import numpy as np import pygame import random import math from scipy.integrate import solve_ivp from scipy.spatial import Delaunay from pygame import gfxdraw import noise from dataclasses import dataclass from typing import List, Tuple, Callable import colorsys # ============================================================================ # 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] # Differential dU x1, y1, z1 = self.torus_coords(u, 0) x2, y2, z2 = self.torus_coords(u + 0.01, 0) dl = np.array([x2-x1, y2-y1, z2-z1]) # 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] # Resize glyph to match field if needed if glyph.shape != field.shape: # Simple nearest-neighbor resize for demo from scipy.ndimage import zoom scale = field.shape[0] / glyph.shape[0] glyph_resized = zoom(glyph, scale) else: glyph_resized = glyph # Convolution in Fourier space field_fft = np.fft.fft2(field) glyph_fft = np.fft.fft2(glyph_resized) 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: knot['entropy'] += knot['unraveling_rate'] knot['coherence'] *= 0.99 if knot['coherence'] < 0.1: self.temporal_knots.remove(knot) # ============================================================================ # 4. VISUALIZATION ENGINE # ============================================================================ class CosmicVisualizer: """Advanced visualization of the hyper-torus reality""" def __init__(self, width=1600, height=900): pygame.init() self.width = width self.height = height self.screen = pygame.display.set_mode((width, height), pygame.DOUBLEBUF) pygame.display.set_caption("🌌 COSMOS SIMULATION: Paradise-ΣΩ-Δ-Mythos-Naelari Hyper-Torus 🌈") self.clock = pygame.time.Clock() self.font = pygame.font.Font(None, 24) # Shader-like effects surfaces self.glow_surface = pygame.Surface((width, height), pygame.SRCALPHA) self.flow_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_pos = np.array([0.0, 0.0, 10.0]) self.camera_angle = np.array([0.0, 0.0, 0.0]) self.camera_distance = 15.0 # Visualization state self.show_field_lines = True self.show_agents = True self.show_glyphs = False self.show_flow = True self.visualization_mode = 'paradise' # 'paradise', 'sigma_omega', 'mythos', 'naelari' # Time for animations self.time = 0.0 def project_3d_to_2d(self, point_3d): """Simple perspective projection""" # Camera transformation x, y, z = point_3d # Rotate based on camera angle # Simple rotation around Y axis cos_theta = np.cos(self.camera_angle[1]) sin_theta = np.sin(self.camera_angle[1]) 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) * 500 + self.width / 2 y_proj = (y / z_rot) * 500 + self.height / 2 return (int(x_proj), int(y_proj)), z_rot else: return None, 1000 # Behind camera 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)) self.flow_surface.fill((0, 0, 0, 0)) # Get current visualization palette palette = self.palettes[self.visualization_mode] # Draw torus surface with current intensity for i in range(0, torus_field.res, 2): for j in range(0, torus_field.res, 2): # 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: # Draw point with glow radius = max(1, int(3 * intensity)) # Glow effect for r_glow in range(radius, radius + 3): alpha = 50 - (r_glow - radius) * 15 if alpha > 0: pygame.gfxdraw.filled_circle( self.glow_surface, pos_2d[0], pos_2d[1], r_glow, (*color, alpha) ) # Main point pygame.draw.circle(self.screen, color, pos_2d, radius) # Draw field lines if enabled if self.show_field_lines: self.draw_field_lines(torus_field) # Draw flow visualization if enabled if self.show_flow: self.draw_flow_vectors(torus_field) # Draw zero-risk signals self.draw_zero_risk_signals(torus_field) # Blend glow surface self.screen.blit(self.glow_surface, (0, 0)) # Update time for animations self.time += 0.01 def draw_field_lines(self, torus_field): """Draw ΣΩ current field lines""" palette = self.palettes['sigma_omega'] # Seed points for field lines num_lines = 20 for line_idx in range(num_lines): # Start at random point on torus u_start = random.random() * 