formatting (#460)

add newline at end of files which had failed Lint test

tests/io/test_dataset.py

@@ -1434,8 +1434,7 @@ def test_remove_untracked_instances(min_tracks_2node_labels):
     labels = min_tracks_2node_labels
 
     # XXX(LM): should I remove multiple tracks and frames?
-    # XXX(LM): if len(labels.labeled_frames)==1,
-    # XXX(LM)       then will not properly test lf.remove_untracked()
+    # XXX(LM): if len(labels.labeled_frames)==1, then will not properly test lf.remove_untracked()
     # Preprocessing
     labels.labeled_frames[0].instances[0].track = None
     labels.labeled_frames[-1].instances = []
@@ -1457,4 +1456,4 @@ def test_remove_untracked_instances(min_tracks_2node_labels):
 
     # Test function with remove_empty_frames=True
     labels.remove_untracked_instances(remove_empty_frames=True)
-    assert all([(len(lf.instances) > 0) for lf in labels.labeled_frames])
+    assert all([(len(lf.instances) > 0) for lf in labels.labeled_frames])

tests/nn/data/test_identity.py

@@ -99,4 +99,4 @@ def test_class_map_generator(min_tracks_2node_labels):
     )
     np.testing.assert_allclose(
         tf.gather_nd(ex["class_maps"], subs), [[0, 1], [1, 0]], atol=1e-2
-    )
+    )

tests/nn/test_inference_identity.py

@@ -72,4 +72,4 @@ def test_classify_peaks_from_maps():
     assert_array_equal(points[0][0], peak_points.numpy()[[1, 2]])
     assert_array_equal(points[0][1], [peak_points.numpy()[0], [np.nan, np.nan]])
     assert_array_equal(points[1][0], [peak_points.numpy()[4], [np.nan, np.nan]])
-    assert_array_equal(points[1][1], [peak_points.numpy()[6], [np.nan, np.nan]])
+    assert_array_equal(points[1][1], [peak_points.numpy()[6], [np.nan, np.nan]])

tests/test_instance.py

@@ -485,8 +485,10 @@ def test_labeledframe_remove_untracked(
     """Test removal of untracked instances on both user-labeled and predicted frames.
 
     Args:
-        min_tracks_2node_labels: Labels object which contains user labeled frames with tracked instances.
-        centered_pair_predictions: Labels object which contains predicted frames with tracked instances.
+        min_tracks_2node_labels: Labels object which contains user labeled frames with
+            tracked instances.
+        centered_pair_predictions: Labels object which contains predicted frames with
+            tracked instances.
     """
     # Load user-labeled frames.
     lf = min_tracks_2node_labels.labeled_frames[0]