New PyGrADS 1.0.8, works on Windows

[Arlindo da Silva]

Dear Pythonistas,

   I just uploaded a new version of PyGrADS to sf.net:


http://sourceforge.net/project/showfiles.php?group_id=161773&package_id=256758

    For the first time I am including a win32 installer as well as a win32
superpack for those of you who do not have no Python whatsoever installed on
your Windows box and would like to give it a try. You can find more
information in the Wiki:


http://opengrads.org/wiki/index.php?title=Python_Interface_to_GrADS#Installation

   This version cleans up some minor API issues and has a more consistent
naming of the main classes. It should be pretty much backward compatible
with v1.0.7, though.

   Be sure to run the examples and check the plots:


http://opengrads.org/wiki/index.php?title=Python_Interface_to_GrADS#Checking_your_Installation

   We are still working on the Examples page in the Wiki. Let me know if any
of you would like to help with the documentation.

   Grads v2 compatibility: like the previous version, only the very basic
GaCore class works with GrADS v2. (The test suite pytests first introduce in
v1.9 has recently been ported to v2; pytests only requires GaCore.) The
ability of python to exchange data with GrADS requires OpenGrADS extensions
not yet available in v2.

   A call to volunteers. Here is a sample of some of the features we would
like to see implemented in PyGrADS:

   - Better handling of satellite imagery, with seamless integration of
   AREA, GeoTiff and some basic KML functionality
   - Integration of VTK or any other volumetric visualization python
   package
   - Neural networks: integrate one of the many available NN python
   modules so that network design and training could be accomplished using
   simple and familiar GrADS expressions
   - Wavelet transforms, including some wavelet based compression
   algorithms
   - Comprehensive set of statistical functions for analysis of climate
   variability: better EOF capabilities, SVD, CCA, POP analysis, kernel density
   estimates, time series analysis, etc
   - Some simple client/server implementation through XML-RPC or
   equivalent protocol
   - Better Java integration through Jython: implement GaNum
   functionality through JNumeric, 3D/volumetric visualization capability
   through visAD, ...
   - Some GUI demonstrations using wxPython or any other python toolkit.
   For example, a "GFS Workbench" tool that would bring together GFS forecasts
   through OPeNDAP, observational data and satellite imagery.

If you have some python experience in any of these areas and would like to
lead or get involved in any of these efforts (or anything else that
interests you)  just drop me a note.

    Cheers!

        Arlindo

--
Arlindo da Silva
dasilva at alum.mit.edu
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