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7648 lines (7590 loc) · 289 KB
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from pathlib import Path
import numpy as np
import pytest
import RATapi
import RATapi.classlist
import RATapi.outputs
import RATapi.rat_core
@pytest.fixture
def input_project():
"""A cut-down version of the input Project object for a reflectivity calculation set out in
"DSPC_standard_layers.py".
"""
project = RATapi.Project(
name="original_dspc_bilayer",
calculation="normal",
model="standard layers",
geometry="substrate/liquid",
absorption=False,
)
# Set up the relevant parameters
project.parameters.append(
name="Oxide Thickness",
min=5.0,
value=19.54,
max=60.0,
fit=True,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="Oxide SLD",
min=3.39e-06,
value=3.39e-06,
max=3.41e-06,
fit=False,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="SAM Tails Thickness",
min=15.0,
value=22.66,
max=40.0,
fit=True,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="SAM Tails SLD",
min=-5e-07,
value=-4.01e-07,
max=-3e-07,
fit=False,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="SAM Tails Hydration",
min=1.0,
value=5.252,
max=50.0,
fit=True,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="SAM Roughness",
min=1.0,
value=5.64,
max=15.0,
fit=True,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="CW Thickness",
min=10.0,
value=17.12,
max=28.0,
fit=True,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="CW SLD",
min=0.0,
value=0.0,
max=1e-09,
fit=False,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="SAM Heads Thickness",
min=5.0,
value=8.56,
max=17.0,
fit=True,
prior_type="gaussian",
mu=10.0,
sigma=2.0,
)
project.parameters.append(
name="SAM Heads SLD",
min=1.0e-07,
value=1.75e-06,
max=2.0e-06,
fit=False,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="SAM Heads Hydration",
min=10.0,
value=45.45,
max=50.0,
fit=True,
prior_type="uniform",
mu=30.0,
sigma=3.0,
)
project.parameters.append(
name="Bilayer Heads Thickness",
min=7.0,
value=10.7,
max=17.0,
fit=True,
prior_type="gaussian",
mu=10.0,
sigma=2.0,
)
project.parameters.append(
name="Bilayer Heads SLD",
min=5.0e-07,
value=1.47e-06,
max=1.5e-06,
fit=False,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="Bilayer Roughness",
min=2.0,
value=6.014,
max=15.0,
fit=True,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="Bilayer Tails Thickness",
min=14.0,
value=17.82,
max=22.0,
fit=True,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="Bilayer Tails SLD",
min=-5.0e-07,
value=-4.61e-07,
max=0.0,
fit=False,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="Bilayer Tails Hydration",
min=10.0,
value=17.64,
max=50.0,
fit=True,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="Bilayer Heads Hydration",
min=10.0,
value=36.15,
max=50.0,
fit=True,
prior_type="gaussian",
mu=30.0,
sigma=3.0,
)
project.parameters.append(
name="CW Hydration",
min=99.9,
value=100.0,
max=100.0,
fit=False,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.append(
name="Oxide Hydration",
min=0.0,
value=23.61,
max=60.0,
fit=True,
prior_type="uniform",
mu=0.0,
sigma=np.inf,
)
project.parameters.set_fields(0, max=10)
del project.bulk_in[0]
project.bulk_in.append(name="Silicon", min=2.0e-06, value=2.073e-06, max=2.1e-06, fit=False)
del project.bulk_out[0]
project.bulk_out.append(name="D2O", min=5.50e-06, value=5.98e-06, max=6.4e-06, fit=True)
project.bulk_out.append(name="SMW", min=1.0e-06, value=2.21e-06, max=4.99e-06, fit=True)
del project.scalefactors[0]
project.scalefactors.append(name="Scalefactor 1", min=0.05, value=0.10, max=0.2, fit=False)
project.scalefactors.append(name="Scalefactor 2", min=0.05, value=0.15, max=0.2, fit=False)
del project.backgrounds[0]
del project.background_parameters[0]
project.background_parameters.append(
name="Background parameter D2O",
min=5.0e-10,
value=2.23e-06,
max=7.0e-06,
fit=True,
)
project.background_parameters.append(
name="Background parameter SMW",
min=1.0e-10,
value=3.38e-06,
max=4.99e-06,
fit=True,
)
return project
@pytest.fixture
def reflectivity_calculation_output_results():
"""The C++ results object for a reflectivity calculation of the project set out in "DSPC_standard_layers.py"."""
