[SciPy-user] MDP 1.0.0 and symeig 1.0 released

[p.berkes at biologie.hu-berlin.de]

Hi,

Tiziano and I are pleased to announce a new release of the

 Modular toolkit for Data Processing (MDP) 1.0.0
 (http://mdp-toolkit.sourceforge.net)

MDP is a Python library based on SciPy to implement data processing
elements (nodes) and combine them into data processing sequences
(flows). Already implemented nodes include Principal Component
Analysis (PCA), Independent Component Analysis (ICA), Slow Feature
Analysis (SFA), and Growing Neural Gas.

In the past there has been some interest on the scipy-user mailing
list concerning the MDP function 'symeig' to solve the standard and
generalized eigenvalue problems for symmetric (hermitian) positive
definite matrices:

http://www.scipy.org/mailinglists/mailman?fn=scipy-user/2004-September/003241.html
http://www.scipy.org/mailinglists/mailman?fn=scipy-user/2004-October/003391.html
http://www.scipy.org/mailinglists/mailman?fn=scipy-user/2004-October/003396.html

This function has been improved in the new release. In particular, it
now correctly handles complex matrices (there seems to be something
wrong with the LAPACK documentation in the complex case). 'symeig' is
now distributed also as an independent package:

http://mdp-toolkit.sourceforge.net/symeig.html

Since the LAPACK functions wrapped by 'symeig' seem to be of general
interest, you might want to consider including it in SciPy. The
distribution includes the .pyf file used to generate the wrappers, the
symeig function itself, and a test suite. Tiziano and I are ready to
help with the inclusion process if needed. 'symeig' is currently released
under LGPL, but we might change the licence if it conflicts with
that of SciPy.

Best Regards,
Pietro Berkes and Tiziano Zito

----------------------------------------
{p.berkes, t.zito}@biologie.hu-berlin.de
Institute for Theoretical Biology
Humboldt University
Invalidenstrasse 43
D-10115 Berlin, Germany
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