File size: 2,745 Bytes
ebc46a9 | 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 | """Lightweight reader for T2 material-loading-memory HDF5 shards."""
from __future__ import annotations
import json
from pathlib import Path
from typing import Iterator
import h5py
import numpy as np
def load_manifest(dataset_root: str | Path) -> dict[str, object]:
root = Path(dataset_root)
return json.loads((root / "manifest.json").read_text(encoding="utf-8"))
def _resolve_shard(root: Path, relative_path: str) -> Path:
candidate = root / "shards" / Path(relative_path).name
if candidate.exists():
return candidate
return root.parents[1] / relative_path
def _group_to_sample(sample_id: str, group: h5py.Group) -> dict[str, object]:
return {
"id": sample_id,
"case_id": str(group.attrs["case_id"]),
"split": str(group.attrs["split"]),
"material_model": str(group.attrs["material_model"]),
"path_family": str(group.attrs["path_family"]),
"parameters": json.loads(str(group.attrs["parameters_json"])),
"metrics": json.loads(str(group.attrs["metrics_json"])),
"history": {name: np.asarray(group[name]) for name in group.keys()},
}
def iter_trajectories(
dataset_root: str | Path,
*,
split: str | None = None,
material_model: str | None = None,
path_family: str | None = None,
) -> Iterator[dict[str, object]]:
"""Yield copied histories, optionally filtered by trajectory metadata."""
root = Path(dataset_root)
manifest = load_manifest(root)
for shard in manifest["shards"]:
with h5py.File(_resolve_shard(root, shard["path"]), "r") as h5:
for sample_id in sorted(h5.keys()):
group = h5[sample_id]
if split is not None and str(group.attrs["split"]) != split:
continue
if (
material_model is not None
and str(group.attrs["material_model"]) != material_model
):
continue
if (
path_family is not None
and str(group.attrs["path_family"]) != path_family
):
continue
yield _group_to_sample(sample_id, group)
def load_trajectory(
dataset_root: str | Path, sample_id: str | int
) -> dict[str, object]:
target = f"{int(sample_id):05d}"
root = Path(dataset_root)
manifest = load_manifest(root)
for shard in manifest["shards"]:
if shard["first_id"] <= target <= shard["last_id"]:
with h5py.File(_resolve_shard(root, shard["path"]), "r") as h5:
if target in h5:
return _group_to_sample(target, h5[target])
raise KeyError(f"Unknown trajectory id: {target}")
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