Fix: Address flake8 and black formatting issues (#395)

This commit applies the black formatter to the codebase and resolves
several flake8 errors.

examples/eval_robustness.py

@@ -1,6 +1,7 @@
 """Script version of the eval robustness tool. This can be used to manually evaluate the
 disturbance if you don't want to implement a disturber.
 """
+
 import os
 from pathlib import Path
 
@@ -470,14 +471,16 @@ def noise_disturbance(mean, std):
 
             # Replace observations and references with short names.
             obs_disturbance_df["observation"] = obs_disturbance_df["observation"].apply(
-                lambda x: "observation"
-                if x == "observation"
-                else x.replace("observation_", "obs_")
+                lambda x: (
+                    "observation"
+                    if x == "observation"
+                    else x.replace("observation_", "obs_")
+                )
             )
             refs_disturbance_df["reference"] = refs_disturbance_df["reference"].apply(
-                lambda x: "reference"
-                if x == "reference"
-                else x.replace("reference_", "ref_")
+                lambda x: (
+                    "reference" if x == "reference" else x.replace("reference_", "ref_")
+                )
             )
 
             # Initialize plot.
@@ -610,19 +613,23 @@ def noise_disturbance(mean, std):
             ref_errors_disturbance_df["reference_error"] = ref_errors_disturbance_df[
                 "reference_error"
             ].apply(
-                lambda x: "reference_error"
-                if x == "reference_error"
-                else x.replace("reference_error_", "ref_error_")
+                lambda x: (
+                    "reference_error"
+                    if x == "reference_error"
+                    else x.replace("reference_error_", "ref_error_")
+                )
             )
 
             # Initialize plot.
             fig, ax = plt.subplots(figsize=(12, 6), tight_layout=True)
 
             # Create plot title.
             plot_title = "Mean {} ".format(
-                "reference errors"
-                if len(available_ref_errors) > 1
-                else "reference error",
+                (
+                    "reference errors"
+                    if len(available_ref_errors) > 1
+                    else "reference error"
+                ),
             )
             plot_title += (
                 "under 'RandomActionNoise' disturber with mean {} and std {}.".format(

examples/manual_env_policy_inference.py

@@ -2,6 +2,7 @@
 the CLI fails. It uses the ``Oscillator-v1`` environment that is found in the
 :stable_gym:`stable_gym <>` package.
 """
+
 import gymnasium as gym
 
 from stable_learning_control.utils.test_policy import (

examples/manual_robustness_eval_plots.py

@@ -2,6 +2,7 @@
 generated by the :ref:`robustness evaluation utility <eval_robustness>` utility. This
 example plots the first observation and reference for each disturbance in the dataset.
 """
+
 import argparse
 from pathlib import Path
 

examples/pytorch/lac_ray_hyper_parameter_tuning.py

@@ -28,6 +28,7 @@
 .. _`Weights & Biases`: https://wandb.ai/site
 .. _`Weights & Biases documentation`: https://docs.wandb.ai/
 """  # noqa: E501
+
 import os.path as osp
 
 import gymnasium as gym
@@ -49,7 +50,6 @@ def train_lac(config):
     Args:
         config (dict): The Ray tuning configuration dictionary.
     """
-
     # Unpack trainable arguments.
     env_name = config.pop("env_name")
 

examples/pytorch/sac_exp_grid_search.py

@@ -13,6 +13,7 @@
 
 .. _`SpinningUp documentation`: https://spinningup.openai.com/en/latest/user/running.html#using-experimentgrid
 """  # noqa
+
 import argparse
 
 import torch

examples/pytorch/sac_ray_hyper_parameter_tuning.py

@@ -28,6 +28,7 @@
 .. _`Weights & Biases`: https://wandb.ai/site
 .. _`Weights & Biases documentation`: https://docs.wandb.ai/
 """  # noqa: E501
+
 import os.path as osp
 
 import gymnasium as gym
@@ -49,7 +50,6 @@ def train_sac(config):
     Args:
         config (dict): The Ray tuning configuration dictionary.
     """
-
     # Unpack trainable arguments.
     env_name = config.pop("env_name")
 

