Using Python as glue

There is no conversation more boring than the one where everybody agrees.

—Michel de Montaigne

Duct tape is like the force. It has a light side, and a dark side, and it holds the universe together.

—Carl Zwanzig

Many people like to say that Python is a fantastic glue language. Hopefully, this Chapter will convince you that this is true. The first adopters of Python for science were typically people who used it to glue together large applicaton codes running on super-computers. Not only was it much nicer to code in Python than in a shell script or Perl, in addition, the ability to easily extend Python made it relatively easy to create new classes and types specifically adapted to the problems being solved. From the interactions of these early contributors, Numeric emerged as an array-like object that could be used to pass data between these applications.

As Numeric has matured and developed into NumPy, people have been able to write more code directly in NumPy. Often this code is fast-enough for production use, but there are still times that there is a need to access compiled code. Either to get that last bit of efficiency out of the algorithm or to make it easier to access widely-available codes written in C/C++ or Fortran.

This chapter will review many of the tools that are available for the purpose of accessing code written in other compiled languages. There are many resources available for learning to call other compiled libraries from Python and the purpose of this Chapter is not to make you an expert. The main goal is to make you aware of some of the possibilities so that you will know what to "Google" in order to learn more.

The http://www.scipy.org website also contains a great deal of useful information about many of these tools. For example, there is a nice description of using several of the tools explained in this chapter at http://www.scipy.org/PerformancePython. This link provides several ways to solve the same problem showing how to use and connect with compiled code to get the best performance. In the process you can get a taste for several of the approaches that will be discussed in this chapter.

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