From fd8f315f09d2d65dc6a514ef3b8505729e9de9c9 Mon Sep 17 00:00:00 2001
From: Giacomo Spigler <spiglerg@gmail.com>
Date: Thu, 23 May 2019 22:25:01 +0200
Subject: [PATCH] lint fixes
---
examples/custom_observation_example.py | 8 +++++---
examples/custom_railmap_example.py | 3 ++-
examples/demo.py | 12 +++++++++---
examples/simple_example_1.py | 5 +----
examples/simple_example_3.py | 4 ++--
examples/training_example.py | 1 -
flatland/envs/rail_env.py | 4 ++--
7 files changed, 21 insertions(+), 16 deletions(-)
diff --git a/examples/custom_observation_example.py b/examples/custom_observation_example.py
index 03bbe4b7..3c4fc819 100644
--- a/examples/custom_observation_example.py
+++ b/examples/custom_observation_example.py
@@ -1,14 +1,15 @@
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])
diff --git a/examples/custom_railmap_example.py b/examples/custom_railmap_example.py
index 71f849de..9d483c0c 100644
--- a/examples/custom_railmap_example.py
+++ b/examples/custom_railmap_example.py
@@ -1,6 +1,5 @@
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(),
diff --git a/examples/demo.py b/examples/demo.py
index 7e3725bc..74427792 100644
--- a/examples/demo.py
+++ b/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)
diff --git a/examples/simple_example_1.py b/examples/simple_example_1.py
index 70d2e73a..7132b533 100644
--- a/examples/simple_example_1.py
+++ b/examples/simple_example_1.py
@@ -1,10 +1,7 @@
-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)
diff --git a/examples/simple_example_3.py b/examples/simple_example_3.py
index 8aac0ccc..b5283df6 100644
--- a/examples/simple_example_3.py
+++ b/examples/simple_example_3.py
@@ -1,9 +1,9 @@
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)
diff --git a/examples/training_example.py b/examples/training_example.py
index 5a8c7c00..d6f2c026 100644
--- a/examples/training_example.py
+++ b/examples/training_example.py
@@ -1,7 +1,6 @@
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)
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index be3f4d6b..d4facaf5 100644
--- a/flatland/envs/rail_env.py
+++ b/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()
--
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