public static ndarray setxor1d(ndarray ar1, ndarray ar2, bool assume_unique = false)
{
/*
* Find the set exclusive-or of two arrays.
*
* Return the sorted, unique values that are in only one (not both) of the
* input arrays.
*
* Parameters
* ----------
* ar1, ar2 : array_like
* Input arrays.
* assume_unique : bool
* If True, the input arrays are both assumed to be unique, which
* can speed up the calculation. Default is False.
*
* Returns
* -------
* setxor1d : ndarray
* Sorted 1D array of unique values that are in only one of the input
* arrays.
*
* Examples
* --------
* >>> a = np.array([1, 2, 3, 2, 4])
* >>> b = np.array([2, 3, 5, 7, 5])
* >>> np.setxor1d(a,b)
* array([1, 4, 5, 7])
*/
if (!assume_unique)
{
ar1 = unique(ar1).data;
ar2 = unique(ar2).data;
}
ndarray aux = np.concatenate(new ndarray[] { ar1, ar2 });
if (aux.size == 0)
{
return(aux);
}
aux = aux.Sort();
ndarray True1 = np.array(new bool[] { true });
ndarray True2 = np.array(new bool[] { true });
ndarray flag = np.concatenate(new ndarray[] { True1, (aux.A("1:")).NotEquals(aux.A(":-1")), True2 });
ndarray mask = flag.A("1:") & flag.A(":-1");
return(aux.A(mask));
}
public static ndarray intersect1d(ndarray ar1, ndarray ar2, bool assume_unique = false)
{
/*
* Find the intersection of two arrays.
*
* Return the sorted, unique values that are in both of the input arrays.
*
* Parameters
* ----------
* ar1, ar2 : array_like
* Input arrays.
* assume_unique : bool
* If True, the input arrays are both assumed to be unique, which
* can speed up the calculation. Default is False.
*
* Returns
* -------
* intersect1d : ndarray
* Sorted 1D array of common and unique elements.
*
* See Also
* --------
* numpy.lib.arraysetops : Module with a number of other functions for
* performing set operations on arrays.
*
* Examples
* --------
* >>> np.intersect1d([1, 3, 4, 3], [3, 1, 2, 1])
* array([1, 3])
*
* To intersect more than two arrays, use functools.reduce:
*
* >>> from functools import reduce
* >>> reduce(np.intersect1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2]))
* array([3])
*/
if (!assume_unique)
{
//Might be faster than unique( intersect1d( ar1, ar2 ) )?
ar1 = unique(ar1).data;
ar2 = unique(ar2).data;
}
ndarray aux = np.concatenate(new ndarray[] { ar1, ar2 });
aux = aux.Sort();
ndarray mask = (aux.A("1:")).Equals(aux.A(":-1"));
return(aux.A(":-1", mask));
}