| """Analyze and visualize DENIM capability-extension v4 without model claims.""" |
|
|
| from __future__ import annotations |
|
|
| import json |
| from collections import defaultdict |
| from pathlib import Path |
|
|
| import h5py |
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
| from src.generate_t2_denim_capability_v4 import ( |
| ARTIFACT_DIR, |
| DATA_PATH, |
| INDEX_PATH, |
| MANIFEST_PATH, |
| _write_json, |
| ) |
|
|
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| SUMMARY_PATH = ARTIFACT_DIR / "capability_summary.json" |
| PREVIEW_PATH = ARTIFACT_DIR / "capability_extension_preview.png" |
|
|
|
|
| def _rmse(left: np.ndarray, right: np.ndarray) -> float: |
| return float(np.sqrt(np.mean(np.square(np.asarray(left) - np.asarray(right))))) |
|
|
|
|
| def analyze() -> dict[str, object]: |
| rows = [json.loads(line) for line in INDEX_PATH.read_text(encoding="utf-8").splitlines() if line] |
| manifest = json.loads(MANIFEST_PATH.read_text(encoding="utf-8")) |
| by_group: dict[str, list[tuple[int, dict[str, object]]]] = defaultdict(list) |
| for index, row in enumerate(rows): |
| by_group[str(row["group_id"])].append((index, row)) |
|
|
| resolution_errors: dict[int, list[float]] = defaultdict(list) |
| active_components = [] |
| observation_keep = [] |
| strain_noise = [] |
| stress_noise_mpa = [] |
| structure_metrics: dict[str, dict[str, float | int]] = {} |
| long_metrics: dict[str, list[float]] = defaultdict(list) |
|
|
| with h5py.File(DATA_PATH, "r") as h5: |
| for group_id, members in by_group.items(): |
| if not group_id.startswith("matched-resolution-"): |
| continue |
| reference_index, _ = next((item for item in members if int(item[1]["points"]) == 481)) |
| reference = h5[f"{reference_index:05d}"] |
| reference_time = reference["time_coordinate"][...] |
| reference_stress = reference["stress_pa"][...] |
| for index, row in members: |
| points = int(row["points"]) |
| group = h5[f"{index:05d}"] |
| time = group["time_coordinate"][...] |
| stress = group["stress_pa"][...] |
| interpolated = np.column_stack( |
| [np.interp(time, reference_time, reference_stress[:, component]) for component in range(6)] |
| ) |
| resolution_errors[points].append(_rmse(stress, interpolated) / 1.0e6) |
| mask = group["observation_mask"][...].astype(bool) |
| observation_keep.append(float(mask.mean())) |
| strain_noise.append( |
| _rmse(group["observed_strain"][...], group["strain"][...]) |
| / max(float(np.max(np.abs(group["strain"][...]))), 1.0e-15) |
| ) |
| stress_noise_mpa.append( |
| _rmse(group["observed_stress_pa"][...], group["stress_pa"][...]) / 1.0e6 |
| ) |
|
|
| for index, row in enumerate(rows): |
| group = h5[f"{index:05d}"] |
| cohort = str(row["cohort"]) |
| if cohort == "full_3d_nonproportional": |
| strain = group["strain"][...] |
| maxima = np.max(np.abs(strain), axis=0) |
| threshold = 0.01 * max(float(maxima.max()), 1.0e-15) |
| active_components.append(int(np.count_nonzero(maxima > threshold))) |
| elif cohort == "structure_derived": |
| entry = structure_metrics.setdefault( |
| str(row["group_id"]), |
| {"trajectory_count": 0, "maximum_peeq": 0.0, "maximum_absolute_stress_mpa": 0.0}, |
| ) |
| entry["trajectory_count"] = int(entry["trajectory_count"]) + 1 |
| entry["maximum_peeq"] = max(float(entry["maximum_peeq"]), float(np.max(group["peeq"][...]))) |
| entry["maximum_absolute_stress_mpa"] = max( |
| float(entry["maximum_absolute_stress_mpa"]), |
| float(np.max(np.abs(group["stress_pa"][...]))) / 1.0e6, |
| ) |
| elif cohort == "long_cycle": |
| long_metrics[str(row["split"])].append(float(group["peeq"][-1])) |
|
|
| summary: dict[str, object] = { |
| "schema": "agentfem.physics-data.denim-capability-extension.analysis", |
