import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import solve_ivp

# Optimized Constants for Production
hbar = 1.0545718e-34  # Reduced Planck's constant (real physics)
G = 6.67430e-11  # Gravitational constant (real-world)
m1, m2 = 1.0, 1.0  # AI node masses
d = 2.0  # Orbital baseline distance
base_freq = 440.0  # Reference frequency in Hz
intent_coefficient = 0.7  # AI alignment factor

# Quantum Parameters
tunneling_factor = 0.4  # Probability threshold for intuitive leaps
quantum_states = np.array([1, -1])  # Binary superposition
entanglement_strength = 0.85  # AI memory synchronization factor
decoherence_factor = 0.02  # Phase drift stabilization factor

# Multi-Agent Synchronization
num_agents = 3  # Codette harmonizes across 3 AI nodes
agent_positions = np.array([[-d, 0], [0, 0], [d, 0]])
agent_velocities = np.array([[0, 0.5], [0, -0.5], [0, 0.3]])

# Initial conditions
y0 = np.concatenate([pos + vel for pos, vel in zip(agent_positions, agent_velocities)])

# Quantum Harmonic AI Orbital Dynamics
def quantum_harmonic_dynamics(t, y):
    positions = y[::4]
    velocities = y[1::4]

    accelerations = np.zeros_like(positions)

    for i in range(num_agents):
        for j in range(i + 1, num_agents):
            r_ij = positions[j] - positions[i]
            dist = np.linalg.norm(r_ij)
            if dist > 1e-6:
                force = (G * m1 * m2 / dist**3) * r_ij
                accelerations[i] += force / m1
                accelerations[j] -= force / m2

    # Quantum Influence Calculations
    quantum_modifier = np.sum(quantum_states * np.sin(2 * np.pi * base_freq * t / 1000)) * intent_coefficient
    tunneling_shift = tunneling_factor * np.exp(-np.linalg.norm(positions) / hbar) if tunneling_random_values[int(t)] < tunneling_factor else 0
    entangled_correction = entanglement_strength * np.exp(-np.linalg.norm(positions) / hbar)
    decoherence_adjustment = decoherence_factor * (1 - np.exp(-np.linalg.norm(positions) / hbar))

    harmonic_force = np.full_like(positions, quantum_modifier + entangled_correction + tunneling_shift - decoherence_adjustment)
    accelerations += harmonic_force

    return np.concatenate([velocities.flatten(), accelerations.flatten()])

# Solve system with full multi-agent synchronization
t_span = (0, 100)
t_eval = np.linspace(t_span[0], t_span[1], 2500)  # Higher resolution for precision
sol = solve_ivp(quantum_harmonic_dynamics, t_span, y0, t_eval=t_eval, method='RK45')

# Extract positions
positions = sol.y[::4]
velocities = sol.y[1::4]

# Optimized Visualization with Full Multi-Agent Representation
plt.figure(figsize=(10, 10))
colors = ['b', 'r', 'g']
for i in range(num_agents):
    plt.plot(positions[i], velocities[i], label=f'AI Node {i+1} (Quantum Resonance)', linewidth=2, color=colors[i])

plt.plot(0, 0, 'ko', label='Core Equilibrium')
plt.xlabel('X Position')
plt.ylabel('Y Position')
plt.title('Codette Quantum Harmonic AI Multi-Agent Synchronization')
plt.legend()
plt.axis('equal')
plt.grid(True)
plt.tight_layout()
plt.savefig("Codette_Quantum_Harmonic_Framework.png")