# [Numpy-discussion] avoiding loops when downsampling arrays

Sturla Molden sturla@molden...
Mon Feb 6 15:12:58 CST 2012

```Something like this:

m,n = data.shape
x = data.reshape((m,n//4,4))
z  = (x[0::4,...] >= t1) & (x[0::4,...] <= t1)
z |= (x[1::4,...] >= t1) & (x[1::4,...] <= t1)
z |= (x[2::4,...] >= t1) & (x[2::4,...] <= t1)
z |= (x[3::4,...] >= t1) & (x[3::4,...] <= t1)
found = np.any(z, axis=2)

Sturla

Den 6. feb. 2012 kl. 21:57 skrev Sturla Molden <sturla@molden.no>:

> Short answer: Create 16 view arrays, each with a stride of 4 in both dimensions. Test them against the conditions and combine the tests with an |= operator. Thus you replace the nested loop with one that has only 16 iterations. Or reshape to 3 dimensions, the last with length 4, and you can do the same with only four view arrays.
>
> Sturla
>
>
> Den 6. feb. 2012 kl. 20:16 skrev "Moroney, Catherine M (388D)" <Catherine.M.Moroney@jpl.nasa.gov>:
>
>> 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)
>>           found[i,j] = True
>>
>> Thank you for any hints and education!
>>
>> Catherine
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```