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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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