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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
1110623911 I_kwDOAMm_X85CMsan 6183 [FEATURE]: dimension attribute are lost when stacking an xarray lanougue 32069530 closed 0     2 2022-01-21T15:49:47Z 2022-03-17T17:11:44Z 2022-03-17T17:11:44Z NONE      

Is your feature request related to a problem?

No

Describe the solution you'd like

Hi all, when stacking an array my_xarray.stack(multi=('x','y')), the attributes of stacked dimension (x and y in the example) are lost. It would be nice to keep them. It would also be nice to have them back when unstacking. Thanks

Describe alternatives you've considered

No response

Additional context

No response

 
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   completed xarray 13221727 issue
371906566 MDU6SXNzdWUzNzE5MDY1NjY= 2494 Concurrent acces with multiple processes using open_mfdataset lanougue 32069530 closed 0     4 2018-10-19T10:52:46Z 2018-10-26T12:37:30Z 2018-10-26T12:37:30Z NONE      

Hi everyone,

First: thanks to the developers for this amazing xarray library ! Great piece of work ! Here comes my troubles: I run several (about 500) independant processes (dask distributed) that need simultaneous reading (only) access to a same (group of) netcdf files. I only pass the files-path strings to the processes to avoid pickling a netcdf python-object (issue).

In each process, I run

python with xr.open_mfdataset(myfiles_path, concat_dim='t', engine='h5netcdf') as myfile: x = myfile['x'].data y = myfile['y'].data

but it leads to typical errors for many concurrent access that fail... : Invalid id or Exception: CancelledError("('mul-484a58bf5830233021e08456b45eb60d', 0, 0)",), ...

I was using netCDF4 module with parallel option set to True, when playing with a single netcdf file and it was running fine: python myfile = Dataset(seedsurf_path,'r', parallel=True) x = myfile['x'] y = myfile['y'] myfile.close() Parallel option for open_mfdataset() seems to be dedicated to multithreaded access only. Is there somthing that can be done for multi-processes access ? Thanks

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.6.final.0 python-bits: 64 OS: Linux OS-release: 3.12.53-60.30-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 xarray: 0.10.8 pandas: 0.23.4 numpy: 1.12.1 scipy: 0.19.1 netCDF4: 1.2.4 h5netcdf: 0.6.2 h5py: 2.7.0 Nio: None zarr: 2.2.0 bottleneck: 1.2.1 cyordereddict: None dask: 0.19.0 distributed: 1.23.0 matplotlib: 2.2.3 cartopy: 0.16.0 seaborn: None setuptools: 40.2.0 pip: 18.0 conda: None pytest: None IPython: 6.5.0 sphinx: None 
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   completed xarray 13221727 issue

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