I missed the original post but I personally find this addition especially 
useful for my work in computational neuroscience.

I did something vaguely similar in a small framework (http://dana.loria.fr/, 
you can look more specifically at http://dana.loria.fr/doc/connection.html for 
details). Examples are available from: http://dana.loria.fr/examples.html

The actual computation can be made in several ways depending on the properties 
of the kernel but the idea is to compute an array "K" such that given an array 
"A" and a kernel "k", A*K holds the expected result. This also work with sparse 
array for example when the kernel is very small. I suspect the PR will be quite 
efficient compared to what I did.


Nicolas


On Oct 10, 2012, at 18:55 , Cera, Tim wrote:

> On Wed, Oct 10, 2012 at 1:58 AM, Travis E. Oliphant 
> <notificati...@github.com> wrote:
> I'm not sure what to make of no comments on this PR. This seems like a useful 
> addition. @timcera are you still interested in having this PR merged?
> 
> 
> Yes.  
> 
> I mentioned in PR comments that the lack of discussion is because my code 
> engenders speechless awe in anyone who looks at it.  :-)   Of course 
> speechless awe can come from two different reasons!  Hopefully it is because 
> my code is so awesome.
> 
> Seriously, I really wanted some input, especially after I found #31.
>  
> 
> On Wed, Oct 10, 2012 at 7:24 AM, Eric Moore <notificati...@github.com> wrote:
> This seems to be trying to solve a very similar problem to #31
> 
> Internally I implemented something like rolling window, but I don't return 
> the windows.  Instead the contents of the windows are used for calculation of 
> each windows 'central' cell in the results array.
> 
> After seeing the rolling window function I thought it might be nice to bring 
> that out into a callable function, so that similar functionality would be 
> available.  That particular function isn't useful to me directly, but perhaps 
> others? 
> 
> Kindest regards,
> Tim
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