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#!/usr/bin/env python3
"""
Minimal working fit examples: finds radius of sphere in Born approximation.
"""
import bornagain as ba
from bornagain import deg, nm, nm2
import lmfit
def get_sample(P):
"""
Returns sample for given set of parameters.
"""
radius = P["radius"]
particle_color = (0.86, 0.24, 0.18)
particle_mat = ba.RefractiveMaterial("Particle", particle_color, 6e-4, 2e-8)
particle = ba.Particle(particle_mat, ba.Sphere(radius))
layer_top = ba.Layer(ba.Vacuum())
layer_bottom = ba.Layer(ba.Vacuum())
layer_top.deposit2D(ba.Dilute2D(0.01/nm2, particle))
sample = ba.Sample()
sample.addLayer(layer_top)
sample.addLayer(layer_bottom)
return sample
def get_simulation(P):
"""
Returns GISAS simulation for given set of parameters.
"""
sample = get_sample(P)
n = 100
beam = ba.Beam(1, 0.1*nm, 0.2*deg)
detector = ba.SphericalDetector(n, -1*deg, 1*deg, n, 0., 2*deg)
simulation = ba.ScatteringSimulation(beam, sample, detector)
return simulation
def fake_data():
"""
Generating "experimental" data by running simulation with default parameters.
"""
simulation = get_simulation({'radius': 5 * nm})
result = simulation.simulate()
return result
if __name__ == '__main__':
data = fake_data()
flat_exp_values = data.intensities().ravel()
def residuals(P):
"""
Runs a simulation for given parameters P, and returns residuals
vector.
"""
sim_values = get_simulation(P.valuesdict()).simulate().intensities()
flat_sim_values = sim_values.ravel()
return flat_exp_values - flat_sim_values
P = lmfit.Parameters()
P.add("radius", 4. * nm, min=0.01)
result = lmfit.minimize(residuals, P)
print(lmfit.fit_report(result))
finalP = result.params.valuesdict()
ba.showSample3D(get_sample(finalP), sample_size=120*nm, seed=0)
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