Test element-wise for negative infinity, return result as bool array. Parameters ---------- x : array_like The input array. out : array_like, optional A location into which the result is stored. If provided, it must have a shape that the input broadcasts
(x, out=None)
| 141 | |
| 142 | @array_function_dispatch(_dispatcher, verify=False, module='numpy') |
| 143 | def 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 |
no outgoing calls