issue_comments: 60332922

For what its worth, I wrote this today. Its a long way from being useful but I find it's working well enough to fill gaps in data after a reindex()

``` py from scipy import interpolate, ndimage

def linterp(data, index, interp_index, order=1): """ Parameters ---------- data: nd-array (cube). index: index (floats) associated with the cube. interp_index: float interpolation poing. Returns ------- interpolated: nd-array An interpolated field. """

# Form a cube of the values, which we will imagine is our function f()
cube = np.array(data, dtype=np.float)

# Form a relationship, and this can be non-linear, between slice indexes and
# the "index".
m = interpolate.interp1d(index, range(len(index)))

# Form a set of coordinates to sample over - x
y, x = np.mgrid[0:data[0].shape[0], 0:data[0].shape[1]]
z = np.ones_like(y) * m(interp_index)

# Perform the sampling f(x), map coordinates is performing a linear
# interpolation of the coordinates in the cube.
return ndimage.map_coordinates(
    cube,
    [z, y, x],
    order=order,
    cval=np.nan)

```