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 mu
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 ov
Just curious, how much faster is it compared to currently recommended `reduce`
approach?
DG
> On 2 Feb 2024, at 17:31, Marten van Kerkwijk wrote:
>
>> For my own work, I required the intersect1d function to work on multiple
>> arrays while returning the indices (using `return_indizes=True`).
> 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/n
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://gith
