climbing-science 0.4.1

Open-source Python library for evidence-based climbing training analysis

climbing-science

Open-source Python library for evidence-based climbing training analysis.

Why this project exists

1. Open Source — Existing climbing analysis tools hide behind paywalls or proprietary code. This library is open, peer-reviewable, and citable.
2. Reproducible — Every formula traces back to a published reference (BibTeX in docs/references.bib), not a black-box implementation.
3. Validatable — Unit tests verify results against published benchmarks (Giles et al., Levernier & Laffaye, Lattice Research).
4. End-to-end — Raw force-gauge data flows through a complete assessment pipeline: import → clean → analyse → report.
5. Dual purpose — A Python library you can pip install and a mathematically rigorous, auto-generated reference manual.

Installation

pip install -e .

For development (includes docs, linting, testing, versioning):

pip install -e ".[dev]"

With plotting support:

pip install -e ".[plot]"

Usage

import climbing_science

Documentation

Full auto-generated documentation: User Manual

Build locally:

make docs

Development

make test        # run tests
make lint        # run linter
make docs        # build documentation
make bump-patch  # bump patch version (0.1.0 → 0.1.1)
make bump-minor  # bump minor version (0.1.0 → 0.2.0)

References

All algorithms and formulas cite peer-reviewed sources. See docs/references.bib for the complete bibliography.

License

GPL-3.0-or-later — see LICENSE.