issue_comments

I think that one big source of confusion has been so far mixing coordinates/variables and indexes. These are really two separate concepts, and the indexes refactoring should address that IMHO.

For example, I think that da[some_name] should never return indexes but only coordinates (and/or data variables for Dataset). That would be much simpler.

Can't claim to be following every detail here, but this sounds very sensible to me FWIW.

Ok, cool. Was wondering if now was right time to revisit that, alongside the work proposed in this PR. Happy to participate in that discussion, still interested in implementing some alternative index classes.

On Tue, 29 May 2018, 15:45 Stephan Hoyer, notifications@github.com wrote:

Yes, the index API still needs to be determined. But I think we want to support something like that. On Tue, May 29, 2018 at 1:20 AM Alistair Miles notifications@github.com wrote:

I see this mentions an Index API, is that still to be decided?

On Tue, 29 May 2018, 05:28 Stephan Hoyer, notifications@github.com wrote:

I started thinking about how to do this incrementally, and it occurs to me that a good place to start would be to write some of the utility functions we'll need for this:

1. Normalizing and creating default indexes in the Dataset/DataArray constructor.
2. Combining indexes from all xarray objects that are inputs for an operations into indexes for the outputs.
3. Extracting MultiIndex objects from arguments into Dataset/DataArray and expanding them into multiple variables.

I drafted up docstrings for each of these functions and did a little bit of working starting to think through implementations in #2195 https://github.com/pydata/xarray/pull/2195. So this would be a great place for others to help out. Each of these could be separate PRs.

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I see this mentions an Index API, is that still to be decided?

On Tue, 29 May 2018, 05:28 Stephan Hoyer, notifications@github.com wrote:

I started thinking about how to do this incrementally, and it occurs to me that a good place to start would be to write some of the utility functions we'll need for this:

1. Normalizing and creating default indexes in the Dataset/DataArray constructor.
2. Combining indexes from all xarray objects that are inputs for an operations into indexes for the outputs.
3. Extracting MultiIndex objects from arguments into Dataset/DataArray and expanding them into multiple variables.

I drafted up docstrings for each of these functions and did a little bit of working starting to think through implementations in #2195 https://github.com/pydata/xarray/pull/2195. So this would be a great place for others to help out. Each of these could be separate PRs.

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/pydata/xarray/issues/1603#issuecomment-392649605, or mute the thread https://github.com/notifications/unsubscribe-auth/AAq8QvMauEPa6hfgorDoShZ2PwyYWk6Tks5t3M6AgaJpZM4PtACU .

Just to say I'm interested in how MultiIndexes are handled also. In our use case, we have two variables conventionally named CHROM (chromosome) and POS (position) which together describe a location in a genome. I want to combine both variables into a multi-index so I can, e.g., select all data from some data variable for chromosome X between positions 100,000-200,000. For all our data variables, this genome location multi-index would be used to index the first dimension.