[Numpy-discussion] Data cube optimization for combination

[Sebastian Berg]

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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