On Thu, 17 Feb 2000, andrew x swan wrote:
is this because the element by element calculations involved are contained in python for loops?
Hi Andrew! I've only just begun using Numeric Python, but I'm a long-time user of GNU Octave and a sporadic user of MatLab. In general, for loops kill the execution speed of interpretive environments like Numpy and Octave. The high-speed comes when one uses vector operations such as Matrix multiplication. If you can vectorize your code, meaning replace all the loops with matrix operations, you should see equivalent speed to Fortran for large data sets. As far as I know, you will never see an interpreted language match a compiled one in the execution of for loops. Thanks. Syrus. -- -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- Syrus Nemat-Nasser <syrus@ucsd.edu> UCSD Physics Dept.