fixed-time-horizon-prediction/Exp_1.2_PenState_2hour.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": [
    "pns = Location(40.798,-77.859, 'US/Eastern', 351.74, 'Penn State')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2009 = pd.DatetimeIndex(start='2009-01-01', end='2010-01-01', freq='1min',\n",
    "                        tz=pns.tz)   # 12 months of 2009 - For testing\n",
    "times2010and2011 = pd.DatetimeIndex(start='2010-01-01', end='2012-01-01', freq='1min',\n",
    "                        tz=pns.tz)   # 24 months of 2010 and 2011 - For training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009 = pns.get_clearsky(times2009) \n",
    "cs_2010and2011 = pns.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 00:00:00-05:00</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 00:01:00-05:00</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 00:02:00-05:00</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 00:03:00-05:00</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 00:04:00-05:00</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 00:00:00-05:00  0.0  2010      1    1     0    0\n",
       "1 2010-01-01 00:01:00-05:00  0.0  2010      1    1     0    1\n",
       "2 2010-01-01 00:02:00-05:00  0.0  2010      1    1     0    2\n",
       "3 2010-01-01 00:03:00-05:00  0.0  2010      1    1     0    3\n",
       "4 2010-01-01 00:04:00-05:00  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": [
       "(1049577, 48)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Penn_State\\\\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": [
       "(524846, 48)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Penn_State\\\\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": [
       "(12, 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": [
       "(1049577, 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": [
       "(524846, 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": [
       "(1049577, 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": [
       "(1039363, 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": [
       "(510853, 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": [
       "13173"
      ]
     },
     "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": [
       "(1039363, 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": [
       "6448"
      ]
     },
     "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": [
       "(1039363, 49)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(510853, 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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WLVrSqlUbXn75dV55ZS6DB19OmzbtaNGiFZs3bwRg+/aqiq1ly1Zs2aLt//vvHHJycgDo\n27cfX3/9FdHR0fTs2Yu1a1dTUlJK48ZNvL6Dzz5bxpgxj/HKK3PZuVOyadMftGrVio0bNwCwc6ck\nLq5xhXOmTZvChAmTmTgxnSZN4su3R0SEbrgP54rjTMAqhFjpvk86ECml/BNACPEVcBGQBHwJIKX8\nSQhxdhhlUigUYSAcZjWr1cq0aS/y0ksvkJd3DLvdQbt27Un3uOaWLZt48MF7iIiI4LHHnqBRozje\nfHMuX375BRZLHe66awSNGjXihhtu5v7778bhcNCqVQseeaSqg/2ss7rz3HNTue22oQC0b9+Bs8/u\nwb333kVJSSlnnJFKfHw8jzzyKJMnP8aSJe/QsGFD6taNrHCdvn37sXHjHwwffjuJiUk0bNgQgKZN\nEyguLqZ793OIjY3FbDbTq1dvn++gbdt2DB9+Gw0bNiI+Pp5OndJITExi2rQpLFnyLna7ncceq7hi\nGThwMHfffQf169enUaPG/P13TlDv3xcRLlfImulVQAjRGegJzAPaAyuAo1LK7u79Q4E2QCLwsZRy\nhXv7PqCNlNJrHJ/d7nDV5iYnCsVpi80GBw5AUlLtXSEpjOK1A2A4Vxw7gF1SShewQwhxDIjz2F8f\nOApY3f8vw+RLaQAhbacYH1+fnJzjIbteuFByhhYlZ+iJj69PToEDYptCgQMKaqfcte2dHjx4kClT\nnqiyvXfv8/j3v++sAYk04uPre90XTsUxFOgM3CuESEZTEAVCiLbAbmAg8CSQAlwBfCCE6AlsCqNM\nCoVCUatITEzklVfmVtle2xScJ+FUHPOBhUKIdYALTZE4gUWAGS2qar0Q4hfgEiHED2hLo5pTsQqF\nQqHwS9gUh5SyBC2CqjI9Kx3nBO4JlxwKhUKhCC2q5IhCoVAoAkJljisUipBhK7WRbTtIgjURa53q\nR1Xt3v0ns2e/RFFREYWFhZx3Xm+GDr2bDRt+49NPP+bJJ58JgdSQm5vL9OlPU1hYiMvlIjExidGj\nx3DkyBEmT57A3LkLA7re/v2ZzJ79EocOHSIqKorIyEhGjnyQNm3aMn/+6zRu3Jirrx5Sfvzdd9/B\nk08+zYEDWSF9rnChFIdCoag2dqed0V+O5pMtS9mfn0mzmBQGtbmM9F5TsZiCG2aOHz9OevoEpk59\nnubNW+BwOHj88Uf59NOPadGiVUjlX7LkbXr0OLd8MJ816wWWLfuY88+/MOBrFRVpxRnHj59EWloX\nALZu3cyMGdN0neCnIkpxKBSKapP+w0TmbjxRkDEjf1/571P6BNd0at261XTr1oPmzVsAYDabywv2\nbdr0R/lxngUMu3TpysiRD3DoUDbTpz9LSUkxeXnHuOOO4Zx/fj9uvfV62rVri9MZwZNPPl1+jYSE\nJL799huaNWtOly5nct99o4iIiCgv5+FwOHj66XRat25L/fqxZGZmcN99o3A4HNx5503Mm/cOdevW\nBeD779fQvXuPcqUB0KlTGi+//HpQ76E2ohSHQqGoFrZSGyt261fHXbFnORPOnRyU2ervv3NITm5W\nYZu1UlJh5QKGTz31uLuAYQQ33ngz3bqdzaZNfzB//uucf34/CgsLuffee4mPb17hOtdcM4TIyEiW\nLHmHxx9/lC5duvLII+MBTWn85z+TOPPMblx77b+w2QoYOvQW7rnnftav/5Fu3c4uVxoAWVlZNGt2\n4vqPPvow+fn5HD78N7Nmacr0vfcW8/XXK8uP+euvPQG/n5pEKQ6FQlEtsm0H2Z+vXx03Kz+TbNtB\nWjcIvDpuQkISO3Zsr3i9rP0VivpVLmBos9nYv38/Xbp05a235vPFF58CEdg9Gkq1bt2a/PyKOca/\n//4rl156GZdffhUlJSUsXvw2L730grvY4Q6io6MpLNQSj63WaLp27cbPP//I8uWfcccdwyvJncD2\n7VvLf3/22RmA5sdwOBwA3HjjTVV8HKcSKqpKoVBUiwRrIs1i9JtOJcekkGANrjpu7959WL/+h/Le\nG3a7nZdffpHdu/8sP6ZyAcMhQ24gNTWNefPmcOmll/H440/RrVvF8ncmU9Vh78MPl7B8+ecA1K1b\nl9at21CnjraKEOIMnn9+Jl99tZxdu3YCcMUV1/D555+Sm5tLu3btK1xLK874M5s3n8hlzszMICfn\nEBERXqt4nFKoFYdCoagW1jpWBrW5rIKPo4xBrQcHHV0VHR3DxIlPMm3aFJxOJzabjd69+3LNNUPY\nsOE3gCoFDJOSkunf/xIuvPAiZs2azjvvvEnTpgkcPXrU573Gjp3ACy88y9KlHxIZGUXDhg0ZM+Yx\nSktLAYiMjGLMmMeYMmUyc+cuJDU1jf37M7jmmn9VfR/u4oxz5rzMnDmHcTjsmM0Wxo2bQGJiUlDv\norYRtiKH4SQn53jIhK7Naf2eKDlDi5IztNiddqZteJJPti4jKz+T5JgUBrUeXK2oqnARinfqdDoZ\nOfIuZsx4mejomBBJVpGa/uzj4+vXSJFDhUJxmmAxWZh56Uwe6vJYSPM4aiNZWfuZMGEsV111bdiU\nRm1HKQ6FQhEyrHWsQTnCTyWSk5uxcOHimhajRlHOcYVCoVAEhFIcCoVCoQgIpTgUCoVCERBKcSgU\nCoUiIJTiUCgUCkVAKMWhUCgUioBQikOhUCgUAaEUh0KhUCgCQikOhUKhUASEUhwKhUKhCAilOBQK\nhUIREEpxKBQKhSIgDBU5FELEABcC7QEnsAv4WkpZFEbZFAqFQlEL8ak4hBBWYDJwLbAR2AvYgV7A\ni0KIT4CnpJT54RZUoVAoFLUDfyuOd4G5wGNSSqfnDiGECbgcWARcFR7xFAqFQlHb8Kc4rpNS6nbb\ncyuSz4QQn4deLIVCoVDUVvw5x81CiFFCiBeEEOd77hBCpAN4UywKhUKh+GfiT3G8DpwFZAFvCSEm\neOy7MmxSKRQKhaLW4s9UdbaU8kwAIcTbwNdCCJuUcibgtZG5QqFQKP65+FtxmIQQ0QBSyhxgMDBa\nCHEzoExUCoVCcRriT3G8DPwuhLgIQEq5H7gUeBo4I8yyKRQKhaIW4lNxSCnnooXc7vTYth1IBR4L\nr2gKhUKhqI34zRyXUu4UQvQRQkyTUv7bvS0fmGnkBkKIpsBvwCVoyYML0cxcm4H7pJROIcRk4DL3\n/tFSyp+DeRiFQqFQhB+vikMIEQncDIwA9gPzAr24EKIOWmRWoXvTDGCSlPI7IcQc4CohxF7gAuBc\noDnwMdAj0HspFAqF4uTga8VxP1q5kWFSyg+CvP50YA4nzFrdgdXu/68ABgASWOnOB9knhLAIIeLd\nznhdGjWyYrGYgxSpKvHx9UN2rXCi5AwtSs7Qc6rIquSsHl4Vh5TyBSHEeuBeIcTjwLtSymlGLyyE\nuAPIkVJ+JYQoUxwRHgmDx4EGQCxw2OPUsu1eFUdurs2oGH6Jj69PTs7xkF0vXCg5Q4uSM/ScKrIq\nOY3f3xs+fRxSynXAOiFEIjA8wPsOBVxCiIuBrsDbQFOP/fWBo0Ce+/+VtysUCoWiFmKorLqU8qAQ\nYlnlsiNSyjU+zik/VgjxHXAP8LwQop+U8jtgEPAtWon254QQ04EUwCSl/DvQB1EoFArFycFoP44l\naP6J/R6bXUD/AO/3CPCGEKIusA34SErpEEKsBX5ECw++L8BrKhQKheIkYkhxoJmazpBSOoK5iZSy\nn8evF+jsTwfSg7m2QqFQKE4uRlvHrgfahVMQhUKhUJwaGF1x/B+wRQiRhZakFwG4pJRtwiaZQqFQ\nKGolRhXHBDR/xt4wyqJQKBSKUwCjiuNvYK1q2qRQKBQKo4pjB/CTEGIVUFK2UUr5n7BIpVAoFIpa\ni1HFsc/9A6qBk0KhUJzW+FQcQoj3geXAHCll9skRSaEIEpsNU/ZBnAmJYLXWtDQKxT8WfyuOR9Ay\nvF8TQsQB36MVJ/xRSukMt3AKhSHsdqLTJxK54gtMmRk4ExMpvmQghfc8gDO5mVIiCkWI8dfIKVNK\n+YaU8jq0fhpfA1cCq92rEYWixolOn4h17mzMGfuIcLkwHziA9e2FxPXqTlzfHkRPGg92e3iFsNkw\n7dkNttAV4FQoaitGfRxIKe3Ad+4fhBDNwiOSQhEANhuRK77Q3RUBmDMysM6dDUDBFMPFnY3judrZ\nn4mzWQrFgy6jIH0qWAx/vRSKUwqjmeNVcPcfVyhqFFP2QUyZGX6Pi1yxPCyrgQqrHacTc8Y+rHNn\nE50+MeT3UihqC0ErDoWiNuBMSMSZmOj3OFNWJqbsg6G9uY/VTrgUlUJRGzCsONwVbRFCtBNCXCaE\nUEpHUfNYrRQPuszvYc7kFC3aKlh0fBim7IOY9mfqHh4WRaVQ1BIMDf5CiCeAt4QQLYA1wEPAi+EU\nTKEwSsGU5yjtlOrzmOKLBwQXXWW3Ez1pPHF9zyHuvG7E9T2n3NnuTEjE2SxF97RAFJWt1MaeY7ux\nlaoViuLUwOiq4Sq0jn43obWQvRjoHTapFIpAsFjIm/c23urhuIDCYSP8X6fSqsJWauOvp+5hy+ez\n2Va0j0JzJR+G1UrxwEG6lyoeeKlfRWV32pm0bjx9l5zDeYu60XfJOUxaNx67M8wRYApFNTEa9mGS\nUhYKIS4HJrnNVNFhlEuhCAhncjOczVtgzthXdV/zllVWBrZSG9sOb6HIXkxRcT62t14m6fvfqXcw\nh5KEprx4cQwr6mdR2LQI7tbOsZbAwB1w02aI//VjivdeST1LNlFJUGSBv+tBk0JoVASxpkL+OrwF\niKBlbCusdaoqkfQfJjJ34+zy3zPy95X/PqVPGCLAFIoQYbisuhBiM2BDM1WtBj4Lm1QKRaC4fR1l\nobee5A6+hG2Feyg6XkxBSQGv/jGLH/avpdBRqB3gAuKAK8rOOAQRh8BJhQI7tkhY2hmWprmP+WIQ\nJFCuWCrgehvefxuAKFMUA1pfyogu9xPjNNO6MApn0wRW7NZ3rK/Ys5wJ507WVTYKRW3AqOKYCrwE\n7JdSOoUQDwC54RNLoQicgvSpgBbRVHQogw2dmvDUgEjWRi+i8P153k8MtPpahJf/ezmmyFnEZ38u\n47NdywCoVwI9cuqQ2axU9/ys/EyybQdp3UC1u1HUTvzVqmqO9qe9HK30SDMhBEAe8BXQMdwCKhRG\nsblK2DTqJl7suY8V+zJwcEjbUVuK47iVRGEkrEkpxZtTJjkmhQRrNSLAFIow42/F8SRwIZCMZqIq\noxTQX2crFCeZ7IKDPPLdg6zNWE2hs7CmxTGOl9XKoNaDlZlKUavxqTiklEMBhBDjpZQVvHVleR0K\nxcnGVmpjb94e8oryePDbe9mT92dNixQy0pp0Jr3X1JoWQ6HwiVEfx9VAueJwR1X9BnQOh1AKhR55\nxXk8/N0DfLVnOcXO4poWJywcLTpKiaMEi0nVuVLUXvz5OL4B+rn/72kptqOiqhQnibziPMasfpBP\ndy3F5TVboxr4uaQpwgROJ87KpqUwtDTLys/SdYzbSm1k2w6SYE1UZixFjePPVNUfQAgxS0o56uSI\npFBoA+WfR3cy6/cZLN/9OXZXiJLiPJSE2QHXbIN7f4YjcXVpeqSEqEq3qdskiYZLv4d6VvZmb+fY\nwT85HA1xsclEWeqVH1dkLyKn8BDLdn3MLwfWk10YXLmR5JjkCo7xsiTBFbu/YH9+JknRyZyX3IuR\nXR+kbcN2SokoagSj6+HxQohrgBjc1aqB1lLKJ8ImmeK0xO6089jasXywffGJPIvq4qqoJI7Wg5Tj\nkPo3WEuhtH0HLJt2EaETfeU6dIgjh/Nwtm7CGSndIKWbz1td2e7q8tVBbN1YDtmyKbIX43Damfvu\ncD6P3IPDjNfVyuA2l1dQBmNWjqmQJLi/IJOPdn7ARzs/IMoUxeC2l/Pc+TOJjYwN5s0oFEFhVHEs\nBhoB7YC1aJFW68IllOL0JK84j4Ef9+PPo7tCcr16xdBvDzyxBrrkaEoCwGU2E+Fy4UhIxDZoMAUT\n0om7sJd+1nkgxRHdrWutCYnlpqbG9ZqU7xvwnoPig/BHPEw5H75rDTZ3iElMnRhuPOPmCo5xW6mN\nZduXeb1dkbOIT3Z+xLKdn3B1+2uVAlGcNIwqji5Ae2AWsACYBKgOgApj+OkFnlecx7g1o/l051Ic\nOKp9uyvaXM3Dif+mx+U3El2i48BwuWDVKo60TS2Xx1vWefGgwf6LIxpo5lTWN8TqgvOy4Iv3wFYH\ndjcEVwQ0XLaKqHYVCzVm2w59oLkAAAAgAElEQVSSccx/rxEnTrcCWcpNZ9zMcxfMVM51RVgxWuTw\nkJTSBWwHukgpdwMqHFfhGx+VZUEzS41d/RAdF7Tik50fVUtpRJoiuartNey6K5P5l75NaocLiEpo\nrnuss1lz6NmzgkIoSJ+K7e6ROJq3xGU242jeEtvdI8uz0X1hpJmTMyERV3TF8m7WUkjLgVRbfaKS\nW1e5boI1kRYNWhh9BThx8O62t+m4oDW5RUcMn6dQBIrRaclmIcTLwGxgkRAimbDElCj+EbhXGPXm\nvIr1zTfKN5cNqAAHHp9YbbNU69g2vNR/NrGRDaoWEvRRu6p40GCsVisUHD+x0WKhYMo0CiZM9rk6\n0ntWX82cCiZMDq6cO2CtY+Wqjlcxa/2sgM7LKzmGWNCKa9sPUeYrRVgwqjhGAr2klFuFEJOBi9BK\nrCsUJ7DbYfRo4j5ZiiljH0RUnVvk1YURBQv5cP7coFYYJkxc3HIAL/R7mYToBJ/HetauMmVl4kxO\noXjQYArSp+J1KLdacbY2XiPKZzOn/RmaEmrdBlP2QSK8dASMKLSVH1eZ6QOmU1hYwqKt71Bgzzcs\nF8AnOz/i013LuLXTbTzdd7oyXylCRoTL5T2IXQjxLbASWCml/O2kSeWHnJzjIQvmj4+vT07Ocf8H\n1jCngpzRk8brzvAB7CYYOQgWngX2IMYvE2aubn9NcDNoHR9LyN6nzUZc33N0Hesus5nC24ZSMHUa\nlJR4Pc7RvCVH1q7XXZmUyZlXnMelH/dn19EdQYnZMe4Mvrn++7Aqj1PhbxSUnAHc36tVyZ+P4xnA\nCrwghMgQQrwnhLjLXfxQoTiBD5NNkRmSHoZ5PQJXGiZMXNt+CDvu2sucSxboKw2dtq4VKFtFBGky\n8omP1rURDgfWN98getI4n8cZccDHRsay5safuD31LuqZ6/k8Vo/tR7bRcX4r5ftQhAR/CYAr0VYc\nZbWpegB9gf8KISKllF6r4wohzMAbgAAcwJ1ofpGFaGlYm4H73GXaJwOXoWWkj5ZS/lzN51KcZLyZ\nbPLqQstRcDTAtl8mIri6/XW+Vxie0UyZGTgTE7VopinPlUcznQwK0qeCvZR6b71JhKOq+a3eW2+C\nK4KCJ72bzoxgMVl4/oIXebLX1PLkyM/+XGpYzrzSPDouaM3tqXcq05WiWvg0VZUhtFrqA9HyN85A\ni65aKaV8zcc5VwNXSimHCiH6ofUpjwBmSCm/E0LMQSvNvheYjuY3aQ58LKXs4UseZaqqhVQy2eTV\nhZGD4b00cAY4PrU/BCtG/EFsUtVII0+8mcZK0zpzdOVqn8oj1O/TtGc3ced1I8LpvYa77e6RFEyZ\n5jc8ORA5j+QfouebnThqLglI3nYN2/Pldd+G1HFe6/9G3Sg5Dd8/OFOVEOJ1IcQutNyNOLQBPlVK\nebUvpQEgpVzGid5oLYFsoDta90CAFcDFQB80JeSSUu4DLEKIeP+PpahVuE0xdhM8OBAaj4XFXQNT\nGmY73PMz/LE2za/S8GUaq7N5E9ETx/u/oT8TVwA4ExKrtKetTOSK5dq9Qmg6a/7sC+RMLWHYL2AJ\noCrLrqM76TC/BeO+vAfnnztC8g4Upw/+nOPZwEbgY+ArKeWeQG8ghHgLuAYYAiyUUia7t/cHhqKt\nXg5LKWe7t68BhkopvcZp2u0Ol8ViDlQURTix2SAjgwfn/4uXozcFdKrZDtdvgTkrIojteCb8+CNE\nRfk+6c8/oX17LZlPj5QUkFJ/cLbbYcwY+PRT2LcPWrSAq66C6dOrZ+IaPRpm+QidNZs1mdq2Df4e\nnths0KkT7N0LBL/K63AIfvkqhdjLr6v+O1D8k/C64vBrqhJCdEMzUw0EmgJlkVbfSCkNraOEEInA\neiBWStnIve0q4BJgBxAlpXzOvX0DcImU8m9v11OmqlqEh5/h0JF9NH8YrRaTQa5tP4Tn0h4nbtc+\n7J1SoXETYyfabMSddxbmAwd0d7vMZo788JtuiKs3E1e5KSlY7HaiJ43z6utwNGvO0cUf4mzZyvBq\nw9fn7s08FoxfqUx5z2g6FPNTM30f7MXUVmv/Riuh5DR8/6CjqpBS/i6lfEZK2Q84B60Px1TA68AO\nIIS4VQjxmPtXG1oDz1/d/g7QWtGuBb4HBgohTEKIFoDJl9JQ1C6i0ydSd95s7k/bR8pDxpVGg0KQ\nN27TIqWSWmPve4FxpeGmpFdfr/u81pjyk7BXLZONxULBszMovG2o7u6IY7nEXdirSgZ9sHgzj8WW\nwIEZ0CSAtA+HBZacCSlNFzDu6wewO3Vk81MJQHH64HdNKoQ4A+jt/jkPyEfzT4zxc+onwJtu01Md\nYDSwDXjDHaG1DfhISukQQqwFfkRTZPcF+SyKk43NhvnL/9J9OGxMMn5a2gH43jWC4rhmgd/TM5Iq\nYx9OiwWTzsDlLcTVZ8JeVqbXRLxAKJg6DepYyqOnXPWsmPKPY8rXRnLPDPpqrXB8ZMdHOTTlMXIQ\nvNkdHAaLCznMsHDHW6zN/p6vhnxXwXleVlqljJA9h+KUw5+P4zBwCPjG/fOtlLLGA8GVqap24Pxz\nBwMWnm1YaVjscJeM5rlGt1Cc/kxFW7rBSCNvZiaXxQIuV8UQVz1bvY+EPV+JeEFhs2Hau4eGN1+P\nObNqsUIj9/P7ubsVadSidzAV6C8xcluncPb9ddl9fHdA4psjzNza6XYtdLfIfwJjfMuEWvc3qkdt\n/C7pUdNy+jJV+VtxdJZSZvk6QAgRJaUsCkoyxSnNmD9nGFYarWNa8X893yVmWDuKPQdKA5Vly/Fh\nZoqw2ym87gbyX5jle+D3U8MqpEmCVitE1cOUtV93d7VXOG5lWzBhMoU33EzcRX10vZkN9x3gh17r\nufCX29h2ZKvhyztcDhZuWcC6fWtYee5CmvhZqdHSdwkYxT8HfwvYl4UQw4UQ9SvvEELUF0LcBywJ\nj2iK2ordaeehbx/g3V2LDR3fsVEnvr/ld2I6dKkyMBupLFtGWWlyb9T9Ya0heQrSp8KoUUFVwg0U\nX2G6zsRkKCoK3K+i42uot2ghzuZeqgEnp2BKSuH/rl/H7e1vDShsF2DX8V20W9mHkddFYdcZMZxJ\nzYz3LFH8I/CnOP6FVj79FyHET0KIj4QQS4QQPwG/o4Vr/SvcQipqD3annYs/6MOibW8ZOr5T41S+\nuWGdfpZygI5qZ0IizkTvA5TpULY28/WHxQIzZ3Jk7XqO/PAbR9au12z04QhD9VWSJEhnua6yXTAP\nZ4OGuseXraQsJgvPX/IqGTlDuel/WiSVUZwmeD3VRuo9WtRWhX0NG4SnnIui1uJTcUgpnVLKV92l\nRUYA7wEfASOklO2llK9IKVVIxWmC3Wmn//t92GrA3GHGzB2pQ/n6X2u9lrYw4qiugI9BGHQiqWqy\nhpUHlXt9OGO0BbwpP9/vKqsKPpSt6egxbHcO97uSMk+ezhtNR5KzKIUOOYE9y46m0Hicli9Stvow\nHTumEghPMwyVHKltKOf4ycfutNPv/fPYkSv9HmvCxB+3S79lz4NyVNvtNBxwAXU2V00yLM/DMOA3\nqZH3GYSzvLKcpl07ievVXdeXUZ67kpBorKSJzYbzQCbj/3qJRTsWY3cFNgdMOwAb3gCzE458/yuN\ne3VX36UQUtNyViuPQ3GaoTNLL7IXkbqgrSGlAXBr6p3+lQYEVzHWYuHoytXazDopWXdmHYjf5KRi\n0Fnui3rzZntN5y1fcflbSR3+G8va1VBow9S2A89f9Arbh/7Fte2HYMZ49ubmJEh8GIrNEDv8DpXP\ncRqhFIdCw0tyl72kiDMXCnJLcg1dpmt8F57p+7zh2wbVstVioWDaCxz58feqPopwJviFAJ/Ocm9J\ni2XYbESuWul1d/HFA3yvMIqKaNi/N03S2tPwuiu0f/v3hqIiYiNjmXPJAuRde2lX0sDo43A4RiuZ\nX7hrM4waZfg8xamNYcUhhOgthLhHCFFXCHF+OIVSnHz0Zul1582m/2sdDCkNix3uOt6JX+75LbBy\n3e6WrUE5qnVm1gH7TU421ejL4evZXEDhsBE+b91w8EXU2byJCIeDCLR+IXU2b6Lh4IvKj4mNjGXN\nvTtJLYnz+yhlHI2GRuPgpqMLyDuabfg8xamLIcUhhBgFTAEeBuoDrwsh/GWOK04VdGbpdhOcNRy2\n1z3q9/T2R0xk5AzlmUe8RE8ZIUSO6mrN6E8SQa2y8PNszVv6rs57+G8s2/SDGizbtsLhE1V+LHWj\nWPXgLm4Xt2ExaLpyWmBJhyLE4o6MWz1av2SJ4h+D0RXHHWhFDguklIfRGjrpF+RRnHJUnsmWKY3N\nBpL7OtRrxerR+7TCeLWhqmo1O+2dFIJdZVXj2Sxbt4BO4UUAHA5tv+fxJovm+7hrL9e2vQ6T9zYj\nFS+FljR48Yd99ZVHCEvZK2oOo4rDIaX07BRThNbVT/EPwHMmG4jSaJwPa87/CEtM6JoBhYJgZ/Qn\nnSBWWcE+m71TqlbWXQ+zWduvQ2xkLO+sbUrus9CwwLCYbD28hQvf73VCeagCif8ojHYAfAHNjHol\nMA6tQdMOKeXo8IqnjwrHDT3Rk8ZTd95sug6HLQaVxt4Pm2Nb/UvtLa3to/5VrZHTT40ur3IG0EWw\njIb9e+uGMZemdeboN9/rn3T4b+L698F8IIsiM6Q8pDnEjdKxUSe+uWEdDZ6YGJ5S9kFQaz57P9S0\nnKEIxx0L7AT+AG4DvsB/dVzFKcSxMeM5a4QxpdHpABycAREDL68dph9vnKQEvwrmF6OmmGBn4GXX\nB//PVkmuvFffoLRTGi6zGRda3kdpWmeOLv8/7/L1743pgFauLsqhfe5pAcQXbM/dSp9F3Sle9Znu\n/toQ6aYIHKNGaStgkVL+SwjRDC2LvC6g1pn/AOxOO/0/vYjtBlIv0g7ArytaUDLsstpn+jnZeCYa\nZmbgitY6J0XYbL6LNQLRE8djffON8t/9ligPpBikH7kKb76V4suvxt65i9ceKJVLqJdhccKGufDA\nQJjXHewGRpDdx/fQ9GYY+SvMWKldo4xQlbJXnFyMrjgWA8nu/x93n/dOWCRSnFTKyohsL/jT77Ft\n6rfh6zt+JW/Nz6Gp7WSzYdq2BdO2rafkrLNCCLPLhSk/338ZEbud6Ecfpt7bC3Sv6W0GHkhSo1+5\n3l5I5NdfeW+c5SMXBrSBf/YKOPwcXP8HmhHbD3YLvNxTUzie1JZIN0VgGFUcLaWUEwGklHlSyklA\niBonK2qKcqWRa6z21Mrr12BKStFyIaoz0NvtRE8YS+O0dsRdcB5xF/SkcVo7oieMrR3OUiPmJj+D\naxmVFUF0+kSsC+bptpYFL7kmgSQ1BilXBRn85Is4EhNxxsQQWwLvL4V7fvF7u3LmnAPDLj9R56rW\nRLopAsKo4nAJITqX/SKE6AiUhkckxcnA7rRz8Yd9DSkNgDtTh5L01FT/NnkDg250+kSs817XZsFo\nJZZN+flY571es2VBAvA7+BpcKxznqQgMDOp6M/BAkhqDkquyDLGxXlcBzqRkjr61hAiPz/flL+GB\nH8FiZESIgPlnQ/d7LeTdPUKZO09RjCqOMcAqIcSvQohfga+AR8InliLcTFg7hq2Ht/g9zhxhYVjn\nEbywyuTbVOIedElN9atYIr/43Ov9Ipf/t8bMVoGYg3wl41U4zkMRGBnU9WbggSQ1OhMScSYl6x7r\n6zzghOIc0K/cIV5FviuuwinOqCCPxQkvfQWHn4d2f+ueVoWNTeycm7pWt7+HovZj6GOTUn4NtEBz\nig8FOkgp14RTMEX4yC7I5p0t/vtptIlthxz6F093f5LoFSt0jykzeZQ7U//6y+ega8o+6LXIH4Ap\nKyu0ZUGMRjkFWuPKT4n3MjwVgS8F4DKbsd05XH8GHkjin9VKSV//FYH0FFQFxekpGxEV80W8yBNb\nAtte0wIojLA9dyv93uulssxPQYyWHGkJTAXuBUYDs4UQ+t49Ra3F7rQzdvVDnPmWwOEnf7Njo06s\nu+lnYiNj/ZtK9u4xPOg6ExJxJjfzel9ncnJonKUBhrsGU+OqQjKeyYQzpj7OmPreE/N8KIDC2++k\nYNoLXgMOAkn8y5/yHE53JFVlvCooH4rTmZTEkZXfVgiIKBg3EWdM1YQOixN+n28i1aDy2HF0O6kL\n2lJkV92nTyWMhsV8AKx1/5x6DTxOU2ylNrJtB0mwJlLXXJeLPzifrUc2+z2vTYO2Fbr2lc2U9fpm\nOJNTgAi/g255uKXVSvFlV+iGegKU9Olr7OH8UDmc1F+4q79n1FVm7tIhBRMmn0jGA5+JeWUDduSK\n5ZiyMnEmp1A8aLB/W7/nvfbuASJwtmylr2hiY3G0boNJJ9nPLjpqCqoSPhXnoWxMeXk4PaKwTIf/\nruDnqCCq3cn/3oDuwzHUkz63JJczFwq2DP0z+FpnipOK0U+pjpRSJfydItiddtJ/mMiK3V+wPz+T\n5OhmFNgLyC0+4vdcM2ZWDlld8QvsninrDfbFgwbjbNkqoEG3IH0qOJ1EvbeYiHwtM9ZlqQNRkUR9\n8B51v1/nMwfCL37MTgUTJqPV6vTAxzM6G8RC3bpVtnue65mH4DMnQU/ZGIwqshXlcXzaOFquWEPM\nvizvuRw2G6Zj+sUpTcePaytAL34Uo5+hz+MTkyi5ZCA/r/iG0Wn7eKM7OPzUSswtyaXf++fx3Q0/\nKuVxCmDUNbVOCHGFEMLHt0dRW0j/YSJzN84mI38fTpxkFmQYUhoAt6TeQWxk1dpTPk0lgRbfs1go\nePp5Dm/eyZHVP1I05AZM9tLgWqnqEGxp9YL0qZSmda6yvc7mTcQ8fH9onfYBZLXbnXYmrRvP+XM7\ncmb8YrpclclDlzhx7ffhR9rvrVnUfv3nD/Qz9HG86eABIr/7BsfFA5gy6TfkrTto08B/9P6OXEmf\nxeeQV5zn91hFzWK0VlUWUHmt7pJSGm8XFkJUrSrv2Ept9F1yDhn5VWeC/ugUl8bX16/xPePzViPJ\nna1sXbkCV0ZGRROMr1VDMO1j/WHgmvEtE6q+Tx/nuQBnSnOKB18e/EooCOLj6zNi6b3M3Vh1JTTq\nR5j5lc57Cvadlmec65jR9J7X8/iMvbqdCctqUdmddvq914sdR7f7fWZzhJk704bxn97PhGX18U/7\nzofx/l5rVame4/+wP6I9x3Zz3qJuODFYB9tNp8apfP2vtdX+osZHmzm8eaehftem7INQVETchb2I\ncFaVt7yHdhDlKKInjfdZVE/vfZr27CbuvG66suhd42QQ3dBMx5fO0J0ItDoCW2ZDPWfV9+Tv+X0S\naAFFj0KIlfFUVHanndQFbQ13kxzWeQRPB9BN0ij/tO98GO/vVXEYGiWEEPHALUAMWr6WGWgtpbwt\nJBIqQkaCNZFmMSmGVxxmzNyaejtP950emtldJXt/FSrXXEpKxmW1EpGfX+XQ6pSjCMYJ7ctu70m5\nn+QkZDwfOH6A/fn6ZreMBnAgBlrFePEjEYQTHvx/hpUw5eV5Nf95BkdYikrYdMEKOq++lNwS/w3C\n3tw0j0fPeVzXdKqoWYyOFO8DGUBPYBlwORBAoQHFyaJusZ1oVx1Dx5rssPH8T0lokIKzqASs4Te/\nVIl28pEQV61yFME4oX04yD05mYX5kuoneZ0IND8GSflQfM0lVZ+xGk74QPHrWG/chOhJ48snCwdS\nmtH1Fv/dJR04GPDRBaz79y/KYV7LMOocT5ZS3g58DnwCnA+cFTapFIFjtxM5aSyDZ7QyVLAQ4O6N\nZsTQkSevsY6vXIGY+jhSmoe28VIQPSvKgwCaNfcad+5MTsEZG3tSOtlZ61gZ1EbfCX3l/hjqiM5E\nrvrK+2cY6tLyegmVfhzr0c9NrZCRH7kvg03PHqVTSSO/t9t97E/lMK+FGFUcZUZJCZzpbh+rqEVE\np09kfObr/K+JsYG/ywF4+b8OzJkZIYlkMoKvaKeIQhtHF30QWCtVb1Sn21xZW9fvf6Hohpt0D3E2\niCVuQD9j1w5Bq9T0XlO5u8tImtdviTnCTPOY5tzd8iaeir2BOps3Yc4Iw2dYWW4/77QgfSqMGlU1\n6m7cRN3JgsUJvy+IpGNsB7+i7M7bhVjQkglrx6os81qC0aiqqUAHtJpVK4Fvga5Syp7hFU8f5Ryv\nRF4epee1p8U9hX7j5QG65Jj55e0o6h6v2gs06EgmI3KGI4JKByOOYUOfu06UkbNBrG4XvSpO50D6\nZ/jAU07PhE5rKeF5l17kxunEOu91n88dH1+fnL3ZFVZ5vgIOXEBptJWuD9ZhW+QxQ+KFwmH+j/jO\nn5z7V68DoLuk+qNSyr3Av9FWHteERjxFdbA77Ux4qR/NR/hXGlazlTtThrByyGrqFBTqHuMrz6Ha\nBJorEAyB1pzyRdnqY+16bSW08ltMx/QHON3y6QYLJhrFWsdK6wZtsNaxBp2r4g9vcke9t0j3+Crv\ntJJpzFd9rgigboGNjdOOGa5v9eamecpsVQvwqTiEEJe7/70N6O3+Nw04DFwSfvEUvrA77Vz8fm/m\nxe3C6WcSe0nLAWwduptpVy7A1Lqd92qriR61okJgZqlMIDWXgiEsA6p7MDTl5Rm7diiVlxcCqZhr\nGB9y60W9gYF3aqAYpMUJG94wVhzRgYML3++llEcN42/F0cP974U6P/3CJ5bCH2X9NLbmbvN7rMUZ\nweyLF2Ct457R+/gyRxzLJXrKZKInjA3OR1CGN6VTeRZfXX9GJUI6oFZ6BqPXDtdqoAKhXr3ZbFh+\n+8VQLw9PjLzTgvSpFN54k88id4Eoj4z8fYgFLRm3erTyedQQPr+tUsrJ7n/vDPTCQog6wAKgFRAJ\nTAG2AgvRzJubgfuklE4hxGTgMrQe5qOllD8Her/TDaP9NADu2mYl1lHxoy6b4UctfhdT/gk7allD\nJU/89sP2xO1E9WvbDzBXwDB+6mpVGVD1Iq98+CeMXDuogolBUK1cjTLsdqInjifyyy8wHTwAJv25\npCumfnldMU8MKSmLhfxnZ1B3zWrMPkrqlymPs4bDZj/FER0uBwu3LODng+tDkriqCAyfb1sIsQfv\n1XBdUkpfBWhuAQ5LKW8VQjQGNgD/AyZJKb8TQswBrhJC7AUuAM4FmgMfc2Klo9DBaD8NcEdPLS0k\nb0ylvAOLhYIJk7XGSToDgh4VEt+8hbqOGRNQVdqQUEkWQwOqDwXns7KukWsHqryCpbq5GnY7DQdc\nUNHZ76WlbdGNN4HJFJyScn8+xQMHY33zDd+P5IRfV7TgzIeikMd2+L301sNbuPiD8/2XylGEFH9v\nuh+aD+sJYDfaasEO3Ay09nPuh8BHHr/bge7AavfvK4ABaI72lVJKF7BPCGERQsRLKXOMP8bpgd1p\n57G1Y3lny5uGSop0OQC/vQERzZrrznL9NVWqcnxWJqb9mdR7a77+iqKkBJYt0z03LNnWPlYGfgdU\nbwrOXkrkqpU+n8HIYF1FwSQmU9K3LwXjQhQq63nvIFdv0ZPG6UaIgVbuBahSryogJVX580luRmlq\nGua9f5X7TPTCdhyXXsa3/55K//f7GGptvPXIZsaufogXL3zZ77GK0GA0HPdXKeXZlbb9JqXsbuDc\n+sBnwBvAdCllsnt7f7RugtvRViaz3dvXAEOllLu8XdNud7gslhqpr1hj2J12ur/ejY2H9L/onpjt\nMPx3rRe0xQmMGgUzZ544wGaDAwegQQPo0QP++suYEK1aweDB8NprVfeNGgUPPAAdOoBerSezGaSE\ntv6rpOpSJnNS0okBa/RomDVLXxbP59W7VqdOsHdv1X3Nmmn3CcUz2GywYwe88AKsWQOZmdCiBVx1\nFUyfHrhfx26HMWPg009h377qXctmg/btIUu/RSwmE6xaBT17Bq/svX0+994LI0dqz/PGG7B8OWRk\nQPPmFZ7H7rRz1pyz2Jzjv4cMwF1n3cWcy+eolUfoqF6tKiBCCNFfSvkNgBBiENoKwidCiObAUuA1\nKeViIcRzHrvrA0eBPCo2Ryjb7pXc3NBF+dR0rLQR7E47ly7tZ0hpmJyw971kkvdk42yWgm3QYArG\nT4a92dpqYc4rRH69Ups1NkvB2aABxgqUgK3/xUR+/l/0VLbjgw85cvvdxLdooauIHMkpHLHEgNF3\nXTarbtyE6OemVl1VjJtI3CdL9WX5ZBlHHnrM64Bn2rObxhkZuvtcBw7gTEjUL9hn9Bk8Z9qV2rDy\n118waxa2whJDpjvPv88q+Sm+ruUna960ZzdxBw54HRkcCYkcaZsKBQ4oMPaZVfgu2WzeP5/Pv+DI\nuCc0udKfhXFPVJQ190So+Mrr1hhuQDZ/w3x+3PeTX5/HqfCdh5qXMz6+vtd9RjPHhwEzhRA5QojD\naI5unw5zIUQCWrLgeCllWZvZDUKIfu7/D0LrKPg9MFAIYRJCtABMUkqDLe9PD8Z99xAbszcaOvbW\nzndR9+vfT0QsuW32cX16ENf7bKzvLMR8IKs8Rr/O5k2UpnWuGB47bAS2YSOqhMwWDhvpPVroQBZx\nl10CDRvq7q9g2/cV5lspQ7lx1466eQUxk8YFHbnkTEjUZut6+5o1p/jS6kUreevd7UnAYblGQ3wN\nZs07ExJxpjT3ervigYOqZVYMKLLMR1kUi8nC19ev4fbUuzAZGK62Ht5C//f7qGirMGNoxSGl3AB0\ncTu5XVJKI12BJgCNgMeFEI+7t40CXnI3hNoGfCSldAgh1gI/oimy+wJ9iH8qZT3CF2035gjvFJfG\nM32fB5Ol3ObtLYvaE9OxPC25LS+vwgy1YNKTFWeCNpvXaKEI0GbpB7IoTeuM6VheVSeqgWzqyo5p\nb/kDddeuxZncDHNm1ZWD38glq1UzieiYUcplrWMJ2hHsbYD3JNBCiUYGYmfrNsZb5vor6FjN8OhQ\nRpZZTBaev+BFxpz9KF3f6ogDfQd+GdtztyqHeZgx6uPoCTxGxbLqLaWUrcIqnRdOh5Ijdqfd8BId\ntDIiXz6yF0uMRwlqH2sDOTMAACAASURBVCU+PAmk74URReRo3lJXEfktBWJQXgCXyUTRv26k3vuL\nvV/PB/GN6mG770HfTYuCKJJotKeH0bIg5X+fRsq1EGAZkrw8GnftiElHOQdTtqTyd6laPUG8MG71\naBZuWeD/QODmM27XdZjX1u98ZWpazmqXHEHLx1iGtkJ5FchE810owkBecR59lvQwpDTMdrjnZ/j1\ndRd1cypa+HzNUj1xNk3QBkcDmeLlmd9JyV7jtE1ZmZrS8DQ/GDC1GJUXgIgIXFH1dE1qhlYGRhIR\ng6gs6ytJ0JOAw3INJPwFmnhoOvw3EV4+61AkKoajSsDTfaeT1qRqe189Fm17i4e/eUCZrcKAUcVR\nLKV8E/gOrVLubcDAcAl1umJ32pmwdixifkt2H/NfGt1kh8wZMHs5mJKqhtwaHsQGDCL66SeNZYqX\nDbjfrMOZlKx7PT1ThCFTi0F5ASIcDqxvzQeTqXpZ6KEuO+5jgHcRUa3B099AHGjWfFjKlngSyioB\n7kmNpaiElUNWc3vqXZh1Xe8VeXf7W1ysfB4hx6jiKBJCxKHlXPSUUjrAwKemCIgnvn+MeZte92vD\nLePu3yDRPWHUncEaqBNUmtYZ6lgCL8jXuAnFV1ylu0tPFkODlK9BN0J/1Ry5Yrl2jVAO/tVEd4Af\nOowjP/xavcHT30AcaBmSQI6vTt2y6ijnvDxiHrhHC+5wT2oaPDGR53s/j7xrL81j9IMcPNmau1U5\nzEOM0b/eF9C6AF4L/CyEuBn4NWxSnYbkFefx5uZ5ho/vetDES6vA0by5T8dteSLa8i8wZe7TchEc\nDpxJSZpjekI6cRf20j3XX9KeXha1+dqrtfDfyhjMpta7Zknv3kR98B7o+ONOZjc+XfT8ICHuvleh\nnHod3wl/gZYh8Xt8iMrDB4z7vlGL36ngg/F09jNlGt/e8ANiQUscLv8O8/7v9+GbG9aFT+bTCKPO\n8cuA5VJKlxAiGq03xx9SSv/py2Hgn+Yctzvt9Fl8DrvzvOY8lmOxwzB3cl/p9TeR/+wMY4NS2QAX\nG1vBae2zX4JRp7nH4BnfMkH/fdrtRD/xGFHvLSqPlHLF1Kfoxpso+M8zVQchzwGZ0PeeqPbnfhIG\nVLvTzrQNT/LJlqXsz8+kWUwKg9pcRnqvqf6jhQJ17Hs5PhAHt+47DSLAwNd9y/D83ANxmHds1IlN\n9/9B7mH9tgK1iZoem0LhHH/OXRIEKWWBlHJDTSmNfxpljnAjSqP9ITj8nObTsDih7vffG79R2Sy1\ncZMTZgO7nXqvvwJezECG7dwGTBHR6ROxznsdU34+EWiheab841qGst5A63nNk9HHI0DC0W+jMuk/\nTGTW+llk5O/DiZOM/H3M3Tib9B8M3CNQ85DHKqbcJFWd8vDV6cJoIKTZ03n/dN/ppDVK839dtJVH\n1zldldmqmhhVHH8KIRYIIUYIIW4r+wmrZP9wAnWEpx2ArXMgtuTENlNWJqa9e4K2PUenT8S6YB4R\nXgrbhWxQzssjavE7uruMJsKFu4+HV/Rs+yeh34at1MaK3fr3WLFnObbSEPc61xnoYx59OOgky+oo\nViPRdZ6TGovJwsob1jAsvxNmA/pgS45KEqwuRhXHYbRJYk9UP46QEIgjPPWAVm7aUmmN56pnpeHN\n1wfXM8PH4Ocym7HdOTxkg3LMpHG6uQIQQNhnmPt4VMHHjDnk/TZ0lFO27SD78/XvkZWfSbZN5x7V\ncGDrDfT13luMy8vEwedqtJqK1Uh0XfEll1SY1FhMFp5+eB2ZOUNpl+s/bme7cphXC6OKY7+U8s5K\nP0PDKtk/mEAc4WkH4H86SgM0U485MyMoU4nPWZ3LReE994VmULbZqLt2jdfdzqTkwMI+Qx0+6wVf\nM+aQhbH6UE4J1kSaxejfIzkmhQRroqHrVCHAFZS3One+VqPVVqx+outcQOSqr6o+o8WC+amZrBm1\nlzb1/Edbbc/dSr/3einlEQRGFccV/9/emYdJUZ37/9PVM8zWMw4ggZlhAI1wjKIxGmMkbqhAAI3L\nNcLlalQk8XoTfzHGSCLmArlglCBRY6JG3G68RIkaF1CBRBMRE6NGE9ejkcVh2BlhmO7Zevn9Ud09\nvdTaXT3dyPk8j4/MTHfVW6eqzvKe9/2+QgjTjRKFc9q72xn/yDjbKBDQB43UlUbM79fdNE3NRAMB\nw+9UrFzhaMYZHXyw+WzSRIY9F7Tt29AMBAMT9Hzl5JIJo01iN2MGT/ZcrAan6vJqJh9qfI7Jh0zp\nq+Zoc5wk4TA1s7+vDyyZK6gtrWgm2fq+UJDOaTN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No+2FdURHC13C5KEH0/ZA2gfAlVNg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\nAElFTkSuQmCC\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('Figure2a.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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kLCHEtBKLGz0Bo7GxXbHq6Anym62HOdlmdFM3mxQ2rZ3DxUsqpGvFBJGEJYSY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\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('Figure3a.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: 15152.440011618102\n",
