Contributing

We welcome contributions to PropFlow! This guide will help you get started.

Getting Started

  1. Fork the repository on GitHub

  2. Clone your fork locally:

git clone https://github.com/YOUR-USERNAME/Belief-Propagation-Simulator.git
cd Belief-Propagation-Simulator

  1. Create a virtual environment and install development dependencies:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e ".[dev]"

  1. Create a new branch for your feature:

git checkout -b feature/your-feature-name

Development Workflow

Running Tests

Run the full test suite:

pytest

Run with coverage:

pytest --cov=src --cov-report=html

Code Quality

Format code with Black:

black src/ tests/

Lint with Flake8:

flake8 src/ tests/

Type check with MyPy:

mypy src/

Or run all checks with pre-commit:

pre-commit run --all-files

Documentation

Build documentation locally:

cd docs
make html
open _build/html/index.html  # On Mac
# Or: start _build/html/index.html  # On Windows

Contribution Guidelines

Code Style

  • Follow PEP 8

  • Use Black for formatting (line length 120)

  • Add type hints to all functions

  • Write descriptive docstrings in Google/NumPy format

Commit Messages

Use conventional commit format:

  • feat: Add new feature

  • fix: Fix bug in module

  • docs: Update documentation

  • test: Add tests

  • refactor: Refactor code

  • chore: Update dependencies

Pull Requests

  1. Ensure all tests pass

  2. Add tests for new features

  3. Update documentation as needed

  4. Keep PRs focused on a single change

  5. Write a clear PR description

Testing

  • Write unit tests for new code

  • Maintain test coverage above 90%

  • Use pytest fixtures for common setups

  • Test edge cases and error handling

What to Contribute

Ideas for contributions:

  • 🐛 Bug fixes: Fix reported issues

  • ✨ New features: Add new algorithms or policies

  • 📚 Documentation: Improve docs and examples

  • 🧪 Tests: Increase test coverage

  • 🎨 Visualization: Add plotting capabilities

  • 🚀 Performance: Optimize algorithms

  • 🔧 Tools: Add utilities and helpers

Need Ideas?

Check out:

  • GitHub Issues

  • Issues labeled “good first issue”

  • Issues labeled “help wanted”

Questions?

  • Open a GitHub Issue for bugs or feature requests

  • Start a GitHub Discussion for questions

  • Contact the maintainers

License

By contributing, you agree that your contributions will be licensed under the MIT License.