#!/usr/bin/env python3 """ ENE TriangleManifold Integration Applies concentric triangular manifold concepts to ENE neural manifold ingestion Per AGENTS.md §6.1: Python shim for integration only """ import sqlite3 from pathlib import Path from typing import Dict, List, Optional, Tuple from dataclasses import dataclass from datetime import datetime import json @dataclass class TriangleShell: """Triangular shell representing a level of neural manifold abstraction""" shell_index: int # k: shell index triangular_number: int # Tₖ = k(k+1)/2 vertices: List[float] # [a, b, c] triangle vertices mass: float # a * b * c (triple product) rotation_angle: float # θ: rotation angle in radians bandwidth: float # Transmission bandwidth latency: float # Transmission latency @dataclass class TransmissionPoint: """Transmission point between neural manifold shells""" source_shell: int target_shell: int vertex: int # Which vertex connects (0, 1, or 2) efficiency: float # bandwidth / latency @dataclass class ENETriangleManifold: """ENE neural manifold modeled as concentric triangular shells""" max_shell: int curvature: float shells: List[TriangleShell] transmission_points: List[TransmissionPoint] total_bandwidth: float total_latency: float class ENETriangleManifoldIntegrator: """ Integrates TriangleManifold concepts with ENE neural manifold ingestion. Key insight: - ENE neural manifold domain modeled as concentric triangular shells - Each shell represents a level of abstraction (session → concept → anchor) - Transmission points represent data flow between abstraction levels - Rotation field represents neural manifold transformation """ def __init__(self, db_path: str = None, max_shell: int = 10, curvature: float = 0.5): self.db_path = db_path or Path("shared-data/data/substrate_index.db") self.max_shell = max_shell self.curvature = curvature self.manifold = None def triangular_number(self, k: int) -> int: """Compute triangular number: Tₖ = k(k+1)/2""" return k * (k + 1) // 2 def create_shell(self, k: int, rotation_angle: float = 0.0) -> TriangleShell: """Create a triangular shell at index k""" t_num = self.triangular_number(k) # Triangle vertices based on shell geometry # a = offset within shell, b = shell width - offset, c = shell index a = float(k % 3) b = float((k + 1) - (k % 3)) c = float(k) mass = a * b * c return TriangleShell( shell_index=k, triangular_number=t_num, vertices=[a, b, c], mass=mass, rotation_angle=rotation_angle, bandwidth=10.0 * (1.0 - self.curvature), # Bandwidth decreases with curvature latency=1.0 + self.curvature # Latency increases with curvature ) def create_transmission_network(self, shells: List[TriangleShell]) -> List[TransmissionPoint]: """Create transmission points between shells""" points = [] for i in range(len(shells) - 1): source = shells[i] target = shells[i + 1] # Create transmission points for each vertex for vertex in range(3): efficiency = target.bandwidth / target.latency points.append(TransmissionPoint( source_shell=source.shell_index, target_shell=target.shell_index, vertex=vertex, efficiency=efficiency )) return points def build_manifold(self) -> ENETriangleManifold: """Build the ENE neural manifold as concentric triangular shells""" shells = [] for k in range(self.max_shell + 1): rotation_angle = float(k) * 0.1 # Incremental rotation per shell shell = self.create_shell(k, rotation_angle) shells.append(shell) transmission_points = self.create_transmission_network(shells) total_bandwidth = sum(tp.efficiency for tp in transmission_points) total_latency = sum(1.0 / tp.efficiency for tp in transmission_points) if transmission_points else 0.0 self.manifold = ENETriangleManifold( max_shell=self.max_shell, curvature=self.curvature, shells=shells, transmission_points=transmission_points, total_bandwidth=total_bandwidth, total_latency=total_latency ) return self.manifold def transmit_data(self, data: float, source_shell: int, target_shell: int) -> float: """Transmit data through the manifold from source to target shell""" if not self.manifold: self.build_manifold() # Find transmission path path = [tp for tp in self.manifold.transmission_points if tp.source_shell == source_shell and tp.target_shell == target_shell] if not path: return data # No direct path tp = path[0] return data * tp.efficiency def compute_rotation_field(self, data: float) -> float: """Compute manifold rotation field for data""" if not self.manifold: self.build_manifold() # Sum over all shells: Σ mass * rotation field_sum = sum(shell.mass * shell.rotation_angle for shell in self.manifold.shells) # Divide by curvature denominator denom = 1.0 + self.curvature ** 2 return field_sum / denom def compute_transmission_field(self, data: float) -> float: """Compute manifold transmission field (rotation + transmission)""" rotation_field = self.compute_rotation_field(data) # Add transmission contribution transmission_sum = sum(self.transmit_data(data, tp.source_shell, tp.target_shell) for tp in self.manifold.transmission_points) return rotation_field + transmission_sum def map_package_to_shell(self, pkg_data: Dict) -> int: """Map an ENE package to a triangular shell based on its properties""" # Use foam_score or metric to determine shell level foam_score = pkg_data.get('foam_score', 0.0) # Map foam_score to shell index (0 to max_shell) shell_index = min(int(foam_score * self.max_shell), self.max_shell) return shell_index def ingest_with_manifold(self, pkg_data: Dict) -> Dict: """Ingest a package with manifold field computation""" if not self.manifold: self.build_manifold() shell_index = self.map_package_to_shell(pkg_data) shell = self.manifold.shells[shell_index] # Compute manifold fields for this package data_value = pkg_data.get('foam_score', 0.0) rotation_field = self.compute_rotation_field(data_value) transmission_field = self.compute_transmission_field(data_value) # Enhance package data with manifold information pkg_data['manifold_shell'] = shell_index pkg_data['manifold_rotation_field'] = rotation_field pkg_data['manifold_transmission_field'] = transmission_field pkg_data['manifold_mass'] = shell.mass pkg_data['manifold_vertices'] = shell.vertices return pkg_data def get_manifold_stats(self) -> Dict: """Get statistics about the neural manifold""" if not self.manifold: self.build_manifold() return { 'max_shell': self.manifold.max_shell, 'curvature': self.manifold.curvature, 'total_shells': len(self.manifold.shells), 'total_transmission_points': len(self.manifold.transmission_points), 'total_bandwidth': self.manifold.total_bandwidth, 'total_latency': self.manifold.total_latency, 'average_mass': sum(s.mass for s in self.manifold.shells) / len(self.manifold.shells), 'total_mass': sum(s.mass for s in self.manifold.shells) } # CLI interface if __name__ == "__main__": print("=" * 60) print("ENE TRIANGLE MANIFOLD INTEGRATION") print("=" * 60) integrator = ENETriangleManifoldIntegrator(max_shell=10, curvature=0.5) manifold = integrator.build_manifold() print(f"\nManifold Statistics:") stats = integrator.get_manifold_stats() for key, value in stats.items(): print(f" {key}: {value}") print(f"\nSample Transmission:") data = 1.0 transmitted = integrator.transmit_data(data, 0, 1) print(f" Data: {data} → Shell 0 to Shell 1: {transmitted}") print(f"\nSample Rotation Field:") rotation_field = integrator.compute_rotation_field(1.0) print(f" Rotation field: {rotation_field}") print(f"\nSample Transmission Field:") transmission_field = integrator.compute_transmission_field(1.0) print(f" Transmission field: {transmission_field}")