def test_quant_zeros(self):
intens = np.array([[0.5, 0.5, 0.5], [0.0, 0.5, 0.5], [0.5, 0.0, 0.5], [0.5, 0.5, 0.0], [0.5, 0.0, 0.0]]).T
with ignore_warning(message="divide by zero encountered", category=RuntimeWarning):
quant = utils_eds.quantification_cliff_lorimer(intens, [1, 1, 3]).T
np.testing.assert_allclose(
quant, np.array([[0.2, 0.2, 0.6], [0.0, 0.25, 0.75], [0.25, 0.0, 0.75], [0.5, 0.5, 0.0], [1.0, 0.0, 0.0]])
)
def test_quant_zeros(self):
intens = np.array([[0.5, 0.5, 0.5], [0.0, 0.5, 0.5], [0.5, 0.0, 0.5],
[0.5, 0.5, 0.0], [0.5, 0.0, 0.0]]).T
with ignore_warning(message="divide by zero encountered",
category=RuntimeWarning):
quant = utils_eds.quantification_cliff_lorimer(intens, [1, 1, 3]).T
np.testing.assert_allclose(
quant,
np.array([[0.2, 0.2, 0.6], [0.0, 0.25, 0.75], [0.25, 0.0, 0.75],
[0.5, 0.5, 0.0], [1.0, 0.0, 0.0]]))
def test_quant_zeros(self):
intens = np.array([[0.5, 0.5, 0.5],
[0., 0.5, 0.5],
[0.5, 0.0, 0.5],
[0.5, 0.5, 0.0],
[0.5, 0.0, 0.0]]).T
assert_true(np.allclose(
utils_eds.quantification_cliff_lorimer(
intens, [1, 1, 3]).T,
np.array([[0.2, 0.2, 0.6],
[0., 0.25, 0.75],
[0.25, 0., 0.75],
[0.5, 0.5, 0.],
[1., 0., 0.]])))
def test_quant_zeros(self):
intens = np.array([[0.5, 0.5, 0.5],
[0., 0.5, 0.5],
[0.5, 0.0, 0.5],
[0.5, 0.5, 0.0],
[0.5, 0.0, 0.0]]).T
assert_true(np.allclose(
utils_eds.quantification_cliff_lorimer(
intens, [1, 1, 3]).T,
np.array([[0.2, 0.2, 0.6],
[0., 0.25, 0.75],
[0.25, 0., 0.75],
[0.5, 0.5, 0.],
[1., 0., 0.]])))
def quantification(self,
intensities,
method,
factors,
composition_units='atomic',
navigation_mask=1.0,
closing=True,
plot_result=False,
**kwargs):
"""
Quantification using Cliff-Lorimer, the zeta-factor method, or
ionization cross sections.
Parameters
----------
intensities: list of signal
the intensitiy for each X-ray lines.
method: 'CL' or 'zeta' or 'cross_section'
Set the quantification method: Cliff-Lorimer, zeta-factor, or
ionization cross sections.
factors: list of float
The list of kfactors, zeta-factors or cross sections in same order
as intensities. Note that intensities provided by Hyperspy are
sorted by the alphabetical order of the X-ray lines.
eg. factors =[0.982, 1.32, 1.60] for ['Al_Ka', 'Cr_Ka', 'Ni_Ka'].
composition_units: 'weight' or 'atomic'
The quantification returns the composition in atomic percent by
default, but can also return weight percent if specified.
navigation_mask : None or float or signal
The navigation locations marked as True are not used in the
quantification. If int is given the vacuum_mask method is used to
generate a mask with the int value as threhsold.
Else provides a signal with the navigation shape.
closing: bool
If true, applied a morphologic closing to the mask obtained by
vacuum_mask.
plot_result : bool
If True, plot the calculated composition. If the current
object is a single spectrum it prints the result instead.
kwargs
The extra keyword arguments are passed to plot.
Returns
------
A list of quantified elemental maps (signal) giving the composition of
the sample in weight or atomic percent.
If the method is 'zeta' this function also returns the mass thickness
profile for the data.
If the method is 'cross_section' this function also returns the atom
counts for each element.
