dpnp.bitwise_and¶

dpnp.bitwise_and = <DPNPBinaryFunc 'bitwise_and'>¶

Computes the bitwise AND 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_and.

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, tuple of ndarray}, optional

Output array to populate. Array must have the correct shape and the expected data type. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

Default: None.

order{None, "C", "F", "A", "K"}, optional

Memory layout of the newly output array, if parameter out is None.

Default: "K".

Returns:

outdpnp.ndarray: An array containing the element-wise results. The data type of the returned array is determined by the Type Promotion Rules.

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_and: Compute the truth value of x1 AND x2 element-wise.
dpnp.bitwise_or: Compute the bit-wise OR 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([3, 14, 16])
>>> np.bitwise_and(x1, x2)
array([ 2,  4, 16])

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

The & operator can be used as a shorthand for bitwise_and on dpnp.ndarray.

>>> x1 & x2
array([ 2,  4, 16])

The number 13 is represented by 00001101. Likewise, 17 is represented by 00010001. The bit-wise AND of 13 and 17 is therefore 000000001, or 1:

>>> np.bitwise_and(np.array(13), 17)
array(1)

>>> np.bitwise_and(np.array(14), 13)
array(12)
>>> np.binary_repr(12)
'1100'
>>> np.bitwise_and(np.array([14, 3]), 13)
array([12,  1])