX9rqj0dE//s3VCCiIf6+GHHr2/pq9TbdOrnuejp/EHfb6fvWGSU1xoRVlPOUjJ+kP7wyTzOXbw77G03zXfhMvjz68/anvHoefG+x3pi7JpSW811Zjay12ntC7WzRyUN7htK8fuAbEIis5njlszM16s+lum3yfF30+Od1SqX+9sX4yt97vFnDZ6YvV//rJuuCf01XdY3Vik07Eg5s/Outrnl+luasdN6Hkal7c1ZuUcn4SaGUnnMP7afPrz9Wlx9dOzDllWw9dEDnmFW2Fq3dFrZ4OFZxg3jN+HZTKJWvvnL3sT5vvYII7y1cp6Ubdui0KFkSfs9ffJhOGOKsJYh2oZoK+XlG1koby23M2eEXLj407CLZywg44vYpYQv3a2qsxtztzIRGzmh6M1L/OX9EaCAo3xjVuOcoT+nVo/TfXx0Wlu4cWa75TLe6Xn3p9ktuO1G/chfj9+7YMmzd46wVW8LSZM92i1R8PP7ouLcH8PvDKdEL6URW14xm1J7dtHTiOPVo3zI0mOlP20vkNey9hg7/05TQgIyfN8uz1+9f13PujHv3KAPXkdk0Hm/T45tfnadzH/lUD72/WAN+O1kL3AGK4/eJvxjLzAljJDkFo7IBgUyChvXrIMkJLLq2baFZNx6v8b50qJ+PdEYTzzioj+b84Xgd3L+T/hlRNcivTYsCzb7xeD380+F1LmC8VKrGjEpcdswgLfrjCZp945ioI+VeepU/lSNeQ3q1DxtVO76eaUqp4bQ1/zT9U76FbXt0b6ulE8fp5UsPj1r5rD7+BX+ebm7aUVFBnhbeckLY36s+sUoU/2rU7pobUZ47CJcPazhY9Xjn02cuOlSDurdVYUH0vvZej563rzxKN53auFGu+tsR+28cWQRAcqbi/RfyN57ccPW2IPjLY0cGH6MjSoIf6Y6UXfv8LJWMnxRWOWvvCa/r5Zm1a6+iVbiLTFmcv2qrTrr3Q43+c6n6Xzc57L7zDivRbu2LG1wwf+WYxq3J271b7BSYAyNKlZaMn6Tr/zdbE16KvTHvfr3bq6RzK43dZzctuHmsJOm9a0bptV8fETpmoG8Ee83WCh3v7jkVySsRLklHxgiEf3poSdhAR6QDbn5Lv3ri8zrlvWO9Ho0xujFKcCQ5F2aTZq3Sbz/cqb+8HX7x+Kfv76szhvfR0onjGhyRX7Zhh5a4o9ZbyytDmydXVdfUSaU8bPf6U5MjL8bfnr9Gx971ng7/0xSNufv90CxS5MxedY2NWWCiID8v6jktchd7b8+Jjdt3aUCX1irMz1PnNi105ZjatGFvYG1kPRkFx971fqg/I4tWNFZNjQ2lUv7n/BFhA0CRvDURXdoU6fX/q319LtuwQ1XVNXplppNK511gxtKuuDD0vgxqMX9jFeQZvX/NaD3tfj6O27dHncXqR/oyAn79tFP2t2T8JH26dGOo0uGffRVVr3jmS9311kId3L+TDvf9/fKM0ZadlaEZNMnJBBnWt2NYNsTWiGDJewlefkz9GRierm1bqFProrAU0h8/PE2V1TVauGZbaNCrZwLXKJIzKzslYsG9FF8FTb9oVTD9e/55AV1D66W9NUL18T/uoG5top5rtlc4r+uj9+oWVnRky85KffzNej3yYd2Uv+MGdw8Vo4lH+1aFeujcA2Oum21qCGQS9PuTBuvNK44My6v95VG7692rjtLzvwwfKWnTokDPXnRo1BzcaG6LuAAatVc3Lb71xEa/qAry82JW9+rcpoXuOeuARpdB9jx14SGhYCaevPD6+E8ssTbGayxvavjJC0bo8fMPDivJKDnBTLwpAv4SxZ5Prjtavxm7V0IjRQ3xshzjGW/1noI3G+ZNGUemDA6I8yR27di9tGc964xitqOe+zq3aaGb6ykzPeGkwQlvHtdYbYujr5GRnJlD/4fWtooqTVu8Qc/EMUMR7bV05kF9ol40R9sU7gdxpv1FK+8784YxMY9v2cCH9qI/nhD2/X+mxl6ztH+fDnr50sNVes1oPXDugaH3bb/OrbV3j9oP6YI8EypjWx9v4fiAetYb5OUZDYmSXuE3efbqsJ2/I6sFRTpvZP86qX+SU7zEW/jsT6kY3q9jveelaCO83mi2kfTkp99qvxvf1MTX6s5K/evnsYM0yfnsePvKo8JmQZa41Ze+XlsWWmvzii+olqSnP/s2bI1SZLrxfr071LnIW+krxvFmRFpcrLTNDu6u7Q0FZJHHJ+rAW2r3wzq8gXRsb5HzdSfsXeci8spnZ+qL5Zt05B5dY35G+hW5o+Dx7vUUr3gebunEcVp064nq27mVDhnQWfNuOj5qRc18N9iRnOIE3pon/z5nXjnq8spqvegWObh2bPiA3qYdu1RVbcPWVHmv/94dW+mN/3MyJfyptQvXOqP+PdsXN/i5Wh0RoEReF13/4hy99KXTthcujp7KHK/+URbcJ8KbjfX400O9jbsbqixYWGD004gsiNk3jtED59T+LQ+57Z3Q17EGgL3qbRceOUC7tS8OPb9v1m0PSwke48vquPvM/ettWzRj9tlNJSleCxYUApkEtSjI1x5RLvgGdG2j4UmmwrQrLtSnvg21igvyU3Khd8rQng1WeYqlRUG+zj/cSXOJdd7624+H6eSAq5LFq2vbFlo6cZwOG9hFRwzqWudk3VizbhwTNs3rzzHdvWswb/ZQumICf2rvpDfh5MG67fR9Q2ugGuviUbvrjSuO1M3fGxJzaj6ahmLCc90y09HKg39/WOPX7iTKPwsTLQj9ePzRYWvebp7UcDnTWB84xhidFzETFs1J+/Woc6FY39o1f/CxdOI4tW9ZqAfOqXthfv24vRu8qCjIzwur2Ob3tx8PU4dWhbp2rLMO4n+XxN48NVK0QMETuYbtltPqnxmsb18gz7F31c76xPPhGyvNIlp1oucbuJiKtg+XN7tRkJent93y5A/7Rkonnr6vlk4cF7MSk9/Abm105Zg9o44uexem6yP2yvooYiPlaIMx/bu01jMXHhIKxLb60kgi91OKluIi1Q5CdWnTQr8ZWztTE60owztX1a1+2Bgbt+8KFZyI3Kg6Gq/aWmR6rCS9PPM7LV63vc4GirFUuI/VJqCBtnjG0P5z/ghde1Ddwc/6guq+nVupb6fYC+O9VKGrnnXSkCaevm+dmdmOrYrUqkW+HnzPCer95X8lac/d2urovbppxrebVTJ+kiqra0IFXO6K44L53EP7aVjfDqE1eh1ahs/YL9u4Xc9OX6GDSzrVaVuyoqWWxiPaonspvKLfD4c3/Dnmf78vnThObYsLNXZID30UZUuIWOeG7u2c4OUQXzWzkoi1lAtvOUFvzXfOOycP7Vkn+6C5IZBpovxrYTon+OZLNW9k4PQYF6In7ttD98ZYeB/pgXMOrLdSWaa1Ky7UtN8eq7euOLJO6cZJlx8RyilNxhMXjNDbVx4VuviMJ57xcnW9n2lXXKizDu6b9MzWuYf0008PK4n7+Hhn5YwxmnL1qLCF5u2THKVtDP8FRLTqd61bFIRVo/LWD9TnNxEl2SNFq1To+dWo3etUv/po/NF6K0rJa0+09JaxQ3bTI2PCP8zi/fCKtu7mBwf21on79tCXE8bUmc2MR6yqQKVXj6qzhq2hdublmdBGbQ2JnM2uz6PnNTwbfcno+J77P2Ns1nrOI9OiXkjHswYuUv8ureusT5y+bJPmfrcllF/vLUz357b/fGT/0IxCUcRrZ8SAzjr/8P6hio7vzF8TNhvjlTWPnLV44oIRdfYb+9Wo2lmjW317GLUqytfPRpY0KrUlmkfdQPDmU/eJ67V90VG7q11xQdgFX6RoZYKjOW1Yb508tKeuOK5xqZ3JOHxQF+3duXFpUFL4xsFf3TI27L55q7aqsromNFtzZpStFooK8rR5R6U+ccsBR0uJ8hcnGvS71/Tzx5ziC4Pi6M/eHVvpv78aGRoMbBOxvnjq4o1at61CPzo4evCQjMiZlcY4JiL1uGT8pLCqi8Pr2ZfGE6uEfLQU/8Yswyn1VbeUnL+hd55tzIBktiKQyQL1Vf3KpD6dnCpliXwoRxo7ZDdddFTjL5jSbVD3tnUqfhQX5gdyMd6xdVHcH6weby1QtAWAXu53Y0bSE9WYk27/Lq1jVu9Ltd6+Mp6J5Lu3aVFQpz8bet3edcb+WjpxnP4dJY0o2qxQrw4t45qFiJSfZ7R04rhQtaZ4Z3H7dW6tBTeP1ewba4PxyDUnibj1tNqg4uGfDNfSieNCsyVX+9b6xBN0eyk4Da0