thomasgit
diff --git a/‎doc/_templates/index.html
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diff --git a/‎doc/api/api_changes.rst
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diff --git a/‎doc/faq/usage_faq.rst
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@@ -9,10 +9,19 @@ <h1>intro</h1>
   publication quality figures in a variety of hardcopy formats and
   interactive environments across platforms.  matplotlib can be used
   in python scripts, the python and <a
-  href="http://ipython.scipy.org">ipython</a> shell (ala matlab or
-  mathematica), web application servers, and six graphical user
+  href="http://ipython.scipy.org">ipython</a> shell (ala
+  MATLAB<sup>&reg;<a name="matlab" href="#ftn.matlab">*</a></sup>
+  or
+  Mathematica<sup>&reg;<a name="mathematica"
+  href="#ftn.mathematica">&#8224;</a></sup>),
+  web application servers, and six graphical user
   interface toolkits.</p>
 
+
+
+
+
+
   <p>matplotlib tries to make easy things easy and hard things possible.
   You can generate plots, histograms, power spectra, bar charts,
   errorcharts, scatterplots, etc, with just a few lines of code.
@@ -35,7 +44,7 @@ <h1>intro</h1>
 
   <p>For the power user, you have full control of line styles, font
   properties, axes properties, etc, via an object oriented interface
-  or via a set of functions familiar to Matlab&reg; users.
+  or via a set of functions familiar to MATLAB users.
   The pylab mode provides all of the <a href="api/pyplot_api.html">pyplot</a> plotting
   functions listed below, as well as non-plotting functions from
   <a href="http://scipy.org/Numpy_Example_List_With_Doc">numpy</a> and
@@ -1193,4 +1202,16 @@ <h3>plotting commands</h3> <br/>
 
     </tr>
   </table>
+
+<div class="footnote"><p>
+<sup><a name="ftn.matlab" href="#matlab">*</a></sup>
+MATLAB is a registered trademark of The MathWorks, Inc.
+</p>
+<p>
+<sup><a name="ftn.mathematica" href="#mathematica">&#8224;</a></sup>
+Mathematica is a registered trademark of Wolfram Research, Inc.
+</p>
+
+
+
 {% endblock %}
@@ -179,7 +179,7 @@ Changes for 0.98.x
   to scale one-sided densities by a factor of 2.  Also, optionally
   scale the densities by the sampling frequency, which gives true values
   of densities that can be integrated by the returned frequency values.
-  This also gives better MatLab compatibility.  The corresponding
+  This also gives better MATLAB compatibility.  The corresponding
   :class:`matplotlib.axes.Axes` methods and :mod:`matplotlib.pyplot`
   functions were updated as well.
 
@@ -1094,7 +1094,7 @@ Changes for 0.72
   - pylab figure now defaults to num=None, which creates a new figure
     with a guaranteed unique number
 
-  - contour method syntax changed - now it is matlab compatible
+  - contour method syntax changed - now it is MATLAB compatible
 
       unchanged: contour(Z)
       old: contour(Z, x=Y, y=Y)
@@ -1183,7 +1183,7 @@ Changes for 0.65
 ::
 
 
-  mpl_connect and mpl_disconnect in the matlab interface renamed to
+  mpl_connect and mpl_disconnect in the MATLAB interface renamed to
   connect and disconnect
 
   Did away with the text methods for angle since they were ambiguous.
@@ -1272,7 +1272,7 @@ Changes for 0.54.3
 Changes for 0.54
 ================
 
-matlab interface
+MATLAB interface
 ----------------
 
 dpi
@@ -1289,7 +1289,7 @@ screen to get true sizes.
 pcolor and scatter
 ~~~~~~~~~~~~~~~~~~
 
-There are two changes to the matlab interface API, both involving the
+There are two changes to the MATLAB interface API, both involving the
 patch drawing commands.  For efficiency, pcolor and scatter have been
 rewritten to use polygon collections, which are a new set of objects
 from matplotlib.collections designed to enable efficient handling of
@@ -1323,7 +1323,7 @@ property, eg to have different line widths, see matplotlib.collections
 for a discussion on how to set the properties as a sequence.
 
 For scatter, the size argument is now in points^2 (the area of the
-symbol in points) as in matlab and is not in data coords as before.
+symbol in points) as in MATLAB and is not in data coords as before.
 Using sizes in data coords caused several problems.  So you will need
 to adjust your size arguments accordingly or use scatter_classic.
 
@@ -1491,7 +1491,7 @@ Changes for 0.50
 
   * backends must implement FigureCanvasBackend (the thing that
     controls the figure and handles the events if any) and
-    FigureManagerBackend (wraps the canvas and the window for matlab
+    FigureManagerBackend (wraps the canvas and the window for MATLAB
     interface).  FigureCanvasBase implements a backend switching
     mechanism
 
@@ -1514,9 +1514,9 @@ Changes for 0.50
 
   Migrating code:
 
-  Matlab interface:
+  MATLAB interface:
 
-    The only API change for those using the matlab interface is in how
+    The only API change for those using the MATLAB interface is in how
     you call figure redraws for dynamically updating figures.  In the
     old API, you did
 
@@ -1653,7 +1653,7 @@ Changes for 0.40
   - new module transforms supplies Bound1D, Bound2D and Transform
     instances and more
 
-  - Changes to the matlab helpers API
+  - Changes to the MATLAB helpers API
 
     * _matlab_helpers.GcfBase is renamed by Gcf.  Backends no longer
       need to derive from this class.  Instead, they provide a factory
 
@@ -17,19 +17,19 @@ that gets
 installed alongside :mod:`matplotlib`; and :mod:`matplotlib.pyplot` is a
 module in matplotlib.
 
-Pyplot provides a Matlab-style state-machine interface to
+Pyplot provides a MATLAB-style state-machine interface to
 the underlying object-oriented plotting library in matplotlib.
 
 Pylab combines the pyplot functionality (for plotting) with the numpy
 functionality (for mathematics and for working with arrays)
 in a single namespace, making that namespace
-(or environment) even more Matlab-like.  This is what you get if
+(or environment) even more MATLAB-like.  This is what you get if
 you use the
 *ipython* shell with the *-pylab* option, which imports everything
 from pylab and makes plotting fully interactive.
 
 We have been gradually converting the matplotlib examples
-from pure Matlab-style, using "from pylab import \*", to a preferred
+from pure MATLAB-style, using "from pylab import \*", to a preferred
 style in which pyplot is used for some convenience functions, either
 pyplot or the object-oriented style is used for the remainder of the
 plotting code, and numpy is used explicitly for numeric array operations.
@@ -42,7 +42,7 @@ In this preferred style, the imports at the top are::
 Then one calls, for example, np.arange, np.zeros, np.pi, plt.figure,
 plt.plot, plt.show, etc.
 
-Example, pure Matlab-style::
+Example, pure MATLAB-style::
 
     from pylab import *
     x = arange(0, 10, 0.2)
@@ -71,7 +71,7 @@ And using pyplot convenience functions, but object-orientation for the rest::
     plt.show()
 
 So, why do all the extra typing required as one moves away from the pure
-matlab-style?  For very simple things like this example, the only
+MATLAB-style?  For very simple things like this example, the only
 advantage is educational: the wordier styles are more explicit, more
 clear as to where things come from and what is going on.  For more
 complicated applications, the explicitness and clarity become