fixed flake8 unusued import error

flyer_env/aircraft/controller.py

@@ -1,6 +1,5 @@
 import numpy as np
-import os
-from typing import List, Tuple, Union, Optional
+from typing import Union
 from flyer_env.utils import Vector
 from simple_pid import PID
 from pyflyer import Aircraft

flyer_env/aircraft/tracking.py

@@ -65,7 +65,6 @@ def _unit_dir_vector(start_point, end_point):
     def arc_path(self, pos):
 
         if self.goal_state < len(self.goal_set) - 2:
-            final_state = False
 
             # Get points defining trajecotries
             point_a = self.goal_set[self.goal_state]
@@ -149,7 +148,6 @@ def arc_path(self, pos):
                 heading = tangent_track + (np.arctan(k_orbit * error))
                 # heading = tangent_track
         else:
-            final_state = True
             points = list(self.goal_set.values())
 
             point_a = points[-2]

flyer_env/envs/common/abstract.py

@@ -1,4 +1,3 @@
-import os
 from typing import List, Tuple, Optional, TypeVar, Dict, Text
 import gymnasium as gym
 import numpy as np

flyer_env/envs/common/action.py

@@ -1,6 +1,5 @@
 import functools
-import itertools
-from typing import TYPE_CHECKING, Optional, Union, Tuple, Callable, List, Dict
+from typing import TYPE_CHECKING, Optional, Union, Tuple, Callable, Dict
 from gymnasium import spaces
 import numpy as np
 

flyer_env/envs/common/observation.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 from gymnasium import spaces
-from typing import List, Dict, TYPE_CHECKING, Tuple, OrderedDict
+from typing import List, Dict, TYPE_CHECKING, OrderedDict
 
 if TYPE_CHECKING:
     from flyer_env.envs.common.abstract import AbstractEnv

flyer_env/envs/flyer_env.py

@@ -1,5 +1,4 @@
 from typing import Dict, Text
-import sys
 import os
 import numpy as np
 from gymnasium import Env

flyer_env/envs/forced_landing_env.py

@@ -1,6 +1,4 @@
-from typing import Dict, Text
 import os
-import numpy as np
 from flyer_env import utils
 from flyer_env.aircraft import ControlledAircraft
 from flyer_env.envs.common.abstract import AbstractEnv

flyer_env/envs/runway_env.py

@@ -1,5 +1,4 @@
 from typing import Dict, Text
-import sys
 import os
 import numpy as np
 from flyer_env import utils

flyer_env/utils.py

@@ -1,5 +1,5 @@
 import numpy as np
-from typing import Tuple, Dict, Callable, List, Optional, Union, Sequence
+from typing import Tuple, List, Union, Sequence
 
 # Useful types
 Vector = Union[np.ndarray, Sequence[float]]

scripts/controller.py

@@ -2,7 +2,7 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 
-from typing import List, Union, DefaultDict
+from typing import Union, DefaultDict
 from flyer_env.utils import Vector
 from simple_pid import PID
 from pyflyer import Aircraft
@@ -255,9 +255,9 @@ def clip_heading(heading: float) -> float:
 def simulate():
 
     # Trim values for given airspeed and conditions
-    pitch = -0.06081844622950141
-    elevator = 0.04055471935347572
-    tla = 0.6730648623679762
+    # pitch = -0.06081844622950141
+    # elevator = 0.04055471935347572
+    # tla = 0.6730648623679762
 
     dt = 0.01
     c_ac = ControlledAircraft(dt)

scripts/pid_landing.py

@@ -3,7 +3,6 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 
-from typing import Dict
 from flyer_env import utils
 from flyer_env.aircraft import TrackPoints
 
@@ -104,7 +103,7 @@ def main():
         obs, reward, terminated, truncated, info = env.step(action)
 
         v_dict = env.unwrapped.vehicle.dict
-        controls = env.unwrapped.vehicle.aircraft.controls
+        # controls = env.unwrapped.vehicle.aircraft.controls
         obs_dict = {"x": v_dict["x"], "y": v_dict["y"], "z": v_dict["z"]}
         # print(f'z: {v_dict["z"]}')
         observations.append(obs_dict)

scripts/pid_traj.py

@@ -3,7 +3,6 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 
-from typing import Dict
 from flyer_env import utils
 
 plt.rcParams.update({"text.usetex": True})
@@ -55,10 +54,10 @@ def main():
             "other_controls": np.array([alt, speed]),
         }
 
-        v_dict = env.unwrapped.vehicle.dict
+        # v_dict = env.unwrapped.vehicle.dict
         controls = env.unwrapped.vehicle.aircraft.controls
 
