Robert C, Robert K, folks,
messing with the nice delaunay/testfuncs.py to time
linear_interpolate_grid nn_interpolate_grid and nn_interpolate_unstructured
in _delaunay, I see linear ~ 100 times faster than the nn_ s:
# from: trigrid Ntri=1000 Ngrid=100 run: 21 Jul 2009 17:33 mac 10.4.11 ppc
time: 0.027 sec trigrid: build Triangulation 1000
time: 0.0059 sec trigrid 100 "linear" corners: 0 1 2 1
time: 0.5 sec trigrid 100 "nn_grid" corners: 0 1 2 1
time: 0.49 sec trigrid 100 "nn_unstruct" corners: 0 1 2 1
Correct me: if all 3 methods do gridpoint-to-triangle in the same way,
then the huge diff is in find-neighboring-triangles (6 on average ?), not in
gridpoint-to-triangle ?
This is with the _delaunay.so that comes with the mac 98.5.3 egg,
however that was compiled (-O3 ?)
What to do ?
1) does it matter, how many people care ? (all who believe in telekinesis,
raise my right hand)
2) natgrid ? don't see it in matplotlib.sf.net
3) stick with fast linear, smooth the triangle planes a la 3t^2 - 2t^3 or
fancier smoothing
In any case, add griddata( ... method = "linear" / "nn" ... ) so users have
a choice.
Can a real user or two tell us about the flow,
with some rough numbers for Ntri Ngrid Npix --
Ntri = nr original sample points, say 1000
Ngrid 100 x 100
Npix 800 x 600 ?
(Ntri -> Ngrid slowly and accurately,
then Ngrid -> Npix w fast inaccurate image interpolation ? hmm.)
cheers
-- denis
--
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