[Numpy-discussion] Data cube optimization for combination
Sebastian Berg
sebastian@sipsolutions....
Tue Mar 6 06:16:00 CST 2012
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 !
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