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  • fsteinmetz · 2 ✖

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  • Many methods are broken (e.g., concat/stack/sortby) when using repeated dimensions · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
295657656 https://github.com/pydata/xarray/issues/1378#issuecomment-295657656 https://api.github.com/repos/pydata/xarray/issues/1378 MDEyOklzc3VlQ29tbWVudDI5NTY1NzY1Ng== fsteinmetz 668201 2017-04-20T09:50:19Z 2017-04-20T09:53:33Z NONE

I cannot see a use case in which repeated dims actually make sense.

In my case this situation originates from h5 files which indeed contains repeated dimensions (variables(dimensions): uint16 B0(phony_dim_0,phony_dim_0), ..., uint8 VAA(phony_dim_1,phony_dim_1)), thus xarray is not to blame here. These are "dummy" dimensions, not associated with physical values. What we do to circumvent this problem is "re-dimension" all variables. Maybe a safe approach would be for open_dataset to raise a warning by default when encountering such variables, with possibly an option to perform automatic or custom dimension naming to avoid repeated dims. I also agree with @shoyer that failing loudly when operating on such DataArrays instead of providing confusing results would be an improvement.

 
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   Many methods are broken (e.g., concat/stack/sortby) when using repeated dimensions 222676855
295593740 https://github.com/pydata/xarray/issues/1378#issuecomment-295593740 https://api.github.com/repos/pydata/xarray/issues/1378 MDEyOklzc3VlQ29tbWVudDI5NTU5Mzc0MA== fsteinmetz 668201 2017-04-20T06:11:02Z 2017-04-20T06:11:02Z NONE

Right, also positional indexing works unexpectedly in this case, though I understand it's tricky and should probably be discouraged: python A[0,:] # returns A A[:,0] # returns A.isel(dim0=0)

 
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   Many methods are broken (e.g., concat/stack/sortby) when using repeated dimensions 222676855

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