def test_keys_to_tuples():
assert (AnalysisResults._keys_to_tuples({}) == {})
assert (AnalysisResults._keys_to_tuples({
'a': 1,
'b': 2
}) == {
('a', 0): 1,
('b', 0): 2
})
assert (AnalysisResults._keys_to_tuples({
('a', 0): 1,
('b', 0): 2
}) == {
('a', 0): 1,
('b', 0): 2
})
assert (AnalysisResults._keys_to_tuples({
('a', 0): 1,
'b': 2
}) == {
('a', 0): 1,
('b', 0): 2
})
def test_plotting_sobols_tree(tmp_path):
results = AnalysisResults()
results.qois = ['g1']
results.inputs = ['a', 'b', 'c', 'd']
def sobols_first(qoi):
return {
'a': np.array([0.1]),
'b': np.array([0.2]),
'c': np.array([0.3]),
'd': np.array([0.3])
}
results.sobols_first = sobols_first
results.plot_sobols_treemap('g1',
filename=os.path.join(tmp_path, 'test.png'),
dpi=10)
assert (os.path.exists(os.path.join(tmp_path, 'test.png')))
def test_plotting_moments(tmp_path, ylabel, xlabel, dpi):
results = AnalysisResults()
results.qois = ['t']
data = np.random.normal(np.arange(100), np.arange(100), size=(100, 100))
def describe(qoi, stat):
if stat == 'mean':
return data.mean(axis=0)
elif stat == 'std':
return data.std(axis=0)
elif stat == '1%':
return data.min(axis=0)
elif stat == '99%':
return data.max(axis=0)
results.describe = describe
results.inputs = ['kappa', 't_env']
results.plot_moments('t',
ylabel,
xlabel,
filename=os.path.join(tmp_path, 'test.png'),
dpi=dpi)
assert (os.path.exists(os.path.join(tmp_path, 'test.png')))
def test_to_tuple():
assert (AnalysisResults._to_tuple('a') == ('a', 0))
assert (AnalysisResults._to_tuple(('a', 0)) == ('a', 0))
with pytest.raises(RuntimeError):
AnalysisResults._to_tuple(3)
def test_plotting(tmp_path, withdots, ylabel, xlabel, dpi):
results = AnalysisResults()
results.qois = ['t']
kappa = np.array([
0.19726384, 0.98001541, 0.97884984, 0.97760245, 0.97626742, 0.97483855,
0.97330924, 0.97167242, 0.96992063, 0.9680459, 0.96603977, 0.96389328,
0.96159695, 0.95914077, 0.95651411, 0.95370585, 0.95070424, 0.94749696,
0.94407112, 0.94041323, 0.93650925, 0.93234459, 0.92790414, 0.92317227,
0.91813298, 0.91276984, 0.90706615, 0.90100496, 0.89456923, 0.88774191,
0.8805061, 0.87284519, 0.86474304, 0.85618416, 0.84715394, 0.83763885,
0.8276267, 0.81710691, 0.80607073, 0.79451156, 0.78242517, 0.76981004,
0.75666756, 0.7430023, 0.72882224, 0.71413895, 0.69896774, 0.68332775,
0.66724206, 0.65073761, 0.63384519, 0.61659926, 0.59903778, 0.5812019,
0.56313564, 0.54488547, 0.52649989, 0.50802887, 0.48952336, 0.4710347,
0.45261407, 0.43431192, 0.4161774, 0.39825788, 0.38059843, 0.36324144,
0.3462262, 0.32958862, 0.313361, 0.29757187, 0.28224586, 0.2674037,
0.25306221, 0.23923444, 0.22592976, 0.21315405, 0.20090991, 0.18919689,
0.17801175, 0.16734875, 0.15719988, 0.14755517, 0.13840294, 0.12973007,
0.12152225, 0.1137642, 0.10643989, 0.09953277, 0.0930259, 0.08690214,
