def test_stack_non_uniform_axis(self):
s = self.signal
s2 = s.deepcopy()
s2.axes_manager[2].offset = 2.5
s.axes_manager[1].convert_to_non_uniform_axis()
s.axes_manager[2].convert_to_non_uniform_axis()
s2.axes_manager[2].convert_to_non_uniform_axis()
# test error for overlapping axes
with pytest.raises(ValueError, match="Signals can only be stacked"):
rs = utils.stack([s, s], axis=2)
# test stacking along non-uniform axis
rs = utils.stack([s, s2], axis=2)
assert rs.axes_manager[2].axis.size == rs.data.shape[2]
# Test stacking without specified axis
rs = utils.stack([s, s])
assert rs.axes_manager.shape == (2, 3, 2, 5)
assert rs.axes_manager[0].axis.size == 2
# Test stacking along uniform axis
rs = utils.stack([s, s], axis=0)
assert rs.axes_manager[0].axis.size == 4
# Test stacking axes with inverse vectors
s.axes_manager[2].axis = s.axes_manager[2].axis[::-1]
s2.axes_manager[2].axis = s2.axes_manager[2].axis[::-1]
rs = utils.stack([s2, s], axis=2)
assert rs.axes_manager[2].axis.size == rs.data.shape[2]
def fem(self, version="omega"):
"""Calculated the variance among some image
Parameters
----------
version : str
The name of the FEM equation to use. 'rings' calculates the mean of the variances of all the patterns at
some k. 'omega' calculates the variance of the annular means for every value of k.
"""
if not self.metadata.has_item('HAADF'):
print("No thickness filter applied...")
if version is 'rings':
var = self.nanmean(axis=-1)
var.map(square)
var = var.nanmean()
center = self.nanmean(axis=-1).nanmean()
center.map(square)
int_vs_k = (var - center) / center
print(int_vs_k.axes_manager)
elif version is 'omega':
var = self.map(square, show_progressbar=False,
inplace=False).nanmean().nanmean(axis=1)
center = self.nanmean(axis=-1)
center.map(square)
center = center.nanmean()
int_vs_k = (var - center) / center
print(int_vs_k.axes_manager)
else:
filt, thickness = self.thickness_filter()
if version is 'rings':
int_vs_k = []
for i, th in enumerate(thickness):
index = np.where(filt.transpose() == i + 1)
index = tuple(zip(index[0], index[1]))
var = stack([self.inav[ind] for ind in index])
v = var.map(square,
inplace=False).nanmean().nanmean(axis=-2)
center = var.nanmean(axis=-2)
center.map(square)
center = center.nanmean()
int_vs_k.append((v - center) / center)
if version is 'omega':
int_vs_k = []
for i, th in enumerate(thickness):
index = np.where(filt.transpose() == i + 1)
index = tuple(zip(index[0], index[1]))
var = stack([self.inav[ind] for ind in index])
v = var.map(square, inplace=False).nanmean(axis=-2)
center = var.nanmean(axis=-2)
center.map(square)
center = center.nanmean()
int_vs_k.append(((v - center) / center).nanmean())
int_vs_k = stack(int_vs_k)
int_vs_k.axes_manager.navigation_axes[0].offset = thickness[0]
int_vs_k.axes_manager.navigation_axes[
0].scale = thickness[1] - thickness[0]
return int_vs_k
def test_stack_of_stack(self):
s = self.signal
s1 = utils.stack([s] * 2)
s2 = utils.stack([s1] * 3)
s3 = s2.split()[0]
s4 = s3.split()[0]
np.testing.assert_array_almost_equal(s4.data, s.data)
assert not hasattr(s4.original_metadata, 'stack_elements')
assert s4.metadata.General.title == 'test'
def test_stack_of_stack(self):
s = self.signal
s1 = utils.stack([s] * 2)
s2 = utils.stack([s1] * 3)
s3 = s2.split()[0]
s4 = s3.split()[0]
np.testing.assert_array_almost_equal(s4.data, s.data)
nt.assert_false(hasattr(s4.original_metadata, 'stack_elements'))
nt.assert_equal(s4.metadata.General.title, 'test')
def test_stack_of_stack(self):
s = self.signal
s1 = utils.stack([s] * 2)
s2 = utils.stack([s1] * 3)
s3 = s2.split()[0]
s4 = s3.split()[0]
