Hello, On Tue, 2012-03-06 at 13:00 +0100, Jose Miguel Ibáñez wrote: > Hello everyone, > > does anyone know of an efficient implementation (maybe using > numpy.where statement) of the next code for data cube (3d array) > combining ? > You use the axis keyword/argument to sum, at which point you want to cast (if you do) to float32 I don't know. result = np.sqrt(cube).sum(axis=0)
> import numpy as np > > def combine( ) > > cube = np.random.rand(32,2048,2048) > result = np.zeros([2048,2048], np.float32) > > for ii in range(2048): > for jj in range(2048): > result[, ii, jj] = np.sqrt((cube[:,ii, jj])).sum() > > > It takes long time to run, however, > > > >> result = np.median(cube,0) > > > only around one second ! where is the point ? any suggestions ? > > > > Thanks ! > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