0.08114432, 0.07573531, 0.07065816, 0.06589622, 0.06143316, 0.05725307,
0.05334053, 0.0496806, 0.04625891, 0.04306163, 0.04007551, 0.0372879,
0.03468673, 0.0322605, 0.0299983, 0.02788979, 0.02592518, 0.02409521,
0.02239114, 0.02080475, 0.01932827, 0.01795441, 0.01667633, 0.01548759,
0.01438215, 0.01335437, 0.01239895, 0.01151093, 0.01068569, 0.00991888,
0.00920647, 0.00854467, 0.00792996, 0.00735905, 0.00682888, 0.00633657,
0.00587946, 0.00545508, 0.00506112, 0.00469541, 0.00435595, 0.00404089,
0.00374848, 0.00347711, 0.00322528, 0.00299161, 0.00277478, 0.00257359,
0.00238693, 0.00221375, 0.00205308, 0.00190403, 0.00176576, 0.00163749,
0.00151852, 0.00140815, 0.00130578, 0.00121084, 0.00112277, 0.00104109
])
t_env = np.array([
0.17448336, 0.01853271, 0.01970001, 0.02094925, 0.02228626, 0.02371725,
0.02524883, 0.02688807, 0.02864246, 0.03051998, 0.0325291, 0.03467878,
0.03697853, 0.03943838, 0.04206895, 0.04488141, 0.04788752, 0.05109961,
0.05453059, 0.05819397, 0.06210382, 0.06627475, 0.0707219, 0.07546089,
0.0805078, 0.08587904, 0.09159135, 0.0976617, 0.10410717, 0.11094482,
0.11819158, 0.12586409, 0.13397851, 0.14255036, 0.15159426, 0.16112375,
0.17115105, 0.18168677, 0.19273964, 0.2043163, 0.21642096, 0.22905515,
0.24221748, 0.25590336, 0.2701048, 0.28481022, 0.30000428, 0.31566781,
0.33177769, 0.34830693, 0.36522471, 0.38249651, 0.4000843, 0.41794688,
0.43604017, 0.4543176, 0.47273062, 0.49122919, 0.50976227, 0.52827844,
0.54672646, 0.56505579, 0.58321722, 0.6011633, 0.61884888, 0.63623155,
0.65327193, 0.66993406, 0.68618559, 0.70199797, 0.71734653, 0.7322105,
0.74657303, 0.76042107, 0.77374523, 0.78653963, 0.79880166, 0.81053178,
0.82173322, 0.83241175, 0.84257538, 0.8522341, 0.86139961, 0.87008505,
0.87830475, 0.88607403, 0.89340891, 0.900326, 0.90684225, 0.91297481,
0.91874091, 0.92415769, 0.92924212, 0.93401089, 0.93848034, 0.9426664,
0.94658452, 0.95024967, 0.95367623, 0.95687806, 0.95986842, 0.96265999,
0.96526485, 0.96769452, 0.96995991, 0.9720714, 0.97403878, 0.97587133,
0.97757779, 0.97916642, 0.98064498, 0.98202076, 0.98330063, 0.98449104,
0.98559802, 0.98662724, 0.98758399, 0.98847325, 0.98929964, 0.99006751,
0.99078091, 0.99144363, 0.99205919, 0.99263089, 0.9931618, 0.99365479,
0.99411252, 0.99453749, 0.994932, 0.99529821, 0.99563813, 0.99595363,
0.99624644, 0.99651818, 0.99677035, 0.99700435, 0.99722148, 0.99742294,
0.99760985, 0.99778327, 0.99794416, 0.99809341, 0.99823187, 0.99836031,
0.99847945, 0.99858996, 0.99869246, 0.99878754, 0.99887573, 0.99895751
])
test_data = {'kappa': kappa, 't_env': t_env}
results.sobols_first = lambda qoi, input_: test_data[input_]
results.inputs = ['kappa', 't_env']
results.plot_sobols_first('t', ['kappa', 't_env'],
withdots,
ylabel,
xlabel,
filename=os.path.join(tmp_path, 'test.png'),
dpi=dpi)
assert (os.path.exists(os.path.join(tmp_path, 'test.png')))