homeF-preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Usage</th>\n",
       "      <th>Generation</th>\n",
       "      <th>WaterHeater</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Refrigerator</th>\n",
       "      <th>Microwave</th>\n",
       "      <th>Furnace</th>\n",
       "      <th>WaterHeater3</th>\n",
       "      <th>WaterHeater2</th>\n",
       "      <th>WaterHeater1</th>\n",
       "      <th>...</th>\n",
       "      <th>visibility</th>\n",
       "      <th>summary</th>\n",
       "      <th>apparentTemperature</th>\n",
       "      <th>pressure</th>\n",
       "      <th>windSpeed</th>\n",
       "      <th>cloudCover</th>\n",
       "      <th>windBearing</th>\n",
       "      <th>precipIntensity</th>\n",
       "      <th>dewPoint</th>\n",
       "      <th>precipProbability</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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>2014-01-01 00:00:00</th>\n",
       "      <td>0.977402</td>\n",
       "      <td>0.009264</td>\n",
       "      <td>0.296840</td>\n",
       "      <td>0.009264</td>\n",
       "      <td>0.059256</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.008474</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>0.020002</td>\n",
       "      <td>0.277369</td>\n",
       "      <td>...</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Clear</td>\n",
       "      <td>8.33</td>\n",
       "      <td>1023.40</td>\n",
       "      <td>12.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>284.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.360</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:15:00</th>\n",
       "      <td>0.377746</td>\n",
       "      <td>0.009904</td>\n",
       "      <td>0.063231</td>\n",
       "      <td>0.009904</td>\n",
       "      <td>0.003734</td>\n",
       "      <td>0.002914</td>\n",
       "      <td>0.008633</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.009642</td>\n",
       "      <td>0.054060</td>\n",
       "      <td>...</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Clear</td>\n",
       "      <td>8.37</td>\n",
       "      <td>1023.39</td>\n",
       "      <td>12.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>284.25</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.255</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:30:00</th>\n",
       "      <td>0.420196</td>\n",
       "      <td>0.010133</td>\n",
       "      <td>0.001511</td>\n",
       "      <td>0.010133</td>\n",
       "      <td>0.121228</td>\n",
       "      <td>0.002630</td>\n",
       "      <td>0.008059</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.001478</td>\n",
       "      <td>...</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Clear</td>\n",
       "      <td>8.41</td>\n",
       "      <td>1023.38</td>\n",
       "      <td>11.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>284.50</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.150</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:45:00</th>\n",
       "      <td>0.290328</td>\n",
       "      <td>0.010029</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.010029</td>\n",
       "      <td>0.016986</td>\n",
       "      <td>0.002910</td>\n",
       "      <td>0.008511</td>\n",
       "      <td>0.000411</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000671</td>\n",
       "      <td>...</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Clear</td>\n",
       "      <td>8.45</td>\n",
       "      <td>1023.37</td>\n",
       "      <td>10.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>284.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.045</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td>0.307616</td>\n",
       "      <td>0.010038</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.010038</td>\n",
       "      <td>0.014140</td>\n",
       "      <td>0.002691</td>\n",
       "      <td>0.008500</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>...</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Clear</td>\n",
       "      <td>8.49</td>\n",
       "      <td>1023.36</td>\n",
       "      <td>10.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>285.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.940</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:55:00</th>\n",
       "      <td>3.963900</td>\n",
       "      <td>0.008100</td>\n",
       "      <td>0.003200</td>\n",
       "      <td>0.008100</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.002783</td>\n",
       "      <td>0.008683</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.73</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>29.47</td>\n",
       "      <td>1011.65</td>\n",
       "      <td>6.45</td>\n",
       "      <td>0.31</td>\n",
       "      <td>186.00</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>31.840</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:56:00</th>\n",
       "      <td>4.150650</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.003167</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.001250</td>\n",
       "      <td>0.002817</td>\n",
       "      <td>0.008733</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.001033</td>\n",
       "      <td>...</td>\n",
       "      <td>8.73</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>29.47</td>\n",
       "      <td>1011.65</td>\n",
       "      <td>6.45</td>\n",
       "      <td>0.31</td>\n",
       "      <td>186.00</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>31.840</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:57:00</th>\n",
       "      <td>4.571883</td>\n",
       "      <td>0.008733</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.008733</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.003783</td>\n",
       "      <td>0.009367</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>...</td>\n",
       "      <td>8.73</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>29.47</td>\n",
       "      <td>1011.65</td>\n",
       "      <td>6.45</td>\n",
       "      <td>0.31</td>\n",
       "      <td>186.00</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>31.840</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:58:00</th>\n",
       "      <td>4.406200</td>\n",
       "      <td>0.008733</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.008733</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.003950</td>\n",
       "      <td>0.009483</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>...</td>\n",
       "      <td>8.73</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>29.47</td>\n",
       "      <td>1011.65</td>\n",
       "      <td>6.45</td>\n",
       "      <td>0.31</td>\n",
       "      <td>186.00</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>31.840</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:59:00</th>\n",
       "      <td>4.124233</td>\n",
       "      <td>0.008100</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.008100</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.002900</td>\n",
       "      <td>0.008667</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.000567</td>\n",
       "      <td>0.001067</td>\n",
       "      <td>...</td>\n",
       "      <td>8.73</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>29.47</td>\n",
       "      <td>1011.65</td>\n",
       "      <td>6.45</td>\n",
       "      <td>0.31</td>\n",
       "      <td>186.00</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>31.840</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Usage  Generation  WaterHeater     Solar  \\\n",
       "time                                                               \n",
       "2014-01-01 00:00:00  0.977402    0.009264     0.296840  0.009264   \n",
       "2014-01-01 00:15:00  0.377746    0.009904     0.063231  0.009904   \n",
       "2014-01-01 00:30:00  0.420196    0.010133     0.001511  0.010133   \n",
       "2014-01-01 00:45:00  0.290328    0.010029     0.000058  0.010029   \n",
       "2014-01-01 01:00:00  0.307616    0.010038     0.000127  0.010038   \n",
       "2016-12-31 23:55:00  3.963900    0.008100     0.003200  0.008100   \n",
       "2016-12-31 23:56:00  4.150650    0.008000     0.003167  0.008000   \n",
       "2016-12-31 23:57:00  4.571883    0.008733     0.000500  0.008733   \n",
       "2016-12-31 23:58:00  4.406200    0.008733     0.001200  0.008733   \n",
       "2016-12-31 23:59:00  4.124233    0.008100     0.003333  0.008100   \n",
       "\n",
       "                     Refrigerator  Microwave   Furnace  WaterHeater3  \\\n",
       "time                                                                   \n",
       "2014-01-01 00:00:00      0.059256   0.002800  0.008474      0.000531   \n",
       "2014-01-01 00:15:00      0.003734   0.002914  0.008633      0.000471   \n",
       "2014-01-01 00:30:00      0.121228   0.002630  0.008059      0.000044   \n",
       "2014-01-01 00:45:00      0.016986   0.002910  0.008511      0.000411   \n",
       "2014-01-01 01:00:00      0.014140   0.002691  0.008500      0.000400   \n",
       "2016-12-31 23:55:00      0.001267   0.002783  0.008683      0.001600   \n",
       "2016-12-31 23:56:00      0.001250   0.002817  0.008733      0.001633   \n",
       "2016-12-31 23:57:00      0.000667   0.003783  0.009367      0.000467   \n",
       "2016-12-31 23:58:00      0.000500   0.003950  0.009483      0.000467   \n",
       "2016-12-31 23:59:00      0.001267   0.002900  0.008667      0.001700   \n",
       "\n",
       "                     WaterHeater2  WaterHeater1  ...  visibility     summary  \\\n",
       "time                                             ...                           \n",
       "2014-01-01 00:00:00      0.020002      0.277369  ...       10.00       Clear   \n",
       "2014-01-01 00:15:00      0.009642      0.054060  ...       10.00       Clear   \n",
       "2014-01-01 00:30:00      0.000011      0.001478  ...       10.00       Clear   \n",
       "2014-01-01 00:45:00      0.000202      0.000671  ...       10.00       Clear   \n",
       "2014-01-01 01:00:00      0.000200      0.000727  ...       10.00       Clear   \n",
       "2016-12-31 23:55:00      0.000600      0.001000  ...        8.73  Light Rain   \n",
       "2016-12-31 23:56:00      0.000500      0.001033  ...        8.73  Light Rain   \n",
       "2016-12-31 23:57:00      0.000100      0.000867  ...        8.73  Light Rain   \n",
       "2016-12-31 23:58:00      0.000367      0.001300  ...        8.73  Light Rain   \n",
       "2016-12-31 23:59:00      0.000567      0.001067  ...        8.73  Light Rain   \n",
       "\n",
       "                     apparentTemperature  pressure  windSpeed  cloudCover  \\\n",
       "time                                                                        \n",
       "2014-01-01 00:00:00                 8.33   1023.40      12.83        0.00   \n",
       "2014-01-01 00:15:00                 8.37   1023.39      12.13        0.00   \n",
       "2014-01-01 00:30:00                 8.41   1023.38      11.43        0.00   \n",
       "2014-01-01 00:45:00                 8.45   1023.37      10.73        0.00   \n",
       "2014-01-01 01:00:00                 8.49   1023.36      10.03        0.00   \n",
       "2016-12-31 23:55:00                29.47   1011.65       6.45        0.31   \n",
       "2016-12-31 23:56:00                29.47   1011.65       6.45        0.31   \n",
       "2016-12-31 23:57:00                29.47   1011.65       6.45        0.31   \n",
       "2016-12-31 23:58:00                29.47   1011.65       6.45        0.31   \n",
       "2016-12-31 23:59:00                29.47   1011.65       6.45        0.31   \n",
       "\n",
       "                     windBearing  precipIntensity  dewPoint  precipProbability  \n",
       "time                                                                            \n",
       "2014-01-01 00:00:00       284.00           0.0000     3.360               0.00  \n",
       "2014-01-01 00:15:00       284.25           0.0000     3.255               0.00  \n",
       "2014-01-01 00:30:00       284.50           0.0000     3.150               0.00  \n",
       "2014-01-01 00:45:00       284.75           0.0000     3.045               0.00  \n",
       "2014-01-01 01:00:00       285.00           0.0000     2.940               0.00  \n",
       "2016-12-31 23:55:00       186.00           0.0153    31.840               0.56  \n",
       "2016-12-31 23:56:00       186.00           0.0153    31.840               0.56  \n",
       "2016-12-31 23:57:00       186.00           0.0153    31.840               0.56  \n",
       "2016-12-31 23:58:00       186.00           0.0153    31.840               0.56  \n",
       "2016-12-31 23:59:00       186.00           0.0153    31.840               0.56  \n",
       "\n",
       "[10 rows x 36 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"./HomeF.csv\"\n",
    "dataset = pd.read_csv(file_path, low_memory=False, index_col=\"time\")\n",
    "\n",
    "dataset.columns = [col.replace(' [kW]', '') for col in dataset.columns]\n",
    "\n",
    "time_index = pd.DatetimeIndex(dataset.index)\n",
    "dataset = dataset.set_index(time_index)\n",
    "dataset.iloc[np.r_[0:5,-5:0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 571987 entries, 2014-01-01 00:00:00 to 2016-12-31 23:59:00\n",
      "Data columns (total 36 columns):\n",
      "Usage                  571987 non-null float64\n",
      "Generation             571987 non-null float64\n",
      "WaterHeater            571987 non-null float64\n",
      "Solar                  571987 non-null float64\n",
      "Refrigerator           571987 non-null float64\n",
      "Microwave              571987 non-null float64\n",
      "Furnace                571987 non-null float64\n",
      "WaterHeater3           571987 non-null float64\n",
      "WaterHeater2           571987 non-null float64\n",
      "WaterHeater1           571987 non-null float64\n",
      "Master_Bdrm            571987 non-null float64\n",
      "Front_Bdrm             571987 non-null float64\n",
      "Kit_StoveWall          571987 non-null float64\n",
      "Dishwasher_Disposal    571987 non-null float64\n",
      "Kit_SinkWall           571987 non-null float64\n",
      "Family_Rm              571987 non-null float64\n",
      "Kit_Half-Bath_Foyer    571987 non-null float64\n",
      "Washing_Machine        571987 non-null float64\n",
      "Guest_Bdrm_SmkDet      571987 non-null float64\n",
      "Dryer                  571987 non-null float64\n",
      "Basement               571987 non-null float64\n",
      "Phase_B                571987 non-null float64\n",
      "Phase_A                571987 non-null float64\n",
      "temperature            571987 non-null float64\n",
      "icon                   571987 non-null object\n",
      "humidity               571987 non-null float64\n",
      "visibility             571987 non-null float64\n",
      "summary                571987 non-null object\n",
      "apparentTemperature    571987 non-null float64\n",
      "pressure               571987 non-null float64\n",
      "windSpeed              571987 non-null float64\n",
      "cloudCover             571987 non-null float64\n",
      "windBearing            571987 non-null float64\n",
      "precipIntensity        571987 non-null float64\n",
      "dewPoint               571987 non-null float64\n",
      "precipProbability      571987 non-null float64\n",
      "dtypes: float64(34), object(2)\n",
      "memory usage: 161.5+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Usage                  0\n",
       "Generation             0\n",
       "WaterHeater            0\n",
       "Solar                  0\n",
       "Refrigerator           0\n",
       "Microwave              0\n",
       "Furnace                0\n",
       "WaterHeater3           0\n",
       "WaterHeater2           0\n",
       "WaterHeater1           0\n",
       "Master_Bdrm            0\n",
       "Front_Bdrm             0\n",
       "Kit_StoveWall          0\n",
       "Dishwasher_Disposal    0\n",
       "Kit_SinkWall           0\n",
       "Family_Rm              0\n",
       "Kit_Half-Bath_Foyer    0\n",
       "Washing_Machine        0\n",
       "Guest_Bdrm_SmkDet      0\n",
       "Dryer                  0\n",
       "Basement               0\n",
       "Phase_B                0\n",
       "Phase_A                0\n",
       "temperature            0\n",
       "icon                   0\n",
       "humidity               0\n",
       "visibility             0\n",
       "summary                0\n",
       "apparentTemperature    0\n",
       "pressure               0\n",
       "windSpeed              0\n",
       "cloudCover             0\n",
       "windBearing            0\n",
       "precipIntensity        0\n",
       "dewPoint               0\n",
       "precipProbability      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 取樣時間 (1min -> 15mins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of 15mins dataset: (105216, 36) --> n_rows = 105216, n_cols = 36\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.resample('15T').first()\n",
    "print(\"Shape of 15mins dataset: {} --> n_rows = {}, n_cols = {}\".format(dataset.shape, dataset.shape[0],dataset.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Date encode to 環形時間"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Usage</th>\n",
       "      <th>Generation</th>\n",
       "      <th>WaterHeater</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Refrigerator</th>\n",
       "      <th>Microwave</th>\n",
       "      <th>Furnace</th>\n",
       "      <th>WaterHeater3</th>\n",
       "      <th>WaterHeater2</th>\n",
       "      <th>WaterHeater1</th>\n",
       "      <th>...</th>\n",
       "      <th>precipIntensity</th>\n",
       "      <th>dewPoint</th>\n",
       "      <th>precipProbability</th>\n",
       "      <th>sin_hr</th>\n",
       "      <th>cos_hr</th>\n",
       "      <th>sin_weekday</th>\n",
       "      <th>cos_weekday</th>\n",
       "      <th>sin_month</th>\n",
       "      <th>cos_month</th>\n",
       "      <th>is_holiday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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>2014-01-01 00:00:00</th>\n",
       "      <td>0.977402</td>\n",
       "      <td>0.009264</td>\n",
       "      <td>0.296840</td>\n",
