def run_fitting():
"""
Setup simulation and fit
"""
real_data = get_real_data_values()
# setting artificial uncertainties (uncertainty sigma equals a half
# of experimental data value)
uncertainties = real_data * 0.5
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(get_simulation, real_data, uncertainties)
plot_observer = ba.PlotterSpecular(units=ba.AxesUnits.RQ4)
fit_objective.initPrint(10)
fit_objective.initPlot(10, plot_observer)
fit_objective.setObjectiveMetric("Chi2", "L1")
params = ba.Parameters()
params.add("ti_thickness", 50 * ba.angstrom,
min=10 * ba.angstrom, max=60 * ba.angstrom)
minimizer = ba.Minimizer()
minimizer.setMinimizer("Genetic", "", "MaxIterations=40;PopSize=10")
result = minimizer.minimize(fit_objective.evaluate, params)
fit_objective.finalize(result)
def run_fitting():
"""
Setup simulation and fit
"""
real_data = load_real_data()
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(get_simulation, real_data)
# Print fit progress on every n-th iteration.
fit_objective.initPrint(10)
# Plot fit progress on every n-th iteration. Will slow down fit.
fit_objective.initPlot(10)
params = ba.Parameters()
params.add("cylinder_height", 4.*nm, min=0.01)
params.add("cylinder_radius", 6.*nm, min=0.01)
params.add("prism_height", 4.*nm, min=0.01)
params.add("prism_base_edge", 6.*nm, min=0.01)
minimizer = ba.Minimizer()
result = minimizer.minimize(fit_objective.evaluate, params)
fit_objective.finalize(result)
print("Fitting completed.")
print("chi2:", result.minValue())
for fitPar in result.parameters():
print(fitPar.name(), fitPar.value, fitPar.error)
def run_fitting():
"""
main function to run fitting
"""
data1 = create_real_data(0.1 * deg)
data2 = create_real_data(0.4 * deg)
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(simulation1, data1, 1.0)
fit_objective.addSimulationAndData(simulation2, data2, 1.0)
fit_objective.initPrint(10)
# creating custom observer which will draw fit progress
plotter = PlotObserver()
fit_objective.initPlot(10, plotter.update)
params = ba.Parameters()
params.add("radius_a", 4.*nm, min=2.0, max=10.0)
params.add("radius_b", 6.*nm, vary=False)
params.add("height", 4.*nm, min=2.0, max=10.0)
minimizer = ba.Minimizer()
result = minimizer.minimize(fit_objective.evaluate, params)
fit_objective.finalize(result)
def run_fitting():
"""
main function to run fitting
"""
real_data = create_real_data()
# prints info about available minimizers
print(ba.MinimizerFactory().catalogToString())
# prints detailed info about available minimizers and their options
print(ba.MinimizerFactory().catalogDetailsToString())
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(get_simulation, real_data, 1.0)
fit_objective.initPrint(10)
params = ba.Parameters()
params.add("cylinder_height", 4. * nm, min=0.01)
params.add("cylinder_radius", 6. * nm, min=0.01)
params.add("prism_height", 4. * nm, min=0.01)
params.add("prism_base_edge", 12. * nm, min=0.01)
minimizer = ba.Minimizer()
# Uncomment one of the line below to adjust minimizer settings
"""
Setting Minuit2 minimizer with Migrad algorithm, limiting number of iterations.
Minimization will try to respect MaxFunctionCalls value.
"""
# minimizer.setMinimizer("Minuit2", "Migrad", "MaxFunctionCalls=50")
"""
Setting two options at once.
Strategy=2 promises more accurate fit.
"""
# minimizer.setMinimizer("Minuit2", "Migrad", "MaxFunctionCalls=500;Strategy=2")
"""
Setting Minuit2 minimizer with Fumili algorithm.
"""
# minimizer.setMinimizer("Minuit2", "Fumili")
"""
Setting Levenberg-Marquardt algorithm.
"""
# minimizer.setMinimizer("GSLLMA")
result = minimizer.minimize(fit_objective.evaluate_residuals, params)
fit_objective.finalize(result)
print("Fitting completed.")
print("chi2:", result.minValue())
for fitPar in result.parameters():
print(fitPar.name(), fitPar.value, fitPar.error)
def run_fitting():
"""
Setups and runs fit.
