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LHL$8HL$Lt$0RLI藉HL$Hx HHIExHIEM3Hx HHcLL-IH="IULIHtA$ID$H5LHHIMI$xHI$IHtIFHEtEIFHhHI9EZHt$8LLt$8MHHD$0 IIxHIuL/IxHIuLMA$tA$Hx HH}HExHHE I$xHI$uLHXL[]A\A]A^A_L^IExHIERAE1E11MtI$xHI$ HtHx HH0MtIx HIDH=*HtHx HHt{HtHExHHEE1#L%LHx HHIExHIEAgfDHHtHExHHE}H]pLHL$CHL$fL(LHHDLJHH9GHE111AH8c5fD3H=H HGH91HEHHHuHHH5DH81T@AE111fDL}LuAtAAtAHExHHEDfInHt$0LfI:"E)D$0萼HIqHIdLW+H=HGHH;r<1H=IH]HAE11M~MfAtAA$tA$Ix HIDfInHt$0LfI:"E )D$0註HIHI LLHL;HIEE11E1E1HIEMtIx HIMtIx HItuAfIHff.@I$xHI$NAHLLHL$AHL$'LHL$HL$?LxHhvLIHHAE1fLhIHAI(A|DHAE1YfHHT$HT$fLHT$HL$HT$HL$XLHT$HL$vHT$HL$LHT$SHT$` will create ``n`` SeedSequences that can be used to seed independent BitGenerators, i.e. for different threads. Parameters ---------- entropy : {None, int, sequence[int]}, optional The entropy for creating a `SeedSequence`. All integer values must be non-negative. spawn_key : {(), sequence[int]}, optional An additional source of entropy based on the position of this `SeedSequence` in the tree of such objects created with the `SeedSequence.spawn` method. Typically, only `SeedSequence.spawn` will set this, and users will not. pool_size : {int}, optional Size of the pooled entropy to store. Default is 4 to give a 128-bit entropy pool. 8 (for 256 bits) is another reasonable choice if working with larger PRNGs, but there is very little to be gained by selecting another value. n_children_spawned : {int}, optional The number of children already spawned. Only pass this if reconstructing a `SeedSequence` from a serialized form. Notes ----- Best practice for achieving reproducible bit streams is to use the default ``None`` for the initial entropy, and then use `SeedSequence.entropy` to log/pickle the `entropy` for reproducibility: >>> sq1 = np.random.SeedSequence() >>> sq1.entropy 243799254704924441050048792905230269161 # random >>> sq2 = np.random.SeedSequence(sq1.entropy) >>> np.all(sq1.generate_state(10) == sq2.generate_state(10)) True numpy.random.bit_generator.BitGeneratorBitGenerator(seed=None) -- Base Class for generic BitGenerators, which provide a stream of random bits based on different algorithms. Must be overridden. 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 `~numpy.random.SeedSequence` to derive the initial `BitGenerator` state. One may also pass in a `SeedSequence` instance. All integer values must be non-negative. Attributes ---------- lock : threading.Lock Lock instance that is shared so that the same BitGenerator 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. See Also -------- SeedSequence Get or set the PRNG state The base BitGenerator.state must be overridden by a subclass Returns ------- state : dict Dictionary containing the information required to describe the state of the PRNG Get the seed sequence used to initialize the bit generator. .. versionadded:: 1.25.0 Returns ------- seed_seq : ISeedSequence The SeedSequence object used to initialize the BitGenerator. This is normally a `np.random.SeedSequence` instance. ctypes interface Returns ------- interface : namedtuple Named tuple containing ctypes wrapper * state_address - Memory address of the state struct * state - pointer to the state struct * next_uint64 - function pointer to produce 64 bit integers * next_uint32 - function pointer to produce 32 bit integers * next_double - function pointer to produce doubles * bitgen - pointer to the bit generator struct CFFI interface Returns ------- interface : namedtuple Named tuple containing CFFI wrapper * state_address - Memory address of the state struct * state - pointer to the state struct * next_uint64 - function pointer to produce 64 bit integers * next_uint32 - function pointer to produce 32 bit integers * next_double - function pointer to produce doubles * bitgen - pointer to the bit generator struct 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_8picklePickleError__pyx_capi____loader__loader__file__origin__package__parent__path__submodule_search_locationsan integer is requiredkeywords must be stringsend'bool''char''signed char''unsigned char''short''unsigned short''int''unsigned int''long''unsigned long''long