examples/tf2/lac_ray_hyper_parameter_tuning.py

@@ -28,6 +28,7 @@
 .. _`Weights & Biases`: https://wandb.ai/site
 .. _`Weights & Biases documentation`: https://docs.wandb.ai/
 """  # noqa: E501
+
 import os.path as osp
 
 import gymnasium as gym
@@ -49,7 +50,6 @@ def train_lac(config):
     Args:
         config (dict): The Ray tuning configuration dictionary.
     """
-
     # Unpack trainable arguments.
     env_name = config.pop("env_name")
 

examples/tf2/sac_exp_grid_search.py

@@ -13,6 +13,7 @@
 
 .. _`SpinningUp documentation`: https://spinningup.openai.com/en/latest/user/running.html#using-experimentgrid
 """  # noqa
+
 import argparse
 
 import tensorflow as tf

examples/tf2/sac_ray_hyper_parameter_tuning.py

@@ -28,6 +28,7 @@
 .. _`Weights & Biases`: https://wandb.ai/site
 .. _`Weights & Biases documentation`: https://docs.wandb.ai/
 """  # noqa: E501
+
 import os.path as osp
 
 import gymnasium as gym
@@ -49,7 +50,6 @@ def train_sac(config):
     Args:
         config (dict): The Ray tuning configuration dictionary.
     """
-
     # Unpack trainable arguments.
     env_name = config.pop("env_name")
 

sandbox/test_finite_horizon_replay_buffer.py

@@ -1,5 +1,6 @@
 """Script used for performing some quick tests on the FiniteHorizonReplayBuffer class.
 """
+
 import gymnasium as gym
 
 # from stable_learning_control.common.buffers import TrajectoryBuffer

sandbox/test_gym_env.py

@@ -2,6 +2,7 @@
 example, we use the ``Oscillator-v1`` environment in the :stable_gym:`stable_gym <>`
 package.
 """
+
 import gymnasium as gym
 import matplotlib.pyplot as plt
 import numpy as np

sandbox/test_replay_buffer.py

@@ -1,5 +1,6 @@
 """Script used for performing some quick tests on the ReplayBuffer class.
 """
+
 import gymnasium as gym
 
 # from stable_learning_control.common.buffers import TrajectoryBuffer

sandbox/test_traj_buffer.py

@@ -2,6 +2,7 @@
 buffer was created for a new monte-carlo algorithm we had in mind. The buffer is
 designed to store trajectories of variable length.
 """
+
 import gymnasium as gym
 
 # from stable_learning_control.common.buffers import TrajectoryBuffer

setup.cfg

@@ -1,10 +1,12 @@
 [flake8]
 max-line-length = 89
-extend-ignore = E203
 exclude =
     docs/source/conf.py,
     sandbox,
     build,
-    node_modules
+    node_modules,
+    tests,
+    stable_learning_control/version.py
 per-file-ignores =
     __init__.py: F401, E501
+extend-ignore = E203, D400, D401, D205

stable_learning_control/__init__.py

@@ -1,4 +1,5 @@
 """Module that Initialises the stable_learning_control package."""
+
 # Put algorithms in main namespace.
 from stable_learning_control.algos.pytorch.lac.lac import lac as lac_pytorch
 from stable_learning_control.algos.pytorch.latc.latc import latc as latc_pytorch

stable_learning_control/algos/__init__.py

@@ -5,6 +5,7 @@
     research. As a result, the TensorFlow implementation has yet to be thoroughly tested,
     and no guarantees can be given about the correctness of these algorithms.
 """
+
 from stable_learning_control.algos.pytorch.lac.lac import LAC as LAC_pytorch
 from stable_learning_control.algos.pytorch.sac.sac import SAC as SAC_pytorch
 from stable_learning_control.utils.import_utils import tf_installed

stable_learning_control/algos/common/helpers.py

@@ -1,4 +1,5 @@
 """Functions that are used in multiple Pytorch and TensorFlow algorithms."""
+
 import importlib
 
