def test_running(self, extrapolate_lowloss):
s = signals.EELSSpectrum(np.arange(200))
gaussian = Gaussian()
gaussian.A.value = 50
gaussian.sigma.value = 10
gaussian.centre.value = 20
s_ll = signals.EELSSpectrum(gaussian.function(np.arange(0, 200, 1)))
s_ll.axes_manager[0].offset = -50
s.fourier_ratio_deconvolution(s_ll,
extrapolate_lowloss=extrapolate_lowloss)
def _create_signal(shape, dim, dtype, metadata):
data = np.arange(np.product(shape)).reshape(shape).astype(dtype)
if dim == 1:
if len(shape) > 2:
s = signals.EELSSpectrum(data)
if metadata:
s.set_microscope_parameters(beam_energy=100.,
convergence_angle=1.,
collection_angle=10.)
else:
s = signals.EDSTEMSpectrum(data)
if metadata:
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 = signals.BaseSignal(data).transpose(signal_axes=dim)
if metadata:
s.metadata.General.date = "2016-08-06"
s.metadata.General.time = "10:55:00"
s.metadata.General.title = "Test title"
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_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`
"""
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 = signals.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_method(self, method):
s = signals.EELSSpectrum(np.zeros((10, 100)))
self.scale = 0.1
self.offset = -2
eaxis = s.axes_manager.signal_axes[0]
eaxis.scale = self.scale
eaxis.offset = self.offset
self.izlp = eaxis.value2index(0)
self.bg = 2
self.ishifts = np.array([0, 4, 2, -2, 5, -2, -5, -9, -9, -8])
self.new_offset = self.offset - self.ishifts.min() * self.scale
s.data[np.arange(10), self.ishifts + self.izlp] = 10
s.data += self.bg
s.axes_manager[-1].offset += 100
self.signal = s
def compare_spectra(s1, s2, same_size=True):
'''Compares two EELS spectra, spectrum lines or spectrum images. Currently same_size=False is not suported.'''
if same_size == False:
raise NameError('Sorry, function under development')
return
if same_size == True and s1.data.shape != s2.data.shape:
raise NameError(
'Actually, what you want to compare is not the same size.')
return
scomp = signals.EELSSpectrum(s1.data + 1j * s2.data)
copy_eaxis(scomp, s1)
scomp.plot()
return scomp
def get_low_loss_eels_signal(add_noise=True, random_state=None):
"""Get an artificial low loss electron energy loss 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
--------
get_core_loss_eels_signal, get_core_loss_eels_model,
get_low_loss_eels_line_scan_signal, get_core_loss_eels_line_scan_signal
"""
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 = zero_loss.function(x)
data += plasmon.function(x)
if add_noise:
data += random_state.uniform(size=len(x)) * 0.7
s = signals.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 setup_method(self, method):
# Create an empty spectrum
s = signals.EELSSpectrum(np.zeros((3, 2, 1024)))
energy_axis = s.axes_manager.signal_axes[0]
energy_axis.scale = 0.02
energy_axis.offset = -5
gauss = Gaussian()
gauss.centre.value = 0
gauss.A.value = 5000
gauss.sigma.value = 0.5
gauss2 = Gaussian()
gauss2.sigma.value = 0.5
# Inflexion point 1.5
gauss2.A.value = 5000
gauss2.centre.value = 5
s.data[:] = (gauss.function(energy_axis.axis) +
gauss2.function(energy_axis.axis))
self.signal = s
def setup_method(self, method):
# Create an empty spectrum
s = signals.EELSSpectrum(np.ones((4, 2, 1024)))
self.signal = s
def setup_method(self, method):
s = signals.EELSSpectrum(0.1 * np.arange(50, 250, 0.5)**-3.)
s.metadata.Signal.binned = False
s.axes_manager[-1].offset = 50
s.axes_manager[-1].scale = 0.5
self.s = s
def setup_method(self, method):
s = signals.EELSSpectrum(np.diag(np.arange(1, 11)))
s.axes_manager[-1].scale = 0.1
s.axes_manager[-1].offset = 100
self.signal = 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
"""
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 = signals.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
"""
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 = signals.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
