def test_get_ensemble_forecast_collection(self):
EPSG = 32633
upper_left_x = 436100.0
upper_left_y = 7417800.0
nx = 74
ny = 94
dx = 1000.0
dy = 1000.0
t0 = api.YMDhms(2015, 7, 26, 0)
n_hours = 30
utc = api.Calendar() # No offset gives Utc
period = api.UtcPeriod(utc.time(t0), utc.time(t0) + api.deltahours(n_hours))
t_c = utc.time(t0) + api.deltahours(1)
base_dir = path.join(shyftdata_dir, "netcdf", "arome")
pattern = "fc2015072600.nc"
bbox = ([upper_left_x, upper_left_x + nx * dx,
upper_left_x + nx * dx, upper_left_x],
[upper_left_y, upper_left_y,
upper_left_y - ny * dy, upper_left_y - ny * dy])
try:
ar1 = AromeDataRepository(EPSG, base_dir, filename=pattern, bounding_box=bbox)
ar2 = AromeDataRepository(EPSG, base_dir, filename=pattern, bounding_box=bbox)
repos = GeoTsRepositoryCollection([ar1, ar2])
data_names = ("temperature", "wind_speed", "relative_humidity")
ensemble = repos.get_forecast_ensemble(data_names, period, t_c, None)
self.assertTrue(isinstance(ensemble, list))
self.assertEqual(len(ensemble), 10)
with self.assertRaises(GeoTsRepositoryCollectionError) as context:
repos = GeoTsRepositoryCollection([ar1, ar2], reduce_type="add")
repos.get_forecast_ensemble(data_names, period, t_c, None)
self.assertEqual("Only replace is supported yet", context.exception.args[0])
except AromeDataRepositoryError as adre:
self.skipTest("(test inconclusive- missing arome-data {0})".format(adre))
def test_get_forecast_collection(self):
n_hours = 30
dt = api.deltahours(1)
utc = api.Calendar() # No offset gives Utc
tc = api.YMDhms(2015, 8, 24, 6)
t0 = utc.time(tc)
period = api.UtcPeriod(t0, t0 + api.deltahours(n_hours))
date_str = "{}{:02}{:02}_{:02}".format(tc.year, tc.month, tc.day, tc.hour)
epsg, bbox = self.arome_epsg_bbox
base_dir = path.join(shyftdata_dir, "repository", "arome_data_repository")
f1 = "arome_metcoop_red_default2_5km_{}.nc".format(date_str)
f2 = "arome_metcoop_red_test2_5km_{}.nc".format(date_str)
ar1 = AromeDataRepository(epsg, base_dir, filename=f1, allow_subset=True)
ar2 = AromeDataRepository(epsg, base_dir, filename=f2, elevation_file=f1, allow_subset=True)
geo_ts_repository = GeoTsRepositoryCollection([ar1, ar2])
source_names = ("temperature", "radiation")
sources = geo_ts_repository.get_forecast(source_names, period, t0,
geo_location_criteria=bbox)
self.assertTrue(all([x in source_names for x in sources]))
geo_ts_repository = GeoTsRepositoryCollection([ar1, ar2], reduce_type="add")
with self.assertRaises(GeoTsRepositoryCollectionError) as context:
sources = geo_ts_repository.get_forecast(("temperature", "radiation"),
period, t0, geo_location_criteria=bbox)
def arome_repository(self):
"""
This shows how we
join together two arome repositories to a repository that gives us all the variabes that we need.
arome_4: all except radiation
arome_rad: ratiation
Return
------
Arome repository (inside a GeoTsRepositoryCollection), with all datafiles that matches
the filepattrns we currently use for arome downloads.
