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The Trignum Pyramid: Core geometric structure.
The pyramid is a tetrahedron with three magnetic faces (α, β, γ) and an apex
where the Trignomed Tensor collapses into Sovereign Reality.
"""
import math
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple
from .faces import FaceAlpha, FaceBeta, FaceGamma
from .magnetic_field import MagneticField
class PyramidState(Enum):
"""States of the Trignum Pyramid."""
IDLE = "idle"
SEPARATING = "separating"
VACUUM_FORMING = "vacuum_forming"
COLLAPSING = "collapsing"
SOVEREIGN = "sovereign"
FROZEN = "frozen"
@dataclass
class TrignumOutput:
"""The output of a Trignum Pyramid processing cycle."""
result: Any
logic_component: float
illogic_component: float
context_component: float
state: PyramidState
confidence: float
freeze_coordinates: Optional[List[float]] = None
metadata: Dict[str, Any] = field(default_factory=dict)
class TrignumPyramid:
"""
The Trignum Pyramid: A tetrahedron with three magnetic faces.
Face α (Alpha, Logic) — Attracts coherent patterns (+)
Face β (Beta, Illogic) — Attracts contradictions (−), creates vacuum
Face γ (Gamma, Context) — Attracts human sovereignty (~)
The Apex is where the three streams reconnect into a Trignomed Tensor.
"""
def __init__(
self,
logic_strength: float = 1.0,
illogic_strength: float = 1.0,
context_strength: float = 1.0,
freeze_threshold: float = 0.7,
):
"""
Initialize the Trignum Pyramid.
Args:
logic_strength: Magnetic strength of Face α (0.0 to 2.0)
illogic_strength: Magnetic strength of Face β (0.0 to 2.0)
context_strength: Magnetic strength of Face γ (0.0 to 2.0)
freeze_threshold: Illogic ratio that triggers THE FREEZE (0.0 to 1.0)
"""
self.face_alpha = FaceAlpha(strength=logic_strength)
self.face_beta = FaceBeta(strength=illogic_strength)
self.face_gamma = FaceGamma(strength=context_strength)
self.magnetic_field = MagneticField(
self.face_alpha, self.face_beta, self.face_gamma
)
self.freeze_threshold = freeze_threshold
self.state = PyramidState.IDLE
self._history: List[TrignumOutput] = []
def process(
self,
data: Any,
human_pulse: Optional[float] = None,
) -> TrignumOutput:
"""
Process data through Magnetic Trillage.
The data enters as undifferentiated Ferro-Data and is separated
by the tri-polar magnetic field into Logic, Illogic, and Context
components.
Args:
data: Input data (Ferro-Data) to process.
human_pulse: Optional human sovereignty signal (0.0 to 1.0).
If None, the system may FREEZE at boundary.
Returns:
TrignumOutput with the processed result.
"""
self.state = PyramidState.SEPARATING
# Phase 1: Injection — data enters as Ferro-Data
ferro_data = self._inject(data)
# Phase 2: Magnetic Separation — data self-orients
logic_pull = self.face_alpha.attract(ferro_data)
illogic_pull = self.face_beta.attract(ferro_data)
context_pull = self.face_gamma.attract(ferro_data)
total_pull = logic_pull + illogic_pull + context_pull
if total_pull == 0:
total_pull = 1e-10 # Avoid division by zero
logic_ratio = logic_pull / total_pull
illogic_ratio = illogic_pull / total_pull
context_ratio = context_pull / total_pull
# Phase 3: Check for THE FREEZE
if illogic_ratio >= self.freeze_threshold:
self.state = PyramidState.FROZEN
freeze_coords = self._compute_freeze_coordinates(
logic_ratio, illogic_ratio, context_ratio
)
output = TrignumOutput(
result=None,
logic_component=logic_ratio,
illogic_component=illogic_ratio,
context_component=context_ratio,
state=PyramidState.FROZEN,
confidence=0.0,
freeze_coordinates=freeze_coords,
metadata={
"message": "🔴 T-CHIP FROZEN. Illogic boundary detected. "
"Human Pulse required.",
"illogic_ratio": illogic_ratio,
"threshold": self.freeze_threshold,
},
)
self._history.append(output)
return output
# Phase 4: Vacuum Formation
self.state = PyramidState.VACUUM_FORMING
vacuum_strength = self.face_beta.create_vacuum(illogic_ratio)
# Phase 5: Apply Human Pulse (if provided)
if human_pulse is not None:
context_ratio *= human_pulse
self.face_gamma.apply_pulse(human_pulse)
# Phase 6: Collapse at Apex — Magnetic Reconnection
self.state = PyramidState.COLLAPSING
result = self._collapse_at_apex(
ferro_data, logic_ratio, illogic_ratio, context_ratio,
vacuum_strength
)
# Compute confidence
confidence = self._compute_confidence(
logic_ratio, illogic_ratio, context_ratio, human_pulse
)
# Determine final state
if human_pulse is not None and human_pulse > 0.5:
self.state = PyramidState.SOVEREIGN
else:
self.state = PyramidState.IDLE
output = TrignumOutput(
result=result,
logic_component=logic_ratio,
illogic_component=illogic_ratio,
context_component=context_ratio,
state=self.state,
confidence=confidence,
metadata={
"vacuum_strength": vacuum_strength,
"human_pulse": human_pulse,
},
)
self._history.append(output)
return output
def _inject(self, data: Any) -> Dict[str, Any]:
"""Convert raw data into Ferro-Data for magnetic processing."""
