11 Modeling - Feature Importances All - New - New .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Early Model Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as dates\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import os\n",
    "import time\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139678, 535)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_file_name = \"lotOfFeatures.csv\"\n",
    "input_file_path = os.path.join(os.path.join(os.getcwd(), \"data\"), input_file_name)\n",
    "\n",
    "original_data = pd.read_csv(input_file_path, index_col=0, parse_dates=True)\n",
    "originalSize = original_data.shape[1]\n",
    "original_data.head()\n",
    "original_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excluding Testing Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spliting the dataset into three parts: training, testing and evaluation (last 10 percent)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "start_time = '2012-02-01 00:00:00'\n",
    "#start_time = '2014-02-01 00:00:00'\n",
    "splitting_point = int(0.9 * original_data.shape[0])\n",
    "splitting_time = original_data.iloc[[splitting_point]].index.to_pydatetime()[0]\n",
    "#data = original_data[:splitting_time]\n",
    "data = original_data[start_time:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x45d17940>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZE5ele2DK5ZKk8OqRiIiIbMZgDxEREfkC03ERERFRqLnY2WGwh4h8jcOVElHoMcpFRERE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\nRvUMTeHpXbWY0Bh5y89uW3UIdz50WPXzsclZPL2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O8k4WAUbqkrnjJbfeWj/m7FqH5WGH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b0dY4ymplrmC3hvKdwBeLays5Qe6hWEMUNq8yp7hjZWZQn1ep1w6gmEmtqqCGpzFiAUBU2+fAH9\nExDlHZR4airDqKnMzkk4FMSAWuMdbWW0vyop6YwaXJP3GaUjNzLZUPJstHGoJ2Xs+/1pqmtDbVUE\nTTrlCcjvqX7XfB18FaOu0KqHPZTSxKwYIuGgdmWRrZ/NoNTP6oqwrjK3EmxZNLSxvw1EIbelWh2Z\nNXpogYykUdqski9BkBVH4RAyym41ajkaCMh5qIwWrgeRcJA5iYiGg8xVaECWkbmw3jdX2Viflg/q\nsKy4FNQrxXp5AYC6PIvQud9rsI4885OSx0kCAQGBEjvupihvFRrrKtBYx95toDf2kO+Fs+6r41D+\nVsshvTQU1P2nnvyQ7yGrX1a+j7ots/piZWzUWBvNUvYMqq/U9OVKfCzUfXluV5P7jLwwJP8dDQez\n2nskp+mHgtkeWVhjOb2xjLIDTSnj+mq5/HLHhwp11dryzZVFikxQywYlT2qX2sGAgEH1lVkD2Po8\nYyFZjgeZsk9PHhar6JHTy98PVFeEUW2wX1KQFzq0/VA4FERDTeG86tVvvfFmNMetsyAImW9ciEBA\nYO40GlSvTauqIoTxw3PH/wFN2LCq4odCAeZ3Y9WfhpoIs1431ETQ2tGXda0yGkR9TRQDc+RGVUVY\n49Jd8eSbS21VGE0NlQWVPbWVYU0Z20lueVVFQ0xZ29RQqTn6WF8dyShiFSoYdVFhYM5OMnW91Rvr\nKQiCoJEV1RUh1FdHUFsVzlL21FZFNO8QDAQ0u2PVi/NVFWGMHdafhwG1Fbo73xSUsmuozo5XeRe9\n3UCZ9FUTL9b4T69tW8ngAVWOLFbpUd4zJDsoMEYsY2PgnkW9QmK3MobsM5cH1MwJz0CVkbAIv3oz\nye1uyS4EH1R65Yf6m9suBTgSEAzs/E8HLD4RCzB9XC6n0bkhwxIJho0nA+/BMnuh2IsKBYSCAkVR\n1oTDRaos/NltWUp5Knts/PCFdg2RIT8Povokdn8fcr1eXtCkgnAb6nEIq/CtskczUXInHwThV7LG\nMibGyU63Nfk4f+H8+U2SJXN/53OzZjFKSsV6b2Mqe9KKo0BAKDgfUqpbuMgd/GQ0vFyVPTZSqE5R\nnfMe6k8SsnswS1t7ygJq54RXsNqIP1G+GD1G4jVqcwwBV5gwcEsQRPbRrKiJHRZ6x2NYhvCVo26V\nEdZxuZwj0jqyqCISynukUMHJI1wscmWSGtY8JJgzqAwGA6YMdfNQmz5GVc04uhc1cBycZdKiJv3+\navuCeiiL5IWOoOlh9xEtP1CWyh6nhr6XnjEZADBdZUW81Ibd5580AV/+BNujgF9Q7+bRs39kWVrp\n/0nVQxCEE1RVhHHW3DG49rzpbmeF8BG3ffW4zN/nzB+HhTNG4IpPTnExR8b5wikTccnpkzO/Lz1j\nMhYeNRzf/sLRWDhjBE6bo/VMRBTH186egvMWyt5pj5kke9tR7EXe+pVjXcsXYS1TxjZCHNWAscPq\nMHtyv6fMkU3yt77uM9n9y6cWyN7yFG9gV5zdLzu+cMpE3HDBTE0aV39qGgDgaLHfgc7/pevQhBxP\ngtd9ZjpGD6nNujZxVAMWHjUcF502KXNt7lTZk7Dam1plNJTxMHx5zvzlG589Kuv3YYNrMHn0AEwY\nWY8LPzYRnz9lIk6eNVKT93woc0FlqrFwxgh87ZypALLbyPcvOgZAtq0zxXbfwPqKjMe4ow4fhPHD\n63DLxbM1aSn9vKJMm2jCA2OFSsn23S/Nyvx9Wdpr3GVnTtbYgh0/oi7jNU/PQ+IihsfPK86egoUz\nRuCsuWMydgXVCrgrz9H2NdMO759LX/PpaQXf57xFslw6dfaogmFLnaK8cYmiGADwawDTAPQCuEyS\npA/yPOIpb1zXfHoaph8+yJY0/rLsPTz/xg4A2Z5PlLTPnT/ONheXXkdtXX3RzBH4wimii7np508v\nvIsX3/oIldEQ7r1uvq1p/fHf7+I///sIP7hkdkED1n7ELW9cXmPpc5uw/O1m1NdEcOfXT3A7OwRR\nErhp4JAgvIAyjlKPL51O+4qzp2QUO6UEyReinGk8Wh5nH3hLf5zd2RPLeFZ2Qwb5GSfki543rmJ3\n9pwNICJJ0vEAFgO4vdiMuYGdRywK2+yxL20/4SlvJ5msOLDfJp0W2XIpdTxUvwmCIAiCIAiCKDuK\nVfbMBfAvAJAk6XUAs/IHLyMKeuOiSSDgLaWX8k2c0L946LUJJyCdHkEQBEEQBEEQLlCs1aI6AG2q\n3wlRFAOSJOUaDC87CioxaLYv46FyEDK7bZxIS05s+du70GDAiJzfqK6OorOzlysOaUerRblxj0yd\ncjcbBEEQBGEptDOZIAjCPxRrs+d2AKskSXos/XuHJElkAYkgCIIgCIIgCIIgCMJlij3GtQLA6QAg\niuKxANZZliOCIAiCIAiCIAiCIAiiaIo9xvUkgI+Jorgi/ftii/JDEARBEARBEARBEARBcFDUMS6C\nIAiCIAiCIAiCIAjCmxR7jIsgCIIgCIIgCIIgCILwIKTsIbIQRfFlURQn6tzbKopixOk8EQRRGpB8\nIQjCLki+EARhFyRfCL9Cyh4ilxT0HaPTmT+CIHgg+UIQhF2QfCEIwi5IvhC+hJQ9BItbRFH8CgCI\nojhJFMWX3M4QQRAlA8kXgiDsguQLQRB2QfKF8B2k7CEIoijSW1pFt/NBEERpQjKGIAi7IPlCEIRd\neEm+kLKHgCiKNaIohtI/BWRvR9TbskgQKdDWVaIAJF8IDkjGEHkh+UJwQPKFyAvJF4IDz8iXUOEg\nRBmwFMCvRFFcDqAJwL8ADEvfm+lWpghf0CSK4i8AVECuM9+RJOkfoiiuA/AygGmQhd0nJUlqcy+b\nhIssBckXonhIxhD5WAqSL0TxkHwh8rEUJF+I4vGEfKGdPQQA3A7g5wBeB/AYgEcBnJ4+izoDHtFM\nEp5kOoDbJUk6BcCXAVyZvl4L4E+SJC0EsBPAae5kj/AAJF8IHkjGEPkg+ULwQPKFyAfJF4IHT8gX\n2tlDQJKklQCOybk8mxFurDM5IryKKIo1AHokSYqnL70GYLEoipdC7vTUMmVN+v8dkLXaRBlC8oUw\nA8kYwgwkXwgzkHwhzEDyhTCDV+UL7ewhCMIMSwGcIIpiAMBgAHcCeESSpC9C3pKolim04kEQhFmW\ngmQMQRD2sBQkXwiCsIel8KB8oZ09BEGY4XYA96T/fgzAZgC/EEXxJgAfARio8xwNmgiCMALJGIIg\n7ILkC0EQduFJ+SKkUiS/CIIgCIIgCIIgCIIgSgU6xkUQBEEQBEEQBEEQBFFCkLKHIAiCIAiCIAiC\nIAiihCCbPQRB5EUUxTCAJQBGA4gC+DGATZANkSUBbABwpSRJqXT4JgArAEyRJKlPFMV6AH+A7Gow\nAuB6SZJWOf0eBEF4DwvkSzWAPwFoANAH4EuSJO1y+j0IgvAevPJFFc8kAKsADFZfJwiifLFg/CJA\ntuXzbjrKlZIk3Wx1PmlnD0EQhbgQQIskSfMBfBzAvZCNkN2cviYA+CQAiKJ4KoB/Q7ZCr3AdgBck\nSVoI4KL08wRBEAC/fLkMwJuSJC2ArFS+wcG8EwThbXjlC0RRrEs/0+NgvgmC8D688mU8gLckSVqU\n/me5ogcgZQ9BEIV5DMD30n8HAMQAzJQkaXn62nMATk7/nQBwEoCDqufvBPBA+u8wgG5bc0sQhJ/g\nki+SJN0N4Kfpn6ORLXsIgihvuORLeuX9fgA3gcYuBEFkwzs/OhrACFEUl4mi+IwoihPtyCQd4yII\nIi+SJHUCgCiKtZAF23cA/EIVpANAfTrsi+mw6ucPpa8NBfB7ANc4kW+CILwPr3xJX0+KorgMwJEA\nTrE/1wRB+AEL5Mv3ATwjSdK69HXB/lwTBOEHLJAvuwD8VJKkJ0RRnAt5d/Jsq/NJO3sIgiiIKIqj\nACwD8IgkSX+GfBZVoRZAa4HnpwJ4EcBNkiS9altGCYLwHbzyBQAkSToRwHwAT9iSSYIgfAmnfLkQ\nwKWiKL4EYCiA523LKEEQvoNTvqwG8BQASJK0AsBwO/JIyh6CIPIiiuIQyOdMb5AkaWn68hpRFBek\n/z4NwHLWs+nnj4Cs8T5fkiQaKBEEkcEC+bJYFMXPp392AojblVeCIPwFr3yRJGmCYk8DwG7QzkGC\nINLwyhfIOwevTcc1HcB2O/JJx7gIgijEzZC3IX5PFEXlbOo1AO4RRTEC4B0Aj+c8k1L9/VPIXrju\nSW9fbJUk6Rx7s0wQhE/glS9LAPxOFMVLAQQBXGxzfgmC8A+88sXIdYIgyhNe+XIrgD+IongGZHs/\nF9mRSSGVItlFEARBEARBEARBEARRKtAxLoIgCIIgCIIgCIIgiBKClD0EQRAEQRAEQRAEQRAlBCl7\nCIIgCIIgCIIgCIIgSghS9hAEQRAEQRAEQRAEQZQQpOwhCIIgCIIgCIIgCIIoIUjZQxAEQRAEQRAE\nQRAEUUKQsocgCIIgCIIgCIIgCKKEIGUPQRAEQRAEQRAEQRBECfH/AWrjrt4VQJkAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201e7b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = original_data[[\"id14061721\"]].plot(figsize=(20,5)) \n",
    "original_data.iloc(splitting_point)\n",
    "f.axvline(start_time, color='g')\n",
    "f.axvline(splitting_time, color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean importances of all sensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoregressive_cols = ['measurement', 'shifted_measurement_1d', 'shifted_measurement_2d', 'shifted_measurement_1w', \n",
    "                       'measurement_roll_mean_1h', 'measurement_roll_mean_6h', 'measurement_roll_mean_d', \n",
    "                       'measurement_roll_mean_w', 'measurement_roll_mean_m', 'measurement_roll_min2_d',\n",
    "                       'measurement_roll_min2_w', 'measurement_roll_max2_d', 'measurement_roll_max2_w',\n",
    "                       'measurement_roll_var_6h', 'measurement_roll_var_d', 'measurement_roll_var_w']\n",
    "\n",
    "wetaher_current_cols =  ['p', 't2m_termin', 'rh_termin', 'padavine', 'veter_hitrost', 'veter_vek_smer', 'energija_gl',\n",
    " 'energija_di', 'p_roll_mean_1h', 'p_roll_mean_6h', 'p_roll_mean_d', 'p_roll_mean_w', 'p_roll_mean_m', 'p_roll_var_1h',\n",
    " 'p_roll_var_d', 't2m_termin_roll_mean_1h', 't2m_termin_roll_mean_6h', 't2m_termin_roll_mean_d', 't2m_termin_roll_mean_w',\n",
    " 't2m_termin_roll_mean_m', 't2m_termin_roll_min2_d', 't2m_termin_roll_min2_w', 't2m_termin_roll_max2_d',\n",
    " 't2m_termin_roll_max2_w', 't2m_termin_roll_var_1h', 't2m_termin_roll_var_d', 'rh_termin_roll_mean_1h',\n",
    " 'rh_termin_roll_mean_6h', 'rh_termin_roll_mean_d', 'rh_termin_roll_mean_w', 'rh_termin_roll_mean_m',\n",
    " 'rh_termin_roll_min2_d', 'rh_termin_roll_min2_w', 'rh_termin_roll_max2_d', 'rh_termin_roll_max2_w',\n",
    " 'rh_termin_roll_var_1h', 'rh_termin_roll_var_d', 'energija_gl_roll_mean_1h', 'energija_gl_roll_mean_6h',\n",
    " 'energija_gl_roll_mean_d', 'energija_gl_roll_mean_w', 'energija_gl_roll_mean_m', 'energija_gl_roll_var_1h',\n",
    " 'energija_gl_roll_var_d']\n",
    "\n",
    "weather_forecasted_cols =  ['shifted_p', 'shifted_t2m_termin', 'shifted_rh_termin', 'shifted_padavine', \n",
    "                            'shifted_veter_hitrost', 'shifted_veter_vek_smer', 'shifted_energija_gl']\n",
    "\n",
    "date_time_cols =  ['shifted_HourOfDay', 'shifted_DayOfWeek', 'shifted_Month', 'shifted_Weekday', 'shifted_Weekend', \n",
    "                   'shifted_Holiday', 'shifted_DayBeforeHoliday', 'shifted_DayAfterHodliay', 'shifted_BusinessDay', \n",
    "                   'shifted_BusinessDay_sum', 'shifted_Holiday_sum']\n",
    "\n",
    "feature_importances = pd.DataFrame(columns=pd.MultiIndex.from_arrays([[],[]], names=['sensor', 'horizon']))\n",
    "feature_importances.index.name = \"Feature\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with sensor: id14061671\n",
      "Horizon: 1h"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\bkazic\\Envs\\py27_64bit\\lib\\site-packages\\pandas\\core\\frame.py:2524: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  **kwargs)\n",
      "D:\\Users\\bkazic\\Envs\\py27_64bit\\lib\\site-packages\\pandas\\core\\frame.py:2148: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
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      "Horizon: 8h\n",
      "Horizon: 9h\n",
      "Horizon: 10h\n",
      "Horizon: 11h\n",
      "Horizon: 12h\n",
      "Horizon: 13h\n",
      "Horizon: 14h\n",
      "Horizon: 15h\n",
      "Horizon: 16h\n",
      "Horizon: 17h\n",
      "Horizon: 18h\n",
      "Horizon: 19h\n",
      "Horizon: 20h\n",
      "Horizon: 21h\n",
      "Horizon: 22h\n",
      "Horizon: 23h\n",
      "Horizon: 24h\n",
      "Working with sensor: id11010018\n",
      "Horizon: 1h\n",
      "Horizon: 2h\n",
      "Horizon: 3h\n",
      "Horizon: 4h\n",
      "Horizon: 5h\n",
      "Horizon: 6h\n",
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      "Horizon: 14h\n",
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      "Horizon: 21h\n",
      "Horizon: 22h\n",
      "Horizon: 23h\n",
      "Horizon: 24h\n",
      "Working with sensor: id12041994\n",
      "Horizon: 1h\n",
      "Horizon: 2h\n",
      "Horizon: 3h\n",
      "Horizon: 4h\n",
      "Horizon: 5h\n",
      "Horizon: 6h\n",
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      "Horizon: 8h\n",
      "Horizon: 9h\n",
      "Horizon: 10h\n",
      "Horizon: 11h\n",
      "Horizon: 12h\n",
      "Horizon: 13h\n",
      "Horizon: 14h\n",
      "Horizon: 15h\n",
      "Horizon: 16h\n",
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      "Horizon: 20h\n",
      "Horizon: 21h\n",
      "Horizon: 22h\n",
      "Horizon: 23h\n",
      "Horizon: 24h\n",
      "Working with sensor: id14061693\n",
      "Horizon: 1h\n",
      "Horizon: 2h\n",
      "Horizon: 3h\n",
      "Horizon: 4h\n",
      "Horizon: 5h\n",
      "Horizon: 6h\n",
      "Horizon: 7h\n",
      "Horizon: 8h\n",
      "Horizon: 9h\n",
      "Horizon: 10h\n",
      "Horizon: 11h\n",
      "Horizon: 12h\n",
      "Horizon: 13h\n",
      "Horizon: 14h\n",
      "Horizon: 15h\n",
      "Horizon: 16h\n",
      "Horizon: 17h\n",
      "Horizon: 18h\n",
      "Horizon: 19h\n",
      "Horizon: 20h\n",
      "Horizon: 21h\n",
      "Horizon: 22h\n",
      "Horizon: 23h\n",
      "Horizon: 24h\n",
      "Working with sensor: id14061706\n",
      "Horizon: 1h\n",
      "Horizon: 2h\n",
      "Horizon: 3h\n",
      "Horizon: 4h\n",
      "Horizon: 5h\n",
      "Horizon: 6h\n",
      "Horizon: 7h\n",
      "Horizon: 8h\n",
      "Horizon: 9h\n",
      "Horizon: 10h\n",
      "Horizon: 11h\n",
      "Horizon: 12h\n",
      "Horizon: 13h\n",
      "Horizon: 14h\n",
      "Horizon: 15h\n",
      "Horizon: 16h\n",
      "Horizon: 17h\n",
      "Horizon: 18h\n",
      "Horizon: 19h\n",
      "Horizon: 20h\n",
      "Horizon: 21h\n",
      "Horizon: 22h\n",
      "Horizon: 23h\n",
      "Horizon: 24h\n",
      "Working with sensor: id14061723\n",
      "Horizon: 1h\n",
      "Horizon: 2h\n",
      "Horizon: 3h\n",
      "Horizon: 4h\n",
      "Horizon: 5h\n",
      "Horizon: 6h\n",
      "Horizon: 7h\n",
      "Horizon: 8h\n",
      "Horizon: 9h\n",
      "Horizon: 10h\n",
      "Horizon: 11h\n",
      "Horizon: 12h\n",
      "Horizon: 13h\n",
      "Horizon: 14h\n",
      "Horizon: 15h\n",
      "Horizon: 16h\n",
      "Horizon: 17h\n",
      "Horizon: 18h\n",
      "Horizon: 19h\n",
      "Horizon: 20h\n",
      "Horizon: 21h\n",
      "Horizon: 22h\n",
      "Horizon: 23h\n",
      "Horizon: 24h\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\bkazic\\Envs\\py27_64bit\\lib\\site-packages\\IPython\\kernel\\__main__.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "sensors_list = list(data)[:30] # Sensor ids (30)\n",
    "\n",
    "for sensor_name in sensors_list:\n",
    "    print \"Working with sensor: {}\".format(sensor_name)\n",
    "    \n",
    "    # Create dataframe for specific sensor\n",
    "    sensor_cols_to_drop = [col for col in list(data) if col.startswith('id') and not col.startswith(sensor_name)]\n",
    "    sensor_data_cols = [col for col in list(data) if col not in sensor_cols_to_drop]\n",
    "    sensor_data = data[sensor_data_cols]\n",
    "    \n",
    "    # Replace 'sensor id' column names with 'measurement'\n",
    "    sensor_data.rename(columns=lambda col: col.replace(sensor_name, 'measurement') if sensor_name in col else col, inplace=True)\n",
    "    \n",
    "    # Prepare columns to shift\n",
    "    columnsToShift = list(sensor_data)\n",
    "    shiftedColumns = [\"shifted_\" + col for col in columnsToShift]\n",
    "    \n",
    "    # Prepare datasets\n",
    "    #measurements_cols = [col for col in list(sensor_data) if col.startswith('measurement')]    \n",
    "    all_cols = autoregressive_cols + wetaher_current_cols + weather_forecasted_cols + date_time_cols \n",
    "\n",
    "    for horizon in range (1,25):\n",
    "        print \"Horizon: {}h\".format(horizon)\n",
    "        \n",
    "        sensor_data[shiftedColumns] = sensor_data[columnsToShift].shift(-1 * horizon, freq='H')\n",
    "        sensor_data.dropna(inplace=True)\n",
    "\n",
    "        training_data = sensor_data[:splitting_time]\n",
    "        testing_data = sensor_data[splitting_time:]\n",
    "\n",
    "        model = RandomForestRegressor(n_estimators = 100, min_samples_leaf = 10, n_jobs=32)\n",
    "\n",
    "        X_train = training_data[all_cols].values\n",
    "        y_train = training_data[[\"shifted_measurement\"]].values.ravel()\n",
    "\n",
    "        X_test = testing_data[all_cols].values\n",
    "        y_test = testing_data[[\"shifted_measurement\"]].values.ravel()\n",
    "\n",
    "        start = time.time() # Start measuring time\n",
    "        model.fit(X_train,y_train)\n",
    "        runtime = time.time() - start # Calculate runtime\n",
    "\n",
    "        feature_importances.loc[:,(sensor_name, horizon)] = model.feature_importances_\n",
    "\n",
    "feature_importances.index = all_cols # Replaces index names\n",
    "#feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output_file_name = \"featureImportanceResultsAllNew.csv\"\n",
    "output_file_path = os.path.join(os.path.join(os.getcwd(), \"data\"), output_file_name)\n",
    "\n",
    "feature_importances.to_csv(output_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute means of all sensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>horizon</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>measurement</th>\n",
       "      <td>0.606680</td>\n",
       "      <td>0.184148</td>\n",
       "      <td>0.063959</td>\n",
       "      <td>0.018727</td>\n",
       "      <td>0.007431</td>\n",
       "      <td>0.004422</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003648</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>0.002749</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004381</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>0.001166</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>0.002003</td>\n",
       "      <td>0.002109</td>\n",
       "      <td>0.002408</td>\n",
       "      <td>0.002269</td>\n",
       "      <td>0.027257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_measurement_1d</th>\n",
       "      <td>0.026476</td>\n",
       "      <td>0.061081</td>\n",
       "      <td>0.076868</td>\n",
       "      <td>0.085147</td>\n",
       "      <td>0.087946</td>\n",
       "      <td>0.088310</td>\n",
       "      <td>0.089914</td>\n",
       "      <td>0.091967</td>\n",
       "      <td>0.092278</td>\n",
       "      <td>0.092898</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098006</td>\n",
       "      <td>0.097015</td>\n",
       "      <td>0.097646</td>\n",
       "      <td>0.095507</td>\n",
       "      <td>0.085684</td>\n",
       "      <td>0.061714</td>\n",
       "      <td>0.048305</td>\n",
       "      <td>0.066870</td>\n",
       "      <td>0.060982</td>\n",
       "      <td>0.027680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_measurement_2d</th>\n",
       "      <td>0.003720</td>\n",
       "      <td>0.006125</td>\n",
       "      <td>0.007120</td>\n",
       "      <td>0.007704</td>\n",
       "      <td>0.008240</td>\n",
       "      <td>0.008556</td>\n",
       "      <td>0.008560</td>\n",
       "      <td>0.008441</td>\n",
       "      <td>0.008881</td>\n",
       "      <td>0.008504</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009038</td>\n",
       "      <td>0.008663</td>\n",
       "      <td>0.008689</td>\n",
       "      <td>0.008699</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.008010</td>\n",
       "      <td>0.007396</td>\n",
       "      <td>0.007561</td>\n",
       "      <td>0.007492</td>\n",
       "      <td>0.007462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_measurement_1w</th>\n",
       "      <td>0.083884</td>\n",
       "      <td>0.396689</td>\n",
       "      <td>0.419818</td>\n",
       "      <td>0.443881</td>\n",
       "      <td>0.445707</td>\n",
       "      <td>0.446498</td>\n",
       "      <td>0.447161</td>\n",
       "      <td>0.447249</td>\n",
       "      <td>0.447320</td>\n",
       "      <td>0.446988</td>\n",
       "      <td>...</td>\n",
       "      <td>0.447301</td>\n",
       "      <td>0.447606</td>\n",
       "      <td>0.447020</td>\n",
       "      <td>0.447291</td>\n",
       "      <td>0.436182</td>\n",
       "      <td>0.434014</td>\n",
       "      <td>0.432529</td>\n",
       "      <td>0.434504</td>\n",
       "      <td>0.434440</td>\n",
       "      <td>0.433886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_mean_1h</th>\n",
       "      <td>0.084864</td>\n",
       "      <td>0.068960</td>\n",
       "      <td>0.032386</td>\n",
       "      <td>0.026575</td>\n",
       "      <td>0.017745</td>\n",
       "      <td>0.019000</td>\n",
       "      <td>0.025674</td>\n",
       "      <td>0.030823</td>\n",
       "      <td>0.031880</td>\n",
       "      <td>0.034622</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026934</td>\n",
       "      <td>0.027868</td>\n",
       "      <td>0.029108</td>\n",
       "      <td>0.029556</td>\n",
       "      <td>0.030834</td>\n",
       "      <td>0.030398</td>\n",
       "      <td>0.027322</td>\n",
       "      <td>0.019684</td>\n",
       "      <td>0.066064</td>\n",
       "      <td>0.077224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_mean_6h</th>\n",
       "      <td>0.029034</td>\n",
       "      <td>0.028670</td>\n",
       "      <td>0.039725</td>\n",
       "      <td>0.021815</td>\n",
       "      <td>0.027316</td>\n",
       "      <td>0.025937</td>\n",
       "      <td>0.017357</td>\n",
       "      <td>0.013096</td>\n",
       "      <td>0.010777</td>\n",
       "      <td>0.006885</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026450</td>\n",
       "      <td>0.029101</td>\n",
       "      <td>0.032741</td>\n",
       "      <td>0.038275</td>\n",
       "      <td>0.065795</td>\n",
       "      <td>0.094549</td>\n",
       "      <td>0.111777</td>\n",
       "      <td>0.085010</td>\n",
       "      <td>0.027825</td>\n",
       "      <td>0.024529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_mean_d</th>\n",
       "      <td>0.005548</td>\n",
       "      <td>0.019698</td>\n",
       "      <td>0.082634</td>\n",
       "      <td>0.095823</td>\n",
       "      <td>0.095722</td>\n",
       "      <td>0.092793</td>\n",
       "      <td>0.083523</td>\n",
       "      <td>0.068029</td>\n",
       "      <td>0.063016</td>\n",
       "      <td>0.062827</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038441</td>\n",
       "      <td>0.036895</td>\n",
       "      <td>0.033354</td>\n",
       "      <td>0.031386</td>\n",
       "      <td>0.028197</td>\n",
       "      <td>0.027340</td>\n",
       "      <td>0.028078</td>\n",
       "      <td>0.028126</td>\n",
       "      <td>0.031200</td>\n",
       "      <td>0.029147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_mean_w</th>\n",
       "      <td>0.004193</td>\n",
       "      <td>0.006218</td>\n",
       "      <td>0.011336</td>\n",
       "      <td>0.015510</td>\n",
       "      <td>0.015687</td>\n",
       "      <td>0.015573</td>\n",
       "      <td>0.016244</td>\n",
       "      <td>0.015183</td>\n",
       "      <td>0.016939</td>\n",
       "      <td>0.017734</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019820</td>\n",
       "      <td>0.019637</td>\n",
       "      <td>0.018609</td>\n",
       "      <td>0.017346</td>\n",
       "      <td>0.011687</td>\n",
       "      <td>0.012160</td>\n",
       "      <td>0.010212</td>\n",
       "      <td>0.013933</td>\n",
       "      <td>0.014674</td>\n",
       "      <td>0.017280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_mean_m</th>\n",
       "      <td>0.002971</td>\n",
       "      <td>0.004406</td>\n",
       "      <td>0.005339</td>\n",
       "      <td>0.005364</td>\n",
       "      <td>0.006354</td>\n",
       "      <td>0.007131</td>\n",
       "      <td>0.007101</td>\n",
       "      <td>0.007046</td>\n",
       "      <td>0.007074</td>\n",
       "      <td>0.007045</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007186</td>\n",
       "      <td>0.008190</td>\n",
       "      <td>0.008239</td>\n",
       "      <td>0.008710</td>\n",
       "      <td>0.008021</td>\n",
       "      <td>0.008147</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>0.010115</td>\n",
       "      <td>0.013462</td>\n",
       "      <td>0.013075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_min2_d</th>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.001756</td>\n",
       "      <td>0.001849</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.002003</td>\n",
       "      <td>0.001983</td>\n",
       "      <td>0.002142</td>\n",
       "      <td>0.002102</td>\n",
       "      <td>0.002263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>0.002124</td>\n",
       "      <td>0.002109</td>\n",
       "      <td>0.002026</td>\n",
       "      <td>0.001815</td>\n",
       "      <td>0.001835</td>\n",
       "      <td>0.001784</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>0.001985</td>\n",
       "      <td>0.001931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_min2_w</th>\n",
       "      <td>0.000516</td>\n",
       "      <td>0.000682</td>\n",
       "      <td>0.002786</td>\n",
       "      <td>0.000805</td>\n",
       "      <td>0.000925</td>\n",
       "      <td>0.000980</td>\n",
       "      <td>0.000940</td>\n",
       "      <td>0.000939</td>\n",
       "      <td>0.000951</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000992</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>0.001020</td>\n",
       "      <td>0.000958</td>\n",
       "      <td>0.001048</td>\n",
       "      <td>0.001077</td>\n",
       "      <td>0.001078</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>0.001121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_max2_d</th>\n",
       "      <td>0.001445</td>\n",
       "      <td>0.004387</td>\n",
       "      <td>0.003164</td>\n",
       "      <td>0.002632</td>\n",
       "      <td>0.004709</td>\n",
       "      <td>0.005542</td>\n",
       "      <td>0.003482</td>\n",
       "      <td>0.002543</td>\n",
       "      <td>0.003118</td>\n",
       "      <td>0.003495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002702</td>\n",
       "      <td>0.002571</td>\n",
       "      <td>0.002900</td>\n",
       "      <td>0.002641</td>\n",
       "      <td>0.003343</td>\n",
       "      <td>0.003169</td>\n",
       "      <td>0.004059</td>\n",
       "      <td>0.004601</td>\n",
       "      <td>0.005648</td>\n",
       "      <td>0.008332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_max2_w</th>\n",
       "      <td>0.000987</td>\n",
       "      <td>0.001615</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003573</td>\n",
       "      <td>0.003407</td>\n",
       "      <td>0.010264</td>\n",
       "      <td>0.024290</td>\n",
       "      <td>0.028335</td>\n",
       "      <td>0.027868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031351</td>\n",
       "      <td>0.032353</td>\n",
       "      <td>0.032203</td>\n",
       "      <td>0.032030</td>\n",
       "      <td>0.032339</td>\n",
       "      <td>0.032328</td>\n",
       "      <td>0.033373</td>\n",
       "      <td>0.035745</td>\n",
       "      <td>0.036777</td>\n",
       "      <td>0.036073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_var_6h</th>\n",
       "      <td>0.005055</td>\n",
       "      <td>0.009427</td>\n",
       "      <td>0.008726</td>\n",
       "      <td>0.012808</td>\n",
       "      <td>0.009782</td>\n",
       "      <td>0.006645</td>\n",
       "      <td>0.008788</td>\n",
       "      <td>0.007735</td>\n",
       "      <td>0.006107</td>\n",
       "      <td>0.004232</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>0.004113</td>\n",
       "      <td>0.003391</td>\n",
       "      <td>0.004028</td>\n",
       "      <td>0.007539</td>\n",
       "      <td>0.005935</td>\n",
       "      <td>0.005381</td>\n",
       "      <td>0.004227</td>\n",
       "      <td>0.004198</td>\n",
       "      <td>0.004196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_var_d</th>\n",
       "      <td>0.004258</td>\n",
       "      <td>0.005903</td>\n",
       "      <td>0.005617</td>\n",
       "      <td>0.006932</td>\n",
       "      <td>0.007196</td>\n",
       "      <td>0.007790</td>\n",
       "      <td>0.006787</td>\n",
       "      <td>0.006968</td>\n",
       "      <td>0.006968</td>\n",
       "      <td>0.006537</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005860</td>\n",
       "      <td>0.005677</td>\n",
       "      <td>0.005564</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>0.004960</td>\n",
       "      <td>0.005197</td>\n",
       "      <td>0.005280</td>\n",
       "      <td>0.007214</td>\n",
       "      <td>0.009383</td>\n",
       "      <td>0.008303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_roll_var_w</th>\n",
       "      <td>0.003355</td>\n",
       "      <td>0.005042</td>\n",
       "      <td>0.006637</td>\n",
       "      <td>0.005800</td>\n",
       "      <td>0.005903</td>\n",
       "      <td>0.006118</td>\n",
       "      <td>0.006131</td>\n",
       "      <td>0.006411</td>\n",
       "      <td>0.006459</td>\n",
       "      <td>0.006387</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007082</td>\n",
       "      <td>0.006937</td>\n",
       "      <td>0.006954</td>\n",
       "      <td>0.006793</td>\n",
       "      <td>0.007396</td>\n",
       "      <td>0.007249</td>\n",
       "      <td>0.006743</td>\n",
       "      <td>0.008185</td>\n",
       "      <td>0.010334</td>\n",
       "      <td>0.010183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>0.000308</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.000627</td>\n",
       "      <td>0.001113</td>\n",
       "      <td>0.001258</td>\n",
       "      <td>0.000853</td>\n",
       "      <td>0.002050</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>0.000797</td>\n",
       "      <td>0.000818</td>\n",
       "      <td>0.000828</td>\n",
       "      <td>0.000908</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>0.000907</td>\n",
       "      <td>0.001011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t2m_termin</th>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.002536</td>\n",
       "      <td>0.002469</td>\n",
       "      <td>0.002602</td>\n",
       "      <td>0.004626</td>\n",
       "      <td>0.004016</td>\n",
       "      <td>0.005016</td>\n",
       "      <td>0.003534</td>\n",
       "      <td>0.006008</td>\n",
       "      <td>0.004778</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002364</td>\n",
       "      <td>0.002277</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.002036</td>\n",
       "      <td>0.002271</td>\n",
       "      <td>0.003045</td>\n",
       "      <td>0.003516</td>\n",
       "      <td>0.003593</td>\n",
       "      <td>0.003142</td>\n",
       "      <td>0.003033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rh_termin</th>\n",
       "      <td>0.001177</td>\n",
       "      <td>0.001376</td>\n",
       "      <td>0.001895</td>\n",
       "      <td>0.002309</td>\n",
       "      <td>0.001697</td>\n",
       "      <td>0.001393</td>\n",
       "      <td>0.001446</td>\n",
       "      <td>0.001412</td>\n",
       "      <td>0.001455</td>\n",
       "      <td>0.001414</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001883</td>\n",
       "      <td>0.001634</td>\n",
       "      <td>0.001469</td>\n",
       "      <td>0.001619</td>\n",
       "      <td>0.001880</td>\n",
       "      <td>0.001431</td>\n",
       "      <td>0.001405</td>\n",
       "      <td>0.001420</td>\n",
       "      <td>0.001578</td>\n",
       "      <td>0.001423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>padavine</th>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000308</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.000858</td>\n",
       "      <td>0.000626</td>\n",
       "      <td>0.000442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veter_hitrost</th>\n",
       "      <td>0.001800</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>0.002638</td>\n",
       "      <td>0.002788</td>\n",
       "      <td>0.002985</td>\n",
       "      <td>0.003349</td>\n",
       "      <td>0.003794</td>\n",
       "      <td>0.002633</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.002397</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003443</td>\n",
       "      <td>0.002466</td>\n",
       "      <td>0.002953</td>\n",
       "      <td>0.002644</td>\n",
       "      <td>0.003597</td>\n",
       "      <td>0.004331</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.005272</td>\n",
       "      <td>0.005304</td>\n",
       "      <td>0.002938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veter_vek_smer</th>\n",
