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The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__int__ returned non-int (type %.200s)C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)%.200s does not export expected C variable %.200sInterpreter change detected - this module can only be loaded into one interpreter per process.Shared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompilingunbound method %.200S() needs an argumentvalue too large to convert to int__annotations__ must be set to a dict object__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function calls__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function callsfunction's dictionary may not be deletedsetting function's dictionary to a non-dict__qualname__ must be set to a string object__name__ must be set to a string object%.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%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%.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'numpy.random._pcg64.PCG64.jump_inplacenumpy.random._pcg64.PCG64DXSM.jump_inplacecan't convert negative value to uint32_tvalue too large to convert to uint32_t%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random._pcg64.PCG64.__setstate_cython__numpy.random._pcg64.PCG64DXSM.__setstate_cython__numpy.random._pcg64.PCG64.jumpedCannot convert %.200s to %.200snumpy.random._pcg64.PCG64DXSM.jumpednumpy.random._pcg64.PCG64.__init__numpy.random._pcg64.PCG64DXSM.__init__numpy.random._pcg64.PCG64DXSM.__reduce_cython__numpy.random._pcg64.PCG64.__reduce_cython__numpy.random._pcg64.PCG64DXSM.advancenumpy.random._pcg64.PCG64.advancenumpy.random._pcg64.PCG64.state.__get__numpy.random._pcg64.PCG64DXSM.state.__get__ while calling a Python objectNULL result without error in PyObject_Callnumpy.random._pcg64.PCG64.state.__set__numpy.random._pcg64.PCG64DXSM.state.__set__Unable to initialize pickling for %.200sModule '_pcg64' has already been imported. Re-initialisation is not supported.compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d0x9e3779b97f4a7c15f39cc0605cedc835_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).PyObject *(PyObject *, PyObject *)C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)%.200s does not export expected C function %.200s_cython_3_1_0.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" `_ 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?d<@;a0/ƸDX =$ p ̾ / u<tXl`0 @`p  4H``|P@`0Dd00Tl p0 < `\ | @ T D X @  , `P d  h0 L0dP $`-6 9`; IVpfPv(xP$hP|zRx $ثFJ w?;*3$"D0OXl{ld4Q{cHT` \  X  T4`&DaLxD` D h&Dax`@\ cA\ d@AGG0k GAF d DAG Z CAJ (047AU J V$PTMAU J \ D Lx|4BDD Y DBE ABPzYF A |A@ G b F 4}BEE A(D0a(D BBB@AdN X jA C H H |X)Ac${pBIC ^ABDð'BEE L(A0A8E@8D0A(B BBB8~BEH A(A0b(D BBB8DaBBE A(A0K(D BBB4BDD  GBG AAB <Dq K K E BA@xBBE B(D0D8Dc 8G0A(B BBBF R 8D0A(B BBBA Q 8I0A(B BBBM @pBAD G DBD ] AEE _ DBK @}DE G (ADD U AAH (ADD U AAH (Do E o I HhDo E ohLhDo E olCBBB E(D0G8X`hApBxAAABAABL`T8D0A(B BBBGD`BHBEE E(A0D8E@[8D0A(B BBBP\I BHE A(D0JGfA\0D(A BBB tD0Z B I G hBBD  EBH T BBP F EBK f BBF Q EBH t BBH x@'BBB E(A0A8D@_ 8A0A(B BBBF _ 8A0A(B BBBG _ 8F0A(B BBBB $cBDA XABH/FBBB E(A0A8G`'8A0A(B BBB0 (D BAG U DBK p 4z` GBBB B(D0D8G` 8A0A(B BBBG  8C0A(B BBBH  e|h dh  \Do E C E < x`P tBBB B(D0D8FP 8A0A(B BBBC N 8A0A(B BBBH L BBB D(GP (A BBBH f (A BBBC L BBB D(GP (A BBBH f (A BBBC T D` D 4p BAD M DBF @ AEJ  zYF A l L|BIB E(A0A8DW_A^ 8C0A(B BBBH W_Al8 \|BIB E(A0A8DW_A^ 8C0A(B BBBH W_A8 l_BBA A(D0U (D ABBH @ !bBB A(A0D@S 0D(A BBBF $( 1WBDA LAB@P TBBE A(A0D@z 0D(A BBBD X BIB B(D0A8D|HYAi 8D0A(B BBBD X  BIB B(D0A8D|HYAi 8D0A(B BBBD XLBIB E(A0D8DHYAf 8D0A(B BBBI XBIB E(A0D8DHYAf 8D0A(B BBBI Dp$BBA Q DBD T(R0`(C A BBB DL&BBA Q DBD T(R0`(C A BBB l)k BIE B(A0A8DGW_Ai 8D0A(B BBBD W_Al6k BIE B(A0A8DGW_Ai 8D0A(B BBBD W_ALtCBBB B(A0A8DY 8D0A(B BBBD LRBBB B(A0A8DY 8D0A(B BBBD L bWBBB B(A0A8D7 8A0A(B BBBA Ld0WBBB B(A0A8D7 8A0A(B BBBA HBBE B(A0A8DP8D0A(B BBB\BBB B(A0A8GXiHHL@8A0A(B BBB`? Tt1EEE B(A0D8 0A(B BBBB A0G(B BBB@hEBBB B(A0A810A(B BBB@t=BBB B(A0A8)0A(B BBBTpgh̢5|+nPn  `r` `0``0`P``p`Ȏ` ` `H ` ````h ```^` X`R`9 2`А ` `` `8 `ݐ` ```x +`L` `  ` ``F`( # Ѝ`P ِ`h `(`؏ ` ``X``` P ȏ ` `p` `@`  ؎ x `` ` @ Jo`  0x8 oXoo@o@6@F@V@f@v@@@@@@@@@AA&A6AFAVAfAvAAAAAAAAABB&B6BFBVBfBvBBBBBBBBBCC&C6CFCVCfCvCCCCCCCCCDD&D6DFDVDfDvDDDDDDDDDEE&E6EFEVEfEvEEEEEEEEEFF&F6FFFVFfFvFFFFFFeM4zB v2|G0z3w@`HI6puS S S@S0T(#Tu@w,Tu@w4Tu>TuGTpqpTT`s^T`sgTqtTqTqTqTqTqT@v@T@v@TT s T@STJTTD fpq@оqU&0qQ0Q`|IQsQUDrrprU`0qQ`Q0@IQ0sQ@sQIQQ`|Q0sQ@IQ0Q0@Q`GCC: (GNU) 10.2.1 20210130 (Red Hat 10.2.1-11)8`@Xx0 @ @ F J P(@ nO: n{f pod oQ @p{ pc 0q\ Pq pq  q  q  q q&=X q pr&  rx; s@\ `sAv s Pu pu u7 uM v F @vz9 v|\ G}u @wA wj z) ?Hp H' I~ TJa+3 0zM {k KB |  }  @ - phK he K MG MM   eG N j P  ' Oc ?PF  0" ЈzK PGq px   Д-  Y  r  z  @|   |&  @_H  !i  QW  Х  `  0  - C ]  о @ 8    0;  k o    x p  0k * h@ `[ Xq P      Rh `W*D] &W0(  Q T H@ Zk @|@>o` @ 3  o    X h`@   - r;  M 0o  0 P  p Ȏ  /  A H T _ o {   h    ^  X  R 9( 27 А D O f r   8  ݐ     x  + L    $  1 < FJ (s # Ѝ P ِ h  (  ؏   ) ? 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