Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/numpy/lib/ufunclike.py: 47%

1""" 

2Module of functions that are like ufuncs in acting on arrays and optionally 

3storing results in an output array. 

4 

5""" 

6__all__ = ['fix', 'isneginf', 'isposinf'] 

7 

8import numpy.core.numeric as nx 

9from numpy.core.overrides import ( 

10 array_function_dispatch, ARRAY_FUNCTION_ENABLED, 

11) 

12import warnings 

13import functools 

14 

15 

16def _deprecate_out_named_y(f): 

17 """ 

18 Allow the out argument to be passed as the name `y` (deprecated) 

19 

20 In future, this decorator should be removed. 

21 """ 

22 @functools.wraps(f) 

23 def func(x, out=None, **kwargs): 

24 if 'y' in kwargs: 

25 if 'out' in kwargs: 

26 raise TypeError( 

27 "{} got multiple values for argument 'out'/'y'" 

28 .format(f.__name__) 

29 ) 

30 out = kwargs.pop('y') 

31 # NumPy 1.13.0, 2017-04-26 

32 warnings.warn( 

33 "The name of the out argument to {} has changed from `y` to " 

34 "`out`, to match other ufuncs.".format(f.__name__), 

35 DeprecationWarning, stacklevel=3) 

36 return f(x, out=out, **kwargs) 

37 

38 return func 

39 

40 

41def _fix_out_named_y(f): 

42 """ 

43 Allow the out argument to be passed as the name `y` (deprecated) 

44 

45 This decorator should only be used if _deprecate_out_named_y is used on 

46 a corresponding dispatcher function. 

47 """ 

48 @functools.wraps(f) 

49 def func(x, out=None, **kwargs): 

50 if 'y' in kwargs: 

51 # we already did error checking in _deprecate_out_named_y 

52 out = kwargs.pop('y') 

53 return f(x, out=out, **kwargs) 

54 

55 return func 

56 

57 

58def _fix_and_maybe_deprecate_out_named_y(f): 

59 """ 

60 Use the appropriate decorator, depending upon if dispatching is being used. 

61 """ 

62 if ARRAY_FUNCTION_ENABLED: 

63 return _fix_out_named_y(f) 

64 else: 

65 return _deprecate_out_named_y(f) 

66 

67 

68@_deprecate_out_named_y 

69def _dispatcher(x, out=None): 

70 return (x, out) 

71 

72 

73@array_function_dispatch(_dispatcher, verify=False, module='numpy') 

74@_fix_and_maybe_deprecate_out_named_y 

75def fix(x, out=None): 

76 """ 

77 Round to nearest integer towards zero. 

78 

79 Round an array of floats element-wise to nearest integer towards zero. 

80 The rounded values are returned as floats. 

81 

82 Parameters 

83 ---------- 

84 x : array_like 

85 An array of floats to be rounded 

86 out : ndarray, optional 

87 A location into which the result is stored. If provided, it must have 

88 a shape that the input broadcasts to. If not provided or None, a 

89 freshly-allocated array is returned. 

90 

91 Returns 

92 ------- 

93 out : ndarray of floats 

94 A float array with the same dimensions as the input. 

95 If second argument is not supplied then a float array is returned 

96 with the rounded values. 

97 

98 If a second argument is supplied the result is stored there. 

99 The return value `out` is then a reference to that array. 

100 

101 See Also 

102 -------- 

103 rint, trunc, floor, ceil 

104 around : Round to given number of decimals 

105 

106 Examples 

107 -------- 

108 >>> np.fix(3.14) 

109 3.0 

110 >>> np.fix(3) 

111 3.0 

112 >>> np.fix([2.1, 2.9, -2.1, -2.9]) 

113 array([ 2., 2., -2., -2.]) 

114 

115 """ 

116 # promote back to an array if flattened 

117 res = nx.asanyarray(nx.ceil(x, out=out)) 

118 res = nx.floor(x, out=res, where=nx.greater_equal(x, 0)) 

119 

120 # when no out argument is passed and no subclasses are involved, flatten 

121 # scalars 

122 if out is None and type(res) is nx.ndarray: 

123 res = res[()] 

124 return res 

125 

126 

127@array_function_dispatch(_dispatcher, verify=False, module='numpy') 

128@_fix_and_maybe_deprecate_out_named_y 

129def isposinf(x, out=None): 

130 """ 

131 Test element-wise for positive infinity, return result as bool array. 

132 

133 Parameters 

134 ---------- 

135 x : array_like 

136 The input array. 

137 out : array_like, optional 

138 A location into which the result is stored. If provided, it must have a 

139 shape that the input broadcasts to. If not provided or None, a 

140 freshly-allocated boolean array is returned. 

