> I have 2D array, let's say: `np.random.random((100,100))` and I want to do
> simple manipulation on each point neighbors, like divide their values by 3.
>
> So for each array value, x, and it neighbors n:
>
> n n n n/3 n/3 n/3
> n x n -> n/3 x n/3
> n n n n/3 n/3 n/3
>
> I searched a bit, and found about scipy ndimage filters, but if I'm not
> wrong, there is no such function. Of course me being wrong is quite possible,
> as I did not comprehend whole ndimage module, but I tried generic filter for
> example and browser other functions.
>
> Is there better way to make above manipulation, instead using for loop over
> every array element?
I am not sure I understand the above manipulation... typically neighborhood
operators take an array element and the its neighborhood and then give a single
output that becomes the value of the new array at that point. That is, a 3x3
neighborhood filter would act as a function F(R^{3x3}) -> R. It appears that
what you're talking about above is a function F(R^{3x3}) -> R^{3x3}. But how is
this output to map onto the original array positions? Is the function to be
applied to non-overlapping neighborhoods? Is it to be applied to all
neighborhoods and then summed at each position to give the output array?
If you can describe the problem in a bit more detail, with perhaps some sample
input and output for what you desire (and/or with some pseudocode describing
how it would work in a looping-over-each-element approach), I'm sure folks can
figure out how best to do this in numpy.
Zach
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