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"""Test the outputs module using the example calculation from "DSPC_standard_layers.py".
We use the example for both a reflectivity calculation, and Bayesian analysis using the Dream algorithm.
"""
import tempfile
from pathlib import Path
import numpy as np
import pytest
import ratapi
import ratapi.outputs
import ratapi.rat_core
from ratapi.utils.enums import Procedures
from tests.utils import check_results_equal
@pytest.fixture
def reflectivity_calculation_str():
"""The string representation of the python results object for a reflectivity calculation of the project set out in
"DSPC_standard_layers.py".
"""
return (
"reflectivity = [Data array: [21 x 2], Data array: [21 x 2]],\n"
"simulation = [Data array: [21 x 2], Data array: [21 x 2]],\n"
"shiftedData = [Data array: [21 x 3], Data array: [21 x 3]],\n"
"backgrounds = [Data array: [82 x 3], Data array: [82 x 3]],\n"
"resolutions = [Data array: [82 x 2], Data array: [82 x 2]],\n"
"sldProfiles = [[Data array: [25 x 2], Data array: [25 x 2]]],\n"
"layers = [[Data array: [8 x 3]], [Data array: [8 x 3]]],\n"
"resampledLayers = [[Data array: [1 x 3]], [Data array: [1 x 3]]],\n"
"calculationResults = CalculationResults(\n"
"\tchiValues = [ 202.83057377 1641.4024969 ],\n"
"\tsumChi = 1844.2330706690975,\n"
"),\n"
"contrastParams = ContrastParams(\n"
"\tscalefactors = [0.1 0.15],\n"
"\tbulkIn = [2.073e-06 2.073e-06],\n"
"\tbulkOut = [5.98e-06 2.21e-06],\n"
"\tsubRoughs = [3. 3.],\n"
"\tresample = [0. 0.],\n"
"),\n"
"fitParams = [3.000e+00 1.954e+01 2.266e+01 5.252e+00 5.640e+00 1.712e+01 8.560e+00\n"
" 4.545e+01 1.070e+01 6.014e+00 1.782e+01 1.764e+01 3.615e+01 2.361e+01\n"
" 2.230e-06 3.380e-06 5.980e-06 2.210e-06],\n"
"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'],\n"
)
@pytest.fixture
def dream_str():
"""The string representation of the python BayesResults object for a Dream optimisation of the project set out in
"DSPC_standard_layers.py".
"""
return (
"reflectivity = [Data array: [21 x 2], Data array: [21 x 2]],\n"
"simulation = [Data array: [21 x 2], Data array: [21 x 2]],\n"
"shiftedData = [Data array: [21 x 3], Data array: [21 x 3]],\n"
"backgrounds = [Data array: [82 x 3], Data array: [82 x 3]],\n"
"resolutions = [Data array: [82 x 2], Data array: [82 x 2]],\n"
"sldProfiles = [[Data array: [29 x 2], Data array: [29 x 2]]],\n"
"layers = [[Data array: [8 x 3]], [Data array: [8 x 3]]],\n"
"resampledLayers = [[Data array: [1 x 3]], [Data array: [1 x 3]]],\n"
"calculationResults = CalculationResults(\n"
"\tchiValues = [4.6077885 7.00028098],\n"
"\tsumChi = 11.608069475997699,\n"
"),\n"
"contrastParams = ContrastParams(\n"
"\tscalefactors = [0.1 0.15],\n"
"\tbulkIn = [2.073e-06 2.073e-06],\n"
"\tbulkOut = [6.01489149e-06 1.59371685e-06],\n"
"\tsubRoughs = [6.19503045 6.19503045],\n"
"\tresample = [0. 0.],\n"
"),\n"
"fitParams = [6.19503045e+00 1.84420960e+01 2.11039621e+01 8.75538121e+00\n"
" 3.72292994e+00 1.84624551e+01 1.02316734e+01 2.31156093e+01\n"
" 1.09906265e+01 5.71005361e+00 1.67933822e+01 1.72009856e+01\n"
" 3.00260126e+01 2.94448999e+01 2.37113128e-06 1.99006694e-06\n"
" 6.01489149e-06 1.59371685e-06],\n"
"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'],\n"
"predictionIntervals = PredictionIntervals(\n"
"\treflectivity = [Data array: [5 x 21], Data array: [5 x 21]],\n"
"\tsld = [[Data array: [5 x 29], Data array: [5 x 29]]],\n"
"\tsampleChi = Data array: [1000],\n"
"),\n"
"confidenceIntervals = ConfidenceIntervals(\n"
"\tpercentile95 = Data array: [2 x 18],\n"
"\tpercentile65 = Data array: [2 x 18],\n"
"\tmean = Data array: [1 x 18],\n"
"),\n"
"dreamParams = DreamParams(\n"
"\tnParams = 18.0,\n"
"\tnChains = 1.0,\n"
"\tnGenerations = 1.0,\n"
"\tparallel = False,\n"
"\tCPU = 1.0,\n"
"\tjumpProbability = 0.5,\n"
"\tpUnitGamma = 0.2,\n"
"\tnCR = 3.0,\n"
"\tdelta = 3.0,\n"
"\tsteps = 50.0,\n"
"\tzeta = 1e-12,\n"
"\toutlier = iqr,\n"
"\tadaptPCR = False,\n"
"\tthinning = 1.0,\n"
"\tepsilon = 0.025,\n"
"\tABC = False,\n"
"\tIO = False,\n"
"\tstoreOutput = False,\n"
"\tR = Data array: [1 x 1],\n"
"),\n"
"dreamOutput = DreamOutput(\n"
