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1"""
2This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
3our sphinx ``conf.py`` during doc builds, where we want to avoid showing
4platform-dependent information.
5"""
6import sys
7import os
8from numpy.core import dtype
9from numpy.core import numerictypes as _numerictypes
10from numpy.core.function_base import add_newdoc
12##############################################################################
13#
14# Documentation for concrete scalar classes
15#
16##############################################################################
18def numeric_type_aliases(aliases):
19 def type_aliases_gen():
20 for alias, doc in aliases:
21 try:
22 alias_type = getattr(_numerictypes, alias)
23 except AttributeError:
24 # The set of aliases that actually exist varies between platforms
25 pass
26 else:
27 yield (alias_type, alias, doc)
28 return list(type_aliases_gen())
31possible_aliases = numeric_type_aliases([
32 ('int8', '8-bit signed integer (``-128`` to ``127``)'),
33 ('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
34 ('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
35 ('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
36 ('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
37 ('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
38 ('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
39 ('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
40 ('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
41 ('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
42 ('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
43 ('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
44 ('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
45 ('float96', '96-bit extended-precision floating-point number type'),
46 ('float128', '128-bit extended-precision floating-point number type'),
47 ('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
48 ('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
49 ('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
50 ('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
51 ])
54def _get_platform_and_machine():
55 try:
56 system, _, _, _, machine = os.uname()
57 except AttributeError:
58 system = sys.platform
59 if system == 'win32':
60 machine = os.environ.get('PROCESSOR_ARCHITEW6432', '') \
61 or os.environ.get('PROCESSOR_ARCHITECTURE', '')
62 else:
63 machine = 'unknown'
64 return system, machine
67_system, _machine = _get_platform_and_machine()
68_doc_alias_string = f":Alias on this platform ({_system} {_machine}):"
71def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
72 # note: `:field: value` is rST syntax which renders as field lists.
73 o = getattr(_numerictypes, obj)
75 character_code = dtype(o).char
76 canonical_name_doc = "" if obj == o.__name__ else \
77 f":Canonical name: `numpy.{obj}`\n "
78 if fixed_aliases:
79 alias_doc = ''.join(f":Alias: `numpy.{alias}`\n "
80 for alias in fixed_aliases)
81 else:
82 alias_doc = ''
83 alias_doc += ''.join(f"{_doc_alias_string} `numpy.{alias}`: {doc}.\n "
84 for (alias_type, alias, doc) in possible_aliases if alias_type is o)
86 docstring = f"""
87 {doc.strip()}
89 :Character code: ``'{character_code}'``
90 {canonical_name_doc}{alias_doc}
91 """
93 add_newdoc('numpy.core.numerictypes', obj, docstring)
96add_newdoc_for_scalar_type('bool_', ['bool8'],
97 """
98 Boolean type (True or False), stored as a byte.
100 .. warning::
102 The :class:`bool_` type is not a subclass of the :class:`int_` type
103 (the :class:`bool_` is not even a number type). This is different
104 than Python's default implementation of :class:`bool` as a
105 sub-class of :class:`int`.
106 """)
108add_newdoc_for_scalar_type('byte', [],
109 """
110 Signed integer type, compatible with C ``char``.
111 """)
113add_newdoc_for_scalar_type('short', [],
114 """
115 Signed integer type, compatible with C ``short``.
116 """)
118add_newdoc_for_scalar_type('intc', [],
119 """
120 Signed integer type, compatible with C ``int``.
121 """)
123add_newdoc_for_scalar_type('int_', [],
124 """
125 Signed integer type, compatible with Python `int` and C ``long``.
126 """)
128add_newdoc_for_scalar_type('longlong', [],
129 """
130 Signed integer type, compatible with C ``long long``.
131 """)
133add_newdoc_for_scalar_type('ubyte', [],
134 """
135 Unsigned integer type, compatible with C ``unsigned char``.
136 """)
138add_newdoc_for_scalar_type('ushort', [],
139 """
140 Unsigned integer type, compatible with C ``unsigned short``.
141 """)
143add_newdoc_for_scalar_type('uintc', [],
144 """
145 Unsigned integer type, compatible with C ``unsigned int``.
146 """)
148add_newdoc_for_scalar_type('uint', [],
149 """
150 Unsigned integer type, compatible with C ``unsigned long``.
151 """)
153add_newdoc_for_scalar_type('ulonglong', [],
154 """
155 Signed integer type, compatible with C ``unsigned long long``.
156 """)
158add_newdoc_for_scalar_type('half', [],
159 """
160 Half-precision floating-point number type.
161 """)
163add_newdoc_for_scalar_type('single', [],
164 """
165 Single-precision floating-point number type, compatible with C ``float``.
166 """)
168add_newdoc_for_scalar_type('double', ['float_'],
169 """
170 Double-precision floating-point number type, compatible with Python `float`
171 and C ``double``.
172 """)
174add_newdoc_for_scalar_type('longdouble', ['longfloat'],
175 """
176 Extended-precision floating-point number type, compatible with C
177 ``long double`` but not necessarily with IEEE 754 quadruple-precision.
178 """)
180add_newdoc_for_scalar_type('csingle', ['singlecomplex'],
181 """
182 Complex number type composed of two single-precision floating-point
183 numbers.
184 """)
186add_newdoc_for_scalar_type('cdouble', ['cfloat', 'complex_'],
187 """
188 Complex number type composed of two double-precision floating-point
189 numbers, compatible with Python `complex`.
190 """)
192add_newdoc_for_scalar_type('clongdouble', ['clongfloat', 'longcomplex'],
193 """
194 Complex number type composed of two extended-precision floating-point
195 numbers.
