matplotlib Code

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@header@ 
<h2>Installing matplotlib</h2>

matplotlib requires at a minimum python 2.3, NumPy or Numeric or 
numarray and freetype.  If you are using python-2.3, you will also need 
to install setuptools, which can be downloaded at 
<a>http://peak.telecommunity.com/dist/ez_setup.py</a>.  Just run 
ez_setup.py to install setuptools. To get 
the most out of matplotlib, you will want to build some of the optional 
GUI and image extensions, discussed below. Matplotlib is known to work 
on linux, unix, win32 and OS X platforms.<p>

There are two kinds of matplotlib backends: vector based and raster
based.  Two vector based backends, SVG and PS, produce ASCII text
output files *.svg and *.ps; the other vector based backends, PDF and 
EMF, produces binary output files *.emf.  The core raster based 
renderer is the <a href=http://antigrain.com>antigrain</a> (agg) 
backend.  This is a high quality 2D library that supports fast 
antialiasing, alpha blending and much more.  If you want to produce 
PNGs or GUI images that support all of matplotlib's features, you 
should compile matplotlib with agg support and use one of the GUI agg 
backends as described below: GTKAgg, WXAgg, TkAgg or FLTKAgg.  All of 
these features are included in the windows installer<p>

<h2><a name=compiling>Compiling</a></h2>

You probably don't need to compile matplotlib unless you want to or
work on an obscure platform.  There are binary builds for Windows, OS
X and many major linux distributions; see below.  If you do want to
compile, read on.<p>

You will need to have recent versions of freetype (>= 2.1.7), libpng
and zlib installed on your system.  If you are using a package
manager, also make sure the devel versions of these packages are also
installed (eg freetype-devel).<p>

The top of setup.py contains some flags controlling which backends
will be built.  If you want to use a GUI backend, you may need to
install dev headers for the GUI toolkits you want to build with (eg
gtk2-dev).  You can choose which backends to enable by setting the
flags in setup.py, but the 'auto' flags will work in most cases, as
matplotlib tries to find a GUI and build the backend acccordingly.  If
you know you don't want a particular backend or extension, you can set
that flag to False.<p>

Most users will want to set <tt>BUILD_AGG = 1</tt> and one or more of
the GUI backends to True.  Exceptions to this are if you know you
don't need a GUI (eg a web server) or you only want to produce
postscript or SVG for example.<p>

If you have installed prerequisites to nonstandard places and need to
inform matplotlib where they are, edit <tt>setupext.py</tt> an add the
base dirs to the <tt>basedir</tt> dictionary entry for your
<tt>sys.platform</tt>.  Eg, if the header to some required library is
in <tt>/some/path/include/somheader.h</tt>, put <tt>/some/path</tt> in
the basedir list for your platform.<p>

matplotlib works with with NumPy, Numeric or numarray.  At compile time,
setup.py will look for both packages and compile the appropriate
extensions into matplotlib.  At runtime, the correct extension code
will be chosen based on your numerix setting in matplotlibrc.  If you
want to be able to use either NumPy, Numeric or numarray efficiently with
matplotlib, it is important that you have *both* present and in your
PYTHONPATH when you compile matplotlib.<p>

Note that if you install matplotlib anywhere other than the default
location, you will need to set the <tt>MATPLOTLIBDATA</tt> environment
variable to point to the install base dir.<p>

Once you have everything above set to your liking,for just do the usual
thing

<pre>
      python setup.py build
      python setup.py install
</pre>
    
There are many defaults that you can control in matplotlib in the <a
href=matplotlibrc>matplotlibrc</a> file.  As for installation, the
most important of these are the <tt>backend</tt> and <tt>numerix</tt>
settings.  Please set these parameters for your particular build after
you install because they are not automatically configured for your
particular installation.

<h2><a name=windows>Windows</a></h2>

If you don't already have python installed, you may want to consider
using the enthought edition of python, which has scipy, Numeric, and
wxpython, plus a lot of other goodies, preinstalled - <a
href=http://www.enthought.com/python> enthought python</a> .  With the
enthought edition of python + matplotlib installer, the following
backends should work out of the box: agg, wx, wxagg, tkagg, ps and
svg.<p>

For standard python installations, you will also need to install
either Numpy, Numeric or numarray in addition to the matplotlib installer.
matplotlib has a <tt>numerix</tt> setting in the matplotlib rc file
(which by default resides in
<tt>c:\python23\share\matplotlitb\matplotlibrc</tt>) and you should
make sure this setting corresponds to your preferred array package.<p>

With a standard python + NumPy/Numeric/numarray + matplotlib, the following
backends should work on windows: agg, tkagg, ps, svg.  If you want
others, eg a wx, wxagg, gtk or gtkagg, you'll need to install the
requisite GUI toolkits, as described on <a
href=backends.html>backends</a>.  This is fairly easy, as both
wxpython and pygtk come with windows friendly installers.  The latter
includes an additional requirement of the GTK runtime.<p>

