FitbitRange.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Collecting Fitbit 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",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!/usr/bin/python      \n",
    "\n",
    "import fitbit\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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   "cell_type": "code",
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      "127.0.0.1 - - [08/Jul/2016:10:15:45] \"GET /?code=373695b353df267b2d8a088d95958d6fd2708f09&state=KlzrxOUS2lsg6r7pXwdmHg7DwbBwn3 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",
   "metadata": {},
   "source": [
    "## 3. Pick a Date Range\n",
    "In order to not exceed the rate limit, weekly and monthly intervals 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": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "start_date = datetime.date(2016, 2, 1)\n",
    "end_date = datetime.date(2016, 3, 1) # end_date not inclusive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "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": 101,
   "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 functions for data collection\n",
    "Some of the following functions retrieve time-series data for each day, while others return summary data for each day. Each function creates a data frame by iterating over the days in the date range. These are then returned as global variables and saved as CSV files.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"Calories Series Function\"\"\"\n",
    "\n",
    "def Calorieseries(start_date, end_date):\n",
    "    Cals_df = pd.DataFrame([]) # initialize an empty data frame \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 calorie log and save as data frame:\n",
    "        fitbit_cals = auth2_client.intraday_time_series('activities/log/calories', base_date=date, detail_level='1min')\n",
    "        Calstats = fitbit_cals['activities-log-calories-intraday']['dataset']\n",
    "        Cals=pd.DataFrame(Calstats)\n",
    "        del Cals['mets'] # delete 'mets' column \n",
    "        del Cals['level'] # delete 'level' column\n",
    "        Cals=Cals.transpose() \n",
    "        Cals.columns = Cals.iloc[0] # use 'time' stamp to rename columns with 00:00, 00:01 ... 23:58, 23:59\n",
    "        Cals=Cals.reindex(Cals.index.drop('time')) # drop redundant information (time now reflected as column name)\n",
    "        Cals=Cals.rename(index={'value': str(date)}) # rename the index (row name) with the corresponding date\n",
    "        Cals_df = Cals_df.append(Cals, 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='Cals_Timeseries/Cals_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",
    "    Cals_df.to_csv(full_path)\n",
    "    \n",
    "    # save 'Cals_df1' as a global variable to explore directly in this notebook\n",
    "    global Cals_df1\n",
    "    Cals_df1 = Cals_df\n",
    "    # save 'full_index' as a global variable to use elsewhere\n",
    "    # (the 'full_index' variable is a 1x1440 vector of column names between 00:00, 00:01 ... 23:58, 23:59\n",
    "    # it comes in handy for the time-series functions (i.e. heart rate, sleep) that do not\n",
    "    # provide values for all 1440 minutes) \n",
    "    global full_index\n",
    "    full_index=Cals.columns.values\n",
    "    return Cals_df1, full_index\n",
    "\n",
    "\"\"\"Steps Series Function\"\"\"\n",
    "\n",
    "def Stepseries(start_date, end_date):\n",
    "    Steps_df = pd.DataFrame([]) # initialize an empty data frame\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 step log and save as data frame:\n",
    "        fitbit_steps = auth2_client.intraday_time_series('activities/steps', base_date=date, detail_level='1min')\n",
    "        Stepsstats = fitbit_steps['activities-steps-intraday']['dataset']\n",
    "        Steps=pd.DataFrame(Stepsstats)\n",
    "        Steps=Steps.transpose()\n",
    "        Steps.columns = Steps.iloc[0] # use 'time' stamp to rename columns with 00:00, 00:01 ... 23:58, 23:59\n",
    "        Steps=Steps.reindex(Steps.index.drop('time')) # drop redundant information (time now reflected as column name)\n",
    "        Steps=Steps.rename(index={'value': str(date)}) # rename the index (row name) with the corresponding date\n",
    "        Steps_df = Steps_df.append(Steps, 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='Steps_Timeseries/Steps_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",
    "    Steps_df.to_csv(full_path)\n",
    "    \n",
