allow passing mask to clustervector (#304)

- passing mask to ClusterVector 
- creates a copy of the ClusterVector

Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>

python/src/bind_ClusterVector.hpp

@@ -35,6 +35,22 @@ void define_ClusterVector(py::module &m, const std::string &typestr) {
 
         .def(py::init()) // TODO change!!!
 
+        .def(
+            "__call__",
+            [](ClusterVector<ClusterType> &self, py::array_t<bool> mask) {
+                return self(make_view_1d(mask));
+            },
+            py::arg("mask"), R"(
+            Create a copy of the clustervector and apply a boolean mask to the ClusterVector.
+
+            Parameters
+            ----------
+
+            mask : 1d boolean numpy array
+                Mask to apply to the ClusterVector. Must be the same length as the number of clusters in the ClusterVector. 
+
+            )")
+
         .def("push_back",
              [](ClusterVector<ClusterType> &self, const ClusterType &cluster) {
                  self.push_back(cluster);

python/tests/test_ClusterVector.py

@@ -109,4 +109,22 @@ def test_3x3_reduction():
     assert reduced_cv.size == 2
     assert reduced_cv[0]["x"] == 5
     assert reduced_cv[0]["y"] == 5
-    assert (reduced_cv[0]["data"] == np.array([[2.0, 1.0, 1.0], [2.0, 3.0, 1.0], [2.0, 1.0, 1.0]], dtype=np.double)).all()
+    assert (reduced_cv[0]["data"] == np.array([[2.0, 1.0, 1.0], [2.0, 3.0, 1.0], [2.0, 1.0, 1.0]], dtype=np.double)).all()
+
+
+def test_masking(): 
+
+    cv = _aare.ClusterVector_Cluster3x3i()
+    cv.push_back(_aare.Cluster3x3i(19, 22, np.array([0,1,0,2,3,0,2,1,0], dtype=np.int32)))
+    cv.push_back(_aare.Cluster3x3i(1, 2, np.ones(9, dtype=np.int32)))
+    assert cv.size == 2
+
+    mask = np.array([False, True], dtype=bool)
+    cv_masked = cv(mask)
+    assert cv_masked.size == 1
+
+    cv_masked_array = np.array(cv_masked, copy=False)
+    
+    assert cv_masked_array[0]["x"] == 1
+    assert cv_masked_array[0]["y"] == 2
+    assert (cv_masked_array[0]["data"] == np.ones((3,3),dtype=np.int32)).all()