Xarray: IndexError when printing dataset from an Argo file
Working with a netcdf Argo data file, I encountered the following error:
# Sample data file here: https://storage.googleapis.com/myargo/sample/4902076_prof.nc
argofile = '4902076_prof.nc'
ds = xr.open_dataset(argofile)
print ds
[...full trace below...]
Out[]:
IndexError: The indexing operation you are attempting to perform is not valid on netCDF4.Variable object. Try loading your data into memory first by calling .load().
Original traceback:
Traceback (most recent call last):
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/backends/netCDF4_.py", line 62, in __getitem__
data = getitem(self.get_array(), key)
File "netCDF4/_netCDF4.pyx", line 3961, in netCDF4._netCDF4.Variable.__getitem__
File "netCDF4/_netCDF4.pyx", line 4796, in netCDF4._netCDF4.Variable._get
IndexError
The error remains the same even if I try to load the data as suggested in the error message.
However, I can keep working with the dataset and access variable.
This only affects the printing of the ds object.
I can't get to determine where in my package updating workflow this really pop out. It used to work very fined up to xarray version 0.9.5. Here I'm using 0.10.0rc1 (see full version details below).
It is worth noting that using the 'scipy' engine solves the issue !
ds = xr.open_dataset(argofile, engine='scipy')
print ds
Out[48]:
<xarray.Dataset>
Dimensions: (N_CALIB: 1, N_HISTORY: 0, N_LEVELS: 1007, N_PARAM: 3, N_PROF: 33)
Dimensions without coordinates: N_CALIB, N_HISTORY, N_LEVELS, N_PARAM, N_PROF
Data variables:
PROFILE_PRES_QC (N_PROF) object ...
DATA_TYPE object ...
JULD (N_PROF) datetime64[ns] .
[...]
I suspect a compatibility issue somewhere with netCDF4
Any ideas ?
thanks
Exact trace:
print ds
Traceback (most recent call last):
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-31-63698f3c943a>", line 1, in <module>
print ds
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/formatting.py", line 64, in __repr__
return ensure_valid_repr(self.__unicode__())
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/dataset.py", line 1050, in __unicode__
return formatting.dataset_repr(self)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/formatting.py", line 426, in dataset_repr
summary.append(data_vars_repr(ds.data_vars, col_width=col_width))
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/formatting.py", line 297, in _mapping_repr
summary += [summarizer(k, v, col_width) for k, v in mapping.items()]
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/formatting.py", line 236, in summarize_datavar
return summarize_variable(name, var.variable, col_width, show_values)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/formatting.py", line 212, in summarize_variable
elif isinstance(var.data, dask_array_type):
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/variable.py", line 308, in data
return self.values
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/variable.py", line 369, in values
return _as_array_or_item(self._data)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/variable.py", line 225, in _as_array_or_item
data = np.asarray(data)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/numpy/core/numeric.py", line 482, in asarray
return array(a, dtype, copy=False, order=order)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py", line 412, in __array__
self._ensure_cached()
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py", line 409, in _ensure_cached
self.array = np.asarray(self.array)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/numpy/core/numeric.py", line 482, in asarray
return array(a, dtype, copy=False, order=order)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py", line 393, in __array__
return np.asarray(self.array, dtype=dtype)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/numpy/core/numeric.py", line 482, in asarray
return array(a, dtype, copy=False, order=order)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py", line 360, in __array__
return np.asarray(array[self.key], dtype=None)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/conventions.py", line 384, in __getitem__
return mask_and_scale(self.array[key], self.fill_value,
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/conventions.py", line 493, in __getitem__
return char_to_bytes(self.array[key])
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/conventions.py", line 635, in char_to_bytes
arr = np.array(arr, copy=False, order='C')
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py", line 360, in __array__
return np.asarray(array[self.key], dtype=None)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/backends/netCDF4_.py", line 73, in __getitem__
raise IndexError(msg)
IndexError: The indexing operation you are attempting to perform is not valid on netCDF4.Variable object. Try loading your data into memory first by calling .load().
