From fbc72c1b1c1b7cea606759faf7ff103c2c1562b4 Mon Sep 17 00:00:00 2001
From: Mikhail Svechnikov <m.svechnikov@fz-juelich.de>
Date: Thu, 12 Sep 2024 16:17:01 +0200
Subject: [PATCH] upd example
---
auto/Examples/varia/RoughSurface.py | 85 ++++++++++++++-----
auto/FigExamples/varia/RoughSurface.py | 87 ++++++++++++++-----
auto/MiniExamples/varia/RoughSurface.py | 87 ++++++++++++++-----
rawEx/varia/RoughSurface.py | 107 +++++++++++++++++-------
4 files changed, 269 insertions(+), 97 deletions(-)
diff --git a/auto/Examples/varia/RoughSurface.py b/auto/Examples/varia/RoughSurface.py
index e941e69426d..c14f8adc7d4 100755
--- a/auto/Examples/varia/RoughSurface.py
+++ b/auto/Examples/varia/RoughSurface.py
@@ -6,10 +6,12 @@ heights statistics and lateral roughness spectrum.
import numpy as np
import bornagain as ba
-from bornagain import nm
+from bornagain import nm, nm2, nm3
from matplotlib import pyplot as plt, gridspec
def plot(h):
+ rms = np.std(surface)
+
fig = plt.figure(figsize=(10, 6))
gs = gridspec.GridSpec(3, 2)
gs.update(left=0.1, right=0.48, wspace=0.15, hspace=0.5)
@@ -34,7 +36,7 @@ def plot(h):
# hist
fig.add_subplot(gs[-1, 1])
- plt.hist(h.flatten(), range=[-4*sigma, 4*sigma],
+ plt.hist(h.flatten(), range=[-4*rms, 4*rms],
bins=300, color='black')
plt.title('Height histogram')
plt.xlabel('h, nm')
@@ -42,27 +44,68 @@ def plot(h):
plt.show()
-# roughness parameters
-sigma = 1*nm
-alpha = 0.5
-xi = 35*nm
+def get_sample():
+ """
+ A sample with one layer on a substrate, with partially
+ correlated roughnesses.
+ """
+ # defining materials
+ vacuum = ba.RefractiveMaterial("ambience", 0, 0)
+ material_layer = ba.RefractiveMaterial("layer", 5e-6, 0)
+ material_substrate = ba.RefractiveMaterial("substrate", 15e-6, 0)
-# sample size
-Lx = 1000*nm
-Ly = 1000*nm
-X_points = 512
-Y_points = 512
+ # defining layers
+ l_ambience = ba.Layer(vacuum)
+ l_layer = ba.Layer(material_layer, 25*nm)
+ l_substrate = ba.Layer(material_substrate)
-# create roughness model
-autocorr = ba.K_CorrelationModel(sigma, alpha, xi)
-height_distribution = ba.ErfInterlayer()
-roughness = ba.LayerRoughness(autocorr, height_distribution)
+ # defining roughness
+ height_distribution = ba.ErfInterlayer()
+ max_spat_freq = 0.5*nm
-# generate roughness map
-roughness_map = ba.RoughnessMap(X_points, Y_points, Lx, Ly, roughness)
-surface = roughness_map.generate()
+ # for layer
+ omega = 1*nm3
+ a1 = 1
+ a2 = 10*nm
+ a3 = 100*nm2
+ a4 = 1000*nm3
+ autocorr_layer = ba.LinearGrowthModel(omega, a1, a2, a3, a4, max_spat_freq)
+ roughness_layer = ba.LayerRoughness(autocorr_layer, height_distribution)
+
+ # for substrate
+ sigma = 0.9*nm
+ alpha = 0.5
+ xi = 35*nm
+ autocorr_sub = ba.K_CorrelationModel(sigma, alpha, xi, max_spat_freq)
+ roughness_sub = ba.LayerRoughness(autocorr_sub, height_distribution)
+
+ # adding layers
+ my_sample = ba.MultiLayer()
+ my_sample.addLayer(l_ambience)
+ my_sample.addLayerWithTopRoughness(l_layer, roughness_layer)
+ my_sample.addLayerWithTopRoughness(l_substrate, roughness_sub)
+
+ return my_sample
+
+if __name__ == '__main__':
+
+ # sample size
+ Lx = 1000*nm
+ Ly = 1000*nm
+ X_points = 512
+ Y_points = 512
+
+ sample = get_sample()
+ interface_index = 0 # top interface
+
+ # generate roughness map
+ roughness_map = ba.RoughnessMap(X_points, Y_points, Lx, Ly,
+ sample, interface_index)
+ surface = roughness_map.generate()
+
+ print("rms = {:.3}".format(np.std(surface)),"nm")
+
+ plot(surface)
+ plt.show()
-print("rms = {:.3}".format(np.std(surface)),"nm")
-plot(surface)
-plt.show()
diff --git a/auto/FigExamples/varia/RoughSurface.py b/auto/FigExamples/varia/RoughSurface.py
index 64ac3864635..753819d7fed 100755
--- a/auto/FigExamples/varia/RoughSurface.py
+++ b/auto/FigExamples/varia/RoughSurface.py
@@ -6,10 +6,12 @@ heights statistics and lateral roughness spectrum.
