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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)%.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.value 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 objectFailed to import '%.20s.decompress' - cannot initialise module strings. 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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 BaseExceptioninvalid vtable found for imported typeunbound method %.200S() needs an argument%s() got an unexpected keyword argument '%U'numpy.random._philox.Philox.jump_inplacecan't convert negative value to uint64_t%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random._philox.Philox.__setstate_cython__can't convert negative value to uint32_tvalue too large to convert to uint32_tnumpy.random._philox.Philox.advancenumpy.random._philox.Philox.jumpedCannot convert %.200s to %.200snumpy.random._philox.Philox.__reduce_cython__numpy.random._philox.Philox.state.__get__ while calling a Python objectNULL result without error in PyObject_Callnumpy.random._philox.Philox.state.__set__join() result is too long for a Python stringnumpy.random._philox.Philox.__init__Module '_philox' 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 1.23) 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 runtime../numpy/__init__.cython-30.pxd_cython_3_2_4.cython_function_or_method_cython_3_2_4._common_types_metatypePhilox(seed=None, counter=None, key=None) -- Container for the Philox (4x64) 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. counter : {None, int, array_like}, optional Counter to use in the Philox state. Can be either a Python int (long in 2.x) in [0, 2**256) or a 4-element uint64 array. If not provided, the RNG is initialized at 0. key : {None, int, array_like}, optional Key to use in the Philox state. Unlike ``seed``, the value in key is directly set. Can be either a Python int in [0, 2**128) or a 2-element uint64 array. `key` and ``seed`` cannot both be used. 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 ----- Philox is a 64-bit PRNG that uses a counter-based design based on weaker (and faster) versions of cryptographic functions [1]_. Instances using different values of the key produce independent sequences. Philox has a period of :math:`2^{256} - 1` and supports arbitrary advancing and jumping the sequence in increments of :math:`2^{128}`. These features allow multiple non-overlapping sequences to be generated. `Philox` 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. **State and Seeding** The `Philox` state vector consists of a 256-bit value encoded as a 4-element uint64 array and a 128-bit value encoded as a 2-element uint64 array. The former is a counter which is incremented by 1 for every 4 64-bit randoms produced. The second is a key which determined the sequence produced. Using different keys produces independent sequences. The input ``seed`` is processed by `SeedSequence` to generate the key. The counter is set to 0. Alternately, one can omit the ``seed`` parameter and set the ``key`` and ``counter`` directly. **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, Philox, SeedSequence >>> sg = SeedSequence(1234) >>> rg = [Generator(Philox(s)) for s in sg.spawn(10)] `Philox` can be used in parallel applications by calling the :meth:`jumped` method to advance the state as-if :math:`2^{128}` random numbers have been generated. Alternatively, :meth:`advance` can be used to advance the counter for any positive step in [0, 2**256). When using :meth:`jumped`, all generators should be chained to ensure that the segments come from the same sequence. >>> from numpy.random import Generator, Philox >>> bit_generator = Philox(1234) >>> rg = [] >>> for _ in range(10): ... rg.append(Generator(bit_generator)) ... bit_generator = bit_generator.jumped() Alternatively, `Philox` can be used in parallel applications by using a sequence of distinct keys where each instance uses different key. >>> key = 2**96 + 2**33 + 2**17 + 2**9 >>> rg = [Generator(Philox(key=key+i)) for i in range(10)] **Compatibility Guarantee** `Philox` makes a guarantee that a fixed ``seed`` will always produce the same random integer stream. Examples -------- >>> from numpy.random import Generator, Philox >>> rg = Generator(Philox(1234)) >>> rg.standard_normal() 0.123 # random References ---------- .. [1] John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw, "Parallel Random Numbers: As Easy as 1, 2, 3," Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC11), New York, NY: ACM, 2011. Get or set the PRNG state Returns ------- state : dict Dictionary containing the information required to describe the state of the PRNG needs an argument%.200s() %stakes no keyword argumentstakes exactly one argument%.200s() %s (%zd given)takes no argumentsBad call flags for CyFunction_cython_3_2_4__pyx_capi____loader__loader__file__origin__package__parent__path__submodule_search_locationsan integer is requireddecompresszlib80000000000000000000000000keywords must be stringsnumpy/random/_philox.pyx__setstate_cython__exactlyadvanceat mostat leastjumpedMissing type objectname '%U' is not defined__reduce_cython____del____init__builtinscython_runtime__builtins__does not matchBitGeneratornumpy.random.bit_generatornumpydtypeflatiterbroadcastndarraygenericnumberunsignedintegerinexactcomplexfloatingflexiblecharacterufuncSeedlessSeedSequenceSeedlessSequencevariablenumpy.random._commonfunctionnumpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is NULL pointernumpy.import_arrayinit numpy.random._philox__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._philox.PhiloxstatexڅSKkA  xİ!*ze#yRv2=f xqsc)9џ`̺@WWWkuv6>n ik-V>mIn YKdN3fڸn>npǪ**dQ+Gɋs&Rڱ.bl1鵠+]Ju0qW܅VC4.Y'wp ˤ8JׁUg_[u&koTֲ`0ATUfvu7<r%pz:$$0|D5;8r{؛$!as%n; @0Np*!@$VT Hh+(r1(?rwF4,qy'0^)xqR|m|4Ǔ³.Bxc2wa '" O          x?'? 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 : Philox 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 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 : Philox New instance of generator jumped iter times @<;|N(X xK+Prh( !@""""#(#(X#@#p$%x'<'P'd'(X(x((x)P)h8,h,X-.Ph/l0081182 2, 3T 5 8P 9d < H=X X? 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