Okay, We’re Fighting China (Help) | Part Five ‘Taking The Simulation?’
things to think about…
what would change about my face?
how fat and fit do you think I could get at the same time?
who goes there?
Check my Wiki in development: Chinese Entrainment of The American People
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
# --- SPECTRAL INITIALIZATION MATRIX ---
np.random.seed(42)
NUM_NODES = 50
STEPS = 200
SWITCH_STEP = 80
MU = 0.5
# Target Stable Horizon (Earth-Cosmic-Humanity Coherence Fixed Point)
TARGET_COHERENCE = 1.0
def transverse_lyapunov_bound(alpha, sigma, lambda_k, mu=MU):
“”“
CORRECTED to match the LaTeX document AND the actual dynamics:
Λ_k = -2α - σμλ_k
Stable when Λ_k < 0 → σμλ_k < 2α → λ_k < 2α/(σμ)
“”“
return -2.0 * alpha - (sigma * mu) * lambda_k
def run_spectral_control_simulation():
# Initialize nodes in a highly fragmented, decoupled state
states = np.random.uniform(-1.5, 2.5, NUM_NODES)
history = np.zeros((STEPS, NUM_NODES))
lyapunov_history = np.zeros(STEPS)
# Construct a Small-World Topology (High clustering, short paths)
G = nx.watts_strogatz_graph(n=NUM_NODES, k=4, p=0.25, seed=42)
L = nx.laplacian_matrix(G).toarray().astype(float)
# Compute graph spectrum (Laplacian eigenvalues)
eigenvalues = np.sort(np.real(np.linalg.eigvalsh(L)))
lambda2 = eigenvalues[1] # Fiedler eigenvalue (algebraic connectivity)
max_lambda = eigenvalues[-1] # Worst-case transverse mode bound
# Baseline Parameters: Phase 1 (Algorithmic Fragmentation)
alpha = 0.1 # Suppressed internal gating
sigma = 0.02 # Weak real relational coupling
dt = 0.05
for t in range(STEPS):
history[t] = states.copy()
# Determine active control parameters based on Protocol Activation
if t >= SWITCH_STEP:
# Phase 2: Protocol Active (Spectral Control Restored)
alpha = 1.5 # Fixed-point reset & kill-switch engaged
sigma = 0.8 # Edge reinforcement & unmediated coupling active
# CORRECTED worst-case Transverse Lyapunov bound tracking
worst_case_exponent = transverse_lyapunov_bound(alpha, sigma, max_lambda)
lyapunov_history[t] = worst_case_exponent
# Intrinsic node dynamics driven by localized recovery field
intrinsic_drift = -2 * alpha * (states - TARGET_COHERENCE)
# Algorithmic injection field (high-frequency noise mimicking short-form content)
if t < SWITCH_STEP:
algorithmic_noise = np.random.normal(0, 1.8, NUM_NODES)
else:
algorithmic_noise = np.zeros(NUM_NODES) # Suppressed via Entrainment Kill-Switch
# Standard structural network coupling via Graph Laplacian
network_coupling = -sigma * (L @ states)
# Unified integration step
states += dt * (intrinsic_drift + network_coupling + algorithmic_noise)
return history, lyapunov_history, lambda2, max_lambda
# Execute Simulation
history, lyapunov, lambda2, max_lambda = run_spectral_control_simulation()
# --- VERIFICATION ENGINE ---
print(”[NETWORK TOPOLOGY SPECTRUM]”)
print(f” Fiedler eigenvalue λ2 (algebraic connectivity): {lambda2:.6f}”)
print(f” Max Laplacian eigenvalue λN: {max_lambda:.6f}”)
pre = np.mean(lyapunov[:SWITCH_STEP])
post = np.mean(lyapunov[SWITCH_STEP:])
print(”\n[TRANSVERSE LYAPUNOV BOUND CHECK]”)
print(f” Mean Λ (pre-switch, t < {SWITCH_STEP}): {pre:.6f} -> {’UNSTABLE (Divergent Fragmentation)’ if pre > 0 else ‘STABLE’}”)
print(f” Mean Λ (post-switch, t ≥ {SWITCH_STEP}): {post:.6f} -> {’UNSTABLE’ if post > 0 else ‘STABLE (Coherence Convergent)’}”)
# --- VISUALIZATION ENGINE ---
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 9), sharex=True)
# Plot 1: Node Trajectories (Sovereignty Matrix)
ax1.plot(history, color=’#0066cc’, alpha=0.25, linewidth=0.9)
ax1.axhline(y=TARGET_COHERENCE, color=’#ff3366’, linestyle=’--’, linewidth=2.5,
label=f’Coherence Fixed Point ({TARGET_COHERENCE:.1f})’)
ax1.set_title(’Spectral Control vs Algorithmic Fragmentation (Standard Form)\n”Collapsing Transverse Modes via Balanced Gating and Relational Coupling”’, fontsize=14)
ax1.set_ylabel(’Node Coherence Trajectories ($Z_k$)’, fontsize=11)
ax1.grid(True, linestyle=’:’, alpha=0.5)
ax1.legend(loc=’upper right’)
ax1.text(10, 2.0, f’Phase 1: Algorithmic Fragmentation\n(Mean $\\Lambda_{{max}}$ = {pre:.3f} > 0)’,
fontsize=10, color=’darkred’, weight=’bold’)
ax1.text(SWITCH_STEP + 15, 2.0, f’Phase 2: Protocol Active\n(Mean $\\Lambda_{{max}}$ = {post:.3f} < 0)’,
fontsize=10, color=’darkgreen’, weight=’bold’)
# Plot 2: Corrected Transverse Lyapunov Stability Horizon
ax2.plot(lyapunov, color=’purple’, linewidth=2, label=’Worst-Case Lyapunov Exponent Bound ($\\Lambda_{max}$)’)
ax2.axhline(y=0, color=’black’, linestyle=’-’, linewidth=1.2)
ax2.set_title(’Standard Transverse Stability Threshold Profile’, fontsize=12)
ax2.set_xlabel(’Discrete Operational Time Steps’, fontsize=11)
ax2.set_ylabel(’Lyapunov Exponent ($\\Lambda_k$)’, fontsize=11)
ax2.grid(True, linestyle=’:’, alpha=0.5)
ax2.legend(loc=’lower left’)
plt.tight_layout()
plt.savefig(’spectral_control_output.png’, dpi=150)
plt.show()
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