def test_deprecated_ks_component(self):
# run same test as above, only with the deprecated component,
# to ensure we get the warning and the correct answer.
# self-contained, to be removed when class name goes away.
from openmdao.components.ks_comp import KSComponent # deprecated
prob = Problem()
prob.model = model = Group()
model.add_subsystem('px', IndepVarComp(name="x", val=np.ones((2, ))))
model.add_subsystem('comp', DoubleArrayComp())
msg = "'KSComponent' has been deprecated. Use 'KSComp' instead."
with assert_warning(DeprecationWarning, msg):
model.add_subsystem('ks', KSComponent(width=2))
model.connect('px.x', 'comp.x1')
model.connect('comp.y2', 'ks.g')
model.add_design_var('px.x')
model.add_objective('comp.y1')
model.add_constraint('ks.KS', upper=0.0)
prob.setup(check=False)
prob.run_driver()
assert_rel_error(self, max(prob['comp.y2']), prob['ks.KS'][0])
def test_double_arraycomp(self):
# Mainly testing an old bug in the array return for multiple arrays
group = Group()
group.add_subsystem('x_param1',
IndepVarComp('x1', np.ones((2))),
promotes=['x1'])
group.add_subsystem('x_param2',
IndepVarComp('x2', np.ones((2))),
promotes=['x2'])
mycomp = group.add_subsystem('mycomp',
DoubleArrayComp(),
promotes=['x1', 'x2', 'y1', 'y2'])
prob = Problem()
model = prob.model = group
model.linear_solver = self.linear_solver_class()
prob.set_solver_print(level=0)
prob.setup(check=False, mode='fwd')
prob.run_model()
Jbase = mycomp.JJ
of = ['y1', 'y2']
wrt = ['x1', 'x2']
J = prob.compute_totals(of=of, wrt=wrt, return_format='flat_dict')
diff = np.linalg.norm(J['y1', 'x1'] - Jbase[0:2, 0:2])
assert_near_equal(diff, 0.0, 1e-8)
diff = np.linalg.norm(J['y1', 'x2'] - Jbase[0:2, 2:4])
assert_near_equal(diff, 0.0, 1e-8)
diff = np.linalg.norm(J['y2', 'x1'] - Jbase[2:4, 0:2])
assert_near_equal(diff, 0.0, 1e-8)
diff = np.linalg.norm(J['y2', 'x2'] - Jbase[2:4, 2:4])
assert_near_equal(diff, 0.0, 1e-8)
def test_vector_bounds_inf(self):
# make sure no overflow when there is no specified upper/lower bound and significatn scaling
prob = om.Problem()
model = prob.model
model.add_subsystem('px', om.IndepVarComp(name="x", val=np.ones(
(2, ))))
comp = model.add_subsystem('comp', DoubleArrayComp())
model.connect('px.x', 'comp.x1')
model.add_design_var('px.x', ref=np.array([.1, 1e-6]))
model.add_constraint('comp.y2', ref=np.array([.2, 2e-6]))
prob.setup()
desvars = model.get_design_vars()
self.assertFalse(np.any(np.isinf(desvars['px.x']['upper'])))
self.assertFalse(np.any(np.isinf(-desvars['px.x']['lower'])))
responses = prob.model.get_responses()
self.assertFalse(np.any(np.isinf(responses['comp.y2']['upper'])))
self.assertFalse(np.any(np.isinf(-responses['comp.y2']['lower'])))
def test_vector_scaled_derivs(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('px', om.IndepVarComp(name="x", val=np.ones(
(2, ))))
comp = model.add_subsystem('comp', DoubleArrayComp())
model.connect('px.x', 'comp.x1')
model.add_design_var('px.x',
ref=np.array([2.0, 3.0]),
ref0=np.array([0.5, 1.5]))
model.add_objective('comp.y1',
ref=np.array([[7.0, 11.0]]),
ref0=np.array([5.2, 6.3]))
model.add_constraint('comp.y2',
lower=0.0,
upper=1.0,
ref=np.array([[2.0, 4.0]]),
ref0=np.array([1.2, 2.3]))
prob.setup()
prob.run_driver()
with assert_warning(
OMDeprecationWarning,
"'global_names' is deprecated in calls to _compute_totals. Use 'use_abs_names' instead."