       "      <td>0.002681</td>\n",
       "      <td>0.002827</td>\n",
       "      <td>0.002724</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>0.002998</td>\n",
       "      <td>0.002727</td>\n",
       "      <td>0.002920</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.002826</td>\n",
       "      <td>0.002980</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003001</td>\n",
       "      <td>0.004604</td>\n",
       "      <td>0.005497</td>\n",
       "      <td>0.003767</td>\n",
       "      <td>0.003958</td>\n",
       "      <td>0.003062</td>\n",
       "      <td>0.002911</td>\n",
       "      <td>0.002847</td>\n",
       "      <td>0.002921</td>\n",
       "      <td>0.002977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energija_gl</th>\n",
       "      <td>0.002476</td>\n",
       "      <td>0.001868</td>\n",
       "      <td>0.001926</td>\n",
       "      <td>0.002319</td>\n",
       "      <td>0.001832</td>\n",
       "      <td>0.001246</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.001132</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>0.003596</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000934</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>0.001020</td>\n",
       "      <td>0.001399</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.002029</td>\n",
       "      <td>0.002982</td>\n",
       "      <td>0.005078</td>\n",
       "      <td>0.003201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energija_di</th>\n",
       "      <td>0.002488</td>\n",
       "      <td>0.002142</td>\n",
       "      <td>0.001976</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>0.005870</td>\n",
       "      <td>0.007285</td>\n",
       "      <td>0.001987</td>\n",
       "      <td>0.001550</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.002145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001258</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.001823</td>\n",
       "      <td>0.002485</td>\n",
       "      <td>0.002648</td>\n",
       "      <td>0.003255</td>\n",
       "      <td>0.005562</td>\n",
       "      <td>0.003428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_roll_mean_1h</th>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.001079</td>\n",
       "      <td>0.001299</td>\n",
       "      <td>0.003571</td>\n",
       "      <td>0.003631</td>\n",
       "      <td>0.001438</td>\n",
       "      <td>0.001357</td>\n",
       "      <td>0.003380</td>\n",
       "      <td>0.002538</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001516</td>\n",
       "      <td>0.001760</td>\n",
       "      <td>0.001871</td>\n",
       "      <td>0.001945</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.001692</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.001636</td>\n",
       "      <td>0.001707</td>\n",
       "      <td>0.001425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_roll_mean_6h</th>\n",
       "      <td>0.001705</td>\n",
       "      <td>0.002778</td>\n",
       "      <td>0.004684</td>\n",
       "      <td>0.003070</td>\n",
       "      <td>0.002666</td>\n",
       "      <td>0.003021</td>\n",
       "      <td>0.002915</td>\n",
       "      <td>0.005613</td>\n",
       "      <td>0.005489</td>\n",
       "      <td>0.005502</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004387</td>\n",
       "      <td>0.003936</td>\n",
       "      <td>0.003549</td>\n",
       "      <td>0.003773</td>\n",
       "      <td>0.003765</td>\n",
       "      <td>0.003415</td>\n",
       "      <td>0.003039</td>\n",
       "      <td>0.002923</td>\n",
       "      <td>0.002751</td>\n",
       "      <td>0.003144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_roll_mean_d</th>\n",
       "      <td>0.002279</td>\n",
       "      <td>0.003077</td>\n",
       "      <td>0.003606</td>\n",
       "      <td>0.004098</td>\n",
       "      <td>0.004445</td>\n",
       "      <td>0.004754</td>\n",
       "      <td>0.004386</td>\n",
       "      <td>0.005236</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.006014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004567</td>\n",
       "      <td>0.004893</td>\n",
       "      <td>0.004840</td>\n",
       "      <td>0.004857</td>\n",
       "      <td>0.004163</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>0.004467</td>\n",
       "      <td>0.004280</td>\n",
       "      <td>0.004414</td>\n",
       "      <td>0.004337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_roll_mean_w</th>\n",
       "      <td>0.002728</td>\n",
       "      <td>0.004158</td>\n",
       "      <td>0.004832</td>\n",
       "      <td>0.005093</td>\n",
       "      <td>0.006101</td>\n",
       "      <td>0.006199</td>\n",
       "      <td>0.006867</td>\n",
       "      <td>0.006232</td>\n",
       "      <td>0.006949</td>\n",
       "      <td>0.008138</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005378</td>\n",
       "      <td>0.005306</td>\n",
       "      <td>0.005535</td>\n",
       "      <td>0.005898</td>\n",
       "      <td>0.005778</td>\n",
       "      <td>0.005831</td>\n",
       "      <td>0.006405</td>\n",
       "      <td>0.007557</td>\n",
       "      <td>0.008012</td>\n",
       "      <td>0.007400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_roll_mean_m</th>\n",
       "      <td>0.003287</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.007085</td>\n",
       "      <td>0.007360</td>\n",
       "      <td>0.007847</td>\n",
       "      <td>0.007336</td>\n",
       "      <td>0.008081</td>\n",
       "      <td>0.008729</td>\n",
       "      <td>0.008090</td>\n",
       "      <td>0.011282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007358</td>\n",
       "      <td>0.007330</td>\n",
       "      <td>0.007383</td>\n",
       "      <td>0.007390</td>\n",
       "      <td>0.008325</td>\n",
       "      <td>0.008542</td>\n",
       "      <td>0.007653</td>\n",
       "      <td>0.007538</td>\n",
       "      <td>0.007649</td>\n",
       "      <td>0.007866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_roll_var_1h</th>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.000560</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.000608</td>\n",
       "      <td>0.000547</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.000579</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rh_termin_roll_min2_w</th>\n",
       "      <td>0.001019</td>\n",
       "      <td>0.001379</td>\n",
       "      <td>0.002305</td>\n",
       "      <td>0.002206</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.002344</td>\n",
       "      <td>0.002181</td>\n",
       "      <td>0.002333</td>\n",
       "      <td>0.002490</td>\n",
       "      <td>0.002280</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002433</td>\n",
       "      <td>0.002522</td>\n",
       "      <td>0.002075</td>\n",
       "      <td>0.001945</td>\n",
       "      <td>0.001522</td>\n",
       "      <td>0.001618</td>\n",
       "      <td>0.002072</td>\n",
       "      <td>0.002108</td>\n",
       "      <td>0.002110</td>\n",
       "      <td>0.003127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rh_termin_roll_max2_d</th>\n",
       "      <td>0.000898</td>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.001524</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.002489</td>\n",
       "      <td>0.002184</td>\n",
       "      <td>0.001707</td>\n",
       "      <td>0.001510</td>\n",
       "      <td>0.001318</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001289</td>\n",
       "      <td>0.001342</td>\n",
       "      <td>0.001436</td>\n",
       "      <td>0.001412</td>\n",
       "      <td>0.001578</td>\n",
       "      <td>0.001628</td>\n",
       "      <td>0.001526</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>0.001480</td>\n",
       "      <td>0.001489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rh_termin_roll_max2_w</th>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.000692</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>0.000863</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.001031</td>\n",
       "      <td>0.001015</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.000889</td>\n",
       "      <td>0.000926</td>\n",
       "      <td>0.000915</td>\n",
       "      <td>0.001012</td>\n",
       "      <td>0.001022</td>\n",
       "      <td>0.000963</td>\n",
       "      <td>0.000990</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.001027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rh_termin_roll_var_1h</th>\n",
       "      <td>0.001773</td>\n",
       "      <td>0.001877</td>\n",
       "      <td>0.001960</td>\n",
       "      <td>0.001839</td>\n",
       "      <td>0.001904</td>\n",
       "      <td>0.001891</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>0.002006</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.001949</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.002074</td>\n",
       "      <td>0.002047</td>\n",
       "      <td>0.001978</td>\n",
       "      <td>0.001927</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>0.001959</td>\n",
       "      <td>0.001959</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.002191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rh_termin_roll_var_d</th>\n",
       "      <td>0.001986</td>\n",
       "      <td>0.002749</td>\n",
       "      <td>0.003714</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>0.008213</td>\n",
       "      <td>0.011977</td>\n",
       "      <td>0.012152</td>\n",
       "      <td>0.009177</td>\n",
       "      <td>0.006578</td>\n",
       "      <td>0.003757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004497</td>\n",
       "      <td>0.004150</td>\n",
       "      <td>0.003910</td>\n",
       "      <td>0.004243</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003794</td>\n",
       "      <td>0.004126</td>\n",
       "      <td>0.004846</td>\n",
       "      <td>0.005478</td>\n",
       "      <td>0.005317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energija_gl_roll_mean_1h</th>\n",
       "      <td>0.001917</td>\n",
       "      <td>0.002274</td>\n",
       "      <td>0.002433</td>\n",
       "      <td>0.003259</td>\n",
       "      <td>0.002254</td>\n",
       "      <td>0.001524</td>\n",
       "      <td>0.001955</td>\n",
       "      <td>0.003346</td>\n",
       "      <td>0.004846</td>\n",
       "      <td>0.002619</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001204</td>\n",
       "      <td>0.001377</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>0.002510</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>0.003495</td>\n",
       "      <td>0.004747</td>\n",
       "      <td>0.004943</td>\n",
       "      <td>0.004015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energija_gl_roll_mean_6h</th>\n",
       "      <td>0.006492</td>\n",
       "      <td>0.006439</td>\n",
       "      <td>0.006106</td>\n",
       "      <td>0.005905</td>\n",
       "      <td>0.006473</td>\n",
       "      <td>0.007681</td>\n",
       "      <td>0.008765</td>\n",
       "      <td>0.007272</td>\n",
       "      <td>0.006343</td>\n",
       "      <td>0.004733</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004654</td>\n",
       "      <td>0.005122</td>\n",
       "      <td>0.007497</td>\n",
       "      <td>0.007323</td>\n",
       "      <td>0.006044</td>\n",
       "      <td>0.005371</td>\n",
       "      <td>0.006903</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.005974</td>\n",
       "      <td>0.003913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energija_gl_roll_mean_d</th>\n",
       "      <td>0.001649</td>\n",
       "      <td>0.003084</td>\n",
       "      <td>0.003618</td>\n",
       "      <td>0.002937</td>\n",
       "      <td>0.002995</td>\n",
       "      <td>0.003609</td>\n",
       "      <td>0.003640</td>\n",
       "      <td>0.002919</td>\n",
       "      <td>0.004788</td>\n",
       "      <td>0.003987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004319</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>0.003520</td>\n",
       "      <td>0.003813</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.008195</td>\n",
       "      <td>0.005009</td>\n",
       "      <td>0.004153</td>\n",
       "      <td>0.003006</td>\n",
       "      <td>0.002999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energija_gl_roll_mean_w</th>\n",
       "      <td>0.002535</td>\n",
       "      <td>0.003389</td>\n",
       "      <td>0.003867</td>\n",
       "      <td>0.004375</td>\n",
       "      <td>0.004161</td>\n",
       "      <td>0.004569</td>\n",
       "      <td>0.004640</td>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.005439</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004721</td>\n",
       "      <td>0.005142</td>\n",
       "      <td>0.004895</td>\n",
       "      <td>0.004676</td>\n",
       "      <td>0.004621</td>\n",
       "      <td>0.005363</td>\n",
       "      <td>0.005671</td>\n",
       "      <td>0.006896</td>\n",
       "      <td>0.007388</td>\n",
       "      <td>0.018686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energija_gl_roll_mean_m</th>\n",
       "      <td>0.002675</td>\n",
       "      <td>0.004336</td>\n",
       "      <td>0.003959</td>\n",
       "      <td>0.005024</td>\n",
       "      <td>0.004350</td>\n",
       "      <td>0.004227</td>\n",
       "      <td>0.004269</td>\n",
       "      <td>0.004251</td>\n",
       "      <td>0.004069</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004290</td>\n",
       "      <td>0.004278</td>\n",
       "      <td>0.004115</td>\n",
       "      <td>0.004399</td>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>0.004928</td>\n",
       "      <td>0.005836</td>\n",
       "      <td>0.006217</td>\n",
       "      <td>0.005415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energija_gl_roll_var_1h</th>\n",
       "      <td>0.001814</td>\n",
       "      <td>0.001998</td>\n",
       "      <td>0.001693</td>\n",
       "      <td>0.001898</td>\n",
       "      <td>0.002539</td>\n",
       "      <td>0.002436</td>\n",
       "      <td>0.001603</td>\n",
       "      <td>0.002115</td>\n",
       "      <td>0.002104</td>\n",
       "      <td>0.001720</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001357</td>\n",
       "      <td>0.001486</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.001809</td>\n",
       "      <td>0.001676</td>\n",
       "      <td>0.001693</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>0.002108</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.003185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energija_gl_roll_var_d</th>\n",
       "      <td>0.001506</td>\n",
       "      <td>0.002354</td>\n",
       "      <td>0.002573</td>\n",
       "      <td>0.002879</td>\n",
       "      <td>0.002999</td>\n",
       "      <td>0.003150</td>\n",
       "      <td>0.002826</td>\n",
       "      <td>0.002743</td>\n",
       "      <td>0.003122</td>\n",
       "      <td>0.002791</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002878</td>\n",
       "      <td>0.004427</td>\n",
       "      <td>0.003561</td>\n",
       "      <td>0.003338</td>\n",
       "      <td>0.003005</td>\n",
       "      <td>0.003208</td>\n",
       "      <td>0.003809</td>\n",
       "      <td>0.003837</td>\n",
       "      <td>0.003117</td>\n",
       "      <td>0.002587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_p</th>\n",
       "      <td>0.000576</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.001654</td>\n",
       "      <td>0.001859</td>\n",
       "      <td>0.002122</td>\n",
       "      <td>0.003481</td>\n",
       "      <td>0.002841</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006515</td>\n",
       "      <td>0.002716</td>\n",
       "      <td>0.004379</td>\n",
       "      <td>0.003633</td>\n",
       "      <td>0.003670</td>\n",
       "      <td>0.003591</td>\n",
       "      <td>0.003685</td>\n",
       "      <td>0.003666</td>\n",
       "      <td>0.003678</td>\n",
       "      <td>0.003774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_t2m_termin</th>\n",
       "      <td>0.004303</td>\n",
       "      <td>0.006925</td>\n",
       "      <td>0.006008</td>\n",
       "      <td>0.008177</td>\n",
       "      <td>0.008496</td>\n",
       "      <td>0.008682</td>\n",
       "      <td>0.010652</td>\n",
       "      <td>0.011150</td>\n",
       "      <td>0.009335</td>\n",
       "      <td>0.009197</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011266</td>\n",
       "      <td>0.013252</td>\n",
       "      <td>0.012719</td>\n",
       "      <td>0.012603</td>\n",
       "      <td>0.008791</td>\n",
       "      <td>0.010763</td>\n",
       "      <td>0.007794</td>\n",
       "      <td>0.008891</td>\n",
       "      <td>0.007149</td>\n",
       "      <td>0.007663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_rh_termin</th>\n",
       "      <td>0.001609</td>\n",
       "      <td>0.002098</td>\n",
       "      <td>0.002499</td>\n",
       "      <td>0.002620</td>\n",
       "      <td>0.002891</td>\n",
       "      <td>0.003011</td>\n",
       "      <td>0.004582</td>\n",
       "      <td>0.005136</td>\n",
       "      <td>0.003247</td>\n",
       "      <td>0.003231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005430</td>\n",
       "      <td>0.005825</td>\n",
       "      <td>0.005758</td>\n",
       "      <td>0.005422</td>\n",
       "      <td>0.003951</td>\n",
       "      <td>0.003936</td>\n",
       "      <td>0.003976</td>\n",
       "      <td>0.003589</td>\n",
       "      <td>0.003357</td>\n",
       "      <td>0.003346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_padavine</th>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>0.000865</td>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.000740</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.000959</td>\n",
       "      <td>0.000862</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.000835</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>0.000823</td>\n",
       "      <td>0.000720</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>0.000722</td>\n",
       "      <td>0.000790</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>0.000804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_veter_hitrost</th>\n",
       "      <td>0.001790</td>\n",
       "      <td>0.002388</td>\n",
       "      <td>0.002641</td>\n",
       "      <td>0.002615</td>\n",
       "      <td>0.002874</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>0.002758</td>\n",
       "      <td>0.002733</td>\n",
       "      <td>0.002844</td>\n",
       "      <td>0.002777</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003412</td>\n",
       "      <td>0.003384</td>\n",
       "      <td>0.003251</td>\n",
       "      <td>0.002975</td>\n",
       "      <td>0.002874</td>\n",
       "      <td>0.003006</td>\n",
       "      <td>0.002905</td>\n",
       "      <td>0.003057</td>\n",
       "      <td>0.003240</td>\n",
       "      <td>0.002795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_veter_vek_smer</th>\n",
       "      <td>0.002670</td>\n",
       "      <td>0.003465</td>\n",
       "      <td>0.003097</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.003046</td>\n",
       "      <td>0.003105</td>\n",
       "      <td>0.003573</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>0.003231</td>\n",
       "      <td>0.003164</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003567</td>\n",
       "      <td>0.003354</td>\n",
       "      <td>0.003295</td>\n",
       "      <td>0.003643</td>\n",
       "      <td>0.003561</td>\n",
       "      <td>0.003402</td>\n",
       "      <td>0.003048</td>\n",
       "      <td>0.003152</td>\n",
       "      <td>0.003045</td>\n",
       "      <td>0.003180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_energija_gl</th>\n",
       "      <td>0.008452</td>\n",
       "      <td>0.013362</td>\n",
       "      <td>0.014567</td>\n",
       "      <td>0.011971</td>\n",
       "      <td>0.011891</td>\n",
       "      <td>0.011508</td>\n",
       "      <td>0.010927</td>\n",
       "      <td>0.009834</td>\n",
       "      <td>0.009487</td>\n",
       "      <td>0.010324</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012871</td>\n",
       "      <td>0.013141</td>\n",
       "      <td>0.011424</td>\n",
       "      <td>0.010783</td>\n",
       "      <td>0.011851</td>\n",
       "      <td>0.010978</td>\n",
       "      <td>0.006751</td>\n",
       "      <td>0.008444</td>\n",
       "      <td>0.006649</td>\n",
       "      <td>0.006788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_HourOfDay</th>\n",
       "      <td>0.020292</td>\n",
       "      <td>0.019208</td>\n",
       "      <td>0.017248</td>\n",
       "      <td>0.015095</td>\n",
       "      <td>0.013266</td>\n",
       "      <td>0.012221</td>\n",
       "      <td>0.011712</td>\n",
       "      <td>0.011394</td>\n",
       "      <td>0.011403</td>\n",
       "      <td>0.012762</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012589</td>\n",
       "      <td>0.011880</td>\n",
       "      <td>0.010984</td>\n",
       "      <td>0.011392</td>\n",
       "      <td>0.011308</td>\n",
       "      <td>0.011150</td>\n",
       "      <td>0.011534</td>\n",
       "      <td>0.011838</td>\n",
       "      <td>0.011505</td>\n",
       "      <td>0.013727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_DayOfWeek</th>\n",
       "      <td>0.002748</td>\n",
       "      <td>0.004916</td>\n",
       "      <td>0.006699</td>\n",
       "      <td>0.009739</td>\n",
       "      <td>0.010639</td>\n",
       "      <td>0.011543</td>\n",
       "      <td>0.011059</td>\n",
       "      <td>0.010474</td>\n",
       "      <td>0.010430</td>\n",
       "      <td>0.011057</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010585</td>\n",
       "      <td>0.010860</td>\n",
       "      <td>0.011373</td>\n",
       "      <td>0.011331</td>\n",
       "      <td>0.011370</td>\n",
       "      <td>0.012653</td>\n",
       "      <td>0.012272</td>\n",
       "      <td>0.011618</td>\n",
       "      <td>0.012253</td>\n",
       "      <td>0.012350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_Month</th>\n",
       "      <td>0.000719</td>\n",
       "      <td>0.001140</td>\n",
       "      <td>0.001158</td>\n",
       "      <td>0.001214</td>\n",
       "      <td>0.001418</td>\n",
       "      <td>0.001410</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>0.001262</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001463</td>\n",
       "      <td>0.001371</td>\n",
       "      <td>0.001312</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>0.001351</td>\n",
       "      <td>0.001341</td>\n",
       "      <td>0.001296</td>\n",
       "      <td>0.001419</td>\n",
       "      <td>0.001486</td>\n",
       "      <td>0.001565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_Weekday</th>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.002200</td>\n",
       "      <td>0.001388</td>\n",
       "      <td>0.001483</td>\n",
       "      <td>0.001471</td>\n",
       "      <td>0.001409</td>\n",
       "      <td>0.001406</td>\n",
       "      <td>0.001430</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001240</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.001316</td>\n",
       "      <td>0.001116</td>\n",
       "      <td>0.001066</td>\n",
       "      <td>0.001147</td>\n",
       "      <td>0.001194</td>\n",
       "      <td>0.001006</td>\n",
       "      <td>0.000772</td>\n",
       "      <td>0.001010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_Weekend</th>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.000849</td>\n",
       "      <td>0.000720</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>0.001671</td>\n",
       "      <td>0.002229</td>\n",
       "      <td>0.001818</td>\n",
       "      <td>0.001501</td>\n",
       "      <td>0.001453</td>\n",
       "      <td>0.001454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>0.001094</td>\n",
       "      <td>0.001097</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.001084</td>\n",
       "      <td>0.001149</td>\n",
       "      <td>0.000894</td>\n",
       "      <td>0.000903</td>\n",
       "      <td>0.000920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_Holiday</th>\n",
       "      <td>0.002137</td>\n",
       "      <td>0.006234</td>\n",
       "      <td>0.009338</td>\n",
       "      <td>0.009640</td>\n",
       "      <td>0.010563</td>\n",
       "      <td>0.011400</td>\n",
       "      <td>0.011755</td>\n",
       "      <td>0.012274</td>\n",
       "      <td>0.012584</td>\n",
       "      <td>0.012751</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012985</td>\n",
       "      <td>0.012912</td>\n",
       "      <td>0.012744</td>\n",
       "      <td>0.012958</td>\n",
       "      <td>0.012944</td>\n",
       "      <td>0.013002</td>\n",
       "      <td>0.012935</td>\n",
       "      <td>0.012999</td>\n",
       "      <td>0.013003</td>\n",
       "      <td>0.013012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_DayBeforeHoliday</th>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>0.000612</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>0.000714</td>\n",
       "      <td>0.000753</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.000750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>0.000772</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>0.000753</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>0.000725</td>\n",
       "      <td>0.000767</td>\n",
       "      <td>0.000803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_DayAfterHodliay</th>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_BusinessDay</th>\n",
       "      <td>0.002449</td>\n",
       "      <td>0.010312</td>\n",
       "      <td>0.014929</td>\n",
       "      <td>0.015745</td>\n",
       "      <td>0.016779</td>\n",
       "      <td>0.016483</td>\n",
       "      <td>0.017595</td>\n",
       "      <td>0.018290</td>\n",
       "      <td>0.018929</td>\n",
       "      <td>0.018845</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018757</td>\n",
       "      <td>0.018897</td>\n",
       "      <td>0.019015</td>\n",
       "      <td>0.018514</td>\n",
       "      <td>0.016915</td>\n",
       "      <td>0.017354</td>\n",
       "      <td>0.019720</td>\n",
       "      <td>0.018875</td>\n",
       "      <td>0.019171</td>\n",
       "      <td>0.019092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_BusinessDay_sum</th>\n",
       "      <td>0.000628</td>\n",
       "      <td>0.000905</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>0.001695</td>\n",
       "      <td>0.001414</td>\n",
       "      <td>0.001214</td>\n",
       "      <td>0.001530</td>\n",
       "      <td>0.001742</td>\n",
       "      <td>0.001562</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001424</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>0.001173</td>\n",
       "      <td>0.001444</td>\n",
       "      <td>0.001223</td>\n",
       "      <td>0.001375</td>\n",
       "      <td>0.001266</td>\n",
       "      <td>0.001558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifted_Holiday_sum</th>\n",
       "      <td>0.000248</td>\n",
       "      <td>0.000607</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>0.001174</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>0.000687</td>\n",
       "      <td>0.000627</td>\n",
       "      <td>0.000637</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001086</td>\n",
       "      <td>0.000771</td>\n",
       "      <td>0.000871</td>\n",
       "      <td>0.000934</td>\n",
       "      <td>0.000874</td>\n",
       "      <td>0.000743</td>\n",
       "      <td>0.000677</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.000743</td>\n",
       "      <td>0.000756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "horizon                         1         2         3         4         5   \\\n",
       "measurement               0.606680  0.184148  0.063959  0.018727  0.007431   \n",
       "shifted_measurement_1d    0.026476  0.061081  0.076868  0.085147  0.087946   \n",
       "shifted_measurement_2d    0.003720  0.006125  0.007120  0.007704  0.008240   \n",
       "shifted_measurement_1w    0.083884  0.396689  0.419818  0.443881  0.445707   \n",
       "measurement_roll_mean_1h  0.084864  0.068960  0.032386  0.026575  0.017745   \n",
       "measurement_roll_mean_6h  0.029034  0.028670  0.039725  0.021815  0.027316   \n",
       "measurement_roll_mean_d   0.005548  0.019698  0.082634  0.095823  0.095722   \n",
       "measurement_roll_mean_w   0.004193  0.006218  0.011336  0.015510  0.015687   \n",
       "measurement_roll_mean_m   0.002971  0.004406  0.005339  0.005364  0.006354   \n",
       "measurement_roll_min2_d   0.000875  0.001756  0.001849  0.002052  0.002080   \n",
       "measurement_roll_min2_w   0.000516  0.000682  0.002786  0.000805  0.000925   \n",
       "measurement_roll_max2_d   0.001445  0.004387  0.003164  0.002632  0.004709   \n",
       "measurement_roll_max2_w   0.000987  0.001615  0.002813  0.003464  0.003573   \n",
       "measurement_roll_var_6h   0.005055  0.009427  0.008726  0.012808  0.009782   \n",
       "measurement_roll_var_d    0.004258  0.005903  0.005617  0.006932  0.007196   \n",
       "measurement_roll_var_w    0.003355  0.005042  0.006637  0.005800  0.005903   \n",
       "p                         0.000308  0.000439  0.000506  0.000627  0.001113   \n",
       "t2m_termin                0.001610  0.002536  0.002469  0.002602  0.004626   \n",
       "rh_termin                 0.001177  0.001376  0.001895  0.002309  0.001697   \n",
       "padavine                  0.000205  0.000243  0.000282  0.000329  0.000338   \n",
       "veter_hitrost             0.001800  0.002202  0.002638  0.002788  0.002985   \n",
       "veter_vek_smer            0.002681  0.002827  0.002724  0.002721  0.002998   \n",
       "energija_gl               0.002476  0.001868  0.001926  0.002319  0.001832   \n",
       "energija_di               0.002488  0.002142  0.001976  0.001851  0.005870   \n",
       "p_roll_mean_1h            0.000704  0.001079  0.001299  0.003571  0.003631   \n",
       "p_roll_mean_6h            0.001705  0.002778  0.004684  0.003070  0.002666   \n",
       "p_roll_mean_d             0.002279  0.003077  0.003606  0.004098  0.004445   \n",
       "p_roll_mean_w             0.002728  0.004158  0.004832  0.005093  0.006101   \n",
       "p_roll_mean_m             0.003287  0.005102  0.007085  0.007360  0.007847   \n",
       "p_roll_var_1h             0.000520  0.000532  0.000560  0.000550  0.000608   \n",
       "...                            ...       ...       ...       ...       ...   \n",
       "rh_termin_roll_min2_w     0.001019  0.001379  0.002305  0.002206  0.002392   \n",
       "rh_termin_roll_max2_d     0.000898  0.001610  0.001524  0.002293  0.002886   \n",
       "rh_termin_roll_max2_w     0.000535  0.000692  0.000782  0.000852  0.000863   \n",
       "rh_termin_roll_var_1h     0.001773  0.001877  0.001960  0.001839  0.001904   \n",
