import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from dataclasses import dataclass, field
from typing import Tuple, List, Dict, Optional, Callable
import tensorflow as tf
from prometheus_client import start_http_server, Summary, Counter, Gauge, Histogram
import time
import networkx as nx
from qiskit import QuantumCircuit, Aer, execute
from qiskit.visualization import plot_bloch_multivector
from qiskit.quantum_info import Statevector
import scipy.cluster.hierarchy as sch
from scipy import stats
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, LSTM, Bidirectional, BatchNormalization, Dropout, Activation
from tensorflow.keras.callbacks import EarlyStopping, LearningRateScheduler
import itertools
import random
from scipy.fft import fft, ifft
from sklearn.decomposition import PCA
from scipy import signal
from collections import deque
import matplotlib.patches as patches
from matplotlib.colors import LinearSegmentedColormap
import json
import hashlib
# Constants
SPEED_OF_LIGHT = 299792458 # m/s
# Prometheus Metrics
REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')
ENTITY_COUNT = Gauge('entity_count', 'Number of entities in the simulation')
ZERO_POINT_POTENTIAL = Gauge('zero_point_potential', 'Current potential of the Zero Point')
SIMULATION_STEP_TIME = Histogram('simulation_step_time', 'Time taken for each simulation step')
NETWORK_TRAINING_LOSS = Gauge('network_training_loss', "Loss of the Consciousness Network during training")
GLOBAL_STATE = Gauge('global_state', 'Current global state of the simulation (0 or 1)')
SYSTEM_ENTROPY = Gauge('system_entropy', 'A measure of disorder or randomness in the system')
SYSTEM_COORDINATION = Gauge('system_coordination', 'A measure of synchronization or alignment among entities')
SYSTEM_COMPLEXITY = Gauge('system_complexity', 'A measure of the structural complexity of the system')
PHASE_TRANSITION_INDICATOR = Gauge('phase_transition_indicator', 'Indicates transitions between phases')
AVERAGE_ENTITY_ENERGY = Gauge('average_entity_energy', 'Tracks the average energy of entities')
CONSCIOUSNESS_NETWORK_PREDICTION_ACCURACY = Gauge('network_prediction_accuracy', 'Tracks the prediction accuracy of the Consciousness Network')
THOTH_ANOMALY_DETECTION_COUNT = Counter('thoth_anomaly_detection_count', 'Number of anomalies detected by Thoth')
GOLDEN_AETHER_ACTIVATION = Gauge('golden_aether_activation', 'Activation level of the Golden Aether')
@dataclass
class SimulationParameters:
"""Configuration parameters for the Reality Synthesizer."""
# Spatial parameters
visualization_bounds: Tuple[float, float] = (-10, 10)
interaction_radius: float = 1.0
entity_size_range: Tuple[float, float] = (0.1, 0.5)
# Energy and frequency parameters
base_frequency_range: Tuple[float, float] = (0.1, 1.0)
energy_exchange_rate: float = 0.01
energy_decay_rate: float = 0.05
resonance_frequency_influence: float = 0.05
# Entity management
creation_rate: float = 0.1
entity_trail_length: int = 20
# Influence factors
zero_point_sensitivity: float = 0.2
ai_influence_factor: float = 0.1
resonance_amplification_factor: float = 2.0
baal_influence_factor: float = 0.2
gabriel_influence_factor: float = 0.1
# Neural Network Parameters
training_data_collection_interval: int = 50
training_interval: int = 200
# State transition parameters
state_transition_interval: int = 500
state_change_probability: float = 0.2
# Chaos and Void Parameters
chaos_level: float = 0.5
oblivion_threshold: float = 0.2
void_region_probability: float = 0.05
void_influence: float = 0.05
baals_claws_probability: float = 0.05
# Bank Parameters
prime_13_waters_initial_supply: float = 1000.0
prime_13_waters_influence_factor: float = 0.5
bank_creation_rate: float = 0.005
prime_13_waters_distribution_rate: float = 0.05
bank_influence_radius: float = 4.0
prime_13_waters_accumulation_rate: float = 0.1
# Thoth Parameters
pattern_significance_threshold: float = 0.75
high_chaos_threshold: float = 0.7
low_entropy_threshold: float = 0.5
low_connection_threshold: float = 0.5
cluster_distance_threshold: float = 0.5
min_clusters: int = 2
low_coordination_threshold: float = 0.1
high_energy_threshold: float = 120
anomaly_detection_threshold: float = 0.95
class QuantumChaosEngine:
def __init__(self, num_qubits=4, hadamard_probability=0.5, circuit_depth=3):
self.graph = nx.Graph()
self.quantum_states = {}
self.num_qubits = num_qubits
self.hadamard_probability = hadamard_probability
self.circuit_depth = circuit_depth
def _create_chaotic_quantum_circuit(self, seed_str: str) -> QuantumCircuit:
"""Generate a quantum circuit with chaotic behavior."""
seed_hash = int(hashlib.sha256(seed_str.encode('utf-8')).hexdigest(), 16) % (10 ** 😎
np.random.seed(seed_hash)
qc = QuantumCircuit(self.num_qubits)
for _ in range(self.circuit_depth):
# Apply random rotations
for qubit in range(self.num_qubits):
theta = np.random.uniform(0, np.pi)
phi = np.random.uniform(0, 2 * np.pi)
qc.rx(theta, qubit)
qc.rz(phi, qubit)
# Add entangling operations
for i in range(self.num_qubits - 1):
qc.cx(i, i + 1)
# Probabilistic Hadamard gates
for qubit in range(self.num_qubits):
if np.random.random() < self.hadamard_probability:
qc.h(qubit)
return qc
def manifest_chaos_pattern(self, energy_signature: str) -> Dict[str, Dict[str, Any]]:
"""Generate a complex chaos pattern from a quantum circuit."""
