860 lines
36 KiB
Python
860 lines
36 KiB
Python
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#!/usr/bin/env python3
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"""
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Comprehensive Data Analysis & Report Generator
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Analyzes the 375-point EM parameter sweep and produces:
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1. Full statistical summary (text)
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2. Interactive HTML visualization dashboard
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"""
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import os
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import sys
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import numpy as np
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import pandas as pd
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from datetime import datetime
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from collections import Counter
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import json
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# ── Config ──────────────────────────────────────────────────────────
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MAGIC_NUMBERS = [2, 8, 20, 28, 50, 82, 126]
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SWEEP_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "sweep_results")
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RESULTS_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "results")
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os.makedirs(RESULTS_DIR, exist_ok=True)
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def load_data(csv_path=None):
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if csv_path is None:
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csvs = sorted([f for f in os.listdir(SWEEP_DIR) if f.startswith("em_direct_sweep") and f.endswith(".csv")])
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if not csvs:
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print("No sweep CSVs found"); sys.exit(1)
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csv_path = os.path.join(SWEEP_DIR, csvs[-1])
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print(f"Auto-selected: {csvs[-1]}")
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df = pd.read_csv(csv_path)
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print(f"Loaded {len(df)} points")
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return df, os.path.basename(csv_path)
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# ═══════════════════════════════════════════════════════════════════
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# PART 1: Comprehensive Text Report
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# ═══════════════════════════════════════════════════════════════════
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def generate_text_report(df, source_name):
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R = []
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ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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R.append("=" * 78)
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R.append(" COMPREHENSIVE PARAMETER SWEEP ANALYSIS — RESONANCE ENGINE")
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R.append("=" * 78)
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R.append(f"Source: {source_name}")
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R.append(f"Generated: {ts}")
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R.append(f"Points: {len(df)}")
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R.append(f"Duration: {df['timestamp'].iloc[0]} → {df['timestamp'].iloc[-1]}")
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R.append("")
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# ── Section 1: Global Statistics ──
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R.append("─" * 78)
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R.append("1. GLOBAL STATISTICS")
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R.append("─" * 78)
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numeric_cols = ['omega', 'khra_amp', 'gixx_amp', 'coherence', 'asymmetry',
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'vorticity_mean', 'gpu_temp_c', 'gpu_power_w', 'cycle']
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R.append(f" {'Column':<18} {'Min':>12} {'Max':>12} {'Mean':>12} {'Std':>12} {'Median':>12}")
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for col in numeric_cols:
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v = df[col]
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R.append(f" {col:<18} {v.min():>12.6f} {v.max():>12.6f} {v.mean():>12.6f} {v.std():>12.6f} {v.median():>12.6f}")
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# ── Section 2: Parameter Space Coverage ──
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R.append("")
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R.append("─" * 78)
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R.append("2. PARAMETER SPACE COVERAGE")
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R.append("─" * 78)
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omega_vals = sorted(df['omega'].unique())
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khra_vals = sorted(df['khra_amp'].unique())
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gixx_vals = sorted(df['gixx_amp'].unique())
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R.append(f" Omega: {len(omega_vals)} values: {[round(x,1) for x in omega_vals]}")
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R.append(f" Khra_amp: {len(khra_vals)} values: {[round(x,3) for x in khra_vals]}")
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R.append(f" Gixx_amp: {len(gixx_vals)} values: {[round(x,4) for x in gixx_vals]}")
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R.append(f" Grid: {len(omega_vals)} × {len(khra_vals)} × {len(gixx_vals)} = {len(omega_vals)*len(khra_vals)*len(gixx_vals)} (actual: {len(df)})")