       "rh_termin_roll_var_d      0.001986  0.002749  0.003714  0.004848  0.008213   \n",
       "energija_gl_roll_mean_1h  0.001917  0.002274  0.002433  0.003259  0.002254   \n",
       "energija_gl_roll_mean_6h  0.006492  0.006439  0.006106  0.005905  0.006473   \n",
       "energija_gl_roll_mean_d   0.001649  0.003084  0.003618  0.002937  0.002995   \n",
       "energija_gl_roll_mean_w   0.002535  0.003389  0.003867  0.004375  0.004161   \n",
       "energija_gl_roll_mean_m   0.002675  0.004336  0.003959  0.005024  0.004350   \n",
       "energija_gl_roll_var_1h   0.001814  0.001998  0.001693  0.001898  0.002539   \n",
       "energija_gl_roll_var_d    0.001506  0.002354  0.002573  0.002879  0.002999   \n",
       "shifted_p                 0.000576  0.001087  0.001276  0.001482  0.001654   \n",
       "shifted_t2m_termin        0.004303  0.006925  0.006008  0.008177  0.008496   \n",
       "shifted_rh_termin         0.001609  0.002098  0.002499  0.002620  0.002891   \n",
       "shifted_padavine          0.000289  0.000487  0.000865  0.000705  0.000740   \n",
       "shifted_veter_hitrost     0.001790  0.002388  0.002641  0.002615  0.002874   \n",
       "shifted_veter_vek_smer    0.002670  0.003465  0.003097  0.003058  0.003046   \n",
       "shifted_energija_gl       0.008452  0.013362  0.014567  0.011971  0.011891   \n",
       "shifted_HourOfDay         0.020292  0.019208  0.017248  0.015095  0.013266   \n",
       "shifted_DayOfWeek         0.002748  0.004916  0.006699  0.009739  0.010639   \n",
       "shifted_Month             0.000719  0.001140  0.001158  0.001214  0.001418   \n",
       "shifted_Weekday           0.000401  0.000739  0.000887  0.002200  0.001388   \n",
       "shifted_Weekend           0.000448  0.000849  0.000720  0.001060  0.001671   \n",
       "shifted_Holiday           0.002137  0.006234  0.009338  0.009640  0.010563   \n",
       "shifted_DayBeforeHoliday  0.000088  0.000307  0.000569  0.000612  0.000624   \n",
       "shifted_DayAfterHodliay   0.000030  0.000066  0.000119  0.000354  0.000367   \n",
       "shifted_BusinessDay       0.002449  0.010312  0.014929  0.015745  0.016779   \n",
       "shifted_BusinessDay_sum   0.000628  0.000905  0.001295  0.001717  0.001695   \n",
       "shifted_Holiday_sum       0.000248  0.000607  0.000820  0.000984  0.001174   \n",
       "\n",
       "horizon                         6         7         8         9         10  \\\n",
       "measurement               0.004422  0.003464  0.003648  0.002131  0.002749   \n",
       "shifted_measurement_1d    0.088310  0.089914  0.091967  0.092278  0.092898   \n",
       "shifted_measurement_2d    0.008556  0.008560  0.008441  0.008881  0.008504   \n",
       "shifted_measurement_1w    0.446498  0.447161  0.447249  0.447320  0.446988   \n",
       "measurement_roll_mean_1h  0.019000  0.025674  0.030823  0.031880  0.034622   \n",
       "measurement_roll_mean_6h  0.025937  0.017357  0.013096  0.010777  0.006885   \n",
       "measurement_roll_mean_d   0.092793  0.083523  0.068029  0.063016  0.062827   \n",
       "measurement_roll_mean_w   0.015573  0.016244  0.015183  0.016939  0.017734   \n",
       "measurement_roll_mean_m   0.007131  0.007101  0.007046  0.007074  0.007045   \n",
       "measurement_roll_min2_d   0.002003  0.001983  0.002142  0.002102  0.002263   \n",
       "measurement_roll_min2_w   0.000980  0.000940  0.000939  0.000951  0.000968   \n",
       "measurement_roll_max2_d   0.005542  0.003482  0.002543  0.003118  0.003495   \n",
       "measurement_roll_max2_w   0.003407  0.010264  0.024290  0.028335  0.027868   \n",
       "measurement_roll_var_6h   0.006645  0.008788  0.007735  0.006107  0.004232   \n",
       "measurement_roll_var_d    0.007790  0.006787  0.006968  0.006968  0.006537   \n",
       "measurement_roll_var_w    0.006118  0.006131  0.006411  0.006459  0.006387   \n",
       "p                         0.001258  0.000853  0.002050  0.001295  0.000594   \n",
       "t2m_termin                0.004016  0.005016  0.003534  0.006008  0.004778   \n",
       "rh_termin                 0.001393  0.001446  0.001412  0.001455  0.001414   \n",
       "padavine                  0.000300  0.000404  0.000319  0.000289  0.000256   \n",
       "veter_hitrost             0.003349  0.003794  0.002633  0.002495  0.002397   \n",
       "veter_vek_smer            0.002727  0.002920  0.002800  0.002826  0.002980   \n",
       "energija_gl               0.001246  0.001182  0.001132  0.001797  0.003596   \n",
       "energija_di               0.007285  0.001987  0.001550  0.002116  0.002145   \n",
       "p_roll_mean_1h            0.001438  0.001357  0.003380  0.002538  0.001282   \n",
       "p_roll_mean_6h            0.003021  0.002915  0.005613  0.005489  0.005502   \n",
       "p_roll_mean_d             0.004754  0.004386  0.005236  0.004641  0.006014   \n",
       "p_roll_mean_w             0.006199  0.006867  0.006232  0.006949  0.008138   \n",
       "p_roll_mean_m             0.007336  0.008081  0.008729  0.008090  0.011282   \n",
       "p_roll_var_1h             0.000547  0.000555  0.000549  0.000579  0.000557   \n",
       "...                            ...       ...       ...       ...       ...   \n",
       "rh_termin_roll_min2_w     0.002344  0.002181  0.002333  0.002490  0.002280   \n",
       "rh_termin_roll_max2_d     0.002489  0.002184  0.001707  0.001510  0.001318   \n",
       "rh_termin_roll_max2_w     0.000887  0.001031  0.001015  0.000977  0.000936   \n",
       "rh_termin_roll_var_1h     0.001891  0.002030  0.002006  0.002082  0.001949   \n",
       "rh_termin_roll_var_d      0.011977  0.012152  0.009177  0.006578  0.003757   \n",
       "energija_gl_roll_mean_1h  0.001524  0.001955  0.003346  0.004846  0.002619   \n",
       "energija_gl_roll_mean_6h  0.007681  0.008765  0.007272  0.006343  0.004733   \n",
       "energija_gl_roll_mean_d   0.003609  0.003640  0.002919  0.004788  0.003987   \n",
       "energija_gl_roll_mean_w   0.004569  0.004640  0.005025  0.005439  0.005172   \n",
       "energija_gl_roll_mean_m   0.004227  0.004269  0.004251  0.004069  0.004210   \n",
       "energija_gl_roll_var_1h   0.002436  0.001603  0.002115  0.002104  0.001720   \n",
       "energija_gl_roll_var_d    0.003150  0.002826  0.002743  0.003122  0.002791   \n",
       "shifted_p                 0.001859  0.002122  0.003481  0.002841  0.002392   \n",
       "shifted_t2m_termin        0.008682  0.010652  0.011150  0.009335  0.009197   \n",
       "shifted_rh_termin         0.003011  0.004582  0.005136  0.003247  0.003231   \n",
       "shifted_padavine          0.000837  0.000959  0.000862  0.000857  0.000852   \n",
       "shifted_veter_hitrost     0.002646  0.002758  0.002733  0.002844  0.002777   \n",
       "shifted_veter_vek_smer    0.003105  0.003573  0.003293  0.003231  0.003164   \n",
       "shifted_energija_gl       0.011508  0.010927  0.009834  0.009487  0.010324   \n",
       "shifted_HourOfDay         0.012221  0.011712  0.011394  0.011403  0.012762   \n",
       "shifted_DayOfWeek         0.011543  0.011059  0.010474  0.010430  0.011057   \n",
       "shifted_Month             0.001410  0.001413  0.001383  0.001274  0.001262   \n",
       "shifted_Weekday           0.001483  0.001471  0.001409  0.001406  0.001430   \n",
       "shifted_Weekend           0.002229  0.001818  0.001501  0.001453  0.001454   \n",
       "shifted_Holiday           0.011400  0.011755  0.012274  0.012584  0.012751   \n",
       "shifted_DayBeforeHoliday  0.000657  0.000714  0.000753  0.000773  0.000750   \n",
       "shifted_DayAfterHodliay   0.000398  0.000385  0.000396  0.000360  0.000384   \n",
       "shifted_BusinessDay       0.016483  0.017595  0.018290  0.018929  0.018845   \n",
       "shifted_BusinessDay_sum   0.001414  0.001214  0.001530  0.001742  0.001562   \n",
       "shifted_Holiday_sum       0.000640  0.000640  0.000687  0.000627  0.000637   \n",
       "\n",
       "horizon                     ...           15        16        17        18  \\\n",
       "measurement                 ...     0.004381  0.001383  0.001166  0.001198   \n",
       "shifted_measurement_1d      ...     0.098006  0.097015  0.097646  0.095507   \n",
       "shifted_measurement_2d      ...     0.009038  0.008663  0.008689  0.008699   \n",
       "shifted_measurement_1w      ...     0.447301  0.447606  0.447020  0.447291   \n",
       "measurement_roll_mean_1h    ...     0.026934  0.027868  0.029108  0.029556   \n",
       "measurement_roll_mean_6h    ...     0.026450  0.029101  0.032741  0.038275   \n",
       "measurement_roll_mean_d     ...     0.038441  0.036895  0.033354  0.031386   \n",
       "measurement_roll_mean_w     ...     0.019820  0.019637  0.018609  0.017346   \n",
       "measurement_roll_mean_m     ...     0.007186  0.008190  0.008239  0.008710   \n",
       "measurement_roll_min2_d     ...     0.002202  0.002124  0.002109  0.002026   \n",
       "measurement_roll_min2_w     ...     0.000992  0.000984  0.001021  0.001020   \n",
       "measurement_roll_max2_d     ...     0.002702  0.002571  0.002900  0.002641   \n",
       "measurement_roll_max2_w     ...     0.031351  0.032353  0.032203  0.032030   \n",
       "measurement_roll_var_6h     ...     0.003800  0.004113  0.003391  0.004028   \n",
       "measurement_roll_var_d      ...     0.005860  0.005677  0.005564  0.005815   \n",
       "measurement_roll_var_w      ...     0.007082  0.006937  0.006954  0.006793   \n",
       "p                           ...     0.000761  0.000763  0.000797  0.000818   \n",
       "t2m_termin                  ...     0.002364  0.002277  0.002000  0.002036   \n",
       "rh_termin                   ...     0.001883  0.001634  0.001469  0.001619   \n",
       "padavine                    ...     0.000237  0.000233  0.000308  0.000316   \n",
       "veter_hitrost               ...     0.003443  0.002466  0.002953  0.002644   \n",
       "veter_vek_smer              ...     0.003001  0.004604  0.005497  0.003767   \n",
       "energija_gl                 ...     0.000934  0.000985  0.000948  0.001020   \n",
       "energija_di                 ...     0.001258  0.001244  0.001298  0.001493   \n",
       "p_roll_mean_1h              ...     0.001516  0.001760  0.001871  0.001945   \n",
       "p_roll_mean_6h              ...     0.004387  0.003936  0.003549  0.003773   \n",
       "p_roll_mean_d               ...     0.004567  0.004893  0.004840  0.004857   \n",
       "p_roll_mean_w               ...     0.005378  0.005306  0.005535  0.005898   \n",
       "p_roll_mean_m               ...     0.007358  0.007330  0.007383  0.007390   \n",
       "p_roll_var_1h               ...     0.000523  0.000549  0.000544  0.000613   \n",
       "...                         ...          ...       ...       ...       ...   \n",
       "rh_termin_roll_min2_w       ...     0.002433  0.002522  0.002075  0.001945   \n",
       "rh_termin_roll_max2_d       ...     0.001289  0.001342  0.001436  0.001412   \n",
       "rh_termin_roll_max2_w       ...     0.000917  0.000889  0.000926  0.000915   \n",
       "rh_termin_roll_var_1h       ...     0.002086  0.002074  0.002047  0.001978   \n",
       "rh_termin_roll_var_d        ...     0.004497  0.004150  0.003910  0.004243   \n",
       "energija_gl_roll_mean_1h    ...     0.001204  0.001377  0.001326  0.001862   \n",
       "energija_gl_roll_mean_6h    ...     0.004654  0.005122  0.007497  0.007323   \n",
       "energija_gl_roll_mean_d     ...     0.004319  0.003425  0.003520  0.003813   \n",
       "energija_gl_roll_mean_w     ...     0.004721  0.005142  0.004895  0.004676   \n",
       "energija_gl_roll_mean_m     ...     0.004290  0.004278  0.004115  0.004399   \n",
       "energija_gl_roll_var_1h     ...     0.001357  0.001486  0.001620  0.001809   \n",
       "energija_gl_roll_var_d      ...     0.002878  0.004427  0.003561  0.003338   \n",
       "shifted_p                   ...     0.006515  0.002716  0.004379  0.003633   \n",
       "shifted_t2m_termin          ...     0.011266  0.013252  0.012719  0.012603   \n",
       "shifted_rh_termin           ...     0.005430  0.005825  0.005758  0.005422   \n",
       "shifted_padavine            ...     0.000887  0.000835  0.000852  0.000823   \n",
       "shifted_veter_hitrost       ...     0.003412  0.003384  0.003251  0.002975   \n",
       "shifted_veter_vek_smer      ...     0.003567  0.003354  0.003295  0.003643   \n",
       "shifted_energija_gl         ...     0.012871  0.013141  0.011424  0.010783   \n",
       "shifted_HourOfDay           ...     0.012589  0.011880  0.010984  0.011392   \n",
       "shifted_DayOfWeek           ...     0.010585  0.010860  0.011373  0.011331   \n",
       "shifted_Month               ...     0.001463  0.001371  0.001312  0.001274   \n",
       "shifted_Weekday             ...     0.001240  0.001373  0.001316  0.001116   \n",
       "shifted_Weekend             ...     0.001060  0.001094  0.001097  0.001053   \n",
       "shifted_Holiday             ...     0.012985  0.012912  0.012744  0.012958   \n",
       "shifted_DayBeforeHoliday    ...     0.000774  0.000782  0.000772  0.000761   \n",
       "shifted_DayAfterHodliay     ...     0.000330  0.000305  0.000328  0.000394   \n",
       "shifted_BusinessDay         ...     0.018757  0.018897  0.019015  0.018514   \n",
       "shifted_BusinessDay_sum     ...     0.001424  0.001335  0.001212  0.001197   \n",
       "shifted_Holiday_sum         ...     0.001086  0.000771  0.000871  0.000934   \n",
       "\n",
       "horizon                         19        20        21        22        23  \\\n",
       "measurement               0.001383  0.002003  0.002109  0.002408  0.002269   \n",
       "shifted_measurement_1d    0.085684  0.061714  0.048305  0.066870  0.060982   \n",
       "shifted_measurement_2d    0.008164  0.008010  0.007396  0.007561  0.007492   \n",
       "shifted_measurement_1w    0.436182  0.434014  0.432529  0.434504  0.434440   \n",
       "measurement_roll_mean_1h  0.030834  0.030398  0.027322  0.019684  0.066064   \n",
       "measurement_roll_mean_6h  0.065795  0.094549  0.111777  0.085010  0.027825   \n",
       "measurement_roll_mean_d   0.028197  0.027340  0.028078  0.028126  0.031200   \n",
       "measurement_roll_mean_w   0.011687  0.012160  0.010212  0.013933  0.014674   \n",
       "measurement_roll_mean_m   0.008021  0.008147  0.009307  0.010115  0.013462   \n",
       "measurement_roll_min2_d   0.001815  0.001835  0.001784  0.001860  0.001985   \n",
       "measurement_roll_min2_w   0.000958  0.001048  0.001077  0.001078  0.001118   \n",
       "measurement_roll_max2_d   0.003343  0.003169  0.004059  0.004601  0.005648   \n",
       "measurement_roll_max2_w   0.032339  0.032328  0.033373  0.035745  0.036777   \n",
       "measurement_roll_var_6h   0.007539  0.005935  0.005381  0.004227  0.004198   \n",
       "measurement_roll_var_d    0.004960  0.005197  0.005280  0.007214  0.009383   \n",
       "measurement_roll_var_w    0.007396  0.007249  0.006743  0.008185  0.010334   \n",
       "p                         0.000828  0.000908  0.000978  0.001060  0.000907   \n",
       "t2m_termin                0.002271  0.003045  0.003516  0.003593  0.003142   \n",
       "rh_termin                 0.001880  0.001431  0.001405  0.001420  0.001578   \n",
       "padavine                  0.000230  0.000262  0.000285  0.000858  0.000626   \n",
       "veter_hitrost             0.003597  0.004331  0.005040  0.005272  0.005304   \n",
       "veter_vek_smer            0.003958  0.003062  0.002911  0.002847  0.002921   \n",
       "energija_gl               0.001399  0.001721  0.002029  0.002982  0.005078   \n",
       "energija_di               0.001823  0.002485  0.002648  0.003255  0.005562   \n",
       "p_roll_mean_1h            0.001500  0.001692  0.002082  0.001636  0.001707   \n",
       "p_roll_mean_6h            0.003765  0.003415  0.003039  0.002923  0.002751   \n",
       "p_roll_mean_d             0.004163  0.004142  0.004467  0.004280  0.004414   \n",
       "p_roll_mean_w             0.005778  0.005831  0.006405  0.007557  0.008012   \n",
       "p_roll_mean_m             0.008325  0.008542  0.007653  0.007538  0.007649   \n",
       "p_roll_var_1h             0.000640  0.000518  0.000532  0.000561  0.000611   \n",
       "...                            ...       ...       ...       ...       ...   \n",
       "rh_termin_roll_min2_w     0.001522  0.001618  0.002072  0.002108  0.002110   \n",
       "rh_termin_roll_max2_d     0.001578  0.001628  0.001526  0.001583  0.001480   \n",
       "rh_termin_roll_max2_w     0.001012  0.001022  0.000963  0.000990  0.001045   \n",
       "rh_termin_roll_var_1h     0.001927  0.001885  0.001959  0.001959  0.002794   \n",
       "rh_termin_roll_var_d      0.003782  0.003794  0.004126  0.004846  0.005478   \n",
       "energija_gl_roll_mean_1h  0.002510  0.002913  0.003495  0.004747  0.004943   \n",
       "energija_gl_roll_mean_6h  0.006044  0.005371  0.006903  0.007812  0.005974   \n",
       "energija_gl_roll_mean_d   0.007660  0.008195  0.005009  0.004153  0.003006   \n",
       "energija_gl_roll_mean_w   0.004621  0.005363  0.005671  0.006896  0.007388   \n",
       "energija_gl_roll_mean_m   0.005025  0.004628  0.004928  0.005836  0.006217   \n",
       "energija_gl_roll_var_1h   0.001676  0.001693  0.001627  0.002108  0.001921   \n",
       "energija_gl_roll_var_d    0.003005  0.003208  0.003809  0.003837  0.003117   \n",
       "shifted_p                 0.003670  0.003591  0.003685  0.003666  0.003678   \n",
       "shifted_t2m_termin        0.008791  0.010763  0.007794  0.008891  0.007149   \n",
       "shifted_rh_termin         0.003951  0.003936  0.003976  0.003589  0.003357   \n",
       "shifted_padavine          0.000720  0.000774  0.000722  0.000790  0.000798   \n",
       "shifted_veter_hitrost     0.002874  0.003006  0.002905  0.003057  0.003240   \n",
       "shifted_veter_vek_smer    0.003561  0.003402  0.003048  0.003152  0.003045   \n",
       "shifted_energija_gl       0.011851  0.010978  0.006751  0.008444  0.006649   \n",
       "shifted_HourOfDay         0.011308  0.011150  0.011534  0.011838  0.011505   \n",
       "shifted_DayOfWeek         0.011370  0.012653  0.012272  0.011618  0.012253   \n",
       "shifted_Month             0.001351  0.001341  0.001296  0.001419  0.001486   \n",
       "shifted_Weekday           0.001066  0.001147  0.001194  0.001006  0.000772   \n",
       "shifted_Weekend           0.000982  0.001084  0.001149  0.000894  0.000903   \n",
       "shifted_Holiday           0.012944  0.013002  0.012935  0.012999  0.013003   \n",
       "shifted_DayBeforeHoliday  0.000753  0.000762  0.000736  0.000725  0.000767   \n",
       "shifted_DayAfterHodliay   0.000399  0.000400  0.000428  0.000460  0.000318   \n",
       "shifted_BusinessDay       0.016915  0.017354  0.019720  0.018875  0.019171   \n",
       "shifted_BusinessDay_sum   0.001173  0.001444  0.001223  0.001375  0.001266   \n",
       "shifted_Holiday_sum       0.000874  0.000743  0.000677  0.000670  0.000743   \n",
       "\n",
       "horizon                         24  \n",
       "measurement               0.027257  \n",
       "shifted_measurement_1d    0.027680  \n",
       "shifted_measurement_2d    0.007462  \n",
       "shifted_measurement_1w    0.433886  \n",
       "measurement_roll_mean_1h  0.077224  \n",
       "measurement_roll_mean_6h  0.024529  \n",
       "measurement_roll_mean_d   0.029147  \n",
       "measurement_roll_mean_w   0.017280  \n",
       "measurement_roll_mean_m   0.013075  \n",
       "measurement_roll_min2_d   0.001931  \n",
       "measurement_roll_min2_w   0.001121  \n",
       "measurement_roll_max2_d   0.008332  \n",
       "measurement_roll_max2_w   0.036073  \n",
       "measurement_roll_var_6h   0.004196  \n",
       "measurement_roll_var_d    0.008303  \n",
       "measurement_roll_var_w    0.010183  \n",
       "p                         0.001011  \n",
       "t2m_termin                0.003033  \n",
       "rh_termin                 0.001423  \n",
       "padavine                  0.000442  \n",
       "veter_hitrost             0.002938  \n",
       "veter_vek_smer            0.002977  \n",
       "energija_gl               0.003201  \n",
       "energija_di               0.003428  \n",
       "p_roll_mean_1h            0.001425  \n",
       "p_roll_mean_6h            0.003144  \n",
       "p_roll_mean_d             0.004337  \n",
       "p_roll_mean_w             0.007400  \n",
       "p_roll_mean_m             0.007866  \n",
       "p_roll_var_1h             0.000592  \n",
       "...                            ...  \n",
       "rh_termin_roll_min2_w     0.003127  \n",
       "rh_termin_roll_max2_d     0.001489  \n",
       "rh_termin_roll_max2_w     0.001027  \n",
       "rh_termin_roll_var_1h     0.002191  \n",
       "rh_termin_roll_var_d      0.005317  \n",
       "energija_gl_roll_mean_1h  0.004015  \n",
       "energija_gl_roll_mean_6h  0.003913  \n",
       "energija_gl_roll_mean_d   0.002999  \n",
       "energija_gl_roll_mean_w   0.018686  \n",
       "energija_gl_roll_mean_m   0.005415  \n",
       "energija_gl_roll_var_1h   0.003185  \n",
       "energija_gl_roll_var_d    0.002587  \n",
       "shifted_p                 0.003774  \n",
       "shifted_t2m_termin        0.007663  \n",
       "shifted_rh_termin         0.003346  \n",
       "shifted_padavine          0.000804  \n",
       "shifted_veter_hitrost     0.002795  \n",
       "shifted_veter_vek_smer    0.003180  \n",
       "shifted_energija_gl       0.006788  \n",
       "shifted_HourOfDay         0.013727  \n",
       "shifted_DayOfWeek         0.012350  \n",
       "shifted_Month             0.001565  \n",
       "shifted_Weekday           0.001010  \n",
       "shifted_Weekend           0.000920  \n",
       "shifted_Holiday           0.013012  \n",
       "shifted_DayBeforeHoliday  0.000803  \n",
       "shifted_DayAfterHodliay   0.000369  \n",
       "shifted_BusinessDay       0.019092  \n",
       "shifted_BusinessDay_sum   0.001558  \n",
       "shifted_Holiday_sum       0.000756  \n",
       "\n",
       "[78 rows x 24 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances_all = feature_importances.groupby(level='horizon',axis=1).mean()\n",
    "feature_importances_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output_file_name = \"featureImportanceResultsNew.csv\"\n",
    "output_file_path = os.path.join(os.path.join(os.getcwd(), \"data\"), output_file_name)\n",
    "\n",
    "feature_importances_all.to_csv(output_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kZtFyN9PScLLXskYd5+ktU9fHTrmo8YFza3ZW6eeA25m80qBB0bm1whOeA5xI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hkiIiJYuXIljz/+eLPrFBcXx2OPPUZKSgr/8z//Q48ePXjjjTd44IEHWryu587+2OL2Ne+s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\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CzZs3s2nTJkJDQ/Hz82P06NF88803vPPOO3z55Zds3ryZ9evX07p1axo1anTDOZc7//Pp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\nABAWWE4AAAAAhIV/AXz9FHaBOcCJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x36bcbb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import seaborn as sns\n",
    "sns.set(context=\"paper\", font=\"monospace\")\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "f, ax = plt.subplots(figsize=(30, 40))\n",
    "sns.heatmap(feature_importances_all, square=True, annot=True, annot_kws={\"size\": 8}, fmt=\".2f\", cbar=False)\n",
    "plt.title(\"Feature Importances\")\n",
    "plt.savefig(\"./figures/featureImportanceAll.pdf\")\n",
    "plt.savefig(\"./figures/featureImportanceAll.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 20 Features of All Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shifted_measurement_1w      0.425377\n",
       "shifted_measurement_1d      0.079981\n",
       "measurement_roll_mean_d     0.050115\n",
       "measurement                 0.039630\n",
       "measurement_roll_mean_1h    0.035302\n",
       "measurement_roll_mean_6h    0.033153\n",
       "measurement_roll_max2_w     0.023567\n",
       "shifted_BusinessDay         0.017166\n",
       "measurement_roll_mean_w     0.015097\n",
       "shifted_HourOfDay           0.013252\n",
       "t2m_termin_roll_mean_m      0.012516\n",
       "shifted_Holiday             0.011641\n",
       "shifted_energija_gl         0.010874\n",
       "shifted_DayOfWeek           0.010318\n",
       "shifted_t2m_termin          0.009457\n",
       "p_roll_var_d                0.008596\n",
       "shifted_measurement_2d      0.007957\n",
       "measurement_roll_mean_m     0.007690\n",
       "p_roll_mean_m               0.007600\n",
       "rh_termin_roll_mean_w       0.006933\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances_all_mean = feature_importances_all.mean(axis=1)\n",
    "feature_importances_all_mean.sort(ascending=False, inplace=True)\n",
    "feature_importances_all_mean[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace index names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "top20_indices = np.argsort(list(feature_importances_all.mean(axis=1)))[::-1][0:20]\n",
    "top20 = feature_importances_all.ix[top20_indices]\n",
    "#top20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wdKhKw3qBfD5jbplmDgGSU9NwdXYEQGNrQ2Z29oMMpRJLlYrcPC1xt29T3cOd\n0QP6cP7yVeJvJ5iekZaOi6ORDB7O0Oth+uhhTBjYl+oez3Dy/IWyZTg5GDJsSutHHi82asitxCTm\nLFvB9fibpKTfNT0jPR1XxwcZWQUZFOtHXp6WFxs14FZiInOWf1eQkf5kZaQVz1A/yNAby0gqVwZA\nSno6roXbvVhf9Hoe5GjzeKFhA24lJTH325XcuHmrTNvkqclIS8fV4cE2yb53D4DG/n4PzfKlpN/F\nxaEqAM4OVUlMTZOMCshITi8lo67fQ7PUKenpuBQs5+LgQFKa6RmV1ZenJaNSjvGVcD5MSknD1dkJ\nADtb22Lnw2LHk7w8LC1VfD52BOM/Gkj63QxizpXhfJiahqtTYUbxfjycAaDV6di8e2/R7HWZMip4\nrACSU1Jwc3Eu6Iut8Toot7AOCuDzWXNRmJW9NH6kxXTjxo159913uXbtGhYWFmg0GoC/LdD0ej32\n9vYMHz6cnj17EhAQgEKhQK/XA6D8m6sQa2trFAoFKpUKADMzMxQKRVF7vXr1IiAggG+//ZbY2Fjq\n1q2LpaUlWq2WMWPG4OnpSXh4OCkpKUV5qampRe1rNBry8/NLrF/xbEtLy6JsvV6PmZkZw4YNo1+/\nfjRq1Kho2cJlTFW4PoVj93fjUBo3V2du37kDQPrdu9ja2AAUnLy13M/JQaVSkZiUzLUbf/DxqKE0\nbdSAA4eiTM5wdXYiITEZgLsZmdiqDVe/KpUF2sIMCwscq9qjtjZcGNnbacrWDydH7iQXZGQWy7Ao\nzMjFUmVBVMxpzl+NBcCpqj1Z2ffKlpGUYiRDZcjIzcXSwoK0uxm80LA+w3q9i0MVO6oVHNRM4ero\nSEKyISMjKxObv/QjJzcXlaogo0EDhvV8B4cqVZ68DKdiGZlZDzJUxTIsLEjLKBirnu8YxsrJ2eQM\nQ18cSCja7lnG+1KQ83yDegx9rwcOdmXdJk9JhpMjCSkFn9+sBxnGlruTYjjGJaak4lxQmEjGI85w\ndHiQkfk3GY6O3ClY7k5KCk5VTc8oXMenYrwqIaNSjvGVcT50cSIhMQmA9GL9sLQolqFScSshkeSC\nsapip0Gr05me4exEQlJBRkbWgwzVg3Nu4Te/+44c5eXnmpWpD1A5YwXg5uLC7TuJBX3JKKUOsiAx\nuaAOGjGUpg3rc+Cw6XUQgHLSpEmTyrx2pTh+/Djff/89OTk5REdH06pVK1xdXdmwYQOtWrXi+PHj\nNGnShOjszLyUAAAgAElEQVToaF555RV+/vln3NzcsLGxYc2aNezcuZPWrVvTuHFjvvzyS06cOEFC\nQgKhoaElsnbu3MlLL72ETqcram/nzp00b96c7OzsovZatWpFamoqZ8+e5cKFC8THxxMWFkZ4eDhJ\nSUmo1Wrat2/P8ePH2bdvH9evX8fb25uMjAyqV6+Op6cnSUlJD7UXGxtL9erVcXV15ddffyUkJISo\nqCi8vb3x8vIiMjKS3bt307RpUzIzMzE3N6dWrVpFy+7YsYOcnBz8/PyMjmNUVFRR+zqdju+++45T\np06hUCh45ZVXjL4nL6vkrJ+3pycRy1dy6Ohxnm3aiI3bdvBc08a4ODvxn29WsHv/QT7o8RZVq1Zh\nzr8jOHX6LDdv3aZrpw5FFyiF8nNyjOZ6uT/Dkh9/5siJUzRrUI/Nu/bybMP6ODs6EvHDT+w5FEWv\nrqE0qRfAj79u5tjps1iqVLza/IUSbem1xnd2r2eqsfSXtUTFnKZZvUC27N1Ps3qBODs6sOTnteyN\nOkrPziHUreHDT5u3cfriZRJT03gz+LWSF3IFFyklMqpVY+nqdUTFnKFZYABb9u2nWb0AnB0cWPLL\nWvYePU7PTiE84+rC0tXrOBpzhlxtHp2KzTI9nGMsw41v1q4nKuYMTQMD2LLvAM0CDRlLVxsy3uvY\nwZCxZh1HT58hV6ul06utjGcY7UclZazZQNTpMzQN9GfL/oM0C/TH2aEqS9esY++x47zX8Q2ecXFh\n6Zr1HD19tiDjFZMzHvTlQc7WghwXBweWrF3PvqPRvBtiyPlmzXqOnjlHbl4eHVubnvNUZazbQNTp\nszQN8Gfrgd9pFuBfdAH/28FDtH/pBVwdHdl28BBHz5wjT6sl5BXj35BJRhkyjO3rbm4sW/8rR88U\nZhykafGM3w/R/kVDxm+/H+bY2bPkabV0KCWjtAmpf+R4VXCGQml80upRHuPNSrmF8FGeDw19KTmJ\n5u3hTuT3P3I4+iTPNqzPph27ea5RA1ycnFi8chV7fj/C+93D8HjGjYiVqzhz4RJZ2ffoUnBP8l8Z\nO+96uVdj6U+rOXKyoB+79/Jsg3q4ODoQ+ePP7D0cRc+wzjg5VGXtth00f7YpVavYGW0fQGFW8vP7\nqMdKWTCxWWK8PD2IWPEdh45G82zjRmz8bTvPNSmog5avYPf+3/mgx5tUta/CnIURnDpzlpu3Euja\n8Y0SdRCAhcbeeB/1+lKqiyfI+vXruXnzZtH/W1tb06tXr8e3Qo/AxYsX2bVr10OvBQcH4+XlVa72\nshP+eBSrVaq8Mn5FXx66+8YL9kcqP7/iMwB9/hO/W5mmEg4P/4BD0JNDxuqJUxn7urFiRBhnZlFh\nf1ehiFJt/d8XegRKK9ofpco475Z2gfMoWWhKL+YfJXU1b6Ov/yOKafHfSTFtIimmy0aK6SeLjNUT\nR4rpJ4sU02UjxXTZlFZMy0NbhBBCCCGEKCcppoUQQgghhCgnKaaFEEIIIYQoJymmhRBCCCGEKCcp\npoUQQgghhCgnKaaFEEIIIYQoJymmhRBCCCGEKCcppoUQQgghhCiniv/r5qJSmFupK7R9hVnJx5o+\navfi4ys8Iz9PW+EZlaaURww/SmbmFb/dlUYe2fqoKcyeknmDp+jhHaU9IvuRZlTCQy/MzJ+S02hl\nbI9KyEDxlOzrlURpVQkPoNFXzsPSHif51AkhhBBCCFFOUkwLIYQQQghRTlJMCyGEEEIIUU5STAsh\nhBBCCFFOUkwLIYQQQghRTlJMCyGEEEIIUU5STAshhBBCCFFOUkwLIYQQQghRTv8zxfStW7ce9yoI\nIYQQQoinzFPy6Kb/buLEiURERJTpPfHx8SxevJiGDRtiaWlJUFBQBa2daXr37k1kZKTJyyfcSWTW\n1/Oxs7Ojpq8Pb4aFAnAo6igbN29Fr4duXTrTsH4gGZmZTPxsKu+/24P6gQGmZyQmMmf+Iuzs7Kjh\nU53uoR0BOHz0OBu3bQe9nq6dQ/D29ODLrxbg5FCV1PS7TBwzAgsTnxx2JyWF+St+QGNrg6+HB13a\ntwEgKuYMW/cfRK/XE9ruVQJr1WTOshVYWFiQkJTMxEH9UJn4BLTElFTmf/8jdrY2+Li7E9q2ddHP\nDp86zca9+/j8o0Hk5+cTvmQZNtbW5Gm1jOj1rsljVXkZq7CzscHHw53Qtq8CcPTMWbYe+B30ejq3\naY2nmxszli7DyaEqVTUa3uvYweSnk1XG9gC4k5zC18tWYGdri6+XB2GvtzfknIphy5796NHTpX07\ncnJz2bx3X8HPTrNg8id4uz9jYkYyX39TmOFJWFBBxskYNu/Zh16vp8vr7QioVZOZEUuxsrTkbmYm\n4wb2w1xp2tMhKy1j6bcPMoJfe5Cxe68hI6i9IeM/S7GyVHE3K4txg56wjKRkvlq6HDuNBl8vT7oW\nZBw5eYotu/Y9yKhdkxn/WYKVpSUZmZmM+7B/mTLmRiylikaDr7cXXTsYjutHok+yeedu9HoIe+N1\n6vv7AbB28zYuXo1l7OABJrUPcCcxiTn/WYKdxpYa1b3p1vENQ8bxE2zavgs9esI6BNMgoC4AazZt\n5eKVq4wbMsjkjMrKuZOYxJxFkdjZaYxk7DSMV0gwdWvV4LNZX+NgXwUzpRlD+v6rTBmzF0VQRVOQ\n0akDAIePR7Ppt13o9Xq6dgymbq2aTJn5FQ5V7VGamTGkXxkzFi6mSmE/OoUYMo5Fs2n7DvR66Nrx\njYKMuTjY26NUmjGkX2/JqKiMCt7mldUXKEcxPW/ePBITE1GpVGRlZeHj40N0dDSvv/46p0+fJjs7\nm7Zt2+Lj48PixYuxsbHh7t27hIeH89lnn6FSqUhMTCQ8PJx+/foRGRnJyZMnOXDgAO7u7mzevJnG\njRvj5eXFiRMnitpLTU1l3759eHl5ERcXh5+fHydOnODjjz/mxo0bbN++nfz8fAIDA/H29iYiIoIm\nTZpw8eJFhg8fzvXr19m0aRPBwcEP9ScuLo6BAwfSsGFDunTpwvLly7G1taV69eq0b9/+b8ciLi6O\noUOH8sorr3DhwgX8/f35888/adOmDf7+/ixcuBALCwssLS0ZPXo0U6dORalUcu3aNcaMGcPGjRtJ\nTU1Fo9GgUCgYOnRoiYy9e/eyYcMGnJycyrqp+GXdenp070qDeoEMHDaSrp07olQqWfHDj3w9Yzo6\nnY6REyYyb2Y4Ed98i3U5Hiu6esMm3u7ahfqB/nw4ahxhHd9AqVTy3U+/MHfa5+h0OsZ8OoXB/Xrz\nwTtvUdPXh/C587h58xbeXp4mZazfsZtur7cnsHZNRkyfSae2rVGamfHj5q2EjxqGTqfjk68WMHnw\nALYfPEzzpo3I02rLVLit372Hrq+1JbBmDUbOmENI65cxVyqJPneBm3cSuXc/B4AT5y/g4erKuyHB\nLFm9jjOXrxBYq+aTk7FrD13btyWwVk1GzphNx1dbGcZqyzamDx+CTqdj4ryFBL/cnAZ16tD1tbb8\nvG070efO0yTA37SMStgeAOt+20H3N16nXp3aDPt8Op3btUWpNOOHXzczY9wodDodE2Z+xczxo2ha\nP5C9R47iX7OGyYU0wLptO+jeIciQMWUqndu3QalU8sOGjcyYMMaQMWMOQa1eRq/Xk5OTSx1fH5ML\nt0rL2LqD7h2CqedXm2GTp9L5tbaGjPUbmfFxQcaXswlq9Qp69OTkPpkZa7dt582QN6jnV5uhk74g\ntDBj3UZmfjIWnU7H+PDZBLd+GfTlzNi8jbc6daBeXT+GfDKF0KD2KJVKvl+7gVmTJqDT6Rj3xZfM\nnvwx0afP8Ef8TZPbLrR601beCu1IfX8/Phr/KV3eeB2lUsnK1euY/dlEdDodYz+bzpzPJnI85jQ3\n4uLLnFFZOas3beGtLh2p71+Xj8ZNfJDxy1pmf/6pIWPKND4e/hFdOrxOw8AARnwyhTyt1uQJk9Ub\nt/B2l07U96/L4LGf0KVDkCHj57XM+WKS4TwyeSqfjBhCWIcgGtYLYHiZMzbzdpfO1A+oy+CxH9Ol\nQ3BBP9Yw5/PJhowpX/DJiKGEdQg2ZHw8STIqLKPit3ll9QXKcZuHQqEgKCiICRMmEBcXR9++falb\nty5jx45FrVbj6OjIwYMHUavVhIaG0rhxY6KjowFD8VmtWjV69OhRos1Cbdq0YcCAAURGRj7UnkKh\noEWLFgwfPpyEhAT69u1LmzZtiImJYfHixWg0Guzs7Dhy5AgAgYGB9O/fH7Vajbm5Od7e3iUK6UK1\natViypQpLF++nEmTJjFlyhSio6PJysr6r+Ph6+vLhx9+iI2NDZ06dWLgwIEcOHCAlStXAqBWq4mL\ni+PWrVu0a9eOF198ETs7O06dOoVCoaBdu3YMGzaMmJgYo+2vWrWKmTNnMmTIEMzLsGEBkpJTcHN1\nBcBOoyEz09AfvV6Pubk5lpaW5OXmATB88EA8PdzL1D5AcnIKrq7ODzKyimcosbRUkZubRw2f6tT0\n9WH/ocOYmZmZXEgDJKel4+LkAIDGxobM7OwHGUollioVuXl56IHpI4cyoX8fqrs/w8nzF03OSElL\nx9WhMENN9r17ADT293toBjkl/S4uDlUBcHaoSmJq2pOVkZ6Oq6NjQYYNWUVjRdFY5WnzeKFhA24l\nJTH325XcuHmLlPS7JmdUxvYw5KThWnARaVdKTp7W8PnV6nRs3r23aPa6bBkF42Vr+yADHupL3O0E\nqnu4M7p/b85fuUr87YQnKyM1DVfnwozStomWuNu3DRkD+nD+8hOaYWys/vLZiruVgLeHO2MG9OHc\nlavE3b5tckZSaiquzgXHLFsbMrOMfK7y8riVcIc9vx+hW0gwer3e5PYBklNScXN2etAPIxm5eXnc\nTEhgz4FDdO/0RpkzKivHkGEYL42m9AxHh6o0DAzg89lfozArW2mRnJJSlGFXPAMe2iaODlVpWC+A\nz2d9ZfI3aQ9luBRud1uj56q