multi-time-horizon-prediction/Exp_2.1_multi-time-scale_All_Locations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {
    "collapsed": true
   },
   "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": 509,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_color_codes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pvlib\n",
    "from pvlib import clearsky, atmosphere\n",
    "from pvlib.location import Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### CONFIGURE RUNS\n",
    "run_train = False # Disables training & processing of train set; Set it to True for the first time to create a model\n",
    "#test_location = \"Bondville\" #Folder name\n",
    "test_location = \"Boulder\" #Folder name\n",
    "#test_location = \"Desert_Rock\" #Folder name\n",
    "#test_location = \"Fort_Peck\" #Folder name\n",
    "#test_location = \"Goodwin_Creek\" #Folder name\n",
    "#test_location = \"Penn_State\" #Folder name\n",
    "#test_location = \"Sioux_Falls\" #Folder name\n",
    "\n",
    "# All_locations\n",
    "#bvl = Location(40.1134,-88.3695, 'US/Central', 217.932, 'Bondville')\n",
    "bvl = Location(40.0150,-105.2705, 'US/Mountain', 1655.064, 'Boulder')\n",
    "#bvl = Location(36.621,-116.043, 'US/Pacific', 1010.1072, 'Desert Rock')\n",
    "# bvl = Location(48,-106.449, 'US/Mountain', 630.0216, 'Fort Peck')\n",
    "# bvl = Location(34.2487,-89.8925, 'US/Central', 98, 'Goodwin Creek')\n",
    "# bvl = Location(40.798,-77.859, 'US/Eastern', 351.74, 'Penn State')\n",
    "# bvl = Location(43.544,-96.73, 'US/Central', 448.086, 'Sioux Falls')\n",
    "\n",
    "\n",
    "\n",
    "test_year = \"2015\"\n",
    "\n",
    "\n",
    "# TEST year 2009\n",
    "times = pd.DatetimeIndex(start='2009-01-01', end='2010-01-01', freq='1min',tz=bvl.tz)   # 12 months\n",
    "#  TEST year 2015\n",
    "#times = pd.DatetimeIndex(start='2015-01-01', end='2016-01-01', freq='1min',tz=bvl.tz)   # 12 months \n",
    "# TEST year 2016\n",
    "#times = pd.DatetimeIndex(start='2016-01-01', end='2017-01-01', freq='1min',tz=bvl.tz)   # 12 months \n",
    "# Test year 2017\n",
    "#times = pd.DatetimeIndex(start='2017-01-01', end='2018-01-01', freq='1min',tz=bvl.tz)   # 12 months \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1051200, 7)\n"
     ]
    }
   ],
   "source": [
    " if run_train:\n",
    "    # TRAIN set\n",
    "    times2010and2011 = pd.DatetimeIndex(start='2010-01-01', end='2012-01-01', freq='1min',\n",
    "                            tz=bvl.tz)   # 24 months of 2010 and 2011 - For training\n",
    "    cs_2010and2011 = bvl.get_clearsky(times2010and2011) # ineichen with climatology table by default\n",
    "    cs_2010and2011.drop(['dni','dhi'],axis=1, inplace=True) #updating the same dataframe by dropping two columns\n",
    "    cs_2010and2011.reset_index(inplace=True)\n",
    "\n",
    "    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)\n",
    "\n",
    "\n",
    "    cs_2010and2011.drop(cs_2010and2011.index[-1], inplace=True)\n",
    "    print(cs_2010and2011.shape)\n",
    "    cs_2010and2011.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(525600, 7)\n"
     ]
    }
   ],
   "source": [
    "# TEST set\n",
    "\n",
    "\n",
    "cs_test = bvl.get_clearsky(times)\n",
    "cs_test.drop(['dni','dhi'],axis=1, inplace=True) #updating the same dataframe by dropping two columns\n",
    "cs_test.reset_index(inplace=True)\n",
    "\n",
    "cs_test['index']=cs_test['index'].apply(lambda x:x.to_datetime())\n",
    "cs_test['year'] = cs_test['index'].apply(lambda x:x.year)\n",
    "cs_test['month'] = cs_test['index'].apply(lambda x:x.month)\n",
    "cs_test['day'] = cs_test['index'].apply(lambda x:x.day)\n",
    "cs_test['hour'] = cs_test['index'].apply(lambda x:x.hour)\n",
    "cs_test['min'] = cs_test['index'].apply(lambda x:x.minute)\n",
    "\n",
    "cs_test.drop(cs_test.index[-1], inplace=True)\n",
    "print(cs_test.shape)"
   ]
  },
  {
   "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": 514,
   "metadata": {
    "collapsed": true
   },
   "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": 515,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_path: ./data/Boulder/Exp_1_train\n",
      "(1050018, 48)\n",
      "(1050018, 50)\n"
     ]
    }
   ],
   "source": [
    " if run_train:\n",
    "    # Train Set\n",
    "    path = r'./data/' + test_location + '/Exp_1_train'\n",
    "    print(\"train_path:\",path)\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",
    "    print(df_big_train.shape)\n",
    "    df_train = pd.merge(df_big_train, cs_2010and2011, on=['year','month','day','hour','min'])\n",
    "    print(df_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/Boulder/Exp_1_test/2009\n",
      "(3702, 48)\n",
      "(525132, 48)\n"
     ]
    }
   ],
   "source": [
    "# Test set\n",
    "path = r'./data/' + test_location + '/Exp_1_test/' + test_year\n",
    "print(path)\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",
    "print(df_big_test[df_big_test['dw_solar']==-9999.9].shape)\n",
    "df_test = pd.merge(df_big_test, cs_test, on=['year','month','day','hour','min'])\n",
    "print(df_big_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging Clear Sky GHI And the big dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [],
   "source": [
    "if run_train:\n",
    "    # TRAIN set\n",
    "    #updating the same dataframe by dropping the index columns from clear sky model\n",
    "    df_train.drop(['index'],axis=1, inplace=True)\n",
    "    # Resetting Index\n",
    "    df_train.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TEST set\n",
    "#updating the same dataframe by dropping the index columns from clear sky model\n",
    "df_test.drop(['index'],axis=1, inplace=True)\n",
    "# Resetting Index\n",
    "df_test.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Managing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1034397, 49)\n"
     ]
    }
   ],
   "source": [
    "if run_train:\n",
    "    # TRAIN set\n",
    "    #Dropping rows with two or more -9999.9 values in columns\n",
    "    missing_data_indices = np.where((df_train <=-9999.9).apply(sum, axis=1)>=2)[0] #Get indices of all rows with 2 or more -9999.9\n",
    "    df_train.drop(missing_data_indices, axis=0, inplace=True) # Drop those inddices\n",
    "    print(df_train.shape)\n",
    "    df_train.reset_index(drop=True, inplace=True) # 2nd time - Resetting index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(505639, 49)\n"
     ]
    }
   ],
   "source": [
    "# TEST set\n",
    "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",
    "print(df_test.shape)\n",
    "df_test.reset_index(drop=True, inplace=True) # 2nd time - Reseting Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First resetting index after dropping rows in the previous part of the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10058\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/praveen/anaconda3/envs/pytorch/lib/python3.6/site-packages/pandas/core/indexing.py:179: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "if run_train:\n",
