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aaaaaaaaa aa aaaaaaaaaaaaaaaa a aa aaa a !$'.5<?B E *5K # #
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.
numpy.random.bit_generator <stringsource> __reduce__ __module__ __doc__ __name__ __qualname__ __dict__ _is_coroutine [0-9]+ 0x ABC
Abstract base class for seed sequences.
``BitGenerator`` implementations should treat any object that adheres to
this interface as a seed sequence.
See Also
--------
SeedSequence, SeedlessSeedSequence
Abstract base class for seed sequences that can spawn.
AssertionError BitGenerator.__getstate__ BitGenerator.__reduce__ BitGenerator.__setstate__ BitGenerator._benchmark _BitGenerator__bit_generator_ctor BitGenerator is a base class and cannot be instantized BitGenerator.random_raw BitGenerator.spawn 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)
DECIMAL_RE ISeedSequence ISeedSequence.generate_state ISpawnableSeedSequence ISpawnableSeedSequence.spawn ImportError Incompatible checksums (0x%x vs (0xe3b0c44, 0xda39a3e, 0xd41d8cd) = ()) Incompatible checksums (0x%x vs (0x3eaa222, 0xc464c19, 0xf88304a) = (entropy, n_children_spawned, pool, pool_size, spawn_key)) Lock NotImplementedError Not implemented in base BitGenerator PickleError SeedSequence SeedSequence.__reduce_cython__ SeedSequence.__setstate_cython__ SeedSequence expects int or sequence of ints for entropy not {} SeedSequence.generate_state SeedSequence.spawn SeedlessSeedSequence SeedlessSeedSequence.__reduce_cython__ SeedlessSeedSequence.__setstate_cython__ SeedlessSeedSequence.generate_state SeedlessSeedSequence.spawn SeedlessSequence The size of the entropy pool should be at least The underlying SeedSequence does not implement spawning. TypeError /Users/appveyor/projects/mobile-forge/build/cp312/numpy/2.2.2/numpy/random/bit_generator.pyx ValueError , )
. * ? ( abc abstractmethod __all__ arr array astype asyncio.coroutines base __bit_generator_ctor cline_in_traceback cnt _coerce_to_uint32_array (line 81) compile concatenate copy cycle data_val _dict disable enable entropy= errstate expected non-negative integer format gc hash_const i i_dst ignore __import__ __init_subclass__ _initializing isenabled itertools __main__ match __metaclass__ method __mro_entries__ n n_children n_children_spawned= n_words __new__ np numpy._core.multiarray failed to import numpy._core.umath failed to import only support uint32 or uint64 out_dtype output over _pickle pickle pool_size= __prepare__ __pyx_PickleError __pyx_checksum __pyx_result __pyx_state __pyx_type __pyx_vtable__ randbits range re __reduce_ex__ register secrets seed seed must be integer seedless SeedSequences cannot generate state self seqs __set_name__ size spawn_key= __spec__ src_cycle startswith state_seed_seq subseqs super sys __test__ threading <u4 <u8 uint32 uint64 unrecognized seed string update use_setstate v view x zeros __getstate__ __setstate__ spawn
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.
random_raw
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.
_benchmark Used in tests s state BitGenerator 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 generate_state
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
See Also
--------
random.Generator.spawn, random.BitGenerator.spawn :
Equivalent method on the generator and bit generator.
__reduce_cython__ __setstate_cython__ entropy spawn_key pool_size n_children_spawned numpy dtype _int_to_uint32_array _coerce_to_uint32_array 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)
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
__pyx_unpickle_SeedlessSeedSequence __pyx_unpickle_SeedSequence bit_generator name loader __loader__ origin __file__ parent __package__ submodule_search_locations __path__ Interpreter change detected - this module can only be loaded into one interpreter per process. Module 'bit_generator' has already been imported. Re-initialisation is not supported. builtins cython_runtime __builtins__ __orig_bases__ init numpy.random.bit_generator bit_generator.pyx __init__.cython-30.pxd bit_generator.pxd compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d does not match _cython_3_0_12 Shared Cython type %.200s is not a type object Shared Cython type %.200s has the wrong size, try recompiling _cython_3_0_12.cython_function_or_method <cyfunction %U at %p> unbound method %.200S() needs an argument keywords must be strings %.200s() takes no arguments (%zd given) %.200s() takes exactly one argument (%zd given) Bad call flags for CyFunction %.200s() takes no keyword arguments func_doc func_name func_dict func_globals __globals__ func_closure __closure__ func_code __code__ func_defaults __defaults__ __kwdefaults__ __annotations__ __name__ must be set to a string object __qualname__ must be set to a string object function's dictionary may not be deleted setting function's dictionary to a non-dict __defaults__ must be set to a tuple object changes to cyfunction.__defaults__ will not currently affect the values used in function calls __kwdefaults__ must be set to a dict object changes to cyfunction.__kwdefaults__ will not currently affect the values used in function calls __annotations__ must be set to a dict object 4294967296 __debug__ name '%U' is not defined numpy.random.bit_generator.BitGenerator
BitGenerator(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
at least at most exactly %.200s() takes %.8s %zd positional argument%.1s (%zd given) %.200s() keywords must be strings %s() got an unexpected keyword argument '%U' numpy.random.bit_generator.BitGenerator.__getstate__ numpy.random.bit_generator.BitGenerator.__setstate__ %s() got multiple values for keyword argument '%U' numpy.random.bit_generator.BitGenerator.__reduce__ numpy.random.bit_generator.BitGenerator.spawn value too large to convert to int int an integer is required __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) instance exception may not have a separate value calling %R should have returned an instance of BaseException, not %R raise: exception class must be a subclass of BaseException numpy.random.bit_generator.BitGenerator.random_raw numpy.random.bit_generator.BitGenerator._benchmark
