Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/numpy/core/_add_newdocs_scalars.py: 92%

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 

11 

12############################################################################## 

13# 

14# Documentation for concrete scalar classes 

15# 

16############################################################################## 

17 

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()) 

29 

30 

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 ]) 

52 

53 

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 

65 

66 

67_system, _machine = _get_platform_and_machine() 

68_doc_alias_string = f":Alias on this platform ({_system} {_machine}):" 

69 

70 

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) 

74 

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) 

85 

86 docstring = f""" 

87 {doc.strip()} 

88 

89 :Character code: ``'{character_code}'`` 

90 {canonical_name_doc}{alias_doc} 

91 """ 

92 

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

94 

95 

96add_newdoc_for_scalar_type('bool_', ['bool8'], 

97 """ 

98 Boolean type (True or False), stored as a byte. 

99 

100 .. warning:: 

101 

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 """) 

107 

108add_newdoc_for_scalar_type('byte', [], 

109 """ 

110 Signed integer type, compatible with C ``char``. 

111 """) 

112 

113add_newdoc_for_scalar_type('short', [], 

114 """ 

115 Signed integer type, compatible with C ``short``. 

116 """) 

117 

118add_newdoc_for_scalar_type('intc', [], 

119 """ 

120 Signed integer type, compatible with C ``int``. 

121 """) 

122 

123add_newdoc_for_scalar_type('int_', [], 

124 """ 

125 Signed integer type, compatible with Python `int` and C ``long``. 

126 """) 

127 

128add_newdoc_for_scalar_type('longlong', [], 

129 """ 

130 Signed integer type, compatible with C ``long long``. 

131 """) 

132 

133add_newdoc_for_scalar_type('ubyte', [], 

134 """ 

135 Unsigned integer type, compatible with C ``unsigned char``. 

136 """) 

137 

138add_newdoc_for_scalar_type('ushort', [], 

139 """ 

140 Unsigned integer type, compatible with C ``unsigned short``. 

141 """) 

142 

143add_newdoc_for_scalar_type('uintc', [], 

144 """ 

145 Unsigned integer type, compatible with C ``unsigned int``. 

146 """) 

147 

148add_newdoc_for_scalar_type('uint', [], 

149 """ 

150 Unsigned integer type, compatible with C ``unsigned long``. 

151 """) 

152 

153add_newdoc_for_scalar_type('ulonglong', [], 

154 """ 

155 Signed integer type, compatible with C ``unsigned long long``. 

156 """) 

157 

158add_newdoc_for_scalar_type('half', [], 

159 """ 

160 Half-precision floating-point number type. 

161 """) 

162 

163add_newdoc_for_scalar_type('single', [], 

164 """ 

165 Single-precision floating-point number type, compatible with C ``float``. 

166 """) 

167 

168add_newdoc_for_scalar_type('double', ['float_'], 

169 """ 

170 Double-precision floating-point number type, compatible with Python `float` 

171 and C ``double``. 

172 """) 

173 

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 """) 

179 

180add_newdoc_for_scalar_type('csingle', ['singlecomplex'], 

181 """ 

182 Complex number type composed of two single-precision floating-point 

183 numbers. 

184 """) 

185 

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 """) 

191 

192add_newdoc_for_scalar_type('clongdouble', ['clongfloat', 'longcomplex'], 

193 """ 

194 Complex number type composed of two extended-precision floating-point 

195 numbers. 

196 """) 

197 

198add_newdoc_for_scalar_type('object_', [], 

199 """ 

200 Any Python object. 

201 """) 

202 

203add_newdoc_for_scalar_type('str_', ['unicode_'], 

204 r""" 

205 A unicode string. 

206 

207 When used in arrays, this type strips trailing null codepoints. 

208 

209 Unlike the builtin `str`, this supports the :ref:`python:bufferobjects`, exposing its 

210 contents as UCS4: 

211 

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 """) 

218 

219add_newdoc_for_scalar_type('bytes_', ['string_'], 

220 r""" 

221 A byte string. 

222 

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

224 """) 

225 

226add_newdoc_for_scalar_type('void', [], 

227 r""" 

228 np.void(length_or_data, /, dtype=None) 

229 

230 Create a new structured or unstructured void scalar. 

231 

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`). 

245 

246 ..versionadded:: 1.24 

247 

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: 

253 

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. 

260 

261 Please see the examples which show all three different conventions. 

262 

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` 

273 

274 """) 

275 

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. 

282 

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') 

289 

290 See :ref:`arrays.datetime` for more information. 

291 """) 

292 

293add_newdoc_for_scalar_type('timedelta64', [], 

294 """ 

295 A timedelta stored as a 64-bit integer. 

296 

297 See :ref:`arrays.datetime` for more information. 

298 """) 

299 

300add_newdoc('numpy.core.numerictypes', "integer", ('is_integer', 

301 """ 

302 integer.is_integer() -> bool 

303 

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

305 

306 .. versionadded:: 1.22 

307 

308 Examples 

309 -------- 

310 >>> np.int64(-2).is_integer() 

311 True 

312 >>> np.uint32(5).is_integer() 

313 True 

314 """)) 

315 

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) 

321 

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. 

325 

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))) 

333 

334 add_newdoc('numpy.core.numerictypes', float_name, ('is_integer', 

335 f""" 

336 {float_name}.is_integer() -> bool 

337 

338 Return ``True`` if the floating point number is finite with integral 

339 value, and ``False`` otherwise. 

340 

341 .. versionadded:: 1.22 

342 

343 Examples 

344 -------- 

345 >>> np.{float_name}(-2.0).is_integer() 

346 True 

347 >>> np.{float_name}(3.2).is_integer() 

348 False 

349 """)) 

350 

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 

357 

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++. 

360 

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 "")))