def test_feature_vector(self):
import openmdao.api as om
from openmdao.core.tests.test_scaling import ScalingExampleVector
prob = om.Problem()
model = prob.model
model.add_subsystem('p', om.IndepVarComp('x', np.ones((2))))
comp = model.add_subsystem('comp', ScalingExampleVector())
model.connect('p.x', 'comp.x')
prob.setup()
prob.run_model()
model.run_apply_nonlinear()
with model._scaled_context_all():
val = model.comp._residuals['y']
assert_near_equal(val[0], (1 - 200.) / 200.)
assert_near_equal(val[1], (1 - 6000.) / 6000.)
val = model.comp._outputs['y']
assert_near_equal(val[0], 2.0)
assert_near_equal(val[1], 6.0)
def test_feature_vector(self):
from openmdao.api import Problem, Group, IndepVarComp
from openmdao.core.tests.test_scaling import ScalingExampleVector
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p', IndepVarComp('x', np.ones((2))))
comp = model.add_subsystem('comp', ScalingExampleVector())
model.connect('p.x', 'comp.x')
prob.setup(check=False)
prob.run_model()
model.run_apply_nonlinear()
with model._scaled_context_all():
val = model.comp._residuals['y']
assert_rel_error(self, val[0], (1 - 200.) / 200.)
assert_rel_error(self, val[1], (1 - 6000.) / 6000.)
val = model.comp._outputs['y']
assert_rel_error(self, val[0], 2.0)
assert_rel_error(self, val[1], 6.0)