fixed-time-horizon-prediction/Exp_1_RNN_Desert_Rock.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 1 Desert Rock summary:\n",
    "1. import bird model ghi values\n",
    "2. import actual data\n",
    "3. merge the two based on the common timestamps\n",
    "4. drop all rows if 'ghi' value is 0\n",
    "5. Drop all rows with dw_solar < 0\n",
    "6. Manage missing values: Drop any row if it has -9999.9\n",
    "7. Make Kt column ('dw_solar'/'ghi') \n",
    "8. Group the data into hourly format (by finding mean)\n",
    "9. assign hierarchical index\n",
    "10. get the subdataframes for each day and shift the Kt values\n",
    "11. drop the year, month, day, hour and minute columns\n",
    "12. feed the data into RNN alrogithm\n",
    "    - RNN seq_lenth = 1\n",
    "    - learning rate = 0.001\n",
    "    - single RNN layer\n",
    "    - num of epochs = 1000\n",
    "\n",
    "** in this experiment we are not giving Kt as one of the features. We are using hierarchical index to make sub dataframe for every day and then shift the Kt values one cell up to make it as one hour forecast. **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE\n",
    "** the test['Kt'].max() values after grouping into hourly data is 7279.907547 = this is a outlier. The reason might be having a dw_solar reading with QC=2 value. Still haven't checked that. But the second highest value of 'Kt' parameter in the testset is 672 (which is way lower) **"
   ]
  },
  {
   "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": [
    "drk = Location(36.621,-116.043, 'US/Pacific', 1010.1072, 'Desert Rock')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2009 = pd.DatetimeIndex(start='2009-01-01', end='2010-01-01', freq='1min',\n",
    "                        tz=drk.tz)   # 12 months of 2009 - For testing\n",
    "times2010and2011 = pd.DatetimeIndex(start='2010-01-01', end='2012-01-01', freq='1min',\n",
    "                        tz=drk.tz)   # 24 months of 2010 and 2011 - For training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009 = drk.get_clearsky(times2009) \n",
    "cs_2010and2011 = drk.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 00:00:00-08:00</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 00:01:00-08:00</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 00:02:00-08:00</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 00:03:00-08:00</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 00:04:00-08:00</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 00:00:00-08:00  0.0  2010      1    1     0    0\n",
       "1 2010-01-01 00:01:00-08:00  0.0  2010      1    1     0    1\n",
       "2 2010-01-01 00:02:00-08:00  0.0  2010      1    1     0    2\n",
       "3 2010-01-01 00:03:00-08:00  0.0  2010      1    1     0    3\n",
       "4 2010-01-01 00:04:00-08:00  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": [
       "(1047761, 48)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Desert_Rock\\\\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": [
       "(525542, 48)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Desert_Rock\\\\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": [
       "(11338, 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": [
       "(1047761, 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": [
       "(525542, 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": [
       "(1047761, 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": [
       "(988587, 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": [
       "(524158, 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": [
       "16067"
      ]
     },
     "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": [
       "(988587, 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": [
       "10590"
      ]
     },
     "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": [
       "(988587, 49)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524158, 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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icOgQNtGEIXc4+UpAUhWl6l0vCZN/MGLatx8aNSp8XkYGDBrEyU3rqNs7BbvG\nV2uwK2aZ99ZonGVERUG/fjB5MuTmQkoKxMXlH4FbrdCiBRw9Wvj8Bg1g9+7CI/bBg2HaNG1C+2vL\nA9aLVvaf2c8jyx/hQNoBv8e7E2aDR3bDrFUQXVv7NXV08BHzF/I5pJTyKSHEcGAzcJOU8ri6awUw\nHfgKcM/kFoWiTLySnh68KmmlPR3USrmQ0xRJbHx9pq47yhs/QkqkUirVfBHs8fGcNkWC+z2oI3XT\nko8YdIuFOY+CQ6PSaHwKtmqcZTgiIsi5t1ferIL0LOV5RtcAix0s+Z9rRLfu+XwcLqzdumMpeLzV\nSrUvVhDoMMZjWx6IjY3Ccs5OHVMjfnlkMyM2DOWTPQs1m7Bcvo/PWsBzRzJISLdgshQtissf5eI7\nii5nINf3RsiiqoQQTwohRqgfrYAD+EIIcb267XZgK7AJuEsIYRRC1AOMUsozoZJLJ4SYzdCrl/Lf\ni9DIbWGdpxKirlDb9o9bmNVRydOkhdYpsGeWd6XhiIzEGRaGvU48Wb37cPZvSea7sxSloQFL4nis\nzw7EHl9faSe+PtZnB2IZlpBXVtaFMfUkxmTPAQGgBEo5TKZLMrnaKkJlPJPRxFu3vsO+fkd5sOED\nhAXw87WZ4L0mabz2TpeAr6ujU5BQzji+AD5UTU8VUPwZScAMIUQucBJ4VkqZIYTYAPyGosheDKFM\nOqFm8mSsWbl5C+a8lhC1WnGuW0mtl+GsRjOxyQb9t8HkrC6YWI8yFsmPEzj//jwc9evjqN+gaGYZ\nkwnLuImF1m5Uu+2mQovsHDVr4ahVi7CUFM9tGQyk/7ABR/2GwVlTYbMRN3Y8n67ZSuYpB9cPgP3e\nS1YXYl7kHnK+fZ5Jd8woc05rnfKDXshJn7YGlTw5/aTAcBzaT4vPO2hWGk1OweY1dQm/814srwyn\nevuWGNUQV3ecYWHgdPpdQR3I84wYNdyz6erZgVjGTSTi1Zcxz5/r8dziRjIVlLOgLDYj/Ocu+KA9\nAYUtN6/egu//tSGoyqPcfUfLOKUtp6+oKn0BoE5ocEUJFXA8G48cxpaZQeffH9OsNK6JEWx48k9s\nP/2pdNRvT/SoNAAMdjsGhyNv7UJEYkLx7sNqJXyN53Qa4WtWg9WKZdwkLrZs5fEYTya6YMpicsDM\nNZA2Ca45pb2pPWd30/WjDjh278hnetPR0YKuOHRCj82WtxLb1LkNnafVZ/95bdFBzau34Mfev2Fs\n1FTpgH105J5wde4BoSo416zJX3guJhPnvv0F69MDsMfVLrYvw5s8xmNHvcoSnQs7Z8Hzm5VIKi3s\nsx7mjqW3UKVVIyJGDtULRumDvUqFAAAgAElEQVRoRjdy6oQclxP8pTyTiv+oHpMdnmj2VKEaFL46\nck/4XLtQ0JzmWo+xZtUlX8ad3XDUqUNYUmEHuKNWbeVcUPwiE9/GMnps8PJD2WwweDDVvlihyBNX\nG6fZjMHLbMs1+5j4A7R/IYyDMf6f8644aNfHwl8fzAajMSQry3UuP/QZh05osVoJW7uS6/rDrI7a\n7PAxFkg61Y9Jd0wvZIN31KyFo47nvEiecNSue6lzd+HqkN1zUY0aTsTrIzDPmUlY0j+XzF3z5+Ko\nEuOxbcP5dCLeGJN/pO7JRFdEIhITYNq0S/IcT/ZqonPhBCLDzOx5105rL/76guyOg7YDIGzNSt1s\npaMJXXHohBRj6kkGtUzmr9rajq9ugUPnBxA2erLnA8xmcrr31Hx9b2HA+Tpk1R9Saeknnu/h3Hms\nTw/AEZk/rt2YmRkcP4onfJjkfEWGGABjlpUKDtj6gXbT1a44aHVvMplH9/k/WOeKR1ccOiHlXCUD\n89ppO7Z5ChxfUhf7yLE+a0l4XGfR/zms/Z8rvPbCQxiwtw7ZmwnIeCKZrL79cMZ4nnkUyY/ih0BN\ncp5wd5w3OOv/+AM1oMmPtzFyw1BsDt3foeMd3cehEzIycjK4bfXdmjK9tkyB7R9AmCHFfz6lguss\n3PwJllFjfPoYitQhGwyY352K8cRxj7tDkQPKZZLzlGzRGRmFwUMxKSeec0RE58Lfs6H6MP9Zd+1G\nJ3N3Ktl93+j0VhEk17kS0GccOkHH5rAxcsNQxLz6JOX4N7S3VpWGyeHFJ+ENT/4EPz4GXz4SZ6Tn\nFAsGu53Kny/D6a3NQGTWig+TXHbvPlifHVjIdOar0kN0Lgz8U/vlP9w5l4ycDO0n6FxRaJpxCCEi\ngduAJijLdQ8C30sps0Mom045JfHXhLxRq0+c8MzW/KnQg7ruwRNqh+xpQV927z7gsFN54YcY7IUj\nkryZskIlsyVxPObKFbF/8WXhVfi5uYSv/gY8zDy8MeVbwAkzO4DNV5ZiwI6d25bdxE+P/nqprrmO\njorPleNCCDMwGngQ2AEcQym21ABoi5JWZKyU0neoR5DRV46XXSJiwmg6TXDc4t8c9MwWmLsKnBhw\nxNe71CmGula2zUbsxDEeO2Rj0j9Uu7EdBof3dLuOyCgMWdb8HXmIZI6NjeL0sdRC5jfjkcN+5cwn\nc0QkWC0YnU4yKsJ1/ZXKgf4IM4TxZPOneKPTZL+rzMvLd1SXU/P1i5wd92NgDjBCSpnvGyqEMAL3\nAJ8AvYorpM7lQcqFFE5YPPsC3GmdAu/tqIu1391k9R+Io3adkkv3bTLB1Kmk/XdEoQ7Zl2/BhbNK\nDOmrvit6LiytWK1w6BSYIgv5T7TI6U72409ieXk4MffcSdThQ+ya5aDdACWayhd2p50Fu+fzx8nN\nQU9RolN+8efjeEhKubag0gCQUjqklF8D94dGNJ0yjdWKce9ujNu3Ydy7B6xWpQrcRv8LyFqmwKbk\nnmRs+BPLhCk4GjcpnRoRXnwk/sJ9jSdPQKVKoZPZbaU9TZt6rhmuMSzZCWQ92keZFVWrxrlft3Jm\n90Gs0+ew7QNooXGtx56zu7nj0856tJUO4N9UZULJVlsP+EpKud5tX6KUMjHkEnpAN1WVIjYbEa+P\noNKSjzFYLHmbL0ZF0P6lcHaFp/k8vXUKbFkcyfnt+zSnOQ8FPp+nzUbEqGFefR2hLMEK/pMq5pMz\nMYHwVd9gPJ7k0TlurxNP2qYthWW1Wql2Y1ucqSm01TDzcPFUi2d469Z3PO4rM99RP+hyar5+kZMc\nzkbxZZwAFgohRrrtuy8IsumUMyISEzDPnY3RYsGAEsljN0L7PhbvSsMJETnw/B/KojTbY0+WqtLw\ni8mEZcIUsv7dz+PukDrwNSRVzMullZuLZdxE0jZtIfvRPp5l7XmPZ1nVGYvJoUS0tdQ48/h49wI9\n2krHr+LoIKXsK6V8G7geeFQIMVjd5yv6T+dyxGolfNXKfJtsRvyOWA1O+O1DAzN21ie3v8bEf2fP\nYNrwC5wtvZpelvETPRd0CkbiQi/4TKp4PInI4S8XSpVCxYpkvjMjYFkt4yZhr1Y9T3loSVFix063\n5bfqJqsrHH+mqp3ADVJKi/q5DkrFvgTgFSmlxjXBwUU3VZUOxiOHqXZDWwzqd8ZmhPYDYIcfM0dc\n5Vps7rSCSrUbKu34SgKYnU1Mj9sx7d0DdjuEhWFr1pxzq39Q/ApBIqDn6ae2SFCxWqnW6XqPTm9H\nZBRGD+G3+UxYgchqtVKt03V5CRxdtT3mtPdfjfGaqs358dGN+ZzlZeE7qgVdTs3XL7KpajqwTQhx\nO4BaL/xu4A2gWdAk1CkXOGrWUqKfVP5zt3+lAXDv6epUaiCIeGPMpdHyzR2I/L/nISO/2SOmx+1U\n2LVTqauBsviuwq6dxPS4Pch3EwBBTFyo5Vrend6ex0v5Up4EIKsyu7kUAedKUXJ8CoT5Say7L30P\nXZfdos88rlB8Kg4p5RyUkNsDbtv2AS2AEd7O07lMMZvJ6XkvABkVYa6G+WbrFHj30wtEJAzPn3n2\neDKVly6meptrLkUMnT2jzDQ8YNq7p3hmK7caG2Ud91xcqGanrN59MHiRPa8uSIB4W0VfywoDtvo/\nf1/6Hj3S6gpFU+lYIcQtwItSysdCL5J/dFNVKWKzEf76cDpU+oADfmpd5+WfwoijZi3CUk54Pdba\n/zmMKScIX7XSo/PMCZz7fCW2TrcGLG+hGhvde2J+711Op2cF1lZJc/YMsSeOcLp2Q6hs9mrCKk6U\nl7cIrothBjr0d2qaUboircrMd9QPupyarx+4qUoIES6E6CeE2Ay8DCwKhXA65QubETq22uRXaTQ9\n5ZZ/qmYtvyPiSksXU8mL0gAUX0fzFgHLG5GYULjGxpyZMGRIwG2VGK51HN26wJ13Uq1bFyLeGEPO\nXd09Hl6cKC9PmYazevfBpKZl1xJtpUdaXXl4nXEIIV5BSTfSX0r5aaANCyHCgA8AAdiBp1EisRag\nDCB3ocxiHEKI0UBPlHQmg6WUf/hqW59xlAJWK46UZLpsfpx956TPQ8NsSirv6Fz11H79Cf/uW9+r\nsfEdpnexZSvO/bgpYJm9jdJp0IDTP/9eOgsP/eB1HUf/58BoJHzN6sK5q4qb8sTdqQ5Uu7EtYSkp\nmqLmAK6u0gj5f/tIP1vGZ3GUgd+SRkpbziLNONQQ3B7A/UKInUKI4QFe9161nZuB14Ep6t8oKWUn\nlH6ilxCiHXAr0BHoDbwX4HV0Qok6+o3ufB13LOjgV2mAYh+PsruFhI6bFFDxJXecwMWmTZWoqgDx\nmUL9qPf63aWKr3Uc69ZiGTmatA2bSft1K2kbNivRVMHIk+XuVHdz0LtCdZuc8n364fOHaDurre7v\nuELQ6uOoBQyQUo4NpHEhhElKaRNCPAXcjDKrqCuldAohegHdAAmYpZQT1HO2A92klKe9tWuz2Z0m\nU1ggougUlcGDsU2fpinsFhR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c6ilVekGcSUmk7TrgM/wyYDmDiPHIYaolJXn8hvuSPbZR\nI0VOSwhkLRiGG1eb3H89Sua4SRAdDelZAER06+65vGy37lgsdrBcUJ7nmIlU++FHwpKTCh1rr12X\nNFMklIHvcEDvvlptWPszMV1vof+2PT5DdAEWtoFxS77Fdiy12Mr+svrNh/j63tC6ALA10BWls5+E\nkqeqga8ThBA1gW+B4VJKV6KfdUII11fkdhQH+ybgLiGEUU2iaJRSntEol44brlDc9zuCw8eEzJyj\nZC71lCq9IEVelVxCqb/LYjbdQqaz48lUXrqYiEn5TWKaF0mazUqItAdKtb5Hcd9xbi5GS6amEF1L\nONzc4wSOFO0VCXVCh1bFcUpK6QT2Aa2llIdR0o/4YiTKLOU1IcTPQoifUaoGTlX/fzMwTkq5FWUW\n8xvwOfBiwHehA8C5qhF82M7r7DKPvmoUlbNChbwOy91x6U7AHVNJp/4ua0WTAklvEkBOrrK0Ej9Y\n79gVeaY1n9XeGjD86LvFEPwywmrFuH0rplUrMf62CeP2bcrf3j0l4vPS6uOYA+QAM4FPgGVAHyll\n69CK55nTpy9oqSmjidKeDmpFi5wvff8sn+5f6vOYNidgi5tDPOuhR8l8expUrKiaV1ZjPJGcf1Vy\nAGGQseNfg2nTCm1X1kBM9HBGEMgzDWmXPVTv3XjkMNVubOcxjNYZFkbar1s1m/3Ag5w