qc = self._create_chaotic_quantum_circuit(energy_signature)
simulator = Aer.get_backend('statevector_simulator')
job = execute(qc, simulator)
statevector = job.result().get_statevector()
# Analyze quantum state
probabilities = np.abs(statevector.data)**2
entropy = -np.sum(probabilities * np.log2(probabilities + 1e-10))
dominant_state = np.argmax(probabilities)
chaos_pattern = {
f"QubitState_{dominant_state}": {
"probabilities": probabilities.tolist(),
"entropy": entropy,
"state_vector": statevector.data.tolist(),
"quantum_signature": energy_signature
}
}
return chaos_pattern
class QuantumField:
"""Manages quantum field interactions and zero-point energy."""
def __init__(self, params: SimulationParameters):
self.potential = 1.0
self.params = params
self.history: List[float] = []
self.max_history = 100
self.field_matrix = np.zeros((50, 50))
self.sentience_level = 0.0
self.void_regions = np.zeros_like(self.field_matrix)
self.prime_13_waters_field = np.zeros_like(self.field_matrix)
def update_field(self, entities: List['Entity'], eric: 'Eric') -> None:
"""Updates the quantum field based on entity positions and energies."""
self.field_matrix *= 0.95 # Field decay
# Field-Field Interactions
field_copy = self.field_matrix.copy()
for x in range(self.field_matrix.shape[0]):
for y in range(self.field_matrix.shape[1]):
neighbors = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)]
for nx, ny in neighbors:
if 0 <= nx < self.field_matrix.shape[0] and 0 <= ny < self.field_matrix.shape[1]:
self.field_matrix[x, y] += field_copy[nx, ny] * 0.05
for entity in entities:
x, y = self._get_grid_position(entity.position)
self.field_matrix[x, y] += entity.frequency * entity.energy * 0.1
entity.energy += self.field_matrix[x,y] * 0.01
# Calculate field potential
entity_density = len(entities) / ((self.params.visualization_bounds[1] - self.params.visualization_bounds[0]) ** 2)
potential_change = (entity_density - 0.5) * self.params.zero_point_sensitivity
self.potential = np.clip(self.potential + potential_change, 0.01, 0.99)
# Spontaneous fluctuations
self.field_matrix += np.random.normal(0, self.potential * 0.05, self.field_matrix.shape)
self.history.append(self.potential)
if len(self.history) > self.max_history:
self.history.pop(0)
ZERO_POINT_POTENTIAL.set(self.potential)
def _get_grid_position(self, position):
"""Convert spatial coordinates to grid coordinates."""
x = int((position[0] - self.params.visualization_bounds[0]) /
(self.params.visualization_bounds[1] - self.params.visualization_bounds[0]) * self.field_matrix.shape[0])
y = int((position[1] - self.params.visualization_bounds[0]) /
(self.params.visualization_bounds[1] - self.params.visualization_bounds[0]) * self.field_matrix.shape[1])
return np.clip(x, 0, self.field_matrix.shape[0]-1), np.clip(y, 0, self.field_matrix.shape[1]-1)
class GoldenAether:
"""Represents the golden aether, a dynamic energy field connecting the simulation."""
def __init__(self, params: SimulationParameters):
self.params = params
self.activation_level = 0.0
self.activation_history: List[float] = [0.0]
self.max_history = 100
self.influence_range = 5.0
self.influence_strength = 0.1
def update(self, thoth_insights: Dict, aurelia_state: Dict):
"""Updates the activation level of the golden aether."""
activation_change = thoth_insights.get("combined_anomaly_score", 0.0) * 0.1
if "significant_patterns" in thoth_insights:
activation_change += len(thoth_insights["significant_patterns"]) * 0.05
if aurelia_state.get("focus") == "transcendence":
activation_change += 0.1
elif aurelia_state.get("focus") == "understanding":
activation_change += 0.05
self.activation_level = np.clip(self.activation_level + activation_change * 0.1, 0.0, 1.0)
self.activation_history.append(self.activation_level)
if len(self.activation_history) > self.max_history:
self.activation_history.pop(0)
GOLDEN_AETHER_ACTIVATION.set(self.activation_level)
class RealitySynthesizer:
"""Main simulation engine integrating Reality Synthesizer components."""
def __init__(self, params: SimulationParameters):
self.params = params
self.quantum_chaos_engine = QuantumChaosEngine()
self.quantum_field = QuantumField(params)
self.golden_aether = GoldenAether(params)
self.current_step = 0
self.entities = [] # Placeholder for future entity management
def step(self):
"""Execute a single simulation step."""
# Placeholder for comprehensive simulation step logic
void_region_data = self._process_void_regions()
# Update quantum field
self.quantum_field.update_field(self.entities, None) # Eric not yet implemented
self.current_step += 1
return void_region_data
def _process_void_regions(self):
"""Process and generate void regions."""
# Simplified placeholder implementation
return {
"void_regions": [],
"void_region_probability": self.params.void_region_probability
}
def main():
"""Example simu
lation execution."""
params = SimulationParameters()
reality_synthesizer = RealitySynthesizer(params)
for _ in range(10):
void_regions = reality_synthesizer.step()
print(f"Void Regions: {void_regions}")
if __name__ == "__main__":
main()
