chironpy 0.28.2 — Enhanced FIT Field Support and Developer Documentation
chironpy 0.28.2 brings support for enhanced FIT file fields and improved documentation for developers working with the library.
Enhanced FIT Field Support
Garmin devices now record enhanced_speed and enhanced_altitude in their FIT files, providing higher-resolution data than legacy fields. This release adds both fields to the DataTypeEnum, allowing chironpy to correctly parse modern Garmin activity files. These enhanced fields are automatically normalized into the standard WorkoutData columns (speed, altitude) during processing.
The library’s example configuration has been updated to reflect this change. The Osaka Marathon 2025 dataset now references the correct column names (enhanced_speed, enhanced_altitude) in examples/index.yml, ensuring consistent data handling across workflows.
Improved Developer Experience
This release includes several additions to make contributing to chironpy more straightforward.
A new lab/ directory provides Jupyter notebooks for rapid prototyping and onboarding. Two notebooks are included: getting_started.ipynb walks through core API usage, while osaka_marathon.ipynb demonstrates real-world running data analysis with the Osaka Marathon 2025 FIT file. Both are documented in CONTRIBUTING.md to help new contributors explore the library’s internals.
The documentation site now includes docs/Example.ipynb, an interactive notebook showcasing the Osaka Marathon dataset directly in the navigation. The release workflow has also been documented in CONTRIBUTING.md under a new Releasing section.
WorkoutData.from_file() now accepts an ExampleData object directly, removing the need to pass .path explicitly when working with example datasets.
Upgrade
Update to chironpy 0.28.2 via pip:
pip install --upgrade chironpyAbout chironpy
chironpy is Chiron’s open source Python library for processing and analysing endurance activity data. It standardises inputs from FIT, GPX, TCX, and Strava into a unified structure with 1Hz time-series data, and handles the metrics and resampling that power Chiron’s training analysis.
Open source is how we give back to the endurance sports data science community. If you work with running or cycling data in Python, check it out.