text: import os
  import math
  from common.realtime import sec_since_boot
  from selfdrive.swaglog import cloudlog
  from selfdrive.controls.lib.lateral_mpc import libmpc_py
  from selfdrive.controls.lib.drive_helpers import MPC_COST_LAT
  from selfdrive.controls.lib.lane_planner import LanePlanner
  import selfdrive.messaging as messaging
  
  LOG_MPC = os.environ.get('LOG_MPC', False)
  
  
  def calc_states_after_delay(states, v_ego, steer_angle, curvature_factor, steer_ratio, delay):
    states[0].x = v_ego * delay
    states[0].psi = v_ego * curvature_factor * math.radians(steer_angle) / steer_ratio * delay
    return states
  
  
  class PathPlanner():
    def __init__(self, CP):
      self.LP = LanePlanner()
  
      self.last_cloudlog_t = 0
  
      self.setup_mpc(CP.steerRateCost)
      self.solution_invalid_cnt = 0
      self.path_offset_i = 0.0
  
    def setup_mpc(self, steer_rate_cost):
      self.libmpc = libmpc_py.libmpc
      self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE, MPC_COST_LAT.HEADING, steer_rate_cost)
  
      self.mpc_solution = libmpc_py.ffi.new("log_t *")
      self.cur_state = libmpc_py.ffi.new("state_t *")
      self.cur_state[0].x = 0.0
      self.cur_state[0].y = 0.0
      self.cur_state[0].psi = 0.0
      self.cur_state[0].delta = 0.0
  
      self.angle_steers_des = 0.0
      self.angle_steers_des_mpc = 0.0
      self.angle_steers_des_prev = 0.0
      self.angle_steers_des_time = 0.0
  
    def update(self, sm, pm, CP, VM):
      v_ego = sm['carState'].vEgo
      angle_steers = sm['carState'].steeringAngle
      active = sm['controlsState'].active
  
      angle_offset = sm['liveParameters'].angleOffset
  
      self.LP.update(v_ego, sm['model'])
  
      # Run MPC
      self.angle_steers_des_prev = self.angle_steers_des_mpc
      VM.update_params(sm['liveParameters'].stiffnessFactor, sm['liveParameters'].steerRatio)
      curvature_factor = VM.curvature_factor(v_ego)
  
      # TODO: Check for active, override, and saturation
      # if active:
      #   self.path_offset_i += self.LP.d_poly[3] / (60.0 * 20.0)
      #   self.path_offset_i = clip(self.path_offset_i, -0.5,  0.5)
      #   self.LP.d_poly[3] += self.path_offset_i
      # else:
      #   self.path_offset_i = 0.0
  
      # account for actuation delay
      self.cur_state = calc_states_after_delay(self.cur_state, v_ego, angle_steers - angle_offset, curvature_factor, VM.sR, CP.steerActuatorDelay)
  
      v_ego_mpc = max(v_ego, 5.0)  # avoid mpc roughness due to low speed
      self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
                          list(self.LP.l_poly), list(self.LP.r_poly), list(self.LP.d_poly),
                          self.LP.l_prob, self.LP.r_prob, curvature_factor, v_ego_mpc, self.LP.lane_width)
  
      # reset to current steer angle if not active or overriding
      if active:
        delta_desired = self.mpc_solution[0].delta[1]
        rate_desired = math.degrees(self.mpc_solution[0].rate[0] * VM.sR)
      else:
        delta_desired = math.radians(angle_steers - angle_offset) / VM.sR
        rate_desired = 0.0
  
      self.cur_state[0].delta = delta_desired
  
      self.angle_steers_des_mpc = float(math.degrees(delta_desired * VM.sR) + angle_offset)
  
      #  Check for infeasable MPC solution
      mpc_nans = any(math.isnan(x) for x in self.mpc_solution[0].delta)
      t = sec_since_boot()
      if mpc_nans:
        self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE, MPC_COST_LAT.HEADING, CP.steerRateCost)
        self.cur_state[0].delta = math.radians(angle_steers - angle_offset) / VM.sR
  
        if t > self.last_cloudlog_t + 5.0:
          self.last_cloudlog_t = t
          cloudlog.warning("Lateral mpc - nan: True")
  
      if self.mpc_solution[0].cost > 20000. or mpc_nans:   # TODO: find a better way to detect when MPC did not converge
        self.solution_invalid_cnt += 1
      else:
        self.solution_invalid_cnt = 0
      plan_solution_valid = self.solution_invalid_cnt < 2
  
      plan_send = messaging.new_message()
      plan_send.init('pathPlan')
      plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState', 'liveParameters', 'model'])
      plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
      plan_send.pathPlan.dPoly = [float(x) for x in self.LP.d_poly]
      plan_send.pathPlan.lPoly = [float(x) for x in self.LP.l_poly]
      plan_send.pathPlan.lProb = float(self.LP.l_prob)
      plan_send.pathPlan.rPoly = [float(x) for x in self.LP.r_poly]
      plan_send.pathPlan.rProb = float(self.LP.r_prob)
  
      plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
      plan_send.pathPlan.rateSteers = float(rate_desired)
      plan_send.pathPlan.angleOffset = float(sm['liveParameters'].angleOffsetAverage)
      plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid)
      plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid)
      plan_send.pathPlan.sensorValid = bool(sm['liveParameters'].sensorValid)
      plan_send.pathPlan.posenetValid = bool(sm['liveParameters'].posenetValid)
  
      pm.send('pathPlan', plan_send)
  
      if LOG_MPC:
        dat = messaging.new_message()
        dat.init('liveMpc')
        dat.liveMpc.x = list(self.mpc_solution[0].x)
        dat.liveMpc.y = list(self.mpc_solution[0].y)
        dat.liveMpc.psi = list(self.mpc_solution[0].psi)
        dat.liveMpc.delta = list(self.mpc_solution[0].delta)
        dat.liveMpc.cost = self.mpc_solution[0].cost
        pm.send('liveMpc', dat)
  