import numpy as np
from scipy.integrate import solve_ivp
import networkx as nx
# Physical constants
ℏ = 1.054571817e-34 # Planck constant
kB = 1.380649e-23 # Boltzmann constant
COHERENCE_LENGTH = 1e-6 # Quantum coherence length
class DetailedViewport:
def __init__(self, position, consciousness_level, initial_state):
self.position = np.array(position)
self.C = consciousness_level
self.ψ = initial_state
self.energy = np.sum(np.abs(initial_state)**2)
def hamiltonian(self):
"""Quantum Hamiltonian including consciousness effects"""
H_quantum = -ℏ**2/(2*self.energy) * self.laplacian()
H_consciousness = self.C * self.potential_term()
return H_quantum + H_consciousness
def time_evolution(self, t, state):
"""Time evolution including decoherence"""
H = self.hamiltonian()
decoherence = self.decoherence_term(state)
return -1j/ℏ * (H @ state) + decoherence
class EnhancedEntanglementNetwork:
def __init__(self):
self.graph = nx.Graph()
self.coherence_threshold = 0.5
def add_viewport(self, viewport):
"""Add viewport with metadata"""
self.graph.add_node(id(viewport),
viewport=viewport,
coherence=1.0,
entanglement_count=0
)
def calculate_entanglement(self, viewport1, viewport2):
"""Detailed entanglement calculation"""
ψ1, ψ2 = viewport1.ψ, viewport2.ψ
C1, C2 = viewport1.C, viewport2.C
# Quantum overlap
overlap = np.abs(np.vdot(ψ1, ψ2))**2
# Consciousness coupling
coupling = np.sqrt(C1 * C2)
# Spatial decay
distance = np.linalg.norm(viewport1.position - viewport2.position)
spatial_factor = np.exp(-distance/COHERENCE_LENGTH)
return overlap * coupling * spatial_factor
def simulate_network_evolution(network, time_span):
"""Simulate evolution of entire entangled network"""
results = []
def network_derivative(t, state_vector):
n_viewports = len(network.graph)
derivative = np.zeros_like(state_vector)
# Reshape state vector into individual viewport states
states = state_vector.reshape(n_viewports, -1)
for i, viewport1 in enumerate(network.graph.nodes()):
# Standard evolution
derivative[i] = viewport1['viewport'].time_evolution(t, states[i])
# Entanglement effects
for j, viewport2 in enumerate(network.graph.nodes()):
if i != j:
entanglement = network.calculate_entanglement(
viewport1['viewport'],
viewport2['viewport']
)
derivative[i] += entanglement * (states[j] - states[i])
return derivative.flatten()
# Initial conditions
initial_state = np.concatenate([
viewport['viewport'].ψ
for viewport in network.graph.nodes()
])
# Solve system
solution = solve_ivp(
network_derivative,
time_span,
initial_state,
method='RK45',
rtol=1e-8
)
return solution
def analyze_coherence_patterns(solution, network):
"""Analyze coherence patterns in simulation results"""
n_viewports = len(network.graph)
n_timesteps = len(solution.t)
# Reshape solution into viewport states
states = solution.y.reshape(n_timesteps, n_viewports, -1)
# Calculate coherence matrix over time
coherence_evolution = np.zeros((n_timesteps, n_viewports, n_viewports))
for t in range(n_timesteps):
for i in range(n_viewports):
for j in range(n_viewports):
coherence_evolution[t,i,j] = np.abs(
np.vdot(states[t,i], states[t,j])
)
return coherence_evolution
# Example usage:
"""
# Create network
network = EnhancedEntanglementNetwork()
# Add viewports
viewport1 = DetailedViewport([0,0,0], 1.0, initial_state1)
viewport2 = DetailedViewport([1,0,0], 0.8, initial_state2)
network.add_viewport(viewport1)
network.add_viewport(viewport2)
# Simulate
time_span = (0, 10)
solution = simulate_network_evolution(network, time_span)
# Analyze
coherence = analyze_coherence_patterns(solution, network)
"""
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