def _create_signal(
shape,
dim,
dtype,
):
data = np.arange(np.product(shape)).reshape(shape).astype(dtype)
if dim == 1:
if len(shape) > 2:
s = EELSSpectrum(data)
s.set_microscope_parameters(beam_energy=100.,
convergence_angle=1.,
collection_angle=10.)
else:
s = EDSTEMSpectrum(data)
s.set_microscope_parameters(beam_energy=100.,
live_time=1.,
tilt_stage=2.,
azimuth_angle=3.,
elevation_angle=4.,
energy_resolution_MnKa=5.)
else:
s = BaseSignal(data)
s.axes_manager.set_signal_dimension(dim)
for i, axis in enumerate(s.axes_manager._axes):
i += 1
axis.offset = i * 0.5
axis.scale = i * 100
axis.name = "%i" % i
if axis.navigate:
axis.units = "m"
else:
axis.units = "eV"
return s
def _create_signal(shape, dim, dtype,):
data = np.arange(np.product(shape)).reshape(
shape).astype(dtype)
if dim == 1:
if len(shape) > 2:
s = EELSSpectrum(data)
s.set_microscope_parameters(
beam_energy=100.,
convergence_angle=1.,
collection_angle=10.)
else:
s = EDSTEMSpectrum(data)
s.set_microscope_parameters(
beam_energy=100.,
live_time=1.,
tilt_stage=2.,
azimuth_angle=3.,
elevation_angle=4.,
energy_resolution_MnKa=5.)
else:
s = BaseSignal(data)
s.axes_manager.set_signal_dimension(dim)
for i, axis in enumerate(s.axes_manager._axes):
i += 1
axis.offset = i * 0.5
axis.scale = i * 100
axis.name = "%i" % i
if axis.navigate:
axis.units = "m"
else:
axis.units = "eV"
return s
def get_core_loss_eels_line_scan_signal():
"""Get an artificial core loss electron energy loss line scan spectrum.
Similar to a Mn-L32 and Fe-L32 edge from a perovskite oxide.
Returns
-------
artificial_core_loss_line_scan_signal : HyperSpy EELSSpectrum
Example
-------
>>> s = hs.datasets.artificial_data.get_core_loss_eels_line_scan_signal()
>>> s.plot()
See also
--------
get_low_loss_eels_model : get a low loss signal
get_core_loss_eels_model : get a model instead of a signal
get_low_loss_eels_line_scan_signal : get low loss signal with the same size
"""
from hyperspy.signals import EELSSpectrum
from hyperspy import components1d
x = np.arange(400, 800, 1)
arctan_mn = components1d.Arctan(A=1, k=0.2, x0=688)
arctan_mn.minimum_at_zero = True
arctan_fe = components1d.Arctan(A=1, k=0.2, x0=612)
arctan_fe.minimum_at_zero = True
mn_l3_g = components1d.Gaussian(A=100, centre=695, sigma=4)
mn_l2_g = components1d.Gaussian(A=20, centre=720, sigma=4)
fe_l3_g = components1d.Gaussian(A=100, centre=605, sigma=4)
fe_l2_g = components1d.Gaussian(A=10, centre=630, sigma=3)
mn_intensity = [1, 1, 1, 1, 1, 1, 0.8, 0.5, 0.2, 0, 0, 0]
fe_intensity = [0, 0, 0, 0, 0, 0, 0.2, 0.5, 0.8, 1, 1, 1]
data = np.zeros((len(mn_intensity), len(x)))
for i in range(len(mn_intensity)):
data[i] += arctan_mn.function(x) * mn_intensity[i]
data[i] += mn_l3_g.function(x) * mn_intensity[i]
data[i] += mn_l2_g.function(x) * mn_intensity[i]
data[i] += arctan_fe.function(x) * fe_intensity[i]
data[i] += fe_l3_g.function(x) * fe_intensity[i]
data[i] += fe_l2_g.function(x) * fe_intensity[i]
data[i] += np.random.random(size=len(x)) * 0.7
s = EELSSpectrum(data)
s.axes_manager.signal_axes[0].offset = x[0]
s.metadata.General.title = 'Artifical core loss EEL spectrum'
s.axes_manager.signal_axes[0].name = 'Electron energy loss'
s.axes_manager.signal_axes[0].units = 'eV'
s.axes_manager.navigation_axes[0].name = 'Probe position'
s.axes_manager.navigation_axes[0].units = 'nm'
s.set_microscope_parameters(
beam_energy=200, convergence_angle=26, collection_angle=20)
return s
def get_low_loss_eels_line_scan_signal(add_noise=True, random_state=None):
"""Get an artificial low loss electron energy loss line scan spectrum.
