Add pluggable metrics architecture with LUFS

Introduce a Metric ABC (compute on worker thread, render on GUI thread)
with a METRICS registry, and refactor the analysis pipeline around it.
RMS power (ported), raw waveform, and BS.1770 LUFS (via pyloudnorm) ship
as the initial three; new metrics drop in by appending to METRICS.

- metrics.py: Metric ABC + RMSPowerMetric, WaveformMetric (locked to
  +/-1.1 y-range for float headroom), LUFSMetric (short-term 3 s window
  + integrated value, with streaming-target reference line).
- plot_control_widget.py: metric selector dropdown + Refresh Plot button
  (moved out of FontControlWidget).
- analysis_results_manager.py: AnalysisResult caches the AudioFile and a
  per-metric data dict; new MetricComputeWorker runs metric switches off
  the GUI thread via metricComputeStarted/metricReady/metricComputeError
  signals, so LUFS on a 12-minute track no longer stalls the UI.
- main.py: all redraw paths funnel through one _render_or_request helper;
  stale-result guards keep slow computes from overwriting fresh selections.
- plotting_engine.py removed (metadata text moved onto AnalysisResult;
  figure construction lives in each Metric).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

CLAUDE.md

@@ -6,7 +6,7 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
 
 ### Core features
 - **Audio Analysis**: Uses librosa to analyze audio files (MP3/WAV/FLAC support)
-- **Power Visualization**: Generates colorized power magnitude graphs over time
+- **Pluggable Metrics**: Switchable visualizations (RMS Power, Waveform, LUFS; DR next) via a `Metric` ABC
 - **Metadata Extraction**: Reads ID3 tags from MP3 files for better file identification
 - **Modular GUI Architecture**: Complete PyQt5 interface with drag-and-drop and file dialog support
 - **Font Management**: Comprehensive CJK-compatible font system with user-provided font support
@@ -29,7 +29,8 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
 
 #### `analysis_results_manager.py`
 - Background threading for audio analysis
-- Results caching and management
+- Caches both the loaded `AudioFile` and per-metric `compute()` output, so
+  metric/font switches re-render from cache without reloading librosa
 - Progress tracking and error handling
 
 #### `audio_visualization_widget.py`
@@ -40,7 +41,18 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
 - Unified font control system with clustered interface
 - Auto-detection of custom fonts from `fonts/` directory
 - System font discovery and CJK compatibility
-- Auto-regeneration of plots when fonts change
+- Font changes trigger a cheap re-render of the cached metric data
+
+#### `plot_control_widget.py`
+- Metric selector dropdown driven by the `metrics.METRICS` registry
+- Houses the `Refresh Plot` button (foundation for upcoming style controls)
+
+#### `metrics.py`
+- Pluggable `Metric` ABC: `compute(audio_file) -> data` (heavy, worker thread)
+  and `render(data, file_path) -> Figure` (cheap, GUI thread)
+- Current registry: `RMSPowerMetric`, `WaveformMetric`, `LUFSMetric`
+  (BS.1770 short-term + integrated, via pyloudnorm) — drop in new ones (DR,
+  spectrum) by appending an instance to `METRICS`
 
 #### `master_core.py`
 - Defines the `AudioFile` class: librosa loading, rolling RMS power, BPM detection
@@ -57,22 +69,20 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
 
 ### GUI features
 - **File management**: Drag-and-drop and file dialog for audio selection
-- **Font control**: Unified font selector with size control and plot regeneration
+- **Font control**: Unified font selector with size control
+- **Plot control**: Metric selector + refresh-plot button
 - **Analysis display**: Real-time visualization with metadata panels
 - **Modular architecture**: Self-contained widgets for easy layout management
 
 ## Future development plans
 
 ### Short-term (urgent)
-1. **Plot control widget cluster**
-   - Move 'Refresh Plot' into dedicated plot/graph widget cluster
-   - Add metric selection widget (choose which analysis to display)
-   - Implement plot style controller (colormap, line vs bar, etc.)
-   - Prepare foundation for mastering comparison features
+1. **Plot control widget cluster** *(metric selector + Refresh Plot done; still TODO)*
+   - Plot style controller (colormap, line vs bar, etc.)
+   - Foundation for mastering comparison features
 
 ### Short-term (not urgent)
-1. **Enhanced metrics**
-   - LUFS loudness measurement implementation
+1. **Enhanced metrics** *(plug new ones into `metrics.METRICS`)*
    - Dynamic range measurement (DR meter)
    - Peak-to-average ratio analysis
    - Frequency spectrum analysis

README.md

@@ -15,7 +15,7 @@ Developed with Claude Code assistance.
 
 ### Roadmap
 See [CLAUDE.md](CLAUDE.md) for the full development roadmap. Near-term:
-plot-control widget cluster, LUFS, dynamic range, interactive axis controls.
+dynamic range, plot-style controls, interactive axis controls.
 
 ## Quick start
 
@@ -40,19 +40,27 @@ selector. The directory is gitignored to avoid bundling licensed font data.
 See [CJK_FONTS.md](CJK_FONTS.md) for details.
 
 ## Dependencies
-`librosa`, `numpy`, `matplotlib`, `mutagen`, `PyQt5` — all pinned through
-`uv.lock`. Python 3.10+.
+`librosa`, `numpy`, `matplotlib`, `mutagen`, `pyloudnorm`, `PyQt5` — all pinned
+through `uv.lock`. Python 3.10+.
 
