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numpy / random / _pcg64.cpython-312.so
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””ª'm__cxa_finalize__cxa_atexit__register_atforkPyInit__pcg64PyModuleDef_InitPyThreadState_GetPyInterpreterState_GetIDPyObject_GetAttrStringPyModule_NewObject_Py_DeallocPyModule_GetDictPyDict_SetItemString_Py_NoneStructPyExc_ImportErrorPyErr_SetStringPyExc_AttributeErrorPyErr_ExceptionMatchesPyErr_ClearPyExc_RuntimeErrorPyImport_AddModulePyObject_SetAttrStringPy_VersionPyOS_snprintfPyErr_WarnExPyTuple_NewPyBytes_FromStringAndSizePyUnicode_FromStringAndSizePyUnicode_FromStringPyImport_ImportModulePyObject_GetAttrPyMemoryView_FromMemoryPyObject_CallFunctionObjArgsPyUnicode_DecodeUTF8PyUnicode_InternInPlacePyObject_HashPyLong_FromLongPyLong_FromStringPyCapsule_TypePyErr_OccurredPyExc_ModuleNotFoundErrorPyCapsule_GetPointerPyErr_FormatPyType_TypePyTuple_PackPyObject_SetAttrPyImport_GetModuleDictPyDict_GetItemStringPyDict_TypePyDict_SetItemPyDict_NewPyObject_GenericGetAttrPyCapsule_NewstrlenPyImport_GetModulePyUnicode_FindCharPyUnicode_SubstringPyImport_ImportModuleLevelObjectPyList_New_PyObject_GC_NewPyObject_GC_TrackPyExc_SystemErrorPyType_ModifiedPyObject_HasAttrPyObject_CallMethodObjArgs_PyThreadState_UncheckedGetPyCode_NewEmptyPyException_SetTracebackPyMem_ReallocPyFrame_NewPyTraceBack_HerePyMem_MallocstrrchrPyDict_GetItemWithErrorPyType_FromMetaclassPyDict_SetDefaultPyExc_TypeErrorPyObject_GC_UnTrackPyObject_ClearWeakRefsPyObject_GC_DelPyUnicode_FromFormatPyTuple_GetSlicePyTuple_GetItem_PyObject_VisitManagedDict_PyObject_ClearManagedDictPyMethod_NewPyDict_SizePyDict_NextPyMem_FreePyErr_NoMemoryPyUnicode_InternFromStringPyExc_RuntimeWarning_Py_FalseStruct_Py_TrueStructPyBytes_AsStringmemsetPyUnstable_Code_NewWithPosOnlyArgsPyExc_ValueErrorPyErr_WarnFormatPyLong_TypePyNumber_LongPyNumber_MultiplyPyObject_VectorcallMethodPyType_ReadyPyGC_DisablePyGC_EnablePyObject_GetItem_PyType_LookupPyBaseObject_Type_PyObject_GenericGetAttrWithDictPyDict_DelItemPyObject_GC_IsFinalizedPyObject_CallFinalizerFromDealloc_PyDict_GetItem_KnownHashPyMethod_TypePyObject_VectorcallPyNumber_FloorDividePyNumber_RemainderPyArg_ValidateKeywordArgumentsPyUnicode_TypememcmpPyObject_RichCompareBoolPyList_TypePyLong_FromSsize_tPyObject_SetItemPyExc_NameErrorPyErr_GivenExceptionMatchesPyCFunction_TypePyObject_VectorcallDictPy_EnterRecursiveCallPy_LeaveRecursiveCallPyObject_IsSubclassPyObject_CallPyErr_SetObjectPyNumber_AddPyObject_RichComparePyObject_IsTruePyExc_NotImplementedErrorPyTuple_TypePyExc_KeyErrorPyLong_AsLongPyExc_OverflowErrorPyLong_AsUnsignedLongPyExc_DeprecationWarningPyCapsule_IsValidPyModule_GetNamePyCapsule_GetNamePyObject_GenericGetDictPyObject_GenericSetDictlibc.soLIBClibpython3.12.soФФऐúð¤ ú¥Ðú¥û ¥0‘(¥‘hëb€ëP÷ˆë°ë¸ë‘Èëp”èëÎRðë ãì <ìQSì`è ì?(ì?^0ìPñHìMPìàñhìÎRpìp)€ì`DˆìQSì°. ìÐF¨ì?^°ì 7ÈìMÐì08èììLííí0í0í)j8í`»€í‡^˜í í¨íp»¸í »Èí;Øíð¼èíà½øí@îî€îî ï(îð¿@îedHî0Āî)j¨îRÐîS ï,R(ïPÄ0ï°ÄHï4hPïPÄXï°Äpï¯^xïŀïPŘï8_ ïŨïPÅÀï[PÈïÀÅÐïàÅèï5^ð5R8ðsc@ðPÆ`ð¹^hðPƈð’SðpÆ°ðpd¸ðpÆØð€càðÆñgañÆ(ñQe0ñ°Æ8ñÇPñm_Xñ°Æ`ñÇxñJj€ñ°ÇˆñÈ ñ}v¨ñ°È°ñðÈÈñ cÐñpÉ0òYjHòÛÈòŸSÐòÛØòÀÛó`õóö@óðÛPóâÐóøOèóP"hôkpôÐ"xô# ôPö°ôðöàô0#ðôP)`õÎRhõ ãxõ <€õQSˆõ`è˜õ? õ?^¨õPñÀõMÈõàñöSöPö€ö¶hPöÎRXöp)hö`DpöQSxö°.ˆöÐFö?^˜ö 7°öM¸ö08ðöSøöà:÷E÷¶h ¦¨¦
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        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.
