It’s been a while since we shared an update—but we’re excited to finally introduce something we’ve been building quietly in the background.
🎉 Meet chironpy, a Python library for analysing endurance sports data—designed by endurance athletes, for endurance athletes.
To advance innovation in endurance sport, we maintain chironpy, an open-source endurance sports analysis library for Python. This project reflects our commitment to fostering collaboration and innovation within the endurance sports science and developer communities.
chironpy was developed to support the Chiron platform in analysing and processing workout data, providing a foundation for performance insights and training management. However, we think the library will be a useful tool for coaches, data scientists, athletes, and developers interested in endurance sports analysis.
chironpy supports formats like .fit, .gpx, .tcx, and even direct Strava API imports. It wraps this data in a familiar pandas-like interface with a standardised structure, allowing you to:
- Smooth and clean activity data
- Calculate meaningful metrics (e.g., elevation gain, pace, power, heart rate zones)
- Find best intervals by time or distance
- Visualize and transform your workouts however you want
We originally forked the excellent but no-longer-maintained sweatpy project, and reoriented it toward running and general endurance training.
You can install it now in python 3.11 and later:
pip install chironpyAnd explore the code and docs on GitHub.
This is just the beginning—we’re working toward a full Workout object that includes athlete context, training zones, and stress/load modeling. If you’re curious, we’d love your feedback or contributions.
Open source is endurance too. Thanks for sticking with us.
— Clive and the Chiron team