higlass-python v2 🔎

a fresh python library for higlass built on top of higlass-schema and higlass-widget.

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)

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:

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]"

Our CI checks formatting (black .), linting (ruff .), and tests (pytest).