    "    # TRAIN set\n",
    "    one_miss_train_idx = np.where((df_train <=-9999.9).apply(sum, axis=1)==1)[0]\n",
    "    print(len(one_miss_train_idx))\n",
    "    df_train.shape\n",
    "\n",
    "    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",
    "\n",
    "    train = np.where((df_train <=-9999.9).apply(sum, axis=1)==1)[0]\n",
    "    print(len(train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/praveen/anaconda3/envs/pytorch/lib/python3.6/site-packages/pandas/core/indexing.py:179: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(505639, 49)"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TEST set\n",
    "one_miss_test_idx = np.where((df_test <=-9999.9).apply(sum, axis=1)==1)[0]\n",
    "len(one_miss_test_idx)\n",
    "col_names_test = df_test.columns\n",
    "\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",
    "    \n",
    "test = np.where((df_test <=-9999.9).apply(sum, axis=1)==1)[0]\n",
    "print(len(test))\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {
    "collapsed": true
   },
   "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": 524,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VEZnqlO2pqh843z8E9nS+C3YOMJdVTpnBYDAYSpywPpN1jj9jEvB1JxV9RYj9\njlZVFZHPAU+IyJvelapqiUik5JGOUJrqfI+yq8FgMBgKRFjN5AzsBI/nqeqHwFDglkw7qao6nx8D\n92ObrT4Skb0BnM+P3c2BfTy7D3XKUo85T1VHqurIeDwesvoGg8FgKCRhhcmlqnqrqj4HoKorsB3l\ngYjIQBEZ7H4HxgKvAQ8CU5zNpgAPON8fBM4UkX4iMhzYHzMg0mAwGLoFYYXJGJ+y8Rn22RN4XkT+\ngS0UHlHVx4AbgTEi8jZQ6/xGVV8H7gXeAB4DLlLV7SHrZzAYDIYuJFPW4GnAdGBfEVniWTUY+FO6\nfVX1PeAQn/JWoCZgnwagIUOdDQaDwVBiZNJM/heYiG2KmuhZvqqqdQWum8FQMiSXJqmeVU3ZzDKq\nZ1WTXJrs6ioZDCVFpqzBa4G1IrJNVVu860RkgapOLmjtDIYSILk0ydSHptK2tQ2AlrUtTH3IjnSv\nG2H6VAYDhPeZdHC2i0gf4Kv5r47BUHrUN9e3CxKXtq1t1DfXd1GNDIbSI8xMiz8GBojIZ05xDNgC\nzCtw3QyGkmDF2hWRyg2G3kgmM9cN2POX3KCqVxapTgZDSTFs12G0rG3xLTcYDDahRsCr6pUisjv2\n2I/+nvI/FqpiBkOp0FDT0MFnAlBZUUlDjQk8NBhcQgkTETkfmIE9Kv3vwNeAPwOjC1c1g6E0cJ3s\n9c31rFi7gmG7DqOhpsE43w0GD2Fzc80ADgdeVNXjRORLwE8KVy2DobSoG1FnhIfBkIaw0VybVHUT\ngIj0U9U3gS8WrloGg8Fg6E6E1UxWichuwO+xs/9+AnT2SBoMBoOhVxLWAf9t5+s1IvI0sCt2/iyD\nwWAwGDKOM5mFnYPrBU86+WeLUTGDoRAklyaNI91gKACZNJN3sKfVvdmZiOpPzvIC8A9V3VHY6hkM\n+cOkRTEYCkdaB7yq/lxVv6uq1cBRwCLg88BvgU8LXz2DIX+YtCgGQ+HIGM0lIjER+TJwAvYc8N/A\n1lh+WuC6GQx5JVJalGQSqquhrMz+TJoswQZDOjL5TJ4AdsEeqPgi8BNV/WcxKmYw5JvQaVGSSZg6\nFdocLaalxf4NUGfMYQaDH5k0k/eAHdhpVPYH9hORIQWvlcFQABpqGqisqOxQ5psWpb5+pyBxaWuz\ny3sgZq4WQz7IlOjxAgAR2QU7hcpRwEUi8m/Aa6o6Jd3+BkMpETotyoqAbMBB5d0YE5RgyBdhBy1u\nBtqAjc5FQ+H1AAAgAElEQVT3oUDfQlXKYCgUodKiDBtmm7b8ynsY6YISjDAxRCGTz+Q2bG1kf+Bv\n2Mkd5wJTVDVUNJeIlAOvAKqqE0RkD2AhUA0sB05X1U+cba8EzgO2Axer6uNZXJPBkBsNDR19JgCV\nlXZ5D8PM1WLIF5l8JsuA6cC/qWqNqv6Xqi4OK0gcZgBep/0VQLOq7g80O78RkQOBM7FndRwHzHEE\nkcFQXOrqYN48qKqCWMz+nDevRzrfg+ZkMXO1GKKSSZg8qKqvqup2v5VO2PDQoJ2ddd8CfuUpPhGY\n73yfjz0o0i2/R1U3q+oy7PDjI0Jcg8GQf+rqYPly2LHD/uyBggQiBCUYDBnI5DO5RUTKgAeAV4F/\nYU+OtR9wHFADXA2sCth/FvCfwGBP2Z6q+oHz/UNgT+e7YIcfu6xyyjogIlOBqc73DNU3GAzpMHO1\nGPJFpmiu0xzzUx1wLrA3tiP+n8CjQIObmj4VEZkAfKyqr4rIsQHHt0TEilJhVZ2HM//8+PHjI+1r\nMBg6Y+ZqMeSDjNFcqvoGkE2A/SjgBBE5Hlub2UVEmoCPRGRvVf1ARPYGPnZPBezj2X+oU2YwGAyG\nEidsaHBkVPVK4EoARzO5TFUnicgtwBTgRufzAWeXB4H/FZFbgQR2BNnLhaqfwZCJ5NIkMxbPoHVj\na3tZfECcQ/c6lKeWPYVFZsU4PiDO7PGzTc/f0OMpmDBJw43AvSJyHvYEW6cDqOrrInIv8AawDbgo\nyPFvMOQLP4FRFivjuOrjeGb5M2y3Oj6CrRtbaV7WHPr4rRtbmbRoEpMWTfJdb4SNoacQs6zu63YY\nP368tXjx4q6uhqEbklya5IKHLmDD1g1dXZUOGOFiKDVE5FVVHZlpu1CaiYjEsJ3wn1fVa0VkGLCX\nqhozlKFk8dM6Sp1UTcYIF0N3IWMKeoc5wL8D33F+rwN+UZAaGQw5kFyaZMjNQ4jNjDFp0aRuJUj8\ncIVLbGaM2MwYQ24eYhIxGkqSsMLkSFW9CNgE4KQ/Mbm5DCVBTxMg6fAKFyNYDKVEWGGy1UltYgE4\nWYPNlL2GLqODALmvMAIkRqxT2aC+g6gZXmOvsTIsBcYrWAbfMNgIFkOXEjaa62fA/cDnRKQBOBX4\nr4LVymBIJZmE+nqmH9hC4+FAzFnwfOaJslgZF3z1AkYNGxU8Mry62j+zsFvdETBjHLRW+qyMYQub\nPNZ7/Zb17b4W42cxdAWho7lE5EvY6VNi2Ikau3zGRRPN1TtINk5nxvJGWgc4BXkWHpCFo7usDILe\nnaqqtIIGy2L6GYOY+6UNWN5ryfN1xYhx4cgLmfOtOfk9sKFXETaaK5SZS0S+hp1C/heq+nNAReTI\nXCtpMATRwYz1UaPdw/dqIzkSHxCn6eQmrKstrKstVv/nauqWEH7e96C5Taqq7MSQVVXB64E5J9zO\njutiWDPBmglNi2DgJvJqJrOwaHyl0TjvDUUhrM+kEVjv+b3eKTMY8kpyaZJBPxnU0ZGerQDxNMy+\nwsOrhbjzvre02BqHO+97kEBpaLDnOPHinfMk0/q6ug6aTd1SWH8jHYRLfAN59b+4Ppbau2vzc0CD\nwUNYYRJT1fZHWlV30DWj5w09FK8QyXkgoQWDNtsNsnVDX6z9mzoLj1Sizvueac6TMHOiBGkv2MJl\n9S0+giUPNC9rNpqKIe+E8pmIyCLgGXZqI9OB41T1pMCdioDxmfQMau+ujZSipBOeRzjeBrMfsxtj\nuyAOq1dnPkaQDyQWs+c0KQSuNpQqxNIRj5N8enZeR+9PGznN+FUMgeTVZwJciD19r2LPM3Ikzpwi\nBkM2eH0iWQsSC/pvdTQQxzy0+haPIAFYsybcsYJ8IIWc9z1Ve4nHoTzD5KKtrdQtgfU/Xo91tUXT\nyU3EB8RzqkbjK40mtNiQMyY3l6Go5CUnluWjgQThOsQzVsxHS6isLP50vUOGQGuGMTMB15Q8bgjn\nHtXKlgqy9jMZLcWQSr6juf5NRH4sIvNE5E53yb2aht5E7d21OflEBvUdZDvRr4111kD88Dq8M1Hs\ned+TSf/IsTCa1IoVvsV1z65h8098nPcR+otu9JfxpxiiEtaJ/gDwHPAkYNLCGyKRXJrk3N+fy5Yd\nW7Lav9MYkGH16cdxgG0ymj07mjCoqyuOFpKqBbmRY2Cb1TJdWzqTXEsLdUs7Ctra8/vSPDTavXcj\nv+762108edaTkfY19E7C+kwqVfVyVb1XVe9zl4LWzNDt8UZoRRIkri/kmTjWfp5ILLc3n6mxdSmE\nYPDTKIK0jKDt00WO+YUUe0mnbQWEIz957J1Z+1aalzUz4PoBRksxZCRsNNf1wJ9U9dHCVyk8xmdS\numQdoWVBzbvwZJPz2/VbQPTIp3z7A/38KhUVtllsi0dYpqtzZWX6a7CsnQJnxQrYYw+7fM0aW/No\naEgvJL37Bmw//ZHpNL4SfZjYoL6DmDthrknT0ssI6zMJK0zWAQOBzcBWnOxCqrpLrhXNBSNMSo+s\nTFrOIxjfXMbsR3Z09oW44zHCaiTtx82zMImiFWVT5/Jy2LYtcrWyIVuBAkao9Dby6oBX1cGqWqaq\nA1R1F+d3lwoSQ2mRlUnLG9p7bYzVN1n+TvUVKwKdzoHEA0w66UxSmYhSh2zqvD2kOzKba0gm7Uix\nWAxiMeZMuZemz03LyvTlJpWc/sj0yPsaei5hfSaIyO4icoSIfN1dClkxQ/dh+iPTo0dpOeasjQ2O\ns3jYsPSO5SjjPSoqbOd7KlFTpvjVIyxlZTtNVGFJMyK+nSjX4AqdWAwmTeoYctzaSt30RlYvP719\nvErfrUSO/DICxeAS1sx1PjADGAr8Hfga8GdVHZ1mn/7AH4F+2FFjv1PVq0VkD2AhUA0sB053JttC\nRK4EzsOOGLtYVR9PVy9j5up6am88iOZNb4Qf12DBoC0w9+GU0N54HE4/HebP9x/rATB5sr/pKh6H\nQYPS+gmAYDNVLmNR0lFRAVu3+q+LxTpeS9gxLWGvIWxdYzFYsMA+byxG7SRo3pdI41SM2atnk+8R\n8DOAw4EWVT0O+ArwaYZ9NgOjVfUQ4FBgnJN9+ArsFPb7A83Ob0TkQOBM4CBgHDDHmZDLUIIklybp\nN7NPJEHSnz40PRtn3Q1Q91rKTq2ttiCZMsV/rEddHVx4oV3upbLS1kKWL7fTnixfHtwgB5mdMpmj\n3B7+5MkwYICtdYQhSJCALUiyGdMS9hr8IsaC6uHJP/ZkU0oG4xC4Zi+TQLJ3E1aYbFLVTQAi0k9V\n3wS+mG4HVbVU1c00XOEsFnAiMN8pnw+4+b1OBO5R1c2qugx4Bzgi9JUYikIH3wjbQwuSmuE1bLx6\nK3VPr7YbMD+TUVsbPPposGCYM8fuRWc7sDCblCmpZqXW1vzk6nI1Cfc6IZwfJNM1RA2fhp2CyPEz\nuRmMowoVE0bcuwkrTFaJyG7A74EnROQBIOPTKiLlIvJ34GPgCVV9CdhTVT9wNvkQ2NPdHFjpPadT\nlnrMqSLyioi80pop7YQhr2TjG3FHrXca+JatllBXF04L8SNTWng/wvbwg4jHM58zrB8kmYT16+mE\nezzvcaLgCqLZszvkBnOFyrSXCS1QNm3fZJzzvZSw0VzfVtVPVfUa4L+BO9ipUaTbb7uqHortazlC\nRA5OWR95tgZVnaeqI1V1ZDwoYseQdyKFkrop4D83jXVXrvO3pQc5p4uZWDGMZhM2IquiAvr27Vjm\nmuDcc4LdWLsDFF1hESb9vSsoUjtQ8fjOa8hG8KXOsTJ/fsdIuHicOY+XRdZSTPLI3kdaYSIiuzif\ne7gLsBR4HhgU9iSq+inwNLYv5CMR2ds57t7YWgvYGYn38ew21CkzdDHJxuk0/iW8IKl5F9svclPA\nGNdkEj77rHN5377hc2llS1TNJki4eX038TjcdRfceWewv8fVitzwX6/2EaRJeAVZOkHhXkPUUGQ/\nYVpXZ6fstyx7Wb0aduzISksxvpTeRSbN5H+dz1eBV5zPVz2/A3GSQ+7mfB8AjAHeBB4EpjibTcHO\n+4VTfqaI9BOR4cD+wMuRrsaQf5JJZixvDOcbSR29HtS41df7O6cHDy5uht4wBKU38UZibdxof6YT\nVEHax4wZnYMKXLyCLOhetrbu1HCiaHWuzybM/faELM9ZbPtS+m7B+FIMHUgrTFR1gvM5XFU/73y6\ny+czHHtv4GkRWQL8Bdtn8jBwIzBGRN4Gap3fqOrrwL3AG8BjwEWqapJKdgHeuUZib0+idUCGHTwz\nG7YLEghu3IIaRr+MubkMMswHqaYxv/lG0s3I6JJOGARNyuXV0tIJCvfcmfJ6hamPHynHrVsKm2dV\nUtP/wNCHML6Unk/acSYicli6nVX1r3mvUQTMOJP8EzXNRmwHLLg/IB18U1Nu4z1KZY4RL0FaBKRP\n3xI1wir1eMmkPfAwqE5uhFkyaWs7Wc6JEkhAzq/k0iQXLDqXDdaW0JF9ZlxK9yJf40x+6iy/AF4C\n5gG/dL7/ItdKGkqLqIKkfHsaQTJwoN34+GkUQVFVxx/fUQuZMSPavOxeUtKHMGRI5my+YQiaCbG8\nPP0xg645KIgkdTR8XV3wtl6tpa7OHsCZjijzvHiP62PCq1sC62/uk5UvxWgpPYtMZq7jnEGKHwCH\nOVFUX8UetGic4z0Ed+xIFEFStgPm/z5AkLgjv4NCXf2iqqZMsSOJvPsE9a79evjehnzIEPt4KelD\nOPfcnSnjU0NxJ03qLHD8CMqftX17+vDeoEiy2bPDhyuH3TadCSvfk345vqBsfCkmHUvPImw6lddV\n9aBMZcXGmLlyJ5vssZVbYN5DKYKkvNzutQ4bZo+F8BME6UwrUcxA3hQgEC3NSaZsvpnMaEH1LC/3\nFzRhzEmuCamlZedxqqr808KESDEfKW1MmOOlo6ysk3kvakoWY/YqbfKdTmWJiPxKRI51ll8CS3Kr\noqGriTp2BAuqPvURJJWVtlbhmkCCpp5N12OO4hBOSQESaXxFpmy+mcxoQeaqII3FbdTTmcAyhQ57\nCRPaHHZwZq6JL8E3MCBqShbX7GXGpXRvwgqTc4DXsXN0zcCOuDqnUJUyFJhkkulnDIo0dmTay2DN\nhOWzUgSJn9kkStoSt5GNOveIVyBEEURhMhCnO16QuSoo428sBtOnd260J0+214WZfTEqYQdn5uOc\nARFk2Y5LmXL/FCNQuimhzFylijFzRSfZOJ0LVjayoS+ZzRCpGX69pqx05pCwUVhRs/B68ZpswprI\n+va1BxZC+vNGjXQC+1qCshoHmcBc0s2+6I3Uyhdes1o+zuk1lZWVdbrW6eOh8QhCm71ixFhw8oLe\nYfbK1cxYBPJq5hKR/UXkdyLyhoi85y65V9NQaJKN06n+UR/KrokxqD7GpI8a2dCPUIJk2svOSHZX\nE9mxI9zI8Vx6xmFINdmEHV9x5507R6TPm+cfHeWXNytM1FddXbB2lWnSq7a24CixfKeXCZO/y7Ki\nRbh5TW/z53cKn3ad82HNXhZW74j2yoeZsYQIa+a6C2gEtgHHAXcDTWn3MHQ5ycbpTNVGWgZtx4oR\nThvBHjvStMhuBDoQpWELY9uPmv7DxS8FSDpTE9jr/NKGNDUFC73p021tI+zLHnT+IEHhZfv2YD9H\nPgduhhXg2TZsAVMFZGP26vH5vfJp2iwBwgqTAaraDMRUtcVJ+PitwlXLkA/q35tHW0W0fdrHjqTO\nN5LN2IRMBAmneDy4AU4VCi6u8GpqipYZOEjoJZMwd25nbSPdyx7k+J46NbPm5PW9eAUb5Lf3GkWA\nZ9OwJZP2NAKWtfM/9AiWqFpKj87vlW3m7BIlrDDZLCJlwNsi8h8i8m0iJHo0dA0tAyNko3FSorSP\nHcl28qYoBDW+s2fb5pKo6eIhWmbgdD3++vpgs1XQyx507jlzOmpOfhN8ubbyVMGW795rVLNZS0uw\nNjR9OvTpY19Pnz5QW9vRhOZqWxde2MGkmI2W0rysuecJlGzm1ylhosy0WAlcDHwVmMTOZI2GEiSS\nvdnPP+KyYEH0eUPCkq7hzyZdvPe4mUxsmezV6XqHZWXpfSd+53bLLSt4gi8/4Zbv3mvUkfjgrw1N\nnw6NjTv9Qdu3Q3Ozv+B79FHfUflzFkcUKO81M/jaHpQw8vjjgzsW3ZCM0VzO1Lk3qeplxalSeEw0\nV2eSS5Nc8NAF4SawSo3W8qMYubC6IqIl08C+IUPS57fK930JioAbMMC/HmHnvQ86V+r9hsyRdd4o\ntz59MgcWuKTOd59anTnTuGBFyMAQh24/0NHv/47FbC1uzpyuq5cPYaO5wo6Af1FVv5aXmuURI0w6\nEnUQ4rSXYc6zlXbqkUcfDY7wySZUNixdlczRZ+Q2sDMsNpMwgfzelyDhNnAgbPDpGPTpA9u27fyd\nj3sWJWQ4XcLLVAYOtP9fv/vt3sPqaqYf2BIphLg8Vs78b8/vngIlSpaCLibfwqQRewrd3wLtT7aq\nLsqlkrlihMlOogiSmAUXvgxz3khJ2ZGpgS0EXfVSZTpv0L3wks/7EuZ8mcjXPQvzn0TRTILwpsVx\nxukkD7Y4dyJsqSBc5GF3HZPSFe9aluQ7nUp/oBUYDUx0lgnZV8+QL6ImaYwPiLPglCbmPGp19id0\nxVS6XRXRkinlSJhrDtomm1DefNxj11meawhxmHQsU6dmW8udWFZHn9KFF9pzpfzEnmStR49J6WHO\ndwghTByfyRJVPSdlObcI9TOkYfoj05m0aFIE/0iMNW2t1N89hWRjysvXVVPpdtVLlcnBn2kgZJCj\nNNuBaNk4xlOJxbIPIfYKwPp62/SZLvghH3b91HE5c+bYod3xOE82RR+TMuTmId3HOR82f1o3IqyZ\n62VVPaII9YlEbzZzRfWP9NkB2zxDNyq3wjyZRt0uo9LbyeNxe3BfoSjFCbBcvI5qV2tbsya9wzsX\ns11Yx3hFhd3Ib9mysyzIyR3GUZ/tfxA107O3fiH/42TjdC5Y1ciGkGYvgJrhNTx51pPhNu5KukEq\nFci/z+Q2oAJYSEefiZlpsQuIKkgGbYH1/TqvqvqsjOVz+6eP4Emdxa8QD383ealCUQhbuN/9eeEF\n/0GVmfA24pkc7kEC0Ltfhkit9nO6QR5R/mPPdU8fZ0VyzncbgdINyLcwedqn2FLV0Wn22Qc77cqe\n2MrqPFWdLSJ7YAulamA5cLqqfuLscyVwHrAduFhVH09Xr94mTLIN+518Mlg+L2HMgh0zMxzHbVCC\neq/ZNBI9mWIEFCSTcM459gRk2VBVZY9xyCSM/ARg1OSc5eX2ANSoz4TPeaImjOz24cMlQl4d8O6M\niylLoCBx2Ab8UFUPBL4GXCQiBwJXAM2quj/Q7PzGWXcmcBAwDpjj+GsMRPePeAchDlvrv1lQeTte\nG27QSOy5c4Pt9PnMKVVo8lXXYtjC6+uzFyRg/09htJphwzrfF7+plF369u34253nJmhApovfOp/n\nLdtULD06v1cJkVYzEZEfpNtZVW8NeyIReQD4ubMcq6ofiMjewDOq+kVHK0FVb3C2fxy4RlX/HHTM\n3qKZ1N5dS/Oy5lDbxnZ0npc9OQKmToQ2z7vuO1uil9SZ/qKGrsbjsG5dR7t+qfhCUsm336bQZrtc\nw4gzpcSHnVrn/PnhtZAg/0y6+wv+g/cyXF9yBMwYD60hkkVDNx+T0sXkSzMZnGEJW5lq7HnjXwL2\nVNUPnFUfYpvBwB7HstKz2yqnLPVYU0XkFRF5pTXToLJuTnJpkn4z+9D8XjhB0p6kMUVA1C21BUfV\np7Zpy3e2RJe+fWHaNPv75Mk7e4pRo6taWzsKEijdjKj5zn8VJp1LLqT7LzJlKE43K6SLG7316KPR\npghobfUXoOnur9+6EIKybimsvhlqQk6Esd3azuRFk42GUkAKPjmWiAwCngUaVHWRiHyqqrt51n+i\nqruLyM+BF1W1ySm/A1isqr8LOnZP1kyycbKnTYsSloED7ZfZzzcSpZcaRAkOyupOA8iAYJ9J376d\nBbgXV9sMcrp7BxFCbhqQV7NLd38h58Gatef3pXlomutOwfWlANQ317Ni7QqG7TqMhpoGo7n4kO9B\ni9lWogK4D0h6Rst/5Ji3cD4/dsoV2Mez+1CnrFcRdRBi2iSN2bBhQ3CyPu+4jDBzdPhRioOyutsA\nsro6uOuujmNQ4nF78q+gOVXcAADvfPNe3LxQYaZfHjgwczoVr2YXdJx8jPoHnrxjK9PeGBhpeuBJ\n901i0n2TaFnbgoVFy9oWpj401WguOVAwYSIiMeAO4J8pvpUH2ZlxeArwgKf8TBHpJyLDgf2BlwtV\nv1IkuTTJlPunhI/W2hwwiVUhWLGio/kmm1QapTooqzsOIHMn97Ise1m9OlhQpF6L34DNBQs6D0QM\nOtbtt3fMfByEm8UgaABorulYXIYNY85v2yI554nRKSqsbWsb9c3FMcMmlyapnlVN2cwyqmdV9wgh\nFmYEfJmInJ7FsUcBk4HRIvJ3ZzkeuBEYIyJvA7XOb1T1deBe4A3gMeAiVc3T01b6JJcmOev+s9hu\nhbhky0430UkbiZJ8z490I669vctkMrtzDRiQXb0KTS7p7rua1EgoCHctYfw6maYIcPcP0obcZyb1\nONlqtRAsKIcNy2qelFRWrC38xFTJpUmmPjS1x2lFYceZvBLGZlZseoLPJNLYEWgXJE/ma9Lk8nK7\nQUg34jo1sinKyOdUSjWiqztSKtkDotYjnXkrnR8lHrcnTvOLlEvxI0Udk9J+igFxVv9nATM+ANWz\nqmlZ2/n9qdq1iuWXLC/oubMh3z6TJ0XkMhHZR0T2cJcc69ircX0jkXJrOWatvAkSsAWJt3cappee\nSxLGUo3o6o4Uew7xoLEiUTW7IB9KVZX9LC5YYKeM8VJRYQsSVyNasMAudyMOoYMfKeqYFJfWtlam\n31rYGR2DtJ9iaEWFJKwwOQO4CPgj8KqzvFKoSvV0Ig1AhPw72b34vdiZTCDp5m7PNNc5dM0c18mk\nPUdJLGYvQ4Z0zSDKfA7kLGbG5UwJLKOEQ2fy67gBBl7hdNddO48ZVBfo4EeqW2Kx/jdVkX0pjZ8V\ndkbHYbv6vz9B5d2FsCPgh/ssny905XoikUJ+YeckVn7WvNQRx1HJ1smcbu72KVMy+1OKHSXlmkC8\n45JaW+Hcc4srULLNKOzumyqEihmFlk8tKIwmk044BdVlypTOQrqhod2X0rQIysJEesdgvbWpYKPn\nG2oaqKzo+P5UVlTSUBP+XSxFB37ocSYicjBwIPbcJgCo6t0FqlcoupPPJBvfSMaxI94Rx2Vl4aJj\nUn0k2drWg0Z5Z/KnFNumn0zajYzn3iRHQH0NrNgVhm0op+GsIo2MzjZvV7q8aKljfwp1f0tpLE6Y\nkGLvffDMmpmtL2VQ30HM3W0ydTfllocuuTRJfXM9LWtbKI+Vs93aTtWuVZHGuLgO/LatO//3yopK\n5k2cV5DnOK8+ExG5Gvh/znIccDNwQk417EXU3l0bzawFEIMdmR74NWt29t7mz89sYnJzJeVjZHZQ\nzzGdiaXYUVJuI5wiSKZOhJbd7OSXLYO2Fy+SJluzVFBP3Dv2B+yOgqst5FvjKqWxOGHO6dWaZs+G\nykqSI2D+V+goSKKMTfmokekHZjlfDB2juMAelR8jxvH7Hx9JCNQ313cQJFDcsOYgwvpMTgVqgA9V\n9RzgEGDXgtWqB5BcmmTIzUOIzYyFzquVSltfqD8jnjn0EvxNB9OmFT/cNZ1ztRCpRdLh0wjX13TM\nUQZFfBGzbZDTCSHv2BJXaGbR0GWklMbiZJq4zMW9b867Uf/N8k7/fSQNJWZrNYOvtDslUc18fkLA\nwmLuK3MjdWZK1YEfVphsVNUdwDYR2QV71Po+GfbplXijtFo3RsgdFtBDWrFtTbgX2c/sNGdOYXNE\n+VFKjY5PI7wioAtUlBcx23uTSQhl68+IEgxQSmNxwo5bSelsrRiUB3NczJ4baNLJjlDZpSV0IEXQ\nM2ZhRerMBDnq9xiwR5f6UcIKk1dEZDfgl9iRXH8FArP59layMmc5Ib/xgJRXw3YdlvlFzsWxm2+y\nbXQKka7epxEOTMdfjEiabO9NJiGUjfksm2em0Aks0+E3ODOdiddHSAf9x7FsBjh6hcp3W0jedk7G\nZzbdM7Zi7YrQ74CfA7+irIJ1W9Z16UDIsNFc01X1U1WdC4wBpjjmrl5PTuYsT8jv7MfstPBeOkR4\neF9kN1lfujkmchlvkGvDHrXRKZQw9GmEG56roDLW0dYRNZImJ7JpkDMJoWzMZ8Ueo5ILYZ4Pb3aF\neNxXSPtGUW2BC1/emVE7vgH6biH82BRXqEzYyuA3JqVtvBtqGogF2NWG9dkj9DtQN6KOeRPnUbVr\nFTFiVO1axS79dmHL9o4NSLH9KJnmMzks3c69edreyNFZXizovw1+9WDneUc6RBl9fip101LyJUWZ\n6S5MpE2qeez444sXIeSSbZRTGHzMf8kv97BssdmMhC+l6KxMpHs+GhoiXbsbTbVi7QqGfWrR0Owf\nLZlt1BcWxGIxLhx5IXO+NafT6umPTGfuK3OxPNKqsqKSeU8MoO4ZH7N4yHegbGZZh2O6xIix4+rc\n/s+8TNsbMF2vS9ppe4tBVwiTnIQIpE+HkjopkN9L4QlzzEg2IadBExPlc9rZVLpTw1aqRJ2QK4wA\n98717k6olTppWr7r5Ue652PYsPAdkdS6rF/v/y4570ByBFzwLdjQj+hCxSE+IM7s8bM7dFY6CDS3\nM3PI5JzegUKmaMnrHPClSjGFSa6aCNh+kdmPBYwbCdOIJ5MwaVK4c4bRJqLk2Cpkw15IzaTQFHpW\nxUKRSZtJpwGH1VTzlTss3fOxYkXwmBN3fZDGXVFhP9feOWAqKjplxc6HUIkRrK0AOb8DhRx7ku9x\nJhUicrGI/M5Z/sOZq6TH4vWFxGbGojvWwTZnbbVH3lozYfUtAYKkvDz4hfA6UdPZs+Px6I7dKGk3\nCqKcHV0AABThSURBVDmeoBQiwLLxE5VK4EM2dc/kh/HzqbiE9a3kyy+T7vkIei5jsY7/y9y5neuy\ndSsMHtzxHuyyS6fBv/nIRmxh0fhKY3t7MuTmIR39