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>

analysis_results_manager.py

@@ -0,0 +1,124 @@
+"""
+Analysis Results Manager - Bridge between audio processing and GUI.
+Manages analysis queue and coordinates between components.
+"""
+
+from PyQt5.QtCore import QObject, pyqtSignal
+from dataclasses import dataclass
+from typing import Optional
+import os
+
+from master_core import AudioFile
+from plotting_engine import PlottingEngine
+
+
+@dataclass
+class AnalysisResult:
+    """Container for audio analysis results."""
+    file_path: str
+    song_name: str
+    bpm: float
+    max_amplitude: float
+    avg_amplitude: float
+    times: list
+    rms_array: list
+    analysis_successful: bool = True
+    error_message: str = ""
+
+
+class AnalysisResultsManager(QObject):
+    """
+    Manages audio file analysis and coordinates between processing and GUI.
+    Threading-ready architecture for future background processing.
+    """
+    
+    # Signals for GUI communication
+    analysisStarted = pyqtSignal(str)  # file_path
+    analysisCompleted = pyqtSignal(str, object)  # file_path, AnalysisResult
+    analysisError = pyqtSignal(str, str)  # file_path, error_message
+    
+    def __init__(self):
+        super().__init__()
+        self.results_cache = {}  # Store analysis results
+        self.plotting_engine = PlottingEngine()
+    
+    def analyze_file(self, file_path: str, window: int = 10, hop: int = 2):
+        """
+        Analyze an audio file and emit results.
+        Currently synchronous - ready for threading later.
+        
+        Args:
+            file_path: Path to audio file
+            window: RMS analysis window size in seconds
+            hop: Analysis hop size in seconds
+        """
+        if not os.path.exists(file_path):
+            error_msg = f"File not found: {file_path}"
+            self.analysisError.emit(file_path, error_msg)
+            return
+            
+        # Emit analysis started signal
+        self.analysisStarted.emit(file_path)
+        
+        try:
+            # Create AudioFile and perform analysis
+            audio_file = AudioFile(file_path)
+            
+            # Get RMS analysis data
+            audio_file.get_energy_levels_over_time(window=window, hop=hop)
+            
+            # Extract analysis results
+            result = AnalysisResult(
+                file_path=file_path,
+                song_name=audio_file.song_name,
+                bpm=audio_file.get_bpm(),
+                max_amplitude=audio_file.max_amplitude,
+                avg_amplitude=audio_file.avg_amplitude,
+                times=audio_file._get_times(),  # We'll need to add this method
+                rms_array=audio_file.rms_array,
+                analysis_successful=True
+            )
+            
+            # Cache the result
+            self.results_cache[file_path] = result
+            
+            # Emit completion signal
+            self.analysisCompleted.emit(file_path, result)
+            
+        except Exception as e:
+            error_msg = f"Analysis failed: {str(e)}"
+            self.analysisError.emit(file_path, error_msg)
+    
+    def get_analysis_figure(self, file_path: str):
+        """
+        Get matplotlib figure for a previously analyzed file.
+        
+        Returns:
+            matplotlib.figure.Figure or None
+        """
+        if file_path not in self.results_cache:
+            return None
+            
+        result = self.results_cache[file_path]
+        return self.plotting_engine.create_power_analysis_figure(
+            result.times, result.rms_array, result.file_path
+        )
+    
+    def get_metadata_text(self, file_path: str) -> str:
+        """Get formatted metadata text for a file."""
+        if file_path not in self.results_cache:
+            return "No analysis data available"
+            
+        result = self.results_cache[file_path]
+        return self.plotting_engine.create_metadata_display_text(
+            result.song_name, result.bpm, 
+            result.max_amplitude, result.avg_amplitude
+        )
+    
+    def clear_cache(self):
+        """Clear all cached analysis results."""
+        self.results_cache.clear()
+    
+    def is_file_analyzed(self, file_path: str) -> bool:
+        """Check if a file has been analyzed."""
+        return file_path in self.results_cache

