Use Python for Scientific Computing

As computers become more and more powerful, scientific computing is becoming a more important part of fundamental research into how our world works. We can do more now than we could even imagine just a mere decade ago.

Most of this work has been done traditionally in more low-level languages, such as C or FORTRAN. Originally, this was done in order to maximize the efficiency of the code and to squeeze out every last bit of work from the computer. With computers now reaching multi-GHz speeds, this is no longer the bottleneck it once was. Other efficiencies come into play, with programmer efficiency being paramount. With this in mind, other languages are being considered that help make the most of a progammer's time and effort.

This article discusses one of these options: Python. Although Python is an interpreted language and suffers, unjustly, from the stigma that entails, it is growing in popularity among scientists for its clarity of style and the availability of many useful packages. The packages I look at in this article specifically are designed to provide fast, robust mathematical and scientific tools that can run nearly as fast as C or FORTRAN code.

The packages I focus on here are called numpy and scipy. They are both available from the main SciPy site (see Resources). But before we download them, what exactly are numpy and scipy?

numpy is a Python package that provides extended math capabilities. These include new data types, such as long integers of unlimited size and complex numbers. It also provides a new array data type that allows for the construction of vectors and matrices. All the basic operations that can be applied to these new data types also are included. With this we can get quite a bit of scientific work done already.

scipy is a further extension built on top of numpy. This package simplifies a lot of the more-common tasks that need to be handled, including tools such as those used to find the roots of polynomials, doing Fourier transformations, doing numerical integrals and enhanced I/O. With these functions, a user can develop very sophisticated scientific applications in relatively short order.

Now that we know what numpy and scipy are, how do we get them and start using them? Most distributions include both of these packages, making this the easy way to install them. Simply use your distribution's package manager to do the install. For example, in Ubuntu, you would type the following in a terminal window:

sudo apt-get install python-scipy

This installs scipy and all of its dependencies.

If you want to use the latest-and-greatest version and don't want to wait for your distribution to get updated, they are available through Subversion. Simply execute the following:

svn co http://svn.scipy.org/svn/numpy/trunk numpy svn co
http://svn.scipy.org/svn/scipy/trunk scipy

Building and installing is handled by a setup.py script in the source directory. For most people, building and installing simply requires:

python setup.py build
python setup.py install    # done as root

If you don't have root access, or don't want to install into the system package directory, you can install into a different directory using:

python setup.py install --prefix=/path/to/install/dir

Other options also are available, which you can find out about by using:

python setup.py --help-commands

Take time to experiment and see whether you can use any of the extra options in your specific case.

Now that we have scipy and numpy installed, let's begin our tour by looking at some of the basic functions that are often used in scientific calculations. One of the most common tasks is matrix mathematics. This is greatly simplified when you use numpy. The most basic code to do a multiplication of two matrices using numpy would look like this:

import numpy
a1=numpy.empty((500,500))
a2=numpy.empty((500,500))
a3=a1*a2

Contrast this to what we would need to write if we did it in C:

#include <stdlib.h>
int main() {
   double a1[500][500];
   double a2[500][500];
   double a3[500][500];
   int i, j, k;
   for (i=0; i<500; i++) {
      for (j=0; j<500; j++) {
         a3[i][j] = 0;
         for (k=0; k<500; k++) {
            a3[i][j] += a1[i][k] * a2[k][j];
         }
      }
   }
}

The Python code is much shorter and cleaner, and the intent of the code is much clearer. This kind of clarity in the code means that the programmer can focus much more on the algorithm rather than the gritty details of the implementation. There are C libraries, such as LAPACK, which help simplify this work in C. But, even these libraries can't match the simplicity of scipy.

“But what about efficiency?”, I hear you ask. Well, let's take a look at it with some timed runs. Taking our above example, we can put some calls around the actual matrix multiplication part and see how long each one takes. See Table 1 for the results.

Although your mileage will vary, because these times depend on your hardware and what other programs also are running on your machine, we can see a general trend. The Python code actually was about eight times faster than the C code compiled with no command-line options. That is actually quite surprising. Once we use the optimization command-line option, we see that the C code is now faster, by a factor of approximately 25. So, we can get faster code using optimized C, but we need to realize that multiplying two matrices with 250,000 elements each in one-quarter of a second is probably fast enough.

As well, we get a certain amount of protection when we use Python. What happens if we try to multiply two matrices where such multiplication doesn't make sense mathematically? When we try to multiply two matrices of different sizes, Python gives us:

ValueError: shape mismatch: objects cannot be 
 broadcast to a single shape

In C, we get no error at all. This due to the fact that when we work with matrices, we actually are using pointer arithmetic. So pretty much anything we do is valid C, even if it makes no sense in the problem domain.

We also can work just as easily with complex numbers. If we wanted to create an array of 64-bit complex numbers, we would write:

a=zeros((500,500), dtype=complex64)