import os
import matplotlib
matplotlib.use('Agg') # Force non-interactive backend
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
from colorama import Fore, Style
class ReportGenerator:
def __init__(self, output_dir):
self.output_dir = output_dir
os.makedirs(output_dir, exist_ok=True)
def _prepare_data(self, filename, metrics, history, reference, strategy_log=None):
from analyzer import AudioAnalyzer
import soundfile as sf
analyzer = AudioAnalyzer()
score, penalties = analyzer.calculate_health_score(metrics)
c_score, c_penalties = analyzer.calculate_coherence_score(metrics)
o_score = analyzer.calculate_overall_score(metrics)
verdict_title, verdict_desc = analyzer.get_verdict(score)
# 7-Band Targets
targets = {"sub": 4.0, "bass": 12.0, "mud": 15.0, "low_mid": 18.0, "mid": 15.0, "presence": 15.0, "air": 21.0}
tonal_dev = {k: metrics['spectrum'].get(k, 0) - v for k, v in targets.items()}
# Load audio data from disk for waveforms
orig_path = history[0]['path']
master_path = metrics['path']
try:
o_data, _ = sf.read(orig_path)
m_data, _ = sf.read(master_path)
o_env = analyzer.get_waveform_envelope(o_data)
m_env = analyzer.get_waveform_envelope(m_data)
except Exception as e:
print(f"Warning: Could not load data for waveform: {e}")
o_env = [0]*150
m_env = [0]*150
return {
'filename': os.path.basename(filename),
'date': datetime.now().strftime("%B %d, %Y"),
'health_score': score,
'coherence_score': c_score,
'overall_score': o_score,
'translation_score': metrics.get('translation_score', 0),
'translation_penalties': metrics.get('translation_penalties', []),
'punch_score': metrics.get('punch_factor', 0),
'punch_penalties': metrics.get('punch_penalties', []),
'cleanliness_score': metrics.get('cleanliness_score', 0),
'cleanliness_penalties': metrics.get('cleanliness_penalties', []),
'verdict': verdict_title,
'verdict_desc': verdict_desc,
'penalties': penalties + c_penalties,
'metrics': metrics,
'history': history,
'tonal_dev': tonal_dev,
'reference': reference,
'strategy_log': strategy_log,
'envelopes': {
'original': o_env,
'master': m_env
}
}
def generate(self, filename, metrics, history, reference, formats, strategy_log=None):
"""Generate high-end visual reports in multiple formats."""
# Ensure formats are clean
formats = [f.strip().lower() for f in formats]
base_name = os.path.splitext(os.path.basename(filename))[0]
data = self._prepare_data(filename, metrics, history, reference, strategy_log=strategy_log)
generated_files = []
# 1. HTML Report (Interactive Storyboard)
if 'html' in formats:
path = os.path.join(self.output_dir, f"{base_name}_Storyboard.html")
self._generate_html(path, data)
generated_files.append(path)
# 2. Graphical Dashboard (PDF/JPG)
if 'pdf' in formats or 'jpg' in formats or 'png' in formats:
fig = self._create_matplotlib_dashboard(data)
if 'pdf' in formats:
path = os.path.join(self.output_dir, f"{base_name}_Dashboard.pdf")
fig.savefig(path, bbox_inches='tight', dpi=300)
generated_files.append(path)
if 'jpg' in formats or 'png' in formats:
ext = 'jpg' if 'jpg' in formats else 'png'
path = os.path.join(self.output_dir, f"{base_name}_Dashboard.{ext}")
fig.savefig(path, bbox_inches='tight', dpi=150)
generated_files.append(path)
plt.close(fig)
return generated_files
def _generate_html(self, path, data):
m = data['metrics']
bg_color = "#131314"
card_bg = "#1e1f20"
accent = "#A8C7FA"
def get_score_color(s):
if s > 85: return "#c4eed0"
if s > 60: return "#fff4a3"
return "#f2b8b5"
score_color = get_score_color(data['health_score'])
# --- Visual Components ---
# 1. Waveform SVG
def make_waveform_svg(env, color):
points = " ".join([f"{i*5},{100-v}" for i, v in enumerate(env)])
return f''
# 2. Soundstage Arcs
low_w = max(15, (1 - m['band_corr']['low']) * 100)
mid_w = max(30, (1 - m['band_corr']['mid']) * 100)
high_w = max(50, (1 - m['band_corr']['high']) * 100)
# 3. Tonal Rows
bands = ["sub", "bass", "mud", "low_mid", "mid", "presence", "air", "sparkle"]
labels = ["Sub", "Bass", "Mud", "Low-Mid", "Mid", "Presence", "Air", "Sparkle"]
tonal_rows = ""
for i, k in enumerate(bands):
dev = data['tonal_dev'].get(k, 0)
width = min(80, abs(dev) * 8)
bar_color = "#7ab3ef" if dev < 0 else "#fbc02d"
if k == "sparkle": bar_color = "#A8C7FA" # Special color for sparkle
align = "flex-end" if dev < 0 else "flex-start"
tonal_rows += f"""
{labels[i]}
{dev:+.1f}%
"""
# 4. Evolution History
evolution_html = ""
for i, h in enumerate(data['history']):
label = "Original" if i == 0 else f"Pass {i}"
s = self._calculate_health_score_simple(h)
evolution_html += f"""
{label}
{s}
"""
# 5. Micro-Metrics HTML
micro_html = ""
if 'micro' in m:
mc = m['micro']
def get_status_col(v, target, inv=False):
return "#c4eed0" if (v > target if not inv else v < target) else "#fff4a3"
# Fidelity Penalties Breakdown for HTML
fid_penalties_html = ""
for fid_type, p_list in [("Translation", data.get('translation_penalties', [])),
("Punch", data.get('punch_penalties', [])),
("Cleanliness", data.get('cleanliness_penalties', []))]:
if p_list:
fid_penalties_html += f'
{fid_type}:{" | ".join(p_list)}
'
micro_html = f"""
High-Fidelity Analytics
{mc['sparkle_energy']:.2f}%
HF Sparkle (12k+)
{mc['hf_sharpness']:.1f}dB
Transient Bite
{mc['phase_drift']:.3f}
Phase Drift (Var)
{mc['spectral_complexity']:.2f}
Complexity (Bits)
{f'
{fid_penalties_html}
' if fid_penalties_html else ''}
"""
html = f"""
Mastering Report | {data['filename']}