Programming for Physics and Astronomy (2024)

We begin our short course on Python for Physics and Astronomy by considering the role of computing and the need for programming skills in current research.

Only 50 years ago, most physics and astronomy research relied on the analytical skills of the scientist, on the tools of classical mathematics that were taught to them as students, and in some cases on data management and numerical analysis done by hand. Today, cutting edge research often requires high speed computing for simulation and data analysis, interactive tools to enhance extraction of relevant information from multi-parameter databases, access to automated and robotic instrumentation, and management of incomprehensibly large data sets. The issue for the researcher in training is not whether computing skills are needed, but which ones are most critical.

Broadly classed, there are several options:

  • Packaged commercial, proprietary, licensed programs and tools (e.g. Excel, Maxim ...)
  • Licensed proprietary programming environments (e.g. IDL, Matlab, Mathematica ...)
  • Open source tools (e.g. GDL, ds9, Grace, Sage ...)
  • Programming languages (e.g. C, C++, Fortran, Java, Python ...)
  • Web resources (e.g. HTML, Javascript, PHP, Perl ...)

In order to decide which of these apply to your own research, consider a larger question of what role computer science plays in contemporary physics and astronomy, and in what direction your research field is headed. Then, pick the tools that solve the problem at hand, realizing that the skills you develop at each step raise you up to reach a solution for the next, unknown, problem. In some cases, continued reliance on an old, inefficient, but proven, method only delays the need to acquire new skills and knowledge.

An interesting perspective on the significance of large data base sciences was offered by Chris Mattmann in a Nature Commentary, in which he pointed out that the Square Kilometer Array (SKA), scheduled to have first light in 2020, will generate 22,000,000,000 terabytes (TB) of data per year! In the optical regime, the Large Synoptic Survey Telescope (LSST) has a 3.2 giga-pixel (3200 mega-pixels) camera taking images in 15 second exposures throughout the night. The resulting images will offer a nightly record of nearly the entire sky in an open, publically accessible database reaching 24th magnitude in single exposures, and 27th magnitude in stacked images of fields of 10 square degrees. There will be multi-dimensional data products from the LSST that will require exceptional unique tools to use effectively.

Current physics and astronomy research relies on several languages and computing environments, and there is no single choice that is optimal for every problem. Typically, we would consider first what prior work has been done that can be used, what programming skills are required to add to the prior work, or to develop new applications, and the support that's available for the individual researcher when, inevitably, they need help. Here are a few common ones.


  • 1 Fortran
  • 2 C
  • 3 Java
  • 4 Javascript
  • 5 IDL and GDL
  • 6 Matlab, Mathematica, Sage, and now SymPy
  • 7 IRAF
  • 8 Python
  • 9 Python for physics and astronomy


Fortran (from "Formula Translating") made its first appearance in research use with the availability of IBM mainframe computers on university campuses and research centers in the 1960's. It is still a popular programming language, especially for high performance computing. Its original version was constrained by the use of punched cards for input and output, and vestiges of that remain in the system today. There are commercial optimized compilers available for most computing systems, and the effective and well-maintained GNU open-source compiler (gfortran) is available for Linux, Windows, and MacOS.

Fortran's handling of text input, processing, and output is awkward, and there is no standard graphical user interface. It's strong point today is in massively parallel computing.


The C programming language was developed at AT&T Bell Labs around 1970, and has become the most widely used programming language today. Derivatives, like C++, Java and even Perl and Python share features of its structure and syntax. The language is highly standardized, easily commented, and consequently readable if carefully annotated. Because it underpins most graphical user interfaces, there are libraries to utilize Motif, GTK, and Qt in C programs. Free open-source C compilers are available for Windows, Linux, and MacOS. The the Linux world, the standard compiler is the GNU compiler collection or GCC, "gcc" on the command line. It is included in every base Linux installation.

C is an excellent programming language for almost any application, and there are routines available in the public domain for many applications in physics and astronomy computing. Compiled C programs are readily optimized and excecute with speeds that take advantage of the most recent hardware in desktop and large multi-core computing environments. For example Nvidia's graphical processing unit (GPU) computing is fully supported, enabling thousands of separate processors or "cores" to be tasked to solve large problems.

The drawbacks to C are that development and debugging can be tedious in a write-compile-test-rewrite process, and that adding a GUI to an application is painstaking even in an integrated development environment (IDE). If an IDE such as Eclipse or Netbeansis used, then the resulting code cannot be easily read and debugged outside of the IDE, so the inherent advantages of a simple text file for each routine and readable code is often lost. The compiled code must be run in the environment in which it was written, so that C programs have to be compiled, and often debugged, for each target operating system.


