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npanpaliya / numpy python

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numpy / random / _pcg64.cpython-311-powerpc64le-linux-gnu.so
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`B`èÿ¡ûøÿáû ;À8àÿûðÿÁû9à;ëåèÄë$é`#<£èC`<í`dßc`ÆxxãÆ|xëç|àÀx¤ëx;¤ÊxxãJ}xó}x[| ;ÆcxPè|Eùxëk}xû)}̟cdÆdEöc`Y)}eù¤]`Ò)}Ò!Ç|Ò}ç|2)}P}:)}Y)}ù%ùàÿëèÿ¡ëðÿÁëøÿáë N``B`#é)é$ù#é)é$ù#é)é$ù#é)é$ù#%#& Nàÿû$éë9@9èÿ¡ûãè ;ðÿÁûøÿáûà;xCxK½û§ûÄëdé#éxóJ}xûk}Iùiù£Ðàÿëèÿ¡ëðÿÁëøÿáë N@¦|B¦h}¦|ðÿèP`}Z`}Ôÿ8éðx¦}ké NÌÿÿKÈÿÿKÄÿÿKÀÿÿK¼ÿÿK¸ÿÿK´ÿÿK°ÿÿK¬ÿÿK¨ÿÿK¤ÿÿK ÿÿKÿÿKÿÿKÿÿKÿÿKÿÿKÿÿKÿÿKÿÿK|ÿÿKxÿÿKtÿÿKpÿÿKlÿÿKhÿÿKdÿÿK`ÿÿK\ÿÿKXÿÿKTÿÿKPÿÿKLÿÿKHÿÿKDÿÿK@ÿÿK<ÿÿK8ÿÿK4ÿÿK0ÿÿK,ÿÿK(ÿÿK$ÿÿK ÿÿKÿÿKÿÿKÿÿKÿÿKÿÿKÿÿKÿÿKÿÿKüþÿKøþÿKôþÿKðþÿKìþÿKèþÿKäþÿKàþÿKÜþÿKØþÿKÔþÿKÐþÿKÌþÿKÈþÿKÄþÿKÀþÿK¼þÿK¸þÿK´þÿK°þÿK¬þÿK¨þÿK¤þÿK þÿKþÿKþÿKþÿKþÿKþÿKþÿKþÿKþÿK|þÿKxþÿKtþÿKpþÿKlþÿKhþÿKdþÿK`þÿK\þÿKXþÿKTþÿKPþÿKLþÿKHþÿKDþÿK@þÿK<þÿK8þÿK4þÿK0þÿK,þÿK(þÿKL<¼½B8¦|ø¡ÿ!ø`!8è¦| N%.200s() takes no arguments (%zd given)%.200s() takes exactly one argument (%zd given)Bad call flags for CyFunction%.200s() takes no keyword arguments<cyfunction %U at %p>base class '%.200s' is not a heap typeextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base type__int__ returned non-int (type %.200s).  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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 PyObject__reduce_cython__%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'instance exception may not have a separate valuecalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseException%s() got multiple values for keyword argument '%U'invalid vtable found for imported typemultiple bases have vtable conflict: '%.200s' and '%.200s'can't convert negative value to uint32_tvalue too large to convert to uint32_tUnable to initialize pickling for %.200s while calling a Python objectNULL result without error in PyObject_Callname '%U' is not definednumpy/random/_pcg64.cpython-311-powerpc64le-linux-gnu.so.p/numpy/random/_pcg64.pyx.c%s (%s:%d)_pcg64.pyx<stringsource>Module '_pcg64' has already been imported. Re-initialisation is not supported.builtinscython_runtime__builtins__does not matchnumpy.random._pcg64compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d_cython_3_0_11Shared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling0x100000000000000000x9e3779b97f4a7c15f39cc0605cedc835numpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is not PyCapsule object_ARRAY_API is NULL pointermodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule was compiled against NumPy C-API version 0x%x (NumPy 1.20) but the running NumPy has C-API version 0x%x. Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem.FATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimenumpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).numpy.random.bit_generatorBitGeneratortypenumpydtypeflatiterbroadcastndarraygenericnumbersignedintegerunsignedintegerinexactfloatingcomplexfloatingflexiblecharacterufuncSeedSequenceSeedlessSequencenumpy.random._commondoublePOISSON_LAM_MAXLEGACY_POISSON_LAM_MAXuint64_tMAXSIZEwrap_int%.200s does not export expected C function %.200sPyObject *(PyObject *, PyObject *)C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)init numpy.random._pcg64at mostat leastjumpednumpy.random._pcg64.PCG64DXSM.jumpedMissing type objectCannot convert %.200s to %.200s%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random._pcg64.PCG64.jumpednumpy.random._pcg64.PCG64DXSM.jump_inplacenumpy.random._pcg64.PCG64.jump_inplacenumpy.random._pcg64.PCG64DXSM.__setstate_cython____setstate_cython__exactlynumpy.random._pcg64.PCG64DXSM.__reduce_cython__numpy.random._pcg64.PCG64.__setstate_cython__numpy.random._pcg64.PCG64.__reduce_cython__advancenumpy.random._pcg64.PCG64DXSM.advancenumpy.random._pcg64.PCG64.advancenumpy.random._pcg64.PCG64.state.__set____del____init__numpy.random._pcg64.PCG64DXSM.__init__numpy.random._pcg64.PCG64.__init__numpy.random._pcg64.PCG64.state.__get__numpy.random._pcg64.PCG64DXSM.state.__get__numpy.random._pcg64.PCG64DXSM.state.__set___cython_3_0_11.cython_function_or_method__reduce____module__func_doc__doc__func_name__name____qualname__func_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations___is_coroutine_pcg64numpy.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>`_
    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"
           <https://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>`_
     <ImportErrorPCG64PCG64DXSMPCG64DXSM.__reduce_cython__PCG64DXSM.__setstate_cython__PCG64DXSM.advancePCG64DXSM.jumpedPCG64.__reduce_cython__PCG64.__setstate_cython__PCG64.advancePCG64.jumpedTypeErrorValueError?*advance__all__asyncio.coroutinesbit_generator__class__cline_in_tracebackddeltadisabledtypeemptyenableformatgcgenerate_stateget__getstate__has_uint32__import__inc__init___initializing_is_coroutineisenabledjumpedjumps__main____name__npnumpynumpy._core.multiarray failed to importnumpy._core.umath failed to importnumpy.random._pcg64__pyx_state__pyx_vtable____reduce____reduce_cython____reduce_ex__seedselfself._bitgen,self.rng_state cannot be converted to a Python object for pickling__setstate____setstate_cython____spec__statestate must be a dictstate must be for a {0} RNG<stringsource>__test__/tmp/pip-install-5nnte3hl/numpy_fbfca956206d496182f3175e68a213df/numpy/random/_pcg64.pyxuint64uinteger
        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 : PCG64
            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.

        Jumps the state as-if jumps * 210306068529402873165736369884012333109
        random numbers have been generated.

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

        Returns
        -------
        bit_generator : PCG64DXSM
            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.
        
        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 : PCG64
            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.

        Jumps the state as-if jumps * 210306068529402873165736369884012333109
        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.
        ;aL<þÿ(=þÿ@X>þÿh@þÿ ü@þÿÐCþÿ<EþÿtFþÿT
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npanpaliya / numpy python

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