Supercharge Your Academic Projects

New Advanced Features in the Academic Project Template

In my previous post, I introduced how Pixi and Quarto can transform your academic workflow by combining powerful Python project management with versatile documentation tools. Today, I'm excited to announce a significant expansion of the Academic Project Template with a comprehensive suite of professional development tools that will take your research projects to the next level.

These new features address the entire lifecycle of academic software development—from dependency management to testing, documentation, security, and publishing. Let's dive into how these additions can revolutionize your workflow.

Core Infrastructure Improvements

Packaging and Dependency Management

• Pixi: Our template continues to leverage Pixi's powerful environment management, now with enhanced configuration options for even more reliable dependency resolution.

• Dependabot Integration: Automatic dependency updates ensure your project stays current without manual intervention.

Testing Framework

• Pytest Integration: Comprehensive test infrastructure with fixtures and parameterization for thorough validation of your research code.

• Nox Automation: Test across multiple Python versions and environments to ensure broad compatibility.

• Coverage.py and Codecov: Track exactly how much of your code is tested, with beautiful visual reports that identify untested sections.

Code Quality and Standards

• Ruff: Lightning-fast Python linter and formatter that replaces multiple tools (flake8, black, isort) with a single unified solution.

• Pre-commit Hooks: Automatically catch style issues, typing problems, and potential bugs before they enter your repository.

• Mypy and Typeguard: Robust static and runtime type-checking to catch errors early and improve code documentation.

Documentation and Publishing

Enhanced Documentation

• Sphinx with MyST: Create beautiful documentation with Markdown or reStructuredText, perfect for academic explanations.

• Furo Theme: A clean, responsive theme that makes your documentation professional and accessible.

• Autodoc and Napoleon: Automatically generate API references from your docstrings, saving hours of manual documentation work.

• Xdoctest: Ensure your code examples actually work by testing them automatically.

Publishing and Distribution

• Automated PyPI Publishing: Seamlessly publish your package to PyPI and TestPyPI with GitHub Actions.

• Read the Docs Integration: Automatically deploy your documentation online whenever you update your code.

Developer Experience

Command-line Interface

• Typer Integration: Build powerful command-line interfaces for your research tools with minimal code.

• Sphinx-Click: Generate comprehensive command-line reference documentation automatically.

Workflow Automation

• GitHub Actions: Continuous integration workflows that test, build, and deploy your project automatically.

• Release Drafter: Automatically generate release notes based on merged pull requests.

• GitHub Labeler: Streamline issue management with automated labeling.

Security

• Bandit and Safety: Proactively identify security vulnerabilities in your code and dependencies.

• Pyupgrade: Automatically upgrade your Python syntax to use newer language features.

Real-World Impact for Academic Research

These features aren't just nice-to-haves—they represent a significant advancement in how academic software can be developed and maintained:

1. Reproducibility: With comprehensive dependency management and testing, others can reliably reproduce your research results.

2. Collaboration: Professional-grade tooling makes it easier for others (including non-academic contributors) to understand and contribute to your work.

3. Maintenance: Automated dependency updates, security checks, and testing reduce the long-term burden of maintaining research software.

4. Time Efficiency: Automated documentation, formatting, and testing free up your time to focus on the science rather than the infrastructure.

5. Research Impact: Well-documented, reliable software is more likely to be used and cited by others in your field.

Example: From Academic Idea to Production-Ready Package

Let's consider a typical machine learning research project workflow with these new tools:

1. Development Phase:

   • Use Pixi to set up your environment with the exact dependencies needed

   • Write your code with type hints for clarity

   • Let pre-commit and Ruff automatically enforce consistent style

2. Validation Phase:

   • Run pytest with coverage to ensure your methods work as expected

   • Use xdoctest to verify your documentation examples

   • Check for security issues with Bandit

3. Documentation Phase:

   • Generate API documentation automatically with autodoc

   • Create command-line reference with sphinx-click

   • Build and preview your docs locally

4. Publication Phase:

   • Automatically publish to PyPI when you create a new release

   • Deploy documentation to Read the Docs

   • Release notes are generated automatically

All of this happens with minimal manual intervention, letting you focus on your research while maintaining professional software standards.

Getting Started with the Enhanced Template

Ready to supercharge your academic project? Here's how to get started:

git clone https://github.com/eLearningHub/academic-project-template.git
cd academic-project-template
pixi install

After installation, explore the pyproject.toml file to see how all these tools are configured and integrated.

Conclusion

The boundary between academic and professional software development is increasingly blurring. With these new features in the Academic Project Template, researchers can adopt industry best practices without the steep learning curve typically associated with professional software development.

By investing a small amount of time in setting up your project with this template, you'll save countless hours down the road while producing more reliable, accessible, and impactful research software.

What challenges do you face in maintaining academic software? Which of these new features are you most excited about? Let's discuss in the comments!

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Note: This template is continuously evolving based on community feedback. If you have suggestions for additional features or improvements, please open an issue on the GitHub repository.