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 every (nx, ny). However, I want to interpolate for a different value in each [i, j].

Here’s some code to exemplify. If I wanted to interpolate to a single value, say new_z, I’d use scipy.interpolate.interp1d like this

# y is a 3D ndarray
# x is a 1D ndarray with the abcissa values
# new_z is a number
f = scipy.interpolate.interp1d(x, y, axis=-1, kind='linear')
result = f(new_z)

However, for this problem what I actually want is to interpolate to a different new_z for each y[i, j]. So I do this:

# y is a 3D ndarray
# x is a 1D ndarray with the abcissa values
# new_z is a 2D array
result = numpy.empty(y.shape[:-1])
for i in range(nx):
    for j in range(ny):
        f = scipy.interpolate.interp1d(x, y[i, j], axis=-1, kind='linear')
        result[i, j] = f(new_z[i, j])

Unfortunately, with multiple loops this becomes inefficient and slow. Is there a better way to do this kind of interpolation? Linear interpolation is sufficient. A possibility is to implement this in Cython, but I was trying to avoid that because I want to have the flexibility of changing to cubic interpolation and don’t want to do it by hand in Cython.

Answer 1

To speedup high order interpolate, you can call interp1d() only once, and then use the _spline attribute and the low level function _bspleval() in the _fitpack module. Here is the code:

from scipy.interpolate import interp1d
import numpy as np

nx, ny, nz = 30, 40, 50
x = np.arange(0, nz, 1.0)
y = np.random.randn(nx, ny, nz)
new_x = np.random.random_integers(1, (nz-1)*10, size=(nx, ny))/10.0

def original_interpolation(x, y, new_x):
    result = np.empty(y.shape[:-1])
    for i in xrange(nx):
        for j in xrange(ny):
            f = interp1d(x, y[i, j], axis=-1, kind=3)
            result[i, j] = f(new_x[i, j])
    return result

def fast_interpolation(x, y, new_x):
    from scipy.interpolate._fitpack import _bspleval
    f = interp1d(x, y, axis=-1, kind=3)
    xj,cvals,k = f._spline
    result = np.empty_like(new_x)
    for (i, j), value in np.ndenumerate(new_x):
        result[i, j] = _bspleval(value, x, cvals[:, i, j], k, 0)
    return result

r1 = original_interpolation(x, y, new_x)
r2 = fast_interpolation(x, y, new_x)

>>> np.allclose(r1, r2)
True

%timeit original_interpolation(x, y, new_x)
%timeit fast_interpolation(x, y, new_x)
1 loops, best of 3: 3.78 s per loop
100 loops, best of 3: 15.4 ms per loop