""" Unit tests for the problem interface."""

import sys
import unittest
import itertools

from io import StringIO
import numpy as np

import openmdao.api as om
from openmdao.core.driver import Driver
from openmdao.test_suite.components.paraboloid import Paraboloid
from openmdao.test_suite.components.sellar import SellarDerivatives, SellarDerivativesConnected
from openmdao.utils.assert_utils import assert_near_equal, assert_warning
import openmdao.utils.hooks as hooks
from openmdao.utils.units import convert_units
from openmdao.utils.om_warnings import DerivativesWarning

try:
    from parameterized import parameterized
except ImportError:
    from openmdao.utils.assert_utils import SkipParameterized as parameterized


class SellarOneComp(om.ImplicitComponent):

    def initialize(self):
        self.options.declare('solve_y1', types=bool, default=True)
        self.options.declare('solve_y2', types=bool, default=True)

    def setup(self):


        # Global Design Variable
        self.add_input('z', val=np.array([-1., -1.]))

        # Local Design Variable
        self.add_input('x', val=2.)

        self.add_output('y1', val=1.0)
        self.add_output('y2', val=1.0)

        self.add_output('R_y1')
        self.add_output('R_y2')

        if self.options['solve_y1']:
            self.declare_partials('y1', ['x', 'z', 'y1', 'y2'])
        else:
            self.declare_partials('y1', 'y1')

        if self.options['solve_y2']:
            self.declare_partials('y2', ['z', 'y1', 'y2'])
        else:
            self.declare_partials('y2', 'y2')

        self.declare_partials('R_y1', ['R_y1', 'x', 'z', 'y1', 'y2'])
        self.declare_partials('R_y2', ['R_y2','z', 'y1', 'y2'])

    def apply_nonlinear(self, inputs, outputs, residuals):

        z0 = inputs['z'][0]
        z1 = inputs['z'][1]
        x = inputs['x']
        y1 = outputs['y1']
        y2 = outputs['y2']

        if self.options['solve_y1']:
            residuals['y1'] = (z0**2 + z1 + x - 0.2*y2) - y1
        else:
            residuals['y1'] = 0

        if self.options['solve_y2']:
            residuals['y2'] = (y1**.5 + z0 + z1) - y2
        else:
            residuals['y2'] = 0

        residuals['R_y1'] = (z0**2 + z1 + x - 0.2*y2) - y1 - outputs['R_y1']
        residuals['R_y2'] = (y1**.5 + z0 + z1) - y2 - outputs['R_y2']

    def linearize(self, inputs, outputs, J):

        # this will look wrong in check_partials if solve_y2 = False, but its not: R['y1'] = y1^* - y1
        J['y1', 'y1'] = -1.
        J['R_y1','R_y1'] = -1

        if self.options['solve_y1']:
            J['y1', 'x'] = [1]
            J['y1', 'z'] = [2*inputs['z'][0], 1]
            J['y1', 'y2'] = -0.2

        J['R_y1', 'x'] = [1]
        J['R_y1', 'z'] = [2*inputs['z'][0], 1]
        J['R_y1', 'y1'] = -1.
        J['R_y1', 'y2'] = -0.2

        # this will look wrong in check_partials if solve_y2 = False, but its not" R['y1'] = y2^* - y2
        J['y2','y2'] = -1

        J['R_y2','R_y2'] = -1
        if self.options['solve_y2']:
            J['y2','z'] = [1, 1]
            J['y2','y1'] = 0.5*outputs['y1']**-0.5

        J['R_y2','y2'] = -1
        J['R_y2','z'] = [1, 1]
        J['R_y2','y1'] = 0.5*outputs['y1']**-0.5

    def solve_nonlinear(self, inputs, outputs):
        z0 = inputs['z'][0]
        z1 = inputs['z'][1]
        x = inputs['x']
        y1 = outputs['y1']
        y2 = outputs['y2']

        outputs['R_y1'] = (z0**2 + z1 + x - 0.2*y2) - y1
        outputs['R_y2'] = (y1**.5 + z0 + z1) - y2


class TestProblem(unittest.TestCase):

    def test_simple_component_model_with_units(self):
        class TestComp(om.ExplicitComponent):
            def setup(self):
                self.add_input('foo', units='N')
                self.add_output('bar', units='N')
                self.declare_partials('bar', 'foo')

            def compute(self, inputs, outputs):
                outputs['bar'] = inputs['foo']

            def compute_partials(self, inputs, J):
                J['bar', 'foo'] = 1.

        p = om.Problem(model=TestComp())
        p.setup()

        p.set_val('foo', 5, units='lbf')
        p.run_model()

        lbf_val = convert_units(5, 'lbf', 'N')
        self.assertEqual(p.get_val('foo'), lbf_val)
        self.assertEqual(p.get_val('bar'), lbf_val)

    def test_feature_simple_run_once_no_promote(self):

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

        model.add_subsystem('comp', Paraboloid())

        model.set_input_defaults('comp.x', 3.0)
        model.set_input_defaults('comp.y', -4.0)

        prob.setup()
        prob.run_model()

        assert_near_equal(prob['comp.f_xy'], -15.0)

    def test_feature_simple_run_once_input_input(self):

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

        # promote the two inputs to the same name
        model.add_subsystem('comp1', Paraboloid(), promotes_inputs=['x'])
        model.add_subsystem('comp2', Paraboloid(), promotes_inputs=['x'])

        model.set_input_defaults('x', 3.0)

        prob.setup()
        prob.run_model()

        assert_near_equal(prob['comp1.f_xy'], 13.0)
        assert_near_equal(prob['comp2.f_xy'], 13.0)

    def test_feature_simple_run_once_compute_totals(self):

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

        model.add_subsystem('comp', Paraboloid())

        model.set_input_defaults('comp.x', 3.0)
        model.set_input_defaults('comp.y', -4.0)

        prob.setup()

        prob.run_model()

        totals = prob.compute_totals(of=['comp.f_xy'], wrt=['comp.x', 'comp.y'])
        assert_near_equal(totals[('comp.f_xy', 'comp.x')][0][0], -4.0)
        assert_near_equal(totals[('comp.f_xy', 'comp.y')][0][0], 3.0)

        totals = prob.compute_totals(of=['comp.f_xy'], wrt=['comp.x', 'comp.y'], return_format='dict')
        assert_near_equal(totals['comp.f_xy']['comp.x'][0][0], -4.0)
        assert_near_equal(totals['comp.f_xy']['comp.y'][0][0], 3.0)

    def test_feature_simple_run_once_compute_totals_scaled(self):

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

        model.add_subsystem('comp', Paraboloid())

        model.set_input_defaults('comp.x', 3.0)
        model.set_input_defaults('comp.y', -4.0)

        model.add_design_var('comp.x', 3.0, ref0=50.0)
        model.add_design_var('comp.y', -4.0)
        model.add_objective('comp.f_xy')

        prob.setup()

        prob.run_model()

        totals = prob.compute_totals(of=['comp.f_xy'], wrt=['comp.x', 'comp.y'], driver_scaling=True)
        assert_near_equal(totals[('comp.f_xy', 'comp.x')][0][0], 196.0)
        assert_near_equal(totals[('comp.f_xy', 'comp.y')][0][0], 3.0)

    def test_feature_simple_run_once_set_deriv_mode(self):

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

        model.add_subsystem('comp', Paraboloid())

        model.set_input_defaults('comp.x', 3.0)
        model.set_input_defaults('comp.y', -4.0)

        prob.setup(mode='rev')
        # prob.setup(mode='fwd')
        prob.run_model()

        assert_near_equal(prob['comp.f_xy'], -15.0)

        prob.compute_totals(of=['comp.f_xy'], wrt=['comp.x', 'comp.y'])

    def test_single_string_wrt_of(self):

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

        model.add_subsystem('p1', om.IndepVarComp('x', 3.0))
        model.add_subsystem('p2', om.IndepVarComp('y', -4.0))
        model.add_subsystem('comp', Paraboloid())

        model.connect('p1.x', 'comp.x')
        model.connect('p2.y', 'comp.y')

        prob.setup()
        prob.run_model()

        totals = prob.compute_totals(of='comp.f_xy', wrt='p1.x')
        assert_near_equal(totals[('comp.f_xy', 'p1.x')][0][0], -4.0)

    def test_two_var_single_string_error(self):

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

        model.add_subsystem('p1', om.IndepVarComp('x', 3.0))
        model.add_subsystem('p2', om.IndepVarComp('y', -4.0))
        model.add_subsystem('comp', Paraboloid())

        model.connect('p1.x', 'comp.x')
        model.connect('p2.y', 'comp.y')

        prob.setup()
        prob.run_model()

        with self.assertRaises(KeyError) as cm:
            totals = prob.compute_totals(of='comp.f_xy', wrt="p1.x, p2.y")
        self.assertEqual(str(cm.exception), "'p1.x, p2.y'")

    def test_compute_totals_cleanup(self):
        p = om.Problem()
        model = p.model
        model.add_subsystem('indeps1', om.IndepVarComp('x', np.ones(5)))
        model.add_subsystem('indeps2', om.IndepVarComp('x', np.ones(3)))

        model.add_subsystem('MP1', om.ExecComp('y=7*x', x=np.zeros(5), y=np.zeros(5)))
        model.add_subsystem('MP2', om.ExecComp('y=-3*x', x=np.zeros(3), y=np.zeros(3)))

        model.add_design_var('indeps1.x')
        model.add_design_var('indeps2.x')

        model.add_constraint('MP1.y')
        model.add_constraint('MP2.y')

        model.connect('indeps1.x', 'MP1.x')
        model.connect('indeps2.x', 'MP2.x')

        p.setup(mode='rev')
        p.run_model()

        J = p.compute_totals()
        assert_near_equal(J[('MP1.y', 'indeps1.x')], np.eye(5)*7., 1e-10)
        assert_near_equal(J[('MP2.y', 'indeps2.x')], np.eye(3)*-3., 1e-10)
        # before the bug fix, the following two derivs contained nonzero values even
        # though the variables involved were not dependent on each other.
        assert_near_equal(J[('MP2.y', 'indeps1.x')], np.zeros((3, 5)), 1e-10)
        assert_near_equal(J[('MP1.y', 'indeps2.x')], np.zeros((5, 3)), 1e-10)

    def test_set_2d_array(self):

        prob = om.Problem()
        model = prob.model
        model.add_subsystem(name='indeps',
                            subsys=om.IndepVarComp(name='X_c', shape=(3, 1)))
        prob.setup()

        new_val = -5*np.ones((3, 1))
        prob['indeps.X_c'] = new_val
        prob.final_setup()

        assert_near_equal(prob['indeps.X_c'], new_val, 1e-10)

        new_val = 2.5*np.ones(3)
        prob['indeps.X_c'][:, 0] = new_val
        prob.final_setup()

        assert_near_equal(prob['indeps.X_c'], new_val.reshape((3, 1)), 1e-10)
        assert_near_equal(prob['indeps.X_c'][:, 0], new_val, 1e-10)

    def test_set_checks_shape(self):

        model = om.Group()

        indep = model.add_subsystem('indep', om.IndepVarComp())
        indep.add_output('num')
        indep.add_output('arr', shape=(10, 1))

