FitbitFullHR.ipynb

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    "# Collecting 1-Second Interval Fitbit Heart Rate Data for a Range of Dates\n",
    "To start saving your own data as a CSV, you really only need to adjust the following variables in this notebook:\n",
    "\n",
    "  * USER_ID, CLIENT_SECRET (your own Fitbit credentials)\n",
    "  * start_date, end_date (dates in date range, note end_date is not inclusive)\n",
    "  * path (specifies where to save your output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import the necessary Python libraries and modules: "
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "#!/usr/bin/python      \n",
    "%matplotlib inline\n",
    "\n",
    "import fitbit\n",
    "import matplotlib.pyplot as plt\n",
    "import gather_keys_oauth2 as Oauth2\n",
    "import numpy as np\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import csv\n",
    "import re\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Access the Fitbit API\n",
    "Replace 'your USER_ID' and 'your CLIENT_SECRET' ([follow this tutorial to obtain these](http://blog.mr-but-dr.xyz/en/programming/fitbit-python-heartrate-howto/)) to access your Fitbit data:"
   ]
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   "execution_count": 144,
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     "text": [
      "127.0.0.1 - - [17/Jul/2016:20:18:34] \"GET /?code=c58094ebedb308ec36f65f04aaec499f2cf6b5f5&state=BhEvba1rieRYQGHae4Mn1I8ZaSnZVu HTTP/1.1\" 200 122 \"\" \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36\"\n"
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   "source": [
    "\"\"\"provide your credentials for OAuth2.0\"\"\"\n",
    "USER_ID = 'your USER_ID' # should look something like this: '123A4B'\n",
    "CLIENT_SECRET = 'your CLIENT_SECRET' # should look something like this: 'c321fvdc59b4cc62156n9luv20k39072'\n",
    "\n",
    "\"\"\"obtain access and refresh tokens\"\"\"\n",
    "server = Oauth2.OAuth2Server(USER_ID, CLIENT_SECRET)\n",
    "server.browser_authorize()\n",
    " \n",
    "ACCESS_TOKEN = server.oauth.token['access_token']\n",
    "REFRESH_TOKEN = server.oauth.token['refresh_token']\n",
    " \n",
    "\"\"\"complete authorization\"\"\"\n",
    "auth2_client = fitbit.Fitbit(USER_ID, CLIENT_SECRET, oauth2=True, access_token=ACCESS_TOKEN, refresh_token=REFRESH_TOKEN)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## 3. Pick a Date Range\n",
    "In order to not exceed the rate limit, intervals shorter than 6 months typically work best (learn more about [Fitbit's rate limiting policy]).   \n",
    "\n",
    "[Fitbit's rate limiting policy]: https://dev.fitbit.com/docs/basics/#rate-limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "start_date = datetime.date(2016, 2, 13)\n",
    "end_date = datetime.date(2016, 2, 17) # end_date not inclusive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "\"\"\"function to iterate over dates (resource: http://stackoverflow.com/a/1060330)\"\"\"\n",
    "\n",
    "def daterange(start_date, end_date):\n",
    "    for n in range(int ((end_date - start_date).days)): # for loop through dates in date range\n",
    "        yield start_date + datetime.timedelta(n) # return date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Define a path to save your data files to\n",
    "If you followed the README to create folders to store your data the end of your path will look like mine, otherwise adjust as necessary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# adjust your path depending on where you want to store your data\n",
    "path='/Users/amandasolis/Dropbox/Projects/python-fitbit/Data/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Define function for data collection\n",
    "When requesting Heart Rate data at the 1-sec interval, Fitbit actually provides data in 10 or 15 second intervals unless the tracker's \"Workout\" mode is activated. In any case, this function extracts all heart rate values provided by Fitbit and linearly interpolates missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "\"\"\"Heart Rate Time Series Function -- Detail Level = 1 second\"\"\"\n",
    "\n",
    "def HR_SeriesSeconds(start_date, end_date):\n",
    "    HR_df = pd.DataFrame([]) # initialize an empty data frame\n",
    "    # create index of all 86400 seconds\n",
    "    full_index=[]\n",
    "    for i in range(0,86400):\n",
    "        sec=str(datetime.timedelta(seconds=i))\n",
    "        full_index.append(sec.zfill(8)) # pad times to have format 00:00:00 rather than 0:00:00\n",
    "    for single_date in daterange(start_date, end_date): # loop through dates in date range\n",
    "        date = single_date.strftime(\"%Y-%m-%d\")\n",
    "        # access Fitbit heart rate log and save as data frame:\n",
    "        fitbit_stats = auth2_client.intraday_time_series('activities/heart', base_date=date, detail_level='1sec')\n",
    "        stats = fitbit_stats['activities-heart-intraday']['dataset']\n",
    "        HR=pd.DataFrame(stats)\n",
    "        try: # try the following:\n",
    "            indexed_HR = HR.set_index(HR['time'])\n",
    "        except KeyError: # if there is no HR['time'] data for this date, create an empty row:\n",
