It gets more comfortable over time, but I remember feeling that way for the first year or three. My wishlist now is for most of numpy to just be a really great einsum implementation, along with a few analogous operations for the rest of the map-reduces numpy accelerates.
I've been writing my own low-level numeric routines lately, so I'm not up-to-date on the latest news, but there have been a few ideas floating around over the last few years about naming your axes and defining operations in terms of those names [0,1,2,3]. That sort of thing looks promising to me, and one of those projects might be a better conceptual fit for you.
I've been writing my own low-level numeric routines lately, so I'm not up-to-date on the latest news, but there have been a few ideas floating around over the last few years about naming your axes and defining operations in terms of those names [0,1,2,3]. That sort of thing looks promising to me, and one of those projects might be a better conceptual fit for you.
[0] https://nlp.seas.harvard.edu/NamedTensor
[1] https://pypi.org/project/named-arrays/
[2] https://pytorch.org/docs/stable/named_tensor.html
[3] https://docs.xarray.dev/en/stable/