Implement modular GUI architecture with embedded matplotlib

Major refactor from popup-based to persistent PyQt5 interface: - Extract plotting logic from AudioFile class into separate PlottingEngine - Create AudioVisualizationWidget with embedded matplotlib canvas - Add AnalysisResultsManager as bridge between processing and GUI - Replace simple drag-drop widget with professional splitter layout - Preserve legacy batch processing mode with execution guard Features: - Drag-and-drop audio analysis (.mp3/.wav/.flac support) - File list with metadata display (BPM, amplitudes, track info) - Persistent visualization area (no more matplotlib popups) - Multi-file support with click-to-view functionality - Threading-ready architecture for future background processing Technical improvements: - Clean separation of concerns (analysis/visualization/GUI) - Qt signal-slot communication pattern - Modular component design ready for multithreading - Proper import guards prevent legacy code interference 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-08-21 22:43:11 +09:00
parent a9a4b1725c
commit b07b363454
5 changed files with 513 additions and 40 deletions
@@ -0,0 +1,79 @@
+"""
+Audio visualization plotting engine.
+Separates plotting logic from audio processing for clean GUI integration.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import matplotlib.cm as cm
+from matplotlib.figure import Figure
+import os
+
+
+class PlottingEngine:
+    """Handles all matplotlib visualization logic for audio analysis."""
+    
+    @staticmethod
+    def create_power_analysis_figure(times, rms_array, file_path, figsize=(10, 4)):
+        """
+        Creates a matplotlib Figure for power analysis visualization.
+        
+        Args:
+            times: Array of time points
+            rms_array: RMS power values over time
+            file_path: Path to the audio file for title
+            figsize: Figure size tuple
+            
+        Returns:
+            matplotlib.figure.Figure: Ready-to-embed figure
+        """
+        # Determine color scale based on headroom detection
+        local_max_power = np.max(rms_array)
+        if local_max_power > 0.3:
+            norm = mcolors.Normalize(vmin=0, vmax=0.6)
+            maxpower = 0.6
+        else:
+            norm = mcolors.Normalize(vmin=0, vmax=0.3)
+            maxpower = 0.3
+        
+        # Create figure and axis
+        fig = Figure(figsize=figsize, facecolor='white')
+        ax = fig.add_subplot(111)
+        
+        # Color map
+        cmap = cm.autumn
+        
+        # Plot power levels as colored bars
+        ax.set_ylim(0., maxpower)
+        for i in range(len(times)-1):
+            ax.fill_between(times[i:i+2], 0, rms_array[0][i], 
+                          color=cmap(norm(rms_array[0][i])), edgecolor='none')
+        
+        # Add colorbar
+        sm = cm.ScalarMappable(cmap=cmap, norm=norm)
+        sm.set_array([])
+        cbar = fig.colorbar(sm, ax=ax, label='RMS Power')
+        
+        # Labels and title
+        ax.set_ylabel('Power')
+        ax.set_xlabel('Time (seconds)')
+        ax.set_title(f'{os.path.basename(file_path)}')
+        
+        # Tight layout for better appearance in GUI
+        fig.tight_layout()
+        
+        return fig
+    
+    @staticmethod
+    def create_metadata_display_text(song_name, bpm, max_amplitude, avg_amplitude):
+        """
+        Creates formatted text for metadata display.
+        
+        Returns:
+            str: Formatted metadata text
+        """
+        return f"""Track: {song_name}
+BPM: {bpm:.1f}
+Max Amplitude: {max_amplitude:.3f}
+Avg Amplitude: {avg_amplitude:.3f}"""