code: import math
  import numpy as np
  from common.realtime import sec_since_boot, DT_MDL
  from common.numpy_fast import interp
  from selfdrive.swaglog import cloudlog
  from selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import LateralMpc
  from selfdrive.controls.lib.drive_helpers import CONTROL_N, MPC_COST_LAT, LAT_MPC_N, CAR_ROTATION_RADIUS
  from selfdrive.controls.lib.lane_planner import LanePlanner, TRAJECTORY_SIZE
  from selfdrive.config import Conversions as CV
  import cereal.messaging as messaging
  from cereal import log
  
  LaneChangeState = log.LateralPlan.LaneChangeState
  LaneChangeDirection = log.LateralPlan.LaneChangeDirection
  
  LANE_CHANGE_SPEED_MIN = 25 * CV.MPH_TO_MS
  LANE_CHANGE_TIME_MAX = 10.
  
  DESIRES = {
    LaneChangeDirection.none: {
      LaneChangeState.off: log.LateralPlan.Desire.none,
      LaneChangeState.preLaneChange: log.LateralPlan.Desire.none,
      LaneChangeState.laneChangeStarting: log.LateralPlan.Desire.none,
      LaneChangeState.laneChangeFinishing: log.LateralPlan.Desire.none,
    },
    LaneChangeDirection.left: {
      LaneChangeState.off: log.LateralPlan.Desire.none,
      LaneChangeState.preLaneChange: log.LateralPlan.Desire.none,
      LaneChangeState.laneChangeStarting: log.LateralPlan.Desire.laneChangeLeft,
      LaneChangeState.laneChangeFinishing: log.LateralPlan.Desire.laneChangeLeft,
    },
    LaneChangeDirection.right: {
      LaneChangeState.off: log.LateralPlan.Desire.none,
      LaneChangeState.preLaneChange: log.LateralPlan.Desire.none,
      LaneChangeState.laneChangeStarting: log.LateralPlan.Desire.laneChangeRight,
      LaneChangeState.laneChangeFinishing: log.LateralPlan.Desire.laneChangeRight,
    },
  }
  
  
  class LateralPlanner:
    def __init__(self, CP, use_lanelines=True, wide_camera=False):
      self.use_lanelines = use_lanelines
      self.LP = LanePlanner(wide_camera)
  
      self.last_cloudlog_t = 0
      self.steer_rate_cost = CP.steerRateCost
  
      self.solution_invalid_cnt = 0
      self.lane_change_state = LaneChangeState.off
      self.lane_change_direction = LaneChangeDirection.none
      self.lane_change_timer = 0.0
      self.lane_change_ll_prob = 1.0
      self.keep_pulse_timer = 0.0
      self.prev_one_blinker = False
      self.desire = log.LateralPlan.Desire.none
  
      self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
      self.path_xyz_stds = np.ones((TRAJECTORY_SIZE, 3))
      self.plan_yaw = np.zeros((TRAJECTORY_SIZE,))
      self.t_idxs = np.arange(TRAJECTORY_SIZE)
      self.y_pts = np.zeros(TRAJECTORY_SIZE)
  
      self.lat_mpc = LateralMpc()
      self.reset_mpc(np.zeros(6))
  
    def reset_mpc(self, x0=np.zeros(6)):
      self.x0 = x0
      self.lat_mpc.reset(x0=self.x0)
  
    def update(self, sm):
      v_ego = sm['carState'].vEgo
      active = sm['controlsState'].active
      measured_curvature = sm['controlsState'].curvature
  
      md = sm['modelV2']
      self.LP.parse_model(sm['modelV2'])
      if len(md.position.x) == TRAJECTORY_SIZE and len(md.orientation.x) == TRAJECTORY_SIZE:
        self.path_xyz = np.column_stack([md.position.x, md.position.y, md.position.z])
        self.t_idxs = np.array(md.position.t)
        self.plan_yaw = list(md.orientation.z)
      if len(md.position.xStd) == TRAJECTORY_SIZE:
        self.path_xyz_stds = np.column_stack([md.position.xStd, md.position.yStd, md.position.zStd])
  
      # Lane change logic
      one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
      below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
  
      if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX):
        self.lane_change_state = LaneChangeState.off
        self.lane_change_direction = LaneChangeDirection.none
      else:
        # LaneChangeState.off
        if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
          self.lane_change_state = LaneChangeState.preLaneChange
          self.lane_change_ll_prob = 1.0
  
        # LaneChangeState.preLaneChange
        elif self.lane_change_state == LaneChangeState.preLaneChange:
          # Set lane change direction
          if sm['carState'].leftBlinker:
            self.lane_change_direction = LaneChangeDirection.left
          elif sm['carState'].rightBlinker:
            self.lane_change_direction = LaneChangeDirection.right
          else:  # If there are no blinkers we will go back to LaneChangeState.off
            self.lane_change_direction = LaneChangeDirection.none
  
          torque_applied = sm['carState'].steeringPressed and \
                           ((sm['carState'].steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or
                            (sm['carState'].steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right))
  
          blindspot_detected = ((sm['carState'].leftBlindspot and self.lane_change_direction == LaneChangeDirection.left) or
                                (sm['carState'].rightBlindspot and self.lane_change_direction == LaneChangeDirection.right))
  
          if not one_blinker or below_lane_change_speed:
            self.lane_change_state = LaneChangeState.off
          elif torque_applied and not blindspot_detected:
            self.lane_change_state = LaneChangeState.laneChangeStarting
  
