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_five_alpha_points(self):
filename = os.path.join(self.csv_dir, 'test_five_alpha_points.csv')
known_points_bottom = np.genfromtxt(filename,
delimiter=',',
usecols=(1))
known_points_right = np.genfromtxt(filename,
delimiter=',',
usecols=(0))
adjusted_points = MetaModelVisualization(self.mm)
adjusted_points.input_point_list = [
6.632653061224477, 3.36734693877551
]
adjusted_points.dist_range = 0.5
right_points = adjusted_points._structured_training_points(
compute_distance=True, source='right')
right_plot = adjusted_points._right_plot()
right_transparency = adjusted_points.right_alphas
bottom_points = adjusted_points._structured_training_points(
compute_distance=True, source='bottom')
bottom_plot = adjusted_points._bottom_plot()
bottom_transparency = adjusted_points.bottom_alphas
assert_almost_equal(known_points_right, right_transparency, decimal=5)
assert_almost_equal(known_points_bottom,
bottom_transparency,
decimal=5)
def test_single_input_parameter(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('simple_x', 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()
with self.assertRaises(Exception) as context:
viz = MetaModelVisualization(interp)
msg = 'Must have more than one input value'
self.assertTrue(msg in str(context.exception))
def test_working_scipy_cubic(self):
# create input param training data, of sizes 25, 5, and 10 points resp.
p1 = np.linspace(0, 100, 25)
p2 = np.linspace(-10, 10, 5)
p3 = np.linspace(0, 1, 10)
# can use meshgrid to create a 3D array of test data
P1, P2, P3 = np.meshgrid(p1, p2, p3, indexing='ij')
f = np.sqrt(P1) + P2 * P3
# Create regular grid interpolator instance
interp = om.MetaModelStructuredComp(method='scipy_cubic')
interp.add_input('p1', 0.5, training_data=p1)
interp.add_input('p2', 0.0, training_data=p2)
interp.add_input('p3', 3.14, training_data=p3)
interp.add_output('f', 0.0, training_data=f)
# Set up the OpenMDAO model
model = om.Group()
model.add_subsystem('comp', interp, promotes=["*"])
prob = om.Problem(model)
prob.setup()
MetaModelVisualization(interp)
def test_working_slinear(self):
num_train = 10
x0_min, x0_max = -5.0, 10.0
x1_min, x1_max = 0.0, 15.0
train_x0 = np.linspace(x0_min, x0_max, num_train)
train_x1 = np.linspace(x1_min, x1_max, num_train)
t_data = np.array([[308.12909601, 253.61567418, 204.6578079, 161.25549718, 123.40874201, 91.1175424, 64.38189835, 43.20180985, 27.5772769, 17.50829952],
[162.89542418, 123.20470795, 89.06954726, 60.48994214, 37.46589257, 19.99739855, 8.08446009, 1.72707719, 0.92524984, 5.67897804,],
[ 90.2866907, 63.02637433, 41.32161352, 25.17240826, 14.57875856, 9.54066442, 10.05812583, 16.13114279, 27.75971531, 44.94384339,],
[ 55.60211264, 38.37989042, 26.71322375, 20.60211264, 20.04655709, 25.04655709, 35.60211264, 51.71322375, 73.37989042, 100.60211264],
[ 22.81724065, 13.24080685, 9.2199286, 10.75460591, 17.84483877, 30.49062719, 48.69197117, 72.4488707, 101.76132579, 136.62933643],
[ 5.11168719, 0.78873608, 2.02134053, 8.80950054, 21.1532161, 39.05248721, 62.50731389, 91.51769611, 126.0836339, 166.20512723],
[ 14.3413983, 12.87962416, 16.97340558, 26.62274256, 41.82763509, 62.58808317, 88.90408682, 120.77564601, 158.20276077, 201.18543108],
