diff --git a/fixtracks/utils/trackingdata.py b/fixtracks/utils/trackingdata.py
index 7c18201..c9c7c17 100644
--- a/fixtracks/utils/trackingdata.py
+++ b/fixtracks/utils/trackingdata.py
@@ -54,6 +54,7 @@ class TrackingData(QObject):
         return self._indices
 
     def setSelectionRange(self, col, start, stop):
+        logging.debug("Trackingdata: set selection range based on column %s to %.2f - %.2f", col, start, stop)
         self._start = start
         self._stop = stop
         self._selection_column = col
@@ -64,7 +65,7 @@ class TrackingData(QObject):
 
     def setUserSelection(self, ids):
         """
-        Set the user selections. That is, e.g. when the user selected a number of ids.
+        Set the user selections. That is, e.g. when the user selected a number of detection ids (aka the index of the original data frame entries).
         Parameters
         ----------
         ids : array-like
@@ -73,16 +74,33 @@ class TrackingData(QObject):
         """
         self._user_selections = ids.astype(int)
 
-    def assignUserSelection(self, track_id:int)-> None:
+    def assignUserSelection(self, track_id:int, userFlag:bool=True)-> None:
         """Assign a new track_id to the user-selected detections
 
         Parameters
         ----------
         track_id : int
             The new track id for the user-selected detections
+        userFlag : bool
+            Should the "userlabeled" state of the detections be set to True or False? 
         """
         self._data["track"][self._user_selections] = track_id
-        self._data["userlabeled"][self._user_selections] = True
+        self.setAssignmentStatus(userFlag)
+
+    def setAssignmentStatus(self, isTrue: bool):
+        self._data["userlabeled"][self._user_selections] = isTrue
+
+    def revertAssignmentStatus(self):
+        self._data["userlabeled"][:] = False
+
+    def deleteDetections(self):
+        # from IPython import embed
+        # if self._user_selections is not None:
+        #     ids = self._user_selections
+        #     for c in self.columns:
+        #     pass
+        # embed()
+        pass
 
     def assignTracks(self, tracks):
         """assignTracks _summary_
@@ -115,7 +133,7 @@ class TrackingData(QObject):
             return 0
         return self._data["keypoints"][0].shape[0]
 
-    def coordinates(self):
+    def coordinates(self, selection=False):
         """
         Returns the coordinates of all keypoints as a NumPy array.
 
@@ -123,7 +141,10 @@ class TrackingData(QObject):
             np.ndarray: A NumPy array of shape (N, M, 2) where N is the number of detections, 
                         and M is number of keypoints
         """
-        return np.stack(self._data["keypoints"]).astype(np.float32)
+        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)
 
     def keypointScores(self):
         """
@@ -202,14 +223,6 @@ class TrackingData(QObject):
     def __getitem__(self, key):
         return self._data[key]
 
-    # def __setitem__(self, key, value):
-    #     self._data[key] = value
-        """ 
-         self._data.setSelectionRange("index", 0, self._data.numDetections)
-        self._data.assignTracks(tracks)
-        self._timeline.setDetectionData(self._data.data)
-        self.update()
-        """
 
 def main():
     import pandas as pd
@@ -217,6 +230,8 @@ def main():
     import matplotlib.pyplot as plt
     from fixtracks.info import PACKAGE_ROOT
 
+    logging.basicConfig(level=logging.DEBUG, force=True)
+
     def as_dict(df:pd.DataFrame):
         d = {c: df[c].values for c in df.columns}
         d["index"] = df.index.values
@@ -251,7 +266,7 @@ def main():
             plt.plot([positions[si, 0], positions[ei, 0]],
                      [positions[si, 1], positions[ei, 1]], color="tab:green")
 
-    datafile = PACKAGE_ROOT / "data/merged2.pkl"
+    datafile = PACKAGE_ROOT / "data/merged_small.pkl"
     with open(datafile, "rb") as f:
         df = pickle.load(f)