bundles / numpy latest / numpy / random / bit_generator / SeedSequence
class
numpy.random.bit_generator:SeedSequence
Signature
class SeedSequence(entropy = None, *, spawn_key = (), pool_size = 4, n_children_spawned = 0)Members
Summary
SeedSequence mixes sources of entropy in a reproducible way to set the initial state for independent and very probably non-overlapping BitGenerators.
Extended Summary
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.
Parameters
entropy: {None, int, sequence[int]}, optionalThe entropy for creating a SeedSequence. All integer values must be non-negative.
spawn_key: {(), sequence[int]}, optionalAn additional source of entropy based on the position of this SeedSequence in the tree of such objects created with the SeedSequence.spawn method. Typically, only SeedSequence.spawn will set this, and users will not.
pool_size: {int}, optionalSize 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.
n_children_spawned: {int}, optionalThe number of children already spawned. Only pass this if reconstructing a SeedSequence from a serialized form.
Notes
Best practice for achieving reproducible bit streams is to use the default None for the initial entropy, and then use SeedSequence.entropy to log/pickle the 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
Aliases
numpy.random.SeedSequence