results = RATapi.rat_core.OutputResult()
results.reflectivity = [
np.array(
[
[1.14030000e-02, 1.00000223e00],
[1.38610000e-02, 1.00000223e00],
[1.68480000e-02, 6.42692703e-02],
[2.04790000e-02, 1.69530247e-02],
[2.48920000e-02, 6.18815370e-03],
[3.02560000e-02, 2.99474163e-03],
[3.67770000e-02, 1.73558265e-03],
[4.47020000e-02, 1.03290554e-03],
[5.43360000e-02, 6.45645642e-04],
[6.60450000e-02, 5.57949197e-04],
[8.02790000e-02, 5.58974858e-04],
[9.75790000e-02, 3.49803750e-04],
[1.18610000e-01, 8.89485853e-05],
[1.44170000e-01, 4.04713900e-05],
[1.75240000e-01, 6.98590391e-06],
[2.13000000e-01, 3.60410795e-06],
[2.58910000e-01, 2.41835671e-06],
[3.14700000e-01, 2.26496635e-06],
[3.82520000e-01, 2.26396064e-06],
[4.64960000e-01, 2.24609517e-06],
[5.65160000e-01, 2.23162115e-06],
],
),
np.array(
[
[1.14030000e-02, 1.53247324e-02],
[1.38610000e-02, 9.89602164e-03],
[1.68480000e-02, 6.26868663e-03],
[2.04790000e-02, 3.83918877e-03],
[2.48920000e-02, 2.21818017e-03],
[3.02560000e-02, 1.15473774e-03],
[3.67770000e-02, 4.92469653e-04],
[4.47020000e-02, 1.37287345e-04],
[5.43360000e-02, 2.28561535e-05],
[6.60450000e-02, 6.63620782e-05],
[8.02790000e-02, 1.36790069e-04],
[9.75790000e-02, 1.15354200e-04],
[1.18610000e-01, 3.73063683e-05],
[1.44170000e-01, 1.21263573e-05],
[1.75240000e-01, 6.59103452e-06],
[2.13000000e-01, 4.38321947e-06],
[2.58910000e-01, 3.40797246e-06],
[3.14700000e-01, 3.41625063e-06],
[3.82520000e-01, 3.40076933e-06],
[4.64960000e-01, 3.38892224e-06],
[5.65160000e-01, 3.38103236e-06],
],
),
]
results.simulation = [
np.array(
[
[1.14030000e-02, 1.00000223e00],
[1.38610000e-02, 1.00000223e00],
[1.68480000e-02, 6.42692703e-02],
[2.04790000e-02, 1.69530247e-02],
[2.48920000e-02, 6.18815370e-03],
[3.02560000e-02, 2.99474163e-03],
[3.67770000e-02, 1.73558265e-03],
[4.47020000e-02, 1.03290554e-03],
[5.43360000e-02, 6.45645642e-04],
[6.60450000e-02, 5.57949197e-04],
[8.02790000e-02, 5.58974858e-04],
[9.75790000e-02, 3.49803750e-04],
[1.18610000e-01, 8.89485853e-05],
[1.44170000e-01, 4.04713900e-05],
[1.75240000e-01, 6.98590391e-06],
[2.13000000e-01, 3.60410795e-06],
[2.58910000e-01, 2.41835671e-06],
[3.14700000e-01, 2.26496635e-06],
[3.82520000e-01, 2.26396064e-06],
[4.64960000e-01, 2.24609517e-06],
[5.65160000e-01, 2.23162115e-06],
],
),
np.array(
[
[1.14030000e-02, 1.53247324e-02],
[1.38610000e-02, 9.89602164e-03],
[1.68480000e-02, 6.26868663e-03],
[2.04790000e-02, 3.83918877e-03],
[2.48920000e-02, 2.21818017e-03],
[3.02560000e-02, 1.15473774e-03],