 ## Architecture
 
 | Module | Responsibility |
 | --- | --- |
 | `main.py` | `MainWindow` + the `ujm` entry point |
-| `analysis_results_manager.py` | Background `QThread` worker, result cache |
+| `analysis_results_manager.py` | Background `QThread` worker, result + metric-data cache |
 | `master_core.py` | `AudioFile`: librosa loading, RMS rolling window, BPM |
-| `plotting_engine.py` | Matplotlib `Figure` builder for the power graph |
+| `metrics.py` | Pluggable `Metric` ABC + registry (RMS Power, Waveform, …) |
 | `audio_visualization_widget.py` | Embedded `FigureCanvasQTAgg` host |
 | `font_manager.py` | Custom + system CJK font discovery, matplotlib/Qt config |
-| `font_control_widget.py` | Font picker, size slider, refresh-plot button |
+| `font_control_widget.py` | Font picker + size slider |
+| `plot_control_widget.py` | Metric selector + refresh-plot button |
 | `logger_setup.py` | CLI log-level parsing + custom TRACE level |
 | `setup_fonts.py` | Diagnostic utility (run standalone) |
+
+### Adding a metric
+
+Subclass `Metric` in `metrics.py`, implement `compute(audio_file) -> data` (the
+heavy part, runs on the worker thread) and `render(data, file_path) -> Figure`
+(cheap, runs on the GUI thread). Register the instance in the `METRICS` dict at
+the bottom of the file — it shows up in the dropdown automatically.

analysis_results_manager.py

@@ -4,180 +4,241 @@ Manages analysis queue and coordinates between components.
 """
 
 from PyQt5.QtCore import QObject, pyqtSignal, QThread
-from dataclasses import dataclass
-from typing import Optional
+from dataclasses import dataclass, field
+from typing import Any, Optional
 import os
 import logging
 
 from master_core import AudioFile
-from plotting_engine import PlottingEngine
+from font_manager import safe_title
+from metrics import METRICS, DEFAULT_METRIC_ID, Metric
 
 
 @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 = ""
+  """Container for audio analysis results."""
+  file_path: str
+  audio_file: AudioFile
+  song_name: str
+  bpm: float
+  max_amplitude: float
+  avg_amplitude: float
+  metric_data: dict[str, Any] = field(default_factory=dict)
+  analysis_successful: bool = True
+  error_message: str = ""
+
+  def metadata_text(self) -> str:
+    return (
+      f"Track: {safe_title(self.song_name)}\n"
+      f"BPM: {self.bpm:.1f}\n"
+      f"Max Amplitude: {self.max_amplitude:.3f}\n"
+      f"Avg Amplitude: {self.avg_amplitude:.3f}"
+    )
 
 
 class AudioAnalysisWorker(QThread):
-    """
-    Worker thread for audio analysis to prevent GUI freezing.
-    Performs heavy librosa operations in background.
-    """
+  """Worker thread that loads audio and computes a single metric."""
 
-    # Signals for communicating with main thread
-    progressUpdate = pyqtSignal(str, int)  # message, percentage
-    analysisCompleted = pyqtSignal(str, object)  # file_path, AnalysisResult
-    analysisError = pyqtSignal(str, str)  # file_path, error_message
+  progressUpdate = pyqtSignal(str, int)  # message, percentage
+  analysisCompleted = pyqtSignal(str, object)  # file_path, AnalysisResult
+  analysisError = pyqtSignal(str, str)  # file_path, error_message
 
-    def __init__(self, file_path: str, window: int = 10, hop: int = 2):
-        super().__init__()
-        self.file_path = file_path
-        self.window = window
-        self.hop = hop
-        self.logger = logging.getLogger(__name__)
+  def __init__(self, file_path: str, metric: Metric):
+    super().__init__()
+    self.file_path = file_path
+    self.metric = metric
+    self.logger = logging.getLogger(__name__)
 
-    def run(self):
-        """Main thread execution - performs audio analysis."""
-        try:
-            self.logger.info(f"Starting analysis of: {os.path.basename(self.file_path)}")
-            self.progressUpdate.emit("Loading audio file...", 10)
+  def run(self):
+    try:
+      self.logger.info(f"Starting analysis of: {os.path.basename(self.file_path)}")
+      self.progressUpdate.emit("Loading audio file...", 10)
 
-            # Create AudioFile and load audio data
-            audio_file = AudioFile(self.file_path)
-            self.progressUpdate.emit("Audio loaded, detecting tempo...", 30)
+      audio_file = AudioFile(self.file_path)
+      self.progressUpdate.emit("Audio loaded, detecting tempo...", 30)
 
-            # BPM is already calculated in __init__, now do RMS analysis
-            self.progressUpdate.emit("Computing RMS power levels...", 60)
-            audio_file.get_energy_levels_over_time(window=self.window, hop=self.hop)
+      self.progressUpdate.emit(f"Computing {self.metric.display_name}...", 60)
+      metric_data = {self.metric.id: self.metric.compute(audio_file)}
 
-            self.progressUpdate.emit("Finalizing analysis...", 90)
+      self.progressUpdate.emit("Finalizing analysis...", 90)
 
-            # Extract analysis results
-            result = AnalysisResult(
-                file_path=self.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(),
-                rms_array=audio_file.rms_array,
-                analysis_successful=True
-            )
+      result = AnalysisResult(
+        file_path=self.file_path,
+        audio_file=audio_file,
+        song_name=audio_file.song_name,
+        bpm=audio_file.get_bpm(),
+        max_amplitude=audio_file.max_amplitude,
+        avg_amplitude=audio_file.avg_amplitude,
+        metric_data=metric_data,
+        analysis_successful=True,
+      )
 
