fixed-time-horizon-prediction/Exp_1_RNN_Bondville.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 1 Bondville 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": 44,
   "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": 45,
   "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": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "sns.set_color_codes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pvlib\n",
    "from pvlib import clearsky, atmosphere\n",
    "from pvlib.location import Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "bvl = Location(40.1134,-88.3695, 'US/Central', 217.932, 'Bondville')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2009 = pd.DatetimeIndex(start='2009-01-01', end='2010-01-01', freq='1min',\n",
    "                        tz=bvl.tz)   # 12 months of 2009 - For testing\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_2009 = bvl.get_clearsky(times2009) \n",
    "cs_2010and2011 = bvl.get_clearsky(times2010and2011) # ineichen with climatology table by default\n",
    "#cs_2011 = bvl.get_clearsky(times2011) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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": 113,
   "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": 114,
   "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": 115,
   "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": 116,
   "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": 117,
   "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": 118,
   "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": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ghi</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01 00:00:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01 00:01:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01 00:02:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01 00:03:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-01 00:04:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      index  ghi  year  month  day  hour  min\n",
       "0 2010-01-01 00:00:00-06:00  0.0  2010      1    1     0    0\n",
       "1 2010-01-01 00:01:00-06:00  0.0  2010      1    1     0    1\n",
       "2 2010-01-01 00:02:00-06:00  0.0  2010      1    1     0    2\n",
       "3 2010-01-01 00:03:00-06:00  0.0  2010      1    1     0    3\n",
       "4 2010-01-01 00:04:00-06:00  0.0  2010      1    1     0    4"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cs_2010and2011.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>2009-01-01 00:00:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</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>2009-01-01 00:01:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</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>2009-01-01 00:02:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</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>2009-01-01 00:03:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</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>2009-01-01 00:04:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</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 2009-01-01 00:00:00-06:00  0.0  2009      1    1     0    0\n",
       "1 2009-01-01 00:01:00-06:00  0.0  2009      1    1     0    1\n",
       "2 2009-01-01 00:02:00-06:00  0.0  2009      1    1     0    2\n",
       "3 2009-01-01 00:03:00-06:00  0.0  2009      1    1     0    3\n",
       "4 2009-01-01 00:04:00-06:00  0.0  2009      1    1     0    4"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cs_2009.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": 120,
   "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1050402, 48)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Bondville\\\\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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>jday</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>min</th>\n",
       "      <th>dt</th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>dw_solar_QC</th>\n",
       "      <th>...</th>\n",
       "      <th>temp</th>\n",
       "      <th>temp_QC</th>\n",
       "      <th>rh</th>\n",
       "      <th>rh_QC</th>\n",
       "      <th>windspd</th>\n",
       "      <th>windspd_QC</th>\n",
       "      <th>winddir</th>\n",
       "      <th>winddir_QC</th>\n",
       "      <th>pressure</th>\n",
       "      <th>pressure_QC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>105.07</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.9</td>\n",
       "      <td>0</td>\n",
       "      <td>75.9</td>\n",
       "      <td>0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0</td>\n",
       "      <td>304.1</td>\n",
       "      <td>0</td>\n",
       "      <td>996.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.017</td>\n",
       "      <td>105.25</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.9</td>\n",
       "      <td>0</td>\n",
       "      <td>75.7</td>\n",
       "      <td>0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0</td>\n",
       "      <td>302.6</td>\n",
       "      <td>0</td>\n",
       "      <td>996.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.033</td>\n",
       "      <td>105.44</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.9</td>\n",
       "      <td>0</td>\n",
       "      <td>75.7</td>\n",
       "      <td>0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0</td>\n",
       "      <td>301.5</td>\n",
       "      <td>0</td>\n",
       "      <td>996.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.050</td>\n",
       "      <td>105.62</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.9</td>\n",
       "      <td>0</td>\n",
       "      <td>75.6</td>\n",
       "      <td>0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0</td>\n",
       "      <td>301.4</td>\n",
       "      <td>0</td>\n",
       "      <td>996.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.067</td>\n",
       "      <td>105.80</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.9</td>\n",
       "      <td>0</td>\n",
       "      <td>75.8</td>\n",
       "      <td>0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0</td>\n",
       "      <td>302.8</td>\n",
       "      <td>0</td>\n",
       "      <td>996.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  jday  month  day  hour  min     dt     zen  dw_solar  dw_solar_QC  \\\n",
       "0  2010     1      1    1     0    0  0.000  105.07      -4.1            0   \n",
       "1  2010     1      1    1     0    1  0.017  105.25      -4.1            0   \n",
       "2  2010     1      1    1     0    2  0.033  105.44      -4.1            0   \n",
       "3  2010     1      1    1     0    3  0.050  105.62      -4.1            0   \n",
       "4  2010     1      1    1     0    4  0.067  105.80      -4.1            0   \n",
       "\n",
       "      ...       temp  temp_QC    rh  rh_QC  windspd  windspd_QC  winddir  \\\n",
       "0     ...       -8.9        0  75.9      0      6.4           0    304.1   \n",
       "1     ...       -8.9        0  75.7      0      6.5           0    302.6   \n",
       "2     ...       -8.9        0  75.7      0      6.6           0    301.5   \n",
       "3     ...       -8.9        0  75.6      0      6.9           0    301.4   \n",
       "4     ...       -8.9        0  75.8      0      6.7           0    302.8   \n",
       "\n",
       "   winddir_QC  pressure  pressure_QC  \n",
       "0           0     996.1            0  \n",
       "1           0     996.1            0  \n",
       "2           0     996.2            0  \n",
       "3           0     996.2            0  \n",
       "4           0     996.2            0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_big_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524936, 48)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = r'.\\\\data\\\\Bondville\\\\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": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 48)"
      ]
     },
     "execution_count": 122,
     "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": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1050402, 50)"
      ]
     },
     "execution_count": 123,
     "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": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>jday</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>min</th>\n",
       "      <th>dt</th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>dw_solar_QC</th>\n",
       "      <th>...</th>\n",
       "      <th>temp</th>\n",
       "      <th>temp_QC</th>\n",
       "      <th>rh</th>\n",
       "      <th>rh_QC</th>\n",
       "      <th>windspd</th>\n",
       "      <th>windspd_QC</th>\n",
       "      <th>winddir</th>\n",
       "      <th>winddir_QC</th>\n",
       "      <th>pressure</th>\n",
       "      <th>pressure_QC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>105.13</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1001.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.017</td>\n",
       "      <td>105.32</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>76.1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0</td>\n",
       "      <td>268.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1001.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.033</td>\n",
       "      <td>105.50</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>77.3</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>272.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1001.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.050</td>\n",
       "      <td>105.68</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0</td>\n",
       "      <td>273.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1001.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.067</td>\n",
       "      <td>105.86</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>278.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1001.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  jday  month  day  hour  min     dt     zen  dw_solar  dw_solar_QC  \\\n",
       "0  2016     1      1    1     0    0  0.000  105.13      -3.4            0   \n",
       "1  2016     1      1    1     0    1  0.017  105.32      -3.6            0   \n",
       "2  2016     1      1    1     0    2  0.033  105.50      -3.6            0   \n",
       "3  2016     1      1    1     0    3  0.050  105.68      -3.7            0   \n",
       "4  2016     1      1    1     0    4  0.067  105.86      -3.8            0   \n",
       "\n",
       "      ...       temp  temp_QC    rh  rh_QC  windspd  windspd_QC  winddir  \\\n",
       "0     ...       -2.2        0  76.5      0      6.8           0    270.0   \n",
       "1     ...       -2.2        0  76.1      0      6.4           0    268.2   \n",
       "2     ...       -2.2        0  77.3      0      6.0           0    272.1   \n",
       "3     ...       -2.2        0  76.7      0      6.6           0    273.9   \n",
       "4     ...       -2.2        0  77.0      0      6.0           0    278.4   \n",
       "\n",
       "   winddir_QC  pressure  pressure_QC  \n",
       "0           0    1001.5            0  \n",
       "1           0    1001.5            0  \n",
       "2           0    1001.5            0  \n",
       "3           0    1001.5            0  \n",
       "4           0    1001.5            0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_big_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>2009-01-01 00:00:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</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>2009-01-01 00:01:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</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>2009-01-01 00:02:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</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>2009-01-01 00:03:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</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>2009-01-01 00:04:00-06:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</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 2009-01-01 00:00:00-06:00  0.0  2009      1    1     0    0\n",
       "1 2009-01-01 00:01:00-06:00  0.0  2009      1    1     0    1\n",
       "2 2009-01-01 00:02:00-06:00  0.0  2009      1    1     0    2\n",
       "3 2009-01-01 00:03:00-06:00  0.0  2009      1    1     0    3\n",
       "4 2009-01-01 00:04:00-06:00  0.0  2009      1    1     0    4"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cs_2009.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524936, 50)"
      ]
     },
     "execution_count": 126,
     "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": 127,
   "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": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1050402, 49)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Managing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows with two or more -9999.9 values in columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1046527, 49)"
      ]
     },
     "execution_count": 131,
     "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": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(516826, 49)"
      ]
     },
     "execution_count": 132,
     "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": 133,
   "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": 134,
   "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": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_miss_train_idx = np.where((df_train <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24739"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_miss_train_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1046527, 49)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:132: 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": 139,
   "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": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = np.where((df_train <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# doing the same thing on test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15271"
      ]
     },
     "execution_count": 143,
     "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": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:132: 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": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = np.where((df_test <=-9999.9).apply(sum, axis=1)==1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1046527, 49)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(516826, 49)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E8uXfMnz4A7Ru3RaXy8XKld8zduzjjBs3kauu6uLHWYJv3atgIEGPEEKIoDFj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\nM7Na+A+mHVkonnifbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b08f4aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.1,0.1,0.8,0.8])\n",
    "#axes2 = fig.add_axes([0.1,0.1,0.8,0.8])\n",
    "\n",
    "axes1.scatter(j_day,dw_solar_everyday,label='Observed dw_solar',color='red')\n",
    "axes1.scatter(j_day, ghi_everyday, label='Clear Sky GHI',color='green')\n",
    "\n",
    "axes1.set_xlabel('Days')\n",
    "axes1.set_ylabel('Solar Irradiance (Watts /m^2)')\n",
    "axes1.set_title('Solar Irradiance - Test Year 2009')\n",
    "axes1.legend(loc='best')\n",
    "\n",
