Xarray's interpolator behavior compared to scipy with a numpy array: new keyword behavior requested

[rebeccaringuette]

I'm having trouble with the differing behavior between giving an xarray object to scipy's interpolating functions (particularly the RegularGridInterpolator and the one xarray's interpn is based on) versus giving a numpy array. When giving the interpolator a numpy array, I get a 1D array returned with one value for every point given. When an xarray object is given instead, I get an N dimensional array, as if a np.meshgrid statement is executed on the given points. I have provided more detail at the link below. This differing return behavior and the additional demand for the calculation for a grid made from the points (rather than the points themselves) is much slower than the numpy approach, but I can't use numpy arrays for medium data (because it won't all fit in my memory). Can a feature be added, maybe a 'numpy-like' keyword, to xarray's version of the scipy interpolator to only execute for the points given rather than a grid made from the points? Such a keyword would enable backwards-compatibility and reduce the computational demand for those interested in interpolating along a curved trajectory (such as in my case).

Note: the same differing behavior occurs when I give scipy's RegularGridInterpolator an xarray object.

scipy/scipy#14824 (comment)