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?? ..
?? __init__.py(5.64 KB)
?? __init__.pyi(126 B)
?? __pycache__
?? _add_newdocs.py(204.07 KB)
?? _add_newdocs_scalars.py(11.82 KB)
?? _asarray.py(3.79 KB)
?? _asarray.pyi(1.06 KB)
?? _dtype.py(10.36 KB)
?? _dtype_ctypes.py(3.59 KB)
?? _exceptions.py(5.25 KB)
?? _internal.py(27.68 KB)
?? _internal.pyi(1.01 KB)
?? _machar.py(11.29 KB)
?? _methods.py(8.41 KB)
?? _multiarray_tests.cpython-311-x86_64-linux-gnu.so(171.4 KB)
?? _multiarray_umath.cpython-311-x86_64-linux-gnu.so(6.64 MB)
?? _operand_flag_tests.cpython-311-x86_64-linux-gnu.so(16.55 KB)
?? _rational_tests.cpython-311-x86_64-linux-gnu.so(58.29 KB)
?? _simd.cpython-311-x86_64-linux-gnu.so(2.47 MB)
?? _string_helpers.py(2.79 KB)
?? _struct_ufunc_tests.cpython-311-x86_64-linux-gnu.so(16.65 KB)
?? _type_aliases.py(7.36 KB)
?? _type_aliases.pyi(404 B)
?? _ufunc_config.py(13.62 KB)
?? _ufunc_config.pyi(1.04 KB)
?? _umath_tests.cpython-311-x86_64-linux-gnu.so(41.01 KB)
?? arrayprint.py(62.12 KB)
?? arrayprint.pyi(4.32 KB)
?? cversions.py(347 B)
?? defchararray.py(71.89 KB)
?? defchararray.pyi(9 KB)
?? einsumfunc.py(50.65 KB)
?? einsumfunc.pyi(4.75 KB)
?? fromnumeric.py(125.8 KB)
?? fromnumeric.pyi(22.96 KB)
?? function_base.py(19.37 KB)
?? function_base.pyi(4.61 KB)
?? generate_numpy_api.py(7.47 KB)
?? getlimits.py(25.26 KB)
?? getlimits.pyi(82 B)
?? include
?? lib
?? memmap.py(11.5 KB)
?? memmap.pyi(55 B)
?? multiarray.py(54.78 KB)
?? multiarray.pyi(24.19 KB)
?? numeric.py(75.21 KB)
?? numeric.pyi(13.9 KB)
?? numerictypes.py(17.67 KB)
?? numerictypes.pyi(3.19 KB)
?? overrides.py(6.93 KB)
?? records.py(36.65 KB)
?? records.pyi(5.56 KB)
?? setup.py(47.05 KB)
?? setup_common.py(16.68 KB)
?? shape_base.py(29.05 KB)
?? shape_base.pyi(2.71 KB)
?? tests
?? umath.py(1.99 KB)
?? umath_tests.py(389 B)

Editing: _add_newdocs_scalars.py

"""
This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
our sphinx ``conf.py`` during doc builds, where we want to avoid showing
platform-dependent information.
"""
import sys
import os
from numpy.core import dtype
from numpy.core import numerictypes as _numerictypes
from numpy.core.function_base import add_newdoc

##############################################################################
#
# Documentation for concrete scalar classes
#
##############################################################################

def numeric_type_aliases(aliases):
    def type_aliases_gen():
        for alias, doc in aliases:
            try:
                alias_type = getattr(_numerictypes, alias)
            except AttributeError:
                # The set of aliases that actually exist varies between platforms
                pass
            else:
                yield (alias_type, alias, doc)
    return list(type_aliases_gen())

possible_aliases = numeric_type_aliases([
    ('int8', '8-bit signed integer (``-128`` to ``127``)'),
    ('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
    ('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
    ('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
    ('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
    ('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
    ('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
    ('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
    ('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
    ('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
    ('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
    ('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
    ('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
    ('float96', '96-bit extended-precision floating-point number type'),
    ('float128', '128-bit extended-precision floating-point number type'),
    ('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
    ('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
    ('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
    ('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
    ])

def _get_platform_and_machine():
    try:
        system, _, _, _, machine = os.uname()
    except AttributeError:
        system = sys.platform
        if system == 'win32':
            machine = os.environ.get('PROCESSOR_ARCHITEW6432', '') \
                    or os.environ.get('PROCESSOR_ARCHITECTURE', '')
        else:
            machine = 'unknown'
    return system, machine

