def test_training_point_array_for_nan_values(self):
# Model
interp = om.MetaModelUnStructuredComp()
# Training Data
x_train = np.linspace(0, 10, 20)
y_train = np.linspace(0, 20, 20)
# Inputs
interp.add_input('x', 0., training_data=x_train)
interp.add_input('y', 0., training_data=x_train)
#Outputs
interp.add_output('cos_x', 0., training_data=.5 * np.cos(y_train))
# Surrogate Model
interp.options['default_surrogate'] = om.ResponseSurface()
prob = om.Problem()
prob.model.add_subsystem('interp', interp)
prob.setup()
viz = MetaModelVisualization(interp)
training_points_output = viz._unstructured_training_points()
for i in range(0, 2):
self.assertFalse(np.any(np.isnan(training_points_output[:, i])))
def test_training_point_array_width(self):
# Model
interp = om.MetaModelUnStructuredComp()
# Training Data
x_train = np.linspace(0, 10, 20)
y_train = np.linspace(0, 20, 20)
# Inputs
interp.add_input('x', 0., training_data=x_train)
interp.add_input('y', 0., training_data=x_train)
#Outputs
interp.add_output('cos_x', 0., training_data=.5 * np.cos(y_train))
# Surrogate Model
interp.options['default_surrogate'] = om.ResponseSurface()
prob = om.Problem()
prob.model.add_subsystem('interp', interp)
prob.setup()
viz = MetaModelVisualization(interp)
training_points_output = viz._unstructured_training_points()
self.assertTrue(training_points_output.shape[1] == 2)
def test_alpha_transparency(self):
adjusted_points = MetaModelVisualization(self.mm)
adjusted_points.input_point_list = [
0.04203304, 2.043553874897959, 0.02435233
]
adjusted_points.dist_range = 0.75
known_points_right = np.array([
0.86567989, 0.78348589, 0.639777, 0.62940986, 0.62587447,
0.56152752, 0.50987878, 0.49376071, 0.48388914, 0.46630121,
0.4640111, 0.45980945, 0.41160175, 0.39350227, 0.39026132,
0.38265725, 0.38192591, 0.36878805, 0.34231505, 0.32336551,
0.30383287, 0.29137243, 0.28730608, 0.2857517, 0.26159429,
0.26053377, 0.22521007, 0.18561353, 0.17247835, 0.13633153,
0.10337783, 0.10021358, 0.08660427, 0.08069668, 0.06691626,
0.05474111, 0.04688808, 0.04670316, 0.01284643, 0.00901992,
0.00842553, 0.00386693
])
known_points_bottom = np.array([
7.65526230e-01, 7.38928632e-01, 7.26005432e-01, 7.15400571e-01,
6.49085815e-01, 6.40394477e-01, 6.28033520e-01, 6.13040213e-01,
5.83063203e-01, 4.39487058e-01, 3.68150531e-01, 3.43219760e-01,
3.23457593e-01, 2.99935268e-01, 2.52810393e-01, 2.44806774e-01,
2.43471983e-01, 2.36658494e-01, 2.33785648e-01, 2.22517218e-01,
2.08587699e-01, 1.99369532e-01, 1.95640614e-01, 1.80272528e-01,
1.74355451e-01, 1.52993016e-01, 1.28729050e-01, 1.28385003e-01,
1.28254220e-01, 1.05787985e-01, 1.01550282e-01, 8.44650788e-02,
6.39578812e-02, 3.34477398e-02, 1.97405267e-02, 6.40957590e-04
])
right_points = adjusted_points._unstructured_training_points(
compute_distance=True, source='right')
right_plot = adjusted_points._right_plot()
right_transparency = adjusted_points.right_alphas
bottom_points = adjusted_points._unstructured_training_points(
compute_distance=True, source='bottom')
bottom_plot = adjusted_points._bottom_plot()
bottom_transparency = adjusted_points.bottom_alphas
assert_rel_error(self, right_transparency, known_points_right, 1.1e-02)
assert_rel_error(self, bottom_transparency, known_points_bottom,
1.6e-02)
def test_in_between_training_points_bottom(self):
filename = os.path.join(self.csv_dir, 'unstructured_test_points_bottom.csv')
known_points = np.genfromtxt(
filename, delimiter=',', skip_header=1)
known_points = np.delete(known_points, 2, 1)
known_points = known_points[known_points[:,0].argsort()[::-1]]
adjusted_points = MetaModelVisualization(self.mm)
adjusted_points.input_point_list = [0.04203304, 2.043553874897959, 0.02435233]
adjusted_points.dist_range = 0.75
new_points = adjusted_points._unstructured_training_points(compute_distance=True, source='bottom')
new_points = np.delete(new_points, 2, 1)
new_points = new_points[new_points[:,0].argsort()[::-1]]
assert_almost_equal(known_points, new_points, decimal=4)
def test_in_between_training_points_right(self):
filename = os.path.join(self.csv_dir, 'unstructured_test_points_right.csv')
known_points = np.genfromtxt(
filename, delimiter=',', skip_header=1)
known_points = np.delete(known_points, 2, 1)
known_points = known_points[known_points[:,1].argsort()[::-1]]
adjusted_points = MetaModelVisualization(self.mm)
adjusted_points.input_point_list = [1.619333019591837, 0.01423411, 0.02435233]
adjusted_points.dist_range = 0.75
new_points = adjusted_points._unstructured_training_points(compute_distance=True, source='right')
new_points = np.delete(new_points, 2, 1)
new_points = new_points[new_points[:,1].argsort()[::-1]]
assert_almost_equal(known_points, new_points, decimal=4)