import numpy as np
import bornagain as ba
-from bornagain import nm
+from bornagain import nm, nm2, nm3
from matplotlib import pyplot as plt, gridspec
def plot(h):
+ rms = np.std(surface)
+
fig = plt.figure(figsize=(10, 6))
gs = gridspec.GridSpec(3, 2)
gs.update(left=0.1, right=0.48, wspace=0.15, hspace=0.5)
@@ -34,7 +36,7 @@ def plot(h):
# hist
fig.add_subplot(gs[-1, 1])
- plt.hist(h.flatten(), range=[-4*sigma, 4*sigma],
+ plt.hist(h.flatten(), range=[-4*rms, 4*rms],
bins=300, color='black')
plt.title('Height histogram')
plt.xlabel('h, nm')
@@ -42,28 +44,69 @@ def plot(h):
plt.show()
-# roughness parameters
-sigma = 1*nm
-alpha = 0.5
-xi = 35*nm
+def get_sample():
+ """
+ A sample with one layer on a substrate, with partially
+ correlated roughnesses.
+ """
+ # defining materials
+ vacuum = ba.RefractiveMaterial("ambience", 0, 0)
+ material_layer = ba.RefractiveMaterial("layer", 5e-6, 0)
+ material_substrate = ba.RefractiveMaterial("substrate", 15e-6, 0)
-# sample size
-Lx = 1000*nm
-Ly = 1000*nm
-X_points = 512
-Y_points = 512
+ # defining layers
+ l_ambience = ba.Layer(vacuum)
+ l_layer = ba.Layer(material_layer, 25*nm)
+ l_substrate = ba.Layer(material_substrate)
-# create roughness model
-autocorr = ba.K_CorrelationModel(sigma, alpha, xi)
-height_distribution = ba.ErfInterlayer()
-roughness = ba.LayerRoughness(autocorr, height_distribution)
+ # defining roughness
+ height_distribution = ba.ErfInterlayer()
+ max_spat_freq = 0.5*nm
-# generate roughness map
-roughness_map = ba.RoughnessMap(X_points, Y_points, Lx, Ly, roughness)
-surface = roughness_map.generate()
+ # for layer
+ omega = 1*nm3
+ a1 = 1
+ a2 = 10*nm
+ a3 = 100*nm2
+ a4 = 1000*nm3
+ autocorr_layer = ba.LinearGrowthModel(omega, a1, a2, a3, a4, max_spat_freq)
+ roughness_layer = ba.LayerRoughness(autocorr_layer, height_distribution)
+
+ # for substrate
+ sigma = 0.9*nm
+ alpha = 0.5
+ xi = 35*nm
+ autocorr_sub = ba.K_CorrelationModel(sigma, alpha, xi, max_spat_freq)
+ roughness_sub = ba.LayerRoughness(autocorr_sub, height_distribution)
+
+ # adding layers
+ my_sample = ba.MultiLayer()
+ my_sample.addLayer(l_ambience)
+ my_sample.addLayerWithTopRoughness(l_layer, roughness_layer)
+ my_sample.addLayerWithTopRoughness(l_substrate, roughness_sub)
+
+ return my_sample
+
+if __name__ == '__main__':
+
+ # sample size
+ Lx = 1000*nm
+ Ly = 1000*nm
+ X_points = 512
+ Y_points = 512
+
+ sample = get_sample()
+ interface_index = 0 # top interface
+
+ # generate roughness map
+ roughness_map = ba.RoughnessMap(X_points, Y_points, Lx, Ly,
+ sample, interface_index)
+ surface = roughness_map.generate()
+
+ print("rms = {:.3}".format(np.std(surface)),"nm")
+
+ from bornagain import ba_plot as bp
+ plotargs = bp.parse_commandline()
+ bp.export(**plotargs)
-print("rms = {:.3}".format(np.std(surface)),"nm")
-from bornagain import ba_plot as bp
-plotargs = bp.parse_commandline()
-bp.export(**plotargs)
diff --git a/auto/MiniExamples/varia/RoughSurface.py b/auto/MiniExamples/varia/RoughSurface.py
index f04346dbc59..8dcf9bed08f 100755
--- a/auto/MiniExamples/varia/RoughSurface.py
+++ b/auto/MiniExamples/varia/RoughSurface.py
@@ -6,10 +6,12 @@ heights statistics and lateral roughness spectrum.