The zero loss peak is offset by 4.1 eV.
Parameters
----------
%s
%s
Example
-------
>>> s = hs.datasets.artificial_data.get_low_loss_eels_signal()
>>> s.plot()
See also
--------
artificial_low_loss_line_scan_signal : :py:class:`~hyperspy._signals.eels.EELSSpectrum`
"""
from hyperspy.signals import EELSSpectrum
from hyperspy import components1d
random_state = check_random_state(random_state)
x = np.arange(-100, 400, 0.5)
zero_loss = components1d.Gaussian(A=100, centre=4.1, sigma=1)
plasmon = components1d.Gaussian(A=100, centre=60, sigma=20)
data_signal = zero_loss.function(x)
data_signal += plasmon.function(x)
data = np.zeros((12, len(x)))
for i in range(12):
data[i] += data_signal
if add_noise:
data[i] += random_state.uniform(size=len(x)) * 0.7
s = EELSSpectrum(data)
s.axes_manager.signal_axes[0].offset = x[0]
s.axes_manager.signal_axes[0].scale = x[1] - x[0]
s.metadata.General.title = 'Artifical low loss EEL spectrum'
s.axes_manager.signal_axes[0].name = 'Electron energy loss'
s.axes_manager.signal_axes[0].units = 'eV'
s.axes_manager.navigation_axes[0].name = 'Probe position'
s.axes_manager.navigation_axes[0].units = 'nm'
s.set_microscope_parameters(beam_energy=200,
convergence_angle=26,
collection_angle=20)
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 = signals.Signal(np.arange(10, 70, 10).reshape((2, 3))) # thickness
t.axes_manager.set_signal_dimension(0)
scale = 0.02
# Create an 3x2x2048 spectrum with Drude plasmon
s = 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 = create_model(s, 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_low_loss_eels_line_scan_signal():
"""Get an artificial low loss electron energy loss line scan spectrum.
The zero loss peak is offset by 4.1 eV.
Returns
-------
artificial_low_loss_line_scan_signal : HyperSpy EELSSpectrum
Example
-------
>>> s = hs.datasets.artificial_data.get_low_loss_eels_signal()
>>> s.plot()
See also
--------
get_core_loss_eels_signal : get a core loss signal
get_core_loss_eels_model : get a core loss model
get_core_loss_eels_line_scan_signal : core loss signal with the same size
"""
from hyperspy.signals import EELSSpectrum
from hyperspy import components1d
x = np.arange(-100, 400, 0.5)
zero_loss = components1d.Gaussian(A=100, centre=4.1, sigma=1)
plasmon = components1d.Gaussian(A=100, centre=60, sigma=20)
data_signal = zero_loss.function(x)
data_signal += plasmon.function(x)
data = np.zeros((12, len(x)))
for i in range(12):
data[i] += data_signal
data[i] += np.random.random(size=len(x)) * 0.7
s = EELSSpectrum(data)
s.axes_manager.signal_axes[0].offset = x[0]
s.axes_manager.signal_axes[0].scale = x[1] - x[0]
s.metadata.General.title = 'Artifical low loss EEL spectrum'
s.axes_manager.signal_axes[0].name = 'Electron energy loss'
s.axes_manager.signal_axes[0].units = 'eV'
s.axes_manager.navigation_axes[0].name = 'Probe position'
s.axes_manager.navigation_axes[0].units = 'nm'
s.set_microscope_parameters(
beam_energy=200, convergence_angle=26, collection_angle=20)
return s
def get_low_loss_eels_signal():
"""Get an artificial low loss electron energy loss spectrum.
The zero loss peak is offset by 4.1 eV.