       "      <td>0.009264</td>\n",
       "      <td>0.059256</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.008474</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>0.020002</td>\n",
       "      <td>0.277369</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.360</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:15:00</th>\n",
       "      <td>0.377746</td>\n",
       "      <td>0.009904</td>\n",
       "      <td>0.063231</td>\n",
       "      <td>0.009904</td>\n",
       "      <td>0.003734</td>\n",
       "      <td>0.002914</td>\n",
       "      <td>0.008633</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.009642</td>\n",
       "      <td>0.054060</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.255</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:30:00</th>\n",
       "      <td>0.420196</td>\n",
       "      <td>0.010133</td>\n",
       "      <td>0.001511</td>\n",
       "      <td>0.010133</td>\n",
       "      <td>0.121228</td>\n",
       "      <td>0.002630</td>\n",
       "      <td>0.008059</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.001478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.150</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:45:00</th>\n",
       "      <td>0.290328</td>\n",
       "      <td>0.010029</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.010029</td>\n",
       "      <td>0.016986</td>\n",
       "      <td>0.002910</td>\n",
       "      <td>0.008511</td>\n",
       "      <td>0.000411</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000671</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.045</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td>0.307616</td>\n",
       "      <td>0.010038</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.010038</td>\n",
       "      <td>0.014140</td>\n",
       "      <td>0.002691</td>\n",
       "      <td>0.008500</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.940</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 22:45:00</th>\n",
       "      <td>4.404933</td>\n",
       "      <td>0.007467</td>\n",
       "      <td>0.006733</td>\n",
       "      <td>0.007467</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.003033</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>0.002667</td>\n",
       "      <td>0.001067</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>31.840</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:00:00</th>\n",
       "      <td>4.326100</td>\n",
       "      <td>0.007600</td>\n",
       "      <td>0.004133</td>\n",
       "      <td>0.007600</td>\n",
       "      <td>0.147567</td>\n",
       "      <td>0.002750</td>\n",
       "      <td>0.008450</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>31.840</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:15:00</th>\n",
       "      <td>4.651833</td>\n",
       "      <td>0.008200</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.008200</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.004300</td>\n",
       "      <td>0.010067</td>\n",
       "      <td>0.001033</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>31.840</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:30:00</th>\n",
       "      <td>4.412017</td>\n",
       "      <td>0.007500</td>\n",
       "      <td>0.004800</td>\n",
       "      <td>0.007500</td>\n",
       "      <td>0.008500</td>\n",
       "      <td>0.002883</td>\n",
       "      <td>0.008733</td>\n",
       "      <td>0.002167</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>31.840</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:45:00</th>\n",
       "      <td>4.094733</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>0.138167</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.008433</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>31.840</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Usage  Generation  WaterHeater     Solar  \\\n",
       "time                                                               \n",
       "2014-01-01 00:00:00  0.977402    0.009264     0.296840  0.009264   \n",
       "2014-01-01 00:15:00  0.377746    0.009904     0.063231  0.009904   \n",
       "2014-01-01 00:30:00  0.420196    0.010133     0.001511  0.010133   \n",
       "2014-01-01 00:45:00  0.290328    0.010029     0.000058  0.010029   \n",
       "2014-01-01 01:00:00  0.307616    0.010038     0.000127  0.010038   \n",
       "2016-12-31 22:45:00  4.404933    0.007467     0.006733  0.007467   \n",
       "2016-12-31 23:00:00  4.326100    0.007600     0.004133  0.007600   \n",
       "2016-12-31 23:15:00  4.651833    0.008200     0.000833  0.008200   \n",
       "2016-12-31 23:30:00  4.412017    0.007500     0.004800  0.007500   \n",
       "2016-12-31 23:45:00  4.094733    0.008333     0.000400  0.008333   \n",
       "\n",
       "                     Refrigerator  Microwave   Furnace  WaterHeater3  \\\n",
       "time                                                                   \n",
       "2014-01-01 00:00:00      0.059256   0.002800  0.008474      0.000531   \n",
       "2014-01-01 00:15:00      0.003734   0.002914  0.008633      0.000471   \n",
       "2014-01-01 00:30:00      0.121228   0.002630  0.008059      0.000044   \n",
       "2014-01-01 00:45:00      0.016986   0.002910  0.008511      0.000411   \n",
       "2014-01-01 01:00:00      0.014140   0.002691  0.008500      0.000400   \n",
       "2016-12-31 22:45:00      0.001500   0.003033  0.009000      0.002667   \n",
       "2016-12-31 23:00:00      0.147567   0.002750  0.008450      0.001833   \n",
       "2016-12-31 23:15:00      0.000550   0.004300  0.010067      0.001033   \n",
       "2016-12-31 23:30:00      0.008500   0.002883  0.008733      0.002167   \n",
       "2016-12-31 23:45:00      0.138167   0.002700  0.008433      0.000500   \n",
       "\n",
       "                     WaterHeater2  WaterHeater1  ...  precipIntensity  \\\n",
       "time                                             ...                    \n",
       "2014-01-01 00:00:00      0.020002      0.277369  ...           0.0000   \n",
       "2014-01-01 00:15:00      0.009642      0.054060  ...           0.0000   \n",
       "2014-01-01 00:30:00      0.000011      0.001478  ...           0.0000   \n",
       "2014-01-01 00:45:00      0.000202      0.000671  ...           0.0000   \n",
       "2014-01-01 01:00:00      0.000200      0.000727  ...           0.0000   \n",
       "2016-12-31 22:45:00      0.001067      0.003000  ...           0.0153   \n",
       "2016-12-31 23:00:00      0.000667      0.001633  ...           0.0153   \n",
       "2016-12-31 23:15:00      0.000133      0.000333  ...           0.0153   \n",
       "2016-12-31 23:30:00      0.000733      0.001900  ...           0.0153   \n",
       "2016-12-31 23:45:00      0.000000      0.000100  ...           0.0153   \n",
       "\n",
       "                     dewPoint  precipProbability    sin_hr    cos_hr  \\\n",
       "time                                                                   \n",
       "2014-01-01 00:00:00     3.360               0.00  0.000000  1.000000   \n",
       "2014-01-01 00:15:00     3.255               0.00  0.000000  1.000000   \n",
       "2014-01-01 00:30:00     3.150               0.00  0.000000  1.000000   \n",
       "2014-01-01 00:45:00     3.045               0.00  0.000000  1.000000   \n",
       "2014-01-01 01:00:00     2.940               0.00  0.258819  0.965926   \n",
       "2016-12-31 22:45:00    31.840               0.56 -0.500000  0.866025   \n",
       "2016-12-31 23:00:00    31.840               0.56 -0.258819  0.965926   \n",
       "2016-12-31 23:15:00    31.840               0.56 -0.258819  0.965926   \n",
       "2016-12-31 23:30:00    31.840               0.56 -0.258819  0.965926   \n",
       "2016-12-31 23:45:00    31.840               0.56 -0.258819  0.965926   \n",
       "\n",
       "                     sin_weekday  cos_weekday     sin_month  cos_month  \\\n",
       "time                                                                     \n",
       "2014-01-01 00:00:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 00:15:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 00:30:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 00:45:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 01:00:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2016-12-31 22:45:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 23:00:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 23:15:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 23:30:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 23:45:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "\n",
       "                     is_holiday  \n",
       "time                             \n",
       "2014-01-01 00:00:00           0  \n",
       "2014-01-01 00:15:00           0  \n",
       "2014-01-01 00:30:00           0  \n",
       "2014-01-01 00:45:00           0  \n",
       "2014-01-01 01:00:00           0  \n",
       "2016-12-31 22:45:00           1  \n",
       "2016-12-31 23:00:00           1  \n",
       "2016-12-31 23:15:00           1  \n",
       "2016-12-31 23:30:00           1  \n",
       "2016-12-31 23:45:00           1  \n",
       "\n",
       "[10 rows x 43 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hours_in_day = 24\n",
    "days_in_week = 7\n",
    "months_in_year = 12\n",
    "mins_in_hour = 60\n",
    "\n",
    "dataset['sin_hr'] = np.sin(2*np.pi*dataset.index.hour/hours_in_day)\n",
    "dataset['cos_hr'] = np.cos(2*np.pi*dataset.index.hour/hours_in_day)\n",
    "dataset['sin_weekday'] = np.sin(2*np.pi*dataset.index.weekday/days_in_week)\n",
    "dataset['cos_weekday'] = np.cos(2*np.pi*dataset.index.weekday/days_in_week)\n",
    "dataset['sin_month'] = np.sin(2*np.pi*dataset.index.month/months_in_year)\n",
    "dataset['cos_month'] = np.cos(2*np.pi*dataset.index.month/months_in_year)\n",
    "# dataset['sin_min'] = np.sin(2*np.pi*dataset.index.minute/mins_in_hour)\n",
    "# dataset['cos_min'] = np.cos(2*np.pi*dataset.index.minute/mins_in_hour)\n",
    "\n",
    "dataset['is_holiday'] = dataset.index.weekday.map(lambda x: 1 if x>4 else 0)\n",
    "\n",
    "dataset.iloc[np.r_[0:5, -5:0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.sample(100).plot.scatter('sin_month','cos_month').set_aspect('equal');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Usage', 'Generation', 'WaterHeater', 'Solar', 'Refrigerator',\n",
       "       'Microwave', 'Furnace', 'WaterHeater3', 'WaterHeater2', 'WaterHeater1',\n",
       "       'Master_Bdrm', 'Front_Bdrm', 'Kit_StoveWall', 'Dishwasher_Disposal',\n",
       "       'Kit_SinkWall', 'Family_Rm', 'Kit_Half-Bath_Foyer', 'Washing_Machine',\n",
       "       'Guest_Bdrm_SmkDet', 'Dryer', 'Basement', 'Phase_B', 'Phase_A',\n",
       "       'temperature', 'icon', 'humidity', 'visibility', 'summary',\n",
       "       'apparentTemperature', 'pressure', 'windSpeed', 'cloudCover',\n",
       "       'windBearing', 'precipIntensity', 'dewPoint', 'precipProbability',\n",
       "       'sin_hr', 'cos_hr', 'sin_weekday', 'cos_weekday', 'sin_month',\n",
       "       'cos_month', 'is_holiday'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    105204.000000\n",
       "mean          0.095905\n",
       "std           0.162381\n",
       "min           0.000000\n",
       "25%           0.008800\n",
       "50%           0.010776\n",
       "75%           0.114442\n",
       "max           0.629318\n",
       "Name: Furnace, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Furnace\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "COL_WEATHER = ['temperature', 'humidity', 'visibility', 'apparentTemperature', 'pressure', 'windSpeed', 'cloudCover','windBearing', 'precipIntensity', 'dewPoint', 'precipProbability']\n",
    "COL_APPLIANCE = ['WaterHeater', 'Refrigerator', 'Microwave', 'Furnace', 'WaterHeater3', 'WaterHeater2', 'WaterHeater1', 'Kit_StoveWall', 'Dishwasher_Disposal', 'Kit_SinkWall', 'Washing_Machine', 'Dryer']\n",
    "# COL_DATE = ['sin_hr', 'cos_hr', 'sin_weekday', 'cos_weekday', 'sin_month', 'cos_month', 'sin_min', 'cos_min']\n",
    "COL_DATE = ['sin_hr', 'cos_hr', 'sin_weekday', 'cos_weekday', 'sin_month', 'cos_month', 'is_holiday']\n",
    "APPLIANCE = 'Furnace'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Norm some weather features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "COL_NORM = ['temperature', 'humidity', 'visibility', 'apparentTemperature', 'pressure', 'windSpeed', 'windBearing', 'precipIntensity', 'dewPoint']\n",
    "X = dataset.loc[:, COL_NORM]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_norm = MinMaxScaler().fit_transform(X)\n",
    "dataset.loc[:, COL_NORM] = ds_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Usage</th>\n",
       "      <th>Generation</th>\n",
       "      <th>WaterHeater</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Refrigerator</th>\n",
       "      <th>Microwave</th>\n",
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       "      <th>WaterHeater2</th>\n",
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       "      <th>...</th>\n",
       "      <th>precipIntensity</th>\n",
       "      <th>dewPoint</th>\n",
       "      <th>precipProbability</th>\n",
       "      <th>sin_hr</th>\n",
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       "      <th>cos_weekday</th>\n",
       "      <th>sin_month</th>\n",
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       "      <th>time</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:00:00</th>\n",
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       "    <tr>\n",
       "      <th>2014-01-01 00:15:00</th>\n",
       "      <td>0.377746</td>\n",
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       "      <td>0.008633</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.009642</td>\n",
       "      <td>0.054060</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.299205</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:30:00</th>\n",
       "      <td>0.420196</td>\n",
       "      <td>0.010133</td>\n",
       "      <td>0.001511</td>\n",
       "      <td>0.010133</td>\n",
       "      <td>0.121228</td>\n",
       "      <td>0.002630</td>\n",
       "      <td>0.008059</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.001478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.298186</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-01 00:45:00</th>\n",
       "      <td>0.290328</td>\n",
       "      <td>0.010029</td>\n",
       "      <td>0.000058</td>\n",
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       "      <td>0.000411</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000671</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.297168</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td>0.307616</td>\n",
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       "      <td>0.000127</td>\n",
       "      <td>0.010038</td>\n",
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       "      <td>0.008500</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.296149</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>9.659258e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-01 01:15:00</th>\n",
       "      <td>0.396122</td>\n",
       "      <td>0.010278</td>\n",
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       "      <td>0.010278</td>\n",
       "      <td>0.124819</td>\n",
       "      <td>0.002258</td>\n",
       "      <td>0.008111</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.001460</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.297798</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:30:00</th>\n",
       "      <td>0.272560</td>\n",
       "      <td>0.010127</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.010127</td>\n",
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       "      <td>0.002678</td>\n",
       "      <td>0.008587</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000576</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.299447</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-01 01:45:00</th>\n",
       "      <td>0.320803</td>\n",
       "      <td>0.010244</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.010244</td>\n",
       "      <td>0.023630</td>\n",
       "      <td>0.002581</td>\n",
       "      <td>0.008497</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.000840</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.301096</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 02:00:00</th>\n",
       "      <td>0.378931</td>\n",
       "      <td>0.010218</td>\n",
       "      <td>0.001389</td>\n",
       "      <td>0.010218</td>\n",
       "      <td>0.114904</td>\n",
       "      <td>0.002516</td>\n",
       "      <td>0.008121</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.001369</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.302745</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 02:15:00</th>\n",
       "      <td>0.301320</td>\n",
       "      <td>0.010136</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.010136</td>\n",
       "      <td>0.002524</td>\n",
       "      <td>0.002759</td>\n",
       "      <td>0.008567</td>\n",
       "      <td>0.000436</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000709</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.304612</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 02:30:00</th>\n",
       "      <td>0.280731</td>\n",
       "      <td>0.010118</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.010118</td>\n",
       "      <td>0.013208</td>\n",
       "      <td>0.002818</td>\n",
       "      <td>0.008553</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.306480</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 02:45:00</th>\n",
       "      <td>0.456980</td>\n",
       "      <td>0.010418</td>\n",
       "      <td>0.001984</td>\n",
       "      <td>0.010418</td>\n",
       "      <td>0.129216</td>\n",
       "      <td>0.002467</td>\n",
       "      <td>0.077179</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.001784</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.308347</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 03:00:00</th>\n",
       "      <td>0.448540</td>\n",
       "      <td>0.010331</td>\n",
       "      <td>0.000869</td>\n",