_system, _machine = _get_platform_and_machine()
_doc_alias_string = f":Alias on this platform ({_system} {_machine}):"

def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
    # note: `:field: value` is rST syntax which renders as field lists.
    o = getattr(_numerictypes, obj)

character_code = dtype(o).char
    canonical_name_doc = "" if obj == o.__name__ else \
                        f":Canonical name: `numpy.{obj}`\n    "
    if fixed_aliases:
        alias_doc = ''.join(f":Alias: `numpy.{alias}`\n    "
                            for alias in fixed_aliases)
    else:
        alias_doc = ''
    alias_doc += ''.join(f"{_doc_alias_string} `numpy.{alias}`: {doc}.\n    "
                         for (alias_type, alias, doc) in possible_aliases if alias_type is o)

docstring = f"""
    {doc.strip()}

:Character code: ``'{character_code}'``
    {canonical_name_doc}{alias_doc}
    """

add_newdoc('numpy.core.numerictypes', obj, docstring)

add_newdoc_for_scalar_type('bool_', [],
    """
    Boolean type (True or False), stored as a byte.

.. warning::

The :class:`bool_` type is not a subclass of the :class:`int_` type
       (the :class:`bool_` is not even a number type). This is different
       than Python's default implementation of :class:`bool` as a
       sub-class of :class:`int`.
    """)

add_newdoc_for_scalar_type('byte', [],
    """
    Signed integer type, compatible with C ``char``.
    """)

add_newdoc_for_scalar_type('short', [],
    """
    Signed integer type, compatible with C ``short``.
    """)

add_newdoc_for_scalar_type('intc', [],
    """
    Signed integer type, compatible with C ``int``.
    """)

add_newdoc_for_scalar_type('int_', [],
    """
    Signed integer type, compatible with Python `int` and C ``long``.
    """)

add_newdoc_for_scalar_type('longlong', [],
    """
    Signed integer type, compatible with C ``long long``.
    """)

add_newdoc_for_scalar_type('ubyte', [],
    """
    Unsigned integer type, compatible with C ``unsigned char``.
    """)

add_newdoc_for_scalar_type('ushort', [],
    """
    Unsigned integer type, compatible with C ``unsigned short``.
    """)

add_newdoc_for_scalar_type('uintc', [],
    """
    Unsigned integer type, compatible with C ``unsigned int``.
    """)

add_newdoc_for_scalar_type('uint', [],
    """
    Unsigned integer type, compatible with C ``unsigned long``.
    """)

add_newdoc_for_scalar_type('ulonglong', [],
    """
    Signed integer type, compatible with C ``unsigned long long``.
    """)

add_newdoc_for_scalar_type('half', [],
    """
    Half-precision floating-point number type.
    """)

add_newdoc_for_scalar_type('single', [],
    """
    Single-precision floating-point number type, compatible with C ``float``.
    """)

add_newdoc_for_scalar_type('double', ['float_'],
    """
    Double-precision floating-point number type, compatible with Python `float`
    and C ``double``.
    """)

add_newdoc_for_scalar_type('longdouble', ['longfloat'],
    """
    Extended-precision floating-point number type, compatible with C
    ``long double`` but not necessarily with IEEE 754 quadruple-precision.
    """)

add_newdoc_for_scalar_type('csingle', ['singlecomplex'],
    """
    Complex number type composed of two single-precision floating-point
    numbers.
    """)

add_newdoc_for_scalar_type('cdouble', ['cfloat', 'complex_'],
    """
    Complex number type composed of two double-precision floating-point
    numbers, compatible with Python `complex`.
    """)

add_newdoc_for_scalar_type('clongdouble', ['clongfloat', 'longcomplex'],
    """
    Complex number type composed of two extended-precision floating-point
    numbers.
    """)

add_newdoc_for_scalar_type('object_', [],
    """
    Any Python object.
    """)

add_newdoc_for_scalar_type('str_', ['unicode_'],
    r"""
    A unicode string.

This type strips trailing null codepoints.

>>> s = np.str_("abc\x00")
    >>> s
    'abc'

Unlike the builtin `str`, this supports the :ref:`python:bufferobjects`, exposing its
    contents as UCS4:

>>> m = memoryview(np.str_("abc"))
    >>> m.format
    '3w'
    >>> m.tobytes()
    b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
    """)

add_newdoc_for_scalar_type('bytes_', ['string_'],
    r"""
    A byte string.

When used in arrays, this type strips trailing null bytes.
    """)

add_newdoc_for_scalar_type('void', [],
    r"""
    np.void(length_or_data, /, dtype=None)

Create a new structured or unstructured void scalar.

