Interpolating with a common chunked dim fails

[aulemahal]

Interpolating a dataarray with another one fails if one of them is a dask array and they share a chunked dimension. Even if the interpolation is independent of that dimension.

MCVE Code Sample

import xarray as xr
import numpy as np

g = xr.DataArray(np.zeros((10, 10)), dims=('x', 'c'), coords={k: np.arange(10) for k in ['x', 'c']})
b = xr.DataArray([5, 6.6, 8.8], dims=('new',)).expand_dims(c=g.c)
gc = g.chunk({'c': 1})
gc.interp(x=b)

Expected Output

An array with coords "new" and "c", with values of g interpolated along x at positions in b, for each c. As there is no interpolation along c, I would expect the fact that it is chunked to be irrelevant.

Problem Description

Raises: NotImplementedError: Chunking along the dimension to be interpolated (1) is not yet supported.;

I didn't see any issue about this, so I thought it ought to be noted as a needed enhancement.

Versions

Output of xr.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.8.2 | packaged by conda-forge | (default, Apr 16 2020, 18:04:51)
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 3.10.0-514.2.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
libhdf5: 1.10.6
libnetcdf: 4.7.4

xarray: 0.15.2.dev42+g0cd14a5
pandas: 1.0.3
numpy: 1.18.1
scipy: 1.4.1
netCDF4: 1.5.3
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.1.1.2
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.1.3
cfgrib: None
iris: None
bottleneck: 1.3.2
dask: 2.14.0
distributed: 2.14.0
matplotlib: 3.2.1
cartopy: 0.17.0
seaborn: None
numbagg: None
pint: 0.11
setuptools: 46.1.3.post20200325
pip: 20.0.2
conda: None
pytest: 5.4.1
IPython: 7.13.0
sphinx: None

[pums974]

I thing this has been solve with v0.16.0 and #4155

[aulemahal]

It doesn't work with 0.16.0, but does with later versions (just tried with the current master). Thanks!