Added #121

utils/db_utils.py

@@ -176,7 +176,24 @@ def query_confirmed_trips(start_date: str, end_date: str, tz: str):
                 # Add primary modes from the sensed, inferred and ble summaries. Note that we do this
                 # **before** filtering the `all_trip_columns` because the
                 # *_section_summary columns are not currently valid
-                get_max_mode_from_summary = lambda md: max(md["distance"], key=md["distance"].get) if len(md["distance"]) > 0 else "INVALID"
+                # Check if 'md' is not a dictionary or does not contain the key 'distance'
+                # or if 'md["distance"]' is not a dictionary.
+                # If any of these conditions are true, return "INVALID".
+                get_max_mode_from_summary = lambda md: (
+                    "INVALID"
+                    if not isinstance(md, dict)
+                    or "distance" not in md
+                    or not isinstance(md["distance"], dict)
+                    # If 'md' is a dictionary and 'distance' is a valid key pointing to a dictionary:
+                    else (
+                        # Get the maximum value from 'md["distance"]' using the values of 'md["distance"].get' as the key for 'max'.
+                        # This operation only happens if the length of 'md["distance"]' is greater than 0.
+                        # Otherwise, return "INVALID".
+                        max(md["distance"], key=md["distance"].get)
+                        if len(md["distance"]) > 0
+                        else "INVALID"
+                    )
+                )
                 df["data.primary_sensed_mode"] = df.cleaned_section_summary.apply(get_max_mode_from_summary)
                 df["data.primary_predicted_mode"] = df.inferred_section_summary.apply(get_max_mode_from_summary)
                 if 'ble_sensed_summary' in df.columns: