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Tidied up some of the documentation.
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‎doc/_templates/index.html

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{% extends "layout.html" %}
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{% set title = 'matplotlib: python plotting' %}
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{% block body %}
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<h1>John Hunter (1968-2012)</h1>
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<table bgcolor="#ddddff">
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<tr>
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<td>
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<img src="_static/John-hunter-crop-2.jpg"/>
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</td>
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<td>
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<p>
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On August 28 2012, John D. Hunter, the creator of matplotlib, died
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from complications arising from cancer treatment, after a brief but
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intense battle with this terrible illness. John is survived by his
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wife Miriam, his three daughters Rahel, Ava and Clara, his sisters
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Layne and Mary, and his mother Sarah.</p>
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<p>
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If you have benefited from John's many contributions, please say
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thanks in the way that would matter most to him. Please consider
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making a donation to
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the <a href="http://numfocus.org/johnhunter/">John Hunter Memorial
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Fund</a>.</p>
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</td>
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</tr>
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</table>
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<h1>Introduction</h1>
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<p>matplotlib is a python 2D plotting library which produces
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properties, axes properties, etc, via an object oriented interface
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or via a set of functions familiar to MATLAB users.</p>
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<div style="float: right; min-width: 450px; width: 50%; padding-left: 5%;">
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<h1>John Hunter (1968-2012)</h1>
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<table bgcolor="#ddddff">
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<tr>
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<td>
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<img src="_static/John-hunter-crop-2.jpg" align="left" />
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</td>
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<td>
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<p>
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On August 28 2012, John D. Hunter, the creator of matplotlib, died
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from complications arising from cancer treatment, after a brief but
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intense battle with this terrible illness. John is survived by his
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wife Miriam, his three daughters Rahel, Ava and Clara, his sisters
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Layne and Mary, and his mother Sarah.</p>
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<p>
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If you have benefited from John's many contributions, please say
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thanks in the way that would matter most to him. Please consider
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making a donation to
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the <a href="http://numfocus.org/johnhunter/">John Hunter Memorial
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Fund</a>.</p>
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</td>
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</tr>
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</table>
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</div>
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<h1>Download</h1>
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Visit the
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<h1>Citing matplotlib</h1>
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<p>
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matplotlib is the brainchild of John Hunter (1968-2012), who has put an
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inordinate amount of effort into producing a piece of software utilized by
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thousands of scientists worldwide.
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matplotlib is the brainchild of John Hunter (1968-2012), who, along with its many
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contributors, have put an immeasurable amount of time and effort into producing a
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piece of software utilized by thousands of scientists worldwide.
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If matplotlib contributes to a project that leads to a scientific publication,
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please acknowledge this fact by citing the project. You can use this
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please acknowledge this work by citing the project. You can use this
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<a href="{{ pathto('citing') }}">ready-made citation entry</a>.
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</p>
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<h1>Open source</h1>
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<p>Please
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consider <a href="http://sourceforge.net/project/project_donations.php?group_id=80706">donating</a>
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to support matplotlib development or to
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the <a href="http://numfocus.org/johnhunter/">John Hunter Memorial
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Fund</a>.</p>
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<p>
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Please consider <a href="http://sourceforge.net/project/project_donations.php?group_id=80706">donating</a>
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to support matplotlib development or to the <a href="http://numfocus.org/johnhunter/">John Hunter Memorial Fund</a>.
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</p>
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<p>The matplotlib <a href="{{ pathto('users/license') }}">license</a>
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is based on the Python Software Foundation
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<a href="http://www.python.org/psf/license">(PSF)</a> license.</p>
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<p>
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The matplotlib <a href="{{ pathto('users/license') }}">license</a> is based on the Python Software Foundation
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<a href="http://www.python.org/psf/license">(PSF)</a> license.
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</p>
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<p>There is an active developer community and a long list of people
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who have made significant <a href="{{ pathto('users/credits')
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}}">contributions</a>.</p>
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<p>
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There is an active developer community and a long list of people
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who have made significant <a href="{{ pathto('users/credits') }}">contributions</a>.
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</p>
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<div class="footnote"><p>
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Mathematica is a registered trademark of Wolfram Research, Inc.
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</p>
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{% endblock %}

