This document provides detailed API documentation for Symphonic-Joules components.
The Symphonic-Joules API is organized into several main modules:
Note: Detailed API documentation will be generated and populated as the codebase develops.
# Example API structure - will be implemented as development progresses
class AudioProcessor:
"""Main class for audio processing operations."""
def __init__(self, sample_rate=44100, channels=1):
"""Initialize audio processor with specified parameters."""
pass
def load_audio(self, file_path):
"""Load audio file from specified path."""
pass
def stream_audio(self, file_path, chunk_size=8192):
"""
Stream audio file in chunks for efficient processing of large files.
Args:
file_path: Path to audio file
chunk_size: Number of samples per chunk
Yields:
Audio chunks as numpy arrays
"""
pass
def process_signal(self, signal, processing_type='default'):
"""Apply signal processing to audio data."""
pass
def extract_features(self, signal, features=None):
"""Extract specified features from audio signal.
Args:
signal: Audio signal data
features: List of features to extract (default: ['energy', 'frequency'])
"""
if features is None:
features = ['energy', 'frequency']
pass
def calculate_energy(self, signal):
"""Calculate energy of audio signal."""
pass
# Example API structure - to be implemented
class EnergyCalculator:
"""Class for energy-related calculations."""
def __init__(self, units='joules'):
"""Initialize with specified energy units."""
pass
def calculate_acoustic_energy(self, signal):
"""Calculate acoustic energy from audio signal."""
pass
def analyze_energy_distribution(self, data):
"""Analyze energy distribution patterns."""
pass
def convert_units(self, value, from_unit, to_unit):
"""Convert between different energy units."""
pass
# Example API structure - to be implemented
class Visualizer:
"""Class for creating visualizations."""
def __init__(self, style='default'):
"""Initialize visualizer with specified style."""
pass
def plot_waveform(self, signal, title='Waveform'):
"""Create waveform visualization."""
pass
def plot_spectrum(self, signal, title='Frequency Spectrum'):
"""Create frequency spectrum plot."""
pass
def plot_energy_distribution(self, energy_data, title='Energy Distribution'):
"""Create energy distribution visualization."""
pass
# Example API structure - to be implemented
class Utils:
"""Utility functions and helpers."""
@staticmethod
def validate_audio_file(file_path):
"""Validate audio file format and integrity."""
pass
@staticmethod
def convert_sample_rate(signal, original_rate, target_rate):
"""Convert signal sample rate."""
pass
@staticmethod
def normalize_signal(signal, method='peak'):
"""Normalize audio signal using specified method."""
pass
class ResultCache:
"""Cache for storing and retrieving processed results."""
def __init__(self, cache_dir='.cache', max_size=1000):
"""
Initialize result cache.
Args:
cache_dir: Directory for cache storage
max_size: Maximum number of cached items
"""
pass
def get(self, key):
"""
Retrieve cached result.
Args:
key: Cache key (typically file path or hash)
Returns:
Cached result or None if not found
"""
pass
def set(self, key, value):
"""
Store result in cache.
Args:
key: Cache key
value: Result to cache
"""
pass
def clear(self):
"""Clear all cached results."""
pass
# Example configuration structure
CONFIG = {
'audio': {
'default_sample_rate': 44100,
'default_channels': 1,
'buffer_size': 1024,
'supported_formats': ['wav', 'mp3', 'flac']
},
'energy': {
'default_units': 'joules',
'precision': 'double',
'calculation_method': 'standard'
},
'visualization': {
'default_style': 'scientific',
'color_scheme': 'viridis',
'figure_size': (10, 6),
'dpi': 300
}
}
# Example exception structure
class SymphonicJoulesError(Exception):
"""Base exception for Symphonic-Joules."""
pass
class AudioProcessingError(SymphonicJoulesError):
"""Raised when audio processing fails."""
pass
class EnergyCalculationError(SymphonicJoulesError):
"""Raised when energy calculations fail."""
pass
class VisualizationError(SymphonicJoulesError):
"""Raised when visualization creation fails."""
pass
# Example usage - to be implemented
from symphonic_joules import AudioProcessor, EnergyCalculator, Visualizer
# Load and process audio
processor = AudioProcessor()
audio_data = processor.load_audio('input.wav')
processed_audio = processor.process_signal(audio_data)
# Calculate energy
calculator = EnergyCalculator()
energy_data = calculator.calculate_acoustic_energy(processed_audio)
# Visualize results
visualizer = Visualizer()
visualizer.plot_waveform(processed_audio)
visualizer.plot_energy_distribution(energy_data)
# Example: Process large audio file efficiently using streaming
from symphonic_joules import AudioProcessor
processor = AudioProcessor()
# ✅ GOOD: Stream processing for large files
for chunk in processor.stream_audio('large_file.wav', chunk_size=8192):
# Process chunk by chunk, avoiding memory issues
features = processor.extract_features(chunk)
energy = processor.calculate_energy(chunk)
# Process or save results incrementally
# ❌ AVOID: Loading entire large file into memory
# audio_data = processor.load_audio('large_file.wav') # May cause memory issues
# Example: Efficient batch processing with result caching
from symphonic_joules import AudioProcessor, ResultCache
from pathlib import Path
processor = AudioProcessor()
cache = ResultCache(cache_dir='.audio_cache')
def process_audio_library(audio_dir):
"""Process multiple audio files with caching."""
audio_files = Path(audio_dir).glob('*.wav')
results = []
for audio_file in audio_files:
file_id = str(audio_file)
# Check cache first
cached_result = cache.get(file_id)
if cached_result is not None:
results.append(cached_result)
continue
# Process and cache result
audio_data = processor.load_audio(audio_file)
features = processor.extract_features(audio_data)
energy = processor.calculate_energy(audio_data)
result = {'file': file_id, 'features': features, 'energy': energy}
cache.set(file_id, result)
results.append(result)
return results
# Example: Process multiple files in parallel
from symphonic_joules import AudioProcessor
from concurrent.futures import ProcessPoolExecutor
import multiprocessing
def process_single_file(file_path):
"""Process a single audio file."""
processor = AudioProcessor()
audio = processor.load_audio(file_path)
return processor.extract_features(audio)
def process_files_parallel(file_paths):
"""Process multiple files in parallel using all CPU cores."""
num_workers = multiprocessing.cpu_count()
with ProcessPoolExecutor(max_workers=num_workers) as executor:
results = list(executor.map(process_single_file, file_paths))
return results
# Usage
audio_files = ['file1.wav', 'file2.wav', 'file3.wav', 'file4.wav']
results = process_files_parallel(audio_files)
API versioning and compatibility information will be maintained here as the project evolves.
Detailed API documentation will be automatically generated from code as development progresses.