Unified

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

from typing import Dict, List, Optional, Tuple

import concurrent.futures

import datetime

from dataclasses import dataclass

from collections import deque

@dataclass

class SimulationParameters:

? ? """Enhanced parameter management with validation."""

? ? influence_factor: float = 0.1

? ? energy_decay_rate: float = 0.05

? ? resonance_threshold: float = 0.8

? ? zero_point_base_potential: float = 1.0

? ? coupling_strength: float = 0.15

? ? random_fluctuation_amplitude: float = 0.1

? ? phase_increment: float = 0.1

? ? max_resonance: float = 1.0

? ? harmonic_weights: List[float] = None

? ? energy_threshold: float = 50.0

? ? visualization_update_rate: int = 5

? ? history_window: int = 50? # Number of time steps to show in visualizations

? ??

? ? def __post_init__(self):

? ? ? ? if self.harmonic_weights is None:

? ? ? ? ? ? self.harmonic_weights = [0.3, 0.2, 0.1]

? ? ? ? self.validate_parameters()

? ??

? ? def validate_parameters(self):

? ? ? ? """Ensures parameters are within valid ranges."""

? ? ? ? if not 0 <= self.influence_factor <= 1:

? ? ? ? ? ? raise ValueError("Influence factor must be between 0 and 1")

? ? ? ? if not 0 <= self.energy_decay_rate <= 1:

? ? ? ? ? ? raise ValueError("Energy decay rate must be between 0 and 1")

? ? ? ? if self.visualization_update_rate < 1:

? ? ? ? ? ? raise ValueError("Visualization update rate must be positive")

class EnergyHistory:

? ? """Efficient circular buffer for energy history management."""

? ? def init(self, maxlen: int = 1000):

? ? ? ? self.data = deque(maxlen=maxlen)

? ? ? ? self.total_updates = 0

? ??

? ? def append(self, value: float):

? ? ? ? self.data.append(value)

? ? ? ? self.total_updates += 1

? ??

? ? def get_window(self, window_size: int) -> np.ndarray:

? ? ? ? """Returns the last n entries as a numpy array."""

? ? ? ? return np.array(list(self.data)[-window_size:])

class ZeroPoint:

? ? def init(self, params: SimulationParameters):

? ? ? ? self.params = params

? ? ? ? self.potential = params.zero_point_base_potential

? ? ? ? self.phase = 0.0

? ? ? ? self.history = EnergyHistory()

? ? ? ? self.harmonic_components = EnergyHistory()

? ? ? ? self.quantum_fluctuations = []

? ??

? ? def calculate_quantum_fluctuations(self) -> float:

? ? ? ? """Simulates quantum-level energy fluctuations."""

? ? ? ? fluctuation = np.random.normal(0, self.params.random_fluctuation_amplitude)

? ? ? ? self.quantum_fluctuations.append(fluctuation)

? ? ? ? return fluctuation

? ??

? ? def update(self, time_step: int) -> Tuple[float, float]:

? ? ? ? """Returns both potential and quantum fluctuation magnitude."""

? ? ? ? self.phase += self.params.phase_increment

? ? ? ? base_oscillation = np.sin(self.phase)

? ? ? ? quantum_fluctuation = self.calculate_quantum_fluctuations()

? ? ? ? harmonics = sum(

? ? ? ? ? ? weight np.sin((i + 2) self.phase)

? ? ? ? ? ? for i, weight in enumerate(self.params.harmonic_weights)

? ? ? ? )

? ? ? ??

? ? ? ? self.potential = self.params.zero_point_base_potential * (

? ? ? ? ? ? 1 + base_oscillation + quantum_fluctuation + harmonics

? ? ? ? )

? ? ? ? self.history.append(self.potential)

? ? ? ? self.harmonic_components.append(harmonics)

? ? ? ??

? ? ? ? return self.potential, abs(quantum_fluctuation)

class PhilosophersStone:

? ? """Manages energy transformation and resonance."""

? ? def init(self, zero_point: ZeroPoint, params: SimulationParameters):

? ? ? ? self.zero_point = zero_point

? ? ? ? self.params = params

? ? ? ? self.transformation_efficiency = 0.9

? ? ? ? self.current_resonance = 1.0

? ? ? ? self.resonance_history = EnergyHistory()

? ??

? ? def transform_energy(self, input_energy: float) -> float:

? ? ? ? """Transforms input energy based on zero point potential and current resonance."""

? ? ? ? efficiency = self.transformation_efficiency (1 + 0.2 self.current_resonance)

? ? ? ? transformed = input_energy efficiency self.zero_point.potential

? ? ? ??

