def _set_metadata_from_mapping(
omd: dict,
md: DictionaryTreeBrowser,
mapping: dict,
):
"""Update metadata dictionary inplace from original metadata
dictionary via a mapping.
Parameters
----------
omd
Dictionary with original metadata.
md
Dictionary with metadata to update.
mapping
Mapping between `omd` and `md`.
"""
for key_out, key_in in mapping.items():
try:
if isinstance(key_in, list):
value = _get_nested_dictionary(omd, key_in)
else:
value = omd[key_in]
md.set_item(key_out, value)
except KeyError:
warnings.warn(f"Could not read {key_in} from file.")
def kikuchipyheader2dicts(
scan_group: h5py.Group, md: DictionaryTreeBrowser
) -> Tuple[DictionaryTreeBrowser, DictionaryTreeBrowser, DictionaryTreeBrowser]:
"""Return scan metadata dictionaries from a kikuchipy h5ebsd file.
Parameters
----------
scan_group : h5py:Group
HDF group of scan data and header.
md
Dictionary with empty fields from kikuchipy's metadata.
Returns
-------
md
kikuchipy ``metadata`` elements available in kikuchipy file.
omd
All metadata available in kikuchipy file.
scan_size
Scan, image, step and detector pixel size available in
kikuchipy file.
"""
# Data sets to not read via hdf5group2dict
pattern_dset_names = list(manufacturer_pattern_names().values())
omd = DictionaryTreeBrowser()
sem_node, ebsd_node = metadata_nodes(["sem", "ebsd"])
md.set_item(
ebsd_node,
hdf5group2dict(
group=scan_group["EBSD/Header"], data_dset_names=pattern_dset_names,
),
)
md = _delete_from_nested_dictionary(md, "Phases")
phase_node = "Sample.Phases"
md.set_item(
sem_node,
hdf5group2dict(
group=scan_group["SEM/Header"], data_dset_names=pattern_dset_names,
),
)
md.set_item(
phase_node,
hdf5group2dict(
group=scan_group["EBSD/Header/Phases"],
data_dset_names=pattern_dset_names,
recursive=True,
),
)
# Get and remove scan info values from metadata
mapping = {
"sx": "pattern_width",
"sy": "pattern_height",
"nx": "n_columns",
"ny": "n_rows",
"step_x": "step_x",
"step_y": "step_y",
"delta": "detector_pixel_size",
}
scan_size = DictionaryTreeBrowser()
for k, v in mapping.items():
scan_size.set_item(k, _get_nested_dictionary(md, ebsd_node + "." + v))
md = _delete_from_nested_dictionary(md, mapping.values())
return md, omd, scan_size