create_summary_visualization.py

#!/usr/bin/env python3
"""
Create comprehensive Phase D visualization showing:
1. Week 1 progress timeline
2. Enhancement scaling with N
3. Gap analysis (current → target)
4. Tier 3 potential scenarios
"""

import matplotlib.pyplot as plt
import numpy as np

def create_phase_d_summary_visualization():
    """Create comprehensive summary visualization."""
    
    fig = plt.figure(figsize=(18, 12))
    
    # ========== Plot 1: Week 1 Timeline ==========
    ax1 = plt.subplot(3, 3, 1)
    days = ['Day 1\nAnalytical', 'Day 2\nScaling', 'Day 3\nTopology', 
            'Days 4-5\nN-scan', 'Day 6\nOptimize', 'Day 7\nValidate']
    enhancements = [0, 52.5, 52.5, 19800, 19800*25, 4.51e8]
    
    colors = ['lightblue', 'lightblue', 'skyblue', 'cornflowerblue', 'royalblue', 'darkblue']
    ax1.bar(range(len(days)), np.log10(np.array(enhancements)+1), color=colors, alpha=0.8)
    ax1.set_xticks(range(len(days)))
    ax1.set_xticklabels(days, fontsize=9, rotation=45, ha='right')
    ax1.set_ylabel('log₁₀(Enhancement + 1)', fontsize=11)
    ax1.set_title('Week 1 Progress Timeline', fontsize=13, fontweight='bold')
    ax1.grid(True, alpha=0.3, axis='y')
    ax1.axhline(6, color='green', linestyle='--', linewidth=2, label='Tier 1 Target (10⁶×)')
    ax1.legend(fontsize=8)
    
    # ========== Plot 2: N Scaling (All Data) ==========
    ax2 = plt.subplot(3, 3, 2)
    
    # Week 1 data
    N_week1 = [4, 6, 8, 10, 15, 20, 30, 50, 75, 100]
    enh_week1 = [9.6, 21.8, 38.5, 60.2, 135.7, 241.2, 543.2, 1498, 3366, 7916]
    
    # High-N data (optimized)
    N_high = [100, 238, 500, 1000, 2000]
    enh_high = [7.92e7, 4.51e8, 2.00e9, 7.99e9, 3.20e10]
    
    ax2.loglog(N_week1, enh_week1, 'o-', color='royalblue', linewidth=2, 
               markersize=6, label='Week 1 (baseline)', alpha=0.7)
    ax2.loglog(N_high, enh_high, 's-', color='darkred', linewidth=3,
               markersize=10, label='High-N (optimized)')
    
    # Fit line
    N_fit = np.logspace(np.log10(4), np.log10(2000), 100)
    enh_fit = 3.1e-117 * 5 * 100 * (N_fit**2.003) / 3.96e-121
    ax2.loglog(N_fit, enh_fit, '--', color='black', linewidth=2, alpha=0.5,
               label='Fit: N^2.003')
    
    # Target line
    ax2.axhline(1e6, color='green', linestyle='--', linewidth=2, label='Tier 1 (10⁶×)')
    
    ax2.set_xlabel('Network Size N', fontsize=12)
    ax2.set_ylabel('Enhancement', fontsize=12)
    ax2.set_title('Scaling Across All N', fontsize=13, fontweight='bold')
    ax2.legend(fontsize=9)
    ax2.grid(True, alpha=0.3, which='both')
    
    # ========== Plot 3: Gap Analysis ==========
    ax3 = plt.subplot(3, 3, 3)
    
    milestones = ['Current\n(Tier 1)', 'Tier 3\nConservative', 'Tier 3\nOptimistic', 
                  'Required\nfor Warp']
    values = [4.51e8, 5.79e11, 4.51e14, 1e71]
    gaps = [values[3]/v for v in values]
    
    x = range(len(milestones))
    colors_gap = ['green', 'yellow', 'orange', 'red']
    
    bars = ax3.bar(x, np.log10(values), color=colors_gap, alpha=0.7)
    ax3.set_xticks(x)
    ax3.set_xticklabels(milestones, fontsize=10)
    ax3.set_ylabel('log₁₀(Enhancement)', fontsize=11)
    ax3.set_title('Enhancement Milestones', fontsize=13, fontweight='bold')
    ax3.grid(True, alpha=0.3, axis='y')
    
    # Add gap labels
    for i, (bar, gap) in enumerate(zip(bars, gaps)):
        height = bar.get_height()
        ax3.text(bar.get_x() + bar.get_width()/2, height + 1,
                f'{np.log10(gap):.0f}\norders\nto warp',
                ha='center', va='bottom', fontsize=8, fontweight='bold')
    
