fixed-time-horizon-prediction/Exp_1_RNN_Sioux_Falls.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import glob\n",
    "import os.path\n",
    "from pandas.compat import StringIO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NREL Bird Model implementation: for obtaining clear sky GHI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "sns.set_color_codes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pvlib\n",
    "from pvlib import clearsky, atmosphere\n",
    "from pvlib.location import Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sif = Location(43.544,-96.73, 'US/Central', 448.086, 'Sioux Falls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2009 = pd.DatetimeIndex(start='2009-01-01', end='2010-01-01', freq='1min',\n",
    "                        tz=sif.tz)   # 12 months of 2009 - For testing\n",
    "times2010and2011 = pd.DatetimeIndex(start='2010-01-01', end='2012-01-01', freq='1min',\n",
    "                        tz=sif.tz)   # 24 months of 2010 and 2011 - For training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009 = sif.get_clearsky(times2009) \n",
    "cs_2010and2011 = sif.get_clearsky(times2010and2011) # ineichen with climatology table by default\n",
    "#cs_2011 = bvl.get_clearsky(times2011) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009.drop(['dni','dhi'],axis=1, inplace=True) #updating the same dataframe by dropping two columns\n",
    "cs_2010and2011.drop(['dni','dhi'],axis=1, inplace=True) #updating the same dataframe by dropping two columns\n",
    "#cs_2011.drop(['dni','dhi'],axis=1, inplace=True) #updating the same dataframe by dropping two columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009.reset_index(inplace=True)\n",
    "cs_2010and2011.reset_index(inplace=True)\n",
    "#cs_2011.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009['index']=cs_2009['index'].apply(lambda x:x.to_datetime())\n",
    "cs_2009['year'] = cs_2009['index'].apply(lambda x:x.year)\n",
    "cs_2009['month'] = cs_2009['index'].apply(lambda x:x.month)\n",
    "cs_2009['day'] = cs_2009['index'].apply(lambda x:x.day)\n",
    "cs_2009['hour'] = cs_2009['index'].apply(lambda x:x.hour)\n",
    "cs_2009['min'] = cs_2009['index'].apply(lambda x:x.minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2010and2011['index']=cs_2010and2011['index'].apply(lambda x:x.to_datetime())\n",
    "cs_2010and2011['year'] = cs_2010and2011['index'].apply(lambda x:x.year)\n",
    "cs_2010and2011['month'] = cs_2010and2011['index'].apply(lambda x:x.month)\n",
    "cs_2010and2011['day'] = cs_2010and2011['index'].apply(lambda x:x.day)\n",
    "cs_2010and2011['hour'] = cs_2010and2011['index'].apply(lambda x:x.hour)\n",
    "cs_2010and2011['min'] = cs_2010and2011['index'].apply(lambda x:x.minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(525601, 7)\n",
      "(1051201, 7)\n"
     ]
    }
   ],
   "source": [
    "print(cs_2009.shape)\n",
    "print(cs_2010and2011.shape)\n",
    "#print(cs_2011.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009.drop(cs_2009.index[-1], inplace=True)\n",
    "cs_2010and2011.drop(cs_2010and2011.index[-1], inplace=True)\n",
    "#cs_2011.drop(cs_2011.index[-1], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(525600, 7)\n",
      "(1051200, 7)\n"
     ]
    }
   ],
   "source": [
    "print(cs_2009.shape)\n",
    "print(cs_2010and2011.shape)\n",
    "#print(cs_2011.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ghi</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01 00:00:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01 00:01:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01 00:02:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01 00:03:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-01 00:04:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      index  ghi  year  month  day  hour  min\n",
       "0 2010-01-01 00:00:00-06:00  0.0  2010      1    1     0    0\n",
       "1 2010-01-01 00:01:00-06:00  0.0  2010      1    1     0    1\n",
       "2 2010-01-01 00:02:00-06:00  0.0  2010      1    1     0    2\n",
       "3 2010-01-01 00:03:00-06:00  0.0  2010      1    1     0    3\n",
       "4 2010-01-01 00:04:00-06:00  0.0  2010      1    1     0    4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cs_2010and2011.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import files from each year in a separate dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "- year            integer\t year, i.e., 1995\n",
    "- jday            integer\t Julian day (1 through 365 [or 366])\n",
    "- month           integer\t number of the month (1-12)\n",
    "- day             integer\t day of the month(1-31)\n",
    "- hour            integer\t hour of the day (0-23)\n",
    "- min             integer\t minute of the hour (0-59)\n",
    "- dt              real\t decimal time (hour.decimalminutes, e.g., 23.5 = 2330)\n",
    "- zen             real\t solar zenith angle (degrees)\n",
    "- dw_solar        real\t downwelling global solar (Watts m^-2)\n",
    "- uw_solar        real\t upwelling global solar (Watts m^-2)\n",
    "- direct_n        real\t direct-normal solar (Watts m^-2)\n",
    "- diffuse         real\t downwelling diffuse solar (Watts m^-2)\n",
    "- dw_ir           real\t downwelling thermal infrared (Watts m^-2)\n",
    "- dw_casetemp     real\t downwelling IR case temp. (K)\n",
    "- dw_dometemp     real\t downwelling IR dome temp. (K)\n",
    "- uw_ir           real\t upwelling thermal infrared (Watts m^-2)\n",
    "- uw_casetemp     real\t upwelling IR case temp. (K)\n",
    "- uw_dometemp     real\t upwelling IR dome temp. (K)\n",
    "- uvb             real\t global UVB (milliWatts m^-2)\n",
    "- par             real\t photosynthetically active radiation (Watts m^-2)\n",
    "- netsolar        real\t net solar (dw_solar - uw_solar) (Watts m^-2)\n",
    "- netir           real\t net infrared (dw_ir - uw_ir) (Watts m^-2)\n",
    "- totalnet        real\t net radiation (netsolar+netir) (Watts m^-2)\n",
    "- temp            real\t 10-meter air temperature (?C)\n",
    "- rh              real\t relative humidity (%)\n",
    "- windspd         real\t wind speed (ms^-1)\n",
    "- winddir         real\t wind direction (degrees, clockwise from north)\n",
    "- pressure        real\t station pressure (mb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['year', 'jday', 'month', 'day','hour','min','dt','zen','dw_solar','dw_solar_QC','uw_solar',\n",
    "       'uw_solar_QC', 'direct_n','direct_n_QC','diffuse', 'diffuse_QC', 'dw_ir', 'dw_ir_QC', 'dw_casetemp',\n",
    "       'dw_casetemp_QC', 'dw_dometemp','dw_dometemp_QC','uw_ir', 'uw_ir_QC', 'uw_casetemp','uw_casetemp_QC',\n",
    "       'uw_dometemp','uw_dometemp_QC','uvb','uvb_QC','par','par_QC','netsolar','netsolar_QC','netir','netir_QC',\n",
    "       'totalnet','totalnet_QC','temp','temp_QC','rh','rh_QC','windspd','windspd_QC','winddir','winddir_QC',\n",
    "       'pressure','pressure_QC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1049906, 48)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Sioux_Falls\\\\Exp_1_train'\n",
    "all_files = glob.glob(path + \"/*.dat\")\n",
    "all_files.sort()\n",
    "\n",
    "df_big_train = pd.concat([pd.read_csv(f, skipinitialspace = True, quotechar = '\"',skiprows=(2),delimiter=' ', \n",
    "                 index_col=False,header=None, names=cols) for f in all_files],ignore_index=True)\n",
    "df_big_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524982, 48)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Sioux_Falls\\\\Exp_1_test'\n",
    "all_files = glob.glob(path + \"/*.dat\")\n",
    "all_files.sort()\n",
    "\n",
    "df_big_test = pd.concat((pd.read_csv(f, skipinitialspace = True, quotechar = '\"',skiprows=(2),delimiter=' ', \n",
    "                 index_col=False,header=None, names=cols) for f in all_files),ignore_index=True)\n",
    "df_big_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(585, 48)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_big_test[df_big_test['dw_solar']==-9999.9].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging Clear Sky GHI And the big dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1049906, 50)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.merge(df_big_train, cs_2010and2011, on=['year','month','day','hour','min'])\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524982, 50)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.merge(df_big_test, cs_2009, on=['year','month','day','hour','min'])\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.drop(['index'],axis=1, inplace=True) #updating the same dataframe by dropping the index columns from clear sky model\n",
    "df_test.drop(['index'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1049906, 49)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Managing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resetting index\n",
    "df_train.reset_index(drop=True, inplace=True)\n",
    "df_test.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows with two or more -9999.9 values in columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1048493, 49)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step1: Get indices of all rows with 2 or more -999\n",
    "missing_data_indices = np.where((df_train <=-9999.9).apply(sum, axis=1)>=2)[0]\n",
    "# Step2: Drop those indices\n",
    "df_train.drop(missing_data_indices, axis=0, inplace=True)\n",
    "# Checking that the rows are dropped\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524310, 49)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_indices_test = np.where((df_test <= -9999.9).apply(sum, axis=1)>=2)[0]\n",
    "df_test.drop(missing_data_indices_test, axis=0, inplace=True)\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the rows with only one cell as -9999.9, replacing this cell with the mean of the column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First resetting index after dropping rows in the previous part of the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2nd time - Reseting Index\n",
    "df_train.reset_index(drop=True, inplace=True)\n",
    "df_test.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_miss_train_idx = np.where((df_train <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6872"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_miss_train_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1048493, 49)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "col_names = df_train.columns\n",
    "from collections import defaultdict\n",
    "stats = defaultdict(int)\n",
    "total_single_missing_values = 0\n",
    "for name in col_names:\n",
    "    col_mean = df_train[~(df_train[name] == -9999.9)][name].mean()\n",
    "    missing_indices = np.where((df_train[name] == -9999.9))\n",
    "    stats[name] = len(missing_indices[0])\n",
    "    df_train[name].loc[missing_indices] = col_mean\n",
    "    total_single_missing_values += sum(df_train[name] == -9999.9)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_col_min = df_train.apply(min, axis=0)\n",
    "df_col_max = df_train.apply(max, axis =0)\n",
    "#print(df_col_min, df_col_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = np.where((df_train <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# doing the same thing on test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1576"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_miss_test_idx = np.where((df_test <=-9999.9).apply(sum, axis=1)==1)[0]\n",
    "len(one_miss_test_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "col_names_test = df_test.columns\n",
    "from collections import defaultdict\n",
    "stats_test = defaultdict(int)\n",
    "total_single_missing_values_test = 0\n",
    "for name in col_names_test:\n",
    "    col_mean = df_test[~(df_test[name] == -9999.9)][name].mean()\n",
    "    missing_indices = np.where((df_test[name] == -9999.9))\n",
    "    stats_test[name] = len(missing_indices[0])\n",
    "    df_test[name].loc[missing_indices] = col_mean\n",
    "    total_single_missing_values_test += sum(df_test[name] == -9999.9)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = np.where((df_test <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1048493, 49)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524310, 49)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "dw_solar_everyday = df_test.groupby(['jday'])['dw_solar'].mean()\n",
    "ghi_everyday = df_test.groupby(['jday'])['ghi'].mean()\n",
    "j_day = df_test['jday'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mjB49huzsbB58cBhDhgxn06YNdO3ag/T0NL75Zj1vvbUQg8HA6NEPcsUVXVi4cA7Dht3P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\ncIQSHAqFQqFwxP8DOob1BZgzGgIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.1,0.1,0.8,0.8])\n",
    "#axes2 = fig.add_axes([0.1,0.1,0.8,0.8])\n",
    "\n",
    "axes1.scatter(j_day,dw_solar_everyday,label='Observed dw_solar',color='red')\n",
    "axes1.scatter(j_day, ghi_everyday, label='Clear Sky GHI',color='green')\n",
    "\n",
    "axes1.set_xlabel('Days')\n",
    "axes1.set_ylabel('Solar Irradiance (Watts /m^2)')\n",
    "axes1.set_title('Solar Irradiance - Test Year 2009')\n",
    "axes1.legend(loc='best')\n",
    "\n",