Examples
--------
>>> s = hs.datasets.example_signals.EDS_TEM_Spectrum()
>>> s.add_lines()
>>> kfactors = [1.450226, 5.075602] #For Fe Ka and Pt La
>>> bw = s.estimate_background_windows(line_width=[5.0, 2.0])
>>> s.plot(background_windows=bw)
>>> intensities = s.get_lines_intensity(background_windows=bw)
>>> res = s.quantification(intensities, kfactors, plot_result=True,
>>> composition_units='atomic')
Fe (Fe_Ka): Composition = 15.41 atomic percent
Pt (Pt_La): Composition = 84.59 atomic percent
See also
--------
vacuum_mask
"""
if self.axes_manager.navigation_size == 0:
navigation_mask = None
elif isinstance(navigation_mask, float):
navigation_mask = self.vacuum_mask(navigation_mask, closing).data
elif navigation_mask is not None:
navigation_mask = navigation_mask.data
composition = utils.stack(intensities, lazy=False)
if method == 'CL':
composition.data = utils_eds.quantification_cliff_lorimer(
composition.data, kfactors=factors,
mask=navigation_mask) * 100.
elif method == 'zeta':
results = utils_eds.quantification_zeta_factor(composition.data,
zfactors=factors,
dose=self._get_dose(
method,
**kwargs))
composition.data = results[0] * 100.
mass_thickness = intensities[0].deepcopy()
mass_thickness.data = results[1]
mass_thickness.metadata.General.title = 'Mass thickness'
elif method == 'cross_section':
results = utils_eds.quantification_cross_section(
composition.data,
cross_sections=factors,
dose=self._get_dose(method, **kwargs))
composition.data = results[0] * 100
number_of_atoms = composition._deepcopy_with_new_data(results[1])
number_of_atoms = number_of_atoms.split()
else:
raise ValueError("Please specify method for quantification, "
"as 'CL', 'zeta' or 'cross_section'.")
composition = composition.split()
if composition_units == 'atomic':
if method != 'cross_section':
composition = utils.material.weight_to_atomic(composition)
else:
if method == 'cross_section':
composition = utils.material.atomic_to_weight(composition)
for i, intensity in enumerate(intensities):
xray_line = intensity.metadata.Sample.xray_lines[0]
element, line = utils_eds._get_element_and_line(xray_line)
composition[i].metadata.General.title = composition_units + \
' percent of ' + element
composition[i].metadata.set_item("Sample.elements", ([element]))
composition[i].metadata.set_item("Sample.xray_lines",
([xray_line]))
if plot_result and \
composition[i].axes_manager.navigation_size == 1:
print("%s (%s): Composition = %.2f %s percent" %
(element, xray_line, composition[i].data,
composition_units))
if method == 'cross_section':
number_of_atoms[i].metadata.General.title = \
'atom counts of ' + element
number_of_atoms[i].metadata.set_item("Sample.elements",
([element]))
number_of_atoms[i].metadata.set_item("Sample.xray_lines",
([xray_line]))
if plot_result and composition[i].axes_manager.navigation_size != 1:
utils.plot.plot_signals(composition, **kwargs)
if method == 'zeta':
self.metadata.set_item("Sample.mass_thickness", mass_thickness)
return composition, mass_thickness
elif method == 'cross_section':
return composition, number_of_atoms
elif method == 'CL':
return composition
else:
raise ValueError("Please specify method for quantification, as "
"'CL', 'zeta' or 'cross_section'.")
def quantification(self,
intensities,
method,
factors='auto',
composition_units='atomic',
navigation_mask=1.0,
closing=True,
plot_result=False,
**kwargs):
"""
Quantification using Cliff-Lorimer, the zeta-factor method, or
ionization cross sections.
Parameters
----------
intensities: list of signal
the intensitiy for each X-ray lines.
method: 'CL' or 'zeta' or 'cross_section'
Set the quantification method: Cliff-Lorimer, zeta-factor, or
ionization cross sections.
factors: list of float
The list of kfactors, zeta-factors or cross sections in same order as
intensities. Note that intensities provided by Hyperspy are sorted
by the alphabetical order of the X-ray lines.
eg. factors =[0.982, 1.32, 1.60] for ['Al_Ka', 'Cr_Ka', 'Ni_Ka'].
composition_units: 'weight' or 'atomic'
The quantification returns the composition in atomic percent by default,
but can also return weight percent if specified.
navigation_mask : None or float or signal
The navigation locations marked as True are not used in the
quantification. If int is given the vacuum_mask method is used to
generate a mask with the int value as threhsold.
Else provides a signal with the navigation shape.
closing: bool
If true, applied a morphologic closing to the mask obtained by
vacuum_mask.
plot_result : bool
If True, plot the calculated composition. If the current
object is a single spectrum it prints the result instead.
kwargs
The extra keyword arguments are passed to plot.
Returns
------
A list of quantified elemental maps (signal) giving the composition of
the sample in weight or atomic percent.
If the method is 'zeta' this function also returns the mass thickness
profile for the data.
If the method is 'cross_section' this function also returns the atom
counts for each element.
