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 execute_refiniment_strategy(self, mesh, test_params, e_distr, m_distr):
# Define the refinement strategy
if test_params['refinement_tag'] == "adaptive":
# Define the distribution (error or majorant) upon which the refinement is based
if test_params['refinement_criteria_tag'] == 'error':
distr = e_distr
elif test_params['refinement_criteria_tag'] == 'majorant':
distr = m_distr
# distr = maj_distr
# elif test_params['refinement_criteria_tag'] == 'e-dwr':
# distr = e_dwr_distr
# elif test_params['refinement_criteria_tag'] == 'residual':
# distr = residual_distr
# Run the marking procedure
theta = 0.2 # percentage of the marked cells
if test_params['marking_tag'] == 'average':
marking = estimates.averaged_marking(mesh, distr)
elif test_params['marking_tag'] == 'predefined':
marking = estimates.predefined_amount_of_elements_marking(
mesh, distr, theta)
elif test_params['marking_tag'] == 'bulk':
marking = estimates.bulk_marking(mesh, distr, theta)
# Refine mesh based on the marking criteria
mesh = refine(mesh, marking)
elif test_params['refinement_tag'] == "uniform":
# Refinement for the comparison with theta < 1.0
# cell_markers = CellFunction("bool", mesh)
# cell_markers.set_all(True)
# mesh = refine(mesh, cell_markers)
mesh = refine(mesh)
return mesh