diff --git a/Sample/Interface/AutocorrelationModels.h b/Sample/Interface/AutocorrelationModels.h
index c32d32c70ce55dee53b9a16ed6e043f0faea1dbe..55b269eb0fdc57ee5aff0d1329b9b5e25e131f4e 100644
--- a/Sample/Interface/AutocorrelationModels.h
+++ b/Sample/Interface/AutocorrelationModels.h
@@ -44,7 +44,7 @@ protected:
//! 14472-14478 (1993)
class K_CorrelationModel : public AutocorrelationModel {
public:
- K_CorrelationModel(double sigma = 0, double hurst = 0.7, double lateralCorrLength = 25,
+ K_CorrelationModel(double sigma, double hurst, double lateralCorrLength,
double maxSpatFrequency = 0.5);
std::string className() const override { return "K_CorrelationModel"; }
std::vector<ParaMeta> parDefs() const final;
diff --git a/Sample/Multilayer/Layer.cpp b/Sample/Multilayer/Layer.cpp
index 58880f2d976bcecc05d3ef3fd3ddd0083a88615f..555be9f02fe38683f927a73f81963463a891d3c6 100644
--- a/Sample/Multilayer/Layer.cpp
+++ b/Sample/Multilayer/Layer.cpp
@@ -22,7 +22,7 @@ namespace {
LayerRoughness* zeroRoughness()
{
- K_CorrelationModel autocorr(0);
+ K_CorrelationModel autocorr(0, 0.7, 25);
ErfInterlayer interlayer;
return new LayerRoughness(&autocorr, &interlayer);
}
diff --git a/Sample/StandardSample/FeNiBilayerBuilder.cpp b/Sample/StandardSample/FeNiBilayerBuilder.cpp
index c6c161ea610d577c4d9d18bb0fbf1f0298b33e59..ef4c99bc8d6be3634843c8563e9d90f70312cb25 100644
--- a/Sample/StandardSample/FeNiBilayerBuilder.cpp
+++ b/Sample/StandardSample/FeNiBilayerBuilder.cpp
@@ -110,7 +110,7 @@ std::unique_ptr<Sample> FeNiBilayer::constructSample()
auto m_Ni = MaterialBySLD("Ni", sldNi.real(), sldNi.imag());
auto m_Substrate = MaterialBySLD("Au", sldAu.real(), sldAu.imag());
- K_CorrelationModel autocorrelation(sigmaRoughness);
+ K_CorrelationModel autocorrelation(sigmaRoughness, 0.7, 25);
LayerRoughness roughness{&autocorrelation, interlayerModel.get()};
Layer l_Fe{m_Fe, thicknessFe, &roughness};
diff --git a/Sample/StandardSample/MagneticLayersBuilder.cpp b/Sample/StandardSample/MagneticLayersBuilder.cpp
index b3da64131ab5426b1ae16a4186db42b62398e86c..4a24d22928ca3d2d15d8e0dcd063ec093e5df8cb 100644
--- a/Sample/StandardSample/MagneticLayersBuilder.cpp
+++ b/Sample/StandardSample/MagneticLayersBuilder.cpp
@@ -135,7 +135,7 @@ Sample* ExemplarySamples::createSimpleMagneticRotationWithRoughness(const std::s
Material substrate_material = RefractiveMaterial("Substrate", 7e-6, 2e-8, substr_field);
Material layer_material = RefractiveMaterial("MagLayer", 6e-4, 2e-8, layer_field);
- K_CorrelationModel autocorrelation(sigmaRoughness);
+ K_CorrelationModel autocorrelation(sigmaRoughness, 0.7, 25);
auto roughness = LayerRoughness(&autocorrelation, interlayerModel.get());
Layer vacuum_layer(vacuum_material);
diff --git a/auto/Examples/fit/specular/Honeycomb_fit.py b/auto/Examples/fit/specular/Honeycomb_fit.py
index 4aa383b2c1ed942a086b9b5503ad98e93c56da09..0e3a5e0541c1bab13e68152ef87049169fda2c3d 100755
--- a/auto/Examples/fit/specular/Honeycomb_fit.py
+++ b/auto/Examples/fit/specular/Honeycomb_fit.py
@@ -8,7 +8,7 @@ data without spin-flip for performance reasons.
"""
import bornagain as ba, numpy as np, os, matplotlib.pyplot as plt, scipy
-from bornagain import angstrom, sample_tools as st
+from bornagain import nm, sample_tools as st
from bornagain.numpyutil import Arrayf64Converter as dac
datadir = os.getenv('BA_DATA_DIR', '')
@@ -46,11 +46,11 @@ def get_sample(P, sign, T):
interlayer_model = ba.ErfInterlayer()
- rPyOx_autocorr = ba.K_CorrelationModel(P["rPyOx"]*angstrom)
- rPy2_autocorr = ba.K_CorrelationModel(P["rPy2"]*angstrom)
- rPy1_autocorr = ba.K_CorrelationModel(P["rPy1"]*angstrom)
- rSiO2_autocorr = ba.K_CorrelationModel(P["rSiO2"]*angstrom)
- rSi_autocorr = ba.K_CorrelationModel(P["rSi"]*angstrom)
+ rPyOx_autocorr = ba.K_CorrelationModel(P["rPyOx"]*nm, 0.7, 25*nm)
+ rPy2_autocorr = ba.K_CorrelationModel(P["rPy2"]*nm, 0.7, 25*nm)
+ rPy1_autocorr = ba.K_CorrelationModel(P["rPy1"]*nm, 0.7, 25*nm)
+ rSiO2_autocorr = ba.K_CorrelationModel(P["rSiO2"]*nm, 0.7, 25*nm)
+ rSi_autocorr = ba.K_CorrelationModel(P["rSi"]*nm, 0.7, 25*nm)
rPyOx = ba.LayerRoughness(rPyOx_autocorr, interlayer_model)
rPy2 = ba.LayerRoughness(rPy2_autocorr, interlayer_model)
@@ -59,10 +59,10 @@ def get_sample(P, sign, T):
rSi = ba.LayerRoughness(rSi_autocorr, interlayer_model)
l_Air = ba.Layer(material_Air)
- l_PyOx = ba.Layer(material_PyOx, P["t_PyOx"]*angstrom, rPyOx)
- l_Py2 = ba.Layer(material_Py2, P["t_Py2"]*angstrom, rPy2)
- l_Py1 = ba.Layer(material_Py1, P["t_Py1"]*angstrom, rPy1)
- l_SiO2 = ba.Layer(material_SiO2, P["t_SiO2"]*angstrom, rSiO2)
+ l_PyOx = ba.Layer(material_PyOx, P["t_PyOx"]*nm, rPyOx)
+ l_Py2 = ba.Layer(material_Py2, P["t_Py2"]*nm, rPy2)
+ l_Py1 = ba.Layer(material_Py1, P["t_Py1"]*nm, rPy1)
+ l_SiO2 = ba.Layer(material_SiO2, P["t_SiO2"]*nm, rSiO2)
l_Si = ba.Layer(material_Si, rSi)
sample = ba.Sample()
@@ -164,10 +164,10 @@ if __name__ == '__main__':
# We start with rather good values so that the example takes not too much time
startPnB = {
"intensity": (0.5, 0.4, 0.6),
- "t_PyOx": (77, 60, 100),
- "t_Py2": (56, 46, 66),
- "t_Py1": (56, 46, 66),
- "t_SiO2": (22, 15, 29),
+ "t_PyOx": (7.7, 6.0, 10.0),
+ "t_Py2": (5.6, 4.6, 6.6),
+ "t_Py1": (5.6, 4.6, 6.6),
+ "t_SiO2": (2.2, 1.5, 2.9),
}
# For fixed parameters, bounds are ignored. We leave them here just
@@ -185,11 +185,11 @@ if __name__ == '__main__':
"sld_PyOx_real": (1.995, 1.92, 2.07),
"sld_Py2_real": (5, 4.7, 5.3),
"sld_Py1_real": (4.62, 4.32, 4.92),
- "rPyOx": (27, 15, 35),
- "rPy2": (12, 2, 20),
- "rPy1": (12, 2, 20),
- "rSiO2": (15, 5, 25),
- "rSi": (15, 5, 25),
+ "rPyOx": (2.7, 1.5, 3.5),
+ "rPy2": (1.2, .2, 2.0),
+ "rPy1": (1.2, .2, 2.0),
+ "rSiO2": (1.5, .5, 2.5),
+ "rSi": (1.5, .5, 2.5),
"msld_PyOx": (0.25, 0, 1),
"msld_Py2": (0.63, 0, 1),
"msld_Py1": (0.64, 0, 1),
diff --git a/auto/Examples/fit/specular/PolarizedSpinAsymmetry.py b/auto/Examples/fit/specular/PolarizedSpinAsymmetry.py
index 620160d0ad1baa63fefb3d1d90ba32ec9b1cdc95..7b90a24fbd198fb8b5c896958725f0517c52edca 100755
--- a/auto/Examples/fit/specular/PolarizedSpinAsymmetry.py
+++ b/auto/Examples/fit/specular/PolarizedSpinAsymmetry.py
@@ -12,7 +12,7 @@ demonstration without the magnetically dead layer.
