Seed Sequences¶
Note
randomgen imports SeedSequece
from NumPy if available, and only falls back to a vendored copy if not. The correct import location is randomgen.seed_sequence
which handles to selection of the correct SeedSequence
.
- class randomgen._seed_sequence.SeedSequence(entropy=None, *, spawn_key=(), pool_size=4)¶
SeedSequence mixes sources of entropy in a reproducible way to set the initial state for independent and very probably non-overlapping BitGenerators.
Once the SeedSequence is instantiated, you can call the generate_state method to get an appropriately sized seed. Calling spawn(n) <spawn> will create
n
SeedSequences that can be used to seed independent BitGenerators, i.e. for different threads.- Parameters:
- entropy{None, int, sequence[int]}, optional
The entropy for creating a SeedSequence.
- spawn_key{(), sequence[int]}, optional
A third source of entropy, used internally when calling SeedSequence.spawn
- pool_size{int}, optional
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.
- n_children_spawned{int}, optional
The 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
Using a SeedSequence¶
| Return the requested number of words for PRNG seeding. |
| Spawn a number of child SeedSequence instances by extending the spawn_key. |
State¶
Get the state of the SeedSequence |