assert_true((s4.data == s.data).all())
assert_true((hasattr(s4.original_metadata, 'stack_elements')is False))
assert_true((s4.metadata.General.title == 'test'))
def test_stack_of_stack(self):
s = self.signal
s1 = utils.stack([s] * 2)
s2 = utils.stack([s1] * 3)
s3 = s2.split()[0]
s4 = s3.split()[0]
assert_true((s4.data == s.data).all())
assert_true((hasattr(s4.original_metadata, 'stack_elements') is False))
assert_true((s4.metadata.General.title == 'test'))
def test_stack_not_default(self):
s = self.signal
s1 = s.deepcopy() + 1
s2 = s.deepcopy() * 4
result_signal = utils.stack([s, s1, s2], axis=1)
result_list = result_signal.split()
assert_true(len(result_list) == 3)
assert_true((result_list[0].data == result_signal[::, 0].data).all())
result_signal = utils.stack([s, s1, s2], axis='y')
assert_true((result_list[0].data == result_signal[::, 0].data).all())
def test_stack_not_default(self):
s = self.signal
s1 = s.deepcopy() + 1
s2 = s.deepcopy() * 4
result_signal = utils.stack([s, s1, s2], axis=1)
result_list = result_signal.split()
assert_true(len(result_list) == 3)
assert_true((result_list[0].data == result_signal[::, 0].data).all())
result_signal = utils.stack([s, s1, s2], axis='y')
assert_true((result_list[0].data == result_signal[::, 0].data).all())
def test_stack_not_default(self):
s = self.signal
s1 = s.deepcopy() + 1
s2 = s.deepcopy() * 4
result_signal = utils.stack([s, s1, s2], axis=1)
axis_size = s.axes_manager[1].size
result_list = result_signal.split()
nt.assert_equal(len(result_list), 3)
np.testing.assert_array_almost_equal(
result_list[0].data, result_signal.inav[:, :axis_size].data)
result_signal = utils.stack([s, s1, s2], axis='y')
np.testing.assert_array_almost_equal(
result_list[0].data, result_signal.inav[:, :axis_size].data)
def test_stack_not_default(self):
s = self.signal
s1 = s.deepcopy() + 1
s2 = s.deepcopy() * 4
result_signal = utils.stack([s, s1, s2], axis=1)
axis_size = s.axes_manager[1].size
result_list = result_signal.split()
nt.assert_equal(len(result_list), 3)
np.testing.assert_array_almost_equal(
result_list[0].data, result_signal.inav[:, :axis_size].data)
result_signal = utils.stack([s, s1, s2], axis='y')
np.testing.assert_array_almost_equal(
result_list[0].data, result_signal.inav[:, :axis_size].data)
def test_stack_functional_data_axis(self):
s = self.signal
s2 = s.deepcopy()
# Test stacking of functional data axes with uniform x vector
s.axes_manager[0].convert_to_functional_data_axis(expression='x')
s2.axes_manager[0].offset = 2
s2.axes_manager[0].convert_to_functional_data_axis(expression='x')
rs = utils.stack([s, s2], axis=0)
assert rs.axes_manager[0].axis.size == rs.data.shape[1]
# Test stacking of functional data axes with uniform x vector
s.axes_manager[0].x.convert_to_non_uniform_axis()
s2.axes_manager[0].x.convert_to_non_uniform_axis()
rs = utils.stack([s, s2], axis=0)
assert rs.axes_manager[0].axis.size == rs.data.shape[1]
def test_stack_stack_metadata_index(self):
s = self.signal
s1 = s.deepcopy() + 1
s1.metadata.General.title = 'first signal'
s1.original_metadata.om_title = 'first signal om'
s2 = s.deepcopy() * 4
s2.metadata.General.title = 'second_signal'
s2.original_metadata.om_title = 'second signal om'
res = utils.stack([s1, s2, s], stack_metadata=0)
assert res.metadata.General.title == s1.metadata.General.title
res2 = utils.stack([s1, s2, s], stack_metadata=2)
assert res2.metadata.General.title == s.metadata.General.title
def get_abs_corr_cross_section(
composition, number_of_atoms, take_off_angle,
probe_area): # take_off_angle, temporary value for testing
"""
Calculate absorption correction terms.