+Qo5LiohR\nwz2a2Bg0iNMJY7U3ZLVSrdP1eSHSNiO0fB6kDzNrmANkv6NEm6sFKPUlyvVvXlXalRbO95p40xkZ\nSXbvx4sdwhwbG+V1FKp1xjEQ+FRKuQcYDcShpFjXKSNk5GTw+YHPvO432mHAlvxKA6Dir+pCsmBk\npLVaYcUKj7tCGv0TiqJJRSTkprPSXonvKwHkV18F9o4LrOh3re+o7KP4k90Id09rgi03W/t1LiMi\nEhMwz/8Ao92uZBPw8GfMzMQ8N7QhzP7WcfwkhBgBtJFSbgCQUn4tpRwkpdwVMql0AsLmsNHtsy4+\na2w8sgvmrCrs1zCeSs2/arqoHZPNRuSrL8M/hRfYQcml/i4T6U3KkuksyPhMAJmUFPA7LriiPzoX\nHtzr+5yDVS7S7b1rrryUJFYr4Su/0nx4+OpvQjZY8zfjeBMwA28LIZKEEEuFEM+o0VI6ZQCbw8Yd\nn3XicIbX6GVwwvBNnncFy4EckZhA5aWLve4v1bKvJUyZ8kcEGV8zKuLjA37HrhX97ry/2n8W3V3h\naST8/HJA1yrvGFNPYkw5of34E/6LphVZFl87pZTfSilfk1J2ARqhhM/GAt8IIfaFRCKdgBi1cRh7\nzu72eUxkLjRN97wvWOVY/dWvuBxG25opQ6azoONjRkWvXoG/Yw/tubLo+mOZXHpFpWB31Kzlsyxz\noeODkeXaC1prjgvgeWAI0Bc4AujhDaVMRk4GS/d6H+W7eGIHhNeoixNwGo04AXt8vaCNgn2ZL5xA\n1qN9LovRdsCUBdNZCPA2o2Ly5KK31/85HJFRyncUmPat0W+IrtWZzd3Lu1w5JiuzmZx7e2k+PKhZ\nrgvgrx7HbJT1FqnAd8Bk4HcpZWBZ9HSCjs1ho/sXXbHafdswW6fAtPWRpP2xXkljER2t/BvEqBxf\n9SscdeLJnDjl8hht6yh4SQBpLuo7NpnAaMxXu6OizcHWD/CbAcGVgv37R9ZfEfU7LInjwWaj0sIP\nMdg9K8y8qKoQDtb8Pen7gR0o6yvWSSmPhEwSnYAYuWEIB9L3+zymZYoSHx9myLpU0Q9wVL8quMKo\n5gZPIZo5PUM36tEpZfwUdNKMF1Onq+SsvxDdPWm7GLFhKG/d+k7xZSnrmExYJryN5fWxGOVejCdO\n4KhWDdS09VSqhKN+g5D/5nwqDillTTV77V3Ah0KIGsBPKCvCf5RSlv1g6MuQjJwMFu3xkrtIRaQq\nPzqTA+zxoXdMu0Y35m/X4ExKKr3qdP5Q10EQ0aS0JdFR8WXq1JqC/ePdC3jthjFEh0eHSMoyhtmM\no217HG3bl8rl/fo4pJTbpJRvqg7y61HShIwH9LQgpYDNYePuz2/zGXprzoE/3FKKlIhjWjVfsHt3\n2XQIF1jtTIsW5SdtvIsSSuNS0viM1EJjCnbs3P35bVeOv6OU8as4hBDNhBD9hRAfAn8CL6CUjdXu\npdEJGqM2DuPguQM+j3lwr/JjA3BERmEZVoKp0cuoQ7hg/iiOHi03aeNLPI1LSeMrUktFSwr2g+cO\nkLBRL0xaEvhbAHgW+AJoC3wD3CSl7CClHCqlXFsSAupcQksUlckG762+9NmQZcV49gqfHAaSP6oM\norl2eTmmUKRW3XpcbNkKe914nMYwjJUj+GOu//UdS/d+ckWF6JYW/uwIraSUPlecCCEqSSmvzPX/\nJYjNYePu5bf5jaLqv/XSbAOurIV33tBSxjUoTt5Q4EfpWUaOLnOzuyLhrVSvKzdX9auIuasL/bcd\n9JmCPctu5e7lXVj/2B9XRJRVaeHPVDVdCDFALcaUDyFElBDiRWBJaETTcWFz2Gg/pz0Hz/s2UbVO\nKVxj44paeOeFsph6XSJINCQAACAASURBVDMpKX6V3mVFQVOn63N0NBlzFvCuhhTsrhBd3d8ROvwp\njn+hpE/fIoT4XQixXAixRAjxO7ANJafWv0It5JXOyA1D2JG6w+cxTXOrsMH5HIY6l1+ai2JTnvNH\nxcWVX6UXZByNGhNmjmTrB/79Ha4QXZ3QoCmtOoAQ4lqgCcrCzoNSyr9DKZgvrqS06hk5GYj59X1G\nUUXkQNLSuth++hOgVNNul9nnWSD1uiE+Xqm0F2Da+JJOax4bG4X1uRc8VwoMZar6IlAS7z5i5FDM\nc2eTUdF/iG4YYchnjhUK0S2z39EClLacvtKqa/7FqIqi1JTFlYiW0FuAp/6CmH9SSFNt9QHZ6907\nQkpX6YSUAjb06i2bKOVZtVKwHGyduuR07xm44ikCrlmjx3ojVxJWK1lPDwC7g4jv1vLU30nM9OHv\nsGOn2/Jb2fjYFt3fEWT0p1mGGbV+iN/Q22tOKaGKASc0c+8Ik5NwRkQAYLBaS7RTLHHcbegB1Bt3\nRTa5cEU2AaEf9XtzHF8peFLa3e5idL8BfLf5cQ5e8J4Z+vD5Q3Rddgs/PrpRVx5BRGshJ50SJiMn\ng6U7fa8OD7PBZrUwU+7NnQLqTPKFeDqdGDMzMWZmFi/c02qFQ4fKfHhrwJSVcN4yukYm1HgMR54/\nl6iPFrD2oe8Jc/gul7wvfY/uLA8ymhWHEOJmIcTzQoiKQojOoRTqSsfmsHH3Z7diDfNtShmght46\nIqPIHD9J+wU0pEGHADpFtwVqNG162S1Q0xLOqxMi/CjtuDHjGfCnf5fnnrRd+uLAIKI1rfogYBzw\nMhAFzBZCDAmlYFcqNoeNrstu4WDGIZ/HuYfe5tzdXenYNI58fVZxcz9OY6foPiKkPC5Q85PKo1yH\n85ZzfCrt40mEr1nJdA0huqAvDgwmWmccfVESHVqklGeB64B+vk4QQlQQQiwSQmwQQvwhhLhPCNFY\nCLFR3TZTCGFUjx2tHvOrEMKHu+vyxlXNb1/6Hp/HiVPw51wDxsqROEwmKi3/lGq33kD1lo2JGDnU\n70jfX26gvOPiNPhNyooZpyj4SuXhrkzKczhvOcen0q5ZC+PJk5gcShboln6UR5bdSo/Pu+omqyCg\nVXHYpZRu65HJBvyFpDwBnJVSdgK6AzOAKcAodZsB6KVm370V6Aj0Bt4LQP7LipEbhvit5heRAz9n\nPo7t4ccwWjIx2myBF6nXkBsItPlNyrMZx1sqj5hutxZSJpZRYy7bcrBlGj9K2zWwyUvB7kd57D+3\nn/+s/k+wpbzi0LSOQwjxNsr6jfuAYcCzwH4p5WAf50QCBinlBSFEdWALEA7UlVI6hRC9gG6ABMxS\nygnqeduBblLK097attnsTpMpTOs9lgsycjKoPrE6Nqfv0dALm+G9N7bDffdBUpLng+rXhz17fHf6\nNhsMGgSzZ4PdwxggKgqSkyHaT5pqqxVatICjRwvva9AAdu8umyNyqxWaN4djx7QdP2gQTJ2qnJeS\nAnFxZfO+LkdsNhgyBL76SvnOx8crZWonT1bey/vvXzrU6L9+R5ghjLThaVdOCvaiU+x1HEOBASjr\nOP4NrAJm+zpBSpkJSmoSYDkwCpgspXRpqgtAFSAaOOt2qmu7V8WRnh4880dpL7IBxUR1y+Lr/SqN\n1ikwZXc9zmbkUC052etbdSYlk7brgP/1HIkTiMi6iPnDDwrtsj72BJYcA2h4NhHdunteoNatu7JW\nIoCw15IiNuMUzn/+8f7LKID9iy9J++8IRVlE14ASuq+y8P3USkhlTRgL/x2RPxw5PQtGjSPmp5+p\nsFcx77rqd9R6BbK8LA60O+20fb8tG/v8WaZDdEv73cfGFso0lYdWU5UZMEkp/wX8B6iJkorEJ0KI\neJTCT4uklIsBh9vuKOAckKH+v+D2KwKXM/xwhvdYdICmpxQ7rv3unjjqN8BRu47XYwNZ02EZP7HY\nJhj3zKaUFzNOXFze2hUtlHWz2xVBwXBkdX2H8cKFvFrloEQaPrjXd1OHMw7Tddktur+jiGhVHIuB\n2ur/L6jnLfJ1ghCiJkqlwOFSyvnq5u1CiC7q/7sDG4BNwF1CCKMQoh5glFJeEXnAtTrDw2yw5dt4\ncvurnXHFijiqVvV6fEBF6tXFZWkbNhe9AJNbG0hZ9oo4BQE9eqrskeejSk7K8/O5eH+1/xTs+9L3\n6MqjiGj9ZdeXUt4HIKXMAEYJIfzU5GIkUBV4TQjxmrptEPCuEKIisBdYLqW0CyE2AL+hKKQXA72J\n8sqo9f6d4QBPZzQm+u+/Oa2myIgYNZz/b+/Mw5wqz4b/m0wGhgygQKcsOoLrjYLWulTFpagVi8tH\na/tRa91FW+rVSj+3KrZgX2krL1qUWtAiYOuGqNVaRNBqFXFXeBXQR19BGLYBGXCYyTBMJvn+OMmQ\nyZwkJ5kk52Tm/l0XF8nZcueZnHM/z72Wrfyo3XGRsjIaL7syu5l+LvpHBwJQ2d+Rect1Nm+mJIOI\nL42e8hhpcpF674FxH5CyBDvEkgNP4aWxmlmeCU6d4yuAS4wxH0XfD8UyPx2fZ/ls6QxFDuua6jjq\ngSEES9P7Nd5ZuD/djbEURzBI31O/ZeVMJNCyXxW1y9519QHntl3WKZUVpbQMPdx2HMM9exHZd198\nmze1rQvlwgqqWMYTCiurb+0a+p50jNXNMYEI1uoj5INjr4YPB6a/3mXDruK/v/2nnMvZEdz+26cq\ncujUVHUD8KKIvCci7wGLgetzIVxX5dZXfpVWaQzfbPk1um3YANdeC6FQ6vDXLZs6hx2+EL21U4R5\n7r7oYmpff9ebvdMVIE0uUon1WHOa3wHwyOqHNDkwAxwpDmPMS8ABwE+xEv8OM8a8lk/BOjN1TXU8\ntebplMcM32zFpfvDUdvtvHlUTJ7YubOYC9xbu1270ninfhetC1U0pMpFiuxdhTjN7whFQtzw6i9z\nKGDnxmnJkcHAFODnwARgpojMSX2WYkcoHGLUgpG0pMifPGzrXqURT/dFVjPxzprFXPDe2rkIDFBc\nw1bxXzmOcFVVm+NiyuPQNM2fnv3ff3D9f65TZ7kDnJqqnsCa+C4FXo37p2SAo9DbCMx/sr3SgL0h\noW1uGJ+PloGDCF45ztvhr+lws3SJri6KEzvF/8e7aRp9XvtDw/De7NSRVhEi/H31XL6z4FRVHmlw\nOrUqM8ZoUcMOEFMa6UJvK/bAIXsqgIZ2+8Jf70+4d2/rhpk8BZpDdH9hIb6aLXR/cQn4y4q2h4aT\n0iUdjvpSOifxEYGhEITDhCsqKGloaBOi6zTSavX2VdrDIw1OVxyvi8j50TBaJUOcKg2AHx51Kb6x\nl9ru823eRN9RI6m47WYqfnsLgbl/pXTzpsKYdfJMp/bdKAWjYvJEArPvx5egNGLMeAGO3pT+OtrD\nIzVOFccPgWeB3SISjv7LoO9m18Vpkh/AEX2H84fTp7c1RcXtL2Fv57nyxx+xvYbnK9ImQyvQKh0l\nhbkzArTsX0Xk4MN4d7azSKvVtSu5ZemNuZWxk+BoHWaMGZT+KMWOW191luQ3tM8RvDT2NWtp7LPa\nkTb86kb6nnEKpZvbT5FK6uttr1PMZh3tra10hJR9Znw+dj74N/CX0eMHZ7OrvL0p2I6HV83jNyfe\nrgURE3CkOESkEqtMek+iE1/gQGOMvU1FAaCmoYa/r5qTdl03tM8RtvZUX11dxnkZBTHrBIP56X3d\n1XtrKx0iZu60S+qMBCrY96pL8W3ayJpBPajex9k1W2jh9PkjeOVHb6jyiMOpqWo+cDSW8qjAMl3Z\nxP0oYJmnbl16I994SGhJpzT2kaROuFR2/0hP+8qVeTXrFCrPQqOclGxIYe701e+yalqFwwysaeCA\nr5xftrp+PTJnMDe9OkF9HlGcKo5BxpjLgOeAp4HTgG/mTaoiZ+LrNzP7o/sJp9Gth22Fd976ZvLI\njVTZzRdeVPDGQgXPs1CUDGmX27FfFeGePdscE2iGMZ9kdt2WSAvzVs1h1JPfVuWBc8WxI/q/Ab4R\nbR+rJBAKh7jp1QnMXdm+v0UipSF4dzYEli1L6cyO3QgMGdJWQfzuD4VNXivmFrHFTCHKr3QmEnI7\ndj66wLaY5bQX4bo3YUgtlLZAz0Znl1/55UfqMMe54nhZRBZglUm/XkRmAQ6Huuvw26W/Zt4qZwn1\nV79vxZX7NqepLxW9EVi1yl5BFMisU8wtYouSqFmQYcMKUn6l0xG9L8KDh9iae/1hmL4YVs0E82fY\n+CeroKgTHl41r8vXtXJaq2oi8GtjzDrgx1grj+/nU7Bio66pjrkfpV9pgPUDnbHYeu3Yme2y3V/z\nLApLzCzIF1+oWbAjpKpphWW2OniHNYlzWhAx5jDvysojpeIQkfOi/18KnBz9fzhWq9ez8i9ecVDX\nVMfpj59Eiy99tfejohVvYyVFiiZHQfMsCoeaBbPHxrTX6vfYr4pkd2iEvTWtnKw8urrDPN2KI9Zv\n43SbfyPzJ1ZxEAqHuPHVXyEPDqa6oTr1wWG46j1LaZSGKY72qgmkrCar5Aw1C2ZBqoi/qLl356ML\nwJf6kRcrxX7Vu5BUy0Tpyg5zR42cvIYXGjmFwiG+88RprK5d6ej4q9+FBxZC8xHDqJv9N6tneCDg\nOCfC7aYubUghs6fkTIGn5UzVrKtqsNWi14MrPDfHtOK2my3TXgLBa8ZbPkFIOa7hnr3w1beVfdx5\n8OBxzj7/J4dfxp9On5Gx3Klw+zeaqpFTyhAcEVlLcr0bMcYc3BHBipVQOMTp80dgdjiL6TtyM9y3\npJTg5ZfR8PtplmM7OkPqvmghvo0bCO+3P01njaJx3Pi9SsWr5KLNrJKcqFnQ7kGoZkEb0pj2Gm6d\nZI1ZinHdfeFFAJQ//iglUQXyl1crePvIbqzsvqPd8Yk88vFDlERg6sg/dYnCiOlMVSOBM4D/AHOw\n8jdGAPcBi/IpmFepa6rjlMeOd6w0htfAsm0XsPPjdTRMnd4aDWWbEzFnNn1HHKsRNEryMGw1C7Yj\nE9New+QpcN117c2tv/sD+Hz46ndRglUeo9uuBpbfuYNhO5zVdn34k4c47fETuoTT3GnP8feMMccl\nbHvfGHOsg3NPAO40xowUkWOwkgg/i+6eaYyZLyKTgHOBEDDBGPNOqmu6YaoKhUPcsvRGHl41L2UT\npniGBg7i5R++hr9nQqmCFEvmNofFLbNb5cxXuY8c4fby2ilFI2dFKdtXfubZv3c8ro1phqa9yspe\nbFtX0/Y+SnGN5tISjhsXcdS7HKC0pJRLjriM3586rUOrD7d/o7noOV4iImfE3ojIaKyHfEpE5CZg\nNlAe3XQMcLcxZmT03/yoMvk2cAJwIdZqxlOEwiHOeOJkHlr1oGOlcUS/Ybx86XvtlQZpirHF0SaC\npsBtVRWPoOVX0pNNxF/CuKa6J/0tEccOc9jrNO/MDaGcKo5xwHQR2SYi24E7gCscnPc5cEHc+2OB\nc0XkNRF5UER6AacAS4wxEWPMesAfLaroCWobaxn64IF8UvuxsxMicPHQy3jp/y5NOttIlRMRT5tl\n9g03aLkPRUlCRyP+Ut6TpaX4wzB7IVz1vnOZVm9fxcmPHtcpTVcZRVWJSD8sp3htBucMAR43xpwo\nIlcAHxpj3heRiUAfYCew3RgzM3r8a8CVxpik/VVDoZaI31/qWO5sqGuqY/y/xvPoykczOu9nqyuY\nOXdr+hnihAlwzz2pjxkyBFZFS7IfcQSsW5f8GJ2RKoq1Qt+8GQYOzPyeSHZPHn00rFgBQMgHx4+D\nFRk0miillLHDxzLrvFnFVmE3u6iqGCJyInAL0bLqIlIKDDbGDMlQkH8YY3bGXgMzsBpExZd67YWl\nTJKyY0fuEqAS7YhWbsYEHv/4EcdmKbB6GY9/D+56sZHt13+WOuooGMQ39hJ61AXp/tISfNXrbP9C\nwVGjaWhowbd2Df2q7fNEItXV1K5M83kFxG27rFNUztzjGVl7fx0aWqDBXpakct48iYrGPe37wdx2\nOxV3TKL78wsp3bCed2bDL8+Gvx4LLQ6eoC208NjKx5i/8gkuHebc9+H2eFZW2lfgBuemqjnAM1iK\n5j5gA9aDP1MWi0is4++ZwPvAMuBsEfGJyAGAzxjzZRbX7jC1jbUcPucgHvn4bxkpDdkK26fCvS+A\nb+B+yctvxPspTv0W3V9aTNNZZ1H72tsEr7g66TI73O9rUFFhe0kt96EoOSKhQGJrXbjycmv76+9Q\n++qb+PoPZOYiqJ0KQ7c6v3wYy/dx8qPH8dG2Dwk2F28FAKcu/yZjzNyo2WkHcCnwURafNx74s4js\nAbYA1xhj6kRkKfAmliK7NovrZkWwOcjKmnVU19Qw64N7+Oe65zI63x+CcR9YfYxbS4icc17SJXJr\n/aEosRBc/GU03HkXDUkipiqmToFd9jMPjetXlBxjl6cUuzcHH0jT+d8j8MBMeu+Bj2bBz0ZHEwWT\nGnbasrZuDWcuOIVyXzmnVY3krpEz6F/Rv8NiB5uD1AS30D8wgEBZfp8JTsNx38QKl/0uMMQY83sR\nMcYYyat0SehoOG4oHOK3y27hsdUP09DSYEVKOPyjx9i3AdbdYxVHA4iUldF42ZVWPLhdafNss4HT\nZLtuX/Ex9PaO3dTt5bVTVM7cUyyyZiRnKETF5IltE3XPHg3hMOXzH6OkwWrh/PNzYNa30lwrBQf2\nPoh7z5hJt9LulPvLGdx7CIMH9U8rZ7A5yOc7P2PG8um8tWkZW4Nb2a/n/ow+6Fwmj5jS0XDgjvk4\ngLuwugBeALwjIj8B3staIpeZ/MZEZn90/94NGSqNfrtgw3Qob7F0zu4x36f+rhng9+OrXm8bc+8k\nScnOT5HqvJLGIL7tXxL2kOJQlM6ErZVg9v0ErxnP7rE/JjDXqog94wUoa4FZx0FzWeafs7ZuDec/\nc3br+3JfOecPPZ/L5af4fX52h3ZT27Sdvt37Uu7vQUs4xJ9X3Mu/1y2mKdzU5lrV9et54ENL5jtO\nuTOLb50ep4qjERhljImIyHHAYcD/5EWiPBNsDrLw88xMUjFKQ3D5Cpj1/F7TVOOV42i4Y2r7Wcno\ncy0fRXT1kaofcio/RbbnKUqnJ9/JsKlKmTz3T4js9YP6w3DvYrjjFRh/Dswf7sxxnozd4d0sWL2A\nBasXZH2NRWuf59YTJuXFbOXUOT7VGBMBMMY0GGOWG2OKsud4TXALmxo2ZnZSC/z4fyxn2Ox/Ravb\nDhwUzeye6qylarZlybWcuaK0pUDJsCmtBJs34tvSvkpx7z3wyDPWs+KwDBzn+WDTrg3UBPNTSdmp\nTvxcROYAbxPX+c8Y87e8SJVH+gcGMKhiEBsdKo996+HzGdA3bjUYHjCQ2pdfh35fc15gDVqjpNqF\n+6VJUmqYPIVAj260PP1MRucpSmfE1nwUfd9aCTcHpFrtp7Nu994Dq2bBL86G2cdCyIW6h1+v6E//\nQH4sEk5XHNuxxupEirwfR6AswJgtfdMeVxqy+mdsu7ut0gBo+j/fs5QGGfZOSBbul+hMT2xG4/fD\n9OmF6y+uKF6lwE2u9ow4JeNzIkC4RwB/GGYuskL1L1oBgSYclSzJFd898Ny8RVc5ffJsNMbclhcJ\nCk0wyN0LdlJ6OMw9GurL2+4O7IExn8CshXERU9F9kZ492X3hT9rM9LPyQSQrS24XwRHzlaQ6T1Fy\nhceLaGYbZJIR8ffhhmrCPXsCJZQEGyActl1tRIDwwEE0jT6HxosvZ9+LfwSNlhKLma+CC+HTPjDl\nFHhqGETyWPxi+NeOZEqeHOPgXHGcLyK/ifk5ihlfzRa6bdjIvevhjy/Dmn2hMToKPUJw0E6rD3E8\noYMOpm7uI4QHD2l/M+Wwd0KqJTj3/8XxdRQlY1JNWjy0si1EsEjifVhSb4XcNv5wLN3eepPSDe2r\nOIQHDGDnvEcIy+GWctu8qd0xgWY4eisseBrq/gVXnwfPCTTGqrZnGN1pR8AfYKxc2OHKvOlweuXt\nwCci8gFtfRxX5kWqPBL/wws0w/BtaY73l7Hz+X9D3+TmrTa+i43VhPsPyNwHoX2mFRcplN+gw+S7\nyVWK+7Db22/TdNZ3W0Nw4ympr6fv6DNbG7KFBw2idGNyP2rvPTD/aQiWWZPXujK458QEReKUEisP\n5IFRczm0j+Q9+Q+cK46H8ipFIUnxw7OjJBLG99VOwikUB36/pSSaQ3R/YSG+mi10f3GJlRHucMaW\nbgnO5s1WDR5FyTUZBHh4gWyDTJyQ7j5sHPdTKPO3fnakRwBf/S580VVJrBpE8/AjUyqOGLHJawQY\nEadIGhMeGbv98GUP+FojlIf2btvVDQ6Z/RJ9hnUg+zALumbP8bo6+h09tPWPnYq0PZ6jNuEes+6z\nnYm06XmcijSZ5aWfrGZbg/P6WW7RKbOHXaQQcvrWrqHvScdQEm4fYR8pLaX2jfcd+Q0KPqZZ+mNS\nyum0UkMwiG/dWvb9yVhb01XL/gfQdOZZlD/1RGsr2kjPXuweeyH4fHRf/AK+jdVEAhUQaqZk9+6s\nLVXBK66m4c67sjw7OfnqOY4xpig9tb7tX1Li0PSTdPmb4EDDZx+g5njGlmYJHggEklb7VJSOULRJ\npvkIFunWjfA++1BqU4zaV7+LiqlTrIlgIADlPfBtsl9V+DZvpPHnv6Dh9in41q0FSvb6SINBGi+5\nnMCM6fR4cn6HRe7+0hIagsGCrgrT2VBGFkKIQpPqRoFohETV4JTL30SbMC32qwFf9TrL2XjoYWnl\nSrUE946hQOl05NtvUERUTJ5I2crk9VvjJ4KOFG4gQPjwYdbGaOJiuslmpuQsmiwDUioOY4xN56BO\nQCBA01mjrMq0NoQHDqJ2ySutuRrtSGETTqQE6DH7fmdLyWieR8OtkzwdEql0PvLpNygaHNzXbR7S\nGSpcp5PNTHFjVeidOLsC0zhuPD3mzLa1K/q21uCrq7P6YNjtd9gzPEbGS0nN11AKjU5aHN3XiQ9p\nxwo3g8lmprixKuyyiiM8aD/CVQdkZddNZ+pKxI2lpKJkRReetDi5r8P79IZucfGyDhVuJpPNiL+M\nklBz8v2lVuagm6tCR0Y2EVmcb0EKTkeKB6Y41w5POxgVRbFwcF+XrfyobfHSuHNj5qtW4koHxZSS\nHZHS0tbOn40XXgThNCasSISdTzzjaukhp96ZgIhU5VUSF2iYPAWuuy5py9Z05wavGd/m3ObhR9oe\nm9FSMrFOlaIoBaP1vt6vKmk4adqEXLvqvb+/3WoAZUPjZVe01qCr/+PdSRVMjPB+VYSOPd5VU6LT\nDoAfY/Xg2IqVOV4CRNwKx+1wHkcclZW92LauJnu7bnwsebdu0RBdG3tnullBmpIPmneQW1TO3FMs\nsjqR0/fxavqePiKr3JaK2262dZgHx/3UyuFI83xIdn7rdZzmhnWQXHQA/G6OZPEmHbHrJpybrYOx\naEo+KEoXIDx4SHa5Lamy8Be/YJmX0jwfYhaPwOLniaxbB6Wl0BImXFXVtuipizg1VW0BjgFOA74N\nnAFclS+hih47e2cqtE6VoniLLH2gjtospHs+RB3urF5N7dsr+HLlZ9S+vZzape94pp2CUwkeA/oA\nhwBLsfpxvO7kRBE5AbjTGDNSRA4B5mHl2K0ErjXGhEVkEnAuEAImGGPeyehbFDmOfmyD+xdYKkXp\n2mST25LTLPw4a0ay1AC3cLriOAprlfEPYCpwMjAk3UkichMwG4h1vbgbuM0YcyqWn2SMiByDtYo5\nAbgQuC8D+TsFqSIuNCJLUVzCaeO1eKIlS+zoTFn4ThXH1mgvjk+Ao4wxawAnxX8/By6Ie38s8Gr0\n9SLgO8ApwBJjTMQYsx7wi0ilQ7k6B9pXXFEKQzAIn3+eN/NvspIlzcOP9IRvIlc4NVWtFJEZwEzg\nEREZhIO2I8aYp0RkSNymkrhmULuAfYDeWP0+SNietFNGnz4B/P7ctc+qrOxlvQgGrfLlAwdm9rDO\n9rx47rsXenSDZ5+F6mqoqoIxYwhMm0YgOsNpldPjqJy5pVjkBA/LGgrBDTdY99f69VQecACMGQPT\npiVfQSScQ7pzgkFY/Lztpcrqd1G5T/eMnw9eHU+nimM8MMIYs1pEfou1Urgoi8+Lj23rBewE6qKv\nE7cnZceO3M0WKit7sW3zjuy6n+W6a9rE/4Jf3dI24mJH4145O0mooxdQOXOPl2VtF+L6xRdwzz0E\nG/ckjVrM9Bzf2jX0ra62by1bXU3tys8yit50ezxTKa2UpioROU1ETsPyaZREX38FPAWk6GyUlOUi\nMjL6ejSWo30ZcLaI+ETkAMBnjPkyi2tnTSwUtrR6PSXhcGsorG2GaA7OS0mmEVmKoqQmm6jFLM7J\nma+yCJKA0/k4bk/xb3IWn3c9cLuIvInlI3nSGPM+lgJ5E0shXZvFdbMn21BYDaFVlKLAUdRiDs7p\nsK8yIeOcYcOouO1my2TmMdKVVT89/r2I9AJKjTEpTUkJ1/gCODH6+lOsCKrEYyaTnSLqOJs3p/2B\n2C0vnfywumqxOEXxEtmEyGYbVtuR8vTtyq5/8YVnk4CdFjk8SETeAb4A1ojIchE5NK+SFYqBA7Na\nXmoIraIUCdmsBLJdPWQTwgtFZ8FwGo57PzDVGNPPGNMX+APQvsF2MZLtD0RDaBWlaIgvSorDgqZ2\nhUydFkHN1FeZlWnMRZwWOVxujPlmwrYPjTFH5U2yFOS8yGFrVFWGxQlbo6qyKGqYjZwejViJR+XM\nLcUiJxSJrMEglaF6tvl7ZlaxOt/NrYJB+p76LVvTWEvVYGqXvl3wyWguihw2icgxxpgPAETkWMBb\na6eOkG33M+2apijFRSAAlf0hEwVXiOZWRdb33animAA8JSK1WIl/fYEf5U0qt8j2B9KFu6YpipIb\nEh3rJVVVBEeN9mTGuSPFYYx5S0QOw+rJ4bM2mT15lUxRFKUrkWDB6Df8UBoa0nQDdIm0znEROU9E\nDjLGNAOHAr8H7/QiFgAADENJREFUJopIWd6lU9pSBIlBilJQOuM9kaYNrRdIlzl+AzAJKBeRo4BH\ngGeBfsB/5188BbASgCZMaNuK0qOJQYpSEOzas3bGe8Kj3zOdqeoS4CRjTFBE/gj80xgzW0RKgNX5\nF68Tk0GkRsXkifDATGJlHW27AxYi8kNRPEJX6Zjp1e+ZzlQVMcbE1kanAy8AxFW4VTIlcQZx8nH0\n/OXPoK7O/vh0iUF1dZ6ckShK3iiyZLms8fD3TKc4QiKyr4jsD3wTWAIgIoOxuvUpGdKuMOLGDfR4\n/FH6HT3U9oGfLjGo58Sbcl9oUVE8TLEly2WNg3JIbpFOcfwRWAG8Bcw2xmwWkbHAv7E6ASqZkGIG\n4auvt33gpyxtMmAQ3ZYttd3n9oxEUfJFlyn3k2U5pEKQUnEYY54ERgDnGGN+Ht1cD4wzxvw938J1\nNlLNlGK0e+CnKG2y59RT8W3aaP9ZnWnmpSjxdJZyP+kipTz8PdPmcRhjNgGb4t7bt7hS0pKq4mYM\nu8q6DZOnEOjRjZann2lb2uSmiXRb9nrGFTwVpdjpSBVa18mgAZxXv2duCyopqUlRViCG7QPf74fp\n06lN7A4IRVWmQFFyRhGX+8koUsqj39NpdVwlR8QqboZ72rdlTFeRNzExqEMVPBWl2Cm2jpnZRkp5\n7Huq4ig00RnE9hUf0/iji2jZv6pjD/xs6/8rilJwso4I81jmuD5d3KJ3b+pnzMpd4p4WWlQUz5Nx\nZ8EM/CGFRFccbuOxJaiiKHkkw0ipdnlfHsnTckVlichy4Kvo27VYHQbvwUoqXGKMud0NuRRFUfKN\n40ipNP6QhlsnuTbhLLjiEJFyAGPMyLhtK4AfAGuAhfFNoxRFUToVTiOlHGSOu2WedmPF8Q0gICJL\nop8/GehujPkcQEQWA2cCqjgURem8pPNLRjPHvZin5YbiCALTgNlY/T0WATvj9u8CUqrRPn0C+P2l\nqQ7JiMpK+9BYr6Fy5haVM/cUi6zFImfpBd+He+6x2f49Kgf3d0EiCzcUx6fA/0Yr7H4qIl9htaKN\n0Yu2iqQdO3bkLiStsrIX2zLpP+wSKmduUTlzT7HIWlRy3jyJisY9lj9kYzXh/gMsf8jNkzLrm57l\n5yfDjaiqK4G7AERkEBAAGkTk4Gifj7MB+8p9XsZjcdaKonQC/H4aJk+h6TujCPcfgK9mC91fXGJF\nVbnYOsGNFceDwDwReR2IYCmSMFZ3wVKsqKq3XZArOzwaZ60oSuegYvJEAnP/2vreC82cCv5kM8bs\nAS6y2XVioWXJBXnr0KUd/RRF8WhIriYAdoR8dOjyaI9hRVEKj1ebVqni6AD5+KMmyxTlhhs6Kq6i\nKEWGV5tWqeLoADn/o6ZYwfDss+p4V5SuhkebOani6Agd/aMmRGKl7BBYXa0d/RSlM5MkMtOLrRM0\n7KeDZNWhK1kk1k0Tk3cIrKrSjn6K0hlJ8jzgvnut/R5s5qSKo6Nk8UdNFYmVtEPgmDGu/1gURck9\nSZ8HPbrBxP/ae6CHWieo4sgVTv+oaSKxal9Z1vo6fgUTmDYNdjTmUmJFUdwmnV/zV7d4csKoiqPA\npI3E2v6l7QomoMmEitLp8G3aiM/ONA2tfk2vrDLiUed4gXEciaUNnhSl09Nj9kxKku30sF9TFUeh\n8Wh4naIoBSYYpPuLS5LvP8e7zwO1f7hAVpFYRUawOUhNcAv9AwMIlHnzx68obpLKbB0BSn75y8IK\nlAGqONzAg+F1uSIUDnHb6zezaM1CNtZvYL+e+zP6oHOZPGIKfp/+3BQlRsxsbduoqWowpVVV0NDi\ngmTpUVOVm3RCP8YNS27ggQ9nUl2/njBhquvX88CHM5n8xkS3RVMUb1HEZmtVHErOCDYHeeaTZ2z3\nLVr7PMFmLZmiKPF4MSvcCWo7UHJGTXAL1V9V2+7bVL+BmuAWDtzHe6GFiuIaRWq21hWHkjP6BwZw\nwD4H2O4b1HN/+ge8GVqoKK5TZGZrVRxKzgiUBRgzdIztvtEHnqPRVYrSSVBTlZJTpo2aRmPjHhat\nfZ5N9RsY1HN/Rh94DpNHeNtmqyiKc1RxKDnF7/Nzxyl3cusJkzSPQ1E6Kao4lLwQKAuoI1xROime\nUBwi4gP+AnwDaALGGWP+112pFEVRFDu84hz/HlBujDkJ+DVwl8vyKIqiKEnwiuI4BXgBwBjzFnCc\nu+IoiqIoyfCEqQroDXwV975FRPzGmJDdwX36BPD7S3P24ZWVvXJ2rXyicuYWlTP3FIusKmfH8Iri\nqAPiR8iXTGkA7NiRu9IVlZW92LZtV86uly9UztyicuaeYpFV5XT++cnwiqlqGXAOgIicCHzkrjiK\noihKMryy4vgHcJaIvAGUAFe4LI+iKIqSBE8oDmNMGPiZ23IoiqIo6SmJRCJuy6AoiqIUEV7xcSiK\noihFgioORVEUJSNUcSiKoigZoYpDURRFyQhVHIqiKEpGqOJQFEVRMkIVh6IoipIRnkgALDTF0P9D\nRJazt/DjWuB+4B4gBCwxxtzulmwAInICcKcxZqSIHALMAyLASuBaY0xYRCYB52LJPMEY847Lch4D\nPAd8Ft090xgz3005RaQMmAMMAboDdwCr8eB4JpF1A94b01Lgr4AALViVKErw2JgmkXMfPDaednRJ\nxUFc/49obay7gDEuy9SKiJQDGGNGxm1bAfwAWAMsFJFjjDEfuCTfTcAlQEN0093AbcaY/4jILGCM\niKwDvg2cAFQBTwHHuyznMcDdxpi74o45xmU5Lwa2G2MuEZF+wHJgBR4czySy/g7vjen5AMaYk0Vk\nJNbvswTvjamdnM/hvfFsR1c1VXm9/8c3gICILBGRl0XkNKC7MeZzY0wEWAyc6aJ8nwMXxL0/Fng1\n+noR8B2sMV5ijIkYY9YDfhGpLKyYtnKeKyKviciDItLLA3IuAH4T9z6Ed8czmayeGlNjzDPANdG3\ng4EaPDimKeT01Hja0VUVh23/D7eEsSEITAPOxqrhNTe6LcYurCWtKxhjngKa4zaVRBUa7JUtcYwL\nLrONnO8ANxpjTsNauU3CZTmNMfXGmF3RB8STwG14dzztZPXcmEZlDYnIQ8CMqKxeHdNEOT05nol0\nVcWRUf8PF/gUeDg6w/gU60fTN25/L2CnK5LZE457HZMtcYy9IPM/jDHvx14D38QDcopIFfAK8Hdj\nzKN4eDxtZPXkmAIYYy4DDsPyI/SwkceLci7x6njG01UVh9f7f1xJtO+6iAwCAkCDiBwsIiVYK5Gl\nLsqXyPKojRZgNJZsy4CzRcQnIgdgKecv3RIwymIR+Vb09ZnA+7gsp4j0B5YANxtj5kQ3e3I8k8jq\nxTG9RERuib4NYini97w2pknkfNpr42mHl8wzhcTr/T8eBOaJyOtYUSBXYv2oHgFKsWYlb7soXyLX\nA38VkW7Ax8CTxpgWEVkKvIk1QbnWTQGjjAf+LCJ7gC3ANcaYOpflvBXoA/xGRGL+g+uAez04nnay\n/j9gusfG9Glgroi8BpQBE7DG0Wu/UTs5q/Heb7QdWlZdURRFyYiuaqpSFEVRskQVh6IoipIRqjgU\nRVGUjFDFoSiKomSEKg5FURQlI7pqOK6idAgRGYKVqLk6uqkH8Abwa2NMjVtyKUoh0BWHomTPJmPM\n0caYo4GhWHH3T7osk6LkHV1xKEoOMMZEoqWva0TkKOAXwHCgP/Ah8GOsBDqfMWYigIjMwyq4Vwrc\nhFVaey1wsTFmd8G/hKI4RFccipIjjDF7sPoofA/YY4w5CTgE2BerxM1c4CIRKRGRAHAG8CxWX4tR\nxphjsRTHUDfkVxSnqOJQlNwSwepT8RcRuRar+dahQE9jzBrgC+A0rN4qC6Mri+eAZSIyFXjKGLPC\nFckVxSGqOBQlR0TrIAlwEFZdsSDWKuM1rJpoYHXQuyj6bx6AMeY6LEWyA3hYRC4uqOCKkiGqOBQl\nB0TbEd8OvAUcDDxhjJmLVf76dCw/BljO8zOBAcaYt0XELyKfAV8aY/4A/A2rlLaieBZ1jitK9gyK\ntvQFSzEsx3KC7w88KiI/BvZglcU+EMAY0ygibxIt5R9t5PNb4EURaQS2ApcX9FsoSoZodVxFKRDR\nXiq9sMpjn2mM2eKySIqSFWqqUpTCcTyWc/wBVRpKMaMrDkVRFCUjdMWhKIqiZIQqDkVRFCUjVHEo\niqIoGaGKQ1EURckIVRyKoihKRvx/n8GpAuLq6boAAAAASUVORK5CYII=\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('Figure3.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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bWng9Z75qzcrZXJUzXzXVQ4CDVfrczKv3YRzsIRpP5i0bsZhM/zMUQogRWJZF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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=dw_solar_everyday,y=ghi_everyday,kind='reg')\n",
    "#plt.title('observed dw_solar vs clear sky ghi')\n",
    "plt.xlabel('Observed global downwelling solar (Watts/m^2)')\n",
    "plt.ylabel('Clear Sky GHI (Watts/m^2)')\n",
    "plt.savefig('Figure3.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### making the Kt (clear sky index at time t) column by first removing rows with ghi==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train[df_train['ghi']!=0]\n",
    "df_test = df_test[df_test['ghi']!=0]\n",
    "df_train['Kt'] = df_train['dw_solar']/df_train['ghi']\n",
    "df_test['Kt'] = df_test['dw_solar']/df_test['ghi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.reset_index(inplace=True)\n",
    "df_test.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Kt max: 934060.7967236277\n",
      "test Kt min: -1212.4523861041143\n",
      "test Kt mean: 6.087694158417696\n",
      "\n",
      "\n",
      "train Kt max: 36737.73884840597\n",
      "train Kt min: -404.8538655568123\n",
      "train Kt mean: 2.4557778502312537\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 0x6c504a8>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REYWBiIgoDEREBIWBiIigMBARERQGkgfzlnyI1zaEPQwRyYHCQHKyZ187Ty3YyC2zqsMe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\nYSAiIigMREQEhYGIiKAwEBER4P8DLgaOgNe8NIcAAAAASUVORK5CYII=\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 0xfad1c50>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Co08e4PorL6Hr3M48l4nPc+LUKD986pf8nQ+u45xlxbytUChw4PUBDhwc4IYr11AoFPK2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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_test['Kt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train= df_train[df_train['Kt']< 5000]\n",
    "df_train= df_train[df_train['Kt']> -1000]\n",
    "df_test= df_test[df_test['Kt']< 5000]\n",
    "df_test= df_test[df_test['Kt']> -1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group the data (train dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "zen = df_train.groupby(['year','month','day','hour'])['zen'].mean()\n",
