"""
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This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
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our sphinx ``conf.py`` during doc builds, where we want to avoid showing
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platform-dependent information.
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"""
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import sys
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import os
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from numpy.core import dtype
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from numpy.core import numerictypes as _numerictypes
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from numpy.core.function_base import add_newdoc
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##############################################################################
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#
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# Documentation for concrete scalar classes
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#
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##############################################################################
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def numeric_type_aliases(aliases):
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def type_aliases_gen():
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for alias, doc in aliases:
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try:
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alias_type = getattr(_numerictypes, alias)
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except AttributeError:
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# The set of aliases that actually exist varies between platforms
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pass
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else:
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yield (alias_type, alias, doc)
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return list(type_aliases_gen())
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|
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possible_aliases = numeric_type_aliases([
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('int8', '8-bit signed integer (``-128`` to ``127``)'),
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('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
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('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
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('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
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('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
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('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
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('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
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('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
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('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
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('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
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('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
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('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
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('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
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('float96', '96-bit extended-precision floating-point number type'),
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('float128', '128-bit extended-precision floating-point number type'),
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('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
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('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
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('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
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('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
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])
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def _get_platform_and_machine():
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try:
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system, _, _, _, machine = os.uname()
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except AttributeError:
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system = sys.platform
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if system == 'win32':
|
machine = os.environ.get('PROCESSOR_ARCHITEW6432', '') \
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or os.environ.get('PROCESSOR_ARCHITECTURE', '')
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else:
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machine = 'unknown'
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return system, machine
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_system, _machine = _get_platform_and_machine()
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_doc_alias_string = f":Alias on this platform ({_system} {_machine}):"
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|
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def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
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# note: `:field: value` is rST syntax which renders as field lists.
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o = getattr(_numerictypes, obj)
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character_code = dtype(o).char
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canonical_name_doc = "" if obj == o.__name__ else \
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f":Canonical name: `numpy.{obj}`\n "
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if fixed_aliases:
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alias_doc = ''.join(f":Alias: `numpy.{alias}`\n "
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for alias in fixed_aliases)
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else:
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alias_doc = ''
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alias_doc += ''.join(f"{_doc_alias_string} `numpy.{alias}`: {doc}.\n "
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for (alias_type, alias, doc) in possible_aliases if alias_type is o)
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docstring = f"""
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{doc.strip()}
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:Character code: ``'{character_code}'``
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{canonical_name_doc}{alias_doc}
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"""
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add_newdoc('numpy.core.numerictypes', obj, docstring)
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add_newdoc_for_scalar_type('bool_', ['bool8'],
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"""
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Boolean type (True or False), stored as a byte.
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.. warning::
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The :class:`bool_` type is not a subclass of the :class:`int_` type
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(the :class:`bool_` is not even a number type). This is different
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than Python's default implementation of :class:`bool` as a
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sub-class of :class:`int`.
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""")
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add_newdoc_for_scalar_type('byte', [],
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"""
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Signed integer type, compatible with C ``char``.
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""")
|
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add_newdoc_for_scalar_type('short', [],
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"""
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Signed integer type, compatible with C ``short``.
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""")
|
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add_newdoc_for_scalar_type('intc', [],
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"""
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Signed integer type, compatible with C ``int``.
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""")
|
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add_newdoc_for_scalar_type('int_', [],
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"""
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Signed integer type, compatible with Python `int` and C ``long``.
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""")
|
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add_newdoc_for_scalar_type('longlong', [],
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"""
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Signed integer type, compatible with C ``long long``.
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""")
|
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add_newdoc_for_scalar_type('ubyte', [],
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"""
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Unsigned integer type, compatible with C ``unsigned char``.
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""")
|
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add_newdoc_for_scalar_type('ushort', [],
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"""
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Unsigned integer type, compatible with C ``unsigned short``.
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""")
|
|
add_newdoc_for_scalar_type('uintc', [],
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"""
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Unsigned integer type, compatible with C ``unsigned int``.
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""")
|
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add_newdoc_for_scalar_type('uint', [],
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"""
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Unsigned integer type, compatible with C ``unsigned long``.
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""")
|
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add_newdoc_for_scalar_type('ulonglong', [],
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"""
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Signed integer type, compatible with C ``unsigned long long``.
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""")
|
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add_newdoc_for_scalar_type('half', [],
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"""
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Half-precision floating-point number type.
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""")
|
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add_newdoc_for_scalar_type('single', [],
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"""
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Single-precision floating-point number type, compatible with C ``float``.
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""")
|
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add_newdoc_for_scalar_type('double', ['float_'],
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"""
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Double-precision floating-point number type, compatible with Python `float`
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and C ``double``.
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""")
|
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add_newdoc_for_scalar_type('longdouble', ['longfloat'],
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"""
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Extended-precision floating-point number type, compatible with C
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``long double`` but not necessarily with IEEE 754 quadruple-precision.
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""")
|
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add_newdoc_for_scalar_type('csingle', ['singlecomplex'],
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"""
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Complex number type composed of two single-precision floating-point
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numbers.
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""")
|
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add_newdoc_for_scalar_type('cdouble', ['cfloat', 'complex_'],
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"""
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Complex number type composed of two double-precision floating-point
|
numbers, compatible with Python `complex`.
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""")
|
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add_newdoc_for_scalar_type('clongdouble', ['clongfloat', 'longcomplex'],
|
"""
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Complex number type composed of two extended-precision floating-point
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numbers.
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""")
|
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add_newdoc_for_scalar_type('object_', [],
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"""
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Any Python object.
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""")
|
|
add_newdoc_for_scalar_type('str_', ['unicode_'],
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r"""
|
A unicode string.
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When used in arrays, this type strips trailing null codepoints.
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Unlike the builtin `str`, this supports the :ref:`python:bufferobjects`, exposing its
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contents as UCS4:
|
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>>> m = memoryview(np.str_("abc"))
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>>> m.format
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'3w'
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>>> m.tobytes()
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b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
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""")
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add_newdoc_for_scalar_type('bytes_', ['string_'],
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r"""
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A byte string.
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When used in arrays, this type strips trailing null bytes.
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""")
|
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add_newdoc_for_scalar_type('void', [],
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r"""
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np.void(length_or_data, /, dtype=None)
|
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Create a new structured or unstructured void scalar.
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Parameters
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----------
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length_or_data : int, array-like, bytes-like, object
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One of multiple meanings (see notes). The length or
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bytes data of an unstructured void. Or alternatively,
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the data to be stored in the new scalar when `dtype`
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is provided.
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This can be an array-like, in which case an array may
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be returned.
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dtype : dtype, optional
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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
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-----
|
For historical reasons and because void scalars can represent both
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arbitrary byte data and structured dtypes, the void constructor
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has three calling conventions:
|
|
1. ``np.void(5)`` creates a ``dtype="V5"`` scalar filled with five
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``\0`` bytes. The 5 can be a Python or NumPy integer.
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2. ``np.void(b"bytes-like")`` creates a void scalar from the byte string.
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The dtype itemsize will match the byte string length, here ``"V10"``.
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3. When a ``dtype=`` is passed the call is rougly 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)
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void(b'\x00\x00\x00\x00\x00')
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>>> np.void(b'abcd')
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void(b'\x61\x62\x63\x64')
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>>> np.void((5, 3.2, "eggs"), dtype="i,d,S5")
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(5, 3.2, b'eggs') # looks like a tuple, but is `np.void`
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>>> np.void(3, dtype=[('x', np.int8), ('y', np.int8)])
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(3, 3) # looks like a tuple, but is `np.void`
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|
""")
|
|
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 "")))
|
