def test_set_observed_state(self):
# set up configuration
config_dir = path.join(path.dirname(__file__), "netcdf")
cfg = orchestration.YAMLConfig("atnsjoen_simulation.yaml",
"atnsjoen",
config_dir=config_dir,
data_dir=shyftdata_dir)
# get a simulator
simulator = cfg.get_simulator()
n_cells = simulator.region_model.size()
state_repos = DefaultStateRepository(cfg.model_t, n_cells)
state = state_repos.get_state(0)
simulator.run(cfg.time_axis, state)
simulator.region_model.get_states(state)
obs_discharge = 0.0
state = simulator.discharge_adjusted_state(obs_discharge, state)
self.assertAlmostEqual(
0.0,
reduce(operator.add,
(state[i].kirchner.q for i in range(state.size()))))
simulator.region_model.get_states(state)
obs_discharge = 10.0 # m3/s
state = simulator.discharge_adjusted_state(obs_discharge, state)
# Convert from l/h to m3/s by dividing by 3.6e6
adj_discharge = reduce(
operator.add,
(state[i].kirchner.q * cell.geo.area()
for (i, cell) in enumerate(simulator.region_model.get_cells())
)) / (3.6e6)
self.assertAlmostEqual(obs_discharge, adj_discharge)
def run_calibration(self, model_t):
# set up configuration
config_dir = path.join(path.dirname(__file__), "netcdf")
cfg = orchestration.YAMLConfig(
"atnsjoen_calibration.yaml", "atnsjoen",
config_dir=config_dir, data_dir=shyftdata_dir,
model_t=model_t)
time_axis = cfg.time_axis
# get a simulator
simulator = cfg.get_simulator()
n_cells = simulator.region_model.size()
state_repos = DefaultStateRepository(cfg.model_t, n_cells)
simulator.run(time_axis, state_repos.get_state(0))
cid = 1
target_discharge_ts = simulator.region_model.statistics.discharge([cid])
target_discharge = api.TsTransform().to_average(time_axis.time(0), time_axis.time(1)-time_axis.time(0), time_axis.size(), target_discharge_ts)
# Perturb parameters
param = simulator.region_model.get_region_parameter()
p_vec_orig = [param.get(i) for i in range(param.size())]
p_vec_min = p_vec_orig[:]
p_vec_max = p_vec_orig[:]
p_vec_guess = p_vec_orig[:]
random.seed(0)
p_names = []
for i in range(4):
p_names.append(param.get_name(i))
p_vec_min[i] *= 0.5
p_vec_max[i] *= 1.5
p_vec_guess[i] = random.uniform(p_vec_min[i], p_vec_max[i])
if p_vec_min[i] > p_vec_max[i]:
p_vec_min[i], p_vec_max[i] = p_vec_max[i], p_vec_min[i]
p_min = simulator.region_model.parameter_t()
p_max = simulator.region_model.parameter_t()
p_guess = simulator.region_model.parameter_t()
p_min.set(p_vec_min)
p_max.set(p_vec_max)
p_guess.set(p_vec_guess)
# Find parameters
target_spec = api.TargetSpecificationPts(target_discharge, api.IntVector([cid]),
1.0, api.KLING_GUPTA)
target_spec_vec = api.TargetSpecificationVector() #([target_spec]) does not yet work
target_spec_vec.append(target_spec)
p_opt = simulator.optimize(time_axis, state_repos.get_state(0),
target_spec_vec, p_guess, p_min, p_max)
simulator.region_model.set_catchment_parameter(cid, p_opt)
simulator.run(time_axis, state_repos.get_state(0))
found_discharge = simulator.region_model.statistics.discharge([cid])
t_vs = np.array([target_discharge.value(i) for i in range(target_discharge.size())])
t_ts = np.array([target_discharge.time(i) for i in range(target_discharge.size())])
f_vs = np.array([found_discharge.value(i) for i in range(found_discharge.size())])
f_ts = np.array([found_discharge.time(i) for i in range(found_discharge.size())])
self.assertTrue(np.linalg.norm(t_ts - f_ts) < 1.0e-10)
self.assertTrue(np.linalg.norm(t_vs - f_vs) < 1.0e-3)
def test_run_geo_ts_data_simulator(self):
# set up configuration
config_dir = path.join(path.dirname(__file__), "netcdf")
cfg = orchestration.YAMLConfig(
"atnsjoen_simulation.yaml", "atnsjoen",
config_dir=config_dir, data_dir=shyftdata_dir)
# get a simulator
simulator = cfg.get_simulator()
n_cells = simulator.region_model.size()
state_repos = DefaultStateRepository(cfg.model_t, n_cells)
simulator.run(cfg.time_axis, state_repos.get_state(0))
sim_copy = simulator.copy()
sim_copy.run(cfg.time_axis,state_repos.get_state(0))
def test_set_observed_state(self):
# set up configuration
config_dir = path.join(path.dirname(__file__), "netcdf")
cfg = orchestration.YAMLConfig("atnsjoen_simulation.yaml",
"atnsjoen",
config_dir=config_dir,
data_dir=shyftdata_dir)
# get a simulator
simulator = cfg.get_simulator()
state_repos = DefaultStateRepository(simulator.region_model)
state = state_repos.get_state(0)
simulator.run(
cfg.time_axis, state
) # Here we have already checked if StateIDs match with model cell ID. Further check is redundant.
