def create_model(self, signal_dict, model_letter):
_logger.debug('Creating model in worker {}'.format(self.identity))
sig = BaseSignal(**signal_dict)
sig._assign_subclass()
self.model = sig.models[model_letter].restore()
for component in self.model:
component.active_is_multidimensional = False
component.active = True
for par in component.parameters:
par.map = par.map.copy()
if self.model.signal.metadata.has_item(
'Signal.Noise_properties.variance'):
var = self.model.signal.metadata.Signal.Noise_properties.variance
if isinstance(var, BaseSignal):
var.data = var.data.copy()
self._array_views_to_copies()
def create_model(self, signal_dict, model_letter):
_logger.debug('Creating model in worker {}'.format(self.identity))
sig = BaseSignal(**signal_dict)
sig._assign_subclass()
self.model = sig.models[model_letter].restore()
self.model.signal.data = self.model.signal.data.copy()
for component in self.model:
component.active_is_multidimensional = False
component.active = True
for par in component.parameters:
par.map = par.map.copy()
var = self.model.signal.metadata.Signal.Noise_properties.variance
if isinstance(var, BaseSignal):
var.data = var.data.copy()
if self.model.low_loss is not None:
self.model.low_loss.data = self.model.low_loss.data.copy()
def as_signal(self, field='values'):
"""Get a parameter map as a signal object.
Please note that this method only works when the navigation
dimension is greater than 0.
Parameters
----------
field : {'values', 'std', 'is_set'}
Raises
------
NavigationDimensionError : if the navigation dimension is 0
"""
from hyperspy.signal import BaseSignal
s = BaseSignal(data=self.map[field],
axes=self._axes_manager._get_navigation_axes_dicts())
if self.component is not None and \
self.component.active_is_multidimensional:
s.data[np.logical_not(self.component._active_array)] = np.nan
s.metadata.General.title = ("%s parameter" % self.name
if self.component is None
else "%s parameter of %s component" %
(self.name, self.component.name))
for axis in s.axes_manager._axes:
axis.navigate = False
if self._number_of_elements > 1:
s.axes_manager._append_axis(
size=self._number_of_elements,
name=self.name,
navigate=True)
s._assign_subclass()
if field == "values":
# Add the variance if available
std = self.as_signal(field="std")
if not np.isnan(std.data).all():
std.data = std.data ** 2
std.metadata.General.title = "Variance"
s.metadata.set_item(
"Signal.Noise_properties.variance", std)
return s
def reconstruct_object(flags, value):
""" Reconstructs the value (if necessary) after having saved it in a
dictionary
"""
if not isinstance(flags, list):
flags = parse_flag_string(flags)
if 'sig' in flags:
if isinstance(value, dict):
from hyperspy.signal import BaseSignal
value = BaseSignal(**value)
value._assign_subclass()
return value
if 'fn' in flags:
ifdill, thing = value
if ifdill is None:
return thing
if ifdill in [True, 'True', b'True']:
return dill.loads(thing)
# should not be reached
raise ValueError("The object format is not recognized")
return value
def reconstruct_object(flags, value):
""" Reconstructs the value (if necessary) after having saved it in a
dictionary
"""
if not isinstance(flags, list):
flags = parse_flag_string(flags)
if 'sig' in flags:
if isinstance(value, dict):
from hyperspy.signal import BaseSignal
value = BaseSignal(**value)
value._assign_subclass()
return value
if 'fn' in flags:
ifdill, thing = value
if ifdill is None:
return thing
if ifdill in [True, 'True', b'True']:
return dill.loads(thing)
# should not be reached
raise ValueError("The object format is not recognized")
return value
