def run_fitting():
"""
main function to run fitting
"""
simulation = get_simulation()
sample = get_sample()
simulation.setSample(sample)
# the core method of this example which adds masks to the simulation
add_mask_to_simulation(simulation)
real_data = create_real_data()
fit_suite = ba.FitSuite()
fit_suite.addSimulationAndRealData(simulation, real_data)
fit_suite.initPrint(10)
draw_observer = ba.DefaultFitObserver(draw_every_nth=10)
fit_suite.attachObserver(draw_observer)
# setting fitting parameters with starting values
fit_suite.addFitParameter("*/Cylinder/Radius", 6. * nm).setLimited(4., 8.)
fit_suite.addFitParameter("*/Cylinder/Height", 9. * nm).setLimited(8., 12.)
# running fit
fit_suite.runFit()
print("Fitting completed.")
fit_suite.printResults()
print("chi2:", fit_suite.getChi2())
print("chi2:", fit_suite.getChi2())
for fitPar in fit_suite.fitParameters():
print(fitPar.name(), fitPar.value(), fitPar.error())
def run_fitting():
"""
main function to run fitting
"""
simulation = get_simulation()
simulation.setSampleBuilder(MySampleBuilder())
real_data = create_real_data()
fit_suite = ba.FitSuite()
fit_suite.addSimulationAndRealData(simulation, real_data)
fit_suite.initPrint(10)
draw_observer = ba.DefaultFitObserver(draw_every_nth=10)
fit_suite.attachObserver(draw_observer)
# setting fitting parameters with starting values
fit_suite.addFitParameter("*radius", 8. * nm,
ba.AttLimits.limited(4., 12.))
fit_suite.addFitParameter("*lattice_constant", 8. * nm,
ba.AttLimits.limited(4., 12.))
print(fit_suite.treeToString())
print(fit_suite.parametersToString())
# running fit
fit_suite.runFit()
print("Fitting completed.")
print("chi2:", fit_suite.getChi2())
for fitPar in fit_suite.fitParameters():
print(fitPar.name(), fitPar.value(), fitPar.error())
def run_fitting():
"""
main function to run fitting
"""
simulation = get_simulation()
sample = get_sample()
simulation.setSample(sample)
real_data = create_real_data()
fit_suite = ba.FitSuite()
#fit_suite.setMinimizer("GSLLMA")
fit_suite.addSimulationAndRealData(simulation, real_data)
fit_suite.initPrint(10)
draw_observer = ba.DefaultFitObserver(draw_every_nth=10)
fit_suite.attachObserver(draw_observer)
fit_suite.addFitParameter("*/FullSphere/Radius", 8. * nm,
ba.AttLimits.limited(4., 12.))
fit_suite.addFitParameter("*HexagonalLattice/LatticeLength", 8. * nm,
ba.AttLimits.limited(4., 12.))
print(fit_suite.treeToString())
print(fit_suite.parametersToString())
# running fit
fit_suite.runFit()
print("Fitting completed.")
print("chi2:", fit_suite.getChi2())
for fitPar in fit_suite.fitParameters():
print(fitPar.name(), fitPar.value(), fitPar.error())
def run_fitting():
simulation = create_simulation()
sample_builder = MySampleBuilder()
simulation.setSampleBuilder(sample_builder)
real_data = load_exp_data()
fit_suite = ba.FitSuite()
draw_observer = ba.DefaultFitObserver(draw_every_nth=10)
fit_suite.attachObserver(draw_observer)
fit_suite.initPrint(10)
fit_suite.addSimulationAndRealData(simulation, real_data)
print("1.8")
# setting fitting parameters with starting values
fit_suite.addFitParameter("*radius", 5.0 * ba.nm,
ba.AttLimits.limited(4.0, 6.0), 0.1 * ba.nm)
fit_suite.addFitParameter("*sigma", 0.55, ba.AttLimits.limited(0.2, 0.8),
0.01 * ba.nm)
fit_suite.addFitParameter("*distance", 27. * ba.nm,
ba.AttLimits.limited(20, 70), 0.1 * ba.nm)
