Starting from a seeded default generator:
>>> # High quality entropy created with: f"0x{secrets.randbits(128):x}"
>>> entropy = 0x3034c61a9ae04ff8cb62ab8ec2c4b501
>>> rng = np.random.default_rng(entropy)
Create two new generators for example for parallel execution:
>>> child_rng1, child_rng2 = rng.spawn(2)
Drawn numbers from each are independent but derived from the initial seeding entropy:
>>> rng.uniform(), child_rng1.uniform(), child_rng2.uniform() (0.19029263503854454, 0.9475673279178444, 0.4702687338396767)
It is safe to spawn additional children from the original rng or the children:
>>> more_child_rngs = rng.spawn(20) >>> nested_spawn = child_rng1.spawn(20)
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4