Advanced workshops/reInvent2019-400/customize/deepracer_racetrack_env_lidar_3cars.py [262:523]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rospy.wait_for_service('/gazebo/set_model_state') rospy.wait_for_service('/gazebo/spawn_urdf_model') self.set_model_state = rospy.ServiceProxy('/gazebo/set_model_state', SetModelState) self.spawn_model_client = rospy.ServiceProxy('gazebo/spawn_urdf_model', SpawnModel) # Spawn the new car robot_description = rospy.get_param('robot_description_bot') car_initial_pose = Pose() car_initial_pose.position.x = 0 car_initial_pose.position.y = 0 car_initial_pose.position.z = 0 self.car_name = "racecar_{}".format(car_id) self.car_section = car_id - 1 # car_id should be 1, 2 and 3 self.is_inner = is_inner self.resp = self.spawn_model_client(self.car_name, robot_description, '/{}'.format(self.car_name), car_initial_pose, '') logger.info('Spawn status: {}'.format(self.resp.status_message)) # Initialize member data and reset car self.car_speed = speed # Initializing the lane that the bot car runs self.lanes_list_np = lanes_list_np self.lanes_one_hot = np.zeros(len(lanes_list_np), dtype=bool) if is_inner: self.lanes_one_hot[0] = True self.shapely_lane = lanes_list_np[self.lanes_one_hot][0] else: self.lanes_one_hot[1] = True self.shapely_lane = lanes_list_np[self.lanes_one_hot][0] # Change the lanes after certain time self.change_lane_freq_sec = 30 if self.car_section==self.stat_sec else 0 self.reverse_dir = False self.car_model_state = ModelState() self.reset_sim(self.sec_map[self.is_inner][self.car_section][0]) def reset_sim(self, start_dist): rostime = rospy.get_rostime() self.car_initial_dist = start_dist self.car_initial_time = rostime.secs + 1.e-9*rostime.nsecs self.next_change_lane_time = self.car_initial_time + self.change_lane_freq_sec self.position_bot_car(start_dist) def update_bot_sim(self, sim_time): current_time = sim_time.clock.secs + 1.e-9*sim_time.clock.nsecs seconds_elapsed = current_time - self.car_initial_time traveled_dist = seconds_elapsed * self.car_speed if self.reverse_dir: current_dist = (self.car_initial_dist - traveled_dist) % self.shapely_lane.length else: current_dist = (self.car_initial_dist + traveled_dist) % self.shapely_lane.length # Check for lane change and section reset if self.car_section == self.stat_sec and current_time > self.next_change_lane_time: # Get the next lane index tmp_lane_np = np.zeros(len(self.lanes_list_np), dtype=bool) next_lane_idx = (int(np.argwhere(self.lanes_one_hot==True)) + 1) % len(self.lanes_list_np) tmp_lane_np[next_lane_idx] = True self.lanes_one_hot = tmp_lane_np self.is_inner = not self.is_inner # Assigns the next lane that the bot car as to travel self.shapely_lane = self.lanes_list_np[self.lanes_one_hot][0] self.reset_sim(self.sec_map[self.is_inner][self.car_section][0]) elif self.car_section != self.stat_sec and current_dist >= self.sec_map[self.is_inner][self.car_section][1]: tmp_lane_np = np.zeros(len(self.lanes_list_np), dtype=bool) next_lane_idx = (int(np.argwhere(self.lanes_one_hot==True)) + 1) % len(self.lanes_list_np) tmp_lane_np[next_lane_idx] = True self.lanes_one_hot = tmp_lane_np self.is_inner = not self.is_inner self.shapely_lane = self.lanes_list_np[self.lanes_one_hot][0] self.reset_sim(self.sec_map[self.is_inner][self.car_section][0]) else: # Position the car on the lane self.position_bot_car(current_dist) def position_bot_car(self, car_dist): # Set the position of the second car _, next_index = self.find_prev_next_lane_points(car_dist, self.reverse_dir) car_position = self.shapely_lane.interpolate(car_dist) car_yaw = math.atan2(self.shapely_lane.coords[next_index][1] - car_position.y, self.shapely_lane.coords[next_index][0] - car_position.x) car_quaternion = Rotation.from_euler('zyx', [car_yaw, 0, 0]).as_quat() self.car_model_state.model_name = self.car_name self.car_model_state.pose.position.x = car_position.x self.car_model_state.pose.position.y = car_position.y self.car_model_state.pose.position.z = 0 self.car_model_state.pose.orientation.x = car_quaternion[0] self.car_model_state.pose.orientation.y = car_quaternion[1] self.car_model_state.pose.orientation.z = car_quaternion[2] self.car_model_state.pose.orientation.w = car_quaternion[3] self.car_model_state.twist.linear.x = 0 self.car_model_state.twist.linear.y = 0 self.car_model_state.twist.linear.z = 0 self.car_model_state.twist.angular.x = 0 self.car_model_state.twist.angular.y = 0 self.car_model_state.twist.angular.z = 0 self.set_model_state(self.car_model_state) def get_lane_dists(self, lane): return [lane.project(Point(p)) for p in lane.coords[:-1]] + [1.0] def find_prev_next_lane_points(self, ndist, reverse_dir): lane_dist = self.get_lane_dists(self.shapely_lane) if reverse_dir: next_index = bisect.bisect_left(lane_dist, ndist) - 1 prev_index = next_index + 1 if next_index == -1: next_index = len(lane_dist) - 1 else: next_index = bisect.bisect_right(lane_dist, ndist) prev_index = next_index - 1 if next_index == len(lane_dist): next_index = 0 return prev_index, next_index ### Gym Env ### class DeepRacerRacetrackEnv(gym.Env): def __init__(self): # Create the observation space # self.observation_space = spaces.Box(low=0, high=255, # shape=(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 3), # dtype=np.uint8) # self.observation_space = spaces.Dict({ # # 'left_camera': spaces.Box(low=0, high=255, # # shape=(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 3), # # dtype=np.uint8), # 'stereo': spaces.Box(low=0, high=255, # shape=(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 2), # dtype=np.uint8) # }) self.observation_space = spaces.Dict({ # 'left_camera': spaces.Box(low=0, high=255, # shape=(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 3), # dtype=np.uint8), 'STEREO_CAMERAS': spaces.Box(low=0, high=255, shape=(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 2), dtype=np.uint8), 'LIDAR': spaces.Box(low=0.15, high=1.0, shape=(64,)) }) # Create the action space self.action_space = spaces.Box(low=np.array([-1, 0]), high=np.array([+1, +1]), dtype=np.float32) if node_type == SIMULATION_WORKER: # ROS initialization rospy.init_node('rl_coach', anonymous=True) # wait for required services rospy.wait_for_service('/deepracer_simulation_environment/get_waypoints') rospy.wait_for_service('/deepracer_simulation_environment/reset_car') rospy.wait_for_service('/gazebo/get_model_state') rospy.wait_for_service('/gazebo/get_link_state') rospy.wait_for_service('/gazebo/clear_joint_forces') rospy.wait_for_service('/gazebo/spawn_urdf_model') self.get_model_state = rospy.ServiceProxy('/gazebo/get_model_state', GetModelState) self.get_link_state = rospy.ServiceProxy('/gazebo/get_link_state', GetLinkState) self.clear_forces_client = rospy.ServiceProxy('/gazebo/clear_joint_forces', JointRequest) self.reset_car_client = rospy.ServiceProxy('/deepracer_simulation_environment/reset_car', ResetCarSrv) get_waypoints_client = rospy.ServiceProxy('/deepracer_simulation_environment/get_waypoints', GetWaypointSrv) # Create the publishers for sending speed and steering info to the car self.velocity_pub_dict = OrderedDict() self.steering_pub_dict = OrderedDict() for