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
def SerializeModelParametersDeterministicAdaptiveRefinement(self):
"""
Method serializing and pickling the Kratos Model and the Kratos Parameters of the problem. It builds self.pickled_model and self.pickled_project_parameters. To be called if the selected refinement strategy is deterministic_adaptive_refinement.
Inputs:
- self: an instance of the class.
"""
self.SerializeModelParametersStochasticAdaptiveRefinement(
) # to prepare parameters and model part of coarsest level
number_levels_to_serialize = self.solverWrapperIndex[0]
# same routine of ExecuteInstanceConcurrentAdaptiveRefinemnt() to build models and parameters, but here we save models and parameters
pickled_coarse_model = self.pickled_model[0]
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
# generate the sample and prepare auxiliary variables we need
fake_sample = self.fake_sample_to_serialize
fake_computational_time = 0.0
if (number_levels_to_serialize > 0):
for current_level in range(number_levels_to_serialize + 1):
fake_qoi,pickled_current_model,fake_computational_time = \
mds.executeInstanceStochasticAdaptiveRefinementMultipleTasks_Wrapper(number_levels_to_serialize,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,fake_sample,current_level,current_analysis,fake_computational_time,mapping_flag=False,print_to_file=False,current_contribution=0)
del (pickled_coarse_model)
pickled_coarse_model = pickled_current_model
# save if current level > 0 (level = 0 has already been saved)
if (current_level > 0):
# save pickled and serialized model and parameters
self.pickled_model.append(pickled_current_model)
self.serialized_model.append(
pickle.loads(pickled_current_model))
del (pickled_current_model)
def SerializeModelParametersDeterministicAdaptiveRefinement(self):
"""
Method serializing and pickling the Kratos Model and the Kratos Parameters of the problem. It builds self.pickled_model and self.pickled_project_parameters. To be called if the selected refinement strategy is deterministic_adaptive_refinement.
Inputs:
"""
# Serialize model and parameters of coarsest level (level = 0).
# If we are running with MPI parallel type,
# the model is being serialized in a MPI task
# with the same number of processes required by level = self.solverWrapperIndex[0].
# This strategy works in both cases the solverWrapper instance is solving level 0
# or if it is solving levels > 0.
self.SerializeModelParametersStochasticAdaptiveRefinement()
# now serialize levels > 0
number_levels_to_serialize = self.solverWrapperIndex[0]
# same routine of executeInstanceStochasticAdaptiveRefinement() to build models and parameters, but here we save models and parameters
pickled_coarse_model = self.pickled_model[0]
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
# generate the sample and prepare auxiliary variables we need
fake_sample = self.fake_sample_to_serialize
fake_computational_time = 0.0
if (number_levels_to_serialize > 0):
for current_level in range(number_levels_to_serialize + 1):
if not self.is_mpi: # serial
fake_qoi,pickled_current_model,fake_computational_time = \
mds.executeInstanceStochasticAdaptiveRefinementMultipleTasks_Wrapper(number_levels_to_serialize,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,fake_sample,current_level,current_analysis,fake_computational_time,mapping_flag=False,print_to_file=False,current_contribution=0)
elif self.is_mpi and current_level == number_levels_to_serialize: # MPI and we serialize level of interest
adaptive_refinement_jump_to_finest_level = self.adaptive_refinement_jump_to_finest_level
pickled_current_model = mpi_mds.SerializeDeterministicAdaptiveRefinementMPIModel_Wrapper(
current_level, pickled_coarse_model,
pickled_coarse_project_parameters,
pickled_custom_metric_refinement_parameters,
pickled_custom_remesh_refinement_parameters,
fake_sample, current_analysis, fake_computational_time,
adaptive_refinement_jump_to_finest_level)
else: # MPI parallel type and we do not serialize since it is not the level of interest
# we set pickled model equal to coarsest model as workaround
pickled_current_model = pickled_coarse_model
del (pickled_coarse_model)
pickled_coarse_model = pickled_current_model
# save if current level > 0 (level = 0 has already been saved)
if (current_level > 0):
# save pickled and serialized model and parameters
self.pickled_model.append(pickled_current_model)
# self.serialized_model.append(pickle.loads(get_value_from_remote(pickled_current_model))) # commented since gives problem when solving with PyCOMPSs
self.pickled_project_parameters.append(
pickled_coarse_project_parameters)
# self.serialized_project_parameters.append(pickle.loads(get_value_from_remote(pickled_coarse_project_parameters))) # commented since gives problem when solving with PyCOMPSs
del (pickled_current_model)
def SerializeModelParametersDeterministicAdaptiveRefinement(self):
self.SerializeModelParametersStochasticAdaptiveRefinement(
) # to prepare parameters and model part of coarsest level
number_levels_to_serialize = self.solverWrapperIndex[0]
# same routine of ExecuteInstanceConcurrentAdaptiveRefinemnt() to build models and parameters, but here we save models and parameters
pickled_coarse_model = self.pickled_model[0]
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
# generate the sample and prepare auxiliary variables we need
fake_sample = self.fake_sample_to_serialize
fake_computational_time = 0.0
if (number_levels_to_serialize > 0):
for current_level in range(number_levels_to_serialize + 1):
if (current_level < number_levels_to_serialize):
fake_qoi,pickled_current_model,fake_computational_time = \
mds.executeInstanceStochasticAdaptiveRefinementMultipleTasks_Wrapper(number_levels_to_serialize,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,fake_sample,current_level,current_analysis,fake_computational_time)
# mds.executeInstanceStochasticAdaptiveRefinementAux_Task(number_levels_to_serialize,pickled_coarse_model,pickled_coarse_project_parameters,pickled_custom_metric_refinement_parameters,pickled_custom_remesh_refinement_parameters,fake_sample,current_level,current_analysis,fake_computational_time)
del (pickled_coarse_model)
pickled_coarse_model = pickled_current_model
elif (current_level == number_levels_to_serialize):
pickled_current_model, fake_computational_time = mds.executeInstanceOnlyAdaptiveRefinement_Wrapper(
pickled_coarse_model,
pickled_coarse_project_parameters,
pickled_custom_metric_refinement_parameters,
pickled_custom_remesh_refinement_parameters,
fake_sample, current_level, current_analysis,
fake_computational_time)
# save if current level > 0
if (current_level > 0):
# save pickled and serialized model and parameters
self.pickled_model.append(pickled_current_model)
self.serialized_model.append(
pickle.loads(pickled_current_model))
del (pickled_current_model)