| "sample_count": len(rows), |
| "cohort_counts": manifest["cohort_counts"], |
| "quality": manifest["quality"], |
| "long_cycle_final_peeq": { |
| split: { |
| "count": len(values), |
| "median": float(np.median(values)), |
| "maximum": float(np.max(values)), |
| } |
| for split, values in sorted(long_metrics.items()) |
| }, |
| "full_3d_component_coverage": { |
| "minimum_active_voigt_components": int(min(active_components)), |
| "median_active_voigt_components": float(np.median(active_components)), |
| "all_six_components_active_fraction": float(np.mean(np.asarray(active_components) == 6)), |
| }, |
| "structure_derived": structure_metrics, |
| "matched_resolution_stress_rmse_mpa_against_481": { |
| str(points): { |
| "median": float(np.median(values)), |
| "maximum": float(np.max(values)), |
| } |
| for points, values in sorted(resolution_errors.items()) |
| }, |
| "observation_protocol": { |
| "retained_fraction_minimum": float(np.min(observation_keep)), |
| "retained_fraction_median": float(np.median(observation_keep)), |
| "retained_fraction_maximum": float(np.max(observation_keep)), |
| "relative_strain_noise_rmse_median": float(np.median(strain_noise)), |
| "stress_noise_rmse_mpa_median": float(np.median(stress_noise_mpa)), |
| }, |
| } |
| _write_json(SUMMARY_PATH, summary) |
| return summary |
|
|
|
|
| def plot() -> Path: |
| ARTIFACT_DIR.mkdir(parents=True, exist_ok=True) |
| with h5py.File(DATA_PATH, "r") as h5: |
| long = h5["00299"] |
| full3d = h5["00300"] |
| structural = h5["00600"] |
| observed = h5["00801"] |
|
|
| figure, axes = plt.subplots(2, 2, figsize=(13.0, 8.2), constrained_layout=True) |
|
|
| axes[0, 0].plot(long["time_coordinate"][...], long["stress_pa"][:, 0] / 1.0e6, lw=1.0, color="#1f6feb") |
| axes[0, 0].set_title("100-cycle long-horizon response") |
| axes[0, 0].set_xlabel("normalized history coordinate") |
| axes[0, 0].set_ylabel(r"$\sigma_{xx}$ (MPa)") |
|
|
| strain = full3d["strain"][...] |
| labels = ("xx", "yy", "zz", "xy", "yz", "xz") |
| for component, label in enumerate(labels): |
| axes[0, 1].plot(full3d["time_coordinate"][...], 100.0 * strain[:, component], lw=1.1, label=label) |
| axes[0, 1].set_title("Full six-component non-proportional path") |
| axes[0, 1].set_xlabel("normalized history coordinate") |
| axes[0, 1].set_ylabel("tensor strain (%)") |
| axes[0, 1].legend(ncol=3, fontsize=8, frameon=False) |
|
|
| axes[1, 0].plot(100.0 * structural["strain"][:, 0], structural["stress_pa"][:, 0] / 1.0e6, lw=1.5, color="#d97706") |
| axes[1, 0].set_title("AgentFEM-derived local structural path") |
| axes[1, 0].set_xlabel(r"$\varepsilon_{xx}$ (%)") |
| axes[1, 0].set_ylabel(r"$\sigma_{xx}$ (MPa)") |
|
|
| time = observed["time_coordinate"][...] |
| mask = observed["observation_mask"][...].astype(bool) |
| axes[1, 1].plot(time, observed["stress_pa"][:, 0] / 1.0e6, lw=1.5, color="#22c55e", label="clean truth") |
| axes[1, 1].scatter( |
| time[mask], observed["observed_stress_pa"][mask, 0] / 1.0e6, |
| s=10, alpha=0.65, color="#ef4444", label="noisy sparse observation", |
| ) |
| axes[1, 1].set_title("Paired observation robustness protocol") |
| axes[1, 1].set_xlabel("normalized history coordinate") |
| axes[1, 1].set_ylabel(r"$\sigma_{xx}$ (MPa)") |
| axes[1, 1].legend(frameon=False, fontsize=8) |
|
|
| for axis in axes.ravel(): |
| axis.grid(alpha=0.20) |
| figure.suptitle("AgentFEM DENIM capability extension v4", fontsize=16, fontweight="bold") |
| figure.savefig(PREVIEW_PATH, dpi=180) |
| plt.close(figure) |
| return PREVIEW_PATH |
|
|
|
|
| if __name__ == "__main__": |
| print(json.dumps(analyze(), indent=2, sort_keys=True)) |
| print(plot()) |
|
|
