issue_comments: 50425930

So I would definitely still wrap the scipy functions for any interpolation routines for xray. It's not worth rewriting any of them twice! Scipy is an optional dependency for xray, but it would be fine to require it for the interpolation routines.

Yes, there do seems to be a few different interpolation routines in scipy. Naming the function you're thinking of something like xray.interpolation.map_coordinates would leave plenty of room for alternative interpolation methods.

In xray, we have DataArray objects (corresponding to a particular variable in a netCDF file along with its coordinates) as well as Datasets (corresponding to entire netCDFs). I think it makes a bit more sense to interpolate individual variables, so I guess you would want this function to have a signature something like the following:

``` Parameters

source : DataArray Gridded source data. destination : ndarray or dict-like (e.g., xray.Dataset or pandas.DataFrame) Array of points of points to sample at (n_obs * n_dim) or mapping from dimension names to coordinate values. order : int, optional mode : {'constant', 'nearest'}, optional cval : scalar, optional Maybe defaulting to NaN instead of 0?

Returns

interpolated : DataArray Data array with interpolated values. Coordinates and dimensions are copied from points. ```

You don't need to necessarily handle all the edge cases of possible input to destination, but it would be nice to handle destinations with labeled dimensions (e.g., in the form of a dictionary or xray.Dataset), and return labeled data in the form of an xray DataArray, with the same coordinate values (if they were supplied).

The DataArray constructor isn't in the currently released version of xray but it will be a major feature of the next release (hopefully to be released in a week or two) and it might be worth using here. It should be clearly documented in the docstring of DataArray.__init__ but let me know if you have any questions.