Suggestions for Improving Project Maintainability and Dissemination

[daharoni]

Improving Project Maintainability and Dissemination

This issue outlines key improvements needed to make minian-bin more maintainable and accessible to the scientific community.

Documentation

• Enhance README.md
  • Project description and purpose
  • Installation instructions (including CUDA setup)
  • Quick start guide
  • Link to documentation
• Create API documentation (Sphinx/MkDocs)
• Add docstrings to key functions/classes
• Create example notebooks

Testing Infrastructure

• Add unit tests for core functionality
• Add integration tests for pipelines
• Set up GitHub Actions CI
• Add test coverage reporting
• Create test data fixtures

Code Organization & Quality

• Add type hints consistently
• Separate public API from internal functions
• Implement consistent logging
• Add code quality tools:

  [tool.pdm.dev-dependencies]
  dev = [
      "black",
      "isort",
      "flake8",
      "mypy",
      "pytest",
      "pytest-cov",
      "sphinx",
  ]

• Set up pre-commit hooks

Development Guidelines

• Create CONTRIBUTING.md
  • Code style guidelines
  • PR process
  • Development setup instructions
• Add issue templates
• Create CHANGELOG.md

Package Distribution

• Complete pyproject.toml metadata
• Add project URLs
• Consider PyPI publication
• Create conda/mamba package recipe

Dependencies

• Review and lock version ranges
• Split requirements (core vs optional)
• Document system requirements
• Add environment.yml

Examples and Tutorials

• Create examples/ directory
  • Jupyter notebooks
  • Sample datasets
  • Common analysis pipelines
  • Performance optimization tips

Community Resources

• Add CODE_OF_CONDUCT.md
• Set up GitHub Discussions
• Create documentation website
• Add CITATION.cff

Performance & Deployment

• Create benchmarking suite
• Document performance expectations
• Create release process
• Set up automated dependency updates

Priority and Timeline

Suggested order of implementation:

1. Essential documentation (README, docstrings)
2. Basic testing infrastructure
3. Code quality tools
4. Examples and tutorials
5. Community resources
6. Advanced features (benchmarking, automation)

Related Resources

• NumPy Documentation Guide
• Scientific Python Development Guide
• Python Packaging Guide

/label documentation
/label enhancement
/label good first issue