83OIZc8vXD8kwPaOCt3ll9QXKec+0tbU1CoUCS0tLQyNmZiiVSoYN\nG0a/fv1o1KgRmzZtIioqCh8fH+zt7dFqtQwaNAh/f38WL17M5cuXi3bq1NTUorY1Gk1Rm8XbK8wt\n/q+ZmRl6vR6dTkf//v0ZOnQozz///EPLmJubk5+fX2pfFAoFtra2AA8dZBQKxd++r3CZ4uuiUqkw\nMzMjPz8fvV5PUFAQI0aM4NVXX0Wv17No0SKsrKyoXbt2UVbx95eWodfrS/3536nm6srtBMOJLP3u\nXWxtbQAKihAt9+/noFKpytxucW6uLiQkJD7IsPlLRk4OKktDxuJlK/gz7iajPirbV5luTo7cSUoB\n4G5mJrZqNQAqCxVarZb7ublYWlgQdeo056/GAuBkb09WQbFqClcnRxJSCjKysrApyDC23J0Uw+c1\nMSUV54Ki94nJcHQgITnZkJH5IENlYYFWqyUnNxeVhQVpGRk836AeQ9/rgYOdHdWcTf/mozK2hyHH\niYSkJADSi+VYFvTlfk5u0Wz3vqhjvPxcszK1D+Dq5ERCUuF4Fe9LYYZhH3G0t0ddsK/a29k9eRnO\nTiQkFmRkFMtQFcuwsMCxqj1qa6uCDM0Tl+HmXGysMjKKZaiKMixVhgybco6Vm7MzCYkFx6yMTGxt\nCj5Xqoe3x5Zde8jIyOTfy1Zw4sw5zlww/WLQzdWZ24lJD/ph86AfhcdFSwsLtuzYw93MTBYs+ZYT\np89y+vyFsvWlEnLcXJy5XTBed++WkqFSkZicwrU//uTj4R/RtEE9Dh45WoZ+uBRl/HWb5BXbJonJ\nyYaMEUNo2rA+Bw6XIcPFhdt3CjMyjJ+rVBaGjBt/8PGIoQUZUZJRERmVsM0rqy8ACn0ZL1Pnz59P\ny5YtqV+/Pn369CEiIoL58+ejUCiIjY013OfStSspKSls27YNV1dXDh8+zIoVK5gxYwbW1tZkZWUx\nZswYli1bxp07dzA3N8fFxQV3d/eie5PXrVvH/v37i9q7fft20c8K7x1eu3YtlpaW2NnZ8dNPP2Ft\nbU3z5s1xdXXl1KlT9OnTh08//ZS+ffsSGRmJp6cnH3zwwUP9iYuLIzIykkmTJnHu3Dm++eYbHBwc\ncHd359VXX/3be6aLv3fcuHGMHDmS+/fvExERQd++fZk6dSpubm5YWFjQp08fRo4cSe3atYmPj6dx\n48ZkZWUVjWVp90MfOnSI1atX4+joSFxcHAsWLDC6XXLTk0q8lpScwoy5X2OjVhNQ149LV64ycuhg\nTsWcYe2GX8nT6uj1zlv4+9UBYGHEUpq/+Dz1jHzdr8vNMZqbnJLCzHkLsVGr8ferzeWrsQz/cAAx\nZ86yduMWtFotPd/qxtVr14lY/h2BBfch9u31Ht6eHg+1dS/e+FeQKWnpfPXtStTW1tT19eHKH3/w\n0btvc/rSFX7dvRetTkePN4LwcHPls38vxtXJgZzcPEb37lXiIiQ/T2s8Iz2dr1euQm1lhZ9Pda7+\nGcfgt7sXfRsw4svZzBo9HIAvly5HZWGBQqFgyDtvGW2vUjKMXKWnpKfz9Xc/GDJ8fbj6x58M7vEm\nZy5f5de9+9BpdbwV/Bo1vDyZ+p9I1NbWKM3MGNbzHaMRZuYlvz5/lNsDwKyUC7rktDS++uZb1FbW\n1K3py5UbfzCk17ucvniJDTt2G3I6voFfDV9mL1lGp3Zt8P3LZ6poqEq5GE1OS2PukuWGvtSswZXr\nNxj6wXvEXLzEhu270Oq0vNMpBB9PD76YvwiNrQ1qKysGvdfDaHsVnmFmfGYmOTWNuUuWPZzxr57E\nXLjEhu070Wp1vNM5BB8vD76YtxCNra0ho2cZ+vGIM4zNMiWnpjEn0vD7AnVrFc+4yIbfCsaqc0d8\nvDz4/OuF2NnaYG1lxYe9jH9+FUZuLUpOTWXOf5aitrbGv3ZNLl+7zrC+HxBz/gLrt25Hq9Xxblhn\n/GrVAOBWwh2W/7S61HumzYx8Y5icksrsRREFGbUMGf17E3PuPOs3/4ZWp+Xdrl2oW9tw+9bNhASW\nr/qlzPdMP9KcUmb9klNSmb1wMWq12pARe41hA/oQc/Y867dsM4xXty64V3Nl6tz5ODk4kJGZxagP\n+xUVLg8iSs+Y9e/FqNXW+NepxeWr1xg+sC8xZ8+zbvM2tFot73Xvgns1N76YMx9nRwcyMjMZ9WH/\nEhkoStnXU1KZ9e9FqK3V+NepzeXYWIYP7EfM2XOs27wVrVbHe93DDBmzvy7IyGLU4AElM0ohGaVk\n6EtOTD7SbQ6Vtt3V1byN5pS5mP4nu3nzJuvXr3/otRYtWhAYGFih7zXV+vXruXnzwf151tbW9OrV\ny6T3GiumH6XSiulHqbRi+lEqrZj+RyrHV15lZayYfuQZ/89vR0xRnq/tnkilFNP/ROX5yrbMGWW8\nT788jBXT/0iVsT0qIaO0oko8RkaK6Ueukra7FNNPOSmmTSPFdNlIMf2EkWK6bBlSTJtOimlRUf4H\nimn51AkhhBBCCFFOUkwLIYQQQghRTlJMCyGEEEIIUU5STAshhBBCCFFOUkwLIYQQQghRTlJMCyGE\nEEIIUU5STAshhBBCCFFOUkwLIYQQQghRTlJMCyGEEEIIUU5PyaObRNP6XSq0/dVTP6rQ9gFcmtSq\n8Axrd8cKzwAwq4QnrynkSV+mq4wnr4n/TZXxEGH5/Arxt7RZmY81X87GQgghhBBClJMU00IIIYQQ\nQpSTFNNCCCGEEEKUkxTTQgghhBBClJMU00IIIYQQQpSTFNNCCCGEEEKUkxTTQgghhBBClJMU0/8Q\nt27detyrIIQQQggh/kIe2vIPMXHiRCIiIsr0HhdXJ0ZMGEh6+l2uXrrOjyvWAfDSy8/Sqm1zzJRm\n7Nt1iD3bD2KrsWHKjDF8s+gHTp88b3JG0t10lm7fgsbaGi9nV4KbPV/0s+NXLvHbiWOM6/o2er2e\nxds2YqE0JzE9jeGdumJhbtrH705yMl9/swI7W1t8vTwJC2oPQNTJGDbv2Yder6fL6+0IqFWTmRFL\nsbK05G5mJuMG9sNcqTQpIyExkTnzF2FnZ0cNn+p0D+0IwOGjx9m4bTvo9XTtHIK3pwdffrUAJ4eq\npKbfZeKYESb3AyDhTiKzvl5AFTs7avj68GZYZ0NO1DF+3bINvV5P9y6daFAvkIzMTCZ+Np33332L\n+oEBZWh/PnZ2dtT09eHNsFAADkUdZePmrej10K1LZxrUC2DSF9OxsbEhNy+Pj0ePKGMfKjajUvvy\n1bwHGV0NDz86dOQoG7dsRa/XF2QEFmSoyc3N4+MxIyVDMv4+o7L2kadlvCRDMh5xBsCdxCRmL1xM\nFTsNNap7061TCACHj0WzafsO9Hro2vEN6taqyZSZc3Gwt0epNGNIv95lyvmfm5meN28eEydO5PPP\nP2fcuHEsXryY/v37s379ej7//HPGjx/P7t27uX79OuPHj+eLL75gzJgxAHz22WeEh4czcuRIdDod\nvXsbBvvkyZPMnz+ftWvX0qdPHxYuXMimTZseam/NmjUMHTqU2bNnM3z4cBYvXsyAAQOIj4/n999/\nZ/LkyXz66af8/PPPREVF0adPHxYtWsSwYcP4888/uX79Ops2bSpTX8Pe7sDKb35h6idzadH6eczM\nDJu7y1tvkJqahtJcyfkzlwDoPegd7mXfL/N4bos+SshzLzIgqCNHL19El68DIOZ6LLdTU7ifmwvA\n/bxc9p45Rca9bPJ0ujIVoOu27aB7hyBG9+/NwWPH0ekMGT9s2MjHgwcwflA/vl29jr1HjqLX68nJ\nyaWOr4/JhTTA6g2beLtrF8aPGML+Q4eLMr776RcmjR3FJ6NH8M13P5CcksoH77zF8A8HYGuj5ubN\nsn1j8Mu6DfTo3pUJo4ez/+DvRTkrfviRyRPG8Om4USxZvhKAyGUrsLa2KmP76+nRvSsfjx7Bvr+2\n//E4Ph0/miXfruDo8Wg8PdwZPewjHOztORlz5onKqLS+rF1Pjze78fGYkX/JWFWQMYYlywsy3N0Z\nPWwIDlXtORlzWjIko/SMytpHnpbxkgzJqIAMgNUbN/N2l86MGzqY/YejinJW/rKGT0eN4JMRQ/nm\n+x/JyMwirEMwwwf25cafceRptWXK+Z8rphUKBUFBQUyYMIG4uDj69u1L3bp1GTt2LGq1GkdHRw4e\nPIharSY0NJTGjRsTHR0NQFxcHNWqVaNHjx4l2izUpk0bBgwYQGRk5EPtKRQKWrRowfDhw0lISKBv\n3760adOGmJgYFi9ejEajwc7OjiNHjgAQGBhI//79UavVmJub4+3tTXBwcJn66ujswO2bdwC4m56B\nrcYGAL+AWkTM/47FXy9n8EjDBcHc6f/hzxvxZR7P1MwMnOyqAGBrbU12Tg4A9av7PjRLrdfDJ93f\nZUhIFzydnDlz45rJGclpabg6GR4DrrG1JTM729AmYK5UYqlSkZuXR9ztBKp7uDO6f2/OX7lK/O0E\n0zOSU3B1dQbATqMhMyurYL31mJsrsbRUkZubRw2f6tT09WH/ocOYmZnh7eVpckZhjpuriyHHTkNm\nZmEOmJubY2lpSW6e4QJk2IcD8PRwL1P7SckpuLm6PuhHZvF+GNrPy80jKTkF14LlXF2cSUxKfKIy\nKq8vyaVk8JeMZFwLtpuriwuJSUmSIRl/k1FZ+8jTMl6SIRmPPgMgOSUFN5eCc7utrdFze15uHo4O\nVWlYL4DPZ81FYVb20vh/rpgGsLa2RqFQYGlpCYCZmRlKpZJhw4bRr18/GjVqxKZNm4iKisLHxwd7\ne3u0Wi2DBg3C39+fxYsXc/nyZfR6PQCpqalFbWs0mqI2i7dXmFv8XzMzM/R6PTqdjv79+zN06FCe\nf/75h5YxNzcnPz+/XP28fTMBt2cMH8Iq9nZkZhg+RDfjE8jNySU9LaNc7RbnXMWepLvpAGTcy0Zt\naXwm9WTsZS7FxwHgoNFwr6DoNoWrkxMJSckA3M3MxFatBkBlYYFWq+V+Tg4qlQpHe3vUBeNmb2dX\npn64ubqQkGA4kaXfvYutjeHCw1KlIq8ww1IFwOJlK/gz7iajPhpUpgwANzdXbicYLnDS0zOwtS3I\nsSzIuZ+DpUpV5nYLVXN15XZCwoN+2P6lH/cNY1XNzZWEO4b1SLiTiIuz8xOVUWl9cSuekWE8w1JF\nNTe3Yhl3JEMy/j6jsvaRp2W8JEMyKiADwM3Fhdt3Cs7tGRnGz+0qCxKTk7l24w8+HjGUpg3rc+Bw\nVJlyFPrCivB/xPz582nZsiX169enT58+REREMH/+fBQKBbGxsej1erp27UpKSgrbtm3D1dWVw4cP\ns2LFCmbMmIG1tTVZWVmMGTOGZcuWcefOHczNzXFxccHd3R1LS0uCgoJYt24d+/fvL2rv9u3bRT/r\n3bs3kZGRrF27FktLS+zs7Pjpp5+wtramefPmuLq6curUKfr06cOnn35K3759iYyMxNPTkw8++MBo\nv+p7v1ziNQenqoz6ZBBZmdmcO32RWn41mPX5Al5p+xKt2jbH3Nyc/8xbTuzlGwD0H9KTA3uOcObU\nhRJtrZ76kdHc1MwMIn/bjLWlJbWquXP9zm3+1Tao6BaLT1cuY3KPXmTn3GfO+l9wsqtCnlbLwOCO\nmCkevpZzaVLLaEZyWhpzlyxHbW1N3Zo1uHL9BkM/eI+Yi5fYsH0XWp2WdzqF4OPpwRfzF6GxtUFt\nZcWg93qUaEvl6Gg8IyWFmfMWYqNW4+9Xm8tXYxn+4QBizpxl7cYtaLVaer7VjavXrhOx/DsC/f0A\n6NvrPbw9PUq0Z2ZhYTwnOYUZX8035NStw+UrVxkx5ENOnT7D2g2bDDk93sTfrw4ACyO/ofkLz1Ev\nwL9EWwpFyWvhpOQUZsz9Ghu1moC6fly6cpWRQwdzKuYMazf8Sp5WR6933sLfrw5Tpn2JpaUKUDBm\n+BCj62tMZWQ88pxi3x6VyJjzNTY2hRlXGDn0I07F/B97dx4XZb3+f/w1rDLAqKgMhoRLmJq5pdXp\nZMgDmMoAACAASURBVN8Wj520cAE1NctMEJSUTRE1QDRPZi4lqQGuZIvmgopLapq4oqKgthzLJQFF\nBJSlkzA4vz9mQMjBZvjBpHg9/+khMPf7vj6f+3PfF/dM3KfYsGkLpRoNI98YdjvDxgYUNRgvyai/\nGQYuobW+RuT4lQzJuGuGprjI4Ndz8/KZu2gJSjslHR5ty9lz5wgeO4b0Mz+wcet2NJoy3hzijWtz\nF96f9wnNmjhRWFTMxHf9KxrvypTN3Q3mPHDNdH1lqJmuTdU107Wpuma6NlXXTNe26prp2mSomRbV\nqKYZEeL/mzkuoXL8CnFX1TXTta26ZlquxkIIIYQQQtSQNNNCCCGEEELUkDTTQgghhBBC1JA000II\nIYQQQtSQNNNCCCGEEELUkDTTQgghhBBC1JA000IIIYQQQtSQNNNCCCGEEELUkDTTQgghhBBC1JA0\n00IIIYQQQtSQPE68nsg7mVKn27ewsanT7QNYN1TVeYbCyqrOMwAUFpZmyalr2ltldR9SVvcZZjnN\n1ZNTqcLSTGvEsu7v5dSby9utelKH9tbfvQf3F0XdrxFzXBNvXrtW5xm3SkvrPAPAqVN3g1+XO9NC\nCCGEEELUkDTTQgghhBBC1JA000IIIYQQQtSQNNNCCCGEEELUkDTTQgghhBBC1JA000IIIYQQQtSQ\nNNNCCCGEEELUUL1spi9fvvx374IQQgghhHgAmOev85tZREQEcXFxJr0mMzOT2NhYunTpgq2tLX36\n9Kmjvbu7U6dOMWfOHFatWkVGRgZxcXFMnz69Rtu6mpdHTMKXODrY07pFC7xe7gVASvppticfQKvV\nMrD3S3T0eIT5KxKwtrYm+1ouEePGYGNtbVxGbh6frEhA5eBA64db4P3Ky7qMtHS27U1Gixavl3tz\ns6SErd/v03/vFJ9Ofw9314eMy8i5xvwl8ahUjrRp6c7gfq8CcOT4CZJ27karBW/PvrT3aMOMuZ/g\n1KghFpYWTPB9x/ixyrnGvEWf0dDRkTatWjJ4gCcAh4+lkrRjF1q0DOr3Gu3bPkL0nPk4NWqEpYUF\nE/x9jM7IzslhfswSVCoVbVq1ZMjAfrqMo8fZsmMnaLUMGuCJu1sLPvz4U5o6NSb/RgERYSFYG/mH\n9c2RYY6xqshZEqfLaenO4P6v6XKOp5L07XdotVoG9etLe49HiP7oY5wa63PGmDbv5ji25n+2FJWj\ng4GM79Cixfs1XR0z5n2MU6NGWFhYMMF3VK1ndH6sPQDrk7bz8y+/Ej5hnEkZ8xbH0rB8rPpXmved\nu9BqYVC/V/XzsUA375YWTBgz2uiM7JxrzP/0M918tHJnyAD98XvsOFt27NIdv/09cXdz5cOPF9G0\niRP5128QERZ8Tx2/Zl0jdTwnZssww1qvNxl1vg7NcB3JzeOTFat0vYObG959KvcO+9Bqwevf+t5h\n776K730aHWF071CeE5Pwha4PcmuB18v/0m0r/TTb9+3X90G96Nj2EeYvX4W1lTXZ164R8a6/0X0Q\n/MWd6YULFxIREcHMmTMJDw8nNjYWPz8/EhMTmTlzJlOmTGHPnj1cuHCBKVOm8P777xMWFgbAjBkz\nmD17NqGhoZSVlTF6tG4iT548SUxMDBs2bMDHx4fFixeTlJRUZXvr168nMDCQefPmERwcTGxsLP7+\n/mRmZnLw4EGmT59OZGQka9euJSUlBR8fH5YsWUJQUBCXLl3iwoULJCUl3VFPRkYGnp6eREREkJaW\nRnBwMBERESxbtuwvByojIwNvb29iYmIICAhg0aJFhIeHs3v3bi5fvkxERAQzZszgww8/BGDWrFnM\nnj0bPz8/zp8/z8KFC4mOjmb+/PksWLDAYMbZs2c5cOAAlpa3n56XmppKdHQ0Y8aMoaio6C/3s7LE\nXXsY/MrLTHxnJAdPnKTslu7pU19v3c4Uv9FM9h1FQuIW/rh5k50HDlNQVESpRmPSAbTx210MefUV\nJo15hwPHT1BWpsv4cvNWpgb4Ee7vy8r1iXTv1JGId8fyf0/2YKRXf5MWw7qkbQz16kf4hHHsP5xC\nmf6Jeau/2UDExCCmhYxnxZdrKCwqxuu1Vwjy9+G3S5mUajTGZ2xOYpj3AMKDx5N86MjtjDXriAwL\n4b3QIJav/orComK8PfsSPG4MFzNMzNiUxLBBXkwJmUDyocMVGZ+v+YaoyRN5b1IIyz//kty8fEa9\nMZTgAH8c7JVkZRn/TotZMswwVgDrtmxjmFd/wgMDSK4872s3EDkpiPdCJ7D8C928e7/Wh2B/H9Pn\nxBzHVtJ2hg7UZxw5ejtj3UYiJgYyLXg8K75aS2FREV6vvkKQ32h+M7kO4zIAjqef4mJGptHbrsjY\nspVhXgMID3y36nx8s57IiSG8FxLI8i++1s9HX4LH+nLxUkYN1sgApgSPJ/ng7WPr86/XERUWynsT\ng1m++kty864z6o3XCR43Rn/8XjE+wxxr3WxrxAxzYpYMM6z1epNhrnVYt9cRXe/Qh0ljRnPgeOrt\n3mFTElMD/Akf68vK9Rt1vcP4sfzfUz0Y6TXApN4BIHHXdwzu8zITR7/NweMnK3K+3rKNKf4+TB7z\nDgkbN+n6oP2H9H1QmUl9EPxFM61QKOjTpw9Tp04lIyMDX19f2rdvz+TJk1EqlTRp0oQDBw6gVCoZ\nOHAg3bp1IzU1FdA1n82bN2f48OF3bLNcr1698Pf3Jz4+vsr2FAoFPXv2JDg4mOzsbHx9fenVqxfp\n6enExsbi6OiISqXiyJEjAHTs2BE/Pz+USiVWVla4u7vTt29fgzV5eHgQHR3NypUriYqKIjo6mtTU\nVIqLi/9ysFq3bk1AQAD29vb079+fsWPHsn//flavXg2AUqkkIyODy5cv07t3b5555hlUKhVpaWko\nFAp69+5NUFAQ6enp1e6bn59flWbaw8ODiIgIOnfuzE8//fSX+1hZ7vUbODd1AsDR3p6i338HdI/Y\ntbK0xNbGhpLSUrTAB6GBTPXzoaXrQ5z88WcTMq6jbtoUAFU1GaUa3WM+NWVlbN3zfcXda6Mz8vJx\nadZMV4ejA0XFhuto4tSYLh0fY+a8T1BYmPYJptzcfFycnXV1ODpSpD8etFotVlaW2NraUFqe8XhH\nZs5ZUOVYNi4jD7W62V0zSkpKadOqJY+0bkXyocNYWFjg/rDbPZZR92MFkJuXVzHvqsrzDrePrYqc\nx5g592PT58Qcx1ZePi7NdGvE0cGYjIUoLGpSx19nZGVns3f/IYb0f9XkR23n5uXh4qyfDwcHw/Ne\nUnk+FtRgrPJQGzq24E/Hr7v++D2iP35bGJ9hlrVuxjVihjkxS0adr/V6lFHX82GO60h+pd7BoXLv\nUHWsoFLv0Me03gH0fVCTJgA4Vs6h8rlRg1YLH0wKYupYX1q2eIiTP5rWb/3lCNvZ2aFQKLC1tdW9\nwMICS0tLgoKCGDNmDF27diUpKYmUlBRatWpFo0aN0Gg0jBs3jg4dOhAbG8vZs2crTtz5+fkV23Z0\ndKzYZuXtledW/q+FhQVarZaysjL8/PwIDAzk6aefrvIzVlZW3NLffTVEoVDg4OAAUOVColAo7vq6\n8p+pvC82NjZYWFhw69YttFotffr0ISQkhJdeegmtVsuSJUto0KABbdu2rciq/HpjlY/RX9VmiEvT\nJly9lgdAQVERDkolADbWNmg0Gv4oKcHW2pqUtFP8+Os5AJo2akTx//5nQkZTsq9dA+BGpQxba2td\nxs2Sit/w9qUc4/+e6mFSDQAuzs24kpOjq6OgEAd7fR02NpRqNPxx8ya2Njbk5OZx/rdLTAseT/fO\nj3PgyFHjM9TNuHL1qq6OggIc7O11dVTKsLGxIedaLucv/sa0iYF079qZ/YdSTMhwJjs75+4ZtjYA\nxK5I4FJGFhPHG/82vPky6n6symspn/cbhUUV825rY101JzdXN+8hE+jepRP7D5sw7+Y6tnJ0a6Sg\nsJoMa+tKGe/SvXOnOsnYtmsvBUVFfLp0FSdOneGUCRcMF2dnrlwtn4/CaubdWjcfF39jWkigfj5M\nXCPlx1a1GeXH7+dcysxi4vixRm9fl2GOtW6mNWKOOTHTvNf5Wq8vGeZah3V9HWlWqXcoLL7dO9gY\n6B2OHK1R7wD6Pig3F/hzH3Q7x9bGmpT0Sn1Q40YU/258HwSg0N7l9kRMTAzPPfccnTp1wsfHh7i4\nOGJiYlAoFJw7d073+Z9Bg8jLy2PHjh2o1WoOHz5MQkICc+bMwc7OjuLiYsLCwlixYgVXr17FysoK\nZ2dnXF1dKz6bvHHjRpKTkyu2d+XKlYrvjR49mvj4eDZs2ICtrS0qlYo1a9ZgZ2fHs88+i1qtJi0t\nDR8fHyIjI/H19SU+Ph43NzdGjar6mcOMjAzi4+OJiorihx9+YPny5Tg5OeHq6spLL710189MV35t\neHg4oaGh/PHHH8TFxeHr68usWbNwcXHB2toaHx8fQkNDadu2LZmZmXTr1o3i4uKKsSyvqTrl36+c\nGRsbS9euXenRw/ABlXfyzoWSd/0GH69ajdLOjvatW/HLb78xfsQwTv33Fzbv+R5NWRnDX+1DCxc1\nMxbFom7qxM2SUiaNHnlHw2+hv4D9We7163y8fBXKBna0f6Q1v1z8jQkjR3Dq5/+yadceXUa/V2nX\npjXzlq6gf+9etHYzfBfJuqHKcEZePvMWx6JUKunQ1oOz584T5O9D+pkfSdy2A42mjBGDvXBtrmbW\nghiaOjlRWFTMxIAxFSeBcopqPs+Vm5fP3JglKJV2dGjXlrO/nid43BjST//AxqTtaMo0vDlkEK4P\nufD+3E9o1sSJwqIiJo4fe0cGgMLC0kBGHh8tXIy9UqnPOEdwgD/pp8+wYcs2NBoNbw0dzK/nLxC3\n8nM6dmgHgO/IN3GvZszqOkN7q6zOx4qyOzMqchbF6nIe9dDljPUl/cyPbNy6A41Gw5tDvHBt7sL7\n82Nu5wT43ZFT3WmuNo8t7paxJA6lnZ0u4/wFgvxGk/7DjyRu/RZNmYYRg8ozPqVpeR3j/Couxn/F\n2Iz2bR8BICs7m5VffWPwM9MKy7uskUVLUNop6fBoW86eO0fw2DGkn/mBjVu3o9GU8eYQb918zCuf\n92ImvutveI1Y3nlTITcvn49i9Mfvo+XHrx/pp39gQ5L++H1df/yuWk3H9uXH7xuGj18Dc1Lrx291\nY1WbGbeqP7Zqc07qPENr+IZQba71u9dxn2UoDK+RWl2HBq6JtX0dualvmqtkXL/Ox8tWobRrQPtH\n2vDLhYtMePtNfe/wHRpNGcP7v6brHeJX0P/l6nsHgFv6u9h/lnf9Bh+v/FyX00bXo4x/czin/nuW\nzd99j0ajYbhnX10f9OlnqJs04WZpKZN83jZ449OpU3eDOXdtpu9nWVlZJCYmVvlaz5496dixY52+\n1liJiYlkZWVV/NvOzo6RI0fWeHuGmunaVF0zXZuqa6ZrU3XNdK3nGGim70eGmulaV00zXZvMcpqr\nJ6fS6prp2s+p+z8mVW8ub9U00/edapppUQ0DzXStR5jhmmioma5t1TXTte2Ba6YfNNJMG0eaadNI\nM21SSN1nmIE00/cgaaYfTNJMG+3vbqbr5d+ZFkIIIYQQwhykmRZCCCGEEKKGpJkWQgghhBCihqSZ\nFkIIIYQQooakmRZCCCGEEKKGpJkWQgghhBCihqSZFkIIIYQQooakmRZCCCGEEKKGpJkWQgghhBCi\nhszzqCtR5xqo1X/3Lvx/M8cTjCysFXWeAaDVaOo+xKLua7Ewx9PwrOr+6ZrmYI4n+pmDwgxPXQPQ\nmuFpeOY4p5jlKaFmYJY6zPBEytLCojrP0BQV13kGQNnNuj9+rR3s6jzDLP2JGa6Hd43/W9OFEEII\nIYS4j0kzLYQQQgghRA1JMy2EEEIIIUQNSTMthBBCCCFEDUkzLYQQQgghRA1JMy2EEEIIIUQNSTMt\nhBBCCCFEDUkzLYQQQgghRA3Vy4e2XL58mebNm//du/G3u5pzjXmLY2mocqRNS3cG9/cE4PCxVJJ2\n7kKrhUH9XqW9xyNEf7QAp0aNsLS0YMKY0fdWxrVrLPhsKSpHR1q3fJjBnq8CcCT1BEm79oBWi/dr\nfejUoT0A67du5+dffiV8/DijM7JzrjH/089QqRxp08qdIQP66es4zpYdu0CrZVB/T9zdXPnw40U0\nbeJE/vUbRIQFY21l3DIy23ws+oyGjo60adWSwQMqZezYhRYtg/q9Rvu2jxA9Z74uw8KCCf4+JoxV\nDvNilujqaNWSIQP76zKOHmfL9m/RomXwgH64u7Vg9oIYmjZx4vr1G0RMDjV6rCpyFi6ioUqly/Ea\noM85psvR6nI6P96RwqIiImfNZuTwoXR6rMO9lXE1h7mffKrLaN2K1731GSnH2LxtB1qtliFe/Ssy\nImZ8wNsjhtKp42P3VMbtnBhUKhWPtG7F694DATiUcpQtW7ej1cJgrwF06VSeM4u3Rwy/p2rJzslh\nfswSVOVzPlC/1o8eZ8uOnbq1PsATd7cWfPjxpzR1akz+jQIiwkJMW+t1vA4rcsxwTpm/JF53bmzp\nzuB++vPv8RMk7dyNVgvenn1p79GGGXM/walRQywsLZjg+45pGZ8tReXoYCDjO7Ro8X6tL50f05/j\nk/Tn+AnGn+Ov5ubxyYpVqBwcaO3mhneflwFISUtn2959aLXg9e/e3CwpYevefRXf+zQ6AnfXh4zK\nyMnLJ+aLr1E52NPK1ZWB/3oRgKOnz7B9/0EABrz0Am4uLsxZvpKmjRvTWOXIm56volAY/9CRnPx8\nFq35Bkd7e1o99BADXny+4ntHTp0mKXk/0WP9ANh24CA/X7hI4PChRm//al4eMQlf4uhgT+sWLfB6\nuRcAKemn2Z58AK1Wy8DeL/Fwcxdmxy2nmZMTjVWOvDXA0+g6zHHsVuQYsRbd3Vow55NFNHFqzPUb\nN3hvkvHXdqind6YjIiJMfk1mZiaRkZFs2LCBrVu31sFe/bUzZ84wbdo0ZsyYwddff12xTzW1bstW\nhnkNIDzwXZIPp1BWpnvC1epv1hM5MYT3QgJZ/sXXFBYV4/1aX4LH+nLxUgalJjy9zxwZ65O2M3RA\nPyaPH8v+I0dvZ6zbSETIBKYGvcuKr9YCkJp+mt8yMo3edkUdm5IYNmgAU4LHk3zwSEXG51+vIyos\nlPcmBrN89Zfk5l1n1BuvEzxuDA72SrKyrhifYY752JzEMO8BhAePJ/nQ7TpWr1lHZFgI74UGsXz1\nV7oMz74EjxvDxYxM0zIStzB8kBdTQgJJPni40litJSp8EhGTQlmW8AW5efmMGjGMkAB/7O2VZGZd\nNjpDl7OZ4YO9mRIaVDXnK31OmC4HIH7l59g1aGDS9s2V8c3GTQwfMoipk4JJPnCwIiPhy6+ZPjWM\nyPCJLF25WpexIgE7u3szQ5eTyPAhg5g2KYR9f86ZFk7klEksXZUAQNzyVdg1MP3panVdi26tezEl\nZALJhyrN+ZpviJo8kfcmhbD88y91x+8bQwkO8NevdeOPX3OsQzDTOSVpG0O9+hE+YRz7q2RsIGJi\nENNCxrPiyzUUFhXj9dorBPn78NslE88pSdsZOlCf8edz/MRApgWPrzjHH08/xcUanOM3fruLIa/2\nYdKY0Rw4nkpZme4JnF9uSmJqgD/hY31ZuX4j3Tt1JGL8WP7vqR6M9BpgdCMNkLhnL4P+/S9CRo7g\n4Mk0ym7pMr7e9i3hPqOYNGokCZuTSPv5v3R+tC2BI4ahtLMj9YcfTapl8/fJePd6ieA3hnEoPR2N\nfrxO/PQzWTnX+N/NmwDsOXqM/IKCiv0wuo5dexj8ystMfGckB0+cvF3H1u1M8RvNZN9RJCRu4eRP\nP9Ol/aMEjXwDezs7jp/5wegMcxy7YPxazM3P5+3hQwgeNwZ7e3uyLht/bYe/uDO9cOFCcnJysLGx\nobi4mFatWpGamsorr7zCqVOn+P333/nXv/5Fq1atiI2Nxd7enoKCAmbPns2MGTOwsbEhJyeH2bNn\nM2bMGOLj4zl58iT79+/H1dWVrVu30q1bNx5++GFOnDhRsb38/Hz27dvHww8/TEZGBu3atePEiRNM\nmzaNixcvsnPnTm7dukXHjh1xd3cnLi6OJ554gp9//png4GAuXLhAUlISffv2rVJPRkYGY8eOpUuX\nLnh5ebFy5UocHBxo2bIlL7/88l0HKiMjg8DAQJ5//nl++uknOnTowKVLl+jVqxcdOnRg8eLFWFtb\nY2try6RJk5g1axaWlpacP3+esLAwtmzZQn5+Po6OjigUCgIDA+/IWLlyJS1btuS3336jS5cuaLVa\nUlNTiY6OJjMzk7lz5+Lg4GD05Obm5eHi3AwAlYMDRcXFNFSp0Gq1WFlZYmVlSWlJKU2cGtPEqTEz\n5y5AYWHa71dmycjPR+3cVJfh6EBR8e80VDmCFqwsLbGytKSktJTL2VfZe/AQQwf0Y7n+xGtKHWpn\nZ32G4+06oKKOkpJS2rRyByD50BEsLCxwf7iFSRl1Pla5+bgYqqNyRmmljDkLTLojAnAtLw+1WleH\nY5WMSmNVWkqbVi0BSD54GEsLS1o+7GZaTm4earXznTlosbKywsrKihL946KDxvmxZOkKk7Zvrozc\n3Dxc9BkqlSNFRcU0bFg+XuUZJbqMAH8Wxy+/JzNAN14u+kcDqxwr59wer9IS3XgFvzuWxXHL7rla\ncnNvH7/VrRHdWm8JQPKhw/q1bvzxa451COY6x+fj0qx8vd8+/2q12irn34qMeZ/UMEN3jnd0qD4j\nKzubvfsPMdSrHyu+NPEcn38ddVP9dcTBnqLff6eho4PuuNJnlOrXuqasjK17vmf25FCTMvKu30Dt\n5KSrw15J8e+/o3Jw0J1PKjI0/KNLJxZ/tZYFq1ajKSujkQnXdYDcghs4OzXW59jz+//+QOVgT9d2\nj9K13aMcTEsD4IUe3blyLZfV27abtv3rN3BuWl6HfqwcHO6Yj2e6dmHRF18zf0UCGk2Z7rpsbIYZ\njl0wfi22aXn72m5pYYG7m/HXdviLO9MKhYI+ffowdepUMjIy8PX1pX379kyePBmlUkmTJk04cOAA\nSqWSgQMH0q1bN1JTUwFd89m8eXOGDx9+xzbL9erVC39/f+Lj46tsT6FQ0LNnT4KDg8nOzsbX15de\nvXqRnp5ObGwsjo6OqFQqjhw5AkDHjh3x8/NDqVRiZWWFu7v7HY10OQ8PD6Kjo1m5ciVRUVFER0eT\nmppKcXHxXw5W69atCQgIwN7env79+zN27Fj279/P6tW6uyRKpZKMjAwuX75M7969eeaZZ1CpVKSl\npaFQKOjduzdBQUGkp6cb3P7PP//M66+/ztSpU5k3bx4KhQIPDw8iIiLo3LkzP/3001/uY2Uuzs5c\nuZoDwI3CQhzs7QGwtbGhVKPhj5s3sbGxJic3l/MXf2NaSCDdu3Ri/+GUeyujWTOyr14DoKCgCAd7\nJQA2NtaVMmzYtnsPBYVFfLp8JSdPn+HUj8aPl4vameyrV/+iDhsAYld8zqXMLCaOH2v09sFMY6Vu\nxpXyOgoKqq0j55o+Y2Ig3bt2Zv8h4zOaq9VkZ+vqKCi4XYdNpQxb/Vh9tnwVv2VmMtGEt2Or5uhq\nKSgsuGtOTZkjw8VFzRV9xo0bhTg46OfEVp/xx/2RAbrxupKdrcspKLidY3M7x+Yer8VF7Vxx/Fa7\nRmzL13oClzKymGjCR8Z0GXW/DsFc5/hmXMmpvN7Lz79V10hObh7nf7vEtODxdO/8OAeOHDU+Q92M\nKzn6c3xhNRnW1mzbtZeCoiI+XbqKE6dMPMc3a0r2NV3GjcJiHJS6DFsbazQaDX/cLMHG2hqAfUeO\n8n9P9TB62+XUTZuQnZenq6O4GHt9ho21LuNmSQk2NtZcLyzk6c6dCHxzOE4NVTTX/yJhdI6TE1fz\n8ivlVP8OkBatyXW4NG3C1Wv6OoqKKsbKxtpGN1YlJdhaW3O9oJB/dOlE0MgRJtdhjmMXjF+LALEr\nddf20Hf9TcoAsIyKioqq7pspKSm0adMGFxcXtm7diqenJ0ePHiU1NZVVq1bRqVMnbt68ycmTJ7l4\n8SLdunXj0KFDDBw4kFatWuHk5ER8fDweHh4cOHCAfv368dNPP3H16lVUKhVKpRIPDw/Wrl3L/Pnz\nK7ZXVlZGgwYN8PDwYNu2bXh6evLzzz9jaWlJeno606dP59lnn6WoqIiGDRuSm5vLE088wb59++jW\nrRt79+7F09PzjnoKCws5deoUL7zwAtu3b+eFF17A1taWbdu20aNHD06fPo2LiwtWVlZ4eHjc8dr0\n9HReeOEFvvvuO3r27MmtW7c4duwYNjY29OrVi8GDB1NSUsJDDz3EnDlzePrpp9FoNNjZ2VFYWFgx\nlps3bza4f7t376Zfv340aNCAnTt38uyzz3L69GleeOEFTpw4gVqtxtXV1eBclRbduONr7m4tiEv4\nnENHU3myW1e2fLuTp57ohnOzpny2MoE9yQcZNfx1GjdqyPzFcaSdPkPW5WwG9XvV6AtibWZoq3kr\nyt3NlbjVX3Hk+Al6dO3Cll27ebJbF9RNmxKb8AV7Dhzi7aGD6f38czz/z3/Q3sOD7JwcvF7tc8e2\nLKr5DFRLNzdiV37O4aPHeeqJrmzZvpOnundD3awpS5av4rt9+3nnjWEcOZbKV+sTUaDgu337eaR1\nSxo1VN25Qe2dJ7Banw8Dd7Lc3dyIW7maQ0eP82T3rmzZsYunuuszliewJ/kAo4YPpXHjhsxfFEfa\nqTNkXb7CoP6vGcwwdCeg5cMtiF2+ikMpx3iqezc2b9/BUz2eQO3clCVLV/LdvmTeGTGcw8eO89W6\njSgUCnZ/n8wjrVsZHqtq7si1fNitUs4TbN62g6d6dEfdrBlLlq3gu++TeefNN2jWtAkAx06c5GG3\nFqj1d9GMUZsZCovq6/hs2UoOHTnKUz2eYPPW7Tz1ZHecnZuxJH45332/j3feeoNm+rtmx1JPZS/C\ngQAAIABJREFU4u7WArWzaXXUVsbd7pC6P/wwny1dzqEjKTzdozubtm7n6Sd7oG7mzJK4pezeu4/R\nI0dUyjmB+8Nu1dRi+CJfm7UYOqe0fLgFsSsSdGu9eze2bP+Wp7o/oVvry/RrfcQwjlQcv/Ddvrsc\nvwbXeu2uw+r6oVo9pxioA8C9RQviE77g8PETPNmtC1u+3c2TT3TFuWlTYletZs/+Q7w9bAiNG6lY\n8Fk8J0//QOaVbAZ59jX+OtLClfjPv+TwsVSe7NqFLTu/48luXXBu1oTYlV+w58BB3h46hN4vPMcL\n//wH7ds+wtWcawbP8bdKSgxmPOzanGVr1nHkZBo9Oj/O1j3f82Tnx3Fu4kT812v5/nAKb3kPoKlT\nYzbs2MWzT3ansaH5Bm7p3325I6O5C8s3biLl1Bm6P9aB7fsP0uOxDjg7ObF0fSL7jqUy4rW+POTs\nzPINiRw9fYYSjYZ+Lz5vcHvaMsPXRDe1Cys3b+HomR/o3r4dOw4epnv7dljoz9c7Dx+h9z+eBqD4\n9/9x6pdfeKZzJ4PbsrSxNlBHc5at20hK+ml6dHyMbfuS6fH4YzRzcmLpNxv4/uhx3urvyUNqZ5at\n28jR9NOUaErp3+tFgxlWBu68m+N6qMsxbi0eOZ7K1+s36df7ftq0Mnxtt3ZoaDBHodVWs4KAmJgY\nnnvuOTp16oSPjw9xcXHExMSgUCg4d+4cWq2WQYMGkZeXx44dO1Cr1Rw+fJiEhATmzJmDnZ0dxcXF\nhIWFsWLFCq5evYqVlRXOzs64urpia2tLnz592LhxI8nJyRXbu3LlSsX3Ro8eTXx8PBs2bMDW1haV\nSsWaNWuws7Pj2WefRa1Wk5aWho+PD5GRkfj6+hIfH4+bmxujRo2qUk9GRgbx8fFERUXxww8/sHz5\ncpycnHB1deWll14iNjaWLl26VGRX99rw8HBCQ0P5448/iIuLw9fXl1mzZuHi4oK1tTU+Pj6EhobS\ntm1bMjMz6datG8XFxRVjWV7Tnx07dowvvvgCKysrvLy8aNGiBXFxcURFRREbG0vXrl3p0cPwb8u/\nX75Y3TTeN26VGj5B1SbLBrZ1ngHVnwRrVTXNW61GWJrh/1FW1I//dUNhWU/qMNN8aLV1v0bMcU7R\n3iqr8wxumX530VRmqaP6dqPWlBYW1XmGpuiv38muDWU36/74tXYw/f9rMFUD/UfD6pQZrocASvXD\nBr9+12b6fpaVlUViYmKVr/Xs2ZOOHTvW6WuNlZiYSFZWVsW/7ezsGDlyZI23J820caSZNjFCmmmj\nSTNtGmmmTSDNtNGkmTaNNNOmeeCa6QeNNNPGkWbaxAhppo0mzbRppJk2gTTTRpNm2jTSTJumuma6\nfpz9hRBCCCGE+BtIMy2EEEIIIUQNSTMthBBCCCFEDUkzLYQQQgghRA1JMy2EEEIIIUQNSTMthBBC\nCCFEDUkzLYQQQgghRA1JMy2EEEIIIUQNmeGJDMIcrOwd/u5duD/IM4pMYo4HOZSV3KzzDHM89KLe\nMMPDVADqy/PCFIq6f1hEfRkrzDBW1irHepFhNmY4trRlmjrPwAzPHLobuTMthBBCCCFEDUkzLYQQ\nQgghRA1JMy2EEEIIIUQNSTMthBBCCCFEDUkzLYQQQgghRA1JMy2EEEIIIUQNSTMthBBCCCFEDUkz\nLYQQQgghRA3V+2b68uXLf/cuCCGEEEKIeqrePwExIiKCuLg4k16TmZlJbGwsXbp0wdbWlj59+tTq\nPsXExHD9+nXy8/N566236NSpU61uv1z21RzmfrwQlUrFI61b8fogLwAOHTnKlm3b0Wq1DPYaQOfH\nOxL1/gfY2yspKSllWljog5nxScztDO+BuoyUo2zZuh2tFn3GY/oMe0pKS5k2KeSBy6jIWbiIhipH\n2rRqxeveAwA4nHKMzdt3oNXCkIH96Px4RwqLioiY+QFvvzGMTh07GJ1xNeca8xZ9RkNHR9q0asng\nAZ66jGOpJO3YhRYtg/q9hrtbC+Z8sogmTo25fuMG700KxtrKuFPb1ZxrzFscq6ujpTuD+1fK2LkL\nrRYG9XuV9h6PEP3RApwaNcLS0oIJY0abVkd9yVgSp5uPlu4M7v+aLuN4KknffodWq2VQv776jI9x\natwISwsLJox5x6SM+UviUZXX0e9VAI4cP0HSzt1oteDt2Zf2Hm2YMfcTnBo1xMLSggm+915GXY+V\nOWuRjAcw47OlqBwdDGR8hxYt3q/1pfNj7QFYn7Sdn3/5lfAJ4+6pDHPmGH1neuHChURERDBz5kzC\nw8OJjY3Fz8+PxMREZs6cyZQpU9izZw8XLlxgypQpvP/++4SFhQEwY8YMZs+eTWhoKGVlZYwerTuB\nnzx5kpiYGDZs2ICPjw+LFy8mKSmpyvbWr19PYGAg8+bNIzg4mNjYWPz9/cnMzOTgwYNMnz6dyMhI\n1q5dS0pKCj4+PixZsoSgoCAuXbrEhQsXSEpKuqOejIwMPD09iYiIIC0tjeDgYCIiIli2bNlfjkVG\nRgbe3t7ExMQQEBDAokWLCA8PZ/fu3XfUX1JSwqhRo8jOzmb8+PFcunSJ1q1bM23aNN5++2127dp1\nx/Z/+uknFixYQElJCU899RQ3b94kJiaGn3/+2djpAuCbDYkMf30w08JC2XfgIGVluudtJnz5FdOn\nhRM5JYylKxM4ejwVN1dXJgVNwKlxI06mn3rwMjYmMnzIIKZNCvlTxtf6jEksXaXPaOHKpKDxODVq\nxMn00w9cBsA3iZsZPsSbqRODST546HbOV2uYPiWMyMmhLF21GoD4FQnY2dmZtH2AdZuTGOY9gPDg\n8SQfOlKRsXrNOiLDQngvNIjlq78iNz+ft4cPIXjcGOzt7cm6fMX4jC1bGeY1gPDAd0k+nHI745v1\nRE4M4b2QQJZ/8TWFRcV4v9aX4LG+XLyUQanG+Mfj1p+MbQzz6k94YEDVjLUbiJwUxHuhE1j+xRp9\nRh+C/X24mJFpWkbSNoZ69SN8wjj2V6ljAxETg5gWMp4VX+oyvF57hSB/H367dA9mmGGszFaLZDyA\nGdsZOlCfceTo7Yx1G4mYGMi04PGs+GotAMfTT3ExI9PobZszw5w5RjfTCoWCPn36MHXqVDIyMvD1\n9aV9+/ZMnjwZpVJJkyZNOHDgAEqlkoEDB9KtWzdSU1MBXfPZvHlzhg8ffsc2y/Xq1Qt/f3/i4+Or\nbE+hUNCzZ0+Cg4PJzs7G19eXXr16kZ6eTmxsLI6OjqhUKo4cOQJAx44d8fPzQ6lUYmVlhbu7O337\n9jVYk4eHB9HR0axcuZKoqCiio6NJTU2luLj4L8ejdevWBAQEYG9vT//+/Rk7diz79++/o34bGxve\ne+89RowYQd++fXFzc6NPnz5cu3aNpUuX8sYbb9yx7Xbt2nHhwgWOHz9O165dOXr0KOfOnePRRx81\ndroAuJabi4taDYDK0ZGiIl1dWi1YWVlha2tLaUkp13JzUaudAVA7O5Nz7doDmJFXTYb2Txl5qPU/\np3ZuRs61nAcuAyA3NxcXZ+e75pSUlgIQFOCPm+tDJm1fl5FfNaO4coYltrY2lJaW0qalO4+0bkXy\noSNYWljg7tbC+Iy8PFycm+kyHBwMZ5SU0sSpMV0ef4yZcxegsDDt03H1KqOZPsPRgaLi33UZgJWl\nJbY2uvm4nfFxlXO8cRn5FRmOlTO02oqMkvKMjo8xc94nNajDHBl1P1bmq0UyHsyMproMh+ozsrKz\n2bv/EEP6v4pWq73nMsyZY9II29nZoVAosLW11b3YwgJLS0uCgoIYM2YMXbt2JSkpiZSUFFq1akWj\nRo3QaDSMGzeODh06EBsby9mzZyt2ND8/v2Lbjo6OFdusvL3y3Mr/tbCwQKvVUlZWhp+fH4GBgTz9\n9NNVfsbKyopbt25VW4tCocDBwQGgysApFIq7vq78Zyrvi42NDRYWFty6deuO+kHXeKjVai5evAjA\n8ePHmTt3LhERETjrm4U/e+ihh9i0aRPBwcGsW7cONze3u+6TIc1d1FzJzgbgRkEhDg72ALoTuUbD\nH3/cxMbWhuYuLmRfvQpA9tWrOOsX6gOVoa6cUWA4w8aG5i7qShk5D2QGgItazRX966vLsbWxMWmb\nd2Y0q5ph/6eMm7paAGJXfs6lzCxC3/U3LcPZmStXdb9I3CgsrCbDmpzcXM5f/I1pIYF079KJ/YdT\nHrwMtTNXcsozinCwV+ozrKvMR05uLud/u8S0kAn6jKMm1NGsIqOgoLAiw6ZSHbY2NuTk5ukygsfT\nvfPjHDhyj2WYYazMVotkPHgZ6mZcydHdjCoorCbD2pptu/ZSUFTEp0tXceLUGU79+NM9lWHOHIXW\nyBY8JiaG5557jk6dOuHj40NcXBwxMTEoFArOnTun+wzYoEHk5eWxY8cO1Go1hw8fJiEhgTlz5mBn\nZ0dxcTFhYWGsWLGCq1evYmVlhbOzM66urhWfTd64cSPJyckV27ty5UrF90aPHk18fDwbNmzA1tYW\nlUrFmjVrsLOz49lnn0WtVpOWloaPjw+RkZH4+voSHx+Pm5sbo0aNqlJPRkYG8fHxREVF8cMPP7B8\n+XKcnJxwdXXlpZdeuutnpiu/Njw8nNDQUP744w9iY2N58skn76g/JCSERYsWER0dTf/+/QkMDOQf\n//gHCoWCp556Cm9v7zvG+/jx48yZM4evvvoKT09PZs2aRceOHaudn5KC3Du+di03jznzP8HeXslj\n7dvx319+ITRwPGnpp9iwaQulGg0j3xhGh3aPEv2fD3XNj0JBWPAEYw6J+zOjmsP9Wm4ecxZ8gr2y\nPONXQgPfJS39NBs2baZUU8bIN4bezrC1AWpQx32Wob1VZvDrubl5zPk4Bnt7JR3atePsL78SMmEc\naadOs2FzEhpNGW8NG0KHdrp3UxbHL+fZfzzF44/d+ZnpspKbhjPy8pkbswSl0o4O7dpy9tfzBI8b\nQ/rpH9iYtB1NmYY3hwzi1wsXiF/1BR3b67J83nrjzrvTtwzPe25ePnMXLUFpp6TDo205e+4cwWPH\nkH7mBzZu3Y5GU8abQ7xxbe7C+/M+oVkTJwqLipn4rn9Fw/pX7rsMreGbCbqMWN18POqhm4+xvqSf\n+ZGNW3eg0Wh4c4iXLmN+jD6jiIkBfgbrMHTpyc3LZ97iWJRKJR3aenD23HmC/H1IP/Mjidt2oNGU\nMWKwF67N1cxaEENTJ30dAWNMGqvazDB0R9kcY3W/jpdk3GMZ1a3DJXEo7ex0GecvEOQ3mvQffiRx\n67doyjSMGORF+7aPAJCVnc3Kr74x6XPG5sioixz7Fm0Mft3oZvp+lpWVRWJiYpWv9ezZ867NaW28\n1liLFy+u8u82bdrQu3dvk7ZhqJkWBtT/w71WVddM16bqmulaVU0zLQyoppmu9Zh6shZr8vEMU9WX\nsRL3IDm2TPJAN9MPAmmmjSSHu0mkmX4ASTNtEmmmxX1Nji2TVNdM1/u/My2EEEIIIURdkWZaCCGE\nEEKIGpJmWgghhBBCiBqSZloIIYQQQogakmZaCCGEEEKIGpJmWgghhBBCiBqSZloIIYQQQogakmZa\nCCGEEEKIGrL6u3dA3B+0ZZq6zzDHgzXM9ECK+sIcD4tQWFjWeYbcNrj31P2jTuoPGSvjmeMhOuYi\nD+u5f8glRgghhBBCiBqSZloIIYQQQogakmZaCCGEEEKIGpJmWgghhBBCiBqSZloIIYQQQogakmZa\nCCGEEEKIGpJmWgghhBBCiBqSZloIIYQQQogaum+b6cuXL//duyCEEEIIIR5w9+QTEBcuXMhzzz1H\n586dAbh27RqbNm3C29sbf39/Bg4cyPbt24mLi7vrdkaPHk18fLzB7aelpdGoUSNyc3MJDw+nbdu2\nf7lfs2fPJiwsrGZFVZKRkcE777xDp06duHXrFo0aNWLq1KlYWNTu7zbZV3OY+/FCVCoVj7RuxeuD\nvAA4dOQoW7ZtR6vVMthrAJ0f70jU+x9gb6+kpKSUaWGhpmUsXERDlSNtWrXide8BABxOOcbm7TvQ\namHIwH50frwjhUVFRMz8gLffGEanjh2Mz8jJYd7CRTRUqWjTqiVDvPQZR4+xZfu3ujoG3M6InDWb\nkcOH0ukxEzNilujraMmQgf31Gcd1Gegy3N1aMHtBDE2bOHH9+g0iJodibWXcMqpPGfNjlqAqn4+B\n/W5n7NgJWi2DBnji7taCDz/+lKZOjcm/UUBEWIjRGebKkQzJuF8z6lMt5sqQ8++DlWHOnHvmzvS3\n335LREQEISEhnDx5khUrVjB58mRiYmK4efMmFy9eZP369fzvf//j+vXrXLhwgZMnT7J69WpmzpxJ\nWFgY6enp/PDDD7z77rvMmjWLwsJCg1kKhYJ3332Xjz76iCFDhrB3715SUlKIjY0FIDIykszMTBYu\nXMgHH3xAYGAgN27c4OzZswC88sorxMbGMn78eC5dusTBgweZPn06kZGRrF27lkuXLjFx4kQiIyP5\n4osv7vi3QqHg6aefZs6cOcydO5fHH3+cDRs2cOLECSIiIpg+fTqzZ8/m3LlzREZGArBo0SLS0tJM\nGtNvNiQy/PXBTAsLZd+Bg5SVlQGQ8OVXTJ8WTuSUMJauTODo8VTcXF2ZFDQBp8aNOJl+yviMxM0M\nH+LN1InBJB88dDvjqzVMnxJG5ORQlq5aDUD8igTs7OxMqgFgXeJmhg/2ZkpoEMkHD1dkfP7VWqLC\nJxERFsqyhC90GSs/x65BgxpkbGH4IC+mhARWzfhanzFJl5Gbl8+oEcMICfDH3l5JZpbx75DUm4xN\nSQwb5MWUkAkkH6qUseYboiZP5L1JISz//EtdxhtDCQ7wx8FeSZYJGebKkQzJuF8z6lMtZsmQ8+8D\nl2HOnHvmznR2djY2NjY8//zzHDx4kOeff55nn32Wd955hwEDdHci//Wvf3H+/HneeecdDh06RNu2\nbXnvvfd44YUXsLKy4sCBA/z666/85z//wd7enpEjR1abt2jRIho1asSJEyeYO3cu//vf/+74mczM\nTFq3bs2LL75YpQl0cnLC19eXxMREjh8/zsaNG+nUqRNarZYjR47Qrl07iouLefHFF+nQoQPXr1+v\n8u8/e/zxx/nmm2/o0aMHnp6eXL58mWXLlhEWFkZBQQG5ubn8+OOPjB071qQxvZabi4taDYDK0ZGi\nomIaNlSh1YKVlRVWVlaUlpRyLTcXtdoZALWzMznXrhmdkZubi4uzs4EMbUVGSWkpAEEB/iyOX25S\nDbo68ir2z9HRkaLiYhqqVGgxkDHOjyVLV5iekZeHWt3szgwtWFlZYmVlSUlpKW1atQQg+eBhLC0s\nafmw2wOXkZt7O0NVJUN7O6OkUsahw1hYWOBuQoa5ciRDMu7XjPpUizky5Pz74GWYM+eeuTPdrVs3\nRowYwfnz57G2tsbR0RHQ3UWujlarpVGjRgQHB/PWW2/x2GOPoVAo0Gq1AFhaWlb72rFjxzJ79mxW\nrVrFnDlzsLS0RKPRAHD9+nUAXn/9dZ555hnWr1/PwYMHK15b3lhbW1uj1WopKyvDz8+PwMBAnn76\naZo0aUJISAharZbo6Og7/v1nqampPPLII6xatYpz587Rvn17bG1tK/YhJCSEV155xZThBKC5i5or\n2dkA3CgoxMHBHgBbGxtKNRr++OMmNrY2NHdxIfvqVQCyr17FuVkzozNc1Gqu6F97o6DAYIatjY3J\n+16lDrWa7GxdRkFhAQ72ugyb8oybtZWRo8soKLxrxmfLV/FbZiYTJ4x7IDNc1M4VGTcKbs+HbaUM\nG1tdRuyKBC5lZDFxvGkZ5sqRDMm4XzPqUy3myJDz74OXYc4chba88/ybbdu2jZ07d6JSqUhPTycq\nKopOnToxevRopk+fTmxsLL6+vsTFxREVFcWIESMYPXo0P//8MxcvXqSwsJCAgAC0Wi2LFy/G2dmZ\nH3/8kYSEhDuyYmJiOHnyJE5OThQVFfHKK6/w4osvMmHCBFq2bMmPP/7Ihx9+yJdffklhYSElJSX4\n+/szY8YM4uLiKj6LvXXrVm7evEmzZs1Ys2YNdnZ29OzZkw4dOvDRRx/h7u6Og4MDr7zySpV/9+/f\nn1GjRlXczba3t2fatGmsWrWKs2fPolQqOXnyJOvWrUOhUODt7c2aNWvu+pnqkoLcO752LTePOfM/\nwd5eyWPt2/HfX34hNHA8aemn2LBpC6UaDSPfGEaHdo8S/Z8PdYtcoSAseMId29KWaQzm5ubmMefj\nGOztlXRo146zv/xKyIRxpJ06zYbNSWg0Zbw1bAgd2j0KwOL45Tz7j6d43MDnmbW3DB+KuXl5fPTJ\npyiVSh5r9yj//eVXQsaPI/3UGTZsSUKj0fDWsNdp/6juc+9Llq7gn/94isc7tL9zY9pbd8lYdDvj\n118JeXcs6afPsGHzVjRlGt4aOoRfzl8gbsXndOzQDoAxb7+Ju1sLg9usDxmGTg+5eXl8tHAx9kol\nHdq15eyv5wgO8NdlbNmmm4+hg/n1/AXiVt7O8B1pfB3mypEMybhfM+pTLbWZUd0NODn/1t85N2eO\nXTNXgzn3TDNdVxITE8nKyqr4t52d3V0//nEv0Wg0hIaG8q9//Yu+ffve9WcNNdO1qbpmulYzqmmm\nazmk7jPqkXp+ehBC1DN3ezf7fiPn33vPA9tMPyikmTY6pO4z6hE5PQgh7ifSTIu6VF0zfc98ZloI\nIYQQQoj7jTTTQgghhBBC1JA000IIIYQQQtSQNNNCCCGEEELUkDTTQgghhBBC1JA000IIIYQQQtSQ\nNNNCCCGEEELUkDTTQgghhBBC1JDV370DopbU9R93N8sfwpc/UH+vqU8PQKhr5njAgjnmQx4UIeqK\nWc4nivpzj9DC0gzr3RwPSzOHv/mBbPXnqBNCCCGEEMLMpJkWQgghhBCihqSZFkIIIYQQooakmRZC\nCCGEEKKGpJkWQgghhBCihqSZFkIIIYQQooakmRZCCCGEEKKGpJkWQgghhBCihup9M3358uW/exeE\nEEIIIUQ9ZRkVFRX1d+9EXQoODsbT09Ok12RmZjJ37lyuX7/O+fPn8fDwqKO9qz1lN3+/42vZV3OI\n/s+HHDmWStblK3Ts0B6AQylHWRK3jN179tGkSRPUzs2Iev8Djh4/wZ7k/Tz3z2cMJBh+SpIuYw4p\nf8o4nHKMxfHL2b13H02bOOGidqawqIjJ70XzUHM1amdnAxHVZOTkMGP2Rxw9fqJqxtFjLFm2gt3f\n76Op0+2M8KgZNHdxQe3czPg6cnKY8eE8jqaeIOvKFTq2b6fPOM6SZSvZvS+Zpk2caGBry/TZc0k7\nfYZt3+6m5zNPY2lh3O+kkmF8Rn2qJTsnh5kfziMl9SRZV7KrZixfxXffJ9NEnxH94TzSTp1m687v\nDGZU9wS5+60Oyfj/z7gfazHL8Xu3jFq7jtx9vGorp9rxuppD9AcfkXLcwHV36XJ2702uet2NiOah\n6mqp5gGI5hiv+/HabqV0NJhj9KpduHAhERERzJw5k/DwcGJjY/Hz8yMxMZGZM2cyZcoU9uzZw4UL\nF5gyZQrvv/8+YWFhAMyYMYPZs2cTGhpKWVkZo0ePBuDkyZPExMSwYcMGfHx8WLx4MUlJSVW2t379\negIDA5k3bx7BwcHExsbi7+9PZmYmBw8eZPr06URGRrJ27VpSUlLw8fFhyZIlBAUFcenSJS5cuEBS\nUtId9WRkZODp6UlERARpaWkEBwcTERHBsmXL/nIsMjIy8Pb2JiYmhoCAABYtWkR4eDi7d+/m8uXL\nREREMGPGDD788EMAZs2axezZs/Hz8+P8+fMsXLiQ6Oho5s+fz4IFC+7YfllZGQEBAQC8/fbbHD58\nmJSUFBISEoydLgC+2ZjI8CGDmDYphH0HDlJWVgZAwpdfM31aOJFTJrF0VQJHj6fi1sKVSUHjcWrU\niJPpp03I2MTwIYOYOimY5D9nTA0jMnwiS1euBiB+RQJ2dg1MqgFgXeJmhg/2ZkpoEMkHD1dkfP7V\nWqLCJxERFsqyhC90GSs/x65BTTK2MHyQF1NCAqtmfK3PmKTLyM3LZ9SIYYQE+GNvryQzy/h3PiTD\ntHeJ6kst6zYlMWyQF1NCJpB8qFLGmm+ImjyR9yaFsPzzL3UZbwwlOMAfB3slWQ9iHZJhdEZ9qsU8\na73uryPmyvkmcTPDh3gzdWIwyQcP3b7ufrWG6VPCiJwcytJVla+7dvdkHfXl2g4mNNMKhYI+ffow\ndepUMjIy8PX1pX379kyePBmlUkmTJk04cOAASqWSgQMH0q1bN1JTUwFd89m8eXOGDx9+xzbL9erV\nC39/f+Lj46tsT6FQ0LNnT4KDg8nOzsbX15devXqRnp5ObGwsjo6OqFQqjhw5AkDHjh3x8/NDqVRi\nZWWFu7s7ffv2NViTh4cH0dHRrFy5kqioKKKjo0lNTaW4uPgvx6N169YEBARgb29P//79GTt2LPv3\n72f1at0BrFQqycjI4PLly/Tu3ZtnnnkGlUpFWloaCoWC3r17ExQURHp6+h3btrS0xMnJiezsbBwd\nHTl48CB79+6ld+/exk2W3rXcPFzUagBUjo4UFenq0mq1WFlZYWtrS2lJKddy81Drf07t3IycazlG\nZ+Tm5uGi1t1lVqkqZ1CRUVJaAkBQgD9uLVxNqqG8DrU+w9HRkSL9/GjRVsoo1WWM88PNtQYZeXmo\n1c3uzNCClZUltrY2lJSW0qZVSzxatyL54GEsLSxp+bCbZNRBRn2qJTf3doaqSob2dkaJLuOR1q1I\nPnQYCwsL3B/AOiTDtDVSX2oxy1o3w3XEXDm5ubm46N/dre7aXpER4I+b60P3ZB315doOJn5m2s7O\nDoVCga2tre7FFhZYWloSFBTEmDFj6Nq1K0lJSaSkpNCqVSsaNWqERqNh3LhxdOjQgdjYWM6ePYtW\n/3Z+fn5+xbYdHR0rtll5e+W5lf9rYWGBVqulrKwMPz8/AgMDefrpp6v8jJWVFbdu3aoLBmSYAAAg\nAElEQVS2FoVCgYODA0DF/pR//W6vK/+ZyvtiY2ODhYUFt27dQqvV0qdPH0JCQnjppZfQarUsWbKE\nBg0a0LZt24qsyq835IUXXmD+/Pl4enqSlZVFTk5ORcNrrOZqNVeyswG4UVCAg4M9ALY2NpRqNPzx\nx01sbGxo7qIm++pVQPf2kXMz49+mcXFRcyVb99obNwpvZ9jezrC1sTFpvw3Vka3PKCgswMFel2FT\nXsfN2srQ/RJRUFB414zPlq/it8xMJk4YJxl1lFGfanFRO1dk3Ci4ffzaVsqwsdVlxK5I4FJGFhPH\nP5h1SIZp6kst5lvrdXsdMVeOi1rNFf01u7pr+/1z3b3/r+1gwmemU1JSaNmyJWq1ms2bN+Pp6cnR\no0fp0aMHX3/9NXv27KF79+5YWFhw5MgRfvvtNy5cuMCrr75KbGwsFy9exNLSkn//+99cvnyZTZs2\ncfHiRezt7VGpVFhZWeHh4YGtrS3x8fEV2ysqKqr43qZNm/D09OSnn37CysqKf/7zn3z88cfs27eP\n1q1b4+DgQPb/Y+/e46Iu8/6PvzjEQcVUhBkPgGZ6V2q7t2BY7Vru6t7d2/1jt7WDpW7e6ppmmeIR\nj4Ssmrr12LTEA6CSRltZiJtpoQIekHPWWrvdrdoBHQ6mYCmgzu+PQdScsZkJBhjfz/8YZq7397q+\nh/nMdy64TCbCw8PZs2cP4eHhfPHFF/z73/+uL8wvqayspKioiPvvv5/Q0FD++te/UlBQwG233cZt\nt91GQUEBRqOxPtvWazMyMrj33ns5f/48hYWF/O///i+vvPIKhYWFfPvtt0RGRrJjxw4qKyv5/PPP\nad++PbW1tfVjealPP9S5c2diY2OZPXs2xcXF3HLLLdf04UrW5kyHhYayOjGZAwdzGdA/gq3vvc+A\nu/pjCAomYW0iGXuyGDtqJHf26c227TvIycujpqaWob+3Nsfc+nykbqEhrE7awIGDeUT2Dyf9vfeJ\nvCuC4OAgEtYlsyszizFPjiCoY0cA8guLCQvpamPulu2MNckbOZCbT2REOOnbdxDZPwJDUBAJSevZ\nlZnNmD+OIKhjoCWjqJjQkK4YrH4osJXR9YqMfqS/v4PI/uEYgjuSkLiBXVnZjBk5nJz8AlLffhcP\nDw8yMrO59ZbutLu5rdU2leF8hjv1pVtoV9asTyEnr4DIiH5se38nkRHhGII6kpC0kV1Zexkz8gkO\n1mfArizrGbbmULa0fijjp2e0xL645Pi1mdGQ7yPXG6+Gy7E9XnXvu7l5REZEkP7ejsvvu4nJdRnD\n7Xzfbfx+uGKsXPXebmvOtIfZbKOCcSMlJSWkpaVd9dgvf/lL+vTp06ivtVdaWholJSX1P/v7+zNq\n1CiH2qg5Xd5g22ON2Xz9u/UNknGh8TNwQT/kxuSKS6mtN9eGdAO8JUgTccXxi4f7/JMyD08XnO8X\n3eR8d9F7u19H61Nmbohi+kagYtreEBXT0jhUTItcn4ppx6iYdkATF9Puc9SJiIiIiLiYimkRERER\nESepmBYRERERcZKKaRERERERJ6mYFhERERFxkoppEREREREnqZgWEREREXGSimkRERERESd5N/UG\nSMO4UH22cQNc8I/w9Q/9pSVzxeHrCu7SD5GWzm0WVHGFJn5vV2UhIiIiIuIkFdMiIiIiIk5SMS0i\nIiIi4iQV0yIiIiIiTlIxLSIiIiLiJBXTIiIiIiJOUjEtIiIiIuIkFdMiIiIiIk5yu2L6+PHjTb0J\nIiIiInKD8IqNjY1t6o2w14oVK/Dz88NoNAJQXl5OamoqPXr0YPTo0QCsXbuWqKio67YzduxYq89Z\nuXIlvr6+9e3bet6P2bRpEykpKXzwwQfs3LmTvn37EhAQwKRJk/jHP/7B6tWr2b9/Px988AHbtm2j\nZ8+edOjQweGcK53/rvKax0xl5cQve4ncwmJKTpygz+23AZCTX0BCcgq7svYS2KEDfr4+xC19iY8+\nOcx7H+zil/dE4uX5g89ZNpZFM5WVEb/0xboM0+WMvAISkjeyKzObwMAO+Pn6Erf0RT76+JO6jAHX\nZNhaAdFUVsbCpS+SV1h0dT/yCkhI2kBGVjYd6zKef+EvfPTJP9i+M8NqxvX6sfCF5eQVFFFy/AR9\n7ri9LiOfhKT1ZGRm0bFDB/z8fHl+yXI++vgTtn/wIb+85+5rM67D3hyjIZiqM2eIiV1IJ6MRQ3CQ\nMpTRpBnu1BdlKEPniDKcyfBu1cbq483+zvTOnTuZP38+U6dOpbi4mPXr1zNr1ixWrlxJdXU1x44d\nY8uWLZw9e5ZTp05x9OhRiouL2bRpE/Hx8cycOZNDhw5x+PBhnn32WRYtWkRVVZVD25CYmFi/DZ99\n9hkrV67ko48+AiwFN8DgwYOZO3cueXl5lJaWsmzZMpYtW8Zzzz3HwoULycnJ4dNPP6V3797cdNNN\n9b//85//zEsvvURNTQ3z5s3jhRdeYPz48Zw6dYpx48Zx7tw5vvnmGxYuXOjw2L299e888chDzI6e\nRPb+g1y4cAGA1954m9iZ05g3PZrkTa9TcfIUo0cMI3riU7Rp3YqSkhMOZgxl9tTnyD6Qcznjb28R\nO2s682ZMJfm116k4+S2jRzxO9DMT6jLs/wbh7bRtDH9kKLOnTiZ7/xUZb7xJbMwM5s+YRlLKZkvG\nyCeY+swEWrduxTcOZaQz/NGHmT1tytUZqXUZM3+Q8ezTtG7lWIYjOQDrNryGv5+fQ+0rQxmNleFO\nfVGGMnSOKKMh93uzL6ZNJhM+Pj48+OCDdO/enaFDh7JkyRKKiorqnzNkyBD69u3LmDFjCAsLo1ev\nXqSmptKqVSvat2/Pvn37SEpKYvHixcTExOB3ncF69dVXiYmJISYmhiNHjnD27FmKi4uJi4tj3rx5\nrFq1Crj2LmpgYCDx8fEcOnSIAQMG1D8eEhJCdXU1AwYMICwsjP/+7/++6nUBAQHU1tZy4cIFfve7\n3xEZGcmFCxf4/PPPGTp0KGlpaWzevJnhw4c7PHYVJ09iCA4GoG1AAGe++w4AM+Dt7YWvrw81NbX0\n6B7