    "fig.savefig('Figure2.png', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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awNe//pVuQcPrbeeOO27jggvO5vTT53PllZfx3nvvdB1fuHAuS5Y8wjnnnMF1\n111HKBTizjt/zvnnL+Kss07hBz/4LvX1dV3nv//+e1x11Rc488yT+eIXL+fdd9/uOnb99d/giSf+\nwHe/ez1nnnkyn//8paxataLHa51//iJ++MP/16Ovd9xxGwsXzu3x6zOfuajXn82mTR8ze/acru+d\nznimTp3Gxo0bepy7a9dOWlqau43qnHXWOfz6178BIDk5FYB///s5TNNk48YNlJeXMXXqtG7vQ28h\nbl/f//jHR7nuuqtZtGgB3/rW1ykvL+313FdeeZGFC+dyyinzuvXzlFPmUV1d3eP81NRUWlqau37W\nS5e+g8/nZerUqb1ef39LljzM8cfP49hjj+96rK2tjdtv/wmLF5/KJZecy3333UVHR8dBr7F79y5y\ncnK7plgPVFVVRTAYZMmSp8jLy//ENknsUI2WiIwZjz/+GLff/kuCwSC33/5TXC4X11xzLdu3b+OO\nO/6Xm276MdOmTWfFivf5/vdv4PHHn6agYDz33Xc3FRV7uO++3xEfH89TT/2JO+/8BfPnL8Buj/41\n+v777/Hoo4+TmOjg2Wf/xvr1H3Hvvb/D6XRyzz2/5De/+TW33fZ/rFnzIbfcchPf/OYNnHjiybz/\n/lJuvfWH/P73j3cFkj//+Y/8v//3A773vR/w8MMP8qtf/YJnn/13Vz/ef/89Hn74j4TD4R59vPHG\n73Httdf3eHzfyMmBGhrqycrK7vZYRkYGdXW1Pc6tqqokOTmFDRvW8/vf/xaPx8Npp53Btdd+m7i4\nOHJzc7nmmuv43e/u58EH78U0Ta666utdAWXcuFxeeOE1kpNTDvoePfnk41x77fX84Ae3sGTJ7/ne\n927gL3/5R9fPeZ8zzzybE088qddrpKWl93hs9uw5XHLJZfzkJzd31Ujdcsv/UlJSQlNT+0HbU11d\nzZtvvs4jj/yx2+P/93+3EYmYPPzwH+no8HPffXdz3313cfPNt/R6nbKy3djtNm666TsYxmYKC4v5\n5jdv4KijZgAwefIU7rzz3oO2Q2KXRrREZMy47robmDlzFnPmHMfVV/8P//73cwD89a9PcuGFl3Lm\nmWdTUDCeT3/6s5xwwnyee+4fAMyZcxzf//6PmDRpMgUF4/nc566kpaWZpqbGrmtffPGnKSwsYtKk\nSezdW4XT6WTcuFyKior50Y9u5Qtf+AoA//znM5x++iIuu+xzjB9fyGc/eyWnnnoGTz/9ZNe15s9f\nwDnnnEet6T7fAAAgAElEQVR+fgFf/vLXqK2toaGhvttrjR9f2KO+B8DlSuw2NbXvV2pqWq8/E7/f\nj8Ph6PZYXJyDQCDY41yv14vf7+ORRx7k+uu/y49+dCvvv/8ev/3t/QCEQiHKy0u5+OLLeOyxJ/jW\nt77Dk08+3jWCZbFYSE/P6BGa9nfiiSdx2WWfo6iohJtvvgWPx8OHH67ocZ7D4ei1n+npGVgsll7b\nXlVVyde+9g0ee+wJvvSlq7jnnl+xe/fug7YF4KWXnmfatOlMmza967HKygqWLVvKT35yGxMmTGTa\ntOl8//s/4uWX/43X23toKysrpa2tjQsvvIS7736AkpKJ3HDDdb0GWhldNKIlImOCxWLh6KNndX0/\nbdpReDxNNDd7KCsr5e233+wKVgDhcIgTTpgPwDnnnMfSpe/w/PP/pLy8FMPY0nnOf6cec3Pzur6+\n+OJP88Ybr3PRRYuZM+c4TjnlND71qQuA6AfuxRd/ulvbjj56Fi+//N8Rq/HjC7u+TkyM1gSFQqFe\nX+tAd9/9f7z22is9Hs/Ly+OJJ/7W43GHw0EgEOj2WDAY6HXUyWazEQgEuPHGm5g9+xgAvvWtG7nt\ntlu48cbv8eqrL2IYW7peZ8oUN7t37+Kpp/7EMccce9A27+/oo2d3fe1yuSgsLKK0tJT58xd0O+/1\n11/lrrvu6PF8i8XCk08+Q07OuG6P/+UvTwDw5S9/rattmzdv5IknnuDb3z548f8777zFxRdf1u2x\nsrJSIpEIF130qR7nV1Ts4YUX/tX1Huxrzw9+8BP8fj8ulwuA733vB3z88XpeffVlvvjFrxz09SX2\nKWiJyJix/0jKvpBksVgJh0NceeWXOeec87qd73TGA3D77T9l06aPWbz4XC655DIyMrK49tqrup27\n/6jQhAkT+fvfX2D58mV88MEyHnnkd7z55us8+ODve4weQbTIfP/QdmAdj2ma7L/KQG/X2Ofqq6/l\n85//4iH7vr/s7BwaGxu6PdbQ0MCUKe4e52ZlZQFQVFTc9VhRUQmBQICmpiYMw2DixMndnjN1qpuN\nG3svOO/Nge2MRMJYrT1HqBYuPJUZM2b2eo0Dp0IBDGMLkyd3r8eaOtVNRUXZQdtSW1tDWVlpjzsN\nw+EQSUnJLFny5x7LP2Rn5/R4D7KysrFarV0ha5/i4hLq6zWiNdopaInImGCaJtu3b+sq/N66dTNZ\nWdmkpKRQVFTM3r1VFBSM7zr/d7+7n6KiEs44YxFvvvkajz76BG53tIZq+fJl+67a62u98sqLWK1x\nnHnmWZx22pls2rSRa6+9iqamJgoLi9m0aSOX7TdIsnHjx93Cy4F6mwo7mLS0NNLSep8m7M2MGUez\nYcO6ru/9fj/btxt87Wvf6HHu1KnTsNvj2LFjG3PnngBE7zR0uVykpqaSlZXFxx93D1VlZbvJyzv4\nmlwH2r59W9fXbW1tVFRUMGnSlB7nJSQkdHu/PklWVjalpbsOaFspxcWFB3kGbN68kZyccT1Gx4qK\nSmhvbwPoasPOnTtYsuQRfvzjW3t9D7797f9hzpzjutbGMk2TnTu3c+mll/e5DxKbVKMlImPG/fff\nzdatW/jww5UsWfIIl176GQAuv/xK3nrrdZ599q9UVlbwzDN/4ZlnnqaoqBiHw0lCQgLvvPMW1dV7\nWblyOffeexdAjym3fdra2njggbtZs+ZDqqoqef31l8nOziEtLY3PfvYK3nnnLf7+979SUbGHv/3t\nKZYufburLb05cNRkMJ133oV8/PF6nnrqT+zevYs77riNgoLxzJlzHAA+nw+PpwmITmNecMFF3Hff\nXWzatJGNGzfw0EMPcsEFl2C1Wjn77E9RWVnBww8/SGVlBa+++hIvvvg8n/nM54DoyF1jY0O3adAD\nvfHGa7z66kuUlu7ml7/8Gfn5+d3u9huo88+/mOXL3+eZZ56mqqqSZ575CytXLueKK64AolOzjY0N\n3e5E3bVrJyUlE3pcq7i4hHnzTuS2225h69bNGMZW7rjjNvx+P4mJSb2+/sknL+SZZ55m2bKllJeX\ncc89d9LW1sa5515w2H2TkU0jWiIyZpxxxlncdNONmKbJJZdc1lWgPmPGTG655Wf84Q+P8Lvf/YaC\nggJuu+0OZs2K1iH95Ce38+CD9/Hss38lLy+fr3zlah599CG2bzcoKiruMeJ02WWfpaamlp///FZa\nWpqZNm06v/zlr7FYLEyf/t/Xeuih31BUVMztt/+yK9j0Nnq1/2P9Gd3qi9zcPH7xi7u4//67efzx\nxzj66NncccfdXceffvrPvPrqS7z99n8AuP767/LQQw/w/e/fAMDixefyjW98E4C8vHzuvfe3/Pa3\n9/Kvf/2dceNy+eEPf9o1+lVbW8NnPnMhv/nNIwet2Tr77HN4/vl/cs89v+SYY47lrrvuP+gdk/0x\nY8ZMfvGLu3jssYd47LGHKSoq5t57f8OkSZNoamrn44/Xc8MN1/LMMy+Qmxtdx6qxsfGgd0j+9Ke3\nc++9d3Hjjddhs9k48cSTuOGG7x/09T/72SsJBILcd99dNDU1Mn36TO6//yESEhJ6nDvY77EML8tQ\n/ktplDCbmtoJhSKffGaMsNutpKcnon7FhtHYryPdp+rqvVx++UXdPkSHwmh8r2Bw+7VkySPMn38y\n06f3rK+6/vpvcOyxxx+xrWdG+fs14LRWV9eqYNAH2dnJffoZa+pQRMYE/aNy+Hm97axdu7prvTCR\nsUBThyIyJmg6Zvi5XIk88MDD2Gy2Xo/rPZLRSEFLREa93Nw8li5dNdzNEDhoyAJ44IGHj2BLRI4M\nTR2KiIiIDBEFLREREZEhoqAlIiIiMkQUtERERESGiIKWiIiIyBBR0BIREREZIgpaIiIiIkNEQUtE\nRERkiChoiYiIiAwRBS0RERGRIaKgJSIiIjJEFLREREREhoiCloiIiMgQUdASERERGSIKWiIiIiJD\nREFLREREZIgoaImIiIgMEQUtERERkSGioCUiIiIyRBS0RERERIaIfbgb0Bdut/ti4J+ACVg6f/+H\nYRiXu93uEuBRYD5QCnzHMIw39nvuIuBeYCKwHLjGMIzdR7QDIiIiMibFyojWdOAFILfzVx5wdeex\n54Eq4DjgSeBfbrd7PIDb7S4E/gUsAY4H6oHnjmjLRUREZMyKiREt4Chgo2EYdfs/6Ha7zwAmACcY\nhuEHful2u88ErgJ+BlwDfGgYxn2d538VqHa73acYhrH0iPZARERExpxYGtHa1svjJwBrO0PWPsuI\nTiPuO94VqAzD8AFr9zsuIiIiMmRiZUTLDZzjdrt/DNiAvwM/JTqFWHXAuTXA+M6vP+m4iIiIyJAZ\n8UHL7XYXAQmAD/gM0anCBzofcwEdBzylA3B2fv1Jx/vEZouVgb++2dcf9Ss2jMZ+jcY+gfoVa0Z7\nvwbKarVgtVoGqTUy4oOWYRjlbrc70zAMT+dDG9xut41o4fsfgfQDnuIEvJ1f++kZqpxAU3/akJKS\n0L9Gxwj1K7aMxn6Nxj6B+hVrRlO/TNOktq6RkxZ/rnDj8uf3DOQaGRmJWCwKWoNlxActgP1C1j5b\ngHigmmih/P5ygb2dX1d2fn/g8Y/68/otLT7C4Uh/njKi2WxWUlIS1K8YMRr7NRr7BOpXrBlt/Wpv\n99LgacORkIgjMWPASamxsV0jWn2Qnp7Yp/NGfNByu91nA38Bxu9X9D6H6FIN7wHfc7vdTsMw9k0R\nLuh8HGBF5/f7ruXqfO6t/WlDOBwhFIr9P4QHUr9iy2js12jsE6hfsSbW+xUOh6mr9xAwLTgcLkzz\n8K4XiZhEIod5Eeky4oMW8AHRqcDH3G73z4BJwK+AO4neUbgHeNztdt8OXAjMBb7S+dw/EA1iNwEv\nEg1YOw3DePeI9kBERGQIeJpbaGnzExefiEPTfSPSiK8ANAyjDVgMZAMfEl0F/mHDMO4xDCNCNFzl\nAquBK4CLDcOo6HxuGXAp0XW1VgFpwCVHvBMiIiKDKBDooLK6jja/iSMhSTVVI1gsjGhhGMYWomGr\nt2O7gNMP8dzXgGlD1DQREZEjxjRNGpo8eP1hHPF9qxGS4RUTQUtERGSs83p9NHhascYl4Ijv1ypF\nMowUtEREREawcDhMXYOHQBgc8UnD3RzpJwUtERGREaq5pZXmVl+02N2uOqxYpKAlIiIywgQCAeoa\nmzEtDhwJGsWKZQpaIiIiI4Rpmniam2nxBnEOoNi9vKaVNdvqhqBlMlAKWiIiIiOA1+ujobkNq93Z\n75DV1NrBa6vK2bCzYYhaJwOloCUiIjKMuhW7O/sXsDqCYZauq+K9DVWEwlrNfSRS0BIRERkm3VZ2\n70exe8Q0+WhbHa9/uIdWb3AIWyiHS0FLRETkCPN3dFDf1ILF2v9i9917W3hpeRlV9e09jrni7Sye\nV8iD6werpXK4FLRERESOkEgkQn1jE76gBWc/pwkbW/y8urKcjbsbexyzWS2cNDOX0+YUkOSK48HB\narAcNgUtERGRI6CltRVPqx+7IwGns+9bDfsDId75qIr3P95LONKzDmt6STqfOqGYzNT4wWyuDBIF\nLRERkSEUCASob2whYonr1/6EkYjJms46rHZfzzqsvEwX584vZlJ+6mA2VwaZgpaIiMgQME2TxiYP\n7f4wjngXfR/Dgp1Vzby8vIy9Dd4ex5IS4jh7biHHTs3GatVq8SOdgpaIiMgga2/30tjc1u8NoBua\n/byysozNpU09jtmsFk4+Oo/T5uQT79DHd6zQOyUiIjJIwuEwdfUegqaFuH5sAO3rCPH2R5Us31jd\nax3WzIkZnDOviIwU1WHFGgUtERGRQdDc0kpzm584p4s4S9+m9MIRk9Vba3lj9R68/lCP4/lZiZw3\nv5gJeSmD3Vw5QhS0REREDkMg0EFdYwtYHf0qdt9e4eHl5WXUNPl6HEt2Reuw5kzNxtrH0CYjk4KW\niIjIAJimSUNnsXt/9ias8/h4ZUUZW8s9PY7ZbRYWzMrn1GPyccbZBrO5MkwUtERERPqpvd1LQ3Mb\ntrgEnH0sdvd1hPjPmgqWb6ohYvasw5o1KZPF84pIT+578byMfApaIiIifRQKhahvaCZoWnD0sdg9\nHImwanMtb66pwNfRsw5rfHYi580voTg3ebCbKyOAgpaIiEgfNHlaaPV29KvY3Shv4uUVZdR5/D2O\npSQ6WDyvkNmTswa1DivQ4Sfo9XQM2gXlsChoiYiIHEIg0EFtQzMWm7PPxe41TV5eWVHGtj3NPY7F\n2aycckw+C2fl4RjEOqxgwI+NCDlZKXz8wb9qBu3CclgUtERERHqxr9jd64/0eZqw3R/krdUVrNpS\nQy/LYXHM5CwWzyskNWnw6rACAT92ImSmJuJKSMBu788a9DLUFLREREQO0N7upam5HUtcPI74Tx51\nCoUjrNxcw1trKvAHwj2OF41L4rz5xRTmDF4dVjDYgdUMkZWahCshYdCuK4NLQUtERKRTOBymrsFD\nMGIhrg/ThKZpsrXcwysryqhv7lmHlZbkYPG8ImZNysQySHVY+wJWRnIiiYnpg3JNGToKWiIiInSu\n7N7qIy4+kTj7J4ei6kYvLy8vY0dlzzosh93KqccUsGBWHnGDNJUXDASwEFTAijEKWiIiMqYFAh3s\nrfVgWhw4Ej65FqvNF+TN1Xv4cGstBy6HZQGOnZrNWXMLSUl0DEr7/huwXCQmpg3KNQ/lL29s44qz\npg7564wVCloiIjImmaZJXX0je+tbsTk+eZowFI6wfGM1/1lbSUewZx1WcW4y588vpiC775tJH/L1\ngkGIBEhPSSDpCASsff6ztlJBaxApaImIyJjj9fpobm0nLSsDhzOBcLiXWwQ7mabJlrLoeliNLT2X\np0pPdnLOCUXMnJAxKHVY+wJWanI8yUnZh329/oqYJqZpDlpN2VinoCUiImNGJBKhtr6JQBgSXInY\nbIe+o3BvQzsvLS9jV1VLj2OOOCunzyngpJmDU4cVCgYxIwHShilg7c8kOg0qh09BS0RExoSW1lY8\nrX7inC4cn1Ds3uoN8MbqCtZsreXAsS4LcJw7WoeV7Dr8OqzoCFaQlCQnKcnDG7D2MU0TNKI1KBS0\nRERkVAsEAtQ3thDG/okruwdDET7YuJd3PqrqtQ5rQl4K580vJj+rbyvEH0r3Eaysw77eYIpEwKZ1\nTweFgpaIiIxKpmniaW6mxRvEGZ/IoXKDaZps3N3IqyvLaWrtWYeVkeLkUycUM70k/bBrl0bSFOHB\nmAfeTikDpqAlIiKjjs/vp6GpFYvdifMTRrEq6tr497JSSqtbexxzxtk449gC5s/MxX6YQzyxELD2\niShoDRoFLRERGTUikQj1jU34ghaczkMHrJb2AM8tK2XFx3t71mFZYO60HBYdX0hSQtxhtWm47yIc\niNAh7sKU/lHQEhGRUaGtvY3GZh92RwJO58FHn4KhCO9tqGLpuioCoUiP45MLUjl3fjG5Ga7Dak8s\nBqx9gr38XGRgFLRERCSmhUIh6ho8hLAdstjdNE027Gzg1ZXlNLcHehzPSo3n3BOLcRelHVYdVixN\nER5MINTzRgAZGAUtERGJSdFi9xZa2gM44l3EHSIc7alt5aXlZZTXtPU4luCwccZx4zlh+rjDqsMK\nh4JEwrEdsPYJBjWiNVgUtEREJOZ0K3ZPOPgoVnNbB6+t2sO6HfU9jlktcMqc8ZwyO5f4uIF/HIZC\nQQgFSE6KJzUltgPWPr1NqcrAKGiJiEjMiEQi1DU04Q8dutg9EAzz3oa9LF1XRTDcMzRMGZ/KBSeX\nMHVCFs3N3kNuwXMw4XAIM9hBclI8KclZo2rLmkAva4jJwChoiYhITGhuaaWlzY/d6cLp7D3UREyT\n9TvqeW3VHlp6qcPKTkvg3BOLcBelY7MNLBiFwyEiwQ6SE52kZo2ugLVPb4u1ysAoaImIyIgWCHRQ\n19iCaXEQd4hi9/KaVl78oJSKuvYexxKcdhYdN55503OwWQdWhxUOhwkH/SS7nKSN0oC1T28hVQYm\npoKW2+1+CagxDOOqzu9LgEeB+UAp8B3DMN7Y7/xFwL3ARGA5cI1hGLuPcLNFRGQATNOkocmD1x8+\n5N2ETa0dvLaqnA07G3ocs1osnDhjHGccOx5X/MA+8sLhMJGgnyRX3KgPWPt4FLQGTcwELbfb/Tng\nU8Dj+z38HLAeOA64BPiX2+2eZhhGhdvtLgT+BfwEeA24tfP82Uey3SIi0n/t7V4am9uwxiXgiHf2\nek5HMMzSdVW8t6Gq1wU2pxWl8akTi8lOSxhQGyKRCKEOL0kuB2mZmVgHOBIWi5rbem5DJAMTE0HL\n7XanA78CVu332BlER6pONAzDD/zS7XafCVwF/Ay4BvjQMIz7Os//KlDtdrtPMQxj6ZHug4iIfLJQ\nKER9QzNB00JcfFKv50RMk4+21fH6h3to9QZ7HM9JT+C8+cVMGZ82oDZEIhFCAR+ueDu5eVljKmDt\n09ymEa3BEhNBC7gbeAIo2O+xE4C1nSFrn2VEpxH3He8KVIZh+Nxu99rO4wpaIiIjTJOnhZb2jkOu\nibV7bwsvLS+jqr5nHZYr3s5Zxxdy/LQcbNb+T+/tG8FyJcSRmzu2RrAOVNPkG+4mjBojPmh1jlwt\nBI4GHt7vUB5QdcDpNcD4Ph4XEZERwN/RQX1TCxar46BrYjW2+Hl1ZTkbdzf2OGazWpg/M5fT5xSQ\n4Oz/x1okEiHgaycxIW7MjmAdqLqxnXAkMuAbB+S/RnTQcrvdTqLh6jrDMDrcbvf+h13AgZPIHYCz\nj8dFRGQY9WUDaH8gxLvrqli2YS/hSM86rOkl6XzqhGIyU+MH9PodvnbibWGyFbC6CYVNKuvaKRqX\nPNxNiXkjOmgB/0u0zurNXo75gYwDHnMC3v2OHxiqnEBTfxthO4wtGUaiff1Rv2LDaOzXaOwTqF/9\n0dLaSlOzD7szAVdcz+tGIiarjVpeW7mHNl/POqy8TBfnn1TC5PGp/X7tfTVYqUnxFI/Poa2tg3Av\ni5rGqsF6n3ZUNTOxoP8/X+lupAetzwLj3G53a+f3TgC3230ZcAcw/YDzc4G9nV9Xdn5/4PGP+tuI\nlJSB3bEy0qlfsWU09ms09gnUr0MJBALU1HswbQ6yc3svdjfKGvn7W9upqO25L2GyK46LTp3ESUfn\nY+1nHVYkEiHg95GS6CAzI6drBGu0vl8DNT4niYraNrZXtPDZsw++rIb0zUgPWqcCcft9/yvABG4C\nSoAfuN1up2EY+6YIFwDvdX69ovN7ANxutwuYQ3SZh35pafGNun/tpKQkqF8xYjT2azT2CdSvQzFN\nk8YmD23+MA5nAhACX6jbOfXNPl5eXsam3T0nHmxWCwtn53H6sQXEO+y0tva9WDsSiRAM+ElKsJOR\nlorVaqW52Tfq36+Bmjkhg4raNtZuraWqunlAdW9jQXp630LoiP7pGYaxZ//vO0e2TMMwdrvd7jJg\nD/C42+2+HbgQmAt8pfP0PwDfc7vdNwEvEg1YOw3DeLe/7QiHI4RG4Qab6ldsGY39Go19AvXrQPuv\niWWzx/XYV9AfCPH22ko+2Fjdax3WzAkZnHNCERkp8Z3t6Nu+hKZpEvB7SYy3k5WVhs1mIxKJBq/B\n6NdodZw7m1dXlhMMR1i5qYYFs/KGu0kxLWYLCQzDiAAXEZ0OXA1cAVxsGEZF5/Ey4FKi62qtAtKI\nLmoqIiJHQDgcprqmgcZWP3HxSdhstu7HIyYrN9dwz1/X8V4vxe75mS6uPn86V5w1tStk9YVpmnT4\n27GZfsbnZpCVmd7jteXgJualkJfpAuCN1Xswzf5vuC3/NaJHtA5kGMZXD/h+F3D6Ic5/DZg21O0S\nEZHumjwttHo7iHP2vibW9goPLy8v63W9puSEOM6eV8icqdlY+7Hdzb4RrHiHlYKcdOz2mPqIGzEs\nFguLji/kz68Z7KltY+PuRo6emDnczYpZ+r9QREQGTUdHB3Wda2L1tj9hncfHKyvK2Fru6XHMbrOw\nYFY+px6TjzOufyNQAb8XZ5yFgnEKWIPhpJm5PP/eLlq8Qf761naOKk7HPsrupj1S9H+jiIgctkgk\nQkOjB1/A7DVg+TpC/GdNBcs31RDpZSpq1qRMFs8rIj25f0sdBvw+nHGQl51KXFzcJz9B+sQZZ+PT\np07ij69sZW+DlzdXV3DOCUXD3ayYpKAlIiKHpa29jcZmH3ZHAo747qMe4UiEVZtreXNNBb6OUI/n\njs9O5Lz5JRTn9m9hzA6/D6cNcrOScDi0DvVQOHlWHm9/VElpdSv/eHcn7qI0JuSlDHezYo6CloiI\nDEgoFKKuwUMIe6+jWNv2eHhpeRl1np51WCmJDhbPK2T25Kx+1WF1dPhxWiPkZSUrYA0xq8XCNRdM\n52ePr6YjGOah5zby06/MJSlBI4f9oaAlIiL9YpomnuZmWrxBnPGJHPixW9Pk5ZUVZWzb09zjuXE2\nKwtn53HK7Hwc/ajDCnT4iLOa5GYk43QqYB0peZmJfGmxm0df3Ex9s59f/20d3//8HK2t1Q/6SYmI\nSJ/5/H4amlqx2J04DxjF8vqDvLmmglWba+hlOSyOmZzF4nmFpCb1PSgFOnzYLSbZ6UkkxPd/P0M5\nfPNn5lJW08rrH+6htLqV+5/dwHc+MxunQ0tm9IWCloiIfKJIJEJNXQNtfrPHBtChcISVm2t4a00F\n/kC4x3MLc5I4/6RiCnP6XocVCPixE1HAGiE+e8ZkfB0h3tuwl217PNz5l7XccNmsfoXmsUpBS0RE\nDqmltRVPayshHDid/62nMk0To9zDyyvKqG/293heaqKDc04oYtakTCx9rMMKBjuwmWGyUhNxJWgP\nwpHCYrHw5XOmEQpHWL6phtLqVn7+xBpu/MwsCrJ737NSohS0RESkV4FAgPrGFrA7yMpKxRfwdm1/\nU93o5eXlZeyo7KUOy27l1GPyWTArD4e9b9NLwWAHVjNERnIiiYmuQe2HDA6r1cLXzp9OZmo8L35Q\nRkOLn58/sYYvnD2Vk2bm9jlMjzUKWiIi0s2Bxe42238/QNt8Qd5cvYcPt9bS284sx07N4uy5RaQk\nOvr0Wt0DVvpgdUGGiNVi4dJTJpGVmsCfXzPoCIZZ8tIWNu1u5IuL3SqS74V+IiIi0sXr9dHQ3Ib1\ngGL3YCjCu+uqeGt1BR3BnnVYxbnJnDe/mPF9nEYKBgNYzaACVow6ZXY+hTlJPPL8Jmo9PlZsrmF7\nhYcvnO1m9uSs4W7eiKKgJSIihMNh6ho8BMLg2K/Y3TRNNu1u4pUV5b2uh5We7OScE4qYOSGjT1NH\nwUAAC/sCVtqg9kGOrAl5Kdz61bk8+brB8k01NLR0cP+zGzjenc3nF03t9yr/o5WClojIGNfc0kpz\nq4+4+EQc9v+Gpb0N7by0vIxdVS09nuOIs3L6nAJOmplHnP2T98D7b8ByKWCNIglOO9dcMIM5U7J5\n6s1tNLcFWG3Usam0kQtOmsCZxxUQ18c6vdFKQUtEZAwKhsKs3lLFrooGsjJSmDEhu2tEqtUb4I3V\nFazZWsuBZVgW4LhpOZx1/HiSXZ9chxUKBiESID0lgSQFrFHr+Gk5TC/J4J9Ld/L22kp8HWGeeXsH\n/1lbwaWnTmTeUeP6tQPAaNLvoOV2ux3AAmA+kAtEgCpgJbDUMIzIoLZQREQGVUcgyEP/WE21J4g9\nzgmVdWzd08qFJ5Wwamst73xU1Wsd1sT8FM49sZj8rJ7b7RwoHAoSCQdIS44nOSl7KLohI4wr3s4X\nznZz0sw8nn5rGzsrW6hv9vP7Fzbzxod7uPTUSUwvTh9zdydazN5uG+mF2+1OA24EvgmkA7uBOsAG\n5ABFQBPwW+BewzA8Q9HgYWA2NbUTCo2e/Gi3W0lPT0T9ig2jsV+jsU/8f/beOzyu+7zz/ZwzZ/qg\nDgAC7P2wiKRIFUqybMtFsiTL3bFvcp04cuxsyjqbxNnk3t2N45vdm+uUvet1nCc3iW1tEudJ4lgu\nsV/1SF8AACAASURBVKppR9Vq7KBYDkmwgChEGcwMMP20+8eZGU4DMAAHAEH9Ps8DCzhzyu93hj7z\nnfd9f9+X5TGv+OQUr5wY4uVT0eIHnm3bZHImOd0kmalu/Bxu9vIz71VZ3xXAmmVapqFjGzmaQj5a\nmufWKHqxWQ7v13zIz2veakbTLtcnDGbAtm1OXIzz1BtDjE/mitvXrwjywG3dbFkVWhLB1draiizP\nnuquh87OpromUFdES1XVDwFfA14DfgV4StO0dMU+7cC7gF8ETquq+muapn1vLoMWCAQCwcKQzWYZ\nj05iSx7iGan4IZczTCYTOXI1hIbX7eLd+1Zx754ewu0h4vEUVCUTHcoFlohgLWd+/MZZAoHGiOR7\nd7RyYSSNNpAkq1tcGkny10/1EW5ys21NkK4Wz6IJrlRqivfeuZX29vZFuV6BelOHvwDcp2la33Q7\naJo2ATwOPK6q6jbgy4AQWgKBQLCEWJZFZCJGOmfjyds1dLb4Ma0Ik0mddLY6giVJcMe2Lt57+xpC\nfneZj1Ylpmlg61khsG4iAoEmQs2Nq6e7tbWNnZsszl6JcfLiBJmcSWRK56enYrQ3e9m5vp113U3I\n8s2ZUqxLaGma9rG5nFTTtDPAh+c1IoFAIBA0hEQyQTSexuXx4/E56RLdsBieSDEWTdds/LxpVTPv\nv3s93e0zu7ObpompZ2gOemnp6HjL1d0I5oZbkdm5oZ2ta1rLBNfEZJaXeoc5em6c7evb2Lyqpa5V\nrMuJORXDq6q6Hqc+q1fTNLPiNTdwt6ZpLzZueAKBQCCYK4ZhMBaJYeDCnY9i2bZNb1+EZ9/oJ5bI\nVR0Tbvby8N3r2ba2dUbRZFkWZi5NKOCmVQgswRwpCC51bSt9g3FOXoySSOsk0joHT49y/Pw429a2\nsW1dKz7PzWGMUG+NVhvwbeDd+U3Dqqr+iqZpT5Ts1g48h1McLxAIBIJFprJ1jju//cpogidfvUT/\nSKLqGJ/HxXtuW83+HStQXNNHEizLIptOEvAqtHaHG1ZQLHhrorhk1LVtbFnTSv9IgpMXIkQms+R0\ni96+CCcvTrB5dQs71rfVZSNyI1OvXPwTYAXwTpxKyN8Dvq+q6uc0TXusZD/x1UYgEAiWgFQ6zUQs\ngVTSOieeyPLsG1c4dn68an9Zgju2r+C9t68m6HNXvV7Atm2y6RQdLS7W9ISxbfGYFzQOWZJY393E\nuhUhRibSvHlxgqHxJKZlo/XHONsfY213Ezs3tNHR4l/q4c6LeoXWQ8AnNU37af7vD6qq+mfAX6uq\nGtc07bv57de9JFQgEAgE9VOrdU7OMHnp+DAvHhtCN6tXE25Z3cLDd69jRdv0dVi2bZPLpvB7ZFb2\nhOnoaL7pbBAENw6SJNEdDtAdDhCdynDyYpSLw5PYNly+OsXlq1OsaPOzc0M7qzqDyyplXa/QCuF4\nZhXRNO13VFUNA/+gquoDwNlGD04gEAgE01PZOseybXrPO3VY8WR1HVZnq4+H71qHunb6Js62baNn\n03jdEqu62lAUBdcMKUWBoNG0Nfm4d3cPt27p4PSlKOcGYhimzUg0zUh0kJaQh53r29mwsgnXMkhh\n1yu0jgK/CvxWxfbPAWuAHwK/3MBxCQQCgWAacrkso5E4yF48/hAA/SNTPPnqZa6MVtdh+b0K771t\nNXfu6JrxgymbSeFVJHo6W3C7p08nCgSLQcjv5o7tXezeHObslRhnLkdJZ03iiRyvvHnVWam4rpWt\na1rxuG/c8vB6hdZ/Bp5VVfVB4NOapr0BoGmaoarqh4GngH9coDEKBAKBACfaFInGSGZMvD5HYMUS\nWZ55vZ/evkjV/rIkcdfOFbx732oCvukf99lsBq9s0dPRhMfjXbDxCwTzwet2sWtjmB3r27gwNMWp\nixPEkznSWYMjZ8c50TfBljUt7FjfPuO/86WiXh+tV1RVvQ34eSBW8VpCVdX34Iixn238EAUCgUBQ\n8MSS3T68Pi9Z3eTF40O8dHwIw6wuj922tpWH7lpHZ+v0BcTZTBqPy6a7vQmvVwgswY2NS5bZsrqF\nzauaGRxLcvLiBCPRNLppcepSFK0/xtY1rezccGMJrrpHomnaWeD3C3+rqtpa6GeoaZoOfCn/IxAI\nFgjdMOnti3B1IkV3e4Dt69o4fTk67d+7N4VxK4sbUq8c43RjyOkmB8+MMjSWKO4HzHps4fz9o5Nc\nGUmSMyzWdoV45J71BGZYPVfvuOo5rtY4AY6eHyee0mkJuLllfVvVfoX3Z3A8SSZr4HbL6LqFz+sq\nFqaPxzN0tPiKv3e2eOhpVcDlxu0LYtk2R86O8ewb/Uyl9KrxhvwKd27r4r69q1CmmV8um0aRbLra\nQ/h9vlnvgUBwIyFJEqu7QqzuCjEWS3Py4gT9IwlMy+b05Shnr9xYgqvuptIFVFXdgtNc+hc0Tbux\nO4Y2BtFUepkw33nN9wN4sSjMa2Rskq//8BTDkRTgpJFSGYOAT0GSpLK/AdJZA59H4eG71rJva+e8\n5lTPvSndp6PFx9Fz44xEr7VC7QkHePShbWXHWdh860fnuDQcp/AIWtHmRF5mOlY3TB57+gyDYwmG\nxlNYlo0kOZ48zUEPX3r0DtyKzJGzYxw/76TS9mwOs2tjmG8dOMtwJIVt23Xfm8L1Su+5R5HJ5Ewy\nObPYjNnncdEa8mJYNm5FxjAtulrL51N4f/xehYnJDLphYVo2sgRutwtsG0mSaG/2MjGZxbZtmn0W\nlg3dnW186N4NvHZyhNdPjTBZQ2C5ZImQ313899DZ6uNj79hYJrZyuQwKFm2tcxNY4pmxvLjeptKP\nPf6a3cgWPItBbCpLb1+ES1enitsUl8QtG9rZsaEdxSWTmIxxzy09Det12NCm0gCqqr4b+G3gfcAB\nRJpQcBNQ+UEKcPjsWJUwuBE4fj5SNs501mAymUOSIOBzF//O5gwyuoVt28hSju++eIHjfREefWgb\nMHvEqMBM96ZwnsHxJKcuOa00JEkildFJZQzCLb7i8uvhSIrevgi3qV1lcxkYnSq73sXhSYCyqFTl\nsb19zj2IJ3JYlo0N2DYYpkVsKst3X7xAIq1z8uIEev6D88SFCD3hIDnDaWYxFkuT1U1sG/7uGY3D\n2hif+8COmvehcD3I10fFM2R15zyWZRdb2CQzBhNTWQJehe5wsOZ8Cu9PTjfRDQvLth2hKEvkdBNs\nkGWJeCJHJpPBMnK4lSaCfh/DkRT//Z+Pk8pU9yWUJQg3+8iZJgGvUrzvY7EMWn+MnRvD6HoWl23S\n0RIk4F+eXkQCwUy0Nnl5x60r2V0iuAzT5tj5CGevxNm7tYOu0NI4UNXrDN8LrAS+Dnxe07SLCzoq\ngWCRKP0gLVBLGCwWtSJISr7v19VIsmJfq+q/hun8FKJElmQ7ve0iKSfKUzHfmUTldPemcJ6h8SSx\nqSzJjIHikuhq86MbFrphkc4aZYLp6kT5eSrnUjqPSkqPLfyeM6wy0z7LdoTQaydHCPhcZedy5p8k\n4FOwLJt09lr3sHTO5MjZMf7mh6fYv2NFmfDUDZPD2hjxRBa3ImPbzrns/LUq+wTaNmR1k2RGx+dx\nVc2n8Hcu/9/Ce2QX/wdsyyKZSGJLLhRvENOSmEzmSKSrI1gAAa+LUMBDOmuQyRrkchatTd6i2Loa\nmWTbah/tTUGCwZl7FwoENwMFwXXLZIZDZ8a4OpEilTX46YmrtIfcbFnTToMCWnVTb0TrArAF2JT/\nEUJLcFNQKQBm276QTBdB+uwHdgAUIyUFCo1XC/+1LBvbhlIfP9t2tkM+IjZRv6ic7h4cPx9hKJIk\nEs+QyZlYlk3OshmNpmkKuPNzKRcZlQ2Ku8NBOFfuVj5dI9nSYwu/u/Ppu0pMyyaVrd5eGFM6Wx0R\nsmw4cWGCsXiGw2fH+NT9WzlxIcJTr/UTT2RJZw0nNYvT+kKSoIYHKODcb1238HlcVfMp/F1IPRbe\nJyn/P0Y2jYGF2+PHtCRs08ZIGzVdoCXJEVmW5UTKCu+xYVpkcgYel41t5VjX3c2q7s7agxUIbmLa\nm33cf8dqBsaSHD4zymRKZyKh8/8+rvHRdxg8cMcaZHlxTE/rcvrSNO3DwA5gAHhcVdU+VVX/jwUd\nmUCwCFQKgNm2LyTTRZBKa416wtfG5fcqNAXc2LbTasWwbGRZKhNaksSsD5PpBNVM9yCdNdANq3gt\nGydSk9VNFEUuExk94WvF4gX2bA6zuutaiadt27QEvXjdLlIZnULtaOWxuzeFWdHmR9drpNBkiaaA\nu2YfML9XoavNXxWFKuBEAW2GxpN89fFevvviBUYmUo6QzEewbMvGsm1cslzzGgUR5nY7c9/Q08yG\nnuayMTQHPbSEvLgVGVmSkGUJy9RBT2JLCpISwLCk4j2tNVyX5PyksyY53SSTNcjmDEzLxjJ1MukE\nLhk2rulm/87VtScsELwFkCSJNV0hPnjvBvZu7UCWwDBtvv3ceb78D0eIJbKLMo65rDq8CPyWqqpf\nBH4J+PfAlxdqYALBYrB7U5jDZ8fKBE4tYbAYVAqeQtH280cGCAW9bOoJ8ehD28oKzw9rY1wecWqd\nFFnCViQCPg+TKT1foyXhVmR6wgH2bAoz/Ea1qJpOUE13b/ZsCnOmPwqQj/TY+fFCKmPSGvLwoXs3\nEJ3KTlsH5lFcfP4Tt/L8oX4GRqY4ma/zKohC24aH9q9h18ZwVSp118Z2jp8fR5LsYvoNoDXkwe9V\nUBSJSDxbNv+NK5v5hfep/PUPT3GiL1IlYGwgEs/g97qYrHBUd8kSPo/LEVGKC8N06qtKU7QAkgzr\nu5vYv2sl7SHPjKsOh8aTpDI6ejZJKmeTzMlcvjpJMmNiTqMGFVkCnIJ5K3/hotC1DCw9S8DvY/fW\nNdymdt5wizoEgqVCliV2bQwTDticGUwxMJbm/GCc//Z3h/jNj+9hdVdoQa8/53WPmqZNAV9RVfV/\nLsB4BIJFxa24ysTLUq46LBU8hcLrQgruX35ylq5WP59+UC2m+Q5ro4zFM8VaqMIxsizREw5UrawD\nqmq0ZhKVlfemYDkwEk3h9yqkMga2bRdTX7IkEfQpBP1uvG4X7797/Yzz9bhd3LGtC8u0OHJuvFhX\nVFrbVVgpWODw2TEs0xFYbpdcJnbS+YJ8v9dFS9BNKmsS8Lr48Ns3cse2LtyKi1/+wA6++I3XiSZy\n5SIJp77KxknJ5XRnRWAhSiXLEi0hLw/euYbLV6d47dQILlliKq0XV6sFfW7CrX4+et9mkolMcXtl\nWvY2tYttq1NMxBPI7h500+ZvnznDZKo6Sue8DxIexUVz0EMmZ2CYNoZh4XXLpDM5ctkUkkvB428i\n3B7g0YdvvIUcAsGNQHNA4fMf2sqLJ2M8+eplJiaz/NG3DvPrH93FzvULV7g1L4MJVVXfD9yiqmqV\nw52maX943aMSCBYRt+JaksL3SkojSIXUnFuR8eftGoYiybJ6qsoImCRJhFt8rAwHWdUZrCka5yoq\nC/emsn5McUl4iwXfVjFyVCjEnkuN20y1YLVqyrwlaUnFJWPZjvBqCXpwKzKSJBH0ewjmF9d53a7i\nHN2KzKZVLfT2TZAzzKLYKogqXTJJ2ddSiVa+5k1xOeJ139ZO9m3tJJbM0TcYB9sZg1uRaWvyMjKR\n4tDpEbavaSkbt244RfdHtVFS6QTqui5u2dTFEW2cA4eu1FxNCI4nVlPAg9ctkzNs/F5HhCqSSTqd\nwt/kRg+1k846tVzb1i6vJfkCwWLjkiU+9s5NrGgL8LfPnCGTM/nad0/wX37hdlZ1BGc/wTyYs9BS\nVfVrwK8Bo0C64mUbEEJLIJgHpRGkF44NAU5dj1xSdFUqSmql/CRJ4ja1c1rhOF9RWVk/JsuOsGhv\n8nLp6pQjCEusBeZS4zbXerjWJi/u8WQx2idLEm63zJquICPRTNX+pfesty/CaCxDc9BNdMrCNJ3E\np5yPWnndLnTDUV8FESdJEnu3dPLx+zYVBdujD23jsafOcKY/iluR8Xlcjjg2LV45McSmnhCFSi7d\nMPn6E6c4rg2imxaK28/ZoQGeeG2waBUxHYZh09Hi48Nv38CFwUlGJhK0+MHjVnj5VJR01iCVNTDy\noly7EuOxp8/ckPYkAsGNxL27e2hr9vKVbx8nmzP52uO9/P6nb5/R9Hi+zCei9bPAr2qa9leNHoxA\nsFTcKKalpULoiVcvV71eKkoWs76sVtRJkiTW9zTh8ypzGoNumBzURjl1OUouZ7JzfRsr2vxVRqXT\n1ZTt3dIBOD5Vhajfhp5m9m7p4Jk3rlTtX3rPCvPwexViiRxQSA86IsvlklFcTuSrGFHMF7GX/ntw\n3qdOhiccA9TxWJqc7lhOHD87RjZjFFN4r58cQLswhIUblyJhmHaxrq0UWZagEEXLbzNtC3VNKx5F\nYutKH3s2hHj8p4MMR6ZIZ3UmUzqmadMa8hL0O0J3Ke1JBDcHE+OjpFKLv/J6ocmkEsRi14x6e5rh\nw/es4vGXBxiJpvmX587z6Ye2N/y68xFaOvBcowciECwVczEtXSxBtn1dGwcOXWE0msbjdtEU8LAy\nHCwTMItZXzZd1GlVR4iH9q+bkwnqN548zcmLE0XB0Xt+nB3r23jwzjWMxzNlLW0Kfl2FVGpXm59d\nG8Ps29pZswXObDVohXlIkkRL0EMskcW2IehTaG3yFu0fKr/Vls6/8G9gcDyJ1y0TiafzLvFOmjGd\nNTlxIcLB08Ns7PJxaXgKy+XH0g2saWwhdm0MYxgm5wfjuKCYDjUNg5PnB7lnRzstHR0cyQvrQv2e\naTmmp4m0TtB/7XG+FPYkgpsH09KxzdrebcsZr9fLm/0p5IHyyPfqDi8D41l++uZVPvrOTTQFPA29\n7nyE1l8A/1lV1V/WNG1x1kYKBAtIvaaljXCRr7elzbcOnCWTM3ErMjnDxOdR+PkHt1btu1j1ZTNF\nz+Yyht6+iBOJMi2kYmrN4tLVKe7cvqKqgP5T92/lq4/3MpnMFb2zvnXgLI8+tK3mNR99aBsHz4zy\n4rEh0jmDJr87H5lyVc0j4FOKwqpQW1awY6iMrhXEWq2WPLl8qtHlkpALrZBSSV7tHWDtA7cwmbZJ\nZoyy4vsCfq+Le3f18PbdPTz+wgXAWX5uWRamkUGSJC6Munmud5S1XRkGS0QnXIt8GWa5SexS2JMI\nbh46u1ax3FrwXA97VR8D45cxTJuXTwzz0P51DT3/fITWt4FXgLiqqleBsu9omqZtbMTABILFol7T\n0ut1ka9XqBWuI0kSAZ8bSYKsbnDqUoy9mzvmMrVZxzOXaNieTeGisdOezeF59U+8OpGq6QCvG1bN\n9+H05ShZ3aIldG3dTek9r5zD5lUtfP+li0WLhpGJNEfPjfOlR+8g4HNPu5KyMpI23X2p/DcgSRKW\n5dhIyJKEoWcwDQNZ8TI2ZfHnj/cyFquuGwNwu5yo2slLUUZjabasbuHkpXH0XBoJCZfbjyRJZHWL\nH70xwIr2QJXjvCRd+ylsXyp7EoFgudLW5KMloBBPGVwZSTT8/PMRWt8CosA3gOo+GgLBMqNe09Lr\ndZGvV6hNe51ICjbXdalZmWu6tHJf+ihaRsyFjhYflmVjmk4z6IKXquKSSKZ1nnz1Upm4me5eDI0n\nMcyrPPVaP5mcUSzEz+lmmQ+WZdtEp7J8/YlT/OqHb8GtuOqKwE33eq3x+L0KWd0gl0lioyC5A9jA\naA2BJUnQnDdV9fvcxcUDo9E0G7u8tAVg3PJh2+Ve0oZpF6NvBWsN53wSPo9MwOdm86oW4Z8lEMwT\nn0cmnoKpadpdXQ/zEVq3AHdomvZmowcjECwF9RaVz9VFvjLaMjhe+3tJ6Ye3bpgk03qxv17ZSr5w\noGE1YnOJzjWqH6RumBw9N15inWBjAV6PC9uGU5ejxbkWRF+te2vbNicvTfDyiSzxhCOqCo2so1PZ\n4krBUo+t0/2NWY1XazxuSaerSSLqCpHOTVOEhRMFfN+dazl2bow3L0aLc7H0DGCDq5OPvmsnf/+j\ns2Rz5jV7CZwf3bAI+CRu39aB1h9nNJou/htZ2REUKw0Fguug0MbKmq59xHUwH6F1GljU5K2qqptw\nasPeBkSAr2ma9mf519YDfwPcDVwCfkvTtAMlx74X+B/ARuBV4HOiKbaglHqLyndvCnPwzGjVarda\naZpaUSCvW3YMPkt75HDtw7twzFDeuiCVMRwB0epjdVcTO9a3ThuFgunTXbWYS3SuUf0ge/sijETT\ndLT6SWX0YgH5plUtxBLZsvtSEHKliwIKosLncZHJmVWNo9NZA09+taBplbu2m6bF0HjyulfjlYpy\n09DJZdN0tDXh8XgZ7ovUPKYl6OHn7t/CmnzLoc4WPxDF0LNItoHH60d2KazsaGL3pjDPHR10Cuht\nsPORP8eV3olyre1q5pG7N9wQq2QFgpsB07KZSDiRrI0rm2fZe+7MR2j9MfCYqqp/CvThrEIsomna\ni40YWAFVVSXgSeB14Fac5tb/pKrqgKZp/wT8ADgG3AZ8BPieqqrbNE0bUFV1DfA94PeBZ4E/AL4P\n7GnkGAXLn0YXldeKAmVyJj6Pi6x+TSCURs5Ka7PCLb5i0fP2tW3s3trF3z2j0TcYL0a5bNumbzDO\nN548TSyRzTcqLo8ITffhO5foXKP6QRaEmSRJhAIeWpscZ3fTsqrEJ8DgeILDZ8eKiwJ0w6KtyYW6\ntoXevmhV02bdsGgJecgZVnXDadsmlTE4rI1dlzhxKy5+8UGVl49eZGA8iUkrb5weJatX13W4ZImm\noIeH71xTFFkAG7r9HA+YRJMuFLfjqlq6sOA3Prabrz7ey0g0TSbfMNrjduH3KvNagCAQCGam/+pU\nsVG8ugCmv/MRWv+Y/+//V+M1G2j016oVwFHg1zRNSwJ9qqr+BLhXVdURYAOwX9O0DPBlVVXfA3wG\nxzj1c8BBTdO+AqCq6qPAVVVV39FoQSi4+SlEZEqX/o9E0zWjJNP5Tu1Y38aqjlDND/vSYwqF8LZt\nc/pylL6hScbjaVJpJ8rV3uxlYjKLblicvDhRjLCFW3x1eSnNxYOrUX5d0wmzNZ0hTl2OlrvhexUy\nWbNsUQBAVrfQdSdUVahVKkS2nJC/xI61bZzqj5LVTSQKZqQy8WSOvqF40W1+ritGARLJBNF4mtaW\nZl7TJjk/eLXmfj6PTGuTjxVtfratc3oe5nIZFCx6OkJ8/hP7p41IBXxuvvDJW+ktsbbweV2s6giJ\nyJVA0GAsy+bY+XEAws0etq1ta/g15iO0NjR8FDOgadpVHJNUAFRVfRvwdhx3+ruAI3mRVeBlnDQi\nwH6gKKg0TUurqnok/7oQWoI5MZcU2ky+U9OJn1rHFAqgPW4XbpcTwdENi3giV7V6r5A+K4iSmVJ7\nc/HgapRfVy3BtjIc5H13ruWQNlZWxG7bTHt+j9uF1y0zGk3j97rweRybBo/bafx8ZTyBLDlCrNBv\n0LJtXLKE33vtkVcQo7s3VTeurry2YRiMR2LEMhbPHxvl4JnRmnYNhbicW3Hxzj097Fzfjm0bmLkM\nHS0hAn5/cd+ZIlKFiNVt6rS7CASCBnDiQoSplJOYe9/tPSgueZYj5k5dQktV1adw0ndPa5p2oeGj\nqBNVVS8Ba4AngO8CXwGGKnYbAVbnf++Z5XWBoG7mkkKbTxSo9BjbdlaZ5XQLj1vGtsHvuxbByeUF\nRCH6oxuOSCkVX7Ol9uaSfmpEqqpUsI3GMmxe28amnhBHtTECPqVoUVCMaOUMUhm9bBvAmf5oWTrR\n73UR8HmRZecBWYh0+b0uJJ+Cbli4ZJmAz1WVoiykJ0tNUQ8c8vMbH9tdjCjG4nGik1kO903x3NHB\n6rRkCU5MzVlFiW2ClaG9KUgw2PhvyQKB4PoYjiQ5ft6prWwPubl108KUn9cb0fo74GHgi6qqRoGn\ngaeAFzRNy814ZGP5KNAN/CVOgXsAqDRNzQIF053ZXq8L1wIo3KWkMB8xr2vkDJPj5yNcjSTpDgfZ\nszmMpyKqsVft5Oi5cYYi11YPrgwH2at2olTUCymKzGc/sCN/zhTd4UDNc9Y65rA2xpOvXALArUjE\nkznSOZOOZh/hVh/pjEFbk49YIlsUH4U+e05T5enHVc88FxJFkdm/sxuXS6a52c/kZJrRWBpZlgj6\nr6VkbdsRVKmMMy+yzsrClZ1BMrpZtn8skcWjuAj4nLlKkkS41ceqjhCrO0N0hwMYpsXTr1W3NMoa\nNkORpOO0ni/SuHR1ij//7gl+/UMqk1MpzgymefqNASLxarsGWYLKRUqWZWBmdSYTGcYSzZy4PLYk\n97qRiGfG8uJ65yNJEi65um7yZmIymeOl48MAeNwy+7e14nG7qp6ZjaAuoZUvOv+nfGH6HTii678B\n21RVfYG88NI0rfpJ1kA0TTsCoKrqbwP/gOPlVflV0QsUwggZqkWVF8cHrG6am/2z77QMEfNyyOkm\n3/z2MQZGp5wN58Y5cWGCz3/iVjzu8g/GL3zqdg6dHmFwLMGqzhC3b19RtU8pD3TOfQVL63ACj9uF\nx+3Csm0yuoWuW2R0k5DfzcaNrfy7j+zir753ojjm7nAQn9fFrVs6WdvdXHNcc5nnYtHc7Gfz2naO\nnhsv257Ie9msCAccsaWbuN0uusNBhitsMnweBV23qkL+9922hnt2rwScuZ++HLs2d2B1VxPtzX5y\nuoVh2kWnetM0GBwe49+OtXC6fxLtcvXjQnE5Qs+2LJIZA9MC2zKxjAwetxvFG6I/kuPccP6RmL/X\n/+4ju+g9P173v58bDfHMeGsQCLgJBOYUj1hWJNM6Pzo4UIxO33/HOsJBk9bWAG1twYZfb041Wpqm\n2cAb+Z8vqaraATxEXnipqjqiadqORg5QVdUu4G5N035QsvkU4AGGgcoOkN357QCD+b8rXz86lzFM\nTqYxzen9cZYbpdEEMS84eGaUS8Pxsm2XhuM8f6ifO7ZVp8u2r2lh+5oWAJKJTMNde8/3T2CUO9N5\nIwAAIABJREFUjD/c7COrm3S1+nnn3lXs2RxGz+p86oEt00bMao1rrvNcSErfq009Ibpa/WWRQo8i\ng+QUqnrdLizLJpszGYk4zvKlGUCPW6Yl6CGrX0vprQwH2dQTIhq9ds5a9+v4+QiZnIGNjW3bGLkU\nSDK6y8/3X6r9vbG7PcAd2zs5cnacVEYH28LMZUCS8fpDuGQnzZlI6WXjvDAY4wv/80XSWaOYCn3h\n8BU+88j2Gz7SJZ4Zy4vCvOZLKqWjuG/ODnuprMGzr/czlXKScft3rKCr1cvUZIxYLIUs+2Y5wzXq\nFWXzKYYvomnaOPD3wN+rqirjFKc3mg3Ad1VVXa1pWkFA3Q6M4hS+/0dVVb0lfRfvBV7K//5a/m8A\nVFUNAHtxbB7qxjStYlHtzYSYl8PQWKJmYfPQWAKjgS1v6qWr1V82HkmCkN/NO/euKrbgMQwLGcn5\nu8QtfqZ5L8Q8r9dA1TSdeXz6QbXsPIZp8cwbV7Asu9hAGcAJWkn5mi5HxawMB/nU/Vs5fTlaNg4Z\nqex+1Lpft6xvo6vVz/n+cSxTR1b8WEhlFhwFpPz1I/E0Lx0bors9QDwexzJNvL4gsuyMa1VXiM2r\nmjl2LlK837ZtMzKRwjDtYkommdaxbZuj2tiysWoQz4y3BrZtYy6AcedSk0jpHDh0pVj8vmdzGHVt\na7E5u2EszL+DOQstVVU9wO8A39Y07byqql8H/jfgp8DPNXh8AAeBQ8A38ynDDcCf4KQuXwSuAP9L\nVdX/CnwQJ7X5i/ljvwn8jqqqv4tTQP8HQJ+maS8swDgFy5Tu9kCx+Ly08HqpGvPWKqRf3dXEns3X\n17+uUX5YBRrRZLtAZbG9bpgc74vQNxgviiy3IhdXVO5Y10bQ7y4Td/MRK7IEn3j7Sv7yiSQTU+6q\neisg31/QsYpw/MssYvE4sp3D5w+ieJyHdEvQwyP3buC9+9fz/KF+jp27ZmCazhpOerIkwlVYJTpX\n41eBQDB3YoksBw4OFFdy79oUXrSeoPOp+vpj4LeBZlVVH8QRNX8ENAF/1rihOWiaZgEfwumr+Arw\n18BXNE37Wv61D+KkAw/hCL0Pa5o2kD/2Mk4B/Wdw0p2tOKamAkGR7evaSGUM4olc2X+3r7tW/qcb\nJoe1UZ589RKHtVF0Y/qVZ9dLYXXeI3ev43a1kw+8bYNTR3Wd6aXdm8L0hMtF1fU0IJ6pNc/1UrgH\nm1a2EPAptIQ8hPMNoNNZgytjiaoI2lzeI9u2icYmOXhykG/92wCRSaMq2ud1u3hw/1pWdQaQ8wrJ\nyKUx9Qwut5+UoRD0e2gJeWlr9iG7ZBSXjMftYs/m8nutGxaKSyqep3T7Ugl6geCtwng8w7OvXymK\nrNvVTvZu6ahplLwQzCd1+DPAz2qadkRV1b8Entc07Y9UVX0Wpyi+4eS9tD4+zWsXgHfNcOyzwLaF\nGJfg5uD05WhNe4HTl6PcpnY1NHJTL6URGkVxPrxLa67mk7JrlB9WgcpITCEq+PyxQYDrNtd07kFn\n0WDUtsvTiE+8ermsBVG971E6k+HS4AQ/OTbG0XMRKoNYkgR3bOvivbevIeR3k0zluHhlHNsyUTwB\nJFnGtGynjqyCwbEEr/QOcb5/gj2bwuzZFGY8niGZ1jl5aaJoMlugq82/aN+qBYK3IlcjKZ47Mohu\nWkjAXbd0s2V1y6KOYT5CK4zT7xDgAeCv8r9HcOwUBIJlxdWJcvfx0u0we1PlRjV6rpfrEX6NbN1S\nGomZSQTVGlPOMIuCpKvVP+09K02jVqZ2oTyCNlvja9M0GRqZ4IUTY7x8YrToRVbKplXNPHzXOnrC\nTpFrNpvhvt3tvHlxjGTmmiRzomzlq7Js2+bkhQl6+yLFhtY94UBRCMaSOSRJKs6jq83x6xJO7wLB\nwjAwmuD5Y0NYlo0swdv3rGRdd9PsBzaY+QitPuCO/GrADTg9BAE+DCyZmalAMF9mq12ayRF+KaJd\nswm/xaJeEVRZe3Xk7BhPv95PznDMWCWkae9ZIQp35OwYT73WT0oxytzdYWYH/MJrE9E4r54c4SfH\nRoklqq3/wi0+Hr5rHdvWtiJJUrFdTnd7E15vO//1s2GeeOUSA2NJVnc6bvb//Nz5svfB53GR1o2y\nFG/pPWhkNFEgEMzMhaFJfnpiGNt27FjeeesqVnU23rqhHuYjtP4Ep9+hBfybpmnHVVX9fZxC8880\ncnACwWIwm4v7TEJsKUTPXFoBLSSlqchCurDQ8LrWmAqitG8wTjyZQ0JCcTkNtCvvWWmUsKPFx9Fz\n42RyjpjTjRzprFHs61j5/pQubIjFE7x89CIHjo5yZbTaiMPncfHufau5a+cKFJeMoetg5+hoDhEI\nXFseH/C5+cS7t5QdWymcBscTHDtfXZ9WuAeiEbRAsDicG4jx6psjgLOI5j23raarbem80uYstDRN\n+ztVVY8CG4Fn8psPAu/TNO0njRycQLAYzFa7NJMQ+9HBKzXPuZCip9GrB6+HUvHwxKvVvlOlYyqI\n0tIaJd281p+xcM8qo4SpjF5spJ3Kt93J6SaxqSwtIS+GabFrY7jYSicSz5DLGdhWhpdP6vzk2GjV\nuGQJ7ty+gvfcvpqgz41pGuiZFK3NfppCneiGyeunrhbbc+zZHGbf1s6yCFQt4VRLaIlid4Fg8dD6\no7x+yvn/vM/j4r23r6a9uX5vrIVgPvYO3wT+g6ZpJwrbNE17RlXVdlVVv69p2ocbOkKBYBGYKdow\nkxCrV/Q0so5rPn0UF5p6xnQtsiOXNcYqCK/CPauMEjpRLItMziTc4iOV0YkndUzLsUt45o0rHO+L\n8Kn7t/LDn17kpaMXkWUXWcuHoVf7NWxd08JDd61jRVsA0zTJZZI0B720dHQiSRK6YfKNJ0/z5oUI\nOd3CBo6eG+OwNsbnPrBj2vdt96YwR8+NMxpLT3sPBILlwMT4KKnU0tqO+H1+pDm2ATo/lKL3ktP9\nweeWefuOVjxkSExWt8+qJJWawmmP3HjqbSr9NmBT/s9PA0dUVZ2s2G078N4Gjk0guGGYTojt3hTm\n4JlRLg5PFmuUNvQ0l324NrqOq9GrBxtBPWMqCKlC02fDtPPHymWCpDIa6M6v7tMNi4BPQpKcZjml\npqXDkRRHtCGuXJ0gY9b2w+psdeqw1LVt2LZNNp0k5Fdo7yhf5t3bF+HCUJxMzixaPuQsmxMXIhw5\nO8b+HZXNJq7dg888sp2+4QTn+6N0tfqW/H0RCOaDaenYpr5k10+lE9y+tZ3W1vqbsT93fLQoslqC\nbn7lkc10tsyljVAPra1L21TaBv5Xye9frbFPAvjTBoxJILipWIg6rhux3me2MZVGvTpa/WR1E48i\n89D+tWVpucpoYEGYlQqu0qJ7yzJJJFJ854U0kymj5rX3be3gA/es4/zAJAdeO0dP2M+9t67H5/VU\n7Xt1IkUqa5aJNQkwTJvj5yPTCi0Aj+Lint0r2b6mRTiNC5YtnV2rCDUvjOioh8RkjNbWNtrb2+va\n/6nXLvPk60OA07Lsd39uL52tN07/ynqbSr9C3txUVVUL6NY0rbrwQSB4i9HbF2Ekmi6zhhiJpstE\nVGmEprRQ+7A29paKeJRGvUZjGTavbWNTTwiZ8vRAZRpSkiR2bmhn75aOoifVqcvRvOlonFROwrRl\noFpkyZLTN3FjTzOPP68xOpHA4/FxYTTHhdFzNaOKHS0+srlys1MbWCRvQ4FAMAdePD7Ed57vA5yo\n9e/+7L6iufGNwnxWHf5fONGrMlRVbQb+UNO037zuUQkEy4R6VgAWIjSVXlN9Q3Eee/rMglpBzJeF\n8gYrRL0URaatLUg0mqyK/MyWhtQNk8HvHOHkpRgWLqBaAbldEm63C68iYZkGzx/uw7RdNIVCZenG\n6aKKLpdU1evNJUvX3QZJIBA0jqNnx/jbZ84A0N7s5fd+bt+SF77Xot4arW1A4Wn0ReCYqqrRit12\nAb8MCKEleMtQTzF8IUJT2bfP71UWzf9qLsJpKbzBKpkuDZnJZvjxwX7ODaWwajy+Al4Fn9eFz6Ng\nGjoTsUlMW8bvDzirFc1MWSufF4456YbS+zEez7Cizc9oNF18v2RZorPVz76tnQs1ZYFAMAeujCb4\nqx+exLYh6FP47U/cekOKLKg/orUJ+CEUu1V8b5r9vnndIxIIbjBmEin1rLYrRGgee+oMZ/qjRZFV\niKwstP/VXIXTjWKIWoppmhw6NcC/vjbEcCRd9bosQVPAw21qB2OxNFfHouiGhaQE8Ofvt27k0A2L\nVMYopm+h2sW+uz2ALMt0tfmJJ3KOsaoi874719xwkUeB4K1IIq3z54/3ktOdHqL/4Wf2sLJjacxI\n66HeGq0nVVVdj1OndQG4Exgr2cUGEpqmTTR8hALBEjKbSKl3BWBl375SFtpnaa7C6UYxRAUn3dp3\nZZTv/3SAU5fjNfcJ+hWa/B4kCdqD8MC+dVwY6ebp16+gT2bwexX8XqUormZzsd++ro1n3+jn8nga\nO28hYcoSJy5McOf2FUJsCQRLiG3bfPPJ04zHHcuGn3+fyuZVi9u7cK7UXaOlaVo/gKqqG4B+TdNq\nLKAWCG4u6hEp9a4ArIx+2baNz+NicDxRfH0hPsTnKpxuFEPU8Wicf/3pZV47HSlaQZTSHHTj8yiO\no3suTXebl3ffvhGXy0Xvq2eqnOTbm31kcgY+j0ImZ9R0sdcNk28dOEt0KotpOf0KXZJEW5OnapGD\nQCBYfF7qHebY+XEA7tu7irfvXrnEI5qd+TjDX1ZV9YOqqu4CCp8KEuAF7tA07f5GDlAgWAymSw82\nMrpTGv0aGk9y8tIEmZzJ0XMRjp6LLFgd1FyF01IboqbTGQ4c7OfHR6+SSFevJOxuD/D+u9exbkWI\nE+eHiU6m2bRmDftUJ9p0WBtlOJIq2kIUnOTjCcdJftvaVrQrsTKRVThv4b2ZTOnY+Rp907QZi2VY\n0R5YkqieQCBwiMQz/OOPzwHOM+mT7968xCOqj/k4w38Z+F1gBKdAfhBYkT/XPzZ0dALBIjBTerDR\n0Z1r0a9RjpwbL/uwX6g6qLkKp8UyRM3pJgfPjDI0lqC7PcDO9a0c1Yb54auDXI1WOzkH/W4euH01\nt6ldmIaObGW5b99a/L7yAtiCGJIkp49iMp0jltBJ50w8WYMz/VHSWbPM8LS0pVI6a2Db5RE0w3Rs\nOUQ7HYFg6fjnfztHVjeRJYnPPrIDr3t5pPHnY+/wvwO/qWnaV1VVvQLci2P38H2c+i2BYFkxU3pw\noaI7tSIjtm1zWBtbEEuFuQqnuRqiztUOImeYfPPbx7g0HMeybNKpJBlDZiptVu3rkiXetquH+/au\nxO0CU0/R1uwnFKxtqFgphhJpAytfazWZzJHOyrQ3e9mxro2g313VUkk3LGRJwpLsojO8JIHPo4h2\nOgLBEnH6cpRDmlMa/q59q9jQ07zEI6qf+QitFcC/5n/vBe7UNO07qqr+J5xVh19s1OAEgsVgpvTg\nbWrXgkR3KsVAwWMrnTWKBfOFqJqSd0S/HhrtJF8qrDpafBw9N85I9NpqwNnSoMfPRxgYnSKbThFL\nmqR1gGqRdcuGdh7cv5bWkAczlybg89Ja0TKnklJxnM46rX4kCeT8MYW+iUG/m/ffvb7q2AOH/Fwa\nnkJxyVi2jSRJtAQ9PHzXWlEILxAsET94yYnjhPxuPvz2DUs8mrkxH6EVBUL5388DO4HvAP3AqgaN\nSyBYNGZLDy5Eu5vKSFk669QiFVbBwbWo2v6d07d8WQwqo1Xb17XxrQNni2NPZXRSGYNwi68uM1CA\n/qEI4xNxJtPU7Evodsms7Q7xM/dtxDKzeGSd9u4wsjy76CyN4D1/bJBsziRT4fSuG1bN992tuPiN\nj+3mq4/3MhpNF1cmruwICg8tgWCJOHslxtkBZ9Xxg/vXEizpxLEcmI/Qeg74Y1VVfxl4HfhPqqr+\nBfBxyi0fBIJlwVIUf1em8wbHkgxFklWRmqUuvq5Vv3bg0BUyObM4Vmdln2ObUNqKqNbYDcPgtTcH\neP74KPEaU5MkaAl68HsVphIpzlwc5l23b0JRFHTD5Oi50boii6Xi+IevXCpz5AfoavNP+/4GfG6+\n8Mlbb6im3QLBW5kDh64AjiHxu/Yuv3jOfITWf8RJHX4C+AvgCziF8QC/3aBxCQSLxmIVfxeojBA9\ncMcap07s1cX32JqNWvVrhUhPQVSVNnsupXTslmWhXRrhB68McHZgqua1FFki3OoD28TUUyiKh4uj\nBrmDA/NKT8I1EQ0U/bO62vz8xsd2N7RGTSAQLAyTqRzHzjl2Dvfu7imL+i8X5mPvcAXYq6qqT9O0\nnKqqbwfeBwxomnaw4SMUCBaBxfpgnW6F46fu39qwqFoj+xTWikq5FblMVBVsFNwltWSFsdu2zfBY\nlCdevcIbZyaw7Oo8YXe7nzWdQa6MJjD1NG5FQfGHiMQz9A3FGZ5IzSs96Yx1cUW0QCBoLAdPjxb7\njt67u2eJRzM/6u11eAJ4FjgAvKhpWlrTtAyApmkppm/JIxAISphuhePpy9GGCIJG9ymsFVHzexXa\nmlxkdUdsSZLEzg3t7N3SwXg8Uxx7Op3mR4cGeP7YKKlstR/Wup5mHt6/htWdIVLJBE+8kiaadpo+\npzJ68VrOvGqnJ4fGkxjmVY6fjwCwZ3OYfVs7y+YqolMCwfLleJ8TzVrVGWR1Z2iWvW9M6o1ovQY8\ngpMazKqq+grwI+CApmlHFmpwAsHNxmwrHK9XEDS6T2Gt+rWVHUE+df9WTl+O1hSF2WyWnx7r5+lD\nw4zFqv2wmgNuHrxrHffdsZbIWBTJyLBuZZhf+3jHtDVrtdKTtm1z4mKEoTeS5HQLGzh6bozD2hif\n+8AOEbUSCJY5Od3kzOUYwLK2Vqm31+HnAFRV7QTeieOd9Ung/1ZVdQL4CdeE18ACjVUgWPYsdHub\nRvcpnCn1VincDMPg1IURnnh9iPOD1XVYbpfM2/f08I49K/G4wcym6GwN4PF4i/sUznlYGy2rWauV\nnvR5XETiGTI5E9t2Gq6als3Rc+N85/k+Pn7fpjKx1ciUqkAgWHguj0xhmM6Xq53r25d4NPNnTjVa\nmqaN4Vg5fAdAVdVm4G3Ax4CvAgGuteURCAQVLPQKx4UQcrOl3kzT5MpwhGcODXNIi9S0a7h1cwcP\n3LmG5oAbI5siFAqwfs0KotEkRkURPVTfp1rpycHxBD86OFAUWcXxWDavvHmVqbReTJnqhsk3njzN\nxeHJYkPpg2dG+aX3bxdiSyC4Qbk4NAk4Pf6Wk0FpJfMq31dVdTVOAfy7gHcAq4FCHZdAIJiGhS7O\nXkyrCtu2iUzE+Lejw7xwYox0ttpwdE1XiEfuWceariaymRQKFt09HXg8Mz966r1P09uWlqdMj5wd\n480LkWKKUQKS6QhHzo6xf8fS+pQJBILaDIwlAehqDyzL1YYF6i2G9+CkDB/EEVjbgXHgx8B/AX6k\nadrVhRqkQHAzMdfi7Jxh8krvEOf7J+hq9c/qH7UYq+xi8UkOaWP86PAI4/HqOqyWoIcH969l96Yw\nhpHD1lP0dDSVpQkrex0WxlnL/qLW+HdvCtMTDnBhaJLSxYyydK2IvpAyPXp2vJhiLJDJmRw9Oy6E\nlkBQwcT4KKnU0nn4ZVIJYjEfg2NORKu7zb9kY2kE9UrECZyU4CvAt3CElSiCFwgWGN0w+dtnNEZj\naQzTwrZnX0W4kKvskskUZy5HeObQVfqGatRhKTLvvHUl9+7uwSXZGLkU7c1BgsHy1GVpr8OC+CnY\nXJS6zgMcPDNaTBl2tDgNpAvpw1//yC7+4ntvcmV0yulRKEt43C4CPufRVkiZxhJZKp0lbNvZLhAI\nyjEtHdvUl+z6Xq+XN/tTDE84vnnN/utvQ7aU1Cu0PDhiaxgYyv8IBIIFprcvwlAkieK69qC5nlWE\n8yWTzXJlOMqPj41ySBuvEi0A+7Z28MAda2kKuMllUgSC7mn7EhZ6HZYyHEnxxCuXGBpPFs1FFZfM\nm7E0F4cn8XsVIvnoWcFPqycc4Dd/ZjcnLkR46rV+MjkDv1cpvlZImbY2OZG00mFLJdsFAsE1OrtW\nEWqu3bR9sbBtm5zumA03B5Zv2hDqF1rtwHtw0oZ/ADymquqbOCsNn8Xx1sotzBAFgpuLuax+a/Qq\nwrmSy+UYHY/x8qkJXuwdqeoZCLBuRRPvv2cdqztD5LJpJMNk1Yo2FGX6x8vVSLLm9v7RRFm7HMu2\nsSw7v9rQKG4v+GkVPMj27+hm39bOae/rro3tHDozWiYQJcnZLhAIbjx0wyourAn63gJCS9O0BPCD\n/A+qqm7Fqdd6APhVQFJV9UXgWU3TvrJAYxUIlj1zNRRdSDuImQSfaZqMRaL0Xohz4MgIkcnqOqzW\nkIcH969j18Z2TMPAyCXpbGvC7/PNeu3ucBDybTVK8ZS4ztt5kWXZkMuZlKok3bBIZXR0w+KwNjat\n5UQpkiSVnaNWpE0gENwYmCXLl0ttXZYj85KJmqadBc4CX1VVVQV+DXgUR3gJoSV4S1JPpGquhqK7\nN4U5em6c0di1Hn+NWEU4neD79Pu2MjWVoG94igNHRrmQX15dikeRuW/vKt62qweXDHo2RUvIR0tz\nZ93X37M5zIkLE1wajpfNq7PVy7mBODndxMyLLICcYWHZNrZtI0kS6axJKuO4zfcNxXns6TMz1q2d\nvBjFJUsUtJYkgSxJnLwY5W27VtY9boFAsDiUtutyycv7S9GchJaqqgpwG3APjn/W24Au4CTwTRzj\nUoHgLUe9kapCys+27WIdkluRGRxP1BRabsXFZx7ZTt9wgvP9UbpafQ1ZRVgQfIVx5HSTWCzGD1+y\nGZ20OXx2rGYdVsCrsL6nibfdsgLLSOP3KnR3h5HluX3j9CguPv+JW3n+UH/ZqsPevgjhlgliU1mS\nGQNFBsuyiwJJliVAws4Pzq3I+L1K3XVrsiTN7AkhEAhuCCzrLSa0VFX9f3DE1e2AD7iCI6q+APxE\n07SRBRuhQLCIzNc9vN5IVXd7wPGfKqlDAjh1KcpD+82a1/IoLu7ZvZLta1pqmnvOh6sTjsgaj6VJ\np5PYloWs+Hn64Cg19BUSTiSrJeQhEp3izMVh3nX7phnrsGbD43Zxx7YujM0dxW0FH7DJZK74cPV4\nXQS8CoZpsXVNK2Bz9kq8KLIKKcCZ6tacCFqk7J67FZk9m5dvWw+B4GbGKnnUyW8FoQV8FngOp9fh\nTzRNO79wQxIIloZ6olLTCbF6i9Z3bwpz4NCVqg/8TM45byGqU3p+ZQHqE7rbA0xOTZFKppEVH7Ys\nM52EkyXnxzB0klNZmpqC5GzvdYms6Sj4gH3vxQu8dmqkSkzdsc0RrSPR6pqxmerW9m3t5Oi58TJn\n+A09zezbWn+6UyAQLB6lJZR2za9/y4d6n5Qb8gXxdaOqakDTtKVzPBMI5siRs2P0DcaLH8SVKamZ\nhFi9RetuxcWO9e1Ep7Jl15EkiaHxZJWr++GzY3z2AzsaOs9kMkU4aCHJbiRFmlFgAWBbmHoGSXYh\ne5pQ3N6G9WashVtx8ZF3bCSWzE3rcD9X93u34uKX3r9d9DoUCJYJiuua0tKNt4bQeiOfPvyWpmkz\nzlhVVTfwaeB3gG3XOT6BYFHQDZOnXusnnrjmUpLKGIRbfMWo1Ezpwbm0vlnVESTgc1dtT2eNmuc/\nfj7CA5319/maLuqWzWaJRKeIpkyePzZGNGHUPN7jlvF7XKQyBqaewbJM3N4AkiTjVuQFa+lTymwO\n9/Nxv19II1eBQNBYXCXegXqDSiaWinqF1oPAXwN/rKrq48CTOL0NR3Ec4zuBfcC7gZ/Nv/ZQw0cr\nECwQvX0RMrly4aEbFumsUYzezJQevE3tqvvDvyDKSo05u9r8uN21U4RXI/UHhmtF3V4/NcQH71pJ\nxpA5dDbK80cHydV4cCkuCZck0d7swzR1Usk0kuymo60FSQKfR+Hhu9ayb2tnQyJB07XgKTCTMBKi\nSSC4uSmNaNV6Xi0n6vXR6gceVFX1XTgF8N8HKr+SZ3F6H/6cpmk/bugoBYIF5upECr9XIZUxyr49\n+TxKMXozW3qw3g9/t+LiU/dv5auP9zKZzBVrtLT+WNG+oOz84frTdKVRN8syyaZTaBMWX4/lGI9n\nipYIpTQF3Gxa1czW1S2cvDjBaCSGW1HoWdGBz+Nix/o2VnWE5pxqm2lhwXQteGayaBAIBG8dZElC\nwunmkNPfAkKrgKZpzwHPqaoawIlgdQMWTmue46ImS7Bc6W4PIEkS4RZfme3Cw3etLX7wzyU9OBun\nL0fJ6hYtoWstYDI5E5/HRbbkodITDsxpZdzViRS2ZZHNpLCAeEpCN2WmRqpLLH0eF+/et5q7dq5A\ncclkM0lu3RBiYGIVI9F0lTjSDZPD2mhd6brZFhaUtuApWEycuRzley9e4CPv2CjElkDwFkeSnL6l\nWd0kla1d5rBcmK9haQp4ucFjEQiWjFIRVaif6gkHylalzVY3NBdqpSElSSpGj0rP76nz/LZtE3Qb\nZDIpkL3EEzlyZvU3QUmCO7ev4L5bexgYTfLCkct0Nivce+taAn4/3TWCcnN1tJ/N7qLQgseqsLp4\n7dQIsWRORLYEAkH+i6dJMlPd+ms5sbwbCAkEDWImEVUrBXa99UHTpSFXdYTmfG7dMHm19zKXr07S\n2hICl4+xaLrmgmiv28VtagcP3rmWf3n+PCNjEyhuD+c9Pi6NX5i3cKpkNruLQguedEWq1q3IS9I0\nWyAQ3Hh4PS5IQrJGycNyQggtgSBPrRqrQiSntHD9wCE/v/Gx3TVXDtZLo9KQE7FJHnvqTcYnLbKG\nxGQqWuaoXEBxSTQHPXjdLjZ0N3Pi/BBjE3F8gaZiTdhswqnSzd7vVaYVVLPVsxVa8IzHr7UWKpyz\ncD2BQPDWxut2vvQJobUIqKq6Evgq8C4gBXwb+D81Tcupqroe+BvgbuAS8Fuaph0oOfaBCleoAAAg\nAElEQVS9wP8ANgKvAp/TNO3iok5AsGzp7YswNJ4sS29dGp7iq4/38oVP3jrv9Nb1piGTySTRyRQn\n+6cYiUM8adRcAu2SJYJ+N0Gf49UVDrnYstLD0b4MXl+wav/pBE5Hi6/KzT6VMehoqd1AejYhWWjB\n8/UfnODVE8NlfmKWZdE3GOe///NR1nSGeOSe9dclagUCwfLE73Weh1MpfYlHcn0sC6EFPA5EcHor\nhoHHAAP4PeAHwDGcHowfAb6nquo2TdMGVFVdA3wP+H3gWeAPcFZM7ln0GQiWJVcnUsUoTimj0fR1\np7fmY1GQTKaITSWxJIWkrvDC8RHG49Uu6ZIEd+3s5p17ergykmA0mqQtAPtvWUlbSzPDMZvUybEq\n09RGGZHWIyQ9bhcfe+dGopOZYt/FRCpHLJFjeDyFJMG5K3EOaWN86dE7hNgSCN5iBPP/n48l9Zor\nspcL9fY6XFvvCfNWEA1DVVUVuBNYoWnaeH7bF4E/VVX1GWADsF/TtAzwZVVV3wN8BvhD4HPAQU3T\nvpI/7lHgqqqq79A07cVGjlNwc9LdHiCnm1iW0wRCwhExbkVe1PRWIpliYHgM3ZKxJB8vHh/ipeND\nGGZ1mtDrdnH/7au5Z1cPAFtWmuzZEKK9rQVZltENk6PnxsusLFIZg50b2qdNXY7HM1UrMv1epabI\nK1CPkPTkBdmRs2M89Vo/qaxRTH1KErhkmEzmeOKVS3zi3VvqulcCgeDmIOh3JIph2kyldJqDniUe\n0fyoN6J1CWZtNlSwvGj0UqGrwIMFkVVCC3AXcCQvsgq8jJNGBNgPFAWVpmlpVVWP5F8XQkswK5tX\ntZDVLYzChz9Og1Ofx7WgbWgKpNJpJhMpQi1NSG4/J86M8ewb/TVD6ZIEbSEva1aEuHN7F7qew2Ub\ndHc04fFcs5Ho7YswEk1XCae9WzqmTV0W7C8qo0qNugeXr04xlcphmNcErW07P5IEA2PJhlxHIBAs\nH0qfN5HJzE0vtN61oKOYAU3T4kBpzZUE/HvgJ0APMFRxyAiwOv/7/8/ence3dZ4Hvv8dHOzcd0rU\nZknWkWRbsmRbthzXdhLHjpcsddok7XRJ0mQ67bTp3tx20nQ6+fTe9nZNem/byWSyNb1t+mmapLUd\n21m8xLa8yJIlW5aPNouUKO4EuGA76/3jACBBgCQIQiYJPd/PR4l1Dpb3ACLx4H2f93kWOy9q3EJF\nMxc7b1o2f/ed17AdF0XxPvRRvEArEvJf1jY0mUyG8fgUpqMQiUbpG0rxT4+/QX+JgMOr2q6i+hSu\n2dLCO/avx7XStDRFqK9rLrp9biZubuC00OxUNWuIzWZkNxu80RvzWv5kA9p8sJW93YaO4nwyIURt\nqwvPhCjjk2muWld+K7LVpNzK8E9d7oEswZ8B+4CbgN/Eq0g/WwbIfX2PLnK+LLN7LtWC3PXU+nUZ\nls1XH9W5NJbAcV1SaYv/eO4899+6hRs0rz5W7nzO0dOjfOyBXQT9KkfPjDIcT3lLhaoPx3VxXYiE\n/Fy7tY3IZcgZMgyDsdgkhq0QDNcxOZnm3753iuNnxkrevj7ipyEaxJftAB1UbZrqFNpaOufNZ1jf\nUY9yaqTkcb+/9L8Jv9/Hx9+zm2NnxhgcS9KdLaRabo2vuXLv0WvnxhkcTxIMqCQzFn6fgpHbNal4\nQWRjXZD33X7VvGNbTa6Un61aUevXVanxsRHS6fm/eL1V7Fk7qGNTmTXxO6CUVZ+jNWccfwp8Evig\nruuva5qWBlrn3CyEtzMRIE1xUBUCYkt53sbGSAWjXf1q/bqeO36J4XgKn09hNJbGNB0mEybfevoc\nJ3vj3LS7i+F4Cv+sX0rD8RRnB6a5dc96JpIm4aCfVNoGBdRs4BJQfWhb2mhpqd4si23bDI/GSBkO\nTW1tpDMWjz5/nu+/eAGrRNHRzd0NGKZNIPuLx7YNsC327b6aq7ctPGF7542bePXceL4yO8CGzgbu\nvHETwcDCgdPc5taGaXP45BD9I9P0dNRz466uBR9j7u1HJtL4VR8N0SDJjIVpOgQDPnyKN2t4+/Xr\n+fDdO6mPrq0lg1r/2ao1tXpdlQoFIbwqfuQUQgGFjOkyOpGo6u/ct9JayNECQNO0vwF+EfhPuq5/\nO3u4H9g956bdeC2Bcue7S5w/upTnnpxMYZf4sFurVNVHY2Ok5q/rTN84lu2QSJkY5kxl4Yxhc35g\ngoxhlgxizvTF2LWxiaZogGDAh19VMGfdrr05zLZ19cRiy88bchyH8fgE0ymLYCiC68LTr7zJYy9c\nYDpVnIfV3RrlPW/bwuZ1DXzzibMMjSewzBSBQIDNPZ3s3NRS1rh+5u6rC2andm9p5snDfQyOJehu\nqytrtiqRNvjrfznOcCyVT45/6uUL+RnBuQzL5ksPneTSWAIFBVVVvBksy8anKLQ1zuSM3XJtNx+4\nYytBv4qZMYll1sb27ivlZ6tW1Pp1Vaq+sZPGpuK0g5XQ3GAwNJ7iwtB0VX7nVlO5gV+lOVoK8Ajw\ncbxg5rLSNO0Pgf8MfEjX9W/NOvU88ClN00K6rueWCG8DfjTr/G2zHieKt+z4h0t5ftt2sNZ49/BS\nav26Opu9wMW0nIKvCX5V8RKtHfINjWfrbA5jWQ7XbmnhpZN14JIPADpbIvzqg9fhQyn7tSuVB+ZX\nfcQnJphKGPhDUfyBAGcuTvLIofNcGivezdgQDfCumzay/+qO/DLhfQe66R2YIGn5WddWx55tbWWP\ny4fCvu3tsL10e52XTg4t2AbHtGz+6hvHOD8wMyuWSHlbsI/qIyULv37r6XO8fn6cgN9HNBxARSFl\n2IQDM/0dIyE/W9dHef/btizpNV5tav1nq9bU6nVVynXdgmW7ldRUF2RoPMXgeGrNvkcV52hpmmYD\nz+u6fq7qoyp8nl3Ap4H/E3hO07SuWaefAi4AX9E07bPAe/Fytz6SPf8l4Lc1Tftd4CG8AOvsKss5\nE5dJLoE7mZ6ZDZldfXzv9jY4y7wJ3nNrQeWKcz5xtL/sAqNzgxjXdXn2lfM8cOsmQuE6gpF6xibT\nPPp8HyfOjxfdX/Up3LZnHe+/82qMjIFtu5iGgYrFxq5mtm3szAdyj790oaL+i0ttr5O7z3Bspqq7\n47qkDZv4VIZLowlu0Ipfg1zCO3jlJLrb6lAUuGZLK+vb65bdP1IIUXua6r3sn9i0ScawvbY8a8xa\nKFj6XsCHF2x9OntMAVxd11VN094PfBE4DJwB3q/r+kUAXdd7NU17EPgc8BngWbyipuIKEJhToylt\nWPnCnF0t3rR6d2uU5rog4ZBKT3t90Yd8rhbUUpsq58wOYsxMCsuyGDTDvDlksm2Dy+Mv9fLca4Ml\nvz1u7KznA7dfxbqOeiJhP+lUGiOdpLkhTGODN61f6bhmW6wv4Xzncvlhlu3kZwYTaYsT58d5982b\n8s+few0CsxJZTdshkTYJB1XWt9dJX0MhREnN9TPJYpfGEmty5+GqD7R0Xf9T4E8XOH+WBcpP6Lr+\nGLDzMgxNrAEBv8rNu7vZv6MjPzPVXB/kqVcG+MYPz+RnuNa313HvzZvnDU4qmfUBLyAxjTSWaaCo\nIUyCmCmLp45d4tvPvFmyh1dA9dFYH8R2XJ545RI/+Y7tpFMJgopJR3cbPt9MwFJqXJdGE3zr6XPU\nRQJlzRAt1pdwvnORkJ/JhFGw/OpXFdKGXfC65AK2SMhfUCTVNB22rmu8rGUyhBBrW3P9zH623qEp\nCbSEWK1mz0z9xTdeoW9oJrcot5y1UNBUyaxPMpnC76axXQU1WEd8KoNh2dgOTKeKA6xwUCUcVPOz\nbgBDY9OcON3Pe+7cxaFXh7h0sq8geJr7/K7rMjaR5vnXh/JT7ovNcJVbI2t2rll7U5ju1igT0xns\nbJFRv6rQ2RJBUZSCceUCNkVRZoqk2g537O/hvls24WNtttUQQlx+kZCfaMhHMuP1QL3z+p6VHtKS\nLSfQcll8J6IQq0YuIbtvaBrHdfFlgxnTckhlrAWDpqXM+mQyGcZiU1j4uGZ7D29cyvDmwCRpwy75\nA+NXFW7bsx6f4vJG3wQArutgmylUVSVjB/nCd05yfmAiP3uUC57mPn8uaT86q9DfYjNv5fQlnL1E\n6bouqYxFKKCyubuR3sFJgoHCAHH2uGYHcrkiqT3tdfynd+8iMZ1eswmuQoi3RmtDgGQmw7lLkys9\nlIqUW0fLoTioUoAzXivCGbqur71MNVHzZidkW5aD7bg4ipuvoWVazoJLZeXM+hhGhvHYFBnHRygU\nJYgX+ERDfiYTpcsTdLdG+Nl7NFoawpw4N8YbfRNYZgbFtQlH6lAUHynTLqh5BTPB09xxze5DONti\nfRln9yUstUsyt0SZmzHLLf+lDZuAvzDImvu6lArk9mkdBAMqq2uzthBiNWptCHJxNMPAWJLplEl9\nZG01mC93RutjyOyVWMNmJ2Qr2Yrjrkt+ZquzJbJgrtBCsz7JVIpnj/UxOJ5mfWcr2qZmbMflxZND\n/ODwRZKZEnlYfh+NdUHeuX8DLQ3ebsZtPfW8csomnlDxB7xk/XVtUSLz7LIZHE9yg9ZZMK5EyuT1\n3lhRVfhcEFlOS6JSyfXdrd54cjNmOZbt0FgXZPfmlgVzwuY2mF6rFZ6FEG+91vqZwOr0xTj7ru5Y\nwdEsXbnlHb5ymcchxGU1NyHbMG1cF/yqj01d9XzyA3sW3aU3N1gwTZP+gXH++YlzxLJxiX7pEs+d\nGGQ6ZTISL25h4fMpNEYDREJ+OlsiaJuacV0XM52gqS7MJz94c1Eg9Nr5GMfPFrfgyQVPs/PPjpwa\n4cT5GKmMmZ9lys0wlbNDcfbMVS6oSqZNGrLfIM05y3xe4KpQFwlw/8Eti7wLQgixdM31fiIhlVTG\n5tiZsdoMtHI0TWvKNnlG07SfAmbP372m6/qRag5OiGopmZBtOdyyu4sHbt3Cyd5Y2XWcLMtiLDZB\nxnQ51Z/KB1mm5TCZMMjMqkKfE1B9vO26brpaIsSmM3Q0eUGWYxv4gZ7uNlTVe865uVR7t7fx6rlx\nzg9M5I/NXZ7LBVGXRhOkMhapjIXrwvtu28KBXV0E/Cov68OL7pwcHC9eHgQ42RtjfXvdvDXJFlp2\nFUKI5fApCrs2NnLkTIxjZ0cLcmzXgrIDLU3Tfg/4jKZpO3RdvwB8AZhdf35Q07Sduq6vzWw1UdNK\nJWSva4vywK1b+Pr3TuUDFNNy+N7hCJ/8wB6ic5pG27bNWCxOKuMQDEcJhhVGJ8dxHJeppFGyVAN4\ngdI9Bzbltylbls3rb47x5OGzbN3Qyk27e/JBVilBv8qvfvB6njzcx6WR6ZLB4PGzY1waTRQESKZl\n8KPjAxzY5dX4LWfnZHdrtGh5ECBj2uy7up19V7cX1SQrtUNRCCGqafdmL9CamDboG5piS/faKfNQ\nbjL8g3gFP3+NmT6CANt1XT+nadpG4BjwCeAvqj5KIZZpvhyrUgHK+YEpPv/N4/zWh64n4FdnAizD\nIRiKEop436Rsx2FsIsNQLFmylc/GznruP7iZTV0N+WOmafEv33+d8ak0gVCUs8NDvNY7tWiB0WBA\n5aadnVjb20ueHxxP5gMk13W9FkPAxZEER06NcPPu7kV3TpqWjWU7GKZT8I0xN3M1OpHm/oNbCmqS\nSSV3IcRbQdvQgOpTsB2Xo6dGay/QAn4J+Kyu61+YdSz/0aLr+gVN0/4S+AASaIlVam6OFRQGKLMN\nx1K8cnqYLR0hkhmbYDhKKOwFHq7rol+I88ihXkYnivOwAn4f73vbFvbt6ChISjfNDG+8OcxkBoLh\nmcngcgqfLqa7NZoPsqxsXSuAtGHx8KFe9u/oWHDn5Oz8rYBfARdcxesz5uVGWPSPJHhZH2bPtjap\n5C6EeEtFQn52bmrmxPkYh04M8r4fu2rNLB+WG2jdgDebNdvcK/wO8NvLHpEQb4Hc7rv+kUS+YGmO\n6zi4VpITZweZmG5jdDJNR1MabVMzY5MZHnm+l9MXJ4oe0+dTuHZLC++9bQvR8EzbCNu2ccw0rU11\nGG4In6949mex8guL2bOtje8djnA2YRRsD3YdGImn8rNa8+2cnJ2/FQ0HSGXsfPA5Pun1a780lmDg\nUHLJLX6EEKIabtuznhPnY4xOpDl5PsY1V7Wu9JDKUm6gFQam5hx7F3Bx1t+nlvB4QrwlSpUzAAqK\nb1q2g2U7qApYZgq/XyUcaaRv1OTMoLdSbjvjPPpiH+NTmZLLhPuubufuA5toqgsWHDfSKaJhH23t\n7SiKUlG7m/xjWTZH9ZGSS3YBv8onP7CH3/vCIaaSXuCoMFPG4tiZMW7e3V1yVg8KA73ZGwbCQT+K\nQkGdrGrMwAkhxFLt39FOXdhPIm3x9LFLNRdoXQJ2ABdyB3Rdf37Oba4Feqs0LiGWbb5yBnu3teWP\nKYpCR3OIS0PjoKo0NTUQDgawHZfRyQx+H9mWOWbJAGtzVwP3H9zMhs76guO2ZYJj0N3eSDA406ur\n3HY3cxmmzZceOkn/6EyJz9zMEpAPJrtaoqQyU7iuF2SVO7U+N9DLbxhojTJQYrZtuTNwQgixVAG/\nyq3XruN7hy9w5NQIU0mDhmhw8TuusHIDrUeBXwd+sMBtPgk8tOwRCVEl8zWCzq2tua6DkUniOC6d\n7S10tdTR0RzmTP8EoxNpUhkLe57uMHVhP/ffspm9V7cX5GG5rouZSdIQDdHSXFzrpZx2N6UcPjnE\npbHCOuoDY0mOnBrh2KzaV7GpNI7j4vMpBcnse7cvHMjNFwDu3dbGwIvFQZWUcxBCrIQf2+sFWrbj\n8sTRft77tqtWekiLKjfQ+kvgiKZp3wB+S9f1/JKhpmldwJ8B+4Cfr/4QhahMriZULtk9t3vOdV2M\ndALbcVADkXyV8muz09DHzo6TNux5g6yGaID6SAD9Ypxrt7bizwZJppkhgENPV+uC5RrmW75bSP/I\nNEDR9Rw9PcpQLJWvfWWYXj9FJzuj1VQXZOv6RvbvWLjA33wBoPd6jC15Bk4IIS6HDR317Nrcwsne\nGN8/fJG7b9pIOLi6s5bKrQx/TtO09wNfB3o1TTsNjACteEuKw8CDuq4PLPAwQryl2pvCBWUbXNdl\nYmKS66+Kksw0MTZp5G/b0Rymp6OOf/z+aUbiqZKPp/q83KVcf8SReBq9L86uLS3YRoqWpij1dXUl\n77tcPR31uC5FhUTP9k9SF/Hngy9FUfD7IBBQCfp93Kh18OO3byXgV+dtvzP3+N03bSyYYatkBk4I\nIS6XXJHp6ZTJU69c4p4Dm1Z6SAsqOwzUdf1JTdN2Ah8E7gC68HKy/gb4p1zFeCFWG9d1sc00rmMT\nCkeJRBv4yTtb0fvijEykaG0IMTaZ4XP/+mrJqu6qT6GzOQyKUtRDcGAkxt6r6mlb1150rppu3NXF\nvz1xuiDI8trfuEXlKRRFoS7sJxoOUBcJ5IOpUvlqP/OuHXz9e6cKjr/0xjD7rm5ndCKdD6wk8V2I\nK8f46DDJ5OrJw0wnp4nHw/m/d9a7bO6M0juc5LvPn2ffVdH8F+Dm5mZ8vtXVS3VJ8226rk8DX8r+\nEWJVG51I0xB2SLomdiBMMBAgHPQTm8rg96vsvqqVE2+O890X+ohNZYrur/oUGuuCbOysY9fmFn50\nfDB/zrEtHDvNtg3raW9ruezXEgyoXLOlhfHJdMEyKJBN0p8pUVGqNc7cfDXXdTnbP8Hn/vU48elM\nfleh67qceHOcNwcm85XxcwHZUtoUidphWjZHz4wykTRpiga4dkuLvPc1znZMXNtc/IZvkVAoxGt9\nSXwXZ+oWrm8N0jucZDJp8c9PvMnW7ijJ5BR3HdhBa+vq2o24uhc2hajQxOQUqpMGJUB9Q6TgXEdT\nhP7RBA8fOs/5gblVSyAUUNm9uYW2phBdLVG0Tc0AvNEXZziWwjZTqD4fWzd2c+Ou9W/F5QDQ09lA\nNDxadPzemzcCLNgaZ/YuQcdxGI6lsGyX+LSBAiTTVr6kQy7PKxfQ9Y9M8/lvHidjzsyaSS2tK0Nu\nJnRwPIlf9WHZDi+dHJL3vsZ1dPZQ39i80sNY0PYGlzf6U8SmMpwZSHPt9nUrPaR5SaAlakp8YpIL\n/WM4SoBrr+5BH8gwEp/5FtRcH+Rkb4yjp0eZW61BUeCmnZ3cdeNG6iMB5nrvwQ2c6RslZXfQ09Hw\nls/q7N3exksnh4oS0/fv6CDgVxdsjZOb2XIcl4GxFFY20992vFfBsh1G42kgV1nexsouR04qEA6q\nNDfMTN1LLa0rQ24mdPaquLz3YjVQFIVrt7byo2MDTKdM3hyYZFa3s1VFAi1REyanpkgkMzS1NuEP\n12HbXgDxgdu3ovfFGYwlGY2nOdkbw7CKtxNuXd/I/Qc3s66tOJndcRxsI0VHc4SrerTLfi3zCS5S\nGqLUbsZ8BfzRBH5VoXd4GsspLgjmuJDMWPh8XvWL2dlmlu2WrCEmtbRqXzmNyIVYKZu7G3jl9ChT\nSZMTb47Ted3qnIVbcqClaZpP1/V5Nr4L8daamp4iPpVG8QUJhevwB4LMzldSVR+26/KyPkJ82ii6\nf1tjmPtu2cTOzS0lk9mNdIpIUGHdZU52L9dSSkPkln0ujXpthmJTmfwM1ryPr/owXQfbdVGyJSL8\nqg/FV3ztUkur9i2nk4EQl5tPUbjmqlaePzFEfNpgKF78O341qGRG66KmaV8FvqLrul7tAQlRjunE\nNPHJFPiCBEKlSypcHJ7m4UO99A4V52GFgyrv2L+BW67pyu9WmS1X2b2rrZFQKFR0/q1kWDbPHb/E\nmb5xOpsjZS9Zer0cpxmOpTAth0ViLMCbvQIKprVUn0JHUxjTnnkAqaV1ZcgVsp09gyXvvVhNtq1v\n5JXTo6QNm9P9icXvsAIqCbT+CPg54FOapr0AfBn4Z13XJ6s6MiFKSCQSxKaSuATmDbAmpjN89/k+\njp4uThz3KXDTri7uunEDdeHiPCzXdTHTCRrqwiUru882X12qajItm68+qjMc9/KqXLf8RPT+US/I\nMkynKB9tPq7r4gKqouDLzmIpCtxx/XrCQb/sOrzC5ArZvnY+JrsOxaqkqj52bW7h6OlRRiZN+oaT\na3/Xoa7r/xP4n5qm7cALuH4P+CtN074NfFnX9e9XeYxCkEgkiU8lcAgQCJYOsAzT5uFnzvHo870F\ndaVyrt7QxH23bKarNYpl2Zw4N8bIRIqOpgjapmYc2yCoQk9324KV3WH+PorV3o11/OwYl8YSBbNu\n5SYjpzP2zAxVGXyKl2CqKtBUH8Sy3XypiPi0wf0H37odlmL1CPhVbtrZSUtLHbFYIr9JQojVYsem\nZl49N4Zluzx7YoTrd25Y6SEVqDgZXtf1U8CnNU37I+C3gD8APqxpWh9eEdPP6bpeXP1RiCVIJlPE\nJqdxFD+BYB2lQhjHdTl+ZozHXuxjIlG8Rt/eFOa+g5vRNjajKAqWZfPNp8/ldyM6zihHT13gEw/s\noamxvuj+pczXR3GhAGgmMX2adMYmHPLT01634OzQcpKRwyE/ftWH4dgo5Fs84svuIgwG1HwpB7+q\n4PP5cB2XYMBHXaSwUavk5AghVqtQQOWqdY2cvjjBa+cnyJg2ocDqmXWtONDSNO1mvBmtDwFh4N+A\nrwAb8ZYXbwJ+avlDFFeiVDpNLD6NhY/gPAEWQN/QFA8f6uXC8HTRuUhI5Z03bOTm3Z2osyoF633x\nfJBlmSlUBaYzUc4MJLmhzEBrqQHQ7MT0XBudgN9HW1M4PxMGFC1FdrdGcVyX6ZRJ2rAIqN4MUzmB\nT097HZ0tkWzNLCe/RT8S8rNrUwvXbWvl1XPjDMVmWg51tXg1x2Yfk5wcIcRqt3W9F2hlTIdXz45x\n487VU36kkl2HnwZ+FtgOHAb+G14LnslZtzGAL1RrkOLKkc5kGI9NYbkKwVCU4Dy3i09nePSFPo6f\nHSs651PgwK5ONnTUM5k0eON8DG1Tc77588hECts2cW2DYDCM6vdytZayZX2pu7FyM2C5djmO65I2\nbOLZivRHTo0UNW9++dQIH3r7dpIpi6mkiYsLrlcJftfmxavR5xKZFYX889qOS3N9kKF4iqGX++lq\nifDuAxsL2u3kxiv5WEKItaKzJULQr2BYLvqF+NoOtIBfxWsu/SVd10/Mc5uTwO9UPCpxxclkMozH\npzBshVB4/gArY9o8fewSPzp2qWT+0XXb2njHDT08eaSf504M4bouacPiB0f6ueP6deze3EJT2EHF\nIRAtrG63lOWxXBAzt3jofDM/uSDOsJx8UjtAIm1hOy5HT48WzCKBtxT52It91EUC+P0+MoaNX1WI\nhPyc7I0VLFHOl5j/0Xt3ekHcmTHGJ9OMT6ULylQMxVL4VR/3H9xS8NxSjFIIsZYoikJrQ4DBmMGZ\ni6ur9XIlgda2bM/DIpqmvV3X9Sd0XT8KHF3e0MSVwDC8GayM4yMUihIq3ggIeHlYr5we5fEX+5hM\nFvfg6myJ8MCtWzhw3XqeOXqRkXga13WJT2WwbIcp1+Dfnz7FD8Mq77ldY0O3tejy2EK7CgOLFA+d\nKxfEuU5h8U9F8VrdlOq1CHBhZBpFgfpIgHBQzd939uxbMm3y+W8eZziWyievz16OPHZ2jIHxJBPT\nGZJpi1TGpq0pnA+4pPikEKIWNNV5gdZIPLX4jd9ClQRa39Q07QFd1/Ofdpqm1QF/AXwC5k2nESIv\nH2BlZ7AWqlTVOzjFQ4fO0z9SXCMlGvLzzhs3cGBXF8GAl4c1GvcCh7RhYdkOjm1jZFJY/gA2Af7j\nuV6uWteYXzJrb/Jayzz+0oWC5bPFdhUupXhobgYsPpVGUbzlPyW7yy+3tJcx7XyfwpyNHfW8cSFe\n9Hi5wM20bD7/zeMFPRuTaa9g6/GzY1i2w9n+CW+5MltIy7QcUhkr3zRaEt2FELUg6Pd+d6YyFo7r\n4lsFRaahskBrM/BtTdPer+u6qWnaXcD/AhqBj1d1dKLmFAVY88xgAcSm0jz6QvkUvKIAACAASURB\nVB+vnhsvOudTFG69tpu37+8hEir8Z9zeHAXGsWwX20xj2zZqMIrf7wVipuXkl8zuvmljyYBq77a2\nJe8qXEhuBuxbT5/j0InBfKCVNmwcx1veTGVsEmmLaMiPZTt0tkS458AmJpMmw/HChPWMafOFfz/B\n+GSaCyPTuK6bD9BygdSl0QSH9REmshXxXdfFcb0CpLnyF5LoLoSoFUq2yrKL19fVp67dQOtO4PvA\ntzRNGwB+AfgG8Gu6rg9XcWyihiwlwMoYNk++0s+zrw6UzMPauamF+27ZRHtzpOT9d25u5tipQaYm\n4yg+P35/GHfWt5tANuDKLZmVCqjmq/C5nGW2gF/lx2/fSjxhMDCWJJk2SWVsggGVaDhAJORnOJbK\nzjb5SRs233jiDD9/r0bfSAr9zTGmkgav98Z45fQItuMFTrnlxIBKUbCVNrzZLcfNLVm6hIJ+dm5q\n4QatQxLdhRA1I5GdzW9pCJXs+LFSKilYOqhp2p14wda7gQd1Xf92tQcmaoNhGNkAi0UDLMdxOXJq\nhMdfusB0qjgPq7s1yn0HN7O9p2nex3BdF9dM87N3beXsUA+PPN/HxHSGtOGVdMvlMIFXX+tlfYSJ\n6Uz++GL9DBdbZlsoryt3rrs1QnNdkL5sSYrc86YyFq7rjTG3rDcwluT183HuvHETT718gdMX44xP\nprFL1Ix0HBc1+w2usyVCOKQSDqqMO25+2RDAtl1+5u4d+ecQQohaEJv2Aq1cmZrVoqxAS9O0nytx\n+GvAHwMf1TStgWxnNF3Xv1a94Ym1yjRNxmKTZCx30QAL4NylSR45dJ5LY8UzRnVhP++6aSM3ap35\ntjClWIaBYiv0dLUAPtrb4LqtbXznmTd58eQwruvSWBcglbEIBVR+8PJF4tkEcfBym3JJ4nu3t8HZ\nmdku13UJB1X6R73gqNRM0ELV4qE45ysUKAzucst5uRm3nMGxJIdPDnFpLIFpOQXJ9LP5s8FiZ0uE\nT35gDyd7Yzz76qBX8d2n5NsXRsNq0a5FIYRYyyYTBuPT3hf0PdvaV3g0hcqd0frKAufek/0D3oKL\nBFpXMMuyGItNkDZdgqEIIf/CM0Rjk2kefb6PE+eL87BU30weVjg4/z9Vx3GwjRTtrXX0rOsgFkuQ\nSpscOTXCI8/3kTYsGuuCpDIm45NGflluOJbC7/fhVxUs280vt23raWL/jg6u29rGQ8+dp294itiU\ngeu6HD09xtHTYyXb7SxULT7337OlDZtwUCVjzgRYuZk113Xzta+m0yZ9g5P525SiKNDZEuW9b9uS\nDwL3bGvj3589j6IY+WKludky2WkohKglJ970PkN8Chy8pmuFR1OorEBL1/XVs9gpVqVcgJUxXQKh\nCKHwwgFW2rB44kg/z702iO0UT9Fcs6WVd9+yibbG8IKPk8mkiQagu7uNYDYYM7IzS2f7J/KJ4Em/\nRSSbZA4uyYzt5TgZNk31QXzZBPGW+hDdrRGOnBrJ17ZKpk0mpg0Cfh+tjSHShs0bvTG+9fQ5fvz2\nrflga6nV4hVFYfeWFnra6xkcT9LSEOLJV/oZiaVIG3Z+GfH1N8epjwawHQfHKZ0+5lMU7r5pQ8Es\nVcCvct8tm/i3p8/lK9HnZtBkp6EQolYMx1KcztbOun5bC031C+1jf+tV3IIHQNM0P7AHGNJ1vb86\nQxJrSX4Gy3AIhqMEFwmwHMflsD7M9166kE9cnG19W5T7Dm5h6/rGBR/Hti1cO0NXayPhUOEP1bEz\n3sySka3A7jguluNgmA6O6xDPBl+5vKXJpEl3a4Rk2iI2neHo6TGS6cH8UmJuSc8wvVmw3NLd868P\nEU8Y+ZmtfK2sWbNRAb+P9qbwvImZPe313KB15pcdDdNbGrRsN1+cdDJhkMpYJNMWybRZtHToVxWC\nAbXkc+zf0VFUcV52GgohakXGsHnu1QEAAqrCA7esX+ERFSs70NI07WeAX8dLfu/TNG0X8F283oau\npmlfAX5RGklfGWzbZiwWJ2U4BENRQpHFt9Ge6Z/gkUO9JWd4GiIB7j6wkX1XdyyYhwWQTk3TVBek\npaOj5PnBsYRXET5jzexadMHBC5gUxUX1KZCtZ2XZDiPxNLbj4jMsDNMuWErMLdflAiDVN7N7cXbJ\nhz3b2njpjWFeOzfmBUx4QdDhN4a5/ur2fBmHXDL+7Jwvy3YYGEuiKAo+n5IvwRCfzuDzKd5Mlut6\nS4CzC57i1RJrbggxOpEuei2WWlhVCCHGR4dJJldPekEkHEEp8blgOy7PnIjli1jvWB+gMbr6NvmU\nmwz/AeCrwL8AuVf/K0AzXn7WBPC/gV8D/rLqoxSrhm3bjMcmSGQsQuG6RZcIAUYnUnz3+T5O9saK\nzvlVhduuW8cd1/cQCi784W+aBn4sNnS14vfP/0+3u63OK1jnuMyJS4CZgGnmgDdb5QJTSYfZV2SY\nNk31oWxFdQsUsB1v3OFg4ZJhwK9y3dZWjp0ZxXFdUMC04ejpUc4PTlIX8RoLOY6XWJ827HzOl+O4\n+QKmAb8vX44hN3vlum7JJVY3+78LLQcupbCqEELYjolrF+/8XgnJ1DQ37milubmwv6tpOXz9B+cZ\nm/LGeds17bzv1h6am5tXYpgLKndG65PAZ3Vd/+8AmqZdC9wE/F+6rj+SPfZp4DNIoFWTbNsmFp8k\nkbYIhqOEIwvnToFXnfeJI/0cOlE6D+u6ra28++ZNtDQs/FiO42AZKVoawzTUL/5DtHd7G996Sp13\nd95cLhTcdvbdEimTgF9F9XlBkOO6gItle7sBw0GVC8PTvKwPs2tzCz86NjCzM9DFawQNxKdNFMVL\nus8YNmnDS0pPpk0My8EwbBQF6rKtdpTs1FUuiV31UbKkA0AyY7OzJSLLgUKIqujo7KG+cXUELNOT\ncZqbW2htbZ05ljL5+28e58xFb5PQDTs6+Mj91y66GrJSyg209gL/Zdbf34H3efQfs469Amyv0rjE\nKuE4DuPxCZJpC38wQiiyeJKh7bi8dHKI7x++SDJTnIfV01HHAwe3sLm7ocS9C2UyaSIBl+7uNny+\n8vdkhIN+b12t1JTWErgoJDOWl9/leGGTV/fTxQaslMVrb44zMJbkGz88QzJtUiKmxHZc4lMGPp+C\n7bikMl45Cct2Z5pMZ/seBgMqjVE/0ymLcMhPQFWYSpnzXojqU9h3dbssBwohat7geJK/+ebxfN7p\nDVoH//k9u1dtkAXlB1pBYHaXxtuBaeClWccCgFGlcYkV5rou8YkJphIG/lCUYLi8XRynLsR55Ple\nhmPFTT0bowHuPrCJ669uX7QHlW1buFaGzpYGIuHFZ89mO3ZmDMOyCQVUMtl8q0r4lGw/QrzXo+Rt\nfAqW5eSLooaCKgp2yZAod0xRwHbBNZ18nhh4AVMk5MfnU9i/o5OJhJHf9WhZ81+DX/WVzM8SQoha\ncujEIF97VCdjeqngd92wgQ+/8+pVHWRB+YGWDtwAnNc0LQzcBfxwTuL7A8CpKo+vgKZpIeAw8F91\nXX86e2wLXq/Fg8B54Dd0Xf/erPvcBfwVsBU4BHxC1/U3L+c41zIvwJpkKmmgBsIEI/Vl3W84nuK7\nh3rRSzRA9qsKt+9dz+171xMMLDzr4rouRjpJY11o3mT3xQyOJVAUhbamMGMTaVzXLrl0uRAFQFHy\nAeF8P8a540Z2Z6LPpxAI+DCytbFyP//ZlC0AggF1pslzdliK4pVo8PkUmupDNNYF+Yk7t3H87BhP\nHbuEablMp4yioFFRvMryUq5BCFGrMqbNlx85yY+Oe7sLVZ/CB9++nbtu3LBoN4/VoNxA68vA5zVN\n24C3bNgI/B2ApmlB4H3Ap7N/LotskPVPwO45p74NHMMLBH8crwfjTl3XL2qathH4FvAHwGPAH2Zv\nv/dyjXOtcl2XickpphIZfIEQwXBdWfdLpi1+cOQiL5wYLLlktnd7G/cc2ERzGXVNDCNNUHHp6WpZ\nMNl9Md1t3tgVRSEa9mNaXoK7ne/3V5pPmekVmAtgHMclEvJjmF4O1ez7K9kZL4Cg30fasAn6fTRG\nIwzHUli2S13YTzCgMpU0CAdVggGVSMhPMu3tbrQdl4xpF/Vh7G6N5pPYfaqPRw71Ylq2N9M3a8yR\noJ+t6xslP0sIUZOGJwz+4l91xqe8BbO2xjD/5f3XsG39/K3YVptyC5Z+XtO0drxAygF+U9f1x7On\nPwf8IvAPwP97OQaZLSXx/5U4/g68mapbdF1PA3+iado7gY8B/wP4BPCSrut/nb39R4FBTdNuz82I\nCbwAazqNEggRKDPAsh2HF14f4gcvXySVKa7osbGznvsPbmZT1+J5WLZt45gp2prqqatb/szM3u1t\nvHRyiIGxZD6oyS39ZUyn5OyWT4FwyE9bY5i0YREO+rn7pg28em6cwfEkqYwXGPl9Cq7j7fJTVQXb\ndgn4fTTVhwhmrHxB0K7WKOGgyjVbWulsieSLn+bk6oQNjicZm0gXFBSdW+dq7/Y2Xj03jmU7JFIm\nybSJqvrQNjZzg9bB/h0dkp8lhKgphmVzRB/h1IWJ/LEbtA4+cu9O6tZYn9aypw10Xf8M3q7Cuf4W\n+Ftd11+t2qiK3QH8AC/Qm13c42bgSDbIynkGbxkxdz4fUOm6ntI07Uj2/BUfaE1NTzMaS+BTg/jL\nDLBc10W/EOeRQ70l84Ka6oLcc/Mm9mxrWzQPC8BIJ4mGVdraO6o2BRycUzuqvcnL8RqKJUlnbFTV\nx4XhacYmUjgu9LRH2bu9PZ/rNLvW1IFdXRw/O8al0QSpjEU4pNLVEi14vEjIz/r2OnZtbuFkb6xk\nvar9OzqKalkBRY/d015fVOcq6Ff51Q9ez5OH+7g0Ml1xLayFGl4LIcRq4Lou5wenOPzGiFdSB6gL\nq/zsPTu5aWfnmlgqnGtZleEBLnOAlXuOv8/9t6Zps0+tAy7NufkQsKHM81ekRCLJZGKaiYRNIFRe\ngAUwNJ7kked7860OZgv4fdxx/Xpu27OOYBkf3pZporgG3e2NBIPVb5dQrdpRuce5QVv8tsC8zznf\neMp97GBA5aadnVjbK2uWulDDawm2hBCrQXw6w4uvDxcUte5pC/Hx+7azuWd19S9cimUHWissCmTm\nHMsAoTLPX1ESiSTxKW8Gq62jGX86iV3GjrzplMkPXr7IiyeHSuY47bu6nbsPbKKpLrjoY7mui5VJ\n0lgfpqmxsmR3sXQLNbyWYqZCiJVkWg7Hz47y+vlY/jOmPhLgwK5OmsMWDZG1tVQ411oPtNJA65xj\nIWaWF9MUB1UhoLhE+QLUefrUrRXJVIrx+DQufoKR+vz1eP8/TxVMvLYwz702yA8OXyRtFOdhbelu\n4IG3bWFjZ3k7E41MmnAAejZ0LKkmVrkKr6t2VOO6huMpSs24D8fT+P1v/esl79XaIte1tiz3ehRF\nybcau5xyy4Qvnhwmme19q/oU9mxr49qtrfhVH1OTcfx+34r8nqqWtR5o9VO8C7EbGJh1vrvE+aNL\neZLGxkhFg1tpyWSKsfgUlqPS2lG85FRfX7o+leu6HDs9yjefOM1IiXpYrY1hHnz7dm4oc73csixc\nK82W9R1Eo5f/tVyr79dilnNd2ze1cvT0aInjLbS0lL98XG3yXq0tcl1Xhmg0QDR6eRd+YpNpnn6l\nn4vD0/ljW9Y1ctve9TTN2qVum0Gam6Mr+ntquZYcaGma9jng87qun70M41mq54FPaZoW0nU9t0R4\nG/CjWedvy91Y07QosA+vzEPZJidT2PP1P1mFMpkMo7FJLMdHMBQGbFLpmWUjVfVRXx9mejpddF0D\nYwn+49nznO2fLHrcoN/H2/f38GN71xHwq0xOFgdhRWNJJ2mIBGhtaSKTcchkEsu+vvmoqo/Gxsia\ne78WU43r2rauns7mCJfGZl7/9W11bFtXTyx2+d6T+ch7tbbIda0tueuqVDJp4g/MzbqpDtNyOHZm\nlBNvjudLAtVHAty8uzO/Sz2ZnHnuZMogHk/i8y2tcPVbodzgr5IZrY/gFQBdDZ4CLgBf0TTts8B7\n8XowfiR7/kvAb2ua9rvAQ3gB1lld159aypPYtoNlrf4fwkwmw3h8CsNWCIUjXn+8kjlY3rXYtpM/\nP50y+d5LFzisDxflYSl4u+bedWAjjdFg9r4L53ZZponPNelsbSAYDGVvv4xeOEuwVt6vpVrOdflQ\n+Pl3a0W7Dn0oK/payXu1tsh1XRnma2K/3MfsG5rmpTdmlgl9PoVrr2rNLxOWek7HcbGstf3+VBJo\nPQL8qqZpf6jr+vSit66+/Duh67qjadr7gP+NVzH+DPB+XdcvZs/3apr2IF6tr88Az+IVNa0phpFh\nPDZFxvERCkUJLSFvMJeH9cSR/nxbg9m2rGvg/oNb6Gkvv/yDmU7Q1BChqbGyHXLi8qjWTkwhhFiK\nyYTBC68PFWzI6Wmv46ZdnTSWsYlqrask0FoHfAj4dU3ThinsgYiu61urMbD56Lquzvn7OeDtC9z+\nMWDn5RzTSjEMg/HYFIYNwXB0SVspXdfltXNjPPxcL+NTxVPELQ0h7r1lM9dsaSm7bomRSRHyQ093\nG6oqJQOEEOJKZtkOr54d48SbMZzsUkld2M9NuzrZ2Fm/JmtiVaKSQOuJ7B+xQizLYiw2Qdp0CYWj\nBJe487V/JMEXHzrJ6RJ9CUMBlbfv6+HW67rxl7lzxbYtHCtDe3M90YgklQohxJVucDzJodcGmUqa\ngNfL9ZqrWrhuW1vZny21YsmBlq7rf3Q5BiIWlw+wDMebwQov7dvAZNLgey9d4Ig+UpQtpShwo9bJ\nXTduoCFa/lRuJp2gMRqgub39ivl2IoQQojSvdc4op2Z9kV/XFuXm3V1XxDJhKRWVd9A0bQ9wHZBb\nH1Lw6lPdpOv6J6o0NpFl2zZjsTgpwyEYihKKLC2gMS2HZ18d4MlX+jHM4oTCresbuf/gZta1lb99\n1jQN/Fis72gmEFjbxeSEEEIs33AsxTPHB5hOebNYwYCPA7s6uWpd4xX9RbyS8g6/Cfx59q8uXpCV\n++8rvn9gNTmOw3h8gkTKqmgGy3VdXj03zqMv9BKfNorOtzWFue/mTezcXH4eluM4WEaK1qYI9XXN\nSxqPEEKI2uM4Lq+eG+P4mbH8asnmrnoO7O4iElrr5TqXr5JX4L8Cfwr8EdCLV5eqDfgn4DvVG9qV\ny3EcYvEJEikTfyhKKLL0wnEXR6Z5+Lleeoemis6FgyoP3LaVfdtbUSg/eDPSKSJBhe7utstS2V0I\nIcTKSyaLPzfmkzZsXjg1wdikN4vlVxX2bW1gY0cEOzPN9DLLcXljWbe8B1lhlQRaG4Av6rqe1jTt\nGN5y4XeyM11/Cfx1VUd4BZkbYAUrCLAmEgaPv9hXsgq4osCBXV3cc2Aj67ubmJgor9