# Basic simulation tutorial

In this example, we simulate the scattering from a mixture of cylindrical and prismatic nanoparticles without any interference between them. These particles are placed in air, on top of a substrate. We will go through each step of the simulation. Each section starts with a Python code snippet, followed by a discussion. The full code can be found at the end of this page.

### Importing Python modules

import numpy
import matplotlib
from matplotlib import pyplot as plt
from bornagain import *

We start by importing basic packages for scientific computing and 2D. Line 4 imports the BornAgain Python API.

### Defining the materials

def get_sample():
"""
Build and return the sample representing cylinders and prisms on top of
substrate without interference.
"""
# defining materials
m_air = HomogeneousMaterial("Air", 0.0, 0.0)
m_substrate = HomogeneousMaterial("Substrate", 6e-6, 2e-8)
m_particle = HomogeneousMaterial("Particle", 6e-4, 2e-8)

Line 13 marks the beginning of the function to define our sample. Lines 19-21 define different materials using class HomogeneousMaterial. The general syntax is the following

<material> = HomogeneousMaterial("name", delta, beta)


where name is the arbitrary name of the material associated with its complex refractive index n = 1 - delta + i*beta. Variable <material> is later used when referring to this particular material. The three materials defined in this example are Air with a refractive index of 1 (delta = beta = 0), a Substrate associated with a complex refractive index equal to 1 - 6x10-6 + i2x10-8, and the material of the particles, whose refractive index is n = 1 - 6x10-4 + i2x10-8.

### Defining the particles

    # collection of particles
cylinder_ff = FormFactorCylinder(5*nanometer, 5*nanometer)
cylinder = Particle(m_particle, cylinder_ff)
prism_ff = FormFactorPrism3(10*nanometer, 5*nanometer)
prism = Particle(m_particle, prism_ff)


We implement two different shapes of particles: cylinders and prisms (i.e. elongated particles with a constant equilateral triangular cross section).

All particles implemented in BornAgain are defined by their form factors (see formfactors), their sizes and the material they are made of. Here, for the cylindrical particle, we input its radius and height.  For the prism, the possible inputs are the length of one side of its equilateral triangular base and its height.

In order to define a particle, we proceed in two steps. For example for the cylindrical particle, we first specify the form factor of a cylinder with its radius and height, both equal to 5 nanometers in this particular case (see line 24). Then we associate this shape with the constituting material as in line 25. The same procedure has been applied for the prism in lines 26 and 27, respectively.

### Characterizing particle assembly

    particle_layout = ParticleLayout()
interference = InterferenceFunctionNone()
particle_layout.setInterferenceFunction(interference)

The object which holds the information about the positions and densities of particles in our sample is called ParticleLayout (line 28). We use the associated function addParticle for each particle shape. Its general syntax is

addParticle(<particle>, abundance=1.0, position=kvector_t(0,0,0), rotation=None)

Here <particle> is the name of the variable used to define the particles (lines 24 and 26). The  abundance is the proportion of given type of particles, normalized to the total number of particles. Here we have 50% of cylinders and 50% of prisms. The parameter position represents coordinates of particle's reference point (expressed in nanometers) in coordinate system of a given layer (the association with a particular layer will be done during the next step). In this example the position is set to the default value (0,0,0) which means particles sitting on top of the interface.

Important
See tutorials Particles positioning and Particles rotation for detailed explanations

Finally, lines 31-32 specify that there is no coherent interference between the waves scattered by these particles. In this case, the intensity is calculated by the incoherent sum of the scattered waves. By default, ParticleLayout object is already in that state, so lines 31-32 can be omitted. Other examples will present more complicated interference cases.

### Multilayer

    # air layer with particles and substrate form multi layer
air_layer = Layer(m_air)
substrate_layer = Layer(m_substrate)
multi_layer = MultiLayer()
return multi_layer

We now have to configure our sample. Our particles, cylinders and prisms, are on top of a substrate in an air layer. Both layers are considered to be semi-infinite. The air layer is constructed on line 35 using the previously defined air material as a constructor parameter. With the next line, the air layer is populated with particles using the previously defined particle layout object. The substrate layer is constructed on line 36 using the substrate material as a constructor parameter.

In the general case, if the user constructs a multilayer with more than 2 layers (taking the air and substrate layers into account), the thickness of intermediate layers has to be specified using the constructor below. The thickness is expressed in nanometers.

<layer> = Layer(<material>, thickness)

Our two layers are now fully characterized. The whole sample is represented by the MultiLayer object which is constructed on line 38. We assemble the sample by adding the top air layer decorated with the particles (line 39) and the bottom substrate layer (line 40). The order in which layers are added to the multilayer is important: we start from the top layer down to the bottom one.

The last line of the function, line 41, returns the fully constructed sample.

### Characterizing the input beam and output detector

def get_simulation():
"""
Create and return GISAXS simulation with beam and detector defined
"""
simulation = GISASSimulation()
simulation.setDetectorParameters(100, phi_min*degree, phi_max*degree, 100, alpha_min*degree, alpha_max*degree)
simulation.setBeamParameters(1.0*angstrom, 0.2*degree, 0.0*degree)
return simulation

The function defined on line 44 creates and returns the simulation object. The first stage is to create the simulation object (line 48) of the GISASSimulation class type. Then we define the detector (line 49) and the beam parameters (line 50) using the corresponding class methods.

