def test_solver_debug_print(self, name, solver):
p = om.Problem()
model = p.model
model.add_subsystem('ground', om.IndepVarComp('V', 0., units='V'))
model.add_subsystem('source', om.IndepVarComp('I', 0.1, units='A'))
model.add_subsystem('circuit', Circuit())
model.connect('source.I', 'circuit.I_in')
model.connect('ground.V', 'circuit.Vg')
p.setup()
nl = model.circuit.nonlinear_solver = solver()
nl.options['debug_print'] = True
nl.options['err_on_non_converge'] = True
if name == 'NonlinearBlockGS':
nl.options['use_apply_nonlinear'] = True
# suppress solver output for test
nl.options['iprint'] = model.circuit.linear_solver.options[
'iprint'] = -1
# For Broydensolver, don't calc Jacobian
try:
nl.options['compute_jacobian'] = False
except KeyError:
pass
# set some poor initial guesses so that we don't converge
p['circuit.n1.V'] = 10.
p['circuit.n2.V'] = 1e-3
opts = {}
# formatting has changed in numpy 1.14 and beyond.
if LooseVersion(np.__version__) >= LooseVersion("1.14"):
opts["legacy"] = '1.13'
with printoptions(**opts):
# run the model and check for expected output file
output = run_model(p, ignore_exception=True)
expected_output = '\n'.join([
self.expected_data,
"Inputs and outputs at start of iteration "
"have been saved to '%s'.\n" % self.filename
])
self.assertEqual(output, expected_output)
with open(self.filename, 'r') as f:
self.assertEqual(f.read(), self.expected_data)
# setup & run again to make sure there is no error due to existing file
p.setup()
with printoptions(**opts):
run_model(p, ignore_exception=False)
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_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 = ScipyKrylov()
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 = ScipyKrylov()
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_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)
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()
g1.linear_solver = om.LinearBlockGS()
g2.nonlinear_solver = om.NewtonSolver()
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)
def test_solver_debug_print(self, name, solver):
p = Problem()
model = p.model
model.add_subsystem('ground', IndepVarComp('V', 0., units='V'))
model.add_subsystem('source', IndepVarComp('I', 0.1, units='A'))
model.add_subsystem('circuit', Circuit())
model.connect('source.I', 'circuit.I_in')
model.connect('ground.V', 'circuit.Vg')
p.setup()
nl = model.circuit.nonlinear_solver = solver()
nl.options['debug_print'] = True
nl.options['err_on_maxiter'] = True
if name == 'NonlinearBlockGS':
nl.options['use_apply_nonlinear'] = True
# suppress solver output for test
nl.options['iprint'] = model.circuit.linear_solver.options['iprint'] = -1
# For Broydensolver, don't calc Jacobian
try:
nl.options['compute_jacobian'] = False
except KeyError:
pass
# set some poor initial guesses so that we don't converge
p['circuit.n1.V'] = 10.
p['circuit.n2.V'] = 1e-3
opts = {}
# formatting has changed in numpy 1.14 and beyond.
if LooseVersion(np.__version__) >= LooseVersion("1.14"):
opts["legacy"] = '1.13'
with printoptions(**opts):
# run the model and check for expected output file
output = run_model(p, ignore_exception=True)
expected_output = '\n'.join([
self.expected_data,
"Inputs and outputs at start of iteration "
"have been saved to '%s'.\n" % self.filename
])
self.assertEqual(output, expected_output)
with open(self.filename, 'r') as f:
self.assertEqual(f.read(), self.expected_data)
def test_solver_debug_print(self, name, solver):
p = Problem()
model = p.model
model.add_subsystem('ground', IndepVarComp('V', 0., units='V'))
model.add_subsystem('source', IndepVarComp('I', 0.1, units='A'))
model.add_subsystem('circuit', Circuit())
model.connect('source.I', 'circuit.I_in')
model.connect('ground.V', 'circuit.Vg')
p.setup()
nl = model.circuit.nonlinear_solver = solver()
nl.options['debug_print'] = True
# suppress solver output for test
nl.options['iprint'] = model.circuit.linear_solver.options['iprint'] = -1
# set some poor initial guesses so that we don't converge
p['circuit.n1.V'] = 10.
p['circuit.n2.V'] = 1e-3
opts = {}
# formatting has changed in numpy 1.14 and beyond.
if LooseVersion(np.__version__) >= LooseVersion("1.14"):
opts["legacy"] = '1.13'
with printoptions(**opts):
# run the model and check for expected output file
output = run_model(p)
expected_output = '\n'.join([
self.expected_data,
"Inputs and outputs at start of iteration "
"have been saved to '%s'.\n" % self.filename
])
self.assertEqual(output, expected_output)
with open(self.filename, 'r') as f:
self.assertEqual(f.read(), self.expected_data)
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 = 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 = ScipyKrylov()
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 = ScipyKrylov()
g2.linear_solver.precon = LinearBlockGS()
g2.linear_solver.precon.options['maxiter'] = 2
prob.set_solver_print(level=2)
prob.setup(check=False)
# 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)