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# ── Section 3: Coherence Analysis ──
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R.append("")
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R.append("─" * 78)
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R.append("3. COHERENCE ANALYSIS")
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R.append("─" * 78)
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# Top 10 coherence values
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top10 = df.nlargest(10, 'coherence')
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R.append(" Top 10 coherence measurements:")
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R.append(f" {'Rank':>4} {'Ω':>5} {'K':>6} {'G':>7} {'Coh':>10} {'Asym':>8} {'Vort':>10}")
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for rank, (_, row) in enumerate(top10.iterrows(), 1):
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R.append(f" {rank:4d} {row['omega']:5.1f} {row['khra_amp']:6.3f} {row['gixx_amp']:7.4f} "
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f"{row['coherence']:10.6f} {row['asymmetry']:8.4f} {row['vorticity_mean']:10.6f}")
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# Bottom 10
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bot10 = df.nsmallest(10, 'coherence')
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R.append("\n Bottom 10 coherence measurements:")
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R.append(f" {'Rank':>4} {'Ω':>5} {'K':>6} {'G':>7} {'Coh':>10} {'Asym':>8} {'Vort':>10}")
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for rank, (_, row) in enumerate(bot10.iterrows(), 1):
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R.append(f" {rank:4d} {row['omega']:5.1f} {row['khra_amp']:6.3f} {row['gixx_amp']:7.4f} "
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f"{row['coherence']:10.6f} {row['asymmetry']:8.4f} {row['vorticity_mean']:10.6f}")
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# ── Section 4: Per-Omega Breakdown ──
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R.append("")
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R.append("─" * 78)
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R.append("4. PER-OMEGA BREAKDOWN")
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R.append("─" * 78)
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R.append(f" {'Ω':>5} {'Coh_min':>10} {'Coh_max':>10} {'Coh_mean':>10} {'Coh_std':>10} "
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f"{'Asym_mean':>10} {'Vort_mean':>10} {'Best K':>7} {'Best G':>8}")
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for omega in omega_vals:
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g = df[df['omega'] == omega]
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best = g.loc[g['coherence'].idxmax()]
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R.append(f" {omega:5.1f} {g['coherence'].min():10.6f} {g['coherence'].max():10.6f} "
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f"{g['coherence'].mean():10.6f} {g['coherence'].std():10.6f} "
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f"{g['asymmetry'].mean():10.4f} {g['vorticity_mean'].mean():10.6f} "
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f"{best['khra_amp']:7.3f} {best['gixx_amp']:8.4f}")
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# ── Section 5: Per-Khra Breakdown ──
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R.append("")
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R.append("─" * 78)
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R.append("5. PER-KHRA BREAKDOWN")
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R.append("─" * 78)
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R.append(f" {'K':>6} {'Coh_min':>10} {'Coh_max':>10} {'Coh_mean':>10} {'Coh_std':>10} {'Best Ω':>6} {'Best G':>8}")
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for khra in khra_vals:
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g = df[df['khra_amp'] == khra]
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best = g.loc[g['coherence'].idxmax()]
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R.append(f" {khra:6.3f} {g['coherence'].min():10.6f} {g['coherence'].max():10.6f} "
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f"{g['coherence'].mean():10.6f} {g['coherence'].std():10.6f} "
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f"{best['omega']:6.1f} {best['gixx_amp']:8.4f}")
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# ── Section 6: Per-Gixx Breakdown ──
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R.append("")
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R.append("─" * 78)
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R.append("6. PER-GIXX BREAKDOWN")
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R.append("─" * 78)
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R.append(f" {'G':>7} {'Coh_min':>10} {'Coh_max':>10} {'Coh_mean':>10} {'Coh_std':>10} {'Best Ω':>6} {'Best K':>7}")
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for gixx in gixx_vals:
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g = df[df['gixx_amp'] == gixx]
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best = g.loc[g['coherence'].idxmax()]
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R.append(f" {gixx:7.4f} {g['coherence'].min():10.6f} {g['coherence'].max():10.6f} "
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f"{g['coherence'].mean():10.6f} {g['coherence'].std():10.6f} "
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f"{best['omega']:6.1f} {best['khra_amp']:7.3f}")
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# ── Section 7: Correlation Matrix ──
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R.append("")
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R.append("─" * 78)
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R.append("7. CORRELATION MATRIX")
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R.append("─" * 78)
