issue_comments: 122294746
This data as json
| 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/471#issuecomment-122294746 | https://api.github.com/repos/pydata/xarray/issues/471 | 122294746 | MDEyOklzc3VlQ29tbWVudDEyMjI5NDc0Ng== | 1050278 | 2015-07-17T14:30:15Z | 2015-07-17T14:30:15Z | CONTRIBUTOR | Yes, the two values in the missing_value attribute indicate two classes of data (not collected vs below minimum detectable threshold) and these data can be access by setting mask_and_scale=False but this also results in a valid data being returned without scaling which makes it less useful. My question is how should xray should handle these cases? Either replace all instances of the values in missing_value with NaN or raise a error message stating that multiple missing_values are not supported similar? I'd be happy to create a PR implementing either case. |
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