audio_visualization_widget.py

@@ -0,0 +1,126 @@
+"""
+Audio visualization widget with embedded matplotlib canvas.
+Pure display responsibility - receives plotting data and shows graphs.
+"""
+
+from PyQt5.QtWidgets import QWidget, QVBoxLayout, QLabel
+from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
+from matplotlib.figure import Figure
+import matplotlib.pyplot as plt
+
+
+class AudioVisualizationWidget(QWidget):
+    """Widget for displaying audio analysis graphs with embedded matplotlib."""
+    
+    def __init__(self, parent=None):
+        super().__init__(parent)
+        self.initUI()
+        
+    def initUI(self):
+        """Initialize the UI components."""
+        layout = QVBoxLayout()
+        
+        # Create matplotlib canvas
+        self.figure = Figure(figsize=(10, 4), facecolor='white')
+        self.canvas = FigureCanvas(self.figure)
+        
+        # Add canvas to layout
+        layout.addWidget(self.canvas)
+        
+        # Status label for feedback
+        self.status_label = QLabel("Ready for audio analysis...")
+        layout.addWidget(self.status_label)
+        
+        self.setLayout(layout)
+        
+        # Initialize with empty plot
+        self._create_empty_plot()
+        
+    def _create_empty_plot(self):
+        """Creates an empty placeholder plot."""
+        self.figure.clear()
+        ax = self.figure.add_subplot(111)
+        ax.text(0.5, 0.5, 'Drop an audio file to see analysis', 
+                ha='center', va='center', transform=ax.transAxes,
+                fontsize=14, alpha=0.7)
+        ax.set_xlim(0, 1)
+        ax.set_ylim(0, 1)
+        ax.set_xticks([])
+        ax.set_yticks([])
+        self.canvas.draw()
+    
+    def display_analysis_figure(self, figure):
+        """
+        Display a matplotlib figure in the widget.
+        
+        Args:
+            figure: matplotlib.figure.Figure to display
+        """
+        # Clear current figure
+        self.figure.clear()
+        
+        # Copy the provided figure to our canvas
+        # Get the subplot from the provided figure
+        source_ax = figure.get_axes()[0]
+        
+        # Create new subplot in our figure
+        ax = self.figure.add_subplot(111)
+        
+        # Copy all the plot elements
+        for child in source_ax.get_children():
+            if hasattr(child, 'get_data'):
+                # Copy line plots
+                try:
+                    x_data, y_data = child.get_data()
+                    ax.plot(x_data, y_data, color=child.get_color(), 
+                           linewidth=child.get_linewidth())
+                except:
+                    pass
+        
+        # Copy collections (fill_between creates PolyCollection)
+        for collection in source_ax.collections:
+            ax.add_collection(collection)
+        
+        # Copy axis properties
+        ax.set_xlim(source_ax.get_xlim())
+        ax.set_ylim(source_ax.get_ylim())
+        ax.set_xlabel(source_ax.get_xlabel())
+        ax.set_ylabel(source_ax.get_ylabel())
+        ax.set_title(source_ax.get_title())
+        
+        # Copy colorbar if it exists
+        if hasattr(figure, '_colorbar') or len(figure.get_axes()) > 1:
+            # Try to copy colorbar
+            try:
+                cbar = figure.colorbar(source_ax.collections[-1], ax=ax, label='RMS Power')
+            except:
+                pass
+        
+        self.figure.tight_layout()
+        self.canvas.draw()
+        self.status_label.setText("Analysis complete - displaying power graph")
+    
+    def display_figure_direct(self, figure):
+        """
+        Display a figure by replacing our canvas figure entirely.
+        More reliable than copying elements.
+        
+        Args:
+            figure: matplotlib.figure.Figure to display
+        """
+        # Remove old canvas
+        layout = self.layout()
+        layout.removeWidget(self.canvas)
+        self.canvas.deleteLater()
+        
+        # Create new canvas with the provided figure
+        self.figure = figure
+        self.canvas = FigureCanvas(self.figure)
+        layout.insertWidget(0, self.canvas)  # Insert at position 0 (before status label)
+        
+        self.canvas.draw()
+        self.status_label.setText("Analysis complete - displaying power graph")
+    
+    def set_status(self, message):
+        """Update the status label."""
+        self.status_label.setText(message)

main.py

@@ -1,41 +1,171 @@
 import sys
-from PyQt5.QtWidgets import QApplication, QWidget, QVBoxLayout, QLabel
+import os
+from PyQt5.QtWidgets import (QApplication, QMainWindow, QWidget, QVBoxLayout, 
+                            QHBoxLayout, QSplitter, QLabel, QListWidget, 
+                            QTextEdit, QListWidgetItem)
 from PyQt5.QtCore import Qt
-import master_core
 
-class AudioDragDropWidget(QWidget):
+from audio_visualization_widget import AudioVisualizationWidget
+from analysis_results_manager import AnalysisResultsManager
+
+
+class MainWindow(QMainWindow):
+    """Main application window with modular audio analysis interface."""
+    
     def __init__(self):
         super().__init__()
+        self.analysis_manager = AnalysisResultsManager()
         self.initUI()
+        self.connect_signals()
     
     def initUI(self):
-        self.setWindowTitle('Drag and Drop Audio Analysis')
-        self.setGeometry(100, 100, 400, 200)  # x, y, width, height
+        """Initialize the user interface."""
+        self.setWindowTitle('Audio Mastering Analysis Toolkit')
+        self.setGeometry(100, 100, 1200, 700)
         self.setAcceptDrops(True)
         