Kzm+A35+lEINYgwibNbgXwEVwHynaDKwXVXP\nL2DdMpJvzcTPnpk1FtR8voYnz3oSpk+3X1S/EepuTy1o9jtvzyTdyN+mpug94rCaSTFGrXdlDz/b\nHnQpaFSFyhycaZR5GE01n+bLoOcjiqnWj9S6ZLju5AiYMc4z93yWmopLBzNYiWq5eTVzicg/VPWQ\nTGXFJh/CJLk0yYzFM6KNCQnCa876S5y6p1enNxe4s9vNmROuUQhqvOJxO8FdVMI486PayLsj2QqF\nUvD1FEqgZepohDl+sYRtaiMcZXqE1LpEMP3mw/wFUEE5fXfE2BDb1qE8XtnZ3xJYF592zM9fkw35\nFiZ/BU5T1Xed358HfqeqaaO9Ck2uwiS5NMk5vz+HrTu25l4Zb0LGigpbXV6zJnPOLG8vauBA6N/f\n3s+vZ1KIXqhP7qp2uoPPIh9kKxRKQTMplEArJZ9JVIL+lzABLlGiJauqYP16kolWzp0IWyrIWVPJ\nGsv/3H3pw50n/zongZLv+Ux+BDwtIs+IyLPAU8APs65diVDfXJ+bILHsJb7BM32uOz92a6v94GZK\nvuh9uDdsgHXr7Lka/MYgFGIUcl1d6EFfPZZs05yUgq+nUDmzvM8a7BxlHuWZ66pR80H/y4UXhpuB\nct68zLNBuv/z7NnUvRZj80/sNiC+gfZ2oagECLEtbKP+wRnFqUKErMH9gC86P99S1c0Fq1VIctVM\ngmKz05IuMmvgQDt0N9tZCF26QiMoUXttUcilB93V961UZlvMhkLeuzDHTrdNOt9JqunXZ8qF5Ai6\nXltxiFmw45rspVteNBMROVxE9gJwhMehwHXALT1hpsXQKTS8/0MMKrf6CJK+feGss3IXJNA1k0d1\nZaqMQpMp2imXHnRX37dSypkVhUJHwmX6XzKdP0rCUp9ErHVL8ddWukBrCUohlG8yDVr8K1CrqmtE\n5OvAPcD3sYXKAap6app97wQmAB+r6sFO2R7AQqAaWA6crqqfOOuuBM4DtgMXq+rjmSqfF5/Joils\nxccU5dyWMvxTwVd9CstnuT+qYL/94Kmngnsz7qCv1M+gbefPL/0GoTvQnXvuPZmu9jdlOn+U5yaK\nn4WOmS72aHMiwwqkvfTdBnc+7wQDZUm+fCblqrrG+X4GME9V71PV/wb2y7Dvr4FxKWVXAM2quj/Q\n7PxGRA4EzgQOcvaZIyIZjJa5Uzeijrue2a1Tz8H1gVgzgzsRK3bFfriammyVN50gcecRsSzYts3+\nnD+/8zzXLtu3d12ixp5Gd8pB1ZvI55TD2YyzyXT+dBqfX9LJefPSn2/gwPavde8NZPmCODuujbH6\n3iqm7VKTX23F047dubiCuvNn5/HgwfTJsL5cRPqo6jbs+Uymht1XVf8oItUpxScCxzrf5wPPAJc7\n5fc4prRlIvIOcARFyExc9+wa6p4JXj9srT2Rkl95+8NVXZ0+rj3I0Qd2kka/lA5ug2d6z9mTTAab\nHbvClGjYSVAIb9TAgVStwDsffLp3J8z56+oyayHu+ebNs0P0/d5ld9KygDD7OcCopUnq757CioHb\n2aMNNpU7IccR6eDLLXZYv2VZgUsikahPJBIvJBKJBxKJxN8SiUTMKd8vkUi8kG5fZ7vqRCLxmuf3\np57vMfd3IpH4eSKRmORZd0cikTg14JhTE4nEK4lE4pXDDz/cypmqKsuyRYHv0jQCq/LHWFyzc6n8\nsV1uVVVZVlOTZcViwceoqspch6D9Y7Hcr6+30tRkWZWVuf0vhs40Ndn3Lhbb+fxne5zU/6eyMvrx\ngt7fTP9vtucPOl88blkVFWnbkoznyPTMhl3y3G4kEolXrAxtvWWfOaNA+Foikfh2IpEY6Cn7QiKR\nOCzEvoHCxPn9iRVRmHiXcePG5X6nQvyBTSOwqi7Bil1tfzaNSHk44vHgPzXMy5HtC1FgmpY0WVW3\nVVmxa2JW1W1VVtOSpvw1JoUmXSchm0arJxP2P82XAIh63nTk0hHL5vzpOo5hl3TvdVNTcHuSj+Nn\nQd6ESS6LjzB5K5FI7O183zuRSLzlfL8ykUhc6dnu8UQi8e+Zjp8XYWJZOx8q9yGM+ufF451fsljM\nsqZNC3/+fL6keaBpSZNV2VDZUSOb2ddq+mpFSdUzkHT/YynWt6uI8uyVWqenqcmyysuLW6cMloyc\nNYdctZMCvI9hhUnYQYv54kFgivN9CvCAp/xMEeknIsOB/YGXi1YrN4zQsuwBg5kGLKWyZk1nZ92C\nBXaalLDnTx0g5vpMusgJ7zdfdZu1hfpjUgZ5lqozO11op58dPKoDt6cQJUAhn05zl2zvveu7CMp3\nV6iBo0EDIuPx8MeIOmkZ2G3CtGmdz11RYZ+7FMLCw0icbJZEIvGbRCLxQSKR2JpIJFYlEonzEolE\nPJFINCcSibcTicSTiURiD8/29YlE4l1Hexkf5hx500xcvBpKV6iVfr2SKBpOHoldE+uglbhL7OqQ\nPa2uNoeF7XGXoFZYVKKYifKtmeRy74PqUl4ebv/U53PatPDPq9+z7XctFRWW1bdvuGfQPV4mbaYL\n3quSMHMVesmrMAmjXsZi4R6ObAl6QcL6XvJI1W1VvsKk6pIQjUmpNNBhXrxSM90UmyjXn+//NZd7\nH0YIBv3/Yd71bK4rSMikewbDmrW68Hk0wiQsYbURt0EvZM8g16iwPJKTzyQfDXSxemAB97xpBJ2D\nD3oiUQVEPv+XXJznmZ6xdNcV1vpQjHcubNvTBdYJFyNMwhDV2VVotTLdg9UFYcJZR3NlEorFjhpK\nh889bxqBVflfHc18lQ2VPVugdIVJMmqnw1vPeDy9lSDdscMG2RTjnQtbl0zmsaimuggYYRKGbPwj\nhTTXpBuz0p3MLunMdWHuZTFNTz6Cq+pSf39R1W0FOH9vpqmp89iMigr/ZyLIJxGP+zee6bSe7qaZ\npNYnbPhwntqqUo3mKi2yiULJFL2US2RQXZ2dJjs1C2l3SwfvF/HiN/tdtlFD+Yy+8kmbsWJXy3fT\nFWvNqPm8k/qs+2TgBfyjnLZuhUGD/JM5pkvN7/d8plKsdy5MXVxWrLCf9XPP9R9pn0qxIy3DSJxS\nXbpEM0mn/ubLPNPVkVD5IPUa0t3L1G2Del3uvSiwCSww+MBoJvkligYa1b+S6TkpkokoFFGe/6gD\nGvNgqjNmrjBkO0AoSP3t7ZFB6UiXhsLPfNGnT8eyvn3TO0/zeI99gw96ss+kq8hHWHI8HiwE0kVz\nlXJnLZ0gzFdbFQEjTMISdWyJ26j5YXJsBRP0goTtabm29CLdY9/gA0N+yTUsOew4Di/TpoX33XUl\nQQIviiApss+kywVCLktex5mEFSjxePAx0qmnBv8XJEr6mqoqo/31JLINS4bgNCrpnoV8BLgUW6tJ\nPd/Agf71Lysz0Vy5LEUftJiuB+wXmZJJkzFE0wpd/0opDIg05IeojXPYwcV+x08ngMImhizmsxek\njaVeR1AEXJ4wwiQbwjx4Uf0l6TQZg/8Lk6n3WOo2b4NNIf6nMJ2PdAMXo2ozYc5dKK0mGz9RATDC\nJFei9kKMvyR7/CJrjPbRvSlULz6TWTTMwMUgjTfbcxdKqymRNsUIk3wQpSdhbPn5xWgf3ZtCvQ/p\nBETYgYupDXPYVCW5XFM2+5ZIm2KESbExtnyDYSeF6lVHec+CGuPy8uw6Kbm849ncj67Mm+bBCJOu\noAf2pvMSIpua/iEe7xH3xpCGQvaqw75nhejgZfuOZ3s/uvJaHYwwiUoPFAS5kpfBeybKrXdSKpp6\nvt/rbI9X6PtRQOFthEkUSuXBLzHyklYkk43b0HPpaR20XNuJQt6PAjrrwwqTmGVZxUsElmfGjx9v\nLV68OPcDVVdDS0vn8qoqO4FcL6VsZhkWnZ+PGDF2XL0j5EHK7Mfaj1jMTtJnMHQHSrmdKGDdRORV\nVR2ZabvenTXYpRBzW/cAhu3qn3k1qNx/4zTbpltnMJQa+Wwn8pn5GoLnpi9itvGSEyYiMk5E3hKR\nd0TkiqKcNF266l5MQ7/jqdzWMSV4ZUUlDTURHtCGBqio6Fzet2/JpdVPLk1SPauaspllVM+qJrm0\n8wseZhtDcSj6f5GvdiKZhKlTSe7SwpDLLGJntxB7exJDrhuc/TX4TKXAvHkd0/IXmJISJiJSDvwC\nGA8cCHxHRA4s+IlLQKqXHMkkdZfNZ94DFlWfQsyCqrUx5u0+hboRER7Qujq46y6Ix3eWxeNw551F\nfdAzkVyaZOpDU2lZ24KFRcvaFqY+NLXDyx1mG0Nx6JL/Ih/tRDIJZ51Fct82zjkRWgcCMXtp3bGe\ncx84N9w1+Gk2dXW2SctvfpciUFI+ExH5d+AaVf2m8/tKAFW9wW/7vPlMwP4z6uttldWdQKeEGrui\nU8r24QJQPav6/7d3tzFylWUYx//Nsost6KJLIeZq2bZJv1SEShokhhhEMZWI1ZjUkhWxGqsJKmv8\noLYfaEzW+EVlUdEsspQ2WxEtQhNRo40RE42ixlhjTWxqEe7UFlErkAb75ofnLMxud17aMzvnGeb6\nJU1nzpxpr3NPZu7z8pxzeOLomcs7PDjMwdGDLc9jnVHZZ1Hmd2JqCjZuhOPHWTYKT1w092xNl6HY\nsplxs7BFi+ZtS6TVYybntf1/LkfAkzXPnwLeOGMGaROwqXjcvv95ZKS3m8dsPXYcqd5dFGuntzKP\ndUZln0WZ34ktW9LdIYG/D9afrekyzHXXyem7Klb4G5bVbq5WRMRERKyJiDVDtbtOrL167DhSK4MN\n2jIgwdqiKz+LmhWxy47Wn63pMmS6opdbMwlgac3zJcU067QeO4409tYxFvXPXN7Zgw1amcc6oys/\ni5oVsbE90H/izFkG+gaaL0OmK3q5NZPHgZWSlksaADYAuyvO1JsyGB3SSSOvH2HipgmGB4dZwAKG\nB4eZuGlixmCDVuaxzujKz6JmZOPIXrjvERh6HqZP5RpaOMTkusnmy5Dpil5WB+ABJN0I3An0AZMR\nUbdCbT0Ab2Y236am4Pbb4Zln0vOhIRgfP/uVtA4OGGr1AHx2zeRsuJmYmc0vnwFvZmYd42ZiZmal\nuZmYmVlpbiZmZlaam4mZmZXmZmJmZqW5mZiZWWluJmZmVlpXn7Qo6Wlgjuukn7WLgX+24d+Zb92S\nE7ona7fkhO7J6pztV2XW4YhY3Gymrm4m7SLpt62c4Vm1bskJ3ZO1W3JC92R1zvbrhqzezWVmZqW5\nmZiZWWluJslE1QFa1C05oXuydktO6J6sztl+2Wf1MRMzMyvNWyZmZlaam4mZmZV2XtUBqiZpLTBO\nurPjtyLiixVHepGkg8CzwEngRESskfQa4DvAMuAgsD4i/t3hXJPAO4EjEXF5Ma1uLkmfAz5cLMcn\nI+LHFWfdCnwEeLqYbXNEPFplVklLge3ApaQbuU5ExHhudW2QcysZ1VTSK4DHgPNJv3Pfi4g7cqtn\nk6xbyaimzfT0lomkPuDrwDuAVcDNklZVm+oMb4mI1TVjzD8L7ImIlcCe4nmnbQPWzpo2Z66inhuA\n1xXvubuoe6ds48ysAF8p6rq65gtaZdYTwKcjYhVwDXBbkSe3utbLCXnV9AXg+oi4ElgNrJV0DfnV\ns1FWyKumDfV0MwGuBvZHxIGI+B/wALCu4kzNrAPuLx7fD7y70wEi4jHgX7Mm18u1DnggIl6IiL8B\n+0l174g6WeupLGtEHIqI3xePnwX2ASKzujbIWU9VOU9HxHPF0/7iz2kyq2eTrPVU+p2qp9ebiYAn\na54/ReMvRqedBn4q6XeSNhXTLo2IQ8Xjf5B2N+SgXq5ca/wJSX+UNCnp1cW0LLJKWga8Afg1Gdd1\nVk7IrKaS+iT9ATgC/CQisq1nnayQWU0b6fVmkrtrI2I1aTfcbZLeXPtiRJym8RpMJXLNVeMbwArS\nLoVDwJeqjfMSSRcCu4DRiPhv7Ws51XWOnNnVNCJOFt+fJcDVki6f9Xo29ayTNbuaNtLrzSSApTXP\nlxTTshARUfx9BPg+aVP2sKTXAhR/H6ku4Qz1cmVX44g4XHx5TwH38NIugkqzSuon/UBPRcRDxeTs\n6jpXzlxrWmT7D/Az0vGF7OpZqzZrzjWdS683k8eBlZKWSxogHdTaXXEmACRdIOmV04+BtwN/IuW7\ntZjtVuCRahKeoV6u3cAGSedLWg6sBH5TQb4XTf+YFN5DqitUmFXSAuBeYF9EfLnmpazqWi9nbjWV\ntFjSRcXjhcANwF/IrJ6NsuZW02Z6/gx4STcCd5KGBk9GxFjFkQCQtIK0NQJpuODOiBiTNAQ8CFxG\nuvz++oho9QBzu7J9G7iOdFnsw8AdwMP1cknaAnyINBJoNCJ+WHHW60i7Dk6Thod+dHo/elVZJV0L\n/ALYC5wqJm8mHY/Ipq4Nct5MRjWVdAXpAHsfaaX5wYj4fKPvT4Wffb2sO8iops30fDMxM7Pyen03\nl5mZtYGbiZmZleZmYmZmpbmZmJlZaW4mZmZWWs9fNdisLEknSUNl+0lDNbeTLtB3quEbzV5G3EzM\nyjtWXAoDSZcAO4FXkc5pMesJPs/ErCRJz0XEhTXPV5CurnAxMAzsAC4oXv54RPxS0nbgoYh4uHjP\nFOlkuv3AfcAAaTf0eyPirx1bGLNz5GMmZm0WEQdIZzNfQrr20w0RcRXwPuCuYrZ7gQ8CSBoE3gT8\nAPgYMF5s6awhXRHWLHtuJmbzqx+4R9Je4Lukm7ARET8nXRduMelSJLsi4gTwK2CzpM8AwxFxrKLc\nZmfFzcSszYrdXCdJWyWfIl0T7ErSlsZAzazbgfcDG4FJgIjYCbwLOAY8Kun6ziU3O3duJmZtVGxp\nfBP4WnG/jEHgUDGy6xbS7q9p24BRgIj4c/H+FcCBiLiLdEXbKzqX3uzceTSXWXkLi7vkTQ8N3gFM\nX579bmCXpA8APwKen35TRByWtI90xeVp64FbJB0n3QnwCx3Ib1aaR3OZVUTSItL5KVdFxNGq85iV\n4d1cZhWQ9DZgH/BVNxJ7OfCWiZmZleYtEzMzK83NxMzMSnMzMTOz0txMzMysNDcTMzMr7f+WhQ0K\nxEz8uwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f079efa7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.1,0.1,0.8,0.8])\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('RNN Paper Results/Exp2_1/' + test_location + '/'+  test_year + 'Figure 2.jpg', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Sv3U/pomHv24mBVfHy7qnTMun+tPG8WQ7HgV9XZPkv/dcNQkWAkhWugL/f3c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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f078ca8e320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=dw_solar_everyday,y=ghi_everyday,kind='reg')\n",
