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Contributing to AstroLab

Thank you for your interest in contributing to AstroLab! This document provides guidelines for contributing to our Astro GNN laboratory for cosmic web exploration.

🚀 Quick Start

  1. Fork the repository
  2. Clone your fork: git clone https://github.com/YOUR_USERNAME/astro-lab.git
  3. Setup the development environment: 
    cd astro-lab
uv sync
  4. Create a feature branch: git checkout -b feature/amazing-feature
  5. Make your changes
  6. Test your changes: uv run pytest -v
  7. Commit with clear messages: git commit -m "feat: add cosmic web clustering algorithm"
  8. Push to your fork: git push origin feature/amazing-feature
  9. Create a Pull Request

🧠 Development Focus Areas

Cosmic Web Analysis

  • Multi-scale clustering algorithms
  • Filament detection methods (MST, Morse theory, Hessian)
  • Structure identification across astronomical scales
  • Interactive 3D visualization improvements

Graph Neural Networks

  • New GNN architectures for spatial data
  • Attention mechanisms for astronomical graphs
  • Multi-scale graph learning
  • Temporal GNNs for variable objects

Data Processing

  • New survey integrations
  • Improved tensor operations
  • Performance optimizations
  • Data validation and quality checks

Visualization

  • New visualization backends
  • Interactive 3D features
  • Scientific color mapping
  • Real-time data streaming

📋 Code Style

Python Code

  • Follow PEP 8 style guidelines
  • Use type hints for all functions
  • Write comprehensive docstrings
  • Keep functions focused and small

Documentation

  • Update docstrings for new functions
  • Add examples in docstrings
  • Update README.md for new features
  • Generate API documentation: python docs/generate_docs.py update

Testing

  • Write tests for new functionality
  • Ensure all tests pass: uv run pytest -v
  • Add integration tests for cosmic web features
  • Test with real astronomical data

🎯 Commit Message Format

Use conventional commit messages:

type(scope): description

feat(cosmic_web): add MST filament detection
fix(gnn): resolve memory leak in graph convolution
docs(readme): update installation instructions
test(data): add Gaia DR3 integration tests

Types:

  • feat: New feature
  • fix: Bug fix
  • docs: Documentation changes
  • style: Code style changes
  • refactor: Code refactoring
  • test: Adding or updating tests
  • chore: Maintenance tasks

🔬 Research Contributions

Astronomical Research

  • Novel clustering algorithms for cosmic web
  • New filament detection methods
  • Multi-scale structure analysis
  • Cross-survey data integration

Machine Learning Research

  • Graph neural network architectures
  • Attention mechanisms for spatial data
  • Multi-modal learning approaches
  • Transfer learning for astronomical data

Visualization Research

  • Scientific visualization techniques
  • Interactive 3D rendering
  • Real-time data visualization
  • Multi-backend rendering systems

🐛 Bug Reports

When reporting bugs, please include:

  1. Environment: OS, Python version, package versions
  2. Steps: Clear steps to reproduce the issue
  3. Expected: What you expected to happen
  4. Actual: What actually happened
  5. Data: Sample data or code to reproduce
  6. Logs: Error messages and stack traces

💡 Feature Requests

For feature requests, please describe:

  1. Problem: What problem does this solve?
  2. Solution: How should it work?
  3. Use Case: Specific astronomical use case
  4. Priority: High/Medium/Low priority
  5. Implementation: Any implementation ideas?

🤝 Community Guidelines

  • Be respectful and inclusive
  • Help others learn and grow
  • Share knowledge and expertise
  • Provide constructive feedback
  • Celebrate contributions and achievements

📚 Resources

🏆 Recognition

Contributors will be recognized in:

  • Project README acknowledgments
  • Release notes
  • Documentation credits
  • Community highlights

Thank you for contributing to the future of astronomical machine learning! 🌌✨