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 _make_mn_eels_spectrum(energy_range=None):
if energy_range is None:
energy_range = (590, 900)
energy = np.arange(energy_range[0], energy_range[1], 1)
mn_arctan = components1d.Arctan(A=2, k=0.2, x0=634)
mn_arctan.minimum_at_zero = True
mn_l3_g = components1d.Gaussian(A=100, centre=642, sigma=1.8)
mn_l2_g = components1d.Gaussian(A=40, centre=652, sigma=1.8)
mn_data = mn_arctan.function(energy)
mn_data += mn_l3_g.function(energy)
mn_data += mn_l2_g.function(energy)
return mn_data
def _make_la_eels_spectrum(energy_range=None):
if energy_range is None:
energy_range = (590, 900)
energy = np.arange(energy_range[0], energy_range[1], 1)
la_arctan = components1d.Arctan(A=1, k=0.2, x0=845)
la_arctan.minimum_at_zero = True
la_l3_g = components1d.Gaussian(A=110, centre=833, sigma=1.5)
la_l2_g = components1d.Gaussian(A=100, centre=850, sigma=1.5)
la_data = la_arctan.function(energy)
la_data += la_l3_g.function(energy)
la_data += la_l2_g.function(energy)
return la_data
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):
"""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
