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As noted in #3956 fill_value="extrapolate" is what we need for 1D.

xarray could use the same interpolation function for both 1d and nd.

Not sure if there's a big performance gain. @jhamman implemented these interpolators originally AFAIK. Perhaps he remembers why we use both.

Nice find! The documentation doesn't explain that at all currently.

But that solution doesn't work for 1d DataArrays. You have to use this kwargs instead: python kwargs = dict(fill_value=(np.min(da.data), np.max(da.data))) So it still isn't as convenient as I would like it to be. The wrapper function can now be simplified to this however:

````python import numpy as np import xarray as xr

def interp(da, coords, extrapolation='clip'): """ Linear interpolation that clips the inputs to the coords min and max value.

Parameters
----------
da : DataArray
    DataArray to interpolate.
coords : dict
    Coordinates for the interpolated value.
"""
if extrapolation == 'clip':
    if len(da.coords) > 1:
        kwargs = dict(fill_value=None)
    else:
        kwargs = dict(fill_value=(np.min(da.data), np.max(da.data)))
return da.interp(coords, kwargs=kwargs)

Create coordinates:

x = np.linspace(1000, 6000, 4) y = np.linspace(100, 1200, 3) z = np.linspace(1, 2, 2)

Create 1D DataArray:

da1 = xr.DataArray(data=2*x, coords=[('x', x)])

Create 2D DataArray:

X = np.meshgrid(*[x, y], indexing='ij') data = X[0] * X[1] da2 = xr.DataArray(data=data, coords=[('x', x), ('y', y)])

Create 3D DataArray:

X = np.meshgrid(*[x, y, z], indexing='ij') data = X[0] * X[1] * X[2] da3 = xr.DataArray(data=data, coords=[('x', x), ('y', y), ('z', z)])

Attempt to extrapolate:

print(interp(da1, {'x': 7000})) print(interp(da2, {'x': 7000, 'y': 375})) print(interp(da3, {'x': 7000, 'y': 375, 'z': 1})) ````

Scipy will do this for you if you ask it to:

da.interp(x=7000, y=375, method="linear", kwargs=dict(fill_value=None))

See https://github.com/pydata/xarray/pull/3956 .