Spice Segmenter

---

Documentation: https://luca-penasa.github.io/spice-segmenter

Source Code: https://github.com/luca-penasa/spice-segmenter

PyPI: https://pypi.org/project/spice-segmenter/

---

Installation

pip install spice-segmenter

Development

• Clone this repository
• Requirements:
  • uv
  • Python 3.10+
• Create a virtual environment and install the dependencies

uv sync --all-extras

• Activate the virtual environment

source .venv/bin/activate

(or .venv\Scripts\activate on Windows)

Testing

pytest

Regression testing

The tests/regression/ suite locks down numerical results for both property evaluation and constraint solving against a fixed set of SPICE kernels. Each test calls compute_all or solves a constraint, then compares the output against a YAML baseline stored in tests/regression/baselines/.

Normal CI run — compare against stored baselines:

pytest tests/regression/

After a kernel delivery / planned geometry change — regenerate all baselines, review the diff, then commit:

pytest tests/regression/ --update-regression
git diff tests/regression/baselines/   # review what changed
git add tests/regression/baselines/
git commit -m "chore: update regression baselines for kernel v<new>"

Regenerate a single case (useful when adding a new property or constraint):

pytest tests/regression/ --update-regression -k "ganymede_start"

Tolerances applied during comparison:

Quantity	Tolerance
Scalar / vector numeric values	1 ppm relative
Solver total coverage duration	0.01 % relative
Individual interval start / end	60 s absolute

Both n_intervals and each interval boundary are stored, so a code change that shifts event times or changes the number of detected intervals will always produce a clear failure message.

Documentation

The documentation is automatically generated from the content of the docs directory and from the docstrings of the public signatures of the source code. The documentation is updated and published as a Github Pages page automatically as part each release.

Releasing

Manual release

Releases are done with the command, e.g. incrementing patch:

just bump patch
# also push, of course:
git push origin main --tags

this will update the changelog, commit it, and make a corresponding tag.

as the CI is not yet configured for publish on pypi it can be done by hand:

uv build
twine upload dist/*

Automatic release - to be fixed

Trigger the Draft release workflow (press Run workflow). This will update the changelog & version and create a GitHub release which is in Draft state.

Find the draft release from the GitHub releases and publish it. When a release is published, it'll trigger release workflow which creates PyPI release and deploys updated documentation.

Updating with copier

To update the skeleton of the project using copier:

 pipx run copier update --defaults

Pre-commit

Pre-commit hooks run all the auto-formatting (ruff format), linters (e.g. ruff and mypy), and other quality checks to make sure the changeset is in good shape before a commit/push happens.

You can install the hooks with (runs for each commit):

pre-commit install

Or if you want them to run only for each push:

pre-commit install -t pre-push

Or if you want e.g. want to run all checks manually for all files:

pre-commit run --all-files

Running tests

uv run pytest

---

This project was generated using a fork of the wolt-python-package-cookiecutter template.