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

        msg = "Incompatible shape for '.*': Expected (.*) but got (.*)"

        # check valid scalar value
        new_val = -10.
        prob['indep.num'] = new_val
        assert_near_equal(prob['indep.num'], new_val, 1e-10)

        # check bad scalar value
        bad_val = -10*np.ones((10))
        prob['indep.num'] = bad_val
        with self.assertRaisesRegex(ValueError,
                "<model> <class Group>: Failed to set value of '.*': could not broadcast input array from shape (.*) into shape (.*)."):
            prob.final_setup()
        prob._initial_condition_cache = {}

        # check assign scalar to array
        arr_val = new_val*np.ones((10, 1))
        prob['indep.arr'] = new_val
        prob.final_setup()
        assert_near_equal(prob['indep.arr'], arr_val, 1e-10)

        # check valid array value
        new_val = -10*np.ones((10, 1))
        prob['indep.arr'] = new_val
        assert_near_equal(prob['indep.arr'], new_val, 1e-10)

        msg = "<model> <class Group>: Failed to set value of '.*': could not broadcast input array from shape (.*) into shape (.*)."
        # check bad array value
        bad_val = -10*np.ones((9,1))
        with self.assertRaisesRegex(ValueError, msg):
            prob['indep.arr'] = bad_val

        # check valid list value
        new_val = new_val.tolist()
        prob['indep.arr'] = new_val
        assert_near_equal(prob['indep.arr'], new_val, 1e-10)

        # check bad list value
        bad_val = bad_val.tolist()
        with self.assertRaisesRegex(ValueError, msg):
            prob['indep.arr'] = bad_val

    def test_compute_totals_basic(self):
        # Basic test for the method using default solvers on simple model.

        prob = om.Problem()
        model = prob.model
        model.add_subsystem('p1', om.IndepVarComp('x', 0.0), promotes=['x'])
        model.add_subsystem('p2', om.IndepVarComp('y', 0.0), promotes=['y'])
        model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])

        prob.setup(check=False, mode='fwd')
        prob.set_solver_print(level=0)
        prob.run_model()

        of = ['f_xy']
        wrt = ['x', 'y']
        derivs = prob.compute_totals(of=of, wrt=wrt)

        assert_near_equal(derivs['f_xy', 'x'], [[-6.0]], 1e-6)
        assert_near_equal(derivs['f_xy', 'y'], [[8.0]], 1e-6)

        prob.setup(check=False, mode='rev')
        prob.run_model()

        of = ['f_xy']
        wrt = ['x', 'y']
        derivs = prob.compute_totals(of=of, wrt=wrt)

        assert_near_equal(derivs['f_xy', 'x'], [[-6.0]], 1e-6)
        assert_near_equal(derivs['f_xy', 'y'], [[8.0]], 1e-6)

    def test_compute_totals_basic_return_dict(self):
        # Make sure 'dict' return_format works.

        prob = om.Problem()
        model = prob.model
        model.add_subsystem('p1', om.IndepVarComp('x', 0.0), promotes=['x'])
        model.add_subsystem('p2', om.IndepVarComp('y', 0.0), promotes=['y'])
        model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])

        prob.setup(check=False, mode='fwd')
        prob.set_solver_print(level=0)
        prob.run_model()

        of = ['f_xy']
        wrt = ['x', 'y']
        derivs = prob.compute_totals(of=of, wrt=wrt, return_format='dict')

        assert_near_equal(derivs['f_xy']['x'], [[-6.0]], 1e-6)
        assert_near_equal(derivs['f_xy']['y'], [[8.0]], 1e-6)

        prob.setup(check=False, mode='rev')
        prob.run_model()

        of = ['f_xy']
        wrt = ['x', 'y']
        derivs = prob.compute_totals(of=of, wrt=wrt, return_format='dict')

        assert_near_equal(derivs['f_xy']['x'], [[-6.0]], 1e-6)
        assert_near_equal(derivs['f_xy']['y'], [[8.0]], 1e-6)

    def test_compute_totals_no_args_no_desvar(self):
        p = om.Problem()

        dv = p.model.add_subsystem('des_vars', om.IndepVarComp())
        dv.add_output('x', val=2.)

        p.model.add_subsystem('calc', om.ExecComp('y=2*x'))

        p.model.connect('des_vars.x', 'calc.x')

        p.model.add_objective('calc.y')

        p.setup()
        p.run_model()

        with self.assertRaises(RuntimeError) as cm:
            p.compute_totals()

        self.assertEqual(str(cm.exception),
                         "Driver is not providing any design variables for compute_totals.")

    def test_compute_totals_no_args_no_response(self):
        p = om.Problem()

        dv = p.model.add_subsystem('des_vars', om.IndepVarComp())
        dv.add_output('x', val=2.)

        p.model.add_subsystem('calc', om.ExecComp('y=2*x'))

        p.model.connect('des_vars.x', 'calc.x')

        p.model.add_design_var('des_vars.x')

        p.setup()
        p.run_model()

        with self.assertRaises(RuntimeError) as cm:
            p.compute_totals()

        self.assertEqual(str(cm.exception),
                         "Driver is not providing any response variables for compute_totals.")

    def test_compute_totals_no_args(self):
        p = om.Problem()

        dv = p.model.add_subsystem('des_vars', om.IndepVarComp())
        dv.add_output('x', val=2.)

        p.model.add_subsystem('calc', om.ExecComp('y=2*x'))

        p.model.connect('des_vars.x', 'calc.x')

        p.model.add_design_var('des_vars.x')
        p.model.add_objective('calc.y')

        p.setup()
        p.run_model()

        derivs = p.compute_totals()

        assert_near_equal(derivs['calc.y', 'des_vars.x'], [[2.0]], 1e-6)

    def test_compute_totals_no_args_promoted(self):
        p = om.Problem()

        dv = p.model.add_subsystem('des_vars', om.IndepVarComp(), promotes=['*'])
        dv.add_output('x', val=2.)

        p.model.add_subsystem('calc', om.ExecComp('y=2*x'), promotes=['*'])

        p.model.add_design_var('x')
        p.model.add_objective('y')

        p.setup()
        p.run_model()

        derivs = p.compute_totals()

        assert_near_equal(derivs['calc.y', 'des_vars.x'], [[2.0]], 1e-6)

    @parameterized.expand(itertools.product(['fwd', 'rev']))
    def test_compute_jacvec_product(self, mode):

        prob = om.Problem()
        prob.model = SellarDerivatives()
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.setup(mode=mode)
        prob.run_model()

        of = ['obj', 'con1']
        wrt = ['_auto_ivc.v1', '_auto_ivc.v0']

        if mode == 'fwd':
            seed_names = wrt
            result_names = of
            rvec = prob.model._vectors['output']['linear']
            lvec = prob.model._vectors['residual']['linear']
        else:
            seed_names = of
            result_names = wrt
            rvec = prob.model._vectors['residual']['linear']
            lvec = prob.model._vectors['output']['linear']

        J = prob.compute_totals(of, wrt, return_format='array')

        seed = []
        for name in seed_names:
            seed.append(np.random.random(rvec[name].size))

        resdict = prob.compute_jacvec_product(of, wrt, mode, seed)
        result = []
        for name in result_names:
            result.append(resdict[name].flat)
        result = np.hstack(result)

        testvec = np.hstack(seed)

        if mode == 'fwd':
            checkvec = J.dot(testvec)
        else:
            checkvec = J.T.dot(testvec)

        np.testing.assert_allclose(checkvec, result)

    def test_feature_set_indeps(self):

        prob = om.Problem()

        model = prob.model
        model.add_subsystem('p1', om.IndepVarComp('x', 0.0), promotes=['x'])
        model.add_subsystem('p2', om.IndepVarComp('y', 0.0), promotes=['y'])
        model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])

        prob.setup()

        prob['x'] = 2.
        prob['y'] = 10.
        prob.run_model()
        assert_near_equal(prob['f_xy'], 214.0, 1e-6)

    def test_feature_set_indeps_auto(self):

        prob = om.Problem()

        prob.model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])

        prob.setup()

        prob.set_val('x', 2.)
        prob.set_val('y', 10.)
        prob.run_model()
        assert_near_equal(prob.get_val('f_xy'), 214.0, 1e-6)

    def test_feature_basic_setup(self):

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

        model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])

        prob.setup()

        prob.set_val('x', 2.)
        prob.set_val('y', 10.)
        prob.run_model()
        assert_near_equal(prob.get_val('f_xy'), 214.0, 1e-6)

        prob.set_val('x', 0.)
        prob.set_val('y', 0.)
        prob.run_model()
        assert_near_equal(prob.get_val('f_xy'), 22.0, 1e-6)

        prob.setup()

        prob.set_val('x', 4.)
        prob.set_val('y', 8.)
        prob.run_model()
        assert_near_equal(prob.get_val('f_xy'), 174.0, 1e-6)

    def test_feature_petsc_setup(self):

        prob = om.Problem()
        model = prob.model
        model.add_subsystem('p1', om.IndepVarComp('x', 0.0), promotes=['x'])
        model.add_subsystem('p2', om.IndepVarComp('y', 0.0), promotes=['y'])
        model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])

        # PETScVectors will be used automatically where needed. No need to set manually.
        prob.setup()
        prob['x'] = 2.
        prob['y'] = 10.

        prob.run_model()
        assert_near_equal(prob['f_xy'], 214.0, 1e-6)

    def test_feature_check_totals_manual(self):

        prob = om.Problem()
        prob.model = SellarDerivatives()
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.setup()
        prob.run_model()

        # manually specify which derivatives to check
        prob.check_totals(of=['obj', 'con1'], wrt=['x', 'z'])

    def test_feature_check_totals_from_driver_compact(self):

        prob = om.Problem()
        prob.model = SellarDerivatives()
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.model.add_design_var('x', lower=-100, upper=100)
        prob.model.add_design_var('z', lower=-100, upper=100)
        prob.model.add_objective('obj')
        prob.model.add_constraint('con1', upper=0.0)
        prob.model.add_constraint('con2', upper=0.0)

        prob.setup()

