Spaces:
Running
Running
File size: 8,186 Bytes
b154e4c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | """noctilith/sim/local_material_map.py — M13.1: per-cell material parameter arrays."""
from __future__ import annotations
import numpy as np
from dataclasses import dataclass, field
from typing import Any, Dict, Optional, Tuple
from engine.sim.material_profiles import MaterialPipelineProfile, PROFILE_CACHE, clamp
SCHEMA_VERSION = "noctilith.schema.v1"
MODULE_NAME = "noctilith.sim.local_material_map"
_DEFAULT_PARAMS = {
"temp_weight": 0.45, "entropy_weight": 0.40, "fatigue_exponent": 1.35,
"risk_gain": 0.65, "fracture_threshold": 0.80, "propagation_gain": 0.35,
"damage_gain": 0.65, "debris_threshold": 0.88, "entropy_gain": 0.40,
"thermal_gain": 0.15, "fragmentation_threshold": 0.50, "fragmentation_gain": 0.40,
}
@dataclass
class LocalMaterialMap:
shape: Tuple[int, ...]
temp_weight: np.ndarray = field(repr=False)
entropy_weight: np.ndarray = field(repr=False)
fatigue_exponent: np.ndarray = field(repr=False)
risk_gain: np.ndarray = field(repr=False)
fracture_threshold: np.ndarray = field(repr=False)
propagation_gain: np.ndarray = field(repr=False)
damage_gain: np.ndarray = field(repr=False)
debris_threshold: np.ndarray = field(repr=False)
entropy_gain: np.ndarray = field(repr=False)
thermal_gain: np.ndarray = field(repr=False)
fragmentation_threshold: np.ndarray = field(repr=False)
fragmentation_gain: np.ndarray = field(repr=False)
unique_material_ids: Tuple[int, ...] = field(default_factory=tuple)
material_diversity: float = 0.0
@classmethod
def from_voxel_ids(cls, voxel_ids: np.ndarray, *, registry=None,
cache=None) -> "LocalMaterialMap":
if cache is None: cache = PROFILE_CACHE
shape = voxel_ids.shape
unique_ids = np.unique(voxel_ids).tolist()
param_names = list(_DEFAULT_PARAMS.keys())
max_id = max(unique_ids) if unique_ids else 0
lut = np.zeros((max_id + 1, len(param_names)), dtype=np.float32)
for vid in unique_ids:
profile = cache.get(int(vid), registry)
if profile is not None:
lut[vid] = _extract_params(profile)
else:
lut[vid] = [float(_DEFAULT_PARAMS[k]) for k in param_names]
ids_flat = np.clip(voxel_ids.ravel().astype(np.int32), 0, max_id)
params_flat = lut[ids_flat]
def _arr(i: int) -> np.ndarray:
return params_flat[:, i].reshape(shape).astype(np.float32)
idx = {name: i for i, name in enumerate(param_names)}
counts = np.bincount(ids_flat, minlength=max_id+1).astype(np.float64)
total = counts.sum() + 1e-12
probs = counts[counts > 0] / total
h = -np.sum(probs * np.log2(probs + 1e-12))
max_h = np.log2(len(unique_ids) + 1e-12)
diversity = float(clamp(h / (max_h + 1e-12), 0.0, 1.0))
return cls(
shape=shape,
temp_weight =_arr(idx["temp_weight"]),
entropy_weight =_arr(idx["entropy_weight"]),
fatigue_exponent =_arr(idx["fatigue_exponent"]),
risk_gain =_arr(idx["risk_gain"]),
fracture_threshold=_arr(idx["fracture_threshold"]),
propagation_gain =_arr(idx["propagation_gain"]),
damage_gain =_arr(idx["damage_gain"]),
debris_threshold =_arr(idx["debris_threshold"]),
entropy_gain =_arr(idx["entropy_gain"]),
thermal_gain =_arr(idx["thermal_gain"]),
fragmentation_threshold=_arr(idx["fragmentation_threshold"]),
fragmentation_gain =_arr(idx["fragmentation_gain"]),
unique_material_ids=tuple(int(v) for v in unique_ids),
material_diversity =diversity,
)
@classmethod
def uniform(cls, material_id: int, shape: Tuple[int,...],
