def setUp(self):
s = Spectrum(np.array([1.0, 2, 4, 7, 12, 7, 4, 2, 1]))
m = create_model(s)
self.model = m
self.A = 38.022476979172588
self.sigma = 1.4764966133859543
self.centre = 4.0000000002462945
def setUp(self):
s = hs.signals.Spectrum([1])
s.axes_manager[0].scale = 0.1
m = hs.create_model(s)
m.append(hs.components.Offset())
m[0].offset.value = 1
self.m = m
def setUp(self):
s = hs.signals.Spectrum(np.empty((10)))
s.axes_manager[0].scale = 0.01
m = hs.create_model(s)
m.append(hs.components.Offset())
m[0].offset.value = 10
self.m = m
def setUp(self):
s = Spectrum(np.array([1.0, 2, 4, 7, 12, 7, 4, 2, 1]))
m = create_model(s)
self.model = m
self.A = 38.022476979172588
self.sigma = 1.4764966133859543
self.centre = 4.0000000002462945
def setUp(self):
self.m = hs.create_model(hs.signals.Spectrum(
np.arange(10).reshape(2, 5)))
self.comps = [hs.components.Offset(), hs.components.Offset()]
self.m.extend(self.comps)
for c in self.comps:
c.offset.value = 2
self.m.assign_current_values_to_all()
def setUp(self):
s = hs.signals.Spectrum(np.empty((1024)))
s.axes_manager[0].offset = -5
s.axes_manager[0].scale = 0.01
m = hs.create_model(s)
m.append(hs.components.Polynomial(order=2))
m[0].coefficients.value = (0.5, 2, 3)
self.m = m
def setUp(self):
s = hs.signals.Spectrum(np.empty((1024)))
s.axes_manager[0].offset = 100
s.axes_manager[0].scale = 0.01
m = hs.create_model(s)
m.append(hs.components.PowerLaw())
m[0].A.value = 10
m[0].r.value = 4
self.m = m
def setUp(self):
s = hs.signals.Spectrum(np.empty((1024)))
s.axes_manager[0].offset = -5
s.axes_manager[0].scale = 0.01
m = hs.create_model(s)
m.append(hs.components.Gaussian())
m[0].sigma.value = 0.5
m[0].centre.value = 1
m[0].A.value = 2
self.m = m
def setUp(self):
np.random.seed(1)
s = hs.signals.SpectrumSimulation(np.arange(10, 100, 0.1))
s.metadata.set_item("Signal.Noise_properties.variance",
hs.signals.Spectrum(np.arange(10, 100, 0.01)))
s.axes_manager[0].scale = 0.1
s.axes_manager[0].offset = 10
s.add_poissonian_noise()
m = hs.create_model(s)
m.append(hs.components.Polynomial(1))
self.m = m
def setUp(self):
s = hs.signals.EELSSpectrum(np.ones(200))
s.set_microscope_parameters(100, 10, 10)
s.axes_manager[-1].offset = 150
CE = hs.utils.material.elements.C.Atomic_properties.Binding_energies.K.onset_energy_eV
BE = hs.utils.material.elements.B.Atomic_properties.Binding_energies.K.onset_energy_eV
s.isig[BE:] += 1
s.isig[CE:] += 1
s.add_elements(("Be", "B", "C"))
self.m = hs.create_model(s, auto_background=False)
self.m.append(hs.components.Offset())
def setUp(self):
g = Gaussian()
g.A.value = 10000.0
g.centre.value = 5000.0
g.sigma.value = 500.0
axis = np.arange(10000)
s = Spectrum(g.function(axis))
m = create_model(s)
self.model = m
self.g = g
self.axis = axis
self.rtol = 0.00
def setUp(self):
g1 = Gaussian()
g2 = Gaussian()
g3 = Gaussian()
s = Spectrum(np.arange(1000).reshape(10, 10, 10))
m = create_model(s)
m.append(g1)
m.append(g2)
m.append(g3)
self.g1 = g1
self.g2 = g2
self.g3 = g3
self.model = m
def setUp(self):
g1 = Gaussian()
g2 = Gaussian()
g3 = Gaussian()
s = Spectrum(np.arange(10))
m = create_model(s)
m.append(g1)
m.append(g2)
m.append(g3)
self.g1 = g1
self.g2 = g2
self.g3 = g3
self.model = m
def setUp(self):
np.random.seed(1)
s = hs.signals.Spectrum(
np.random.normal(
scale=2,
size=10000)).get_histogram()
s.metadata.Signal.binned = True
g = hs.components.Gaussian()
m = hs.create_model(s)
m.append(g)
g.sigma.value = 1
g.centre.value = 0.5
g.A.value = 1e3
self.m = m
def setUp(self):
g = Gaussian()
g.A.value = 10000.0
g.centre.value = 5000.0
g.sigma.value = 500.0
axis = np.arange(10000)
s = Spectrum(g.function(axis))
m = create_model(s)
self.A = g.A.value
self.centre = g.centre.value
self.sigma = g.sigma.value
self.model = m
self.g = g
self.axis = axis
self.rtol = 0.00
def setUp(self):
variance = hs.signals.SpectrumSimulation(
np.arange(
100, 300).reshape(
(2, 100)))
s = variance.deepcopy()
np.random.seed(1)
std = 10
s.add_gaussian_noise(std)
s.add_poissonian_noise()
s.metadata.set_item("Signal.Noise_properties.variance",
variance + std ** 2)
m = hs.create_model(s)
m.append(hs.components.Polynomial(order=1))
self.s = s
self.m = m
def setUp(self):
