def test_double_arraycomp(self):
# Mainly testing a bug in the array return for multiple arrays
group = Group()
group.add('x_param1', IndepVarComp('x1', np.ones((2))), promotes=['*'])
group.add('x_param2', IndepVarComp('x2', np.ones((2))), promotes=['*'])
group.add('mycomp', DoubleArrayComp(), promotes=['*'])
prob = Problem(impl=impl)
prob.root = group
prob.root.ln_solver = PetscKSP()
prob.setup(check=False)
prob.run()
Jbase = group.mycomp.JJ
J = prob.calc_gradient(['x1', 'x2'], ['y1', 'y2'], mode='fwd',
return_format='array')
diff = np.linalg.norm(J - Jbase)
assert_rel_error(self, diff, 0.0, 1e-8)
J = prob.calc_gradient(['x1', 'x2'], ['y1', 'y2'], mode='fd',
return_format='array')
diff = np.linalg.norm(J - Jbase)
assert_rel_error(self, diff, 0.0, 1e-8)
J = prob.calc_gradient(['x1', 'x2'], ['y1', 'y2'], mode='rev',
return_format='array')
diff = np.linalg.norm(J - Jbase)
assert_rel_error(self, diff, 0.0, 1e-8)
def __init__(self):
super(ConvergeDivergeGroups, self).__init__()
self.add('p', ParamComp('x', 2.0))
sub1 = self.add('sub1', Group())
sub1.add('comp1', Comp1())
sub2 = sub1.add('sub2', Group())
sub2.add('comp2', Comp2())
sub2.add('comp3', Comp3())
sub1.add('comp4', Comp4())
sub3 = self.add('sub3', Group())
sub3.add('comp5', Comp5())
sub3.add('comp6', Comp6())
self.add('comp7', Comp7())
self.connect("p.x", "sub1.comp1.x1")
self.connect('sub1.comp1.y1', 'sub1.sub2.comp2.x1')
self.connect('sub1.comp1.y2', 'sub1.sub2.comp3.x1')
self.connect('sub1.sub2.comp2.y1', 'sub1.comp4.x1')
self.connect('sub1.sub2.comp3.y1', 'sub1.comp4.x2')
self.connect('sub1.comp4.y1', 'sub3.comp5.x1')
self.connect('sub1.comp4.y2', 'sub3.comp6.x1')
self.connect('sub3.comp5.y1', 'comp7.x1')
self.connect('sub3.comp6.y1', 'comp7.x2')
def test_simple_array_model(self):
prob = Problem()
prob.root = Group()
prob.root.add(
'comp',
ExecComp(['y[0]=2.0*x[0]+7.0*x[1]', 'y[1]=5.0*x[0]-3.0*x[1]'],
x=np.zeros([2]),
y=np.zeros([2])))
prob.root.add('p1', ParamComp('x', np.ones([2])))
prob.root.connect('p1.x', 'comp.x')
prob.setup(check=False)
prob.run()
data = prob.check_partial_derivatives(out_stream=None)
assert_rel_error(self, data['comp'][('y', 'x')]['abs error'][0], 0.0,
1e-5)
assert_rel_error(self, data['comp'][('y', 'x')]['abs error'][1], 0.0,
1e-5)
assert_rel_error(self, data['comp'][('y', 'x')]['abs error'][2], 0.0,
1e-5)
assert_rel_error(self, data['comp'][('y', 'x')]['rel error'][0], 0.0,
1e-5)
assert_rel_error(self, data['comp'][('y', 'x')]['rel error'][1], 0.0,
1e-5)
assert_rel_error(self, data['comp'][('y', 'x')]['rel error'][2], 0.0,
1e-5)
def test_parab_FD_subbed_Pcomps(self):
model = Problem(impl=impl)
root = model.root = Group()
par = root.add('par', ParallelGroup())
par.add('s1', MP_Point(root=2.0))
par.add('s2', MP_Point(root=3.0))
root.add('sumcomp', ExecComp('sum = x1+x2'))
root.connect('par.s1.c.y', 'sumcomp.x1')
root.connect('par.s2.c.y', 'sumcomp.x2')
driver = model.driver = pyOptSparseDriver()
driver.add_param('par.s1.p.x', low=-100, high=100)
driver.add_param('par.s2.p.x', low=-100, high=100)
driver.add_objective('sumcomp.sum')
root.fd_options['force_fd'] = True
model.setup(check=False)
model.run()
if not MPI or self.comm.rank == 0:
assert_rel_error(self, model['par.s1.p.x'], 2.0, 1.e-6)
if not MPI or self.comm.rank == 1:
assert_rel_error(self, model['par.s2.p.x'], 3.0, 1.e-6)
def test_simple(self):
group = Group()
group.add('x_param', IndepVarComp('x', 1.0), promotes=['*'])
group.add('mycomp', SimpleCompDerivMatVec(), promotes=['x', 'y'])
prob = Problem(impl=impl)
prob.root = group
prob.root.ln_solver = PetscKSP()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
def test_parab_FD(self):
model = Problem(impl=impl)
root = model.root = Group()
par = root.add('par', ParallelGroup())
par.add('c1', Parab1D(root=2.0))
par.add('c2', Parab1D(root=3.0))
root.add('p1', ParamComp('x', val=0.0))
root.add('p2', ParamComp('x', val=0.0))
root.connect('p1.x', 'par.c1.x')
