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Expected %zd from C header, got %zd from PyObject__reduce_cython__%s() got an unexpected keyword argument '%U'%.200s() keywords must be strings%s() got multiple values for keyword argument '%U'invalid vtable found for imported typemultiple bases have vtable conflict: '%.200s' and '%.200s'__annotations__ must be set to a dict object__qualname__ must be set to a string object__name__ 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 callsvalue too large to convert to uint32_tcan't convert negative value to uint32_tfunction's dictionary may not be deletedsetting function's dictionary to a non-dictinstance 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__setstate_cython__exactly%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random._pcg64.PCG64.__setstate_cython__numpy.random._pcg64.PCG64DXSM.__setstate_cython__numpy/random/_pcg64.pyxnumpy.random._pcg64.PCG64.jump_inplacenumpy.random._pcg64.PCG64DXSM.jump_inplaceat mostat leastjumpednumpy.random._pcg64.PCG64.jumpedMissing type objectCannot convert %.200s to %.200snumpy.random._pcg64.PCG64DXSM.jumpednumpy.random._pcg64.PCG64.__reduce_cython__numpy.random._pcg64.PCG64DXSM.__reduce_cython__name '%U' is not definedadvancenumpy.random._pcg64.PCG64.advancenumpy.random._pcg64.PCG64DXSM.advance__init__numpy.random._pcg64.PCG64DXSM.__init__numpy.random._pcg64.PCG64.__init__numpy.random._pcg64.PCG64DXSM.state.__get__numpy.random._pcg64.PCG64.state.__get__ while calling a Python objectNULL result without error in PyObject_Callnumpy.random._pcg64.PCG64DXSM.state.__set____del__numpy.random._pcg64.PCG64.state.__set__Unable to initialize pickling for %.200sModule '_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_1_00x100000000000000000x9e3779b97f4a7c15f39cc0605cedc835numpy._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 '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_intPyObject *(PyObject *, PyObject *)C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)init numpy.random._pcg64%.200s does not export expected C function %.200s_cython_3_1_0.cython_function_or_method__reduce____module____dictoffset____vectorcalloffset____weaklistoffset__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" `_ .. [2] O'Neill, Melissa E. `"PCG: A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation" `_ 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" `_ .. [2] O'Neill, Melissa E. `"PCG: A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation" `_ E̟d߅C]`#@14 JaYd!]!1qAH BfASb %vT %vSQat<~]!1 #1q +QAH BfASb %vT %vS$l. Qa #1q 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. self._bitgen,self.rng_state cannot be converted to a Python object for picklingnumpy._core.umath failed to importnumpy._core.multiarray failed to importPCG64DXSM.__setstate_cython__state must be for a {0} RNGPCG64DXSM.__reduce_cython__numpy/random/_pcg64.pyxPCG64.__setstate_cython__PCG64.__reduce_cython__state must be a dictnumpy.random._pcg64cline_in_tracebackasyncio.coroutinesPCG64DXSM.advancePCG64DXSM.jumped__setstate_cython__generate_state__reduce_cython__bit_generatorPCG64.advance_is_coroutine_initializingPCG64.jumpedImportError__pyx_vtable__has_uint32ValueError__reduce_ex____pyx_stateisenabledTypeErrorPCG64DXSMuinteger__setstate____set_name____qualname____getstate__disableadvanceuint64__reduce____module__jumpedformatenablestatenumpyjumpsemptydtypedelta__class__PCG64__test____spec__selfseed__name____main____init____func__popincget__all__npgc?dXB ;Dg(h$4XHD`  P ,H\p0` $84L``t0@,Pt\p00d4x$ P 0 p 0 @D P @ 4 p T  tD l   0,0 D t`h$|+`.p02P4A4NpW`xoHP(4<zRx \0,x< @HA BNd|T|plPxth$$ 0 D X( l88A BJTAI A  08A BJ LDA BI A lHA BJ A @0A@BCR A L A Z A t PA BJ A tA BJ A h0 A0BBI A s< ,HI B_ A (P A0BBe@|AA B CE C_,xA BR A N A |A CZ0@hAP CEP,HtA@BBBa(xA0Cb0@A0BCu A C$ AM D \ A tA[ $4pABCN A Z A A JAAE A CAM B aAAD M AAA D AAA 8xA Bk A J A I A TTA@S0<A@CDB_,pA"!B BD{4dA0[ A B A AO B A A A 8A@BBERDA@U A EBA@KA@B ` A D[<@Ap C EF$`A BK A  p A` B BCBEhEA`  n A D CA Jtp|RLL[L$AL D \ A p A@BBF@CA@UAA@NRAd 0x AI A  dSE PAW A $ AS A K$ (AS A K$( A C] A $P $A C] A 8x A BS A Z A J A $ AS A K A A0CP B EhAA0F B J A DA0C AA E A B AA CAG AA p I B_ A , \A Bt A D A <A BKP A@BI A PCu AAA J AA N AB FBp< AP BCCD A AsOAP ICV A E A | HAB E d A CY CA IAA GAA h B ` AA J AA |0 TAB E d A CY CA IAA GAA i B ` AA J AA p dAP BCBD A AtOAP JCV A E A <$<AP BCBI A k A <d <AP BCBI A k A  AC F LG\ A ANDA AlFh A B A VB8lAC F LG\ A ANDA AlFh A B A VB\xA0GOgSBA0HAGCAP A GD A H B F A \,xA0GOgSBA0HAGCAP A GD A H B F A XJ@BG A Dj A I A B A G A AM A E B GA \\A CR\ h4A@EP A FjFI A BMG A AM A E B GAl< ABG d A M~L CA I CA  A [ A UE, ABG d A ML CA I CA  A [ A UEt9ABFH H GAE AA AYA A eAH \ `AA Q HF GG BAA W Ip Eh T@ABFH H GAE AA AYA A eAH \ `AA Q HF GG BAA W Ip Eh 4HAB E lLGeP A FoyA FSbPCGLvWCo B F A F A k A T A E A G A d A F A F A B A A A F A CWAB E lLGeP A FoyA FSbPCGLvWCo B F A F A k A T A E A G A d A F A F A B A A A F A C۔fAB BA B BHAAACA ` ABAAA H lȂAB BA B BHAAACA ` ABAAA H 8AP BCBB<@4AB&%B"!F$# pt\D jPj ؇ `H`X `````P`0``ȇ `H ` ` `؉`І`x`8 ```0` (` ` ` ` H`Љ` ` `ȉ` ```8 `` ` ` ```( P# ` `( `` `Ј ````x`p`P  `x `0`` ``p Ѕ  P `Ȉ`h ` : tFox(  0P ohooLo(;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;@\M(iDj8 `o]D^nPnH  nR  S@}xLPdRz SuxLP]8 4Br2TvGPt3@H I 60vh\@~x\ \@\0\(\0r\0r\q\q]m]o@ ]o@0]m@]mP]m`]mp]m]m] ] ]r]ot]PGQi0  iRz SuxLPi& iR  S@}xLPdGCC: (GNU) 10.2.1 20210130 (Red Hat 10.2.1-11)(xLh0 : ; A tF F h(X i i : tF& : |F; jH jTt k| kl kP @lx* lhY 0m$ Tm$ m  m  m / m(I n8w Pn n `o8 oD% oH? 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