Original traceback:
Traceback (most recent call last):
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/backends/netCDF4_.py", line 62, in __getitem__
data = getitem(self.get_array(), key)
File "netCDF4/_netCDF4.pyx", line 3961, in netCDF4._netCDF4.Variable.__getitem__
File "netCDF4/_netCDF4.pyx", line 4796, in netCDF4._netCDF4.Variable._get
IndexError
Expected Output:
In [26]: print ds
<xarray.Dataset>
Dimensions: (N_CALIB: 1, N_HISTORY: 0, N_LEVELS: 1007, N_PARAM: 3, N_PROF: 33)
Dimensions without coordinates: N_CALIB, N_HISTORY, N_LEVELS, N_PARAM, N_PROF
Data variables:
DATA_TYPE object 'Argo profile '
FORMAT_VERSION object '3.1 '
HANDBOOK_VERSION object '1.2 '
REFERENCE_DATE_TIME object '19500101000000'
DATE_CREATION object '20150402105716'
DATE_UPDATE object '20160127150022'
PLATFORM_NUMBER (N_PROF) object '4902076 ' '4902076 ' ...
PROJECT_NAME (N_PROF) object 'US ARGO PROJECT ' ...
PI_NAME (N_PROF) object 'GREGORY C. JOHNSON ' ...
STATION_PARAMETERS (N_PROF, N_PARAM) object 'PRES ' ...
CYCLE_NUMBER (N_PROF) float64 1.0 2.0 4.0 5.0 6.0 7.0 ...
DIRECTION (N_PROF) object 'A' 'A' 'A' 'A' 'A' 'A' ...
DATA_CENTRE (N_PROF) object 'AO' 'AO' 'AO' 'AO' 'AO' ...
DC_REFERENCE (N_PROF) object '5448_0469_001 ' ...
DATA_STATE_INDICATOR (N_PROF) object '2B ' '2B ' '2B ' ...
DATA_MODE (N_PROF) object 'A' 'A' 'A' 'A' 'A' 'A' ...
PLATFORM_TYPE (N_PROF) object 'NAVISIR ' ...
FLOAT_SERIAL_NO (N_PROF) object '0469 ' ...
FIRMWARE_VERSION (N_PROF) object '011514 ' ...
WMO_INST_TYPE (N_PROF) object '863 ' '863 ' '863 ' ...
JULD (N_PROF) datetime64[ns] 2015-03-03T08:56:39.984000 ...
JULD_QC (N_PROF) object '1' '1' '1' '1' '1' '1' ...
JULD_LOCATION (N_PROF) datetime64[ns] 2015-03-03T09:12:09.993600 ...
LATITUDE (N_PROF) float64 20.26 20.11 21.53 22.0 ...
LONGITUDE (N_PROF) float64 -121.5 -121.5 -121.1 ...
POSITION_QC (N_PROF) object '1' '1' '1' '1' '1' '1' ...
POSITIONING_SYSTEM (N_PROF) object 'GPS ' 'GPS ' ...
PROFILE_PRES_QC (N_PROF) object 'A' 'A' 'A' 'A' 'A' 'A' ...
PROFILE_TEMP_QC (N_PROF) object 'A' 'A' 'A' 'A' 'A' 'A' ...
PROFILE_PSAL_QC (N_PROF) object 'A' 'A' 'A' 'A' 'A' 'A' ...
VERTICAL_SAMPLING_SCHEME (N_PROF) object 'Primary sampling: averaged [] ' ...
CONFIG_MISSION_NUMBER (N_PROF) float64 1.0 2.0 4.0 5.0 6.0 7.0 ...
PRES (N_PROF, N_LEVELS) float64 4.0 6.0 8.0 ...
PRES_QC (N_PROF, N_LEVELS) object '1' '1' '1' '1' ...
PRES_ADJUSTED (N_PROF, N_LEVELS) float64 3.73 5.73 7.73 ...
PRES_ADJUSTED_QC (N_PROF, N_LEVELS) object '1' '1' '1' '1' ...
PRES_ADJUSTED_ERROR (N_PROF, N_LEVELS) float64 nan nan nan nan ...
TEMP (N_PROF, N_LEVELS) float64 22.24 22.24 ...
