dpnp.bitwise_or

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

Computes the bitwise OR 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_or.

Parameters:

  • x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have an integer or boolean data type.

  • x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have an integer or boolean data type.

  • 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 ({None, "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_or

Compute the truth value of x1 OR x2 element-wise.

dpnp.bitwise_and

Compute the bit-wise AND of two arrays element-wise.

dpnp.bitwise_xor

Compute the bit-wise XOR of two arrays element-wise.

dpnp.binary_repr

Return the binary representation of the input number as a string.

Notes

At least one of x1 or x2 must be an array.

If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

Examples

>>> import dpnp as np
>>> x1 = np.array([2, 5, 255])
>>> x2 = np.array([4])
>>> np.bitwise_or(x1, x2)
array([  6,   5, 255])

The | operator can be used as a shorthand for bitwise_or on dpnp.ndarray.

>>> x1 | x2
array([  6,   5, 255])

The number 13 has the binary representation 00001101. Likewise, 16 is represented by 00010000. The bit-wise OR of 13 and 16 is then 00011101, or 29:

>>> np.bitwise_or(np.array(13), 16)
array(29)
>>> np.binary_repr(29)
'11101'