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$LD$H/H_H5.H8QL $LD$ LT$H_H5LD$L $f.HH@HHxuH@HHHHHHH9rIHPHOHHHHH;PvYH@HHOHPHHHHH;PvZH@HHWHPH@HHOHPHHHHH;PwHtHPHOHHHHH;PwHWHPHtHH__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)%.200s does not export expected C variable %.200sC variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)%.200s does not export expected C function %.200sC function %.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%.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%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'calling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptioncan't convert negative value to uint64_tcan't convert negative value to uint32_tvalue too large to convert to uint32_tnumpy.random._philox.Philox.advanceModule '_philox' has already been imported. Re-initialisation is not supported.compiletime version %s of module '%.100s' does not match runtime version %s340282366920938463463374607431768211456Unable to initialize pickling for %sinvalid vtable found for imported typePyObject *(PyObject *, PyObject *)PyArrayObject *(PyObject *, PyObject *, PyObject *, PyObject *)_ARRAY_API is not PyCapsule objectmodule 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 while calling a Python objectNULL result without error in PyObject_Callnumpy.random._philox.Philox.__setstate_cython__numpy.random._philox.Philox.__reduce_cython__numpy.random._philox.Philox.jump_inplacenumpy.random._philox.Philox.jumpedCannot convert %.200s to %.200s%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random._philox.Philox.state.__get__numpy.random._philox.Philox.__init__numpy.random._philox.Philox.state.__set__ Philox(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 ``jumped`` method to advances the state as-if :math:`2^{128}` random numbers have been generated. Alternatively, ``advance`` can be used to advance the counter for any positive step in [0, 2**256). When using ``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 __pyx_capi__name__loader__loader__file__origin__package__parent__path__submodule_search_locationsan integer is requiredname '%U' is not definednumpy/random/_philox.c%s (%s:%d)_philox.pyx%d.%dbuiltinscython_runtime__builtins__numpy.random.bit_generatorBitGeneratornumpydtypeflatiterbroadcastndarraygenericnumberunsignedintegerinexactcomplexfloatingflexiblecharacterufuncSeedSequenceSeedlessSequencenumpy.random._commondoubleLEGACY_POISSON_LAM_MAXuint64_tMAXSIZEwrap_intint_to_arraynumpy.core._multiarray_umath_ARRAY_API_ARRAY_API not found_ARRAY_API is NULL pointer__init__.pxdnumpy.import_arrayinit numpy.random._philoxstringsourceat leastat mostjumpedMissing type object__init____del__numpy.random._philox.Philoxstateadvance__reduce_cython____setstate_cython__D0DCCCl4̯hȯ(_W3|Yself._bitgen,self.rng_state cannot be converted to a Python object for picklingseed and key cannot be both usednumpy.core.umath failed to importnumpy.core.multiarray failed to importstate must be for a {0} PRNGstate must be a dictcline_in_traceback__setstate_cython__generate_state__reduce_cython__bit_generatorImportError__pyx_vtable__has_uint32buffer_posValueError__reduce_ex__TypeErroruinteger__setstate____getstate__counteradvanceuint64__reduce____import__formatbufferPhiloxstaterangenumpyjumpsemptydtype__class____test__stepseed__name____main____init__keyget__all__np<@;D'h`!o!l"\i##G%S&&= =>H>h>>?H8AA8B(BPXEHF IdJJKOp(RS8ZX[\$x]T8bi 8T x , zRx $FJ w?;*3$"DX<0Xt<&Dap< <A@ G h H $$WBDA LABDSBEL E(A0A8E@8D0A(B BBBD BEL E(A0A8E@8D0A(B BBB8` ~BEH A(A0b(D BBB8 `BBE A(A0J(D BBB(;BAG U DBK <H<AD A AI G AH V AI G AC PD! BHE A(D0JHfA\0D(A BBBo"aoq8=D` D =x$>At K ` H Qd >`BBB B(D0D8Hh 8A0A(B BBBF  8F0A(B BBBA Pt@BBA s EBI A BBC D BBH H IBM T4A4PBB B(A0A8Z0A(M BHBDP80 DtBEG e EBF lEB8ThDBEA G(G0s (D ABBF <D/AD h AJ F AA J AM G AH XE EBB B(A0A8q 0A(g BBBD w 0A(G EEBE D,H EBB B(A0A8 0A(O BBBB 4thK|BAD B DBA @ AEJ dLBBE E(D0A8F 8A0A(B BBBH d 8A0A(B BBBJ LRqBBA D(J0 (D ABBA \ (D ABBK HdaBBB B(A0D8GB8A0A(B BBB,SBHH h CBA ,lTBHH h CBA XUBBB E(A0D@w 0A(B BBBH  0C(B BBBD TlYBBB E(A0A8DPeXI`YXFP_ 8D0A(B BBBF H`4BBB B(A0A8D`P 8D0A(B BBBE \wABBB B(A0A8G 8D0A(B BBBG WRFHp̕+BBB B(A0A8D`w 8D0A(B BBBF  3 XdRPR0= 0  =(=o`  @pp' oooo8=60F0V0f0v00000000011&161F1V1f1v11111111122&262F2V2f2v22222222233&363F3V3f3v33333333344&464F4V4f4v44444444455&565F5V5f5v555555 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 an 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 XXXxX@Y( 8Y 0Y (Y  Yh YYYYXXX X8 XXXX.X(XXXX XH X X X"xXpXhXp `X` XXPXHX@'@X"8X80X(X  XXxXVX!X`PW WWWWWpWQWH WW 0W`S8l;VDpVUU@UR~@ VvHn CPqppGCC: (GNU) 10.2.1 20210130 (Red Hat 10.2.1-11) R0@ R&lYY S S 5W/`X> '6Z $7w !8~ 8`CY S PT 9  ;a<Sf U pVx V W` Z5 [4c @^txY ^Y p_/ `  c  f|/ `hBYWpVlhVXY nqxWpYW*W9hYf l;aYJ WWWWWpWhX! Y6XYO0X_(YsPY@YHYXhW`WXWW2PWAYOYgHWvX@W8WHX0W@X& WR 0r8X8YXX(X XW W* `YU Y Y xY PX XX 0Y Y# Y/ W>  pp|  Pq Y  v X W J. X>  ~4r V V X V V W X V V6 VK Ve Vz V V xV X pX W X X X. WA  At X X xX X V V X J  @+DXRWxXRh (     h      - X @ 8 N i y . 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