      "test Kt min: -34.79740973618779\n",
      "test Kt mean: 1.9145243277345676\n",
      "\n",
      "\n",
      "train Kt max: 110113.08976621665\n",
      "train Kt min: -167.27791726909712\n",
      "train Kt mean: 2.453877360947302\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 0xa3bb828>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pf2UlT72wl7++5RH+8t8eOum67Ye72N3YxjMn2UJcs3E3S/71Qe597GU+9ZWVfPJLDwDQ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\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 0xfe934a8>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "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": [
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       "<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>143.425455</td>\n",
       "      <td>-1.595455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.354545</td>\n",
       "      <td>-0.481818</td>\n",
       "      <td>294.027273</td>\n",
       "      <td>272.977273</td>\n",
       "      <td>272.813636</td>\n",
       "      <td>303.809091</td>\n",
       "      <td>270.872727</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.786364</td>\n",
       "      <td>-9.786364</td>\n",
       "      <td>-1.918182</td>\n",
       "      <td>94.595455</td>\n",
       "      <td>1.645455</td>\n",
       "      <td>251.290909</td>\n",
       "      <td>969.427273</td>\n",
       "      <td>46.729148</td>\n",
       "      <td>-0.035871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>135.844000</td>\n",
       "      <td>-1.611667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.188333</td>\n",
       "      <td>-0.465000</td>\n",
       "      <td>293.376667</td>\n",
       "      <td>272.786667</td>\n",
       "      <td>272.631667</td>\n",
       "      <td>303.080000</td>\n",
       "      <td>270.731667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.711667</td>\n",
       "      <td>-9.711667</td>\n",
       "      <td>-1.998333</td>\n",
       "      <td>95.076667</td>\n",
       "      <td>1.708333</td>\n",
       "      <td>244.951667</td>\n",
       "      <td>969.338333</td>\n",
       "      <td>116.574405</td>\n",
       "      <td>-0.015675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>124.525667</td>\n",
       "      <td>-1.760000</td>\n",
       "      <td>-0.013333</td>\n",
       "      <td>2.090000</td>\n",
       "      <td>-0.393333</td>\n",
       "      <td>299.085000</td>\n",
       "      <td>272.953333</td>\n",
       "      <td>272.795000</td>\n",
       "      <td>305.198333</td>\n",
       "      <td>270.860000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.116667</td>\n",
       "      <td>-6.116667</td>\n",
       "      <td>-1.755000</td>\n",
       "      <td>95.043333</td>\n",
       "      <td>2.110000</td>\n",
       "      <td>239.411667</td>\n",
       "      <td>968.896667</td>\n",
       "      <td>258.670822</td>\n",
       "      <td>-0.006941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>113.255500</td>\n",
       "      <td>-1.645000</td>\n",
       "      <td>-0.045000</td>\n",
       "      <td>3.075000</td>\n",
       "      <td>-0.441667</td>\n",
       "      <td>298.563333</td>\n",
       "      <td>273.190000</td>\n",
       "      <td>273.025000</td>\n",
       "      <td>305.490000</td>\n",
       "      <td>271.108333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.930000</td>\n",
       "      <td>-6.930000</td>\n",
       "      <td>-1.506667</td>\n",
       "      <td>94.693333</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>262.800000</td>\n",
       "      <td>969.090000</td>\n",
       "      <td>375.577358</td>\n",
       "      <td>-0.004403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>102.321167</td>\n",
       "      <td>-1.561667</td>\n",
       "      <td>-0.045000</td>\n",
       "      <td>3.716667</td>\n",
       "      <td>-0.470000</td>\n",
       "      <td>306.001667</td>\n",
       "      <td>273.680000</td>\n",
       "      <td>273.568333</td>\n",
       "      <td>308.616667</td>\n",
       "      <td>271.521667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.611667</td>\n",
       "      <td>-2.611667</td>\n",
       "      <td>-0.810000</td>\n",
       "      <td>93.096667</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>255.361667</td>\n",
       "      <td>969.346667</td>\n",
       "      <td>445.293463</td>\n",
       "      <td>-0.003517</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   143.425455 -1.595455  0.000000  2.354545 -0.481818   \n",
       "                 8.0   135.844000 -1.611667  0.000000  2.188333 -0.465000   \n",
       "                 9.0   124.525667 -1.760000 -0.013333  2.090000 -0.393333   \n",
       "                 10.0  113.255500 -1.645000 -0.045000  3.075000 -0.441667   \n",
       "                 11.0  102.321167 -1.561667 -0.045000  3.716667 -0.470000   \n",
       "\n",
       "                            dw_ir  dw_casetemp  dw_dometemp       uw_ir  \\\n",
       "year   month day hour                                                     \n",
       "2010.0 1.0   1.0 7.0   294.027273   272.977273   272.813636  303.809091   \n",
       "                 8.0   293.376667   272.786667   272.631667  303.080000   \n",
       "                 9.0   299.085000   272.953333   272.795000  305.198333   \n",
       "                 10.0  298.563333   273.190000   273.025000  305.490000   \n",
       "                 11.0  306.001667   273.680000   273.568333  308.616667   \n",
       "\n",
       "                       uw_casetemp    ...     netsolar     netir  totalnet  \\\n",
       "year   month day hour                 ...                                    \n",
       "2010.0 1.0   1.0 7.0    270.872727    ...          0.0 -9.786364 -9.786364   \n",
       "                 8.0    270.731667    ...          0.0 -9.711667 -9.711667   \n",
       "                 9.0    270.860000    ...          0.0 -6.116667 -6.116667   \n",
       "                 10.0   271.108333    ...          0.0 -6.930000 -6.930000   \n",
       "                 11.0   271.521667    ...          0.0 -2.611667 -2.611667   \n",
       "\n",
       "                           temp         rh   windspd     winddir    pressure  \\\n",
       "year   month day hour                                                          \n",
       "2010.0 1.0   1.0 7.0  -1.918182  94.595455  1.645455  251.290909  969.427273   \n",
       "                 8.0  -1.998333  95.076667  1.708333  244.951667  969.338333   \n",
       "                 9.0  -1.755000  95.043333  2.110000  239.411667  968.896667   \n",
       "                 10.0 -1.506667  94.693333  1.555000  262.800000  969.090000   \n",
       "                 11.0 -0.810000  93.096667  0.730000  255.361667  969.346667   \n",
       "\n",
       "                              ghi        Kt  \n",
       "year   month day hour                        \n",
       "2010.0 1.0   1.0 7.0    46.729148 -0.035871  \n",
       "                 8.0   116.574405 -0.015675  \n",
       "                 9.0   258.670822 -0.006941  \n",
       "                 10.0  375.577358 -0.004403  \n",
       "                 11.0  445.293463 -0.003517  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Groupdata - test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_zen = df_test.groupby(['month','day','hour'])['zen'].mean()\n",
    "test_dw_solar = df_test.groupby(['month','day','hour'])['dw_solar'].mean()\n",