ehbZm4doau\ntiZCoSuyTaQQQlwxbtzRQXNzy6K3uziS5MuPv8lEwguytnTV8dPv2ERrQzU/J9bR3Ly2V08qCbQS\nzORmnQGuwZvJOglsqc6wriyO4zAxOclUwkANRioKsAzL5pnjAzz1yiXMEoXdrt7QxL23bKa7NYqq\nljeL5bouRjpJY12IlmapvySEEFeC5uYWWlvnthEu9NIbw3zxoTP5z5t7b9nEg7dvRZXVjiKVBFrP\nAv+Hpmm/gtcz8Bc0TfsTvFY3xX1bxLxc1yU+McFU0kQNhAlGymvOPPcxjp0d47EX+phIFOdhtTeF\nue/gZrSNzUtKRjTNDAFserpa8PtljV0IIYTnqVf6+dqjOi5ea7aP3LeTW3bPbSsscir5BP094HG8\nXK2/A/4bMA7UAX9WvaHVLi/AmmQqaXgBVriyLa99Q1M8fKiXC8PFBfojIZV33rCBm3d3LekbhuM4\n2EaKlqYI9XWLTx0LIYS4cjz2Yh/f+OEZABqjAX79g3vZ0t24wqNa3Sqpo/WapmnbgDpd16c1TbsZ\n+Gnggq7r/1r1EdYQ13WZmJxiMpHJBliVdSOPT2d47MU+jp0ZKzrnU+Dma7p55/4NRMNLe3sl2V0I\nIcR8nnqlPx9ktTSE+O0PX7+kskBXqorWhHRdT2ma1qFp2j14JR3+Udf14eoOrbZMTE4xOZ3GFwhV\nHGBlTJunj13imWMDmCU6zWsbm7n3ls10tiytOrslye5CCCEWcPzsKP/w2CkA2hpDfOqn99PeLJ1A\nylFJHa0g8DXgg4AD7AD+XNO0BuADuq5LntYsE5NTTE2nUfxBAhUGWI7r8srpUR5/sY/JbDuD2Tpb\nItx3y2Z2bFzazgzXdcmkEpLsLoQQYl4DYwn+7tsncFyXurCf3/jg9RJkLUElM1qfBvYC7wAeyh77\nPPBl4E+AX67O0Na2yakpJqczKGoAf4UBFkDv4BQPHTpP/0ii6Fw05OedN27gwK4uVN/SipkaRgbs\nlCS7CyGEmJdlO3zhP14nY9qoPoVfefA61rfLcuFSVPIJ+1PAL+m6/qSmaS5A9r8/jjfTdUUHWlPT\nU8Sn0ii+IP5QtOLHiU2lefSFPl49N150zqco3HptN2/f37PkqruO4+CYaTZ2dWBFw1glSkEIIYQQ\nAN955k16B70Cpg/esRVtk2ySWqpKAq0evPpZc/UBCxfeqGHTiWnikynwBQmEKo/2M4bNk6/08+yr\nA1glionu2tzCvTdvqmjaNpfs3rWunYaGemKx4lkyIYQQAmA4nuKxF/sA77PnngObVnhEa1Mlgdbr\nwF3AF+cc/3D23BUlkUgQm0riElhWgOU4LkdOjfD4SxfyDTln626Nct/BzWzvaVryY9u2hWOl6Wxr\nIhwKyY5CIYQQi/rXJ89i2S6qT+Fn79HwXcGNoZejkkDrvwPf0DRtd/b+P69pmgb8BPChKo5tVUsk\nksSnEjhKgEBweevV5y5N8sih81waSxadqwv7eddNG7lR68S3xDysgsruHZLsLoQQojwDYwkOv+EV\nE3j7/h66WytPhbnSVVJH6yFN0z4A/D5gA78DvAZ8SNf1b1Z5fKtOMpUiPpHAVlQCwbp8L6JKjE2m\nefT5Pk6cL87DUn0zeVjh4NLjYansLoQQolLff/ki4H0W3X/L5hUezdpWaR2tR4FHqzyWVS2VThOL\nT2PhIxiMspzFt7Rh8eTRfp59dRDbKc7DuuaqVu69eROtjeElP7ZUdhdCCLEcpuXw3GuDABzY1UlT\nvdRXXI6yAi1N036u3AfUdf1rlQ9n9Uml0vQPjpAxIRiKUlmzHI/juBzWh/ne4YskSuRhrWuLcv/B\nLWxdX1k7A6nsLoQQYrlOXZwiY9gA3LZn/QqPZu0rd0brK2XezsUr8VAzxuNT+AJRgr7imaelONs/\nwcOHehkcL87Dqo8EuPumjezf0bHkPCzwkt1dO0NnWyNhqewuhBBiGV47PwF4n007Ni59A5YoVFag\npev6FTs9oigKXvxYmdGJFN99vo+TvbGic35V4bbr1nHH9T2EgpVle2XSCRqjAVo6OioeoxBCCJFz\nbmAagGu3tqLK6siyLSlHS9O0KJDSdd2ddWwXcF7X9VS1B7eWpTIWTxzp59CJ0nlY12718rBaGpae\nhwVgGgaqYtLTKcnuQgghqmdsygBgx4altXUTpZX9Ca1p2k8BnwPuBV6edeqvgRs1Tfu4ruvfqvL4\n1hzbcXnpjSG+/9JFkhmr6HxPRx33H9zMlu7K8rBc18VMJ2hujNDYID8EQgghLo9K84VFoXKT4e8E\n/gH4KtA/5/SvAZ8C/kXTtDt0XX+uqiNcQ05fjPPwoV6GY8WTe43RAHcf2MT1V7dXXPTNyKQIqtDT\n3YaqLqewhBBCCDE/BaR2VpWUO6P1e8Df6Lr+G3NP6Lr+BvDRbN/DTwP3VXF8a8JwPMV3n+9F74sX\nnfOrCrfvXc/te9cTDFQWHNm2jWOmaG9uIBqVjulCCCEur9bGcMWfWaJQuYHWPuC3FrnN3wIPLW84\na0sybfGDIxd54cQQjluch7V3exv3HNhE8zJqkBjpFNGwj7b2jmxivhBCCHF5tTbKDvZqKTfQCgOL\nJbuPA1fEPKPtOLzw+hA/ePkiqYxddH5jZz33H9zMpq6Gyp/DMnHtDF1tTYSkZIMQQoi30HImCESh\ncgMtHTgInF3gNrcCvcse0Srmui6nLnh5WKMT6aLzTXVB7rl5E3u2tVWch1XQn7BZ+hMKIYR460XD\nspu9Wsp9Jb8OfFbTtB/qun5p7klN03qAzwJfqubgVpOh8SSPPN/L6YsTRecCfh93XL+e2/asI+iv\nfE1b+hMKIYRYDUKSn1U15X6a/z/ATwAnNE37EvAcEAPagLcBHwFOAX9+Gca4oqaTJt9+upcXXh+i\nRDks9l3dzt0HNtFUV3lzHsdxsIwUrdKfUAghxCoQrrCItihWbmV4W9O0u/BmrT4GzN59OIQXiP1x\nLRYt/fQXXy6Zh7Wpq54HDm5hQ2f9sh4/k0kTCbjSn1AIIcSqEQ7Kqkq1lP1K6rqeAX5X07TfB7YC\nrcAIcG52pfhaMzfIaq4Pcs8BLw9rObsAbdvCtTJ0tjQQCVdWHV4IIYS4HPyq7HKvliWHrLquW3jL\nhFeUoN/Hnft6eNt16wj4lzfzlOtP2NzeLiUbhBBCrDo1O3uyAmRucBGKAjdoHdx140Yao5XnYQGY\npoEfS/oTCiGEWN0k0qoa+bRfxOc+eQspQ8G2K/9XV5jsLv0JhRBCiCuFBFqLCAVVUoZT8f0l2V0I\nIcRaIxNa1SOB1mVi2xauLcnuQggh1qASbeVEZSTQqrKZyu5BWjo6Vno4QgghxJJJmFU9EmhVkVR2\nF0IIIcRsEg1UgVR2F0IIUUtk5bB6JNBapkw6RTSoSLK7EEIIIYpIoFUh2zJxHYOutkbCodBKD0cI\nIYQQq1DNB1qapoWAvwUeBJLAX+i6/peVPt5MsnuIlmZJdhdCCCHE/K6Eta4/B/YDdwK/DPyhpmkP\nVvJApplBsVL0dLXQ0txYxSEKIYQQq4dPusNVTU3PaGmaFgV+AbhH1/VjBul2lQAAIABJREFUwDFN\n0/5v4FeAfyv3cRzHwUwnaZFkdyGEEFcARSKtqqnpQAvYi3eNh2Ydewb4/XIfwLYtgopJ5zppAL3a\nGZbNc8cvcaZvnM7mCHu2tRHwqys9LCGEWHN88nlXNbUeaK0DRnVdt2YdGwLCmqa16bo+ttgDbFjf\nRTyexLIqb8MjLj/TsvnqozrD8RSW7eC68PKpET56704JtoQQYol8MqNVNbUeaEWBzJxjub+XtVVQ\nURRUtbZS2XLXU0vXdfTMKANjSVRVQUEBxWVwPMlr52PctLNzpYe3LLX4ftXiNYFc11pT69e1HH7V\nh99fW6/LSqn1QCtNcUCV+3uy3AdpbIxUbUCrSS1d10TSRFW9b2De/yv54y0tdSs4suqppfcrpxav\nCeS61ppava7laKgP1czvzpVW64FWP9CuaZpP1/Xc2l83kNJ1PV7ug0xOprDt2lk6VFUfjY2Rmrqu\npmgA23ZRVQXbdnGznbqaogFiscQKj255avH9qsVrArmutabWr2s5Uiljzf/uvNzKDURrPdB6BTCB\nW4Dnssd+DHhpKQ9i205N5mjV0nVdu6WFl98YZjiewsXFdWFdW5Rrt7TUzDXW0vuVU4vXBHJda02t\nXteyuMhrUiU1HWjpup7SNO1rwN9rmvYxYAPwW8DPr+zIRLUF/Cofe2AXZwemOdMXo7M5LLsOhRCi\nQoqkZ1VNTQdaWb+JVxn+h8AE8Ae6rn9nZYckLoegX+XWPevZtbFJvokJIcQyqNK7t2pqPtDSdT0F\nfDT7RwghhBCLkOoO1SMhqxBCCCEKSMHS6pFASwghhBAFpAVP9UigJYQQQogCMqNVPRJoCSGEEKKA\n5MJXj7yUQgghhCggM1rVI4GWEEIIIQpIoFU9EmgJIYQQooBPkuGrRgItIYQQQhSQGa3qkUBLCCGE\nEAWkBU/1yEsphBBCiAIyo1U9EmgJIYQQooAEWtUjgZYQQgghCkgyfPVIoCWEEEKIAhJnVY8EWkII\nIYQoIDNa1SOBlhBCCCEKSI5W9UigJYQQQogCisxoVY0EWkIIIYQoIHFW9UigJYQQQogCsnRYPRJo\nCSGEEKKAJMNXjwRaQgghhCggM1rVI4GWEEIIIQpInFU9EmgJIYQQooAikVbVSKAlhBBCiAISZ1WP\nBFpCCCGEKCBxVvVIoCWEEEKIArJ0WD0SaAkhhBBCXCYSaAkhhBCigJR3qB4JtIQQQghRSOKsqpFA\nSwghhBAFpDB89UigJYQQQog5JNKqFgm0hBBCCFFAUrSqRwItIYQQQhSQQKt6JNASQgghRAFFlg6r\nRgItIYQQQhSQGa3qkUBLCCGEEAWkMnz1SKAlhBBCiDwJsapLAi0hhBBCzJBIq6ok0BJCCCFEXndr\ndKWHUFMk0BJCCCFE3u/81L6VHkJNkUBLCCGEEHnN9aGVHkJNkUBLCCGEEOIykUBLCCGEEOIy8a/0\nAJZC07THgH/Udf1rs461Av8LeBcwAnxG1/V/nHV+H/B3wHXAa8Av6bp+5C0duBBCCCGuSGtiRkvT\nNEXTtL8B7ipx+qtAA3Az8MfAFzVNuzF7vyjwMPAUsB84BDysaVrkLRm4EEIIIa5oq35GS9O09cDX\ngauA+JxzW4H7gc26rl8ATmqadhD4ZeBjwIeBpK7rn8re5dc1TbsP+EngawghhBBCXEZrYUZrP9AH\n3ABMzjl3M9CXDbJyngEOzjr/zJz7PDvrvBBCCCHEZbPqZ7R0XX8IeAhA07S5p9cBl+YcGwI2zDr/\nWonz11R3lEIIIYQQxVY80NI0LQz0zHN6QNf15AJ3jwKZOccyQKjM80IIIYQQl82KB1p4y3tPAG6J\ncz8O/PsC901THDSFgGSZ58uiqmthhbV8/397dx5v13T3cfxzExWzokgMRZRfVT2GxJQYaijB86ip\nLdWaShEqqaEaFFVKENEYQnlqKFXzGK2QhxCk5iHor2YRScxDiSHJff74rZ1sO+feexLZuXef+32/\nXnnl3r32OWetvfY9+7d/a+29s/aoXdXQiO1qxDaB2lU1jd6uOdWlSxNduuiBh3NLuwda7j6aOZ8r\nNgHoXljWHZhYZ3ldFlusMS9SVLuqpRHb1YhtArWrahq1XXNqySUXpqlJgdbc0u6B1lc0FljJzJZz\n92yu1iZpeVZ+dOE1fYGTZ+dDPvxwCtOmTf9KFe1IunbtwmKLLah2VUQjtqsR2wRqV9U0ervm1Lvv\nfqyMVh2WWGLhutardKDl7i+nm5heYWYDgA2APYDN0irXAaea2VDgT8BBxLyta2bnc6ZNm87UqY3z\nR5hRu6qlEdvViG0CtatqGrVdc2r69GamT681m0fmRNUGpmv1/F7EbR/GAoOAfd39UQB3/wj4byLw\neoQIxLZz9ynzproiIiLSmVUqo+XuPWssexvYqZXXPELcg0tERERknqpaRktERESkMhRoiYiIiJRE\ngZaIiIhISRRoiYiIiJREgZaIiIhISRRoiYiIiJREgZaIiIhISRRoiYiIiJREgZaIiIhISRRoiYiI\niJREgZaIiIhISRRoiYiIiJREgZaIiIhISRRoiYiIiJREgZaIiIhISRRoiYiIiJREgZaIiIhISRRo\niYiIiJREgZaIiIhISRRoiYiIiJREgZaIiIhISRRoiYiIiJREgZaIiIhISRRoiYiIiJREgZaIiIhI\nSRRoiYiIiJREgZaIiIhISRRoiYiIiJREgZaIiIhISRRoiYiIiJREgZaIiIhISRRoiYiIiJREgZaI\niIhISRRoiYiIiJREgZaIiIhISRRoiYiIiJREgZaIiIhISRRoiYiIiJREgZaIiIhISRRoiYiIiJRE\ngZaIiIhISRRoiYiIiJREgZaIiIhISeZr7wq0xcwWB4YA/00EhiOAge7+QSpfErgI+D7wFnC8u1+Z\ne/26wHBgLWAccLC7PzZPGyEiIiKdUhUyWhcSQVI/YBtgDSKwylwGLApsCJwCXGxmvQHMbCEiMBsN\nrAc8CIwwswXnWe1FRESk0+rQGa0UKO0C9HH3J9KygcC9ZjY/sCKwA7CSu48HnjOzjYH+wH7A7sAn\n7n50esuBZrY98EPg8nnbGhEREelsOnpGazoxZPhkblkT0BVYBNgAeC0FWZkxwMbp5w3T73n358pF\nREREStOhM1ru/ikwsrB4APCUu79rZj2ANwrlk4EV0s89iHlZxfI153ZdRURERIraPdAyswWA5Vso\nnujun+TWPRTYDdg2LVoI+Kzwms+AbnWW16Vr146e+Js9WXvUrmpoxHY1YptA7aqaRm/XnOrSpYku\nXZrmUm2k3QMtYnjvbqC5RtnOwC0AZtYf+CMwwN1HpfJPmTVo6gZ8Umd5PZoWW6wx586rXdXSiO1q\nxDaB2lU1jdquObXUUosoypqL2j3QcvfRtDFXzMyOBE4HjnD3c3NFE4DuhdW7AxPrLBcREREpTYfP\nl5rZ3sBgIpM1tFA8FljJzJbLLdskLc/K+xRe0zdXLiIiIlKapubmWiN2HYOZLQG8ClwHDCoUv+nu\nzWZ2O7AAMUl+A2AYsJm7P2pmiwLPA1cBfwIOIuZ4fcvdp8yjZoiIiEgn1dEzWtsACwN7E1cXvkEM\n+71B3EOLVPYhkaUaBOzr7o8CuPtHxO0hNgMeIQKx7RRkiYiIyLzQoTNaIiIiIlXW0TNaIiIiIpWl\nQEtERESkJAq0REREREqiQEtERESkJAq0RERERErS7neG7wjMbHFgCHEriC7ACGCgu3+QypcELgK+\nD7wFHO/uV+Zevy4wHFiLeIj1we7+2DxtRBvM7A7gSne/PLes8u3KmFk34HxgF+IRS0Pc/az2rVX9\nUv0fAQ5x93vTspWJ/tkYeAX4lbvfmXvN1sBQoCfwIHCAu788b2s+q3QD4WHAFkRfXAMMcvfPq9om\nADNbFTiPuOnxO8C57n5mKluZirYrz8xGAJPdfb/0+8pUtF1mthNwA/F4t6b0//Xu/qOKt2t+om57\nEM/u/bO7H5vKVqai7WpkymiFC4lgoh9x7641iJ01cxmwKPFcxlOAi82sN4CZLUQEZqOB9Yidd4SZ\ndYiHZ5lZk5mdA2xdo7iy7arhTKKe3wP6AyeY2S7tWqM6pSDrKuA7haKbiHvG9QKuAG40sxXSa1YE\nbgT+F+gNvJ3W7wiuJ24i3BfYHfgf4Pep7GYq2CYzayL+HiYD6xA3Pz7OzHZPq1SyXXmpLdsVFld1\nH4T4e7qFeOxad6AHsH8qq3J/DQO2Ik6QfwIcYGYHpLIqt6thdfqMVgoodgH6uPsTadlA4N505rAi\nsAOwkruPB54zs42Jg/l+xIHkE3c/Or3lQDPbHvghcDntKGUWrgBWAd4vlPWkou0qSn34c2Bbd38S\neNLMTgcOJc5oOywzWwP4a43lWxJnnRu5+6fAaWa2FdE3JwEHAA+7+9lp/X2BSWa2WZYRaw9mZsSN\ngZd197fTsuOBM8zsH8S+uGGV2pQsCzwO9Hf3j4EXzWwUsImZTaa67QJmPIXjdOCh3LJK7oM5awDj\n3P2t/MLUrkr2V+qn/YAtsxtzm9mZwIZm9gIVbVejU0YLphNDhk/mljUBXYFFiIPGaykYyYwhUrMQ\n2aAxhfe8P1fentYDXiPObj4slG1IddtVtDZx0vBgbtkYog0d3ebAKGK7NuWWbwg8lr4wM8X+mfHl\nmJ528Bjt3z+TgH5ZkJWzOLAR1WwT7j7J3fdIQRZm1hfYFLiHCrcr50ziBOq53LKq7oOZ7wD/rrG8\nyu3aBHjf3Wd8N7v76e6+P42xHzakTp/RSjvlyMLiAcBT7v6umfUgUrF5k4EV0s89iPlLxfI153Zd\nZ5e73wbcBhCJhi+pbLtq6AG87e5Tc8smAwuY2VLu/k471atN7n5B9nOhj+rpn9bK20Wa15ifE9JE\nZBZHUdE2FZnZK0Sm+zYiY3o2FW5XyvBsSkyfuCBXVPX+MqCfmR1LnDhfCxxPtdvVE3jFzH4GHAPM\nD1xCTP2ocrsaWqcItMxsAWD5FoonuvsnuXUPJR48vW1atBAx4TDvM6BbneWlmZ121dBh2zUHWqor\ndMz61qNR+ucMYF1gfeBwGqNNuxBzfoYTE4sr21dpfuAFxJDoZ4Vgv8rt+iawIDCFmO6wCjG3aUEq\n3C5ilGV14BfAPkTwdCFx0UmV29XQOkWgRaRM7yauOinamZgwiZn1B/4IDHD3Uan8U2bdEbsRO3Y9\n5WWqq10t6Mjtml0t1RU6Zn3r8SmwZGFZPf3zXsn1qpuZDQYOA37k7s+aWeXbBJBdeWtmhwNXEpOL\nlyisVpV2nUjM27mrRlll+8vdX0vZ7Gxu6lNm1pWYs3oJ1e2vqcQFTHu4++sAZrYSMbd2JLBUYf2q\ntKuhdYpAy91H08Z8NDM7kpgMeoS7n5srmkCcveZ1BybWWV6aetrVig7brjkwAfiGmXVx9+lpWXdg\nSu6LtmomMOtViPX0z+Ml16su6UrXA4E93T27sqmybTKzZYCN3f3m3OJniaGbicTE67xKtAv4MbCs\nmX2Ufu8GYGa7AX+gov0FUONv/zniathJVLe/JgKfZkFW4sTw3wRmndpRlXY1NE2GB8xsb2Awkcka\nWigeC6yUruDLbJKWZ+V9Cq/pmyvvqBqpXU8AXxCTQTObAg+3T3XmirHAemloJ1Psn02ygnTl5bp0\ngP4xsxOIoY0fu/u1uaLKtokYerohzdnM9AbeJCYc96pouzYn5matnf7dQtwiYG3gn1S0v8xsGzN7\nO02vyKxL3NLgPqrbX2OJuaffyi37DnHPrLFUt10Nram5udaoU+eRLpd9FbgOGFQoftPdm83sduJM\naABxFeIwYDN3f9TMFgWeJ+6D9Cfi/jq7Ad9KV3V0CGb2MnBC4YallW9XxsyGE4HgfsTZ3aXA3oUM\nRIdmZtOB77n7vWbWhbgSdhxxD6odif1zTXd/PQ0XPAv8jpiUfQKwmruv1z61D+l2FU8R2ZDzC8Vv\nUcE2AaT+eBB4l5hrtgoxZHgK0c6ngKepWLuKzOwSoNnd96vqPghgZosQdbuXuLXBqsS9EYemf5Xt\nLzO7hRjS7U/M0bqcaONwKtyuRqaMVtygdGFgb+KKjDeIVOsbxJVFpLIPich/ELBvdg8Td/+IuD3E\nZsSdvTcAtuuAwUitiHovqt+uzOHAo8D/AecAv61SkJXM6KM0BPoDIrX/CHFjwp2yIQN3f5WYlL0f\nce+jrxPz8trbjsT3ynEU/p5Sm3aiem3K98fHwAPEycfZ7n5uKtuRCrarNRXeB3H3/xAXNC1NZLYv\nAi5w9yEN0F97Ai8QmblLgWHufl4DtKthdfqMloiIiEhZlNESERERKYkCLREREZGSKNASERERKYkC\nLREREZGSKNASERERKYkCLREREZGSKNASERERKYkCLREREZGSKNASERERKcl87V0BaV/peWYHAvsQ\nDyedSjwP62J3vyS33krAy6Rn8bVDVeeaudGW9OzIS9z9pDLWn0uf2bB9ZmaXAt909y2r0E4z2xy4\nG1jZ3V8r4f1vJB7HshnwE3fvUSj/G/Aj4vmff8kt70s8ymX97PFbbXzOikAfd786/b4k8ZiXP89G\nXb9GPPdydXd/s97XFd5jJ+AGoJe7P55bvgRwKrADsBjx7L/fuPv9qXxH4lFjevSMzDPKaHViZjYf\ncCtwIvElvQ6wEXANcJaZ3WhmTbmXNNLzmhqpLa1ppHY2t/DzeOL5bg/M2+rMtlL6wsz2ABZPz/Yc\nBSxjZqvkypuArYHXgH6Fl28KvFNPkJVcRjxDMHMm8NPZrPKmwPNzGmQlA4DJwK8Ky68mvsN+DPQC\nngBGmtlqAO5+C7C4me3+FT5bZLYoo9W5HQv0BXq7+wu55W5mo4mHTR8FnJ6WN9E4GqktrWmkdtZs\nS3qY7lc5aFdWykj/Hjg0LboXmEb8Xb+clq0PLAL8Fvhd4S02JYKzehX7YE72r+2Bv8/B6wAws3WA\nDYFtgLvM7NfuPsnMVgW2Avq6+9i0+i/NrB/xIOYT07IhwDAzu9rdG+lERDooBVqdVDrLPZQYinqh\nWO7uT5jZX4BfMjPQAuhrZucABjwNHOXu96T3XBo4D9gCWBh4DDgmG85JQwYnE196i6fXn+Dud6by\nvYHjgBHEUOY9wFrANe4+KFf3vYDzge7u/h8z25cICFcmDi4XAudkX6JmtiYwjPhyfgM4jTayC2a2\nDTEE8R3geeAs4M+0MPRjZhuntvUCviAyhUe6+7u51ZYzs9vT9pkInOnu5+feY39ie68GTE/b71f1\nZhvqaWfadocDqxMZgYuBP6TiScCp7n52WncAMJQIxB9Ly64H3gFOIbb1bsCviWzoROAP7n6RmR0K\nnAQs5e7NaX97C3jA3XdM77UW8CSwortPaKsfW2l3cVjxbuIkYWlgVyJzfytwoLt/nF7Ti+jT3qne\nxxPZmq1qDT+a2YLAOcSQ1NeB54Dfu/uNqbwLkWU5EFgJeBUY6u4XtlDnrwNnANsBywDvATcDh7n7\np2mo8S7iZOjXwEvuvkGNt9oVWCKtS/p7eIgItK5I6/Qjsn03AeeZWW93fyT1SV9if8i+E34D7E30\nwWfA/cAh7v5y2q6bA5uZ2feA0WldzGyau3dNmaNhwMZpuz9A/B2My9V5e2Df9Lq7gYeBHsBOwEdE\nMPg08V2yOpGV2svdX0yvHwDc4O5jzOxB4nvsOODt1D/Fv5fmtI0ydxB9uAtwfY1tKjJXaeiw81od\nWIr4Im3JKCI46JlbdiRxAP0v4HFghJl1T2UXAAsQZ8nfBf4N3JQOUhAHsq2BPYgD8zXArWa2Xe79\nVyW+dNcGBqXXFNP8ewLXp4PKL4hA8AQiKDoOOJoIkjCzxVI73iMOqgcTZ/YtSmfMtwEjUztPJs6C\nax7wzWwDYv7N00SQs1v6/47C0Ov+xMFpLeIg/0cz+0F6j52IA9RpRBC7JbEtL2qtrrk6tNlOMxtI\nBC/DUx2OJQKbISmYuR34fu4lWxMB3xbp9fOlZTfl1jmLyKh8m9hm56fA51YimO6d1luPONhtmtsm\n2wOPpiCr1X6sQ7FvBhIBVG9if9mJNMxkZj2IbfUSERgfQgSbrX0fnkzs0/1SW/8O/M3MvpnbDsem\n+n8XOJfo38NaeL9LiX18J+Bbqb57Ab/IrdOV2EYbEvtOLT8A7nT3qblldwF9cr9vC9zh7hOBccwc\nPlwHWBS4M/0+ADiC2E6rpfdendj3IQKTB4m/297AYennB4ihW4C/Aa8T/b0BkV27IauIma1MBMAP\n5ep3GHFSsRaxb51DnEgdRnyX9CD+LjCzZYnvgz+l1w4HDjSzbu7+gbv/w92/yH3ersR3yowMWtpW\nI1P7REqnjFbntWT6/51W1nk7/b80ke0AON7drwcws4OJA3N/IiPQk5h8+ko6Kx9AnFVPS2n93YF1\n3P2p9F5np6DmKGZ+ETYDJ7n7q+kzLgOON7NN0hnsskQQsk1a/zgis3Bt+v0VM1ucOOAfTwR1CwH7\nuPt/gH+lgGPGl38NA4GHc1m059Pnnt3C+kcAT7r7wPS7p3kzTxAHuX+k5Te6++D087lmtlF67c1E\nP/zc3a9K5ePN7M/EQace9bTzaGBYLsvyopl9AzjdzE4AbgEuT5nHZiJ7cTMRaA1Jv3chgpTswDrE\n3UcAmNmxRNCykbtfbWbjiH56mNhPskCuF/AIkX3Igra2+rEtxSGsZ909CzRfNLORRPYG4CDgfWB/\nd5+WttUv+XIAWdSTyLa84u4fmNlviYzre2a2KBHYDswmiRP925M4WRhW4/1GAqPd/Zn0+2spKFur\nsN4ZuUxOLRsBlxSWjQKOS8F3VyLgOSSV3UHskycTE+f/7e7jU9nzROYo+1scb2bXEicOuPt7ZvY5\nMCXL1JrZFOBzd38rt53uAF5z96kpS/ntXN22A0YWspSPu/vQ9H7nEv0zzN3vS8uuYWZQdBDRB1nW\n8QYi67oXhZMSM+tDZKGvd/d/8GXjiKy5SOkUaHVeWRC1eCvrZOn2t3LLZmTA3H2amT1GnMFDpPyv\nAH5oZmOIL9y/uvvnZrZuWmdMIcszH5GFyZsxlOnur6b5YnsCY4CfABPc/e4UJKwAnGpmp+Re3wWY\nH1gl1e3fKfjIPEDrc0vWIw6Eea1dzfZdoq0zuPtTZvYBceDMvuSL2cN/EhkL3P0+M/u2mR1HHJhW\nI7Jp9WadW21nGtZdtkYdRgNfS585kuiPvkQm4iMiA3ZtGhrbnjhIfmZm2ev/lWvzh2n5/GnRrURg\ndUr6/2oii7qlmb1ADC/1r7MfP61zO8xSr+QDZu7r6wKPpCArcy+t7xODiUD0LTP7J7Gt/uruH5nZ\n+sR2q7VtB6RtXzQc2DEFIqsBaxLDdc/l1mkm97fQgu7MOj9tLDCF2L6LAu+6+xOpbCQxb2lBIluU\nZbNw9xFmtoGZ/Y7Iqlqq1+tt1CHvGOCPwCFmdg+x71+VK9+e2A/y8m38OP3/Um7ZFKBbquPvyM0z\nS9mp5YuVSJniK4krKmtN1n+LmScLIqXS0GHn9SIxtLJZK+tkc4lezi2bVlinKzGXA3e/iUjz751e\n8ysiW7AGsa81A5sQQybZvzWJA8IM7v5Z4TMuJYK3rxGB1mVpebb/Diy851rEkMdL6TOL+/kXtG5q\njde0pqUDdFPhs1rcdmb2EyIb2JM4YB9BmjtTp7ba2VIdu2T1TPOX7iYyHlsD/0cEt/MTWZEdiAxX\nXrGv8p91C7CRmS1DDGWNSu+5BTF8NT7N3WmrH1vL6LSktXrNbv+SJlevSAyfPUpkUJ4zsy3S+9ba\nvtlnfGl/SycaI4iA5HNiuG0Hal81OaWNqk0n9qN8Xb8g+q0PkVG8M1d8L9H+vsTf4owTCjP7DdH/\nSxHDjwcSVxXWzd2HA8sRcw3fJ6YZPGtmS5tZNyIrWswu1fp7nD47n5uX5gdeT+yr/+Pun9dYretX\n+QyR2aFAq5NKV2oNBfY3s28Xy9PE6r2YdTJyr9w63Yi5Gk+b2fxmNgRY1d2vdfcDibknzcRBZBxx\nMFrO3V/K/gE/J02MbcV1RMbgACLbdFlqw5vEmemqhfdcn8iiQAzfrW5xv5/M+rQ+Gf5JYl5MXp9a\nKyZPEQetGcxsbeI+Ps/kFvfiyzYl5nVBDOtd5O77uftwdx9DbL96tdrOtK0mF+tJBNqfMTOYuZU4\nOG8BjHL3T4jM24FEEHhbvRVy94eIbOUxwOQ0BHYX0e5dSEFbHf04t6+cfBJYz8zyAUofWtknzOxE\nYFN3vy0NERuxzXYlslBfUHvbTnL39wvL1yECzd3c/Zg0XPwS0d+z29aJxNB+0V1EcLw5uWAqncTc\nRwzjL0EEVplBwInufqi7X5z6zwp1Km6jGb+nYOocoJu7X+7uexMBc/dUj+8Bz7n725QkTWcYRgw9\n7lmYu5a3DHHBiEjpNHTYuQ0hAqXR6UCSfSFvS6Tn7+TLVxwCnGZm7xLp/t8SKf3z0vDg+sAmaa7J\nJGKYYGHiSrNnzew24IJ0xvkM8EMiwNintUq6+xQzu46YGH1/OghnBgMnm9l4Yp7X2sRE2hvd/QuL\nGzUeC1xlZkcRB5eW5lplzgQeN7NTiTke32XmcEWtg/FZwH1mNix9dndibtWjRAYns4eZPUUEKzsT\n8062SGXjiSs61yWGuX5AmldjZvO3cFaeV087zyC21UtE325ITN6+0N0/Suvcmuo+lRiuhchEnQCM\n8S9fRVmP24gg7a/p92w4c2e+PPG+rX6czY9t1flEtvViMxtMDFtmc+FaCrZ6AnumSfsvEnOjvkns\njx+Z2YXASelv42EikDqICF4yWcAyiQjMfmxmbwPfIILRZUlDZIX1W/NP4uSjaBRxO4MFmXUYfCSR\naRqbXYWZjAe2SX+n04gTrZ2ZOT8T4D/Ayma2vLtPSL8vlya5jydOqnqa2THE0PM+RCD/KJGxvL2O\nNs0RM1ud2OdvAAaneZWZKe7+Ye739YhtJ1I6ZbQ6MXdvdvfdiWG5DVl8AAACl0lEQVSqPYgDxCPp\n56PcfedCNquZOOAOJr44lyMuh/8glf+IODO/mZgj8wviLtUP5MqvJ65OfAb4GbCfu19B2y4h7gX0\npYm/7n4WMcR2CHFH+6Hp/Q9O5Z8Qk+c/J4ZTLkv1b227PEMcYHYgslUnMPNAnAU8zbn1HyIOrL2I\nq6f+lj7r+7l5QM1EoLMDkVHZB9gjm/BLXKI+mZhgPZYIUn+WytYvfmaNOrfZzrStjiQOeM8QweOp\n5G766O6vpza/lpskfRdx0C9OFq9Vn+KyW4mhx1Hp/bP6vU9u3ltb/djK5+XL6ronUpq43Q9Yg7hy\ndjgRfMHM/i3qn9rwF8CJbffr3MULA4mhwNOI7O2BxG0R8sFull2cSAyv70i09RpiHtRQZl6lOWP9\nNtxEXMlZHD58ghh2HOfukwqvuYO4cKIYgP00LX+YmF+2ZmrHMma2QlrnAmJI98k0BHoZcTI1jsis\nbUcMyd1FZGu3ArZ395eJbV7P/bPm9N5WuxLJg52JbFX+34x+sLh6ti+tX/wgMtc0NTfrfm0ieWbW\nG5iam0CczaG6GFgkDbtKRaU5g0vkTgCyK9TuIx7rM6HdKjebUtDgxIlRa1fSSmJmu5Fuo1K4IEKk\nFBo6FJnVusQtD/Yi5j6tRgzDXKUgqyGsQNz/7edE5mZ5Yvj3nioFWRBX3aWrBA+n9VuWyEwDiLlo\nCrJkntDQoUiBu19EzF8bStx09RJiyLN/e9ZL5g6PJxH8krgL+r+IAOUJYuipctz9cuBdM9ulvevS\n0VncGPi9OqcriMwVGjoUERERKYkyWiIiIiIlUaAlIiIiUhIFWiIiIiIlUaAlIiIiUhIFWiIiIiIl\nUaAlIiIiUhIFWiIiIiIlUaAlIiIiUpL/B7lzwslqQBUhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b08f4a080>"
      ]
     },
     "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('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": 152,
   "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": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.reset_index(inplace=True)\n",
    "df_test.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Kt max: 44687.079003\n",
      "test Kt min: -869.110494468\n",
      "test Kt mean: 2.789494437955276\n",
      "\n",
      "\n",
      "train Kt max: 13966.2364604\n",
      "train Kt min: -1176.76984694\n",
      "train Kt mean: 2.4471862343120523\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": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23b04598278>]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MFgGXA5c554Zl8zOIiIhIYcp2S0srsAu41DlX7pxzwOnAO3gtI4uTbufMxbtVBF6yMydR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\niEgoKGkRERGRUPj/FStYEH0l8O0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b047920f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_train['Kt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23b051ab630>]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gSSfbDjWGrQSm93htOrC6Cu0rSU4vTSeZV1NuK2XWlyRJI9TuHCOa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\nBhpJkpR7BhpJkpR7/xc4ug4NtagvTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b0467d630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_test['Kt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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": 158,