    "    # save 'Steps_df1' as a global variable to explore directly in this notebook\n",
    "    global Steps_df1\n",
    "    Steps_df1 = Steps_df\n",
    "    return Steps_df1\n",
    "\n",
    "\"\"\"Heart Rate Series Function\"\"\"\n",
    "\n",
    "def HRseries(start_date, end_date):\n",
    "    HR_df = pd.DataFrame([]) # initialize an empty data frame\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='1min')\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(1400, 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 from the Calorieseries function \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_Timeseries/HR_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_df1\n",
    "    HR_df1 = HR_df\n",
    "    return HR_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Sleep Series & Summary Function\"\"\"\n",
    "\n",
    "def Sleep(start_date, end_date):\n",
    "    Sleep_df = pd.DataFrame([]) # initialize an empty data frame\n",
    "    SleepSummary_df = pd.DataFrame([]) # initialize an empty data frame\n",
    "\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 sleep log and save as data frame:\n",
    "        fitbit_sleep = auth2_client.sleep(date)\n",
    "        try: # try the following:\n",
    "            sleepstats = fitbit_sleep['sleep'][0]['minuteData']\n",
    "        except IndexError: # create empty/filler data if no timeseries data collected:\n",
    "            filler=pd.DataFrame(np.empty(1400, 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",
    "            Sleep_df = Sleep_df.append(filler, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "        else: # check for multiple sleep records\n",
    "            if len(fitbit_sleep['sleep']) >= 2: # if multiple sleep records exist for one day, combine all records:\n",
    "                for record in range(1,len(fitbit_sleep['sleep'])-1): # loop through sleep records\n",
    "                    temp=fitbit_sleep['sleep'][record]['minuteData'] # create temporary sleep record\n",
    "                    sleepstats = sleepstats + [x for x in temp if x not in sleepstats] # append temporary record to full sleepstats\n",
    "            else: # if there are not multiple records, continue with initial sleepstats variable:\n",
    "                pass   \n",
    "            Sleep=pd.DataFrame(sleepstats)\n",
    "            Sleep['dateTime']=Sleep['dateTime'].astype(str)\n",
    "            i=0\n",
    "            for val in Sleep['dateTime']: # loop through time stamps, for values that end with --:30 replace with --:00:\n",
    "                val2=re.sub('30$', '00', val)\n",
    "                Sleep.set_value(i,'dateTime',val2)\n",
    "                i=i+1\n",
    "            Sleep=Sleep.transpose()\n",
    "            Sleep.columns = Sleep.iloc[0] # use 'time' stamp to rename columns with 00:00, 00:01 ... 23:58, 23:59\n",
    "            Sleep=Sleep.reindex(Sleep.index.drop('dateTime')) # drop redundant information (time now reflected as column name)\n",
    "            Sleep2=Sleep.transpose()\n",
    "            full_Sleep=Sleep2.reindex(full_index) # reindex with the full_index of times from the Calorieseries function \n",
    "            Sleep=full_Sleep.transpose()\n",
    "            Sleep=Sleep.rename(index={'value': str(date)}) # rename the index (row name) with the corresponding date\n",
    "            Sleep_df = Sleep_df.append(Sleep, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "        try: # try the following:\n",
    "            SleepSumm = fitbit_sleep['sleep']\n",
    "        except IndexError: # if no entry exists for this date, create a filler row:\n",
    "            filler=pd.DataFrame(np.empty(14, dtype=object))\n",
    "            filler=filler.transpose()\n",
    "            filler=filler.rename(index={0: str(date)}) # rename the index (row name) with the corresponding date\n",
    "            col_names=[u'awakeCount', u'awakeDuration', u'awakeningsCount', u'duration',u'efficiency', u'isMainSleep', u'minutesAfterWakeup', u'minutesAsleep', u'minutesAwake', u'minutesToFallAsleep', u'restlessCount',u'restlessDuration', u'startTime', u'timeInBed']\n",
    "            filler.columns = col_names # rename the column names with the ones defined in 'col_names'\n",
    "            SleepSummary_df = SleepSummary_df.append(filler, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "        else: # check for multiple sleep records\n",
    "            if len(fitbit_sleep['sleep']) >= 2: # if multiple sleep records exist for one day, combine all records:\n",
