On Tue, Jun 21, 2022 at 9:54 AM Pieter Eendebak <pieter.eende...@gmail.com>
wrote:
> Hi Robert,
>
> Thanks for the information and the offer. My use case is development of
> algorithms where I want to be able to test variations of the algorithm
> while keeping the same (simulated or generated) data. Related to that are
> example scripts part of the documentation that I want to always have the
> same result.
>
> Creating a generator and passing that (someway or the other) is a good
> approach, but it requires some refactoring of the code. Especially for
> third-party code I would like to avoid this (to be honest, most external
> code is still using the old numpy random number interface, so there we can
> use the old style of setting the seed)
>
Ah, okay, so you're replacing the old global with a new global Generator
instance in your own code as an easier transitional step.
# global instance
rng = np.random.default_rng()
def seed_global_rng(seed=None):
# Create the same type of BitGenerator with the given seed, and then
copy its state over to the global.
bg_cls = type(rng.bit_generator)
bg = bg_cls(seed)
rng.bit_generator.state = bg.state
I do recommend moving to passing around a Generator when you can; even in
the older system, we've always recommended passing around a RandomState
instance.
--
Robert Kern
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