numpy.heaviside¶

numpy.
heaviside
(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'heaviside'>¶ Compute the Heaviside step function.
The Heaviside step function is defined as:
0 if x1 < 0 heaviside(x1, x2) = x2 if x1 == 0 1 if x1 > 0
where x2 is often taken to be 0.5, but 0 and 1 are also sometimes used.
Parameters:  x1 : array_like
Input values.
 x2 : array_like
The value of the function when x1 is 0. If
x1.shape != x2.shape
, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshlyallocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
 where : array_like, optional
This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default
out=None
, locations within it where the condition is False will remain uninitialized. **kwargs
For other keywordonly arguments, see the ufunc docs.
Returns:  out : ndarray or scalar
The output array, elementwise Heaviside step function of x1. This is a scalar if both x1 and x2 are scalars.
Notes
New in version 1.13.0.
References
Examples
>>> np.heaviside([1.5, 0, 2.0], 0.5) array([ 0. , 0.5, 1. ]) >>> np.heaviside([1.5, 0, 2.0], 1) array([ 0., 1., 1.])