gos is a declarative genomics visualization library for Python. It is built on top of the Gosling JSON specification, providing a simplified interface for authoring interactive genomic visualizations.

The gos API is under active development. Feedback is appreciated and welcomed.

pip install gosling[all]

See the Documentation Site for more information.

import gosling as gos data = gos.multivec( url="https://server.gosling-lang.org/api/v1/tileset_info/?d=cistrome-multivec", row="sample", column="position", value="peak", categories=["sample 1", "sample 2", "sample 3", "sample 4"], binSize=5, ) base_track = gos.Track(data, width=800, height=100) heatmap = base_track.mark_rect().encode( x=gos.X("start:G", axis="top"), xe="end:G", row=gos.Row("sample:N", legend=True), color=gos.Color("peak:Q", legend=True), ) bars = base_track.mark_bar().encode( x=gos.X("position:G", axis="top"), y="peak:Q", row="sample:N", color=gos.Color("sample:N", legend=True), ) lines = base_track.mark_line().encode( x=gos.X("position:G", axis="top"), y="peak:Q", row="sample:N", color=gos.Color("sample:N", legend=True), ) gos.vertical(heatmap, bars, lines).properties( title="Visual Encoding", subtitle="Gosling provides diverse visual encoding methods", layout="linear", centerRadius=0.8, xDomain=gos.GenomicDomain(chromosome="1", interval=[1, 3000500]), )

We have started a gallery of community examples in gosling/examples/. If you are interested in contributing, please feel free to submit a PR! Checkout the existing JSON examples if you are looking for inspiration.

Check the GitHub Releases for a detailed changelog.

The source code for gos is a hybrid of Python and TypeScript (used for the anywidget component). It requires both:

• uv (for Python development)
• Deno (for building the widget)

Please ensure both are installed before proceeding.

Tests

Run the test suite with:

uv run pytest

Notebooks

To try out the library in the example notebooks/, launch Jupyter Lab with:

uv run jupyter lab

Widget

The widgets implementation is split between ./gosling/_widget.py (the Python component) and ./frontend/widget.ts (the TypeScript component).

To modify the widget's behavior in the front end, edit ./frontend/widget.ts and compile with:

deno task build

Use deno task dev to watch for changes and recompile automatically.

Docs

To build and preview the documentation locally:

uv run docs/build.py uv run python -m http.server -d docs/dist

Open your browser to http://localhost:8000 to view the generated docs.

Auto-generate Schema Bindings

Much of the Python code in this repository is automatically generated from the Gosling schema to keep the bindings in sync. This includes both the bindings in gosling/schema/ and the corresponding API documentation in doc/user_guide/API.rst.

Do not edit these files manually. Instead, regenerate them using:

# Update gosling/schema/* uv run tools/generate_schema_wrapper.py <tag_name> # Update API docs uv run tools/generate_api_docs.py

Use a tag_name that corresponds to a valid Gosling.js Release (e.g., v0.12.3).

You must commit the changes and create a new release. Schema updates usually require at least a minor version bump, but the exact versioning is up to the maintainer.

Releases are managed via the GitHub UI. The release tag determines the package version published to PyPI.

Draft a new release:

1. Create a tag

   • Click "Choose a tag", then type a new tag in the format v[major].[minor].[patch] to create it.
   • Note: The UI is not obvious about this. You can create a tag here, not just select one.

2. Generate release notes

   • Click "Generate Release Notes" to auto-summarize changes from merged PRs.
   • Edit to exclude irrelevant changes for end users (e.g., docs or CI).

3. Document significant changes

   • Add migration steps or noteworthy updates.
   • Ensure PR titles are clear and consistent.

4. Publish the release

   • Click Publish release to make it public.
   • This triggers a workflow that builds the package and publishes it to PyPI using the new tag.

gos is inspired by and borrows heavily from Altair both in project philosophy and implementation. The internal Python API is auto-generated from the Gosling specification using code adapted directly from Altair to generate Vega-Lite bindings. This design choice guarantees that visualizations are type-checked in complete concordance with the Gosling specification, and that the Python API remains consistent with the evolving schema over time. Special thanks to Jake Vanderplas and others on schemapi.