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,
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.fetch_stored_values()
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.fetch_stored_values()
s.metadata.General.title = self.name.replace('_',
' ') + ' fine structure'
return s
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,
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.fetch_stored_values()
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.fetch_stored_values()
s.metadata.General.title = self.name.replace(
'_', ' ') + ' fine structure'
return s
def setup_method(self, method):
s_eels = EELSSpectrum([list(range(10))] * 3)
s_eels.metadata.set_item(
'Acquisition_instrument.TEM.Detector.EELS.collection_angle', 3.0)
s_eels.metadata.set_item('Acquisition_instrument.TEM.beam_energy', 1.0)
s_eels.metadata.set_item(
'Acquisition_instrument.TEM.convergence_angle', 2.0)
self.eels_m = s_eels.create_model(auto_background=False)
def setUp(self):
s_eels = EELSSpectrum([list(range(10))] * 3)
s_eels.metadata.set_item(
'Acquisition_instrument.TEM.Detector.EELS.collection_angle',
3.0)
s_eels.metadata.set_item('Acquisition_instrument.TEM.beam_energy', 1.0)
s_eels.metadata.set_item(
'Acquisition_instrument.TEM.convergence_angle',
2.0)
self.eels_m = s_eels.create_model(auto_background=False)
def setup_method(self, method):
data = np.random.random((10, 10, 600))
s = EELSSpectrum(data)
s.axes_manager[-1].offset = -150.
s.axes_manager[-1].scale = 0.5
s.metadata.set_item(
'Acquisition_instrument.TEM.Detector.EELS.collection_angle', 3.0)
s.metadata.set_item('Acquisition_instrument.TEM.beam_energy', 1.0)
s.metadata.set_item('Acquisition_instrument.TEM.convergence_angle',
2.0)
m = s.create_model(ll=s + 1,
auto_background=False,
auto_add_edges=False)
g = Gaussian()
m.append(g)
self.model = m
def setUp(self):
data = np.random.random((10, 10, 600))
s = EELSSpectrum(data)
s.axes_manager[-1].offset = -150.
s.axes_manager[-1].scale = 0.5
s.metadata.set_item(
'Acquisition_instrument.TEM.Detector.EELS.collection_angle',
3.0)
s.metadata.set_item('Acquisition_instrument.TEM.beam_energy', 1.0)
s.metadata.set_item(
'Acquisition_instrument.TEM.convergence_angle',
2.0)
m = s.create_model(
ll=s + 1,
auto_background=False,
auto_add_edges=False)
g = Gaussian()
m.append(g)
self.model = m
