spatialdata-plot-tutorials

Executable notebooks demonstrating spatialdata-plot on real spatial-omics datasets. Rendered into the spatialdata-plot documentation as a gallery.

Layout

tutorials/ # end-to-end workflows on real datasets (Visium, Xenium, MERFISH, ...)
examples/ # short, focused notebooks demonstrating one feature at a time

Each notebook is committed with outputs so the spatialdata-plot docs build performs no execution. Outputs are kept fresh by a scheduled CI job that re-executes every notebook against the latest spatialdata-plot release.

Running notebooks locally

git clone https://github.com/scverse/spatialdata-plot-tutorials.git
cd spatialdata-plot-tutorials
pip install -e ".[exec]"
jupyter lab

The exec extra pulls spatialdata-plot, squidpy (for dataset loaders), and jupyter. Datasets are fetched on first run via each library's built-in caching (pooch), then re-used across runs.

Contributing a notebook

See CONTRIBUTING.md. Short version:

1. Add tutorials/<topic>.ipynb (workflow) or examples/<group>/<topic>.ipynb (focused).
2. Re-execute end-to-end and commit with outputs.
3. Add the notebook to tutorials/index.md or examples/index.md.
4. Open a PR — lint.yaml checks structure; execute.yaml re-runs notebooks on the PR.

Datasets and attribution

Notebooks use public datasets distributed via squidpy.datasets and spatialdata.datasets. Per-dataset citations live in the markdown header of each notebook; please follow the same convention when contributing.

License

BSD-3-Clause. See LICENSE.