def test_compute_totals_return_array_non_square(self):
prob = Problem()
prob.model = model = Group()
model.add_subsystem('px', IndepVarComp(name="x", val=np.ones((2, ))))
comp = model.add_subsystem('comp', NonSquareArrayComp())
model.connect('px.x', 'comp.x1')
model.add_design_var('px.x')
model.add_objective('px.x')
model.add_constraint('comp.y1')
model.add_constraint('comp.y2')
prob.setup(check=False)
prob.run_driver()
derivs = prob.driver._compute_totals(of=['comp.y1'],
wrt=['px.x'],
return_format='array')
J = comp.JJ[0:3, 0:2]
assert_rel_error(self, J, derivs, 1.0e-3)
# Support for a name to be in 'of' and 'wrt'
derivs = prob.driver._compute_totals(of=['comp.y2', 'px.x', 'comp.y1'],
wrt=['px.x'],
return_format='array')
assert_rel_error(self, J, derivs[3:, :], 1.0e-3)
assert_rel_error(self, comp.JJ[3:4, 0:2], derivs[0:1, :], 1.0e-3)
assert_rel_error(self, np.eye(2), derivs[1:3, :], 1.0e-3)
def test_vector_scaled_derivs_diff_sizes(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('px', om.IndepVarComp(name="x", val=np.ones(
(2, ))))
comp = model.add_subsystem('comp', NonSquareArrayComp())
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, 2.0]]),
ref0=np.array([5.2, 6.3, 1.2]))
model.add_constraint('comp.y2',
lower=0.0,
upper=1.0,
ref=np.array([[2.0]]),
ref0=np.array([1.2]))
prob.setup()
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), 1.0 / (2.0 - 1.2)])
iscale = np.array([2.0 - 0.5, 3.0 - 1.5])
J = comp.JJ[0:3, 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]
J[2, 0] *= oscale[2] * iscale[0]
J[2, 1] *= oscale[2] * 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),
(prob['comp.y1'][2] - 1.2) / (2.0 - 1.2)])
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)])
assert_near_equal(con['comp.y2'], con_base, 1.0e-3)