ConvNetSetup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1014,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "import time\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from datetime import datetime, date, timedelta\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1017,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pandas.read_csv('./combined_new.csv')\n",
    "df = df.dropna(subset=['correcttimestamp'])\n",
    "df['correcttimestamp']\n",
    "start_date = time.mktime(time.strptime('9/1/2017', '%m/%d/%Y'))\n",
    "end_date = time.mktime(time.strptime('11/17/2017', '%m/%d/%Y'))\n",
    "\n",
    "df['unixtime'] = df['correcttimestamp'].apply(lambda x: time.mktime(time.strptime(x, '%m/%d/%Y %H:%M:%S')))\n",
    "df = df[(df['unixtime'] > start_date) & (df['unixtime'] < end_date)].sort_values('unixtime')\n",
    "\n",
    "df['date'] = df['unixtime'].apply(lambda x: date.fromtimestamp(x))\n",
    "\n",
    "df = df.drop(df['humalog'].idxmax())\n",
    "\n",
    "\n",
    "df['is_sugar'] = df['meal'] == 'sugar to treat'\n",
    "df['is_breakfast'] = df['meal'] == 'breakfast'\n",
    "df['is_lunch'] = df['meal'] == 'lunch'\n",
    "df['is_dinner'] = df['meal'] == 'dinner'\n",
    "df['30min_block'] = df['unixtime'] / (60 * 30)\n",
    "df['hour_block'] = df['unixtime'] / (60 * 60)\n",
    "df['30min_block'] = (df['30min_block'] - min(df['30min_block'])).apply(lambda x: math.floor(x))\n",
    "df['hour_block'] = (df['hour_block'] - min(df['hour_block'])).apply(lambda x: math.floor(x))\n",
    "\n",
    "\n",
    "df = df[['date', 'time', 'hour', 'hour_block', '30min_block', 'unixtime', 'mealcarbs', 'humalog', 'glucose', 'is_sugar', 'is_breakfast', 'is_lunch', 'is_dinner']]\n",
    "df_to_plot = df.dropna(subset = ['glucose']).reset_index()\n",
    "daily_means = df_to_plot.groupby('date')['mealcarbs', 'glucose', 'humalog'].mean()\n",
    "\n",
    "weekly_means = df_to_plot\n",
    "\n",
    "monthly_means = df_to_plot\n",
    "monthly_means['month'] = (df_to_plot['unixtime']).map(lambda x: datetime.fromtimestamp(x), replace(hour=0, minute=0, second=0, microsecond=0))\n",
    "monthly_means['month'] = (monthly_means['month']).map(lambda x: datetime.fromtimestamp(x), replace(hour=0, minute=0, second=0, microsecond=0))\n",
    "\n",
    "\n",
    "#weekly_means['week'] = (df_to_plot['unixtime']).map(lambda x: datetime.fromtimestamp(x).replace(hour=0, minute=0, second=0, microsecond=0))\n",
    "#weekly_means['week'] = weekly_means['week'].map(lambda x: x - timedelta(days=x.weekday()))\n",
    "#weekly_means = weekly_means.groupby('week')['glucose', 'humalog', 'mealcarbs'].mean()\n",
    "#plt.rcParams.update({'font.size': 12})\n",
    "#\n",
    "#plt.figure(figsize=(10,10))\n",
    "#plt.subplot(311)\n",
    "#plt.title('humalog')\n",
    "#plt.ylabel('units')\n",
    "#plt.xlabel('date')\n",
    "#daily_means['humalog'].plot(rot = 45)\n",
    "\n",
    "#plt.subplot(312)\n",
    "#plt.title('glucose')\n",
    "#plt.ylabel('mg/dL')\n",
    "#plt.xlabel('date')\n",
    "#daily_means['glucose'].plot(color='r', rot=45)\n",
    "\n",
    "#plt.subplot(313)\n",
    "#plt.title('mealcarbs')\n",
    "#plt.ylabel('grams')\n",
    "#plt.xlabel('date')\n",
    "#daily_means['mealcarbs'].plot(color='g', rot=45)\n",
    "#plt.tight_layout()\n",
    "#plt.show()\n",
    "#\n",
    "#daily_means\n",
    "#plt.figure(figsize=(25, 5))\n",
    "#plt.title('Hourly, Daily, and Weekly Glucose Trends')\n",
    "#plt.plot(df_to_plot['date'] , df_to_plot['glucose'], 'r--')\n",
    "#plt.ylabel('mg/dL')\n",
    "#plt.xlabel('date')\n",
    "#plt.plot(daily_means['glucose'])\n",
    "#weekly_means = weekly_means[~pandas.isnull(weekly_means['glucose'])]\n",
    "#plt.plot(weekly_means['glucose'][1:], 'g-')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1022,
   "metadata": {},
   "outputs": [
    {
     "data": {
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7h+qCb7+2q6KV6HANa3LdGyYaS1JMOMuy4qWOWDLjONrQTYhasDD94oHlzIRI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L8LLkhulOiQQ8Ll1xtuCGW9csVf/hJG57Zu3bmUV1344INwW9sjxBD6d8mS8KXs4ovDCOgvflH7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\nZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7a8c70c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7a8c7cf0f0>]"
      ]
     },
     "execution_count": 1022,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TPUm+bJm7faSl6YeKggWjG5st7JlNRLH00kvAn39qBfAHHwDnnRf/GGrWBF58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ova1EKHcpkFT26xLn5epT0zG5As/pgYd977gQfkXM3JqjdAzs2bNAlMMPvRRyVpwclxwI5j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\nRERERBTyGMwmIiIiIiIiIiIiopD3//Ip01APPXNgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7a8c225e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pandas.read_csv('./combined_new.csv')\n",
    "df = df.dropna(subset=['correcttimestamp'])\n",
    "df['correcttimestamp']\n",
    "start_date = time.mktime(time.strptime('9/1/2017', '%m/%d/%Y'))\n",
    "end_date = time.mktime(time.strptime('11/17/2017', '%m/%d/%Y'))\n",
    "\n",
    "df['unixtime'] = df['correcttimestamp'].apply(lambda x: time.mktime(time.strptime(x, '%m/%d/%Y %H:%M:%S')))\n",
    "df = df[(df['unixtime'] > start_date) & (df['unixtime'] < end_date)].sort_values('unixtime')\n",
    "\n",
    "df['date'] = df['unixtime'].apply(lambda x: date.fromtimestamp(x))\n",
    "\n",
    "df = df.drop(df['humalog'].idxmax())\n",
    "\n",
    "\n",
    "df['is_sugar'] = df['meal'] == 'sugar to treat'\n",
    "df['is_breakfast'] = df['meal'] == 'breakfast'\n",
    "df['is_lunch'] = df['meal'] == 'lunch'\n",
    "df['is_dinner'] = df['meal'] == 'dinner'\n",
    "df['30min_block'] = df['unixtime'] / (60 * 30)\n",
    "df['hour_block'] = df['unixtime'] / (60 * 60)\n",
    "df['30min_block'] = (df['30min_block'] - min(df['30min_block'])).apply(lambda x: math.floor(x))\n",
    "df['hour_block'] = (df['hour_block'] - min(df['hour_block'])).apply(lambda x: math.floor(x))\n",
    "\n",
    "\n",
    "df = df[['date', 'time', 'hour', 'hour_block', '30min_block', 'unixtime', 'mealcarbs', 'humalog', 'glucose', 'is_sugar', 'is_breakfast', 'is_lunch', 'is_dinner']]\n",
    "df_to_plot = df.dropna(subset = ['glucose']).reset_index()\n",
    "daily_means = df_to_plot.groupby('date')['mealcarbs', 'glucose', 'humalog'].mean()\n",
    "\n",
    "weekly_means = df_to_plot\n",
    "\n",
    "weekly_means['week'] = (df_to_plot['unixtime']).map(lambda x: datetime.fromtimestamp(x).replace(hour=0, minute=0, second=0, microsecond=0))\n",
    "weekly_means['week'] = weekly_means['week'].map(lambda x: x - timedelta(days=x.weekday()))\n",
    "weekly_means = weekly_means.groupby('week')['glucose', 'humalog', 'mealcarbs'].mean()\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(311)\n",
    "plt.title('humalog')\n",
    "plt.ylabel('units')\n",
    "plt.xlabel('date')\n",
    "daily_means['humalog'].plot(rot = 45)\n",
    "\n",
    "plt.subplot(312)\n",
    "plt.title('glucose')\n",
    "plt.ylabel('mg/dL')\n",
    "plt.xlabel('date')\n",
    "daily_means['glucose'].plot(color='r', rot=45)\n",
    "\n",
    "plt.subplot(313)\n",
    "plt.title('mealcarbs')\n",
    "plt.ylabel('grams')\n",
    "plt.xlabel('date')\n",
    "daily_means['mealcarbs'].plot(color='g', rot=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "daily_means\n",
    "plt.figure(figsize=(25, 5))\n",
    "plt.title('Hourly, Daily, and Weekly Glucose Trends')\n",
    "plt.plot(df_to_plot['date'] , df_to_plot['glucose'], 'r--')\n",
    "plt.ylabel('mg/dL')\n",
    "plt.xlabel('date')\n",
    "plt.plot(daily_means['glucose'])\n",
    "weekly_means = weekly_means[~pandas.isnull(weekly_means['glucose'])]\n",
    "plt.plot(weekly_means['glucose'][1:], 'g-')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1016,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['humalog'][pandas.isnull(df['humalog'])] = 0\n",
    "df['mealcarbs'][pandas.isnull(df['mealcarbs'])] = 0\n",
    "df['glucose'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1002,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n",
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:8: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  \n",
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:12: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_block</th>\n",
       "      <th>30min_block</th>\n",
       "      <th>unixtime</th>\n",
       "      <th>mealcarbs</th>\n",
       "      <th>humalog</th>\n",
       "      <th>glucose</th>\n",
       "      <th>is_sugar</th>\n",
       "      <th>is_breakfast</th>\n",
       "      <th>is_lunch</th>\n",
       "      <th>is_dinner</th>\n",
       "      <th>glucose_scaled</th>\n",
       "      <th>mealcarbs_scaled</th>\n",
       "      <th>humalog_scaled</th>\n",
       "      <th>30min_diff</th>\n",
       "      <th>30min_block_diff</th>\n",
       "      <th>glucose_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>06:31 am</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.504247e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.46</td>\n",
       "      <td>387.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.216430</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>2.021288</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>12:25 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>1.504269e+09</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.31</td>\n",
       "      <td>147.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.045700</td>\n",
       "      <td>0.969233</td>\n",
       "      <td>0.571112</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>03:49 pm</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>1.504281e+09</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>241.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.840301</td>\n",
       "      <td>0.179223</td>\n",
       "      <td>0.949419</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>06:38 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>1.504291e+09</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.58</td>\n",
       "      <td>115.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.347317</td>\n",
       "      <td>2.302373</td>\n",
       "      <td>0.911588</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>01:12 am</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>1.504315e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>189.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.350173</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-0.374655</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>03:03 am</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>41</td>\n",
       "      <td>1.504321e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>149.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.026849</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>06:51 am</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>48</td>\n",
       "      <td>1.504335e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>120.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.300189</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>07:41 am</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>1.504338e+09</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>138.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.130529</td>\n",
       "      <td>1.067984</td>\n",
       "      <td>1.125962</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>10:00 am</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>54</td>\n",
       "      <td>1.504346e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>293.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.330429</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>01:12 pm</td>\n",