       "      <td>0.010331</td>\n",
       "      <td>0.002491</td>\n",
       "      <td>0.002868</td>\n",
       "      <td>0.164730</td>\n",
       "      <td>0.000380</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.001409</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.310214</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 03:15:00</th>\n",
       "      <td>0.268943</td>\n",
       "      <td>0.009836</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.009836</td>\n",
       "      <td>0.002509</td>\n",
       "      <td>0.002748</td>\n",
       "      <td>0.008424</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.310627</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 03:30:00</th>\n",
       "      <td>0.410630</td>\n",
       "      <td>0.010162</td>\n",
       "      <td>0.001587</td>\n",
       "      <td>0.010162</td>\n",
       "      <td>0.119947</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.008009</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.001520</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.311039</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 03:45:00</th>\n",
       "      <td>0.497172</td>\n",
       "      <td>0.010458</td>\n",
       "      <td>0.001358</td>\n",
       "      <td>0.010458</td>\n",
       "      <td>0.016851</td>\n",
       "      <td>0.002819</td>\n",
       "      <td>0.232191</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.001744</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.311451</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 04:00:00</th>\n",
       "      <td>0.270416</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>0.002511</td>\n",
       "      <td>0.002799</td>\n",
       "      <td>0.008456</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.000607</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.311863</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 04:15:00</th>\n",
       "      <td>0.821217</td>\n",
       "      <td>0.011289</td>\n",
       "      <td>0.003778</td>\n",
       "      <td>0.011289</td>\n",
       "      <td>0.091657</td>\n",
       "      <td>0.002538</td>\n",
       "      <td>0.460180</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.003429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.312615</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 04:30:00</th>\n",
       "      <td>0.785542</td>\n",
       "      <td>0.011202</td>\n",
       "      <td>0.003256</td>\n",
       "      <td>0.011202</td>\n",
       "      <td>0.044146</td>\n",
       "      <td>0.002762</td>\n",
       "      <td>0.503950</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.003209</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.313367</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 04:45:00</th>\n",
       "      <td>0.791664</td>\n",
       "      <td>0.011256</td>\n",
       "      <td>0.002858</td>\n",
       "      <td>0.011256</td>\n",
       "      <td>0.002423</td>\n",
       "      <td>0.002857</td>\n",
       "      <td>0.522060</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.003080</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.314119</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 05:00:00</th>\n",
       "      <td>0.834181</td>\n",
       "      <td>0.011278</td>\n",
       "      <td>0.003624</td>\n",
       "      <td>0.011278</td>\n",
       "      <td>0.063359</td>\n",
       "      <td>0.002792</td>\n",
       "      <td>0.534906</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.003504</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.314871</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 05:15:00</th>\n",
       "      <td>0.833748</td>\n",
       "      <td>0.011160</td>\n",
       "      <td>0.003647</td>\n",
       "      <td>0.011160</td>\n",
       "      <td>0.064278</td>\n",
       "      <td>0.002686</td>\n",
       "      <td>0.520154</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.317344</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 05:30:00</th>\n",
       "      <td>0.789072</td>\n",
       "      <td>0.011136</td>\n",
       "      <td>0.003022</td>\n",
       "      <td>0.011136</td>\n",
       "      <td>0.011891</td>\n",
       "      <td>0.002898</td>\n",
       "      <td>0.521420</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.003147</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.319818</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 05:45:00</th>\n",
       "      <td>0.905009</td>\n",
       "      <td>0.011164</td>\n",
       "      <td>0.082649</td>\n",
       "      <td>0.011164</td>\n",
       "      <td>0.083032</td>\n",
       "      <td>0.002637</td>\n",
       "      <td>0.508458</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.082351</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.322291</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 06:00:00</th>\n",
       "      <td>0.819421</td>\n",
       "      <td>0.011156</td>\n",
       "      <td>0.003767</td>\n",
       "      <td>0.011156</td>\n",
       "      <td>0.090353</td>\n",
       "      <td>0.002714</td>\n",
       "      <td>0.460004</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.003496</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.324765</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 06:15:00</th>\n",
       "      <td>0.631073</td>\n",
       "      <td>0.010547</td>\n",
       "      <td>0.002113</td>\n",
       "      <td>0.010547</td>\n",
       "      <td>0.025774</td>\n",
       "      <td>0.002799</td>\n",
       "      <td>0.354993</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.002358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.324983</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 06:30:00</th>\n",
       "      <td>0.787369</td>\n",
       "      <td>0.011020</td>\n",
       "      <td>0.003189</td>\n",
       "      <td>0.011020</td>\n",
       "      <td>0.035307</td>\n",
       "      <td>0.002868</td>\n",
       "      <td>0.480657</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.003196</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.325201</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 06:45:00</th>\n",
       "      <td>0.682386</td>\n",
       "      <td>0.010782</td>\n",
       "      <td>0.003202</td>\n",
       "      <td>0.010782</td>\n",
       "      <td>0.111310</td>\n",
       "      <td>0.002609</td>\n",
       "      <td>0.329049</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.002944</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.325420</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 07:00:00</th>\n",
       "      <td>0.587707</td>\n",
       "      <td>0.010971</td>\n",
       "      <td>0.001840</td>\n",
       "      <td>0.010971</td>\n",
       "      <td>0.003817</td>\n",
       "      <td>0.002833</td>\n",
       "      <td>0.335748</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.325638</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 07:15:00</th>\n",
       "      <td>0.757860</td>\n",
       "      <td>0.009478</td>\n",
       "      <td>0.003562</td>\n",
       "      <td>0.009478</td>\n",
       "      <td>0.109891</td>\n",
       "      <td>0.002433</td>\n",
       "      <td>0.370949</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.003224</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.326923</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 16:30:00</th>\n",
       "      <td>1.776200</td>\n",
       "      <td>0.008767</td>\n",
       "      <td>0.005800</td>\n",
       "      <td>0.008767</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>0.003117</td>\n",
       "      <td>0.331267</td>\n",
       "      <td>0.002167</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 16:45:00</th>\n",
       "      <td>1.528633</td>\n",
       "      <td>0.007800</td>\n",
       "      <td>0.007067</td>\n",
       "      <td>0.007800</td>\n",
       "      <td>0.146450</td>\n",
       "      <td>0.058700</td>\n",
       "      <td>0.008850</td>\n",
       "      <td>0.002167</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.004067</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 17:00:00</th>\n",
       "      <td>1.403200</td>\n",
       "      <td>0.007933</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.007933</td>\n",
       "      <td>0.138567</td>\n",
       "      <td>0.002667</td>\n",
       "      <td>0.008683</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>0.004367</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 17:15:00</th>\n",
       "      <td>1.309867</td>\n",
       "      <td>0.007767</td>\n",
       "      <td>0.009067</td>\n",
       "      <td>0.007767</td>\n",
       "      <td>0.008633</td>\n",
       "      <td>0.002733</td>\n",
       "      <td>0.053533</td>\n",
       "      <td>0.002433</td>\n",
       "      <td>0.001133</td>\n",
       "      <td>0.005500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 17:30:00</th>\n",
       "      <td>1.508617</td>\n",
       "      <td>0.007767</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.007767</td>\n",
       "      <td>0.148050</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.008783</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.004433</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 17:45:00</th>\n",
       "      <td>1.604033</td>\n",
       "      <td>0.008267</td>\n",
       "      <td>0.007933</td>\n",
       "      <td>0.008267</td>\n",
       "      <td>0.018867</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.307883</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.001033</td>\n",
       "      <td>0.004400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 18:00:00</th>\n",
       "      <td>1.353933</td>\n",
       "      <td>0.007933</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>0.007933</td>\n",
       "      <td>0.008483</td>\n",
       "      <td>0.003133</td>\n",
       "      <td>0.117200</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.000933</td>\n",
       "      <td>0.005100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.836970e-16</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 18:15:00</th>\n",
       "      <td>1.870267</td>\n",
       "      <td>0.009100</td>\n",
       "      <td>0.003633</td>\n",
       "      <td>0.009100</td>\n",
       "      <td>0.137617</td>\n",
       "      <td>0.002617</td>\n",
       "      <td>0.505350</td>\n",
       "      <td>0.001333</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.001933</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.836970e-16</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 18:30:00</th>\n",
       "      <td>1.474317</td>\n",
       "      <td>0.008367</td>\n",
       "      <td>0.006900</td>\n",
       "      <td>0.008367</td>\n",
       "      <td>0.008550</td>\n",
       "      <td>0.003217</td>\n",
       "      <td>0.327767</td>\n",
       "      <td>0.002333</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>0.003833</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.836970e-16</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 18:45:00</th>\n",
       "      <td>1.953217</td>\n",
       "      <td>0.008500</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.008500</td>\n",
       "      <td>0.146633</td>\n",
       "      <td>0.003933</td>\n",
       "      <td>0.009667</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.002367</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.836970e-16</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 19:00:00</th>\n",
       "      <td>1.312517</td>\n",
       "      <td>0.007200</td>\n",
       "      <td>0.010233</td>\n",
       "      <td>0.007200</td>\n",
       "      <td>0.001417</td>\n",
       "      <td>0.003200</td>\n",
       "      <td>0.009283</td>\n",
       "      <td>0.002833</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.006200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 19:15:00</th>\n",
       "      <td>1.463267</td>\n",
       "      <td>0.007400</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.007400</td>\n",
       "      <td>0.008533</td>\n",
       "      <td>0.002783</td>\n",
       "      <td>0.009383</td>\n",
       "      <td>0.002667</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.006133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 19:30:00</th>\n",
       "      <td>1.879917</td>\n",
       "      <td>0.008800</td>\n",
       "      <td>0.005433</td>\n",
       "      <td>0.008800</td>\n",
       "      <td>0.138033</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>0.327567</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 19:45:00</th>\n",
       "      <td>1.595483</td>\n",
       "      <td>0.007933</td>\n",
       "      <td>0.008067</td>\n",
       "      <td>0.007933</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.003650</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.002167</td>\n",
       "      <td>0.000767</td>\n",
       "      <td>0.005133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 20:00:00</th>\n",
       "      <td>1.832150</td>\n",
       "      <td>0.009467</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>0.009467</td>\n",
       "      <td>0.149167</td>\n",
       "      <td>0.002833</td>\n",
       "      <td>0.504017</td>\n",
       "      <td>0.001167</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.001567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 20:15:00</th>\n",
       "      <td>1.816100</td>\n",
       "      <td>0.009467</td>\n",
       "      <td>0.003567</td>\n",
       "      <td>0.009467</td>\n",
       "      <td>0.146033</td>\n",
       "      <td>0.002683</td>\n",
       "      <td>0.512300</td>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 20:30:00</th>\n",
       "      <td>1.901800</td>\n",
       "      <td>0.009067</td>\n",
       "      <td>0.002200</td>\n",
       "      <td>0.009067</td>\n",
       "      <td>0.142500</td>\n",
       "      <td>0.003717</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 20:45:00</th>\n",
       "      <td>2.024450</td>\n",
       "      <td>0.009033</td>\n",
       "      <td>0.002167</td>\n",
       "      <td>0.009033</td>\n",
       "      <td>0.031533</td>\n",
       "      <td>0.004533</td>\n",
       "      <td>0.052567</td>\n",
       "      <td>0.000567</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 21:00:00</th>\n",
       "      <td>2.144767</td>\n",
       "      <td>0.009100</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.009100</td>\n",
       "      <td>0.146050</td>\n",
       "      <td>0.004267</td>\n",
       "      <td>0.009883</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.001133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 21:15:00</th>\n",
       "      <td>1.473400</td>\n",
       "      <td>0.008200</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.008200</td>\n",
       "      <td>0.140133</td>\n",
       "      <td>0.002550</td>\n",
       "      <td>0.009067</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.004267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 21:30:00</th>\n",
       "      <td>1.271233</td>\n",
       "      <td>0.007833</td>\n",
       "      <td>0.008967</td>\n",
       "      <td>0.007833</td>\n",
       "      <td>0.001483</td>\n",
       "      <td>0.003167</td>\n",
       "      <td>0.009400</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.005467</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 21:45:00</th>\n",
       "      <td>1.955783</td>\n",
       "      <td>0.009500</td>\n",
       "      <td>0.003267</td>\n",
       "      <td>0.009500</td>\n",
       "      <td>0.147983</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>0.510933</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.001733</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 22:00:00</th>\n",
       "      <td>5.482367</td>\n",
       "      <td>0.006367</td>\n",
       "      <td>0.008767</td>\n",
       "      <td>0.006367</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>0.004183</td>\n",
       "      <td>0.009817</td>\n",
       "      <td>0.002900</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>0.004500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 22:15:00</th>\n",
       "      <td>5.306383</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.006467</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.007583</td>\n",
       "      <td>0.004467</td>\n",
       "      <td>0.010100</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.000933</td>\n",
       "      <td>0.003500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 22:30:00</th>\n",
       "      <td>5.674317</td>\n",
       "      <td>0.008267</td>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.008267</td>\n",
       "      <td>0.135317</td>\n",
       "      <td>0.004083</td>\n",
       "      <td>0.009283</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 22:45:00</th>\n",
       "      <td>4.404933</td>\n",
       "      <td>0.007467</td>\n",
       "      <td>0.006733</td>\n",
       "      <td>0.007467</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.003033</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>0.002667</td>\n",
       "      <td>0.001067</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:00:00</th>\n",
       "      <td>4.326100</td>\n",
       "      <td>0.007600</td>\n",
       "      <td>0.004133</td>\n",
       "      <td>0.007600</td>\n",
       "      <td>0.147567</td>\n",
       "      <td>0.002750</td>\n",
       "      <td>0.008450</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:15:00</th>\n",
       "      <td>4.651833</td>\n",
       "      <td>0.008200</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.008200</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.004300</td>\n",
       "      <td>0.010067</td>\n",
       "      <td>0.001033</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:30:00</th>\n",
       "      <td>4.412017</td>\n",
       "      <td>0.007500</td>\n",
       "      <td>0.004800</td>\n",
       "      <td>0.007500</td>\n",
       "      <td>0.008500</td>\n",
       "      <td>0.002883</td>\n",
       "      <td>0.008733</td>\n",
       "      <td>0.002167</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:45:00</th>\n",