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corr_cols = ['omega', 'khra_amp', 'gixx_amp', 'coherence', 'asymmetry', 'vorticity_mean']
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corr = df[corr_cols].corr()
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R.append(f" {'':>14}" + "".join(f"{c:>14}" for c in corr_cols))
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for row_name in corr_cols:
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vals = "".join(f"{corr.loc[row_name, c]:14.4f}" for c in corr_cols)
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R.append(f" {row_name:>14}{vals}")
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# ── Section 8: Coherence Sensitivity ──
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R.append("")
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R.append("─" * 78)
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R.append("8. PARAMETER SENSITIVITY (effect on coherence)")
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R.append("─" * 78)
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# Omega sensitivity: variance of mean coherence across omega
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omega_means = df.groupby('omega')['coherence'].mean()
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khra_means = df.groupby('khra_amp')['coherence'].mean()
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gixx_means = df.groupby('gixx_amp')['coherence'].mean()
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omega_range = omega_means.max() - omega_means.min()
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khra_range = khra_means.max() - khra_means.min()
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gixx_range = gixx_means.max() - gixx_means.min()
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total_range = omega_range + khra_range + gixx_range
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R.append(f" Omega effect: range={omega_range:.6f} ({100*omega_range/total_range:.1f}% of total)")
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R.append(f" Khra effect: range={khra_range:.6f} ({100*khra_range/total_range:.1f}% of total)")
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R.append(f" Gixx effect: range={gixx_range:.6f} ({100*gixx_range/total_range:.1f}% of total)")
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R.append(f" Most influential: {'omega' if omega_range >= max(khra_range, gixx_range) else 'khra_amp' if khra_range >= gixx_range else 'gixx_amp'}")
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# Per-omega sensitivity to khra and gixx
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R.append(f"\n Per-omega sensitivity (coherence std when varying K,G):")
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R.append(f" {'Ω':>5} {'Std(coh)':>10} {'Sensitivity':>12}")
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for omega in omega_vals:
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g = df[df['omega'] == omega]
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s = g['coherence'].std()
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bar = "█" * int(s * 10000)
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R.append(f" {omega:5.1f} {s:10.6f} {bar}")
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# ── Section 9: Thermal & Power Profile ──
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R.append("")
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R.append("─" * 78)
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R.append("9. THERMAL & POWER PROFILE")
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R.append("─" * 78)
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R.append(f" GPU Temperature:")
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R.append(f" Min: {df['gpu_temp_c'].min():.0f}°C Max: {df['gpu_temp_c'].max():.0f}°C Mean: {df['gpu_temp_c'].mean():.1f}°C")
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R.append(f" Points above 60°C: {(df['gpu_temp_c'] > 60).sum()} ({100*(df['gpu_temp_c'] > 60).mean():.1f}%)")
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R.append(f" GPU Power:")
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R.append(f" Min: {df['gpu_power_w'].min():.1f}W Max: {df['gpu_power_w'].max():.1f}W Mean: {df['gpu_power_w'].mean():.1f}W")
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R.append(f" High power (>250W): {(df['gpu_power_w'] > 250).sum()} ({100*(df['gpu_power_w'] > 250).mean():.1f}%)")
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R.append(f" Idle (<100W): {(df['gpu_power_w'] < 100).sum()} ({100*(df['gpu_power_w'] < 100).mean():.1f}%)")
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# Temperature by omega
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R.append(f"\n Temperature by omega:")
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R.append(f" {'Ω':>5} {'Temp_mean':>10} {'Power_mean':>11}")
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for omega in omega_vals:
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g = df[df['omega'] == omega]
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R.append(f" {omega:5.1f} {g['gpu_temp_c'].mean():10.1f} {g['gpu_power_w'].mean():11.1f}")
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# ── Section 10: Asymmetry & Vorticity Analysis ──
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R.append("")
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R.append("─" * 78)
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R.append("10. ASYMMETRY & VORTICITY ANALYSIS")
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R.append("─" * 78)
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R.append(f" Asymmetry: min={df['asymmetry'].min():.4f} max={df['asymmetry'].max():.4f} mean={df['asymmetry'].mean():.4f}")
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R.append(f" Vorticity: min={df['vorticity_mean'].min():.6f} max={df['vorticity_mean'].max():.6f} mean={df['vorticity_mean'].mean():.6f}")
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# Best asymmetry (lowest = most symmetric)
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best_sym = df.nsmallest(5, 'asymmetry')