    "dw_solar = df_train.groupby(['year','month','day','hour'])['dw_solar'].mean()\n",
    "uw_solar = df_train.groupby(['year','month','day','hour'])['uw_solar'].mean()\n",
    "direct_n = df_train.groupby(['year','month','day','hour'])['direct_n'].mean()\n",
    "diffuse = df_train.groupby(['year','month','day','hour'])['diffuse'].mean()\n",
    "dw_ir = df_train.groupby(['year','month','day','hour'])['dw_ir'].mean()\n",
    "dw_casetemp = df_train.groupby(['year','month','day','hour'])['dw_casetemp'].mean()\n",
    "dw_dometemp = df_train.groupby(['year','month','day','hour'])['dw_dometemp'].mean()\n",
    "uw_ir = df_train.groupby(['year','month','day','hour'])['uw_ir'].mean()\n",
    "uw_casetemp = df_train.groupby(['year','month','day','hour'])['uw_casetemp'].mean()\n",
    "uw_dometemp = df_train.groupby(['year','month','day','hour'])['uw_dometemp'].mean()\n",
    "uvb = df_train.groupby(['year','month','day','hour'])['uvb'].mean()\n",
    "par = df_train.groupby(['year','month','day','hour'])['par'].mean()\n",
    "netsolar = df_train.groupby(['year','month','day','hour'])['netsolar'].mean()\n",
    "netir = df_train.groupby(['year','month','day','hour'])['netir'].mean()\n",
    "totalnet = df_train.groupby(['year','month','day','hour'])['totalnet'].mean()\n",
    "temp = df_train.groupby(['year','month','day','hour'])['temp'].mean()\n",
    "rh = df_train.groupby(['year','month','day','hour'])['rh'].mean()\n",
    "windspd = df_train.groupby(['year','month','day','hour'])['windspd'].mean()\n",
    "winddir = df_train.groupby(['year','month','day','hour'])['winddir'].mean()\n",
    "pressure = df_train.groupby(['year','month','day','hour'])['pressure'].mean()\n",
    "ghi = df_train.groupby(['year','month','day','hour'])['ghi'].mean()\n",
    "Kt = df_train.groupby(['year','month','day','hour'])['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_train = pd.concat([zen,dw_solar,uw_solar,direct_n,diffuse,dw_ir,dw_casetemp,dw_dometemp,uw_ir,uw_casetemp,uw_dometemp,\n",
    "                    uvb,par,netsolar,netir,totalnet,temp,rh,windspd,winddir,pressure,ghi,Kt], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th>6.0</th>\n",
       "      <td>162.750000</td>\n",
       "      <td>229.275124</td>\n",
       "      <td>-1.300000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>243.300000</td>\n",
       "      <td>276.200000</td>\n",
       "      <td>275.700000</td>\n",
       "      <td>308.800000</td>\n",
       "      <td>274.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-65.500000</td>\n",
       "      <td>-65.500000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.400000</td>\n",
       "      <td>915.400000</td>\n",
       "      <td>8.485002</td>\n",
       "      <td>27.021223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>165.356667</td>\n",
       "      <td>229.275124</td>\n",
       "      <td>-1.366667</td>\n",
       "      <td>1.018333</td>\n",
       "      <td>-0.885000</td>\n",
       "      <td>241.946667</td>\n",
       "      <td>275.456667</td>\n",
       "      <td>274.983333</td>\n",
       "      <td>307.460000</td>\n",
       "      <td>274.423333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-65.521667</td>\n",
       "      <td>-65.521667</td>\n",
       "      <td>2.268333</td>\n",
       "      <td>41.808333</td>\n",
       "      <td>2.255000</td>\n",
       "      <td>115.338333</td>\n",
       "      <td>915.410000</td>\n",
       "      <td>72.956955</td>\n",
       "      <td>4.371045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>163.399000</td>\n",
       "      <td>229.275124</td>\n",
       "      <td>-1.450000</td>\n",
       "      <td>0.781667</td>\n",
       "      <td>-0.781667</td>\n",
       "      <td>244.203333</td>\n",
       "      <td>275.143333</td>\n",
       "      <td>274.721667</td>\n",
       "      <td>304.498333</td>\n",
       "      <td>273.966667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-60.288333</td>\n",
       "      <td>-60.288333</td>\n",
       "      <td>1.918333</td>\n",
       "      <td>43.545000</td>\n",
       "      <td>0.883333</td>\n",
       "      <td>167.170000</td>\n",
       "      <td>915.458333</td>\n",
       "      <td>234.169751</td>\n",
       "      <td>1.026259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>154.172667</td>\n",
       "      <td>229.275124</td>\n",
       "      <td>-1.561667</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>-0.815000</td>\n",
       "      <td>245.158333</td>\n",
       "      <td>274.786667</td>\n",
       "      <td>274.335000</td>\n",
       "      <td>303.886667</td>\n",
       "      <td>273.730000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-58.726667</td>\n",
       "      <td>-58.726667</td>\n",
       "      <td>2.043333</td>\n",
       "      <td>43.063333</td>\n",
       "      <td>1.690000</td>\n",
       "      <td>130.491667</td>\n",
       "      <td>915.133333</td>\n",
       "      <td>388.224711</td>\n",
       "      <td>0.596671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>142.737000</td>\n",
       "      <td>229.275124</td>\n",
       "      <td>-1.428333</td>\n",
       "      <td>1.516667</td>\n",
       "      <td>-0.661667</td>\n",
       "      <td>256.343333</td>\n",
       "      <td>274.830000</td>\n",
       "      <td>274.453333</td>\n",
       "      <td>305.071667</td>\n",
       "      <td>273.793333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-48.730000</td>\n",
       "      <td>-48.730000</td>\n",
       "      <td>1.671667</td>\n",
       "      <td>44.681667</td>\n",
       "      <td>1.256667</td>\n",
       "      <td>104.535000</td>\n",
       "      <td>915.161667</td>\n",
       "      <td>496.624772</td>\n",
       "      <td>0.462711</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 6.0   162.750000  229.275124 -1.300000  1.800000 -0.700000   \n",
       "                 7.0   165.356667  229.275124 -1.366667  1.018333 -0.885000   \n",
       "                 8.0   163.399000  229.275124 -1.450000  0.781667 -0.781667   \n",
       "                 9.0   154.172667  229.275124 -1.561667  0.983333 -0.815000   \n",
       "                 10.0  142.737000  229.275124 -1.428333  1.516667 -0.661667   \n",
       "\n",
       "                            dw_ir  dw_casetemp  dw_dometemp       uw_ir  \\\n",
       "year   month day hour                                                     \n",
       "2010.0 1.0   1.0 6.0   243.300000   276.200000   275.700000  308.800000   \n",
       "                 7.0   241.946667   275.456667   274.983333  307.460000   \n",
       "                 8.0   244.203333   275.143333   274.721667  304.498333   \n",
       "                 9.0   245.158333   274.786667   274.335000  303.886667   \n",
       "                 10.0  256.343333   274.830000   274.453333  305.071667   \n",
       "\n",
       "                       uw_casetemp    ...      netsolar      netir   totalnet  \\\n",
       "year   month day hour                 ...                                       \n",
       "2010.0 1.0   1.0 6.0    274.700000    ...           0.0 -65.500000 -65.500000   \n",
       "                 7.0    274.423333    ...           0.0 -65.521667 -65.521667   \n",
       "                 8.0    273.966667    ...           0.0 -60.288333 -60.288333   \n",
       "                 9.0    273.730000    ...           0.0 -58.726667 -58.726667   \n",
       "                 10.0   273.793333    ...           0.0 -48.730000 -48.730000   \n",
       "\n",
       "                           temp         rh   windspd     winddir    pressure  \\\n",
       "year   month day hour                                                          \n",
       "2010.0 1.0   1.0 6.0   3.600000  39.000000  1.000000   56.400000  915.400000   \n",
       "                 7.0   2.268333  41.808333  2.255000  115.338333  915.410000   \n",
       "                 8.0   1.918333  43.545000  0.883333  167.170000  915.458333   \n",
       "                 9.0   2.043333  43.063333  1.690000  130.491667  915.133333   \n",
       "                 10.0  1.671667  44.681667  1.256667  104.535000  915.161667   \n",
       "\n",
       "                              ghi         Kt  \n",
       "year   month day hour                         \n",
       "2010.0 1.0   1.0 6.0     8.485002  27.021223  \n",
       "                 7.0    72.956955   4.371045  \n",
       "                 8.0   234.169751   1.026259  \n",
       "                 9.0   388.224711   0.596671  \n",
       "                 10.0  496.624772   0.462711  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Groupdata - test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_zen = df_test.groupby(['month','day','hour'])['zen'].mean()\n",
    "test_dw_solar = df_test.groupby(['month','day','hour'])['dw_solar'].mean()\n",
    "test_uw_solar = df_test.groupby(['month','day','hour'])['uw_solar'].mean()\n",
    "test_direct_n = df_test.groupby(['month','day','hour'])['direct_n'].mean()\n",
    "test_diffuse = df_test.groupby(['month','day','hour'])['diffuse'].mean()\n",
    "test_dw_ir = df_test.groupby(['month','day','hour'])['dw_ir'].mean()\n",
    "test_dw_casetemp = df_test.groupby(['month','day','hour'])['dw_casetemp'].mean()\n",
    "test_dw_dometemp = df_test.groupby(['month','day','hour'])['dw_dometemp'].mean()\n",
    "test_uw_ir = df_test.groupby(['month','day','hour'])['uw_ir'].mean()\n",
    "test_uw_casetemp = df_test.groupby(['month','day','hour'])['uw_casetemp'].mean()\n",
    "test_uw_dometemp = df_test.groupby(['month','day','hour'])['uw_dometemp'].mean()\n",
    "test_uvb = df_test.groupby(['month','day','hour'])['uvb'].mean()\n",