*, registry=None, cache=None) -> "LocalMaterialMap":
ids = np.full(shape, material_id, dtype=np.int16)
return cls.from_voxel_ids(ids, registry=registry, cache=cache)
def compute_fatigue_vectorized(self, T: np.ndarray, S: np.ndarray,
phi_rms_total: float = 0.0, *,
ambient_temperature: float = 293.15,
temperature_scale_K: float = 500.0,
entropy_scale: float = 1.0,
phi_rms_scale: float = 1.0,
max_fatigue_multiplier: float = 100.0) -> np.ndarray:
dT = np.maximum(0.0, T - ambient_temperature) / max(temperature_scale_K, 1e-8)
s_term = np.maximum(0.0, S) / max(entropy_scale, 1e-8)
rms = max(0.0, phi_rms_total) / max(phi_rms_scale, 1e-8)
rms_w = np.clip(1.0 - self.temp_weight - self.entropy_weight, 0.0, 1.0)
raw = self.temp_weight * dT + self.entropy_weight * s_term + rms_w * rms
fatigue= 1.0 + np.power(np.maximum(0.0, raw), self.fatigue_exponent.astype(np.float64))
return np.clip(fatigue, 1.0, max_fatigue_multiplier)
def get_fracture_threshold_field(self) -> np.ndarray:
return self.fracture_threshold.astype(np.float64)
def summary(self) -> Dict[str, Any]:
return {
"schema_version": SCHEMA_VERSION,
"module_name": MODULE_NAME,
"shape": self.shape,
"unique_materials": list(self.unique_material_ids),
"n_materials": len(self.unique_material_ids),
"material_diversity": float(self.material_diversity),
"mean_fracture_threshold": float(self.fracture_threshold.mean()),
"mean_entropy_gain": float(self.entropy_gain.mean()),
"mean_fatigue_exponent": float(self.fatigue_exponent.mean()),
}
def _extract_params(profile: MaterialPipelineProfile) -> list:
d = profile.degradation; f = profile.fracture
w = profile.world_reaction; c = profile.coupling
return [
float(d.temp_weight), float(d.entropy_weight), float(d.fatigue_exponent),
float(d.risk_gain), float(f.fracture_risk_threshold), float(f.propagation_gain),
float(w.damage_gain), float(w.debris_threshold), float(c.entropy_gain),
float(c.thermal_gain), float(c.fragmentation_strength_threshold),
float(c.fragmentation_strength_gain),
]
def evaluate_degradation_local(thermal_grid: Any, entropy_field: Any,
material_map: LocalMaterialMap, *,
phi_rms_total: float = 0.0,
ambient_temperature: float = 293.15,
temperature_scale_K: float = 500.0,
entropy_scale: float = 1.0,
risk_bias: float = 0.0) -> Dict[str, Any]:
T = _resolve_arr(thermal_grid)
S = _resolve_arr(entropy_field)
fatigue = material_map.compute_fatigue_vectorized(
T, S, phi_rms_total, ambient_temperature=ambient_temperature,
temperature_scale_K=temperature_scale_K, entropy_scale=entropy_scale)
x = np.maximum(0.0, fatigue - 1.0)
risk = 1.0 - np.exp(-(material_map.risk_gain * x + risk_bias))
risk = np.clip(risk, 0.0, 1.0)
return {
"schema_version": SCHEMA_VERSION, "module_name": MODULE_NAME,
"risk_field": risk.astype(np.float32, copy=True),
"risk_max": float(np.max(risk)), "risk_mean": float(np.mean(risk)),
"fatigue_max": float(np.max(fatigue)), "fatigue_mean": float(np.mean(fatigue)),
"material_diversity": float(material_map.material_diversity),
}
def _resolve_arr(obj: Any) -> np.ndarray:
if isinstance(obj, np.ndarray): return obj.astype(np.float64, copy=False)
for name in ("T", "S", "values", "temperatures", "entropy", "E"):
arr = getattr(obj, name, None)
if arr is not None and isinstance(arr, np.ndarray):
return arr.astype(np.float64, copy=False)
raise AttributeError(f"Cannot resolve array from {type(obj)}")
|