issue_comments: 365841787

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https://github.com/pydata/xarray/issues/1536#issuecomment-365841787 https://api.github.com/repos/pydata/xarray/issues/1536 365841787 MDEyOklzc3VlQ29tbWVudDM2NTg0MTc4Nw== 1217238 2018-02-15T07:03:04Z 2018-02-15T07:03:04Z MEMBER

@crusaderky In case adding this to the netCDF4 library doesn't work out:

I'm not sure I understood your latest comment - are you implying that to_hdf5 should internally use the h5netcdf module? I understand the rationale but it sounds a bit counter-intuitive to me?

Yes, I would suggest that to_hdf5() using h5netcdf, but with invalid_netcdf=True.

Also, to allow for non-zlib compression we need to either tap into the new h5netcdf API, or into h5py directly - so I'm afraid to_hdf5 can't be a simple wrapper around to_netcdf.

Yes, this is unfortunately true.

new method Dataset.to_hdf5 - starts as a copy-paste of to_netcdf, including the backend functions underneath

Yes

new unit tests, starting as a copy-paste of all unit tests for to_netcdf

Yes

change open_dataset and open_mfdataset: add new possible value for the engine field, "hdf5" if engine is None and file name terminates with .nc, use the current algorithm to choose default engine if engine is None and file name terminates with .h5, use h5py if engine is not None, ignore file extension

I think this is a little easier than that. h5netcdf will always be able to read invalid netCDF files, so we can just continue to use engine='h5netcdf'.

As for picking the default engine, see https://github.com/pydata/xarray/pull/1682, which is pretty close, though I need to think a little bit harder about the API to make sure it's right.

add to high level documentation and tutorials

Yes

 
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