       "      <td>13</td>\n",
       "      <td>30</td>\n",
       "      <td>61</td>\n",
       "      <td>1.504358e+09</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>85.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.630083</td>\n",
       "      <td>1.759242</td>\n",
       "      <td>0.495450</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>04:52 pm</td>\n",
       "      <td>17</td>\n",
       "      <td>34</td>\n",
       "      <td>68</td>\n",
       "      <td>1.504371e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.88</td>\n",
       "      <td>214.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.585812</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>0.028872</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>06:30 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>35</td>\n",
       "      <td>71</td>\n",
       "      <td>1.504377e+09</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>63.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.837445</td>\n",
       "      <td>-0.512035</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>06:45 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>1.504378e+09</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1.431254</td>\n",
       "      <td>2.894880</td>\n",
       "      <td>1.289895</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>08:24 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>37</td>\n",
       "      <td>75</td>\n",
       "      <td>1.504384e+09</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.431254</td>\n",
       "      <td>1.314862</td>\n",
       "      <td>0.344128</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>10:16 pm</td>\n",
       "      <td>22</td>\n",
       "      <td>39</td>\n",
       "      <td>79</td>\n",
       "      <td>1.504391e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>176.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.227641</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-0.904285</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>03:57 am</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>1.504411e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>329.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.669749</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>1.062911</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>07:42 am</td>\n",
       "      <td>8</td>\n",
       "      <td>49</td>\n",
       "      <td>98</td>\n",
       "      <td>1.504425e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>243.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.859152</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>0.180195</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>09:10 am</td>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>101</td>\n",
       "      <td>1.504430e+09</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>53.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.931700</td>\n",
       "      <td>-0.610786</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>09:23 am</td>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>101</td>\n",
       "      <td>1.504431e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>132.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.187083</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>12:50 pm</td>\n",
       "      <td>13</td>\n",
       "      <td>54</td>\n",
       "      <td>108</td>\n",
       "      <td>1.504443e+09</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.714913</td>\n",
       "      <td>-0.561410</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>01:05 pm</td>\n",
       "      <td>13</td>\n",
       "      <td>54</td>\n",
       "      <td>109</td>\n",
       "      <td>1.504444e+09</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.677211</td>\n",
       "      <td>0.574228</td>\n",
       "      <td>-0.147671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>01:22 pm</td>\n",
       "      <td>13</td>\n",
       "      <td>54</td>\n",
       "      <td>109</td>\n",
       "      <td>1.504445e+09</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.431254</td>\n",
       "      <td>-0.018279</td>\n",
       "      <td>-0.450317</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>01:32 pm</td>\n",
       "      <td>14</td>\n",
       "      <td>55</td>\n",
       "      <td>110</td>\n",
       "      <td>1.504446e+09</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.431254</td>\n",
       "      <td>-0.018279</td>\n",
       "      <td>-0.551199</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>03:34 pm</td>\n",
       "      <td>16</td>\n",
       "      <td>57</td>\n",
       "      <td>114</td>\n",
       "      <td>1.504453e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>340.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.773429</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>0.785486</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>06:09 pm</td>\n",
       "      <td>18</td>\n",
       "      <td>59</td>\n",
       "      <td>119</td>\n",
       "      <td>1.504462e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>206.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.510407</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-0.362045</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>07:42 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>61</td>\n",
       "      <td>122</td>\n",
       "      <td>1.504468e+09</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1.93</td>\n",
       "      <td>153.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.010854</td>\n",
       "      <td>2.401125</td>\n",
       "      <td>1.352946</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>09:07 pm</td>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>125</td>\n",
       "      <td>1.504473e+09</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.743189</td>\n",
       "      <td>-0.363908</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>07:55 am</td>\n",
       "      <td>8</td>\n",
       "      <td>73</td>\n",
       "      <td>146</td>\n",
       "      <td>1.504512e+09</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>151.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.007997</td>\n",
       "      <td>0.129848</td>\n",
       "      <td>0.281077</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>12:15 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>77</td>\n",
       "      <td>155</td>\n",
       "      <td>1.504527e+09</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.38</td>\n",
       "      <td>325.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.632046</td>\n",
       "      <td>0.574228</td>\n",
       "      <td>1.920406</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>03:41 pm</td>\n",
       "      <td>16</td>\n",
       "      <td>81</td>\n",
       "      <td>162</td>\n",
       "      <td>1.504540e+09</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.42</td>\n",
       "      <td>219.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.632939</td>\n",
       "      <td>0.179223</td>\n",
       "      <td>0.709824</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>12:24 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>1733</td>\n",
       "      <td>3467</td>\n",
       "      <td>1.510489e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.52</td>\n",
       "      <td>326.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.641472</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>0.835927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>01:46 pm</td>\n",
       "      <td>14</td>\n",
       "      <td>1735</td>\n",
       "      <td>3470</td>\n",
       "      <td>1.510494e+09</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.431254</td>\n",
       "      <td>0.919857</td>\n",
       "      <td>0.142364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>06:51 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>1740</td>\n",
       "      <td>3480</td>\n",
       "      <td>1.510513e+09</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>245.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.878003</td>\n",
       "      <td>1.314862</td>\n",
       "      <td>1.945627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>07:31 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>1740</td>\n",
       "      <td>3481</td>\n",
       "      <td>1.510515e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.431254</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-0.576419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>08:17 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>1741</td>\n",
       "      <td>3483</td>\n",
       "      <td>1.510518e+09</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.431254</td>\n",
       "      <td>-0.314532</td>\n",
       "      <td>-0.689911</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>09:01 pm</td>\n",
       "      <td>21</td>\n",
       "      <td>1742</td>\n",
       "      <td>3485</td>\n",
       "      <td>1.510521e+09</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.431254</td>\n",
       "      <td>-0.512035</td>\n",
       "      <td>-0.841234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>08:01 am</td>\n",
       "      <td>8</td>\n",
       "      <td>1753</td>\n",
       "      <td>3506</td>\n",
       "      <td>1.510560e+09</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.66</td>\n",
       "      <td>203.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.482131</td>\n",
       "      <td>-0.067655</td>\n",
       "      <td>1.012470</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>12:15 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>1757</td>\n",
       "      <td>3515</td>\n",
       "      <td>1.510575e+09</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>265.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.066514</td>\n",
       "      <td>0.919857</td>\n",
       "      <td>1.504269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>01:38 pm</td>\n",
       "      <td>14</td>\n",
       "      <td>1759</td>\n",
       "      <td>3518</td>\n",
       "      <td>1.510580e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>369.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.046770</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>02:14 pm</td>\n",
       "      <td>14</td>\n",
       "      <td>1759</td>\n",
       "      <td>3519</td>\n",
       "      <td>1.510582e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>279.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.198472</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>03:29 pm</td>\n",
       "      <td>15</td>\n",
       "      <td>1760</td>\n",
       "      <td>3521</td>\n",
       "      <td>1.510587e+09</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>224.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.680067</td>\n",
       "      <td>0.228599</td>\n",
       "      <td>0.671994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>06:48 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>1764</td>\n",
       "      <td>3528</td>\n",
       "      <td>1.510599e+09</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3.61</td>\n",
       "      <td>350.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.867685</td>\n",
       "      <td>2.055496</td>\n",
       "      <td>3.471465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>06:35 am</td>\n",
       "      <td>7</td>\n",
       "      <td>1776</td>\n",
       "      <td>3552</td>\n",
       "      <td>1.510641e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.18</td>\n",