Parameters
    ----------
    length_or_data : int, array-like, bytes-like, object
       One of multiple meanings (see notes).  The length or
       bytes data of an unstructured void.  Or alternatively,
       the data to be stored in the new scalar when `dtype`
       is provided.
       This can be an array-like, in which case an array may
       be returned.
    dtype : dtype, optional
        If provided the dtype of the new scalar.  This dtype must
        be "void" dtype (i.e. a structured or unstructured void,
        see also :ref:`defining-structured-types`).

..versionadded:: 1.24

Notes
    -----
    For historical reasons and because void scalars can represent both
    arbitrary byte data and structured dtypes, the void constructor
    has three calling conventions:

1. ``np.void(5)`` creates a ``dtype="V5"`` scalar filled with five
       ``\0`` bytes.  The 5 can be a Python or NumPy integer.
    2. ``np.void(b"bytes-like")`` creates a void scalar from the byte string.
       The dtype itemsize will match the byte string length, here ``"V10"``.
    3. When a ``dtype=`` is passed the call is roughly the same as an
       array creation.  However, a void scalar rather than array is returned.

Please see the examples which show all three different conventions.

Examples
    --------
    >>> np.void(5)
    void(b'\x00\x00\x00\x00\x00')
    >>> np.void(b'abcd')
    void(b'\x61\x62\x63\x64')
    >>> np.void((5, 3.2, "eggs"), dtype="i,d,S5")
    (5, 3.2, b'eggs')  # looks like a tuple, but is `np.void`
    >>> np.void(3, dtype=[('x', np.int8), ('y', np.int8)])
    (3, 3)  # looks like a tuple, but is `np.void`

""")

add_newdoc_for_scalar_type('datetime64', [],
    """
    If created from a 64-bit integer, it represents an offset from
    ``1970-01-01T00:00:00``.
    If created from string, the string can be in ISO 8601 date
    or datetime format.

>>> np.datetime64(10, 'Y')
    numpy.datetime64('1980')
    >>> np.datetime64('1980', 'Y')
    numpy.datetime64('1980')
    >>> np.datetime64(10, 'D')
    numpy.datetime64('1970-01-11')

See :ref:`arrays.datetime` for more information.
    """)

add_newdoc_for_scalar_type('timedelta64', [],
    """
    A timedelta stored as a 64-bit integer.

See :ref:`arrays.datetime` for more information.
    """)

add_newdoc('numpy.core.numerictypes', "integer", ('is_integer',
    """
    integer.is_integer() -> bool

Return ``True`` if the number is finite with integral value.

.. versionadded:: 1.22

Examples
    --------
    >>> np.int64(-2).is_integer()
    True
    >>> np.uint32(5).is_integer()
    True
    """))

# TODO: work out how to put this on the base class, np.floating
for float_name in ('half', 'single', 'double', 'longdouble'):
    add_newdoc('numpy.core.numerictypes', float_name, ('as_integer_ratio',
        """
        {ftype}.as_integer_ratio() -> (int, int)

Return a pair of integers, whose ratio is exactly equal to the original
        floating point number, and with a positive denominator.
        Raise `OverflowError` on infinities and a `ValueError` on NaNs.

>>> np.{ftype}(10.0).as_integer_ratio()
        (10, 1)
        >>> np.{ftype}(0.0).as_integer_ratio()
        (0, 1)
        >>> np.{ftype}(-.25).as_integer_ratio()
        (-1, 4)
        """.format(ftype=float_name)))

add_newdoc('numpy.core.numerictypes', float_name, ('is_integer',
        f"""
        {float_name}.is_integer() -> bool

Return ``True`` if the floating point number is finite with integral
        value, and ``False`` otherwise.

.. versionadded:: 1.22

Examples
        --------
        >>> np.{float_name}(-2.0).is_integer()
        True
        >>> np.{float_name}(3.2).is_integer()
        False
        """))

for int_name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32',
        'int64', 'uint64', 'int64', 'uint64', 'int64', 'uint64'):
    # Add negative examples for signed cases by checking typecode
    add_newdoc('numpy.core.numerictypes', int_name, ('bit_count',
        f"""
        {int_name}.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input.
        Analogous to the builtin `int.bit_count` or ``popcount`` in C++.

Examples
        --------
        >>> np.{int_name}(127).bit_count()
        7""" +
        (f"""
        >>> np.{int_name}(-127).bit_count()
        7
        """ if dtype(int_name).char.islower() else "")))

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