    "test_par = df_test.groupby(['month','day','hour'])['par'].mean()\n",
    "test_netsolar = df_test.groupby(['month','day','hour'])['netsolar'].mean()\n",
    "test_netir = df_test.groupby(['month','day','hour'])['netir'].mean()\n",
    "test_totalnet = df_test.groupby(['month','day','hour'])['totalnet'].mean()\n",
    "test_temp = df_test.groupby(['month','day','hour'])['temp'].mean()\n",
    "test_rh = df_test.groupby(['month','day','hour'])['rh'].mean()\n",
    "test_windspd = df_test.groupby(['month','day','hour'])['windspd'].mean()\n",
    "test_winddir = df_test.groupby(['month','day','hour'])['winddir'].mean()\n",
    "test_pressure = df_test.groupby(['month','day','hour'])['pressure'].mean()\n",
    "test_ghi = df_test.groupby(['month','day','hour'])['ghi'].mean()\n",
    "test_Kt = df_test.groupby(['month','day','hour'])['Kt'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_test = pd.concat([test_zen,test_dw_solar,test_uw_solar,test_direct_n,test_diffuse,test_dw_ir,\n",
    "                         test_dw_casetemp,test_dw_dometemp,test_uw_ir,test_uw_casetemp,test_uw_dometemp,\n",
    "                    test_uvb,test_par,test_netsolar,test_netir,test_totalnet,test_temp,test_rh,\n",
    "                         test_windspd,test_winddir,test_pressure,test_ghi,test_Kt], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>150.286429</td>\n",
       "      <td>-5.457143</td>\n",
       "      <td>4.758099e-17</td>\n",
       "      <td>-0.175000</td>\n",
       "      <td>-0.053571</td>\n",
       "      <td>238.189286</td>\n",
       "      <td>280.017857</td>\n",
       "      <td>279.328571</td>\n",
       "      <td>327.503571</td>\n",
       "      <td>278.535714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-89.325000</td>\n",
       "      <td>-89.325000</td>\n",
       "      <td>5.610714</td>\n",
       "      <td>61.664286</td>\n",
       "      <td>10.210714</td>\n",
       "      <td>198.364286</td>\n",
       "      <td>895.860714</td>\n",
       "      <td>33.869158</td>\n",
       "      <td>-0.199644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>154.152333</td>\n",
       "      <td>-5.783333</td>\n",
       "      <td>-3.450000e-01</td>\n",
       "      <td>0.123333</td>\n",
       "      <td>-0.021667</td>\n",
       "      <td>238.683333</td>\n",
       "      <td>279.945000</td>\n",
       "      <td>279.300000</td>\n",
       "      <td>327.176667</td>\n",
       "      <td>278.526667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-88.500000</td>\n",
       "      <td>-88.500000</td>\n",
       "      <td>5.720000</td>\n",
       "      <td>59.485000</td>\n",
       "      <td>9.733333</td>\n",
       "      <td>198.575000</td>\n",
       "      <td>896.445000</td>\n",
       "      <td>156.492998</td>\n",
       "      <td>-0.043188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>154.031833</td>\n",
       "      <td>-5.948333</td>\n",
       "      <td>-2.000000e-01</td>\n",
       "      <td>-0.068333</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>234.365000</td>\n",
       "      <td>279.816667</td>\n",
       "      <td>279.126667</td>\n",
       "      <td>325.691667</td>\n",
       "      <td>278.390000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-91.336667</td>\n",
       "      <td>-91.336667</td>\n",
       "      <td>5.523333</td>\n",
       "      <td>57.200000</td>\n",
       "      <td>9.743333</td>\n",
       "      <td>206.088333</td>\n",
       "      <td>897.225000</td>\n",
       "      <td>358.157279</td>\n",
       "      <td>-0.016985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>147.761500</td>\n",
       "      <td>-6.048333</td>\n",
       "      <td>-2.966667e-01</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.048333</td>\n",
       "      <td>234.465000</td>\n",
       "      <td>279.631667</td>\n",
       "      <td>278.981667</td>\n",
       "      <td>324.173333</td>\n",
       "      <td>278.226667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-89.698333</td>\n",
       "      <td>-89.698333</td>\n",
       "      <td>5.428333</td>\n",
       "      <td>54.283333</td>\n",
       "      <td>7.906667</td>\n",
       "      <td>204.571667</td>\n",
       "      <td>898.161667</td>\n",
       "      <td>534.190398</td>\n",
       "      <td>-0.011397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>137.994833</td>\n",
       "      <td>-6.270000</td>\n",
       "      <td>-2.583333e-01</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.168333</td>\n",
       "      <td>233.670000</td>\n",
       "      <td>279.040000</td>\n",
       "      <td>278.473333</td>\n",
       "      <td>321.188333</td>\n",
       "      <td>277.590000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-87.516667</td>\n",
       "      <td>-87.516667</td>\n",
       "      <td>4.856667</td>\n",
       "      <td>58.343333</td>\n",
       "      <td>4.830000</td>\n",
       "      <td>221.958333</td>\n",
       "      <td>898.420000</td>\n",
       "      <td>660.519935</td>\n",
       "      <td>-0.009504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>126.708167</td>\n",
       "      <td>-6.398333</td>\n",
       "      <td>-4.233333e-01</td>\n",
       "      <td>0.073333</td>\n",
       "      <td>0.118333</td>\n",
       "      <td>232.613333</td>\n",
       "      <td>278.768333</td>\n",
       "      <td>278.190000</td>\n",
       "      <td>318.916667</td>\n",
       "      <td>277.463333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-86.303333</td>\n",
       "      <td>-86.303333</td>\n",
       "      <td>4.821667</td>\n",
       "      <td>56.403333</td>\n",
       "      <td>3.730000</td>\n",
       "      <td>242.768333</td>\n",
       "      <td>898.488333</td>\n",
       "      <td>725.681709</td>\n",
       "      <td>-0.008817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>114.824167</td>\n",
       "      <td>-6.381667</td>\n",
       "      <td>-4.466667e-01</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.161667</td>\n",
       "      <td>231.091667</td>\n",
       "      <td>277.991667</td>\n",
       "      <td>277.468333</td>\n",
       "      <td>317.415000</td>\n",
       "      <td>276.856667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-86.321667</td>\n",
       "      <td>-86.321667</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>57.873333</td>\n",
       "      <td>4.493333</td>\n",
       "      <td>240.988333</td>\n",
       "      <td>898.961667</td>\n",
       "      <td>724.381774</td>\n",
       "      <td>-0.008812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>102.804000</td>\n",
       "      <td>-6.466667</td>\n",
       "      <td>-3.716667e-01</td>\n",
       "      <td>0.581667</td>\n",
       "      <td>0.128333</td>\n",
       "      <td>231.006667</td>\n",
       "      <td>277.816667</td>\n",
       "      <td>277.293333</td>\n",
       "      <td>317.900000</td>\n",
       "      <td>276.565000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-86.886667</td>\n",
       "      <td>-86.886667</td>\n",
       "      <td>3.931667</td>\n",
       "      <td>59.148333</td>\n",
       "      <td>4.121667</td>\n",
       "      <td>251.095000</td>\n",
       "      <td>899.751667</td>\n",
       "      <td>656.728953</td>\n",
       "      <td>-0.009876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>90.783167</td>\n",
       "      <td>4.851667</td>\n",
       "      <td>2.676667e+00</td>\n",
       "      <td>113.013333</td>\n",
       "      <td>7.931667</td>\n",
       "      <td>232.438333</td>\n",
       "      <td>278.385000</td>\n",
       "      <td>277.873333</td>\n",
       "      <td>319.696667</td>\n",
       "      <td>276.965000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.883333</td>\n",
       "      <td>-87.261667</td>\n",
       "      <td>-75.386667</td>\n",
       "      <td>4.480000</td>\n",
       "      <td>54.673333</td>\n",
       "      <td>4.691667</td>\n",
       "      <td>230.323333</td>\n",
       "      <td>900.423333</td>\n",
       "      <td>528.243267</td>\n",
       "      <td>0.011476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>79.467333</td>\n",
       "      <td>134.226667</td>\n",
       "      <td>2.904833e+01</td>\n",
       "      <td>583.338333</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>236.191667</td>\n",
       "      <td>280.461667</td>\n",
       "      <td>279.906667</td>\n",
       "      <td>325.915000</td>\n",
       "      <td>277.828333</td>\n",
       "      <td>...</td>\n",
       "      <td>123.505000</td>\n",
       "      <td>-89.710000</td>\n",
       "      <td>33.791667</td>\n",
       "      <td>5.283333</td>\n",
       "      <td>51.800000</td>\n",
       "      <td>3.570000</td>\n",
       "      <td>233.085000</td>\n",
       "      <td>901.383333</td>\n",
       "      <td>350.685281</td>\n",
       "      <td>0.411660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>68.964167</td>\n",
       "      <td>355.316667</td>\n",
       "      <td>7.125333e+01</td>\n",
       "      <td>835.673333</td>\n",
       "      <td>65.670000</td>\n",
       "      <td>238.995000</td>\n",
       "      <td>283.240000</td>\n",
       "      <td>282.521667</td>\n",
       "      <td>346.746667</td>\n",
       "      <td>279.658333</td>\n",
       "      <td>...</td>\n",
       "      <td>294.840000</td>\n",
       "      <td>-107.756667</td>\n",
       "      <td>187.081667</td>\n",
       "      <td>6.493333</td>\n",
       "      <td>47.625000</td>\n",
       "      <td>5.473333</td>\n",
       "      <td>193.385000</td>\n",
       "      <td>902.025000</td>\n",
       "      <td>149.085976</td>\n",
       "      <td>3.013727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>62.158462</td>\n",
