Fast interpolation over 3D array

Question

I have a 3D array that I need to interpolate over one axis (the last dimension). Let\'s say y.shape = (nx, ny, nz), I want to interpolate in nz for

Answer 1

Although there are several nice answers, they're still doing 250k interpolations in a fixed 500-long array:

j250k = np.searchsorted( X500, X250k )  # indices in [0, 500)

This can be sped up with a LUT, LookUp Table, with say 5k slots:

lut = np.interp( np.arange(5000), X500, np.arange(500) ).round().astype(int)
xscale = (X - X.min()) * (5000 - 1) \
        / (X.max() - X.min()) 
j = lut.take( xscale.astype(int), mode="clip" )  # take(floats) in numpy 1.7 ?

#---------------------------------------------------------------------------
# X     |    |       | |             |
# j     0    1       2 3             4 ...
# LUT   |....|.......|.|.............|....  -> int j (+ offset in [0, 1) )
#---------------------------------------------------------------------------

searchsorted is pretty fast, time ~ ln2 500, so this is probably not much faster.
But LUTs are very fast in C, a simple speed / memory tradeoff.