gap_startstop_heartrate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the `gap_startstop` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%autoreload\n",
    "from inspectEHR.CCD import CCD\n",
    "from inspectEHR.data_classes import DataRaw, ContMixin, CatMixin\n",
    "\n",
    "n0108 = ccd.extract('NIHR_HIC_ICU_0108', as_type=np.int, drop_miss=False)\n",
    "d0108 = DataRaw(n0108, spec=refs['NIHR_HIC_ICU_0108'])\n",
    "\n",
    "df = d0108.gap_startstop(ccd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>start</th>\n",
       "      <th>stop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B1</th>\n",
       "      <td>0.583333</td>\n",
       "      <td>1.996667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B2</th>\n",
       "      <td>-0.833056</td>\n",
       "      <td>8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B3</th>\n",
       "      <td>0.166944</td>\n",
       "      <td>1.786667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D4</th>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.499722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D5</th>\n",
       "      <td>-0.500000</td>\n",
       "      <td>1.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       start      stop\n",
       "id                    \n",
       "B1  0.583333  1.996667\n",
       "B2 -0.833056  8.500000\n",
       "B3  0.166944  1.786667\n",
       "D4  0.933333  1.499722\n",
       "D5 -0.500000  1.055556"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x137f097f0>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD5CAYAAAA6JL6mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\np0QuIuI5JXIREc/9f6qwXWQ99LwgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b0372b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df['start'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x129572e80>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD3CAYAAADmBxSSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12723f7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df['stop'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.cla()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:cchic]",
   "language": "python",
   "name": "conda-env-cchic-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}