       "      <td>500.811538</td>\n",
       "      <td>9.932308e+01</td>\n",
       "      <td>911.876923</td>\n",
       "      <td>77.473077</td>\n",
       "      <td>241.038462</td>\n",
       "      <td>284.615385</td>\n",
       "      <td>283.865385</td>\n",
       "      <td>363.803846</td>\n",
       "      <td>280.730769</td>\n",
       "      <td>...</td>\n",
       "      <td>404.415385</td>\n",
       "      <td>-122.773077</td>\n",
       "      <td>281.650000</td>\n",
       "      <td>7.173077</td>\n",
       "      <td>46.588462</td>\n",
       "      <td>5.292308</td>\n",
       "      <td>201.319231</td>\n",
       "      <td>902.446154</td>\n",
       "      <td>31.373484</td>\n",
       "      <td>21.307784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             zen    dw_solar      uw_solar    direct_n    diffuse       dw_ir  \\\n",
       "hour                                                                            \n",
       "6.0   150.286429   -5.457143  4.758099e-17   -0.175000  -0.053571  238.189286   \n",
       "7.0   154.152333   -5.783333 -3.450000e-01    0.123333  -0.021667  238.683333   \n",
       "8.0   154.031833   -5.948333 -2.000000e-01   -0.068333   0.016667  234.365000   \n",
       "9.0   147.761500   -6.048333 -2.966667e-01    0.083333   0.048333  234.465000   \n",
       "10.0  137.994833   -6.270000 -2.583333e-01    0.140000   0.168333  233.670000   \n",
       "11.0  126.708167   -6.398333 -4.233333e-01    0.073333   0.118333  232.613333   \n",
       "12.0  114.824167   -6.381667 -4.466667e-01    0.233333   0.161667  231.091667   \n",
       "13.0  102.804000   -6.466667 -3.716667e-01    0.581667   0.128333  231.006667   \n",
       "14.0   90.783167    4.851667  2.676667e+00  113.013333   7.931667  232.438333   \n",
       "15.0   79.467333  134.226667  2.904833e+01  583.338333  46.980000  236.191667   \n",
       "16.0   68.964167  355.316667  7.125333e+01  835.673333  65.670000  238.995000   \n",
       "17.0   62.158462  500.811538  9.932308e+01  911.876923  77.473077  241.038462   \n",
       "\n",
       "      dw_casetemp  dw_dometemp       uw_ir  uw_casetemp    ...      \\\n",
       "hour                                                       ...       \n",
       "6.0    280.017857   279.328571  327.503571   278.535714    ...       \n",
       "7.0    279.945000   279.300000  327.176667   278.526667    ...       \n",
       "8.0    279.816667   279.126667  325.691667   278.390000    ...       \n",
       "9.0    279.631667   278.981667  324.173333   278.226667    ...       \n",
       "10.0   279.040000   278.473333  321.188333   277.590000    ...       \n",
       "11.0   278.768333   278.190000  318.916667   277.463333    ...       \n",
       "12.0   277.991667   277.468333  317.415000   276.856667    ...       \n",
       "13.0   277.816667   277.293333  317.900000   276.565000    ...       \n",
       "14.0   278.385000   277.873333  319.696667   276.965000    ...       \n",
       "15.0   280.461667   279.906667  325.915000   277.828333    ...       \n",
       "16.0   283.240000   282.521667  346.746667   279.658333    ...       \n",
       "17.0   284.615385   283.865385  363.803846   280.730769    ...       \n",
       "\n",
       "        netsolar       netir    totalnet      temp         rh    windspd  \\\n",
       "hour                                                                       \n",
       "6.0     0.000000  -89.325000  -89.325000  5.610714  61.664286  10.210714   \n",
       "7.0     0.000000  -88.500000  -88.500000  5.720000  59.485000   9.733333   \n",
       "8.0     0.000000  -91.336667  -91.336667  5.523333  57.200000   9.743333   \n",
       "9.0     0.000000  -89.698333  -89.698333  5.428333  54.283333   7.906667   \n",
       "10.0    0.000000  -87.516667  -87.516667  4.856667  58.343333   4.830000   \n",
       "11.0    0.000000  -86.303333  -86.303333  4.821667  56.403333   3.730000   \n",
       "12.0    0.000000  -86.321667  -86.321667  4.230000  57.873333   4.493333   \n",
       "13.0    0.000000  -86.886667  -86.886667  3.931667  59.148333   4.121667   \n",
       "14.0   11.883333  -87.261667  -75.386667  4.480000  54.673333   4.691667   \n",
       "15.0  123.505000  -89.710000   33.791667  5.283333  51.800000   3.570000   \n",
       "16.0  294.840000 -107.756667  187.081667  6.493333  47.625000   5.473333   \n",
       "17.0  404.415385 -122.773077  281.650000  7.173077  46.588462   5.292308   \n",
       "\n",
       "         winddir    pressure         ghi         Kt  \n",
       "hour                                                 \n",
       "6.0   198.364286  895.860714   33.869158  -0.199644  \n",
       "7.0   198.575000  896.445000  156.492998  -0.043188  \n",
       "8.0   206.088333  897.225000  358.157279  -0.016985  \n",
       "9.0   204.571667  898.161667  534.190398  -0.011397  \n",
       "10.0  221.958333  898.420000  660.519935  -0.009504  \n",
       "11.0  242.768333  898.488333  725.681709  -0.008817  \n",
       "12.0  240.988333  898.961667  724.381774  -0.008812  \n",
       "13.0  251.095000  899.751667  656.728953  -0.009876  \n",
       "14.0  230.323333  900.423333  528.243267   0.011476  \n",
       "15.0  233.085000  901.383333  350.685281   0.411660  \n",
       "16.0  193.385000  902.025000  149.085976   3.013727  \n",
       "17.0  201.319231  902.446154   31.373484  21.307784  \n",
       "\n",
       "[12 rows x 23 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test.loc[2].xs(17,level='day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shifting Kt values to make 1 hour ahead forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels_index= []\n",
    "for m in df_new_train.index.levels:\n",
    "    levels_index.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in levels_index[0]:\n",
    "    for j in levels_index[1]:\n",
    "        df_new_train.loc[i].loc[j]['Kt'] = df_new_train.loc[i].loc[j]['Kt'].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_train = df_new_train[~(df_new_train['Kt'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels_index2= []\n",
    "for m in df_new_test.index.levels:\n",
    "    levels_index2.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in levels_index2[0]:\n",
    "    for j in levels_index2[1]:\n",
    "        df_new_test.loc[i].loc[j]['Kt'] = df_new_test.loc[i].loc[j]['Kt'].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_test = df_new_test[~(df_new_test['Kt'].isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <th>16.0</th>\n",
       "      <th>19.0</th>\n",
       "      <td>16.453833</td>\n",
       "      <td>1036.853333</td>\n",
       "      <td>207.88</td>\n",
       "      <td>937.083333</td>\n",
       "      <td>94.176667</td>\n",
       "      <td>392.505</td>\n",
       "      <td>316.651667</td>\n",
       "      <td>315.851667</td>\n",
       "      <td>654.351667</td>\n",
       "      <td>313.416667</td>\n",
       "      <td>...</td>\n",
       "      <td>784.853333</td>\n",
       "      <td>-261.845</td>\n",
       "      <td>523.001667</td>\n",
       "      <td>37.598333</td>\n",
       "      <td>11.703333</td>\n",
       "      <td>5.456667</td>\n",
       "      <td>240.191667</td>\n",
       "      <td>903.088333</td>\n",
       "      <td>56.199403</td>\n",
       "      <td>4900.219009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       zen     dw_solar  uw_solar    direct_n    diffuse  \\\n",
       "month day  hour                                                            \n",
       "7.0   16.0 19.0  16.453833  1036.853333    207.88  937.083333  94.176667   \n",
       "\n",
       "                   dw_ir  dw_casetemp  dw_dometemp       uw_ir  uw_casetemp  \\\n",
       "month day  hour                                                               \n",
       "7.0   16.0 19.0  392.505   316.651667   315.851667  654.351667   313.416667   \n",
       "\n",
       "                    ...         netsolar    netir    totalnet       temp  \\\n",
       "month day  hour     ...                                                    \n",
       "7.0   16.0 19.0     ...       784.853333 -261.845  523.001667  37.598333   \n",
       "\n",
       "                        rh   windspd     winddir    pressure        ghi  \\\n",
       "month day  hour                                                           \n",
       "7.0   16.0 19.0  11.703333  5.456667  240.191667  903.088333  56.199403   \n",
       "\n",
       "                          Kt  \n",
       "month day  hour               \n",
       "7.0   16.0 19.0  4900.219009  \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": [
       "2"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fractions import gcd\n",
    "gcd(train_norm.shape[0],test_norm.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def roundup(x):\n",
    "    return int(math.ceil(x / 100.0)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_lim = roundup(train_norm.shape[0])\n",
    "test_lim = roundup(test_norm.shape[0])\n",
    "\n",
    "train_random = train_norm.sample(train_lim-train_norm.shape[0])\n",
    "test_random = test_norm.sample(test_lim-test_norm.shape[0])\n",
    "\n",
    "train_norm = train_norm.append(train_random)\n",
    "test_norm = test_norm.append(test_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = train_norm.drop('Kt',axis=1)\n",
    "y1 = train_norm['Kt']\n",
    "\n",
    "X2 = test_norm.drop('Kt',axis=1)\n",
    "y2 = test_norm['Kt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1_train shape is (9000, 22)\n",
      "y1_train shape is (9000,)\n",
      "X2_test shape is (4500, 22)\n",
      "y2_test shape is (4500,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X1_train shape is {}\".format(X1.shape))\n",
    "print(\"y1_train shape is {}\".format(y1.shape))\n",