[ 20.18431209, 19.1914092, 23.75406186, 33.87227009, 49.54603386, 70.77535319, 97.56022808, 129.90065853, 167.79664453, 211.24818608],
[ 8.48953212, 5.57319475, 8.21241294, 16.40718668, 30.15751598, 49.46340083, 74.32484124, 104.74183721, 140.71438873, 182.2424958 ],
[ 10.96088904, 3.72881146, 2.05228945, 5.93132298, 15.36591208, 30.35605673, 50.90175693, 77.00301269, 108.65982401, 145.87219088]])
prob = om.Problem()
ivc = om.IndepVarComp()
ivc.add_output('x0', 0.0)
ivc.add_output('x1', 0.0)
prob.model.add_subsystem('p', ivc, promotes=['*'])
mm = prob.model.add_subsystem('mm', om.MetaModelStructuredComp(method='slinear'),
promotes=['x0', 'x1'])
mm.add_input('x0', 0.0, train_x0)
mm.add_input('x1', 0.0, train_x1)
mm.add_output('f', 0.0, t_data)
prob.setup()
prob.final_setup()
MetaModelVisualization(mm)
def test_working_slinear(self):
num_train = 10
x0_min, x0_max = -5.0, 10.0
x1_min, x1_max = 0.0, 15.0
train_x0 = np.linspace(x0_min, x0_max, num_train)
train_x1 = np.linspace(x1_min, x1_max, num_train)
t_data = self.grid_data
prob = om.Problem()
ivc = om.IndepVarComp()
ivc.add_output('x0', 0.0)
ivc.add_output('x1', 0.0)
prob.model.add_subsystem('p', ivc, promotes=['*'])
mm = prob.model.add_subsystem(
'mm',
om.MetaModelStructuredComp(method='slinear'),
promotes=['x0', 'x1'])
mm.add_input('x0', 0.0, train_x0)
mm.add_input('x1', 0.0, train_x1)
mm.add_output('f', 0.0, t_data)
prob.setup()
prob.final_setup()
MetaModelVisualization(mm)
def test_working_scipy_slinear(self):
# Create regular grid interpolator instance
xor_interp = om.MetaModelStructuredComp(method='scipy_slinear')
# set up inputs and outputs
xor_interp.add_input('x',
0.0,
training_data=np.array([0.0, 1.0]),
units=None)
xor_interp.add_input('y',
1.0,
training_data=np.array([0.0, 1.0]),
units=None)
xor_interp.add_output('xor',
1.0,
training_data=np.array([[0.0, 1.0], [1.0, 0.0]]),
units=None)
# Set up the OpenMDAO model
model = om.Group()
ivc = om.IndepVarComp()
ivc.add_output('x', 0.0)
ivc.add_output('y', 1.0)
model.add_subsystem('ivc', ivc, promotes=["*"])
model.add_subsystem('comp', xor_interp, promotes=["*"])
prob = om.Problem(model)
prob.setup()
MetaModelVisualization(xor_interp)
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)
def test_basic(self):
# Tests that semi structured grids load without error.
grid = np.array([
[1.0, 5.0, 8.0],
[1.0, 5.0, 9.0],
[1.0, 5.0, 10.0],
[1.0, 5.0, 20.0],
[1.0, 5.3, 8.0],
[1.0, 5.3, 9.0],
[1.0, 5.3, 10.0],
[1.0, 5.3, 20.0],
[1.0, 5.6, 8.0],
[1.0, 5.6, 9.0],
[1.0, 5.6, 10.0],
[1.0, 5.6, 20.0],
[1.0, 6.0, 8.0],
[1.0, 6.0, 9.0],
[1.0, 6.0, 10.0],
[1.0, 6.0, 20.0],
[2.0, 7.0, 13.0],
[2.0, 7.0, 14.0],
[2.0, 7.0, 15.0],
[2.0, 7.0, 16.0],
[2.0, 8.0, 13.0],
[2.0, 8.0, 14.0],
[2.0, 8.0, 15.0],
[2.0, 8.0, 16.0],
[2.0, 8.5, 13.0],
[2.0, 8.5, 14.0],
[2.0, 8.5, 15.0],
[2.0, 8.5, 16.0],
[2.0, 9.0, 13.0],
[2.0, 9.0, 14.0],
[2.0, 9.0, 15.0],
[2.0, 9.0, 16.0],
])
values = 15.0 + 2 * np.random.random(32)
prob = om.Problem()
model = prob.model
interp = om.MetaModelSemiStructuredComp(vec_size=3,
training_data_gradients=True)
interp.add_input('x', training_data=grid[:, 0])
interp.add_input('y', training_data=grid[:, 1])
interp.add_input('z', training_data=grid[:, 2])
interp.add_output('f', training_data=values)
model.add_subsystem('interp', interp)
prob.setup(force_alloc_complex=True)
prob.run_model()
