AxisArrays.jl

It seems the main complaint is about confusing shapes / dimensions. Xarray has already be mentioned, but this is a broader concept, called named dimensions, sometimes also named tensors, named axes, labeled tensors, etc, which has often been proposed before, and many implementations exists. https://nlp.seas.harvard.edu/NamedTensor https://namedtensor.github.io/ https://docs.pytorch.org/docs/stable/named_tensor.html https://github.com/JuliaArrays/AxisArrays.jl https://github.com/ofnote/tsalib https://github.com/google-deepmind/tensor_annotations https://github.com/google-deepmind/penzai https://github.com/tensorflow/mesh/ https://github.com/facebookresearch/torchdim https://xarray.dev/ https://returnn.readthedocs.io/en/latest/getting_started/dat... (that's my own development; unfortunately the doc is very much outdated on that)

Similar functionality is available in Julia via [NamedDims.jl](https://github.com/invenia/NamedDims.jl/) And in other packages, such as [AxisArrays.jl](https://github.com/JuliaArrays/AxisArrays.jl/) and [AxisKeys.jl](https://github.com/mcabbott/AxisKeys.jl). In fact, the AxisKeys README has [a nice overview of packages with similar functionality](https://github.com/mcabbott/AxisKeys.jl#elsewhere).

I got fed up with Python when I found out that xarray couldn't support unitful quantities. In Julia, it just works. In Python, it turns into a massive project due to duck-typing & single-dispatch. It's been 5 years and xarray still doesn't have units, although it's getting close. Julia's equivalent to xarray: https://github.com/JuliaArrays/AxisArrays.jl Original issue explaining the problem: https://github.com/pydata/xarray/issues/525 Latest issue detailing the work: https://github.com/pydata/xarray/issues/3594

There are a bunch of packages exploring ideas around labelled dimensions, although we are still far from the vision of caring as little as we do about registers: * https://github.com/JuliaArrays/AxisArrays.jl * https://github.com/davidavdav/NamedArrays.jl * https://github.com/invenia/NamedDims.jl * https://github.com/rafaqz/DimensionalData.jl * https://github.com/ITensor/ITensors.jl While these don't pass dimension labels along in the type, there are also a number of Einsum-like packages which should work on any storage order, and could be taught to interface with such types: * https://github.com/Jutho/TensorOperations.jl * https://github.com/under-Peter/OMEinsum.jl * https://github.com/mcabbott/TensorCast.jl

There's also a lot of work going on in that direction right now. I'd check out NamedDims.jl[1] and AxisArrays.jl[2]. This approach is more similar to an EinSum type apprach than you seem to realize though. The main difference between this sort of approach and the approach in an EinSum esque package is that these packages actually encode the name of the dimensions in the type signature of the array whereas an Einsum-esque approach, the names are just temporary labels for the purposes of the macro call. [1] https://github.com/invenia/NamedDims.jl [2] https://github.com/JuliaArrays/AxisArrays.jl

Sounds like https://github.com/JuliaArrays/AxisArrays.jl/ ;)