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R.append(f"\n Most symmetric configurations (lowest asymmetry):")
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for _, row in best_sym.iterrows():
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R.append(f" Ω={row['omega']:.1f} K={row['khra_amp']:.3f} G={row['gixx_amp']:.4f} "
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f"Asym={row['asymmetry']:.4f} Coh={row['coherence']:.6f}")
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# Highest vorticity
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high_vort = df.nlargest(5, 'vorticity_mean')
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R.append(f"\n Highest vorticity configurations:")
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for _, row in high_vort.iterrows():
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R.append(f" Ω={row['omega']:.1f} K={row['khra_amp']:.3f} G={row['gixx_amp']:.4f} "
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f"Vort={row['vorticity_mean']:.6f} Coh={row['coherence']:.6f}")
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# Coherence-Asymmetry relationship
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coh_asym_corr = df['coherence'].corr(df['asymmetry'])
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coh_vort_corr = df['coherence'].corr(df['vorticity_mean'])
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asym_vort_corr = df['asymmetry'].corr(df['vorticity_mean'])
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R.append(f"\n Cross-correlations:")
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R.append(f" Coherence ↔ Asymmetry: {coh_asym_corr:+.4f}")
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R.append(f" Coherence ↔ Vorticity: {coh_vort_corr:+.4f}")
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R.append(f" Asymmetry ↔ Vorticity: {asym_vort_corr:+.4f}")
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# ── Section 11: Optimal Operating Regions ──
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R.append("")
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R.append("─" * 78)
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R.append("11. OPTIMAL OPERATING REGIONS")
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R.append("─" * 78)
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# Multi-objective: high coherence + low asymmetry
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df_copy = df.copy()
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df_copy['score'] = (df_copy['coherence'] - df_copy['coherence'].min()) / (df_copy['coherence'].max() - df_copy['coherence'].min()) - \
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0.5 * (df_copy['asymmetry'] - df_copy['asymmetry'].min()) / (df_copy['asymmetry'].max() - df_copy['asymmetry'].min())
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best_multi = df_copy.nlargest(10, 'score')
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R.append(f" Top 10 by composite score (high coherence + low asymmetry):")
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R.append(f" {'Ω':>5} {'K':>6} {'G':>7} {'Coh':>10} {'Asym':>8} {'Vort':>10} {'Score':>8}")
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for _, row in best_multi.iterrows():
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R.append(f" {row['omega']:5.1f} {row['khra_amp']:6.3f} {row['gixx_amp']:7.4f} "
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f"{row['coherence']:10.6f} {row['asymmetry']:8.4f} {row['vorticity_mean']:10.6f} {row['score']:8.4f}")
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# Recommend optimal settings
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best_overall = best_multi.iloc[0]
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R.append(f"\n ★ RECOMMENDED OPERATING POINT:")
|
|||
|
|
R.append(f" Ω = {best_overall['omega']:.1f}")
|
|||
|
|
R.append(f" khra_amp = {best_overall['khra_amp']:.3f}")
|
|||
|
|
R.append(f" gixx_amp = {best_overall['gixx_amp']:.4f}")
|
|||
|
|
R.append(f" Expected coherence: {best_overall['coherence']:.6f}")
|
|||
|
|
R.append(f" Expected asymmetry: {best_overall['asymmetry']:.4f}")
|
|||
|
|
|
|||
|
|
# ── Section 12: Data Quality Assessment ──
|
|||
|
|
R.append("")
|
|||
|
|
R.append("─" * 78)
|
|||
|
|
R.append("12. DATA QUALITY ASSESSMENT")
|
|||
|
|
R.append("─" * 78)
|
|||
|
|
|
|||
|
|
# Check for duplicate telemetry (stale reads)
|
|||
|
|
# Count consecutive identical coherence values
|
|||
|
|
coh_vals = df['coherence'].values
|
|||
|
|
stale_runs = []
|
|||
|
|
run_len = 1
|
|||
|
|
for i in range(1, len(coh_vals)):
|
|||
|
|
if coh_vals[i] == coh_vals[i-1]:
|
|||
|
|
run_len += 1
|
|||
|
|
else:
|
|||
|
|
if run_len > 1:
|
|||
|
|
stale_runs.append(run_len)
|
|||
|
|
run_len = 1
|
|||
|
|
if run_len > 1:
|
|||
|
|
stale_runs.append(run_len)
|
|||
|
|
|
|||
|
|
total_stale = sum(stale_runs)
|
|||
|
|
R.append(f" Consecutive identical readings: {len(stale_runs)} runs")
|
|||
|
|
R.append(f" Total stale points: {total_stale}/{len(df)} ({100*total_stale/len(df):.1f}%)")
|
|||
|
|
if stale_runs:
|
|||
|
|
R.append(f" Longest stale run: {max(stale_runs)} points")
|
|||
|
|
R.append(f" Mean stale run length: {np.mean(stale_runs):.1f}")
|
|||
|
|
|
|||
|
|
# Unique coherence values
|
|||
|
|
n_unique = df['coherence'].nunique()
|
|||
|
|
R.append(f" Unique coherence values: {n_unique}/{len(df)} ({100*n_unique/len(df):.1f}%)")
|
|||
|
|
|
|||
|
|
# Check cycle progression
|
|||
|
|
cycles = df['cycle'].values
|
|||
|
|
cycle_gaps = np.diff(cycles)
|
|||
|
|
R.append(f"\n Cycle progression:")
|
|||
|
|
R.append(f" Start cycle: {int(cycles[0])}")
|
|||
|
|
R.append(f" End cycle: {int(cycles[-1])}")
|
|||
|
|
R.append(f" Total cycles: {int(cycles[-1] - cycles[0])}")
|
|||
|
|
R.append(f" Mean gap: {cycle_gaps.mean():.0f} cycles/point")
|
|||
|
|
R.append(f" Backwards jumps: {(cycle_gaps < 0).sum()}")
|
|||
|
|
|
|||
|
|
R.append("")
|
|||
|
|
R.append("=" * 78)
|
|||
|
|
R.append("END OF REPORT")
|
|||
|
|
R.append("=" * 78)
|
|||
|
|
|
|||
|
|
return "\n".join(R)
