issue_comments

I note that xarray.Variable also provides attrs. Would it make sense to separate this aspect from the labelled dims? That is, instead of extracting this as a single library, turn it into two, such that users can pick one or both depending on their needs.

This is something I am getting more and more interested in. We (scipp) currently have a C++ implementation (with Pything bindings) of a simpler version of xarray.Variable. I am starting considerations of moving more of this to the Python side. So I would like to hear about the status of this?

While I am still far from having reached a conclusion (or convincing anyone here to support this), investing in technology that is adopted and carried by the community is considered important here. In other words, we may in principle be able to help out and invest some time into this.

One important precondition would be full compatibility with other custom array containers: For our applications we do not just need to add labelled axes, but also units, masks, bin edges, and ragged data support. I am currently toying with the idea of a "stack" of Python array libraries (I guess you would call them duck arrays?) that add these features one by one, selectively, but can all be used also independently --- unlike Scipp, where you get all or nothing, and lose the ability of using NumPy (or other) array libraries under the hood. Each of those libraries could be small and simple, focussing one just one specific aspect, but everything should be composable. For example, we can imagine a Variable with a pint array for having units as well as labelled dimensions.

I can't make the calls next week - but maybe @andy-sweet can report back and we'll take it from there. Thanks!!

We have a bi-weekly developer call on Wednesday mornings (#4001), one idea would be devote 10-15 minutes of our next meeting to this topic. Is that something you and/or @andy-sweet would be up for joining?

Sounds good to me. Looks like the next one is on December 7th at 13:30 UTC? If so, should I just jump on the Zoom meeting or do I need to add an item to the agenda?

I'm still fairly new to xarray and have mostly poked around a little at the feasibility of using DataSet and DataArray in napari, but I think that just about gives me enough time to look at Variable in a bit more detail to understand how much that would help us.

hi all, just wanted to check in if there's been any further thoughts/ progress here. We're having more and more need for an xarray-lite style package - so just simple around dimension naming/indexing - and we potentially have some engineering time to devote to this effort cc @andy-sweet. Would it make sense for the interested parties to hop on a call sometime to discuss?

@shoyer - thanks for the feedback. I guess this means that it's unlikely this will be ready in time for our own CZI grant to finish (around June 2022)?

We were just talking about this over at https://github.com/nipy/nibabel - because we are about commit ourselves to an array-axis-labelling API. Is xarray-lite on the near or the distant horizon? Should we wait, to make our decisions? Mentioning @effigies because he reminded me about this thread.

Just to add - we at Nibabel are very interested in adding labelled arrays with Xarray, but for us, the Pandas dependency is a serious problem. We're a base library for reading brain imaging formats, and we sit at the bottom of several imaging stacks, so it is very important to us that we don't introduce heavy dependencies - because we pass these on to all the libraries that depend on us. We've looked enviously at Xarray for a while, but the Pandas dependency is a serious-enough problem that we've held off from using it. Just for example, the Pandas dependency of Xarray was the reason that I was working on Datarray, to see if we could use that instead (we couldn't).

To add another use case, the NiBabel package has considered how to label axes (https://github.com/nipy/nibabel/wiki/BIAP6, https://github.com/nipy/nibabel/issues/412), but it's fallen by the wayside. We considered xarray when it was still xray, but the pandas dependency has always been a sticking point. This is partially due to a desire to keep dependencies minimal, and partly due to the size of pandas causing significant overhead at import time for what is a relatively small component.

If it's useful to go further into our use case, we can, but this is just to put in a vote for making pandas an optional dependency, if possible.

FYI the conversation on sklearn is far from resolved, and at this point I think the added pandas dependency is not what will keep us from using xarray. I think right now we're most concerned about sparse data representations (and I was considering asking you folks if you'd support scipy.sparse ;)