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): 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): 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)
""" 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")