-            self.progressUpdate.emit("Analysis complete!", 100)
-            self.logger.info(f"Analysis completed: {os.path.basename(self.file_path)} (BPM: {result.bpm:.1f})")
+      self.progressUpdate.emit("Analysis complete!", 100)
+      self.logger.info(
+        f"Analysis completed: {os.path.basename(self.file_path)} (BPM: {result.bpm:.1f})"
+      )
+      self.analysisCompleted.emit(self.file_path, result)
 
-            # Emit success signal
-            self.analysisCompleted.emit(self.file_path, result)
+    except Exception as e:
+      error_msg = f"Analysis failed: {str(e)}"
+      self.logger.error(f"Analysis error for {self.file_path}: {error_msg}")
+      self.analysisError.emit(self.file_path, error_msg)
 
-        except Exception as e:
-            error_msg = f"Analysis failed: {str(e)}"
-            self.logger.error(f"Analysis error for {self.file_path}: {error_msg}")
-            self.analysisError.emit(self.file_path, error_msg)
+
+class MetricComputeWorker(QThread):
+  """Worker thread that computes a single metric against an already-loaded AudioFile."""
+
+  completed = pyqtSignal(str, str, object)  # file_path, metric_id, data
+  failed = pyqtSignal(str, str, str)         # file_path, metric_id, error_message
+
+  def __init__(self, file_path: str, audio_file: AudioFile, metric: Metric):
+    super().__init__()
+    self.file_path = file_path
+    self.audio_file = audio_file
+    self.metric = metric
+    self.logger = logging.getLogger(__name__)
+
+  def run(self):
+    try:
+      self.logger.info(
+        f"Computing {self.metric.display_name} for {os.path.basename(self.file_path)}"
+      )
+      data = self.metric.compute(self.audio_file)
+      self.completed.emit(self.file_path, self.metric.id, data)
+    except Exception as e:
+      msg = f"{self.metric.display_name} compute failed: {e}"
+      self.logger.error(msg)
+      self.failed.emit(self.file_path, self.metric.id, str(e))
 
 
 class AnalysisResultsManager(QObject):
+  """Manages audio file analysis and coordinates between processing and GUI."""
+
+  # Full-analysis (load + initial metric) signals.
+  analysisStarted = pyqtSignal(str)
+  analysisCompleted = pyqtSignal(str, object)
+  analysisError = pyqtSignal(str, str)
+  progressUpdate = pyqtSignal(str, int)
+
+  # Metric-only signals (used for switches after analysis has completed).
+  metricComputeStarted = pyqtSignal(str, str)   # file_path, metric_id
+  metricReady = pyqtSignal(str, str)            # file_path, metric_id
+  metricComputeError = pyqtSignal(str, str, str)  # file_path, metric_id, error
+
+  def __init__(self):
+    super().__init__()
+    self.results_cache: dict[str, AnalysisResult] = {}
+    self.current_worker: Optional[AudioAnalysisWorker] = None
+    self.metric_workers: dict[tuple[str, str], MetricComputeWorker] = {}
+    self.logger = logging.getLogger(__name__)
+
+  def analyze_file(self, file_path: str, metric_id: str = DEFAULT_METRIC_ID):
+    """Kick off background analysis for the given file and metric."""
+    if not os.path.exists(file_path):
+      error_msg = f"File not found: {file_path}"
+      self.logger.error(error_msg)
+      self.analysisError.emit(file_path, error_msg)
+      return
+
+    metric = METRICS.get(metric_id)
+    if metric is None:
+      error_msg = f"Unknown metric: {metric_id}"
+      self.logger.error(error_msg)
+      self.analysisError.emit(file_path, error_msg)
+      return
+
+    if self.current_worker and self.current_worker.isRunning():
+      self.logger.info("Stopping previous analysis to start new one")
+      self.current_worker.quit()
+      self.current_worker.wait()
+
+    self.analysisStarted.emit(file_path)
+    self.logger.info(
+      f"Queuing analysis: {os.path.basename(file_path)} ({metric.display_name})"
+    )
+
+    self.current_worker = AudioAnalysisWorker(file_path, metric)
+    self.current_worker.progressUpdate.connect(self.progressUpdate.emit)
+    self.current_worker.analysisCompleted.connect(self._on_worker_completed)
+    self.current_worker.analysisError.connect(self.analysisError.emit)
+    self.current_worker.start()
+
+  def _on_worker_completed(self, file_path: str, result: AnalysisResult):
+    self.results_cache[file_path] = result
+    self.analysisCompleted.emit(file_path, result)
+
+  def request_metric(self, file_path: str, metric_id: str) -> bool:
+    """Ensure the metric's data exists for the file; emit metricReady when ready.
+
+    Returns True if the data was already cached (metricReady emitted synchronously)
+    or successfully kicked off (will emit later). Returns False if the file hasn't
+    been analysed yet or the metric id is unknown — in that case the caller
+    should wait for analysisCompleted or correct the metric id.
     """
-    Manages audio file analysis and coordinates between processing and GUI.
-    Now uses background threads to prevent GUI freezing.
+    result = self.results_cache.get(file_path)
+    if result is None:
+      return False
+
+    metric = METRICS.get(metric_id)
+    if metric is None:
+      self.logger.warning(f"Unknown metric requested: {metric_id}")
+      return False
+
+    if metric_id in result.metric_data:
+      # Cached — emit immediately so the caller can re-render.
+      self.metricReady.emit(file_path, metric_id)
+      return True
+
+    key = (file_path, metric_id)
+    existing = self.metric_workers.get(key)
+    if existing is not None and existing.isRunning():
+      self.logger.debug(f"Metric compute already in flight: {metric_id} for {os.path.basename(file_path)}")
+      return True
+
+    worker = MetricComputeWorker(file_path, result.audio_file, metric)
+    worker.completed.connect(self._on_metric_completed)
+    worker.failed.connect(self._on_metric_failed)
+    self.metric_workers[key] = worker
+    self.metricComputeStarted.emit(file_path, metric_id)
+    worker.start()
+    return True
+
+  def _on_metric_completed(self, file_path: str, metric_id: str, data: object):
+    result = self.results_cache.get(file_path)
+    if result is not None:
+      result.metric_data[metric_id] = data
+    self.metric_workers.pop((file_path, metric_id), None)
+    self.metricReady.emit(file_path, metric_id)
+
+  def _on_metric_failed(self, file_path: str, metric_id: str, error_message: str):
+    self.metric_workers.pop((file_path, metric_id), None)
+    self.metricComputeError.emit(file_path, metric_id, error_message)
+
+  def get_metric_figure(self, file_path: str, metric_id: str):
+    """Render a Figure from cached metric data. Returns None if not cached.
+
+    Never triggers compute — call `request_metric` first and listen for
+    `metricReady` if you need on-demand computation.
     """
+    result = self.results_cache.get(file_path)
+    if result is None:
+      return None
+    metric = METRICS.get(metric_id)
+    if metric is None:
+      return None
+    data = result.metric_data.get(metric_id)
+    if data is None:
+      return None
+    return metric.render(data, file_path)
 