[3.67770000e-02, 4.92469653e-04],
[4.47020000e-02, 1.37287345e-04],
[5.43360000e-02, 2.28561535e-05],
[6.60450000e-02, 6.63620782e-05],
[8.02790000e-02, 1.36790069e-04],
[9.75790000e-02, 1.15354200e-04],
[1.18610000e-01, 3.73063683e-05],
[1.44170000e-01, 1.21263573e-05],
[1.75240000e-01, 6.59103452e-06],
[2.13000000e-01, 4.38321947e-06],
[2.58910000e-01, 3.40797246e-06],
[3.14700000e-01, 3.41625063e-06],
[3.82520000e-01, 3.40076933e-06],
[4.64960000e-01, 3.38892224e-06],
[5.65160000e-01, 3.38103236e-06],
],
),
]
results.shiftedData = [
np.array(
[
[1.1403e-02, 1.0063e00, 1.9003e-02],
[1.3861e-02, 9.0118e-01, 1.1774e-02],
[1.6848e-02, 7.0455e-02, 1.3083e-03],
[2.0479e-02, 1.7544e-02, 5.1254e-04],
[2.4892e-02, 6.4257e-03, 2.6236e-04],
[3.0256e-02, 2.7746e-03, 8.5758e-05],
[3.6777e-02, 1.8591e-03, 4.9391e-05],
[4.4702e-02, 1.1002e-03, 3.2644e-05],
[5.4336e-02, 6.6691e-04, 2.1365e-05],
[6.6045e-02, 6.0729e-04, 1.1791e-05],
[8.0279e-02, 5.8755e-04, 1.5569e-05],
[9.7579e-02, 3.2700e-04, 6.5280e-06],
[1.1861e-01, 7.8205e-05, 2.5881e-06],
[1.4417e-01, 3.3455e-05, 1.5143e-06],
[1.7524e-01, 4.9313e-06, 5.6663e-07],
[2.1300e-01, 4.1948e-06, 6.8549e-07],
[2.5891e-01, 3.9863e-06, 8.2061e-07],
[3.1470e-01, 2.0861e-06, 6.1379e-07],
[3.8252e-01, 2.1154e-06, 6.3084e-07],
[4.6496e-01, 1.9906e-06, 6.0793e-07],
[5.6516e-01, 2.3816e-06, 7.0610e-07],
],
),
np.array(
[
[1.14030000e-02, 1.20493333e-02, 7.32200000e-04],
[1.38610000e-02, 6.71800000e-03, 3.71853333e-04],
[1.68480000e-02, 4.10506667e-03, 2.23106667e-04],
[2.04790000e-02, 2.43306667e-03, 1.46873333e-04],
[2.48920000e-02, 1.40940000e-03, 9.59200000e-05],
[3.02560000e-02, 7.43400000e-04, 3.50226667e-05],
[3.67770000e-02, 3.51466667e-04, 1.59573333e-05],
[4.47020000e-02, 9.80666667e-05, 7.29466667e-06],
[5.43360000e-02, 1.73500000e-05, 2.59200000e-06],
[6.60450000e-02, 4.86286667e-05, 2.46453333e-06],
[8.02790000e-02, 9.71733333e-05, 4.68166667e-06],
[9.75790000e-02, 7.06533333e-05, 2.32180000e-06],
[1.18610000e-01, 1.93046667e-05, 1.00813333e-06],
[1.44170000e-01, 6.61193333e-06, 5.29773333e-07],
[1.75240000e-01, 3.23726667e-06, 3.65933333e-07],
[2.13000000e-01, 3.29920000e-06, 4.74273333e-07],
[2.58910000e-01, 1.71180000e-06, 4.24720000e-07],
[3.14700000e-01, 2.24020000e-06, 5.08946667e-07],
[3.82520000e-01, 2.73306667e-06, 5.71513333e-07],
[4.64960000e-01, 2.36153333e-06, 5.24806667e-07],
[5.65160000e-01, 2.17460000e-06, 5.31346667e-07],
],
),
]
results.backgrounds = [
np.array(
[
[1.1403e-02, 2.2300e-06, 0.0000e00],
[1.1973e-02, 2.2300e-06, 0.0000e00],
