diff --git a/src/mdevaluate/extra/water.py b/src/mdevaluate/extra/water.py
index 0a64f3b..0e72f22 100644
--- a/src/mdevaluate/extra/water.py
+++ b/src/mdevaluate/extra/water.py
@@ -273,7 +273,9 @@ def LSI(
     return pd.DataFrame({"I": I, "P": P})
 
 
-def HDL_LDL_positions(frame, trajectory, selector=None):
+def HDL_LDL_positions(
+    frame: CoordinateFrame, selector: Optional[Callable] = None
+) -> Tuple[NDArray, NDArray]:
     atoms_PBC = pbc_points(frame, frame.box, thickness=0.7)
     atoms_tree = KDTree(atoms_PBC)
     if selector:
@@ -290,8 +292,12 @@ def HDL_LDL_positions(frame, trajectory, selector=None):
     return pos_HDL, pos_LDL
 
 
-def HDL_LDL_gr(trajectory, segments=10000, skip=0):
-    def gr_frame(frame, trajectory, bins):
+def HDL_LDL_gr(
+    trajectory: Coordinates, segments: int = 10000, skip: float = 0.1
+) -> pd.DataFrame:
+    def gr_frame(
+        frame: CoordinateFrame, trajectory: Coordinates, bins: ArrayLike
+    ) -> NDArray:
         atoms_ALL = frame
         atoms_HDL, atoms_LDL = HDL_LDL_positions(frame, trajectory)
 
@@ -334,20 +340,13 @@ def HDL_LDL_gr(trajectory, segments=10000, skip=0):
             dist_HDL_LDL, bins=bins, range=(0, bins[-1]), density=False
         )[0]
 
-        n = [len(atoms_ALL), len(atoms_HDL), len(atoms_LDL)] / np.prod(
-            np.diag(frame.box)
-        )
-
-        return (
-            np.array(
-                [
-                    hist_ALL_ALL / len(atoms_ALL),
-                    hist_HDL_HDL / len(atoms_HDL),
-                    hist_LDL_LDL / len(atoms_LDL),
-                    hist_HDL_LDL / len(atoms_HDL),
-                ]
-            ),
-            n,
+        return np.array(
+            [
+                hist_ALL_ALL / len(atoms_ALL),
+                hist_HDL_HDL / len(atoms_HDL),
+                hist_LDL_LDL / len(atoms_LDL),
+                hist_HDL_LDL / len(atoms_HDL),
+            ]
         )
 
     start_frame = trajectory[int(len(trajectory) * skip)]
@@ -358,24 +357,25 @@ def HDL_LDL_gr(trajectory, segments=10000, skip=0):
     )
 
     gr = []
-    ns = []
     for frame_index in frame_indices:
-        hists, n = gr_frame(trajectory[frame_index], trajectory, bins)
+        hists = gr_frame(trajectory[frame_index], trajectory, bins)
         gr.append(hists)
-        ns.append(n)
 
     gr = np.mean(gr, axis=0)
     gr = gr / (4 / 3 * np.pi * bins[1:] ** 3 - 4 / 3 * np.pi * bins[:-1] ** 3)
     r = bins[1:] - (bins[1] - bins[0]) / 2
-    n = np.mean(ns, axis=0)
 
     return pd.DataFrame(
         {"r": r, "gr_ALL": [0], "gr_HDL": gr[1], "gr_LDL": gr[2], "gr_MIX": gr[3]}
     )
 
 
-def HDL_LDL_concentration(trajectory, segments=10000, skip=0):
-    def HDL_LDL_concentration_frame(frame, trajectrory, bins):
+def HDL_LDL_concentration(
+    trajectory: Coordinates, segments: int = 10000, skip: float = 0.1
+) -> pd.DataFrame:
+    def HDL_LDL_concentration_frame(
+        frame: CoordinateFrame, bins: ArrayLike
+    ) -> Tuple[NDArray, NDArray]:
         atoms_HDL, atoms_LDL = HDL_LDL_positions(frame, trajectory)
         atoms_PBC_HDL = pbc_points(atoms_HDL, frame.box, thickness=0.61)
         atoms_PBC_LDL = pbc_points(atoms_LDL, frame.box, thickness=0.61)