Grbd0J/vAQTw9PQkL7Wp/RsVJDIagazPM5h9kdKvLyKnLCLE7o/zk5YyAqzKu6EetJaPnLd3J\n3p+Dl6cX3RzJqDiJwRB8bQZmvL298fX1vZzR4xZLhpdjGY7kAEyZOJ6QLl0cal8ZymisDHfqizKU\noXNEGQ2535t9Md2vXz9GjhzJkSNHuOmmmwgICABsTwkASyHXrl07oqOjefLJJ+nduzceHh6YzWYA\nvLy8bL524sSJLF68mMWLF9O9e/f611zKNJvNeHl5UVNTg9lsprLSMr3i0nbdcccd5OTk1L/mxIkT\ntGrVymbeqVOn8PT05PPPP2fz5s3cfPPNdOvWDbPZzJAhQ8jOzqa0tJRbbrnFjtG6mtEQjKm0FIDT\nVVW0ad0aAF8fH2rPn+dcdTU+Pj4ArFn/Gl99U8L0SU87nmEqs2RUVlrP8L2UkcJXX5cwfdJEhzI6\nGQz1GZWVl/vhc0WGb10/Vidv5MtvvmH6c85kWMaqsqryRzI28OXXXzP9uWccynA0x1nKUEZjZLgq\nRxnKaKkZrspRRvPKgBYwZ7qgoIDNmzdTXV1NYWEhgwYNwmAwsHXrVgYNGkRBQQHh4eEUFhZy//33\n8+abb2I0GmndujVbtmwhIyODX/3qV/Tr14+lS5dSVFSEyWTiD3/4wzVZubm5hIaG1s+ZTk9PZ+jQ\noZSUlPD3v/+dPXv28NRTT9GlSxdWrlzJJ598QlVVFb///e/ZunUrUVFRhISE8Nlnn5GSksKePXvI\nzs4mJiaGNm3akJ6eTlRUFFu2bGHfvn1kZGTw/vvvM2XKFHx9ffnwww85deoU//rXv+jVqxe33nor\nn376Kb/+9a8JDQ297jhZmzPdLSSENRteIyevgMjw/2Tb+x8QGdEPQ1BHy3zmrL2MGfEEB/MLSd2S\nhgce7Mray623dKPdzW2vbszGh5duoV1Zsz7FkhHRj23v7yQyItySkVSXMfIJDuYXkPr2u3h4wK6s\nbG69pfs1GbY+IHUL7cqa5I0cyM0nMqIf6e/vILJ/OIbgjiQkbmBXVjZjRg4npz7Dg4xM6xm2+xFy\nRUY46dt3ENk/AkNQEAlJ69mVmc2YP44gJy+f1LffwQPIyMzi1h63XJtxHfbmBHUMBCC/qJjQkK4Y\nguyfI6YMZTRGhjv1RRnK0DmiDGcybM2Z9jBfeev1BpKWlkZJSUn9z/7+/owaNarpNsiKzZs388kn\nn7Bo0aIffe7Z0q8ad2M8Gv9LjOt929BwIc3+yxgRERFphvwCjVYfv2GLaXejYtreEBXTIiIi4jhb\nxbQqCxERERERJ6mYFhERERFxkoppEREREREnqZgWEREREXGSimkRERERESepmBYRERERcZKKaRER\nERERJ6mYFhERERFxkndTb4A0DC+/Vo0b4IK1fczmi42e4U48XLAAjfaJA1xxjlx0nzW2zBcvuEWG\nK7hkQSt34YoFxjy9Gj3DrbjifaSJF2TTnWkRERERESepmBYRERERcZKKaRERERERJ6mYFhERERFx\nkoppEREREREnqZgWEREREXGSimkRERERESepmBYRERERcVKDFdM1NTVUVFQ0eRsiIiIiIq7iFRsb\nG9sQDW3dupWvvvqKdevWUVRUxLZt27jvvvvw8rJ/paCtW7dy4sQJevbs6dQ2vP322/j6+tKhQwen\nXn/J2LFjCQ8P5y9/+QuDBg36SW25yoXqs9c8ZiotI27RCxzML6Dk+HH69L4DgAMH80hYl0TG7kwC\nAztgCA4m9s9LyCsoZHfWXgb+4h67c02lZcQtXsrB/EJKjp+gzx23WzJy80hYm0TG7iwCAwMxBAfV\nZRSxO3svA++1lmF9dTdLxjJyf5CRk5vPqnXJZOzJomNgB4yGYKrOnGHWvDg6dzJgCA62ux+uymnI\nDFurotm7Ty5nxNK5k9FGP5pun7hVxpLl5BZYyUhMJmNP9tUZ8+PobDRiCA66tjEbCyCayspY+MJy\n8gqKrs7IyychaT0ZmVl07HA5IyZ2IZ1sZVyvLw2ZY2XFSFNZGfFLXyS3sJiSEyb63H5bXfsFJCRv\nZFdmNoGBHfDz9SVu6Yt89PEnvPfBLn55zwC8PK3cG7KSUVpWzsLlL5FXWMzxEyZ63/4floz8QlYn\np7Arey+BHSwZC5e9xEef/IPtH+7iF3dHWs+woqEzbJ7rZeXEL3upbrxOXB6v/AISklPYlXUpx4e4\npS/x0SeH68bL/r60uAybY9Vwx9b1Vp91xbnY8jKsn+sLl75IXmHR1fs8r4CEpA1kZFmui36+vjz/\nwl8s58jODNvn+nX2e0OOlXerNlYfb7A70/v37+eDDz5g3LhxzJw5kzZt2vD1118zYcIEXnrpJSZP\nnszy5ct59dVXmTNnjs023n//fc6cOcPzzz9PfHw88+fPp6amhmHDhhETE0NKSgqjR4/m5ZdfZvLk\nyaxatYrJkydz6NAhjh8/zpkzZxg/fjyvvPIKs2fPJiMjw2rWsGHDmD17Np999hnTpk0jPj6euXPn\ncuHCjy8/+/jjj/Pyyy8zYcIEVqxYwUsvvcSLL77IxYsXWbJkCfHx8cTExHDq1Clef/11Fi9ezJQp\nU9i+fTu5ubn86U9/IiEhgSlTpnDx4rXLbG7ZsoXJkyfz4osvEh0dzZo1a5gwYQLffPONQ/vkrXfS\nGD7sUebOnEbWvv31fUt5PZXn58awYPZMEjekkFdQSEiXLsyY8hwd2rej+NDH9me8m8bwxx5h7oyp\nP8h4oy5jBokb6zK6dmHGlEl0aNeO4kOfOJCxleGPPcKcGdFk/zBjzkwWxEwnccMmANatT8Hf38/u\ntl2d45oM+/YJwNrkjfj7+TfTfrhJRlo6wx97mDnTo8nef+ByRurfeH72TBbMmkbixiszHN8fb6el\nM/zRh5k9bQrZ+3PqM15LfZPYmBnMnzmNpJTNlowNr+Hv59w50tg5b2/9O088MpTZU58j+8AV7f/t\nLWJnTWfejKkkv/Y6FSe/ZfSIx4l+ZgJtWreipOS4/Rnpf+eJhx8iJnoS2QcO1mds+tvbLJg5lXnT\nppC8KZWKb7/lf4c/RvTEp2jdujUlx080qwy4NF4PMTt6Etn7L+e89sbbxM6cxrzp0SRvep2Kk6cY\nPWIY0ROfqhsvB/riVhmNe2yBa85Fd8h4O20bwx8Zyuypk69u/4269mdY2q84+S2jRz7B1Gcm0Lp1\nK75phvsDGrCYvueee/jtb39Lr169yMzMxNPTk+7du3P27FmeffZZhg0bhp+fH08//TQmk8lmGw88\n8ADp6emcPn0af39/zp49y+eff47ZbGbx4sX8x3/8Bz//+c+ZNGkS3377LU899RTDhg0jNze3vp1z\n587x1FNPMWnSJJvFtNlsZtGiRRQWFvLAAw8wd+5cbr31Vvbu3fujffX09GTSpEkMHjyYkJAQpkyZ\nwuHDh9m7dy9ffPEFrVq1wtPTk48++og+ffowePBgbr/9drKysgDo06cP48ePp1WrVlbHwsPDg1/+\n8pdER0djMpkYN24cgwcP5tChQ/bsinrlFRUYDQYA2gYEcObMd3V9B29vb3x9famtqaW8ogKDwXKn\nzRAcTFl5uQMZJ21kmH+QcRJD3fMMwUGUlZfZnVFRcRJj3fa1bWu9HzW1NQBMeWYCIV272N22q3Nc\nkWHvPgGIfvbpZtsP98mowFh3J9vW/qiprb2c0aWzwxmW88uSERAQwJnv6jKwkjFxPCFdnDtHGjun\nouIkBoPljlDbK9s3m/H29sLX14eamlp6dO/Grbd0J/tADp6enoSFhjiQ8e3V+8NKRm1tLT26hdVl\nHMTL05OwkK7NKgOg4uTJ+m9JrsqBH4zX5RzLeDnQF3fJcMGxBa45F90ho/zk5f1xVfvmK/Z5rWV/\n9LylO9n7c/Dy9KJbM9wf0IDFtIeHB2azmVdeeYVjx44xe/ZswPKG5O3tjYeHBz4+Pj/aBsDFixe5\n9957mTp1Kv/93/+NwWAgICAAsBz4fnWfHG666SY8PT3x9PS86g7vpUwvLy/MVr7mA+rbs9aHH3Pp\nzpGnp+dVfTKbzfTt25fo6GgeeughQkJCWL58Od9//z19+/a95vXe3t5W70xf+Zwrs+zZtit1Mho4\nUVesn66sok2b1gD4+vhQe/48585V4+PrQyejEVNpKQCm0lKCg+z/KqiT4cqMSusZPj50MhquyChz\nKMNoNHDCZHnt6dNX9MP3cobvjxxbzSXHFRn27pOfwl3GyiUZBgMn6o59W/ujIfa5qa4flVWVtGlt\nyfC5lFHdMOdIY+cYDcGYTJYP2qcrL7fve0X7Pr6W9tesT+Grr0uYPmmigxlBV+8Paxl1fViz4TW+\n+qaEac9OaHYZlpzg+uvq6aqq6+est+RMn/T0jZvRyMcWuOZcdIcMS/uW/VFZWXXd9lcnb+TLb75h\n+nPNc39AA86ZrqqqYvz48ZTWnRAffPABvXr1Ijs7m6ioKEpKSjCZTISHh7N161aioqKstrFx40bG\njBnDpk2bKCws5J///CcPPvgg6enpNtv55ptvOHHiBBcuXCAsLIy8vDyioqL4/vvvOXDgAIMHD74m\n69Jru3fvTlJSEkVFRZw6dYonnniCbdu2MWjQIAoKCqzOmb60LZ999hne3t707NmT9PR0nn76ad55\n5x1yc3M5ePAgUVFR5OTkUFFRwb/+9S8qKyu5884767d/z549hIeH07Zt26vav7LdS9t55WPWWJsz\nHRYayup1yRw4mMuA/hFsfW87A+7qjyE4yDJ3dk8mY0f9kTv79Gbb9h3k5OZRU1vL0N9fu29sCQsN\nZXXilRnvWzKCgklYm0jGnizGjhp5OSMvj5oaWxnWPyx0Cw1hddIGDhzMI7J/OOnvvU/kXREEBweR\nsC6ZXZlZjHlyBEEdOwKQX1hMWEhXh+eDuiKnITNszaO0d59czigiLDTE7rluLXGsmkVGbh6RERGk\nv7fjckZiMrsysxnzx+H2Zdj4PN0tNIQ1yRs5kJtPZEQ46dt3ENk/AkNQEAlJ6+syRhDUMdCSUVRM\naEhXDA58qG3wHCs3B7qFdmXN+hRy8gqIjOjHtvd3EhkRjiGoIwlJG9mVtZcxI5/gYH4BqW+/i4cH\n7MrK5tZbutPu5rZ2ZYSFhLB2wyYO5BVwV8R/sm3Hh0RG9CM4qCOrk1PYnb2P0cMf52BBIW9s2VqX\nsZce3btZz7CioTNsnevdQkJYs+E1y3iF/yfb3v+AyIh+lvFKrhuvEU9wML+Q1C1peODBrqy93HqL\n/X1pcRm2xqoBj63rzZl2xbnY8jJsnOv17fcj/f0dRPYPxxDckYTEDezKymbMyOHk1O8PDzIyr3Ou\n29zvDTtWtuZMe5gdvd3ZQHbv3s1nn3121WN/+tOf8Pb2bvCsVatWXfVzjx49+M1vftPor7VHfn4+\neXl5Vz02bNgw2rdv71A7NZWN/F9QXHCYmM3W79KLdde7oDcU7RMHuOIcudgkl+tGYb7443+f0hIy\nXMFWMS1WuOC66OFp/z9WEMAV7yMu2O8AfoFG6/FNVUxLw1IxfeNRMd3MqJh2iIpp+6mYdoCK6ebn\nBiimtWiLiIiIiIiTVEyLiIiIiDhJxbSIiIiIiJNUTIuIiIiIOEnFtIiIiIiIk1RMi4iIiIg4ScW0\niIiIiIiTVEyLiIiIiDhJxbSIiIiIiJMafu1ucUsuWQnPjRbjdMVKdWa0OqG0XK5YRU4r1Yk0Ay5a\nnbApuX8PRUREREQaiYppEREREREnqZgWEREREXGSimkRERERESepmBYRERERcZKKaRERERERJ6mY\nFhERERFxkorpOsePH2/qTRARERGRFsYrNjY2tqk3orGtWLECPz8/jEYjAOXl5aSmptKjRw9Gjx4N\nwNq1a4mKirpuO2PHjrX6nJUrV+Lr61vf/o89b8OGDfziF7+of3zLli0cOXKEnj17Ot3HC9Vnr3nM\nVFpG3KIXOJhfQMnx4/TpfQcABw7mkbAuiYzdmQQGdsAQHEzsn5eQV1DI7qy9DPzFPdcG2FhQxVRa\nRtziZeTmF1Jy/AR97rgdgJzcfFatSyZjTxYdAztgNARTdeYMs+bF0bmTAUNwsN19M5WWEbdkObkF\nVjISk8nYk311xvw4OhuNGIKD7M5o8Bwba7aYyspY+MJy8gqKrs7IyychaT0ZmVl07HA5IyZ2IZ0c\n7IsylNEYGe7UF2UoQ+eIMpzJ8G7VxurjbntneufOncyfP5+pU6dSXFzM+vXrmTVrFitXrqS6uppj\nx46xZcsWzp49y6lTpzh69CjFxcVs2rSJ+Ph4Zs6cyaFDhzh8+DDPPvssixYtoqqqyqFtSExMrN+G\nzz77rP7xzz//HID4+Hji4+PZtm0bAB9++CFxcXHExMSwYcMG9u7dy+rVqwFYsGABJpPJofy33klj\n+LBHmTtzGln79nPhwgUAUl5P5fm5MSyYPZPEDSnkFRQS0qULM6Y8R4f27Sg+9LH9Ge9uZfhjjzBn\nRjTZV2W8wfNzZrIgZjqJGzYBsG59Cv7+fg71AeCttHSGP/Ywc6ZHk73/wOWM1L/x/OyZLJg1jcSN\nV2b4O5zhqpy309IZ/ujDzJ42hez9OfUZr6W+SWzMDObPnEZSymZLxobX8PdzfLyUoYzGyHCnvihD\nGTpHlNGQ+91ti2mTyYSPjw8PPvgg3bt3Z+jQoSxZsoSioqL65wwZMoS+ffsyZswYwsLC6NWrF6mp\nqbRq1Yr27duzb98+kpKSWLx4MTExMfhdZ5BfffVVYmJiiImJ4ciRI5w9e5bi4mLi4uKYN28eq1at\nuur5R44cwdvbm7lz5zJkyBAAunbtyv/8z//Qr18/du/ezS9+8QsKCgqoqKigpqYGg8Hg0BiUV1Rg\nrHtN24AAzpz5DrDcZPb29sbX15famlrKKyowGCx3ig3BwZSVl9udUVFxEmPda9u2tZ5RU1sDwJRn\nJhDStYtDfbBkVGCsu5N9dT/MV2TUXs7o0tnhDFfllFecrB/rgIAAznxXl4GVjInjCeni+HgpQxmN\nkeFOfVGGMnSOKKMh97vbFtP9+vVj5MiRHDlyhJtuuomAgAAAPDw8bL7GbDbTrl07oqOjefLJJ+nd\nuzceHh6Y66Y4eHl52XztxIkTWbx4MYsXL6Z79+71r7mUaf7BNAlPT89r2n311VcpLS3lZz/7Wf3v\nHnjgAaZNm8awYcMcHoNORgMn6u5mn66sok2b1gD4+vhQe/48585V4+PrQyejEVNpKQCm0lKCg+z/\nCsVoNHDCZHnt6dNXZPhezvD18XF426/KMBg4Ubd9pysrrfbjp2a4KqeTwYCpbrwqqypp09qS4XMp\no1oZymieGa7KUYYyWmqGq3KU0bwywI3nTBcUFLB582aqq6spLCxk0KBBGAwGtm7dyqBBgygoKCA8\nPJzCwkLuv/9+3nzzTYxGI61bt2bLli1kZGTwq1/9in79+rF06VKKioowmUz84Q9/uCYrNzeX0NDQ\n+jnT6enpDB06lJKSEv7+97+zZ88exo0bxxdffEFYWBh5eXmMHDmSnJwcMjMzKSws5LbbbqOqqoqv\nvvqKjz/+mFOnTvHQQw8RFhbGli1bmDhx4nX7a23OdFhoKKvXJXPgYC4D+kew9b3tDLirP4bgIBLW\nJpGxJ5Oxo/7InX16s237DnJy86iprWXo763MHbcxZ7pbaAirkzZw4GAekf3DSX/vfSLviiA4OIiE\ndcnsysxizJMjCOrYEYD8wmLCQro6NOepPiM3j8iICNLf23E5IzGZXZnZjPnj8J+U0eA5NuZMdwsN\nYU3yRg7k5hMZEU769h1E9o/AEBREQtL6uowRBHUMtGQUFRMa0hWDAx9wlKGMxshwp74oQxk6R5Th\nTIatOdMe5h/eMpXrSktLo6SkpP5nf39/Ro0a1ShZpaWlxMbGMnr0aCIiIq773HLhuF8AACAASURB\nVJrKikbZhkvMFy80avuWEPc5FM0X3acvIiIiAn6BRquPq5h2EyqmmxcV0yIiIu7FVjHttnOmRURE\nREQam4ppEREREREnqZgWEREREXGSimkRERERESepmBYRERERcZKKaRERERERJ6mYFhERERFxkopp\nEREREREnqZgWEREREXGSd1NvgDQM84Xzjdu+VvSTRuLh6dHUm9Bi6DwUuXF4eLngfqcrVh72cP9r\nvO5Mi4iIiIg4ScW0iIiIiIiTVEyLiIiIiDhJxbSIiIiIiJNUTIuIiIiIOEnFtIiIiIiIk1RMi4iI\niIg4yW2L6ePHjzf1JoiIiIiIm/OKjY2NbeqNcNSKFSvw8/PDaDQCUF5eTmpqKj169GD06NEArF27\nlqioqOu2M3bs2B99TkPIysqipKSE9957j549e+Ln5+dwG++88w5HjhyhZ8+eVn9/4dx31zxmKi0j\nbslycgsKKTl+gj533A5ATm4+qxKTydiTTcfADhgNwVSdOcOs+XF0NhoxBAddG2Dj/7qbyspY+MJy\n8gqKrs7IyychaT0ZmVl07HA5IyZ2IZ1sZdjgigx36ktLy/Cw8Q/9G/T4tdWPlpbRhOehq3KUoYyW\nmtHQObYWtDKVlhG3eBm5+VauKeuSydiTdfU1ZV4cnTsZMAQH29+PhrxuXe8a74p+NGCGt19rq4+3\nmDvTO3fuZP78+UydOpXi4mLWr1/PrFmzWLlyJdXV1Rw7dowtW7Zw9uxZTp06xdGjRykuLmbTpk3E\nx8czc+ZMDh06xOHDh3n22WdZtGgRVVVVVrP279/P888/z4IFC3jzzTfJzc3lT3/6EwkJCUyZMoWL\nFy9e0+6KFSuYPHkyr7/+OuvXr2f+/PnExMSwYMECKioq+Pbbbzl69Ci1tbW88sorvPDCCzzzzDPk\n5+db3YYftuGMt9LSGf7Yw8yZHk32/gNcuHABgJTUv/H87JksmDWNxI2bAFi3PgV/f3+HM95OS2f4\now8ze9oUsvfn1Ge8lvomsTEzmD9zGkkpmy0ZG17D34kPEq7IcKe+uEuGK45fd8nQOaIMZTR9hqty\n3np3K8Mfe4Q5M6LJ3rf/8jXl9Td4fs5MFsRMJ3HDldeUZnr9dUU/XJABLaiYNplM+Pj48OCDD9K9\ne3eGDh3KkiVLKCoqqn/OkCFD6Nu3L2PGjCEsLIxevXqRmppKq1ataN++Pfv27SMpKYnFixcTExNj\n8w7x6tWrCQgIoG3bthw8eBCAPn36MH78eFq1aoXJZLqmXQ8PDx5++GGGDh1KcXExcXFxPPbYY1bb\nv/vuuxk4cCBdu3YlJyfnmt9XV1f/aBv2qKiowFj36aptQABnzljuXpvNZry9vfH19aWmthaAKc9M\nIKRLZ4czyitOYjBYMgICAjjzXV0GVjImjiekS5dmmeFOfXGXDFccv+6SoXNEGcpo+gxX5VRUnMRY\nl9G27ZXXFK7IqLFkPDOBkK7N9frrin40fga0oGK6X79+jBw5kiNHjnDTTTcREBAA2P6KGCw7vV27\ndkRHR/Pkk0/Su3dvPDw8MNetRe/l5WX1dRcvXmT8+PFMnjyZAQMGANR/6vL29qaqquqadsFy4tTW\n1ta37+l57fCazWZeeuklAO644w6r+efPn79uG/YyGgycKC0F4HRlJW3aWL6e8PXxofb8ec6dq8bX\nx8fp9gE6GQyYTJaMyqpK2rS2ZPhcyqhuGRmuylGG/Vxx/LpLhs4RZSij6TNclWM0GjhRl3H6dNXl\na4pvy7puuaQfLsiAFjRnuqCggM2bN1NdXU1hYSGDBg3CYDCwdetWBg0aREFBAeHh4RQWFnL//ffz\n5ptvYjQaad26NVu2bCEjI4Nf/epX9OvXj6VLl1JUVITJZOIPf/jDNVnBwcH89a9/JSsri1tuuYU2\nbdpgMpkIDw9nz5493H333VRXV9e3O2jQIP7973/TrVs3QkJC+Oqrr9i+fTs5OTncfPPNdOzYEW9v\nb7788kvuvfdeMjMzOX36NF988QUXL15k4MCBV+X7+PjYbMOROdPdQkNYnbSBA7l5REZEkP7eDiLv\niiA4OIiExGR2ZWYz5o/DCerYEYD8wmLCQro6NFezW2gIa5I3ciA3n8iIcNK37yCyfwSGoCASktbX\nZYwgqGOgJaOomNCQrhiC7J+H5ooMd+pLS8uw9YG4QY/f6/SjRWU04XnoqhxlKKOlZjR0jq050/XX\nlIN5RPYPJ/299y9fU9YlsyszizFPjmg+160fu8a7oh8NlGFrzrSH+dIt0BtUWloaJSUl9T/7+/sz\natSon9TmX//6V86dO0dlZSUjRozg9ttvJycnh1WrVpGUlHTNHfFVq1Zd9XOPHj349NNPr2njeqq/\nNf2kbf4x5os39GEijcjWG4ZcS+ehyI3Dw8sFkwdcUQJeZwZBS+PbzvofJt7wxbS7UDEtLZWKafvp\nPBS5caiYbn5sFdMtZs60iIiIiEhzo2JaRERERMRJKqZFRERERJykYlpERERExEkqpkVEREREnKRi\nWkRERETESSqmRUREREScpGJaRERERMRJKqZFRERERJzk3dQbIA3Dw6txd6WHZ+OvkmQ2X2z0DJes\n9oRWqnOEK8bKFassuss+d6cVKd1ln8iNyXzBBe+JLuH+56HuTIuIiIiIOEnFtIiIiIiIk1RMi4iI\niIg4ScW0iIiIiIiTVEyLiIiIiDhJxbSIiIiIiJNUTIuIiIiIOEnFtIiIiIiIk7xiY2Njm3ojfqrj\nx48TEBDQ1JvRpC5Un73mMVNpGXGLXuBgfgElx4/Tp/cdABw4mEfCuiQydmcSGNgBQ3AwsX9eQl5B\nIbuz9jLwF/fYnWsqLSNu8VIO5hdScvwEfe643ZKRm0fC2iQydmcRGBiIITioLqOI3dl7GXivtQzr\n/9jdkrGM3B9k5OTms2pdMhl7sugY2AGjIZiqM2eYNS+Ozp0MGIKD7e5Hfc6S5eQWWMlJTCZjT/bV\nOfPj6Gw0YggOsrcrmMrKWPjCcvIKiq7OyMsnIWk9GZlZdOxwOSMmdiGdbGXY6ocyrmnLw8P6QiTa\n5/aPVYOPlysy3GSfKKNlZLhTX5RhPcO7VRurjzf7O9MrVqzgo48+qv+5vLycpKQkKisrGT58OG+/\n/Tbz58//0XbGjh1rs/2xY8cyffp0Jk2axI4dO+zetk2bNjF9+nSmT5/OtGnTOH78OACTJk1iypQp\nzJo1C4DDhw8TGRnJmTNnAPjf//1fzHasxGdrm+311jtpDB/2KHNnTiNr334uXLgAQMrrqTw/N4YF\ns2eSuCGFvIJCQrp0YcaU5+jQvh3Fhz62P+PdNIY/9ghzZ0z9QcYbdRkzSNxYl9G1CzOmTKJDu3YU\nH/rEgYytDH/sEebMiCb7hxlzZrIgZjqJGzYBsG59Cv7+fna3fVVOWjrDH3uYOdOjyd5/4HJO6t94\nfvZMFsyaRuLGK3P8Hc54Oy2d4Y8+zOxpU8jen1Of8Vrqm8TGzGD+zGkkpWy2ZGx4DX8/x/uiDPtp\nnzvGFeOlfaKMlprhTn1RhmOaZTG9c+dO5s+fz9SpUykuLmb9+vXMmjWLlStXUl1dzbFjx9iyZQtn\nz57l1KlTHD16lOLiYjZt2kR8fDwzZ87k0KFDHD58mGeffZZFixZRVVVlNcvDw4NJkyaxbNkyXn75\nZd555x3Onj3LK6+8wgsvvMAzzzxDfn4+L774IkVFRZjNZp5++mny8vIoLS1l2bJlLFu2jOeee46F\nCxeSk5PDp59+yn/9139RXl4OwI4dO3jqqafIyMigrKyMzp07889//pMFCxbw/PPPs2bNGi5evMiS\nJUuIj48nJiaGU6dOAXDq1CkmTZrEl19+6fA4lldUYDQYAGgbEMCZM98BlhW1vb298fX1pbamlvKK\nCgwGy11cQ3AwZXXbbV/GSRsZ5h9knMRQ9zxDcBBl5WV2Z1RUnMRYt31t21rvR01tDQBTnplASNcu\ndrd9dU4Fxrq72bb6UlNbezmnS2eHMyzjYMkICAjgzHd1GVjJmDiekC6O90UZ9tM+d4wrxkv7RBkt\nNcOd+qIMxzTLYtpkMuHj48ODDz5I9+7dGTp0KEuWLKGoqKj+OUOGDKFv376MGTOGsLAwevXqRWpq\nKq1ataJ9+/bs27ePpKQkFi9eTExMDH7X+bRx5V3iW2+9la+++oq7776bgQMH0rVrV3JychgxYgR/\n+9vfyMrKYuDAgRw6dIgBAwbUvy4kJITq6moGDBhAWFgYDzzwAH369OH//u//+Prrr3n88cfJzs4m\nOzubwYMHs3r1atq0aUObNm0oKioiMzOTL774glatWuHp6clHH33ExYsXGTVqFBMnTiQ0NNThcexk\nNHDCZALgdGUVbdq0BsDXx4fa8+c5d64aH18fOhmNmEpLLWNfWkpwkP1foXQyXJlRaT3Dx4dORsMV\nGWUOZRiNBk6YLK89ffqKfvhezvD18bG7PZs5BgMn6rbRVl9+ak4ngwFTXV8qqypp09qS4XMpo1oZ\nrszQPneMK8ZL+0QZLTXDVTnKaF4Z0EznTHt7e9O/f38KCwuprq6md+/eGI1G0tPTGTRoEAUFBYSH\nh1NQUMCgQYPYunUrDzzwAB9++CHLli2jR48eeHl58a9//Yv77rsPHx8f0tPT+d3vfndNVm5uLmFh\nYRiNRgCSkpJ49NFHmTt3Lv3796d169acPn2aQYMGkZWVRV5eHpMnT+b8+fPk5ORw9913A3DixAly\ncnL47W9/y9atW4mKiqJVq1a88cYbhISEcM8997B7925OnDjByJEj2blzJ+PGjeOBBx7g+++/JyAg\nALPZzOTJk7n55psxGo1kZWUxadIkXnvtNX7zm99cd8yszZkOCw1l9bpkDhzMZUD/CLa+t50Bd/XH\nEBxkmc+8J5Oxo/7InX16s237DnJy86iprWXo76Ps3ldhoaGsTrwy431LRlAwCWsTydiTxdhRIy9n\n5OVRU2Mrw/rUl26hIaxO2sCBg3lE9g8n/b33ibwrguDgIBLWJbMrM4sxT44gqGNHAPILiwkL6erw\nXLf6nNw8IiMiSH9vx+WcxGR2ZWYz5o/D7cuxMYunW2gIa5I3ciA3n8iIcNK37yCyfwSGoCASktbX\nZYwgqGOgJaOomNCQrhgc+PChDPvnAWufOzZnukHHyxUZbrJPlNEyMtypL8pwbM60h9meybsutn37\ndj744APatm3LoUOHiI2N5c4772Ts2LH10yLGjRvH2rVriY2NZeTIkYwdO5Z//vOfHDt2jKqqKp55\n5hnMZjOrVq0iODiYTz/9lJSUlGuyVq5cSXFxMR06dODs2bM88MAD/OY3v2HixIl0796dyspKWrdu\nzdy5c8nKyiI/P5/o6GgAkpOT+fjjjy1fE9TUMGPGDAwGA2PHjmXdunWYzWZ+/etfs2bNGm699Vbe\neecd9u3bx/Llyzl8+DCvvvoqHTp0oFu3bjz55JPExMRw8803U15eTnx8PJMnT2bt2rW88sorhIaG\n8v/+3/+zOWY1lRWNtj8Ay5yKRmY2X2z0DFf0A8B8sdmdVjc0D0/bBWJDcZd97oqxchV32Sci0jz4\nBRqtPt4si+nGkpaWRklJSf3P/v7+jBo1yq7XHjx4kOTkZBYtWkSHDh0aaQudp2La7pDGz0Bv4s2N\nimn7qZgWEbFOxbSbUzFtd0jjZ6A38eZGxbT9VEyLiFhnq5huln+AKCIiIiLSEqiYFhERERFxkopp\nEREREREnqZgWEREREXGSimkRERERESepmBYRERERcZKKaRERERERJ6mYFhERERFxkndTb4A0kEZe\njMQlC6q4EXdZ+EKLXtjPXfa5q+jYksbgTgs0eXi54H6nKxYy83D/a6PuTIuIiIiIOEnFtIiIiIiI\nk1RMi4iIiIg4ScW0iIiIiIiTVEyLiIiIiDhJxbSIiIiIiJNUTIuIiIiIOEnFtIiIiIiIk9yymD5+\n/LjTr/3uu++oqqr6ydvQUO2IiIiISPPlFRsbG9vUG+GIFStW4Ofnh9FoBKC8vJzU1FR69OjB6NGj\nAVi7di1RUVHXbWfs2LFWn7NmzRratm3LuXPniI+PJz8/nz179nDfffc5tJ2X2rm0nY5avXo1PXv2\nxM/Pz67nX6j+/prHTKVlxC1eysH8QkqOn6DPHbcDcCA3j4S1SWTsziIwMBBDcBCxf15CXkERu7P3\nMvDee6wkWF8lyZKxjNwfZOTk5rNqXTIZe7LoGNgBoyGYqjNnmDUvjs6dDBiCg+0bCBdl1OcsWU5u\ngZWcxGQy9mRfnTM/js5GI4bgIPfNsLE4lqmsjIUvLCevoOjqjLx8EpLWk5GZRccOlzNiYhfSydF+\nNGCGh40VuFrc/mjCjAbPcZNjSxnNK8Ml5/p1Fg1s0L7YWM3RZe+7DTVe19snLax+8PZrbfXxFnFn\neufOncyfP5+pU6dSXFzM+vXrmTVrFitXrqS6uppjx46xZcsWzp49y6lTpzh69CjFxcVs2rSJ+Ph4\nZs6cyaFDhzh8+DDPPvssixYtsnrX+OLFi+Tk5PDuu+9y8uRJpk2bxuzZszl+/DjV1dU8/vjjvPzy\ny0yYMIEVK1bw0ksv8eKLL17TjtlsJicnh7S0NE6cOMH8+fNZuHAhS5cuBWDw4MHMmTOH1atXM3ny\nZF588UWio6NZs2YNEyZM4JtvvuHo0aPU1tYybNgwEhISmDp1KocOHXJo3N56N43hjz3C3BlTydq3\nnwsXLgCQ8vobPD83hgWzZ5C4MYW8gkJCunZhxpRJdGjXjuJDnziQsZXhjz3CnBnRZP8wY85MFsRM\nJ3HDJgDWrU/B39++DweuzgB4Ky2d4Y89zJzp0WTvP3A5J/VvPD97JgtmTSNx45U5/jdsxttp6Qx/\n9GFmT5tC9v6c+ozXUt8kNmYG82dOIyllsyVjw2v42/mh0NUZ7rI/XJHhqhx3ObaU0bwyXHWOuKQv\nrnjfdcV1y43qhxZRTJtMJnx8fHjwwQfp3r07Q4cOZcmSJRQVFdU/Z8iQIfTt25cxY8YQFhZGr169\nSE1NpVWrVrRv3559+/aRlJTE4sWLiYmJsXrH19PTkwEDBvDQQw/Rr18/jEYjb731Fj//+c/x9fXF\n09OTSZMmMXjwYEJCQpgyZQqHDx++ph0PDw8GDBjA7373O1JSUgBo1aoVX3/9NaWlpQQGBvLnP/+Z\n4OBgfvnLXxIdHY3JZGLcuHEMHjz4qqLZy8uL8ePH8/jjj5Odne3QuJVXnMRoMADQNiCAM2e+AyzF\nvre3N76+vtTW1FJecRJD3fMMwUGUlZfZnVFRcRKjwfIJrm3bKzOoz6iprQFgyjMTCOnaxaE+uCrD\nklOBse7TqK3xqqmtvZzTpfMNm2E5ZiwZAQEBnPmuLgMrGRPHE9LF8X3iigx32R+uyHBVjrscW8po\nXhmuOkdc0xdXvO+64rrlPvVDiyim+/Xrx8iRIzly5Ag33XQTAQEBgO2vc8Cyw9u1a0d0dDRPPvkk\nvXv3xsPDA7PZ8v2Ml5fXdV9bW1vLokWL8Pf3Z9y4cQD1n7w8PT3x8fGxa9vNZjO//e1vmTp1Kr/+\n9a+5+eab67f/yjavbPvSNl75uLe391WP26OTwcAJkwmA05WVtGlj+XrC18eH2vPnOXeuGh8fHzoZ\nDZhKSwHLVyLBQfZ/PWc0Gjhhsrz29Omqyxm+lzN87RyrpswAMBoMnKgbB1vj9ZP74iYZnQwGTHX7\npLKqkjatLRk+lzKqW0aGu+wPV2S4Ksddji1lNK8MV50jLumLK953XXHdcqP6oUXMmS4oKGDz5s1U\nV1dTWFjIoEGDMBgMbN26lUGDBlFQUEB4eDiFhYXcf//9vPnmmxiNRlq3bs2WLVvIyMjgV7/6Ff36\n9WPp0qUUFRVhMpn4wx/+cE2WyWTizTff5NixY+zdu5fKyko++OADBgwYwM6dO4mKiuKzzz7D29ub\nnj17snXrVqtzry+1M3HiRF555RUKCwv59ttvue++++pfY62dS499+eWX3HPPPXz44YdERUVhMpn4\n8ssvueuuu6yOkbU502GhoaxOTObAwVwG9I9g63vvM+Cu/hiCgklYm0jGnizGjhrJnX16s237DnLy\n8qipqWXo763NN7deyHcLDWF10gYOHMwjsn846e+9T+RdEQQHB5GwLpldmVmMeXIEQR07ApBfWExY\nSFeH5tO5IuOqnNw8IiMiSH9vx+WcxGR2ZWYz5o/DG6YvLSXDxue3bqEhrEneyIHcfCIjwknfvoPI\n/hEYgoJISFpflzGCoI6BloyiYkJDumJw4INaQ2bY+uDd4vZHE2Y0eI6bHFvKaF4ZLjnXr3Nfq0H7\nYmPOtEvfdxtivH5sn7Sg+sHWnGkPs6O3O91IWloaJSUl9T/7+/szatSoJmvnp6g5Xd6o7ZvNFxu1\nfZe5cQ93p5gvusd42XpTkqbjLseWNC+uONdddex6eLlg8oAr3hOvM4ugpfFtZ/0PE2/oYtqdqJi2\nkw53h7hLwaNiuvlxl2NLmhcV0w5SMe0QW8V0i5gzLSIiIiLSHKmYFhERERFxkoppEREREREnqZgW\nEREREXGSimkRERERESepmBYRERERcZKKaRERERERJ6mYFhERERFxkndTb4A0kEb+p+geLvjcZb54\nwQUZWijiRqT93vy4ZCEdFywW4eGhe1I3HA83WcQM8PD0aupNaBBNvbCcrgIiIiIiIk5SMS0iIiIi\n4iQV0yIiIiIiTlIxLSIiIiLiJBXTIiIiIiJOUjEtIiIiIuIkFdMiIiIiIk5SMS0iIiIi4iQV0yIi\nIiIiTvKKjY2NbeqNaMliYmL4+c9/zqRJk4iKimq0nC1btnDkyBF69uxp9fcXqs9e85iptIy4RS9w\nML+AkuPH6dP7DgAOHMwjYV0SGbszCQzsgCE4mNg/LyGvoJDdWXsZ+It77N4uU2kZcYuXcjC/kJLj\nJ+hzx+2WjNw8EtYmkbE7i8DAQIyGYKrOnGHWvFg6dzJiCA6+tjGz9VXqTKVlxC1ZTm7B1Rk5ufms\nSkwmY082HQM7XM6YH0dnoxFDcJCVjOv0payMhS8sJ6+g6OqcvHwSktaTkZlFxw6Xc2JiF9LJVk4T\nZdjbvp+fL88vWc5HH3/C9g8+5Jf33I2Xp/2frd1hrJTROBn2HlseNlYnbNDz/XoZi5eRm28lY10y\nGXuyrs6YF0fnTgar163r9qOhro02KKOpMmy/kTTkseWKjKY6fu1t3xAcVFefFLE7ey8D77VVn1yn\nfmjA/eHt19rq47ozDaxYsYJ58+bx6quvsn79+mt+P2vWLGbPns327dt54YUXiIuLY+rUqRw/fvxH\n237qqacwm8189dVXLFy4kA8//JC4uDhiYmLYsGED33zzDVFRUcyfP5+ampprXh8fH098fDzbtm1z\nuF9vvZPG8GGPMnfmNLL27efCBcty3Smvp/L83BgWzJ5J4oYU8goKCenShRlTnqND+3YUH/rY/ox3\n0xj+2CPMnTH1Bxlv1GXMIHFjCgBrkzfi7+fveD/S0hn+2MPMmR5N9v4DlzNS/8bzs2eyYNY0Ejdu\nAmDd+hT8/R3PAHg7LZ3hjz7M7GlTyN6fU5/zWuqbxMbMYP7MaSSlbLbkbHgNfz+/Zpdhb/sVJ79l\n9MgnmPrs07Ru1YpvSn78WHZlP5TRcjN+6rHlivP9rXe3MvyxR5gzI5rsH1635sxkQcx0EjdcmeH4\neLnk2qiMZpVhyXHFsdXyj197288rKCSkaxdmTJlEh3btKD70iYM5jT9WoGIasHwye+ihh3j66acp\nLi6mtrb2mt//6U9/olevXpw/f5758+czZswYNm7c+KNtDxw4kAMHDvDuu+/y8MMP07VrV/7nf/6H\nfv36sXv3bgBuvfVW4uLi8PHxueq1//73v/H29mbu3LkMGTLE4X6VV1RgNBgAaBsQwJkz3wGWG8De\n3t74+vpSW1NLeUUFBoPlU5ghOJiy8nIHMk7ayDBflQEQ/ezThHTt4nA/KioqMNZ9SrSVUVO3z6Y8\nM4GQLp0dzrjUl0vjEBAQwJnv6nKwkjNxPCFdHO9LY2fY236P7t3o2eMWsvfn4OXlRbfQkGbVD2W0\n3Iyfemy54nyvqDiJsa4vbdtavzbW1NZcznDiuuWKa6MymlcGuObYcofj1972Ldcdy/MMwUGUlZc5\nlOOKsQIV0/UuXrwIwPnz5/Hy8rrm9wEBAcDlr0RsfTXyQ1FRUWzfvp3/+7//4/bbb+fVV1+ltLSU\nn/3sZ5jrpjVcavuHvLy86p9jbZt+TCejgRMmEwCnK6to08by9YSvjw+1589z7lw1Pr4+dDIaMZWW\nAmAqLSU4yP6vfjsZrsyotJ7xgw8JjjIaDJyo2z5bGb4/MQMsfTGZLDmVVZW0aW3J8bmUU/3Tcxo7\nw5H2Vydv4Muvv2b6c880u34oo2Vn/JRjyxXnu9Fo4ERdX06fvuLa6NtwGa64NiqjeWWAa44tdzh+\n7W2/k9FwRX1S5lB9Aq4ZK9CcaQAOHjzInj17OHjwIP/5n//JnXfeedXvMzIyuPfee+nSpQv79+8n\nOzub/Px8nnrqKXJycrjnnnv48MMPrc6Z9vX15cMPPyQ8PJy+fftSWFjIV199xccff8ypU6f49a9/\nTUFBAYMGDbrmte3atSMnJ4fMzEwKCwu57bbbHJozHRYayup1yRw4mMuA/hFsfW87A+7qjyE4yDIn\naU8mY0f9kTv79Gbb9h3k5OZRU1vL0N/bP/c7LDSU1YlXZrxvyQgKJmFtIhl7shg7aiRBHTsCkF9Y\nRFhoiI35zNbnPHULDWF10gYO5OYRGRFB+ns7iLwrguDgIBISk9mVmc2YPw6/IqOYsJCuDs+Z7hYa\nwprkjRzIzScyIpz07TuI7B+BISiIhKT1dTkjCOoYaMkpKiY0pCsGB07uxs6wt/2cvHxS334HDyAj\nM4tbe9xCu5vbNpt+KKPlZth7bNm6IdGg5/uPZRzMI7J/OOnvvX85Y10ymPnpOAAAIABJREFUuzKz\nGPPkCLsybPWjQa+NNiijqTJsv5E05LHlioymOn7tbb++PsnLo6bmevXJj9QPDbQ/bM2Z9jCbbVQw\nN5CVK1cycOBA7rzzTtLS0igpKan/nb+/P6NGjbKrnZ/y2jNnzpCSknLVYz//+c+5++677Xp9TWWF\nXc9zmgsOE/PFCy7IuOEPd5FmwcPTvm/3flpI42d4eOgL3huN2XyxqTehwbjL8euqfeLbzvofJqqY\ndhMqpu3N0OEu0hyomJaWSsV089PUxbR7jKKIiIiISBNQMS0iIiIi4iQV0yIiIiIiTlIxLSIiIiLi\nJBXTIiIiIiJOUjEtIiIiIuIkFdMiIiIiIk5SMS0iIiIi4iTvpt4AaSCNvKiKS/4huisWWPBywUIR\n4JJFbkTkR7hisSlz4y82Jc2MC96rXMWdFqBpSrozLSIiIiLiJBXTIiIiIiJOUjEtIiIiIuIkFdMi\nIiIiIk5SMS0iIiIi4iQV0yIiIiIiTlIxLSIiIiLiJBXTIiIiIiJOatJi+vjx400Z32Ru1H6LiIiI\nuBuv2NjY2MZoeMWKFfj5+WE0GgEoLy8nNTWVHj16MHr0aADWrl1LVFTUddsZO3bsjz6npYmOjrba\np5MnTzJv3jyKiorYtm0b9913H15eXgB8/fXX/OUvf2HQoEFW27xQ/f01j5lKy4hbvJSD+YWUHD9B\nnztuB+BAbh4Ja5PI2J1FYGAghuAgYv+8hLyCInZn72XgvfdYSbC+kpglYxm5P8jIyc1n1bpkMvZk\n0TGwA0ZDMFVnzjBrXhydOxkwBAfbM1Quy3BpX5YsJ7fASkZiMhl7sq/OmB9HZ6MRQ3CQMpTRpBnu\n1BdlKOMnZVxnBUR3eU9UhvUMb7/W1h+3e4vssHPnTvbu3ct3333HqVOn+Pe//42vry9du3bloYce\n4tixY2zZsoWzZ89y6tQpjh49SnFxMf/4xz84cuQIVVVVDB8+HG9vb1atWkWnTp2oqqqymjVr1iyC\ngoIoLy+ndevWBAUF8cknn7BixQo2bdp0VXsXLlzgnXfewcvLCz8/P6ZNm8acOXNo164d586dIzY2\nlrFjx7Ju3Tr+/ve/U1NTw9dff80XX3xBZGQkFy9evKq9zMxMysrK8PHx4bvvvqN79+4UFhbyl7/8\nhT179lBUVMT333/PkCFD+Pbbb9m3bx+33HILJpOJxx57jKNHj5KVlcXAgQOv6tPJkycZN24cvXr1\nIj4+nq+//pqCggIKCgrw9PTE29ux3fXWu2kMf+wRfta3D09PmcYjD/0OLy8vUl5/g5eXLeHChQtM\nmzOfkcMeJaRrF8aO+iOvrkmk+NAn/PzOPnZmbK3L6M3EKdN55KGo+oy/LlvMhQsXmD4nlpeXL2bd\n+hT8/f0c6oOrMlzWl7R0hj/2MD/r05uJ0TMuZ6T+jb8uXWTJmBvLy8suZfgrQxnNIsOd+qIMZTTa\nOeIm74nKcEyDTvMwmUz4+Pjw4IMP0r17d4YOHcqSJUsoKiqqf86QIUPo27cvY8aMISwsjF69epGa\nmkqrVq1o3749+/btIykpicWLFxMTE4Ofn/WOeXh48Oijj/L00/+/vTuPi7re9zj+GphAQExFGEAB\nbXUhc8HsZnbs5u2cOj04LbZdj8U9mihZR7FccEmtk0t1T4spKohrp27mRmYeIxVzYXXJ7rFbuRQC\nw6IJaMr2u38giAoF08yo0/v5jzrM7/v+fb+/33fmM7/5yi+GU6dOER0djbe3N3l5eXXttW7dmh07\nduDn50dkZCS9evVi9+7dVFZWkp+fT6dOnRg0aNAl7db+OWjQIP70pz9dsn8mk4n777+fSZMmkZOT\nw/Dhw+natSvfffcdCQkJeHt74+fnV/fcO+64g2effZacnBy6detGWFjYJYU0wA033MBNN93Etm3b\ncHNzo1OnTmzevJmZM2fWXc1vjqLi4wRaLAC08vWlrOwUAIZhYDab8fT0pKK8gqLi41jOPc8S4E9h\nUWGTM4qLjxNoqfkE16pV/QzqMsorygEYM2okIR3aN7sfzshwXl+KCTz3ibexY1JeUXE+o32wMpRx\nRWS4Ul+UoQzHzRHXeE9URvPYtZju1asXQ4YM4fDhw1xzzTX4+voC5wvUhhiGQevWrYmNjeXpp5+m\nW7dumEwmDKNmWUHtMoeGeHl5YTKZ8PDwAMDNzQ2TyVTXXlRUFN26dWPZsmUcOnSILl264OnpSWVl\nJePHjyckJITZs2dz/PjxurwTJ07Ute/r60t1dfUl+1c/29PTsy7bMAzc3NwYM2YM0dHR9OzZs+65\ntc/5Je+++y5Hjx4lLi7ugrH7uXFoTJDFQr7VCsDJkhJatqz5esLTw4OKykrOnDmLh4cHQYEWrAUF\nQM1XIgH+Tf/qLDDQQr61ZtuTJ0vPZ3iez/A8d3xs5YwMZ+UEWizknxvrxo6JMpRxJWY4K0cZyrha\nM8B13hOV0Tx2XTOdlZXFe++9x9mzZ8nOzubuu+/GYrGwfv167r77brKysujduzfZ2dkMGDCADz/8\nkMDAQHx8fFi9ejUpKSn8+7//O7169WLOnDns2bMHq9XKww8/fElWSkoK/fr1o6qqqq69lJQU7rzz\nTk6fPl3X3t13382JEyf46quvOHjwIMeOHWPQoEHMnj2boqIivL29+f3vf09WVhapqakcOXKEsLAw\nSktL6dixIyEhIRQVFV3Q3qFDh+jYsSMWi4Xk5GQiIyNJT08nLCyM0NBQEhIS2LJlCxEREZSVlWE2\nm7nxxhvrnvvZZ59x9uxZOnfufEGf1q5dy8qVKzGZTGzevJmbbroJX19fVqxYwb59+zCZTAwYMKDB\nsW9ozXRYaCgLEpPYlZbO7X0iWP/Jp9x+Wx8s/gHEL0okZWsqw6KG0D28Gx9v3MTujAzKyyt45MGG\n1qg3vGa6Y2gICxYvZVdaBn379Cb5k0/pe1sEAQH+xCck8fm2VIY+/Wf827UDIDN7L2EhHZq11s0Z\nGU7vS3oGfSMiSP5k0/mMxCQ+37adoU8NVoYyrrgMV+qLMpTxqzJ+5gKhq7wnKqPhjMbWTJuM2kuy\nV7B169aRm5tb928vLy+ioqIu3w7Zwddff83nn39+wWMPPPAAISEhNrVXfrLIHrvVKMOodmj7LufK\nn1YiImKLnymmxbV5tm74PyZeFcW0/DIV01cYTSsREdekYvo3q7FiWjdtERERERGxkYppEREREREb\nqZgWEREREbGRimkRERERERupmBYRERERsZGKaRERERERG6mYFhERERGxkYppEREREREbmS/3Doid\nOPiXyJv0uatZDHSTG7lKOeuGQ7rxRdPpJlBN54zzSsejWYxq1x8vVUgiIiIiIjZSMS0iIiIiYiMV\n0yIiIiIiNlIxLSIiIiJiIxXTIiIiIiI2UjEtIiIiImIjFdMiIiIiIjZSMS0iIiIiYiOXLqbz8vIu\n9y6IiIiIiAtznzZt2rTLvROOEhsbS2RkZLO2OXbsGG+88QY//vgjhw8f5sYbb3TQ3tlX1dmfLnnM\nWlDIjFdnk5aZRW5eHuHdugKwKy2D+ITFpGzZhp9fWywBAUz72ywysrLZkvoFd915R5NzrQWFzJg5\nh7TMbHLz8gnv2qUmIz2D+EWLSdmSip+fH5YA/3MZe9iy/Qvu6mf/jEBLAKVlZUyYMo3goEAsAQFN\nzrB/TsN3fKrJeI30izJ2p2cyPyGJlK2ptPNrWy9jBsFBlmb1RRnKcERGXc6s10nPaiAnMYmUrdsv\nzJk6g+DAQCwB/pc21sid6lxlvJyWYa/j4SoZl/G8uirHyxkZjdwA0VpYyMuzXycja8+FGRmZxC9e\nQsq2VNq1PZ8xcdrLBDW3H3bOMHu3bPjxpuzMO++8Q2FhIR4eHpw6dYpOnTqRnZ3Nfffdx5dffsnp\n06f5j//4Dzp16sTChQvx8fGhpKSE2bNn8/LLL+Ph4UFhYSGzZ88mOjqahIQE9u7dyxdffEH79u35\n5JNP6NWrF6GhoezZs6euvRMnTpCamkpoaCg5OTl07tyZPXv2MHnyZI4ePcrmzZuprq4mPDycsLAw\nFi1aRO/evfn666+JjY3lyJEjbNiwgT/+8Y8X9CcnJ4eYmBh69OjBI488wtKlS2nZsiUdO3bk97//\n/c+ORU5ODqNHj2bAgAEcPHiQrl278sMPPzBw4EC6du3K/Pnzueaaa/D09GTcuHG8+uqruLu7c/jw\nYcaPH8/HH3/MiRMn8PX1xWQyMXr06EsyJkyYgL+/P0VFRfj4+ODv78+BAwd45513mnK46qxas47B\nTzzGrbeEEzN6LI8+/CDu7u4s/8f7vP36bKqqqnghbgpDnnyckPbtGfZfTzFvYQJ7939Jj+63NC1j\n7ToGP/5oTcaYF3j0oT+dy/iAt1+bVZMxaSpDnniMkA7tGRb1FPMWJrJ3/wF6dA+3a8Y7r89mUdIy\nvFp4NWucnJmzau36cxndeHbMizz6UGRdxluvzaSqqooXJ03j7ddnkrBkOV5eLZShjCsiA2DVumQG\nPz6IW8O78WzsuPM57/8Pb815tSZn8jTefq02R3PEoRnOOB6ukuFKc8RFMj5al8zgxwbRPbwbo8aO\nZ9CDNRkr3v+QN2f/jaqqKsZNmc5bc14lYekKvFo0/5g4IwOauMzDZDJx//33M2nSJHJychg+fDhd\nunRhwoQJeHt74+fnx44dO/D29ubhhx+mV69eZGdnAzXFZ1BQEIMHD76kzVoDBw5k5MiRJCQkXNCe\nyWSif//+xMbGYrVaGT58OAMHDmT//v0sXLgQX19fWrVqRVpaGgDh4eGMGDECb29vzGYzYWFhlxTS\ntW688UZmzJjB0qVLmTZtGjNmzCA7O5tTp0794nhcd911jBo1Ch8fHx588EFiYmL44osvWLlyJQDe\n3t7k5OSQl5fHvffeyx133EGrVq3Yt28fJpOJe++9lzFjxrB///5Gx/uxxx4jJiaGU6dOER0djbe3\nN8ePH//lg1VPUXExgRYLAK18fSkrq+mbYYDZbMbT05OK8gqKiouxWGo+fVsCAigsKmpGxvFGMoyL\nMo5jOfc8S4A/hUWFds8AiH0uhpAO7ZvctrNziouPE3hurFu1aviYlFeUAzBm1EhlKOOKyajJKSbw\n3JW6xuZIeUXF+Zz2wVdkX1wnwxnHw1UyXGmOuEZGTV1Qk+Hr60vZufrLoIGMZ0cQ0r75x8QZGdCM\nNdNeXl6YTCY8PT1rNnRzw93dnTFjxhAdHU3Pnj3ZsGED6enpdOrUidatW1NZWcmzzz5L165dWbhw\nId988w2GUXO9/8SJE3Vt+/r61rVZv73a3Pp/urm5YRgGVVVVjBgxgtGjR3P77bdf8Byz2Ux1dXWj\nfTGZTLRsWXOpvnZ/ah//ue1qn1N/Xzw8PHBzc6O6uhrDMLj//vsZO3Ys99xzD4ZhEB8fT4sWLbjp\nppvqsupv/0vj7eHhUffcX9q3iwUFWsi3WgE4WVJKy5Y+AHh6eFBRWcmZM2fx8PQgKDAQa0EBANaC\nAgL8m/4VSpClfkZJwxkeHgQFWuplFDok49dyRk5goIV8a804nDxZ75h4ns/wVIYyrsAMgECLhfxz\n87ixOXI19MVlMpxxPFwlw5XmiItkBFksWM8dk5LSElr61GR41GacvToyoIlrptPT0+nYsSMWi4Xk\n5GQiIyPJyMigT58+fPDBB2zZsoWIiAjc3NxIS0vj+++/58iRIzzwwAMsXLiQo0eP4u7uzh/+8Afy\n8vJYv349R48excfHh1atWmE2m7nxxhvx9PQkISGhrr2ysrK6n61fv57IyEgOHjyI2WymX79+vPXW\nW6SmpnLdddfRsmVLrFYrvXv3ZuvWrfTu3ZvvvvuOQ4cO1RXmtUpKStizZw8DBgwgNDSUt956i6ys\nLDp37kznzp3JysoiMDCwLruxbVNSUujXrx+VlZVkZ2fzX//1X7z77rtkZ2dz4sQJ+vbty6ZNmygp\nKeGbb76hTZs2VFRU1I1lbZ8uVttuVVUV2dnZF2R5e3s3eIwaWjMdFhrKgoQkdqWlc3ufCNZ/spHb\nb+uDJcC/Zg3w1m0Mi3qK7uHd+HjjJnanZ1BeUcEjDzZ9nXlYaCgLEutnfFqT4R9A/KJEUramMixq\nyPmMjAzKyx2T4d+uHQCZ2XsICw1p1roq++c0vEisY2gICxYvZVdaBn379Cb5k0/pe1sEAQH+xCck\n8fm2VIY+/ed6GXsJC+nQrL4oQxmOyLggJz2DvhERJH+y6XxOYhKfb9vO0KcGNy2nkbWtrjJeTs2w\nx/FwlYzLeF5dlePljIxG1kx3DA1hYdIydqVn0jeiN8kbN9G3TwQWf3/iFy85l/Fn/Nv51WTs2Uto\nSAcszbgYZ++MxtZMm4z6l2ZdUG5uLuvWrbvgsf79+xMe/svrdX/Ntk21bt06cnNz6/7t5eVFVFRU\ns9spLym22z41yLVPE7szjOZ9iyByxXDWXG+k6JEG6PW36ZxxXul4NItR7Trj1cIvsMHHXb6Y/q1Q\nMX1lUTEtVy0V01cevf42nYrpK85voZh26d8zLSIiIiLiSCqmRURERERspGJaRERERMRGKqZFRERE\nRGykYlpERERExEYqpkVEREREbKRiWkRERETERiqmRURERERsZL7cOyBSSzc6+Y1ykRsgOOPGBCY3\nJ9yQQjdTufLomDSdi7yeAC5z3E3urtGPn6Mr0yIiIiIiNlIxLSIiIiJiIxXTIiIiIiI2UjEtIiIi\nImIjFdMiIiIiIjZSMS0iIiIiYiMV0yIiIiIiNlIxLSIiIiJiIxXTIiIiIiI2cp82bdq0y70TV5qJ\nEyfSo0cPnn/+eSIjIy/37jRJ1dmfLnnMWlDIjFdnk5aZRW5eHuHdugKwKy2D+ITFpGzZhp9fWywB\nAUz72ywysrLZkvoFd915R5NzrQWFzJg5h7TMbHLz8gnv2qUmIz2D+EWLSdmSip+fH4GWAErLypgw\nZRrBQYFYAgIaaK3hO1fVZLxG+kUZu9MzmZ+QRMrWVNr5ta2XMYPgIEsjGb/UF8fmKKOZGbNeJz2r\ngYzEJFK2br8wY+oMggMDsQT4X56MRm68Zi0s5OXZr5ORtefCjIxM4hcvIWVbKu3ans+YOO1lghrJ\nMDVyRzS79uNn7rrmUueWMn57GQ5+PbF7zs/Nd1c5JldZhrmFT4OPu+yV6XfeeYcpU6Ywb948lixZ\ncsnPJ0yYQFxcHBs3bmT27NnMmDGDsWPHkpeX94ttT5gwgTfeeIOJEyfyyiuvsGDBAp577jkAVq5c\nySuvvML48ePZv38/e/bsYerUqUyfPp3Zs2cDcN9997Fw4UKef/55fvjhh0vaz8nJYdCgQcydO5dR\no0Yxb948Jk6cSEpKSrPGYNWadQx+4jEmj3+B1B07qaqqAmD5P95n+uSJvBQ3nsSly8nIyiakfXvG\njfkrbdu0Zu/+L5uesXYdgx9/lMnjxl6U8cG5jHEkLlsOwKKkZXi18GpWH2oy1jP48UeZNC6W7Rdn\nTBrPSxNfJHHpSgASlizHy6tFszOclaOMZmSsS2bw44OY9GIs23fuOp/x/v8wPW48L014gcRl9TNs\nOLeckPHRumQGPzaIuBfGsH3n7rqMFe9/yLSJ45g6/gUWL3+vJmPpCrxaXJljBS50binjt5fhrDni\njNctVzkmLpIBLlxMm0wmHnroIWJiYti7dy8VFRWX/PyZZ57hpptuorKykqlTpzJ06FCWLVvWpLYf\ne+wxYmJiOHXqFNHR0Xh7e5OXl8f777+Pt7c3rVu3ZseOHfj5+REZGUmvXr3YvXs3AG3btmX48OHc\nc889ZGVlNZhx3XXXMWrUKHx8fHjwwQeJiYnhiy++aNYYFBUXE2ixANDK15eyslMAGAaYzWY8PT2p\nKK+gqLgYi6XmU5glIIDCoqJmZBxvJMO4IAMg9rkYQjq0b1YfAIqLjxN4bv9atWq4H+UV5QCMGTXS\npgxn5SijORnFBJ67OtDYuVV+bl6PGTWSkPbBV2RGUfHxuvnl6+tL2alzGTSQ8ewIQtpfmWNVk+Mq\n55YyfnsZzpojznjdcpVj4hoZ4MLFNEB1dTUAlZWVuLu7X/JzX19f4PxXp419hdoQLy8vTCYTHh4e\nALi5uWEymWjdujWxsbFERUXRrVs3li1bxqFDh+jSpQuenp512wJcc801GMal3w2bTKa657i5ueHh\n4YHJZKrrT1MFBVrIt1oBOFlSSsuWNV9PeHp4UFFZyZkzZ/Hw9CAoMBBrQQEA1oICAvyb/rVWkKV+\nRknDGefGyFaBgRbyrTX7d/JkvX54ns/w/JUZzspRRjMyLBbyz52XjZ1bV0NGkMWC9dxYlZSW0NKn\nJsOjNuPs1dEPcKFzSxm/vQxnzRFnvG65yjFxkQxw4TXTaWlpbN26lbS0NHr27En37t0v+HlKSgr9\n+vWjffv27Ny5k+3bt5OZmUl0dDS7d+/mjjvu4LPPPmtwzXTttlVVVWRnZzNgwABSUlK48847OX36\nNKtXryYlJYW7776bEydO8NVXX3Hw4EGOHTvGo48+SnJyMpGRkXzzzTdUVVXRpUuXC9ovKSlhz549\nde3WZmVlZXH33Xc32N+G1kyHhYayICGJXWnp3N4ngvWfbOT22/pgCfCvWc+8dRvDop6ie3g3Pt64\nid3pGZRXVPDIg01fJx4WGsqCxPoZn9Zk+AcQvyiRlK2pDIsagn+7dgBkZu8hLDSkkXVoDS867Rga\nwoLFS9mVlkHfPr1J/uRT+t4WQUCAP/EJSXy+LZWhT/+5XsZewkI6NHutmzNylGFDRnoGfSMiSP5k\n0/mMxCQ+37adoU8NvnIyGlkz3TE0hIVJy9iVnknfiN4kb9xE3z4RWPz9iV+85FzGn/Fv51eTsWcv\noSEdsDTwobaxD/x27cfPXFRwuXNLGb+9DAe+ntg955fmu6sck6soo7E10yajoUujLmDu3Lncdddd\ndO/enXXr1pGbm1v3My8vL6KioprUzq/Ztilyc3NZt27dBY/179+f8PDwZrVTXlJst31qkBNOE8No\n3pV3cREu8hJkVDu+Hya3pn97ZnuIEzJEHMVFXk8AzcUrkGfrhv9jossW0781KqblquUiL0EqpkWu\nAC7yegJoLl6BGiumXXrNtIiIiIiII6mYFhERERGxkYppEREREREbqZgWEREREbGRimkRERERERup\nmBYRERERsZGKaRERERERG6mYFhERERGxkYppEREREREbmS/3DojUMpkc/9lOd1m8ArnIXb6ccPq6\nzFiJXNWcNQ+dcTdHZ/TFle5K2QhdmRYRERERsZGKaRERERERG6mYFhERERGxkYppEREREREbqZgW\nEREREbGRimkRERERERupmBYRERERsdEVV0zn5eVd7l0QEREREWkS92nTpk1zVtjQoUP505/+9LPP\niY2NJTIy0uaM2bNnc+edd9q8PcCaNWs4fPgwixcvpkePHnh7e/+q9pyh6uxPlzxmLShkxquzScvM\nIjcvj/BuXQHYlZZBfMJiUrZsw8+vLZaAAKb9bRYZWdlsSf2Cu+68o8m51oJCZsycQ1pmNrl5+YR3\n7VKTkZ5B/KLFpGxJxc/PD0uA/7mMPWzZ/gV39bN/RqAlgNKyMiZMmUZwUCCWgIAGWmv8l8fX5LxG\n+kU5u9MzmZ+QRMrWVNr5ta2XM4PgIEsjOcpQRr2MWa+TntVARmISKVu3X5gxdQbBgYFYAvwvbayR\nGyw4ox9OHS9lKMMRGQ6eh67Ul6uuH07KMHu1bPBxp1yZfueddxg9ejQHDhzglVdeYejQofzv//7v\nJc87cOAAR44cYdu2bWzYsIFXXnmFuLg4tmzZwurVq3nmmWeYP38+MTEx/P3vf2f06NG8/vrrzJs3\nj0mTJgHwzTffkJ6ezjPPPEN8fDxjxoyhuvrSu97Vb2/t2rVMnDiRF154gZ07d/5if1avXs3o0aP5\n7//+b2JjY1m4cCEjR47k2LFj7Ny5k+nTp/PSSy/x4Ycfcvz4ceLi4pg1axbPPvsslZWVjBgxgnff\nfZe4uDhSUlIuaf/gwYO8+eablJeX07dvX86ePcvcuXP5+uuvmzXuq9asY/ATjzF5/Auk7thJVVUV\nAMv/8T7TJ0/kpbjxJC5dTkZWNiHt2zNuzF9p26Y1e/d/2fSMtesY/PijTB439qKMD85ljCNx2bmM\nDu0ZN+Z52rZuzd79B+yeAbAoaRleLbya3PaFOesZ/PijTBoXy/aLcyaN56WJL5K4dCUACUuW4+XV\nQhnK+OWMdckMfnwQk16MZfvOXecz3v8fpseN56UJL5C4rH5G889fZ/TDWTnKUMbVOg9dqS8u0w8n\nHXenFNMmk4lHHnmETp06MXnyZEaOHMnWrVsveV54eDhhYWH87ne/IyEhAW9vb/z8/NixYwcmk4mB\nAwcycuRITp8+zXPPPccTTzxBixYtiImJwWq1XtLWiBEj8Pb2vuRntftU215ycjIzZ85k1qxZJCYm\nNqk//fv3JzY2FqvVyvDhwxk4cCD79+9n4cKF+Pr60qpVK9LS0nB3d+fhhx8mIiICq9VKQUEBZ86c\nITo6mueff77BYrpz584cOXKErKwsevbsSUZGBocOHeLmm29u+qADRcXFBFosALTy9aWs7BRQc2dP\ns9mMp6cnFeUVFBUXY7HUfJq0BARQWFTUjIzjjWQYF2Ucx3LueZYAfwqLCu2eARD7XAwhHdo3ue36\niouPE3huHFq1ani8yivKARgzaqRNOcr4LWYUE3juak1j5295RcX5jPbBV2Q/nJWjDGVcrfPQlfri\nOv1wznF32prpVq1a0bJlzeVxs9nc4NXi+tzc3BgzZgzR0dH07NkTAF9f37rtzWYzJpMJDw+PBrev\n/XTxc1m17Rk23De+tv3aP93c3DAMg6qqKkaMGMHo0aO5/fbb2b17Nxs3biQwMJDg4OC6A2g2m3F3\nd280Ozg4mPXr1xMbG8tHH31ESEhIs/cxKNBC/rkPEidLSmnZ0gdEyMA6AAAMuElEQVQATw8PKior\nOXPmLB6eHgQFBmItKADAWlBAgH/Tv0IJstTPKGk4w8ODoEBLvYxCh2T8WoGBFvKtNft48mS98fI8\nn+P5K3OU8RvMsFjIP3fuN3b+Xg39cFaOMpRxtc5DcJ2+uEw/nHTcnbJmOj09nbCwMDIyMoiMjMRq\ntfL9999z2223XfLczz77jLNnz3LrrbeSkJDAli1biIiIoKysDLPZzI033sj69euJjIwkNzcXq9VK\n7969SU5OJjIykvXr19O7d++6x7du3Urv3r1p1arVBTkHDx6sa88wDFauXMm2bdv4z//8T0pLSzGb\nzXz//ffccccdl6yZrr9t7b7UPtavXz/eeustUlNTuf7662nXrh2pqakUFhby7bff0rdvX9LS0oiM\njOT06dPs2rWLgQMHXjIOLVq0YMOGDURHRzNv3jyGDx9OwM+sRWpozXRYaCgLEpLYlZbO7X0iWP/J\nRm6/rQ+WAP+atcZbtzEs6im6h3fj442b2J2eQXlFBY882PQ162GhoSxIrJ/xaU2GfwDxixJJ2ZrK\nsKgh5zMyMigvd0yGf7t2AGRm7yEsNKSRNU+Nf3DqGBrCgsVL2ZWWQd8+vUn+5FP63hZBQIA/8QlJ\nfL4tlaFP/7lezl7CQjo0a/2WMn7DGekZ9I2IIPmTTeczEpP4fNt2hj41uGkZjaxvdEY/nD5eylDG\nVTYPXakvV10/nJTR2Jppk2HLZVk7KCsrY/ny5Rc81qNHD/7t3/7N7lmZmZlkZGRc8NgTTzxBmzZt\nHLptU82fP/+Cf19//fXce++9zWqjvKTYbvvToMtzmtidYfz8NyIiNnPGHPmZN3ERwbXmoav0xUXq\nBwDPNpYGH79sxbTYl4rpplExLQ7jKm98IlczV5qHrtIXF6kfoPFi+or7PdMiIiIiIlcLFdMiIiIi\nIjZSMS0iIiIiYiMV0yIiIiIiNlIxLSIiIiJiIxXTIiIiIiI2UjEtIiIiImIjFdMiIiIiIjbSTVtE\nRERERGykK9MiIiIiIjZSMS0iIiIiYiMV0yIiIiIiNlIxLSIiIiJiIxXTIiIiIiI2Ml/uHZDL4+jR\no4wePZo1a9bYve3s7Gw++OADfHx88PPz49lnn7V7xpEjR3j77bdp06YN4eHhPPTQQ3bPqDV27Fju\nuece7r//foe0f+zYMWJiYujatSv+/v7ExsbaPSMnJ4d58+bh6+vLtddeS0xMjN0zVq5cyYEDBygv\nL2fPnj18/vnnds/Iz89n7ty5tGzZEpPJxPjx4+2esXfvXpYsW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x221c9828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#top20_indices = np.argsort(list(feature_importances_all.mean(axis=1)))[::-1][0:20]\n",
