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  • jmunroe · 2 ✖

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  • use dask to open datasets in parallel · 2 ✖

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  • CONTRIBUTOR · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
373806224 https://github.com/pydata/xarray/issues/1981#issuecomment-373806224 https://api.github.com/repos/pydata/xarray/issues/1981 MDEyOklzc3VlQ29tbWVudDM3MzgwNjIyNA== jmunroe 6181563 2018-03-16T18:34:19Z 2018-03-16T18:34:19Z CONTRIBUTOR

distributed

 
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   use dask to open datasets in parallel 304201107
373794415 https://github.com/pydata/xarray/issues/1981#issuecomment-373794415 https://api.github.com/repos/pydata/xarray/issues/1981 MDEyOklzc3VlQ29tbWVudDM3Mzc5NDQxNQ== jmunroe 6181563 2018-03-16T17:53:44Z 2018-03-16T17:53:44Z CONTRIBUTOR

For what's worth, this is exactly the workflow I use (https://github.com/OceansAus/cosima-cookbook) when opening a large number of netCDF files:

    bag = dask.bag.from_sequence(ncfiles)

    load_variable = lambda ncfile: xr.open_dataset(ncfile, 
                       chunks=chunks, 
                       decode_times=False)[variables]

    bag = bag.map(load_variable)

    dataarrays = bag.compute()

and then

dataarray = xr.concat(dataarrays,
                      dim='time', coords='all', )

and it appears to work well.

Code snippets from cosima-cookbook/cosima_cookbook/netcdf_index.py

 
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   use dask to open datasets in parallel 304201107

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