"""
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(get_simulation, real_data())
fit_objective.initPrint(10)
params = ba.Parameters()
params.add("radius", 4. * nm, min=0.01)
minimizer = ba.Minimizer()
result = minimizer.minimize(fit_objective.evaluate, params)
fit_objective.finalize(result)
def test_DecayingSinFit(self):
params = ba.Parameters()
params.add(ba.Parameter('amp', 1, ba.AttLimits.positive()))
params.add(ba.Parameter('decay', 0.1, ba.AttLimits.positive()))
params.add(ba.Parameter('phase', 0.1, ba.AttLimits.limited(0.0, 3.1)))
params.add(ba.Parameter('frequency', 1.0, ba.AttLimits.positive()))
model = DecayingSin()
minimizer = ba.Minimizer()
result = minimizer.minimize(model.objective_function, params)
print(result.toString())
# check found parameter values
np.testing.assert_almost_equal(result.parameters().values(),
model.m_params.values(), 3)
def run_fitting():
real_data = load_real_data()
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(create_simulation, real_data, 1.0)
fit_objective.initPrint(10)
fit_objective.initPlot(10)
params = ba.Parameters()
params.add("radius", 5. * nm, min=4.0, max=6.0, step=0.1 * nm)
params.add("sigma", 0.55, min=0.2, max=0.8, step=0.01)
params.add("distance", 27. * nm, min=20.0, max=70.0)
minimizer = ba.Minimizer()
result = minimizer.minimize(fit_objective.evaluate, params)
fit_objective.finalize(result)
def run_fitting():
"""
Runs fitting.
"""
real_data = create_real_data()
objective = MyObjective()
objective.addSimulationAndData(get_simulation, real_data, 1.0)
objective.initPrint(10)
params = ba.Parameters()
params.add('radius', value=7 * nm, min=5 * nm, max=8 * nm)
params.add('length', value=10 * nm, min=8 * nm, max=14 * nm)
minimizer = ba.Minimizer()
result = minimizer.minimize(objective.evaluate_residuals, params)
objective.finalize(result)
def run_fitting():
"""
main function to run fitting
"""
real_data = create_real_data()
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(get_simulation, real_data, 1.0)
fit_objective.initPrint(10)
fit_objective.initPlot(10)
params = ba.Parameters()
params.add("radius", 6.*nm, min=4.0, max=8.0)
params.add("height", 9.*nm, min=8.0, max=12.0)
minimizer = ba.Minimizer()
result = minimizer.minimize(fit_objective.evaluate, params)
fit_objective.finalize(result)
def run_fit():
ref_data = np.loadtxt("experimental_data.txt.gz")
objective = ba.FitObjective()
objective.initPlot(10)
objective.initPrint(10)
objective.addSimulationAndData(get_simulation, ref_data)
params = ba.Parameters()
params.add("radius", value=9.5*nm, min=9*nm, max=11*nm)
params.add("length", value=31.0*nm, min=30*nm, max=35*nm)
minimizer = ba.Minimizer()
minimizer.setMinimizer("Genetic")
result = minimizer.minimize(objective.evaluate, params)
objective.finalize(result)
print("Done")
def test_SimpleMinimizer(self):
minimizer = ba.Minimizer()
minimizer.setMinimizer("Test")
pars = ba.Parameters()
pars.add(ba.Parameter("par0", 0.0))
pars.add(ba.Parameter("par1", 1.0))
pars.add(ba.Parameter("par2", 2.0))
helper = TestMinimizerHelper()
result = minimizer.minimize(helper.objective_function, pars)
# return value of objective function was propagated to MinimizerResult
self.assertEqual(result.minValue(), 42.0)
# objective function was called twice
#(once by test minimizer, and second time during return type deduction)
self.assertEqual(helper.m_ncalls, 2)
# starting values of fit parameters were correctly send to objective func
self.assertEqual(list(helper.m_pars.values()), [0.0, 1.0, 2.0])
def run_fitting():
"""
main function to run fitting
"""
real_data = create_real_data()
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(get_simulation, real_data, 1.0)
fit_objective.initPrint(10)
fit_objective.initPlot(10)
params = ba.Parameters()
params.add("radius", 5. * nm, vary=False)
params.add("height", 9. * nm, min=8. * nm, max=12. * nm)
params.add("scale", 1.5, min=1.0, max=3.0)
params.add("background", 200, min=100.0, max=2000.0, step=100.0)
minimizer = ba.Minimizer()
result = minimizer.minimize(fit_objective.evaluate, params)
fit_objective.finalize(result)
def run_fitting():
"""
Setup simulation and fit
"""
real_data = get_real_data_values()
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(get_simulation, real_data, 1.0)
plot_observer = ba.PlotterSpecular()
fit_objective.initPrint(10)
fit_objective.initPlot(10, plot_observer)
params = ba.Parameters()
params.add("ti_thickness", 50 * ba.angstrom,
min=10 * ba.angstrom, max=60 * ba.angstrom)
minimizer = ba.Minimizer()
result = minimizer.minimize(fit_objective.evaluate, params)
fit_objective.finalize(result)
def run_fitting():
"""
main function to run fitting
"""
real_data = create_real_data()
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(get_simulation, real_data, 1.0)
fit_objective.initPrint(10)
fit_objective.initPlot(10)
"""
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.