Returns
-------
artificial_low_loss_signal : :py:class:`~hyperspy._signals.eels.EELSSpectrum`
Example
-------
>>> s = hs.datasets.artificial_data.get_low_loss_eels_signal()
>>> s.plot()
See also
--------
get_core_loss_eels_signal, get_core_loss_eels_model,
get_low_loss_eels_line_scan_signal, get_core_loss_eels_line_scan_signal
"""
from hyperspy.signals import EELSSpectrum
from hyperspy import components1d
x = np.arange(-100, 400, 0.5)
zero_loss = components1d.Gaussian(A=100, centre=4.1, sigma=1)
plasmon = components1d.Gaussian(A=100, centre=60, sigma=20)
data = zero_loss.function(x)
data += plasmon.function(x)
data += np.random.random(size=len(x)) * 0.7
s = EELSSpectrum(data)
s.axes_manager[0].offset = x[0]
s.axes_manager[0].scale = x[1] - x[0]
s.metadata.General.title = 'Artifical low loss EEL spectrum'
s.axes_manager[0].name = 'Electron energy loss'
s.axes_manager[0].units = 'eV'
s.set_microscope_parameters(beam_energy=200,
convergence_angle=26,
collection_angle=20)
return s
def get_core_loss_eels_signal():
"""Get an artificial core loss electron energy loss spectrum.
Similar to a Mn-L32 edge from a perovskite oxide.
Returns
-------
artificial_core_loss_signal : HyperSpy EELSSpectrum
Example
-------
>>> s = hs.datasets.artificial_data.get_core_loss_eels_signal()
>>> s.plot()
See also
--------
get_low_loss_eels_model : get a low loss signal
get_core_loss_eels_model : get a model instead of a signal
get_low_loss_eels_line_scan_signal : get EELS low loss line scan
get_core_loss_eels_line_scan_signal : get EELS core loss line scan
"""
x = np.arange(400, 800, 1)
arctan = components1d.Arctan(A=1, k=0.2, x0=688)
arctan.minimum_at_zero = True
mn_l3_g = components1d.Gaussian(A=100, centre=695, sigma=4)
mn_l2_g = components1d.Gaussian(A=20, centre=720, sigma=4)
data = arctan.function(x)
data += mn_l3_g.function(x)
data += mn_l2_g.function(x)
data += np.random.random(size=len(x)) * 0.7
s = EELSSpectrum(data)
s.axes_manager[0].offset = x[0]
s.metadata.General.title = 'Artifical core loss EEL spectrum'
s.axes_manager[0].name = 'Electron energy loss'
s.axes_manager[0].units = 'eV'
s.set_microscope_parameters(beam_energy=200,
convergence_angle=26,
collection_angle=20)
return s
def get_core_loss_eels_signal(add_powerlaw=False,
add_noise=True,
random_state=None):
"""Get an artificial core loss electron energy loss spectrum.
Similar to a Mn-L32 edge from a perovskite oxide.
Some random noise is also added to the spectrum, to simulate
experimental noise.
Parameters
----------
%s
%s
%s
Example
-------
>>> import hs.datasets.artifical_data as ad
>>> s = ad.get_core_loss_eels_signal()
>>> s.plot()
With the powerlaw background
>>> s = ad.get_core_loss_eels_signal(add_powerlaw=True)
>>> s.plot()
To make the noise the same for multiple spectra, which can
be useful for testing fitting routines
>>> s1 = ad.get_core_loss_eels_signal(random_state=10)
>>> s2 = ad.get_core_loss_eels_signal(random_state=10)
>>> (s1.data == s2.data).all()
True
See also
--------
get_core_loss_eels_line_scan_signal, get_low_loss_eels_line_scan_signal,
get_core_loss_eels_model
"""
from hyperspy.signals import EELSSpectrum
from hyperspy import components1d
random_state = check_random_state(random_state)
x = np.arange(400, 800, 1)
arctan = components1d.EELSArctan(A=1, k=0.2, x0=688)
mn_l3_g = components1d.Gaussian(A=100, centre=695, sigma=4)
mn_l2_g = components1d.Gaussian(A=20, centre=720, sigma=4)
data = arctan.function(x)
data += mn_l3_g.function(x)
data += mn_l2_g.function(x)
if add_noise:
data += random_state.uniform(size=len(x)) * 0.7
if add_powerlaw:
powerlaw = components1d.PowerLaw(A=10e8, r=3, origin=0)
data += powerlaw.function(x)
s = EELSSpectrum(data)
s.axes_manager[0].offset = x[0]
s.metadata.General.title = 'Artifical core loss EEL spectrum'
s.axes_manager[0].name = 'Electron energy loss'
s.axes_manager[0].units = 'eV'
s.set_microscope_parameters(beam_energy=200,
convergence_angle=26,
collection_angle=20)
return s
def get_core_loss_eels_line_scan_signal(add_powerlaw=False,
add_noise=True,
random_state=None):
"""Get an artificial core loss electron energy loss line scan spectrum.
