Reformat metrics file

Author: sergiopaniego

behavior_metrics/utils/metrics.py

@@ -105,25 +105,6 @@ def get_statistics(stats_filename, perfect_lap_checkpoints, circuit_diameter):
     for index, row in dataframe_pose.iterrows():
         checkpoints.append(row)
 
-    # average_speed
-    lap_statistics = get_distance_completed(lap_statistics, checkpoints)
-    lap_statistics, previous_lap_point = get_average_speed(lap_statistics, stats_filename, checkpoints)
-    # percentage_completed
-    lap_statistics = get_percentage_completed(lap_statistics, checkpoints, perfect_lap_checkpoints)
-    # additional stats
-    lap_statistics = get_lap_completed_stats(lap_statistics, circuit_diameter, previous_lap_point)
-
-    shutil.rmtree(stats_filename.split('.bag')[0])
-    return lap_statistics
-
-
-def get_distance_completed(lap_statistics, checkpoints):
-    end_point = checkpoints[len(checkpoints) - 1]
-    lap_statistics['completed_distance'] = circuit_distance_completed(checkpoints, end_point)
-    return lap_statistics
-
-
-def get_average_speed(lap_statistics, stats_filename, checkpoints):
     data_file = stats_filename.split('.bag')[0] + '/clock.csv'
     dataframe_pose = pd.read_csv(data_file)
     clock_points = []
@@ -144,11 +125,28 @@ def get_average_speed(lap_statistics, stats_filename, checkpoints):
             previous_lap_point = ckp_iter
     seconds_start = start_clock['clock.secs']
     seconds_end = clock_points[len(clock_points) - 1]['clock.secs']
+
+    lap_statistics = get_distance_completed(lap_statistics, checkpoints)
+    lap_statistics = get_average_speed(lap_statistics, seconds_start, seconds_end)
+    lap_statistics = get_percentage_completed(lap_statistics, checkpoints, perfect_lap_checkpoints, seconds_start, seconds_end, laps)
+    lap_statistics = get_lap_completed_stats(lap_statistics, circuit_diameter, previous_lap_point, lap_point)
+
+    shutil.rmtree(stats_filename.split('.bag')[0])
+    return lap_statistics
+
+
+def get_distance_completed(lap_statistics, checkpoints):
+    end_point = checkpoints[len(checkpoints) - 1]
+    lap_statistics['completed_distance'] = circuit_distance_completed(checkpoints, end_point)
+    return lap_statistics
+
+
+def get_average_speed(lap_statistics, seconds_start, seconds_end):
     lap_statistics['average_speed'] = lap_statistics['completed_distance'] / (seconds_end - seconds_start)
-    return lap_statistics, previous_lap_point
+    return lap_statistics
 
 
-def get_percentage_completed(lap_statistics, checkpoints, perfect_lap_checkpoints):
+def get_percentage_completed(lap_statistics, checkpoints, perfect_lap_checkpoints, seconds_start, seconds_end, laps):
     # Find last and first checkpoints for retrieving percentage completed
     first_checkpoint = checkpoints[0]
     first_checkpoint = np.array([first_checkpoint['pose.pose.position.x'], first_checkpoint['pose.pose.position.y']])
@@ -174,12 +172,12 @@ def get_percentage_completed(lap_statistics, checkpoints, perfect_lap_checkpoint
             if dist < min_distance_last:
                 min_distance_last = dist
                 last_perfect_checkpoint_position = i
-
     if first_perfect_checkpoint_position > last_perfect_checkpoint_position and lap_statistics['completed_distance'] > MIN_COMPLETED_DISTANCE_EXPERIMENT and seconds_end - seconds_start > MIN_EXPERIMENT_TIME:
         lap_statistics['percentage_completed'] = (((len(perfect_lap_checkpoints) - first_perfect_checkpoint_position + last_perfect_checkpoint_position) / len(perfect_lap_checkpoints)) * 100) + laps * 100
     else:
         if seconds_end - seconds_start > MIN_EXPERIMENT_TIME:
-            lap_statistics['percentage_completed'] = (((last_perfect_checkpoint_position - first_perfect_checkpoint_position) / len(perfect_lap_checkpoints)) * 100) + laps * 100
+            lap_statistics['percentage_completed'] = (((last_perfect_checkpoint_position - first_perfect_checkpoint_position) / len(
+                perfect_lap_checkpoints)) * 100) + laps * 100
         else:
             lap_statistics['percentage_completed'] = (((last_perfect_checkpoint_position - first_perfect_checkpoint_position) / len(perfect_lap_checkpoints)) * 100)
     lap_statistics = get_robot_position_deviation_score(perfect_lap_checkpoints, checkpoints, lap_statistics)
@@ -269,7 +267,7 @@ def get_robot_position_deviation_score(perfect_lap_checkpoints, checkpoints, lap
     return lap_statistics
 
 
-def get_lap_completed_stats(lap_statistics, circuit_diameter, previous_lap_point):
+def get_lap_completed_stats(lap_statistics, circuit_diameter, previous_lap_point, lap_point):
     # If lap is completed, add more statistic information
     if type(lap_point) is not int and lap_statistics['percentage_completed'] > LAP_COMPLETED_PERCENTAGE:
         if abs(((lap_statistics['completed_distance'] / circuit_diameter) * 100) - lap_statistics['percentage_completed']) > 5:
@@ -283,4 +281,4 @@ def get_lap_completed_stats(lap_statistics, circuit_diameter, previous_lap_point
     else:
         logger.info('Lap not completed')
 
-    return lap_statistics
+    return lap_statistics