contribution offer: griddata with gaussian average

Community matplotlib-users
1

>> about a year ago I developed for my own purposes a routine for averaging
>> irregularly-sampled data using gaussian average.
>
> is this similar to Kernel Density estimation?
>
> http://www.scipy.org/doc/api_docs/SciPy.stats.kde.gaussian_kde.html

No. It is probably closer to radial basis function interpolation (in fact, it
almost certainly is a form of RBFs):

http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html#id1

I checked the official terminology, it is a kernel average smoother
(in the sense of [1]) with special weight function exp(-(x-x0)^2/const),
operating on irregularly-spaced data in 2d.

I am not sure if that is the same as what scipy.stats.kde.gaussian_kde does,
the documentation is terse. Can I be enlightened here?

Cheers, Vaclav

[1] Kernel smoother - Wikipedia

2

gaussian_kde does kernel density estimation. While many of the intermediate computations are similar, they have entirely different purposes.

···

On 2009-10-05 15:54 PM, VáclavŠmilauer wrote:

I am not sure if that is the same as what scipy.stats.kde.gaussian_kde does,
the documentation is terse. Can I be enlightened here?

--
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
  that is made terrible by our own mad attempt to interpret it as though it had
  an underlying truth."
   -- Umberto Eco

3

VáclavŠmilauer wrote:

I checked the official terminology, it is a kernel average smoother
(in the sense of [1]) with special weight function exp(-(x-x0)^2/const),
operating on irregularly-spaced data in 2d.

I am not sure if that is the same as what scipy.stats.kde.gaussian_kde does,
the documentation is terse. Can I be enlightened here?

It looks like they are both part of a similar class of methods:

As such, it makes sense to me to have them all in SciPy, sharing code and API.

-Chris

···

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