On Fri, Feb 2, 2024 at 6:34 AM Stephan Kuschel via NumPy-Discussion <
numpy-discussion@python.org> wrote:

> All the Best
>
Stephan
> _______________________________________________
> NumPy-Discussion mailing li

Dear Community,
>
> 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
>
> My motivation for the change may also apply to a larger group of people
> as it is important for lots of simulation data analysis:
>
> In various simulations there is often the case that many entities
> (particles, cells, vehicles, whatever the simulation consists of) are
> being tracked throughout the simulation. A typical approach is to assign
> a unique ID to every entity which stays constant and unique throughout
> the simulation and is written together with other properties of the
> entities on every simulation snapshot in time. Note, that during the
> simulation new entities may enter or leave the simulation and due to
> parallelization the order of those entities is not conserved.
> Tracking the position of entities over, lets say, 100 snapshots requires
> the intersection of 100 id lists instead of only two.
>
> Consequently I changed the intersect1d function from
> `intersect1d(ar1, ar2, assume_unique=False, return_indices=False)` to
> `intersect1d(*ars, assume_unique=False, return_indices=False)`.
>
> Please let me know if there is any interest in those changes -- be it in
> this form or another.
>
>
Seems reasonable. I don't know if it is faster, but NumPy is all about
vectorization.

Chuck
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