[1.2572e-02, 2.2300e-06, 0.0000e00],
[1.3201e-02, 2.2300e-06, 0.0000e00],
[1.3861e-02, 2.2300e-06, 0.0000e00],
[1.4554e-02, 2.2300e-06, 0.0000e00],
[1.5281e-02, 2.2300e-06, 0.0000e00],
[1.6045e-02, 2.2300e-06, 0.0000e00],
[1.6848e-02, 2.2300e-06, 0.0000e00],
[1.7690e-02, 2.2300e-06, 0.0000e00],
[1.8575e-02, 2.2300e-06, 0.0000e00],
[1.9503e-02, 2.2300e-06, 0.0000e00],
[2.0479e-02, 2.2300e-06, 0.0000e00],
[2.1502e-02, 2.2300e-06, 0.0000e00],
[2.2578e-02, 2.2300e-06, 0.0000e00],
[2.3706e-02, 2.2300e-06, 0.0000e00],
[2.4892e-02, 2.2300e-06, 0.0000e00],
[2.6136e-02, 2.2300e-06, 0.0000e00],
[2.7443e-02, 2.2300e-06, 0.0000e00],
[2.8815e-02, 2.2300e-06, 0.0000e00],
[3.0256e-02, 2.2300e-06, 0.0000e00],
[3.1769e-02, 2.2300e-06, 0.0000e00],
[3.3357e-02, 2.2300e-06, 0.0000e00],
[3.5025e-02, 2.2300e-06, 0.0000e00],
[3.6777e-02, 2.2300e-06, 0.0000e00],
[3.8615e-02, 2.2300e-06, 0.0000e00],
[4.0546e-02, 2.2300e-06, 0.0000e00],
[4.2573e-02, 2.2300e-06, 0.0000e00],
[4.4702e-02, 2.2300e-06, 0.0000e00],
[4.6937e-02, 2.2300e-06, 0.0000e00],
[4.9284e-02, 2.2300e-06, 0.0000e00],
[5.1748e-02, 2.2300e-06, 0.0000e00],
[5.4336e-02, 2.2300e-06, 0.0000e00],
[5.7052e-02, 2.2300e-06, 0.0000e00],
[5.9905e-02, 2.2300e-06, 0.0000e00],
[6.2900e-02, 2.2300e-06, 0.0000e00],
[6.6045e-02, 2.2300e-06, 0.0000e00],
[6.9348e-02, 2.2300e-06, 0.0000e00],
[7.2815e-02, 2.2300e-06, 0.0000e00],
[7.6456e-02, 2.2300e-06, 0.0000e00],
[8.0279e-02, 2.2300e-06, 0.0000e00],
[8.4292e-02, 2.2300e-06, 0.0000e00],
[8.8507e-02, 2.2300e-06, 0.0000e00],
[9.2932e-02, 2.2300e-06, 0.0000e00],
[9.7579e-02, 2.2300e-06, 0.0000e00],
[1.0246e-01, 2.2300e-06, 0.0000e00],
[1.0758e-01, 2.2300e-06, 0.0000e00],
[1.1296e-01, 2.2300e-06, 0.0000e00],
[1.1861e-01, 2.2300e-06, 0.0000e00],
[1.2454e-01, 2.2300e-06, 0.0000e00],
[1.3077e-01, 2.2300e-06, 0.0000e00],
[1.3730e-01, 2.2300e-06, 0.0000e00],
[1.4417e-01, 2.2300e-06, 0.0000e00],
[1.5138e-01, 2.2300e-06, 0.0000e00],
[1.5895e-01, 2.2300e-06, 0.0000e00],
[1.6689e-01, 2.2300e-06, 0.0000e00],
[1.7524e-01, 2.2300e-06, 0.0000e00],
[1.8400e-01, 2.2300e-06, 0.0000e00],
[1.9320e-01, 2.2300e-06, 0.0000e00],
[2.0286e-01, 2.2300e-06, 0.0000e00],
[2.1300e-01, 2.2300e-06, 0.0000e00],
[2.2365e-01, 2.2300e-06, 0.0000e00],
[2.3484e-01, 2.2300e-06, 0.0000e00],
[2.4658e-01, 2.2300e-06, 0.0000e00],
[2.5891e-01, 2.2300e-06, 0.0000e00],
[2.7185e-01, 2.2300e-06, 0.0000e00],
[2.8544e-01, 2.2300e-06, 0.0000e00],
[2.9972e-01, 2.2300e-06, 0.0000e00],
[3.1470e-01, 2.2300e-06, 0.0000e00],
