Coding Essentials for Astronomers

Foundational Python, scientific computing, and tooling guidance presented as an open textbook for astronomers.

📖 Read the textbook online: https://tingyuansen.github.io/coding_essential_for_astronomers/

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Author

Yuan-Sen Ting
The Ohio State University
Email: ting.74@osu.edu

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About

This repository contains the complete lecture materials for Coding Essentials for Astronomers, a comprehensive open textbook covering Python programming, scientific computing, and modern data analysis techniques specifically tailored for astronomical research.

What's Inside

• Python programming patterns applied to observational and simulation workflows
• Numerical, visualization, and automation techniques for modern data volumes
• Data analysis techniques across all modalities: images, spectra, and time series
• Best practices for collaboration, version control, and human-AI co-development

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Lecture Contents

#	Title	Summary
1	Welcome & Setting Up	Course orientation, environment setup, and AI-supported coding.
2	Variables & Collections	Core Python syntax, data structures, and debugging patterns.
3	Control Flow & File Operations	Control flow, file handling, and automation for data workflows.
4	Numerical Computing	NumPy arrays, vectorisation, and numerical best practices.
5	Functions & Object-Oriented Programming	Function design, modular code, and object-oriented programming.
6	Data Visualization	Matplotlib workflows, visual diagnostics, and plotting techniques.
7	LLM API Basics	LLM API authentication, structured prompting, and cost-aware use.
8	LLM Function Tools & RAG	Function calling, retrieval-augmented generation, and vector search.
9	Model Context Protocol	Model Context Protocol: building AI tools and external integrations.
10	Git & GitHub	Git and GitHub fundamentals, documentation, and Pages publishing.
11	Streamlit	Streamlit interfaces, state management, and deploying research tools.
12	Pandas Basics	Pandas DataFrames, catalog operations, and data manipulation.
13	Advanced Pandas	GroupBy, merges, pivot tables, and advanced Pandas transformations.
14	Astroquery, Units & Coordinates	Astroquery archive access, Astropy units, coordinates, cross-matching.
15	Astropy Time, Observation Planning & FITS	Astropy Time, observation planning, visibility, and FITS files.
16	Skyfield Ephemerides	Skyfield ephemerides, planetary positions, and rise/set/transit.
17	SciPy: Interpolation, Differentiation & Integration	SciPy interpolation, differentiation, and integration.
18	Random Variables & Measurement Uncertainty	Statistics, random variables, uncertainty, and error propagation.
19	Optimization & Curve Fitting	SciPy optimization, curve fitting, and parameter estimation.
20	Time-Series Fitting: Exoplanet Transits	Time-series analysis and exoplanet transit light-curve fitting.
21	Image Fitting & Photometry	Image fitting, point spread functions, and photometry.
22	Spectroscopic Fitting	Spectroscopic fitting, equivalent widths, and line profiles.

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Repository Structure

The site is a lightweight static reader: a landing page (index.html) and a single client-side reader (reader.html) that renders each lecture from JSON — code is re-highlighted with highlight.js and LaTeX is typeset with KaTeX in the browser.

coding_essential_for_astronomers/
├── README.md                 # This file
├── build_content.py          # Builds docs/content/*.json + book-data.js from build_src
├── build_slides.sh           # Builds Slidev decks into docs/slides
├── build_src/
│   └── lectures/             # Executed lecture HTML (build source; not published)
├── docs/                     # GitHub Pages website (published root)
│   ├── index.html            # Landing page (table of contents)
│   ├── reader.html           # Client-side lecture reader
│   ├── assets/
│   │   ├── style.css         # Design system
│   │   ├── render.js         # Notebook + Markdown + LaTeX renderer
│   │   ├── theme.js          # Light / dark toggle
│   │   └── book-data.js      # Generated table-of-contents manifest
│   ├── content/              # Generated per-lecture JSON (one per lecture)
│   ├── slides/               # Built Slidev decks (LectureN_Slides/)
│   ├── data_gaia_dr3.csv     # Sample dataset for the Pandas lectures
│   └── .nojekyll             # Serve assets verbatim on GitHub Pages
└── ...

Build dependency: build_content.py only needs beautifulsoup4 (pip install beautifulsoup4).

Building

python3 build_content.py   # regenerate docs/content/*.json and docs/assets/book-data.js
./build_slides.sh          # (re)build the Slidev decks into docs/slides

To preview locally, serve docs/ over HTTP (the reader fetches lecture JSON, so file:// will not work):

cd docs && python3 -m http.server 8000   # then open http://localhost:8000

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License & Usage

This textbook is made available for educational purposes. If you wish to use these lectures in your teaching or research, please cite and refer to the original materials rather than creating derivative versions. This ensures students and readers can access the most up-to-date and authoritative content.

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AI Collaboration Acknowledgment

This textbook was written in collaboration with Claude Opus 4, Opus 4.5, Sonnet 4, and Sonnet 4.5, developed by Anthropic.

While AI assisted with the writing process, all material has been carefully designed, curated, and reviewed by the author. The pedagogical structure, topic selection, scientific content, and teaching philosophy reflect the author's expertise developed through years of research and teaching in astrophysics and data science.

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How to Cite This Work

If you use this material in your research, teaching, or other work, please cite it appropriately.

APA Style

Ting, Y.-S. (2025). Coding Essentials for Astronomers. The Ohio State University. https://doi.org/10.5281/zenodo.17850426

BibTeX

@misc{ting2025coding,
  author       = {Ting, Yuan-Sen},
  title        = {Coding Essentials for Astronomers},
  year         = {2025},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17850426},
  url          = {https://doi.org/10.5281/zenodo.17850426},
  note         = {Open educational resource. Written in collaboration with Claude (Anthropic).}
}

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Contact

For questions, feedback, or collaboration inquiries, please contact:

Yuan-Sen Ting
Department of Astronomy
The Ohio State University
Email: ting.74@osu.edu

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Acknowledgments

This work was developed as part of the Astron 1221 course at The Ohio State University. The author thanks the students whose questions and feedback have helped shape this material, and Anthropic for developing the Claude AI models that assisted in the writing process.

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Made with ❤️ for the astronomy community