"""
params = ba.Parameters()
params.add("height", 1. * nm, min=0.01, max=30.0, step=0.05 * nm)
params.add("radius", 20. * nm, min=0.01, max=30.0, step=0.05 * nm)
"""
Now we run first minimization round using the Genetic minimizer.
The Genetic minimizer is able to explore large parameter space
without being trapped by some local minimum.
"""
minimizer = ba.Minimizer()
minimizer.setMinimizer("Genetic", "", "MaxIterations=2;RandomSeed=1")
result = minimizer.minimize(fit_objective.evaluate, params)
fit_objective.finalize(result)
best_params_so_far = result.parameters()
"""
Second fit will use another minimizer.
It starts from best parameters found in previous minimization
and then continues until fit converges.
"""
minimizer.setMinimizer("Minuit2", "Migrad")
result = minimizer.minimize(fit_objective.evaluate, best_params_so_far)
fit_objective.finalize(result)
def run_fitting():
"""
main function to run fitting
"""
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(get_simulation_1, data_1[:, 1], 1.0)
fit_objective.addSimulationAndData(get_simulation_2, data_2[:, 1], 1.0)
fit_objective.initPrint(10)
# creating custom observer which will draw fit progress
plotter = PlotterSpecular()
fit_objective.initPlot(10, plotter)
params = ba.Parameters()
params.add("Ag_dens_f", 1.0, min=0.5, max=2.0)
params.add("SiO_dens_f", 1.0, min=0.95, max=1.1)
params.add("Ag_thick", 20.0 * nm, min=15 * nm, max=30 * nm)
params.add("SiO_thick", 5.0 * nm, min=3 * nm, max=8 * nm)
minimizer = ba.Minimizer()
result = minimizer.minimize(fit_objective.evaluate, params)
fit_objective.finalize(result)
def test_RosenbrockFit(self):
"""
Testing fit of rosenbrock function
"""
params = ba.Parameters()
params.add(
ba.Parameter("x", -1.2, ba.AttLimits.limited(-5.0, 5.0), 0.01))
params.add(
ba.Parameter("y", 1.0, ba.AttLimits.limited(-5.0, 5.0), 0.01))
model = Rosenbrock()
minimizer = ba.Minimizer()
result = minimizer.minimize(model.objective_function, params)
print(result.toString())
# check found parameter values
np.testing.assert_almost_equal(result.parameters().values(),
model.m_expected_params, 3)
# check found minimum
np.testing.assert_almost_equal(result.minValue(),
model.m_expected_minimum, 3)
def run_fitting(i_img): # for this thesis just batch simulations, no fitting.