    "\n",
    "f, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(top20, square=True, annot=True, annot_kws={\"size\": 8}, fmt=\".2f\", cbar=False)\n",
    "plt.title(\"Feature Importances: Top 20\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./figures/featureImportanceTop20.pdf\")\n",
    "plt.savefig(\"./figures/featureImportanceTop20.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>horizon</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>measurement_1w_back</th>\n",
       "      <td>0.083884</td>\n",
       "      <td>0.396689</td>\n",
       "      <td>0.419818</td>\n",
       "      <td>0.443881</td>\n",
       "      <td>0.445707</td>\n",
       "      <td>0.446498</td>\n",
       "      <td>0.447161</td>\n",
       "      <td>0.447249</td>\n",
       "      <td>0.447320</td>\n",
       "      <td>0.446988</td>\n",
       "      <td>...</td>\n",
       "      <td>0.447301</td>\n",
       "      <td>0.447606</td>\n",
       "      <td>0.447020</td>\n",
       "      <td>0.447291</td>\n",
       "      <td>0.436182</td>\n",
       "      <td>0.434014</td>\n",
       "      <td>0.432529</td>\n",
       "      <td>0.434504</td>\n",
       "      <td>0.434440</td>\n",
       "      <td>0.433886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_1d_back</th>\n",
       "      <td>0.026476</td>\n",
       "      <td>0.061081</td>\n",
       "      <td>0.076868</td>\n",
       "      <td>0.085147</td>\n",
       "      <td>0.087946</td>\n",
       "      <td>0.088310</td>\n",
       "      <td>0.089914</td>\n",
       "      <td>0.091967</td>\n",
       "      <td>0.092278</td>\n",
       "      <td>0.092898</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098006</td>\n",
       "      <td>0.097015</td>\n",
       "      <td>0.097646</td>\n",
       "      <td>0.095507</td>\n",
       "      <td>0.085684</td>\n",
       "      <td>0.061714</td>\n",
       "      <td>0.048305</td>\n",
       "      <td>0.066870</td>\n",
       "      <td>0.060982</td>\n",
       "      <td>0.027680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_MA_d</th>\n",
       "      <td>0.005548</td>\n",
       "      <td>0.019698</td>\n",
       "      <td>0.082634</td>\n",
       "      <td>0.095823</td>\n",
       "      <td>0.095722</td>\n",
       "      <td>0.092793</td>\n",
       "      <td>0.083523</td>\n",
       "      <td>0.068029</td>\n",
       "      <td>0.063016</td>\n",
       "      <td>0.062827</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038441</td>\n",
       "      <td>0.036895</td>\n",
       "      <td>0.033354</td>\n",
       "      <td>0.031386</td>\n",
       "      <td>0.028197</td>\n",
       "      <td>0.027340</td>\n",
       "      <td>0.028078</td>\n",
       "      <td>0.028126</td>\n",
       "      <td>0.031200</td>\n",
       "      <td>0.029147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement</th>\n",
       "      <td>0.606680</td>\n",
       "      <td>0.184148</td>\n",
       "      <td>0.063959</td>\n",
       "      <td>0.018727</td>\n",
       "      <td>0.007431</td>\n",
       "      <td>0.004422</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003648</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>0.002749</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004381</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>0.001166</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>0.002003</td>\n",
       "      <td>0.002109</td>\n",
       "      <td>0.002408</td>\n",
       "      <td>0.002269</td>\n",
       "      <td>0.027257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_MA_h</th>\n",
       "      <td>0.084864</td>\n",
       "      <td>0.068960</td>\n",
       "      <td>0.032386</td>\n",
       "      <td>0.026575</td>\n",
       "      <td>0.017745</td>\n",
       "      <td>0.019000</td>\n",
       "      <td>0.025674</td>\n",
       "      <td>0.030823</td>\n",
       "      <td>0.031880</td>\n",
       "      <td>0.034622</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026934</td>\n",
       "      <td>0.027868</td>\n",
       "      <td>0.029108</td>\n",
       "      <td>0.029556</td>\n",
       "      <td>0.030834</td>\n",
       "      <td>0.030398</td>\n",
       "      <td>0.027322</td>\n",
       "      <td>0.019684</td>\n",
       "      <td>0.066064</td>\n",
       "      <td>0.077224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_MA_6h</th>\n",
       "      <td>0.029034</td>\n",
       "      <td>0.028670</td>\n",
       "      <td>0.039725</td>\n",
       "      <td>0.021815</td>\n",
       "      <td>0.027316</td>\n",
       "      <td>0.025937</td>\n",
       "      <td>0.017357</td>\n",
       "      <td>0.013096</td>\n",
       "      <td>0.010777</td>\n",
       "      <td>0.006885</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026450</td>\n",
       "      <td>0.029101</td>\n",
       "      <td>0.032741</td>\n",
       "      <td>0.038275</td>\n",
       "      <td>0.065795</td>\n",
       "      <td>0.094549</td>\n",
       "      <td>0.111777</td>\n",
       "      <td>0.085010</td>\n",
       "      <td>0.027825</td>\n",
       "      <td>0.024529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_MAX_w</th>\n",
       "      <td>0.000987</td>\n",
       "      <td>0.001615</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003573</td>\n",
       "      <td>0.003407</td>\n",
       "      <td>0.010264</td>\n",
       "      <td>0.024290</td>\n",
       "      <td>0.028335</td>\n",
       "      <td>0.027868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031351</td>\n",
       "      <td>0.032353</td>\n",
       "      <td>0.032203</td>\n",
       "      <td>0.032030</td>\n",
       "      <td>0.032339</td>\n",
       "      <td>0.032328</td>\n",
       "      <td>0.033373</td>\n",
       "      <td>0.035745</td>\n",
       "      <td>0.036777</td>\n",
       "      <td>0.036073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>business_day</th>\n",
       "      <td>0.002449</td>\n",
       "      <td>0.010312</td>\n",
       "      <td>0.014929</td>\n",
       "      <td>0.015745</td>\n",
       "      <td>0.016779</td>\n",
       "      <td>0.016483</td>\n",
       "      <td>0.017595</td>\n",
       "      <td>0.018290</td>\n",
       "      <td>0.018929</td>\n",
       "      <td>0.018845</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018757</td>\n",
       "      <td>0.018897</td>\n",
       "      <td>0.019015</td>\n",
       "      <td>0.018514</td>\n",
       "      <td>0.016915</td>\n",
       "      <td>0.017354</td>\n",
       "      <td>0.019720</td>\n",
       "      <td>0.018875</td>\n",
       "      <td>0.019171</td>\n",
       "      <td>0.019092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_MA_w</th>\n",
       "      <td>0.004193</td>\n",
       "      <td>0.006218</td>\n",
       "      <td>0.011336</td>\n",
       "      <td>0.015510</td>\n",
       "      <td>0.015687</td>\n",
       "      <td>0.015573</td>\n",
       "      <td>0.016244</td>\n",
       "      <td>0.015183</td>\n",
       "      <td>0.016939</td>\n",
       "      <td>0.017734</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019820</td>\n",
       "      <td>0.019637</td>\n",
       "      <td>0.018609</td>\n",
       "      <td>0.017346</td>\n",
       "      <td>0.011687</td>\n",
       "      <td>0.012160</td>\n",
       "      <td>0.010212</td>\n",
       "      <td>0.013933</td>\n",
       "      <td>0.014674</td>\n",
       "      <td>0.017280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour_of_day</th>\n",
       "      <td>0.020292</td>\n",
       "      <td>0.019208</td>\n",
       "      <td>0.017248</td>\n",
       "      <td>0.015095</td>\n",
       "      <td>0.013266</td>\n",
       "      <td>0.012221</td>\n",
       "      <td>0.011712</td>\n",
       "      <td>0.011394</td>\n",
       "      <td>0.011403</td>\n",
       "      <td>0.012762</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012589</td>\n",
       "      <td>0.011880</td>\n",
       "      <td>0.010984</td>\n",
       "      <td>0.011392</td>\n",
       "      <td>0.011308</td>\n",
       "      <td>0.011150</td>\n",
       "      <td>0.011534</td>\n",
       "      <td>0.011838</td>\n",
       "      <td>0.011505</td>\n",
       "      <td>0.013727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperature_MA_m</th>\n",
       "      <td>0.002832</td>\n",
       "      <td>0.004480</td>\n",
       "      <td>0.005977</td>\n",
       "      <td>0.007386</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>0.005696</td>\n",
       "      <td>0.007405</td>\n",
       "      <td>0.006446</td>\n",
       "      <td>0.007595</td>\n",
       "      <td>0.009182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023303</td>\n",
       "      <td>0.021439</td>\n",
       "      <td>0.020572</td>\n",
       "      <td>0.020799</td>\n",
       "      <td>0.018806</td>\n",
       "      <td>0.013880</td>\n",
       "      <td>0.013955</td>\n",
       "      <td>0.013405</td>\n",
       "      <td>0.015472</td>\n",
       "      <td>0.012070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>0.002137</td>\n",
       "      <td>0.006234</td>\n",
       "      <td>0.009338</td>\n",
       "      <td>0.009640</td>\n",
       "      <td>0.010563</td>\n",
       "      <td>0.011400</td>\n",
       "      <td>0.011755</td>\n",
       "      <td>0.012274</td>\n",
       "      <td>0.012584</td>\n",
       "      <td>0.012751</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012985</td>\n",
       "      <td>0.012912</td>\n",
       "      <td>0.012744</td>\n",
       "      <td>0.012958</td>\n",
       "      <td>0.012944</td>\n",
       "      <td>0.013002</td>\n",
       "      <td>0.012935</td>\n",
       "      <td>0.012999</td>\n",
       "      <td>0.013003</td>\n",
       "      <td>0.013012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecasted_global_radiation</th>\n",
       "      <td>0.008452</td>\n",
       "      <td>0.013362</td>\n",
       "      <td>0.014567</td>\n",
       "      <td>0.011971</td>\n",
       "      <td>0.011891</td>\n",
       "      <td>0.011508</td>\n",
       "      <td>0.010927</td>\n",
       "      <td>0.009834</td>\n",
       "      <td>0.009487</td>\n",
       "      <td>0.010324</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012871</td>\n",
       "      <td>0.013141</td>\n",
       "      <td>0.011424</td>\n",
       "      <td>0.010783</td>\n",
       "      <td>0.011851</td>\n",
       "      <td>0.010978</td>\n",
       "      <td>0.006751</td>\n",
       "      <td>0.008444</td>\n",
       "      <td>0.006649</td>\n",
       "      <td>0.006788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_week</th>\n",
       "      <td>0.002748</td>\n",
       "      <td>0.004916</td>\n",
       "      <td>0.006699</td>\n",
       "      <td>0.009739</td>\n",
       "      <td>0.010639</td>\n",
       "      <td>0.011543</td>\n",
       "      <td>0.011059</td>\n",
       "      <td>0.010474</td>\n",
       "      <td>0.010430</td>\n",
       "      <td>0.011057</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010585</td>\n",
       "      <td>0.010860</td>\n",
       "      <td>0.011373</td>\n",
       "      <td>0.011331</td>\n",
       "      <td>0.011370</td>\n",
       "      <td>0.012653</td>\n",
       "      <td>0.012272</td>\n",
       "      <td>0.011618</td>\n",
       "      <td>0.012253</td>\n",
       "      <td>0.012350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecasted_temperature</th>\n",
       "      <td>0.004303</td>\n",
       "      <td>0.006925</td>\n",
       "      <td>0.006008</td>\n",
       "      <td>0.008177</td>\n",
       "      <td>0.008496</td>\n",
       "      <td>0.008682</td>\n",
       "      <td>0.010652</td>\n",
       "      <td>0.011150</td>\n",
       "      <td>0.009335</td>\n",
       "      <td>0.009197</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011266</td>\n",
       "      <td>0.013252</td>\n",
       "      <td>0.012719</td>\n",
       "      <td>0.012603</td>\n",
       "      <td>0.008791</td>\n",
       "      <td>0.010763</td>\n",
       "      <td>0.007794</td>\n",
       "      <td>0.008891</td>\n",
       "      <td>0.007149</td>\n",
       "      <td>0.007663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preassure_VAR_d</th>\n",
       "      <td>0.002987</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>0.009102</td>\n",
       "      <td>0.010520</td>\n",
       "      <td>0.012564</td>\n",
       "      <td>0.015033</td>\n",
       "      <td>0.012805</td>\n",
       "      <td>0.012017</td>\n",
       "      <td>0.013610</td>\n",
       "      <td>0.013845</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005151</td>\n",
       "      <td>0.005055</td>\n",
       "      <td>0.005075</td>\n",
       "      <td>0.005058</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>0.007034</td>\n",
       "      <td>0.005846</td>\n",
       "      <td>0.005492</td>\n",
       "      <td>0.004953</td>\n",
       "      <td>0.006295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_2d_back</th>\n",
       "      <td>0.003720</td>\n",
       "      <td>0.006125</td>\n",
       "      <td>0.007120</td>\n",
       "      <td>0.007704</td>\n",
       "      <td>0.008240</td>\n",
       "      <td>0.008556</td>\n",
       "      <td>0.008560</td>\n",
       "      <td>0.008441</td>\n",
       "      <td>0.008881</td>\n",
       "      <td>0.008504</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009038</td>\n",
       "      <td>0.008663</td>\n",
       "      <td>0.008689</td>\n",
       "      <td>0.008699</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.008010</td>\n",
       "      <td>0.007396</td>\n",
       "      <td>0.007561</td>\n",
       "      <td>0.007492</td>\n",
       "      <td>0.007462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measurement_MA_m</th>\n",
       "      <td>0.002971</td>\n",
       "      <td>0.004406</td>\n",
       "      <td>0.005339</td>\n",
       "      <td>0.005364</td>\n",
       "      <td>0.006354</td>\n",
       "      <td>0.007131</td>\n",
       "      <td>0.007101</td>\n",
       "      <td>0.007046</td>\n",
       "      <td>0.007074</td>\n",
       "      <td>0.007045</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007186</td>\n",
       "      <td>0.008190</td>\n",
       "      <td>0.008239</td>\n",
       "      <td>0.008710</td>\n",
       "      <td>0.008021</td>\n",
       "      <td>0.008147</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>0.010115</td>\n",
       "      <td>0.013462</td>\n",
       "      <td>0.013075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preassure_MA_m</th>\n",
       "      <td>0.003287</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.007085</td>\n",
       "      <td>0.007360</td>\n",
       "      <td>0.007847</td>\n",
       "      <td>0.007336</td>\n",
       "      <td>0.008081</td>\n",
       "      <td>0.008729</td>\n",
       "      <td>0.008090</td>\n",
       "      <td>0.011282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007358</td>\n",
       "      <td>0.007330</td>\n",
       "      <td>0.007383</td>\n",
       "      <td>0.007390</td>\n",
       "      <td>0.008325</td>\n",
       "      <td>0.008542</td>\n",
       "      <td>0.007653</td>\n",
       "      <td>0.007538</td>\n",
       "      <td>0.007649</td>\n",
       "      <td>0.007866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_humidity_MA_w</th>\n",
       "      <td>0.002404</td>\n",
       "      <td>0.004148</td>\n",
       "      <td>0.005181</td>\n",
       "      <td>0.005835</td>\n",
       "      <td>0.005970</td>\n",
       "      <td>0.006354</td>\n",
       "      <td>0.007106</td>\n",
       "      <td>0.006914</td>\n",
       "      <td>0.006776</td>\n",
       "      <td>0.008647</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006855</td>\n",
       "      <td>0.007099</td>\n",
       "      <td>0.007464</td>\n",
       "      <td>0.008058</td>\n",
       "      <td>0.008101</td>\n",
       "      <td>0.007785</td>\n",
       "      <td>0.009085</td>\n",
       "      <td>0.007540</td>\n",
       "      <td>0.008388</td>\n",
       "      <td>0.008470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "horizon                            1         2         3         4         5   \\\n",
       "measurement_1w_back          0.083884  0.396689  0.419818  0.443881  0.445707   \n",
       "measurement_1d_back          0.026476  0.061081  0.076868  0.085147  0.087946   \n",
       "measurement_MA_d             0.005548  0.019698  0.082634  0.095823  0.095722   \n",
       "measurement                  0.606680  0.184148  0.063959  0.018727  0.007431   \n",
       "measurement_MA_h             0.084864  0.068960  0.032386  0.026575  0.017745   \n",
       "measurement_MA_6h            0.029034  0.028670  0.039725  0.021815  0.027316   \n",
       "measurement_MAX_w            0.000987  0.001615  0.002813  0.003464  0.003573   \n",
       "business_day                 0.002449  0.010312  0.014929  0.015745  0.016779   \n",
       "measurement_MA_w             0.004193  0.006218  0.011336  0.015510  0.015687   \n",
       "hour_of_day                  0.020292  0.019208  0.017248  0.015095  0.013266   \n",
       "temperature_MA_m             0.002832  0.004480  0.005977  0.007386  0.005865   \n",
       "holiday                      0.002137  0.006234  0.009338  0.009640  0.010563   \n",
       "forecasted_global_radiation  0.008452  0.013362  0.014567  0.011971  0.011891   \n",
       "day_of_week                  0.002748  0.004916  0.006699  0.009739  0.010639   \n",
       "forecasted_temperature       0.004303  0.006925  0.006008  0.008177  0.008496   \n",
       "preassure_VAR_d              0.002987  0.005488  0.009102  0.010520  0.012564   \n",
       "measurement_2d_back          0.003720  0.006125  0.007120  0.007704  0.008240   \n",
       "measurement_MA_m             0.002971  0.004406  0.005339  0.005364  0.006354   \n",
       "preassure_MA_m               0.003287  0.005102  0.007085  0.007360  0.007847   \n",
       "relative_humidity_MA_w       0.002404  0.004148  0.005181  0.005835  0.005970   \n",
       "\n",
       "horizon                            6         7         8         9         10  \\\n",
       "measurement_1w_back          0.446498  0.447161  0.447249  0.447320  0.446988   \n",
       "measurement_1d_back          0.088310  0.089914  0.091967  0.092278  0.092898   \n",
       "measurement_MA_d             0.092793  0.083523  0.068029  0.063016  0.062827   \n",
       "measurement                  0.004422  0.003464  0.003648  0.002131  0.002749   \n",
       "measurement_MA_h             0.019000  0.025674  0.030823  0.031880  0.034622   \n",
       "measurement_MA_6h            0.025937  0.017357  0.013096  0.010777  0.006885   \n",
       "measurement_MAX_w            0.003407  0.010264  0.024290  0.028335  0.027868   \n",
       "business_day                 0.016483  0.017595  0.018290  0.018929  0.018845   \n",
       "measurement_MA_w             0.015573  0.016244  0.015183  0.016939  0.017734   \n",
       "hour_of_day                  0.012221  0.011712  0.011394  0.011403  0.012762   \n",
       "temperature_MA_m             0.005696  0.007405  0.006446  0.007595  0.009182   \n",
       "holiday                      0.011400  0.011755  0.012274  0.012584  0.012751   \n",
       "forecasted_global_radiation  0.011508  0.010927  0.009834  0.009487  0.010324   \n",
       "day_of_week                  0.011543  0.011059  0.010474  0.010430  0.011057   \n",
       "forecasted_temperature       0.008682  0.010652  0.011150  0.009335  0.009197   \n",
       "preassure_VAR_d              0.015033  0.012805  0.012017  0.013610  0.013845   \n",
       "measurement_2d_back          0.008556  0.008560  0.008441  0.008881  0.008504   \n",
       "measurement_MA_m             0.007131  0.007101  0.007046  0.007074  0.007045   \n",
       "preassure_MA_m               0.007336  0.008081  0.008729  0.008090  0.011282   \n",
       "relative_humidity_MA_w       0.006354  0.007106  0.006914  0.006776  0.008647   \n",
       "\n",
       "horizon                        ...           15        16        17        18  \\\n",
       "measurement_1w_back            ...     0.447301  0.447606  0.447020  0.447291   \n",
       "measurement_1d_back            ...     0.098006  0.097015  0.097646  0.095507   \n",
       "measurement_MA_d               ...     0.038441  0.036895  0.033354  0.031386   \n",
       "measurement                    ...     0.004381  0.001383  0.001166  0.001198   \n",
       "measurement_MA_h               ...     0.026934  0.027868  0.029108  0.029556   \n",
       "measurement_MA_6h              ...     0.026450  0.029101  0.032741  0.038275   \n",
       "measurement_MAX_w              ...     0.031351  0.032353  0.032203  0.032030   \n",
       "business_day                   ...     0.018757  0.018897  0.019015  0.018514   \n",
       "measurement_MA_w               ...     0.019820  0.019637  0.018609  0.017346   \n",
       "hour_of_day                    ...     0.012589  0.011880  0.010984  0.011392   \n",
       "temperature_MA_m               ...     0.023303  0.021439  0.020572  0.020799   \n",
       "holiday                        ...     0.012985  0.012912  0.012744  0.012958   \n",
       "forecasted_global_radiation    ...     0.012871  0.013141  0.011424  0.010783   \n",
       "day_of_week                    ...     0.010585  0.010860  0.011373  0.011331   \n",
       "forecasted_temperature         ...     0.011266  0.013252  0.012719  0.012603   \n",
       "preassure_VAR_d                ...     0.005151  0.005055  0.005075  0.005058   \n",
       "measurement_2d_back            ...     0.009038  0.008663  0.008689  0.008699   \n",
       "measurement_MA_m               ...     0.007186  0.008190  0.008239  0.008710   \n",
       "preassure_MA_m                 ...     0.007358  0.007330  0.007383  0.007390   \n",
       "relative_humidity_MA_w         ...     0.006855  0.007099  0.007464  0.008058   \n",
       "\n",
       "horizon                            19        20        21        22        23  \\\n",
       "measurement_1w_back          0.436182  0.434014  0.432529  0.434504  0.434440   \n",
       "measurement_1d_back          0.085684  0.061714  0.048305  0.066870  0.060982   \n",
       "measurement_MA_d             0.028197  0.027340  0.028078  0.028126  0.031200   \n",
       "measurement                  0.001383  0.002003  0.002109  0.002408  0.002269   \n",
       "measurement_MA_h             0.030834  0.030398  0.027322  0.019684  0.066064   \n",
       "measurement_MA_6h            0.065795  0.094549  0.111777  0.085010  0.027825   \n",
       "measurement_MAX_w            0.032339  0.032328  0.033373  0.035745  0.036777   \n",
       "business_day                 0.016915  0.017354  0.019720  0.018875  0.019171   \n",
       "measurement_MA_w             0.011687  0.012160  0.010212  0.013933  0.014674   \n",
       "hour_of_day                  0.011308  0.011150  0.011534  0.011838  0.011505   \n",
       "temperature_MA_m             0.018806  0.013880  0.013955  0.013405  0.015472   \n",
       "holiday                      0.012944  0.013002  0.012935  0.012999  0.013003   \n",
       "forecasted_global_radiation  0.011851  0.010978  0.006751  0.008444  0.006649   \n",
       "day_of_week                  0.011370  0.012653  0.012272  0.011618  0.012253   \n",
       "forecasted_temperature       0.008791  0.010763  0.007794  0.008891  0.007149   \n",
       "preassure_VAR_d              0.005164  0.007034  0.005846  0.005492  0.004953   \n",
       "measurement_2d_back          0.008164  0.008010  0.007396  0.007561  0.007492   \n",
       "measurement_MA_m             0.008021  0.008147  0.009307  0.010115  0.013462   \n",
       "preassure_MA_m               0.008325  0.008542  0.007653  0.007538  0.007649   \n",
       "relative_humidity_MA_w       0.008101  0.007785  0.009085  0.007540  0.008388   \n",
       "\n",
       "horizon                            24  \n",
       "measurement_1w_back          0.433886  \n",
       "measurement_1d_back          0.027680  \n",
       "measurement_MA_d             0.029147  \n",
       "measurement                  0.027257  \n",
       "measurement_MA_h             0.077224  \n",
       "measurement_MA_6h            0.024529  \n",
       "measurement_MAX_w            0.036073  \n",
       "business_day                 0.019092  \n",
       "measurement_MA_w             0.017280  \n",
       "hour_of_day                  0.013727  \n",
       "temperature_MA_m             0.012070  \n",
       "holiday                      0.013012  \n",
       "forecasted_global_radiation  0.006788  \n",
       "day_of_week                  0.012350  \n",
       "forecasted_temperature       0.007663  \n",
       "preassure_VAR_d              0.006295  \n",
       "measurement_2d_back          0.007462  \n",
       "measurement_MA_m             0.013075  \n",
       "preassure_MA_m               0.007866  \n",
       "relative_humidity_MA_w       0.008470  \n",
       "\n",
       "[20 rows x 24 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top20_indices = np.argsort(list(feature_importances_all.mean(axis=1)))[::-1][0:20]\n",
    "top20 = feature_importances_all.ix[top20_indices]\n",
    "\n",
    "new_index_names = [\"measurement_1w_back\", \"measurement_1d_back\", \"measurement_MA_d\", \"measurement\", \"measurement_MA_h\",\n",
    "                   \"measurement_MA_6h\", \"measurement_MAX_w\", \"business_day\", \"measurement_MA_w\", \"hour_of_day\",\n",
    "                   \"temperature_MA_m\", \"holiday\", \"forecasted_global_radiation\", \"day_of_week\", \"forecasted_temperature\",\n",
    "                   \"preassure_VAR_d\", \"measurement_2d_back\", \"measurement_MA_m\", \"preassure_MA_m\", \"relative_humidity_MA_w\"]\n",
    "\n",
    "top20.index=new_index_names\n",
    "top20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2bdpk9R6lUsmlS9ZTMtdi8ODB/POf/yQgIIDt27cjk8lwdXVtti0A7OzsKCkp4fPPPyc4\nOJh77rkHSZKorr6ygnFeXh4AkyZNYtOmTTz55JOoVKpW1UmpVCBJEqUmMz3uvovqmmqM5WZM5eVo\n1Wq27vuR3v93Dzq1GmN5eYuuSzWWaSWVzoni9AI8+nbD0Lk97T06UFNVfVV5a1xOejWFafl087qb\n9nfewR0erlRfrCY17mcefGIgu7/8/poxXqqx7Ce1Xk3eyTy69+9Ohy4dcO3akeqqajp4uNL5Hnc8\nenrS6S43CjOar9u1XBVlFZTklnCh4gJyRcu/DWrrpvScDBbXPd7d6eBR57p4xfWrcAmXcN1Wrobj\nRMGpPO7y7s4dXTrQwdOVmqpq7ujSAbfunenS04OOd3WiKKP5a0Ov5cr+KYsTPx6ntqYWj/s9yUrO\nbNZVH2MDV/s7O+Dq6Up1VTU5DV29PMlKad5ly3rdqjE2xObX9CQkJODp6YmrqysxMTEEBARw+PBh\nfHx8WL9+PXv27KF///7I5XLi4+PJzc0lOzubMWPGEBYWRk5ODgqFAn9/fwoLC4mOjiYnJwcnJye0\nWi1KpZLu3btjb29PeHh4va+ioqK+LDo6moCAAFJTU1EqlQwcOJBPPvmE/fv3061bN9RqNcXFxXh7\ne7N37168vb3JyMggMzMTLy+vJuO67OrevTtubm4sWrSI+fPnk5ycTLdu3ZrcLjU1lZ07d5KXl0e7\ndu0YNGgQO3bswGw2k5aWhrOzM6NHj+azzz5j9+7ddO7cGaPRSO/evenXrx8rV65k8ODBTdan8TU9\nTo6OrIyMoqamlsIzZxj+yAC+3bKNg0kpvBAwBns7O76OiqGktJQn/Ychb3D2rPE1PVW/XuSBJweh\nUCrQddBzbPsRso6modKrObE7mUpjBQ88OQi5QkHawRNW2za+pqfq/EUGPvsYinYKdK4GEmMTSE84\niZNBw7EfkvB/+XEUSgXdvLtTklXMxcoLV7ZtdE3PhfMXGDx+CEqlAkNHA/HRh0g99AtaZw1Htx/h\n2J4UTiWcpIOnK4k7jjTZbtdyaepcDk4OPDJuENr2WhK2xLfsqrzAkAlDUCgVOHc0cOiyy3DF5fuk\ncAmXcN3qrsbX9Fw8f4FBzw9GoVRi6GjgcEw8p+JT0ThrSdxxlON7j5F2+BR3eHYg+fvEFuvVvEtD\n4o6juHbryINjH8alc3tSdiZRW31l/Gt8vcvFygsMem4winZK9K4GDm+xuNQGDUnfH8W1awPXD9Yu\nW9brVo2xpWt6ZNL1nBf6k1NQUMDmzZut/ubr60uvXr2uue2RI0c4fPiw1d+effZZDIaWr1P5rYhn\nb908xDdGIPhzIp69dfP4o569JR442gA3Nzdmzpz5m7bt378//fv3t3GNBAKBQCAQ2AqxTo9AIBAI\nBII2gUh6BAKBQCAQtAlE0iMQCAQCgaBNIJIegUAgEAgEbQKR9AgEAoFAIGgTiFvWb1POF+fazFVt\nMtnMVXvhos1cXOeikS3R8JletxQ2/Pq1ia9yW4jRhtiy38vkNryn+BZF3s52NzQrVI42c8nbtbOZ\ny5ZjtOwaizteD+00Wpu5VJ08mi0TZ3oEAoFAIBC0CUTSIxAIBAKBoE0gkh6BQCAQCARtApH0CAQC\ngUAgaBOIpEcgEAgEAkGbQCQ9DSgsLLzZVRAIBAKBQPA7IR442oCFCxeyYsWKJssuXrzIhx9+SP/+\n/Rk1apRV2bRp0wgPD79qm+b+3hK/ZRuAo8nHiP3+BxwdHHjrtZepqqri/Y8/AWDqC8+TX1DIrn0H\ncHCw581XW36oauLxE2zbsw9HBwdCXppCVXU1S/4dBsCUp4P49eJFNmzdQXV1Da+9OBG9VtOsK+nE\nL2zffwAHewfmTHmBqupqloatBGBS0FjyioqJTzlGqdHE/JnBONjbteBKZfuPcTjY2zNn8gSLa8WX\n9a7jp9JIST3FHc4Gpj75RIsxJv1icTna2zN7Up3ri68AmBwYwE9p6Rw7eYo7DAZeHBf4x7oOHLS4\nJo5v3uVs4MWgll3Jv5xk+4E4HBzsmf2CxfWPlass7TX2cY6npXPsVBrtDQZeDBp7e7pST7L9wEEc\n7O2Z/cLzAJwzmvjwiy9Z+sbsJsvbumtH3P/Dwd6O1yc0cK38iqUhrzdZfjvGaCuXLcccW46riT/9\nzNZde3B0cOCNGdOoqqpm8fL/APDic0+TX1jEt1ExOOt1vP7SFPTa5m8HT/r5BNv27sfB3oGQaZMt\n9fqv5Tg45akgDqcc4/ipdNKysgmZNhmvnj3+kBiPphwj9vtdlmPaqzMtx7RlnwIwdcJz5BcWsmv/\nARzs7XnzldY/KFyxaNGiRa1+dytZvnw5sbGxxMXFsW3bNjIzM1mxYgW1tbVERkayfft25HI5MpmM\npUuXcujQIWJjYxk2bBjvvvsuhw4dIioqiqFDhxIcHExAQADJyclERkaSn5/PsmXLyM/Pp6SkhO++\n+67el5SUxOeff86JEyeIiIggJyeH8PBwvLy8OH78OCtXrmTPnj2cPXuWyspKFi1aREFBAWvXrqVn\nz56sWrUKFxcX7rnnnqti+uKLL/j111/p1KkT3bt3Z9++fSxfvpzExETKysoICAi4apuoqCh++ukn\ntmzZglqtpra29qp4v/zyS7Zt20ZERAT9+vVj9+7deHl58de//pUHH3wQR8em13qorrReW+erb9bz\nlzdnk5mdg16n5VR6Bu6d3Qh6fBQRUTF0ce/MM0EBxMUfxqdfXxQKRf22ly5ar9vwdWQU816ZTubp\nPPRaLacys+jcsSOB/n5Ebt1OenYuTipH7O3sGPSgj9W2Uk2t1es1UVuYO30q2Xn56LUa0rJz6Ozq\nyli/IWzYsZNSk5H0nFzkcjkjfAdaB9loTZY1m7cwN/hFi0ujIS07l84dOzDWbzAbdvxAdn4B86ZP\nJTu/AEd7e+sveqPlStZEb2HuSy+SlV/nysmlc4cOBA59jA3f7yI7P5/Ql160uBzsWhw0bOqKiWXu\ntClk5RWg16ivdhUUXHHZ27c4aKzZspW3p04mO78AnUZNWs5pOrvewdghj7Fh526y8wuYO23K7e2K\nsXbp1GrWb/ueCxcv8qiPd5PlbcrVuN9v2cbbL06qe68GnVrNd9u/58LFKh7t791k+WVkMut1em6Z\nGG3oarzuzI2MOY3X1rmRcVXWYLwGWBWxkfmvvUxmzmnLeJ+ZhXunjjwxagQRMVsxl5dTVV2Ni8HA\noIcesNq28Ri9euNmQl8OJut0HjqthlNZ2bh3dCVwuB8btu1gYlAgD/S5n+Kz5xg7fKh1vRqt3XQj\nMSrs7a1ef/Xtev7yxmwyc3LQ63SWY5pbJ8sxbfMWyzEtMIC4hCP4ePWxOqa10+hpjt9leksmkzFq\n1CgWLFhAXl4ewcHB3HfffYSGhqJSqXBxcSEuLg6VSkVQUBD9+vUjMTERgLy8PDp16sT48eOvcl7G\nz8+PmTNnEh4ebuWTyWT4+voSEhJCcXExwcHB+Pn5cezYMcLCwtBoNGi1WuLj4wHo1asXM2bMQKVS\noVQq8fDwYPTo0U3GNH36dHr27Fn/et26dXz00Ue8/vrrKJVNnzCTyWS8+eabvP/++3z55ZdXxXvx\n4kVSUlKYP38+f/nLX1AoFBiNRqZNm8aiRYtwdna+7rZ3MRgoMxoxmswY9Dp0Wi0mczk+/fqyc89+\n8goKkclbt9td9DrKjCaM5nIMOi06jQZTeQWZuacZHxiAh7sbicdPtMrlrNdRarK49FoNOo2a8opK\nfHrfz5K35vBgn/s5mZnVOpdOS6nJjLG8HL1Gi06tpryykpGDHmHx519wMjMbU0VlK106ysxmTOXl\nGLQatA1dYV+QmpWF+aa4tHWuCmuX70AWr1hJalY25oqKVrkMOi1lpnJMde112eXvO5AlK1ZyMiu7\n1fW6pV3mcgCmPBGAo4NDs+XCdaV/geUMoqODfbPlf2S9blWXLcccW46rLgYdpUYTRrMZvU6HTqPB\nXFHBIw/6sOC1l3F368TRY8dbF6NeR5nJhMlcjl6nRadR148xW3bvZcSjj7TKY/sYDZSVXT6m6a8c\n07z6snPvfvLyW39Mg9/xmh5HR0dkMhn2ddmbXC5HoVAwZ84cpk+fjpeXF7GxsSQkJNC1a1f0ej01\nNTXMmjWLHj16EBYWRlpaWv0qs2VlZfVujUZT72zou/y5Df+Vy+VIkkRtbS0zZsxg9uzZPPTQQ1bv\nUSqVXLrO1X9lMhmSJCFvobEVCgV2dnbI5XLatWvXZLyX47tw4QLnzp2jXbt2hISE8MUXX1xXfZQK\nBZIkca6sDINej4uzwdLpTGZ0Wg3HT6QyYuhjBI7252jysZZdyjqX0YRBr8PFoKfMZMZoLken0WDQ\nabGza4fayemaK+Rerlep0YSzToeLXo/RbDnQadVqtu7Zj127dug0GozlLQ9Al+tVajLjrNPiotdh\nLDfXu8rPVzJv+lTu7NSRO5ybz/QtLmWdy4RBq8VZp6esrl4atRMVleeZFzyVLp060t5g+ONciisx\nGrRay0BkLsdUXoFGrba4XnqRLh1b7yozmTHoNA3aqwKt2omK8+cJfelF7uzUkfaGa7TX7eBq4ozQ\ntcrbnEtp3b+ut/y2iPF3aq8bH3NsP66eKzPhrNfhYjBgNFmSH61GTWp6BjKZDG3dDENLKJRXxmiD\nzlIvo8mMyWyJESDrdB73dPVs0WPzGOvG1XNlZRgMl49pxivHtF9SGTHkMQJHj7jmMc0q3t9jeish\nIQFPT09cXV2JiYkhICCAw4cP4+Pjw/r169mzZw/9+/dHLpcTHx9Pbm4u2dnZjBkzhrCwMHJyclAo\nFPj7+1NYWEh0dDQ5OTk4OTmh1WpRKpV0794de3t7wsPD630VFRX1ZdHR0QQEBJCamopSqWTgwIF8\n8skn7N+/n27