-    # Signals for GUI communication
-    analysisStarted = pyqtSignal(str)  # file_path
-    analysisCompleted = pyqtSignal(str, object)  # file_path, AnalysisResult
-    analysisError = pyqtSignal(str, str)  # file_path, error_message
-    progressUpdate = pyqtSignal(str, int)  # message, percentage
+  def get_metadata_text(self, file_path: str) -> str:
+    result = self.results_cache.get(file_path)
+    if result is None:
+      return "No analysis data available"
+    return result.metadata_text()
 
-    def __init__(self):
-        super().__init__()
-        self.results_cache = {}  # Store analysis results
-        self.plotting_engine = PlottingEngine()
-        self.current_worker = None  # Track active worker thread
-        self.logger = logging.getLogger(__name__)
+  def clear_cache(self):
+    self.results_cache.clear()
 
-    def analyze_file(self, file_path: str, window: int = 10, hop: int = 2):
-        """
-        Analyze an audio file using background thread to prevent GUI freezing.
-        
-        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.logger.error(error_msg)
-            self.analysisError.emit(file_path, error_msg)
-            return
-        
-        # Stop any existing worker
-        if self.current_worker and self.current_worker.isRunning():
-            self.logger.info("Stopping previous analysis to start new one")
-            self.current_worker.quit()
-            self.current_worker.wait()
-            
-        # Emit analysis started signal
-        self.analysisStarted.emit(file_path)
-        self.logger.info(f"Queuing analysis: {os.path.basename(file_path)}")
-        
-        # Create and start worker thread
-        self.current_worker = AudioAnalysisWorker(file_path, window, hop)
-        
-        # Connect worker signals
-        self.current_worker.progressUpdate.connect(self.progressUpdate.emit)
-        self.current_worker.analysisCompleted.connect(self._on_worker_completed)
-        self.current_worker.analysisError.connect(self.analysisError.emit)
-        
-        # Start the background analysis
-        self.current_worker.start()
-    
-    def _on_worker_completed(self, file_path: str, result: AnalysisResult):
-        """Handle completion of worker thread analysis."""
-        # Cache the result
-        self.results_cache[file_path] = result
-        
-        # Forward the signal to GUI
-        self.analysisCompleted.emit(file_path, result)
-    
-    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
+  def is_file_analyzed(self, file_path: str) -> bool:
+    return file_path in self.results_cache

font_control_widget.py

@@ -6,7 +6,7 @@ Self-contained widget for easy layout management.
 import logging
 from typing import List, Dict, Optional
 from PyQt5.QtWidgets import (QWidget, QComboBox, QVBoxLayout, QHBoxLayout,
-                            QLabel, QSlider, QPushButton, QGroupBox)
+                            QLabel, QSlider, QGroupBox)
 from PyQt5.QtCore import pyqtSignal, Qt
 
 from font_manager import get_font_manager
@@ -19,13 +19,11 @@ class FontControlWidget(QWidget):
     Provides clustered interface for:
     - Font selection (unified for Qt and matplotlib)
     - Qt font size adjustment
-    - Plot regeneration controls
     """
 
     # Signals
     fontChanged = pyqtSignal(str, str)  # (font_name, font_type)
     fontSizeChanged = pyqtSignal(int)   # font_size
-    plotRefreshRequested = pyqtSignal() # manual refresh request
     
     def __init__(self, parent=None):
         """Initialize the font control widget."""
@@ -60,10 +58,6 @@ class FontControlWidget(QWidget):
         size_section = self._create_font_size_section()
         group_layout.addWidget(size_section)
 
