Merge pull request #355 from smithandrewl/master

Capitalization / Spelling

docs/scenarios/admin.rst

@@ -6,7 +6,7 @@ Fabric
 
 `Fabric <http://docs.fabfile.org>`_ is a library for simplifying system
 administration tasks. While Chef and Puppet tend to focus on managing servers
-and system libraries, fabric is more focused on application level tasks such
+and system libraries, Fabric is more focused on application level tasks such
 as deployment.
 
 Install Fabric:
@@ -77,7 +77,7 @@ It supports remote command execution from a central point (master host) to multi
 hosts (minions). It also supports system states which can be used to configure
 multiple servers using simple template files.
 
-Salt supports python versions 2.6 and 2.7 and can be installed via pip:
+Salt supports Python versions 2.6 and 2.7 and can be installed via pip:
 
 .. code-block:: console
 
@@ -95,7 +95,7 @@ The following command lists all available minion hosts, using the ping module.
 The host filtering is accomplished by matching the minion id, or using the grains system.
 The `grains <http://docs.saltstack.org/en/latest/topics/targeting/grains.html>`_ system
 uses static host information like the operating system version or the CPU architecture to
-provide a host taxonomy for the salt modules.
+provide a host taxonomy for the Salt modules.
 
 The following command lists all available minions running CentOS using the grains system:
 
@@ -119,7 +119,7 @@ and start the Apache server:
         - require:
           - pkg: apache
 
-State files can be written using YAML, the Jinja2 template system or pure python.
+State files can be written using YAML, the Jinja2 template system or pure Python.
 
     `Salt Documentation <http://docs.saltstack.com>`_
 

docs/scenarios/ci.rst

@@ -47,7 +47,7 @@ which provides the following features:
   interpreters
 * Running tests in each of the environments, configuring your test tool of
   choice
-* Acting as a frontend to Continuous Integration servers, reducing boilerplate
+* Acting as a front-end to Continuous Integration servers, reducing boilerplate
   and merging CI and shell-based testing.
 
 

docs/scenarios/scientific.rst

@@ -9,7 +9,7 @@ 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.
 
-Due to its high performance nature, scientific computing in python often refers
+Due to its high performance nature, scientific computing in Python often refers
 to 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
@@ -24,11 +24,11 @@ Tools
 IPython
 -------
 
-`IPytthon <http://ipython.org/>`_ is an enhanced version of Python interpreter.
+`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`
 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
+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.
 
@@ -64,7 +64,7 @@ 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 programing, for
+with modules for various commonly used tasks in scientific programming, for
 example: linear algebra, integration (calculus), ordinary differential equation
 solvers and signal processing.
 
@@ -86,7 +86,7 @@ based on Numpy and 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-alignement of data.
+and auto-alignment of data.
 
 Rpy2
 ----
@@ -120,7 +120,7 @@ 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
+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)

docs/scenarios/web.rst

@@ -271,10 +271,10 @@ and to the templates themselves.
   templates. This convenience can lead to uncontrolled
   increase in complexity, and often harder to find bugs.
 
-- It is often necessary to mix javascript templates with
+- It is often necessary to mix JavaScript templates with
   HTML templates. A sane approach to this design is to isolate
   the parts where the HTML template passes some variable content
-  to the javascript code.
+  to the JavaScript code.
 
 
 
@@ -370,7 +370,7 @@ The `base.html` file can be used as base for all site pages which are for exampl
     </body>
 
 
-The next listing is our site page (`site.html`) loaded in the python app which extends `base.html`. The content block is
+The next listing is our site page (`site.html`) loaded in the Python app which extends `base.html`. The content block is
 automatically set into the corresponding block in the base.html page.
 
 .. code-block:: html

docs/scenarios/xml.rst

@@ -8,7 +8,7 @@ untangle
 an XML document and returns a Python object which mirrors the nodes and
 attributes in its structure.
 
-For example, an xml file like this:
+For example, an XML file like this:
 
 .. code-block:: xml
 
@@ -36,9 +36,9 @@ xmltodict
 ---------
 
 `xmltodict <http://github.com/martinblech/xmltodict>`_ is another simple
-library that aims at making xml feel like working with json.
+library that aims at making XML feel like working with JSON.
 
-An xml file like this:
+An XML file like this:
 
 .. code-block:: xml
 
@@ -52,7 +52,7 @@ An xml file like this:
       </plus>
     </mydocument>
 
-can be loaded into a python dict like this:
+can be loaded into a Python dict like this:
 
 .. code-block:: python
 
@@ -68,6 +68,6 @@ and then you can access elements, attributes and values like this:
     doc['mydocument']['plus']['@a'] # == u'complex'
     doc['mydocument']['plus']['#text'] # == u'element as well'
 
-xmltodict also lets you roundtrip back to xml with the unparse function,
+xmltodict also lets you roundtrip back to XML with the unparse function,
 has a streaming mode suitable for handling files that don't fit in memory
 and supports namespaces.