ANN: matplotlib-0.98.3 - plotting for python

[jdh2358 at gmail.com]

matplotlib is a 2D plotting library for python for use in scripts,
applications, interactive shell work or web application servers.
matplotlib 0.98.3 is a major but stable release which brings
many new features detailed below.


 Homepage: http://matplotlib.sourceforge.net/

 Downloads: http://sourceforge.net/project/showfiles.php?group_id=80706&package_id=278194&release_id=617552

 Screenshots: http://matplotlib.sourceforge.net/screenshots.html


Thanks to Charlie Moad for the release and for all the matplotlib
developers for the feature enhancements and bug fixes.

The following "what's new" summary is also online at
http://matplotlib.sourceforge.net/whats_new.html.

What's new
==========

delaunay triangularization

 Jeffrey Whitaker has added support for gridding irregularly spaced
 data using the Matlab (TM) equivalent griddata function.  This is a
 long-standing feature request for matplotlib and a major
 enhancement.  matplotlib now ships with Robert Kern's delaunay
 triangularization code (BSD license), which supports the default
 griddata implementation, but there are some known corner cases where
 this routine fails.  As such, Jeff has provided a python wrapper to
 the NCAR natgrid routines, whose licensing terms are a bit murkier,
 for those who need bullet proof gridding routines.  If the NCAR
 toolkit is installed, griddata will detect it and use it.  See
 http://matplotlib.sf.net/matplotlib.mlab.html#-griddata for details.
 Thanks Robert and Jeff.

proper paths

 For the first time, matplotlib supports spline paths across
 backends, so you can pretty much draw anything.  See the
 http://matplotlib.sf.net/screenshots.html#path_patch_demo. Thanks to
 Michael Droettboom and http://www.stsci.edu (STScI).

better transformations

 In what has been described as open-heart surgery on matplotlib,
 Michael Droettboom, supported by http://www.stsci.edu (STSci) , has
 rewritten the transformation infrastructure from the ground up,
 which not only makes the code more intuitive, it supports custom
 user projections and scales.  See
 http://matplotlib.sf.net/doc/devel/add_new_projection.rst and the
 http://matplotlib.sf.net/matplotlib.transforms.html module
 documentation.

histogram enhancements

 hist (http://matplotlib.sf.net/matplotlib.pyplot.html#-hist) can
 handle 2D arrays and create side-by-side or stacked histograms, as
 well as cumulative filled and unfilled histograms; see
 http://matplotlib.sf.net/examples/pylab_examples/histogram_demo_extended.py

ginput function

 ginput (http://matplotlib.sf.net/matplotlib.pyplot.html#-ginput) is
 a blocking function for interactive use to get input from the user.
 A long requested feature submitted by Gael Varoquaux.  See
 http://matplotlib.sf.net/examples/pylab_examples/ginput_demo.py

wind barbs

 Ryan May has added support for wind barbs, which are popular among
 meterologists.  These are similar to direction fields or quiver
 plots but contain extra information about wind speed and other
 attributes.  See
 http://matplotlib.sf.net/examples/pylab_examples/barb_demo.py

external backends

 backend developers and users can now use custom backends outside the
 matplotlib tree, by using the special syntax
 module://my_backend for the backend setting in the rc
 file, the use directive, or in -d command line argument to
 pylab/pyplot scripts

findobj

 Introduced a recursive object search method to find all objects that
 meet some matching criterion, ef to find all text instances in a
 figure.  See
 http://matplotlib.sf.net/examples/pylab_examples/findobj_demo.py


saving transparent figures

 http://matplotlib.sf.net/matplotlib.pyplot.html#-savefig now
 supports a *transparent* keyword argument to set the figure an axes
 backgrounds transparent.  Useful when you want to embed matplotlib
 figures with transparent backgrounds into other documents

axes3d support removed

 Amid considerable controversy from the users, we decided to pull the
 experimental 3D support from matplotlib.  Although basic 3D support
 remains a goal, the 3D support we had was mainly orphaned, and we
 need a developer with interest to step up and maintain it.

mathtext outside matplotlib

 The mathtext support in matplotlib is very good, and some folks want
 to be able to use it outside of matplotlib figures.  We added some
 helper functions to get the mathtext rendered pixel buffer as a
 numpy array, with an example at
 http://matplotlib.sf.net/examples/api/mathtext_asarray.py


image optimizations

 enhancements to speed up color mapping and panning and zooming on
 dense images


better savefig

 http://matplotlib.sf.net/matplotlib.pyplot.html#-savefig now
 supports save to file handles (great for web app servers) or unicode
 filenames on all backends

record array functions

 some more helper functions to facilitate work with record arrays:
 http://matplotlib.sf.net/matplotlib.mlab.html#-rec_groupby,
 http://matplotlib.sf.net/matplotlib.mlab.html#-rec2txt,
 http://matplotlib.sf.net/matplotlib.mlab.html#-rec_summarize

accurate elliptical arcs

 In support of the http://www.jpl.nasa.gov/news/phoenix/main.php
 (Phoenix mission) to Mars, which used matplotlib in ground tracking
 of the spacecraft, Michael Droettboom built on work by Charlie Moad
 to provide an extremely accurate 8-spline approximation to
 elliptical arcs
 http://matplotlib.sf.net/matplotlib.patches.html#Arc-draw in the
 viewport.  This provides a scale free, accurate graph of the arc
 regardless of zoom level.  See
 http://matplotlib.sf.net/screenshots.html#ellipse_demo

imread enhanced

 imread (http://matplotlib.sf.net/matplotlib.image.html) now will use
 PIL when available to load images and return numpy arrays

postscript enhancements

 the postscript backend has clipping to paths (useful for polar
 plots

PDF enhancements

 The PDF backend handles composite glyphs properly, usetex fixes

SVG enhancements

 clip to path (useful for polar plots), inkscape cut-and-paste fixes.

QT enhancements

 Fixed a duplicate draw bug that slowed performance.  Native qt
 toolbars and status bars used for the toolbar controls.


bug fixes and minor enhancements

 Lots of bug fixes and feature enhancements: memory leaks, math
 rendering, UI specific problems, dpi scaling problems, better
 support for relative font sizes, patch collections, better
 http://matplotlib.sf.net/matplotlib.pyplot.html#-pie chart label
 alignment, better baseline text alignment support, support for image
 downsampling, more better
 http://matplotlib.sf.net/matplotlib.pyplot.html#-hist functionality,
 image rendering fixes...  For details, see
 http://matplotlib.sf.net/CHANGELOG