       "      <td>270.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.113642</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>0.407179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>07:46 am</td>\n",
       "      <td>8</td>\n",
       "      <td>1777</td>\n",
       "      <td>3554</td>\n",
       "      <td>1.510646e+09</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.431254</td>\n",
       "      <td>-0.067655</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>12:21 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>1781</td>\n",
       "      <td>3563</td>\n",
       "      <td>1.510662e+09</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>187.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.331322</td>\n",
       "      <td>0.919857</td>\n",
       "      <td>0.873757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>02:15 pm</td>\n",
       "      <td>14</td>\n",
       "      <td>1783</td>\n",
       "      <td>3567</td>\n",
       "      <td>1.510669e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>263.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.047663</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>06:24 pm</td>\n",
       "      <td>18</td>\n",
       "      <td>1787</td>\n",
       "      <td>3575</td>\n",
       "      <td>1.510684e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.45</td>\n",
       "      <td>309.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.481238</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>0.747655</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>08:02 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>1789</td>\n",
       "      <td>3579</td>\n",
       "      <td>1.510690e+09</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>178.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.246492</td>\n",
       "      <td>2.104871</td>\n",
       "      <td>1.315115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>08:12 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>1789</td>\n",
       "      <td>3579</td>\n",
       "      <td>1.510690e+09</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1.431254</td>\n",
       "      <td>-0.363908</td>\n",
       "      <td>-0.715132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>07:39 am</td>\n",
       "      <td>8</td>\n",
       "      <td>1801</td>\n",
       "      <td>3602</td>\n",
       "      <td>1.510732e+09</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.32</td>\n",
       "      <td>155.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.029705</td>\n",
       "      <td>-0.067655</td>\n",
       "      <td>0.583722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>09:15 am</td>\n",
       "      <td>9</td>\n",
       "      <td>1802</td>\n",
       "      <td>3605</td>\n",
       "      <td>1.510737e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>235.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.783748</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>10:00 am</td>\n",
       "      <td>10</td>\n",
       "      <td>1803</td>\n",
       "      <td>3606</td>\n",
       "      <td>1.510740e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>269.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.104216</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>12:22 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>1805</td>\n",
       "      <td>3611</td>\n",
       "      <td>1.510749e+09</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.22</td>\n",
       "      <td>283.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.236174</td>\n",
       "      <td>0.919857</td>\n",
       "      <td>1.718643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>03:37 pm</td>\n",
       "      <td>16</td>\n",
       "      <td>1809</td>\n",
       "      <td>3618</td>\n",
       "      <td>1.510760e+09</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.53</td>\n",
       "      <td>280.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.207897</td>\n",
       "      <td>-0.265157</td>\n",
       "      <td>0.848537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>06:25 pm</td>\n",
       "      <td>18</td>\n",
       "      <td>1811</td>\n",
       "      <td>3623</td>\n",
       "      <td>1.510770e+09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>95.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.535828</td>\n",
       "      <td>-0.709537</td>\n",
       "      <td>-1.080828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>11:01 pm</td>\n",
       "      <td>23</td>\n",
       "      <td>1816</td>\n",
       "      <td>3632</td>\n",
       "      <td>1.510787e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.41</td>\n",
       "      <td>270.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.113642</td>\n",
       "      <td>-0.808288</td>\n",
       "      <td>0.697214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>07:45 am</td>\n",
       "      <td>8</td>\n",
       "      <td>1825</td>\n",
       "      <td>3650</td>\n",
       "      <td>1.510818e+09</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.97</td>\n",
       "      <td>248.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.906280</td>\n",
       "      <td>-0.067655</td>\n",
       "      <td>1.403387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>12:15 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>1829</td>\n",
       "      <td>3659</td>\n",
       "      <td>1.510834e+09</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>238.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.812024</td>\n",
       "      <td>0.574228</td>\n",
       "      <td>1.088131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>06:30 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>1835</td>\n",
       "      <td>3671</td>\n",
       "      <td>1.510857e+09</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2.23</td>\n",
       "      <td>160.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.076832</td>\n",
       "      <td>1.956744</td>\n",
       "      <td>1.731253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>08:18 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>1837</td>\n",
       "      <td>3675</td>\n",
       "      <td>1.510863e+09</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.431254</td>\n",
       "      <td>-0.462659</td>\n",
       "      <td>-0.752962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>594 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date      time  hour  hour_block  30min_block      unixtime  \\\n",
       "629  2017-09-01  06:31 am     7           0            0  1.504247e+09   \n",
       "628  2017-09-01  12:25 pm    12           5           11  1.504269e+09   \n",
       "627  2017-09-01  03:49 pm    16           9           18  1.504281e+09   \n",
       "626  2017-09-01  06:38 pm    19          12           24  1.504291e+09   \n",
       "625  2017-09-02  01:12 am     1          18           37  1.504315e+09   \n",
       "624  2017-09-02  03:03 am     3          20           41  1.504321e+09   \n",
       "623  2017-09-02  06:51 am     7          24           48  1.504335e+09   \n",
       "622  2017-09-02  07:41 am     8          25           50  1.504338e+09   \n",
       "621  2017-09-02  10:00 am    10          27           54  1.504346e+09   \n",
       "620  2017-09-02  01:12 pm    13          30           61  1.504358e+09   \n",
       "619  2017-09-02  04:52 pm    17          34           68  1.504371e+09   \n",
       "618  2017-09-02  06:30 pm    19          35           71  1.504377e+09   \n",
       "617  2017-09-02  06:45 pm    19          36           72  1.504378e+09   \n",
       "616  2017-09-02  08:24 pm    20          37           75  1.504384e+09   \n",
       "615  2017-09-02  10:16 pm    22          39           79  1.504391e+09   \n",
       "614  2017-09-03  03:57 am     4          45           90  1.504411e+09   \n",
       "613  2017-09-03  07:42 am     8          49           98  1.504425e+09   \n",
       "612  2017-09-03  09:10 am     9          50          101  1.504430e+09   \n",
       "611  2017-09-03  09:23 am     9          50          101  1.504431e+09   \n",
       "610  2017-09-03  12:50 pm    13          54          108  1.504443e+09   \n",
       "609  2017-09-03  01:05 pm    13          54          109  1.504444e+09   \n",
       "608  2017-09-03  01:22 pm    13          54          109  1.504445e+09   \n",
       "607  2017-09-03  01:32 pm    14          55          110  1.504446e+09   \n",
       "606  2017-09-03  03:34 pm    16          57          114  1.504453e+09   \n",
       "605  2017-09-03  06:09 pm    18          59          119  1.504462e+09   \n",
       "604  2017-09-03  07:42 pm    20          61          122  1.504468e+09   \n",
       "603  2017-09-03  09:07 pm    21          62          125  1.504473e+09   \n",
       "602  2017-09-04  07:55 am     8          73          146  1.504512e+09   \n",
       "601  2017-09-04  12:15 pm    12          77          155  1.504527e+09   \n",
       "600  2017-09-04  03:41 pm    16          81          162  1.504540e+09   \n",
       "..          ...       ...   ...         ...          ...           ...   \n",
       "64   2017-11-12  12:24 pm    12        1733         3467  1.510489e+09   \n",
       "63   2017-11-12  01:46 pm    14        1735         3470  1.510494e+09   \n",
       "62   2017-11-12  06:51 pm    19        1740         3480  1.510513e+09   \n",
       "61   2017-11-12  07:31 pm    20        1740         3481  1.510515e+09   \n",
       "60   2017-11-12  08:17 pm    20        1741         3483  1.510518e+09   \n",
       "59   2017-11-12  09:01 pm    21        1742         3485  1.510521e+09   \n",
       "58   2017-11-13  08:01 am     8        1753         3506  1.510560e+09   \n",
       "57   2017-11-13  12:15 pm    12        1757         3515  1.510575e+09   \n",
       "56   2017-11-13  01:38 pm    14        1759         3518  1.510580e+09   \n",
       "55   2017-11-13  02:14 pm    14        1759         3519  1.510582e+09   \n",
       "54   2017-11-13  03:29 pm    15        1760         3521  1.510587e+09   \n",
       "53   2017-11-13  06:48 pm    19        1764         3528  1.510599e+09   \n",
       "52   2017-11-14  06:35 am     7        1776         3552  1.510641e+09   \n",
       "51   2017-11-14  07:46 am     8        1777         3554  1.510646e+09   \n",
       "50   2017-11-14  12:21 pm    12        1781         3563  1.510662e+09   \n",
       "49   2017-11-14  02:15 pm    14        1783         3567  1.510669e+09   \n",
       "48   2017-11-14  06:24 pm    18        1787         3575  1.510684e+09   \n",
       "47   2017-11-14  08:02 pm    20        1789         3579  1.510690e+09   \n",
       "46   2017-11-14  08:12 pm    20        1789         3579  1.510690e+09   \n",
       "45   2017-11-15  07:39 am     8        1801         3602  1.510732e+09   \n",
       "44   2017-11-15  09:15 am     9        1802         3605  1.510737e+09   \n",
       "43   2017-11-15  10:00 am    10        1803         3606  1.510740e+09   \n",
       "42   2017-11-15  12:22 pm    12        1805         3611  1.510749e+09   \n",
       "41   2017-11-15  03:37 pm    16        1809         3618  1.510760e+09   \n",
       "40   2017-11-15  06:25 pm    18        1811         3623  1.510770e+09   \n",
       "39   2017-11-15  11:01 pm    23        1816         3632  1.510787e+09   \n",
       "38   2017-11-16  07:45 am     8        1825         3650  1.510818e+09   \n",
       "37   2017-11-16  12:15 pm    12        1829         3659  1.510834e+09   \n",
       "36   2017-11-16  06:30 pm    19        1835         3671  1.510857e+09   \n",
