issues

Is your feature request related to a problem? Please describe. It would be useful if I could retrieve the underlying store from an xarray Dataset object. There was previously a workaround that allowed this, but that was recently removed.

python dataset._file_obj.ds # Doesn't work anymore :(

Describe the solution you'd like Ideally I'd like the API to be extended so the underlying store can be retrieved. Something like:

```python

dataset = xr.open_dataset('/path/to/file.nc') nc_dataset = dataset.store type(nc_dataset) <class 'netCDF4._netCDF4.Dataset'> ```

Describe alternatives you've considered I'd be fine using the old workaround if that was still an option. If anyone knows of a different workaround I would be fine with that, but a better long-term solution would be an actual user-facing function for accessing the store.

I might be using the 'store' terminology incorrectly, but hopefully my request is clear with the above example.

What happened?

In the following conditions, I am unable to open a NetCDF file in xarray with mask_and_scale=True and decode_times=True:

• Unusual timestamp format in time variable units attr, e.g. seconds since 2000-1-1 0:0:0 0
• First and last value in time matches _FillValue

The following changes to the NetCDF file allows me to open the file in xarray:

• Update timestamp format in units attr to something more common (e.g. seconds since 2000-01-01T00:00:00)

OR

• Update first and last value in time variable to value that is not equal to _FillValue

What did you expect to happen?

I should be able to open the NetCDF file with mask_and_scale=True and decode_times=True without errors.

Minimal Complete Verifiable Example

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

fill_val = -99999.0 time_vals = np.random.rand(10) time_vals[0] = fill_val time_vals[-1] = fill_val

data_vars = { 'foo': (['x'], np.random.rand(10)), 'time': ( ['x'], time_vals, { 'units': 'seconds since 2000-1-1 0:0:0 0', '_FillValue': fill_val, 'standard_name': 'time', 'calendar': 'standard' } ), }

ds = xr.Dataset( data_vars=data_vars, coords={'x': (['x'], np.arange(10))} )

nc_out_location = '/path/to/example.nc' ds.to_netcdf(nc_out_location) ds = xr.open_dataset(nc_out_location) # This line results in an error ```

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 dateutil.parser._parser.ParserError: Unknown string format: 2000-1-1 0:0:0 0 ... ValueError: could not convert string to Timestamp ... pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime ... TypeError: unsupported operand type(s) for +: 'cftime._cftime.DatetimeGregorian' and 'NoneType' ... ValueError: unable to decode time units 'seconds since 2000-1-1 0:0:0 0' with "calendar 'standard'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.

Anything else we need to know?

Looking at the code, it seems like this is happening because _decode_cf_datetime_dtype in times.py is only selecting the first and last values from the data, which in this case are masked due to _FillValue.

Environment