issue_comments: 336381864

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https://github.com/pydata/xarray/issues/1603#issuecomment-336381864 https://api.github.com/repos/pydata/xarray/issues/1603 336381864 MDEyOklzc3VlQ29tbWVudDMzNjM4MTg2NA== 6815844 2017-10-13T08:09:25Z 2017-10-13T08:09:25Z MEMBER

Thanks for the details. (Sorry for my late responce. It took a long for me to understand what does it look like.)

I am wondering what the advantageous cases which are realized with this Index concept are. As far as my understanding is correct,

  1. It will enable more flexible indexing, e.g. more than one Indexes are associated with one dimension and we can select from these coordinate values very flexibly.
  2. It will naturally integrate more advanced Indexes such as KDTree

Are they correct?

Probably the most elegant rule would again be to check all indexed variables for exact matches.

That sounds reasonable.

In principle, this data model would allow for two mostly equivalent indexing schemes: MultiIndex[time, space] vs two indexes Index[time] and Index[space].

I like the latter one, as it is easier to understand even for non-pandas users.

What does the actual implementation look like? xr.Dataset.indexes will be an OrderedDict that maps from variable's name to its associated dimension? Actual instance of Index will be one of xr.Dataset.variables?

 
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