content: import numpy as np
  import matplotlib.pyplot as plt
  from pyquaternion import Quaternion
  
  from constants import SIM_TASK_CONFIGS
  from ee_sim_env import make_ee_sim_env
  
  import IPython
  e = IPython.embed
  
  
  class BasePolicy:
      def __init__(self, inject_noise=False):
          self.inject_noise = inject_noise
          self.step_count = 0
          self.left_trajectory = None
          self.right_trajectory = None
  
      def generate_trajectory(self, ts_first):
          raise NotImplementedError
  
      @staticmethod
      def interpolate(curr_waypoint, next_waypoint, t):
          t_frac = (t - curr_waypoint["t"]) / (next_waypoint["t"] - curr_waypoint["t"])
          curr_xyz = curr_waypoint['xyz']
          curr_quat = curr_waypoint['quat']
          curr_grip = curr_waypoint['gripper']
          next_xyz = next_waypoint['xyz']
          next_quat = next_waypoint['quat']
          next_grip = next_waypoint['gripper']
          xyz = curr_xyz + (next_xyz - curr_xyz) * t_frac
          quat = curr_quat + (next_quat - curr_quat) * t_frac
          gripper = curr_grip + (next_grip - curr_grip) * t_frac
          return xyz, quat, gripper
  
      def __call__(self, ts):
          # generate trajectory at first timestep, then open-loop execution
          if self.step_count == 0:
              self.generate_trajectory(ts)
  
          # obtain left and right waypoints
          if self.left_trajectory[0]['t'] == self.step_count:
              self.curr_left_waypoint = self.left_trajectory.pop(0)
          next_left_waypoint = self.left_trajectory[0]
  
          if self.right_trajectory[0]['t'] == self.step_count:
              self.curr_right_waypoint = self.right_trajectory.pop(0)
          next_right_waypoint = self.right_trajectory[0]
  
          # interpolate between waypoints to obtain current pose and gripper command
          left_xyz, left_quat, left_gripper = self.interpolate(self.curr_left_waypoint, next_left_waypoint, self.step_count)
          right_xyz, right_quat, right_gripper = self.interpolate(self.curr_right_waypoint, next_right_waypoint, self.step_count)
  
          # Inject noise
          if self.inject_noise:
              scale = 0.01
              left_xyz = left_xyz + np.random.uniform(-scale, scale, left_xyz.shape)
              right_xyz = right_xyz + np.random.uniform(-scale, scale, right_xyz.shape)
  
          action_left = np.concatenate([left_xyz, left_quat, [left_gripper]])
          action_right = np.concatenate([right_xyz, right_quat, [right_gripper]])
  
          self.step_count += 1
          return np.concatenate([action_left, action_right])
  
  
  class PickAndTransferPolicy(BasePolicy):
  
      def generate_trajectory(self, ts_first):
          init_mocap_pose_right = ts_first.observation['mocap_pose_right']
          init_mocap_pose_left = ts_first.observation['mocap_pose_left']
  
          box_info = np.array(ts_first.observation['env_state'])
          box_xyz = box_info[:3]
          box_quat = box_info[3:]
          # print(f"Generate trajectory for {box_xyz=}")
  
          gripper_pick_quat = Quaternion(init_mocap_pose_right[3:])
          gripper_pick_quat = gripper_pick_quat * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60)
  
          meet_left_quat = Quaternion(axis=[1.0, 0.0, 0.0], degrees=90)
  
          meet_xyz = np.array([0, 0.5, 0.25])
  
          self.left_trajectory = [
              {"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep
              {"t": 100, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # approach meet position
              {"t": 260, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # move to meet position
              {"t": 310, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 0}, # close gripper
              {"t": 360, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # move left
              {"t": 400, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # stay
          ]
  
          self.right_trajectory = [
              {"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep
              {"t": 90, "xyz": box_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat.elements, "gripper": 1}, # approach the cube
              {"t": 130, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 1}, # go down
              {"t": 170, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 0}, # close gripper
              {"t": 200, "xyz": meet_xyz + np.array([0.05, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 0}, # approach meet position
              {"t": 220, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 0}, # move to meet position
              {"t": 310, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 1}, # open gripper
              {"t": 360, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # move to right
              {"t": 400, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # stay
          ]
  
  
  class InsertionPolicy(BasePolicy):
  
      def generate_trajectory(self, ts_first):
          init_mocap_pose_right = ts_first.observation['mocap_pose_right']
          init_mocap_pose_left = ts_first.observation['mocap_pose_left']
  
          peg_info = np.array(ts_first.observation['env_state'])[:7]
          peg_xyz = peg_info[:3]
          peg_quat = peg_info[3:]
  
          socket_info = np.array(ts_first.observation['env_state'])[7:]
          socket_xyz = socket_info[:3]
          socket_quat = socket_info[3:]
  
          gripper_pick_quat_right = Quaternion(init_mocap_pose_right[3:])
          gripper_pick_quat_right = gripper_pick_quat_right * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60)
  
          gripper_pick_quat_left = Quaternion(init_mocap_pose_right[3:])
          gripper_pick_quat_left = gripper_pick_quat_left * Quaternion(axis=[0.0, 1.0, 0.0], degrees=60)
  
          meet_xyz = np.array([0, 0.5, 0.15])
          lift_right = 0.00715
  
          self.left_trajectory = [
              {"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep
              {"t": 120, "xyz": socket_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # approach the cube
              {"t": 170, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # go down
              {"t": 220, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # close gripper
              {"t": 285, "xyz": meet_xyz + np.array([-0.1, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # approach meet position
              {"t": 340, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements,"gripper": 0},  # insertion
              {"t": 400, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0},  # insertion
          ]
  
          self.right_trajectory = [
              {"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep
              {"t": 120, "xyz": peg_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # approach the cube
              {"t": 170, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # go down
              {"t": 220, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # close gripper
              {"t": 285, "xyz": meet_xyz + np.array([0.1, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # approach meet position
              {"t": 340, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0},  # insertion
              {"t": 400, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0},  # insertion
  
          ]
  
  
  def test_policy(task_name):
      # example rolling out pick_and_transfer policy
      onscreen_render = True
      inject_noise = False
  
      # setup the environment
      episode_len = SIM_TASK_CONFIGS[task_name]['episode_len']
      if 'sim_transfer_cube' in task_name:
          env = make_ee_sim_env('sim_transfer_cube')
      elif 'sim_insertion' in task_name:
          env = make_ee_sim_env('sim_insertion')
      else:
          raise NotImplementedError
  
      for episode_idx in range(2):
          ts = env.reset()
          episode = [ts]
          if onscreen_render:
              ax = plt.subplot()
              plt_img = ax.imshow(ts.observation['images']['angle'])
              plt.ion()
  
          policy = PickAndTransferPolicy(inject_noise)
          for step in range(episode_len):
              action = policy(ts)
              ts = env.step(action)
              episode.append(ts)
              if onscreen_render:
                  plt_img.set_data(ts.observation['images']['angle'])
                  plt.pause(0.02)
          plt.close()
  
          episode_return = np.sum([ts.reward for ts in episode[1:]])
          if episode_return > 0:
              print(f"{episode_idx=} Successful, {episode_return=}")
          else:
              print(f"{episode_idx=} Failed")
  
  
  if __name__ == '__main__':
      test_task_name = 'sim_transfer_cube_scripted'
      test_policy(test_task_name)
  
  