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