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T@!@4x  @рC@ ?@@@77@  sEtA@@`@` "ҙ4dT`0@@@ ?@@G?!T T@ 6 @TT@TT* T@рď@@C) x* T@рC) x* TG!!`@@р]@KF# @@ @ !ORsE`}@wK@!ORsE^q`T>G!!@& 5z@@@vWT@рsEaT  0@@@ ?@@?aTܫ@* 5k[`G!!@d7$7@; @ sE {S[6,c!w @C sT'Tks@C`@`@~}:T@ 56Gb)_*SA[BcC{˨_-|*#%c/!`@'7@cO@TGqT1TG@!!"@nC@kDsE@O@@@x#!TC@3 `@kDsE&F]Rg,r@O CR `@``܍pBR" B >)qЍb!GC @ ƍbkA@E @KARkfA @ @@Ѡ@"  @ `kD@!ѡf!GC @ kDsEAR5B\G4A@@ @@р`BRmBRA@CE @.BRWBR|A#U;G<k 47kw @`j"!@d\T@`C@kDsETC@C#P4K@!@TA@@'!G7c@_Tq7@`T5G@K@!@!"jkDsEkDsEq7@T{{_Shared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling%.200s() %stakes no keyword argumentstakes exactly one argument%.200s() %s (%zd given)%.200s() keywords must be strings%s() got multiple values for keyword argument '%U'takes no argumentsBad call flags for CyFunction_cython_3_2_4base 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 type__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.__int__ returned non-int (type %.200s)picklePickleErrorIncompatible checksums (0x%x vs (0x%x, 0x%x, 0x%x) = (%s))__pyx_capi__%.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.name__loader__loader__file__origin__package__parent__path__submodule_search_locationsmetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its basesan integer is requiredvalue 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 objectcannot import name %S%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectcalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseException%s() got an unexpected keyword argument '%U'invalid vtable found for imported typeUnexpected format string character: '%c'keywords must be stringsunbound method %.200S() needs an argumentjoin() result is too long for a Python stringcan't convert negative value to uint32_tvalue too large to convert to uint32_tend''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 dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringDoes not understand character buffer dtype format string ('%c')Expected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'Buffer has wrong number of dimensions (expected %d, got %d)buffer dtypenpy_uint32Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s) while calling a Python objectNULL result without error in PyObject_Callnumpy/random/bit_generator.pyxnumpy.random.bit_generator.BitGenerator.ctypes.__get__numpy.random.bit_generator.BitGenerator.cffi.__get__numpy.random.bit_generator.SeedSequence.state.__get__exactly%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random.bit_generator.BitGenerator.__reduce__Out of bounds on buffer access (axis %d)numpy.random.bit_generator.SeedSequence.mix_entropynumpy.random.bit_generator.__defaults__numpy.random.bit_generator.BitGenerator.__getstate____pyx_unpickle_SeedlessSeedSequencenumpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequence__pyx_stateArgument '%.200s' has incorrect type (expected %.200s, got %.200s)%s%Unumpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequence__set_statenumpy/random/bit_generator.pxdnumpy.random.bit_generator.SeedSequence.n_children_spawned.__get__numpy.random.bit_generator.SeedSequence.pool_size.__get__numpy.random.bit_generator.SeedSequence.__repr__strings are too large to concattupleExpected %s, got %.200snumpy.random.bit_generator.__pyx_unpickle_SeedSequence__set_statenumpy.random.bit_generator.BitGenerator.state.__get__numpy.random.bit_generator.BitGenerator.state.__set____del__name '%U' is not definednumpy.random.bit_generator.ISpawnableSeedSequence.spawnnumpy.random.bit_generator.SeedSequence.spawn_int_to_uint32_arraynumpy.random.bit_generator._int_to_uint32_arrayat leastat mostnumpy.random.bit_generator.ISeedSequence.generate_statenumpy.random.bit_generator.SeedlessSeedSequence.spawnnumpy.random.bit_generator.BitGenerator.random_rawcannot pass None into a C function argument that is declared 'not None'numpy.random.bit_generator.SeedSequence.__setstate_cython__numpy.random.bit_generator.SeedlessSeedSequence.__setstate_cython__numpy.random.bit_generator.BitGenerator._benchmarkMissing type objectCannot convert %.200s to %.200snumpy.random.bit_generator.SeedSequence.__init____mro_entries__ must return a tuplenumpy.random.bit_generator.SeedlessSeedSequence.__reduce_cython__numpy.random.bit_generator.SeedlessSeedSequence.generate_statenumpy.random.bit_generator.BitGenerator.__setstate____pyx_unpickle_SeedSequencenumpy.random.bit_generator.__pyx_unpickle_SeedSequencenumpy.random.bit_generator.SeedSequence.get_assembled_entropynumpy.random.bit_generator.SeedSequence.__reduce_cython__numpy.random.bit_generator.SeedSequence.generate_state%.200s object is not an iterator_coerce_to_uint32_arraynumpy.random.bit_generator._coerce_to_uint32_arraynumpy.random.bit_generator.BitGenerator.spawnnumpy.random.bit_generator.BitGenerator.__init__Unable to initialize pickling for %.200sModule 'bit_generator' has already been imported. Re-initialisation is not supported.builtinscython_runtime__builtins__does not matchnumpy.random.bit_generatorcompile time Python version %d.%d of module '%.100s' %s runtime version %d.%ddecompresszlibFailed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'.multiple bases have vtable conflict: '%.200s' and '%.200s'numpydtypeflatiterbroadcastndarraygenericnumbersignedintegerunsignedintegerinexactfloatingcomplexfloatingflexiblecharacterufuncvariablenumpy.random._commonfunctionnumpy._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 1.23) 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.pxdnumpy.import_array__orig_bases__init numpy.random.bit_generator_cython_3_2_4.cython_function_or_method__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_cython_3_2_4._common_types_metatypebit_generatornumpy.random.bit_generator.__pyx_defaultsnumpy.random.bit_generator.SeedlessSequencenumpy.random.bit_generator.SeedlessSeedSequenceSeedlessSeedSequence() -- A seed sequence for BitGenerators with no need for seed state. See Also -------- SeedSequence, ISeedSequence numpy.random.bit_generator.SeedSequenceSeedSequence(entropy=None, *, spawn_key=(), pool_size=4, n_children_spawned=0) -- SeedSequence mixes sources of entropy in a reproducible way to set the initial state for independent and very probably non-overlapping BitGenerators. Once the SeedSequence is instantiated, you can call the `generate_state` method to get an appropriately sized seed. Calling `spawn(n) ` 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 stateentropyspawn_keypool_sizepooln_children_spawnednumpy.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 seed_seq 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 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 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 _seed_seqlock_ctypes_cfficapsuleJSM258;>ADGP%, / rol~{xue^WTQNK;99777=]B;997WWWWWWW=WWWWWWWWWWWWWWWWWWWWW =WWWWWW00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899entropy, n_children_spawned, pool, pool_size, spawn_keyBitGenerator@6$=08!  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JrM+O^dNa۱N[/quuG'_a)3// N>GS o9joq'`ALPBgp g%{xC"@AUsed 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) 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. @"D ;;t , H T x $ tܫ8hpxTl$X%X@%l0xD0000l @,`@Th| @`Lt(<P@t`@L`|@$ H @| @  4 @T  ( L $  L  (  4 @\  @ @P $ l  !! 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