fitout 0.1.0

A Python library to extract FitBit Google Takeout data.

pyFitOut

The pyFitOut project is an open source Python library for extracting FitBit data from Google Takeout.

Installation

Use pip to install:

pip install fitout

Example

How to use pyFitOut:

Export

Export your FitBit data, using Google Takeout.

Note: Currently only export to zip is supported, and the zip files must be extracted to your local drive.

Once the export is complete, download the zip file and extract it. I use C:/Dev/Fitbit/Google/. This directory is the takeout_dir.

Trivial Example

import fitout as fo
from datetime import date

def main():
    # Specify the location where the Takeout zip files was extracted
    takeout_dir = 'C:/Dev/Fitbit/Google/'
    # Use the NativeFileLoader to load the data from the extracted files
    data_source = fo.NativeFileLoader(takeout_dir)
    
    # Specify the desired date range.
    start_date = date(2024, 10, 1)
    end_date = date(2024, 10, 31)
    
    # Generate a list of dates for the date range, for informational or plotting purposes.
    dates = fo.dates_array(start_date, end_date)
    print("Dates:", dates)
    
    # Create the breathing rate importer and fetch the data.
    breather_importer = fo.BreathingRate(data_source, 1)
    breathing_data = breather_importer.get_data(start_date, end_date)
    print("Breathing rate:", breathing_data)
    
    # Create the heart rate variability importer and fetch the data.
    hrv_importer = fo.HeartRateVariability(data_source)
    hrv_data = hrv_importer.get_data(start_date, end_date)
    print("HRV:", hrv_data)
    
    # Create the resting heart rate importer and fetch the data.
    rhr_importer = fo.RestingHeartRate(data_source)
    rhr_data = rhr_importer.get_data(start_date, end_date)
    print("RHR:", rhr_data)


if __name__ == "__main__":
    main()

Plotting Example with Numpy and Matplotlib

Note: To run this example, you will need to install the dependencies:

pip install matplotlib numpy

from datetime import date
import numpy as np
import matplotlib.pyplot as plt
import fitout as fo

def main():
    # Specify the location where the Takeout zip files was extracted
    takeout_dir = 'C:/Dev/Fitbit/Google/'
    # Use the NativeFileLoader to load the data from the extracted files
    data_source = fo.NativeFileLoader(takeout_dir)

    # Specify the desired date range.
    start_date = date(2024, 10, 1)
    end_date = date(2024, 10, 31)

    # Generate a list of dates for the date range, for informational or plotting purposes.
    dates = fo.dates_array(start_date, end_date)

    # Create the breathing rate importer and fetch the data.
    breather_importer = fo.BreathingRate(data_source, 1)
    breathing_data = breather_importer.get_data(start_date, end_date)

    # Create the heart rate variability importer and fetch the data.
    hrv_importer = fo.HeartRateVariability(data_source)
    hrv_data = hrv_importer.get_data(start_date, end_date)

    # Create the resting heart rate importer and fetch the data.
    rhr_importer = fo.RestingHeartRate(data_source)
    rhr_data = rhr_importer.get_data(start_date, end_date)

    # Fill in missing values with the mean of the neighbouring values
    breathing_data = fo.fill_missing_with_neighbours(breathing_data)
    hrv_data = fo.fill_missing_with_neighbours(hrv_data)
    rhr_data = fo.fill_missing_with_neighbours(rhr_data)

    # Adjust buggy data (typically values that are too high or too low) to the mean of the neighbouring values
    # These values depend on your personal ranges.
    breathing_data = fo.fix_invalid_data_points(breathing_data, 10, 20)
    hrv_data = fo.fix_invalid_data_points(hrv_data, 20, 50)
    rhr_data = fo.fix_invalid_data_points(rhr_data, 46, 54)

    # Convert lists to numpy arrays
    dates_array = np.asarray(dates)
    breathing_data_array = np.array(breathing_data).astype(float)
    hrv_data_array = np.array(hrv_data).astype(float)
    rhr_data_array = np.array(rhr_data).astype(float)


    # Create a combined calmness index as follows: 100-(RHR/2 + breathing rate*2 - HRV)
    calmness_index = 100 - (rhr_data_array / 2. + breathing_data_array * 2. - hrv_data_array)

    # Plot the calmness index
    plt.figure(figsize=(10, 6))
    plt.plot(dates_array, calmness_index, marker='o', linestyle='-', color='b')
    plt.xlabel('Date')
    plt.ylabel('Calmness Index')
    plt.title('Calmness Index Over Time')
    plt.ylim(60, 95)  # Set the y-range
    plt.grid(True)
    plt.xticks(rotation=45)
    plt.tight_layout()  
    # Fit a 4th order polynomial to the calmness index data
    dates_axis = np.arange(len(dates_array))
    polynomial_coefficients = np.polyfit(dates_axis, calmness_index, 4)
    polynomial = np.poly1d(polynomial_coefficients)
    fitted_calmness_index = polynomial(dates_axis)

    # Plot the fitted polynomial
    plt.plot(dates_array, fitted_calmness_index, linestyle='--', color='r', label='4th Order Polynomial Fit')
    plt.legend()

    plt.show()

    plt.show()
if __name__ == "__main__":
    main()

More Examples

For more examples, see the examples directory.

Contributing

If you'd like to contribute to pyFitOut, follow the guidelines outlined in the Contributing Guide.

License

See LICENSE.txt for more information.

Contact

For inquiries and discussion, use pyFitOut Discussions.

Issues

For issues related to this Python implementation, visit the Issues page.