BUG: Fix T-DaTing labels when continuing tracking (#280)

* BUG: Fix T-DaTing labels when continuing tracking

When continuing tracking from previous list, the track labels
could be duplicated because the labeling started always from 0.
Instead, start from maximum label of previous cells.

* Add test for multi-step t-dating

* Update variable name

Author: ritvje

pysteps/tests/test_tracking_tdating.py

@@ -15,6 +15,56 @@
     ("mch", True),
 ]
 
+arg_names_multistep = ("source", "len_timesteps")
+arg_values_multistep = [
+    ("mch", 6),
+]
+
+
+@pytest.mark.parametrize(arg_names_multistep, arg_values_multistep)
+def test_tracking_tdating_dating_multistep(source, len_timesteps):
+    pytest.importorskip("skimage")
+
+    input_fields, metadata = get_precipitation_fields(
+        0, len_timesteps, True, True, 4000, source
+    )
+    input_fields, __ = to_reflectivity(input_fields, metadata)
+
+    timelist = metadata["timestamps"]
+
+    # First half of timesteps
+    tracks_1, cells, labels = dating(
+        input_fields[0 : len_timesteps // 2],
+        timelist[0 : len_timesteps // 2],
+        mintrack=1,
+    )
+    # Second half of timesteps
+    tracks_2, cells, _ = dating(
+        input_fields[len_timesteps // 2 - 2 :],
+        timelist[len_timesteps // 2 - 2 :],
+        mintrack=1,
+        start=2,
+        cell_list=cells,
+        label_list=labels,
+    )
+
+    # Since we are adding cells, number of tracks should increase
+    assert len(tracks_1) <= len(tracks_2)
+
+    # Tracks should be continuous in time so time difference should not exceed timestep
+    max_track_step = max([t.time.diff().max().seconds for t in tracks_2 if len(t) > 1])
+    timestep = np.diff(timelist).max().seconds
+    assert max_track_step <= timestep
+
+    # IDs of unmatched cells should increase in every timestep
+    for prev_df, cur_df in zip(cells[:-1], cells[1:]):
+        prev_ids = set(prev_df.ID)
+        cur_ids = set(cur_df.ID)
+        new_ids = list(cur_ids - prev_ids)
+        prev_unmatched = list(prev_ids - cur_ids)
+        if len(prev_unmatched):
+            assert np.all(np.array(new_ids) > max(prev_unmatched))
+
 
 @pytest.mark.parametrize(arg_names, arg_values)
 def test_tracking_tdating_dating(source, dry_input):

pysteps/tracking/tdating.py

@@ -152,7 +152,10 @@ def dating(
         raise ValueError("start > len(timelist)")
 
     oflow_method = motion.get_method("LK")
-    max_ID = 0
+    if len(label_list) == 0:
+        max_ID = 0
+    else:
+        max_ID = np.nanmax([np.nanmax(np.unique(label_list)), 0])
     for t in range(start, len(timelist)):
         cells_id, labels = tstorm_detect.detection(
             input_video[t, :, :],