issue_comments

Just wondering what the status of this is. I've been running into bugs trying to model symmetric distance matrices using the same dimension. Interestingly, it does work very well for selecting, e.g. if use .sel(nodes=node_list) on a square matrix i correctly get a square matrix subset 👍 But unfortunately a lot of other things seems to break, e.g. concatenating fails with ValueError: axes don't match array :( What would need to happen to make this work?

I'm not too fond of having multiple dimensions with the same name because, whenever you need to operate on one but not the other, you have little to no choice but revert to positional indexing.

Consider also how many methods expect either **kwargs or a dict-like parameter with the dimension or variable names as the keys. I would not be surprised to find that many API design choices fall apart in the face of this use case.

Also, having two non positional (as it should always be in xarray!) dimensions with the same name only makes sense when modelling symmetric N:N relationships. Two good examples are covariance matrices and the weights for a Dijkstra algorithm.

The problems start when the object represents an asymmetric relationship, e.g: - Cost (for the purpose of graph resolution, so time/money/other) of transportation via river, where going from A->B (downstream) is cheaper than going back from B->A (upstream) - Currency conversion, where EUR->USD is not identical to 1/(USD->EUR) because of arbitrage and illiquidity - In financial Monte Carlo simulations, I had to deal with credit rating transition matrices which define the probability of a company to change its credit rating. In unfavourable market conditions, the chances of being downgraded from AAA to AA are higher than being promoted from AA to AAA.

I could easily come up with many other cases. In case of asymmetric N:N relationships, it is highly desirable to share the same index across multiple dimensions with different names (that would typically convey the direction of the relationship, e.g. "from" and "to").

What if, instead of allowing for duplicate dimensions, we allowed sharing an index across different dimensions?

Something like python river_transport = Dataset( coords={ 'station': ['Kingston', 'Montreal'], 'station_from': ('station', ) 'station_to': ('station', ) }, data_vars={ cost=(('station_from', 'station_to'), [[0, 20], [15, 0]]), } } or, for DataArrays: python river_transport = DataArray( [[0, 20], [15, 0]], dims=('station_from', 'station_to'), coords={ 'station': ['Kingston', 'Montreal'], 'station_from': ('station', ) 'station_to': ('station', ) }, }

Note how this syntax doesn't exist as of today: python 'station_from': ('station', ) 'station_to': ('station', ) From an implementation point of view, I think it could be easily implemented by keeping track of a map of aliases and with some __geitem__ magic. More effort would be needed to convince DataArrays to accept (and not accidentally drop) a coordinate whose dims don't match any of the data variable's.

This design would not resolve the issue of compatibility with NetCDF though. I'd be surprised if the NetCDF designers never came across this - maybe it's a good idea to have a chat with them?

This also affects the stack method.

@jhamman Ok, good to hear it's not slated to be removed. I would love to work on this, I wish I had the time! I'll keep it in mind if I do find some spare time.

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

I use repeated dimensions to store a covariance matrix. The data variable containing the covariance matrix has 4 dimensions, of which the last 2 are repeated. For example, I have a data variable with dimensions (channel, scanline, element, element), storing an element-element covariance matrix for every scanline in satellite data.

This is valid NetCDF and should be valid in xarray. It would be a significant problem for me if they became disallowed.