topic in VELOCITY_TOPICS: self.velocity_pub_dict[topic] = rospy.Publisher(topic, Float64, queue_size=1) for topic in STEERING_TOPICS: self.steering_pub_dict[topic] = rospy.Publisher(topic, Float64, queue_size=1) # Read in parameters self.world_name = rospy.get_param('WORLD_NAME') self.job_type = rospy.get_param('JOB_TYPE') self.aws_region = rospy.get_param('AWS_REGION') self.metrics_s3_bucket = rospy.get_param('METRICS_S3_BUCKET') self.metrics_s3_object_key = rospy.get_param('METRICS_S3_OBJECT_KEY') self.metrics = [] self.simulation_job_arn = 'arn:aws:robomaker:' + self.aws_region + ':' + \ rospy.get_param('ROBOMAKER_SIMULATION_JOB_ACCOUNT_ID') + \ ':simulation-job/' + rospy.get_param('AWS_ROBOMAKER_SIMULATION_JOB_ID') # Setup SIM_TRACE_LOG data upload to s3 # self.setup_simtrace_data_upload_to_s3() if self.job_type == TRAINING_JOB: from custom_files.customer_reward_function import reward_function self.reward_function = reward_function self.target_number_of_episodes = rospy.get_param('NUMBER_OF_EPISODES') self.target_reward_score = rospy.get_param('TARGET_REWARD_SCORE') else: from markov.defaults import reward_function self.reward_function = reward_function self.number_of_trials = 0 self.target_number_of_trials = rospy.get_param('NUMBER_OF_TRIALS') # Request the waypoints waypoints = None try: resp = get_waypoints_client() waypoints = np.array(resp.waypoints).reshape(resp.row, resp.col) except Exception as ex: utils.json_format_logger("Unable to retrieve waypoints: {}".format(ex), **utils.build_system_error_dict(utils.SIMAPP_ENVIRONMENT_EXCEPTION, utils.SIMAPP_EVENT_ERROR_CODE_500)) is_loop = np.all(waypoints[0,:] == waypoints[-1,:]) if 'inch' in self.world_name: coef = 0.5 else: coef = 0.6 if is_loop: self.center_line = LinearRing(waypoints[:,0:2]) self.inner_border = LinearRing(waypoints[:,2:4]) self.outer_border = LinearRing(waypoints[:,4:6]) self.left_lane = LinearRing(waypoints[:, 2:4]*coef + waypoints[:, 0:2]*(1-coef)) self.right_lane = LinearRing(waypoints[:, 4:6]*coef + waypoints[:, 0:2]*(1-coef)) self.road_poly = Polygon(self.outer_border, [self.inner_border]) else: self.center_line = LineString(waypoints[:,0:2]) self.inner_border = LineString(waypoints[:,2:4]) self.outer_border = LineString(waypoints[:,4:6]) self.left_lane = LinearRing(waypoints[:, 2:4]*coef + waypoints[:, 0:2]*(1-coef)) self.right_lane = LinearRing(waypoints[:, 4:6]*coef + waypoints[:, 0:2]*(1-coef)) self.road_poly = Polygon(np.vstack((self.outer_border, np.flipud(self.inner_border)))) self.center_dists = [self.center_line.project(Point(p), normalized=True) for p in self.center_line.coords[:-1]] + [1.0] self.track_length = self.center_line.length self.safe_angle = 30 # Queue used to maintain image consumption synchronicity self.image_queue_left_camera = queue.Queue(IMG_QUEUE_BUF_SIZE) rospy.Subscriber('/racecar/camera/zed/rgb/image_rect_color', sensor_image, self.callback_image_left_camera) self.image_queue_right_camera = queue.Queue(IMG_QUEUE_BUF_SIZE) rospy.Subscriber('/racecar/camera/zed_right/rgb/image_rect_color_right', sensor_image, self.callback_image_right_camera) print(self) # Initialize state data self.episodes = 0 self.reverse_dir = False # if self.reverse_dir: # self.start_ndist_map = [0.28, 0.5, 0.97] # else: # self.start_ndist_map = [0.2, 0.42, 0.78] # 36inch # # self.start_ndist_map = [0.19, 0.67, 0.87] # base if 'base' in self.world_name: self.start_ndist_map = [0.19, 0.67, 0.87] elif 'inch' in self.world_name: self.start_ndist_map = [0.23, 0.45, 0.93] elif 'mirror' in self.world_name: self.start_ndist_map = [0.12, 0.34, 