   "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": 159,
   "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": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>zen</th>\n",
       "      <th>dw_solar</th>\n",
       "      <th>uw_solar</th>\n",
       "      <th>direct_n</th>\n",
       "      <th>diffuse</th>\n",
       "      <th>dw_ir</th>\n",
       "      <th>dw_casetemp</th>\n",
       "      <th>dw_dometemp</th>\n",
       "      <th>uw_ir</th>\n",
       "      <th>uw_casetemp</th>\n",
       "      <th>...</th>\n",
       "      <th>netsolar</th>\n",
       "      <th>netir</th>\n",
       "      <th>totalnet</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>windspd</th>\n",
       "      <th>winddir</th>\n",
       "      <th>pressure</th>\n",
       "      <th>ghi</th>\n",
       "      <th>Kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th>7.0</th>\n",
       "      <td>152.659762</td>\n",
       "      <td>-4.019048</td>\n",
       "      <td>0.026190</td>\n",
       "      <td>1.554762</td>\n",
       "      <td>-0.202381</td>\n",
       "      <td>182.345238</td>\n",
       "      <td>261.159524</td>\n",
       "      <td>260.650000</td>\n",
       "      <td>253.014286</td>\n",
       "      <td>259.557143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-70.657143</td>\n",
       "      <td>-70.657143</td>\n",
       "      <td>-13.607143</td>\n",
       "      <td>83.883333</td>\n",
       "      <td>5.145238</td>\n",
       "      <td>293.504762</td>\n",
       "      <td>999.397619</td>\n",
       "      <td>61.060726</td>\n",
       "      <td>-0.069829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>143.730167</td>\n",
       "      <td>-3.900000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>1.731667</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>181.795000</td>\n",
       "      <td>260.780000</td>\n",
       "      <td>260.283333</td>\n",
       "      <td>251.493333</td>\n",
       "      <td>259.196667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-69.693333</td>\n",
       "      <td>-69.693333</td>\n",
       "      <td>-13.956667</td>\n",
       "      <td>84.893333</td>\n",
       "      <td>4.805000</td>\n",
       "      <td>296.086667</td>\n",
       "      <td>999.803333</td>\n",
       "      <td>166.338212</td>\n",
       "      <td>-0.025197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>132.476167</td>\n",
       "      <td>-3.825000</td>\n",
       "      <td>0.011667</td>\n",
       "      <td>1.841667</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>181.033333</td>\n",
       "      <td>260.256667</td>\n",
       "      <td>259.761667</td>\n",
       "      <td>249.923333</td>\n",
       "      <td>258.665000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-68.890000</td>\n",
       "      <td>-68.890000</td>\n",
       "      <td>-14.476667</td>\n",
       "      <td>85.781667</td>\n",
       "      <td>4.898333</td>\n",
       "      <td>295.981667</td>\n",
       "      <td>999.783333</td>\n",
       "      <td>306.455432</td>\n",
       "      <td>-0.012680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>121.024000</td>\n",
       "      <td>-3.793333</td>\n",
       "      <td>-0.003333</td>\n",
       "      <td>1.783333</td>\n",
       "      <td>-0.210000</td>\n",
       "      <td>183.566667</td>\n",
       "      <td>259.941667</td>\n",
       "      <td>259.446667</td>\n",
       "      <td>249.775000</td>\n",
       "      <td>258.315000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-66.203333</td>\n",
       "      <td>-66.203333</td>\n",
       "      <td>-14.826667</td>\n",
       "      <td>85.966667</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>298.381667</td>\n",
       "      <td>999.968333</td>\n",
       "      <td>411.028556</td>\n",
       "      <td>-0.009267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>109.724667</td>\n",
       "      <td>-3.703333</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>1.758333</td>\n",
       "      <td>-0.210000</td>\n",
       "      <td>191.770000</td>\n",
       "      <td>259.800000</td>\n",
       "      <td>259.353333</td>\n",
       "      <td>250.373333</td>\n",
       "      <td>258.070000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-58.600000</td>\n",
       "      <td>-58.600000</td>\n",
       "      <td>-15.123333</td>\n",
       "      <td>86.431667</td>\n",
       "      <td>4.973333</td>\n",
       "      <td>299.090000</td>\n",
       "      <td>1000.193333</td>\n",
       "      <td>465.002071</td>\n",
       "      <td>-0.007967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              zen  dw_solar  uw_solar  direct_n   diffuse  \\\n",
       "year   month day hour                                                       \n",
       "2010.0 1.0   1.0 7.0   152.659762 -4.019048  0.026190  1.554762 -0.202381   \n",
       "                 8.0   143.730167 -3.900000  0.030000  1.731667 -0.200000   \n",
       "                 9.0   132.476167 -3.825000  0.011667  1.841667 -0.200000   \n",
       "                 10.0  121.024000 -3.793333 -0.003333  1.783333 -0.210000   \n",
       "                 11.0  109.724667 -3.703333  0.006667  1.758333 -0.210000   \n",
       "\n",
       "                            dw_ir  dw_casetemp  dw_dometemp       uw_ir  \\\n",
       "year   month day hour                                                     \n",
       "2010.0 1.0   1.0 7.0   182.345238   261.159524   260.650000  253.014286   \n",
       "                 8.0   181.795000   260.780000   260.283333  251.493333   \n",
       "                 9.0   181.033333   260.256667   259.761667  249.923333   \n",
       "                 10.0  183.566667   259.941667   259.446667  249.775000   \n",
       "                 11.0  191.770000   259.800000   259.353333  250.373333   \n",
       "\n",
       "                       uw_casetemp    ...     netsolar      netir   totalnet  \\\n",
       "year   month day hour                 ...                                      \n",
       "2010.0 1.0   1.0 7.0    259.557143    ...          0.0 -70.657143 -70.657143   \n",
       "                 8.0    259.196667    ...          0.0 -69.693333 -69.693333   \n",
       "                 9.0    258.665000    ...          0.0 -68.890000 -68.890000   \n",
       "                 10.0   258.315000    ...          0.0 -66.203333 -66.203333   \n",
       "                 11.0   258.070000    ...          0.0 -58.600000 -58.600000   \n",
       "\n",
       "                            temp         rh   windspd     winddir  \\\n",
       "year   month day hour                                               \n",
       "2010.0 1.0   1.0 7.0  -13.607143  83.883333  5.145238  293.504762   \n",
       "                 8.0  -13.956667  84.893333  4.805000  296.086667   \n",
       "                 9.0  -14.476667  85.781667  4.898333  295.981667   \n",
       "                 10.0 -14.826667  85.966667  5.083333  298.381667   \n",
       "                 11.0 -15.123333  86.431667  4.973333  299.090000   \n",
       "\n",
       "                          pressure         ghi        Kt  \n",
       "year   month day hour                                     \n",
       "2010.0 1.0   1.0 7.0    999.397619   61.060726 -0.069829  \n",
       "                 8.0    999.803333  166.338212 -0.025197  \n",
       "                 9.0    999.783333  306.455432 -0.012680  \n",
       "                 10.0   999.968333  411.028556 -0.009267  \n",
       "                 11.0  1000.193333  465.002071 -0.007967  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Groupdata - test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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": 162,
   "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": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>150.182143</td>\n",
       "      <td>-4.521429</td>\n",
       "      <td>-0.471429</td>\n",
       "      <td>1.007143</td>\n",
       "      <td>0.421429</td>\n",
       "      <td>216.700000</td>\n",
       "      <td>272.085714</td>\n",
       "      <td>271.642857</td>\n",
       "      <td>286.392857</td>\n",
       "      <td>270.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-69.692857</td>\n",
       "      <td>-69.692857</td>\n",
       "      <td>-2.271429</td>\n",
       "      <td>89.750000</td>\n",
       "      <td>2.871429</td>\n",
       "      <td>174.300000</td>\n",
       "      <td>998.471429</td>\n",
       "      <td>37.874247</td>\n",
       "      <td>-0.120932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>146.446833</td>\n",
       "      <td>-4.301667</td>\n",
       "      <td>-0.345000</td>\n",
       "      <td>0.326667</td>\n",
       "      <td>0.518333</td>\n",
       "      <td>216.170000</td>\n",
       "      <td>271.606667</td>\n",
       "      <td>271.146667</td>\n",
       "      <td>284.053333</td>\n",
       "      <td>269.938333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-67.881667</td>\n",
       "      <td>-67.881667</td>\n",
       "      <td>-2.773333</td>\n",
       "      <td>92.965000</td>\n",
       "      <td>3.066667</td>\n",
       "      <td>146.796667</td>\n",
       "      <td>997.966667</td>\n",
       "      <td>108.318844</td>\n",
       "      <td>-0.050473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>137.938333</td>\n",
       "      <td>-4.831667</td>\n",
       "      <td>-0.351667</td>\n",
       "      <td>1.045000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>216.516667</td>\n",
       "      <td>271.153333</td>\n",
       "      <td>270.733333</td>\n",
       "      <td>288.993333</td>\n",
       "      <td>269.398333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-72.466667</td>\n",
       "      <td>-72.466667</td>\n",
       "      <td>-3.256667</td>\n",
       "      <td>94.690000</td>\n",
       "      <td>3.873333</td>\n",
       "      <td>152.585000</td>\n",
       "      <td>996.900000</td>\n",
       "      <td>284.228645</td>\n",
       "      <td>-0.017408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>127.563000</td>\n",
       "      <td>-5.281667</td>\n",
       "      <td>-0.308333</td>\n",
       "      <td>0.951667</td>\n",
       "      <td>0.436667</td>\n",
       "      <td>223.628333</td>\n",
       "      <td>271.841667</td>\n",
       "      <td>271.390000</td>\n",
       "      <td>294.260000</td>\n",
       "      <td>269.860000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-70.631667</td>\n",
       "      <td>-70.631667</td>\n",
       "      <td>-2.865000</td>\n",
       "      <td>91.560000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>164.426667</td>\n",
       "      <td>995.828333</td>\n",
       "      <td>448.178675</td>\n",
       "      <td>-0.011885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>116.367333</td>\n",
       "      <td>-5.035000</td>\n",
       "      <td>-0.243333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.373333</td>\n",
       "      <td>236.190000</td>\n",
       "      <td>272.423333</td>\n",
       "      <td>271.983333</td>\n",
       "      <td>296.791667</td>\n",
       "      <td>270.326667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-60.611667</td>\n",
       "      <td>-60.611667</td>\n",
       "      <td>-2.485000</td>\n",
       "      <td>89.688333</td>\n",
       "      <td>4.981667</td>\n",
       "      <td>173.578333</td>\n",
       "      <td>995.345000</td>\n",
       "      <td>570.306262</td>\n",
       "      <td>-0.008854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>104.909333</td>\n",
       "      <td>-5.066667</td>\n",
       "      <td>-0.135000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.401667</td>\n",
       "      <td>233.243333</td>\n",
       "      <td>272.581667</td>\n",
       "      <td>272.093333</td>\n",
       "      <td>297.226667</td>\n",
       "      <td>270.480000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-63.985000</td>\n",
       "      <td>-63.985000</td>\n",
       "      <td>-2.386667</td>\n",
       "      <td>92.431667</td>\n",
       "      <td>5.595000</td>\n",
       "      <td>167.585000</td>\n",
       "      <td>994.613333</td>\n",
       "      <td>638.422279</td>\n",
       "      <td>-0.007937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>93.430833</td>\n",
       "      <td>-2.668333</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>3.091667</td>\n",
       "      <td>238.826667</td>\n",
       "      <td>272.686667</td>\n",
       "      <td>272.196667</td>\n",
       "      <td>298.433333</td>\n",
       "      <td>270.543333</td>\n",
       "      <td>...</td>\n",
       "      <td>2.351667</td>\n",
       "      <td>-59.603333</td>\n",
       "      <td>-57.251667</td>\n",
       "      <td>-2.320000</td>\n",
       "      <td>92.493333</td>\n",
       "      <td>6.421667</td>\n",
       "      <td>167.895000</td>\n",
       "      <td>993.798333</td>\n",
       "      <td>646.734117</td>\n",
       "      <td>-0.004067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>82.473333</td>\n",
       "      <td>50.488333</td>\n",
       "      <td>14.828333</td>\n",
       "      <td>7.531667</td>\n",
       "      <td>57.045000</td>\n",
       "      <td>245.631667</td>\n",
       "      <td>273.396667</td>\n",
       "      <td>272.983333</td>\n",
       "      <td>302.830000</td>\n",
       "      <td>271.061667</td>\n",
       "      <td>...</td>\n",
       "      <td>43.403333</td>\n",
       "      <td>-57.188333</td>\n",
       "      <td>-13.786667</td>\n",
       "      <td>-1.801667</td>\n",
       "      <td>91.796667</td>\n",
       "      <td>6.791667</td>\n",
       "      <td>167.960000</td>\n",
       "      <td>993.278333</td>\n",
       "      <td>594.568596</td>\n",
       "      <td>0.086837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>72.405000</td>\n",
       "      <td>136.151667</td>\n",
       "      <td>36.918333</td>\n",
       "      <td>13.550000</td>\n",
       "      <td>140.373333</td>\n",
       "      <td>286.001667</td>\n",
       "      <td>275.108333</td>\n",
       "      <td>274.810000</td>\n",
       "      <td>312.523333</td>\n",
       "      <td>272.315000</td>\n",
       "      <td>...</td>\n",
       "      <td>107.461667</td>\n",
       "      <td>-26.526667</td>\n",
       "      <td>80.938333</td>\n",
       "      <td>-0.561667</td>\n",
       "      <td>101.838333</td>\n",
       "      <td>7.736667</td>\n",
       "      <td>175.430000</td>\n",
       "      <td>992.623333</td>\n",
       "      <td>486.293072</td>\n",
       "      <td>0.287014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>63.572667</td>\n",
       "      <td>249.433333</td>\n",
       "      <td>63.790000</td>\n",
       "      <td>22.685000</td>\n",
       "      <td>249.351667</td>\n",
       "      <td>287.831667</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>276.723333</td>\n",
       "      <td>322.285000</td>\n",
       "      <td>273.716667</td>\n",
       "      <td>...</td>\n",
       "      <td>195.410000</td>\n",
       "      <td>-34.446667</td>\n",
       "      <td>160.953333</td>\n",
       "      <td>0.821667</td>\n",
       "      <td>109.766667</td>\n",
       "      <td>8.668333</td>\n",
       "      <td>182.706667</td>\n",
       "      <td>991.760000</td>\n",
       "      <td>332.051692</td>\n",
       "      <td>0.774799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>56.723667</td>\n",
       "      <td>288.480000</td>\n",
       "      <td>73.990000</td>\n",
       "      <td>45.445000</td>\n",
       "      <td>272.428333</td>\n",
       "      <td>299.576667</td>\n",
       "      <td>278.595000</td>\n",
       "      <td>278.275000</td>\n",
       "      <td>331.316667</td>\n",
       "      <td>274.995000</td>\n",
       "      <td>...</td>\n",
       "      <td>222.996667</td>\n",
       "      <td>-31.736667</td>\n",
       "      <td>191.260000</td>\n",
       "      <td>1.906667</td>\n",
       "      <td>107.371667</td>\n",
       "      <td>9.136667</td>\n",
       "      <td>179.690000</td>\n",
       "      <td>990.573333</td>\n",
       "      <td>153.597510</td>\n",
       "      <td>2.067046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>53.264194</td>\n",
       "      <td>314.187097</td>\n",
       "      <td>79.038710</td>\n",
       "      <td>4.867742</td>\n",
       "      <td>319.570968</td>\n",
       "      <td>309.864516</td>\n",
       "      <td>279.222581</td>\n",
       "      <td>278.980645</td>\n",
       "      <td>336.116129</td>\n",
       "      <td>275.416129</td>\n",
       "      <td>...</td>\n",
       "      <td>243.445161</td>\n",
       "      <td>-26.254839</td>\n",
       "      <td>217.200000</td>\n",
       "      <td>2.474194</td>\n",
       "      <td>111.645161</td>\n",
       "      <td>9.616129</td>\n",
       "      <td>176.358065</td>\n",
       "      <td>989.632258</td>\n",
       "      <td>45.373147</td>\n",
       "      <td>7.893298</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   150.182143   -4.521429  -0.471429   1.007143    0.421429  216.700000   \n",