Parameters
----------
number_of_atoms: list of signal
Stack of maps with number of atoms per pixel.
take_off_angle: float
X-ray take-off angle in degrees.
"""
toa_rad = np.radians(take_off_angle)
Av = constants.Avogadro
elements = [
intensity.metadata.Sample.elements[0] for intensity in number_of_atoms
]
lines = [
intensity.metadata.Sample.xray_lines[0]
for intensity in number_of_atoms
]
atomic_weights = np.array([
elements_db[element]['General_properties']['atomic_weight']
for element in elements
])
number_of_atoms = utils.stack(number_of_atoms).data
#calculate the total_mass per pixel, or mass thicknessself.
total_mass = np.zeros_like(number_of_atoms[0], dtype='float')
for i, (weight) in enumerate(atomic_weights):
total_mass += (number_of_atoms[i] * weight / Av / probe_area / 1E-15)
# determine mass absorption coefficients and convert from cm^2/g to m^2/atom.
mac = utils.stack(
utils.material.mass_absorption_mixture(
weight_percent=utils.material.atomic_to_weight(composition))) * 0.1
acf = np.zeros_like(number_of_atoms)
constant = 1 / (Av * math.sin(toa_rad) * probe_area * 1E-16)
#determine an absorption coeficcient per element per pixel.
for i, (weight) in enumerate(atomic_weights):
expo = (mac.data[i] * total_mass * constant)
acf[i] = expo / (1 - math.e**(-expo))
return acf
def test_with_signals_examples(self):
from hyperspy.misc.example_signals_loading import \
load_1D_EDS_SEM_spectrum as EDS_SEM_Spectrum
s = EDS_SEM_Spectrum()
np.testing.assert_allclose(
utils.stack(s.get_lines_intensity()).data.squeeze(),
np.array([84163, 89063, 96117, 96700, 99075]))
def test_with_signals_examples(self):
from hyperspy.misc.example_signals_loading import load_1D_EDS_SEM_spectrum as EDS_SEM_Spectrum
s = EDS_SEM_Spectrum()
np.testing.assert_allclose(
utils.stack(s.get_lines_intensity()).data.squeeze(), np.array([84163, 89063, 96117, 96700, 99075])
)
def get_abs_corr_zeta(
weight_percent, mass_thickness,
take_off_angle): # take_off_angle, temporary value for testing
"""
Calculate absorption correction terms.
Parameters
----------
weight_percent: list of signal
Composition in weight percent.
mass_thickness: signal
Density-thickness map in kg/m^2
take_off_angle: float
X-ray take-off angle in degrees.
"""
toa_rad = np.radians(take_off_angle)
csc_toa = 1.0 / np.sin(toa_rad)
# convert from cm^2/g to m^2/kg
mac = utils.stack(
utils.material.mass_absorption_mixture(
weight_percent=weight_percent)) * 0.1
acf = mac.data * mass_thickness.data * csc_toa
acf = acf / (1.0 - np.exp(-(acf)))
return acf
def test_function_nd(lazy):
s = Signal1D(np.empty((200, )))
axis = s.axes_manager.signal_axes[0]
axis.scale = .05
axis.offset = -5
A, sigma1, sigma2, fraction, centre = 5, 0.3, 0.75, 0.5, 1
g1 = SplitVoigt(A=A,
sigma1=sigma1,
sigma2=sigma2,
fraction=fraction,
centre=centre)
s.data = g1.function(axis.axis)
s2 = stack([s] * 2)
if lazy:
s2 = s2.as_lazy()
g2 = SplitVoigt()
assert g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
g2.A.map['values'] = [A] * 2
g2.sigma1.map['values'] = [sigma1] * 2
g2.sigma2.map['values'] = [sigma2] * 2
g2.fraction.map['values'] = [fraction] * 2
g2.centre.map['values'] = [centre] * 2
np.testing.assert_allclose(g2.function_nd(axis.axis), s2.data)
def get_strain_maps(
self,
rot_matr
):
"""Obtain strain maps from the displacement gradient tensor at each
navigation position in the small strain approximation.
