id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
786839234,MDU6SXNzdWU3ODY4MzkyMzQ=,4816,Possible bug with da.interp_like(),56925856,closed,0,,,1,2021-01-15T11:54:50Z,2021-01-15T12:17:18Z,2021-01-15T12:17:18Z,CONTRIBUTOR,,,,"<!-- Please include a self-contained copy-pastable example that generates the issue if possible.

Please be concise with code posted. See guidelines below on how to provide a good bug report:

- Craft Minimal Bug Reports: http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports
- Minimal Complete Verifiable Examples: https://stackoverflow.com/help/mcve

Bug reports that follow these guidelines are easier to diagnose, and so are often handled much more quickly.
-->

**What happened**:
When interpolating a multidimensional array, I used the `fill_value=None` kwarg, as [in the scipy docs](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interpn.html) 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 `nan`s 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**:

<details><summary>Output of <tt>xr.show_versions()</tt></summary>

<!-- Paste the output here xr.show_versions() here -->
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.12 |Anaconda, Inc.| (default, Sep  8 2020, 23:10:56) 
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 3.10.0-1127.19.1.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
libhdf5: 1.10.5
libnetcdf: 4.6.2

xarray: 0.16.1
pandas: 1.1.5
numpy: 1.19.2
scipy: 1.5.2
netCDF4: 1.5.1.2
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.3.0
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.1.8
cfgrib: None
iris: None
bottleneck: 1.3.2
dask: 2.30.0
distributed: 2.30.1
matplotlib: 3.3.3
cartopy: 0.18.0
seaborn: None
numbagg: None
pint: None
setuptools: 51.0.0.post20201207
pip: 20.3.3
conda: None
pytest: None
IPython: 7.16.1
sphinx: None

</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4816/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
617476316,MDU6SXNzdWU2MTc0NzYzMTY=,4055,Automatic chunking of arrays ?,56925856,closed,0,,,11,2020-05-13T14:02:41Z,2020-05-25T19:23:45Z,2020-05-25T19:23:45Z,CONTRIBUTOR,,,,"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!","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4055/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
568378007,MDU6SXNzdWU1NjgzNzgwMDc=,3784,Function for regressing/correlating multiple fields?,56925856,closed,0,,,6,2020-02-20T15:24:14Z,2020-05-25T16:55:33Z,2020-05-25T16:55:33Z,CONTRIBUTOR,,,,"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","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3784/reactions"", ""total_count"": 1, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 1, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue