Testing

This guide covers testing the Remote OpenFAST Plotter application.

Testing Framework

The project uses pytest as its testing framework. Tests are located in the tests/ directory and can be run using the pytest command.

Running Tests

To run the full test suite:

python -m pytest

To run with coverage reporting:

python -m pytest --cov=. --cov-report=term

To run a specific test file:

python -m pytest tests/test_utils.py

To run a specific test function:

python -m pytest tests/test_utils.py::test_function_name -v

Test Files

For running tests that require OpenFAST output files, you can download test files using:

python utils/download_test_files.py

This will download sample .outb files to the test_files directory.

Test Structure

The test suite is organized by module:

• test_app.py: Tests for the main application setup

• test_app_with_files.py: Integration tests using actual OpenFAST files

• test_utils.py: Tests for utility functions

• test_fft_analysis.py: Tests for FFT analysis functionality

• test_html_export.py: Tests for HTML export functionality

• test_preferences.py: Tests for user preferences management

• test_callback_modules.py: Tests for callback modules

• test_signal_selection.py: Tests for signal selection functionality

Writing Tests

When writing tests for Remote OpenFAST Plotter:

1. Unit Tests: Test individual functions in isolation

   def test_unique_identifiers():
       """Test the generation of unique identifiers from file paths."""
       paths = ['/path/to/file1.outb', '/path/to/file2.outb']
       identifiers = get_unique_identifiers(paths)
       assert len(identifiers) == 2
       assert identifiers[0] != identifiers[1]
   

2. Integration Tests: Test components working together

   def test_file_loading(app_instance):
       """Test loading of OpenFAST files."""
       # Arrange
       file_paths = ['test_files/5MW_Land_DLL_WTurb.outb']
   
       # Act
       store_dataframes(file_paths)
   
       # Assert
       assert len(DATAFRAMES) == 1
       assert 'Time' in DATAFRAMES[file_paths[0]].columns
   

3. Mocking Dependencies: Use pytest fixtures and mocks when needed

   @pytest.fixture
   def mock_dataframe():
       """Create a mock dataframe for testing."""
       return pd.DataFrame({
           'Time': [0, 1, 2],
           'Signal1': [10, 20, 30]
       })
   

Test Coverage

Aim for high test coverage of the codebase:

• Run coverage reports to identify untested code

• Focus on testing complex logic and edge cases

• Include tests for error handling

• Test both positive and negative scenarios

# Generate HTML coverage report
python -m pytest --cov=. --cov-report=html

# View the report in browser
open htmlcov/index.html

Continuous Integration

The project uses GitHub Actions for continuous integration:

• Tests run automatically on pull requests

• Code quality checks are performed

• Test failures block merging of pull requests

See .github/workflows/ci-cd.yml for the CI configuration.