| |
| """Show two zero-strain states with different stresses in published train data. |
| |
| Install: pip install numpy matplotlib huggingface_hub |
| Run: python show_material_memory.py |
| Offline: python show_material_memory.py --shard /path/to/part-00002.h5 |
| Code: Apache-2.0; source data: CC BY 4.0, Haoming Luo / AgentFEM. |
| This reads a synthetic material-point history, not a DENIM prediction. |
| """ |
| import argparse |
| import hashlib |
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
|
|
| REPO = "HaomingLuo/AgentFEM-Material-Loading-Memory" |
| REVISION = "cc9644aa5b51ac4f0ff5cccc9a42452de0d220a3" |
| FILE = "data/t2_material_loading_memory_v1/shards/part-00002.h5" |
| SHA256 = "952b90eed1ce3ac288487cf31180384fc12f79aa03d95e4885bfd73b85c9a46d" |
| SAMPLE = "00252" |
| EXCERPT_REVISION = "62cedc709b159c9faa652e0aff34ff48771b3f07" |
| EXCERPT_FILE = "example/training-history-00252.npz" |
| EXCERPT_SHA256 = "c16bd5da346dd6926d32c730ad9ad040c5bd9a51dccba92b1f8c4ae1dce8717f" |
| ARRAY_NAMES = ("total_strain", "signed_equivalent_strain", |
| "signed_equivalent_stress_pa", "equivalent_plastic_strain") |
|
|
|
|
| def read_sample(path, excerpt=False): |
| digest = hashlib.sha256(Path(path).read_bytes()).hexdigest() |
| if digest != (EXCERPT_SHA256 if excerpt else SHA256): |
| raise ValueError("Input checksum mismatch; use the pinned published file.") |
| if excerpt: |
| with np.load(path, allow_pickle=False) as packed: |
| if (str(packed["split"]) != "train" or str(packed["sample_id"]) != SAMPLE |
| or str(packed["source_sha256"]) != SHA256): |
| raise ValueError("Unexpected excerpt provenance.") |
| arrays = {name: packed[name] for name in ARRAY_NAMES} |
| case_id = str(packed["case_id"]) |
| else: |
| import h5py |
| with h5py.File(path, "r") as f: |
| g = f[SAMPLE] |
| if str(g.attrs["split"]) != "train": |
| raise ValueError("Illustration must use training data only.") |
| arrays = {name: g[name][:] for name in ARRAY_NAMES} |
| case_id = str(g.attrs["case_id"]) |
| if not all(np.isfinite(a).all() for a in arrays.values()): |
| raise ValueError("Non-finite trajectory.") |
| |
| zero = np.flatnonzero(np.max(np.abs(arrays["total_strain"]), axis=(1, 2)) < 1e-12) |
| zero = zero[zero > 0] |
| if len(zero) < 2: |
| raise ValueError("No two noninitial zero-strain states.") |
| a, b = map(int, zero[:2]) |
| if not np.allclose(arrays["total_strain"][a], arrays["total_strain"][b], atol=1e-12, rtol=0): |
| raise ValueError("The complete strain tensors differ.") |
| return arrays, a, b, case_id |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description=__doc__) |
| inputs = parser.add_mutually_exclusive_group() |
| inputs.add_argument("--shard", type=Path, help="Original HDF5 shard (requires h5py)") |
| inputs.add_argument("--sample", type=Path, help="Local 4.5 KB NPZ training excerpt") |
| parser.add_argument("--out", type=Path, default=Path("memory-example")) |
| args = parser.parse_args() |
| if args.shard is None and args.sample is None: |
| from huggingface_hub import hf_hub_download |
| args.sample = Path(hf_hub_download(REPO, EXCERPT_FILE, repo_type="dataset", revision=EXCERPT_REVISION)) |
| using_excerpt = args.sample is not None |
| d, a, b, case_id = read_sample(args.sample if using_excerpt else args.shard, excerpt=using_excerpt) |
| strain = d["signed_equivalent_strain"] * 100 |
| stress = d["signed_equivalent_stress_pa"] / 1e6 |
| report = {"repository": REPO, "revision": REVISION, "shard_sha256": SHA256, |
