a.schmolck@gmx.net [mailto:a.schmolck@gmx.net] wrote:

Numeric is an impressively powerful and in many respects easy and comfortable to use package (e.g. it's sophisticated slicing operations, not to mention the power and elegance of the underlying python language) and one would hope that it can one day replace Matlab (which is both expensive and a nightmare as a programming language) as a standard platform for numerical calculations.

I'm in much the same boat, only with Gauss as the language I want to replace.

There is however a problem that, for the use to which I want to put Numeric, runs deeper and provides me with quite a headache:

Two essential matrix operations (matrix-multiplication and transposition (which is what I am mainly using) are both considerably

a) less efficient and b) less notationally elegant

under Numeric than under Matlab.

These are my two problems as well. I can live with the clumsy function call interface to the matrix ops, but the loss of efficiency is a real killer for me. In my code, Gauss is about 8-10x faster than Numpy, which is a killer speed loss. (And Gauss is modestly slower than C, though I don't care about this because the Gauss is fast enough.) Right now, I have a data-mining program that I prototyped in Numpy and am now rewriting in C. Because Numpy isn't fast enough, I have wasted close to a week on this rewrite. This sounds bitter, but it's not meant to. I have to deploy on VMS, and after we had gotten Numpy working on OpenVMS I really hoped that the Alpha would fast enough that I could just use the Python prototype. -- Neel Krishnaswami neelk@cswcasa.com