import os
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from bornagain.numpyutil import Arrayf64Converter as dac
# q-range on which the simulation and fitting are to be performed
@@ -46,8 +46,8 @@ def get_sample(P):
material_layer = ba.MaterialBySLD("(Mg,Al,Fe)3O4", P["rho_Mafo"]*1e-6, 0, B)
material_substrate = ba.MaterialBySLD("MgAl2O4", *sldMao)
- r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*angstrom)
- r_substrate_autocorr = ba.K_CorrelationModel(P["r_Mao"]*angstrom)
+ r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*nm, 0.7, 25*nm)
+ r_substrate_autocorr = ba.K_CorrelationModel(P["r_Mao"]*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
@@ -55,7 +55,7 @@ def get_sample(P):
r_substrate = ba.LayerRoughness(r_substrate_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_Mafo"]*angstrom, r_Mafo)
+ layer = ba.Layer(material_layer, P["t_Mafo"]*nm, r_Mafo)
substrate_layer = ba.Layer(material_substrate, r_substrate)
sample = ba.Sample()
@@ -200,9 +200,9 @@ if __name__ == '__main__':
'q_offset': 7.971243487467318e-05,
'rho_Mafo': 6.370140108715461,
'rhoM_Mafo': 0.27399566816062926,
- 't_Mafo': 137.46913056084736,
- 'r_Mao': 8.60487712674644,
- 'r_Mafo': 3.7844265311293483
+ 't_Mafo': 13.746913056084736,
+ 'r_Mao': 0.860487712674644,
+ 'r_Mafo': 0.37844265311293483
}
def run_Simulation_pp(qzs, P):
diff --git a/auto/Examples/fit/specular/PolarizedSpinAsymmetryFit.py b/auto/Examples/fit/specular/PolarizedSpinAsymmetryFit.py
index 12abd08485d1dd3797a6e0bb533491acab1f5f4a..1102cf911bc4c38bfb10a0af2fdf09c9fae92a90 100755
--- a/auto/Examples/fit/specular/PolarizedSpinAsymmetryFit.py
+++ b/auto/Examples/fit/specular/PolarizedSpinAsymmetryFit.py
@@ -58,9 +58,9 @@ if __name__ == '__main__':
"q_offset": (0, -0.002, 0.002),
"rho_Mafo": (6.3649, 2, 7),
"rhoM_Mafo": (0, 0, 2),
- "t_Mafo": (150, 60, 180),
- "r_Mao": (1, 0, 12),
- "r_Mafo": (1, 0, 12),
+ "t_Mafo": (15, 6, 18),
+ "r_Mao": (0.1, 0, 1.2),
+ "r_Mafo": (0.1, 0, 1.2),
}
PInitial = {d: v[0] for d, v in startParams.items()}
diff --git a/auto/Examples/fit/specular/Pt_layer_fit.py b/auto/Examples/fit/specular/Pt_layer_fit.py
index 56edceb5767bfb368b769abcf00228f28b44c466..4a3abd3db4445aee4a1c6a33e8eac9d28eb0ea0b 100755
--- a/auto/Examples/fit/specular/Pt_layer_fit.py
+++ b/auto/Examples/fit/specular/Pt_layer_fit.py
@@ -9,7 +9,7 @@ band of roughly 4-7 Ã… in 100 steps of 2theta.
"""
import bornagain as ba, numpy as np, os, matplotlib.pyplot as plt
-from bornagain import angstrom
+from bornagain import nm
from bornagain.numpyutil import Arrayf64Converter as dac
datadir = os.getenv('BA_DATA_DIR', '')
@@ -32,14 +32,14 @@ def get_sample(P):
interlayer = ba.TanhInterlayer()
- si_autocorr = ba.K_CorrelationModel(P["r_si/nm"])
- pt_autocorr = ba.K_CorrelationModel(P["r_pt/nm"])
+ si_autocorr = ba.K_CorrelationModel(P["r_si/nm"]*nm, 0.7, 25*nm)
+ pt_autocorr = ba.K_CorrelationModel(P["r_pt/nm"]*nm, 0.7, 25*nm)
r_si = ba.LayerRoughness(si_autocorr, interlayer)
r_pt = ba.LayerRoughness(pt_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_pt/nm"], r_pt)
+ layer = ba.Layer(material_layer, P["t_pt/nm"]*nm, r_pt)
substrate_layer = ba.Layer(material_substrate, r_si)
sample = ba.Sample()
diff --git a/auto/Examples/fit/specular/TREFF_Ni_film.py b/auto/Examples/fit/specular/TREFF_Ni_film.py
index 2a613fc1e434cd2a59e9fc3eab7612eba34e25c0..682511f59b1750a1f210003649367ae4e87f4a77 100755
--- a/auto/Examples/fit/specular/TREFF_Ni_film.py
+++ b/auto/Examples/fit/specular/TREFF_Ni_film.py
@@ -28,10 +28,10 @@ def get_sample(P):
# Layers and interfaces
interlayer = ba.TanhInterlayer()
- Ni_autocorr = ba.K_CorrelationModel(P["sigma_Ni"])
+ Ni_autocorr = ba.K_CorrelationModel(P["sigma_Ni"], 0.7, 25*nm)
roughness_Ni = ba.LayerRoughness(Ni_autocorr, interlayer)
- sub_autocorr = ba.K_CorrelationModel(P["sigma_Substrate"])
+ sub_autocorr = ba.K_CorrelationModel(P["sigma_Substrate"], 0.7, 25*nm)
roughness_Substrate = ba.LayerRoughness(sub_autocorr, interlayer)
layer_Ni = ba.Layer(material_Ni_58, P["thickness"], roughness_Ni)
diff --git a/auto/Examples/specular/MagneticLayerImperfect.py b/auto/Examples/specular/MagneticLayerImperfect.py
index 918c4dca34c1c063cb03ee9ec6041aed7c4b1ee8..2b5abd8bdfaef3a1393bccf493f7b1fda98cee62 100755
--- a/auto/Examples/specular/MagneticLayerImperfect.py
+++ b/auto/Examples/specular/MagneticLayerImperfect.py
@@ -6,7 +6,7 @@ similar to Devishvili et al., Rev. Sci. Instrum. 84, 025112 (2013).
"""
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from math import sin, cos
def get_sample():
@@ -19,14 +19,14 @@ def get_sample():
material_Fe = ba.MaterialBySLD("Fe", 8.0241e-06, 6.0448e-10, B)
material_substrate = ba.MaterialBySLD("MgO", 5.9803e-06, 9.3996e-12)
- autocorr = ba.K_CorrelationModel(20*angstrom)
+ autocorr = ba.K_CorrelationModel(2*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
roughness = ba.LayerRoughness(autocorr, interlayer)
# Layers
layer_vacuum = ba.Layer(vacuum)
- layer_Pd = ba.Layer(material_Pd, 120*angstrom, roughness)
- layer_Fe = ba.Layer(material_Fe, 1000*angstrom, roughness)
+ layer_Pd = ba.Layer(material_Pd, 12*nm, roughness)
+ layer_Fe = ba.Layer(material_Fe, 100*nm, roughness)
layer_substrate = ba.Layer(material_substrate, roughness)
# Multilayer
diff --git a/auto/Examples/specular/PolarizedSpinAsymmetry.py b/auto/Examples/specular/PolarizedSpinAsymmetry.py
index 2410578dc111e8b6cc22a6dc494a24d9ed5c6ce5..0e0261ae2f0ef008559241ac540ce101e423c329 100755
--- a/auto/Examples/specular/PolarizedSpinAsymmetry.py
+++ b/auto/Examples/specular/PolarizedSpinAsymmetry.py
@@ -12,7 +12,7 @@ demonstration without the magnetically dead layer.
import os
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from bornagain.numpyutil import Arrayf64Converter as dac
@@ -47,8 +47,8 @@ def get_sample(P):
material_layer = ba.MaterialBySLD("(Mg,Al,Fe)3O4", P["rho_Mafo"]*1e-6, 0, B)
material_substrate = ba.MaterialBySLD("MgAl2O4", *sldMao)
- r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*angstrom)
- r_sub_autocorr = ba.K_CorrelationModel(P["r_Mao"]*angstrom)
+ r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*nm, 0.7, 25*nm)
+ r_sub_autocorr = ba.K_CorrelationModel(P["r_Mao"]*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
@@ -56,7 +56,7 @@ def get_sample(P):
r_substrate = ba.LayerRoughness(r_sub_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_Mafo"]*angstrom, r_Mafo)
+ layer = ba.Layer(material_layer, P["t_Mafo"]*nm, r_Mafo)
substrate_layer = ba.Layer(material_substrate, r_substrate)
sample = ba.Sample()
@@ -201,9 +201,9 @@ if __name__ == '__main__':
'q_offset': 7.971243487467318e-05,
'rho_Mafo': 6.370140108715461,
'rhoM_Mafo': 0.27399566816062926,
- 't_Mafo': 137.46913056084736,
- 'r_Mao': 8.60487712674644,
- 'r_Mafo': 3.7844265311293483
+ 't_Mafo': 13.746913056084736,
+ 'r_Mao': 0.860487712674644,
+ 'r_Mafo': 0.37844265311293483
}
def run_Simulation_pp(qzs, P):
diff --git a/auto/Examples/specular/RoughnessModel.py b/auto/Examples/specular/RoughnessModel.py
index df5342a2bc9a96ea283ee2da81dde4dc86e58cc0..745a79a7fbc4afb5344e4015e19a7c81c21c775f 100755
--- a/auto/Examples/specular/RoughnessModel.py
+++ b/auto/Examples/specular/RoughnessModel.py
@@ -5,7 +5,7 @@ tanh and Nevot-Croce roughness model using BornAgain.
"""
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg
+from bornagain import angstrom, nm, ba_plot as bp, deg
def get_sample(interlayer):
@@ -16,13 +16,13 @@ def get_sample(interlayer):
material_substrate = ba.MaterialBySLD("SiSubstrate", 2.0704e-06, 0)
# Roughness
- autocorr = ba.K_CorrelationModel(10*angstrom)
+ autocorr = ba.K_CorrelationModel(1*nm, 0.7, 25*nm)
roughness = ba.LayerRoughness(autocorr, interlayer)
# create layers
ambient_layer = ba.Layer(vacuum)
- ti_layer = ba.Layer(material_ti, 30*angstrom, roughness)
- ni_layer = ba.Layer(material_ni, 70*angstrom, roughness)
+ ti_layer = ba.Layer(material_ti, 3*nm, roughness)
+ ni_layer = ba.Layer(material_ni, 7*nm, roughness)
substrate_layer = ba.Layer(material_substrate, roughness)
# create periodic stack
diff --git a/auto/Examples/specular/SpecularSimulationWithRoughness.py b/auto/Examples/specular/SpecularSimulationWithRoughness.py
index 845bd4f47ee372dd159e78bc4b4b0f9a43465248..ea29ced604bfdc1a586ef62bcdb4a0339a4c6d1d 100755
--- a/auto/Examples/specular/SpecularSimulationWithRoughness.py
+++ b/auto/Examples/specular/SpecularSimulationWithRoughness.py
@@ -5,7 +5,7 @@ with a rough sample using BornAgain.
"""
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, nm
+from bornagain import nm, ba_plot as bp, deg, nm
def get_sample():
@@ -16,14 +16,14 @@ def get_sample():
material_substrate = ba.MaterialBySLD("SiSubstrate", 2.0704e-06, 0)
# roughness
- autocorr = ba.K_CorrelationModel(1*nm)
+ autocorr = ba.K_CorrelationModel(1*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
roughness = ba.LayerRoughness(autocorr, interlayer)
# Layers
ambient_layer = ba.Layer(vacuum)
- ti_layer = ba.Layer(material_ti, 30*angstrom, roughness)
- ni_layer = ba.Layer(material_ni, 70*angstrom, roughness)
+ ti_layer = ba.Layer(material_ti, 3*nm, roughness)
+ ni_layer = ba.Layer(material_ni, 7*nm, roughness)
substrate_layer = ba.Layer(material_substrate, roughness)
# Periodic stack
@@ -44,7 +44,7 @@ def get_sample():
def get_simulation(sample):
n = 500
scan = ba.AlphaScan(n, 2*deg/n, 2*deg)
- scan.setWavelength(1.54*angstrom)
+ scan.setWavelength(0.154*nm)
return ba.SpecularSimulation(scan, sample)
diff --git a/auto/FigExamples/fit/specular/Honeycomb_fit.py b/auto/FigExamples/fit/specular/Honeycomb_fit.py
index fee2317e0356eac81bd6a69f13c2d80a43535aa4..fe04348a1e4b6a2deda9f3cba43f2c4f355d4ac5 100755
--- a/auto/FigExamples/fit/specular/Honeycomb_fit.py
+++ b/auto/FigExamples/fit/specular/Honeycomb_fit.py
@@ -8,7 +8,7 @@ data without spin-flip for performance reasons.