viz = MetaModelVisualization(interp)
def test_flip_inputs_aligned_points(self):
known_points_right = np.array(
[[6.66666667e+00, 0.00000000e+00, 2.26757370e-03, 2.01843121e+01],
[6.66666667e+00, 1.66666667e+00, 2.26757370e-03, 1.91914092e+01],
[6.66666667e+00, 3.33333333e+00, 2.26757370e-03, 2.37540619e+01],
[6.66666667e+00, 5.00000000e+00, 2.26757370e-03, 3.38722701e+01],
[6.66666667e+00, 6.66666667e+00, 2.26757370e-03, 4.95460339e+01],
[6.66666667e+00, 8.33333333e+00, 2.26757370e-03, 7.07753532e+01],
[6.66666667e+00, 1.00000000e+01, 2.26757370e-03, 9.75602281e+01],
[6.66666667e+00, 1.16666667e+01, 2.26757370e-03, 1.29900659e+02],
[6.66666667e+00, 1.33333333e+01, 2.26757370e-03, 1.67796645e+02],
[6.66666667e+00, 1.50000000e+01, 2.26757370e-03, 2.11248186e+02]])
known_points_bottom = np.array(
[[-5.00000000e+00, 3.33333333e+00, 2.26757370e-03, 2.04657808e+02],
[-3.33333333e+00, 3.33333333e+00, 2.26757370e-03, 8.90695473e+01],
[-1.66666667e+00, 3.33333333e+00, 2.26757370e-03, 4.13216135e+01],
[0.00000000e+00, 3.33333333e+00, 2.26757370e-03, 2.67132238e+01],
[1.66666667e+00, 3.33333333e+00, 2.26757370e-03, 9.21992860e+00],
[3.33333333e+00, 3.33333333e+00, 2.26757370e-03, 2.02134053e+00],
[5.00000000e+00, 3.33333333e+00, 2.26757370e-03, 1.69734056e+01],
[6.66666667e+00, 3.33333333e+00, 2.26757370e-03, 2.37540619e+01],
[8.33333333e+00, 3.33333333e+00, 2.26757370e-03, 8.21241294e+00],
[1.00000000e+01, 3.33333333e+00, 2.26757370e-03, 2.05228945e+00]])
adjusted_points = MetaModelVisualization(self.mm)
adjusted_points.input_point_list = [
6.632653061224477, 3.36734693877551
]
right_points = adjusted_points._structured_training_points(
compute_distance=True, source='right')
bottom_points = adjusted_points._structured_training_points(
compute_distance=True, source='bottom')
assert_almost_equal(known_points_right, right_points, decimal=5)
assert_almost_equal(known_points_bottom, bottom_points, decimal=5)
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_single_line_of_alpha_points(self):
adjusted_points = MetaModelVisualization(self.mm)
adjusted_points.input_point_list = [
6.632653061224477, 3.36734693877551
]
right_points = adjusted_points._structured_training_points(
compute_distance=True, source='right')
right_plot = adjusted_points._right_plot()
bottom_points = adjusted_points._structured_training_points(
compute_distance=True, source='bottom')
bottom_plot = adjusted_points._bottom_plot()
self.assertTrue(len(adjusted_points.right_alphas) == 10)
self.assertTrue(len(adjusted_points.bottom_alphas) == 10)
def test_updated_scatter_distance(self):
filename = os.path.join(self.csv_dir, 'updated_scatter_distance.csv')
known_points_bottom = np.genfromtxt(filename,
delimiter=',',
usecols=(5, 6, 7, 8))
known_points_right = np.genfromtxt(filename,
delimiter=',',
usecols=(0, 1, 2, 3))
adjusted_points = MetaModelVisualization(self.mm)
adjusted_points.input_point_list = [
6.632653061224477, 3.36734693877551
]
adjusted_points.dist_range = 0.5
right_points = adjusted_points._structured_training_points(
compute_distance=True, source='right')
bottom_points = adjusted_points._structured_training_points(
compute_distance=True, source='bottom')
assert_almost_equal(known_points_right, right_points, decimal=5)
assert_almost_equal(known_points_bottom, bottom_points, decimal=5)