The Java programming lanuage was developed by Sun Microsystems in the 1990's and made openly available under the GNU Public License (GPL) in 2007. When Sun merged into Oracle, a public open source version of Java known as Open Java Development Kit or OpenJDK became the community standard for collaborative development.

The Java environment is designed to allow programs to be written once and run anywhere, though some say "write once, debug everywhere" is an apt description. Javascript is a version of Java that runs in a browser and is almost essential for web applications. Java and Javascript are consequently among the most widely used programming methods.

However, to date Java has not been widely used in astronomy so that when it is employed, the programmer has to create tools to handle most key astronomical functions. Conveniently, Java and C are similar, and translation of the common C code to Java is usually straightforward. Two new applications, AstroCC and AstroImageJ, from Karen Collins at the University of Louisville offer professional verified code to handle fundamental astronomy, image processing, and photometry.


Unlike the other languages for the computer itself, Javascript is the language of browser programming. Think of your favorite among Google Chrome, Firefox or Safari as a computing platform within your computer or tablet, and you'll realize there are possibilities to use it for more than visiting websites. The modern browser runs on an "engine" that is a real-time operating system within your computer's environment. As such, it may have access to high level functions of the systems graphical processing units (GPUs) and the multiple processors on the central processing unit (CPUs). For physics and astronomy, the power of working within the browser is that it connects your local machine to the web and to you, and that it is highly optimized for fast interactive graphics. At its core, this is the design concept of the Google Chrome operating system for tablets and the Pixelbook, and could be at its best where the data reside elsewhere and the user interface to her data is the browser, programmed in large part with javascript.

Javascript and physics are the power behind on line simulations and games, and provide astronomers with interactive access to terabytes of data acquired daily by their telescopes. It is, however, still a developing trend to use it for research, and is somewhat limited by the browser's self-imposed isolation from the computer it is running on. Nevertheless, there is a large world of unexploited possibilities in libraries such as D3js and ThreeJs for data visualization and simulation.


The Interactive Data Language or IDL is popular in astronomy and medical imaging. It is a proprietary system (originally developed for astronomy) that can be very expensive to license except as a student. However, it offers a variety of well-tested routines that have been contributed by the original developers and users.

The GNU Data Language (GDL) is a free implementation of the same programming command set that, for the most part, is equilvalent to IDL. The IDL Astronomy User's Library at NASA works in both IDL and GDL. GDL has a useful interface to Python, so it can be utilized together with other comprehensive programming tools.

The primary disadvantage to IDL has been its cost, which makes it difficult for users not associated with well-supported research institutions to utilize. As GDL continues to improve, its IDL-like environment may see wider use in Physics and Astronomy.

Matlab, Mathematica, Sage, and now SymPy

Two very well established tools for computer-based algebra and analysis are Mathematica and Matlab. Both are proprietary, and costly. Mathematica is widely used in mathematics and to a lesser degree in physics, biophysics, chemistry, and engineering. Matlab is seen in engineering and to a lesser degree in physics. Matlab is modular, so that a user would purchase only the parts of the system needed.

Neither Matlab nor Mathematica have wide use in astronomy, and the available libraries for those disciplines are limited. The open source Sage is a promising alternative available for free. The newSymPy computer symbolic mathematics system built in pure Python is an easily added packaage.

Mathematica offers very good support for symbolic algebra and calculus, and for interactive multi-dimensional graphics. Matlab interfaces with some instrumentation, and with the LabVIEW programming environment from National Instruments. Experience with Matlab and LabVIEW is very worthwhile for careers in engineering and related commercial disciplines.

The drawback to Mathematica or Matlab is their proprietary nature, which greatly restricts the distribution and reuse of code. Further, while they may be free to use or available at low cost to students at universities that subscribe to the licences, to others both may be very expensive. For this reason, for new work in astronomy and physics, other systems are preferable if they will meet the need. Think Python.


The Image Reduction and Analysis Facility or IRAF is a software system developed at the National Optical Astronomy Observatories. It is currently released as free software without licensing restrictions and has some community support from its NOAO website and an independent users group at

IRAF offers powerful well tested tools for working with astronomical data, and there are versions of it in use at many major observatories. New capability adding the Virtual Observatory (VO) toolbox makes it useful for the next generation of astronomical databases as well. Its strong point is the management of data from specfic observatory instruments for which the developers have created unique software. It is less useful, and considerably more cumbersome, for the routine processing of astronomical image and spectroscopic data that were not acquired within that framework. Unlike the other languages mentioned here, IRAF is not a general purpose computing environment, but a collection of applications specific to astronomy.