"""
import bornagain as ba, numpy as np, os, matplotlib.pyplot as plt, scipy
-from bornagain import angstrom, sample_tools as st
+from bornagain import nm, sample_tools as st
from bornagain.numpyutil import Arrayf64Converter as dac
datadir = os.getenv('BA_DATA_DIR', '')
@@ -46,11 +46,11 @@ def get_sample(P, sign, T):
interlayer_model = ba.ErfInterlayer()
- rPyOx_autocorr = ba.K_CorrelationModel(P["rPyOx"]*angstrom)
- rPy2_autocorr = ba.K_CorrelationModel(P["rPy2"]*angstrom)
- rPy1_autocorr = ba.K_CorrelationModel(P["rPy1"]*angstrom)
- rSiO2_autocorr = ba.K_CorrelationModel(P["rSiO2"]*angstrom)
- rSi_autocorr = ba.K_CorrelationModel(P["rSi"]*angstrom)
+ rPyOx_autocorr = ba.K_CorrelationModel(P["rPyOx"]*nm, 0.7, 25*nm)
+ rPy2_autocorr = ba.K_CorrelationModel(P["rPy2"]*nm, 0.7, 25*nm)
+ rPy1_autocorr = ba.K_CorrelationModel(P["rPy1"]*nm, 0.7, 25*nm)
+ rSiO2_autocorr = ba.K_CorrelationModel(P["rSiO2"]*nm, 0.7, 25*nm)
+ rSi_autocorr = ba.K_CorrelationModel(P["rSi"]*nm, 0.7, 25*nm)
rPyOx = ba.LayerRoughness(rPyOx_autocorr, interlayer_model)
rPy2 = ba.LayerRoughness(rPy2_autocorr, interlayer_model)
@@ -59,10 +59,10 @@ def get_sample(P, sign, T):
rSi = ba.LayerRoughness(rSi_autocorr, interlayer_model)
l_Air = ba.Layer(material_Air)
- l_PyOx = ba.Layer(material_PyOx, P["t_PyOx"]*angstrom, rPyOx)
- l_Py2 = ba.Layer(material_Py2, P["t_Py2"]*angstrom, rPy2)
- l_Py1 = ba.Layer(material_Py1, P["t_Py1"]*angstrom, rPy1)
- l_SiO2 = ba.Layer(material_SiO2, P["t_SiO2"]*angstrom, rSiO2)
+ l_PyOx = ba.Layer(material_PyOx, P["t_PyOx"]*nm, rPyOx)
+ l_Py2 = ba.Layer(material_Py2, P["t_Py2"]*nm, rPy2)
+ l_Py1 = ba.Layer(material_Py1, P["t_Py1"]*nm, rPy1)
+ l_SiO2 = ba.Layer(material_SiO2, P["t_SiO2"]*nm, rSiO2)
l_Si = ba.Layer(material_Si, rSi)
sample = ba.Sample()
@@ -162,10 +162,10 @@ if __name__ == '__main__':
# We start with rather good values so that the example takes not too much time
startPnB = {
"intensity": (0.5, 0.4, 0.6),
- "t_PyOx": (77, 60, 100),
- "t_Py2": (56, 46, 66),
- "t_Py1": (56, 46, 66),
- "t_SiO2": (22, 15, 29),
+ "t_PyOx": (7.7, 6.0, 10.0),
+ "t_Py2": (5.6, 4.6, 6.6),
+ "t_Py1": (5.6, 4.6, 6.6),
+ "t_SiO2": (2.2, 1.5, 2.9),
}
# For fixed parameters, bounds are ignored. We leave them here just
@@ -183,11 +183,11 @@ if __name__ == '__main__':
"sld_PyOx_real": (1.995, 1.92, 2.07),
"sld_Py2_real": (5, 4.7, 5.3),
"sld_Py1_real": (4.62, 4.32, 4.92),
- "rPyOx": (27, 15, 35),
- "rPy2": (12, 2, 20),
- "rPy1": (12, 2, 20),
- "rSiO2": (15, 5, 25),
- "rSi": (15, 5, 25),
+ "rPyOx": (2.7, 1.5, 3.5),
+ "rPy2": (1.2, .2, 2.0),
+ "rPy1": (1.2, .2, 2.0),
+ "rSiO2": (1.5, .5, 2.5),
+ "rSi": (1.5, .5, 2.5),
"msld_PyOx": (0.25, 0, 1),
"msld_Py2": (0.63, 0, 1),
"msld_Py1": (0.64, 0, 1),
diff --git a/auto/FigExamples/fit/specular/PolarizedSpinAsymmetry.py b/auto/FigExamples/fit/specular/PolarizedSpinAsymmetry.py
index b80b6280d028b83839cfed34f9f77dca3cfe6766..86c264e9b68c1e19ec10ec307628fd7c822dbba7 100755
--- a/auto/FigExamples/fit/specular/PolarizedSpinAsymmetry.py
+++ b/auto/FigExamples/fit/specular/PolarizedSpinAsymmetry.py
@@ -12,7 +12,7 @@ demonstration without the magnetically dead layer.
import os
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from bornagain.numpyutil import Arrayf64Converter as dac
# q-range on which the simulation and fitting are to be performed
@@ -46,8 +46,8 @@ def get_sample(P):
material_layer = ba.MaterialBySLD("(Mg,Al,Fe)3O4", P["rho_Mafo"]*1e-6, 0, B)
material_substrate = ba.MaterialBySLD("MgAl2O4", *sldMao)
- r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*angstrom)
- r_substrate_autocorr = ba.K_CorrelationModel(P["r_Mao"]*angstrom)
+ r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*nm, 0.7, 25*nm)
+ r_substrate_autocorr = ba.K_CorrelationModel(P["r_Mao"]*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
@@ -55,7 +55,7 @@ def get_sample(P):
r_substrate = ba.LayerRoughness(r_substrate_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_Mafo"]*angstrom, r_Mafo)
+ layer = ba.Layer(material_layer, P["t_Mafo"]*nm, r_Mafo)
substrate_layer = ba.Layer(material_substrate, r_substrate)
sample = ba.Sample()
@@ -202,9 +202,9 @@ if __name__ == '__main__':
'q_offset': 7.971243487467318e-05,
'rho_Mafo': 6.370140108715461,
'rhoM_Mafo': 0.27399566816062926,
- 't_Mafo': 137.46913056084736,
- 'r_Mao': 8.60487712674644,
- 'r_Mafo': 3.7844265311293483
+ 't_Mafo': 13.746913056084736,
+ 'r_Mao': 0.860487712674644,
+ 'r_Mafo': 0.37844265311293483
}
def run_Simulation_pp(qzs, P):
diff --git a/auto/FigExamples/fit/specular/PolarizedSpinAsymmetryFit.py b/auto/FigExamples/fit/specular/PolarizedSpinAsymmetryFit.py
index 5bee9fca96ee8343bf3d7b43d078b03190151ad0..227ebbb3d5bb2c19ae2e05d2ad79f56e07cd7668 100755
--- a/auto/FigExamples/fit/specular/PolarizedSpinAsymmetryFit.py
+++ b/auto/FigExamples/fit/specular/PolarizedSpinAsymmetryFit.py
@@ -58,9 +58,9 @@ if __name__ == '__main__':
"q_offset": (0, -0.002, 0.002),
"rho_Mafo": (6.3649, 2, 7),
"rhoM_Mafo": (0, 0, 2),
- "t_Mafo": (150, 60, 180),
- "r_Mao": (1, 0, 12),
- "r_Mafo": (1, 0, 12),
+ "t_Mafo": (15, 6, 18),
+ "r_Mao": (0.1, 0, 1.2),
+ "r_Mafo": (0.1, 0, 1.2),
}
PInitial = {d: v[0] for d, v in startParams.items()}
diff --git a/auto/FigExamples/fit/specular/Pt_layer_fit.py b/auto/FigExamples/fit/specular/Pt_layer_fit.py
index 24bf5f3aa85e2a0e5d3568f612482eb2808d3d86..f7b5fc034a53437244c372641cbd2b3ced960897 100755
--- a/auto/FigExamples/fit/specular/Pt_layer_fit.py
+++ b/auto/FigExamples/fit/specular/Pt_layer_fit.py
@@ -9,7 +9,7 @@ band of roughly 4-7 Ã… in 100 steps of 2theta.
"""
import bornagain as ba, numpy as np, os, matplotlib.pyplot as plt
-from bornagain import angstrom
+from bornagain import nm
from bornagain.numpyutil import Arrayf64Converter as dac
datadir = os.getenv('BA_DATA_DIR', '')
@@ -32,14 +32,14 @@ def get_sample(P):
interlayer = ba.TanhInterlayer()
- si_autocorr = ba.K_CorrelationModel(P["r_si/nm"])
- pt_autocorr = ba.K_CorrelationModel(P["r_pt/nm"])
+ si_autocorr = ba.K_CorrelationModel(P["r_si/nm"]*nm, 0.7, 25*nm)
+ pt_autocorr = ba.K_CorrelationModel(P["r_pt/nm"]*nm, 0.7, 25*nm)
r_si = ba.LayerRoughness(si_autocorr, interlayer)
r_pt = ba.LayerRoughness(pt_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_pt/nm"], r_pt)
+ layer = ba.Layer(material_layer, P["t_pt/nm"]*nm, r_pt)
substrate_layer = ba.Layer(material_substrate, r_si)
sample = ba.Sample()
diff --git a/auto/FigExamples/fit/specular/TREFF_Ni_film.py b/auto/FigExamples/fit/specular/TREFF_Ni_film.py
index 69b801eabc7b93562385198ac160057fab7dba35..8f47984d1286faebdba03b7e536b140252b434e1 100755
--- a/auto/FigExamples/fit/specular/TREFF_Ni_film.py
+++ b/auto/FigExamples/fit/specular/TREFF_Ni_film.py
@@ -28,10 +28,10 @@ def get_sample(P):
# Layers and interfaces
interlayer = ba.TanhInterlayer()
- Ni_autocorr = ba.K_CorrelationModel(P["sigma_Ni"])
+ Ni_autocorr = ba.K_CorrelationModel(P["sigma_Ni"], 0.7, 25*nm)
roughness_Ni = ba.LayerRoughness(Ni_autocorr, interlayer)
- sub_autocorr = ba.K_CorrelationModel(P["sigma_Substrate"])
+ sub_autocorr = ba.K_CorrelationModel(P["sigma_Substrate"], 0.7, 25*nm)
roughness_Substrate = ba.LayerRoughness(sub_autocorr, interlayer)
layer_Ni = ba.Layer(material_Ni_58, P["thickness"], roughness_Ni)
diff --git a/auto/FigExamples/specular/MagneticLayerImperfect.py b/auto/FigExamples/specular/MagneticLayerImperfect.py
index edced923301ef14b517446d1d573325cac5ac503..8a65a11c227af45588a98a7b3ab9341a71fd2726 100755
--- a/auto/FigExamples/specular/MagneticLayerImperfect.py
+++ b/auto/FigExamples/specular/MagneticLayerImperfect.py
@@ -6,7 +6,7 @@ similar to Devishvili et al., Rev. Sci. Instrum. 84, 025112 (2013).
