File size: 4,836 Bytes
d8cc85c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
#!/usr/bin/env python3
"""Validate the standardized PDENNEval dataset package."""

from __future__ import annotations

import argparse
import hashlib
import json
import math
import sys
from pathlib import Path

import h5py
import numpy as np


REPO_ROOT = Path(__file__).resolve().parents[1]
DATA_ROOT = REPO_ROOT / "data"
CHECKSUM_PATH = REPO_ROOT / "files_sha256.jsonl"

EXPECTED_FILES = {
    "1D_Burgers_Sols_Nu0.001.hdf5": {
        "datasets": {
            "tensor": {"ndim": 3, "dtype": "float32"},
            "x-coordinate": {"ndim": 1, "dtype": "float32"},
            "t-coordinate": {"ndim": 1, "dtype": "float32"},
        },
        "attrs": {"Nu": 0.001},
    },
    "2D_DarcyFlow_beta0.1_Train.hdf5": {
        "datasets": {
            "tensor": {"ndim": 4, "dtype": "float32"},
            "nu": {"ndim": 3, "dtype": "float32"},
            "x-coordinate": {"ndim": 1, "dtype": "float32"},
            "y-coordinate": {"ndim": 1, "dtype": "float32"},
        },
        "attrs": {"beta": 0.1},
    },
    "1D_Advection_Sols_beta1.0.hdf5": {
        "datasets": {
            "tensor": {"ndim": 3, "dtype": "float32"},
            "x-coordinate": {"ndim": 1, "dtype": "float32"},
            "t-coordinate": {"ndim": 1, "dtype": "float32"},
        },
        "attrs": {},
    },
}


def fail(message: str) -> None:
    print(f"[FAIL] {message}")
    raise SystemExit(1)


def ok(message: str) -> None:
    print(f"[OK] {message}")


def warn(message: str) -> None:
    print(f"[WARN] {message}")


def sha256_file(path: Path) -> str:
    digest = hashlib.sha256()
    with path.open("rb") as handle:
        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
            digest.update(chunk)
    return digest.hexdigest()


def as_float(value: object) -> float | None:
    try:
        return float(value)
    except (TypeError, ValueError):
        return None


def validate_hdf5_file(path: Path, spec: dict[str, object]) -> None:
    if not path.is_file():
        fail(f"missing expected HDF5 file: {path}")
    with h5py.File(path, "r") as handle:
        for attr_name, expected in spec.get("attrs", {}).items():
            actual = as_float(handle.attrs.get(attr_name))
            if actual is None or not math.isclose(actual, float(expected), rel_tol=1e-6, abs_tol=1e-12):
                fail(f"{path.name} attr {attr_name!r} expected {expected}, got {handle.attrs.get(attr_name)!r}")
        for dataset_name, dataset_spec in spec["datasets"].items():
            if dataset_name not in handle:
                fail(f"{path.name} missing dataset {dataset_name!r}")
            dataset = handle[dataset_name]
            if dataset.ndim != dataset_spec["ndim"]:
                fail(f"{path.name}/{dataset_name} ndim expected {dataset_spec['ndim']}, got {dataset.ndim}")
            if str(dataset.dtype) != dataset_spec["dtype"]:
                fail(f"{path.name}/{dataset_name} dtype expected {dataset_spec['dtype']}, got {dataset.dtype}")
            if any(dim <= 0 for dim in dataset.shape):
                fail(f"{path.name}/{dataset_name} has invalid shape {dataset.shape}")
            probe = np.asarray(dataset[0])
            if not np.isfinite(probe).all():
                fail(f"{path.name}/{dataset_name} first slice contains non-finite values")
        ok(f"{path.name} HDF5 schema is readable")


def verify_checksums(full_hash: bool) -> None:
    if not CHECKSUM_PATH.exists():
        warn(f"checksum manifest is not present: {CHECKSUM_PATH}")
        return
    records = [json.loads(line) for line in CHECKSUM_PATH.read_text(encoding="utf-8").splitlines() if line.strip()]
    if not records:
        fail("checksum manifest is empty")
    for record in records:
        path = REPO_ROOT / record["path"]
        if not path.is_file():
            fail(f"checksum entry points to missing file: {path}")
        size = path.stat().st_size
        if size != record["size"]:
            fail(f"size mismatch for {path}: expected {record['size']}, got {size}")
        if full_hash:
            digest = sha256_file(path)
            if digest != record["sha256"]:
                fail(f"sha256 mismatch for {path}")
    mode = "size+sha256" if full_hash else "size"
    ok(f"checksum manifest verified in {mode} mode: {len(records)} files")


def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--full-hash", action="store_true", help="verify SHA256 for all large HDF5 files")
    args = parser.parse_args()

    if not DATA_ROOT.is_dir():
        fail(f"dataset data root does not exist: {DATA_ROOT}")
    for filename, spec in EXPECTED_FILES.items():
        validate_hdf5_file(DATA_ROOT / filename, spec)
    verify_checksums(args.full_hash)
    ok("dataset validation completed")
    return 0


if __name__ == "__main__":
    sys.exit(main())