fixed-time-horizon-prediction/Exp_1_RNN_Goodwin_Creek.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": [
    "gwc = Location(34.2487,-89.8925, 'US/Central', 98, 'Goodwin Creek')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2009 = pd.DatetimeIndex(start='2009-01-01', end='2010-01-01', freq='1min',\n",
    "                        tz=gwc.tz)   # 12 months of 2009 - For testing\n",
    "times2010and2011 = pd.DatetimeIndex(start='2010-01-01', end='2012-01-01', freq='1min',\n",
    "                        tz=gwc.tz)   # 24 months of 2010 and 2011 - For training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009 = gwc.get_clearsky(times2009) \n",
    "cs_2010and2011 = gwc.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": [
       "(1029728, 48)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Goodwin_Creek\\\\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": [
       "(513149, 48)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Goodwin_Creek\\\\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": [
       "(1155, 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": [
       "(1029728, 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": [
       "(513149, 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": [
       "(1029728, 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": [
       "(987521, 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": [
       "(496347, 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": [
       "2007"
      ]
     },
     "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": [
       "(987521, 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": [
       "2206"
      ]
     },
     "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": [
       "(987521, 49)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(496347, 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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pGTDwb0qtQAC+OrCKGz/pRPPFDXjm5ycwWzzs1NZvHS/l19CwoykOjYDgmIug\nQ1Ea1zwK864Dsxe+3IgQI4MTh7Dhvj9KrzAcmTkT06MjKEiojzUkBEuU86Y1eb16OzV5WeJqYanj\nPHfB6mJfdvRnz2G4fxjvb6vPqZk6BuyNwGApfShWbkEuy/57n+aLG5KRe6bkCnZl3rkdMe1bE9O5\nHZGTxoE5SEu3aAQtmnNcc5T5FXvfaUeHeG4I1H0aTnsRtXhHrZt5uusU6kc3CEh0TuH1tDvSq9cg\ncsY0JWrKQ0SWHZfRYkOHo8s6rzSWcrKdNSSEM5u2YYmrVejEzwwxM3b9KNYe+Iacghy/nOPtje9g\nZOvRNK7aBGMlo8fottJSoZ5RTU41x9eiqlxR3jdHLUEt5+lTGHYlY26RSGzzhpz+4x9i2rdGZ7Fg\n1kOtZ7xTGi1PGVj3zCEMUX6cYRTD5fX0pvqsY4hucWWTn09Mp7ZOI68KEupzZsMWp/s3XTCx58x/\nDP9+KAcz95dY7gvhulD617+DhS9sJuyId/J4Q1A/ow5ocqo+vhZVpREAcnOp2r0jNVo2pepdt1Gj\nZVO45hosVatiqVOXzFC44n/eKY2rjsJv+Y8EVGm4xUNEVhEMBrKnTufMhi2c2bSNMxu2KCN3gwGM\nRpeRV67MX6BkYbeKa8OWB7bz38MH+HLAl/RueBsR+gify57kWvNZduhjat2TwpmwksuDoWzJZcUl\n4GMKjswhjYpBsdF41d43UsmhHg8FBfD330Td1YfHupxnUUMoUPmEGczwiIxkRrUHyEsK7iY2JbAr\nm2LY+507nZGooHpEDZrXu43rY7qRuy+ZY3e15/47bVnqPgz5zkZBjbEwfBu8+e3FkibWCGO5ly25\nLAjy0vjeULGk1SgfnD3wN3TH8N+ukqvq4fqOyexQWZJHj547GvRlZpNniRrahLwgak5UamwzkuwJ\nk0vdfCm8dkNahySw5+0Ujhlh2G3wYyNbcqEXfnVrCMxrBz83gD8WKvkmGmWDPWDETjCVxvcWzVSl\n4ZEiEVIWi/LAv79EmWE4YNZDq2GoVhrRlaLZM1Cy4IrJRNVtElQd7fyKN+YvZ5jNRL48Bd3ZDABq\nmeCrlUpy4dZ5UKUg1MMOSiJrQsxYGNEbCnKyNFNVoDGZlDL5TiiRZFoB0BSHhnvcPPCOmPVwzTBI\nVqk0qoVW4+Dhe2lw441aaKgH7Iq7eGn18LDKtOg7gv+Gp/JAnX5e54QUGJTZR+LjOs5Wi/SjxBrF\ncVdBoSL6mAJmqhJChADvAgIoAB5GmVQvQXHz7QQel1JahBCTgT6AGRglpfwjUHJpeIe7B96OXWns\nVKk0mlVtztZdXYlecLGVZoUgHYRpAAAgAElEQVSYtpdHv283ittapSrZEyZjCA1n9k1zeeOGP3ji\n6jRWXQEmL0xYe2MsiBXNebDFIF7uPDPoqtleCtjzfpzVbAuG0vjeomrGIYSIEkLcJoR4RggxSghx\nqxAi3MNmtwFIKTsCLwCzbf8mSSk7ozzWfYUQrYGuwHXAAOBtH89FIwC4S3Sz1EkgJzHRK6XRonoi\nv9z+A5Fr1zpdHpTT9kAmznmKsDl61PVI9Vj6xZGq0UjYTbfz4So4aTNhVfXiMhZYC1iSvJjuH3f0\nnHmu4T1GI3m9+jhd5C7KLlhxm8chhDACk4E7gR3AYZRZQQPgGuBz4CUppdMqdUIIg5TSLIQYBHRE\nmVXUlVJahRB9gZ6ABIxSyldt22wHekopT7qSy2wusBoMpa1ZraGaUaOcliA3j3ySNs1/ZcfxHZ73\nYYVHrhnCvNvmYzh4GJo1A4uTin8hISAlNG7sB8F9xGSCo0chPl55oV2cPyNHwuuv+3aMzExl+59+\ngtRUpXZW374wc2bRCBuTCRIT4dChkvto0ACSky9+dMxmGD0aVq+GlBTMdWsz4pp03mtZoDq6DaBq\naFX2j9pPTESM55U11FPs/pCQ4PyeBw8u56yepF0GLADGSymLvOVCCD1wK/Ah4LTwj01pLAX6Af2B\nW6WUdk11HqgCRAOnHTaz/+5ScWRk+G9EWt5JNmopVznHTSYyJ79EWOnw68+yQ6pQGsDglkN4pcPr\nZJzOAUMUMS6m7QW1anM27RQWQ1RAR2GqG07d1JOw77/D2TCl4PNVnHl6vHdy2o4RvvyDoj6LQ4dg\nzhxMOflFTHWxsZUx9ezlPOO7Zy+yswsg2+E8Jr4ET48vNKnNeXkKs2bMpe1Q2FNTnYhn889SY0YN\nBiU+7JXpSnuXVFDs/mA0QobzSgHlfT1jY12XzvFkqrpLSvltcaUBIKW0SCm/BO5wtwMp5SCgGYq/\nI8JhUWXgLJBp+//iv2sEC8US3U78+huP3mhimfzA46YhOgNDrxzOy51nKj+YTMpHubLzBD/duQxi\nbuhQLs5yp9Fjixeid1GC3RenpitHtx1nprrspGlF6moVJNRXyoS4ygdxiOLKTpqGYfAIdnxdjyuP\nqpfTipUlyYvp8tF1Wul2f1PaKLsgwJOpygA8DtQDVksp1zssS5JSJrnZ9kEUs9QrQoho4B9gHzBN\nSvmLEGIe8LPttxnATUBd4Csp5dXuhNZKjpQfZouZHh93YdcZz+1dG0U3Yd3dvyjFCR1H87bCh2oI\nVGtUbxpOWUNC0BULPQYfSnW4OYbjsc5s2laYUFhEztI4500mLEdTGXfoDT7cs9yrfu4huhBVjvNg\neUY9ocmp+vg+lxyZj+LLSAeWCiEmOCy73cO2nwPXCCHWA98Bo1CU0BQhxGYgFPhUSrkN2ABsBj6z\nraMRhJgtZrqv7KROaVRpzMaBFyvaFq+Wq5aycpa7jR5zojTAe6emPj0NvRulAR4ibEozUjUa0Tdu\nxms3vsXuIYe4s2l/9Cqj8e2O8x6fdNYc5xqAZx/HtfbRvxDifeAHIYRJSvk6HoL9pJTZwD1OFnV1\nsm4SkKRGYI3ywa40dmeUzBYvTgghrOv/68XRqcpcEGfYzUHOSnp4jeOInaL2W7fhknXrkXfTzYT9\nsM6n0iF2IhbO9ag0SygjW9SVP0OAo8OimXfTYmZ0eZ3W7yeSeeGcqu12nU7mhpUd+PneTVrI7mWO\npyGHXggRCWCLcuoNjBJC3I/PJdc0Khpmi5ken3RWpTQAHkgcXKR3hppcEFf4JcbdSTgto0YV9Z+4\nC5fs3Yfs6bOcFzNUi8lE2PfrXC62gtJR0GJR5LLJTGJiwBIko8Oi2fnwXmLC1EdPyYzddFraisyz\nx/0mh0bFw9OT/ybwlxDiMSnlj1LKNCHELSimJ5UxGhoVnQkbRrPrdLKqdVvVasUrnV8r8pslrhaW\n+NqE+KA8/BHj7qxGEHPmEFksgsljUUIXxQwBj/4HT8pTB+iysjAunA96ZTxnl1lH4BIkww3h7Hx4\nH2N+HslH/31AgQrr1YGcIzT/oCmDTC146elfMIR6SunSuNRw+5hIKReghNzudfhtN5AIjA+saBrB\nQGZeJh/sWupxvRCdgcGJQ9g6bGtJM4bRSH7nLi63tQI5dw/ANHS4+sghtXhTI8hdmXRXqEwOdJdI\nWUKub74mbM3X7mX2pjS3s3UdfjPoDSz4OYozr0LzE6pExBwCiyrv4pa3r9D8Hpchqho5CSE6oZQH\nuS/wInlGi6oqG8wWM52Wt+NA5j636zlGT7mUMzOT6q2aOw1DLaiTwJnftiojdT+X9dAfPFDYVKo4\nxSOYfMGbrnqu1i0hl23G4VRmvZ7cuwcQummj59LczvJSbu4FQNh3a0vmqqSkYNbD/3rBomtRXbLk\n/qYD+b+b5mnvkp8pbzndRVW5HEoJIcKA+4HhQBqw0P+iaQQrdme4R6VRpTEb7/vDs7M0OprcgQ86\n/XDm9bn1opJwZw7ygYDWCPIwm8meMLmI8itiCktLAZ3OaZivJb4O6HDaPdBqjCRi5fLCv92ZsCIn\njsP43rtF1104v8g69lwV+0jMYIGF30Alq1IAUQ0f7l2OLqQS792tfSIuF9yZqp4AXgdmSSnvlFKu\nKSOZNMoZtRFUJaKnPOB1Ips/CGCNIK8rnjqawjb/Rc5DQ5zL1edWl90DXcWkFDG7mc1EPvcMEe8v\nVnMaTnnzW3hyMxguqFt/2e6ltFnQRjNbXSa4VBxSylkoUVR3CCH+FUKMKzuxNMoUB3u3NxFUxaOn\nPOKLD8EPZCdNwzR0OJaoylixfXorV74YweQjbgtAqsjHyJ423aUitStZGjQoXJYzYCA6Fz4NR0UV\nmTQR4+KFTmczrihukzBY4I3v4PRr6v0eO47voPvKTpryuAzw5BzfKKUciJLV7X23GI3gxolj9/nX\nOqmKoGoR07JE9JRqyrrkgsEAej36rPNK9BLA+fMYF84nMmmi7/st7WzGnSK1LSM5uXBZ1quzPSuq\nUuTMWHUlTdrR+fDvPHhkK6oC8Hdn7KLbRx005XGJoyp1VEp5DFglhOji+C/AsmkEmOK1mbKPH2Fp\nhOeZRvNqLfjhnvVlmwTmTRSRk20D1X3NL+Y3R0Va/Dwdl6lQVKXJmcFFoIzd7/HIX+p2s+fsbhIX\nNybXnOubHJcbpXm2ywlVb74QYgXQBsVJbscKdA+EUBplQLGPqVkP1w4Fs4cnonm1Fvx070YMufno\njx8JfFMjZ5FBvfqQPXYi+tOnVB1fjS+ihENebXSXv/qKu6jMy9jREF61cJ+eck3cBQMAXBBXEJKW\n4jS6zZ4hH/7JR+izSkbzvHmoJdvid/J3bc+nk5GfwdVLBMlD9mtZ5q5w9Ww7i5ALMtSG4/4HtJRS\nqjeaBhAtHLf0OIapqu3g16hKYzbevZkqL052+bDHRoZweudevykUVyGslqgodCaTupfNTXHBEoUK\ny+lldheqW5BQr6QMzhSbyYT+8EGM/zeTiFWfudhXffJ69CwSbWWnMIQ4M5OoiWMJ/W0D+qPpF5XT\npCmETX2esRnLWCiyVfX4aFZN8Mu9m4NKeQTLO+8plLu85XQXjqtWcSwBXpFSSj/K5TOa4vADto+p\nNe2IKqURQgjykcPEvzTN+cM+dDjo9RjXrcV65Ih/PrgqqskWruqhiq7afAtv8jL8hsrzdCmD2Uzk\nC+MJ/+hDdA4zCWdvvTUkhDPrtxCxdFFhWLAlrpaiGKbOKNFEyuksymQiK3UvPbYM4sD5Ax5Pr3CW\nGiTKIyjeeRWDmdj6cUGrONSVx4QfgWQhxBEhxAEhxEEhhOcnRiN4CQ0lv2q06ravDyQOJrrA4NJX\nEP7RcuWDe+jQxV4WC+aWyvnsjb0+fPkypaueC4r7ImjQoKQvwp++EC/s1mrP05UMkUkTMS6cjz4r\nq9D57+qNt9Sui6VOXbKTppHXoyeWuFrojx8j7Pt1yr0qVr/LaRCD0UhUs6vZeP+fNKva3KPcmsO8\nJF6HcgcZahXHBBR/RmfgBqCb7b8aFZSwpPFc32GnKqXRIqYlr7SZgmHbVpcPu86JTRxK53z2pkyH\nPus8URPHul6hWAQTycklQoH98jL70J9c7Xk6lcFkIuybrzzLZcPuRI9MmojxvXcJOZruXNHbFd/p\nUy4VoEFv4JcBm6gWWs3jcTWHeVF8DuUOEtQqjlPABinlYcd/gRRMI4CYTIzNWMYOFUqjebXmbN7Z\nkZpdO1C1/+3gJGTTHao+uK5G526iiJwR+tsGz0rKTSiwu5fZGmHEUr2GRxmcdhH0NPNSeZ7OPij6\n48fQp6e52EKJYLHqi0V7uZtZrfmGyHHPEtOpLTHXtaJGy6bEXNeKmM5tnSpAg97AP4OlKuVhd5hr\nMw8CmphaFqhVHHuA34UQU4UQL9j/BVIwjcCRlbqPRSLb43rNq7VgS3JXohfMVz6EVqvLpDJrVJTT\n392OnlSMzoubmCzGSJfy6tPTSjfFd/My67POEznDQ4htKUxdRc7TxTrOPiiWuFpYatdxuV9LrXjO\n/PxbkRwRtzOr1CPKTCQ1RTF5FRTYqvOmuFSA4YZwkofsp0WNFi7lsJORn0G3le015UE5VVLwE2oV\nxxFgDXABPJpRNYIYs8VMjy0Pqgu7vW0dkWvXOl1uDQkp8rDnDrjf6Xp5vXoDOJ1RqBqdFzMxnd7y\nt9K3whk6HRHz3i5VNnj22IlYIp0rp7A137j9+JfK1OV4nr/9ienhYRQk1AdPHxSjkbw+t7ncrS7r\nPBEfLoVQW/6uyQS5OS6VjaeX2pUCNOgN/DPiH5pX86w89mRILcMcyq2Sgj9wK6EQYiWKwpgnpdQ6\nt1RwCgsXnj/odr1GVRrz070bCT18xLXT1mrl7CerMbdpq4yCzeaLUVUpKUoI5823gMVCTOd2JUNb\n8/Ndj86/+bpEgUDH4oeuiiXqCgqUMNNKBp8joPSnT6HLdj4b06cecduN0C8FFY1GLE2bkT19Ftkm\nE7HmLM4YotyaLrKTpoHFQvhHy9HZsuMLZc7KUq6VxQJ6fWGYsTUiwrMsTtCnpbi8Bga9gZ/u3Ui3\njzqw5+xut/vZnbGL7is7BVW0Vbnh58KeZYGnGcezKKVG3hFC/GwzVXUUQqidqWiUI6YLJg6eO4Dp\ngsmnwoVuHXh1Ei4qDXBaIgO9HuPC+U5nFG5H52kpRI17xuXMITtpGqYhQ5XoKCeUGBV7EeFkiY4G\nF/slJERZ7gp/262NRmjcWF3pkpdf4/TWf7DUcq6c7FFv9nuhd6EcPWE1RrpVgN44zO3K47KfeVRA\nPNWqSpVSviulvAulXtUPwO3Ar7bZiEYQYraYmbRxHJ1XtKP9h63ptLwtie818b5woS8fQofRkzt7\nvyU62qVS0gERK5e7digbDOQMf8JliYxCs5APEU76zExwVRywwKIsd0N52q31mZnoTzivSOgq6i0Q\neOMw352xix4fd7moPCpg+Y3LEdVzRCmlGfjF9g8hhGuPnLK8ErAYaACEAVOBVOArLnYUnCulXCmE\nmAz0AczAKCnlH96chEZRkjZNZMGOi6ac1OySfR2c4axwocd2qi7waO/PzCSvVx+3jY2c9bSwo8Ys\n5KxlbOHf899xekxLXC0sCQmEpJS8ZpaEBM/mJn+VIPEBT+VG1HKhaTMMe/c49XfockxuzXV27A7z\nbivbsyfDfd7wrjM7GfPzSBb8HFUhy29cjvhscpJSuo4BVHgAOC2l7Az0At4CWgOzpZTdbP9WCiFa\nA12B64ABwNu+yqShmKfWHvC+OqrLwoU+OvDUxKlnJ00jZ8BAl1FEbh3KnmZDuJ/xuBzRGo3k9XLe\nCyOvVx/1SsDbCsD+GGm7uSYuo96iKlNQN6HI7Ojs2p+w1E1wvr4XOQYGvYFf7t2symH+4e4PeCp9\nLtY0L8KYNcqNQPoqPgGed/jbjFIosY8QYr0QYpEQojLQCVgnpbRKKY8ABiFEbADluqQ5bjpGWpZ3\n1VE9loTwpZ2rGjOXwaCUCvfxI+XOLORpxsPRoz7t1+/4YE5zik3xZI+d6FR2V1FvuQMf4MzGrUUH\nBdHRLhtJeeursTvMPSoPndKuts0wpeCmndJWL9YIDKpqVQEIIUKllPlCiCaAANZKKUs2RS65XWXg\nS+BdFJPVDinlNiHERKAacBZlZjLXtv56YIiU0mXPUrO5wGowuHBgXuaYLphIfDuRQ+cOqVq/WUwz\nkh9Pdq40zGYYPRpWr4YjR6BePejbF2bOVGc+cNw+JQUSEpxvP2oUzJlTcvuRI+H11z0fx2RSFEF8\nfJGCfyQmwqFDJddv0ACSkz1/AJ3t19+U9txd3aMXX4STJxXZQ0PhmWdgyRI4b/N1REfDoEEwe7bz\ne6n23qnEbDHT4u0W7D2z1+O6LY/C9neVcu6EhICUSpCARllT6iKHLwBXAOOA34FdQLKUcqSH7RKA\nL4B3pJSLhRBVpZRnbctaAG8Cq4FwKeUM2+/bgZuklKdc7VcrcugEh1nBpL+mFPFxuMJeuNBVFz+X\nBf+GDCVn+BNOZyBO5fQ0YymsSOvEj1IK+7a7goXG+e+U/X0vfh38UOhOTVHGUhVu9HTv7Mujo4mt\nZOGkm9DhzLxMxOL6FFg9F9m+6ihsexd0cbU589NGUJG1r5ZL5p0P/PFLXeSwLzAEGAgsk1L2ADq6\n20AIEQesA8ZJKe3Nj78TQrSz/f+NwDbgN+BmIYReCFEP0LtTGhrFcGLqeO1bC82reS4+57b1q5ss\n6Iil73lnVvFk71frR/HSDxA0mbkuzFH69LTS1cZSk6le2sKNru6d/ZwcSpPQpInL0iQA0WHRPNhi\nkPvj2dgRD0/eDPqj6cT07Oqb+U4jYKgdzumllDlCiFuBSbY8Dte1HxQmoJiinhdC2H0dzwCvCyHy\ngWPAo1LKTCHEBmAziiJ73OuzuIwpHjlkTTvC6LT5yHjcpgE3q9rMbetXdz4Ce9kRxyglv5Qcd5UI\n5WuPjHKMcHLEZXSX+YLryLCace5zRlCfqe51E6viOJl1FD8nCp+JFLfPxMudZ/Ln8a3sPPWv+2MC\n77aB6T9CtId9apQ9ak1VM4FbABNwPfArsElKOS6w4jlHM1XZKGbqMOuh7VA8dmiLCDHy7+A9RBcY\nXH9QveiF4dgMKRDXMxA9MsrsvnswR7lqqmQFLAn1CLmzHyfHTXauINU0qAL1TayK46b7YswNHdw+\nG+72bbaYGb9hDMuSl1CAe7NVsxOQPE/xd3iUVyUV+p0v2+OX2lQ1DegNtLc5xJ8EnAfCa5QZ+vQ0\n9A4v75O3eFYaAAPEfcS/NM19JI8XlWkLR7cmE+zf798omAD2C3c8RqCSzjzNCnKGDsc0dDiWqMpF\nwpKVwoJHYM4c1yGpaiLXSpHN7qqWWNSksR77h7gztRn0Bl7r+n/IRw6TEOk8os7OnppKd0qzvmL0\nqbhccKs4hBAJNr/DBpRBUB3b35nAd2Ugn4YbIhbOLbRGZYbCwtaet2lZ40pe/8GgqvR3ER+BXu+y\nxIclvg4R894mpnM7aNbM95BSJwS04Y2/QmHd4DGfpU5d0OvRF6sx5Yg7BanGj+OTr8eNwg7dsMFt\nRd7Cc/OQ7xEdFs2v7Vdg8HC5d8YryuOCMUJVaXuNwONpxjEFxSzVFFhv+/9fgW8B52VTNcoGk4mw\n79cBymis3VDcVrzVoePBFg+zrvdalxVvS3ygHJ3Wm/8i56EhTrezVK2ilOJOOQJ+Tt4KZMMbn3pn\neEspEhXtuFWQagILfEjidKuwj6WT37GzW5nV5ntE1W3CI9KTu1RRHm0HZhE24yWP62oEHk+1qoZI\nKRsCL0gpGzr8a4YSmqtRTji+2CNvBlnT/fp9m/RjVrc5hJ485f0I3ua0zp42veTIdchQ9OfOOt2f\nX0xJgWp4UxYmMBu+JiraUaUg1WSqe5HN7qmpVdaUl5VzqltPaRYVEgI6HQUJ9byLXDMamVHtAa5y\nnY9ZyI54GJuxTEsIDALUOsc3SynbO/ytB/6RUl4ZSOFcoTnHKXSMZh8/QvyzYApzva1BZ2D3kENK\n6K0ah6qKxDi7U11//Bgx7Vujs5TMBbWGhHBm07bSl4wOQJ5HbOYJrM2aBVbu4jjLiVARhFCaIIDS\n4CooARxk8iKPwyVmM2FJ4+kQupDkGu6d5SFm2HvLBqKaXe3dMRyosO982R/fN+e4EOInIYQFuE4I\nYbH/A3IB95XLNAKL0Uh2r160G+peaQDc32JQ6SreOjm2feRaJr2T/dHwprgDPD6+7Hs+Oxvxu7kf\nlqjKMHJkuXWEyx470WXTrMJZmf2cqtdQVwLeGQYDeVNf4/tnDtM80n2GeIEBev7xsFaKvZzxZKrq\nLqXUA29KKfUO/0KllP3LSEYNJ5gtZtonbvBoomqZH+O04q3fEuPKsneyO1OLq8goVw7w0NCg6flc\n4n7USSBnwEBO//2fUnaknKrD6k+fQufCLBSICCdDVDQ/PbiVRtFN3K63L3MfEzdqlvLyRK2pKhyl\nwm0UtkhBoKGUslz6jmumKhj76yiWJC92swWI4/DPN/XIXP+H+3IRpU2MczAlhaSnUuCnkiGqyMwk\nauJYQn/bgD49rURyoEtzy8iRnBw3OSClTnzGyf0o1+fTS7Omv2RVU5okPrI2mwf+hbGS989tRX3n\ny+H4Lk1Vat+O5ShZ4E1QQnNvADaWXjQNX8jMy+SDXUvdrhOZB38sgtCCNNfZwf5qWemQoa2m1alf\nsCmr8OUfoM/KKvy5SDb7hMmuI5ZWr4anxwdFZnkhwdZC1DabdKZ4Azkrs5cmcTcwOp59jG3Ht9Im\nrq1PykOjdKhNALwK6I5SsHAGSp2qBgGSScMNZouZnp9081gobtDfEJ0fQHu9M9S2OvUD9lBaR6Xh\nSNjaNegPH3IdsZSSctHU4inaqDy60gVJJ7wyrfflcM4vd55Ji+qJblfv/+XtdFx+LZM2jtN8HmWM\nWsVxQkppBXYDV0kpD6D0ItcoQ8wWMz0+6cyBTJcV5wFofgLm2NIzy9peXya4CaW1o09PBawuHeCo\n6eZXBgmCno5JYmL5FvhzFpgwYbJSscBfSs3Jda7ywkR+6PczLWJaOt3EggUrVtKyU1mwYy4TNoz2\njywaqlBrqtophHgTmAt8KISojdsSehqBYNLGsew6nex2nZAC+H2xDl2dephUtHitiKjOfajf0HV7\n2r59PSpUd61nAxUeW6J44KFDwVHgz2jEklDPt2KTHnB3nX94cT0TN47j24PfcDz7GBactwBakrwY\nc0EBM7r9n+uGZBp+Q+2MYwTwsZRyFzAZiEcpsa5RRqhtCftAi4cw/7rdt5DVCoIlrhaWePdFufJ6\n9FRCll2YWpg50/1ByjBBsFyP6QUBybT3cM6G3Hymd5nF5oF/saz3Sre7WrZ7KT0+6ayZrcoAT3kc\nPwshxgOtpJQbAKSUX0opR0opd5aJhBoAHD1/lGMm9+GPLWJa8soNr3vX69pflKVN3mgkv3MXl4ut\nQM7Q4cofPuaABLRGVhAdsxBP9y9ASk3tORsrGalT2X1BRIBdp5PpvrKTpjwCjKcZxyuAEZglhEgR\nQnwkhHjE1tlPowyJrxxP3SjXl715tRb8cM/6sp+ml5NNPmvqDJfJaZaE+opvw/Fj6EW5DQhsjSxX\nH+kySaYsjko/TqCUmjfnXD+6AUaD5/u3O2MXPT5ohzkr0yeZNDzjKQFwnZTyeSllN6AxSqvXWOBr\nIcTuMpBPw4axkpFejZwnrLWonshP924sF9tucfOF3Sbv10KBzoiOJnfgg04X5d18C5EvTymdUzsQ\niY2ePtJlmUxpQ635KWBKzYtzNlYyco8YoGq3u7L38eKo+kROGKN1DgwAIUlJSR5XEkIIYABwP3AT\nsA9Y/uSTT24NqHQuMJnyk/y1r8jIMEymfH/tLmBERoZxbUwHzudncsJ0kqz889SKjOcecS9Leq0o\nH4egyUTlCWPRZ54rsUh/4iQ5Dw6GSpUCdvgLXW5Adz4T/YmT6LKzsNStR+6AgWC1Ynx3HvrMc+is\nVvSZ56i07U905zO50P0mQN19d7X/7KRpoFfrHrxI5AvjlRBiN3IVP6aufn1M99zn8zHd4s39q1QJ\nfcphKm37s8S6uQMGcuHm3j6/S95c5xvq9eC7Q2s4YTrhcb/bY62MeWcb4WcvXl+oWO98ecoZGRk2\nxdUyt5njQoj5KL3BjwPf2/79LqX03G0+gFwumeOmCyaOm44RZ6xF/dpxhXI6/l6eyU/6gwcCX+BQ\nDY4Z16jreOfVffdHhr23xSVtx6zesiknswPzunl9/8xmIl8YT/hHy9FlKdfOGhVF7oD7yX7xFWLj\nq5XuXVJ5nb3tIPjvB5Gc2y7B1oo3mN95R8pbztJ0ALwDOAh8ACyVUv5W3krjcsBsMTNu/bO0X96a\n9h+2pvOKdoz6dlShw89YyUjDKo3KPWO2XGzyznDwXwTEFu+lf8Tpsb2Vyw/H9ITX989gKNJ0Sgfo\ns7IwLpzvH9OkynMu0kEwqp7bdffUhDYDswmfqOV5+BNPPo44lL4b1YD3hBC7hBBvCyH6CiEql4mE\nlxlmi5keH3fhvZ3vcjQ7HQsWUrKOMGfLHJI2Bdhv4C0B7JXha4RW0Ciz4scORrm8vX9BFi4cHRbN\nz/duIkTnvDOlnZ3x0D72E81Z7kc8GsallH8BfwGvCCGigHtQepA3BVwW9BZCVAIWo5QmCQOmAruA\nJSgRkzuBx6WUFiHEZKAPYAZGSSn/8P2UKi7mrEx6fN6NXVnOM8PXHlzDhOsml/tMwxF7gqG9UKAu\nIQFTz16+JR4WFkssRYJZOdVX8kiQylX8/hUp9FgMT7Mmjh6FaA/lmv2MmrpWAMk1Cuj+aRd+eqik\nj0bDezy+jUKIK1BqU3UE2gNZKG1jPc39HgBOSykfFEJUB7YDfwOTpJS/CCHmAX2FEIeBrsB1QALw\nGdDWx/OpmNg+mKMz3iu2+1MAACAASURBVGdXYrbL1dLPp3LcdIyGVYKoEJ5DgUO7TT7bR5u8vzK1\ns5OmgcVSwhaPxaJE2JRTUqQ3H+kyo9j9c+dfsM+anPlprBFGdLGxkBdogUvycueZ/JH+O7sydrld\nb7fpAD3faML2yWllJNmli6cEwNPA58A1wNdABynltVLKMVLKbz3s+xPgeYe/zUAblJ7loCifHkAn\nYJ2U0iqlPAIYhBCx3p9KxSUyaSLmJXN5v4lrpQFQMzKOOGP5mFo8Utw+7a25yZ0Z5KvVcPqUelkC\nbYv3FX80pAoUKlvPujJt6bPOw/jx5VKY0aA38MO9G2lerYXHdXeGnuHJF9qVgVSXNp6iqmpLKdPd\n7UAIES6lzHWzvDLwJfAuMFNKWdv2e3dgCErhxNNSyrm239cDQ6SULiv5mc0FVoPBvV2zwmAyQYsW\nDL76MEtbu1/1sWsf4+0+b5eNXL5iNsPo0UrZ8iNHoF49pS7UzJnuP5D790OzZsqswBl16kD//p73\nA4XXlMOHSy5r0ACSky+9wo9lRWYm1K0L551E+4SEgNWq/p67w2RSTF/x8arvldli5pp5rdh50nM9\ntzNPHyW6WpAOwoIHn/txvCmE+Bb4SEpZ5EmxKYSHUGYN/ZxtbMsw/wJ4R0q5XAgxw2FxZeAskGn7\n/+K/uyQjw38jmvIOedMfPIDh2GE+vc/9eledNjCpxYSgDyOMnfY8zJlz8YdDh2DOHEw5+e7NTYYo\nYlyYQQBIS1O3H2xhpikpTp96a0oKZ3bupXq7q4P+WkL5P5/F0R88SEx2tvMvSoHNRKn2njujlH6u\ndXdtoPvKTux2Y7YqCIFr3m3HxkH/BHVBxPK+97GxruOfPIXj3o1SPn2rEOJ3IcSnQogVQojfURzm\nOts6JRBCxAHrgHFSSrvnarsQopvt/3uhNIX6DbhZCKEXQtQD9FJKL+wSFYxiJpz82Bq0Gx5Cdrjr\nTcRx2DbXQujJIL8sJhOsWuV0kceoGzdmEK/2Q5BGMF0iuLu2xfEl0qq0hRQNegM/3buRRpUbul3v\nQE6KVtOqFHgKx7VIKd+WUjYHhgMfAZ8Cw6WUTaWUb0kpXV35CShhvM8LIX4RQvwCTAKmCCE2oyik\nT6WU21AUyGYUx/jj/jixoMNJuYmwSWPo/lUP9sS4diZH5Sid/Ax16wX9B09//BikpDhfpiKHorCS\nbXxtXBlQVeVilEPpjssGlQoefMib8VO4r0FvYN09GwixuO/8sDtjl6Y8fERVz/FgoyJmjhfvfW3W\nQ5thsCPe/XaDt8N7q1F6ZE98yf3K/uoh7ismE7HdrldMFcVwmh3titOniOneiZCjJd1rqvfj0Afd\nWT/x8jYDqCUo5XS8tmkpgA6dpeTgx6t7jv8rEYxb/TDvpX3mcb0WMS0DWyDUx/eyvO99aTLHNfyB\nk5HUk7d4VhpReTB7R13P/SPKo1OdM4xGxSnqBK9G+tVrkHdbKffjrwimIGnhGlTYr+3Pv5F79wCs\nxginq3k7u/O3iXHajXO46pTnIJpdZ3YyfsMYr/atimB5LwOApjjKgOKJU5mhsNBDBBXAPU3vwfzz\nnx4/eAFpsOMrM2f6pUe133pd+1q64xJ+6f1F5IxpRKxcXqLvuyWqss/3yp8mRkNUNBvyh9LyqOd1\nlyUvITPPv5nlQfVe+hnVpiohREfgSpRs8OullOsDKZg7KpypyqHAnVkPLf4Hez0k2BafPruU09vi\neU6296d5q1BOf+03QOY3T/e9uGmxUJxHR5RpC9fyNle4xN1zVyeBM79t9b30vBsToy/7C0saT4fQ\nhSTXcJ+Y2qhKYzbet9U/JqvSvpeU/70vtalKCDESpWTIMyjhsvOFEFrVMLXYRlJmPVwzzLPSaBDd\nkIU3LyW/wHNJZZ+L+gV6RO2vIn1lUOyvBEFWkykYcfvcHUv3vVuhv5MkDQbypr7G988cplG4+/5z\nB87tp9OHbcjas6PU97hcuzmWAWpNVYOBm4FsKeVplJIgQwIl1KXIudHjuGa4UnDNExcsF+i0oi2d\nV7Rj0sZxbqM+fLULX8rT6NJyqb/0/iDgIc/+HDCYzVR5dRp/zrdg8DAuOnD+IM2+7cTzYxoTNsn3\nJlCXeki4WsVRIKV0HP7mgodC+BqFmC1muq++kZ1x6tZPy0otrIq7YMdc91VxfbELqx1RX6aO4Uv9\npfcLFSjk2T5IqnYwjaF/eV7fbID5idl0ZT5hSeN9O2gFuj6+oFZx/CqEmAlECiHuQCkh8mPgxLp0\nMFvM9PioE7uz93tc15Vtde3BNZguuP54e+tI9jiiTkslctyzignrcnQMX+Ivvb8o/tzRoIFvTvFA\nUmyQ9Oa3cJUKZznA37VhbMYynwdOfgvwCEJUOceFEHpgGEp5kRAUpTHfTfJfQKkwznGzmfGzOrGo\nsvuqnQBNT8D+mnoslIxhDyEE+aQkusCDc0StI9md465uPSzR0VTatbPkZiocw+Xt0FOLRzn97aQN\nlJzBQBl0K/QVZ7khdl+jGrNxiBn23rKBqGZX+y7EZZzHYQQMUsq7gSeBOJTMbw03nJ8yiiVGz0qj\n5VHYsrYudS84f6jqmo3EV1bxlKu1C7sZUVuiKztVGnCZOYaDuZJtsFEeAQwqcWZ2NFhg+7uoCtMt\nMEDXTfeVLlQ3iK+Pr6hVHMuB2rb/P2/b7oOASHQJYLaYGfPjEzSt+T4FHvKPWh5VHuKwzj244z/n\nE6m+u8F4wb8yOp1GDxmKPuOMy20uS8fwJfjSX1a4GCTZlYcas1WKKRWx+P/bO/P4KMr78b93NyFk\nswkQjDlICHjweGC9quKB4oUGbPHbQ61HPYpa9ddK/VovbIEWWrHoV2w9Wg/s19aq9a6CoPVErDff\nCtjHg2sDAZUrZBeSbHZ/f8xustmdmZ3dnd2dJM/79coLdmd25jPPPDPP83zORq57fZpKTxLFqqrq\n/6SUByd8t1xKeUjOJDPByaqqWOnXVVv1Z+3x7PsVfPxcA+4hQ3Fv+Zrw5hZ+fio8K8A/BBp2wBQJ\nv/unm+L/fMpXmVRXS7VMjtvu3ryJynGH4jLoE121dWx9+0PTl2ihl9dWUXLaj2NlTVA7urze7rTw\nITf8uAke+CYmScR7GLvHQSz53ut5yapb6Pa0Q1UVEUIcFPsghNgPsHkO3PcJhUOc8vfxlgYNTwgW\nf+dluiacTPGKj/G0tFAchjsWw8p7QP5B+/eOxeCubdDqEqQljMU4jbgZdbiignCN8XnaT5/c92fe\nA9RTbECToHakuVlbbdfV4wnD/S/Ajz6wdqgVX3+cnJ5kAPYpqwPHtcBLQoj3hRDvA4uB/86dWH2T\nm968llVbzIvIxLj0Q6i5+0+UPpKs8fN2wt7betRTmXjypBWnERtkJk7ArZNYEKBz7EEE5uQvYtp2\nEgZSDjxwYHmKKXomSRUVBGbPZfvfngC39gq8d6F1b6uHVy5gc2DzgE5Lk07KkUFoKUc6ASmlLEB1\nYQ1Hqaqiqp6WcheHPH4YXRbCW77RAu894mPQzjbDfSJAuLaO9m9NITBzDlW1w6zLmWa6A6P0Gt0y\nNE0iMPtWS4bhQi+vjXBKCpF0cVR7plB7piVrATM5x6fFiX9O0vK2wsNFOwX3/s8qihIcIe3qU4W+\n93akHGkE5gBXAtOAe4QQD5r/qp8TnW1UHH8EN888hIP/drDlQeOD+6AoxdAXrq1j6ytLM/LkcW/c\ngNugkl6SgdskGBC3m/ZTTrM8aDgWlUIkO+ycWTtplu710j7h5O6P6RjMu+jigfJVHH6pNuDEMxD6\nlFVV1eNopqM3gdfj/gYsZTOnM+j+eziyyc+9R0E4hfeUpwsufw/eXTSSzrPOxZWiY7V/awoM3yMj\n2Urvv8fQzpcY+WwWDOgKh/E+vKDPpyFRKUQsYqCrtzM9jWNS3UQHsJKXXuxVNKworE3sfvQeGFYT\ni+PftdoqJX7wGAh9yuo0slhKqZIaxggG8bz4vOVlrRs3y89cxohDvqL1gAOh1Mugt5bqqpIiHg+7\nfnhJ5tGlwSAlLy0x3Nx+ysReqoGYn7thrW+0GVTgphnZqxQKpJ4wu0aVQgTzOt8dHaarNcN+oXev\nU6z8bOljFokNYHoURQ3muOGBw1Mfa0V08PjoPu236fapYGeQzcFNVHtr8Bb3DecTqyuOpUKIb0Xt\nHAOecEszhzf5LQ0aAJe07cf+Z57F0O9PoXLiBMp+M4v205p099114cUE5t6WsWrIbHYdAXZNvbz3\nlxZKgbo3+HGvW5ORPID2YipkChOVQsQUs1VA2qs1E1WUY1Z+ZurZOH6/5kDLBvMVtXSrraz2qVA4\nxM1Lr2f8347k6L8eZimpqVOwOnB8D3gW2C2ECEf/nJVbIE+0trdyzLKzWGlx0BjbUck9t69KeigB\n/Tw2s2/NSj7TBH0NjbrbAjPnELxkqpZvSPeHYYaed1ZmL/tQiKETT8C74D48LRsLpp7oE3mVrGKn\n+2eKVUC4oiKthI9mg5Bjkke2tJhOrrpq6whedgWRI4/hA4s2D9DUVlddc6DlPjVz2XT+9O978Let\nt57U1CFYGjiklHVSSnfCX+qajP2IUFsr1/3jEsQDjazeudrSbw4YegDv/6UsyesCoGTxiwRummF/\nSotMZtdFRQRuuZ1dP9TPlO8CPM3+jF72ZTdfR/GKj3W35dWImOjLv3Jl30shkgPDcspVQGur9f6U\nygkBjI916qnaiiMf/aG21ngAizml3DSDkpcWd9s8fvyOFnuViuf23MInOz5NSkoa7AyyZsdqgq1f\n416j/btotX5bpUpq6gQsPTVCiCrgfMBH9D0CjJZS/tDCb48C5kopJwghDgP+AXwW3XyPlPIxIcQM\nYDIQAqZJKd9N/1JyRLSC2EklD7BiuLUH1NMFFwUPYM4pCxjUfLTuPrGleXj0XppvuY3EZjy6CfrM\nfjdnLhQXUfLC87g3+HUN7GnpolOoBOLbIG/EpxAJOMTN1SKJevn41Wum7p9W7D+W+9Pq1SlVUUnH\nqh1BeOgQSl5aTOlDD/a2r+RqUI9OrvRsHDGnFPfnn3V7JhaF4Z5FMPefcMRU+NQkgcOm4CZOeGwc\new6u5viGE5h93K3c/sFcFq1+ng07/Yxs8/DtlV1c7q9lw/dadKPVN7Y1szm4iUYs1mEoAFbvzGOA\nHxgHPAOcAbyX6kdCiOuAC4BA9KvDgNullLfF7XMYcAJwFNAAPIlWKMoRtM+6hjFDHmJ7mbX9PZ3Q\n/D9QE1xFcNt9hTHKRmfXgZtmpGeMLioicNMM2iecxNDzz4Zw8lIpnZe9e/Mm3JuM9dbhPauVYdoq\nuTIsm71E41YUpv0palznxRd0+wzE9feEvll67114F9zXvZ8dg6EVUg2Gep6JFR2w8l5rsR5f7t7M\nE589zhOfPd7zpQvWlndx5zgIuVoYuR3WDkv+bZ2vnmqvs58LqzaOOinlhWirhaeA44FDLfzuC+A7\ncZ8PByYLId4QQjwghCgHjgOWSCkjUsr1QFF0hVNQWttb+fGiC2motj5oAFz5PtREV5klL71E+ymn\n6e6XU6NsTAcO1hP0xalBhp53Frj0HXrTGfDC1TWE643LdfaLFCZ5IpeGZct1IwwSPnavhNavN3QD\nT+rvXi/h6hpKXl6su7+pGtMOG49Z9mMTz8R0Yj3MWLgvTPpUf1vT6EmO966yuuLYFv1XAgdLKd8R\nQqT8kZTySSHEqLiv3gXul1J+IISYDswAtgNb4vbZCQwBvjI67rBhXoqK7DOxVFWVd/