nlcpy.logical_xor
- nlcpy.logical_xor = <ufunc 'nlcpy_logical_xor'>
Computes the logical XOR of two arrays element-wise.
- Parameters
- x1, x2array_like
Input arrays or scalars. If
x1.shape != x2.shape
, they must be broadcastable to a common shape (which becomes the shape of the output).- outndarray or 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.
- wherearray_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 section Optional Keyword Arguments.
- Returns
- yndarray
Boolean result of the logical XOR operation applied to the elements of x1 and x2; the shape is determined by broadcasting. If x1 and x2 are both scalars, this function returns the result as a 0-dimension ndarray.
参考
logical_or
Computes the logical OR of two arrays element-wise.
logical_not
Computes the logical NOT of the input array element-wise.
logical_xor
Computes the logical XOR of two arrays element-wise.
bitwise_and
Computes the bit-wise AND of two arrays element-wise.
Examples
>>> import nlcpy as vp >>> vp.logical_xor(True, False) array(True) >>> vp.logical_xor([True, True, False, False], [True, False, True, False]) array([False, True, True, False]) >>> x = vp.arange(5) >>> vp.logical_xor(x < 1, x > 3) array([ True, False, False, False, True])
Simple example showing support of broadcasting
>>> vp.logical_xor(0, vp.eye(2)) array([[ True, False], [False, True]])