| Name | fitout JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | A Python library to extract FitBit Google Takeout data. |
| upload_time | 2024-11-25 11:12:44 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | None |
| keywords |
fitbitbit
google takeout
data processing
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
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|
# FitOut
[](https://github.com/kev-m/FitOut/blob/development/LICENSE.txt)
[](https://pypi.org/project/fitout/)
[](https://semver.org/)
[](https://github.com/kev-m/FitOut/releases)
[](https://pypi.org/project/autopep8/)
<!--  -->
The **FitOut** project is an open source Python library for extracting FitBit data from Google Takeout.
<!-- For detailed documentation, refer to the [FitOut Documentation](https://FitOut.readthedocs.io/). -->
## Installation
Use pip to install:
```bash
pip install fitout
```
## Example
How to use FitOut:
### Export
Export your [FitBit data](https://www.fitbit.com/settings/data/export), using [Google Takeout](https://takeout.google.com/settings/takeout/custom/fitbit?pli=1).
**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
```python
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:
```bash
pip install matplotlib numpy
```
```python
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](https://github.com/kev-m/FitOut/tree/development/examples) directory.
## Contributing
If you'd like to contribute to **FitOut**, follow the guidelines outlined in the [Contributing Guide](https://github.com/kev-m/FitOut/blob/development/CONTRIBUTING.md).
## License
See [`LICENSE.txt`](https://github.com/kev-m/FitOut/blob/development/LICENSE.txt) for more information.
## Contact
For inquiries and discussion, use [FitOut Discussions](https://github.com/kev-m/FitOut/discussions).
## Issues
For issues related to this Python implementation, visit the [Issues](https://github.com/kev-m/FitOut/issues) page.
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"description": "# FitOut\n[](https://github.com/kev-m/FitOut/blob/development/LICENSE.txt)\n[](https://pypi.org/project/fitout/)\n[](https://semver.org/)\n[](https://github.com/kev-m/FitOut/releases)\n[](https://pypi.org/project/autopep8/)\n\n<!--  -->\n\nThe **FitOut** project is an open source Python library for extracting FitBit data from Google Takeout.\n\n<!-- For detailed documentation, refer to the [FitOut Documentation](https://FitOut.readthedocs.io/). -->\n\n## Installation\n\nUse pip to install:\n```bash\npip install fitout\n```\n\n## Example\n\nHow to use FitOut:\n\n### Export\nExport your [FitBit data](https://www.fitbit.com/settings/data/export), using [Google Takeout](https://takeout.google.com/settings/takeout/custom/fitbit?pli=1).\n\n**Note:** Currently only export to zip is supported, and the zip files must be extracted to your local drive.\n\nOnce the export is complete, download the zip file and extract it. I use `C:/Dev/Fitbit/Google/`. \nThis directory is the `takeout_dir`.\n\n### Trivial Example\n```python\nimport fitout as fo\nfrom datetime import date\n\ndef main():\n # Specify the location where the Takeout zip files was extracted\n takeout_dir = 'C:/Dev/Fitbit/Google/'\n # Use the NativeFileLoader to load the data from the extracted files\n data_source = fo.NativeFileLoader(takeout_dir)\n \n # Specify the desired date range.\n start_date = date(2024, 10, 1)\n end_date = date(2024, 10, 31)\n \n # Generate a list of dates for the date range, for informational or plotting purposes.\n dates = fo.dates_array(start_date, end_date)\n print(\"Dates:\", dates)\n \n # Create the breathing rate importer and fetch the data.\n breather_importer = fo.BreathingRate(data_source, 1)\n breathing_data = breather_importer.get_data(start_date, end_date)\n print(\"Breathing rate:\", breathing_data)\n \n # Create the heart rate variability importer and fetch the data.\n hrv_importer = fo.HeartRateVariability(data_source)\n hrv_data = hrv_importer.get_data(start_date, end_date)\n print(\"HRV:\", hrv_data)\n \n # Create the resting heart rate importer and fetch the data.