Python in Science: How long until a Nobel Prize?

As I write this, the Nobel Prizes for 2007 are being
announced. During the week of announcements, each day includes news
of another award being bestowed for outstanding contributions in
physics, chemistry, physiology or medicine, literature, peace, and
economics. As a technophile, the science awards have always been the
most interesting to me. This year, prior to the awards, new releases
of several scientific packages on PyPI caught my eye and I was
struck by the coincidence. I started to wonder: How long before a
Nobel Prize is awarded to a scientist who uses Python for their work
in some significant way?

It should come as no surprise that Python can be found in many
scientific environments. The language is powerful enough to do the
necessary work, and simple enough to allow the focus to remain on the
science, instead of the programming. It is portable, making sharing
code between researchers easy. Its ability to interface with other
systems, through C libraries or network protocols, also makes Python
well suited for building on existing legacy tools. The list of tools
highlighted here is by no means exhaustive, but is intended to
introduce the wide variety of packages available for scientific work
in Python, ranging from general mathematics tools to narrowly focused,
specialty libraries.

The home for scientific programming in Python is the SciPy home page. SciPy aims to collect references to all of
the scientific libraries and serve as a central hub for sharing code
in the scientific programming community. There is even a SciPy
conference! The SciPy site is an excellent source of information about
scientific programming in general, and using Python specifically. It’s
a good starting point for a survey to examine how Python is used in
scientific research work.

General Purpose Toolkits

Most scientific work includes some data collection and management,
along with number crunching to analyze that data. There are several
general purpose libraries for working with datasets from any
scientific field.

The NumPy library is designed as “the
fundamental package needed for scientific computing with Python”. It
includes powerful data management and manipulation features, including
multi-dimensional arrays. Once your data is collected, it can be
processed by NumPy using linear algebra, Fourier transforms, a random
number generator, and FORTRAN integration. NumPy is the foundation of
several libraries also hosted on or otherwise associated with the
SciPy site.

The main SciPy library uses the array manipulation features of NumPy
to implement more advanced mathematical, scientific, and engineering
functions. It provides routines for numerical integration and
optimization, signal processing, and statistics.

PyTables is a data management package
specifically designed to handle large datasets. It manages data file
access with the HDF5 library, and uses NumPy for in-memory
datasets. Since it is designed for use with large amounts of data, it
is well suited for applications which produce or collect a lot of
data, even outside the scientific arena.

The ScientificPython
package is another broad ranging collection of modules for scientific
computing. It includes an input/output library; geometric,
mathematical, and statistical functions; as well as several general
purpose modules to assist in programming tasks such as threading and
parallel programming.


Besides working with observed data, scientists often use simulations
to understand the rules governing the operation of a system. If you
can construct a simulation that accurately predicts the outcome of a
set of inputs, then you have a higher level of confidence that you
understand the way the parts of a system interact. There are
basically two types of simulation, discrete and continuous, and there
are Python packages for working with both.

SimPy is a simulation language
based on standard Python. You can use SimPy to simulate activities
like traffic flow patterns, queues at retail stores, and other
discrete events. SimPy represents independent active components of
the simulation as “processes” that can interact with each other
(queuing, passing data or resources, etc.). Limited capacity is
represented through “resources”, and requests for resources are
maintained for you.

is a suite of computational tools for modeling dynamic systems and
physical processes being developed at Cornell. It supports discrete or
continuous simulation, and a wide range of mathematical operations and
constraints. One especially interesting feature is the use of
automatically generated and compiled C code for the “generators” that
produce input data used by the rest of the tool.


Once your data is collected, it needs to be analyzed. That may involve
statistical calculations, or you might be trying to uncover an
underlying relationship or formula. In either case, there is a Python
library with the tools you need.

SymPy – not to be confused with
SimPy – is a full featured computer algebra system, for symbolic
mathematics. It supports algebraic formula expansion and reduction, as
well as calculus operations such as differentials, derivatives, series
and limits, and integration.

If you need even more powerful mathematics, or just want to take
advantage of previous work done with the ubiquitous MATLAB, you can
use pymatl to drive the MATLIB
engine directly from your Python program. It will send matrices of
data back and forth between your Python program and MATLIB, allowing
the two programs to act together on the data.


Once your data is collected and you have completed your calculations,
the next step is generally to produce graphical views of the data to
make it easier to interpret the results and spot trends. The 2-D
plotting and visualization matplotlib is used in a lot of different
application areas. It produces publication quality figures in a
variety formats, and you can control it from scripts, GUIs, through
the web, or interactively through the python or ipython shells. Figure
1 shows a sample plot created with matplotlib by Jeff Whitaker of
NOAA’s Earth System Research Lab in Boulder, CO (and author of the
basemap toolkit for matplotlib).

A matplotlib example from Jeff Whitaker.