8/FA5xxTOX\n8tDHDxOiy/LQWhSCK96H21/q+c6zsRnvT6+ESCe8+io0N0NDA0yZgnfePLxpLsXj5dQlFIJrr4Vn\nn4X162HkSJgyBebNS73snzYN4medXfq+D57vnElVo/kSukfOcvjOf8H8+ck7HXII3j/dnXYb2EnK\n9nQIVVXlULavdj/Xrk3a7mpoYPjYfbMbhP94t/YibmnBU1uL1+vF0tGCQVi80Hh7YyOceaZ+f//i\nSzAYDD0bm6kKtUFVXF/Lpn8n0KuPJvZnE7mgJ9bj6tPguf1ggw8ibiwlSYzhHwI/fQeKgWdPqscf\nbKFhSANTxBTmTZzXnUTRqX3Uamu/IoT4O1rOqiVR9dKuDM73tJRye+z/wO/RvLXiW6ccbTAxZNs2\n+wxH8WH9W9u+ZNyCA9ju6Ujxq96IL+Hd+7WlbDzhUi+Rb30b98YNhGvr6Pj+2bTNvhUqKmBbes3X\nK/2AQTxEUlqNtWth/nyCuzrMl/3BIJVPPY3eUBzztOpeyl8/A0zSICSlSbh+BmW7OjSVwAY/4eqa\nnhQmabaBnRQ6nYNV4uUsm9iknzZlYhOBQJc9NpuKPSGNY7nXrKbSr28Pi7jdbP3fRwnvf6D+vS7y\nUWmgyu2qq2drka9XX8u4fyeQ8t6byNW9SxjuWgS/exleHwmTzrd8ekDLfN2wE25b0cjPfv8qmyOt\n3XEc27bssiZnjjEbtKx6VU0HbpBSrgN+gLby+K8MZFkshDgy+v+TgQ+At4DThBBuIcRIwC2l/DqD\nY6dNsDPIis0reL/lXaYuvpD9HtonrUGjKASXryzjozfHJg0aAO62nXia/Zpb4oZmSh99hLJbs3D/\nNPOqCQYpWfi87s9KFr5guqw3U4MQibD98Wcy9/qKVwm8/SFb3/6QwC239y1vJofgxFKkpi62IxoI\nN442/nE6HoD5TBtjIbape9dOOGE9+NLM3DfpM2jxwbZJp+Kt2IPRQ/YyVU91e2U5xNvK9OkVQpwh\npXxeCPHD6Odjo5u2AKcC/5vm+a4A/iCE6AA2AZdJKVuFEG8Cb6MNZFelecy0CYVD/PKtG/nbqr8Q\n6Ar0bLBq8QEqispZfsoifFP3oW3QICLxZUZr6nDt2Ia7LTmJYTaGTDOvml0/uhx3s1/3d+7m9RR9\n8B6hw4/QPa+pZ82IBsPfpUVMP54tBUyOV3AydXrIJRaN60ZY9diyYuOx3L+iNhKz9uuWy8DDMFis\nvfhr27TB4+Ll8Ptx1k5f3An/OMDDvUd2UetbxPh/Bpk+bhbBUCApejwUDjFz2XQWrX6BDW3NjPDV\n07TXZGYeM6dXTZBgZ5B1rWsAF40Vo3JuIzHNjiuEmCWlnCGEWKCzOSKl1Hf8zzHZZse9+Y2f86cV\nf8z498NDJXx02RoGD/b13hB9qbF7N5UnHtOrznGMiMfD1mUfpP0SrSrz0LXf/sYZb59dyB5HfAOX\njm0iAuB2m7o62pU9NmfLa7O0GBmsXgqtBrBKfCZXxwwWiUTvjXfJIiJ+f2b12S0UHEsn43NKOdev\nt9SH3J+s6vUsh9xw7anw7H6wfgiM3KFV6LzlZbjuFFhwCLQNJi17B4ALFxEijCgbwfj6E5g+bhaU\n7mbWK7N54tPHkva/ZOxUzhbn4d/p56nP/s5r/n8SDGmrEV+xj3P2O49fHfvbrApOmWXHtZxW3Ulk\nM3AEO4Mcd8/eNBcFUu+cgCcEFy3Xcvd3TDV5oZp18miAkWkCQ52HqKr1SyJjxhgORtsff4ah3/2W\npf4avOyK5FlrQpW0jB5+cvRCDgbxXX8NpY89krwpwxTWBRs40hwAqoaVErzqp7YNmLmkqszDlhWf\n5Wxwy3Zyk9bvY/epooLKiRO6n+Vpp8F8ndCsK3eO5Y6FYTo/X8Wnw2DOeHjqgNTJT3PJZd+4gtnH\nZe7SnPHAIYRYg3GOyIiUcu+MpcqCbAaONZtWMe6JcZoXhFW64Acr4N4XegzgqWY5pjUuGkbqP/wm\ns+qqISXmK44lr1I58QQ8fn11VTxhXzmRoUM1o33iiyjLma2tL+RYe7zwD9wbmnUHRcuzzVzKaYUM\nV0xVc36h65XmxDoiOW/TbCY3VlcsOvcpPGQIxSs+JlgMB1wJ63RiLxrKG3nz+29Rd/rpFK/SKoBe\n1QR3H2XHhWdGva+BpT94L2O1VTb1OCYAJwGvAQ+ixW8cA9wFLMpImgJT2xahYYf1/Ye2wZZb4ZGn\ne3tNpfKd72XIjPveRY9touzm63r5o5umnE5lSBy+B+1NZ1i6pl5G+0RZSCP2I8d0t4fBoAE2JcfL\nQ+nPjNKJB4PwzDO6m/pUzQe99s2kzc1iL1JgNQ5G7z4Vr/iYzgPHsmHPMvxD9I+/sa2Zze1fs/3l\nNwhefCldtXXcscTFT/9dRmOnDw8eRvga8BX79A+QAza2bWRzMDeJI00HDinlOinlWuAbUsrZUspm\nKeWmaOS3fi4NhzO4bjRnbkh98zxdcMn78NXtUKnjMRGurSNcUWHc+WOdfMmrhGvrdM9R+ucFPd5R\nN1xDySIDr6joSyKVV02v7S4XEbf1ZVUvWQpRWCfxRWIxg2lWEfj5KiqUoUeQe/MmMFhBOrrmQ+xe\ntrYmt+9NP6fspp9n1+YGgYhmWEqwaHKfPOvWMeLLACMNJp3d0d5FRQTmzKX99Mm496zljmd28fFf\nh7J889m8/J1XmLzXty3LnC11vrqcRaBbVZK6hBAnSSlfARBCNKHllep7eL38uvI8Im//sceQFUdp\nUSlNe03m7qe7qH7+acPDhL2lWo1uM7VDMEjRqpWGD3jMkO3xr8f74P2GOkH3xmZoaYGKPc29aoqK\nNBk6Q5S8+IJh/fCUslhN+WCHwdZAhbProqnGLsJxZBOBn4vcT3pk6hEUrq4xDPpzZB2RWHGk6L2M\neL29PAs9/vV47+/tlJKvFCNWvL/ca4xzbbnaduJFM4Tr2TiOrTu2+/9lM6f3SqNSur6Z+1c/wtMP\nPoe/KICvyMfu8O6cp0+ftNcZOfOusmQcF0IcCvwZqEVbpawFLpBSrsqJVCnIuuZ49GXVteR51u1u\npq2mms5jjiFy5TU0DtsHbydUHvtNPAadKFxUhFtnhtStd45/GTb7we3W9XZKJOLx6O7X1dCI5z+r\n+CqQ+hjGthUX4RH1hm7Ceuc0tB2Y2WLSqY1uIm/wkqmUvLREv9gVEK5voH3SGZl7VaXyUsvAbmJI\nFh5Blm0cDvC6MpLVCum0ecaFj1J5f5ncpwiamrnbq0poXlVlHRDxlRHoClLjraGp4XTuvv5lStb3\nrBSNDOr7b4bAIO04QFrhAGYUu4u58MBLnONVJYQYjmYU35qxNDaQ9cARIxikKtTGV0W+Xh3WvWY1\nlUcfpuvBBBD2luEOJntlxTp/2W9mGVYXMyPWOZPEvOwKvH+8O/UL2YI3V9n//M6SbGZuw2beKZbk\ntCJvQyPtp0zsNXOLseucc2m75fasXpCpvNQycZk2I1OPoB6vKgODsM1uyhkTDFJ1wlGwbl1GP7fS\n5lZjGuJl0htMzby/jO5T2OfrNeEKFsNVk+AhnYx9h7TAe9FqgGYG9VFbtf12DIaydjjpQvgknYS4\nCW9Bbwd8W8JvrlmKb8w30jiQPmYDh9W06uOAG4mmVRdCeIBGKeWorKUrJF6vlgsn4UVnGhDnLcO1\nWz9VhntjM+51awz1pBGPByIRcLl0Vxbh+pG0n3oaJS8vSXpJWHlFmqpEvtyMu7W1d8DVBr+xLEaq\nEKv6egsz4FQqnF1TL9fSvGfpIqxLtCZDvjIXZ5rqPlXQX77Ubakws8dYwUqbxwofxYgVPgJ6u52m\nGkxNglGN7hPhcJKa7dVR+nIur9XyWN21SAsSNDKo+4dog8be27QBJmixvmrFbjj//+D8f8OmMthj\nFwzbDXtth5KaRrbW72PtQFlg9el7EPgdcBFwJ1rG2w9zJFPhMdGH7j7nB4YqlHBdPeAyT9/x92cp\nef453Zl0+6TJ2ksiQ7WDpdraKdJad8sSLayTKEOqlz1+P2W3z7c0A04p74j63EVKZxnxnDbZRn3r\nvewcVMM7PHwPKCuDnZm546Zq82Bn0LTw0U1HzehWW2U1mBrdp1AI3O7uAaV532rWDzW2IT63n5bH\nqrZNCxLUS5/esEPbDuYDDBHwhGHEDjhxLdz5IvjCxbhDnUm7BvNUCtmqVq1dSrkAzS13G/BDQD9f\neD/B0INp9q2mbrHhxlGmuXtChx9BYM5c85xDGXiNxH5nOfdP9BxJstSPpHPsQZS8tFjX6yWVdwp3\n3mnd7dSqvJm2RwoKkvvJxmtxTA1v0HKwGQwaYV95T/tOvZzg1MvTbvPNwU1saNO/1ljhI4JB3J+s\nNM7Zlo4Lc+J9SnAFLnluKTVlxkU5Wiqged86SsMevr1BP1ngFKmlK4GeAUaPkdtg+b3wyT3w0HNa\nWMDuiy4heOGPCHvLiBC1+/l8Wj2UPHhEWl1x7BZCVKIlNxwnpXwlqq7qv5jMEE3VDkVF2RfGyYK0\nVSLpFNaJ7tN+ymmGqxTvC+nNgDNW4diBE3M/pUF4+B5EvF5cOs4OefW6Mln5hH3lbHn7Q9zBQO9n\n6OZZabV5tbeGEb56/G3Jq9O6shHsNe8PDFu4RHNGMbDbdg+mKcoCmBIdULxA016TeXDF/bq71VU0\nUvLcq2zd0soNVXuwe/kcFq1ZyMa2Zup89TQ1ns6vNkJXw4u4NzZTUlPPpKIK7ia5zPJ/SRgbLTIR\n9vnYfe4FBGbOoWz69b1sra62Nk2d5nbnXE1p1avqe8DlaCqqd4EwsFxKeV5OpTPANuM4BtGuVlVF\nRvvZlL4jpZxmZKLuMjFW94o2rxtBeOhQ3Dt2RD9r17froqkMP+4I3SpwKY2fevLm0FMoL+1pA4Zy\nhkIMnXiCYT33fEaWmzmT2OlocPPS63vZOGJcufMg7rpNvx3iiTmvVDVW2xLhHgqHmPjECaz4Ovnc\neuk+dL3B4vpVaPAgzfi/ZiEbdzbT0FXGt1eFmfd0G0W4IRwmPGKEFugb6qT04T8bemHa4RWYtVeV\nEGIysFBKGRFClAFjgP+TUuq7HeWYnA0cdnuoWHnZWHwh5SNFRipvskSCl0xl1+X/r0f2YJCqCeN0\n4w7S6sx23QeTtrXcngX2WjKSs+yGa/A+qD/bDfvK2bL8E63uSz6wIwGhBbq9quJn7vUTufPnL/Zy\nfzUUMzqY2vkszCPLoQAAIABJREFUhcIhpi+9nhfXvMCXgc3UldfTNHqSsaeXmXzRgaViUAWtHa2M\nbdwXLp+mu7o3w67B2o6BY6WU8sCspLCRXA0cdmWItUSaL6S85FYyeQHoofdSsCO3Utb3wULbWm3P\nvPYJHYxWxJVHH4qnRb9+aS7ciVORz3aKn7n7mjcZr3YA3J7o/e9Z8efiWco4tgRjN+O7Tr0F1wEH\nWX4eY+RjxWF1SPxCCPEg8A5xlf+klOnW43AuefZQcYobZS9MPI300I16njePYKziXyY2Cxvug21t\n6yCvpXjcmzfh3mRs+A7vWZ33qPLAzDl4SwfR9dQz6d33DFSA3mIvo4dofS5VLZntj/ydcOOonN+n\neJnSxcjNuLRjF/9jIXtCIjnxCkzAqlfVFrTYtHHAidG/CTmSqSDk1UMln9XM0iTJ02hEg+atoYOu\nATaLRHRgw32wsW0d47WUkMcrXF1DuL7BcPf20yen/+LINtFjURHccYf5fY8/h115wsw88yafQXj/\nAxzt8GDmZvzg6ifZ3mjsuZVIxOMhePGleXEssbri2CClvDmnkhQYSzEQNmFrNTO70fE0MoqEN53Z\nZFjxL9v7YGfb5rNP6BIKwbRpVD71dJLKzWhl2Dn2IAJz0lhV2W3D0bvvCSl4wjU1hIdVUrxqZfcu\n2ay4C+qZlyVmbsY7O3by0+/U8vBtPd8lVh6MZ9eFFxO45TbygdUVx7eEEGnWtOpjpBMDkSWWMnUW\nmjg/9rzGO2R5H2xt2zz2CT3KZk6H+fN1Y2J63RO3m67aOoKXTGX7ktfTeuFnlO49g+voPkckgqel\npdegEU9GK+4sV7lZkeVKrdpbQ22ZfvZsgDf5nJ17VhJyazmvDrwSxvxE+/fqM4rpLHL3ijHLF1Zb\ndgvwHyHEh/S2cRSkdGyuyNvMJd9Ry9mSbrxDlq6rWd0Hm9u2YLNZC/aVrGNQ8mHDsZgeP0ZWK267\n6tpbIYuVWqIhfXz98Twqk6tbAjR7w3zZuZXfn9o7UeLaYXDnNzvZfebZzD5lft7fGVa9qi7U+15K\n+WfbJbJAweI47PTlTzPWo0/UyA6FqJo7iy4d1UrO3JkN5EjVtk6P48hHbITd59BrU/ea1VSOOxSX\nxWSqtmcm1sGOZykTL7JE76nasjrG1x/P9Uf+gvGPHkFbZ3IgZywR4jcv00+UOLK0jjfO/zAn6dNV\nzXETLHWiXPryZxrH4YA02okU2nU1+cQ2xHEUinzERth8jkxch5NEykNfyfreZ9huRgGMvmIfo4aM\n1g0kvPpt+Mm7mnoqrGNY8OBh2XkfZOzRZUbG7rgpao4jpUwprRDiKGCulHKCEGIf4KHoMVcAV0kp\nw0KIGcBktOJQ06SU76Y6bj7JqetsusvrQqfRTnwZxz5XVDjPdTWfqgu7yYc6M5/nMAhWjHi0zEV9\nyaBt5IARLIaNu/yUbFzD4H16wt6CnUG+2P45j6x6WPd4bZ1trPj6Y8YOG0vbuk/wl3XRsEPLZTXv\nJejwGCdKrCuvz1mVPzNSvWkmZHNwIcR1wAVALKHK7cDNUsrXhBD3AlOEEOuAE4CjgAbgSeCIbM5r\nKyY62sGP/IXAT65JysOTSwoW/5E4YHWnHdmOe8MGwtXVuA1mlQX3FOsL6KyOMo6NSIOc23BCIS0I\nz6D42a4LL+6dfaAPkOht113caT9YPyTMiGVn0bTpDG4eN4vZ/5rBotXP49/p1y+2E8eO0E6WBi6g\nc8FDvbymisJw+teV3DssuQxS0+hJOavyZ0ZOVVVCiO8C/wYellKOE0JsAOqjqUumABPREid6pZS3\nRH/zETBRSvmV0XFDoa5IUVGecix+8QWMGaObfwkAn0/zqBg5EqZMgXnzcjfzDwbhgAP0i+WMGgUr\nV+bu4Zs2LePqbjmXrS8TCsG118Kzz8L69fr9KBjUSgfX1uauDXN1DqN+U1EBF1+c2+cll8Rdl1GF\nv0NqDmH5puWWD+lxeZBXrGTvOfdo/cHvJzSynmvPGsYz1VtZ17oej8tNOBJh5JCRnLnfmcybOC+r\nKn8pyDpyPCOklE8KIUbFCyKljI1UO4EhQAWa1xYJ3xsOHNu22Rcgl1LfWeSjsrbOsIwsscyka9fC\n/PkEd3XkZOZfVVXOlhWfUen3697NiN/P1hWf5WZWHwxS+dTTZDpUByc2EQh0QcA5NgWn2DiS7EIJ\n/aiqqlwrGVyxJ+S6DbM8h54dzqjfdJUPYevPboRt+kXRcokt9/76GZTt6qBryfM8s59+nqyPN6dO\nvBhPna+eos6hfDX91/CzG6kKtXHVB3P50yf3Qau2T1dEm8Ce3HAq0w//Ndu25K79qqr008GDbVVu\nLRM/bS8HtqM1SbnO987A66Vj/PGWd89l5Heh4j/Mgur06Kqty199i76MgzMI2IFpMOamjXmtF2I7\nURf1T577O/5h+q/Rrkhy5lozeqmdvF6CI2tZ5F+su+9L614i2Fm4/mFp4BBC6EufPh8JISZE/98E\nvAm8BZwmhHALIUYCbinl1zadzxbaZt9qmHYjEfcGf+4eiAIFpJkNWEmMGsXWV5bmPxCrD+KYlCY5\nIuVEp6IiuzQnDqB6+ChG+PSv0eNKvUZ3hWFUq4crdx7EzCNn9drWsrMldfGqAmF1xeEVQhgnx7HO\nfwOzhBBvA4OAJ6SUH6ANIG+jGcavsuE89lJRwe5zL7C2r8tF6b13ZV6FK0UkaqGq1hkNWElMmQLD\n98hJxb7+Rp/IIJANJv0mPKSCyokTsstT5QC8xV6a9tK/xv2HH6D7vSusxWdc+h4sfhjeu6eLu277\nmCG/mtFrv9ryWsNBqc5XGG+qGFangnsAa4UQX6JFjruAiBV3XCnlWrTkiEgpP0XzoErcZyYw06Is\nBSHR+yRS6sXdlqwndXV1afnzi4vSs3VYdbMtUNW6JO+b2hGEhw5JKubknTevIHrrPklfyyCQAYHr\npuPasYNBb72Ju2WjNiAOqehVgMoRmaGzYOYx2rPRq07I6EncPG4WM5ZN12p1tG2kYaeHpv90ceX7\ncM/hsHAMPHC45mo75T8w98UXIM5tPTYo6cV+FMqbKobVyPFGve+llDruPbkn7wGA8cTcJofvQdlv\nZlL65wW2VOFKFTznFGOuYRxH9LNj5EyBY+RMEeXuGDktYFoUrbaOjvHH0zZ9FpWTTs550SfLctpI\nfCqRQZ5B3VHizW1+arw1NDWczi2uM5i94LvcqeOF9dN34Jczl3c7uFRVldOyeVty8aoMC0Wlix2F\nnEqASYAPbbXhAUZLKX9pl5DpUNCBIw7bUjZYiES1q9xlrukrLzrHyWkQ5e44OU2wUhRt1znnMvjx\nR3NeZtaqnLnCKEr80tb9WdL1iW76kFGtHl678jO8FXskyZlNoahMsaOQ09+AYcA+aPaIE4Gl2YvW\nt7Er7bYlI2ljdVayKhxOX45yT8TEW2zQm28SrhuBpznZhbVf2HUwr7Gx0CVpGaL/O395F5uDXzI6\nOnDEE18oqhCDSCJWjePfAE4CngZuBY4FRuVIpr6DTV5O/d5IqhhQpHLD7Rino6eh/9h1zGpstHjD\n1Bosdhq2wf7f/p6ho0AoHOLmpdcz/m9HcvRfD2P8347k5qXXEwrn36nA6sDxZTRw7z/AN6SUq9G8\nogY8tng5Fbjug0JhJ2YToUipl0FvLyOCppqK4KKrYWS/ivep9tYYekONDHiY8h/9302RUL622bAe\nSqzErL9tPWHC3SVmZy6zr3aKVawOHCuEEL8HXgN+JoS4gZSZVwYINhWRyWgAyrbcp0KRC0wmQu62\nnXg2NONC80B0EaH91InZxfvk6jnI8LhmLrqTXAcwf7GW9XbUVvB0af9e/baW0DBGYgComfpr0ZqF\neQ8GtHqnrgCOkVKuEkL8EjgFODd3YvVBstVRp+NmW+gMuQqFGaEQhMOEfT5c0ZQ8kTIfuMDdllxz\nomTxYnZduIpw46gM667Y/BzYcFxDF90jZ9Gxcwa3LVrInFfX01IWoU6nDKx7gx9Wr4ZqzaHVTP0V\nCwbMRWp1I0y9qoQQprk2pJRv2C6RBZziVWVIDmplWPFYKVjdizj6iheQktN+YrIa9c8I+mqKCIDb\nnd4LOhjEd/01lD6WXDkv1XOQqk3tfL4MDdnBIO51axh63lm6jgIRwDVyJMHTtfYIRjoY/7cj8bcl\nO+I0lDfy5jnv2G4oN/OqSqWqmmXyN9Mm+foPoRBlN19P5bHfpHLcoVQe+037I2L7eX4jRR/HrFSs\nRz8Fhwus1zuPe8YG6wwakOVzYPPzFfOGSnqpe72E9z+Q9kln6P7OBbC+pz3M1F/f2liB15Vfk7Pp\nsC6lPDH+sxCiHPBIKZ2ThNBBlP3yRrz3/7H7s2eDZugiHCbwm98Z/zCNFYoV192UKjMHVg9U9A9M\nE2LqBMrqYVb4K7Eeja4M0XxxmaiOM36+zJ4pk20xG+bgvz6MO5CsxoOe9ph5zByK3lrKws6P8Q8h\nrtjTx3TsnJ5XbYPVJId7CSHeBdYCq4UQHwkh9s2pZH2NYJDBj/5Vd9PgRx/Rn6nEZk/jj7Scsycr\n190MzqdQpINp/6wfSfDiSzUHELfbsLSoYYJHs9VMPFnki0v7+Yp/psYdSuXRh1J2wzXaua08b0VF\nBG6aQaTcJIV5tD2Kdndw5+M7WHkPyD/AynvgjsVaoad8axuselX9EbhVSjlcSlkJ/Ba4L3di9T3c\n69Z0GwITcbXtxL1uTdL3sdmTx7/e+lI9C9fdjM6nUKSDWf+cNJnA3Ns0D8RX3yJcr5831WgCZDW9\nfyxfXEb9Os3nq9