root.connect('p2.x', 'par.c2.x')
root.add('sumcomp', ExecComp('sum = x1+x2'))
root.connect('par.c1.y', 'sumcomp.x1')
root.connect('par.c2.y', 'sumcomp.x2')
driver = model.driver = pyOptSparseDriver()
driver.add_param('p1.x', low=-100, high=100)
driver.add_param('p2.x', low=-100, high=100)
driver.add_objective('sumcomp.sum')
root.fd_options['force_fd'] = True
model.setup(check=False)
model.run()
if not MPI or self.comm.rank == 0:
assert_rel_error(self, model['p1.x'], 2.0, 1.e-6)
assert_rel_error(self, model['p2.x'], 3.0, 1.e-6)
def test_simple_jac(self):
group = Group()
group.add('x_param', IndepVarComp('x', 1.0), promotes=['*'])
group.add('mycomp', ExecComp(['y=2.0*x']), promotes=['x', 'y'])
prob = Problem()
prob.root = group
prob.root.ln_solver = DirectSolver()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
def test_simple_array_model2(self):
prob = Problem()
prob.root = Group()
comp = prob.root.add(
'comp',
ExecComp('y = mat.dot(x)',
x=np.zeros((2, )),
y=np.zeros((2, )),
mat=np.array([[2., 7.], [5., -3.]])))
p1 = prob.root.add('p1', ParamComp('x', np.ones([2])))
prob.root.connect('p1.x', 'comp.x')
prob.setup(check=False)
prob.run()
data = prob.check_partial_derivatives(out_stream=None)
assert_rel_error(self, data['comp'][('y', 'x')]['abs error'][0], 0.0,
1e-5)
assert_rel_error(self, data['comp'][('y', 'x')]['abs error'][1], 0.0,
1e-5)
assert_rel_error(self, data['comp'][('y', 'x')]['abs error'][2], 0.0,
1e-5)
assert_rel_error(self, data['comp'][('y', 'x')]['rel error'][0], 0.0,
1e-5)
assert_rel_error(self, data['comp'][('y', 'x')]['rel error'][1], 0.0,
1e-5)
assert_rel_error(self, data['comp'][('y', 'x')]['rel error'][2], 0.0,
1e-5)
def test_unequal_training_outputs(self):
meta = MetaModel()
meta.add_param('x', 0.)
meta.add_param('y', 0.)
meta.add_output('f', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [1.0, 1.0, 1.0, 1.0]
prob['meta.train:y'] = [1.0, 2.0, 3.0, 4.0]
prob['meta.train:f'] = [1.0, 1.0]
prob['meta.x'] = 1.0
prob['meta.y'] = 1.0
with self.assertRaises(RuntimeError) as cm:
prob.run()
expected = "MetaModel: Each variable must have the same number" \
" of training points. Expected 4 but found" \
" 2 points for 'f'."
self.assertEqual(str(cm.exception), expected)
def test_simple_in_group_matvec(self):
group = Group()
sub = group.add('sub', Group(), promotes=['x', 'y'])
group.add('x_param', ParamComp('x', 1.0), promotes=['*'])
sub.add('mycomp', SimpleCompDerivMatVec(), promotes=['x', 'y'])
prob = Problem()
prob.root = group
prob.root.ln_solver = ExplicitSolver()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
def test_simple_matvec_subbed(self):
group = Group()
group.add('mycomp', SimpleCompDerivMatVec(), promotes=['x', 'y'])
prob = Problem()
prob.root = Group()
prob.root.add('x_param', ParamComp('x', 1.0), promotes=['*'])
prob.root.add('sub', group, promotes=['*'])
prob.root.ln_solver = ExplicitSolver()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
def test_array2D(self):
group = Group()
group.add('x_param', IndepVarComp('x', np.ones((2, 2))), promotes=['*'])
group.add('mycomp', ArrayComp2D(), promotes=['x', 'y'])
prob = Problem(impl=impl)
prob.root = group
prob.root.ln_solver = PetscKSP()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
Jbase = prob.root.mycomp._jacobian_cache
diff = np.linalg.norm(J['y']['x'] - Jbase['y', 'x'])
assert_rel_error(self, diff, 0.0, 1e-8)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
diff = np.linalg.norm(J['y']['x'] - Jbase['y', 'x'])
assert_rel_error(self, diff, 0.0, 1e-8)
def test_math(self):
prob = Problem(root=Group())
C1 = prob.root.add('C1', ExecComp('y=sin(x)', x=2.0))
self.assertTrue('x' in C1._params_dict)
self.assertTrue('y' in C1._unknowns_dict)
prob.setup(check=False)
prob.run()
assert_rel_error(self, C1.unknowns['y'], math.sin(2.0), 0.00001)
def test_array_to_scalar(self):
root = Group()
root.add('P1', ParamComp('x', np.array([2., 3.])))
