def test_hierarchy_iprint3(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('pz', om.IndepVarComp('z', np.array([5.0, 2.0])))
sub1 = model.add_subsystem('sub1', om.Group())
sub2 = sub1.add_subsystem('sub2', om.Group())
g1 = sub2.add_subsystem('g1', SubSellar())
g2 = model.add_subsystem('g2', SubSellar())
model.connect('pz.z', 'sub1.sub2.g1.z')
model.connect('sub1.sub2.g1.y2', 'g2.x')
model.connect('g2.y2', 'sub1.sub2.g1.x')
model.nonlinear_solver = om.NonlinearBlockJac()
sub1.nonlinear_solver = om.NonlinearBlockJac()
sub2.nonlinear_solver = om.NonlinearBlockJac()
g1.nonlinear_solver = om.NonlinearBlockJac()
g2.nonlinear_solver = om.NonlinearBlockJac()
prob.set_solver_print(level=2)
prob.setup()
output = run_model(prob)
# Check that certain things show up in our outputs
self.assertGreaterEqual(output.count('sub1'), 2)
self.assertGreaterEqual(output.count('sub1.sub2'), 2)
self.assertGreaterEqual(output.count('sub1.sub2.g1'), 2)
self.assertGreaterEqual(output.count('g2'), 2)
self.assertGreaterEqual(output.count('NL: NLBJ'), 2)
def test_feature_set_solver_print1(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.double_sellar import SubSellar
prob = om.Problem()
model = prob.model
sub1 = model.add_subsystem('sub1', om.Group(), promotes_inputs=['z'])
sub2 = sub1.add_subsystem('sub2', om.Group(), promotes_inputs=['z'])
g1 = sub2.add_subsystem('g1', SubSellar(), promotes_inputs=['z'])
g2 = model.add_subsystem('g2', SubSellar())
model.connect('sub1.sub2.g1.y2', 'g2.x')
model.connect('g2.y2', 'sub1.sub2.g1.x')
model.nonlinear_solver = om.NewtonSolver()
model.linear_solver = om.ScipyKrylov()
model.nonlinear_solver.options['solve_subsystems'] = True
model.nonlinear_solver.options['max_sub_solves'] = 0
g1.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g1.linear_solver = om.LinearBlockGS()
g2.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g2.linear_solver = om.ScipyKrylov()
g2.linear_solver.precon = om.LinearBlockGS()
g2.linear_solver.precon.options['maxiter'] = 2
prob.set_solver_print(level=2)
prob.setup()
prob.set_val('z', np.array([5.0, 2.0]))
prob.run_model()
def test_feature_max_sub_solves(self):
prob = Problem()
model = prob.model = Group()
model.add_subsystem('g1', SubSellar())
model.add_subsystem('g2', SubSellar())
model.connect('g1.y2', 'g2.x')
model.connect('g2.y2', 'g1.x')
# Converge the outer loop with Gauss Seidel, with a looser tolerance.
model.nonlinear_solver = NewtonSolver()
model.linear_solver = DirectSolver()
g1 = model.get_subsystem('g1')
g1.nonlinear_solver = NewtonSolver()
g1.nonlinear_solver.options['rtol'] = 1.0e-5
g1.linear_solver = DirectSolver()
g2 = model.get_subsystem('g2')
g2.nonlinear_solver = NewtonSolver()
g2.nonlinear_solver.options['rtol'] = 1.0e-5
g2.linear_solver = DirectSolver()
model.nonlinear_solver = NewtonSolver()
model.linear_solver = ScipyIterativeSolver()
model.nonlinear_solver.options['solve_subsystems'] = True
model.nonlinear_solver.options['max_sub_solves'] = 0
prob.setup()
prob.run_model()
def test_hierarchy_iprint3(self):
prob = Problem()
model = prob.model
model.add_subsystem('pz', IndepVarComp('z', np.array([5.0, 2.0])))
sub1 = model.add_subsystem('sub1', Group())
sub2 = sub1.add_subsystem('sub2', Group())
g1 = sub2.add_subsystem('g1', SubSellar())
g2 = model.add_subsystem('g2', SubSellar())