        # We don't call run_driver() here because we don't
        # actually want the optimizer to run
        prob.run_model()

        # check derivatives of all obj+constraints w.r.t all design variables
        prob.check_totals(compact_print=True)

    def test_feature_check_totals_from_driver(self):

        prob = om.Problem()
        prob.model = SellarDerivatives()
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.model.add_design_var('x', lower=-100, upper=100)
        prob.model.add_design_var('z', lower=-100, upper=100)
        prob.model.add_objective('obj')
        prob.model.add_constraint('con1', upper=0.0)
        prob.model.add_constraint('con2', upper=0.0)

        prob.setup()

        # We don't call run_driver() here because we don't
        # actually want the optimizer to run
        prob.run_model()

        # check derivatives of all obj+constraints w.r.t all design variables
        prob.check_totals()

    def test_feature_check_totals_from_driver_scaled(self):

        prob = om.Problem()
        prob.model = SellarDerivatives()
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.model.add_design_var('x', lower=-100, upper=100, ref=100.0, ref0=-100.0)
        prob.model.add_design_var('z', lower=-100, upper=100)
        prob.model.add_objective('obj')
        prob.model.add_constraint('con1', upper=0.0, ref=3.0)
        prob.model.add_constraint('con2', upper=0.0, ref=20.0)

        prob.setup()

        # We don't call run_driver() here because we don't
        # actually want the optimizer to run
        prob.run_model()

        # check derivatives of all driver vars using the declared scaling
        prob.check_totals(driver_scaling=True)

    def test_feature_check_totals_suppress(self):

        prob = om.Problem()
        prob.model = SellarDerivatives()
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.model.add_design_var('x', lower=-100, upper=100)
        prob.model.add_design_var('z', lower=-100, upper=100)
        prob.model.add_objective('obj')
        prob.model.add_constraint('con1', upper=0.0)
        prob.model.add_constraint('con2', upper=0.0)

        prob.setup()

        # We don't call run_driver() here because we don't
        # actually want the optimizer to run
        prob.run_model()

        # check derivatives of all obj+constraints w.r.t all design variables
        totals = prob.check_totals(out_stream=None)
        print(totals)

    def test_feature_check_totals_cs(self):

        prob = om.Problem()
        prob.model = SellarDerivatives()
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.model.add_design_var('x', lower=-100, upper=100)
        prob.model.add_design_var('z', lower=-100, upper=100)
        prob.model.add_objective('obj')
        prob.model.add_constraint('con1', upper=0.0)
        prob.model.add_constraint('con2', upper=0.0)

        prob.setup(force_alloc_complex=True)

        # We don't call run_driver() here because we don't
        # actually want the optimizer to run
        prob.run_model()

        prob.model.nonlinear_solver.options['atol'] = 1e-15
        prob.model.nonlinear_solver.options['rtol'] = 1e-15

        # check derivatives with complex step
        prob.check_totals(method='cs')

    def test_check_totals_user_detect(self):

        class SimpleComp(om.ExplicitComponent):

            def setup(self):
                self.add_input('x', val=1.0)
                self.add_output('y', val=1.0)

                self.declare_partials(of='y', wrt='x')

                if not self._force_alloc_complex:
                    raise RuntimeError('force_alloc_complex not set in component.')

            def compute(self, inputs, outputs):
                outputs['y'] = 3.0*inputs['x']

                if inputs.iscomplex() and not self.under_complex_step:
                    raise RuntimeError('under_complex_step not set in component.')

            def compute_partials(self, inputs, partials):
                partials['y', 'x'] = 3.

        prob = om.Problem()
        prob.model.add_subsystem('px', om.IndepVarComp('x', 2.0))
        prob.model.add_subsystem('comp', SimpleComp())
        prob.model.connect('px.x', 'comp.x')

        prob.model.add_design_var('px.x', lower=-100, upper=100)
        prob.model.add_objective('comp.y')

        prob.setup(force_alloc_complex=True)

        prob.run_model()

        # check derivatives with complex step and a larger step size.
        prob.check_totals(method='cs', out_stream=None)
        self.assertFalse(prob.model.under_complex_step,
                         msg="The under_complex_step flag should be reset.")

    def test_feature_check_totals_user_detect_forced(self):

        class SimpleComp(om.ExplicitComponent):

            def setup(self):
                self.add_input('x', val=1.0)
                self.add_output('y', val=1.0)

                self.declare_partials(of='y', wrt='x')

                if self._force_alloc_complex:
                    print("Vectors allocated for complex step.")

            def compute(self, inputs, outputs):
                outputs['y'] = 3.0*inputs['x']

            def compute_partials(self, inputs, partials):
                partials['y', 'x'] = 3.

        prob = om.Problem()
        prob.model.add_subsystem('comp', SimpleComp())

        prob.model.add_design_var('comp.x', lower=-100, upper=100)
        prob.model.add_objective('comp.y')

        prob.setup(force_alloc_complex=True)

        prob.run_model()

        prob.check_totals(method='cs')

    def test_set_cs_error_messages(self):
        prob = om.Problem()
        prob.model.add_subsystem('comp', Paraboloid())
        prob.setup()
        prob.run_model()
        msg = "Problem .*: To enable complex step, specify 'force_alloc_complex=True' when calling " + \
            "setup on the problem, e\.g\. 'problem\.setup\(force_alloc_complex=True\)'"
        with self.assertRaisesRegex(RuntimeError, msg):
            prob.set_complex_step_mode(True)

        prob = om.Problem()
        prob.model.add_subsystem('comp', Paraboloid())
        msg = "Problem .*: set_complex_step_mode cannot be called before `Problem\.run_model\(\)`, " + \
            "`Problem\.run_driver\(\)`, or `Problem\.final_setup\(\)`."
        with self.assertRaisesRegex(RuntimeError, msg) :
            prob.set_complex_step_mode(True)

    def test_feature_run_driver(self):

        prob = om.Problem(model=SellarDerivatives())
        model = prob.model
        model.nonlinear_solver = om.NonlinearBlockGS()

        prob.driver = om.ScipyOptimizeDriver()
        prob.driver.options['optimizer'] = 'SLSQP'
        prob.driver.options['tol'] = 1e-9

        model.add_design_var('z', lower=np.array([-10.0, 0.0]), upper=np.array([10.0, 10.0]))
        model.add_design_var('x', lower=0.0, upper=10.0)
        model.add_objective('obj')
        model.add_constraint('con1', upper=0.0)
        model.add_constraint('con2', upper=0.0)

        prob.setup()
        prob.run_driver()

        assert_near_equal(prob.get_val('x'), 0.0, 1e-5)
        assert_near_equal(prob.get_val('y1'), 3.160000, 1e-2)
        assert_near_equal(prob.get_val('y2'), 3.755278, 1e-2)
        assert_near_equal(prob.get_val('z'), [1.977639, 0.000000], 1e-2)
        assert_near_equal(prob.get_val('obj'), 3.18339395, 1e-2)

    def test_feature_promoted_sellar_set_get_outputs(self):

        prob = om.Problem(model=SellarDerivatives())
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.setup()

        prob.set_val('x', 2.75)

        prob.run_model()

        assert_near_equal(prob.get_val('y1'), 27.3049178437, 1e-6)

    def test_feature_not_promoted_sellar_set_get_outputs(self):

        prob = om.Problem(model= SellarDerivativesConnected())
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.setup()

        prob.set_val('x', 2.75)

        prob.run_model()

        assert_near_equal(prob.get_val('x'), 2.75, 1e-6)

        assert_near_equal(prob.get_val('d1.y1'), 27.3049178437, 1e-6)

    def test_feature_promoted_sellar_set_get_inputs(self):

        prob = om.Problem(model=SellarDerivatives())
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.setup()

        prob['x'] = 2.75

        prob.run_model()

        assert_near_equal(prob['x'], 2.75, 1e-6)

        # the output variable, referenced by the promoted name
        assert_near_equal(prob['y1'], 27.3049178437, 1e-6)
        # the connected input variable, referenced by the absolute path
        assert_near_equal(prob['d2.y1'], 27.3049178437, 1e-6)

    def test_get_set_with_units_exhaustive(self):

        prob = om.Problem()
        prob.model.add_subsystem('comp', om.ExecComp('y=x-25.',
                                                     x={'val': 77.0, 'units': 'degF'},
                                                     y={'val': 0.0, 'units': 'degC'}))
        prob.model.add_subsystem('prom', om.ExecComp('yy=xx-25.',
                                                     xx={'val': 77.0, 'units': 'degF'},
                                                     yy={'val': 0.0, 'units': 'degC'}),
                                 promotes=['xx', 'yy'])
        prob.model.add_subsystem('acomp', om.ExecComp('y=x-25.',
                                                      x={'val': np.array([77.0, 95.0]), 'units': 'degF'},
                                                      y={'val': 0.0, 'units': 'degC'}))
        prob.model.add_subsystem('aprom', om.ExecComp('ayy=axx-25.',
                                                      axx={'val': np.array([77.0, 95.0]), 'units': 'degF'},
                                                      ayy={'val': 0.0, 'units': 'degC'}),
                                 promotes=['axx', 'ayy'])

        prob.setup()

        # Make sure everything works before final setup with caching system.

        # Gets

        assert_near_equal(prob.get_val('comp.x'), 77.0, 1e-6)
        assert_near_equal(prob.get_val('comp.x', 'degC'), 25.0, 1e-6)
        assert_near_equal(prob.get_val('comp.y'), 0.0, 1e-6)
        assert_near_equal(prob.get_val('comp.y', 'degF'), 32.0, 1e-6)

        assert_near_equal(prob.get_val('xx'), 77.0, 1e-6)
        assert_near_equal(prob.get_val('xx', 'degC'), 25.0, 1e-6)
        assert_near_equal(prob.get_val('yy'), 0.0, 1e-6)
        assert_near_equal(prob.get_val('yy', 'degF'), 32.0, 1e-6)

        assert_near_equal(prob.get_val('acomp.x', indices=0), 77.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.x', indices=[1]), 95.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.x', 'degC', indices=[0]), 25.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.x', 'degC', indices=1), 35.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.y', indices=0), 0.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.y', 'degF', indices=0), 32.0, 1e-6)

        assert_near_equal(prob.get_val('axx', indices=0), 77.0, 1e-6)
        assert_near_equal(prob.get_val('axx', indices=1), 95.0, 1e-6)
        assert_near_equal(prob.get_val('axx', 'degC', indices=0), 25.0, 1e-6)
        assert_near_equal(prob.get_val('axx', 'degC', indices=np.array([1])), 35.0, 1e-6)
        assert_near_equal(prob.get_val('ayy', indices=0), 0.0, 1e-6)
        assert_near_equal(prob.get_val('ayy', 'degF', indices=0), 32.0, 1e-6)