    "fig.savefig('Figure2.png', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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8BU6qSz3sb+4dVsw8U8l20sWPgDuA44N/nwO8MPj748CVwDpgs67rlq7rRwC7\npmlVWbZLEASBgWCEbl9qQXCvP8SdG3ezp6kbgAKXnU9uWM7ZS4fflgrcdsq8rkmtrxoPS+eWEggZ\nNLf7p+X4U03WQoKapn0caNd1/UlN0/518GFF1/X4R8MHlADFQGfSpvHH20fad1lZAXZ7Zt9mqqq8\n47Q895Fzyg/knHIb/0CYbl+IsvLCxGOHW/q4Y+Nuev2xgtyaigI+//7VVJUVpGyrDIYA03W0mCrK\nygo4Z0UNW95u4Xh3kHNWzp42W6aKbK5hfRKwNE27ElgD3AtUJz3vBXqAvsHfhz4+It3dmaVxVlV5\naW/3jcPk3EfOKT+Qc8pt4jVW5eWFdA0mLTQc6uQvzx0kEo1lCC6eU8KHrlyCzbISr4GTXdb7fUH6\nfcFJtWs8Xwi6uweoLY0Ng9y+p5Xzl82MwNRo70HWBEvX9Uviv2ua9jzwWeA/NE27VNf154F3A88B\nB4Afapr2I6AOUHVd78iWXYIgnL6km2NlWRYvvHWcza8fTTy2dnk1N1w0f1iob6pS1jOlqtRDdamH\nhqYuIkYUR4aRp3xlqgOvXwH+r6ZpWwEn8ICu69uBl4CtwIPA56fYJkEQTgNMy6LbF0oRq9gMq4MJ\nsVIU2HBBPTeuWzBMrLwFDsq8rpwRKwBFUThbqyIUjtLQ2DXd5mSdKanD0nX90qQ/16d5/tvAt6fC\nFkEQTj/S1Vj1ByPc/dg7HGjuBcDpUPngFUtYNq8sZVtVgZIiF64pzgLMlPOWVfPEa0fY2tDKWUtm\nRlhwJKRwWBCEGU26GqsT3QHufWIvXb4QAKVFTv722mWJdkdxHDaVUu/kdlmfbObXeJlTWciO/R34\nBsJ4C4Z335gp5O5VEARBOEUiRpSuITVW+5t7uOORhoRYza0u4nM3rRwmVh6XnfLi6UtZzxRFUbh4\n9WyipsWLO4+PvUEek9tXQhAEYYKEwlG6+kIk19S+uqeVex7fm1jHOnf5LP7u+hUpXokClBQ6KSl0\nTvpIkGyx7sxa3E4bz2xvxoiaY2+Qp4hgCYIw4xgIGnT7Q4nhiqZpsemVJjZuaUoI2OVnz+FT7zkD\nh/3kbdCmKpRnuct6Nihw27lk9Wx6/GG2vN0y9gZ5igiWIAgzCn8gQt/AyUm8wbDBfU/qvNLQCoDd\npvA3ly/mynPnpnhQLoeNimJ3ioDlE9e+ax5Ou8qmV5qIGDPTy8rPKyMIgpCG3v4w/sDJCcHdviD/\n/chu9KOxXgSFHgd/d/0KVi+uTNmuyDOYsq7mRwgwHaVFLi4/u46uvhBPbz869gZ5iAiWIAh5jzVY\nYxUInRy6eKTNx68e3k1bd6zR7IiWAAAgAElEQVT39qwyD/9w0xnMm3Wyk4KqKJQVuSjyzIyJvRsu\nrKfI4+CvLzfR6w9NtzmTjgiWIAh5jWladPWFCEVOFgTvPNDBbzbtoX/Q29LmlvKZG8+gzOtOvMZu\nU6gq8+By5mZ91UQodDu4+ZKFBMNR/vTsgek2Z9IRwRIEIW8xoiZdfcFE/z/LsnhmezN/evZAokj4\nwpU13HqNhtt5MpHC44ytV9ltM+8WuH71bBbUFvPqnjZ2HphZXe5m3tUSBOG0IGLECoKNwbS/iGHy\np2cP8Mz2ZiDWoeLGdQu4/sL5ibUpBSgucFBS5MqblPXxoqoKn7huGTZV4d4n9ZQ1vXxHBEsQhLwj\nHInS5TtZEOwbCPObTXvYdTA2qcjttPHxdy/nXStmJbZRVYUy7/SOBJkq6qqKuHHdArp9Ie55Yi+W\nNTMGPIpgCYKQVwTDRsrQxdauAX79cANHT8SGGJZ7XXz2xpUsritJbOO0q1QUT+0I++nmuvPrWVpX\nwna9nRfemhkdMESwBEHIGwaCBj3+cKIgeO+Rbu54pIGewYGL82u8fO7mlVSXeRLbTPdU4OlCVRX+\n/oYzKHTb+Z+n93HwWO90m3TKnF5XUBCEvCW5INiyLF5+u4X7ntQJR2IJF2ctqeSTG5ZTOBjyU5RY\ni6XigvxpsTTZVJS4+exNK4maFr946G168jzVXQRLEIScJ7kgOGqaPLKlkUe3Hk6EBa8+by7vv3RR\nIuvPripU5GGLpWxwxvxyPnDpYnr9YX71cENe9xoUwRIEIWcZWhAcCBnc87jOtndOALHxHx++cgmX\nnjUn4UW5HDbKS2ZmyvpEuWbtXNYur+ZAcy//8/T+vE3CkK8fgiDkJKYZE6t4jVVnX5B7n9hLe08Q\niE0AvvUajbqqosQ2RR7HjOlaMZkoisIn3r2c4x0DPL/jGDVlHq5eO2+6zRo38hVEEIScY2hBcGNL\nH79+qCEhVrMrCviHm1YmxEpVoMw7c1osZQOX08aXPrCKkiInf3r2AG/sPTHdJo0bESxBEHKKiBET\nq3hB8Hb9BHc/+g4Dg2HB5fVl/P17zqCkyAXEwoIVJe6cHWGfS5QXu/nS+1fjdNq4a9MeDuRZ5qAI\nliAIOUMoPFgQbIFpWTy57QgPvnCI6KB4Xbyqlo9cvTQhTvkyFTiXqK/x8rkbVxKNWvz8gV20dQ1M\nt0kZI1dZEIScIBAy6PHHCoLDkSh/eGp/ouBVVRTee8lC3n1+PaqinGyxlEdTgXOJVYsq+Og1S/EH\nIvzkLztT5oflMiJYgiBMO/5AhN7+WEFwX3+Yu/66h91NXQB4XDY+uWEZ5y6rBmIFseXFp0eLpWxy\n6Zo5bLignhPdAf7rgV2Ek7rd5yoiWIIgTCt9STVWxzv6+dXDDRzr6AegssTN525aycLZsTZLTrtK\nZbEbh13WqyaD916ykPNXzOLg8T7u/OueRG/GXEUESxCEaSFeYxVPptjT1MV/b9xNX38sPLVwdjGf\nu2kllSWxNkvxFkv5PBU411AUhU9ct5xl80p5c187f3x2/3SbNCoiWIIgTDnJQxcty+LFt45z/+Z9\nRIxYGvt5y6r5xHXL8LjsKEiLpWzisKt84b1nMqeykKffaGbztiPTbdKIiGAJgjClJNdYGVGT/33x\nEE9sO4JFbF7VdefXc9PFC7Cp6uB6lbRYyjYFbgdf+sDqnK/REsESBGHKSK6xGghG+O1j77Bdbwdi\n61MfvUZj3apaFEVJWq+S29RUUFHi5p8+EKvRuvOvezh4PPdqtOSTIAjClBCKnKyxau8J8OuHd9PY\n4gNiIb/P3HgGy+vLAChwyXrVdDBvlpd/uGklUdPkVw81JNYTcwXxswUhD2lo7GTLrhbaewJUlXpY\nt6qWlQsqptusEQmEDPoG09YPHOvlf57aRzAcS6Ouqyrk1ms0vAVOFMBb4KTALbem6eLMhRW895KF\nPPjCIe54pIGvfHBNzhRmy6dCGBcTuVFu2trEU9uO0h+MoCgKtRUF3HL5YlYuqKChsZPXn9Bpbusb\nc3/pjg2My55TvdHnglA0NHby4AuHEn+3dQcSf0/03BsaO3n0lSaa2vxEIiYo4LApzK/xsuHC+Sn7\nzeQ61FUX0XzCT3tPgDKvi9WLK1lSV8q2d9rYuKUJc7BbeEmhE8uy2PRKE+ctr+Zdy2dJynoOcN35\n9Rw63seO/R387wuH+MBli6fbJACUfGwz397uy8joqiov7e2+bJszpUznOQ29UcZ53/qFI94oN21t\nYuOWRqLR1EtWUuTk8nPq2K6347CrieywkfaX7tjBkIEFwxbkR7JnIvZPdPtMr9NEBPCORxpo6w4Q\nCBn4AxGMqIndpjK3qpB/+cg5o9o+dJtr3zWP+TVefr95X2xtach1UlWoLPHw0auXctna+Ty3rWnM\n6xAIGfT4QpR5XTjsKqYVS2F32FWOtPkT27mdNsq8LkKRKP6BCBHDxOm0MX9W0TCRzBa5do+oqvJm\nHAPN9D44UQaCBrff8zpt3QE+f/NKztGqs3m4BKO9B+JhCRmzZVfLiI+PdHN5atvRYTdBBfANRHh+\nxzG8Bc6M9pfu2L7BYtOhgjWSPROxfzK3H8pInlJTqy/hnaQTsfaeQEIU4hiGSWOrj4bGzhHPfeg2\n4UiUR15qxG5TMKJW2qJR04Ref5gtu1q4bO38jK6DPxDBsix6B8KUe92YpkVnXzDlSwlAMBylvSdA\n1LQSgxhD4Sj60V72/3knF6ys4VMbVmTyVp6WPP/WsawfY+2KWTz+6mHu2rSHtu5A1kK1l66Zk9Hr\nRLCEjGnvCRAc7PcWv/k47LbEWsRQGho78Qcjwx63iDU27Q9E0gpWfITE0GMPZaTJqem2H2kfo71+\nqPdzuM2H2zn8n8xI249Fupt/MGTwxGtHqCqNFcumC/dVlXpoa+zCtGIiE08HdzhsI4pne08g0U0C\nYu9/NGqBAhEjJhgjfV2PGNHEOWZyHSJG7PMQjZoMBCP0+EdeuB/6ZeakfbC1oZVZ5QVcf8H8EbcX\nskuZ18XZS6vY9s4JXt3dymVnz5nWWrjcWEkT8gKn3UZnb5BwxIzd4AablPb4QzQ0dg57/ZZdLagj\nfLhVRaFwhNlFTofKHY80cPs9r3PHIw00NHYmbuDJ2G1q2qmyVaXutPtNt4+RXh/3ftq6A5hWTDh8\nA5HE5NtMjjcW6W7+Pf4QwZBBS2d/wpOCVHFbt6qWcCRKNDromQxei2jUTAm5xc/jjkcaONEd+7IR\nXzuKe1PxqzPaPciyTp7jWNfBsqzEAr1pMapYjYUFPL8j+16EMDravFJqKgpobu/n8JDP11QjgiWM\nAytxwxvycFpvob0ngLcgvSh5CxxcetbwMEAwZNDtC6UIxYMvHKKuumj4PkaYLltXXTRM8IBEcsBQ\n0j0eD6G19wQS4uG0qyleymjbj0VDYye9/eHEvoMhg0DIIBwPm1mxMF/P4Hj4ZC9u5YIKPE57TGSU\nmNjYbAqqohA2oinHiItu/MtBNBq7hvGrqKoKDrttxC8WEDtG/BzTnWv8OliWhRG1KHDbMU1r2Lrl\neFGA/jTvtzC1KIrC+StmoSoKb+w9MWJkYyqQkKCQMWHDRFWVlDCUqipYWGnDYlWlHuLLIn394cTv\nse7by1m5oIL5NV7e0Ds42ubD6VDp6gvGFuEDEbweB+7BdZHmE37et37hYIguSFWpe0h2Wuyxuuqi\nRCEqpA+pDd1HuhDavuYe+vzhxHmapoVhmBR6HMwq84y5/WjEhcRuUxPC1O0LoQyOzRhae+QPRJhf\n4015rMBjJ5Smu3Zyhl3ylwiPy4630ImvP4xpWqiKgqXE161MbDYVwxyhW7cFTa2xxIR072FddRFv\nH+yg+UQ/lmXhdtnJVKsUBUbK+1LVkb1wYWopLnSyYn4ZDY1d7GnqZtWi6SmhEMESMqaq1ENbdwBj\nyOK53aamDYutW1XLgy8coqTIlZgOC6lZdSsXVKRkn4Ui0ZSbeBngdtlp7wmyckFFWnFIfuyORxrS\n2h5f2xlpH8k0NHbi648kbqQWMc8EW+wG+9kbV466/Vj7/t3je/EHIthtKh63nYgRa1FkRE2KC50M\nBFPDjkbUHObZ1M/yYlmkZPwVeRzUzzrpicZDismv8RY6MQwTp8NGrz+EqsY8M8uyUJXYt+n4F5I4\nigJPvHaE1dos5pZ7Ut7DhsZO/vLcQaKmRanXRY8/lBIGVBh5bQygejDEGAgZ9A1EEsezDdqVzgsX\npoeVi8rZ19zDnqYulteXTUsHEhEsIWPWraqlqdWXkmkGsZBQulDReDyauDdgt6kpgugLRHC77Bmv\nE403sSIdW3a1pF3TMQez2e54pGFCdVhxz8ofiCRE2TBMSr0uPC47HT0BSopcuBw2fMnp6tVFw46x\nblUtbd2BYRmSydfBaVc5NiST0DBMFtR6KSlycbjVl3KcAredvv5w7NyTVEZVFYyoyTPbjvDxa7WU\n4z2/4xgDwQj+QCS2tjn4uKJAocdBIBBJ622paqz1Un2NN/EebtraxPM7jtEfiFDoiYWMJeEid3Da\nbayoL+OtA53sO9rDGQvKp9wGESwhY1YuqOCjVy/l0VeaaG6PzSuqqy5iwwX1I960M/Fo4KTQFHkc\nqenag/HyTNeJ4l5gMsGQQSRqcvs9r2ckMu09AZwOG2GiKeFPFIVQJJrYf6YFu3FGEmV/IILHZaeu\nqpBgxMTtsidCoQAbLqgftq/MvgyMsC6lKLT3BIYdB2Kp5uFINLY2BgkPzG5TOXisJ0Wsz9GqONzm\no9cfYojTjapAcYETl8NGry+Y8rxtMOTpdqUWCF9/wXwRqBxHqy9j16Eu9h/tYcX8sinPGBTBEsZF\npgI0XuJCk1zLY0RNijyOjAt74WQYMk48iaPU60pJ4oify0i29AcNegwT1XbyH2TUtPCmWVMZqw4r\nnh7/1oEO7DYVpz1VsOKivOHC+Yn9ZbJGNta1CBtRyryuFC/K63EQjphphR2gvsbL0RP+YWFfp12l\n1x8mYpj4AxGa2/3sOtgZC2WmWYOPmtDRE6DU62JOtZcefwhff3gwu9RCVRUGAgZNrb6EHbncWkqI\n4XLYqJ9VRGOLjxM9AWaVFUzp8UWwhJwgWWg8LntCuJLFKpOuEEM9j0j0ZMgtmdFEJh5ug9Q1okKH\nbZhHAqOHG5OLg+OelWGYFLjthAfXroaK8nhaU43WMslpt2G6GGZzXAjTde3YcEE9Ta0+nnjtSIrI\n+QIR3E4b3X0nzzUciTLagFojaia85YgR21fUtBIeFpz0LidafC1MPQtnl9DY4uNomz/3BEvTNAfw\nYeA9wBLABA4AjwB/1HVd8k6FcZN8c62bVcx5WmXaLMBkscq0f16y53H7Pa+nvamOJjIjhdu27GpJ\n65WMtr6WnKmXHO4MG2aipikTD3Ks80/3fCBkoDBcsJLf15He77lVRTS3x2puSouchIwovf4Q8Yzm\nsZIpFOXk83HRjz+eTPzxiRZfC1NPTbkHm6pwrKOfc6f42KMKlqZpG4BvAC8BvwMOAwYwH7gM+EdN\n027XdX1jds0UZhJDb64tHX4ebO3jfesXjpiBN9G2SCOFvsZK4hgp3JbOKxltfS05CSQ53BmNmswq\nyzxpY6zzT/e8x2XH7VApKXKlFaV055h8bSoHBbXbH8bfHyG5/CaTrHW7TcVuV4kOepEOmxoLTybF\nEOMFxxMtvhamHptNpbrMQ0vnAOFIFKdj6poVj+VhLQEuSeNF7QEe0zTNCXwxK5YJM5aJiM9Es/9G\nCn1NpNh3PFmPcYYKZjzcOavMM670+LHOf6Tnw4Y1ruPEr00gZAyuWY0e9lOVeBbhkBcNrlV5PY5E\nJmD8OnQnJdXEC78ncj2E6aO82EVL5wDdvhCzyqcuLDiqYOm6/tMxng8D/zmpFgkzgtHWWyYiPqfi\nKcH4RGas/Y1n28kSzLHOf6znM+0KH6/d6uoLxlo/jWGXTVUocNvxDRgpXVDinTd8gciwEKQy2JHD\nYY8t4Of6LC9hOKWDdZW9/eHcESxN01YB9wJzgYeBL+m67ht87k1d18/OvolCvjHWestExOdUbvzZ\nymzMhMkSzLHOf7Tnx7P+V1Xq4fiBjhGb0g6lwG0nEIpiYQ1b17LbVbwFzlFDkEJ+Em8CPVLj62wx\nVqnyr4F/ApYCIeA5TdPipfQyu1pIy2ghPxhfT784KxdU8L71C5lV5kFVFGaVxeqAtuxqGdYzMHeZ\neG+9dOc/NLNwpOfHuh7JOOwqocjoveLsamy4Y5HHTiQ6pIkusTCh02GjqtST0nlDmDm4nbF1q9AU\nC9ZYa1gFuq4/N/j7P2ia9iNgo6Zp12TZLiGPGSvkN9TrmF1ZxLla5Zjfvoe2BJrI1N2pZjLtHMtD\nGen5TEOwDY2d7NjfMaYdDoeN4gInF62qZdMrTUBsHSve7DbeGQNkbWqmMl0TRsYSrD5N064FntR1\n3dJ1/auapt0PPAhMbQK+kDdkEvJLvrlOZOrrZA9TzBbJdgZDRqKI93eP7+Xj7142NVN1R7keDY2d\nPPrKYZrb/QRCBqY1esq6TVWYV31yIvDuQ10cbfdjRGMNdLHAwhp3wbeQX0zXoPqxBOuzwJ1ANbG1\nLIC/JZZo8e4s2iXkMZOZmTcSk9EzcCqI2xnvuBHHH4hMukc4UmJF/HoMbYRbV1XI3Y++k9JJH0YW\nK5dDxQLae0++xxsurE97rUWsZjbxSQFOx9Q2wB0rS/Ad4GIATdNKdV3v0XU9CnxJ07TvToWBQv4x\n2Zl56Zho1uBUE7fTN2SuU7z+aLI8wrFCj+m6V7yxt51wJJrRylrM3NgskHRim81rLeQe8WQLl3Pq\narAgs04Xq4B/BNYTq8sCQNf1sYPdwmlLtjPCpsKLmwzidg4dehevP5osj3CsEGnzCf+wacGRnkBG\nYhWbE3ly0WKo2Er23+lHfJBpugGq2WREwdI07Xzg/wF+4C7gM1NllHB6EA9hdfvDlBU5x/XNPF++\n2cftSZ6BVeRxJLpeTJZHOFqItKGxk92NXUSSvCu3yz5qQXAyFmCYFlErikIslT2+b+H0pK8/NvOs\nuMA5pccdzcMqAkqBg0D7YChQmAFkWkSabRviHpLDrtLWHeD3m/dRVuQibEQzsitfvtmvXFDBx9+9\nLKse4UghUqdD5cEXDsU8qaTBmCUZrJoPTb5QUFBVGAgasa7dQ6YgC6cPnb1B7DaFooIc8bB0XX8a\nOEvTtKuBf9U0baGu6xMftSrkBLmSDj40hBUIGfT4QvgDkcTNdzrsypaYZ9sjHClEGk/nSm68a1kW\nvf3h4a9NIl3WssOuYg3uL97BQjj9CIWj9PaHqa0oQM21eVi6rm8GNmuaNnyK3ChommYjFkrUgCjw\nCWL/Dn5H7ItbA/B5XddNTdO+BWwg1lj3S7qubxvPsYTMyZV08KEhrHhMfOhaz1TalW0xn0yPMJ2w\nput2/9CLJ0e2APgGwoQjJtHISd8pNpIeTJMRw4SqGhOssBGNhRWTOlgIpxftvbF/u0PXRKeCjOZh\naZqmAZ/WNK0s+XFd1z85ymY3DL7mIk3TLgV+TEywvqHr+vOapt0B3Khp2mFiCR3vItYC6kHgvPGe\niJAZuZIOPjSEFReq+IL+dNiVK2I+FiMJ69Bu9w2NnfT2h/EHIoM9/xw4HTYCoZPRfbfTRqnXhcOm\nEo5EUz4f8aihqsRa8dRUFBAZ7LQ+q2zqb1ZCbhD/NzkdgpVpEv1DQC/wwpCfEdF1/WHg04N/1gNt\nwDlJ2z0OXAmsAzYPFiYfAeyaplWN5ySEzBnpQzbV6eBDw0lxoRo60Xcy7Wpo7OSORxpGbOWUK2I+\nFpm0WoqLmsOmYpkWkUiU7r4gvf6ToUBvgYOacg9etx27TaW+xsuy+tjYc4tYx4qCweeGXhcJB56+\ntHT0oyjTU0KS6cThHl3XvzPeneu6bmiadg9wM/B+4Hpd1+NBBx9QAhQDyXeO+OPtI+23rKwAuz2z\n/P+qqpm3MHwq57Th4kXc99ieYY9rCyr47RM6rZ391FQUcuXaeZytVZ+KmaNyWZWXkpICntl2hNau\nfhbOLqGrL5jIQEu2dzKu4Zv6CTa+3ATE5vl0+UJsfLmJkpKCxHnWzSqmpcM/bNvZlUUTsmGidr+p\nn+DpbUdGvBbd/jAO+/Dvmj394cQxX39Cx2FXsdscRC2L7r5QSjf18mIX3gInNpuCpSh8/IYVANz3\n2B5mlRfQ1x8mEjVRFYWLz5pDJGLS2tVPfU0xV2T5szHV5Os9orDAiapmv3D32gvmJ37v7gty7xM6\nqxZX8pHrzsj6sYeSqWD9TtO07wHPEFtnAkDX9RfH2lDX9Y9pmvYvwGtA8td7L9AD9A3+PvTxEenu\nHsjI6Im0/Ml1TvWc5pZ7eM9F81PWOuqqi3jxzebEa4609nH3xgZ6s9ytYG65h49fqyXO6eS6zMk1\nmLnlnkm5ho++dDARzhr6+Nzy2MfyPK2SB1v7hr3mXK1y3DZM9DoNDfeluxZlRc60GYGzyk6+V81t\nfRimha8/TG9/OKWVjs0GAyEjESYEi0dfOgjERtlHoyamZWFZFqZl0dzm418+fHbKOc2Uf1e5do8Y\nj3j2D4yeODNZJL8/L+48DsCK+rKsvW+jvQeZCtaFwEWD/49jAZePtIGmabcCdbqu/z9gADCBNzRN\nu1TX9eeJtXZ6DjgA/HCwsW4doEpRcnaJL/7HBeLJ145gQUp9EEz92k0209QzCfflQm1XJutomRRN\nl3td7D/WS1//0NmrgAlm1KLXH0ZRFDwu++D7YA1rIWUYJo0tfTQ0dnJZnnoiwuTx1mBz5DWLp2dN\nN1PBOlvX9SVjvyyF/wV+q2nai4AD+BLwDnDX4KTid4AHdF2Papr2ErCV2Jra58d5HGECJH+Tj0RN\nsEikPcdFK9fWbk6FTFs5TZZovqmf4NGXDo47Pf5wmw/fQCSlhZI7ISgnbYSRhdU3EKa40JlerEit\nrfIHInhc9qRBj13DXm+3qWzZ1cJla+dnePbCTCQUibK7qYs5lYVUl01P7/NMBWu3pmmrdF3flemO\ndV3vB25J89T6NK/9NvDtTPctnDrJ3+TtNhVjMFwWv4FB7vXlOxWmspVTQ2MnG19uSoQgM02P37S1\niY6eIKZpoSgxL6jbMCmDYUW6qaIVYMuuFizLYk5lEZtfP8pzO44nXqsqsXR1mwqKcnIMCDBsDMib\n+9oxzdikYYVY4kWRxzGjvrwIE2NPYxcRw2TNksppsyFTwVoG7NA0rQUIM1gEr+v6wqxZJmSV5BCZ\n1+NIhIGS66BmUibYVIb7JpIe39DYyROvHUnMGbIsiI5SpDt0rau1a4B7n9hLKGziDyaWmSkudFLk\nsdPtCyWEyuZQsdliM6uKPI7EIMzDbb6YWMX1bLDVhcLM+vIiTIwdB+LhwBwVLE3TvLqu+4Cbpsge\nYYpIDpG5XXbKiN0YFWKL97nYly9TRupWMVWtnNp7ArHZUMMeH9lL2bKrBWMwKw8bCS/HgrRFusmi\naFmx5Ipuf+oivNtlw2lXsdtUiguciS8lJUXOhBd9jlbFdj2WkOsbiCQGMdpUBVWNqad0tRBM02Ln\ngQ6KC50smF08bXaM5WH9RdM0hVh24OO6rr89BTYJU8DQEJnbZcftsk/ZHKNkUambVcx5GUwcznS/\nU916aqhAOu0q0TQdI0bzUtp7AonQrKooqLZBV0sh7Zj5uIdsmhbBcHSYWNlViBombqdKTXkB7T1B\nSr0usCzChpXwMJOFL1kwrcGYoHS1EAAOtfThG4hwyeraKW/HlMxY87Cu1TStELgC+AdN084klizx\nOPC0ruvDc4CFvGA6M+KGikpLhz+RTn6qxz+VbhUT6SOYTiCDIQObTcExpFZwNC+lqtTDQDA1Qw9i\ngpFuu6pSD43H++jpDw9L17cPrlUpikK3L8x3PjVyC9B466b4seKCiQq1FYWAdLUQkrMDp7enQya9\nBPuBjYM/aJq2nFhK+h+B67JqnZBVpqvb+Uii8ujWw6fceHai3Som6pmlOxe3y4630EmB05bxl4F1\nq2pp6w4kQrPxLMGzllSyZVcLD714KOU9qSz18MbeE2l7/8XFKhOSQ8PJa5nJLbIkHCi8daADp11l\n+fyysV+cRTLtJVhOLLX9aWLrWWcDX8ymYcLMJZ2oBEMGLZ39iW/1Ew3lTXQS8UQ9s5EEMhyJ8uUP\nrB71mMmk83jrqosS60sQe08eeP4gvf4wr+1uTS9WkCJWZV4XdzzSMOKXgOTQcPJaprfAmfdrmcLk\ncKInwPGOflYvqsDlmNoJw0PJNEvwD8BTsR64fAD4CfAb4LIs2SXMYNKJim9wuOFQxlu8PNH09Yl6\nZiMJZE154ajbpWOox3vHIw0pz1uWRcQw+dOzBxLd7SEmSqqq0OMLpqw9Oe0qEcNM2JfuS8BQoayv\n8YpICSnsGswOXD2N6exxMhWsMl3Xf6Rp2n8Bv9N1/T5N027LpmHCzCWdqBhRk9Ii17DXjrf+Z6Jr\ncxP1zEYSyCvWzkv5eyLrY6md02PJFV19QYykjA6nXcWmKhR6HNjUWKp6SaGLqlI3vf4wwcjwuatx\nb3K6h3gK+cHOg7FWr6sX5Y9gqZqmnUMsHLhe07Q149hWEFIYKiqzK4uwKRCMDO/1N5H6n4mszU3U\nMxtJIM/WqnluW1Oivsk3EEm0vso03BkXUdO0CIQMOvuCKT0BVWJC1tcfRlVjLZaSszxvv+f1tPs9\n0ubPiSGeQu4TMUz0I93Mm1VEmXf4F8qpJlPR+RrwH8CPdF0/pGnaq8A/Zc8sYaaTLCpVVV6e29Y0\nKZ0oxuvJJL/e7bABqWnfmdzA0wnkm/qJxPn4BiIYhjms9dVY4c51q2r583MH8A8Y9PiHZA+qJEaA\nWMQ81KElCXHBC4aMlEQOm6rgcg5fi8i1uV/C9NPS2Y8RtViVA94VZC5Yc3VdTzS61XX9fE3TPk+s\nea0gnDKTkWY/3ky/oVv3XGMAACAASURBVK+Ph88moxbt6W1HEr8ndw+Jt74KhAx2N3Zx+z2vpxVW\n07KYXVFIZbGb5hMnp+8onGyxpCoKNptKVakHVVES28dF+HCbjx5fmGjUTBQBG4ZJ2Io1uXW7Uv/5\nS/slYSjN7f0ArJ6mZrdDGavTxZeIzav6rKZp9UO2+wjwyyzaJpxmnGqa/Xgz/bI5Ybi1sz/xe3Kv\nRiNqEggZdPUGsYBjHf20dQdoavXx0auXsnJBBVHT5ET3AH985iBvHzopViWFTkJGlKhhJjIB42J4\nsnntSRF2O+1AODYHywKnw0aRx4E/EMEXiAwTLGm/JCRjWRbH2/sp8jhYUDt93S2SGcvD2g+cy2C2\nbNLjIeDjWbJJECbEeDP9sjlhuKaikCODxdBD65t6/SGipoXNpoBFIlz452f382L5cY519NPjDyVG\n2budNmrLC4iYJu6ILaW4OJ5ZGQ+dDhVhCwu7TcVuV1OmTQ8NMSbvQxAgFsoeCBmct6x6WrtbJDNW\np4tHgUc1TevWdf3nyc9pmvberFomCONkvJl+E80MzIQr187j7o2xtPSh9U0dPQFsNiXlJmCaFsfa\nBwhFLLr6gkQHi6y8BQ7+7voVhI0om145jM2lphQXz60qZMOF8xMe4VARtszY+JiwYdJ8wo+3wEFJ\nkYuyIiclRa5pm/sl5D6tXbFBucvmlU6zJScZKyT4N4AL+LKmaclTgO3A14nNvBKEnGC8mX7ZHDly\ntlZN7/qFaeubvvjTFwlFohhRMzHGI14E3NEbSGQCKoP/MaIm5yytpqVzgOd3HKM/EKHQ4+DSs+Zx\nfdL4ckgV4V5/KDbrbJCoadEz2HPwfRuWi0AJo9LaOShY9dPb3SKZsUKCXmKThr2kFgkbwL9lyyhB\nmAjjTdzIdj/Fkdbkyrwujg0uZsc7ssdJiJVCLNU/ZLDx5SaOdfSzXW/HW+DEW+AEYLvezvwa74id\nK/r6049QD0WiwxI0pB5LSMayLNq6B/C4bNSUT8+wxnSMFRL8DfAbTdOu0HX9mSmySRAmzHgTN6aj\nn2Kh247NljpIMRlVif0oyskGus/vOJYQqmSSE0Ti4hMMG0QMM6V1U8oCdDiaeL3UYwnp6OsPEwhF\nmV/jzbgv5VSQaVp7v6ZpjwBFDGbWAvW6rs/PlmGCkCn55iWEDZMyr4u+/jBhw0wpBrapJNa2FCU2\n7RdiN5BgOJqopfJ6HLhd9kSCyNDsQLcTuvpiiRVDbzfxG1A2sySF/Ka1KxZWrqnIHe8KMhesu4kV\nDn8c+DnwXuDNLNkkCBkz2V7CVIhfebGb/qBBNGm6r6oq1JS5GQhFGQgZ2G0qRR4HCrG1BCNqYZoG\nqqpgWCbdvhBlQH2NF0gvPo7BXoJDqR28CWUzS1LIbzoGPxvVpbk1WiZTwQrpuv5bTdPmA93A3wIy\nzHGGkG8eSjKT6SVMRYhsIGhQXerhzX3tCbFy2FTKi13cvH4RLoctccxgKDYfKzZYMba+ZUQtFCVW\nV9XRG+SiwQSRdOJT7nXR0RsTH9OyUBUFb4GDWy5fDGQ3S1LIbzr6gthtCsVFw8PQ00mmghUcHDGi\nA+fruv6spmnT22demBTyfR1jMr2EbIbILMvi9b1t/PWVw4mECwCP08aiOcVcdnYdZy05ORxvy64W\ndjd2YberCbExorHwoWWdXOOKJ16kEx+3y87iuhJKCp1pk0qymSUp5C8Rw6TXH2ZWmSdn6q/iZCpY\nPwb+RCwUuE3TtI8Ab2TNKmHKyPd1jMn0ErIVIjNNi1f3tPDHZw6mjAUp8ji45fJFnL+iJmW0SjwR\n5PZ7Xse0YiHBiBFNSaKIFwND7FqNJD4bLqiftixJIT/p7It93itKcs/THqsOa56u60d0Xf+LpmkP\n6LpuaZp2LrAU2Dk1JgrZJN/XMSbTS8hGiMyImrR1DfDnZ1PFym5TCEUMHnrxEKVFrrQiUVXq4XCr\nj2g0NTEDYiE+72BCRntPcMLiM11Tp4Xc4tI1cxK/P/FarA/mJatns3b5rOkyKS1jeVhbNU3zA5uJ\nDXB8Vtd1P7Aj+6YJU0G+r2NMppcw2SGyUDjK4RM+7n1Cp28gkvKcEbVQiLW/GSkEu25VLQ2NXScb\n1w66WIoCNlVN9AKMXysRH2EyaBpsKTY/R/oHJjNWHdYcTdMWARcTm4X175qmtQNPAU/puv7qFNgo\nTCJDEyzqqovSClaurGNkkhAyWTfqyRQ//0CYx147zOOvHk4MXFQUUrtYQGLWVboQ7MoF/z97bx4e\nx3Xdab9V1SuAxkpwX0CK5KVEWvtqrdRGyVIky463WJKdxHHs2DPxJN8zM5mxnWUyTxKPJzNJPjtK\n8jmWZXmLJSuyLUvUZmrfSEmmKYmXG7iABEns6AZ6rarvj+oqdDca6AYIoLuB+z4PJXR1deNWA+hf\nn3PP+Z02InV+ZzyJaRE0NEzL2c+yc9qNq+VnpZgfHDkZpT7ko73WUoIAUsqDwEHgfiFEM3An8MfA\nl3FsmxQ1QrECi1MDcS4S7XSdjlXdPsZMFoSUWwl5puLnDlR86PlDPP7yYU9W/IZG2hWunPN1XSMW\nT0+Ygl2zJJL3gSKezBCLp9GAJS21VdGpqH7SGZPTg3E2rGiqqoZhl1J7WD7gKuAWYBsQBp4Gvgo8\nO+urU8woExVYdJ2O8bk7t8zxakozUwUhc1UJaVoWA8NJnnmzi+2vH/OO65ojTLrbd6U5oqXrmlf9\nlzseZLIIOBz0jZssPNE112qrgqJynOx3vCyXL6qv9FKKUirCGgBeBh4C7pJSHp71FSlmjVorsJip\n9c5FJWQqbdI7nOAnzx3izX093nHXZsnK7j/pupY1u7UxTRtLs/H79Ox+1cxEwLXeqqCoHMd7Y0Dt\nCtY/ATcAvwOsFEI8CbwipRzfPq+oes60wGKuP7XPVEHIbAv1SCLNqf5RHnxqH4e7o97xQpslXXeE\nS9OdOVhubjAc9HH4ZJQdbx0nFk/nWS/B1CPgWm9VUFSOE72OQ3tNCpaU8v8BEEIsw0kJfhH4jhDi\n18B2KeV9s79ExUxxJlVws/2pvZgYTne9hc8V8Okk0uM/Y51pJaRt2wyNpDh6OsYDT+z1vPuaGwLo\nmkZ01HFLd/cCdE1DMzT8Pj3PExCcUuKMaXkDHV3rpVy/wHKptUhaUT109zpN7TUpWC5Sym4hxPdx\nJhBfiWPNdBmgBKuGOJMquNn81P6mPM3Dzx0ikcwQjac53jvCns5+brlsNR/OmSk13XRYIpnBxolk\ncpludd2ezj6e/9UJTvaN4vfpHO8ZIZX17FvZXs892wTPvnWCzhNDxLKDFl1vwJF4mkUF/mw9g3Hv\nnEyO9587xn6qwlrrrQqKynGib4S6oI+m+uqyZHIpVXRxB45AXQWsA17FKbb4mJTyndlfnmKmmW4V\n3Gx+an/69aMMxZIMj6TGBhpaNk+8dpTP3bn5jNNhoaCPUMCY0KJoKuzp7OPHOw5imjYjiTRDsbGZ\nU+9b18ZvXncWLZEgH7hyLf/60z3jRLKlIUgibeYdyxW0wWgy7zhMXViV5ZJiOli2Tc9gnFWLG6qy\nQhBKR1hfxBGoLwG71N7VwmU2P7UfOj7oiFW2BtwGTNMmhTnlCG4iYU2lrRmphNzx1nEyGYvhkRQj\niYx3fHFziI/fsJ7uvhEee+UwA7EUIb8B2KQytieSwDgxccXKFTc3KmsI+0tWAxZDWS4ppsNgNEnG\ntFnUVF0O7bmUEqx/kFL+bLIThBB3SCl/OoNrUswi0y2cmM1P7cm0RbFRhrY99QhutoTV7a862TdK\n/3CSZE6U1NwQJOD3cbJ/lJ+9fARwRnsksum9YqKTKyYXiXbvZ+JGW80NQe6+eeO0RUa5Xiimiuvs\nv6iKU8elBKsjWxn4EPA80AWkgQ7geuBjwCOzuUDFzDHVwolCcZutBuOAX0eDcaKlaVMXmtkQ1oxp\nMRhL0jOYoD86Jla65sy2CvgNlraFee3dU0Uf70aJha/nXdes9Y6/VJDKrM6EjGI+0zvkfNBrr9UI\nS0r5D0KIHwFfAH4AbABMHOeLn+HsZRX/K1VUHVMpnJioJ2g6KapSnLWimZF4mmg2LahpTkVdwG9M\nWWhmOh2WTJsMxZIcORXlu9v3kciOl/cZGq2NIfw+HUPXuOa85Tzy/HihBCdKnOzDwou7u519toL9\nLlWGrphLerPZjEVVaMnkUo4102ngT7P/FDXMVAon5rKX58ZLV3P05DBBv5FXVXfLZaun9b1mKh0W\ni6eJxdPsPtjLQzsOep6AK9rraaoPEB1Ns7Q1zKolEV7c3c3pgTg2ztgQv2+syqq9OTTp66nK0BXV\nQE82wiqsYq0myp2HpZgHTGV/ZzbeRCfaP7tQLGZoiuXrs4mV3a+KJzM8++ZxntnV5d13xZalfODy\nNUTq/DTWBbzIKZ7MkDFtUmmTRDJDxrSI1DmiddW5yyaNvlQZuqIacPsI2xqr1yJWCdYCYir7OzP9\nJjpZSmxre6RqigTc/ap40uQnzx/kVwf6AGe/6vb3d3D55qU01vmpCzkNvy/u7iaezHjl6IahYVk2\nA9Ek6bRFXdjHi7u7J21edn8ubh+aG2FeJNrHna9QzBZDIynqQz78vuodJq+XPkUxX9iyto0PX7vO\nG329pCU84Z7URHtH0y1emCwlVi0k0yb9wwkGYym+9di7nlgF/QafunUTV2xeSnNDwBMrcCLR3MGM\nuuY429q2Y9kUHU1z5GSUwZgTsRXiRpIXifY8sWoI+9kle9jT2Tf7F65QAEOxJE0N1RtdQenG4Xsn\nu19K+cDMLkcx25Qbycx08UK179O44nKyf5QHntjLYLYhuCUS5N5bBMta62iJBMd9+mxvDnM8a2cD\nTjrRstyvIZHMEE9kCPgN2ptDLGkJF309u07HaC+yd6AKLxRzQTpjMZLIsHpJpNJLmZRSKcGtk9xn\nA0qw5hmzZXBbrfs0rh9gImUijw7ww2cOeGXra5ZG+ORNG2mqD9ASCeIzxick3KnArqWS68o+9vzO\n/905Qx+9fn3R17PaBV0xv3F9L6vVksmlVFn7b8/VQhSVZzYNbqvRLsi0HJPZdMbilXdO8dgrhz2B\nuWDDIu66Zh3hgI+WSNAbU1/IlrVt3HLZ6jHzWvInC7vYOI4WE0VM1SroioXB0IgjWI21LFhCiK9O\ndr+U8i9mdjmKSjITpewTRWjVZheUSpsMxpKkTYufv3wkr+n35ktWce35ywkFfDQ3BEr6qt1+RQcd\nS53S9nc6+8mYFqmcAgs7+59U2uSdzn72dPaNu+5qFHTFwsH1xKzpCIv8hvvfx5mPpZinnGlaqlSE\nNp1KwNlIUY4mMkRHU4wmM/zg6f0cOD4EgN/Q+c2tZ/G+dW3UBX1T+rTpXpv7GpzqHyWVNnEzhEZ2\nurANRaPWahN0xcJiJOEUDtWH/SXOrCylUoJ/7n4thPhg7m3F/ONM01Iz3Ww83RTlRCJn2zbDo2ni\nyQz9wwm+84T0RDoS9nPPNsHKxQ00hP00TPMP113Xk290se/YAKZp4wZoGdPyHLEfe+XIuGuoltJ+\nxcJjNGvkXBes7k6nqZS1F/MnVcwjzrSUfaYLB6ZTCu9FOANxLHtM5HYf7KV/OEk8meHwyWG++cge\nb73L2ur4/F1bWLW4gab6wLTFymXL2jb+6gtX8cUPvY9QwKkqtCwbXXOirEzGorN7WJWsK6qG0WzL\nRV2ougWrulenmFMK01IBnwZoPPL8IV7c3V0yRTXThQPTEcBiYmZZNs++2cXHb9jIm/t6eOT5Q5jZ\nXN3Za1r46PXrCQUMmhuCBP3Tb5rMjezqwwHSaedNwLad/ixd17Jl7873vv/xvXz61k0qqlJUHC/C\nqmXBKujDaivsy1J9WPOPwr0Yl3LScTNdODAdASwUOdNyBKJvKMGTrx9lx9snvPu2rG0FbL7183dZ\n2lbHNectn7Z45L5eiWSGrtMjmJaFpmmeOOrY2NlaDEPXiMXTM1aFqVCcCaNJZw+r2lOCU+nDerbg\nturDmsdMZz9qpgsHpiOArsjZto1p2di208w7mkh7YqVrGldsWUJndxRNc8SjZzAxTjzKLfjY09nH\n/Y/vJRZP4zN00mmTjGV75YFumbtlORZPbrTl9nWp5mBFpTl6KgaQ5+JSjag+LEVRprsfNZOFA9MR\nwKvOXZbnrG5aFv3DTq8VQDho8Fs3beT1d095YpVbtp47u6qcCNM9LxZPgw2ZjEU6+73dGV+GrmGa\ntrcJnLFssGwylkXPYJxkamwYpEJRCdLZHkJ3z7VaKRn/CSFuBeJSyh1CiIeAVpyZWHerWVjzl8J0\nnGvMqgH3PbpnzkqupyqAZy1v4oaLVrJz72lO9Y8yEE15f4xtTSE+dYtgUVOYJ18/Ok6sYEyQH3v5\ncN4EYHeEfWE05EaiPkP33C5y0XAiKlvPpidzSpdsC1Ipk2FSRXuzFIq5wjQtNI2ibi7VxKSrE0L8\nLvDXgPvOdTbw58BO4E9md2mKSpKbdkskMwxEk2QyFvVhvxdtVFOVmzvCfmgkxYaVzZy3fhEDsTGx\nWre8kc/fuYVFTWHqQj6WtdUXbQhubw6xp7OPzpNRR4CyUdNg1KkwLIwwc8vix60p+8+ybGzbxu/T\n0R1vXK/B0bJtGsL+qjIBViw8TMvGmMDNpZooFWF9CbhJSnkyezslpXxOCPE68Kvs/YoqZrqNt7np\nuHc6+/H5xqIMl2rZe7Esm8FYklTGwrZtXtzdzROvHfVScBdvWsydV3Vg6Lo3GmSy/bEXd3cXjZhi\n8TQdS/PNQd1INBT00QIMxpLefa4eWrYNmmOk2z+ccASMbPSla4SDPuUZqKgojmBVd3QFpfuwtByx\nAvhXACllnLGoS1GlTNSTVG5ktGVtG5+7cwuLW8K0N4fzxAqqw5g1nbHoG06QylhkTItHnj/E41mx\n0oBbL1vNXVevxafreaNBJhu1cuRUFNO0SWef0y1Dz5jWuIKP3NuhoA/D0An4dFobQ4SDPvw+nVDQ\nR33IEfuA38Bn6PgNHZ+hE8iW0SvPQEUlMU0bw6j9CCsghAhIKVMAUsp/ABBChMi3bVJUIe5wwVjB\nnKWpRkbVaswaT2YYHklh4/SRfO+pfXR2DwMQ8Ol87Pr1nN3Riq5Bc0PQEweXYvtjezr7iI6msW3b\nG8Zo2jaGptGxrLGoOwWMFYZoOJFUpD5AOjOWJnQLKyJhPwPRsSjMbVJWnoGKSmJaVtXvX0FpwXoU\n+DshxBellCaAEEIDvg78dLIHCiH8OBFZBxAE/hJ4F7gfJ7W/B/iClNISQvwpcBuQAb4kpXx9uhek\nGOPIqag3CRfG9mL2ZSzue3TPpGnC3FRiwKeTSGYIFURYlXqTtW2b6Gja687vHYzzne2SviEn4muq\nD3DPNsHyRfUYujbhaJBivLi72xMVXdPQs586DUPntivWFH1MrvDd9+ieouK+ekmDl27UNI1UxsTv\nM1iTPV4NqVXFwsW0bIL+6o9BSgnWnwKPAJ1CiFdxhOYy4B3grhKPvRvok1LeI4RoA94C3ga+nK04\nvA+4UwhxBLg2+7yrgIeBS6Z7QYox0kWq1izLJjqS8t5Ui5VrF5Z0J9IWNk7JayptVdSY1bQshmIp\nUtlrO3h8iO8/vY940olgVrbXc/c2QWNdAJ/hiNVUcvM9g2P7UbkTgBvrAl65+2R7gpPtjSmvQEW1\nYtuUnEpQDZTqwxoFtgkhrgAuzx7+Bynli2U894+Bh3JuZ4CLgOeytx8HbgYk8KSU0gaOCiF8Qoh2\nKWXPFK5DUQS/b/wbtWXb6EXewHPThMUq1sJBH031AT5355Ypr2OmHNdTaZPBkZS3p/TG3tM8+kKn\nU9SA41zxm1vPIuAzCPh0miNBZ2T9FMgtosiNKJe0hMvqzXL/v1P2cuxUVLmuK2qDGnGKLcuHQ0r5\nCvDKVJ5YShkDEEJEcITry8DXs8IEEAWagEYgtwrAPT6hYLW01OHzldfg1t5e3SOfp0O517RxdSsH\nuwYZHnHKu/2Gjg0E/cY4MRscSXnPOxBLFRW73HPK5U15mp++dBhw0mr90SQ/fekwTU11XCgWl7ym\nN+Vpnn79KMd7orQ2hrnifcs4u6OVn+w4wNOvH/XOu+WKDu64Zh26plEX9NEcCZb9idH9Hif7Rgj6\nDdIZa5yn2m1Xn8XTrx8t+rrslL1svbTDu721PZJ3ez6xkP+eqo36ukDRD5/TQnP+Pqv9tZhV4ygh\nxCqclOI3pZTfF0J8LefuCDAIDGe/Ljw+IQMDo2V9//b2CD090SmtudqZyjVdIhZx9OQwbU1jxRE9\ng3HqQ75x6cIlLWHveVsaAkX3YXLPKZfHXjhYNDX52AsHWdUaBia+pj2dfTy04yBWtuH2RE+MHz+z\nD7+hc+y0YyVj6BofumYdF2xsZ3BglIawnwA2vVlvtFIURk2ptEnGtDB0LS/9uao1TNep4bzGX5dj\np6Lj1r/Qf/dqhWq7pqkIxkh2rP1MYNs2lmVVxWsx2Wswa4IlhFgCPAl8UUr5TPbwW0KI66SUO4Bb\ngV8CB4CvCSG+DqwEdCll72ytayGQm4IL+Q3AJpWxaW8OcZFoZ1eRbGtuAcVMmtieyciRF351wvMD\nBKcbv2846Y2irwv5uPvmjXQsbQSgsS4wZbfpctOfezr7GBpJeX6BkbDfSxlWulpSoZgJqn8Hq0zB\nEkJ8A7hfSvnGFJ77vwEtwFeEEF/JHvtD4O+FEAHgPeAhKaUphHgBJ+WoA1+YwvdQFDC+YMIpRnB7\njABvnPtk/nwhv0FXjxPFrGyv57b3d0xrH2a6JfHJtEl336gnVrF4iuGRsaipqSHA791+Dq2NIbTs\n7VBg6p+/yhFU9zX1GbrnfDEQTdKC03ulStIVirmh3L/w14G/FkIsxnFo/25BQ/E4pJR/iCNQhVxb\n5Nw/A/6szLUoJqEcl/XJqtVyBW9Rs5OyS6THp/TKZTrRWiyeJhZP0xIJ0jecZHgkSSye8e7XNQgH\nfPQNJ1jUFCraY1Uu5Qiq+5qGgz6SaZPYaBrLthmIJrldFVQo5gm1UHdR1o6dlPI7UsobgA/gRI4v\nCyF+LoT44KyuTjFlznTq73Sm/E7GZI4ShbgiEIs7kdRFop3oaGqcWDU1BNB1jTdlD62NoWmLFYz3\nTOwZjNPdN8JQLOk5grivaTyZIZ7IYOgafkMHDXbJnqryVFQopoOh697ctmqm7ByKEGItTm/VJ3D2\nnX4CfFQI8SEp5b2TPlgxZ5ypK8VMj7mH8hzXM6bT1JzJ/tGkMxZv73dcJ1x8hkZjvZP60zQYGklN\nuTu/WIn9h69dx2OvHKG7bwSfodPcECSRtrzI0H1NXSHNnRrcMxjnsVeOqChLUdMYhjMCp9op669d\nCPEi8FT25i1SypullP8K3Atsm63FKabORKm2cvdZ2rNpwPHHZ6+wIJ7M0Dec8MQqFk/zrcfe5e0D\nTu2NrkFrY5DFLXWeWBm6xuKW4mudiIm8FcFxx1jWVj/OM/HF3d2sXNxAz2CceDJD2rTImE4hiK5r\nZDIWnd3DKspS1DSGrs2rCOurUspncw8IIZZLKU8AS2Z+WYrpcqZTf2d6zH0poqMpUmheccWpgVEe\neEJ6fnvNDQGuu2AFr793GiBv6GKpNRVGU0M5Tuq5uOcU48ipGKcG4kTCfpIp0/uj1jTHNcTERtc0\nFWUpahpD10imq3+QaLmC9btCiF+6Tb9CiC8CX0GJVVVyJhZAMz3mfiJyR4IE64IA7O8a5PtP7ff+\ncOqCPsJBHwePD7F5bQu9gwkGY6my1lTMlaK7b4TmhmBR1/mJUqnpjEkoYBAK+ljUFOL0YNwZd2+P\nlQFrGl6UpURLUYsYuualuauZcgWrB/ihEOKvgW8CMeCqWVuVoqLMtuddOmMyEEshjw6wc+9phkfT\npNMm3f1jZezhoEFTQwBN0+gbTjIQS/HRrWeVva5iRSI+QycWT48TLFcAi0WWuc4WoaCPgM/wBFXT\nnGnCuq7hM/SqmQ+mUEwVw9AxTWfQaDV7CpZrzfSlrKP6G8DvSSm/PbvLUswWM+XrN93nGU1kiI6m\n2Nc1yPbXj3nO625BA8DilnDe+HpDd0RhKoJQLMUXCfsZKJIWzF17YWT54u7uvMiruSHA6cE4Gvnj\nxBvC/qqYD6ZQTIeAz7FtiyfNKTffzyWTrkwI8W3GyvM1oBf4rBDiagAp5e/M7vIUM0k55q2z9Ty2\nbTM8miaeHQmyc+9pLMspY8/Nna9aXO+l3GBMrKB0pWKuiA6NpPAbep6BbSjoY20kSFN9oGi6c6LI\nMvda3SgLnKnB7oyxcNCnHC8UNUsw4LSGxBLp2hUsYEeJ24o5ZE9nH288Iek6NTyt6KicpuKpPE/h\ncMjHXj5c9HlMy2Iw6hjwuvQMxukdSpDJltLqmkZrYxDbxmsY9hlaXnpiMkEoFFGfoee5UbjcdsWa\nKV1rscirHHsrhaKWCGZ7GWOjaRZPUClcDZQaL/Kd3NtCCB24ADgopZzUoFYxs7hvyH6fPq4k+0zS\nZM7x8lNZezr7eKezn2TaxLJsbw8nk7HoPBkdV3iQTJsMxZJ5prFHTkbpGUx4FXd+n+4NWWyJBLn0\nnCU89caxcbn0yQShUIzdfaq0aVGnaV40BZQcXllIscirHHsrhaJW8AQrXp5pdKUolRJcD/wQZ5Dj\n08DzwGLAEEJ8Qkr50uwvUQEzEx0FfDrHeka8iGiqqSxXNG2cKj/bBjNbJVGs8GAkkc5r/AV4a38P\nP3nukCdWQb9Be3PIE7TLNy/hsnOW0lQfKEsQ3DTg2wd6x5nShoM+dE3jK5+6OG/9LtNNibrnK4FS\nzBfclOBILQsW8PfA14FfAL8DNAAbgHXAt4ErZ3V1Co8zjY72dPYxGEuRyY76yGQcZwkoP5XlimYk\n7Pf2oiA7FBKNSLbwwLJthkdSJFJm3jnP7Ozil28d945tXtuCBsQSGSJhP1dsWcplZy9B07SyBCFX\ngHyGPs6UFop7VUTfZQAAIABJREFUAha7LiU+ioWMG2FFa1ywVkgpfwgghLgJx109A+wTQjTN+uoU\nHmdqufTi7u6io98DPicqeuT5QyVTZK5ouoUH6YzpVeS0RIKEgj7amoL0D425VoBjs/TQjgP8+lA/\n4OxX3XFVB5ee7bTxtbbWkxhJ0lgfKPPVGLsml4aw3xPgaDztCVauGE8m+jNVPalQ1CLhoCNYEzXX\nVwulBEsDEEJowFbgGzm362d3aYpcztSBIlds3DfzeDLD6cE4RrY8u1SKLFc0mxsCnhuFz+dU41mW\nzfvWteWJ1fBoige3S7p6RpzvHzC49vzlHDw+xM69p2mJBPnAVesQyxvLuo5i15TIFn9YtpOmtNMW\nS1rGi85Eoh/w6zOWKlQoapH6sB+AvuHqbs0o5SW4WwjxX4CvAkngpewsqz8BXp3txSnGcF3Ply9q\nKOl6XoxiHoHuMMJCiqXO9nT2MRRL0t034glFSySIz+fshbVGgtx48Uo2rGz2HtPdN8I/PrLHE6vW\nxiC3Xr6aXx/qp2/YKcQYjKV4+Nn90/Lia28Ok0hmGIgmyWQsdE1znNT9OisXN/Di7m7+x3fe4L5H\n97Cns29icbeLd/hP16Feoag1wkEfhq7RO1TdglUqwvoC8FfAUuCDUkpLCPF/gbOBj8/24hT5bFnb\nxtZLO6Y1xrpYhJYxLVoaguPOLdwXy90ram4IEounGYglWbuskU/cuIGV7Q2kMvkzs947MsCPntnv\nHe9YFuHumzby6Iud3jmGoaFnKwGns4901bnL2NPZP+6436fzxGtHPZF2I6YPX7uOD1+7blwxxyPP\nj49ci70OCsV8xW0r6atlwZJSDgF/UHDsDyY4XVHFFOsnCvkNbyJxLoX7YrmRRjjr7wdO8cXStvo8\nsbJtm5d+fZLHXz3i7W9duLGdD1691uuNAsb1WE2lKTh3jylS5yc6ms6rfHR7w3JJJDPc//hemuoD\ntDeHueuatXmvyZnsDyoU84FFTWHeOzJAMmV6VYPVRvW2NCtmnMLKu8Iyb5fC1FlhsUI86dgrneiN\nkUybXLxpMRtWNmNaFj998TBv7M06qwPbLl3N1ect88SprSnEQDQ5rsdqKk3BuXtMa5ZExonNYCyZ\nl+p004ZoEKkLcGogzvee3EdzQ4BUxiLg00kkM3kNxsVeB4ViPrO8rZ73jgzQ3T9Cx9Kp7ynPBUqw\n5jmTVb+V68yeW6wwmkh71XiGodM3nGT768dIpk1efecUh04MA05a7qNb17N5bav3PAGfzvUXruAn\nz3dSyFSagnOPF0t1uv1YLm6pritiroBF42lnHyxtYeMUhKTSlmoEVixIli+qA+B4T40LlhDi96WU\n/zTbi1HMLOU0ypbT7+SKgm3beX0a9VnPsYxp8fCOgyTTThqusc7PPbdsYsWisULScMCgsT5Aa2MI\nTdPyRPK2q89iVevEdjC51YC5JfnJlFmWdZKbHmzIiph7Dblpw3DQR1N9gM/duWXS10KhmK8sz/69\nnugbqfBKJqbcCOs/AEqwaoyZapTdsraNdMbkubdP0N03gmHo1Id8BAM+kmmT/uGEV2i3fFE992wT\nNOX0VNWHfETqAnnPl1du3h6ZtJCkvTnMkZNRb/8LnMbn4dEUP3/lMF2nY+MiyFzrpIawH5+he3tv\nrlAVVkiqIgvFQsYVrOM9tS9Yx4QQzwKvAd6GgZTyL2ZlVYoZYSa8A8Epf1/W1sDHb9jID57eR9+w\nIxyjiTSDsZR33uaOVj6y9SwC/rEN28a6wBm7PxdWA1qWjWXbmJbNvz/fid+nYeNEkEdORvnkzRvz\nRLGYMW4mY3kRl4sqslAsZCJ1AVobgxw+Ga3auVjlvpPk9lxV31UoijJVd4zC/a4r37eUle2RvPEf\nF29azBOvHWV4NJ3nO3buWW189Pr1Xpm6BjQ1BAgFznybNLcaMJU2HSsoXcPM+hmm0jaGrpGxHWum\nwnH1hWnDVYsbGIgmxw1yVEUWioXO2mWN7JI99A8naWuqvg9w5Q5w/PPc21mni7WzsiLFjDEVd4zC\nKORk/yg/evYg2y5dldcMvHpJBL9P98RKA648dykfuLzDO0fTnH6toH/i0thccVy5pJFLxKJJ05Ru\nNWDPYHzMDzGn4deybbCd6Gv/sUHue3TPpLOuxr6/cltXKFzWZQWrs3u4dgVLCPFZHBPcXDumTmD9\nbCxKMTOUWwXonuNiWbbnpr5z72lPsIZiSb67XXKibxQAXXPGbOQKmq45Dhh+3+RilSuO3b0xHj45\nnLfmQlzxLeyv8tZsg52draVppe2VlNu6QjGetcuc6sBDJ4a5eNPiCq9mPOXma/4EOA/4S+C/Ax9A\nObXXBOW+MfcMxrFtO7s/NHbcLXQ43hPjge3SGxfiMzRaG0Mk0hbbXz8GwKbVLd5cq8mYTjGIe/z+\nx/d6llI2eNEW5I/GHoolSWUs/unRd9i8tlVFUApFGaxd1oiha+w9OlDppRSllJegy2kpZSfwa+B9\nUspv4giYYp6wqCmEWSBW4ERLezr7+eefvuuJVcCvs6gpnCdMb+7roa0xVFKsYPrFIFvWtvHpWzex\nrK2e9uYwrZEgmj4mVC6apjEUS5FKmaRNy4u2puNXqFAsJIIBg/UrmjhyMlqVwxzLjbBGhBBbgd3A\nB4UQbwDVO0dZMSXe2t9D71CC0wOjGLpOXchHKODDtm0CfoPvP7XPO7c+5KOxPpBXQaRpMBRLoetj\nxyZrWD6TUSm5ac4jp2L4fQbptOXsYWWxsqpr2TYBYyw1qeZeKRSlOWdtK/LYIO8dGeCSKksLTqUP\n6zPAHwO/C0icKcSKKmG685x2ylP8+wuHAacEfSTh2C41hn34fD6vAVfT4AOXr+HIyWH6o2Ol7JoG\nhq6xuGXs88tkDcuA5/ruOlL4fU6PVrlVem6a875H9xAKGHT3jWBZYGb3sGyctKBtk1e6rvqsFIrS\nbO5o5ZHnD/FOZ19tCpaU8h3gPwkhWqSUH57lNSmmSDGByPXKKyZg7lTgF341tp8UDDjNwKZlc3oo\nyWjCmSoc8Ot8/IYNbFrdwuKWsLdnpWt4s7RyxWaiParHXjniTSHOdX1vb63jjitXTjn6cVOLPkMn\nY1tgONGVbTtC6vcZeaXrqs9KoShNx9II9SEf73QOVF0/VrlVgucDPwTqhBCXA88DH5VSvjmbi1OU\nR6FAFHrlFVbM/epgLzveOk7fUIK+oQThbAoQnOnA/cMJr0qwuSHAvbdsYmmr4zPmVgS+vb+XwViq\naOXhRHtUXadjLMqO/Mh1fW+NhPKafMuNFN1ri4T9DEST6JqGnnWBt22bpob8Ccaqz0qhKI2ua5y9\npoWdsodTA3Hvb78aKDcl+PfAXcD3pZQnhBCfB+4DLp21lSnKplAginnlgSNs6YzJw8+Nmc/aQHTE\nTfFpDETHbJZWLW7g7ps35tkqAVy0sZ2rz10+4Xom2qOaiJP9jhVMOd6HuXgehziFFumME70tX1TH\npecszVo2qT4rhWKqnLO2lZ2yh3c6+6tKsMqtEqyTUr7n3pBSPgWMn/ynqAiF04Qn8so72T/KjrdO\n5B1zDWyHR1J5noBrl0X4zO3n5ImVBjTVB6gL5VsaFXLVuctIJDP0DMa9CcWJZIaV7fVFz1/a6hyf\nrNy9GFvWtnGRaCcWT2NjEwr6aG8OYxgGHUsjfO7OLXzlUxfzuTu3KLFSKKbA5g5nysK7h8cPSK0k\n5QpWvxDiPLIVxEKITwLVdSULmMJUlytUbsGBbdtkTIum+kCegSxAwG9gGDoZc6zK7vwNi/jM7efg\n9439erjuFYV2RhNRWGpuA5vXOaJRKGZrljvNitMpd+86HfPMbTOmRTSeJp7MqPH2CsUZ0N4cZnGL\nM9Bxomb9SlBuSvDzwHeAzUKIQWA/cPesrUoxJSbzynPEypGPizctZufe0555bTyZZiiW8nqvDF3j\nN687i/PWL8p7/nLcK3J5cXd33h6VS9fpGBeJdp547WjehOBXdp+gPRKYUrm7u9f15r4eLMvxFtQ1\njUzGYjCa5EgVbRQrFLXI5o5WfvnWcTq7h/PcbCpJuVWCB4GrhBD1gCGlHJ7dZSmmSjGvvOfeOs6p\ngThtjUFvKjDA9tePMZJwxMpFy4pSobO6oWtluVfkUipSKkxhQv4wxngy44259xk6F4n2vOt67OXD\ndJ6M4jN0LMspXzdNGww88113P0uhUEyPc7KC9U5nf20IlhDil4zP7iCEAEBKef3sLEtxJti2zerF\nET583Xirxw0rmzk9EOcXrxzxjhm6RluT41KR6x3oMxyxMvTyxQombwwuFLN4MkPfUIajp5x5WCvb\n63lrf68nVpGwn12yh46lEQAefu6Q8xy2Y8vkNgxrOCXtuuEIVqDMaFChUBTn7DXN6JrGO4f7+eDV\n6yq9HKB0hPVnc7EIxcxhWhaD0RTpCfLOvzrQy/bXj3qfQoJ+g5ZI0HOpcPe4/Iaed3wqTOYS/+Lu\nbk/M4skMg9EkmqbhM3TPjT2Q3Ttz96RgrPAinsyQSGac4pCcpdnZ//h8jsitXtIw5XUrFIox6kJ+\n1i6P0Hkiymgic8Zz7WaCSVcgpXxOCNGCkwbsBRBCXAu8K2XODHJFVZBKmwzGkuP8AMGJup598zjP\n7OryjhWzWWqJBAn4dJojQS+9NlVKucS7YuZ6lblWSt19I6QzFvEEXsFHJuPMuNI0DRubwayg2t5/\nHDSctGYk7CcU9KmeK4ViBjhnTSsHjw+zr2uQ8wv2titBqZTgBcAvgN8Gnsgevhn4vhDiVinl7lle\nn6JMRhJpYqPp8flbnGbgh587yO6DjvmrrsFl5yzhyKnYuHOv2LKUlkjwjLvbJ3KJzxWz7r4RNM3R\nHTub2nPL6t1CCoCMZXF6YNQT4mJBn6Y5DY9p0+KT165TZewKxRTZ8fbxcccSacft5umdxxiMJcfd\nP1WuO3/FGT2+VIz3deATUsod7gEp5X8XQjwP/C1w4xl9d8UZ41osuZZHhURHUzz45D6OnXbEKRQw\n+MSNG9iwspn9XYPs3HuagWiSlkiQK9+3lEvPXjor6yzmYOEc7wfT9gQr97qwySu3H7sv/7amwaLm\nMOGgD13TlFgpFDNEe3MYXdM42T9a6aUApQWrJVesXKSU24UQfzM7S1KUS8Z0SrgzxXKAOI3CDzyx\nl8FsNWBrJMi9t2zyjGo3rGz2Ciwawv48o1iYvqFuIcUcLB58ch8Bn048mUFDQ89GSLrmuFZYtu2Z\n2U6E+5iAf8wzUPkFKhQzh8/QaW8OcWogTiptEphkivhcUKr8yy+EGHdO9ligyPmKOSKezNA3nJhQ\nrOTRAe57dI8nVh1LI3z+ri15ruoujXXFxerh5w5xaiCOZXNGM6UKm3jjyQz9wwm6s5OLLdsmY9lo\nGjTWBzCyPVXGBAUf7lGfoaNrWt7a1d6VQjGzuO8ZvUOVn3ZQKsJ6DmeMSOEokS8DO2dlRYpJsW2b\noZEU8WRmwvtf3nOSX7x6xNsPunDjIj549Tp8hp6XBmxtDHLNectZunH8CIHpTAWeCLeUPZHMeE4U\n7tp8huYY7doAGk0NQYJ+g/5JxFjTnH6rtcsbwbZJZWzlF6hQzBItjU7WYjCaZPmi4vZqc0UpwfoT\n4BdCiE8BbwMJ4ELgNHDHLK9NUUDGtOgZjHtiVbgHdeHGdt47MsDr7532HrPt0lVcc95yNE1jf9eg\nNxoEYCCW4mcvHyHgN8a90U93KnAx2pvDHDkZ9UrmC7ar8Bk6pmmRzpgsaQmzcnEDP3/pMGA7c60Y\nf/7tV3Zw+xUdU16LQqGYGi0Njm1soa1bJShV1h4VQlwDbAUuACzgG1LKF+ZicYoxkimToZEkzS1O\nDnl/1yCPvtjJaCKDaTlC5vh+OW/vfkPnI1vPYsu6MSHauXdMyHzZMRxQPGqa6lTgyfa7rjp3mVNc\nUQTLsvH7dDRDx+/TvR4ut+iiEF3XlFgpFHNIpN6PoWsMzECV4JlSshNMSmkDz2b/KSpAdDTFSCI/\nBbjjzS5vLIht2yRz0meROj/3bhOsaM9vnh2IJtEAI0esoHjUNFnzbyETjQU5fDKaHfERxzQtr5dK\n15xKPw3n/+mMhW3bBP0Gj718GHBMeVOYXo+WrmksagqxZmlEiZVCMYfomkZTQ4ChWKriAx2n5rmj\nmFMsy6Z/ODFOrABO9jvRj1OwMHZc0+APPrhlnFgBtDWFxokVFI+a3NEd0dEU3X0jREdTXCTai+4R\nFdvvSiQzPPHaUa9oA5x5VW5Elev2ZNk2NpDKWBw8MUw8mSHg053pwbhNwZpqCFYoKkRdyI9p2aTS\nlXVuV4JVpaTSJr3DCVKZiX9BLMsm14FJA4I+naaG8aPKgn6D6y9cUfTT0URR0y7ZQ6QuwLK2eiJ1\nAXbJnqJVgsX2u6JZ81pwxMs0LWx7bIS9nuOr5LpUpNImGdPm9ECcoZGUt9dl204UOZFgKhSK2SUc\ncLYi4qnixV5zReXNoRTjmMy1ApyIJOA3iOc0C+ua829ZkSqecMCgsT5AS2QRmqZNaJmUy1SqBIvt\nd7nmteCIl647vVWWDWnTcmyfNPBpTpVgYSGGnU0ZYjspzNbGEF2nxztzKBSK2cf9Wy7VGznr66jo\nd1fkUcq1Apziix88tZ+hkbHRIIYOfp9BfcjHdRfkW5/UhXw05kwNnsgyqZBiruqxeJruvhHue3RP\nntCtXNzAns7+PId1d9YVOOLlRVZazi9/xiZTqFQFaJrjJh8O+qZVoahQKM4ctyfSnKDVZK5QglUl\nlHKtAGeM/X0/fYejJ51RHAG/zopF9aQzFi2R/JlXUNy9AspzsMiNmlxXdXDc0N2iCpddsoeGsN+b\nYRWNp7lgwyIOdA3RMxjPjgFxzs1tBi5Wsp6La4DrnqNcLBSKyuAK1UTN/HOFEqwqIJHK5O3ZFON4\n7wjf3S4ZzkZWi5pCXHP+cvYfGyzaH9FYFyg6DmCiij5woi9XzI6cihIdTRPJCpFLJEcAc9OGhROG\ne4cSntDouoaVTSVYto1l2V6VYDm4EZkquFAoKoM7rsj9EFkplGBVmGIl64W8e7ifHz17gHS2AGPd\n8kYu37yEHW+d8M7pG056TcEXi8V54pEbUQ2NpPAZ+rjx9a74uOIVCviwbWf/KZ228Pt1b3SHi5Oi\nc4TIdbFw04KWZbOktc77Pif7RkmlTSyLrF8gE4ZXGk6fmM+nkzEtVi1u4LYr1qiCC4WiQrhmBaFA\nZb0ElWBVCMtyLJaS6Yn3q2zb5oVfdecNXLzqvOVsu2Ql//bsgaKP2X2wj6vPXe7d/vkrh3nitaOe\nkKTSpjfnKlwgPoWFFm7UFB1NEakbbx3ppuhyXSzAmWGVMS0SyYwncE0NAXqz+2I+QydtWl7Jeq5u\nuRWDNs4fx3UXrFZ9VwpFhYmOpgn6jYqb3yrBqgDpjMVgLDnpBmbGtHj0hU527XPmZGrArZev4Teu\nPYuBgdGiaUCfodE/7Bzf09nHYy8fYX/XIOA0/2Vsp/gBzRmemCtYhePrcyMmDcc5I1QQleWNCCnA\n79OJxtPeY8JBX7ZT2Ekv5KY/XZGybKffalFzyFvbLtlDx9KIiq4UigqRzlhER1MsahpvnD3XzLpg\nCSEuA/5GSnmdEGI9cD/OB+g9wBeklJYQ4k+B24AM8CUp5euzva5KEU9mGB5JTVpsMJpI872n9tHZ\nnS2u8Ol87IYNnL2mxeujaokE6RseEy3Xaqm9OeTtU/UMxr3JvGZWIXRdw7Jsr0cKHHEaiiU50TdK\nJpt2tG07O+5Dw/DpXsSTSlvjyuEjdX6io2lSadMpR9fA0JxoLve63RJ1XdM80bJxxNRnaGRMC79P\nK5quVIKlUFSGvqEEtl0dRU+zKlhCiP8M3AOMZA/9LfBlKeUOIcR9wJ1CiCPAtcBlwCrgYeCS2VxX\nJbBtm+homtEJXNZdegbjfOeJvV6k1FQf4N5bBMva8vurLt60mO2vHxtntbRycQP3P77XqdjLWHn5\nNsu28ek66E4Foa5pBPzOTKrBWAozY3nuEgC2aYPhnBsO+miqD/C5O7eMW/OaJREOn4wymLG8Sgrb\ntjF0HdM06R1KksyW6pvZ58zF0J39KtfxYvxrosrZFYpK0d3nvH0XG00018x2hHUQ+BDw3ezti3BG\nlgA8DtwMSODJrGfhUSGETwjRLqXsmeW1zRmmZTEUS03qWgFw4PgQ339qn9eHtbK9nru3ibw+KpcN\nK5sxdI1fHeildyhJe3OIlYsb2CV7GB5JeZV4LhpjLukBv8Gnb93ElrVt3PfoHhIpk57BeM5IeufE\n3B6oeDLDO539/I/vvEHAZwA2qYxFe3PY68MqJBw06BlKYlv5QuQa9LoDGNcsi5DOWBM6xFfDJzuF\nYqFy9FQMQ9fGfWiuBLMqWFLKh4UQHTmHtKwwAUSBJqARyPX7cY9PKFgtLXX4fOVt/rW3R6ay5Bkn\nmTYZGE7Q0Dj5el94+zg/eFJ6Zq8XblrMp287p+gmZ2trPaGAwZaNi7lz60bv+Ne+u5N0xsLKBlZ5\nBQ3ZSb4Bv8FdW9ez9dIOwBkx4vfpmKZjamkYGpZtYWPj9xmgOZWMg1GnnL67bxTTdNKFbU1B+qNJ\n+qNJwkEfSc0kmTaxbadJ2B0eWQxHDHWC2aojv0+nuSEAaONKZ2+7+qyK/xynQy2uuRTqmqqH+roA\nuj67ZeZuZfG65U20Nted8fOd6Ws910UXuSFGBBgEhrNfFx6fkIGB0bK+WXt7hJ6e6BSXOHOMJtJE\nJ7FYAqda8PHXjvDSr096x7ZesIIbLl5JLDo+FdbaWk98JEmAAL29+VZFXaeGGYwl0XUwC4oPNWDD\nikZue38HW9a2ea9LS0OAUwNxDEPz9q9cN3XbduZRDUaT2Nn9p2R2nwoTuntHMQxHBG3bxrKcCkG0\n8TOvCrGzirpiUR22ptFcH+COKzsAxllHrWoNV/TnOB0q/bs3G6hrmn2m8oY+MjrxB8KZ4m15CoCO\npQ1EY2eemi/ntZ7sNZhrwXpLCHGdlHIHcCvwS+AA8DUhxNeBlYAupeyd43XNKHbWYik+icUSODZL\nP3p2P3uPOvps6BofunYdF2xon/AxkboAAeyibhXtzWGO9444ZesGeaM5IvUBmhqCPPL8IV7c3e0V\nTbhjRCJhP31DCcfXD0fgMqaFkW3aNQwte20514mzJ5W0zfyKxzLdWyzL5rb3d7D10o68X2RVYKFQ\nVJ5EyuTQiWHqQz6Wt1c+HQhzL1h/DPyLECIAvAc8JKU0hRAvAK/guMd/YY7XNKNkTKdkPVPCJHIw\nluSBJyQn+51osS7k456bBWuWFv90oQGN9QEa6wP88p0TRd0qLhLtjqdfxjGX1bMiEw75SKVNz2qp\n0N0C4LGXD+e5U2ia803dakKriEGty5n4i724u9tLTyoUiurhvcP9ZEyb8ze0eL2blWbWBUtKeRi4\nPPv1PpyKwMJz/gz4s9ley2yTTJsMxZKUev8+djrGg9sl0azl0eKWMPduE7Q2Fi8u0DRorg96+z2F\nDb6uMe32147SGgk6AxMt26v4S8VS+A2nGjCZNomNpjEtm//7b7+iqSHIhpVNjCTS2DmKZNl4kZK7\nF1bsss7ECtOybVUBqFBUIYlUhr1HBgkFDDauai79gDlCNQ7PELF4Os9zbyJ2H+zjoR0HvAhsw8om\nPnHjBkKB4j8KXXN6rvw5RSa51XS5xrRoYGRtl0biGWxsT2wypkXPQDxPYCzb2Z9653A/I/GJy+1t\nnHTlTDs1V0tvh0KhyOft/b2kTYvzNy72vDyrASVYZ0g5I0HA2df65VvHeXpnl3fs8s1LuO2Kjgkd\nkH26RnMkOO4XJtdJPVck3fNSGQvLtvFnb2dymnSLMTqJWOWtx9A954uZki5laKtQVBf9wwn2Hxui\nqT6AqKLoCpRgnRHljAQBx9rkkecP8fYBp5ZE0+D2Kzq4YsvSCR/jN3RaIkGvNyoXt1DCXYNLJHf+\nlG1jZLt4dV2bdPBaOeJjGE4fxsn+UdJupeAZouuaKrBQKKbJdeevKH3SFDEti//5wC5s4HdvP7vq\n/j6VYE2TZMpkcCRZ8o07Fk/z4JOSo6ecEvSg3+ATN26YNC8c9Bs0NwSKjrOHsUKJF3d305u1X8p1\nUvcZep5AuVWDhYUgWs7/XS+/YuQOXexY0sCxnhGvBL5U9DYZy9vOvK9DoVDMHE+90cXhk1Gu2Ly0\n6sQKlGBNi3L3q071j/LAdukZ1bZEgty7TbCkdeI3atcCqRTu5ODC+VbgiFfApzOaM7ZE1zTqQobj\n72fnV/bVhX0kkyaaNr7iT8u6UbgjPgC+9+Q+75p0TcO0bQxdw8apJHT9BH2GTlO9n6GRtDcaxXmM\nU/H40evXl7xOhUIxNxw6MczDzx2ksc7Px2+ozr9NJVhTwLJthmKTjwRx2XdskB88vd87d/WSBu6+\nWRSdAOwy0YTgyciNtnKbbQEee+UIXaedyG5lez23vb+Dwyej7HjrONGRFJqmUR/2s2FlEysXN/DO\noT6OnIo5oqZpBHw6a5ZGxs2i+uTNG/OeuyUSoD4cyNpPmfh9BmuWNOQZ5I71jSXGmecqFIrKMppI\nc9+je7Asm8/esbnoOKFqQLNnYjNijunpiZa16JnsYi93vwrglT0n+fkrh7104fnrF3HXNesmnNbp\n9lgVupQXo9o682cCdU21gbqm2ae9PVJ2w1O574OlsG2bbzyyhzf39XDHlR188Op1M/G002ay10BF\nWGVQzgh7cNJpj718mFffPeUdu/HilWy9YMWE+1GFPVYKhUIxlzy9q4s39/WwaXUzd1y5ttLLmRQl\nWJNg2zbReDpvL2giEqkMP3h6P/u7hgBnPtVHtq7nfesmTnsV67FSKBSKuaKze5h/e/YAjXV+PnvH\n5qJVydWEEqwJKHckCDh9Cw9sl5zO9kZFwn7u3iZYtbhhwsdM1GOlUCgUc8FoIs0//ruzb/V7v7GZ\n5oZgpZdsLyGoAAAWw0lEQVRUEiVYRUilTQazM6VKcfjkMA8+uc+Lwpa11XHPNjHpD3+yHiuFQqGY\nbWzb5tu/2EvvUILfeH8Hm9e2VnpJZaEEq4ByRoK4vLWvh588f8grBd+0uoWP3bCeYJEZVi6leqwU\nCoVitnlmVxe79vUgVjVz51XVvW+VixKsLOWOBAGnvP3pnV3seOu4d+yqc5dxy6WrJ42ayu2xUigU\nitmis3uYHz17gEiN7FvlogSL8keCAKQyJg/tOMieQ85IeF3TuPOqDi45e8mkj5tOj5VCoVDMJPn7\nVufQEqn+fatcFrxgJVMmQyOlR4IADI+meHC7pKtnBIBQwOCTN23krBVNkz6usS5AXWjBv9QKhaKC\n2LbNtx939q1uf/+ammzcX9DvouVaLAGc6B3hu9slQyPOWOq2phCf2iZY1Bye8DEa0NQQmHB0iEKh\nUMwVz755nF2yh401tm+Vy4J8J52KxRI4kzd/9OwBr8R97bJGPnnTBupCE6f4NA2aG4KTFmAoFArF\nXHDkZJQfPbufhrCf379jM4Zem+00C06wpmKxZNs2L/66mydePepVDV4k2rnzqrWT9k+phmCFQlEt\npNIm//yzd8iYtblvlcuCEqxyLZbAEbafvnSYnXtPA05675bLVnPVucsmLUk3dI0W1RCsUCiqhB/v\nOEh33yg3XrRyUuedWmDBCFZ0NMVIGRZLAKOJDN9/eh+HTgwD4PfpfOz69ZzTMXlznU/XaGkM1my4\nrVAo5hd7DvXxzK4uli+q5zevO6vSyzlj5r1gWZbN0Ej5+1W9Q3EeeELSO5QAHBf1e7cJli+qn/Rx\nyr1CoVBUE7F4mm/94j0MXeP3bj+HwDzYT5/XgpXOmPQNJ8YNJZyIQyeG+N5T+4gnHXFb0V7PPTcL\nGks0+yr3CoVCUW38+JcHGIql+PC161izNFLp5cwI81aw4skM6YF42WK1c+9p/v2FTqzsBtfmta18\nZOtZBEoUToQCBk31SqwUCkX1cPD4EC/s7mZlez23XLa60suZMeadYNm2TXQ0zWgyQ2uwtLOEZdls\nf/0oL+zu9o5de/5ybrpkFXoJEaoL+kpGXwqFQjGXWJbNd5+UANx9s5hXe+rzSrCmMhIEIJk2+bdn\nD/DekQHAqfC765p1XLixveRjldWSQqGoRna8fZyjp2K8f8tSNq5qrvRyZpR5I1hTGQkCMBRL8sB2\nSXffKOBES5+8eSNrlzWWfKyyWlIoFNXIaCLNI88fIhz08ZGt6yu9nBlnXrzrjiTSxMocCQLQ1RPj\nu9sl0VHHlqm9OcS9t2yirTE06eM0nKrBcHBevGwKhWKesf31Y4wkMnzkurPm5WSImn7ntW2nZD1R\nxkgQl18f6uOhXx4kbTppw/UrmvjEjRtKipCyWlIoFNXMSCLNkzuP0Vgf4PoLV1Z6ObNCzQrWVEaC\ngCNuz719giffOOYdu+ycJdz+/g6MEr1TympJoVBUO7988zjJlMmdV64lGJif71U1KVjJlMngSLIs\niyVwxO2R5w/x1v5ewImWbruigys2LylZjq6slhQKRbWTzlg8vauLcNDg2vOXV3o5s0ZNCtZoMlO2\nWI0k0jz45D6OnIwCTpPvx29Yj1jdUvKxPsMRq/lUFqpQKOYfb+3vYXgkxc2XrJrXe+zz98qAE70x\n/vGRPfRHkwA0NwS495ZNLG2tK/nYgE+nORIs2YulUCgUlea5t08AzOvoCuaxYO3vGuQHz+wnkbVZ\nWrW4gbtv3kikrnTljLJaUigUtcJANMneIwOsX9nEsrbJPU9rnXkpWK++e5Kfv3TYG3t/7lltfPja\ns/D7Sqf2lHuFQqGoJXbuPY0NXHb2kkovZdaZV4JlWja/ePUIr+w56R274aKVXH/hirKiJeVeoVAo\nao23DzjFZBeL0g49tc68EaxEKsMPnznAvmODgFMw8anbzuGsMl2KlXuFQqGoNVJpk/1dQ6xa3EBT\nQ+1OEi6XefEOPRBN8J0nJKcH4gDUh/3cc/NGzj97Kf39I5M+ViPbEDxP+xYUCsX85cDxITKmxdlr\nSlc9zwdqXrCOnory3e3Smya8tLWOe7YJWiKlP23oWfeK+TDYTKFQLDzePewYd5eahj5fqGnBevtA\nLz957qDndiFWN/Px6zeUFS3pukaraghWKBQ1jDw2gKFrbFzVVOmlzAk1KVi2bfP0zmM8++Zx79iV\nW5Zy6+VryhpRrxqCFQpFrWPZNl2nR1jWVkcoUJNv5VOmJq/ywSf3eZUxuga3X9nB5ecsLeuxqiFY\noVDMB3oH4yTTJivbGyq9lDmjJgXLFatQwOATN25gw8ryhpSphmCFQjFf6OpxCspWtM/vZuFcalKw\nAFojQe69dROLm8Nlna8aghUKxXyiqycGOC4+C4WaFKwPX7uOs9e0lt03pRqCFQrFfKNn0GnjWdJS\n2ht1vlCTgvX+LctIpssb2qgaghUKxXxkIGvqXU4Lz3xh3r6Ta0BbY4goZc4hUSgUihqifzhJQ9i/\noPpI52Vdt65Ba2OQ0DyeC6NQKBY2A9EkrQsouoJ5GGGpCcEKhWIhkEybC8I/MJd5JViqIVihUCwk\n6hfY/vy8uVrVEKxQKBYaC62gbF5crWoIVigUCxElWDWGaghWKBQLlfACKyyr6atVDcEKhWIh419g\nxWU1KViaphqCFQqFwudTglX1ROr8qhJQoVAseHwL7H2wKgRLCKED3wTOA5LAZ6SUByY6X4mVQqFQ\nOK08C4lqeef/IBCSUl4B/Ffgf1d4PQqFQlH1LDSDhGq52quAJwCklK8CF1d2OQqFQlH9GAsswqqK\nlCDQCAzl3DaFED4pZabYyS0tdfh85Rk+trdHZmB51YW6ptpAXVNtUKvXtOWsNi7ZspyWxlCllzJn\nVItgDQO5vzX6RGIFMDAwWtaTtrdH6OmJnuHSqgt1TbWBuqbaoNquaSri+UcfOY9MMk1PT3oWVzT3\nTPYaVEtK8CXgAwBCiMuBX1d2OQqFQqGoNqolwnoEuEkI8TLOKKvfrvB6FAqFQlFlVIVgSSkt4HOV\nXodCoVAoqpdqSQkqFAqFQjEpSrAUCoVCURMowVIoFApFTaAES6FQKBQ1gRIshUKhUNQESrAUCoVC\nURMowVIoFApFTaAES6FQKBQ1gRIshUKhUNQEmm3blV6DQqFQKBQlURGWQqFQKGoCJVgKhUKhqAmU\nYCkUCoWiJlCCpVAoFIqaQAmWQqFQKGoCJVgKhUKhqAmUYCkUCoWiJqiKicMzhRDiLuAjUsrfyt6+\nHPg7IAM8KaX8cyGEDnwTOA9IAp+RUh6o1JrLpVbXnYsQ4jLgb6SU1wkh1gP3AzawB/iClNISQvwp\ncBvOz+xLUsrXK7bgSRBC+IF/BTqAIPCXwLvU9jUZwL8AAjCB3wY0aviaXIQQi4FdwE04a76fGr+m\nhci8ibCEEH8H/BX513Qf8FvAVcBlQogLgQ8CISnlFcB/Bf73XK91mtTqugEQQvxn4P8DQtlDfwt8\nWUp5Nc6b4p3Zn8+1wGXAx4FvVGKtZXI30Jdd/63A/0vtX9NvAEgprwS+inM9tX5N7oeLfwLi2UM1\nf00LlXkjWMDLwOfdG0KIRiAopTwopbSB7cANOOL1BICU8lXg4gqsdTrU6rpdDgIfyrl9EfBc9uvH\ngRtxrvFJKaUtpTwK+IQQ7XO7zLL5MfCVnNsZavyapJT/Dnw2e3MNcIoav6YsX8f58Hoie3s+XNOC\npOYESwjxu0KIPQX/LpFS/ggnxHdpBIZzbkeBpuzxoZzjphCiFlKjtbpuAKSUDwPpnENa9oMETPyz\ncY9XHVLKmJQyKoSIAA8BX6bGrwlASpkRQnwH+Aec66rpaxJCfBrokVJuzzlc09e0kKmZNzwXKeW3\ngG+VceowEMm5HQEGgbqC47qUMjNzK5w1Cq+nVtY9EVbO1+7PZqKfWVUihFgFPAJ8U0r5fSHE13Lu\nrslrApBSfkoI8V+A14Bwzl21eE2/A9hCiBuB84EHgMU599fiNS1Yai7CKhcp5TCQEkKcJYTQgG3A\nC8BLwAfAK8r4deVWOSVqdd0T8ZYQ4rrs17cy9rPZJoTQhRCrcUS5t1ILnAwhxBLgSeC/SCn/NXu4\n1q/pHiHEn2RvjuJ8qNhZy9ckpbxGSnmtlPI64G3gXuDxWr6mhUzNRVhT5HPA9wADJz/9mhDiDeAm\nIcTLOBuuv13JBU6BR6jNdU/EHwP/IoQIAO8BD0kpTSHEC8ArOB+mvlDJBZbgvwEtwFeEEO5e1h8C\nf1/D1/QT4NtCiOcBP/AlnOuo5Z9TMWr9d2/BosaLKBQKhaImmLcpQYVCoVDML5RgKRQKhaImUIKl\nUCgUippACZZCoVAoagIlWAqFomYRQlwihLi10utQzA1KsGYJIUSDEOIbQogDQohfCSFeEELckHP/\njpxekKpACFF2yagQ4tNCiPtLnDOlayzzOa8TQuwo9znngtx1CyEOCyE6hBB3CCH+Yo7X0SGEOHyG\nz3GHEOI/CiH+RQjxpZzjXxRC2EKIFTnHXpns5yuEuF0I8UfZry8VQvxNGd//W0KIc6aw5P8JfD1r\n3IsQYpkQ4gkhxNtCiDeFENdnj39ICPHFKTyvogpRgjULZBuVfwakgHOklOcB/xH4brWJlGJ2kFL+\nVEr51UqvYyoIIYI4xsr/CDwLXJlz9zacRult2XPDwCYcD8+JuBjH8gjgHGBJGcs4R0r5bpnr3YTT\nY7mDrHEv8L+An0kpzwc+AXxfCGFIKX8CfCjr2q6oUeZ743CluBbHPPR617NMSvmWEOIvcQxTd2TP\n+6wQ4v9kv/5PUsod2Sjsazi+iAPAJ6SUvUKIe3EaOXWcMQlfkFImhBA9wE5gGdAJPJj17UMIsQv4\nDI4v2j8CbTgOBv8hu54O4EGgAXi12IUIIZpw7GzWA4eAlcBdBee4Y1xCQC/w+zmjT4pd4woce61m\nYDlw/2Rv7kKIm4H/AySAvTnHNwL/DLQCIzgfCiwcq6TLhBD12dfw6mzT+D8BT+OMkBjCMUFdAfwF\n8HPgbSnliuxzHwf+SEr5o6z7Qyb7Gn4D2ML/397Zx3ZZXXH8o20MurpkaIYz8W0DjxNQqYDFyavi\ntmTBCEYCbRSjRjTotkiIEVNx4tBNM7IRxRnZCMy3qfNdQ7ViBRUtUBCZ31TFglF8gxiNIk3T/XHO\nY5/+fNqauYb9zP3809/vee7r89zec8+593eOT5Q3Sbq7hzbPAiZImhVazwp8sv8ecJ6k9WY2DA9z\nUYl7W/ilpMEl5cwE5uHhPrYBdfHer8Y9xnfggmReSb5huD/AKtwV0SJJS81sAVADHAn8RdJtuWy1\nQJOkdjNrxJ3GEj+w/SkwB//B+rIo4wVJe81sPK7pHIS/098CrfgP9zGzT+JalZnNxxdzf41+7wEu\nkNRqZicCmyLPTuAh3Hv6zqjzCnz8zZL0HP7j3mXAa9HWh/Af2DdGf97Ax2QV/r4fjDzXFryyRBmQ\nNKz+YRTQnHOwmdEU9zI+kzQCOB9YGSvca4DZkkYCDUC1mQ0FLgZOjZXjB8DcKONQfOLM/KTNADCz\nIXg4ko3AcmCepGrcG/c9kXcJLixOwl3TFFEPSNJQ4DpgeP5mTGb3AHNCk1wK5Cfxoj7OAO6WVBPl\n/cbMDi2qPNIvB86RdDJdISLAhe2fJZ2AT4j34/GNDg9BOxYXWOMj/STcaz/AEXF/CnCzpA+BHWY2\nLFbulbl8v8AF2jXA+mjHOGC+mf24h+dWyseSRsfzuTquLQfq4/m/RfECciFwZtS5DTgu9mym4BrM\nCHwxMbsk30XAQkmjgIm45pExQNLxJcKKKLMJQNL7wG4zOwbXtF7EPZyfZh6bbRw+PgEux+OzVefq\n3Rp9XSrpT/g4ekTSDfi7uiXG+B248AN3k/RkfB4EPBljZwBwdoQDWYCPl6po74OSWoDvm9nxkh6Q\ntDvKmAtslJQ5tW2KPIkyJQms/qGT4snnALp7lL8TQNJmXAgdBzwC/MvMluD/bKvwCWcI8JKZtQBn\nRdqMdfH3cWCMuQfxGbiAqMKF5N8i7134SvcQYAJwb+T9B929qWdMxrUDJDXzdR+GxwK7Jb0Saf4J\nDA6BUdhHSTcD281sLq6ZHYBrHkUMB96V9O/4vhx8jxAYHKaeLOTKLjz4YEP0bRKwGBgf+yLbw8ck\nRCgJXMANjGtP4CFoJkW7xkY/BkX9ZwCz4zk2RZuH9tDuUp6Kv1uAgWY2EDha0hNxfVlxNh4F1po7\n1n0gJufTcYH/eThAXhbX8lwJDAjtcCGuZWSso5ghwDu578/gwurn+PP6AndldAIu7FdFujpgWLio\nurKkriIeB5aY2Z245nNXXD+dLu0IuoRXW+56G/CD8JZ/VLQJSaPzpsTYf7sE9x1ILu+QPtqW+D8m\nCaz+YR0w0jxwXJ4xuPkuI+9tfX+gPVajE3Bzxh/ChFIB3CfppFiNj8bNMwDk/mn34hPcFOBcfCKo\nAPZkeSP/Kfjk3knXGOjEzUuldND7OCm6t1/UW9hHM7sFN++04ZPpR5GniM6Se1l5PdVbiU+IZ+CT\n6q34/smvcC0pYw9AiRac5ZuIm4868ACgmVZWgZvksudYQ5cg6os9Jf3poOc+f4WkXwPTcE1xpZnV\n8fW+Z/3Ocx9uut0KzC+59wXFdNJ90dKIj9nJdGlTq/DYUUfkBMTz+Jhcj5sGe+2XpPuBauBlXNta\nah6/rkPSp7l0e3PZvnFkghDuFwPjJO3I3Wqne5SARJmRBFY/IOl53K6+OBNaZnYyblK6Ppe0Nu6N\nxMMZtJrZOuBgSYvxfZtqfM/rbDP7YRzouA3fzypiBb7K/VhSW5hDWmOiw8wmE2YffD+nLj5PpSsa\ncJ6n8UkbMxuO79/kJ3kBh5jZqEhzLtAmaVdPfcQnwD+GNmb4PlIFxWwGBsX+BoTJMzSlt8xsapRf\nAxyGazANuFbQEf1vwR3TPkbvbMA1xmMlvQ48i7+zLF8jESTUzH4UbTuyjzILiXa9aV1HsmfS/bli\nZpVm1gp8JGkRbvIdEe2YYWYHmsdEuyDammcybm58GDe1kZ2k64U3gKNz31fjZtH9JO2Maw245vJC\nlDkQf2b1uEZ0Ft0XK5Wln83sXmCUpNvxPd3qaO/TfbSvT0Kzmgj8TNI7JbePiT4mypQksPqPqcCX\nwBYz24qbmOokrc6lqTKzjbitf6akdnx/4+9xYOJC4CpJm/D9o0ZcEFYANxZVKmktHnhuZe5yLXCR\nmW0GFgHTQ7OYA0wzs0146JJPS8vDBezgyPs7fAP8qxW6pC+B6biJZ0uUOb2PPi7CT0xm6ZvxyaSo\nP+24kFphZhvwjf2MOuAKM3sV34+bKmlvCLMdwJpI14jvpbUW1ZGrqzPyZObHRvyUWxad9jrgwGh3\nI74v+GZvZfbBeUB99OsUSjSfMPfVAw1m1oxrdDdJegwXos34eNiOH7DIswBYE2NvLPA2PTzjHI/i\nk31W/yf4YZZncmla8EMcDZFmF272fQ1/bgcDB8WBlyag1swux7WpGjO7Efg9vv+3AT9gdCkuVL+p\ntlpILOaujfatjqPtLWZ2eCSZCDz8bepI7FuSt/ZEr4Rmtk3SWvM4Qc8BP5GUTCvfEjOrB+6Q9F5o\nirWSpu3D9gzABfaYWCh8pzCzNfii5oN93ZbEf0c61p7oi9fxPYYK3P5/SRJW/zO249pTO75HdeG+\nbEwcl78BuAy3CHxnMLNz8LhXSViVMUnDSiQSiURZkPawEolEIlEWJIGVSCQSibIgCaxEIpFIlAVJ\nYCUSiUSiLEgCK5FIJBJlQRJYiUQikSgL/gORpGYcDPS+XQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=dw_solar_everyday,y=ghi_everyday,kind='reg')\n",
    "#plt.title('observed dw_solar vs clear sky ghi')\n",
    "plt.xlabel('Observed global downwelling solar (Watts/m^2)')\n",
    "plt.ylabel('Clear Sky GHI (Watts/m^2)')\n",
    "plt.savefig('Figure2.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### making the Kt (clear sky index at time t) column by first removing rows with ghi==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train[df_train['ghi']!=0]\n",
    "df_test = df_test[df_test['ghi']!=0]\n",
    "df_train['Kt'] = df_train['dw_solar']/df_train['ghi']\n",
    "df_test['Kt'] = df_test['dw_solar']/df_test['ghi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.reset_index(inplace=True)\n",
    "df_test.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Kt max: 16815.23562858055\n",
      "test Kt min: -59.82928662131299\n",
      "test Kt mean: 2.5854594971725517\n",
      "\n",
      "\n",
      "train Kt max: 35403.71260380253\n",
      "train Kt min: -1080.8061639132102\n",
      "train Kt mean: 2.849421609595172\n"
     ]
    }
   ],
   "source": [
    "print(\"test Kt max: \"+str(df_test['Kt'].max()))\n",
    "print(\"test Kt min: \"+str(df_test['Kt'].min()))\n",
    "print(\"test Kt mean: \"+str(df_test['Kt'].mean()))\n",
    "print(\"\\n\")\n",
    "print(\"train Kt max: \"+str(df_train['Kt'].max()))\n",
    "print(\"train Kt min: \"+str(df_train['Kt'].min()))\n",
    "print(\"train Kt mean: \"+str(df_train['Kt'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xba43d30>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EIiIjzY1Tz6GxYXRRy2YL/3Je7TMBSD2IGTWzOnfPuFnb2DiOurqB71pPGF8/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\nkQBS+IuIBJDCX0QkgBT+IiIB9P8B8A+REn1VHc0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_train['Kt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xa36b4a8>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J7D/cws/ve7XndSaUxfA8j3jC49s3LgHgq595Dx//4DRKY/6Gwd/AtjF1sj/M\n0/05/NInT+HTHz6RhuYOdtc08z+Or2BCWQk3zF/DOadOwwNOtyq+d0t1T0zlZTHaO+McV1lOe0d8\nWBu2/qqqKlLuNRay+M8GVjnnHgge73HOvWOg/pkW/9d3HM77mbIiMrZE6D0jvRAu/6dTM/72MFDx\nL+QPvpOA5ENW4mYWc86lHLysrCwnFhv+1m9Tta77IiLZKfSFK044bhJVVRU5XWYhi38jkJxNdKDC\nD1BXl9nJLxeedRIXfdqoqzvCoYY2KieVEQFKolEikd6jHxJe71EHLW2dlI8v9YMKtpmRSIRYSZTO\nrkTPfAkPSqIRPM8Ljkp5G/F4gkgkQjzhBfNG8PDo3vR6+L8JdMUTdHYlGBcroSueIBqNEI1EiET8\nI3q8YNnxhEckQjB/pOe1O7sSjCv1N4aeB+UTx3OkuZVI0KelrYt4IsHECeOC+Anm7ZtzNNIdnz9f\nVzxBPOFRVlpCZ1ei3+8Xnj/2GewGdceW/OUx4XnsqWlmYvk4KieW9cnB8zw64/50LGgnaV1059I9\n7ccRZdveRt5+TDmlsRKqqiZSU9PU876WBCvI8+DJFTsoKy3h02eceNTfgR+j1/Me9n/Ow+vze040\nGum5hnxJNEJXsF7TFYlAInH0clPFNa40SkdngmjUfzx16kRqapp7lpN6vr65JL9vydPdjrR14nbV\nM/09VQPGEk94PX/P0LuA7hyikchRP6xGoxHicS+pJ0Tw+0Ui/t9X93vXPV0ai/bk2PvZiPT8rSQ8\nr8/nrjs2/3Pgr/dEonfdd+tKeMSi0Z75k28BEI34z5eWRIknPGIlEbrifr+S4HPd/b7Ggj69nw2/\nT2lJlNaOLiL01otoNEJXV4JINEIE//ab48eV+O1xj6lTJ1Jb66/LhOf1fIOIRiJ0dvnvRff7VBqL\n0tEZp7m1k7JxsZ78ykqjlMZKgr/74Rtoo1HI4r8c+D/An4Ix//Uj8SKRSIQpFWV0tnUwcUJpWvMM\n1q97fK+/k94ezFOa3reTsjT7pauqcgJ09W47J5Rlv2onlGU2n72zMuvXHmh55eNLezbM/f3jzFNy\n+rrJxuV4ffWXvJH1c8ztR3NCWYypH5iQ02X2SO9j1cdI5JiNdD+Pbxvgb28gFeXjaEuz7oD/dzDQ\n33euFfLdfwT4tJm9iL+b8c0CxiIiEioFK/7OuQTw3UK9vohImOkkLxGREFLxFxEJIRV/EZEQUvEX\nEQkhFX8RkRBS8RcRCaGCXdtHREQKR3v+IiIhpOIvIhJCKv4iIiGk4i8iEkIq/iIiIaTiLyISQsVz\nQe0RUOw3iR+Mma2h905n24HA51YVAAAC/ElEQVRfA7cCXcBi59z1A+UX3B8hrb55TSpgZjOAG5xz\nM83sFGAe/j0uNgCXOucSZnYd8Lkgh8ucc6ty0beAeU4HngC2BE/f5ZxbMFrzNLNS4F7gJKAM+Bnw\nRrZxj4IcdzNG1uNY3/P/AjDeOfdR4EfAzQWOJy1mNh7AOTcz+PdN4G7gK8BZwIygmAyU33D65pWZ\nXQXcA4wPmmYD1zrnPo5/X4cLgnjPAWYAFwG/ykXfkc4tWYo8pwOzk9bpglGe51eBQ8Hrng/ckW3c\noyTHMbMex/SeP37xewrAObfSzM4ocDzpOhUoN7PF+OtoFlDmnNsKYGZPA+cCJ9AvPzOblG7f/KbU\nYytwIfCH4PHpwNJgehHwGcDhf2PxgF1mFjOzqhz0fWREM+srVZ5mZhfg7zVehv/3OVrzfBB4KOlx\nVw7iHi05jon1ONb3/FPeJL5QwQxDC3ATcB7+DW/mBm3dmoDJpMgvaGtMp28h3gvn3EKgM6kpEnwQ\nYOBYu9uz7Zs3KfJcBVzpnDsb2AZcxyjO0znX7JxrMrMK/AJ5bQ7iHg05jpn1ONaL/7BuEl9ENgP3\nOec859xm/D+WY5KerwDqSZFfirYB+xbJe5E8rjlQrN3t2fYtpEecc6u7p4EPMcrzNLMTgReAPzjn\n5g8Qy1jLccysx7Fe/JcDnwUYyZvEj4CLCcbkzWwaUA4cMbN3mVkE/xtBNSnyc841Ah3p9M1vSgNa\nY2Yzg+nz6Y31PDOLmtk78TdUtTnoW0hPm9lHgulzgdWM4jzN7HhgMfBD59y9QfOYWpcD5Dhm1uNo\nGALJxmi9SfxvgXlmtgz/1/+L8fcM7gdK8McMXzKzl0md33eH0bfQrgDmmNk4YCPwkHMubmbVwAr8\nHZRLc9E3bxmldglwh5l1APuBbzvnGkdxnlcDlcCPzezHQdsPgNvG0LpMleO/A7eMhfWoq3qKiITQ\nWB/2ERGRFFT8RURCSMVfRCSEVPxFREJIxV9EJIRU/EVEQkjFX0QkhFT8RURC6P8Dl+B6jIPoW+IA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_test['Kt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train= df_train[df_train['Kt']< 5000]\n",
    "df_train= df_train[df_train['Kt']> -1000]\n",
    "df_test= df_test[df_test['Kt']< 5000]\n",
    "df_test= df_test[df_test['Kt']> -1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group the data (train dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "zen = df_train.groupby(['year','month','day','hour'])['zen'].mean()\n",
    "dw_solar = df_train.groupby(['year','month','day','hour'])['dw_solar'].mean()\n",
    "uw_solar = df_train.groupby(['year','month','day','hour'])['uw_solar'].mean()\n",
    "direct_n = df_train.groupby(['year','month','day','hour'])['direct_n'].mean()\n",
    "diffuse = df_train.groupby(['year','month','day','hour'])['diffuse'].mean()\n",
    "dw_ir = df_train.groupby(['year','month','day','hour'])['dw_ir'].mean()\n",
    "dw_casetemp = df_train.groupby(['year','month','day','hour'])['dw_casetemp'].mean()\n",
    "dw_dometemp = df_train.groupby(['year','month','day','hour'])['dw_dometemp'].mean()\n",
    "uw_ir = df_train.groupby(['year','month','day','hour'])['uw_ir'].mean()\n",
    "uw_casetemp = df_train.groupby(['year','month','day','hour'])['uw_casetemp'].mean()\n",
    "uw_dometemp = df_train.groupby(['year','month','day','hour'])['uw_dometemp'].mean()\n",
    "uvb = df_train.groupby(['year','month','day','hour'])['uvb'].mean()\n",
    "par = df_train.groupby(['year','month','day','hour'])['par'].mean()\n",
    "netsolar = df_train.groupby(['year','month','day','hour'])['netsolar'].mean()\n",
    "netir = df_train.groupby(['year','month','day','hour'])['netir'].mean()\n",
    "totalnet = df_train.groupby(['year','month','day','hour'])['totalnet'].mean()\n",
    "temp = df_train.groupby(['year','month','day','hour'])['temp'].mean()\n",
    "rh = df_train.groupby(['year','month','day','hour'])['rh'].mean()\n",
    "windspd = df_train.groupby(['year','month','day','hour'])['windspd'].mean()\n",
    "winddir = df_train.groupby(['year','month','day','hour'])['winddir'].mean()\n",
    "pressure = df_train.groupby(['year','month','day','hour'])['pressure'].mean()\n",
    "ghi = df_train.groupby(['year','month','day','hour'])['ghi'].mean()\n",
    "Kt = df_train.groupby(['year','month','day','hour'])['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_train = pd.concat([zen,dw_solar,uw_solar,direct_n,diffuse,dw_ir,dw_casetemp,dw_dometemp,uw_ir,uw_casetemp,uw_dometemp,\n",
    "                    uvb,par,netsolar,netir,totalnet,temp,rh,windspd,winddir,pressure,ghi,Kt], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th>8.0</th>\n",
       "      <td>147.640000</td>\n",
       "      <td>-3.319298</td>\n",
       "      <td>-0.385965</td>\n",
       "      <td>2.254386</td>\n",
       "      <td>0.217544</td>\n",
       "      <td>240.949123</td>\n",
       "      <td>257.463158</td>\n",
       "      <td>257.122807</td>\n",
       "      <td>241.624561</td>\n",
       "      <td>255.159649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.271930</td>\n",
       "      <td>-1.271930</td>\n",
       "      <td>-17.843860</td>\n",
       "      <td>86.363158</td>\n",
       "      <td>3.459649</td>\n",
       "      <td>322.194737</td>\n",
       "      <td>971.714035</td>\n",
       "      <td>56.746103</td>\n",
       "      <td>-0.069423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>137.909167</td>\n",
       "      <td>-3.186667</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>1.803333</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>245.716667</td>\n",
       "      <td>258.640000</td>\n",
       "      <td>258.251667</td>\n",
       "      <td>245.268333</td>\n",
       "      <td>256.210000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.618333</td>\n",
       "      <td>-0.618333</td>\n",
       "      <td>-16.991667</td>\n",
       "      <td>84.353333</td>\n",
       "      <td>3.388333</td>\n",
       "      <td>340.455000</td>\n",
       "      <td>972.018333</td>\n",
       "      <td>173.258555</td>\n",
       "      <td>-0.019518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>127.232667</td>\n",
       "      <td>-3.213333</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>1.315000</td>\n",
       "      <td>0.186667</td>\n",
       "      <td>234.545000</td>\n",
       "      <td>258.533333</td>\n",
       "      <td>258.103333</td>\n",
       "      <td>243.210000</td>\n",
       "      <td>256.148333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.660000</td>\n",
       "      <td>-8.660000</td>\n",
       "      <td>-17.196667</td>\n",
       "      <td>84.298333</td>\n",
       "      <td>3.696667</td>\n",
       "      <td>342.185000</td>\n",
       "      <td>972.093333</td>\n",
       "      <td>291.141970</td>\n",
       "      <td>-0.011144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>116.433167</td>\n",
       "      <td>-3.265000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.536667</td>\n",
       "      <td>0.241667</td>\n",
       "      <td>225.940000</td>\n",
       "      <td>258.170000</td>\n",
       "      <td>257.753333</td>\n",
       "      <td>240.096667</td>\n",
       "      <td>255.845000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-14.153333</td>\n",
       "      <td>-14.153333</td>\n",
       "      <td>-17.428333</td>\n",
       "      <td>84.266667</td>\n",
       "      <td>3.226667</td>\n",
       "      <td>335.816667</td>\n",
       "      <td>972.341667</td>\n",
       "      <td>369.552873</td>\n",
       "      <td>-0.008872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>105.860667</td>\n",
       "      <td>-3.126667</td>\n",
       "      <td>-0.001667</td>\n",
       "      <td>1.645000</td>\n",
       "      <td>0.226667</td>\n",
       "      <td>240.656667</td>\n",
       "      <td>258.306667</td>\n",
       "      <td>257.915000</td>\n",
       "      <td>243.390000</td>\n",
       "      <td>255.910000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.736667</td>\n",
       "      <td>-2.736667</td>\n",
       "      <td>-17.298333</td>\n",
       "      <td>84.818333</td>\n",
       "      <td>3.295000</td>\n",
       "      <td>331.678333</td>\n",
       "      <td>972.923333</td>\n",
       "      <td>397.404717</td>\n",
       "      <td>-0.007868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              zen  dw_solar  uw_solar  direct_n   diffuse  \\\n",
       "year   month day hour                                                       \n",
