# 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][].

[pyopensci tutorials]: https://www.pyopensci.org/learn.html
[scientific Python tutorials]: https://learn.scientific-python.org/development/tutorials/
[scanpy developer guide]: https://scanpy.readthedocs.io/en/latest/dev/index.html

:::{tip} The *hatch* project manager

We highly recommend to familiarize yourself with [`hatch`][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`.

:::

[hatch]: https://hatch.pypa.io/latest/

## Installing dev dependencies

In addition to the packages needed to _use_ this package,
you need additional python packages to [run tests](#writing-tests) and [build the documentation](#docs-building).

:::::{tabs}
::::{group-tab} Hatch

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:

```bash
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]
```

When using an IDE such as VS Code,
you'll have to point the editor at the paths to the virtual environments manually.
The environment you typically want to use as your main development environment is the `hatch-test`
environment with the latest Python version.

To get a list of all environments for your projects, run

```bash
hatch env show -i
```

From the `Envs` column, select the environment name you want to use for development.

Next, create the environment with

```bash
hatch env create <env-name>
```

Then, obtain the path to the environment using

```bash
hatch env find <env-name>
```

In case you are using VScode, now open the command palette (Ctrl+Shift+P) and search for `Python: Select Interpreter`.
Choose `Enter Interpreter Path` and paste the path to the virtual environment from above.

::::

::::{group-tab} uv

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.

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

```bash
uv sync --all-extras
```

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

::::

::::{group-tab} Pip

Pip is nowadays mostly superseded by environment managers 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`:

```bash
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,test,doc]"
```

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

::::
:::::

[hatch environments]: https://hatch.pypa.io/latest/tutorials/environment/basic-usage/
[uv]: https://docs.astral.sh/uv/

## Code-style

This package uses [pre-commit][] to enforce consistent code-styles.
On every commit, pre-commit checks will either automatically fix issues with the code, or raise an error message.

To enable pre-commit locally, simply run

```bash
pre-commit install
```

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

Alternatively, you can rely on the [pre-commit.ci][] service enabled on GitHub.
If you didn't run `pre-commit` before pushing changes to GitHub it will automatically commit fixes to your pull request, or show an error message.

If pre-commit.ci added a commit on a branch you still have been working on locally, simply use

```bash
git pull --rebase
```

to integrate the changes into yours.
While the [pre-commit.ci][] is useful, we strongly encourage installing and running pre-commit locally first to understand its usage.

Finally, most editors have an _autoformat on save_ feature.
Consider enabling this option for [ruff][ruff-editors] and [biome][biome-editors].

[pre-commit]: https://pre-commit.com/
[pre-commit.ci]: https://pre-commit.ci/
[ruff-editors]: https://docs.astral.sh/ruff/integrations/
[biome-editors]: https://biomejs.dev/guides/integrate-in-editor/

(writing-tests)=

## Writing tests

This package uses [pytest][] for automated testing.
Please write {doc}`scanpy:dev/testing` for every function added to the package.

Most IDEs integrate with pytest and provide a GUI to run tests.
Just point yours to one of the environments returned by

```bash
hatch env create hatch-test  # create test environments for all supported versions
hatch env find hatch-test  # list all possible test environment paths
```

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

:::::{tabs}
::::{group-tab} Hatch

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

::::

::::{group-tab} uv

```bash
uv run pytest
```

::::

::::{group-tab} Pip

```bash
source .venv/bin/activate
pytest
```

::::
:::::

in the root of the repository.

[pytest]: https://docs.pytest.org/

### Testing the correctness of the plots

Many tests will produce plots and check that they are correct by comparing them with a previously saved and serialized version of the same plots. The ground truth images are located in `tests/_images`. Different OS/versions may produce similar but not identical plots (for instance the ticks/padding could vary). To take into account for this please consider the following:

