more ruff stuff

.github/workflows/ruff.yml

@@ -11,8 +11,15 @@ jobs:
     - uses: actions/setup-python@v2
     - uses: chartboost/ruff-action@v1
       with:
-        args: --check .
-        fix_args: --fix .
+        args: check .
+  fix:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - uses: actions/setup-python@v2
+    - uses: chartboost/ruff-action@v1
+      with:
+        args: format .
     - uses: stefanzweifel/git-auto-commit-action@v4
       with:
         commit_message: 'style fixes by ruff'

CompositeEnvironment.py

@@ -24,24 +24,18 @@ def forward(self, y_pred, y_true):
 
 
 class Environment:
-    def __init__(
-        self, image: Image, noisy: Image, radius: int, center: None | tuple
-    ) -> None:
+    def __init__(self, image: Image, noisy: Image, radius: int, center: None | tuple) -> None:
         self.image = image.copy()
         self.radius = radius
         self.noisy_image = noisy.copy()
         self.center = center
 
     def generate(self) -> Image:
-        masked = get_visible_image(
-            self.image, self.radius, self.noisy_image, self.center
-        )
+        masked = get_visible_image(self.image, self.radius, self.noisy_image, self.center)
         return masked
 
 
-def create_circular_mask(
-    h: int, w: int, radius: int, center: None | tuple = None
-) -> np.ndarray:
+def create_circular_mask(h: int, w: int, radius: int, center: None | tuple = None) -> np.ndarray:
     if center is None:  # use the middle of the image
         center = (w / 2, h / 2)
 
@@ -52,9 +46,7 @@ def create_circular_mask(
     return mask
 
 
-def get_visible_image(
-    image: Image, radius: int, noisy: Image, center: None | tuple
-) -> Image:
+def get_visible_image(image: Image, radius: int, noisy: Image, center: None | tuple) -> Image:
     # Find the size of the image
     image = np.abs(image)
 
@@ -112,9 +104,7 @@ def dNoiseVis(self, inputpic):
         ax[1][1].set_title("De-Noised Image Histogram")
 
         fig.suptitle(
-            "Image Size: 256 x 256\nNoise Level: {}%\nAccuracy: {:.2f}%".format(
-                noise_level, loss
-            ),
+            "Image Size: 256 x 256\nNoise Level: {}%\nAccuracy: {:.2f}%".format(noise_level, loss),
             fontsize=16,
             y=0.9,
         )

DNoise/dnoise.py

@@ -77,8 +77,6 @@ def create_synced_dictionary(folder_name):
                     except ValueError:
                         continue
 
-
-
                     if image_type == "clean.jpeg":
                         # Add the clean image to the dictionary
                         sync_dir_inner[name] = file

DNoise/helper_functions.py

@@ -19,6 +19,7 @@
 
 from pathlib import Path
 
+
 def walk_through_dir(dir_path):
     """
     Walks through dir_path returning its contents.
@@ -32,9 +33,7 @@ def walk_through_dir(dir_path):
       name of each subdirectory
     """
     for dirpath, dirnames, filenames in os.walk(dir_path):
-        print(
-            f"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'."
-        )
+        print(f"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.")
 
 
 def plot_decision_boundary(model: torch.nn.Module, X: torch.Tensor, y: torch.Tensor):
@@ -74,9 +73,7 @@ def plot_decision_boundary(model: torch.nn.Module, X: torch.Tensor, y: torch.Ten
 
 
 # Plot linear data or training and test and predictions (optional)
-def plot_predictions(
-    train_data, train_labels, test_data, test_labels, predictions=None
-):
+def plot_predictions(train_data, train_labels, test_data, test_labels, predictions=None):
     """
     Plots linear training data and test data and compares predictions.
     """
@@ -222,9 +219,7 @@ def pred_and_plot_image(
     target_image_pred_label = torch.argmax(target_image_pred_probs, dim=1)
 
     # 8. Plot the image alongside the prediction and prediction probability
-    plt.imshow(
-        target_image.squeeze().permute(1, 2, 0)
-    )  # make sure it's the right size for matplotlib
+    plt.imshow(target_image.squeeze().permute(1, 2, 0))  # make sure it's the right size for matplotlib
     if class_names:
         title = f"Pred: {class_names[target_image_pred_label.cpu()]} | Prob: {target_image_pred_probs.max().cpu():.3f}"
     else:

Pathfinding/Environment.py

@@ -39,10 +39,7 @@ class ACTS(enum.Enum):
 def circleUpdate(base, add, radius, coordinates: Tuple[int, int]):
     for y, ny in enumerate(base):
         for x, nx in enumerate(base):
-            distance = sqrt(
-                (coordinates[0] - x) * (coordinates[0] - x)
-                + (coordinates[1] - y) * (coordinates[1] - y)
-            )
+            distance = sqrt((coordinates[0] - x) * (coordinates[0] - x) + (coordinates[1] - y) * (coordinates[1] - y))
             if distance < radius:
                 base[y][x] = add[y][x]
 

