diff --git a/Examples/python/fitting/ex10_FitGALAXIData/FitGALAXIData_new.py b/Examples/python/fitting/ex10_FitGALAXIData/FitGALAXIData_new.py
index a9d158af52deb5eb00275a4dfe18b984e032268e..c68f50e9cfad9a4c1892742d182e8991bcd6bbed 100644
--- a/Examples/python/fitting/ex10_FitGALAXIData/FitGALAXIData_new.py
+++ b/Examples/python/fitting/ex10_FitGALAXIData/FitGALAXIData_new.py
@@ -5,7 +5,8 @@ import matplotlib
from matplotlib import pyplot as plt
import numpy
import bornagain as ba
-from SampleBuilder import MySampleBuilder
+from bornagain import nm as nm
+from SampleBuilder_new import MySampleBuilder
wavelength = 1.34*ba.angstrom
alpha_i = 0.463*ba.deg
@@ -29,7 +30,7 @@ def create_detector():
return detector
-def create_simulation():
+def create_simulation(params):
"""
Creates and returns GISAS simulation with beam and detector defined
"""
@@ -47,6 +48,11 @@ def create_simulation():
# beam divergence
# alpha_distr = ba.DistributionGaussian(alpha_i, 0.02*ba.deg)
# simulation.addParameterDistribution("*/Beam/Alpha", alpha_distr, 5)
+
+ sample_builder = MySampleBuilder()
+ sample = sample_builder.create_sample(params)
+ simulation.setSample(sample)
+
return simulation
@@ -55,47 +61,27 @@ def load_real_data(filename="galaxi_data.tif.gz"):
Fill histogram representing our detector with intensity data from tif file.
Returns cropped version of it, which represent the area we are interested in.
"""
- hist = ba.IHistogram.createFrom(filename)
+ hist = ba.IHistogram.createFrom(filename).array()
return hist
def run_fitting():
- simulation = create_simulation()
- sample_builder = MySampleBuilder()
- simulation.setSampleBuilder(sample_builder)
-
real_data = load_real_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)
-
- # 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)
-
- use_two_minimizers_strategy = False
- if use_two_minimizers_strategy:
- 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()
+ 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)
+
if __name__ == '__main__':
run_fitting()
diff --git a/Examples/python/fitting/ex10_FitGALAXIData/SampleBuilder_new.py b/Examples/python/fitting/ex10_FitGALAXIData/SampleBuilder_new.py
index 4561e114cb4ad1245f096b13a20c3e26da34d234..7cb141e5f99df8505c0845ce32e0b6d2498b5cd8 100644
--- a/Examples/python/fitting/ex10_FitGALAXIData/SampleBuilder_new.py
+++ b/Examples/python/fitting/ex10_FitGALAXIData/SampleBuilder_new.py
@@ -3,37 +3,30 @@
Air layer is populated with spheres with some size distribution.
"""
import bornagain as ba
-import ctypes
-class MySampleBuilder(ba.IMultiLayerBuilder):
+class MySampleBuilder():
"""
"""
def __init__(self):
- ba.IMultiLayerBuilder.__init__(self)
- self.sample = None
-
# parameters describing the sample
- self.radius = ctypes.c_double(5.75*ba.nm)
- self.sigma = ctypes.c_double(0.4)
- self.distance = ctypes.c_double(53.6*ba.nm)
- self.disorder = ctypes.c_double(10.5*ba.nm)
- self.kappa = ctypes.c_double(17.5)
- self.ptfe_thickness = ctypes.c_double(22.1*ba.nm)
- self.hmdso_thickness = ctypes.c_double(18.5*ba.nm)
+ self.radius = 5.75*ba.nm
+ self.sigma = 0.4
+ self.distance = 53.6*ba.nm
+ self.disorder = 10.5*ba.nm
+ self.kappa = 17.5
+ self.ptfe_thickness = 22.1*ba.nm
+ self.hmdso_thickness = 18.5*ba.nm
- # register parameters
- self.registerParameter("radius", ctypes.addressof(self.radius))
- self.registerParameter("sigma", ctypes.addressof(self.sigma))
- self.registerParameter("distance", ctypes.addressof(self.distance))
- self.registerParameter("disorder", ctypes.addressof(self.disorder))
- self.registerParameter("kappa", ctypes.addressof(self.kappa))
- self.registerParameter("tptfe", ctypes.addressof(self.ptfe_thickness))
- self.registerParameter("thmdso", ctypes.addressof(self.hmdso_thickness))
+ def create_sample(self, params):
+ self.radius = params["radius"]
+ self.sigma = params["sigma"]
+ self.distance = params["distance"]
+ return self.multilayer()
# constructs the sample for current values of parameters
- def buildSample(self):
+ def multilayer(self):
# defining materials
m_air = ba.HomogeneousMaterial("Air", 0.0, 0.0)
m_Si = ba.HomogeneousMaterial("Si", 5.78164736e-6, 1.02294578e-7)
@@ -44,18 +37,18 @@ class MySampleBuilder(ba.IMultiLayerBuilder):
# collection of particles with size distribution
nparticles = 20
nfwhm = 2.0
- sphere_ff = ba.FormFactorFullSphere(self.radius.value)
+ sphere_ff = ba.FormFactorFullSphere(self.radius)
# sphere_ff = ba.FormFactorTruncatedSphere(
- # self.radius.value, self.radius.value*1.5)
+ # self.radius, self.radius*1.5)
sphere = ba.Particle(m_Ag, sphere_ff)
position = ba.kvector_t(0*ba.nm, 0*ba.nm,
- -1.0*self.hmdso_thickness.value)
+ -1.0*self.hmdso_thickness)
sphere.setPosition(position)
- ln_distr = ba.DistributionLogNormal(self.radius.value, self.sigma.value)
+ ln_distr = ba.DistributionLogNormal(self.radius, self.sigma)
par_distr = ba.ParameterDistribution(
"/Particle/FullSphere/Radius", ln_distr, nparticles, nfwhm,
- ba.RealLimits.limited(0.0, self.hmdso_thickness.value/2.0))
+ ba.RealLimits.limited(0.0, self.hmdso_thickness/2.0))
# par_distr = ba.ParameterDistribution(
# "/Particle/TruncatedSphere/Radius", ln_distr, nparticles, nfwhm)
# par_distr.linkParameter("/Particle/TruncatedSphere/Height")
@@ -63,10 +56,10 @@ class MySampleBuilder(ba.IMultiLayerBuilder):
# interference function
interference = ba.InterferenceFunctionRadialParaCrystal(
- self.distance.value, 1e6*ba.nm)
- interference.setKappa(self.kappa.value)
+ self.distance, 1e6*ba.nm)
+ interference.setKappa(self.kappa)
interference.setDomainSize(20000.0)
- pdf = ba.FTDistribution1DGauss(self.disorder.value)
+ pdf = ba.FTDistribution1DGauss(self.disorder)
interference.setProbabilityDistribution(pdf)
# assembling particle layout
@@ -82,9 +75,9 @@ class MySampleBuilder(ba.IMultiLayerBuilder):
# layers
air_layer = ba.Layer(m_air)
- hmdso_layer = ba.Layer(m_HMDSO, self.hmdso_thickness.value)
+ hmdso_layer = ba.Layer(m_HMDSO, self.hmdso_thickness)
hmdso_layer.addLayout(particle_layout)
- ptfe_layer = ba.Layer(m_PTFE, self.ptfe_thickness.value)
+ ptfe_layer = ba.Layer(m_PTFE, self.ptfe_thickness)
substrate_layer = ba.Layer(m_Si)
# assembling multilayer