\n rhr_importer = fo.RestingHeartRate(data_source)\n rhr_data = rhr_importer.get_data(start_date, end_date)\n print(\"RHR:\", rhr_data)\n\n\nif __name__ == \"__main__\":\n main()\n```\n\n### Plotting Example with Numpy and Matplotlib\n**Note:** To run this example, you will need to install the dependencies:\n```bash\npip install matplotlib numpy\n```\n\n```python\nfrom datetime import date\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport fitout as fo\n\ndef main():\n # Specify the location where the Takeout zip files was extracted\n takeout_dir = 'C:/Dev/Fitbit/Google/'\n # Use the NativeFileLoader to load the data from the extracted files\n data_source = fo.NativeFileLoader(takeout_dir)\n\n # Specify the desired date range.\n start_date = date(2024, 10, 1)\n end_date = date(2024, 10, 31)\n\n # Generate a list of dates for the date range, for informational or plotting purposes.\n dates = fo.dates_array(start_date, end_date)\n\n # Create the breathing rate importer and fetch the data.\n breather_importer = fo.BreathingRate(data_source, 1)\n breathing_data = breather_importer.get_data(start_date, end_date)\n\n # Create the heart rate variability importer and fetch the data.\n hrv_importer = fo.HeartRateVariability(data_source)\n hrv_data = hrv_importer.get_data(start_date, end_date)\n\n # Create the resting heart rate importer and fetch the data.\n rhr_importer = fo.RestingHeartRate(data_source)\n rhr_data = rhr_importer.get_data(start_date, end_date)\n\n # Fill in missing values with the mean of the neighbouring values\n breathing_data = fo.fill_missing_with_neighbours(breathing_data)\n hrv_data = fo.fill_missing_with_neighbours(hrv_data)\n rhr_data = fo.fill_missing_with_neighbours(rhr_data)\n\n # Adjust buggy data (typically values that are too high or too low) to the mean of the neighbouring values\n # These values depend on your personal ranges.\n breathing_data = fo.fix_invalid_data_points(breathing_data, 10, 20)\n hrv_data = fo.fix_invalid_data_points(hrv_data, 20, 50)\n rhr_data = fo.fix_invalid_data_points(rhr_data, 46, 54)\n\n # Convert lists to numpy arrays\n dates_array = np.asarray(dates)\n breathing_data_array = np.array(breathing_data).astype(float)\n hrv_data_array = np.array(hrv_data).astype(float)\n rhr_data_array = np.array(rhr_data).astype(float)\n\n\n # Create a combined calmness index as follows: 100-(RHR/2 + breathing rate*2 - HRV)\n calmness_index = 100 - (rhr_data_array / 2. + breathing_data_array * 2. - hrv_data_array)\n\n # Plot the calmness index\n plt.figure(figsize=(10, 6))\n plt.plot(dates_array, calmness_index, marker='o', linestyle='-', color='b')\n plt.xlabel('Date')\n plt.ylabel('Calmness Index')\n plt.title('Calmness Index Over Time')\n plt.ylim(60, 95) # Set the y-range\n plt.grid(True)\n plt.xticks(rotation=45)\n plt.tight_layout() \n # Fit a 4th order polynomial to the calmness index data\n dates_axis = np.arange(len(dates_array))\n polynomial_coefficients = np.polyfit(dates_axis, calmness_index, 4)\n polynomial = np.poly1d(polynomial_coefficients)\n fitted_calmness_index = polynomial(dates_axis)\n\n # Plot the fitted polynomial\n plt.plot(dates_array, fitted_calmness_index, linestyle='--', color='r', label='4th Order Polynomial Fit')\n plt.legend()\n\n plt.show()\n\n plt.show()\nif __name__ == \"__main__\":\n main()\n```\n\n### More Examples\n\nFor more examples, see the [examples](https://github.com/kev-m/FitOut/tree/development/examples) directory.\n\n## Contributing\n\nIf you'd like to contribute to **FitOut**, follow the guidelines outlined in the [Contributing Guide](https://github.com/kev-m/FitOut/blob/development/CONTRIBUTING.md).\n\n## License\n\nSee [`LICENSE.txt`](https://github.com/kev-m/FitOut/blob/development/LICENSE.txt) for more information.\n\n## Contact\n\nFor inquiries and discussion, use [FitOut Discussions](https://github.com/kev-m/FitOut/discussions).\n\n## Issues\n\nFor issues related to this Python implementation, visit the [Issues](https://github.com/kev-m/FitOut/issues) page.\n\n\n",
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