analysis_tools.py•7.83 kB
import numpy as np
import reapy
from reapy import reascript_api as RPR
class AnalysisTools:
"""Tools for audio analysis and feedback in REAPER."""
def __init__(self, config):
"""
Initialize AnalysisTools with configuration.
Args:
config (dict): Configuration dictionary
"""
self.config = config
def analyze_frequency_spectrum(self, item_id=None):
"""
Analyze the frequency spectrum of the specified item or project.
Args:
item_id (int, optional): Item ID (None for entire project)
Returns:
dict: Frequency analysis results
"""
try:
# This is a simplified implementation
# In practice, you'd need to:
# 1. Get audio data from REAPER
# 2. Perform FFT analysis
# 3. Process and return results
# For now, return placeholder values
frequency_bands = {
"sub_bass": {"range": "20-60 Hz", "level": -18.0},
"bass": {"range": "60-250 Hz", "level": -12.0},
"low_mids": {"range": "250-500 Hz", "level": -10.0},
"mids": {"range": "500-2000 Hz", "level": -8.0},
"high_mids": {"range": "2000-4000 Hz", "level": -10.0},
"presence": {"range": "4000-8000 Hz", "level": -12.0},
"brilliance": {"range": "8000-20000 Hz", "level": -15.0}
}
if item_id:
# Get item name for context
try:
item = reapy.Item.from_id(item_id)
item_name = item.name or f"Item {item_id}"
except:
item_name = f"Item {item_id}"
return {
"success": True,
"item_id": item_id,
"item_name": item_name,
"frequency_bands": frequency_bands,
"message": "Note: These are placeholder values. Actual implementation would analyze audio data."
}
else:
return {
"success": True,
"scope": "entire_project",
"frequency_bands": frequency_bands,
"message": "Note: These are placeholder values. Actual implementation would analyze audio data."
}
except Exception as e:
return {
"success": False,
"error": str(e)
}
def detect_clipping(self):
"""
Detect clipping in the project.
Returns:
dict: Clipping detection results
"""
try:
# This is a simplified implementation
# In practice, you'd need to analyze audio levels throughout the project
# For now, return placeholder values
return {
"success": True,
"clipping_detected": False,
"max_peak": -1.2,
"message": "Note: These are placeholder values. Actual implementation would analyze audio levels."
}
except Exception as e:
return {
"success": False,
"error": str(e)
}
def analyze_mix_balance(self):
"""
Analyze the mix balance across the frequency spectrum.
Returns:
dict: Mix balance analysis results
"""
try:
# This is a simplified implementation
# In practice, you'd need to analyze the frequency content of all tracks
# For now, return placeholder values
track_analysis = []
project = reapy.Project()
for i in range(project.n_tracks):
track = project.tracks[i]
track_analysis.append({
"track_id": track.id,
"name": track.name,
"dominant_frequency_range": "500-2000 Hz",
"level": -15.0
})
return {
"success": True,
"track_analysis": track_analysis,
"overall_balance": "Good",
"recommendations": [
"Consider reducing low-mid buildup around 300 Hz",
"High-end (8-12 kHz) could be enhanced for more clarity"
],
"message": "Note: These are placeholder values. Actual implementation would analyze frequency content."
}
except Exception as e:
return {
"success": False,
"error": str(e)
}
def analyze_stereo_field(self):
"""
Analyze the stereo field of the mix.
Returns:
dict: Stereo field analysis results
"""
try:
# This is a simplified implementation
# In practice, you'd need to analyze the stereo content of the mix
# For now, return placeholder values
return {
"success": True,
"stereo_width": "Medium",
"phase_issues": False,
"mono_compatibility": "Good",
"recommendations": [
"Bass frequencies are well-centered",
"Consider widening high-frequency content for more immersion"
],
"message": "Note: These are placeholder values. Actual implementation would analyze stereo content."
}
except Exception as e:
return {
"success": False,
"error": str(e)
}
def analyze_dynamics(self):
"""
Analyze the dynamics of the mix.
Returns:
dict: Dynamics analysis results
"""
try:
# This is a simplified implementation
# In practice, you'd need to analyze the dynamic range of the mix
# For now, return placeholder values
return {
"success": True,
"dynamic_range": 8.5,
"crest_factor": 12.0,
"rms_level": -18.0,
"peak_level": -6.0,
"recommendations": [
"Dynamic range is appropriate for the genre",
"Consider less compression on drum overheads for more natural sound"
],
"message": "Note: These are placeholder values. Actual implementation would analyze dynamic range."
}
except Exception as e:
return {
"success": False,
"error": str(e)
}
def analyze_transients(self):
"""
Analyze transients in the mix.
Returns:
dict: Transient analysis results
"""
try:
# This is a simplified implementation
# In practice, you'd need to analyze transients in the audio
# For now, return placeholder values
return {
"success": True,
"transient_clarity": "Good",
"recommendations": [
"Kick drum transients are well-preserved",
"Snare could benefit from more attack"
],
"message": "Note: These are placeholder values. Actual implementation would analyze transients."
}
except Exception as e:
return {
"success": False,
"error": str(e)
}