       "      <td>4.094733</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>0.138167</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.008433</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>0.576487</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105204 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Usage  Generation  WaterHeater     Solar  \\\n",
       "time                                                               \n",
       "2014-01-01 00:00:00  0.977402    0.009264     0.296840  0.009264   \n",
       "2014-01-01 00:15:00  0.377746    0.009904     0.063231  0.009904   \n",
       "2014-01-01 00:30:00  0.420196    0.010133     0.001511  0.010133   \n",
       "2014-01-01 00:45:00  0.290328    0.010029     0.000058  0.010029   \n",
       "2014-01-01 01:00:00  0.307616    0.010038     0.000127  0.010038   \n",
       "2014-01-01 01:15:00  0.396122    0.010278     0.001511  0.010278   \n",
       "2014-01-01 01:30:00  0.272560    0.010127     0.000118  0.010127   \n",
       "2014-01-01 01:45:00  0.320803    0.010244     0.000298  0.010244   \n",
       "2014-01-01 02:00:00  0.378931    0.010218     0.001389  0.010218   \n",
       "2014-01-01 02:15:00  0.301320    0.010136     0.000058  0.010136   \n",
       "2014-01-01 02:30:00  0.280731    0.010118     0.000078  0.010118   \n",
       "2014-01-01 02:45:00  0.456980    0.010418     0.001984  0.010418   \n",
       "2014-01-01 03:00:00  0.448540    0.010331     0.000869  0.010331   \n",
       "2014-01-01 03:15:00  0.268943    0.009836     0.000147  0.009836   \n",
       "2014-01-01 03:30:00  0.410630    0.010162     0.001587  0.010162   \n",
       "2014-01-01 03:45:00  0.497172    0.010458     0.001358  0.010458   \n",
       "2014-01-01 04:00:00  0.270416    0.009938     0.000124  0.009938   \n",
       "2014-01-01 04:15:00  0.821217    0.011289     0.003778  0.011289   \n",
       "2014-01-01 04:30:00  0.785542    0.011202     0.003256  0.011202   \n",
       "2014-01-01 04:45:00  0.791664    0.011256     0.002858  0.011256   \n",
       "2014-01-01 05:00:00  0.834181    0.011278     0.003624  0.011278   \n",
       "2014-01-01 05:15:00  0.833748    0.011160     0.003647  0.011160   \n",
       "2014-01-01 05:30:00  0.789072    0.011136     0.003022  0.011136   \n",
       "2014-01-01 05:45:00  0.905009    0.011164     0.082649  0.011164   \n",
       "2014-01-01 06:00:00  0.819421    0.011156     0.003767  0.011156   \n",
       "2014-01-01 06:15:00  0.631073    0.010547     0.002113  0.010547   \n",
       "2014-01-01 06:30:00  0.787369    0.011020     0.003189  0.011020   \n",
       "2014-01-01 06:45:00  0.682386    0.010782     0.003202  0.010782   \n",
       "2014-01-01 07:00:00  0.587707    0.010971     0.001840  0.010971   \n",
       "2014-01-01 07:15:00  0.757860    0.009478     0.003562  0.009478   \n",
       "...                       ...         ...          ...       ...   \n",
       "2016-12-31 16:30:00  1.776200    0.008767     0.005800  0.008767   \n",
       "2016-12-31 16:45:00  1.528633    0.007800     0.007067  0.007800   \n",
       "2016-12-31 17:00:00  1.403200    0.007933     0.007000  0.007933   \n",
       "2016-12-31 17:15:00  1.309867    0.007767     0.009067  0.007767   \n",
       "2016-12-31 17:30:00  1.508617    0.007767     0.007000  0.007767   \n",
       "2016-12-31 17:45:00  1.604033    0.008267     0.007933  0.008267   \n",
       "2016-12-31 18:00:00  1.353933    0.007933     0.008333  0.007933   \n",
       "2016-12-31 18:15:00  1.870267    0.009100     0.003633  0.009100   \n",
       "2016-12-31 18:30:00  1.474317    0.008367     0.006900  0.008367   \n",
       "2016-12-31 18:45:00  1.953217    0.008500     0.002700  0.008500   \n",
       "2016-12-31 19:00:00  1.312517    0.007200     0.010233  0.007200   \n",
       "2016-12-31 19:15:00  1.463267    0.007400     0.010000  0.007400   \n",
       "2016-12-31 19:30:00  1.879917    0.008800     0.005433  0.008800   \n",
       "2016-12-31 19:45:00  1.595483    0.007933     0.008067  0.007933   \n",
       "2016-12-31 20:00:00  1.832150    0.009467     0.003067  0.009467   \n",
       "2016-12-31 20:15:00  1.816100    0.009467     0.003567  0.009467   \n",
       "2016-12-31 20:30:00  1.901800    0.009067     0.002200  0.009067   \n",
       "2016-12-31 20:45:00  2.024450    0.009033     0.002167  0.009033   \n",
       "2016-12-31 21:00:00  2.144767    0.009100     0.000800  0.009100   \n",
       "2016-12-31 21:15:00  1.473400    0.008200     0.006567  0.008200   \n",
       "2016-12-31 21:30:00  1.271233    0.007833     0.008967  0.007833   \n",
       "2016-12-31 21:45:00  1.955783    0.009500     0.003267  0.009500   \n",
       "2016-12-31 22:00:00  5.482367    0.006367     0.008767  0.006367   \n",
       "2016-12-31 22:15:00  5.306383    0.006667     0.006467  0.006667   \n",
       "2016-12-31 22:30:00  5.674317    0.008267     0.001200  0.008267   \n",
       "2016-12-31 22:45:00  4.404933    0.007467     0.006733  0.007467   \n",
       "2016-12-31 23:00:00  4.326100    0.007600     0.004133  0.007600   \n",
       "2016-12-31 23:15:00  4.651833    0.008200     0.000833  0.008200   \n",
       "2016-12-31 23:30:00  4.412017    0.007500     0.004800  0.007500   \n",
       "2016-12-31 23:45:00  4.094733    0.008333     0.000400  0.008333   \n",
       "\n",
       "                     Refrigerator  Microwave   Furnace  WaterHeater3  \\\n",
       "time                                                                   \n",
       "2014-01-01 00:00:00      0.059256   0.002800  0.008474      0.000531   \n",
       "2014-01-01 00:15:00      0.003734   0.002914  0.008633      0.000471   \n",
       "2014-01-01 00:30:00      0.121228   0.002630  0.008059      0.000044   \n",
       "2014-01-01 00:45:00      0.016986   0.002910  0.008511      0.000411   \n",
       "2014-01-01 01:00:00      0.014140   0.002691  0.008500      0.000400   \n",
       "2014-01-01 01:15:00      0.124819   0.002258  0.008111      0.000049   \n",
       "2014-01-01 01:30:00      0.002522   0.002678  0.008587      0.000460   \n",
       "2014-01-01 01:45:00      0.023630   0.002581  0.008497      0.000344   \n",
       "2014-01-01 02:00:00      0.114904   0.002516  0.008121      0.000011   \n",
       "2014-01-01 02:15:00      0.002524   0.002759  0.008567      0.000436   \n",
       "2014-01-01 02:30:00      0.013208   0.002818  0.008553      0.000413   \n",
       "2014-01-01 02:45:00      0.129216   0.002467  0.077179      0.000122   \n",
       "2014-01-01 03:00:00      0.002491   0.002868  0.164730      0.000380   \n",
       "2014-01-01 03:15:00      0.002509   0.002748  0.008424      0.000473   \n",
       "2014-01-01 03:30:00      0.119947   0.002482  0.008009      0.000044   \n",
       "2014-01-01 03:45:00      0.016851   0.002819  0.232191      0.000289   \n",
       "2014-01-01 04:00:00      0.002511   0.002799  0.008456      0.000469   \n",
       "2014-01-01 04:15:00      0.091657   0.002538  0.460180      0.000162   \n",
       "2014-01-01 04:30:00      0.044146   0.002762  0.503950      0.000029   \n",
       "2014-01-01 04:45:00      0.002423   0.002857  0.522060      0.000198   \n",
       "2014-01-01 05:00:00      0.063359   0.002792  0.534906      0.000022   \n",
       "2014-01-01 05:15:00      0.064278   0.002686  0.520154      0.000044   \n",
       "2014-01-01 05:30:00      0.011891   0.002898  0.521420      0.000158   \n",
       "2014-01-01 05:45:00      0.083032   0.002637  0.508458      0.000116   \n",
       "2014-01-01 06:00:00      0.090353   0.002714  0.460004      0.000136   \n",
       "2014-01-01 06:15:00      0.025774   0.002799  0.354993      0.000184   \n",
       "2014-01-01 06:30:00      0.035307   0.002868  0.480657      0.000071   \n",
       "2014-01-01 06:45:00      0.111310   0.002609  0.329049      0.000153   \n",
       "2014-01-01 07:00:00      0.003817   0.002833  0.335748      0.000276   \n",
       "2014-01-01 07:15:00      0.109891   0.002433  0.370949      0.000202   \n",
       "...                           ...        ...       ...           ...   \n",
       "2016-12-31 16:30:00      0.008333   0.003117  0.331267      0.002167   \n",
       "2016-12-31 16:45:00      0.146450   0.058700  0.008850      0.002167   \n",
       "2016-12-31 17:00:00      0.138567   0.002667  0.008683      0.001900   \n",
       "2016-12-31 17:15:00      0.008633   0.002733  0.053533      0.002433   \n",
       "2016-12-31 17:30:00      0.148050   0.002800  0.008783      0.001900   \n",
       "2016-12-31 17:45:00      0.018867   0.002933  0.307883      0.002500   \n",
       "2016-12-31 18:00:00      0.008483   0.003133  0.117200      0.002300   \n",
       "2016-12-31 18:15:00      0.137617   0.002617  0.505350      0.001333   \n",
       "2016-12-31 18:30:00      0.008550   0.003217  0.327767      0.002333   \n",
       "2016-12-31 18:45:00      0.146633   0.003933  0.009667      0.000467   \n",
       "2016-12-31 19:00:00      0.001417   0.003200  0.009283      0.002833   \n",
       "2016-12-31 19:15:00      0.008533   0.002783  0.009383      0.002667   \n",
       "2016-12-31 19:30:00      0.138033   0.002717  0.327567      0.001500   \n",
       "2016-12-31 19:45:00      0.001000   0.003650  0.009950      0.002167   \n",
       "2016-12-31 20:00:00      0.149167   0.002833  0.504017      0.001167   \n",
       "2016-12-31 20:15:00      0.146033   0.002683  0.512300      0.001400   \n",
       "2016-12-31 20:30:00      0.142500   0.003717  0.053333      0.000600   \n",
       "2016-12-31 20:45:00      0.031533   0.004533  0.052567      0.000567   \n",
       "2016-12-31 21:00:00      0.146050   0.004267  0.009883      0.000033   \n",
       "2016-12-31 21:15:00      0.140133   0.002550  0.009067      0.001700   \n",
       "2016-12-31 21:30:00      0.001483   0.003167  0.009400      0.002500   \n",
       "2016-12-31 21:45:00      0.147983   0.002717  0.510933      0.001200   \n",
       "2016-12-31 22:00:00      0.000867   0.004183  0.009817      0.002900   \n",
       "2016-12-31 22:15:00      0.007583   0.004467  0.010100      0.002033   \n",
       "2016-12-31 22:30:00      0.135317   0.004083  0.009283      0.000400   \n",
       "2016-12-31 22:45:00      0.001500   0.003033  0.009000      0.002667   \n",
       "2016-12-31 23:00:00      0.147567   0.002750  0.008450      0.001833   \n",
       "2016-12-31 23:15:00      0.000550   0.004300  0.010067      0.001033   \n",
       "2016-12-31 23:30:00      0.008500   0.002883  0.008733      0.002167   \n",
       "2016-12-31 23:45:00      0.138167   0.002700  0.008433      0.000500   \n",
       "\n",
       "                     WaterHeater2  WaterHeater1  ...  precipIntensity  \\\n",
       "time                                             ...                    \n",
       "2014-01-01 00:00:00      0.020002      0.277369  ...         0.000000   \n",
       "2014-01-01 00:15:00      0.009642      0.054060  ...         0.000000   \n",
       "2014-01-01 00:30:00      0.000011      0.001478  ...         0.000000   \n",
       "2014-01-01 00:45:00      0.000202      0.000671  ...         0.000000   \n",
       "2014-01-01 01:00:00      0.000200      0.000727  ...         0.000000   \n",
       "2014-01-01 01:15:00      0.000002      0.001460  ...         0.000000   \n",
       "2014-01-01 01:30:00      0.000233      0.000576  ...         0.000000   \n",
       "2014-01-01 01:45:00      0.000198      0.000840  ...         0.000000   \n",
       "2014-01-01 02:00:00      0.000009      0.001369  ...         0.000000   \n",
       "2014-01-01 02:15:00      0.000216      0.000709  ...         0.000000   \n",
       "2014-01-01 02:30:00      0.000213      0.000704  ...         0.000000   \n",
       "2014-01-01 02:45:00      0.000078      0.001784  ...         0.000000   \n",
       "2014-01-01 03:00:00      0.000160      0.001409  ...         0.000000   \n",
       "2014-01-01 03:15:00      0.000240      0.000567  ...         0.000000   \n",
       "2014-01-01 03:30:00      0.000022      0.001520  ...         0.000000   \n",
       "2014-01-01 03:45:00      0.000098      0.001744  ...         0.000000   \n",
       "2014-01-01 04:00:00      0.000262      0.000607  ...         0.000000   \n",
       "2014-01-01 04:15:00      0.000187      0.003429  ...         0.000000   \n",
       "2014-01-01 04:30:00      0.000076      0.003209  ...         0.000000   \n",
       "2014-01-01 04:45:00      0.000024      0.003080  ...         0.000000   \n",
       "2014-01-01 05:00:00      0.000098      0.003504  ...         0.000000   \n",
       "2014-01-01 05:15:00      0.000118      0.003484  ...         0.000000   \n",
       "2014-01-01 05:30:00      0.000033      0.003147  ...         0.000000   \n",
       "2014-01-01 05:45:00      0.000182      0.082351  ...         0.000000   \n",
       "2014-01-01 06:00:00      0.000136      0.003496  ...         0.000000   \n",
       "2014-01-01 06:15:00      0.000060      0.002358  ...         0.000000   \n",
       "2014-01-01 06:30:00      0.000064      0.003196  ...         0.000000   \n",
       "2014-01-01 06:45:00      0.000104      0.002944  ...         0.000000   \n",
       "2014-01-01 07:00:00      0.000087      0.002202  ...         0.000000   \n",
       "2014-01-01 07:15:00      0.000136      0.003224  ...         0.000000   \n",
       "...                           ...           ...  ...              ...   \n",
       "2016-12-31 16:30:00      0.000700      0.002933  ...         0.041475   \n",
       "2016-12-31 16:45:00      0.000833      0.004067  ...         0.041475   \n",
       "2016-12-31 17:00:00      0.000733      0.004367  ...         0.041475   \n",
       "2016-12-31 17:15:00      0.001133      0.005500  ...         0.041475   \n",
       "2016-12-31 17:30:00      0.000667      0.004433  ...         0.041475   \n",
       "2016-12-31 17:45:00      0.001033      0.004400  ...         0.041475   \n",
       "2016-12-31 18:00:00      0.000933      0.005100  ...         0.041475   \n",
       "2016-12-31 18:15:00      0.000367      0.001933  ...         0.041475   \n",
       "2016-12-31 18:30:00      0.000733      0.003833  ...         0.041475   \n",
       "2016-12-31 18:45:00      0.000133      0.002367  ...         0.041475   \n",
       "2016-12-31 19:00:00      0.001200      0.006200  ...         0.041475   \n",
       "2016-12-31 19:15:00      0.001200      0.006133  ...         0.041475   \n",
       "2016-12-31 19:30:00      0.000600      0.003333  ...         0.041475   \n",
       "2016-12-31 19:45:00      0.000767      0.005133  ...         0.041475   \n",
       "2016-12-31 20:00:00      0.000333      0.001567  ...         0.041475   \n",
       "2016-12-31 20:15:00      0.000333      0.001833  ...         0.041475   \n",
       "2016-12-31 20:30:00      0.000033      0.001633  ...         0.041475   \n",
       "2016-12-31 20:45:00      0.000100      0.001700  ...         0.041475   \n",
       "2016-12-31 21:00:00      0.000367      0.001133  ...         0.041475   \n",
       "2016-12-31 21:15:00      0.000600      0.004267  ...         0.041475   \n",
       "2016-12-31 21:30:00      0.001000      0.005467  ...         0.041475   \n",
       "2016-12-31 21:45:00      0.000333      0.001733  ...         0.041475   \n",
       "2016-12-31 22:00:00      0.001367      0.004500  ...         0.041475   \n",
       "2016-12-31 22:15:00      0.000933      0.003500  ...         0.041475   \n",
       "2016-12-31 22:30:00      0.000333      0.001267  ...         0.041475   \n",
       "2016-12-31 22:45:00      0.001067      0.003000  ...         0.041475   \n",
       "2016-12-31 23:00:00      0.000667      0.001633  ...         0.041475   \n",
       "2016-12-31 23:15:00      0.000133      0.000333  ...         0.041475   \n",
       "2016-12-31 23:30:00      0.000733      0.001900  ...         0.041475   \n",
       "2016-12-31 23:45:00      0.000000      0.000100  ...         0.041475   \n",
       "\n",
       "                     dewPoint  precipProbability    sin_hr        cos_hr  \\\n",
       "time                                                                       \n",
       "2014-01-01 00:00:00  0.300223               0.00  0.000000  1.000000e+00   \n",
       "2014-01-01 00:15:00  0.299205               0.00  0.000000  1.000000e+00   \n",
       "2014-01-01 00:30:00  0.298186               0.00  0.000000  1.000000e+00   \n",
       "2014-01-01 00:45:00  0.297168               0.00  0.000000  1.000000e+00   \n",
       "2014-01-01 01:00:00  0.296149               0.00  0.258819  9.659258e-01   \n",
       "2014-01-01 01:15:00  0.297798               0.00  0.258819  9.659258e-01   \n",
       "2014-01-01 01:30:00  0.299447               0.00  0.258819  9.659258e-01   \n",
       "2014-01-01 01:45:00  0.301096               0.00  0.258819  9.659258e-01   \n",
       "2014-01-01 02:00:00  0.302745               0.00  0.500000  8.660254e-01   \n",
       "2014-01-01 02:15:00  0.304612               0.00  0.500000  8.660254e-01   \n",
       "2014-01-01 02:30:00  0.306480               0.00  0.500000  8.660254e-01   \n",
       "2014-01-01 02:45:00  0.308347               0.00  0.500000  8.660254e-01   \n",
       "2014-01-01 03:00:00  0.310214               0.00  0.707107  7.071068e-01   \n",
       "2014-01-01 03:15:00  0.310627               0.00  0.707107  7.071068e-01   \n",
       "2014-01-01 03:30:00  0.311039               0.00  0.707107  7.071068e-01   \n",
       "2014-01-01 03:45:00  0.311451               0.00  0.707107  7.071068e-01   \n",
       "2014-01-01 04:00:00  0.311863               0.00  0.866025  5.000000e-01   \n",
       "2014-01-01 04:15:00  0.312615               0.00  0.866025  5.000000e-01   \n",
       "2014-01-01 04:30:00  0.313367               0.00  0.866025  5.000000e-01   \n",
       "2014-01-01 04:45:00  0.314119               0.00  0.866025  5.000000e-01   \n",
       "2014-01-01 05:00:00  0.314871               0.00  0.965926  2.588190e-01   \n",
       "2014-01-01 05:15:00  0.317344               0.00  0.965926  2.588190e-01   \n",
       "2014-01-01 05:30:00  0.319818               0.00  0.965926  2.588190e-01   \n",
       "2014-01-01 05:45:00  0.322291               0.00  0.965926  2.588190e-01   \n",
       "2014-01-01 06:00:00  0.324765               0.00  1.000000  6.123234e-17   \n",
       "2014-01-01 06:15:00  0.324983               0.00  1.000000  6.123234e-17   \n",