        # Sets

        prob.set_val('comp.x', 30.0, 'degC')
        assert_near_equal(prob['comp.x'], 86.0, 1e-6)
        assert_near_equal(prob.get_val('comp.x', 'degC'), 30.0, 1e-6)

        prob.set_val('xx', 30.0, 'degC')
        assert_near_equal(prob['xx'], 86.0, 1e-6)
        assert_near_equal(prob.get_val('xx', 'degC'), 30.0, 1e-6)

        prob.set_val('acomp.x', 30.0, 'degC', indices=[0])
        assert_near_equal(prob['acomp.x'][0], 86.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.x', 'degC', indices=0), 30.0, 1e-6)

        prob.set_val('axx', 30.0, 'degC', indices=0)
        assert_near_equal(prob['axx'][0], 86.0, 1e-6)
        assert_near_equal(prob.get_val('axx', 'degC', indices=np.array([0])), 30.0, 1e-6)

        prob.final_setup()

        # Now we do it all over again for coverage.

        # Gets

        assert_near_equal(prob.get_val('comp.x'), 86.0, 1e-6)
        assert_near_equal(prob.get_val('comp.x', 'degC'), 30.0, 1e-6)
        assert_near_equal(prob.get_val('comp.y'), 0.0, 1e-6)
        assert_near_equal(prob.get_val('comp.y', 'degF'), 32.0, 1e-6)

        assert_near_equal(prob.get_val('xx'), 86.0, 1e-6)
        assert_near_equal(prob.get_val('xx', 'degC'), 30.0, 1e-6)
        assert_near_equal(prob.get_val('yy'), 0.0, 1e-6)
        assert_near_equal(prob.get_val('yy', 'degF'), 32.0, 1e-6)

        assert_near_equal(prob.get_val('acomp.x', indices=0), 86.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.x', indices=[1]), 95.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.x', 'degC', indices=[0]), 30.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.x', 'degC', indices=1), 35.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.y', indices=0), 0.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.y', 'degF', indices=0), 32.0, 1e-6)

        assert_near_equal(prob.get_val('axx', indices=0), 86.0, 1e-6)
        assert_near_equal(prob.get_val('axx', indices=1), 95.0, 1e-6)
        assert_near_equal(prob.get_val('axx', 'degC', indices=0), 30.0, 1e-6)
        assert_near_equal(prob.get_val('axx', 'degC', indices=np.array([1])), 35.0, 1e-6)
        assert_near_equal(prob.get_val('ayy', indices=0), 0.0, 1e-6)
        assert_near_equal(prob.get_val('ayy', 'degF', indices=0), 32.0, 1e-6)

        # Sets

        prob.set_val('comp.x', 35.0, 'degC')
        assert_near_equal(prob['comp.x'], 95.0, 1e-6)
        assert_near_equal(prob.get_val('comp.x', 'degC'), 35.0, 1e-6)

        prob.set_val('xx', 35.0, 'degC')
        assert_near_equal(prob['xx'], 95.0, 1e-6)
        assert_near_equal(prob.get_val('xx', 'degC'), 35.0, 1e-6)

        prob.set_val('acomp.x', 35.0, 'degC', indices=[0])
        assert_near_equal(prob['acomp.x'][0], 95.0, 1e-6)
        assert_near_equal(prob.get_val('acomp.x', 'degC', indices=0), 35.0, 1e-6)

        prob.set_val('axx', 35.0, 'degC', indices=0)
        assert_near_equal(prob['axx'][0], 95.0, 1e-6)
        assert_near_equal(prob.get_val('axx', 'degC', indices=np.array([0])), 35.0, 1e-6)

    def test_feature_get_set_with_units_diff_err(self):

        prob = om.Problem()
        prob.model.add_subsystem('C1', om.ExecComp('y=x*2.',
                                                     x={'val': 1.0, 'units': 'ft'},
                                                     y={'val': 0.0, 'units': 'ft'}),
                                 promotes=['x'])
        prob.model.add_subsystem('C2', om.ExecComp('y=x*3.',
                                                     x={'val': 1.0, 'units': 'inch'},
                                                     y={'val': 0.0, 'units': 'inch'}),
                                 promotes=['x'])

        try:
            prob.setup()
        except RuntimeError as err:
            self.assertEqual(str(err), "<model> <class Group>: The following inputs, ['C1.x', 'C2.x'], promoted to 'x', are connected but their metadata entries ['units', 'val'] differ. Call <group>.set_input_defaults('x', units=?, val=?), where <group> is the model to remove the ambiguity.")
        else:
            self.fail("Exception expected.")

    def test_feature_get_set_with_units_diff(self):

        prob = om.Problem()
        G1 = prob.model.add_subsystem('G1', om.Group())
        G1.add_subsystem('C1', om.ExecComp('y=x*2.',
                                            x={'val': 1.0, 'units': 'cm'},
                                            y={'val': 0.0, 'units': 'cm'}),
                         promotes=['x'])
        G1.add_subsystem('C2', om.ExecComp('y=x*3.',
                                            x={'val': 1.0, 'units': 'mm'},
                                            y={'val': 0.0, 'units': 'mm'}),
                         promotes=['x'])

        # units and value to use for the _auto_ivc output are ambiguous.  This fixes that.
        G1.set_input_defaults('x', units='m', val=1.0)

        prob.setup()

        # set G1.x to 2.0 m, based on the units we gave in the set_input_defaults call
        prob.set_val('G1.x', 2.)

        prob.run_model()

        # we gave 'G1.x' units of 'm' in the set_input_defaults call
        assert_near_equal(prob.get_val('G1.x'), 2.0, 1e-6)

        # using absolute value will give us the value of the input C1.x, in its units of 'cm'
        assert_near_equal(prob.get_val('G1.C1.x'), 200.0, 1e-6)

        # using absolute value will give us the value of the input C2.x, in its units of 'mm'
        assert_near_equal(prob.get_val('G1.C2.x'), 2000.0, 1e-6)

    def test_feature_get_set_with_src_indices_diff(self):

        prob = om.Problem()
        G1 = prob.model.add_subsystem('G1', om.Group())
        G1.add_subsystem('C1', om.ExecComp('y=x*2.',
                                            x={'val': 1.0, 'units': 'cm', 'src_indices': [0], 'flat_src_indices': True},
                                            y={'val': 0.0, 'units': 'cm'}),
                         promotes=['x'])
        G1.add_subsystem('C2', om.ExecComp('y=x*3.',
                                            x={'val': np.ones(2), 'units': 'mm', 'src_indices': [1,2], 'flat_src_indices': True},
                                            y={'val': np.zeros(2), 'units': 'mm'}),
                         promotes=['x'])
        G1.add_subsystem('C3', om.ExecComp('y=x*4.',
                                            x={'val': np.ones(3), 'units': 'mm'},
                                            y={'val': np.zeros(3), 'units': 'mm'}),
                         promotes=['x'])

        # units and value to use for the _auto_ivc output are ambiguous.  This fixes that.
        G1.set_input_defaults('x', units='m', val=np.ones(3))

        prob.setup()

        # set G1.x to 2.0 m, based on the units we gave in the set_input_defaults call
        prob['G1.x'] = np.ones(3) * 2.0

        prob.run_model()

        # we gave 'G1.x' units of 'm' in the set_input_defaults call
        assert_near_equal(prob['G1.x'], np.ones(3) * 2.0, 1e-6)

        # using absolute value will give us the value of the input C1.x, in its units of 'cm'
        assert_near_equal(prob['G1.C1.x'], 200.0, 1e-6)

        assert_near_equal(prob['G1.C1.y'], 400.0, 1e-6)

        # using absolute value will give us the value of the input C2.x, in its units of 'mm'
        assert_near_equal(prob['G1.C2.x'], np.ones(2) * 2000.0, 1e-6)

        assert_near_equal(prob['G1.C2.y'], np.ones(2) * 6000.0, 1e-6)

    def test_feature_get_set_with_units_prom_plus_explicit(self):

        prob = om.Problem()
        prob.model.add_subsystem('indeps', om.IndepVarComp('x', val=1.0, units='m'))
        G1 = prob.model.add_subsystem('G1', om.Group())
        G1.add_subsystem('C1', om.ExecComp('y=x*2.',
                                            x={'val': 1.0, 'units': 'cm'},
                                            y={'val': 0.0, 'units': 'cm'}),
                         promotes=['x'])
        G1.add_subsystem('C2', om.ExecComp('y=x*3.',
                                            x={'val': 1.0, 'units': 'mm'},
                                            y={'val': 0.0, 'units': 'mm'}),
                         promotes=['x'])

        # connect IVC to promoted inputs
        prob.model.connect('indeps.x', 'G1.x')

        # units and value to use for the _auto_ivc output are ambiguous.  This fixes that.
        G1.set_input_defaults('x', units='dm', val=1.0)

        prob.setup()

        prob['indeps.x'] = 2.0

        prob.run_model()

        assert_near_equal(prob['indeps.x'], 2.0, 1e-6)

        # using the promoted name of the inputs will give the value
        # in the units set in set_input_defaults, which is 'dm'
        assert_near_equal(prob['G1.x'], 20.0, 1e-6)

        # get value from lower level group
        assert_near_equal(G1.get_val('x'), 20.0, 1e-6)

        # using absolute value will give us the value of the input C1.x, in its units of 'inch'
        assert_near_equal(prob['G1.C1.x'], 200.0, 1e-6)

        # using absolute value will give us the value of the input C2.x, in its units of 'ft'
        assert_near_equal(prob['G1.C2.x'], 2000.0, 1e-6)

    def test_feature_get_set_with_units_prom_plus_explicit_err(self):

        prob = om.Problem()
        prob.model.add_subsystem('indeps', om.IndepVarComp('x', val=1.0, units='m'))
        G1 = prob.model.add_subsystem('G1', om.Group())
        G1.add_subsystem('C1', om.ExecComp('y=x*2.',
                                            x={'val': 1.0, 'units': 'cm'},
                                            y={'val': 0.0, 'units': 'cm'}),
                         promotes=['x'])
        G1.add_subsystem('C2', om.ExecComp('y=x*3.',
                                            x={'val': 1.0, 'units': 'mm'},
                                            y={'val': 0.0, 'units': 'mm'}),
                         promotes=['x'])

        # connect IVC to promoted inputs
        prob.model.connect('indeps.x', 'G1.x')

        prob.setup()

        prob['indeps.x'] = 2.0

        prob.run_model()

        assert_near_equal(prob['indeps.x'], 2.0, 1e-6)

