lint fixes

examples/custom_observation_example.py

 import random
 
-from flatland.envs.generators import random_rail_generator, random_rail_generator
+from flatland.envs.generators import random_rail_generator
 from flatland.envs.rail_env import RailEnv
-from flatland.utils.rendertools import RenderTool
 from flatland.core.env_observation_builder import ObservationBuilder
+
 import numpy as np
 
 random.seed(100)
 np.random.seed(100)
 
+
 class CustomObs(ObservationBuilder):
     def __init__(self):
         self.observation_space = [5]
@@ -20,6 +21,7 @@ class CustomObs(ObservationBuilder):
         observation = handle*np.ones((5,))
         return observation
 
+
 env = RailEnv(width=7,
               height=7,
               rail_generator=random_rail_generator(),
@@ -29,4 +31,4 @@ env = RailEnv(width=7,
 # Print the observation vector for each agents
 obs, all_rewards, done, _ = env.step({0: 0})
 for i in range(env.get_num_agents()):
-    print("Agent ", i,"'s observation: ", obs[i])
+    print("Agent ", i, "'s observation: ", obs[i])

examples/custom_railmap_example.py

 import random
 
-from flatland.envs.generators import random_rail_generator, random_rail_generator
 from flatland.envs.rail_env import RailEnv
 from flatland.core.transitions import RailEnvTransitions
 from flatland.core.transition_map import GridTransitionMap
@@ -10,6 +9,7 @@ import numpy as np
 random.seed(100)
 np.random.seed(100)
 
+
 def custom_rail_generator():
     def generator(width, height, num_agents=0, num_resets=0):
         rail_trans = RailEnvTransitions()
@@ -24,6 +24,7 @@ def custom_rail_generator():
         return grid_map, agents_positions, agents_direction, agents_target
     return generator
 
+
 env = RailEnv(width=6,
               height=4,
               rail_generator=custom_rail_generator(),

examples/demo.py

@@ -48,7 +48,11 @@ class Scenario_Generator:
     def generate_complex_scenario(number_of_agents=3):
         env = RailEnv(width=15,
                       height=15,
-                      rail_generator=complex_rail_generator(nr_start_goal=6, nr_extra=30, min_dist=10, max_dist=99999, seed=0),
+                      rail_generator=complex_rail_generator(nr_start_goal=6,
+                                                            nr_extra=30,
+                                                            min_dist=10,
+                                                            max_dist=99999,
+                                                            seed=0),
                       number_of_agents=number_of_agents)
 
         return env
@@ -150,7 +154,9 @@ class Demo:
         obs = self.env.reset(False, False)
 
         for a in range(self.env.get_num_agents()):
-            data, distance = self.env.obs_builder.split_tree(tree=np.array(obs[a]), num_features_per_node=5, current_depth=0)
+            data, distance = self.env.obs_builder.split_tree(tree=np.array(obs[a]),
+                                                             num_features_per_node=5,
+                                                             current_depth=0)
 
             data = norm_obs_clip(data)
             distance = norm_obs_clip(distance)
@@ -174,7 +180,7 @@ class Demo:
                 action_prob[action] += 1
                 action_dict.update({a: action})
 
-            self.renderer.renderEnv(show=True,action_dict=action_dict)
+            self.renderer.renderEnv(show=True, action_dict=action_dict)
 
             # Environment step
             next_obs, all_rewards, done, _ = self.env.step(action_dict)

examples/simple_example_1.py

-import random
-
-from flatland.envs.generators import random_rail_generator, rail_from_manual_specifications_generator
+from flatland.envs.generators import rail_from_manual_specifications_generator
 from flatland.envs.rail_env import RailEnv
 from flatland.envs.observations import TreeObsForRailEnv
 from flatland.utils.rendertools import RenderTool
-import numpy as np
 
 # Example generate a rail given a manual specification,
 # a map of tuples (cell_type, rotation)

examples/simple_example_3.py

 import random
 
-from flatland.envs.generators import random_rail_generator, random_rail_generator
+from flatland.envs.generators import random_rail_generator
 from flatland.envs.rail_env import RailEnv
 from flatland.utils.rendertools import RenderTool
-from flatland.core.env_observation_builder import ObservationBuilder
+from flatland.core.env_observation_builder import TreeObsForRailEnv
 import numpy as np
 
 random.seed(100)

examples/training_example.py

 from flatland.envs.generators import complex_rail_generator
 from flatland.envs.rail_env import RailEnv
 import numpy as np
-from flatland.utils.rendertools import RenderTool
 
 np.random.seed(1)
 

flatland/envs/rail_env.py

@@ -91,7 +91,7 @@ class RailEnv(Environment):
         self.obs_builder._set_env(self)
 
         self.action_space = [1]
-        self.observation_space = self.obs_builder.observation_space # updated on resets?
+        self.observation_space = self.obs_builder.observation_space  # updated on resets?
 
         self.actions = [0] * number_of_agents
         self.rewards = [0] * number_of_agents
@@ -163,7 +163,7 @@ class RailEnv(Environment):
 
         # Reset the state of the observation builder with the new environment
         self.obs_builder.reset()
-        self.observation_space = self.obs_builder.observation_space # <-- change on reset?
+        self.observation_space = self.obs_builder.observation_space  # <-- change on reset?
 
         # Return the new observation vectors for each agent
         return self._get_observations()