-        # Control buttons section
-        button_section = self._create_button_section()
-        group_layout.addWidget(button_section)
-        
         layout.addWidget(group_box)
         
     def _create_font_selector_section(self) -> QWidget:
@@ -119,20 +113,6 @@ class FontControlWidget(QWidget):
         
         return section
     
-    def _create_button_section(self) -> QWidget:
-        """Create the control buttons section."""
-        section = QWidget()
-        layout = QHBoxLayout(section)
-        layout.setContentsMargins(0, 0, 0, 0)
-        
-        # Refresh plot button
-        self.refresh_button = QPushButton("Refresh Plot")
-        self.refresh_button.setToolTip("Regenerate current plot with new font settings")
-        self.refresh_button.clicked.connect(self.on_refresh_plot_clicked)
-        layout.addWidget(self.refresh_button)
-        
-        return section
-    
     def refresh_font_list(self):
         """Refresh the list of available fonts."""
         self.logger.debug("Refreshing font list...")
@@ -243,11 +223,6 @@ class FontControlWidget(QWidget):
         # Emit signal for external listeners
         self.fontSizeChanged.emit(size)
     
-    def on_refresh_plot_clicked(self):
-        """Handle manual plot refresh button click."""
-        self.logger.info("Manual plot refresh requested")
-        self.plotRefreshRequested.emit()
-    
     def _apply_font_change(self, font_name: str, font_type: str):
         """Apply the font change to both Qt and matplotlib."""
         try:

main.py

@@ -11,6 +11,7 @@ from analysis_results_manager import AnalysisResultsManager
 from logger_setup import setup_logging, parse_log_args
 from font_manager import initialize_fonts, get_font_manager
 from font_control_widget import FontControlWidget
+from plot_control_widget import PlotControlWidget
 
 
 class MainWindow(QMainWindow):
@@ -63,9 +64,14 @@ class MainWindow(QMainWindow):
         self.font_control = FontControlWidget()
         self.font_control.fontChanged.connect(self.on_font_changed)
         self.font_control.fontSizeChanged.connect(self.on_font_size_changed)
-        self.font_control.plotRefreshRequested.connect(self.on_plot_refresh_requested)
         layout.addWidget(self.font_control)
 
+        # Plot control cluster (metric selector + refresh)
+        self.plot_control = PlotControlWidget()
+        self.plot_control.metricChanged.connect(self.on_metric_changed)
+        self.plot_control.plotRefreshRequested.connect(self.on_plot_refresh_requested)
+        layout.addWidget(self.plot_control)
+        
         # File list
         self.file_list_label = QLabel("Analyzed Files:")
         layout.addWidget(self.file_list_label)
@@ -99,6 +105,9 @@ class MainWindow(QMainWindow):
         self.analysis_manager.analysisCompleted.connect(self.on_analysis_completed)
         self.analysis_manager.analysisError.connect(self.on_analysis_error)
         self.analysis_manager.progressUpdate.connect(self.on_progress_update)
+        self.analysis_manager.metricComputeStarted.connect(self.on_metric_compute_started)
+        self.analysis_manager.metricReady.connect(self.on_metric_ready)
+        self.analysis_manager.metricComputeError.connect(self.on_metric_compute_error)
     
     def dragEnterEvent(self, event):
         """Handle drag enter event for file drops."""
@@ -124,7 +133,7 @@ class MainWindow(QMainWindow):
             # TODO: Add support for multiple file queue
             file_path = audio_files[0]
             self.logger.info(f"Starting analysis of dropped file: {os.path.basename(file_path)}")
-            self.analysis_manager.analyze_file(file_path)
+            self.analysis_manager.analyze_file(file_path, self.plot_control.current_metric_id())
         else:
             self.visualization_widget.set_status("No audio files detected in drop")
             self.logger.warning("No supported audio files found in drop")
@@ -140,7 +149,7 @@ class MainWindow(QMainWindow):
         
         if file_path:  # User selected a file (didn't cancel)
             self.logger.info(f"File selected via dialog: {os.path.basename(file_path)}")
-            self.analysis_manager.analyze_file(file_path)
+            self.analysis_manager.analyze_file(file_path, self.plot_control.current_metric_id())
     
     def on_analysis_started(self, file_path):
         """Called when analysis starts."""
@@ -159,11 +168,6 @@ class MainWindow(QMainWindow):
             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)
@@ -175,6 +179,9 @@ class MainWindow(QMainWindow):
                 self.file_list.setCurrentItem(item)
                 break
 
+        # Render the currently-selected metric (cached, or async-compute it)
+        self._render_or_request(file_path)
+    
     def on_analysis_error(self, file_path, error_message):
         """Called when analysis fails."""
         filename = os.path.basename(file_path)
@@ -190,53 +197,81 @@ class MainWindow(QMainWindow):
         """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)
 
+        # Render the currently-selected metric (cached, or async-compute it)
+        self._render_or_request(file_path)
+    
     def on_font_changed(self, font_name: str, font_type: str):
         """Called when font selection changes."""
         self.logger.info(f"Font changed via GUI: {font_name} ({font_type})")
-        # Auto-regenerate current plot with new font
-        self._regenerate_current_plot()
+        # Cheap re-render — cached metric data, redraws under the new font.
+        self._render_or_request(self._current_file_path())
 
     def on_font_size_changed(self, font_size: int):
         """Called when Qt font size changes."""
         self.logger.info(f"Qt font size changed via GUI: {font_size}pt")
         # Qt font size doesn't affect matplotlib plots, so no regeneration needed
 
+    def on_metric_changed(self, metric_id: str):
+        """Called when the metric selector changes."""
+        self.logger.info(f"Metric changed via GUI: {metric_id}")
+        self._render_or_request(self._current_file_path())
+
     def on_plot_refresh_requested(self):
         """Called when manual plot refresh is requested."""
         self.logger.info("Manual plot refresh requested via GUI")
-        self._regenerate_current_plot()
+        self._render_or_request(self._current_file_path())
 
-    def _regenerate_current_plot(self):
-        """Regenerate the current plot with updated font settings."""
-        try:
-            # Get the currently selected file
-            current_item = self.file_list.currentItem()
-            if not current_item:
-                self.logger.debug("No file selected for plot regeneration")
-                return
+    def on_metric_compute_started(self, file_path: str, metric_id: str):
+        """Called when an off-thread metric compute starts."""
+        if file_path != self._current_file_path():
+            return  # selection moved on; status bar shouldn't lie
+        from metrics import METRICS
+        metric = METRICS.get(metric_id)
+        display = metric.display_name if metric else metric_id
+        self.visualization_widget.set_status(f"Computing {display}...")
 