czFYngaWnB++D9DD1lvKaBsPC8uTdvMs2EHN6zmnB1Tff1ezth7229Der5zqZs\ndeDYQ0r5ROyDlPJxoDI3IvVVUnknJ2zPQpeaqetuv7CNKBdkx5Oyf6bQ7xtNgNJK70/m/dry82Xy\nTBWvWknpQw9YkivVdbWfPllrMwelqbc6cLQLIQ6LfRBCHA6oJzeOcOMoIgY1OyK+cs3VMI6sajFk\nEDvS52s/KDVb38Fi/0x7ApROen+y6NcW5Xdv3oRbxyMqhsugbybJ5fUSHjpUd9+uyuEE5szt3s8p\naeqtOjtPA54UQmxFmzpXAmfnTKq+iNfL7nPO62Ucj7H7nHOTZlC25LlKI3bErrxahSLRKNrXU3EP\nCFL1zwxKBCSl96+pw7Vjm258SNb9OoX84eoawjU1eFpa0jpsklzBIO4dBv5GZWXQ0dE9aCVdf4HS\n1FtacUgp/wWMAX4IXAiMkVK+kxOJ+jCBX/1WM/7V1BJxu+mqb9BmUL/6bfLO+U4z0pfTmvQXNZtC\nn9gL2kofTFwNvPWeYZG1nPfrNFdAMRLl0rQBG3T3dW/cgHvdmh4VVOL1L3kV944dur/N5bORcuAQ\nQpwhhNhLStkJ7Av8BpguhCjOiUR9lVi06cuLo7OnatonnmYazJTvan4FqR5oA6ZqNv+6tOqhK/oJ\ncYNNIdP1BGbfSucBBxpuD/vK6apvMJUrVW6voeedlayCil6/u7W1ICroVJHj16KppC5EU2u9DVwN\nHAKEpJTTciJVCpwYOZ5VtKmFuApbI4hzGMeRq0h8o3olAMGLLyUw97a0DtlXIrL7ipzgAFmt9OtQ\niKq5s+h66mn70pSEQgw95XiKV61IFumyKyyp4ozeH3r0eqdYqOWT6TOeTeT4BcAJUspVaLmpnpNS\n3g/8BDgtI2n6I9mqUtJZqttBvs+XLV4v7adONNxc8vISpa5SWOrXZTOnw/z52bukx69YiorY/vIb\nmpq6ti55dWFBrqRV04gGMHC26fVOKZAKOtXAEZFSxp7IE4EXAaIp1hVR+rzHUh9g19Qr0g8YUyji\nscNWZuTdB1qMytsf9vbE6uiwphKLt1288Q4d444Bg7iwxP5eCBV0qrVZSAgxFK1k7KHAEuiuQZ6x\nr5cQ4iMgZtFZgxZgOD96zCVSylmZHrsQ9HWPJUeSoHYI140g3DBStXE8KnVMWtiRrield19sdREd\nYNLOsOv1UvrnByh98jHDXZL6ewbeadmSasVxC7Ac+Bdwv5SyRQhxFvBPtEqAaSOEGAwgpZwQ/bsY\nuBdNFXYccFR8zEifoC97LDkNoxndoEGqjWMMtJiWYBD3Jytxf7IqK5Vk1pU201ixZJylwUJalY5j\nj9XfkEcVdKokh08IIZahRY7/O/p1GzBVSvlahuc8GPAKIZZEzz8TKJFSfgEghFgMnAx8mOHxC4Ke\nf3V70yTHeyw5DdMZnWpjYADFtIRClP3yRgY/+teeuh4+H7vPOU9zcU/XmB2d4OkZoa1MPiyvWFIM\nMEYJHFOdIwJEvGUMfvxRBr21tLA1eCKRSF7/xowZc9CYMWMuHTNmjGuMxhdjxoz5IG77JWPGjJlt\ndozOzlDEsQQCkcjnn2v/OhEnyxcIRCKNjZEIJP+NGtUjs5OvIddYbaP+wNVX618naNsyobNT++2o\nUZGIx6P9e/XV2vepCAS0/VO1/eefRyJut/5+Ho+2PZNz2NkO1jB8BxeiXNynwOdRA/unQogd9M57\nVQ6Ypm3fts0+D5qcuBBW7Alf7cS9+bPs9I1xOuyqxurs5MxT1cBs2tO9ZjWVfr9+sR+/n60rPuvR\nQVfsCYEuCGR2roK7jlokUc602ijP6LZppnaYYJDKJ55Ev4IHdD35FFt/fDXu1ta0j111xx189bMb\ne8u1bZel35ZNbNJ3u5/YRCDWH4t8VBrYPLvq6tla5AO9vhdtq9ITT8W7wFoO2a6nnmHrz27MiXqq\nqsokY6/tZ0vNJcBtAEKIOsALBIQQewshXGhuvm8WQC57sEP/rHMMpk3LSoddNv16x2fGzVoHPQDo\nM22U5XPg3rwJ90b9aGoAd7OfyuPHZf6MZWgPsOTBlK7NM6GtSl56kc6xB0Fj9By1dY7zKCzEwPEA\nMFQIsRR4DG0gmQr8FXgX+KgvpzOxI3W53jGYP1//GKkiYEMhym64htL/fVB3s6NSdigng9T0kTbK\n9jkIV9cQrhthuN0FeL76Mv+ToJjb7JJX2f74M2xd8mrWCRyT2qrZr+WemjxZc+19ZSnhhpG64hRq\nsmAaOe5UbI0cL/OwZUWWKqUYdkRxWj2GRdVTqojUiMfD1mUf2KbeyFoFFLuuhc/j3riRcF0d7ZPO\ncJRKLZ/oytl973WcBAphKI3SLatN0czpRFObHjtBXZbVvU9X5ZtKVWeWFWHUKL567V/g9Rq2RefY\ng9i+5PWc3PdsIsf7L9HlIQcemFy5K0PsCAS0egxLMzoLrn2OUm/EE4lAJKz9q+hNBmn184ldAbGB\nmXMITr2csK9c8yiK/hmed4O/d8G0HLgtp72SSqESMy1O5fd3t1Vg5hxNfZVA8YqPC6JuHrADR7dL\n49q1vSt3TTwh445lh/7Z0jEs+pNbqZjmJPUGxD2YG5q1+xKt2+4kW4xjcGjqGNvsMEVFBH7zO7as\n+Iytr7/N9keeAJdJwbRwmKHnndU9OBi95Ln22gyuipxkaTYt4tTQ0NNWHR0FyYJrxMAcOMwqd634\nmLLp12d2XDv0zxaOYVZAxr2hZ5ZimnXT5SJ40Y+cFQOh0qf3D+y2w0QrBoaOOc6w3CxE7R7Nfm2i\ncdO1DH7kYf0dn302o76Uk9RCZqnZp0zpbiunpTUakANHqspdJS++kPFLyo68MXrH4Oqru48Rrq4h\nUlam+9uIt6xnlmLSKV2RCAwa5Bj1Bjjv4VBkTk7yJ6VR/2Lw44/pFncCeqmA0iFXHm1GbcW8eTk/\nd6YMTON4MEjl0YcaVu6yxWBsRx4hoziOYJDhY/fRr3rmK2fLis96ztnayvCDBe5AIGnfrvqRbF36\nrq2qjqwMjzlMEZ1InzaOOxRb4ziMiHcMaF4PkYh+TAvofg9AYyNfvZ5ZX8qqfEIqUhjxc3puHZRx\nPJEUMxdbRnA79M8Gx3Bv3oTLYEXk2hXsNZtyb/kal86gAeBuXu+sWXwfcTVVpIHddph4x4DXlpmq\nrgw58cSM5bF1JZXoSp+irZxUiM05eoo8E5h9K8XvvtOrVm8Mp7+k0snGG66oAI8HurqSD+TxaNsd\nhMpHpbBE1O7RPukM3Vl4xOfrzm8VT9hXjnv+fGjP8Lx2ZKLNNItDAbLgGjEwVxygFV9Z8jpceaUW\nmel201VbR/CSqalfUjaVncyYNGbm7tZW/UEDoCusbXcSDnc1VTiLwHXT2XX2uUnlWXefc57u/rvP\nPR/smCxlsZLKOkjYAd50A/tpLCqC+fNp39VJyYsv4N68iZKXlkBRsf7on6d8T1awOjMPV9cQbmjA\n4092BgjHu/s5jdjDoVDokfgs1tax+/tn0zb7Vm1gCIXA7dZ9PgqqS8gic66TGNgDB8C11/ZKKGaW\notpR6aytLlu9Xtqb9Jfz7U2T+0QnVSgSSXoWNzRT+ugjRCqGdK9QnaLWiceOYlJOYOCqqkBTNT3z\njO6mkkULYcvXPSopp8YYZFLPuIBGNYUiLfTUwuk8i0bPR4HUzTlzq83z9QzogcO9eRPoqHAA3P51\nVJ50XHeqAt8N1/TdGANlN1D0NUzShWQV79PaChdfTOVxR2SehiSTl3TsN2Cv52CBqkEO6DdHuLoG\nRo6EtWuTtrkAT8tGQFNJlT76CGEjTw2n5ntKRNkNFH0EU7XwTTMsexV2E7WJDH7kYWhr667zkZa6\nORMbp95vTmsiOPVySha/mLXnYKHU5wN6xYHXq4X1W0Y/Hsbp7rsKRZ8ilSqK9GftsResUTS5FXVz\nJt5Qur+5/4/gdmevASig+nxgDxwA8+ZZzsDpCgY01z9lK1AocoYVVVRadjsLWaJTqrgyeUlbGACz\ncastZIqeAa2qArRR3u3G3ZY6rUN4RANtc28HcJSnhkLRn7AU4JqG15SVLNGp1M2ZeEPl2oMqnUBg\nu1ErDguzkRjdy2AHBOAoFP2WdFLPWHgWTVOXGx03jWMYvaRznpiwgCl61MDR0mI4K4iAFlGuVFIK\nRV6x1YXc5AUb9pVbO24mL+k8vNgL5Wo/MLPjxlFV5qFrv/31SzcCu757Nm23zS/46qKvZElVctpL\nX5ETciSrXdl147LqejY201VTR8f48T2R5mkew3LJ3izK/KbVnnZnIcY8O64aOKrKCf7oUrwP3q+7\n3e503ukS7AyyObiJsY37Etje1f252luDt9h5qrJsXx75ur6+8kLuK3JCH5E1GKQq1MZXRT5byh1Y\nPkYGvyl0e5oNHI4wjgsh3MDdwMFoeSunSik/z9f5d029gtIH79d1ti1UGoBQOMTMZdNZtPoFNrQ1\n01DRQPmgCnbs3s6Gtg2M8NXTtNdkZh4zhyK3I25jViReb3+7PoVD8HqhqhqyeSFnEg/Vz2KonPJE\nngkMllIeLYQYB9wGpBNgkRXhuhGEG0YWxDvBiJnLpvOnf/cE9qxrXddru79tfff22cflOU9WDki8\n3v52fQpFf8IpxvHjgBcBpJT/Ar6Z17M7rIBQsDPIotXWPL0WrVlIsLNv1+I2u97+cH0KRX/DKSuO\nCmBH3OcuIUSRlFI34cqwYV6Kijx6mzKiqqoc7roTSgdphez9fmhogClT8M6bhzfPOZ2+2PolG9rM\n/c5jbGxrJjS4jarK6hxLZZ2qqvK09je73lxeX7pyFoq+Iif0HVmVnNnhlIGjFYhvIbfRoAGwbZt9\nM9BeBqjpv4af3djbiLVtl23nskpRp4/6kJf1RfrpEeKp89VTtNvnGKNkJga9ok4fI3z1+NuSVYW5\nur5CGx6t0lfkhL4jq5LT+vmNcIqq6i1gEkDUxpFczzVfOCC4z9sJZ/7H2r5Noyc50rsqHbzFXpr2\n0lcV9ofrUyj6G05ZcTwNnCqEWIaWSfDiAstTUNybNzHv6QCunfCsAP8QqN8Bw3bDtlJoHuahrrye\nptGTmHlM/whKjF3HojUL2djWTJ2vf12fQtGfUHEcTly2BoNUjj8Sj389wWJo8UFtm7YS2TmqgU+e\n+zvVw0c5ciau4jjspa/ICX1HViWn5fMbxnE4RVWliCfOy8vbCXtv0/4F8Ew8g9E1Bzhy0LADb7GX\n0UP26rfXp1D0B5yiqlIkEMs1E0tV4GpoIDixSeXLUigUBUcNHE4lIW308LH7Egh0FVoqhUKhUKoq\nx+MALy+FQqGIRw0cCoVCoUgLNXAoFAqFIi3UwKFQKBSKtFADh0KhUCjSQg0cCoVCoUiLPhk5rlAo\nFIrCoVYcCoVCoUgLNXAoFAqFIi3UwKFQKBSKtFADh0KhUCjSQg0cCoVCoUgLNXAoFAqFIi3UwKFQ\nKBSKtBiQadWFEG7gbuBgoB2YKqX8vLBS9UYI8RGwI/pxDfBHYD4QApZIKWcVSjYAIcRRwFwp5QQh\nxD7AQ0AEWAFcJaUMCyFmAJPRZJ4mpXy3wHIeBvwD+Cy6+R4p5WOFlFMIUQw8CIwCSoDZwCoc2J4G\nsjbjvDb1APcBAuhCK0XtwmFtaiDnEBzWnnoMyIEDOBMYLKU8WggxDrgNmFJgmboRQgwGkFJOiPtu\nOfBdYDXwghDiMCnlhwWS7zrgAiAQ/ep24GYp5WtCiHuBKUKIdcAJwFFAA/AkcESB5TwMuF1KeVvc\nPocVWM7zgS1SyguEEMOBj4DlOLA9DWT9Fc5r028BSCmPFUJMQOufLpzXpnpy/gPntWcSA1VVdRzw\nIoCU8l/ANwsrThIHA14hxBIhxCtCiOOBEinlF1LKCLAYOLmA8n0BfCfu8+HA69H/LwJOQWvjJVLK\niJRyPVAkhKjKr5i6ck4WQrwhhHhACFHuADn/Dvwi7nMI57ankayOalMp5TPAZdGPjcBmHNimJnI6\nqj31GKgDRwU9aiCALiGEk1ZfQWAecBrwY2BB9LsYO9GWtAVBSvkk0Bn3lSs6oEGPbIltnHeZdeR8\nF/i5lPJ4tJXbDAosp5SyTUq5M/qCeAK4Gee2p56sjmvTqKwhIcSfgd9HZXVqmybK6cj2TGSgDhyt\nQHncZ7cP0nwHAAADZElEQVSUMlQoYXT4FPhLdIbxKVqnqYzbXg5sL4hk+oTj/h+TLbGNnSDz01LK\nD2L/Bw7FAXIKIRqAV4GHpZSP4OD21JHVkW0KIKW8EBiDZkco1ZHHiXIucWp7xjNQB463gEkAURvH\nx4UVJ4lL0OwuCCHqAC8QEELsLYRwoa1E3iygfIl8FNXRAjShyfYWcJoQwi2EGIk2OH9dKAGjLBZC\nHBn9/8nABxRYTiFENbAEuF5K+WD0a0e2p4GsTmzTC4QQN0Y/BtEG4ved1qYGcj7ltPbUw0nqmXzy\nNHCqEGIZmtHs4gLLk8gDwENCiKVoXiCXoHWqvwIetFnJOwWUL5H/Bu4TQgwCPgGekFJ2CSHeBN5G\nm6BcVUgBo1wB/EEI0QFsAi6TUrYWWM6bgGHAL4QQMfvB1cCdDmxPPVmvAe5wWJs+BSwQQrwBFAPT\n0NrRaX1UT04/zuujSai06gqFQqFIi4GqqlIoFApFhqiBQ6FQKBRpoQYOhUKhUKSFGjgUCoVCkRZq\n4FAoFApFWgxUd1yFIiuEEKPQAjVXRb8qBZYBN0gpNxdKLoUiH6gVh0KRORullIdIKQ8B9kPzu3+i\nwDIpFDlHrTgUChuQUkaiqa83CyG+AfwEGAtUA/8GfoAWQOeWUk4HEEI8hJZwzwNch5Zaew1wvpRy\nd94vQqGwiFpxKBQ2IaXsQKujcCbQIaU8GtgHGIqW4mYBcK4QwiWE8AInAc+i1bWYKKU8HG3g2K8Q\n8isUVlEDh0JhLxG0OhV3CyGuQiu+tS/gk1KuBtYCx6PVVnkhurL4B/CWEOJW4Ekp5fKCSK5QWEQN\nHAqFTUTzIAlgL7S8YkG0VcYbaDnRQKugd2707yEAKeXVaAPJNuAvQojz8yq4QpEmauBQKGwgWo54\nFvAvYG/gcSnlArT01yei2TFAM56fDNRIKd8RQhQJIT4DvpZS/hb4X7RU2gqFY1HGcYUic+qiJX1B\nGxg+QjOC1wOPCCF+AHSgpcUeDSCl3CWEeJtoKv9oIZ9fAi8JIXYBXwIX5fUqFIo0UdlxFYo8Ea2l\nUo6WHvtkKeWmAoukUGSEUlUpFPnjCDTj+J/UoKHoy6gVh0KhUCjSQq04FAqFQpEWauBQKBQKRVqo\ngUOhUCgUaaEGDoVCoVCkhRo4FAqFQpEW/x/dHxv/nh9duQAAAABJRU5ErkJggg==\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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5K728cPAKHb3B5PtOh+k43Li0GEWenqYhiizzkQdW8NUfHObRPecpznOwvKZg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\nfwN89pZH2Aq09gAAAABJRU5ErkJggg==\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('Figure3.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: 16565.62428392545\n",
      "test Kt min: -858.197482511317\n",
      "test Kt mean: 2.3067127385087867\n",
      "\n",
      "\n",
      "train Kt max: 154389.83695910865\n",
      "train Kt min: -244.67261793490562\n",
      "train Kt mean: 3.284695720674705\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 0xb17d048>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mexBfBOJmNhc4H3gIOCnl8mrgENCYPN13PJZGrYiI5EFG70G4+xXuPtvd5wCvAtcBy8xs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\nREQkkAJCREQC/X+Taznq3XBGqgAAAABJRU5ErkJggg==\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 0x12276cc0>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nzIwfH1UeztmHcDjUZR2HCXVpU14WIRL27kdinXWMqfCet6qynHM+cnzeOqNZ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\nv4hIAP1/Utf10Ubxek4AAAAASUVORK5CYII=\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>7.0</th>\n",
       "      <td>157.281154</td>\n",
       "      <td>-4.348077</td>\n",
       "      <td>0.090385</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>-1.123077</td>\n",
       "      <td>320.623077</td>\n",
       "      <td>280.961538</td>\n",
       "      <td>280.546154</td>\n",
       "      <td>335.715385</td>\n",
       "      <td>277.776923</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.086538</td>\n",
       "      <td>-15.086538</td>\n",
       "      <td>5.005769</td>\n",
       "      <td>79.425000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>69.582692</td>\n",
       "      <td>1013.025000</td>\n",
       "      <td>58.673464</td>\n",
       "      <td>-0.092544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>146.229333</td>\n",
       "      <td>-4.288333</td>\n",
       "      <td>0.113333</td>\n",
       "      <td>0.156667</td>\n",
       "      <td>-1.106667</td>\n",
       "      <td>322.270000</td>\n",
       "      <td>280.761667</td>\n",
       "      <td>280.345000</td>\n",
       "      <td>334.833333</td>\n",
       "      <td>277.581667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-12.571667</td>\n",
       "      <td>-12.571667</td>\n",
       "      <td>4.783333</td>\n",
       "      <td>75.178333</td>\n",
       "      <td>2.541667</td>\n",
       "      <td>84.746667</td>\n",
       "      <td>1013.470000</td>\n",
       "      <td>199.226885</td>\n",