Examples
--------
>>> s = hs.datasets.example_signals.EDS_TEM_Spectrum()
>>> s.add_lines()
>>> kfactors = [1.450226, 5.075602] #For Fe Ka and Pt La
>>> bw = s.estimate_background_windows(line_width=[5.0, 2.0])
>>> s.plot(background_windows=bw)
>>> intensities = s.get_lines_intensity(background_windows=bw)
>>> res = s.quantification(intensities, kfactors, plot_result=True,
>>> composition_units='atomic')
Fe (Fe_Ka): Composition = 15.41 atomic percent
Pt (Pt_La): Composition = 84.59 atomic percent
See also
--------
vacuum_mask
"""
if isinstance(navigation_mask, float):
navigation_mask = self.vacuum_mask(navigation_mask, closing).data
elif navigation_mask is not None:
navigation_mask = navigation_mask.data
xray_lines = self.metadata.Sample.xray_lines
composition = utils.stack(intensities)
if method == 'CL':
composition.data = utils_eds.quantification_cliff_lorimer(
composition.data, kfactors=factors,
mask=navigation_mask) * 100.
elif method == 'zeta':
results = utils_eds.quantification_zeta_factor(
composition.data, zfactors=factors,
dose=self._get_dose(method))
composition.data = results[0] * 100.
mass_thickness = intensities[0].deepcopy()
mass_thickness.data = results[1]
mass_thickness.metadata.General.title = 'Mass thickness'
elif method == 'cross_section':
results = utils_eds.quantification_cross_section(composition.data,
cross_sections=factors,
dose=self._get_dose(method))
composition.data = results[0] * 100
number_of_atoms = utils.stack(intensities)
number_of_atoms.data = results[1]
number_of_atoms = number_of_atoms.split()
else:
raise ValueError ('Please specify method for quantification, as \'CL\', \'zeta\' or \'cross_section\'')
composition = composition.split()
if composition_units == 'atomic':
if method != 'cross_section':
composition = utils.material.weight_to_atomic(composition)
else:
if method == 'cross_section':
composition = utils.material.atomic_to_weight(composition)
for i, xray_line in enumerate(xray_lines):
element, line = utils_eds._get_element_and_line(xray_line)
composition[i].metadata.General.title = composition_units + \
' percent of ' + element
composition[i].metadata.set_item("Sample.elements", ([element]))
composition[i].metadata.set_item(
"Sample.xray_lines", ([xray_line]))
if plot_result and \
composition[i].axes_manager.signal_dimension == 0:
print("%s (%s): Composition = %.2f %s percent"
% (element, xray_line, composition[i].data,
composition_units))
if method=='cross_section':
for i, xray_line in enumerate(xray_lines):
element, line = utils_eds._get_element_and_line(xray_line)
number_of_atoms[i].metadata.General.title = 'atom counts of ' +\
element
number_of_atoms[i].metadata.set_item("Sample.elements",
([element]))
number_of_atoms[i].metadata.set_item(
"Sample.xray_lines", ([xray_line]))
if plot_result and composition[i].axes_manager.signal_dimension != 0:
utils.plot.plot_signals(composition, **kwargs)
if method=='zeta':
self.metadata.set_item("Sample.mass_thickness", mass_thickness)
return composition, mass_thickness
elif method == 'cross_section':
return composition, number_of_atoms
elif method == 'CL':
return composition
else:
raise ValueError ('Please specify method for quantification, as \'CL\', \'zeta\' or \'cross_section\'')
def quantification(
self,
intensities,
kfactors,
composition_units="weight",
navigation_mask=1.0,
closing=True,
plot_result=False,
**kwargs
):
"""
Quantification of intensities to return elemental composition
Method: Cliff-Lorimer
Parameters
----------
intensities: list of signal
the intensitiy for each X-ray lines.
kfactors: list of float
The list of kfactor in same order as intensities. Note that
intensities provided by hyperspy are sorted by the aplhabetical
order of the X-ray lines. eg. kfactors =[0.982, 1.32, 1.60] for
['Al_Ka','Cr_Ka', 'Ni_Ka'].
composition_units: 'weight' or 'atomic'
Quantification returns weight percent. By choosing 'atomic', the
return composition is in atomic percent.
navigation_mask : None or float or signal
The navigation locations marked as True are not used in the
quantification. If int is given the vacuum_mask method is used to
generate a mask with the int value as threhsold.
Else provides a signal with the navigation shape.
closing: bool
If true, applied a morphologic closing to the mask obtained by
vacuum_mask.
plot_result : bool
If True, plot the calculated composition. If the current
object is a single spectrum it prints the result instead.
kwargs
The extra keyword arguments are passed to plot.
Return
------
A list of quantified elemental maps (signal) giving the composition of
the sample in weight or atomic percent.