# set fit strategies
# strategy1 = ba.AdjustMinimizerStrategy("Genetic")
# fit_suite.addFitStrategy(strategy1)
# Second fit strategy will use another algorithm.
# It will use best parameters found from previous minimization round.
# strategy2 = ba.AdjustMinimizerStrategy("Minuit2", "Migrad")
# fit_suite.addFitStrategy(strategy2)
# running fit
fit_suite.runFit()
plt.show()
def run_fitting():
"""
main function to run fitting
"""
simulation = get_simulation()
sample = get_sample()
simulation.setSample(sample)
real_data = create_real_data()
fit_suite = ba.FitSuite()
fit_suite.addSimulationAndRealData(simulation, real_data)
fit_suite.initPrint(10)
draw_observer = ba.DefaultFitObserver(draw_every_nth=10)
fit_suite.attachObserver(draw_observer)
# setting fitting parameters with starting values
# Here we select starting values being quite far from true values
# to puzzle our minimizer's as much as possible
fit_suite.addFitParameter("*Height", 1.*nm).setLimited(0.01, 30.)\
.setStep(0.05*nm)
fit_suite.addFitParameter("*Radius", 20.*nm).setLimited(0.01, 30.)\
.setStep(0.05*nm)
# Now we create first fig strategy which will run first minimization round
# using the Genetic minimizer.
# The Genetic minimizer is able to explore large parameter space
# without being trapped by some local minima.
strategy1 = ba.AdjustMinimizerStrategy("Genetic", "",
"MaxIterations=2;RandomSeed=1")
fit_suite.addFitStrategy(strategy1)
# Second fit strategy will use another minimizer.
# It starts from best parameters found in previous minimization
# and then continues until fit converges.
strategy2 = ba.AdjustMinimizerStrategy("Minuit2", "Migrad")
fit_suite.addFitStrategy(strategy2)
# running fit
fit_suite.runFit()
print("Fitting completed.")
print("chi2:", fit_suite.getChi2())
for fitPar in fit_suite.fitParameters():
print(fitPar.name(), fitPar.value(), fitPar.error())
def run_fitting():
"""
main function to run fitting
"""
ax_values, real_data = create_real_data()
axis = make_axis(ax_values)
simulation = get_simulation(axis)
sample = get_sample()
simulation.setSample(sample)
print(simulation.treeToString())
print(simulation.parametersToString())
fit_suite = ba.FitSuite()
fit_suite.addSimulationAndRealData(simulation, real_data)
fit_suite.initPrint(10)
draw_observer = ba.DefaultFitObserver(draw_every_nth=10,
SimulationType='Specular')
fit_suite.attachObserver(draw_observer)
fitPar = ba.FitParameter(5. * ba.nm,
ba.AttLimits.limited(1. * ba.nm, 7. * ba.nm))
fitPar.setName("thickness")
for odd in [1, 3, 5, 7,
9]: # adding patterns for all odd layers' thicknesses
fitPar.addPattern("*" + str(odd) + "/Thickness*")
fit_suite.addFitParameter(fitPar)
strategy1 = ba.AdjustMinimizerStrategy("Minuit2", "Migrad",
"Strategy=2;Tolerance=1e-5")
fit_suite.addFitStrategy(strategy1)
# prints defined fit parameters and their relation to instrument parameters
print(fit_suite.setupToString())
# running fit
print("Starting the fitting")
fit_suite.runFit()
print("Fitting completed.")
