| |
| """Compute paper-protocol peak-field normalization from training cases only.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| from pathlib import Path |
|
|
| import torch |
|
|
| from build_peak_targets import select_peak |
| from load_case import canonical_geometry, load_case, load_split |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--dataset-root", type=Path, default=Path(".")) |
| parser.add_argument("--geometry", required=True) |
| parser.add_argument("--output", type=Path, required=True) |
| args = parser.parse_args() |
|
|
| geometry = canonical_geometry(args.geometry) |
| train_cases = load_split(args.dataset_root)["train"] |
| target_sum_sq = torch.zeros(4, dtype=torch.float64) |
| target_count = torch.zeros(4, dtype=torch.int64) |
| condition_rows = [] |
|
|
| for offset, case_id in enumerate(train_cases, start=1): |
| data = load_case(args.dataset_root, geometry, case_id) |
| _, _, displacement, stress = select_peak(data) |
| for component in range(3): |
| values = displacement[:, component].to(torch.float64) |
| target_sum_sq[component] += torch.sum(values.square()) |
| target_count[component] += values.numel() |
| stress64 = stress.to(torch.float64) |
| target_sum_sq[3] += torch.sum(stress64.square()) |
| target_count[3] += stress64.numel() |
| condition_rows.append( |
| torch.cat( |
| ( |
| data["velocity_xyz"].to(torch.float64).reshape(3), |
| data["mass_ratio"].to(torch.float64).reshape(1), |
| data["material_young_mpa"].to(torch.float64).reshape(1), |
| data["material_poisson"].to(torch.float64).reshape(1), |
| ) |
| ) |
| ) |
| if offset % 50 == 0: |
| print(f"[{geometry}] normalization {offset}/{len(train_cases)}") |
|
|
| scale = torch.sqrt(target_sum_sq / target_count) |
| conditions = torch.stack(condition_rows) |
| payload = { |
| "geometry": geometry, |
| "split": "train", |
| "train_cases": len(train_cases), |
| "target_scale_definition": ( |
| "training-split RMS of peak-state x/y/z displacement and " |
| "same-state max-IP von Mises effective stress" |
| ), |
| "target_feature_names": ["disp_x", "disp_y", "disp_z", "effective_stress"], |
| "target_scale": scale.tolist(), |
| "condition_feature_names": [ |
| "velocity_x", |
| "velocity_y", |
| "velocity_z", |
| "mass_ratio", |
| "material_young_mpa", |
| "material_poisson", |
| ], |
| "condition_mean": conditions.mean(dim=0).tolist(), |
| "condition_std_sample": conditions.std(dim=0, unbiased=True).tolist(), |
| } |
| args.output.parent.mkdir(parents=True, exist_ok=True) |
| args.output.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") |
| print(json.dumps(payload, indent=2)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