Pathfinding/astar.py

@@ -45,10 +45,7 @@ def a_star(self):
                 neighbor = (x + dx, y + dy)
 
                 if (0 <= neighbor[0] < 256) and (0 <= neighbor[1] < 256):
-                    if (
-                        self.terrain[neighbor[0], neighbor[1]] == 0
-                        and neighbor not in visited
-                    ):
+                    if self.terrain[neighbor[0], neighbor[1]] == 0 and neighbor not in visited:
                         distance = self.heuristic(neighbor, self.end)
                         heapq.heappush(queue, (distance, neighbor))
                         self.path.append(neighbor)
@@ -84,9 +81,9 @@ def animate(self):
         self.ax[0][0].set_title("De-Noised Image")
 
         self.fig.suptitle(
-            "A* Pathfinding Example"
-            "\nImage Size: 256 x 256\n"
-            "Noise Level: {}%\nAccuracy: {:.2f}%".format(noise_level, loss),
+            "A* Pathfinding Example" "\nImage Size: 256 x 256\n" "Noise Level: {}%\nAccuracy: {:.2f}%".format(
+                noise_level, loss
+            ),
             fontsize=16,
         )
 

Terrain/blobcheck.py

@@ -47,14 +47,7 @@ def mark_island(array, visited, i, j):
     neighbors = [(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)]
     for x, y in neighbors:
         # If the adjacent cell is land and has not been visited yet, it's part of the same island
-        if (
-            x >= 0
-            and x < len(array)
-            and y >= 0
-            and y < len(array[i])
-            and array[x][y] == 1
-            and not visited[x][y]
-        ):
+        if x >= 0 and x < len(array) and y >= 0 and y < len(array[i]) and array[x][y] == 1 and not visited[x][y]:
             mark_island(array, visited, x, y)
 
 
@@ -74,10 +67,6 @@ def mark_island(array, visited, i, j):
     axes[1].imshow(noisepic)
     axes[1].set_title("Noisy")
 
-    print(
-        "Clean pic islands: {}, Noisy pic islands: {}".format(
-            pic_islands, noisepic_islands
-        )
-    )
+    print("Clean pic islands: {}, Noisy pic islands: {}".format(pic_islands, noisepic_islands))
 
     plt.show()

Terrain/border.py

@@ -25,9 +25,7 @@ def bordercheck(pic: Union[list[list[int]], np.ndarray]):
 
     for i in range(len(pic)):
         pic[i] = [abs(ele) for ele in pic[i]]
-    borderpic = copy.deepcopy(
-        pic
-    )  # create a deep copy so that stuff doesn't get messed up
+    borderpic = copy.deepcopy(pic)  # create a deep copy so that stuff doesn't get messed up
 
     for x in range(len(pic)):
         for y in range(len(pic)):

Terrain/map.py

@@ -38,10 +38,7 @@ def from_file(cls, filename):
     def thresholded(self):
         """Get the thresholded map"""
         return np.array(
-            [
-                [1 if self.map[y][x] > self.threshold else 0 for x in range(self.width)]
-                for y in range(self.height)
-            ]
+            [[1 if self.map[y][x] > self.threshold else 0 for x in range(self.width)] for y in range(self.height)]
         )
 
     @property
@@ -72,9 +69,7 @@ def __neg__(self):
 
     def build(self, octaves):
         """build the map"""
-        self.map = terraingen.terrain(
-            self.width, self.height, octaves, seed=random.randint(0, 1000000)
-        )
+        self.map = terraingen.terrain(self.width, self.height, octaves, seed=random.randint(0, 1000000))
 
     def __mul__(self, other):
         """Multiply the map by a given value"""

Terrain/pathcheck.py

@@ -72,12 +72,7 @@ def isPath(arr):
             seed = random.randint(1, x * y * 1000)
         if failedSeeds.count(seed) == 0 and setseed == 0:
             solved = isPath(terraingen.terrain(x, y, octaves, seed=seed))
-            sys.stdout.write(
-                "\rChecking seed: "
-                + str(seed)
-                + ", Number: "
-                + str(len(failedSeeds) + 1)
-            )
+            sys.stdout.write("\rChecking seed: " + str(seed) + ", Number: " + str(len(failedSeeds) + 1))
             sys.stdout.flush()
             if solved:
                 print("\nWorking Seed: " + str(seed))

Terrain/sumPerlin.py

@@ -37,11 +37,7 @@ def noiseMaps(
     map2 = PerlinNoise(octaves1, seed + seedDifference)
     discrete1 = [[map1([i / x, j / y]) for j in range(y)] for i in range(x)]
     discrete2 = [
-        [
-            (difference * map2([i / x, j / y])) + (1 - difference) * (discrete1[i][j])
-            for j in range(y)
-        ]
-        for i in range(x)
+        [(difference * map2([i / x, j / y])) + (1 - difference) * (discrete1[i][j]) for j in range(y)] for i in range(x)
     ]
 
     return discrete1, discrete2

Terrain/terraingen.py

@@ -27,15 +27,10 @@ def terrain(x: int, y: int, octaves: int, progress: bool = False, seed: int = 0)
 
     noise = PerlinNoise(octaves=octaves, seed=seed)
     if progress:
-        picarr = [
-            [int(np.floor(noise([i / x, j / y]))) for j in range(y)]
-            for i in tqdm(range(x))
-        ]
+        picarr = [[int(np.floor(noise([i / x, j / y]))) for j in range(y)] for i in tqdm(range(x))]
         return picarr
     if not progress:
-        picarr = [
-            [int(np.floor(noise([i / x, j / y]))) for j in range(y)] for i in range(x)
-        ]
+        picarr = [[int(np.floor(noise([i / x, j / y]))) for j in range(y)] for i in range(x)]
         return picarr
 
 

Terrain/timers.py

@@ -2,6 +2,7 @@
 
 from math import floor, log10
 
+
 class BaseTimer:
     """class for timing things such as testing performance