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https://github.com/pydata/xarray/pull/2195#issuecomment-442554179 https://api.github.com/repos/pydata/xarray/issues/2195 442554179 MDEyOklzc3VlQ29tbWVudDQ0MjU1NDE3OQ== 1217238 2018-11-28T18:28:50Z 2018-11-28T18:29:58Z MEMBER

I've pushed a few commits with some additional progress here. Here's my current plan:

  1. Automatically create new variables for MultiIndex levels, and store these in Dataset._variables / DataArray._coords. This lets us use the normal variable consistency checks and indexing operations, rather than requiring explicit checks like assert_unique_multiindex_level_names or "virtual variables". This is mostly complete (still under-tested) as of the last commit, but it should be a big win for consistency when using a MultiIndex since the level variables will be built into xarray's data model. The MultiIndex itself will still be an xarray variable (an object array of tuples).
  2. Add explicit indexes, as a map from coordinate names to a pandas.Index or pandas.MultiIndex used for indexing. Note that if a MultiIndex is used, coordinates corresponding to levels will all point to the same MultiIndex, e.g., if MultiIndex foo has levels x and y, indexes will look like {'foo': foo, 'x': foo, 'y': foo}.
  3. Remove/fixup code that assumes that only dimension names are used for indexes. Instead, we look for indexes by looking up coordinate names in .indexes.
  4. Add an xarray specific Index API so we can add our own index classes, e.g., a KDTree. This will happen in a later PR.
 
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