    "print(\"X2_test shape is {}\".format(X2.shape))\n",
    "print(\"y2_test shape is {}\".format(y2.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array(X1)\n",
    "y_train  = np.array(y1)\n",
    "X_test = np.array(X2)\n",
    "y_test = np.array(y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### start of RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as dsets\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNModel(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):\n",
    "        super(RNNModel, self).__init__()\n",
    "        #Hidden Dimension\n",
    "        self.hidden_dim = hidden_dim\n",
    "        \n",
    "        # Number of hidden layers\n",
    "        self.layer_dim = layer_dim\n",
    "        \n",
    "        #Building the RNN\n",
    "        self.rnn = nn.RNN(input_dim, hidden_dim, layer_dim, batch_first=True, nonlinearity='relu')\n",
    "        \n",
    "        # Readout layer\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # Initializing the hidden state with zeros\n",
    "        # (layer_dim, batch_size, hidden_dim)\n",
    "        h0 = Variable(torch.zeros(self.layer_dim, x.size(0), self.hidden_dim))\n",
    "        \n",
    "        #One time step (the last one perhaps?)\n",
    "        out, hn = self.rnn(x, h0)\n",
    "        \n",
    "        # Indexing hidden state of the last time step\n",
    "        # out.size() --> ??\n",
    "        #out[:,-1,:] --> is it going to be 100,100\n",
    "        out = self.fc(out[:,-1,:])\n",
    "        # out.size() --> 100,1\n",
    "        return out\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiating Model Class\n",
    "input_dim = 22\n",
    "hidden_dim = 15\n",
    "layer_dim = 1\n",
    "output_dim = 1\n",
    "batch_size = 100\n",
    "\n",
    "model = RNNModel(input_dim, hidden_dim, layer_dim, output_dim)\n",
    "\n",
    "# Instantiating Loss Class\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "# Instantiate Optimizer Class\n",
    "learning_rate = 0.001\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# converting numpy array to torch tensor\n",
    "X_train = torch.from_numpy(X_train)\n",
    "y_train = torch.from_numpy(y_train)\n",
    "X_test = torch.from_numpy(X_test)\n",
    "y_test = torch.from_numpy(y_test)\n",
    "\n",
    "# initializing lists to store losses over epochs:\n",
    "train_loss = []\n",
    "test_loss = []\n",
    "train_iter = []\n",
    "test_iter = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Iteration: 100. Train_MSE: 0.004700318910181522. Test_MSE: 0.00019951935628665442\n",
      "Epoch: 2 Iteration: 200. Train_MSE: 0.012488487176597118. Test_MSE: 0.0002042209739458771\n",
      "Epoch: 3 Iteration: 300. Train_MSE: 0.0015307371504604816. Test_MSE: 0.00024397069262226752\n",
      "Epoch: 4 Iteration: 400. Train_MSE: 0.0006486308411695063. Test_MSE: 0.0002769354265236081\n",
      "Epoch: 5 Iteration: 500. Train_MSE: 0.003521236125379801. Test_MSE: 0.00030508233865041465\n",
      "Epoch: 6 Iteration: 600. Train_MSE: 0.003108575940132141. Test_MSE: 0.00033278958745323574\n",
      "Epoch: 7 Iteration: 700. Train_MSE: 0.0023963607382029295. Test_MSE: 0.00035171668768771983\n",
      "Epoch: 8 Iteration: 800. Train_MSE: 0.0009026312036439776. Test_MSE: 0.00035802223106929423\n",
      "Epoch: 10 Iteration: 900. Train_MSE: 0.0014665904454886913. Test_MSE: 0.0003586035647292295\n",
      "Epoch: 11 Iteration: 1000. Train_MSE: 0.00286492588929832. Test_MSE: 0.00036656470342391937\n",
      "Epoch: 12 Iteration: 1100. Train_MSE: 0.00044321443419903517. Test_MSE: 0.00036956018491858864\n",
      "Epoch: 13 Iteration: 1200. Train_MSE: 0.0007179246749728918. Test_MSE: 0.0003637647699589236\n",
      "Epoch: 14 Iteration: 1300. Train_MSE: 0.0025133518502116203. Test_MSE: 0.00035894156705093864\n",
      "Epoch: 15 Iteration: 1400. Train_MSE: 0.007280575577169657. Test_MSE: 0.0003616355362978199\n",
      "Epoch: 16 Iteration: 1500. Train_MSE: 0.0006070469389669597. Test_MSE: 0.0003614296498447059\n",
      "Epoch: 17 Iteration: 1600. Train_MSE: 0.0007038762560114264. Test_MSE: 0.00035531229726237047\n",
      "Epoch: 19 Iteration: 1700. Train_MSE: 0.0005442762048915029. Test_MSE: 0.00034629062676857874\n",
      "Epoch: 20 Iteration: 1800. Train_MSE: 0.0020178707782179117. Test_MSE: 0.000346490035022187\n",
      "Epoch: 21 Iteration: 1900. Train_MSE: 0.00029939322848804295. Test_MSE: 0.00034432169322267395\n",
      "Epoch: 22 Iteration: 2000. Train_MSE: 0.0006309495074674487. Test_MSE: 0.00033676385218504027\n",
      "Epoch: 23 Iteration: 2100. Train_MSE: 0.0020029686857014894. Test_MSE: 0.00032998093215555105\n",
      "Epoch: 24 Iteration: 2200. Train_MSE: 0.005599563010036945. Test_MSE: 0.00033097847839998125\n",
      "Epoch: 25 Iteration: 2300. Train_MSE: 0.0006645724060945213. Test_MSE: 0.00032939242702437443\n",
      "Epoch: 26 Iteration: 2400. Train_MSE: 0.0004984806291759014. Test_MSE: 0.000324062198705246\n",
      "Epoch: 28 Iteration: 2500. Train_MSE: 0.0003220937796868384. Test_MSE: 0.0003160050229789456\n",
      "Epoch: 29 Iteration: 2600. Train_MSE: 0.0016157282516360283. Test_MSE: 0.0003157846446916299\n",
      "Epoch: 30 Iteration: 2700. Train_MSE: 0.0004873348807450384. Test_MSE: 0.00031376165732094466\n",
      "Epoch: 31 Iteration: 2800. Train_MSE: 0.0003610151179600507. Test_MSE: 0.0003079378699439396\n",
      "Epoch: 32 Iteration: 2900. Train_MSE: 0.001150466618128121. Test_MSE: 0.0003023330744433832\n",
      "Epoch: 33 Iteration: 3000. Train_MSE: 0.001494324766099453. Test_MSE: 0.0003034759973440894\n",
      "Epoch: 34 Iteration: 3100. Train_MSE: 0.0007958323694765568. Test_MSE: 0.00030201926165080345\n",
      "Epoch: 35 Iteration: 3200. Train_MSE: 0.0005022017867304385. Test_MSE: 0.0002981440418758764\n",
      "Epoch: 37 Iteration: 3300. Train_MSE: 0.0005435631610453129. Test_MSE: 0.0002914465992208103\n",
      "Epoch: 38 Iteration: 3400. Train_MSE: 0.000862924731336534. Test_MSE: 0.00029133595563218633\n",
      "Epoch: 39 Iteration: 3500. Train_MSE: 0.0017122456338256598. Test_MSE: 0.00028999617538165873\n",
      "Epoch: 40 Iteration: 3600. Train_MSE: 0.0004844327340833843. Test_MSE: 0.0002856021040828156\n",
      "Epoch: 41 Iteration: 3700. Train_MSE: 0.0007191619952209294. Test_MSE: 0.0002807425179112515\n",
      "Epoch: 42 Iteration: 3800. Train_MSE: 0.0013895132578909397. Test_MSE: 0.00028221561327414435\n",
      "Epoch: 43 Iteration: 3900. Train_MSE: 0.0004007766838185489. Test_MSE: 0.000281164713978264\n",
      "Epoch: 44 Iteration: 4000. Train_MSE: 0.00038612436037510633. Test_MSE: 0.00027842200988538956\n",
      "Epoch: 46 Iteration: 4100. Train_MSE: 0.0006976104341447353. Test_MSE: 0.00027294878532164464\n",
      "Epoch: 47 Iteration: 4200. Train_MSE: 0.001544868922792375. Test_MSE: 0.00027287766740107157\n",
      "Epoch: 48 Iteration: 4300. Train_MSE: 0.00033841084223240614. Test_MSE: 0.00027183191177916345\n",
      "Epoch: 49 Iteration: 4400. Train_MSE: 0.0004895165329799056. Test_MSE: 0.00026866804485568043\n",
      "Epoch: 50 Iteration: 4500. Train_MSE: 0.0003994944563601166. Test_MSE: 0.00026441460657141265\n",
      "Epoch: 51 Iteration: 4600. Train_MSE: 0.0016330700600519776. Test_MSE: 0.0002658591675792583\n",
      "Epoch: 52 Iteration: 4700. Train_MSE: 0.0007058937335386872. Test_MSE: 0.0002652486531107345\n",
      "Epoch: 53 Iteration: 4800. Train_MSE: 0.007154480088502169. Test_MSE: 0.00026319506786705126\n",
      "Epoch: 55 Iteration: 4900. Train_MSE: 0.0003755477082449943. Test_MSE: 0.00025880774794403966\n",
      "Epoch: 56 Iteration: 5000. Train_MSE: 0.0006379889673553407. Test_MSE: 0.000258510562701715\n",
      "Epoch: 57 Iteration: 5100. Train_MSE: 0.011756127700209618. Test_MSE: 0.00025789583048349394\n",
      "Epoch: 58 Iteration: 5200. Train_MSE: 0.0005147535121068358. Test_MSE: 0.0002553662684000733\n",
      "Epoch: 59 Iteration: 5300. Train_MSE: 0.0003501909668557346. Test_MSE: 0.0002518598993337428\n",
      "Epoch: 60 Iteration: 5400. Train_MSE: 0.002022106433287263. Test_MSE: 0.0002530547862227728\n",
      "Epoch: 61 Iteration: 5500. Train_MSE: 0.000859572202898562. Test_MSE: 0.0002529456948934962\n",
      "Epoch: 62 Iteration: 5600. Train_MSE: 0.002152825938537717. Test_MSE: 0.00025155990515661296\n",
      "Epoch: 64 Iteration: 5700. Train_MSE: 0.001426803064532578. Test_MSE: 0.00024763851957200175\n",
      "Epoch: 65 Iteration: 5800. Train_MSE: 0.0008022236870601773. Test_MSE: 0.000247350375386861\n",
      "Epoch: 66 Iteration: 5900. Train_MSE: 0.004228692501783371. Test_MSE: 0.00024729022953156964\n",
      "Epoch: 67 Iteration: 6000. Train_MSE: 0.0007597797084599733. Test_MSE: 0.0002449752132387617\n",
      "Epoch: 68 Iteration: 6100. Train_MSE: 0.0003154170117340982. Test_MSE: 0.00024191921296267804\n",
      "Epoch: 69 Iteration: 6200. Train_MSE: 0.0010850647231563926. Test_MSE: 0.00024282636816484567\n",
      "Epoch: 70 Iteration: 6300. Train_MSE: 0.00029107843874953687. Test_MSE: 0.00024313930634832045\n",
      "Epoch: 71 Iteration: 6400. Train_MSE: 0.0003339037939440459. Test_MSE: 0.00024223884983275666\n",
      "Epoch: 73 Iteration: 6500. Train_MSE: 0.0002948205219581723. Test_MSE: 0.00023898721658287836\n",
      "Epoch: 74 Iteration: 6600. Train_MSE: 0.0009593008435331285. Test_MSE: 0.00023838832408945346\n",
      "Epoch: 75 Iteration: 6700. Train_MSE: 0.0009088316583074629. Test_MSE: 0.00023864417359759294\n",
      "Epoch: 76 Iteration: 6800. Train_MSE: 0.00034212638274766505. Test_MSE: 0.00023676207444612858\n",
      "Epoch: 77 Iteration: 6900. Train_MSE: 0.0009802505373954773. Test_MSE: 0.0002337869689395839\n",
      "Epoch: 78 Iteration: 7000. Train_MSE: 0.0007700427668169141. Test_MSE: 0.00023461560475903314\n",
      "Epoch: 79 Iteration: 7100. Train_MSE: 0.006948874797672033. Test_MSE: 0.0002351534161242035\n",
