def test_create_TargetSpecificationPts(self):
t = api.TargetSpecificationPts();
t.scale_factor = 1.0
t.calc_mode = api.NASH_SUTCLIFFE
t.calc_mode = api.KLING_GUPTA;
t.s_r = 1.0 # KGEs scale-factors
t.s_a = 2.0
t.s_b = 3.0
self.assertAlmostEqual(t.scale_factor, 1.0)
# create a ts with some points
cal = api.Calendar();
start = cal.time(api.YMDhms(2015, 1, 1, 0, 0, 0))
dt = api.deltahours(1)
tsf = api.TsFactory();
times = api.UtcTimeVector()
times.push_back(start + 1 * dt);
times.push_back(start + 3 * dt);
times.push_back(start + 4 * dt)
values = api.DoubleVector()
values.push_back(1.0)
values.push_back(3.0)
values.push_back(np.nan)
tsp = tsf.create_time_point_ts(api.UtcPeriod(start, start + 24 * dt), times, values)
# convert it from a time-point ts( as returned from current smgrepository) to a fixed interval with timeaxis, needed by calibration
tst = api.TsTransform()
tsa = tst.to_average(start, dt, 24, tsp)
# tsa2 = tst.to_average(start,dt,24,tsp,False)
# tsa_staircase = tst.to_average_staircase(start,dt,24,tsp,False) # nans infects the complete interval to nan
# tsa_staircase2 = tst.to_average_staircase(start,dt,24,tsp,True) # skip nans, nans are 0
# stuff it into the target spec.
# also show how to specify snow-calibration
cids = api.IntVector([0, 2, 3])
t2 = api.TargetSpecificationPts(tsa,cids, 0.7, api.KLING_GUPTA, 1.0, 1.0, 1.0, api.SNOW_COVERED_AREA)
t2.catchment_property = api.SNOW_WATER_EQUIVALENT
self.assertEqual(t2.catchment_property, api.SNOW_WATER_EQUIVALENT)
self.assertIsNotNone(t2.catchment_indexes)
for i in range(len(cids)):
self.assertEqual(cids[i],t2.catchment_indexes[i])
t.ts = tsa
#TODO: Does not work, list of objects are not yet convertible tv = api.TargetSpecificationVector([t, t2])
tv=api.TargetSpecificationVector()
tv.append(t)
tv.append(t2)
# now verify we got something ok
self.assertEqual(2, tv.size())
self.assertAlmostEqual(tv[0].ts.value(1), 1.5) # average value 0..1 ->0.5
self.assertAlmostEqual(tv[0].ts.value(2), 2.5) # average value 0..1 ->0.5
self.assertAlmostEqual(tv[0].ts.value(3), 3.0) # average value 0..1 ->0.5
# and that the target vector now have its own copy of ts
tsa.set(1, 3.0)
self.assertAlmostEqual(tv[0].ts.value(1), 1.5) # make sure the ts passed onto target spec, is a copy
self.assertAlmostEqual(tsa.value(1), 3.0) # and that we really did change the source
def _create_target_specvect(self):
print("Creating TargetSpecificationVector...")
tv = api.TargetSpecificationVector()
tst = api.TsTransform()
cid_map = self.region_model.catchment_id_map
for repo in self.target_repo:
tsp = repo['repository'].read([ts_info['uid'] for ts_info in repo['1D_timeseries']], self.time_axis.total_period())
for ts_info in repo['1D_timeseries']:
if np.count_nonzero(np.in1d(cid_map, ts_info['catch_id'])) != len(ts_info['catch_id']):
raise ConfigSimulatorError("Catchment ID {} for target series {} not found.".format(
','.join([str(val) for val in [i for i in ts_info['catch_id'] if i not in cid_map]]), ts_info['uid']))
period = api.UtcPeriod(ts_info['start_datetime'],
ts_info['start_datetime'] + ts_info['number_of_steps'] * ts_info['run_time_step'])
if not self.time_axis.total_period().contains(period):
raise ConfigSimulatorError(
"Period {} for target series {} is not within the calibration period {}.".format(
period.to_string(), ts_info['uid'], self.time_axis.total_period().to_string()))
#tsp = repo['repository'].read([ts_info['uid']], period)[ts_info['uid']]
t = api.TargetSpecificationPts()
t.uid = ts_info['uid']
t.catchment_indexes = api.IntVector(ts_info['catch_id'])
t.scale_factor = ts_info['weight']
t.calc_mode = self.obj_funcs[ts_info['obj_func']['name']]
[setattr(t, nm, ts_info['obj_func']['scaling_factors'][k]) for nm, k in zip(['s_r','s_a','s_b'], ['s_corr','s_var','s_bias'])]
t.ts = api.TimeSeries(tst.to_average(ts_info['start_datetime'], ts_info['run_time_step'], ts_info['number_of_steps'], tsp[ts_info['uid']]))
tv.append(t)
return tv
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)