       "35   2017-11-16  08:18 pm    20        1837         3675  1.510863e+09   \n",
       "\n",
       "     mealcarbs  humalog  glucose  is_sugar  is_breakfast  is_lunch  is_dinner  \\\n",
       "629        0.0     2.46    387.0     False         False     False      False   \n",
       "628       36.0     1.31    147.0     False         False      True      False   \n",
       "627       20.0     1.61    241.0     False         False     False      False   \n",
       "626       63.0     1.58    115.0     False         False     False       True   \n",
       "625        0.0     0.56    189.0     False         False     False      False   \n",
       "624        0.0     0.00    149.0     False         False     False      False   \n",
       "623        0.0     0.00    120.0     False         False     False      False   \n",
       "622       38.0     1.75    138.0     False          True     False      False   \n",
       "621        0.0     0.00    293.0     False         False     False      False   \n",
       "620       52.0     1.25     85.0     False         False      True      False   \n",
       "619        0.0     0.88    214.0     False         False     False      False   \n",
       "618        6.0     0.00     63.0      True         False     False      False   \n",
       "617       75.0     1.88      0.0     False         False     False       True   \n",
       "616       43.0     1.13      0.0     False         False     False      False   \n",
       "615        0.0     0.14    176.0     False         False     False      False   \n",
       "614        0.0     1.70    329.0     False         False     False      False   \n",
       "613        0.0     1.00    243.0     False         False     False      False   \n",
       "612        4.0     0.00     53.0      True         False     False      False   \n",
       "611        0.0     0.00    132.0     False         False     False      False   \n",
       "610        5.0     0.00     76.0      True         False     False      False   \n",
       "609       28.0     0.74     80.0     False         False      True      False   \n",
       "608       16.0     0.50      0.0     False         False      True      False   \n",
       "607       16.0     0.42      0.0     False         False      True      False   \n",
       "606        0.0     1.48    340.0     False         False     False      False   \n",
       "605        0.0     0.57    206.0     False         False     False      False   \n",
       "604       65.0     1.93    153.0     False         False     False       True   \n",
       "603        9.0     0.00     73.0      True         False     False      False   \n",
       "602       19.0     1.08    151.0     False          True     False      False   \n",
       "601       28.0     2.38    325.0     False         False      True      False   \n",
       "600       20.0     1.42    219.0     False         False     False      False   \n",
       "..         ...      ...      ...       ...           ...       ...        ...   \n",
       "64         0.0     1.52    326.0     False         False     False      False   \n",
       "63        35.0     0.97      0.0     False         False      True      False   \n",
       "62        43.0     2.40    245.0     False         False     False       True   \n",
       "61         0.0     0.40      0.0     False         False     False      False   \n",
       "60        10.0     0.31      0.0     False         False     False      False   \n",
       "59         6.0     0.19      0.0     False         False     False      False   \n",
       "58        15.0     1.66    203.0     False          True     False      False   \n",
       "57        35.0     2.05    265.0     False         False      True      False   \n",
       "56         0.0     0.00    369.0     False         False     False      False   \n",
       "55         0.0     0.00    279.0     False         False     False      False   \n",
       "54        21.0     1.39    224.0     False         False     False      False   \n",
       "53        58.0     3.61    350.0     False         False     False       True   \n",
       "52         0.0     1.18    270.0     False         False     False      False   \n",
       "51        15.0     0.94      0.0     False          True     False      False   \n",
       "50        35.0     1.55    187.0     False         False      True      False   \n",
       "49         0.0     0.00    263.0     False         False     False      False   \n",
       "48         0.0     1.45    309.0     False         False     False      False   \n",
       "47        59.0     1.90    178.0     False         False     False       True   \n",
       "46         9.0     0.29      0.0     False         False     False       True   \n",
       "45        15.0     1.32    155.0     False          True     False      False   \n",
       "44         0.0     0.00    235.0     False         False     False      False   \n",
       "43         0.0     0.00    269.0     False         False     False      False   \n",
       "42        35.0     2.22    283.0     False         False      True      False   \n",
       "41        11.0     1.53    280.0     False         False     False      False   \n",
       "40         2.0     0.00     95.0      True         False     False      False   \n",
       "39         0.0     1.41    270.0     False         False     False      False   \n",
       "38        15.0     1.97    248.0     False          True     False      False   \n",
       "37        28.0     1.72    238.0     False         False      True      False   \n",
       "36        56.0     2.23    160.0     False         False     False       True   \n",
       "35         7.0     0.26      0.0     False         False     False      False   \n",
       "\n",
       "     glucose_scaled  mealcarbs_scaled  humalog_scaled  30min_diff  \\\n",
       "629        2.216430         -0.808288        2.021288       -11.0   \n",
       "628       -0.045700          0.969233        0.571112        -7.0   \n",
       "627        0.840301          0.179223        0.949419        -6.0   \n",
       "626       -0.347317          2.302373        0.911588       -13.0   \n",
       "625        0.350173         -0.808288       -0.374655        -4.0   \n",
       "624       -0.026849         -0.808288       -1.080828        -7.0   \n",
       "623       -0.300189         -0.808288       -1.080828        -2.0   \n",
       "622       -0.130529          1.067984        1.125962        -4.0   \n",
       "621        1.330429         -0.808288       -1.080828        -7.0   \n",
       "620       -0.630083          1.759242        0.495450        -7.0   \n",
       "619        0.585812         -0.808288        0.028872        -3.0   \n",
       "618       -0.837445         -0.512035       -1.080828        -1.0   \n",
       "617       -1.431254          2.894880        1.289895        -3.0   \n",
       "616       -1.431254          1.314862        0.344128        -4.0   \n",
       "615        0.227641         -0.808288       -0.904285       -11.0   \n",
       "614        1.669749         -0.808288        1.062911        -8.0   \n",
       "613        0.859152         -0.808288        0.180195        -3.0   \n",
       "612       -0.931700         -0.610786       -1.080828         0.0   \n",
       "611       -0.187083         -0.808288       -1.080828        -7.0   \n",
       "610       -0.714913         -0.561410       -1.080828        -1.0   \n",
       "609       -0.677211          0.574228       -0.147671         0.0   \n",
       "608       -1.431254         -0.018279       -0.450317        -1.0   \n",
       "607       -1.431254         -0.018279       -0.551199        -4.0   \n",
       "606        1.773429         -0.808288        0.785486        -5.0   \n",
       "605        0.510407         -0.808288       -0.362045        -3.0   \n",
       "604        0.010854          2.401125        1.352946        -3.0   \n",
       "603       -0.743189         -0.363908       -1.080828       -21.0   \n",
       "602       -0.007997          0.129848        0.281077        -9.0   \n",
       "601        1.632046          0.574228        1.920406        -7.0   \n",
       "600        0.632939          0.179223        0.709824        -5.0   \n",
       "..              ...               ...             ...         ...   \n",
       "64         1.641472         -0.808288        0.835927         NaN   \n",
       "63        -1.431254          0.919857        0.142364         NaN   \n",
       "62         0.878003          1.314862        1.945627         NaN   \n",
       "61        -1.431254         -0.808288       -0.576419         NaN   \n",
       "60        -1.431254         -0.314532       -0.689911         NaN   \n",
       "59        -1.431254         -0.512035       -0.841234         NaN   \n",
       "58         0.482131         -0.067655        1.012470         NaN   \n",
       "57         1.066514          0.919857        1.504269         NaN   \n",
       "56         2.046770         -0.808288       -1.080828         NaN   \n",
       "55         1.198472         -0.808288       -1.080828         NaN   \n",
       "54         0.680067          0.228599        0.671994         NaN   \n",
       "53         1.867685          2.055496        3.471465         NaN   \n",
       "52         1.113642         -0.808288        0.407179         NaN   \n",
       "51        -1.431254         -0.067655        0.104533         NaN   \n",
       "50         0.331322          0.919857        0.873757         NaN   \n",
       "49         1.047663         -0.808288       -1.080828         NaN   \n",
       "48         1.481238         -0.808288        0.747655         NaN   \n",
       "47         0.246492          2.104871        1.315115         NaN   \n",
       "46        -1.431254         -0.363908       -0.715132         NaN   \n",
       "45         0.029705         -0.067655        0.583722         NaN   \n",
       "44         0.783748         -0.808288       -1.080828         NaN   \n",
       "43         1.104216         -0.808288       -1.080828         NaN   \n",
       "42         1.236174          0.919857        1.718643         NaN   \n",
       "41         1.207897         -0.265157        0.848537         NaN   \n",
       "40        -0.535828         -0.709537       -1.080828         NaN   \n",
       "39         1.113642         -0.808288        0.697214         NaN   \n",
       "38         0.906280         -0.067655        1.403387         NaN   \n",
       "37         0.812024          0.574228        1.088131         NaN   \n",
       "36         0.076832          1.956744        1.731253         NaN   \n",
       "35        -1.431254         -0.462659       -0.752962         NaN   \n",
       "\n",
       "     30min_block_diff  glucose_category  \n",
       "629              11.0               2.0  \n",
       "628              11.0               2.0  \n",
       "627               7.0               2.0  \n",
       "626               6.0               1.0  \n",
       "625              13.0               2.0  \n",
       "624               4.0               2.0  \n",
       "623               7.0               1.0  \n",
       "622               2.0               2.0  \n",
       "621               4.0               2.0  \n",
       "620               7.0               1.0  \n",
       "619               7.0               2.0  \n",
       "618               3.0               0.0  \n",
       "617               1.0               0.0  \n",
       "616               3.0               0.0  \n",
       "615               4.0               2.0  \n",