Similar to a Mn-L32 and Fe-L32 edge from a perovskite oxide.
Parameters
----------
%s
%s
%s
Example
-------
>>> s = hs.datasets.artificial_data.get_core_loss_eels_line_scan_signal()
>>> s.plot()
See also
--------
get_low_loss_eels_line_scan_signal, get_core_loss_eels_model
"""
from hyperspy.signals import EELSSpectrum
from hyperspy import components1d
random_state = check_random_state(random_state)
x = np.arange(400, 800, 1)
arctan_mn = components1d.EELSArctan(A=1, k=0.2, x0=688)
arctan_fe = components1d.EELSArctan(A=1, k=0.2, x0=612)
mn_l3_g = components1d.Gaussian(A=100, centre=695, sigma=4)
mn_l2_g = components1d.Gaussian(A=20, centre=720, sigma=4)
fe_l3_g = components1d.Gaussian(A=100, centre=605, sigma=4)
fe_l2_g = components1d.Gaussian(A=10, centre=630, sigma=3)
mn_intensity = [1, 1, 1, 1, 1, 1, 0.8, 0.5, 0.2, 0, 0, 0]
fe_intensity = [0, 0, 0, 0, 0, 0, 0.2, 0.5, 0.8, 1, 1, 1]
data = np.zeros((len(mn_intensity), len(x)))
for i in range(len(mn_intensity)):
data[i] += arctan_mn.function(x) * mn_intensity[i]
data[i] += mn_l3_g.function(x) * mn_intensity[i]
data[i] += mn_l2_g.function(x) * mn_intensity[i]
data[i] += arctan_fe.function(x) * fe_intensity[i]
data[i] += fe_l3_g.function(x) * fe_intensity[i]
data[i] += fe_l2_g.function(x) * fe_intensity[i]
if add_noise:
data[i] += random_state.uniform(size=len(x)) * 0.7
if add_powerlaw:
powerlaw = components1d.PowerLaw(A=10e8, r=3, origin=0)
data += powerlaw.function_nd(np.stack([x] * len(mn_intensity)))
if add_powerlaw:
powerlaw = components1d.PowerLaw(A=10e8, r=3, origin=0)
data += powerlaw.function(x)
s = EELSSpectrum(data)
s.axes_manager.signal_axes[0].offset = x[0]
s.metadata.General.title = 'Artifical core loss EEL spectrum'
s.axes_manager.signal_axes[0].name = 'Electron energy loss'
s.axes_manager.signal_axes[0].units = 'eV'
s.axes_manager.navigation_axes[0].name = 'Probe position'
s.axes_manager.navigation_axes[0].units = 'nm'
s.set_microscope_parameters(beam_energy=200,
convergence_angle=26,
collection_angle=20)
return s
def get_core_loss_eels_signal(add_powerlaw=False):
"""Get an artificial core loss electron energy loss spectrum.
Similar to a Mn-L32 edge from a perovskite oxide.
Some random noise is also added to the spectrum, to simulate
experimental noise.
Parameters
----------
add_powerlaw : bool
If True, adds a powerlaw background to the spectrum.
Default False.
