diff --git a/src/mdevaluate/extra/chill.py b/src/mdevaluate/extra/chill.py
index f626915..11ec8b2 100644
--- a/src/mdevaluate/extra/chill.py
+++ b/src/mdevaluate/extra/chill.py
@@ -11,7 +11,7 @@ from mdevaluate.coordinates import CoordinateFrame, Coordinates
 from mdevaluate.pbc import pbc_points
 
 
-def a_ij(atoms: ArrayLike, N: int = 4, l: int = 3) -> tuple[NDArray, NDArray]:
+def calc_aij(atoms: ArrayLike, N: int = 4, l: int = 3) -> tuple[NDArray, NDArray]:
     tree = KDTree(atoms)
 
     dist, indices = tree.query(atoms, N + 1)
@@ -84,18 +84,18 @@ def count_ice_types(iceTypes: NDArray) -> NDArray:
 
 
 def selector_ice(
-    start_frame: CoordinateFrame,
-    traj: Coordinates,
+    oxygen_atoms_water: CoordinateFrame,
     chosen_ice_types: ArrayLike,
     combined: bool = True,
+    next_neighbor_distance: float = 0.35,
 ) -> NDArray:
-    oxygen = traj.subset(atom_name="OW")
-    atoms = oxygen[start_frame.step]
-    atoms = atoms % np.diag(atoms.box)
-    atoms_PBC = pbc_points(atoms, thickness=1)
-    aij, indices = a_ij(atoms_PBC)
+    atoms = oxygen_atoms_water
+    atoms_PBC = pbc_points(atoms, thickness=next_neighbor_distance * 2.2)
+    aij, indices = calc_aij(atoms_PBC)
     tree = KDTree(atoms_PBC)
-    neighbors = tree.query_ball_point(atoms_PBC, 0.35, return_length=True)
+    neighbors = tree.query_ball_point(
+        atoms_PBC, next_neighbor_distance, return_length=True
+    ) - 1
     index_SOL = atoms_PBC.tolist().index(atoms[0].tolist())
     index_SOL = np.arange(index_SOL, index_SOL + len(atoms))
     ice_Types = classify_ice(aij, indices, neighbors, index_SOL)
@@ -117,9 +117,9 @@ def selector_ice(
 def ice_types(trajectory: Coordinates, segments: int = 10000) -> pd.DataFrame:
     def ice_types_distribution(frame: CoordinateFrame, selector: Callable) -> NDArray:
         atoms_PBC = pbc_points(frame, thickness=1)
-        aij, indices = a_ij(atoms_PBC)
+        aij, indices = calc_aij(atoms_PBC)
         tree = KDTree(atoms_PBC)
-        neighbors = tree.query_ball_point(atoms_PBC, 0.35, return_length=True)
+        neighbors = tree.query_ball_point(atoms_PBC, 0.35, return_length=True) - 1
         index = selector(frame, atoms_PBC)
         ice_types_data = classify_ice(aij, indices, neighbors, index)
         ice_parts_data = count_ice_types(ice_types_data)