196 """)
198add_newdoc_for_scalar_type('object_', [],
199 """
200 Any Python object.
201 """)
203add_newdoc_for_scalar_type('str_', ['unicode_'],
204 r"""
205 A unicode string.
207 When used in arrays, this type strips trailing null codepoints.
209 Unlike the builtin `str`, this supports the :ref:`python:bufferobjects`, exposing its
210 contents as UCS4:
212 >>> m = memoryview(np.str_("abc"))
213 >>> m.format
214 '3w'
215 >>> m.tobytes()
216 b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
217 """)
219add_newdoc_for_scalar_type('bytes_', ['string_'],
220 r"""
221 A byte string.
223 When used in arrays, this type strips trailing null bytes.
224 """)
226add_newdoc_for_scalar_type('void', [],
227 r"""
228 np.void(length_or_data, /, dtype=None)
230 Create a new structured or unstructured void scalar.
232 Parameters
233 ----------
234 length_or_data : int, array-like, bytes-like, object
235 One of multiple meanings (see notes). The length or
236 bytes data of an unstructured void. Or alternatively,
237 the data to be stored in the new scalar when `dtype`
238 is provided.
239 This can be an array-like, in which case an array may
240 be returned.
241 dtype : dtype, optional
242 If provided the dtype of the new scalar. This dtype must
243 be "void" dtype (i.e. a structured or unstructured void,
244 see also :ref:`defining-structured-types`).
246 ..versionadded:: 1.24
248 Notes
249 -----
250 For historical reasons and because void scalars can represent both
251 arbitrary byte data and structured dtypes, the void constructor
252 has three calling conventions:
254 1. ``np.void(5)`` creates a ``dtype="V5"`` scalar filled with five
255 ``\0`` bytes. The 5 can be a Python or NumPy integer.
256 2. ``np.void(b"bytes-like")`` creates a void scalar from the byte string.
257 The dtype itemsize will match the byte string length, here ``"V10"``.
258 3. When a ``dtype=`` is passed the call is rougly the same as an
259 array creation. However, a void scalar rather than array is returned.
261 Please see the examples which show all three different conventions.
263 Examples
264 --------
265 >>> np.void(5)
266 void(b'\x00\x00\x00\x00\x00')
267 >>> np.void(b'abcd')
268 void(b'\x61\x62\x63\x64')
269 >>> np.void((5, 3.2, "eggs"), dtype="i,d,S5")
270 (5, 3.2, b'eggs') # looks like a tuple, but is `np.void`
271 >>> np.void(3, dtype=[('x', np.int8), ('y', np.int8)])
272 (3, 3) # looks like a tuple, but is `np.void`
274 """)
276add_newdoc_for_scalar_type('datetime64', [],
277 """
278 If created from a 64-bit integer, it represents an offset from
279 ``1970-01-01T00:00:00``.
280 If created from string, the string can be in ISO 8601 date
281 or datetime format.
283 >>> np.datetime64(10, 'Y')
284 numpy.datetime64('1980')
285 >>> np.datetime64('1980', 'Y')
286 numpy.datetime64('1980')
287 >>> np.datetime64(10, 'D')
288 numpy.datetime64('1970-01-11')
290 See :ref:`arrays.datetime` for more information.
291 """)
293add_newdoc_for_scalar_type('timedelta64', [],
294 """
295 A timedelta stored as a 64-bit integer.
297 See :ref:`arrays.datetime` for more information.
298 """)
300add_newdoc('numpy.core.numerictypes', "integer", ('is_integer',
301 """
302 integer.is_integer() -> bool
304 Return ``True`` if the number is finite with integral value.
306 .. versionadded:: 1.22
308 Examples
309 --------
310 >>> np.int64(-2).is_integer()
311 True
312 >>> np.uint32(5).is_integer()
313 True
314 """))
316# TODO: work out how to put this on the base class, np.floating
317for float_name in ('half', 'single', 'double', 'longdouble'):
318 add_newdoc('numpy.core.numerictypes', float_name, ('as_integer_ratio',
319 """
320 {ftype}.as_integer_ratio() -> (int, int)
322 Return a pair of integers, whose ratio is exactly equal to the original
323 floating point number, and with a positive denominator.
324 Raise `OverflowError` on infinities and a `ValueError` on NaNs.
326 >>> np.{ftype}(10.0).as_integer_ratio()
327 (10, 1)
328 >>> np.{ftype}(0.0).as_integer_ratio()
329 (0, 1)
330 >>> np.{ftype}(-.25).as_integer_ratio()
331 (-1, 4)
332 """.format(ftype=float_name)))
334 add_newdoc('numpy.core.numerictypes', float_name, ('is_integer',
335 f"""
336 {float_name}.is_integer() -> bool
338 Return ``True`` if the floating point number is finite with integral
339 value, and ``False`` otherwise.
341 .. versionadded:: 1.22
343 Examples
344 --------
345 >>> np.{float_name}(-2.0).is_integer()
346 True
347 >>> np.{float_name}(3.2).is_integer()
348 False
349 """))
351for int_name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32',
352 'int64', 'uint64', 'int64', 'uint64', 'int64', 'uint64'):
353 # Add negative examples for signed cases by checking typecode
354 add_newdoc('numpy.core.numerictypes', int_name, ('bit_count',
355 f"""
356 {int_name}.bit_count() -> int
358 Computes the number of 1-bits in the absolute value of the input.
359 Analogous to the builtin `int.bit_count` or ``popcount`` in C++.
361 Examples
362 --------
363 >>> np.{int_name}(127).bit_count()
364 7""" +
365 (f"""
366 >>> np.{int_name}(-127).bit_count()
367 7
368 """ if dtype(int_name).char.islower() else "")))