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import shutil
import tempfile
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import numpy as np
import pytest
import ratapi
import ratapi.classlist
import ratapi.outputs
import ratapi.rat_core
@pytest.fixture
def temp_dir():
path = tempfile.mkdtemp()
yield path
shutil.rmtree(path)
@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],
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results.backgrounds = [
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results.resolutions = [
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),
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results.sldProfiles = [
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],
]
results.layers = [
[
np.array(
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np.array(
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]
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.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],
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