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
1574694462,I_kwDOAMm_X85d2-4-,7513,"intermittent failures with h5netcdf, h5py on macos",2448579,closed,0,,,5,2023-02-07T16:58:43Z,2024-04-28T23:35:21Z,2024-04-28T23:35:21Z,MEMBER,,,,"### What is your issue?

cc @hmaarrfk @kmuehlbauer 

Passed: https://github.com/pydata/xarray/actions/runs/4115923717/jobs/7105298426
Failed: https://github.com/pydata/xarray/actions/runs/4115946392/jobs/7105345290

Versions:
```
h5netcdf                  1.1.0              pyhd8ed1ab_0    conda-forge
h5py                      3.8.0           nompi_py310h5555e59_100    conda-forge
hdf4                      4.2.15               h7aa5921_5    conda-forge
hdf5                      1.12.2          nompi_h48135f9_101    conda-forge
```

```
=================================== FAILURES ===================================
_____________ test_open_mfdataset_manyfiles[h5netcdf-20-True-5-5] ______________
[gw1] darwin -- Python 3.10.9 /Users/runner/micromamba-root/envs/xarray-tests/bin/python

readengine = 'h5netcdf', nfiles = 20, parallel = True, chunks = 5
file_cache_maxsize = 5

    @requires_dask
    @pytest.mark.filterwarnings(""ignore:use make_scale(name) instead"")
    def test_open_mfdataset_manyfiles(
        readengine, nfiles, parallel, chunks, file_cache_maxsize
    ):
        # skip certain combinations
        skip_if_not_engine(readengine)
    
        if ON_WINDOWS:
            pytest.skip(""Skipping on Windows"")
    
        randdata = np.random.randn(nfiles)
        original = Dataset({""foo"": (""x"", randdata)})
        # test standard open_mfdataset approach with too many files
        with create_tmp_files(nfiles) as tmpfiles:
            writeengine = readengine if readengine != ""pynio"" else ""netcdf4""
            # split into multiple sets of temp files
            for ii in original.x.values:
                subds = original.isel(x=slice(ii, ii + 1))
                if writeengine != ""zarr"":
                    subds.to_netcdf(tmpfiles[ii], engine=writeengine)
                else:  # if writeengine == ""zarr"":
                    subds.to_zarr(store=tmpfiles[ii])
    
            # check that calculation on opened datasets works properly
>           with open_mfdataset(
                tmpfiles,
                combine=""nested"",
                concat_dim=""x"",
                engine=readengine,
                parallel=parallel,
                chunks=chunks if (not chunks and readengine != ""zarr"") else ""auto"",
            ) as actual:

/Users/runner/work/xarray/xarray/xarray/tests/test_backends.py:3267: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/backends/api.py:991: in open_mfdataset
    datasets, closers = dask.compute(datasets, closers)
/Users/runner/micromamba-root/envs/xarray-tests/lib/python3.10/site-packages/dask/base.py:599: in compute
    results = schedule(dsk, keys, **kwargs)
/Users/runner/micromamba-root/envs/xarray-tests/lib/python3.10/site-packages/dask/threaded.py:89: in get
    results = get_async(
/Users/runner/micromamba-root/envs/xarray-tests/lib/python3.10/site-packages/dask/local.py:511: in get_async
    raise_exception(exc, tb)
/Users/runner/micromamba-root/envs/xarray-tests/lib/python3.10/site-packages/dask/local.py:319: in reraise
    raise exc
/Users/runner/micromamba-root/envs/xarray-tests/lib/python3.10/site-packages/dask/local.py:224: in execute_task
    result = _execute_task(task, data)
/Users/runner/micromamba-root/envs/xarray-tests/lib/python3.10/site-packages/dask/core.py:119: in _execute_task
    return func(*(_execute_task(a, cache) for a in args))
/Users/runner/micromamba-root/envs/xarray-tests/lib/python3.10/site-packages/dask/utils.py:72: in apply
    return func(*args, **kwargs)
/Users/runner/work/xarray/xarray/xarray/backends/api.py:526: in open_dataset
    backend_ds = backend.open_dataset(
/Users/runner/work/xarray/xarray/xarray/backends/h5netcdf_.py:417: in open_dataset
    ds = store_entrypoint.open_dataset(
/Users/runner/work/xarray/xarray/xarray/backends/store.py:32: in open_dataset
    vars, attrs = store.load()
/Users/runner/work/xarray/xarray/xarray/backends/common.py:129: in load
    (_decode_variable_name(k), v) for k, v in self.get_variables().items()
/Users/runner/work/xarray/xarray/xarray/backends/h5netcdf_.py:220: in get_variables
    return FrozenDict(
/Users/runner/work/xarray/xarray/xarray/core/utils.py:471: in FrozenDict
    return Frozen(dict(*args, **kwargs))
/Users/runner/work/xarray/xarray/xarray/backends/h5netcdf_.py:221: in <genexpr>
    (k, self.open_store_variable(k, v)) for k, v in self.ds.variables.items()
/Users/runner/work/xarray/xarray/xarray/backends/h5netcdf_.py:200: in open_store_variable
    elif var.compression is not None:
/Users/runner/micromamba-root/envs/xarray-tests/lib/python3.10/site-packages/h5netcdf/core.py:394: in compression
    return self._h5ds.compression
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <[AttributeError(""'NoneType' object has no attribute '_root'"") raised in repr()] Variable object at 0x151378970>

    @property
    def _h5ds(self):
        # Always refer to the root file and store not h5py object
        # subclasses:
>       return self._root._h5file[self._h5path]
E       AttributeError: 'NoneType' object has no attribute '_h5file'

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