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V:R6:R[` @ T@a@ @4 @!A !F|  !@TBG`T!@?TT T@X6@hadA?`@``4@A@7T@6:R(=oV:Rcv:RMk"r@ 3HxV:Ry@T@+TG!`(@>'tG!8@@@v:R$fl``v:RH@A@A7aT@}C) x* `T`G!@ C) x* Tr:R9R:R#9R(>t@Koa!GT,@ ?@T@B)99R9R  `pG!7@P`G!'@I`pG!7@@`@`C@р`R4T0@@@@ ?@bBG3?AT@T@6 @ TT T`T* T@р ҿp@C) x* T@р C) x* @T`G!@@р@O:R@Ka!GT?!@[l0@` @@!  ? @bBG3?T@T@ 6 @+TTTTZC)@6xT@yyw@`RRk@^@TT`G!`(@@T:R9!'`GJ`G!@u`G!@iATݿ}0@@@a ?@3@?aTU@* @@!`G0@`@@! ?@3@T3*3@#@` @<@`G!@3*B)9qܾ~@@рiҾPH+   }ě"6%b“ }cA %PHj}ě.|ě4)$j)}ƛ$ -6k} 5}||˛" |  `A_֊|ě4$$b“L}ƛcAӬ1 G1F}|%@@0J@!&A lFȞҀ(Ҁpf|D6ēg|Af#} |O4FK}-}I- A}|~ț@~ 0J_|K4Fē|Aj)( ||%@@0J@!&A tFҠ(@Rf|D6ēg|Af#} |O4FK}-}I- A}|~ț@~ 0J_|K4Fē|Aj)( ||D@Ȟ& @ЅӄǓA@pcmFb|ś@`|bB_@@$@@$@ @$@@$ @A @`_@&@@%A )_{{_Shared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling%.200s() %stakes no keyword argumentstakes exactly one argument%.200s() %s (%zd given)%.200s() keywords must be strings%s() got multiple values for keyword argument '%U'takes no argumentsBad call flags for CyFunction_cython_3_2_4base 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.__int__ returned non-int (type %.200s)__pyx_capi__%.200s does not export expected C %.8s %.200sC %.8s %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Interpreter change detected - this module can only be loaded into one interpreter per process.name__loader__loader__file__origin__package__parent__path__submodule_search_locationsan integer is requiredvalue 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 calls__qualname__ must be set to a string object__name__ must be set to a string objectdecompresszlibFailed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'.%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectinstance 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__reduce_cython__%s() got an unexpected keyword argument '%U'invalid vtable found for imported typemultiple bases have vtable conflict: '%.200s' and '%.200s'keywords must be stringsunbound method %.200S() needs an argumentjoin() result is too long for a Python stringcan't convert negative value to uint32_tvalue too large to convert to uint32_tnumpy/random/_pcg64.pyxnumpy.random._pcg64.PCG64.jump_inplacenumpy.random._pcg64.PCG64DXSM.jump_inplace__setstate_cython__exactly%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random._pcg64.PCG64DXSM.__setstate_cython__numpy.random._pcg64.PCG64.__setstate_cython__name '%U' is not definedat mostat leastjumpednumpy.random._pcg64.PCG64DXSM.jumpedMissing type objectCannot convert %.200s to %.200snumpy.random._pcg64.PCG64.jumpednumpy.random._pcg64.PCG64DXSM.__reduce_cython__numpy.random._pcg64.PCG64.__reduce_cython__advancenumpy.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.PCG64.state.__set____del__numpy.random._pcg64.PCG64DXSM.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.%dnumpy._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 1.23) 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 runtimeBitGeneratornumpy.random.bit_generatornumpydtypeflatiterbroadcastndarraygenericnumbersignedintegerunsignedintegerinexactfloatingcomplexfloatingflexiblecharacterufuncSeedlessSeedSequenceSeedlessSequencevariablenumpy.random._commonfunctioninit numpy.random._pcg64numpy.