"""
base_dir = path.join(self.statkraft_data_dir, "repository",
"arome_data_repository")
epsg = self.grid_spec.epsg()
bbox = self.grid_spec.bounding_box(epsg)
f1 = "arome_metcoop_default2_5km_20151001_00.nc"
f2 = "arome_metcoop_test2_5km_20151001_00.nc"
arome_4 = AromeDataRepository(epsg,
base_dir,
filename=f1,
bounding_box=bbox,
allow_subset=True)
arome_rad = AromeDataRepository(epsg,
base_dir,
filename=f2,
elevation_file=f1,
bounding_box=bbox,
allow_subset=True)
return GeoTsRepositoryCollection([arome_4, arome_rad])
def test_compute_lwc_percentiles(self):
# Simulation time axis
year, month, day, hour = 2010, 9, 1, 0
dt = api.deltahours(24)
n_steps = 400
utc = api.Calendar() # No offset gives Utc
t0 = utc.time(api.YMDhms(year, month, day, hour))
time_axis = api.Timeaxis(t0, dt, n_steps)
# Some fake ids
region_id = 0
interpolation_id = 0
# Simulation coordinate system
epsg = "32633"
# Model
model_t = pt_gs_k.PTGSKModel
# Configs and repositories
dataset_config_file = path.join(path.dirname(__file__), "netcdf",
"atnsjoen_datasets.yaml")
region_config_file = path.join(path.dirname(__file__), "netcdf",
"atnsjoen_calibration_region.yaml")
region_config = RegionConfig(region_config_file)
model_config = ModelConfig(self.model_config_file)
dataset_config = YamlContent(dataset_config_file)
region_model_repository = RegionModelRepository(
region_config, model_config, model_t, epsg)
interp_repos = InterpolationParameterRepository(model_config)
netcdf_geo_ts_repos = []
for source in dataset_config.sources:
station_file = source["params"]["stations_met"]
netcdf_geo_ts_repos.append(
GeoTsRepository(source["params"], station_file, ""))
geo_ts_repository = GeoTsRepositoryCollection(netcdf_geo_ts_repos)
# Construct target discharge series
simulator = DefaultSimulator(region_id, interpolation_id,
region_model_repository,
geo_ts_repository, interp_repos, None)
n_cells = simulator.region_model.size()
state_repos = DefaultStateRepository(model_t, n_cells)
cid = 1
simulator.region_model.set_state_collection(cid, True)
simulator.run(time_axis, state_repos.get_state(0))
percentile_list = [10, 25, 50, 75, 90]
# From here, things could be calculated without copies (except for 't')
# TODO: Graham optimize with numba :-)
cells = simulator.region_model.get_cells()
lwcs = [np.array(cell.sc.gs_lwc.v) for cell in cells] # Contiguous
t = np.array([
cells[0].sc.gs_lwc.time(i)
for i in range(cells[0].sc.gs_lwc.size())
])
percentiles = np.percentile(np.array(lwcs), percentile_list, 0)
def test_compute_lwc_percentiles(self):
# Simulation time axis
year, month, day, hour = 2010, 9, 1, 0
dt = api.deltahours(24)
n_steps = 400
utc = api.Calendar() # No offset gives Utc
t0 = utc.time(year, month, day, hour)
time_axis = api.TimeAxisFixedDeltaT(t0, dt, n_steps)
# Some fake ids
region_id = 0
interpolation_id = 0
# Simulation coordinate system
epsg = "32633"
# Model
model_t = pt_gs_k.PTGSKModel
# Configs and repositories
dataset_config_file = path.join(path.dirname(__file__), "netcdf",
"atnsjoen_datasets.yaml")
region_config_file = path.join(path.dirname(__file__), "netcdf",
"atnsjoen_calibration_region.yaml")
region_config = RegionConfig(region_config_file)
model_config = ModelConfig(self.model_config_file)
dataset_config = YamlContent(dataset_config_file)
region_model_repository = RegionModelRepository(
region_config, model_config, model_t, epsg)
interp_repos = InterpolationParameterRepository(model_config)