"""
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from math import sin, cos
def get_sample():
@@ -19,14 +19,14 @@ def get_sample():
material_Fe = ba.MaterialBySLD("Fe", 8.0241e-06, 6.0448e-10, B)
material_substrate = ba.MaterialBySLD("MgO", 5.9803e-06, 9.3996e-12)
- autocorr = ba.K_CorrelationModel(20*angstrom)
+ autocorr = ba.K_CorrelationModel(2*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
roughness = ba.LayerRoughness(autocorr, interlayer)
# Layers
layer_vacuum = ba.Layer(vacuum)
- layer_Pd = ba.Layer(material_Pd, 120*angstrom, roughness)
- layer_Fe = ba.Layer(material_Fe, 1000*angstrom, roughness)
+ layer_Pd = ba.Layer(material_Pd, 12*nm, roughness)
+ layer_Fe = ba.Layer(material_Fe, 100*nm, roughness)
layer_substrate = ba.Layer(material_substrate, roughness)
# Multilayer
diff --git a/auto/FigExamples/specular/PolarizedSpinAsymmetry.py b/auto/FigExamples/specular/PolarizedSpinAsymmetry.py
index 51a7201e6bab67a2eba0719c62bad91e0488d0f7..55dba54082c812904d38c7980905265ada4a1666 100755
--- a/auto/FigExamples/specular/PolarizedSpinAsymmetry.py
+++ b/auto/FigExamples/specular/PolarizedSpinAsymmetry.py
@@ -12,7 +12,7 @@ demonstration without the magnetically dead layer.
import os
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from bornagain.numpyutil import Arrayf64Converter as dac
@@ -47,8 +47,8 @@ def get_sample(P):
material_layer = ba.MaterialBySLD("(Mg,Al,Fe)3O4", P["rho_Mafo"]*1e-6, 0, B)
material_substrate = ba.MaterialBySLD("MgAl2O4", *sldMao)
- r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*angstrom)
- r_sub_autocorr = ba.K_CorrelationModel(P["r_Mao"]*angstrom)
+ r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*nm, 0.7, 25*nm)
+ r_sub_autocorr = ba.K_CorrelationModel(P["r_Mao"]*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
@@ -56,7 +56,7 @@ def get_sample(P):
r_substrate = ba.LayerRoughness(r_sub_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_Mafo"]*angstrom, r_Mafo)
+ layer = ba.Layer(material_layer, P["t_Mafo"]*nm, r_Mafo)
substrate_layer = ba.Layer(material_substrate, r_substrate)
sample = ba.Sample()
@@ -203,9 +203,9 @@ if __name__ == '__main__':
'q_offset': 7.971243487467318e-05,
'rho_Mafo': 6.370140108715461,
'rhoM_Mafo': 0.27399566816062926,
- 't_Mafo': 137.46913056084736,
- 'r_Mao': 8.60487712674644,
- 'r_Mafo': 3.7844265311293483
+ 't_Mafo': 13.746913056084736,
+ 'r_Mao': 0.860487712674644,
+ 'r_Mafo': 0.37844265311293483
}
def run_Simulation_pp(qzs, P):
diff --git a/auto/FigExamples/specular/RoughnessModel.py b/auto/FigExamples/specular/RoughnessModel.py
index 72a3a1342d1a895c4f87a86824a068c664dce4c1..93ddc33ae7a0b018fedfc7724d5110a708eb70e9 100755
--- a/auto/FigExamples/specular/RoughnessModel.py
+++ b/auto/FigExamples/specular/RoughnessModel.py
@@ -5,7 +5,7 @@ tanh and Nevot-Croce roughness model using BornAgain.
"""
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg
+from bornagain import angstrom, nm, ba_plot as bp, deg
def get_sample(interlayer):
@@ -16,13 +16,13 @@ def get_sample(interlayer):
material_substrate = ba.MaterialBySLD("SiSubstrate", 2.0704e-06, 0)
# Roughness
- autocorr = ba.K_CorrelationModel(10*angstrom)
+ autocorr = ba.K_CorrelationModel(1*nm, 0.7, 25*nm)
roughness = ba.LayerRoughness(autocorr, interlayer)
# create layers
ambient_layer = ba.Layer(vacuum)
- ti_layer = ba.Layer(material_ti, 30*angstrom, roughness)
- ni_layer = ba.Layer(material_ni, 70*angstrom, roughness)
+ ti_layer = ba.Layer(material_ti, 3*nm, roughness)
+ ni_layer = ba.Layer(material_ni, 7*nm, roughness)
substrate_layer = ba.Layer(material_substrate, roughness)
# create periodic stack
diff --git a/auto/FigExamples/specular/SpecularSimulationWithRoughness.py b/auto/FigExamples/specular/SpecularSimulationWithRoughness.py
index 92323c2379a8f2232f28058c0c40000489f6a12e..250b1546e5e56f4965eff4b7cad3d9105377479f 100755
--- a/auto/FigExamples/specular/SpecularSimulationWithRoughness.py
+++ b/auto/FigExamples/specular/SpecularSimulationWithRoughness.py
@@ -5,7 +5,7 @@ with a rough sample using BornAgain.
"""
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, nm
+from bornagain import nm, ba_plot as bp, deg, nm
def get_sample():
@@ -16,14 +16,14 @@ def get_sample():
material_substrate = ba.MaterialBySLD("SiSubstrate", 2.0704e-06, 0)
# roughness
- autocorr = ba.K_CorrelationModel(1*nm)
+ autocorr = ba.K_CorrelationModel(1*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
roughness = ba.LayerRoughness(autocorr, interlayer)
# Layers
ambient_layer = ba.Layer(vacuum)
- ti_layer = ba.Layer(material_ti, 30*angstrom, roughness)
- ni_layer = ba.Layer(material_ni, 70*angstrom, roughness)
+ ti_layer = ba.Layer(material_ti, 3*nm, roughness)
+ ni_layer = ba.Layer(material_ni, 7*nm, roughness)
substrate_layer = ba.Layer(material_substrate, roughness)
# Periodic stack
@@ -44,7 +44,7 @@ def get_sample():
def get_simulation(sample):
n = 500
scan = ba.AlphaScan(n, 2*deg/n, 2*deg)
- scan.setWavelength(1.54*angstrom)
+ scan.setWavelength(0.154*nm)
return ba.SpecularSimulation(scan, sample)
diff --git a/auto/MiniExamples/fit/specular/Honeycomb_fit.py b/auto/MiniExamples/fit/specular/Honeycomb_fit.py
index fee2317e0356eac81bd6a69f13c2d80a43535aa4..fe04348a1e4b6a2deda9f3cba43f2c4f355d4ac5 100755
--- a/auto/MiniExamples/fit/specular/Honeycomb_fit.py
+++ b/auto/MiniExamples/fit/specular/Honeycomb_fit.py
@@ -8,7 +8,7 @@ data without spin-flip for performance reasons.
