File size: 3,923 Bytes
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 | """Run the frozen T4 v1 time-step refinement cohort."""
from __future__ import annotations
import argparse
import json
import os
from pathlib import Path
import tempfile
import h5py
import numpy as np
try:
from .t4_structural_dynamics import run_trajectory
from .t4_structural_dynamics_v1 import (
CASE_DIR,
DATA_DIR,
case_config,
configuration_design,
load_config,
)
except ImportError: # Direct script execution.
from t4_structural_dynamics import run_trajectory
from t4_structural_dynamics_v1 import (
CASE_DIR,
DATA_DIR,
case_config,
configuration_design,
load_config,
)
AUDIT_DIR = DATA_DIR / "time_refinement"
def run_audit(configuration_id: int) -> dict:
global_config = load_config()
rows = configuration_design(global_config)
row = rows[configuration_id]
if configuration_id not in global_config["quality"]["time_refinement_configuration_ids"]:
raise ValueError(f"Configuration {configuration_id} is not in the frozen audit cohort.")
output = AUDIT_DIR / f"{row['case_id']}.json"
if output.is_file():
cached = json.loads(output.read_text(encoding="utf-8"))
if (
cached.get("generator_fix") == "component-preserving-kinematic-bcs"
and
float(cached.get("coarse_dt_s", -1.0)) == float(global_config["dynamics"]["dt_s"])
and float(cached.get("fine_dt_s", -1.0))
== float(global_config["quality"]["time_refinement_dt_s"])
):
return cached
with h5py.File(CASE_DIR / f"{row['case_id']}.h5", "r") as h5:
frequencies = np.asarray(h5["modal/frequencies_hz"])
coarse_time = np.asarray(h5["trajectories/half_sine_pulse/time_s"])
coarse_sensors = np.asarray(
h5["trajectories/half_sine_pulse/sensor_displacement_m"]
)
config = case_config(global_config, row)
with tempfile.TemporaryDirectory(prefix=f"refine_{row['case_id']}_", dir=DATA_DIR) as scratch:
fine = run_trajectory(
config,
config["excitations"][0],
frequencies_hz=frequencies,
output_dir=Path(scratch),
dt=float(global_config["quality"]["time_refinement_dt_s"]),
save_fields=False,
)
fine_on_coarse = np.column_stack(
[
np.interp(coarse_time, fine["time_s"], fine["sensor_displacement_m"][:, index])
for index in range(coarse_sensors.shape[1])
]
)
relative_l2 = float(
np.linalg.norm(coarse_sensors - fine_on_coarse)
/ max(np.linalg.norm(fine_on_coarse), np.finfo(float).eps)
)
record = {
"configuration_id": configuration_id,
"case_id": row["case_id"],
"generator_fix": "component-preserving-kinematic-bcs",
"coarse_dt_s": float(config["dynamics"]["dt_s"]),
"fine_dt_s": float(global_config["quality"]["time_refinement_dt_s"]),
"five_sensor_history_relative_l2": relative_l2,
"threshold": float(global_config["quality"]["maximum_time_refinement_relative_l2"]),
"passed": relative_l2
<= float(global_config["quality"]["maximum_time_refinement_relative_l2"]),
}
AUDIT_DIR.mkdir(parents=True, exist_ok=True)
temporary = output.with_suffix(".json.tmp")
temporary.write_text(json.dumps(record, indent=2, sort_keys=True) + "\n", encoding="utf-8")
os.replace(temporary, output)
return record
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("configuration_ids", nargs="*", type=int)
args = parser.parse_args()
selected = args.configuration_ids or load_config()["quality"]["time_refinement_configuration_ids"]
for configuration_id in selected:
print(json.dumps(run_audit(configuration_id), sort_keys=True), flush=True)
if __name__ == "__main__":
main()
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