text: #!/usr/bin/env python
  
  import rospy
  import numpy as np
  from geometry_msgs.msg import PoseStamped
  from styx_msgs.msg import Lane, Waypoint
  from std_msgs.msg import Int32
  
  from scipy.spatial import KDTree
  import math
  
  '''
  This node will publish waypoints from the car's current position to some `x` distance ahead.
  
  As mentioned in the doc, you should ideally first implement a version which does not care
  about traffic lights or obstacles.
  
  Once you have created dbw_node, you will update this node to use the status of traffic lights too.
  
  Please note that our simulator also provides the exact location of traffic lights and their
  current status in `/vehicle/traffic_lights` message. You can use this message to build this node
  as well as to verify your TL classifier.
  
  TODO (for Yousuf and Aaron): Stopline location for each traffic light.
  '''
  
  LOOKAHEAD_WPS = 20 # Number of waypoints we will publish. You can change this number
  MAX_DECEL = 0.5
  
  class WaypointUpdater(object):
      def __init__(self):
          rospy.init_node('waypoint_updater')
  
          rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)
          rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)
  
          # TODO: Add a subscriber for /traffic_waypoint and /obstacle_waypoint below
          #rospy.Subscriber('/obstacle_waypoint', PoseStamped, self.obstacle_cb)
          rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)
  
          # Publisher
          self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane, queue_size=1)
  
          # TODO: Add other member variables you need below
          self.base_lane = None
          self.pose = None
          self.stopline_wp_idx = -1
          self.waypoints_2d = None
          self.waypoint_tree = None
  
          # Gives us control over the publishing frequency
          self.loop()
  
      def loop(self):
          rate = rospy.Rate(50)
          while not rospy.is_shutdown():
              if self.pose and self.base_lane:
                  self.publish_waypoints()
              rate.sleep()
  
      def get_closest_waypoint_idx(self):
          x = self.pose.pose.position.x
          y = self.pose.pose.position.y
          closest_idx = self.waypoint_tree.query([x,y],1)[1]
  
          closest_coord = self.waypoints_2d[closest_idx]
          prev_coord = self.waypoints_2d[closest_idx-1]
  
          cl_vect = np.array(closest_coord)
          prev_vect = np.array(prev_coord)
          pos_vect = np.array([x,y])
  
          val = np.dot(cl_vect-prev_vect, pos_vect-cl_vect)
  
          if val > 0:
              closest_idx = (closest_idx+1) % len(self.waypoints_2d)
  
          return closest_idx
  
      def publish_waypoints(self):
          final_lane = self.generate_lane()
          self.final_waypoints_pub.publish(final_lane)
  
      def generate_lane(self):
          lane = Lane()
          # Get the closest index
          closest_idx = self.get_closest_waypoint_idx()
          # Set the farthest index
          farthest_idx = closest_idx + LOOKAHEAD_WPS
          base_waypoints = self.base_lane.waypoints[closest_idx:farthest_idx]
  
          # If no traffic light was detected, publish the base_waypoints as it is
          if (self.stopline_wp_idx == -1) or (self.stopline_wp_idx >= farthest_idx):
              lane.waypoints = base_waypoints
          else:
              lane.waypoints = self.decelerate_waypoints(base_waypoints, closest_idx)
  
          return lane
  
      def decelerate_waypoints(self, waypoints, closest_idx):
          temp = []
          for i , wp in enumerate(waypoints):
              p = Waypoint()
              p.pose = wp.pose
  
              # 2 is subtracted to ensure that the front of the car stops at the stop line
              stop_idx = max(self.stopline_wp_idx - closest_idx - 2, 0)
              # Sum of distance between waypoints (sum of line segments)
              dist = self.distance(waypoints, i, stop_idx)
  
              # We can experiment with other functions too as this is quite steep
              vel = math.sqrt(2 * MAX_DECEL * dist)
              if vel < 1.:
                  vel = 0
  
              p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)
              temp.append(p)
  
          return temp
  
      def pose_cb(self, msg):
          # TODO: Implement
          self.pose = msg
  
      def waypoints_cb(self, waypoints):
          # TODO: Implement
          self.base_lane = waypoints
          if not self.waypoints_2d:
              self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.pose.pose.position.y] for waypoint in waypoints.waypoints]
              self.waypoint_tree = KDTree(self.waypoints_2d)
  
      def traffic_cb(self, msg):
          # TODO: Callback for /traffic_waypoint message. Implement
          self.stopline_wp_idx = msg.data
  
      def obstacle_cb(self, msg):
          # TODO: Callback for /obstacle_waypoint message. We will implement it later
          pass
  
      def get_waypoint_velocity(self, waypoint):
          return waypoint.twist.twist.linear.x
  
      def set_waypoint_velocity(self, waypoints, waypoint, velocity):
          waypoints[waypoint].twist.twist.linear.x = velocity
  
      def distance(self, waypoints, wp1, wp2):
          dist = 0
          dl = lambda a, b: math.sqrt((a.x-b.x)**2 + (a.y-b.y)**2  + (a.z-b.z)**2)
          for i in range(wp1, wp2+1):
              dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose.pose.position)
              wp1 = i
          return dist
  
  
  if __name__ == '__main__':
      try:
          WaypointUpdater()
      except rospy.ROSInterruptException:
          rospy.logerr('Could not start waypoint updater node.')
  