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test_problem.py
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test_problem.py
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""" Unit tests for the problem interface."""
import sys
import unittest
import itertools
from io import StringIO
import numpy as np
from collections import defaultdict
import openmdao.api as om
from openmdao.core.problem import _default_prob_name
from openmdao.core.driver import Driver
from openmdao.test_suite.components.paraboloid import Paraboloid
from openmdao.test_suite.components.misc_components import MultComp
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
from openmdao.utils.testing_utils import use_tempdirs
from openmdao.utils.tests.test_hooks import hooks_active
try:
from parameterized import parameterized
except ImportError:
from openmdao.utils.assert_utils import SkipParameterized as parameterized
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()
p.model.add_subsystem('comp', TestComp(), promotes=['*'])
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.model._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.model.linear_solver = om.ScipyKrylov()
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 = f"Problem {prob._get_inst_id()}: 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.assertRaises(RuntimeError) as err:
prob.set_complex_step_mode(True)
self.assertEqual(str(err.exception), msg)
prob = om.Problem()
prob.model.add_subsystem('comp', Paraboloid())
msg = f"Problem {prob._get_inst_id()}: set_complex_step_mode cannot be called before `Problem.run_model()`, " + \
"`Problem.run_driver()`, or `Problem.final_setup()`."
with self.assertRaises(RuntimeError) as err:
prob.set_complex_step_mode(True)
self.assertEqual(str(err.exception), msg)
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(name="get_set_with_units_diff_err")
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 Exception as err:
self.assertEqual(str(err),
"\nCollected errors for problem 'get_set_with_units_diff_err':"
"\n <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)