def get_electron_energy_loss_spectrum(self, zlp, t):
data = ((-1 / self.data).imag * eels_constant(self, zlp, t).data *
self.axes_manager.signal_axes[0].scale)
s = self._deepcopy_with_new_data(data)
s.set_signal_type("EELS")
s.metadata.General.title = ("EELS calculated from " +
self.metadata.General.title)
return s
def get_electron_energy_loss_spectrum(self, zlp, t):
data = ((-1 / self.data).imag * eels_constant(self, zlp, t).data *
self.axes_manager.signal_axes[0].scale)
s = self._deepcopy_with_new_data(data)
s.set_signal_type("EELS")
s.metadata.General.title = ("EELS calculated from " +
self.metadata.General.title)
return s
def setUp(self):
"""To test the kramers_kronig_analysis we will generate 3
EELSSpectrum instances. First a model energy loss function(ELF),
in our case following the Drude bulk plasmon peak. Second, we
simulate the inelastic scattering to generate a model scattering
distribution (SPC). Finally, we use a lorentzian peak with
integral equal to 1 to simulate a ZLP.
"""
# Parameters
i0 = 1.
t = hs.signals.Signal(np.arange(10, 70, 10).reshape((2, 3)))
t.axes_manager.set_signal_dimension(0)
scale = 0.02
# Create an 3x2x2048 spectrum with Drude plasmon
s = hs.signals.EELSSpectrum(np.zeros((2, 3, 2 * 2048)))
s.set_microscope_parameters(
beam_energy=300.0,
convergence_angle=5,
collection_angle=10.0)
s.axes_manager.signal_axes[0].scale = scale
k = eels_constant(s, i0, t)
vpm = VolumePlasmonDrude()
m = s.create_model(auto_background=False)
m.append(vpm)
vpm.intensity.map['values'][:] = 1
vpm.plasmon_energy.map['values'] = np.array([[8., 18.4, 15.8],
[16.6, 4.3, 3.7]])
vpm.fwhm.map['values'] = np.array([[2.3, 4.8, 0.53],
[3.7, 0.3, 0.3]])
vpm.intensity.map['is_set'][:] = True
vpm.plasmon_energy.map['is_set'][:] = True
vpm.fwhm.map['is_set'][:] = True
s.data = (m.as_signal() * k).data
# Create ZLP
z = s.deepcopy()
z.axes_manager.signal_axes[0].scale = scale
z.axes_manager.signal_axes[0].offset = -10
zlp = Lorentzian()
zlp.A.value = i0
zlp.gamma.value = 0.2
zlp.centre.value = 0.0
z.data[:] = zlp.function(z.axes_manager[-1].axis).reshape((1, 1, -1))
z.data *= scale
self.s = s
self.thickness = t
self.k = k
self.zlp = z
def get_electron_energy_loss_spectrum(self, zlp, t):
for axis in self.axes_manager.signal_axes:
if not axis.is_uniform:
raise NotImplementedError(
"The function is not implemented for non-uniform axes.")
data = ((-1 / self.data).imag * eels_constant(self, zlp, t).data *
self.axes_manager.signal_axes[0].scale)
s = self._deepcopy_with_new_data(data)
s.data = s.data.real
s.set_signal_type("EELS")
s.metadata.General.title = ("EELS calculated from " +
self.metadata.General.title)
return s