Merge pull request #246 from esc/refactor/scenarios/scientific

Refactor/scenarios/scientific

docs/scenarios/scientific.rst

@@ -10,15 +10,19 @@ is widely used in academia and scientific projects because it is easy to write,
 and it performs really well.
 
 Due to its high performance nature, scientific computing in python often refers
-to external libraries, typically written in faster languages (like C, or FORTRAN
+to external libraries, typically written in faster languages (like C, or
-for matrix operations). The main libraries used are `NumPy`_ and
+FORTRAN for matrix operations). The main libraries used are `NumPy`_, `SciPy`_
-`SciPy`_.
+and `Matplotlib`_. Going into detail about these libraries is beyond the scope
+of the Python guide. However, a comprehensive introduction to the scientific
+Python ecosystem can be found in the `Python Scientific Lecture Notes
+<http://scipy-lectures.github.com/>`_
 
 Libraries
 :::::::::
 
 NumPy
 -----
+
 `NumPy <http://numpy.scipy.org/>`_ is a low level library written in C (and
 FORTRAN) for high level mathematical functions. NumPy cleverly overcomes the
 problem of running slower algorithms on Python by using multidimensional arrays
@@ -33,14 +37,44 @@ NumPy is compatible with Python versions 2.4 through to 2.7.2 and 3.1+.
 
 SciPy
 -----
-`SciPy <http://scipy.org/>`_ is a library that uses Numpy for more mathematical
-function. SciPy uses NumPy arrays as its basic data structure. SciPy comes with
-modules for various commonly used tasks in scientific programing like linear
-algebra, integration (calculus), ordinary differential equation solvers and
-signal processing.
 
-Enthought
+`SciPy <http://scipy.org/>`_ is a library that uses Numpy for more mathematical
----------
+functions. SciPy uses NumPy arrays as the basic data structure. SciPy comes
+with modules for various commonly used tasks in scientific programing, for
+example: linear algebra, integration (calculus), ordinary differential equation
+solvers and signal processing.
+
+Matplotlib
+----------
+
+`Matplotlib <http://matplotlib.sourceforge.net/>`_ is a flexible plotting
+library for creating interactive 2D and 3D plots that can also be saved as
+manuscript-quality figures.  The API in many ways reflects that of `MATLAB
+<http://www.mathworks.com/products/matlab/>`_, easing transition of MATLAB
+users to Python.  Many examples, along with the source code to re-create them,
+can be browsed at the `matplotlib gallery
+<http://matplotlib.sourceforge.net/gallery.html>`_.
+
+Resources
+:::::::::
+
+Installation of scientific  Python packages can be troublesome. Many of these
+packages are implemented as Python C extensions which need to be compiled.
+This section lists various so-called Python distributions which provide precompiled and 
+easy-to-install collections of scientific Python packages.
+
+Unofficial Windows Binaries for Python Extension Packages
+---------------------------------------------------------
+
+Many people who do scientific computing are on Windows. And yet many of the
+scientific computing packages are notoriously difficult to build and install.
+`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_ however, has
+compiled a list of Windows binaries for many useful Python packages. 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.
+
+Enthought Python Distribution (EPD)
+-----------------------------------
 
 Installing NumPy and SciPy can be a daunting task. Which is why the
 `Enthought Python distribution <http://enthought.com/>`_ was created. With
@@ -50,25 +84,13 @@ variants: a free version `EPD Free <http://enthought.com/products/epd_free.php>`
 and a paid version with various `pricing options.
 <http://enthought.com/products/epd_sublevels.php>`_
 
-Matplotlib
+Anaconda
-----------
+--------
 
-`matplotlib <http://matplotlib.sourceforge.net/>`_ is a flexible plotting
+`Continuum Analytics <http://continuum.io/>`_ offers the `Anaconda
-library for creating interactive 2D and 3D plots that can also be saved as
+Python Distribution <https://store.continuum.io/cshop/anaconda>`_ which
-manuscript-quality figures.  The API in many ways reflects that of `MATLAB <http://www.mathworks.com/products/matlab/>`_,
+includes all the common scientific python packages and additionally many
-easing transition of MATLAB users to Python.  Many examples, along with the
+packages related to data analytics and big data. Anaconda comes in two
-source code to re-create them, can be browsed at the `matplotlib gallery <http://matplotlib.sourceforge.net/gallery.html>`_.
+flavours, a paid for version and a completely free and open source community
-
+edition, Anaconda CE, which contains a slightly reduced feature set. Free
-Resources
+licences for the paid-for version are available for academics and researchers.
-:::::::::
-
-Many people who do scientific computing are on Windows. And yet many of the
-scientific computing packages are notoriously difficult to build and install.
-`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_ however, has
-compiled a list of Windows binaries for many useful Python packages. 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.
-
-For a quick introduction to scientific python:
-
-http://scipy-lectures.github.com