    "                for record in range(0,len(fitbit_sleep['sleep'])-1): # loop through sleep records\n",
    "                    SleepSumm=fitbit_sleep['sleep'][record] \n",
    "                    # delete redundant data:\n",
    "                    del SleepSumm['minuteData'] \n",
    "                    del SleepSumm['logId']\n",
    "                    del SleepSumm['dateOfSleep']\n",
    "                    SleepSumm=pd.DataFrame([SleepSumm])\n",
    "                    SleepSumm=SleepSumm.rename(index={0: str(date)}) # rename the index (row name) with the corresponding date\n",
    "                    SleepSummary_df = SleepSummary_df.append(SleepSumm, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "            else: # if there are not multiple records:\n",
    "                try: \n",
    "                    # delete redundant data:\n",
    "                    del SleepSumm[0]['minuteData']\n",
    "                    del SleepSumm[0]['logId']\n",
    "                    del SleepSumm[0]['dateOfSleep']\n",
    "                except IndexError: # if no entry exists for this date, create a filler row:\n",
    "                    filler=pd.DataFrame(np.empty(14, dtype=object))\n",
    "                    filler=filler.transpose()\n",
    "                    filler=filler.rename(index={0: str(date)}) # rename the index (row name) with the corresponding date\n",
    "                    col_names=[u'awakeCount', u'awakeDuration', u'awakeningsCount', u'duration',u'efficiency', u'isMainSleep', u'minutesAfterWakeup', u'minutesAsleep', u'minutesAwake', u'minutesToFallAsleep', u'restlessCount',u'restlessDuration', u'startTime', u'timeInBed']\n",
    "                    filler.columns = col_names # rename the column names with the ones defined in 'col_names'\n",
    "                    SleepSummary_df = SleepSummary_df.append(filler, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "                else:\n",
    "                    SleepSumm=pd.DataFrame(SleepSumm)\n",
    "                    SleepSumm=SleepSumm.rename(index={0: str(date)}) # rename the index (row name) with the corresponding date\n",
    "                    SleepSummary_df = SleepSummary_df.append(SleepSumm, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "    \n",
    "    # the 'name1' variable reflects the folder and file name we are saving the full summary data frame to:\n",
    "    name1='Sleep_Summary/Sleep_Summary_', str(start_date),'.csv'\n",
    "    name1=\"\".join(name1)\n",
    "    # adding in the full path saves the file to the exact location specified earlier:\n",
    "    full_path1=path, name1\n",
    "    full_path1=\"\".join(full_path1)\n",
    "    SleepSummary_df.to_csv(full_path1)\n",
    "\n",
    "    # the 'name2' variable reflects the folder and file name we are saving the full time-series data frame to:\n",
    "    name2='Sleep_Timeseries/Sleep_Timeseries_', str(start_date),'.csv'\n",
    "    name2=\"\".join(name2)\n",
    "    # adding in the full path saves the file to the exact location specified earlier:\n",
    "    full_path2=path, name2\n",
    "    full_path2=\"\".join(full_path2)\n",
    "    Sleep_df.to_csv(full_path2)\n",
    "    \n",
    "    # save 'SleepSummary_df1' as a global variable to explore directly in this notebook\n",
    "    global SleepSummary_df1\n",
    "    SleepSummary_df1 = SleepSummary_df\n",
    "    # save 'Sleep_df1' as a global variable to explore directly in this notebook\n",
    "    global Sleep_df1\n",
    "    Sleep_df1 = Sleep_df\n",
    "    return SleepSummary_df1, Sleep_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\" Activities Summary (Distances, Heart Rate Zones, Remaining Activity) Function\"\"\"\n",
    "\n",
    "def ActivitiesSummary(start_date, end_date):\n",
    "    DistancesSummary_df = pd.DataFrame([]) # initialize an empty data frame\n",
    "    HRSummary_df = pd.DataFrame([]) # initialize an empty data frame\n",
    "    ActivitySummary_df = pd.DataFrame([]) # initialize an empty data frame\n",
    "    \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 activity summary and save subfields as individual data frames:\n",
    "        active_list = auth2_client.activities(date)\n",
    "        activities_summary=active_list['summary']\n",
    "        distances=activities_summary['distances']        \n",
    "        try: # try the following:\n",
    "            HRzones=activities_summary['heartRateZones'] \n",
    "        except KeyError: # if no summary exists, create fillder data:\n",