-        obs, reward, terminated, truncated, info = env.step(action)
+        _, reward, terminated, truncated, info = env.step(action)
         obs_dict = {
             "elevator": controls["elevator"],
             "aileron": controls["aileron"],

scripts/sac_control_plot.py

@@ -45,16 +45,16 @@ def main():
     }
 
     c_env = gym.make("control-v1", config=control_env_config)
-    c_obs, c_info = c_env.reset()
-    c_done = False
+    _, _ = c_env.reset()
+    # c_done = False
     c_action = [
         0.0,
         1.0,
         lmap(-1000.0, [0.0, -10000], [-1.0, 1.0]),
         lmap(100.0, [60.0, 110.0], [-1.0, 1.0]),
     ]
 
-    obs, info = env.reset()
+    obs, _ = env.reset()
     done = False
 
     observations = []

scripts/sac_train.py

@@ -49,7 +49,7 @@ def main():
         render=False,
     )
 
-    model = SAC("MlpPolicy", env, verbose=1, tensorboard_log=f".runs/sac")
+    model = SAC("MlpPolicy", env, verbose=1, tensorboard_log=".runs/sac")
     model.learn(
         total_timesteps=config["total_timesteps"],
         log_interval=4,

scripts/test_plot.py

@@ -1,4 +1,3 @@
-import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 import gymnasium as gym

scripts/train.py

@@ -1,9 +1,9 @@
 import gymnasium as gym
 import hydra
 from pathlib import Path
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import OmegaConf
 
-from stable_baselines3 import HerReplayBuffer, SAC, PPO, DDPG
+from stable_baselines3 import HerReplayBuffer, SAC
 from stable_baselines3.common.env_util import make_vec_env
 from stable_baselines3.common.callbacks import EvalCallback
 from stable_baselines3.common.monitor import Monitor
@@ -65,11 +65,11 @@ def __init__(self, cfg):
                     n_sampled_goal=4, goal_selection_strategy="future"
                 ),
                 learning_starts=self.cfg.learning_starts,
-                tensorboard_log=f".runs/sac",
+                tensorboard_log=".runs/sac",
             )
         else:
             self.model = SAC(
-                "MlpPolicy", self.train_env, verbose=1, tensorboard_log=f".runs/sac"
+                "MlpPolicy", self.train_env, verbose=1, tensorboard_log=".runs/sac"
             )
         return
 

scripts/trim_disturbance.py

@@ -24,7 +24,7 @@
 def simulate():
 
     # Trim values for given airspeed and conditions
-    pitch = -0.06081844622950141
+    # pitch = -0.06081844622950141
     elevator = 0.04055471935347572
     tla = 0.6730648623679762
 

scripts/wb_test.py

@@ -1,13 +1,12 @@
 import gymnasium as gym
 import wandb
-import numpy as np
 from stable_baselines3 import PPO
 from stable_baselines3.common.vec_env import DummyVecEnv
 
 
 def make_env():
     env = gym.make("CartPole-v1", render_mode="rgb_array")
-    env = gym.wrappers.RecordVideo(env, f"videos")  # record videos
+    env = gym.wrappers.RecordVideo(env, "videos")  # record videos
     env = gym.wrappers.RecordEpisodeStatistics(env)  # record stats such as returns
     return env
 
@@ -23,6 +22,6 @@ def make_env():
 )
 
 env = DummyVecEnv([make_env])
-model = PPO(config["policy"], env, verbose=1, tensorboard_log=f"runs/ppo")
+model = PPO(config["policy"], env, verbose=1, tensorboard_log="runs/ppo")
 model.learn(total_timesteps=config["total_timesteps"])
 wandb.finish()

tests/aircraft/test_controller.py

@@ -1,4 +1,3 @@
-import pytest
 from pyflyer import Aircraft
 
 

tests/aircraft/test_trajectory.py

@@ -1,4 +1,3 @@
-import pytest
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -40,7 +39,7 @@ def step(self, hdg: float, alt: float, speed: float):
 def test_trajectory():
 
     dt = 0.01
-    start_pos = np.array([0.0, 0.0])
+    # start_pos = np.array([0.0, 0.0])
     # target_points = [
     #     [0.0, 0.0],
     #     [24060.25390625, -15000.0],

tests/aircraft/test_trim.py

@@ -1,4 +1,3 @@
-import pytest
 from pyflyer import Aircraft
 
 

tests/envs/test_gym.py

@@ -1,9 +1,6 @@
 import gymnasium as gym
 import pytest
 import flyer_env
-import numpy as np
-from flyer_env.envs.flyer_env import FlyerEnv
-from PIL import Image
 
 flyer_env.register_flyer_envs()
 
@@ -15,7 +12,7 @@ def test_env_step(env_spec):
 
     env_configuration = {"screen_size": 256, "duration": 10.0}
     env = gym.make(env_spec, config=env_configuration, render_mode="rgb_array")
-    obs, info = env.reset()
+    obs, _ = env.reset()
     assert env.observation_space.contains(obs)
     observations = []
     terminated = truncated = False