        # using absolute value will give us the value of the input C1.x, in its units of 'inch'
        assert_near_equal(prob['G1.C1.x'], 200.0, 1e-6)

        # using absolute value will give us the value of the input C2.x, in its units of 'ft'
        assert_near_equal(prob['G1.C2.x'], 2000.0, 1e-6)

        # using the promoted name of the inputs will raise an exception because the two promoted
        # inputs have different units and set_input_defaults was not called to disambiguate.
        with self.assertRaises(RuntimeError) as cm:
            x = prob['G1.x']

        msg = "<model> <class Group>: The following inputs, ['G1.C1.x', 'G1.C2.x'], promoted to 'G1.x', are connected but their metadata entries ['units'] differ. Call <group>.set_input_defaults('x', units=?), where <group> is the Group named 'G1' to remove the ambiguity."
        self.assertEqual(cm.exception.args[0], msg)

    def test_get_set_with_units_error_messages(self):

        prob = om.Problem()
        prob.model.add_subsystem('comp', om.ExecComp('y=x+1.',
                                                     x={'val': 100.0, 'units': 'cm'},
                                                     y={'units': 'm'}))
        prob.model.add_subsystem('no_unit', om.ExecComp('y=x+1.', x={'val': 100.0}))

        prob.setup()
        prob.run_model()

        msg = "Can't express variable 'comp.x' with units of 'cm' in units of 'degK'."
        with self.assertRaisesRegex(TypeError, msg):
            prob.get_val('comp.x', 'degK')

        with self.assertRaisesRegex(TypeError, msg):
            prob.set_val('comp.x', 55.0, 'degK')

        msg = "Can't express variable 'no_unit.x' with units of 'None' in units of 'degK'."
        with self.assertRaisesRegex(TypeError, msg):
            prob.get_val('no_unit.x', 'degK')

        with self.assertRaisesRegex(TypeError, msg):
            prob.set_val('no_unit.x', 55.0, 'degK')

    def test_feature_get_set_with_units(self):

        prob = om.Problem()
        prob.model.add_subsystem('comp', om.ExecComp('y=x+1.',
                                                     x={'val': 100.0, 'units': 'cm'},
                                                     y={'units': 'm'}))

        prob.setup()
        prob.run_model()

        assert_near_equal(prob.get_val('comp.x'), 100, 1e-6)
        assert_near_equal(prob.get_val('comp.x', 'm'), 1.0, 1e-6)
        prob.set_val('comp.x', 10.0, 'mm')
        assert_near_equal(prob.get_val('comp.x'), 1.0, 1e-6)
        assert_near_equal(prob.get_val('comp.x', 'm'), 1.0e-2, 1e-6)

    def test_feature_get_set_array_with_units(self):

        prob = om.Problem()
        prob.model.add_subsystem('comp', om.ExecComp('y=x+1.',
                                                     x={'val': np.array([100.0, 33.3]), 'units': 'cm'},
                                                     y={'shape': (2, ), 'units': 'm'}))

        prob.setup()
        prob.run_model()

        assert_near_equal(prob.get_val('comp.x'), np.array([100, 33.3]), 1e-6)
        assert_near_equal(prob.get_val('comp.x', 'm'), np.array([1.0, 0.333]), 1e-6)
        assert_near_equal(prob.get_val('comp.x', 'km', indices=[0]), 0.001, 1e-6)

        prob.set_val('comp.x', 10.0, 'mm')
        assert_near_equal(prob.get_val('comp.x'), np.array([1.0, 1.0]), 1e-6)
        assert_near_equal(prob.get_val('comp.x', 'm', indices=0), 1.0e-2, 1e-6)

        prob.set_val('comp.x', 50.0, 'mm', indices=[1])
        assert_near_equal(prob.get_val('comp.x'), np.array([1.0, 5.0]), 1e-6)
        assert_near_equal(prob.get_val('comp.x', 'm', indices=1), 5.0e-2, 1e-6)

    def test_feature_get_set_array_with_slicer(self):

        prob = om.Problem()
        prob.model.add_subsystem('comp', om.ExecComp('y=x+1.',
                                                     x={'val': np.array([[1., 2.], [3., 4.]]), },
                                                     y={'shape': (2, 2), }))

        prob.setup()
        prob.run_model()

        assert_near_equal(prob.get_val('comp.x', indices=om.slicer[:, 0]), [1., 3.], 1e-6)
        assert_near_equal(prob.get_val('comp.x', indices=om.slicer[0, 1]), 2., 1e-6)
        assert_near_equal(prob.get_val('comp.x', indices=om.slicer[1, -1]), 4., 1e-6)

        prob.set_val('comp.x', [5., 6.], indices=om.slicer[:,0])
        assert_near_equal(prob.get_val('comp.x', indices=om.slicer[:, 0]), [5., 6.], 1e-6)
        prob.run_model()
        assert_near_equal(prob.get_val('comp.y', indices=om.slicer[:, 0]), [6., 7.], 1e-6)

    def test_feature_set_get_array(self):

        prob = om.Problem(model=SellarDerivatives())
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.setup()

        # default value from the class definition
        assert_near_equal(prob.get_val('x'), 1.0, 1e-6)

        prob.set_val('x', 2.75)
        assert_near_equal(prob.get_val('x'), 2.75, 1e-6)

        # default value from the class definition
        assert_near_equal(prob.get_val('z'), [5.0, 2.0], 1e-6)

        prob.set_val('z', [1.5, 1.5])
        assert_near_equal(prob.get_val('z'), [1.5, 1.5], 1e-6)

        prob.run_model()
        assert_near_equal(prob.get_val('y1'), 5.43379016853, 1e-6)
        assert_near_equal(prob.get_val('y2'), 5.33104915618, 1e-6)

        prob.set_val('z', np.array([2.5, 2.5])) # for convenience we convert the list to an array.
        assert_near_equal(prob.get_val('z'), [2.5, 2.5], 1e-6)

        prob.run_model()
        assert_near_equal(prob.get_val('y1'), 9.87161739688, 1e-6)
        assert_near_equal(prob.get_val('y2'), 8.14191301549, 1e-6)

    def test_feature_residuals(self):

        prob = om.Problem(model=SellarDerivatives())
        prob.model.nonlinear_solver = om.NonlinearBlockGS()

        prob.setup()

        prob.set_val('z', [1.5, 1.5])
        prob.run_model()

        inputs, outputs, residuals = prob.model.get_nonlinear_vectors()

        self.assertLess(residuals['y1'], 1e-6)
        self.assertLess(residuals['y2'], 1e-6)

    def test_setup_bad_mode(self):
        # Test error message when passing bad mode to setup.

        prob = om.Problem(name='foo')

        try:
            prob.setup(mode='junk')
        except ValueError as err:
            msg = "Problem foo: Unsupported mode: 'junk'. Use either 'fwd' or 'rev'."
            self.assertEqual(str(err), msg)
        else:
            self.fail('Expecting ValueError')

    def test_setup_bad_mode_direction_fwd(self):

        prob = om.Problem()
        prob.model.add_subsystem("indep", om.IndepVarComp("x", np.ones(99)))
        prob.model.add_subsystem("C1", om.ExecComp("y=2.0*x", x=np.zeros(10), y=np.zeros(10)))

        prob.model.connect("indep.x", "C1.x", src_indices=list(range(10)))

        prob.model.add_design_var("indep.x")
        prob.model.add_objective("C1.y")

        prob.setup(mode='fwd')

        msg = "Inefficient choice of derivative mode.  " \
              "You chose 'fwd' for a problem with 99 design variables and 10 " \
              "response variables (objectives and nonlinear constraints)."

        with assert_warning(DerivativesWarning, msg):
            prob.final_setup()

    def test_setup_bad_mode_direction_rev(self):

        prob = om.Problem()
        prob.model.add_subsystem("indep", om.IndepVarComp("x", np.ones(10)))
        prob.model.add_subsystem("C1", om.ExecComp("y=2.0*x", x=np.zeros(10), y=np.zeros(10)))
        prob.model.add_subsystem("C2", om.ExecComp("y=2.0*x", x=np.zeros(10), y=np.zeros(10)))

        prob.model.connect("indep.x", ["C1.x", "C2.x"])

        prob.model.add_design_var("indep.x")
        prob.model.add_constraint("C1.y")
        prob.model.add_constraint("C2.y")

        prob.setup(mode='rev')

        msg = "Inefficient choice of derivative mode.  " \
              "You chose 'rev' for a problem with 10 design variables and 20 " \
              "response variables (objectives and nonlinear constraints)."

        with assert_warning(DerivativesWarning, msg):
            prob.final_setup()

    def test_run_before_setup(self):
        # Test error message when running before setup.

        prob = om.Problem()

        msg = "Problem .*: The `setup` method must be called before `run_model`."
        with self.assertRaisesRegex(RuntimeError, msg):
            prob.run_model()

        msg = "Problem .*: The `setup` method must be called before `run_driver`."
        with self.assertRaisesRegex(RuntimeError, msg):
            prob.run_driver()

    def test_run_with_invalid_prefix(self):
        # Test error message when running with invalid prefix.

        prob = om.Problem()

        msg = "Problem .*: The 'case_prefix' argument should be a string."
        with self.assertRaisesRegex(TypeError, msg):
            prob.setup()
            prob.run_model(case_prefix=1234)
        with self.assertRaisesRegex(TypeError, msg):
            prob.setup()
            prob.run_driver(case_prefix=12.34)

    def test_args(self):
        # defaults
        prob = om.Problem()
        self.assertTrue(isinstance(prob.model, om.Group))
        self.assertTrue(isinstance(prob.driver, Driver))

        # model
        prob = om.Problem(SellarDerivatives())
        self.assertTrue(isinstance(prob.model, SellarDerivatives))
        self.assertTrue(isinstance(prob.driver, Driver))

        # driver
        prob = om.Problem(driver=om.ScipyOptimizeDriver())
        self.assertTrue(isinstance(prob.model, om.Group))
        self.assertTrue(isinstance(prob.driver, om.ScipyOptimizeDriver))

        # model and driver
        prob = om.Problem(model=SellarDerivatives(), driver=om.ScipyOptimizeDriver())
        self.assertTrue(isinstance(prob.model, SellarDerivatives))
        self.assertTrue(isinstance(prob.driver, om.ScipyOptimizeDriver))

        # invalid model
        msg = "Problem .*: The value provided for 'model' is not a valid System."
        with self.assertRaisesRegex(TypeError, msg) :
            prob = om.Problem(om.ScipyOptimizeDriver())