TEMP_QC (N_PROF, N_LEVELS) object '1' '1' '1' '1' ...
TEMP_ADJUSTED (N_PROF, N_LEVELS) float64 22.24 22.24 ...
TEMP_ADJUSTED_QC (N_PROF, N_LEVELS) object '1' '1' '1' '1' ...
TEMP_ADJUSTED_ERROR (N_PROF, N_LEVELS) float64 nan nan nan nan ...
PSAL (N_PROF, N_LEVELS) float64 34.45 34.45 ...
PSAL_QC (N_PROF, N_LEVELS) object '1' '1' '1' '1' ...
PSAL_ADJUSTED (N_PROF, N_LEVELS) float64 34.45 34.45 ...
PSAL_ADJUSTED_QC (N_PROF, N_LEVELS) object '1' '1' '1' '1' ...
PSAL_ADJUSTED_ERROR (N_PROF, N_LEVELS) float64 nan nan nan nan ...
PARAMETER (N_PROF, N_CALIB, N_PARAM) object 'PRES ' ...
SCIENTIFIC_CALIB_EQUATION (N_PROF, N_CALIB, N_PARAM) object 'PRES_ADJUSTED = PRES - surface_pressure ' ...
SCIENTIFIC_CALIB_COEFFICIENT (N_PROF, N_CALIB, N_PARAM) object 'surface_pressure=0.27 dbar ' ...
SCIENTIFIC_CALIB_COMMENT (N_PROF, N_CALIB, N_PARAM) object 'Pressure adjusted at real time based on most recent valid surface pressure ' ...
SCIENTIFIC_CALIB_DATE (N_PROF, N_CALIB, N_PARAM) object '20150514141619' ...
HISTORY_INSTITUTION (N_HISTORY, N_PROF) object
HISTORY_STEP (N_HISTORY, N_PROF) object
HISTORY_SOFTWARE (N_HISTORY, N_PROF) object
HISTORY_SOFTWARE_RELEASE (N_HISTORY, N_PROF) object
HISTORY_REFERENCE (N_HISTORY, N_PROF) object
HISTORY_DATE (N_HISTORY, N_PROF) object
HISTORY_ACTION (N_HISTORY, N_PROF) object
HISTORY_PARAMETER (N_HISTORY, N_PROF) object
HISTORY_START_PRES (N_HISTORY, N_PROF) float64
HISTORY_STOP_PRES (N_HISTORY, N_PROF) float64
HISTORY_PREVIOUS_VALUE (N_HISTORY, N_PROF) float64
HISTORY_QCTEST (N_HISTORY, N_PROF) object
Attributes:
title: Argo float vertical profile
institution: Coriolis GDAC
source: Argo float
history: 2016-01-27T15:00:22Z creation
references: http://www.argodatamgt.org/Documentation
user_manual_version: 3.1
Conventions: Argo-3.1 CF-1.6
featureType: trajectoryProfile
Output of xr.show_versions()
INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
xarray: 0.10.0rc1
pandas: 0.21.0
numpy: 1.11.3
scipy: 0.18.1
netCDF4: 1.3.1
h5netcdf: 0.3.1
Nio: None
bottleneck: 1.2.0
cyordereddict: 1.0.0
dask: 0.16.0
matplotlib: 1.5.3
cartopy: 0.15.1
seaborn: 0.7.1
setuptools: 36.5.0
pip: 9.0.1
conda: None
pytest: None
IPython: 5.2.2
sphinx: 1.5.2
gmaze
All 14 comments
Guillaume--this is very troubling! I use xarray frequently on ARGO netCDF files. It would be a shame if we broke something related to reading them. It sounds like this could be related to the indexing changes in #1676.
