Function default_rng

dask/array/_array_expr/random.py:378–470

Construct a new Generator with the default BitGenerator (PCG64). Parameters ---------- seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional A seed to initialize the `BitGenerator`. If None, then fresh, unpredictable entropy will be p

(seed=None)

def default_rng(seed=None):
    """
    Construct a new Generator with the default BitGenerator (PCG64).

    Parameters
    ----------
    seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
        A seed to initialize the `BitGenerator`. If None, then fresh,
        unpredictable entropy will be pulled from the OS. If an ``int`` or
        ``array_like[ints]`` is passed, then it will be passed to
        `SeedSequence` to derive the initial `BitGenerator` state. One may
        also pass in a `SeedSequence` instance.
        Additionally, when passed a `BitGenerator`, it will be wrapped by
        `Generator`. If passed a `Generator`, it will be returned unaltered.

    Returns
    -------
    Generator
        The initialized generator object.

    Notes
    -----
    If ``seed`` is not a `BitGenerator` or a `Generator`, a new
    `BitGenerator` is instantiated. This function does not manage a default
    global instance.

    Examples
    --------
    ``default_rng`` is the recommended constructor for the random number
    class ``Generator``. Here are several ways we can construct a random
    number generator using ``default_rng`` and the ``Generator`` class.

    Here we use ``default_rng`` to generate a random float:

    >>> import dask.array as da
    >>> rng = da.random.default_rng(12345)
    >>> print(rng)
    Generator(PCG64)
    >>> rfloat = rng.random().compute()
    >>> rfloat
    array(0.86999885)
    >>> type(rfloat)
    <class 'numpy.ndarray'>

    Here we use ``default_rng`` to generate 3 random integers between 0
    (inclusive) and 10 (exclusive):

    >>> import dask.array as da
    >>> rng = da.random.default_rng(12345)
    >>> rints = rng.integers(low=0, high=10, size=3).compute()
    >>> rints
    array([2, 8, 7])
    >>> type(rints[0])
    <class 'numpy.int64'>

    Here we specify a seed so that we have reproducible results:

    >>> import dask.array as da