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Jens Rantil
68 lines
2.7 KiB
ReStructuredText
68 lines
2.7 KiB
ReStructuredText
=======================
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Scientific Applications
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=======================
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Context
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Python is frequently used for high-performance scientific applications. Python
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is widely used in academia and scientific projects because it is easy to write,
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and it performs really well.
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Due to its high performance nature, scientific computing in python often refers
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to external libraries, typically written in faster languages (like C, or FORTRAN
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for matrix operations). The main libraries used are `NumPy`_ and
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`SciPy`_.
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Libraries
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NumPy
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-----
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`NumPy <http://numpy.scipy.org/>`_ is a low level library written in C (and
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FORTRAN) for high level mathematical functions. NumPy cleverly overcomes the
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problem of running slower algorithms on Python by using multidimensional arrays
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and functions that operate on arrays. Any algorithm can then be expressed as a
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function on arrays, allowing the algorithms to be run quickly.
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NumPy is part of the SciPy project, and is released as a separate library so
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people who only need the basic requirements can just use NumPy.
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NumPy is compatible with Python versions 2.4 through to 2.7.2 and 3.1+.
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SciPy
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-----
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`SciPy <http://scipy.org/>`_ is a library that uses Numpy for more mathematical
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function. SciPy uses NumPy arrays as its basic data structure. SciPy comes with
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modules for various commonly used tasks in scientific programing like linear
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algebra, integration (calculus), ordinary differential equation solvers and
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signal processing.
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Enthought
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---------
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Installing NumPy and SciPy can be a daunting task. Which is why the
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`Enthought Python distribution <http://enthought.com/>`_ was created. With
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Enthought, scientific python has never been easier (one click to install about
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100 scientific python packages). User beware: Enthought is not free.
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Matplotlib
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----------
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`matplotlib <http://matplotlib.sourceforge.net/>`_ is a flexible plotting
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library for creating interactive 2D and 3D plots that can also be saved as
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manuscript-quality figures. The API in many ways reflects that of `MATLAB <http://www.mathworks.com/products/matlab/>`_,
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easing transition of MATLAB users to Python. Many examples, along with the
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source code to re-create them, can be browsed at the `matplotlib gallery <http://matplotlib.sourceforge.net/gallery.html>`_.
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Resources
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Many people who do scientific computing are on Windows. And yet many of the
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scientific computing packages are notoriously difficult to build and install.
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`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_ however, has
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compiled a list of Windows binaries for many useful Python packages. The list
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of packages has grown from a mainly scientific python resource to a more
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general list. It might be a good idea to check it out if you're on Windows.
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