dpnp.logical_and

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

Computes the logical AND 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_and.

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 AND 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_or

Compute the truth value of x1 OR 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_and

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

Examples

>>> import dpnp as np
>>> x1 = np.array([True, False])
>>> x2 = np.array([False, False])
>>> np.logical_and(x1, x2)
array([False, False])

>>> x = np.arange(5)
>>> np.logical_and(x > 1, x < 4)
array([False, False,  True,  True, False])

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

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