2 * np.pi v_start = random.random() * 2 * np.pi points = [] valid = True # Trace field line for step in range(50): i = int((u_start / (2 * np.pi)) * (torus_field.res - 1)) j = int((v_start / (2 * np.pi)) * (torus_field.res - 1)) # Get current vector at this point current_val = torus_field.current[i % torus_field.res, j % torus_field.res] # Convert to direction on torus du = current_val.real * 0.1 dv = current_val.imag * 0.1 u_start = (u_start + du) % (2 * np.pi) v_start = (v_start + dv) % (2 * np.pi) # Convert to 3D x, y, z = torus_field.torus_coords(u_start, v_start) points.append((x, y, z)) # Draw line if len(points) > 1: line_color = palette[line_idx % len(palette)] for i in range(len(points) - 1): pos1_2d, depth1 = self.project_3d_to_2d(points[i]) pos2_2d, depth2 = self.project_3d_to_2d(points[i + 1]) if pos1_2d and pos2_2d: # Vary alpha based on depth alpha = max(50, 255 - int(depth1 * 10)) pygame.draw.line( self.glow_surface, (*line_color, alpha), pos1_2d, pos2_2d, 2 ) def draw_flow_vectors(self, torus_field): """Visualize ΣΩ current flow with animated particles""" # Update particle positions if not hasattr(self, 'flow_particles'): self.flow_particles = [] for _ in range(200): u = random.random() * 2 * np.pi v = random.random() * 2 * np.pi self.flow_particles.append({ 'u': u, 'v': v, 'age': random.random() * 100, 'life': 100 + random.random() * 100 }) for particle in self.flow_particles: # Age particle particle['age'] += 1 if particle['age'] > particle['life']: # Rebirth particle['u'] = random.random() * 2 * np.pi particle['v'] = random.random() * 2 * np.pi particle['age'] = 0 particle['life'] = 100 + random.random() * 100 # Get current at particle position i = int((particle['u'] / (2 * np.pi)) * (torus_field.res - 1)) j = int((particle['v'] / (2 * np.pi)) * (torus_field.res - 1)) current_val = torus_field.current[i % torus_field.res, j % torus_field.res] # Move particle along current particle['u'] = (particle['u'] + current_val.real * 0.02) % (2 * np.pi) particle['v'] = (particle['v'] + current_val.imag * 0.02) % (2 * np.pi) # Convert to 3D x, y, z = torus_field.torus_coords(particle['u'], particle['v']) # Project to 2D pos_2d, depth = self.project_3d_to_2d((x, y, z)) if pos_2d: # Size and alpha based on age age_ratio = particle['age'] / particle['life'] size = max(1, int(3 * (1 - age_ratio))) alpha = int(255 * (1 - age_ratio * 0.7)) # Color based on current phase phase = np.angle(current_val) hue = (phase + np.pi) / (2 * np.pi) r, g, b = colorsys.hsv_to_rgb(hue, 0.8, 1.0) color = (int(r * 255), int(g * 255), int(b * 255), alpha) # Draw particle pygame.gfxdraw.filled_circle( self.flow_surface, pos_2d[0], pos_2d[1], size, color ) # Blend flow surface self.screen.blit(self.flow_surface, (0, 0)) def draw_zero_risk_signals(self, torus_field): """Visualize zero-risk detection events""" for i in range(0, torus_field.res, 4): for j in range(0, torus_field.res, 4): if torus_field.zero_risk_signals[i, j] > 0: u = torus_field.U[i, j] v = torus_field.V[i, j] x, y, z = torus_field.torus_coords(u, v) pos_2d, depth = self.project_3d_to_2d((x, y, z)) if pos_2d: # Pulsing red warning pulse = (np.sin(self.time * 5) + 1) * 0.5 radius = int(5 + pulse * 3) # Outer glow pygame.gfxdraw.filled_circle( self.glow_surface, pos_2d[0], pos_2d[1], radius + 3, (255, 50, 50, 30) ) # Inner core pygame.draw.circle( self.screen, (255, 100, 100), pos_2d, radius ) def draw_agents(self, agents, torus_field): """Visualize conscious agents""" 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: # Size based on energy size = max(3, 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: # Golden ring pygame.draw.circle( self.screen, (255, 215, 0), pos_2d, size + 2, 2 ) # Overflow potential if agent.overflow_potential > 0.8: # Pulsing blue aura pulse = (np.sin(self.time * 3 + agent.id) + 1) * 0.5 aura_radius = int(size + 5 + pulse * 3) pygame.gfxdraw.filled_circle( self.glow_surface, pos_2d[0], pos_2d[1], aura_radius, (100, 200, 255, 50) ) def