[3.3044e-01, 2.2300e-06, 0.0000e00],
[3.4696e-01, 2.2300e-06, 0.0000e00],
[3.6431e-01, 2.2300e-06, 0.0000e00],
[3.8252e-01, 2.2300e-06, 0.0000e00],
[4.0165e-01, 2.2300e-06, 0.0000e00],
[4.2173e-01, 2.2300e-06, 0.0000e00],
[4.4282e-01, 2.2300e-06, 0.0000e00],
[4.6496e-01, 2.2300e-06, 0.0000e00],
[4.8821e-01, 2.2300e-06, 0.0000e00],
[5.1262e-01, 2.2300e-06, 0.0000e00],
[5.3825e-01, 2.2300e-06, 0.0000e00],
[5.6516e-01, 2.2300e-06, 0.0000e00],
[5.9342e-01, 2.2300e-06, 0.0000e00],
]
),
np.array(
[
[1.1403e-02, 3.3800e-06, 0.0000e00],
[1.1973e-02, 3.3800e-06, 0.0000e00],
[1.2572e-02, 3.3800e-06, 0.0000e00],
[1.3201e-02, 3.3800e-06, 0.0000e00],
[1.3861e-02, 3.3800e-06, 0.0000e00],
[1.4554e-02, 3.3800e-06, 0.0000e00],
[1.5281e-02, 3.3800e-06, 0.0000e00],
[1.6045e-02, 3.3800e-06, 0.0000e00],
[1.6848e-02, 3.3800e-06, 0.0000e00],
[1.7690e-02, 3.3800e-06, 0.0000e00],
[1.8575e-02, 3.3800e-06, 0.0000e00],
[1.9503e-02, 3.3800e-06, 0.0000e00],
[2.0479e-02, 3.3800e-06, 0.0000e00],
[2.1502e-02, 3.3800e-06, 0.0000e00],
[2.2578e-02, 3.3800e-06, 0.0000e00],
[2.3706e-02, 3.3800e-06, 0.0000e00],
[2.4892e-02, 3.3800e-06, 0.0000e00],
[2.6136e-02, 3.3800e-06, 0.0000e00],
[2.7443e-02, 3.3800e-06, 0.0000e00],
[2.8815e-02, 3.3800e-06, 0.0000e00],
[3.0256e-02, 3.3800e-06, 0.0000e00],
[3.1769e-02, 3.3800e-06, 0.0000e00],
[3.3357e-02, 3.3800e-06, 0.0000e00],
[3.5025e-02, 3.3800e-06, 0.0000e00],
[3.6777e-02, 3.3800e-06, 0.0000e00],
[3.8615e-02, 3.3800e-06, 0.0000e00],
[4.0546e-02, 3.3800e-06, 0.0000e00],
[4.2573e-02, 3.3800e-06, 0.0000e00],
[4.4702e-02, 3.3800e-06, 0.0000e00],
[4.6937e-02, 3.3800e-06, 0.0000e00],
[4.9284e-02, 3.3800e-06, 0.0000e00],
[5.1748e-02, 3.3800e-06, 0.0000e00],
[5.4336e-02, 3.3800e-06, 0.0000e00],
[5.7052e-02, 3.3800e-06, 0.0000e00],
[5.9905e-02, 3.3800e-06, 0.0000e00],
[6.2900e-02, 3.3800e-06, 0.0000e00],
[6.6045e-02, 3.3800e-06, 0.0000e00],
[6.9348e-02, 3.3800e-06, 0.0000e00],
[7.2815e-02, 3.3800e-06, 0.0000e00],
[7.6456e-02, 3.3800e-06, 0.0000e00],
[8.0279e-02, 3.3800e-06, 0.0000e00],
[8.4292e-02, 3.3800e-06, 0.0000e00],
[8.8507e-02, 3.3800e-06, 0.0000e00],
[9.2932e-02, 3.3800e-06, 0.0000e00],
[9.7579e-02, 3.3800e-06, 0.0000e00],
[1.0246e-01, 3.3800e-06, 0.0000e00],
[1.0758e-01, 3.3800e-06, 0.0000e00],
[1.1296e-01, 3.3800e-06, 0.0000e00],
[1.1861e-01, 3.3800e-06, 0.0000e00],
[1.2454e-01, 3.3800e-06, 0.0000e00],
[1.3077e-01, 3.3800e-06, 0.0000e00],
[1.3730e-01, 3.3800e-06, 0.0000e00],
[1.4417e-01, 3.3800e-06, 0.0000e00],
[1.5138e-01, 3.3800e-06, 0.0000e00],
[1.5895e-01, 3.3800e-06, 0.0000e00],