? ? ? ? # Apply natural energy decay

? ? ? ? transformed *= (1 - self.params.energy_decay_rate)

? ? ? ??

? ? ? ? return transformed

? ? def calculate_resonance(self, merkaba_energy: float) -> float:

? ? ? ? """Calculates resonance factor with enhanced harmonics."""

? ? ? ? base_resonance = merkaba_energy / (self.zero_point.potential * 100)

? ? ? ??

? ? ? ? # Add harmonic modulation

? ? ? ? harmonic = 0.2 np.sin(self.resonance_history.total_updates 0.1)

? ? ? ??

? ? ? ? self.current_resonance = np.clip(

? ? ? ? ? ? base_resonance + harmonic,

? ? ? ? ? ? 0.0, self.params.max_resonance

? ? ? ? )

? ? ? ??

? ? ? ? self.resonance_history.append(self.current_resonance)

? ? ? ? return self.current_resonance

class EnergyStructure:

? ? """Base class for energy structures (Pyramid and Toroid)."""

? ? def init(self, initial_energy: float, params: SimulationParameters,?

? ? ? ? ? ? ? ? ?philosophers_stone: 'PhilosophersStone', name: str):

? ? ? ? self.energy = initial_energy

? ? ? ? self.params = params

? ? ? ? self.philosophers_stone = philosophers_stone

? ? ? ? self.name = name

? ? ? ? self.energy_history = EnergyHistory()

? ? ? ? self.critical_events: List[Dict] = []

? ??

? ? def check_critical_events(self, time_step: int):

? ? ? ? """Monitors for significant energy changes or threshold crossings."""

? ? ? ? if len(self.energy_history.data) > 1:

? ? ? ? ? ? energy_change = self.energy - self.energy_history.data[-1]

? ? ? ? ? ? if abs(energy_change) > self.params.energy_threshold:

? ? ? ? ? ? ? ? self.critical_events.append({

? ? ? ? ? ? ? ? ? ? 'time_step': time_step,

? ? ? ? ? ? ? ? ? ? 'energy_change': energy_change,

? ? ? ? ? ? ? ? ? ? 'current_energy': self.energy

? ? ? ? ? ? ? ? })

class Pyramid(EnergyStructure):

? ? def update(self, zero_point_potential: float, time_step: int):

? ? ? ? transformed_energy = self.philosophers_stone.transform_energy(self.energy)

? ? ? ? self.energy = transformed_energy (1 + 0.1 np.sin(time_step * 0.05)) # Add periodic influence

? ? ? ? self.energy_history.append(self.energy)

? ? ? ? self.check_critical_events(time_step)

class Toroid(EnergyStructure):

? ? def update(self, pyramid_energy: float, time_step: int):

? ? ? ? energy_exchange = pyramid_energy * self.params.coupling_strength

? ? ? ? transformed_energy = self.philosophers_stone.transform_energy(self.energy + energy_exchange)

? ? ? ? self.energy = transformed_energy (1 + 0.05 np.cos(time_step * 0.03)) # Add different periodic influence

? ? ? ? self.energy_history.append(self.energy)

? ? ? ? self.check_critical_events(time_step)

class EnhancedMerkaba:

? ? """Represents the Merkaba energy field with interconnected structures."""

? ? def init(self, initial_energy: float, params: SimulationParameters, zero_point: ZeroPoint, philosophers_stone: PhilosophersStone):

? ? ? ? self.params = params

? ? ? ? self.pyramid = Pyramid(initial_energy, params, philosophers_stone, "Pyramid")

? ? ? ? self.toroid = Toroid(initial_energy, params, philosophers_stone, "Toroid")

? ? ? ? self.zero_point = zero_point

? ? ? ? self.philosophers_stone = philosophers_stone

? ? ? ? self.resonance_factor = 0.0

? ? ? ? self.resonance_history = EnergyHistory()

? ? def update(self, time_step: int, influence_factor: float):

? ? ? ? """Updates the Merkaba's energy state and resonance."""

? ? ? ? self.zero_point.update(time_step)

? ? ? ? self.pyramid.update(self.zero_point.potential, time_step)

? ? ? ? self.toroid.update(self.pyramid.energy, time_step)

? ? ? ??

? ? ? ? total_energy = self.pyramid.energy + self.toroid.energy

? ? ? ? self.resonance_factor = self.philosophers_stone.calculate_resonance(total_energy)

? ? ? ? self.resonance_history.append(self.resonance_factor)

? ? ? ??