    "            HRfiller=pd.DataFrame(np.empty(16, dtype=object))\n",
    "            HRfiller=HRfiller.transpose()\n",
    "            HRfiller=HRfiller.rename(index={0: str(date)}) # rename the index (row name) with the corresponding date\n",
    "            HRcol_names= [u'OutRange.caloriesOut', u'OR.max', u'OR.min', u'OR.minutes', u'FatBurn.caloriesOut', u'FB.max', u'FB.min', u'FB.minutes', u'Cardio.caloriesOut', u'C.max', u'C.min', u'C.minutes',u'Peak.caloriesOut', u'P.max', u'P.min', u'P.minutes']\n",
    "            HRfiller.columns = HRcol_names # rename the column names with the ones defined in 'HRcol_names'\n",
    "            HRSummary_df = HRSummary_df.append(HRfiller, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "            \n",
    "            DistSumm=pd.DataFrame(distances)\n",
    "            DistSumm=DistSumm.transpose()\n",
    "            DistSumm.columns = DistSumm.iloc[0] # rename the columns w/ names from row 0\n",
    "            DistSumm=DistSumm.reindex(DistSumm.index.drop('activity')) # drop redundant information (now reflected as column names)\n",
    "            DistSumm=DistSumm.rename(index={'distance': str(date)}) # rename the index (row name) with the corresponding date            \n",
    "            DistancesSummary_df = DistancesSummary_df.append(DistSumm, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "\n",
    "            del activities_summary['distances']\n",
    "            \n",
    "            ActivitiesSumm=pd.DataFrame(activities_summary.items())\n",
    "            ActivitiesSumm=ActivitiesSumm.transpose()\n",
    "            ActivitiesSumm.columns = ActivitiesSumm.iloc[0] # rename the columns w/ names from row 0\n",
    "            ActivitiesSumm=ActivitiesSumm.reindex(ActivitiesSumm.index.drop(0)) # drop redundant information (now reflected as column names)\n",
    "            ActivitiesSumm=ActivitiesSumm.rename(index={1: str(date)}) # rename the index (row name) with the corresponding date\n",
    "            ActivitySummary_df = ActivitySummary_df.append(ActivitiesSumm, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "    \n",
    "            \n",
    "        else:\n",
    "            DistSumm=pd.DataFrame(distances)\n",
    "            DistSumm=DistSumm.transpose()\n",
    "            DistSumm.columns = DistSumm.iloc[0] # rename the columns w/ names from row 0\n",
    "            DistSumm=DistSumm.reindex(DistSumm.index.drop('activity')) # drop redundant information (now reflected as column names)\n",
    "            DistSumm=DistSumm.rename(index={'distance': str(date)}) # rename the index (row name) with the corresponding date            \n",
    "            DistancesSummary_df = DistancesSummary_df.append(DistSumm, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "    \n",
    "\n",
    "            HRSumm=pd.DataFrame(HRzones) \n",
    "            HRSumm.rows = HRSumm.iloc[0] # rename the rows w/ names from row 0\n",
    "            HRSumm=HRSumm.rename(index={0:\"Out of Range\", 1:\"Fat Burn\", 2:\"Cardio\", 3:\"Peak\"}) # rename the index           \n",
    "            del HRSumm['name'] # delete duplicated info now that indexes are named\n",
    "            HRflat=HRSumm.values.flatten() # flatten dataframe into single row\n",
    "            HRSumm2=pd.DataFrame(HRflat)\n",
    "            HRSumm2=HRSumm2.transpose()\n",
    "            # rename column names:\n",
    "            HRSumm2=HRSumm2.rename(columns={0:\"OutRange.caloriesOut\", 1:\"OR.max\", 2:\"OR.min\", 3:\"OR.minutes\", 4:\"FatBurn.caloriesOut\", 5:\"FB.max\", 6:\"FB.min\", 7:\"FB.minutes\", 8:\"Cardio.caloriesOut\", 9:\"C.max\", 10:\"C.min\", 11:\"C.minutes\", 12:\"Peak.caloriesOut\", 13:\"P.max\", 14:\"P.min\", 15:\"P.minutes\"})\n",
    "            HRSumm2=HRSumm2.rename(index={0: str(date)}) # rename the index (row name) with the corresponding date\n",
    "            HRSummary_df = HRSummary_df.append(HRSumm2, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "    \n",
    "            # delete data that we have already used:\n",
    "            del activities_summary['distances']\n",
    "            del activities_summary['heartRateZones']\n",
    "\n",
    "            ActivitiesSumm=pd.DataFrame(activities_summary.items())\n",
    "            ActivitiesSumm=ActivitiesSumm.transpose()\n",
    "            ActivitiesSumm.columns = ActivitiesSumm.iloc[0] # rename the columns w/ names from row 0\n",
    "            ActivitiesSumm=ActivitiesSumm.reindex(ActivitiesSumm.index.drop(0)) # drop redundant information (now reflected as column names)\n",
