Also, I don’t know if this could be of value, but my use case for this is to find overlaps, then split arrays into overlapping and non-overlapping segments.
Thus, it might be useful for `return_indices=True` to return indices of all instances, not only the first. Also, in my case I need both overlapping and non-overlapping indices, but this would become ambiguous with more than 2 arrays. If it was left with 2 array input, then it can be extended to return both overlapping and non-overlapping parts. I think it could be another potential path to consider. E.g. what would be the speed comparison: intr = intersect1d(arr1, arr2, assume_unique=False) intr = intersect1d(intr, np.unique(arr3), assume_unique=True) # VS new intr = intersect1d(arr1, arr2, arr3, assume_unique=False) Then, does the gain from such generalisation justify constriction it introduces? Regards, DG > On 2 Feb 2024, at 17:31, Marten van Kerkwijk <m...@astro.utoronto.ca> wrote: > >> For my own work, I required the intersect1d function to work on multiple >> arrays while returning the indices (using `return_indizes=True`). >> Consequently I changed the function in numpy and now I am seeking >> feedback from the community. >> >> This is the corresponding PR: https://github.com/numpy/numpy/pull/25688 > > <snip> > > To me this looks like a very sensible generalization. In terms of numpy > API, the only real change is that, effectively, the assume_unique and > return_indices arguments become keyword-only, i.e., in the unlikely case > that someone passed those as positional, a trivial backward-compatible > change will fix it. > > -- Marten > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: dom.grigo...@gmail.com
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