       "614              11.0               2.0  \n",
       "613               8.0               2.0  \n",
       "612               3.0               0.0  \n",
       "611               0.0               2.0  \n",
       "610               7.0               1.0  \n",
       "609               1.0               1.0  \n",
       "608               0.0               0.0  \n",
       "607               1.0               0.0  \n",
       "606               4.0               2.0  \n",
       "605               5.0               2.0  \n",
       "604               3.0               2.0  \n",
       "603               3.0               1.0  \n",
       "602              21.0               2.0  \n",
       "601               9.0               2.0  \n",
       "600               7.0               2.0  \n",
       "..                ...               ...  \n",
       "64               18.0               2.0  \n",
       "63                3.0               0.0  \n",
       "62               10.0               2.0  \n",
       "61                1.0               0.0  \n",
       "60                2.0               0.0  \n",
       "59                2.0               0.0  \n",
       "58               21.0               2.0  \n",
       "57                9.0               2.0  \n",
       "56                3.0               2.0  \n",
       "55                1.0               2.0  \n",
       "54                2.0               2.0  \n",
       "53                7.0               2.0  \n",
       "52               24.0               2.0  \n",
       "51                2.0               0.0  \n",
       "50                9.0               2.0  \n",
       "49                4.0               2.0  \n",
       "48                8.0               2.0  \n",
       "47                4.0               2.0  \n",
       "46                0.0               0.0  \n",
       "45               23.0               2.0  \n",
       "44                3.0               2.0  \n",
       "43                1.0               2.0  \n",
       "42                5.0               2.0  \n",
       "41                7.0               2.0  \n",
       "40                5.0               1.0  \n",
       "39                9.0               2.0  \n",
       "38               18.0               2.0  \n",
       "37                9.0               2.0  \n",
       "36               12.0               2.0  \n",
       "35                4.0               0.0  \n",
       "\n",
       "[594 rows x 19 columns]"
      ]
     },
     "execution_count": 1002,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "glucose_scaler = StandardScaler()\n",
    "glucose_scaler.fit(df['glucose'].reshape(-1, 1))\n",
    "df['glucose_scaled'] = glucose_scaler.transform(df[['glucose']])\n",
    "\n",
    "mealcarbs_scaler = StandardScaler()\n",
    "mealcarbs_scaler.fit(df['mealcarbs'].reshape(-1, 1))\n",
    "df['mealcarbs_scaled'] = mealcarbs_scaler.transform(df[['mealcarbs']])\n",
    "\n",
    "humalog_scaler = StandardScaler()\n",
    "humalog_scaler.fit(df['humalog'].reshape(-1, 1))\n",
    "df['humalog_scaled'] = humalog_scaler.transform(df[['humalog']])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 870,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "\n",
    "#only grab the row with glucose measurements\n",
    "only_glucose = df[df['glucose'] !=0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1058,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "\n",
    "gaussian_x = only_glucose[['30min_block']]\n",
    "gaussian_y = only_glucose['glucose']\n",
    "\n",
    "gaussian_x\n",
    "gaussian_y\n",
    "x_train, x_test, y_train, y_test = train_test_split(gaussian_x, gaussian_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1044,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/gaussian_process/gpr.py:457: UserWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 143, 'grad': array([  1.39586413e+10,  -0.00000000e+00]), 'warnflag': 2, 'nit': 8}\n",
      "  \" state: %s\" % convergence_dict)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/gaussian_process/gpr.py:457: UserWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'grad': array([  6.43827137e+07,  -8.71363316e+00]), 'warnflag': 2, 'nit': 10}\n",
      "  \" state: %s\" % convergence_dict)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/gaussian_process/gpr.py:457: UserWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 70, 'grad': array([-2608899.80882757,  -890844.55075297]), 'warnflag': 2, 'nit': 3}\n",
      "  \" state: %s\" % convergence_dict)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Found array with dim 3. Estimator expected <= 2.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1044-b4ab81a282f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m#create a vector from 0 to max of '30min_block'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/sklearn/gaussian_process/gpr.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    179\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rng\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_random_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_numeric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0;31m# Normalize target value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    540\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    541\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m                     ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[1;32m    543\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    418\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_nd\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    419\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n\u001b[0;32m--> 420\u001b[0;31m                              % (array.ndim, estimator_name))\n\u001b[0m\u001b[1;32m    421\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m             \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found array with dim 3. Estimator expected <= 2."
     ]
    }
   ],
   "source": [
    "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\n",
    "# Instanciate a Gaussian Process model\n",
    "kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))\n",
    "d = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9, normalize_y=True).fit(gaussian_x, gaussian_y)\n",
    "\n",
    "#d = GaussianProcessRegressor(normalize_y = True).fit(gaussian_x, gaussian_y)\n",
    "x = np.atleast_2d(np.linspace(0, 10, 1000)).T\n",
    "import numpy\n",
    "\n",
    "d.fit(x_train, y_train)\n",
    "\n",
    "#create a vector from 0 to max of '30min_block'\n",
    "start_block = min(only_glucose['30min_block'])\n",
    "end_block = max(only_glucose['30min_block'])\n",
    "\n",
    "times = numpy.linspace(start=start_block, stop=end_block, num=end_block+1).reshape(-1, 1)\n",
    "pred, sigma = d.predict(times, return_std=True)\n",
    "\n",
    "times = times[:100]\n",
    "pred = pred[:100]\n",
    "sigma = sigma[:100]\n",
    "\n",
    "\n",
    "only_first_100 = gaussian_x[gaussian_x['30min_block'] <100]\n",
    "only_first_100_y = gaussian_y[:len(only_first_100)]\n",
    "\n",
    "\n",
    "print(np.concatenate([pred - 1.9600 * 100* sigma,\n",
    "                        (pred + 1.9600 * 50 * sigma)[::-1]]))\n",
    "print(np.concatenate([times, times[::-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1059,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7a8a4800b8>"
      ]
     },
     "execution_count": 1059,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hNcf+DnhCRApEZBfwCnBjR+Q5PByOFZn0WvCaprmsbjCJi1Okp3etD6YebTNR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7a8a4fccc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot(times, pred, 'b-', label=u'Prediction')\n",
    "plt.fill(np.concatenate([times, times[::-1]]),\n",
    "         np.concatenate([pred - 1.9600 * sigma,\n",
    "                        (pred + 1.9600 * sigma)[::-1]]),\n",
    "         alpha=.8, fc='g', ec='None', label='95% confidence interval')\n",
    "plt.plot(only_first_100, only_first_100_y, 'r.', markersize=10, label=u'Observations')\n",
    "\n",
    "plt.xlabel('$time\\_block$')\n",
    "plt.ylabel('$glucose$')\n",
    "plt.ylim(0, 500)\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1075,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "629\n",
      "411\n"
     ]
    }
   ],
   "source": [
    "#foreach glucose measurement, get the last 24 hours of samples\n",
    "#array_samples\n",
    "#the glucose indexes are what we are going to predict\n",
    "import keras\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "length_30_min = max(df['30min_block'] + 1)\n",
    "length_hour = max(df['hour_block'] + 1)\n",
    "#0.0\t2.46\t387.0\tFalse\tFalse\tFalse\tFalse\n",
    "#mealcarbs, humalog, glucose, is_sugar, is_breakfast, is_lunch, is_dinner\n",
    "depth = 4\n",
    "import numpy as np\n",
    "results = np.zeros((length_hour, depth))\n",
    "#iterate through df\n",
    "glucose_indexes = []\n",
    "df.reset_index()\n",
    "df.reindex(index=df.index[::-1])\n",
    "\n",
    "for idx, row in df.iterrows():\n",
    "    glucose_category = 2\n",
    "    if row['glucose'] > 130:\n",
    "        df.loc[idx, 'glucose_category'] = 2\n",
    "    elif row['glucose'] < 70:\n",
    "        df.loc[idx, 'glucose_category'] = 0\n",
    "    else:\n",
    "        df.loc[idx, 'glucose_category'] = 1\n",
    "    try:\n",
    "        df.loc[idx, '30min_block_diff'] = df.loc[idx, '30min_block'] - df.loc[idx+1, '30min_block']\n",
    "    except:\n",
    "        print(idx)\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    #just try and predict lunch from breakfast?\n",
    "    if (row['glucose'] != 0):\n",
    "        glucose_indexes.append(row['hour_block'])\n",
    "    #results[index] = row[['mealcarbs_scaled','humalog_scaled','glucose_scaled', 'is_sugar','is_breakfast', 'is_lunch', 'is_dinner']]\n",
    "    results[row['hour_block']] = row[['mealcarbs','humalog','glucose', 'glucose_category']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1076,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_block</th>\n",
       "      <th>30min_block</th>\n",
       "      <th>unixtime</th>\n",
       "      <th>mealcarbs</th>\n",
       "      <th>humalog</th>\n",
       "      <th>glucose</th>\n",
       "      <th>is_sugar</th>\n",
       "      <th>is_breakfast</th>\n",
       "      <th>is_lunch</th>\n",
       "      <th>is_dinner</th>\n",
       "      <th>glucose_category</th>\n",
       "      <th>30min_block_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>06:31 am</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.504247e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.46</td>\n",
       "      <td>387.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>12:25 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>1.504269e+09</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.31</td>\n",
       "      <td>147.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>03:49 pm</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>1.504281e+09</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>241.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>06:38 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>1.504291e+09</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.58</td>\n",
       "      <td>115.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>01:12 am</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>1.504315e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.56</td>\n",
       "      <td>189.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>03:03 am</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>41</td>\n",