root.add('C1', SimpleComp())
root.add('C2', ExecComp('y = x * 3.', y=0., x=0.))
root.connect('P1.x', 'C1.x', src_indices=[0,])
root.connect('P1.x', 'C2.x', src_indices=[1,])
prob = Problem(root)
prob.setup(check=False)
prob.run()
self.assertAlmostEqual(root.C1.params['x'], 2.)
self.assertAlmostEqual(root.C2.params['x'], 3.)
def test_fd_options_meta_step_size(self):
class MetaParaboloid(Component):
""" Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3 """
def __init__(self):
super(MetaParaboloid, self).__init__()
# Params
self.add_param('x', 1.0, fd_step_size=1.0e5)
self.add_param('y', 1.0, fd_step_size=1.0e5)
# Unknowns
self.add_output('f_xy', 0.0)
def solve_nonlinear(self, params, unknowns, resids):
"""f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
Optimal solution (minimum): x = 6.6667; y = -7.3333
"""
x = params['x']
y = params['y']
f_xy = ((x - 3.0)**2 + x * y + (y + 4.0)**2 - 3.0)
unknowns['f_xy'] = f_xy
def jacobian(self, params, unknowns, resids):
"""Analytical derivatives"""
x = params['x']
y = params['y']
J = {}
J['f_xy', 'x'] = (2.0 * x - 6.0 + y)
J['f_xy', 'y'] = (2.0 * y + 8.0 + x)
return J
prob = Problem()
prob.root = Group()
comp = prob.root.add('comp', MetaParaboloid())
prob.root.add('p1', ParamComp('x', 15.0))
prob.root.add('p2', ParamComp('y', 15.0))
prob.root.connect('p1.x', 'comp.x')
prob.root.connect('p2.y', 'comp.y')
comp.fd_options['force_fd'] = True
prob.setup(check=False)
prob.run()
# Make sure bad meta step_size is used
# Derivative should be way high with this.
J = prob.calc_gradient(['p1.x'], ['comp.f_xy'], return_format='dict')
self.assertGreater(J['comp.f_xy']['p1.x'][0][0], 1000.0)
def __init__(self):
super(TestProb, self).__init__()
self.root = root = Group()
root.add('c1', SimpleArrayComp())
root.add('p1', ParamComp('p', 1 * np.ones(2)))
root.connect('p1.p', 'c1.x')
root.add('ci1', SimpleImplicitComp())
root.add('pi1', ParamComp('p', 1.))
root.connect('pi1.p', 'ci1.x')
def test_array(self):
prob = Problem(root=Group())
C1 = prob.root.add('C1',
ExecComp('y=x[1]', x=np.array([1., 2., 3.]), y=0.0))
self.assertTrue('x' in C1._params_dict)
self.assertTrue('y' in C1._unknowns_dict)
prob.setup(check=False)
prob.run()
assert_rel_error(self, C1.unknowns['y'], 2.0, 0.00001)
def test_prom_conns(self):