-            file_path = current_item.data(Qt.UserRole)
-            if not file_path:
-                self.logger.debug("No file path found for current selection")
-                return
+    def on_metric_ready(self, file_path: str, metric_id: str):
+        """Called when metric data is available (cached hit or async finish)."""
+        if file_path != self._current_file_path():
+            return  # stale — user moved on
+        if metric_id != self.plot_control.current_metric_id():
+            return  # user already switched to a different metric
+        figure = self.analysis_manager.get_metric_figure(file_path, metric_id)
+        if figure:
+            self.visualization_widget.display_figure_direct(figure)
 
-            self.logger.info(f"Regenerating plot for: {os.path.basename(file_path)}")
+    def on_metric_compute_error(self, file_path: str, metric_id: str, error_message: str):
+        self.logger.error(f"Metric compute failed ({metric_id} / {os.path.basename(file_path)}): {error_message}")
+        if file_path == self._current_file_path():
+            self.visualization_widget.set_status(f"Error computing {metric_id}: {error_message}")
 
-            # Re-analyze the file to regenerate plots with new font
-            self.analysis_manager.analyze_file(file_path)
+    def _current_file_path(self):
+        item = self.file_list.currentItem()
+        return item.data(Qt.UserRole) if item else None
 
-        except Exception as e:
-            self.logger.error(f"Error regenerating plot: {e}")
-            self.visualization_widget.set_status(f"Error regenerating plot: {e}")
+    def _render_or_request(self, file_path):
+        """Render the current metric from cache, or kick off async compute if missing.
+
+        Falls back to a full analyse_file if the file hasn't been processed yet
+        (e.g. font change on an empty session — defensive).
+        """
+        if not file_path:
+            return
+        metric_id = self.plot_control.current_metric_id()
+        figure = self.analysis_manager.get_metric_figure(file_path, metric_id)
+        if figure:
+            self.visualization_widget.display_figure_direct(figure)
+            return
+        # Not cached yet — try async compute if the file has been loaded.
+        if self.analysis_manager.is_file_analyzed(file_path):
+            self.analysis_manager.request_metric(file_path, metric_id)
+        else:
+            # No AudioFile yet either; kick off a full analysis with this metric.
+            self.analysis_manager.analyze_file(file_path, metric_id)
 
 
 def main():