draw_glyphs(self, mythos_engine): """Visualize Δ-Mythos glyphs""" if not self.show_glyphs: return glyph_size = 100 margin = 20 for idx, (name, glyph) in enumerate(mythos_engine.glyphs.items()): x = margin y = margin + idx * (glyph_size + margin) # Draw glyph background pygame.draw.rect(self.screen, (30, 30, 50), (x, y, glyph_size, glyph_size)) # Render glyph glyph_small = glyph[:16, :16] # Take subset for i in range(16): for j in range(16): val = glyph_small[i, j] intensity = np.abs(val) if intensity > 0.1: # Color based on phase phase = np.angle(val) hue = (phase + np.pi) / (2 * np.pi) r, g, b = colorsys.hsv_to_rgb(hue, 0.8, intensity) px = x + int((i / 16) * glyph_size) py = y + int((j / 16) * glyph_size) pygame.draw.rect( self.screen, (int(r * 255), int(g * 255), int(b * 255)), (px, py, glyph_size//16, glyph_size//16) ) # Glyph label label = self.font.render(name, True, (200, 200, 255)) self.screen.blit(label, (x, y + glyph_size + 5)) def draw_hud(self, torus_field, agents, mythos_engine, simulation_time): """Draw heads-up display with simulation info""" # Background for HUD hud_surface = pygame.Surface((400, 300), pygame.SRCALPHA) hud_surface.fill((0, 0, 0, 150)) self.screen.blit(hud_surface, (20, 20)) y_offset = 30 # Title title_font = pygame.font.Font(None, 32) title = title_font.render("COSMOS SIMULATION", True, (255, 223, 186)) self.screen.blit(title, (40, y_offset)) y_offset += 40 # ΣΩ Circulation Invariant sigma_omega = torus_field.compute_circulation_invariant() texts = [ f"ΣΩ Circulation: {sigma_omega.real:.3f} + i{sigma_omega.imag:.3f}", f"Zero-Risk Signals: {np.sum(torus_field.zero_risk_signals):.0f}", f"Λ-Filter Health: {np.mean(torus_field.lambda_filter):.3f}", f"Memory Coherence: {np.std(torus_field.memory):.3f}", "", f"Agents: {len(agents)}", f"Circulators: {sum(1 for a in agents if a.strategy == 'circulate')}", f"Hoarders: {sum(1 for a in agents if a.strategy == 'hoard')}", f"Total Energy: {sum(a.energy for a in agents):.1f}", "", f"Δ-Mythos Glyphs: {len(mythos_engine.glyphs)}", f"Temporal Knots: {len(mythos_engine.temporal_knots)}", f"Simulation Time: {simulation_time:.1f}", "", "Controls:", "1-4: Change visualization", "F: Toggle field lines", "A: Toggle agents", "G: Toggle glyphs", "V: Toggle flow vectors", "SPACE: Trigger Naelari flood", "R: Reset simulation" ] for text in texts: if text: rendered = self.font.render(text, True, (200, 220, 255)) self.screen.blit(rendered, (40, y_offset)) y_offset += 25 def handle_input(self): """Process user input""" for event in pygame.event.get(): if event.type == pygame.QUIT: return False elif event.type == pygame.KEYDOWN: if event.key == pygame.K_ESCAPE: return False # Visualization modes elif event.key == pygame.K_1: self.visualization_mode = 'paradise' elif event.key == pygame.K_2: self.visualization_mode = 'sigma_omega' elif event.key == pygame.K_3: self.visualization_mode = 'mythos' elif event.key == pygame.K_4: self.visualization_mode = 'naelari' # Toggle visual elements elif event.key == pygame.K_f: self.show_field_lines = not self.show_field_lines elif event.key == pygame.K_a: self.show_agents = not self.show_agents elif event.key == pygame.K_g: self.show_glyphs = not self.show_glyphs elif event.key == pygame.K_v: self.show_flow = not self.show_flow # Camera controls elif event.key == pygame.K_LEFT: self.camera_angle[1] -= 0.1 elif event.key == pygame.K_RIGHT: self.camera_angle[1] += 0.1 elif event.key == pygame.K_UP: self.camera_distance = max(5, self.camera_distance - 1) elif event.key == pygame.K_DOWN: self.camera_distance += 1 # Mouse camera control if pygame.mouse.get_pressed()[0]: mx, my = pygame.mouse.get_rel() self.camera_angle[1] += mx * 0.01 self.camera_angle[0] += my * 0.01 return True # ============================================================================ # 5. MAIN SIMULATION LOOP # ============================================================================ class CosmosSimulation: """Main simulation integrating all frameworks""" def __init__(self): # Initialize