# this test mimics some of the connections found in test_nozzle in pycycle. The bug was that
# an unknown that was connected to one parameter
# (desVars.Ps_exhaust to nozzle.press_calcs.Ps_exhaust), was not being connected to the
# other parameters ('nozzle.ideal_flow.chem_eq.n2ls.P', 'nozzle.ideal_flow.mach_calc.Ps',
# and 'nozzle.ideal_flow.props.tp2props.P') that were connected via input-input connections
# to nozzle.press_calcs.Ps_exhaust.
prob = Problem(root=Group())
root = prob.root
desVars = root.add("desVars",
ParamComp('Ps_exhaust', 1.0),
promotes=('Ps_exhaust', ))
nozzle = root.add("nozzle", Group())
press_calcs = nozzle.add('press_calcs',
ExecComp('out=Ps_exhaust'),
promotes=('Ps_exhaust', ))
ideal_flow = nozzle.add("ideal_flow", Group())
chem_eq = ideal_flow.add('chem_eq', Group(), promotes=('P', ))
n2ls = chem_eq.add("n2ls", ExecComp('out=P'), promotes=('P', ))
props = ideal_flow.add("props", Group(), promotes=('P', ))
tp2props = props.add("tp2props", ExecComp('out=P'), promotes=('P', ))
mach_calc = ideal_flow.add("mach_calc",
ExecComp('out=Ps'),
promotes=('Ps', ))
nozzle.connect('Ps_exhaust', 'ideal_flow.Ps')
root.connect('Ps_exhaust', 'nozzle.Ps_exhaust')
ideal_flow.connect('Ps', 'P')
prob.setup(check=False)
expected_targets = set([
'nozzle.ideal_flow.chem_eq.n2ls.P',
'nozzle.press_calcs.Ps_exhaust', 'nozzle.ideal_flow.mach_calc.Ps',
'nozzle.ideal_flow.props.tp2props.P'
])
self.assertEqual(set(prob.root.connections), expected_targets)
for tgt in expected_targets:
self.assertTrue('desVars.Ps_exhaust' in prob.root.connections[tgt])
def setUp(self):
self.startdir = os.getcwd()
self.tempdir = tempfile.mkdtemp(prefix='test_extcode-')
os.chdir(self.tempdir)
shutil.copy(os.path.join(DIRECTORY, 'external_code_for_testing.py'),
os.path.join(self.tempdir, 'external_code_for_testing.py'))
self.extcode = ExternalCodeForTesting()
self.top = Problem()
self.top.root = Group()
self.top.root.add('extcode', self.extcode)
def test_double_arraycomp(self):
# Mainly testing a bug in the array return for multiple arrays
group = Group()
group.add('x_param1', ParamComp('x1', np.ones((2))), promotes=['*'])
group.add('x_param2', ParamComp('x2', np.ones((2))), promotes=['*'])
group.add('mycomp', DoubleArrayComp(), promotes=['*'])
prob = Problem(impl=impl)
prob.root = group
prob.root.ln_solver = PetscKSP()
prob.setup(check=False)
prob.run()
Jbase = group.mycomp.JJ
J = prob.calc_gradient(['x1', 'x2'], ['y1', 'y2'],
mode='fwd',
return_format='array')
diff = np.linalg.norm(J - Jbase)
assert_rel_error(self, diff, 0.0, 1e-8)
J = prob.calc_gradient(['x1', 'x2'], ['y1', 'y2'],
mode='fd',
return_format='array')
diff = np.linalg.norm(J - Jbase)
assert_rel_error(self, diff, 0.0, 1e-8)
J = prob.calc_gradient(['x1', 'x2'], ['y1', 'y2'],
mode='rev',
return_format='array')
diff = np.linalg.norm(J - Jbase)
assert_rel_error(self, diff, 0.0, 1e-8)
def test_array_lhs(self):
prob = Problem(root=Group())
C1 = prob.root.add(
'C1',
ExecComp(['y[0]=x[1]', 'y[1]=x[0]'],
x=np.array([1., 2., 3.]),
y=np.array([0., 0.])))
self.assertTrue('x' in C1._params_dict)
self.assertTrue('y' in C1._unknowns_dict)
prob.setup(check=False)
prob.run()
assert_rel_error(self, C1.unknowns['y'], np.array([2., 1.]), 0.00001)
def test_complex_step(self):
prob = Problem(root=Group())
C1 = prob.root.add('C1', ExecComp(['y=2.0*x+1.'], x=2.0))
self.assertTrue('x' in C1._params_dict)
self.assertTrue('y' in C1._unknowns_dict)
prob.setup(check=False)
prob.run()
assert_rel_error(self, C1.unknowns['y'], 5.0, 0.00001)
J = C1.jacobian(C1.params, C1.unknowns, C1.resids)
assert_rel_error(self, J[('y', 'x')], 2.0, 0.00001)
def test_linear_system(self):
root = Group()
root.add('lin', LinearSystem(3))
x = np.array([1, 2, -3])
A = np.array([[5.0, -3.0, 2.0], [1.0, 7.0, -4.0], [1.0, 0.0, 8.0]])
b = A.dot(x)
root.add('p1', ParamComp('A', A))
root.add('p2', ParamComp('b', b))
root.connect('p1.A', 'lin.A')
root.connect('p2.b', 'lin.b')
prob = Problem(root)
prob.setup(check=False)
prob.run()
# Make sure it gets the right answer
assert_rel_error(self, prob['lin.x'], x, .0001)
assert_rel_error(self, np.linalg.norm(prob.root.resids.vec), 0.0,
1e-10)
# Compare against calculated derivs
Ainv = np.linalg.inv(A)
dx_dA = np.outer(Ainv, -x).reshape(3, 9)
dx_db = Ainv
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'],
mode='fwd',
return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'],
mode='rev',
