I don’t have an issue with cumsum0 if it is approached as a request for a
useful utility function.
But arguing that this is what a cumulative sum function should be doing is a
very big stretch. Cumulative sum has its foundational meaning and purpose which
is clearly reflected in its name, which
On Mon, Aug 21, 2023 at 4:37 AM Ralf Gommers wrote:
> Hi all,
>
> On behalf of the steering council, I am very happy to announce that Andrew
> is joining the Maintainers team. Andrew has been contributing to our CI
> setup in particular for the past year, and has contributed for example the
> Cir
Congratulations Andrew!
On Tue, Aug 22, 2023 at 9:44 PM Daniela Cialfi
wrote:
>
> Welcome on board
>
> Daniela
>
>
> On Tue, 22 Aug 2023 at 16:06, Charles R Harris
> wrote:
>
>>
>>
>> On Mon, Aug 21, 2023 at 10:09 PM Andrew Nelson
>> wrote:
>>
>>> On Mon, 21 Aug 2023 at 18:39, Ralf Gommers
>>
Welcome on board
Daniela
On Tue, 22 Aug 2023 at 16:06, Charles R Harris
wrote:
>
>
> On Mon, Aug 21, 2023 at 10:09 PM Andrew Nelson wrote:
>
>> On Mon, 21 Aug 2023 at 18:39, Ralf Gommers
>> wrote:
>>
>>> Hi all,
>>>
>>> On behalf of the steering council, I am very happy to announce that
>>>
`cumsum` provides a sequence of partial sums, exactly as expected.
https://reference.wolfram.com/language/ref/Accumulate.html
https://www.mathworks.com/help/matlab/ref/cumsum.html
https://docs.julialang.org/en/v1/base/arrays/#Base.cumsum
https://hackage.haskell.org/package/base-4.12.0.0/docs/Data-
Dom Grigonis wrote:
> 1. Dimension length stays constant, while cumusm0 extends length to n+1, then
> np.diff, truncates it back. This adds extra complexity, while things are very
> convenient to work with when dimension length stays constant throughout the
> code.
For n values there are n-1 di
On Mon, Aug 21, 2023 at 10:09 PM Andrew Nelson wrote:
> On Mon, 21 Aug 2023 at 18:39, Ralf Gommers wrote:
>
>> Hi all,
>>
>> On behalf of the steering council, I am very happy to announce that
>> Andrew is joining the Maintainers team. Andrew has been contributing to our
>> CI setup in particula
On 22/8/23 02:25, Dylon Edwards wrote:
It is my understanding that Numpy accelerates array operations with BLAS where
possible, but BLAS does not support all the dtypes that Numpy does. How does
Numpy model non-float arrays like arrays of dtype=bool or dtype=object?
Numpy only uses BLAS wher
It is my understanding that Numpy accelerates array operations with BLAS where
possible, but BLAS does not support all the dtypes that Numpy does. How does
Numpy model non-float arrays like arrays of dtype=bool or dtype=object?
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