        # invalid driver
        msg = "Problem .*: The value provided for 'driver' is not a valid Driver."
        with self.assertRaisesRegex(TypeError, msg) :
            prob = om.Problem(driver=SellarDerivatives())

    def test_relevance(self):
        p = om.Problem()
        model = p.model

        model.add_subsystem("indep1", om.IndepVarComp('x', 1.0))
        G1 = model.add_subsystem('G1', om.Group())
        G1.add_subsystem('C1', om.ExecComp(['x=2.0*a', 'y=2.0*b', 'z=2.0*a']))
        G1.add_subsystem('C2', om.ExecComp(['x=2.0*a', 'y=2.0*b', 'z=2.0*b']))
        model.add_subsystem("C3", om.ExecComp(['x=2.0*a', 'y=2.0*b+3.0*c']))
        model.add_subsystem("C4", om.ExecComp(['x=2.0*a', 'y=2.0*b']))
        model.add_subsystem("indep2", om.IndepVarComp('x', 1.0))
        G2 = model.add_subsystem('G2', om.Group())
        G2.add_subsystem('C5', om.ExecComp(['x=2.0*a', 'y=2.0*b+3.0*c']))
        G2.add_subsystem('C6', om.ExecComp(['x=2.0*a', 'y=2.0*b+3.0*c']))
        G2.add_subsystem('C7', om.ExecComp(['x=2.0*a', 'y=2.0*b']))
        model.add_subsystem("C8", om.ExecComp(['y=1.5*a+2.0*b']))
        model.add_subsystem("Unconnected", om.ExecComp('y=99.*x'))

        model.connect('indep1.x', 'G1.C1.a')
        model.connect('indep2.x', 'G2.C6.a')
        model.connect('G1.C1.x', 'G1.C2.b')
        model.connect('G1.C2.z', 'C4.b')
        model.connect('G1.C1.z', ('C3.b', 'C3.c', 'G2.C5.a'))
        model.connect('C3.y', 'G2.C5.b')
        model.connect('C3.x', 'C4.a')
        model.connect('G2.C6.y', 'G2.C7.b')
        model.connect('G2.C5.x', 'C8.b')
        model.connect('G2.C7.x', 'C8.a')

        p.setup(check=False, mode='rev')

        relevant = model.get_relevant_vars({'indep1.x': {}, 'indep2.x': {}},
                                           {'C8.y': {}, 'Unconnected.y': {}}, mode='rev')

        indep1_ins = set(['C3.b', 'C3.c', 'C8.b', 'G1.C1.a', 'G2.C5.a', 'G2.C5.b'])
        indep1_outs = set(['C3.y', 'C8.y', 'G1.C1.z', 'G2.C5.x', 'indep1.x'])
        indep1_sys = set(['C3', 'C8', 'G1.C1', 'G2.C5', 'indep1', 'G1', 'G2', ''])

        dct, systems = relevant['C8.y']['indep1.x']
        inputs = dct['input']
        outputs = dct['output']

        self.assertEqual(inputs, indep1_ins)
        self.assertEqual(outputs, indep1_outs)
        self.assertEqual(systems, indep1_sys)

        dct, systems = relevant['C8.y']['indep1.x']
        inputs = dct['input']
        outputs = dct['output']

        self.assertEqual(inputs, indep1_ins)
        self.assertEqual(outputs, indep1_outs)
        self.assertEqual(systems, indep1_sys)

        indep2_ins = set(['C8.a', 'G2.C6.a', 'G2.C7.b'])
        indep2_outs = set(['C8.y', 'G2.C6.y', 'G2.C7.x', 'indep2.x'])
        indep2_sys = set(['C8', 'G2.C6', 'G2.C7', 'indep2', 'G2', ''])

        dct, systems = relevant['C8.y']['indep2.x']
        inputs = dct['input']
        outputs = dct['output']

        self.assertEqual(inputs, indep2_ins)
        self.assertEqual(outputs, indep2_outs)
        self.assertEqual(systems, indep2_sys)

        dct, systems = relevant['C8.y']['indep2.x']
        inputs = dct['input']
        outputs = dct['output']

        self.assertEqual(inputs, indep2_ins)
        self.assertEqual(outputs, indep2_outs)
        self.assertEqual(systems, indep2_sys)

        dct, systems = relevant['C8.y']['@all']
        inputs = dct['input']
        outputs = dct['output']

        self.assertEqual(inputs, indep1_ins | indep2_ins)
        self.assertEqual(outputs, indep1_outs | indep2_outs)
        self.assertEqual(systems, indep1_sys | indep2_sys)

    def test_relevance_with_component_model(self):
        # Test relevance when model is a Component
        SOLVE_Y1 = False
        SOLVE_Y2 = True

        p_opt = om.Problem()

        p_opt.model = SellarOneComp(solve_y1=SOLVE_Y1, solve_y2=SOLVE_Y2)

        if SOLVE_Y1 or SOLVE_Y2:
            newton = p_opt.model.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
            newton.options['iprint'] = 0

        # NOTE: need to have this direct solver attached to the sellar comp until I define a solve_linear for it
        p_opt.model.linear_solver = om.DirectSolver(assemble_jac=True)

        p_opt.driver = om.ScipyOptimizeDriver()
        p_opt.driver.options['disp'] = False

        if not SOLVE_Y1:
            p_opt.model.add_design_var('y1', lower=-10, upper=10)
            p_opt.model.add_constraint('R_y1', equals=0)

        if not SOLVE_Y2:
            p_opt.model.add_design_var('y2', lower=-10, upper=10)
            p_opt.model.add_constraint('R_y2', equals=0)

        # this objective doesn't really matter... just need something there
        p_opt.model.add_objective('y2')

        p_opt.setup()

        # set
        p_opt['y2'] = 5
        p_opt['y1'] = 5

        p_opt.run_driver()

        np.testing.assert_almost_equal(p_opt['y1'][0], 2.109516506074582, decimal=5)
        np.testing.assert_almost_equal(p_opt['y2'][0], -0.5475825303740725, decimal=5)
        np.testing.assert_almost_equal(p_opt['x'][0], 2.0, decimal=5)
        np.testing.assert_almost_equal(p_opt['z'], np.array([-1., -1.]), decimal=5)

    def test_system_setup_and_configure(self):
        # Test that we can change solver settings on a subsystem in a system's setup method.
        # Also assures that highest system's settings take precedence.

        class ImplSimple(om.ImplicitComponent):

            def setup(self):
                self.add_input('a', val=1.)
                self.add_output('x', val=0.)

            def apply_nonlinear(self, inputs, outputs, residuals):
                residuals['x'] = np.exp(outputs['x']) - \
                    inputs['a']**2 * outputs['x']**2

            def linearize(self, inputs, outputs, jacobian):
                jacobian['x', 'x'] = np.exp(outputs['x']) - \
                    2 * inputs['a']**2 * outputs['x']
                jacobian['x', 'a'] = -2 * inputs['a'] * outputs['x']**2

        class Sub(om.Group):

            def setup(self):
                self.add_subsystem('comp', ImplSimple())

                # This will not solve it
                self.nonlinear_solver = om.NonlinearBlockGS()

            def configure(self):
                # This will not solve it either.
                self.nonlinear_solver = om.NonlinearBlockGS()

        class Super(om.Group):

            def setup(self):
                self.add_subsystem('sub', Sub())

            def configure(self):
                # This will solve it.
                self.sub.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
                self.sub.linear_solver = om.ScipyKrylov()

        top = om.Problem(model=Super())

        top.setup()

        self.assertTrue(isinstance(top.model.sub.nonlinear_solver, om.NewtonSolver))
        self.assertTrue(isinstance(top.model.sub.linear_solver, om.ScipyKrylov))

    def test_post_setup_solver_configure(self):
        # Test that we can change solver settings after we have instantiated our model.

        class ImplSimple(om.ImplicitComponent):

            def setup(self):
                self.add_input('a', val=1.)
                self.add_output('x', val=0.)

            def apply_nonlinear(self, inputs, outputs, residuals):
                residuals['x'] = np.exp(outputs['x']) - \
                    inputs['a']**2 * outputs['x']**2

            def linearize(self, inputs, outputs, jacobian):
                jacobian['x', 'x'] = np.exp(outputs['x']) - \
                    2 * inputs['a']**2 * outputs['x']
                jacobian['x', 'a'] = -2 * inputs['a'] * outputs['x']**2

        class Sub(om.Group):

            def setup(self):
                self.add_subsystem('comp', ImplSimple())

                # This solver will get over-ridden below
                self.nonlinear_solver = om.NonlinearBlockGS()

            def configure(self):
                # This solver will get over-ridden below
                self.nonlinear_solver = om.NonlinearBlockGS()

        class Super(om.Group):

            def setup(self):
                self.add_subsystem('sub', Sub())

        top = om.Problem(model=Super())

        top.setup()

        # These solvers override the ones set in the setup method of the 'sub' groups
        top.model.sub.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
        top.model.sub.linear_solver = om.ScipyKrylov()

        self.assertTrue(isinstance(top.model.sub.nonlinear_solver, om.NewtonSolver))
        self.assertTrue(isinstance(top.model.sub.linear_solver, om.ScipyKrylov))

    def test_configure_add_indep_var(self):
        # add outputs to an IndepVarComp in Group configure

        class Model(om.Group):
            def initialize(self):
                self.options.declare('where_to_add', values=('setup', 'configure'))

            def setup(self):
                comp1 = self.add_subsystem('comp1', om.IndepVarComp())
                comp2 = self.add_subsystem('comp2', om.IndepVarComp())

                comp1.add_output('foo', val=1.0)
                comp2.add_output('foo', val=1.0)

                self.add_subsystem('comp3', om.ExecComp('y=a+b'))

                if self.options['where_to_add'] == 'setup':
                    comp1.add_output('a', val=2.0)
                    comp2.add_output('b', val=3.0)

                    self.connect('comp1.a', 'comp3.a')
                    self.connect('comp2.b', 'comp3.b')

            def configure(self):
                if self.options['where_to_add'] == 'configure':
                    self.comp1.add_output('a', val=2.0)
                    self.comp2.add_output('b', val=3.0)

                    self.connect('comp1.a', 'comp3.a')
                    self.connect('comp2.b', 'comp3.b')

        for where in ('setup', 'configure'):
            p = om.Problem(Model(where_to_add=where))
            p.setup()
            p.run_model()

            inputs = p.model.list_inputs(out_stream=None)
            self.assertEqual(sorted(inputs), [
                ('comp3.a', {'val': [2.]}),
                ('comp3.b', {'val': [3.]})
            ], "Inputs don't match when added in %s." % where)

            outputs = p.model.list_outputs(out_stream=None)
            self.assertEqual(sorted(outputs), [
                ('comp1.a',   {'val': [2.]}),
                ('comp1.foo', {'val': [1.]}),
                ('comp2.b',   {'val': [3.]}),
                ('comp2.foo', {'val': [1.]}),
                ('comp3.y',   {'val': [5.]})
            ], "Outputs don't match when added in %s." % where)

    def test_configure_add_input_output(self):
        # add inputs and outputs to an ExplicitComponent in Group configure

        class MyComp(om.ExplicitComponent):
            def setup(self):
                self.add_input('a', val=0.)
                self.add_output('a2', val=0.)

            def compute(self, inputs, outputs):
                outputs['a2'] = inputs['a'] * 2.
                if 'b' in inputs:
                    outputs['b2'] = inputs['b'] * 2.

        class Model(om.Group):
            def initialize(self):
                self.options.declare('add_b2', default=False)

            def setup(self):
                self.add_subsystem('indep', om.IndepVarComp(), promotes=['*'])
                self.add_subsystem('mcomp', MyComp(), promotes=['*'])

                self.add_subsystem('sub', om.Group(), promotes_inputs=['*'])
                self.sub.add_subsystem('mcomp', MyComp(), promotes=['*'])

                self.indep.add_output('a', val=2.0)

            def configure(self):
                if self.options['add_b2']:
                    self.indep.add_output('b', val=3.0)

                    self.mcomp.add_input('b', val=0.)
                    self.mcomp.add_output('b2', val=0.)

                    self.sub.mcomp.add_input('b', val=0.)
                    self.sub.mcomp.add_output('b2', val=0.)