    "plt.xlabel('Observed global downwelling solar (Watts/m^2)')\n",
    "plt.ylabel('Clear Sky GHI (Watts/m^2)')\n",
    "plt.savefig('RNN Paper Results/Exp2_1/' + test_location + '/'+  test_year + 'Figure 3', 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": 526,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Kt max: 275783.511009\n",
      "train Kt min: -1561.16866474\n",
      "train Kt mean: 3.97720123135\n"
     ]
    }
   ],
   "source": [
    "if run_train:\n",
    "    # TRAIN dataset\n",
    "    df_train = df_train[df_train['ghi']!=0]\n",
    "    df_train['Kt'] = df_train['dw_solar']/df_train['ghi']\n",
    "    df_train.reset_index(inplace=True)\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": 527,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Kt max: 18622.0035826\n",
      "test Kt min: -1997.84146167\n",
      "test Kt mean: 3.20825588533\n"
     ]
    }
   ],
   "source": [
    "# TEST dataset\n",
    "df_test = df_test[df_test['ghi']!=0]\n",
    "df_test['Kt'] = df_test['dw_solar']/df_test['ghi']\n",
    "df_test.reset_index(inplace=True)\n",
    "\n",
    "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()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if run_train:\n",
    "    # TRAIN dataset\n",
    "    df_train= df_train[df_train['Kt']< 5000]\n",
    "    df_train= df_train[df_train['Kt']> -1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Test dataset\n",
    "df_test= df_test[df_test['Kt']< 5000]\n",
    "df_test= df_test[df_test['Kt']> -1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making 4 Kt columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if run_train:\n",
    "    # Train dataset\n",
    "    df_train['Kt_2'] = df_train['Kt']\n",
    "    df_train['Kt_3'] = df_train['Kt']\n",
    "    df_train['Kt_4'] = df_train['Kt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Test dataset\n",
    "df_test['Kt_2'] = df_test['Kt']\n",
    "df_test['Kt_3'] = df_test['Kt']\n",
    "df_test['Kt_4'] = df_test['Kt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group the data (train dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {
    "collapsed": true
   },
   "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()\n",
    "Kt_2 = df_train.groupby(['year','month','day','hour'])['Kt_2'].mean()\n",
    "Kt_3 = df_train.groupby(['year','month','day','hour'])['Kt_3'].mean()\n",
    "Kt_4 = df_train.groupby(['year','month','day','hour'])['Kt_4'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "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,Kt_2,Kt_3,Kt_4], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Groupdata - test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {
    "collapsed": true
   },
   "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()\n",
    "test_Kt_2 = df_test.groupby(['month','day','hour'])['Kt_2'].mean()\n",
    "test_Kt_3 = df_test.groupby(['month','day','hour'])['Kt_3'].mean()\n",
    "test_Kt_4 = df_test.groupby(['month','day','hour'])['Kt_4'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {
    "collapsed": true
   },
   "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,test_Kt_2,test_Kt_3,test_Kt_4], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [],
   "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": 537,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if run_train:\n",
    "    levels_index= []\n",
    "    for m in df_new_train.index.levels:\n",
    "        levels_index.append(m)\n",
    "    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)\n",
    "            df_new_train.loc[i].loc[j]['Kt_2'] = df_new_train.loc[i].loc[j]['Kt_2'].shift(-2)\n",
    "            df_new_train.loc[i].loc[j]['Kt_3'] = df_new_train.loc[i].loc[j]['Kt_3'].shift(-3)\n",
    "            df_new_train.loc[i].loc[j]['Kt_4'] = df_new_train.loc[i].loc[j]['Kt_4'].shift(-4)\n",
    "    df_new_train = df_new_train[~(df_new_train['Kt_4'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "levels_index2= []\n",
    "for m in df_new_test.index.levels:\n",
    "    levels_index2.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {
    "collapsed": true
   },
   "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)\n",
    "        df_new_test.loc[i].loc[j]['Kt_2'] = df_new_test.loc[i].loc[j]['Kt_2'].shift(-2)\n",
    "        df_new_test.loc[i].loc[j]['Kt_3'] = df_new_test.loc[i].loc[j]['Kt_3'].shift(-3)\n",
    "        df_new_test.loc[i].loc[j]['Kt_4'] = df_new_test.loc[i].loc[j]['Kt_4'].shift(-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_new_test = df_new_test[~(df_new_test['Kt_4'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize train and test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if run_train:\n",
    "    # TRAIN set\n",
    "    train_norm = (df_new_train - df_new_train.mean()) / (df_new_train.max() - df_new_train.min())\n",
    "    train_norm.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST set\n",
    "test_norm =  (df_new_test - df_new_test.mean()) / (df_new_test.max() - df_new_test.min())\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": 543,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "def roundup(x):\n",
    "    return int(math.ceil(x / 100.0)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1_train shape is (9400, 22)\n",
      "y1_train shape is (9400, 4)\n"
     ]
    }
   ],
   "source": [
    "if run_train:\n",
    "    # TRAIN set\n",
    "    train_lim = roundup(train_norm.shape[0])\n",
    "    train_random = train_norm.sample(train_lim-train_norm.shape[0])\n",
    "    train_norm = train_norm.append(train_random)\n",
    "\n",
    "    X1 = train_norm.drop(['Kt','Kt_2','Kt_3','Kt_4'],axis=1)\n",
    "    y1 = train_norm[['Kt','Kt_2','Kt_3','Kt_4']]\n",
    "\n",
    "    print(\"X1_train shape is {}\".format(X1.shape))\n",
    "    print(\"y1_train shape is {}\".format(y1.shape))\n",
    "\n",
    "    X_train = np.array(X1)\n",
    "    y_train  = np.array(y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X2_test shape is (3200, 22)\n",
      "y2_test shape is (3200, 4)\n"
     ]
    }
   ],
   "source": [
    "# TEST set\n",