s = hs.signals.Spectrum(np.empty((2, 200)))
s.axes_manager[-1].offset = 1
s.data[:] = 2 * s.axes_manager[-1].axis ** (-3)
m = hs.create_model(s)
m.append(hs.components.PowerLaw())
m[0].A.value = 2
m[0].r.value = 2
m.store_current_values()
m.axes_manager.indices = (1,)
m[0].r.value = 100
m[0].A.value = 2
m.store_current_values()
m[0].A.free = False
self.m = m
m.axes_manager.indices = (0,)
m[0].A.value = 100
def setUp(self):
gs1 = Gaussian()
gs1.A.value = 10000.0
gs1.centre.value = 5000.0
gs1.sigma.value = 500.0
gs2 = Gaussian()
gs2.A.value = 60000.0
gs2.centre.value = 2000.0
gs2.sigma.value = 300.0
gs3 = Gaussian()
gs3.A.value = 20000.0
gs3.centre.value = 6000.0
gs3.sigma.value = 100.0
axis = np.arange(10000)
total_signal = (gs1.function(axis) +
gs2.function(axis) +
gs3.function(axis))
s = Spectrum(total_signal)
m = create_model(s)
g1 = Gaussian()
g2 = Gaussian()
g3 = Gaussian()
m.append(g1)
m.append(g2)
m.append(g3)
self.model = m
self.gs1 = gs1
self.gs2 = gs2
self.gs3 = gs3
self.g1 = g1
self.g2 = g2
self.g3 = g3
self.axis = axis
self.rtol = 0.01
def setUp(self):
gs1 = Gaussian()
gs1.A.value = 10000.0
gs1.centre.value = 5000.0
gs1.sigma.value = 500.0
gs2 = Gaussian()
gs2.A.value = 60000.0
gs2.centre.value = 2000.0
gs2.sigma.value = 300.0
gs3 = Gaussian()
gs3.A.value = 20000.0
gs3.centre.value = 6000.0
gs3.sigma.value = 100.0
axis = np.arange(10000)
total_signal = (gs1.function(axis) + gs2.function(axis) +
gs3.function(axis))
s = Spectrum(total_signal)
m = create_model(s)
g1 = Gaussian()
g2 = Gaussian()
g3 = Gaussian()
m.append(g1)
m.append(g2)
m.append(g3)
self.model = m
self.gs1 = gs1
self.gs2 = gs2
self.gs3 = gs3
self.g1 = g1
self.g2 = g2
self.g3 = g3
self.axis = axis
self.rtol = 0.01
def setUp(self):
s = hs.signals.Spectrum(np.empty(1))
m = hs.create_model(s)
self.model = m
def setUp(self):
s = Spectrum(np.empty(1))
m = create_model(s)
self.model = m
def setUp(self):
self.m = hs.create_model(hs.signals.Spectrum(
np.arange(10).reshape(2, 5)))
self.comps = [hs.components.Offset(), hs.components.Offset()]
self.m.extend(self.comps)
def setUp(self):
self.m = hs.create_model(hs.signals.Spectrum(np.arange(10)))
self.o = hs.components.Offset()
self.m.append(self.o)
def setUp(self):
s = hs.signals.SpectrumSimulation(np.ones(100))
m = hs.create_model(s)
m.append(hs.components.Offset())
self.s = s
self.m = m
""" Loads hyperspy as a regular python library, loads spectrums from files, does curve fitting, and plotting the model and original spectrum to a png file"""
import hyperspy.hspy as hspy
import matplotlib.pyplot as plt
coreLossSpectrumFileName = "coreloss_spectrum.msa"
lowlossSpectrumFileName = "lowloss_spectrum.msa"
s = hspy.load(coreLossSpectrumFileName).to_EELS()
s.add_elements(("Mn", "O"))
s.set_microscope_parameters(
beam_energy=300,
convergence_angle=24.6,
collection_angle=13.6)
ll = hspy.load(lowlossSpectrumFileName).to_EELS()
m = hspy.create_model(s, ll=ll)
m.enable_fine_structure()
m.multifit(kind="smart")
m.plot()
plt.savefig("model_original_spectrum_plot.png")
def setUp(self):
s = hs.signals.EELSSpectrum(np.empty(200))
s.set_microscope_parameters(100, 10, 10)
s.axes_manager[-1].offset = 150
s.add_elements(("B", "C"))
self.m = hs.create_model(s)
def setUp(self):
s = hs.signals.EELSSpectrum(np.empty(200))
s.set_microscope_parameters(100, 10, 10)
s.axes_manager[-1].offset = 150
s.add_elements(("B", "C"))
self.m = hs.create_model(s)
def setUp(self):
s = Spectrum(np.empty(1))
m = create_model(s)
self.model = m
""" Loads hyperspy as a regular python library, loads spectrums from files, does curve fitting, and plotting the model and original spectrum to a png file"""
import hyperspy.hspy as hspy
import matplotlib.pyplot as plt
coreLossSpectrumFileName = "coreloss_spectrum.msa"
lowlossSpectrumFileName = "lowloss_spectrum.msa"
s = hspy.load(coreLossSpectrumFileName).to_EELS()
s.add_elements(("Mn", "O"))
s.set_microscope_parameters(beam_energy=300,
convergence_angle=24.6,
collection_angle=13.6)
ll = hspy.load(lowlossSpectrumFileName).to_EELS()
m = hspy.create_model(s, ll=ll)
m.enable_fine_structure()
m.multifit(kind="smart")
m.plot()
plt.savefig("model_original_spectrum_plot.png")