Returns
-------
artificial_core_loss_signal : HyperSpy EELSSpectrum
Example
-------
>>> import hs.datasets.artifical_data as ad
>>> s = ad.get_core_loss_eels_signal()
>>> s.plot()
With the powerlaw background
>>> s = ad.get_core_loss_eels_signal(add_powerlaw=True)
>>> s.plot()
To make the noise the same for multiple spectra, which can
be useful for testing fitting routines
>>> np.random.seed(seed=10)
>>> s1 = ad.get_core_loss_eels_signal()
>>> np.random.seed(seed=10)
>>> s2 = ad.get_core_loss_eels_signal()
>>> (s1.data == s2.data).all()
True
See also
--------
get_low_loss_eels_model : get a low loss signal
get_core_loss_eels_model : get a model instead of a signal
get_low_loss_eels_line_scan_signal : get EELS low loss line scan
get_core_loss_eels_line_scan_signal : get EELS core loss line scan
"""
from hyperspy.signals import EELSSpectrum
from hyperspy import components1d
x = np.arange(400, 800, 1)
arctan = components1d.Arctan(A=1, k=0.2, x0=688)
arctan.minimum_at_zero = True
mn_l3_g = components1d.Gaussian(A=100, centre=695, sigma=4)
mn_l2_g = components1d.Gaussian(A=20, centre=720, sigma=4)
data = arctan.function(x)
data += mn_l3_g.function(x)
data += mn_l2_g.function(x)
data += np.random.random(size=len(x)) * 0.7
if add_powerlaw:
powerlaw = components1d.PowerLaw(A=10e8, r=3, origin=0)
data += powerlaw.function(x)
s = EELSSpectrum(data)
s.axes_manager[0].offset = x[0]
s.metadata.General.title = 'Artifical core loss EEL spectrum'
s.axes_manager[0].name = 'Electron energy loss'
s.axes_manager[0].units = 'eV'
s.set_microscope_parameters(
beam_energy=200, convergence_angle=26, collection_angle=20)
return s
def get_core_loss_eels_signal(add_powerlaw=False):
"""Get an artificial core loss electron energy loss spectrum.
Similar to a Mn-L32 edge from a perovskite oxide.
Some random noise is also added to the spectrum, to simulate
experimental noise.
Parameters
----------
add_powerlaw : bool
If True, adds a powerlaw background to the spectrum.
Default False.
Returns
-------
artificial_core_loss_signal : HyperSpy EELSSpectrum
Example
-------
>>> import hs.datasets.artifical_data as ad
>>> s = ad.get_core_loss_eels_signal()
>>> s.plot()
With the powerlaw background
>>> s = ad.get_core_loss_eels_signal(add_powerlaw=True)
>>> s.plot()
To make the noise the same for multiple spectra, which can
be useful for testing fitting routines
>>> np.random.seed(seed=10)
>>> s1 = ad.get_core_loss_eels_signal()
>>> np.random.seed(seed=10)
>>> s2 = ad.get_core_loss_eels_signal()
>>> (s1.data == s2.data).all()
True
See also
--------
get_low_loss_eels_model : get a low loss signal
get_core_loss_eels_model : get a model instead of a signal
get_low_loss_eels_line_scan_signal : get EELS low loss line scan
get_core_loss_eels_line_scan_signal : get EELS core loss line scan
"""
x = np.arange(400, 800, 1)
arctan = components1d.Arctan(A=1, k=0.2, x0=688)
arctan.minimum_at_zero = True
mn_l3_g = components1d.Gaussian(A=100, centre=695, sigma=4)
mn_l2_g = components1d.Gaussian(A=20, centre=720, sigma=4)
data = arctan.function(x)
data += mn_l3_g.function(x)
data += mn_l2_g.function(x)
data += np.random.random(size=len(x)) * 0.7
if add_powerlaw:
powerlaw = components1d.PowerLaw(A=10e8, r=3, origin=0)
data += powerlaw.function(x)
s = EELSSpectrum(data)
s.axes_manager[0].offset = x[0]
s.metadata.General.title = 'Artifical core loss EEL spectrum'
s.axes_manager[0].name = 'Electron energy loss'
s.axes_manager[0].units = 'eV'
s.set_microscope_parameters(beam_energy=200,
convergence_angle=26,
collection_angle=20)
return s