‎doc/_templates/layout.html

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<li><a href="{{ pathto('search') }}">search</a>|&nbsp;</li>
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<li><a href="{{ pathto('examples/index') }}">examples</a>|&nbsp;</li>
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<li><a href="{{ pathto('gallery') }}">gallery</a>|&nbsp;</li>
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<li><a href="{{ pathto('api/pyplot_summary') }}">pyplot</a>|&nbsp;</li>
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<li><a href="{{ pathto('citing') }}">citation</a>|&nbsp;</li>
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<li><a href="{{ pathto('contents') }}">docs</a> &raquo;</li>
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{% endblock %}

‎doc/api/cm_api.rst

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cm (colormap)
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*************
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:mod:`matplotlib.cm`
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====================
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‎doc/api/colors_api.rst

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colors
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******
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For a plot showing the available matplotlib colormaps see the
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:ref:`colormap <pylab_examples-show_colormaps>` example.
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:mod:`matplotlib.colors`
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========================

‎doc/api/matplotlib_configuration_api.rst

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*************
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configuration
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*************
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:mod:`matplotlib`
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=================
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The top level :mod:`matplotlib` module
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======================================
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.. automodule:: matplotlib
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:members: rc, rcdefaults, use
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:members:
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:undoc-members:
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:show-inheritance:
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‎doc/api/nxutils_api.rst

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‎doc/mpl_toolkits/index.rst

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.. _toolkit_basemap:
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Basemap
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=======
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Basemap (*Not distributed with matplotlib*)
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============================================
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Plots data on map projections, with continental and political
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boundaries, see `basemap <http://matplotlib.github.com/basemap>`_
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boundaries, see `basemap <http://matplotlib.org/basemap>`_
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docs.
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.. image:: http://matplotlib.org/basemap/_images/contour1.png
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:height: 400px
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Cartopy (*Not distributed with matplotlib*)
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============================================
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An alternative mapping library written for matplotlib ``v1.2`` and beyond.
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`Cartopy <http://scitools.org.uk/cartopy/docs/latest>`_ builds on top of
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matplotlib to provide object oriented map projection definitions and close
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integration with Shapely for powerful yet easy-to-use vector data processing
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tools. An example plot from the
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`Cartopy gallery <http://scitools.org.uk/cartopy/docs/latest/gallery.html>`_:
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.. image:: http://scitools.org.uk/cartopy/docs/latest/_images/hurricane_katrina_01_00.png
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:height: 400px
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.. _toolkit_gtk:
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GTK Tools
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This toolkit ships with matplotlib, but requires `pygtk
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<http://www.pygtk.org/>`_.
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.. _toolkit_excel:
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Excel Tools
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Excel. This toolkit ships with matplotlib, but requires
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`xlwt <http://pypi.python.org/pypi/xlwt>`_
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.. _toolkit_natgrid:
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Natgrid
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========
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Natgrid (*Not distributed with matplotlib*)
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===========================================
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mpl_toolkits.natgrid is an interface to natgrid C library for gridding
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irregularly spaced data. This requires a separate installation of the
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natgrid toolkit from the sourceforge `download
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<http://sourceforge.net/project/showfiles.php?group_id=80706&package_id=142792>`_
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page.
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.. _toolkit_mplot3d:
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mplot3d
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===========
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mpl_toolkits.mplot3d provides some basic 3D plotting (scatter, surf,
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:ref:`mpl_toolkits.mplot3d <toolkit_mplot3d-index>` provides some basic 3D plotting (scatter, surf,
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line, mesh) tools. Not the fastest or feature complete 3D library out
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there, but ships with matplotlib and thus may be a lighter weight
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solution for some use cases.
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See :ref:`toolkit_mplot3d-index` for more documentation and examples.
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.. image:: /_images/contourf3d_demo21.png
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.. _toolkit_axes_grid:
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AxesGrid
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========
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The matplotlib AxesGrid toolkit is a collection of helper classes to
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The matplotlib :ref:`AxesGrid <toolkit_axesgrid-index>` toolkit is a collection of helper classes to
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ease displaying multiple images in matplotlib. The AxesGrid toolkit is
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distributed with matplotlib source.
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.. image:: ../_static/demo_axes_grid.png
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See :ref:`toolkit_axesgrid-index` for documentations.
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.. image:: /_static/demo_axes_grid.png

