diff --git a/Examples/python/fitting/ex08_SimultaneousFitOfTwoDatasets/SimultaneousFitOfTwoDatasets_new.py b/Examples/python/fitting/ex08_SimultaneousFitOfTwoDatasets/SimultaneousFitOfTwoDatasets_new.py
index b197d0eab50a95e103b76e5bc8dcea83f19c82db..ea6d0a21324456958a99be225f6495dd476becec 100644
--- a/Examples/python/fitting/ex08_SimultaneousFitOfTwoDatasets/SimultaneousFitOfTwoDatasets_new.py
+++ b/Examples/python/fitting/ex08_SimultaneousFitOfTwoDatasets/SimultaneousFitOfTwoDatasets_new.py
@@ -9,10 +9,14 @@ import bornagain as ba
from bornagain import deg, angstrom, nm
-def get_sample(radius_a=4.0*nm, radius_b=4.0*nm, height=4.0*nm):
+def get_sample(params):
"""
Returns a sample with uncorrelated cylinders and pyramids.
"""
+ radius_a = params["radius_a"]
+ radius_b = params["radius_b"]
+ 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)
@@ -33,26 +37,39 @@ def get_sample(radius_a=4.0*nm, radius_b=4.0*nm, height=4.0*nm):
return multi_layer
-def get_simulation(incident_alpha=0.2):
+def get_simulation(params):
"""
Returns a GISAXS simulation with beam and detector defined.
"""
+ incident_angle = params["incident_angle"]
+
simulation = ba.GISASSimulation()
simulation.setDetectorParameters(50, -1.5*deg, 1.5*deg,
50, 0.0*deg, 2.0*deg)
- simulation.setBeamParameters(1.0*angstrom, incident_alpha, 0.0*deg)
+ simulation.setBeamParameters(1.0*angstrom, incident_angle, 0.0*deg)
simulation.setBeamIntensity(1e+08)
+ simulation.setSample(get_sample(params))
return simulation
+def simulation1(params):
+ params["incident_angle"] = 0.1*deg
+ return get_simulation(params)
+
+
+def simulation2(params):
+ params["incident_angle"] = 0.4*deg
+ return get_simulation(params)
+
+
def create_real_data(incident_alpha):
"""
Generating "real" data by adding noise to the simulated data.
"""
- sample = get_sample(
- radius_a=5.0*nm, radius_b=6.0*nm, height=8.0*nm)
- simulation = get_simulation(incident_alpha)
- simulation.setSample(sample)
+ params = {'radius_a': 5.0*nm, 'radius_b': 6.0*nm,
+ 'height': 8.0*nm, "incident_angle": incident_alpha}
+
+ simulation = get_simulation(params)
simulation.runSimulation()
# retrieving simulated data in the form of numpy array
@@ -65,26 +82,21 @@ def create_real_data(incident_alpha):
return noisy
-class DrawObserver(ba.IFitObserver):
+class PlotObserver():
"""
Draws fit progress every nth iteration. Real data, simulated data
and chi2 map will be shown for both datasets.
"""
def __init__(self, draw_every_nth=10):
- import matplotlib
- from matplotlib import pyplot as plt
- global matplotlib, plt
-
- ba.IFitObserver.__init__(self, draw_every_nth)
self.fig = plt.figure(figsize=(12.8, 10.24))
self.fig.canvas.draw()
plt.ion()
- def plot_datasets(self, fit_suite, canvas):
- for i_dataset in range(0, fit_suite.numberOfFitObjects()):
- real_data = fit_suite.experimentalData(i_dataset)
- simul_data = fit_suite.simulationResult(i_dataset)
- chi2_map = fit_suite.relativeDifference(i_dataset)
+ def plot_datasets(self, fit_objective, canvas):
+ for i_dataset in range(0, fit_objective.fitObjectCount()):
+ real_data = fit_objective.experimentalData(i_dataset)
+ simul_data = fit_objective.simulationResult(i_dataset)
+ chi2_map = fit_objective.relativeDifference(i_dataset)
zmax = real_data.histogram2d().getMaximum()
@@ -98,16 +110,22 @@ class DrawObserver(ba.IFitObserver):
ba.plot_colormap(chi2_map, title="Chi2 map - #"+str(i_dataset+1),
zmin=0.001, zmax=10.0, zlabel="")
- def plot_fit_parameters(self, fit_suite, canvas):
+ def plot_fit_parameters(self, fit_objective, canvas):
# fit parameters
plt.subplot(canvas[6:])
plt.axis('off')
- plt.text(0.01, 0.95, "Iterations " + '{:d} '.