import numpy as np
import bornagain as ba
-from bornagain import nm
+from bornagain import nm, nm2, nm3
from matplotlib import pyplot as plt, gridspec
def plot(h):
+ rms = np.std(surface)
+
fig = plt.figure(figsize=(10, 6))
gs = gridspec.GridSpec(3, 2)
gs.update(left=0.1, right=0.48, wspace=0.15, hspace=0.5)
@@ -34,7 +36,7 @@ def plot(h):
# hist
fig.add_subplot(gs[-1, 1])
- plt.hist(h.flatten(), range=[-4*sigma, 4*sigma],
+ plt.hist(h.flatten(), range=[-4*rms, 4*rms],
bins=300, color='black')
plt.title('Height histogram')
plt.xlabel('h, nm')
@@ -42,28 +44,69 @@ def plot(h):
plt.show()
-# roughness parameters
-sigma = 1*nm
-alpha = 0.5
-xi = 35*nm
+def get_sample():
+ """
+ A sample with one layer on a substrate, with partially
+ correlated roughnesses.
+ """
+ # defining materials
+ vacuum = ba.RefractiveMaterial("ambience", 0, 0)
+ material_layer = ba.RefractiveMaterial("layer", 5e-6, 0)
+ material_substrate = ba.RefractiveMaterial("substrate", 15e-6, 0)
-# sample size
-Lx = 1000*nm
-Ly = 1000*nm
-X_points = 10
-Y_points = 10
+ # defining layers
+ l_ambience = ba.Layer(vacuum)
+ l_layer = ba.Layer(material_layer, 25*nm)
+ l_substrate = ba.Layer(material_substrate)
-# create roughness model
-autocorr = ba.K_CorrelationModel(sigma, alpha, xi)
-height_distribution = ba.ErfInterlayer()
-roughness = ba.LayerRoughness(autocorr, height_distribution)
+ # defining roughness
+ height_distribution = ba.ErfInterlayer()
+ max_spat_freq = 0.5*nm
-# generate roughness map
-roughness_map = ba.RoughnessMap(X_points, Y_points, Lx, Ly, roughness, 0)
-surface = roughness_map.generate()
+ # for layer
+ omega = 1*nm3
+ a1 = 1
+ a2 = 10*nm
+ a3 = 100*nm2
+ a4 = 1000*nm3
+ autocorr_layer = ba.LinearGrowthModel(omega, a1, a2, a3, a4, max_spat_freq)
+ roughness_layer = ba.LayerRoughness(autocorr_layer, height_distribution)
+
+ # for substrate
+ sigma = 0.9*nm
+ alpha = 0.5
+ xi = 35*nm
+ autocorr_sub = ba.K_CorrelationModel(sigma, alpha, xi, max_spat_freq)
+ roughness_sub = ba.LayerRoughness(autocorr_sub, height_distribution)
+
+ # adding layers
+ my_sample = ba.MultiLayer()
+ my_sample.addLayer(l_ambience)
+ my_sample.addLayerWithTopRoughness(l_layer, roughness_layer)
+ my_sample.addLayerWithTopRoughness(l_substrate, roughness_sub)
+
+ return my_sample
+
+if __name__ == '__main__':
+
+ # sample size
+ Lx = 1000*nm
+ Ly = 1000*nm
+ X_points = 10
+ Y_points = 10
+
+ sample = get_sample()
+ interface_index = 0 # top interface
+
+ # generate roughness map
+ roughness_map = ba.RoughnessMap(X_points, Y_points, Lx, Ly,
+ sample, interface_index)
+ surface = roughness_map.generate()
+
+ print("rms = {:.3}".format(np.std(surface)),"nm")
+
+ from bornagain import ba_plot as bp
+ plotargs = bp.parse_commandline()
+ bp.export(**plotargs)
-print("rms = {:.3}".format(np.std(surface)),"nm")
-from bornagain import ba_plot as bp
-plotargs = bp.parse_commandline()
-bp.export(**plotargs)
diff --git a/rawEx/varia/RoughSurface.py b/rawEx/varia/RoughSurface.py
index 1e22299eaa0..c12911350fe 100755
--- a/rawEx/varia/RoughSurface.py
+++ b/rawEx/varia/RoughSurface.py
@@ -6,10 +6,12 @@ heights statistics and lateral roughness spectrum.
