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