       "2014-01-01 06:30:00  0.325201               0.00  1.000000  6.123234e-17   \n",
       "2014-01-01 06:45:00  0.325420               0.00  1.000000  6.123234e-17   \n",
       "2014-01-01 07:00:00  0.325638               0.00  0.965926 -2.588190e-01   \n",
       "2014-01-01 07:15:00  0.326923               0.00  0.965926 -2.588190e-01   \n",
       "...                       ...                ...       ...           ...   \n",
       "2016-12-31 16:30:00  0.576487               0.56 -0.866025 -5.000000e-01   \n",
       "2016-12-31 16:45:00  0.576487               0.56 -0.866025 -5.000000e-01   \n",
       "2016-12-31 17:00:00  0.576487               0.56 -0.965926 -2.588190e-01   \n",
       "2016-12-31 17:15:00  0.576487               0.56 -0.965926 -2.588190e-01   \n",
       "2016-12-31 17:30:00  0.576487               0.56 -0.965926 -2.588190e-01   \n",
       "2016-12-31 17:45:00  0.576487               0.56 -0.965926 -2.588190e-01   \n",
       "2016-12-31 18:00:00  0.576487               0.56 -1.000000 -1.836970e-16   \n",
       "2016-12-31 18:15:00  0.576487               0.56 -1.000000 -1.836970e-16   \n",
       "2016-12-31 18:30:00  0.576487               0.56 -1.000000 -1.836970e-16   \n",
       "2016-12-31 18:45:00  0.576487               0.56 -1.000000 -1.836970e-16   \n",
       "2016-12-31 19:00:00  0.576487               0.56 -0.965926  2.588190e-01   \n",
       "2016-12-31 19:15:00  0.576487               0.56 -0.965926  2.588190e-01   \n",
       "2016-12-31 19:30:00  0.576487               0.56 -0.965926  2.588190e-01   \n",
       "2016-12-31 19:45:00  0.576487               0.56 -0.965926  2.588190e-01   \n",
       "2016-12-31 20:00:00  0.576487               0.56 -0.866025  5.000000e-01   \n",
       "2016-12-31 20:15:00  0.576487               0.56 -0.866025  5.000000e-01   \n",
       "2016-12-31 20:30:00  0.576487               0.56 -0.866025  5.000000e-01   \n",
       "2016-12-31 20:45:00  0.576487               0.56 -0.866025  5.000000e-01   \n",
       "2016-12-31 21:00:00  0.576487               0.56 -0.707107  7.071068e-01   \n",
       "2016-12-31 21:15:00  0.576487               0.56 -0.707107  7.071068e-01   \n",
       "2016-12-31 21:30:00  0.576487               0.56 -0.707107  7.071068e-01   \n",
       "2016-12-31 21:45:00  0.576487               0.56 -0.707107  7.071068e-01   \n",
       "2016-12-31 22:00:00  0.576487               0.56 -0.500000  8.660254e-01   \n",
       "2016-12-31 22:15:00  0.576487               0.56 -0.500000  8.660254e-01   \n",
       "2016-12-31 22:30:00  0.576487               0.56 -0.500000  8.660254e-01   \n",
       "2016-12-31 22:45:00  0.576487               0.56 -0.500000  8.660254e-01   \n",
       "2016-12-31 23:00:00  0.576487               0.56 -0.258819  9.659258e-01   \n",
       "2016-12-31 23:15:00  0.576487               0.56 -0.258819  9.659258e-01   \n",
       "2016-12-31 23:30:00  0.576487               0.56 -0.258819  9.659258e-01   \n",
       "2016-12-31 23:45:00  0.576487               0.56 -0.258819  9.659258e-01   \n",
       "\n",
       "                     sin_weekday  cos_weekday     sin_month  cos_month  \\\n",
       "time                                                                     \n",
       "2014-01-01 00:00:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 00:15:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 00:30:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 00:45:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 01:00:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 01:15:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 01:30:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 01:45:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 02:00:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 02:15:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 02:30:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 02:45:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 03:00:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 03:15:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 03:30:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 03:45:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 04:00:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 04:15:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 04:30:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 04:45:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 05:00:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 05:15:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 05:30:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 05:45:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 06:00:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 06:15:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 06:30:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 06:45:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 07:00:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "2014-01-01 07:15:00     0.974928    -0.222521  5.000000e-01   0.866025   \n",
       "...                          ...          ...           ...        ...   \n",
       "2016-12-31 16:30:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 16:45:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 17:00:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 17:15:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 17:30:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 17:45:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 18:00:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 18:15:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 18:30:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 18:45:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 19:00:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 19:15:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 19:30:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 19:45:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 20:00:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 20:15:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 20:30:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 20:45:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 21:00:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 21:15:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 21:30:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 21:45:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 22:00:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 22:15:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 22:30:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 22:45:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 23:00:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 23:15:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 23:30:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "2016-12-31 23:45:00    -0.974928    -0.222521 -2.449294e-16   1.000000   \n",
       "\n",
       "                     is_holiday  \n",
       "time                             \n",
       "2014-01-01 00:00:00           0  \n",
       "2014-01-01 00:15:00           0  \n",
       "2014-01-01 00:30:00           0  \n",
       "2014-01-01 00:45:00           0  \n",
       "2014-01-01 01:00:00           0  \n",
       "2014-01-01 01:15:00           0  \n",
       "2014-01-01 01:30:00           0  \n",
       "2014-01-01 01:45:00           0  \n",
       "2014-01-01 02:00:00           0  \n",
       "2014-01-01 02:15:00           0  \n",
       "2014-01-01 02:30:00           0  \n",
       "2014-01-01 02:45:00           0  \n",
       "2014-01-01 03:00:00           0  \n",
       "2014-01-01 03:15:00           0  \n",
       "2014-01-01 03:30:00           0  \n",
       "2014-01-01 03:45:00           0  \n",
       "2014-01-01 04:00:00           0  \n",
       "2014-01-01 04:15:00           0  \n",
       "2014-01-01 04:30:00           0  \n",
       "2014-01-01 04:45:00           0  \n",
       "2014-01-01 05:00:00           0  \n",
       "2014-01-01 05:15:00           0  \n",
       "2014-01-01 05:30:00           0  \n",
       "2014-01-01 05:45:00           0  \n",
       "2014-01-01 06:00:00           0  \n",
       "2014-01-01 06:15:00           0  \n",
       "2014-01-01 06:30:00           0  \n",
       "2014-01-01 06:45:00           0  \n",
       "2014-01-01 07:00:00           0  \n",
       "2014-01-01 07:15:00           0  \n",
       "...                         ...  \n",
       "2016-12-31 16:30:00           1  \n",
       "2016-12-31 16:45:00           1  \n",
       "2016-12-31 17:00:00           1  \n",
       "2016-12-31 17:15:00           1  \n",
       "2016-12-31 17:30:00           1  \n",
       "2016-12-31 17:45:00           1  \n",
       "2016-12-31 18:00:00           1  \n",
       "2016-12-31 18:15:00           1  \n",
       "2016-12-31 18:30:00           1  \n",
       "2016-12-31 18:45:00           1  \n",
       "2016-12-31 19:00:00           1  \n",
       "2016-12-31 19:15:00           1  \n",
       "2016-12-31 19:30:00           1  \n",
       "2016-12-31 19:45:00           1  \n",
       "2016-12-31 20:00:00           1  \n",
       "2016-12-31 20:15:00           1  \n",
       "2016-12-31 20:30:00           1  \n",
       "2016-12-31 20:45:00           1  \n",
       "2016-12-31 21:00:00           1  \n",
       "2016-12-31 21:15:00           1  \n",
       "2016-12-31 21:30:00           1  \n",
       "2016-12-31 21:45:00           1  \n",
       "2016-12-31 22:00:00           1  \n",
       "2016-12-31 22:15:00           1  \n",
       "2016-12-31 22:30:00           1  \n",
       "2016-12-31 22:45:00           1  \n",
       "2016-12-31 23:00:00           1  \n",
       "2016-12-31 23:15:00           1  \n",
       "2016-12-31 23:30:00           1  \n",
       "2016-12-31 23:45:00           1  \n",
       "\n",
       "[105204 rows x 43 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN by weather and date\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training\n",
    "neighs = NearestNeighbors(n_neighbors=200, algorithm='auto', n_jobs=-1).fit(dataset.loc[:, COL_WEATHER+COL_DATE])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get neighbors\n",
    "target_neighbors = neighs.kneighbors(dataset.loc[:, COL_WEATHER+COL_DATE], return_distance=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0,      1,      2,      3,  71899,  71898,  71897,  71896,\n",
       "            4,      5,      6,      7,  71901,  71902,  71900,  71903,\n",
       "        36959,  36958,    672,  36957,    673,    674,    675,  36956,\n",
       "         2688,   2689,  37628,   2783,  35704,  35705,   2690,  35706,\n",
       "         2782,  35707,   2781,  36961,  36962,  36960,   2780,  36963,\n",
       "        71991,  71990,   2691,  37629,    678,    679,    677,    676,\n",
       "        72660,   2108,   2109,   2110,   2111,  71989,  37720,  37721,\n",
       "          767,  37722,  72661,  37723,   2695,  71988,    766,  36287,\n",
       "        71231,   2694,  71316,  71317,    765,  70552,  72662,   2693,\n",
       "        71318,  37630,    764,  71319,  70644,  71907,  70553,  71906,\n",
       "        71230,  71905,  71904,      8,  72663,      9,     10,   2692,\n",
       "        36286,     11,  70554,  36289,  37716,   2776,  37635,  71984,\n",
       "         2777,  37717,  70559,   2778,  70555,  70645,  35700,  71985,\n",
       "        35701,  35702,  36288,  35703,  71229,   2779,  37718,  37631,\n",
       "        40408,  40409,  72656,  71986,  36964,  70558,  72657,  72568,\n",
       "        36965,  40410,  36966,  72658, 104148,  71235,   2104,  37719,\n",
       "        37634,   2105,  36967,  40411,    683, 104149,  71234,  72659,\n",
       "          682,   2106,    681,  71233,   2107,    680, 104150,  72569,\n",
       "        71987,  71232,   5376,  37639,  36290, 104151,  71227,  70557,\n",
       "        35612,  71312,  72575, 104820,  71313,  37638,   5377,  72570,\n",
       "        71314,  35613,  70646,  71228,  71315, 104821,  37637,   2696,\n",
       "        35614,  70556,   5378,  72571,   2697,  72574,    760,    761,\n",
       "         2698,   1347,   1346,  37633,   1345,    762,  35615,   1344,\n",
       "         4035,   2699,    763,   4034,   4033,  37636,   4032,   5379,\n",
       "        70642,  70643,  34940,  70641,  70640,  71226, 104152, 104153])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_neighbors[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_tolerable_power = lambda x: dataset[\"Furnace\"].iloc[x].quantile(.25)\n",
    "tolerable_power_df = pd.DataFrame([get_tolerable_power(row) for row in target_neighbors], index=dataset.index, columns=[\"Furnace\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
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       "      <th>2014-01-01 17:15:00</th>\n",
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       "      <th>2014-01-01 17:45:00</th>\n",
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       "      <th>2014-01-01 19:15:00</th>\n",
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       "      <th>2014-01-01 22:30:00</th>\n",
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       "      <th>2014-01-02 03:30:00</th>\n",
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       "    <tr>\n",
       "      <th>2014-01-02 04:45:00</th>\n",
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       "    <tr>\n",
       "      <th>2014-01-02 05:00:00</th>\n",
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       "      <td>0.077138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 05:45:00</th>\n",
       "      <td>0.077138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 06:00:00</th>\n",
       "      <td>0.079459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 06:15:00</th>\n",
       "      <td>0.109667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 06:30:00</th>\n",
       "      <td>0.114899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 06:45:00</th>\n",
       "      <td>0.109667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 07:00:00</th>\n",
       "      <td>0.016352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 07:15:00</th>\n",
       "      <td>0.011198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 07:30:00</th>\n",
       "      <td>0.009842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 07:45:00</th>\n",
       "      <td>0.009842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 08:00:00</th>\n",
       "      <td>0.008881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 08:15:00</th>\n",
       "      <td>0.008881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 08:30:00</th>\n",
       "      <td>0.008881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 08:45:00</th>\n",
       "      <td>0.008881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 09:00:00</th>\n",
       "      <td>0.008687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 09:15:00</th>\n",
       "      <td>0.008689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 09:30:00</th>\n",
       "      <td>0.008689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 09:45:00</th>\n",
       "      <td>0.008689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 10:00:00</th>\n",
       "      <td>0.008633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 10:15:00</th>\n",
       "      <td>0.008633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 10:30:00</th>\n",
       "      <td>0.008633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 10:45:00</th>\n",
       "      <td>0.008633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 11:00:00</th>\n",
       "      <td>0.008617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 11:15:00</th>\n",
       "      <td>0.008617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 11:30:00</th>\n",
       "      <td>0.008617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 11:45:00</th>\n",
       "      <td>0.008617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 12:00:00</th>\n",
       "      <td>0.008656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 12:15:00</th>\n",
       "      <td>0.008650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 12:30:00</th>\n",
       "      <td>0.008643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 12:45:00</th>\n",
       "      <td>0.008629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 13:00:00</th>\n",
       "      <td>0.008643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 13:15:00</th>\n",
       "      <td>0.008655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 13:30:00</th>\n",
       "      <td>0.008656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 13:45:00</th>\n",
       "      <td>0.008650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 14:00:00</th>\n",
       "      <td>0.008722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 14:15:00</th>\n",
       "      <td>0.008724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 14:30:00</th>\n",
       "      <td>0.008730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 14:45:00</th>\n",
       "      <td>0.008730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 15:00:00</th>\n",
       "      <td>0.008836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 15:15:00</th>\n",
       "      <td>0.008836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 15:30:00</th>\n",
       "      <td>0.008836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 15:45:00</th>\n",
       "      <td>0.008836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 16:00:00</th>\n",
       "      <td>0.010273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 16:15:00</th>\n",
       "      <td>0.009766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 16:30:00</th>\n",
       "      <td>0.009766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 16:45:00</th>\n",
       "      <td>0.010273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 17:00:00</th>\n",
       "      <td>0.084694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 17:15:00</th>\n",
       "      <td>0.084694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 17:30:00</th>\n",
       "      <td>0.084694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 17:45:00</th>\n",
       "      <td>0.086936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 18:00:00</th>\n",
