Summary

In [4]:

    

%load_ext pypath_magic


    

In [2]:

    

%pypath -a /Users/rbussman/Projects/sleep-bit


    

    




ERROR:root:Line magic function `%pypath` not found.

In [1]:

    

from src.data import get_fitbit
import matplotlib.pyplot as plt
%matplotlib inline

import seaborn as sns
sns.set_context('poster')


    

In [30]:

    

resources = [
    'activities/calories',  
    'activities/caloriesBMR',
    'activities/steps',
    'activities/distance',
    'activities/minutesSedentary',
    'activities/minutesLightlyActive',
    'activities/minutesFairlyActive',
    'activities/minutesVeryActive',
#     'activities/activityCalories'
]
df_activity = pd.DataFrame({})
for resource in resources:
    activity = resource.split('/')[1]
    resource_name = "-".join(resource.split('/'))
    activity_time_series = authd_client.time_series(resource, base_date='today', end_date='2016-11-09')
    df_activity[activity] = [resource_dict['value'] for resource_dict in activity_time_series[resource_name]]
df_activity['datetime'] = [resource_dict['dateTime'] for resource_dict in activity_time_series[resource_name]]


    

In [32]:

    

df_activity['datetime'] = pd.to_datetime(df_activity['datetime'])
df_activity = df_activity.set_index('datetime')


    

In [33]:

    

df_activity.head()


    

    
Out[33]:







  

    

      

      
calories
      
caloriesBMR
      
steps
      
distance
      
minutesSedentary
      
minutesLightlyActive
      
minutesFairlyActive
      
minutesVeryActive
    
    

      
datetime
      

      

      

      

      

      

      

      

    
  
  

    

      
2016-11-09
      
3200
      
1735
      
17760
      
8.47530422009856
      
712
      
301
      
29
      
68
    
    

      
2016-11-10
      
3733
      
1733
      
21888
      
10.47757346692784
      
449
      
322
      
98
      
106
    
    

      
2016-11-11
      
3094
      
1732
      
15847
      
7.69073609823168
      
572
      
284
      
66
      
51
    
    

      
2016-11-12
      
2994
      
1731
      
15215
      
7.26079694306304
      
620
      
273
      
61
      
31
    
    

      
2016-11-13
      
3009
      
1730
      
14414
      
6.91717867388704
      
616
      
307
      
32
      
49
    
  

In [34]:

    

data_path = os.path.join(os.getcwd(), os.pardir, 'data', 'interim', 'activity_data.csv')
df_activity.to_csv(data_path, index_label='datetime')


    

In [62]:

    

intraday = authd_client.intraday_time_series(
    'activities/steps', base_date='2017-08-03', detail_level='1min')


    

In [63]:

    

intraday.keys()


    

    
Out[63]:





dict_keys(['activities-steps', 'activities-steps-intraday'])

In [77]:

    

intraday.keys()


    

    
Out[77]:





dict_keys(['activities-steps', 'activities-steps-intraday'])

In [80]:

    

intraday['activities-steps-intraday'].keys()


    

    
Out[80]:





dict_keys(['dataset', 'datasetInterval', 'datasetType'])

In [82]:

    

intraday['activities-steps-intraday']['datasetInterval']


    

    
Out[82]:





1

In [83]:

    

import json


    

In [84]:

    

with open('test.json', 'w') as testfile:
    json.dump(intraday, testfile)


    

In [65]:

    

df_day = pd.DataFrame(intraday['activities-steps-intraday']['dataset'])


    

In [66]:

    

df_day['time'] = pd.to_datetime(df_day['time'])
df_day = df_day.set_index('time')


    

In [67]:

    

df15min = df_day.resample('10T').sum()


    

In [68]:

    

df15min.plot()


    

    
Out[68]:





<matplotlib.axes._subplots.AxesSubplot at 0x115de1668>






    

In [69]:

    

intraday = authd_client.intraday_time_series(
    'activities/steps', base_date='2017-08-04', detail_level='1min')


    

In [70]:

    

df_day4 = pd.DataFrame(intraday['activities-steps-intraday']['dataset'])


    

In [71]:

    

df_day4['time'] = pd.to_datetime(df_day4['time'])
df_day4 = df_day4.set_index('time')


    

In [72]:

    

dfday415min = df_day4.resample('10T').sum()


    

In [75]:

    

plt.plot(dfday415min)
plt.plot(df15min)


    

    
Out[75]:





[<matplotlib.lines.Line2D at 0x116577c50>]






    

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