# passing the simulation through the fitting mechanism
# still useful for processing experimental and
# simulation patterns simultaneously
global first_run
global batch_sim
global init_radius
global init_height
global init_height_flattening_ratio
global init_lattice_length
global init_damping_length
global init_disorder_parameter
global init_beam_intensity
if batch_sim == True: # setting the simulation parameters
d_eff = 11.6 * (0.1 * (i_img - 11)) / 150
init_lattice_length = 2 * np.pi / (0.628 +
1.499 * math.exp(-d_eff / 3.31))
init_height = 1.519 + 1.367 * d_eff - 0.038 * (d_eff**2)
init_radius = init_height / 3.1
init_height_flattening_ratio = 0.66
init_disorder_parameter = 0.25
init_damping_length = 30 * nm
init_beam_intensity = 4.15e+09
real_data = load_real_data(i_img)
fit_objective = ba.FitObjective()
fit_objective.addSimulationAndData(get_simulation, real_data)
fit_objective.initPrint(1)
fit_objective.initPlot(1, PlotObserver(
)) #plotting frequency on screen (increase if OpenGL error)
params = ba.Parameters(
) #parameter limits only relevant for full fitting, otherwise ignored
min_height = 0.1
max_height = 20
min_lattice_length = 0.1
max_lattice_length = 20
min_radius = 0.1
max_radius = 20
min_damping_length = 10
max_damping_length = 1000
min_disorder_parameter = 0.1
max_disorder_parameter = 0.5
min_beam_intensity = 1e+8
max_beam_intensity = 1e+12
params.add("radius",
value=init_radius,
min=min_radius,
max=max_radius,
step=0.1 * nm,
vary=True)
params.add("height",
value=init_height,
min=min_height,
max=max_height,
step=0.1 * nm,
vary=True)
params.add("height_flattening_ratio",
value=init_height_flattening_ratio,
min=0.55,
max=0.75,
step=0.01,
vary=True)
params.add("lattice_length",
value=init_lattice_length,
min=min_lattice_length,
max=max_lattice_length,
step=0.1 * nm,
vary=True)
params.add("damping_length",
value=init_damping_length,
min=min_damping_length,
max=max_damping_length,
step=10 * nm,
vary=True)
params.add("disorder_parameter",
value=init_disorder_parameter,
min=min_disorder_parameter,
max=max_disorder_parameter,
step=0.01,
vary=True)
params.add("beam_intensity",
init_beam_intensity,
min=min_beam_intensity,
max=max_beam_intensity,
vary=False)
minimizer = ba.Minimizer()
minimizer.setMinimizer("Test") # normal simulation (no fitting)
#minimizer.setMinimizer("Minuit2", "Migrad", "MaxFunctionCalls=0;Strategy=2;") # minimizer for fitting
result = minimizer.minimize(fit_objective.evaluate,
params) # runs the simulation
fit_objective.finalize(result)
fig_name = output_folder + 'fitting_img_#32_' + repr(i_img) + '.png'
plt.savefig(fig_name, dpi=500)
plt.close(1)
print("Fitting completed.")
print("chi2:", result.minValue())
for fitPar in result.parameters():
print(fitPar.name(), fitPar.value, fitPar.error)
experimental_data = fit_objective.experimentalData()
simulation_data = fit_objective.simulationResult()
difference = fit_objective.relativeDifference()
zmin = 0.1 # range of the colormap
zmax = 100
mpl.rcParams['image.cmap'] = 'jet' # saves figures in jet colormap
plt.figure(2, figsize=(8, 6))
ba.plot_colormap(experimental_data,
title="Experimental data",
zmin=zmin,
zmax=zmax,
units=ba.AxesUnits.QSPACE,
xlabel=None,
ylabel=None,
zlabel='',
aspect=None)
fig_name = output_folder + 'exp_data_#32_' + repr(i_img) + '_jet' + '.png'
plt.savefig(fig_name, dpi=500)
plt.close(2)
plt.figure(3, figsize=(8, 6))
ba.plot_colormap(simulation_data,
title="Simulation result",
zmin=zmin,
zmax=zmax,
units=ba.AxesUnits.QSPACE,
xlabel=None,
ylabel=None,
zlabel='',
aspect=None)
fig_name = output_folder + 'sim_data_#32_' + repr(i_img) + '_jet' + '.png'
plt.savefig(fig_name, dpi=500)
plt.close(3)
mpl.rcParams[
'image.cmap'] = 'nipy_spectral' #saves figures in spectral colormap
plt.figure(4, figsize=(8, 6))
ba.plot_colormap(experimental_data,
title="Experimental data",
zmin=zmin,
zmax=zmax,
units=ba.AxesUnits.QSPACE,
xlabel=None,
ylabel=None,
zlabel='',
aspect=None)
fig_name = output_folder + 'exp_data_#32_' + repr(
i_img) + '_spectrum' + '.png'
plt.savefig(fig_name, dpi=500)
plt.close(4)
plt.figure(5, figsize=(8, 6))
ba.plot_colormap(simulation_data,
title="Simulation result",
zmin=zmin,
zmax=zmax,
units=ba.AxesUnits.QSPACE,
xlabel=None,
ylabel=None,
zlabel='',
aspect=None)
fig_name = output_folder + 'sim_data_#32_' + repr(
i_img) + '_spectrum' + '.png'
plt.savefig(fig_name, dpi=500)
plt.close(5)