Examples
--------
>>> s = utils.example_signals.EDS_TEM_Spectrum()
>>> s.add_lines()
>>> kfactors = [1.450226, 5.075602] #For Fe Ka and Pt La
>>> bw = s.estimate_background_windows(line_width=[5.0, 2.0])
>>> s.plot(background_windows=bw)
>>> intensities = s.get_lines_intensity(background_windows=bw)
>>> res = s.quantification(intensities, kfactors, plot_result=True,
>>> composition_units='atomic')
Fe (Fe_Ka): Composition = 15.41 atomic percent
Pt (Pt_La): Composition = 84.59 atomic percent
See also
--------
vacuum_mask
"""
if isinstance(navigation_mask, float):
navigation_mask = self.vacuum_mask(navigation_mask, closing).data
elif navigation_mask is not None:
navigation_mask = navigation_mask.data
xray_lines = self.metadata.Sample.xray_lines
composition = utils.stack(intensities)
composition.data = (
utils_eds.quantification_cliff_lorimer(composition.data, kfactors=kfactors, mask=navigation_mask) * 100.0
)
composition = composition.split()
if composition_units == "atomic":
composition = utils.material.weight_to_atomic(composition)
for i, xray_line in enumerate(xray_lines):
element, line = utils_eds._get_element_and_line(xray_line)
composition[i].metadata.General.title = composition_units + " percent of " + element
composition[i].metadata.set_item("Sample.elements", ([element]))
composition[i].metadata.set_item("Sample.xray_lines", ([xray_line]))
if plot_result and composition[i].axes_manager.signal_dimension == 0:
print(
"%s (%s): Composition = %.2f %s percent"
% (element, xray_line, composition[i].data, composition_units)
)
if plot_result and composition[i].axes_manager.signal_dimension != 0:
utils.plot.plot_signals(composition, **kwargs)
return composition
def quantification(self,
intensities,
kfactors,
composition_units='weight',
navigation_mask=1.0,
closing=True,
plot_result=False,
**kwargs):
"""
Quantification of intensities to return elemental composition
Method: Cliff-Lorimer
Parameters
----------
intensities: list of signal
the intensitiy for each X-ray lines.
kfactors: list of float
The list of kfactor in same order as intensities. Note that
intensities provided by hyperspy are sorted by the aplhabetical
order of the X-ray lines. eg. kfactors =[0.982, 1.32, 1.60] for
['Al_Ka','Cr_Ka', 'Ni_Ka'].
composition_units: 'weight' or 'atomic'
Quantification returns weight percent. By choosing 'atomic', the
return composition is in atomic percent.
navigation_mask : None or float or signal
The navigation locations marked as True are not used in the
quantification. If int is given the vacuum_mask method is used to
generate a mask with the int value as threhsold.
Else provides a signal with the navigation shape.
closing: bool
If true, applied a morphologic closing to the mask obtained by
vacuum_mask.
plot_result : bool
If True, plot the calculated composition. If the current
object is a single spectrum it prints the result instead.
kwargs
The extra keyword arguments are passed to plot.
Return
------
A list of quantified elemental maps (signal) giving the composition of
the sample in weight or atomic percent.
Examples
--------
>>> s = utils.example_signals.EDS_TEM_Spectrum()
>>> s.add_lines()
>>> kfactors = [1.450226, 5.075602] #For Fe Ka and Pt La
>>> bw = s.estimate_background_windows(line_width=[5.0, 2.0])
>>> s.plot(background_windows=bw)
>>> intensities = s.get_lines_intensity(background_windows=bw)
>>> res = s.quantification(intensities, kfactors, plot_result=True,
>>> composition_units='atomic')
Fe (Fe_Ka): Composition = 15.41 atomic percent
Pt (Pt_La): Composition = 84.59 atomic percent
See also
--------
vacuum_mask
"""
if isinstance(navigation_mask, float):
navigation_mask = self.vacuum_mask(navigation_mask, closing).data
elif navigation_mask is not None:
navigation_mask = navigation_mask.data
xray_lines = self.metadata.Sample.xray_lines
composition = utils.stack(intensities)
composition.data = utils_eds.quantification_cliff_lorimer(
composition.data, kfactors=kfactors,
mask=navigation_mask) * 100.
composition = composition.split()
if composition_units == 'atomic':
composition = utils.material.weight_to_atomic(composition)
for i, xray_line in enumerate(xray_lines):
element, line = utils_eds._get_element_and_line(xray_line)
composition[i].metadata.General.title = composition_units + \
' percent of ' + element
composition[i].metadata.set_item("Sample.elements", ([element]))
composition[i].metadata.set_item(
"Sample.xray_lines", ([xray_line]))
if plot_result and \
composition[i].axes_manager.signal_dimension == 0:
print("%s (%s): Composition = %.2f %s percent"
% (element, xray_line, composition[i].data,
composition_units))
if plot_result and composition[i].axes_manager.signal_dimension != 0:
utils.plot.plot_signals(composition, **kwargs)
return composition