output_configs=hmc_output_configs) # Create sequential DA # processing object: # --------------------- # Here this is a filtering_process object; from filtering_process import FilteringProcess experiment = FilteringProcess( assimilation_configs=dict( model=model, filter=filter_obj, obs_checkpoints=obs_checkpoints, da_checkpoints=da_checkpoints, forecast_first=True, ref_initial_condition=model._reference_initial_condition.copy(), ref_initial_time= 0, # should be obtained from the model along with the ref_IC random_seed=0), output_configs=dict(scr_output=True, scr_output_iter=1, file_output=True, file_output_iter=1)) # run the sequential filtering over the timespan created by da_checkpoints experiment.recursive_assimilation_process() # # Clean executables and temporary modules # --------------------- utility.clean_executable_files() #
experiment_timespan=[ analysis_trajectory_timespan[0], analysis_trajectory_timespan[-1] ], obs_checkpoints=obs_checkpoints, observations_list=None, da_checkpoints=da_checkpoints, initial_forecast=forecast_state, ref_initial_condition=ref_IC, ref_initial_time=analysis_trajectory_timespan[0], analysis_timespan=analysis_trajectory_timespan, # random_seed=_random_seed ) experiment = SmoothingProcess(assimilation_configs=experiment_configs, output_configs={ 'file_output': True, 'verbose': False }) # # print("Terminated per request...") # sys.exit() # run the sequential filtering over the timespan created by da_checkpoints experiment.recursive_assimilation_process() # # Clean executables and temporary modules # --------------------- utility.clean_executable_files("src") #