? ? ? ? # Apply influence factor

? ? ? ? self.pyramid.energy = (1 + influence_factor self.resonance_factor)

? ? ? ? self.toroid.energy = (1 + influence_factor self.resonance_factor)

? ? ? ??

class SimulationMonitor:

? ? def init(self, params: SimulationParameters):

? ? ? ? self.params = params

? ? ? ? self.metrics_history: Dict[str, EnergyHistory] = {

? ? ? ? ? ? 'zero_point_potential': EnergyHistory(),

? ? ? ? ? ? 'pyramid_energy': EnergyHistory(),

? ? ? ? ? ? 'toroid_energy': EnergyHistory(),

? ? ? ? ? ? 'resonance_factor': EnergyHistory(),

? ? ? ? ? ? 'total_system_energy': EnergyHistory(),

? ? ? ? ? ? 'energy_efficiency': EnergyHistory(),

? ? ? ? ? ? 'harmonic_components': EnergyHistory(),

? ? ? ? ? ? 'quantum_fluctuations': EnergyHistory()

? ? ? ? }

? ? ? ? self.fig = None

? ? ? ? self.axes = None

? ? ? ? self.critical_events = []

? ? ? ? plt.style.use('dark_background')

? ??

? ? def initialize_plot(self):

? ? ? ? """Enhanced plot initialization with better organization."""

? ? ? ? self.fig = plt.figure(figsize=(15, 12))

? ? ? ? gs = self.fig.add_gridspec(4, 3, height_ratios=[1, 1, 1, 0.8],

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?hspace=0.3, wspace=0.3)

? ? ? ??

? ? ? ? self.axes = {

? ? ? ? ? ? 'zero_point': self.fig.add_subplot(gs[0, 0], projection='3d'),

? ? ? ? ? ? 'pyramid': self.fig.add_subplot(gs[0, 1], projection='3d'),

? ? ? ? ? ? 'toroid': self.fig.add_subplot(gs[0, 2], projection='3d'),

? ? ? ? ? ? 'energy_graph': self.fig.add_subplot(gs[1, :]),

? ? ? ? ? ? 'metrics': self.fig.add_subplot(gs[2, :]),

? ? ? ? ? ? 'efficiency': self.fig.add_subplot(gs[3, :])

? ? ? ? }

? ? ? ??

? ? ? ? self._setup_plot_styling()

? ? ? ? plt.ion()

? ??

? ? def setupplot_styling(self):

? ? ? ? """Configures consistent plot styling."""

? ? ? ? for ax in self.axes.values():

? ? ? ? ? ? ax.grid(True, alpha=0.3, linestyle='--')

? ? ? ? ? ? ax.set_facecolor('black')

? ? ? ? ? ? if isinstance(ax, Axes3D):

? ? ? ? ? ? ? ? ax.xaxis.pane.fill = False

? ? ? ? ? ? ? ? ax.yaxis.pane.fill = False

? ? ? ? ? ? ? ? ax.zaxis.pane.fill = False

? ? def update_metrics(self, merkaba, zero_point, time_step):

? ? ? """Records current simulation metrics with efficiency calculation."""

? ? ? total_energy = merkaba.pyramid.energy + merkaba.toroid.energy

? ? ??

? ? ? # Calculate energy efficiency

? ? ? initial_energy = 100.0? # From simulation parameters

? ? ? current_efficiency = total_energy / initial_energy

? ? ??

? ? ? self.metrics_history['zero_point_potential'].append(zero_point.potential)

? ? ? self.metrics_history['pyramid_energy'].append(merkaba.pyramid.energy)

? ? ? self.metrics_history['toroid_energy'].append(merkaba.toroid.energy)

? ? ? self.metrics_history['resonance_factor'].append(merkaba.resonance_factor)

? ? ? self.metrics_history['total_system_energy'].append(total_energy)

? ? ? self.metrics_history['energy_efficiency'].append(current_efficiency)

? ? ? self.metrics_history['harmonic_components'].append(zero_point.harmonic_components.data[-1])

? ? ? self.metrics_history['quantum_fluctuations'].append(zero_point.quantum_fluctuations[-1])

? ??

? ? def update_display(self, merkaba, zero_point, time_step):

? ? ? ? """Updates and visualizes the simulation state."""