Arguments
---------
rot_matr : DisplacementGradientMap
Object containing information on the rotation of the grid
Returns
-------
strain_results : BaseSignal
Signal of shape < 4 | , > , navigation order is e11,e22,e12,theta
"""
# This may need to be eliminated if an existing rotation matrix is used
R, U = self.polar_decomposition()
e11 = -U.isig[0, 0].T + 1
e12 = U.isig[0, 1].T
e21 = U.isig[1, 0].T
e22 = -U.isig[1, 1].T + 1
theta = rot_matr.map(_get_rotation_angle, inplace=False)
theta.axes_manager.set_signal_dimension(2)
strain_results = stack([e11, e22, e12, theta])
return StrainMap(strain_results)
def test_stack_bigger_than_ten(self):
s = self.signal
list_s = [s] * 12
list_s.append(s.deepcopy() * 3)
s1 = utils.stack(list_s)
res = s1.split()
np.testing.assert_array_almost_equal(list_s[-1].data, res[-1].data)
assert res[-1].metadata.General.title == 'test'
def test_plot_xray_lines():
# It should be the same image as with previous test (test_plot_eds_lines)
a = EDS_TEM_Spectrum()
s = stack([a, a * 5])
s.plot()
s._plot_xray_lines(xray_lines=True)
s.axes_manager.navigation_axes[0].index = 1
return s._plot.signal_plot.figure
def fem(self, version="omega", indicies=None):
"""Calculated the variance among some image
Parameters
----------
version : str
The name of the FEM equation to use. 'rings' calculates the mean of the variances of all the patterns at
some k. 'omega' calculates the variance of the annular means for every value of k.
patterns: indicies
Calculates the FEM pattern using only some of the patterns based on their indexes
"""
print("Here")
if version is "omega":
if indicies:
var = stack([self.inav[ind] for ind in indicies])
annular_mean = var.nanmean(axis=-2)
annular_mean_squared = annular_mean.nanmean()**2
v = (annular_mean**2).nanmean()
int_vs_k = (annular_mean_squared / v) - 1
else:
with self.unfolded(unfold_navigation=True,
unfold_signal=False):
annular_mean = self.nanmean(axis=-2)
annular_mean_squared = annular_mean.nanmean()**2
v = (annular_mean**2).nanmean()
int_vs_k = (annular_mean_squared / v) - 1
self.set_signal_type("PolarSignal")
if version is 'rings':
if indicies:
s = stack([self.inav[ind] for ind in indicies])
ring_squared_average = (s**2).nanmean(axis=-2)
ring_squared = s.nanmean(axis=-2)**2
int_vs_k = (ring_squared_average / ring_squared) - 1
else:
with self.unfolded(unfold_navigation=True,
unfold_signal=False):
ring_squared_average = (self**2).nanmean(axis=-2)
ring_squared = self.nanmean(axis=-2)**2
int_vs_k = (ring_squared_average / ring_squared) - 1
self.set_signal_type("PolarSignal")
int_vs_k.axes_manager[0].units = "$nm^{-1}$"
int_vs_k.axes_manager[0].name = "k"
return int_vs_k
def test_stack_bigger_than_ten(self):
s = self.signal
list_s = [s] * 12
list_s.append(s.deepcopy() * 3)
list_s[-1].metadata.General.title = 'test'
s1 = utils.stack(list_s)
res = s1.split()
assert_true((list_s[-1].data == res[-1].data).all())
assert_true((res[-1].metadata.General.title == 'test'))
def test_stack_not_default(self):
s = self.signal
s1 = s.inav[:, :-1] + 1
s2 = s.inav[:, ::2] * 4
result_signal = utils.stack([s, s1, s2], axis=1)
axis_size = s.axes_manager[1].size
axs1 = s1.axes_manager[1].size
result_list = result_signal.split()
assert len(result_list) == 3