print("chi2:", fit_suite.getChi2())
for fitPar in fit_suite.fitParameters():
print(fitPar.name(), fitPar.value(), fitPar.error())
def run_fitting():
"""
main function to run fitting
"""
simulation = get_simulation()
sample = get_sample()
simulation.setSample(sample)
print(simulation.treeToString())
print(simulation.parametersToString())
real_data = create_real_data()
fit_suite = ba.FitSuite()
fit_suite.addSimulationAndRealData(simulation, real_data)
fit_suite.initPrint(10)
draw_observer = ba.DefaultFitObserver(draw_every_nth=10)
fit_suite.attachObserver(draw_observer)
# this fit parameter fits 4 sample parameter with one value
fit_suite.addFitParameter("*Lattice/LatticeLength*", 9.*nm).\
setLimited(6., 10.).addPattern("*Cylinder/Radius").\
addPattern("*Cylinder/Height").setName("custom_length")
# alternatively, following syntax is possible
# fitPar = ba.FitParameter(10.*nm, ba.AttLimits.limited(4., 12.))
# fitPar.setName("custom_length")
# fitPar.addPattern("*Lattice/LatticeLength*")
# fitPar.addPattern("*Cylinder/Radius").addPattern("*Cylinder/Height")
# fit_suite.addFitParameter(fitPar)
# prints defined fit parameters and their relation to instrument parameters
print(fit_suite.setupToString())
# running fit
print("Starting the fitting")
fit_suite.runFit()
print("Fitting completed.")
print("chi2:", fit_suite.getChi2())
for fitPar in fit_suite.fitParameters():
print(fitPar.name(), fitPar.value(), fitPar.error())
def run_fitting():
"""
main function to run fitting
"""
simulation = get_simulation()
sample = get_sample()
simulation.setSample(sample)
real_data = create_real_data()
fit_suite = ba.FitSuite()
fit_suite.addSimulationAndRealData(simulation, real_data)
chiModule = ba.ChiSquaredModule()
chiModule.setIntensityNormalizer(ba.IntensityScaleAndShiftNormalizer())
fit_suite.setChiSquaredModule(chiModule)
fit_suite.initPrint(10)
draw_observer = ba.DefaultFitObserver(draw_every_nth=10)
fit_suite.attachObserver(draw_observer)
# print all defined parameters for sample and simulation
print(fit_suite.parametersToString())
# setting fitting parameters with starting values
fit_suite.addFitParameter("*/Cylinder/Radius", 6. * nm,
ba.AttLimits.limited(4., 8.))
fit_suite.addFitParameter("*/Cylinder/Height", 9. * nm,
ba.AttLimits.limited(8., 12.))
fit_suite.addFitParameter("*/Normalizer/scale", 1.5,
ba.AttLimits.limited(1.0, 3.0))
fit_suite.addFitParameter("*/Normalizer/shift", 50.,
ba.AttLimits.limited(1, 500.))
# running fit
fit_suite.runFit()
print("Fitting completed.")
print("chi2:", fit_suite.getChi2())
for fitPar in fit_suite.fitParameters():
print(fitPar.name(), fitPar.value(), fitPar.error())
def run_fitting():
"""
Setup simulation and fit
"""
sample = get_sample()
simulation = get_simulation()
simulation.setSample(sample)
real_data = create_real_data()
fit_suite = ba.FitSuite()
fit_suite.addSimulationAndRealData(simulation, real_data)
# fit_suite.setMinimizer("Minuit2", "Migrad") # ba.Default
# fit_suite.setMinimizer("Minuit2", "Fumili")
# fit_suite.setMinimizer("GSLLMA")
fit_suite.initPrint(10)
# setting fitting parameters with starting values
fit_suite.addFitParameter("*Cylinder/Height",
4. * nm).setLowerLimited(0.01)
fit_suite.addFitParameter("*Cylinder/Radius",
6. * nm).setLowerLimited(0.01)
fit_suite.addFitParameter("*Prism3/Height", 4. * nm).setLowerLimited(0.01)
fit_suite.addFitParameter("*Prism3/BaseEdge",
12. * nm).setLowerLimited(0.01)
draw_observer = ba.DefaultFitObserver(draw_every_nth=10)
fit_suite.attachObserver(draw_observer)
fit_suite.runFit()
print("Fitting completed.")
print("chi2:", fit_suite.getChi2())
for fitPar in fit_suite.fitParameters():
print(fitPar.name(), fitPar.value(), fitPar.error())
return fit_suite