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:
Please ensure both are installed before proceeding.
Tests
Run the test suite with:
uv run pytest
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.
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.
-
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.
- Click "Choose a tag", then type a new tag in the format
-
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).
-
Document significant changes
- Add migration steps or noteworthy updates.
- Ensure PR titles are clear and consistent.
-
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
.