       "7.0   146.446833   -4.301667  -0.345000   0.326667    0.518333  216.170000   \n",
       "8.0   137.938333   -4.831667  -0.351667   1.045000    0.490000  216.516667   \n",
       "9.0   127.563000   -5.281667  -0.308333   0.951667    0.436667  223.628333   \n",
       "10.0  116.367333   -5.035000  -0.243333   0.833333    0.373333  236.190000   \n",
       "11.0  104.909333   -5.066667  -0.135000   0.480000    0.401667  233.243333   \n",
       "12.0   93.430833   -2.668333   0.580000   1.250000    3.091667  238.826667   \n",
       "13.0   82.473333   50.488333  14.828333   7.531667   57.045000  245.631667   \n",
       "14.0   72.405000  136.151667  36.918333  13.550000  140.373333  286.001667   \n",
       "15.0   63.572667  249.433333  63.790000  22.685000  249.351667  287.831667   \n",
       "16.0   56.723667  288.480000  73.990000  45.445000  272.428333  299.576667   \n",
       "17.0   53.264194  314.187097  79.038710   4.867742  319.570968  309.864516   \n",
       "\n",
       "      dw_casetemp  dw_dometemp       uw_ir  uw_casetemp    ...       netsolar  \\\n",
       "hour                                                       ...                  \n",
       "6.0    272.085714   271.642857  286.392857   270.300000    ...       0.000000   \n",
       "7.0    271.606667   271.146667  284.053333   269.938333    ...       0.000000   \n",
       "8.0    271.153333   270.733333  288.993333   269.398333    ...       0.000000   \n",
       "9.0    271.841667   271.390000  294.260000   269.860000    ...       0.000000   \n",
       "10.0   272.423333   271.983333  296.791667   270.326667    ...       0.000000   \n",
       "11.0   272.581667   272.093333  297.226667   270.480000    ...       0.000000   \n",
       "12.0   272.686667   272.196667  298.433333   270.543333    ...       2.351667   \n",
       "13.0   273.396667   272.983333  302.830000   271.061667    ...      43.403333   \n",
       "14.0   275.108333   274.810000  312.523333   272.315000    ...     107.461667   \n",
       "15.0   277.000000   276.723333  322.285000   273.716667    ...     195.410000   \n",
       "16.0   278.595000   278.275000  331.316667   274.995000    ...     222.996667   \n",
       "17.0   279.222581   278.980645  336.116129   275.416129    ...     243.445161   \n",
       "\n",
       "          netir    totalnet      temp          rh   windspd     winddir  \\\n",
       "hour                                                                      \n",
       "6.0  -69.692857  -69.692857 -2.271429   89.750000  2.871429  174.300000   \n",
       "7.0  -67.881667  -67.881667 -2.773333   92.965000  3.066667  146.796667   \n",
       "8.0  -72.466667  -72.466667 -3.256667   94.690000  3.873333  152.585000   \n",
       "9.0  -70.631667  -70.631667 -2.865000   91.560000  4.900000  164.426667   \n",
       "10.0 -60.611667  -60.611667 -2.485000   89.688333  4.981667  173.578333   \n",
       "11.0 -63.985000  -63.985000 -2.386667   92.431667  5.595000  167.585000   \n",
       "12.0 -59.603333  -57.251667 -2.320000   92.493333  6.421667  167.895000   \n",
       "13.0 -57.188333  -13.786667 -1.801667   91.796667  6.791667  167.960000   \n",
       "14.0 -26.526667   80.938333 -0.561667  101.838333  7.736667  175.430000   \n",
       "15.0 -34.446667  160.953333  0.821667  109.766667  8.668333  182.706667   \n",
       "16.0 -31.736667  191.260000  1.906667  107.371667  9.136667  179.690000   \n",
       "17.0 -26.254839  217.200000  2.474194  111.645161  9.616129  176.358065   \n",
       "\n",
       "        pressure         ghi        Kt  \n",
       "hour                                    \n",
       "6.0   998.471429   37.874247 -0.120932  \n",
       "7.0   997.966667  108.318844 -0.050473  \n",
       "8.0   996.900000  284.228645 -0.017408  \n",
       "9.0   995.828333  448.178675 -0.011885  \n",
       "10.0  995.345000  570.306262 -0.008854  \n",
       "11.0  994.613333  638.422279 -0.007937  \n",
       "12.0  993.798333  646.734117 -0.004067  \n",
       "13.0  993.278333  594.568596  0.086837  \n",
       "14.0  992.623333  486.293072  0.287014  \n",
       "15.0  991.760000  332.051692  0.774799  \n",
       "16.0  990.573333  153.597510  2.067046  \n",
       "17.0  989.632258   45.373147  7.893298  \n",
       "\n",
       "[12 rows x 23 columns]"
      ]
     },
     "execution_count": 163,
     "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": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels_index= []\n",
    "for m in df_new_train.index.levels:\n",
    "    levels_index.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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": 166,
   "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": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels_index2= []\n",
    "for m in df_new_test.index.levels:\n",
    "    levels_index2.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_test = df_new_test[~(df_new_test['Kt'].isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>8.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>19.0</th>\n",
       "      <td>30.434833</td>\n",
       "      <td>800.548333</td>\n",
       "      <td>153.193333</td>\n",
       "      <td>601.871667</td>\n",
       "      <td>280.84</td>\n",
       "      <td>384.561667</td>\n",
       "      <td>304.885</td>\n",
       "      <td>304.643333</td>\n",
       "      <td>482.225</td>\n",
       "      <td>302.208333</td>\n",
       "      <td>...</td>\n",
       "      <td>645.503333</td>\n",
       "      <td>-97.671667</td>\n",
       "      <td>547.838333</td>\n",
       "      <td>26.02</td>\n",
       "      <td>63.08</td>\n",
       "      <td>2.26</td>\n",
       "      <td>103.323333</td>\n",
       "      <td>992.138333</td>\n",
       "      <td>44.570245</td>\n",
       "      <td>1146.230573</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",
       "8.0   5.0 19.0  30.434833  800.548333  153.193333  601.871667   280.84   \n",
       "\n",
       "                     dw_ir  dw_casetemp  dw_dometemp    uw_ir  uw_casetemp  \\\n",
       "month day hour                                                               \n",
       "8.0   5.0 19.0  384.561667      304.885   304.643333  482.225   302.208333   \n",
       "\n",
       "                   ...         netsolar      netir    totalnet   temp     rh  \\\n",
       "month day hour     ...                                                         \n",
       "8.0   5.0 19.0     ...       645.503333 -97.671667  547.838333  26.02  63.08   \n",
       "\n",
       "                windspd     winddir    pressure        ghi           Kt  \n",
       "month day hour                                                           \n",
       "8.0   5.0 19.0     2.26  103.323333  992.138333  44.570245  1146.230573  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 170,
     "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": 171,
   "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": 172,
   "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": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:2: DeprecationWarning: fractions.gcd() is deprecated. Use math.gcd() instead.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 173,
     "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": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def roundup(x):\n",
    "    return int(math.ceil(x / 100.0)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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": 176,
   "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": 177,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1_train shape is (9600, 22)\n",
      "y1_train shape is (9600,)\n",
      "X2_test shape is (4400, 22)\n",
      "y2_test shape is (4400,)\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": 178,
   "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": 179,
   "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": 180,
   "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": 181,
   "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": 188,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Iteration: 100. Train_MSE: 0.009215903468430042. Test_MSE: 0.00529575115069747\n",
      "Epoch: 2 Iteration: 200. Train_MSE: 0.0061593265272676945. Test_MSE: 0.004371232353150845\n",
      "Epoch: 3 Iteration: 300. Train_MSE: 0.0027144080959260464. Test_MSE: 0.003831080161035061\n",
      "Epoch: 4 Iteration: 400. Train_MSE: 0.0037528714165091515. Test_MSE: 0.0034912065602838993\n",
      "Epoch: 5 Iteration: 500. Train_MSE: 0.0038438993506133556. Test_MSE: 0.003262390149757266\n",
      "Epoch: 6 Iteration: 600. Train_MSE: 0.013666358776390553. Test_MSE: 0.0030873597133904696\n",
      "Epoch: 7 Iteration: 700. Train_MSE: 0.003519519465044141. Test_MSE: 0.0029423863161355257\n",
      "Epoch: 8 Iteration: 800. Train_MSE: 0.0021539567969739437. Test_MSE: 0.0028148391284048557\n",
      "Epoch: 9 Iteration: 900. Train_MSE: 0.0038854663725942373. Test_MSE: 0.002696497831493616\n",
      "Epoch: 10 Iteration: 1000. Train_MSE: 0.003347498131915927. Test_MSE: 0.002590596443042159\n",
      "Epoch: 11 Iteration: 1100. Train_MSE: 0.0016097460174933076. Test_MSE: 0.002499358495697379\n",
      "Epoch: 12 Iteration: 1200. Train_MSE: 0.001478787511587143. Test_MSE: 0.0024178409948945045\n",
      "Epoch: 13 Iteration: 1300. Train_MSE: 0.005909610539674759. Test_MSE: 0.002342446707189083\n",
      "Epoch: 14 Iteration: 1400. Train_MSE: 0.002041962230578065. Test_MSE: 0.0022704279981553555\n",
      "Epoch: 15 Iteration: 1500. Train_MSE: 0.0032672984525561333. Test_MSE: 0.002201232360675931\n",
      "Epoch: 16 Iteration: 1600. Train_MSE: 0.0014488236047327518. Test_MSE: 0.00213678739964962\n",
      "Epoch: 17 Iteration: 1700. Train_MSE: 0.001070733997039497. Test_MSE: 0.0020780100021511316\n",
      "Epoch: 18 Iteration: 1800. Train_MSE: 0.0014801662182435393. Test_MSE: 0.002022120403125882\n",
      "Epoch: 19 Iteration: 1900. Train_MSE: 0.001216000528074801. Test_MSE: 0.00196811999194324\n",
      "Epoch: 21 Iteration: 2000. Train_MSE: 0.0015646411338821054. Test_MSE: 0.0019170403247699142\n",
      "Epoch: 22 Iteration: 2100. Train_MSE: 0.001973925158381462. Test_MSE: 0.0018703859532251954\n",
      "Epoch: 23 Iteration: 2200. Train_MSE: 0.0010979383951053023. Test_MSE: 0.0018262603553012013\n",
      "Epoch: 24 Iteration: 2300. Train_MSE: 0.0025699574034661055. Test_MSE: 0.0017834381433203816\n",
      "Epoch: 25 Iteration: 2400. Train_MSE: 0.0020880349911749363. Test_MSE: 0.0017411266453564167\n",
      "Epoch: 26 Iteration: 2500. Train_MSE: 0.011824551038444042. Test_MSE: 0.001702168257907033\n",
      "Epoch: 27 Iteration: 2600. Train_MSE: 0.0029722703620791435. Test_MSE: 0.0016660388791933656\n",
      "Epoch: 28 Iteration: 2700. Train_MSE: 0.0013518482446670532. Test_MSE: 0.0016325169708579779\n",
      "Epoch: 29 Iteration: 2800. Train_MSE: 0.0017881094245240092. Test_MSE: 0.0015992788830772042\n",
      "Epoch: 30 Iteration: 2900. Train_MSE: 0.0015011749928817153. Test_MSE: 0.0015673484886065125\n",
      "Epoch: 31 Iteration: 3000. Train_MSE: 0.0008187620551325381. Test_MSE: 0.0015381422126665711\n",
      "Epoch: 32 Iteration: 3100. Train_MSE: 0.0009277431527152658. Test_MSE: 0.0015107091749086976\n",
      "Epoch: 33 Iteration: 3200. Train_MSE: 0.005306551232933998. Test_MSE: 0.0014845018740743399\n",
      "Epoch: 34 Iteration: 3300. Train_MSE: 0.0012990743853151798. Test_MSE: 0.0014589527854695916\n",
      "Epoch: 35 Iteration: 3400. Train_MSE: 0.0017996510723605752. Test_MSE: 0.0014339493354782462\n",
      "Epoch: 36 Iteration: 3500. Train_MSE: 0.0008639426669105887. Test_MSE: 0.0014104117872193456\n",
      "Epoch: 37 Iteration: 3600. Train_MSE: 0.0008239051094278693. Test_MSE: 0.0013888073153793812\n",
      "Epoch: 38 Iteration: 3700. Train_MSE: 0.0008436978096142411. Test_MSE: 0.0013680739793926477\n",
      "Epoch: 39 Iteration: 3800. Train_MSE: 0.0006429171771742404. Test_MSE: 0.001347732380963862\n",
      "Epoch: 41 Iteration: 3900. Train_MSE: 0.0008333298028446734. Test_MSE: 0.0013282581930980086\n",
      "Epoch: 42 Iteration: 4000. Train_MSE: 0.0013595032505691051. Test_MSE: 0.001310168532654643\n",
      "Epoch: 43 Iteration: 4100. Train_MSE: 0.0007292324444279075. Test_MSE: 0.0012929006479680538\n",
      "Epoch: 44 Iteration: 4200. Train_MSE: 0.0018181272316724062. Test_MSE: 0.0012759279925376177\n",
      "Epoch: 45 Iteration: 4300. Train_MSE: 0.0013197866501286626. Test_MSE: 0.0012587964301928878\n",
      "Epoch: 46 Iteration: 4400. Train_MSE: 0.010764692910015583. Test_MSE: 0.0012428879272192717\n",
      "Epoch: 47 Iteration: 4500. Train_MSE: 0.002806546166539192. Test_MSE: 0.0012280514929443598\n",
      "Epoch: 48 Iteration: 4600. Train_MSE: 0.0011033982736989856. Test_MSE: 0.0012141064507886767\n",
      "Epoch: 49 Iteration: 4700. Train_MSE: 0.00105660455301404. Test_MSE: 0.0012000466231256723\n",
      "Epoch: 50 Iteration: 4800. Train_MSE: 0.000854359706863761. Test_MSE: 0.0011864763218909502\n",
      "Epoch: 51 Iteration: 4900. Train_MSE: 0.0005297217867337167. Test_MSE: 0.0011738929897546768\n",
      "Epoch: 52 Iteration: 5000. Train_MSE: 0.0006982508348301053. Test_MSE: 0.0011618933640420437\n",
      "Epoch: 53 Iteration: 5100. Train_MSE: 0.005080935545265675. Test_MSE: 0.0011502668494358659\n",
      "Epoch: 54 Iteration: 5200. Train_MSE: 0.0009702856768853962. Test_MSE: 0.001138819381594658\n",
      "Epoch: 55 Iteration: 5300. Train_MSE: 0.0012094647390767932. Test_MSE: 0.0011274921707808971\n",
      "Epoch: 56 Iteration: 5400. Train_MSE: 0.0006412257789634168. Test_MSE: 0.0011167360935360193\n",
      "Epoch: 57 Iteration: 5500. Train_MSE: 0.0006904439069330692. Test_MSE: 0.0011067792074754834\n",
      "Epoch: 58 Iteration: 5600. Train_MSE: 0.0005635120905935764. Test_MSE: 0.0010971293086186051\n",
      "Epoch: 59 Iteration: 5700. Train_MSE: 0.00042672231211327016. Test_MSE: 0.0010876171290874481\n",
      "Epoch: 61 Iteration: 5800. Train_MSE: 0.0005270502297207713. Test_MSE: 0.001078435336239636\n",
      "Epoch: 62 Iteration: 5900. Train_MSE: 0.0010363742476329207. Test_MSE: 0.001069764606654644\n",
      "Epoch: 63 Iteration: 6000. Train_MSE: 0.0005691892583854496. Test_MSE: 0.001061448478139937\n",
      "Epoch: 64 Iteration: 6100. Train_MSE: 0.001516483025625348. Test_MSE: 0.001053216285072267\n",
      "Epoch: 65 Iteration: 6200. Train_MSE: 0.0010415608994662762. Test_MSE: 0.0010448044631630182\n",
      "Epoch: 66 Iteration: 6300. Train_MSE: 0.010314728133380413. Test_MSE: 0.0010369367664679885\n",
      "Epoch: 67 Iteration: 6400. Train_MSE: 0.002756477566435933. Test_MSE: 0.001029555220156908\n",
      "Epoch: 68 Iteration: 6500. Train_MSE: 0.000978649128228426. Test_MSE: 0.0010225363075733185\n",
      "Epoch: 69 Iteration: 6600. Train_MSE: 0.0006935420678928494. Test_MSE: 0.001015400979667902\n",
      "Epoch: 70 Iteration: 6700. Train_MSE: 0.0005479879910126328. Test_MSE: 0.001008498715236783\n",
      "Epoch: 71 Iteration: 6800. Train_MSE: 0.0003789989568758756. Test_MSE: 0.0010020231129601598\n",
      "Epoch: 72 Iteration: 6900. Train_MSE: 0.0005633337423205376. Test_MSE: 0.0009957958245649934\n",
      "Epoch: 73 Iteration: 7000. Train_MSE: 0.004997127689421177. Test_MSE: 0.0009897325653582811\n",
      "Epoch: 74 Iteration: 7100. Train_MSE: 0.0007966920966282487. Test_MSE: 0.0009837555699050426\n",
      "Epoch: 75 Iteration: 7200. Train_MSE: 0.0009227722184732556. Test_MSE: 0.0009777829982340336\n",
      "Epoch: 76 Iteration: 7300. Train_MSE: 0.0005283975624479353. Test_MSE: 0.000972046225797385\n",
      "Epoch: 77 Iteration: 7400. Train_MSE: 0.0005963462754152715. Test_MSE: 0.0009667135891504586\n",
      "Epoch: 78 Iteration: 7500. Train_MSE: 0.0004160054086241871. Test_MSE: 0.0009615024318918586\n",
      "Epoch: 79 Iteration: 7600. Train_MSE: 0.00033153739059343934. Test_MSE: 0.0009563472704030573\n",
      "Epoch: 81 Iteration: 7700. Train_MSE: 0.0003782568091992289. Test_MSE: 0.0009513484546914697\n",
      "Epoch: 82 Iteration: 7800. Train_MSE: 0.0008398671634495258. Test_MSE: 0.0009465563925914466\n",
      "Epoch: 83 Iteration: 7900. Train_MSE: 0.000478638568893075. Test_MSE: 0.0009419600828550756\n",
      "Epoch: 84 Iteration: 8000. Train_MSE: 0.0013714422238990664. Test_MSE: 0.0009373952052555978\n",
      "Epoch: 85 Iteration: 8100. Train_MSE: 0.000917217112146318. Test_MSE: 0.0009326791041530669\n",
      "Epoch: 86 Iteration: 8200. Train_MSE: 0.010098746046423912. Test_MSE: 0.0009282449027523398\n",
      "Epoch: 87 Iteration: 8300. Train_MSE: 0.002745197154581547. Test_MSE: 0.0009240720537491143\n",
      "Epoch: 88 Iteration: 8400. Train_MSE: 0.0009010823559947312. Test_MSE: 0.0009200641652569175\n",
      "Epoch: 89 Iteration: 8500. Train_MSE: 0.000493774889037013. Test_MSE: 0.0009159813052974641\n",
      "Epoch: 90 Iteration: 8600. Train_MSE: 0.00038289400981739163. Test_MSE: 0.000912049668841064\n",
      "Epoch: 91 Iteration: 8700. Train_MSE: 0.00028966052923351526. Test_MSE: 0.0009083243785426021\n",
      "Epoch: 92 Iteration: 8800. Train_MSE: 0.0004742439487017691. Test_MSE: 0.0009047235944308341\n",
