issue_comments: 512005032
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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/2064#issuecomment-512005032 | https://api.github.com/repos/pydata/xarray/issues/2064 | 512005032 | MDEyOklzc3VlQ29tbWVudDUxMjAwNTAzMg== | 10638475 | 2019-07-16T22:01:59Z | 2019-07-16T22:50:39Z | NONE |
Here is the most specific thing I can say: If I switch the default value for data_vars to 'minimal' for concat() and open_mfdataset(), then I get a lot of failing unit tests (when running "pytest xarray -n 4". I may be wrong about why they are failing. The unit tests have comments in them, like "Check pandas compatibility"; see for example, line 370 in test_duck_array_ops.py for an example instruction that raises a ValueError exception. Many failures appear to be caused by a ValueError exception being raised, like in the final example you have in your notebook. I hope this is specific enough; I realize that I'm not deeply comprehending what the unit tests are actually supposed to be testing. UPDATE: @shoyer it could be that unit tests are failing because, as your final example shows, you get an error for data_vars='minimal' if any variables have different values across datasets, when adding a new concatentation dimension. If this is the reason so many unit tests are failing, then the failures are a red herring and should probably be ignored/rewritten. |
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