-        # Layout and label for displaying messages
-        layout = QVBoxLayout()
-        self.label = QLabel('Drag and drop an audio file here', self)
-        self.label.setAlignment(Qt.AlignCenter)
-        layout.addWidget(self.label)
-        self.setLayout(layout)
+        # Create central widget with splitter
+        central_widget = QWidget()
+        self.setCentralWidget(central_widget)
+        
+        # Main horizontal layout
+        main_layout = QHBoxLayout(central_widget)
+        splitter = QSplitter(Qt.Horizontal)
+        main_layout.addWidget(splitter)
+        
+        # Left panel - File list and metadata
+        left_panel = self.create_left_panel()
+        splitter.addWidget(left_panel)
+        
+        # Right panel - Visualization
+        self.visualization_widget = AudioVisualizationWidget()
+        splitter.addWidget(self.visualization_widget)
+        
+        # Set splitter proportions
+        splitter.setSizes([300, 900])  # Left panel narrower than visualization
+    
+    def create_left_panel(self):
+        """Create the left panel with file list and metadata."""
+        panel = QWidget()
+        layout = QVBoxLayout(panel)
+        
+        # File list
+        self.file_list_label = QLabel("Analyzed Files:")
+        layout.addWidget(self.file_list_label)
+        
+        self.file_list = QListWidget()
+        self.file_list.itemClicked.connect(self.on_file_selected)
+        layout.addWidget(self.file_list)
+        
+        # Metadata display
+        self.metadata_label = QLabel("File Information:")
+        layout.addWidget(self.metadata_label)
+        
+        self.metadata_display = QTextEdit()
+        self.metadata_display.setReadOnly(True)
+        self.metadata_display.setMaximumHeight(150)
+        layout.addWidget(self.metadata_display)
+        
+        # Instructions
+        instructions = QLabel(
+            "Drag and drop audio files (.mp3, .wav) onto this window to analyze them."
+        )
+        instructions.setWordWrap(True)
+        instructions.setStyleSheet("color: gray; font-style: italic;")
+        layout.addWidget(instructions)
+        
+        return panel
+    
+    def connect_signals(self):
+        """Connect analysis manager signals to GUI updates."""
+        self.analysis_manager.analysisStarted.connect(self.on_analysis_started)
+        self.analysis_manager.analysisCompleted.connect(self.on_analysis_completed)
+        self.analysis_manager.analysisError.connect(self.on_analysis_error)
     
     def dragEnterEvent(self, event):
+        """Handle drag enter event for file drops."""
         if event.mimeData().hasUrls():
-            event.accept()
-        else:
-            event.ignore()
+            # Check if any files have audio extensions
+            urls = event.mimeData().urls()
+            for url in urls:
+                file_path = url.toLocalFile()
+                if file_path.lower().endswith(('.mp3', '.wav', '.flac')):
+                    event.accept()
+                    return
+        event.ignore()
     
     def dropEvent(self, event):
+        """Handle file drop event."""
         files = [u.toLocalFile() for u in event.mimeData().urls()]
-        for file_path in files:
-            self.label.setText(f'File dropped: {file_path}')
-            # Here, you would call your plot function with the dropped file path
-            # For example: plot_macro_time_power_graph_colormap(file_path)
-            break  # This example only processes the first dropped file
+        audio_files = [f for f in files if f.lower().endswith(('.mp3', '.wav', '.flac'))]
+        
+        if audio_files:
+            # Analyze the first audio file
+            # TODO: Add support for multiple file queue
+            file_path = audio_files[0]
+            self.analysis_manager.analyze_file(file_path)
+        else:
+            self.visualization_widget.set_status("No audio files detected in drop")
+    
+    def on_analysis_started(self, file_path):
+        """Called when analysis starts."""
+        filename = os.path.basename(file_path)
+        self.visualization_widget.set_status(f"Analyzing: {filename}...")
+    
+    def on_analysis_completed(self, file_path, result):
+        """Called when analysis completes successfully."""
+        filename = os.path.basename(file_path)
+        
+        # Add to file list if not already there
+        existing_items = [self.file_list.item(i).text() 
+                         for i in range(self.file_list.count())]
+        if filename not in existing_items:
+            item = QListWidgetItem(filename)
+            item.setData(Qt.UserRole, file_path)  # Store full path
+            self.file_list.addItem(item)
+        
+        # Get and display the analysis figure
+        figure = self.analysis_manager.get_analysis_figure(file_path)
+        if figure:
+            self.visualization_widget.display_figure_direct(figure)
+        
+        # Update metadata display
+        metadata_text = self.analysis_manager.get_metadata_text(file_path)
+        self.metadata_display.setText(metadata_text)
+        
+        # Select the analyzed file in the list
+        for i in range(self.file_list.count()):
+            item = self.file_list.item(i)
+            if item.data(Qt.UserRole) == file_path:
+                self.file_list.setCurrentItem(item)
+                break
+    
+    def on_analysis_error(self, file_path, error_message):
+        """Called when analysis fails."""
+        filename = os.path.basename(file_path)
+        self.visualization_widget.set_status(f"Error analyzing {filename}: {error_message}")
+    
+    def on_file_selected(self, item):
+        """Called when a file is selected from the list."""
+        file_path = item.data(Qt.UserRole)
+        
+        # Display the analysis figure
+        figure = self.analysis_manager.get_analysis_figure(file_path)
+        if figure:
+            self.visualization_widget.display_figure_direct(figure)
+        
+        # Update metadata display
+        metadata_text = self.analysis_manager.get_metadata_text(file_path)
+        self.metadata_display.setText(metadata_text)
+
 