    "test_uw_solar = df_test.groupby(['month','day','hour'])['uw_solar'].mean()\n",
    "test_direct_n = df_test.groupby(['month','day','hour'])['direct_n'].mean()\n",
    "test_diffuse = df_test.groupby(['month','day','hour'])['diffuse'].mean()\n",
    "test_dw_ir = df_test.groupby(['month','day','hour'])['dw_ir'].mean()\n",
    "test_dw_casetemp = df_test.groupby(['month','day','hour'])['dw_casetemp'].mean()\n",
    "test_dw_dometemp = df_test.groupby(['month','day','hour'])['dw_dometemp'].mean()\n",
    "test_uw_ir = df_test.groupby(['month','day','hour'])['uw_ir'].mean()\n",
    "test_uw_casetemp = df_test.groupby(['month','day','hour'])['uw_casetemp'].mean()\n",
    "test_uw_dometemp = df_test.groupby(['month','day','hour'])['uw_dometemp'].mean()\n",
    "test_uvb = df_test.groupby(['month','day','hour'])['uvb'].mean()\n",
    "test_par = df_test.groupby(['month','day','hour'])['par'].mean()\n",
    "test_netsolar = df_test.groupby(['month','day','hour'])['netsolar'].mean()\n",
    "test_netir = df_test.groupby(['month','day','hour'])['netir'].mean()\n",
    "test_totalnet = df_test.groupby(['month','day','hour'])['totalnet'].mean()\n",
    "test_temp = df_test.groupby(['month','day','hour'])['temp'].mean()\n",
    "test_rh = df_test.groupby(['month','day','hour'])['rh'].mean()\n",
    "test_windspd = df_test.groupby(['month','day','hour'])['windspd'].mean()\n",
    "test_winddir = df_test.groupby(['month','day','hour'])['winddir'].mean()\n",
    "test_pressure = df_test.groupby(['month','day','hour'])['pressure'].mean()\n",
    "test_ghi = df_test.groupby(['month','day','hour'])['ghi'].mean()\n",
    "test_Kt = df_test.groupby(['month','day','hour'])['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_test = pd.concat([test_zen,test_dw_solar,test_uw_solar,test_direct_n,test_diffuse,test_dw_ir,\n",
    "                         test_dw_casetemp,test_dw_dometemp,test_uw_ir,test_uw_casetemp,test_uw_dometemp,\n",
    "                    test_uvb,test_par,test_netsolar,test_netir,test_totalnet,test_temp,test_rh,\n",
    "                         test_windspd,test_winddir,test_pressure,test_ghi,test_Kt], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>140.031091</td>\n",
       "      <td>-2.469091</td>\n",
       "      <td>-0.358182</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>0.745455</td>\n",
       "      <td>195.056364</td>\n",
       "      <td>266.727273</td>\n",
       "      <td>266.285455</td>\n",
       "      <td>269.809091</td>\n",
       "      <td>265.814545</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-74.750909</td>\n",
       "      <td>-74.750909</td>\n",
       "      <td>-6.736364</td>\n",
       "      <td>72.103636</td>\n",
       "      <td>0.458182</td>\n",
       "      <td>193.252727</td>\n",
       "      <td>980.114545</td>\n",
       "      <td>75.495960</td>\n",
       "      <td>-0.036719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>130.593167</td>\n",
       "      <td>-2.996667</td>\n",
       "      <td>-0.261667</td>\n",
       "      <td>5.101667</td>\n",
       "      <td>0.508333</td>\n",
       "      <td>194.861667</td>\n",
       "      <td>266.001667</td>\n",
       "      <td>265.620000</td>\n",
       "      <td>269.271667</td>\n",
       "      <td>264.505000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-74.410000</td>\n",
       "      <td>-74.410000</td>\n",
       "      <td>-7.518333</td>\n",
       "      <td>77.925000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>205.293333</td>\n",
       "      <td>980.090000</td>\n",
       "      <td>232.239353</td>\n",
       "      <td>-0.013559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>119.707500</td>\n",
       "      <td>-4.038333</td>\n",
       "      <td>-0.643333</td>\n",
       "      <td>4.806667</td>\n",
       "      <td>0.148333</td>\n",
       "      <td>195.403333</td>\n",
       "      <td>266.540000</td>\n",
       "      <td>266.166667</td>\n",
       "      <td>273.628333</td>\n",
       "      <td>264.931667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-78.231667</td>\n",
       "      <td>-78.231667</td>\n",
       "      <td>-6.785000</td>\n",
       "      <td>75.850000</td>\n",
       "      <td>1.795000</td>\n",
       "      <td>260.378333</td>\n",
       "      <td>980.315000</td>\n",
       "      <td>404.694221</td>\n",
       "      <td>-0.010033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>108.404667</td>\n",
       "      <td>-3.563333</td>\n",
       "      <td>-0.198333</td>\n",
       "      <td>2.993333</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>197.165000</td>\n",
       "      <td>267.168333</td>\n",
       "      <td>266.723333</td>\n",
       "      <td>277.640000</td>\n",
       "      <td>265.658333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-80.465000</td>\n",
       "      <td>-80.465000</td>\n",
       "      <td>-6.968333</td>\n",
       "      <td>77.696667</td>\n",
       "      <td>2.896667</td>\n",
       "      <td>294.021667</td>\n",
       "      <td>980.585000</td>\n",
       "      <td>540.919433</td>\n",
       "      <td>-0.006609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>97.092500</td>\n",
       "      <td>-2.285000</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>1.671667</td>\n",
       "      <td>1.081667</td>\n",
       "      <td>197.653333</td>\n",
       "      <td>266.230000</td>\n",
       "      <td>265.790000</td>\n",
       "      <td>273.118333</td>\n",
       "      <td>264.701667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.703333</td>\n",
       "      <td>-75.468333</td>\n",
       "      <td>-74.763333</td>\n",
       "      <td>-8.111667</td>\n",
       "      <td>84.283333</td>\n",
       "      <td>1.836667</td>\n",
       "      <td>234.206667</td>\n",
       "      <td>980.305000</td>\n",
       "      <td>625.970675</td>\n",
       "      <td>-0.003699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>85.866500</td>\n",
       "      <td>32.508333</td>\n",
       "      <td>9.730000</td>\n",
       "      <td>70.833333</td>\n",
       "      <td>29.548333</td>\n",
       "      <td>196.690000</td>\n",
       "      <td>265.670000</td>\n",
       "      <td>265.330000</td>\n",
       "      <td>273.705000</td>\n",
       "      <td>263.960000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.581667</td>\n",
       "      <td>-77.023333</td>\n",
       "      <td>-47.456667</td>\n",
       "      <td>-8.293333</td>\n",
       "      <td>85.805000</td>\n",
       "      <td>0.763333</td>\n",
       "      <td>140.258333</td>\n",
       "      <td>980.138333</td>\n",
       "      <td>652.453536</td>\n",
       "      <td>0.049939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>75.707667</td>\n",
       "      <td>210.618333</td>\n",
       "      <td>64.191667</td>\n",
       "      <td>636.253333</td>\n",
       "      <td>73.140000</td>\n",
       "      <td>198.428333</td>\n",
       "      <td>268.711667</td>\n",
       "      <td>268.348333</td>\n",
       "      <td>295.406667</td>\n",
       "      <td>265.903333</td>\n",
       "      <td>...</td>\n",
       "      <td>169.485000</td>\n",
       "      <td>-96.983333</td>\n",
       "      <td>72.503333</td>\n",
       "      <td>-5.795000</td>\n",
       "      <td>77.586667</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>235.083333</td>\n",
       "      <td>980.205000</td>\n",
       "      <td>618.211997</td>\n",
       "      <td>0.343985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>66.538667</td>\n",
       "      <td>390.026667</td>\n",
       "      <td>101.055000</td>\n",
       "      <td>775.546667</td>\n",