    "test_lim = roundup(test_norm.shape[0])\n",
    "test_random = test_norm.sample(test_lim-test_norm.shape[0])\n",
    "test_norm = test_norm.append(test_random)\n",
    "\n",
    "X2 = test_norm.drop(['Kt','Kt_2','Kt_3','Kt_4'],axis=1)\n",
    "y2 = test_norm[['Kt','Kt_2','Kt_3','Kt_4']]\n",
    "\n",
    "print(\"X2_test shape is {}\".format(X2.shape))\n",
    "print(\"y2_test shape is {}\".format(y2.shape))\n",
    "\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": 546,
   "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": 547,
   "metadata": {
    "collapsed": true
   },
   "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": 548,
   "metadata": {},
   "outputs": [],
   "source": [
    "if run_train:\n",
    "    # Instantiating Model Class\n",
    "    input_dim = 22\n",
    "    hidden_dim = 15\n",
    "    layer_dim = 1\n",
    "    output_dim = 4\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",
    "    # initializing lists to store losses over epochs:\n",
    "    train_loss = []\n",
    "    train_iter = []\n",
    "else:\n",
    "    model = torch.load('RNN Paper Results/Exp2_1/' + test_location + '/'+  test_year + 'torch_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TEst set\n",
    "\n",
    "\n",
    "test_loss = []\n",
    "test_iter = []\n",
    "# converting numpy array to torch tensor\n",
    "\n",
    "X_test = torch.from_numpy(X_test)\n",
    "y_test = torch.from_numpy(y_test)\n",
    "\n",
    "# Convert to Float tensor\n",
    "\n",
    "X_test = X_test.type(torch.FloatTensor)\n",
    "y_test = y_test.type(torch.FloatTensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Iteration: 100. Train_MSE: 0.03832287713885307. Test_MSE: [[[ 0.12061296  0.03091992  0.00825078  0.04777953]]]\n",
      "Epoch: 2 Iteration: 200. Train_MSE: 0.03326038271188736. Test_MSE: [[[ 0.10679609  0.03094405  0.00756924  0.04006466]]]\n",
      "Epoch: 3 Iteration: 300. Train_MSE: 0.028695719316601753. Test_MSE: [[[ 0.09557553  0.03094332  0.00702937  0.03376288]]]\n",
      "Epoch: 4 Iteration: 400. Train_MSE: 0.02027653157711029. Test_MSE: [[[ 0.08644794  0.03094414  0.00658457  0.0286114 ]]]\n",
      "Epoch: 5 Iteration: 500. Train_MSE: 0.016247019171714783. Test_MSE: [[[ 0.07900257  0.03093149  0.00622283  0.02439137]]]\n",
      "Epoch: 6 Iteration: 600. Train_MSE: 0.017917541787028313. Test_MSE: [[[ 0.07290123  0.03089711  0.00593458  0.02090653]]]\n",
      "Epoch: 7 Iteration: 700. Train_MSE: 0.015244700014591217. Test_MSE: [[[ 0.06784897  0.03084437  0.00570457  0.01798662]]]\n",
      "Epoch: 8 Iteration: 800. Train_MSE: 0.011180789209902287. Test_MSE: [[[ 0.06374811  0.03078108  0.00552332  0.01559514]]]\n",
      "Epoch: 9 Iteration: 900. Train_MSE: 0.017052482813596725. Test_MSE: [[[ 0.06039879  0.03070905  0.0053782   0.01363007]]]\n",
      "Epoch: 10 Iteration: 1000. Train_MSE: 0.009396160021424294. Test_MSE: [[[ 0.05762663  0.03063303  0.00526112  0.01201377]]]\n",
      "Epoch: 11 Iteration: 1100. Train_MSE: 0.007917770184576511. Test_MSE: [[[ 0.05534184  0.03056885  0.00516074  0.01068815]]]\n",
      "Epoch: 12 Iteration: 1200. Train_MSE: 0.007108100224286318. Test_MSE: [[[ 0.05345024  0.03050339  0.00507925  0.00958829]]]\n",
      "Epoch: 13 Iteration: 1300. Train_MSE: 0.0073609668761491776. Test_MSE: [[[ 0.0518748   0.03043022  0.00501434  0.00865212]]]\n",
      "Epoch: 15 Iteration: 1400. Train_MSE: 0.00575594836845994. Test_MSE: [[[ 0.0505561   0.03035449  0.00496173  0.00786631]]]\n",
      "Epoch: 16 Iteration: 1500. Train_MSE: 0.005472333636134863. Test_MSE: [[[ 0.04948306  0.03027593  0.00492142  0.00722339]]]\n",
      "Epoch: 17 Iteration: 1600. Train_MSE: 0.005932149942964315. Test_MSE: [[[ 0.04859237  0.03019387  0.00489014  0.00669282]]]\n",
      "Epoch: 18 Iteration: 1700. Train_MSE: 0.005012026987969875. Test_MSE: [[[ 0.04784129  0.03012386  0.00486253  0.00625407]]]\n",
      "Epoch: 19 Iteration: 1800. Train_MSE: 0.005514926742762327. Test_MSE: [[[ 0.04720683  0.03006039  0.00483864  0.00588984]]]\n",
      "Epoch: 20 Iteration: 1900. Train_MSE: 0.004411553964018822. Test_MSE: [[[ 0.04667065  0.03000018  0.00481845  0.00557823]]]\n",
      "Epoch: 21 Iteration: 2000. Train_MSE: 0.003900592913851142. Test_MSE: [[[ 0.0462036   0.02993725  0.0048026   0.00529941]]]\n",
      "Epoch: 22 Iteration: 2100. Train_MSE: 0.0038534754421561956. Test_MSE: [[[ 0.04581331  0.02987176  0.00479073  0.00506529]]]\n",
      "Epoch: 23 Iteration: 2200. Train_MSE: 0.004704623483121395. Test_MSE: [[[ 0.04548445  0.02980573  0.00478283  0.00487256]]]\n",
      "Epoch: 24 Iteration: 2300. Train_MSE: 0.004746641032397747. Test_MSE: [[[ 0.04519672  0.02974102  0.00477614  0.00471212]]]\n",
      "Epoch: 25 Iteration: 2400. Train_MSE: 0.004309520591050386. Test_MSE: [[[ 0.04494235  0.02968989  0.0047694   0.00457692]]]\n",
      "Epoch: 26 Iteration: 2500. Train_MSE: 0.005965297110378742. Test_MSE: [[[ 0.04471593  0.02963676  0.00476367  0.0044614 ]]]\n",
      "Epoch: 27 Iteration: 2600. Train_MSE: 0.003149581840261817. Test_MSE: [[[ 0.04451418  0.02958916  0.00475895  0.00435388]]]\n",
      "Epoch: 29 Iteration: 2700. Train_MSE: 0.0026089369785040617. Test_MSE: [[[ 0.04433104  0.0295381   0.00475535  0.00425519]]]\n",
      "Epoch: 30 Iteration: 2800. Train_MSE: 0.0037896353751420975. Test_MSE: [[[ 0.04416877  0.0294851   0.00475379  0.00417285]]]\n",
      "Epoch: 31 Iteration: 2900. Train_MSE: 0.005740323569625616. Test_MSE: [[[ 0.04401943  0.02943016  0.00475388  0.0041022 ]]]\n",
      "Epoch: 32 Iteration: 3000. Train_MSE: 0.003627569880336523. Test_MSE: [[[ 0.04387838  0.02938499  0.00475324  0.00404158]]]\n",
      "Epoch: 33 Iteration: 3100. Train_MSE: 0.00477349990978837. Test_MSE: [[[ 0.04374591  0.02934067  0.00475266  0.00398876]]]\n",
      "Epoch: 34 Iteration: 3200. Train_MSE: 0.0028716258239001036. Test_MSE: [[[ 0.04361986  0.02930277  0.00475125  0.0039393 ]]]\n",
      "Epoch: 35 Iteration: 3300. Train_MSE: 0.0018153678392991424. Test_MSE: [[[ 0.0435029   0.02926181  0.00475101  0.00388992]]]\n",
      "Epoch: 36 Iteration: 3400. Train_MSE: 0.002428604755550623. Test_MSE: [[[ 0.04339289  0.02921925  0.00475118  0.00384351]]]\n",
      "Epoch: 37 Iteration: 3500. Train_MSE: 0.0035504354164004326. Test_MSE: [[[ 0.0432864   0.02917691  0.00475259  0.00380344]]]\n",
      "Epoch: 38 Iteration: 3600. Train_MSE: 0.004078075289726257. Test_MSE: [[[ 0.04318218  0.02912952  0.00475449  0.00376828]]]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch: 959 Iteration: 89200. Train_MSE: 0.00024873114307411015. Test_MSE: [[[ 0.02717029  0.0223906   0.00488253  0.00187572]]]\n",
      "Epoch: 960 Iteration: 89300. Train_MSE: 0.00041008181869983673. Test_MSE: [[[ 0.02716699  0.0223899   0.00488234  0.00187559]]]\n",
      "Epoch: 961 Iteration: 89400. Train_MSE: 0.00036819675005972385. Test_MSE: [[[ 0.0271648   0.02238935  0.00488204  0.00187563]]]\n",