metrics.py

@@ -0,0 +1,231 @@
+"""
+Pluggable analysis metrics.
+
+A `Metric` knows how to compute a series from an `AudioFile` and how to render
+that series into a matplotlib `Figure`. Compute is the heavy step (runs on the
+worker thread); render is cheap and reruns on font / refresh.
+
+To add a metric: subclass `Metric`, implement `compute` and `render`, and
+register the instance in `METRICS` at the bottom of this file.
+"""
+
+from __future__ import annotations
+
+import os
+import warnings
+from abc import ABC, abstractmethod
+from typing import Any
+
+import numpy as np
+import matplotlib.colors as mcolors
+import matplotlib.cm as cm
+from matplotlib.figure import Figure
+import pyloudnorm as pyln
+
+from font_manager import safe_title
+from master_core import AudioFile
+
+
+class Metric(ABC):
+  """A pluggable analysis metric."""
+
+  id: str
+  display_name: str
+
+  @abstractmethod
+  def compute(self, audio_file: AudioFile) -> Any:
+    """Compute and return the metric's data from a loaded AudioFile.
+
+    The returned object is cached and later passed to `render`. This is the
+    heavy step and runs on the worker thread.
+    """
+
+  @abstractmethod
+  def render(self, data: Any, file_path: str, figsize=(10, 4)) -> Figure:
+    """Render a Figure from precomputed data. Cheap; runs on the GUI thread."""
+
+
+class RMSPowerMetric(Metric):
+  id = "rms_power"
+  display_name = "RMS Power"
+
+  def __init__(self, window: int = 10, hop: int = 2):
+    self.window = window
+    self.hop = hop
+
+  def compute(self, audio_file: AudioFile):
+    audio_file.get_energy_levels_over_time(window=self.window, hop=self.hop)
+    return {
+      "times": audio_file.get_times(),
+      "rms_array": audio_file.rms_array,
+    }
+
+  def render(self, data, file_path, figsize=(10, 4)) -> Figure:
+    times = data["times"]
+    rms_array = data["rms_array"]
+
+    # Adaptive colour scale: bump headroom for loud masters.
+    maxpower = 0.6 if np.max(rms_array) > 0.3 else 0.3
+    norm = mcolors.Normalize(vmin=0, vmax=maxpower)
+    cmap = cm.autumn
+
+    fig = Figure(figsize=figsize, facecolor="white")
+    ax = fig.add_subplot(111)
+    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",
+      )
+    sm = cm.ScalarMappable(cmap=cmap, norm=norm)
+    sm.set_array([])
+    fig.colorbar(sm, ax=ax, label="RMS Power")
+    ax.set_ylabel("Power")
+    ax.set_xlabel("Time (seconds)")
+    ax.set_title(safe_title(os.path.basename(file_path)))
+    fig.tight_layout()
+    return fig
+
+
+class WaveformMetric(Metric):
+  """Raw mono waveform with a min/max envelope downsample for plotting speed."""
+
+  id = "waveform"
+  display_name = "Waveform"
+
+  def __init__(self, target_columns: int = 4000):
+    self.target_columns = target_columns
+
+  def compute(self, audio_file: AudioFile):
+    y = audio_file.y_mono
+    sr = audio_file.sr
+    n = len(y)
+    if n <= self.target_columns:
+      times = np.arange(n) / sr
+      return {"times": times, "lo": y, "hi": y}
+
+    chunk = n // self.target_columns
+    trimmed = y[: chunk * self.target_columns]
+    reshaped = trimmed.reshape(self.target_columns, chunk)
+    lo = reshaped.min(axis=1)
+    hi = reshaped.max(axis=1)
+    times = (np.arange(self.target_columns) * chunk + chunk / 2) / sr
+    return {"times": times, "lo": lo, "hi": hi}
+
+  def render(self, data, file_path, figsize=(10, 4)) -> Figure:
+    times = data["times"]
+    lo = data["lo"]
+    hi = data["hi"]
+
+    fig = Figure(figsize=figsize, facecolor="white")
+    ax = fig.add_subplot(111)
+    ax.fill_between(times, lo, hi, color="#3a7ad6", linewidth=0)
+    ax.axhline(0, color="black", linewidth=0.5, alpha=0.3)
+    # Fixed full-scale range with a touch of headroom for float-wav signals.
+    ax.set_ylim(-1.1, 1.1)
+    ax.set_xlim(times[0], times[-1])
+    ax.set_ylabel("Amplitude")
+    ax.set_xlabel("Time (seconds)")
+    ax.set_title(safe_title(os.path.basename(file_path)))
+    fig.tight_layout()
+    return fig
+
+
+class LUFSMetric(Metric):
+  """ITU-R BS.1770 loudness: short-term (3 s) time series + integrated value.
+
+  Powered by pyloudnorm. The time series slides `meter.integrated_loudness`
+  across the track because pyloudnorm doesn't expose a per-block series.
+  Slightly redundant work, but the per-call cost is small.
+  """
+
+  id = "lufs"
+  display_name = "LUFS"
+
+  # Short-term as defined by EBU R128 / BS.1770: 3-second window.
+  WINDOW_S = 3.0
+  HOP_S = 0.5
+  SILENCE_FLOOR = -70.0  # BS.1770 absolute gate
+
+  def compute(self, audio_file: AudioFile):
+    y = audio_file.y_mono.astype(np.float64, copy=False)
+    sr = audio_file.sr
+    meter = pyln.Meter(sr)
+
+    # pyloudnorm warns on clipping and on too-short audio; we handle both.
+    with warnings.catch_warnings():
+      warnings.simplefilter("ignore")
+      integrated = self._safe_integrated(meter, y)
+
+      window_n = int(self.WINDOW_S * sr)
+      hop_n = int(self.HOP_S * sr)
+
+      if len(y) < window_n:
+        # Track shorter than 3 s — just one data point at the centre.
+        times = np.array([len(y) / (2.0 * sr)])
+        lufs = np.array([integrated if np.isfinite(integrated) else self.SILENCE_FLOOR])
+      else:
+        n_windows = 1 + (len(y) - window_n) // hop_n
+        lufs = np.empty(n_windows)
+        for i in range(n_windows):
+          start = i * hop_n
+          lufs[i] = self._safe_integrated(meter, y[start:start + window_n])
+        times = (np.arange(n_windows) * hop_n + window_n / 2.0) / sr
+
+    lufs = np.where(np.isfinite(lufs), lufs, self.SILENCE_FLOOR)
+    lufs = np.clip(lufs, self.SILENCE_FLOOR, 0.0)
+
+    return {
+      "times": times,
+      "lufs": lufs,
+      "integrated": float(integrated),
+    }
+
+  @staticmethod
+  def _safe_integrated(meter: "pyln.Meter", segment: np.ndarray) -> float:
+    try:
+      return float(meter.integrated_loudness(segment))
+    except (ValueError, FloatingPointError):
+      return float("-inf")
+
+  def render(self, data, file_path, figsize=(10, 4)) -> Figure:
+    times = data["times"]
+    lufs = data["lufs"]
+    integrated = data["integrated"]
+
+    fig = Figure(figsize=figsize, facecolor="white")
+    ax = fig.add_subplot(111)
+    ax.plot(times, lufs, color="#2a9d8f", linewidth=1.4, label="Short-term (3 s)")
+
+    if np.isfinite(integrated):
+      ax.axhline(
+        integrated, color="#e76f51", linestyle="--", linewidth=1.5,
+        label=f"Integrated: {integrated:.1f} LUFS",
+      )
+
+    # Streaming target reference (Spotify normalises to -14 LUFS).
+    ax.axhline(-14.0, color="gray", linestyle=":", linewidth=0.8, alpha=0.6)
+    ax.text(
+      times[-1], -14.0, "  -14 LUFS (streaming target)",
+      va="center", ha="left", fontsize=8, alpha=0.6,
+    )
+
+    ax.set_ylim(-50.0, 0.0)
+    ax.set_xlim(times[0], times[-1])
+    ax.set_ylabel("LUFS")
+    ax.set_xlabel("Time (seconds)")
+    ax.set_title(safe_title(os.path.basename(file_path)))
+    ax.grid(True, alpha=0.3)
+    ax.legend(loc="lower right", fontsize=8)
+    fig.tight_layout()
+    return fig
+
+
+METRICS: dict[str, Metric] = {
+  m.id: m for m in (
+    RMSPowerMetric(),
+    WaveformMetric(),
+    LUFSMetric(),
+  )
+}
+DEFAULT_METRIC_ID = "rms_power"