Since rc1, xarray 0.10.0 has gone through another release candidate (rc2) and is now in full release. Can you try upgrading to the just-released 0.10.0 and verify whether the problem is still present?
rabernat
on 21 Nov 2017
Yes, please upgrade to 0.10.0 and report back. I cannot reproduce your issue with this release version.
jhamman
on 21 Nov 2017
Ok, upgrading to 0.10.0 solve the issue ! Thanks
Should have tried this in the 1st place
gmaze
on 21 Nov 2017
It's good to see that the release-candidate process actually works! 馃槈
rabernat
on 21 Nov 2017
👍1
Sorry guys, just found out that the issue is still going on with some of the variables in the dataset:
Works ok for temperature TEMP for instance:
ds = xr.open_dataset(argofile, autoclose=True, decode_cf=True)
ds['TEMP']
Out[89]:
<xarray.DataArray 'TEMP' (N_PROF: 338, N_LEVELS: 51)>
array([[ 27.393 , 27.392 , 27.393 , ..., 3.597 , 3.34 ,
nan],
[ 27.57 , 27.572001, 27.570999, ..., 3.543 , 3.265 ,
nan],
[ 28.094999, 28.091999, 28.096001, ..., 3.544 , 3.287 ,
nan],
...,
[ 27.157 , 27.156 , 27.159 , ..., 3.318 , nan,
nan],
[ 27.608999, 27.610001, 27.608999, ..., 3.419 , nan,
nan],
[ 27.569 , 27.566999, 27.561001, ..., 3.422 , nan,
nan]])
Dimensions without coordinates: N_PROF, N_LEVELS
Attributes:
long_name: Sea temperature in-situ ITS-90 scale
standard_name: sea_water_temperature
units: degree_Celsius
valid_min: -2.5
valid_max: 40.0
C_format: %9.3f
FORTRAN_format: F9.3
resolution: 0.001
but for the variable "HISTORY_STEP", I get the error:
ds['HISTORY_STEP']
Out[90]: Traceback (most recent call last):
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/IPython/core/formatters.py", line 190, in catch_format_error
r = method(self, *args, **kwargs)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/IPython/core/formatters.py", line 672, in __call__
printer.pretty(obj)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/IPython/lib/pretty.py", line 383, in pretty
return _default_pprint(obj, self, cycle)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/IPython/lib/pretty.py", line 503, in _default_pprint
_repr_pprint(obj, p, cycle)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/IPython/lib/pretty.py", line 701, in _repr_pprint
output = repr(obj)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/common.py", line 100, in __repr__
return formatting.array_repr(self)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/formatting.py", line 393, in array_repr
summary.append(short_array_repr(arr.values))
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/dataarray.py", line 412, in values
return self.variable.values
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/variable.py", line 396, in values
return _as_array_or_item(self._data)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/variable.py", line 217, in _as_array_or_item
data = np.asarray(data)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/numpy/core/numeric.py", line 482, in asarray
return array(a, dtype, copy=False, order=order)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py", line 557, in __array__
self._ensure_cached()
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py", line 554, in _ensure_cached
self.array = NumpyIndexingAdapter(np.asarray(self.array))
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/numpy/core/numeric.py", line 482, in asarray
return array(a, dtype, copy=False, order=order)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py", line 538, in __array__
return np.asarray(self.array, dtype=dtype)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/numpy/core/numeric.py", line 482, in asarray
return array(a, dtype, copy=False, order=order)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py", line 505, in __array__
return np.asarray(array[self.key], dtype=None)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/conventions.py", line 388, in __getitem__
return mask_and_scale(self.array[key], self.fill_value,
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/conventions.py", line 498, in __getitem__
return char_to_bytes(self.array[key])
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/conventions.py", line 640, in char_to_bytes
arr = np.array(arr, copy=False, order='C')
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/core/indexing.py", line 505, in __array__
return np.asarray(array[self.key], dtype=None)
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/backends/netCDF4_.py", line 72, in __getitem__
raise IndexError(msg)
IndexError: The indexing operation you are attempting to perform is not valid on netCDF4.Variable object. Try loading your data into memory first by calling .load().
