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Buffer dtype mismatch; next field is at offset %zd but %zd expected Big-endian buffer not supported on little-endian compiler Buffer acquisition: Expected '{' after 'T' Cannot handle repeated arrays in format string Does not understand character buffer dtype format string ('%c') 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 ')' 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) value too large to convert to uint32_t can't convert negative value to uint32_t value too large to convert to int %s() got multiple values for keyword argument '%U' Unable to initialize pickling for %.200s __mro_entries__ must return a tuple numpy/random/bit_generator.cpython-312-x86_64-linux-gnu.so.p/numpy/random/bit_generator.pyx.c 'NoneType' object is not subscriptable hasattr(): attribute name must be string numpy.random.bit_generator.__pyx_unpickle_SeedSequence__set_state object of type 'NoneType' has no len() numpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequence__set_state %.200s() takes %.8s %zd positional argument%.1s (%zd given) numpy.random.bit_generator.ISpawnableSeedSequence.spawn numpy.random.bit_generator.__defaults__ numpy.random.bit_generator.ISeedSequence.generate_state numpy.random.bit_generator.SeedlessSeedSequence.__setstate_cython__ numpy.random.bit_generator.SeedlessSeedSequence.__reduce_cython__ numpy.random.bit_generator.SeedlessSeedSequence.spawn numpy.random.bit_generator.SeedSequence.n_children_spawned.__get__ numpy.random.bit_generator.SeedSequence.pool_size.__get__ numpy.random.bit_generator.SeedSequence.state.__get__ numpy.random.bit_generator.SeedSequence.__setstate_cython__ numpy.random.bit_generator.SeedSequence.__reduce_cython__ numpy.random.bit_generator.SeedSequence.spawn %.200s object is not an iterator numpy.random.bit_generator.SeedSequence.generate_state strings are too large to concat numpy.random.bit_generator.SeedSequence.__repr__ numpy.random.bit_generator.SeedSequence.get_assembled_entropy Out of bounds on buffer access (axis %d) numpy.random.bit_generator.SeedSequence.mix_entropy numpy.random.bit_generator.BitGenerator.__init__ numpy.random.bit_generator.BitGenerator.cffi.__get__ numpy.random.bit_generator.BitGenerator.ctypes.__get__ numpy.random.bit_generator.BitGenerator._benchmark numpy.random.bit_generator.BitGenerator.random_raw numpy.random.bit_generator.BitGenerator.spawn numpy.random.bit_generator.BitGenerator.__reduce__ numpy.random.bit_generator.BitGenerator.__setstate__ numpy.random.bit_generator.BitGenerator.__getstate__ Module 'bit_generator' has already been imported. Re-initialisation is not supported. compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d Shared Cython type %.200s is not a type object Shared Cython type %.200s has the wrong size, try recompiling multiple bases have vtable conflict: '%.200s' and '%.200s' PyObject *(bitgen_t *, PyObject *, Py_ssize_t, PyObject *) PyObject *(bitgen_t *, PyObject *, PyObject *, PyObject *) _ARRAY_API is not PyCapsule object module compiled against ABI version 0x%x but this version of numpy is 0x%x module 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 endian FATAL: module compiled as little endian, but detected different endianness at runtime init numpy.random.bit_generator numpy.random.bit_generator.__pyx_unpickle_SeedSequence __pyx_unpickle_SeedlessSeedSequence numpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequence numpy.random.bit_generator.BitGenerator.state.__set__ numpy.random.bit_generator.BitGenerator.state.__get__ numpy.random.bit_generator.SeedlessSeedSequence.generate_state numpy.random.bit_generator._coerce_to_uint32_array numpy.random.bit_generator._int_to_uint32_array numpy.random.bit_generator.SeedSequence.__init__ Cannot convert %.200s to %.200s _cython_3_0_8.cython_function_or_method numpy.random.bit_generator.SeedlessSeedSequence
A seed sequence for BitGenerators with no need for seed state.
See Also
--------
SeedSequence, ISeedSequence
numpy.random.bit_generator.SeedlessSequence 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.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
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
Bad call flags for CyFunction <cyfunction %U at %p> __pyx_capi__ __loader__ loader __file__ origin __package__ parent __path__ submodule_search_locations %.200s() needs an argument keywords must be strings end unparsable format string 'complex double' 'signed char' 'unsigned char' 'short' 'unsigned short' 'int' 'unsigned int' 'long' 'unsigned long' 'long long' 'unsigned long long' 'double' 'complex long double' 'bool' 'char' 'complex float' 'float' a struct Python object a pointer a string 'long double' buffer dtype int an integer is required cannot import name %S name '%U' is not defined %s (%s:%d) tuple Expected %s, got %.200s <stringsource> exactly spawn bit_generator.pyx at most at least generate_state __setstate_cython__ __reduce_cython__ bit_generator.pxd __init__ BitGenerator _benchmark random_raw __reduce__ __setstate__ __getstate__ builtins cython_runtime __builtins__ numpy.random.bit_generator does not match _cython_3_0_8 4294967296 __debug__ numpy flatiter broadcast ndarray generic number unsignedinteger inexact complexfloating flexible character ufunc numpy.random._common double LEGACY_POISSON_LAM_MAX uint64_t MAXSIZE PyObject *(bitgen_t *) prepare_cffi prepare_ctypes numpy.core._multiarray_umath _ARRAY_API _ARRAY_API is NULL pointer __init__.cython-30.pxd numpy.import_array __orig_bases__ __pyx_unpickle_SeedSequence __del__ _coerce_to_uint32_array _int_to_uint32_array Missing type object __module__ 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 entropy spawn_key pool_size pool n_children_spawned __repr__ _seed_seq lock capsule npy_uint32 POOPOOOOO0POOOOOOOOOOOOOOOOOOOOOOOOOPPOOO P0POOOOOOOPOOPfldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldldfldldtfldldldldldDfPfldld\fldldhfeeldldeldldldldldldldldldldldldldefeldf*ffeldldeldldlddflddrcvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbvbcvbvb~cvbvbvbvbvbccvbvbcvbvb6cBcNcvbvbZcvbvbvbvbvbvbvbvbvbvbvbvbvbfccbvbbc*cbvbvbcvbvbvbbcvbbeddedddddffddfddfffddddddddddddddddeegdgfffddfdddefdecfcccfccccccfcfcccfcc}f}fcfccccccccccccccccYfgmfcmfmfYfYfccYfcccYfYfcYfDd4c4cDd4c4c4c4c4cee4c4ce4c4ceee4c4c4c4c4c4c4c4c4c4c4c4c4c4c4c4cDdDd f4c feee4c4ce4c4c4cDde4cDdgfffffffffgffgffffffffffffffffffggffffff gfffffffffffffffffifhggpfgfpffffffpfpfffpfffpffpfff"ifffffhfffgfffpfpfpffpfpfpfpfffpffffpfpffGjffffQhffffh 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 Used 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)
Incompatible checksums (0x%x vs (0x3eaa222, 0xc464c19, 0xf88304a) = (entropy, n_children_spawned, pool, pool_size, spawn_key))
Abstract base class for seed sequences that can spawn.
seedless SeedSequences cannot generate state numpy.core.umath failed to import The underlying SeedSequence does not implement spawning. The size of the entropy pool should be at least SeedSequence expects int or sequence of ints for entropy not {} Not implemented in base BitGenerator Incompatible checksums (0x%x vs (0xe3b0c44, 0xda39a3e, 0xd41d8cd) = ()) BitGenerator is a base class and cannot be instantized /project/numpy/random/bit_generator.pyx numpy.core.multiarray failed to import 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
http://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.
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
__pyx_unpickle_SeedlessSeedSequence _coerce_to_uint32_array (line 81) only support uint32 or uint64 expected non-negative integer ISpawnableSeedSequence.spawn ISeedSequence.generate_state numpy.random.bit_generator __pyx_unpickle_SeedSequence unrecognized seed string _coerce_to_uint32_array ISpawnableSeedSequence seed must be integer n_children_spawned= SeedlessSeedSequence _int_to_uint32_array NotImplementedError n_children_spawned cline_in_traceback __bit_generator_ctor asyncio.coroutines SeedlessSequence __setstate_cython__ __pyx_PickleError generate_state abstractmethod AssertionError __reduce_cython__ __init_subclass__ bit_generator ISeedSequence <stringsource> __pyx_checksum _is_coroutine _initializing SeedSequence BitGenerator spawn_key= pool_size= __mro_entries__ concatenate PickleError ImportError startswith __pyx_vtable__ __pyx_result n_children ValueError DECIMAL_RE threading spawn_key __reduce_ex__ __pyx_state pool_size __metaclass__ itertools isenabled entropy= TypeError __setstate__ __set_name__ register randbits __qualname__ __pyx_type pickle __getstate__ errstate subseqs secrets __prepare__ n_words entropy disable compile update uint64 uint32 __reduce__ _pickle output __module__ method __import__ ignore format enable astype zeros super state spawn range numpy match dtype cycle array view __test__ __spec__ size self seed over __name__ __main__ __dict__ copy base Lock sys __new__ __doc__ cnt arr __all__ abc ? * .
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