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# numpy.random.SeedSequence

class numpy.random.SeedSequence(entropy=None, *, spawn_key=(), pool_size=4, n_children_spawned=0)

  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)` 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`. All integer values must be non-negative.

    spawn_key{(), sequence[int]}, optional

      An 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}, 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.

  Attributes:
    entropy
    n_children_spawned
    pool
    pool_size
    spawn_key
    state

  Methods

  `generate_state`(n_words[, dtype]) | Return the requested number of words for PRNG seeding.
  `spawn`(n_children) | Spawn a number of child `SeedSequence` s by extending the `spawn_key`.

  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