[1.6689e-01, 3.3800e-06, 0.0000e00],
[1.7524e-01, 3.3800e-06, 0.0000e00],
[1.8400e-01, 3.3800e-06, 0.0000e00],
[1.9320e-01, 3.3800e-06, 0.0000e00],
[2.0286e-01, 3.3800e-06, 0.0000e00],
[2.1300e-01, 3.3800e-06, 0.0000e00],
[2.2365e-01, 3.3800e-06, 0.0000e00],
[2.3484e-01, 3.3800e-06, 0.0000e00],
[2.4658e-01, 3.3800e-06, 0.0000e00],
[2.5891e-01, 3.3800e-06, 0.0000e00],
[2.7185e-01, 3.3800e-06, 0.0000e00],
[2.8544e-01, 3.3800e-06, 0.0000e00],
[2.9972e-01, 3.3800e-06, 0.0000e00],
[3.1470e-01, 3.3800e-06, 0.0000e00],
[3.3044e-01, 3.3800e-06, 0.0000e00],
[3.4696e-01, 3.3800e-06, 0.0000e00],
[3.6431e-01, 3.3800e-06, 0.0000e00],
[3.8252e-01, 3.3800e-06, 0.0000e00],
[4.0165e-01, 3.3800e-06, 0.0000e00],
[4.2173e-01, 3.3800e-06, 0.0000e00],
[4.4282e-01, 3.3800e-06, 0.0000e00],
[4.6496e-01, 3.3800e-06, 0.0000e00],
[4.8821e-01, 3.3800e-06, 0.0000e00],
[5.1262e-01, 3.3800e-06, 0.0000e00],
[5.3825e-01, 3.3800e-06, 0.0000e00],
[5.6516e-01, 3.3800e-06, 0.0000e00],
[5.9342e-01, 3.3800e-06, 0.0000e00],
]
),
]
results.layerSlds = [
[
np.array(
[
[1.954000e01, 4.001499e-06, 3.000000e00],
[2.266000e01, -6.586988e-08, 3.000000e00],
[8.560000e00, 3.672535e-06, 5.640000e00],
[1.712000e01, 5.980000e-06, 5.640000e00],
[1.070000e01, 3.100365e-06, 6.014000e00],
[1.782000e01, 6.751924e-07, 6.014000e00],
[1.782000e01, 6.751924e-07, 6.014000e00],
[1.070000e01, 3.100365e-06, 6.014000e00],
],
),
],
[
np.array(
[
[1.9540000e01, 3.1114020e-06, 3.0000000e00],
[2.2660000e01, -2.6387028e-07, 3.0000000e00],
[8.5600000e00, 1.9590700e-06, 5.6400000e00],
[1.7120000e01, 2.2100000e-06, 5.6400000e00],
[1.0700000e01, 1.7375100e-06, 6.0140000e00],
[1.7820000e01, 1.0164400e-08, 6.0140000e00],
[1.7820000e01, 1.0164400e-08, 6.0140000e00],
[1.0700000e01, 1.7375100e-06, 6.0140000e00],
],
),
],
]
results.sldProfiles = [
[
np.array(
[
[0.00000000e00, 2.07300000e-06],
[1.10000000e01, 2.07300000e-06],
[2.20000000e01, 2.07300000e-06],
[3.30000000e01, 2.07300001e-06],
[4.40000000e01, 2.11687361e-06],
[5.50000000e01, 3.90933280e-06],
[6.60000000e01, 3.51748792e-06],
[7.70000000e01, -2.64615370e-08],
[8.80000000e01, 8.14665196e-07],
[9.90000000e01, 4.11508879e-06],
[1.10000000e02, 5.58392432e-06],
[1.21000000e02, 3.71785214e-06],
[1.32000000e02, 1.39304140e-06],
[1.43000000e02, 6.95745588e-07],
[1.54000000e02, 7.84170141e-07],
[1.65000000e02, 2.15552213e-06],
[1.76000000e02, 4.68458332e-06],
[1.87000000e02, 5.91563638e-06],
[1.98000000e02, 5.97982117e-06],
[2.09000000e02, 5.97999998e-06],
[2.20000000e02, 5.98000000e-06],
