#!/usr/bin/env python3
"""
Mass Quality Measure — Measure theta on every organ of every model
Find the organs closest to theta=90 (pure signal)
CSCI v1.0 — Cross-Scale Coherence Index
"""
import subprocess, os, json, sys
from organ_measure import measure_directory, compute_z_measure, read_organ_data_f32


all_results = {}

models = sorted(os.listdir(ORGANS_DIR))
for model_name in models:
    model_path = os.path.join(ORGANS_DIR, model_name)
    manifest_path = os.path.join(model_path, "manifest.json")
    
    if not os.path.isdir(model_path) or not os.path.exists(manifest_path):
        continue
    
    print(f"\n[QUALITY-MEASURE] {model_name}")
    print(f"  Measuring organs...")
    
    results = measure_directory(model_path)
    
    if not results:
        print(f"  [SKIP] No measurable organs")
        continue
    
    # Group by type
    groups = {}
    for r in results:
        dirname = os.path.dirname(r['file']).split('/')[-1]
        if dirname not in groups:
            groups[dirname] = []
        groups[dirname].append(r)
    
    model_summary = {
        "model": model_name,
        "total_tensors": len(results),
        "avg_theta": sum(r['theta_deg'] for r in results) / len(results),
        "avg_signal": sum(r['signal_ratio'] for r in results) / len(results),
        "groups": {}
    }
    
    for gname in ['skeleton', 'organs', 'embed', 'norm']:
        if gname in groups:
            g = groups[gname]
            avg_t = sum(r['theta_deg'] for r in g) / len(g)
            avg_s = sum(r['signal_ratio'] for r in g) / len(g)
            best = max(g, key=lambda r: r['theta_deg'])
            worst = min(g, key=lambda r: r['theta_deg'])
            model_summary["groups"][gname] = {
                "count": len(g),
                "avg_theta": round(avg_t, 1),
                "avg_signal": round(avg_s, 3),
                "best_theta": round(best['theta_deg'], 1),
                "best_name": best['name'],
                "worst_theta": round(worst['theta_deg'], 1),
                "worst_name": worst['name']
            }
            print(f"  {gname:12s}: {len(g):3d} tensors | avg theta={avg_t:5.1f} | signal={avg_s:.3f} | best={best['theta_deg']:.1f}")
    
    print(f"  GLOBAL: theta={model_summary['avg_theta']:.1f} | signal={model_summary['avg_signal']:.3f}")
    
    all_results[model_name] = model_summary

# Rank models by signal quality
print(f"\n{'='*70}")
print(f"  QUALITY RANKING — ALL MODELS")
print(f"{'='*70}")

ranked = sorted(all_results.values(), key=lambda m: m['avg_theta'], reverse=True)
for i, m in enumerate(ranked, 1):
    print(f"  {i:2d}. theta={m['avg_theta']:5.1f}  signal={m['avg_signal']:.3f}  {m['model']}")

# Find best organs across ALL models for each type
print(f"\n{'='*70}")
print(f"  BEST ORGANS ACROSS ALL MODELS (theta closest to 90)")
print(f"{'='*70}")

for organ_type in ['skeleton', 'organs', 'embed']:
    print(f"\n  [{organ_type.upper()}]")
    candidates = []
    for model_name, summary in all_results.items():
        if organ_type in summary['groups']:
            g = summary['groups'][organ_type]
            candidates.append((model_name, g['best_theta'], g['best_name'], g['avg_theta']))
    
    candidates.sort(key=lambda c: c[1], reverse=True)
    for c in candidates[:5]:
        print(f"    theta={c[1]:5.1f}  avg={c[3]:5.1f}  {c[0]:30s}  {c[2][:40]}")

print(f"{'='*70}")

# Save full report
    json.dump(all_results, f, indent=2)
# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
# Licensed under Business Source License 1.1 — https://inference-x.com
# ─────────────────────────────────────────────────────────
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# VERIFY: python3 verify_authorship.py mass_z_measure.py