src/convert_well_active_matter.py

#!/usr/bin/env python3
"""
Convert The Well active_matter nested HDF5 into flat (T, C, H, W) files
for LocalWellHDF5 / run_full.

Default C=11 (full spatiotemporal state, float32, lossless gzip):
  concentration, vx, vy, D00, D01, D10, D11, E00, E01, E10, E11

Does not reduce velocity to speed, does not drop tensors, does not
downcast precision. Truncated/corrupt source files are skipped.

Example:
  python scripts/convert_well_active_matter.py \\
    --src data/well/datasets/active_matter/data/train \\
    --out data/well_simple \\
    --max-trajs 16
"""
from __future__ import annotations

import argparse
from pathlib import Path

import h5py
import numpy as np

CHANNEL_NAMES = [
    "concentration", "vx", "vy",
    "D00", "D01", "D10", "D11",
    "E00", "E01", "E10", "E11",
]


def convert_one(fp: Path, out_dir: Path, n_written: int, max_trajs: int) -> int:
    try:
        with h5py.File(fp, "r") as f:
            conc = f["t0_fields/concentration"][:]
            vel = f["t1_fields/velocity"][:]
            D = f["t2_fields/D"][:]
            E = f["t2_fields/E"][:]
            meta = {}
            for k in ("L", "alpha", "zeta"):
                if f"scalars/{k}" in f:
                    meta[k] = float(f[f"scalars/{k}"][()])
    except OSError as e:
        print(f"SKIP truncated/corrupt: {fp.name} | {e}")
        return n_written

    N, T, H, W = conc.shape
    for i in range(N):
        if n_written >= max_trajs:
            break
        chans = [
            conc[i],
            vel[i, ..., 0], vel[i, ..., 1],
            D[i, ..., 0, 0], D[i, ..., 0, 1], D[i, ..., 1, 0], D[i, ..., 1, 1],
            E[i, ..., 0, 0], E[i, ..., 0, 1], E[i, ..., 1, 0], E[i, ..., 1, 1],
        ]
        fields = np.stack(chans, axis=1).astype(np.float32)
        assert fields.shape == (T, 11, H, W), fields.shape

        out = out_dir / f"traj_{n_written:03d}.hdf5"
        with h5py.File(out, "w") as g:
            g.create_dataset("fields", data=fields, compression="gzip")
            g.attrs["channel_names"] = np.array(CHANNEL_NAMES, dtype="S")
            g.attrs["source_file"] = fp.name
            for k, v in meta.items():
                g.attrs[k] = v
        print(f"wrote {out.name} {fields.shape}")
        n_written += 1
    return n_written


def main():
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument("--src", type=Path, required=True,
                    help="Directory of active_matter train *.hdf5 files")
    ap.add_argument("--out", type=Path, required=True,
                    help="Output directory for flat traj_*.hdf5")
    ap.add_argument("--max-trajs", type=int, default=16)
    ap.add_argument("--clear-out", action="store_true",
                    help="Delete existing traj_*.hdf5 in --out first")
    args = ap.parse_args()

    args.out.mkdir(parents=True, exist_ok=True)
    if args.clear_out:
        for old in args.out.glob("traj_*.hdf5"):
            old.unlink()

    n = 0
    for fp in sorted(args.src.glob("*.hdf5")):
        n = convert_one(fp, args.out, n, args.max_trajs)
        if n >= args.max_trajs:
            break
    print(f"total trajectories: {n}")
    print(f"out_dir: {args.out}")
    print("Train with: python -m src.run_full --data-root <out> --channels 11")


if __name__ == "__main__":
    main()