TrG/D10hH79Xweeua4+MbXKlvn5fI53fp6IF1NjKO15Be7eusJxp3z4d63l0c7PWTt2brpP166Pdu1aUXLj5MU2LM1HrpLef/a7reXeAUtXjnqqNC6a2RBVtGDuwSdb8xL1B6x32MhWu2aceu6rDNcBNJzqqqrglVzjsnzhTrYX07ataNx9dpuz9Fu1+MqoCR2rQo0L1nHRB3engm+dcktijIr1Oa+Q53ncxtp+8bdcY28qXsX1foOevgvlEHghLZS8dbr7t/nw5hZbOPieP92VjxvOdjufaEvTSkV+x1LrHSJq3vFX/Sfk5wMSbKNdPSiePCgqXGVsfzCv14xXV+d+LeevKCEYHub9RUEchkgUQuapAbLjpq95gpKn8/50AtnTguNKra1Fw/bu+w6nrp0Kl1kZbcdmKDedNti6NfXLdpUZBwfx65R9eY5csb67zDSsICBT8vgKlpYH8Bv+LCfLUtLgylfVVVJ78W4AxfqsWAiPTLS4+uLcYRK23Jz2vNkJ7t6n3NNHYhb30ubcQAkrcw+eiIUVtJoSDDE7mpcmNFC4hXuYuEvTUc3h4f9509LHRBdGC/jjGLu/gHZ57+bLnatCjQ7l3b6AD3tT62gQXxngJfXuxnq6v0srsZsbe3RqLrKWe6my9ePWaPhMsfX3XcHrp+3N5hKXCRVeeag8gCEXl5Rk9cMCJUnvkht8jKoTFmqj76ZkPY99H6u7gwX3NvGlun0mMif5tB3drq+8N6664zhmq0r4pZkDP1vx7WQr9McqB0j+5t9eplR+iRGOuKGzr3GBkd3L+TFtw8Vg/9JPpj+Gdsjmig8EskryS6t1Fmt3bFOqyRj5GtCGSAJio7tqJK3AVHDNBlUarLpVo8H7ZP/eKQOreddXBfPfZzJ21j0R9P0F9/tH+jN6Lr6AZSybrxlH1irm3xAoNEClEUukFQrBSIxogseBLp2L27qXProrgWW//00H7KM86FZ7JrEKP5e5TF040ZfT98YBf9eETfmMFlutTY+Hf8jvS1b+2VNzP8+5MG68sJx8W9ltL/zrr/ywrdN2WRBnRpHbY7eSK+XO5cpJ2WxHq6y44ZpAuOGBBWMKNtM1w7kJ9ndP24vfXkL2pLuY8c2EUX+y7kiwvzYs5Gxap2F833D+wdSncsilEWuCFFBXm684yhGtC1TdLFIGI5oFtB3FVKG3LM3t015epRWnzriQmllNcX8Hgl8V/81WGNTmPs4Z6vEkkFznbN713cjDz/y0P1fkRJTOSeNOy/hgiRi+9nThgTNkJYkJ/XqM1S/Ywx+uVRu4d2zA7aZUcPUo/2LUMbNzbGQf07KT/PhO1GHYRWUS4Y7z5zf+30LZatz+nDnDU7xYX56tKmhebddLyK8vP09vw1zl4P93+UVGnfE/btoaUTx2nOyi2a8MwnmrG2us4+Q/UpzM/TH+MIYu4+s/7NfJNVY21ooX28C9k9hfl5Oqikoz5buim0eW5+nmlwF3S/nx5Wor+VfqOhfTpopls4Id6KibFUVdforXmr1aN9cULbBUQq9M0apWNzy0y44Ii6697y8pzS4ze+Mk9d2rQI7Ll7ac1B1CNK53rJZHhbRrRvVah7zzogtEYtWWOH7JZwlTUvK6NlYe5d1jMj04QNL+kUljMKNFXN8XrASxP62ciSwD9gbQrn24oL83XOIf0SqnTYpU0LfXPriQmNoEd7Tt4obbQy0G2LC+Pa4NcTWaa9ID9PY4f0ULWbTlXfOpVYIvP8h/RqLy+rLtELgvqKCOzfJ5gqTLedvq/OP7x/WLWjdsUFqrFWj7hpZXf8sPFBk7cnRtcEC8xcc/ye+vqPJ+ilS0bqpAHOe2a39omtB5r73Rbt2FWlXz0xQ1MXbwws17+5Bi/x6Oj2YecA10307thKnVsX6ZGfxt4nL17eDFm09TdN1clDe8a9biuVjh3sfF7FSotuznLvGQMIXLJ7CTVFhwzopHcXrFV+Ci58ThzSQw++t1jH7F13TUW28/fWK5certkrtzS4KW4yvPU8ifyOd64aFVZCVVIokGlRmNg439F7dXcKG4yfVOe+/gHty3DWwXXTCnt2aKlt5VWaucJJw+rTsfGzF9vdvVcSLUZgjAnNeHhvm06tG/9Y5ZXVGnfPh9qvd3vNcp9P64BKH3tO3Df+nc6bC68YQzxB4W7tivWnOKp8tSzK1+cBrf0zxujBcw8MbUWA+F1+zCCdMrRXs1z31RACGaCpyqJFMs1xkLO80hmdTsWfYWifDoFs1NbU7blbW+25W+M3WG0Mbx+naBf3DYm2DsbbsK8wL7sSFnp2aKl5321VVU2NRu/ZNaGqfDvcTQ4T3V/Mr7zK6cd2CYwQb3D3x/GCGCmYdVueb249sRkOvTTMSxM8KqLyXTRTf3tMg8ekQqw9nlC/FgX5KT/XNlUEMkATlw0fuM0xkPFG+IMoRYzU6dymhRbfemJgmwZ3bZWn+Rtr1DuBGY1MOO2AXnrxi5Vq37JQq7eWyxhp3ygbYMZj925t9N2W8kDSuHa6W9k0NtXFWqvxL8yqc/uvAywMksoZwqbs0N0764WLD6u38uI/f3ZQ2F5CQFOXXUNOAJqk5phadvw+Tq3/Hx3U+JF+pFdQQYwknb1XkZ696NDQnjdN3fXj9tYrlx4eKgFtbeI7mN931jA9ccGIQEr+j+rtBDCHDwof/be2/jnOia8v0AcRRW6W3HaiRkcpbZ3tGuqLVDiwX8d6A7nRe3bTbacnvnEkkG7MyABNXDZkmDXHGZmB3drmRPoXwhUXOPs9JOv1/ztC7321Tre9tiCAVsXWuU0LdW7TQj19aXLxbDIaTftWhRoZ0N4TAzvm13n/xHOe+O+MlZKczTzPO6xEXdsGV2GrqWhezwbILAIZoKnKok+7LGoqkBZ77dZOe+3WThceOUD9r5uc8t/n34zy0N2T27clUybNWqV12yp09F7ddN/Zdff2AbJONoxEZjlSy4CmihMgkPXSNZvw4aLaHdmD2G8lE56dvlyS9Jcf7Z/ZhgABa2aTik0KMzJAE8f5LzkZSEMHwlw7di8dMiD5dLX6HNK/U2gTymz1xbebdPaIvmpXnB0bIwLIvLgCGWPMWEl/lZQv6WFr7cQox5wh6UY548gzrbVnB9hOAE1YUxxtaoJNQo66eNTuKf8d147dS0fu0VX9OmfnPhKbd+zS1vIqDciSIgsAmoYGAxljTL6k+yUdJ2mFpM+MMS9ba+f5jhkk6TpJI621m4wxza+8CIB6EDbkOma+MisvzwS2UD8Tlm3YIUnq15lABkD84lkjc7CkRdbaxdbaXZKelnRqxDG/kHS/tXaTJFlr1wbbTCD32CxaJNMUZ2SQGc2twhSCFeus9u1GJ5Dpm4M7kwNIXDyBTC9Jy33fr3Bv89tD0h7GmI+MMVPdVDQAAciGC8Om30IATZkXyPTplJ2FCgBkRlCL/QskDZI0SlJvSe8bY/a11m72H2SMuVDShZLUvXt3lZaWBvTrk1dWVtak2pON6MNgeP24ZctOSdIXX3yhHcvyM9yq+n366af6tnXTKYJYVlam9RvKJUmz58xWwdr5GW5R6mX6/Td/ZaUkafWa1Vl9Hsh0PzYX0fpx08Zy7ayyUft32twKtSsy+vTjD9PTwAxasbJCkvT1okUqrVwW8zhei8HIZD9+u3yXJGnxN9+o1C5v4Oimqym/FuMJZFZK6uP7vrd7m98KSdOstZWSlhhjFsoJbD7zH2StfUjSQ5I0fPhwO2rUqASbHbzS0lI1pfZkI/owGF4/3r/gY2nTJh1wwAGBbNAXmNcn1bnpkBEjmtRO6KWlperSubW0bq32HbKvRg3unukmpVym338bZ6yQZs/Ubt1306hR+2esHcnKdD82F9H68ZFvpqmwokqjRo2sc/xDX0/VwN2qo97X3EzZMkf6dpkGDRyoUSP7xzyO12IwMtmPn+yYLy1ZrAG7765RR6W+6EeqNOXXYjxDqJ9JGmSM6W+MKZL0I0kvRxzzPzmzMTLGdJGTarY4uGYCuce4CVs2C1ZRtyxq2jNGSL0+7tqGwT3aZbglyEart5arR/vcSitr+md2oOlrcEbGWltljLlU0htyyi8/aq2da4y5SdJ0a+3L7n1jjDHzJFVLusZauyH2owJoyJ1nDNUD732jA/t1zHRTwjx+/sHq3q5YS9dv14H9OmrOd1vVvV1xppuFDDuopJNevexwAhkkZMn67RrRlGaeUygb1j0iGASrqRfXGhlr7WRJkyNum+