def test_scalar_with_guess_func_additional_input(self):
n = 1
prob = Problem(model=Group())
bal = BalanceComp()
bal.add_balance('x',
guess_func=lambda inputs, resids: inputs['guess_x'])
bal.add_input('guess_x', val=0.0)
ivc = IndepVarComp()
ivc.add_output(name='y_tgt', val=4)
ivc.add_output(name='guess_x', val=2.5)
exec_comp = ExecComp('y=x**2', x={'value': 1}, y={'value': 1})
prob.model.add_subsystem(name='ivc',
subsys=ivc,
promotes_outputs=['y_tgt', 'guess_x'])
prob.model.add_subsystem(name='exec', subsys=exec_comp)
prob.model.add_subsystem(name='balance', subsys=bal)
prob.model.connect('guess_x', 'balance.guess_x')
prob.model.connect('y_tgt', 'balance.rhs:x')
prob.model.connect('balance.x', 'exec.x')
prob.model.connect('exec.y', 'balance.lhs:x')
prob.model.linear_solver = DirectSolver()
prob.model.nonlinear_solver = NewtonSolver()
prob.model.nonlinear_solver.options['maxiter'] = 100
prob.model.nonlinear_solver.options['iprint'] = 0
prob.model.jacobian = DenseJacobian()
prob.setup()
prob['balance.x'] = np.random.rand(n)
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
with printoptions(linewidth=1024):
cpd = prob.check_partials()
for (of, wrt) in cpd['balance']:
assert_almost_equal(cpd['balance'][of, wrt]['abs error'],
0.0,
decimal=5)
def test_scalar_with_guess_func_additional_input(self):
model = Group(assembled_jac_type='dense')
bal = BalanceComp()
bal.add_balance('x')
bal.add_input('guess_x', val=0.0)
ivc = IndepVarComp()
ivc.add_output(name='y_tgt', val=4)
ivc.add_output(name='guess_x', val=2.5)
exec_comp = ExecComp('y=x**2', x={'value': 1}, y={'value': 1})
model.add_subsystem(name='ivc',
subsys=ivc,
promotes_outputs=['y_tgt', 'guess_x'])
model.add_subsystem(name='exec', subsys=exec_comp)
model.add_subsystem(name='balance', subsys=bal)
model.connect('guess_x', 'balance.guess_x')
model.connect('y_tgt', 'balance.rhs:x')
model.connect('balance.x', 'exec.x')
model.connect('exec.y', 'balance.lhs:x')
model.linear_solver = DirectSolver(assemble_jac=True)
model.nonlinear_solver = NewtonSolver(maxiter=100, iprint=0)
prob = Problem(model)
prob.setup()
# run problem without a guess function
prob['balance.x'] = .5
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
iters_no_guess = model.nonlinear_solver._iter_count
# run problem with same initial value and a guess function
def guess_function(inputs, outputs, resids):
outputs['x'] = inputs['guess_x']
bal.options['guess_func'] = guess_function
prob['balance.x'] = .5
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
iters_with_guess = model.nonlinear_solver._iter_count
# verify it converges faster with the guess function
self.assertTrue(iters_with_guess < iters_no_guess)
def test_scalar_with_guess_func_additional_input(self):
model = Group(assembled_jac_type='dense')
bal = BalanceComp()
bal.add_balance('x')
bal.add_input('guess_x', val=0.0)
ivc = IndepVarComp()
ivc.add_output(name='y_tgt', val=4)
ivc.add_output(name='guess_x', val=2.5)
exec_comp = ExecComp('y=x**2', x={'value': 1}, y={'value': 1})
model.add_subsystem(name='ivc', subsys=ivc, promotes_outputs=['y_tgt', 'guess_x'])
model.add_subsystem(name='exec', subsys=exec_comp)
model.add_subsystem(name='balance', subsys=bal)
model.connect('guess_x', 'balance.guess_x')
model.connect('y_tgt', 'balance.rhs:x')
model.connect('balance.x', 'exec.x')
model.connect('exec.y', 'balance.lhs:x')
model.linear_solver = DirectSolver(assemble_jac=True)
model.nonlinear_solver = NewtonSolver(maxiter=100, iprint=0)
prob = Problem(model)
prob.setup()
# run problem without a guess function
prob['balance.x'] = .5
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
iters_no_guess = model.nonlinear_solver._iter_count
# run problem with same initial value and a guess function
def guess_function(inputs, outputs, resids):
outputs['x'] = inputs['guess_x']
bal.options['guess_func'] = guess_function
prob['balance.x'] = .5
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
iters_with_guess = model.nonlinear_solver._iter_count
# verify it converges faster with the guess function
self.assertTrue(iters_with_guess < iters_no_guess)
def test_scalar_with_guess_func_additional_input(self):
n = 1
prob = Problem(model=Group())
bal = BalanceComp()
bal.add_balance('x', guess_func=lambda inputs, resids: inputs['guess_x'])
bal.add_input('guess_x', val=0.0)
ivc = IndepVarComp()
ivc.add_output(name='y_tgt', val=4)
ivc.add_output(name='guess_x', val=2.5)
exec_comp = ExecComp('y=x**2', x={'value': 1}, y={'value': 1})
prob.model.add_subsystem(name='ivc', subsys=ivc, promotes_outputs=['y_tgt', 'guess_x'])
prob.model.add_subsystem(name='exec', subsys=exec_comp)
prob.model.add_subsystem(name='balance', subsys=bal)
prob.model.connect('guess_x', 'balance.guess_x')
prob.model.connect('y_tgt', 'balance.rhs:x')
prob.model.connect('balance.x', 'exec.x')
prob.model.connect('exec.y', 'balance.lhs:x')
prob.model.linear_solver = DirectSolver()
prob.model.nonlinear_solver = NewtonSolver()
prob.model.nonlinear_solver.options['maxiter'] = 100
prob.model.nonlinear_solver.options['iprint'] = 0
prob.model.jacobian = DenseJacobian()
prob.setup()
prob['balance.x'] = np.random.rand(n)
prob.run_model()
assert_almost_equal(prob['balance.x'], 2.0, decimal=7)
np.set_printoptions(linewidth=1024)
cpd = prob.check_partials()
for (of, wrt) in cpd['balance']:
assert_almost_equal(cpd['balance'][of, wrt]['abs error'], 0.0, decimal=5)
