Use sgkit.distarray for PCA #69

	name: Cubed

	on:
	push:
	pull_request:
	# manual trigger
	workflow_dispatch:

	jobs:
	build:
	# This workflow only runs on the origin org
	# if: github.repository_owner == 'sgkit-dev'
	runs-on: ubuntu-latest
	strategy:
	matrix:
	python-version: ["3.11"]

	steps:
	- uses: actions/checkout@v4
	- name: Set up Python ${{ matrix.python-version }}
	uses: actions/setup-python@v5
	with:
	python-version: ${{ matrix.python-version }}

	- name: Install deps and sgkit
	run: \|
	python -m pip install --upgrade pip
	python -m pip install -r requirements.txt -r requirements-dev.txt
	python -m pip install -U git+https://github.com/cubed-dev/cubed.git -U git+https://github.com/cubed-dev/cubed-xarray.git -U git+https://github.com/pydata/xarray.git

	- name: Test with pytest
	run: \|
	pytest -v sgkit/tests/test_{aggregation,association,hwe,pca}.py -k 'test_count_call_alleles or test_gwas_linear_regression or test_hwep or test_sample_stats or (test_count_variant_alleles and not test_count_variant_alleles__chunked[call_genotype]) or (test_variant_stats and not test_variant_stats__chunks[chunks2-False]) or (test_pca__array_backend and tsqr)' --use-cubed

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