D72kq60v0HIAB9OrXSH0/bN9PNqOOIQV0lSXt0bytJOmqPrplsDpqQIb3aZ7oJyEKzV2yRtdLCNWWZbgqQEoSuqdN0VucCAIBmLVqm7ILVWyVJPxtZkt7GAMh6BDIAACDlYqVUrStzqngdvRd7aQNoHAIZAACQMau3lKtdcYFaFQW1IwSAXEEgAwAAMmbN1nIKhgBICMMfAAAgY96Yu0btirkcAdB4zMgAAICM2lpelekmAMhCDIEAAICMqKmxKsgzuuCIAZluCoAsxIwMAADIiA3bd6mqxqpHe9bIAGg8AhkAAJAWkdvIVFRVS5JaFuWnvzEZFm1PHTQvlj9yyhHIAACAlIu2i8w2d21MdQ0XfGi+YmyhhAAQyAAAgIzYULZLkrQb5ZcBJIBABgAAZMSC1VsliX1kACSEQAYAAGRERVWNJGlA19YZbgmAbEQgAwAAMmLj9l1qXZSv4sLcW+wPIHkEMgAAICM2bd+ljq2LMt0MAFmKQAYAAKRHRDnajTt2qROBDIAEEcgAAICUi1aCdtP2XerYikAGQGIIZAAAQEYwIwMgGQQyAAAgIzZtr2RGBkDCCGQAAEDalVdWq6yiSpXVNZluCpASEUvCkAIEMgAAIO2+XlMmyQlochHXuLnDKMoCMQSCQAYAAKRdeZUTwJw8tGeGW5Je0YoeAEgMgQwAAEi7rTsrJUntWxZmuCUAshWBDAAASAt/OlVZRZUkqXWLgsw0BkDWI5AB0KyRhw40DZEZVdsrnNSyNgQyABJEIAOgWSIPHWjatodmZPIz3BIA2YpABgAApF0otayIGRkAiSGQAQAAabe9okqtivKVl8f0KYDEEMgAAIC0276rioX+yAmW1ZopQyADAADSrqyimoX+AJJCIAMAANLC+gamt1dUsdAfOcHUqdmHoBDIAACAlDMRpQTLKqpyeqG/taQbAckikAEAAGm3vaIqJ1PLGJ0HgkMgAwAA0s5JLcu9QAZAcAhkAABA2pVVVBPIAEgKgQwAAEg7J7WMxf4AEkcgAwAA0qq6xmpnJTMyAJJDIAMAANLC2xhw+64qScrJxf4AgkMgAwAAUs5fq2t7hRPIMCMDIBkEMgAAIK0IZJAL2Cko9QhkAABAWpVVVEsSi/2REwxbB6UMgQwAAEir0IxMETMyABJHIAMAANKqjNQyAAEgkAEAAGnlzcjkYtUy0oyA4BDIAACAtGKxP4AgEMgAAIC0sG4Zp9rF/gQyABJHIAMAAFLOn1K1vaJKeUYqLuQyBEDiOIMAAIC0KquoUusWBTIsGAGQBAIZAACQVtsrqkgrA5A0AhkAAJBWOyqr1bKIzTDRvHlrwpA6BDIAACCtKiqrVVxAIAMgOQQyAAAgrSqqaljoDyBpnEUAAEBaeKk25ZXVasGMDIAkEcgAAIA0qK1QtmLTzpyfkWH9BJC83D6LAACAtMvPM9q0ozLTzcgICk4DwSGQAQAAaVVdYzWwW5tMNwNAliOQAQAAaVVeWa1WlF8GkCQCGQAAkFY7K6tVXEggAyA5BDIAmjXLilqgSampsSqvrCGQAZA0AhkAzRRLaoGmxsrZQ0aSWhLIoJmzYiAt1QhkAABAyhl3bGFnZbUkqWWOl19G7jCGgbVU4SwCAADSptwLZFjsDyBJBDIAACBtvBkZ1sgASFZcgYwxZqwx5itjzCJjzPh6jvu+McYaY4YH10QAANBc7NxFIAMgGA0GMsaYfEn3SzpB0mBJZxljBkc5rq2kX0uaFnQjAQBA8xBKLcvxQIaF4EDy4pmROVjSImvtYmvtLklPSzo1ynE3S/qTpPIA2wcAAJqRnTm+RoZ130BwCuI4ppek5b7vV0ga4T/AGDNMUh9r7SRjzDWxHsgYc6GkCyWpe/fuKi0tbXSDU6WsrKxJtScb0YfBoB+TV1ZWpg0bnDGVOXPmqGjdggy3KPV43QSDfgxGtH5cv75cZTutPpsxU5I0d+YX2r4094KZ5csrJEnffLNYpTXLYx7HazEYmezHFSucv/WiRYtUWrUsI20IQlN+LcYTyNTLGJMn6S5J5zV0rLX2IUkPSdLw4cPtqFGjkv31gSktLVVTak82og+DQT8mr7S0VJ07t5HWrtGQIUM0ap/dMt2klON1Ewz6MRjR+vGJb6dr58Yd6tq3r/TFXB1+6MEa2K1tZhqYQR9tnyctXaLddx+gUUfuHvM4XovByGQ/vrdtrrRsqQYOHKhRh/fPSBuC0JRfi/Gklq2U1Mf3fW/3Nk9bSUMklRpjlko6RNLLLPgHAAAeL6Nqh7vYv3WLpMdSAeS4eAKZzyQNMsb0N8YUSfqRpJe9O621W6y1Xay1JdbaEklTJZ1irZ2ekhYDAICsddtrTqpnh5ZFGW4JkFqWeg4p12AgY62tknSppDckzZf0rLV2rjHmJmPMKaluIAAAaH6KC9nKDrmB+g6pE9e8rrV2sqTJEbdNiHHsqOSbBQAAmqP8PKPqGitD+S4ASSJBFQAApE2/Tq00uGe7TDcDQDPAvC4AAEib8spqFef4ZpgAgkEgAwAA0ua7LeVqSSADIAAEMgCaKcrFAE2JtySmqCBPm3dWZrYxAJoFAhkAzRoLioGmpbrGqm+nlpluBoBmgEAGAACkRXWNVXWNVVE+qWUAkkcgAwAA0qKqxkn5LCxgphRA8ghkAABAWuyqqpEkFeVz+QEgeZxJAABAWlRWO4FMIYEMgABwJgEAAGmxyw1kigq4/EDuoOZM6nAmAQAAaUFqGYAgcSYBAAApZ2RU4QYyhczIAAgAZxIAAJAW1W7VMmZkAASBMwkAAEirFszIAAgAZxIAAJBWVC2TrM10C5Au/K1ThzMJAABIq1yuWmYoYQUEJnfPJAAAICNyOZABEBzOJAAAIK0K85mVAJA8AhkAAJBy/owqFvsDCAJnEgAAkFZF+fmZbgKQNiyLSh0CGQDNmqVcDNDkFBZwZQcgeQQyAJopLpSApooNMQEEgTMJAABIq0LWyAAIAGcSAACQVszIAAgCZxIAAJBWBDIAgsCZBAAApJxXuam4ME95eaxhowwJkDwCGQAAkDYtC3O79DIhHBAcAhkAAJA2uR7IAAgOgQwAAEiblkUEMsgN7GOWegQyAAAgbb5Ztz3TTQDSinTC1CGQAQAAAJB1CGQAAAAAZB0CGQAAkDYnD+2Z6SYAaCYIZAAAQMoZd6VAAXvIAAgIgQwAAEibfAIZAAEhkAEAAGlTmE8gAyAYBDIAACBtmJFxsMUIkDwCGQAAkDYFeTl+6UEcBwQmx88mAAAgnVjsj1zBpFvqEcgAAIC0yWeNDHKMMbzmU4VABgAApJ57LceMDICgEMgAAIDUc/Ns8nN9jQyAwHA2AQAAKVdd40QyhczIAAgIgQwAAEi5KjeQYY0MgKAQyAAAgJSrqqmRxBoZAMEhkAEAACnnpZaxRgZAUDibAACAlKuqdtfIkFoGICAEMgAAIOWqrTcjQyAjSZbtEoGkEcgAAICUq6pmjQxyiyVWTTkCGQAAkHLeGpmCHF8jY0Qgl2sMf/KUye2zCQAASAuv/HIBa2QABIRABgAApJy32J81MgCCQiADAABSjn1kAASNQAYAAKQca2QABI2zCYBmjaIxQNPgrZHJZ40MgIAQyABolqgSAzQttTMyvDkBBINABgAApFwVqWUAAsbZBAAApFw15ZcBBIxABgAApFxltVO1jPLLyBWWVZopRyADAABSjjUy4SzXuDmDV3zqEMgAAICUY42Mg0IkQHBy+2wCAADSgjUyAIIWVyBjjBlrjPnKGLPIGDM+yv1XGmPmGWNmGWPeMcb0C76pAAAgW3mBDGtkAASlwUDGGJMv6X5JJ0gaLOksY8zgiMO+kDTcWrufpOcl3R50QwEAQPZjjQyAoMQzI3OwpEXW2sXW2l2SnpZ0qv8Aa+0Ua+0O99upknoH20wAANAcMCMDICjxBDK9JC33fb/CvS2W8yW9lkyjAABA81SUz/JcAMEoCPLBjDHnSBou6agY918o6UJJ6t69u0pLS4P89UkpKytrUu3JRvRhMOjH5JWVlWn9+nJJ0pw5c9Ri3YIMtyj1eN0Eg34MRn39OHXqJ2pXlLuzMt8u2yVJWrJ4sUrNipjH8VoMRib78buVFZKkhV9/rdKKpRlpQxCa8msxnkBmpaQ+vu97u7eFMcYcK+l3ko6y1lZEeyBr7UOSHpKk4cOH21GjRjW2vSlTWlqqptSebEQfBoN+TF5paam6dGkjrV2jIUOGaNQ+u2W6SSnH6yYY9GMwovbj65MkSUcePlIdWhWlv1FNxLTyBdKSb9R/wACNGjUw5nG8FoORyX58e/Nsafm32mPQII06tCQjbQhCU34txjO/+5mkQcaY/saYIkk/kvSy/wBjzAGSHpR0irV2bfDNBAAAzQFrZAAEpcFAxlpbJelSSW9Imi/pWWvtXGPMTcaYU9zD7pDURtJzxpgvjTEvx3g4AACQw3J9Q0zkDmsz3YLmL641MtbayZImR9w2wff1sQG3CwAANEPEMcg5hlnIVOF0AgAA0ibXZ2S4pAWCk9tnEwAAkFYskUHOIccsZQhkAABA2hjSbJAjeKmnHoEMAAAAgKxDIAMAAAAg6xDIAAAAAMg6BDIAAAAAsg6BDAAAAICsQyADAAAABIyqy6lHIAMAAACkCnWYU4ZABgAAIM0sw/VA0ghkAAAA0oTBeSA4BDIAAAAAsk5BphsAAACav/9dMlKrNu/MdDMANCMEMgAAIOX279NB+/fpkOlmAGhGSC0DAAAAkHUIZAAAAABkHQIZAM0aFU4BAGieCGQANEtUOAUAZBLjaKlHIAMAAACkCANrqUMgAwAAACDrEMgAAACkGev3gOQRyAAAAKSJIdEICAyBDAAAAICsQyADAAAAIOsQyAAAAADIOgQyAAAAALIOgQwAAACArEMgAwAAAASMEtupRyADAAAApIih4nbKEMgAAAAAyDoEMgAAAACyDoEMAABAmrF8AkgegQwAAECasF4CCA6BDAAAAICsQyADAAAAIOsQyAAAAADIOgQyAAAAALIOgQwAAAAQOGrTpRqBDAAAAJAiRpSqSxUCGQAAAABZh0AGAAAAQNYhkAEAAACQdQhkAAAA0syyDhxIGoEMAABAmrDsGwgOgQwAAACArEMgAwAAACDrEMgAAAAAyDoEMgAAAEDAKOiQegQyAAAAQIoYKjykDIEMAAAAgKxDIAOgWRq9VzdJ0sBurTPcEgAAkAoFmW4AAKTCjw7qoxP37aH2LQsz3RQAAJACzMgAaJaMMQQxAAA0YwQyAAAAaWZFSSsgWQQyAAAA6UIJKyAwBDIAAAAAsg6BDAAAAICsQyADAAAAIOtQfhkAAAAI2P8du4fWl1Xo5KE9M92UZotABgAAAAjYbu2L9fBPD8p0M5o1UssAAAAAZJ24AhljzFhjzFfGmEXGmPFR7m9hjHnGvX+aMaYk8JYCAAAAgKvBQMYYky/pfkknSBos6SxjzOCIw86XtMlaO1DS3ZL+FHRDAQAAAMATz4zMwZIWWWsXW2t3SXpa0qkRx5wq6V/u189LOsYYdnwCAAAAkBrxBDK9JC33fb/CvS3qMdbaKklbJHUOooEAAADNRbtip85SmxbUWwKSZay19R9gzA8kjbXWXuB+f66kEdbaS33HzHGPWeF+/417zPqIx7pQ0oWS1L179wOffvrpIJ9LUsrKytSmTZtMNyOr0YfBoB+Tl4t9mIvPORXox2DQj7FV1VhN+bZKo/sWqCAvdvIKfRgM+jF5me7D0aNHf26tHR7tvniGA1ZK6uP7vrd7W7RjVhhjCiS1l7Qh8oGstQ9JekiShg8fbkeNGhXHr0+P0tJSNaX2ZCP6MBj0Y/JysQ9z8TmnAv0YDPqxfsfGcQx9GAz6MXlNuQ/jSS37TNIgY0x/Y0yRpB9JejnimJcl/dT9+geS3rUNTfUAAAAAQIIanJGx1lYZYy6V9IakfEmPWmvnGmNukjTdWvuypEckPW6MWSRpo5xgBwAAAABSIq6VZtbayZImR9w2wfd1uaQfBts0AAAAAIgurg0xAQAAAKApIZABAAAAkHUIZAAAAABkHQIZAAAAAFmHQAYAAABA1iGQAQAAAJB1CGQAAAAAZB0CGQAAAABZh0AGAAAAQNYhkAEAAACQdQhkAAAAAGQdAhkAAAAAWYdABgAAAEDWMdbazPxiY9ZJWpaRXx5dF0nrM92ILEcfBoN+TF4u9mEuPudUoB+DQT8mjz4MBv2YvEz3YT9rbddod2QskGlqjDHTrbXDM92ObEYfBoN+TF4u9mEuPudUoB+DQT8mjz4MBv2YvKbch6SWAQAAAMg6BDIAAAAAsg6BTK2HMt2AZoA+DAb9mLxc7MNcfM6pQD8Gg35MHn0YDPoxeU22D1kjAwAAACDrMCMDAAAAIOvkVCBjjDnMGHOW+3V+ptuTrYwxBZluQ7YzxuxjjCnJdDuynTGmW6bbgOxkjNnTGNMl0+3IZsaYju7/JtNtyWbGmN0y3YZsx/VdMLLx+q7ZBzLGmF7GmAnGmPckPSPpekmy1lZntmXZxRjTzhhzqzHmc0lHZbo92cgY08kYc7Mx5kNJd0nqmOk2ZSNjTJEx5hpjzDRJDxpjfpjpNqWa+9r5kzHmY2PMgEy3J1sZY7oZY/5gjPlE0t2S2mW6TdnGGFNsjPm9MeZjSfcbYw635Kg3mvtavMntx0eMMZdluk3Zhuu7YGT79V3WRV6NYYzJk3SZJCPpl5L2lDTcGNPOWrs1o43LAsaYfN8JYT85F97rJO1hjJlmrS3LXOuyg9eH7ojlqZLOknSBtbY0sy3LLhGvxRJJIyRdLGdT3WeMMVXW2heNMaa5XFQZYwqstVXut6MltZfUWtIBxpglzeV5pprXj+578FJJ4yRdbq39KMNNyxoRr8X9Je0t6ReS2ki6whjT0lr7VnN6/6XByZJayrk22S7pSWPMHGvtlMw2KztwfZec5nR916xmZIwx3zPGTPZGLK21Ndba8dbaa6218yX1kDTUWruVqcfYjDGnGmP+K+kfxpgR7s1zrLUXS/qHpCGSemWsgVkgog8PcT/cZ0p6QdJq95h9M9nGbBDjtfg9SUustTOstRskfSZpfKbaGDRjzBhjzGRJ//E954+stb+U9ICk4+QENKhHRD9678Hpkl6WNNc4Bma2lU1bjNfijyV9Y62da62dJme370u8H8lEO5u6yGsT1xPW2mustbMktZD0haQ5mWlh08f1XTCa4/VdswlkjDEnSzpH0mBJV7m3Ffj/lzRD7h+IqcfojDHXSrpS0r8lzZJ0tTHmMGvtZveQD+WkYwzKTAubvhh9OFzSfEnVkt5xp3BvMMbcaYxpmbnWNl1R+vFaY8xekj6QNNIYM8Q9tEpSP2NMz2wfDTbGtJb0E0nPS3pL0mXGmNOstavdQybJGXnsl6EmZoUo/Xi5MeZ4Se/JmdH7SE5Qc6sx5gpjTFGm2tpURenDK4wxh0t6VtKZxpgW7gVjlaRRxpjW1tqazLW4aYpxbZJvrS1302QvlvS2nFHxM4wxLTLX2qaJ67tgNNfru6wNZNw0Af8iw08lnS/paEnflyRvKtw3Jd5H0gfGmE7pbW3TFaUfX5B0jrX2f3Ki851yUxDdtIE1kr6VtK9xF1q7U7w5K44+3CGplbV2p5wPrPGShku6SNJBcqbF6ceG+3G7pK7W2k8kvSjpd8aYOZLWSPpKzkxNVi08jtLWoZIGWmsflfNh819JPzbGFEqStfZbSd9JOsK74Mmm55sqcfTjC3JSobZJel3SRGvtgZLukDPDdbb7ODn7HoyjD5+T9Gtr7QeSXnVvmy1nJuEzZeH7LxXivDapdv/fJekda21P9/5xclJmeS2K67tk5cr1XZNvoMcY09YYc55xq1J4I6++/9dYa7dYaxdL2mmMOc79uTzfH7FYzol5Yzb8cVIhjn5cZK1dbowpttZulzRAta8Tb7r2Q0m9vcfMtVG4BPvQ67uPrLWPW8cGSU9KOsL9OfpR9fZjf0neBf0dkn4vaYy19j45F6d5/p9viozjB8aY292bIs9DcyS1NMbsZq2tlPS5pEo5F9ue1yWNsNZWSE37+aZKAv04Xc5s6LHW2mestY9LkrX2MzkzfPu53+fMezDBPrTGmDHW2islTZR0tLX2X5K+lLRFyr3XY6x+jOPaJN+9f6F7EVku6RVJx7i35/xrkeu7xomjH5vl9V2T/2MbY1q5f5QPJY2RdLxxqs609h3jRZ3eFOPjchaBSbV/HMmZeiyRsuOPE6TG9KMkudPeh0raKmmme6L1RkBek5Pa8h9jzFxjTNe0PpkMSbIPv3T7sMJ37O6SDpez7iFnJNmPX3j3uSfl74wxe8uZ2XoxrU8kMS3kzMRdbZxUnFAKhPu8qiRNk3SSe/MmOReQXi6z3Ivw/Y0xzxhj3jRuCdwck0g/fipppP9BjLNGZrikJ9LR6CYmkT6cJuecJWvtF9ba1caYfeSkouRq8YSG+jHWtUno+staa42z9uNQSX9LS6ublkT