141 

142 Returns 

143 ------- 

144 out : ndarray 

145 A boolean array with the same dimensions as the input. 

146 If second argument is not supplied then a boolean array is returned 

147 with values True where the corresponding element of the input is 

148 positive infinity and values False where the element of the input is 

149 not positive infinity. 

150 

151 If a second argument is supplied the result is stored there. If the 

152 type of that array is a numeric type the result is represented as zeros 

153 and ones, if the type is boolean then as False and True. 

154 The return value `out` is then a reference to that array. 

155 

156 See Also 

157 -------- 

158 isinf, isneginf, isfinite, isnan 

159 

160 Notes 

161 ----- 

162 NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic 

163 (IEEE 754). 

164 

165 Errors result if the second argument is also supplied when x is a scalar 

166 input, if first and second arguments have different shapes, or if the 

167 first argument has complex values 

168 

169 Examples 

170 -------- 

171 >>> np.isposinf(np.PINF) 

172 True 

173 >>> np.isposinf(np.inf) 

174 True 

175 >>> np.isposinf(np.NINF) 

176 False 

177 >>> np.isposinf([-np.inf, 0., np.inf]) 

178 array([False, False, True]) 

179 

180 >>> x = np.array([-np.inf, 0., np.inf]) 

181 >>> y = np.array([2, 2, 2]) 

182 >>> np.isposinf(x, y) 

183 array([0, 0, 1]) 

184 >>> y 

185 array([0, 0, 1]) 

186 

187 """ 

188 is_inf = nx.isinf(x) 

189 try: 

190 signbit = ~nx.signbit(x) 

191 except TypeError as e: 

192 dtype = nx.asanyarray(x).dtype 

193 raise TypeError(f'This operation is not supported for {dtype} values ' 

194 'because it would be ambiguous.') from e 

195 else: 

196 return nx.logical_and(is_inf, signbit, out) 

197 

198 

199@array_function_dispatch(_dispatcher, verify=False, module='numpy') 

200@_fix_and_maybe_deprecate_out_named_y 

201def isneginf(x, out=None): 

202 """ 

203 Test element-wise for negative infinity, return result as bool array. 

204 

205 Parameters 

206 ---------- 

207 x : array_like 

208 The input array. 

209 out : array_like, optional 

210 A location into which the result is stored. If provided, it must have a 

211 shape that the input broadcasts to. If not provided or None, a 

212 freshly-allocated boolean array is returned. 

213 

214 Returns 

215 ------- 

216 out : ndarray 

217 A boolean array with the same dimensions as the input. 

218 If second argument is not supplied then a numpy boolean array is 

219 returned with values True where the corresponding element of the 

220 input is negative infinity and values False where the element of 

221 the input is not negative infinity. 

222 

223 If a second argument is supplied the result is stored there. If the 

224 type of that array is a numeric type the result is represented as 

225 zeros and ones, if the type is boolean then as False and True. The 

226 return value `out` is then a reference to that array. 

227 

228 See Also 

229 -------- 

230 isinf, isposinf, isnan, isfinite 

231 

232 Notes 

233 ----- 

234 NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic 

235 (IEEE 754). 

236 

237 Errors result if the second argument is also supplied when x is a scalar 

238 input, if first and second arguments have different shapes, or if the 

239 first argument has complex values. 

240 

241 Examples 

242 -------- 

243 >>> np.isneginf(np.NINF) 

244 True 

245 >>> np.isneginf(np.inf) 

246 False 

247 >>> np.isneginf(np.PINF) 

248 False 

249 >>> np.isneginf([-np.inf, 0., np.inf]) 

250 array([ True, False, False]) 

251 

252 >>> x = np.array([-np.inf, 0., np.inf]) 

253 >>> y = np.array([2, 2, 2]) 

254 >>> np.isneginf(x, y) 

255 array([1, 0, 0]) 

256 >>> y 

257 array([1, 0, 0]) 

258 

259 """ 

260 is_inf = nx.isinf(x) 

261 try: 

262 signbit = nx.signbit(x) 

263 except TypeError as e: 

264 dtype = nx.asanyarray(x).dtype 

265 raise TypeError(f'This operation is not supported for {dtype} values ' 

266 'because it would be ambiguous.') from e 

267 else: 

268 return nx.logical_and(is_inf, signbit, out)