netcdf_geo_ts_repos = []
for source in dataset_config.sources:
station_file = source["params"]["stations_met"]
netcdf_geo_ts_repos.append(
GeoTsRepository(source["params"], station_file, ""))
geo_ts_repository = GeoTsRepositoryCollection(netcdf_geo_ts_repos)
# Construct target discharge series
simulator = DefaultSimulator(region_id, interpolation_id,
region_model_repository,
geo_ts_repository, interp_repos, None)
n_cells = simulator.region_model.size()
state_repos = DefaultStateRepository(model_t, n_cells)
cid = 1
simulator.region_model.set_state_collection(cid, True)
simulator.run(time_axis, state_repos.get_state(0))
self.assertAlmostEqual(
simulator.region_model.cells[0].rc.pe_output.values[0],
0.039768354, 5) # just to verify pot.evap by regression, mm/h
percentile_list = [10, 25, 50, 75, 90]
def construct_geots_repo(datasets_config, epsg=None):
""" iterates over the different sources that are provided
and prepares the repository to read the data for each type"""
geo_ts_repos = []
src_types_to_extract = []
for source in datasets_config['sources']:
if epsg is not None:
source['params'].update({'epsg': epsg})
# note that here we are instantiating the different source repositories
# to place in the geo_ts list
geo_ts_repos.append(source['repository'](**source['params']))
src_types_to_extract.append(source['types'])
return GeoTsRepositoryCollection(geo_ts_repos, src_types_per_repo=src_types_to_extract)
def run_simulator(self, model_t):
# Simulation time axis
dt0 = api.YMDhms(2015, 8, 24, 6)
n_hours = 30
dt = api.deltahours(1)
utc = api.Calendar() # No offset gives Utc
t0 = utc.time(dt0)
time_axis = api.Timeaxis(t0, dt, n_hours)
# Some dummy ids not needed for the netcdf based repositories
region_id = 0
interpolation_id = 0
# Simulation coordinate system
epsg = "32633"
# Configs and repositories
region_config = RegionConfig(self.region_config_file)
model_config = ModelConfig(self.model_config_file)
region_model_repository = RegionModelRepository(
region_config, model_config, model_t, epsg)
interp_repos = InterpolationParameterRepository(model_config)
date_str = "{}{:02}{:02}_{:02}".format(dt0.year, dt0.month, dt0.day,
dt0.hour)
base_dir = path.join(shyftdata_dir, "repository",
"arome_data_repository")
f1 = "arome_metcoop_red_default2_5km_{}.nc".format(date_str)
f2 = "arome_metcoop_red_test2_5km_{}.nc".format(date_str)
ar1 = AromeDataRepository(epsg,
base_dir,
filename=f1,
allow_subset=True)
ar2 = AromeDataRepository(epsg,
base_dir,
filename=f2,
elevation_file=f1,
allow_subset=True)
geo_ts_repository = GeoTsRepositoryCollection([ar1, ar2])
simulator = DefaultSimulator(region_id, interpolation_id,
region_model_repository,
geo_ts_repository, interp_repos, None)
n_cells = simulator.region_model.size()
state_repos = DefaultStateRepository(model_t, n_cells)
simulator.run(time_axis, state_repos.get_state(0))
def test_get_ensemble_forecast_collection(self):
EPSG = 32633
upper_left_x = 436100.0
upper_left_y = 7417800.0
nx = 74
ny = 94
dx = 1000.0
dy = 1000.0
t0 = api.YMDhms(2015, 7, 26, 0)
n_hours = 30
utc = api.Calendar() # No offset gives Utc
period = api.UtcPeriod(utc.time(t0),
utc.time(t0) + api.deltahours(n_hours))
t_c = utc.time(t0) + api.deltahours(1)
base_dir = path.join(shyftdata_dir, "netcdf", "arome")
pattern = "fc2015072600.nc"
bbox = ([
upper_left_x, upper_left_x + nx * dx, upper_left_x + nx * dx,
upper_left_x
], [