       "      <td>0.116629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 18:15:00</th>\n",
       "      <td>0.117250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 18:30:00</th>\n",
       "      <td>0.097036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 18:45:00</th>\n",
       "      <td>0.071132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 19:00:00</th>\n",
       "      <td>0.137870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 19:15:00</th>\n",
       "      <td>0.114905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 19:30:00</th>\n",
       "      <td>0.019756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 19:45:00</th>\n",
       "      <td>0.013910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 20:00:00</th>\n",
       "      <td>0.008827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 20:15:00</th>\n",
       "      <td>0.008802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 20:30:00</th>\n",
       "      <td>0.008827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 20:45:00</th>\n",
       "      <td>0.008827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 21:00:00</th>\n",
       "      <td>0.008634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 21:15:00</th>\n",
       "      <td>0.008646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 21:30:00</th>\n",
       "      <td>0.008654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 21:45:00</th>\n",
       "      <td>0.008654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 22:00:00</th>\n",
       "      <td>0.008507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 22:15:00</th>\n",
       "      <td>0.008506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 22:30:00</th>\n",
       "      <td>0.008506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 22:45:00</th>\n",
       "      <td>0.008506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 23:00:00</th>\n",
       "      <td>0.008436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 23:15:00</th>\n",
       "      <td>0.008442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 23:30:00</th>\n",
       "      <td>0.008442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 23:45:00</th>\n",
       "      <td>0.008436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 00:00:00</th>\n",
       "      <td>0.008413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 00:15:00</th>\n",
       "      <td>0.008407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 00:30:00</th>\n",
       "      <td>0.008407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 00:45:00</th>\n",
       "      <td>0.008396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 01:00:00</th>\n",
       "      <td>0.008387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 01:15:00</th>\n",
       "      <td>0.008387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 01:30:00</th>\n",
       "      <td>0.008390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 01:45:00</th>\n",
       "      <td>0.008413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 02:00:00</th>\n",
       "      <td>0.008444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 02:15:00</th>\n",
       "      <td>0.008444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 02:30:00</th>\n",
       "      <td>0.008449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 02:45:00</th>\n",
       "      <td>0.008444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 03:00:00</th>\n",
       "      <td>0.008513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 03:15:00</th>\n",
       "      <td>0.008523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 03:30:00</th>\n",
       "      <td>0.008523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 03:45:00</th>\n",
       "      <td>0.008546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 04:00:00</th>\n",
       "      <td>0.008931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 04:15:00</th>\n",
       "      <td>0.008996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 04:30:00</th>\n",
       "      <td>0.009000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 04:45:00</th>\n",
       "      <td>0.008979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 05:00:00</th>\n",
       "      <td>0.161402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 05:15:00</th>\n",
       "      <td>0.161402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 05:30:00</th>\n",
       "      <td>0.128184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 05:45:00</th>\n",
       "      <td>0.171184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 06:00:00</th>\n",
       "      <td>0.075222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 06:15:00</th>\n",
       "      <td>0.119090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 06:30:00</th>\n",
       "      <td>0.097061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 06:45:00</th>\n",
       "      <td>0.123138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 07:00:00</th>\n",
       "      <td>0.053893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 07:15:00</th>\n",
       "      <td>0.064753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 07:30:00</th>\n",
       "      <td>0.134315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 07:45:00</th>\n",
       "      <td>0.165980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 08:00:00</th>\n",
       "      <td>0.009469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 08:15:00</th>\n",
       "      <td>0.009947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 08:30:00</th>\n",
       "      <td>0.010015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 08:45:00</th>\n",
       "      <td>0.009947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 09:00:00</th>\n",
       "      <td>0.009363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 09:15:00</th>\n",
       "      <td>0.009372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 09:30:00</th>\n",
       "      <td>0.009428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 09:45:00</th>\n",
       "      <td>0.009438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 10:00:00</th>\n",
       "      <td>0.009299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 10:15:00</th>\n",
       "      <td>0.009346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 10:30:00</th>\n",
       "      <td>0.009392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 10:45:00</th>\n",
       "      <td>0.009428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 11:00:00</th>\n",
       "      <td>0.009642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 11:15:00</th>\n",
       "      <td>0.009709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 11:30:00</th>\n",
       "      <td>0.009714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 11:45:00</th>\n",
       "      <td>0.009737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 12:00:00</th>\n",
       "      <td>0.009616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 12:15:00</th>\n",
       "      <td>0.009628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 12:30:00</th>\n",
       "      <td>0.009647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 12:45:00</th>\n",
       "      <td>0.009696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 13:00:00</th>\n",
       "      <td>0.009440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 13:15:00</th>\n",
       "      <td>0.009417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 13:30:00</th>\n",
       "      <td>0.009417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 13:45:00</th>\n",
       "      <td>0.009417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 14:00:00</th>\n",
       "      <td>0.009175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 14:15:00</th>\n",
       "      <td>0.009175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 09:30:00</th>\n",
       "      <td>0.008796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 09:45:00</th>\n",
       "      <td>0.008796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 10:00:00</th>\n",
       "      <td>0.008678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 10:15:00</th>\n",
       "      <td>0.008591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 10:30:00</th>\n",
       "      <td>0.008549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 10:45:00</th>\n",
       "      <td>0.008544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 11:00:00</th>\n",
       "      <td>0.008458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 11:15:00</th>\n",
       "      <td>0.008458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 11:30:00</th>\n",
       "      <td>0.008462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 11:45:00</th>\n",
       "      <td>0.008462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 12:00:00</th>\n",
       "      <td>0.008470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 12:15:00</th>\n",
       "      <td>0.008471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 12:30:00</th>\n",
       "      <td>0.008484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 12:45:00</th>\n",
       "      <td>0.008501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 13:00:00</th>\n",
       "      <td>0.008505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 13:15:00</th>\n",
       "      <td>0.008517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 13:30:00</th>\n",
       "      <td>0.008517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 13:45:00</th>\n",
       "      <td>0.008547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 14:00:00</th>\n",
       "      <td>0.008584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 14:15:00</th>\n",
       "      <td>0.008571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 14:30:00</th>\n",
       "      <td>0.008571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 14:45:00</th>\n",
       "      <td>0.008584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 15:00:00</th>\n",
       "      <td>0.008577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 15:15:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 15:30:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 15:45:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 16:00:00</th>\n",
       "      <td>0.008629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 16:15:00</th>\n",
       "      <td>0.008626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 16:30:00</th>\n",
       "      <td>0.008679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 16:45:00</th>\n",
       "      <td>0.008671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 17:00:00</th>\n",
       "      <td>0.008766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 17:15:00</th>\n",
       "      <td>0.008787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 17:30:00</th>\n",
       "      <td>0.008787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 17:45:00</th>\n",
       "      <td>0.008787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 18:00:00</th>\n",
       "      <td>0.009208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 18:15:00</th>\n",
       "      <td>0.008993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 18:30:00</th>\n",
       "      <td>0.008829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 18:45:00</th>\n",
       "      <td>0.008763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 19:00:00</th>\n",
       "      <td>0.008919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 19:15:00</th>\n",
       "      <td>0.009134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 19:30:00</th>\n",
       "      <td>0.008961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 19:45:00</th>\n",
       "      <td>0.008951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 20:00:00</th>\n",
       "      <td>0.008829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 20:15:00</th>\n",
       "      <td>0.008833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 20:30:00</th>\n",
       "      <td>0.008817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 20:45:00</th>\n",
       "      <td>0.008768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 21:00:00</th>\n",
       "      <td>0.008681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 21:15:00</th>\n",
       "      <td>0.008681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 21:30:00</th>\n",
       "      <td>0.008705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 21:45:00</th>\n",
       "      <td>0.008761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 22:00:00</th>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 22:15:00</th>\n",
       "      <td>0.008658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 22:30:00</th>\n",
       "      <td>0.008681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 22:45:00</th>\n",
       "      <td>0.008679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 23:00:00</th>\n",
       "      <td>0.008582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 23:15:00</th>\n",
       "      <td>0.008582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 23:30:00</th>\n",
       "      <td>0.008582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29 23:45:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 00:00:00</th>\n",
       "      <td>0.008511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 00:15:00</th>\n",
       "      <td>0.008511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 00:30:00</th>\n",
       "      <td>0.008505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 00:45:00</th>\n",
       "      <td>0.008505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 01:00:00</th>\n",
       "      <td>0.008563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 01:15:00</th>\n",
       "      <td>0.008561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 01:30:00</th>\n",
       "      <td>0.008563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 01:45:00</th>\n",
       "      <td>0.008563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 02:00:00</th>\n",
       "      <td>0.008619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 02:15:00</th>\n",
       "      <td>0.008619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 02:30:00</th>\n",
       "      <td>0.008622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 02:45:00</th>\n",
       "      <td>0.008622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 03:00:00</th>\n",
       "      <td>0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 03:15:00</th>\n",
       "      <td>0.008686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 03:30:00</th>\n",
       "      <td>0.008686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 03:45:00</th>\n",
       "      <td>0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 04:00:00</th>\n",
       "      <td>0.008963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 04:15:00</th>\n",
       "      <td>0.008963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 04:30:00</th>\n",
       "      <td>0.008963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 04:45:00</th>\n",
       "      <td>0.008963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 05:00:00</th>\n",
       "      <td>0.009080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 05:15:00</th>\n",
       "      <td>0.009108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 05:30:00</th>\n",
       "      <td>0.009637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 05:45:00</th>\n",
       "      <td>0.009842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 06:00:00</th>\n",
       "      <td>0.017861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 06:15:00</th>\n",
       "      <td>0.017861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 06:30:00</th>\n",
       "      <td>0.010537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 06:45:00</th>\n",
       "      <td>0.010196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 07:00:00</th>\n",
       "      <td>0.009392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 07:15:00</th>\n",
       "      <td>0.009392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 07:30:00</th>\n",
       "      <td>0.009374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 07:45:00</th>\n",
       "      <td>0.009374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 08:00:00</th>\n",
       "      <td>0.008978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 08:15:00</th>\n",
       "      <td>0.008978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 08:30:00</th>\n",
       "      <td>0.008978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 08:45:00</th>\n",
       "      <td>0.008978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 09:00:00</th>\n",
       "      <td>0.008848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 09:15:00</th>\n",
       "      <td>0.008848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 09:30:00</th>\n",
       "      <td>0.008848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 09:45:00</th>\n",
       "      <td>0.008848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 10:00:00</th>\n",
       "      <td>0.008812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 10:15:00</th>\n",
       "      <td>0.008812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 10:30:00</th>\n",
       "      <td>0.008796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 10:45:00</th>\n",
       "      <td>0.008778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 11:00:00</th>\n",
       "      <td>0.008793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 11:15:00</th>\n",
       "      <td>0.008783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 11:30:00</th>\n",
       "      <td>0.008736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 11:45:00</th>\n",
       "      <td>0.008736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 12:00:00</th>\n",
       "      <td>0.008753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 12:15:00</th>\n",
       "      <td>0.008761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 12:30:00</th>\n",
       "      <td>0.008743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 12:45:00</th>\n",
