SeedSequence(entropy=None, *, spawn_key=(), pool_size=4)
Once the SeedSequence is instantiated, you can call the generate_state
method to get an appropriately sized seed. Calling spawn(n)
will create n
SeedSequences that can be used to seed independent BitGenerators, i.e. for different threads.
Best practice for achieving reproducible bit streams is to use the default None
for the initial entropy, and then use :None:None:`SeedSequence.entropy` to log/pickle the :None:None:`entropy` for reproducibility:
>>> sq1 = np.random.SeedSequence() >>> sq1.entropy 243799254704924441050048792905230269161 # random >>> sq2 = np.random.SeedSequence(sq1.entropy) >>> np.all(sq1.generate_state(10) == sq2.generate_state(10)) True
The entropy for creating a SeedSequence
.
A third source of entropy, used internally when calling SeedSequence.spawn
Size of the pooled entropy to store. Default is 4 to give a 128-bit entropy pool. 8 (for 256 bits) is another reasonable choice if working with larger PRNGs, but there is very little to be gained by selecting another value.
The number of children already spawned. Only pass this if reconstructing a SeedSequence
from a serialized form.
SeedSequence mixes sources of entropy in a reproducible way to set the initial state for independent and very probably non-overlapping BitGenerators.
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.random.bit_generator.BitGenerator
numpy.random.bit_generator.SeedlessSeedSequence
numpy.random._pcg64.PCG64DXSM
numpy.random._sfc64.SFC64
numpy.random._philox.Philox
numpy.random._generator.default_rng
numpy.random._mt19937.MT19937
numpy.random.bit_generator.SeedSequence
numpy.random.bit_generator.ISpawnableSeedSequence.spawn
numpy.random.bit_generator.ISeedSequence
numpy.random._pcg64.PCG64
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