0.83] else: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Advanced workshops/reInvent2019-400/src/markov/environments/deepracer_racetrack_env.py [143:402]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rospy.wait_for_service('/gazebo/set_model_state') rospy.wait_for_service('/gazebo/spawn_urdf_model') self.set_model_state = rospy.ServiceProxy('/gazebo/set_model_state', SetModelState) self.spawn_model_client = rospy.ServiceProxy('gazebo/spawn_urdf_model', SpawnModel) # Spawn the new car robot_description = rospy.get_param('robot_description_bot') car_initial_pose = Pose() car_initial_pose.position.x = 0 car_initial_pose.position.y = 0 car_initial_pose.position.z = 0 self.car_name = "racecar_{}".format(car_id) self.car_section = car_id - 1 # car_id should be 1, 2 and 3 self.is_inner = is_inner self.resp = self.spawn_model_client(self.car_name, robot_description, '/{}'.format(self.car_name), car_initial_pose, '') logger.info('Spawn status: {}'.format(self.resp.status_message)) # Initialize member data and reset car self.car_speed = speed # Initializing the lane that the bot car runs self.lanes_list_np = lanes_list_np self.lanes_one_hot = np.zeros(len(lanes_list_np), dtype=bool) if is_inner: self.lanes_one_hot[0] = True self.shapely_lane = lanes_list_np[self.lanes_one_hot][0] else: self.lanes_one_hot[1] = True self.shapely_lane = lanes_list_np[self.lanes_one_hot][0] # Change the lanes after certain time self.change_lane_freq_sec = 30 if self.car_section==self.stat_sec else 0 self.reverse_dir = False self.car_model_state = ModelState() self.reset_sim(self.sec_map[self.is_inner][self.car_section][0]) def reset_sim(self, start_dist): rostime = rospy.get_rostime() self.car_initial_dist = start_dist self.car_initial_time = rostime.secs + 1.e-9*rostime.nsecs self.next_change_lane_time = self.car_initial_time + self.change_lane_freq_sec self.position_bot_car(start_dist) def update_bot_sim(self, sim_time): current_time = sim_time.clock.secs + 1.e-9*sim_time.clock.nsecs seconds_elapsed = current_time - self.car_initial_time traveled_dist = seconds_elapsed * self.car_speed if self.reverse_dir: current_dist = (self.car_initial_dist - traveled_dist) % self.shapely_lane.length else: current_dist = (self.car_initial_dist + traveled_dist) % self.shapely_lane.length # Check for lane change and section reset if self.car_section == self.stat_sec and current_time > self.next_change_lane_time: # Get the next lane index tmp_lane_np = np.zeros(len(self.lanes_list_np), dtype=bool) next_lane_idx = (int(np.argwhere(self.lanes_one_hot==True)) + 1) % len(self.lanes_list_np) tmp_lane_np[next_lane_idx] = True self.lanes_one_hot = tmp_lane_np self.is_inner = not self.is_inner # Assigns the next lane that the bot car as to travel self.shapely_lane = self.lanes_list_np[self.lanes_one_hot][0] self.reset_sim(self.sec_map[self.is_inner][self.car_section][0]) elif self.car_section != self.stat_sec and current_dist >= self.sec_map[self.is_inner][self.car_section][1]: tmp_lane_np = np.zeros(len(self.lanes_list_np), dtype=bool) next_lane_idx = (int(np.argwhere(self.lanes_one_hot==True)) + 1) % len(self.lanes_list_np) tmp_lane_np[next_lane_idx] = True self.lanes_one_hot = tmp_lane_np self.is_inner = not self.is_inner self.shapely_lane = self.lanes_list_np[self.lanes_one_hot][0] self.reset_sim(self.sec_map[self.is_inner][self.car_section][0]) else: # Position the car on the lane self.position_bot_car(current_dist) def position_bot_car(self, car_dist): # Set the position of the second car _, next_index = self.find_prev_next_lane_points(car_dist, self.reverse_dir) car_position = self.shapely_lane.interpolate(car_dist) car_yaw = math.atan2(self.shapely_lane.coords[next_index][1] - car_position.y, self.shapely_lane.coords[next_index][0] - car_position.x) car_quaternion = Rotation.from_euler('zyx', [car_yaw, 0, 0]).as_quat() self.car_model_state.model_name = self.car_name self.car_model_state.pose.position.x = car_position.x self.car_model_state.pose.position.y = car_position.y self.car_model_state.pose.position.z = 0 self.car_model_state.pose.orientation.x = car_quaternion[0] self.car_model_state.pose.orientation.y = car_quaternion[1] self.car_model_state.pose.orientation.z = car_quaternion[2] self.car_model_state.pose.orientation.w = car_quaternion[3] self.car_model_state.twist.linear.x = 0 self.car_model_state.twist.linear.y = 0 self.car_model_state.twist.linear.z = 0 self.car_model_state.twist.angular.x = 0 self.car_model_state.twist.angular.y = 0 self.car_model_state.twist.angular.z = 0 self.set_model_state(self.car_model_state) def get_lane_dists(self, lane): return [lane.project(Point(p)) for p in lane.coords[:-1]] + [1.0] def find_prev_next_lane_points(self, ndist, reverse_dir): lane_dist = self.get_lane_dists(self.shapely_lane) if reverse_dir: next_index = bisect.bisect_left(lane_dist, ndist) - 1 prev_index = next_index + 1 if next_index == -1: next_index = len(lane_dist) - 1 else: next_index = bisect.bisect_right(lane_dist, ndist) prev_index = next_index - 1 if next_index == len(lane_dist): next_index = 0 return prev_index, next_index ### Gym Env ### class DeepRacerRacetrackEnv(gym.Env): def __init__(self): # Create the observation space # self.observation_space = spaces.Box(low=0, high=255, # shape=(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 3), # dtype=np.uint8) # self.observation_space = spaces.Dict({ # # 'left_camera': spaces.Box(low=0, high=255, # # shape=(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 3), # # dtype=np.uint8), # 'stereo': spaces.Box(low=0, high=255, # shape=(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 2), # dtype=np.uint8) # }) self.observation_space = spaces.Dict({ 'STEREO_CAMERAS': spaces.Box(low=0, high=255, shape=(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 2), dtype=np.uint8), 'LIDAR': spaces.Box(low=0.15, high=1.0, shape=(64,)) }) # Create the action space self.action_space = spaces.Box(low=np.array([-1, 0]), high=np.array([+1, +1]), dtype=np.float32) if node_type == SIMULATION_WORKER: # ROS initialization rospy.init_node('rl_coach', anonymous=True) # wait for required services rospy.wait_for_service('/deepracer_simulation_environment/get_waypoints') rospy.wait_for_service('/deepracer_simulation_environment/reset_car') rospy.wait_for_service('/gazebo/get_model_state') rospy.wait_for_service('/gazebo/get_link_state') rospy.wait_for_service('/gazebo/clear_joint_forces') rospy.wait_for_service('/gazebo/spawn_urdf_model') self.get_model_state = rospy.ServiceProxy('/gazebo/get_model_state', GetModelState) self.get_link_state = rospy.ServiceProxy('/gazebo/get_link_state', GetLinkState) self.clear_forces_client = rospy.ServiceProxy('/gazebo/clear_joint_forces', JointRequest) self.reset_car_client = rospy.ServiceProxy('/deepracer_simulation_environment/reset_car', ResetCarSrv) get_waypoints_client = rospy.ServiceProxy('/deepracer_simulation_environment/get_waypoints', GetWaypointSrv) # Create the publishers for sending speed and steering info to the car self.velocity_pub_dict = OrderedDict() self.steering_pub_dict = OrderedDict() for topic in VELOCITY_TOPICS: self.velocity_pub_dict[topic] = rospy.Publisher(topic, Float64, queue_size=1) for topic in STEERING_TOPICS: self.steering_pub_dict[topic] = rospy.Publisher(topic, Float64, queue_size=1) # Read in parameters self.world_name = rospy.get_param('WORLD_NAME') self.job_type = rospy.get_param('JOB_TYPE') self.aws_region = rospy.get_param('AWS_REGION') self.metrics_s3_bucket = rospy.get_param('METRICS_S3_BUCKET') self.metrics_s3_object_key = rospy.get_param('METRICS_S3_OBJECT_KEY') self.metrics = [] self.simulation_job_arn = 'arn:aws:robomaker:' + self.aws_region + ':' + \ rospy.get_param('ROBOMAKER_SIMULATION_JOB_ACCOUNT_ID') + \ ':simulation-job/' + rospy.get_param('AWS_ROBOMAKER_SIMULATION_JOB_ID') # Setup SIM_TRACE_LOG data upload to s3 # self.setup_simtrace_data_upload_to_s3() if self.job_type == TRAINING_JOB: from custom_files.customer_reward_function import reward_function self.reward_function = reward_function self.target_number_of_episodes = rospy.get_param('NUMBER_OF_EPISODES') self.target_reward_score = rospy.get_param('TARGET_REWARD_SCORE') else: from markov.defaults import reward_function self.reward_function = reward_function self.number_of_trials = 0 self.target_number_of_trials = rospy.get_param('NUMBER_OF_TRIALS') # Request the waypoints waypoints = None try: resp = get_waypoints_client() waypoints = np.array(resp.waypoints).reshape(resp.row, resp.col) except Exception as ex: utils.json_format_logger("Unable to retrieve waypoints: {}".format(ex), **utils.build_system_error_dict(utils.SIMAPP_ENVIRONMENT_EXCEPTION, utils.SIMAPP_EVENT_ERROR_CODE_500)) is_loop = np.all(waypoints[0,:] == waypoints[-1,:]) if 'inch' in self.world_name: coef = 0.5 else: coef = 0.6 if is_loop: self.center_line = LinearRing(waypoints[:,0:2]) self.inner_border = LinearRing(waypoints[:,2:4]) self.outer_border = LinearRing(waypoints[:,4:6]) self.left_lane = LinearRing(waypoints[:, 2:4]*coef + waypoints[:, 0:2]*(1-coef)) self.right_lane = LinearRing(waypoints[:, 4:6]*coef + waypoints[:, 0:2]*(1-coef)) self.road_poly = Polygon(self.outer_border, [self.inner_border]) else: self.center_line = LineString(waypoints[:,0:2]) self.inner_border = LineString(waypoints[:,2:4]) self.outer_border = LineString(waypoints[:,4:6]) self.left_lane = LinearRing(waypoints[:, 2:4]*coef + waypoints[:, 0:2]*(1-coef)) self.right_lane = LinearRing(waypoints[:, 4:6]*coef + waypoints[:, 0:2]*(1-coef)) self.road_poly = Polygon(np.vstack((self.outer_border, np.flipud(self.inner_border)))) self.center_dists = [self.center_line.project(Point(p), normalized=True) for p in self.center_line.coords[:-1]] + [1.0] self.track_length = self.center_line.length self.safe_angle = 30 # Queue used to maintain image consumption synchronicity self.image_queue_left_camera = queue.Queue(IMG_QUEUE_BUF_SIZE) rospy.Subscriber('/racecar/camera/zed/rgb/image_rect_color', sensor_image, self.callback_image_left_camera) self.image_queue_right_camera = queue.Queue(IMG_QUEUE_BUF_SIZE) rospy.Subscriber('/racecar/camera/zed_right/rgb/image_rect_color_right', sensor_image, self.callback_image_right_camera) print(self) # Initialize state data self.episodes = 0 self.reverse_dir = False # if self.reverse_dir: # self.start_ndist_map = [0.28, 0.5, 0.97] # else: # self.start_ndist_map = [0.2, 0.42, 0.78] # 36inch # # self.start_ndist_map = [0.19, 0.67, 0.87] # base if 'base' in self.world_name: self.start_ndist_map = [0.19, 0.67, 0.87] elif 'inch' in self.world_name: self.start_ndist_map = [0.23, 0.45, 0.93] elif 'mirror' in self.world_name: self.start_ndist_map = [0.12, 0.34, 0.83] else: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -