formatting

selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py

@@ -39,7 +39,6 @@ DANGER_ZONE_COST = 100.
 CRASH_DISTANCE = .5
 LIMIT_COST = 1e6
 
-
 # Less timestamps doesn't hurt performance and leads to
 # much better convergence of the MPC with low iterations
 N = 12
@@ -53,12 +52,15 @@ T_FOLLOW = 1.45
 COMFORT_BRAKE = 2.5
 STOP_DISTANCE = 6.0
 
+
 def get_stopped_equivalence_factor(v_lead):
   return (v_lead ** 2) / (2 * COMFORT_BRAKE)
 
+
 def get_safe_obstacle_distance(v_ego):
   return (v_ego ** 2) / (2 * COMFORT_BRAKE) + T_FOLLOW * v_ego + STOP_DISTANCE
 
+
 def desired_follow_distance(v_ego, v_lead):
   return get_safe_obstacle_distance(v_ego) - get_stopped_equivalence_factor(v_lead)
 
@@ -144,7 +146,7 @@ def gen_long_mpc_solver():
   # Constraints on speed, acceleration and desired distance to
   # the obstacle, which is treated as a slack constraint so it
   # behaves like an assymetrical cost.
-  constraints = vertcat((v_ego),
+  constraints = vertcat(v_ego,
                         (a_ego - a_min),
                         (a_max - a_ego),
                         ((x_obstacle - x_ego) - (3 / 4) * (desired_dist_comfort)) / (v_ego + 10.))
@@ -190,7 +192,7 @@ def gen_long_mpc_solver():
   return ocp
 
 
-class LongitudinalMpc():
+class LongitudinalMpc:
   def __init__(self, e2e=False):
     self.e2e = e2e
     self.reset()
@@ -355,7 +357,6 @@ class LongitudinalMpc():
                                   self.prev_a[:, None]], axis=1)
     self.run()
 
-
   def run(self):
     for i in range(N + 1):
       self.solver.set(i, 'p', self.params[i])