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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.__%.4s__ returned non-%.4s (type %.200s)value too large to convert to uint32_tcan't convert negative value to uint32_t%.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 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)can't convert negative value to uint64_tvalue too large to convert to intInterpreter change detected - this module can only be loaded into one interpreter per process.%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 BaseExceptionnumpy.random._philox.Philox.state.__get__numpy.random._philox.Philox.__setstate_cython__numpy.random._philox.Philox.__reduce_cython__numpy.random._philox.Philox.advanceCannot convert %.200s to %.200snumpy.random._philox.Philox.jumped%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random._philox.Philox.jump_inplaceModule '_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%xFATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimenumpy.random._philox.Philox.state.__set__numpy.random._philox.Philox.__init__ 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 an integer is required__pyx_capi__name '%U' is not definedcannot import name %Sname__loader__loader__file__origin__package__parent__path__submodule_search_locationsnumpy/random/_philox.c%s (%s:%d)_philox.pyxstringsourceat mostat leastjumpedMissing type object%d.%d%sbuiltinscython_runtime__builtins____init__.pxdBitGeneratornumpy.random._bit_generatortype.pxdnumpydtypeflatiterbroadcastndarrayufunc_bit_generator.pxdSeedSequenceSeedlessSequencedoubleLEGACY_POISSON_LAM_MAXuint64_tMAXSIZEwrap_intint_to_arraynumpy.core._multiarray_umath_ARRAY_API_ARRAY_API not found_ARRAY_API is NULL pointernumpy.import_arrayinit numpy.random._philoxnumpy.random._common__del____init__numpy.random._philox.Philoxstateadvance__reduce_cython____setstate_cython__l@l0llPlĸll|xFormat string allocated too short.self._bitgen,self.rng_state cannot be converted to a Python object for picklingseed and key cannot be both usednumpy.core.umath failed to importNon-native byte order not supportedFormat string allocated too short, see comment in numpy.pxdunknown dtype code in numpy.pxd (%d)numpy.core.multiarray failed to importstate must be for a {0} PRNGstate must be a dictcline_in_traceback__setstate_cython__dummy_threadinggenerate_state__reduce_cython__bit_generatorRuntimeErrorImportError__pyx_vtable__has_uint32buffer_posValueErrorthreading__reduce_ex__TypeErroruinteger__setstate____getstate__counteradvanceuint64__reduce____import__formatbufferPhiloxstaterangenumpyjumpsemptydtype__class____test__stepseed__name____main____init__Lockkeyget__all__np<;7p.3 ;455pq667 9h:U:::(6;P;x;;>>XyADB`B b cPd8dPdhde(fXfghPii0pjn`q tH u px y0 { 0 8 0x ` P  8 zRx $,pFJ w?;*3$"DaS\1[tb&Da(b(@bTHbBEE A(A0G@j 0A(A BBBI { 0A(A BBBE [1A,42cBID D0K AAB,dbAD  AK w AA pcD 2BEH D(D0G0A(A BBB$2GADA |AC<2DBED D(G`'(A ABBD\3BLE E(D0A8DP8A0A(B BBBD4BLE E(D0A8DP8A0A(B BBBN5O$5?AGA pDA4,5ZBGD ~ ABE CAB$d5HADA |DA$5WAGD0EDA 6A[$XaMD ~A5;Ay<a3AD A AI G AH  AH ^ AI ,T5YBDG D0C AAB45BEG D(D@p(A ABB,C6TBAA LAB<g7BEH A(D0g(D BBB,awD` A L8bwDb A _ A $lbAt B \ A GLE7BEB B(A0E8G8A0A(B BBBTbIEE B(A0A8Q0A(B BPEV8\<q9BBA D(E0{ (A ABBE ^ (G ABBE A(A ABBL9|BEA D(E0O (A ABBE J(A ABBLHe\bEB B(A0A8 0A(S KVFF D<XhdBEE B(A0A8Z 0A(B BBBM TkBAA z ABG D ABI A OBN A DBE LlBBB E(D0A8Gp 8D0A(B BBBG <,xnOBBA A(D@ (A ABBI Llo!BED A(G@ (D ABBG l (D ABBK dhq@BBB E(D0C8F 8A0A(B BBBF / 8A0A(B BBBG L$ @weBBB E(A0A8D`J 8D0A(B BBBH <t `BBA A(D0 (C ABBE < BBA A(D0 (C ABBE , BAD  ABB d$ pBBE B(A0A8G` 8C0A(B BBBA  8D0A(B BBBH D (BBB D(A0D@ 0A(A BBBJ L 6 BBB B(A0A8J8A0A(B BBBL$ BBB B(A0D8DP 8A0A(B BBBG dt pBBB B(A0D8G 8C0A(B BBBD  8F0A(B BBBA  /U   ff(! x1 !!o   !h)p oooo0!11111122&262F2V2f2v22222222233&363F3V3f3v33333333344&464F4V4f4v44444444455&565F5V5f5v55555555566&666F6V6f6v666666666 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 big 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 /!.!00!/!01!`< 1!#1! 1!0! $0!0!0! 0!1 0! 0!0!f0! 0!p0!`0! P0! @0!P00!^ 0!^0!0!0!/!/!/!/!Q /! /! /! /!/!p/!`/! P/! @/!0/! /!'/!"/!.!.!u .!.!#.!.!!.!@P.!D p.!p`.!P.!@.!00.! .!.! .! .!n-!; -!%-,!(!?E[lDplk*!`*!pgwP0`!}!PGCC: (GNU) 4.1.2 20080704 (Red Hat 4.1.2-55)GCC: (GNU) 4.8.2 20140120 (Red Hat 4.8.2-15)GCC: (GNU) 4.1.2 20080704 (Red Hat 4.1.2-55).shstrtab.note.gnu.build-id.gnu.hash.dynsym.dynstr.gnu.version.gnu.version_r.rela.dyn.rela.plt.init.text.fini.rodata.eh_frame_hdr.eh_frame.init_array.fini_array.jcr.data.rel.ro.dynamic.got.got.plt.data.bss.comment $oH(  0 8oEo Tp^Bh)h)hx1x1c11pn77t z`$   $ !! ! (!(0!0!!! `+!`+x `++