def test_function():
g = Lorentzian()
g.A.value = 1.5 * np.pi
g.gamma.value = 1
g.centre.value = 2
np.testing.assert_allclose(g.function(2), 1.5)
np.testing.assert_allclose(g.function(4), 0.3)
def test_estimate_parameters_binned(only_current, binned, lazy, uniform):
s = Signal1D(np.empty((250, )))
s.axes_manager.signal_axes[0].is_binned = binned
axis = s.axes_manager.signal_axes[0]
axis.scale = .2
axis.offset = -15
g1 = Lorentzian(52342, 2, 10)
s.data = g1.function(axis.axis)
if not uniform:
axis.convert_to_non_uniform_axis()
if lazy:
s = s.as_lazy()
g2 = Lorentzian()
if binned and uniform:
factor = axis.scale
elif binned:
factor = np.gradient(axis.axis)
else:
factor = 1
assert g2.estimate_parameters(s,
axis.low_value,
axis.high_value,
only_current=only_current)
assert g2._axes_manager[-1].is_binned == binned
np.testing.assert_allclose(g1.A.value, g2.A.value * factor, 0.1)
assert abs(g2.centre.value - g1.centre.value) <= 0.2
assert abs(g2.gamma.value - g1.gamma.value) <= 0.1
def setup_method(self, method):
"""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.BaseSignal(np.arange(10, 70, 10).reshape((2, 3)))
t = t.transpose(signal_axes=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 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.BaseSignal(np.arange(10, 70, 10).reshape((2, 3)))
t = t.transpose(signal_axes=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(show_progressbar=None) * 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 test_function_nd(binned, lazy):
s = Signal1D(np.empty((250,)))
axis = s.axes_manager.signal_axes[0]
axis.scale = .2
axis.offset = -15
g1 = Lorentzian(52342, 2, 10)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
if lazy:
s2 = s2.as_lazy()
g2 = Lorentzian()
factor = axis.scale if binned else 1
g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
np.testing.assert_allclose(g2.function_nd(axis.axis) * factor, s2.data,0.16)
def test_estimate_parameters_binned(only_current, binned, lazy):
s = Signal1D(np.empty((250,)))
s.metadata.Signal.binned = binned
axis = s.axes_manager.signal_axes[0]
axis.scale = .2
axis.offset = -15
g1 = Lorentzian(52342, 2, 10)
s.data = g1.function(axis.axis)
if lazy:
s = s.as_lazy()
g2 = Lorentzian()
factor = axis.scale if binned else 1
assert g2.estimate_parameters(s, axis.low_value, axis.high_value,
only_current=only_current)
assert g2.binned == binned
np.testing.assert_allclose(g1.A.value, g2.A.value * factor,0.1)
assert abs(g2.centre.value - g1.centre.value) <= 0.2
assert abs(g2.gamma.value - g1.gamma.value) <= 0.1