#simulator.region_model.get_states(state.state_vector)
state = simulator.reg_model_state
obs_discharge = 0.0
state = simulator.discharge_adjusted_state(obs_discharge, state)
self.assertAlmostEqual(
0.0,
reduce(operator.add,
(state[i].state.kirchner.q for i in range(len(state)))))
#simulator.region_model.get_states(state.state_vector)
state = simulator.reg_model_state
obs_discharge = 10.0 # m3/s
state = simulator.discharge_adjusted_state(obs_discharge, state)
# Convert from l/h to m3/s by dividing by 3.6e6
adj_discharge = reduce(
operator.add,
(state[i].state.kirchner.q * cell.geo.area()
for (i, cell) in enumerate(simulator.region_model.get_cells())
)) / (3.6e6)
self.assertAlmostEqual(obs_discharge, adj_discharge)
def run_calibration(self, model_t):
# set up configuration
config_dir = path.join(path.dirname(__file__), "netcdf")
cfg = orchestration.YAMLConfig("atnsjoen_calibration.yaml",
"atnsjoen",
config_dir=config_dir,
data_dir=shyftdata_dir,
model_t=model_t)
time_axis = cfg.time_axis
# get a simulator
simulator = cfg.get_simulator()
n_cells = simulator.region_model.size()
state_repos = DefaultStateRepository(cfg.model_t, n_cells)
s0 = state_repos.get_state(0)
param = simulator.region_model.get_region_parameter()
# not needed, we auto initialize to default if not done explicitely
#if model_t in [pt_hs_k.PTHSKOptModel]:
# for i in range(len(s0)):
# s0[i].snow.distribute(param.hs)
simulator.run(time_axis, s0)
cid = 1
target_discharge_ts = simulator.region_model.statistics.discharge(
[cid])
target_discharge = api.TsTransform().to_average(
time_axis.time(0),
time_axis.time(1) - time_axis.time(0), time_axis.size(),
target_discharge_ts)
# Perturb parameters
p_vec_orig = [param.get(i) for i in range(param.size())]
p_vec_min = p_vec_orig[:]
p_vec_max = p_vec_orig[:]
p_vec_guess = p_vec_orig[:]
random.seed(0)
p_names = []
for i in range(4):
p_names.append(param.get_name(i))
p_vec_min[i] *= 0.5
p_vec_max[i] *= 1.5
p_vec_guess[i] = random.uniform(p_vec_min[i], p_vec_max[i])
if p_vec_min[i] > p_vec_max[i]:
p_vec_min[i], p_vec_max[i] = p_vec_max[i], p_vec_min[i]
p_min = simulator.region_model.parameter_t()
p_max = simulator.region_model.parameter_t()
p_guess = simulator.region_model.parameter_t()
p_min.set(p_vec_min)
p_max.set(p_vec_max)
p_guess.set(p_vec_guess)
# Find parameters
target_spec = api.TargetSpecificationPts(target_discharge,
api.IntVector([cid]), 1.0,
api.KLING_GUPTA)
target_spec_vec = api.TargetSpecificationVector(
) # ([target_spec]) does not yet work
target_spec_vec.append(target_spec)
self.assertEqual(simulator.optimizer.trace_size,
0) # before optmize, trace_size should be 0
p_opt = simulator.optimize(time_axis, s0, target_spec_vec, p_guess,
p_min, p_max)
self.assertGreater(simulator.optimizer.trace_size,
0) # after opt, some trace values should be there
# the trace values are in the order of appearance 0...trace_size-1
#
goal_fn_values = simulator.optimizer.trace_goal_function_values.to_numpy(
) # all of them, as np array
self.assertEqual(len(goal_fn_values), simulator.optimizer.trace_size)
p_last = simulator.optimizer.trace_parameter(
simulator.optimizer.trace_size -
1) # get out the last (not neccessary the best)
self.assertIsNotNone(p_last)
simulator.region_model.set_catchment_parameter(cid, p_opt)
simulator.run(time_axis, s0)
found_discharge = simulator.region_model.statistics.discharge([cid])
t_vs = np.array([
target_discharge.value(i) for i in range(target_discharge.size())
])
t_ts = np.array(
[target_discharge.time(i) for i in range(target_discharge.size())])
f_vs = np.array(
[found_discharge.value(i) for i in range(found_discharge.size())])
f_ts = np.array(
[found_discharge.time(i) for i in range(found_discharge.size())])
self.assertTrue(np.linalg.norm(t_ts - f_ts) < 1.0e-10)
self.assertTrue(np.linalg.norm(t_vs - f_vs) < 1.0e-3)