issue_comments: 1026774266
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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/6196#issuecomment-1026774266 | https://api.github.com/repos/pydata/xarray/issues/6196 | 1026774266 | IC_kwDOAMm_X849M1T6 | 32069530 | 2022-02-01T12:07:51Z | 2022-02-01T12:07:51Z | NONE | Thanks for the enlightening. Actually, this coordinates dependency with singleton dimension caused me a problem when using the to_netcdf() function. No problem playing whith the xr.Dataset but I get some error when trying to write on disk using to_netcdf(). For now, I wasn't able to reproduce a minimalist example because the error disappears with minimalist example. I wasn't able to find the fundamental difference between the dataset causing the error and the minimalist one. Printing them are exactly the same. I have to do deeper inspection. Concerning the philosophy of what a coordinate should be: For me the "label" idea is understandable at a dataset level. A singleton dimension become a (shared) "label' for the whole dataset. This is ok for me. However, I do not understand why it should also be a "label" of the other coordinates of the dataset. A singleton dimension should not be "more important" than the other (not singleton) dimensions. Why the singleton dimension should become a "label" of another dimension while the other dimensions are not. This do not seem logical to me. |
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