Download Cozie-Fitbit data

Extracting Data

Data can be extracted via our web API:

Parameter	Description / Value
API URL	https://6uc3obiy9f.execute-api.ap-southeast-1.amazonaws.com/default/cozie-fitbit-researcher-read-influx
API key	Reach out to cozie.app@gmail.com for an API key
experiment_id ‌ ‌ ‌	Value entered in the Cozie settings
user_id	Value entered in the Cozie settings (optional, if not included all users are extracted)
weeks	Weeks of data (optional, default is 2 weeks, time frame 2 weeks from now until now)

Extracting Data with Python

The Python script below is all you need to download data logged with the Cozie clock face. Make sure to configure at least ID_EXPERIMENT, ID_PARTICIPANT, and API_KEY before executing the script.

import requests
import json
import pandas as pd
import matplotlib.pyplot as plt

# Settings
YOUR_TIMEZONE = 'Asia/Singapore'
ID_PARTICIPANT = 'alpha'
ID_EXPERIMENT = 'alpha01'
WEEKS = "100"  # Number of weeks from which the data is retrieved, starting from now
API_KEY = '' # reach out to cozie.app@gmail.com for an API_KEY
API_URL = 'https://6uc3obiy9f.execute-api.ap-southeast-1.amazonaws.com/default/cozie-fitbit-researcher-read-influx'

# Assemble request
payload = {'experiment_id': ID_EXPERIMENT, 'weeks': WEEKS, 'user_id':ID_PARTICIPANT}
headers = {"Accept": "application/json", 'x-api-key': API_KEY}

# Query data
response = requests.get(API_URL, params=payload, headers=headers)

# Convert response to Pandas dataframe
my_json = response.content.decode('utf8').replace("'", '"')
data = json.loads(my_json, )
df = pd.read_json(data[1]['data']).T

df.index = pd.to_datetime(df['index'], unit='ms')
df = df.tz_localize('UTC').tz_convert(YOUR_TIMEZONE)
df = df.drop(columns=['index'])

# Display dataframe
df.head()

Some mild data processing

The raw data has the watch survey responses encoded as numbers between 9 and 12. These values can be converted back into the words shown on the clock face with the code snippet below.

# Translate integer values into strings for main question flow
translation_table = {'comfort':      { 9: 'Not Comfy',
                                      10: 'Comfy'},
                     'thermal':      { 9: 'No change',
                                      10: 'Warmer',
                                      11: 'Cooler',
                                      12: 'Something else'},
                     'indoorOutdoor':{10: 'Outdoor',
                                      11: 'Indoor'},
                     'location':     { 9: 'Neither',
                                      10: 'Office',
                                      11: 'Home'},
                     'clothing':     { 9: 'Medium',
                                      10: 'Heavy',
                                      11: 'Light'},
                     'airSpeed':     {10: 'Yes',
                                      11: 'No'},
                     'met':          { 9: 'Sitting',
                                      10: 'Standing',
                                      11: 'Resting'},
                     'anyChange':    {10: 'No',
                                      11: 'Yes'},
                     'mood':         { 9: 'Neutral',
                                      10: 'Bad',
                                      11: 'Good'},
                     'noise':        { 9: 'No Change',
                                      10: 'Louder',
                                      11: 'Quieter'},
                     'light':        { 9: 'No Change',
                                      10: 'Brighter',
                                      11: 'Dimmer'}
                     }

df = df.replace(translation_table)

Additionally, the column names can also be replaced with the question show on the clock face:

# Change column names
df = df.rename(columns={'comfort':'Clock face',
                        'thermal':'Would you prefer to be?',
                        'indoorOutdoor':'Are you?', 
                        'location':'Where are you?', 
                        'clothing':'What are you wearing?', 
                        'airSpeed':'Can you perceive air movement around you?', 
                        'met':'Activity, lat 10-min?', 
                        'anyChange':'Any changes in clo, loc, or met past 10-m?', 
                        'mood':'What mood are you in?', 
                        'noise':'Sound preference', 
                        'light':'Light preference'})

df.head()