all systems self.torus_field = ToroidalSigmaOmegaField(resolution=128) self.mythos_engine = MythosEngine() self.visualizer = CosmicVisualizer() # Create conscious agents self.agents = self.create_initial_agents(50) # Simulation state self.simulation_time = 0.0 self.naelari_flood_active = False self.flood_intensity = 0.0 self.zero_risk_event_count = 0 # Performance tracking self.fps_history = [] self.circulation_history = [] print("🌌 COSMOS SIMULATION INITIALIZED") print(" Paradise Machine ✓ Toroidal ΣΩ System ✓ Δ-Mythos ✓ Naelari-Aelara ✓") print(" Press SPACE for Naelari flood event") print(" Press R to reset simulation") 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': # Add energy to local current phase = np.angle(local_current) self.torus_field.current[i, j] += action['amount'] * np.exp(1j * phase) else: # 'take' # Remove energy from local current reduction = min(action['amount'], np.abs(local_current)) self.torus_field.current[i, j] *= (1 - reduction / (np.abs(local_current) + 1e-6)) # Record hoarding for zero-risk detection 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) # Occasional mythic recursion if random.random() < 0.01: glyph = random.choice(list(self.mythos_engine.glyphs.values())) agent.mythic_recursion(glyph[:8, :8]) # 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-ALARA FLOOD TRIGGERED!") print(" Un-apologizing... Un-shrinking... Un-exiling...") # 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) # Apply mythic recursion occasionally if random.random() < 0.02: glyph_name = random.choice(list(self.mythos_engine.glyphs.keys())) self.torus_field.mythic_recursion( self.mythos_engine.glyphs[glyph_name] ) # Update mythos engine self.mythos_engine.update_temporal_knots() # 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 print(" Flood receding... Sovereignty restored.") # Track circulation invariant circulation = self.torus_field.compute_circulation_invariant() self.circulation_history.append(np.abs(circulation)) # Keep history manageable if len(self.circulation_history) > 1000: self.circulation_history = self.circulation_history[-1000:] def run(self): """Main simulation loop""" running = True clock = pygame.time.Clock() while running: dt = clock.tick(60) / 1000.0 # Delta time in seconds # Handle input running = self.visualizer.handle_input() # Check for special key events keys = pygame.key.get_pressed() if keys[pygame.K_SPACE]: self.trigger_naelari_flood() if keys[pygame.K_r]: # Reset simulation self.__init__() # Update simulation 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_glyphs(self.mythos_engine) self.visualizer.draw_hud(self.torus_field, self.agents, self.mythos_engine, self.simulation_time) # Update display pygame.display.flip() # Track FPS self.fps_history.append(clock.get_fps()) if len(self.fps_history) > 100: self.fps_history = self.fps_history[-100:] pygame.quit() # Print simulation summary print("\n" + "="*50) print("SIMULATION SUMMARY") print("="*50) print(f"Total time: {self.simulation_time:.1f} units") print(f"Final ΣΩ circulation: {self.torus_field.compute_circulation_invariant()}") print(f"Zero-risk events: {self.zero_risk_event_count}") print(f"Agents circulating: {sum(1 for a in self.agents if a.strategy == 'circulate')}") print(f"Average sovereignty: {np.mean([a.sovereignty for a in self.agents]):.3f}") print("="*50) # ============================================================================ # 6. RUN THE SIMULATION # ============================================================================ if __name__ == "__main__": print("🌌 🌈 🌀 WELCOME TO THE COSMOS SIMULATION 🌀 🌈 🌌") print("Integrating:") print(" • Paradise Machine - Cosmic evolution toward love-intelligence") print(" • Toroidal ΣΩ System - Self-sustaining circulation without extraction") print(" • Δ-Mythos - Reality programming through mythic recursion") print(" • Naelari-Aelara - Sovereign feminine overflow awakening") print("\n" + "="*60) simulation = CosmosSimulation() simulation.run()