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caa4bd5 7b0b96f caa4bd5 7b0b96f caa4bd5 7b0b96f caa4bd5 7b0b96f caa4bd5 7b0b96f caa4bd5 7b0b96f caa4bd5 7b0b96f caa4bd5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 | """Audit 128 T4 configurations and pack them into eight publication shards."""
from __future__ import annotations
import hashlib
import csv
import json
import os
from pathlib import Path
import shutil
import h5py
import numpy as np
try:
from .audit_t4_modal_transient_consistency import measured_frequency
except ImportError:
from audit_t4_modal_transient_consistency import measured_frequency
try:
from .t4_structural_dynamics_v1 import (
CASE_DIR,
DATA_DIR,
configuration_design,
load_config,
valid_case_file,
)
except ImportError:
from t4_structural_dynamics_v1 import (
CASE_DIR,
DATA_DIR,
configuration_design,
load_config,
valid_case_file,
)
SHARD_DIR = DATA_DIR / "shards"
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as stream:
for chunk in iter(lambda: stream.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def audit_cases() -> tuple[list[dict], dict]:
config = load_config()
rows = configuration_design(config)
index: list[dict] = []
missing: list[str] = []
invalid: list[str] = []
frequency_errors: list[float] = []
modal_transient_frequency_errors: list[float] = []
energy_residuals: list[float] = []
for row in rows:
path = CASE_DIR / f"{row['case_id']}.h5"
if not path.is_file():
missing.append(row["case_id"])
continue
if not valid_case_file(path):
invalid.append(row["case_id"])
continue
with h5py.File(path, "r") as h5:
quality = json.loads(h5.attrs["quality_json"])
frequency_errors.append(float(quality["first_frequency_relative_error"]))
energy_residuals.append(float(quality["maximum_energy_balance_relative_residual"]))
case_config = json.loads(h5.attrs["config_json"])
modal_hz = float(h5["modal/frequencies_hz"][0])
pulse = h5["trajectories/half_sine_pulse"]
transient_hz = measured_frequency(
np.asarray(pulse["time_s"], dtype=np.float64),
np.asarray(pulse["sensor_displacement_m"], dtype=np.float64)[:, -1],
modal_hz,
)
modal_transient_frequency_errors.append(abs(transient_hz - modal_hz) / modal_hz)
for name in sorted(h5["trajectories"]):
trajectory = h5[f"trajectories/{name}"]
required = (
"time_s",
"force_scale",
"sensor_displacement_m",
"fields/displacement_m",
"fields/velocity_m_per_s",
"fields/acceleration_m_per_s2",
)
finite = all(np.all(np.isfinite(trajectory[key][...])) for key in required)
if not finite:
invalid.append(f"{row['case_id']}:{name}:nonfinite")
sensors = np.asarray(trajectory["sensor_displacement_m"])
spec = json.loads(trajectory.attrs["spec_json"])
index.append(
{
"trajectory_id": f"{row['case_id']}__{name}",
"configuration_id": int(row["configuration_id"]),
"case_id": row["case_id"],
"split": row["split"],
"protocol_role": row["protocol_role"],
"excitation": name,
"excitation_spec": spec,
"length_m": case_config["geometry"]["length_m"],
"height_m": case_config["geometry"]["height_m"],
"young_pa": case_config["material"]["young_pa"],
"density_kg_m3": case_config["material"]["density_kg_m3"],
"damping_ratio": case_config["dynamics"]["target_damping_ratio"],
"traction_amplitude_pa": case_config["dynamics"]["traction_amplitude_pa"],
"first_frequency_hz": float(h5["modal/frequencies_hz"][0]),
"time_states": int(trajectory["time_s"].shape[0]),
"field_frames": int(trajectory["fields/time_s"].shape[0]),
"sensor_count": int(sensors.shape[1]),
"max_abs_tip_displacement_m": float(np.max(np.abs(sensors[:, -1]))),
"energy_balance_relative_residual": float(
np.max(np.abs(trajectory["energy_balance_relative_residual"][...]))
