ssapy_toolkit.IO.hdf5_to_csv

h5_to_csv.py

Library + script:

• Import and call: hdf5_to_csv_per_key(“input.h5”)

• Or run directly: python h5_to_csv.py (uses the hard-coded path in main()).

Functions

dataset_to_python(ds)
hdf5_to_csv_per_key(h5_filename, *[, encoding])	Callable API (for importing).
iter_datasets(h5)
key_to_filename(key[, max_len])
main()
normalize_grid(grid)
place_cell(grid, r, c, val)
write_block(grid, top, left, key, arr2d)
write_column(grid, top, col, key, values)
write_dataset_csv(out_path, key, data[, ...])	Returns (written, warning).
write_grid_csv(path, grid[, encoding])

ssapy_toolkit.IO.hdf5_to_csv.dataset_to_python(ds: Dataset) → Any

ssapy_toolkit.IO.hdf5_to_csv.hdf5_to_csv_per_key(h5_filename: str | Path, *, encoding: str = 'utf-8') → Path

Callable API (for importing).

Always: - creates directory <input_stem>/ next to the input file - writes one CSV per dataset key path inside that directory

Returns:

Path to the output directory.

ssapy_toolkit.IO.hdf5_to_csv.iter_datasets(h5: File) → List[Tuple[str, Dataset]]

ssapy_toolkit.IO.hdf5_to_csv.key_to_filename(key: str, max_len: int = 180) → str

ssapy_toolkit.IO.hdf5_to_csv.main() → None

ssapy_toolkit.IO.hdf5_to_csv.normalize_grid(grid: List[List[str]]) → List[List[str]]

ssapy_toolkit.IO.hdf5_to_csv.place_cell(grid: List[List[str]], r: int, c: int, val: str) → None

ssapy_toolkit.IO.hdf5_to_csv.write_block(grid: List[List[str]], top: int, left: int, key: str, arr2d: Any) → Tuple[int, int]

ssapy_toolkit.IO.hdf5_to_csv.write_column(grid: List[List[str]], top: int, col: int, key: str, values: List[Any]) → int

ssapy_toolkit.IO.hdf5_to_csv.write_dataset_csv(out_path: Path, key: str, data: Any, encoding: str = 'utf-8') → Tuple[bool, str | None]

Returns (written, warning). written=False if skipped due to ndim>2.

ssapy_toolkit.IO.hdf5_to_csv.write_grid_csv(path: Path, grid: List[List[str]], encoding: str = 'utf-8') → None