def test_scaling(self):
# Make sure values are unscaled/dimensional.
def custom_method(d_outputs, d_residuals, mode):
if d_outputs['out_var'][0] != 12.0:
raise ValueError('This value should be unscaled.')
return False, 0, 0
class ScaledComp(ImplicitComponent):
def setup(self):
self.add_input('a', val=10., units='m')
self.add_output('states', val=20.0, ref=3333.0)
self.add_output('out_var', val=20.0, ref=12.0)
p = Problem()
p.model.add_subsystem('des_vars',
IndepVarComp('a', val=10., units='m'),
promotes=['*'])
p.model.add_subsystem('icomp', ScaledComp(), promotes=['*'])
model = p.model
model.linear_solver = LinearUserDefined(custom_method)
p.setup(mode='rev', check=False)
p.run_model()
jac = p.compute_totals(of=['out_var'], wrt=['a'], return_format='dict')
def setup(self):
self.add_input('a', val=10., units='m')
rank = self.comm.rank
GLOBAL_SIZE = 15
sizes, offsets = evenly_distrib_idxs(self.comm.size, GLOBAL_SIZE)
self.add_output('states', shape=int(sizes[rank]))
self.add_output('out_var', shape=1)
self.local_size = sizes[rank]
self.linear_solver = PETScKrylov()
self.linear_solver.precon = LinearUserDefined(self.mysolve)
def test_method_default(self):
# Uses `solve_linear` by default
p = Problem()
p.model.add_subsystem('des_vars', IndepVarComp('a', val=10., units='m'), promotes=['*'])
p.model.add_subsystem('icomp', DistribStateImplicit(), promotes=['*'])
model = p.model
model.linear_solver = PETScKrylov()
model.linear_solver.precon = LinearRunOnce()
p.setup(vector_class=PETScVector, mode='rev', check=False)
model.icomp.linear_solver.precon = LinearUserDefined()
p.run_model()
jac = p.compute_totals(of=['out_var'], wrt=['a'], return_format='dict')
assert_rel_error(self, 15.0, jac['out_var']['a'][0][0])