    # ========== Plot 4: Mechanism Breakdown ==========
    ax4 = plt.subplot(3, 3, 4)
    
    mechanisms = ['Tier 1\nN-scaling', 'Tier 1\nParameters', 
                  'Tier 3\nCasimir', 'Tier 3\nTopology', 'Tier 3\nBackreaction']
    boosts = [19800, 500, 236, 5.5, 1.0]
    
    colors_mech = ['darkblue', 'royalblue', 'orange', 'coral', 'lightcoral']
    ax4.barh(range(len(mechanisms)), np.log10(boosts), color=colors_mech, alpha=0.8)
    ax4.set_yticks(range(len(mechanisms)))
    ax4.set_yticklabels(mechanisms, fontsize=10)
    ax4.set_xlabel('log₁₀(Boost Factor)', fontsize=11)
    ax4.set_title('Mechanism Contributions', fontsize=13, fontweight='bold')
    ax4.grid(True, alpha=0.3, axis='x')
    
    # Add values
    for i, boost in enumerate(boosts):
        ax4.text(np.log10(boost) + 0.1, i, f'{boost:.1f}×',
                va='center', fontsize=9, fontweight='bold')
    
    # ========== Plot 5: Parameter Optimization ==========
    ax5 = plt.subplot(3, 3, 5)
    
    lambda_vals = [0.5, 1.0, 2.0, 5.0, 10.0]
    enh_lambda = [3.04e3, 6.08e3, 1.22e4, 3.04e4, 6.08e4]
    
    ax5.loglog(lambda_vals, enh_lambda, 'o-', color='purple',
               linewidth=3, markersize=10)
    ax5.set_xlabel('Coupling Constant λ', fontsize=12)
    ax5.set_ylabel('Enhancement (N=20)', fontsize=12)
    ax5.set_title('λ Optimization (Linear!)', fontsize=13, fontweight='bold')
    ax5.grid(True, alpha=0.3, which='both')
    
    # Fit line
    ax5.loglog(lambda_vals, 6.08e3 * np.array(lambda_vals), '--',
               color='black', alpha=0.5, linewidth=2, label='g ∝ λ¹·⁰⁰⁰')
    ax5.legend(fontsize=10)
    
    # ========== Plot 6: Energy Optimization ==========
    ax6 = plt.subplot(3, 3, 6)
    
    energy_vals = [1e-16, 1e-15, 1e-14, 1e-13, 1e-12]
    enh_energy = [6.08e2, 6.08e3, 6.08e4, 6.08e5, 6.08e6]
    
    ax6.loglog(energy_vals, enh_energy, 's-', color='green',
               linewidth=3, markersize=10)
    ax6.set_xlabel('Matter Energy E (J)', fontsize=12)
    ax6.set_ylabel('Enhancement (N=20)', fontsize=12)
    ax6.set_title('Energy Optimization (Linear!)', fontsize=13, fontweight='bold')
    ax6.grid(True, alpha=0.3, which='both')
    
    # Fit line
    ax6.loglog(energy_vals, 6.08e18 * np.array(energy_vals), '--',
               color='black', alpha=0.5, linewidth=2, label='g ∝ E¹·⁰⁰⁰')
    ax6.legend(fontsize=10)
    
    # ========== Plot 7: Tier 3 Scenarios ==========
    ax7 = plt.subplot(3, 3, 7)
    
    scenarios = ['Current\n(Tier 1)', 'Conservative\nTier 3', 'Optimistic\nTier 3',
                 'Aggressive\nTier 3', 'Warp\nTarget']
    tier3_boosts = [1, 1e3, 1e6, 1e10, 1e62]  # Multipliers on Tier 1
    totals = [4.51e8 * b for b in tier3_boosts]
    
    x = range(len(scenarios))
    colors_scenario = ['blue', 'cyan', 'yellow', 'orange', 'red']
    
    ax7.bar(x, np.log10(totals), color=colors_scenario, alpha=0.7)
    ax7.set_xticks(x)
    ax7.set_xticklabels(scenarios, fontsize=9)
    ax7.set_ylabel('log₁₀(Total Enhancement)', fontsize=11)
    ax7.set_title('Tier 3 Scenarios', fontsize=13, fontweight='bold')
    ax7.axhline(71, color='red', linestyle='--', linewidth=3, label='Warp (10⁷¹×)')
    ax7.grid(True, alpha=0.3, axis='y')
    ax7.legend(fontsize=9)
    
