issue_comments: 364802374
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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/pull/1528#issuecomment-364802374 | https://api.github.com/repos/pydata/xarray/issues/1528 | 364802374 | MDEyOklzc3VlQ29tbWVudDM2NDgwMjM3NA== | 2443309 | 2018-02-11T23:54:01Z | 2018-02-11T23:54:01Z | MEMBER | @martindurant - If I understand your question correctly, I think you should be able to follow a pretty standard xarray workflow: ```Python ds = xr.Dataset() ds['your_varname'] = xr.DataArray(some_dask_array, dims=['dimname0', 'dimname1', ...], coords=dict_of_preknown_coords) repeat for each variable you want in your datasetds.to_zarr(some_zarr_store) then to opends2 = xr.open_zarr(some_zarr_store) ``` Two things to note: 1) if you are looking for decent performance when writing to a remote store, make sure you're working off xarray@master as #1800 fixed a number of choke points in the to_zarr implementation
2) if you are pushing to GCS, |
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