dpnp.logical_or
- dpnp.logical_or(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)
Computes the logical OR for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.
For full documentation refer to
numpy.logical_or
.- Parameters:
x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array. Both inputs x1 and x2 can not be scalars at the same time.
x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array. Both inputs x1 and x2 can not be scalars at the same time. If
x1.shape != x2.shape
, they must be broadcastable to a common shape (which becomes the shape of the output).out ({None, dpnp.ndarray, usm_ndarray}, optional) -- Output array to populate. Array must have the correct shape and the expected data type. Default:
None
.order ({"C", "F", "A", "K"}, optional) -- Memory layout of the newly output array, if parameter out is
None
. Default:"K"
.
- Returns:
out -- An array containing the element-wise logical OR results. The shape is determined by broadcasting.
- Return type:
dpnp.ndarray
Limitations
Parameters where and subok are supported with their default values. Otherwise
NotImplementedError
exception will be raised.See also
dpnp.logical_and
Compute the truth value of x1 AND x2 element-wise.
dpnp.logical_not
Compute the truth value of NOT x element-wise.
dpnp.logical_xor
Compute the truth value of x1 XOR x2, element-wise.
dpnp.bitwise_or
Compute the bit-wise OR of two arrays element-wise.
Examples
>>> import dpnp as np >>> x1 = np.array([True, False]) >>> x2 = np.array([False, False]) >>> np.logical_or(x1, x2) array([ True, False])
>>> x = np.arange(5) >>> np.logical_or(x < 1, x > 3) array([ True, False, False, False, True])
The
|
operator can be used as a shorthand forlogical_or
on booleandpnp.ndarray
.>>> a = np.array([True, False]) >>> b = np.array([False, False]) >>> a | b array([ True, False])