if isinstance(data, str):
return {
"raw": data,
"tokens": data.split(),
"length": len(data),
"entropy": self._estimate_entropy(data),
}
elif isinstance(data, (int, float)):
return {
"raw": data,
"tokens": [str(data)],
"length": 1,
"entropy": 0.0,
}
elif isinstance(data, dict):
return {
"raw": data,
"tokens": list(data.keys()),
"length": len(data),
"entropy": self._estimate_entropy(str(data)),
}
else:
return {
"raw": data,
"tokens": [str(data)],
"length": 1,
"entropy": 0.5,
}
def _estimate_entropy(self, text: str) -> float:
"""Estimate Shannon entropy of text as proxy for information density."""
if not text:
return 0.0
freq: Dict[str, int] = {}
for char in text:
freq[char] = freq.get(char, 0) + 1
length = len(text)
entropy = 0.0
for count in freq.values():
p = count / length
if p > 0:
entropy -= p * math.log2(p)
return entropy
def _compute_freeze_coordinates(
self, logic: float, illogic: float, context: float
) -> List[float]:
"""Compute the Semantic Boundary Coordinates of a Freeze event."""
return [logic, illogic, context]
def _collapse_at_apex(
self,
ferro_data: Dict[str, Any],
logic_ratio: float,
illogic_ratio: float,
context_ratio: float,
vacuum_strength: float,
) -> Any:
"""
Perform Magnetic Reconnection at the Apex.
The three streams reconnect in a single point, producing
the Trignomed Tensor — the output.
"""
# The result is the raw data, weighted by the dominant face
result = {
"trignomed_tensor": ferro_data["raw"],
"dominant_face": (
"α (Logic)" if logic_ratio >= max(illogic_ratio, context_ratio)
else "β (Illogic)" if illogic_ratio >= context_ratio
else "γ (Context)"
),
"vacuum_applied": vacuum_strength > 0,
"face_weights": {
"alpha": round(logic_ratio, 4),
"beta": round(illogic_ratio, 4),
"gamma": round(context_ratio, 4),
},
}
return result
def _compute_confidence(
self,
logic: float,
illogic: float,
context: float,
pulse: Optional[float],
) -> float:
"""
Compute confidence score for the output.
High confidence when:
- Logic is dominant
- Illogic is low (well-filtered)
- Context is present (Human Pulse applied)
"""
base = logic * (1 - illogic)
if pulse is not None:
base *= (0.5 + 0.5 * pulse)
return min(max(base, 0.0), 1.0)
@property
def history(self) -> List[TrignumOutput]:
"""Return processing history."""
return self._history.copy()
def reset(self) -> None:
"""Reset pyramid to IDLE state and clear history."""
self.state = PyramidState.IDLE
self._history.clear()
self.face_alpha.reset()
self.face_beta.reset()
self.face_gamma.reset()
def __repr__(self) -> str:
return (
f"TrignumPyramid(state={self.state.value}, "
f"faces=[α={self.face_alpha.strength:.2f}, "
f"β={self.face_beta.strength:.2f}, "
f"γ={self.face_gamma.strength:.2f}])"
)
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