7kOu7cA32o9T8ru+aZCBjjDnIODXBjbV2h6Q3JR1hrT1bTom93nIq+EgKGwHy/mgPSTrSva/S99D7S5pscmTvgCT60XOSpGnW2k3efcYp0/e0nBPubEnnWmvX+d8kzUmK+vAYY8zrclI1lkqamoanklEp6se9jDH/kzRZzqj6ujQ8lbh5z9n3fZ6kzpJekhOceCmF3iyTdfumVNJp7m1bJbWStNodbettjHleThrFd5KuttZuaq7vPyn4fnSPPd0Y87ac196XctavNVsB9+Eq99gRxphJctLMJsm5KGrWEulH99CY1ybGmFOMMW/JWXv0rZp5QJiKPnTtrxy8vvN9H28/eprP9Z21tsn8k3OSfFHSXElvyFnU1cJ3/xA5U6/3RPzcaDn5z/5jp0t6TNKfJA13b2uf6eeYJf3YUk7e5FNyUi7GSLpBUn/3uL0z/RyztA9vlNRdTorUIZl+jlnej33kVO7aN9PPMY7nfKWklu59P5Gz18FoSQvc20zEc+4k5+L6RPf2pyV93/26s5zKPBl/nlnajz90vz5ITiCd8eeZhX34A/frfpKGZfo5Zkk/Rrs2uUPSQDnpPYdl+jlmaR9yfdf4fmx213dNbUbmcEnV1tp95FzwDJL0c0kyTmWnS+SUsG1lnM17Ohlj/izpz3KmvauMMZ2NMb+TU+e+v5w83xmSZK3dku4nlCHJ9OMW6yxKP0PSmZIekfNGmS1n9kDWKXXY3KWiD2dJWmutXWKtbfazMK5U9eMKa+12a+3stD+jhkU+5z0k/cy9r1jSDOvsFdHVGHOqpBbGmDsk3Slpm7V2o6RbJZ1qjFklp6Tt55Jkrd1grZ2Z3qeTManox88kZ22MdRatN3ep6MPpkmStXWatnZHep5MxifZjfdcms+WUsV5srf04zc8nE1LRh1zfNa4fm+f1XQajyjzf117EeIikD73bJP1QzjRij4if7SJnXcF5UR63laTTJfXMdJSY5f14gKQ/SOqS6edIH2bHv1zsx0Y853/IKWt5v5x9OM6StFZSjZw1Gm2iPHZ7Sd0z/Rzpx+z4Rx827X5UDl2b0IdNvh+b7GdqIv/SNiNjjNnPGPOYMebv7k3Wu8+6PSupQtJiY8y+7m0L5ZSHO9h9DG+R73pJRarN1TWmtgLIDmvtf62136XjeaVbGvvxC2vtDe4xzQp9GIxc7McknvMOOZXHtku6T9KxcqqurbHWTrfWlvmfs/t4W6xTDrPZoR+TRx8GI1392JyvTejDYKSxH5vMZ2oQ0hLIGGe/g6vkRIdHG2P6WGutdxFj3MVIchbrfid3IZechZnb5Na5ltTfGHOacRYjdZO7SNo6mv0GSPRj8ujDYORiPybxnNfIKU2bL+mv1tqO1trzrbUPSqowTtWnJvmcU4F+TB59GAz6MXn0YTDox8QFHsgYY1obYy4wxjxnjDnJGNPBOiVnJ1prfy7pfbmbj6m2xrVXeWKXnPKOxxkT2pznMLn16eUsVPqlnCpFp9lmnBNJPyaPPgxGLvZjwM95tZy85g3W2pXu43u7dw+w1s5N1/NKN/oxefRhMOjH5NGHwaAfgxVoIONGfs/LqY4wWU6u3g3u3Qvd/1+VdLLk7G5rnA2NLjbGTJWzYOkNOXmQfzLG3CwnOl3l/uwEa+3x1tp/2/Cye80K/Zg8+jAYudiPKXzOK73fYWs3s2y2+x3Qj8mjD4NBPyaPPgwG/ZgCNrEFSN6io4Mk3a3acozdJR3uO+4QORUU/D/bXs7GZAe73+fJqWJ0tO+YXpIukLMoeEgibcyGf/QjfdhU/uViP+bic6Yfm+Y/+pB+bCr/6EP6Mdv+eR0dN3cqyxpjjpRTIeFVSR0k7bDWXmWcxUQ17jGnSTpC0rXWN9pqnPKqFdba3xlj8qwvavQev1GNykL0Y/Low2DkYj/m4nNOBfoxefRhMOjH5NGHwaAf06ug4UMkY0wrSedKOkrSFGPMY5KGSXrAWnu/Maaje/soa22pcRYlVUo6XtK3tm7KyOOSJhljekiaaox5xLqLkJrzH4d+TB59GIxc7MdcfM6pQD8mjz4MBv2YPPowGPRj5jS4RsYYs5ucaHKUnI69UE4d70MlVUmStXaTpJckXeZ+X2mM6StppLX21ojHO0PSk3IW+66Q9LxtppUU/OjH5NGHwcjFfszF55wK9GPy6MNg0I/Jow+DQT9mVoOpZcbZfXs/a+009/vzJA2WNFfSJdZabz+IHpK+sNbu5n5/rpxN7h6Sk9s3y1r7njFmhKRymzu7U0uiH4NAHwYjF/sxF59zKtCPyaMPg0E/Jo8+DAb9mGE2jgVL3j/3+2GS3nO/3iBpN9+xb0g6yv16mpz61h9Lek7SsIZ+V3P+Rz/Sh03lXy72Yy4+Z/qxaf6jD+nHpvKPPqQfm8O/BlPLrMt3068l/df9erKkKyTJGNNJ0nJJ840xvSV9JulySaOttT+01s5o6Hc1Z/Rj8ujDYORiP+bic04F+jF59GEw6Mfk0YfBoB8zq1FVy9yOf0TSZdbahcaY3eXkAu4jpxTcF9bZzAf1oB+TRx8GIxf7MRefcyrQj8mjD4NBPyaPPgwG/Zh+cVUt8zlA0mxJS4wxF8hZhPRbSWdKWkA0GTf6MXn0YTBysR9z8TmnAv2YPPowGPRj8ujDYNCPadbYGZmPJA2QtFTSd5L+YK2dlZqmNV/0Y/Low2DkYj/m4nNOBfoxefRhMOjH5NGHwaAf0y/uQMY4Na9vkLRE0n+stRWpbFhzRT8mjz4MRi72Yy4+51SgH5NHHwaDfkwefRgM+jEzGjUjAwAAAABNQYNVywAAAACgqSGQAQAAAJB1CGQAAAAAZB0CGQAAAABZh0AGAAAAQNYhkAEANIoxptoY86UxZq4xZqYx5ipjTL2fJ8aYEmPM2Q0cs6/7uF8aYzYaY5a4X79tjDnFGDM+2GcCAMhmlF8GADSKMabMWtvG/bqbpCclfWStvaGenxkl6Wpr7Ulx/o7HJL1qrX0+6QYDAJolZmQAAAmz1q6VdKGkS42jxBjzgTFmhvvvMPfQiZKOcGdYrjDG5Btj7jDGfGaMmWWMuai+32OMOc8Yc5/79WPGmL8bY6YaYxYbY0YZYx41xsx3AyDvZ8YYYz5x2/GcMaZNiroBAJABBDIAgKRYaxdLypfUTdJaScdZa4dJOlPSPe5h4yV9YK3d31p7t6TzJW2x1h4k6SBJvzDG9G/Er+0o6VBJV0h6WdLdkvaRtK8xZn9jTBdJ10s61m3LdElXJvlUAQBNSEGmGwAAaFYKJd1njNlfUrWkPWIcN0bSfsaYH7jft5c0SNKSOH/PK9Zaa4yZLWmNtXa2JBlj5koqkdRb0mBJHxljJKlI0ieNfjYAgCaLQAYAkBRjzAA5QctaSTdIWiNpqJxZ//JYPybpMmvtGwn+2gr3/xrf1973BW573rLWnpXg4wMAmjhSywAACTPGdJX0gKT7rFM9pr2kVdbaGknnykk5k6Rtktr6fvQNSRcbYwrdx9nDGNM6wKZNlTTSGDPQffzWxphYs0MAgCzEjAwAoLFaGmO+lJNGViXpcUl3uff9TdILxpifSHpd0nb39lmSqo0xMyU9JumvclLAZhgn92udpO8F1UBr7TpjzHmSnjLGtHBvvl7SwqB+BwAgsyi/DAAAACDrkFoGAAAAIOsQyAAAAADIOgQyAAAAALIOgQwAAACArEMgAwAAACDrEMgAAAAAyDoEMgAAAACyDoEMAAAAgKzz/x5X4L/5ZPftAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_train_df[sample_sensor].plot(x = 'DateTime', y = 'Value', kind = 'line')\n",
    "plt.xticks(rotation=20)\n",
    "plt.title('Normalized data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create input and target dataframe - Window generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_hour_data = 65 # 65 entries ~ 1 hour - There is a variable sampling frecuency\n",
    "n_hours_in  = 3\n",
    "n_hours_out = 1\n",
    "\n",
