Visualization of heart rate data from Google Fit

fit_app.py

import datetime
import json
import os

import matplotlib.pyplot as plt
import pandas as pd
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from googleapiclient.discovery import build

CREDENTIALS_JSON = 'token.json'
SCOPES = ['https://www.googleapis.com/auth/fitness.heart_rate.read']


def get_credentials():

    credentials = None

    if os.path.exists(CREDENTIALS_JSON):
        credentials = Credentials.from_authorized_user_file(
            CREDENTIALS_JSON, SCOPES)

    if not credentials or not credentials.valid:
        if credentials and credentials.expired and credentials.refresh_token:
            credentials.refresh(Request())
        else:
            flow = InstalledAppFlow.from_client_secrets_file(
                'client_secrets.json', scopes=SCOPES)
            flow.run_local_server(prompt='consent')

            credentials = flow.credentials

            with open(CREDENTIALS_JSON, 'w') as token:
                token.write(credentials.to_json())

    return credentials


def request_fit_data(start: int, end: int, filename: str = None):

    response = None

    with build('fitness', 'v1', credentials=get_credentials()) as service:

        body = {
            'aggregateBy': {
                'dataTypeName': 'com.google.heart_rate.bpm'
            },
            'startTimeMillis': start,
            'endTimeMillis': end
        }

        response = service.users().dataset().aggregate(userId='me', body=body).execute()

    if filename is not None:
        with open(filename, 'w') as f:
            json.dump(response, f, indent=4)

    return response


def convert_to_dataframe(response: dict):

    data = []

    for bucket in response['bucket']:
        for dataset in bucket['dataset']:
            data.extend(list(map(lambda x: {
                        'time': x['startTimeNanos'], 'fpVal': x['value'][0]['fpVal']}, dataset['point'])))

    df = pd.DataFrame(data)
    df['time'] = df['time'].astype(
        int) + (9 * 60 * 60 * 1000 * 1000 * 1000)    # 9時間ずらす
    df['time'] = pd.to_datetime(df['time'])

    return df


def save_png(filename: str, df: pd.DataFrame):

    plt.figure()
    df.plot(x='time', y='fpVal')
    plt.savefig(filename)
    plt.close('all')


if __name__ == '__main__':

    start = int(datetime.datetime(year=2022, month=6,
                day=20, hour=0).timestamp()) * 1000
    end = int(datetime.datetime(year=2022, month=6,
                                day=21, hour=0).timestamp()) * 1000

    data = request_fit_data(start=start, end=end, filename='response.json')
    df = convert_to_dataframe(data)
    save_png('chart.png', df)

requirements.txt

google-api-python-client
google-auth
google-auth-oauthlib
pandas
matplotlib