Fitting example explaining how to set minimizer options.

Examples/python/fitting/ex02_FitBasics/MinimizerOptions.py

+"""
+Fitting example: running same fit using various minimizer and their settings.
+"""
+import bornagain as ba
+from bornagain import deg, angstrom, nm
+
+
+def get_sample(cylinder_height=5.0*nm, cylinder_radius=5.0*nm,
+               prism_length=5.0*nm, prism_height=5.0*nm):
+    """
+    Returns a sample with uncorrelated cylinders and prisms on a substrate.
+    """
+    # defining materials
+    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)
+
+    # collection of particles
+    cylinder_ff = ba.FormFactorCylinder(cylinder_radius, cylinder_height)
+    cylinder = ba.Particle(m_particle, cylinder_ff)
+    prism_ff = ba.FormFactorPrism3(prism_length, prism_height)
+    prism = ba.Particle(m_particle, prism_ff)
+    particle_layout = ba.ParticleLayout()
+    particle_layout.addParticle(cylinder, 0.5)
+    particle_layout.addParticle(prism, 0.5)
+    interference = ba.InterferenceFunctionNone()
+    particle_layout.setInterferenceFunction(interference)
+
+    # air layer with particles and substrate form multi layer
+    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, -1.0*deg, 1.0*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 run_fitting():
+    """
+    run fitting
+    """
+
+    # prints info about available minimizers
+    print(ba.MinimizerFactory().catalogueToString())
+
+    # prints detailed info about available minimizers and their options
+    print(ba.MinimizerFactory().catalogueDetailsToString())
+
+    sample = get_sample()
+    simulation = get_simulation()
+    simulation.setSample(sample)
+
+    real_data = ba.IntensityDataIOFactory.readIntensityData(
+        'refdata_fitcylinderprisms.int.gz')
+
+    fit_suite = ba.FitSuite()
+    fit_suite.addSimulationAndRealData(simulation, real_data)
+    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)
+
+    # 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 and will stop around this value
+    # fit_suite.setMinimizer("Minuit2", "Migrad", "MaxFunctionCalls=100")
+
+    # Setting two options at once.
+    # Strategy=2 promises more accurate fit.
+    # fit_suite.setMinimizer("Minuit2", "Simplex", "MaxFunctionCalls=100;Strategy=2")
+
+    # setting Minuit2 minimizer with Fumili algorithm
+    # fit_suite.setMinimizer("Minuit2", "Fumili")
+
+    # Setting Levenberg-Marquardt algorithm
+    # fit_suite.setMinimizer("GSLLMA")
+
+    # Setting Genetic algorithm
+    # It requires all parameters to be limited, so we recreate parameters with min and max defined
+    # fit_suite.fitParameters().clear()
+    # fit_suite.addFitParameter("*Cylinder/Height", 4.*nm).setLimited(3.0, 8.0)
+    # fit_suite.addFitParameter("*Cylinder/Radius", 6.*nm).setLimited(3.0, 8.0)
+    # fit_suite.addFitParameter("*Prism3/Height", 4.*nm).setLimited(3.0, 8.0)
+    # fit_suite.addFitParameter("*Prism3/BaseEdge", 4.*nm).setLimited(3.0, 8.0)
+    # fit_suite.setMinimizer("Genetic", "Default", "MaxIterations=2;PopSize=200;RandomSeed=1")
+
+    # running fit with default minimizer
+    fit_suite.runFit()
+
+    print("Fitting completed.")
+    print("chi2:", fit_suite.getChi2())
+    for par in fit_suite.fitParameters():
+        print(par.name(), par.value(), par.error())
+
+if __name__ == '__main__':
+    run_fitting()