issues

What happened?

When running the broadcast in the sample code, I end up with nan in the output when there are not any in the original source array. While I know the construction is really odd (this came from user-submitted code), I'm shocked that it resulted in nans the resulting broadcasted data and honestly assumed MetPy's code was doing something dumb for quite awhile. I would have expected (regardless of the nature of the coordinates) that the result for broad_a be [[1, 2], [1, 2]].

What did you expect to happen?

No response

Minimal Complete Verifiable Example

```Python levs = np.array([100000, 85000]) a = xr.Dataset({'a': (('lev',), [1, 2])}, coords={'lev': levs}).to_array() b = xr.Dataset({'b': (('lev',), [3, 4])}, coords={'lev': levs}).to_array()

broad_a, broad_b = xr.broadcast(a, b) print(broad_a) ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

Python <xarray.DataArray (variable: 2, lev: 2)> array([[ 1., 2.], [nan, nan]]) Coordinates: * lev (lev) int64 100000 85000 * variable (variable) object 'a' 'b'

Anything else we need to know?

No response

Environment

What happened?

When subsetting a DataArray along a dimension down to a single item, the other coordinate variables gain this scalar coordinate.

What did you expect to happen?

Coordinate variables should not have their coordinates changed.

Minimal Complete Verifiable Example

```Python import numpy as np import xarray as xr

lat = np.array([25, 35, 45]) lon = np.array([-105, -95, -85, -75]) time = np.array([0, 1])

data = np.arange(lat.size * lon.size * time.size) test_data = xr.DataArray(data.reshape((time.size, lat.size, lon.size)), coords=dict(lat=lat, lon=lon, time=time), dims=('time', 'lat', 'lon'))

print(test_data.lat.coords) # Only 'lat' print(test_data.isel(time=0).lat.coords) # Has both 'lat' and 'time' pritn(test_data.isel(time=[0, 1]).lat.coords) # Only 'lat' ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

No response

Anything else we need to know?

This occurs with both the latest 2022.11.0 release and current main.

Environment

I ran into this issue with a netCDF file with the following time variable: ``` double time1(time1) ; time1:_FillValue = NaN ; time1:standard_name = "time" ; time1:long_name = "time" ; time1:udunits = "Hour since 2017-09-05T12:00:00Z" ; time1:units = "Hour since 2017-09-05T12:00:00+00:00" ; time1:calendar = "proleptic_gregorian" ;

time1 = 0, 3, 6, 9, 12, 15, 18, 21, 24 ; but we can reproduce the problem with something as simple as:python import numpy as np import xarray as xr

t = np.array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000'], dtype='datetime64[ns]') da = xr.DataArray(np.ones(t.shape), dims=('time',), coords=(t,))

da.loc[{'time':t[0]}] # Works on pandas 1.0.5 this produces:pytb

KeyError Traceback (most recent call last) <ipython-input-11-3e0afa0bd195> in <module> ----> 1 da.loc[{'time':t[0]}]

~/miniconda3/envs/py38/lib/python3.8/site-packages/xarray/core/dataarray.py in getitem(self, key) 196 labels = indexing.expanded_indexer(key, self.data_array.ndim) 197 key = dict(zip(self.data_array.dims, labels)) --> 198 return self.data_array.sel(**key) 199 200 def setitem(self, key, value) -> None:

~/miniconda3/envs/py38/lib/python3.8/site-packages/xarray/core/dataarray.py in sel(self, indexers, method, tolerance, drop, **indexers_kwargs) 1147 1148 """ -> 1149 ds = self._to_temp_dataset().sel( 1150 indexers=indexers, 1151 drop=drop,

~/miniconda3/envs/py38/lib/python3.8/site-packages/xarray/core/dataset.py in sel(self, indexers, method, tolerance, drop, **indexers_kwargs) 2099 """ 2100 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "sel") -> 2101 pos_indexers, new_indexes = remap_label_indexers( 2102 self, indexers=indexers, method=method, tolerance=tolerance 2103 )

~/miniconda3/envs/py38/lib/python3.8/site-packages/xarray/core/coordinates.py in remap_label_indexers(obj, indexers, method, tolerance, **indexers_kwargs) 394 } 395 --> 396 pos_indexers, new_indexes = indexing.remap_label_indexers( 397 obj, v_indexers, method=method, tolerance=tolerance 398 )

~/miniconda3/envs/py38/lib/python3.8/site-packages/xarray/core/indexing.py in remap_label_indexers(data_obj, indexers, method, tolerance) 268 coords_dtype = data_obj.coords[dim].dtype 269 label = maybe_cast_to_coords_dtype(label, coords_dtype) --> 270 idxr, new_idx = convert_label_indexer(index, label, dim, method, tolerance) 271 pos_indexers[dim] = idxr 272 if new_idx is not None:

~/miniconda3/envs/py38/lib/python3.8/site-packages/xarray/core/indexing.py in convert_label_indexer(index, label, index_name, method, tolerance) 187 indexer = index.get_loc(label.item()) 188 else: --> 189 indexer = index.get_loc( 190 label.item(), method=method, tolerance=tolerance 191 )

~/miniconda3/envs/py38/lib/python3.8/site-packages/pandas/core/indexes/datetimes.py in get_loc(self, key, method, tolerance) 620 else: 621 # unrecognized type --> 622 raise KeyError(key) 623 624 try:

KeyError: 1504612800000000000 ```

what's interesting is changing the units of datetime64 to [s] works: ```python import numpy as np import xarray as xr

t = np.array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000'], dtype='datetime64[s]') da = xr.DataArray(np.ones(t.shape), dims=('time',), coords=(t,)) da.loc[{'time':t[0]}] # Works ```

Environment: Python 3.8 from conda-forge on macOS 10.15.4

There's no easy way currently to control the conversion to string of time values/series. For this purpose, Panda's own .dt attribute has an implementation of strftime.

Is there interest in adding similar functionality to xarray?

So this exception: ``` ValueError: Conflicting _FillValue and missing_value attrs on a variable 'MergedBaseReflectivityQC_altitude_above_msl': -999.0 vs. -99.0

Consider opening the offending dataset using decode_cf=False, correcting the attrs and decoding explicitly using xarray.decode_cf(). `` Why is having_FillValueandmissing_valuedifferent considered an error in decoding CF? It's perfectly CF-compliant, especially since_FillValueis a scalar (used by the netCDF library to initialize an array), andmissing_value` can be a vector (representing one or more undefined or invalid values).

This happens in this case because the source GRIB file has one value specified for "missing" (maps to missing_value) and another for "no coverage" (which has been mapped to _FillValue).

Is this a technical limitation? Or just something that needs an implementation?

It would be really handy to be able to initialize a xarray.Dataset instance from an already opened instance of netCDF4.Dataset. I have a lot of code where I'm already returning an opened netCDF4 file and this would streamline the process of hooking xarray into that.

It seems like the quick solution here would be to make NetCDF4DataStore accept a netCDF4.Dataset instance as filename, which would bypass the creation of a new instance. Thoughts?