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  • Cross-platform in-memory serialization of netcdf4 (like the current scipy-based dumps) · 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
36186205 https://github.com/pydata/xarray/issues/23#issuecomment-36186205 https://api.github.com/repos/pydata/xarray/issues/23 MDEyOklzc3VlQ29tbWVudDM2MTg2MjA1 ebrevdo 1794715 2014-02-26T22:32:06Z 2014-02-26T22:32:06Z CONTRIBUTOR

Looks like this may be the only option. Based on my tests, netCDF4 is strongly antithetical to any kind of streams/piped buffers. If we go the hdf5 route, we'd have to reimplement the CDM/netcdf4 on top of hdf5, no?

 
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   Cross-platform in-memory serialization of netcdf4 (like the current scipy-based dumps) 28375178
36185024 https://github.com/pydata/xarray/issues/23#issuecomment-36185024 https://api.github.com/repos/pydata/xarray/issues/23 MDEyOklzc3VlQ29tbWVudDM2MTg1MDI0 akleeman 514053 2014-02-26T22:21:01Z 2014-02-26T22:21:01Z CONTRIBUTOR

Another similar option would be to use in-memory HDF5 objects for which Todd Small found an option:

Writing to a string:

h5_file = tables.open_file("in-memory", title=my_title, mode="w", 12 driver="H5FD_CORE", driver_core_backing_store=0) ... [add variables] ... image = h5_file.get_file_image()

Reading from a string

h5_file = tables.open_file("in-memory", mode="r", driver="H5FD_CORE", driver_core_image=image, driver_core_backing_store=0)

 
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   Cross-platform in-memory serialization of netcdf4 (like the current scipy-based dumps) 28375178

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