Should `np.ndarray.bit_count()` exist? I tend against this;
Thanks for the info Sebastian, I agree with this as we can stick to what
Python offers.
Should `np.bit_count` exist? Having it on the int* types may be sufficient.
Hey Stephan, regarding this, I felt we could support it in the same lines NumPy
Mathematical Functions
https://numpy.org/doc/stable/reference/routines.math.html, something like
GCD perhaps, where we do not have `np.ndarray.gcd` but do have an `np.gcd`
What is the max value of the count? 64? If so it can go in a uint8.
This makes sense yeah, will make this change, thanks for the suggestion.
Also, an interesting future proposal can be to club all the bitwise
functions into a single "namespace" of sorts and have np.bits.*. This has
already been suggested in this comment
https://github.com/numpy/numpy/issues/16325#issuecomment-869182363 and I
feel this would be a clean addition and we can support other useful
functions as well.
Regards,
Ganesh
On Tue, Aug 3, 2021 at 9:18 PM Sebastian Berg
On Mon, 2021-08-02 at 13:10 -0700, Stefan van der Walt wrote:
On Mon, Aug 2, 2021, at 10:50, Sebastian Berg wrote:
* Should `np.ndarray.bit_count()` exist? I tend against this; but we should have it on (integer) scalars to mirror the Python `int`.
Should `np.bit_count` exist? Having it on the int* types may be sufficient.
Right, we could add it only to the integer scalars mostly for Python compatibility. The PR suggests to create a ufunc to make the feature available to typical NumPy code (allow using it with arrays).
* The return value is currently the same type as the input. That means that: `np.bit_count(uint8)` returns the count as `uint8` while `np.bit_count(int32)` returns it as `int32`, etc.
What is the max value of the count? 64? If so it can go in a uint8.
Yes, uint8 would even work for 128 bit integers. I was a bit unsure about this, since we rarely create non-default integer arrays unless prompted, but it is a good option as well.
Cheers,
Sebastian
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