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Contributing to kooplearn

Thank you for considering a contribution to kooplearn. Bug reports, documentation improvements, examples, tests, and code changes are all welcome.

Development setup

Clone the repository and install the project from source with the optional runtime dependencies and development tools:

git clone https://github.com/Machine-Learning-Dynamical-Systems/kooplearn.git
cd kooplearn
uv sync --all-extras --all-groups

If you prefer pip, use an editable install. Dependency groups require pip>=25.1.

python -m pip install -U pip
python -m pip install -e ".[torch,jax]" --group dev --group docs --group examples

The core package does not require torch or jax. Install only the extras you need when working on a narrower change:

uv sync --extra torch --group dev
uv sync --extra jax --group dev

Tests and linting

Run the default test suite with:

uv run pytest

Run the linter before opening a pull request:

ruff check

When changing optional PyTorch or JAX functionality, include the matching extra in your local environment and run the relevant tests:

uv run --extra torch pytest tests/test_torch_losses.py tests/test_feature_map_embedder.py
uv run --extra jax pytest tests/test_jax_losses.py tests/test_nnx_feature_map_embedder.py

Reporting issues

When filing a bug report, include:

  • The installed kooplearn version.
  • Your Python version and operating system.
  • The install command you used, including optional extras.
  • A minimal code example or notebook cell that reproduces the problem.
  • The full traceback or error message.

Feature requests should describe the use case, the current workaround if any, and the expected behavior.

Pull requests

Please keep pull requests focused on one change. Include tests or documentation updates when the behavior changes, and mention any optional dependency needed to run the new code.

Maintainer releases

The maintainer release process is documented separately in RELEASE.md.