higlass-python 🔎

A fresh Python library for higlass built on top of:

higlass-schema: Pydantic models for HiGlass.
higlass-widget: A cross-platform AnyWidget for Jupyter environments.

Installation

pip install higlass-python

Usage

import higlass as hg

# Remote data source (tileset)
tileset1 = hg.remote(
    uid="CQMd6V_cRw6iCI_-Unl3PQ",
    server="https://higlass.io/api/v1/",
    name="Rao et al. (2014) GM12878 MboI (allreps) 1kb",
)

# Local tileset
tileset2 = hg.cooler("../data/dataset.mcool")

# Create a `hg.HeatmapTrack` for each tileset
track1 = tileset1.track("heatmap")
track2 = tileset2.track("heatmap")

# Create two independent `hg.View`s, one for each heatmap
view1 = hg.view(track1, width=6)
view2 = hg.view(track2, width=6)

# Lock zoom & location for each `View`
view_lock = hg.lock(view1, view2)

# Concatenate views horizontally and apply synchronization lock
(view1 | view2).locks(view_lock)

To learn more about the new API, check out the updated documentation.

Upgrade Guide

higlass-python v1.0 is a total rewrite of our prior implementation, aimed to offer a more ergonomic and flexible API. While this might present challenges when upgrading existing code, we've prepared documentation to guide you through the new API usage.

If you find a missing feature, please open an issue – we're committed to supporting your use cases with the new API.

Despite the large changes in v1.0, we will strive to avoid breaking changes going forward. However, because of the complete rewrite, the v1.0 release doesn't strictly adhere to semantic versioning. You can think of it as a pre-1.0 release, with breaking changes and new features included in minor releases, and bug fixes in patch releases.

We will aim for strict semantic versioning with the v2.0 release. Your feedback and understanding are greatly appreciated.

Development

higlass-python uses the recommended hatchling build-system, which is convenient to use via the hatch CLI. We recommend installing hatch globally (e.g., via pipx) and running the various commands defined within pyproject.toml. hatch will take care of creating and synchronizing a virtual environment with all dependencies defined in pyproject.toml.

Commands Cheatsheet

All commands are run from the root of the project, from a terminal:

Command	Action
`hatch run fix`	Format project with `black .` and apply linting with `ruff --fix .`
`hatch run lint`	Lint project with `ruff .`.
`hatch run test`	Run unit tests with `pytest` in latest Python version.
`hatch run test:test`	Run unit tests with `pytest` in all target Python versions.
`hatch run docs:build`	Build the documentation in `docs/_build/html`.
`hatch run docs:serve`	Start an dev-server for live editing RST files in `docs/`.

Note: hatch build and hatch publish are available to build and publish the project to PyPI, but all releases are handled automatically via CI.

Alternatively, you can develop higlass-python by manually creating a virtual environment and managing installation and dependencies with pip. For example, create a virtual environment with conda:

conda create -n higlass python=3.11
conda activate higlass

and install higlass-python in editable mode with all optional dependencies:

pip install -e ".[dev,fuse,docs]"