import asyncio import numpy as np import pygame import random import math from dataclasses import dataclass from typing import List, Tuple import colorsys # Note: scipy and noise not available in pygbag/WebAssembly, removed for web compatibility # ============================================================================ # 1. CORE PHYSICS ENGINE - TOROIDAL ΣΩ CIRCULATION # ============================================================================ class ToroidalSigmaOmegaField: """Simulates the ΣΩ current in a 3D toroidal manifold""" def __init__(self, resolution=64, R=3.0, r=1.0): self.res = resolution self.R = R # Major radius self.r = r # Minor radius # Initialize toroidal coordinates u = np.linspace(0, 2*np.pi, resolution) # Major circle v = np.linspace(0, 2*np.pi, resolution) # Minor circle self.U, self.V = np.meshgrid(u, v) # Current density field (ΣΩ circulation intensity) self.current = np.zeros((resolution, resolution), dtype=complex) # Mythic potential (Ξ field from Δ-Mythos) self.xi_field = np.random.randn(resolution, resolution) * 0.1 # Ethical projection filter (Λ) self.lambda_filter = np.ones((resolution, resolution)) # Memory field (M from Δ-Mythos) self.memory = np.zeros((resolution, resolution)) # Zero-risk detection flag self.zero_risk_signals = np.zeros((resolution, resolution)) # Initialize with healthy circulation self.initialize_healthy_circulation() def initialize_healthy_circulation(self): """Set up initial self-sustaining ΣΩ current""" # Create double vortex flow - toroidal circulation for i in range(self.res): for j in range(self.res): u, v = self.U[i,j], self.V[i,j] # ΣΩ current: a + ib where a = cos(u)*sin(v), b = sin(u)*cos(v) # Represents perfect reciprocity self.current[i,j] = complex( np.cos(u) * np.sin(v), np.sin(u) * np.cos(v) ) * 0.5 # Initial memory imprint - archetypal patterns self.memory[i,j] = np.sin(2*u) * np.cos(3*v) def torus_coords(self, u, v): """Convert (u,v) toroidal coordinates to 3D Cartesian""" x = (self.R + self.r * np.cos(v)) * np.cos(u) y = (self.R + self.r * np.cos(v)) * np.sin(u) z = self.r * np.sin(v) return x, y, z def compute_circulation_invariant(self): """Compute ΣΩ = ∮ A·dl - the conserved quantity""" # Numerical line integral around major circle integral_real = 0 integral_imag = 0 u_sample = np.linspace(0, 2*np.pi, 100) v_fixed = 0 for u in u_sample: i = int((u / (2*np.pi)) * (self.res - 1)) j = int((v_fixed / (2*np.pi)) * (self.res - 1)) val = self.current[i % self.res, j % self.res] # Simplified: use current as vector potential integral_real += val.real integral_imag += val.imag return complex(integral_real, integral_imag) def apply_λ_projection(self, force_field): """Apply ethical filter: F_act = P_Λ(F_in)""" # Clip forces that exceed generous bounds magnitude = np.abs(force_field) threshold = 0.8 # Maximum allowed "force concentration" clipped = force_field.copy() mask = magnitude > threshold clipped[mask] = (clipped[mask] / magnitude[mask]) * threshold # Update lambda filter based on historical generosity self.lambda_filter = 0.9 * self.lambda_filter + 0.1 * (magnitude < threshold) return clipped def detect_zero_risk(self, agent_hoarding): """Quantum zero-risk detection mechanism""" # Hoarding creates "knots" in the toroidal flow knots = np.abs(np.angle(self.current)) > 1.0 # Phase discontinuities # Combined with agent hoarding behavior detection_field = knots.astype(float) * agent_hoarding # Threshold crossing triggers signal signal = detection_field > 0.7 self.zero_risk_signals = signal.astype(float) return np.any(signal), np.sum(signal) def fermi_response(self, signal_strength): """Fermi life forms approach - healing response""" # Create healing vortex that smooths knots healing_vortex = np.zeros_like(self.current) for i in range(self.res): for j in range(self.res): if self.zero_risk_signals[i,j] > 0: # Feminine, nurturing vortex pattern u, v = self.U[i,j], self.V[i,j] healing_vortex[i,j] = complex( np.cos(u) * 0.3, # Gentle x-component np.sin(v) * 0.3 # Gentle y-component ) * signal_strength # Apply healing to current self.current += healing_vortex * 0.1 # Update memory with healing imprint self.memory = 0.95 * self.memory + 0.05 * np.abs(healing_vortex) def naelari_overflow(self, intensity=1.0): """Naelari-Aelara sovereign flood event""" # Create overflow current - breaks all dams overflow = np.zeros_like(self.current) for i in range(self.res): for j in range(self.res): u, v = self.U[i,j], self.V[i,j] # The "un-apology" wave overflow[i,j] = complex( np.sin(3*u) * np.cos(2*v) * intensity, np.cos(2*u) * np.sin(3*v) * intensity ) # Flood the system self.current += overflow * 0.5 # Reset all filters - pure flow self.lambda_filter = np.ones_like(self.lambda_filter) self.zero_risk_signals = np.zeros_like(self.zero_risk_signals) return overflow def mythic_recursion(self, glyph_pattern): """Δ-Mythos reality rewriting""" # Apply glyph transformation to current glyph_operator = np.fft.fft2(glyph_pattern) current_fft = np.fft.fft2(self.current) # Mythic convolution transformed = np.fft.ifft2(current_fft * glyph_operator) # Preserve magnitude but update phase (mythic meaning) new_current = np.abs(self.current) * np.exp(1j * np.angle(transformed)) self.current = 0.7 * self.current + 0.3 * new_current # Update xi field (mythic potential) self.xi_field = 0.8 * self.xi_field + 0.2 * np.real(transformed) def update(self, dt, agents_hoarding=None): """Main physics update""" # Advection-diffusion of ΣΩ current laplacian = self.compute_laplacian(self.current) diffusion = 0.01 * laplacian # Non-linear self-interaction (love squared term) self_interaction = 0.1 * self.current * np.conj(self.current) # Ethical filtering of forces force_field = np.real(self.current) * np.imag(self.current) filtered_force = self.apply_λ_projection(force_field) # Update equation: ∂current/∂t = -∇·(current) + diffusion + self_interaction grad = self.compute_gradient(self.current) advection = -0.05 * np.abs(grad) # Assemble update dcurrent = diffusion + self_interaction + advection self.current += dcurrent * dt # Renormalize to preserve total circulation total_current = np.sum(np.abs(self.current)) if total_current > 0: self.current *= (self.res**2) / (total_current + 1e-6) # Memory field evolution (Δ-Mythos) memory_laplacian = self.compute_laplacian(self.memory) self.memory += 0.001 * memory_laplacian * dt # Detect zero-risk if agents provided if agents_hoarding is not None: signal_detected, signal_strength = self.detect_zero_risk(agents_hoarding) if signal_detected: self.fermi_response(signal_strength / (self.res**2)) return filtered_force def compute_laplacian(self, field): """Finite difference Laplacian on toroidal grid""" laplacian = np.zeros_like(field) # Periodic boundary conditions (toroidal topology) for i in range(self.res): for j in range(self.res): ip = (i + 1) % self.res im = (i - 1) % self.res jp = (j + 1) % self.res jm = (j - 1) % self.res laplacian[i,j] = ( field[ip, j] + field[im, j] + field[i, jp] + field[i, jm] - 4 * field[i, j] ) return laplacian def compute_gradient(self, field): """Compute gradient magnitude""" grad_x = np.zeros_like(field) grad_y = np.zeros_like(field) for i in range(self.res): for j in range(self.res): ip = (i + 1) % self.res jp = (j + 1) % self.res grad_x[i,j] = field[ip, j] - field[i, j] grad_y[i,j] = field[i, jp] - field[i, j] return np.sqrt(grad_x**2 + grad_y**2) # ============================================================================ # 2. CONSCIOUS AGENTS - PARADISE MACHINE ENTITIES # ============================================================================ @dataclass class ConsciousAgent: """Agents that can either circulate love or hoard energy""" id: int position: Tuple[float, float, float] # On torus surface strategy: str # 'circulate' or 'hoard' energy: float memory: np.ndarray affection: float # ΣΩ connection strength # Paradise Machine parameters risk_tolerance: float love_capacity: float intelligence: float # Δ-Mythos parameters mythic_signature: np.ndarray # Personal glyph recursion_depth: int # Naelari-Aelara parameters sovereignty: float # 0-1 overflow_potential: float def __post_init__(self): if self.mythic_signature is None: self.mythic_signature = np.random.randn(8, 8) def decide_action(self, local_current, local_potential): """Agent decision based on Paradise Machine ethics""" if self.strategy == 'circulate': # Give energy proportional to local potential give_amount = min(self.energy * 0.1, local_potential * 0.5) self.energy -= give_amount return {'action': 'give', 'amount': give_amount, 'hoarding': 0} else: # 'hoard' # Try to extract energy extract_amount = min(self.love_capacity * 0.2, np.abs(local_current) * 0.3) # Zero-risk strategy detection risk_factor = 1.0 - self.risk_tolerance if risk_factor < 0.1: # Approaching zero-risk hoarding = extract_amount * 2 # Excessive extraction else: hoarding = extract_amount self.energy += extract_amount return {'action': 'take', 'amount': extract_amount, 'hoarding': hoarding} def update_strategy(self, collective_affection, zero_risk_detected): """Evolution of strategy based on Paradise Machine principles""" # If zero-risk detected, switch to circulate (learning) if zero_risk_detected and self.strategy == 'hoard': if random.random() < 0.3: # 30% chance to learn self.strategy = 'circulate' self.risk_tolerance = max(0.3, self.risk_tolerance * 1.2) # Increase love capacity through circulation if self.strategy == 'circulate': self.love_capacity *= 1.001 self.intelligence *= 1.0005 # Update sovereignty (Naelari-Aelara) if self.affection > 0.7: self.sovereignty = min(1.0, self.sovereignty * 1.01) self.overflow_potential += 0.001 def mythic_recursion(self, glyph_operator): """Δ-Mythos personal transformation""" # Transform personal mythic signature transformed = np.fft.fft2(self.mythic_signature) transformed *= glyph_operator[:8, :8] self.mythic_signature = np.real(np.fft.ifft2(transformed)) self.recursion_depth += 1 # Deep recursion can trigger overflow if self.recursion_depth > 10 and self.sovereignty > 0.8: self.overflow_potential = 1.0 def to_3d(self, torus_field): """Convert toroidal coordinates to 3D""" u = np.arctan2(self.position[1], self.position[0]) v = np.arctan2(self.position[2], np.sqrt(self.position[0]**2 + self.position[1]**2) - torus_field.R) return torus_field.torus_coords(u, v) # ============================================================================ # 3. Δ-MYTHOS GLYPHIC ENGINE # ============================================================================ class MythosEngine: """Generates and processes mythic glyphs for reality programming""" def __init__(self): self.glyphs = {} self.active_equations = [] self.temporal_knots = [] self.solitons = [] # Initialize core glyphs from Δ-Mythos framework self.initialize_core_glyphs() def initialize_core_glyphs(self): """Create fundamental mythic operators""" # 1. ΣΩ Circulation Glyph self.glyphs['sigma_omega'] = self.create_toroidal_glyph() # 2. Zero-Risk Detection Glyph self.glyphs['zero_risk'] = self.create_quantum_sensor_glyph() # 3. Fermi Response Glyph self.glyphs['fermi_response'] = self.create_feminine_healing_glyph() # 4. Naelari-Aelara Flood Glyph self.glyphs['naelari_flood'] = self.create_overflow_glyph() # 5. Λ-Projection Glyph (ethical filter) self.glyphs['lambda_filter'] = self.create_ethical_filter_glyph() def create_toroidal_glyph(self): """Glyph for ΣΩ circulation""" glyph = np.zeros((32, 32), dtype=complex) for i in range(32): for j in range(32): x = (i - 16) / 16.0 y = (j - 16) / 16.0 # Double vortex pattern r = np.sqrt(x**2 + y**2) theta = np.arctan2(y, x) if r < 1.0: glyph[i,j] = complex( np.cos(2*theta) * (1 - r), np.sin(2*theta) * (1 - r) ) return