Hi,
this should work:
import numpy as np
ndim = 20
cube = np.random.rand(32,ndim, ndim)
result = np.zeros([ndim, ndim], np.float32)
def combine(cube, result):
for ii in range(ndim):
for jj in range(ndim):
result[ii, jj] = np.sqrt((cube[:,ii, jj])).sum()
return
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 w
Hello everyone,
does anyone know of an efficient implementation (maybe using
numpy.where statement) of the next code for data cube (3d array)
combining ?
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