       "      <td>0.008696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 13:00:00</th>\n",
       "      <td>0.008618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 13:15:00</th>\n",
       "      <td>0.008612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 13:30:00</th>\n",
       "      <td>0.008614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 13:45:00</th>\n",
       "      <td>0.008619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 14:00:00</th>\n",
       "      <td>0.008461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 14:15:00</th>\n",
       "      <td>0.008455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 14:30:00</th>\n",
       "      <td>0.008461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 14:45:00</th>\n",
       "      <td>0.008462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 15:00:00</th>\n",
       "      <td>0.008396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 15:15:00</th>\n",
       "      <td>0.008393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 15:30:00</th>\n",
       "      <td>0.008393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 15:45:00</th>\n",
       "      <td>0.008393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 16:00:00</th>\n",
       "      <td>0.008432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 16:15:00</th>\n",
       "      <td>0.008424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 16:30:00</th>\n",
       "      <td>0.008432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 16:45:00</th>\n",
       "      <td>0.008449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 17:00:00</th>\n",
       "      <td>0.008611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 17:15:00</th>\n",
       "      <td>0.008667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 17:30:00</th>\n",
       "      <td>0.008622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 17:45:00</th>\n",
       "      <td>0.008622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 18:00:00</th>\n",
       "      <td>0.008744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 18:15:00</th>\n",
       "      <td>0.008788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 18:30:00</th>\n",
       "      <td>0.008842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 18:45:00</th>\n",
       "      <td>0.008898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:00:00</th>\n",
       "      <td>0.009242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:15:00</th>\n",
       "      <td>0.008928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:30:00</th>\n",
       "      <td>0.008898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:45:00</th>\n",
       "      <td>0.008856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 20:00:00</th>\n",
       "      <td>0.009246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 20:15:00</th>\n",
       "      <td>0.009246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 20:30:00</th>\n",
       "      <td>0.009232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 20:45:00</th>\n",
       "      <td>0.009221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 21:00:00</th>\n",
       "      <td>0.008929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 21:15:00</th>\n",
       "      <td>0.008929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 21:30:00</th>\n",
       "      <td>0.008981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 21:45:00</th>\n",
       "      <td>0.008954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 22:00:00</th>\n",
       "      <td>0.008745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 22:15:00</th>\n",
       "      <td>0.008778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 22:30:00</th>\n",
       "      <td>0.008780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 22:45:00</th>\n",
       "      <td>0.008778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 23:00:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 23:15:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 23:30:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 23:45:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 00:00:00</th>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 00:15:00</th>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 00:30:00</th>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 00:45:00</th>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 01:00:00</th>\n",
       "      <td>0.008636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 01:15:00</th>\n",
       "      <td>0.008636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 01:30:00</th>\n",
       "      <td>0.008636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 01:45:00</th>\n",
       "      <td>0.008636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 02:00:00</th>\n",
       "      <td>0.008665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 02:15:00</th>\n",
       "      <td>0.008665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 02:30:00</th>\n",
       "      <td>0.008665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 02:45:00</th>\n",
       "      <td>0.008665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 03:00:00</th>\n",
       "      <td>0.008732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 03:15:00</th>\n",
       "      <td>0.008732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 03:30:00</th>\n",
       "      <td>0.008732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 03:45:00</th>\n",
       "      <td>0.008732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 04:00:00</th>\n",
       "      <td>0.008711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 04:15:00</th>\n",
       "      <td>0.008711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 04:30:00</th>\n",
       "      <td>0.008711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 04:45:00</th>\n",
       "      <td>0.008711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 05:00:00</th>\n",
       "      <td>0.008820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 05:15:00</th>\n",
       "      <td>0.008820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 05:30:00</th>\n",
       "      <td>0.008820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 05:45:00</th>\n",
       "      <td>0.008820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 06:00:00</th>\n",
       "      <td>0.009124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 06:15:00</th>\n",
       "      <td>0.009124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 06:30:00</th>\n",
       "      <td>0.009124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 06:45:00</th>\n",
       "      <td>0.009124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 07:00:00</th>\n",
       "      <td>0.057446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 07:15:00</th>\n",
       "      <td>0.057446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 07:30:00</th>\n",
       "      <td>0.057446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 07:45:00</th>\n",
       "      <td>0.057446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 08:00:00</th>\n",
       "      <td>0.021459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 08:15:00</th>\n",
       "      <td>0.021459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 08:30:00</th>\n",
       "      <td>0.021459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 08:45:00</th>\n",
       "      <td>0.021459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 09:00:00</th>\n",
       "      <td>0.009398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 09:15:00</th>\n",
       "      <td>0.009398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 09:30:00</th>\n",
       "      <td>0.009398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 09:45:00</th>\n",
       "      <td>0.009398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 10:00:00</th>\n",
       "      <td>0.009050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 10:15:00</th>\n",
       "      <td>0.009050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 10:30:00</th>\n",
       "      <td>0.009050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 10:45:00</th>\n",
       "      <td>0.009050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 11:00:00</th>\n",
       "      <td>0.008813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 11:15:00</th>\n",
       "      <td>0.008813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 11:30:00</th>\n",
       "      <td>0.008813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 11:45:00</th>\n",
       "      <td>0.008813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 12:00:00</th>\n",
       "      <td>0.008799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 12:15:00</th>\n",
       "      <td>0.008799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 12:30:00</th>\n",
       "      <td>0.008799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 12:45:00</th>\n",
       "      <td>0.008799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 13:00:00</th>\n",
       "      <td>0.008741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 13:15:00</th>\n",
       "      <td>0.008741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 13:30:00</th>\n",
       "      <td>0.008741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 13:45:00</th>\n",
       "      <td>0.008741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 14:00:00</th>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 14:15:00</th>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 14:30:00</th>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 14:45:00</th>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 15:00:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 15:15:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 15:30:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 15:45:00</th>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 16:00:00</th>\n",
       "      <td>0.008600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 16:15:00</th>\n",
       "      <td>0.008600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 16:30:00</th>\n",
       "      <td>0.008600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 16:45:00</th>\n",
       "      <td>0.008600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 17:00:00</th>\n",
       "      <td>0.008680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 17:15:00</th>\n",
       "      <td>0.008680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 17:30:00</th>\n",
       "      <td>0.008680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 17:45:00</th>\n",
       "      <td>0.008680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 18:00:00</th>\n",
       "      <td>0.009162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 18:15:00</th>\n",
       "      <td>0.009162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 18:30:00</th>\n",
       "      <td>0.009162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 18:45:00</th>\n",
       "      <td>0.009162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 19:00:00</th>\n",
       "      <td>0.009808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 19:15:00</th>\n",
       "      <td>0.009808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 19:30:00</th>\n",
       "      <td>0.009808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 19:45:00</th>\n",
       "      <td>0.009808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 20:00:00</th>\n",
       "      <td>0.009975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 20:15:00</th>\n",
       "      <td>0.009975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 20:30:00</th>\n",
       "      <td>0.009975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 20:45:00</th>\n",
       "      <td>0.009975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 21:00:00</th>\n",
       "      <td>0.009176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 21:15:00</th>\n",
       "      <td>0.009176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 21:30:00</th>\n",
       "      <td>0.009176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 21:45:00</th>\n",
       "      <td>0.009176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 22:00:00</th>\n",
       "      <td>0.008902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 22:15:00</th>\n",
       "      <td>0.008902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 22:30:00</th>\n",
       "      <td>0.008902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 22:45:00</th>\n",
       "      <td>0.008902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:00:00</th>\n",
       "      <td>0.008758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:15:00</th>\n",
       "      <td>0.008758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:30:00</th>\n",
       "      <td>0.008758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31 23:45:00</th>\n",
       "      <td>0.008758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105204 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Furnace\n",
       "time                         \n",
       "2014-01-01 00:00:00  0.008594\n",
       "2014-01-01 00:15:00  0.008594\n",
       "2014-01-01 00:30:00  0.008594\n",
       "2014-01-01 00:45:00  0.008594\n",
       "2014-01-01 01:00:00  0.008606\n",
       "2014-01-01 01:15:00  0.008594\n",
       "2014-01-01 01:30:00  0.008591\n",
       "2014-01-01 01:45:00  0.008591\n",
       "2014-01-01 02:00:00  0.008650\n",
       "2014-01-01 02:15:00  0.008633\n",
       "2014-01-01 02:30:00  0.008633\n",
       "2014-01-01 02:45:00  0.008633\n",
       "2014-01-01 03:00:00  0.009081\n",
       "2014-01-01 03:15:00  0.009074\n",
       "2014-01-01 03:30:00  0.009088\n",
       "2014-01-01 03:45:00  0.009088\n",
       "2014-01-01 04:00:00  0.045634\n",
       "2014-01-01 04:15:00  0.045634\n",
       "2014-01-01 04:30:00  0.045634\n",
       "2014-01-01 04:45:00  0.045634\n",
       "2014-01-01 05:00:00  0.290939\n",
       "2014-01-01 05:15:00  0.290939\n",
       "2014-01-01 05:30:00  0.290939\n",
       "2014-01-01 05:45:00  0.290939\n",
       "2014-01-01 06:00:00  0.334073\n",
       "2014-01-01 06:15:00  0.338592\n",
       "2014-01-01 06:30:00  0.344746\n",
       "2014-01-01 06:45:00  0.338592\n",
       "2014-01-01 07:00:00  0.195783\n",
       "2014-01-01 07:15:00  0.196271\n",
       "2014-01-01 07:30:00  0.196271\n",
       "2014-01-01 07:45:00  0.196271\n",
       "2014-01-01 08:00:00  0.009694\n",
       "2014-01-01 08:15:00  0.009647\n",
       "2014-01-01 08:30:00  0.009617\n",
       "2014-01-01 08:45:00  0.009617\n",
       "2014-01-01 09:00:00  0.009141\n",
       "2014-01-01 09:15:00  0.009141\n",
       "2014-01-01 09:30:00  0.009116\n",
       "2014-01-01 09:45:00  0.009195\n",
       "2014-01-01 10:00:00  0.009032\n",
       "2014-01-01 10:15:00  0.009028\n",
       "2014-01-01 10:30:00  0.009028\n",
       "2014-01-01 10:45:00  0.009000\n",
       "2014-01-01 11:00:00  0.009124\n",
       "2014-01-01 11:15:00  0.009124\n",
       "2014-01-01 11:30:00  0.009124\n",
       "2014-01-01 11:45:00  0.009168\n",
       "2014-01-01 12:00:00  0.009110\n",
       "2014-01-01 12:15:00  0.009110\n",
       "2014-01-01 12:30:00  0.009099\n",
       "2014-01-01 12:45:00  0.009110\n",
       "2014-01-01 13:00:00  0.008830\n",
       "2014-01-01 13:15:00  0.008858\n",
       "2014-01-01 13:30:00  0.008858\n",
       "2014-01-01 13:45:00  0.008872\n",
       "2014-01-01 14:00:00  0.008755\n",
       "2014-01-01 14:15:00  0.008755\n",
       "2014-01-01 14:30:00  0.008782\n",
       "2014-01-01 14:45:00  0.008782\n",
       "2014-01-01 15:00:00  0.008791\n",
       "2014-01-01 15:15:00  0.008796\n",
       "2014-01-01 15:30:00  0.008796\n",
       "2014-01-01 15:45:00  0.008796\n",
       "2014-01-01 16:00:00  0.008845\n",
       "2014-01-01 16:15:00  0.008829\n",
       "2014-01-01 16:30:00  0.008817\n",
       "2014-01-01 16:45:00  0.008817\n",
       "2014-01-01 17:00:00  0.084674\n",
       "2014-01-01 17:15:00  0.064896\n",
       "2014-01-01 17:30:00  0.010296\n",
       "2014-01-01 17:45:00  0.009976\n",
       "2014-01-01 18:00:00  0.155090\n",
       "2014-01-01 18:15:00  0.157949\n",
       "2014-01-01 18:30:00  0.159459\n",
       "2014-01-01 18:45:00  0.159459\n",
       "2014-01-01 19:00:00  0.182408\n",
       "2014-01-01 19:15:00  0.188554\n",
       "2014-01-01 19:30:00  0.191795\n",
       "2014-01-01 19:45:00  0.185280\n",
       "2014-01-01 20:00:00  0.147101\n",
       "2014-01-01 20:15:00  0.137640\n",
       "2014-01-01 20:30:00  0.129983\n",
       "2014-01-01 20:45:00  0.129983\n",
       "2014-01-01 21:00:00  0.009200\n",
       "2014-01-01 21:15:00  0.009479\n",
       "2014-01-01 21:30:00  0.010292\n",
       "2014-01-01 21:45:00  0.009625\n",
       "2014-01-01 22:00:00  0.008808\n",
       "2014-01-01 22:15:00  0.008796\n",
       "2014-01-01 22:30:00  0.008796\n",
       "2014-01-01 22:45:00  0.008779\n",
       "2014-01-01 23:00:00  0.008645\n",
       "2014-01-01 23:15:00  0.008636\n",
       "2014-01-01 23:30:00  0.008635\n",
       "2014-01-01 23:45:00  0.008638\n",
       "2014-01-02 00:00:00  0.008557\n",
       "2014-01-02 00:15:00  0.008532\n",
       "2014-01-02 00:30:00  0.008447\n",
       "2014-01-02 00:45:00  0.008411\n",
       "2014-01-02 01:00:00  0.008429\n",
       "2014-01-02 01:15:00  0.008417\n",
       "2014-01-02 01:30:00  0.008417\n",
       "2014-01-02 01:45:00  0.008417\n",
       "2014-01-02 02:00:00  0.008452\n",
       "2014-01-02 02:15:00  0.008447\n",
       "2014-01-02 02:30:00  0.008447\n",
       "2014-01-02 02:45:00  0.008444\n",
       "2014-01-02 03:00:00  0.008488\n",
       "2014-01-02 03:15:00  0.008488\n",
       "2014-01-02 03:30:00  0.008488\n",
       "2014-01-02 03:45:00  0.008488\n",
       "2014-01-02 04:00:00  0.009026\n",
       "2014-01-02 04:15:00  0.009047\n",
       "2014-01-02 04:30:00  0.009013\n",
       "2014-01-02 04:45:00  0.008992\n",
       "2014-01-02 05:00:00  0.058298\n",
       "2014-01-02 05:15:00  0.077138\n",
       "2014-01-02 05:30:00  0.077138\n",
       "2014-01-02 05:45:00  0.077138\n",
       "2014-01-02 06:00:00  0.079459\n",
       "2014-01-02 06:15:00  0.109667\n",
       "2014-01-02 06:30:00  0.114899\n",
       "2014-01-02 06:45:00  0.109667\n",