return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'],
mode='fd',
return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
def __init__(self):
super(SingleDiamondGrouped, self).__init__()
self.add('p', ParamComp('x', 2.0))
sub1 = self.add('sub1', Group())
sub1.add('comp1', Comp1())
sub1.add('comp2', Comp2())
sub1.add('comp3', Comp3())
self.add('comp4', Comp4())
self.connect("p.x", "sub1.comp1.x1")
self.connect('sub1.comp1.y1', 'sub1.comp2.x1')
self.connect('sub1.comp1.y2', 'sub1.comp3.x1')
self.connect('sub1.comp2.y1', 'comp4.x1')
self.connect('sub1.comp3.y1', 'comp4.x2')
def test_fd_options_form(self):
prob = Problem()
prob.root = Group()
comp = prob.root.add('comp', Paraboloid())
prob.root.add('p1', ParamComp('x', 15.0))
prob.root.add('p2', ParamComp('y', 15.0))
prob.root.connect('p1.x', 'comp.x')
prob.root.connect('p2.y', 'comp.y')
comp.fd_options['force_fd'] = True
comp.fd_options['form'] = 'forward'
param_list = ['p1.x']
unknowns_list = ['comp.f_xy']
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(param_list, unknowns_list, return_format='dict')
assert_rel_error(self, J['comp.f_xy']['p1.x'][0][0], 39.0, 1e-6)
# Make sure it gives good result with small stepsize
comp.fd_options['form'] = 'backward'
J = prob.calc_gradient(['p1.x'], ['comp.f_xy'], return_format='dict')
assert_rel_error(self, J['comp.f_xy']['p1.x'][0][0], 39.0, 1e-6)
# Make sure it gives good result with small stepsize
comp.fd_options['form'] = 'central'
J = prob.calc_gradient(['p1.x'], ['comp.f_xy'], return_format='dict')
assert_rel_error(self, J['comp.f_xy']['p1.x'][0][0], 39.0, 1e-6)
# Now, Make sure we really are going foward and backward
comp.fd_options['form'] = 'forward'
comp.fd_options['step_size'] = 1e3
J = prob.calc_gradient(['p1.x'], ['comp.f_xy'], return_format='dict')
self.assertGreater(J['comp.f_xy']['p1.x'][0][0], 0.0)
comp.fd_options['form'] = 'backward'
J = prob.calc_gradient(['p1.x'], ['comp.f_xy'], return_format='dict')
self.assertLess(J['comp.f_xy']['p1.x'][0][0], 0.0)
# Central should get pretty close even for the bad stepsize
comp.fd_options['form'] = 'central'
J = prob.calc_gradient(['p1.x'], ['comp.f_xy'], return_format='dict')
assert_rel_error(self, J['comp.f_xy']['p1.x'][0][0], 39.0, 1e-1)
def test_indices(self):
size = 10
root = Group()
root.add('P1', ParamComp('x', np.zeros(size)))
root.add('C1', ExecComp('y = x * 2.', y=np.zeros(size//2), x=np.zeros(size//2)))
root.add('C2', ExecComp('y = x * 3.', y=np.zeros(size//2), x=np.zeros(size//2)))
root.connect('P1.x', "C1.x", src_indices=list(range(size//2)))
root.connect('P1.x', "C2.x", src_indices=list(range(size//2, size)))
prob = Problem(root)
prob.setup(check=False)
root.P1.unknowns['x'][0:size//2] += 1.0
root.P1.unknowns['x'][size//2:size] -= 1.0
prob.run()
assert_rel_error(self, root.C1.params['x'], np.ones(size//2), 0.0001)
assert_rel_error(self, root.C2.params['x'], -np.ones(size//2), 0.0001)
def __init__(self):
super(FanOutGrouped, self).__init__()
sub = self.add('sub', ParallelGroup())
pgroup = sub.add('pgroup', Group())
pgroup.add('p', ParamComp('x', 1.0))
pgroup.add('comp1', ExecComp(['y=3.0*x']))
sub.add('comp2', ExecComp(['y=-2.0*x']))
sub.add('comp3', ExecComp(['y=5.0*x']))
self.add('c2', ExecComp(['y=x']))
self.add('c3', ExecComp(['y=x']))
self.connect('sub.comp2.y', 'c2.x')
self.connect('sub.comp3.y', 'c3.x')
self.connect("sub.pgroup.comp1.y", "sub.comp2.x")
self.connect("sub.pgroup.comp1.y", "sub.comp3.x")
self.connect("sub.pgroup.p.x", "sub.pgroup.comp1.x")
def test_complex_step2(self):
prob = Problem(Group())
comp = prob.root.add('comp', ExecComp('y=x*x + x*2.0'))
prob.root.add('p1', ParamComp('x', 2.0))
prob.root.connect('p1.x', 'comp.x')
comp.fd_options['force_fd'] = False
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['p1.x'], ['comp.y'],
mode='fwd',
return_format='dict')
assert_rel_error(self, J['comp.y']['p1.x'], np.array([6.0]), 0.00001)
J = prob.calc_gradient(['p1.x'], ['comp.y'],
mode='rev',
return_format='dict')
assert_rel_error(self, J['comp.y']['p1.x'], np.array([6.0]), 0.00001)
def test_overrides(self):
class OverrideComp(Component):
def __init__(self):
super(OverrideComp, self).__init__()
# Params
self.add_param('x', 3.0)
# Unknowns
self.add_output('y', 5.5)
def solve_nonlinear(self, params, unknowns, resids):
""" Doesn't do much. """
unknowns['y'] = 7.0 * params['x']
def apply_linear(self, params, unknowns, dparams, dunknowns,
dresids, mode):
"""Never Call."""