        # LaneChangeState.laneChangeStarting
        elif self.lane_change_state == LaneChangeState.laneChangeStarting:
          # fade out over .5s
          self.lane_change_ll_prob = max(self.lane_change_ll_prob - 2 * DT_MDL, 0.0)
  
          # 98% certainty
          lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
          if lane_change_prob < 0.02 and self.lane_change_ll_prob < 0.01:
            self.lane_change_state = LaneChangeState.laneChangeFinishing
  
        # LaneChangeState.laneChangeFinishing
        elif self.lane_change_state == LaneChangeState.laneChangeFinishing:
          # fade in laneline over 1s
          self.lane_change_ll_prob = min(self.lane_change_ll_prob + DT_MDL, 1.0)
          if self.lane_change_ll_prob > 0.99:
            self.lane_change_direction = LaneChangeDirection.none
            if one_blinker:
              self.lane_change_state = LaneChangeState.preLaneChange
            else:
              self.lane_change_state = LaneChangeState.off
  
      if self.lane_change_state in [LaneChangeState.off, LaneChangeState.preLaneChange]:
        self.lane_change_timer = 0.0
      else:
        self.lane_change_timer += DT_MDL
  
      self.prev_one_blinker = one_blinker
  
      self.desire = DESIRES[self.lane_change_direction][self.lane_change_state]
  
      # Send keep pulse once per second during LaneChangeStart.preLaneChange
      if self.lane_change_state in [LaneChangeState.off, LaneChangeState.laneChangeStarting]:
        self.keep_pulse_timer = 0.0
      elif self.lane_change_state == LaneChangeState.preLaneChange:
        self.keep_pulse_timer += DT_MDL
        if self.keep_pulse_timer > 1.0:
          self.keep_pulse_timer = 0.0
        elif self.desire in [log.LateralPlan.Desire.keepLeft, log.LateralPlan.Desire.keepRight]:
          self.desire = log.LateralPlan.Desire.none
  
      # Turn off lanes during lane change
      if self.desire == log.LateralPlan.Desire.laneChangeRight or self.desire == log.LateralPlan.Desire.laneChangeLeft:
        self.LP.lll_prob *= self.lane_change_ll_prob
        self.LP.rll_prob *= self.lane_change_ll_prob
      if self.use_lanelines:
        d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
        self.lat_mpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, self.steer_rate_cost)
      else:
        d_path_xyz = self.path_xyz
        path_cost = np.clip(abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5, 1.5) * MPC_COST_LAT.PATH
        # Heading cost is useful at low speed, otherwise end of plan can be off-heading
        heading_cost = interp(v_ego, [5.0, 10.0], [MPC_COST_LAT.HEADING, 0.0])
        self.lat_mpc.set_weights(path_cost, heading_cost, self.steer_rate_cost)
      y_pts = np.interp(v_ego * self.t_idxs[:LAT_MPC_N + 1], np.linalg.norm(d_path_xyz, axis=1), d_path_xyz[:, 1])
      heading_pts = np.interp(v_ego * self.t_idxs[:LAT_MPC_N + 1], np.linalg.norm(self.path_xyz, axis=1), self.plan_yaw)
      self.y_pts = y_pts
  
      assert len(y_pts) == LAT_MPC_N + 1
      assert len(heading_pts) == LAT_MPC_N + 1
      self.x0[4] = v_ego
      self.lat_mpc.run(self.x0,
                       v_ego,
                       CAR_ROTATION_RADIUS,
                       y_pts,
                       heading_pts)
      # init state for next
      self.x0[3] = interp(DT_MDL, self.t_idxs[:LAT_MPC_N + 1], self.lat_mpc.x_sol[:, 3])
  
      #  Check for infeasible MPC solution
      mpc_nans = any(math.isnan(x) for x in self.lat_mpc.x_sol[:, 3])
      t = sec_since_boot()
      if mpc_nans or self.lat_mpc.solution_status != 0:
        self.reset_mpc()
        self.x0[3] = measured_curvature
        if t > self.last_cloudlog_t + 5.0:
          self.last_cloudlog_t = t
          cloudlog.warning("Lateral mpc - nan: True")
  
      if self.lat_mpc.cost > 20000. or mpc_nans:
        self.solution_invalid_cnt += 1
      else:
        self.solution_invalid_cnt = 0
  
    def publish(self, sm, pm):
      plan_solution_valid = self.solution_invalid_cnt < 2
      plan_send = messaging.new_message('lateralPlan')
      plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState', 'modelV2'])
      plan_send.lateralPlan.laneWidth = float(self.LP.lane_width)
      plan_send.lateralPlan.dPathPoints = [float(x) for x in self.y_pts]
      plan_send.lateralPlan.psis = [float(x) for x in self.lat_mpc.x_sol[0:CONTROL_N, 2]]
      plan_send.lateralPlan.curvatures = [float(x) for x in self.lat_mpc.x_sol[0:CONTROL_N, 3]]
      plan_send.lateralPlan.curvatureRates = [float(x) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]] + [0.0]
      plan_send.lateralPlan.lProb = float(self.LP.lll_prob)
      plan_send.lateralPlan.rProb = float(self.LP.rll_prob)
      plan_send.lateralPlan.dProb = float(self.LP.d_prob)
  
      plan_send.lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
  
      plan_send.lateralPlan.desire = self.desire
      plan_send.lateralPlan.useLaneLines = self.use_lanelines
      plan_send.lateralPlan.laneChangeState = self.lane_change_state
      plan_send.lateralPlan.laneChangeDirection = self.lane_change_direction
  
      pm.send('lateralPlan', plan_send)
  
apis: numpy.ones numpy.zeros numpy.column_stack numpy.arange numpy.array numpy.linalg.norm