      "Epoch: 962 Iteration: 89500. Train_MSE: 0.0006165553350001574. Test_MSE: [[[ 0.02716166  0.02238663  0.00488175  0.00187584]]]\n",
      "Epoch: 963 Iteration: 89600. Train_MSE: 0.004470381885766983. Test_MSE: [[[ 0.02715807  0.02238365  0.00488155  0.00187599]]]\n",
      "Epoch: 964 Iteration: 89700. Train_MSE: 0.0002574716054368764. Test_MSE: [[[ 0.02715601  0.02238162  0.00488156  0.00187561]]]\n",
      "Epoch: 965 Iteration: 89800. Train_MSE: 0.0009543782216496766. Test_MSE: [[[ 0.02715334  0.02237953  0.00488156  0.00187533]]]\n",
      "Epoch: 966 Iteration: 89900. Train_MSE: 0.00039731402648612857. Test_MSE: [[[ 0.02714997  0.02237846  0.00488156  0.001875  ]]]\n",
      "Epoch: 967 Iteration: 90000. Train_MSE: 0.00044186730519868433. Test_MSE: [[[ 0.02714672  0.02237711  0.00488129  0.00187503]]]\n",
      "Epoch: 968 Iteration: 90100. Train_MSE: 0.00041171739576384425. Test_MSE: [[[ 0.02714432  0.02237551  0.00488101  0.0018751 ]]]\n",
      "Epoch: 969 Iteration: 90200. Train_MSE: 0.00026202143635600805. Test_MSE: [[[ 0.02714086  0.02237177  0.00488079  0.0018753 ]]]\n",
      "Epoch: 970 Iteration: 90300. Train_MSE: 0.0002916278608608991. Test_MSE: [[[ 0.02713831  0.02236987  0.00488071  0.00187515]]]\n",
      "Epoch: 972 Iteration: 90400. Train_MSE: 0.0002621837775222957. Test_MSE: [[[ 0.02713664  0.02236777  0.00488068  0.00187481]]]\n",
      "Epoch: 973 Iteration: 90500. Train_MSE: 0.0005301244091242552. Test_MSE: [[[ 0.02713419  0.02236631  0.00488062  0.00187458]]]\n",
      "Epoch: 974 Iteration: 90600. Train_MSE: 0.0008708235109224916. Test_MSE: [[[ 0.027131    0.02236547  0.00488053  0.00187439]]]\n",
      "Epoch: 975 Iteration: 90700. Train_MSE: 0.0003649175923783332. Test_MSE: [[[ 0.02712889  0.02236545  0.00488027  0.00187437]]]\n",
      "Epoch: 976 Iteration: 90800. Train_MSE: 0.00035203355946578085. Test_MSE: [[[ 0.0271261   0.02236289  0.00487995  0.00187458]]]\n",
      "Epoch: 977 Iteration: 90900. Train_MSE: 0.00036166873178444803. Test_MSE: [[[ 0.02712285  0.02236017  0.00487976  0.00187474]]]\n",
      "Epoch: 978 Iteration: 91000. Train_MSE: 0.00023366538516711444. Test_MSE: [[[ 0.02712041  0.022358    0.00487972  0.00187452]]]\n",
      "Epoch: 979 Iteration: 91100. Train_MSE: 0.0003224406682420522. Test_MSE: [[[ 0.0271181   0.02235587  0.00487971  0.0018742 ]]]\n",
      "Epoch: 980 Iteration: 91200. Train_MSE: 0.00041420827619731426. Test_MSE: [[[ 0.02711501  0.0223545   0.00487973  0.00187392]]]\n",
      "Epoch: 981 Iteration: 91300. Train_MSE: 0.00046453613322228193. Test_MSE: [[[ 0.02711177  0.02235303  0.00487957  0.00187384]]]\n",
      "Epoch: 982 Iteration: 91400. Train_MSE: 0.0005226420471444726. Test_MSE: [[[ 0.02710938  0.02235232  0.00487926  0.00187385]]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 983 Iteration: 91500. Train_MSE: 0.000330286449752748. Test_MSE: [[[ 0.02710618  0.02234835  0.00487901  0.00187404]]]\n",
      "Epoch: 984 Iteration: 91600. Train_MSE: 0.0003279226366430521. Test_MSE: [[[ 0.02710366  0.02234654  0.0048789   0.00187403]]]\n",
      "Epoch: 986 Iteration: 91700. Train_MSE: 0.00028435405693016946. Test_MSE: [[[ 0.02710187  0.02234434  0.00487884  0.0018737 ]]]\n",
      "Epoch: 987 Iteration: 91800. Train_MSE: 0.002074234886094928. Test_MSE: [[[ 0.02709984  0.0223426   0.00487881  0.0018735 ]]]\n",
      "Epoch: 988 Iteration: 91900. Train_MSE: 0.0011727442033588886. Test_MSE: [[[ 0.02709693  0.02234181  0.00487878  0.0018732 ]]]\n",
      "Epoch: 989 Iteration: 92000. Train_MSE: 0.0003280674573034048. Test_MSE: [[[ 0.02709447  0.02234171  0.00487853  0.00187317]]]\n",
      "Epoch: 990 Iteration: 92100. Train_MSE: 0.008978566154837608. Test_MSE: [[[ 0.02709192  0.02233967  0.00487818  0.00187337]]]\n",
      "Epoch: 991 Iteration: 92200. Train_MSE: 0.00025990515132434666. Test_MSE: [[[ 0.02708877  0.02233698  0.00487793  0.00187355]]]\n",
      "Epoch: 992 Iteration: 92300. Train_MSE: 0.0003588796535041183. Test_MSE: [[[ 0.02708624  0.02233488  0.00487788  0.00187345]]]\n",
      "Epoch: 993 Iteration: 92400. Train_MSE: 0.00036913526128046215. Test_MSE: [[[ 0.02708436  0.02233274  0.00487786  0.00187314]]]\n",
      "Epoch: 994 Iteration: 92500. Train_MSE: 0.0003811231581494212. Test_MSE: [[[ 0.02708136  0.0223311   0.00487788  0.00187286]]]\n",
      "Epoch: 995 Iteration: 92600. Train_MSE: 0.0006602706853300333. Test_MSE: [[[ 0.02707816  0.02232954  0.00487776  0.00187271]]]\n",
      "Epoch: 996 Iteration: 92700. Train_MSE: 0.00041486843838356435. Test_MSE: [[[ 0.0270757   0.02232902  0.00487753  0.00187266]]]\n",
      "Epoch: 997 Iteration: 92800. Train_MSE: 0.00024588132509961724. Test_MSE: [[[ 0.0270728   0.02232568  0.00487724  0.00187284]]]\n",
      "Epoch: 998 Iteration: 92900. Train_MSE: 0.00026624518795870245. Test_MSE: [[[ 0.02707033  0.02232371  0.00487712  0.00187291]]]\n",
      "Epoch: 999 Iteration: 93000. Train_MSE: 0.0003266619169153273. Test_MSE: [[[ 0.02706849  0.02232151  0.00487706  0.00187266]]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/praveen/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/serialization.py:147: UserWarning: Couldn't retrieve source code for container of type RNNModel. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    }
   ],
   "source": [
    "if run_train:\n",
    "    X_train = torch.from_numpy(X_train)\n",
    "    y_train = torch.from_numpy(y_train)\n",
    "    X_train = X_train.type(torch.FloatTensor)\n",
    "    y_train = y_train.type(torch.FloatTensor)\n",
    "    # Training the model\n",
    "    seq_dim = 1\n",
    "\n",
    "    n_iter =0\n",
    "    num_samples = len(X_train)\n",
    "    test_samples = len(X_test)\n",
    "    batch_size = 100\n",
    "    num_epochs = 1000\n",
    "    feat_dim = X_train.shape[1]\n",
    "\n",
    "\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",
    "            test_batch_mse =list()    \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",
    "                    test_batch_mse.append(np.mean([(Kt_test.data.numpy() - outputs.data.numpy().squeeze())**2],axis=1))\n",
    "\n",
    "                test_iter.append(n_iter)\n",
    "                test_loss.append(np.mean([test_batch_mse],axis=1))\n",
    "\n",
    "                print('Epoch: {} Iteration: {}. Train_MSE: {}. Test_MSE: {}'.format(epoch, n_iter, loss.data[0], test_loss[-1]))       \n",
    "    torch.save(model,'RNN Paper Results/Exp2_1/' + test_location + '/'+  test_year + 'torch_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [],
   "source": [
    "# JUST TEST CELL\n",
    "\n",
    "batch_size = 100\n",
    "seq_dim = 1\n",
    "test_samples = len(X_test)\n",
    "batch_size = 100\n",
    "feat_dim = X_test.shape[1]\n",
    "\n",
    "# initializing lists to store losses over epochs:\n",
    "test_loss = []\n",
    "test_iter = []\n",
    "\n",
    "\n",
    "\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",
    "    test_batch_mse.append(np.mean([(Kt_test.data.numpy() - outputs.data.numpy().squeeze())**2],axis=1))\n",
