issue_comments

Thanks @shoyer, I think it's the files written by the 'scipy' engine and read by the 'netcdf4' engine. Here is a minimal example: ``` In [1]: import xarray as xr

In [2]: da = xr.DataArray([1, 2, 3], dims=['x'])

In [3]: da.to_netcdf('test_netcdf4.nc', format='NETCDF3_CLASSIC', engine='netcdf4')

In [4]: ds = xr.open_dataset('test_netcdf4.nc', engine='netcdf4')

In [5]: ds = xr.open_dataset('test_netcdf4.nc', engine='scipy')

In [6]: da.to_netcdf('test_scipy.nc', format='NETCDF3_CLASSIC', engine='scipy')

In [7]: ds = xr.open_dataset('test_scipy.nc', engine='scipy')

In [8]: ds = xr.open_dataset('test_scipy.nc', engine='netcdf4')

IOError Traceback (most recent call last) <ipython-input-8-13ddc3810e0b> in <module>() ----> 1 ds = xr.open_dataset('test_scipy.nc', engine='netcdf4')

//anaconda/envs/research/lib/python2.7/site-packages/xarray/backends/api.pyc in open_dataset(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables) 280 if engine == 'netcdf4': 281 store = backends.NetCDF4DataStore(filename_or_obj, group=group, --> 282 autoclose=autoclose) 283 elif engine == 'scipy': 284 store = backends.ScipyDataStore(filename_or_obj,

//anaconda/envs/research/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in init(self, filename, mode, format, group, writer, clobber, diskless, persist, autoclose) 208 diskless=diskless, persist=persist, 209 format=format) --> 210 self.ds = opener() 211 self._autoclose = autoclose 212 self._isopen = True

//anaconda/envs/research/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in _open_netcdf4_group(filename, mode, group, kwargs) 183 import netCDF4 as nc4 184 --> 185 ds = nc4.Dataset(filename, mode=mode, kwargs) 186 187 with close_on_error(ds):

netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Dataset.init()

netCDF4/_netCDF4.pyx in netCDF4._netCDF4._ensure_nc_success()

IOError: [Errno -36] NetCDF: Invalid argument: '/Users/skc/test_scipy.nc' ``` Here's a link to the issue upstream: https://github.com/Unidata/netcdf-c/issues/657.

@ocefpaf thanks, empirically the original did the trick, but I have edited it for correctness.

@shoyer I initially ran into that too. I think depending on the libraries you have installed (or seek to install) conda may not let you upgrade to libnetcdf 4.5.0. To get libnetcdf 4.5.0 I created a clean environment with the following specification file: name: test_xarray_libnetcdf45 channels: - conda-forge dependencies: - python=3.6 - netcdf4 - libnetcdf=4.5.0 - numpy - mock - pandas - pytest - pip - dask - scipy Then from within that environment I did an editable install of the xarray master branch and ran the test suite.