raise RuntimeError(
"This should have been overridden by force_fd.")
def jacobian(self, params, unknowns, resids):
"""Never Call."""
raise RuntimeError(
"This should have been overridden by force_fd.")
prob = Problem()
prob.root = Group()
comp = prob.root.add('comp', OverrideComp())
prob.root.add('p1', ParamComp('x', 2.0))
prob.root.connect('p1.x', 'comp.x')
comp.fd_options['force_fd'] = True
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['p1.x'], ['comp.y'],
mode='fwd',
return_format='dict')
assert_rel_error(self, J['comp.y']['p1.x'][0][0], 7.0, 1e-6)
def test_warm_start(self):
# create metamodel with warm_restart = True
meta = MetaModel()
meta.add_param('x1', 0.)
meta.add_param('x2', 0.)
meta.add_output('y1', 0.)
meta.add_output('y2', 0.)
meta.default_surrogate = ResponseSurface()
meta.warm_restart = True
# add to problem
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
# provide initial training data
prob['meta.train:x1'] = [1.0, 3.0]
prob['meta.train:x2'] = [1.0, 4.0]
prob['meta.train:y1'] = [3.0, 1.0]
prob['meta.train:y2'] = [1.0, 7.0]
# run against a data point and check result
prob['meta.x1'] = 2.0
prob['meta.x2'] = 3.0
prob.run()
assert_rel_error(self, prob['meta.y1'], 1.9085, .001)
assert_rel_error(self, prob['meta.y2'], 3.9203, .001)
# Add 3rd training point, moves the estimate for that point
# back to where it should be.
prob['meta.train:x1'] = [2.0]
prob['meta.train:x2'] = [3.0]
prob['meta.train:y1'] = [2.0]
prob['meta.train:y2'] = [4.0]
meta.train = True # currently need to tell meta to re-train
prob.run()
assert_rel_error(self, prob['meta.y1'], 2.0, .00001)
assert_rel_error(self, prob['meta.y2'], 4.0, .00001)
def test_simple_jac(self):
group = Group()
group.add('x_param', ParamComp('x', 1.0), promotes=['*'])
group.add('mycomp', ExecComp(['y=2.0*x']), promotes=['x', 'y'])
prob = Problem()
prob.root = group
prob.root.ln_solver = ExplicitSolver()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
def test_no_derivatives(self):
prob = Problem()
prob.root = Group()
comp = prob.root.add('comp', ExecComp('y=x*2.0'))
prob.root.add('p1', ParamComp('x', 2.0))
prob.root.connect('p1.x', 'comp.x')
comp.fd_options['force_fd'] = True
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['p1.x'], ['comp.y'],
mode='fwd',
return_format='dict')
assert_rel_error(self, J['comp.y']['p1.x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['p1.x'], ['comp.y'],
mode='rev',
return_format='dict')
assert_rel_error(self, J['comp.y']['p1.x'][0][0], 2.0, 1e-6)
def test_simple_in_group_matvec(self):
group = Group()
sub = group.add('sub', Group(), promotes=['x', 'y'])
group.add('x_param', ParamComp('x', 1.0), promotes=['*'])
sub.add('mycomp', SimpleCompDerivMatVec(), promotes=['x', 'y'])
prob = Problem(impl=impl)
prob.root = group
prob.root.ln_solver = PetscKSP()
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['x'], ['y'], mode='fwd', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
J = prob.calc_gradient(['x'], ['y'], mode='rev', return_format='dict')
assert_rel_error(self, J['y']['x'][0][0], 2.0, 1e-6)
def test_sin_metamodel(self):
# create a MetaModel for Sin and add it to a Problem
sin_mm = MetaModel()
sin_mm.add_param('x', 0.)
