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PDAF_system.py
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PDAF_system.py
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"""This file is part of pyPDAF
Copyright (C) 2022 University of Reading and
National Centre for Earth Observation
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import numpy as np
import collector
import config
import distributor
import filter_options
import localisation
import model
import obs_factory
import parallelisation
import prepost_processing
import state_vector
import pyPDAF.PDAF as PDAF
class PDAF_system:
"""PDAF system
Attributes
----------
pe : parallelisation.parallelisation
parallelisation instance
model_ens : list[model.model]
list of model instances
sv : state_vector.state_vector
state vector
local : localisation.localisation
localisation
obs : obs_factory.obs_factory
observation factory
filter_options : filter_options.filter_options
filter options
"""
def __init__(self, pe:parallelisation.parallelisation, model_ens:list[model.model]) -> None:
self.pe:parallelisation.parallelisation = pe
self.model_ens:list[model.model] = model_ens
self.filter_options = filter_options.filter_options()
self.sv = state_vector.state_vector(model_ens[0], dim_ens=pe.dim_ens)
self.local = localisation.localisation(sv=self.sv)
# here, observation only uses the domain observation of the model ensemble.
# Therefore, only one ensemble member (i.e., model_ens[0]) is passed to obs_factory.
# In a more complicated system, it is possible to have domain class for model,
# in which case only the domain object is required here.
self.obs = obs_factory.obs_factory(self.pe, self.model_ens[0], self.local)
# initial time step
self.steps_for = config.init_step
def init_pdaf(self, screen:int) -> None:
"""constructor
Parameters
----------
screen : int
verbosity of PDAF screen output
"""
filter_param_i:np.ndarray
filter_param_r:np.ndarray
if self.filter_options.filtertype == 2:
# EnKF with Monte Carlo init
filter_param_i, filter_param_r = self.setEnKFOptions(6, 2)
else:
# All other filters
filter_param_i, filter_param_r = self.setETKFOptions(7, 2)
status:int = 0
cltor:collector.collector = collector.collector(self.model_ens[0], self.pe)
# initialise PDAF filters, communicators, ensemble
_, _, status = PDAF.init(self.filter_options.filtertype,
self.filter_options.subtype,
0,
filter_param_i,
filter_param_r,
self.pe.comm_model.py2f(),
self.pe.comm_filter.py2f(),
self.pe.comm_couple.py2f(), self.pe.task_id,
self.pe.n_modeltasks, self.pe.filter_pe,
cltor.init_ens_pdaf, screen)
assert status == 0, f'ERROR {status} \
in initialization of PDAF - stopping! \
(PE f{self.pe.mype_ens})'
lfilter = PDAF.get_localfilter()
self.local.local_filter = lfilter == 1
# PDAF distribute the initial ensemble to model field
doexit:int = 0
prepost:prepost_processing.prepost = prepost_processing.prepost(self.model_ens[0], self.pe)
for i in range(self.pe.dim_ens_l):
dist = distributor.distributor(self.model_ens[i])
self.steps_for, time, doexit, status = PDAF.get_state(self.steps_for, doexit,
dist.next_observation,
dist.distribute_state,
prepost.initial_process,
status)
# set local domain on each model process
if self.local.local_filter:
self.local.set_lim_coords(self.model_ens[0].nx_p, self.model_ens[0].ny_p, self.pe)
def setEnKFOptions(self, dim_pint:int, dim_preal:int) -> tuple[np.ndarray, np.ndarray]:
"""set ensemble kalman filter options
Parameters
----------
dim_pint : int
size of integer filter options
dim_preal : int
size of float filter options
"""
filter_param_i:np.ndarray = np.zeros(dim_pint, dtype=np.intc)
filter_param_r:np.ndarray = np.zeros(dim_preal)
filter_param_i[0] = self.sv.dim_state_p
filter_param_i[1] = self.sv.dim_ens
filter_param_i[2] = self.filter_options.rank_analysis_enkf
filter_param_i[3] = self.filter_options.incremental
filter_param_i[4] = 0
