Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.9/dist-packages/numpy/lib/_ufunclike_impl.py: 38%

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.inf) 

115 True 

116 >>> np.isposinf(-np.inf) 

117 False 

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

119 array([False, False, True]) 

120 

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

122 >>> y = np.array([2, 2, 2]) 

123 >>> np.isposinf(x, y) 

124 array([0, 0, 1]) 

125 >>> y 

126 array([0, 0, 1]) 

127 

128 """ 

129 is_inf = nx.isinf(x) 

130 try: 

131 signbit = ~nx.signbit(x) 

132 except TypeError as e: 

133 dtype = nx.asanyarray(x).dtype 

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

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

136 else: 

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

138 

139 

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

141def isneginf(x, out=None): 

142 """ 

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

144 

145 Parameters 

146 ---------- 

147 x : array_like 

148 The input array. 

149 out : array_like, optional 

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

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

152 freshly-allocated boolean array is returned. 

153 

154 Returns 

155 ------- 

156 out : ndarray 

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

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

159 returned with values True where the corresponding element of the 

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

161 the input is not negative infinity. 

162 

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

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

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

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

167 

168 See Also 

169 -------- 

170 isinf, isposinf, isnan, isfinite 

171 

172 Notes 

173 ----- 

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

175 (IEEE 754). 

176 

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

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

179 first argument has complex values. 

180 

181 Examples 

182 -------- 

183 >>> np.isneginf(-np.inf) 

184 True 

185 >>> np.isneginf(np.inf) 

186 False 

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

188 array([ True, False, False]) 

189 

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

191 >>> y = np.array([2, 2, 2]) 

192 >>> np.isneginf(x, y) 

193 array([1, 0, 0]) 

194 >>> y 

195 array([1, 0, 0]) 

196 

197 """ 

198 is_inf = nx.isinf(x) 

199 try: 

200 signbit = nx.signbit(x) 

201 except TypeError as e: 

202 dtype = nx.asanyarray(x).dtype 

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

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

205 else: 

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