       "2010.0 1.0   1.0 8.0   147.640000 -3.319298 -0.385965  2.254386  0.217544   \n",
       "                 9.0   137.909167 -3.186667 -0.033333  1.803333  0.180000   \n",
       "                 10.0  127.232667 -3.213333  0.013333  1.315000  0.186667   \n",
       "                 11.0  116.433167 -3.265000  0.000000  1.536667  0.241667   \n",
       "                 12.0  105.860667 -3.126667 -0.001667  1.645000  0.226667   \n",
       "\n",
       "                            dw_ir  dw_casetemp  dw_dometemp       uw_ir  \\\n",
       "year   month day hour                                                     \n",
       "2010.0 1.0   1.0 8.0   240.949123   257.463158   257.122807  241.624561   \n",
       "                 9.0   245.716667   258.640000   258.251667  245.268333   \n",
       "                 10.0  234.545000   258.533333   258.103333  243.210000   \n",
       "                 11.0  225.940000   258.170000   257.753333  240.096667   \n",
       "                 12.0  240.656667   258.306667   257.915000  243.390000   \n",
       "\n",
       "                       uw_casetemp    ...     netsolar      netir   totalnet  \\\n",
       "year   month day hour                 ...                                      \n",
       "2010.0 1.0   1.0 8.0    255.159649    ...          0.0  -1.271930  -1.271930   \n",
       "                 9.0    256.210000    ...          0.0  -0.618333  -0.618333   \n",
       "                 10.0   256.148333    ...          0.0  -8.660000  -8.660000   \n",
       "                 11.0   255.845000    ...          0.0 -14.153333 -14.153333   \n",
       "                 12.0   255.910000    ...          0.0  -2.736667  -2.736667   \n",
       "\n",
       "                            temp         rh   windspd     winddir    pressure  \\\n",
       "year   month day hour                                                           \n",
       "2010.0 1.0   1.0 8.0  -17.843860  86.363158  3.459649  322.194737  971.714035   \n",
       "                 9.0  -16.991667  84.353333  3.388333  340.455000  972.018333   \n",
       "                 10.0 -17.196667  84.298333  3.696667  342.185000  972.093333   \n",
       "                 11.0 -17.428333  84.266667  3.226667  335.816667  972.341667   \n",
       "                 12.0 -17.298333  84.818333  3.295000  331.678333  972.923333   \n",
       "\n",
       "                              ghi        Kt  \n",
       "year   month day hour                        \n",
       "2010.0 1.0   1.0 8.0    56.746103 -0.069423  \n",
       "                 9.0   173.258555 -0.019518  \n",
       "                 10.0  291.141970 -0.011144  \n",
       "                 11.0  369.552873 -0.008872  \n",
       "                 12.0  397.404717 -0.007868  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Groupdata - test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_zen = df_test.groupby(['month','day','hour'])['zen'].mean()\n",
    "test_dw_solar = df_test.groupby(['month','day','hour'])['dw_solar'].mean()\n",
    "test_uw_solar = df_test.groupby(['month','day','hour'])['uw_solar'].mean()\n",
    "test_direct_n = df_test.groupby(['month','day','hour'])['direct_n'].mean()\n",
    "test_diffuse = df_test.groupby(['month','day','hour'])['diffuse'].mean()\n",
    "test_dw_ir = df_test.groupby(['month','day','hour'])['dw_ir'].mean()\n",
    "test_dw_casetemp = df_test.groupby(['month','day','hour'])['dw_casetemp'].mean()\n",
    "test_dw_dometemp = df_test.groupby(['month','day','hour'])['dw_dometemp'].mean()\n",
    "test_uw_ir = df_test.groupby(['month','day','hour'])['uw_ir'].mean()\n",
    "test_uw_casetemp = df_test.groupby(['month','day','hour'])['uw_casetemp'].mean()\n",
    "test_uw_dometemp = df_test.groupby(['month','day','hour'])['uw_dometemp'].mean()\n",
    "test_uvb = df_test.groupby(['month','day','hour'])['uvb'].mean()\n",
    "test_par = df_test.groupby(['month','day','hour'])['par'].mean()\n",
    "test_netsolar = df_test.groupby(['month','day','hour'])['netsolar'].mean()\n",
    "test_netir = df_test.groupby(['month','day','hour'])['netir'].mean()\n",
    "test_totalnet = df_test.groupby(['month','day','hour'])['totalnet'].mean()\n",
    "test_temp = df_test.groupby(['month','day','hour'])['temp'].mean()\n",
    "test_rh = df_test.groupby(['month','day','hour'])['rh'].mean()\n",
    "test_windspd = df_test.groupby(['month','day','hour'])['windspd'].mean()\n",
    "test_winddir = df_test.groupby(['month','day','hour'])['winddir'].mean()\n",
    "test_pressure = df_test.groupby(['month','day','hour'])['pressure'].mean()\n",
    "test_ghi = df_test.groupby(['month','day','hour'])['ghi'].mean()\n",
    "test_Kt = df_test.groupby(['month','day','hour'])['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_test = pd.concat([test_zen,test_dw_solar,test_uw_solar,test_direct_n,test_diffuse,test_dw_ir,\n",
    "                         test_dw_casetemp,test_dw_dometemp,test_uw_ir,test_uw_casetemp,test_uw_dometemp,\n",
    "                    test_uvb,test_par,test_netsolar,test_netir,test_totalnet,test_temp,test_rh,\n",
    "                         test_windspd,test_winddir,test_pressure,test_ghi,test_Kt], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>145.536667</td>\n",
       "      <td>-5.036111</td>\n",
       "      <td>-0.005556</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>-1.691667</td>\n",
       "      <td>225.650000</td>\n",
       "      <td>271.297222</td>\n",
       "      <td>270.644444</td>\n",
       "      <td>290.319444</td>\n",
       "      <td>268.880556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-64.661111</td>\n",
       "      <td>-64.661111</td>\n",
       "      <td>-4.211111</td>\n",
       "      <td>89.900000</td>\n",
       "      <td>3.958333</td>\n",
       "      <td>150.894444</td>\n",
       "      <td>952.938889</td>\n",
       "      <td>38.153234</td>\n",
       "      <td>-0.171464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>140.396000</td>\n",
       "      <td>-4.896667</td>\n",
       "      <td>-0.068333</td>\n",
       "      <td>1.795000</td>\n",
       "      <td>-1.573333</td>\n",
       "      <td>236.036667</td>\n",
       "      <td>270.815000</td>\n",
       "      <td>270.221667</td>\n",
       "      <td>288.866667</td>\n",
       "      <td>268.450000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-52.828333</td>\n",
       "      <td>-52.828333</td>\n",
       "      <td>-4.593333</td>\n",
       "      <td>91.183333</td>\n",
       "      <td>3.235000</td>\n",
       "      <td>148.135000</td>\n",
       "      <td>952.245000</td>\n",
       "      <td>148.814364</td>\n",
       "      <td>-0.037425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>131.753500</td>\n",
       "      <td>-4.581667</td>\n",
       "      <td>-0.096667</td>\n",
       "      <td>3.088333</td>\n",
       "      <td>-1.426667</td>\n",
       "      <td>247.976667</td>\n",
       "      <td>270.580000</td>\n",
       "      <td>269.986667</td>\n",
       "      <td>288.435000</td>\n",
       "      <td>268.101667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-40.468333</td>\n",
       "      <td>-40.468333</td>\n",
       "      <td>-4.898333</td>\n",
       "      <td>92.058333</td>\n",
       "      <td>2.476667</td>\n",
       "      <td>131.661667</td>\n",
       "      <td>951.585000</td>\n",
       "      <td>314.244805</td>\n",
       "      <td>-0.015046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>121.700500</td>\n",
       "      <td>-4.665000</td>\n",
       "      <td>-0.340000</td>\n",
       "      <td>3.340000</td>\n",
       "      <td>-1.415000</td>\n",
       "      <td>241.571667</td>\n",
       "      <td>270.898333</td>\n",
       "      <td>270.323333</td>\n",
       "      <td>287.598333</td>\n",
       "      <td>268.296667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-46.030000</td>\n",
       "      <td>-46.030000</td>\n",
       "      <td>-4.451667</td>\n",
       "      <td>91.293333</td>\n",
       "      <td>2.421667</td>\n",
       "      <td>145.975000</td>\n",
       "      <td>950.940000</td>\n",
       "      <td>454.239484</td>\n",
       "      <td>-0.010324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>111.024500</td>\n",
       "      <td>-4.480000</td>\n",
       "      <td>-0.051667</td>\n",
       "      <td>4.360000</td>\n",
       "      <td>-1.233333</td>\n",
       "      <td>271.590000</td>\n",
       "      <td>271.455000</td>\n",
       "      <td>270.916667</td>\n",
       "      <td>294.383333</td>\n",
       "      <td>268.693333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-22.785000</td>\n",
       "      <td>-22.785000</td>\n",
       "      <td>-4.213333</td>\n",
       "      <td>91.341667</td>\n",
       "      <td>2.233333</td>\n",
       "      <td>118.871667</td>\n",
       "      <td>950.326667</td>\n",
       "      <td>548.885814</td>\n",
       "      <td>-0.008169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>100.208500</td>\n",
       "      <td>-4.666667</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>3.253333</td>\n",
       "      <td>-1.396667</td>\n",
       "      <td>240.323333</td>\n",
       "      <td>271.436667</td>\n",
       "      <td>270.723333</td>\n",
       "      <td>287.046667</td>\n",
       "      <td>268.766667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-46.735000</td>\n",
       "      <td>-46.735000</td>\n",
       "      <td>-4.025000</td>\n",
       "      <td>91.908333</td>\n",
       "      <td>2.181667</td>\n",
       "      <td>99.051667</td>\n",
       "      <td>949.630000</td>\n",
       "      <td>589.040966</td>\n",
       "      <td>-0.007920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>89.412833</td>\n",
       "      <td>6.941667</td>\n",
       "      <td>2.568333</td>\n",
       "      <td>37.210000</td>\n",
       "      <td>9.313333</td>\n",
       "      <td>229.436667</td>\n",
       "      <td>270.868333</td>\n",
       "      <td>270.236667</td>\n",
       "      <td>288.171667</td>\n",
       "      <td>268.686667</td>\n",
       "      <td>...</td>\n",
       "      <td>9.796667</td>\n",
       "      <td>-58.736667</td>\n",
       "      <td>-48.943333</td>\n",
       "      <td>-4.318333</td>\n",
       "      <td>94.170000</td>\n",
       "      <td>3.183333</td>\n",
       "      <td>115.316667</td>\n",
       "      <td>948.886667</td>\n",
       "      <td>571.239451</td>\n",
       "      <td>0.012708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>79.509000</td>\n",
       "      <td>66.015000</td>\n",
       "      <td>14.283333</td>\n",
       "      <td>39.245000</td>\n",
       "      <td>70.763333</td>\n",
       "      <td>269.898333</td>\n",
       "      <td>272.405000</td>\n",
       "      <td>272.001667</td>\n",
       "      <td>301.948333</td>\n",
       "      <td>269.031667</td>\n",
       "      <td>...</td>\n",
       "      <td>61.178333</td>\n",
       "      <td>-32.058333</td>\n",
       "      <td>29.125000</td>\n",
       "      <td>-3.945000</td>\n",
       "      <td>94.998333</td>\n",
       "      <td>2.643333</td>\n",
       "      <td>128.601667</td>\n",
       "      <td>948.573333</td>\n",
       "      <td>497.007676</td>\n",
       "      <td>0.135205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>70.543833</td>\n",
       "      <td>184.720000</td>\n",
       "      <td>39.195000</td>\n",
       "      <td>60.050000</td>\n",
       "      <td>178.240000</td>\n",
       "      <td>284.471667</td>\n",
       "      <td>275.201667</td>\n",
       "      <td>274.968333</td>\n",
       "      <td>316.086667</td>\n",
       "      <td>271.068333</td>\n",
       "      <td>...</td>\n",
       "      <td>160.998333</td>\n",
       "      <td>-31.616667</td>\n",
       "      <td>129.378333</td>\n",
       "      <td>-2.130000</td>\n",
       "      <td>93.173333</td>\n",
       "      <td>2.938333</td>\n",
       "      <td>140.046667</td>\n",
       "      <td>947.636667</td>\n",
       "      <td>373.110606</td>\n",
       "      <td>0.519083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>63.113167</td>\n",
       "      <td>392.156667</td>\n",
       "      <td>84.996667</td>\n",
       "      <td>374.610000</td>\n",
       "      <td>232.673333</td>\n",
       "      <td>275.468333</td>\n",
       "      <td>278.751667</td>\n",
       "      <td>278.428333</td>\n",
       "      <td>335.985000</td>\n",
       "      <td>273.831667</td>\n",
       "      <td>...</td>\n",
       "      <td>325.068333</td>\n",
       "      <td>-60.511667</td>\n",
       "      <td>264.566667</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>86.426667</td>\n",
       "      <td>2.755000</td>\n",
       "      <td>146.100000</td>\n",
       "      <td>946.630000</td>\n",
       "      <td>213.855930</td>\n",
       "      <td>2.104288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>57.950847</td>\n",
       "      <td>600.271186</td>\n",
       "      <td>128.689831</td>\n",
       "      <td>738.779661</td>\n",
       "      <td>221.118644</td>\n",
       "      <td>273.759322</td>\n",
       "      <td>284.210169</td>\n",
       "      <td>283.540678</td>\n",
       "      <td>361.523729</td>\n",
       "      <td>277.961017</td>\n",
       "      <td>...</td>\n",
       "      <td>484.508475</td>\n",
       "      <td>-87.757627</td>\n",
       "      <td>396.744068</td>\n",
