diff --git a/Examples/python/fitting/ex06_FitStrategies/FitStrategyAdjustMinimizer.py b/Examples/python/fitting/ex06_FitStrategies/FitStrategyAdjustMinimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec0aaa95d10b67dc76c294c88cf322058e3a8ddf
--- /dev/null
+++ b/Examples/python/fitting/ex06_FitStrategies/FitStrategyAdjustMinimizer.py
@@ -0,0 +1,122 @@
+"""
+Two parameter fit of cylinders.
+In this example we are trying to find cylinder's height and radius
+using chain of minimizers.
+
+During the first fit round Genetic minimizer will be used.
+It will roughly look for possible local minimas.
+After it is done, the second Minuit2 minimizer will continue
+to find the precise location of best minima found on previous step.
+"""
+
+import numpy as np
+from matplotlib import pyplot as plt
+import bornagain as ba
+from bornagain import deg, angstrom, nm
+
+
+def get_sample(radius=5.0*nm, height=5.0*nm):
+ """
+ Returns a sample with uncorrelated cylinders and pyramids on a substrate.
+ """
+ m_air = ba.HomogeneousMaterial("Air", 0.0, 0.0)
+ m_substrate = ba.HomogeneousMaterial("Substrate", 6e-6, 2e-8)
+ m_particle = ba.HomogeneousMaterial("Particle", 6e-4, 2e-8)
+
+ cylinder_ff = ba.FormFactorCylinder(radius, height)
+ cylinder = ba.Particle(m_particle, cylinder_ff)
+
+ particle_layout = ba.ParticleLayout()
+ particle_layout.addParticle(cylinder)
+
+ air_layer = ba.Layer(m_air)
+ air_layer.addLayout(particle_layout)
+
+ substrate_layer = ba.Layer(m_substrate, 0)
+ multi_layer = ba.MultiLayer()
+ multi_layer.addLayer(air_layer)
+ multi_layer.addLayer(substrate_layer)
+ return multi_layer
+
+
+def get_simulation():
+ """
+ Returns a GISAXS simulation with beam and detector defined.
+ """
+ simulation = ba.GISASSimulation()
+ simulation.setDetectorParameters(100, 0.0*deg, 2.0*deg,
+ 100, 0.0*deg, 2.0*deg)
+ simulation.setBeamParameters(1.0*angstrom, 0.2*deg, 0.0*deg)
+ simulation.setBeamIntensity(1e+08)
+ return simulation
+
+
+def create_real_data():
+ """
+ Generating "real" data by adding noise to the simulated data.
+ """
+ sample = get_sample(5.0*nm, 5.0*nm)
+ simulation = get_simulation()
+ simulation.setSample(sample)
+ simulation.runSimulation()
+
+ # retrieving simulated data in the form of numpy array
+ real_data = simulation.result().array()
+
+ # spoiling simulated data with the noise to produce "real" data
+ noise_factor = 0.1
+ noisy = np.random.normal(real_data, noise_factor*np.sqrt(real_data))
+ noisy[noisy < 0.1] = 0.1
+ return noisy
+
+
+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())
+
+
+if __name__ == '__main__':
+ run_fitting()
+ plt.show()
diff --git a/Examples/python/fitting/ex06_FitStrategies/FitStrategyAdjustMinimizer_new.py b/Examples/python/fitting/ex06_FitStrategies/FitStrategyAdjustMinimizer_new.py
index 5462bc64e46b8ab2429e10d4efc513418f8e90ee..76e2a229de1b222f836931004a8f0af4f21d5b8f 100644
--- a/Examples/python/fitting/ex06_FitStrategies/FitStrategyAdjustMinimizer_new.py
+++ b/Examples/python/fitting/ex06_FitStrategies/FitStrategyAdjustMinimizer_new.py
@@ -15,10 +15,13 @@ import bornagain as ba
from bornagain import deg, angstrom, nm
-def get_sample(radius=5.0*nm, height=5.0*nm):
+def get_sample(params):
"""
Returns a sample with uncorrelated cylinders and pyramids on a substrate.
"""
+ radius = params["radius"]
+ height = params["height"]
+
m_air = ba.HomogeneousMaterial("Air", 0.0, 0.0)
m_substrate = ba.HomogeneousMaterial("Substrate", 6e-6, 2e-8)
m_particle = ba.HomogeneousMaterial("Particle", 6e-4, 2e-8)
@@ -39,7 +42,7 @@ def get_sample(radius=5.0*nm, height=5.0*nm):
return multi_layer
-def get_simulation():
+def get_simulation(params):
"""
Returns a GISAXS simulation with beam and detector defined.
"""
@@ -48,6 +51,7 @@ def get_simulation():
100, 0.0*deg, 2.0*deg)
simulation.setBeamParameters(1.0*angstrom, 0.2*deg, 0.0*deg)
simulation.setBeamIntensity(1e+08)
+ simulation.setSample(get_sample(params))
return simulation
@@ -55,9 +59,9 @@ def create_real_data():
"""
Generating "real" data by adding noise to the simulated data.
"""
- sample = get_sample(5.0*nm, 5.0*nm)
- simulation = get_simulation()
- simulation.setSample(sample)
+ params = {'radius': 5.0*nm, 'height': 5.0*nm}
+
+ simulation = get_simulation(params)
simulation.runSimulation()
# retrieving simulated data in the form of numpy array
@@ -74,47 +78,43 @@ 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")
- 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())
+ 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 minima.
+ """
+ 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)
if __name__ == '__main__':