issue_comments: 1133833687

You're right, thanks for the help. It seems that orthogonal dimensions are now interpolated independently by calling SciPy's interp1d multiple times, which explains why passing 'extrapolate' works. This behaviour has been introduced in #4155, in particular 7daad4fce3bf8ad9b9bc8e7baa104c476437e68d.

The documentation should be adapted to use @keewis's approach. I'd suggest including an example combining "advanced interpolation" and extrapolation, such as the one below:

python da = xr.DataArray( np.sin(0.3 * np.arange(20).reshape(5, 4)), [("x", np.arange(5)), ("y", [0.1, 0.2, 0.3, 0.4])], ) x = xr.DataArray([0.5, 1.5, 2.5, 3.5], dims="z") y = xr.DataArray([0.15, 0.25, 0.35, 0.45], dims="z")

Without extrapolation: python da.interp(x=x, y=y) <xarray.DataArray (z: 4)> array([ 0.55626357, 0.63496063, -0.46643289, nan]) Coordinates: x (z) float64 0.5 1.5 2.5 3.5 y (z) float64 0.15 0.25 0.35 0.45 Dimensions without coordinates: z

With extrapolation: python da.interp(x=x, y=y, kwargs={'fill_value': None}) <xarray.DataArray (z: 4)> array([ 0.55626357, 0.63496063, -0.46643289, -0.73507668]) Coordinates: x (z) float64 0.5 1.5 2.5 3.5 y (z) float64 0.15 0.25 0.35 0.45 Dimensions without coordinates: z