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ═══════════════════════════════════════════════════════════════════
|
|||
|
|
# PART 2: Interactive HTML Dashboard
|
|||
|
|
# ═══════════════════════════════════════════════════════════════════
|
|||
|
|
def generate_html_report(df, source_name):
|
|||
|
|
ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
|||
|
|
omega_vals = sorted(df['omega'].unique())
|
|||
|
|
khra_vals = sorted(df['khra_amp'].unique())
|
|||
|
|
gixx_vals = sorted(df['gixx_amp'].unique())
|
|||
|
|
|
|||
|
|
# Prepare data for JS
|
|||
|
|
# 1. Per-omega stats
|
|||
|
|
omega_stats = []
|
|||
|
|
for omega in omega_vals:
|
|||
|
|
g = df[df['omega'] == omega]
|
|||
|
|
best = g.loc[g['coherence'].idxmax()]
|
|||
|
|
omega_stats.append({
|
|||
|
|
'omega': omega,
|
|||
|
|
'coh_min': round(g['coherence'].min(), 6),
|
|||
|
|
'coh_max': round(g['coherence'].max(), 6),
|
|||
|
|
'coh_mean': round(g['coherence'].mean(), 6),
|
|||
|
|
'coh_std': round(g['coherence'].std(), 6),
|
|||
|
|
'asym_mean': round(g['asymmetry'].mean(), 4),
|
|||
|
|
'vort_mean': round(g['vorticity_mean'].mean(), 6),
|
|||
|
|
'temp_mean': round(g['gpu_temp_c'].mean(), 1),
|
|||
|
|
'power_mean': round(g['gpu_power_w'].mean(), 1),
|
|||
|
|
'best_k': round(best['khra_amp'], 3),
|
|||
|
|
'best_g': round(best['gixx_amp'], 4),
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 2. Heatmap data: omega × khra → max coherence (across gixx)
|
|||
|
|
heatmap_ok = []
|
|||
|
|
for omega in omega_vals:
|
|||
|
|
row = []
|
|||
|
|
for khra in khra_vals:
|
|||
|
|
g = df[(df['omega'] == omega) & (df['khra_amp'] == khra)]
|
|||
|
|
row.append(round(g['coherence'].max(), 6))
|
|||
|
|
heatmap_ok.append(row)
|
|||
|
|
|
|||
|
|
# 3. Heatmap: omega × gixx → max coherence (across khra)
|
|||
|
|
heatmap_og = []
|
|||
|
|
for omega in omega_vals:
|
|||
|
|
row = []
|
|||
|
|
for gixx in gixx_vals:
|
|||
|
|
g = df[(df['omega'] == omega) & (df['gixx_amp'] == gixx)]
|
|||
|
|
row.append(round(g['coherence'].max(), 6))
|
|||
|
|
heatmap_og.append(row)
|
|||
|
|
|
|||
|
|
# 4. All data points for scatter
|
|||
|
|
scatter_data = []
|
|||
|
|
for _, row in df.iterrows():
|
|||
|
|
scatter_data.append({
|
|||
|
|
'o': round(row['omega'], 1),
|
|||
|
|
'k': round(row['khra_amp'], 3),
|
|||
|
|
'g': round(row['gixx_amp'], 4),
|
|||
|
|
'c': round(row['coherence'], 6),
|
|||
|
|
'a': round(row['asymmetry'], 4),
|
|||
|
|
'v': round(row['vorticity_mean'], 6),
|
|||
|
|
't': int(row['gpu_temp_c']),
|
|||
|
|
'p': round(row['gpu_power_w'], 1),
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 5. Coherence distribution histogram
|
|||
|
|
coh_vals = df['coherence'].values
|
|||
|
|
hist_bins = 30
|
|||
|
|
hist_counts, hist_edges = np.histogram(coh_vals, bins=hist_bins)
|
|||
|
|
hist_centers = [round(0.5*(hist_edges[i] + hist_edges[i+1]), 6) for i in range(hist_bins)]
|
|||
|
|
|
|||
|
|
# 6. Top configurations
|
|||
|
|
top20 = df.nlargest(20, 'coherence')
|
|||
|
|
top_configs = []
|
|||
|
|
for _, row in top20.iterrows():
|
|||
|
|
top_configs.append({
|
|||
|
|
'omega': round(row['omega'], 1),
|
|||
|
|
'khra': round(row['khra_amp'], 3),
|
|||
|
|
'gixx': round(row['gixx_amp'], 4),
|
|||
|
|
'coh': round(row['coherence'], 6),
|
|||
|
|
'asym': round(row['asymmetry'], 4),
|
|||
|
|
'vort': round(row['vorticity_mean'], 6),
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 7. Nuclear magic analysis data
|
|||
|
|
resolution = df['coherence'].std() * 0.1
|
|||
|
|
if resolution < 1e-6:
|
|||
|
|
resolution = 1e-4
|
|||
|
|
mode_counts = {}
|
|||
|
|
for omega in omega_vals:
|
|||
|
|
g = df[df['omega'] == omega]
|
|||
|
|
coh_sorted = np.sort(g['coherence'].values)
|
|||
|
|
modes = [coh_sorted[0]]
|
|||
|
|
for c in coh_sorted[1:]:
|
|||
|
|
if c - modes[-1] > resolution:
|
|||
|
|
modes.append(c)
|
|||
|
|
mode_counts[round(omega, 1)] = len(modes)
|
|||
|
|
|
|||
|
|
html = f"""<!DOCTYPE html>
|
|||
|
|
<html lang="en">
|
|||
|
|
<head>
|
|||
|
|
<meta charset="UTF-8">
|
|||
|
|
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
|||
|
|
<title>Resonance Engine — Sweep Analysis Dashboard</title>
|
|||
|
|
<style>
|
|||
|
|
:root {{
|
|||
|
|
--bg: #0a0e17;
|
|||
|
|
--card: #111827;
|
|||
|
|
--border: #1f2937;
|
|||
|
|
--text: #e5e7eb;
|
|||
|
|
--dim: #9ca3af;
|
|||
|
|
--accent: #60a5fa;
|
|||
|
|
--gold: #fbbf24;
|
|||
|
|
--green: #34d399;
|
|||
|
|
--red: #f87171;
|
|||
|
|
--purple: #a78bfa;
|
|||
|
|
}}
|
|||
|
|
* {{ margin: 0; padding: 0; box-sizing: border-box; }}
|
|||
|
|
body {{ background: var(--bg); color: var(--text); font-family: 'Segoe UI', system-ui, sans-serif; padding: 20px; }}
|
|||
|
|
h1 {{ text-align: center; font-size: 1.8em; margin: 20px 0 5px; color: var(--accent); }}
|
|||
|
|
.subtitle {{ text-align: center; color: var(--dim); margin-bottom: 30px; font-size: 0.9em; }}
|
|||
|
|
.grid {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(450px, 1fr)); gap: 20px; margin-bottom: 20px; }}
|
|||
|
|
.card {{ background: var(--card); border: 1px solid var(--border); border-radius: 12px; padding: 20px; }}
|
|||
|
|
.card h2 {{ color: var(--gold); font-size: 1.1em; margin-bottom: 15px; border-bottom: 1px solid var(--border); padding-bottom: 8px; }}
|
|||
|
|
.stat-grid {{ display: grid; grid-template-columns: repeat(3, 1fr); gap: 10px; }}
|
|||
|
|
.stat {{ background: var(--bg); padding: 12px; border-radius: 8px; text-align: center; }}
|
|||
|
|
.stat .label {{ color: var(--dim); font-size: 0.75em; margin-bottom: 4px; }}
|
|||
|
|
.stat .value {{ font-size: 1.4em; font-weight: bold; color: var(--accent); }}
|
|||
|
|
.stat .value.gold {{ color: var(--gold); }}
|
|||
|
|
.stat .value.green {{ color: var(--green); }}
|
|||
|
|
canvas {{ width: 100% !important; height: auto !important; }}
|
|||
|
|
table {{ width: 100%; border-collapse: collapse; font-size: 0.85em; }}
|
|||
|
|
th {{ background: var(--bg); padding: 8px 6px; text-align: right; color: var(--gold); position: sticky; top: 0; }}
|
|||
|
|
td {{ padding: 6px; text-align: right; border-bottom: 1px solid var(--border); }}
|
|||
|
|
tr:hover td {{ background: rgba(96,165,250,0.08); }}
|
|||
|
|