plot_control_widget.py

@@ -0,0 +1,61 @@
+"""
+Plot control widget: pick which metric to display and refresh the current plot.
+
+Mirrors FontControlWidget's clustered-groupbox style so the two sit naturally
+next to each other in the left panel.
+"""
+
+import logging
+from PyQt5.QtWidgets import (
+  QWidget, QVBoxLayout, QHBoxLayout, QLabel, QComboBox, QPushButton, QGroupBox,
+)
+from PyQt5.QtCore import pyqtSignal
+
+from metrics import METRICS, DEFAULT_METRIC_ID
+
+
+class PlotControlWidget(QWidget):
+  """Metric selector + manual plot refresh."""
+
+  metricChanged = pyqtSignal(str)        # metric_id
+  plotRefreshRequested = pyqtSignal()
+
+  def __init__(self, parent=None):
+    super().__init__(parent)
+    self.logger = logging.getLogger(__name__)
+    self.initUI()
+
+  def initUI(self):
+    layout = QVBoxLayout(self)
+    layout.setContentsMargins(5, 5, 5, 5)
+
+    group_box = QGroupBox("Plot")
+    group_layout = QVBoxLayout(group_box)
+
+    group_layout.addWidget(QLabel("Metric:"))
+    self.metric_combo = QComboBox()
+    for metric_id, metric in METRICS.items():
+      self.metric_combo.addItem(metric.display_name, metric_id)
+    default_idx = self.metric_combo.findData(DEFAULT_METRIC_ID)
+    if default_idx >= 0:
+      self.metric_combo.setCurrentIndex(default_idx)
+    self.metric_combo.currentIndexChanged.connect(self._on_metric_changed)
+    group_layout.addWidget(self.metric_combo)
+
+    button_row = QHBoxLayout()
+    self.refresh_button = QPushButton("Refresh Plot")
+    self.refresh_button.setToolTip("Re-render the current plot with current settings")
+    self.refresh_button.clicked.connect(self.plotRefreshRequested.emit)
+    button_row.addWidget(self.refresh_button)
+    group_layout.addLayout(button_row)
+
+    layout.addWidget(group_box)
+
+  def _on_metric_changed(self, _index: int):
+    metric_id = self.metric_combo.currentData()
+    if metric_id:
+      self.logger.info(f"Metric changed: {metric_id}")
+      self.metricChanged.emit(metric_id)
+
+  def current_metric_id(self) -> str:
+    return self.metric_combo.currentData() or DEFAULT_METRIC_ID

plotting_engine.py

@@ -1,80 +0,0 @@
-"""
-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
-from font_manager import safe_title
-
-
-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(safe_title(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: {safe_title(song_name)}
-BPM: {bpm:.1f}
-Max Amplitude: {max_amplitude:.3f}
-Avg Amplitude: {avg_amplitude:.3f}"""

pyproject.toml

@@ -11,6 +11,7 @@ dependencies = [
     "numpy",
     "matplotlib",
     "mutagen",
+    "pyloudnorm",
     "PyQt5>=5.15.10",
     # 5.15.2 is the only pyqt5-qt5 release with a Windows wheel; later
     # versions are Linux/macOS only.
@@ -26,9 +27,10 @@ py-modules = [
     "analysis_results_manager",
     "audio_visualization_widget",
     "master_core",
-    "plotting_engine",
+    "metrics",
     "font_manager",
     "font_control_widget",
+    "plot_control_widget",
     "logger_setup",
     "setup_fonts",
 ]

uv.lock

@@ -1187,6 +1187,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0c/c3/44f3fbbfa403ea2a7c779186dc20772604442dde72947e7d01069cbe98e3/pycparser-3.0-py3-none-any.whl", hash = "sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992", size = 48172, upload-time = "2026-01-21T14:26:50.693Z" },
 ]
 
+[[package]]
+name = "pyloudnorm"
+version = "0.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/23/00/f915eaa75326f4209941179c2b93ac477f2040e4aeff5bb21d16eb8058f9/pyloudnorm-0.2.0.tar.gz", hash = "sha256:8bf597658ea4e1975c275adf490f6deb5369ea409f2901f939915efa4b681b16", size = 14037, upload-time = "2026-01-04T11:43:35.265Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/aa/b6/65a49a05614b2548edbba3aab118f2ebe7441dfd778accdcdce9f6567f20/pyloudnorm-0.2.0-py3-none-any.whl", hash = "sha256:9bb69afb904f59d007a7f9ba3d75d16fb8aeef35c44d6df822a9f192d69cf13f", size = 10879, upload-time = "2026-01-04T11:43:34.534Z" },
+]
+
 [[package]]
 name = "pyparsing"
 version = "3.3.2"
@@ -1642,6 +1657,7 @@ dependencies = [
     { name = "mutagen" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "pyloudnorm" },
     { name = "pyqt5" },
     { name = "pyqt5-qt5", marker = "sys_platform == 'win32'" },
 ]
@@ -1652,6 +1668,7 @@ requires-dist = [
     { name = "matplotlib" },
     { name = "mutagen" },
     { name = "numpy" },
+    { name = "pyloudnorm" },
     { name = "pyqt5", specifier = ">=5.15.10" },
     { name = "pyqt5-qt5", marker = "sys_platform == 'win32'", specifier = "==5.15.2" },
 ]