       "      <td>97.310000</td>\n",
       "      <td>202.463333</td>\n",
       "      <td>271.936667</td>\n",
       "      <td>271.526667</td>\n",
       "      <td>313.186667</td>\n",
       "      <td>269.253333</td>\n",
       "      <td>...</td>\n",
       "      <td>306.830000</td>\n",
       "      <td>-110.721667</td>\n",
       "      <td>196.103333</td>\n",
       "      <td>-3.860000</td>\n",
       "      <td>69.966667</td>\n",
       "      <td>0.835000</td>\n",
       "      <td>207.591667</td>\n",
       "      <td>980.203333</td>\n",
       "      <td>526.060053</td>\n",
       "      <td>0.751055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>59.110333</td>\n",
       "      <td>552.710000</td>\n",
       "      <td>133.133333</td>\n",
       "      <td>867.756667</td>\n",
       "      <td>115.838333</td>\n",
       "      <td>209.016667</td>\n",
       "      <td>274.416667</td>\n",
       "      <td>273.986667</td>\n",
       "      <td>331.186667</td>\n",
       "      <td>271.878333</td>\n",
       "      <td>...</td>\n",
       "      <td>428.315000</td>\n",
       "      <td>-122.181667</td>\n",
       "      <td>306.143333</td>\n",
       "      <td>-2.396667</td>\n",
       "      <td>63.925000</td>\n",
       "      <td>1.126667</td>\n",
       "      <td>202.361667</td>\n",
       "      <td>979.830000</td>\n",
       "      <td>384.153979</td>\n",
       "      <td>1.473137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>54.222000</td>\n",
       "      <td>620.096667</td>\n",
       "      <td>147.301667</td>\n",
       "      <td>827.500000</td>\n",
       "      <td>139.430000</td>\n",
       "      <td>213.890000</td>\n",
       "      <td>275.918333</td>\n",
       "      <td>275.506667</td>\n",
       "      <td>343.101667</td>\n",
       "      <td>273.151667</td>\n",
       "      <td>...</td>\n",
       "      <td>476.185000</td>\n",
       "      <td>-129.215000</td>\n",
       "      <td>346.971667</td>\n",
       "      <td>-1.636667</td>\n",
       "      <td>60.098333</td>\n",
       "      <td>1.731667</td>\n",
       "      <td>214.810000</td>\n",
       "      <td>979.293333</td>\n",
       "      <td>209.013003</td>\n",
       "      <td>3.211572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>52.557917</td>\n",
       "      <td>641.820833</td>\n",
       "      <td>151.570833</td>\n",
       "      <td>843.958333</td>\n",
       "      <td>132.360417</td>\n",
       "      <td>215.918750</td>\n",
       "      <td>277.354167</td>\n",
       "      <td>276.941667</td>\n",
       "      <td>351.164583</td>\n",
       "      <td>274.708333</td>\n",
       "      <td>...</td>\n",
       "      <td>493.879167</td>\n",
       "      <td>-135.239583</td>\n",
       "      <td>358.635417</td>\n",
       "      <td>-0.281250</td>\n",
       "      <td>51.289583</td>\n",
       "      <td>1.729167</td>\n",
       "      <td>266.472917</td>\n",
       "      <td>978.422917</td>\n",
       "      <td>65.876009</td>\n",
       "      <td>11.914275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             zen    dw_solar    uw_solar    direct_n     diffuse       dw_ir  \\\n",
       "hour                                                                           \n",
       "7.0   140.031091   -2.469091   -0.358182    2.360000    0.745455  195.056364   \n",
       "8.0   130.593167   -2.996667   -0.261667    5.101667    0.508333  194.861667   \n",
       "9.0   119.707500   -4.038333   -0.643333    4.806667    0.148333  195.403333   \n",
       "10.0  108.404667   -3.563333   -0.198333    2.993333    0.030000  197.165000   \n",
       "11.0   97.092500   -2.285000    0.116667    1.671667    1.081667  197.653333   \n",
       "12.0   85.866500   32.508333    9.730000   70.833333   29.548333  196.690000   \n",
       "13.0   75.707667  210.618333   64.191667  636.253333   73.140000  198.428333   \n",
       "14.0   66.538667  390.026667  101.055000  775.546667   97.310000  202.463333   \n",
       "15.0   59.110333  552.710000  133.133333  867.756667  115.838333  209.016667   \n",
       "16.0   54.222000  620.096667  147.301667  827.500000  139.430000  213.890000   \n",
       "17.0   52.557917  641.820833  151.570833  843.958333  132.360417  215.918750   \n",
       "\n",
       "      dw_casetemp  dw_dometemp       uw_ir  uw_casetemp    ...      \\\n",
       "hour                                                       ...       \n",
       "7.0    266.727273   266.285455  269.809091   265.814545    ...       \n",
       "8.0    266.001667   265.620000  269.271667   264.505000    ...       \n",
       "9.0    266.540000   266.166667  273.628333   264.931667    ...       \n",
       "10.0   267.168333   266.723333  277.640000   265.658333    ...       \n",
       "11.0   266.230000   265.790000  273.118333   264.701667    ...       \n",
       "12.0   265.670000   265.330000  273.705000   263.960000    ...       \n",
       "13.0   268.711667   268.348333  295.406667   265.903333    ...       \n",
       "14.0   271.936667   271.526667  313.186667   269.253333    ...       \n",
       "15.0   274.416667   273.986667  331.186667   271.878333    ...       \n",
       "16.0   275.918333   275.506667  343.101667   273.151667    ...       \n",
       "17.0   277.354167   276.941667  351.164583   274.708333    ...       \n",
       "\n",
       "        netsolar       netir    totalnet      temp         rh   windspd  \\\n",
       "hour                                                                      \n",
       "7.0     0.000000  -74.750909  -74.750909 -6.736364  72.103636  0.458182   \n",
       "8.0     0.000000  -74.410000  -74.410000 -7.518333  77.925000  0.680000   \n",
       "9.0     0.000000  -78.231667  -78.231667 -6.785000  75.850000  1.795000   \n",
       "10.0    0.000000  -80.465000  -80.465000 -6.968333  77.696667  2.896667   \n",
       "11.0    0.703333  -75.468333  -74.763333 -8.111667  84.283333  1.836667   \n",
       "12.0   29.581667  -77.023333  -47.456667 -8.293333  85.805000  0.763333   \n",
       "13.0  169.485000  -96.983333   72.503333 -5.795000  77.586667  1.020000   \n",
       "14.0  306.830000 -110.721667  196.103333 -3.860000  69.966667  0.835000   \n",
       "15.0  428.315000 -122.181667  306.143333 -2.396667  63.925000  1.126667   \n",
       "16.0  476.185000 -129.215000  346.971667 -1.636667  60.098333  1.731667   \n",
       "17.0  493.879167 -135.239583  358.635417 -0.281250  51.289583  1.729167   \n",
       "\n",
       "         winddir    pressure         ghi         Kt  \n",
       "hour                                                 \n",
       "7.0   193.252727  980.114545   75.495960  -0.036719  \n",
       "8.0   205.293333  980.090000  232.239353  -0.013559  \n",
       "9.0   260.378333  980.315000  404.694221  -0.010033  \n",
       "10.0  294.021667  980.585000  540.919433  -0.006609  \n",
       "11.0  234.206667  980.305000  625.970675  -0.003699  \n",
       "12.0  140.258333  980.138333  652.453536   0.049939  \n",
       "13.0  235.083333  980.205000  618.211997   0.343985  \n",
       "14.0  207.591667  980.203333  526.060053   0.751055  \n",
       "15.0  202.361667  979.830000  384.153979   1.473137  \n",
       "16.0  214.810000  979.293333  209.013003   3.211572  \n",
       "17.0  266.472917  978.422917   65.876009  11.914275  \n",
       "\n",
       "[11 rows x 23 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test.loc[2].xs(17,level='day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shifting Kt values to make 1 hour ahead forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels_index= []\n",