.highlight {{ color: var(--green); font-weight: bold; }}
|
|||
|
|
.heatmap-container {{ overflow-x: auto; }}
|
|||
|
|
.heatmap {{ border-collapse: collapse; margin: 0 auto; }}
|
|||
|
|
.heatmap td {{ width: 60px; height: 32px; text-align: center; font-size: 0.75em; font-weight: bold; border: 1px solid var(--bg); }}
|
|||
|
|
.heatmap th {{ padding: 4px 8px; font-size: 0.8em; color: var(--dim); }}
|
|||
|
|
.magic-bar {{ display: flex; align-items: center; gap: 8px; margin: 4px 0; }}
|
|||
|
|
.magic-bar .bar {{ height: 20px; background: var(--accent); border-radius: 4px; transition: width 0.3s; }}
|
|||
|
|
.magic-bar .label {{ font-size: 0.8em; color: var(--dim); min-width: 40px; }}
|
|||
|
|
.magic-bar .count {{ font-size: 0.8em; min-width: 20px; }}
|
|||
|
|
.magic-hit {{ background: var(--gold) !important; color: #000 !important; }}
|
|||
|
|
.full-width {{ grid-column: 1 / -1; }}
|
|||
|
|
.recommend {{ background: linear-gradient(135deg, #1a2744, #1a3a2a); border: 2px solid var(--green); }}
|
|||
|
|
.recommend h2 {{ color: var(--green); }}
|
|||
|
|
.tab-bar {{ display: flex; gap: 4px; margin-bottom: 12px; }}
|
|||
|
|
.tab {{ padding: 6px 14px; border-radius: 6px 6px 0 0; cursor: pointer; background: var(--bg); color: var(--dim); border: 1px solid var(--border); border-bottom: none; font-size: 0.85em; }}
|
|||
|
|
.tab.active {{ background: var(--card); color: var(--accent); }}
|
|||
|
|
.tab-content {{ display: none; }}
|
|||
|
|
.tab-content.active {{ display: block; }}
|
|||
|
|
</style>
|
|||
|
|
</head>
|
|||
|
|
<body>
|
|||
|
|
<h1>⚛ Resonance Engine — Parameter Sweep Dashboard</h1>
|
|||
|
|
<div class="subtitle">{source_name} • {len(df)} points • {ts}</div>
|
|||
|
|
|
|||
|
|
<!-- Key Metrics -->
|
|||
|
|
<div class="grid">
|
|||
|
|
<div class="card full-width">
|
|||
|
|
<h2>Key Metrics</h2>
|
|||
|
|
<div class="stat-grid">
|
|||
|
|
<div class="stat"><div class="label">Peak Coherence</div><div class="value gold">{df['coherence'].max():.6f}</div></div>
|
|||
|
|
<div class="stat"><div class="label">Mean Coherence</div><div class="value">{df['coherence'].mean():.6f}</div></div>
|
|||
|
|
<div class="stat"><div class="label">Coherence Range</div><div class="value">{df['coherence'].max()-df['coherence'].min():.6f}</div></div>
|
|||
|
|
<div class="stat"><div class="label">Best Omega</div><div class="value green">{top_configs[0]['omega']:.1f}</div></div>
|
|||
|
|
<div class="stat"><div class="label">Points</div><div class="value">{len(df)}</div></div>
|
|||
|
|
<div class="stat"><div class="label">Magic Matches</div><div class="value gold">{sum(1 for n in mode_counts.values() if n in MAGIC_NUMBERS)}/15</div></div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<div class="grid">
|
|||
|
|
|
|||
|
|
<!-- Coherence vs Omega -->
|
|||
|
|
<div class="card">
|
|||
|
|
<h2>Coherence vs Omega</h2>
|
|||
|
|
<canvas id="chartOmega" height="280"></canvas>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- Coherence Distribution -->
|
|||
|
|
<div class="card">
|
|||
|
|
<h2>Coherence Distribution</h2>
|
|||
|
|
<canvas id="chartHist" height="280"></canvas>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- Heatmap: Omega × Khra -->
|
|||
|
|
<div class="card">
|
|||
|
|
<h2>Heatmap: Ω × Khra → Peak Coherence</h2>
|
|||
|
|
<div class="heatmap-container" id="heatmapOK"></div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- Heatmap: Omega × Gixx -->
|
|||
|
|
<div class="card">
|
|||
|
|
<h2>Heatmap: Ω × Gixx → Peak Coherence</h2>
|
|||
|
|
<div class="heatmap-container" id="heatmapOG"></div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- Nuclear Magic Modes -->
|
|||
|
|
<div class="card">
|
|||
|
|
<h2>Mode Count vs Nuclear Magic Numbers</h2>
|
|||
|
|
<canvas id="chartMagic" height="280"></canvas>
|
|||
|
|
<div id="magicBars" style="margin-top:12px;"></div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- Asymmetry & Vorticity -->
|
|||
|
|
<div class="card">
|
|||
|
|
<h2>Asymmetry & Vorticity vs Omega</h2>
|
|||
|
|
<canvas id="chartAsymVort" height="280"></canvas>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- Thermal Profile -->
|
|||
|
|
<div class="card">
|
|||
|
|
<h2>Thermal & Power Profile</h2>
|
|||
|
|
<canvas id="chartThermal" height="280"></canvas>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- Recommended Config -->
|
|||
|
|
<div class="card recommend">
|
|||
|
|
<h2>★ Recommended Operating Point</h2>
|
|||
|
|
<div class="stat-grid" style="margin-top:10px;">
|
|||
|
|
<div class="stat"><div class="label">Omega (Ω)</div><div class="value green">{top_configs[0]['omega']:.1f}</div></div>
|
|||
|
|
<div class="stat"><div class="label">Khra Amp</div><div class="value green">{top_configs[0]['khra']:.3f}</div></div>
|
|||
|
|
<div class="stat"><div class="label">Gixx Amp</div><div class="value green">{top_configs[0]['gixx']:.4f}</div></div>
|
|||
|
|
</div>
|
|||
|
|
<div class="stat-grid" style="margin-top:10px;">
|
|||
|
|
<div class="stat"><div class="label">Coherence</div><div class="value gold">{top_configs[0]['coh']:.6f}</div></div>
|
|||
|
|
<div class="stat"><div class="label">Asymmetry</div><div class="value">{top_configs[0]['asym']:.4f}</div></div>
|
|||
|
|
<div class="stat"><div class="label">Vorticity</div><div class="value">{top_configs[0]['vort']:.6f}</div></div>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- Top Configurations Table -->
|
|||
|
|
<div class="grid">
|
|||
|
|
<div class="card full-width">
|
|||
|
|
<h2>Top 20 Configurations</h2>
|
|||
|
|
<table>
|
|||
|
|
<thead><tr><th>#</th><th>Ω</th><th>K</th><th>G</th><th>Coherence</th><th>Asymmetry</th><th>Vorticity</th></tr></thead>
|
|||
|
|
<tbody id="topTable"></tbody>
|
|||
|
|
</table>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<!-- Per-Omega Detail Table -->
|
|||
|
|
<div class="grid">
|
|||
|
|
<div class="card full-width">
|
|||
|
|
<h2>Per-Omega Summary</h2>
|
|||
|
|
<table>
|
|||
|
|
<thead><tr><th>Ω</th><th>Coh Min</th><th>Coh Max</th><th>Coh Mean</th><th>Coh Std</th><th>Asym Mean</th><th>Vort Mean</th><th>Best K</th><th>Best G</th></tr></thead>
|
|||
|
|
<tbody id="omegaTable"></tbody>
|
|||
|
|
</table>
|
|||
|
|
</div>