"""
import bornagain as ba, numpy as np, os, matplotlib.pyplot as plt, scipy
-from bornagain import angstrom, sample_tools as st
+from bornagain import nm, sample_tools as st
from bornagain.numpyutil import Arrayf64Converter as dac
datadir = os.getenv('BA_DATA_DIR', '')
@@ -46,11 +46,11 @@ def get_sample(P, sign, T):
interlayer_model = ba.ErfInterlayer()
- rPyOx_autocorr = ba.K_CorrelationModel(P["rPyOx"]*angstrom)
- rPy2_autocorr = ba.K_CorrelationModel(P["rPy2"]*angstrom)
- rPy1_autocorr = ba.K_CorrelationModel(P["rPy1"]*angstrom)
- rSiO2_autocorr = ba.K_CorrelationModel(P["rSiO2"]*angstrom)
- rSi_autocorr = ba.K_CorrelationModel(P["rSi"]*angstrom)
+ rPyOx_autocorr = ba.K_CorrelationModel(P["rPyOx"]*nm, 0.7, 25*nm)
+ rPy2_autocorr = ba.K_CorrelationModel(P["rPy2"]*nm, 0.7, 25*nm)
+ rPy1_autocorr = ba.K_CorrelationModel(P["rPy1"]*nm, 0.7, 25*nm)
+ rSiO2_autocorr = ba.K_CorrelationModel(P["rSiO2"]*nm, 0.7, 25*nm)
+ rSi_autocorr = ba.K_CorrelationModel(P["rSi"]*nm, 0.7, 25*nm)
rPyOx = ba.LayerRoughness(rPyOx_autocorr, interlayer_model)
rPy2 = ba.LayerRoughness(rPy2_autocorr, interlayer_model)
@@ -59,10 +59,10 @@ def get_sample(P, sign, T):
rSi = ba.LayerRoughness(rSi_autocorr, interlayer_model)
l_Air = ba.Layer(material_Air)
- l_PyOx = ba.Layer(material_PyOx, P["t_PyOx"]*angstrom, rPyOx)
- l_Py2 = ba.Layer(material_Py2, P["t_Py2"]*angstrom, rPy2)
- l_Py1 = ba.Layer(material_Py1, P["t_Py1"]*angstrom, rPy1)
- l_SiO2 = ba.Layer(material_SiO2, P["t_SiO2"]*angstrom, rSiO2)
+ l_PyOx = ba.Layer(material_PyOx, P["t_PyOx"]*nm, rPyOx)
+ l_Py2 = ba.Layer(material_Py2, P["t_Py2"]*nm, rPy2)
+ l_Py1 = ba.Layer(material_Py1, P["t_Py1"]*nm, rPy1)
+ l_SiO2 = ba.Layer(material_SiO2, P["t_SiO2"]*nm, rSiO2)
l_Si = ba.Layer(material_Si, rSi)
sample = ba.Sample()
@@ -162,10 +162,10 @@ if __name__ == '__main__':
# We start with rather good values so that the example takes not too much time
startPnB = {
"intensity": (0.5, 0.4, 0.6),
- "t_PyOx": (77, 60, 100),
- "t_Py2": (56, 46, 66),
- "t_Py1": (56, 46, 66),
- "t_SiO2": (22, 15, 29),
+ "t_PyOx": (7.7, 6.0, 10.0),
+ "t_Py2": (5.6, 4.6, 6.6),
+ "t_Py1": (5.6, 4.6, 6.6),
+ "t_SiO2": (2.2, 1.5, 2.9),
}
# For fixed parameters, bounds are ignored. We leave them here just
@@ -183,11 +183,11 @@ if __name__ == '__main__':
"sld_PyOx_real": (1.995, 1.92, 2.07),
"sld_Py2_real": (5, 4.7, 5.3),
"sld_Py1_real": (4.62, 4.32, 4.92),
- "rPyOx": (27, 15, 35),
- "rPy2": (12, 2, 20),
- "rPy1": (12, 2, 20),
- "rSiO2": (15, 5, 25),
- "rSi": (15, 5, 25),
+ "rPyOx": (2.7, 1.5, 3.5),
+ "rPy2": (1.2, .2, 2.0),
+ "rPy1": (1.2, .2, 2.0),
+ "rSiO2": (1.5, .5, 2.5),
+ "rSi": (1.5, .5, 2.5),
"msld_PyOx": (0.25, 0, 1),
"msld_Py2": (0.63, 0, 1),
"msld_Py1": (0.64, 0, 1),
diff --git a/auto/MiniExamples/fit/specular/PolarizedSpinAsymmetry.py b/auto/MiniExamples/fit/specular/PolarizedSpinAsymmetry.py
index b80b6280d028b83839cfed34f9f77dca3cfe6766..86c264e9b68c1e19ec10ec307628fd7c822dbba7 100755
--- a/auto/MiniExamples/fit/specular/PolarizedSpinAsymmetry.py
+++ b/auto/MiniExamples/fit/specular/PolarizedSpinAsymmetry.py
@@ -12,7 +12,7 @@ demonstration without the magnetically dead layer.
import os
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from bornagain.numpyutil import Arrayf64Converter as dac
# q-range on which the simulation and fitting are to be performed
@@ -46,8 +46,8 @@ def get_sample(P):
material_layer = ba.MaterialBySLD("(Mg,Al,Fe)3O4", P["rho_Mafo"]*1e-6, 0, B)
material_substrate = ba.MaterialBySLD("MgAl2O4", *sldMao)
- r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*angstrom)
- r_substrate_autocorr = ba.K_CorrelationModel(P["r_Mao"]*angstrom)
+ r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*nm, 0.7, 25*nm)
+ r_substrate_autocorr = ba.K_CorrelationModel(P["r_Mao"]*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
@@ -55,7 +55,7 @@ def get_sample(P):
r_substrate = ba.LayerRoughness(r_substrate_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_Mafo"]*angstrom, r_Mafo)
+ layer = ba.Layer(material_layer, P["t_Mafo"]*nm, r_Mafo)
substrate_layer = ba.Layer(material_substrate, r_substrate)
sample = ba.Sample()
@@ -202,9 +202,9 @@ if __name__ == '__main__':
'q_offset': 7.971243487467318e-05,
'rho_Mafo': 6.370140108715461,
'rhoM_Mafo': 0.27399566816062926,
- 't_Mafo': 137.46913056084736,
- 'r_Mao': 8.60487712674644,
- 'r_Mafo': 3.7844265311293483
+ 't_Mafo': 13.746913056084736,
+ 'r_Mao': 0.860487712674644,
+ 'r_Mafo': 0.37844265311293483
}
def run_Simulation_pp(qzs, P):
diff --git a/auto/MiniExamples/fit/specular/PolarizedSpinAsymmetryFit.py b/auto/MiniExamples/fit/specular/PolarizedSpinAsymmetryFit.py
index 5bee9fca96ee8343bf3d7b43d078b03190151ad0..227ebbb3d5bb2c19ae2e05d2ad79f56e07cd7668 100755
--- a/auto/MiniExamples/fit/specular/PolarizedSpinAsymmetryFit.py
+++ b/auto/MiniExamples/fit/specular/PolarizedSpinAsymmetryFit.py
@@ -58,9 +58,9 @@ if __name__ == '__main__':
"q_offset": (0, -0.002, 0.002),
"rho_Mafo": (6.3649, 2, 7),
"rhoM_Mafo": (0, 0, 2),
- "t_Mafo": (150, 60, 180),
- "r_Mao": (1, 0, 12),
- "r_Mafo": (1, 0, 12),
+ "t_Mafo": (15, 6, 18),
+ "r_Mao": (0.1, 0, 1.2),
+ "r_Mafo": (0.1, 0, 1.2),
}
PInitial = {d: v[0] for d, v in startParams.items()}
diff --git a/auto/MiniExamples/fit/specular/Pt_layer_fit.py b/auto/MiniExamples/fit/specular/Pt_layer_fit.py
index 24bf5f3aa85e2a0e5d3568f612482eb2808d3d86..f7b5fc034a53437244c372641cbd2b3ced960897 100755
--- a/auto/MiniExamples/fit/specular/Pt_layer_fit.py
+++ b/auto/MiniExamples/fit/specular/Pt_layer_fit.py
@@ -9,7 +9,7 @@ band of roughly 4-7 Ã… in 100 steps of 2theta.
"""
import bornagain as ba, numpy as np, os, matplotlib.pyplot as plt
-from bornagain import angstrom
+from bornagain import nm
from bornagain.numpyutil import Arrayf64Converter as dac
datadir = os.getenv('BA_DATA_DIR', '')
@@ -32,14 +32,14 @@ def get_sample(P):
interlayer = ba.TanhInterlayer()
- si_autocorr = ba.K_CorrelationModel(P["r_si/nm"])
- pt_autocorr = ba.K_CorrelationModel(P["r_pt/nm"])
+ si_autocorr = ba.K_CorrelationModel(P["r_si/nm"]*nm, 0.7, 25*nm)
+ pt_autocorr = ba.K_CorrelationModel(P["r_pt/nm"]*nm, 0.7, 25*nm)
r_si = ba.LayerRoughness(si_autocorr, interlayer)
r_pt = ba.LayerRoughness(pt_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_pt/nm"], r_pt)
+ layer = ba.Layer(material_layer, P["t_pt/nm"]*nm, r_pt)
substrate_layer = ba.Layer(material_substrate, r_si)
sample = ba.Sample()
diff --git a/auto/MiniExamples/fit/specular/TREFF_Ni_film.py b/auto/MiniExamples/fit/specular/TREFF_Ni_film.py
index ed8088a84f77c807e257746e5119c9aaec7a3906..6ac5f481d56f5486528eed503b80f724d5c21c23 100755
--- a/auto/MiniExamples/fit/specular/TREFF_Ni_film.py
+++ b/auto/MiniExamples/fit/specular/TREFF_Ni_film.py
@@ -28,10 +28,10 @@ def get_sample(P):
# Layers and interfaces
interlayer = ba.TanhInterlayer()
- Ni_autocorr = ba.K_CorrelationModel(P["sigma_Ni"])
+ Ni_autocorr = ba.K_CorrelationModel(P["sigma_Ni"], 0.7, 25*nm)
roughness_Ni = ba.LayerRoughness(Ni_autocorr, interlayer)
- sub_autocorr = ba.K_CorrelationModel(P["sigma_Substrate"])
+ sub_autocorr = ba.K_CorrelationModel(P["sigma_Substrate"], 0.7, 25*nm)
roughness_Substrate = ba.LayerRoughness(sub_autocorr, interlayer)
layer_Ni = ba.Layer(material_Ni_58, P["thickness"], roughness_Ni)
diff --git a/auto/MiniExamples/specular/MagneticLayerImperfect.py b/auto/MiniExamples/specular/MagneticLayerImperfect.py
index edced923301ef14b517446d1d573325cac5ac503..8a65a11c227af45588a98a7b3ab9341a71fd2726 100755
--- a/auto/MiniExamples/specular/MagneticLayerImperfect.py
+++ b/auto/MiniExamples/specular/MagneticLayerImperfect.py
@@ -6,7 +6,7 @@ similar to Devishvili et al., Rev. Sci. Instrum. 84, 025112 (2013).
"""
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from math import sin, cos
def get_sample():
@@ -19,14 +19,14 @@ def get_sample():
material_Fe = ba.MaterialBySLD("Fe", 8.0241e-06, 6.0448e-10, B)
material_substrate = ba.MaterialBySLD("MgO", 5.9803e-06, 9.3996e-12)
- autocorr = ba.K_CorrelationModel(20*angstrom)
+ autocorr = ba.K_CorrelationModel(2*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
roughness = ba.LayerRoughness(autocorr, interlayer)
# Layers
layer_vacuum = ba.Layer(vacuum)
- layer_Pd = ba.Layer(material_Pd, 120*angstrom, roughness)
- layer_Fe = ba.Layer(material_Fe, 1000*angstrom, roughness)
+ layer_Pd = ba.Layer(material_Pd, 12*nm, roughness)
+ layer_Fe = ba.Layer(material_Fe, 100*nm, roughness)
layer_substrate = ba.Layer(material_substrate, roughness)
# Multilayer
diff --git a/auto/MiniExamples/specular/PolarizedSpinAsymmetry.py b/auto/MiniExamples/specular/PolarizedSpinAsymmetry.py
index 51a7201e6bab67a2eba0719c62bad91e0488d0f7..55dba54082c812904d38c7980905265ada4a1666 100755
--- a/auto/MiniExamples/specular/PolarizedSpinAsymmetry.py
+++ b/auto/MiniExamples/specular/PolarizedSpinAsymmetry.py
@@ -12,7 +12,7 @@ demonstration without the magnetically dead layer.