       "      <td>-0.022905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>133.906167</td>\n",
       "      <td>-4.328333</td>\n",
       "      <td>0.178333</td>\n",
       "      <td>0.701667</td>\n",
       "      <td>-1.108333</td>\n",
       "      <td>318.151667</td>\n",
       "      <td>280.491667</td>\n",
       "      <td>280.046667</td>\n",
       "      <td>333.480000</td>\n",
       "      <td>277.301667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.333333</td>\n",
       "      <td>-15.333333</td>\n",
       "      <td>4.445000</td>\n",
       "      <td>76.843333</td>\n",
       "      <td>2.831667</td>\n",
       "      <td>159.561667</td>\n",
       "      <td>1013.861667</td>\n",
       "      <td>357.002941</td>\n",
       "      <td>-0.012248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>121.558667</td>\n",
       "      <td>-4.560000</td>\n",
       "      <td>0.316667</td>\n",
       "      <td>-0.558333</td>\n",
       "      <td>-1.170000</td>\n",
       "      <td>300.935000</td>\n",
       "      <td>279.631667</td>\n",
       "      <td>279.101667</td>\n",
       "      <td>328.243333</td>\n",
       "      <td>276.451667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-27.313333</td>\n",
       "      <td>-27.313333</td>\n",
       "      <td>3.465000</td>\n",
       "      <td>78.943333</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>133.698333</td>\n",
       "      <td>1014.648333</td>\n",
       "      <td>475.457844</td>\n",
       "      <td>-0.009625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>109.427167</td>\n",
       "      <td>-4.868333</td>\n",
       "      <td>0.553333</td>\n",
       "      <td>-0.383333</td>\n",
       "      <td>-1.276667</td>\n",
       "      <td>241.326667</td>\n",
       "      <td>277.370000</td>\n",
       "      <td>276.700000</td>\n",
       "      <td>314.453333</td>\n",
       "      <td>274.423333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-73.125000</td>\n",
       "      <td>-73.125000</td>\n",
       "      <td>1.351667</td>\n",
       "      <td>75.265000</td>\n",
       "      <td>3.646667</td>\n",
       "      <td>135.245000</td>\n",
       "      <td>1015.618333</td>\n",
       "      <td>539.708973</td>\n",
       "      <td>-0.009017</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 7.0   157.281154 -4.348077  0.090385  0.150000 -1.123077   \n",
       "                 8.0   146.229333 -4.288333  0.113333  0.156667 -1.106667   \n",
       "                 9.0   133.906167 -4.328333  0.178333  0.701667 -1.108333   \n",
       "                 10.0  121.558667 -4.560000  0.316667 -0.558333 -1.170000   \n",
       "                 11.0  109.427167 -4.868333  0.553333 -0.383333 -1.276667   \n",
       "\n",
       "                            dw_ir  dw_casetemp  dw_dometemp       uw_ir  \\\n",
       "year   month day hour                                                     \n",
       "2010.0 1.0   1.0 7.0   320.623077   280.961538   280.546154  335.715385   \n",
       "                 8.0   322.270000   280.761667   280.345000  334.833333   \n",
       "                 9.0   318.151667   280.491667   280.046667  333.480000   \n",
       "                 10.0  300.935000   279.631667   279.101667  328.243333   \n",
       "                 11.0  241.326667   277.370000   276.700000  314.453333   \n",
       "\n",
       "                       uw_casetemp    ...     netsolar      netir   totalnet  \\\n",
       "year   month day hour                 ...                                      \n",
       "2010.0 1.0   1.0 7.0    277.776923    ...          0.0 -15.086538 -15.086538   \n",
       "                 8.0    277.581667    ...          0.0 -12.571667 -12.571667   \n",
       "                 9.0    277.301667    ...          0.0 -15.333333 -15.333333   \n",
       "                 10.0   276.451667    ...          0.0 -27.313333 -27.313333   \n",
       "                 11.0   274.423333    ...          0.0 -73.125000 -73.125000   \n",
       "\n",
       "                           temp         rh   windspd     winddir     pressure  \\\n",
       "year   month day hour                                                           \n",
       "2010.0 1.0   1.0 7.0   5.005769  79.425000  2.300000   69.582692  1013.025000   \n",
       "                 8.0   4.783333  75.178333  2.541667   84.746667  1013.470000   \n",
       "                 9.0   4.445000  76.843333  2.831667  159.561667  1013.861667   \n",
       "                 10.0  3.465000  78.943333  3.700000  133.698333  1014.648333   \n",
       "                 11.0  1.351667  75.265000  3.646667  135.245000  1015.618333   \n",
       "\n",
       "                              ghi        Kt  \n",
       "year   month day hour                        \n",
       "2010.0 1.0   1.0 7.0    58.673464 -0.092544  \n",
       "                 8.0   199.226885 -0.022905  \n",
       "                 9.0   357.002941 -0.012248  \n",
       "                 10.0  475.457844 -0.009625  \n",
       "                 11.0  539.708973 -0.009017  \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>6.0</th>\n",
       "      <td>156.063125</td>\n",
       "      <td>-5.293750</td>\n",
       "      <td>-0.418750</td>\n",
       "      <td>1.537500</td>\n",
       "      <td>-0.381250</td>\n",
       "      <td>234.900000</td>\n",
       "      <td>272.150000</td>\n",
       "      <td>271.862500</td>\n",
       "      <td>291.925000</td>\n",
       "      <td>272.281250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-57.043750</td>\n",
       "      <td>-57.043750</td>\n",
       "      <td>0.593750</td>\n",
       "      <td>61.012500</td>\n",
       "      <td>1.137500</td>\n",
       "      <td>116.625000</td>\n",
       "      <td>1013.318750</td>\n",
       "      <td>30.663084</td>\n",
       "      <td>-0.182511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>151.636833</td>\n",
       "      <td>-5.338333</td>\n",
       "      <td>-0.555000</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>-0.473333</td>\n",
       "      <td>235.656667</td>\n",
       "      <td>272.565000</td>\n",
       "      <td>272.235000</td>\n",
       "      <td>291.751667</td>\n",
       "      <td>271.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-56.091667</td>\n",
       "      <td>-56.091667</td>\n",
       "      <td>0.088333</td>\n",
       "      <td>63.146667</td>\n",
       "      <td>0.618333</td>\n",
       "      <td>112.858333</td>\n",
       "      <td>1013.015000</td>\n",
       "      <td>115.766684</td>\n",
       "      <td>-0.058923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>141.881000</td>\n",
       "      <td>-5.150000</td>\n",
       "      <td>-0.876667</td>\n",
       "      <td>0.881667</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>236.418333</td>\n",
       "      <td>272.275000</td>\n",
       "      <td>271.936667</td>\n",
       "      <td>291.446667</td>\n",
       "      <td>271.781667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-55.028333</td>\n",
       "      <td>-55.028333</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>57.490000</td>\n",
       "      <td>1.448333</td>\n",
       "      <td>132.271667</td>\n",
       "      <td>1011.976667</td>\n",
       "      <td>310.197139</td>\n",
       "      <td>-0.017190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>130.346167</td>\n",
       "      <td>-5.693333</td>\n",
       "      <td>-0.785000</td>\n",
       "      <td>2.293333</td>\n",
       "      <td>-0.325000</td>\n",
       "      <td>242.050000</td>\n",
       "      <td>272.941667</td>\n",
       "      <td>272.681667</td>\n",
       "      <td>295.341667</td>\n",
       "      <td>273.095000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-53.295000</td>\n",
       "      <td>-53.295000</td>\n",
       "      <td>1.676667</td>\n",
       "      <td>53.623333</td>\n",
       "      <td>2.033333</td>\n",
       "      <td>137.526667</td>\n",
       "      <td>1011.566667</td>\n",
       "      <td>491.373002</td>\n",
       "      <td>-0.011644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>118.142333</td>\n",
       "      <td>-5.468333</td>\n",
       "      <td>-0.343333</td>\n",
       "      <td>2.310000</td>\n",
       "      <td>-0.261667</td>\n",
       "      <td>293.071667</td>\n",
       "      <td>275.710000</td>\n",
       "      <td>275.516667</td>\n",
       "      <td>308.071667</td>\n",
       "      <td>273.635000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-16.146667</td>\n",
       "      <td>-16.146667</td>\n",
       "      <td>1.591667</td>\n",
       "      <td>56.341667</td>\n",
       "      <td>1.473333</td>\n",
       "      <td>151.643333</td>\n",
       "      <td>1011.851667</td>\n",
       "      <td>628.392880</td>\n",
       "      <td>-0.008778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>105.758667</td>\n",
       "      <td>-4.468333</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>1.616667</td>\n",
       "      <td>-0.338333</td>\n",
       "      <td>321.446667</td>\n",
       "      <td>277.086667</td>\n",
       "      <td>276.876667</td>\n",
       "      <td>315.970000</td>\n",
       "      <td>273.856667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.773333</td>\n",
       "      <td>59.400000</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>162.733333</td>\n",
       "      <td>1012.278333</td>\n",
       "      <td>707.999345</td>\n",
       "      <td>-0.006309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>93.360000</td>\n",
       "      <td>-5.085000</td>\n",
       "      <td>0.435000</td>\n",
       "      <td>0.523333</td>\n",
       "      <td>0.573333</td>\n",
       "      <td>324.706667</td>\n",
       "      <td>277.511667</td>\n",
       "      <td>277.048333</td>\n",
       "      <td>318.663333</td>\n",
       "      <td>274.298333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.543333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.543333</td>\n",
       "      <td>2.150000</td>\n",
       "      <td>62.286667</td>\n",
       "      <td>1.993333</td>\n",
