import numpy as np import bornagain as ba from bornagain import deg, nm import matplotlib import matplotlib.pyplot as plt # parameters of 3D particle arrangement particle_radius = 2.5*nm lattice_length = 10.0*nm # mesocrystal size meso_height = 50.0*nm meso_width = 100.0*nm meso_length = meso_width datafile = "fake_data.npy" def get_sample(): # Defining Materials m_air = ba.HomogeneousMaterial("Air", 0.0, 0.0) m_particle = ba.HomogeneousMaterial("Particle", 0.0006, 2e-08) m_substrate = ba.HomogeneousMaterial("Substrate", 6e-06, 2e-08) # Defining Layers l_air = ba.Layer(m_air) l_substrate = ba.Layer(m_substrate) # Spherical particles, form the base of the mesocrystal ff_sphere = ba.FormFactorFullSphere(particle_radius) sphere = ba.Particle(m_particle, ff_sphere) # mesocrystal lattice (cubic lattice for this example) lattice_a = ba.kvector_t(lattice_length, 0.0, 0.0) lattice_b = ba.kvector_t(0.0, lattice_length, 0.0) lattice_c = ba.kvector_t(0.0, 0.0, lattice_length) lattice = ba.Lattice(lattice_a, lattice_b, lattice_c) # crystal structure crystal = ba.Crystal(sphere, lattice) # uncomment to add variance of displacement in each dimension (sigma^2) # crystal.setDWFactor(2) # for BornAgain 1.16 and below # crystal.setPositionVariance(2) # for BornAgain 1.17+ # mesocrystal meso_ff = ba.FormFactorLorentz(meso_width, meso_height) # Lorentz meso = ba.MesoCrystal(crystal, meso_ff) layout = ba.ParticleLayout() layout.addParticle(meso) # Adding layouts to layers l_air.addLayout(layout) # Defining Multilayers multilayer = ba.MultiLayer() multilayer.addLayer(l_air) multilayer.addLayer(l_substrate) # print(multilayer.parametersToString()) return multilayer def get_simulation(): simulation = ba.GISASSimulation() simulation.setDetectorParameters(400, -2.0*deg, 2.0*deg, 400, 0.0*deg, 3.0*deg) simulation.setBeamParameters(0.1*nm, 0.2*deg, 0.0*deg) simulation.setBeamIntensity(1.0e+06) # simulation.getOptions().setMonteCarloIntegration(True, 20) # uncomment for large mesocrystals simulation.setTerminalProgressMonitor() return simulation def run_simulation(): sample = get_sample() simulation = get_simulation() simulation.setSample(sample) simulation.runSimulation() return simulation.result() def load_data(filename=datafile): """ Loads experimental data and returns numpy array. """ simulation = get_simulation() data = np.load(filename) # this loads numpy binary format # data = np.loadtxt(filename) # to load ascii data # for any other format you can use fabio library as follows # import fabio # img = fabio.open(filename) # data = np.array(img.data, dtype='float') return ba.ConvertData(simulation, data, True) def plot(fontsize=16): """ Creates 4 plots: 2D experiment, 2D simulation, slice along Qz and slice along Qy """ plt.style.use('seaborn-talk') matplotlib. rcParams['xtick.labelsize'] = fontsize matplotlib. rcParams['ytick.labelsize'] = fontsize data = load_data() plt.figure(figsize=(22, 17)) zmin = 0.1 zmax = 2.0e+06 qz_slice = 0.4 qy_slice = 0.64 # ==================== # Experiment 2D # ==================== plt.subplot(2, 2, 1) ba.plot_colormap(data, units=ba.AxesUnits.QSPACE, zmin=zmin, zmax=zmax) plt.axhline(y=qz_slice, color='0.5', linestyle='--', linewidth=1) plt.axvline(x=qy_slice, color='0.5', linestyle='--', linewidth=1) plt.title("Experiment", fontsize=fontsize) # ax.tick_params(axis='both', which='major', labelsize=fontsize) # ==================== # Simulation 2D # ==================== result = run_simulation() axes_labels = ba.get_axes_labels(result, ba.AxesUnits.QSPACE) plt.subplot(2, 2, 2) ba.plot_colormap(result, units=ba.AxesUnits.QSPACE, zmin=zmin, zmax=zmax) plt.axhline(y=qz_slice, color='0.5', linestyle='--', linewidth=1) plt.axvline(x=qy_slice, color='0.5', linestyle='--', linewidth=1) plt.title("BornAgain simulation", fontsize=fontsize) # ==================== # projection along Qz # ==================== plt.subplot(2, 2, 3) exp_qz = data.histogram2d(ba.AxesUnits.QSPACE).projectionY(qy_slice) sim_qz = result.histogram2d(ba.AxesUnits.QSPACE).projectionY(qy_slice) plt.semilogy(exp_qz.getBinCenters(), exp_qz.getBinValues(), color='k', marker='.', markersize=5, linestyle='None', label="Experiment") plt.semilogy(sim_qz.getBinCenters(), sim_qz.getBinValues(), color='g', linewidth=2, label="Simulation") # plt.xlim(0.1, 1.21) # uncomment and edit to set x axis limits # plt.ylim(0.5, zmax) # uncomment and edit to set y axis limits plt.xlabel(axes_labels[1], fontsize=fontsize) plt.ylabel(r'$\mathrm{I(Q_z)}$, a.u.', fontsize=fontsize) plt.legend(fontsize=fontsize) plt.title(r"Slice along $Q_z$", fontsize=fontsize) # ===================== # projection along Qy # ===================== plt.subplot(2, 2, 4) exp_qy = data.histogram2d(ba.AxesUnits.QSPACE).projectionX(qz_slice) sim_qy = result.histogram2d(ba.AxesUnits.QSPACE).projectionX(qz_slice) plt.semilogy(exp_qy.getBinCenters(), exp_qy.getBinValues(), color='k', marker='.', markersize=5, linestyle='None', label="Experiment") plt.semilogy(sim_qy.getBinCenters(), sim_qy.getBinValues(), color='g', linewidth=2, label="Simulation") # plt.xlim(-0.81, 0.71) # uncomment and edit to set x axis limits # plt.ylim(0.5, zmax) # uncomment and edit to set y axis limits plt.xlabel(axes_labels[0], fontsize=fontsize) plt.ylabel(r'$\mathrm{I(Q_y)}$, a.u.', fontsize=fontsize) plt.legend(fontsize=fontsize) plt.title(r"Slice along $Q_y$", fontsize=fontsize) # plt.savefig("{}.png".format("my_meso")) # uncomment to save figure # uncomment to save slices to text files # np.savetxt("{}_slice_qz.txt".format("my_meso"), np.column_stack([sim_qz.getBinCenters(), sim_qz.getBinValues()])) # np.savetxt("{}_slice_qy.txt".format("my_meso"), np.column_stack([sim_qy.getBinCenters(), sim_qy.getBinValues()])) plt.show() if __name__ == '__main__': plot()