Merge pull request #18 from gituser12981u2/release-v0.2.0

Release v0.2.0

Author: gituser12981u2

audio_visualizer/__init__.py

@@ -11,7 +11,7 @@ def main():
     parser = argparse.ArgumentParser(description="Terminal Audio Visualizer")
     parser.add_argument(
         "--mode",
-        choices=["vertical", "horizontal"],
+        choices=["vertical", "horizontal-ltr", "horizontal-rtl"],
         default="vertical",
         help="Choose visualization mode: vertical or horizontal",
     )

audio_visualizer/horizontal_left_to_right_visualizer.py

@@ -0,0 +1,96 @@
+"""
+horizontal_left_to_right_visualizer.py
+
+This module visualizes audio data in a horizontal bar chart.
+"""
+
+import numpy as np
+import os
+import time
+
+# NOTE rows are the width and cols are the height for os.get_terminal_size
+
+
+def visualize_horizontal_left_to_right(
+        stream, chunk, rate, alpha, bar_count, window, smoothed_fft):
+    # Initialize smoothed FFT with zeros
+    smoothed_fft = np.zeros(chunk // 2 + 1)
+
+    while True:
+        data = stream.read_data()
+        if data is None:
+            continue
+
+        data = np.frombuffer(data, dtype=np.int16)
+        data = data.reshape(-1, 2).mean(axis=1)  # Average the two channels
+
+        # Apply window function if needed
+        windowed_data = data * window
+        fft_data = np.abs(np.fft.rfft(windowed_data))
+
+        # Apply exponential moving average for smoothing
+        smoothed_fft = alpha * smoothed_fft + (1 - alpha) * fft_data
+
+        cols, rows = os.get_terminal_size()
+        max_fft = np.max(smoothed_fft, initial=1)  # Avoid division by zero
+        scaled_fft = np.int16((smoothed_fft / max_fft) * cols)
+
+        frame_buffer = [' ' * cols for _ in range(rows)]
+        for row in range(min(rows, len(scaled_fft))):
+            bar_width = scaled_fft[row]
+            frame_buffer[row] = '█' * bar_width + ' ' * (cols - bar_width)
+
+        os.system('cls' if os.name == 'nt' else 'clear')
+        print('\n'.join(frame_buffer), end='', flush=True)
+
+        time.sleep(0.1)  # control frame rate
+
+
+# def visualize_horizontal(
+#         stream, chunk, rate, alpha, bar_count, window, smoothed_fft):
+#     """
+#     Visualizes audio data in a vertical bar chart from bottom to top.
+#     """
+#     rows, cols = os.get_terminal_size()  # Get terminal size dynamically
+#     bar_count = rows  # Assume bar_count should use all available rows
+
+#     while True:
+#         data = stream.read_data()
+#         if data is None:
+#             break
+#         data = np.frombuffer(data, dtype=np.int16)
+#         data = data.reshape(-1, 2).mean(axis=1)  # Average two channels
+
+#         windowed_data = data * window
+#         fft = np.abs(np.fft.fft(windowed_data).real)
+# [:len(windowed_data) // 2]
+
+#         smoothed_fft = alpha * smoothed_fft + (1 - alpha) * fft
+#         max_fft = max(np.max(smoothed_fft), 1)  # Normalize the max value
+
+#         indices = np.logspace(0, np.log10(len(smoothed_fft)),
+#                               num=bar_count + 1,
+#                               endpoint=True, base=10).astype(int)
+#         indices = np.unique(np.clip(indices, 0, len(smoothed_fft) - 1))
+
+#         current_frame = {}
+#         for i in range(len(indices) - 1):
+#             bar_values = smoothed_fft[indices[i]:indices[i + 1]]
+#             bar_value = np.average(bar_values, weights=np.linspace(
+#                 1, 0.1, num=len(bar_values))) if bar_values.size > 0 else 0
+#             num_chars = int((bar_value / max_fft) * cols)
+
+#             for j in range(cols - num_chars, cols):
+#                 current_frame[(i, j)] = '█'
+
+#         frame_buffer = [' ' * rows for _ in range(cols)]
+#         for x in range(cols):  # Iterate over each bar
+#             for y in range(rows):  # Iterate over each character in the bar
+#                 if (x, y) in current_frame:
+#                     frame_buffer[x] = frame_buffer[x][:y] + \
+#                         current_frame[(x, y)] + frame_buffer[x][y+1:]
+
+#         os.system('cls' if os.name == 'nt' else 'clear')
+#         print('\n'.join(frame_buffer), end="", flush=True)
+
+#         time.sleep(0.1)

audio_visualizer/horizontal_right_to_left_visualizer.py

@@ -0,0 +1,47 @@
+"""
+horizontal_right_to_left_visualizer.py
+
+This module visualizes audio data in a horizontal bar chart
+from right to left.
+"""
+
+import numpy as np
+import os
+import time
+
+# NOTE rows are the width and cols are the height for os.get_terminal_size
+
+
+def visualize_horizontal_right_to_left(
+        stream, chunk, rate, alpha, bar_count, window, smoothed_fft):
+    # Initialize smoothed FFT with zeros
+    smoothed_fft = np.zeros(chunk // 2 + 1)
+
+    while True:
+        data = stream.read_data()
+        if data is None:
+            continue
+
+        data = np.frombuffer(data, dtype=np.int16)
+        data = data.reshape(-1, 2).mean(axis=1)  # Average the two channels
+
+        # Apply window function if needed
+        windowed_data = data * window
+        fft_data = np.abs(np.fft.rfft(windowed_data))
+
+        # Apply exponential moving average for smoothing
+        smoothed_fft = alpha * smoothed_fft + (1 - alpha) * fft_data
+
+        cols, rows = os.get_terminal_size()
+        max_fft = np.max(smoothed_fft, initial=1)  # Avoid division by zero
+        scaled_fft = np.int16((smoothed_fft / max_fft) * cols)
+
+        frame_buffer = [' ' * cols for _ in range(rows)]
+        for row in range(min(rows, len(scaled_fft))):
+            bar_width = scaled_fft[row]
+            frame_buffer[row] = ' ' * (cols - bar_width) + '█' * bar_width
+
+        os.system('cls' if os.name == 'nt' else 'clear')
+        print('\n'.join(frame_buffer), end='', flush=True)
+
+        time.sleep(0.1)  # control frame rate