),
}
)
counts = {
"configuration_count": len(rows) - len(missing),
"trajectory_count": len(index),
"split_trajectories": {
split: sum(item["split"] == split for item in index)
for split in ("train", "validation", "test")
},
"protocol_trajectories": {
role: sum(item["protocol_role"] == role for item in index)
for role in ("id", "excitation_ood", "parameter_ood")
},
}
refinement_records = []
refinement_missing = []
for configuration_id in config["quality"]["time_refinement_configuration_ids"]:
audit_path = DATA_DIR / "time_refinement" / f"config_{configuration_id:04d}.json"
if not audit_path.is_file():
refinement_missing.append(configuration_id)
else:
refinement_records.append(json.loads(audit_path.read_text(encoding="utf-8")))
refinement_values = [
float(record["five_sensor_history_relative_l2"])
for record in refinement_records
]
quality = {
**counts,
"missing_configurations": missing,
"invalid_entries": sorted(set(invalid)),
"maximum_first_frequency_relative_error": max(frequency_errors, default=float("nan")),
"median_first_frequency_relative_error": float(np.median(frequency_errors))
if frequency_errors
else float("nan"),
"maximum_modal_transient_frequency_relative_difference": max(
modal_transient_frequency_errors, default=float("nan")
),
"median_modal_transient_frequency_relative_difference": float(
np.median(modal_transient_frequency_errors)
)
if modal_transient_frequency_errors
else float("nan"),
"maximum_energy_balance_relative_residual": max(energy_residuals, default=float("nan")),
"median_energy_balance_relative_residual": float(np.median(energy_residuals))
if energy_residuals
else float("nan"),
"time_refinement": {
"configuration_ids": config["quality"]["time_refinement_configuration_ids"],
"missing_configuration_ids": refinement_missing,
"maximum_five_sensor_history_relative_l2": max(
refinement_values, default=float("nan")
),
"median_five_sensor_history_relative_l2": float(np.median(refinement_values))
if refinement_values
else float("nan"),
"records": refinement_records,
},
}
quality["gates"] = {
"complete": not missing and counts["configuration_count"] == 128 and counts["trajectory_count"] == 512,
"valid": not invalid,
"frequency": quality["maximum_first_frequency_relative_error"]
<= float(config["quality"]["maximum_first_frequency_analytical_relative_error"]),
"modal_transient_consistency": quality[
"maximum_modal_transient_frequency_relative_difference"
]
<= 0.005,
"energy": quality["maximum_energy_balance_relative_residual"]
<= float(config["quality"]["maximum_energy_balance_relative_residual"]),
"splits": counts["split_trajectories"] == {"train": 384, "validation": 64, "test": 64},
"time_refinement": not refinement_missing
and all(record["passed"] for record in refinement_records),
}
quality["passed"] = bool(all(quality["gates"].values()))
return index, quality
def pack_shards(index: list[dict], quality: dict) -> dict:
if not quality["passed"]:
raise RuntimeError(f"Cannot pack failed T4 campaign: {quality}")
config = load_config()
rows = configuration_design(config)
per_shard = int(config["storage"]["formal_shard_configuration_count"])
temporary_dir = DATA_DIR / "shards_building"
if temporary_dir.exists():
shutil.rmtree(temporary_dir)
temporary_dir.mkdir(parents=True)
shard_records = []
for start in range(0, len(rows), per_shard):
selected = rows[start : start + per_shard]
shard_path = temporary_dir / f"t4_structural_dynamics_v1_{start // per_shard:02d}.h5"
with h5py.File(shard_path, "w") as target:
target.attrs["schema"] = "agentfem.physics-data.structural-dynamics-v1-shard"
target.attrs["schema_version"] = "1.1.0"
target.attrs["configuration_start"] = start
target.attrs["configuration_stop"] = start + len(selected)
for row in selected:
with h5py.File(CASE_DIR / f"{row['case_id']}.h5", "r") as source:
destination = target.create_group(row["case_id"])
for key, value in source.attrs.items():
destination.attrs[key] = value
for name in source:
source.copy(name, destination, name=name)
shard_records.append(
{
"path": f"shards/{shard_path.name}",
"configuration_start": start,
"configuration_stop": start + len(selected),
"configuration_count": len(selected),
"trajectory_count": 4 * len(selected),
"bytes": shard_path.stat().st_size,
"sha256": sha256(shard_path),
}
)
if SHARD_DIR.exists():
shutil.rmtree(SHARD_DIR)
os.replace(temporary_dir, SHARD_DIR)
for item in index:
shard = int(item["configuration_id"]) // per_shard
item["shard"] = f"shards/t4_structural_dynamics_v1_{shard:02d}.h5"
item["group"] = f"{item['case_id']}/trajectories/{item['excitation']}"
(DATA_DIR / "index.jsonl").write_text(
"".join(json.dumps(item, sort_keys=True) + "\n" for item in index), encoding="utf-8"
)
with (DATA_DIR / "index.csv").open("w", encoding="utf-8", newline="") as stream:
flattened = []
for item in index:
row = dict(item)
row["excitation_spec"] = json.dumps(row["excitation_spec"], sort_keys=True)
flattened.append(row)
writer = csv.DictWriter(stream, fieldnames=list(flattened[0]))
writer.writeheader()
writer.writerows(flattened)
manifest = {
"dataset": "AgentFEM-Structural-Dynamics-Virtual-Sensing",
"release": "v1.1.0-local-candidate",
"configuration_count": 128,
"trajectory_count": 512,
"shards": shard_records,
"software": config["software"],
}
(DATA_DIR / "manifest.json").write_text(
json.dumps(manifest, indent=2, sort_keys=True) + "\n", encoding="utf-8"
)
(DATA_DIR / "quality.json").write_text(
json.dumps(quality, indent=2, sort_keys=True) + "\n", encoding="utf-8"
)
return manifest
def main() -> None:
index, quality = audit_cases()
print(json.dumps(quality, indent=2, sort_keys=True))
if quality["passed"]:
print(json.dumps(pack_shards(index, quality), indent=2, sort_keys=True))
if __name__ == "__main__":
main()
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