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issue 3

  • fix multiIndex min max issue #2923 2
  • Calling max() on a stacked dimension raises exception 1
  • Variable objects not displayable in notebook 1

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  • arabidopsis · 4 ✖

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  • CONTRIBUTOR 4
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
613845875 https://github.com/pydata/xarray/issues/3972#issuecomment-613845875 https://api.github.com/repos/pydata/xarray/issues/3972 MDEyOklzc3VlQ29tbWVudDYxMzg0NTg3NQ== arabidopsis 9948595 2020-04-15T06:36:48Z 2020-04-15T06:36:48Z CONTRIBUTOR

OK PR done #3973 Sorry I forgot to create a new branch... hope that's OK.

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  Variable objects not displayable in notebook 599993415
487329592 https://github.com/pydata/xarray/pull/2924#issuecomment-487329592 https://api.github.com/repos/pydata/xarray/issues/2924 MDEyOklzc3VlQ29tbWVudDQ4NzMyOTU5Mg== arabidopsis 9948595 2019-04-28T00:21:15Z 2019-04-28T00:21:15Z CONTRIBUTOR

No problem. Thanks for xarray!

Just a question. I put a guard in to test just for tuples (because that's what was breaking for me) but it seems to me that _nan_minmax_object should just return utils.to_0d_object_array(data) unconditionally at that point no? It's a max or a min right.

Wasn't sure.

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  fix multiIndex min max issue #2923 437956917
487298047 https://github.com/pydata/xarray/pull/2924#issuecomment-487298047 https://api.github.com/repos/pydata/xarray/issues/2924 MDEyOklzc3VlQ29tbWVudDQ4NzI5ODA0Nw== arabidopsis 9948595 2019-04-27T16:04:45Z 2019-04-27T16:04:45Z CONTRIBUTOR

Oops... I usually use black -- which I turned off since it would really have stuffed the code :)

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  fix multiIndex min max issue #2923 437956917
487291300 https://github.com/pydata/xarray/issues/2923#issuecomment-487291300 https://api.github.com/repos/pydata/xarray/issues/2923 MDEyOklzc3VlQ29tbWVudDQ4NzI5MTMwMA== arabidopsis 9948595 2019-04-27T14:38:12Z 2019-04-27T14:38:12Z CONTRIBUTOR

Seems that the problem begins when xarray.core.nanops._nan_minmax_object converts the max index value (2,99) tuple (which is correct) to np.array((0,99), dtype='O') which is not because np.array converts this from dims=() to dims=(2,).

Which is now incorrect. Interesting... how do you tell numpy to create a 0 dimension array with a tuple?

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  Calling max() on a stacked dimension raises exception 437940426

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