tidy up and fix bug of messing up an empty MultiIndex

python-sdk/nuscenes/eval/tracking/mot.py

@@ -57,7 +57,6 @@ def new_event_dataframe_with_data(indices, events):
         ]
 
         idx = pd.MultiIndex.from_arrays(
-            # TODO What types are the indices FrameId and Event? string or int?
             [indices[field] for field in _INDEX_FIELDS],
             names=_INDEX_FIELDS)
         df = pd.concat(series, axis=1)
@@ -129,46 +128,11 @@ def merge_event_dataframes(dfs, update_frame_indices=True, update_oids=True, upd
             # Update index
             if update_frame_indices:
                 # pylint: disable=cell-var-from-loop
-                # TODO TypeError: can only concatenate tuple (not "int") to tuple
-                # This is likely because we have a multi-index dataframe r here (see new_event_dataframe())
-                # See also https://stackoverflow.com/questions/39080555/pandas-get-level-values-for-multiple-columns
-                # Playground code: https://onecompiler.com/python/422kn8tev
-                """
-
-                import pandas as pd
-
-                a={"gk":[15,12,13,22,32,12],"mk":[12,21,23,22,56,12], "sf": [1,2,3,4,5,5]}
-                df=pd.DataFrame(a)
-
-                # B=df[df["mk"]>=21]
-
-                # print(df)
-                # print(B)
-
-                df = df.set_index(["gk", "sf"])
-
-                print(df)
-
-                print("Experiment")
-                print(df.index.get_level_values(1))
-                print("First argument of max")
-                print(df.index.get_level_values(0).max())
-                print(df.index.get_level_values(0).max() +1) # the maximum value of the 0th index column incremented by 1
-                print(df.index.get_level_values(1).max())
-                print(df.index.get_level_values(1).max() +1)
-                print("Second argument of max")
-                print(df.index.get_level_values(0))
-                print(df.index.get_level_values(0).unique())
-                print(df.index.get_level_values(0).unique().shape)
-                print(df.index.get_level_values(0).unique().shape[0])  # number of unique values in the 0th index column
-                print("Final max evaluation")
-                print(max(df.index.get_level_values(0).max() +1,df.index.get_level_values(0).unique().shape[0]))
-                """
-
                 next_frame_id = max(r.index.get_level_values(0).max() + 1, r.index.get_level_values(0).unique().shape[0])
                 if np.isnan(next_frame_id):
                     next_frame_id = 0
-                copy.index = copy.index.map(lambda x: (x[0] + next_frame_id, x[1]))
+                if not copy.index.empty:
+                    copy.index = copy.index.map(lambda x: (x[0] + next_frame_id, x[1]))
                 infos['frame_offset'] = next_frame_id
 
             # Update object / hypothesis ids