    "input_width     = n_hour_data * n_hours_in \n",
    "label_width     = n_hour_data * n_hours_out \n",
    "offset_width    = 1 # 1 as default\n",
    "\n",
    "def f_window_gen(df, label_target, input_width, label_width, offset_width):\n",
    "\n",
    "    time_range = df.index\n",
    "    data = []\n",
    "    target = []\n",
    "\n",
    "    data_time = []\n",
    "    target_time = []\n",
    "\n",
    "    for i in range(len(time_range) - (input_width + offset_width + label_width)):\n",
    "        range_data = df.loc[time_range[i]:time_range[input_width + i]].values\n",
    "        data.append(range_data)\n",
    "        data_time.append(time_range[i:input_width + i])\n",
    "\n",
    "        range_target = df[label_target].loc[time_range[input_width + offset_width + i: input_width + offset_width + label_width + i]].values\n",
    "        target.append(range_target.flatten('F'))\n",
    "        target_time.append(time_range[input_width + offset_width + i: input_width + offset_width + label_width + i])\n",
    "\n",
    "    data = np.array(data)\n",
    "    target = np.array(target)\n",
    "\n",
    "    return data, target, data_time, target_time\n",
    "\n",
    "train_data, train_target, train_data_time, train_target_time = f_window_gen(norm_train_df, label_target, input_width, label_width, offset_width)\n",
    "val_data, val_target, val_data_time, val_target_time = f_window_gen(norm_val_df, label_target, input_width, label_width, offset_width)\n",
    "test_data, test_target, test_data_time, test_target_time = f_window_gen(norm_test_df, label_target, input_width, label_width, offset_width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting - Normalized values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(label_target, label_time, label, results, label_width):\n",
    "    n_label = np.int(len(label_target))\n",
    "\n",
    "    sensor_tag = list(label_target.values())\n",
    "\n",
    "    print(label_time.shape)\n",
    "    \n",
    "    fig, ax = plt.subplots(n_label, sharex = 'all')\n",
    "    \n",
    "    for i in range(n_label):\n",
    "\n",
    "        label_plot = label[((i) * label_width): ((i + 1)* label_width)]\n",
    "        ax.plot(label_time, label_plot, color = 'blue', label = 'Real')\n",
    "        \n",
    "        results_plot = results[((i) * label_width): ((i + 1)* label_width)]\n",
    "        ax.plot(label_time, results_plot, color = 'orange', label = 'Prediction')\n",
    "\n",
    "        ax.set_ylabel(sensor_tag[i])\n",
    "\n",
    "    lines, labels = fig.axes[-1].get_legend_handles_labels()\n",
    "    fig.legend(lines, labels, loc = 'center right')\n",
    "    plt.xticks(rotation=20)\n",
    "    plt.xlabel('Day - Time')\n",
    "    plt.show()\n",
    "\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting - History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "\n",
    "    acc = history.history['mean_absolute_error']\n",
    "    val_acc = history.history['val_mean_absolute_error']\n",
    "\n",
    "    loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "\n",
    "    # Get number of epochs\n",
    "    epochs = range(len(acc))\n",
    "\n",
    "    fig, ax = plt.subplots(2, sharex = 'all')\n",
    "\n",
    "    # Plot training and validation accuracy per epoch\n",
    "    ax[0].plot(epochs, acc, label = 'Training')\n",
    "    ax[0].plot(epochs, val_acc, label = 'Validation')\n",
    "    ax[0].title.set_text('Training and validation MAE')\n",
    "\n",
    "    # Plot training and validation loss per epoch\n",
    "    ax[1].plot(epochs, loss, label = 'Training')\n",
    "    ax[1].plot(epochs, val_loss, label = 'Validation')\n",
    "    ax[1].title.set_text('Training and validation loss')\n",
    "\n",
    "    lines, labels = fig.axes[-1].get_legend_handles_labels()\n",
    "    fig.legend(lines, labels, loc = 'center right')\n",
    "    plt.xticks(rotation=20)\n",
    "    plt.xlabel('epochs')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Architecture and Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scheduler(epoch, lr):\n",
    "    if epoch < 10:\n",
    "        return lr\n",
    "    else:\n",