import os
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from bornagain.numpyutil import Arrayf64Converter as dac
@@ -47,8 +47,8 @@ def get_sample(P):
material_layer = ba.MaterialBySLD("(Mg,Al,Fe)3O4", P["rho_Mafo"]*1e-6, 0, B)
material_substrate = ba.MaterialBySLD("MgAl2O4", *sldMao)
- r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*angstrom)
- r_sub_autocorr = ba.K_CorrelationModel(P["r_Mao"]*angstrom)
+ r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*nm, 0.7, 25*nm)
+ r_sub_autocorr = ba.K_CorrelationModel(P["r_Mao"]*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
@@ -56,7 +56,7 @@ def get_sample(P):
r_substrate = ba.LayerRoughness(r_sub_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_Mafo"]*angstrom, r_Mafo)
+ layer = ba.Layer(material_layer, P["t_Mafo"]*nm, r_Mafo)
substrate_layer = ba.Layer(material_substrate, r_substrate)
sample = ba.Sample()
@@ -203,9 +203,9 @@ if __name__ == '__main__':
'q_offset': 7.971243487467318e-05,
'rho_Mafo': 6.370140108715461,
'rhoM_Mafo': 0.27399566816062926,
- 't_Mafo': 137.46913056084736,
- 'r_Mao': 8.60487712674644,
- 'r_Mafo': 3.7844265311293483
+ 't_Mafo': 13.746913056084736,
+ 'r_Mao': 0.860487712674644,
+ 'r_Mafo': 0.37844265311293483
}
def run_Simulation_pp(qzs, P):
diff --git a/auto/MiniExamples/specular/RoughnessModel.py b/auto/MiniExamples/specular/RoughnessModel.py
index 49deb3d398ea75baf09b8286963644b88e6d7386..2157b328dab968beb38953ff84de052aef8a6f33 100755
--- a/auto/MiniExamples/specular/RoughnessModel.py
+++ b/auto/MiniExamples/specular/RoughnessModel.py
@@ -5,7 +5,7 @@ tanh and Nevot-Croce roughness model using BornAgain.
"""
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg
+from bornagain import angstrom, nm, ba_plot as bp, deg
def get_sample(interlayer):
@@ -16,13 +16,13 @@ def get_sample(interlayer):
material_substrate = ba.MaterialBySLD("SiSubstrate", 2.0704e-06, 0)
# Roughness
- autocorr = ba.K_CorrelationModel(10*angstrom)
+ autocorr = ba.K_CorrelationModel(1*nm, 0.7, 25*nm)
roughness = ba.LayerRoughness(autocorr, interlayer)
# create layers
ambient_layer = ba.Layer(vacuum)
- ti_layer = ba.Layer(material_ti, 30*angstrom, roughness)
- ni_layer = ba.Layer(material_ni, 70*angstrom, roughness)
+ ti_layer = ba.Layer(material_ti, 3*nm, roughness)
+ ni_layer = ba.Layer(material_ni, 7*nm, roughness)
substrate_layer = ba.Layer(material_substrate, roughness)
# create periodic stack
diff --git a/auto/MiniExamples/specular/SpecularSimulationWithRoughness.py b/auto/MiniExamples/specular/SpecularSimulationWithRoughness.py
index 5c4fc002d6378eae2040786b2b8ccf3c30019647..7876785f6eb2edf45f0a0b154ad3e3e7c68e448a 100755
--- a/auto/MiniExamples/specular/SpecularSimulationWithRoughness.py
+++ b/auto/MiniExamples/specular/SpecularSimulationWithRoughness.py
@@ -5,7 +5,7 @@ with a rough sample using BornAgain.
"""
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, nm
+from bornagain import nm, ba_plot as bp, deg, nm
def get_sample():
@@ -16,14 +16,14 @@ def get_sample():
material_substrate = ba.MaterialBySLD("SiSubstrate", 2.0704e-06, 0)
# roughness
- autocorr = ba.K_CorrelationModel(1*nm)
+ autocorr = ba.K_CorrelationModel(1*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
roughness = ba.LayerRoughness(autocorr, interlayer)
# Layers
ambient_layer = ba.Layer(vacuum)
- ti_layer = ba.Layer(material_ti, 30*angstrom, roughness)
- ni_layer = ba.Layer(material_ni, 70*angstrom, roughness)
+ ti_layer = ba.Layer(material_ti, 3*nm, roughness)
+ ni_layer = ba.Layer(material_ni, 7*nm, roughness)
substrate_layer = ba.Layer(material_substrate, roughness)
# Periodic stack
@@ -44,7 +44,7 @@ def get_sample():
def get_simulation(sample):
n = 50
scan = ba.AlphaScan(n, 2*deg/n, 2*deg)
- scan.setWavelength(1.54*angstrom)
+ scan.setWavelength(0.154*nm)
return ba.SpecularSimulation(scan, sample)
diff --git a/auto/Wrap/libBornAgainSample.py b/auto/Wrap/libBornAgainSample.py
index ffd2e351953f0185b67955028c5ab047ca23d1b6..1ca96dcab97475edcf99f603931806604a74eba6 100644
--- a/auto/Wrap/libBornAgainSample.py
+++ b/auto/Wrap/libBornAgainSample.py
@@ -3912,8 +3912,8 @@ class K_CorrelationModel(AutocorrelationModel):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
- def __init__(self, sigma=0, hurst=0.7, lateralCorrLength=25, maxSpatFrequency=0.5):
- r"""__init__(K_CorrelationModel self, double sigma=0, double hurst=0.7, double lateralCorrLength=25, double maxSpatFrequency=0.5) -> K_CorrelationModel"""
+ def __init__(self, sigma, hurst, lateralCorrLength, maxSpatFrequency=0.5):
+ r"""__init__(K_CorrelationModel self, double sigma, double hurst, double lateralCorrLength, double maxSpatFrequency=0.5) -> K_CorrelationModel"""
_libBornAgainSample.K_CorrelationModel_swiginit(self, _libBornAgainSample.new_K_CorrelationModel(sigma, hurst, lateralCorrLength, maxSpatFrequency))
def className(self):
diff --git a/auto/Wrap/libBornAgainSample_wrap.cpp b/auto/Wrap/libBornAgainSample_wrap.cpp
index 658835f88154bd9da036fff2501370aa57b81e29..79ec3e111d377d16c24670fd998ff2a51b01e89d 100644
--- a/auto/Wrap/libBornAgainSample_wrap.cpp
+++ b/auto/Wrap/libBornAgainSample_wrap.cpp
@@ -45486,96 +45486,6 @@ fail:
}
-SWIGINTERN PyObject *_wrap_new_K_CorrelationModel__SWIG_2(PyObject *self, Py_ssize_t nobjs, PyObject **swig_obj) {
- PyObject *resultobj = 0;
- double arg1 ;
- double arg2 ;
- double val1 ;
- int ecode1 = 0 ;
- double val2 ;
- int ecode2 = 0 ;
- K_CorrelationModel *result = 0 ;
-
- (void)self;
- if ((nobjs < 2) || (nobjs > 2)) SWIG_fail;
- ecode1 = SWIG_AsVal_double(swig_obj[0], &val1);
- if (!SWIG_IsOK(ecode1)) {
- SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_K_CorrelationModel" "', argument " "1"" of type '" "double""'");
- }
- arg1 = static_cast< double >(val1);
- ecode2 = SWIG_AsVal_double(swig_obj[1], &val2);
- if (!SWIG_IsOK(ecode2)) {
- SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "new_K_CorrelationModel" "', argument " "2"" of type '" "double""'");
- }
- arg2 = static_cast< double >(val2);
- {
- try {
- result = (K_CorrelationModel *)new K_CorrelationModel(arg1,arg2);
- } catch (const bug& ex) {
- SWIG_exception(SWIG_RuntimeError, bug_msg(ex).c_str());
- } catch (const std::exception& ex) {
- SWIG_exception(SWIG_RuntimeError, exception_msg(ex).c_str());
- }
- }
- resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_K_CorrelationModel, SWIG_POINTER_NEW | 0 );
- return resultobj;
-fail:
- return NULL;
-}
-
-
-SWIGINTERN PyObject *_wrap_new_K_CorrelationModel__SWIG_3(PyObject *self, Py_ssize_t nobjs, PyObject **swig_obj) {
- PyObject *resultobj = 0;
- double arg1 ;
- double val1 ;
- int ecode1 = 0 ;
- K_CorrelationModel *result = 0 ;
-
- (void)self;
- if ((nobjs < 1) || (nobjs > 1)) SWIG_fail;
- ecode1 = SWIG_AsVal_double(swig_obj[0], &val1);
- if (!SWIG_IsOK(ecode1)) {
- SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_K_CorrelationModel" "', argument " "1"" of type '" "double""'");
- }
- arg1 = static_cast< double >(val1);
- {
- try {
- result = (K_CorrelationModel *)new K_CorrelationModel(arg1);
- } catch (const bug& ex) {
- SWIG_exception(SWIG_RuntimeError, bug_msg(ex).c_str());
- } catch (const std::exception& ex) {
- SWIG_exception(SWIG_RuntimeError, exception_msg(ex).c_str());
- }
- }
- resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_K_CorrelationModel, SWIG_POINTER_NEW | 0 );
- return resultobj;
-fail:
- return NULL;
-}
-
-
-SWIGINTERN PyObject *_wrap_new_K_CorrelationModel__SWIG_4(PyObject *self, Py_ssize_t nobjs, PyObject **SWIGUNUSEDPARM(swig_obj)) {
- PyObject *resultobj = 0;
- K_CorrelationModel *result = 0 ;
-
- (void)self;
- if ((nobjs < 0) || (nobjs > 0)) SWIG_fail;
- {
- try {
- result = (K_CorrelationModel *)new K_CorrelationModel();
- } catch (const bug& ex) {
- SWIG_exception(SWIG_RuntimeError, bug_msg(ex).c_str());
- } catch (const std::exception& ex) {
- SWIG_exception(SWIG_RuntimeError, exception_msg(ex).c_str());
- }
- }
- resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_K_CorrelationModel, SWIG_POINTER_NEW | 0 );
- return resultobj;
-fail:
- return NULL;
-}
-
-
SWIGINTERN PyObject *_wrap_new_K_CorrelationModel(PyObject *self, PyObject *args) {
Py_ssize_t argc;
PyObject *argv[5] = {
@@ -45584,35 +45494,6 @@ SWIGINTERN PyObject *_wrap_new_K_CorrelationModel(PyObject *self, PyObject *args
if (!(argc = SWIG_Python_UnpackTuple(args, "new_K_CorrelationModel", 0, 4, argv))) SWIG_fail;