       "      <td>1.504321e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>06:51 am</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>48</td>\n",
       "      <td>1.504335e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>07:41 am</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>1.504338e+09</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>138.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>10:00 am</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>54</td>\n",
       "      <td>1.504346e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>01:12 pm</td>\n",
       "      <td>13</td>\n",
       "      <td>30</td>\n",
       "      <td>61</td>\n",
       "      <td>1.504358e+09</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>85.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>04:52 pm</td>\n",
       "      <td>17</td>\n",
       "      <td>34</td>\n",
       "      <td>68</td>\n",
       "      <td>1.504371e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.88</td>\n",
       "      <td>214.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>06:30 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>35</td>\n",
       "      <td>71</td>\n",
       "      <td>1.504377e+09</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>06:45 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>1.504378e+09</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>08:24 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>37</td>\n",
       "      <td>75</td>\n",
       "      <td>1.504384e+09</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>10:16 pm</td>\n",
       "      <td>22</td>\n",
       "      <td>39</td>\n",
       "      <td>79</td>\n",
       "      <td>1.504391e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.14</td>\n",
       "      <td>176.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>03:57 am</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>1.504411e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>329.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>07:42 am</td>\n",
       "      <td>8</td>\n",
       "      <td>49</td>\n",
       "      <td>98</td>\n",
       "      <td>1.504425e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>243.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>09:10 am</td>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>101</td>\n",
       "      <td>1.504430e+09</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>09:23 am</td>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>101</td>\n",
       "      <td>1.504431e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>12:50 pm</td>\n",
       "      <td>13</td>\n",
       "      <td>54</td>\n",
       "      <td>108</td>\n",
       "      <td>1.504443e+09</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>01:05 pm</td>\n",
       "      <td>13</td>\n",
       "      <td>54</td>\n",
       "      <td>109</td>\n",
       "      <td>1.504444e+09</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>01:22 pm</td>\n",
       "      <td>13</td>\n",
       "      <td>54</td>\n",
       "      <td>109</td>\n",
       "      <td>1.504445e+09</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>01:32 pm</td>\n",
       "      <td>14</td>\n",
       "      <td>55</td>\n",
       "      <td>110</td>\n",
       "      <td>1.504446e+09</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>03:34 pm</td>\n",
       "      <td>16</td>\n",
       "      <td>57</td>\n",
       "      <td>114</td>\n",
       "      <td>1.504453e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.48</td>\n",
       "      <td>340.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>06:09 pm</td>\n",
       "      <td>18</td>\n",
       "      <td>59</td>\n",
       "      <td>119</td>\n",
       "      <td>1.504462e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.57</td>\n",
       "      <td>206.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>07:42 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>61</td>\n",
       "      <td>122</td>\n",
       "      <td>1.504468e+09</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1.93</td>\n",
       "      <td>153.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>09:07 pm</td>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>125</td>\n",
       "      <td>1.504473e+09</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>07:55 am</td>\n",
       "      <td>8</td>\n",
       "      <td>73</td>\n",
       "      <td>146</td>\n",
       "      <td>1.504512e+09</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>151.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>12:15 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>77</td>\n",
       "      <td>155</td>\n",
       "      <td>1.504527e+09</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.38</td>\n",
       "      <td>325.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>03:41 pm</td>\n",
       "      <td>16</td>\n",
       "      <td>81</td>\n",
       "      <td>162</td>\n",
       "      <td>1.504540e+09</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.42</td>\n",
       "      <td>219.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>12:24 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>1733</td>\n",
       "      <td>3467</td>\n",
       "      <td>1.510489e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.52</td>\n",
       "      <td>326.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>01:46 pm</td>\n",
       "      <td>14</td>\n",
       "      <td>1735</td>\n",
       "      <td>3470</td>\n",
       "      <td>1.510494e+09</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>06:51 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>1740</td>\n",
       "      <td>3480</td>\n",
       "      <td>1.510513e+09</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>245.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>07:31 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>1740</td>\n",
       "      <td>3481</td>\n",
       "      <td>1.510515e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>08:17 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>1741</td>\n",
       "      <td>3483</td>\n",
       "      <td>1.510518e+09</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>09:01 pm</td>\n",
       "      <td>21</td>\n",
       "      <td>1742</td>\n",
       "      <td>3485</td>\n",
       "      <td>1.510521e+09</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>08:01 am</td>\n",
       "      <td>8</td>\n",
       "      <td>1753</td>\n",
       "      <td>3506</td>\n",
       "      <td>1.510560e+09</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.66</td>\n",
       "      <td>203.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>12:15 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>1757</td>\n",
       "      <td>3515</td>\n",
       "      <td>1.510575e+09</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>265.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>01:38 pm</td>\n",
       "      <td>14</td>\n",
       "      <td>1759</td>\n",
       "      <td>3518</td>\n",
       "      <td>1.510580e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>369.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>02:14 pm</td>\n",
       "      <td>14</td>\n",
       "      <td>1759</td>\n",
       "      <td>3519</td>\n",
       "      <td>1.510582e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>279.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>03:29 pm</td>\n",
       "      <td>15</td>\n",
       "      <td>1760</td>\n",
       "      <td>3521</td>\n",
       "      <td>1.510587e+09</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>224.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>06:48 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>1764</td>\n",
       "      <td>3528</td>\n",
       "      <td>1.510599e+09</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3.61</td>\n",
       "      <td>350.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>06:35 am</td>\n",
       "      <td>7</td>\n",
       "      <td>1776</td>\n",
       "      <td>3552</td>\n",
       "      <td>1.510641e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.18</td>\n",
       "      <td>270.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>07:46 am</td>\n",
       "      <td>8</td>\n",
       "      <td>1777</td>\n",
       "      <td>3554</td>\n",
       "      <td>1.510646e+09</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>12:21 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>1781</td>\n",
       "      <td>3563</td>\n",
       "      <td>1.510662e+09</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>187.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>02:15 pm</td>\n",
       "      <td>14</td>\n",
       "      <td>1783</td>\n",
       "      <td>3567</td>\n",
       "      <td>1.510669e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>06:24 pm</td>\n",
       "      <td>18</td>\n",
       "      <td>1787</td>\n",
       "      <td>3575</td>\n",
       "      <td>1.510684e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.45</td>\n",
       "      <td>309.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>08:02 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>1789</td>\n",
       "      <td>3579</td>\n",
       "      <td>1.510690e+09</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>178.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>08:12 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>1789</td>\n",
       "      <td>3579</td>\n",
       "      <td>1.510690e+09</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>07:39 am</td>\n",
       "      <td>8</td>\n",
       "      <td>1801</td>\n",
       "      <td>3602</td>\n",
       "      <td>1.510732e+09</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.32</td>\n",
       "      <td>155.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>09:15 am</td>\n",
       "      <td>9</td>\n",
       "      <td>1802</td>\n",
       "      <td>3605</td>\n",
       "      <td>1.510737e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>235.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>10:00 am</td>\n",
       "      <td>10</td>\n",
       "      <td>1803</td>\n",
       "      <td>3606</td>\n",
       "      <td>1.510740e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>269.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>12:22 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>1805</td>\n",
       "      <td>3611</td>\n",
       "      <td>1.510749e+09</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.22</td>\n",
       "      <td>283.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>03:37 pm</td>\n",
       "      <td>16</td>\n",
       "      <td>1809</td>\n",
       "      <td>3618</td>\n",
       "      <td>1.510760e+09</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.53</td>\n",
       "      <td>280.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>06:25 pm</td>\n",
       "      <td>18</td>\n",
       "      <td>1811</td>\n",
       "      <td>3623</td>\n",
       "      <td>1.510770e+09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>11:01 pm</td>\n",
       "      <td>23</td>\n",
       "      <td>1816</td>\n",
       "      <td>3632</td>\n",
       "      <td>1.510787e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.41</td>\n",
       "      <td>270.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>07:45 am</td>\n",
       "      <td>8</td>\n",
       "      <td>1825</td>\n",
       "      <td>3650</td>\n",
       "      <td>1.510818e+09</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.97</td>\n",
