Re: [Numpy-discussion] numpy vs numeric benchmarks

favorable numpy creates arrays much faster, fft seems a tad faster a useful metric, I think, for O-scope and ADC apps I get 0.0039054614015815738 0.0019759541205486885 0.023268623246481726 0.0023570392204637913 from the below on a PIII 600... from time import * n=4096 r = range(n) #numpy import numpy arr = numpy.array # array creation t0 = clock() for i in r: a = arr(r) (clock()-t0)/float(n) #fft of n fftn = numpy.fft t0 = clock() for i in r: f = fftn(a) (clock()-t0)/float(n) #Numeric import Numeric arr = Numeric.array # array creation t0 = clock() for i in r: a = arr(r) (clock()-t0)/float(n) #fft of n from FFT import * t0 = clock() for i in r: f = fft(a) (clock()-t0)/float(n)