[Numpy-discussion] the neighbourhood of each element of an array

Francesc Altet faltet@carabos....
Fri Feb 23 13:18:48 CST 2007

A Divendres 23 Febrer 2007 17:38, joris@ster.kuleuven.ac.be escrigué:
> Hi,
> Given a (possibly masked) 2d array x, is there a fast(er) way in Numpy to
> obtain the same result as the following few lines?
> d = 1                                  # neighbourhood 'radius'
> Nrow = x.shape[0]
> Ncol = x.shape[1]
> y = array([[x[i-d:i+d+1,j-d:j+d+1].ravel() for j in range(d,Ncol-d)]      \
>                                            for i in range(d,Nrow-d)])
> What you get is an array containing all the elements in a neighbourhood for
> each element, disregarding the edges to avoid out-of-range problems. The
> code above becomes quite slow for e.g. a 2000x2000 array. Does anyone know
> a better approach?

Well, it seems that copying data here is taking most of the CPU. Perhaps you 
may want to try getting *references* to the original slices better. For 
example, if rd = 2+d, you can write:

def get_neighbors_views_ravel(x):
    # The next is for an array of references to *views* of neighborgs
    y = numpy.empty((Nrow-2*d, Ncol-2*d), dtype='object')

    for i in xrange(0, Nrow-2*d):
        x2 = x[i:i+rd]   # Get a view of the first dimension slice
        for j in xrange(0, Ncol-2*d):
            y[i, j] = x2[:,j:j+rd].ravel()
    return y

which is a 1.34x (on my machine) faster than your current approach.

If you want more speed, you may want to not .ravel() in the new array creation 
time (you can always use .ravel() when you are going to use the data). The 
removal of the .ravel() call makes the above function 2.56x faster.

Finally, if your machine has an x86 architecture, you can also take advantge 
of Psyco so as to accelerate a bit more. With Psyco and not raveling, you can 
get up to 3x better times than your original approach (without using Psyco). 
Of course, more speed-ups could be possible by using Pyrex or any other easy 
method for doing C-extensions.

I'm attaching a small benchmark, and here are the results for my machine:

ref time--> 3.021
views (ravel) time--> 2.258   speed-up--> 1.34
views (no ravel) time--> 1.179   speed-up--> 2.56

and if we use psyco:

ref time--> 2.368
views (ravel) time--> 1.636   speed-up--> 1.45
views (no ravel) time--> 0.935   speed-up--> 2.53


>0,0<   Francesc Altet     http://www.carabos.com/
V   V   Cárabos Coop. V.   Enjoy Data
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