    "        return lr * 0.9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compile_and_fit(model, train_data, train_target, val_data, val_target, \n",
    "                    test_data, test_target, max_epochs, steps_epoch, batch_size, test_entry, label_width):\n",
    "\n",
    "    # Add callbacks\n",
    "    early_stop = tf.keras.callbacks.EarlyStopping(monitor = \"val_loss\", \n",
    "                                                  patience = 5)\n",
    "    lr_scheduler = tf.keras.callbacks.LearningRateScheduler(scheduler, verbose = 0)\n",
    "    \n",
    "    broadcast_variables = hvd.callbacks.BroadcastGlobalVariablesCallback(0)\n",
    "    \n",
    "    metric_average = hvd.callbacks.MetricAverageCallback()\n",
    "    \n",
    "    callbacks = [early_stop, lr_scheduler, broadcast_variables, metric_average]\n",
    "\n",
    "    # Set optimizer\n",
    "    opt = tf.keras.optimizers.Nadam(learning_rate = 0.001 * hvd.size()) # Check!!!\n",
    "    opt = hvd.DistributedOptimizer(opt)\n",
    "    \n",
    "    # Model compile\n",
    "    model.compile(loss      = tf.losses.MeanSquaredError(),\n",
    "                  optimizer = opt,\n",
    "                  metrics   = [tf.metrics.MeanAbsoluteError()])\n",
    "\n",
    "    model.fit(x = train_data,\n",
    "              y = train_target,\n",
    "              steps_per_epoch =  steps_per_epoch // hvd.size(), # TODO\n",
    "              callbacks = callbacks, \n",
    "              epochs  = max_epochs,\n",
    "              verbose = 1,\n",
    "              workers=4,\n",
    "              shuffle = False,\n",
    "              validation_data = (val_data, val_target),\n",
    "              validation_steps= 3 * steps_per_epoch // hvd.size()) # TODO\n",
    "\n",
    "    model.summary()\n",
    "    \n",
    "    # Model evaluate\n",
    "    model.evaluate(test_data, test_target)\n",
    "\n",
    "    # Test model\n",
    "    test_data = np.reshape(test_data[test_entry], (1, test_data[0].shape[0], test_data[0].shape[1]))\n",
    "    test_prediction = model.predict(test_data)\n",
    "    \n",
    "    test_prediction = np.squeeze(test_prediction)\n",
    "    print(test_prediction.shape)\n",
    "    \n",
    "    # Plot results\n",
    "    plot_results(label_target, test_target_time[test_entry], test_target[test_entry], test_prediction, label_width)\n",
    "\n",
    "    plot_history(history)\n",
    "\n",
    "    return history, test_prediction\n",
    "\n",
    "test_entry = np.random.randint(0, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_linear_model(label_target, label_width):\n",
    "    model = tf.keras.Sequential([\n",
    "            tf.keras.layers.Flatten(),\n",
    "            \n",
    "            tf.keras.layers.Dense(units = 100, kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "            tf.keras.layers.Dropout(0.2),\n",
    "        \n",
    "            tf.keras.layers.Dense(units = 300, kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "            tf.keras.layers.Dropout(0.2),\n",
    "        \n",
    "            tf.keras.layers.Dense(units = len(label_target) * label_width, kernel_initializer='random_normal', bias_initializer='zeros')])\n",
    "    return model\n",
    "\n",
    "linear_model = f_linear_model(label_target, label_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'horovod.tensorflow' has no attribute 'callbacks'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-117-f51d2cf2f5ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m linear_history, linear_prediction = compile_and_fit(linear_model, train_data, train_target, val_data, val_target, \n\u001b[0;32m----> 7\u001b[0;31m                                                     test_data, test_target, max_epochs, steps_epoch, batch_size, test_entry, label_width)\n\u001b[0m",