upper_left_y, upper_left_y, upper_left_y - ny * dy,
upper_left_y - ny * dy
])
try:
ar1 = AromeDataRepository(EPSG,
base_dir,
filename=pattern,
bounding_box=bbox)
ar2 = AromeDataRepository(EPSG,
base_dir,
filename=pattern,
bounding_box=bbox)
repos = GeoTsRepositoryCollection([ar1, ar2])
data_names = ("temperature", "wind_speed", "relative_humidity")
ensemble = repos.get_forecast_ensemble(data_names, period, t_c,
None)
self.assertTrue(isinstance(ensemble, list))
self.assertEqual(len(ensemble), 10)
with self.assertRaises(GeoTsRepositoryCollectionError) as context:
repos = GeoTsRepositoryCollection([ar1, ar2],
reduce_type="add")
repos.get_forecast_ensemble(data_names, period, t_c, None)
self.assertEqual("Only replace is supported yet",
context.exception.args[0])
except AromeDataRepositoryError as adre:
self.skipTest(
"(test inconclusive- missing arome-data {0})".format(adre))
def test_get_timeseries_collection(self):
tc = api.YMDhms(2015, 8, 24, 6)
n_hours = 30
dt = api.deltahours(1)
utc = api.Calendar() # No offset gives Utc
t0 = utc.time(tc)
period = api.UtcPeriod(t0, t0 + api.deltahours(n_hours))
date_str = "{}{:02}{:02}_{:02}".format(tc.year, tc.month, tc.day,
tc.hour)
epsg, bbox = self.arome_epsg_bbox
base_dir = path.join(shyftdata_dir, "repository",
"arome_data_repository")
f1 = "arome_metcoop_red_default2_5km_{}.nc".format(date_str)
f2 = "arome_metcoop_red_test2_5km_{}.nc".format(date_str)
ar1 = AromeDataRepository(epsg,
base_dir,
filename=f1,
allow_subset=True)
ar2 = AromeDataRepository(epsg,
base_dir,
filename=f2,
elevation_file=f1,
allow_subset=True)
geo_ts_repository = GeoTsRepositoryCollection([ar1, ar2])
sources = geo_ts_repository.get_timeseries(
("temperature", "radiation"), period, geo_location_criteria=bbox)
with self.assertRaises(GeoTsRepositoryCollectionError) as context:
GeoTsRepositoryCollection([ar1, ar2], reduce_type="foo")
geo_ts_repository = GeoTsRepositoryCollection([ar1, ar2],
reduce_type="add")
with self.assertRaises(GeoTsRepositoryCollectionError) as context:
sources = geo_ts_repository.get_timeseries(
("temperature", "radiation"),
period,
geo_location_criteria=bbox)
def test_compute_lwc_percentiles(self):
# Simulation time axis
year, month, day, hour = 2013, 9, 1, 0
dt = api.deltahours(24)
n_steps = 364
utc = api.Calendar() # No offset gives Utc
t0 = utc.time(year, month, day, hour)
time_axis = api.TimeAxisFixedDeltaT(t0, dt, n_steps)
# Some fake ids
region_id = 0
interpolation_id = 0
# Model
region_model_repository = CFRegionModelRepository(
self.region_config, self.model_config)
interp_repos = InterpolationParameterRepository(
self.interpolation_config)
netcdf_geo_ts_repos = [
CFDataRepository(32633,
source["params"]["filename"],
padding=source["params"]['padding'])
for source in self.dataset_config.sources
]
geo_ts_repository = GeoTsRepositoryCollection(netcdf_geo_ts_repos)
# Construct target discharge series
simulator = DefaultSimulator(region_id, interpolation_id,
region_model_repository,
geo_ts_repository, interp_repos, None)
state_repos = DefaultStateRepository(simulator.region_model)
cid = 1228
simulator.region_model.set_state_collection(cid, True)
simulator.run(time_axis=time_axis, state=state_repos.get_state(0))
# TODO: Update the regression test below with correct result
# self.assertAlmostEqual(simulator.region_model.cells[0].rc.pe_output.values[0], 0.039768354, 5) # just to verify pot.evap by regression, mm/h
percentile_list = [10, 25, 50, 75, 90]
