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
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
|
#!/usr/bin/env python3
"""
Large cylinders in DWBA.
This example demonstrates that for large particles (~1000nm) the form factor
oscillates rapidly within one detector bin and analytical calculations
(performed for the bin center) give completely wrong intensity pattern.
In this case Monte-Carlo integration over detector bin should be used.
"""
import bornagain as ba
from bornagain import angstrom, ba_plot as bp, deg, nm
default_cylinder_radius = 10*nm
default_cylinder_height = 20*nm
def get_sample(cylinder_radius, cylinder_height):
# Materials
vacuum = ba.RefractiveMaterial("Vacuum", 0, 0)
material_substrate = ba.RefractiveMaterial("Substrate", 6e-6, 2e-8)
material_particle = ba.RefractiveMaterial("Particle", 6e-4, 2e-8)
# Particle layout
cylinder_ff = ba.Cylinder(cylinder_radius, cylinder_height)
cylinder = ba.Particle(material_particle, cylinder_ff)
particle_layout = ba.ParticleLayout()
particle_layout.addParticle(cylinder)
# Layers
vacuum_layer = ba.Layer(vacuum)
vacuum_layer.addLayout(particle_layout)
substrate_layer = ba.Layer(material_substrate)
# Sample
sample = ba.Sample()
sample.addLayer(vacuum_layer)
sample.addLayer(substrate_layer)
return sample
def get_simulation(sample, integration_flag):
"""
A GISAXS simulation with defined beam and detector.
If integration_flag=True, the simulation will integrate over detector bins.
"""
beam = ba.Beam(1, 1*angstrom, 0.2*deg)
n = <%= test_mode ? 21 : 201 %>
det = ba.SphericalDetector(n, -1.5*deg, 1.5*deg, n, 0, 3*deg)
simulation = ba.ScatteringSimulation(beam, sample, det)
simulation.options().setMonteCarloIntegration(integration_flag, <%= test_mode ? 500 : 50 %>)
if not "__no_terminal__" in globals():
simulation.setTerminalProgressMonitor()
return simulation
def simulate():
ret = []
# conditions to define cylinders scale factor and integration flag
conditions = [{
'title': "Small cylinders, analytical calculations",
'scale': 1,
'integration': False,
'zmin': 1e-5,
'zmax': 1e2
}, {
'title': "Small cylinders, Monte-Carlo integration",
'scale': 1,
'integration': True,
'zmin': 1e-5,
'zmax': 1e2
}, {
'title': "Large cylinders, analytical calculations",
'scale': 100,
'integration': False,
'zmin': 1e-5,
'zmax': 1e10
}, {
'title': "Large cylinders, Monte-Carlo integration",
'scale': 100,
'integration': True,
'zmin': 1e-5,
'zmax': 1e10
}]
# run simulation 4 times
for i_plot, condition in enumerate(conditions):
scale = condition['scale']
integration_flag = condition['integration']
sample = get_sample(default_cylinder_radius*scale,
default_cylinder_height*scale)
simulation = get_simulation(sample, integration_flag)
result = simulation.simulate()
result.setTitle(condition['title'])
ret.append(result)
return ret
if __name__ == '__main__':
results = simulate()
<%- if test_mode -%>
from bornagain import ba_check
ba_check.persistence_test(results)
<%- elsif figure_mode -%>
plotargs = bp.parse_commandline()
plotargs['fontsize'] = 12
bp.plot_to_grid(results, 2, **plotargs)
bp.export(**plotargs)
<%- else -%>
bp.plot_to_grid(results, 2)
bp.plt.show()
<%- end -%>
|
