What happened: When interpolating a multidimensional array, I used the fill_value=None kwarg, as in the scipy docs it says that If None, values outside the domain are extrapolated.

What you expected to happen: However, this did not work and the array was filled with nans instead of being extrapolated. I had to explicitly add fill_value='extrapolate' in order to get extrapolation to work.

However, as far as I can tell, in this context interp_like() should be calling scipy.interpolate.interpn(), which doesn't seem to require fill_value='extrapolate'?

I might be misunderstanding something here, but if not then I think there might be a bug? Happy to be corrected if I've misunderstood the docs!

Minimal Complete Verifiable Example:

```python

Put your MCVE code here

import xarray as xr import numpy as np

da = xr.DataArray( np.array(([1,2,3,4,0],[1,2,3,9,0])), dims=["x", "y"], coords={"x": [0,1],"y": [0,1,2,3,4]} )

da2 = xr.DataArray( np.array(([2,3,0,4,5,6],[2,3,0,4,5,6],[2,3,0,4,5,6])), dims=["x", "y"], coords={"x": [0,1,2],"y": [0,1,2,3,4,5]} )

print(da.interp_like(da2)) <xarray.DataArray (x: 3, y: 6)> array([[ 1., 2., 3., 4., 0., nan], [ 1., 2., 3., 9., 0., nan], [nan, nan, nan, nan, nan, nan]]) Coordinates: * x (x) int64 0 1 2 * y (y) int64 0 1 2 3 4 5

print(da.interp_like(da2, kwargs={'fill_value':None})) <xarray.DataArray (x: 3, y: 6)> array([[ 1., 2., 3., 4., 0., nan], [ 1., 2., 3., 9., 0., nan], [nan, nan, nan, nan, nan, nan]]) Coordinates: * x (x) int64 0 1 2 * y (y) int64 0 1 2 3 4 5

print(da.interp_like(da2, kwargs={'fill_value':'extrapolate'})) <xarray.DataArray (x: 3, y: 6)> array([[ 1., 2., 3., 4., 0., -4.], [ 1., 2., 3., 9., 0., -9.], [ 1., 2., 3., 14., 0., -14.]]) Coordinates: * x (x) int64 0 1 2 * y (y) int64 0 1 2 3 4 5 ```

Environment:

Hi there,

Hopefully this turns out to be a basic issue, but I was wondering why the chunks='auto' that dask seems to provide (https://docs.dask.org/en/latest/array-chunks.html#automatic-chunking) isn't an option for xarray? I'm not 100% sure of how dask decides how to automatically chunk its arrays, so maybe there's a compatibility issue?

I get the impression that the dask method automatically tries to prevent the issues of "too many chunks" or "too few chunks" which can sometimes happen when choosing chunk sizes automatically. If so, it would maybe be a useful thing to include in future versions?

Happy to be corrected if I've misunderstood something here though, still getting my head around how the dask/xarray compatibility really works...

Cheers!

I came across this StackOverflow thread about applying linear regression across multi-dimensional arrays and there were some very efficient, xarray-based solutions suggested to the OP which leveraged vectorized operations. I was wondering if there was any interest in adding a function like this to future releases of xarray? It seems like a very neat and useful functionality to have, and not just for meteorological applications!

https://stackoverflow.com/questions/52108417/how-to-apply-linear-regression-to-every-pixel-in-a-large-multi-dimensional-array

Most answers draw from this blog post: https://hrishichandanpurkar.blogspot.com/2017/09/vectorized-functions-for-correlation.html