for rs in [result_signal, utils.stack([s, s1, s2], axis='y')]:
np.testing.assert_array_almost_equal(
result_list[0].data, rs.inav[:, :axis_size].data)
np.testing.assert_array_almost_equal(
s.data, rs.inav[:, :axis_size].data)
np.testing.assert_array_almost_equal(
s1.data, rs.inav[:, axis_size:axis_size + axs1].data)
np.testing.assert_array_almost_equal(
s2.data, rs.inav[:, axis_size + axs1:].data)
def test_stack_bigger_than_ten(self):
s = self.signal
list_s = [s] * 12
list_s.append(s.deepcopy() * 3)
list_s[-1].metadata.General.title = 'test'
s1 = utils.stack(list_s)
res = s1.split()
np.testing.assert_array_almost_equal(list_s[-1].data, res[-1].data)
nt.assert_equal(res[-1].metadata.General.title, 'test')
def test_stack_bigger_than_ten(self):
s = self.signal
list_s = [s] * 12
list_s.append(s.deepcopy() * 3)
list_s[-1].metadata.General.title = 'test'
s1 = utils.stack(list_s)
res = s1.split()
assert_true((list_s[-1].data == res[-1].data).all())
assert_true((res[-1].metadata.General.title == 'test'))
def test_stack_not_default(self):
s = self.signal
s1 = s.inav[:, :-1] + 1
s2 = s.inav[:, ::2] * 4
result_signal = utils.stack([s, s1, s2], axis=1)
axis_size = s.axes_manager[1].size
axs1 = s1.axes_manager[1].size
axs2 = s2.axes_manager[1].size
result_list = result_signal.split()
assert len(result_list) == 3
for rs in [result_signal, utils.stack([s, s1, s2], axis='y')]:
np.testing.assert_array_almost_equal(
result_list[0].data, rs.inav[:, :axis_size].data)
np.testing.assert_array_almost_equal(
s.data, rs.inav[:, :axis_size].data)
np.testing.assert_array_almost_equal(
s1.data, rs.inav[:, axis_size:axis_size + axs1].data)
np.testing.assert_array_almost_equal(
s2.data, rs.inav[:, axis_size + axs1:].data)
def test_stack_default(self):
s = self.signal
s1 = s.deepcopy() + 1
s2 = s.deepcopy() * 4
test_axis = s.axes_manager[0].index_in_array
result_signal = utils.stack([s, s1, s2])
result_list = result_signal.split()
nt.assert_equal(test_axis, s.axes_manager[0].index_in_array)
nt.assert_equal(len(result_list), 3)
np.testing.assert_array_almost_equal(
result_list[0].data, result_signal.inav[:, :, 0].data)
def test_stack_default(self):
s = self.signal
s1 = s.deepcopy() + 1
s2 = s.deepcopy() * 4
test_axis = s.axes_manager[0].index_in_array
result_signal = utils.stack([s, s1, s2])
result_list = result_signal.split()
assert_true(test_axis == s.axes_manager[0].index_in_array)
assert_true(len(result_list) == 3)
assert_true(
(result_list[0].data == result_signal[::, ::, 0].data).all())
def test_stack_default(self):
s = self.signal
s1 = s.deepcopy() + 1
s2 = s.deepcopy() * 4
test_axis = s.axes_manager[0].index_in_array
result_signal = utils.stack([s, s1, s2])
result_list = result_signal.split()
assert test_axis == s.axes_manager[0].index_in_array
assert len(result_list) == 3
np.testing.assert_array_almost_equal(
result_list[0].data, result_signal.inav[:, :, 0].data)
def test_stack_default(self):
s = self.signal
s1 = s.deepcopy() + 1
s2 = s.deepcopy() * 4
test_axis = s.axes_manager[0].index_in_array
result_signal = utils.stack([s, s1, s2])
result_list = result_signal.split()
assert_true(test_axis == s.axes_manager[0].index_in_array)
assert_true(len(result_list) == 3)