- format(fit_suite.numberOfIterations()))
- plt.text(0.01, 0.70, "Chi2 " + '{:8.4f}'.format(fit_suite.getChi2()))
- for index, fitPar in enumerate(fit_suite.fitParameters()):
- plt.text(0.01, 0.30 - index*0.3,
- '{:40.40s}: {:6.3f}'.format(fitPar.name(), fitPar.value()))
+
+ iteration_info = fit_objective.iterationInfo()
+
+ plt.text(0.01, 0.95, "Iterations " + '{:d}'.
+ format(iteration_info.iterationCount()))
+ plt.text(0.01, 0.70, "Chi2 " + '{:8.4f}'.format(iteration_info.chi2()))
+ index = 0
+ params = iteration_info.parameterMap()
+ for key in params:
+ plt.text(0.01, 0.30 - index * 0.3, '{:30.30s}: {:6.3f}'.format(key, params[key]))
+ index = index + 1
+
def update(self, fit_suite):
self.fig.clf()
@@ -124,8 +142,6 @@ class DrawObserver(ba.IFitObserver):
plt.draw()
plt.pause(0.01)
- if fit_suite.isLastIteration():
- plt.ioff()
def run_fitting():
@@ -133,35 +149,27 @@ def run_fitting():
main function to run fitting
"""
- incident_alpha_angles = [0.1*deg, 0.4*deg]
- fit_suite = ba.FitSuite()
- sample = get_sample()
-
- for alpha in incident_alpha_angles:
- real_data = create_real_data(incident_alpha=alpha)
- simulation = get_simulation(incident_alpha=alpha)
- simulation.setSample(sample)
- fit_suite.addSimulationAndRealData(simulation, real_data)
-
- fit_suite.initPrint(10)
- draw_observer = DrawObserver(draw_every_nth=10)
- fit_suite.attachObserver(draw_observer)
+ data1 = create_real_data(0.1 * deg)
+ data2 = create_real_data(0.4 * deg)
- # setting fitting parameters with starting values
- fit_suite.addFitParameter("*/HemiEllipsoid/RadiusX", 4.*nm).setLimited(2., 10.)
- fit_suite.addFitParameter("*/HemiEllipsoid/RadiusY", 6.*nm).setFixed()
- fit_suite.addFitParameter("*/HemiEllipsoid/Height", 4.*nm).setLimited(2., 10.)
+ fit_objective = ba.FitObjective()
+ fit_objective.addSimulationAndData(simulation1, data1, 1.0)
+ fit_objective.addSimulationAndData(simulation2, data2, 1.0)
+ fit_objective.initPrint(10)
- print(fit_suite.treeToString())
- print(fit_suite.parametersToString())
+ # creating custom observer which will draw fit progress
+ plotter = PlotObserver()
+ fit_objective.initPlot(10, plotter.update)
- # running fit
- fit_suite.runFit()
+ 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)
- print("Fitting completed.")
- print("chi2:", fit_suite.getChi2())
- for fitPar in fit_suite.fitParameters():
- print(fitPar.name(), fitPar.value(), fitPar.error())
+ print("begin")
+ minimizer = ba.Minimizer()
+ result = minimizer.minimize(fit_objective.evaluate, params)
+ fit_objective.finalize(result)
if __name__ == '__main__':