model.connect('pz.z', 'sub1.sub2.g1.z')
model.connect('sub1.sub2.g1.y2', 'g2.x')
model.connect('g2.y2', 'sub1.sub2.g1.x')
model.nonlinear_solver = NonlinearBlockJac()
sub1.nonlinear_solver = NonlinearBlockJac()
sub2.nonlinear_solver = NonlinearBlockJac()
g1.nonlinear_solver = NonlinearBlockJac()
g2.nonlinear_solver = NonlinearBlockJac()
prob.set_solver_print(level=2)
prob.setup(check=False)
output = run_model(prob)
def test_hierarchy_iprint(self):
prob = Problem()
model = prob.model
model.add_subsystem('pz', IndepVarComp('z', np.array([5.0, 2.0])))
sub1 = model.add_subsystem('sub1', Group())
sub2 = sub1.add_subsystem('sub2', Group())
g1 = sub2.add_subsystem('g1', SubSellar())
g2 = model.add_subsystem('g2', SubSellar())
model.connect('pz.z', 'sub1.sub2.g1.z')
model.connect('sub1.sub2.g1.y2', 'g2.x')
model.connect('g2.y2', 'sub1.sub2.g1.x')
model.nonlinear_solver = NewtonSolver()
model.linear_solver = ScipyIterativeSolver()
model.nonlinear_solver.options['solve_subsystems'] = True
model.nonlinear_solver.options['max_sub_solves'] = 0
g1.nonlinear_solver = NewtonSolver()
g1.linear_solver = LinearBlockGS()
g2.nonlinear_solver = NewtonSolver()
g2.linear_solver = ScipyIterativeSolver()
g2.linear_solver.precon = LinearBlockGS()
g2.linear_solver.precon.options['maxiter'] = 2
prob.set_solver_print(level=2)
prob.setup(check=False)
output = run_model(prob)
def test_feature_set_solver_print4(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('pz', om.IndepVarComp('z', np.array([5.0, 2.0])))
sub1 = model.add_subsystem('sub1', om.Group())
sub2 = sub1.add_subsystem('sub2', om.Group())
g1 = sub2.add_subsystem('g1', SubSellar())
g2 = model.add_subsystem('g2', SubSellar())
model.connect('sub1.sub2.g1.y2', 'g2.x')
model.connect('g2.y2', 'sub1.sub2.g1.x')
model.nonlinear_solver = om.NewtonSolver()
model.linear_solver = om.ScipyKrylov()
model.nonlinear_solver.options['solve_subsystems'] = True
model.nonlinear_solver.options['max_sub_solves'] = 0
g1.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g1.linear_solver = om.LinearBlockGS()
g2.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g2.linear_solver = om.ScipyKrylov()
g2.linear_solver.precon = om.LinearBlockGS()
g2.linear_solver.precon.options['maxiter'] = 2
prob.set_solver_print(level=-1, type_='all')
g2.set_solver_print(level=2, type_='NL')
prob.setup()
prob.run_model()
def test_hierarchy_iprint(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('pz', om.IndepVarComp('z', np.array([5.0, 2.0])))
sub1 = model.add_subsystem('sub1', om.Group())
sub2 = sub1.add_subsystem('sub2', om.Group())
g1 = sub2.add_subsystem('g1', SubSellar())
g2 = model.add_subsystem('g2', SubSellar())
model.connect('pz.z', 'sub1.sub2.g1.z')
model.connect('sub1.sub2.g1.y2', 'g2.x')
model.connect('g2.y2', 'sub1.sub2.g1.x')
model.nonlinear_solver = om.NewtonSolver()
model.linear_solver = om.LinearBlockGS()
model.nonlinear_solver.options['solve_subsystems'] = True
model.nonlinear_solver.options['max_sub_solves'] = 0
g1.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g1.linear_solver = om.LinearBlockGS()
g2.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g2.linear_solver = om.PETScKrylov()
g2.linear_solver.precon = om.LinearBlockGS()
g2.linear_solver.precon.options['maxiter'] = 2
prob.set_solver_print(level=2)
prob.setup()
