html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/191#issuecomment-60332922,https://api.github.com/repos/pydata/xarray/issues/191,60332922,MDEyOklzc3VlQ29tbWVudDYwMzMyOTIy,377869,2014-10-24T01:19:47Z,2014-10-24T01:19:47Z,NONE,"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)
```
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