       "      <td>152.851667</td>\n",
       "      <td>1012.153333</td>\n",
       "      <td>723.565963</td>\n",
       "      <td>-0.007017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>81.477500</td>\n",
       "      <td>10.581667</td>\n",
       "      <td>3.041667</td>\n",
       "      <td>0.323333</td>\n",
       "      <td>15.745000</td>\n",
       "      <td>326.311667</td>\n",
       "      <td>277.568333</td>\n",
       "      <td>277.051667</td>\n",
       "      <td>320.978333</td>\n",
       "      <td>274.485000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.793333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.793333</td>\n",
       "      <td>2.198333</td>\n",
       "      <td>71.641667</td>\n",
       "      <td>2.133333</td>\n",
       "      <td>160.955000</td>\n",
       "      <td>1012.345000</td>\n",
       "      <td>673.851495</td>\n",
       "      <td>0.015885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>70.368167</td>\n",
       "      <td>44.298333</td>\n",
       "      <td>7.703333</td>\n",
       "      <td>1.603333</td>\n",
       "      <td>47.981667</td>\n",
       "      <td>327.441667</td>\n",
       "      <td>277.428333</td>\n",
       "      <td>276.891667</td>\n",
       "      <td>323.908333</td>\n",
       "      <td>274.638333</td>\n",
       "      <td>...</td>\n",
       "      <td>40.848333</td>\n",
       "      <td>-0.015000</td>\n",
       "      <td>40.835000</td>\n",
       "      <td>2.188333</td>\n",
       "      <td>82.856667</td>\n",
       "      <td>2.678333</td>\n",
       "      <td>166.960000</td>\n",
       "      <td>1012.361667</td>\n",
       "      <td>562.891372</td>\n",
       "      <td>0.082049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>60.402833</td>\n",
       "      <td>56.543333</td>\n",
       "      <td>8.775000</td>\n",
       "      <td>0.658333</td>\n",
       "      <td>59.681667</td>\n",
       "      <td>331.398333</td>\n",
       "      <td>278.208333</td>\n",
       "      <td>277.673333</td>\n",
       "      <td>327.438333</td>\n",
       "      <td>275.178333</td>\n",
       "      <td>...</td>\n",
       "      <td>51.280000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.280000</td>\n",
       "      <td>2.543333</td>\n",
       "      <td>89.940000</td>\n",
       "      <td>2.470000</td>\n",
       "      <td>150.240000</td>\n",
       "      <td>1012.115000</td>\n",
       "      <td>400.469352</td>\n",
       "      <td>0.149594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>52.384000</td>\n",
       "      <td>187.235000</td>\n",
       "      <td>29.745000</td>\n",
       "      <td>2.215000</td>\n",
       "      <td>185.133333</td>\n",
       "      <td>335.448333</td>\n",
       "      <td>279.595000</td>\n",
       "      <td>279.348333</td>\n",
       "      <td>337.640000</td>\n",
       "      <td>276.196667</td>\n",
       "      <td>...</td>\n",
       "      <td>156.750000</td>\n",
       "      <td>-2.490000</td>\n",
       "      <td>154.268333</td>\n",
       "      <td>3.485000</td>\n",
       "      <td>91.268333</td>\n",
       "      <td>3.808333</td>\n",
       "      <td>153.068333</td>\n",
       "      <td>1011.073333</td>\n",
       "      <td>205.793937</td>\n",
       "      <td>1.063090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>47.737500</td>\n",
       "      <td>163.261364</td>\n",
       "      <td>27.654545</td>\n",
       "      <td>0.834091</td>\n",
       "      <td>162.845455</td>\n",
       "      <td>339.390909</td>\n",
       "      <td>281.243182</td>\n",
       "      <td>280.965909</td>\n",
       "      <td>342.279545</td>\n",
       "      <td>277.679545</td>\n",
       "      <td>...</td>\n",
       "      <td>135.852273</td>\n",
       "      <td>-3.006818</td>\n",
       "      <td>132.838636</td>\n",
       "      <td>4.775000</td>\n",
       "      <td>86.770455</td>\n",
       "      <td>4.115909</td>\n",
       "      <td>165.429545</td>\n",
       "      <td>1010.206818</td>\n",
       "      <td>55.699158</td>\n",
       "      <td>3.182868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             zen    dw_solar   uw_solar  direct_n     diffuse       dw_ir  \\\n",
       "hour                                                                        \n",
       "6.0   156.063125   -5.293750  -0.418750  1.537500   -0.381250  234.900000   \n",
       "7.0   151.636833   -5.338333  -0.555000  1.555000   -0.473333  235.656667   \n",
       "8.0   141.881000   -5.150000  -0.876667  0.881667   -0.500000  236.418333   \n",
       "9.0   130.346167   -5.693333  -0.785000  2.293333   -0.325000  242.050000   \n",
       "10.0  118.142333   -5.468333  -0.343333  2.310000   -0.261667  293.071667   \n",
       "11.0  105.758667   -4.468333  -0.058333  1.616667   -0.338333  321.446667   \n",
       "12.0   93.360000   -5.085000   0.435000  0.523333    0.573333  324.706667   \n",
       "13.0   81.477500   10.581667   3.041667  0.323333   15.745000  326.311667   \n",
       "14.0   70.368167   44.298333   7.703333  1.603333   47.981667  327.441667   \n",
       "15.0   60.402833   56.543333   8.775000  0.658333   59.681667  331.398333   \n",
       "16.0   52.384000  187.235000  29.745000  2.215000  185.133333  335.448333   \n",
       "17.0   47.737500  163.261364  27.654545  0.834091  162.845455  339.390909   \n",
       "\n",
       "      dw_casetemp  dw_dometemp       uw_ir  uw_casetemp    ...       netsolar  \\\n",
       "hour                                                       ...                  \n",
       "6.0    272.150000   271.862500  291.925000   272.281250    ...       0.000000   \n",
       "7.0    272.565000   272.235000  291.751667   271.600000    ...       0.000000   \n",
       "8.0    272.275000   271.936667  291.446667   271.781667    ...       0.000000   \n",
       "9.0    272.941667   272.681667  295.341667   273.095000    ...       0.000000   \n",
       "10.0   275.710000   275.516667  308.071667   273.635000    ...       0.000000   \n",
       "11.0   277.086667   276.876667  315.970000   273.856667    ...       0.000000   \n",
       "12.0   277.511667   277.048333  318.663333   274.298333    ...       0.543333   \n",
       "13.0   277.568333   277.051667  320.978333   274.485000    ...      12.793333   \n",
       "14.0   277.428333   276.891667  323.908333   274.638333    ...      40.848333   \n",
       "15.0   278.208333   277.673333  327.438333   275.178333    ...      51.280000   \n",
       "16.0   279.595000   279.348333  337.640000   276.196667    ...     156.750000   \n",
       "17.0   281.243182   280.965909  342.279545   277.679545    ...     135.852273   \n",
       "\n",
       "          netir    totalnet      temp         rh   windspd     winddir  \\\n",
       "hour                                                                     \n",
       "6.0  -57.043750  -57.043750  0.593750  61.012500  1.137500  116.625000   \n",
       "7.0  -56.091667  -56.091667  0.088333  63.146667  0.618333  112.858333   \n",
       "8.0  -55.028333  -55.028333  0.830000  57.490000  1.448333  132.271667   \n",
       "9.0  -53.295000  -53.295000  1.676667  53.623333  2.033333  137.526667   \n",
       "10.0 -16.146667  -16.146667  1.591667  56.341667  1.473333  151.643333   \n",
       "11.0   0.000000    0.000000  1.773333  59.400000  0.791667  162.733333   \n",
       "12.0   0.000000    0.543333  2.150000  62.286667  1.993333  152.851667   \n",
       "13.0   0.000000   12.793333  2.198333  71.641667  2.133333  160.955000   \n",
       "14.0  -0.015000   40.835000  2.188333  82.856667  2.678333  166.960000   \n",
       "15.0   0.000000   51.280000  2.543333  89.940000  2.470000  150.240000   \n",
       "16.0  -2.490000  154.268333  3.485000  91.268333  3.808333  153.068333   \n",
       "17.0  -3.006818  132.838636  4.775000  86.770455  4.115909  165.429545   \n",
       "\n",
       "         pressure         ghi        Kt  \n",
       "hour                                     \n",
       "6.0   1013.318750   30.663084 -0.182511  \n",
       "7.0   1013.015000  115.766684 -0.058923  \n",
       "8.0   1011.976667  310.197139 -0.017190  \n",
       "9.0   1011.566667  491.373002 -0.011644  \n",
       "10.0  1011.851667  628.392880 -0.008778  \n",
       "11.0  1012.278333  707.999345 -0.006309  \n",
       "12.0  1012.153333  723.565963 -0.007017  \n",
       "13.0  1012.345000  673.851495  0.015885  \n",
       "14.0  1012.361667  562.891372  0.082049  \n",
       "15.0  1012.115000  400.469352  0.149594  \n",
       "16.0  1011.073333  205.793937  1.063090  \n",
       "17.0  1010.206818   55.699158  3.182868  \n",
       "\n",
       "[12 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>7.0</th>\n",
       "      <th>31.0</th>\n",
       "      <th>19.0</th>\n",
       "      <td>24.717</td>\n",
       "      <td>619.511667</td>\n",
       "      <td>113.963333</td>\n",
       "      <td>292.741667</td>\n",
       "      <td>347.925</td>\n",
       "      <td>421.721667</td>\n",
       "      <td>308.51</td>\n",
       "      <td>308.396667</td>\n",
       "      <td>479.341667</td>\n",
       "      <td>304.19</td>\n",
       "      <td>...</td>\n",
       "      <td>500.905</td>\n",
       "      <td>-57.635</td>\n",
       "      <td>443.276667</td>\n",
       "      <td>28.41</td>\n",
       "      <td>62.248333</td>\n",
       "      <td>1.366667</td>\n",
       "      <td>291.705</td>\n",
       "      <td>1005.448333</td>\n",
       "      <td>45.154656</td>\n",
       "      <td>1801.872849</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  \\\n",
       "month day  hour                                                        \n",
       "7.0   31.0 19.0  24.717  619.511667  113.963333  292.741667  347.925   \n",
       "\n",
       "                      dw_ir  dw_casetemp  dw_dometemp       uw_ir  \\\n",
       "month day  hour                                                     \n",
       "7.0   31.0 19.0  421.721667       308.51   308.396667  479.341667   \n",
       "\n",
       "                 uw_casetemp     ...       netsolar   netir    totalnet  \\\n",
