Contributing guide

Contributing guide#

This document aims at summarizing the most important information for getting you started on contributing to this project. We assume that you are already familiar with git and with making pull requests on GitHub.

For more extensive tutorials, that also cover the absolute basics, please refer to other resources such as the pyopensci tutorials, the scientific Python tutorials, or the scanpy developer guide.

Tip

The hatch project manager

We highly recommend to familiarize yourself with hatch. Hatch is a Python project manager that

  • manages virtual environments, separately for development, testing and building the documentation. Separating the environments is useful to avoid dependency conflicts.

  • allows to run tests locally in different environments (e.g. different python versions)

  • allows to run tasks defined in pyproject.toml, e.g. to build documentation.

While the project is setup with hatch in mind, it is still possible to use different tools to manage dependencies, such as uv or pip.

Installing dev dependencies#

In addition to the packages needed to use this package, you need additional python packages to run tests and build the documentation.

On the command line, you typically interact with hatch through its command line interface (CLI). Running one of the following commands will automatically resolve the environments for testing and building the documentation in the background:

hatch test  # defined in the table [tool.hatch.envs.hatch-test] in pyproject.toml
hatch run docs:build  # defined in the table [tool.hatch.envs.docs]

VS Code

If you are using VS code, install the hatch-code extension. Additionally, make sure that the vscode-python-environments extension is installed (should be by default) and "python.useEnvironmentsExtension": true is activated in your settings.json.

Next, open the “Python Environment Managers” sidebar. You can do so by opening the command palette (Ctrl+Shift+P) and searching for Python: Focus on Environment Managers View. It will show a collapsible list where you can expand “Hatch” and activate an environment by clicking on the checkmark next to it. As the main development environment, we recommend to use hatch-test with the latest supported Python version.

Other IDEs

For other IDEs, you’ll have to point the editor at the paths to the virtual environments manually. To get a list of all environments for your projects, run

hatch env show -i

This will list “Standalone” environments and a table of “Matrix” environments like the following:

+------------+---------+--------------------------+----------+---------------------------------+-------------+
| Name       | Type    | Envs                     | Features | Dependencies                    | Scripts     |
+------------+---------+--------------------------+----------+---------------------------------+-------------+
| hatch-test | virtual | hatch-test.py3.12-stable | dev      | coverage-enable-subprocess==1.0 | cov-combine |
|            |         | hatch-test.py3.14-stable | test     | coverage[toml]~=7.4             | cov-report  |
|            |         | hatch-test.py3.14-pre    |          | pytest-mock~=3.12               | run         |
|            |         |                          |          | pytest-randomly~=3.15           | run-cov     |
|            |         |                          |          | pytest-rerunfailures~=14.0      |             |
|            |         |                          |          | pytest-xdist[psutil]~=3.5       |             |
|            |         |                          |          | pytest~=8.1                     |             |
+------------+---------+--------------------------+----------+---------------------------------+-------------+

From the Envs column, select the environment name you want to use for development. As the main development environment, we recommend to use hatch-test with the latest supported Python version. In this example, it would be hatch-test.py3.14-stable.

Next, create the environment with

hatch env create hatch-test.py3.14-stable

Then, obtain the path to the environment using

hatch env find hatch-test.py3.14-stable

and manually point it to the python binary.

A popular choice for managing virtual environments is uv. The main disadvantage compared to hatch is that it supports only a single environment per project at a time, which requires you to mix the dependencies for running tests and building docs. This can have undesired side-effects, such as requiring to install a lower version of a library your project depends on, only because an outdated sphinx plugin pins an older version.

To initialize a virtual environment in the .venv directory of your project, simply run

uv sync --group=test --group=doc --extra=torch

The .venv directory is typically automatically discovered by IDEs such as VS Code.

Pip is nowadays mostly superseded by environment manager such as hatch. However, for the sake of completeness, and since it’s ubiquitously available, we describe how you can manage environments manually using pip:

python3 -m venv .venv
source .venv/bin/activate
pip install -e . --group dev --group test --group doc

The .venv directory is typically automatically discovered by IDEs such as VS Code.

Code-style#

This package uses pre-commit-style hooks to enforce consistent code-styles. We recommend running them with prek, a fast, drop-in replacement for pre-commit that reads the same .pre-commit-config.yaml. On every commit, the checks will either automatically fix issues with the code, or raise an error message.

To enable the checks locally, run

hatch run hatch-check-code:prek install
# or
uvx prek install

in the root of the repository. prek will automatically download all dependencies when it is run for the first time.

If you didn’t run the checks locally, the Pre-commit checks job of the GitHub Actions CI runs them on your pull request and reports any failures. We strongly encourage installing and running the checks locally first to understand their usage.

Finally, most editors have an autoformat on save feature. Consider enabling this option for ruff and biome.

Writing tests#

This package uses pytest for automated testing. Please write Tests for every function added to the package.

Most IDEs integrate with pytest and provide a GUI to run tests. If you set up your virtual environments as described in installing dev dependencies, test cases should be automatically discovered by your IDE.

