71 lines
2.8 KiB
Python
71 lines
2.8 KiB
Python
"""
|
|
FieldState — the complete cognitive field at one moment.
|
|
|
|
Invariant: versor_condition(F) < 1e-6 always.
|
|
This is checked at injection and maintained structurally by versor_apply().
|
|
|
|
FieldState is immutable by design (frozen=True, slots=True).
|
|
The np.ndarray F is copied and validated at construction — the copy() call
|
|
is the explicit contract boundary. Callers must not retain a mutable
|
|
reference to the array passed in and expect coherence.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
from dataclasses import dataclass
|
|
from typing import TYPE_CHECKING
|
|
import numpy as np
|
|
|
|
if TYPE_CHECKING:
|
|
from core.physics.energy import EnergyProfile
|
|
from core.physics.valence import ValenceBundle
|
|
|
|
_EXPECTED_COMPONENTS = 32
|
|
|
|
|
|
@dataclass(frozen=True, slots=True)
|
|
class FieldState:
|
|
F: np.ndarray # shape (32,) float32/float64 — Cl(4,1) multivector on the versor manifold
|
|
node: int = 0 # current node index in the vocabulary manifold
|
|
step: int = 0 # number of propagation steps taken
|
|
holonomy: np.ndarray | None = None
|
|
energy: EnergyProfile | None = None
|
|
valence: ValenceBundle | None = None
|
|
|
|
def __post_init__(self) -> None:
|
|
# Enforce copy + dtype + shape at the construction boundary.
|
|
# frozen=True prevents reassignment, but ndarray contents are still
|
|
# mutable via the array object; copy() here is the defence.
|
|
# slots=True closes __dict__ so no incidental attributes can be added.
|
|
f_dtype = np.asarray(self.F).dtype
|
|
if f_dtype not in (np.dtype(np.float32), np.dtype(np.float64)):
|
|
f_dtype = np.dtype(np.float32)
|
|
F = np.array(self.F, dtype=f_dtype).copy()
|
|
if F.shape != (_EXPECTED_COMPONENTS,):
|
|
raise ValueError(
|
|
f"FieldState.F must have shape ({_EXPECTED_COMPONENTS},), "
|
|
f"got {F.shape}."
|
|
)
|
|
# Bypass frozen to store the validated copy.
|
|
object.__setattr__(self, "F", F)
|
|
if self.holonomy is not None:
|
|
h_dtype = np.asarray(self.holonomy).dtype
|
|
if h_dtype not in (np.dtype(np.float32), np.dtype(np.float64)):
|
|
h_dtype = np.dtype(np.float32)
|
|
H = np.array(self.holonomy, dtype=h_dtype).copy()
|
|
if H.shape != (_EXPECTED_COMPONENTS,):
|
|
raise ValueError(
|
|
f"FieldState.holonomy must have shape ({_EXPECTED_COMPONENTS},), "
|
|
f"got {H.shape}."
|
|
)
|
|
object.__setattr__(self, "holonomy", H)
|
|
|
|
def advance(self, new_F: np.ndarray, new_node: int) -> FieldState:
|
|
"""Return a new FieldState after one propagation step."""
|
|
return FieldState(
|
|
F=new_F,
|
|
node=new_node,
|
|
step=self.step + 1,
|
|
holonomy=self.holonomy,
|
|
energy=self.energy,
|
|
valence=self.valence,
|
|
)
|