Fix: seed sequence permutation tests and make results independent of n_jobs

[selmanozleyen]

Fixes: #1233 and fixes #1232 as a side effect.

For each permutation we have a separate seed. The seeds are generated by SeeqSequence(root_seed).spawn(x) routine. This will prevent having correlated results from sequential seeds mentioned in the numpy docs. I tried to use the best practice but since we need to use numba in the future our seeds can be only Sequence[int], instead of Sequence[np.random.SeedSequence]. Hence:

def _spawn_seeds(seed: int | None, n: int) -> list[int]:
    # uint32 integer seeds, derived from independent SeedSequence children (avoids the correlation
    # of sequential seeds). uint32 is required to stay reproducible with numba/legacy RandomState,
    # which only accept uint32 integer seeds.
    return [int(s.generate_state(1)[0]) for s in np.random.SeedSequence(seed).spawn(n)]

Once you confirm. I will write warnings in docstrings and in notebooks about this behavioral change to users.

[flying-sheep]

I tried to use the best practice but since we need to use numba in the future our seeds can be only Sequence[int], instead of Sequence[np.random.SeedSequence]. Hence:

please explain: why does the seed have to go inside of numba? Numba supports numpy.random.Generator instances (to a degree).

[selmanozleyen]

please explain: why does the seed have to go inside of numba? Numba supports numpy.random.Generator instances (to a degree).

Oof, I didn't know about this. Thank you! Then we can fully modernize as well

[flying-sheep]

first check if the methods you need work, I tried before and it failed since whatever distribution I tried didn’t work

[selmanozleyen]

now I remember why I discarded generators. It was because it was documented that Generator was not thread safe but I guess it should be fine if we have a generator for each permutation that's used once I guess...

But another point is ripley uses .uniform(). Numba doesn't guarantee bit by bit alignment with numpy on that. But I guess we can document it once we numbafy ripley. As long as it stays reproducable per version it's good enough for me. (Which is the modern numpy Generator guarantee now anyway from what I understand...)