xolars 0.2.1

Xarray + Polars: an xarray Dataset paired with per-dimension Polars frames that stay aligned under selection.

xolars

Xarray + polars: an xarray Dataset paired with one Polars frame per dimension, kept aligned to the Dataset's coordinate order — including under isel/sel selection and zarr + parquet round-trips.

Install

uv add xolars      # or: pip install xolars

Usage

import numpy as np
import polars as pl
import xarray as xr
from xolars import Xolars

ds = xr.Dataset(
    {"expr": (["gene_id", "sample_id"], np.arange(12.0).reshape(3, 4))},
    coords={"gene_id": ["G1", "G2", "G3"], "sample_id": ["S1", "S2", "S3", "S4"]},
)
genes = pl.DataFrame({"gene_id": ["G1", "G2", "G3"], "chrom": ["c1", "c2", "c3"]})

xol = Xolars(ds=ds, df={"gene_id": genes})

# Selection filters the Dataset AND every per-dimension frame, together:
sub = xol.sel(gene_id=["G3", "G1"])
assert list(sub.df["gene_id"]["gene_id"]) == list(sub.ds["gene_id"].values)

# Persist to zarr (Dataset) + parquet (per-dim frames), then reopen lazily:
xol.write("mydata.xolars", mode="w")
reloaded = Xolars.open("mydata.xolars")        # frames are pl.LazyFrame
eager = reloaded.collect()                      # -> pl.DataFrame

Xolars is a frozen, generic container: Xolars[pl.LazyFrame] after open, Xolars[pl.DataFrame] after collect. Construction validates that each frame's dim column exactly matches the Dataset coordinate (same set, same multiplicity) and reorders rows to the Dataset's order.

Development

uv sync                          # create .venv with runtime + dev deps
uv run pytest -q                 # tests
uv run prek run --all-files      # ruff + pyrefly + hygiene hooks
uv run prek install              # enable git hooks locally

Releasing

Releases run via the manual Release GitHub Actions workflow (workflow_dispatch), which uses commitizen to bump the version from conventional commits, tag, create a GitHub release, and publish to PyPI via OIDC trusted publishing. Before the workflow can succeed, the following one-time setup is required on GitHub (out of scope for the initial extraction):

1. Push this repository to https://github.com/d-laub/xolars.
2. Add a GH_ACTIONS repository secret (a PAT able to push to main) so the bump commit + tag can be pushed past branch protection.
3. Configure a pypi environment and enable PyPI trusted publishing for the xolars project pointing at the publish job.