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npanpaliya / numpy python

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numpy / random / bit_generator.cpython-311-powerpc64le-linux-gnu.so
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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 PyObjectUnexpected format string character: '%c'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'%s() got multiple values for keyword argument '%U'instance exception may not have a separate valuecalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptioninvalid vtable found for imported typejoin() result is too long for a Python stringcannot import name %Send''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 dtypeItem size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)intan integer is requiredcan't convert negative value to uint32_tvalue too large to convert to uint32_tvalue too large to convert to intUnable to initialize pickling for %.200s__mro_entries__ must return a tuplenumpy/random/bit_generator.cpython-311-powerpc64le-linux-gnu.so.p/numpy/random/bit_generator.pyx.c%s (%s:%d)exactly%.200s() takes %.8s %zd positional argument%.1s (%zd given)bit_generator.pyxnumpy.random.bit_generator.BitGenerator.__getstate__numpy.random.bit_generator.__defaults__at leastat mostnumpy.random.bit_generator.ISeedSequence.generate_stateOut of bounds on buffer access (axis %d)numpy.random.bit_generator.SeedSequence.mix_entropy'NoneType' object is not subscriptabletupleExpected %s, got %.200shasattr(): attribute name must be string<stringsource>numpy.random.bit_generator.__pyx_unpickle_SeedSequence__set_stateobject of type 'NoneType' has no len()numpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequence__set_statenumpy.random.bit_generator.BitGenerator.__reduce__numpy.random.bit_generator.BitGenerator.__setstate__numpy.random.bit_generator.SeedSequence.spawnnumpy.random.bit_generator.SeedSequence.state.__get__strings are too large to concatnumpy.random.bit_generator.SeedSequence.__repr__numpy.random.bit_generator.BitGenerator.cffi.__get__numpy.random.bit_generator.BitGenerator.ctypes.__get__numpy.random.bit_generator.BitGenerator.state.__set____del__numpy.random.bit_generator.BitGenerator.state.__get__bit_generator.pxdnumpy.random.bit_generator.SeedSequence.n_children_spawned.__get__numpy.random.bit_generator.SeedSequence.pool_size.__get__numpy.random.bit_generator.SeedlessSeedSequence.spawnname '%U' is not definednumpy.random.bit_generator.SeedlessSeedSequence.__setstate_cython__numpy.random.bit_generator.SeedSequence.__setstate_cython__numpy.random.bit_generator.SeedlessSeedSequence.generate_stateBitGeneratornumpy.random.bit_generator.BitGenerator.__init__numpy.random.bit_generator.BitGenerator._benchmarknumpy.random.bit_generator.BitGenerator.random_rawnumpy.random.bit_generator.ISpawnableSeedSequence.spawnnumpy.random.bit_generator.SeedSequence.get_assembled_entropy__pyx_unpickle_SeedSequencenumpy.random.bit_generator.__pyx_unpickle_SeedSequence__pyx_unpickle_SeedlessSeedSequencenumpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequencenumpy.random.bit_generator.BitGenerator.spawn_coerce_to_uint32_arraynumpy.random.bit_generator._coerce_to_uint32_array%.200s object is not an iteratornumpy.random.bit_generator.SeedSequence.generate_statenumpy.random.bit_generator.SeedSequence.__init__Missing type objectCannot convert %.200s to %.200snumpy.random.bit_generator.SeedlessSeedSequence.__reduce_cython__numpy.random.bit_generator.SeedSequence.__reduce_cython___int_to_uint32_arraynumpy.random.bit_generator._int_to_uint32_arrayModule 'bit_generator' has already been imported. 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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__init__.cython-30.pxdnumpy.import_array__orig_bases__init numpy.random.bit_generator_cython_3_0_11.cython_function_or_method__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_coroutinebit_generatornumpy.random.bit_generator.SeedlessSeedSequence
    A seed sequence for BitGenerators with no need for seed state.

    See Also
    --------
    SeedSequence, ISeedSequence
    numpy.random.bit_generator.SeedlessSequencenumpy.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
    stateentropyspawn_keypool_sizepooln_children_spawnednumpy.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
        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 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
        _seed_seqlock_ctypes_cfficapsulenpy_uint32[0-9]+0xABC
    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.
    AssertionErrorBitGeneratorBitGenerator.__getstate__BitGenerator.__reduce__BitGenerator.__setstate__BitGenerator._benchmark_BitGenerator__bit_generator_ctorBitGenerator is a base class and cannot be instantizedBitGenerator.random_rawBitGenerator.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_REISeedSequenceISeedSequence.generate_stateISpawnableSeedSequenceISpawnableSeedSequence.spawnImportErrorIncompatible checksums (0x%x vs (0xe3b0c44, 0xda39a3e, 0xd41d8cd) = ())Incompatible checksums (0x%x vs (0x3eaa222, 0xc464c19, 0xf88304a) = (entropy, n_children_spawned, pool, pool_size, spawn_key))LockNotImplementedErrorNot implemented in base BitGeneratorPickleErrorSeedSequenceSeedSequence.__reduce_cython__SeedSequence.__setstate_cython__SeedSequence expects int or sequence of ints for entropy not {}SeedSequence.generate_stateSeedSequence.spawnSeedlessSeedSequenceSeedlessSeedSequence.__reduce_cython__SeedlessSeedSequence.__setstate_cython__SeedlessSeedSequence.generate_stateSeedlessSeedSequence.spawnSeedlessSequenceThe size of the entropy pool should be at least The underlying SeedSequence does not implement spawning.TypeErrorValueError,)
.*?(abcabstractmethod__all__arrarrayastypeasyncio.coroutinesbase_benchmark__bit_generator_ctorcline_in_tracebackcnt_coerce_to_uint32_array_coerce_to_uint32_array (line 81)compileconcatenatecopycycledata_val__dict___dictdisable__doc__dtypeenableentropy    entropy=errstateexpected non-negative integerformatgcgenerate_state__getstate__hash_constii_dstignore__import____init_subclass___initializing_int_to_uint32_array_is_coroutineisenableditertools__main__match__metaclass__method__module____mro_entries__nn_childrenn_children_spawned    n_children_spawned=n_words__name____new__npnumpynumpy._core.multiarray failed to importnumpy._core.umath failed to importnumpy.random.bit_generatoronly support uint32 or uint64out_dtypeoutputover_picklepicklepool_size    pool_size=__prepare____pyx_PickleError__pyx_checksum__pyx_result__pyx_state__pyx_type__pyx_unpickle_SeedSequence__pyx_unpickle_SeedlessSeedSequence__pyx_vtable____qualname__randbitsrandom_rawrangere__reduce____reduce_cython____reduce_ex__registerssecretsseedseed must be integerseedless SeedSequences cannot generate stateselfseqs__set_name____setstate____setstate_cython__sizespawnspawn_key    spawn_key=__spec__src_cyclestartswithstatestate_seed_seq<stringsource>subseqssupersys__test__threading/tmp/pip-install-5nnte3hl/numpy_fbfca956206d496182f3175e68a213df/numpy/random/bit_generator.pyx<u4<u8uint32uint64unrecognized seed stringupdateuse_setstatevviewxzeros00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899Used 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.
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