    "            ActivitiesSumm=ActivitiesSumm.rename(index={1: str(date)}) # rename the index (row name) with the corresponding date\n",
    "            ActivitySummary_df = ActivitySummary_df.append(ActivitiesSumm, ignore_index=False) # add this row to the data frame initialized before the for loop\n",
    "    \n",
    "    # the 'name1' variable reflects the folder and file name we are saving the full Distances summary data frame to:\n",
    "    name1='Distances_Summary/Distances_Summary_', str(start_date),'.csv'\n",
    "    name1=\"\".join(name1)\n",
    "    # adding in the full path saves the file to the exact location specified earlier:\n",
    "    full_path1=path, name1\n",
    "    full_path1=\"\".join(full_path1)\n",
    "    DistancesSummary_df.to_csv(full_path1)\n",
    "    \n",
    "    # the 'name2' variable reflects the folder and file name we are saving the full HR summary data frame to:\n",
    "    name2='HR_Summary/HR_Summary_', str(start_date),'.csv'\n",
    "    name2=\"\".join(name2)\n",
    "    # adding in the full path saves the file to the exact location specified earlier:\n",
    "    full_path2=path, name2\n",
    "    full_path2=\"\".join(full_path2)\n",
    "    HRSummary_df.to_csv(full_path2)\n",
    "    \n",
    "    # the 'name3' variable reflects the folder and file name we are saving the full Activities summary data frame to:\n",
    "    name3='Activities_Summary/Activities_Summary_', str(start_date),'.csv'\n",
    "    name3=\"\".join(name3)\n",
    "    # adding in the full path saves the file to the exact location specified earlier:\n",
    "    full_path3=path, name3\n",
    "    full_path3=\"\".join(full_path3)\n",
    "    ActivitySummary_df.to_csv(full_path3)\n",
    "    \n",
    "    # save 'DistancesSummary_df1' as a global variable to explore directly in this notebook\n",
    "    global DistancesSummary_df1\n",
    "    DistancesSummary_df1 = DistancesSummary_df\n",
    "    # save 'HRSummary_df1' as a global variable to explore directly in this notebook\n",
    "    global HRSummary_df1\n",
    "    HRSummary_df1 = HRSummary_df\n",
    "    # save 'ActivitySummary_df1' as a global variable to explore directly in this notebook\n",
    "    global ActivitySummary_df1\n",
    "    ActivitySummary_df1 = ActivitySummary_df\n",
    "    return DistancesSummary_df1, HRSummary_df1, ActivitySummary_df1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Combine above functions into main function\n",
    "If you don't want to collect all of these measures, feel free to comment any of them out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def main():\n",
    "    Calorieseries(start_date, end_date) # obtain calories time-series data\n",
    "    HRseries(start_date,end_date) # obtain heart rate time-series data\n",
    "    Stepseries(start_date, end_date) # obtain steps time-series data\n",
    "    ActivitiesSummary(start_date, end_date) # obtain activity, HR, and distances summary data\n",
    "    Sleep(start_date, end_date) # obtain sleep time-series and summary data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Run Main and Explore Files/Global Variables! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activity Summary\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marginalCalories</th>\n",
       "      <th>elevation</th>\n",
       "      <th>sedentaryMinutes</th>\n",
       "      <th>lightlyActiveMinutes</th>\n",
       "      <th>caloriesOut</th>\n",
       "      <th>caloriesBMR</th>\n",
       "      <th>fairlyActiveMinutes</th>\n",
       "      <th>veryActiveMinutes</th>\n",
       "      <th>activityCalories</th>\n",
       "      <th>steps</th>\n",
       "      <th>floors</th>\n",
       "      <th>activeScore</th>\n",
       "      <th>restingHeartRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>418</td>\n",
       "      <td>260</td>\n",
       "      <td>724</td>\n",
       "      <td>244</td>\n",
       "      <td>2094</td>\n",
       "      <td>1437</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>870</td>\n",
       "      <td>6902</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>332</td>\n",
       "      <td>190</td>\n",
       "      <td>818</td>\n",
       "      <td>185</td>\n",
       "      <td>1955</td>\n",
       "      <td>1437</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>673</td>\n",
       "      <td>5558</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-03</th>\n",
       "      <td>455</td>\n",
       "      <td>290</td>\n",
       "      <td>779</td>\n",
