[trackingdata] implement better selection handling ...

allow deletion of entries

fixtracks/utils/trackingdata.py

@@ -37,17 +37,22 @@ class TrackingData(QObject):
     def numDetections(self):
         return self._data["track"].shape[0]
 
-    @property
+    def _find(self, ids):
-    def selectionRange(self):
+        ids = np.sort(ids)
-        return self._start, self._stop
+        indexes = np.ones_like(ids, dtype=int) * -1
-
+        j = 0
-    @property
+        for idx, i in enumerate(self._indices):
-    def selectionRangeColumn(self):
+            if i == ids[j]:
-        return self._selection_column
+                indexes[j] = idx
+                j += 1
+            if j == len(indexes):
+                break
+        indexes = indexes[indexes >= 0]
+        return indexes
 
     @property
     def selectionIndices(self):
-        return self._indices
+        return self._selection
 
     def setSelectionRange(self, col, start, stop):
         logging.debug("Trackingdata: set selection range based on column %s to %.2f - %.2f", col, start, stop)
@@ -59,18 +64,19 @@ class TrackingData(QObject):
     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]
+        return self[col][self._selection]
 
-    def setUserSelection(self, ids):
+    def setSelection(self, 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).
+        Set the selection based on the detection IDs.
+
         Parameters
         ----------
         ids : array-like
             An array-like object containing the IDs to be set as user selections. 
-            The IDs will be converted to integers.
         """
-        self._user_selections = ids.astype(int)
+        self._selection = self._find(ids)
+        self._selected_ids = ids
 
     def setTrack(self, track_id:int, setUserLabeled:bool=True)-> None:
         """Assign a new track_id to the user-selected detections
@@ -111,14 +117,15 @@ class TrackingData(QObject):
         logging.debug("TrackingData: Reverting all track assignments!")
         self["track"][:] = -1
 
-    def deleteDetections(self):
+    def deleteDetections(self, ids=None):
-        # from IPython import embed
+        if ids is not None:
-        # if self._user_selections is not None:
+            del_indices = self._find(ids)
-        #     ids = self._user_selections
+        else:
-        #     for c in self.columns:
+            del_indices = self._indices
-        #     pass
+        for c in self._columns:
-        # embed()
+            self._data[c] = np.delete(self._data[c], del_indices, axis=0)
-        pass
+        self._indices = self["index"]
+        self._selected_ids = np.setdiff1d(self._selected_ids, del_indices)
 
     def assignTracks(self, tracks:np.ndarray):
         """assigns the given tracks to the user-selected detections. If the sizes of 
@@ -299,22 +306,26 @@ def main():
             plt.plot([positions[si, 0], positions[ei, 0]],
                      [positions[si, 1], positions[ei, 1]], color="tab:green")
 
-    datafile = PACKAGE_ROOT / "data/merged_small.pkl"
+    datafile = PACKAGE_ROOT / "data/merged_small_tracked.pkl"
     with open(datafile, "rb") as f:
         df = pickle.load(f)
 
+    data = TrackingData(as_dict(df))
+    test_indices = [32, 88, 99, 2593]
+    data.deleteDetections(test_indices)
     
-    data = TrackingData()
+    embed()
-    data.setData(as_dict(df))
+    data.deleteDetections(test_indices)
+    data.setSelection(test_indices)
     all_cogs = data.centerOfGravity()
     orientations = data.orientation()
     lengths = data.animalLength()
     frames = data["frame"]
     tracks = data["track"]
     bendedness = data.bendedness()
+    indices = data._indices
     # positions = data.coordinates()[[160388, 160389]]
 
-    embed()
     tracks = data["track"]
     cogs = all_cogs[tracks==1]
     all_dists = neighborDistances(cogs, 2, False)