availble solutions for 2D interpolation on non monotonic scattered data
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I have been reading many posts regarding the interpolation of data of the form Z(x,y), being x y scattered data points which can not be transformed to monotonic vectors. However I still have some questions. My data are vectors of X, Y (coordinates), and associated Z, R.... of property values. Moreover the domain defined by X, Y vectors is not square. In this situation:
1. when I triangulate the data the triangles in the borders (actually in one border in my particular case) are sliver-like and connect points that are remote from each other. But also when creating an equi-spaciated grid in which to interpolate, I get quite a lot of NaNs for those triangles out of my triangulated region.
For getting rid of those problems I could: define a triangle shape factor criteria to eliminate "bad triangles", still have to figure out how. (the NaNs values I do not see it a real problem so far, one I know their reason)
2. Regarding the interpolation methods available I would like to know is there are more than the three available in TriScatteredInterp (linear, nearest and natural), or the 'cubic' available in griddata. I am referring mainly to the use of splines.
I would really appreciate any information on how I could optimize the use of those tools for my particular case, of if there is any other option that I am missing
Dr. Seis on 8 Nov 2012
Edited: Dr. Seis on 8 Nov 2012
I posted this a while back, perhaps it could be useful here:
It should be parameterized similar to interp2 (except you won't be giving it input data on a regular grid)... but, like any interpolation algorithm, it might behave oddly in the same areas you are getting NaNs (since it is not good at extrapolation).