# Convolution-like operations¶

Users frequently want to break an array up into overlapping chunks, then apply the same operation to each chunk. You can generate a dynamical power spectrum, for example, by taking an FFT of each chunk, or you can construct a convolution using a dot product. Some of these operations already exist in numpy and scipy, but others don't. One way to attack the problem would be to make a matrix in which each column was a starting location, and each row was a chunk. This would normally require duplicating some data, potentially a lot of data if there's a lot of overlap, but numpy's striding can be used to do this. The simplification of striding doesn't come for free; if you modify the array, all shared elements will be modified. Nevertheless, it's a useful operation. Find attached the code, segmentaxis.py . Example usage:

In [ ]:
In [1]: import numpy as N
In [2]: import segmentaxis
In [3]: a = N.zeros(30)
In [4]: a[15] = 1
In [5]: filter = N.array([0.1,0.5,1,0.5,0.1])
In [6]: sa = segmentaxis.segment_axis(a,len(filter),len(filter)-1)
In [7]: sa
Out[7]:
array([[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  1.],
[ 0.,  0.,  0.,  1.,  0.],
[ 0.,  0.,  1.,  0.,  0.],
[ 0.,  1.,  0.,  0.,  0.],
[ 1.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.,  0.]])
In [8]: N.dot(sa[::2,:],filter)
Out[8]:
array([ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0.5,  0.5,  0. ,  0. ,  0. ,
0. ,  0. ])
In [9]: N.dot(sa[1::2,:],filter)
Out[9]:
array([ 0. ,  0. ,  0. ,  0. ,  0. ,  0.1,  1. ,  0.1,  0. ,  0. ,  0. ,
0. ,  0. ])
In [10]: N.dot(sa,filter)
Out[10]:
array([ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,
0.1,  0.5,  1. ,  0.5,  0.1,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,
0. ,  0. ,  0. ,  0. ])


Section author: AMArchibald

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