duqFWqykuLsbb25u9e/fi7e1NRkYGmZmZ9QlUYy67unfvjrOzM59++ikpKSlUVFQ0\neYYoJiaGzMxM9u3bx7hx4wCs4h07dizFxcVs376d3bt3M2zYMA4cOMCcOXPYvn07Wq0WNze3JuvS\neHrLycmJsK9WU11dQ0FxMSP9hrB6fSQHDsUz+flnyMjO5pvITeSczmPsKH/s7a5cO9N4ekutciT8\n20hqamooLClhxKO+rI2K4eDhRCY9GYiHe2f+u/pbSs6eI2jkcKvEr/GpUyeVIysjNllcZ84w/JEB\nfBuzlYOJybwQ+Dj2dnZ8vSmakrPneNJ/OPKGp0sbfSmcHB1ZGRlFTU3tFdeWbRxMSuGFgDGUV57n\nq42bqb10iWEDHrZusEbfL7XKkZUbo6iuqaHozFmGDXyYdbENXZV8tSma2ku1DBvwUJP74Hd11dZS\ndPYswwY8zLqt2zmYnMILAaMbuC5d0+Xk6MjKTZvr2v4cwwY8VO+a8PhoyivPsyrK4vJ7+MHb21Vb\nS8GZs/S91zI1fTA5hYFeferLay/VMuzhVrbXn8klXe36Miqa6poaCs+cpc//XXEN6NunvrymtpZh\njdq+8fTWLROjDV2Np7duZMxpPL11I+Nq4+ktJycVK9aus7iKSxgxeBBrNmzmQMJRJj0dxK8XLrI6\nchMlZ8/x1OOjrPZd4zFarVLxxfrL9TrD8EGP8M3mLcQdTWRi0FjUTir2xR/G94H+V7V14+mtG4mx\n8fSWk5OKsFVrqK6poaCoiJFDh7A6IpIDhxKY/PzTZGTn8E3kRnJO5zN25AirY1pL01vi2VuNKCgo\nYPPmzVZ/8/X1pVevXi1ut3btWswNTgd36NChPtH5PRDP3ro+xLO3/iS0hRhtiHj21vUhnr11fdyO\nz94SSc9tikh6rg+R9PxJaAsx2hCR9FwfIum5Pm7HpEes0yMQCAQCgaBNIJIegUAgEAgEbQKR9AgE\nAoFAIGgTiKRHIBAIBAJBm0AkPQKBQCAQCNoE4tlbtylKB5XNXDK54tpvaiW/5ufbzHWpusZmLpsi\ns91dLHKl7dpeYdf8c8uul+tZ4fQP5Ra9g6jxGjY35LLhnTryZlaLv+nYsr1s6EJ2i/Z7G6JwsN1d\nZUi2u8P2j+LPv4cFAoFAIBAIEEmPQCAQCASCNoJIegQCgUAgELQJRNIjEAgEAoGgTSCSHoFAIBAI\nBG0CkfRjQEoHAAAgAElEQVT8BgoLC292FQQCgUAgEFwnt+j9jLc2CxcuZMWKFU2W9ejRg2XLluHv\n7w/ApEmTGDhwIMHBwdTU1BAYGEhwcDABAQHX/Jxp06YRHh7eqjodSUxmy7btODo6MDdkNlVVVfx9\nyT8ACJ4yiS53urN95y4Ki4qY8sL4Fl1Hk1PYsmMnjg4OvP36K1RVVfHeR/+01GniBH45mUZiSgqV\nledZOPcN7Fq4VTrpRCrbf4zDwd6eOZMnUFVdzdIVX1raJmgseUXFxKcco9RoYv7Ml3BowZWcepLt\nBw7iYG/P7BeeB+Cc0cSHX3zJ0jdmN1n+h7h+Ocn2A3E4ONgz+4XxVFVX84+Vqywxjn2c42npHDuV\nRnuDgReDxrbosmV7Jf18gm17f8TBwZ6QqZOpqq5myf8s/XbKk0EcTvmJ42lppGXlEDJ1El49ezTr\nSvz5BNv27MfRwZ6QaVMsrv+EWVxPB/HrhYts2PY91dXVvDZlInqt5o9xHT/Btj37cHRwIOSlOte/\nG7guXmTD1h1UV9fw2ot/pOtntu62xPhG8ItUVVez+N+fA/Di0+MsMW7dQVVNNa+/OKll10/H2frD\nXotr5ktUVVWz+NP/WFzPPcWdnd3IKyjk0y9WsfSvoc16AI4e+4mtO3fj4ODAW7OmU1VVzfv/Wg7A\n1PHP0qWzG6cLCvk0bCX/WLTgj3OlXHbZ89YrM6iqquL9f35mcU14lvyCQnbVfS/enDX9mq7Ynbtw\ndHBo4Kqr14TnLK79B3BwcGiF6xix39e5Xp1pcS379IqrsM5lb8+br8wUrlu07RuiWLRo0aJWv9vG\nLF++nNjYWOLi4ti2bRuZmZmsWLGC2tpaIiMj2b59O3K5HJlMxtKlSzl06BCxsbEMGzaMd999l0OH\nDhEVFcXQoUPrE4nk5GQiIyPJz89n2bJl5OfnU1JSwnfffVfvS0pK4vPPP+fEiRNERESQk5NDeHg4\nXl5eHD9+nJUrV7Jnzx7Onj1LZWUlixYtoqCggLVr19KzZ09WrVqFi4sL99xzz1UxHTx4kKKiIvz9\n/cnOziYuLg53d3e8vb3ZunUrnp6exMTEEBQU1GSbnDhxgr///e8kJyeTm5vLU0891eT7ai+et3q9\n8us1LJz3NhmZ2Rj0Ok6eSsO9c2eeChzL+siN3HHHHRw+mohCoaBf395W20q1tVavv1yzjr++HUJm\ndg56nY6T6em4u7kxLmAM322MxrPLnTz/VBBFJWfQ6bQY9Lr6bWvKy61cazZvYW7wi2Tn5aPXaEjL\nzqVzxw6M9RvMhh0/UGo0kp6Ti1wuZ8QjA6zr1egp62titvL21Mlk5xeg06jRqdWs3/Y9Fy5e5FEf\n7ybLm+OGXI3WBVmzxfq9aTmn6ex6B2OHPMaGnbvJzi9g7rQpZOcX4Ghvb3WAa7wezo20l0xhvebP\n6k2bCZ0ZTNbpPHRaDaeys3Hv2JHAYX5s2PY9E4PG8kCf+yk+e46xw4ZauxrF+PXGKOa9PJ3M03no\ntVpOZWXTuaMrgSP8iNy6g/TsHJxUjtjb2THoQZ9m2/2GXY3rFRnFvFcauDKz6NyxI4H+fkRu3U56\ndm7r63UDrsbttSoyivmvzCAr9zR6rYZTmdm4d3TlCf9hRMRuJz0nB5WjI/b2djza2NVoP676bgPz\nX59FZu5p9DpLvdzdOvLEyBFEbNnGA159WB0ZhQwZvg81cjXqX6vWRbJgzqtk5eSi12k5mZGJe6eO\nBI0eSUR0LA/068vq7zYik8Gghx9ssb1uyNW4vdZFsCDktSuu9Ezc3ToRNGYkEdFb6NK5M88EPs7B\nw0fx6dsbRYM2atz2X30bwV/eeK1+/DpV7xpFxOYtdHG3uOISjuDj1cfK1bheX327nr+8MZvMnMuu\nDIvr8YaugKZdjfhzuqRbsu3bafTNxntTp7dkMhmjRo1iwYIF5OXlERwczH333UdoaCgqlQoXFxfi\n4uJQqVQEBQXRr18/EhMTAcjLy6NTp06MHz/+Kudl/Pz8mDlzJuHh4VY+mUyGr68vISEhFBcXExwc\njJ+fH8eOHSMsLAyNRoNWqyU+Ph6AXr16MWPGDFQqFUqlEg8PD0aPHt1kTI6Ojjg7O1NcXExUVBRB\nQUFIkqVjrFu3jieeeIJ7772XvXv3Nrn9ypUrWbx4MfPmzcPBweG629TFxUBpWRllRhPOBj06nRaT\n2Uw3Tw8CH2+6zs3h7GygtMyI0WjGoNej02oxl5sZ8KAPxWfOkJ17mq4eXVrn0mkpNZkxlpej12jR\nqdWUV1bi07sXS96czYN97udkZnarXAadljKzJbma8kQAjo3aqWH5H+4ylWOqi1FbF6O/70CWrFjJ\nyaxszBWVrXLZsr2c9TrKTCZM5nL0Wg06jRpzRQUAW3bvY8SgR1rlAXDR6SkzmTCazRh0WnQaDaby\nCjJP5zF+7ON4dO5M4s8n/niXXkeZ0YTRXG7tyj3N+MAAPNzdSDz+x7uc9XrKTOY6lw6dRoO5vILM\nnNNMeCLAEuPxn1tXL4OeUqMRo8mMXqdFp9VgLi9n846djBr62HWt9+dsMNS5TBj0unpX1LbvGeU3\n+LoW/LOtqy5Gs/mKy1yOj1cfdu77kdMFha1eONPFYKCszNJeBp3FZSqvc+39kbzf6qobC03mcny8\n+rJz737y8oWrWdct1PZwC1zT4+joiEwmw97eHgC5XI5CoWDOnDlMnz4dLy8vYmNjSUhIoGvXruj1\nempqapg1axY9evQgLCyMtLS0+sSirKys3q3RaOqdDX2XP7fhv3K5HEmSqK2tZcaMGcyePZuHHnrI\n6j1KpZJLl669AuW4ceOIiIjAbDbTvn17AI4ePcqZM2dYtmwZZ8+ebXZ6TCaT1cfSUnbeGKVSiSRJ\nnDtXhrPBQHsXl7qExYROqwWo97bWVVpahrNBT3sXA2V1g5pWo6WgqIgvvv6GN1pxSlGpVFhcJjPO\nOi0ueh3GcjOm8nK0ajVb9/2IXbt26NRqjOUtJxdKhcVVZjJjaGI64Frlf4hLp2kQYwVatRMV588T\n+tKL3NmpI+0Nzf8CAdu2l0JRtx+NJgw6HS56PUZzOSazxQWQdTqPe7p6tuhpGOM5k9HiMlw5mOs0\nagxaLXZ27VA7qeAa/cymrrr2Ome0HHCtXRoMussupz/WVRdjqdGIQaetc1kSKZ1WjUGnw86uHRon\np2uprny3y4w46/W4OOsxmswYzWa0Gg2Jx46zLiqGpOMnSE3LaF29ysrqXAZLgmeqc6X8xLpN0ST9\n9DO/nEr/41yXx5zLMRoauLQajv9ykhGDHyVw5HASU366hqtuP5aVYTDU1ctkcek0da4hjxI4agRH\nU461ql5WrrrEU6fVcPyXVEYMeYzA0SM4mixct2rbN+SmTm8lJCTg6emJq6srMTExBAQEcPjwYXx8\nfFi/fj179uyhf//+yOVy4uPjyc3NJTs7mzFjxhAWFkZOTg4KhQJ/f38KCwuJjo4mJycHJycntFot\nSqWS7t27Y29vT3h4eL2voqKiviw6OpqAgABSU1NRKpUMHDiQTz75hP3799OtWzfUajXFxcV4e3uz\nd+9evL29ycjIIDMzsz6BakhMTAxTp05l2bJlPPvssygUCoqKiti1axcLFy7kySefxN/fn/3799Oh\nQwfc3Nystvfw8GDp0qUkJSVRXFzc7DRY4+kttdqJ/4WvpLqmhoLCQkaNGMbXa9ex/+BBpk6agFqt\npqKikvSMzGtOb6mdVIR9uZrq6hoKiooZ6TeU1esiOPD/4pn8/DN88PEn1NTUcOBQPP93911oGyQF\njae3nBwdWRkZRU1NLYVnzjD8kQF8u2UbB5NSeCFgDPZ2dnwdFUNJaSlP+g9D3uBXYePpLSdHR1Zu\n2kxNbS0FZ87S917L9OLB5BQGevWpL6+9VMuwhx9qst1s4mr0y7XeVVND4ZlzDBvwEOu2budgcgoT\nHh9NeeV5VkVFU3vpEn6NTvM3/oVyI+3VeFpErXLki+8i6+p1huGDHuGbzVuIO5rExKAA1CoV+xKO\n4OvjfVX7NP51rlapCF8XYXGVnGHEoEdYuzmGg0cTmRQUiIe7G/9ds46Sc+cI8h+OvIVfXjfkuqpe\njoR/WxdjSQkjHvVlbVQMBw8nMunJQDzcO/Pf1d9ScvYcQSOvVa/f7mrcXk5OKsK/jaD6coyP+bJ2\nUzRxR44y8ckn8HTvzH9Wf0vJ2bOMa+xqtB+dVCpWrF1HTXUNhcUljBj8KGs2RHEg4QiTnh7H48OH\n4vugDxnZOQSOHG69HxvFq3ZSEfb1N5ZxorgY/6GDWROxiQPxCUx+9ikeH+GH70MPkJGdwxOj/Ztt\nqxt2NdG/wlY3dm3kwKHDTH7uaTKyc/hmQxS5efkE+A/HvsG1bFe3vRNhX6+1uIqKGek3mNXfbbDU\n67mnycjO5pvIKHLy8hjrP8LKddV320lF2Ko1da4iRg4dwuqISA4cSmDy83X1itxIzul8xo5s5GrE\nn9MlNXLdGm3f0vSWTGrtz3+BFQUFBWzevNnqb76+vvTq1avVjj179pCammr1t5deegllK56XU2U6\n2+rPuRa1VRdt5hLP3ro+bPnsLbl49tZNQzx76zoRz976c2DLZ2/ZsO1VnTya/xiR9NyeiKTnJiKS\nnpuHSHquC5H0XK/sFu33tyq3YdIj9rBAIBAIBII2gUh6BAKBQCAQtAlE0iMQCAQCgaBNIJIegUAg\nEAgEbQKR9AgEAoFAIGgT3KKX9guuRf/e42zm2vDBazZzdfDubjOXY2cXm7nkNrwjRtYW7vCw5R0x\ngpuHLW/OFX1C8DtSU1nxh3xOGxi9BQKBQCAQCETSIxAIBAKBoI0gkh6BQCAQCARtApH0CAQCgUAg\naBOIpEcgEAgEAkGbQCQ9tyiFhYU3uwoCgUAgEPypELes36IsXLiQFStWtPr93g/0ZkzQCH49/ytL\n//4ZAHMXvQaSxNGEFH7Ytp/howfj5u7KV5+va9F1PCeL3ceScLCzI3jEGADKKsr5NGYTf3tuIkfT\nT5GYkUZZRTmvB4zDvoXbwRN/PsG2PftxdLAnZNoUqqqrWfKfMACmPB3ErxcusmHb91RXV/PalIno\ntZpmXUeTU9iyYyeODg68/forVFVV8d5H/wRg2sQJ/HIyjcSUFCorz7Nw7hvYtfAAziNJyWzZtgNH\nB0fmhrxGVVUV7y75CICXpkyky53u7PhhNwWFRUx54fkW2+tIYjJbtm3H0dGBuSGzqaqq4u9L/gFA\n8JRJlJw5a1V+e7qS2LJ1O46Ojsx9o861eKnF9eJkSs6csSoXrlvVJfqEcN0erqMpx4j9fheODg68\n9epMqqqqeH/ZpwBMnfAc+YWF7Np/AAd7e958ZWaLroYoFi1atKjV774NWb58ObGxscTFxbFt2zYy\nMzNZsWIFtbW1REZGsn37duRyOTKZjKVLl3Lo0CFiY2MZNmwY7777LocOHSIqKoqhQ4cSHBxMQEAA\nycnJREZGkp+fz7Jly8jPz6ekpITvvvuu3peUlMTnn3/OiRMniIiIICcnh/DwcLy8vDh+/DgrV65k\nz549nD17lsrKShYtWkRBQQFr166lZ8+erFq1ChcXF+65554m4/rvv76yev3izOd5d/7HdOvuibHU\nSLe7Pelx///Rrl07dm7dh3N7Aw887EVtbS3JR45bbfvM0AetXkce3M8rY54g90wJWpUKrUpF1KE4\nLlZXMeC+nuz9KZms4kIUcjmP3d/XalsnN+u1db7eGMW8l6eTeToPvVbLqaxsOnd0JXCEH5Fbd5Ce\nnYOTyhF7OzsGPehjta1CpbJ6/eWadfz17RAys3PQ63ScTE/H3c2NcQFj+G5jNJ5d7uT5p4IoKjmD\nTqfFoNfVbytTWD/NfOXqtSwMfYvMrCyL61Q6d7q78WRgAOs3bKLDHe1JOJqEUqHAq09vq20bP9V5\n5ddrWDjvbTIyszHodZw8lYZ75848FTiWdZEbOPFLKn8NfYuMrBwMeh16nY7muGVcjWNctYaF8+eS\nkZWFQa+3uNw789QTgayLqHPNe5uMrGwMen3L9RKum+cSfUK4blHXpeoqK9dX367nL2/MJjPHMt6f\nSs/A3a0TQY+PImLzFrq4d+aZwADiEo7g49UHRYMxvp1G32wd//TTWzKZjFGjRrFgwQLy8vIIDg7m\nvvvuIzQ0FJVKhYuLC3FxcahUKoKCgujXrx+JiYkA5OXl0alTJ8aPH3+V8zJ+fn7MnDmT8PBwK59M\nJsPX15eQkBCKi4sJDg7Gz8+PY8eOERYWhkajQavVEh8fD0CvXr2YMWMGKpUKpVKJh4cHo0ePvu54\nz50txbm9AddOd3Dyl3SW/v0zJkx9iuyMXKIitl2XS69WY6qsBOC5QUNwqDtz4tXtbv7y9AS87upO\nemF+q1wuOj1lJhNGsxmDTotOo8FUXkHm6TzGj30cj86dSfz5RKtczs4GSsuMGI1mDHo9Oq0Wc7mZ\nAQ/6UHzmDNm5p+nq0aV19XJ2prSsjDKTyeLSaTGZzXT19CBwzKhWOepdLgaLy2jC2VDnMplBJkMm\nk9WX39auy+1lNOFsMNS3V72rrly4bnGX6BPCdbu4DAbKyowYTVfGe5O5HB+vvuzcu5+8/EJk8tan\nMn/6pAfA0dERmUyGvb09AHK5HIVCwZw5c5g+fTpeXl7ExsaSkJBA165d0ev11NTUMGvWLHr06EFY\nWBhpaWlIdaubljXYWRqNpt7Z0Hf5cxv+K5fLkSSJ2tpaZsyYwezZs3nooYes3qNUKrl06dJ1x1hT\nXQOAS3tnSs+WUVZq4uKFKi78eoGaGktZ4zMUzaGoq6exogKdk9NV5T+kJNJOqUTr6Ij5/PkWXUqF\nAkmSOGcyYtDpcDHoKTOZMZrL0WnUGLRa7OzaoXZSXXP1WKVSiSRJlJaW4WzQ097FQJnRiNFkQqvR\nUlBUxBdff8MbrTjVqVRYXOdKS3E2GGjv4lzv0mm1APX7+5quunqdO1dW53KpS8pM6HU62ikVVuW3\ntatBe5WWldW5tLRrVC5ct7hL9Anhul1cZWUYDHpcnC+P92Z0Wg3Hf0llxJDHCBw9gqPJx1p0NeRP\nP72VkJCAp6cnrq6uxMTEEBAQwOHDh/Hx8WH9+vXs2bOH/v37I5fLiY+PJzc3l+zsbMaMGUNYWBg5\nOTkoFAr8/f0pLCwkOjqanJwcnJyc0Gq1KJVKunfvjr29PeHh4fW+ioqK+rLo6GgCAgJITU1FqVQy\ncOBAPvnkE/bv30+3bt1Qq9UUFxfj7e3N3r178fb2JiMjg8zMzPoEqjGNp7cqKiqZOXsy7eza0dm9\nE7FROxk/ZRyP+g1g07otnC0pRa1x4u7/63rN6S0newe+3b+bmtpaSoxl9PTwBOBwWioP3HMvNbW1\nRMTt46zZxBifh5E3SKYaT2+pVSrC10VQU1NDYckZRgx6hLWbYzh4NJFJQYF4uLvx3zXrKDl3jiD/\n4cgbZOyNp7fUTirCvlxNdXUNBUXFjPQbyup1ERz4f/FMfv4ZPvj4E2pqajhwKJ7/u/sutA2uD2o8\nvaVWO/G/L76kpqaGgsIiRg334+tv1vNj3P/jxYnjUavVVFRUkp6Zdc3pLbXaif+Fr6S6poaCwkJG\njRjG12vXsf/gQaZOmkBHV1f+F76SmtpaRo0Y1nhX3pquxjE6qS2u6mpLe112xR1k6sQXrrhqaq5d\nL+G6eS7RJ4TrFnU1nt5yclIRtmqNpa8WFTFy6BBWR0Ry4FACk59/mozsHL6J3EjO6XzGjhyBfYNr\nOFua3pJJrf05K7il6O3xqM1ct+qzt+xcxLO3bhriOUt/DsSztwS3CbZ89paqk0ezZW1g9BYIBAKB\nQCAQSY9AIBAIBII2gkh6BAKBQCAQtAlE0iMQCAQCgaBNIJIegUAgEAgEbQKR9AgEAoFAIGgTiFvW\nb1NKkxNs5pK38Iyq66WdTmszl0xpu0fDyeSKa7/pJiBdqrWdrNZ2LpsOC7foECNT2LB/KWz3+/GW\nHZIv3aL1kq5/MdfbDhsuk2HLcfXi2bM2c12qrraZy7l3/2bLxJkegUAgEAgEbQKR9AgEAoFAIGgT\niKRHIBAIBAJBm0AkPQKBQCAQCNoEIukRCAQCgUDQJhBJz2+gsLDwZldBIBAIBALBdWK7e9faEAsX\nLmTFihVNlvXo0YNly5bh7+8PwKRJkxg4cCDBwcHU1NQQGBhIcHAwAQEBTW7/2Wef4evrS58+fa6r\nTkknUtn+YxwO9vbMmTyBqupqlq740lKHoLHkFRUTn3KMUqOJ+TNfwqGF29STfj7Btr0/4uBgT8jU\nyVRVV7Pkf5Z4pzwZxOGUnzielkZaVg4hUyfh1bNHs66jKT+xdeduHBzseeuVGVRVVfH+Pz8DYOqE\nZ8kvKGRXXb3fnDW9xRiPJh8j9vsfcHRw4K3XXra4Pv7E4nrheYtr3wEcHOx589WZ13ClsGXHThwd\nHHj79VeoqqrivY/+CcC0iRP45WQaiSkpVFaeZ+HcN7Brob1s67JhjCk/Ebtzl8VV3/bLLa4Jz1lc\n+w/g4OBw7ba35X48dtnlwFuzplNVVc37/6qr1/hnyS8sYtf+OEuMLwffkKtLZzdOFxTyadhK/rFo\nwTViPEbs93Xt9epMS4zLPr3SXoV17WVvz5uvXKt/HbP0CUdH3q7bj+99ZNmP0yY+zy+n0khM+YnK\nykoWvh3yx/UJm/YvG7aXTV227fe3psuWfdV245fl2LEfB3sHQqbVHTv+W3fseCqIwynHOH4qnbSs\nbEKmTW7x2JF04he27z+Ag70Dc6a8YDmmha0EmjqmBeNg37qlVxSLFi1a1Kp3/g4sX76c2NhY4uLi\n2LZtG5mZmaxYsYLa2loiIyPZvn07crkcmUzG0qVLOXToELGxsQwbNox3332XQ4cOERUVxdChQ+sT\nieTkZCIjI8nPz2fZsmXk5+dTUlLCd999V+9LSkri888/58SJE0RERJCTk0N4eDheXl4cP36clStX\nsmfPHs6ePUtlZSWLFi2ioKCAtWvX0rNnT1atWoWLiwv33HPPVTEdPHiQoqIi/P39yc7OJi4uDnd3\nd7y9vdm6dSuenp7ExMQQFBTUZJskJCSwZ88e9u7dS2pqKg888ECT7/u1KN/q9ZrNW5gb/CLZefno\nNRrSsnPp3LEDY/0Gs2HHD5QajaTn5CKXyxnxyACrbWUK6zVsVm/aTOjMYLJO56HTajiVnY17x44E\nDvNjw7bvmRg0lgf63E/x2XOMHTbUaluFg73V61XrIlgQ8hpZObnodVpOpmfi7taJoDEjiYjeQpfO\nnXkm8HEOHj6KT9/eKBrURSa3PhH51Tfr+cubs8nMzkGv03IqPQP3zm4EPT6KiKgYurh35pmgAOLi\nD+PTr6+1q9E6F1+uWcdf3w6pc+k4mZ6Ou5sb4wLG8N3GaDy73MnzTwVRVHIGnU6LQa9rcj/csKvR\nmiw3EuNVrm8j+Msbr9XX61R9248iYvMWiyvwceISjuDj1cfa1Ygb2Y9XuyJZMOfVK66MTNw7dSRo\n9EgiomPp0tmNZwLHcDDhCD59r1Wvll0P9OvL6u82IpPBoIcftNr2qv717Xr+8sZsMnMut1eGJcbH\nG7ZXQJPtJZPLrFxfrl3HX9+aQ2ZWTl17ZdT1iVF8tykGzzvdef7JJyx9Qtty/7qhPmFLV6Nlem6k\nva6q1w25fr9+f8u4ZLJGrhvpq7YbC2vPn7dyrd64mdCXGxw7srJx7+hK4HA/NmzbwcSgwCvHjuHW\nxw7pkvV6S2uitjB3+lTLMU2rIS07h86uroz1G8KGHTspNTU4pvkOtNrW0dWt2f1wU6e3ZDIZo0aN\nYsGCBeTl5REcHMx9991HaGgoKpUKFxcX4uLiUKlUBAUF0a9fPxITEwHIy8ujU6dOjB8//irnZfz8\n/Jg5cybh4eFWPplMhq+vLyEhIRQXFxMcHIyfnx/Hjh0jLCwMjUaDVqslPj4egF69ejFjxgxUKhVK\npRIPDw9Gjx7dZEyOjo44OztTXFxMVFQUQUFB9YuNrVu3jieeeIJ7772XvXv3Ntsu48aNY8mSJSQl\nJV13mzrrtJSazBjLy9FrtOjUasorK/Hp3Yslb87mwT73czIzu3UuvY4ykwmTuRy9VoNOo8ZcUQHA\nlt37GDHokdbXy6Cn1GjEaDZj0OvQaTWYzeX4ePVh574fOV1QeNWXsTlcDAbKjEaMpssuLSZzOT79\n+rJzz37yrsPl7GygtMyI0WjGoNej02oxl5sZ8KAPxWfOkJ17mq4eXf5wly1jdDEYKCurc+ksbW8q\nr2v7vT9eX3vZcD86GwwWl8l0xVXe2CW7tqgFV9S27xnlN9hqXLgWVu1Vtx9N5nJ8vPqyc+9+8vJ/\nQ/+qr5cWs/lynzh7c/vE79G/brC9fjfXDfb728Jlq75qi7Gw4bFDp2107NjLiEev49ih11FqMmFs\ncBwqr6jEp/f9LHlrTt0xLavVvpt+TY+joyMymQx7e8sZArlcjkKhYM6cOUyfPh0vLy9iY2NJSEig\na9eu6PV6ampqmDVrFj169CAsLIy0tLT6xKKsrKzerdFo6p0NfZc/t+G/crkcSZKora1lxowZzJ49\nm4ceesjqPUqlkkuXrr3657hx44iIiMBsNtO+fXsAjh49ypkzZ1i2bBlnz55tdnqsYb2vZ6BWKhVI\nkkSpyYyzTouLXoex3IypvBytWs3WfT9i164dOrUaY3l5iy6FQmlxGU0YdDpc9HqM5nJMZosLIOt0\nHvd09WxFvepcZUac9fq6wdaE0WRGq9Vw/JeTjBj8KIEjh5OY8lPLLoUlxnNlZRj0elycr7h0Wg3H\nT6QyYuhjBI7252jysdbVq7QMZ4Oe9i6XDwImtBotBUVFfPH1N7xxjVPDNnfZNMYGLkOdy1Tn0tS1\n/ZBHCRw1gqMprWwvG+7H0rIyi6tBjFqNhuOpda5RI27YlZjyE+s2RZP008/8ciq9VTFatVddYqDT\nas8MAMUAACAASURBVDj+SyojhjxG4OgR19+/nJ3r6mVCq9VSUFTMF6u/4Y1ZM1r0NIzRJn3id/gO\n2bK9bOOyZb+/VV2/Y1+9gfFLcfk4dPnYYdBjNJl/27FDccXl3PA4dPmYtme/5Zim0VzzmGZVx5s5\nvZWQkICnpyeurq7ExMQQEBDA4cOH8fHxYf369ezZs4f+/fsjl8uJj48nNzeX7OxsxowZQ1hYGDk5\nOSgUCvz9/SksLCQ6OpqcnBycnJzQarUolUq6d++Ovb094eHh9b6Kior6sujoaAICAkhNTUWpVDJw\n4EA++eQT9u/fT7du3VCr1RQXF+Pt7c3evXvx9vYmIyODzMzM+gSqITExMUydOpVly5bx7LPPolAo\nKCoqYteuXSxcuPD/s/fmAVFW++P/CwaVZZgFTE1RtNLKLCO1LLUyMREVFe3mvpbZet1SKzW+n7rd\nVsuW+0tCVHBBcUdSyy0EBZRF7ebGqgIDKMOw2A0G+f0xI8IoCDkpNu/XPzrzPM9r3ufMmTNvnjPn\nHEaOHImPjw9RUVG0aNGC1q1b11onV2K7HpbDWy5OTgRv2ILRWEFOfj7P936Ktdt3cDDpKOP9BtOs\naVNCtkSQV1DASJ/+2FdLqCyHt5TOTixbvwGj0WhyPd2bNVu3E5OQxAR/P5TOzvwSf4Q+PbpdE5fl\n8JbS2ZnA0DWUG41k5+bi068vq8I3ER17mEmj/0FqRiZrNm7h7Pks/Hyep1m18WLLv1BcXFwIXBFK\nebnJNdD7OULXbSA6No5JY14kNSODNRs2k3nuPEN9fWq6LIa3lC7OBC43u3S5DPTuR2hYONGHTK6P\nvliC0WgkOjaO+++7F5XK9brvw027LIakbqaM13WFrDbVvS6Xgd59CV2/kei4eHPdZ7BmwxYyz59n\nqM+Ami7LMt7E+3jd+gqxdG02xTXqBZNr01bOnjt/064hA7zp0/NxUjMyGT7Ip8a117YvZwJXrjLX\nl46B/Z4jNHwD0bHxTBpjLuOGTWSey2LowAEWbbXmHyhKFxdTm7ji8u5H6LrwKtdHXyzBWGEkOjae\n+zveg8q19vZ1U23Cmq5KS9efr69r47oZ11/X7huNy+IP4Jtrq9brCy2Ht5TOzixbZ/7uyKv+3ZHI\nBP+hKF2c+SXuMH0ev3abCMvhLRdnJ4LDN1/9Hur9FGsjfuRgYjLjhw0xfadt3kbehYuM9Hke+2qf\nwbqGt2TvrT9JdnY2W7durfFcnz596NKlS70d+/bt4+TJkzWemzZtWp3jwleQvbca6JK9txqE7L3V\nUJfsvXXbkL23Gqay8b23JOm5Q5Gkp4EuSXoahCQ9DXVJ0nPbkKSnYSobT3pu+296BEEQBEEQbgWS\n9AiCIAiCYBNI0iMIgiAIgk0gSY8gCIIgCDaBJD2CIAiCINgEsvfWHYpjy5a3O4TrYs1f4Ns3qf/i\njDei0mi0mot6rhJcL5UVZxDhYL1ZeNbEmjObrInl2k03Q6UVZxBZ8zNk1dmBVsSqcVlxtlt5cYnV\nXMaSUqu5Kv6wXptoonSymsuq30NW7FfrfJlb8iqCIAiCIAi3GUl6BEEQBEGwCSTpEQRBEATBJpCk\nRxAEQRAEm0CSHkEQBEEQbAJJeupBTk7O7Q5BEARBEISbRKas14NFixbxww8/XPdY586dWbx4MT4+\nPgBMnDiRXr16MW3aNIxGI8OGDWPatGn4+fld9/rz58/zn//8B1dXV9RqNa+99hpTp05l2bJlDYox\n4egxIn/ag5OjI2+/+SplZWX8a/HXAEwdN5qsnBz2REXj2KwZc9549Za5Eo/9SuTuvTg5OjLntWmU\nlZXz0ZJvTa4xL9K2TWvOZefwTdByPl30bt1xJR9j+66fcXJyYu5br1FWVsaHny8B4KUJYzhx+gyJ\nR49TWlrKormzaFrHRqpWra/kY0T+tNvkMsf1ry9McU0dP4as7Bz2/BKNo2Mz5rx5I9dRtu/8GSdH\nR+bOeIOysjI++OxLAF6eOI4Tp06TkHyM0kulvD9vTt1lTDrK9p27TPU1402T69MvTK5J42nn4cGu\nPXvJ0eUyaezouuOyoutIUjLbd+zCydGJebPeMrk+/tzkmjyBdm092LV7L9k5OiaPH3PrXInJbN+x\nEycnR+bNmkFZWRn/9/FnAEybPJF2bT3Y+fMecnQ6Jo8fe8viSkg+amr3jo7M/ecb5nZvahMvTRjH\niVNnSDx6lNLSSyyaN7vuNmHNtmrVPuc4P/68F0fHZrz9xnST60tzPzFulCmuAzEm1+uv1O06dsXl\nyNuvv0JZWTn/+uobk2vsKNqZ+5yvA4P5LOC9Ol1J//2NHfujcGzmyKyXJlFWXs7H/5/pu2DyC/4c\nPnqMX0+ncCY9g1kvTcLroc61upJPnmJn9EEcmzVjxvgxlJWX81lwCAAThw7m15RUjp06TXM3LVOG\nD60zLoDkU6f56VAsjs2a8dboFwG4aDDw2cpQPn7rDUp//513v/0PS96eXXcZfzvJTnPdzpw0jrLy\ncj79YbkpLv+h/Hr6DEdPnuYuNy1TRw6v03Ur+9WTp1NIPHqMkkuXWPT2zDrbfXUUAQEBAfU60wp8\n8803REZGEhMTw44dO0hLS+OHH36goqKCDRs2sHPnTuzt7bGzs+PTTz8lNjaWyMhI+vfvzwcffEBs\nbCxbtmyhX79+VYlEcnIyGzZsICsri8WLF5OVlUVeXh7r16+v8iUlJbF06VJ+++03wsPDyczMJCgo\nCC8vL3799VeCg4PZt28fFy5coLS0lICAALKzs1m9ejUPPfQQK1euxN3dnU6dOl1TpoMHD6LT6fDx\n8SEjI4OYmBg8PDzo1q0bP/74I+3btyciIgJ/f//r1smSJUto3bo1eXl5PPfcc3h4ePD1119z7tw5\nVq5cyeOPP46rq+s115WXGGo8XrF2HQtmzyAtMxONWs3plFQ8Wt+N/xBfwrdup51HG14c5kdM/BF6\neHVFoah91/GbcVVerrleyYp14bw3403SMs+iUak4lZpmcvn6EB4RyeNej7IqfBN22NGn5+M1rrW3\n2A14+eowFr49k7T0TDRqFadSUvFo3ZoRfr6s3xxB+7YejBk5HF1ePmqVCq1GffVii7U8bqq+7Gqu\nJ7FizToWzJlBWoYprtMpqXi0aW1ybYkwufz9iIk7TI/HHq3hsrOvebN1+aq1LJw7m9SMDDRqNafO\npNC2TWtGDh3M+k1b8WzXlrEvjECXl4dGra5ZRou4lq9aw8J5c0hNz0Cj0Vx1DRvC+o1baNG8OYcT\nk1EoFHh1fZi6uBmXncX6G8Ghq1k0/23S0tNNZTydQluP1owc5se6jZtpcVdz4hOScFAo8Or6SJ1x\n3YzLzqK+gkNWseiduaSmZaDVqDl1+gwebdrwwrChrNuwibvuuovDCYkoFAoee9Qyrprt62bisvwM\nLV8VxsK5s8ztS82plBRzux/M+k3baN+uLWNe8De1e/UN2v1NtFWLIt7cZ8girpVh4bw36y3SM8+a\nP9vmfmLwQMK3baddmza8OGwIBw8n0OPRR+rsv1aGbeC9mW9edaWm4XF3K/wHDSR8WySPP/Yooes3\nYWcHTz/5RI1rL5eV1Xgcumkr81+bRvq586hVrpxOz8CjVUuGPe/Nxh27mOA/jMe7PkzuhYsMfb6f\nhavm2jqrIn5k7tRJZGRlo3ZVcubsOdq0aMHQ555h4897ycjOZt5Lk8nIzsGpWTM0qqv9fmXFtetA\nrdmxkzkTx5ORnYPaVYlaqST85938748/eLrbY2zau4/C4hL6Pd6jxnWKpk1qxrV1O/OmTSHjfBYa\nV1fOZJylTasWDPXuy8Zdu8nIyuadV6aSkZVtjktVda2DUlnDdSv7Vc92Hoyupb9volRTG7d0eMvO\nzg5fX1/ee+89zp8/z7Rp03jwwQeZP38+zs7OuLu7ExMTg7OzM/7+/jz22GMkJiYCpjsid999N2PH\njr3GeQVvb29effVVgoKCavjs7Ozo06cPs2bNIjc3l2nTpuHt7c2xY8cIDAzE1dUVlUpFXFwcAF26\ndGH69Ok4Ozvj4OCAp6cngwYNum6ZnJyccHNzIzc3ly1btuDv70+l+QMdFhbG8OHDeeCBB9i/f/91\nrz979izPPvssH3zwAd999x0AzZs3Z8GCBfj5+REfH9+gOnbXatHrCyk0FKHVaFCrVBiKiunh9Sg/\n74/ifFbONV+0t8LlptVQUGigsKgIrVqNWuWKobiErTt/wrdf32safJ0uNy0F+kIKDQa0GjVqlYqi\noiKeeqIHufkXyDh7jg6e7W55Gd21WvSFV1zqq67HHuXnfVGcz26Yq0Cvr1FGQ1ExvZ54nNz8fDIb\nUkY3N7OrEK3WXMbiIjq092TooIH1cvyVLr3BYKp7tQpDkck1bLDv7XO5m+peX2jATXvVdU97T4YN\nuX4/cCviqmr3hVfbalHxlXaf3/B2b8W2at1+otDUT2hM/URRUTE9vLry8y8HONeAuNy0WpOr6jPk\nSlFxMVt2/ISvd99rkt46XRo1eoMBQ1ExGrUKtauSohLTQobb9+5nwDO96+3SqlXoi4oxFBejUbmi\nUiopvnQJn969+DhoOafSMygqrf/Chlq1isKiYgAmDhmMk6MjAKMGPI9btQTlhmVUqygwFFFYXIzG\nVYVaqaS4tJSBT/fm30uXcSotA0M9F1y8Ff3qU4/3IC//QoP6QrgNv+lxcnLCzs6OZs2amQKwt0eh\nUDBz5kxeeeUVvLy8iIyMJD4+ng4dOqDRaDAajbz++ut07tyZwMBAzpw5U5VY6PX6KveVOyL29vY1\nfFdet/q/9vb2VFZWUlFRwfTp05kxYwY9e/ascY6DgwOXL994pdURI0YQHh5OUVERzZs3ByAhIYH8\n/HwWL17MhQsXah0ea968OS4uLjg4OODi4lKjHE2aNKnX61+JtbKykot6PVqtBne3qw1FrXLl1xMn\nGfDcswwbNICE5GO3zqUwuQr0hbhp1KYGbDAlP2pXJQnHfmXtlgiSf/0vJ86k1CuuggI9bloNzd3c\n0BcaKDQYUKlUZOtyWRa6htmvT7+19aVQXHVprrgMV12/nWRAv2cZNsinQXXvptXi7n61jGqVK9k6\nHctCVjP7jdfqX8aCApPLzd3cERlQu5o6w0rLP99vhUtR09Xc3c1c9wbU5k66sp6r7FrVdaWMF/Vm\nl7s5yWgccVW1e3dtlUvlqjK3iTXMvsFwgSmuv6atWqvPMfUTGvMXnSkulcqVX0+cYkDfZxg28HkS\njx6vVxkL9HqTq1oZVa6uJB49TtjmbSQd/y8nTtfd5ygczK5CA1q1GnethkJDEYaiYlTmuxzp587T\nqUP7Oj3V49IbitCqXHFXqyksKsZQXILKxYWSS5eY/9Jk2rZqRXON5oY+RTVf9btCDcXhShkNRbip\nVbhr1BQWF2EoLjYnZKW888pU2t7dirvc6o7rVvarV/r7WfXo76tzS4e34uPjad++PS1btiQiIgI/\nPz8OHz5Mjx49WLduHfv27aN79+7Y29sTFxfH2bNnycjIYPDgwQQGBpKZmYlCocDHx4ecnBy2bdtG\nZmYmLi4uqFQqHBwc6NixI82aNSMoKKjKV1JSUnVs27Zt+Pn5cfLkSRwcHOjVqxdLliwhKiqKe+65\nB6VSSW5uLt26dWP//v1069aN1NRU0tLSqhKo6kRERDB16lQWL17MqFGjUCgU6HQ69uzZw6JFixg5\nciQ+Pj5ERUXRokULWrduXeP6Dh068NVXXxEdHc1zzz1Hp06dqmI8c+YMly9f5oEHHrjmdS2Ht1xc\nnAlcuYpyo5FsnY6B/Z4jNHwD0bHxTBrzD1IzMlmzYROZ57IYOnAAzeoY/7wZl+WteaWLM4Gha02u\n3Dx8nnuW1Rs2ER13mIkvvsCQ573p0/NxUjIyGO7rU+Nay+EtpYsLgctDr8bl3Y/QdeFVcX30xRKM\nFUaiY+O5v+M9qKoPC1p8wdxUfVn8heji4kLgilDKy41k5+Yy0Ps5QtdtIDo2jkljXiQ1I4M1GzaT\nee48Q319args/9pRuriwNDiEcqORnBwdA/t7ExK2nuhDcUwaM4qPPv8KY4WRA4fiuP++e1FV7+ws\n4lIqXVgavJLyciM5Oh0D+/cjJGw9Bw4eYsr4sSiVLpSUlJKSln7D4a2bcVkObymVLny/bDlGo5Hs\nHB2+z3sTsmYdB2IOMWXCWJRKZTVX3cNbN+Oy/EtfqXTh+6BgU5vIycF3QH9CVocRdfAgUyeOu+pK\nTbvh8NbNxHXdz9Byc/vS5ZrafVi4uU28aGr3RiPRsddpE9e0+z/fVi1z2pv6DFnEpXR2JjB0jbmf\nyMWnX19WhW8iOvYwk0abXRu3cPZ8Fn4+z9fZfyldnAkMsXRtJjounkmjXmDIAFOfk5qRyfBBNfsc\ny+EtpbMzy9ZtwGg0kpOXz/NP92bN1u3EJCQywX8oShdnfok7TJ/Hu18Th+XwlouTE8Gbt2KsqCDn\nwgX6P9WTsB27OJh8lHFDfCm+dImVWyKouFyBt8Ww2/WGt1ycnFgRsZ3yigp0Fy7QtVNHAA4dO85T\n5jZV/f9XsBzecnFyInjDFozGCnLy83m+91Os3b6Dg0lHGe83mOLSS6zYtJWKy5fp/9STNa61HN66\nJf1qXDwTR7/Ivxd/jbGigpjYeDrdd2+N/r6u4S27yvr+uWHjZGdns3Xr1hrP9enThy5dutTbsW/f\nPk6ePFnjuZdffhkHh4b/nvxSTmaDr7kVWHPfIIVjM6u5rtdp/Gka695bVtxLyprI3lsNo9HuvXXZ\nel8VsvdWw5C9txqGc8vah7sk6blDkaSnYUjSc/uQpKdhSNLTUJkkPQ3B1pOextkbCYIgCIIgWBlJ\negRBEARBsAkk6REEQRAEwSaQpEcQBEEQBJtAkh5BEARBEGwC2XvrDsXBRXnjk+50bGBioTVnsVSU\n/WE1lzVn6jRarDjjqrFOgm3IqsM3orGWsSGrud+IJjexyN9f6bIqVnwfKyuMVnNhxQl9dSF3egRB\nEARBsAkk6REEQRAEwSaQpEcQBEEQBJtAkh5BEARBEGwCSXoEQRAEQbAJJOmphZycnNsdgiAIgiAI\nVkSmrNfCokWL+OGHH657rHPnzixevBgfHx8AJk6cSK9evZg2bRpGo5Fhw4Yxbdo0/Pz8KCsr4/XX\nX+edd97hnnvuISAggKFDh+Ll5WXVeI8kJrH9x504OTkxb/YMysrK+L9/fwrAtCmTyMvPr3H8znQl\ns33HTpycHJk3y+z6+DOTa/JE8vIv1Dh+R7qSktm+4yfTuTPfoqysjA8++QKAlyeNp11bD3bt3ku2\nLpfJ40bX6UpIPkbkT7txcnTk7bdeo6ysjH99sQSAqePHcPJ0ColHj1Fy6RKL3p5J06ZNa3cdPUbk\nT3tMrjdfNbkWf21yjRtNVk4Oe6KicWzWjDlvvFp3XI3WdZzIn82uN6abXF9+c9WVbXY5OjLn9Vdu\n6Prx5704Ojar5vrW7Bplch2IMcV1i122UEZxNcB17IrLkbdff4WysnL+9ZW5TYwdRbs2rTmXncPX\ngcF8FvDeHemqjiIgICCg3mf/Cb755hsiIyOJiYlhx44dpKWl8cMPP1BRUcGGDRvYuXMn9vb22NnZ\n8emnnxIbG0tkZCT9+/fngw8+IDY2li1bttCvX7+qRCI5OZkNGzaQlZXF4sWLycrKIi8vj/Xr11f5\nkpKSWLp0Kb/99hvh4eFkZmYSFBSEl5cXv/76K8HBwezbt48LFy5QWlpKQEAA2dnZrF69moceeoiV\nK1fi7u5Op06drinTwYMH0el0+Pj4kJGRQUxMDB4eHnTr1o0ff/yR9u3bExERgb+/PwqFgl69erFg\nwQLS09N55JFHePrpp69xhoSEcOnSJXJzc5k7dy7+/v7MmDGD559//rprbVT88XuNx8ErV7Ho3Xmk\npqej1Wg4dfoMHh5teGH4MMLCN/LbiZMsfGcuqekZaDUaNGp1re9Zo3WFrGLRO3NJTctAq1GbXG3a\n8MKwoYRtMLvmv01qeiZajfrOcFmsmREcuoZF8+eQlp6BRq3m1JkU2rZpw8ihQ1i3cQst7mpOfEIS\nDgoFXl0fqXFtZUXNhS5WrFnHgjkzSMvIRKNWcTolFY82rfEf4kv4lgg823kweuRwdHn5qFUqtJrq\ncdUs44q161gwewZpmZlo1GqTq/XdJtfW7bTzaMOLw/yIiT9CD6+uKBSKWuur8bgqLVzhLJj9lrm+\n1JxOSTO5Bld3DalXXCvDwnlv1lukZ55Fo1Zxqso1kPBt22nXxuQ6eDiBHo8+8pe5LPuOv2MZxXWz\nrg28N/PNq67UNDzuboX/oIGEb4vk8cceJXT9Juzs4Oknn6jV05hcTVVutXr/8uEtOzs7fH19ee+9\n9zh//jzTpk3jwQcfZP78+Tg7O+Pu7k5MTAzOzs74+/vz2GOPkZiYCMD58+e5++67GTt27DXOK3h7\ne/Pqq68SFBRUw2dnZ0efPn2YNWsWubm5TJs2DW9vb44dO0ZgYCCurq6oVCri4uIA6NKlC9OnT8fZ\n2RkHBwc8PT0ZNGjQdcvk5OSEm5sbubm5bNmyBX9//6qFu8LCwhg+fDgPPPAA+/fvB8Dd3Z0nn3yS\nffv2MXDgwOs6vb29OXDgAAcPHkSr1aLT6dBoNNjbN+wtcndzo0CvR19owE2rRa1WYSgqAjs77Ozs\nqo7f0S53bTWXxuQyVHOZj9/RLjctBfpC9IUGtBpNVX11aO/JsMG+9XJUubRa9IWFFBqK0GrUqFUq\nDEXFPPV4D/LyL5B59hwdPNvV36W/4tJUuXp4PcrP+6M4n5WDXT3b7B3hUqtRq1wxFBfTw6srP+8/\nwPns+rvctBoKCgspLLpS964UFZldvxzg3G1y2UIZxdVQl9bkMhiuuoqL2bLjJ3y9+zZoocvG6oJb\n9JseJycn7OzsaNasmelF7e1RKBTMnDmTV155BS8vLyIjI4mPj6dDhw5oNBqMRiOvv/46nTt3JjAw\nkDNnzlQlFvpqXxyurq5Vzuq+K69b/V97e3sqKyupqKhg+vTpzJgxg549e9Y4x8HBgcuXb7xS64gR\nIwgPD6eoqIjmzZsDkJCQQH5+PosXL+bChQtVw2NJSUmcPn2al19+mc8+++y6vtatW3PhwgXy8/Px\n8fHhk08+wdvbu9517ODgQGVlJRcLCnDTamnubkoiCgsNaNQqmlgcv6NdF/VmlzsF+kKzS00TB0WN\n43emy3xugR43rYbm7m7mhMWAWqUC6r8yroPC7NLr0Wo0uLtp0RcaKDQUoVa5kq3LZVnoGma9Pv3G\nritl1OvRaq+4Cqtcv544yYDnnmXYoAEkJB+7Q12Ka10Gc325uvLriVMMeO4ZhvkOIOFo/eIq0Bfi\nptGYE0+TS6Uyu/o+w7CBz5N49PgtdNlCGcXVIJe5nyjQ602uav2EytWVxKPHCdu8jaTj/+XE6ZQ7\n0lWdv3x4Kz4+nvbt29OyZUsiIiLw8/Pj8OHD9OjRg3Xr1rFv3z66d++Ovb09cXFxnD17loyMDAYP\nHkxgYCCZmZkoFAp8fHzIyclh27ZtZGZm4uLigkqlwsHBgY4dO9KsWTOCgoKqfCUlJVXHtm3bhp+f\nHydPnsTBwYFevXqxZMkSoqKiuOeee1AqleTm5tKtWzf2799Pt27dSE1NJS0t7bq/vYmIiGDq1Kks\nXryYUaNGoVAo0Ol07Nmzh0WLFjFy5Eh8fHw4cOAAzZs357vvvuOLL77goYceIjo6mgsXLvDAAw9c\n401JSUGhUODn58fHH3/M+++/X+udHsvhLaWLku+DgikvLyc7R4fvgP6ErA4jKuYgUyeMp1XLlnwf\nFIzRaMR3QP8637NG61K6mFxGI9k5OVddBw8ydeK4q66KijvHZZHAKF1c+H7ZCozGcrJ1ufg+703I\n2nUciIllyoQxKJVKSkpKSUlLv+HwlouLC4ErQikvN5Kdm8tA7+cIXbeB6Lh4Jo5+kX8v/hpjRQUx\nsfF0uu9eVK7Vls23yKtcXJwJXLnKVEadjoH9niM0fAPRsfFMGvMPUjMyWbNhE5nnshg6cADN6vh9\nUONxVVq4XAgMWW125TLQuy+h6zcSHRfPpNH/IDUjgzUbtpB5/jxDfeqOS+nsTGDoGpMrNxeffn1Z\nFb6J6NjDZlcmazZu4ez5LPx8nv/LXJZ/Bf8dyyium3S5OBMYYunabGoTo15gyABv+vR8nNSMTIYP\n8qnV05hcdQ1v2VU22g1Vbj/Z2dls3bq1xnN9+vShS5cut91bVnTxpmK4I7CBpil7b91GZO+tBtFY\nyyg0EBt4H1087q31mCQ9dyiS9Pw9kKTnNiJJT4NorGUUGogNvI91JT2yTo8gCIIgCDaBJD2CIAiC\nINgEkvQIgiAIgmATSNIjCIIgCIJNIEmPIAiCIAg2gey9JVBZYbSey5qzfqw4u6axYs0ZMXb2tS81\n32Dkz6EGYb05Uo0XmyijFWe7WROZOWc9pGsTBEEQBMEmkKRHEARBEASbQJIeQRAEQRBsAkl6BEEQ\nBEGwCSTpEQRBEATBJpCkRxAEQRAEm+BvP2X922+/pU+fPnTt2rVe5y9dupRRo0ahVqv/0rheeukl\ngoKCrOY7kpjE9h934uTkxLzZMygrK+P//v0pANOmTCIvP7/G8TpdScls3/ETTk6OzJv5FmVlZXzw\nyRcAvDxpPO3aerBr916ydblMHje6TldC0lG279yFk5MTc2e8aXJ9Ws3l4cGuPXvJ0eUyaewNXMlH\n2b7zZ5wcHZk74w2T67MvTa6J4zhx6jQJyccovVTK+/Pm0LRp0zvTtcvs+qfJ9eHnJtdLE8Zx4tQZ\nEo8epbT0EovmzRaXuMT1V7ukn7jjXdVRBAQEBNTrzDuU+Ph4du/ezZEjRzh9+jTbtm2jb9++BAYG\nUlFRwf79+4mIiGDTpk107tyZ3bt388QTTzBlyhQuXLjA2rVrufvuu9Hr9XzzzTdERUVx+vRpKVqV\n+QAAIABJREFU7rvvPhYuXMiRI0dITEzkwQcfrPG4Z8+e18Tyyy+/8M0335CYmIher6d379588MEH\nHD58mLVr1zJgwABeeuklhg4dypEjR9i+fTvdunW7brkq/vi9xuPglatY9O48UtPT0Wo0nDp9Bg+P\nNrwwfBhh4Rv57cRJFr4zl9T0DLQaDZrqSZ3FejjBoWtYNH8OaekZaNRqTp1JoW2bNowcOoR1G7fQ\n4q7mxCck4aBQ4NX1kZqBWSwnsXzVGhbOm0NqegYajcbsas3IYUNYv3ELLZo353BiMgqFAq+uD1OX\nbPmqtSycO5vUjOpxtWbk0MGs37QVz3ZtGfvCCHR5eWjUarSa2hPXxusKY+HcWaRlZJpcKSl4tG7N\nCL/BrN+0jfbt2jLmBX90efmo1SpxiUtcVnRZrtMj/cSd6WrioqrVaxPDW6NHj65KSCwbdVZWFkql\nkpEjR9K8efOq5xUKBdOnT2f06NEcOHCAwMBAlEolSqWS5ORkfv/9dwoKCujcuTO+vr7XPL4eYWFh\nfP755/zzn//EwcEBhUKBv78/3bt3Jzc3l7y8PHr37k10dDQbNmxg1KhRDS6ru5sbBXo9+kIDblot\narUKQ1ER2NlhZ2dXdbx+Li0F+kL0hQa0Gk2Vq0N7T4YNvn4ZbxRXoaEQrVaDWqXCUGxyDR00sGEu\nrdbsMqDVqE2uomJ6PfE4ufn5ZJ49RwfPdne0y81c94WFRaa6V6koKi7iqSd6kJufT4a4xCWuW+aS\nfuLv4QIbSXocHR0B+OOPPygvLwegsLAQAB8fHwYPHszBgwfZvHlz1TVOTk4ANGnShMrKSi5fvsyo\nUaOYPXs2zz77LE2bNmXu3LmoVCoWLVpEkyZNajy+3gqadnZ2VFZWYm9vqvbY2Fh27NhBq1ataN26\nNZWVlfzjH/9g1apVKJVKVKras1VLHBwcqKys5GJBAW5aLc3dzUlGoQGNWkUTi+N1uxTmc/W4aTU0\nd3dDX1hIocGA2hxTfVcItYzL3c0dvd7scjW7LG8P3cil15tc7m7oCw3muFzJ1ulYFrKa2W+8dse7\nCqrqXltV9ypXldm1htlvvCoucYnrFrmkn7izXdWxieGtuLg4Tp48yYMPPkhqaioJCQlkZmby2GOP\ncfz4cX766SfKy8vp27cvv/76K0899RS7d+/Gz8+P3Nxczp49y8iRI/nqq6+IjY3F2dmZBx54gI8/\n/pjS0lLuuusuevToUePx008/fU0sbm5ufP311xw9epSSkhJ69uxJVFQU+fn5pKSk0LNnT9q0acO+\nffuYMmUKbm5utZbLcnhL6aLk+6BgysvLyc7R4TugPyGrw4iKOcjUCeNp1bIl3wcFYzQa8R3Qv6bM\nYnhL6eLC98tWYDSWk63Lxfd5b0LWruNATCxTJoxBqVRSUlJKSlr6DYe3lEoXlgavpLzcSI5Ox8D+\n/QgJW8+Bg4eYMn4sSqVLNVfdw1tKFxeWBodQbjSSk6NjYH9vQsLWE30ojkljRvHR519hrDBy4FAc\n9993LyqVa63113hdzgQuD6W83Ei2LpeB3v0IDQs3u17koy+WYDQaiY4Vl7jEZW2X5UiA9BN3pquu\n4S27StnU4y9h69atZGdnVz12cnJi0qRJN7zu22+/5ffff+ftt9+u87yyoos3G2IVsvfW7UM+foLQ\neJC9t/4eON3VptZjkvTcoUjS8/dAPn6C0HiQpOfvQV1Jj038pkcQBEEQBEGSHkEQBEEQbAJJegRB\nEARBsAkk6REEQRAEwSaQpEcQBEEQBJvgb7/31t8Wa/6a36ozFmSWQUNorLNFrIk1Z55Ys75kRszf\nA6t+huwa530Ae4UV2701Z9hak1s0W7dxvsOCIAiCIAhWRpIeQRAEQRBsAkl6BEEQBEGwCSTpEQRB\nEATBJpCkRxAEQRAEm0CSngaQk5Nzu0MQBEEQBOFPIlPWG8CiRYv44Ycfrnusc+fOLF68GB8fHwAm\nTpxIr169mDZtGkajkWHDhjFt2jT8/Pz+ktiOJCazfcdOnJwcmTdrBmVlZfzfx58BMG3yRPLyL9Q4\nXqcrKZntO3bh5OjEvFlvUVZWxgcffw7Ay5Mn0K6tB7t27yU7R8fk8WPqdCUkHWX7zl04OTkxd8ab\nJtenX5hck8bTzsODXXv2kqPLZdLY0XW7ko+yfefPODk6MnfGGybXZ1+aXBPHceLUaRKSj1F6qZT3\n582hadOm4mqsrl1m1z9Nrg8/N7lemjCOE6fOkHj0KKWll1g0b/adG5e4bp/LWm3Cmv2XFV2mPvon\nU38+09xHf1LNdaWP1uUyedyti6ux9vfVUQQEBATU60wr8s033xAZGUlMTAw7duwgLS2NH374gYqK\nCjZs2MDOnTuxt7fHzs6OTz/9lNjYWCIjI+nfvz8ffPABsbGxbNmyhX79+lUlEsnJyWzYsIGsrCwW\nL15MVlYWeXl5rF+/vsqXlJTE0qVL+e233wgPDyczM5OgoCC8vLz49ddfCQ4OZt++fVy4cIHS0lIC\nAgLIzs5m9erVPPTQQ6xcuRJ3d3c6dep0TZkOHjyITqfDx8eHjIwMYmJi8PDwoFu3bvz444+0b9+e\niIgI/P39r1sn06dPZ/DgwSxYsAC9Xo9Go+G7776jd+/e1z2/4o9LNR4Hh6xi0TtzSU3LQKtRc+r0\nGTzatOGFYUMJ27CR306cZOH8t0lNz0SrUaNRq6tdXXPdhuDQ1Sya/zZp6elo1GpOnU6hrUdrRg7z\nY93GzbS4qznxCUk4KBR4dX2kZmAWa58sX7WGhfPmkJqegUaj4dSZFNq2ac3IYUNYv3ELLZo353Bi\nMgqFAq+uD1uU0tK1loVzZ5OakWGK64pr6GDWb9qKZ7u2jH1hBLq8PDRqNVqNmtoQ1+10hbFw7izS\nMjJNrpQUPFq3ZoTfYNZv2kb7dm0Z84I/urx81GpVDZflmiyNJS5x3T6XVdvENa6b6b8sy/jnXZZl\nDA5dw6L5c0hLr17GNowcOoR1G7fcoI+2XlzWLKM1+3sHZ9daY7wtw1t2dnb4+vry3nvvcf78eaZN\nm8aDDz7I/PnzcXZ2xt3dnZiYGJydnfH39+exxx4jMTERgPPnz3P33XczduzYa5xX8Pb25tVXXyUo\nKKiGz87Ojj59+jBr1ixyc3OZNm0a3t7eHDt2jMDAQFxdXVGpVMTFxQHQpUsXpk+fjrOzMw4ODnh6\nejJo0KDrlsnJyQk3Nzdyc3PZsmUL/v7+VYufhYWFMXz4cB544AH2799/3esffvhhzpw5g9FoJCEh\ngb179zJgwIAG1627u5YCvR59oQE3rQa1WoXBUAR2dtjZ2VUdr5fLzc3kMhjQasyuoiI6tPdk2GDf\nhsVldhUaCtFqNahVKgzFJtfQQQMb5tJqzS4DWo3a5CoqptcTj5Obn0/m2XN08GwnrkbucnPTUqAv\npLCwyNS+VCqKiot46oke5Obnk/E3iEtct89l1XZvzf7Lqi5TfekLG1kf3Uj7e7iNv+lxcnLCzs6O\nZs2amQKxt0ehUDBz5kxeeeUVvLy8iIyMJD4+ng4dOqDRaDAajbz++ut07tyZwMBAzpw5U5VY6Kt9\nkbu6ulY5q/uuvG71f+3t7amsrKSiooLp06czY8YMevbsWeMcBwcHLl++8WqRI0aMIDw8nKKiIpo3\nbw5AQkIC+fn5LF68mAsXLtQ6PNa/f3+WLVvGAw88gNFo5OjRo1Ux1wcHBwcqKyu5eFGPm1ZLc3d3\nc+dhQKNW08RBUeN4nS6F2VVQYHa5oS8spNBgQK1SAfVfzbYqLrPL3c0dvd7scjW76rmKc5VLbyqD\nu7sb+kKDOS5XsnU6loWsZvYbr4nrDnAVFOhx02po7q6tal8qV5XZtYbZb7x6x8clrtvnsmq7t2b/\nZRWXuT+vqq9G1kc3sv6+OrdleCs+Pp727dvTsmVLIiIi8PPz4/Dhw/To0YN169axb98+unfvjr29\nPXFxcZw9e5aMjAwGDx5MYGAgmZmZKBQKfHx8yMnJYdu2bWRmZuLi4oJKpcLBwYGOHTvSrFkzgoKC\nqnwlJSVVx7Zt24afnx8nT57EwcGBXr16sWTJEqKiorjnnntQKpXk5ubSrVs39u/fT7du3UhNTSUt\nLe26yUhERARTp05l8eLFjBo1CoVCgU6nY8+ePSxatIiRI0fi4+NDVFQULVq0oHXr1jWud3d357PP\nPuO1117j0qVL/PHHHzzzzDO11qHl8JZS6cL3QcGUG41k5+TgO6A/IavDiDp4kKkTx9GqZUu+DwrG\nWFGB74D+FrbKa13LlmM0GsnO0eH7vDcha9ZxIOYQUyaMRalUUlJSSkpa+g2Ht5RKF5YGr6S83EiO\nTsfA/v0ICVvPgYOHmDJ+LEqlSzVX3bc7lS4uLA0OodxoJCdHx8D+3oSErSf6UByTxozio8+/wlhh\n5MChOO6/715UqtpvcYrrdrqcCVweSnm5kWxdLgO9+xEaFm52vchHXyzBaDQSHXuty/I2f2OJS1y3\nz2XVNmHpuqn+C6u5rlfG75etwGgsJ1uXa+qj167jQEwsUyaMuUEfbb24rFlGa/b3dQ1v2VXKBjQN\nIjs7m61bt9Z4rk+fPnTp0qXejn379nHy5Mkaz7388ss4ONT/d+Vlhgv1PvdGVFpxz5PKCivun3KL\n9mIR/lpk7y3hr8QW9t6ys5e9txqCY/PWtR6TpOcORZIe4U5Bkh7hr0SSnoZh60lP43yHBUEQBEEQ\nrIwkPYIgCIIg2ASS9AiCIAiCYBNI0iMIgiAIgk0gSY8gCIIgCDaB7L11h1Lxx+/Wk1lxxoItzKQQ\nGoY1m4Q1aaxxCYIljXbGlTW5Rf29fKsIgiAIgmATSNIjCIIgCIJNIEmPIAiCIAg2gSQ9giAIgiDY\nBJL0CIIgCIJgE0jSIwiCIAiCTWCTU9a//fZb+vTpQ9euXf8S/5YtW9i9ezezZs3innvuue45mzZt\nwtHREV9fX6u8ZkLyMbbv+hknJyfmvvUaZWVlfPj5EgBemjCGE6fPkHj0OKWlpSyaO4umTZvW4Tpq\ncjk6Mvefb5hdX5pd4zhx6gyJR49SWnqJRfNm39i10+yaYXJ98JnJ9fLEcZw4dZqE5GOUXirl/Xlz\n6nYlHWX7zl2mMs540+T69AuTa9L4q67SUt6f//ZNudp5eLBrz15ydLlMGju6Vo+4xCUucYmrcbuq\nowgICAio99l/E+Lj49m3bx/79+/n+PHjREZGcvDgQbZt28ZTTz3Fa6+9hp+fH/n5+Xz44YeoVCrm\nzJlDSkoKvXv3ruEyGo0sXLiQQ4cOsW3bNh5++GG++uorWrZsyfDhw7G3r3kz7cMPPyQqKoq4uDg6\nd+5MZmYmq1atYufOnWRlZVFSUsKOHTvo3r0777//Pp07d0apVF5TBmNpUY3Hy1eHsfDtmaSlZ6JR\nqziVkopH69aM8PNl/eYI2rf1YMzI4ejy8lGrVGg16qsXWyxYsnxVGAvnziItIxONWs2plBSzazDr\nN22jfbu2jHnB3+RS13RZrtOzfNVaFs6dTWpGhsl1JoW2bVozcuhg1m/aime7tox9YQS6vDw0avUN\n4lrDwnlzSE3PQKPRXHUNG8L6jVvwbNeOsf8YgS43D41KhVajqbUN3MjVonlzDicmo1Ao8Or6cK0e\ncYlLXOISV+NyOThf+515BZsd3hoxYgQff/wxQUFBdOzYkQULFjBgwAC2bt163fO7d+/O/Pnzr3k+\nJiaGTp06VV2/d+9ennzySUaNGoVCoahxblpaGg4ODixYsID+/fsD4OHhweDBg3nsscfYt28fvXv3\nJiEhgYsXL1JWVkbLli0bVC43Ny0F+kIKDQa0GjVqlYqioiKeeqIHufkXyDh7jg6e7RrmKixCq9GY\nXMVXXPkNcrlrtRTo9TXiMhQV0+uJx8nNzyezIS43N7OrEK3WFJehuIhePau52nvelKtDe0+GDhpY\nL4e4xCUucYmr8bvAhpMelUoFmO5MXLk7Uf0uRWVlJYWFhVWPXV1db+i0vN4ShUJR9fyVhOg///kP\neXl5dO3ateqYj48Pc+bMYdSoUfUuj4ODA5WVlRQU6HHTamju5oa+0EChwYBKpSJbl8uy0DXMfn16\nw13uWvSFpkRK5aoiW6djWcgaZr/xar1dF/V63LRa3N2vxqVWuZpdq5n9xmv1dxUUmFxu7ujNCZ7a\nVUV2jo5lK1cx+82bdwFUUr9VUMUlLnGJS1yN11Udmx3e8vT0pGXLlhw+fBij0cjhw4dJSUlh6tSp\nVFRUEBwczPnz5/nf//7Hgw8+iE6no1u3bte42rZty8aNG2tcn5ycjKenJ61atapxrkajITY2ll9+\n+YXExEQeeOABiouLOXfuHMePH6ewsJDhw4fj6enJpk2beP3112stg+XwltLFhcDloZQbjWTrdAz0\n7kfounCiY+OZNOYffPTFEowVRqJj47m/4z2oqidxFsNIShdnk6vcSLYu1+QKCyf6UByTxrxochmN\nRMfGcf9996JSuVZTWbpcWBocQrnRSE6OjoH9vQkJW292jeKjz7/CWGHkwKFrXdfEpXRhafBKysuN\n5Oh0DOzfz+yKZdLY0Xz0+ZdmVyz3d7yvpsuC2lwHDh5iyvixKJUulJSUkpKWfsPbsOISl7jEJa7G\n46preMuu8nq3JITrkp2dfc3wV58+fejSpcs155aUlBAaGlrjuUcffZQnn3yyztfIy8sjICCAKVOm\n0L1791rP+z3vXAMivwGy95YgCILwN8HRvVWtxyTpuUORpEcQBEEQrqWupEe+VQRBEARBsAkk6REE\nQRAEwSaQpEcQBEEQBJtAkh5BEARBEGwCSXoEQRAEQbAJJOkRBEEQBMEmsMkNR/8OKBydrSez4qoF\nlZWXreZqrNhZcSq9LdSXVdvX5ca5wkbl5YpG6bImVl2OorFizeU77BU3PulOx5r91y1aokTu9AiC\nIAiCYBNI0iMIgiAIgk0gSY8gCIIgCDaBJD2CIAiCINgEjSbpKSsr4+LFi7f0NXU63S19PUEQBEEQ\nbh+NZvZWZGQkzZo1w9fX95a8XlZWFoGBgfy///f/btrVv39/Ro0axdSpUwF49913adKkSZV76tSp\ndO/enVdfffWmX6s2jiQmsf3HnTg5OTFv9gzKysr4v39/CsC0KZPIy8+vcbxuVzLbd+zEycmRebPM\nro8/M7kmTyQv/0KN43W6kpLZvmMXTo5OzJv1FmVlZXzw8ecAvDx5Au3aerBr916yc3RMHj/mznTd\noL7atfVg5897yNHpmDx+7J1ZRqu7fjLV10yz65MvTK5J46+6dLlMHje6TldC0lG279yFk5MTc2e8\naXJ9Ws3l4cGuPXvJ0eUyaewtdCUfZfuun3FydGTuP9+grKyMDz//EoCXJozjxKkzJB49SmnpJRbN\nm03Tpk3rcB0j8qfdODk68vZbr1FWVsa/vlgCwNTxYzh5OoXEo8couXSJRW/PvKWu7bt+NtWX2fXh\n50vMZRzDidNnSDx6nNLSUhbNnXWHuqz4PjbWtmrtdr/TXF8zTPX1wWem+np54jhOnDpNQvIxSi+V\n8v68ObesvqqjCAgICKj32X8hwcHBnDx5kpiYGA4dOsTu3bvp1asX48aNIzs7mxUrVpCens7Bgwc5\ndOgQ7dq1Y9KkSZSUlLB582Z69uzJTz/9xPr169m5cyf29vYkJSWxePFisrKySElJYfv27WzatInK\nykpSU1PZs2cP3bt3Z+7cufj5+REZGcl///tfdu/ezdq1a9Hr9Rw7doxt27YRGRlJy5Ytadmy5TWx\n//LLL+Tl5TFo0CBKSkr48ccfcXZ25tlnnyUpKYmioiKOHDnCoEGDcHC4Ns/ctGkTS5cu5