public static ndarray in1d(ndarray ar1, ndarray ar2, bool assume_unique = false, bool invert = false)
{
/*
* Test whether each element of a 1-D array is also present in a second array.
*
* Returns a boolean array the same length as `ar1` that is True
* where an element of `ar1` is in `ar2` and False otherwise.
*
* We recommend using :func:`isin` instead of `in1d` for new code.
*
* Parameters
* ----------
* ar1 : (M,) array_like
* Input array.
* ar2 : array_like
* The values against which to test each value of `ar1`.
* assume_unique : bool, optional
* If True, the input arrays are both assumed to be unique, which
* can speed up the calculation. Default is False.
* invert : bool, optional
* If True, the values in the returned array are inverted (that is,
* False where an element of `ar1` is in `ar2` and True otherwise).
* Default is False. ``np.in1d(a, b, invert=True)`` is equivalent
* to (but is faster than) ``np.invert(in1d(a, b))``.
*
* .. versionadded:: 1.8.0
*
* Returns
* -------
* in1d : (M,) ndarray, bool
* The values `ar1[in1d]` are in `ar2`.
*
* See Also
* --------
* isin : Version of this function that preserves the
* shape of ar1.
* numpy.lib.arraysetops : Module with a number of other functions for
* performing set operations on arrays.
*
* Notes
* -----
* `in1d` can be considered as an element-wise function version of the
* python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly
* equivalent to ``np.array([item in b for item in a])``.
* However, this idea fails if `ar2` is a set, or similar (non-sequence)
* container: As ``ar2`` is converted to an array, in those cases
* ``asarray(ar2)`` is an object array rather than the expected array of
* contained values.
*
* .. versionadded:: 1.4.0
*
* Examples
* --------
* >>> test = np.array([0, 1, 2, 5, 0])
* >>> states = [0, 2]
* >>> mask = np.in1d(test, states)
* >>> mask
* array([ True, False, True, False, True])
* >>> test[mask]
* array([0, 2, 0])
* >>> mask = np.in1d(test, states, invert=True)
* >>> mask
* array([False, True, False, True, False])
* >>> test[mask]
* array([1, 5])
*/
// Ravel both arrays, behavior for the first array could be different
ar1 = np.asarray(ar1).ravel();
ar2 = np.asarray(ar2).ravel();
// This code is run when
// a) the first condition is true, making the code significantly faster
// b) the second condition is true (i.e. `ar1` or `ar2` may contain
// arbitrary objects), since then sorting is not guaranteed to work
if (len(ar2) < 10 * Math.Pow(len(ar1), 0.145))
{
ndarray mask;
if (invert)
{
mask = np.ones(new shape(len(ar1)), dtype: np.Bool);
foreach (dynamic a in ar2)
{
if (ar1.TypeNum == NPY_TYPES.NPY_STRING)
{
string aa = a.ToString();
ndarray temp = ar1.NotEquals(aa);
mask &= temp;
}
else
{
ndarray temp = ar1 != a;
mask &= temp;
}
}
}
else
{
mask = np.zeros(new shape(len(ar1)), dtype: np.Bool);
foreach (dynamic a in ar2)
{
if (ar1.TypeNum == NPY_TYPES.NPY_STRING)
{
string aa = a.ToString();
ndarray temp = ar1.Equals(aa);
mask |= temp;
}
else
{
ndarray temp = ar1 == a;
mask |= temp;
}
}
}
return(mask);
}
// Otherwise use sorting
ndarray rev_idx = null;
if (!assume_unique)
{
var temp = np.unique(ar1, return_inverse: true, return_index: true);
ar1 = temp.data;
rev_idx = temp.indices;
ar2 = np.unique(ar2).data;
}
ndarray ar = np.concatenate(ar1, ar2);
// We need this to be a stable sort, so always use 'mergesort'
// here. The values from the first array should always come before
// the values from the second array.
ndarray order = ar.ArgSort(kind: NPY_SORTKIND.NPY_MERGESORT);
ndarray bool_ar;
ndarray sar = ar.A(order);
if (invert)
{
bool_ar = (sar.A("1:")).NotEquals(sar.A(":-1"));
}
else
{
bool_ar = (sar.A("1:")).Equals(sar.A(":-1"));
}
ndarray flag = np.concatenate(new ndarray[] { bool_ar, np.array(new bool[] { invert }) });
ndarray ret = np.empty(ar.shape, dtype: np.Bool);
ret[order] = flag;
if (assume_unique)
{
return(ret.A(":" + len(ar1).ToString()));
}
else
{
return(ret.A(rev_idx));
}
}
