From: Denis-B <den...@t-...> - 2009-07-21 16:32:20
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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 -- View this message in context: http://www.nabble.com/speeding-up-griddata%28%29-tp24467055p24591133.html Sent from the matplotlib - users mailing list archive at Nabble.com. |