Original traceback:
Traceback (most recent call last):
File "/Users/gmaze/anaconda/envs/obidam/lib/python2.7/site-packages/xarray/backends/netCDF4_.py", line 61, in __getitem__
data = getitem(self.get_array(), key)
File "netCDF4/_netCDF4.pyx", line 3961, in netCDF4._netCDF4.Variable.__getitem__
File "netCDF4/_netCDF4.pyx", line 4796, in netCDF4._netCDF4.Variable._get
IndexError
The new state of the versions:
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
xarray: 0.10.0
pandas: 0.21.0
numpy: 1.11.3
scipy: 0.18.1
netCDF4: 1.3.1
h5netcdf: 0.3.1
Nio: None
bottleneck: 1.2.0
cyordereddict: 1.0.0
dask: 0.16.0
matplotlib: 1.5.3
cartopy: 0.15.1
seaborn: 0.7.1
setuptools: 36.5.0
pip: 9.0.1
conda: None
pytest: None
IPython: 5.2.2
sphinx: 1.5.2
~It's good to see that the release-candidate process actually works!~ 馃槶
rabernat
on 21 Nov 2017
😄1
Reproduced also in my environment.
Selected repr of your dataset is
<xarray.Dataset>
Dimensions: (N_CALIB: 1, N_HISTORY: 0, N_LEVELS: 1007, N_PARAM: 3, N_PROF: 33)
Dimensions without coordinates: N_CALIB, N_HISTORY, N_LEVELS, N_PARAM, N_PROF
Data variables:
...
SCIENTIFIC_CALIB_DATE (N_PROF, N_CALIB, N_PARAM) object b'20150514141619' ...
HISTORY_INSTITUTION (N_HISTORY, N_PROF) object ...
It seems the data variable in concern, such as ds['HISTORY_STEP'], is 2-dimensional but one of the dimensions N_HISTORY is size 0.
Currently, xarray does not handle such zero-length n-dimensional arrays.
Maybe we need to drop such variables when reading?
EDIT: This was wrong. xarray should handle 0-size nd-array.
@shoyer, @fmaussion, any idea?
fujiisoup
on 21 Nov 2017
Currently, xarray does not handle such zero-length n-dimensional arrays.
Maybe we need to drop such variables when reading?
Rather than drop a variable the user could read with no problem under xarray 0.9.6, I propose we examine our new indexing wrappers and figure out how to fix this bug.
rabernat
on 21 Nov 2017
👍1
But is this really the issue? Because this works:
import numpy as np
import xarray as xr
a = np.zeros((0, 2))
da = xr.DataArray(a)
da.to_netcdf('test.nc')
xr.open_dataarray('test.nc')
Out[16]:
<xarray.DataArray (dim_0: 0, dim_1: 2)>
array([], shape=(0, 2), dtype=float64)
Dimensions without coordinates: dim_0, dim_1
fmaussion
on 21 Nov 2017
👍1
I tried with xarray v0.9.6 but it raises the same error.
Maybe also related to #1719 .
fujiisoup
on 21 Nov 2017
This might be specific to string variables (character arrays) and netCDF4. My guess is that netCDF4 may not handle indexing on zero-dimensional arrays properly.
shoyer
on 21 Nov 2017
This is pretty clearly a netCDF4-Python bug, which I have reported upstream: https://github.com/Unidata/netcdf4-python/issues/743
If desired, we probably could pretty easily add a work around for this behavior (e.g., to create an empty numpy array of the appropriate shape) to our netCDF4-python wrapper:
https://github.com/pydata/xarray/blob/9d09c1659741dafb1fadeed49c81f9e90a548b07/xarray/backends/netCDF4_.py#L50
shoyer
on 21 Nov 2017
👍2
Marking this as closed since the issue will be fixed in the next netcdf4-python release. Feel free to submit a PR with a work around for xarray if using the scipy backend is not a good alternative for you.
shoyer
on 25 Nov 2017
👍1
The scipy backend has shown to be a good alternative as of now, if not I'll write a work around though.
Thanks for your help !
gmaze
on 27 Nov 2017
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Most helpful comment
This is pretty clearly a netCDF4-Python bug, which I have reported upstream: https://github.com/Unidata/netcdf4-python/issues/743
If desired, we probably could pretty easily add a work around for this behavior (e.g., to create an empty numpy array of the appropriate shape) to our netCDF4-python wrapper:
https://github.com/pydata/xarray/blob/9d09c1659741dafb1fadeed49c81f9e90a548b07/xarray/backends/netCDF4_.py#L50