[SciPy-User] kriging module

[Anand Patil]

Hi everyone,


I'm the author PyMC's GP module. Sorry to come late to this thread.
The discussion of my module has been on target, and thanks very much
for the kind words... as everyone here knows it's nice when people
notice code that you've worked hard on. I have a couple of hopefully
relevant things to say about it.


First, the GP module is broader in scope than what people typically
mean by GP regression and kriging. The statistical model underlying
typical GPR/K says that the data are normally distributed with
expectations equal to the GP's value at particular, known locations.
Further, the mean and covariance parameters of the field, as well as
the variance of the data, are typically fixed before starting the
regression.

With the GP module, the mean and covariance parameters can be unknown,
and the data can depend on the field in any way; as a random example,
each data point could be Gamma distributed, with parameters determined
by a nonlinear transformation of the field's value at several unknown
locations.

That said, the module has a very pronounced fast path that restricts
its practical model space to Bayesian geostatistics, which means the
aforementioned locations have to be known before starting the
regression. This is still a superset of GPR/K. There are numerous
examples of the GP module in use for Bayesian geostatistics at
github.com/malaria-atlas-project.


Second, the parts of the GP module that would help with GPR/K are not
very tightly bound to either the rest of PyMC or the Bayesian
paradigm, and could be pulled out. These parts are the Mean,
Covariance and Realization objects, functions like observe and
point_predict, and their components; but not the GP submodels and step
methods mentioned in the user guide.


Any questions on the GP module are welcome at groups.google.com/p/
pymc. I'm looking forward to checking out the work in progress on the
scikit.

Cheers,
Anand

On Nov 23, 2:45 pm, "Dharhas Pothina"
<Dharhas.Poth... at twdb.state.tx.us> wrote:
> We were planning to project our irregular data onto a cartesian grid and try and use matplotlib to visualize the variograms. I don't think I know enough about the math ofkrigingto be of much help in the coding but I might be able to give your module a try if I can find time between deadlines.
>
> - dharhas
>
> >>> Lionel Roubeyrie <lionel.roubey... at gmail.com> 11/22/2010 9:15 AM >>>
>
> I have tried hpgl and had some discussions with one of the main
> developper, but hpgl works only on cartesian (regular) grid where I
> want to have the possibility to have predictions on irregular points
> and have the possibility to visualize variograms
>
> 2010/11/22 Dharhas Pothina <Dharhas.Poth... at twdb.state.tx.us>:
>
>
>
>
>
>
>
>
>
>
>
> > What about this package?http://hpgl.sourceforge.net/
>
> > I was looking for a kridging module recently and came across this. I haven't tried it out yet but am getting ready to. It uses numpy arrays and also is able to read/write GSLib files. GSLib seems to be a fairly established command line library in the Geostats world.
>
> > - dharhas
>
> > On Sat, Nov 20, 2010 at 12:56 PM, Lionel Roubeyrie <
> > lionel.roubey... at gmail.com> wrote:
>
> >> Hi all,
> >> I have written a simple module forkrigingcomputation (ordinary
> >>krigingfor the moment), it's not optimized and maybe some minors
> >> errors are inside but I think it delivers corrects results. Is there
> >> some people here that can help me for optimize the code or just to
> >> have a try? I don't know the politic of this mailing-list against
> >> joined files, so I don't send it here for now.
> >> Thanks
>
> >> --
> >> Lionel Roubeyrie
> >> lionel.roubey... at gmail.com
> >>http://youarealegend.blogspot.com
> >> _______________________________________________
> >> SciPy-User mailing list
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>
> > _______________________________________________
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>
> --
> Lionel Roubeyrie
> lionel.roubey... at gmail.comhttp://youarealegend.blogspot.com
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