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Re: 2D interpolation with sparse data [message #54093]

Wed, 23 May 2007 07:42

Ken G
Messages: 14
Registered: September 2006

Junior Member

Thank you both for the suggestions!

I did try a strictly-x line-by-line interpolation and that certainly
does work (and quickly) to fill in the missing points. Yet, a problem
might occur when there's a non-x-dependence to the original data. But
I came up with a different idea.

I was originally been using the TRIANGULATE -> TRIGRID path to solve
this interpolation and fill-in the missing data. TRIANGULATE computes
the 'triangles' connectivity based on the simple distance between
points, which means we can trick it to believe that points in x or y
are closer or farther apart. I simply added a constant multiplier to
the y positions in the input arguments to the TRIANGULATE procedure
and it completely changes the triangulation being used. I got the idea
when I looked at the matrix formulation in this article
http://en.wikipedia.org/wiki/Delaunay_triangulation

So the triangulate call looks like this:
TRIANGULATE, x, y*scaling_factor, triangles, boundary

and the whole interpolation looks more or less like this (pared down)

w = where(img NE 0)
wx = w mod Nx
wy = w / Nx
scaling = 4L
TRIANGULATE, wx, wy*scaling, triangles, boundary
img2 = TRIGRID(wx,wy,img(w), triangles, [1,1], [0,0,Nx-1,Ny-1], Nx=Nx,
Ny=Ny)

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[Message index]

		Re: 2D interpolation with sparse data By: Ken G on Wed, 23 May 2007 07:42
		Re: 2D interpolation with sparse data By: cmancone on Wed, 23 May 2007 05:39
		Re: 2D interpolation with sparse data By: ben.bighair on Tue, 22 May 2007 10:06
		Re: 2D interpolation with sparse data By: cmancone on Wed, 23 May 2007 11:58
		Re: 2D interpolation with sparse data By: cmancone on Wed, 23 May 2007 11:57

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