Project workflow for astrophysics analysis

A good project workflow keeps your data, code, notebooks, and results organized while making it easier to share and reproduce work. Install Git and a Python environment first; this page is about how you use them together.

Recommended layout

project/
├── notebooks/
├── scripts/
├── data/
├── results/
├── README.md
├── .gitignore
├── environment.yml   # or pyproject.toml / uv.lock
└── .venv/

Good habits

• Use notebooks for exploration and figures
• Move reusable code into scripts or modules
• Record dependencies and environment setup in a reproducible format
• Commit often and push to GitHub before sharing
• Document assumptions, data sources, and processing steps

Typical cycle

1. Create or activate the project environment
2. Open the notebook or script in VS Code
3. Run analysis and generate figures
4. Move cleaned-up logic into a script or module
5. Commit the updates and push them to GitHub