[2.31000000e02, 5.98000000e-06],
[2.42000000e02, 5.98000000e-06],
[2.53000000e02, 5.98000000e-06],
[2.64000000e02, 5.98000000e-06],
],
),
np.array(
[
[0.00000000e00, 2.07300000e-06],
[1.10000000e01, 2.07300000e-06],
[2.20000000e01, 2.07300000e-06],
[3.30000000e01, 2.07300001e-06],
[4.40000000e01, 2.09662378e-06],
[5.50000000e01, 3.06177428e-06],
[6.60000000e01, 2.70974796e-06],
[7.70000000e01, -2.34283042e-07],
[8.80000000e01, 2.46446429e-07],
[9.90000000e01, 1.80004279e-06],
[1.10000000e02, 2.14886270e-06],
[1.21000000e02, 1.70090192e-06],
[1.32000000e02, 5.06554250e-07],
[1.43000000e02, 2.47801381e-08],
[1.54000000e02, 8.73866316e-08],
[1.65000000e02, 9.86362863e-07],
[1.76000000e02, 1.96411923e-06],
[1.87000000e02, 2.19933821e-06],
[1.98000000e02, 2.20997064e-06],
[2.09000000e02, 2.21000000e-06],
[2.20000000e02, 2.21000000e-06],
[2.31000000e02, 2.21000000e-06],
[2.42000000e02, 2.21000000e-06],
[2.53000000e02, 2.21000000e-06],
[2.64000000e02, 2.21000000e-06],
],
),
],
]
results.resampledLayers = [[np.array([[0.0, 0.0, 0.0]])], [np.array([[0.0, 0.0, 0.0]])]]
results.calculationResults = RATapi.rat_core.Calculation()
results.calculationResults.chiValues = np.array([202.83057377, 1641.4024969])
results.calculationResults.sumChi = 1844.2330706690975
results.contrastParams = RATapi.rat_core.ContrastParams()
results.contrastParams.scalefactors = np.array([0.1, 0.15])
results.contrastParams.bulkIn = np.array([2.073e-06, 2.073e-06])
results.contrastParams.bulkOut = np.array([5.98e-06, 2.21e-06])
results.contrastParams.resolutionParams = np.array([0.03, 0.03])
results.contrastParams.subRoughs = np.array([3.0, 3.0])
results.contrastParams.resample = np.array([0.0, 0.0])
results.fitParams = np.array(
[
3.000e00,
1.954e01,
2.266e01,
5.252e00,
5.640e00,
1.712e01,
8.560e00,
4.545e01,
1.070e01,
6.014e00,
1.782e01,
1.764e01,
3.615e01,
2.361e01,
2.230e-06,
3.380e-06,
5.980e-06,
2.210e-06,
],
)
results.fitNames = [
"Substrate Roughness",
"Oxide Thickness",
"SAM Tails Thickness",
"SAM Tails Hydration",
"SAM Roughness",
"CW Thickness",
"SAM Heads Thickness",
"SAM Heads Hydration",
"Bilayer Heads Thickness",
"Bilayer Roughness",
"Bilayer Tails Thickness",
"Bilayer Tails Hydration",
"Bilayer Heads Hydration",
"Oxide Hydration",
"Background parameter D2O",
"Background parameter SMW",
"D2O",
"SMW",
]
return results
@pytest.fixture
def reflectivity_calculation_results():
"""The python results object for a reflectivity calculation of the project set out in "DSPC_standard_layers.py"."""
