def test_samples_z2():
dist1 = Uniform(loc=0.0, scale=5.0)
dist2 = Uniform(loc=0.0, scale=3.0)
ntf_obj = Nataf(distributions=[dist1, dist2])
samples_z = np.array([[0.3, 1.2], [0.2, 2.4]]).T
ntf_obj.run(samples_z=samples_z, jacobian=True)
g = []
for i in range(2):
if i == 0:
g.append((ntf_obj.samples_x[i] == np.array(
[3.089557110944763, 1.737779128317309])).all())
elif i == 1:
g.append((ntf_obj.samples_x[i] == np.array(
[4.424651648891459, 2.9754073922262116])).all())
assert np.all(g)
def test_non_identity_correlation_uniform_x():
dist1 = Uniform(loc=0.0, scale=1.0)
dist2 = Uniform(loc=0.0, scale=1.0)
rz = np.array([[1.0, 0.8], [0.8, 1.0]])
ntf_obj = Nataf(distributions=[dist1, dist2], corr_z=rz)
assert (ntf_obj.corr_x == [[1., 0.7859392826067285],
[0.7859392826067285, 1.]]).all()
def test_non_identity_correlation_uniform_z():
dist1 = Uniform(loc=0.0, scale=1.0)
dist2 = Uniform(loc=0.0, scale=1.0)
rx = np.array([[1.0, 0.8], [0.8, 1.0]])
ntf_obj = Nataf(distributions=[dist1, dist2], corr_x=rx)
np.testing.assert_allclose(
ntf_obj.corr_z, [[1., 0.8134732861515996], [0.8134732861515996, 1.]],
rtol=1e-09)
def test_samples_x1():
dist1 = Uniform(loc=0.0, scale=5.0)
dist2 = Uniform(loc=0.0, scale=3.0)
ntf_obj = Nataf(distributions=[dist1, dist2])
samples_x = np.array([[0.3, 1.2, 3.5], [0.2, 2.4, 0.9]]).T
ntf_obj.run(samples_x=samples_x, jacobian=True)
g = []
for i in range(3):
if i == 0:
g.append((ntf_obj.samples_z[i] == np.array(
[-1.5547735945968535, -1.501085946044025])).all())
elif i == 1:
g.append((ntf_obj.samples_z[i] == np.array(
[-0.7063025628400874, 0.841621233572914])).all())
else:
g.append((ntf_obj.samples_z[i] == np.array(
[0.5244005127080407, -0.5244005127080409])).all())
assert np.all(g)
def test_derivatives_1_no_samples():
dist1 = Normal(loc=200, scale=20)
dist2 = Normal(loc=150, scale=10)
rx = np.array([[1.0, 0.0], [0.0, 1.0]])
ntf_obj = Nataf(distributions=[dist1, dist2], corr_x=rx)
for file_name in glob.glob("Model_Runs_*"):
shutil.rmtree(file_name)
with pytest.raises(Exception):
assert TaylorSeries._derivatives(nataf_object=ntf_obj, runmodel_object=model_i)
def test_samples_z_jzx2():
dist1 = Uniform(loc=0.0, scale=5.0)
dist2 = Uniform(loc=0.0, scale=3.0)
ntf_obj = Nataf(distributions=[dist1, dist2])
samples_z = np.array([[0.3, 1.2], [0.2, 2.4]]).T
ntf_obj.run(samples_z=samples_z, jacobian=True)
g = []
for i in range(2):
if i == 0:
g.append(
(ntf_obj.jzx[i] == np.array([[0.524400601939789, 0.0],
[0.0,
0.8524218415758338]])).all())
else:
g.append(
(ntf_obj.jzx[i] == np.array([[1.0299400748281828, 0.0],
[0.0,
14.884586948005541]])).all())
assert np.all(g)
def test_derivatives_6_second():
model = PythonModel(model_script='pfn2.py', model_object_name='model_j', delete_files=True)
h_func = RunModel(model=model)
dist1 = Normal(loc=500, scale=100)
