def SerializeModelParameters(self):
with open(self.project_parameters_path,'r') as parameter_file:
parameters = KratosMultiphysics.Parameters(parameter_file.read())
# create wrapper instance to modify current project parameters
self.wrapper = ParametersWrapper(parameters)
# save problem name
self.problem_name = parameters["problem_data"]["problem_name"].GetString()
# serialize parmeters (to avoid adding new data dependent on the application)
parameters = self.wrapper.SetModelImportSettingsInputType("use_input_model_part")
serialized_project_parameters = KratosMultiphysics.StreamSerializer()
serialized_project_parameters.Save("ParametersSerialization",parameters)
self.serialized_project_parameters = serialized_project_parameters
# reset to read the model part
parameters = self.wrapper.SetModelImportSettingsInputType("mdpa")
# prepare the model to serialize
model = KratosMultiphysics.Model()
fake_sample = generator.GenerateSample(self.problem_name) # only used to serialize
simulation = self.analysis(model,parameters,fake_sample)
simulation.Initialize()
# reset general flags
main_model_part_name = self.wrapper.GetModelPartName()
simulation.model.GetModelPart(main_model_part_name).ProcessInfo.SetValue(KratosMultiphysics.IS_RESTARTED,True)
# serialize model
serialized_model = KratosMultiphysics.StreamSerializer()
serialized_model.Save("ModelSerialization",simulation.model)
self.serialized_model = serialized_model
# pickle model and parameters
pickled_model = pickle.dumps(serialized_model, 2) # second argument is the protocol and is NECESSARY (according to pybind11 docs)
pickled_project_parameters = pickle.dumps(serialized_project_parameters, 2)
self.pickled_model = pickled_model
self.pickled_project_parameters = pickled_project_parameters
self.is_project_parameters_pickled = True
self.is_model_pickled = True
print("\n","#"*50," SERIALIZATION COMPLETED ","#"*50,"\n")
def ExecuteInstance(self):
# ensure working level is level 0
current_level = self.current_level
sample = generator.GenerateSample(self.problem_name)
if (current_level != 0):
raise Exception ("current work level must be = 0 in the Monte Carlo algorithm")
return (ExecuteInstanceAux_Task(self.pickled_model,self.pickled_project_parameters,sample,self.GetAnalysis(),self.current_level),current_level)
def SerializeModelParameters(self):
with open(self.project_parameters_path, 'r') as parameter_file:
parameters = KratosMultiphysics.Parameters(parameter_file.read())
model = KratosMultiphysics.Model()
fake_sample = generator.GenerateSample() # only used to serialize
simulation = self.analysis(model, parameters, fake_sample)
simulation.Initialize()
serialized_model = KratosMultiphysics.StreamSerializer()
serialized_model.Save("ModelSerialization", simulation.model)
serialized_project_parameters = KratosMultiphysics.StreamSerializer()
serialized_project_parameters.Save("ParametersSerialization",
simulation.project_parameters)
# pickle dataserialized_data
pickled_model = pickle.dumps(
serialized_model, 2
) # second argument is the protocol and is NECESSARY (according to pybind11 docs)
pickled_project_parameters = pickle.dumps(
serialized_project_parameters, 2)
self.pickled_model = pickled_model
self.pickled_project_parameters = pickled_project_parameters
self.is_project_parameters_pickled = True
self.is_model_pickled = True
print("\n", "#" * 50, " SERIALIZATION COMPLETED ", "#" * 50, "\n")
def ExecuteInstanceAux_Task(pickled_model, pickled_project_parameters,
current_analysis_stage, current_level):
# overwrite the old model serializer with the unpickled one
model_serializer = pickle.loads(pickled_model)
current_model = KratosMultiphysics.Model()
model_serializer.Load("ModelSerialization", current_model)
del (model_serializer)
# overwrite the old parameters serializer with the unpickled one
serialized_project_parameters = pickle.loads(pickled_project_parameters)
current_project_parameters = KratosMultiphysics.Parameters()
serialized_project_parameters.Load("ParametersSerialization",
current_project_parameters)
del (serialized_project_parameters)
# initialize the MonteCarloResults class
mc_results_class = MonteCarloResults(current_level)
sample = generator.GenerateSample()
simulation = current_analysis_stage(current_model,
current_project_parameters, sample)
simulation.Run()
QoI = simulation.EvaluateQuantityOfInterest()
mc_results_class.QoI[current_level].append(
QoI
) # saving results in the corresponding list, for MC only list of level 0
return mc_results_class