def execute_refinement_strategy(self, problem_params, mesh, e_distr,
md_distr, maj_distr, error_k, majorant_k,
i_eff_maj_k, error_d_k, majorant_d_k,
h_max_k, h_min_k, t_T, k, nt, project_path,
results_folder, init_data_info):
if problem_params['refinement_tag'] == "adaptive":
# Define the distribution (error or majorant) upon which the refinement is based
if problem_params['refinement_criteria_tag'] == 'error':
distr = e_distr
elif problem_params['refinement_criteria_tag'] == 'majorant':
distr = md_distr
#if self.dim == 2: distr = md_distr
#elif self.dim == 3: distr = md_distr
refinement_tag = problem_params[
'refinement_criteria_tag'] + '-' + problem_params[
'marking_tag'] + '-marking'
# Define the marking algorithm
if problem_params['marking_tag'] == 'average':
marking = estimates.averaged_marking(mesh, distr)
#refinement_tag = problem_params['refinement_criteria_tag'] + '-' + problem_params[
# 'marking_tag'] + '-marking'
#refinement_tag = refinement_tag + '-marking'
elif problem_params['marking_tag'] == 'predefined':
marking = estimates.predefined_amount_of_elements_marking(
mesh, distr, problem_params['percentage_value'])
refinement_tag = refinement_tag + '-proc-%d' % (
problem_params['percentage_value'] * 100)
elif problem_params['marking_tag'] == 'bulk':
marking = estimates.bulk_marking(
mesh, distr, problem_params['percentage_value'])
refinement_tag = refinement_tag + '-bulk-%d' % (
problem_params['percentage_value'] * 100)
elif problem_params['refinement_tag'] == "uniform":
marking = CellFunction("bool", mesh)
marking.set_all(True)
refinement_tag = problem_params['refinement_tag']
if k % 1 == 0 and problem_params[
'solving_strategy_tag'] == "with-refinement":
results_path = project_path + results_folder + 'e-maj-distr-hist-' + refinement_tag + init_data_info
postprocess.plot_histogram(mesh, e_distr, distr, results_path)
postprocess.save_distr_to_mat_file(e_distr, distr, results_path)
mesh = refine(mesh, marking)
postprocess.document_results_with_refinement(
problem_params, mesh, e_distr, md_distr, error_k, majorant_k,
i_eff_maj_k, error_d_k, majorant_d_k, h_max_k, h_min_k, t_T, k, nt,
refinement_tag, project_path, results_folder, init_data_info, dim)
return mesh
def execute_refinement_strategy(self, problem_params, mesh, e_distr,
md_distr, project_path, results_folder,
init_data_info):
# error_k, majorant_k, i_eff_maj_k, error_d_k, majorant_d_k, h_max_k, h_min_k, t_T, k, nt,
# Define the refinement strategy
if problem_params['refinement_tag'] == "adaptive":
# Define the distribution (error or majorant) upon which the refinement is basedy^
if problem_params['refinement_criteria_tag'] == 'error':
distr = e_distr
elif problem_params['refinement_criteria_tag'] == 'majorant':
distr = md_distr
if problem_params['marking_tag'] == 'average':
marking = estimates.averaged_marking(mesh, distr)
refinement_tag = problem_params[
'refinement_criteria_tag'] + '-' + problem_params[
'marking_tag'] + '-marking'
elif problem_params['marking_tag'] == 'predefined':
marking = estimates.predefined_amount_of_elements_marking(
mesh, distr, problem_params['percentage_value'])
refinement_tag = problem_params[
'refinement_criteria_tag'] + '-' + problem_params[
'marking_tag'] + '-marking' + '-proc-%d' % (
problem_params['percentage_value'] * 100)
elif problem_params['marking_tag'] == 'bulk':
marking = estimates.bulk_marking(
mesh, distr, problem_params['percentage_value'])
refinement_tag = problem_params[
'refinement_criteria_tag'] + '-' + problem_params[
'marking_tag'] + '-marking' + '-bulk-%d' % (
problem_params['percentage_value'] * 100)
print("% ----------------")
print("% Refine the mesh ")
print("% ----------------")
# Refine mesh based on the marking criteria
mesh = refine(mesh, marking)
print('mesh parameters: num_cells = %d, num_vertices = %d' %
(mesh.num_cells(), mesh.num_vertices()))
# Define the name of the mat-file
mat_file_tag = project_path + results_folder + 'results-on-mesh-based-' + refinement_tag + init_data_info
elif problem_params['refinement_tag'] == "uniform":
cell_markers = CellFunction("bool", mesh)
cell_markers.set_all(True)
mesh = refine(mesh, cell_markers)
refinement_tag = problem_params['refinement_tag']
# Define the name of the matfile
mat_file_tag = project_path + results_folder + 'results-on-mesh-uniform' + init_data_info
return mesh, mat_file_tag
def bulk_marking(mesh, distr, theta):
marking = CellFunction("bool", mesh)
distr_0 = sorted(distr, reverse=True)[int(len(distr) * theta)]
for cell in cells(mesh):
marking[cell] = distr[cell.index()] > distr_0
return marking
def get_2d_slice_of_3d_function_on_Oz(mesh, u, T, dim, v_degree):
# create a boundary mesh
bmesh = BoundaryMesh(mesh, "exterior")
cell_func = CellFunction('size_t', bmesh, 0)
coordinates = bmesh.coordinates()
z_indeces = postprocess.allocate_array(dim)
for cell in cells(bmesh):
indx = 0
for vertex in vertices(cell):
z_indeces[indx] = coordinates[vertex.index()][dim - 1]
indx += 1
#print "Vertex index with coordinates", vertex.index(), coordinates[vertex.index()][dim-1]
if (dim == 3 and near(z_indeces[0], T) and near(z_indeces[1], T)
and near(z_indeces[2], T)) or (dim == 2 and near(
z_indeces[0], T) and near(z_indeces[1], T)):
#print "right cell", cell.index()
cell_func[cell] = 1
submesh = SubMesh(bmesh, cell_func, 1)
# create a FunctionSpace on the submesh-
Vs = FunctionSpace(submesh, "Lagrange", v_degree)
us = interpolate(u, Vs)
return us, submesh
def averaged_marking(mesh, distr):
cells_num = mesh.num_cells()
marking = CellFunction("bool", mesh)
distr_aver = sum(distr) / cells_num
for c in cells(mesh):
marking[c] = distr[c.index()] >= distr_aver
return marking
def predefined_amount_of_elements_marking(mesh, distr, theta):
cells_num = mesh.num_cells()
marking = CellFunction("bool", mesh)
marking.set_all(False)
i_cut = int(math.floor(theta * cells_num))
index_sorted = sorted(range(len(distr)),
key=lambda k: distr[k],
reverse=True)
cells_indices_to_refine = index_sorted[0:i_cut]
for cell in cells(mesh):
if cell.index() in cells_indices_to_refine:
marking[cell] = True
return marking