    "                \n",
    "    test_iter.append(n_iter)\n",
    "    test_loss.append(np.mean([test_batch_mse],axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93000\n"
     ]
    },
    {
     "data": {
      "image/png": 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UM5UN/j/81fPKLkGSelJlg/+NC+aUXYIk9aTKBv/rhw1+STqUygb/KXOGyi5BknpSZYNf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\nSTVj8EtSzRj8klQzBr8k1YzBL0k18/8BILiabzYgrCAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f079932deb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if run_train:\n",
    "    print(len(train_loss))\n",
    "    plt.plot(train_loss,'-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n"
     ]
    },
    {
     "data": {
      "image/png": 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1ind1MXDgAPHuHsxkclS5XKHQyAPc6lW231dTPaq6JxWJ0PX2O5x85TWGPvsc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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f079d3f9908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(len(test_loss))\n",
    "figLoss = plt.figure()\n",
    "plt.plot(np.array(test_loss).squeeze(),'r')\n",
    "\n",
    "figLoss.savefig('RNN Paper Results/Exp2_1/' + test_location + '/'+  test_year + 'test_loss.jpg', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Demornamization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.164525\n",
      "0.149404\n",
      "0.069836\n",
      "0.0432742\n"
     ]
    }
   ],
   "source": [
    "mse_1 = np.array(test_loss).squeeze()[-1][0]\n",
    "mse_2 = np.array(test_loss).squeeze()[-1][1]\n",
    "mse_3 = np.array(test_loss).squeeze()[-1][2]\n",
    "mse_4 = np.array(test_loss).squeeze()[-1][3]\n",
    "\n",
    "rmse_1 = np.sqrt(mse_1)\n",
    "rmse_2 = np.sqrt(mse_2)\n",
    "rmse_3 = np.sqrt(mse_3)\n",
    "rmse_4 = np.sqrt(mse_4)\n",
    "\n",
    "print(rmse_1)\n",
    "print(rmse_2)\n",
    "print(rmse_3)\n",
    "print(rmse_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.572121648329\n",
      "1.04362327401\n",
      "4.46312278844\n",
      "54.6116210989\n"
     ]
    }
   ],
   "source": [
    "rmse_denorm1 = (rmse_1 * (df_new_test['Kt'].max() - df_new_test['Kt'].min()))+ df_new_test['Kt'].mean()\n",
    "rmse_denorm2 = (rmse_2 * (df_new_test['Kt_2'].max() - df_new_test['Kt_2'].min()))+ df_new_test['Kt_2'].mean()\n",
    "rmse_denorm3 = (rmse_3 * (df_new_test['Kt_3'].max() - df_new_test['Kt_3'].min()))+ df_new_test['Kt_3'].mean()\n",
    "rmse_denorm4 = (rmse_4 * (df_new_test['Kt_4'].max() - df_new_test['Kt_4'].min()))+ df_new_test['Kt_4'].mean()\n",
    "\n",
    "print(rmse_denorm1)\n",
    "print(rmse_denorm2)\n",
    "print(rmse_denorm3)\n",
    "print(rmse_denorm4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.1726222024\n"
     ]
    }
   ],
   "source": [
    "rmse_mean = np.mean([rmse_denorm1, rmse_denorm2, rmse_denorm3, rmse_denorm4])\n",
    "print(rmse_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    3163.000000\n",
      "mean        0.169756\n",
      "std         0.393624\n",
      "min        -0.267460\n",
      "25%        -0.009081\n",
      "50%        -0.004916\n",
      "75%         0.125252\n",
      "max         2.178158\n",
      "Name: Kt, dtype: float64\n",
      "\n",
      "\n",
      "count    3163.000000\n",
      "mean        0.342026\n",
      "std         0.703577\n",
      "min        -0.031211\n",
      "25%        -0.006896\n",
      "50%        -0.002210\n",
      "75%         0.367659\n",
      "max         4.664769\n",
      "Name: Kt_2, dtype: float64\n",
      "\n",
      "\n",
      "count    3163.000000\n",
      "mean        1.086772\n",
      "std         3.345456\n",
      "min        -0.017466\n",
      "25%        -0.005377\n",
      "50%         0.066067\n",
      "75%         0.771512\n",
      "max        48.329415\n",
      "Name: Kt_3, dtype: float64\n",
      "\n",
      "\n",
      "count    3163.000000\n",
      "mean       10.176067\n",
      "std        45.253311\n",
      "min        -0.014020\n",
      "25%        -0.003442\n",
      "50%         0.266864\n",
      "75%         1.723366\n",
      "max      1026.823214\n",
      "Name: Kt_4, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df_new_test['Kt'].describe())\n",
    "print('\\n')\n",
    "print(df_new_test['Kt_2'].describe())\n",
    "print('\\n')\n",
    "print(df_new_test['Kt_3'].describe())\n",
    "print('\\n')\n",
    "print(df_new_test['Kt_4'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Write to file\n",
    "#f=open('RNN Paper Results/Exp2_1/' + test_location + '/'+  test_year + 'results.txt', \"a+\")\n",
    "#f.write(...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Saving train and test losses to a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [],
   "source": [
    "if run_train:\n",
    "    df_trainLoss = pd.DataFrame(data={'Train Loss':train_loss}, columns=['Train Loss'])\n",
    "    df_trainLoss.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [],
   "source": [
    "testloss_unsqueezed = np.array(test_loss).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse1</th>\n",
       "      <th>mse2</th>\n",
       "      <th>mse3</th>\n",
       "      <th>mse4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.026378</td>\n",
       "      <td>0.021807</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>0.001834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.025729</td>\n",
       "      <td>0.021338</td>\n",
       "      <td>0.004686</td>\n",
       "      <td>0.001822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.025105</td>\n",
       "      <td>0.020901</td>\n",
       "      <td>0.004580</td>\n",
       "      <td>0.001783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.024517</td>\n",
       "      <td>0.020494</td>\n",
       "      <td>0.004473</td>\n",
       "      <td>0.001749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.024026</td>\n",
       "      <td>0.020282</td>\n",
       "      <td>0.004464</td>\n",
       "      <td>0.001989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mse1      mse2      mse3      mse4\n",
       "0  0.026378  0.021807  0.004765  0.001834\n",
       "1  0.025729  0.021338  0.004686  0.001822\n",
       "2  0.025105  0.020901  0.004580  0.001783\n",
       "3  0.024517  0.020494  0.004473  0.001749\n",
       "4  0.024026  0.020282  0.004464  0.001989"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_testLoss = pd.DataFrame(data=testloss_unsqueezed,columns=['mse1','mse2', 'mse3', 'mse4'])\n",
    "df_testLoss.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_testLoss.to_csv('RNN Paper Results/Exp2_1/' + test_location + '/' +  test_year + '_TestLoss.csv')\n",
    "if run_train:\n",
    "    df_trainLoss.to_csv('RNN Paper Results/Exp2_1/' + test_location + '/'+  test_year + '_TrainLoss.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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