assert_true((result_list[0].data == result_signal.inav[::, ::,
0].data).all())
def test_stack_not_default(self):
s = self.signal
# Add variance to metadata to check that it also stacks correctly
s.metadata.set_item("Signal.Noise_properties.variance", s.deepcopy())
def get_variance_data(s):
return s.metadata.Signal.Noise_properties.variance.data
s1 = s.inav[:, :-1]
s1.data += 1
s2 = s.inav[:, ::2]
s2.data *= 4
result_signal = utils.stack([s, s1, s2], axis=1)
axis_size = s.axes_manager[1].size
axs1 = s1.axes_manager[1].size
result_list = result_signal.split()
assert len(result_list) == 3
for rs in [result_signal, utils.stack([s, s1, s2], axis='y')]:
np.testing.assert_array_almost_equal(
result_list[0].data, rs.inav[:, :axis_size].data)
np.testing.assert_array_almost_equal(
s.data, rs.inav[:, :axis_size].data)
np.testing.assert_array_almost_equal(
s1.data, rs.inav[:, axis_size:axis_size + axs1].data)
np.testing.assert_array_almost_equal(
s2.data, rs.inav[:, axis_size + axs1:].data)
np.testing.assert_array_almost_equal(
get_variance_data(result_list[0]), get_variance_data(rs.inav[:, :axis_size]))
np.testing.assert_array_almost_equal(
get_variance_data(s),
get_variance_data(rs.inav[:, :axis_size])
)
np.testing.assert_array_almost_equal(
get_variance_data(s1),
get_variance_data(rs.inav[:, axis_size:axis_size + axs1])
)
np.testing.assert_array_almost_equal(
get_variance_data(s2),
get_variance_data(rs.inav[:, axis_size + axs1:])
)
def test_function_nd(binned):
s = Signal1D(np.empty((100,)))
axis = s.axes_manager.signal_axes[0]
axis.scale = 1
axis.offset = -20
g1 = Gaussian(50015.156, 10/sigma2fwhm, 10)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
g2 = Gaussian()
factor = axis.scale if binned else 1
g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
assert_allclose(g2.function_nd(axis.axis) * factor, s2.data)
def test_function_nd(binned):
s = Signal1D(np.empty((100, )))
axis = s.axes_manager.signal_axes[0]
axis.scale = 0.02
axis.offset = 1
g1 = PowerLaw(50015.156, 1.2)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
g2 = PowerLaw()
factor = axis.scale if binned else 1
g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
assert_allclose(g2.function_nd(axis.axis) * factor, s2.data, rtol=0.05)
def test_function_nd(binned):
s = Signal1D(np.empty((100,)))
axis = s.axes_manager.signal_axes[0]
axis.scale = 2.
axis.offset = -30
g1 = GaussianHF(50015.156, 23, 10)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
g2 = GaussianHF()
factor = axis.scale if binned else 1
g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
# TODO: sort out while the rtol to be so high...
assert_allclose(g2.function_nd(axis.axis) * factor, s2.data, rtol=0.05)
def test_function_nd(binned, lazy):
s = Signal1D(np.empty((300,)))
axis = s.axes_manager.signal_axes[0]
axis.scale = 0.2
axis.offset = -10
g1 = SkewNormal(A=2, x0=2, scale=10, shape=5)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
if lazy:
s2 = s2.as_lazy()
g2 = SkewNormal()
factor = axis.scale if binned else 1
assert g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
assert_allclose(g2.function_nd(axis.axis) * factor, s2.data, 0.06)
def test_function_nd(binned):
s = Signal1D(np.empty((100, )))
axis = s.axes_manager.signal_axes[0]
axis.scale = 2.