--argc;
- if (argc == 0) {
- return _wrap_new_K_CorrelationModel__SWIG_4(self, argc, argv);
- }
- if (argc == 1) {
- int _v = 0;
- {
- int res = SWIG_AsVal_double(argv[0], NULL);
- _v = SWIG_CheckState(res);
- }
- if (_v) {
- return _wrap_new_K_CorrelationModel__SWIG_3(self, argc, argv);
- }
- }
- if (argc == 2) {
- int _v = 0;
- {
- int res = SWIG_AsVal_double(argv[0], NULL);
- _v = SWIG_CheckState(res);
- }
- if (_v) {
- {
- int res = SWIG_AsVal_double(argv[1], NULL);
- _v = SWIG_CheckState(res);
- }
- if (_v) {
- return _wrap_new_K_CorrelationModel__SWIG_2(self, argc, argv);
- }
- }
- }
if (argc == 3) {
int _v = 0;
{
@@ -45668,10 +45549,7 @@ fail:
SWIG_Python_RaiseOrModifyTypeError("Wrong number or type of arguments for overloaded function 'new_K_CorrelationModel'.\n"
" Possible C/C++ prototypes are:\n"
" K_CorrelationModel::K_CorrelationModel(double,double,double,double)\n"
- " K_CorrelationModel::K_CorrelationModel(double,double,double)\n"
- " K_CorrelationModel::K_CorrelationModel(double,double)\n"
- " K_CorrelationModel::K_CorrelationModel(double)\n"
- " K_CorrelationModel::K_CorrelationModel()\n");
+ " K_CorrelationModel::K_CorrelationModel(double,double,double)\n");
return 0;
}
@@ -66172,7 +66050,7 @@ static PyMethodDef SwigMethods[] = {
{ "AutocorrelationModel_parDefs", _wrap_AutocorrelationModel_parDefs, METH_O, "AutocorrelationModel_parDefs(AutocorrelationModel self) -> std::vector< ParaMeta,std::allocator< ParaMeta > >"},
{ "delete_AutocorrelationModel", _wrap_delete_AutocorrelationModel, METH_O, "delete_AutocorrelationModel(AutocorrelationModel self)"},
{ "AutocorrelationModel_swigregister", AutocorrelationModel_swigregister, METH_O, NULL},
- { "new_K_CorrelationModel", _wrap_new_K_CorrelationModel, METH_VARARGS, "K_CorrelationModel(double sigma=0, double hurst=0.7, double lateralCorrLength=25, double maxSpatFrequency=0.5)"},
+ { "new_K_CorrelationModel", _wrap_new_K_CorrelationModel, METH_VARARGS, "K_CorrelationModel(double sigma, double hurst, double lateralCorrLength, double maxSpatFrequency=0.5)"},
{ "K_CorrelationModel_className", _wrap_K_CorrelationModel_className, METH_O, "K_CorrelationModel_className(K_CorrelationModel self) -> std::string"},
{ "K_CorrelationModel_parDefs", _wrap_K_CorrelationModel_parDefs, METH_O, "K_CorrelationModel_parDefs(K_CorrelationModel self) -> std::vector< ParaMeta,std::allocator< ParaMeta > >"},
{ "K_CorrelationModel_validate", _wrap_K_CorrelationModel_validate, METH_O, "K_CorrelationModel_validate(K_CorrelationModel self) -> std::string"},
diff --git a/rawEx/fit/specular/Honeycomb_fit.py b/rawEx/fit/specular/Honeycomb_fit.py
index bcf4127182c87f040c61ab34f035115afe77dddd..8d8ceb653587fdb496e45ed518fb533d8764fcaf 100755
--- a/rawEx/fit/specular/Honeycomb_fit.py
+++ b/rawEx/fit/specular/Honeycomb_fit.py
@@ -8,7 +8,7 @@ data without spin-flip for performance reasons.
"""
import bornagain as ba, numpy as np, os, matplotlib.pyplot as plt, scipy
-from bornagain import angstrom, sample_tools as st
+from bornagain import nm, sample_tools as st
from bornagain.numpyutil import Arrayf64Converter as dac
datadir = os.getenv('BA_DATA_DIR', '')
@@ -46,11 +46,11 @@ def get_sample(P, sign, T):
interlayer_model = ba.ErfInterlayer()
- rPyOx_autocorr = ba.K_CorrelationModel(P["rPyOx"]*angstrom)
- rPy2_autocorr = ba.K_CorrelationModel(P["rPy2"]*angstrom)
- rPy1_autocorr = ba.K_CorrelationModel(P["rPy1"]*angstrom)
- rSiO2_autocorr = ba.K_CorrelationModel(P["rSiO2"]*angstrom)
- rSi_autocorr = ba.K_CorrelationModel(P["rSi"]*angstrom)
+ rPyOx_autocorr = ba.K_CorrelationModel(P["rPyOx"]*nm, 0.7, 25*nm)
+ rPy2_autocorr = ba.K_CorrelationModel(P["rPy2"]*nm, 0.7, 25*nm)
+ rPy1_autocorr = ba.K_CorrelationModel(P["rPy1"]*nm, 0.7, 25*nm)
+ rSiO2_autocorr = ba.K_CorrelationModel(P["rSiO2"]*nm, 0.7, 25*nm)
+ rSi_autocorr = ba.K_CorrelationModel(P["rSi"]*nm, 0.7, 25*nm)
rPyOx = ba.LayerRoughness(rPyOx_autocorr, interlayer_model)
rPy2 = ba.LayerRoughness(rPy2_autocorr, interlayer_model)
@@ -59,10 +59,10 @@ def get_sample(P, sign, T):
rSi = ba.LayerRoughness(rSi_autocorr, interlayer_model)
l_Air = ba.Layer(material_Air)
- l_PyOx = ba.Layer(material_PyOx, P["t_PyOx"]*angstrom, rPyOx)
- l_Py2 = ba.Layer(material_Py2, P["t_Py2"]*angstrom, rPy2)
- l_Py1 = ba.Layer(material_Py1, P["t_Py1"]*angstrom, rPy1)
- l_SiO2 = ba.Layer(material_SiO2, P["t_SiO2"]*angstrom, rSiO2)
+ l_PyOx = ba.Layer(material_PyOx, P["t_PyOx"]*nm, rPyOx)
+ l_Py2 = ba.Layer(material_Py2, P["t_Py2"]*nm, rPy2)
+ l_Py1 = ba.Layer(material_Py1, P["t_Py1"]*nm, rPy1)
+ l_SiO2 = ba.Layer(material_SiO2, P["t_SiO2"]*nm, rSiO2)
l_Si = ba.Layer(material_Si, rSi)
sample = ba.Sample()
@@ -170,10 +170,10 @@ if __name__ == '__main__':
# We start with rather good values so that the example takes not too much time
startPnB = {
"intensity": (0.5, 0.4, 0.6),
- "t_PyOx": (77, 60, 100),
- "t_Py2": (56, 46, 66),
- "t_Py1": (56, 46, 66),
- "t_SiO2": (22, 15, 29),
+ "t_PyOx": (7.7, 6.0, 10.0),
+ "t_Py2": (5.6, 4.6, 6.6),
+ "t_Py1": (5.6, 4.6, 6.6),
+ "t_SiO2": (2.2, 1.5, 2.9),
}
# For fixed parameters, bounds are ignored. We leave them here just
@@ -191,11 +191,11 @@ if __name__ == '__main__':
"sld_PyOx_real": (1.995, 1.92, 2.07),
"sld_Py2_real": (5, 4.7, 5.3),
"sld_Py1_real": (4.62, 4.32, 4.92),
- "rPyOx": (27, 15, 35),
- "rPy2": (12, 2, 20),
- "rPy1": (12, 2, 20),
- "rSiO2": (15, 5, 25),
- "rSi": (15, 5, 25),
+ "rPyOx": (2.7, 1.5, 3.5),
+ "rPy2": (1.2, .2, 2.0),
+ "rPy1": (1.2, .2, 2.0),
+ "rSiO2": (1.5, .5, 2.5),
+ "rSi": (1.5, .5, 2.5),
"msld_PyOx": (0.25, 0, 1),
"msld_Py2": (0.63, 0, 1),
"msld_Py1": (0.64, 0, 1),
diff --git a/rawEx/fit/specular/PolarizedSpinAsymmetry.py b/rawEx/fit/specular/PolarizedSpinAsymmetry.py
index ebde5a32c15dbc87de626486ed449e6f7d28e8d0..4adcb72c5d03dcdd973862c16a6f20aa2adab2f6 100755
--- a/rawEx/fit/specular/PolarizedSpinAsymmetry.py
+++ b/rawEx/fit/specular/PolarizedSpinAsymmetry.py
@@ -12,7 +12,7 @@ demonstration without the magnetically dead layer.
import os
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from bornagain.numpyutil import Arrayf64Converter as dac
# q-range on which the simulation and fitting are to be performed
@@ -46,8 +46,8 @@ def get_sample(P):
material_layer = ba.MaterialBySLD("(Mg,Al,Fe)3O4", P["rho_Mafo"]*1e-6, 0, B)
material_substrate = ba.MaterialBySLD("MgAl2O4", *sldMao)
- r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*angstrom)
- r_substrate_autocorr = ba.K_CorrelationModel(P["r_Mao"]*angstrom)
+ r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*nm, 0.7, 25*nm)
+ r_substrate_autocorr = ba.K_CorrelationModel(P["r_Mao"]*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
@@ -55,7 +55,7 @@ def get_sample(P):
r_substrate = ba.LayerRoughness(r_substrate_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_Mafo"]*angstrom, r_Mafo)
+ layer = ba.Layer(material_layer, P["t_Mafo"]*nm, r_Mafo)
substrate_layer = ba.Layer(material_substrate, r_substrate)
sample = ba.Sample()
@@ -206,9 +206,9 @@ if __name__ == '__main__':
'q_offset': 7.971243487467318e-05,
'rho_Mafo': 6.370140108715461,
'rhoM_Mafo': 0.27399566816062926,
- 't_Mafo': 137.46913056084736,
- 'r_Mao': 8.60487712674644,
- 'r_Mafo': 3.7844265311293483
+ 't_Mafo': 13.746913056084736,
+ 'r_Mao': 0.860487712674644,
+ 'r_Mafo': 0.37844265311293483
}
def run_Simulation_pp(qzs, P):
diff --git a/rawEx/fit/specular/PolarizedSpinAsymmetryFit.py b/rawEx/fit/specular/PolarizedSpinAsymmetryFit.py
index ef4459ab14b3357941431b308c190630a8530910..9a997bc3322939e90eaa7e2f84305b1c64a00b70 100755
--- a/rawEx/fit/specular/PolarizedSpinAsymmetryFit.py
+++ b/rawEx/fit/specular/PolarizedSpinAsymmetryFit.py
@@ -58,9 +58,9 @@ if __name__ == '__main__':
"q_offset": (0, -0.002, 0.002),
"rho_Mafo": (6.3649, 2, 7),
"rhoM_Mafo": (0, 0, 2),
- "t_Mafo": (150, 60, 180),
- "r_Mao": (1, 0, 12),
- "r_Mafo": (1, 0, 12),
+ "t_Mafo": (15, 6, 18),
+ "r_Mao": (0.1, 0, 1.2),
+ "r_Mafo": (0.1, 0, 1.2),
}
PInitial = {d: v[0] for d, v in startParams.items()}
diff --git a/rawEx/fit/specular/Pt_layer_fit.py b/rawEx/fit/specular/Pt_layer_fit.py
index 76da673f4818f762366aa6842461cfe644c8d8ff..7c15cc20783cad9308dfa729e53f306e029332c3 100755
--- a/rawEx/fit/specular/Pt_layer_fit.py
+++ b/rawEx/fit/specular/Pt_layer_fit.py
@@ -9,7 +9,7 @@ band of roughly 4-7 Ã… in 100 steps of 2theta.