       "      <td>3.664407</td>\n",
       "      <td>71.598305</td>\n",
       "      <td>3.384746</td>\n",
       "      <td>126.791525</td>\n",
       "      <td>945.572881</td>\n",
       "      <td>62.208640</td>\n",
       "      <td>13.667905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             zen    dw_solar    uw_solar    direct_n     diffuse       dw_ir  \\\n",
       "hour                                                                           \n",
       "7.0   145.536667   -5.036111   -0.005556    1.050000   -1.691667  225.650000   \n",
       "8.0   140.396000   -4.896667   -0.068333    1.795000   -1.573333  236.036667   \n",
       "9.0   131.753500   -4.581667   -0.096667    3.088333   -1.426667  247.976667   \n",
       "10.0  121.700500   -4.665000   -0.340000    3.340000   -1.415000  241.571667   \n",
       "11.0  111.024500   -4.480000   -0.051667    4.360000   -1.233333  271.590000   \n",
       "12.0  100.208500   -4.666667   -0.300000    3.253333   -1.396667  240.323333   \n",
       "13.0   89.412833    6.941667    2.568333   37.210000    9.313333  229.436667   \n",
       "14.0   79.509000   66.015000   14.283333   39.245000   70.763333  269.898333   \n",
       "15.0   70.543833  184.720000   39.195000   60.050000  178.240000  284.471667   \n",
       "16.0   63.113167  392.156667   84.996667  374.610000  232.673333  275.468333   \n",
       "17.0   57.950847  600.271186  128.689831  738.779661  221.118644  273.759322   \n",
       "\n",
       "      dw_casetemp  dw_dometemp       uw_ir  uw_casetemp    ...      \\\n",
       "hour                                                       ...       \n",
       "7.0    271.297222   270.644444  290.319444   268.880556    ...       \n",
       "8.0    270.815000   270.221667  288.866667   268.450000    ...       \n",
       "9.0    270.580000   269.986667  288.435000   268.101667    ...       \n",
       "10.0   270.898333   270.323333  287.598333   268.296667    ...       \n",
       "11.0   271.455000   270.916667  294.383333   268.693333    ...       \n",
       "12.0   271.436667   270.723333  287.046667   268.766667    ...       \n",
       "13.0   270.868333   270.236667  288.171667   268.686667    ...       \n",
       "14.0   272.405000   272.001667  301.948333   269.031667    ...       \n",
       "15.0   275.201667   274.968333  316.086667   271.068333    ...       \n",
       "16.0   278.751667   278.428333  335.985000   273.831667    ...       \n",
       "17.0   284.210169   283.540678  361.523729   277.961017    ...       \n",
       "\n",
       "        netsolar      netir    totalnet      temp         rh   windspd  \\\n",
       "hour                                                                     \n",
       "7.0     0.000000 -64.661111  -64.661111 -4.211111  89.900000  3.958333   \n",
       "8.0     0.000000 -52.828333  -52.828333 -4.593333  91.183333  3.235000   \n",
       "9.0     0.000000 -40.468333  -40.468333 -4.898333  92.058333  2.476667   \n",
       "10.0    0.000000 -46.030000  -46.030000 -4.451667  91.293333  2.421667   \n",
       "11.0    0.000000 -22.785000  -22.785000 -4.213333  91.341667  2.233333   \n",
       "12.0    0.000000 -46.735000  -46.735000 -4.025000  91.908333  2.181667   \n",
       "13.0    9.796667 -58.736667  -48.943333 -4.318333  94.170000  3.183333   \n",
       "14.0   61.178333 -32.058333   29.125000 -3.945000  94.998333  2.643333   \n",
       "15.0  160.998333 -31.616667  129.378333 -2.130000  93.173333  2.938333   \n",
       "16.0  325.068333 -60.511667  264.566667  0.220000  86.426667  2.755000   \n",
       "17.0  484.508475 -87.757627  396.744068  3.664407  71.598305  3.384746   \n",
       "\n",
       "         winddir    pressure         ghi         Kt  \n",
       "hour                                                 \n",
       "7.0   150.894444  952.938889   38.153234  -0.171464  \n",
       "8.0   148.135000  952.245000  148.814364  -0.037425  \n",
       "9.0   131.661667  951.585000  314.244805  -0.015046  \n",
       "10.0  145.975000  950.940000  454.239484  -0.010324  \n",
       "11.0  118.871667  950.326667  548.885814  -0.008169  \n",
       "12.0   99.051667  949.630000  589.040966  -0.007920  \n",
       "13.0  115.316667  948.886667  571.239451   0.012708  \n",
       "14.0  128.601667  948.573333  497.007676   0.135205  \n",
       "15.0  140.046667  947.636667  373.110606   0.519083  \n",
       "16.0  146.100000  946.630000  213.855930   2.104288  \n",
       "17.0  126.791525  945.572881   62.208640  13.667905  \n",
       "\n",
       "[11 rows x 23 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test.loc[2].xs(17,level='day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shifting Kt values to make 1 hour ahead forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels_index= []\n",
    "for m in df_new_train.index.levels:\n",
    "    levels_index.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in levels_index[0]:\n",
    "    for j in levels_index[1]:\n",
    "        df_new_train.loc[i].loc[j]['Kt'] = df_new_train.loc[i].loc[j]['Kt'].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_train = df_new_train[~(df_new_train['Kt'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels_index2= []\n",
    "for m in df_new_test.index.levels:\n",
    "    levels_index2.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in levels_index2[0]:\n",
    "    for j in levels_index2[1]:\n",
    "        df_new_test.loc[i].loc[j]['Kt'] = df_new_test.loc[i].loc[j]['Kt'].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_test = df_new_test[~(df_new_test['Kt'].isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <th>18.0</th>\n",
       "      <th>17.0</th>\n",
       "      <td>57.5765</td>\n",
       "      <td>566.591667</td>\n",
       "      <td>116.545</td>\n",
       "      <td>929.813333</td>\n",
       "      <td>71.201667</td>\n",
       "      <td>193.94</td>\n",
       "      <td>271.468333</td>\n",
       "      <td>270.183333</td>\n",
       "      <td>306.486667</td>\n",
       "      <td>266.746667</td>\n",
       "      <td>...</td>\n",
       "      <td>453.206667</td>\n",
       "      <td>-112.551667</td>\n",
       "      <td>340.67</td>\n",
       "      <td>-7.156667</td>\n",
       "      <td>56.335</td>\n",
       "      <td>9.213333</td>\n",
       "      <td>342.223333</td>\n",
       "      <td>957.198333</td>\n",
       "      <td>64.16627</td>\n",
       "      <td>2382.577671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     zen    dw_solar  uw_solar    direct_n    diffuse   dw_ir  \\\n",
       "month day  hour                                                                 \n",
       "2.0   18.0 17.0  57.5765  566.591667   116.545  929.813333  71.201667  193.94   \n",
       "\n",
       "                 dw_casetemp  dw_dometemp       uw_ir  uw_casetemp  \\\n",
       "month day  hour                                                      \n",
       "2.0   18.0 17.0   271.468333   270.183333  306.486667   266.746667   \n",
       "\n",
       "                    ...         netsolar       netir  totalnet      temp  \\\n",
       "month day  hour     ...                                                    \n",
       "2.0   18.0 17.0     ...       453.206667 -112.551667    340.67 -7.156667   \n",
       "\n",
       "                     rh   windspd     winddir    pressure       ghi  \\\n",
       "month day  hour                                                       \n",
       "2.0   18.0 17.0  56.335  9.213333  342.223333  957.198333  64.16627   \n",
       "\n",
       "                          Kt  \n",
       "month day  hour               \n",
       "2.0   18.0 17.0  2382.577671  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test[df_new_test['Kt']==df_new_test['Kt'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize train and test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_norm = (df_new_train - df_new_train.mean()) / (df_new_train.max() - df_new_train.min())\n",
    "test_norm =  (df_new_test - df_new_test.mean()) / (df_new_test.max() - df_new_test.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_norm.reset_index(inplace=True,drop=True)\n",
    "test_norm.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Making train and test sets with train_norm and test_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### finding the gcf (greatest common factor) of train and test dataset's length and chop off the extra rows to make it divisible with the batchsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\s269596\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: fractions.gcd() is deprecated. Use math.gcd() instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fractions import gcd\n",
    "gcd(train_norm.shape[0],test_norm.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def roundup(x):\n",
    "    return int(math.ceil(x / 100.0)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_lim = roundup(train_norm.shape[0])\n",
    "test_lim = roundup(test_norm.shape[0])\n",
    "\n",
    "train_random = train_norm.sample(train_lim-train_norm.shape[0])\n",
    "test_random = test_norm.sample(test_lim-test_norm.shape[0])\n",
    "\n",
    "train_norm = train_norm.append(train_random)\n",
    "test_norm = test_norm.append(test_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = train_norm.drop('Kt',axis=1)\n",
    "y1 = train_norm['Kt']\n",
    "\n",
    "X2 = test_norm.drop('Kt',axis=1)\n",
    "y2 = test_norm['Kt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1_train shape is (9600, 22)\n",
      "y1_train shape is (9600,)\n",
      "X2_test shape is (4500, 22)\n",
      "y2_test shape is (4500,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X1_train shape is {}\".format(X1.shape))\n",
    "print(\"y1_train shape is {}\".format(y1.shape))\n",
    "print(\"X2_test shape is {}\".format(X2.shape))\n",
    "print(\"y2_test shape is {}\".format(y2.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array(X1)\n",
    "y_train  = np.array(y1)\n",
    "X_test = np.array(X2)\n",
    "y_test = np.array(y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### start of RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as dsets\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNModel(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):\n",
    "        super(RNNModel, self).__init__()\n",
    "        #Hidden Dimension\n",
    "        self.hidden_dim = hidden_dim\n",
    "        \n",
    "        # Number of hidden layers\n",
    "        self.layer_dim = layer_dim\n",
    "        \n",
    "        #Building the RNN\n",
    "        self.rnn = nn.RNN(input_dim, hidden_dim, layer_dim, batch_first=True, nonlinearity='relu')\n",
    "        \n",
    "        # Readout layer\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # Initializing the hidden state with zeros\n",
    "        # (layer_dim, batch_size, hidden_dim)\n",
    "        h0 = Variable(torch.zeros(self.layer_dim, x.size(0), self.hidden_dim))\n",
    "        \n",
    "        #One time step (the last one perhaps?)\n",
    "        out, hn = self.rnn(x, h0)\n",
    "        \n",
    "        # Indexing hidden state of the last time step\n",
    "        # out.size() --> ??\n",
    "        #out[:,-1,:] --> is it going to be 100,100\n",
    "        out = self.fc(out[:,-1,:])\n",
    "        # out.size() --> 100,1\n",
    "        return out\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiating Model Class\n",
    "input_dim = 22\n",
    "hidden_dim = 15\n",
    "layer_dim = 1\n",
    "output_dim = 1\n",
    "batch_size = 100\n",
    "\n",
    "model = RNNModel(input_dim, hidden_dim, layer_dim, output_dim)\n",
    "\n",
    "# Instantiating Loss Class\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "# Instantiate Optimizer Class\n",
    "learning_rate = 0.001\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# converting numpy array to torch tensor\n",
    "X_train = torch.from_numpy(X_train)\n",
    "y_train = torch.from_numpy(y_train)\n",
    "X_test = torch.from_numpy(X_test)\n",
    "y_test = torch.from_numpy(y_test)\n",
    "\n",
    "# initializing lists to store losses over epochs:\n",
    "train_loss = []\n",
    "test_loss = []\n",
    "train_iter = []\n",
    "test_iter = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Iteration: 100. Train_MSE: 0.052252646535634995. Test_MSE: 0.0018477092542767708\n",
      "Epoch: 2 Iteration: 200. Train_MSE: 0.027763137593865395. Test_MSE: 0.0011385973112716468\n",
      "Epoch: 3 Iteration: 300. Train_MSE: 0.010347426868975163. Test_MSE: 0.0007095602160455708\n",
      "Epoch: 4 Iteration: 400. Train_MSE: 0.01433667540550232. Test_MSE: 0.0004574013971920029\n",
      "Epoch: 5 Iteration: 500. Train_MSE: 0.006544806994497776. Test_MSE: 0.00032817472761559037\n",
      "Epoch: 6 Iteration: 600. Train_MSE: 0.0038624608423560858. Test_MSE: 0.00027853543742052797\n",
      "Epoch: 7 Iteration: 700. Train_MSE: 0.0030486576724797487. Test_MSE: 0.0002694218783193861\n",
      "Epoch: 8 Iteration: 800. Train_MSE: 0.0014516778755933046. Test_MSE: 0.00027403886381348417\n",
      "Epoch: 9 Iteration: 900. Train_MSE: 0.000693789916113019. Test_MSE: 0.0002794819994955784\n",
      "Epoch: 10 Iteration: 1000. Train_MSE: 0.0009710873709991574. Test_MSE: 0.00028467685795046554\n",
      "Epoch: 11 Iteration: 1100. Train_MSE: 0.002551464829593897. Test_MSE: 0.000289764280421301\n",