def test_get_forecast_collection(self):
n_hours = 30
dt = api.deltahours(1)
utc = api.Calendar() # No offset gives Utc
tc = api.YMDhms(2015, 8, 24, 6)
t0 = utc.time(tc)
period = api.UtcPeriod(t0, t0 + api.deltahours(n_hours))
date_str = "{}{:02}{:02}_{:02}".format(tc.year, tc.month, tc.day,
tc.hour)
epsg, bbox, bpoly = self.arome_epsg_bbox
base_dir = path.join(shyftdata_dir, "repository",
"arome_data_repository")
f1_elev = "arome_metcoop_red_default2_5km_{}.nc".format(date_str)
f1 = "arome_metcoop_red_default2_5km_(\d{4})(\d{2})(\d{2})[T_](\d{2})Z?.nc$"
#f2 = "arome_metcoop_red_test2_5km_{}.nc".format(date_str)
f2 = "arome_metcoop_red_test2_5km_(\d{4})(\d{2})(\d{2})[T_](\d{2})Z?.nc$"
ar1 = MetNetcdfDataRepository(epsg,
base_dir,
filename=f1,
allow_subset=True)
ar2 = MetNetcdfDataRepository(epsg,
base_dir,
filename=f2,
elevation_file=f1_elev,
allow_subset=True)
geo_ts_repository = GeoTsRepositoryCollection([ar1, ar2])
source_names = ("temperature", "radiation")
sources = geo_ts_repository.get_forecast(source_names,
period,
t0,
geo_location_criteria=bpoly)
self.assertTrue(all([x in source_names for x in sources]))
geo_ts_repository = GeoTsRepositoryCollection([ar1, ar2],
reduce_type="add")
with self.assertRaises(GeoTsRepositoryCollectionError) as context:
sources = geo_ts_repository.get_forecast(
("temperature", "radiation"),
period,
t0,
geo_location_criteria=bpoly)
def test_snow_and_ground_water_response_calibration(self):
"""
Test dual calibration strategy:
* First fit the three Kirchner parameters for
ground water response during July, August, and
September.
* Then fit two snow routine parameters (tx and max_water)
from November to April.
"""
# Simulation time axis
year, month, day, hour = 2010, 9, 1, 0
dt = api.deltahours(24)
n_steps = 400
utc = api.Calendar() # No offset gives Utc
t0 = utc.time(api.YMDhms(year, month, day, hour))
time_axis = api.Timeaxis(t0, dt, n_steps)
# Some fake ids
region_id = 0
interpolation_id = 0
# Simulation coordinate system
epsg = "32633"
# Model
model_t = pt_gs_k.PTGSKOptModel
# Configs and repositories
dataset_config_file = path.join(path.dirname(__file__), "netcdf",
"atnsjoen_datasets.yaml")
region_config_file = path.join(path.dirname(__file__), "netcdf",
"atnsjoen_calibration_region.yaml")
region_config = RegionConfig(region_config_file)
model_config = ModelConfig(self.model_config_file)
dataset_config = YamlContent(dataset_config_file)
region_model_repository = RegionModelRepository(
region_config, model_config, model_t, epsg)
interp_repos = InterpolationParameterRepository(model_config)
netcdf_geo_ts_repos = []
for source in dataset_config.sources:
station_file = source["params"]["stations_met"]
netcdf_geo_ts_repos.append(
GeoTsRepository(source["params"], station_file, ""))
geo_ts_repository = GeoTsRepositoryCollection(netcdf_geo_ts_repos)
# Construct target discharge series
simulator = DefaultSimulator(region_id, interpolation_id,
region_model_repository,
geo_ts_repository, interp_repos, None)
n_cells = simulator.region_model.size()
state_repos = DefaultStateRepository(model_t, n_cells)
simulator.run(time_axis, state_repos.get_state(0))
cid = 1
target_discharge = simulator.region_model.statistics.discharge([cid])
# Construct kirchner parameters
param = simulator.region_model.parameter_t(
simulator.region_model.get_region_parameter())
print_param("True solution", param)
