matplotlib Code

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===========================================
 Practical Python for Scientific Computing
===========================================


This is a 2-day, hands-on workshop focused on practical techniques useful for
scientific computing.  The topics and examples covered will be generic enough
that they should be useful to students coming from a wide range of disciplines.

Some of the topics covered in the examples will include: numerical integration,
data reading and plotting, data fitting, simple text processing, linear
algebra and FFT-based image denoising.  We will also discuss how to access
low-level languages (C, C++ and Fortran) from within Python, both for the
purposes of accessing existing libraries and for speeding up critical portions
of programs.

Apart from some introductory material presented on day 1, the workshop will
consist entirely of hands-on exercises.  Simple problems will be presented and
students will be given approximately 20 minutes to solve each, starting from an
incomplete code fragment containing some hints.  Then, a brief discussion of
the solution will follow.

We have already used this format on previous occasions and the results were
very positive, with students finding that this approach kept them engaged with
actively learning new material for 2 days.  It allows us to cycle through a
number of topics of interest to a wide audience, and leaves the students with a
small body of code they can later take home to further study and develop.


Prerequisites and Resources
===========================

The workshop is targetted at students (graduate or undergraduate) and
researchers in engineering, mathematics or physics interested in learning about
Python as a tool for their daily work.  A working knowledge of basic
programming is assumed, as well as familiarity with calculus, basic linear
algebra, FFTs, and other similar topics.  The minimum level of mathematical
proficiency required is thus roughly that of an upper undergraduate in applied
mathematics or physics.

The course will stay reasonably basic in the Python techniques used, but the
core language itself will *not* be taught.  There is simply no time, in two
days, to properly cover both all the language and a sufficient amount of
scientifically relevant material.

Python is an easy language to learn, so students are assummed to, at the very
least, work *thoroughly* through the official Python tutorial_, as well as to
have familiarized themselves with Perry Greenfield's `data analysis tutorial`_.
In addition, the SciPy website has a list of additional `documentation
resources`_, as well as a collection of `tutorials and software`_ useful for a
wide variety of scientific fields.


.. _tutorial: http://docs.python.org/tut
.. _`data analysis tutorial`: http://new.scipy.org/wikis/topical_software/Tutorial
.. _`documentation resources`: http://new.scipy.org/Documentation
.. _`tutorials and software`: http://new.scipy.org/Topical_Software


Presenters
==========

The workshop will be conducted by:

John Hunter

  John was trained as a computational neuroscientist and has developed a number
  of Python-based packages for processing Electroencephalography and brain
  imaging datasets.  He is also the original author of the Matplotlib_ library,
  the most widely used library for publication quality 2-D scientific plotting
  in Python.  John currently works at Tradelink.

  
Fernando Perez

  Fernando is a physicist who uses Python extensively as part of his research
  in the development of new algorithms for the fast application of
  multidimensional integral operators.  He is also the original author of
  IPython_, the de facto standard interactive shell for scientific computing,
  with support for interactive plotting with Matplotlib as well as new
  facilities for high-level control of distributed and parallel codes.
  Fernando currently works in the Applied Mathematics Department at the
  University of Colorado, Boulder.

.. _Matplotlib: http://matplotlib.sourceforge.net
.. _IPython: http://ipython.scipy.org