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<script>
|
|||
|
|
// ── Data ──
|
|||
|
|
const omegaStats = {json.dumps(omega_stats)};
|
|||
|
|
const heatmapOK = {json.dumps(heatmap_ok)};
|
|||
|
|
const heatmapOG = {json.dumps(heatmap_og)};
|
|||
|
|
const topConfigs = {json.dumps(top_configs)};
|
|||
|
|
const modeCounts = {json.dumps(mode_counts)};
|
|||
|
|
const histCenters = {json.dumps(hist_centers)};
|
|||
|
|
const histCounts = {json.dumps(hist_counts.tolist())};
|
|||
|
|
const omegaLabels = {json.dumps([round(o,1) for o in omega_vals])};
|
|||
|
|
const khraLabels = {json.dumps([round(k,3) for k in khra_vals])};
|
|||
|
|
const gixxLabels = {json.dumps([round(g,4) for g in gixx_vals])};
|
|||
|
|
const magicNumbers = {json.dumps(MAGIC_NUMBERS[:6])};
|
|||
|
|
|
|||
|
|
// ── Minimal Canvas Chart Library ──
|
|||
|
|
function drawChart(canvasId, config) {{
|
|||
|
|
const canvas = document.getElementById(canvasId);
|
|||
|
|
const ctx = canvas.getContext('2d');
|
|||
|
|
const dpr = window.devicePixelRatio || 1;
|
|||
|
|
const rect = canvas.getBoundingClientRect();
|
|||
|
|
canvas.width = rect.width * dpr;
|
|||
|
|
canvas.height = rect.height * dpr;
|
|||
|
|
ctx.scale(dpr, dpr);
|
|||
|
|
const W = rect.width, H = rect.height;
|
|||
|
|
const pad = {{top: 20, right: 20, bottom: 40, left: 70}};
|
|||
|
|
const pW = W - pad.left - pad.right;
|
|||
|
|
const pH = H - pad.top - pad.bottom;
|
|||
|
|
|
|||
|
|
// Background
|
|||
|
|
ctx.fillStyle = '#0a0e17';
|
|||
|
|
ctx.fillRect(0, 0, W, H);
|
|||
|
|
|
|||
|
|
// Find data bounds
|
|||
|
|
let allY = [];
|
|||
|
|
config.datasets.forEach(ds => ds.data.forEach(v => allY.push(v)));
|
|||
|
|
let yMin = config.yMin !== undefined ? config.yMin : Math.min(...allY);
|
|||
|
|
let yMax = config.yMax !== undefined ? config.yMax : Math.max(...allY);
|
|||
|
|
if (yMin === yMax) {{ yMin -= 0.0001; yMax += 0.0001; }}
|
|||
|
|
const yRange = yMax - yMin;
|
|||
|
|
|
|||
|
|
// Grid lines
|
|||
|
|
ctx.strokeStyle = '#1f2937';
|
|||
|
|
ctx.lineWidth = 1;
|
|||
|
|
for (let i = 0; i <= 5; i++) {{
|
|||
|
|
const y = pad.top + pH - (i/5) * pH;
|
|||
|
|
ctx.beginPath(); ctx.moveTo(pad.left, y); ctx.lineTo(W - pad.right, y); ctx.stroke();
|
|||
|
|
ctx.fillStyle = '#9ca3af';
|
|||
|
|
ctx.font = '11px monospace';
|
|||
|
|
ctx.textAlign = 'right';
|
|||
|
|
ctx.fillText((yMin + (i/5) * yRange).toFixed(config.yDecimals || 4), pad.left - 6, y + 4);
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
// X labels
|
|||
|
|
ctx.textAlign = 'center';
|
|||
|
|
ctx.fillStyle = '#9ca3af';
|
|||
|
|
config.labels.forEach((lbl, i) => {{
|
|||
|
|
const x = pad.left + (i / (config.labels.length - 1)) * pW;
|
|||
|
|
ctx.fillText(lbl, x, H - pad.bottom + 18);
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// Axis labels
|
|||
|
|
if (config.xLabel) {{
|
|||
|
|
ctx.fillText(config.xLabel, pad.left + pW/2, H - 4);
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
// Datasets
|
|||
|
|
config.datasets.forEach(ds => {{
|
|||
|
|
ctx.strokeStyle = ds.color || '#60a5fa';
|
|||
|
|
ctx.lineWidth = ds.lineWidth || 2;
|
|||
|
|
ctx.beginPath();
|
|||
|
|
ds.data.forEach((v, i) => {{
|
|||
|
|
const x = pad.left + (i / (ds.data.length - 1)) * pW;
|
|||
|
|
const y = pad.top + pH - ((v - yMin) / yRange) * pH;
|
|||
|
|
if (i === 0) ctx.moveTo(x, y); else ctx.lineTo(x, y);
|
|||
|
|
}});
|
|||
|
|
ctx.stroke();
|
|||
|
|
|
|||
|
|
// Points
|
|||
|
|
if (ds.points !== false) {{
|
|||
|
|
ctx.fillStyle = ds.color || '#60a5fa';
|
|||
|
|
ds.data.forEach((v, i) => {{
|
|||
|
|
const x = pad.left + (i / (ds.data.length - 1)) * pW;
|
|||
|
|
const y = pad.top + pH - ((v - yMin) / yRange) * pH;
|
|||
|
|
ctx.beginPath(); ctx.arc(x, y, 4, 0, Math.PI * 2); ctx.fill();
|
|||
|
|
}});
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
// Label
|
|||
|
|
if (ds.label) {{
|
|||
|
|
ctx.fillStyle = ds.color || '#60a5fa';
|
|||
|
|
ctx.textAlign = 'left';
|
|||
|
|
ctx.font = '11px sans-serif';
|
|||
|
|
const lastY = pad.top + pH - ((ds.data[ds.data.length-1] - yMin) / yRange) * pH;
|
|||
|
|
ctx.fillText(ds.label, W - pad.right + 4, lastY + 4);
|
|||
|
|
}}
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// Magic number horizontal lines
|
|||
|
|
if (config.magicLines) {{
|
|||
|
|
ctx.setLineDash([4, 4]);
|
|||
|
|
ctx.strokeStyle = '#fbbf2480';
|
|||
|
|
ctx.lineWidth = 1;
|
|||
|
|
magicNumbers.forEach(mn => {{
|
|||
|
|
if (mn >= yMin && mn <= yMax) {{
|
|||
|
|
const y = pad.top + pH - ((mn - yMin) / yRange) * pH;
|
|||
|
|
ctx.beginPath(); ctx.moveTo(pad.left, y); ctx.lineTo(W-pad.right, y); ctx.stroke();
|
|||
|
|
ctx.fillStyle = '#fbbf24';
|
|||
|
|
ctx.textAlign = 'right';
|
|||
|
|
ctx.fillText(mn, pad.left - 4, y + 4);
|
|||
|
|
}}
|
|||
|
|
}});
|
|||
|
|
ctx.setLineDash([]);
|
|||
|
|
}}
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
function drawBarChart(canvasId, labels, data, color, yLabel) {{
|
|||
|
|
const canvas = document.getElementById(canvasId);
|
|||
|
|
const ctx = canvas.getContext('2d');
|
|||
|
|
const dpr = window.devicePixelRatio || 1;
|
|||
|
|
const rect = canvas.getBoundingClientRect();
|
|||
|
|
canvas.width = rect.width * dpr;
|
|||
|
|
canvas.height = rect.height * dpr;
|
|||
|
|
ctx.scale(dpr, dpr);
|
|||
|
|
const W = rect.width, H = rect.height;
|
|||
|
|
const pad = {{top: 20, right: 20, bottom: 40, left: 60}};
|
|||
|
|
const pW = W - pad.left - pad.right;
|
|||
|
|
const pH = H - pad.top - pad.bottom;
|
|||
|
|
|
|||
|
|
ctx.fillStyle = '#0a0e17';
|
|||
|
|
ctx.fillRect(0, 0, W, H);
|
|||
|
|
|
|||
|
|
const maxVal = Math.max(...data) * 1.1;
|
|||
|
|
const barW = pW / data.length * 0.7;
|
|||
|
|
const gap = pW / data.length * 0.3;
|
|||
|
|
|
|||
|
|
data.forEach((v, i) => {{
|
|||
|
|
const x = pad.left + (i / data.length) * pW + gap/2;
|
|||
|
|
const barH = (v / maxVal) * pH;
|
|||
|
|
const y = pad.top + pH - barH;
|
|||
|
|
ctx.fillStyle = color || '#60a5fa';
|
|||
|
|
ctx.fillRect(x, y, barW, barH);
|
|||
|
|
ctx.fillStyle = '#9ca3af';
|
|||
|
|
ctx.font = '10px monospace';
|
|||
|
|
ctx.textAlign = 'center';
|
|||
|
|
ctx.fillText(labels[i], x + barW/2, H - pad.bottom + 16);
