Python for IDL Users I: Ecosystem
Python is often the language of choice for today’s cutting-edge astronomical software. Scientists wishing to take advantage of this powerful and growing ecosystem face the hurdle of learning a new programming language. Thankfully, with the rapid growth of scientific Python, a number of excellent comprehensive tutorials have been developed, many particularly for astronomers:
- A CfA tutorial
- Josh Bloom’s Python Bootcamp
- Scientific Computing with Python tutorials
- Scipy lectures
- Astropython is a directory of python software packages.
- AstroBetter maintains a wiki of tips and tutorials.
All of these are great resources for getting up to speed in Python. Here, I’d like to cover similar ground (installation, packaging, language features, etc.), but focusing explicitly on how Python differs from the language most astronomers know best: IDL. My hope is that direct comparison and contrast with a familiar language will make the learning curve easier.
The installation process for IDL is more straightforward than that of Python, and that fact likely deters busy scientists from even giving Python a try . IDL programs run on a proprietary interpreter sold by
RSI ITT Exelis. Accordingly, your system administrator has to manage software licenses, so they have likely already installed IDL on public servers and maybe even set your
IDL_PATH to point to the IDL Astronomy User’s Library. In this case, you don’t install IDL at all! Likewise, installing IDL on your laptop generally just requires a simple point-and-click installer, configuring a license file, and setting your
IDL_PATH to point to appropriate libraries, which are just directories of IDL programs.
Python, in contrast, is free software that is under active development. Moreover, the packages useful for science are not part of the core language runtime and are developed independently. This situation is an advantage for Python insofar as it enables a broad software ecosystem, but it makes installation more complex. (Also, Python packages often glue in existing C, C++, and FORTRAN libraries which need to compile during installation.)
One additional complication–hopefully to resolve itself naturally soon–is the Python version split. Python 3 introduced backwards-incompatible changes to the language. Since most of the power of Python is in the external packages, scientific migration to Python 3 has been slow. Accordingly, for now it’s best to continue working in Python 2.7 (the current and final pre-Python 3 release) rather than Python 3.3 (the newest version).
There are several methods of installing Python and its external packages on Mac OS X and Linux; I have ordered these roughly by popularity. (I don’t have enough experience with Python on Windows to make recommendations.)
- Use a package manager. In my opinion, this is the best choice for long-term Python use. Mac OS X programs like Macports (or familiar Linux equivalents like
yum) make it easy to keep installed packages up-to-date. These work primarily on the command line (and install non-python software as well). You choose which packages to install (e.g.,
sudo port install py27-numpy), and at later times a simple
sudo port selfupdatecommand will bring all of your libraries up to date. The package manager handles all the tricky details of library dependencies, compilers, and paths for you. An excellent step-by-step guide for installing astronomical python via Macports is here.
- Use an all-in-one installer. Many astronomers appreciate the one-click convenience provided by the Enthought Python Distribution (EPD) , which bundles together many (but not all) of the major scientific python libraries. I have personally found it convenient for getting a new version of python on shared servers where I didn’t have root access. However, EPD costs $199 for a single-user license. While this fee supports further Python development, it means that the EPD distribution requires license files, even for the free version offered to academics. My experience has been that this licensing complicates upgrading packages or EPD itself and negates Python’s advantage of being free to install on all of your systems.
- Maintain your installations manually. It’s possible–but far more complicated–to maintain your packages manually. Manual installation isn’t beginner-friendly, though–not all programs are packaged in the same way, and you have to manage dependencies, upgrades, and paths yourself. If you go this route, pip is currently the installation method of choice, and virtualenv is recommended to create isolated Python environments. See the installation section of this guide for more tips.
- Use the built-in system Python. Not recommended! While both Mac OS X and Linux are likely to have system versions of Python installed, they may change in system upgrades and break your code. Moreover, the packages you install may cause conflicts with routines your computer depends on. However, if you just want to play with the language, though, simply typing
pythonin a terminal will give you a way to start.
Next time: IDL and Python, head to head!