bfffiM8\nPJzMzEyCgoLw8vJCpVJdt74q/vi9Zv2tXMWid+eRmp6OVqPh1OkzeHi04YXhwwgL38hvJ06y8J25\npKZnoNVo0KjVtb8XIatY9M5cUtMy0GrUJlebNrwwbChhG8yu+W+Tmp6JVqO2cNWcUhwcuppF898m\nLT0djVrNqdMptPVozchhfqzbuJkWdzUnPiEJB4UCr66P1NFCGo/LcupuXfW1bsMm7rrrLg4nJKJQ\nKHjsUcu4/v71da1rDYvmzyEtPcPkOpNC2zZtGDl0COs2bqnbZTFjffmqNSycN4fU9Aw0Go3Z1ZqR\nw4awfuMWWjRvzuHEZBQKBV5dH64zrptyWUzLX74qjIVzZ5GWkWkqY0oKHq1bM8JvMOs3baN9u7aM\necEfXV4+arUKrUZdq2vFmnUsmDPD7FJxOiUVjzat8R/iS/iWCDzbeTB65HCTS2XhsuBmXJbtfvnq\nMBa+PZO0dJPrVEqquYy+rN8cQfu2HoypZ1yNxmVZxpt4Hy2Xtmg0bdWqLst2v5aFc2eTmlH9s92a\nkUMHs37TVjzbtWXsCyPQ5eWhUatvUPd/Pi4HZ2Wt5W00w1tPPfUUDz/8ML///jtOTk78/vvvnDlz\nBnt7e9566y28vb1p27YtM2fO5LfffgOgc+fOTJ06lWeeeYZdu3axbNkynJ2dcXd3JyYmBjs7O7y9\nvXn11Vfp0qUL3t7ePPjgg0RFRVW93r333lsVw5UPtZ2dHSNHjmTo0KGEhYXh7OyMRqMhJibmurHb\n2dnh5eVFcnIyERERDBkyhEpzx7VixQr8/f157rnn2LBhQ63X9+nTh1mzZpGbm8u0adPw9vbm2LFj\nDa5Hdzc3CvR69IUG3LRa1GoVhqIisLPDzs6u6ni9XO7aai6NyWWo5jIfb1BcBgNajaYqrg7tPRk2\nuGF39xqt63r1VVTEPe09GTZk0O2Lq9G6tBToC9EXWi+uQkMhWq0GtUqFodjkGjpo4G1zuZnLWFhY\nZCqjSkVRcRFPPdGD3Px8Ms6eo4Nnu/rFpdWiLyyk0FCEVqM2xVVUzFOP9yAv/wKZt8lVVUaDocpV\nVHSljBcaVMZG77LG+9hI26pVXVqt2WWo0b56PfE4ufn5DWurVowLGlHSY2dnx+XLl+nVqxezZ89m\n4MCBtGzZEicnJwDs7e2vuRV2+bJpYaTff/+dpk2bYm9vz8yZM3nllVfw8vICwNXVFYDPP/+cS5cu\n8fDDD1e93hWuJCj6al/grq6uXL58GY1Gw6xZs5g4cSIPPfRQrfEPHz6cLVu2cOLEiarzsrKySE5O\nJiQkhBMnThAaGkpFxfUXHrtSzurlrWzAom4ODg5UVlZysaAAN62W5u7mhlJoQKNW0cTieL1cF/Vm\nl7v5A29Ao1bTxEFR43idLsW1cZk6WwNq812s+paz0brqqK+/TRmtWl/m9lOgx02rsU7dm+Nyd3NH\nb/6iU7uaXZa3h26hq6CqjNqqMqpcVWTrdCwLWcPsN2485O2gMNeXXo9Wo8HdTYu+0EChoQi1ypVs\nXS7LQtcw6/Xpt9ZlWUY3N7PLgEqlqnLN/ju5buZ9bORt1aouvakvdHe/Wvem9qVjWchqZr/x2i2N\nqzqNZniruLiYjRs3cubMGf773/9y6tQpBg0aREREBH5+fpw8eRIHBwc6duzItm3b6Nu3L0FBQZw7\nd460tDQmT56Ms7MzQUFB7Nu3j+7du1NSUlJ1zYEDB7h48SKnT5+mqKgIX19fli9fTqdOnTh79ixR\nUVFkZGTg6elJcXEx7du3p23btly4cIFNmzaxd+9ennvuOdzd3a+JPSIiglGjRhEREUG3bt1o27Yt\nCQkJnDhxgrFjxzJ58mT69+9PdnY2er2e+++/v8b1lmWzLO/1sBzeUroo+T4omPLycrJzdPgO6E/I\n6jCiYg4ydcJ4WrVsyfdBwRiNRnwH9K/zvVAqXUwuo5HsnJyrroMHmTpx3FVXRcV1XJXXupYtx2g0\nmuJ63puQNes4EHOIKRPGolQqKSkpJSUt/YbDIo3FZXmb/0b1VeVKTbvh8FZjKeNf6nJx4ftlKzAa\ny8nW5Zpca9dxICaWKRPG1O2y6OOUSheWBq+kvNxIjk7HwP79CAlbz4GDh5gyfixKpUs1V923+W/K\nZZGkKV2cCVweSnm5kWxdLgO9+xEaFk70oTgmjXmRj75YgtFoJDo2jvvvuxeVyrVWl4uLC4ErzK7c\nXAZ6P0foug1Ex8UzcfSL/Hvx1xgrKoiJjafTffeicnWlNm7GdU27d3ExldFoJFunM5VxXTjRsfFM\nGvMPUxkrjETHxnN/x3vqjKvRuK4p459/Hy2HtxpNW7Wqy7Ldu7A0OIRyo5GcHB0D+3sTErbeXF+j\n+OjzrzBWGDlw6Drt/jr96p+Nq67hLbvKhtxOaERkZWXxww8/cCtztpKSEkJDQ2s89+ijj/Lkk0/W\n23HkyBEOHz5c47lRo0ahvcEdE0vKiqw40022oWgQsg1FA5FtKG6by5rINhQNVck2FA3CinXv6N6q\n9pe5U5MeW0eSntuHJD0NRJKe2+ayJpL0NFQlSU+DuEVJT6P5TY8gCIIgCMJfiSQ9giAIgiDYBJL0\nCIIgCIJgE0jSIwiCIAiCTSBJjyAIgiAINoEkPYIgCIIg2ASNZsNR4fZh1WnTjXQFBGtOda7EBqaZ\nCw3CmtOTbWKqs/D3wIrTzG8Vd17EgiAIgiAIfwJJegRBEARBsAkk6REEQRAEwSaQpEcQBEEQBJtA\nkh5BEARBEGwCRcCt3Kb8b8i3335Ls2bNaNXq+hucvfTSS/j5+fHJJ5/Qu3fvqufnz5/Po48+irOz\n85963Yo/fq/x+EhiEoHLVhB3OIHeT/WkrKyM9z/8N3t/ieK+e+/hTGpqjeM1sJhxdSQpmcDlK4k7\nnEjvJ5+grKyMgH99wr5fDnDfPR1Qq1Xs2r2XAzGH8Or6cJ1xmlwhxB1JuOr66FP2RUXXdB2MxeuR\nv9BlMXkrIekoP6wIIT4hkV49za5/V3OpVOzas5foQ7E8eoO4xCUucYlLXI3H5eCsrNUrd3qswIoV\nK5g/fz5ffvklCxYs4MMPP2TOnDmUlJRUnXPmzBlKSkr45z//yUcffcTx48cB+O677/i40lCtAAAg\nAElEQVTkk0944403OHLkCIsXLyYpKYnKykpee+21esew86fdBCx4h1atWpJ59hxHEpPo+0wf3p7x\nFpu3bWfnT7t5/735VcfrdP28h4B353F3qxZmVzJ9n+nNnH++wZbtP5KekUlqegaV9ZievvPnvQS8\nO5e7W5rjSkqm79O9mfPW62zZvuOq6/KNp4Fb1bV7D++/M5dWLVuQee48R5KO0rePybXV7EpLz+Ry\nPaa6i0tc4hKXuBqvqzqS9FiBESNG8PHHHxMUFETHjh1ZsGABAwYMYOvWrTXOi4yMZNiwYbz77rt0\n6tQJgCeffJKnn34aDw8PYmNjGTduHOvXrycqKopnnnmmwbG4u7lRoNejLzTgptWiVqswFBWBnR12\ndnZVxxvkMhjQajRVrg7tPRk22LeBcWkp0BeiL2xsLlMZCw2FaLUa1CoVhmKTa+iggeISl7jEJa6/\niQsk6bEKKpUKADtzYnHl/5bY29tz2XwXQqFQUFlZyZdffglA586dAWjRogVNmjRh3bp1DBs2rN4x\nODg4UFlZycWCAty0Wpq7mxtKoQGNWkUTi+N1uhTXuvSFhRQaDKjNZa3PXR5TXAqzS4+bVtOIXDXL\n6O7mjl5vdrmaXZZjYuISl7jEJa47zlUd+U3PTRIfH4+npyctW7bk8OHDGI1GDh8+TEpKClOnTmXX\nrl34+fmxbds23nzzTZYtW0ZSUhLHjx9n4MCBREdHYzAYSE1N5fLlyzz99NMoFAr+97//0adPn1pf\n1/I3PUoXJd8HBVNeXk52jg7fAf0JWR1GVMxBpk4YT6uWLfk+KBij0YjvgP41ZRaJglLpwvfLlmM0\nGk2u570JWbOOAzGHmDJhLEqlkpKSUlLS0vHq+kid9aN0ceH7ZSswGsvJ1uWaXGvXcSAmlikTxtw6\nl8VnQ6l0YWnwSsrLjeTodAzs34+QsPUcOHiIKePHolS6VHPVPfYsLnGJS1ziajyuun7TY1dZ3z+N\nhVtCXFwcy5cv56OPPsLNza3W88qKLlrtNSsvV1jNZQvbUAiCIAiNF0f3608sAkl67lgk6WkYkvQI\ngiDYBnUlPfKbHkEQBEEQbAJJegRBEARBsAkk6REEQRAEwSaQpEcQBEEQBJtAkh5BEARBEGwCSXoE\nQRAEQbAJHG53AMKfo7LCaD2XTOf+W2Bnf+0q4H83pK0Ktoidwor3J6y5rMh1dh5o7MidHkEQBEEQ\nbAJJegRBEARBsAkk6REEQRAEwSaQpEcQBEEQBJvgtic9lZWV5Obm3u4wbgm2VFZBEARBaGwoAgIC\nAmo7mJaWxltvvYVGo6FDhw5/SQCHDx/ml19+oVu3brWec/78eb744gv69u17Q9/mzZtJT0+nY8eO\nNZ6Pi4vjxx9/rPN1aru2vmzatIn09HT27t1Lx44dcXR0rHH8Slk9PT0JCwvDy8vrT70OQMX/Sms8\nPpKUTODyEOKOJND7yScoKysj4KNP2RcVzX33dECtVrFr914OHIzF65GHa8osfsyfkPT/s3fnAVGV\n+x/H38OMETAwM4CSCoKGlrgi92amVqSWK+LSvZWVZWbbvffX1crdyJtL3luZyy9XVFRywxWvO6iI\nsshiP8sFAS1lUWEGxCy2+f0BEgyIApOOzvf1T82cmc88z3Oec+brHM45J1i6MoS4hES6P1meNatS\nlpMTew5EcORYDJ1Ns5Cse5WlMDmTokFzwoTFZMlclSwrzDI9M7NsG1pFbHzi79vQjC+IPBRVdRuK\nPoZvpz9we6xxn2POdtUvS/Wwwy1zay16Fi5cSG5uLhkZGcTFxbF3716eeeYZXnnlFRITE3F3d2fh\nwoUcOXKE/fv30717d0JCQti1axcbN26kS5curFq1isjISNatW0eTJk3Izs5m3rx5HDhwAICEhATi\n4uJ46qmnWLBgAQcPHmTXrl386U9/Ijw8nNWrV5OQkICNjU2NRc/KlSvZvHkze/fuJSoqikceeQSV\nSsXly5eZN28ekZGR/PLLLzg6OrJmzRrS0tLYtWsX/v7+/O///m+VtuXn56NSqaoVPRcvXuTVV1/l\n1KlTaLVavv32Ww4fPkxsbCw9evTg888/r3js4+PD999/T9euXVmxYkWV/OjoaOLi4mjfvj0xMTH4\n+PgwdepU4uPjiY2NpXv37vTr149ffvmFVatW0b59ezQaTY3rxrToCV4dyrQJH5GWfh6tRsOZlHN4\nNG/O8MGDWB+2lSaNXYlLSEKlVOLbqWPVMJMvkhVrQpk6/iNS08+j1WrLs5oxPHAQG8K20sTVlfjE\nZJRK5W0nrmTdvSzToqdBc8KExWTJXJUsK8wyLXqCV69l2oSPSUtPL9uGzp7Dw70ZwwMDWB+25e5t\nj9X2OeZsV/2zait6aj289fzzz9OjRw/69u3LlClT8Pb2JioqCqPRyMyZM0lISCAvLw97e3tu3LjB\nyZMnOXHiBJMmTWLy5MkolUq6devG008/jbu7OzExMeTk5FBUVESvXr3o0KEDTz31FN27d+fMmTOk\npqZib2+PjY0NJ06cYN++fcyaNYtRo0bV2L7ffvuN5ORkpk+fzl//+tcqy0JCQpgzZw6zZs1i8+bN\nAHTr1o3x48fj6elJbGxstbbVxtvbm+nTp+Pi4kJAQABdunQhJiaG9PR0VCoVU6ZMoU+fPlXeY5p/\ns6+urq4ArF69mnfeeYepU6dSWFjIuXPncHZ2ZsyYMfTq1YuEhIRa21QTF2cduXoDekMeOq0WjcaJ\nvPx8Wnp5Ejiwfx2znMnV6zHkGdDptGicnMi7VpY1eEA/ybpvssw5Jyw1y1LHXrIk64/P0udZ5vZo\nnnaZLwvu4G96jJUuZHTzX5KOjo4Vy7p3787YsWPp168fzZo1q3j9b7/9xtWrV/n6668B8PHxAaBl\ny5Z88MEHGAwG5syZU5FpNBrp0KEDY8eOZciQIXh4eFQsUyqVNbatuLi44vNsbKp2pbS0tNrr7e3t\nAWjUqBFFRUXV2nYrCoWios+rVq0iLS2Ntm3bYmtri42NTUUbKrfTaDRWyzf9l3hlN3Ps7Owq2mis\nw0WkVColRqORnFw9zjotri7O6A0GDHl5aJycKtp0Z1mq8qxcnHU6XJxd0OvLsxzLs0z/yS1ZFphl\nzjlhqVmWOvaSJVl/YJayapbFbI/mbJcZsyqr9fDWpUuX+Pnnn0lMTCQpKQmDwcArr7xCeHg4AQEB\ntGrViuDgYBITEzlz5gyDBg3ip59+Yvfu3URERNCrVy+OHDlCXl4eqamplJaW4u7uzqJFiygqKqJd\nu3a0bduW4OBghgwZQkREBHFxccTExBAQEIBSqWTNmjWcOHEChULBs88+W6V9Dz30ED///DO7du0i\nJiYGjUaDq6srKpWKrl278u2333Ls2DH69OmDvb09mzZtIicnh8uXL/PXv/6VvXv3VmlbkyZNajy8\nlZ+fT1JSEs8++yznzp3jhx9+4PTp01y6dIm3336b2NhYDh06RGJiIo8//jg//fQTf/7zn6v1vXv3\n7gQHB9OrVy8SExN58803WbhwIUlJSdjb29O/f3+2b99OQEAAKSkplJaW8vjjj9e4bkwPb6kdHFi0\nfCXFxUVkZGXT//nehHy3nqjoGEa9/gpqtZqCguucS0u/7SEDtdqBxcGrKCoqJjMri359ehGybgNR\nR48x6rURqNUOlbJq/0lXsu5elmlR3aA5YdouS8mSuSpZVphlenhLrXZg0fIVFBcXk5GZVbYNha4n\nKvoYo14fcfe2R9N9jjnb1YCs2g5vKYz1KZXukW+//bbK40cffZRTp07x66+/kp+fz6uvvkrbtm0b\n9BkZGRls27atynM9e/akffv2Dco1t9/05jsLTC7t/2CQ21AI8WCS21DUja22yS2X3VdFj/idFD3C\nlBQ9QjyYpOipm9qKnnt+nR4hhBBCiLtBih4hhBBCWAUpeoQQQghhFaToEUIIIYRVkKJHCCGEEFZB\nih4hhBBCWAXVvW6AqB+F0nyrTmFjvlMYjcbqV8JuQJj5oqzgVGdz9tGcp79b6thb6in+ljpe4t4x\nlphxv2pW999clV96hBBCCGEVpOgRQgghhFWQokcIIYQQVkGKHiGEEEJYBSl6hBBCCGEVlEFBQUH3\nuhF32+jRowkICGhwztatW1mwYAGPP/44Op3ODC2DBQsWYGtryyOPPFLr60p+u1Hl8fHEJJYsX0ls\nfAI9nnqSwsJCPv18FhGHDuP9aCtSUlOrLK/N8cRklgSvJPb4cXp0K8+aMZuIQ1F4t2pFSmpaleVV\nVf1r/uNJySxZsYrY+ER6dOtKYWEhQTO+IPJQFN6tWqLROLFnfwRR0cfw7dSh9nYlJbNkRQixxxN+\nz5o5h8jDR6pmHY3Bt6NJlslJBglJJ1i6MoS4hES6P1meNatSlpMTew5EcORYDJ1Ns3jwsxQmNxJ8\nEMferH008SCOl2RJ1v2SpbJX3zLXaoqeH3/8kenTp5OcnMy5c+f44YcfiI+PZ82aNfTo0YN//OMf\n9OnTh+zsbL755hueeeaZKu8vLi5m6tSpHDt2jO3bt9OhQwfmzp2Lm5sbQ4YMwcbm9x/NDhw4QEJC\nAi4uLgQEBDBy5EimTp1K27Zt+eqrrzhy5Aj79++ne/furFu3ju3bt7Nz507c3Ny4cOECHh4eLFu2\nDKPRiJeXV439MS16gletYdqk8aSmp6PTajlzNgV39+a8OCSQdRvD+PHUaaZO/ITU9PPotFq0Gs0t\nxyo4ZA3TJn5Catp5dFpNWVbz5rwYOJh1m8qzJnxMavoFdFqNSVbVPXbw6rVMm/AxaenpaDUazpw9\nh4d7M4YHBrA+bAtNGrsSl5CESqnEt1PHWtdh8OpQpk34iLT082VZKefwaN6c4YMHsT5sa+1ZJl8k\nK9aEMnX8R6Smn0er1ZZnNWN44CA2hG2liasr8YnJKJXK2xZjD2KWaUHwII69Wfto4kEcL8mSrPsl\nq7aix2oObwUHBzNr1iwmTpyIo6MjgwcPpmvXrpSUlJCSksKwYcPYtm0boaGhjBgxotr7o6OjadOm\nDVOmTOGFF14gIiKCbt268dJLL6FUKqu8tkePHiQkJBAVFUWbNm1ITU3lxo0bHDx4kLy8POzs7Lhx\n4wYnTpxg3bp12Nvbo9PpiI6OBuDzzz/H09OTZ599ts79dHF2JlevR2/Iw1mnQ6NxIi8/HxQKFApF\nxfI7ynLRVcrSlmXlVcoqX16nduXlodNqK9rV0suTwIH969hHHbl6A3qDObLK2mXIM6DTadE4OZF3\nrSxr8IB+klUtyxrG3px9tIbxkizJuj+ywIqKHoVCgbH8YndGo5HQ0FA0Gg1eXl4YjUb69OlDVFQU\nly9fplWrVrfNuslYwwX0bG1tsbGxITk5mTFjxrBgwQL+/Oc/U1paSvfu3Rk3bhz9+vXDw8MDrVbL\n2LFjGTlyJO3atQNg5MiRREZG8ssvv9xx/1QqFUajkZzcXJx1OlxdyieKIQ+txolGJsvvKCtHX57l\nQq7eUJ6loZFKWWV5rVnK6u3SGwwY8vLQODndcgxrblf55+bqcdZpG5hVtV0uzi7o9eVZjuVZd3jh\nLevIsoaxN2cfrWG8JEuy7o+syqzm8Janpydz5swhKSmJmJgYnJycMBgMnD17ljZt2uDt7c2pU6fo\n1asXLVq0qPZ+Dw8PwsLCiI+P59y5c7z11lskJyfj6elZ49/fXL9+ndTUVF5//XU+++wzpk2bRrt2\n7QgODiYxMZEzZ84waNAgcnJy2Lx5MwcOHMDf35+0tDQ6duxIly5dCA4Oxt/fv8b+mB7eUjuoWbQs\nmKKiIjIys+j/Qh9C1q7jcPRR3nr9NR5xc2PRsmCKi4vp/0KfWsdKrXYoyyouJiMz8/eso0d5a+Sr\nv2eVlNSQZayetXwFxcXFZe16vjchoeuJij7GqNdHoFarKSi4zrm09NseMlA7OLBo+UqKi4vIyMou\ny/puPVHRMYx6/ZXas0y2DbXagcXBqygqKiYzK4t+fXoRsm4DUUePMeq1EajVDpWyav8Z9kHMMj30\n8yCOvVn7aNquB3C8JEuy7pes2g5vKYx3+s+NB1xoaCgnT55k5syZZGRksG3btirLe/bsSfv27au9\nr6CggNWrV1d5rnPnznTr1u0PbW9hfo75wsx5uwe5DcUDQW5Dce9Y6ngJcb942OXWJwJJ0XOfkqKn\njlHyRVInUvTcO5Y6XkLcL2oreqzmb3qEEEIIYd2k6BFCCCGEVZCiRwghhBBWQYoeIYQQQlgFKXqE\nEEIIYRWk6BFCCCGEVVDd6waIerLU08wtlJyefO9Y6tibkzWsR2tgqZdqUCjN+PuEOa9So7j/tm35\npUcIIYQQVkGKHiGEEEJYBSl6hBBCCGEVpOgRQgghhFW4L4seo9FIdnZ2vd+fmZlpxtbcmevXr3Pt\n2rW7/rlCCCGEKKMMCgoKMmdgWloa//jHP9BqtbRs2dKc0RXi4+M5dOgQfn5+t3zNxYsX+fLLL/H3\n96+2bOzYsQQEBPwhbbuVJUuW4OTkxCOP3PpGaHVR8tsvVR4fT0xmSfBKYo8fp0e3JyksLOTTGbOJ\nOBSFd6tWpKSmVVleVdW/5j+elMySFauIjU+kR7euFBYWEjTjCyIPReHdqiUajRN79kcQFX0M304d\nam2n+bNCiD2e8HvWzDlEHj5SNetoDL4d75MskxMpEpJOsHRlCHEJiXR/sjxrVqUsJyf2HIjgyLEY\nOt+mXQ3JUpiclWEx42WpWRa6HiXrHs57M84J07PKLGa/WuN43fvvDtXDDrfMNXvRs3DhQnJzc8nI\nyCAuLo69e/fyzDPP8Morr5CYmIi7uzsLFy7kyJEj7N+/n+7duxMSEsKuXbvYuHEjXbp0YdWqVURG\nRrJu3TqaNGlCdnY28+bN48CBAwAkJCQQFxfHU089xYIFCzh48CC7du3iT3/6E+Hh4axevZqEhARs\nbGyqFT0//fQTISEhuLi4cPXqVYKDg4mMjOTq1atcv36dqVOncuHCBb777jvS09NZu3YtzZo1Y8OG\nDYSHh3P69Gl++OEHOnbsyBdffFHlsz/88EOOHj2Kra0tS5cuJT4+njVr1tCjRw+WLFnClStX0Ov1\npKen07p1a0aPHk1AQAAvvfTSLcdGqVTWOM6mRU9wyBqmTfyE1LTz6LQazpxNwb15c14MHMy6TWH8\neOo0Uyd8TGr6BXRaDVqNptK7q26dwavXMm3Cx6Slp6PVaDhz9hwe7s0YHhjA+rAtNGnsSlxCEiql\nEt9OHWudD+bNCmXahI9ISz9flpVyDo/mzRk+eBDrw7ben1kmO8YVa0KZOv4jUtPPo9Vqy7OaMTxw\nEBvCttLE1ZX4xGSUSuVtdxoNyTLd+VvMeFlqloWuR8m6h/PejHPCtOixmP1qtfGyjO+O2ooesx/e\nev755+nRowd9+/ZlypQpeHt7ExUVhdFoZObMmSQkJJCXl4e9vT03btzg5MmTnDhxgkmTJjF58mSU\nSiXdunXj6aefxt3dnZiYGHJycigqKqJXr1506NCBp556iu7du3PmzBlSU1Oxt7fHxsaGEydOsG/f\nPmbNmsWoUaNqbF+LFi3w9PRkwIABLF68GEdHR5ycnIiNjQWgc+fO/OMf/0Cv1/POO+/w0ksvERcX\nh0KhYMiQIbz//vskJycTGRlZ7bNv3LjBp59+ip+fH4MHD6Zr166UlJSQkpLCk08+SWBgYI1tutXY\npKSk1Hn8XVx05Or16A15OOu0aDRO5OXlg0KBQqGoWH5HWc7OZVl5eei05Vn5+bT08iRwYP+6tcus\nWTpy9Qb0hgc5q2y8DHkGdDotGicn8q6VZQ0e0O8eZlnqeFlqlqWuR8mqW5ZlzwnL269a5ncH/EF/\n02OsdPGjm5Wzo6NjxbLu3bszduxY+vXrR7NmzSpe/9tvv3H16lW+/vprAHx8fABo2bIlH3zwAQaD\ngTlz5lRkGo1GOnTowNixYxkyZAgeHh4Vy271C0llpaWlvPvuu3z44Yc8+WTZIZ+HH34YgEaNGmFj\nY4ONjQ2lpaUVrwcoLi4GqPbZjRo1wtbWlrNnzxIaGopGo8HLy6vKeCiVSoqKigAwGAy1jo2bm9ud\nDjkqlQqj0UhOjh5nnQ5XFxdy9QYMhjy0Gg2NVMoqy2vNUpZn5eaWZzmjNxgw5OWhcXKqaOsdtcuc\nWTf7kKvHWad9QLOqjpeLswt6fXmWY3mW6T8h70qWpY6XpWZZ6nqUrLplWeicsNT9qoV+d1Rm9sNb\nly5d4ueffyYxMZGkpCQMBgOvvPIK4eHhBAQE0KpVK4KDg0lMTOTMmTMMGjSIn376id27dxMREUGv\nXr04cuQIeXl5pKamUlpairu7O4sWLaKoqIh27drRtm1bgoODGTJkCBEREcTFxRETE0NAQABKpZI1\na9Zw4sQJFAoFzz77bLU2JiUlkZaWxgsvvMA333zD4cOHadWqFWq1muzsbPz8/Ni+fTsBAQFcunSJ\nrKwsSkpKiIyMJDY2li5dujBgwAC2bNlCXFwcsbGxBAQEsHv3bgICArh+/Tr79+/HYDBw9uxZWrdu\njVqtZuPGjfz1r39l0aJFnDx5kkuXLvHSSy9VfJbp2AwYMKDaz603mR7eUqsdWLQsmKLiYjIyM+n/\nQh9C1q7j8NGjvDXyVR5xc2PRsmCKS0ro/0IfkzRj9azlKyguLiYjM4v+z/cmJHQ9UdHHGPX6CNRq\nNQUF1zmXln7bnyjNmuXgwKLlKykuLiIjK7ss67v1REXHMOr1V+7PLJNtVq12YHHwKoqKisnMyqJf\nn16ErNtA1NFjjHptBGq1Q6Ws2n/mb0iW6byzmPGy1CwLXY+SdQ/nvRnnhOnhLYvZr5qOl4V8d9R2\neEthrE+pdB/59ttvqzx+9NFHef755+ucs2DBAp5++mk6dqx9Rd0thXlXzZZlsbeheLCnJmC5ty+w\nhltHmJOlrkdRN3Ibijqy0NtQ2Gqb3HLZA1/0PKik6HkwWOqXpRQ9dWOp61HUjRQ9dXQfFj335XV6\nhBBCCCHqSooeIYQQQlgFKXqEEEIIYRWk6BFCCCGEVZCiRwghhBBWQYoeIYQQQlgF1b1ugKgnM54q\nqDBj7WssLTFjlpwGfK/I2NeNWU/xN+e2rZB/194zCsu8FIjC5vZ3K7gX7talU2SLEEIIIYRVkKJH\nCCGEEFZBih4hhBBCWAUpeoQQQghhFaToEUIIIYRVkLO3GqC0tJRXXnmF1atX06hRIwDefPNNlixZ\nwqZNm9i8eTMbNmxAoVCwefNmwsPD8fDwoKSkhBs3bjBz5kxsbW1vmT9//nyefvppOnXqdNu2HE9M\nIvy/u7Gzs2P8uA8pLCxk+qw5AIwZ9QaXr1ypsrz2rGTCd+3Gzu5hxo8tz5r977KsN0fSwsOd3fsO\nkJmVxZuvjag9KymZ8F17y7L++Q8KCwv51xdfAvD2G6/RwsOdPfsjyMjK5s1XX641KyHpBOG792Bn\nZ8cnH/69LGtOpSx3d/YciCAzK5s3RlhO1qkzZ0lI/p7r16/z6YSPeeihhyyiXZJVt6y6rEdzzvuy\nrD3YPWzH+LHlWbP/U5b15uu/Z2Vm8eZrr9SeZc5tW7LqsS8003q8T+fE5StXqyy/W32sTBkUFBR0\nx6++R+bPn094eDinT5/m5MmTfPrpp5w6dYqmTZsyf/58Dh8+zNmzZ+nQoQNBQUHEx8ezZs0aevTo\nQVhYGDt27GDz5s34+PgQHh5e5fHs2bPx9fXF3t6e0aNH4+fnx6uvvlpjvp+fX5V2KRQKbty4QWZm\nJq1bt+bYsWM89NBDPPHEE8yZMwd/f39ycnLw9vbm9OnTdOzYkTFjxvDcc88RExND06ZNady4cZXM\noqIiJk+eTHx8PLGxsTzzzDO4ublVG5OS325UeRy8ag3TJo0nNT0dnVbLmbMpuLs358UhgazbGMaP\np04zdeInpKafR6fVotVobjnewSFrmDbxE1LTzqPTasqymjfnxcDBrN+0mcaNGxOfkIhSqaRL545V\n32xyB9/g1aFMm/ARaenn0Wo0nEk5h0fz5gwfPIj1YVtp0tiVuIQkVEolvp1Ms6o+XLEmlKnjPyI1\n/TxarbY8qxnDAwexIWwrTVxdiU9MRqlU4tupwy37d7ezPFu0YMRfhpGVfRmtkxM6rdYi2iVZdcuq\nbT0qTE4zb9C8r5a1lmkTPiYtPb0s6+w5PNybMTwwgPVhW2rNqtauhmzbJiTrdlmm+8L6r8dq7boP\n58S6TeXfQxM+JjX9AjqtxuR7yHzjpXrY4ZZtvC8ObykUCoYMGcL7779PcnIyXl5eTJ8+ncWLF6NW\nq1Gr1SQlJVFYWMjgwYPp2rUrJSUlpKSkcOnSJdRqNcOHD6dx48ZVHru6utb4ed7e3jXml5RUvwbN\nsGHD2LlzJwBhYWG89NJLHDp0iI4dOzJkyBBWrFgBgNFo5LvvvmPcuHEMHTqU5s2b4+PjUy0vJiaG\n9u3bM3HiRJ544gmMxrpdL8XF2ZlcvR69IQ9nnQ6Nxom8/HxQKFAoFBXL7yjLRVcpS1uR1crLk8BB\nA+rYLh25egN6Qx467e9ZLb08CRzYv159NOQZ0Om0aJycyLtWljV4QD+Ly+r+5BNkX7nChZ9+pqWX\np8W0S7LqllW/9Wj+ea/PM0OWObdtyapbljnX4/00J/IqfQ+VL7/bfYT7pOiBskNJACUlJWjKq8PS\n0lJeeuklxo0bh7+/P2lpaYSGhqLRaPDy8sJoNNK3b18GDhxIdHQ0mzdvrnh89OhRtmzZglKppKio\niOvXr1NUVASAo6NjjflKZfWLOtnb2+Pl5cXhw4dxdnbGycmJVatWkZWVxYIFC8jNzeXYsWMoFApe\nfvllvvzyS15++WWysrJq7KdSqazoa02fdysqlQqj0UhObi7OOh2uLuU7bkMeWo0TjUyW31FWjr48\ny4VcvQGDIQ+NkxPAHRdjKpWy/HP1OOu0uLo4ozcYMOTVJ6tqH1ycXdDry7Mcy7y3MEgAACAASURB\nVLNMfx6ygKyMzCyWr1rDuL+/b1Htkqy6ZdVtPZpx3iurb9sN3obMsm1LVp2yzLke78M5odVoaHRz\nuyhffrf6WNl9cXgrNjaWgwcPEhsbi6+vL7m5uTz77LN4enoyd+5cYmJisLe3p3Xr1uzfvx+DwcDZ\ns2dp3bo1KSkp7N27l6KiIvz9/UlOTq54/Nxzz9G0aVMWLlzImTNnKCoqokePHiQkJODv718t39fX\nt8b2Pfroo7z33nt89tlnZGdnk5yczNy5c/H39+eJJ55g/vz5tGnTBpVKRevWrWnXrh2HDh2ioKCA\nNm3aVMlq2rQpW7ZsIS4ujqSkJHr06HFHh7fUDmoWLQumqKiIjMws+r/Qh5C16zgcfZS3Xn+NR9zc\nWLQsmOLiYvq/0KfW8VarHcqyiovJyMz8PevoUd4a+SpqtZqCguucS0277eEttYMDi5avpLi4iIys\nbPo/35uQ79YTFR3DqNdf+T0rLf22h7fUagcWB6+iqKiYzKws+vXpRci6DUQdPcao10agVjtUyqr9\nUMbdyDpyLIY3RrzMzP98TXFJMVHHYnistTdOTo73tF2SVbesO1mPpocMGjTvTbPUDixavoLi4uKy\nbfv53oSEricq+hijXh9Ra1a1djVk265hvCSrtixj9ax6rsca23UfzomK76GSkhq+h8w3XrUd3lIY\n61Mq3WULFizg6aefpmPH2gf8j7Zt2zYyMjIqHtvZ2fHGG2/UOy8jI4Nt27ZVea5nz560b9/+tu8t\nzM+p9+dWY8YpILehENZIbkMhTN2t2yrUlaXOCXOOl622yS2X3RdFj6hOih4hLIcUPcKUFD11c7eK\nHsvsvRBCCCGEmUnRI4QQQgirIEWPEEIIIayCFD1CCCGEsApS9AghhBDCKkjRI4QQQgirIDccvV+Z\n8zRzc55aac7TbZVmPA1Yrswg7hdm3bbNdwkJUUdm3Beak6WeSn+3yC89QgghhLAKUvQIIYQQwipI\n0SOEEEIIqyBFjxBCCCGsghQ9lWRmZt7rJgghhBDiD6IMCgoKuteNsBRjx44lICCg2vO//fYbU6dO\nJSEhgbCwMLp168bDDz9csXz06NE1vu9Wz9fmTt9T8tsvVR4fT0xmSfBKYo8fp0e3JyksLOTTGbOJ\nOBSFd6tWpKSmVVleVdWzRY4nJbNkxSpi4xPp0a0rhYWFBM34gshDUXi3aolG48Se/RFERR/Dt1OH\nWttp2VkhxB5P+D1r5hwiDx+pmnU0Bt+OkiVZkiVZdcwyOXvLsveFD1aW6mGHW+b+IUXP/Pnz2blz\nJ9HR0ezatYu0tDSWLl1KSUkJmzZtYvfu3djY2KBQKJgzZw4xMTHs3LmTPn368K9//YuYmBi2bt1K\nr169GDNmDAEBASQnJ7Np0yYuXbrEV199xaVLl7h8+TIbNmyoyEtKSmLx4sX8+OOPbNy4kQsXLrBs\n2TJ8fX05efIkwcHBREZGcvXqVa5fv05QUBAZGRmsXbuWdu3asWrVKlxcXGjTpk2V/uTk5ODu7s6L\nL75IZmYmxcXFpKenM3/+fBITE9Hr9TUWKlu3buX//u//CA8PR61WU1JSUq2/K1asYNeuXWzcuJEu\nXboQERGBr68vU6dOpWvXrtjZ2dU4xqZFT3DIGqZN/ITUtPPotBrOnE3BvXlzXgwczLpNYfx46jRT\nJ3xMavoFdFoNWo2m0rurFj3Bq9cybcLHpKWno9VoOHP2HB7uzRgeGMD6sC00aexKXEISKqUS304d\na50LlpsVyrQJH5GWfr4sK+UcHs2bM3zwINaHbZUsyZIsyWpYlknRY7n7wgcvq7ai5w85vKVQKOjf\nvz+TJ0/m4sWLjBkzhrZt2zJhwgTs7e1xcXEhOjoae3t7hg4dSpcuXUhMTATg4sWLNG3alBEjRlTL\nvKl379689957LFu2rEqeQqGgZ8+ejB07luzsbMaMGUPv3r35/vvvWbJkCY6Ojjg5OREbGwtA+/bt\neffdd7G3t0elUuHp6cmAAQOq9cfNzQ0/Pz++//57zpw5Q7du3Vi3bh3/+c9/+J//+R9Uqpovd6RQ\nKPjoo4+YMWMGK1asqNbf3377jRMnTjBp0iSmTJmCUqnEYDAwevRogoKCcHZ2rvPYu7joyNXr0Rvy\ncNZp0WicyMvLB4UChUJRsfyOspydy7Ly8tBpy7Py82np5UngwP51a5fFZunI1RvQGyRLsiRLsu5G\nlqXuCx/8LPgD/6bHzs4OhUKBra1t2QfZ2KBUKvnnP//JO++8g6+vLzt37iQuLo6WLVui1WopLi7m\ngw8+wMfHhyVLlpCSkoKx/EJd+kpf1I6OjhWZlfNufm7l/9rY2GA0GikpKeHdd9/lww8/5Mknn6zy\nGpVKRWlp7RdsWrduHZGRkcyYMQOlUolCocBoNGJjc+shVCqVPPTQQ9jY2NCoUaMa+3uzf7/++is5\nOTk0atSIsWPHsnz58jqNt0qlwmg0kpOjx1mnw9XFhVy9AYMhD61GQyOVssryWrOU5Vm5ueVZzugN\nBgx5eWicnAAq2n3bdllq1s3xyNXjrNNKlmRJlmT9sVmWui+0gqzK/pDDW3FxcXh5eeHm5saOHTsI\nCAggPj6eP//5z6xfv57IyEj+9Kc/YWNjQ2xsLD/99BPnz59n4MCBLFmyhAsXLqBUKunbty+ZmZls\n376dCxcu4ODggJOTEyqVitatW2Nra8uyZcsq8goKCiqWbd++nYCAAE6fPo1KpaJ79+588803HD58\nmFatWqFWq8nOzsbPz4+DBw/i5+dHamoqaWlpFQVU5f7MmDEDjUbD/v37ady4Me3atWPevHmcOHGC\ngoKCGn8h2rFjB2lpaRw6dIhhw4YBVOnv4MGDyc7OZvfu3URERNCnTx+OHDnCP//5T3bv3o2TkxPN\nmjWrcYxND2+p1Q4sWhZMUXExGZmZ9H+hDyFr13H46FHeGvkqj7i5sWhZMMUlJfR/oY9JmrF61vIV\nFBcXk5GZRf/nexMSup6o6GOMen0EarWagoLrnEtLv+1PlBab5eDAouUrKS4uIiMruyzru/VERccw\n6vVXJEuyJEuyGpZlcnjLYveFD2BWbYe3FMb6lEoPsIyMDLZt21bluZ49e9K+ffta37d27Vry8/Mr\nHjdp0qSi0PkjFOZdNVuWVVyWXKa5EOJustDbUFgDW22TWy6Touc+JUVPHck0F0LcTVL03DO1FT1y\nnR4hhBBCWAUpeoQQQghhFaToEUIIIYRVkKJHCCGEEFZBih4hhBBCWAUpeoQQQghhFWq+f4KwfGY8\nHVJhBbWvESs4LV/UjTkvY2ANpydbw2UfzLkerWC8jKX3Xx8f/G87IYQQQgik6BFCCCGElZCiRwgh\nhBBWQYoeIYQQQlgFKXrqIDMz8143QQghhBD1pAwKCgq61424X4wdO5aAgIAal/n4+PDoo4/i7e0N\nwMiRI7ly5Qp+fn4UFxcTEBCAWq3mscceM0tbSn67UeXx8cQklixfSWx8Aj2eepLCwkI+/XwWEYcO\n4/1oK1JSU6ssr83xxGSWBK8k9vhxenQrz5oxm4hDUXi3akVKalqV5Q3J0mic2L3vAFFHj+HbqeMf\nmFX1LIPjScksWbGK2PhEenTrSmFhIUEzviDyUBTerVqi0TixZ38EUdHH8O3UofZ2SdYDlBVC7PGE\n37NmziHy8JGqWUdj8O1okmVy1o9l99ECxstSs/7w9WgBfTRnlsnJWwlJJ1i6MoS4hES6P1meNatS\nlpMTew5EcORYDJ1v066GZKns1bfMvSdFz/z589m5cyfR0dHs2rWLtLQ0li5dSklJCZs2bWL37t3Y\n2NigUCiYM2cOMTEx7Ny5kz59+vCvf/2LmJgYtm7dSq9evRgzZgwBAQEkJyezadMmLl26xFdffcWl\nS5e4fPkyGzZsqMhLSkpi8eLF/Pjjj2zcuJELFy6wbNkyfH19OXnyJMHBwURGRnL16lWuX79OUFAQ\nGRkZrF27lnbt2rFq1SpcXFxo06ZNtT4dPXqUrKws+vbty/nz54mOjsbd3R0/Pz/++9//4uXlxY4d\nOxg6dGidxsTf35+HHnqo2utNi57gVWuYNmk8qenp6LRazpxNwd29OS8OCWTdxjB+PHWaqRM/ITX9\nPDqtFq1Gc8v1ExyyhmkTPyE17Tw6raYsq3lzXgwczLpN5VkTPiY1/QI6rabeWes3baZx48bEJySi\nVCrp0rn2oqdhWVW3zuDVa5k24WPS0tPRajScOXsOD/dmDA8MYH3YFpo0diUuIQmVUnnbYkyyHpSs\nUKZN+Ii09PNlWSnn8GjenOGDB7E+bGvtWSZflpbbRwsZL0vN+kPXo4X00ZxZJkXPijWhTB3/Eanp\n59FqteVZzRgeOIgNYVtp4upKfGIySqXytkViQ7JqK3ruyeEthUJB//79mTx5MhcvXmTMmDG0bduW\nCRMmYG9vj4uLC9HR0djb2zN06FC6dOlCYmIiABcvXqRp06aMGDGiWuZNvXv35r333mPZsmVV8hQK\nBT179mTs2LFkZ2czZswYevfuzffff8+SJUtwdHTEycmJ2NhYANq3b8+7776Lvb09KpUKT09PBgwY\nUGOf7OzscHZ2Jjs7m61btzJ06FCM5ddpWLduHUOGDOHxxx/n4MGDdzwmPj4+pKam1mlsXZydydXr\n0RvycNbp0GicyMvPB4UChUJRsfyOslx0lbK0ZVl5lbLKl9c7Kz+fVl6eBA6qeUzvStbN8crLQ6f9\nPaullyeBA/tLllVm6cjVG9AbLK1dlpplzvGy1CwZr7pllY2XIc+ATqdF4+RE3rWyrMED+t2zLLiH\nf9NjZ2eHQqHA1ta2rCE2NiiVSv75z3/yzjvv4Ovry86dO4mLi6Nly5ZotVqKi4v54IMP8PHxYcmS\nJaSkpFQUFvpKX76Ojo4VmZXzbn5u5f/a2NhgNBopKSnh3Xff5cMPP+TJJ5+s8hqVSkVp6e0vbjds\n2DA2btxIfn4+rq6uACQkJHDlyhW++uorrl69ytKlS+s0JsY7vMCVSqXCaDSSk5uLs06Hq0v5RDHk\nodU40chk+R1l5ejLs1zI1RvKszQ0UimrLK9vlsbJCaDufTRHlrL6eOkNBgx5kmW1WTfnda4eZ53W\nctplqVnmHC9LzZLxqmNW1fFycXZBry/PcizPMv156C5kVXbPip6bv8xU/u97773HRx99xLRp03B2\ndqZJkyacPn2asLAwfv31VwoKCli/fj27d+9Gq9XyyCOP0KFDByZNmkRERES1z3jttdeq5NXmnXfe\nYcKECUyaNKmi6FCY/NTp4eFBcHDwLfvj4+PDkSNH6NWrV8Xzq1evZt68eXz22WfMnTsXV1dXEhIS\n7mhMamrDrfTt05ugGbO5fPkK+w5E4ufbmUNR0Xzx1Vz+MnQIL/TuRdCM2Vy5chXPFh63ySp77eUr\nV9gXEYmfb6eyrK+/4S/DAn/PutrwrLr10ZxZzxE08wsuX7nKvoiD+HXuxKGoo8z5ah5/GTpYsqwx\nq/dzBM2cUza/Ig+VZR2JZs7X8/nL0IAHo4+WOl6WmiXjVaesF3o/x2ez/83lK1fZH3kQv84dORR9\nlDlz5/PikPLx4u5nVaYw3mkJJwDIyMhg27ZtVZ7r2bMn7du3v+OMyMhITp8+XeW5MWPGoFQq7zij\nMD/njl97W1YwBYxGuQ2FMCG3oagbK9hPyG0o6sZSb0PxsMsjt1wmRc99SoqeupGiR1QjRU/dWMF+\nQoqeurkfix65To8QQgghrIIUPUIIIYSwClL0CCGEEMIqSNEjhBBCCKsgRY8QQgghrIIUPUIIIYSw\nCqp73QDxYJFTw+8hCz1F1pyntSpszHhKsTWcZm5O1jBeFroNWerYK5SW2a7ayC89QgghhLAKUvQI\nIYQQwipI0SOEEEIIqyBFjxBCCCGsghQ9QgghhLAKcvaWGcyfP5/9+/dX3H09Li6OMWPGkJycDMB3\n333H5s2b2bBhA4o/6K/wjycmEf7f3djZ2TF+3IcUFhYyfdYcAMaMeoPLV65UWV57VjLhu3ZjZ/cw\n48eWZ83+d1nWmyNp4eHO7n0HyMzK4s3XRtSelZRM+K492D1sx/ix/6CwsJB/zf4PAG+/+TotPNzZ\nsz+CjMws3nztFckye9besvX4z/KsL74sy3rjtd+zsrJ589WX71pWQtIJwnfvwc7Ojk8+/HtZ1pxK\nWe7u7DkQQWZWNm+MuHvtsuz1KFmyDd0v42V5WZUpg4KCgu741ffQ/PnzCQ8P5/Tp05w8eZJPP/2U\nU6dO0bRpU+bPn8/hw4c5e/YsHTp0ICgoiPj4eNasWUOPHj0ICwtjx44dbN68GR8fH8LDw6s8nj17\nNr6+vtjb2zN69Gj8/Px49dVXa8z38/Or1rb4+Hhu3LhBixYtcHNzY/ny5ahUKoYOHYrRaGTOnDn4\n+/uTk5ODt7d3jf3r27cvBoOBFStW8NNPP7Fnzx5+/vlnOnbsWOPrS367UeVx8Ko1TJs0ntT0dHRa\nLWfOpuDu3pwXhwSybmMYP546zdSJn5Cafh6dVotWo7nlWAeHrGHaxE9ITTuPTqspy2renBcDB7N+\n02YaN25MfEIiSqWSLp1N21f1lM/g1WuZNuFj0tLT0Wo0nDl7Dg/3ZgwPDGB92BaaNHYlLiEJlVKJ\nb6ea+ypZ9c0KZdqEj0hLP1+WlXIOj+bNGT54EOvDtt69LJOzgFesCWXq+I9ITT+PVqstz2rG8MBB\nbAjbShNXV+ITk1Eqlfh26lDlvab/aGhQu6plWep6lCyr34ZkrtYpS/Wwwy1z75vDWwqFgiFDhvD+\n+++TnJyMl5cX06dPZ/HixajVatRqNUlJSRQWFjJ48GC6du1KSUkJKSkpXLp0CbVazfDhw2ncuHGV\nx66urjV+nre3d435JSUlNb5+6NChbN26lfz8fOzs7LC1tQXg8OHDdOzYkaFDh7JixYpb9s/FxYW/\n/e1vtG3blj//+c9MnjyZQ4cO1XmcXJydydXr0RvycNbp0GicyMvPB4UChUJRsfyOslx0lbK0FVmt\nvDwJHDSgfu3Ky0On/T2rpZcngQP7S9YfmqUjV29Ab7C0rLI+GvIM6HRaNE5O5F0ryxo8oN89b5fl\nrUfJundZlr0NWd54WWYW3EdFD0BpadmF70pKStCU/1JRWlrKSy+9xLhx4/D39yctLY3Q0FA0Gg1e\nXl4YjUb69u3LwIEDiY6OZvPmzRWPjx49ypYtW1AqlRQVFXH9+nWKiooAcHR0rDFfqVTW2DY3Nzeu\nXbvG+vXrGTx4cMXzq1atIisri/nz55Obm8uxY8dqfL+dnR0ANjY2PPTQQygUior+3gmVSoXRaCQn\nNxdnnQ5Xl/IvFEMeWo0TjUyW31FWjr48y4VcvQGDIQ+NkxMAxju8iJdKWb1deoMBQ55k/eFZKmV5\nlh5nndaCsqr20cXZBb2+PMuxPMv056G70S5LXY+Sde+yLHUbstTxstCsyu6bw1uxsbEcPHiQ2NhY\nfH19yc3N5dlnn8XT05O5c+cSExODvb09rVu3Zv/+/RgMBs6ePUvr1q1JSUlh7969FBUV4e/vT3Jy\ncsXj5557jqZNm7Jw4ULOnDlDUVERPXr0ICEhAX9//2r5vr6+1doWFxeHp6cnzZs3Z8eOHYwePZrt\n27fTtm1bkpOTmTt3Lv7+/jzxxBPMnz+/SlF0044dOwgICCAuLg4vLy/c3NzYvn07AQEBNY6H6eEt\ntYOaRcuCKSoqIiMzi/4v9CFk7ToORx/lrddf4xE3NxYtC6a4uJj+L/SpdazVaoeyrOJiMjIzf886\nepS3Rr6KWq2moOA651LTbnt4S612YNHyFRQXF5e16/nehISuJyr6GKNeH/F7Vlr6bX/ulKw6Zjk4\nsGj5SoqLi8jIyi7L+m49UdExjHr9lbuXZbJfUqsdWBy8iqKiYjKzsujXpxch6zYQdfQYo14bgVrt\nUCmr9sNbDWqXaZalrkfJkm1I5mqdsmo7vKUw1qdUugcWLFjA008/fcu/cblbtm3bRkZGRsVjOzs7\n3njjjTt+f0FBAatXr67yXOfOnenWrVud2lGYn1On19fKjFNAbkNxD1nopiy3oRD3DQvdhmSu1o2t\ntsktl903RY+oSooeUY2FbspS9Ij7hoVuQzJX66a2oue++pseIYQQQoj6kqJHCCGEEFZBih4hhBBC\nWAUpeoQQQghhFaToEUIIIYRVkKJHCCGEEFZBbjgqzEqhMF8dLae/15GFntZqxilhsX0UohpzzlVz\nnkpvqe26S+SXHiGEEEJYBSl6hBBCCGEVpOgRQgghhFWQokcIIYQQVuGeFj1ZWVn38uNvKzMz8143\nQQghhBBmogwKCgr6Iz9gwYIF2Nra8sgjj1R5/tKlSyxevBh/f3+++OILevToUefsLVu2kJ6eTuvW\nrevVNtPP3bx5M+np6URERNC6dWsmTpxIQEDAHWX5+Pjw6KOP4u3tDcDIkSO5cuUKfn5+FBcXExAQ\ngFqt5rHHHqtXW02V/HajyuPjiUksWb6S2PgEejz1JIWFhXz6+SwiDh3G+9FWpKSmVllem+OJySwJ\nXkns8eP06FaeNWM2EYei8G7VipTUtCrLG5Kl0Tixe98Boo4ew7dTR5N3Vz0z4HhSMktWrCI2PpEe\n3bpSWFhI0IwviDwUhXerlmg0TuzZH0FU9DF8O3WovV2SdY+zQog9nvB71sw5RB4+UjXraAy+HU2y\nTM48sew+StaDkWWpc/UBa5cZs1R26lvmmq3omT9/Pt999x05OTls376dnTt34ubmxoULF/D09CQy\nMpLw8HA2b96M0WgkNTWVAwcO8Kc//Ylt27aRm5tLQUEBLVq04O9//ztPP/00s2bN4siRI+zfv5/u\n3bujVCqrfObp06fZsWMHcXFxHDp0CH9/f0aPHk1AQAA7d+7khx9+4MCBA4SHhxMdHc2uXbtIS0tj\n6dKl+Pv7s3btWgICAvj88885fPgwsbGx+Pj48P3339O0aVM2bdqEs7Mz//nPf+jTpw/Z2dl88803\nPPPMM9X6f/ToUbKysujbty/nz58nOjoad3d3/Pz8+O9//4uXlxc7duxg6NChNY7fu+++y8CBA5ky\nZQp6vR6tVsvChQtvWQyaFj3Bq9YwbdJ4UtPT0Wm1nDmbgrt7c14cEsi6jWH8eOo0Uyd+Qmr6eXRa\nLVqN5pbrMjhkDdMmfkJq2nl0Wk1ZVvPmvBg4mHWbyrMmfExq+gV0Wk29s9Zv2kzjxo2JT0hEqVTS\npXPtRU/w6rVMm/AxaenpaDUazpw9h4d7M4YHBrA+bAtNGrsSl5CESqmsoYBCsiwmK5RpEz4iLf18\nWVbKOTyaN2f44EGsD9tae5bJDtty+yhZD0aWpc7VB7BdZsyqregx2+EthUJBYGAg69atw97eHp1O\nR3R0dMXy9u3b07t3b9q2bcvhw4d56qmn6NChA48++igAgYGB/Pe//+Xnn3+madOm7Nixg7y8POzt\n7blx4wYpKSk1fm7v3r35/PPPuXjxYrX23NS/f38mT57MxYsXGTNmDD4+PqSmpgKQnp6OSqViypQp\n9OnTp+I9LVq0wNPTk4EDBzJs2DC2bdtGaGgoI0aMqLEddnZ2ODs7k52dzdatWxk6dCjG8msYrFu3\njiFDhvD4449z8ODBGt/foUMHUlJSKC4uJiEhgYiICF544YXbjHp1Ls7O5Or16A15OOt0aDRO5OXn\ng0KBQqGoWH5HWS66Slnasqy8Slnly+udlZ9PKy9PAgcNqF8f8/LQaX/PaunlSeDA/pJ132TpyNUb\n0BssrV2SJVmmWZY6V62hXebLAjP/TY9arUar1TJ27FhGjhxJu3btKpZ9+eWX/PLLL3ToUPYzlMKk\n2tRqtSgUCkJDQxk2bBilpaV0796dsWPH0q9fP9zc3Gr8TEdHxyp5NwsNfaUvYzs7OxQKBba2tgDY\n2NhUvK7y/5v+knRTnz59OHLkCJcvX6ZVq1a37P+wYcPYuHEj+fn5uLq6ApCQkMCVK1f46quvuHr1\nKkuXLr3lZyxfvpzHH3+c4uJiTpw4ga+v7y0/y5RKpcJoNJKTm4uzToerS9kENhjy0GqcaGSy/I6y\ncvTlWS7k6g3lWRoaqZRVltc3S+PkBPy+zm7bR2X1PuoNBgx5knVfZd2cP7l6nHVay2mXZEmWaZal\nzlVraJcZsyoza9HTqFEjevbsyeTJk5k1a1aVQsXZ2Zno6Gj27NnD9evXcXJy4syZM3z//fcVBcug\nQYNITk7mscceY/DgwRw6dIgZM2awf/9+nJ2da/3smxlNmzYlKCiIhISEastMCy2FQoGnpycqlYqZ\nM2eyf//+Kss9PDxYsWIFNjY2eHl5MXjw4Fo/38fHhyNHjtCrV6+K51evXs28efP47LPPmDt3Lq6u\nrlXadlObNm1ISkqiZ8+etG3bFqfylXqn+vbpTdCM2Vy+fIV9ByLx8+3MoahovvhqLn8ZOoQXevci\naMZsrly5imcLj9tklb328pUr7IuIxM+3U1nW19/wl2GBv2ddbXjWzbG7sz4+R9DML7h85Sr7Ig7i\n17kTh6KOMuerefxl6GDJul+yej9H0Mw5ZXMi8lBZ1pFo5nw9n78MDXgw+ihZD0aWpc5Va2iXGbMq\nUxjrUyrdA8ePHyc+Pr7Kcy+99BK62/zSYA6hoaGcPHmSmTNnkpGRwbZt26os79mzJ+3bt7/jvMjI\nSE6fPl3lubfffhuV6s7vClKYn3PHr70tC50CchuKB4SlXkJfCFOWOletoV1mZKur+cgQ3EdFj6hK\nih5x37DUHbYQpix1rlpDu8yotqJHLk4ohBBCCKsgRY8QQgghrIIUPUIIIYSwClL0CCGEEMIqSNEj\nhBBCCKsgZ28JIYQQwirILz1CCCGEsApS9AghhBDCKkjRI4QQQgirIEWPEEIIIayCFD1CCCGEsAp3\nfodLcd+5cOECH374IVu2bGlQTmJiIuvXr8fBwQEXFxc++OCDemedP3+erHPqUwAACS5JREFUefPm\nodPpaN++PUOGDGlQ2wDGjRtHr1696N+/f70zLl26xPvvv4+Pjw+NGzdm7Nix9c66ePEi//u//4uj\noyMajYb333+/3llr167l5MmTFBYWkpSURERERL2zsrKyWLBgAWq1GoVCwfjx4+udlZyczMqVK3F1\ndcXX15cBAwbUK6fyHF22bBkZGRlcu3aNiRMn4uzs3KC8//u//+Pf//43ISEhDcr517/+RaNGjcjK\nymLcuHF4eHjUO2vu3Ln88ssvXLt2jUmTJuHo6FjvLIBTp04xZcoUwsLC6pRjmjVp0iRKS0tRKBS8\n/PLLdOzYsd5Zy5Yt4+rVq+Tk5PD3v/+dFi1a1Ctr0aJFzJ07F4CTJ0/yxhtvMGzYsHq3a+XKlWRn\nZ3P58mXGjBnDY489Vu+szz77DBsbG2xsbPjb3/6GRqO54xzT/enDDz/MpUuX6jXvTbPee+89li5d\nio2NDW+//Xad+lc5y9nZGYPBgEqlqte8N21XcXEx169fr9e8r+n7pyHzXn7peUBdvXqVTZs2YW9v\n3+Csa9eu8emnnzJt2jQSExMblFVQUMC4ceOYMGECe/fubXDbVqxYgYODQ4Nzjh8/TuPGjQHw9fVt\ncJtatGhBfn4+fn5+DcoaMWIEs2bNomnTpixcuLBBWWlpacTExJCdnV2nnXRNdu7cyXvvvceUKVM4\ncOAAv/76a50zKs/RwsJCjh8/zrRp0xg+fDgbN26sd56dnR0pKSlER0ejVCob1K5ffvmFnj17MmHC\nBPr378/Ro0frnQXQtm1bJk2ahLe3Nz/88EODsq5evUpYWBg6na5OOTVlnT17Fnt7exwcHPD29q53\nVk5ODomJiZSUlODs7EzTpk3rneXm5sasWbP429/+hq+vb50LHtM+7tu3j6ysLDIzM3F1da13Vn5+\nPleuXGHq1KkMHz6clStX1inLdH/akHlvmrV+/Xp+/fVX6nMlmspZ8fHxDZr3pu1qyLw3zcrJyan3\nvAfAKB5ob731lllySktLjd9++61xy5YtDc7Kysoyjho1yrh48eIG5ezfv9+4adMm4+bNm407d+5s\nUFZaWprxypUrxtLSUuPrr79uLC4urnfW6NGjjadOnTIWFRUZX3vttQa1y2g0Gs+dO2ecPXt2g3PS\n0tKMP//8s9FoNBpHjRplvHHjRr2zLl68aJw4caJx9uzZxnfeeceYnZ1d76y33nrLmJ2dbZw0aZLR\naDQa09PTjdOnT29QXk3/35Cc8+fPG//nf/7HeP369QZnhYaGGocMGWK8fPlyvbMKCwuNU6ZMMRYU\nFJilj7GxsUaj0WiMjIw0Llq0qN5ZJ06cML733ntGo9Fo3LRpk3HTpk0NapfRaDROnjzZeOXKlXrl\nVM46fPiw0Wg0GqOioowLFixoUNaaNWuMU6dONS5atMg4derUOudU3p82dN6b7ptjY2PrvW81zWrI\nvDfNasi8r5zV0Hkvv/SI2yooKGDy5Ml06tSJwMDABmWdPn0aW1tbli9fzsmTJ7l27Vq9s8LDw/n+\n++/ZsmULmzZtwmAw1Dvr1KlTFBYWolAosLe3p7S0tN5Zrq6uODg4oFKpzPIrVGhoKK+99lqDc9au\nXVsx3g4ODpSUlNQ7KzMzk/fee4/x48djNBrrdSiqMhcXl4r1l5WVhZubW4PyzGn//v2sXr2a2bNn\nN+iX0+LiYuLj43n55ZeZNWsWS5curXdWTEwMeXl5zJkzh9TUVLZu3VrvrIKCAs6dOweARqOhqKio\n3lmurq4Vhy50Ol2DtiOAK1euoFAo6vzLjCmDwcB3331X0caCgoIG5dnZ2TF9+nQ6d+5M8+bN6/Te\nyvvTgQMHNmjem3PfbJrVkHlv2sfjx4/Xe95XznJ2dm7wvJe/6RG3NXPmTH766Sc2b97Mtm3bmD17\ndr2zCgsLmTZtGm5ubnh4eNT5bxoq+/rrrwHYsmULtra2aLXaemd5enoyZ84cnJ2defbZZ2nUqFG9\ns0aPHs2XX36JWq2u99+6VJaamkqzZs0anDN06FC++eYbmjVrRufOnRtUkLm5uTFnzhwcHR3p3bs3\nKlX9dyUKhQKlUknXrl357LPPyM/P57PPPqt3nrkoFAp++uknpk6dSs+ePZk6dSp9+/alV69e9cpS\nqVTs3r2b3bt3U1BQwBtvvFHvtvXs2ZOePXsC8Pbbb9f7C0+hUKBWq0lLS2PGjBlcu3aNTz75pN5Z\nzZo145FHHmH69Olcv36dTz/9tN5ZACdOnODxxx+vV0blLK1WS7NmzZg2bRo3btxg3LhxDWpXRkYG\n06ZNo6CgoM59NN2fNmTem3PfXDkrLCyM6Ojoes9703Y5ODiwa9eues37ylkqlYp58+YB9Z/3chsK\nIYQQQlgFObwlhBBCCKsgRY8QQgghrIIUPUIIIYSwClL0CCGEEMIqSNEjhBBCCKsgRY8QQpTbvHkz\na9euvePXb9myheTk5D+wRUIIc5Lr9AghRLmb12G5U+a4d5wQ4u6RokcIISo5cOAAe/fupbS0lC+/\n/JKPP/6YkpISPDw8mDlzJgsWLODEiRPY29vTpk0b2rdvz549e7h48SLXr1+npKSErVu3Mn78eDIy\nMrC1tWXWrFmkp6ezYsUKioqKyMvL49tvv62435sQ4u6Qw1tCCFGJu7s7q1at4oknnuCNN95g9OjR\nrFmzBhcXF/bt24dCoaBnz54VV4YFmDVrFiEhITRr1ozPP/+cffv24ebmxpo1axg5ciRLlizh/9u7\nQxsFgjCAwm+hBhwoxBqGFjCEhAIQZMNKNA3QAS1soAcaoAa2gUWswYJaQRAkhLvj5B1i3mcnvxgz\neRnzJ0nC7XajKAomkwmHw+Fzl5QiZfRI0os0TYHH7qi6rhkOhwCEEKiqCoBer/djbrPZMBqNCCFw\nOp0IIQAwGAyec/1+H4BOp0PTNH98E0nfGT2S9It2u83xeAQee6C63S4ArdbXp3O/33O5XJjNZsBj\nl1tZlgCUZfmck/RZRo8kvZEkCcvlku12y3w+53q9Mp1On2ev1us1VVWxWCzI85zxeMz5fCbLMna7\nHavV6u2cpP/lwlFJkhQFf3okSVIUjB5JkhQFo0eSJEXB6JEkSVEweiRJUhSMHkmSFAWjR5IkRcHo\nkSRJUbgDHZBD/9hmaz0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f1c64e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#top20_indices = np.argsort(list(feature_importances_all.mean(axis=1)))[::-1][0:20]\n",
    "\n",
    "f, ax = plt.subplots(figsize=(8, 6.5))\n",
    "sns.heatmap(top20, square=True, annot=True, annot_kws={\"size\": 6.5}, fmt=\".2f\", cbar=False)\n",
    "plt.title(\"Feature Importances: Top 20\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./figures/featureImportanceTop20.pdf\")\n",
    "plt.savefig(\"./figures/featureImportanceTop20.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ooeMa7f7iF2TmKRQhaDXF6tA+5Wdzy9ePUoSg0ygC25ChgDVQzrU3cCHFCtmd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x452c9c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ax = feature_importances_all_mean[:20][::-1].plot(kind='barh', figsize=(13,8))\n",
    "sns.set_style(\"whitegrid\")\n",
    "    \n",
    "plt.savefig(\"./figures/listOfTop20Features.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nlH0mIiKSFSWI6xYrx59fqtZe6LgFZzxwlbs/bGb3mdl17t4EfBP4r+L9dxJ2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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x39dfd978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#ax = feature_importances_all_mean[:20].plot(kind='bar', figsize=(12,5), title=\"Feature Importance: Top 20\")\n",
    "ax = top20.mean(axis=1).plot(kind='bar', figsize=(8,4), title=\"Feature Importance: Top 20\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(\"%.2f\" % p.get_height()), (p.get_x() , p.get_height() * 1.02), fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./figures/listOfTop20Features.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'top20' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-356ae5e3d6b9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m#ax = feature_importances_all_mean[:20].plot(kind='bar', figsize=(12,5), title=\"Feature Importance: Top 20\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtop20\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Feature Importance: Top 20\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"whitegrid\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatches\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'top20' is not defined"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#ax = feature_importances_all_mean[:20].plot(kind='bar', figsize=(12,5), title=\"Feature Importance: Top 20\")\n",
    "ax = top20.mean(axis=1).plot(kind='bar', figsize=(8,4), title=\"Feature Importance: Top 20\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(\"%.2f\" % p.get_height()), (p.get_x() , p.get_height() * 1.02), fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./figures/listOfTop20Features.png\", dpi=300)"
   ]
  }
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