Bill, Thanks for your prompt and patient help but I need some clarification
of your answer.
We dont quite understand:
> Then you can resample that IrregularSet back to your original GriddedSet.
This is what we have done:
1. create new griddedset of points have non-missing values.
2. create new field from original by resampling with new set.
3. create new irregular set of points we want.
4. create new field by resampling with new irregular set.
This seems to work quite well for interpolation but does not fill in values
for points on the end of the irregular set that have missing values
(i.e. extrapolate). I only need to fill in the end samples with
nearest neighbor or something simmilar. Can this be done?
Your mail seemed to suggest that we use an irregular set and then a gridded
set however this gives a nullpointer exception at
Are we going about this the right way?
> Hi Cameron,
> > I have a field where some of the values are NaN.
> > I would like to remove these missing values by changing them
> > to their nearest neighbor (or some other algorithm).
> > Is there a visad way of doing this?
> > Or do I have to define some sort of custom interpolation?
> It all depends on how dense your missing points are. If
> most points are missing, then you could treat the non-missing
> points as samples of an IrregularSet. The IrregularSet
> constructor will invoke a Delaunay algorithm, which will be
> quite slow. Then you can resample that IrregularSet back
> to your original GriddedSet. A faster way (still for most
> points missing) would be to use an objective analysis
> algorithm. We've got some Fortran for this at:
> Also, I believe your advisor James Kelly is an expert on
> objective analysis.
> If your missing points are sparse, then you should be
> probably just use a simple spot noise filter. For example,
> replace each missing point by the average of adjacent
> non-missing points.
> Bill Hibbard, SSEC, 1225 W. Dayton St., Madison, WI 53706
> hibbard@xxxxxxxxxxxxxxxxx 608-263-4427 fax: 608-263-6738