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Download Data

Download the data collected using the Cozie Apple Watch survey​

You can download the data you have collected using the Cozie Apple Watch survey using the following Python code. Please note that you will have to specify your ID_PARTICIPANT, ID_EXPERIMENT and API_KEY. You can request an API_KEY by emailing us a request at cozie.app@gmail.com

All the data you have queried are saved inside the Pandas dataframe called df.

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

# Settings
YOUR_TIMEZONE = 'Asia/Singapore'
ID_PARTICIPANT = 'ExternalUser'
ID_EXPERIMENT = 'AppleStore'
WEEKS = "2"  # Number of weeks from which the data is retrived, starting from now
API_KEY = '' # reach out to cozie.app@gmail.com for an API_KEY

# Query data
payload = {'id_participant': ID_PARTICIPANT,'id_experiment': ID_EXPERIMENT, 'weeks': WEEKS}
headers = {"Accept": "application/json", 'x-api-key': API_KEY}
response = requests.get('https://m7cy76lxmi.execute-api.ap-southeast-1.amazonaws.com/default/cozie-apple-researcher-read-influx', params=payload, headers=headers)
data = json.loads(response.content)

# Convert response in Pandas Dataframe
df = pd.DataFrame.from_dict(data).T
df.index = pd.to_datetime(df.index, unit='ms')
df.index = df.index.tz_localize('UTC').tz_convert(YOUR_TIMEZONE)
pd.options.display.max_columns = None

# Display dataframe
df.head()

Watch survey data​

If you want to focus on the analysis of the watch-based survey data use the code below to filter the dataframe retrieved above.

# Get only question flow responses
df_questions = df[~df["vote_count"].isna()]
df_questions.style

Physiological data​

Use the code below to plot noise and heart rate data contained in the dataframe retrieved above. 

# Plot time-series data
fig, ax = plt.subplots(1,2, figsize =(15, 7))

# Heart rate
df["heart_rate"].plot(ax=ax[0], style='.')
ax[0].set_title("Heart Rate", fontsize=18)
ax[0].set_ylabel("Heart Rate [bpm]", fontsize=14)
ax[0].set_xlabel("Time", fontsize=14)

# Sound pressure
df["sound_pressure"].plot(ax=ax[1], style='.')
ax[1].set_title("Sound Pressure", fontsize=18)
ax[1].set_ylabel("Sound Pressure [dB?]", fontsize=14)
ax[1].set_xlabel("Time", fontsize=14)

Features & watch surveys​

The description for all features and the watch survey questions are available on separate pages: