Mikkeli Matlock
b07b363454
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>
79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
"""
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Audio visualization plotting engine.
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Separates plotting logic from audio processing for clean GUI integration.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.colors as mcolors
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import matplotlib.cm as cm
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from matplotlib.figure import Figure
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import os
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class PlottingEngine:
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"""Handles all matplotlib visualization logic for audio analysis."""
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@staticmethod
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def create_power_analysis_figure(times, rms_array, file_path, figsize=(10, 4)):
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"""
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Creates a matplotlib Figure for power analysis visualization.
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Args:
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times: Array of time points
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rms_array: RMS power values over time
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file_path: Path to the audio file for title
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figsize: Figure size tuple
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Returns:
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matplotlib.figure.Figure: Ready-to-embed figure
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"""
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# Determine color scale based on headroom detection
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local_max_power = np.max(rms_array)
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if local_max_power > 0.3:
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norm = mcolors.Normalize(vmin=0, vmax=0.6)
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maxpower = 0.6
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else:
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norm = mcolors.Normalize(vmin=0, vmax=0.3)
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maxpower = 0.3
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# Create figure and axis
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fig = Figure(figsize=figsize, facecolor='white')
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ax = fig.add_subplot(111)
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# Color map
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cmap = cm.autumn
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# Plot power levels as colored bars
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ax.set_ylim(0., maxpower)
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for i in range(len(times)-1):
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ax.fill_between(times[i:i+2], 0, rms_array[0][i],
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color=cmap(norm(rms_array[0][i])), edgecolor='none')
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# Add colorbar
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sm = cm.ScalarMappable(cmap=cmap, norm=norm)
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sm.set_array([])
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cbar = fig.colorbar(sm, ax=ax, label='RMS Power')
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# Labels and title
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ax.set_ylabel('Power')
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ax.set_xlabel('Time (seconds)')
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ax.set_title(f'{os.path.basename(file_path)}')
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# Tight layout for better appearance in GUI
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fig.tight_layout()
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return fig
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@staticmethod
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def create_metadata_display_text(song_name, bpm, max_amplitude, avg_amplitude):
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"""
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Creates formatted text for metadata display.
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Returns:
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str: Formatted metadata text
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"""
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return f"""Track: {song_name}
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BPM: {bpm:.1f}
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Max Amplitude: {max_amplitude:.3f}
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Avg Amplitude: {avg_amplitude:.3f}""" |