👷 Setup GitHub Actions Continuous Integration tests

.github/workflows/test.yml

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+# This workflow will install Python dependencies and run tests on a single version of Python
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Test
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  test:
+    name: ${{ matrix.os }} - Python ${{ matrix.python-version }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11"]
+        os: [ubuntu-22.04]
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    steps:
+      # Checkout current git repository
+      - name: Checkout
+        uses: actions/checkout@8e5e7e5ab8b370d6c329ec480221332ada57f0ab # v3.5.2
+
+      # Install Micromamba with conda-forge dependencies
+      - name: Setup Micromamba
+        uses: mamba-org/setup-micromamba@1887e3afc05fd11eb40c9542c2939ac04234546e # v1.2.1
+        with:
+          environment-name: chabud
+          environment-file: conda-lock.yml
+
+      # Run the unit tests
+      - name: Test with pytest
+        run: |
+            micromamba install python=${{ matrix.python-version }} pytest
+            python -m pytest --verbose chabud/tests/

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+
 # Data files and folders
 data/**
 !data/**/
+
+# Unit test / coverage reports
+.pytest_cache/

chabud/tests/test_datapipe.py

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+import lightning as L
+import torch
+
+from chabud.datapipe import ChaBuDDataPipeModule
+
+
+# %%
+def test_datapipemodule():
+    """
+    Ensure that ChaBuDDataPipeModule works to load data from a nested HDF5 file
+    into torch.Tensor and list objects.
+    """
+    datamodule: L.LightningDataModule = ChaBuDDataPipeModule(
+        hdf5_urls=[
+            "https://huggingface.co/datasets/chabud-team/chabud-extra/resolve/main/california_2.hdf5"
+        ]
+    )
+    datamodule.setup()
+
+    it = iter(datamodule.train_dataloader())
+    pre_image, post_image, mask, metadata = next(it)
+
+    assert pre_image.shape == (32, 512, 512, 12)
+    assert pre_image.dtype == torch.int16
+
+    assert post_image.shape == (32, 512, 512, 12)
+    assert post_image.dtype == torch.int16
+
+    assert mask.shape == (32, 512, 512)
+    assert mask.dtype == torch.uint8
+
+    assert len(metadata) == 32
+    assert set(metadata[0].keys()) == {"filename", "uuid"}