"""
import bornagain as ba, numpy as np, os, matplotlib.pyplot as plt
-from bornagain import angstrom
+from bornagain import nm
from bornagain.numpyutil import Arrayf64Converter as dac
datadir = os.getenv('BA_DATA_DIR', '')
@@ -32,14 +32,14 @@ def get_sample(P):
interlayer = ba.TanhInterlayer()
- si_autocorr = ba.K_CorrelationModel(P["r_si/nm"])
- pt_autocorr = ba.K_CorrelationModel(P["r_pt/nm"])
+ si_autocorr = ba.K_CorrelationModel(P["r_si/nm"]*nm, 0.7, 25*nm)
+ pt_autocorr = ba.K_CorrelationModel(P["r_pt/nm"]*nm, 0.7, 25*nm)
r_si = ba.LayerRoughness(si_autocorr, interlayer)
r_pt = ba.LayerRoughness(pt_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_pt/nm"], r_pt)
+ layer = ba.Layer(material_layer, P["t_pt/nm"]*nm, r_pt)
substrate_layer = ba.Layer(material_substrate, r_si)
sample = ba.Sample()
diff --git a/rawEx/fit/specular/TREFF_Ni_film.py b/rawEx/fit/specular/TREFF_Ni_film.py
index d99dc2b8a9de8b3d27d630a019413f0a2cd70dd7..5f437c91d8b55669d29568d8633aeb0f44a157f7 100755
--- a/rawEx/fit/specular/TREFF_Ni_film.py
+++ b/rawEx/fit/specular/TREFF_Ni_film.py
@@ -28,10 +28,10 @@ def get_sample(P):
# Layers and interfaces
interlayer = ba.TanhInterlayer()
- Ni_autocorr = ba.K_CorrelationModel(P["sigma_Ni"])
+ Ni_autocorr = ba.K_CorrelationModel(P["sigma_Ni"], 0.7, 25*nm)
roughness_Ni = ba.LayerRoughness(Ni_autocorr, interlayer)
- sub_autocorr = ba.K_CorrelationModel(P["sigma_Substrate"])
+ sub_autocorr = ba.K_CorrelationModel(P["sigma_Substrate"], 0.7, 25*nm)
roughness_Substrate = ba.LayerRoughness(sub_autocorr, interlayer)
layer_Ni = ba.Layer(material_Ni_58, P["thickness"], roughness_Ni)
diff --git a/rawEx/specular/MagneticLayerImperfect.py b/rawEx/specular/MagneticLayerImperfect.py
index 892cddd12692fd9b6cf6aac3d2371c54a0f12eb5..95ac6267d0983f8fa410a17615b21daff61b5de0 100755
--- a/rawEx/specular/MagneticLayerImperfect.py
+++ b/rawEx/specular/MagneticLayerImperfect.py
@@ -6,7 +6,7 @@ similar to Devishvili et al., Rev. Sci. Instrum. 84, 025112 (2013).
"""
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from math import sin, cos
def get_sample():
@@ -19,14 +19,14 @@ def get_sample():
material_Fe = ba.MaterialBySLD("Fe", 8.0241e-06, 6.0448e-10, B)
material_substrate = ba.MaterialBySLD("MgO", 5.9803e-06, 9.3996e-12)
- autocorr = ba.K_CorrelationModel(20*angstrom)
+ autocorr = ba.K_CorrelationModel(2*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
roughness = ba.LayerRoughness(autocorr, interlayer)
# Layers
layer_vacuum = ba.Layer(vacuum)
- layer_Pd = ba.Layer(material_Pd, 120*angstrom, roughness)
- layer_Fe = ba.Layer(material_Fe, 1000*angstrom, roughness)
+ layer_Pd = ba.Layer(material_Pd, 12*nm, roughness)
+ layer_Fe = ba.Layer(material_Fe, 100*nm, roughness)
layer_substrate = ba.Layer(material_substrate, roughness)
# Multilayer
diff --git a/rawEx/specular/PolarizedSpinAsymmetry.py b/rawEx/specular/PolarizedSpinAsymmetry.py
index 1297a8969d9c087f4ec859c43086a5ef89d13378..0ea01ac6bec1ad0dff4d58828b5656017144e838 100755
--- a/rawEx/specular/PolarizedSpinAsymmetry.py
+++ b/rawEx/specular/PolarizedSpinAsymmetry.py
@@ -12,7 +12,7 @@ demonstration without the magnetically dead layer.
import os
import numpy
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, R3
+from bornagain import nm, ba_plot as bp, deg, R3
from bornagain.numpyutil import Arrayf64Converter as dac
@@ -47,8 +47,8 @@ def get_sample(P):
material_layer = ba.MaterialBySLD("(Mg,Al,Fe)3O4", P["rho_Mafo"]*1e-6, 0, B)
material_substrate = ba.MaterialBySLD("MgAl2O4", *sldMao)
- r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*angstrom)
- r_sub_autocorr = ba.K_CorrelationModel(P["r_Mao"]*angstrom)
+ r_Mafo_autocorr = ba.K_CorrelationModel(P["r_Mafo"]*nm, 0.7, 25*nm)
+ r_sub_autocorr = ba.K_CorrelationModel(P["r_Mao"]*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
@@ -56,7 +56,7 @@ def get_sample(P):
r_substrate = ba.LayerRoughness(r_sub_autocorr, interlayer)
ambient_layer = ba.Layer(vacuum)
- layer = ba.Layer(material_layer, P["t_Mafo"]*angstrom, r_Mafo)
+ layer = ba.Layer(material_layer, P["t_Mafo"]*nm, r_Mafo)
substrate_layer = ba.Layer(material_substrate, r_substrate)
sample = ba.Sample()
@@ -207,9 +207,9 @@ if __name__ == '__main__':
'q_offset': 7.971243487467318e-05,
'rho_Mafo': 6.370140108715461,
'rhoM_Mafo': 0.27399566816062926,
- 't_Mafo': 137.46913056084736,
- 'r_Mao': 8.60487712674644,
- 'r_Mafo': 3.7844265311293483
+ 't_Mafo': 13.746913056084736,
+ 'r_Mao': 0.860487712674644,
+ 'r_Mafo': 0.37844265311293483
}
def run_Simulation_pp(qzs, P):
diff --git a/rawEx/specular/RoughnessModel.py b/rawEx/specular/RoughnessModel.py
index 90905aa67c65cb9563fdc8ae6a5e1b97a711170d..1a98b14dadfada6a841f37940b8841eae2cd29ca 100755
--- a/rawEx/specular/RoughnessModel.py
+++ b/rawEx/specular/RoughnessModel.py
@@ -5,7 +5,7 @@ tanh and Nevot-Croce roughness model using BornAgain.
"""
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg
+from bornagain import angstrom, nm, ba_plot as bp, deg
def get_sample(interlayer):
@@ -16,13 +16,13 @@ def get_sample(interlayer):
material_substrate = ba.MaterialBySLD("SiSubstrate", 2.0704e-06, 0)
# Roughness
- autocorr = ba.K_CorrelationModel(10*angstrom)
+ autocorr = ba.K_CorrelationModel(1*nm, 0.7, 25*nm)
roughness = ba.LayerRoughness(autocorr, interlayer)
# create layers
ambient_layer = ba.Layer(vacuum)
- ti_layer = ba.Layer(material_ti, 30*angstrom, roughness)
- ni_layer = ba.Layer(material_ni, 70*angstrom, roughness)
+ ti_layer = ba.Layer(material_ti, 3*nm, roughness)
+ ni_layer = ba.Layer(material_ni, 7*nm, roughness)
substrate_layer = ba.Layer(material_substrate, roughness)
# create periodic stack
diff --git a/rawEx/specular/SpecularSimulationWithRoughness.py b/rawEx/specular/SpecularSimulationWithRoughness.py
index 1cd1319c263ca6a4b035da5819bf8592ee26250a..60d59fbb9275d83da76751129b63df001a5f9562 100755
--- a/rawEx/specular/SpecularSimulationWithRoughness.py
+++ b/rawEx/specular/SpecularSimulationWithRoughness.py
@@ -5,7 +5,7 @@ with a rough sample using BornAgain.
"""
import bornagain as ba
-from bornagain import angstrom, ba_plot as bp, deg, nm
+from bornagain import nm, ba_plot as bp, deg, nm
def get_sample():
@@ -16,14 +16,14 @@ def get_sample():
material_substrate = ba.MaterialBySLD("SiSubstrate", 2.0704e-06, 0)
# roughness
- autocorr = ba.K_CorrelationModel(1*nm)
+ autocorr = ba.K_CorrelationModel(1*nm, 0.7, 25*nm)
interlayer = ba.TanhInterlayer()
roughness = ba.LayerRoughness(autocorr, interlayer)
# Layers
ambient_layer = ba.Layer(vacuum)
- ti_layer = ba.Layer(material_ti, 30*angstrom, roughness)
- ni_layer = ba.Layer(material_ni, 70*angstrom, roughness)
+ ti_layer = ba.Layer(material_ti, 3*nm, roughness)
+ ni_layer = ba.Layer(material_ni, 7*nm, roughness)
substrate_layer = ba.Layer(material_substrate, roughness)
# Periodic stack
@@ -44,7 +44,7 @@ def get_sample():
def get_simulation(sample):
n = <%= test_mode ? 50 : 500 %>
scan = ba.AlphaScan(n, 2*deg/n, 2*deg)
- scan.setWavelength(1.54*angstrom)
+ scan.setWavelength(0.154*nm)
return ba.SpecularSimulation(scan, sample)