CS across nested problem with derivs.

gistfile1.txt

import numpy as np

import openmdao.api as om
from openmdao.test_suite.components.paraboloid import Paraboloid


class SubProblem(om.ExplicitComponent):

    def setup(self):
        self.add_input('x', val=0.0)
        self.add_input('y', val=0.0)

        self.add_output('df_dx', val=0.0)

        # Setup sub-problem
        self._problem = prob = om.Problem()
        model = prob.model

        model.add_subsystem('sub', Paraboloid(), promotes=['*'])

        prob.setup(force_alloc_complex=True)

    def compute(self, inputs, outputs):
        prob = self._problem
        under_cs = self.under_complex_step

        if under_cs:
            prob.set_complex_step_mode(True)

        # Set inputs
        prob.set_val('x', inputs['x'])
        prob.set_val('y', inputs['y'])

        # Run model
        prob.run_model()
        totals = prob.compute_totals(of=['f_xy'],
                                     wrt=['x'])
        # Extract outputs
        outputs['df_dx'] = totals['f_xy', 'x']

        if under_cs:
            prob.set_complex_step_mode(False)


prob = om.Problem()
model = prob.model

model.add_subsystem('comp', SubProblem())
model.approx_totals(method='cs')
prob.setup(force_alloc_complex=True)

prob.run_model()
totals = prob.compute_totals('comp.df_dx', 'comp.x')

# Should be 2.0 (verify by setting to fd). It's 0 because imaginary part is lost when propagated 
# through sub-problem's linear vector.
print(totals)

print('done')