):
derivs = prob.driver._compute_totals(of=['comp.y1'],
wrt=['px.x'],
global_names=True,
return_format='dict')
oscale = np.array([1.0 / (7.0 - 5.2), 1.0 / (11.0 - 6.3)])
iscale = np.array([2.0 - 0.5, 3.0 - 1.5])
J = comp.JJ[0:2, 0:2]
# doing this manually so that I don't inadvertantly make an error in the vector math in both the code and test.
J[0, 0] *= oscale[0] * iscale[0]
J[0, 1] *= oscale[0] * iscale[1]
J[1, 0] *= oscale[1] * iscale[0]
J[1, 1] *= oscale[1] * iscale[1]
assert_near_equal(J, derivs['comp.y1']['px.x'], 1.0e-3)
obj = prob.driver.get_objective_values()
obj_base = np.array([(prob['comp.y1'][0] - 5.2) / (7.0 - 5.2),
(prob['comp.y1'][1] - 6.3) / (11.0 - 6.3)])
assert_near_equal(obj['comp.y1'], obj_base, 1.0e-3)
con = prob.driver.get_constraint_values()
con_base = np.array([(prob['comp.y2'][0] - 1.2) / (2.0 - 1.2),
(prob['comp.y2'][1] - 2.3) / (4.0 - 2.3)])
assert_near_equal(con['comp.y2'], con_base, 1.0e-3)
def test_vector_scaled_derivs(self):
prob = Problem()
prob.model = model = Group()
model.add_subsystem('px', IndepVarComp(name="x", val=np.ones((2, ))))
comp = model.add_subsystem('comp', DoubleArrayComp())
model.connect('px.x', 'comp.x1')
model.add_design_var('px.x',
ref=np.array([2.0, 3.0]),
ref0=np.array([0.5, 1.5]))
model.add_objective('comp.y1',
ref=np.array([[7.0, 11.0]]),
ref0=np.array([5.2, 6.3]))
model.add_constraint('comp.y2',
lower=0.0,
upper=1.0,
ref=np.array([[2.0, 4.0]]),
ref0=np.array([1.2, 2.3]))
prob.setup(check=False)
prob.run_driver()
derivs = prob.driver._compute_totals(of=['comp.y1'],
wrt=['px.x'],
return_format='dict')
oscale = np.array([1.0 / (7.0 - 5.2), 1.0 / (11.0 - 6.3)])
iscale = np.array([2.0 - 0.5, 3.0 - 1.5])
J = comp.JJ[0:2, 0:2]
# doing this manually so that I don't inadvertantly make an error in the vector math in both the code and test.
J[0, 0] *= oscale[0] * iscale[0]
J[0, 1] *= oscale[0] * iscale[1]
J[1, 0] *= oscale[1] * iscale[0]
J[1, 1] *= oscale[1] * iscale[1]
assert_rel_error(self, J, derivs['comp.y1']['px.x'], 1.0e-3)
obj = prob.driver.get_objective_values()
obj_base = np.array([(prob['comp.y1'][0] - 5.2) / (7.0 - 5.2),
(prob['comp.y1'][1] - 6.3) / (11.0 - 6.3)])
assert_rel_error(self, obj['comp.y1'], obj_base, 1.0e-3)
con = prob.driver.get_constraint_values()
con_base = np.array([(prob['comp.y2'][0] - 1.2) / (2.0 - 1.2),
(prob['comp.y2'][1] - 2.3) / (4.0 - 2.3)])
assert_rel_error(self, con['comp.y2'], con_base, 1.0e-3)
def test_basic_ks(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('px', om.IndepVarComp(name="x", val=np.ones((2, ))))
model.add_subsystem('comp', DoubleArrayComp())
model.add_subsystem('ks', om.KSComp(width=2))
model.connect('px.x', 'comp.x1')
model.connect('comp.y2', 'ks.g')
model.add_design_var('px.x')
model.add_objective('comp.y1')
model.add_constraint('ks.KS', upper=0.0)
prob.setup()
prob.run_driver()
assert_near_equal(max(prob['comp.y2']), prob['ks.KS'][0])
def test_basic_ks(self):
prob = Problem()
prob.model = model = Group()
model.add_subsystem('px', IndepVarComp(name="x", val=np.ones((2, ))))
model.add_subsystem('comp', DoubleArrayComp())
model.add_subsystem('ks', KSComponent(width=2))
model.connect('px.x', 'comp.x1')
model.connect('comp.y2', 'ks.g')
model.add_design_var('px.x')
model.add_objective('comp.y1')
model.add_constraint('ks.KS', upper=0.0)
prob.setup(check=False)
prob.run_driver()
assert_rel_error(self, max(prob['comp.y2']), prob['ks.KS'])