long''unsigned long long''complex float''float''complex double''double''complex long double''long double'a structPython objecta pointera stringunparsable format stringbuffer dtypenpy_uint32__pyx_statecannot import name %Sname '%U' is not definedat leastat mostgenerate_statespawnexactly__init__Missing type object__getstate____setstate____reduce__random_raw__del____reduce_cython__tupleExpected %s, got %.200s__setstate_cython____pyx_unpickle_SeedSequence_coerce_to_uint32_array_benchmark_int_to_uint32_arraybuiltinscython_runtime__builtins__numpy.random.bit_generatordoes not matchdecompresszlib__debug__numpyflatiterbroadcastndarraygenericnumberunsignedintegerinexactcomplexfloatingflexiblecharacterufuncvariablenumpy.random._commonfunctionnumpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is NULL pointernumpy.import_array__orig_bases____module____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____annotate___is_coroutineentropyspawn_keypool_sizepooln_children_spawned_seed_seqlock_ctypes_cfficapsuleB`B`BB`B`B`B`B`BBB`B`BB`B`BBBB`B`B`B`B`B`B`B`B`B`B`B`B`B`B`B`BBBB`BBBBB`B`BB`B`B`BBB`BBXe,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,c,cpe,c,cnd,c,c,c,c,cee,c,ce,c,c(edd,c,cd,c,c,c,c,c,c,c,c,c,c,c,c,cdded,cdd4e@e,c,cLe,c,c,c]czd,c]c2b6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6a6ab6a6a>b6a6a6a6a6abbnb6a6azb6a6aabb6a6ab6a6a6a6a6a6a6a6a6a6a6a6a6a&bba6aaaaa6a6aJb6a6a6aaVb6aadccdccccc,eeccecc e e eccccccccccccccccddece6e,eeccecccd ecd(ebb(ebbbbb8eXfbbebbeeebbbbbbbbbbbbbbbb(e(ehebUeBe8eXfbbebbb(eeb(eidjdjdjdjdjdjdjdjdjWfdjdjWfdjdjdjdjdjdjdjdjdjdjdjdjdjdjdjdjdjdjWfhdjdjdjdjdjdjhdjdjdjdjdjdjdjdjdjdjdjdjdjdjdjdjdjhdjQf*ghxڽXKsF&c:-[ d-+^-ڒUCx7^/4$,0DjuGqGuԑG{|@6"8_o~H.KmŖ QI cRѴ$F = e˜xgfQZ6ITuZMl4ʦmQbKĨKf-pjZI$ͰU$ Hx`-J[Q$)có#*ĐX):CP0:&G&P/fNJIJH=o;baG$*bkh8tJeI,^O X ӸkX~r;jH.;U\1` b6J3JC,]I׾ 6nZn+@),YTh>%7yAYtLB/ Bx(eà8b  L R@]>܎8nPJN`M 2T!J=`},u1BX1P\";p!=7 .dM6CLdžHLII*kH F Α aB% g R0i8czNAYSJՊEDmXe b  Ԥ>O > j;~4 s!l5A<,hZP}mTKEˬ!eVRp VYBMYl.dÜ~'r21ϼIJ* M6G k#s %DHJ3 K7gKJ|飑jC0 ?ܱ IJNfš0-/g痓RY΀Mr#%qBٹ\.)eم9.Qvn~!`~ayq^ ߐ*B >`P-2. pBJƊؘHwsK9-X["Ng˂6jFTHv'y;0K"_'kءjv茦"I5+QZ@ QZ˦+06R`+˄X BV5M#B)a1Tju%Df@QU9%݁BN1R)T}x @Q=UIј`Q Y)k J@GB\LhPb "0LT4\):"W5eW|G1cSE&(k+Š]u7=z=hCa~7؉bhs.߿tdypsb,;}7'?|4|)9,Z(g9\!nh^C69MsMWV??_(YIRo!#ScԬD75@:њ_<1a[4 OM|joay35jn#FsXgHNwZ|HA!в” r/j<ؐ V7Lq2#OD%ka#Z ʮbd5ŬT!; cdJ]Al"OŘ-H{ԲRt E&>ye \n5MVY2 Kid}5V<, eVh i+ekj|-[1UG2eVl R$kf TpM@VfU"AVaq;[~rUYQ{$.s*͢ 2F!Šcat:%^޷1wd{х PUKgNApX^U0Y33~:3( =կRd?0pǯcS{<4P2Dȉ2T!h}~2{QdI2 g#Ii0}x 0k/bQ/2V*TF"B]َ^l.t R6[Lq$<No6AB4rqhK͗hZ[A4y2ib8]"dAX[\S+8UCHi0[sbͺ@Ь&xé8μ毭yꆫy{~D&4Brvb_yfO)" p;@xz?ľIgퟏv;/?C̿;GFge7V9sƯ5Z!΅,>;6:|NWó:ĈnO8lF׭ƵFӨn`^t`W 8KyA$/< ݁v6~z7p(@6$.=08!  '", !   &(#"$             #        ? eYZ-N  ::7nC"@A00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899Used in tests random_raw(self, size=None) Return randoms as generated by the underlying BitGenerator Parameters ---------- size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. output : bool, optional Output values. Used for performance testing since the generated values are not returned. Returns ------- out : uint or ndarray Drawn samples. Notes ----- This method directly exposes the raw underlying pseudo-random number generator. All values are returned as unsigned 64-bit values irrespective of the number of bits produced by the PRNG. See the class docstring for the number of bits returned. spawn(n_children) Create new independent child bit generators. See :ref:`seedsequence-spawn` for additional notes on spawning children. Some bit generators also implement ``jumped`` as a different approach for creating independent streams. .. versionadded:: 1.25.0 Parameters ---------- n_children : int Returns ------- child_bit_generators : list of BitGenerators Raises ------ TypeError When the underlying SeedSequence does not implement spawning. See Also -------- random.Generator.spawn, random.SeedSequence.spawn : Equivalent method on the generator and seed sequence. spawn(n_children) Spawn a number of child `SeedSequence` s by extending the `spawn_key`. See :ref:`seedsequence-spawn` for additional notes on spawning children. Parameters ---------- n_children : int Returns ------- seqs : list of `SeedSequence` s See Also -------- random.Generator.spawn, random.BitGenerator.spawn : Equivalent method on the generator and bit generator. generate_state(n_words, dtype=np.uint32) Return the requested number of words for PRNG seeding. A BitGenerator should call this method in its constructor with an appropriate `n_words` parameter to properly seed itself. Parameters ---------- n_words : int dtype : np.uint32 or np.uint64, optional The size of each word. This should only be either `uint32` or `uint64`. Strings (`'uint32'`, `'uint64'`) are fine. Note that requesting `uint64` will draw twice as many bits as `uint32` for the same `n_words`. This is a convenience for `BitGenerator`\ s that express their states as `uint64` arrays. Returns ------- state : uint32 or uint64 array, shape=(n_words,) spawn(n_children) Spawn a number of child `SeedSequence` s by extending the `spawn_key`. See :ref:`seedsequence-spawn` for additional notes on spawning children. Parameters ---------- n_children : int Returns ------- seqs : list of `SeedSequence` s generate_state(n_words, dtype=np.uint32) Return the requested number of words for PRNG seeding. A BitGenerator should call this method in its constructor with an appropriate `n_words` parameter to properly seed itself. Parameters ---------- n_words : int dtype : np.uint32 or np.uint64, optional The size of each word. This should only be either `uint32` or `uint64`. Strings (`'uint32'`, `'uint64'`) are fine. Note that requesting `uint64` will draw twice as many bits as `uint32` for the same `n_words`. This is a convenience for `BitGenerator`\ s that express their states as `uint64` arrays. Returns ------- state : uint32 or uint64 array, shape=(n_words,) Coerce an input to a uint32 array. If a `uint32` array, pass it through directly. If a non-negative integer, then break it up into `uint32` words, lowest bits first. If a string starting with "0x", then interpret as a hex integer, as above. If a string of decimal digits, interpret as a decimal integer, as above. If a sequence of ints or strings, interpret each element as above and concatenate. Note that the handling of `int64` or `uint64` arrays are not just straightforward views as `uint32` arrays. If an element is small enough to fit into a `uint32`, then it will only take up one `uint32` element in the output. This is to make sure that the interpretation of a sequence of integers is the same regardless of numpy's default integer type, which differs on different platforms. Parameters ---------- x : int, str, sequence of int or str Returns ------- seed_array : uint32 array Examples -------- >>> import numpy as np >>> from numpy.random.bit_generator import _coerce_to_uint32_array >>> _coerce_to_uint32_array(12345) array([12345], dtype=uint32) >>> _coerce_to_uint32_array('12345') array([12345], dtype=uint32) >>> _coerce_to_uint32_array('0x12345') array([74565], dtype=uint32) >>> _coerce_to_uint32_array([12345, '67890']) array([12345, 67890], dtype=uint32) >>> _coerce_to_uint32_array(np.array([12345, 67890], dtype=np.uint32)) array([12345, 67890], dtype=uint32) >>> _coerce_to_uint32_array(np.array([12345, 67890], dtype=np.int64)) array([12345, 67890], dtype=uint32) >>> _coerce_to_uint32_array([12345, 0x10deadbeef, 67890, 0xdeadbeef]) array([ 12345, 3735928559, 16, 67890, 3735928559], dtype=uint32) >>> _coerce_to_uint32_array(1234567890123456789012345678901234567890) array([3460238034, 2898026390, 3235640248, 2697535605, 3], dtype=uint32) entropy, n_children_spawned, pool, pool_size, spawn_key BitGenerator base class and SeedSequence used to seed the BitGenerators. SeedSequence is derived from Melissa E. O'Neill's C++11 `std::seed_seq` implementation, as it has a lot of nice properties that we want. https://gist.github.com/imneme/540829265469e673d045 https://www.pcg-random.org/posts/developing-a-seed_seq-alternative.html The MIT License (MIT) Copyright (c) 2015 Melissa E. O'Neill Copyright (c) 2019 NumPy Developers Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 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