GitHub Actions: Format

smarts/benchmark/driving_smarts/v2023/metric_formula_platoon.py

@@ -66,7 +66,7 @@ def params(self) -> Params:
             gap_between_vehicles=GapBetweenVehicles(
                 active=False,
                 interest="Leader-007",
-            ), # TODO: Activate after implementing gap_between_vehicles cost function.
+            ),  # TODO: Activate after implementing gap_between_vehicles cost function.
             steps=Steps(
                 active=False,
             ),
@@ -143,9 +143,7 @@ def score(self, records_sum: Dict[str, Dict[str, Record]]) -> Score:
 
 
 def _humanness(costs: Costs) -> float:
-    humanness = np.array(
-        [costs.comfort, costs.lane_center_offset]
-    )
+    humanness = np.array([costs.comfort, costs.lane_center_offset])
     humanness = np.mean(humanness, dtype=float)
     return 1 - humanness
 

smarts/env/gymnasium/wrappers/metric/costs.py

@@ -30,9 +30,8 @@
 from smarts.core.utils.math import running_mean
 from smarts.core.vehicle_index import VehicleIndex
 from smarts.env.gymnasium.wrappers.metric.params import Params
-from smarts.env.gymnasium.wrappers.metric.utils import SlidingWindow
 from smarts.env.gymnasium.wrappers.metric.types import Costs
-
+from smarts.env.gymnasium.wrappers.metric.utils import SlidingWindow
 
 Done = NewType("Done", bool)
 
@@ -52,8 +51,8 @@ def func(road_map: RoadMap, done: Done, obs: Observation) -> Costs:
 
 def _comfort() -> Callable[[RoadMap, Done, Observation], Costs]:
     jerk_linear_max = np.linalg.norm(np.array([0.9, 0.9, 0]))  # Units: m/s^3
-    acc_linear_max = np.linalg.norm(np.array([2.0,1.47,0]))  # Units: m/s^2
-    T_p = 30 # Penalty time steps = penalty time / delta time step = 3s / 0.1s = 30
+    acc_linear_max = np.linalg.norm(np.array([2.0, 1.47, 0]))  # Units: m/s^2
+    T_p = 30  # Penalty time steps = penalty time / delta time step = 3s / 0.1s = 30
     T_u = 0
     step = 0
     dyn_window = SlidingWindow(size=T_p)
@@ -65,12 +64,12 @@ def func(road_map: RoadMap, done: Done, obs: Observation) -> Costs:
 
         jerk_linear = np.linalg.norm(obs.ego_vehicle_state.linear_jerk)
         acc_linear = np.linalg.norm(obs.ego_vehicle_state.linear_acceleration)
-        dyn = max(jerk_linear/jerk_linear_max, acc_linear/acc_linear_max)
+        dyn = max(jerk_linear / jerk_linear_max, acc_linear / acc_linear_max)
 
         dyn_window.move(dyn)
         u_t = 1 if dyn_window.max() > 1 else 0
         T_u += u_t
-    
+
         if not done:
             return Costs(comfort=-1)
         else:

smarts/env/gymnasium/wrappers/metric/utils.py

@@ -100,9 +100,10 @@ def multiply(value: Union[int, float], multiplier: Union[int, float]) -> float:
 class SlidingWindow:
     """A sliding window which moves to the right by accepting new elements. The
     maximum value within the sliding window can be queried at anytime by calling
-    the max() method. 
+    the max() method.
     """
-    def __init__(self, size:int):
+
+    def __init__(self, size: int):
         """
         Args:
             size (int): Size of the sliding window.
@@ -112,7 +113,7 @@ def __init__(self, size:int):
         self._size = size
         self._time = -1
 
-    def move(self, x:Union[int,float]):
+    def move(self, x: Union[int, float]):
         """Moves the sliding window one step to the right by appending the new
         element x and discarding the oldest element on the left.
 
@@ -121,29 +122,27 @@ def move(self, x:Union[int,float]):
         """
         self._time += 1
 
-        # When values deque is full, remove head element of max_candidates deque 
+        # When values deque is full, remove head element of max_candidates deque
         # if it matches head element of values deque.
         if len(self._values) == self._size:
             if self._values[0][0] == self._max_candidates[0][0]:
                 self._max_candidates.popleft()
         # Append x to values deque.
-        self._values.append((self._time,x))
+        self._values.append((self._time, x))
 
-	# Remove elements from max_candidates deque's tail which are less than x.
+        # Remove elements from max_candidates deque's tail which are less than x.
         while self._max_candidates and self._max_candidates[-1][1] < x:
             self._max_candidates.pop()
         # Append x to max_candidates deque.
-        self._max_candidates.append((self._time,x))
-    
+        self._max_candidates.append((self._time, x))
+
     def max(self):
-        """ Returns the maximum element within the sliding window.
-        """ 
+        """Returns the maximum element within the sliding window."""
         return self._max_candidates[0][1]
-    
+
     def display(self):
-        """Print the contents of the sliding window.
-        """
+        """Print the contents of the sliding window."""
         print("[", end="")
         for i in self._values:
-            print(i, end=' ')
+            print(i, end=" ")
         print("]")