       "month day  hour                  ...                                      \n",
       "7.0   31.0 19.0       304.19     ...        500.905 -57.635  443.276667   \n",
       "\n",
       "                  temp         rh   windspd  winddir     pressure        ghi  \\\n",
       "month day  hour                                                                \n",
       "7.0   31.0 19.0  28.41  62.248333  1.366667  291.705  1005.448333  45.154656   \n",
       "\n",
       "                          Kt  \n",
       "month day  hour               \n",
       "7.0   31.0 19.0  1801.872849  \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 (9000, 22)\n",
      "y1_train shape is (9000,)\n",
      "X2_test shape is (4300, 22)\n",
      "y2_test shape is (4300,)\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: 0.015558886341750622. Test_MSE: 0.0025503040942971653\n",
      "Epoch: 2 Iteration: 200. Train_MSE: 0.004604177549481392. Test_MSE: 0.0020506100860697225\n",
      "Epoch: 3 Iteration: 300. Train_MSE: 0.0032947715371847153. Test_MSE: 0.0017051522417383647\n",
      "Epoch: 4 Iteration: 400. Train_MSE: 0.011328373104333878. Test_MSE: 0.0014336881187855186\n",
      "Epoch: 5 Iteration: 500. Train_MSE: 0.002388679888099432. Test_MSE: 0.001236024412817214\n",
      "Epoch: 6 Iteration: 600. Train_MSE: 0.0007716209511272609. Test_MSE: 0.001116978564283721\n",
      "Epoch: 7 Iteration: 700. Train_MSE: 0.0011255486169829965. Test_MSE: 0.001036727939696899\n",
      "Epoch: 8 Iteration: 800. Train_MSE: 0.006541411858052015. Test_MSE: 0.0009643539664770516\n",
      "Epoch: 10 Iteration: 900. Train_MSE: 0.0016913466388359666. Test_MSE: 0.0008938629658557895\n",
      "Epoch: 11 Iteration: 1000. Train_MSE: 0.0010945528047159314. Test_MSE: 0.000864773612551133\n",
      "Epoch: 12 Iteration: 1100. Train_MSE: 0.001325509394519031. Test_MSE: 0.0008475916630837387\n",
      "Epoch: 13 Iteration: 1200. Train_MSE: 0.00499088317155838. Test_MSE: 0.0008195420966831146\n",
      "Epoch: 14 Iteration: 1300. Train_MSE: 0.0012502808822318912. Test_MSE: 0.000789639541039516\n",
      "Epoch: 15 Iteration: 1400. Train_MSE: 0.0018785340944305062. Test_MSE: 0.00077856463282153\n",
      "Epoch: 16 Iteration: 1500. Train_MSE: 0.0012798559619113803. Test_MSE: 0.0007736817318428178\n",
      "Epoch: 17 Iteration: 1600. Train_MSE: 0.002960616024211049. Test_MSE: 0.0007595177447390017\n",
      "Epoch: 19 Iteration: 1700. Train_MSE: 0.0014976868405938148. Test_MSE: 0.0007304006166239755\n",
      "Epoch: 20 Iteration: 1800. Train_MSE: 0.0011273921700194478. Test_MSE: 0.0007249747420803838\n",
      "Epoch: 21 Iteration: 1900. Train_MSE: 0.001032331376336515. Test_MSE: 0.0007244798845044878\n",
      "Epoch: 22 Iteration: 2000. Train_MSE: 0.0025383708998560905. Test_MSE: 0.0007143075425272141\n",
      "Epoch: 23 Iteration: 2100. Train_MSE: 0.00122454937081784. Test_MSE: 0.0006953996762621382\n",
      "Epoch: 24 Iteration: 2200. Train_MSE: 0.0006897720159031451. Test_MSE: 0.0006901804039188575\n",
      "Epoch: 25 Iteration: 2300. Train_MSE: 0.0007432717247866094. Test_MSE: 0.0006895476997716145\n",
      "Epoch: 26 Iteration: 2400. Train_MSE: 0.0012310941237956285. Test_MSE: 0.0006822095207578043\n",
      "Epoch: 28 Iteration: 2500. Train_MSE: 0.0008049879688769579. Test_MSE: 0.0006598330819041421\n",
      "Epoch: 29 Iteration: 2600. Train_MSE: 0.0014985372545197606. Test_MSE: 0.0006552114863261707\n",
      "Epoch: 30 Iteration: 2700. Train_MSE: 0.0010624034330248833. Test_MSE: 0.0006559396715195382\n",
      "Epoch: 31 Iteration: 2800. Train_MSE: 0.0015431479550898075. Test_MSE: 0.0006497733158995041\n",
      "Epoch: 32 Iteration: 2900. Train_MSE: 0.0016115104081109166. Test_MSE: 0.0006343963355789229\n",
      "Epoch: 33 Iteration: 3000. Train_MSE: 0.0014043196570128202. Test_MSE: 0.0006299624733572259\n",
      "Epoch: 34 Iteration: 3100. Train_MSE: 0.0006364044384099543. Test_MSE: 0.0006296110758117011\n",
      "Epoch: 35 Iteration: 3200. Train_MSE: 0.00168648362159729. Test_MSE: 0.0006240726584860652\n",
      "Epoch: 37 Iteration: 3300. Train_MSE: 0.0017637324053794146. Test_MSE: 0.000606304228051678\n",
      "Epoch: 38 Iteration: 3400. Train_MSE: 0.0007741882000118494. Test_MSE: 0.0006018766328536525\n",
      "Epoch: 39 Iteration: 3500. Train_MSE: 0.0010036296444013715. Test_MSE: 0.0006025782975444527\n",
      "Epoch: 40 Iteration: 3600. Train_MSE: 0.0012842569267377257. Test_MSE: 0.000598294743125085\n",
      "Epoch: 41 Iteration: 3700. Train_MSE: 0.0009219128405675292. Test_MSE: 0.0005858115661356615\n",
      "Epoch: 42 Iteration: 3800. Train_MSE: 0.0008632889366708696. Test_MSE: 0.0005822112001180112\n",
      "Epoch: 43 Iteration: 3900. Train_MSE: 0.0005782795487903059. Test_MSE: 0.0005818606867411539\n",
      "Epoch: 44 Iteration: 4000. Train_MSE: 0.0012043133610859513. Test_MSE: 0.0005774070633981517\n",
      "Epoch: 46 Iteration: 4100. Train_MSE: 0.0020835809409618378. Test_MSE: 0.0005638424124400776\n",
      "Epoch: 47 Iteration: 4200. Train_MSE: 0.002791895531117916. Test_MSE: 0.0005589034051037414\n",
      "Epoch: 48 Iteration: 4300. Train_MSE: 0.0006601981585845351. Test_MSE: 0.0005594240555573135\n",
      "Epoch: 49 Iteration: 4400. Train_MSE: 0.0015604713698849082. Test_MSE: 0.0005572249643346194\n",
      "Epoch: 50 Iteration: 4500. Train_MSE: 0.002172840991988778. Test_MSE: 0.0005457769498341085\n",
      "Epoch: 51 Iteration: 4600. Train_MSE: 0.0008259561145678163. Test_MSE: 0.0005430454419920413\n",
      "Epoch: 52 Iteration: 4700. Train_MSE: 0.0014403548557311296. Test_MSE: 0.0005428698643701365\n",
      "Epoch: 53 Iteration: 4800. Train_MSE: 0.0009219893254339695. Test_MSE: 0.000539597853980088\n",
      "Epoch: 55 Iteration: 4900. Train_MSE: 0.002839997410774231. Test_MSE: 0.0005289076253208527\n",
      "Epoch: 56 Iteration: 5000. Train_MSE: 0.0016895298613235354. Test_MSE: 0.0005239260512453308\n",
      "Epoch: 57 Iteration: 5100. Train_MSE: 0.0006252311286516488. Test_MSE: 0.0005240207484135189\n",
      "Epoch: 58 Iteration: 5200. Train_MSE: 0.0009561079787090421. Test_MSE: 0.0005232670666264087\n",
      "Epoch: 59 Iteration: 5300. Train_MSE: 0.0012216352624818683. Test_MSE: 0.0005135726234511359\n",
      "Epoch: 60 Iteration: 5400. Train_MSE: 0.0008105009910650551. Test_MSE: 0.0005108657618127922\n",
      "Epoch: 61 Iteration: 5500. Train_MSE: 0.007728679105639458. Test_MSE: 0.0005105100462362896\n",
      "Epoch: 62 Iteration: 5600. Train_MSE: 0.0010505398968234658. Test_MSE: 0.000508498493666985\n",
      "Epoch: 64 Iteration: 5700. Train_MSE: 0.002378086792305112. Test_MSE: 0.0005002540722679169\n",
      "Epoch: 65 Iteration: 5800. Train_MSE: 0.0004910534480586648. Test_MSE: 0.0004947643628032292\n",
      "Epoch: 66 Iteration: 5900. Train_MSE: 0.013115502893924713. Test_MSE: 0.0004945149139185629\n",
      "Epoch: 67 Iteration: 6000. Train_MSE: 0.000471468607429415. Test_MSE: 0.0004942568599144686\n",
      "Epoch: 68 Iteration: 6100. Train_MSE: 0.001684844377450645. Test_MSE: 0.00048644281368699063\n",
      "Epoch: 69 Iteration: 6200. Train_MSE: 0.0004363102780189365. Test_MSE: 0.00048412135150084656\n",
      "Epoch: 70 Iteration: 6300. Train_MSE: 0.0011899222154170275. Test_MSE: 0.0004836491107151808\n",
      "Epoch: 71 Iteration: 6400. Train_MSE: 0.00033142033498734236. Test_MSE: 0.000482295460553898\n",
      "Epoch: 73 Iteration: 6500. Train_MSE: 0.0010176058858633041. Test_MSE: 0.00047589959096310853\n",
      "Epoch: 74 Iteration: 6600. Train_MSE: 0.0007050561835058033. Test_MSE: 0.00046997439120168043\n",
      "Epoch: 75 Iteration: 6700. Train_MSE: 0.0016538500785827637. Test_MSE: 0.00047002915848669345\n",
      "Epoch: 76 Iteration: 6800. Train_MSE: 0.0003542100021149963. Test_MSE: 0.0004700431657774756\n",
      "Epoch: 77 Iteration: 6900. Train_MSE: 0.0018622128991410136. Test_MSE: 0.0004636033880784169\n",
      "Epoch: 78 Iteration: 7000. Train_MSE: 0.0005620373995043337. Test_MSE: 0.00046124314254340694\n",
      "Epoch: 79 Iteration: 7100. Train_MSE: 0.000940892321523279. Test_MSE: 0.0004607686628872755\n",
      "Epoch: 80 Iteration: 7200. Train_MSE: 0.00043270879541523755. Test_MSE: 0.0004595919092955187\n",
      "Epoch: 82 Iteration: 7300. Train_MSE: 0.0018275593174621463. Test_MSE: 0.00045459340405188233\n",
      "Epoch: 83 Iteration: 7400. Train_MSE: 0.0004049254348501563. Test_MSE: 0.0004491579203492796\n",
      "Epoch: 84 Iteration: 7500. Train_MSE: 0.0005957306711934507. Test_MSE: 0.000448968977411996\n",
      "Epoch: 85 Iteration: 7600. Train_MSE: 0.00046982194180600345. Test_MSE: 0.00044926136673924564\n",
      "Epoch: 86 Iteration: 7700. Train_MSE: 0.0018139092717319727. Test_MSE: 0.0004445258428749062\n",
      "Epoch: 87 Iteration: 7800. Train_MSE: 0.0008489580359309912. Test_MSE: 0.0004417778414889063\n",
      "Epoch: 88 Iteration: 7900. Train_MSE: 0.0005791077273897827. Test_MSE: 0.00044112619937506856\n",
      "Epoch: 89 Iteration: 8000. Train_MSE: 0.0002706671366468072. Test_MSE: 0.00044045117155024684\n",
      "Epoch: 91 Iteration: 8100. Train_MSE: 0.002325597917661071. Test_MSE: 0.0004366086809758285\n",
      "Epoch: 92 Iteration: 8200. Train_MSE: 0.0005566838663071394. Test_MSE: 0.00043117124929580654\n",
      "Epoch: 93 Iteration: 8300. Train_MSE: 0.0004883635556325316. Test_MSE: 0.0004308199889771486\n",
      "Epoch: 94 Iteration: 8400. Train_MSE: 0.00047097555943764746. Test_MSE: 0.0004313853044781707\n",
      "Epoch: 95 Iteration: 8500. Train_MSE: 0.0014417286729440093. Test_MSE: 0.0004277141217633859\n",
      "Epoch: 96 Iteration: 8600. Train_MSE: 0.0007263406296260655. Test_MSE: 0.00042465926990645767\n",
      "Epoch: 97 Iteration: 8700. Train_MSE: 0.0010498439660295844. Test_MSE: 0.000424196135031516\n",
      "Epoch: 98 Iteration: 8800. Train_MSE: 0.00040213725878857076. Test_MSE: 0.0004236970818295858\n",
      "Epoch: 99 Iteration: 8900. Train_MSE: 0.000895331206265837. Test_MSE: 0.0004208563478474793\n",
      "Epoch: 101 Iteration: 9000. Train_MSE: 0.0005598215502686799. Test_MSE: 0.0004155657269015303\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 102 Iteration: 9100. Train_MSE: 0.00043001066660508513. Test_MSE: 0.0004150601612288223\n",