‎examples/pylab_examples/marker_path.py

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star = mpath.Path.unit_regular_star(6)
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circle = mpath.Path.unit_circle()
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# concatenate the star with an internal cutout of the circle
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verts = np.concatenate([star.vertices, circle.vertices[::-1, ...]])
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codes = np.concatenate([star.codes, circle.codes])
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# concatenate the circle with an internal cutout of the star
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verts = np.concatenate([circle.vertices, star.vertices[::-1, ...]])
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codes = np.concatenate([circle.codes, star.codes])
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cut_star = mpath.Path(verts, codes)
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‎examples/pylab_examples/pcolor_demo.py

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#!/usr/bin/env python
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"""
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See pcolor_demo2 for an alternative way of generating pcolor plots
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using imshow that is likely faster for large grids
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Demonstrates similarities between pcolor, pcolormesh, imshow and pcolorfast
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for drawing quadrilateral grids.
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"""
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from __future__ import division
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from matplotlib.patches import Patch
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from pylab import *
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import matplotlib.pyplot as plt
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import numpy as np
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def func3(x,y):
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return (1- x/2 + x**5 + y**3)*exp(-x**2-y**2)
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# make these smaller to increase the resolution
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dx, dy = 0.15, 0.05
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# generate 2 2d grids for the x & y bounds
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y, x = np.mgrid[slice(-3, 3 + dy, dy),
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slice(-3, 3 + dx, dx)]
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z = (1 - x / 2. + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)
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# x and y are bounds, so z should be the value *inside* those bounds.
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# Therefore, remove the last value from the z array.
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z = z[:-1, :-1]
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z_min, z_max = -np.abs(z).max(), np.abs(z).max()
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plt.subplot(2, 2, 1)
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plt.pcolor(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
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plt.title('pcolor')
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# set the limits of the plot to the limits of the data
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plt.axis([x.min(), x.max(), y.min(), y.max()])
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plt.colorbar()
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plt.subplot(2, 2, 2)
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plt.pcolormesh(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
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plt.title('pcolormesh')
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# set the limits of the plot to the limits of the data
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plt.axis([x.min(), x.max(), y.min(), y.max()])
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plt.colorbar()
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plt.subplot(2, 2, 3)
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plt.imshow(z, cmap='RdBu', vmin=z_min, vmax=z_max,
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extent=[x.min(), x.max(), y.min(), y.max()],
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interpolation='nearest', origin='lower')
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plt.title('image (interp. nearest)')
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plt.colorbar()
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dx, dy = 0.05, 0.05
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x = arange(-3.0, 3.0001, dx)
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y = arange(-3.0, 3.0001, dy)
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X,Y = meshgrid(x, y)
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Z = func3(X, Y)
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pcolor(X, Y, Z, cmap=cm.RdBu, vmax=abs(Z).max(), vmin=-abs(Z).max())
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colorbar()
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axis([-3,3,-3,3])
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ax = plt.subplot(2, 2, 4)
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ax.pcolorfast(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
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plt.title('pcolorfast')
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plt.colorbar()
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show()
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plt.show()

‎examples/pylab_examples/pcolor_demo2.py

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