def write_signal(self, signal, group, write_dataset=True, chunks=None,
**kwds):
"Writes a hyperspy signal to a hdf5 group"
group.attrs.update(get_object_package_info(signal))
if Version(version) < Version("1.2"):
metadata = "mapped_parameters"
original_metadata = "original_parameters"
else:
metadata = "metadata"
original_metadata = "original_metadata"
for axis in signal.axes_manager._axes:
axis_dict = axis.get_axis_dictionary()
group_name = f'axis-{axis.index_in_array}'
# delete existing group in case the file have been open in 'a' mode
# and we are saving a different type of axis, to avoid having
# incompatible axis attributes from previously saved axis.
if group_name in group.keys():
del group[group_name]
coord_group = group.create_group(group_name)
self.dict2group(axis_dict, coord_group, **kwds)
mapped_par = group.require_group(metadata)
metadata_dict = signal.metadata.as_dictionary()
if write_dataset:
self.overwrite_dataset(
group,
signal.data,
'data',
signal_axes=signal.axes_manager.signal_indices_in_array,
chunks=chunks,
**kwds
)
if default_version < Version("1.2"):
metadata_dict["_internal_parameters"] = \
metadata_dict.pop("_HyperSpy")
self.dict2group(metadata_dict, mapped_par, **kwds)
original_par = group.require_group(original_metadata)
self.dict2group(signal.original_metadata.as_dictionary(), original_par,
**kwds)
learning_results = group.require_group('learning_results')
self.dict2group(signal.learning_results.__dict__,
learning_results, **kwds)
if hasattr(signal, 'peak_learning_results'): # pragma: no cover
peak_learning_results = group.require_group(
'peak_learning_results')
self.dict2group(signal.peak_learning_results.__dict__,
peak_learning_results, **kwds)
if len(signal.models):
model_group = self.file.require_group('Analysis/models')
self.dict2group(signal.models._models.as_dictionary(),
model_group, **kwds)
for model in model_group.values():
model.attrs['_signal'] = group.name
def as_dictionary(self, fullcopy=True):
"""Returns component as a dictionary
For more information on method and conventions, see
:meth:`hyperspy.misc.export_dictionary.export_to_dictionary`
Parameters
----------
fullcopy : Bool (optional, False)
Copies of objects are stored, not references. If any found,
functions will be pickled and signals converted to dictionaries
Returns
-------
dic : dictionary
A dictionary, containing at least the following fields:
parameters : list
a list of dictionaries of the parameters, one per
_whitelist : dictionary
a dictionary with keys used as references saved attributes, for
more information, see
:meth:`hyperspy.misc.export_dictionary.export_to_dictionary`
* any field from _whitelist.keys() *
"""
dic = {
'parameters': [p.as_dictionary(fullcopy) for p in self.parameters]
}
dic.update(get_object_package_info(self))
export_to_dictionary(self, self._whitelist, dic, fullcopy)
from hyperspy.model import _COMPONENTS
if self._id_name not in _COMPONENTS:
import dill
dic['_class_dump'] = dill.dumps(self.__class__)
return dic
def write_signal(signal, group, **kwds):
"Writes a hyperspy signal to a hdf5 group"
group.attrs.update(get_object_package_info(signal))
if default_version < LooseVersion("1.2"):
metadata = "mapped_parameters"
original_metadata = "original_parameters"
else:
metadata = "metadata"
original_metadata = "original_metadata"
if 'compression' not in kwds:
kwds['compression'] = 'gzip'
for axis in signal.axes_manager._axes:
axis_dict = axis.get_axis_dictionary()
coord_group = group.create_group(
'axis-%s' % axis.index_in_array)
dict2hdfgroup(axis_dict, coord_group, **kwds)
mapped_par = group.create_group(metadata)
metadata_dict = signal.metadata.as_dictionary()
overwrite_dataset(group, signal.data, 'data',
signal_axes=signal.axes_manager.signal_indices_in_array,
**kwds)
if default_version < LooseVersion("1.2"):
metadata_dict["_internal_parameters"] = \
metadata_dict.pop("_HyperSpy")
# Remove chunks from the kwds since it wouldn't have the same rank as the
# dataset and can't be used
kwds.pop('chunks', None)
dict2hdfgroup(metadata_dict, mapped_par, **kwds)
original_par = group.create_group(original_metadata)
dict2hdfgroup(signal.original_metadata.as_dictionary(), original_par,
**kwds)
learning_results = group.create_group('learning_results')
dict2hdfgroup(signal.learning_results.__dict__,
learning_results, **kwds)
if hasattr(signal, 'peak_learning_results'):
peak_learning_results = group.create_group(
'peak_learning_results')
dict2hdfgroup(signal.peak_learning_results.__dict__,
peak_learning_results, **kwds)
if len(signal.models):
model_group = group.file.require_group('Analysis/models')
dict2hdfgroup(signal.models._models.as_dictionary(),
model_group, **kwds)
for model in model_group.values():
model.attrs['_signal'] = group.name