Programming for Astronomy and Astrophysics

The best way to learn how to program is to do something useful, so this introduction to Python is built around a common scientific task: data analysis.

We will get to some astronomy-specific examples later on, but for the first part of the course we will focus on the data set used for the Software Carpentry introduction to Python programming, which is a data set studying inflammation in patients who have been given a new treatment for arthritis. Our goal will be to analyze the first dozen data sets of their daily inflammation. The data sets are stored in comma-separated values (CSV) format:

each row holds information for a single patient,
columns represent successive days.

The first three rows of our first file look like this:

0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0
0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1
0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1

Each number represents the number of inflammation bouts that a particular patient experienced on a given day. For example, value “6” at row 3 column 7 of the data set above means that the third patient was experiencing inflammation six times on the seventh day of the clinical study.

So, we want to:

Calculate the average inflammation per day across all patients.
Plot the result to discuss and share with colleagues.

To do all that, we’ll have to learn a little bit about programming.

Following this introductory material, we will briefly introduce some tips on good Python programming style, before introducing the important Python modules for numerical work and data analysis in Astronomy and Astrophysics. These are Numpy, Scipy and Astropy.

Prerequisites

You need to understand the concepts of files and directories and how to start a Python interpreter before tackling this lesson. This lesson sometimes references JupyterLab and Jupyter Notebook although in practice you can use any Python interpreter.

The commands in this lesson pertain to Python 3.

Getting Started

To get started, follow the directions on the “Setup” page to download data and install a Python interpreter.

Schedule

	Setup	Download files required for the lesson
00:00	1. Writing and running code in Jupyter Notebooks	How can I easily write, test and run Python programs?
00:30	2. Python Fundamentals	What basic data types can I work with in Python? How can I create a new variable in Python? Can I change the value associated with a variable after I create it?
01:00	3. Data Types and Type Conversion	What kinds of data do programs store? How can I convert one type to another?
01:30	4. Libraries	How can I use software that other people have written? How can I find out what that software does?
01:50	5. Analyzing Patient Data	How can I process tabular data files in Python?
02:50	6. Visualizing Tabular Data	How can I visualize tabular data in Python? How can I group several plots together?
03:40	7. Repeating Actions with Loops	How can I do the same operations on many different values?
04:10	8. Storing Multiple Values in Lists	How can I store many values together?
04:55	9. Analyzing Data from Multiple Files	How can I do the same operations on many different files?
05:15	10. Beyond Lists - Tuples, Sets and Dictionaries	What other methods can I use to store information? How can I more efficiently summarise and recall the stored data?
05:55	11. Making Choices	How can my programs do different things based on data values?
06:45	12. Creating Functions	How can I define new functions? What’s the difference between defining and calling a function? What happens when I call a function?
07:35	13. Simple Input/Output	How can I write and read data to and from files?
08:05	14. Programming Style	How can I make my programs more readable? How do most programmers format their code?
08:35	15. Working with Numpy Arrays	How do I create, modify and select from numpy arrays?
09:55	16. Array Calculations with Numpy	How can I perform calculations on large arrays quickly, using numpy functions?
10:45	17. Numerical Methods with Scipy	What numerical methods are available in the Scipy library?
11:25	18. Introduction to Astropy	How can the Astropy library help me with astronomical calculations and tasks?
12:05	19. Working with FITS Data	How do I access the data in FITS files?
12:35	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.