def test_all(self):
cls = self.__class__
rank = cls.comm.getRank()
file_dir = os.path.dirname(__file__)
# Create an Albany problem:
filename = 'input_conductivity_dist_paramT.yaml'
problem = Utils.createAlbanyProblem(file_dir+'/'+filename, cls.parallelEnv)
n_directions = 4
parameter_map = problem.getParameterMap(0)
directions = Tpetra.MultiVector(parameter_map, n_directions, dtype="d")
directions[0,:] = 1.
directions[1,:] = -1.
directions[2,:] = 3.
directions[3,:] = -3.
problem.setDirections(0, directions)
problem.performSolve()
response = problem.getResponse(0)
sensitivity = problem.getSensitivity(0, 0)
hessian = problem.getReducedHessian(0, 0)
g_target = 3.23754626955999991e-01
norm_target = 8.94463776843999921e-03
h_target = np.array([0.009195356672103817, 0.009195356672103817, 0.027586070971800013, 0.027586070971800013])
g_data = response.getData()
norm = Utils.norm(sensitivity.getData(0), cls.comm)
print("g_target = " + str(g_target))
print("g_data[0] = " + str(g_data[0]))
print("norm = " + str(norm))
print("norm_target = " + str(norm_target))
hessian_norms = np.zeros((n_directions,))
for i in range(0,n_directions):
hessian_norms[i] = Utils.norm(hessian.getData(i), cls.comm)
tol = 1e-8
if rank == 0:
self.assertTrue(np.abs(g_data[0]-g_target) < tol)
self.assertTrue(np.abs(norm-norm_target) < tol)
for i in range(0,n_directions):
self.assertTrue(np.abs(hessian_norms[i]-h_target[i]) < tol)
def test_all(self):
comm = Teuchos.DefaultComm.getComm()
rank = comm.getRank()
file_dir = os.path.dirname(__file__)
# Create an Albany problem:
filename = 'input_conductivity_dist_paramT.yaml'
problem = Utils.createAlbanyProblem(file_dir + '/' + filename)
n_directions = 4
parameter_map = problem.getParameterMap(0)
directions = Tpetra.MultiVector(parameter_map, n_directions, dtype="d")
directions[0, :] = 1.
directions[1, :] = -1.
directions[2, :] = 3.
directions[3, :] = -3.
problem.setDirections(0, directions)
problem.performSolve()
response = problem.getResponse(0)
sensitivity = problem.getSensitivity(0, 0)
hessian = problem.getReducedHessian(0, 0)
g_target = 3.23754626955999991e-01
norm_target = 8.94463776843999921e-03
h_target = np.array([
4.2121719763904516e-05, -4.21216874727712e-05,
0.00012636506241831498, -0.00012636506241831496
])
g_data = response.getData()
norm = Utils.norm(sensitivity.getData(0), comm)
print("g_target = " + str(g_target))
print("g_data[0] = " + str(g_data[0]))
print("norm = " + str(norm))
print("norm_target = " + str(norm_target))
tol = 1e-8
if rank == 0:
self.assertTrue(np.abs(g_data[0] - g_target) < tol)
self.assertTrue(np.abs(norm - norm_target) < tol)
for i in range(0, n_directions):
self.assertTrue(np.abs(hessian[i, 0] - h_target[i]) < tol)
def test_all(self):
cls = self.__class__
rank = cls.comm.getRank()
file_dir = os.path.dirname(__file__)
# Create an Albany problem:
filename = "input_dirichlet_mixed_paramsT.yaml"
parameter = Utils.createParameterList(
file_dir + "/" + filename, cls.parallelEnv
)
parameter.sublist("Discretization").set("1D Elements", 10)
parameter.sublist("Discretization").set("2D Elements", 10)
problem = Utils.createAlbanyProblem(parameter, cls.parallelEnv)
g_target_before = 0.35681844
g_target_after = 0.17388298
g_target_2 = 0.19570272
p_0_target = 0.39886689
p_1_norm_target = 5.37319376038225
tol = 1e-8
problem.performSolve()
response_before_analysis = problem.getResponse(0)
problem.performAnalysis()
para_0 = problem.getParameter(0)
para_1 = problem.getParameter(1)
print(para_0.getData())
print(para_1.getData())
para_1_norm = Utils.norm(para_1.getData(), cls.comm)
print(para_1_norm)
if rank == 0:
self.assertTrue(np.abs(para_0[0] - p_0_target) < tol)
self.assertTrue(np.abs(para_1_norm - p_1_norm_target) < tol)
problem.performSolve()
response_after_analysis = problem.getResponse(0)
print("Response before analysis " + str(response_before_analysis.getData()))
print("Response after analysis " + str(response_after_analysis.getData()))
if rank == 0:
self.assertTrue(np.abs(response_before_analysis[0] - g_target_before) < tol)
self.assertTrue(np.abs(response_after_analysis[0] - g_target_after) < tol)
parameter_map_0 = problem.getParameterMap(0)
para_0_new = Tpetra.Vector(parameter_map_0, dtype="d")
para_0_new[:] = 0.0
problem.setParameter(0, para_0_new)
problem.performSolve()
response = problem.getResponse(0)
print("Response after setParameter " + str(response.getData()))
if rank == 0:
self.assertTrue(np.abs(response[0] - g_target_2) < tol)
