def executeInstanceStochasticAdaptiveRefinement(self, random_variable):
"""
Method executing an instance of the UQ algorithm, i.e. a single MC realization and eventually the refinement (that occurs before the simulation run). To be called if the selected refinement strategy is stochastic_adaptive_refinement.
Inputs:
- self: an instance of the class.
Outputs:
- qoi: list. It contains the quantities of interest.
- time_for_qoi: float. Measure of time to generate the sample.
"""
current_index = self.solverWrapperIndex[0]
# local variables
pickled_coarse_model = self.pickled_model[0]
pickled_reference_model_mapping = pickled_coarse_model
pickled_coarse_project_parameters = self.pickled_project_parameters[0]
pickled_custom_metric_refinement_parameters = self.pickled_custom_metric_refinement_parameters
pickled_custom_remesh_refinement_parameters = self.pickled_custom_remesh_refinement_parameters
current_analysis = self.analysis
different_tasks = self.different_tasks
mapping_flag = self.mapping_output_quantities
adaptive_refinement_jump_to_finest_level = self.adaptive_refinement_jump_to_finest_level
print_to_file = self.print_to_file
current_local_contribution = self.current_local_contribution
time_for_qoi = 0.0
if (different_tasks is False): # tasks all at once
qoi,time_for_qoi = \
mds.executeInstanceStochasticAdaptiveRefinementAllAtOnce_Wrapper(current_index,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,random_variable,current_analysis,time_for_qoi,mapping_flag,adaptive_refinement_jump_to_finest_level,print_to_file,current_local_contribution)
elif (different_tasks is True): # multiple tasks
if (current_index == 0): # index = 0
current_local_index = 0
qoi,pickled_current_model,time_for_qoi = \
mds.executeInstanceStochasticAdaptiveRefinementMultipleTasks_Wrapper(current_index,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,random_variable,current_local_index,current_analysis,time_for_qoi,mapping_flag,print_to_file,current_local_contribution)
delete_object(pickled_current_model)
else: # index > 0
for current_local_index in range(current_index + 1):
if ((adaptive_refinement_jump_to_finest_level is False) or
(adaptive_refinement_jump_to_finest_level is True and
(current_local_index == 0
or current_local_index == current_index))):
if (mapping_flag is False):
qoi,pickled_current_model,time_for_qoi = \
mds.executeInstanceStochasticAdaptiveRefinementMultipleTasks_Wrapper(current_index,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,random_variable,current_local_index,current_analysis,time_for_qoi,mapping_flag,print_to_file,current_local_contribution)
elif (mapping_flag is True):
qoi,pickled_current_model,time_for_qoi = \
mds.executeInstanceStochasticAdaptiveRefinementMultipleTasks_Wrapper(current_index,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,random_variable,current_local_index,current_analysis,time_for_qoi,mapping_flag,print_to_file,current_local_contribution,pickled_mapping_reference_model=pickled_reference_model_mapping)
delete_object(pickled_coarse_model)
del (pickled_coarse_model)
pickled_coarse_model = pickled_current_model
del (pickled_current_model)
else: # not running since we jump from coarsest to finest level
pass
delete_object(pickled_coarse_model)
else:
raise Exception(
"Boolean variable different task is not a boolean, instead is equal to",
different_tasks)
return qoi, time_for_qoi
def executeInstanceStochasticAdaptiveRefinement(self, random_variable):
current_index = self.solverWrapperIndex[0]
# local variables
pickled_coarse_model = self.pickled_model[0]
pickled_reference_model_mapping = pickled_coarse_model
pickled_coarse_project_parameters = self.pickled_project_parameters[0]
pickled_custom_metric_refinement_parameters = self.pickled_custom_metric_refinement_parameters
pickled_custom_remesh_refinement_parameters = self.pickled_custom_remesh_refinement_parameters
current_analysis = self.analysis
different_tasks = self.different_tasks
mapping_flag = self.mapping_output_quantities
adaptive_refinement_jump_to_finest_level = self.adaptive_refinement_jump_to_finest_level
print_to_file = self.print_to_file
current_local_contribution = self.current_local_contribution
time_for_qoi = 0.0
if (different_tasks is False): # tasks all at once
qoi,time_for_qoi = \
mds.executeInstanceStochasticAdaptiveRefinementAllAtOnce_Wrapper(current_index,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,random_variable,current_analysis,time_for_qoi,mapping_flag,adaptive_refinement_jump_to_finest_level,print_to_file,current_local_contribution)
elif (different_tasks is True): # multiple tasks
if (current_index == 0): # index = 0
current_local_index = 0
qoi,pickled_current_model,time_for_qoi = \
mds.executeInstanceStochasticAdaptiveRefinementMultipleTasks_Wrapper(current_index,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,random_variable,current_local_index,current_analysis,time_for_qoi,mapping_flag,print_to_file,current_local_contribution)
delete_object(pickled_current_model)
else: # index > 0
for current_local_index in range(current_index + 1):
if ((adaptive_refinement_jump_to_finest_level is False) or
(adaptive_refinement_jump_to_finest_level is True and
(current_local_index == 0
or current_local_index == current_index))):
if (mapping_flag is False):
qoi,pickled_current_model,time_for_qoi = \
mds.executeInstanceStochasticAdaptiveRefinementMultipleTasks_Wrapper(current_index,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,random_variable,current_local_index,current_analysis,time_for_qoi,mapping_flag,print_to_file,current_local_contribution)
elif (mapping_flag is True):
qoi,pickled_current_model,time_for_qoi = \
mds.executeInstanceStochasticAdaptiveRefinementMultipleTasks_Wrapper(current_index,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,random_variable,current_local_index,current_analysis,time_for_qoi,mapping_flag,print_to_file,current_local_contribution,pickled_mapping_reference_model=pickled_reference_model_mapping)
delete_object(pickled_coarse_model)
del (pickled_coarse_model)
pickled_coarse_model = pickled_current_model
del (pickled_current_model)
else: # not running since we jump from coarsest to finest level
pass
delete_object(pickled_coarse_model)
else:
raise Exception(
"Boolean variable different task is not a boolean, instead is equal to",
different_tasks)
return qoi, time_for_qoi