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Over the past two decades, Python has become the de facto standard language of data science both in industry and astronomy (with the exception of simulations and other extreme scale computing problems). This course text is a full introduction to programming in Python with an explicit focus on astrophysical applications. The book covers the fundamentals of Python, including the native data types and operations, and how the language, interpreter, and operating system work together. Leaning heavily on standard packages used in astronomy, the book covers the installation and basic structure of the language and libraries; script writing, conditional statements, loops, and other code structures that allow for complex outcome management; the creation and use of functions and classes within Python; the creation of packages and the methods for re-using, importing, and otherwise standardizing code; and plotting. Finally, the book contains several higher level chapters that carry students from the beginner stage of programming into the intermediate.
Key Features
Imad Pasha is an NSF Graduate Research Fellow and PhD candidate at Yale University. Before Yale, he earned Bachelors degrees in Physics and Astrophysics from University of California, Berkeley, as well as a minor in Creative Writing. He worked as a reporter, senior editor, and photographer at The Daily Californian, the newspaper of record in Berkeley, CA. At Yale, his research has focused broadly on the processes driving galaxy evolution. He is interested in particular in how gas is accreted onto galaxies from the cosmic web, processed into stars, and (partially) expelled back out into the intergalactic medium, to be potentially later re-accreted.
1 Introduction
Part I Unix and Basic Python
2 Essential Unix Skills
3 Installing Python and the Astronomy Stack
4 Introduction to Python
Part II Core Research Libraries
5 Visualization with Matplotlib
6 Numpy
7 SciPy
8 Astropy and Associated Packages
Part III Intermediate Applications and Patterns
9 Functions and Functional Programming
10 Classes and Object Oriented Programming
11 Data Science with Astronomical Catalogs
12 Vectorization and Runtime Improvements
13 Astronomical Inference
14 Software Development
15 Conclusions and Next Steps
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