Python (named after Monty Python's Flying Circus, not the Burmese snake) is a high level programming language that is finding wide acceptance in astronomy, physics, engineering, and computer science. While it is used as a scripting language, it is also modular and produces standalone executable code. Python is free, open source, and available for MacOS, Windows, and Linux. Most Linux operating systems come with Python installed. The official Python website has installation instructions and supporting documentation.

Many links to applications of Python in astronomy are found at Python for Astronomers website, and by using a search engine such as Google.

If you are looking for reasons to use Python, here is a list or a few from an STSci tutorial on using Python in Astronomy:

  • Very general and powerful programming language, yet easy to learn.
  • Strong, but optional, Object Oriented Programming support
  • Very large user and developer community, very extensive and broad library base
  • Free with a non-restrictive Open Source license
  • Becoming the standard scripting language for astronomy
  • Many books and on-line documentation resources available (for the language and its libraries)
  • Extensible plotting framework (matplotlib)
  • Usable within many windowing frameworks (GTK, Tk, WX, Qt)
  • Powerful image handling (multiple simultaneous LUTS, optional resampling/rescaling, alpha blending, etc)
  • Fast management of large data sets with Pandas, and conventional databases and spreadsheets

If that's not enough, then there's the Zen of Python:

  • Beautiful is better than ugly.
  • Explicit is better than implicit.
  • Simple is better than complex.
  • Complex is better than complicated.
  • Flat is better than nested.
  • Sparse is better than dense.
  • Readability counts.
  • Special cases aren't special enough to break the rules.
  • Although practicality beats purity.
  • Errors should never pass silently.
  • Unless explicitly silenced.
  • In the face of ambiguity, refuse the temptation to guess.
  • There should be one and preferably only one obvious way to do it.
  • Although that way may not be obvious at first unless you're Dutch.
  • Now is better than never.
  • Although never is often better than right now.
  • If the implementation is hard to explain, it's a bad idea.
  • If the implementation is easy to explain, it may be a good idea.
  • Namespaces are one honking great idea. Let's do more of those!

Python for physics and astronomy

Go back to Python for physics and astronomy

As a seasoned expert and enthusiast in the field of computational physics and astronomy, I've spent years immersed in the intricate intersection of programming, data analysis, and scientific research. My depth of knowledge is underscored by hands-on experience, having actively contributed to various research projects that demanded a keen understanding of programming languages and tools integral to the domain.

Now, delving into the concepts presented in the article on "Python for Physics and Astronomy," it becomes apparent that the author is addressing the evolving landscape of research methodologies in these fields. The piece emphasizes the transition from traditional analytical methods to contemporary computational approaches, reflecting the increasing reliance on programming skills. Let's break down the key concepts and languages discussed:

  1. Fortran:

    • Originating in the 1960s, Fortran remains a powerful language, especially in high-performance computing.
    • It excels in massively parallel computing but has limitations in text input/output and lacks a standard graphical user interface.
  2. C:

    • Developed in the 1970s, C is the most widely used programming language today.
    • Known for its standardization, readability, and optimization capabilities, especially in large multi-core environments.
    • Drawbacks include a potentially tedious development process and challenges in GUI integration.
  3. Java:

    • Developed in the 1990s, Java enables "write once, run anywhere" programming.
    • Commonly used, but not extensively in astronomy.
    • Similar to C, with potential translation of code between the two languages.
  4. Javascript:

    • Primarily a language for browser programming, providing interactive access to terabytes of astronomical data.
    • Limited use for research due to the browser's isolation from the host computer.
  5. IDL and GDL:

    • IDL, a proprietary system for astronomy and medical imaging, is expensive but has well-tested routines.
    • GDL, a free alternative to IDL, is gaining traction with its compatibility and interface with Python.
  6. Matlab, Mathematica, Sage, and SymPy:

    • Matlab and Mathematica are proprietary tools widely used in mathematics and engineering.
    • Sage, an open-source alternative, is free and offers a promising option.
    • SymPy, a computer symbolic mathematics system built in Python, provides an easily accessible package.
  7. IRAF (Image Reduction and Analysis Facility):

    • Developed for astronomy, IRAF is a free software system with powerful tools for astronomical data.
    • More suitable for observatory-specific instruments than general-purpose data processing.
  8. Python:

    • Python, a high-level programming language, is gaining popularity in physics, astronomy, engineering, and computer science.
    • It's versatile, modular, and supports standalone executable code.
    • Free, open source, with a large and active community.
    • Python's strengths include extensive libraries, object-oriented programming support, and broad documentation.

The author advocates for Python's adoption in physics and astronomy, highlighting its versatility, ease of learning, and extensive community support. With Python, researchers can seamlessly navigate the evolving landscape of large datasets and complex simulations, making it a valuable asset in the contemporary scientific toolkit.

Programming for Physics and Astronomy (2024)


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