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Added Web.py in web.rst
Added links to support in web.rst Added a chapter for Scientific/HighPerformance Python Signed-off-by: Chewxy <chewxy@gmail.com>
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docs/scenarios/scientific.rst

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=======================
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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 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.

docs/scenarios/web.rst

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many popular modules, `Extensions <http://flask.pocoo.org/extensions/>`_ may
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already exist to suit your needs.
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**Support** for flask can best be found in its mailing list. Just shoot an email to
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flask@librelist.com and reply to the confirmation email.
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Pyramid
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-------
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.. todo:: Explian Pyramid
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Web.py
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------
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`web.py <http://webpy.org>`_ is a minimalist web framework that is somewhere between Django and Flask.
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The premise of web.py is that it is flexible - code your webapp any way you want it, in just python and python alone.
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web.py comes with some nifty tools built in, like database connection tools and a mini http server.
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**Support** for web.py is quite sparse, but you can look for support in the `mailing list <http://groups.google.com/group/webpy>`_ .
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Web Servers
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WebFaction
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~~~~~~~~~~~
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`Webfaction <http://www.webfaction.com/>`_ started off as a dedicated python hosting company.
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In fact it used to be called python-hosting.com. Webfaction supports Python versions 2.4 through to 2.7.2
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as well as Python 3 versions.
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Webfaction has a very extensive `user guide <http://docs.webfaction.com/user-guide/>`_
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and specific stack (`Django <http://docs.webfaction.com/software/django/index.html> `_, `Pylons <http://docs.webfaction.com/software/pylons.html>`_,
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`Pyramid <http://docs.webfaction.com/software/pyramid.html>`_, `TurboGears <http://docs.webfaction.com/software/turbogears.html>`_
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and `vanilla python <http://docs.webfaction.com/software/python.html>`_) guides.
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It also has a stack-overflow style `community <http://community.webfaction.com/>`_ that is quite useful.
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Twisted
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