SYWTDIAWP

So You Want to Do Image Analysis with Python.

This repository hosts a GitHub Pages course for biological researchers learning to work with Python locally for image analysis and related workflows.

The course is paced as a multi-day self-study resource rather than a short single-session workshop.

The current course covers:

• installing Python locally
• working with virtual environments using venv and conda
• learning core Python programming basics such as variables, data types, and functions
• learning the main scientific Python libraries used in research
• running Jupyter notebooks
• using pandas and matplotlib for tabular data and plotting
• installing napari and launching the napari-mAIcrobe plugin
• creating a Python package from the HenriquesLab cookiecutter template
• continuing into segmentation, classification, QC, neural networks, and pretrained models based on the BioimageCourseGIMM notebooks

Structure

• index.html is the landing page and course map
• resources.html is the central resource hub for notebooks and quick links
• glossary.html contains beginner-friendly definitions for recurring terms
• troubleshooting.html collects common setup and workflow recovery steps
• 404.html is the fallback page for broken GitHub Pages links
• assets/css/site.css contains the shared site styles
• tutorials/install-python.html covers local Python installation
• tutorials/virtual-environments.html covers venv and conda
• tutorials/python-basics.html covers Python fundamentals and exercises
• tutorials/scientific-python-libraries.html introduces the main scientific Python tools
• tutorials/jupyter-notebooks.html covers notebook setup, examples, and exercises
• tutorials/pandas-and-matplotlib.html covers tabular analysis and plotting
• tutorials/napari-maicrobe.html covers napari and napari-mAIcrobe
• tutorials/create-python-package.html covers package creation with cookiecutter
• tutorials/advanced-*.html pages extend the same course path with BioimageCourseGIMM-inspired lessons
• notebooks/ contains sample notebooks linked from the Jupyter lesson

Publishing With GitHub Pages

Because the site is plain HTML and CSS, it can be published directly from the repository without a build step.

1. Push the repository to GitHub.
2. In the repository settings, open Pages.
3. Set the source to deploy from the main branch and the repository root.
4. Save the settings and wait for GitHub to publish the site.

Local Editing

You can edit the HTML files directly and refresh them in a browser, or run a simple local server from the repo root:

python -m http.server 8000

Then open http://localhost:8000.

Content Notes

• The lesson pages are written for researchers who may be new to programming.
• Commands are intentionally copy-paste friendly and organised as a guided path.
• The modules now include slower pacing, checkpoints, and suggested stopping points.
• If the HenriquesLab cookiecutter repository path changes, update the command in tutorials/create-python-package.html.
• The public site navigation now points to HTML pages rather than raw Markdown files so the GitHub Pages experience stays consistent.