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")
#