axis.offset = -30
g1 = GaussianHF(50015.156, 23, 10)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
g2 = GaussianHF()
factor = axis.scale if binned else 1
g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
# TODO: sort out while the rtol to be so high...
assert_allclose(g2.function_nd(axis.axis) * factor, s2.data, rtol=0.05)
def test_function_nd(binned, lazy):
s = Signal1D(np.empty((200, )))
s.metadata.Signal.binned = binned
axis = s.axes_manager.signal_axes[0]
axis.scale = .05
axis.offset = -5
g1 = Voigt(centre=1, area=5, gamma=0, sigma=0.5, legacy=False)
s.data = g1.function(axis.axis)
s2 = stack([s] * 2)
if lazy:
s2 = s2.as_lazy()
g2 = Voigt(legacy=False)
factor = axis.scale if binned else 1
g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
assert_allclose(g2.function_nd(axis.axis) * factor, s2.data)
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_stack_stack_metadata_value(self):
s = BaseSignal(1)
s.metadata.General.title = 'title 1'
s.original_metadata.set_item('a', 1)
s2 = BaseSignal(2)
s2.metadata.General.title = 'title 2'
s2.original_metadata.set_item('a', 2)
stack_out = utils.stack([s, s2], stack_metadata=True)
elem0 = stack_out.original_metadata.stack_elements.element0
elem1 = stack_out.original_metadata.stack_elements.element1
for el, _s in zip([elem0, elem1], [s, s2]):
assert el.original_metadata.as_dictionary() == \
_s.original_metadata.as_dictionary()
assert el.metadata.as_dictionary() == _s.metadata.as_dictionary()
def test_function_nd(binned, lazy):
s = Signal1D(np.empty((100, )))
axis = s.axes_manager.signal_axes[0]
axis.scale = 0.2
axis.offset = 15
g1 = Exponential(A=10005.7, tau=214.3)
s.data = g1.function(axis.axis)
s.metadata.Signal.binned = binned
s2 = stack([s] * 2)
if lazy:
s2 = s2.as_lazy()
g2 = Exponential()
factor = axis.scale if binned else 1.
g2.estimate_parameters(s2, axis.low_value, axis.high_value, False)
assert g2.binned == binned
assert_allclose(g2.function_nd(axis.axis) * factor, s2.data, rtol=0.05)
def test_stack_stack_metadata(self, stack_metadata):
s = self.signal
s1 = s.deepcopy() + 1
s2 = s.deepcopy() * 4
test_axis = s.axes_manager[0].index_in_array
result_signal = utils.stack([s, s1, s2], stack_metadata=stack_metadata)
result_list = result_signal.split()
assert test_axis == s.axes_manager[0].index_in_array
assert len(result_list) == 3
np.testing.assert_array_almost_equal(
result_list[0].data, result_signal.inav[:, :, 0].data)
if stack_metadata is True:
om = result_signal.original_metadata.stack_elements.element0.original_metadata
elif stack_metadata in [0, 1]:
om = result_signal.original_metadata
if stack_metadata is False:
assert om.as_dictionary() == {}
else:
assert om.as_dictionary() == s.original_metadata.as_dictionary()
def test_stack_broadcast_number(self):
s = self.signal
rs = utils.stack([5, s])
np.testing.assert_array_equal(
rs.inav[..., 0].data, 5 * np.ones((3, 2, 5)))
def test_stack_broadcast_number_not_default(self):
s = self.signal
rs = utils.stack([5, s], axis='E')
np.testing.assert_array_equal(rs.isig[0].data, 5 * np.ones((3, 2)))
def test_plot_eds_lines():
a = EDS_TEM_Spectrum()
s = stack([a, a * 5])
s.plot(True)
s.axes_manager.navigation_axes[0].index = 1
return s._plot.signal_plot.figure
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,
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\'')