code/load_t4_structural_dynamics_v1.py

"""Lightweight h5py reader for the sharded T4 v1 dataset."""

from __future__ import annotations

import json
from pathlib import Path

import h5py
import numpy as np


DEFAULT_DATA_DIR = Path(__file__).resolve().parents[1] / "data" / "t4_structural_dynamics_v1"


def load_index(data_dir: str | Path = DEFAULT_DATA_DIR) -> list[dict]:
    path = Path(data_dir) / "index.jsonl"
    return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line]


def trajectory_ids(data_dir: str | Path = DEFAULT_DATA_DIR) -> tuple[str, ...]:
    return tuple(item["trajectory_id"] for item in load_index(data_dir))


def load_trajectory(
    trajectory_id: str,
    data_dir: str | Path = DEFAULT_DATA_DIR,
    *,
    include_fields: bool = True,
) -> dict[str, object]:
    root = Path(data_dir)
    record = next(item for item in load_index(root) if item["trajectory_id"] == trajectory_id)
    with h5py.File(root / record["shard"], "r") as h5:
        group = h5[record["group"]]
        case = h5[record["case_id"]]
        arrays = {
            name: np.asarray(value)
            for name, value in group.items()
            if isinstance(value, h5py.Dataset)
        }
        if include_fields:
            for name in ("time_s", "displacement_m", "velocity_m_per_s", "acceleration_m_per_s2"):
                arrays[f"field_{name}" if name == "time_s" else name] = np.asarray(
                    group[f"fields/{name}"]
                )
            arrays["reference_geometry_m"] = np.asarray(case["common/reference_geometry_m"])
            arrays["topology"] = np.asarray(case["common/topology"])
            arrays["sensor_coordinates_m"] = np.asarray(case["common/sensor_coordinates_m"])
        return {
            "record": record,
            "configuration": json.loads(case.attrs["config_json"]),
            "quality": json.loads(case.attrs["quality_json"]),
            "excitation": json.loads(group.attrs["spec_json"]),
            "arrays": arrays,
        }


if __name__ == "__main__":
    ids = trajectory_ids()
    sample = load_trajectory(ids[0], include_fields=False)
    print(len(ids), ids[0])
    print({name: value.shape for name, value in sample["arrays"].items()})