dist2 = Normal(loc=1000, scale=100)
point_u = np.array([1.73673009, 0.16383283])
rx = np.array([[1.0, 0.0], [0.0, 1.0]])
ntf_obj = Nataf(distributions=[dist1, dist2], corr_x=rx)
hessian = TaylorSeries._derivatives(point_u=point_u, runmodel_object=h_func,
nataf_object=ntf_obj, order='second')
np.testing.assert_allclose(hessian, [[-0.00720754, 0.00477726], [0.00477726, -0.00316643]], rtol=1e-04)
def test_samples_x_jxz2():
dist1 = Uniform(loc=0.0, scale=5.0)
dist2 = Uniform(loc=0.0, scale=3.0)
ntf_obj = Nataf(distributions=[dist1, dist2])
samples_x = np.array([[0.3, 1.2, 3.5], [0.2, 2.4, 0.9]]).T
ntf_obj.run(samples_x=samples_x, jacobian=True)
g = []
for i in range(3):
if i == 0:
g.append((ntf_obj.jxz[i] == np.array([[1.6789373877365803, 0.0],
[0.0,
2.577850090371836]])).all())
elif i == 1:
g.append(
(ntf_obj.jxz[i] == np.array([[0.6433491348614259, 0.0],
[0.0,
1.1906381155257868]])).all())
else:
g.append((ntf_obj.jxz[i] == np.array([[0.5752207318528584, 0.0],
[0.0,
0.958701219754764]])).all())
assert np.all(g)
def test_derivatives_5_run_model(setup):
dist1 = Normal(loc=200, scale=20)
dist2 = Normal(loc=150, scale=10)
point_u = np.array([-2, 1])
rx = np.array([[1.0, 0.0], [0.0, 1.0]])
ntf_obj = Nataf(distributions=[dist1, dist2], corr_x=rx)
gradient, qoi, array_of_samples = TaylorSeries._derivatives(point_u=point_u,
runmodel_object=setup,
nataf_object=ntf_obj)
for file_name in glob.glob("Model_Runs_*"):
shutil.rmtree(file_name)
np.testing.assert_allclose(array_of_samples[0], [160, 160], rtol=1e-09)
np.testing.assert_allclose(gradient, [20, -10], rtol=1e-09)
def test_distortion_z2x_finite_moments():
dist1 = Lognormal(s=0.0, loc=0.0, scale=1.0)
dist2 = Uniform(loc=0.0, scale=1.0)
rz = np.array([[1.0, 0.8], [0.8, 1.0]])
with pytest.raises(Exception):
assert Nataf(distributions=[dist1, dist2], corr_z=rz)
# The two distributions are correlated according to the correlation matrix :code:`Rx`. # %% dist1 = Gamma(4.0, loc=0.0, scale=1.0) dist2 = Lognormal(s=2., loc=0., scale=np.exp(1)) Rx = np.array([[1.0, 0.9], [0.9, 1.0]]) # %% md # # Next, we'll construct a :code:`Nataf` object :code:`nataf_obj`. Here, we provide the distribution of the random # variables :code:`distributions` and the correlation matrix :code:`corr_x` in the parameter space. # %% nataf_obj = Nataf(distributions=[dist1, dist2], corr_x=Rx) # %% md # # We can use the :code:`rvs` method of the :code:`nataf_obj` object to draw random samples from the two distributions. # %% samples_x = nataf_obj.rvs(1000) # %% md # # We can visualize the samples by plotting them on axes of each distribution's range. # %%
def test_samples_x():
dist1 = Normal(loc=0.0, scale=1.0)
ntf_obj = Nataf(distributions=[dist1])
assert ntf_obj.samples_x is None
def test_attribute_h():
dist1 = Normal(loc=0.0, scale=1.0)
dist2 = Normal(loc=0.0, scale=1.0)
rz = np.array([[1.0, 0.8], [0.8, 1.0]])
ntf_obj = Nataf(distributions=[dist1, dist2], corr_z=rz)
np.testing.assert_allclose(ntf_obj.H, [[1., 0.], [0.8, 0.6]], rtol=1e-09)
def test_correct_distribution_dimensions():
dist1 = Normal(loc=0.0, scale=1.0)
dist2 = Normal(loc=0.0, scale=1.0)
rx = np.array([[1.0, 0.0], [0.0, 1.0]])
ntf_obj = Nataf(distributions=[dist1, dist2], corr_x=rx)
assert ntf_obj.n_dimensions == 2
def test_wrong_distribution_in_list():
dist1 = Normal(loc=0.0, scale=1.0)
rx = np.array([[1.0, 0.0], [0.0, 1.0]])
with pytest.raises(Exception):
assert Nataf(distributions=[dist1, 'Beta'], corr_x=rx)
def test_samples_z_jzx1():
dist1 = Normal(loc=0.0, scale=5.0)
ntf_obj = Nataf(distributions=[dist1])
samples_z = np.array([0.3, 1.2, 3.5])
ntf_obj.run(samples_z=samples_z, jacobian=False)
assert ntf_obj.jzx is None
def test_non_identity_correlation_normal():
dist1 = Normal(loc=0.0, scale=1.0)
dist2 = Normal(loc=0.0, scale=1.0)
rx = np.array([[1.0, 0.8], [0.8, 1.0]])
ntf_obj = Nataf(distributions=[dist1, dist2], corr_x=rx)
assert np.all(np.equal(ntf_obj.corr_z, rx))
def test_identity_correlation_uniform_z():
dist1 = Uniform(loc=0.0, scale=1.0)
dist2 = Uniform(loc=0.0, scale=1.0)
rx = np.array([[1.0, 0.0], [0.0, 1.0]])
ntf_obj = Nataf(distributions=[dist1, dist2], corr_x=rx)
assert np.all(np.equal(ntf_obj.corr_z, rx))
def test_identity_correlation_z_normal():
dist1 = Normal(loc=0.0, scale=1.0)
dist2 = Normal(loc=0.0, scale=1.0)
ntf_obj = Nataf(distributions=[dist1, dist2])
assert np.all(np.equal(ntf_obj.corr_z, np.eye(2)))
def test_itam_threshold2_1():
dist1 = Uniform(loc=0.0, scale=5.0)
rx = np.array([[1.0, 0.0], [0.0, 1.0]])
ntf_obj = Nataf(distributions=[dist1], corr_x=rx, itam_threshold2=0.3)
assert ntf_obj.itam_threshold2 == 0.3
def test_itam_max_iteration2():
dist1 = Uniform(loc=0.0, scale=5.0)
rx = np.array([[1.0, 0.0], [0.0, 1.0]])
ntf_obj = Nataf(distributions=[dist1], corr_x=rx, itam_max_iter=10)
assert ntf_obj.itam_max_iter == 10
def test_itam_beta1():
dist1 = Uniform(loc=0.0, scale=5.0)
rx = np.array([[1.0, 0.0], [0.0, 1.0]])
ntf_obj = Nataf(distributions=[dist1], corr_x=rx, itam_beta=2.0)
assert ntf_obj.itam_beta == 2.0
def test_wrong_distribution():
rx = np.array([[1.0, 0.0], [0.0, 1.0]])
with pytest.raises(Exception):
assert Nataf(distributions='Normal', corr_x=rx)
def distortion_z2x_dist_object():
dist1 = Lognormal(s=0.0, loc=0.0, scale=1.0)
rz = np.array([[1.0, 0.8], [0.8, 1.0]])
with pytest.raises(Exception):
assert Nataf.distortion_z2x(distributions=[dist1, 'Beta'], corr_z=rz)
def test_samples_z_shape():
dist1 = Normal(loc=0.0, scale=5.0)
ntf_obj = Nataf(distributions=[dist1])
samples_z = np.array([0.3, 1.2, 3.5])
ntf_obj.run(samples_z)
assert ntf_obj.samples_z.shape == (3, 1)
