feat: Set up comprehensive Python testing infrastructure

[llbbl]

About UnitSeeker

Hi! This PR is part of the UnitSeeker project, a human-guided initiative to help Python repositories establish testing infrastructure.

Key points:

• Human-approved: Every PR is manually approved before work begins
• Semi-automated with oversight: Created and controlled via a homegrown wrapper around Claude Code with human quality control
• Infrastructure only: This PR intentionally contains only the testing setup without actual unit tests
• Your repository, your rules: Feel free to modify, reject, or request changes - all constructive feedback is welcome
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Set up comprehensive Python testing infrastructure

Summary

This PR establishes a complete testing infrastructure for the langtons-emergence research project, providing a robust foundation for test-driven development and code quality assurance.

Changes Made

Package Management

• Poetry Configuration: Set up Poetry as the package manager with comprehensive pyproject.toml
• Dependencies: Added core dependencies (numpy, matplotlib, scipy) and testing dependencies (pytest, pytest-cov, pytest-mock)
• Development Scripts: Configured poetry run test and poetry run tests commands for easy test execution

Testing Framework

• pytest Configuration: Comprehensive pytest setup with strict settings and custom markers
• Coverage Reporting: Configured 80% coverage threshold with HTML, XML, and terminal reporting
• Test Markers: Added unit, integration, and slow markers for selective test execution
• Test Discovery: Configured automatic test discovery patterns for Python files, classes, and functions

Directory Structure

tests/
├── __init__.py
├── conftest.py           # Shared fixtures and test utilities
├── unit/                 # Unit tests
│   └── __init__.py
├── integration/          # Integration tests
│   └── __init__.py
└── test_setup_validation.py  # Infrastructure validation tests

Shared Fixtures (tests/conftest.py)

• Sample Data: Grid data, spiral patterns, projected data, statistical data
• Metrics Fixtures: Spiral metrics, 3D metrics, statistical metrics
• Utility Fixtures: Temporary directories, mock objects, loggers
• Performance Fixtures: Large datasets for performance testing
• Parametrized Fixtures: Multiple test scenarios for spiral types, dimensions, grid sizes

Quality Assurance

• Code Coverage: Configured to track coverage across toolkit modules
• Gitignore: Updated with testing artifacts, cache files, and development entries
• Validation Tests: 22 validation tests verify all infrastructure components work correctly

Testing Instructions

Basic Test Execution

# Run all tests
poetry run test

# Run with verbose output
poetry run pytest -v

# Run without coverage (for faster execution)
poetry run pytest --no-cov

Selective Test Execution

# Run only unit tests
poetry run pytest -m "unit"

# Run only integration tests  
poetry run pytest -m "integration"

# Run only slow tests
poetry run pytest -m "slow"

# Run specific test file
poetry run pytest tests/test_setup_validation.py

Coverage Reporting

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

# View coverage in browser
open htmlcov/index.html

# Generate XML coverage for CI/CD
poetry run pytest --cov-report=xml

Validation Results

All 22 validation tests pass successfully, confirming:

• ✅ Package imports work correctly
• ✅ Shared fixtures are available and functional
• ✅ Temporary directory handling works
• ✅ Matplotlib backend is properly configured
• ✅ Test markers function as expected
• ✅ Parametrized fixtures work across scenarios
• ✅ Mock functionality is available
• ✅ Toolkit modules can be imported
• ✅ Data structures validate correctly
• ✅ Performance fixtures handle large datasets

Dependencies and Configuration

Production Dependencies

• numpy ^1.21.0 - Numerical computing
• matplotlib ^3.5.0 - Visualization
• scipy ^1.7.0 - Scientific computing

Development Dependencies

• pytest ^7.0.0 - Testing framework
• pytest-cov ^4.0.0 - Coverage reporting
• pytest-mock ^3.10.0 - Mocking utilities

Configuration Notes

• Minimum Python version: 3.8
• Coverage threshold: 80%
• Test discovery includes test_*.py and *_test.py patterns
• Warnings filtered for cleaner test output
• Poetry lock file managed automatically

Next Steps

With this testing infrastructure in place, developers can now:

1. Write comprehensive tests using the provided fixtures and utilities
2. Maintain code quality through automated coverage reporting
3. Execute tests efficiently using selective markers and Poetry scripts
4. Integrate with CI/CD using standardized pytest configuration
5. Debug effectively with detailed test output and coverage reports

The testing framework is ready for immediate use and will scale with the project's testing needs.

🤖 Generated with Claude Code