        
        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.
         <Module '_pcg64' has already been imported. Re-initialisation is not supported.numpy/random/_pcg64.pyxexactlyMissing type objectname '%U' is not definednumpy.random._pcg64.PCG64.state.__set___cython_3_2_4._common_types_metatype__setstate_cython__dtypeSeedSequence__name__ must be set to a string object%s() got multiple values for keyword argument '%U'numpy.random._pcg64.PCG64.__setstate_cython____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.numpy.random._pcg64.PCG64DXSM.__init__ndarraydoes not matchnumpy.core._multiarray_umathShared Cython type %.200s has the wrong size, try recompiling%.200s() %snumpy.random._pcg64.PCG64DXSM.advanceflexibleFailed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'.%.200s() keywords must be stringsnumpy.random._pcg64.PCG64DXSMnumpy.random._pcg64.PCG64DXSM.__reduce_cython__complexfloatingzlib__qualname__multiple bases have vtable conflict: '%.200s' and '%.200s'integerloader__package__<stringsource>module 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.__vectorcalloffset__func_doc__dict__at most%.200s() takes %.8s %zd positional argument%.1s (%zd given)floatingvariablenumpy._core._multiarray_umath_ARRAY_API is NULL pointerjumpedan integer is requirednumpy.random._pcg64.PCG64DXSM.jump_inplacestate_ARRAY_APIFATAL: module compiled as unknown endianadvancesSeedlessSequencetakes no arguments__weaklistoffset__func_closure
    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>`_
    needs an argument__file__parentsubmodule_search_locations%.200s() %s (%zd given)functionfunc_dict__reduce_cython__NULL result without error in PyObject_Call__loader___cython_3_2_4.cython_function_or_methodfunc_name__globals__numpy.random._pcg64.PCG64DXSM.state.__set__C %.8s %.200s.%.200s has wrong signature (expected %.500s, got %.500s)__name__numpy.random._pcg64.PCG64.__init__flatiter__defaults__changes to cyfunction.__kwdefaults__ will not currently affect the values used in function callsnumpy.random._pcg64.PCG64DXSM.jumpednumbercython_runtimetakes no keyword arguments%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject__init____path__module compiled against ABI version 0x%x but this version of numpy is 0x%xinvalid vtable found for imported typecharacterufuncShared Cython type %.200s is not a type object__code____kwdefaults__ must be set to a dict objectBitGeneratornumpy.random._pcg64.PCG64.jumpednumpy.random._pcg64.PCG64.advanceUnable to initialize pickling for %.200s_pcg64originnumpy.random._pcg64compile time Python version %d.%d of module '%.100s' %s runtime version %d.%dnumpy.random._pcg64.PCG64.state.__get____int__ returned non-int (type %.200s)can't convert negative value to uint32_t_ARRAY_API is not PyCapsule object_is_coroutineinstance exception may not have a separate valuenumpy_cython_3_2_4func_globalsfunc_code%.200s.%.200s is not a type objectnumpy.random._pcg64.PCG64.jump_inplacenumpy.random._pcg64.PCG64.__reduce_cython__calling %R should have returned an instance of BaseException, not %Rnametakes exactly one argument__reduce____closure____qualname__ must be set to a string objectat leastvalue too large to convert to uint32_tnumpy.random._pcg64.PCG64DXSM.__setstate_cython__unsignedinteger%.200s does not export expected C %.8s %.200sbuiltinsfunc_defaults__defaults__ must be set to a tuple objectextension 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 typetypedecompressFATAL: module compiled as little endian, but detected different endianness at runtimeunbound method %.200S() needs an argument__annotations__ must be set to a dict objectvalue too large to convert to intnumpy.random._pcg64.PCG64DXSM.state.__get__broadcastinit numpy.random._pcg64numpy.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).__doc__changes to cyfunction.__defaults__ will not currently affect the values used in function callsnumpy.random.bit_generator
        Get or set the PRNG state

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        base class '%.200s' is not a heap typenumpy.random._commonInterpreter change detected - this module can only be loaded into one interpreter per process.__builtins____module__<cyfunction %U at %p>__kwdefaults__numpy.random._pcg64.PCG64%s() got an unexpected keyword argument '%U' while calling a Python objectraise: exception class must be a subclass of BaseException__del__
    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>`_
    Bad call flags for CyFunctionCannot convert %.200s to %.200ssignedintegerinexactkeywords must be strings__annotations__generic__pyx_capi__;Ðylì|Œœ(¬<ÌPÜdìx̸.,.0ì0\Ü3˜8Ä<9Ì9(;hü;„|=Ð>LBplD¼¼DÔÌDèüDELFŒ<GÄLIèlIüìJ\	ŒM¬	¬MÀ	Nè	lN
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