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Chewxy
858aff1a7c
Added links to support in web.rst Added a chapter for Scientific/HighPerformance Python Signed-off-by: Chewxy <chewxy@gmail.com>
50 lines
2.3 KiB
ReStructuredText
50 lines
2.3 KiB
ReStructuredText
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Scientific Applications
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Context
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Python is frequently used for high-performance scientific applications. Python is widely used in academia
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and scientific projects because it is easy to write, and it performs really well.
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Due to its high performance nature, scientific computing in python often refers to external libraries, typically
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written in faster languages (like C, or FORTRAN for matrix operations). The main libraries used are NumPy and 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 FORTRAN) for high level mathematical functions.
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NumPy cleverly overcomes the problem of running slower algorithms on Python by using multidimensional arrays and functions that operate on arrays.
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Any algorithm can then be expressed as a 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 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 function. SciPy uses NumPy arrays as its basic data structure.
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SciPy comes with modules for various commonly used tasks in scientific programing like linear algebra, integration (calculus),
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ordinary differential equation solvers and 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 `Enthought Python distribution <http://enthought.com/>`_ was created. With Enthought,
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scientific python has never been easier (one click to install about 100 scientific python packages). User beware: Enthought is not free.
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Matplotlib
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----------
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.. todo:: write about matplotlib.
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Resources
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Many people who do scientific computing are on Windows. And yet many of the 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 compiled a list of Windows binaries for many useful Python packages.
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The list of packages has grown from a mainly scientific python resource to a more general list. It might be a good idea to check it out if you're on Windows. |