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/1626#issuecomment-427195935,https://api.github.com/repos/pydata/xarray/issues/1626,427195935,MDEyOklzc3VlQ29tbWVudDQyNzE5NTkzNQ==,23484003,2018-10-04T22:59:19Z,2018-10-08T15:10:54Z,NONE,"I just got bit with this as well. I was basically using tuples of indices as coordinates in order to implement a [multidimensional sparse array](https://github.com/pydata/xarray/issues/1375) .  

My workaround is to use plain dimension `index_dim` to index the points in the N-dimensional space that I actually populate, and to have several coordinates (say `X,Y`) that all have `index_dim` as their only dimension. It's easy enough to see what the coordinates are once you select a value along `index_dim`, but I have to go outside `xarray` to locate a populated point based on it's `X,Y`-coordinates, because I can't slice along those arrays as (A) they aren't aliased to a dimension (B) they have non-unique values. 

I've come up with an ugly method for selecting by `tuples` of `X,Y`-coordinates:


    pairs = zip(x_wanted,y_wanted)

    pair2index = {(dataset.x[i].item(), dataset.y[i].item()):i for i in dataset.index_dim.data}

    try:

         found_indices = [pair2index[p] for p in pairs]

         found = dataset.isel(index_dim=found_indices)

    except KeyError:

         print ""Coordinate {} not found in dataset."".format(p)

         raise
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