On Thursday, July 23, 2015 at 1:43:00 PM UTC+2, Jeremy Sanders wrote: > Heli Nix wrote: > > > Is there any way that I can optimize this if statement. > > Array processing is much faster in numpy. Maybe this is close to what you > want > > import numpy as N > # input data > vals = N.array([42, 1, 5, 3.14, 53, 1, 12, 11, 1]) > # list of items to exclude > exclude = [1] > # convert to a boolean array > exclbool = N.zeros(vals.shape, dtype=bool) > exclbool[exclude] = True > # do replacement > ones = vals==1.0 > # Note: ~ is numpy.logical_not > vals[ones & (~exclbool)] = 1e-20 > > I think you'll have to convert your HDF array into a numpy array first, > using numpy.array(). > > Jeremy
Dear all, I tried the sorted python list, but this did not really help the runtime. I haven´t had time to check the sorted collections. I solved my runtime problem by using the script from Jeremy up here. It was a life saviour and it is amazing how powerful numpy is. Thanks a lot Jeremy for this. By the way, I did not have to do any array conversion. The array read from hdf5 file using h5py is already a numpy array. The runtime over an array of around 16M reduced from around 12 hours (previous script) to 3 seconds using numpy on the same machine. Thanks alot for your help, -- https://mail.python.org/mailman/listinfo/python-list
