Formatting

pyproject.toml

@@ -144,11 +144,11 @@ PYDEVD_DISABLE_FILE_VALIDATION="1"
 test = "pytest test --cov=armscan_env --cov-report=xml --cov-report=term-missing --durations=0 -v --color=yes"
 # Adjust to a smaller set of tests if appropriate
 test-subset = "pytest test --color=yes"
-_black_check = "black --check ."
-_ruff_check = "ruff check ."
+_black_check = "black --check src scripts docs"
+_ruff_check = "ruff check src scripts docs"
 _ruff_check_nb = "nbqa ruff docs"
-_black_format = "black ."
-_ruff_format = "ruff --fix ."
+_black_format = "black src scripts docs"
+_ruff_format = "ruff --fix src scripts docs"
 _ruff_format_nb = "nbqa ruff --fix docs"
 lint = ["_black_check", "_ruff_check", "_ruff_check_nb"]
 _poetry_install_sort_plugin = "poetry self add poetry-plugin-sort"

src/armscan_env/envs/base.py

@@ -2,8 +2,9 @@
 from dataclasses import dataclass, field
 from typing import Any, Generic, TypeVar
 
-import gymnasium as gym
 import numpy as np
+
+import gymnasium as gym
 from gymnasium.core import ActType as TAction
 from gymnasium.core import ObsType as TObs
 

src/armscan_env/envs/labelmaps_navigation.py

@@ -3,7 +3,6 @@
 from copy import copy, deepcopy
 from typing import Any, ClassVar, Literal
 
-import gymnasium as gym
 import numpy as np
 import SimpleITK as sitk
 from armscan_env.clustering import TissueClusters
@@ -19,13 +18,15 @@
 from armscan_env.slicing import slice_volume
 from armscan_env.util.visualizations import show_clusters
 from celluloid import Camera
-from gymnasium.core import ObsType as TObs
 from IPython.core.display import HTML
 from matplotlib import pyplot as plt
 from matplotlib.animation import ArtistAnimation
 from matplotlib.axes import Axes
 from matplotlib.figure import Figure
 
+import gymnasium as gym
+from gymnasium.core import ObsType as TObs
+
 log = logging.getLogger(__name__)
 
 _VOL_NAME_TO_OPTIMAL_ACTION = {

src/armscan_env/envs/observations.py

@@ -8,13 +8,14 @@
     cast,
 )
 
-import gymnasium as gym
 import numpy as np
 from armscan_env.clustering import TissueClusters, TissueLabel
 from armscan_env.envs.base import ArrayObservation, DictObservation
 from armscan_env.envs.state_action import LabelmapStateAction
 from armscan_env.util.img_processing import crop_center
 
+import gymnasium as gym
+
 
 class ChanneledLabelmapsObsWithActReward(TypedDict):
     """TypeDict for LabelmapSliceAsChannelsObservation.

src/armscan_env/wrapper.py

@@ -4,7 +4,6 @@
 from abc import ABC, abstractmethod
 from typing import Any, Literal, SupportsFloat, cast
 
-import gymnasium as gym
 import numpy as np
 import SimpleITK as sitk
 from armscan_env.envs.base import Observation, RewardMetric, TerminationCriterion
@@ -15,10 +14,11 @@
 from armscan_env.envs.observations import MultiBoxSpace
 from armscan_env.envs.rewards import LabelmapClusteringBasedReward
 from armscan_env.envs.state_action import LabelmapStateAction
+
+import gymnasium as gym
 from gymnasium import Env, Wrapper
 from gymnasium.core import ActType, ObsType, WrapperActType
 from gymnasium.wrappers import FrameStackObservation
-
 from tianshou.highlevel.env import (
     EnvFactory,
     EnvMode,