issues: 175188832

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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
175188832 MDU6SXNzdWUxNzUxODg4MzI= 997 Discovered conflicting _FillValue and missing_value, but both are NaN 204523 closed 0     0 2016-09-06T08:29:01Z 2016-09-07T16:20:24Z 2016-09-07T16:20:24Z CONTRIBUTOR      

When a variable contains both attributes and both are NaN the comparison fails. This happens when with engine='netcdf4' and not with engine='h5netcdf.

Example file: ftp://anon-ftp.ceda.ac.uk/neodc/esacci/aerosol/data/MERIS_ALAMO/L3/v2.2/DAILY/2008/01/20080101-ESACCI-L3C_AEROSOL-AOD-MERIS_ENVISAT-ALAMO-fv2.2.nc

ncdump -h 20080101-ESACCI-L3C_AEROSOL-AOD-MERIS_ENVISAT-ALAMO-fv2.2.nc netcdf \20080101-ESACCI-L3C_AEROSOL-AOD-MERIS_ENVISAT-ALAMO-fv2.2 { dimensions: latitude = 180 ; longitude = 360 ; variables: float AOD550(latitude, longitude) ; AOD550:_FillValue = NaNf ; AOD550:long_name = "Mean Effective Aerosol Optical Thickness at 550nm over ocean - Best solution" ; AOD550:standard_name = "atmosphere_optical_thickness_due_to_ambient_aerosol" ; AOD550:units = "1" ; AOD550:coordinates = "latitude longitude" ; AOD550:valid_range = -3.402823e+38f, 3.402823e+38f ; AOD550:missing_value = NaNf ; }

```

import xarray as xr ds = xr.open_dataset('20080101-ESACCI-L3C_AEROSOL-AOD-MERIS_ENVISAT-ALAMO-fv2.2.nc', engine='h5netcdf', decode_cf=False) ds.variables['AOD550'].attrs['_FillValue'] array([ nan], dtype=float32) ds.variables['AOD550'].attrs['missing_value'] array([ nan], dtype=float32) type(ds.variables['AOD550'].attrs['missing_value']) <class 'numpy.ndarray'> xr.core.utils.equivalent(ds.variables['AOD550'].attrs['_FillValue'], ds.variables['AOD550'].attrs['missing_value']) True

```

```

ds = xr.open_dataset('20080101-ESACCI-L3C_AEROSOL-AOD-MERIS_ENVISAT-ALAMO-fv2.2.nc', engine='netcdf4', decode_cf=False) ds.variables['AOD550'].attrs['_FillValue'] nan ds.variables['AOD550'].attrs['missing_value'] nan type(ds.variables['AOD550'].attrs['missing_value']) <class 'numpy.float32'> xr.core.utils.equivalent(ds.variables['AOD550'].attrs['_FillValue'], ds.variables['AOD550'].attrs['missing_value']) False

```

 
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   completed 13221727 issue

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