return RATapi.outputs.Results(
reflectivity=[
np.array(
[
[1.14030000e-02, 1.00000223e00],
[1.38610000e-02, 1.00000223e00],
[1.68480000e-02, 6.42692703e-02],
[2.04790000e-02, 1.69530247e-02],
[2.48920000e-02, 6.18815370e-03],
[3.02560000e-02, 2.99474163e-03],
[3.67770000e-02, 1.73558265e-03],
[4.47020000e-02, 1.03290554e-03],
[5.43360000e-02, 6.45645642e-04],
[6.60450000e-02, 5.57949197e-04],
[8.02790000e-02, 5.58974858e-04],
[9.75790000e-02, 3.49803750e-04],
[1.18610000e-01, 8.89485853e-05],
[1.44170000e-01, 4.04713900e-05],
[1.75240000e-01, 6.98590391e-06],
[2.13000000e-01, 3.60410795e-06],
[2.58910000e-01, 2.41835671e-06],
[3.14700000e-01, 2.26496635e-06],
[3.82520000e-01, 2.26396064e-06],
[4.64960000e-01, 2.24609517e-06],
[5.65160000e-01, 2.23162115e-06],
],
),
np.array(
[
[1.14030000e-02, 1.53247324e-02],
[1.38610000e-02, 9.89602164e-03],
[1.68480000e-02, 6.26868663e-03],
[2.04790000e-02, 3.83918877e-03],
[2.48920000e-02, 2.21818017e-03],
[3.02560000e-02, 1.15473774e-03],
[3.67770000e-02, 4.92469653e-04],
[4.47020000e-02, 1.37287345e-04],
[5.43360000e-02, 2.28561535e-05],
[6.60450000e-02, 6.63620782e-05],
[8.02790000e-02, 1.36790069e-04],
[9.75790000e-02, 1.15354200e-04],
[1.18610000e-01, 3.73063683e-05],
[1.44170000e-01, 1.21263573e-05],
[1.75240000e-01, 6.59103452e-06],
[2.13000000e-01, 4.38321947e-06],
[2.58910000e-01, 3.40797246e-06],
[3.14700000e-01, 3.41625063e-06],
[3.82520000e-01, 3.40076933e-06],
[4.64960000e-01, 3.38892224e-06],
[5.65160000e-01, 3.38103236e-06],
],
),
],
simulation=[
np.array(
[
[1.14030000e-02, 1.00000223e00],
[1.38610000e-02, 1.00000223e00],
[1.68480000e-02, 6.42692703e-02],
[2.04790000e-02, 1.69530247e-02],
[2.48920000e-02, 6.18815370e-03],
[3.02560000e-02, 2.99474163e-03],
[3.67770000e-02, 1.73558265e-03],
[4.47020000e-02, 1.03290554e-03],
[5.43360000e-02, 6.45645642e-04],
[6.60450000e-02, 5.57949197e-04],
[8.02790000e-02, 5.58974858e-04],
[9.75790000e-02, 3.49803750e-04],
[1.18610000e-01, 8.89485853e-05],
[1.44170000e-01, 4.04713900e-05],
[1.75240000e-01, 6.98590391e-06],
[2.13000000e-01, 3.60410795e-06],
[2.58910000e-01, 2.41835671e-06],
[3.14700000e-01, 2.26496635e-06],
[3.82520000e-01, 2.26396064e-06],
[4.64960000e-01, 2.24609517e-06],
[5.65160000e-01, 2.23162115e-06],
],
),
np.array(
[
[1.14030000e-02, 1.53247324e-02],
[1.38610000e-02, 9.89602164e-03],
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