The GISAS setup and the coordinate system used in BornAgain. The incoming beam propagates with the incidence angles alpha_i and phi_i with respect to the sample axes as shown. A scattered (outgoing) beam, characterized by alpha_f and phi_f propagates toward the area detector. The angles alpha_i and alpha_f are defined in such a way that those shown in the Figure are positive.

The detector parameters are set using ranges of angles via the method:

setDetectorParameters(n_phi, phi_f_min, phi_f_max, n_alpha, alpha_f_min, alpha_f_max)

where the number of bins n_phi, the low edge of first bin phi_f_min and the upper edge of last bin phi_f_max define the phi_f detector axis, while n_alpha, alpha_f_min and alpha_f_max are related to the alpha_f detector axis.

Important
See Detector types tutorial for more details

To characterize the beam we use the following method

setBeamParameters(wavelength, alpha_i, phi_i)

where wavelength is the incident beam wavelength, alpha_i is the incident grazing angle on the surface of the sample, phi_i is the in-plane direction of the incident beam (measured with respect to the x-axis).

Scattering vector
In BornAgain, the wave vector q is defined as ki - kf, where ki is the incident wave vector and kf the scattered one.

### Running the simulation  and plotting the results

def run_simulation():
"""
Run simulation and plot results
"""
sample = get_sample()
simulation = get_simulation()
simulation.setSample(sample)
simulation.runSimulation()
result = simulation.getIntensityData()

The function, whose definition starts from line 54, gathers all items. We create the sample and the simulation objects at the lines 58 and 59, using calls to the previously defined functions. We assign the sample to the simulation at line 60 and finally launch the simulation at line 61. In line 62 we obtain the result of the simulation as an IntensityData object, which contains the axes definition and the simulated intensity as a function of outgoing angles phi_f and alpha_f.

    im = plt.imshow(result.getArray(),
norm=matplotlib.colors.LogNorm(1.0, result.getMaximum()),
extent=[result.getXmin()/deg, result.getXmax()/deg, result.getYmin()/deg, result.getYmax()/deg],
aspect='auto')
cb = plt.colorbar(im)
cb.set_label(r'Intensity (arb. u.)', size=16)
plt.xlabel(r'$\phi_f (^{\circ})$', fontsize=16)
plt.ylabel(r'$\alpha_f (^{\circ})$', fontsize=16)
plt.show()


To be able to plot intensity data using matplotlib routines, we convert our IntensityData object into standard numpy array with intensities on line 65. The rest of the program contains several typical calls for plotting a numpy array as a heat map.

As a result of executing the script in the python interpreter

$python CylindersAndPrisms.py  The following image should be displayed on the screen The full code is given below """ Mixture of cylinders and prisms without interference """ import numpy import matplotlib from matplotlib import pyplot as plt from bornagain import * phi_min, phi_max = -1.0, 1.0 alpha_min, alpha_max = 0.0, 2.0 def get_sample(): """ Build and return the sample representing cylinders and prisms on top of substrate without interference. """ # defining materials m_air = HomogeneousMaterial("Air", 0.0, 0.0) m_substrate = HomogeneousMaterial("Substrate", 6e-6, 2e-8) m_particle = HomogeneousMaterial("Particle", 6e-4, 2e-8) # collection of particles cylinder_ff = FormFactorCylinder(5*nanometer, 5*nanometer) cylinder = Particle(m_particle, cylinder_ff) prism_ff = FormFactorPrism3(10*nanometer, 5*nanometer) prism = Particle(m_particle, prism_ff) particle_layout = ParticleLayout() particle_layout.addParticle(cylinder, 0.5) particle_layout.addParticle(prism, 0.5) interference = InterferenceFunctionNone() particle_layout.setInterferenceFunction(interference) # air layer with particles and substrate form multi layer air_layer = Layer(m_air) air_layer.addLayout(particle_layout) substrate_layer = Layer(m_substrate) multi_layer = MultiLayer() multi_layer.addLayer(air_layer) multi_layer.addLayer(substrate_layer) multi_layer.printParameters() return multi_layer def get_simulation(): """ Create and return GISAXS simulation with beam and detector defined """ simulation = GISASSimulation() simulation.setDetectorParameters(100, phi_min*degree, phi_max*degree, 100, alpha_min*degree, alpha_max*degree) simulation.setBeamParameters(1.0*angstrom, 0.2*degree, 0.0*degree) return simulation def run_simulation(): """ Run simulation and plot results """ sample = get_sample() simulation = get_simulation() simulation.setSample(sample) simulation.runSimulation() result = simulation.getIntensityData() im = plt.imshow(result.getArray(), norm=matplotlib.colors.LogNorm(1.0, result.getMaximum()), extent=[result.getXmin()/deg, result.getXmax()/deg, result.getYmin()/deg, result.getYmax()/deg], aspect='auto') cb = plt.colorbar(im) cb.set_label(r'Intensity (arb. u.)', size=16) plt.xlabel(r'$\phi_f (^{\circ})$', fontsize=16) plt.ylabel(r'$\alpha_f (^{\circ})\$', fontsize=16)
plt.show()

if __name__ == '__main__':
run_simulation()