dpnp.bitwise_xor

dpnp.bitwise_xor(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)

Computes the bitwise XOR of the underlying binary representation of 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.bitwise_xor.

Parameters:

x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have integer or boolean data type. Both inputs x1 and x2 can not be scalars at the same time.
x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have integer or boolean data type. 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 results. The data type of the returned array is determined by the Type Promotion Rules.

Return type:

dpnp.ndarray

Limitations

Parameters where and subok are supported with their default values. Keyword argument kwargs is currently unsupported. Otherwise NotImplementedError exception will be raised.

See also

dpnp.logical_xor: Compute the truth value of x1 XOR x2, element-wise.
dpnp.bitwise_and: Compute the bit-wise AND of two arrays element-wise.
dpnp.bitwise_or: Compute the bit-wise OR of two arrays element-wise.
dpnp.binary_repr: Return the binary representation of the input number as a string.

Examples

>>> import dpnp as np
>>> x1 = np.array([31, 3])
>>> x2 = np.array([5, 6])
>>> np.bitwise_xor(x1, x2)
array([26,  5])

>>> a = np.array([True, True])
>>> b = np.array([False, True])
>>> np.bitwise_xor(a, b)
array([ True, False])

The ^ operator can be used as a shorthand for bitwise_xor on dpnp.ndarray.

>>> a ^ b
array([ True, False])

The number 13 is represented by 00001101. Likewise, 17 is represented by 00010001. The bit-wise XOR of 13 and 17 is therefore 00011100, or 28:

>>> np.bitwise_xor(np.array(13), 17)
array(28)
>>> np.binary_repr(28)
'11100'