> On 16 Feb 2024, at 2:48 am, Marten van Kerkwijk
> wrote:
>
>> In [45]: %timeit np.add.reduce(a, axis=None)
>> 42.8 µs ± 2.44 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
>>
>> In [43]: %timeit dotsum(a)
>> 26.1 µs ± 718 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops ea
On 13 Mar 2024, at 6:01 PM, Dom Grigonis wrote:
So my array sizes in this case are 3e8. Thus, 32bit ints would be needed. So it
is not a solution for this case.
Nevertheless, such concept would still be worthwhile for cases where integers
are say max 256bits (or unlimited), then even if memory
On 22 Jan 2023, at 10:40 am, Samuel Dupree
mailto:sdup...@speakeasy.net>> wrote:
I believe I know what is going on, but I don't understand why.
The line for the first derivative that failed to coincide with the points in
the plot for the cosine is actually the interpolated first derivative sca
On 11 Aug 2023, at 7:52 pm, Robert Kern
mailto:robert.k...@gmail.com>> wrote:
>>> np.cumsum([[1, 2, 3], [4, 5, 6]])
array([ 1, 3, 6, 10, 15, 21])
```
which matches your example in the cumsum0() documentation. Did something change
in a recent release?
That's not what's in his example.
The exa
