feat: Set up complete Python testing infrastructure with Poetry

[llbbl]

Set up Python Testing Infrastructure

Summary

This PR establishes a comprehensive testing infrastructure for the CrossNet project using Poetry as the package manager. The setup provides a robust foundation for writing and running tests with coverage reporting.

Changes Made

Package Management

• Created pyproject.toml with Poetry configuration
• Migrated dependencies from README requirements to structured Poetry format
• Added testing dependencies: pytest, pytest-cov, pytest-mock

Testing Infrastructure

• Created testing directory structure:

  • tests/ (root directory with __init__.py)
  • tests/unit/ (for unit tests)
  • tests/integration/ (for integration tests)

• Configured pytest settings in pyproject.toml:

  • Test discovery patterns
  • Coverage reporting (HTML, XML, terminal)
  • 80% coverage threshold
  • Custom markers: unit, integration, slow
  • Strict configuration for better test reliability

Shared Testing Resources

• Created tests/conftest.py with comprehensive shared fixtures:
  • temp_dir: Temporary directory management
  • sample_tensor, sample_flow: PyTorch tensor fixtures
  • mock_config: Configuration dictionaries
  • sample_hdf5_file: HDF5 test data creation
  • mock_cuda_device: GPU/CPU device handling
  • Environment setup with deterministic random seeds

Project Configuration

• Updated .gitignore with:
  • Testing artifacts (.pytest_cache/, .coverage, htmlcov/)
  • Development files (IDE settings, OS files)
  • Project-specific exclusions (checkpoints, model files)
  • Claude settings exclusion

Validation

• Created tests/test_setup_validation.py to verify:
  • All dependencies import correctly
  • Fixtures work as expected
  • PyTorch operations function properly
  • HDF5 file creation and reading
  • Project structure validation
  • Basic model creation capabilities

Running Tests

# Install dependencies
poetry install

# Run all tests
poetry run pytest

# Run tests with verbose output
poetry run pytest -v

# Run specific test markers
poetry run pytest -m unit
poetry run pytest -m integration
poetry run pytest -m slow

# Run tests without coverage (for quick validation)
poetry run pytest --no-cov

# Generate coverage report
poetry run pytest --cov-report=html

Coverage Reporting

The setup generates multiple coverage report formats:

• Terminal: Shows missing lines during test runs
• HTML: Detailed report in htmlcov/ directory
• XML: Machine-readable report in coverage.xml

Coverage is configured to:

• Monitor Model/ and ref_utils/ source directories
• Exclude test files and cache directories
• Require 80% minimum coverage threshold
• Show precise line-by-line coverage information

Dependencies Installed

Core Dependencies

• torch ^2.0.0 (PyTorch for deep learning)
• opencv-python ^4.0.0 (Computer vision operations)
• h5py ^3.0.0 (HDF5 file format support)
• numpy ^1.20.0 (Numerical computing)

Test Dependencies

• pytest ^7.0.0 (Main testing framework)
• pytest-cov ^4.0.0 (Coverage reporting)
• pytest-mock ^3.10.0 (Mocking utilities)

Notes

• Poetry lock file is included and should be committed for reproducible builds
• The existing Python files use Python 2 syntax (e.g., cPickle) but the testing infrastructure supports Python 3.8+
• OpenCV import may fail in headless environments - the validation tests handle this gracefully
• Coverage warnings about unparseable files are expected for legacy Python 2 code

Testing Philosophy

This infrastructure follows testing best practices:

• Separation of concerns: Unit tests for individual components, integration tests for system interactions
• Reproducible tests: Fixed random seeds and deterministic behavior
• Comprehensive fixtures: Reusable test resources to reduce code duplication
• Flexible execution: Multiple ways to run tests based on development needs
• Quality gates: Coverage thresholds to maintain code quality

The setup is ready for immediate use - developers can start writing tests using the established patterns and fixtures.