       "2014-01-02 07:00:00  0.016352\n",
       "2014-01-02 07:15:00  0.011198\n",
       "2014-01-02 07:30:00  0.009842\n",
       "2014-01-02 07:45:00  0.009842\n",
       "2014-01-02 08:00:00  0.008881\n",
       "2014-01-02 08:15:00  0.008881\n",
       "2014-01-02 08:30:00  0.008881\n",
       "2014-01-02 08:45:00  0.008881\n",
       "2014-01-02 09:00:00  0.008687\n",
       "2014-01-02 09:15:00  0.008689\n",
       "2014-01-02 09:30:00  0.008689\n",
       "2014-01-02 09:45:00  0.008689\n",
       "2014-01-02 10:00:00  0.008633\n",
       "2014-01-02 10:15:00  0.008633\n",
       "2014-01-02 10:30:00  0.008633\n",
       "2014-01-02 10:45:00  0.008633\n",
       "2014-01-02 11:00:00  0.008617\n",
       "2014-01-02 11:15:00  0.008617\n",
       "2014-01-02 11:30:00  0.008617\n",
       "2014-01-02 11:45:00  0.008617\n",
       "2014-01-02 12:00:00  0.008656\n",
       "2014-01-02 12:15:00  0.008650\n",
       "2014-01-02 12:30:00  0.008643\n",
       "2014-01-02 12:45:00  0.008629\n",
       "2014-01-02 13:00:00  0.008643\n",
       "2014-01-02 13:15:00  0.008655\n",
       "2014-01-02 13:30:00  0.008656\n",
       "2014-01-02 13:45:00  0.008650\n",
       "2014-01-02 14:00:00  0.008722\n",
       "2014-01-02 14:15:00  0.008724\n",
       "2014-01-02 14:30:00  0.008730\n",
       "2014-01-02 14:45:00  0.008730\n",
       "2014-01-02 15:00:00  0.008836\n",
       "2014-01-02 15:15:00  0.008836\n",
       "2014-01-02 15:30:00  0.008836\n",
       "2014-01-02 15:45:00  0.008836\n",
       "2014-01-02 16:00:00  0.010273\n",
       "2014-01-02 16:15:00  0.009766\n",
       "2014-01-02 16:30:00  0.009766\n",
       "2014-01-02 16:45:00  0.010273\n",
       "2014-01-02 17:00:00  0.084694\n",
       "2014-01-02 17:15:00  0.084694\n",
       "2014-01-02 17:30:00  0.084694\n",
       "2014-01-02 17:45:00  0.086936\n",
       "2014-01-02 18:00:00  0.116629\n",
       "2014-01-02 18:15:00  0.117250\n",
       "2014-01-02 18:30:00  0.097036\n",
       "2014-01-02 18:45:00  0.071132\n",
       "2014-01-02 19:00:00  0.137870\n",
       "2014-01-02 19:15:00  0.114905\n",
       "2014-01-02 19:30:00  0.019756\n",
       "2014-01-02 19:45:00  0.013910\n",
       "2014-01-02 20:00:00  0.008827\n",
       "2014-01-02 20:15:00  0.008802\n",
       "2014-01-02 20:30:00  0.008827\n",
       "2014-01-02 20:45:00  0.008827\n",
       "2014-01-02 21:00:00  0.008634\n",
       "2014-01-02 21:15:00  0.008646\n",
       "2014-01-02 21:30:00  0.008654\n",
       "2014-01-02 21:45:00  0.008654\n",
       "2014-01-02 22:00:00  0.008507\n",
       "2014-01-02 22:15:00  0.008506\n",
       "2014-01-02 22:30:00  0.008506\n",
       "2014-01-02 22:45:00  0.008506\n",
       "2014-01-02 23:00:00  0.008436\n",
       "2014-01-02 23:15:00  0.008442\n",
       "2014-01-02 23:30:00  0.008442\n",
       "2014-01-02 23:45:00  0.008436\n",
       "2014-01-03 00:00:00  0.008413\n",
       "2014-01-03 00:15:00  0.008407\n",
       "2014-01-03 00:30:00  0.008407\n",
       "2014-01-03 00:45:00  0.008396\n",
       "2014-01-03 01:00:00  0.008387\n",
       "2014-01-03 01:15:00  0.008387\n",
       "2014-01-03 01:30:00  0.008390\n",
       "2014-01-03 01:45:00  0.008413\n",
       "2014-01-03 02:00:00  0.008444\n",
       "2014-01-03 02:15:00  0.008444\n",
       "2014-01-03 02:30:00  0.008449\n",
       "2014-01-03 02:45:00  0.008444\n",
       "2014-01-03 03:00:00  0.008513\n",
       "2014-01-03 03:15:00  0.008523\n",
       "2014-01-03 03:30:00  0.008523\n",
       "2014-01-03 03:45:00  0.008546\n",
       "2014-01-03 04:00:00  0.008931\n",
       "2014-01-03 04:15:00  0.008996\n",
       "2014-01-03 04:30:00  0.009000\n",
       "2014-01-03 04:45:00  0.008979\n",
       "2014-01-03 05:00:00  0.161402\n",
       "2014-01-03 05:15:00  0.161402\n",
       "2014-01-03 05:30:00  0.128184\n",
       "2014-01-03 05:45:00  0.171184\n",
       "2014-01-03 06:00:00  0.075222\n",
       "2014-01-03 06:15:00  0.119090\n",
       "2014-01-03 06:30:00  0.097061\n",
       "2014-01-03 06:45:00  0.123138\n",
       "2014-01-03 07:00:00  0.053893\n",
       "2014-01-03 07:15:00  0.064753\n",
       "2014-01-03 07:30:00  0.134315\n",
       "2014-01-03 07:45:00  0.165980\n",
       "2014-01-03 08:00:00  0.009469\n",
       "2014-01-03 08:15:00  0.009947\n",
       "2014-01-03 08:30:00  0.010015\n",
       "2014-01-03 08:45:00  0.009947\n",
       "2014-01-03 09:00:00  0.009363\n",
       "2014-01-03 09:15:00  0.009372\n",
       "2014-01-03 09:30:00  0.009428\n",
       "2014-01-03 09:45:00  0.009438\n",
       "2014-01-03 10:00:00  0.009299\n",
       "2014-01-03 10:15:00  0.009346\n",
       "2014-01-03 10:30:00  0.009392\n",
       "2014-01-03 10:45:00  0.009428\n",
       "2014-01-03 11:00:00  0.009642\n",
       "2014-01-03 11:15:00  0.009709\n",
       "2014-01-03 11:30:00  0.009714\n",
       "2014-01-03 11:45:00  0.009737\n",
       "2014-01-03 12:00:00  0.009616\n",
       "2014-01-03 12:15:00  0.009628\n",
       "2014-01-03 12:30:00  0.009647\n",
       "2014-01-03 12:45:00  0.009696\n",
       "2014-01-03 13:00:00  0.009440\n",
       "2014-01-03 13:15:00  0.009417\n",
       "2014-01-03 13:30:00  0.009417\n",
       "2014-01-03 13:45:00  0.009417\n",
       "2014-01-03 14:00:00  0.009175\n",
       "2014-01-03 14:15:00  0.009175\n",
       "...                       ...\n",
       "2016-12-29 09:30:00  0.008796\n",
       "2016-12-29 09:45:00  0.008796\n",
       "2016-12-29 10:00:00  0.008678\n",
       "2016-12-29 10:15:00  0.008591\n",
       "2016-12-29 10:30:00  0.008549\n",
       "2016-12-29 10:45:00  0.008544\n",
       "2016-12-29 11:00:00  0.008458\n",
       "2016-12-29 11:15:00  0.008458\n",
       "2016-12-29 11:30:00  0.008462\n",
       "2016-12-29 11:45:00  0.008462\n",
       "2016-12-29 12:00:00  0.008470\n",
       "2016-12-29 12:15:00  0.008471\n",
       "2016-12-29 12:30:00  0.008484\n",
       "2016-12-29 12:45:00  0.008501\n",
       "2016-12-29 13:00:00  0.008505\n",
       "2016-12-29 13:15:00  0.008517\n",
       "2016-12-29 13:30:00  0.008517\n",
       "2016-12-29 13:45:00  0.008547\n",
       "2016-12-29 14:00:00  0.008584\n",
       "2016-12-29 14:15:00  0.008571\n",
       "2016-12-29 14:30:00  0.008571\n",
       "2016-12-29 14:45:00  0.008584\n",
       "2016-12-29 15:00:00  0.008577\n",
       "2016-12-29 15:15:00  0.008583\n",
       "2016-12-29 15:30:00  0.008583\n",
       "2016-12-29 15:45:00  0.008583\n",
       "2016-12-29 16:00:00  0.008629\n",
       "2016-12-29 16:15:00  0.008626\n",
       "2016-12-29 16:30:00  0.008679\n",
       "2016-12-29 16:45:00  0.008671\n",
       "2016-12-29 17:00:00  0.008766\n",
       "2016-12-29 17:15:00  0.008787\n",
       "2016-12-29 17:30:00  0.008787\n",
       "2016-12-29 17:45:00  0.008787\n",
       "2016-12-29 18:00:00  0.009208\n",
       "2016-12-29 18:15:00  0.008993\n",
       "2016-12-29 18:30:00  0.008829\n",
       "2016-12-29 18:45:00  0.008763\n",
       "2016-12-29 19:00:00  0.008919\n",
       "2016-12-29 19:15:00  0.009134\n",
       "2016-12-29 19:30:00  0.008961\n",
       "2016-12-29 19:45:00  0.008951\n",
       "2016-12-29 20:00:00  0.008829\n",
       "2016-12-29 20:15:00  0.008833\n",
       "2016-12-29 20:30:00  0.008817\n",
       "2016-12-29 20:45:00  0.008768\n",
       "2016-12-29 21:00:00  0.008681\n",
       "2016-12-29 21:15:00  0.008681\n",
       "2016-12-29 21:30:00  0.008705\n",
       "2016-12-29 21:45:00  0.008761\n",
       "2016-12-29 22:00:00  0.008649\n",
       "2016-12-29 22:15:00  0.008658\n",
       "2016-12-29 22:30:00  0.008681\n",
       "2016-12-29 22:45:00  0.008679\n",
       "2016-12-29 23:00:00  0.008582\n",
       "2016-12-29 23:15:00  0.008582\n",
       "2016-12-29 23:30:00  0.008582\n",
       "2016-12-29 23:45:00  0.008583\n",
       "2016-12-30 00:00:00  0.008511\n",
       "2016-12-30 00:15:00  0.008511\n",
       "2016-12-30 00:30:00  0.008505\n",
       "2016-12-30 00:45:00  0.008505\n",
       "2016-12-30 01:00:00  0.008563\n",
       "2016-12-30 01:15:00  0.008561\n",
       "2016-12-30 01:30:00  0.008563\n",
       "2016-12-30 01:45:00  0.008563\n",
       "2016-12-30 02:00:00  0.008619\n",
       "2016-12-30 02:15:00  0.008619\n",
       "2016-12-30 02:30:00  0.008622\n",
       "2016-12-30 02:45:00  0.008622\n",
       "2016-12-30 03:00:00  0.008715\n",
       "2016-12-30 03:15:00  0.008686\n",
       "2016-12-30 03:30:00  0.008686\n",
       "2016-12-30 03:45:00  0.008715\n",
       "2016-12-30 04:00:00  0.008963\n",
       "2016-12-30 04:15:00  0.008963\n",
       "2016-12-30 04:30:00  0.008963\n",
       "2016-12-30 04:45:00  0.008963\n",
       "2016-12-30 05:00:00  0.009080\n",
       "2016-12-30 05:15:00  0.009108\n",
       "2016-12-30 05:30:00  0.009637\n",
       "2016-12-30 05:45:00  0.009842\n",
       "2016-12-30 06:00:00  0.017861\n",
       "2016-12-30 06:15:00  0.017861\n",
       "2016-12-30 06:30:00  0.010537\n",
       "2016-12-30 06:45:00  0.010196\n",
       "2016-12-30 07:00:00  0.009392\n",
       "2016-12-30 07:15:00  0.009392\n",
       "2016-12-30 07:30:00  0.009374\n",
       "2016-12-30 07:45:00  0.009374\n",
       "2016-12-30 08:00:00  0.008978\n",
       "2016-12-30 08:15:00  0.008978\n",
       "2016-12-30 08:30:00  0.008978\n",
       "2016-12-30 08:45:00  0.008978\n",
       "2016-12-30 09:00:00  0.008848\n",
       "2016-12-30 09:15:00  0.008848\n",
       "2016-12-30 09:30:00  0.008848\n",
       "2016-12-30 09:45:00  0.008848\n",
       "2016-12-30 10:00:00  0.008812\n",
       "2016-12-30 10:15:00  0.008812\n",
       "2016-12-30 10:30:00  0.008796\n",
       "2016-12-30 10:45:00  0.008778\n",
       "2016-12-30 11:00:00  0.008793\n",
       "2016-12-30 11:15:00  0.008783\n",
       "2016-12-30 11:30:00  0.008736\n",
       "2016-12-30 11:45:00  0.008736\n",
       "2016-12-30 12:00:00  0.008753\n",
       "2016-12-30 12:15:00  0.008761\n",
       "2016-12-30 12:30:00  0.008743\n",
       "2016-12-30 12:45:00  0.008696\n",
       "2016-12-30 13:00:00  0.008618\n",
       "2016-12-30 13:15:00  0.008612\n",
       "2016-12-30 13:30:00  0.008614\n",
       "2016-12-30 13:45:00  0.008619\n",
       "2016-12-30 14:00:00  0.008461\n",
       "2016-12-30 14:15:00  0.008455\n",
       "2016-12-30 14:30:00  0.008461\n",
       "2016-12-30 14:45:00  0.008462\n",
       "2016-12-30 15:00:00  0.008396\n",
       "2016-12-30 15:15:00  0.008393\n",
       "2016-12-30 15:30:00  0.008393\n",
       "2016-12-30 15:45:00  0.008393\n",
       "2016-12-30 16:00:00  0.008432\n",
       "2016-12-30 16:15:00  0.008424\n",
       "2016-12-30 16:30:00  0.008432\n",
       "2016-12-30 16:45:00  0.008449\n",
       "2016-12-30 17:00:00  0.008611\n",
       "2016-12-30 17:15:00  0.008667\n",
       "2016-12-30 17:30:00  0.008622\n",
       "2016-12-30 17:45:00  0.008622\n",
       "2016-12-30 18:00:00  0.008744\n",
       "2016-12-30 18:15:00  0.008788\n",
       "2016-12-30 18:30:00  0.008842\n",
       "2016-12-30 18:45:00  0.008898\n",
       "2016-12-30 19:00:00  0.009242\n",
       "2016-12-30 19:15:00  0.008928\n",
       "2016-12-30 19:30:00  0.008898\n",
       "2016-12-30 19:45:00  0.008856\n",
       "2016-12-30 20:00:00  0.009246\n",
       "2016-12-30 20:15:00  0.009246\n",
       "2016-12-30 20:30:00  0.009232\n",
       "2016-12-30 20:45:00  0.009221\n",
       "2016-12-30 21:00:00  0.008929\n",
       "2016-12-30 21:15:00  0.008929\n",
       "2016-12-30 21:30:00  0.008981\n",
       "2016-12-30 21:45:00  0.008954\n",
       "2016-12-30 22:00:00  0.008745\n",
       "2016-12-30 22:15:00  0.008778\n",
       "2016-12-30 22:30:00  0.008780\n",
       "2016-12-30 22:45:00  0.008778\n",
       "2016-12-30 23:00:00  0.008583\n",
       "2016-12-30 23:15:00  0.008583\n",
       "2016-12-30 23:30:00  0.008583\n",
       "2016-12-30 23:45:00  0.008583\n",
       "2016-12-31 00:00:00  0.008649\n",
       "2016-12-31 00:15:00  0.008649\n",
       "2016-12-31 00:30:00  0.008649\n",
       "2016-12-31 00:45:00  0.008649\n",
       "2016-12-31 01:00:00  0.008636\n",
       "2016-12-31 01:15:00  0.008636\n",
       "2016-12-31 01:30:00  0.008636\n",
       "2016-12-31 01:45:00  0.008636\n",
       "2016-12-31 02:00:00  0.008665\n",
       "2016-12-31 02:15:00  0.008665\n",
       "2016-12-31 02:30:00  0.008665\n",
       "2016-12-31 02:45:00  0.008665\n",
       "2016-12-31 03:00:00  0.008732\n",
       "2016-12-31 03:15:00  0.008732\n",
       "2016-12-31 03:30:00  0.008732\n",
       "2016-12-31 03:45:00  0.008732\n",
       "2016-12-31 04:00:00  0.008711\n",
       "2016-12-31 04:15:00  0.008711\n",
       "2016-12-31 04:30:00  0.008711\n",
       "2016-12-31 04:45:00  0.008711\n",
       "2016-12-31 05:00:00  0.008820\n",
       "2016-12-31 05:15:00  0.008820\n",
       "2016-12-31 05:30:00  0.008820\n",
       "2016-12-31 05:45:00  0.008820\n",
       "2016-12-31 06:00:00  0.009124\n",
       "2016-12-31 06:15:00  0.009124\n",
       "2016-12-31 06:30:00  0.009124\n",
       "2016-12-31 06:45:00  0.009124\n",
       "2016-12-31 07:00:00  0.057446\n",
       "2016-12-31 07:15:00  0.057446\n",
       "2016-12-31 07:30:00  0.057446\n",
       "2016-12-31 07:45:00  0.057446\n",
       "2016-12-31 08:00:00  0.021459\n",
       "2016-12-31 08:15:00  0.021459\n",
       "2016-12-31 08:30:00  0.021459\n",
       "2016-12-31 08:45:00  0.021459\n",
       "2016-12-31 09:00:00  0.009398\n",
       "2016-12-31 09:15:00  0.009398\n",
       "2016-12-31 09:30:00  0.009398\n",
       "2016-12-31 09:45:00  0.009398\n",
       "2016-12-31 10:00:00  0.009050\n",
       "2016-12-31 10:15:00  0.009050\n",
       "2016-12-31 10:30:00  0.009050\n",
       "2016-12-31 10:45:00  0.009050\n",
       "2016-12-31 11:00:00  0.008813\n",
       "2016-12-31 11:15:00  0.008813\n",
       "2016-12-31 11:30:00  0.008813\n",
       "2016-12-31 11:45:00  0.008813\n",
       "2016-12-31 12:00:00  0.008799\n",
       "2016-12-31 12:15:00  0.008799\n",
       "2016-12-31 12:30:00  0.008799\n",
       "2016-12-31 12:45:00  0.008799\n",
       "2016-12-31 13:00:00  0.008741\n",
       "2016-12-31 13:15:00  0.008741\n",
       "2016-12-31 13:30:00  0.008741\n",
       "2016-12-31 13:45:00  0.008741\n",
       "2016-12-31 14:00:00  0.008649\n",
       "2016-12-31 14:15:00  0.008649\n",
       "2016-12-31 14:30:00  0.008649\n",
       "2016-12-31 14:45:00  0.008649\n",
       "2016-12-31 15:00:00  0.008583\n",
       "2016-12-31 15:15:00  0.008583\n",
       "2016-12-31 15:30:00  0.008583\n",
       "2016-12-31 15:45:00  0.008583\n",
       "2016-12-31 16:00:00  0.008600\n",
       "2016-12-31 16:15:00  0.008600\n",
       "2016-12-31 16:30:00  0.008600\n",
       "2016-12-31 16:45:00  0.008600\n",
       "2016-12-31 17:00:00  0.008680\n",
       "2016-12-31 17:15:00  0.008680\n",
       "2016-12-31 17:30:00  0.008680\n",
       "2016-12-31 17:45:00  0.008680\n",
       "2016-12-31 18:00:00  0.009162\n",
       "2016-12-31 18:15:00  0.009162\n",
       "2016-12-31 18:30:00  0.009162\n",
       "2016-12-31 18:45:00  0.009162\n",
       "2016-12-31 19:00:00  0.009808\n",
       "2016-12-31 19:15:00  0.009808\n",
       "2016-12-31 19:30:00  0.009808\n",
       "2016-12-31 19:45:00  0.009808\n",
       "2016-12-31 20:00:00  0.009975\n",
       "2016-12-31 20:15:00  0.009975\n",
       "2016-12-31 20:30:00  0.009975\n",
       "2016-12-31 20:45:00  0.009975\n",
       "2016-12-31 21:00:00  0.009176\n",
       "2016-12-31 21:15:00  0.009176\n",
       "2016-12-31 21:30:00  0.009176\n",
       "2016-12-31 21:45:00  0.009176\n",
       "2016-12-31 22:00:00  0.008902\n",
       "2016-12-31 22:15:00  0.008902\n",
       "2016-12-31 22:30:00  0.008902\n",
       "2016-12-31 22:45:00  0.008902\n",
       "2016-12-31 23:00:00  0.008758\n",
       "2016-12-31 23:15:00  0.008758\n",
       "2016-12-31 23:30:00  0.008758\n",
       "2016-12-31 23:45:00  0.008758\n",
       "\n",
       "[105204 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tolerable_power_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 原始用電 v.s. 容忍值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,10))\n",
    "plt.plot(dataset['Furnace'].iloc[:96*2])\n",
    "plt.plot(tolerable_power_df['Furnace'].iloc[:96*2])\n",
    "# plt.plot(dataset['temperature'].iloc[:24])\n",
    "plt.legend(['powers', 'tolerable_powers', 'temperature'], fontsize='xx-large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,10))\n",
    "plt.plot(dataset['Furnace'].resample('H').mean())\n",
    "plt.plot(tolerable_power_df['Furnace'].resample('H').mean())\n",
    "# plt.plot(dataset['temperature'].resample('D').mean())\n",
    "plt.legend(['old_powers', 'tolerable_powers', 'temperature'], fontsize='xx-large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    105204.000000\n",
       "mean          0.016824\n",
       "std           0.036156\n",
       "min           0.007951\n",
       "25%           0.008533\n",
       "50%           0.008983\n",
       "75%           0.010758\n",
       "max           0.370100\n",
       "Name: Furnace, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 容忍值\n",
    "tolerable_power_df['Furnace'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    105204.000000\n",
       "mean          0.095905\n",
       "std           0.162381\n",
       "min           0.000000\n",
       "25%           0.008800\n",
       "50%           0.010776\n",
       "75%           0.114442\n",
       "max           0.629318\n",
       "Name: Furnace, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 原始用電\n",
    "dataset['Furnace'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find appliance on/off threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118d085d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.loc[(dataset.loc[:, 'Furnace'] < 0.015) & (dataset.loc[:, 'Furnace'] > 0.007), 'Furnace'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter closed furnace (<0.013)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop(dataset.loc[dataset.loc[:, 'Furnace'] < 0.013, :].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "tolerable_power_df.loc[tolerable_power_df.loc[:, 'Furnace'] < 0.013, 'Furnace'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Furnace    98908\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tolerable_power_df == 0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.to_csv('F_features.csv', columns=COL_WEATHER+COL_DATE+[\"Furnace\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "tolerable_power_df.to_csv('F_furnace_tolerable_power.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}