"\tallChains = Data array: [1 x 20 x 1],\n"
"\toutlierChains = Data array: [1 x 2],\n"
"\truntime = 2.6e-06,\n"
"\titeration = 2.0,\n"
"\tAR = Data array: [1 x 2],\n"
"\tR_stat = Data array: [1 x 19],\n"
"\tCR = Data array: [1 x 4],\n"
"),\n"
"nestedSamplerOutput = NestedSamplerOutput(\n"
"\tlogZ = 0.0,\n"
"\tlogZErr = 0.0,\n"
"\tnestSamples = Data array: [1 x 2],\n"
"\tpostSamples = Data array: [1 x 2],\n"
"),\n"
"chain = Data array: [2 x 18],\n"
)
@pytest.mark.parametrize(
["test_value", "array_limit", "expected_field_string"],
[
(5, 100, "test_field = 5,\n"),
([], 100, "test_field = [],\n"),
([[], [], []], 100, "test_field = [[], [], []],\n"),
([1, 2, 3], 100, "test_field = [1, 2, 3],\n"),
([[1], [2], [3]], 100, "test_field = [[1], [2], [3]],\n"),
(np.array([]), 100, "test_field = [],\n"),
(np.array([[], []]), 100, "test_field = Data array: [2 x 0],\n"),
(np.array([1, 2, 3]), 100, "test_field = [1 2 3],\n"),
(np.array([1, 2, 3]), 3, "test_field = Data array: [3],\n"),
(np.array([1, 2, 3]), 2, "test_field = Data array: [3],\n"),
(np.array([[1, 2, 3], [4, 5, 6]]), 100, "test_field = Data array: [2 x 3],\n"),
(np.array([[[1], [2], [3]], [[4], [5], [6]]]), 100, "test_field = Data array: [2 x 3 x 1],\n"),
([np.array([])], 100, "test_field = [Data array: [0]],\n"),
([np.array([1, 2, 3])], 100, "test_field = [Data array: [3]],\n"),
([np.array([[1], [2], [3]])], 100, "test_field = [Data array: [3 x 1]],\n"),
([[np.array([[1, 2, 3], [4, 5, 6]])]], 100, "test_field = [[Data array: [2 x 3]]],\n"),
([[np.array([1, 2, 3]), np.array([4, 5, 6])]], 100, "test_field = [[Data array: [3], Data array: [3]]],\n"),
(
[[np.array([[1, 2], [3, 4]]), np.array([[5, 6], [7, 8]])]],
100,
"test_field = [[Data array: [2 x 2], Data array: [2 x 2]]],\n",
),
],
)
def test_get_field_string(test_value, array_limit, expected_field_string) -> None:
"""For the string representation of output classes, we represent multidimensional and large arrays by their shape,
with other variables printed normally.
"""
field_string = ratapi.outputs.get_field_string("test_field", test_value, array_limit)
assert field_string == expected_field_string
@pytest.mark.parametrize(
["test_procedure", "test_output_results", "test_bayes", "test_results"],
[
(Procedures.Calculate, "reflectivity_calculation_output_results", None, "reflectivity_calculation_results"),
(Procedures.DREAM, "dream_output_results", "dream_bayes", "dream_results"),
],
)
def test_make_results(test_procedure, test_output_results, test_bayes, test_results, request) -> None:
"""The python results object should contain the relevant parameters defined in the C++ results and bayes objects."""
test_output_results = request.getfixturevalue(test_output_results)
if test_bayes:
test_bayes = request.getfixturevalue(test_bayes)
test_results = request.getfixturevalue(test_results)
results = ratapi.outputs.make_results(test_procedure, test_output_results, test_bayes)
check_results_equal(test_results, results)
@pytest.mark.parametrize(
["test_output_results", "test_str"],
[
("reflectivity_calculation_results", "reflectivity_calculation_str"),
("dream_results", "dream_str"),
],
)
def test_results_str(test_output_results, test_str, request) -> None:
"""The string representation of the python results object should represent large and multidimensional arrays by
their shape.
"""
test_output_results = request.getfixturevalue(test_output_results)
test_str = request.getfixturevalue(test_str)
assert test_output_results.__str__() == test_str
@pytest.mark.parametrize("result_class", [ratapi.Results, ratapi.BayesResults])
@pytest.mark.parametrize("test_results", ["reflectivity_calculation_results", "dream_results"])
def test_save_load(result_class, test_results, request):
"""Test that saving and loading an output object returns the same object."""
test_results = request.getfixturevalue(test_results)
with tempfile.TemporaryDirectory() as tmp:
# ignore relative path warnings
path = Path(tmp, "results.json")
test_results.save(path)
loaded_results = result_class.load(path)
check_results_equal(test_results, loaded_results)
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