 if __name__ == '__main__':
     app = QApplication(sys.argv)
-    ex = AudioDragDropWidget()
-    ex.show()
+    
+    # Set application style
+    app.setStyle('Fusion')  # Modern cross-platform style
+    
+    window = MainWindow()
+    window.show()
+    
     sys.exit(app.exec_())

master_core.py

@@ -69,6 +69,12 @@ class AudioFile:
         
         # Calculate RMS over the rolling windows
         self.rms_array = librosa.feature.rms(y=self.y, frame_length=window_samples, hop_length=hop_samples)
+  
+  def _get_times(self):
+    """Get time array for RMS data. Internal method for GUI integration."""
+    if not hasattr(self, 'rms_array'):
+      self.get_energy_levels_over_time()
+    return librosa.frames_to_time(np.arange(self.rms_array.shape[1]), sr=self.sr, hop_length=self.hop*self.sr)
     
   def plot_energy_levels_over_time(self, display='window'):  
     """_summary_
@@ -202,24 +208,32 @@ def find_mp3_files(directory):
     return mp3_files
 
 
-# Replace 'path/to/your/audiofile.mp3' with the path to your audio file
-file_path = []
-with open('./files.txt', 'r') as f:
-  for line in f:
-    if line[0] != '#' and line[0] != ';':
-      file_path.append(line.strip())
+if __name__ == '__main__':
+  # Legacy batch processing mode - runs when master_core.py is executed directly
+  # For GUI usage, run main.py instead
+  
+  print("Running legacy batch analysis mode...")
+  print("For the new GUI interface, please run: python main.py")
+  print()
+  
+  # Replace 'path/to/your/audiofile.mp3' with the path to your audio file
+  file_path = []
+  with open('./files.txt', 'r') as f:
+    for line in f:
+      if line[0] != '#' and line[0] != ';':
+        file_path.append(line.strip())
 
-for file in file_path:
-  # max_amplitude, avg_amplitude, avg_power, avg_power_stft = analyze_track_librosa(file)
-  # # read_mp3_tags(file)
-  # print(f"Maximum Amplitude: {max_amplitude:.2f} dBFS")
-  # print(f"Average Amplitude: {avg_amplitude:.2f} dBFS")
-  # print(f"Average Power: {avg_power:.2f} dBFS")
-  # print(f"Average Power (STFT): {avg_power_stft:.2f} dBFS")
-  currentsong = AudioFile(file)
-  currentsong.display_song_name()
-  print(f"BPM: {currentsong.get_bpm()}")
-  currentsong.plot_energy_levels_over_time()
-  # plot_macro_time_power_graph(file)
+  for file in file_path:
+    # max_amplitude, avg_amplitude, avg_power, avg_power_stft = analyze_track_librosa(file)
+    # # read_mp3_tags(file)
+    # print(f"Maximum Amplitude: {max_amplitude:.2f} dBFS")
+    # print(f"Average Amplitude: {avg_amplitude:.2f} dBFS")
+    # print(f"Average Power: {avg_power:.2f} dBFS")
+    # print(f"Average Power (STFT): {avg_power_stft:.2f} dBFS")
+    currentsong = AudioFile(file)
+    currentsong.display_song_name()
+    print(f"BPM: {currentsong.get_bpm()}")
+    currentsong.plot_energy_levels_over_time()
+    # plot_macro_time_power_graph(file)
 
-plt.show()
+  plt.show()

plotting_engine.py

@@ -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}"""