All of the GUI backends run on windows, but TkAgg is probably the best
for interactive use from the standard python shell or ipython.  The
windows installer (*.exe or *.egg) on the download page contains all the code
you need to get up and running.  However, there are many <a
href=examples>examples</a> that are not included in the windows
installer.  If you want to try the many demos that come in the
matplotlib src distribution, download the zip file and look in the
examples subdir.<p>

<i>Important: There are known conflicts with some of the backends with
some python IDEs such as pycrust, idle.  If you want to use matplotlib
from an IDE, please consult <a
href=backends.html>backends</a> for
compatibility information.  You will have the greatest likelihood of
success if you run the examples from the command shell or by double
clicking on them, rather than from an IDE.  If you are interactively
generating plots, your best bet is TkAgg from the standard python
shell or ipython.</i>

<h3><a name=osx>OS X</a></h3>

All of the backends run on OS X.  Chris Barker has built a binary
package (fink users see below) for matplotlib which is hosted on <a
href=http://pythonmac.org/packages>pythonmac</a>, and works with Agg,
Wx and Tk; see the <a
href=http://geosci.uchicago.edu/~tobis/pylab.html>step-by-step
instructions</a> kindly provided by Michael Tobis.  It does not
included the GTK* backends, because pygtk is usually provided through
fink and/or darwinport.  This version should work on an stock OS-X
10.3.*, and you need Numeric or numarray (available on the same site),
and TK or wxPython, if you want to use those back ends.<p>

There are other binary packages to look at which each have different
components included.  Robery Kern has built an all-in-one installer
which includes scipy, Numeric, numarray, matplotlib, ipython, VTK,
MayaVi, PIL, the enthought tool suite and much more; see <a
href=http://www.scipy.org/wikis/featurerequests/MacEnthon/view?searchterm=macenthon>>MacEnthon</a>.
Charlie Moad has bundled matplotlib with freetype, png, wx, numarray,
numeric and doesn't rely on fink or darwinports; available in the <a
href=http://sda.iu.edu/projects.html>Tools Section</a>.  Another
bundle is <a href=http://www.stecf.org/macosxscisoft>scisoft</a> which
packages many scientific packages for python on OS X, including
matplotlib, although it is designed for astronomical analysis.  </p>

If you want to compile yourself on OS X, make sure you read the
compiling instructions above.  You will need to install freetype2,
libpng and zlib via fink or from src, or from a package manager such
as fink or <a href=http://ii2.sourceforge.net>i-Installer</a>.  You
will also need the base libraries for a given backend.  Eg, if you
want to run TkAgg, you will need a python with Tkinter; if you want to
use WxAgg, install wxpython.  See <a href=backends.html>backends</a>
for a more comprehensive discussion of the various backend
requirements.  Edit setup.py to configure the backends you want to
build as described above.<p>

Note when running a GUI backend in OSX, you should launch your
programs with pythonw rather than python, or you may get
nonresponsive GUIs.

<h2>Package Managers: (rpms, apt, fink)</h2>

<h3><a name=rpms>RPMS</a></h3>

To build all the backends on a binary linux distro such as redhat, you
need to install a number of the devel libs (and whatever dependencies
they require), I suggest
<ul> 
	
  <li>matplotlib core: zlib, zlib-devel, libpng, libpng-devel,
      freetype, freetype-devel, freetype-utils</li>

  <li>gtk/gtkagg backend: gtk2-devel, gtk+-devel, pygtk2, glib-devel,
      pygtk2-devel, gnome-libs-devel, pygtk2-libglade</li>

  <li>tk backend: tcl, tk, tkinter</li>

  <li>wx/wxagg backends - the wxpython rpms from <a
  href=http://wxpython.org</a>wxpython</a></li>

</ul>

<h3><a name=debian>Debian</a></h3>

Vittorio Palmisano <redclay@email.it> maintails the debian
packages 

<ul>
    <li> add these lines to your /etc/apt/sources.list: 
<pre>
  deb http://anakonda.altervista.org/debian packages/
  deb-src http://anakonda.altervista.org/debian sources/
</pre> 
    </li>
   
      <li> then run:
<pre>   
  # apt-get update
  # apt-get install python-matplotlib python-matplotlib-doc
</pre>         
  </li>
</ul>


<h3><a name=fink>Fink</a></h3>

fink users should use Jeffrey Whitaker's fink distribution, which
supports the GTK, TkAgg, GTKAgg, PS, WX, WXAgg and Agg backends. See
<a
href=http://fink.sourceforge.net/pdb/package.php/matplotlib-py23>matplotlib
fink</a>
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