chironpy

chironpy is a Python library for analysing endurance sports data. Load workouts from .fit, .gpx, .tcx, or the Strava API and analyse them with a familiar pandas-based interface — compute best intervals, elevation gain, speed, power, and more.

Installation

# with uv (recommended)
uv add chironpy

# with pip
pip install chironpy

Quickstart

from chironpy import WorkoutData

# Load a workout file
data = WorkoutData.from_file("my_workout.fit")

# Best efforts — time-based and distance-based
data.best_intervals([60, 300, 1200], stream="power")
data.fastest_distance_intervals([1000, 5000, 10000])

# Elevation gain
data.elevation_gain()

# Resample to 10-second buckets
data.resample("10s")

Key features

• Multi-format loading — .fit, .gpx, .tcx, and Strava activity streams
• pandas-native — WorkoutData subclasses pd.DataFrame; use any pandas method directly
• Standardised columns — speed, power, heartrate, cadence, elevation, distance, latitude, longitude regardless of source format
• Best intervals — time-based and distance-based personal bests
• Elevation analysis — gain, smoothed elevation, grade
• Resampling — downsample to any frequency with semantically correct per-column aggregations

Documentation

Full documentation at chironpy.chironapp.com

Contributing

See CONTRIBUTING.md.

Attribution

chironpy is a maintained fork of sweatpy by Maksym Sladkov and Aart Goossens. The original project focused on cycling analysis; chironpy extends it with an emphasis on long-distance running.

With thanks to Aaron Schroeder for work on running power and elevation metrics in heartandsole and spatialfriend.

License

MIT