[SciPy-User] variable smoothing kernel
Nicolau Werneck
nwerneck@gmail....
Sat Mar 26 22:01:10 CDT 2011
On Sun, Mar 27, 2011 at 12:54:58PM +1100, Wolfgang Kerzendorf wrote:
> Well, your time is my wavelength. It should vary on wavelength.
OK, but what kind of data do you have? It is a 1-dimensional signal,
and you have taken its Fourier transform and now you are filtering in
the frequency domain?...
> I agree implementing it with cython makes it probably faster. I do
> suspect however, that it won't be as fast as the normal smoothing function.
>
> I have learned that multiplying functions in fourier space is the same
> as convoluting them. I believe that is how the ndimage kernels work so
> incredibly fast.
> I wanted to see if there's a similar shortcut for a variable kernel.
Implementing in Cython will make it _definitely_ better!... Whenever
you have large loops running over vectors or arrays Cython will give
you great speedups.
And as I was saying later, it will never be as fast because applying a
linear filter is something inherently easier... Because you can use the FFT
and multiply in the transform domain, as you said.
In your case maybe you could consider to filter the signal with a
filter bank, and then pick up the values from the result according to
the formula you use for calculating your kernel. It may or may not be
quicker, but it's not possible if you need infinite precision in the
parameters of your filter.
> I have copied my previous attempts (which were very simply written and
> take a long time) into this pastebin: http://pastebin.com/KkcEATs7
Thanks for sending it... But it's not clear to me how it works in a
first glance. Can you send a small sample with a synthetic signal
(randn, whatever) showing how to run the procedures?
And question: is there any chance you could in your problem first
apply some mapping of your signal, a change of variables (like
x->log(x) )and then apply a normal linear time-invariant filter with
this transform, and then apply the inverse transform? In that case you
would first use interpolation to perform the mapping, then apply the
fast filtering procedure, and do the inverse interpolation...
++nic
>
> Thanks for your help
> Wolfgang
> On 27/03/11 12:09 PM, Nicolau Werneck wrote:
> > If I understand correctly, you want a filter that varies on "time".
> > This non-linearity will cause it to be inherently more complicated to
> > calculate than a normal linear time-invariant filter.
> >
> > I second Christopher's suggestion, try Cython out, it's great for this
> > kind of thing. Or perhaps scipy.weave.
> >
> > ++nic
> >
> > On Sat, Mar 26, 2011 at 9:52 AM, Wolfgang Kerzendorf
> > <wkerzendorf@googlemail.com> wrote:
> >> Hello,
> >>
> >> I'm interested in having a gaussian smoothing where the kernel depends
> >> (linearly in this case) on the index where it is operating on. I
> >> implemented it myself (badly probably) and it takes for ever, compared
> >> to the gaussian smoothing with a fixed kernel in ndimage.
> >>
> >> I could interpolate the array to be smoothed onto a log space and not
> >> change the kernel, but that is complicated and I'd rather avoid it.
> >>
> >> Is there a good way of doing that?
> >>
> >> Cheers
> >> Wolfgang
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> >
> >
>
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--
Nicolau Werneck <nwerneck@gmail.com> C3CF E29F 5350 5DAA 3705
http://www.lti.pcs.usp.br/~nwerneck 7B9E D6C4 37BB DA64 6F15
Linux user #460716
"A huge gap exists between what we know is possible with today's machines and what we have so far been able to finish."
-- Donald Knuth
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