testing.yml

name: Dev tests
on:
  workflow_call:

env:
  TSHARK_PATH: ""
  NWB_BENCHMARKS_NETWORK_INTERFACE: ""

jobs:

  run:
    # Will read on PR dashboard as 'Deploy / DevTests / ubuntu'
    # Action dashboard identified by 'Dev tests'
    # Requirement settings identified as 'DevTests / ubuntu'
    name: ubuntu
    runs-on: ubuntu-latest
    strategy:
      fail-fast: false
    defaults:
      run:
        shell: bash -el {0}

    steps:
      - name: Clone repository
        run: |
          git clone https://github.com/neurodatawithoutborders/nwb_benchmarks.git
          cd nwb_benchmarks
          BRANCH="${GITHUB_HEAD_REF:-$GITHUB_REF_NAME}"
          git checkout $BRANCH

      - name: Install conda
        uses: conda-incubator/setup-miniconda@v2
        with:
          activate-environment: nwb_benchmarks
          auto-activate-base: false

      # `$CONDA` is set by the runner image, but `${{ env.* }}` expressions only see variables written to GITHUB_ENV
      - name: Collect the conda environment cache key inputs
        id: cache-inputs
        run: |
          echo "week=$(date -u '+%G-W%V')" >> "$GITHUB_OUTPUT"
          echo "CONDA=$CONDA" >> "$GITHUB_ENV"

      # Reuse the environment built by an earlier run with the same dependency files. The pip dependencies are
      # unpinned, so the key also includes the ISO week to pick up new upstream releases at least weekly.
      - name: Restore cached conda environment
        id: env-cache
        uses: actions/cache@v4
        with:
          path: ${{ env.CONDA }}/envs/nwb_benchmarks
          key: conda-env-${{ runner.os }}-${{ runner.arch }}-${{ steps.cache-inputs.outputs.week }}-${{ hashFiles('nwb_benchmarks/environments/nwb_benchmarks.yaml', 'nwb_benchmarks/pyproject.toml') }}

      - name: Install special conda environment
        if: steps.env-cache.outputs.cache-hit != 'true'
        run: conda env update --name nwb_benchmarks --file nwb_benchmarks/environments/nwb_benchmarks.yaml

      - name: Display installed packages and their sources for debugging
        run: |
          conda info
          conda list

      # `params.py` hardcodes the DANDI API download URLs so that importing the benchmark suite does not look every asset
      # up. Check they still match what the API resolves to, so a replaced asset shows up here instead of in a benchmark.
      - name: Check the hardcoded DANDI URLs are current
        working-directory: nwb_benchmarks
        run: python -m nwb_benchmarks.scripts.check_dandi_urls

      # `--group` installs a dependency group from `pyproject.toml` and needs pip 25.1 or newer.
      - name: Run unit tests
        working-directory: nwb_benchmarks
        run: |
          python -m pip install --upgrade "pip>=25.1"
          python -m pip install --group test
          python -m pytest tests

      # This run is a smoke test of the benchmark machinery, not a measurement, so each benchmark is restricted to a
      # single parameter set. ASV matches the `--bench` pattern against `module.Class.method({parameters})`, which
      # lets the pattern pick one parametrization. Every parameter set opens the full remote NWB file in `setup`,
      # and the ophys file is by far the fastest of the three test files to open. Both benchmarks are selected in
      # one run, with `|` joining the two patterns, because ASV discovers and imports the suite once per run; the
      # benchmarks themselves still execute one after another.
      - name: Run basic file read and slicing tests
        working-directory: nwb_benchmarks
        run: |
          READ="time_remote_file_reading.HDF5PyNWBRemfilePreloadedNoCacheFileReadBenchmark.time_read_hdf5_pynwb_remfile_preloaded_no_cache\(\{'name': 'OphysTestCase'"
          SLICE="time_remote_slicing.HDF5PyNWBRemfilePreloadedNoCacheContinuousSliceBenchmark.time_slice\(\{'name': 'OphysTestCase1'"
          nwb_benchmarks run --bench "$READ|$SLICE" --debug