### Plotting the sample profile

This short tutorial demonstrates how to visualize the Scattering Length Density (SLD) profile of a Multilayer sample. For more details about preparing a sample and carrying on a reflectometry simulated experiment, read the reflectometry simulation tutorial.

To obtain the figure above, one must run the script below, which is basically about defining a sample with interfacial roughness and plotting right away its sld profile.

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51  #!/usr/bin/env python3 """ Basic example for producing a profile of SLD of a sample. """ import bornagain as ba from bornagain import angstrom, ba_plot as bp, sample_tools import numpy as np import matplotlib.pyplot as plt def get_sample(): """ Defines sample and returns it """ # create materials m_ambient = ba.MaterialBySLD("Ambient", 0, 0) m_ti = ba.MaterialBySLD("Ti", -1.9493e-06, 0) m_ni = ba.MaterialBySLD("Ni", 9.4245e-06, 0) m_substrate = ba.MaterialBySLD("SiSubstrate", 2.0704e-06, 0) # create layers ambient_layer = ba.Layer(m_ambient) ti_layer = ba.Layer(m_ti, 30*angstrom) ni_layer = ba.Layer(m_ni, 70*angstrom) substrate_layer = ba.Layer(m_substrate) # create sample sample = ba.MultiLayer() sample.addLayer(ambient_layer) roughness = ba.LayerRoughness(5*angstrom, 0.5, 10*angstrom) for _ in range(4): sample.addLayerWithTopRoughness(ti_layer, roughness) sample.addLayerWithTopRoughness(ni_layer, roughness) sample.addLayer(substrate_layer) return sample if __name__ == '__main__': bp.parse_args(sim_n=400) n = bp.simargs['n'] sample = get_sample() zpoints, slds = sample_tools.materialProfile(sample, n) plt.figure() plt.plot(zpoints, np.real(slds)) bp.show_or_export() 
Examples/varia/MaterialProfile.py