issue_comments: 591388766
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/3213#issuecomment-591388766 | https://api.github.com/repos/pydata/xarray/issues/3213 | 591388766 | MDEyOklzc3VlQ29tbWVudDU5MTM4ODc2Ng== | 18172466 | 2020-02-26T11:54:40Z | 2020-02-26T11:54:40Z | NONE | Thank you @crusaderky, unfortunately some obstacles appeared using your loading technique. As thousands of .h5 files are the datasource for my use case and they have various - and sometimes different paths to - datasets, using the xarray.open_mfdatasets(...) function seems not to be possible straight forward. But: 1) I have a routine merging all .h5 datasets into corresponding dask arrays, wrapping dense numpy arrays implicitly 2) I "manually" slice out a part of the the huge lazy dask array and wrap that into an xarray.DataArray/Dataset 3) But applying xr.apply_ufunc(sparse.COO, ds, dask='allowed') on that slice then results in an NotImplementedError: Format not supported for conversion. Supplied type is <class 'dask.array.core.Array'>, see help(sparse.as_coo) for supported formats. (I am not sure, if this is the right place to discuss, so I would be thankful for a response on SO in that case: https://stackoverflow.com/questions/60117268/how-to-make-use-of-xarrays-sparse-functionality-when-combining-differently-size) |
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