import numpy as np
import bornagain as ba
-from bornagain import nm
+from bornagain import nm, nm2, nm3
from matplotlib import pyplot as plt, gridspec
def plot(h):
+ rms = np.std(surface)
+
fig = plt.figure(figsize=(10, 6))
gs = gridspec.GridSpec(3, 2)
gs.update(left=0.1, right=0.48, wspace=0.15, hspace=0.5)
@@ -34,7 +36,7 @@ def plot(h):
# hist
fig.add_subplot(gs[-1, 1])
- plt.hist(h.flatten(), range=[-4*sigma, 4*sigma],
+ plt.hist(h.flatten(), range=[-4*rms, 4*rms],
bins=300, color='black')
plt.title('Height histogram')
plt.xlabel('h, nm')
@@ -42,33 +44,74 @@ def plot(h):
plt.show()
-# roughness parameters
-sigma = 1*nm
-alpha = 0.5
-xi = 35*nm
-
-# sample size
-Lx = 1000*nm
-Ly = 1000*nm
-X_points = <%= test_mode ? 10 : 512 %>
-Y_points = <%= test_mode ? 10 : 512 %>
-
-# create roughness model
-autocorr = ba.K_CorrelationModel(sigma, alpha, xi)
-height_distribution = ba.ErfInterlayer()
-roughness = ba.LayerRoughness(autocorr, height_distribution)
-
-# generate roughness map
-roughness_map = ba.RoughnessMap(X_points, Y_points, Lx, Ly, roughness<%= test_mode ? ", 0" : "" %>)
-surface = roughness_map.generate()
-
-print("rms = {:.3}".format(np.std(surface)),"nm")
-
-<%- if test_mode or figure_mode -%>
-from bornagain import ba_plot as bp
-plotargs = bp.parse_commandline()
-bp.export(**plotargs)
-<%- else -%>
-plot(surface)
-plt.show()
-<%- end -%>
+def get_sample():
+ """
+ A sample with one layer on a substrate, with partially
+ correlated roughnesses.
+ """
+ # defining materials
+ vacuum = ba.RefractiveMaterial("ambience", 0, 0)
+ material_layer = ba.RefractiveMaterial("layer", 5e-6, 0)
+ material_substrate = ba.RefractiveMaterial("substrate", 15e-6, 0)
+
+ # defining layers
+ l_ambience = ba.Layer(vacuum)
+ l_layer = ba.Layer(material_layer, 25*nm)
+ l_substrate = ba.Layer(material_substrate)
+
+ # defining roughness
+ height_distribution = ba.ErfInterlayer()
+ max_spat_freq = 0.5*nm
+
+ # for layer
+ omega = 1*nm3
+ a1 = 1
+ a2 = 10*nm
+ a3 = 100*nm2
+ a4 = 1000*nm3
+ autocorr_layer = ba.LinearGrowthModel(omega, a1, a2, a3, a4, max_spat_freq)
+ roughness_layer = ba.LayerRoughness(autocorr_layer, height_distribution)
+
+ # for substrate
+ sigma = 0.9*nm
+ alpha = 0.5
+ xi = 35*nm
+ autocorr_sub = ba.K_CorrelationModel(sigma, alpha, xi, max_spat_freq)
+ roughness_sub = ba.LayerRoughness(autocorr_sub, height_distribution)
+
+ # adding layers
+ my_sample = ba.MultiLayer()
+ my_sample.addLayer(l_ambience)
+ my_sample.addLayerWithTopRoughness(l_layer, roughness_layer)
+ my_sample.addLayerWithTopRoughness(l_substrate, roughness_sub)
+
+ return my_sample
+
+if __name__ == '__main__':
+
+ # sample size
+ Lx = 1000*nm
+ Ly = 1000*nm
+ X_points = <%= test_mode ? 10 : 512 %>
+ Y_points = <%= test_mode ? 10 : 512 %>
+
+ sample = get_sample()
+ interface_index = 0 # top interface
+
+ # generate roughness map
+ roughness_map = ba.RoughnessMap(X_points, Y_points, Lx, Ly,
+ sample, interface_index)
+ surface = roughness_map.generate()
+
+ print("rms = {:.3}".format(np.std(surface)),"nm")
+
+ <%- if test_mode or figure_mode -%>
+ from bornagain import ba_plot as bp
+ plotargs = bp.parse_commandline()
+ bp.export(**plotargs)
+ <%- else -%>
+ plot(surface)
+ plt.show()
+ <%- end -%>
+
+
--
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