    "for m in df_new_train.index.levels:\n",
    "    levels_index.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in levels_index[0]:\n",
    "    for j in levels_index[1]:\n",
    "        df_new_train.loc[i].loc[j]['Kt'] = df_new_train.loc[i].loc[j]['Kt'].shift(-2)"
   ]
  },
  {
   "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(-2)"
   ]
  },
  {
   "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>4.0</th>\n",
       "      <th>25.0</th>\n",
       "      <th>18.0</th>\n",
       "      <td>32.4985</td>\n",
       "      <td>603.748333</td>\n",
       "      <td>128.64</td>\n",
       "      <td>211.105</td>\n",
       "      <td>421.24</td>\n",
       "      <td>379.026667</td>\n",
       "      <td>304.211667</td>\n",
       "      <td>304.038333</td>\n",
       "      <td>457.716667</td>\n",
       "      <td>300.86</td>\n",
       "      <td>...</td>\n",
       "      <td>469.681667</td>\n",
       "      <td>-78.693333</td>\n",
       "      <td>390.996667</td>\n",
       "      <td>27.27</td>\n",
       "      <td>28.423333</td>\n",
       "      <td>8.353333</td>\n",
       "      <td>238.773333</td>\n",
       "      <td>977.523333</td>\n",
       "      <td>227.473301</td>\n",
       "      <td>653.28121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     zen    dw_solar  uw_solar  direct_n  diffuse       dw_ir  \\\n",
       "month day  hour                                                                 \n",
       "4.0   25.0 18.0  32.4985  603.748333    128.64   211.105   421.24  379.026667   \n",
       "\n",
       "                 dw_casetemp  dw_dometemp       uw_ir  uw_casetemp    ...      \\\n",
       "month day  hour                                                       ...       \n",
       "4.0   25.0 18.0   304.211667   304.038333  457.716667       300.86    ...       \n",
       "\n",
       "                   netsolar      netir    totalnet   temp         rh  \\\n",
       "month day  hour                                                        \n",
       "4.0   25.0 18.0  469.681667 -78.693333  390.996667  27.27  28.423333   \n",
       "\n",
       "                  windspd     winddir    pressure         ghi         Kt  \n",
       "month day  hour                                                           \n",
       "4.0   25.0 18.0  8.353333  238.773333  977.523333  227.473301  653.28121  \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 (9500, 22)\n",
      "y1_train shape is (9500,)\n",
      "X2_test shape is (4000, 22)\n",
      "y2_test shape is (4000,)\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": [
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch: 884 Iteration: 83100. Train_MSE: 0.0001779744343366474. Test_MSE: 9.270609530890971e-05\n",
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      "Epoch: 886 Iteration: 83300. Train_MSE: 0.00019781551964115351. Test_MSE: 9.2535603180816e-05\n",
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      "Epoch: 888 Iteration: 83500. Train_MSE: 0.0005077020614407957. Test_MSE: 9.240376888968032e-05\n",
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      "Epoch: 893 Iteration: 84000. Train_MSE: 7.95350715634413e-05. Test_MSE: 9.203681626007235e-05\n",
      "Epoch: 894 Iteration: 84100. Train_MSE: 0.0001733451645122841. Test_MSE: 9.196099638394511e-05\n",
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      "Epoch: 898 Iteration: 84500. Train_MSE: 0.00028068554820492864. Test_MSE: 9.17005308018465e-05\n",
      "Epoch: 900 Iteration: 84600. Train_MSE: 7.573414768557996e-05. Test_MSE: 9.162527690443087e-05\n",
      "Epoch: 901 Iteration: 84700. Train_MSE: 0.0008048619492910802. Test_MSE: 9.15475643103583e-05\n",
      "Epoch: 902 Iteration: 84800. Train_MSE: 0.00021556467982009053. Test_MSE: 9.14669304029444e-05\n",
      "Epoch: 903 Iteration: 84900. Train_MSE: 0.0005191129166632891. Test_MSE: 9.139114474990461e-05\n",
      "Epoch: 904 Iteration: 85000. Train_MSE: 0.0002830972371157259. Test_MSE: 9.132856816341695e-05\n",
      "Epoch: 905 Iteration: 85100. Train_MSE: 0.0011093857465311885. Test_MSE: 9.126302406418482e-05\n",
      "Epoch: 906 Iteration: 85200. Train_MSE: 0.004045937675982714. Test_MSE: 9.11938257786879e-05\n",
      "Epoch: 907 Iteration: 85300. Train_MSE: 0.00021465680038090795. Test_MSE: 9.112225517147546e-05\n",
      "Epoch: 908 Iteration: 85400. Train_MSE: 0.0002967108739539981. Test_MSE: 9.103817393715943e-05\n",
      "Epoch: 909 Iteration: 85500. Train_MSE: 0.0001828608801588416. Test_MSE: 9.096857308247208e-05\n",
      "Epoch: 910 Iteration: 85600. Train_MSE: 0.000146316786413081. Test_MSE: 9.09057140367067e-05\n",
      "Epoch: 911 Iteration: 85700. Train_MSE: 0.00031642511021345854. Test_MSE: 9.083222161774244e-05\n",
      "Epoch: 912 Iteration: 85800. Train_MSE: 0.00019802041060756892. Test_MSE: 9.077166022312875e-05\n",
      "Epoch: 913 Iteration: 85900. Train_MSE: 0.011022775433957577. Test_MSE: 9.070041091438114e-05\n",
      "Epoch: 914 Iteration: 86000. Train_MSE: 6.344062421703711e-05. Test_MSE: 9.064452702682302e-05\n",
      "Epoch: 915 Iteration: 86100. Train_MSE: 6.197837501531467e-05. Test_MSE: 9.057415860406734e-05\n",
      "Epoch: 917 Iteration: 86200. Train_MSE: 0.00010076770558953285. Test_MSE: 9.050281727970314e-05\n",
      "Epoch: 918 Iteration: 86300. Train_MSE: 0.00018174159049522132. Test_MSE: 9.042344124735292e-05\n",
      "Epoch: 919 Iteration: 86400. Train_MSE: 0.001723713823594153. Test_MSE: 9.034636490267587e-05\n",
      "Epoch: 920 Iteration: 86500. Train_MSE: 0.00017225077317561954. Test_MSE: 9.028059551791396e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 921 Iteration: 86600. Train_MSE: 0.00020036545174662024. Test_MSE: 9.021875973481917e-05\n",
      "Epoch: 922 Iteration: 86700. Train_MSE: 0.0011884925188496709. Test_MSE: 9.015265938493077e-05\n",
      "Epoch: 923 Iteration: 86800. Train_MSE: 7.453028956661001e-05. Test_MSE: 9.008907801719291e-05\n",
      "Epoch: 924 Iteration: 86900. Train_MSE: 7.769098738208413e-05. Test_MSE: 9.001426370512997e-05\n",
      "Epoch: 925 Iteration: 87000. Train_MSE: 0.000147753773489967. Test_MSE: 8.99347673139644e-05\n",
      "Epoch: 926 Iteration: 87100. Train_MSE: 0.00019206744036637247. Test_MSE: 8.98728853320018e-05\n",
      "Epoch: 927 Iteration: 87200. Train_MSE: 0.001525042811408639. Test_MSE: 8.980232589512442e-05\n",
      "Epoch: 928 Iteration: 87300. Train_MSE: 0.00016968930140137672. Test_MSE: 8.973850484472765e-05\n",
      "Epoch: 929 Iteration: 87400. Train_MSE: 0.00048358854837715626. Test_MSE: 8.96772091074758e-05\n",
      "Epoch: 930 Iteration: 87500. Train_MSE: 0.00015683066158089787. Test_MSE: 8.961104261659787e-05\n",
      "Epoch: 931 Iteration: 87600. Train_MSE: 0.00014742868370376527. Test_MSE: 8.955099384227014e-05\n",
      "Epoch: 932 Iteration: 87700. Train_MSE: 0.00018265876860823482. Test_MSE: 8.948562414624665e-05\n",
      "Epoch: 934 Iteration: 87800. Train_MSE: 0.00016684303409419954. Test_MSE: 8.941534585341608e-05\n",
      "Epoch: 935 Iteration: 87900. Train_MSE: 0.00010436719458084553. Test_MSE: 8.93361309587478e-05\n",
      "Epoch: 936 Iteration: 88000. Train_MSE: 0.0001958452194230631. Test_MSE: 8.926374870418462e-05\n",
      "Epoch: 937 Iteration: 88100. Train_MSE: 0.0002562465379014611. Test_MSE: 8.920266765314378e-05\n",
      "Epoch: 938 Iteration: 88200. Train_MSE: 0.00050580152310431. Test_MSE: 8.914172947297163e-05\n",
      "Epoch: 939 Iteration: 88300. Train_MSE: 0.0002463701239321381. Test_MSE: 8.907764577967369e-05\n",
      "Epoch: 940 Iteration: 88400. Train_MSE: 0.00027631534612737596. Test_MSE: 8.901084430565014e-05\n",
