diff --git a/fixtracks/utils/trackingdata.py b/fixtracks/utils/trackingdata.py
index a41c120..e3d8625 100644
--- a/fixtracks/utils/trackingdata.py
+++ b/fixtracks/utils/trackingdata.py
@@ -5,6 +5,7 @@ import pandas as pd
 
 from PySide6.QtCore import QObject
 
+
 class TrackingData(QObject):
     def __init__(self, parent=None):
         super().__init__(parent)
@@ -58,9 +59,14 @@ class TrackingData(QObject):
         self._start = start
         self._stop = stop
         self._selection_column = col
-        self._indices = np.where((self._data[col] >= self._start) & (self._data[col] < self._stop))[0]
-
-    def selectedData(self, col):
+        col_indices = np.where((self._data[col] >= self._start) & (self._data[col] < self._stop))[0]
+        self._indices = self["index"][col_indices]
+        if len(col_indices) < 1:
+            logging.warning("TrackingData: Selection range is empty!")
+
+    def selectedData(self, col:str):
+        if col not in self.columns:
+            logging.error("TrackingData:selectedData: Invalid column name! %s", col)
         return self[col][self._indices]
 
     def setUserSelection(self, ids):
@@ -148,11 +154,13 @@ class TrackingData(QObject):
                         and M is number of keypoints
         """
         if selection:
-            return np.stack(self._data["keypoints"][self._start:self._stop, :, :]).astype(np.float32)
-        else:
-            return np.stack(self._data["keypoints"]).astype(np.float32)
+            if len(self._indices) < 1:
+                logging.info("TrackingData.coordinates returns empty array, not detections in range!")
+                return np.ndarray([])
+            return np.stack(self._data["keypoints"][self._indices]).astype(np.float32)
+        return np.stack(self._data["keypoints"]).astype(np.float32)
 
-    def keypointScores(self):
+    def keypointScores(self, selection=False):
         """
         Returns the keypoint scores as a NumPy array of type float32.
 
@@ -161,10 +169,15 @@ class TrackingData(QObject):
         numpy.ndarray
             A NumPy array of type float32 containing the keypoint scores of the shape (N, M) 
             with N the number of detections and M the number of keypoints.
-        """
+        """ 
+        if selection:
+            if len(self._indices) < 1:
+                logging.info("TrackingData.scores returns empty array, not detections in range!")
+                return np.ndarray([])
+            return np.stack(self._data["keypoint_score"][self._indices]).astype(np.float32)
         return np.stack(self._data["keypoint_score"]).astype(np.float32)
 
-    def centerOfGravity(self, threshold=0.8):
+    def centerOfGravity(self, selection=False, threshold=0.8, nodes=[0,1,2]):
         """
         Calculate the center of gravity of keypoints weighted by their scores. Ignores keypoints that have a score
         less than threshold.
@@ -172,16 +185,19 @@ class TrackingData(QObject):
         Parameters:
         -----------
         threshold: float 
-        keypoints with a score less than threshold are ignored
+            nodes with a score less than threshold are ignored
+        nodes: list
+            nodes/keypoints to consider for estimation. Defaults to [0,1,2]
 
         Returns:
         --------
         np.ndarray: 
         A NumPy array of shape (N, 2) containing the center of gravity for each detection.
         """
-        scores = self.keypointScores()
+        scores = self.keypointScores(selection)
         scores[scores < threshold] = 0.0
-        weighted_coords = self.coordinates() * scores[:, :, np.newaxis]
+        scores[:, np.setdiff1d(np.arange(scores.shape[1]), nodes)] = 0.0
+        weighted_coords = self.coordinates(selection=selection) * scores[:, :, np.newaxis]
         sum_scores = np.sum(scores, axis=1, keepdims=True)
         center_of_gravity = np.sum(weighted_coords, axis=1) / sum_scores
         return center_of_gravity
@@ -285,7 +301,7 @@ def main():
     frames = data["frame"]
     tracks = data["track"]
     bendedness = data.bendedness()
-    positions = data.coordinates()[[160388, 160389]]
+    # positions = data.coordinates()[[160388, 160389]]
 
     embed()
     tracks = data["track"]
@@ -309,7 +325,6 @@ def main():
     #     return distances
     # print("estimating neighorhood distances")
     # neighbor_distances = compute_neighbor_distances(cogs)
-    embed()
 
 if __name__ == "__main__":
     main()
\ No newline at end of file