# numpy.isposinf¶

numpy.isposinf(x, y=None)

Shows which elements of the input are positive infinity.

Returns a numpy array resulting from an element-wise test for positive infinity.

Parameters: x : array_like The input array. y : array_like A boolean array with the same shape as x to store the result. y : ndarray A numpy boolean array with the same dimensions as the input. If second argument is not supplied then a numpy boolean array is returned with values True where the corresponding element of the input is positive infinity and values False where the element of the input is not positive infinity. If second argument is supplied then an numpy integer array is returned with values 1 where the corresponding element of the input is positive positive infinity.

isinf
Shows which elements are negative or positive infinity.
isneginf
Shows which elements are negative infinity.
isnan
Shows which elements are Not a Number (NaN).
isfinite
Shows which elements are not: Not a number, positive and negative infinity

Notes

Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity.

Errors result if second argument is also supplied with scalar input or if first and second arguments have different shapes.

Numpy’s definitions for positive infinity (PINF) and negative infinity (NINF) may be change in the future versions.

Examples

```>>> np.isposinf(np.PINF)
array(True, dtype=bool)
>>> np.isposinf(np.inf)
array(True, dtype=bool)
>>> np.isposinf(np.NINF)
array(False, dtype=bool)
>>> np.isposinf([-np.inf, 0., np.inf])
array([False, False,  True], dtype=bool)
>>> x=np.array([-np.inf, 0., np.inf])
>>> y=np.array([2,2,2])
>>> np.isposinf(x,y)
array([1, 0, 0])
>>> y
array([1, 0, 0])
```

numpy.isneginf

numpy.iscomplex