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