diff --git a/audio_visualizer/standalone_visualizers/horizontal.py b/audio_visualizer/standalone_visualizers/horizontal.py
index ca0f17d..a2b3134 100644
--- a/audio_visualizer/standalone_visualizers/horizontal.py
+++ b/audio_visualizer/standalone_visualizers/horizontal.py
@@ -34,22 +34,33 @@
         # Get the audio data and apply the FFT
         data = np.frombuffer(stream.read(CHUNK), dtype=np.int16)
         fft = np.abs(np.fft.fft(data * np.hamming(CHUNK)).real)[:CHUNK // 2]
-        
+
         alpha = 0.2
-        smoothed_fft = alpha * smoothed_fft + (1 - alpha) * fft  # Apply smoothing
-        max_fft = np.max(smoothed_fft) if np.max(smoothed_fft) > 0 else 1  # Normalize
-        
+        smoothed_fft = (alpha * smoothed_fft
+                        + (1 - alpha) * fft)  # Apply smoothing
+        max_fft = (np.max(smoothed_fft)
+                   if np.max(smoothed_fft) > 0 else 1)  # Normalize
+
         # Calculate the height of each bar according to the FFT results
-        log_scale_index = np.logspace(0, np.log10(len(smoothed_fft)), num=bar_count, endpoint=True, base=10).astype(int) - 1
-        log_scale_index = np.unique(np.clip(log_scale_index, 0, len(smoothed_fft)-1))  # Ensure unique indices and within bounds
+        log_scale_index = np.logspace(0, np.log10(len(smoothed_fft)),
+                                      num=bar_count, endpoint=True,
+                                      base=10).astype(int) - 1
+        log_scale_index = np.unique(np.clip(log_scale_index, 0,
+                                            # unique indices within bounds
+                                            len(smoothed_fft)-1))
 
         for i in range(len(log_scale_index) - 1):
             bar_values = smoothed_fft[log_scale_index[i]:log_scale_index[i+1]]
-            bar_height = int(np.mean(bar_values) / max_fft * max_bar_height) if bar_values.size > 0 else 0
+            bar_height = (
+                int(np.mean(bar_values) / max_fft * max_bar_height)
+                if bar_values.size > 0 else 0
+            )
+
             bar_heights[i] = bar_height
 
         # Clear the frame buffer
-        frame_buffer = "\n".join("".join("█" if bar_heights[col] >= row else " " for col in range(bar_count - 1)) for row in range(max_bar_height - 1, -1, -1))
+        frame_buffer = "\n".join("".join("█" if bar_heights[col] >= row else " "  # noqa: E501
+                                         for col in range(bar_count - 1)) for row in range(max_bar_height - 1, -1, -1))  # noqa: E501
 
         # Clear the terminal and print the entire frame at once
         os.system('cls' if os.name == 'nt' else 'clear')
@@ -59,4 +70,4 @@
 finally:
     stream.stop_stream()
     stream.close()
-    audio.terminate()
\ No newline at end of file
+    audio.terminate()
diff --git a/audio_visualizer/standalone_visualizers/vertical.py b/audio_visualizer/standalone_visualizers/vertical.py
index f5ad104..27b0250 100644
--- a/audio_visualizer/standalone_visualizers/vertical.py
+++ b/audio_visualizer/standalone_visualizers/vertical.py
@@ -28,20 +28,26 @@
         windowed_data = data * window
         fft = np.abs(np.fft.fft(windowed_data).real)
         fft = fft[:int(len(fft)/2)]  # keep only the first half
-        
+
         # Smoothing factor
         alpha = 0.2
         smoothed_fft = alpha * smoothed_fft + (1 - alpha) * fft
-        
-        max_fft = np.max(smoothed_fft) if np.max(smoothed_fft) > 0 else 1  # Avoid division by zero
+
+        max_fft = (
+            np.max(smoothed_fft) if np.max(smoothed_fft)
+            > 0 else 1  # Avoid division by zero
+        )
 
         # Clear the screen
         os.system('cls' if os.name == 'nt' else 'clear')
 
         # Visualization logic
         bar_count = 75
-        indices = np.logspace(0, np.log10(len(smoothed_fft)), num=bar_count + 1, endpoint=True, base=10).astype(int) - 1
-        indices = np.unique(np.clip(indices, 0, len(smoothed_fft)-1))  # Ensure unique indices and within bounds
+        indices = np.logspace(0, np.log10(len(smoothed_fft)),
+                              num=bar_count + 1, endpoint=True,
+                              base=10).astype(int) - 1
+        # Ensure unique indices within bounds
+        indices = np.unique(np.clip(indices, 0, len(smoothed_fft)-1))
 
         for i in range(len(indices)-1):
             bar_values = smoothed_fft[indices[i]:indices[i+1]]
@@ -53,8 +59,11 @@
                 bar_value = 0
 
             # Calculate the number of characters to print
-            num_chars = int(np.sqrt(bar_value / max_fft) * 50) if not np.isnan(bar_value) else 0
-            print('█' * num_chars)  # Normalize and apply sqrt to enhance visibility
+            num_chars = (int(np.sqrt(bar_value / max_fft) * 50)
+                         # Normalize and apply sqrt to enhance visibility
+                         if not np.isnan(bar_value) else 0
+                         )
+            print('█' * num_chars)
 
         time.sleep(0.07)  # control frame rate
 finally:
diff --git a/audio_visualizer/visualizer.py b/audio_visualizer/visualizer.py
index f5790b6..1663c3e 100644
--- a/audio_visualizer/visualizer.py
+++ b/audio_visualizer/visualizer.py
@@ -101,7 +101,7 @@ def visualize_vertical(self):
             indices = np.logspace(0, np.log10(len(self.smoothed_fft)),
                                   num=self.bar_count + 1, endpoint=True,
                                   base=10).astype(int) - 1
-            # Ensure unique indices and within bounds
+            # Ensure unique indices within bounds
             indices = np.unique(np.clip(indices, 0, len(self.smoothed_fft)-1))
 
             for i in range(len(indices) - 1):
@@ -149,7 +149,7 @@ def visualize_horizontal(self):
                                           num=bar_count, endpoint=True,
                                           base=10).astype(int) - 1
             log_scale_index = np.unique(np.clip(log_scale_index, 0,
-                                                # unique indices in bounds
+                                                # unique indices within bounds
                                                 len(self.smoothed_fft)-1))
 
             for i in range(len(log_scale_index) - 1):