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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)an integer is requiredvalue too large to convert to intInterpreter change detected - this module can only be loaded into one interpreter per process.name__loader__loader__file__origin__package__parent__path__submodule_search_locationsShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompilingkeywords must be stringsunbound 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__qualname__ must be set to a string object__name__ must be set to a string 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-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_tnumpy/random/_mt19937.pyxnumpy.random._mt19937.MT19937.jump_inplace__setstate_cython__exactly%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random._mt19937.MT19937.__setstate_cython__name '%U' is not definednumpy.random._mt19937.MT19937.state.__get____reduce_cython__numpy.random._mt19937.MT19937.__reduce_cython__at mostat leastjumpednumpy.random._mt19937.MT19937.jumpedMissing type objectCannot convert %.200s to %.200s__init__numpy.random._mt19937.MT19937.__init__hasattr(): attribute name must be stringnumpy.random._mt19937.MT19937._legacy_seeding_legacy_seedingnumpy.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.%d_cython_3_1_04294967295numpy.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_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 runtime../numpy/__init__.cython-30.pxdnumpy.import_arrayinit numpy.random._mt19937_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_mt19937numpy.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. .. 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