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_seq lock _ctypes _cffi capsule numpy.random.bit_generator.BitGenerator.state.__get__ __del__ numpy.random.bit_generator.BitGenerator.state.__set__ numpy.random.bit_generator.BitGenerator.ctypes.__get__ numpy.random.bit_generator.BitGenerator.cffi.__get__ __init__ numpy.random.bit_generator.BitGenerator.__init__ base class '%.200s' is not a heap type extension 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 numpy.random.bit_generator.SeedSequence.mix_entropy Buffer has wrong number of dimensions (expected %d, got %d) Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s) buffer dtype Big-endian buffer not supported on little-endian compiler Buffer acquisition: Expected '{' after 'T' end ' Buffer dtype mismatch, expected %s%s%s but got %s Buffer dtype mismatch, expected '%s' but got %s in '%s.%s' '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 struct Python object a pointer a string unparsable format string Expected a dimension of size %zu, got %zu Expected %d dimensions, got %d Buffer dtype mismatch; next field is at offset %zd but %zd expected Python does not define a standard format string size for long double ('g').. Unexpected format string character: '%c' Cannot handle repeated arrays in format string Expected a dimension of size %zu, got %d Expected a comma in format string, got '%c' Expected %d dimension(s), got %d Unexpected end of format string, expected ')' Does not understand character buffer dtype format string ('%c') npy_uint32 Out of bounds on buffer access (axis %d) numpy.random.bit_generator.SeedSequence.get_assembled_entropy numpy.random.bit_generator.SeedSequence
SeedSequence(entropy=None, *, spawn_key=(), pool_size=4)
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) <spawn>` 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.SeedSequence.__repr__ strings are too large to concat join() result is too long for a Python string __repr__ numpy.random.bit_generator.SeedSequence.generate_state %.200s object is not an iterator value too large to convert to uint32_t can't convert negative value to uint32_t numpy.random.bit_generator.SeedSequence.spawn numpy.random.bit_generator.SeedSequence.__reduce_cython__ numpy.random.bit_generator.SeedSequence.__setstate_cython__ tuple Expected %s, got %.200s 'NoneType' object is not subscriptable object of type 'NoneType' has no len() numpy.random.bit_generator.__pyx_unpickle_SeedSequence__set_state hasattr(): attribute name must be string pool numpy.random.bit_generator.SeedSequence.state.__get__ numpy.random.bit_generator.SeedSequence.pool_size.__get__ numpy.random.bit_generator.SeedSequence.n_children_spawned.__get__ numpy.random.bit_generator.SeedSequence.__init__ Missing type object Cannot convert %.200s to %.200s multiple bases have vtable conflict: '%.200s' and '%.200s' invalid vtable found for imported type Unable to initialize pickling for %.200s numpy.random.bit_generator.SeedlessSequence numpy.random.bit_generator.SeedlessSeedSequence
A seed sequence for BitGenerators with no need for seed state.
See Also
--------
SeedSequence, ISeedSequence
numpy.random.bit_generator.SeedlessSeedSequence.generate_state numpy.random.bit_generator.SeedlessSeedSequence.spawn numpy.random.bit_generator.SeedlessSeedSequence.__reduce_cython__ numpy.random.bit_generator.SeedlessSeedSequence.__setstate_cython__ numpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequence__set_state type flatiter broadcast ndarray generic number integer signedinteger unsignedinteger inexact floating complexfloating flexible character ufunc %.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 numpy.random._common POISSON_LAM_MAX double LEGACY_POISSON_LAM_MAX MAXSIZE uint64_t __pyx_capi__ %.200s does not export expected C variable %.200s C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s) benchmark PyObject *(bitgen_t *, PyObject *, Py_ssize_t, PyObject *) PyObject *(bitgen_t *, PyObject *, PyObject *, PyObject *) prepare_cffi PyObject *(bitgen_t *) prepare_ctypes %.200s does not export expected C function %.200s C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s) cannot import name %S numpy.import_array numpy._core._multiarray_umath numpy.core._multiarray_umath _ARRAY_API _ARRAY_API is not PyCapsule object _ARRAY_API is NULL pointer module compiled against ABI version 0x%x but this version of numpy is 0x%x module was compiled against NumPy C-API version 0x%x (NumPy 1.20) 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 endian FATAL: module compiled as little endian, but detected different endianness at runtime while calling a Python object NULL result without error in PyObject_Call %.200s() needs an argument numpy.random.bit_generator._int_to_uint32_array numpy.random.bit_generator._coerce_to_uint32_array __mro_entries__ must return a tuple metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases numpy.random.bit_generator.ISeedSequence.generate_state numpy.random.bit_generator.__defaults__ numpy.random.bit_generator.ISpawnableSeedSequence.spawn numpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequence numpy.random.bit_generator.__pyx_unpickle_SeedSequence %s (%s:%d) numpy/random/bit_generator.cpython-312-iphonesimulator.so.p/numpy/random/bit_generator.pyx.c 8 8 üL \ \ Ð \ n Ä @ P ¤P XT j Èo $r Èr ¼x (y üy È{ `~ ( ( D
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