      "Epoch: 80 Iteration: 7200. Train_MSE: 0.0003054545959457755. Test_MSE: 0.00023451092540261215\n",
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      "Epoch: 84 Iteration: 7500. Train_MSE: 0.0011679711751639843. Test_MSE: 0.00023153127247862572\n",
      "Epoch: 85 Iteration: 7600. Train_MSE: 0.0005333563894964755. Test_MSE: 0.00022990204238119272\n",
      "Epoch: 86 Iteration: 7700. Train_MSE: 0.0007273408118635416. Test_MSE: 0.00022739513893078937\n",
      "Epoch: 87 Iteration: 7800. Train_MSE: 0.0008401602972298861. Test_MSE: 0.00022783992412515937\n",
      "Epoch: 88 Iteration: 7900. Train_MSE: 0.0024960709270089865. Test_MSE: 0.00022883293194542715\n",
      "Epoch: 89 Iteration: 8000. Train_MSE: 0.00019422158948145807. Test_MSE: 0.00022802531726816167\n",
      "Epoch: 91 Iteration: 8100. Train_MSE: 0.0008669436210766435. Test_MSE: 0.00022562590525276166\n",
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      "Epoch: 95 Iteration: 8500. Train_MSE: 0.0009182741632685065. Test_MSE: 0.00022191309311327925\n",
      "Epoch: 96 Iteration: 8600. Train_MSE: 0.0007084391545504332. Test_MSE: 0.0002220260935059966\n",
      "Epoch: 97 Iteration: 8700. Train_MSE: 0.0007418199093081057. Test_MSE: 0.00022335241975531613\n",
      "Epoch: 98 Iteration: 8800. Train_MSE: 0.0004689056077040732. Test_MSE: 0.00022256075601037448\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 99 Iteration: 8900. Train_MSE: 0.00047664064913988113. Test_MSE: 0.00022064910122448017\n",
      "Epoch: 101 Iteration: 9000. Train_MSE: 0.0009640545467846096. Test_MSE: 0.0002193198664067997\n",
      "Epoch: 102 Iteration: 9100. Train_MSE: 0.011478479020297527. Test_MSE: 0.00022051205019290102\n",
      "Epoch: 103 Iteration: 9200. Train_MSE: 0.000360287434887141. Test_MSE: 0.0002192498705292263\n",
      "Epoch: 104 Iteration: 9300. Train_MSE: 0.0005225582281127572. Test_MSE: 0.00021739197039148073\n",
      "Epoch: 105 Iteration: 9400. Train_MSE: 0.0008762446814216673. Test_MSE: 0.00021702176732094042\n",
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    {
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      "Epoch: 4959 Iteration: 441400. Train_MSE: 3.452386226854287e-05. Test_MSE: 4.986489937093715e-05\n",
      "Epoch: 4960 Iteration: 441500. Train_MSE: 0.00023257042630575597. Test_MSE: 5.029015836391498e-05\n",
      "Epoch: 4961 Iteration: 441600. Train_MSE: 0.00022025764337740839. Test_MSE: 5.0327339321098035e-05\n",
      "Epoch: 4962 Iteration: 441700. Train_MSE: 0.0017586481990292668. Test_MSE: 5.056875885716157e-05\n",
      "Epoch: 4964 Iteration: 441800. Train_MSE: 0.0004961511003784835. Test_MSE: 5.007301587016403e-05\n",
      "Epoch: 4965 Iteration: 441900. Train_MSE: 0.0001661359565332532. Test_MSE: 4.997618817987503e-05\n",
      "Epoch: 4966 Iteration: 442000. Train_MSE: 0.004345763009041548. Test_MSE: 4.996377117469412e-05\n",
      "Epoch: 4967 Iteration: 442100. Train_MSE: 2.570715696492698e-05. Test_MSE: 4.987390668404778e-05\n",
      "Epoch: 4968 Iteration: 442200. Train_MSE: 3.291995744802989e-05. Test_MSE: 4.980981500481659e-05\n",
      "Epoch: 4969 Iteration: 442300. Train_MSE: 0.00014617839769925922. Test_MSE: 5.015180913759967e-05\n",
      "Epoch: 4970 Iteration: 442400. Train_MSE: 0.00023992275237105787. Test_MSE: 5.0194081753363066e-05\n",
      "Epoch: 4971 Iteration: 442500. Train_MSE: 7.449902477674186e-05. Test_MSE: 5.062573019931e-05\n",
      "Epoch: 4973 Iteration: 442600. Train_MSE: 3.186887261108495e-05. Test_MSE: 5.0131848788874626e-05\n",
      "Epoch: 4974 Iteration: 442700. Train_MSE: 0.00015213378355838358. Test_MSE: 4.994128935363687e-05\n",
      "Epoch: 4975 Iteration: 442800. Train_MSE: 0.0008140791906043887. Test_MSE: 5.00483600890494e-05\n",
      "Epoch: 4976 Iteration: 442900. Train_MSE: 2.9689117582165636e-05. Test_MSE: 4.979691006112272e-05\n",
      "Epoch: 4977 Iteration: 443000. Train_MSE: 4.241817077854648e-05. Test_MSE: 4.9775300996388604e-05\n",
      "Epoch: 4978 Iteration: 443100. Train_MSE: 0.00033458240795880556. Test_MSE: 5.005783908382385e-05\n",
      "Epoch: 4979 Iteration: 443200. Train_MSE: 0.006866494193673134. Test_MSE: 5.0066097166454676e-05\n",
      "Epoch: 4980 Iteration: 443300. Train_MSE: 8.422729297308251e-05. Test_MSE: 5.058262562672903e-05\n",
      "Epoch: 4982 Iteration: 443400. Train_MSE: 2.5066281523322687e-05. Test_MSE: 5.007573626542463e-05\n",
      "Epoch: 4983 Iteration: 443500. Train_MSE: 0.0002105703460983932. Test_MSE: 4.9895845703939844e-05\n",
      "Epoch: 4984 Iteration: 443600. Train_MSE: 0.0008980592247098684. Test_MSE: 5.0034943081844994e-05\n",
      "Epoch: 4985 Iteration: 443700. Train_MSE: 8.689379319548607e-05. Test_MSE: 4.9745193833309936e-05\n",
      "Epoch: 4986 Iteration: 443800. Train_MSE: 5.36605111847166e-05. Test_MSE: 4.974202263586624e-05\n",
      "Epoch: 4987 Iteration: 443900. Train_MSE: 0.00026619547861628234. Test_MSE: 4.9907070780341564e-05\n",
      "Epoch: 4988 Iteration: 444000. Train_MSE: 0.0017274846322834492. Test_MSE: 5.0252606027903546e-05\n",
      "Epoch: 4989 Iteration: 444100. Train_MSE: 8.368155977223068e-05. Test_MSE: 5.051774677778729e-05\n",
      "Epoch: 4991 Iteration: 444200. Train_MSE: 0.000186632270924747. Test_MSE: 5.007622866538766e-05\n",
      "Epoch: 4992 Iteration: 444300. Train_MSE: 0.000362342078005895. Test_MSE: 4.989976049054011e-05\n",
      "Epoch: 4993 Iteration: 444400. Train_MSE: 0.004774034488946199. Test_MSE: 4.999248334556029e-05\n",
      "Epoch: 4994 Iteration: 444500. Train_MSE: 0.0007620940450578928. Test_MSE: 4.973569182542387e-05\n",
      "Epoch: 4995 Iteration: 444600. Train_MSE: 4.134000846534036e-05. Test_MSE: 4.974727399652884e-05\n",
      "Epoch: 4996 Iteration: 444700. Train_MSE: 0.0002051212068181485. Test_MSE: 4.975250855368932e-05\n",
      "Epoch: 4997 Iteration: 444800. Train_MSE: 0.0004959184443578124. Test_MSE: 5.030958788776474e-05\n",
      "Epoch: 4998 Iteration: 444900. Train_MSE: 9.099546878132969e-05. Test_MSE: 5.044784495515026e-05\n",
      "Epoch: 4999 Iteration: 445000. Train_MSE: 2.347949157410767e-05. Test_MSE: 5.0146588048798875e-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 = 5000\n",
    "feat_dim = X_train.shape[1]\n",
    "\n",
    "X_train = X_train.type(torch.FloatTensor)\n",
    "y_train = y_train.type(torch.FloatTensor)\n",
    "X_test = X_test.type(torch.FloatTensor)\n",
    "y_test = y_test.type(torch.FloatTensor)\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for i in range(0, int(num_samples/batch_size -1)):\n",
    "        \n",
    "        \n",
    "        features = Variable(X_train[i*batch_size:(i+1)*batch_size, :]).view(-1, seq_dim, feat_dim)\n",
    "        Kt_value = Variable(y_train[i*batch_size:(i+1)*batch_size])\n",
    "        \n",
    "        #print(\"Kt_value={}\".format(Kt_value))\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        outputs = model(features)\n",
    "        #print(\"outputs ={}\".format(outputs))\n",
    "        \n",
    "        loss = criterion(outputs, Kt_value)\n",
    "        \n",
    "        train_loss.append(loss.data[0])\n",
    "        train_iter.append(n_iter)\n",
    "\n",
    "        #print(\"loss = {}\".format(loss))\n",
    "        loss.backward()\n",
    "        \n",
    "        optimizer.step()\n",
    "        \n",
    "        n_iter += 1  \n",
    "            \n",
    "        if n_iter%100 == 0:\n",
    "            for i in range(0,int(test_samples/batch_size -1)):\n",
    "                features = Variable(X_test[i*batch_size:(i+1)*batch_size, :]).view(-1, seq_dim, feat_dim)\n",
    "                Kt_test = Variable(y_test[i*batch_size:(i+1)*batch_size])\n",
    "                \n",
    "                outputs = model(features)\n",
    "                \n",
    "                mse = np.sqrt(np.mean((Kt_test.data.numpy() - outputs.data.numpy().squeeze())**2)/num_samples)\n",
    "                \n",
    "                test_iter.append(n_iter)\n",
    "                test_loss.append(mse)\n",
    "                \n",
    "            print('Epoch: {} Iteration: {}. Train_MSE: {}. Test_MSE: {}'.format(epoch, n_iter, loss.data[0], mse))\n",
    "               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "195800\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x50c77358>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N6/cfB74E/C/gdzPzUOeHJElqpWd29uy9Y3J0dHxJnbvt/n2d7ooknXY7t169pHb1+kBP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\ngg==\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 0xceae7f0>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U9M6NV85wJ5I0t15x8xa6Txk8YbzF9yg6uqSgX7iTJJUyKCRpEZmPk0AGhSSplEEhSSpl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\nklTKoJAklTIoJEml/j/XlurLViDgNwAAAABJRU5ErkJggg==\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": [
       "41.210071429887584"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_denorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4436.000000\n",
       "mean        6.507812\n",
       "std        78.342619\n",
       "min        -0.243412\n",
       "25%        -0.012431\n",
       "50%        -0.007427\n",
       "75%         0.755307\n",
       "max      4900.219009\n",
       "Name: Kt, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_test['Kt'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Saving train and test losses to a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trainLoss = pd.DataFrame(data={'Train Loss':train_loss}, columns=['Train Loss'])\n",
    "df_trainLoss.to_csv('RNN Paper Results/Exp1_DesertRock_5000Epoch_TrainLoss.csv')\n",
    "df_testLoss = pd.DataFrame(data={'Test Loss':test_loss}, columns=['Test Loss'])\n",
    "df_testLoss.to_csv('RNN Paper Results/Exp1_DesertRock_5000EpochTestLoss.csv')"
   ]
  }
 ],
 "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
}