[AstroPy] ANN matplotlib-0.60.2: python graphs and charts

Paul Barrett barrett at stsci.edu
Wed Jul 14 18:10:16 CDT 2004

matplotlib is a 2D plotting library for python.  You can use
matplotlib interactively from a python shell or IDE, or embed it in
GUI applications (WX, GTK, and Tkinter).  matplotlib supports many
plot types: line plots, bar charts, log plots, images, pseudocolor
plots, legends, date plots, finance charts and more.

What's new since matplotlib 0.50

   This is the first wide release in 5 months and there has been a
   tremendous amount of development since then, with new backends, many
   optimizations, new plotting types, new backends and enhanced text
   support. See http://matplotlib.sourceforge.net/whats_new.html for

  * Todd Miller's tkinter backend (tkagg) with good support for
    interactive plotting using the standard python shell, ipython or
    others.  matplotlib now runs on windows out of the box with python
    + numeric/numarry

  * Full Numeric / numarray integration with Todd Miller's numerix
    module.  Prebuilt installers for numeric and numarray on win32.
    Others, please set your numerix settings before building
    matplotlib, as described on

  * Mathtext: you can write TeX style math expressions anywhere in your

  * Images - figure and axes images with optional interpolated
    resampling, alpha blending of multiple images, and more with the
    imshow and figimage commands.  Interactive control of colormaps,
    intensity scaling and colorbars -

  * Text: freetype2 support, newline separated strings with arbitrary
    rotations, Paul Barrett's cross platform font

  * Jared Wahlstrand's SVG backend (alpha)

  * Support for popular financial plot types -

  * Many optimizations and extension code to remove performance
    bottlenecks.  pcolors and scatters are an order of magnitude

  * GTKAgg, WXAgg, TkAgg backends for http://antigrain.com (agg)
    rendering in the GUI canvas.  Now all the major GUIs (WX, GTK, Tk)
    can be used with a common (agg) renderer.

  * Many new examples and demos - see http://matplotlib.sf.net/examples
    or download the src distribution and look in the examples dir.

Documentation and downloads available at

John Hunter

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