flopscope.numpy.logical

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

Adapted from NumPy docs np.logical_and

Areacore

Typecounted

Cost

\text{numel}(\text{output})

Flopscope Context

Element-wise logical AND.

Parameters

x1, x2:array_like: Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
out:ndarray, None, or tuple of ndarray and None, optional: A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
where:array_like, optional: This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.
**kwargs: For other keyword-only arguments, see the ufunc docs.

y:ndarray or bool: Boolean result of the logical AND operation applied to the elements of x1 and x2; the shape is determined by broadcasting. This is a scalar if both x1 and x2 are scalars.

>>> import flopscope.numpy as fnp
>>> flops.logical_and(True, False)
False
>>> flops.logical_and([True, False], [False, False])
array([False, False])

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

The & operator can be used as a shorthand for flops.logical_and on boolean ndarrays.

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