Alternatively, you can run all tests from the command line by executing

hatch test  # test with the highest supported Python version
# or
hatch test --all  # test with all supported Python versions

uv run pytest

source .venv/bin/activate
pytest

in the root of the repository.

Continuous integration#

Continuous integration via GitHub actions will automatically run the tests on all pull requests and test against the minimum and maximum supported Python version.

Additionally, there’s a CI job that tests against pre-releases of all dependencies (if there are any). The purpose of this check is to detect incompatibilities of new package versions early on and gives you time to fix the issue or reach out to the developers of the dependency before the package is released to a wider audience.

The CI job is defined in .github/workflows/test.yaml, however the single point of truth for CI jobs is the Hatch test matrix defined in pyproject.toml. This means that local testing via hatch and remote testing on CI tests against the same python versions and uses the same environments.

Integration testing#

Cross-repo integration testing is available in the spatialdata-integration-testing repo. Please follow the instructions in its readme, which also includes a video overview.

Publishing a release#

Choosing the version number#

spatialdata derives its version from the git tag through hatch-vcs, so cutting a release amounts to creating a tag. Please adhere to [Semantic Versioning][semver], in brief

Given a version number MAJOR.MINOR.PATCH, increment the:

  1. MAJOR version when you make incompatible API changes,

  2. MINOR version when you add functionality in a backwards compatible manner, and

  3. PATCH version when you make backwards compatible bug fixes.

Additional labels for pre-release and build metadata are available as extensions to the MAJOR.MINOR.PATCH format.

For pre-releases please use the aX suffix, such as v0.7.0a0 or v0.7.0a1. Do not use the devX suffix, since it does not support multiple incremental versions. The valid version numbers page lists the labels you can choose from. The naming of the tag matters: it determines whether the package is displayed as pre-release or release on PyPI.

Making a release on GitHub and publishing to PyPI#

Writing release notes#

We recommend using the “Generate release notes” button to automatically collect the information of all pull requests that are part of the release. The release notes serve as a changelog for the users of the package, so it is important to have them curated and well-organized.

The automatically generated notes are grouped by change type through our release configuration file, which infers the type from GitHub labels and ignores pull requests opened by bots. We recommend opening the pull requests included in the release and adding the appropriate release labels before generating the notes.

Use informative titles for pull requests, as these serve as the entries in the release notes; rename them if necessary. You can also manually edit the release notes before publishing them to improve readability.

Some additional considerations:

  • Important! If a pull request is large and its title is not informative or requires multiple lines, do not add a release label. Instead, at the end of the first message of the pull request discussion, include a markdown section titled # Release notes with a brief description of the intended release notes. This allows the person making the release to manually add the content to the release notes.

  • Please avoid redundancy and do not repeat the same release notes across consecutive pre-releases, releases and post-releases.

  • When generating the release notes, you can use the “Previous tag: …” button to choose which pull requests are included.

  • You can see an example of a release in action in this short video tutorial.

Publishing to conda-forge#

Shortly after the release lands on PyPI, a pull request is automatically opened in the conda-forge feedstock repository for the package. The pull request contains a checklist of the tasks that need to be done before it can be merged. Once it is merged, the new version becomes available in the conda-forge channel.

In practice, the change that is usually needed is reconciling the requirements in pyproject.toml with the packages and versions in the feedstock’s meta.yaml. After updating meta.yaml, the CI runs, and if it is green the pull request can be merged.

Writing documentation#

Please write documentation for new or changed features and use-cases. This project uses sphinx with the following features:

See scanpy’s Documentation for more information on how to write your own.

Tutorials with myst-nb and jupyter notebooks#

The documentation is set-up to render jupyter notebooks stored in the docs/notebooks directory using myst-nb. Currently, only notebooks in .ipynb format are supported that will be included with both their input and output cells. It is your responsibility to update and re-run the notebook whenever necessary.

If you are interested in automatically running notebooks as part of the continuous integration, please check out this feature request in the cookiecutter-scverse repository.

Hints#

  • If you refer to objects from other packages, please add an entry to intersphinx_mapping in docs/conf.py. Only if you do so can sphinx automatically create a link to the external documentation.

  • If building the documentation fails because of a missing link that is outside your control, you can add an entry to the nitpick_ignore list in docs/conf.py

Building the docs locally#

hatch run docs:build
hatch run docs:open

cd docs
uv run sphinx-build -M html . _build -W
(xdg-)open _build/html/index.html

source .venv/bin/activate
cd docs
sphinx-build -M html . _build -W
(xdg-)open _build/html/index.html

Debugging and profiling#

Various tools are available to help you understand the existing code base and your own contributions. For debugging, see the resources from Scientific Python, VS Code and PyCharm.

To find out the time or memory performance of your code, profilers can help. Again, there are resources from Scientific Python, napari, PyCharm and Dask. The benchmark dependency group and the profiling pixi environment declared in pyproject.toml provide asv, memray and py-spy; see benchmarks/README.md for how to run the benchmark suite.