Some copyediting changes

Author: davecom

Impressionist/impressionist.py

@@ -166,4 +166,5 @@ def create_output(self, out_file: str, height: int, vector: bool, animation_leng
         if animation_frames is not None:
             animation_frames[0].save(out_file + ".gif", save_all=True,
                                      append_images=animation_frames[1:], optimize=False,
-                                     duration=animation_length, loop=0, transparency=0, disposal=2)
+                                     duration=animation_length, loop=0,
+                                     transparency=0, disposal=2)

KNN/__main__.py

@@ -36,7 +36,7 @@ def run():
     digits_file = (Path(__file__).resolve().parent
                    / "datasets" / "digits" / "digits.csv")
     digits_knn = KNN(Digit, digits_file, has_header=False)
-    # Startup Pygame, create the window
+    # Start up Pygame, create the window
     pygame.init()
     screen = pygame.display.set_mode(size=(PIXEL_WIDTH, PIXEL_HEIGHT),
                                      flags=pygame.SCALED | pygame.RESIZABLE)
@@ -59,7 +59,8 @@ def run():
                     pixels = digit_pixels.transpose((1, 0, 2))[:, :, 0].flatten() * P_TO_D
                     predicted_pixels = digits_knn.predict_array(K, Digit("", pixels), "pixels")
                     predicted_pixels = predicted_pixels.reshape((PIXEL_HEIGHT, PIXEL_WIDTH)).transpose((1, 0)) * D_TO_P
-                    digit_pixels = np.stack((predicted_pixels, predicted_pixels, predicted_pixels), axis=2)
+                    digit_pixels = np.stack((predicted_pixels, predicted_pixels,
+                                             predicted_pixels), axis=2)
             # Handle mouse events
             elif ((event.type == pygame.MOUSEBUTTONDOWN) or
                   (event.type == pygame.MOUSEMOTION and pygame.mouse.get_pressed()[0])):

KNN/knn.py

@@ -56,15 +56,15 @@ def classify(self, k: int, data_point: DP) -> str:
         neighbors = self.nearest(k, data_point)
         return Counter(neighbor.kind for neighbor in neighbors).most_common(1)[0][0]
 
-    # Predict a numeric property of a data point based on the k nearest neighbors
-    # Find the average of that property from the neighbors and return it
+    # Predict a numeric property of a data point based on the k-nearest neighbors.
+    # Find the average of that property from the neighbors and return it.
     def predict(self, k: int, data_point: DP, property_name: str) -> float:
         neighbors = self.nearest(k, data_point)
         return (sum([getattr(neighbor, property_name) for neighbor in neighbors])
                 / len(neighbors))
 
-    # Predict a NumPy array property of a data point based on the k nearest neighbors
-    # Find the average of that property from the neighbors and return it
+    # Predict a NumPy array property of a data point based on the k-nearest neighbors.
+    # Find the average of that property from the neighbors and return it.
     def predict_array(self, k: int, data_point: DP, property_name: str) -> np.ndarray:
         neighbors = self.nearest(k, data_point)
         return (np.sum([getattr(neighbor, property_name) for neighbor in neighbors], axis=0)