      "Epoch: 941 Iteration: 88500. Train_MSE: 4.8171561502385885e-05. Test_MSE: 8.893528228062066e-05\n",
      "Epoch: 942 Iteration: 88600. Train_MSE: 0.00017938809469342232. Test_MSE: 8.886302169613399e-05\n",
      "Epoch: 943 Iteration: 88700. Train_MSE: 7.635002111783251e-05. Test_MSE: 8.88030708304093e-05\n",
      "Epoch: 944 Iteration: 88800. Train_MSE: 0.00017045503773260862. Test_MSE: 8.873495354969001e-05\n",
      "Epoch: 945 Iteration: 88900. Train_MSE: 0.0003002978628501296. Test_MSE: 8.867556387726007e-05\n",
      "Epoch: 946 Iteration: 89000. Train_MSE: 0.0006181529024615884. Test_MSE: 8.86145397941155e-05\n",
      "Epoch: 947 Iteration: 89100. Train_MSE: 0.00024245092936325818. Test_MSE: 8.855311905231323e-05\n",
      "Epoch: 948 Iteration: 89200. Train_MSE: 0.00027299352223053575. Test_MSE: 8.849255578883842e-05\n",
      "Epoch: 950 Iteration: 89300. Train_MSE: 7.087460107868537e-05. Test_MSE: 8.842290006278395e-05\n",
      "Epoch: 951 Iteration: 89400. Train_MSE: 0.0007967877900227904. Test_MSE: 8.835240925258318e-05\n",
      "Epoch: 952 Iteration: 89500. Train_MSE: 0.0002079312689602375. Test_MSE: 8.828068661770954e-05\n",
      "Epoch: 953 Iteration: 89600. Train_MSE: 0.0005157701671123505. Test_MSE: 8.821342922888367e-05\n",
      "Epoch: 954 Iteration: 89700. Train_MSE: 0.0002808225399348885. Test_MSE: 8.81550782921308e-05\n",
      "Epoch: 955 Iteration: 89800. Train_MSE: 0.0011094468645751476. Test_MSE: 8.809443699747228e-05\n",
      "Epoch: 956 Iteration: 89900. Train_MSE: 0.004037303850054741. Test_MSE: 8.803054358623737e-05\n",
      "Epoch: 957 Iteration: 90000. Train_MSE: 0.0002074126387014985. Test_MSE: 8.796434002101173e-05\n",
      "Epoch: 958 Iteration: 90100. Train_MSE: 0.0002871492179110646. Test_MSE: 8.78889893427005e-05\n",
      "Epoch: 959 Iteration: 90200. Train_MSE: 0.00017316752928309143. Test_MSE: 8.782256133754939e-05\n",
      "Epoch: 960 Iteration: 90300. Train_MSE: 0.00014425093831960112. Test_MSE: 8.776449158204125e-05\n",
      "Epoch: 961 Iteration: 90400. Train_MSE: 0.00031177830533124506. Test_MSE: 8.769846270295497e-05\n",
      "Epoch: 962 Iteration: 90500. Train_MSE: 0.00019558891654014587. Test_MSE: 8.764180944955743e-05\n",
      "Epoch: 963 Iteration: 90600. Train_MSE: 0.011016570031642914. Test_MSE: 8.757761642686607e-05\n",
      "Epoch: 964 Iteration: 90700. Train_MSE: 6.123787170508876e-05. Test_MSE: 8.752374208460196e-05\n",
      "Epoch: 965 Iteration: 90800. Train_MSE: 5.9325069742044434e-05. Test_MSE: 8.745812693727635e-05\n",
      "Epoch: 967 Iteration: 90900. Train_MSE: 9.480454173171893e-05. Test_MSE: 8.739169130742997e-05\n",
      "Epoch: 968 Iteration: 91000. Train_MSE: 0.00017577988910488784. Test_MSE: 8.732098639688398e-05\n",
      "Epoch: 969 Iteration: 91100. Train_MSE: 0.0017111547058448195. Test_MSE: 8.725313407740094e-05\n",
      "Epoch: 970 Iteration: 91200. Train_MSE: 0.00017044234846252948. Test_MSE: 8.71932753044473e-05\n",
      "Epoch: 971 Iteration: 91300. Train_MSE: 0.00019863928901031613. Test_MSE: 8.713551571471523e-05\n",
      "Epoch: 972 Iteration: 91400. Train_MSE: 0.0011866329004988074. Test_MSE: 8.707502195625878e-05\n",
      "Epoch: 973 Iteration: 91500. Train_MSE: 7.226506568258628e-05. Test_MSE: 8.701544113941056e-05\n",
      "Epoch: 974 Iteration: 91600. Train_MSE: 7.183948764577508e-05. Test_MSE: 8.6946870303941e-05\n",
      "Epoch: 975 Iteration: 91700. Train_MSE: 0.00013830310490448028. Test_MSE: 8.68743531400463e-05\n",
      "Epoch: 976 Iteration: 91800. Train_MSE: 0.0001879868214018643. Test_MSE: 8.681543741486781e-05\n",
      "Epoch: 977 Iteration: 91900. Train_MSE: 0.0015237119514495134. Test_MSE: 8.675207637326936e-05\n",
      "Epoch: 978 Iteration: 92000. Train_MSE: 0.00016838354349602014. Test_MSE: 8.669353694304747e-05\n",
      "Epoch: 979 Iteration: 92100. Train_MSE: 0.0004793578991666436. Test_MSE: 8.66366022619763e-05\n",
      "Epoch: 980 Iteration: 92200. Train_MSE: 0.00015489627548959106. Test_MSE: 8.657642780078763e-05\n",
      "Epoch: 981 Iteration: 92300. Train_MSE: 0.0001388344244332984. Test_MSE: 8.651925228877763e-05\n",
      "Epoch: 982 Iteration: 92400. Train_MSE: 0.00016968176350928843. Test_MSE: 8.645675051425857e-05\n",
      "Epoch: 984 Iteration: 92500. Train_MSE: 0.00015681581862736493. Test_MSE: 8.639241276112837e-05\n",
      "Epoch: 985 Iteration: 92600. Train_MSE: 0.00010122238745680079. Test_MSE: 8.632348011034743e-05\n",
      "Epoch: 986 Iteration: 92700. Train_MSE: 0.00019400562450755388. Test_MSE: 8.625958900939547e-05\n",
      "Epoch: 987 Iteration: 92800. Train_MSE: 0.0002550267381593585. Test_MSE: 8.620223000175595e-05\n",
      "Epoch: 988 Iteration: 92900. Train_MSE: 0.0005039709503762424. Test_MSE: 8.614437937366185e-05\n",
      "Epoch: 989 Iteration: 93000. Train_MSE: 0.00024184490030165762. Test_MSE: 8.608443914351921e-05\n",
      "Epoch: 990 Iteration: 93100. Train_MSE: 0.0002665171923581511. Test_MSE: 8.602213338967772e-05\n",
      "Epoch: 991 Iteration: 93200. Train_MSE: 4.67447389382869e-05. Test_MSE: 8.595252062633307e-05\n",
      "Epoch: 992 Iteration: 93300. Train_MSE: 0.00016735270037315786. Test_MSE: 8.588356486297564e-05\n",
      "Epoch: 993 Iteration: 93400. Train_MSE: 7.342080789385363e-05. Test_MSE: 8.582619549329689e-05\n",
      "Epoch: 994 Iteration: 93500. Train_MSE: 0.00016759807476773858. Test_MSE: 8.576441654646108e-05\n",
      "Epoch: 995 Iteration: 93600. Train_MSE: 0.0002992320223711431. Test_MSE: 8.570770912013247e-05\n",
      "Epoch: 996 Iteration: 93700. Train_MSE: 0.0006153309950605035. Test_MSE: 8.565033373641218e-05\n",
      "Epoch: 997 Iteration: 93800. Train_MSE: 0.0002377555356360972. Test_MSE: 8.559260223574699e-05\n",
      "Epoch: 998 Iteration: 93900. Train_MSE: 0.00026602097204886377. Test_MSE: 8.553386471752212e-05\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",
    "            \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",
    "        n_iter += 1         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36660\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xffd0588>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SpLl0M72zA3gYIDOfpAr4E84HRjJzMjMPAaPA2VQHgHuBWzrqbgdGM3M8M48B+4GLlt8F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\nS1JBDH1JKoihL0kFMfQlqSD/H3LZoSOb0CffAAAAAElFTkSuQmCC\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 0xcd968d0>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T6KfHgg+IRResor+z6KOiRObHePhU4YkjR5g4cgSA0WrWMwNd0w+pupr7g6CI2db51lvv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\npIAQEZFECggREUn0/wEyHrW4/iCSJgAAAABJRU5ErkJggg==\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": [
       "9.769484181106005"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_denorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3915.000000\n",
       "mean        3.727410\n",
       "std        18.112833\n",
       "min        -0.025284\n",
       "25%         0.000251\n",
       "50%         0.270952\n",
       "75%         1.258475\n",
       "max       653.281210\n",
       "Name: Kt, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test['Kt'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Saving train and test losses to a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trainLoss = pd.DataFrame(data={'Train Loss':train_loss, 'iteration': train_iter}, columns=['Train Loss','iteration'])\n",
    "df_trainLoss.to_csv('RNN Paper Results/Exp1_PennState_TrainLoss.csv')\n",
    "df_testLoss = pd.DataFrame(data={'Test Loss':test_loss, 'iteration': test_iter}, columns=['Test Loss','iteration'])\n",
    "df_testLoss.to_csv('RNN Paper Results/Exp1_PenState_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
}