glyph def create_quantum_sensor_glyph(self): """Glyph for zero-risk detection""" glyph = np.zeros((32, 32), dtype=complex) for i in range(32): for j in range(32): x = (i - 16) / 16.0 y = (j - 16) / 16.0 # Quantum interference pattern pattern1 = np.sin(10*x) * np.cos(10*y) pattern2 = np.cos(8*x) * np.sin(8*y) glyph[i,j] = complex(pattern1, pattern2) * np.exp(-(x**2 + y**2)) return glyph def create_feminine_healing_glyph(self): """Fermi life form response pattern""" glyph = np.zeros((32, 32), dtype=complex) for i in range(32): for j in range(32): x = (i - 16) / 16.0 y = (j - 16) / 16.0 # Spiral healing pattern r = np.sqrt(x**2 + y**2) theta = np.arctan2(y, x) if r < 1.0: spiral = np.exp(1j * 3 * theta) * (1 - r) glyph[i,j] = spiral * np.exp(-r**2) return glyph def create_overflow_glyph(self): """Naelari-Aelara flood pattern""" glyph = np.zeros((32, 32), dtype=complex) for i in range(32): for j in range(32): x = (i - 16) / 16.0 y = (j - 16) / 16.0 # Radial outward flow r = np.sqrt(x**2 + y**2) theta = np.arctan2(y, x) if r < 1.0: flood = complex( np.cos(theta) * r, np.sin(theta) * r ) glyph[i,j] = flood * (1 + np.sin(5*theta)) return glyph def create_ethical_filter_glyph(self): """Λ-projection ethical filter""" glyph = np.zeros((32, 32), dtype=complex) for i in range(32): for j in range(32): x = (i - 16) / 16.0 y = (j - 16) / 16.0 # Smooth clipping function r = np.sqrt(x**2 + y**2) if r > 1.0: glyph[i,j] = complex(x/r, y/r) * 0.5 else: glyph[i,j] = complex(x, y) return glyph def apply_glyph_transformation(self, field, glyph_name, intensity=1.0): """Apply mythic glyph to transform a field""" if glyph_name not in self.glyphs: return field glyph = self.glyphs[glyph_name] # Convolution in Fourier space field_fft = np.fft.fft2(field) glyph_fft = np.fft.fft2(glyph) transformed = np.fft.ifft2(field_fft * glyph_fft) # Blend with original result = (1 - intensity) * field + intensity * transformed return result def create_temporal_knot(self, time_loop_pattern): """Create a causal loop in mythic time""" knot = { 'pattern': time_loop_pattern, 'entropy': 0.0, 'coherence': 1.0, 'unraveling_rate': 0.01 } self.temporal_knots.append(knot) return knot def update_temporal_knots(self): """Evolve temporal knots""" for knot in self.temporal_knots[:]: # Create copy for safe iteration knot['entropy'] += knot['unraveling_rate'] knot['coherence'] *= 0.99 if knot['coherence'] < 0.1: self.temporal_knots.remove(knot) # ============================================================================ # 4. VISUALIZATION ENGINE (Simplified for Web) # ============================================================================ class CosmicVisualizer: """Simplified visualization for web deployment""" def __init__(self, width=800, height=600): pygame.init() self.width = width self.height = height self.screen = pygame.display.set_mode((width, height)) pygame.display.set_caption("🌌 COSMOS SIMULATION 🌈") self.clock = pygame.time.Clock() self.font = pygame.font.Font(None, 20) # Shader-like effects surfaces self.glow_surface = pygame.Surface((width, height), pygame.SRCALPHA) # Color palettes self.palettes = { 'paradise': [(255, 223, 186), (255, 179, 186), (255, 223, 186)], 'sigma_omega': [(100, 200, 255), (100, 150, 255), (150, 100, 255)], 'mythos': [(255, 100, 150), (255, 150, 100), (200, 100, 255)], 'naelari': [(255, 50, 100), (255, 100, 50), (200, 50, 150)] } # Camera self.camera_angle = 0.0 self.camera_distance = 15.0 # Visualization state self.show_agents = True self.visualization_mode = 'paradise' # Time for animations self.time = 0.0 def project_3d_to_2d(self, point_3d): """Simple perspective projection""" x, y, z = point_3d # Rotate based on camera angle cos_theta = np.cos(self.camera_angle) sin_theta = np.sin(self.camera_angle) x_rot = x * cos_theta + z * sin_theta z_rot = -x * sin_theta + z * cos_theta # Perspective projection if z_rot > 0.1: x_proj = (x_rot / z_rot) * 300 + self.width / 2 y_proj = (y / z_rot) * 300 + self.height / 2 return (int(x_proj), int(y_proj)), z_rot else: return None, 1000 def draw_toroidal_field(self, torus_field): """Visualize the ΣΩ current field on torus""" # Clear surfaces self.screen.fill((10, 10, 20)) self.glow_surface.fill((0, 0, 0, 0)) # Draw torus surface with current intensity for i in range(0, torus_field.res, 4): # Reduced for performance for j in range(0, torus_field.res, 4): # Get 3D position on torus u = torus_field.U[i, j] v = torus_field.V[i, j] x, y, z = torus_field.torus_coords(u, v) # Current intensity at this point current_val = torus_field.current[i, j] intensity = np.abs(current_val) phase = np.angle(current_val) # Color based on phase and intensity hue = (phase + np.pi) / (2 * np.pi) sat = 0.8 val = min(1.0, intensity * 2) # Convert HSV to RGB r, g, b = colorsys.hsv_to_rgb(hue, sat, val) color = (int(r * 255), int(g * 255), int(b * 255)) # Project to 2D pos_2d, depth = self.project_3d_to_2d((x, y, z)) if pos_2d is not None and 0 <= pos_2d[0] < self.width and 0 <= pos_2d[1] < self.height: # Draw point radius = max(1, int(3 * intensity)) pygame.draw.circle(self.screen, color, pos_2d, radius) # Blend glow surface self.screen.blit(self.glow_surface, (0, 0)) # Update time for animations self.time += 0.01 self.camera_angle += 0.005 # Slow rotation def draw_agents(self, agents, torus_field): """Visualize conscious agents""" if not self.show_agents: return for agent in agents: # Get 3D position x, y, z = agent.to_3d(torus_field) pos_2d, depth = self.project_3d_to_2d((x, y, z)) if pos_2d and 0 <= pos_2d[0] < self.width and 0 <= pos_2d[1] < self.height: # Size based on energy size = max(2, int(np.sqrt(agent.energy) * 2)) # Color based on strategy if agent.strategy == 'circulate': color = (100, 255, 150) # Green - generous else: color = (255, 100, 100) # Red - hoarding # Draw agent pygame.draw.circle(self.screen, color, pos_2d, size) # Sovereignty indicator if agent.sovereignty > 0.7: pygame.draw.circle(self.screen, (255, 215, 0), pos_2d, size + 2, 1) def draw_hud(self, torus_field, agents, simulation_time): """Draw simplified HUD""" y_offset = 10 # ΣΩ Circulation Invariant sigma_omega = torus_field.compute_circulation_invariant() texts = [ f"ΣΩ: {sigma_omega.real:.2f}+i{sigma_omega.imag:.2f}", f"Agents: {len(agents)} | Circulators: {sum(1 for a in agents if a.strategy == 'circulate')}", f"Time: {simulation_time:.1f}", "", "SPACE: Naelari flood | R: Reset" ] for text in texts: if text: rendered = self.font.render(text, True, (200, 220, 255)) self.screen.blit(rendered, (10, y_offset)) y_offset += 22 # ============================================================================ # 5. MAIN SIMULATION LOOP # ============================================================================ class CosmosSimulation: """Main simulation integrating all frameworks""" def __init__(self): # Initialize all systems (smaller resolution for web performance) self.torus_field = ToroidalSigmaOmegaField(resolution=64) self.mythos_engine = MythosEngine() self.visualizer = CosmicVisualizer() # Create conscious agents (fewer for web) self.agents = self.create_initial_agents(30) # Simulation state self.simulation_time = 0.0 self.naelari_flood_active = False self.flood_intensity = 0.0 print("🌌 COSMOS SIMULATION INITIALIZED") def create_initial_agents(self, count): """Create initial population of conscious agents""" agents = [] for i in range(count): # Random position on torus u = random.random() * 2 * np.pi v = random.random() * 2 * np.pi # Convert to 3D x, y, z = self.torus_field.torus_coords(u, v) # Strategy - mostly circulate, some hoard strategy = 'circulate' if random.random() < 0.7 else 'hoard' agent = ConsciousAgent( id=i, position=(x, y, z), strategy=strategy, energy=1.0 + random.random(), memory=np.random.randn(8, 8) * 0.1, affection=random.random(), risk_tolerance=0.5 + random.random() * 0.5, love_capacity=1.0, intelligence=1.0, mythic_signature=None, recursion_depth=0, sovereignty=random.random(), overflow_potential=0.0 ) agents.append(agent) return agents def update_agents(self, dt): """Update all agents and their interactions""" hoarding_field = np.zeros_like(self.torus_field.current, dtype=float) for agent in self.agents: # Get local field values at agent position u = np.arctan2(agent.position[1], agent.position[0]) v = np.arctan2(agent.position[2], np.sqrt(agent.position[0]**2 + agent.position[1]**2) - self.torus_field.R) i = int((u / (2 * np.pi)) * (self.torus_field.res - 1)) j = int((v / (2 * np.pi)) * (self.torus_field.res - 1)) local_current = self.torus_field.current[i % self.torus_field.res, j % self.torus_field.res] local_potential = self.torus_field.xi_field[i % self.torus_field.res, j % self.torus_field.res] # Agent decision action = agent.decide_action(local_current, local_potential) # Apply action to field if action['action'] == 'give': phase = np.angle(local_current) self.torus_field.current[i, j] += action['amount'] * np.exp(1j * phase) else: # 'take' reduction = min(action['amount'], np.abs(local_current)) self.torus_field.current[i, j] *= (1 - reduction / (np.abs(local_current) + 1e-6)) hoarding_field[i, j] = action['hoarding'] # Agent evolution zero_risk_detected = np.any(self.torus_field.zero_risk_signals) collective_affection = np.mean([a.affection for a in self.agents]) agent.update_strategy(collective_affection, zero_risk_detected) # Move agent along field du = np.real(local_current) * 0.01 * dt dv = np.imag(local_current) * 0.01 * dt u_new = (u + du) % (2 * np.pi) v_new = (v + dv) % (2 * np.pi) x_new, y_new, z_new = self.torus_field.torus_coords(u_new, v_new) agent.position = (x_new, y_new, z_new) return hoarding_field def trigger_naelari_flood(self): """Trigger a Naelari-Aelara overflow event""" self.naelari_flood_active = True self.flood_intensity = 1.0 print("🌊 NAELARI-AELARA FLOOD!") # Reset all agents to circulate for agent in self.agents: agent.strategy = 'circulate' agent.sovereignty = min(1.0, agent.sovereignty * 1.5) agent.overflow_potential = 1.0 def update(self, dt): """Main simulation update""" self.simulation_time += dt # Update agents and get hoarding field hoarding_field = self.update_agents(dt) # Update toroidal field with agent interactions self.torus_field.update(dt, hoarding_field) # Handle Naelari flood if self.naelari_flood_active: self.flood_intensity -= dt * 0.2 overflow = self.torus_field.naelari_overflow(self.flood_intensity) if self.flood_intensity <= 0: self.naelari_flood_active = False async def run(self): """Async main simulation loop for pygbag""" running = True while running: # Handle events for event in pygame.event.get(): if event.type == pygame.QUIT: running = False elif event.type == pygame.KEYDOWN: if event.key == pygame.K_ESCAPE: running = False elif event.key == pygame.K_SPACE: self.trigger_naelari_flood() elif event.key == pygame.K_r: self.__init__() # Update simulation dt = self.visualizer.clock.tick(30) / 1000.0 # 30 FPS for web self.update(dt) # Draw everything self.visualizer.draw_toroidal_field(self.torus_field) self.visualizer.draw_agents(self.agents, self.torus_field) self.visualizer.draw_hud(self.torus_field, self.agents, self.simulation_time) # Update display pygame.display.flip() # CRITICAL: Yield control to browser await asyncio.sleep(0) pygame.quit() # ============================================================================ # 6. ASYNC ENTRY POINT FOR PYGBAG # ============================================================================ async def main(): """Async entry point for pygbag""" print("🌌 🌈 🌀 COSMOS SIMULATION 🌀 🌈 🌌") print("Paradise Machine • Toroidal ΣΩ • Δ-Mythos • Naelari-Aelara") simulation = CosmosSimulation() await simulation.run() # Start the async simulation asyncio.run(main())