For 3-D visualization, you will want to check out Mayavi, from
Enthought. Mayavi is a general purpose visualization engine, and it
can be used with scalar or vector data to create and manipulate three
dimensional representations of your dataset for visualization.


In addition to the many general purpose libraries suitable for
scientific work, there are quite a few application specific libraries
available in different fields from the macroscopic to the

The AstroPy
project from the Astronomy Department at the University of Washington
in Seattle promotes the use of Python for astronomy research. Their
home page lists several packages for accessing legacy systems through
Python wrappers, as well as pure Python libraries for working with
astronomical data. For example, AstroLib is a
collection of 4 components which provide features such as manipulating
the ASCII tables commonly used to exchange data between scientists,
synthetic photometry (for analyzing intensity or apparent magnitude
measurements), and coordinate conversion and manipulation. The target
user is a “typical astronomer” preparing for observation runs or
working with observed or catalog data.

PyNOVAS is a library for
calculating the positions of the sun, moon, planets, and other
celestial objects. It is based on a C library, called NOVAS, and is a
good example of wrapping existing libraries in Python to make them
available to a wider range of scientists.

The Space Telescope Science Institute manages the operation of the
Hubble Space Telescope. In addition to using Python for many of their
internal tools, they have released a library for working with
astronomical data and images, called stsci_python.


If astronomy isn’t your thing, maybe you want to look at scientific
applications a little closer to home. Climate research is a hot topic
these days, both in the news and in Python development.

The tools in the Climate Data Analysis Tools (CDAT) system from
Lawrence Livermore National Laboratory are specifically designed for
working with climate data. There are separate components for reading
and writing data, performing climate-specific calculations, and
general statistical analysis.

PyClimate from Universidad del País
Vasco in Spain is focused on analyzing and modeling climate data,
combining data from different sources in different formats and
measurements to look for variability, especially human induced change.

Bjørn Ådlandsvik’s seawater module
implements functions for computing properties of the ocean, using
standard formulas defined by UNESCO reports, while the fluid package is a more general set of
procedures for studying fluid interactions.

Biology / Health

If you are more interested in animated creatures than inanimate
objects, you will be pleased to know that there is a thriving
community of biology researchers using Python for their work.

Biopython hosts a set of tools for
“computational molecular biology” for bioinformatics. Contributors are
distributed around the world, mostly at research universities. The
library they have produced includes tools for parsing bioinformatics
files from a wide range of sources, offline and online. It also
contains classes for representing and manipulating DNA sequences.

EpiGrass is used for network
epidemiology simulations. The results can be fed to the GRASS
geographic information system
plotted on maps to track or predict the spread of disease.

Molecular Modeling

If we continue this trend toward studying smaller objects, we soon
reach the microscopic and sub-microscopic scales and find Python hard
at work there, too.

The Scripps Research Institute has
released several tools for visualizing and analyzing molecular
structures. Their Python Molecular Viewer draws an interactive three
dimensional representation of a molecule. It is also scriptable, using
built-in or user defined commands dynamically loaded from plug-ins.

The Molecular Modeling Toolkit
by Konrad Hinsen is another simulation application, this time
specifically intended for simulating molecules and their interactions.

Chimera, from the University of
California, San Francisco, is an alternate interactive visualization
and analysis tool for molecular structures. It produces high quality
images and animations, and can be driven by a command interface or


Although I have barely scratched the surface, I hope this list of
packages illustrates the wide array of application areas where Python
is being used for scientific research. From the macroscopic to
microscopic, simulation to computation, it fills gaps left by other
tools and serves as the foundation technology for entirely new
tools. Whether it is reading and writing standard (or ad hoc) data
files, controlling equipment, or performing calculations directly,
Python has an important place in science. It can take several decades
before the impact of fundamental research is evaluated and recognized
as worthy of a Nobel Prize, and Python is still young enough that the
research being done using needs to mature before it would be
considered. But that day is coming, and it is entirely possible that a
Nobel Prize will be awarded to a scientist who uses Python within my

Update on the GIL

Thanks to everyone who sent a message or link after last month’s
column! The responses were generally positive. One of the corrections
came from Adam Olsen, who reported that he is working on a branch
of the C interpreter which removes the GIL
. I missed the
discussion on the python-dev list, so I wasn’t aware of his
project. According to Adam, the code is pre-alpha status, and still
needs work in areas such as deadlock management and weakrefs. He does
have some performance numbers, gathered using the pystone benchmarks
running on a dual core system. As a baseline, an unmodified version of
Python 3000 yields 28000 pystones/second. His GIL-free version
produces 18800 pystones/second for one thread, and 36700
pystones/second for two threads.

As always, if there is something you would like for me to cover in
this column, send a note with the details to doug dot hellmann at
pythonmagazine dot com and let me know, or add the link to your account with the tag pymagdifferent.

Originally published in Python Magazine Volume 1 Number 11 , November, 2007