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// Y axis
|
|||
|
|
for (let i = 0; i <= 4; i++) {{
|
|||
|
|
const y = pad.top + pH - (i/4) * pH;
|
|||
|
|
ctx.strokeStyle = '#1f2937';
|
|||
|
|
ctx.beginPath(); ctx.moveTo(pad.left, y); ctx.lineTo(W-pad.right, y); ctx.stroke();
|
|||
|
|
ctx.fillStyle = '#9ca3af';
|
|||
|
|
ctx.textAlign = 'right';
|
|||
|
|
ctx.font = '11px monospace';
|
|||
|
|
ctx.fillText((maxVal * i / 4).toFixed(0), pad.left - 6, y + 4);
|
|||
|
|
}}
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
// ── Render Charts ──
|
|||
|
|
window.addEventListener('load', () => {{
|
|||
|
|
// Coherence vs Omega
|
|||
|
|
drawChart('chartOmega', {{
|
|||
|
|
labels: omegaLabels,
|
|||
|
|
xLabel: 'Omega (Ω)',
|
|||
|
|
yDecimals: 4,
|
|||
|
|
datasets: [
|
|||
|
|
{{ data: omegaStats.map(s => s.coh_max), color: '#fbbf24', label: 'Max', lineWidth: 2 }},
|
|||
|
|
{{ data: omegaStats.map(s => s.coh_mean), color: '#60a5fa', label: 'Mean', lineWidth: 2 }},
|
|||
|
|
{{ data: omegaStats.map(s => s.coh_min), color: '#f87171', label: 'Min', lineWidth: 1 }},
|
|||
|
|
]
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// Histogram
|
|||
|
|
drawBarChart('chartHist', histCenters.map(c => c.toFixed(4)), histCounts, '#60a5fa');
|
|||
|
|
|
|||
|
|
// Mode counts with magic lines
|
|||
|
|
drawChart('chartMagic', {{
|
|||
|
|
labels: omegaLabels,
|
|||
|
|
xLabel: 'Omega (Ω)',
|
|||
|
|
yDecimals: 0,
|
|||
|
|
yMin: 0,
|
|||
|
|
yMax: 30,
|
|||
|
|
magicLines: true,
|
|||
|
|
datasets: [
|
|||
|
|
{{ data: omegaLabels.map(o => modeCounts[o]), color: '#34d399', label: 'Modes', lineWidth: 2 }},
|
|||
|
|
]
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// Asymmetry & Vorticity
|
|||
|
|
drawChart('chartAsymVort', {{
|
|||
|
|
labels: omegaLabels,
|
|||
|
|
xLabel: 'Omega (Ω)',
|
|||
|
|
yDecimals: 2,
|
|||
|
|
datasets: [
|
|||
|
|
{{ data: omegaStats.map(s => s.asym_mean), color: '#a78bfa', label: 'Asymmetry' }},
|
|||
|
|
]
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// Thermal
|
|||
|
|
drawChart('chartThermal', {{
|
|||
|
|
labels: omegaLabels,
|
|||
|
|
xLabel: 'Omega (Ω)',
|
|||
|
|
yDecimals: 0,
|
|||
|
|
datasets: [
|
|||
|
|
{{ data: omegaStats.map(s => s.temp_mean), color: '#f87171', label: 'Temp °C' }},
|
|||
|
|
{{ data: omegaStats.map(s => s.power_mean / 5), color: '#fbbf24', label: 'Power/5' }},
|
|||
|
|
]
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
// Heatmaps
|
|||
|
|
renderHeatmap('heatmapOK', heatmapOK, omegaLabels, khraLabels, 'Ω', 'K');
|
|||
|
|
renderHeatmap('heatmapOG', heatmapOG, omegaLabels, gixxLabels, 'Ω', 'G');
|
|||
|
|
|
|||
|
|
// Tables
|
|||
|
|
renderTopTable();
|
|||
|
|
renderOmegaTable();
|
|||
|
|
}});
|
|||
|
|
|
|||
|
|
function renderHeatmap(containerId, data, rowLabels, colLabels, rowName, colName) {{
|
|||
|
|
const container = document.getElementById(containerId);
|
|||
|
|
const allVals = data.flat();
|
|||
|
|
const vMin = Math.min(...allVals);
|
|||
|
|
const vMax = Math.max(...allVals);
|
|||
|
|
const range = vMax - vMin || 0.0001;
|
|||
|
|
|
|||
|
|
let html = '<table class="heatmap"><tr><th>' + rowName + '\\\\' + colName + '</th>';
|
|||
|
|
colLabels.forEach(c => html += '<th>' + c + '</th>');
|
|||
|
|
html += '</tr>';
|
|||
|
|
|
|||
|
|
data.forEach((row, i) => {{
|
|||
|
|
html += '<tr><th>' + rowLabels[i] + '</th>';
|
|||
|
|
row.forEach(v => {{
|
|||
|
|
const t = (v - vMin) / range;
|
|||
|
|
const r = Math.round(30 + 50 * (1-t));
|
|||
|
|
const g = Math.round(80 + 160 * t);
|
|||
|
|
const b = Math.round(120 + 130 * t);
|
|||
|
|
html += '<td style="background:rgb(' + r + ',' + g + ',' + b + ');color:' + (t > 0.5 ? '#000' : '#fff') + '">' + v.toFixed(4) + '</td>';
|
|||
|
|
}});
|
|||
|
|
html += '</tr>';
|
|||
|
|
}});
|
|||
|
|
html += '</table>';
|
|||
|
|
container.innerHTML = html;
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
function renderTopTable() {{
|
|||
|
|
const tbody = document.getElementById('topTable');
|
|||
|
|
topConfigs.forEach((c, i) => {{
|
|||
|
|
tbody.innerHTML += '<tr><td>' + (i+1) + '</td><td>' + c.omega + '</td><td>' + c.khra + '</td><td>' + c.gixx + '</td><td class="highlight">' + c.coh.toFixed(6) + '</td><td>' + c.asym + '</td><td>' + c.vort + '</td></tr>';
|
|||
|
|
}});
|
|||
|
|
}}
|
|||
|
|
|
|||
|
|
function renderOmegaTable() {{
|
|||
|
|
const tbody = document.getElementById('omegaTable');
|
|||
|
|
omegaStats.forEach(s => {{
|
|||
|
|
tbody.innerHTML += '<tr><td>' + s.omega + '</td><td>' + s.coh_min.toFixed(6) + '</td><td>' + s.coh_max.toFixed(6) + '</td><td>' + s.coh_mean.toFixed(6) + '</td><td>' + s.coh_std.toFixed(6) + '</td><td>' + s.asym_mean + '</td><td>' + s.vort_mean + '</td><td>' + s.best_k + '</td><td>' + s.best_g + '</td></tr>';
|
|||
|
|
}});
|
|||
|
|
}}
|
|||
|
|
</script>
|
|||
|
|
</body>
|
|||
|
|
</html>"""
|
|||
|
|
return html
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ═══════════════════════════════════════════════════════════════════
|
|||
|
|
# Main
|
|||
|
|
# ═══════════════════════════════════════════════════════════════════
|
|||
|
|
def main():
|
|||
|
|
csv_path = sys.argv[1] if len(sys.argv) > 1 else None
|
|||
|
|
df, source = load_data(csv_path)
|
|||
|
|
|
|||
|
|
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
|||
|
|
|
|||
|
|
# Generate text report
|
|||
|
|
print("Generating text report...")
|
|||
|
|
text_report = generate_text_report(df, source)
|
|||
|
|
text_path = os.path.join(RESULTS_DIR, f"sweep_analysis_{timestamp}.txt")
|
|||
|
|
with open(text_path, 'w', encoding='utf-8') as f:
|
|||
|
|
f.write(text_report)
|
|||
|
|
print(f" Saved: {text_path}")
|
|||
|
|
|
|||
|
|
# Generate HTML dashboard
|
|||
|
|
print("Generating HTML dashboard...")
|
|||
|
|
html_report = generate_html_report(df, source)
|
|||
|
|
html_path = os.path.join(RESULTS_DIR, f"sweep_dashboard_{timestamp}.html")
|
|||
|
|
with open(html_path, 'w', encoding='utf-8') as f:
|
|||
|
|
f.write(html_report)
|
|||
|
|
print(f" Saved: {html_path}")
|
|||
|
|
|
|||
|
|
# Print summary to stdout
|
|||
|
|
print("\n" + text_report)
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main()
|