 import numpy as np

stable_learning_control/algos/pytorch/__init__.py

@@ -1,4 +1,4 @@
-"""Contains the Pytorch implementations of the RL algorithms.
-"""
+"""Contains the Pytorch implementations of the RL algorithms."""
+
 from stable_learning_control.algos.pytorch.lac.lac import LAC
 from stable_learning_control.algos.pytorch.sac.sac import SAC

stable_learning_control/algos/pytorch/common/__init__.py

@@ -1,5 +1,5 @@
-"""Contains several functions that are used across all the RL algorithms.
-"""
+"""Contains several functions that are used across all the RL algorithms."""
+
 from stable_learning_control.algos.pytorch.common.get_lr_scheduler import (
     get_lr_scheduler,
 )

stable_learning_control/algos/pytorch/common/buffers.py

@@ -1,5 +1,5 @@
-"""Contains several replay buffers used in the Pytorch algorithms.
-"""
+"""Contains several replay buffers used in the Pytorch algorithms."""
+
 import torch
 
 from stable_learning_control.algos.common.buffers import (

stable_learning_control/algos/pytorch/common/get_lr_scheduler.py

@@ -1,4 +1,5 @@
 """Contains functions used for creating Pytorch learning rate schedulers."""
+
 from decimal import Decimal
 
 import numpy as np
@@ -22,7 +23,7 @@ def get_exponential_decay_rate(lr_start, lr_final, steps):
 
 
 def calc_linear_decay_rate(lr_init, lr_final, steps):
-    """Returns the linear decay factor (G) needed to achieve a given final learning
+    r"""Returns the linear decay factor (G) needed to achieve a given final learning
     rate at a certain step. This decay factor can for example be used with a
     :class:`torch.optim.lr_scheduler.LambdaLR` scheduler. Keep in mind that this
     function assumes the following formula for the learning rate decay.

stable_learning_control/algos/pytorch/common/helpers.py

@@ -1,5 +1,5 @@
-"""Contains several Pytorch helper functions.
-"""
+"""Contains several Pytorch helper functions."""
+
 import numpy as np
 import torch
 import torch.nn as nn

stable_learning_control/algos/pytorch/lac/__init__.py

@@ -1,3 +1,3 @@
-"""A Lyapunov (soft) Actor-Critic Agent.
-"""
+"""A Lyapunov (soft) Actor-Critic Agent."""
+
 from stable_learning_control.algos.pytorch.lac.lac import LAC, lac

stable_learning_control/algos/pytorch/latc/__init__.py

@@ -1,3 +1,3 @@
-"""A Lyapunov (soft) Actor-Twin Critic Agent.
-"""
+"""A Lyapunov (soft) Actor-Twin Critic Agent."""
+
 from stable_learning_control.algos.pytorch.latc.latc import latc

stable_learning_control/algos/pytorch/policies/__init__.py

@@ -1,5 +1,5 @@
-"""Policies and networks used to create the RL agents.
-"""
+"""Policies and networks used to create the RL agents."""
+
 from stable_learning_control.algos.pytorch.policies.actors.squashed_gaussian_actor import (
     SquashedGaussianActor,
 )

stable_learning_control/algos/pytorch/policies/actors/__init__.py

@@ -1,5 +1,5 @@
-"""Actor network structures.
-"""
+"""Actor network structures."""
+
 from stable_learning_control.algos.pytorch.policies.actors.squashed_gaussian_actor import (
     SquashedGaussianActor,
 )

stable_learning_control/algos/pytorch/policies/actors/squashed_gaussian_actor.py

@@ -3,6 +3,7 @@
 This module contains a Pytorch implementation of the Squashed Gaussian Actor policy of
 `Haarnoja et al. 2019 <https://arxiv.org/abs/1812.05905>`_.
 """
+
 import numpy as np
 import torch
 import torch.nn as nn

stable_learning_control/algos/pytorch/policies/critics/L_critic.py

@@ -3,6 +3,7 @@
 This module contains a Pytorch implementation of the Lyapunov Critic policy of
 `Han et al. 2020 <https://arxiv.org/abs/2004.14288>`_.
 """
+
 import torch
 import torch.nn as nn
 

