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H2wHD$H@H5)H8qLT$LT$LT$HHPH5H89LT$H@`HHHLT$LLT$HHtiHD$H9GuGLT$H|$Q@H|$LT$HHHLT$LT$t?LT$HHuLT$HLT$HH{H5H8dLT$H@`HHHLLT$(L\$ L\$ LT$(HHthHD$H9GLT$(L\$ H|$dH|$L\$ LT$(HHHT$T$L\$ LT$(LT$ L\$eL\$LT$ H}HH58E1<2LT$HLT$Hf։T1ielHH=quLJ pDAVAй+S+f.1iel  HHpuApApLJ pEM1E1fHcƒL1i fIcA3 D4EADA o~ E9DNAuoHc fDHc‹LL1ieX]A3)ЃApu u[A^ffoJfoJHHfo%Jfo-JffooHHo$ffffoffrffofffof@H9uf% у3 ߰1ʋʁ Љƒ3 %߰1Љ% ȉƒ3 %߰1H Ho@oHffffofofrffodfff@H9u LJ % Љƒ30%߰1Љ f f.HHff.HVHH)Hv+oHH= u닆 fDHH= uff. BHcH Q% Љ34LeHA3 Dn(HcH Q% Љ3tot H HLJ % Љƒ303 AWp AVAU)ATUSD D)A9\HcMcȽoLND)N,MM)INdE1H@CoCo fCII9uAAtQFC,McHc,B1,EZD9~3B,EAHcMcFD1A9}FEMcMcFF1D9LcҽoENO$)N<I)N?L|$N&DMM)I\A9AEFAAN&@A)9EME1EAIfDCoCofCIM9uEAEE9tSFGdMcMcF$F1$EZA9}3E$ EAMcMcFF1A9}FEMcMcFF19¸MT*oLcځpApLML@IJDLH)Ho)ЃApJ@A)ӁoDO1EAIfDoAofAHL9uD؃A9tO F HMcF D1 Bot0D DMcHB1BntAHcMcB1[]A\A]A^A_@mHcMcȽoLN)N,MM)INdE1HCoCo$fCII9uAAtQFC,McHc,B1,EZD9~3B,EAHcMcFD1D9~FEMcMcFF1D9 LcAoDNKl E)N@L4A)LL)H9AA@AAֽA)9DMMNpA$ oHHH~ ADŽ$ HLHH]A\NHHtakes no arguments%.200s() %s (%zd given)takes exactly one argumentBad call flags for CyFunctiontakes no keyword arguments%.200s() %san integer is required__loader__loader__file__origin__package__parent__path__submodule_search_locationsneeds an argumentkeywords must be strings__setstate_cython__exactlynumpy/random/_mt19937.pyxname '%U' is not defined__reduce_cython__at leastat mostjumpedMissing type object__init___legacy_seeding__del__builtinscython_runtime__builtins__does not match_cython_3_1_04294967295numpy.random.bit_generatorBitGeneratornumpydtypeflatiterbroadcastndarraygenericnumberunsignedintegerinexactcomplexfloatingflexiblecharacterufuncSeedSequenceSeedlessSequencenumpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is NULL pointernumpy.import_arrayinit numpy.random._mt19937__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_coroutinenumpy.random._mt19937.MT19937state__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)value too large to convert to intInterpreter 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 argument%.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 multiple values for keyword argument '%U'invalid vtable found for imported type%s() got an unexpected keyword argument '%U'__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-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 while calling a Python objectNULL result without error in PyObject_Callvalue too large to convert to uint32_tcan't convert negative value to uint32_t%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random._mt19937.MT19937.__setstate_cython__numpy.random._mt19937.MT19937.jump_inplacenumpy.random._mt19937.MT19937.state.__get__numpy.random._mt19937.MT19937.__reduce_cython__numpy.random._mt19937.MT19937.jumpedCannot convert %.200s to %.200snumpy.random._mt19937.MT19937.__init__hasattr(): attribute name must be stringnumpy.random._mt19937.MT19937._legacy_seedingnumpy.random._mt19937.MT19937.state.__set__Module '_mt19937' has already been imported. Re-initialisation is not supported.compile time Python version %d.%d of module '%.100s' %s runtime version %d.%dbase 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 typemultiple bases have vtable conflict: '%.200s' and '%.200s'Unable to initialize pickling for %.200s_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 runtime../numpy/__init__.cython-30.pxd_cython_3_1_0.cython_function_or_method MT19937(seed=None) Container for the Mersenne Twister 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. Attributes ---------- lock: threading.Lock Lock instance that is shared so that the same bit git generator can be used in multiple Generators without corrupting the state. Code that generates values from a bit generator should hold the bit generator's lock. Notes ----- `MT19937` provides a capsule containing function pointers that produce doubles, and unsigned 32 and 64- bit integers [1]_. These are not directly consumable in Python and must be consumed by a `Generator` or similar object that supports low-level access. The Python stdlib module "random" also contains a Mersenne Twister pseudo-random number generator. **State and Seeding** The `MT19937` state vector consists of a 624-element array of 32-bit unsigned integers plus a single integer value between 0 and 624 that indexes the current position within the main array. The input seed is processed by `SeedSequence` to fill the whole state. The first element is reset such that only its most significant bit is set. **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, MT19937, SeedSequence >>> sg = SeedSequence(1234) >>> rg = [Generator(MT19937(s)) for s in sg.spawn(10)] Another method is to use `MT19937.jumped` which advances the state as-if :math:`2^{128}` random numbers have been generated ([1]_, [2]_). This allows the original sequence to be split so that distinct segments can be used in each worker process. All generators should be chained to ensure that the segments come from the same sequence. >>> from numpy.random import Generator, MT19937, SeedSequence >>> sg = SeedSequence(1234) >>> bit_generator = MT19937(sg) >>> rg = [] >>> for _ in range(10): ... rg.append(Generator(bit_generator)) ... # Chain the BitGenerators ... bit_generator = bit_generator.jumped() **Compatibility Guarantee** `MT19937` makes a guarantee that a fixed seed will always produce the same random integer stream. References ---------- .. [1] Hiroshi Haramoto, Makoto Matsumoto, and Pierre L'Ecuyer, "A Fast Jump Ahead Algorithm for Linear Recurrences in a Polynomial Space", Sequences and Their Applications - SETA, 290--298, 2008. .. [2] Hiroshi Haramoto, Makoto Matsumoto, Takuji Nishimura, François Panneton, Pierre L'Ecuyer, "Efficient Jump Ahead for F2-Linear Random Number Generators", INFORMS JOURNAL ON COMPUTING, Vol. 20, No. 3, Summer 2008, pp. 385-390. Get or set the PRNG state Returns ------- state : dict Dictionary containing the information required to describe the state of the PRNG jumped(jumps=1) Returns a new bit generator with the state jumped The state of the returned bit generator is jumped as-if 2**(128 * jumps) random numbers have been generated. Parameters ---------- jumps : integer, positive Number of times to jump the state of the bit generator returned Returns ------- bit_generator : MT19937 New instance of generator jumped iter times Notes ----- The jump step is computed using a modified version of Matsumoto's implementation of Horner's method. The step polynomial is precomputed to perform 2**128 steps. The jumped state has been verified to match the state produced using Matsumoto's original code. References ---------- .. [1] Matsumoto, M, Generating multiple disjoint streams of pseudorandom number sequences. Accessed on: May 6, 2020. http://www.math.sci.hiroshima-u.ac.jp/m-mat/MT/JUMP/ .. [2] Hiroshi Haramoto, Makoto Matsumoto, Takuji Nishimura, François Panneton, Pierre L'Ecuyer, "Efficient Jump Ahead for F2-Linear Random Number Generators", INFORMS JOURNAL ON COMPUTING, Vol. 20, No. 3, Summer 2008, pp. 385-390. _legacy_seeding(seed) Seed the generator in a backward compatible way. For modern applications, creating a new instance is preferable. Calling this overrides self._seed_seq Parameters ---------- seed : {None, int, array_like} Random seed initializing the pseudo-random number generator. Can be an integer in [0, 2**32-1], array of integers in [0, 2**32-1], a `SeedSequence, or ``None``. If `seed` is ``None``, then fresh, unpredictable entropy will be pulled from the OS. 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