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ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿnumpy\random\_pcg64.c_pcg64.pyxstringsource__init__.pxdtype.pxdbit_generator.pxd__init__numpy.random._pcg64.PCG64.__init__numpy.random._pcg64.PCG64.jump_inplacejumpednumpy.random._pcg64.PCG64.jumpednumpy.random._pcg64.PCG64.state.__get__numpy.random._pcg64.PCG64.state.__set__numpy.random._pcg64.PCG64.advancenumpy.random._pcg64.PCG64.__reduce_cython__numpy.random._pcg64.PCG64.__setstate_cython__numpy.random._pcg64.PCG64DXSM.__init__numpy.random._pcg64.PCG64DXSM.jump_inplacenumpy.random._pcg64.PCG64DXSM.jumpednumpy.random._pcg64.PCG64DXSM.state.__get__numpy.random._pcg64.PCG64DXSM.state.__set__numpy.random._pcg64.PCG64DXSM.advancenumpy.random._pcg64.PCG64DXSM.__reduce_cython__numpy.random._pcg64.PCG64DXSM.__setstate_cython____del__advance__reduce_cython____setstate_cython__state
        Get or set the PRNG state

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        numpy.random._pcg64.PCG64
    PCG64(seed=None)

    BitGenerator for the PCG-64 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.

    Notes
    -----
    PCG-64 is a 128-bit implementation of O'Neill's permutation congruential
    generator ([1]_, [2]_). PCG-64 has a period of :math:`2^{128}` and supports
    advancing an arbitrary number of steps as well as :math:`2^{127}` streams.
    The specific member of the PCG family that we use is PCG XSL RR 128/64
    as described in the paper ([2]_).

    ``PCG64`` 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.

    Supports the method :meth:`advance` to advance the RNG an arbitrary number of
    steps. The state of the PCG-64 RNG is represented by 2 128-bit unsigned
    integers.

    **State and Seeding**

    The ``PCG64`` state vector consists of 2 unsigned 128-bit values,
    which are represented externally as Python ints. One is the state of the
    PRNG, which is advanced by a linear congruential generator (LCG). The
    second is a fixed odd increment used in the LCG.

    The input seed is processed by `SeedSequence` to generate both values. The
    increment is not independently settable.

    **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, PCG64, SeedSequence
    >>> sg = SeedSequence(1234)
    >>> rg = [Generator(PCG64(s)) for s in sg.spawn(10)]

    **Compatibility Guarantee**

    ``PCG64`` makes a guarantee that a fixed seed will always produce
    the same random integer stream.

    References
    ----------
    .. [1] `"PCG, A Family of Better Random Number Generators"
           <http://www.pcg-random.org/>`_
    .. [2] O'Neill, Melissa E. `"PCG: A Family of Simple Fast Space-Efficient
           Statistically Good Algorithms for Random Number Generation"
           <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>`_
    numpy.random._pcg64.PCG64DXSM
    PCG64DXSM(seed=None)

    BitGenerator for the PCG-64 DXSM 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.

    Notes
    -----
    PCG-64 DXSM is a 128-bit implementation of O'Neill's permutation congruential
    generator ([1]_, [2]_). PCG-64 DXSM has a period of :math:`2^{128}` and supports
    advancing an arbitrary number of steps as well as :math:`2^{127}` streams.
    The specific member of the PCG family that we use is PCG CM DXSM 128/64. It
    differs from ``PCG64`` in that it uses the stronger DXSM output function,
    a 64-bit "cheap multiplier" in the LCG, and outputs from the state before
    advancing it rather than advance-then-output.

    ``PCG64DXSM`` 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.

    Supports the method :meth:`advance` to advance the RNG an arbitrary number of
    steps. The state of the PCG-64 DXSM RNG is represented by 2 128-bit unsigned
    integers.

    **State and Seeding**

    The ``PCG64DXSM`` state vector consists of 2 unsigned 128-bit values,
    which are represented externally as Python ints. One is the state of the
    PRNG, which is advanced by a linear congruential generator (LCG). The
    second is a fixed odd increment used in the LCG.

    The input seed is processed by `SeedSequence` to generate both values. The
    increment is not independently settable.

    **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, PCG64DXSM, SeedSequence
    >>> sg = SeedSequence(1234)
    >>> rg = [Generator(PCG64DXSM(s)) for s in sg.spawn(10)]

    **Compatibility Guarantee**

    ``PCG64DXSM`` makes a guarantee that a fixed seed will always produce
    the same random integer stream.

    References
    ----------
    .. [1] `"PCG, A Family of Better Random Number Generators"
           <http://www.pcg-random.org/>`_
    .. [2] O'Neill, Melissa E. `"PCG: A Family of Simple Fast Space-Efficient
           Statistically Good Algorithms for Random Number Generation"
           <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>`_
    _pcg6418446744073709551616210306068529402873165736369884012333109numpy.random.bit_generatorBitGeneratorbuiltinstypenumpydtypeflatiterbroadcastndarraygenericnumberintegersignedintegerunsignedintegerinexactfloatingcomplexfloatingflexiblecharacterufuncSeedSequenceSeedlessSequencenumpy.random._commondoublePOISSON_LAM_MAXLEGACY_POISSON_LAM_MAXuint64_tMAXSIZEPyObject *(PyObject *, PyObject *)wrap_intInterpreter change detected - this module can only be loaded into one interpreter per process.name__loader__loader__file__origin__package__parent__path__submodule_search_locationsModule '_pcg64' has already been imported. Re-initialisation is not supported.cython_runtime__builtins__numpy.random._pcg64init numpy.random._pcg64name '%U' is not defined%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'at leastat mosts%.200s() takes %.8s %zd positional argument%.1s (%zd given) while calling a Python objectNULL result without error in PyObject_CallMissing type objectCannot convert %.200s to %.200scalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseException%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectUnable to initialize pickling for %sinvalid vtable found for imported type%s (%s:%d)can't convert negative value to uint32_t%d.%dcompiletime version %s of module '%.100s' does not match runtime version %s__pyx_capi__%.200s does not export expected C variable %.200sC variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)%.200s does not export expected C function %.200sC function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__%.4s__ returned non-%.4s (type %.200s)intan integer is requiredcline_in_tracebackgethas_uint32µ€
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jumps__setstate_cython__numpyuint64npImportErrorinc__setstate____main__emptyformatbit_generator__reduce_cython__ð´€
ValueError__reduce__state must be for a {0} RNGstate must be a dict__all____import__generate_state__name__uinteger__pyx_vtable__seed__test____getstate__PCG64self._bitgen,self.rng_state cannot be converted to a Python object for picklingPCG64DXSMadvanceTypeErrorstate__init__numpy.core.multiarray failed to import__reduce_ex____class__numpy.core.umath failed to importdtype <3z³b
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        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
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        self : PCG64
            RNG advanced delta steps

        Notes
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        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.

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        random numbers have been generated.

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

        Returns
        -------
        bit_generator : PCG64
            New instance of generator jumped iter times

        Notes
        -----
        The step size is phi-1 when multiplied by 2**128 where phi is the
        golden ratio.
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        jumped(jumps=1)

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        bit_generator : PCG64DXSM
            New instance of generator jumped iter times

        Notes
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        The step size is phi-1 when multiplied by 2**128 where phi is the
        golden ratio.
        
        advance(delta)

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

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        ----------
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            Number of draws to advance the RNG. Must be less than the
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