Method Development Template Repository

This repository provides a standardized template for building, testing, documenting, and releasing Python-based methods. It includes recommended layouts, tooling integrations, and CI/CD workflows to streamline development.

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📁 Repository Structure

├── src/                   # Source code
│   └── my_package/        # Your Python package modules
├── test/                  # Pytest test suite
├── doc/                   # Sphinx documentation source
├── .github/               # GitHub workflows and configuration
│   ├── workflows/         # CI/CD pipelines
│   │   ├── publish-package.yml
│   │   └── docker-publish.yml
│   └── dependabot.yml     # Dependabot configuration
├── env.yml                # Conda environment spec
├── requirements.txt       # pip dependencies
├── lint.sh                # Run Flake8
├── build_doc.sh           # Build Sphinx HTML docs
├── pyproject.toml         # Package metadata & build config
└── README.md              # This file

⚙️ Installation

Conda (preferred for RDKit compatibility):

conda env create -f env.yml
conda activate template-env

Other

python -m venv .venv
source .venv/bin/activate  # macOS/Linux
.\venv\Scripts\activate # Windows
pip install --upgrade pip
pip install -r requirements.txt

🛠 Development Workflow

1. Source Layout
   Store all library code under src/ (rename folder as desired).

2. Unit Tests
   Keep tests in tests/ and run via pytest:

   pytest --maxfail=1 --disable-warnings -q
   

3. Linting & Formatting

   • Follow PEP 8 style and PEP 257 docstrings.
   • Use black for automatic formatting and flake8 to enforce quality.
   • Run both via:

     ./lint.sh

   • Edit lint.sh to adjust rules or exclude files.

4. Documentation

   • Write docs in docs/ using Sphinx.
   • Build locally with:

     ./build_doc.sh

   • Automate publishing via ReadTheDocs with .readthedocs.yml

5. Dependency Management

   • Use env.yml for Conda (pins rdkit>=2025.3.1, pytest, black, flake8).
   • Alternatively, manage with requirements.txt for pip users.

6. Release & Packaging

   • Define package metadata in pyproject.toml.
   • Local install:

     pip install .

   • CI/CD workflows:
     • PyPI: .github/workflows/publish-package.yml
     • Docker: .github/workflows/docker-publish.yml

7. Automated Dependency Updates

   • .github/dependabot.yml configured for weekly checks and PRs.

Feel free to adapt any naming conventions or tooling versions to fit your project needs.

License

This project is licensed under MIT License - see the License file for details.

Acknowledgments

This project has received funding from the European Unions Horizon Europe Doctoral Network programme under the Marie-Skłodowska-Curie grant agreement No 101072930 (TACsy -- Training Alliance for Computational)