      "Epoch: 103 Iteration: 9200. Train_MSE: 0.0010319550056010485. Test_MSE: 0.00041553217136354416\n",
      "Epoch: 104 Iteration: 9300. Train_MSE: 0.0010595557978376746. Test_MSE: 0.00041281883874733294\n",
      "Epoch: 105 Iteration: 9400. Train_MSE: 0.0005688960663974285. Test_MSE: 0.000410193553242442\n",
      "Epoch: 106 Iteration: 9500. Train_MSE: 0.0005015544011257589. Test_MSE: 0.00040952120228146896\n",
      "Epoch: 107 Iteration: 9600. Train_MSE: 0.0003941172326449305. Test_MSE: 0.0004089018268505511\n",
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      "Epoch: 110 Iteration: 9800. Train_MSE: 0.0007390262908302248. Test_MSE: 0.0004016393608556996\n",
      "Epoch: 111 Iteration: 9900. Train_MSE: 0.00033009768230840564. Test_MSE: 0.0004012300768139955\n",
      "Epoch: 112 Iteration: 10000. Train_MSE: 0.00039372974424622953. Test_MSE: 0.000401645495975243\n",
      "Epoch: 113 Iteration: 10100. Train_MSE: 0.0014142919098958373. Test_MSE: 0.00039969342220983617\n",
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      "Epoch: 920 Iteration: 81900. Train_MSE: 0.00020851526642218232. Test_MSE: 0.00018549277676981255\n",
      "Epoch: 921 Iteration: 82000. Train_MSE: 0.00016777031123638153. Test_MSE: 0.0001854362231052339\n",
      "Epoch: 922 Iteration: 82100. Train_MSE: 0.000200085953110829. Test_MSE: 0.0001853984035179723\n",
      "Epoch: 923 Iteration: 82200. Train_MSE: 0.00015359497047029436. Test_MSE: 0.00018559667653708889\n",
      "Epoch: 924 Iteration: 82300. Train_MSE: 0.00025264197029173374. Test_MSE: 0.00018551132491791185\n",
      "Epoch: 925 Iteration: 82400. Train_MSE: 0.00013806449715048075. Test_MSE: 0.0001851006612854342\n",
      "Epoch: 926 Iteration: 82500. Train_MSE: 0.00016298131959047168. Test_MSE: 0.00018510074863677464\n",
      "Epoch: 928 Iteration: 82600. Train_MSE: 0.00013758230488747358. Test_MSE: 0.00018487458018768397\n",
      "Epoch: 929 Iteration: 82700. Train_MSE: 0.0007836039294488728. Test_MSE: 0.00018469917830859028\n",
      "Epoch: 930 Iteration: 82800. Train_MSE: 0.0002212025283370167. Test_MSE: 0.00018460013385829976\n",
      "Epoch: 931 Iteration: 82900. Train_MSE: 0.00016014209541026503. Test_MSE: 0.00018460023896411799\n",
      "Epoch: 932 Iteration: 83000. Train_MSE: 0.00019796665583271533. Test_MSE: 0.00018477623354093184\n",
      "Epoch: 933 Iteration: 83100. Train_MSE: 0.001100056106224656. Test_MSE: 0.00018478038996892857\n",
      "Epoch: 934 Iteration: 83200. Train_MSE: 0.00010592229955364019. Test_MSE: 0.00018432979636316506\n",
      "Epoch: 935 Iteration: 83300. Train_MSE: 0.0002123816084349528. Test_MSE: 0.00018422075861569735\n",
      "Epoch: 937 Iteration: 83400. Train_MSE: 0.00015643573715351522. Test_MSE: 0.00018412914932298993\n",
      "Epoch: 938 Iteration: 83500. Train_MSE: 0.00025335271493531764. Test_MSE: 0.00018393067448351706\n",
      "Epoch: 939 Iteration: 83600. Train_MSE: 0.0001294494140893221. Test_MSE: 0.0001837648598750958\n",
      "Epoch: 940 Iteration: 83700. Train_MSE: 0.00013432835112325847. Test_MSE: 0.00018376310893888527\n",
      "Epoch: 941 Iteration: 83800. Train_MSE: 0.00019430910469964147. Test_MSE: 0.00018391758466470898\n",
      "Epoch: 942 Iteration: 83900. Train_MSE: 0.0003018027055077255. Test_MSE: 0.00018405336053579078\n",
      "Epoch: 943 Iteration: 84000. Train_MSE: 0.00011886733409482986. Test_MSE: 0.0001835633310747749\n",
      "Epoch: 944 Iteration: 84100. Train_MSE: 0.00013775361003354192. Test_MSE: 0.00018334267404631873\n",
      "Epoch: 946 Iteration: 84200. Train_MSE: 0.00016695752856321633. Test_MSE: 0.0001833439439625481\n",
      "Epoch: 947 Iteration: 84300. Train_MSE: 0.002590419724583626. Test_MSE: 0.00018312993592577636\n",
      "Epoch: 948 Iteration: 84400. Train_MSE: 9.598708129487932e-05. Test_MSE: 0.0001829716582035032\n",
      "Epoch: 949 Iteration: 84500. Train_MSE: 0.0005732262507081032. Test_MSE: 0.00018305928919522732\n",
      "Epoch: 950 Iteration: 84600. Train_MSE: 0.0003562177298590541. Test_MSE: 0.00018303816936103025\n",
      "Epoch: 951 Iteration: 84700. Train_MSE: 0.0004312677774578333. Test_MSE: 0.00018324872876434242\n",
      "Epoch: 952 Iteration: 84800. Train_MSE: 0.0009201125940307975. Test_MSE: 0.00018280285978699873\n",
      "Epoch: 953 Iteration: 84900. Train_MSE: 0.00011615354742389172. Test_MSE: 0.0001825542010545223\n",
      "Epoch: 955 Iteration: 85000. Train_MSE: 0.00020899309311062098. Test_MSE: 0.00018258950157604446\n",
      "Epoch: 956 Iteration: 85100. Train_MSE: 0.0014690384268760681. Test_MSE: 0.00018245776732328224\n",
      "Epoch: 957 Iteration: 85200. Train_MSE: 8.955932571552694e-05. Test_MSE: 0.00018220607310573728\n",
      "Epoch: 958 Iteration: 85300. Train_MSE: 0.00019553185848053545. Test_MSE: 0.00018233957647382758\n",
      "Epoch: 959 Iteration: 85400. Train_MSE: 0.0002437429502606392. Test_MSE: 0.00018215380718013488\n",
      "Epoch: 960 Iteration: 85500. Train_MSE: 0.00020938973466400057. Test_MSE: 0.00018249629377186416\n",
      "Epoch: 961 Iteration: 85600. Train_MSE: 0.007566017564386129. Test_MSE: 0.0001820750471499712\n",
      "Epoch: 962 Iteration: 85700. Train_MSE: 0.00014016329078003764. Test_MSE: 0.0001818421457552026\n",
      "Epoch: 964 Iteration: 85800. Train_MSE: 0.00026755203725770116. Test_MSE: 0.00018190012795119375\n",
      "Epoch: 965 Iteration: 85900. Train_MSE: 0.00014202165766619146. Test_MSE: 0.0001817397293897689\n",
      "Epoch: 966 Iteration: 86000. Train_MSE: 0.013239245861768723. Test_MSE: 0.00018143358602751538\n",
      "Epoch: 967 Iteration: 86100. Train_MSE: 9.6087773272302e-05. Test_MSE: 0.00018155022035470393\n",
      "Epoch: 968 Iteration: 86200. Train_MSE: 0.00017153378576040268. Test_MSE: 0.00018132699766764624\n",
      "Epoch: 969 Iteration: 86300. Train_MSE: 0.00015294244803953916. Test_MSE: 0.0001817719325034201\n",
      "Epoch: 970 Iteration: 86400. Train_MSE: 0.0010573803447186947. Test_MSE: 0.00018143766753460588\n",
      "Epoch: 971 Iteration: 86500. Train_MSE: 0.00011742244532797486. Test_MSE: 0.00018114382281660604\n",
      "Epoch: 973 Iteration: 86600. Train_MSE: 0.0001868050021585077. Test_MSE: 0.0001812008506167624\n",
      "Epoch: 974 Iteration: 86700. Train_MSE: 0.00021882705914322287. Test_MSE: 0.00018093854369753947\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 975 Iteration: 86800. Train_MSE: 0.0014764608349651098. Test_MSE: 0.0001808412305360992\n",
      "Epoch: 976 Iteration: 86900. Train_MSE: 9.935961861629039e-05. Test_MSE: 0.0001808494738381976\n",
      "Epoch: 977 Iteration: 87000. Train_MSE: 0.0002483181597199291. Test_MSE: 0.000180623532165035\n",
      "Epoch: 978 Iteration: 87100. Train_MSE: 0.0001329137885477394. Test_MSE: 0.00018103479531350334\n",
      "Epoch: 979 Iteration: 87200. Train_MSE: 0.0007504434906877577. Test_MSE: 0.00018073720031533275\n",
      "Epoch: 980 Iteration: 87300. Train_MSE: 0.00011309063847875223. Test_MSE: 0.00018036997761541974\n",
      "Epoch: 982 Iteration: 87400. Train_MSE: 0.0002237101289210841. Test_MSE: 0.00018047927235285172\n",
      "Epoch: 983 Iteration: 87500. Train_MSE: 0.00023237681307364255. Test_MSE: 0.00018025728883014022\n",
      "Epoch: 984 Iteration: 87600. Train_MSE: 0.0003937310248147696. Test_MSE: 0.00018013851589626417\n",
      "Epoch: 985 Iteration: 87700. Train_MSE: 0.00014741138147655874. Test_MSE: 0.0001801297194400407\n",
      "Epoch: 986 Iteration: 87800. Train_MSE: 0.00035914365435019135. Test_MSE: 0.00017998766001902107\n",
      "Epoch: 987 Iteration: 87900. Train_MSE: 0.000250212469836697. Test_MSE: 0.00018029125442321656\n",
      "Epoch: 988 Iteration: 88000. Train_MSE: 0.00047756702406331897. Test_MSE: 0.00018002332010634922\n",
      "Epoch: 989 Iteration: 88100. Train_MSE: 8.816095942165703e-05. Test_MSE: 0.00017965596119205035\n",
      "Epoch: 991 Iteration: 88200. Train_MSE: 0.00045639582094736397. Test_MSE: 0.0001797549954145589\n",
      "Epoch: 992 Iteration: 88300. Train_MSE: 0.00016986153787001967. Test_MSE: 0.00017955095607533696\n",
      "Epoch: 993 Iteration: 88400. Train_MSE: 0.00035340694012120366. Test_MSE: 0.0001794099704591756\n",
      "Epoch: 994 Iteration: 88500. Train_MSE: 0.0001120680826716125. Test_MSE: 0.00017939061119791056\n",
      "Epoch: 995 Iteration: 88600. Train_MSE: 0.0002390881272731349. Test_MSE: 0.00017929400898781227\n",
      "Epoch: 996 Iteration: 88700. Train_MSE: 0.00022271960915531963. Test_MSE: 0.00017950239385413515\n",
      "Epoch: 997 Iteration: 88800. Train_MSE: 0.0009338147356174886. Test_MSE: 0.00017932854473189848\n",
      "Epoch: 998 Iteration: 88900. Train_MSE: 0.00018257343617733568. Test_MSE: 0.0001789282251258521\n",
      "Epoch: 999 Iteration: 89000. Train_MSE: 0.0001459860213799402. Test_MSE: 0.00017896077152937227\n"
     ]
    }
   ],
   "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": [
      "37380\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x170c7b70>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XklRb3fw2chtwvPL8XEQ0MnOqw9g4sH3B8tllSxoa2kqj0ddV0QvtufON3P/JQ3zs3ht5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\nGe6SVKD/B2sGZzTxAoKzAAAAAElFTkSuQmCC\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 0xe080eb8>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cbWZ2MzDk7g+b2d2kL94l4LbsnUA7gPuzdxQdBq6ZZ/4vZWP3AtMLjBURkQ6LTsfv7h0e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\n0JtvxAL6pPgAAAAASUVORK5CYII=\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": [
       "29.548728001081535"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_denorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4202.000000\n",
       "mean        5.440156\n",
       "std        36.662539\n",
       "min        -0.283893\n",
       "25%        -0.009338\n",
       "50%         0.105658\n",
       "75%         1.152612\n",
       "max      1801.872849\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}, columns=['Train Loss'])\n",
    "df_trainLoss.to_csv('RNN Paper Results/Exp1_GoodwinCreek_TrainLoss.csv')\n",
    "df_testLoss = pd.DataFrame(data={'Test Loss':test_loss}, columns=['Test Loss'])\n",
    "df_testLoss.to_csv('RNN Paper Results/Exp1_GoodwinCreek_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
}