# Conclude setup but don't run model.
prob.final_setup()
# if USE_PROC_FILES is not set, solver convergence messages
# should only appear on proc 0
output = run_model(prob)
if model.comm.rank == 0 or os.environ.get('USE_PROC_FILES'):
self.assertTrue(output.count('\nNL: Newton Converged') == 1)
else:
self.assertTrue(output.count('\nNL: Newton Converged') == 0)
def test_hierarchy_iprint(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('pz', om.IndepVarComp('z', np.array([5.0, 2.0])))
sub1 = model.add_subsystem('sub1', om.Group())
sub2 = sub1.add_subsystem('sub2', om.Group())
g1 = sub2.add_subsystem('g1', SubSellar())
g2 = model.add_subsystem('g2', SubSellar())
model.connect('pz.z', 'sub1.sub2.g1.z')
model.connect('sub1.sub2.g1.y2', 'g2.x')
model.connect('g2.y2', 'sub1.sub2.g1.x')
model.nonlinear_solver = om.NewtonSolver()
model.linear_solver = om.ScipyKrylov()
model.nonlinear_solver.options['solve_subsystems'] = True
model.nonlinear_solver.options['max_sub_solves'] = 0
g1.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g1.linear_solver = om.LinearBlockGS()
g2.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g2.linear_solver = om.ScipyKrylov()
g2.linear_solver.precon = om.LinearBlockGS()
g2.linear_solver.precon.options['maxiter'] = 2
prob.set_solver_print(level=2)
prob.setup()
output = run_model(prob)
# Check that certain things show up in our outputs
self.assertGreaterEqual(output.count('sub1.sub2.g1'), 1)
self.assertGreaterEqual(output.count('g2'), 1)
self.assertGreaterEqual(output.count('NL: Newton'), 2)
self.assertGreaterEqual(output.count('LN: SCIPY'), 2)
self.assertGreaterEqual(output.count('precon: LNBGS'), 2)
def test_feature_set_solver_print3(self):
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, NewtonSolver, ScipyIterativeSolver, LinearBlockGS
from openmdao.test_suite.components.double_sellar import SubSellar
prob = Problem()
model = prob.model
model.add_subsystem('pz', IndepVarComp('z', np.array([5.0, 2.0])))
sub1 = model.add_subsystem('sub1', Group())
sub2 = sub1.add_subsystem('sub2', Group())
g1 = sub2.add_subsystem('g1', SubSellar())
g2 = model.add_subsystem('g2', SubSellar())
model.connect('pz.z', 'sub1.sub2.g1.z')
model.connect('sub1.sub2.g1.y2', 'g2.x')
model.connect('g2.y2', 'sub1.sub2.g1.x')
model.nonlinear_solver = NewtonSolver()
model.linear_solver = ScipyIterativeSolver()
model.nonlinear_solver.options['solve_subsystems'] = True
model.nonlinear_solver.options['max_sub_solves'] = 0
g1.nonlinear_solver = NewtonSolver()
g1.linear_solver = LinearBlockGS()
g2.nonlinear_solver = NewtonSolver()
g2.linear_solver = ScipyIterativeSolver()
g2.linear_solver.precon = LinearBlockGS()
g2.linear_solver.precon.options['maxiter'] = 2
prob.set_solver_print(level=0)
prob.set_solver_print(level=2, depth=2)
prob.setup()
prob.run_model()
def test_feature_max_sub_solves(self):
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, NewtonSolver, LinearBlockGS, ExecComp, DirectSolver, ScipyKrylov
from openmdao.test_suite.components.double_sellar import SubSellar
prob = Problem()
model = prob.model
model.add_subsystem('g1', SubSellar())
model.add_subsystem('g2', SubSellar())
model.connect('g1.y2', 'g2.x')
model.connect('g2.y2', 'g1.x')
# Converge the outer loop with Gauss Seidel, with a looser tolerance.
model.nonlinear_solver = NewtonSolver()
model.linear_solver = DirectSolver()
g1 = model.g1
g1.nonlinear_solver = NewtonSolver()
g1.nonlinear_solver.options['rtol'] = 1.0e-5
g1.linear_solver = DirectSolver()
g2 = model.g2
g2.nonlinear_solver = NewtonSolver()
g2.nonlinear_solver.options['rtol'] = 1.0e-5
g2.linear_solver = DirectSolver()
model.nonlinear_solver = NewtonSolver()
model.linear_solver = ScipyKrylov()
model.nonlinear_solver.options['solve_subsystems'] = True
model.nonlinear_solver.options['max_sub_solves'] = 0
prob.setup()
prob.run_model()
def test_feature_max_sub_solves(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.double_sellar import SubSellar
prob = om.Problem()
model = prob.model
model.add_subsystem('g1', SubSellar())
model.add_subsystem('g2', SubSellar())
model.connect('g1.y2', 'g2.x')
model.connect('g2.y2', 'g1.x')