        # add inputs/outputs in setup only
        p = om.Problem(Model(add_b2=False))
        p.setup()
        p.run_model()

        inputs = p.model.list_inputs(out_stream=None)
        self.assertEqual(sorted(inputs), [
            ('mcomp.a',     {'val': [2.]}),
            ('sub.mcomp.a', {'val': [2.]}),
        ])

        outputs = p.model.list_outputs(out_stream=None)
        self.assertEqual(sorted(outputs), [
            ('indep.a',      {'val': [2.]}),
            ('mcomp.a2',     {'val': [4.]}),
            ('sub.mcomp.a2', {'val': [4.]}),
        ])

        # add inputs/outputs in configure
        p = om.Problem(Model(add_b2=True))
        p.setup()
        p.run_model()

        inputs = p.model.list_inputs(out_stream=None)
        self.assertEqual(sorted(inputs), [
            ('mcomp.a',     {'val': [2.]}),
            ('mcomp.b',     {'val': [3.]}),
            ('sub.mcomp.a', {'val': [2.]}),
            ('sub.mcomp.b', {'val': [3.]}),
        ])

        outputs= p.model.list_outputs(out_stream=None)
        self.assertEqual(sorted(outputs), [
            ('indep.a',      {'val': [2.]}),
            ('indep.b',      {'val': [3.]}),
            ('mcomp.a2',     {'val': [4.]}),
            ('mcomp.b2',     {'val': [6.]}),
            ('sub.mcomp.a2', {'val': [4.]}),
            ('sub.mcomp.b2', {'val': [6.]}),
        ])

    def test_feature_post_setup_solver_configure(self):

        class ImplSimple(om.ImplicitComponent):

            def setup(self):
                self.add_input('a', val=1.)
                self.add_output('x', val=0.)

            def apply_nonlinear(self, inputs, outputs, residuals):
                residuals['x'] = np.exp(outputs['x']) - \
                    inputs['a']**2 * outputs['x']**2

            def linearize(self, inputs, outputs, jacobian):
                jacobian['x', 'x'] = np.exp(outputs['x']) - \
                    2 * inputs['a']**2 * outputs['x']
                jacobian['x', 'a'] = -2 * inputs['a'] * outputs['x']**2

        class Sub(om.Group):

            def setup(self):
                self.add_subsystem('comp', ImplSimple())

                # This will not solve it
                self.nonlinear_solver = om.NonlinearBlockGS()

            def configure(self):
                # This will not solve it either.
                self.nonlinear_solver = om.NonlinearBlockGS()

        class Super(om.Group):

            def setup(self):
                self.add_subsystem('sub', Sub())

        top = om.Problem(model=Super())

        top.setup()

        # This will solve it.
        top.model.sub.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
        top.model.sub.linear_solver = om.ScipyKrylov()

        self.assertTrue(isinstance(top.model.sub.nonlinear_solver, om.NewtonSolver))
        self.assertTrue(isinstance(top.model.sub.linear_solver, om.ScipyKrylov))

    def test_post_final_setup_hook(self):
        def hook_func(prob):
            prob['p2.y'] = 5.0

        hooks.use_hooks = True
        hooks._register_hook('final_setup', class_name='Problem', post=hook_func)
        try:
            prob = om.Problem()
            model = prob.model

            model.add_subsystem('p1', om.IndepVarComp('x', 3.0))
            model.add_subsystem('p2', om.IndepVarComp('y', -4.0))
            model.add_subsystem('comp', om.ExecComp("f_xy=2.0*x+3.0*y"))

            model.connect('p1.x', 'comp.x')
            model.connect('p2.y', 'comp.y')

            prob.setup()
            prob.run_model()

            assert_near_equal(prob['p2.y'], 5.0)
            assert_near_equal(prob['comp.f_xy'], 21.0)
        finally:
            hooks._unregister_hook('final_setup', class_name='Problem')
            hooks.use_hooks = False

    def test_list_problem_vars(self):
        model = SellarDerivatives()
        model.nonlinear_solver = om.NonlinearBlockGS()

        prob = om.Problem(model)
        prob.driver = om.ScipyOptimizeDriver()
        prob.driver.options['optimizer'] = 'SLSQP'
        prob.driver.options['tol'] = 1e-9

        model.add_design_var('z', lower=np.array([-10.0, 0.0]), upper=np.array([10.0, 10.0]))
        model.add_design_var('x', lower=0.0, upper=10.0)
        model.add_objective('obj')
        model.add_constraint('con1', upper=0.0)
        model.add_constraint('con2', upper=0.0)

        prob.setup()
        prob.run_driver()

        # First, with no options
        stdout = sys.stdout
        strout = StringIO()
        sys.stdout = strout
        try:
            prob.list_problem_vars()
        finally:
            sys.stdout = stdout
        output = strout.getvalue().split('\n')
        self.assertEquals(output[1], r'Design Variables')
        self.assertRegex(output[5], r'^z +\|[0-9. e+-]+\| +2')
        self.assertEquals(output[9], r'Constraints')
        self.assertRegex(output[14], r'^con_cmp2.con2 +\[[0-9. e+-]+\] +1')
        self.assertEquals(output[17], r'Objectives')
        self.assertRegex(output[21], r'^obj_cmp.obj +\[[0-9. e+-]+\] +1')

        # With show_promoted_name=False
        stdout = sys.stdout
        strout = StringIO()
        sys.stdout = strout
        try:
            prob.list_problem_vars(show_promoted_name=False)
        finally:
            sys.stdout = stdout
        output = strout.getvalue().split('\n')
        self.assertRegex(output[5], r'^z +\|[0-9. e+-]+\| +2')
        self.assertRegex(output[14], r'^con2 +\[[0-9. e+-]+\] +1')
        self.assertRegex(output[21], r'^obj +\[[0-9. e+-]+\] +1')

        # With all the optional columns
        stdout = sys.stdout
        strout = StringIO()
        sys.stdout = strout
        try:
            prob.list_problem_vars(
                desvar_opts=['lower', 'upper', 'ref', 'ref0',
                             'indices', 'adder', 'scaler',
                             'parallel_deriv_color',
                             'cache_linear_solution'],
                cons_opts=['lower', 'upper', 'equals', 'ref', 'ref0',
                           'indices', 'adder', 'scaler', 'linear',
                           'parallel_deriv_color',
                           'cache_linear_solution'],
                objs_opts=['ref', 'ref0',
                           'indices', 'adder', 'scaler',
                           'parallel_deriv_color',
                           'cache_linear_solution'],
            )
        finally:
            sys.stdout = stdout
        output = strout.getvalue().split('\n')
        self.assertRegex(output[3],
                    r'^name\s+val\s+size\s+lower\s+upper\s+ref\s+ref0\s+'
                    r'indices\s+adder\s+scaler\s+parallel_deriv_color\s+'
                    r'cache_linear_solution')
        self.assertRegex(output[5],
                    r'^z\s+\|[0-9.e+-]+\|\s+2\s+\|10.0\|\s+\|[0-9.e+-]+\|\s+None\s+'
                    r'None\s+None\s+None\s+None\s+None\s+False')

        # With all the optional columns and print_arrays
        stdout = sys.stdout
        strout = StringIO()
        sys.stdout = strout
        try:
            prob.list_problem_vars(print_arrays=True,
                                   desvar_opts=['lower', 'upper', 'ref', 'ref0',
                                                'indices', 'adder', 'scaler',
                                                'parallel_deriv_color',
                                                'cache_linear_solution'],
                                   cons_opts=['lower', 'upper', 'equals', 'ref', 'ref0',
                                              'indices', 'adder', 'scaler', 'linear',
                                              'parallel_deriv_color',
                                              'cache_linear_solution'],
                                   objs_opts=['ref', 'ref0',
                                              'indices', 'adder', 'scaler',
                                              'parallel_deriv_color',
                                              'cache_linear_solution'],
                                   )
        finally:
            sys.stdout = stdout
        output = strout.getvalue().split('\n')
        self.assertRegex(output[6], r'^\s+val:')
        self.assertRegex(output[7], r'^\s+array+\(+\[[0-9., e+-]+\]+\)')
        self.assertRegex(output[9], r'^\s+lower:')
        self.assertRegex(output[10], r'^\s+array+\(+\[[0-9., e+-]+\]+\)')
        self.assertRegex(output[12], r'^\s+upper:')
        self.assertRegex(output[13], r'^\s+array+\(+\[[0-9., e+-]+\]+\)')

    def test_list_problem_vars_driver_scaling(self):
        model = SellarDerivatives()
        model.nonlinear_solver = om.NonlinearBlockGS()

        prob = om.Problem(model)
        prob.driver = om.ScipyOptimizeDriver()
        prob.driver.options['optimizer'] = 'SLSQP'
        prob.driver.options['tol'] = 1e-9

        model.add_design_var('z', lower=np.array([-10.0, 0.0]),
                             upper=np.array([10.0, 10.0]), ref=1.5)
        model.add_design_var('x', lower=0.0, upper=10.0)
        model.add_objective('obj', ref=3.0)
        model.add_constraint('con1', upper=0.0)
        model.add_constraint('con2', upper=0.0, ref=2.0)

        prob.setup()
        prob.run_driver()

