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    Philox(seed=None, counter=None, key=None)

    Container for the Philox (4x64) pseudo-random number generator.

    Parameters
    ----------
    seed : {None, int, array_like[ints], SeedSequence}, 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.
    counter : {None, int, array_like}, optional
        Counter to use in the Philox state. Can be either
        a Python int (long in 2.x) in [0, 2**256) or a 4-element uint64 array.
        If not provided, the RNG is initialized at 0.
    key : {None, int, array_like}, optional
        Key to use in the Philox state.  Unlike ``seed``, the value in key is
        directly set. Can be either a Python int in [0, 2**128) or a 2-element
        uint64 array. `key` and ``seed`` cannot both be used.

    Attributes
    ----------
    lock: threading.Lock
        Lock instance that is shared so that the same bit git generator can
        be used in multiple Generators without corrupting the state. Code that
        generates values from a bit generator should hold the bit generator's
        lock.

    Notes
    -----
    Philox is a 64-bit PRNG that uses a counter-based design based on weaker
    (and faster) versions of cryptographic functions [1]_. Instances using
    different values of the key produce independent sequences.  Philox has a
    period of :math:`2^{256} - 1` and supports arbitrary advancing and jumping
    the sequence in increments of :math:`2^{128}`. These features allow
    multiple non-overlapping sequences to be generated.

    ``Philox`` provides a capsule containing function pointers that produce
    doubles, and unsigned 32 and 64- bit integers. These are not
    directly consumable in Python and must be consumed by a ``Generator``
    or similar object that supports low-level access.

    **State and Seeding**

    The ``Philox`` state vector consists of a 256-bit value encoded as
    a 4-element uint64 array and a 128-bit value encoded as a 2-element uint64
    array. The former is a counter which is incremented by 1 for every 4 64-bit
    randoms produced. The second is a key which determined the sequence
    produced. Using different keys produces independent sequences.

    The input ``seed`` is processed by `SeedSequence` to generate the key. The
    counter is set to 0.

    Alternately, one can omit the ``seed`` parameter and set the ``key`` and
    ``counter`` directly.

    **Parallel Features**

    The preferred way to use a BitGenerator in parallel applications is to use
    the `SeedSequence.spawn` method to obtain entropy values, and to use these
    to generate new BitGenerators:

    >>> from numpy.random import Generator, Philox, SeedSequence
    >>> sg = SeedSequence(1234)
    >>> rg = [Generator(Philox(s)) for s in sg.spawn(10)]

    ``Philox`` can be used in parallel applications by calling the ``jumped``
    method  to advances the state as-if :math:`2^{128}` random numbers have
    been generated. Alternatively, ``advance`` can be used to advance the
    counter for any positive step in [0, 2**256). When using ``jumped``, all
    generators should be chained to ensure that the segments come from the same
    sequence.

    >>> from numpy.random import Generator, Philox
    >>> bit_generator = Philox(1234)
    >>> rg = []
    >>> for _ in range(10):
    ...    rg.append(Generator(bit_generator))
    ...    bit_generator = bit_generator.jumped()

    Alternatively, ``Philox`` can be used in parallel applications by using
    a sequence of distinct keys where each instance uses different key.

    >>> key = 2**96 + 2**33 + 2**17 + 2**9
    >>> rg = [Generator(Philox(key=key+i)) for i in range(10)]

    **Compatibility Guarantee**

    ``Philox`` makes a guarantee that a fixed ``seed`` will always produce
    the same random integer stream.

    Examples
    --------
    >>> from numpy.random import Generator, Philox
    >>> rg = Generator(Philox(1234))
    >>> rg.standard_normal()
    0.123  # random

    References
    ----------
    .. [1] John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw,
           "Parallel Random Numbers: As Easy as 1, 2, 3," Proceedings of
           the International Conference for High Performance Computing,
           Networking, Storage and Analysis (SC11), New York, NY: ACM, 2011.
    
        Get or set the PRNG state

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        __pyx_capi__name__loader__loader__file__origin__package__parent__path__submodule_search_locationsan integer is requiredname '%U' is not defined%s (%s:%d)advanceexactly_philox.pyxat leastat mostjumpedMissing type objectbuiltinscython_runtime__builtins__does not matchnumpy.random.bit_generatorBitGeneratornumpydtypeflatiterbroadcastndarraygenericnumberunsignedintegerinexactcomplexfloatingflexiblecharacterufuncSeedSequenceSeedlessSequencenumpy.random._commondoubleLEGACY_POISSON_LAM_MAXuint64_tMAXSIZEwrap_intint_to_arraynumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is NULL pointer__init__.cython-30.pxdnumpy.import_arrayinit numpy.random._philox__setstate_cython__<stringsource>__reduce_cython____init____del__numpy.random._philox.Philoxstate
        advance(delta)

        Advance the underlying RNG as-if delta draws have occurred.

        Parameters
        ----------
        delta : integer, positive
            Number of draws to advance the RNG. Must be less than the
            size state variable in the underlying RNG.

        Returns
        -------
        self : Philox
            RNG advanced delta steps

        Notes
        -----
        Advancing a RNG updates the underlying RNG state as-if a given
        number of calls to the underlying RNG have been made. In general
        there is not a one-to-one relationship between the number output
        random values from a particular distribution and the number of
        draws from the core RNG.  This occurs for two reasons:

        * The random values are simulated using a rejection-based method
          and so, on average, more than one value from the underlying
          RNG is required to generate an single draw.
        * The number of bits required to generate a simulated value
          differs from the number of bits generated by the underlying
          RNG.  For example, two 16-bit integer values can be simulated
          from a single draw of a 32-bit RNG.

        Advancing the RNG state resets any pre-computed random numbers.
        This is required to ensure exact reproducibility.
        
        jumped(jumps=1)

        Returns a new bit generator with the state jumped

        The state of the returned bit generator is jumped as-if
        (2**128) * jumps random numbers have been generated.

        Parameters
        ----------
        jumps : integer, positive
            Number of times to jump the state of the bit generator returned

        Returns
        -------
        bit_generator : Philox
            New instance of generator jumped iter times
        self._bitgen,self.rng_state cannot be converted to a Python object for picklingseed and key cannot be both usednumpy.core.umath failed to importnumpy.core.multiarray failed to importstate must be for a {0} PRNGstate must be a dictcline_in_traceback__setstate_cython__generate_state__reduce_cython__bit_generator_initializingImportError__pyx_vtable__has_uint32buffer_posValueError__reduce_ex____pyx_stateisenabledTypeErroruinteger__setstate____getstate__disablecounteradvanceuint64__reduce____import__formatenablebufferPhiloxstaterangenumpyjumpsemptydtypedelta__class____test__step__spec__seed__name____main____init__keyget__all__?npgc*<@;\*x	Xw|p%4q<	L0P$P  #$P%P'Xp*-P/@ 346\8BHSW	[	0_T
 
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generated by cgit v1.2.3 (git 2.47.1) at 2025-07-27 05:52:18 +0000