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IFH;5HHpHHyHL$x1IH.HL$LHLQImHI`LD$D$KL LD$M1۽E1E1Ix HILMtIx HISHtHx HHH ~GH=/UM4 ID$x HIMtI$xHI$D$H8[]A\A]A^A_fLLLx]H@hH H@(H L1LЅ IExHIE^ H5HH9] 5IM H5 LI9] `5IIEM xHIEV H5ߠL/IH Ix HI_ LL6 IExHIEH5*HH9] 4IM H5-LI9] 4IIEM xHIEH5"LBIHr Ix HILL5t IExHIEuLH5iHH9] 4IM H5lLI9] 3IIEM xHIEuLLH5]LIH&Ix HIm LL4 IExHIE H5HH9] F3IM L1Aǃ IExHIEi H5HH9] 2IM L> IExHIE H|$IvDHhIUbDL KLVH߉T$T$LT$LT$T$LT$L׉T$T$L1LIHf.LH-AL%HEHHH=w" 1HLHH1Hu>HExHHEH BH=*PD$0DI{IpH *BH=*SPD$fD1E1E1E1E1IEx HIEtMLT$LT$?T$LT$@H AH=*OD$QfDIH1E1E1E1eD{I1E1E1Yf1E1E1fLxHhTKIH, 1E1ҽ>fDL L)MfDI1ۅH _@H=(NI~IVAEH:tAEIVL*H[HHNDH=IH5"HGHHIMH|$H5HGHHIMIBLT$LH57HHLT$IMIx HI HiI9EImHEMEtEAtAIExHIEKfInfHnLǺflHt$ LD$)D$ FLD$IHExHHEIx HIHMIx HIH=ʙHt$(HHD$ L|$(EHHIx HI1H:HEMHHEHT$t$t$T$DLLLIH]fLHHuLIIH0]LI1E1E12ifL5KHhLIIHE1ҽ7 @LpIE19fDL8I>1E1E1fOH j<H=$JD$fD1E1ҽQL@D$1AIHLHLGI+MTfH1LHHDH ;H=$ID$afDa I1E1ҽcl@IMffDLi1E1E1fW1E1E1fLXd>IsAL/I1E1ҽuHH5<H8D$1LLD$LD$LLL$L$[HH_H H5H8IDMxzLjT{1E1E1LLD$2LD$L SHt$(LHD$ Lt$(@MIHqH5"1E1H8E1ҺSI1E1ҽW#HLD$LD$:1E1ҽSII1E1E1I1E1E1ILT$I1E1R]H1E1E1-yI1E1ME1 D$L$H1E1E1E1E17`HD$H}1H5A>H8LT$u<1E1aHfHAWH AVIAUATUHSHt$8H|$@HD$IIҹHD$1AtPILLl$HMMIHIHHD$HLIIHD$ILHHHD$HT$LLIHHLMHIIHHHLLIMMIILMML UHLHIHHLHD$HT$[]A\A]A^A_Ð[HL]A\A]A^A_ff.@AWAVAUATUSHLLVL@LHL0LxHD$LL E1E11HE̟d߅CH]`#AtFHHHHHHHHHHHILHHLHIIIMMHHLHHHHD$HD$HT$IMIHIIIHIHHHHHLHHL PLHIHHLHIIIMMHD$L0Lx[]A\A]A^A_ff.AWAVAUATUSHLLVL@LHL0LxHD$LL E1E11HXB 1AtFHHHHHHHHHHHILHHLHIIIMMHHLHHHHD$HD$HT$IMIHIIIHIHHHHHLHHL PLHIHHLHIIIMMHD$L0Lx[]A\A]A^A_IHFHHIpI8HHIIHyH]`#ILLQHHHE̟d߅CHHHHLLHHQfHH@HHHHFHH@HFHH@HFGG ff.LLNHLL@LFHHvL@HpWO HH%.200s() takes no arguments (%zd given)%.200s() takes exactly one argument (%zd given)%.200s() takes no keyword argumentsbase 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). 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)%.200s does not export expected C variable %.200sC variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Interpreter change detected - this module can only be loaded into one interpreter per process.unbound method %.200S() needs an argumentvalue too large to convert to int%.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 PyObject%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'invalid vtable found for imported typemultiple bases have vtable conflict: '%.200s' and '%.200s'__annotations__ must be set to a dict object__name__ must be set to a string object__qualname__ must be set to a string object__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function callsfunction's dictionary may not be deletedsetting function's dictionary to a non-dictvalue too large to convert to uint32_tcan't convert negative value to uint32_tinstance 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' while calling a Python objectNULL result without error in PyObject_CallUnable to initialize pickling for %.200snumpy/random/_pcg64.cpython-312-x86_64-linux-gnu.so.p/numpy/random/_pcg64.pyx.cModule '_pcg64' has already been imported. Re-initialisation is not supported.compile time Python version %d.%d of module '%.100s' %s runtime version %d.%dShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling0x9e3779b97f4a7c15f39cc0605cedc835_ARRAY_API is not PyCapsule objectmodule 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 'numpy._import_array' to disable if you are certain you don't need it).%.200s does not export expected C function %.200sPyObject *(PyObject *, PyObject *)C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Cannot convert %.200s to %.200snumpy.random._pcg64.PCG64DXSM.advance%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random._pcg64.PCG64DXSM.jumpednumpy.random._pcg64.PCG64.advancenumpy.random._pcg64.PCG64.jumpednumpy.random._pcg64.PCG64DXSM.jump_inplacenumpy.random._pcg64.PCG64.jump_inplacenumpy.random._pcg64.PCG64DXSM.__setstate_cython__numpy.random._pcg64.PCG64DXSM.__reduce_cython__numpy.random._pcg64.PCG64.__setstate_cython__numpy.random._pcg64.PCG64.__reduce_cython__numpy.random._pcg64.PCG64.state.__get__numpy.random._pcg64.PCG64.__init__numpy.random._pcg64.PCG64DXSM.__init__numpy.random._pcg64.PCG64DXSM.state.__get__numpy.random._pcg64.PCG64DXSM.state.__set__numpy.random._pcg64.PCG64.state.__set___cython_3_0_11.cython_function_or_method 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" `_ .. [2] O'Neill, Melissa E. `"PCG: A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation" `_ Get or set the PRNG state Returns ------- state : dict Dictionary containing the information required to describe the state of the PRNG 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" `_ .. [2] O'Neill, Melissa E. `"PCG: A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation" `_ Bad call flags for CyFunction__pyx_capi____loader__loader__file__origin__package__parent__path__submodule_search_locations%.200s() needs an argumentkeywords must be stringsan integer is required__reduce_cython__name '%U' is not defined%s (%s:%d)_pcg64.pyxbuiltinscython_runtime__builtins__does not match_cython_3_0_110x10000000000000000numpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is NULL pointernumpy.random.bit_generatorBitGeneratornumpydtypeflatiterbroadcastndarraygenericnumberunsignedintegerinexactcomplexfloatingflexiblecharacterufuncSeedSequenceSeedlessSequencenumpy.random._commondoubleLEGACY_POISSON_LAM_MAXuint64_tMAXSIZEwrap_intinit numpy.random._pcg64advanceMissing type objectexactlyat leastat mostjumped__setstate_cython____init____del____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_coroutinenumpy.random._pcg64.PCG64DXSMstatenumpy.random._pcg64.PCG64 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. 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