ssapy_toolkit.IO.dict_to_from_hdf5

Utilities for saving/loading nested Python dictionaries to/from HDF5.

Public API:

save_dict_to_hdf5(filename, data, …) load_dict_from_hdf5(filename)

Functions

load_dict_from_hdf5(filename)

Load a dictionary previously stored with save_dict_to_hdf5(filename, data, ...).

save_dict_to_hdf5(filename, data[, mode, ...])

Save a (possibly nested) Python dictionary to an HDF5 file.
ssapy_toolkit.IO.dict_to_from_hdf5.load_dict_from_hdf5(filename: str) → dict[source]

Load a dictionary previously stored with save_dict_to_hdf5(filename, data, …).

Behavior:

  • Groups marked with __is_sequence__=True are reconstructed as lists, even if empty.

  • Groups marked with __is_sequence__=False are reconstructed as dicts, even if empty.

  • Groups without the attribute:

    • if keys are 0..n-1 as strings, treated as lists

    • otherwise, treated as dicts.

  • Supported special types: astropy.time.Time, datetime.*, bytes, pickled objects.

ssapy_toolkit.IO.dict_to_from_hdf5.save_dict_to_hdf5(filename: str, data: Mapping[str, Any], mode: str = 'w', *, pickle_objects: bool = True, compression: str | None = 'gzip', compression_opts: int | None = 4) → None[source]

Save a (possibly nested) Python dictionary to an HDF5 file.

Signature:

save_dict_to_hdf5(filename, data, …)

Supported types:

  • dict -> HDF5 group

  • list/tuple -> HDF5 group with numeric keys (“0”, “1”, …)

  • numpy arrays -> datasets (with compression, if requested)

  • scalars: int/float/bool, numpy scalar types -> scalar datasets (no compression)

  • str -> variable-length UTF-8 datasets

  • bytes/bytearray/memoryview -> bytes datasets

  • datetime.datetime/date/time -> stored as ISO strings

  • astropy.time.Time -> stored as (mjd, scale, format, meta)

  • other objects -> pickled if pickle_objects=True