sin_mm.add_output('f_x', 0.)
prob = Problem(Group())
prob.root.add('sin_mm', sin_mm)
# check that missing surrogate is detected in check_setup
stream = cStringIO()
prob.setup(out_stream=stream)
msg = ("No default surrogate model is defined and the "
"following outputs do not have a surrogate model:\n"
"['f_x']\n"
"Either specify a default_surrogate, or specify a "
"surrogate model for all outputs.")
self.assertTrue(msg in stream.getvalue())
# check that output with no specified surrogate gets the default
sin_mm.default_surrogate = FloatKrigingSurrogate()
prob.setup(check=False)
surrogate = prob.root.unknowns.metadata('sin_mm.f_x').get('surrogate')
self.assertTrue(isinstance(surrogate, FloatKrigingSurrogate),
'sin_mm.f_x should get the default surrogate')
# train the surrogate and check predicted value
prob['sin_mm.train:x'] = np.linspace(0, 10, 200)
prob['sin_mm.train:f_x'] = .5 * np.sin(prob['sin_mm.train:x'])
prob['sin_mm.x'] = 2.22
prob.run()
self.assertAlmostEqual(prob['sin_mm.f_x'],
.5 * np.sin(prob['sin_mm.x']),
places=5)
def test_fd_options_step_size(self):
prob = Problem()
prob.root = Group()
comp = prob.root.add('comp', Paraboloid())
prob.root.add('p1', ParamComp([('x', 15.0), ('y', 15.0)]))
prob.root.connect('p1.x', 'comp.x')
prob.root.connect('p1.y', 'comp.y')
comp.fd_options['force_fd'] = True
prob.setup(check=False)
prob.run()
J = prob.calc_gradient(['p1.x'], ['comp.f_xy'], return_format='dict')
assert_rel_error(self, J['comp.f_xy']['p1.x'][0][0], 39.0, 1e-6)
# Make sure step_size is used
# Derivative should be way high with this.
comp.fd_options['step_size'] = 1e5
J = prob.calc_gradient(['p1.x'], ['comp.f_xy'], return_format='dict')
self.assertGreater(J['comp.f_xy']['p1.x'][0][0], 1000.0)
def test_array_outputs(self):
meta = MetaModel()
meta.add_param('x', np.zeros((2, 2)))
meta.add_output('y', np.zeros(2, ))
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [[[1.0, 1.0], [1.0, 1.0]],
[[2.0, 1.0], [1.0, 1.0]],
[[1.0, 2.0], [1.0, 1.0]],
[[1.0, 1.0], [2.0, 1.0]],
[[1.0, 1.0], [1.0, 2.0]]]
prob['meta.train:y'] = [[3.0, 1.0], [2.0, 4.0], [1.0, 7.0],
[6.0, -3.0], [-2.0, 3.0]]
prob['meta.x'] = [[1.0, 2.0], [1.0, 1.0]]
prob.run()
assert_rel_error(self, prob['meta.y'], np.array([1.0, 7.0]), .00001)
def test_vector_inputs(self):
meta = MetaModel()
meta.add_param('x', np.zeros(4))
meta.add_output('y1', 0.)
meta.add_output('y2', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [[1.0, 1.0, 1.0, 1.0], [2.0, 1.0, 1.0, 1.0],
[1.0, 2.0, 1.0, 1.0], [1.0, 1.0, 2.0, 1.0],
[1.0, 1.0, 1.0, 2.0]]
prob['meta.train:y1'] = [3.0, 2.0, 1.0, 6.0, -2.0]
prob['meta.train:y2'] = [1.0, 4.0, 7.0, -3.0, 3.0]
prob['meta.x'] = [1.0, 2.0, 1.0, 1.0]
prob.run()
assert_rel_error(self, prob['meta.y1'], 1.0, .00001)
assert_rel_error(self, prob['meta.y2'], 7.0, .00001)
def test_linear_system(self):
root = Group()
root.add('lin', LinearSystem(3))
x = np.array([1, 2, -3])
A = np.array([[ 5.0, -3.0, 2.0], [1.0, 7.0, -4.0], [1.0, 0.0, 8.0]])
b = A.dot(x)
root.add('p1', IndepVarComp('A', A))
root.add('p2', IndepVarComp('b', b))
root.connect('p1.A', 'lin.A')
root.connect('p2.b', 'lin.b')
prob = Problem(root)
prob.setup(check=False)
prob.run()
# Make sure it gets the right answer
assert_rel_error(self, prob['lin.x'], x, .0001)
assert_rel_error(self, np.linalg.norm(prob.root.resids.vec), 0.0, 1e-10)
# Compare against calculated derivs
Ainv = np.linalg.inv(A)
dx_dA = np.outer(Ainv, -x).reshape(3, 9)
dx_db = Ainv
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'], mode='fwd', return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'], mode='rev', return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
J = prob.calc_gradient(['p1.A', 'p2.b'], ['lin.x'], mode='fd', return_format='dict')
assert_rel_error(self, J['lin.x']['p1.A'], dx_dA, .0001)
assert_rel_error(self, J['lin.x']['p2.b'], dx_db, .0001)
def test_indices_connect_error(self):
root = Group()
P = root.add('P', IndepVarComp('x', np.array([1., 2., 3., 4., 5.])))