- you should not use locally generated plots as ground truth images, but you should commit images that have been generated by a GitHub Action. The recommended workflow is to go to the ["actions" page for the repo](https://github.com/scverse/spatialdata-plot/actions/workflows/test.yaml), download the artifacts, and upload them as ground truth (after having reviewed them).
- the ground truth images need to be updated when a new test is passing, or when a test starts producing a slightly different (but consistent) plot.
- please never replace the ground truth images without having manually reviewed them.
- if you run the tests locally in macOS or Windows they will likely fail because the ground truth images are generated using Ubuntu. To overcome this you can use `act`, which will generate a Docker reproducing the environment used in the GitHub Action. After the Docker container is generated you can use it within IDEs to run tests and debug code.
- in the case of PyCharm, it is easier to create a container from a `Dockerfile` instead of using `act`. Please in such case use the `Dockerfile` made available in the repository. In this [thread](https://github.com/scverse/spatialdata-plot/pull/397) you can find extra details, and the process is shown [in this Loom recording](https://www.loom.com/share/172e309e5803419bb3b3107eee1f3a4e).
- If you are encountering problems with `act` or `docker`, please [get in touch with the developers via Zulip](https://scverse.zulipchat.com/#narrow/channel/443514-spatialdata-dev) and we will help troubleshoot the issue.

### 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.

## Publishing a release

### Updating the version number

Before making a release, you need to update the version number in the `pyproject.toml` file.
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.

Once you are done, commit and push your changes and navigate to the "Releases" page of this project on GitHub.
Specify `vX.X.X` as a tag name and create a release.
For more information, see [managing GitHub releases][].
This will automatically create a git tag and trigger a Github workflow that creates a release on [PyPI][].

[semver]: https://semver.org/
[managing GitHub releases]: https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository
[pypi]: https://pypi.org/

## Writing documentation

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

- The [myst][] extension allows to write documentation in markdown/Markedly Structured Text
- [Numpy-style docstrings][numpydoc] (through the [napoleon][numpydoc-napoleon] extension).
- Jupyter notebooks as tutorials through [myst-nb][] (See [Gallery notebooks (submodule)](#gallery-notebooks-submodule))
- [sphinx-autodoc-typehints][], to automatically reference annotated input and output types
- Citations (like {cite:p}`Virshup_2023`) can be included with [sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/)

See scanpy's {doc}`scanpy:dev/documentation` for more information on how to write your own.

[sphinx]: https://www.sphinx-doc.org/en/master/
[myst]: https://myst-parser.readthedocs.io/en/latest/intro.html
[myst-nb]: https://myst-nb.readthedocs.io/en/latest/
[numpydoc-napoleon]: https://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html
[numpydoc]: https://numpydoc.readthedocs.io/en/latest/format.html
[sphinx-autodoc-typehints]: https://github.com/tox-dev/sphinx-autodoc-typehints

### Gallery notebooks (submodule)

The gallery rendered into the docs lives in a separate repository,
[`scverse/spatialdata-plot-notebooks`][notebooks-repo], mounted here as a git
submodule at `docs/notebooks/`. This follows the scverse convention used by
`anndata`, `spatialdata`, `scvi-tools`, and `squidpy`.

Notebooks are pre-executed by humans and committed with their outputs; the
docs build performs no execution and pulls no data. A scheduled CI job in the
notebooks repo re-executes every notebook against the latest
`spatialdata-plot` release and fails on output drift.

#### Working with the gallery locally

```bash
# First-time clone — pull this repo with submodules
git clone --recurse-submodules https://github.com/scverse/spatialdata-plot.git

# Already cloned without submodules — initialise after the fact
git submodule update --init --recursive

# Pull the latest gallery content from the notebooks repo's main branch
git submodule update --remote docs/notebooks
```

ReadTheDocs is configured (`submodules: include: all` in `.readthedocs.yaml`)
to fetch the submodule on every build, so PR previews always render the
current pinned gallery content.

#### Adding or editing a notebook

Notebook changes are made in the [notebooks repo][notebooks-repo], not here.
See its `CONTRIBUTING.md` for the workflow. After your notebook PR merges
there, open a small follow-up PR in this repo bumping the submodule pin:

```bash
git submodule update --remote docs/notebooks
git add docs/notebooks
git commit -m "Bump notebooks submodule"
```

[notebooks-repo]: https://github.com/scverse/spatialdata-plot-notebooks

#### 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`

(docs-building)=

### Building the docs locally

:::{important}
The docs include a git submodule (`docs/notebooks` → `spatialdata-plot-notebooks`).
If you cloned this repo without `--recurse-submodules`, initialise it once before building:

```bash
git submodule update --init --recursive
```

Without the submodule, the gallery pages will be missing and sphinx will warn about broken toctree entries.
:::

:::::{tabs}
::::{group-tab} Hatch

```bash
hatch run docs:build
hatch run docs:open
```

::::

::::{group-tab} uv

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

::::

::::{group-tab} Pip

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

::::
:::::