      "Epoch: 93 Iteration: 8900. Train_MSE: 0.004969752859324217. Test_MSE: 0.0009012027294375002\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 94 Iteration: 9000. Train_MSE: 0.0006923566106706858. Test_MSE: 0.0008977365796454251\n",
      "Epoch: 95 Iteration: 9100. Train_MSE: 0.0007616707589477301. Test_MSE: 0.0008942426065914333\n",
      "Epoch: 96 Iteration: 9200. Train_MSE: 0.00045754536404274404. Test_MSE: 0.0008908663294278085\n",
      "Epoch: 97 Iteration: 9300. Train_MSE: 0.0005233740666881204. Test_MSE: 0.0008877204963937402\n",
      "Epoch: 98 Iteration: 9400. Train_MSE: 0.0003299737290944904. Test_MSE: 0.0008846363052725792\n",
      "Epoch: 99 Iteration: 9500. Train_MSE: 0.0002837417705450207. Test_MSE: 0.0008815937326289713\n",
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      "Epoch: 863 Iteration: 82000. Train_MSE: 0.00013496197061613202. Test_MSE: 0.0006408257759176195\n",
      "Epoch: 864 Iteration: 82100. Train_MSE: 0.0010588424047455192. Test_MSE: 0.0006407178589142859\n",
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      "Epoch: 866 Iteration: 82300. Train_MSE: 0.009563121013343334. Test_MSE: 0.0006404796149581671\n",
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      "Epoch: 868 Iteration: 82500. Train_MSE: 0.0005812637391500175. Test_MSE: 0.0006402849685400724\n",
      "Epoch: 869 Iteration: 82600. Train_MSE: 7.392784027615562e-05. Test_MSE: 0.0006401989376172423\n",
      "Epoch: 870 Iteration: 82700. Train_MSE: 6.407329783542082e-05. Test_MSE: 0.000640103651676327\n",
      "Epoch: 871 Iteration: 82800. Train_MSE: 5.96592653892003e-05. Test_MSE: 0.0006400127313099802\n",
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      "Epoch: 873 Iteration: 83000. Train_MSE: 0.0050135119818151. Test_MSE: 0.0006398296100087464\n",
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      "Epoch: 877 Iteration: 83400. Train_MSE: 0.00013215737999416888. Test_MSE: 0.0006394010852091014\n",
      "Epoch: 878 Iteration: 83500. Train_MSE: 9.49573441175744e-05. Test_MSE: 0.0006393193616531789\n",
      "Epoch: 879 Iteration: 83600. Train_MSE: 0.00011220794840482995. Test_MSE: 0.0006392282084561884\n",
      "Epoch: 881 Iteration: 83700. Train_MSE: 0.00011082842684118077. Test_MSE: 0.0006391312344931066\n",
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      "Epoch: 884 Iteration: 84000. Train_MSE: 0.0010574633488431573. Test_MSE: 0.0006388669135048985\n",
      "Epoch: 885 Iteration: 84100. Train_MSE: 0.0006756114307790995. Test_MSE: 0.0006387464236468077\n",
      "Epoch: 886 Iteration: 84200. Train_MSE: 0.009556835517287254. Test_MSE: 0.0006386330933310091\n",
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      "Epoch: 888 Iteration: 84400. Train_MSE: 0.0005798966740258038. Test_MSE: 0.0006384428706951439\n",
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      "Epoch: 891 Iteration: 84700. Train_MSE: 5.877714647795074e-05. Test_MSE: 0.0006381776765920222\n",
      "Epoch: 892 Iteration: 84800. Train_MSE: 0.00014026559074409306. Test_MSE: 0.000638097058981657\n",
      "Epoch: 893 Iteration: 84900. Train_MSE: 0.005012493580579758. Test_MSE: 0.0006379989208653569\n",
      "Epoch: 894 Iteration: 85000. Train_MSE: 0.0003780566621571779. Test_MSE: 0.0006378791877068579\n",
      "Epoch: 895 Iteration: 85100. Train_MSE: 0.0003930651000700891. Test_MSE: 0.0006377861718647182\n",
      "Epoch: 896 Iteration: 85200. Train_MSE: 0.00022527282999362797. Test_MSE: 0.0006376802339218557\n",
      "Epoch: 897 Iteration: 85300. Train_MSE: 0.000130282060126774. Test_MSE: 0.0006375793018378317\n",
      "Epoch: 898 Iteration: 85400. Train_MSE: 9.386450255988166e-05. Test_MSE: 0.0006374999065883458\n",
      "Epoch: 899 Iteration: 85500. Train_MSE: 0.00011057196388719603. Test_MSE: 0.0006374107324518263\n",
      "Epoch: 901 Iteration: 85600. Train_MSE: 0.00010926212416961789. Test_MSE: 0.0006373157375492156\n",
      "Epoch: 902 Iteration: 85700. Train_MSE: 0.0001754054392222315. Test_MSE: 0.0006372260395437479\n",
      "Epoch: 903 Iteration: 85800. Train_MSE: 0.00013062222569715232. Test_MSE: 0.000637161370832473\n",
      "Epoch: 904 Iteration: 85900. Train_MSE: 0.0010561262024566531. Test_MSE: 0.0006370573537424207\n",
      "Epoch: 905 Iteration: 86000. Train_MSE: 0.0006741010583937168. Test_MSE: 0.0006369390175677836\n",
      "Epoch: 906 Iteration: 86100. Train_MSE: 0.009550641290843487. Test_MSE: 0.0006368275498971343\n",
      "Epoch: 907 Iteration: 86200. Train_MSE: 0.002658738987520337. Test_MSE: 0.0006367311580106616\n",
      "Epoch: 908 Iteration: 86300. Train_MSE: 0.0005785595858469605. Test_MSE: 0.0006366410525515676\n",
      "Epoch: 909 Iteration: 86400. Train_MSE: 7.237483077915385e-05. Test_MSE: 0.0006365592707879841\n",
      "Epoch: 910 Iteration: 86500. Train_MSE: 6.300929089775309e-05. Test_MSE: 0.0006364682922139764\n",
      "Epoch: 911 Iteration: 86600. Train_MSE: 5.792232695966959e-05. Test_MSE: 0.0006363815627992153\n",
      "Epoch: 912 Iteration: 86700. Train_MSE: 0.0001387175143463537. Test_MSE: 0.0006363025750033557\n",
      "Epoch: 913 Iteration: 86800. Train_MSE: 0.005011501256376505. Test_MSE: 0.000636206183116883\n",
      "Epoch: 914 Iteration: 86900. Train_MSE: 0.0003764556022360921. Test_MSE: 0.0006360881961882114\n",
      "Epoch: 915 Iteration: 87000. Train_MSE: 0.00039191459654830396. Test_MSE: 0.0006359968683682382\n",
      "Epoch: 916 Iteration: 87100. Train_MSE: 0.00022432481637224555. Test_MSE: 0.0006358927930705249\n",
      "Epoch: 917 Iteration: 87200. Train_MSE: 0.00012846579193137586. Test_MSE: 0.000635793840046972\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 918 Iteration: 87300. Train_MSE: 9.280457015847787e-05. Test_MSE: 0.0006357164238579571\n",
      "Epoch: 919 Iteration: 87400. Train_MSE: 0.00010899025073740631. Test_MSE: 0.0006356294034048915\n",
      "Epoch: 921 Iteration: 87500. Train_MSE: 0.00010772120003821328. Test_MSE: 0.0006355361547321081\n",
      "Epoch: 922 Iteration: 87600. Train_MSE: 0.00017305446090176702. Test_MSE: 0.0006354487268254161\n",
      "Epoch: 923 Iteration: 87700. Train_MSE: 0.0001285634789383039. Test_MSE: 0.0006353860371746123\n",
      "Epoch: 924 Iteration: 87800. Train_MSE: 0.0010547521524131298. Test_MSE: 0.0006352838245220482\n",
      "Epoch: 925 Iteration: 87900. Train_MSE: 0.0006726133287884295. Test_MSE: 0.0006351673509925604\n",
      "Epoch: 926 Iteration: 88000. Train_MSE: 0.009544490836560726. Test_MSE: 0.0006350578623823822\n",
      "Epoch: 927 Iteration: 88100. Train_MSE: 0.002656170167028904. Test_MSE: 0.0006349632749333978\n",
      "Epoch: 928 Iteration: 88200. Train_MSE: 0.0005772770964540541. Test_MSE: 0.0006348754977807403\n",
      "Epoch: 929 Iteration: 88300. Train_MSE: 7.163217378547415e-05. Test_MSE: 0.0006347956950776279\n",
      "Epoch: 930 Iteration: 88400. Train_MSE: 6.250929436646402e-05. Test_MSE: 0.0006347069866023958\n",
      "Epoch: 931 Iteration: 88500. Train_MSE: 5.7092798670055345e-05. Test_MSE: 0.0006346226437017322\n",
      "Epoch: 932 Iteration: 88600. Train_MSE: 0.00013715583190787584. Test_MSE: 0.0006345462170429528\n",
      "Epoch: 933 Iteration: 88700. Train_MSE: 0.005010547582060099. Test_MSE: 0.0006344522698782384\n",
      "Epoch: 934 Iteration: 88800. Train_MSE: 0.0003748830349650234. Test_MSE: 0.000634336203802377\n",
      "Epoch: 935 Iteration: 88900. Train_MSE: 0.0003907419741153717. Test_MSE: 0.0006342470296658576\n",
      "Epoch: 936 Iteration: 89000. Train_MSE: 0.00022340375289786607. Test_MSE: 0.0006341453990899026\n",
      "Epoch: 937 Iteration: 89100. Train_MSE: 0.00012672401499003172. Test_MSE: 0.0006340484833344817\n",
      "Epoch: 938 Iteration: 89200. Train_MSE: 9.177738684229553e-05. Test_MSE: 0.000633973628282547\n",
      "Epoch: 939 Iteration: 89300. Train_MSE: 0.000107454092358239. Test_MSE: 0.0006338890525512397\n",
      "Epoch: 941 Iteration: 89400. Train_MSE: 0.00010623786511132494. Test_MSE: 0.0006337984232231975\n",
      "Epoch: 942 Iteration: 89500. Train_MSE: 0.00017079738609027117. Test_MSE: 0.0006337131489999592\n",
      "Epoch: 943 Iteration: 89600. Train_MSE: 0.00012658997729886323. Test_MSE: 0.0006336532533168793\n",
      "Epoch: 944 Iteration: 89700. Train_MSE: 0.0010533707682043314. Test_MSE: 0.0006335533107630908\n",
      "Epoch: 945 Iteration: 89800. Train_MSE: 0.0006710638990625739. Test_MSE: 0.0006334390491247177\n",
      "Epoch: 946 Iteration: 89900. Train_MSE: 0.009538416750729084. Test_MSE: 0.0006333317724056542\n",
      "Epoch: 947 Iteration: 90000. Train_MSE: 0.002653655596077442. Test_MSE: 0.0006332396878860891\n",
      "Epoch: 948 Iteration: 90100. Train_MSE: 0.0005760389030911028. Test_MSE: 0.0006331545882858336\n",
      "Epoch: 949 Iteration: 90200. Train_MSE: 7.090854342095554e-05. Test_MSE: 0.0006330774049274623\n",
      "Epoch: 950 Iteration: 90300. Train_MSE: 6.201882206369191e-05. Test_MSE: 0.0006329907337203622\n",
      "Epoch: 951 Iteration: 90400. Train_MSE: 5.6288758059963584e-05. Test_MSE: 0.0006329083116725087\n",
      "Epoch: 952 Iteration: 90500. Train_MSE: 0.0001356677385047078. Test_MSE: 0.0006328339222818613\n",
      "Epoch: 953 Iteration: 90600. Train_MSE: 0.005009602755308151. Test_MSE: 0.0006327416631393135\n",
      "Epoch: 954 Iteration: 90700. Train_MSE: 0.0003733462071977556. Test_MSE: 0.0006326275179162621\n",
      "Epoch: 955 Iteration: 90800. Train_MSE: 0.0003895694680977613. Test_MSE: 0.0006325403228402138\n",
      "Epoch: 956 Iteration: 90900. Train_MSE: 0.00022251822520047426. Test_MSE: 0.000632440613117069\n",
      "Epoch: 957 Iteration: 91000. Train_MSE: 0.00012503672041930258. Test_MSE: 0.0006323459092527628\n",
      "Epoch: 958 Iteration: 91100. Train_MSE: 9.078163566300645e-05. Test_MSE: 0.0006322732078842819\n",
      "Epoch: 959 Iteration: 91200. Train_MSE: 0.00010595969069981948. Test_MSE: 0.0006321907858364284\n",
      "Epoch: 961 Iteration: 91300. Train_MSE: 0.00010475254384800792. Test_MSE: 0.0006321023683995008\n",
      "Epoch: 962 Iteration: 91400. Train_MSE: 0.0001686363248154521. Test_MSE: 0.0006320196553133428\n",
      "Epoch: 963 Iteration: 91500. Train_MSE: 0.00012461158621590585. Test_MSE: 0.0006319622043520212\n",
      "Epoch: 964 Iteration: 91600. Train_MSE: 0.001052028383128345. Test_MSE: 0.0006318645318970084\n",
      "Epoch: 965 Iteration: 91700. Train_MSE: 0.0006695602787658572. Test_MSE: 0.0006317527149803936\n",
      "Epoch: 966 Iteration: 91800. Train_MSE: 0.009532437659800053. Test_MSE: 0.0006316476501524448\n",
      "Epoch: 967 Iteration: 91900. Train_MSE: 0.0026511987671256065. Test_MSE: 0.0006315580103546381\n",
      "Epoch: 968 Iteration: 92000. Train_MSE: 0.0005748430849052966. Test_MSE: 0.0006314754718914628\n",
      "Epoch: 969 Iteration: 92100. Train_MSE: 7.020100019872189e-05. Test_MSE: 0.0006314008496701717\n",
      "Epoch: 970 Iteration: 92200. Train_MSE: 6.154562288429588e-05. Test_MSE: 0.000631316564977169\n",
      "Epoch: 971 Iteration: 92300. Train_MSE: 5.550854984903708e-05. Test_MSE: 0.0006312367622740567\n",
      "Epoch: 972 Iteration: 92400. Train_MSE: 0.00013423318159766495. Test_MSE: 0.0006311647011898458\n",
      "Epoch: 973 Iteration: 92500. Train_MSE: 0.00500869145616889. Test_MSE: 0.0006310750031843781\n",
      "Epoch: 974 Iteration: 92600. Train_MSE: 0.0003718483494594693. Test_MSE: 0.0006309633608907461\n",
      "Epoch: 975 Iteration: 92700. Train_MSE: 0.00038843846414238214. Test_MSE: 0.0006308789015747607\n",
      "Epoch: 976 Iteration: 92800. Train_MSE: 0.0002216915600001812. Test_MSE: 0.0006307816947810352\n",
      "Epoch: 977 Iteration: 92900. Train_MSE: 0.0001234063965966925. Test_MSE: 0.0006306898430921137\n",
      "Epoch: 978 Iteration: 93000. Train_MSE: 8.980705752037466e-05. Test_MSE: 0.0006306197610683739\n",
      "Epoch: 979 Iteration: 93100. Train_MSE: 0.00010450885747559369. Test_MSE: 0.000630539667326957\n",
      "Epoch: 981 Iteration: 93200. Train_MSE: 0.00010330072836950421. Test_MSE: 0.0006304532871581614\n",
      "Epoch: 982 Iteration: 93300. Train_MSE: 0.0001665539457462728. Test_MSE: 0.0006303729023784399\n",
      "Epoch: 983 Iteration: 93400. Train_MSE: 0.00012270781735423952. Test_MSE: 0.000630317663308233\n",
      "Epoch: 984 Iteration: 93500. Train_MSE: 0.0010507218539714813. Test_MSE: 0.0006302222027443349\n",
      "Epoch: 985 Iteration: 93600. Train_MSE: 0.0006681284285150468. Test_MSE: 0.0006301128887571394\n",
      "Epoch: 986 Iteration: 93700. Train_MSE: 0.009526558220386505. Test_MSE: 0.000630010268650949\n",
      "Epoch: 987 Iteration: 93800. Train_MSE: 0.0026488250587135553. Test_MSE: 0.0006299227825365961\n",
      "Epoch: 988 Iteration: 93900. Train_MSE: 0.000573695928324014. Test_MSE: 0.0006298428634181619\n",
      "Epoch: 989 Iteration: 94000. Train_MSE: 6.950367969693616e-05. Test_MSE: 0.0006297708605416119\n",
      "Epoch: 990 Iteration: 94100. Train_MSE: 6.108716479502618e-05. Test_MSE: 0.0006296888459473848\n",
      "Epoch: 991 Iteration: 94200. Train_MSE: 5.474821591633372e-05. Test_MSE: 0.000629611371550709\n",
      "Epoch: 992 Iteration: 94300. Train_MSE: 0.0001328523940173909. Test_MSE: 0.0006295418716035783\n",
      "Epoch: 993 Iteration: 94400. Train_MSE: 0.005007818806916475. Test_MSE: 0.0006294545019045472\n",
      "Epoch: 994 Iteration: 94500. Train_MSE: 0.00037039228482171893. Test_MSE: 0.0006293449550867081\n",
      "Epoch: 995 Iteration: 94600. Train_MSE: 0.000387347157811746. Test_MSE: 0.0006292627658694983\n",
      "Epoch: 996 Iteration: 94700. Train_MSE: 0.00022088045079726726. Test_MSE: 0.0006291678873822093\n",
      "Epoch: 997 Iteration: 94800. Train_MSE: 0.00012182581122033298. Test_MSE: 0.0006290782475844026\n",
      "Epoch: 998 Iteration: 94900. Train_MSE: 8.885021088644862e-05. Test_MSE: 0.0006290105520747602\n",
      "Epoch: 999 Iteration: 95000. Train_MSE: 0.00010309459321433678. Test_MSE: 0.0006289327284321189\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "seq_dim = 1\n",
    "\n",
    "n_iter =0\n",
    "num_samples = len(X_train)\n",
    "test_samples = len(X_test)\n",
    "batch_size = 100\n",
    "num_epochs = 1000\n",
    "feat_dim = X_train.shape[1]\n",
    "\n",
    "X_train = X_train.type(torch.FloatTensor)\n",
    "y_train = y_train.type(torch.FloatTensor)\n",
    "X_test = X_test.type(torch.FloatTensor)\n",
    "y_test = y_test.type(torch.FloatTensor)\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for i in range(0, int(num_samples/batch_size -1)):\n",
    "        \n",
    "        \n",
    "        features = Variable(X_train[i*batch_size:(i+1)*batch_size, :]).view(-1, seq_dim, feat_dim)\n",
    "        Kt_value = Variable(y_train[i*batch_size:(i+1)*batch_size])\n",
    "        \n",
    "               \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        outputs = model(features)\n",
    "        \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",
    "                #mse_old = np.sqrt(np.mean((Kt_test.data.numpy() - outputs.data.numpy().squeeze())**2)/num_samples)\n",
    "                test_batch_mse.append(np.mean((Kt_test.data.numpy() - outputs.data.numpy().squeeze())**2))\n",
    "            test_iter.append(n_iter)\n",
    "            test_loss.append(np.mean(test_batch_mse))\n",
    "            \n",
    "            print('Epoch: {} Iteration: {}. Train_MSE: {}. Test_MSE: {}'.format(epoch, n_iter, loss.data[0], test_loss[-1]))\n",
    "               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "950\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23b077f4e48>]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WP5tHkqQqMKA0ueSba7o9BUmShAHlaW77+cOeLCtJUgUYUFosXf6Lbk9BkqRpz4DS4ls3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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b04d23518>"
      ]
     },
     "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": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23b0782a7f0>]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2zuBzm1VL3nkxC895IwBj3/suz950g6dmJElqQ7srIScBZZJVjYaHSFYyWp2W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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b078104a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test_loss,'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Demornamization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = np.sqrt(test_loss[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.131595733546838"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_denorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4362.000000\n",
       "mean        5.380703\n",
       "std        29.307744\n",
       "min        -0.203877\n",
       "25%        -0.008714\n",
       "50%         0.102974\n",
       "75%         1.071525\n",
       "max      1146.230573\n",
       "Name: Kt, dtype: float64"
      ]
     },
     "execution_count": 194,
     "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": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trainLoss = pd.DataFrame(data={'Train Loss':train_loss}, columns=['Train Loss'])\n",
    "df_trainLoss.to_csv('RNN Paper Results/Exp1_Bondville_1000Epoch_TrainLoss.csv')\n",
    "df_testLoss = pd.DataFrame(data={'Test Loss':test_loss}, columns=['Test Loss'])\n",
    "df_testLoss.to_csv('RNN Paper Results/Exp1_Bondville_1000EpochTestLoss.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}