       "      <td>261</td>\n",
       "      <td>2152</td>\n",
       "      <td>1437</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>928</td>\n",
       "      <td>8726</td>\n",
       "      <td>29</td>\n",
       "      <td>-1</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>318</td>\n",
       "      <td>210</td>\n",
       "      <td>785</td>\n",
       "      <td>199</td>\n",
       "      <td>1964</td>\n",
       "      <td>1437</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>685</td>\n",
       "      <td>6098</td>\n",
       "      <td>21</td>\n",
       "      <td>-1</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-05</th>\n",
       "      <td>306</td>\n",
       "      <td>220</td>\n",
       "      <td>746</td>\n",
       "      <td>205</td>\n",
       "      <td>1949</td>\n",
       "      <td>1437</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>650</td>\n",
       "      <td>5578</td>\n",
       "      <td>22</td>\n",
       "      <td>-1</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0          marginalCalories elevation sedentaryMinutes lightlyActiveMinutes  \\\n",
       "2016-02-01              418       260              724                  244   \n",
       "2016-02-02              332       190              818                  185   \n",
       "2016-02-03              455       290              779                  261   \n",
       "2016-02-04              318       210              785                  199   \n",
       "2016-02-05              306       220              746                  205   \n",
       "\n",
       "0          caloriesOut caloriesBMR fairlyActiveMinutes veryActiveMinutes  \\\n",
       "2016-02-01        2094        1437                  10                11   \n",
       "2016-02-02        1955        1437                   9                 5   \n",
       "2016-02-03        2152        1437                   4                10   \n",
       "2016-02-04        1964        1437                   9                 1   \n",
       "2016-02-05        1949        1437                   0                 0   \n",
       "\n",
       "0          activityCalories steps floors activeScore restingHeartRate  \n",
       "2016-02-01              870  6902     26          -1               63  \n",
       "2016-02-02              673  5558     19          -1               63  \n",
       "2016-02-03              928  8726     29          -1               62  \n",
       "2016-02-04              685  6098     21          -1               62  \n",
       "2016-02-05              650  5578     22          -1               64  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distances Summary\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>activity</th>\n",
       "      <th>total</th>\n",
       "      <th>tracker</th>\n",
       "      <th>loggedActivities</th>\n",
       "      <th>veryActive</th>\n",
       "      <th>moderatelyActive</th>\n",
       "      <th>lightlyActive</th>\n",
       "      <th>sedentaryActive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>2.83</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>2.28</td>\n",
       "      <td>2.28</td>\n",
       "      <td>0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-03</th>\n",
       "      <td>3.58</td>\n",
       "      <td>3.58</td>\n",
       "      <td>0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.13</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-05</th>\n",
       "      <td>2.29</td>\n",
       "      <td>2.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "activity   total tracker loggedActivities veryActive moderatelyActive  \\\n",
       "2016-02-01  2.83    2.83                0       0.42             0.22   \n",
       "2016-02-02  2.28    2.28                0       0.18             0.24   \n",
       "2016-02-03  3.58    3.58                0       0.43             0.13   \n",
       "2016-02-04   2.5     2.5                0       0.02             0.24   \n",
       "2016-02-05  2.29    2.29                0          0                0   \n",
       "\n",
       "activity   lightlyActive sedentaryActive  \n",
       "2016-02-01           2.2               0  \n",
       "2016-02-02          1.86               0  \n",
       "2016-02-03          3.02               0  \n",
       "2016-02-04          2.24               0  \n",
       "2016-02-05          2.29               0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heart Rate Zones Summary\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>OutRange.caloriesOut</th>\n",