filter_param_r[0] = self.filter_options.forget
return filter_param_i, filter_param_r
def setETKFOptions(self, dim_pint:int, dim_preal:int) -> tuple[np.ndarray, np.ndarray]:
"""Summary
Parameters
----------
dim_pint : int
size of integer filter options
dim_preal : int
size of float filter options
"""
filter_param_i:np.ndarray = np.zeros(dim_pint, dtype=np.intc)
filter_param_r:np.ndarray = np.zeros(dim_preal)
filter_param_i[0] = self.sv.dim_state_p
filter_param_i[1] = self.sv.dim_ens
filter_param_i[2] = 0
filter_param_i[3] = self.filter_options.incremental
filter_param_i[4] = self.filter_options.type_forget
filter_param_i[5] = self.filter_options.type_trans
filter_param_i[6] = self.filter_options.type_sqrt
filter_param_r[0] = self.filter_options.forget
return filter_param_i, filter_param_r
def assimilate_full_parallel(self) -> None:
"""Assimilation function for the full parallel implementation
"""
doexit:int = 0
status:int = 0
cltor:collector.collector = collector.collector(self.model_ens[0], self.pe)
prepost:prepost_processing.prepost = prepost_processing.prepost(self.model_ens[0], self.pe)
dist = distributor.distributor(self.model_ens[0])
if self.local.local_filter:
status = \
PDAF.localomi_assimilate(cltor.collect_state,
dist.distribute_state,
self.obs.init_dim_obs_pdafomi,
self.obs.obs_op_pdafomi,
prepost.prepostprocess,
self.local.init_n_domains_pdaf,
self.local.init_dim_l_pdaf,
self.obs.init_dim_obs_l_pdafomi,
dist.next_observation, status)
else:
if self.filter_options.filtertype == 8:
status = \
PDAF.omi_assimilate_lenkf(cltor.collect_state,
dist.distribute_state,
self.obs.init_dim_obs_pdafomi,
self.obs.obs_op_pdafomi,
prepost.prepostprocess,
self.obs.localize_covar_pdafomi,
dist.next_observation)
else:
status = \
PDAF.omi_assimilate_global(cltor.collect_state,
dist.distribute_state,
self.obs.init_dim_obs_pdafomi,
self.obs.obs_op_pdafomi,
prepost.prepostprocess,
dist.next_observation)
assert status == 0, f'ERROR {status} in PDAF_put_state - stopping! (PE {self.pe.mype_ens})'
def assimilate_flexible(self) -> None:
"""This function implement the assimilation in flexibble implementation.
The put_state_XXX functions put model fields into PDAF state vectors using
i.e. PDAF will collect state vectors from models (from a user-supplied functions p.o.v.).
When all ensemble members are collected, the PDAF distribute each model fields
"""
doexit:int = 0
status:int = 0
cltor: collector.collector
prepost:prepost_processing.prepost = prepost_processing.prepost(self.model_ens[0], self.pe)
if self.local.local_filter:
for i in range(self.pe.dim_ens_l):
cltor = collector.collector(self.model_ens[i], self.pe)
status = PDAF.localomi_put_state(cltor.collect_state,
self.obs.init_dim_obs_pdafomi,
self.obs.obs_op_pdafomi,
prepost.prepostprocess,
self.local.init_n_domains_pdaf,
self.local.init_dim_l_pdaf,
self.obs.init_dim_obs_l_pdafomi,
status)
else:
if self.filter_options.filtertype == 8:
for i in range(self.pe.dim_ens_l):
cltor = collector.collector(self.model_ens[i], self.pe)
status = PDAF.omi_put_state_lenkf(cltor.collect_state,
self.obs.init_dim_obs_pdafomi, self.obs.obs_op_pdafomi,
prepost.prepostprocess,
self.obs.localize_covar_pdafomi)
else:
for i in range(self.pe.dim_ens_l):
cltor = collector.collector(self.model_ens[i], self.pe)
status = PDAF.omi_put_state_global(cltor.collect_state,
self.obs.init_dim_obs_pdafomi, self.obs.obs_op_pdafomi,
prepost.prepostprocess)
for i in range(self.pe.dim_ens_l):
dist = distributor.distributor(self.model_ens[i])
time:float
self.steps_for, time, doexit, status = PDAF.get_state(self.steps_for, doexit,
dist.next_observation,
dist.distribute_state,
prepost.prepostprocess,
status)
assert status == 0, f'ERROR {status} in PDAF_put_state - stopping! (PE {self.pe.mype_ens})'
def finalise(self) -> None:
PDAF.print_info(11)
if (self.pe.mype_ens == 0): PDAF.print_info(3)
PDAF.deallocate()