# Converge the outer loop with Gauss Seidel, with a looser tolerance.
model.nonlinear_solver = om.NewtonSolver()
model.linear_solver = om.DirectSolver()
g1 = model.g1
g1.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g1.nonlinear_solver.options['rtol'] = 1.0e-5
g1.linear_solver = om.DirectSolver()
g2 = model.g2
g2.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g2.nonlinear_solver.options['rtol'] = 1.0e-5
g2.linear_solver = om.DirectSolver()
model.nonlinear_solver = om.NewtonSolver()
model.linear_solver = om.ScipyKrylov()
model.nonlinear_solver.options['solve_subsystems'] = True
model.nonlinear_solver.options['max_sub_solves'] = 0
prob.setup()
prob.run_model()
def test_hierarchy_list_vars_options(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('pz', om.IndepVarComp('z', np.array([5.0, 2.0])))
sub1 = model.add_subsystem('sub1', om.Group())
sub2 = sub1.add_subsystem('sub2', om.Group())
g1 = sub2.add_subsystem('g1', SubSellar())
g2 = model.add_subsystem('g2', SubSellar())
model.connect('pz.z', 'sub1.sub2.g1.z')
model.connect('sub1.sub2.g1.y2', 'g2.x')
model.connect('g2.y2', 'sub1.sub2.g1.x')
model.nonlinear_solver = om.NewtonSolver()
model.linear_solver = om.ScipyKrylov()
model.nonlinear_solver.options['solve_subsystems'] = True
model.nonlinear_solver.options['max_sub_solves'] = 0
g1.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g1.linear_solver = om.LinearBlockGS()
g2.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
g2.linear_solver = om.ScipyKrylov()
g2.linear_solver.precon = om.LinearBlockGS()
g2.linear_solver.precon.options['maxiter'] = 2
prob.setup()
prob.run_driver()
# logging inputs
# out_stream - not hierarchical - extras - no print_arrays
stream = StringIO()
prob.model.list_inputs(values=True,
units=True,
hierarchical=False,
print_arrays=False,
out_stream=stream)
text = stream.getvalue()
self.assertEqual(1, text.count("10 Input(s) in 'model'"))
# make sure they are in the correct order
self.assertTrue(
text.find("sub1.sub2.g1.d1.z") < text.find('sub1.sub2.g1.d1.x') <
text.find('sub1.sub2.g1.d1.y2') < text.find('sub1.sub2.g1.d2.z') <
text.find('sub1.sub2.g1.d2.y1') < text.find('g2.d1.z') < text.find(
'g2.d1.x') < text.find('g2.d1.y2') < text.find(
'g2.d2.z') < text.find('g2.d2.y1'))
num_non_empty_lines = sum([1 for s in text.splitlines() if s.strip()])
self.assertEqual(14, num_non_empty_lines)
# out_stream - hierarchical - extras - no print_arrays
stream = StringIO()
prob.model.list_inputs(values=True,
units=True,
hierarchical=True,
print_arrays=False,
out_stream=stream)
text = stream.getvalue()
self.assertEqual(1, text.count("10 Input(s) in 'model'"))
num_non_empty_lines = sum([1 for s in text.splitlines() if s.strip()])
self.assertEqual(22, num_non_empty_lines)
self.assertEqual(1, text.count('\nsub1'))
self.assertEqual(1, text.count('\n sub2'))
self.assertEqual(1, text.count('\n g1'))
self.assertEqual(1, text.count('\n d1'))
self.assertEqual(2, text.count('\n z'))
# logging outputs
# out_stream - not hierarchical - extras - no print_arrays
stream = StringIO()
prob.model.list_outputs(values=True,
units=True,
shape=True,
bounds=True,
residuals=True,
scaling=True,
hierarchical=False,
print_arrays=False,
out_stream=stream)
text = stream.getvalue()
self.assertEqual(text.count('5 Explicit Output'), 1)
# make sure they are in the correct order
self.assertTrue(
text.find("pz.z") < text.find('sub1.sub2.g1.d1.y1') < text.find(
'sub1.sub2.g1.d2.y2') < text.find('g2.d1.y1') < text.find(
'g2.d2.y2'))
num_non_empty_lines = sum([1 for s in text.splitlines() if s.strip()])
self.assertEqual(11, num_non_empty_lines)
# Hierarchical
stream = StringIO()
prob.model.list_outputs(values=True,
units=True,
shape=True,
bounds=True,
residuals=True,
scaling=True,
hierarchical=True,
print_arrays=False,
out_stream=stream)
text = stream.getvalue()
self.assertEqual(text.count('\n y1'), 1)
self.assertEqual(text.count('\ng2'), 1)
num_non_empty_lines = sum([1 for s in text.splitlines() if s.strip()])
self.assertEqual(num_non_empty_lines, 20)
