Apparently, I can't spell... fixing my mistakes and others in the faqs.

WeatherGod · WeatherGod · commit 30dc21a6306e · 2011-03-14T14:55:31.000-05:00
diff --git a/doc/faq/howto_faq.rst b/doc/faq/howto_faq.rst
@@ -151,7 +151,7 @@ where all values are in fractional (0 to 1) coordinates.  See
 Automatically make room for tick labels
 ----------------------------------------------------
 
-In most use cases, it is enough to simpy change the subplots adjust
+In most use cases, it is enough to simply change the subplots adjust
 parameters as described in :ref:`howto-subplots-adjust`.  But in some
 cases, you don't know ahead of time what your tick labels will be, or
 how large they will be (data and labels outside your control may be
@@ -232,7 +232,7 @@ When plotting time series, eg financial time series, one often wants
 to leave out days on which there is no data, eg weekends.  By passing
 in dates on the x-xaxis, you get large horizontal gaps on periods when
 there is not data. The solution is to pass in some proxy x-data, eg
-evenly sampled indicies, and then use a custom formatter to format
+evenly sampled indices, and then use a custom formatter to format
 these as dates. The example below shows how to use an 'index formatter'
 to achieve the desired plot::
 
@@ -650,7 +650,7 @@ or by saving to a file handle::
     fig.savefig(sys.stdout)
 
 Here is an example using the Python Imaging Library PIL.  First the figure
-is saved to a StringIO objectm which is then fed to PIL for further
+is saved to a StringIO object which is then fed to PIL for further
 processing::
 
     import StringIO, Image
diff --git a/doc/faq/installing_faq.rst b/doc/faq/installing_faq.rst
@@ -162,7 +162,7 @@ generate postscript images from some numerical simulations, and still
 others in web application servers to dynamically serve up graphs.
 
 To support all of these use cases, matplotlib can target different
-outputs, and each of these capabililities is called a backend; the
+outputs, and each of these capabilities is called a backend; the
 "frontend" is the user facing code, ie the plotting code, whereas the
 "backend" does all the hard work behind-the-scenes to make the
 figure.  There are two types of backends: user interface backends (for
@@ -209,7 +209,7 @@ For the rendering engines, one can also distinguish between `vector
 <http://en.wikipedia.org/wiki/Raster_graphics>`_ renderers.  Vector
 graphics languages issue drawing commands like "draw a line from this
 point to this point" and hence are scale free, and raster backends
-generate a pixel represenation of the line whose accuracy depends on a
+generate a pixel representation of the line whose accuracy depends on a
 DPI setting.
 
 Here is a summary of the matplotlib renderers (there is an eponymous
@@ -323,7 +323,7 @@ mpkd directory, which will have a name like
 file:`matplotlib-0.99.0.rc1-py2.5-macosx10.5.mpkg`, it will run the
 Installer.app, prompt you for a password if you need system wide
 installation privileges, and install to a directory like
-file:`/Library/Python/2.5/site-packages/`, again depedending on your
+file:`/Library/Python/2.5/site-packages/`, again depending on your
 python version.  This directory may not be in your python path, so you
 should test your installation with::
 
diff --git a/doc/faq/usage_faq.rst b/doc/faq/usage_faq.rst
@@ -22,8 +22,8 @@ the axes objects are responsible for other components that handle axis
 labeling, ticks, title, and plot legends. A *figure* is the container
 for one or more axes objects.
 
-Everything in matplotlib is organized in a heirarchy. At the top
-of the heirarchy is the matplotlib state-machine environment. This
+Everything in matplotlib is organized in a hierarchy. At the top
+of the hierarchy is the matplotlib state-machine environment. This
 environment is responsible for managing the figures and axes
 that have been created and modified by you. The behavior of the matplotlib
 environment is similar to MATLAB and therefore should be most familiar to
