def test_correct_vals_in_jac(self):
params_dict = OrderedDict()
params_dict['x'] = { 'val': np.ones((2)),
'pathname' : 'x',
'promoted_name' : 'x',
'shape': 2, 'size' : 2 }
unknowns_dict = OrderedDict()
unknowns_dict['y'] = { 'val': np.zeros((2)),
'pathname' : 'y',
'promoted_name' : 'y',
'shape': 2, 'size' : 2 }
resids_dict = OrderedDict()
resids_dict['y'] = { 'val': np.zeros((2)),
'pathname' : 'y',
'promoted_name' : 'y',
'shape': 2, 'size' : 2 }
params = SrcVecWrapper()
params.setup(params_dict, store_byobjs=True)
unknowns = SrcVecWrapper()
unknowns.setup(unknowns_dict, store_byobjs=True)
resids = SrcVecWrapper()
resids.setup(resids_dict, store_byobjs=True)
self.p.root.c1.solve_nonlinear(params, unknowns, resids)
jac = self.p.root.c1.fd_jacobian(params, unknowns, resids)
expected_jac = {('y', 'x'): np.array([[ 2., 7.],[ 5., -3.]])}
assert_equal_jacobian(self, jac, expected_jac, 1e-8)
#Got lucky that the way this comp was written, it would accept any square
# matrix. But provided jacobian would be really wrong!
params_dict = OrderedDict()
params_dict['x'] = { 'val': np.ones((2, 2)),
'pathname' : 'x',
'promoted_name' : 'x',
'shape': (2,2), 'size' : 4 }
unknowns_dict = OrderedDict()
unknowns_dict['y'] = { 'val': np.zeros((2, 2)),
'pathname' : 'y',
'promoted_name' : 'y',
'shape': (2,2), 'size' : 4 }
resids_dict = OrderedDict()
resids_dict['y'] = { 'val': np.zeros((2, 2)),
'pathname' : 'y',
'promoted_name' : 'y',
'shape': (2,2), 'size' : 4 }
params = SrcVecWrapper()
params.setup(params_dict, store_byobjs=True)
unknowns = SrcVecWrapper()
unknowns.setup(unknowns_dict, store_byobjs=True)
resids = SrcVecWrapper()
resids.setup(resids_dict, store_byobjs=True)
self.p.root.c1.solve_nonlinear(params, unknowns, resids)
jac = self.p.root.c1.fd_jacobian(params, unknowns, resids)
expected_jac = {('y', 'x'): np.array([[ 2, 0, 7, 0],
[ 0, 2, 0, 7],
[ 5, 0, -3, 0],
[ 0, 5, 0, -3]
])}
assert_equal_jacobian(self, jac, expected_jac, 1e-8)
def test_correct_vals_in_jac_implicit(self):
params_dict = OrderedDict()
params_dict['x'] = { 'val': np.array([0.5]),
'pathname' : 'x',
'promoted_name' : 'x',
'shape': (1,), 'size' : 1 }
unknowns_dict = OrderedDict()
unknowns_dict['y'] = { 'val': np.array([0.0]),
'pathname' : 'y',
'promoted_name' : 'y',
'shape': (1,), 'size' : 1 }
unknowns_dict['z'] = { 'val': np.array([0.0]),
'pathname' : 'z',
'promoted_name' : 'z',
'shape': (1,), 'size' : 1 }
resids_dict = OrderedDict()
resids_dict['y'] = { 'val': np.array([0.0]),
'pathname' : 'y',
'promoted_name' : 'y',
'shape': (1,), 'size' : 1 }
resids_dict['z'] = { 'val': np.array([0.0]),
'pathname' : 'z',
'promoted_name' : 'z',
'shape': (1,), 'size' : 1 }
params = SrcVecWrapper()
params.setup(params_dict, store_byobjs=True)
unknowns = SrcVecWrapper()
unknowns.setup(unknowns_dict, store_byobjs=True)
resids = SrcVecWrapper()
resids.setup(resids_dict, store_byobjs=True)
# Partials
self.p.root.ci1.solve_nonlinear(params, unknowns, resids)
jac = self.p.root.ci1.fd_jacobian(params, unknowns, resids)
expected_jac = {}
# Output equation
expected_jac[('y', 'x')] = 1.
expected_jac[('y', 'z')] = 2.0
# State equation
expected_jac[('z', 'z')] = params['x'] + 1
expected_jac[('z', 'x')] = unknowns['z']
assert_equal_jacobian(self, jac, expected_jac, 1e-8)
# Totals
# Really tighten this up
self.p.root.ci1.atol = 1e-14
self.p.root.ci1.solve_nonlinear(params, unknowns, resids)
jac = self.p.root.ci1.fd_jacobian(params, unknowns, resids, total_derivs=True)
expected_jac = {}
expected_jac[('y', 'x')] = -2.5555555555555554
expected_jac[('z', 'x')] = -1.7777777777777777
assert_equal_jacobian(self, jac, expected_jac, 1e-5)