       "      <th>OR.max</th>\n",
       "      <th>OR.min</th>\n",
       "      <th>OR.minutes</th>\n",
       "      <th>FatBurn.caloriesOut</th>\n",
       "      <th>FB.max</th>\n",
       "      <th>FB.min</th>\n",
       "      <th>FB.minutes</th>\n",
       "      <th>Cardio.caloriesOut</th>\n",
       "      <th>C.max</th>\n",
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       "      <th>2016-02-01</th>\n",
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       "      <th>2016-02-02</th>\n",
       "      <td>1755.93824</td>\n",
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       "      <td>30</td>\n",
       "      <td>1374</td>\n",
       "      <td>132.81380</td>\n",
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       "      <th>2016-02-03</th>\n",
       "      <td>1910.82110</td>\n",
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       "      <td>1374</td>\n",
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       "    <tr>\n",
       "      <th>2016-02-05</th>\n",
       "      <td>1837.92330</td>\n",
       "      <td>99</td>\n",
       "      <td>30</td>\n",
       "      <td>1392</td>\n",
       "      <td>86.77834</td>\n",
       "      <td>139</td>\n",
       "      <td>99</td>\n",
       "      <td>23</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>169</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>220</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "            OutRange.caloriesOut  OR.max  OR.min  OR.minutes  \\\n",
       "2016-02-01            1790.68952      99      30        1349   \n",
       "2016-02-02            1755.93824      99      30        1374   \n",
       "2016-02-03            1910.82110      99      30        1374   \n",
       "2016-02-04            1808.16502      99      30        1386   \n",
       "2016-02-05            1837.92330      99      30        1392   \n",
       "\n",
       "            FatBurn.caloriesOut  FB.max  FB.min  FB.minutes  \\\n",
       "2016-02-01            271.51934     139      99          59   \n",
       "2016-02-02            132.81380     139      99          31   \n",
       "2016-02-03            226.68220     139      99          51   \n",
       "2016-02-04            130.81660     139      99          28   \n",
       "2016-02-05             86.77834     139      99          23   \n",
       "\n",
       "            Cardio.caloriesOut  C.max  C.min  C.minutes  Peak.caloriesOut  \\\n",
       "2016-02-01             0.00000    169    139          0                 0   \n",
       "2016-02-02            33.15352    169    139          4                 0   \n",
       "2016-02-03             0.00000    169    139          0                 0   \n",
       "2016-02-04             0.00000    169    139          0                 0   \n",
       "2016-02-05             0.00000    169    139          0                 0   \n",
       "\n",
       "            P.max  P.min  P.minutes  \n",
       "2016-02-01    220    169          0  \n",
       "2016-02-02    220    169          0  \n",
       "2016-02-03    220    169          0  \n",
       "2016-02-04    220    169          0  \n",
       "2016-02-05    220    169          0  "
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      "Sleep Summary\n"
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       "      <th>awakeningsCount</th>\n",
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       "            awakeCount  awakeDuration  awakeningsCount  duration  efficiency  \\\n",
       "2016-02-01           2              8               34  30180000          88   \n",
       "2016-02-02           1              3               16  11220000          89   \n",
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       "\n",
       "           isMainSleep  minutesAfterWakeup  minutesAsleep  minutesAwake  \\\n",
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       "\n",
       "            minutesToFallAsleep  restlessCount  restlessDuration  \\\n",
       "2016-02-01                    0             32                50   \n",
       "2016-02-02                    0             15                18   \n",
       "2016-02-03                    0             25                48   \n",
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       "2016-02-05                    0             22                30   \n",
       "\n",
       "                          startTime  timeInBed  \n",
