def get_fine_structure_as_spectrum(self):
"""Returns a spectrum containing the fine structure.
Notes
-----
The fine structure is corrected from multiple scattering if
the model was convolved with a low-loss spectrum
"""
from hyperspy.signals.eels import EELSSpectrum
channels = int(np.floor(
self.fine_structure_width / self.energy_scale))
data = np.zeros(self.fine_structure_coeff.map.shape +
(channels,))
s = EELSSpectrum({
'data' : data,
'axes' : self.intensity._axes_manager._get_axes_dicts()})
s.get_dimensions_from_data()
s.axes_manager.signal_axes[0].offset = self.onset_energy.value
# Backup the axes_manager
original_axes_manager = self._axes_manager
self._axes_manager = s.axes_manager
for spectrum in s:
self.charge_value_from_map()
spectrum.data[:] = self.function(
s.axes_manager.signal_axes[0].axis)
# Restore the axes_manager and the values
self._axes_manager = original_axes_manager
self.charge_value_from_map()
s.mapped_parameters.title = self.name.replace(
'_',' ') + ' fine structure'
return s
def to_EELS(self):
from hyperspy.signals.eels import EELSSpectrum
dic = self._get_signal_dict()
dic['mapped_parameters']['signal_type'] = 'EELS'
eels = EELSSpectrum(dic)
if hasattr(self, 'learning_results'):
eels.learning_results = copy.deepcopy(self.learning_results)
return eels
def load_with_reader(filename, reader, record_by=None, signal=None, **kwds):
from hyperspy.signals.image import Image
from hyperspy.signals.spectrum import Spectrum
from hyperspy.signals.eels import EELSSpectrum
messages.information(reader.description)
file_data_list = reader.file_reader(filename, record_by=record_by, **kwds)
objects = []
for file_data_dict in file_data_list:
if record_by is not None:
file_data_dict['mapped_parameters']['record_by'] = record_by
# The record_by can still be None if it was not defined by the reader
if file_data_dict['mapped_parameters']['record_by'] is None:
print "No data type provided. Defaulting to image."
file_data_dict['mapped_parameters']['record_by'] = 'image'
if signal is not None:
file_data_dict['mapped_parameters']['signal'] = signal
if file_data_dict['mapped_parameters']['record_by'] == 'image':
s = Image(file_data_dict)
else:
if file_data_dict['mapped_parameters']['signal'] == 'EELS':
s = EELSSpectrum(file_data_dict)
else:
s = Spectrum(file_data_dict)
if defaults.plot_on_load is True:
s.plot()
objects.append(s)
if len(objects) == 1:
objects = objects[0]
return objects
def to_EELS(self):
from hyperspy.signals.eels import EELSSpectrum
dic = self._get_signal_dict()
dic['mapped_parameters']['signal'] = 'EELS'
return EELSSpectrum(dic)