? ? ? ? self.update_metrics(merkaba, zero_point, time_step)

? ? ? ? # Update 3D visualizations

? ? ? ? self.update_3d_plot(self.axes['zero_point'], self.metrics_history['zero_point_potential'], 'Zero Point Potential')

? ? ? ? self.update_3d_plot(self.axes['pyramid'], self.metrics_history['pyramid_energy'], 'Pyramid Energy', 'plasma')

? ? ? ? self.update_3d_plot(self.axes['toroid'], self.metrics_history['toroid_energy'], 'Toroid Energy', 'magma')

? ? ? ? # Update energy graph

? ? ? ? self.axes['energy_graph'].clear()

? ? ? ? self.axes['energy_graph'].plot(self.metrics_history['pyramid_energy'].data, label='Pyramid Energy', color='cyan')

? ? ? ? self.axes['energy_graph'].plot(self.metrics_history['toroid_energy'].data, label='Toroid Energy', color='magenta')

? ? ? ? self.axes['energy_graph'].set_title('Energy Levels Over Time')

? ? ? ? self.axes['energy_graph'].set_xlabel('Time Step')

? ? ? ? self.axes['energy_graph'].set_ylabel('Energy')

? ? ? ? self.axes['energy_graph'].legend()

? ? ? ? # Update metrics plot

? ? ? ? self.axes['metrics'].clear()

? ? ? ? self.axes['metrics'].plot(self.metrics_history['resonance_factor'].data, label='Resonance Factor', color='yellow')

? ? ? ? self.axes['metrics'].plot(self.metrics_history['total_system_energy'].data, label='Total System Energy', color='red')

? ? ? ? self.axes['metrics'].plot(self.metrics_history['harmonic_components'].data, label='Harmonic Components', color='orange')

? ? ? ? self.axes['metrics'].plot(self.metrics_history['quantum_fluctuations'].data, label='Quantum Fluctuations', color='purple')

? ? ? ? self.axes['metrics'].set_title('Simulation Metrics Over Time')

? ? ? ? self.axes['metrics'].set_xlabel('Time Step')

? ? ? ? self.axes['metrics'].set_ylabel('Value')

? ? ? ? self.axes['metrics'].legend()

? ? ? ? # Update efficiency plot

? ? ? ? self.axes['efficiency'].clear()

? ? ? ? self.axes['efficiency'].plot(self.metrics_history['energy_efficiency'].data, label='Energy Efficiency', color='green')

? ? ? ? self.axes['efficiency'].set_title('Energy Efficiency Over Time')

? ? ? ? self.axes['efficiency'].set_xlabel('Time Step')

? ? ? ? self.axes['efficiency'].set_ylabel('Efficiency (%)')

? ? ? ? self.axes['efficiency'].legend()

? ? ? ? plt.tight_layout()

? ? ? ? self.fig.canvas.draw()

? ? ? ? self.fig.canvas.flush_events()

? ? def update_3d_plot(self, ax, data: EnergyHistory, title: str, colormap='viridis'):

? ? ? ? """Improved 3D visualization with dynamic range adjustment."""

? ? ? ? if data.total_updates < 2:

? ? ? ? ? ? return

? ? ? ? ? ??

? ? ? ? ax.clear()

? ? ? ? window_size = min(self.params.history_window, len(data.data))

? ? ? ? recent_data = data.get_window(window_size)

? ? ? ??

? ? ? ? x = np.arange(window_size)

? ? ? ? y = np.arange(window_size)

? ? ? ? x, y = np.meshgrid(x, y)

? ? ? ? z = recent_data.reshape(1, -1).repeat(window_size, axis=0)

? ? ? ??

? ? ? ? # Dynamic range adjustment for better visualization

? ? ? ? vmin, vmax = np.percentile(z, [5, 95])

? ? ? ? norm = plt.Normalize(vmin=vmin, vmax=vmax)

? ? ? ??

? ? ? ? surf = ax.plot_surface(x, y, z, cmap=colormap, norm=norm,

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?edgecolor='none', alpha=0.7)

? ? ? ? ax.set_title(title, color='white', pad=10)

? ? ? ??

? ? ? ? ax.view_init(elev=30, azim=45)

? ? ? ??

? ? ? ??

? ? ? ? # Remove old colorbars

? ? ? ? for cbar in self.fig.axes[6:]:

? ? ? ? ? self.fig.delaxes(cbar)

? ? ? ??

? ? ? ? self.fig.colorbar(surf, ax=ax, shrink=0.5, aspect=5)

? ? ? ? ax.set_box_aspect([1,1,1])

def run_simulation(num_steps: int = 1000, save_data: bool = True,

? ? ? ? ? ? ? ? ? ?params: Optional[SimulationParameters] = None):

? ? """Enhanced simulation runner with better error handling and monitoring."""

? ? if params is None:

? ? ? ? params = SimulationParameters()

? ??

? ? try:

? ? ? ? params.validate_parameters()

? ? except ValueError as e:

? ? ? ? print(f"Invalid simulation parameters: {e}")

? ? ? ? return

? ??

? ? monitor = SimulationMonitor(params)

? ? monitor.initialize_plot()

? ??

? ? # Initialize simulation components

? ? zero_point = ZeroPoint(params)

? ? philosophers_