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       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>1.09846</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.9986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1440 columns</p>\n",
       "</div>"
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       "time       00:00:00 00:01:00 00:02:00 00:03:00 00:04:00 00:05:00 00:06:00  \\\n",
       "2016-02-01   0.9986   0.9986   0.9986   0.9986   0.9986   0.9986   0.9986   \n",
       "2016-02-02   0.9986   0.9986   0.9986   0.9986   0.9986   0.9986   0.9986   \n",
       "2016-02-03   0.9986  2.39664   0.9986   0.9986  1.09846  2.39664  1.09846   \n",
       "2016-02-04   0.9986   0.9986   0.9986   0.9986   0.9986   0.9986  1.09846   \n",
       "2016-02-05   0.9986   0.9986   0.9986   0.9986   0.9986   0.9986   0.9986   \n",
       "\n",
       "time       00:07:00 00:08:00 00:09:00   ...    23:50:00 23:51:00 23:52:00  \\\n",
       "2016-02-01   0.9986   0.9986   0.9986   ...      0.9986   0.9986   0.9986   \n",
       "2016-02-02   0.9986   0.9986   0.9986   ...      0.9986  1.09846  2.59636   \n",
       "2016-02-03  1.09846   0.9986   0.9986   ...     1.09846  1.09846   0.9986   \n",
       "2016-02-04  2.59636   0.9986   0.9986   ...      0.9986   0.9986   0.9986   \n",
       "2016-02-05   0.9986   0.9986   0.9986   ...      0.9986   0.9986   0.9986   \n",
       "\n",
       "time       23:53:00 23:54:00 23:55:00 23:56:00 23:57:00 23:58:00 23:59:00  \n",
       "2016-02-01   0.9986   0.9986   0.9986   0.9986   0.9986   0.9986   0.9986  \n",
       "2016-02-02  2.39664  1.09846  2.59636  2.59636  3.59496  2.79608  1.09846  \n",
       "2016-02-03   0.9986  1.09846   0.9986   0.9986   0.9986  1.09846  2.39664  \n",
       "2016-02-04   0.9986   0.9986   0.9986   0.9986   0.9986   0.9986   0.9986  \n",
       "2016-02-05   0.9986   0.9986   0.9986   0.9986  1.09846   0.9986   0.9986  \n",
       "\n",
       "[5 rows x 1440 columns]"
      ]
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      "Steps Time-Series\n"
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      "Heart Rate Time-Series\n"
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       "<p>5 rows × 1440 columns</p>\n",
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       "time        00:00:00  00:01:00  00:02:00  00:03:00  00:04:00  00:05:00  \\\n",
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       "2016-02-02        64        63        64        65        65        64   \n",
       "2016-02-03        62        65        60        60        64        73   \n",
       "2016-02-04        68        64        67        63        65        64   \n",
       "2016-02-05        59        62        61        62        63        65   \n",
       "\n",
       "time        00:06:00  00:07:00  00:08:00  00:09:00    ...     23:50:00  \\\n",
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       "\n",
       "time        23:51:00  23:52:00  23:53:00  23:54:00  23:55:00  23:56:00  \\\n",
       "2016-02-01        70        66        67        65        62        59   \n",
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       "2016-02-04        59        59        58        60        62        63   \n",
       "2016-02-05        69        69        68        68        68        68   \n",
       "\n",
       "time        23:57:00  23:58:00  23:59:00  \n",
       "2016-02-01        61        63        63  \n",
       "2016-02-02        87        66        57  \n",
       "2016-02-03        73        72        65  \n",
       "2016-02-04        63        63        64  \n",
       "2016-02-05        69        65        66  \n",
       "\n",
       "[5 rows x 1440 columns]"
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   ],
   "source": [
    "\"\"\"preview the first 5 rows of each data frame\"\"\"\n",
    "\n",
    "print \"Activity Summary\"\n",
    "display(ActivitySummary_df1.head())\n",
    "\n",
    "print \"Distances Summary\"\n",
    "display(DistancesSummary_df1.head())\n",
    "\n",
    "print \"Heart Rate Zones Summary\"\n",
    "display(HRSummary_df1.head())\n",
    "\n",
    "print \"Sleep Summary\"\n",
    "display(SleepSummary_df1.head())\n",
    "\n",
    "print \"Sleep Time-Series\"\n",
    "display(Sleep_df1.head())\n",
    "\n",
    "print \"Calories Time-Series\"\n",
    "display(Cals_df1.head())\n",
    "\n",
    "print \"Steps Time-Series\"\n",
    "display(Steps_df1.head())\n",
    "\n",
    "print \"Heart Rate Time-Series\"\n",
    "display(HR_df1.head())\n"
   ]
  }
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