On 7/25/11 2:20 PM, Christopher Barrington-Leigh wrote:
The following code:from pylab import arange nSegments=5.0 print arange(0,1.0+1.0/nSegments,1.0/nSegments) nSegments=6.0 print arange(0,1.0+1.0/nSegments,1.0/nSegments) nSegments=8.0 print arange(0,1.0+1.0/nSegments,1.0/nSegments) nSegments=10.0 print arange(0,1.0+1.0/nSegments,1.0/nSegments) gives an output of: [ 0. 0.2 0.4 0.6 0.8 1. ] [ 0. 0.16666667 0.33333333 0.5 0.66666667 0.83333333 1. 1.16666667] [ 0. 0.125 0.25 0.375 0.5 0.625 0.75 0.875 1. ] [ 0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ] These arrays have lengths, 6, 8, 9, and 11, in stead of 6, 7, 9, and 11. What is going on for the case of n=6?
Floating point computations are not always accurate, and when one tries to compute "the same thing" two different ways, one may get inconsistent results. This is what is happening with n=6. 1+1./6 happens to be slightly greater than 7*(1./6) while 1+1./5 happens to be slightly less than 6*(1./5), etc. The trick of using 1.0+1.0/nSegments/2 tends to work better.
Nonetheless, if you want to get exactly nSegments segments with exact endpoints, you should use numpy.linspace(0.0, 1.0, nSegments+1). That's a much better API for what you want.
Also, you will want to ask numpy questions on the numpy-discussion mailing list, not here.
http://www.scipy.org/Mailing_Lists -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