      "Epoch: 12 Iteration: 1200. Train_MSE: 0.0021729799918830395. Test_MSE: 0.0002923496874020531\n",
      "Epoch: 13 Iteration: 1300. Train_MSE: 0.002828632015734911. Test_MSE: 0.0002934385256888255\n",
      "Epoch: 14 Iteration: 1400. Train_MSE: 0.002916770288720727. Test_MSE: 0.0002919463606902007\n",
      "Epoch: 15 Iteration: 1500. Train_MSE: 0.004116058815270662. Test_MSE: 0.0002893243869235772\n",
      "Epoch: 16 Iteration: 1600. Train_MSE: 0.0022435630671679974. Test_MSE: 0.00028717147471899146\n",
      "Epoch: 17 Iteration: 1700. Train_MSE: 0.001675860257819295. Test_MSE: 0.0002850486001581176\n",
      "Epoch: 18 Iteration: 1800. Train_MSE: 0.0008878692169673741. Test_MSE: 0.00028200781119253965\n",
      "Epoch: 19 Iteration: 1900. Train_MSE: 0.0005896757356822491. Test_MSE: 0.00027798820580468374\n",
      "Epoch: 21 Iteration: 2000. Train_MSE: 0.0015172665007412434. Test_MSE: 0.0002765289918258732\n",
      "Epoch: 22 Iteration: 2100. Train_MSE: 0.0016446798108518124. Test_MSE: 0.0002760463422939814\n",
      "Epoch: 23 Iteration: 2200. Train_MSE: 0.002192713087424636. Test_MSE: 0.0002735746941896012\n",
      "Epoch: 24 Iteration: 2300. Train_MSE: 0.011509153991937637. Test_MSE: 0.00027134732054734904\n",
      "Epoch: 25 Iteration: 2400. Train_MSE: 0.0033063034061342478. Test_MSE: 0.00026859065107648166\n",
      "Epoch: 26 Iteration: 2500. Train_MSE: 0.0021501132287085056. Test_MSE: 0.0002660306485449907\n",
      "Epoch: 27 Iteration: 2600. Train_MSE: 0.002169898711144924. Test_MSE: 0.0002634143873655541\n",
      "Epoch: 28 Iteration: 2700. Train_MSE: 0.000919307756703347. Test_MSE: 0.00026128083121279284\n",
      "Epoch: 29 Iteration: 2800. Train_MSE: 0.0006170937558636069. Test_MSE: 0.00025755743680411583\n",
      "Epoch: 30 Iteration: 2900. Train_MSE: 0.0006842398433946073. Test_MSE: 0.000255852139473938\n",
      "Epoch: 31 Iteration: 3000. Train_MSE: 0.0016533422749489546. Test_MSE: 0.00025589508930162037\n",
      "Epoch: 32 Iteration: 3100. Train_MSE: 0.0015544539783149958. Test_MSE: 0.00025503362484074576\n",
      "Epoch: 33 Iteration: 3200. Train_MSE: 0.001998084830120206. Test_MSE: 0.00025398647864500426\n",
      "Epoch: 34 Iteration: 3300. Train_MSE: 0.0021379992831498384. Test_MSE: 0.0002515645465095954\n",
      "Epoch: 35 Iteration: 3400. Train_MSE: 0.003292463719844818. Test_MSE: 0.00024876886566799656\n",
      "Epoch: 36 Iteration: 3500. Train_MSE: 0.0015756364446133375. Test_MSE: 0.0002468404771844129\n",
      "Epoch: 37 Iteration: 3600. Train_MSE: 0.0012122447369620204. Test_MSE: 0.00024517936071603874\n",
      "Epoch: 38 Iteration: 3700. Train_MSE: 0.0006627035909332335. Test_MSE: 0.0002427816691463067\n",
      "Epoch: 39 Iteration: 3800. Train_MSE: 0.0004524046089500189. Test_MSE: 0.00023960803553963795\n",
      "Epoch: 41 Iteration: 3900. Train_MSE: 0.001230666064657271. Test_MSE: 0.0002386949336273668\n",
      "Epoch: 42 Iteration: 4000. Train_MSE: 0.0010948796989396214. Test_MSE: 0.000238661807288385\n",
      "Epoch: 43 Iteration: 4100. Train_MSE: 0.001612493651919067. Test_MSE: 0.00023680613446735765\n",
      "Epoch: 44 Iteration: 4200. Train_MSE: 0.010893319733440876. Test_MSE: 0.00023528233562117656\n",
      "Epoch: 45 Iteration: 4300. Train_MSE: 0.0025984360836446285. Test_MSE: 0.0002333422986977923\n",
      "Epoch: 46 Iteration: 4400. Train_MSE: 0.001522150938399136. Test_MSE: 0.0002315848535306118\n",
      "Epoch: 47 Iteration: 4500. Train_MSE: 0.0016750459326431155. Test_MSE: 0.00022978429896490223\n",
      "Epoch: 48 Iteration: 4600. Train_MSE: 0.0006554147112183273. Test_MSE: 0.00022838445768855927\n",
      "Epoch: 49 Iteration: 4700. Train_MSE: 0.0004846024967264384. Test_MSE: 0.000225421201370776\n",
      "Epoch: 50 Iteration: 4800. Train_MSE: 0.0005565507453866303. Test_MSE: 0.00022426712233566083\n",
      "Epoch: 51 Iteration: 4900. Train_MSE: 0.0012100869789719582. Test_MSE: 0.00022459644328127153\n",
      "Epoch: 52 Iteration: 5000. Train_MSE: 0.0011475689243525267. Test_MSE: 0.00022410794515103909\n",
      "Epoch: 53 Iteration: 5100. Train_MSE: 0.0015097246505320072. Test_MSE: 0.00022346219334261387\n",
      "Epoch: 54 Iteration: 5200. Train_MSE: 0.0016323025338351727. Test_MSE: 0.00022165199354844307\n",
      "Epoch: 55 Iteration: 5300. Train_MSE: 0.002734697423875332. Test_MSE: 0.00021953174693327749\n",
      "Epoch: 56 Iteration: 5400. Train_MSE: 0.0011734007857739925. Test_MSE: 0.0002182442712152873\n",
      "Epoch: 57 Iteration: 5500. Train_MSE: 0.0009163666400127113. Test_MSE: 0.00021717465529627763\n",
      "Epoch: 58 Iteration: 5600. Train_MSE: 0.0005158812273293734. Test_MSE: 0.0002153426452556028\n",
      "Epoch: 59 Iteration: 5700. Train_MSE: 0.00036697505856864154. Test_MSE: 0.00021272142625698313\n",
      "Epoch: 61 Iteration: 5800. Train_MSE: 0.0010402621701359749. Test_MSE: 0.00021212363106602017\n",
      "Epoch: 62 Iteration: 5900. Train_MSE: 0.0007671270286664367. Test_MSE: 0.0002123459536390769\n",
      "Epoch: 63 Iteration: 6000. Train_MSE: 0.00123188819270581. Test_MSE: 0.00021086022374968043\n",
      "Epoch: 64 Iteration: 6100. Train_MSE: 0.010464143939316273. Test_MSE: 0.00020975305811266854\n",
      "Epoch: 65 Iteration: 6200. Train_MSE: 0.002152932807803154. Test_MSE: 0.0002083157272943047\n",
      "Epoch: 66 Iteration: 6300. Train_MSE: 0.0011320107150822878. Test_MSE: 0.00020706008880232672\n",
      "Epoch: 67 Iteration: 6400. Train_MSE: 0.001344962976872921. Test_MSE: 0.00020576963783139614\n",
      "Epoch: 68 Iteration: 6500. Train_MSE: 0.0004888481344096363. Test_MSE: 0.00020480971751249657\n",
      "Epoch: 69 Iteration: 6600. Train_MSE: 0.00039494605152867734. Test_MSE: 0.00020227781463922687\n",
      "Epoch: 70 Iteration: 6700. Train_MSE: 0.00046436101547442377. Test_MSE: 0.00020140386855459035\n",
      "Epoch: 71 Iteration: 6800. Train_MSE: 0.000926563807297498. Test_MSE: 0.0002018372629306823\n",
      "Epoch: 72 Iteration: 6900. Train_MSE: 0.000869413313921541. Test_MSE: 0.00020151781450110113\n",
      "Epoch: 73 Iteration: 7000. Train_MSE: 0.0011783509980887175. Test_MSE: 0.0002010754091931756\n",
      "Epoch: 74 Iteration: 7100. Train_MSE: 0.0012883683666586876. Test_MSE: 0.00019963241177374942\n",
      "Epoch: 75 Iteration: 7200. Train_MSE: 0.00234427978284657. Test_MSE: 0.00019794594998360994\n",
      "Epoch: 76 Iteration: 7300. Train_MSE: 0.0009092558757402003. Test_MSE: 0.00019708432928411965\n",
      "Epoch: 77 Iteration: 7400. Train_MSE: 0.000715063710231334. Test_MSE: 0.00019639622266391353\n",
      "Epoch: 78 Iteration: 7500. Train_MSE: 0.00041410644189454615. Test_MSE: 0.0001949278575541972\n",
      "Epoch: 79 Iteration: 7600. Train_MSE: 0.00030812370823696256. Test_MSE: 0.00019264427347622382\n",
      "Epoch: 81 Iteration: 7700. Train_MSE: 0.0009113719570450485. Test_MSE: 0.00019220515045387487\n",
      "Epoch: 82 Iteration: 7800. Train_MSE: 0.0005597157287411392. Test_MSE: 0.00019254338055247537\n",
      "Epoch: 83 Iteration: 7900. Train_MSE: 0.0009684014949016273. Test_MSE: 0.00019128163962015617\n",
      "Epoch: 84 Iteration: 8000. Train_MSE: 0.01015504915267229. Test_MSE: 0.00019043097128802167\n",
      "Epoch: 85 Iteration: 8100. Train_MSE: 0.001853585708886385. Test_MSE: 0.0001893257782386545\n",
      "Epoch: 86 Iteration: 8200. Train_MSE: 0.0008749706903472543. Test_MSE: 0.0001884028286099696\n",
      "Epoch: 87 Iteration: 8300. Train_MSE: 0.0011143276933580637. Test_MSE: 0.00018745719066078453\n",
      "Epoch: 88 Iteration: 8400. Train_MSE: 0.0003788026806432754. Test_MSE: 0.00018678607639995582\n",
      "Epoch: 89 Iteration: 8500. Train_MSE: 0.00032934031332843006. Test_MSE: 0.00018454331540323997\n",
      "Epoch: 90 Iteration: 8600. Train_MSE: 0.00039970423677004874. Test_MSE: 0.00018383464562609468\n",
      "Epoch: 91 Iteration: 8700. Train_MSE: 0.0007436546147800982. Test_MSE: 0.00018428482292929564\n",
      "Epoch: 92 Iteration: 8800. Train_MSE: 0.0006733537302352488. Test_MSE: 0.00018403864369192707\n",
      "Epoch: 93 Iteration: 8900. Train_MSE: 0.0009431242942810059. Test_MSE: 0.00018370338674570145\n",
      "Epoch: 94 Iteration: 9000. Train_MSE: 0.001047885394655168. Test_MSE: 0.00018250682942338233\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 95 Iteration: 9100. Train_MSE: 0.002063416177406907. Test_MSE: 0.00018111734095469615\n",
      "Epoch: 96 Iteration: 9200. Train_MSE: 0.0007277397671714425. Test_MSE: 0.0001805410853939828\n",
      "Epoch: 97 Iteration: 9300. Train_MSE: 0.0005727705429308116. Test_MSE: 0.00018010854222937151\n",
      "Epoch: 98 Iteration: 9400. Train_MSE: 0.000340766622684896. Test_MSE: 0.00017890032059088588\n",
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      "Epoch: 101 Iteration: 9600. Train_MSE: 0.0008220250019803643. Test_MSE: 0.00017650770063344857\n",
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      "Epoch: 105 Iteration: 10000. Train_MSE: 0.0016444697976112366. Test_MSE: 0.00017422514408063067\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 922 Iteration: 87600. Train_MSE: 7.113027095329016e-05. Test_MSE: 7.121763699067834e-05\n",
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     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "seq_dim = 1\n",
    "\n",
    "n_iter =0\n",
    "num_samples = len(X_train)\n",
    "test_samples = len(X_test)\n",
    "batch_size = 100\n",
    "num_epochs = 1000\n",
    "feat_dim = X_train.shape[1]\n",
    "\n",
    "X_train = X_train.type(torch.FloatTensor)\n",
    "y_train = y_train.type(torch.FloatTensor)\n",
    "X_test = X_test.type(torch.FloatTensor)\n",
    "y_test = y_test.type(torch.FloatTensor)\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for i in range(0, int(num_samples/batch_size -1)):\n",
    "        \n",
    "        \n",
    "        features = Variable(X_train[i*batch_size:(i+1)*batch_size, :]).view(-1, seq_dim, feat_dim)\n",
    "        Kt_value = Variable(y_train[i*batch_size:(i+1)*batch_size])\n",
    "        \n",
    "        #print(\"Kt_value={}\".format(Kt_value))\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        outputs = model(features)\n",
    "        #print(\"outputs ={}\".format(outputs))\n",
    "        \n",
    "        loss = criterion(outputs, Kt_value)\n",
    "        \n",
    "        train_loss.append(loss.data[0])\n",
    "        train_iter.append(n_iter)\n",
    "\n",
    "        #print(\"loss = {}\".format(loss))\n",
    "        loss.backward()\n",
    "        \n",
    "        optimizer.step()\n",
    "        \n",
    "        n_iter += 1  \n",
    "            \n",
    "        if n_iter%100 == 0:\n",
    "            for i in range(0,int(test_samples/batch_size -1)):\n",
    "                features = Variable(X_test[i*batch_size:(i+1)*batch_size, :]).view(-1, seq_dim, feat_dim)\n",
    "                Kt_test = Variable(y_test[i*batch_size:(i+1)*batch_size])\n",
    "                \n",
    "                outputs = model(features)\n",
    "                \n",
    "                mse = np.sqrt(np.mean((Kt_test.data.numpy() - outputs.data.numpy().squeeze())**2)/num_samples)\n",
    "                \n",
    "                test_iter.append(n_iter)\n",
    "                test_loss.append(mse)\n",
    "                \n",
    "            print('Epoch: {} Iteration: {}. Train_MSE: {}. Test_MSE: {}'.format(epoch, n_iter, loss.data[0], mse))\n",
    "               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41800\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xfef4b70>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JBTLcJalARYX729Mz3S5BkraEosL9wptvdbsESdoSigr3M1/+WrdLkKQtocqpkFva33zP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\nIMNdkgpkuEtSgf4/4SrmdBHEL1MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(len(test_loss))\n",
    "#plt.plot(test_loss)\n",
    "plt.plot(train_loss,'-')\n",
    "#plt.ylim([0.000,0.99])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xbaa7438>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z8ESbsfcD/6Qx9h8B3+nusCRJi6GbINgJjEXEU8AXgZsj4paI+PnMfB24h/oL/Z8At2fm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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test_loss,'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Demornamization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_denorm = (rmse * (df_new_test['Kt'].max() - df_new_test['Kt'].min()))+ df_new_test['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.80224850350189"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_denorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4440.000000\n",
       "mean        6.944326\n",
       "std        53.755166\n",
       "min        -0.127833\n",
       "25%        -0.002593\n",
       "50%         0.102688\n",
       "75%         1.168942\n",
       "max      2382.577671\n",
       "Name: Kt, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test['Kt'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Saving train and test losses to a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trainLoss = pd.DataFrame(data={'Train Loss':train_loss, 'iteration':train_iter}, columns=['Train Loss','iteration'])\n",
    "df_trainLoss.to_csv('RNN Paper Results/Exp1_SiouxFalls_TrainLoss.csv')\n",
    "df_testLoss = pd.DataFrame(data={'Test Loss':test_loss, 'iteration':test_iter}, columns=['Test Loss','iteration'])\n",
    "df_testLoss.to_csv('RNN Paper Results/Exp1_SiouxFalls_TestLoss.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}