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Contributing to RAWSim-O Python MVP

Thank you for your interest in contributing! This project is an MVP implementation and welcomes contributions.

Getting Started

  1. Fork the repository
  2. Clone your fork: git clone https://github.com/YOUR_USERNAME/rawsim-o-python-mvp.git
  3. Create a branch: git checkout -b feature/your-feature-name
  4. Install dependencies: pip install -r requirements.txt

Development Guidelines

Code Style

  • Follow PEP 8 Python style guide
  • Use type hints where appropriate
  • Add docstrings to classes and functions
  • Keep functions focused and concise

Project Structure

  • core/ - Core simulation elements (bots, pods, waypoints, etc.)
  • pathfinding/ - Pathfinding algorithms
  • control/ - Control systems and strategies
  • simulation/ - Simulation execution engine
  • statistics/ - Statistics tracking and export
  • visualization/ - 2D visualization with Pygame
  • utils/ - Utility functions
  • generator/ - Instance generation

Adding New Features

  1. New Pathfinding Algorithm

    • Add to pathfinding/ directory
    • Implement interface compatible with existing algorithms
    • Update PathPlanner to support new method

  2. New Control Strategy

    • Add to control/ directory
    • Follow existing patterns (TaskManager, PodSelector)
    • Document algorithm and parameters

  3. New Visualization

    • Consider web-based (Three.js) or 3D (Panda3D)
    • Maintain compatibility with existing simulation

Testing

Before submitting:

  1. Test your changes locally
  2. Run CLI mode: python cli.py --generate
  3. Run visual mode: python visualization.py --generate
  4. Verify no errors in logs

Areas for Contribution

High Priority

  • Additional pathfinding algorithms (CBS, PAS, BCP)
  • Order generation system
  • Item picking/storing logic
  • Performance optimizations
  • Unit tests

Medium Priority

  • 3D visualization (web-based or Panda3D)
  • Configuration validation
  • More statistical metrics
  • Multi-threaded simulation
  • Real-time monitoring dashboard

Nice to Have

  • Machine learning integration
  • Benchmark suite
  • Docker containerization
  • Documentation website
  • Tutorial notebooks

Pull Request Process

  1. Update README.md if you've added features
  2. Update docstrings and comments
  3. Ensure code follows style guidelines
  4. Test thoroughly
  5. Submit PR with clear description

Questions?

Open an issue or discussion on GitHub.

License

By contributing, you agree that your contributions will be licensed under GPL-3.0.