So, I have three numpy arrays which store latitude, longitude, and some property value on a grid — that is, I have LAT(y,x), LON(y,x), and, say temperature T(y,x), for some limits of x and y. The grid isn’t necessarily regular — in fact, it’s tripolar.
I then want to interpolate these property (temperature) values onto a bunch of different lat/lon points (stored as lat1(t), lon1(t), for about 10,000 t…) which do not fall on the actual grid points. I’ve tried matplotlib.mlab.griddata, but that takes far too long (it’s not really designed for what I’m doing, after all). I’ve also tried scipy.interpolate.interp2d, but I get a MemoryError (my grids are about 400×400).
Is there any sort of slick, preferably fast way of doing this? I can’t help but think the answer is something obvious… Thanks!!
Try the combination of inverse-distance weighting and
scipy.spatial.KDTree
described in SO
inverse-distance-weighted-idw-interpolation-with-python.
Kd-trees
work nicely in 2d 3d …, inverse-distance weighting is smooth and local,
and the k= number of nearest neighbours can be varied to tradeoff speed / accuracy.