G = root.add('G', Group())
C = root.add('C', SimpleComp())
A = G.add('A', SimpleArrayComp())
root.connect('P.x', 'G.A.x', src_indices=[0])
root.connect('P.x', 'C.x', src_indices=[2,])
expected_error_message = py3fix("Size 1 of the indexed sub-part of "
"source 'P.x' must match the size "
"'2' of the target 'G.A.x'")
prob = Problem(root)
with self.assertRaises(ConnectError) as cm:
prob.setup(check=False)
self.assertEqual(str(cm.exception), expected_error_message)
# now try the same thing with promoted var
root = Group()
P = root.add('P', IndepVarComp('x', np.array([1., 2., 3., 4., 5.])))
G = root.add('G', Group())
C = root.add('C', SimpleComp())
A = G.add('A', SimpleArrayComp(), promotes=['x', 'y'])
root.connect('P.x', 'G.x', src_indices=[0,1,2])
root.connect('P.x', 'C.x', src_indices=[2,])
expected_error_message = py3fix("Size 3 of the indexed sub-part of "
"source 'P.x' must match the size "
"'2' of the target 'G.x'")
prob = Problem(root)
with self.assertRaises(ConnectError) as cm:
prob.setup(check=False)
self.assertEqual(str(cm.exception), expected_error_message)
self.connect("nozzle_air.Fl_O:tot:T","tm.nozzle_air_Tt")
self.connect("nozzle_air.Fl_O:tot:Cp","tm.nozzle_air_Cp")
self.connect("nozzle_air.Fl_O:stat:W","tm.nozzle_air_W")
self.connect("bearing_air.Fl_O:tot:T","tm.bearing_air_Tt")
self.connect("bearing_air.Fl_O:tot:Cp","tm.bearing_air_Cp")
self.connect("bearing_air.Fl_O:stat:W","tm.bearing_air_W")
self.connect('tm.ss_temp_residual','tmp_balance.ss_temp_residual')
self.connect('tmp_balance.temp_boundary','tm.temp_boundary')
#run stand-alone component
if __name__ == "__main__":
root = Group()
root.add('fs', FlowStuff())
prob = Problem(root)
prob.root.nl_solver = Newton()
prob.root.nl_solver.options['atol'] = 1e-5
prob.root.nl_solver.options['iprint'] = 1
prob.root.nl_solver.options['rtol'] = 1e-5
prob.root.nl_solver.options['maxiter'] = 50
params = (
('P', 0.3, {'units':'psi'}),
('T', 1500.0, {'units':'degR'}),
('W', 1.0, {'units':'lbm/s'})
)
def test_subarray_to_promoted_var(self):
root = Group()
P = root.add('P', ParamComp('x', np.array([1., 2., 3.])))
G = root.add('G', Group())
C = root.add('C', SimpleComp())
A = G.add('A', SimpleArrayComp()) # , promotes=['x', 'y'])
root.connect('P.x', 'G.A.x', src_indices=[0,1])
root.connect('P.x', 'C.x', src_indices=[2,])
prob = Problem(root)
prob.setup(check=False)
prob.run()
assert_rel_error(self, root.G.A.params['x'], np.array([1., 2.]), 0.0001)
self.assertAlmostEqual(root.C.params['x'], 3.)
# no try the same thing with promoted var
root = Group()
P = root.add('P', ParamComp('x', np.array([1., 2., 3.])))
G = root.add('G', Group())
C = root.add('C', SimpleComp())
A = G.add('A', SimpleArrayComp(), promotes=['x', 'y'])
root.connect('P.x', 'G.x', src_indices=[0,1])
root.connect('P.x', 'C.x', src_indices=[2,])
prob = Problem(root)
prob.setup(check=False)
prob.run()
assert_rel_error(self, root.G.A.params['x'], np.array([1., 2.]), 0.0001)
self.assertAlmostEqual(root.C.params['x'], 3.)
