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A.`B7 @7 #!`A#@@7`@7`#@7Ѡ"@T@.+0 Ҹ#RV(R Հ@7р  @7 ` es SD[Eu*6#R*R`@7` V֢A֮A`@@@T%@*%5?|%`@7`#R%Rq#R(Rc3=Q.Ұ`#R)Rc3=Bt#R6*RxG!*@z,xG!*@"RR#R*Rp9 lA!@@BH@@?@"H@@@?!@"H@A@? @7  @G?T@@ @1@T@@1@T@@7р`cB@7!`@7` @@y7@@֢AA @7 `n@@7@ #R1Rk'"$R3RdQ"RRH8$R4R@ "RVR@@!`AAT{`@֢@"A!T`@7` 4*1T@7р + @sH6@#RR8#R R8#RV R8#R R"R!R|G!=@.ҩ#RA%RnF=xG!*@eLln"IF@$R!,R$Rv,RZ$R,RS#RRL$Rv/RF#R0RbJsH$RA8RW$R8RsHN}X sH$R8R"8#R R#RV!R8#R!RS[s {{_Interpreter change detected - this module can only be loaded into one interpreter per process.name__loader__loader__file__origin__package__parent__path__submodule_search_locationsinvalid vtable found for imported type%.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__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)intan integer is requiredvalue too large to convert to uint32_tcan't convert negative value to uint32_tvalue too large to convert to int while calling a Python objectNULL result without error in PyObject_Callinstance 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'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'name '%U' is not definednumpy/random/_mt19937.cpython-312-aarch64-linux-gnu.so.p/numpy/random/_mt19937.pyx.c%s (%s:%d)at mostat least__init___mt19937.pyxnumpy.random._mt19937.MT19937.__init__%.200s() takes %.8s %zd positional argument%.1s (%zd given)_legacy_seedinghasattr(): attribute name must be stringnumpy.random._mt19937.MT19937._legacy_seedingMissing type objectCannot convert %.200s to %.200sexactlynumpy.random._mt19937.MT19937.jump_inplacejumpednumpy.random._mt19937.MT19937.jumped__reduce_cython__numpy.random._mt19937.MT19937.__reduce_cython__numpy.random._mt19937.MT19937.__setstate_cython____setstate_cython__numpy.random._mt19937.MT19937.state.__get__numpy.random._mt19937.MT19937.state.__set____del__Module '_mt19937' has already been imported. Re-initialisation is not supported.builtinscython_runtime__builtins__does not matchnumpy.random._mt19937compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d4294967295numpy.random.bit_generatorBitGeneratorbase 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 %.200stypenumpydtypeflatiterbroadcastndarraygenericnumbersignedintegerunsignedintegerinexactfloatingcomplexfloatingflexiblecharacterufuncSeedSequenceSeedlessSequencenumpy.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 compiled against API version 0x%x but this version of numpy is 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__init__.cython-30.pxdnumpy.import_arrayinit numpy.random._mt19937state Get or set the PRNG state Returns ------- state : dict Dictionary containing the information required to describe the state of the PRNG numpy.random._mt19937.MT19937 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. _mt19937߰߰߰߰@߰CImportErrorMT19937Seed array must be 1-dSeed must be between 0 and 2**32 - 1Seed must be non-emptyTypeErrorValueError*?__all__anyasarrayastypebit_generatorcasting__class__cline_in_tracebackdisabledtypeenable__enter____exit__formatgcgenerate_stateget__getstate____import__index__init___initializingint64isenabledjumpskey__main____name__npnumpynumpy.core.multiarray failed to importnumpy.core.umath failed to importoperatororderpos__pyx_state__pyx_vtable__range__reduce____reduce_cython____reduce_ex__safeseedself._bitgen cannot be converted to a Python object for pickling__setstate____setstate_cython____spec__squeezestatestate is not a legacy MT19937 statestate must be a dictstate must be for a {0} PRNG__test__uint32unsafezeros 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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