Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.9/dist-packages/numpy/lib/ufunclike.py: 56%

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 array_function_dispatch 

10import warnings 

11import functools 

12 

13 

14def _dispatcher(x, out=None): 

15 return (x, out) 

16 

17 

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

19def fix(x, out=None): 

20 """ 

21 Round to nearest integer towards zero. 

22 

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

24 The rounded values are returned as floats. 

25 

26 Parameters 

27 ---------- 

28 x : array_like 

29 An array of floats to be rounded 

30 out : ndarray, optional 

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

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

33 freshly-allocated array is returned. 

34 

35 Returns 

36 ------- 

37 out : ndarray of floats 

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

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

40 with the rounded values. 

41 

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

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

44 

45 See Also 

46 -------- 

47 rint, trunc, floor, ceil 

48 around : Round to given number of decimals 

49 

50 Examples 

51 -------- 

52 >>> np.fix(3.14) 

53 3.0 

54 >>> np.fix(3) 

55 3.0 

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

57 array([ 2., 2., -2., -2.]) 

58 

59 """ 

60 # promote back to an array if flattened 

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

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

63 

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

65 # scalars 

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

67 res = res[()] 

68 return res 

69 

70 

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

72def isposinf(x, out=None): 

73 """ 

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

75 

76 Parameters 

77 ---------- 

78 x : array_like 

79 The input array. 

80 out : array_like, optional 

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

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

83 freshly-allocated boolean array is returned. 

84 

85 Returns 

86 ------- 

87 out : ndarray 

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

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

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

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

92 not positive infinity. 

93 

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

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

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

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

98 

99 See Also 

100 -------- 

101 isinf, isneginf, isfinite, isnan 

102 

103 Notes 

104 ----- 

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

106 (IEEE 754). 

107 

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

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

110 first argument has complex values 

111 

112 Examples 

113 -------- 

114 >>> np.isposinf(np.PINF) 

115 True 

116 >>> np.isposinf(np.inf) 

117 True 

118 >>> np.isposinf(np.NINF) 

119 False 

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

121 array([False, False, True]) 

122 

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

124 >>> y = np.array([2, 2, 2]) 

125 >>> np.isposinf(x, y) 

126 array([0, 0, 1]) 

127 >>> y 

128 array([0, 0, 1]) 

129 

130 """ 

131 is_inf = nx.isinf(x) 

132 try: 

133 signbit = ~nx.signbit(x) 

134 except TypeError as e: 

135 dtype = nx.asanyarray(x).dtype 

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

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

138 else: 

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

140 

141 

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

143def isneginf(x, out=None): 

144 """ 

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

146 

147 Parameters 

148 ---------- 

149 x : array_like 

150 The input array. 

151 out : array_like, optional 

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

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

154 freshly-allocated boolean array is returned. 

155 

156 Returns 

157 ------- 

158 out : ndarray 

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

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

161 returned with values True where the corresponding element of the 

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

163 the input is not negative infinity. 

164 

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

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

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

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

169 

170 See Also 

171 -------- 

172 isinf, isposinf, isnan, isfinite 

173 

174 Notes 

175 ----- 

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

177 (IEEE 754). 

178 

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

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

181 first argument has complex values. 

182 

183 Examples 

184 -------- 

185 >>> np.isneginf(np.NINF) 

186 True 

187 >>> np.isneginf(np.inf) 

188 False 

189 >>> np.isneginf(np.PINF) 

190 False 

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

192 array([ True, False, False]) 

193 

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

195 >>> y = np.array([2, 2, 2]) 

196 >>> np.isneginf(x, y) 

197 array([1, 0, 0]) 

198 >>> y 

199 array([1, 0, 0]) 

200 

201 """ 

202 is_inf = nx.isinf(x) 

203 try: 

204 signbit = nx.signbit(x) 

205 except TypeError as e: 

206 dtype = nx.asanyarray(x).dtype 

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

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

209 else: 

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