| "input_kind": "lossless_training_excerpt" if using_excerpt else "original_shard", |
| "input_sha256": EXCERPT_SHA256 if using_excerpt else SHA256, |
| "excerpt_revision": EXCERPT_REVISION if using_excerpt else None, |
| "sample_id": SAMPLE, "case_id": case_id, "split": "train", |
| "selection": "First two noninitial returns to zero strain; no interpolation", |
| "states": [{"index": i, "signed_equivalent_strain": float(strain[i]/100), |
| "signed_equivalent_stress_MPa": float(stress[i]), |
| "equivalent_plastic_strain": float(d["equivalent_plastic_strain"][i])} |
| for i in (a, b)], |
| "stress_difference_MPa": float(abs(stress[b]-stress[a])), |
| "scope": "Synthetic proportional deviatoric J2 material-point history; not experiment or model accuracy."} |
| args.out.mkdir(parents=True, exist_ok=True) |
| (args.out / "memory-evidence.json").write_text(json.dumps(report, indent=2)+"\n") |
| np.savetxt(args.out / "memory-history.csv", np.column_stack((np.arange(len(strain)), strain/100, stress, |
| d["equivalent_plastic_strain"])), delimiter=",", comments="", |
| header="state,signed_equivalent_strain,signed_equivalent_stress_MPa,equivalent_plastic_strain") |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| plt.rcParams.update({"font.family": "DejaVu Sans", "font.size": 11}) |
| bg, panel, fg, muted = "#081418", "#102127", "#edf3f1", "#97afb2" |
| colors = ["#85d6cb", "#e9b16d"] |
| fig, axes = plt.subplots(1, 2, figsize=(12, 5.7), facecolor=bg) |
| fig.subplots_adjust(left=.085, right=.97, bottom=.23, top=.76, wspace=.3) |
| for ax in axes: |
| ax.set_facecolor(panel) |
| ax.tick_params(colors=muted) |
| ax.xaxis.label.set_color(fg); ax.yaxis.label.set_color(fg) |
| for spine in ax.spines.values(): spine.set_color("#344a50") |
| ax.grid(alpha=.12, color=muted) |
| steps = np.arange(b+1) |
| axes[0].plot(steps, strain[:b+1], color=fg, lw=2) |
| axes[0].axhline(0, color=muted, lw=.8, ls="--") |
| axes[0].set(xlabel="Loading state (not physical time)", ylabel="Signed equivalent strain / %") |
| axes[1].plot(strain[:a+1], stress[:a+1], color=colors[0], lw=2) |
| axes[1].plot(strain[a:b+1], stress[a:b+1], color=colors[1], lw=2) |
| axes[1].axvline(0, color=muted, lw=.8, ls="--") |
| axes[1].set(xlabel="Signed equivalent strain / %", ylabel="Signed equivalent stress / MPa") |
| for label, i, color in zip(["A", "B"], (a, b), colors): |
| axes[0].scatter(i, strain[i], color=color, s=65, zorder=5) |
| axes[0].annotate(label, (i, strain[i]), xytext=(0, 12), textcoords="offset points", ha="center", color=color) |
| axes[1].scatter(strain[i], stress[i], color=color, s=65, zorder=5) |
| axes[1].annotate(f"{label} {stress[i]:+.1f} MPa", (strain[i], stress[i]), |
| xytext=(12, 5), textcoords="offset points", color=color, fontsize=10, |
| bbox={"facecolor": panel, "edgecolor": "none", "pad": 2}) |
| fig.text(.085, .92, "Same strain. Different stress.", color=fg, size=25, weight="bold") |
| fig.text(.085, .85, "The loading path leaves a material memory.", color=muted, size=14) |
| fig.text(.085, .105, "A and B: zero total strain, different plastic states. No interpolation or neural prediction.", color=fg, size=10) |
| fig.text(.085, .055, "AgentFEM | Synthetic J2 training history 00252 | Data: CC BY 4.0", color=muted, size=9) |
| fig.savefig(args.out / "material-memory.png", dpi=160, facecolor=bg) |
| plt.close(fig) |
| print(json.dumps(report, indent=2)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
