def test_space(settings_ishigami, seed):
corners = settings_ishigami['space']['corners']
space = Space(corners)
assert space.max_points_nb == np.inf
space = Space(corners, sample=10)
assert space.max_points_nb == 10
space = Space(corners, sample=10, nrefine=6,
plabels=['x', 'y', 'z'])
assert space.max_points_nb == 16
space += (1, 2, 3)
npt.assert_array_equal(space.values, [(1, 2, 3)])
space.empty()
npt.assert_array_equal(space.values, np.empty((0, 3)))
space += [(1, 2, 3), (1, 1, 3)]
npt.assert_array_equal(space.values, [(1, 2, 3), (1, 1, 3)])
space2 = Space(corners, space.values)
npt.assert_array_equal(space2.values, [(1, 2, 3), (1, 1, 3)])
s1 = space.sampling()
assert len(s1) == 10
space2 = Space(corners,
sample=settings_ishigami['space']['sampling']['init_size'],
nrefine=settings_ishigami['space']['resampling']['resamp_size'])
s2 = space2.sampling(10, kind='lhsc')
assert len(s2) == 10
assert np.any(s1 != s2)
space.empty()
space += (1, 2, 3)
space += (1, 2, 3)
assert len(space) == 1
space = Space(corners, sample=16, duplicate=True)
space += (1, 2, 3)
space += (1, 2, 3)
assert len(space) == 2
with pytest.raises(ValueError):
space += (1, 2)
assert len(space) == 2
space += (1, 7, 3)
assert len(space) == 2
space.sampling(17)
assert len(space) == 16
space.empty()
dists = ['Uniform(0., 1.)', 'Uniform(-1., 2.)', 'Uniform(-2., 3.)']
space.sampling(5, kind='halton', dists=dists)
out = [(0.5, 0.0, -1.0), (0.25, 1.0, 0.0), (0.75, -0.67, 1.0),
(0.125, 0.33, 2.0), (0.625, 1.33, -1.8)]
npt.assert_almost_equal(space, out, decimal=1)
space = Space(corners, sample=np.array([(1, 2, 3), (1, 1, 3)]))
assert space.doe_init == 2
assert space.max_points_nb == 2
test_settings = copy.deepcopy(settings_ishigami)
test_settings['space']['corners'][1] = [np.pi, -np.pi, np.pi]
with pytest.raises(ValueError):
Space(test_settings['space']['corners'])
for s, r in zip(sample_success, density_success):
r = r if r > min_radius else min_radius
sample_ += random_uniform_ring(center=s,
r_outer=r,
n_samples=n_resamples)
for s, r in zip(sample_failure, density_failure):
r = r if r > min_radius else min_radius
sample_ += random_uniform_ring(center=s,
r_outer=r,
n_samples=n_resamples)
sample_scaled = np.concatenate([sample_scaled, sample_])
data = np.concatenate([data, fun(scaler_inverse_transform(sample_))])
sample.empty()
sample += scaler_inverse_transform(sample_scaled)
doe(sample, fname='final_doe.pdf')
# Analysis
print(f'\n########### N-samples ###########\n {sample.shape}')
# Filtering in/out
mask_threashold = data >= threashold
mask_threashold = mask_threashold.flatten()
inv_mask_threashold = np.logical_not(mask_threashold)
sample_in = sample[mask_threashold]
sample_out = sample[inv_mask_threashold]
ratio = len(sample_in) / len(sample) * 100
