tests.yml

name: Tests

on:
  push:
    branches: [ main, dev ]
  pull_request:
  workflow_dispatch:

jobs:
  pytest:
    # Lightweight test suite -- no tensorflow. Runs everything that doesn't
    # need the model itself; the tensorflow-only tests (test_model.py) are
    # auto-skipped here via importorskip and exercised in the model-smoke job.
    runs-on: ubuntu-latest

    steps:
    - name: Checkout repository
      uses: actions/checkout@v4

    - name: Set up Python
      uses: actions/setup-python@v5
      with:
        python-version: '3.11'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements-dev.txt

    # Network-dependent tests (live calls to USGS / NRCS APIs) are deselected
    # here to keep CI deterministic; run them locally with `pytest -m network`.
    - name: Run tests
      run: python -m pytest -v -m "not network"

  model-smoke:
    # Exercises the Phase 3 encoder-decoder model in the core training env
    # (tensorflow installed). Builds the model, fits one step, predicts for
    # an "unseen station" via basin fallback. This is the layer that catches
    # the val_loss-explosion class of model-architecture regression that
    # broke the old training pipeline.
    runs-on: ubuntu-latest

    steps:
    - name: Checkout repository
      uses: actions/checkout@v4

    - name: Set up Python
      uses: actions/setup-python@v5
      with:
        python-version: '3.11'

    - name: Install core training dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r openFlowML/requirements.txt
        pip install pytest

    - name: Import training pipeline (smoke)
      working-directory: openFlowML
      run: python -c "import combine_data, normalize_data, windowing, model, train, baselines; print('training pipeline imports OK')"

    - name: Run model + windowing tests
      run: python -m pytest -v tests/test_model.py tests/test_windowing.py