       "      <td>248.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>12:15 pm</td>\n",
       "      <td>12</td>\n",
       "      <td>1829</td>\n",
       "      <td>3659</td>\n",
       "      <td>1.510834e+09</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>238.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>06:30 pm</td>\n",
       "      <td>19</td>\n",
       "      <td>1835</td>\n",
       "      <td>3671</td>\n",
       "      <td>1.510857e+09</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2.23</td>\n",
       "      <td>160.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>08:18 pm</td>\n",
       "      <td>20</td>\n",
       "      <td>1837</td>\n",
       "      <td>3675</td>\n",
       "      <td>1.510863e+09</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>594 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date      time  hour  hour_block  30min_block      unixtime  \\\n",
       "629  2017-09-01  06:31 am     7           0            0  1.504247e+09   \n",
       "628  2017-09-01  12:25 pm    12           5           11  1.504269e+09   \n",
       "627  2017-09-01  03:49 pm    16           9           18  1.504281e+09   \n",
       "626  2017-09-01  06:38 pm    19          12           24  1.504291e+09   \n",
       "625  2017-09-02  01:12 am     1          18           37  1.504315e+09   \n",
       "624  2017-09-02  03:03 am     3          20           41  1.504321e+09   \n",
       "623  2017-09-02  06:51 am     7          24           48  1.504335e+09   \n",
       "622  2017-09-02  07:41 am     8          25           50  1.504338e+09   \n",
       "621  2017-09-02  10:00 am    10          27           54  1.504346e+09   \n",
       "620  2017-09-02  01:12 pm    13          30           61  1.504358e+09   \n",
       "619  2017-09-02  04:52 pm    17          34           68  1.504371e+09   \n",
       "618  2017-09-02  06:30 pm    19          35           71  1.504377e+09   \n",
       "617  2017-09-02  06:45 pm    19          36           72  1.504378e+09   \n",
       "616  2017-09-02  08:24 pm    20          37           75  1.504384e+09   \n",
       "615  2017-09-02  10:16 pm    22          39           79  1.504391e+09   \n",
       "614  2017-09-03  03:57 am     4          45           90  1.504411e+09   \n",
       "613  2017-09-03  07:42 am     8          49           98  1.504425e+09   \n",
       "612  2017-09-03  09:10 am     9          50          101  1.504430e+09   \n",
       "611  2017-09-03  09:23 am     9          50          101  1.504431e+09   \n",
       "610  2017-09-03  12:50 pm    13          54          108  1.504443e+09   \n",
       "609  2017-09-03  01:05 pm    13          54          109  1.504444e+09   \n",
       "608  2017-09-03  01:22 pm    13          54          109  1.504445e+09   \n",
       "607  2017-09-03  01:32 pm    14          55          110  1.504446e+09   \n",
       "606  2017-09-03  03:34 pm    16          57          114  1.504453e+09   \n",
       "605  2017-09-03  06:09 pm    18          59          119  1.504462e+09   \n",
       "604  2017-09-03  07:42 pm    20          61          122  1.504468e+09   \n",
       "603  2017-09-03  09:07 pm    21          62          125  1.504473e+09   \n",
       "602  2017-09-04  07:55 am     8          73          146  1.504512e+09   \n",
       "601  2017-09-04  12:15 pm    12          77          155  1.504527e+09   \n",
       "600  2017-09-04  03:41 pm    16          81          162  1.504540e+09   \n",
       "..          ...       ...   ...         ...          ...           ...   \n",
       "64   2017-11-12  12:24 pm    12        1733         3467  1.510489e+09   \n",
       "63   2017-11-12  01:46 pm    14        1735         3470  1.510494e+09   \n",
       "62   2017-11-12  06:51 pm    19        1740         3480  1.510513e+09   \n",
       "61   2017-11-12  07:31 pm    20        1740         3481  1.510515e+09   \n",
       "60   2017-11-12  08:17 pm    20        1741         3483  1.510518e+09   \n",
       "59   2017-11-12  09:01 pm    21        1742         3485  1.510521e+09   \n",
       "58   2017-11-13  08:01 am     8        1753         3506  1.510560e+09   \n",
       "57   2017-11-13  12:15 pm    12        1757         3515  1.510575e+09   \n",
       "56   2017-11-13  01:38 pm    14        1759         3518  1.510580e+09   \n",
       "55   2017-11-13  02:14 pm    14        1759         3519  1.510582e+09   \n",
       "54   2017-11-13  03:29 pm    15        1760         3521  1.510587e+09   \n",
       "53   2017-11-13  06:48 pm    19        1764         3528  1.510599e+09   \n",
       "52   2017-11-14  06:35 am     7        1776         3552  1.510641e+09   \n",
       "51   2017-11-14  07:46 am     8        1777         3554  1.510646e+09   \n",
       "50   2017-11-14  12:21 pm    12        1781         3563  1.510662e+09   \n",
       "49   2017-11-14  02:15 pm    14        1783         3567  1.510669e+09   \n",
       "48   2017-11-14  06:24 pm    18        1787         3575  1.510684e+09   \n",
       "47   2017-11-14  08:02 pm    20        1789         3579  1.510690e+09   \n",
       "46   2017-11-14  08:12 pm    20        1789         3579  1.510690e+09   \n",
       "45   2017-11-15  07:39 am     8        1801         3602  1.510732e+09   \n",
       "44   2017-11-15  09:15 am     9        1802         3605  1.510737e+09   \n",
       "43   2017-11-15  10:00 am    10        1803         3606  1.510740e+09   \n",
       "42   2017-11-15  12:22 pm    12        1805         3611  1.510749e+09   \n",
       "41   2017-11-15  03:37 pm    16        1809         3618  1.510760e+09   \n",
       "40   2017-11-15  06:25 pm    18        1811         3623  1.510770e+09   \n",
       "39   2017-11-15  11:01 pm    23        1816         3632  1.510787e+09   \n",
       "38   2017-11-16  07:45 am     8        1825         3650  1.510818e+09   \n",
       "37   2017-11-16  12:15 pm    12        1829         3659  1.510834e+09   \n",
       "36   2017-11-16  06:30 pm    19        1835         3671  1.510857e+09   \n",
       "35   2017-11-16  08:18 pm    20        1837         3675  1.510863e+09   \n",
       "\n",
       "     mealcarbs  humalog  glucose  is_sugar  is_breakfast  is_lunch  is_dinner  \\\n",
       "629        NaN     2.46    387.0     False         False     False      False   \n",
       "628       36.0     1.31    147.0     False         False      True      False   \n",
       "627       20.0     1.61    241.0     False         False     False      False   \n",
       "626       63.0     1.58    115.0     False         False     False       True   \n",
       "625        NaN     0.56    189.0     False         False     False      False   \n",
       "624        NaN      NaN    149.0     False         False     False      False   \n",
       "623        NaN      NaN    120.0     False         False     False      False   \n",
       "622       38.0     1.75    138.0     False          True     False      False   \n",
       "621        NaN      NaN    293.0     False         False     False      False   \n",
       "620       52.0     1.25     85.0     False         False      True      False   \n",
       "619        NaN     0.88    214.0     False         False     False      False   \n",
       "618        6.0      NaN     63.0      True         False     False      False   \n",
       "617       75.0     1.88      NaN     False         False     False       True   \n",
       "616       43.0     1.13      NaN     False         False     False      False   \n",
       "615        NaN     0.14    176.0     False         False     False      False   \n",
       "614        NaN     1.70    329.0     False         False     False      False   \n",
       "613        NaN     1.00    243.0     False         False     False      False   \n",
       "612        4.0      NaN     53.0      True         False     False      False   \n",
       "611        NaN      NaN    132.0     False         False     False      False   \n",
       "610        5.0      NaN     76.0      True         False     False      False   \n",
       "609       28.0     0.74     80.0     False         False      True      False   \n",
       "608       16.0     0.50      NaN     False         False      True      False   \n",
       "607       16.0     0.42      NaN     False         False      True      False   \n",
       "606        NaN     1.48    340.0     False         False     False      False   \n",
       "605        NaN     0.57    206.0     False         False     False      False   \n",
       "604       65.0     1.93    153.0     False         False     False       True   \n",
       "603        9.0      NaN     73.0      True         False     False      False   \n",
       "602       19.0     1.08    151.0     False          True     False      False   \n",
       "601       28.0     2.38    325.0     False         False      True      False   \n",
       "600       20.0     1.42    219.0     False         False     False      False   \n",
       "..         ...      ...      ...       ...           ...       ...        ...   \n",
       "64         NaN     1.52    326.0     False         False     False      False   \n",
       "63        35.0     0.97      NaN     False         False      True      False   \n",
       "62        43.0     2.40    245.0     False         False     False       True   \n",
       "61         NaN     0.40      NaN     False         False     False      False   \n",
       "60        10.0     0.31      NaN     False         False     False      False   \n",
       "59         6.0     0.19      NaN     False         False     False      False   \n",
       "58        15.0     1.66    203.0     False          True     False      False   \n",
       "57        35.0     2.05    265.0     False         False      True      False   \n",
       "56         NaN      NaN    369.0     False         False     False      False   \n",
       "55         NaN      NaN    279.0     False         False     False      False   \n",
       "54        21.0     1.39    224.0     False         False     False      False   \n",
       "53        58.0     3.61    350.0     False         False     False       True   \n",
       "52         NaN     1.18    270.0     False         False     False      False   \n",
       "51        15.0     0.94      NaN     False          True     False      False   \n",
       "50        35.0     1.55    187.0     False         False      True      False   \n",
       "49         NaN      NaN    263.0     False         False     False      False   \n",
       "48         NaN     1.45    309.0     False         False     False      False   \n",
       "47        59.0     1.90    178.0     False         False     False       True   \n",
       "46         9.0     0.29      NaN     False         False     False       True   \n",
       "45        15.0     1.32    155.0     False          True     False      False   \n",
       "44         NaN      NaN    235.0     False         False     False      False   \n",
       "43         NaN      NaN    269.0     False         False     False      False   \n",
       "42        35.0     2.22    283.0     False         False      True      False   \n",
       "41        11.0     1.53    280.0     False         False     False      False   \n",
       "40         2.0      NaN     95.0      True         False     False      False   \n",
       "39         NaN     1.41    270.0     False         False     False      False   \n",
       "38        15.0     1.97    248.0     False          True     False      False   \n",
       "37        28.0     1.72    238.0     False         False      True      False   \n",
       "36        56.0     2.23    160.0     False         False     False       True   \n",