    # ========== Plot 8: Computational Scaling ==========
    ax8 = plt.subplot(3, 3, 8)
    
    N_comp = [100, 238, 500, 1000, 2000]
    times = [0.53, 3.16, 12.36, 33.01, 102.45]
    
    ax8.loglog(N_comp, times, 'o-', color='darkgreen',
               linewidth=3, markersize=10)
    ax8.set_xlabel('Network Size N', fontsize=12)
    ax8.set_ylabel('Computation Time (s)', fontsize=12)
    ax8.set_title('Computational Cost (t ∝ N^1.74)', fontsize=13, fontweight='bold')
    ax8.grid(True, alpha=0.3, which='both')
    
    # Fit line
    ax8.loglog(N_comp, 0.00005 * np.array(N_comp)**1.742, '--',
               color='black', alpha=0.5, linewidth=2, label='N^1.74')
    ax8.legend(fontsize=10)
    
    # ========== Plot 9: Summary Text ==========
    ax9 = plt.subplot(3, 3, 9)
    ax9.axis('off')
    
    summary_text = """
PHASE D WEEK 1 - COMPLETE SUMMARY

✅ TIER 1 SUCCESS
• Target: 10⁶×
• Achieved: 4.51×10⁸× (451× margin!)
• Scaling: g ∝ N^2.003 (perfect!)
• Optimal: N=238, λ=5, E=10⁻¹³J, j=2

📊 KEY RESULTS
• Tested N up to 2,000 (3.2×10¹⁰×)
• Parameter boost: 500× (λ, E linear)
• Computation: <2min for N=2000
• Universal exponent: α same for all j

⚠️ GAP ANALYSIS
• Current: 10⁹× (rounded)
• Warp target: 10⁷¹×
• Gap: 10⁶²× (62 orders!)

🎯 TIER 3 OUTLOOK
• Conservative: 10³× → total 10¹²×
• Optimistic: 10⁶× → total 10¹⁵×
• Required: 10⁶²× → CHALLENGING!

📅 NEXT STEPS
• Weeks 2-4: Tier 3 design
• Weeks 5-12: Implementation
• Week 12: GO/NO-GO decision
"""
    
    ax9.text(0.1, 0.95, summary_text, transform=ax9.transAxes,
            fontsize=10, verticalalignment='top', family='monospace',
            bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))
    
    # Overall title
    fig.suptitle('PHASE D: LQG MACROSCOPIC COHERENCE - WEEK 1 COMPLETE SUMMARY\nOctober 14, 2025',
                fontsize=16, fontweight='bold', y=0.995)
    
    plt.tight_layout(rect=[0, 0, 1, 0.99])
    plt.savefig('phase_d_week1_summary.png', dpi=150, bbox_inches='tight')
    print("✅ Summary visualization saved: phase_d_week1_summary.png")
    
    # Also create a simple status badge
    create_status_badge()


def create_status_badge():
    """Create simple status badge."""
    fig, ax = plt.subplots(figsize=(8, 4))
    ax.axis('off')
    
    # Main text
    status_text = """
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
    PHASE D: LQG MACROSCOPIC COHERENCE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

    WEEK 1 STATUS: ✅ COMPLETE (7/7 days)
    
    TIER 1 TARGET: ✅ EXCEEDED (451× margin)
    
    CURRENT ENHANCEMENT: 4.51 × 10⁸×
    
    WARP THRESHOLD: 1.00 × 10⁷¹×
    
    GAP REMAINING: 2.22 × 10⁶²× (62 orders)
    
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
    NEXT: TIER 3 DESIGN (Weeks 2-12)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
"""
    
    ax.text(0.5, 0.5, status_text, transform=ax.transAxes,
           fontsize=13, verticalalignment='center', horizontalalignment='center',
           family='monospace', fontweight='bold',
           bbox=dict(boxstyle='round,pad=1', facecolor='lightgreen', alpha=0.9,
                    edgecolor='darkgreen', linewidth=3))
    
    plt.tight_layout()
    plt.savefig('phase_d_status_badge.png', dpi=150, bbox_inches='tight')
    print("✅ Status badge saved: phase_d_status_badge.png")


if __name__ == "__main__":
    print("="*70)
    print("CREATING PHASE D WEEK 1 SUMMARY VISUALIZATION")
    print("="*70)
    
    create_phase_d_summary_visualization()
    
    print("\n" + "="*70)
    print("✅ ALL VISUALIZATIONS COMPLETE")
    print("="*70)