def _make_float_vlarray(h5file: tables.File, name: str,
attribute: np.ndarray) -> None:
vlarray = h5file.create_vlarray(h5file.root,
name=name,
atom=tables.Float64Atom(shape=()))
for a in attribute:
vlarray.append(a)
def _make_str_vlarray(h5file: tables.File, name: str,
attribute: List[str]) -> None:
vlarray = h5file.create_vlarray(h5file.root,
name=name,
atom=tables.VLStringAtom())
for a in attribute:
vlarray.append(a)
def _add_vlarray(
file: tables.File,
where: tables.group,
name: str,
to_store: List[Union[str, HomList]],
) -> None:
"""
Adds a ragged array to a tables file. Each row in the array is
populated by an element of `to_store`, which can contain either strings or
lists of any of the types supported by PyTables. This includes
floats, integers and other scalar data types.
Parameters
----------
file : tables.File
where : tables.Group
name : str
to_store : list of either str or lists of scalars
"""
if to_store:
if isinstance(to_store[0], str):
to_store = [s.encode("utf-8") for s in to_store]
atom = tables.VLStringAtom()
else:
to_store = [np.array(ll) for ll in to_store]
atom = tables.Atom.from_dtype(to_store[0].dtype)
else:
atom = tables.StringAtom(itemsize=1)
vla = file.create_vlarray(
where,
name,
atom=atom,
filters=compression_filter,
expectedrows=len(to_store),
)
for s in to_store:
vla.append(s)