      "\u001b[0;32m<ipython-input-115-80511b393c59>\u001b[0m in \u001b[0;36mcompile_and_fit\u001b[0;34m(model, train_data, train_target, val_data, val_target, test_data, test_target, max_epochs, steps_epoch, batch_size, test_entry, label_width)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mlr_scheduler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLearningRateScheduler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscheduler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mbroadcast_variables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhvd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBroadcastGlobalVariablesCallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mmetric_average\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhvd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMetricAverageCallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'horovod.tensorflow' has no attribute 'callbacks'"
     ]
    }
   ],
   "source": [
    "max_epochs = 50\n",
    "steps_epoch = 512\n",
    "batch_size = 50\n",
    "\n",
    "\n",
    "linear_history, linear_prediction = compile_and_fit(linear_model, train_data, train_target, val_data, val_target, \n",
    "                                                    test_data, test_target, max_epochs, steps_epoch, batch_size, test_entry, label_width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_mlp_model(label_target, label_width):\n",
    "    model = tf.keras.Sequential([\n",
    "            tf.keras.layers.Flatten(),\n",
    "        \n",
    "            tf.keras.layers.Dense(units = 200, activation = 'tanh', kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "            tf.keras.layers.Dropout(0.2),\n",
    "        \n",
    "            tf.keras.layers.Dense(units = 200, activation = 'tanh', kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "            tf.keras.layers.Dropout(0.2),\n",
    "        \n",
    "            tf.keras.layers.Dense(units = 200, activation = 'tanh', kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "            tf.keras.layers.Dropout(0.2),\n",
    "\n",
    "        \n",
    "            tf.keras.layers.Dense(units = len(label_target) * label_width)])\n",
    "    return model\n",
    "    \n",
    "mlp_model = f_mlp_model(label_target, label_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "max_epochs = 30\n",
    "batch_size = 50\n",
    "mlp_history, mlp_prediction = compile_and_fit(mlp_model, train_data, train_target, val_data, val_target, \n",
    "                                              test_data, test_target, max_epochs, batch_size, test_entry, label_width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM RNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_lstm_model(input_shape, output_shape):\n",
    "    model = tf.keras.Sequential([\n",
    "            tf.keras.layers.LSTM(units = 200, input_shape = input_shape, kernel_initializer='random_normal', bias_initializer='zeros', dropout=0.2, return_sequences=False),\n",
    "        \n",
    "            tf.keras.layers.Dense(output_shape)])\n",
    "    return model\n",
    "\n",
    "input_shape = train_data[0].shape\n",
    "output_shape = train_target.shape[1]\n",
    "lstm_model = f_lstm_model(input_shape, output_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "max_epochs = 20\n",
    "batch_size = 50\n",
    "lstm_history, lstm_prediction = compile_and_fit(lstm_model, train_data, train_target, val_data, val_target, \n",
    "                                                test_data, test_target, max_epochs, batch_size, test_entry, label_width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GRU RNN model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_gru_model(input_shape, output_shape):\n",
    "    model = tf.keras.Sequential([\n",
    "            tf.keras.layers.GRU(units = 200, input_shape = input_shape, kernel_initializer='random_normal', bias_initializer='zeros', dropout=0.2, return_sequences=False),\n",
    "\n",
    "            tf.keras.layers.Dense(output_shape)])\n",
    "    return model\n",
    "\n",
    "input_shape = train_data[0].shape\n",
    "output_shape = train_target.shape[1]\n",
    "gru_model = f_gru_model(input_shape, output_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "max_epochs = 20\n",
    "batch_size = 50\n",
    "gru_history, gru_prediction = compile_and_fit(gru_model, train_data, train_target, val_data, val_target, \n",
    "                                              test_data, test_target, max_epochs, batch_size, test_entry, label_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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