issue_comments: 495871201

@fspaolo I never tried using my algorithm to perform 2D interpolation, but this should work: ``` from xarray_extras.interpolate import splrep, splev

da = splev(x_new, splrep(da, 'x')) da = splev(y_new, splrep(da, 'y')) da = splev(t_new, splrep(da, 't')) ``` Add k=1 to downgrade from cubic to linear interpolation and get a speed boost.

You can play around with dask to increase performance by using all your CPUs (or more with dask distributed), although you have to remember that an original dim can't be broken on multiple chunks when you apply splrep to it:

from xarray_extras.interpolate import splrep, splev da = da.chunk(t=TCHUNK) da = splev(x_new, splrep(da, 'x')) da = splev(y_new, splrep(da, 'y')) da = da.chunk(x=SCHUNK, y=SCHUNK).chunk(t=-1) da = splev(t_new, splrep(da, 't')) da = da.compute() where TCHUNK and SCHUNK are integers you'll have to play with. The rule of thumb is that you want to have your chunks 5~100 MBs each.

If you end up finding out that chunking along an interpolation dimension is important for you, it is possible to implement it with dask ghosting techniques, just painfully complicated.