issue_comments

In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity

If this issue remains relevant, please comment here or remove the stale label; otherwise it will be marked as closed automatically

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.

I ran our test suite with libnetcdf 4.5.0 and it turned up two failures, for GenericNetCDFDataTest.test_cross_engine_read_write_netcdf3 and GenericNetCDFDataTestAutocloseTrue.test_cross_engine_read_write_netcdf3: https://travis-ci.org/shoyer/xarray/jobs/303718182

I'll have to do a little digging to turn up exactly which cross read/write cases failed. (It would probably be best to edit the test cases to use parameterized tests of some sort rather than the explicit "for" loop)

@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.

@spencerkclark conda-forge has netcdf4 built with both 4.5.0 and 4.4.1.1, so conda can get one or the other depending on the scenario. The only way to ensure you are building an env with libnetcdf 4.5.0 is to add it explicitly in that list.

@spencerahill how did you install libnetcdf 4.5.0? I'm still getting 4.4.1 with conda, on both the default and conda-forge channel.