        # Driver Scaling
        stdout = sys.stdout
        strout = StringIO()
        sys.stdout = strout

        try:
            prob.list_problem_vars()
        finally:
            sys.stdout = stdout
        output = strout.getvalue().split('\n')

        self.assertTrue('1.31' in output[5]) # z
        self.assertTrue('-10.' in output[14]) # con
        self.assertTrue('1.06' in output[21]) # obj

        # Model Scaling
        stdout = sys.stdout
        strout = StringIO()
        sys.stdout = strout

        try:
            prob.list_problem_vars(driver_scaling=False)
        finally:
            sys.stdout = stdout
        output = strout.getvalue().split('\n')

        self.assertTrue('1.9' in output[5]) # z
        self.assertTrue('-20.' in output[14]) # con
        self.assertTrue('3.18' in output[21]) # obj

    def test_feature_list_problem_vars(self):

        prob = om.Problem(model=SellarDerivatives())
        model = prob.model
        model.nonlinear_solver = om.NonlinearBlockGS()

        prob.driver = om.ScipyOptimizeDriver()
        prob.driver.options['optimizer'] = 'SLSQP'
        prob.driver.options['tol'] = 1e-9

        model.add_design_var('z', lower=np.array([-10.0, 0.0]), upper=np.array([10.0, 10.0]))
        model.add_design_var('x', lower=0.0, upper=10.0)
        model.add_objective('obj')
        model.add_constraint('con1', upper=0.0)
        model.add_constraint('con2', upper=0.0)

        prob.setup()
        prob.run_driver()

        prob.list_problem_vars(print_arrays=True,
                               desvar_opts=['lower', 'upper', 'ref', 'ref0',
                                            'indices', 'adder', 'scaler',
                                            'parallel_deriv_color'],
                               cons_opts=['lower', 'upper', 'equals', 'ref', 'ref0',
                                          'indices', 'adder', 'scaler', 'linear'],
                               objs_opts=['ref', 'ref0',
                                          'indices', 'adder', 'scaler',
                                          'parallel_deriv_color',
                                          'cache_linear_solution'])

    def test_error_msg_set_val_before_setup(self):
        prob = om.Problem()
        model = prob.model
        model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])

        msg = "Problem .*: 'x' Cannot call set_val before setup."
        with self.assertRaisesRegex(RuntimeError, msg):
            prob.set_val('x', 0.)

    def test_design_var_connected_to_output_as_input_err(self):
        prob = om.Problem()
        root = prob.model

        prob.driver = om.ScipyOptimizeDriver()

        root.add_subsystem('initial_comp', om.ExecComp(['x = 10']), promotes_outputs=['x'])

        outer_group = root.add_subsystem('outer_group', om.Group(), promotes_inputs=['x'])
        inner_group = outer_group.add_subsystem('inner_group', om.Group(), promotes_inputs=['x'])

        c1 = inner_group.add_subsystem('c1', om.ExecComp(['y = x * 2.0',  'z = x ** 2']),
                                       promotes_inputs=['x'])

        c1.add_design_var('x', lower=0, upper=5)

        msg = "Design variable 'initial_comp.x' is not an IndepVarComp or ImplicitComp output."
        with self.assertRaises(RuntimeError) as cm:
            prob.setup()
        self.assertEqual(str(cm.exception), msg)

    def test_design_var_connected_to_output(self):
        prob = om.Problem()
        root = prob.model

        prob.driver = om.ScipyOptimizeDriver()

        root.add_subsystem('ivc', om.IndepVarComp('x', val=10.))

        outer_group = root.add_subsystem('outer_group', om.Group(), promotes_inputs=['x'])
        inner_group = outer_group.add_subsystem('inner_group', om.Group(), promotes_inputs=['x'])

        c1 = inner_group.add_subsystem('c1', om.ExecComp(['y = x * 2.0',  'z = x ** 2']),
                                       promotes_inputs=['x'])

        c1.add_design_var('x', lower=0, upper=5)

        try:
            prob.setup()
        except RuntimeError:
            self.fail("'setup raised RuntimeError unexpectedly")

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

        root.add_subsystem('ivc', om.IndepVarComp('x', val=10.))

        outer_group = root.add_subsystem('outer_group', om.Group(), promotes_inputs=['x'])
        inner_group = outer_group.add_subsystem('inner_group', om.Group(), promotes_inputs=['x'])

        c1 = inner_group.add_subsystem('c1', om.ExecComp(['y = x * 2.0',  'z = x ** 2']),
                                       promotes_inputs=['x'])

        c1.add_design_var('x', lower=0, upper=5)
        root.connect('ivc.x', 'x')

        try:
            prob.setup()
        except RuntimeError:
            self.fail("'setup raised RuntimeError unexpectedly")


class NestedProblemTestCase(unittest.TestCase):

    def test_nested_prob(self):

        class _ProblemSolver(om.NonlinearRunOnce):
            def solve(self):
                # create a simple subproblem and run it to test for global solver_info bug
                p = om.Problem()
                p.model.add_subsystem('indep', om.IndepVarComp('x', 1.0))
                p.model.add_subsystem('comp', om.ExecComp('y=2*x'))
                p.model.connect('indep.x', 'comp.x')
                p.setup()
                p.run_model()

                return super().solve()

        p = om.Problem()
        p.model.add_subsystem('indep', om.IndepVarComp('x', 1.0))
        G = p.model.add_subsystem('G', om.Group())
        G.add_subsystem('comp', om.ExecComp('y=2*x'))
        G.nonlinear_solver = _ProblemSolver()
        p.model.connect('indep.x', 'G.comp.x')
        p.setup()
        p.run_model()

    def test_cs_across_nested(self):

        class NestedAnalysis(om.ExplicitComponent):

            def __init__(self):
                super().__init__()
                self._problem = None

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

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

                # Setup sub-problem
                self._problem = prob = om.Problem()
                model = prob.model
                model.add_subsystem('parab', Paraboloid(), promotes=['*'])
                prob.setup(force_alloc_complex=True)

            def setup_partials(self):
                self.declare_partials(of='*', wrt='*')

            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()

                # Extract outputs
                outputs['f_xy'] = prob.get_val('f_xy')

                if under_cs:
                    prob.set_complex_step_mode(False)

            def compute_partials(self, inputs, partials):
                totals = self._problem.compute_totals(of='f_xy', wrt=['x', 'y'])
                partials['f_xy', 'x'] = totals['f_xy', 'x']
                partials['f_xy', 'y'] = totals['f_xy', 'y']

        prob = om.Problem()
        model = prob.model
        model.add_subsystem('nested', NestedAnalysis(), promotes=['*'])

        prob.setup(force_alloc_complex=True)

        prob.set_val('x', 3.5)
        prob.set_val('y', 1.5)

        prob.run_model()

        totals = prob.check_totals(of='f_xy', wrt=['x', 'y'], method='cs', out_stream=None)
        for key, val in totals.items():
            assert_near_equal(val['rel error'][0], 0.0, 1e-12)

    def test_nested_prob_default_naming(self):
        import openmdao.core.problem
        class _ProblemSolver(om.NonlinearRunOnce):

            def __init__(self, prob_name=None):
                super(_ProblemSolver, self).__init__()
                self.prob_name = prob_name
                self._problem = None

            def solve(self):
                # create a simple subproblem and run it to test for global solver_info bug
                p = om.Problem(name=self.prob_name)
                self._problem = p
                p.model.add_subsystem('indep', om.IndepVarComp('x', 1.0))
                p.model.add_subsystem('comp', om.ExecComp('y=2*x'))
                p.model.connect('indep.x', 'comp.x')
                p.setup()
                p.run_model()

                print(p.msginfo)

                return super().solve()

        # Initially use the default names
        openmdao.core.problem._problem_names = []  # need to reset these to simulate separate runs
        p = om.Problem()
        p.model.add_subsystem('indep', om.IndepVarComp('x', 1.0))
        G = p.model.add_subsystem('G', om.Group())
        G.add_subsystem('comp', om.ExecComp('y=2*x'))
        G.nonlinear_solver = _ProblemSolver()
        p.model.connect('indep.x', 'G.comp.x')
        p.setup()
        p.run_model()  # need to do run_model in this test so sub problem is created

        self.assertEqual(p._get_inst_id(), 'problem1')
        self.assertEqual(G.nonlinear_solver._problem._get_inst_id(), 'problem2')

        # If the second Problem uses the default name of the first
        openmdao.core.problem._problem_names = []  # need to reset these to simulate separate runs
        p = om.Problem()
        p.model.add_subsystem('indep', om.IndepVarComp('x', 1.0))
        G = p.model.add_subsystem('G', om.Group())
        G.add_subsystem('comp', om.ExecComp('y=2*x'))
        G.nonlinear_solver = _ProblemSolver(prob_name='problem1')
        p.model.connect('indep.x', 'G.comp.x')
        p.setup()

        with self.assertRaises(Exception) as context:
            p.run_model()
        self.assertEqual(str(context.exception), "The problem name 'problem1' already exists")

        # If the first Problem uses the default name of 'problem2'
        openmdao.core.problem._problem_names = []  # need to reset these to simulate separate runs
        p = om.Problem(name='problem2')
        p.model.add_subsystem('indep', om.IndepVarComp('x', 1.0))
        G = p.model.add_subsystem('G', om.Group())
        G.add_subsystem('comp', om.ExecComp('y=2*x'))
        G.nonlinear_solver = _ProblemSolver()
        p.model.connect('indep.x', 'G.comp.x')
        p.setup()
        p.run_model()

        self.assertEqual(p._get_inst_id(), 'problem2')
        self.assertEqual(G.nonlinear_solver._problem._get_inst_id(), 'problem2.1')


class SystemInTwoProblemsTestCase(unittest.TestCase):
    def test_2problems(self):
        prob = om.Problem()
        G1 = prob.model.add_subsystem("G1", om.Group())
        G2 = G1.add_subsystem('G2', om.Group(), promotes_inputs=['x'])
        G2.add_subsystem('C1', om.ExecComp('y = 2 * x'), promotes_inputs=['x'])
        G2.add_subsystem('C2', om.ExecComp('y = 3 * x'), promotes_inputs=['x'])

        prob.setup()
        prob.run_model()

        # 2nd problem
        prob = om.Problem()
        prob.model = G2

        prob.setup()
        prob.run_model()

        np.testing.assert_allclose(prob['C1.y'], 2.0)
        np.testing.assert_allclose(prob['C2.y'], 3.0)


if __name__ == "__main__":
    unittest.main()