    "            filler=pd.DataFrame(np.empty(86400, dtype=object))\n",
    "            full_filler=filler.reindex(full_index) # reindex with the full_index of times from the Calorieseries function \n",
    "            filler=full_filler.transpose()\n",
    "            filler=filler.rename(index={0: str(date)}) # rename the index (row name) with the corresponding date\n",
    "            HR_df = HR_df.append(filler, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "        else:\n",
    "            del indexed_HR['time']\n",
    "            full_HR=indexed_HR.reindex(full_index) # reindex with the full_index of times\n",
    "            full_HR=full_HR.interpolate() # interpolate missing values\n",
    "            full_HR=full_HR.fillna(method='bfill') # back fill rows that start with nan\n",
    "            HR2=full_HR.transpose()\n",
    "            HR2=HR2.rename(index={'value': str(date)}) # rename the index (row name) with the corresponding date\n",
    "            HR_df = HR_df.append(HR2, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "    \n",
    "    # the 'name' variable reflects the folder and file name we are saving the full data frame to:\n",
    "    name='HR_Second_Timeseries/HR_Second_Timeseries_', str(start_date),'.csv'\n",
    "    name=\"\".join(name)\n",
    "    # adding in the full path saves the file to the exact location specified earlier:\n",
    "    full_path=path, name\n",
    "    full_path=\"\".join(full_path)\n",
    "    HR_df.to_csv(full_path)\n",
    "    \n",
    "    # save 'HR_df1' as a global variable to explore directly in this notebook\n",
    "    global HR_Seconds_df1\n",
    "    HR_Seconds_df1 = HR_df\n",
    "    return HR_Seconds_df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Run HR_SeriesSeconds and Explore CSV File/Global Variable! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
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       "      <th>23:59:50</th>\n",
       "      <th>23:59:51</th>\n",
       "      <th>23:59:52</th>\n",
       "      <th>23:59:53</th>\n",
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       "      <th>23:59:56</th>\n",
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       "      <th>23:59:58</th>\n",
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       "      <th>2016-02-13</th>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
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       "      <td>58</td>\n",
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       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58.2</td>\n",
       "      <td>58.4</td>\n",
       "      <td>58.6</td>\n",
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       "      <td>62.6</td>\n",
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       "time        00:00:00  00:00:01  00:00:02  00:00:03  00:00:04  00:00:05  \\\n",
       "2016-02-13        59        59        59        59        59        59   \n",
       "2016-02-14        58        58        58        58        58        58   \n",
       "2016-02-15        62        62        62        62        62        62   \n",
       "2016-02-16        73        73        73        73        73        73   \n",
       "\n",
       "time        00:00:06  00:00:07  00:00:08  00:00:09    ...     23:59:50  \\\n",
       "2016-02-13      59.0      59.0      59.0      59.0    ...           58   \n",
       "2016-02-14      58.2      58.4      58.6      58.8    ...           63   \n",
       "2016-02-15      62.2      62.4      62.6      62.8    ...           74   \n",
       "2016-02-16      73.0      73.0      73.0      73.0    ...           54   \n",
       "\n",
       "time        23:59:51  23:59:52  23:59:53  23:59:54  23:59:55  23:59:56  \\\n",
       "2016-02-13        58        58        58        58        58        58   \n",
       "2016-02-14        63        63        63        63        63        63   \n",
       "2016-02-15        74        74        74        74        74        74   \n",
       "2016-02-16        54        54        54        54        54        54   \n",
       "\n",
       "time        23:59:57  23:59:58  23:59:59  \n",
       "2016-02-13        58        58        58  \n",
       "2016-02-14        63        63        63  \n",
       "2016-02-15        74        74        74  \n",
       "2016-02-16        54        54        54  \n",
       "\n",
       "[4 rows x 86400 columns]"
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     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "HR_SeriesSeconds(start_date,end_date) # obtain heart rate time-series data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Plot Heart Rate Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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lCALgtkZwpJLq1D3BnOGj61iwZDu3Pfgu3/nTkoTv7GzlTpgb66Llu/n+X97m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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209152f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time=pd.to_datetime(full_index)\n",
    "plt.plot(time, HR_Seconds_df1.iloc[1]) # plot one date of heart rate data\n",
    "plt.gcf().autofmt_xdate()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}