logical_xor= <xarray.ufuncs._UFuncDispatcher object>¶
xarray specific variant of numpy.logical_xor. Handles xarray.Dataset, xarray.DataArray, xarray.Variable, numpy.ndarray and dask.array.Array objects with automatic dispatching.
Documentation from numpy:
logical_xor(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])
Compute the truth value of x1 XOR x2, element-wise.
- x1, x2 : array_like
Logical XOR is applied to the elements of x1 and x2. They must be broadcastable to the same shape.
- 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
Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.
For other keyword-only arguments, see the ufunc docs.
- y : bool or ndarray of bool
Boolean result of the logical XOR operation applied to the elements of x1 and x2; the shape is determined by whether or not broadcasting of one or both arrays was required.
>>> np.logical_xor(True, False) True >>> np.logical_xor([True, True, False, False], [True, False, True, False]) array([False, True, True, False])
>>> x = np.arange(5) >>> np.logical_xor(x < 1, x > 3) array([ True, False, False, False, True])
Simple example showing support of broadcasting
>>> np.logical_xor(0, np.eye(2)) array([[ True, False], [False, True]])