Rewording and wrapping in scenarios/scientific.

Author: rshipp

docs/scenarios/scientific.rst

@@ -5,12 +5,12 @@ Scientific Applications
 Context
 :::::::
 
-Python is frequently used for high-performance scientific applications. Python
-is widely used in academia and scientific projects because it is easy to write,
-and it performs really well.
+Python is frequently used for high-performance scientific applications. It
+is widely used in academia and scientific projects because it is easy to write
+and performs well.
 
-Due to its high performance nature, scientific computing in Python often refers
-to external libraries, typically written in faster languages (like C, or
+Due to its high performance nature, scientific computing in Python often
+utilizes external libraries, typically written in faster languages (like C, or
 FORTRAN for matrix operations). The main libraries used are `NumPy`_, `SciPy`_
 and `Matplotlib`_. Going into detail about these libraries is beyond the scope
 of the Python guide. However, a comprehensive introduction to the scientific
@@ -24,13 +24,14 @@ Tools
 IPython
 -------
 
-`IPython <http://ipython.org/>`_ is an enhanced version of Python interpreter.
-The features it provides are of great interest for the scientists. The `inline mode`
+`IPython <http://ipython.org/>`_ is an enhanced version of Python interpreter,
+which provides features of great interest to scientists. The `inline mode`
 allow graphics and plots to be displayed in the terminal (Qt based version).
-Moreover the `notebook` mode supports literate programming and reproducible science
-generating a web-based Python notebook. This notebook allowing to store chunk of
-Python code along side to the results and additional comments (HTML, LaTeX, Markdown).
-The notebook could be shared and exported in various file formats.
+Moreover, the `notebook` mode supports literate programming and reproducible
+science generating a web-based Python notebook. This notebook allows you to
+store chunks of Python code along side the results and additional comments
+(HTML, LaTeX, Markdown). The notebook can then be shared and exported in various
+file formats.
 
 
 Libraries
@@ -45,9 +46,9 @@ problem of running slower algorithms on Python by using multidimensional arrays
 and functions that operate on arrays. Any algorithm can then be expressed as a
 function on arrays, allowing the algorithms to be run quickly.
 
-
 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.
+people who only need the basic requirements can use it without installing the
+rest of SciPy.
 
 NumPy is compatible with Python versions 2.4 through to 2.7.2 and 3.1+.
 
@@ -56,88 +57,90 @@ Numba
 
 `Numba <http://numba.pydata.org>`_ is an Numpy aware Python compiler
 (just-in-time (JIT) specializing compiler) which compiles annotated Python (and
-Numpy) code to LLVM (Low Level Virtual Machine) (through special decorators).
-Briefly, Numba using system that compiles Python code with LLVM to code which
+Numpy) code to LLVM (Low Level Virtual Machine) through special decorators.
+Briefly, Numba uses a system that compiles Python code with LLVM to code which
 can be natively executed at runtime.
 
 SciPy
 -----
 
-`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 programming, for
-example: linear algebra, integration (calculus), ordinary differential equation
-solvers and signal processing.
+`SciPy <http://scipy.org/>`_ is a library that uses NumPy for more mathematical
+functions. SciPy uses NumPy arrays as the basic data structure, and comes
+with modules for various commonly used tasks in scientific programming,
+including linear algebra, integration (calculus), ordinary differential equation
+solving 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
+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
+users to Python. Many examples, along with the source code to re-create them,
+are available in the `matplotlib gallery
 <http://matplotlib.sourceforge.net/gallery.html>`_.
 
 Pandas
 ------
+
 `Pandas <http://pandas.pydata.org/>`_ is data manipulation library
-based on Numpy and which provides many useful functions for accessing,
+based on Numpy which provides many useful functions for accessing,
 indexing, merging and grouping data easily. The main data structure (DataFrame)
-is close to what could be found in the R statistical package, that is
-an heterogeneous data tables with name indexing, time series operations
-and auto-alignment of data.
+is close to what could be found in the R statistical package; that is,
+heterogeneous data tables with name indexing, time series operations and
+auto-alignment of data.
 
 Rpy2
 ----
+
 `Rpy2 <http://rpy.sourceforge.net/rpy2.html>`_ is a Python binding for the R
-statistical package allowing to execute R functions from Python and passing
-data back and forth the two environments. Rpy2 is the object oriented
-implementation of the binding based on `Rpy <http://rpy.sourceforge.net/rpy.html>`_.
+statistical package allowing the execution of R functions from Python and passing
+data back and forth between the two environments. Rpy2 is the object oriented
+implementation of the `Rpy <http://rpy.sourceforge.net/rpy.html>`_ bindings.
 
 PsychoPy
 --------
 
 `PsychoPy <http://www.psychopy.org/>`_ is a library for cognitive scientists
-allowing to create cognitive psychology and neuroscience experiments. The library
-handles both presentation of stimuli, scripting of experimental design and
-data collection.
+allowing the creation of cognitive psychology and neuroscience experiments.
+The library handles presentation of stimuli, scripting of experimental design
+and data collection.
 
 
 Resources
 :::::::::
 
-Installation of scientific  Python packages can be troublesome. Many of these
-packages are implemented as Python C extensions which need to be compiled.
+Installation of scientific  Python packages can be troublesome, as many of
+these packages are implemented as Python C extensions which need to be compiled.
 This section lists various so-called scientific 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.
+Many people who do scientific computing are on Windows, yet many of the
+scientific computing packages are notoriously difficult to build and install
+on this platform. `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. If you're on Windows, you may want to check it out.
 
 Anaconda
 --------
 
 `Continuum Analytics <http://continuum.io/>`_ offers the `Anaconda
 Python Distribution <https://store.continuum.io/cshop/anaconda>`_ which
-includes all the common scientific Python packages and additionally many
-packages related to data analytics and big data. Anaconda itself is free, and
-Continuum sells a number of proprietary add-ons. Free
-licenses for the add-ons are available for academics and researchers.
+includes all the common scientific Python packages as well as many packages
+related to data analytics and big data. Anaconda itself is free, and
+Continuum sells a number of proprietary add-ons. Free licenses for the
+add-ons are available for academics and researchers.
 
 Canopy
 ------
 
-`Canopy <https://www.enthought.com/products/canopy/>'_ is another scientific Python
-distribution, produced by `Enthought <https://www.enthought.com/>`_. A limited
-'Canopy Express' variant is available for free, and Enthought charge for the
-full distribution. Free licenses are available for academics.
+`Canopy <https://www.enthought.com/products/canopy/>`_ is another scientific
+Python distribution, produced by `Enthought <https://www.enthought.com/>`_.
+A limited 'Canopy Express' variant is available for free, but Enthought
+charges for the full distribution. Free licenses are available for academics.