Numpy Two-Dimensional Moving Average

I have a 2d numpy array. I want to take the average value of the n nearest entries to each entry, just like taking a sliding average over a one-dimensional array. What is the cleanest way to do this?

Solution 1:

This is a similar concept to applying a filter to an image.

Fortunately, scipy.ndimage.filters has a bunch of functions to do that. The one you're after is scipy.ndimage.uniform_filter.

Can be used like this:

a
=> 
array([[  0.,   1.,   2.,   3.,   4.],
       [  5.,   6.,   7.,   8.,   9.],
       [ 10.,  11.,  12.,  13.,  14.],
       [ 15.,  16.,  17.,  18.,  19.],
       [ 20.,  21.,  22.,  23.,  24.]])

uniform_filter(a, size=3, mode='constant')
=> 
array([[  1.33333333,   2.33333333,   3.        ,   3.66666667,          2.66666667],
       [  3.66666667,   6.        ,   7.        ,   8.        ,          5.66666667],
       [  7.        ,  11.        ,  12.        ,  13.        ,          9.        ],
       [ 10.33333333,  16.        ,  17.        ,  18.        ,         12.33333333],
       [  8.        ,  12.33333333,  13.        ,  13.66666667,          9.33333333]])

If you want a 5x5 filter, use size=5. The mode option controls how the edges are treated. You didn't specify how you want to handle the edges. In this example, the "constant" mode means it treats each item outside the bounds of the array as a constant value of 0 (0 is the default, which can be overridden).