stable_learning_control/algos/pytorch/policies/critics/Q_critic.py

@@ -3,6 +3,7 @@
 This module contains a Pytorch implementation of the Q Critic policy of
 `Haarnoja et al. 2019 <https://arxiv.org/abs/1812.05905>`_.
 """
+
 import torch
 import torch.nn as nn
 

stable_learning_control/algos/pytorch/policies/critics/__init__.py

@@ -1,4 +1,4 @@
-"""Critic network structures.
-"""
+"""Critic network structures."""
+
 from stable_learning_control.algos.pytorch.policies.critics.L_critic import LCritic
 from stable_learning_control.algos.pytorch.policies.critics.Q_critic import QCritic

stable_learning_control/algos/pytorch/policies/soft_actor_critic.py

@@ -5,6 +5,7 @@
 This module contains a Pytorch implementation of the Soft Actor Critic policy of
 `Haarnoja et al. 2019 <https://arxiv.org/abs/1812.05905>`_.
 """
+
 import torch
 import torch.nn as nn
 

stable_learning_control/algos/pytorch/sac/__init__.py

@@ -1,3 +1,3 @@
-"""A Soft Actor-Critic Agent.
-"""
+"""A Soft Actor-Critic Agent."""
+
 from stable_learning_control.algos.pytorch.sac.sac import sac

stable_learning_control/algos/tf2/__init__.py

@@ -1,4 +1,4 @@
-"""Contains the TensorFlow 2.x implementations of the RL algorithms.
-"""
+"""Contains the TensorFlow 2.x implementations of the RL algorithms."""
+
 from stable_learning_control.algos.tf2.lac.lac import LAC
 from stable_learning_control.algos.tf2.sac.sac import SAC

stable_learning_control/algos/tf2/common/__init__.py

@@ -1,3 +1,3 @@
-"""Contains several functions that are used across all the RL algorithms.
-"""
+"""Contains several functions that are used across all the RL algorithms."""
+
 from stable_learning_control.algos.tf2.common.get_lr_scheduler import get_lr_scheduler

stable_learning_control/algos/tf2/common/bijectors.py

@@ -4,6 +4,7 @@
 `the TensorFlow documentation <https://www.tensorflow.org/probability/api_docs/python/tfp/bijectors/Bijector>`_ and
 `this stackoverflow question <https://stackoverflow.com/questions/56425301/what-is-bijectors-in-layman-terms-in-tensorflow-probability>`_.
 """  # noqa: E501
+
 import numpy as np
 import tensorflow as tf
 import tensorflow_probability as tfp
@@ -12,7 +13,8 @@
 
 class SquashBijector(tfp.bijectors.Bijector):
     """A squash bijector used to keeps track of the distribution properties when the
-    distribution is transformed using the tanh squash function."""
+    distribution is transformed using the tanh squash function.
+    """
 
     def __init__(self, validate_args=False, name="tanh"):
         """Initiate squashed bijector object.

stable_learning_control/algos/tf2/common/get_lr_scheduler.py

@@ -1,4 +1,5 @@
 """Module used for creating TensorFlow learning rate schedulers."""
+
 import numpy as np
 
 from stable_learning_control.utils.import_utils import import_tf

stable_learning_control/algos/tf2/common/helpers.py

@@ -1,5 +1,5 @@
-"""Several TensorFlow helper functions.
-"""
+"""Several TensorFlow helper functions."""
+
 import numpy as np
 import tensorflow as tf
 

stable_learning_control/algos/tf2/lac/__init__.py

@@ -1,3 +1,3 @@
-"""A Lyapunov Actor Critic Agent.
-"""
+"""A Lyapunov Actor Critic Agent."""
+
 from stable_learning_control.algos.tf2.lac.lac import LAC, lac

stable_learning_control/algos/tf2/latc/__init__.py

@@ -1,3 +1,3 @@
-"""A Lyapunov (soft) Actor-Twin Critic Agent.
-"""
+"""A Lyapunov (soft) Actor-Twin Critic Agent."""
+
 from stable_learning_control.algos.tf2.latc.latc import latc