       "35         7.0     0.26      NaN     False         False     False      False   \n",
       "\n",
       "     glucose_category  30min_block_diff  \n",
       "629               2.0               NaN  \n",
       "628               2.0              11.0  \n",
       "627               2.0               7.0  \n",
       "626               1.0               6.0  \n",
       "625               2.0              13.0  \n",
       "624               2.0               4.0  \n",
       "623               1.0               7.0  \n",
       "622               2.0               2.0  \n",
       "621               2.0               4.0  \n",
       "620               1.0               7.0  \n",
       "619               2.0               7.0  \n",
       "618               0.0               3.0  \n",
       "617               1.0               1.0  \n",
       "616               1.0               3.0  \n",
       "615               2.0               4.0  \n",
       "614               2.0              11.0  \n",
       "613               2.0               8.0  \n",
       "612               0.0               3.0  \n",
       "611               2.0               0.0  \n",
       "610               1.0               7.0  \n",
       "609               1.0               1.0  \n",
       "608               1.0               0.0  \n",
       "607               1.0               1.0  \n",
       "606               2.0               4.0  \n",
       "605               2.0               5.0  \n",
       "604               2.0               3.0  \n",
       "603               1.0               3.0  \n",
       "602               2.0              21.0  \n",
       "601               2.0               9.0  \n",
       "600               2.0               7.0  \n",
       "..                ...               ...  \n",
       "64                2.0              18.0  \n",
       "63                1.0               3.0  \n",
       "62                2.0              10.0  \n",
       "61                1.0               1.0  \n",
       "60                1.0               2.0  \n",
       "59                1.0               2.0  \n",
       "58                2.0              21.0  \n",
       "57                2.0               9.0  \n",
       "56                2.0               3.0  \n",
       "55                2.0               1.0  \n",
       "54                2.0               2.0  \n",
       "53                2.0               7.0  \n",
       "52                2.0              24.0  \n",
       "51                1.0               2.0  \n",
       "50                2.0               9.0  \n",
       "49                2.0               4.0  \n",
       "48                2.0               8.0  \n",
       "47                2.0               4.0  \n",
       "46                1.0               0.0  \n",
       "45                2.0              23.0  \n",
       "44                2.0               3.0  \n",
       "43                2.0               1.0  \n",
       "42                2.0               5.0  \n",
       "41                2.0               7.0  \n",
       "40                1.0               5.0  \n",
       "39                2.0               9.0  \n",
       "38                2.0              18.0  \n",
       "37                2.0               9.0  \n",
       "36                2.0              12.0  \n",
       "35                1.0               4.0  \n",
       "\n",
       "[594 rows x 15 columns]"
      ]
     },
     "execution_count": 1076,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1077,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "590\n"
     ]
    }
   ],
   "source": [
    "# feed in last 24 hours of measurments\n",
    "last_24 = 24*2\n",
    "last_12 = 12\n",
    "\n",
    "final_glucose_indexes = []\n",
    "for i, glucose_index in enumerate(glucose_indexes):\n",
    "    if (glucose_index-last_12 > 0):\n",
    "        final_glucose_indexes.append(glucose_index)\n",
    "print(len(final_glucose_indexes))\n",
    "y = np.zeros((len(final_glucose_indexes)))\n",
    "x = np.zeros((len(final_glucose_indexes), 12, 3))\n",
    "glucose_category = np.zeros((len(final_glucose_indexes)))\n",
    "\n",
    "for i, glucose_index in enumerate(final_glucose_indexes):\n",
    "    x[i, :, :] = np.array(results[glucose_index-last_12:glucose_index, :3])\n",
    "    y[i] = results[glucose_index][2]\n",
    "    glucose_category[i] = results[glucose_index][3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1078,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "590"
      ]
     },
     "execution_count": 1078,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1079,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "442\n",
      "148\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y)\n",
    "#input_shape = (len(x_train[0]), len(x_train[0][0]))\n",
    "print(len(x_train))\n",
    "print(len(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1082,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, 12, 3)\n",
      "(None, 1)\n",
      "(442, 12, 3)\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_81 (Flatten)         (None, 36)                0         \n",
      "_________________________________________________________________\n",
      "dense_221 (Dense)            (None, 100)               3700      \n",
      "_________________________________________________________________\n",
      "dense_222 (Dense)            (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dense_223 (Dense)            (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 13,901\n",
      "Trainable params: 13,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv1D, MaxPooling1D, Dropout, Dense, Flatten, Input\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Flatten(input_shape=(12, 3)))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "#model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "\n",
    "model.compile(loss='mse',\n",
    "              optimizer='Adam')\n",
    "print(model.input_shape)\n",
    "print(model.output_shape)\n",
    "print(x_train.shape)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1065,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Error when checking input: expected flatten_78_input to have 3 dimensions, but got array with shape (375, 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1065-a9a1b9910312>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m    958\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    959\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 960\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m    961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    962\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1572\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1573\u001b[0m             \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1574\u001b[0;31m             batch_size=batch_size)\n\u001b[0m\u001b[1;32m   1575\u001b[0m         \u001b[0;31m# Prepare validation data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1576\u001b[0m         \u001b[0mdo_validation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)\u001b[0m\n\u001b[1;32m   1405\u001b[0m                                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_feed_input_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m                                     \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m                                     exception_prefix='input')\n\u001b[0m\u001b[1;32m   1408\u001b[0m         y = _standardize_input_data(y, self._feed_output_names,\n\u001b[1;32m   1409\u001b[0m                                     \u001b[0moutput_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_input_data\u001b[0;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[1;32m    139\u001b[0m                                  \u001b[0;34m' to have '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshapes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m                                  \u001b[0;34m' dimensions, but got array with shape '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m                                  str(array.shape))\n\u001b[0m\u001b[1;32m    142\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mref_dim\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshapes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Error when checking input: expected flatten_78_input to have 3 dimensions, but got array with shape (375, 1)"
     ]
    }
   ],
   "source": [
    "model.fit(x=np.array(x_train), y=np.array(y_train), validation_data=(np.array(x_test), np.array(y_test)), epochs=100)\n",
    "x_train.shape\n",
    "y_train.shape\n",
    "print(len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1010,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Error when checking input: expected flatten_68_input to have 3 dimensions, but got array with shape (1,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1010-ec5c39d8b11d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/models.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight)\u001b[0m\n\u001b[1;32m    987\u001b[0m                                    \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    988\u001b[0m                                    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 989\u001b[0;31m                                    sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    991\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps)\u001b[0m\n\u001b[1;32m   1714\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1715\u001b[0m             \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1716\u001b[0;31m             batch_size=batch_size)\n\u001b[0m\u001b[1;32m   1717\u001b[0m         \u001b[0;31m# Prepare inputs, delegate logic to `_test_loop`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1718\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muses_learning_phase\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_phase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)\u001b[0m\n\u001b[1;32m   1405\u001b[0m                                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_feed_input_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m                                     \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m                                     exception_prefix='input')\n\u001b[0m\u001b[1;32m   1408\u001b[0m         y = _standardize_input_data(y, self._feed_output_names,\n\u001b[1;32m   1409\u001b[0m                                     \u001b[0moutput_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_input_data\u001b[0;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[1;32m    139\u001b[0m                                  \u001b[0;34m' to have '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshapes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m                                  \u001b[0;34m' dimensions, but got array with shape '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m                                  str(array.shape))\n\u001b[0m\u001b[1;32m    142\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mref_dim\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshapes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Error when checking input: expected flatten_68_input to have 3 dimensions, but got array with shape (1,)"
     ]
    }
   ],
   "source": [
    "model.evaluate(x=x_test, y=y_test)\n",
    "predictions = model.predict(x_test)\n",
    "print(predictions)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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