[Numpy-discussion] avoiding loops when downsampling arrays

eat e.antero.tammi@gmail....
Mon Feb 6 16:27:16 CST 2012


Hi,

On Mon, Feb 6, 2012 at 9:16 PM, Moroney, Catherine M (388D) <
Catherine.M.Moroney@jpl.nasa.gov> wrote:

> Hello,
>
> I have to write a code to downsample an array in a specific way, and I am
> hoping that
> somebody can tell me how to do this without the nested do-loops.  Here is
> the problem
> statement:  Segment a (MXN) array into 4x4 squares and set a flag if any
> of the pixels
> in that 4x4 square meet a certain condition.
>
> Here is the code that I want to rewrite avoiding loops:
>
> shape_out = (data_in.shape[0]/4, data_in.shape[1]/4)
> found = numpy.zeros(shape_out).astype(numpy.bool)
>
> for i in xrange(0, shape_out[0]):
>        for j in xrange(0, shape_out[1]):
>
>                excerpt = data_in[i*4:(i+1)*4, j*4:(j+1)*4]
>                mask = numpy.where( (excerpt >= t1) & (excerpt <= t2),
> True, False)
>                if (numpy.any(mask)):
>                        found[i,j] = True
>
> Thank you for any hints and education!
>
> Catherine
>
Following Warrens answer a slight demonstration of code like this:

import numpy as np

def ds_0(data_in, t1= 1, t2= 4):

shape_out= (data_in.shape[0]/ 4, data_in.shape[1]/ 4)

found= np.zeros(shape_out).astype(np.bool)

for i in xrange(0, shape_out[0]):

for j in xrange(0, shape_out[1]):

excerpt= data_in[i* 4: (i+ 1)* 4, j* 4: (j+ 1)* 4]

mask= np.where((excerpt>= t1)& (excerpt<= t2), True, False)

if (np.any(mask)):

found[i, j]= True

return found

# with stride_tricks you may cook up something like this:

from numpy.lib.stride_tricks import as_strided as ast

def _ss(dt, ds, s):

return {'shape': (ds[0]/ s[0], ds[1]/ s[1])+ s,

'strides': (s[0]* dt[0], s[1]* dt[1])+ dt}

def _view(D, shape= (4, 4)):

return ast(D, **_ss(D.strides, D.shape, shape))

def ds_1(data_in, t1= 1, t2= 4):

# return _view(data_in)

excerpt= _view(data_in)

mask= np.where((excerpt>= t1)& (excerpt<= t2), True, False)

return mask.sum(2).sum(2).astype(np.bool)

 if __name__ == '__main__':

from numpy.random import randint

r= randint(777, size= (64, 288)); print r

print np.allclose(ds_0(r), ds_1(r))




My 2 cents,
eat

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