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)._cython_3_2_4.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_cython_3_2_4._common_types_metatype_pcg64numpy.random._pcg64.PCG64DXSMPCG64DXSM(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.PCG64PCG64(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]`#XB @ '"O          +?'[[ xڥRMO1M$TʡjqBR@@zJhؓ`صۛf/U9=rc~ 4N]g̛?2I#@ζ(9b"*aN"$:=#=FRaqH&yhkGIGcM D !=JkX$EWK̑l#5^S*/$࢕'qjn޹h-k\Ь׷Pb@4̷8 kAqrqllxNd#"J:v6v6Ga$щ\v 26\-cTH@۬'a`3IF&hƂŸ\gBF? GDC̅Y|],4sY[:$B$RjFUl'S} gw;Ȝ䆭UfZ_2ǠKgmgT1Z &M"ma2(r0͵v6mجnVuٮƷgU04JddPz;HY6a6-\vOV> ^)-ey)mfլ~vq^n5-Ov{ga0(\̼OlN9}g>I]Lg0000000000004u6tsrivqafgav7760c1eeri1l 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 a 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 a 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. ;Hh@d@BO`\Hem`~L܏8$8`L`zRx 0,< @HA BNd(|0Plxh$L`td$,$<0@DHXT$ ld8A BJ |8A BJAI A  t@A BH A  |A]$,pAM D \ A lT8$A@BBERDA@T A DCA@HA@A@BCR A L A J A LP 0HlA BI A T hLA BI A 4A BP A Y A M0 A0BBI A s PH B[ A ,0A BP A O A ,`A@BBB`\QE,\$A Bf A D A P<A BK@AA BCD BBY HpA C]4lAP CECV((A0BBe(A0BB_0pA0BCu A C80<A BU A D A V A lAP A $A B[ A $A B[ A $0|AQ A K$|AQ A K<,$A43C.-C,+F210/*)lPA@R0A@CDBV,DA@BBDz|A0BOPG B E A F B HUBA0C AA H A A BA MB4pA0Z A B A AP B A A A l|A DY<<Ap C BCE  @ABCB N A Y A A HDA P j yAD CI B C $ xA BJ A 8 AP BDBsp Ap B E EBc BDA XAFAp  ] AA D `AP BBBCi A m A  p d|TJ ]J$( DAL D \ A P 4d A BP A Y A M0 @xA BU A W A p `AP BCB~ A DvK A Cc A b A R A M A A A N A <D p AP BCBA A J A < (AP BCBA A J A  AI A LH B[ A  ABE EB qA] A GA AA^ \ AA M AA P AA a AA q AA A AA ABE EB oA] A GA AA^ \ AA M AA P AA a AA o AA A AA <p|lA0BH A PfIJ A A A @J@BG A DAv AA M AA pA CWTXXA@CO A EAvAMFCG AA p\ABEE A pAM A C AAa A Q \ AA MAD By GA[ a AA p AA AAR 4#ABEE A pAM A C AAa A Q \ AA MAD B| GA[ a AA n AA AAR P)<A0EDnABA0cP A GPL+<A0EDnABA0cP A G,- ACELA hQs AOA ABMC_A MHC ^ AA N AA PAAC C AA CBA vCT Fh f A @ AA B AA ~ AA F AA ,`9 ACELA hQs AOA ABMC_A MHC ^ AA N AA PAAC C AA CBA vCT Fh f A @ AA B AA ~ AA F AA EABCEY ^| A b A Q ] A Mu CD NG DH Wl Dۈ@MABCEY _| A b A Q ] A Mu CD NG DH Wl DtUAB CB E }L] UeCaa A DAKA QhC[LSLVNHV D M A KDU A U B F D LFB C VCK B A B E B J C H B F A ` B IeAB CB E }L] UeCaa A DAKA QhC[LSLVNHV D M A KDU A U B F D LFB C VCK B A B E B J C H B F A ` B I۔u AB BAA B AAACA D AAABA H $ AB BAA B AAACA D AAABA H 8  AP BBD@\\AB('B$#F&%"!  ԫĬ\ D< `2 2  H o`( ( H. o`oo$o                                                                                                                        L;>0L;>8;`FH;`FX,5EhxD5D5`5`5550= E0= Ea`D07C(j8X M p"@<D"6 685hUD,U`H 5 w > $8 @f p`6 1~ a` 4AB,<2IG@A3>@P H I6;I @0(Ix @;L!`! p`6 1~ a!``! w > $8 @fGCC: (GNU) 14.2.1 20250110 (Red Hat 14.2.1-7)GA$3a1|11GA$3a1 GA$3a1H P GA$3a11d2GA$3a1H H GA$3a1H H GA$3a1 GA$3a1P X 8`($`H.    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