kirchner_param_min = simulator.region_model.parameter_t(param)
kirchner_param_max = simulator.region_model.parameter_t(param)
# Kichner parameters are quite abstract (no physical meaning), so simply scale them
kirchner_param_min.kirchner.c1 *= 0.8
kirchner_param_min.kirchner.c2 *= 0.8
kirchner_param_min.kirchner.c3 *= 0.8
kirchner_param_max.kirchner.c1 *= 1.2
kirchner_param_max.kirchner.c2 *= 1.2
kirchner_param_max.kirchner.c3 *= 1.2
# kirchner_t_start = utc.time(api.YMDhms(2011, 4, 1, 0))
# kirchner_time_axis = api.Timeaxis(kirchner_t_start, dt, 150)
kirchner_time_axis = time_axis
# Construct gamma snow parameters (realistic tx and max_lwc)
gamma_snow_param_min = simulator.region_model.parameter_t(param)
gamma_snow_param_max = simulator.region_model.parameter_t(param)
gamma_snow_param_min.gs.tx = -1.0 # Min snow/rain temperature threshold
gamma_snow_param_min.gs.max_water = 0.05 # Min 8% max water in snow in costal regions
gamma_snow_param_max.gs.tx = 1.0
gamma_snow_param_max.gs.max_water = 0.25 # Max 35% max water content, or we get too little melt
gs_t_start = utc.time(api.YMDhms(2010, 11, 1, 0))
gs_time_axis = api.Timeaxis(gs_t_start, dt, 250)
# gs_time_axis = time_axis
# Find parameters
target_spec = api.TargetSpecificationPts(target_discharge,
api.IntVector([cid]), 1.0,
api.KLING_GUPTA)
target_spec_vec = api.TargetSpecificationVector(
) # TODO: We currently dont fix list initializer for vectors
target_spec_vec.append(target_spec)
# Construct a fake, perturbed starting point for calibration
p_vec = [param.get(i) for i in range(param.size())]
for i, name in enumerate(
[param.get_name(i) for i in range(len(p_vec))]):
if name not in ("c1" "c2", "c3", "TX", "max_water"):
next
if name in ("c1", "c2", "c3"):
p_vec[i] = random.uniform(0.8 * p_vec[i], 1.2 * p_vec[i])
elif name == "TX":
p_vec[i] = random.uniform(gamma_snow_param_min.gs.tx,
gamma_snow_param_max.gs.tx)
elif name == "max_water":
p_vec[i] = random.uniform(gamma_snow_param_min.gs.max_water,
gamma_snow_param_max.gs.max_water)
param.set(p_vec)
print_param("Initial guess", param)
# Two pass optimization, once for the ground water response, and second time for
kirchner_p_opt = simulator.optimize(kirchner_time_axis,
state_repos.get_state(0),
target_spec_vec, param,
kirchner_param_min,
kirchner_param_max)
gamma_snow_p_opt = simulator.optimize(gs_time_axis,
state_repos.get_state(0),
target_spec_vec, kirchner_p_opt,
gamma_snow_param_min,
gamma_snow_param_max)
print_param("Half way result", kirchner_p_opt)
print_param("Result", gamma_snow_p_opt)
simulator.region_model.set_catchment_parameter(cid, gamma_snow_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.v)
t_ts = np.array(
[target_discharge.time(i) for i in range(target_discharge.size())])
f_vs = np.array(found_discharge.v)
f_ts = np.array(
[found_discharge.time(i) for i in range(found_discharge.size())])
def test_snow_and_ground_water_response_calibration(self):
"""
Test dual calibration strategy:
* First fit the three Kirchner parameters for
ground water response during July, August, and
September.
* Then fit two snow routine parameters (tx and max_water)
from November to April.
"""
# Simulation time axis
dt = api.deltahours(24)
n_steps = 364
utc = api.Calendar() # No offset gives Utc
t0 = utc.time(2013, 9, 1, 0)
time_axis = api.TimeAxisFixedDeltaT(t0, dt, n_steps)
# Some fake ids
region_id = 0
interpolation_id = 0
opt_model_t = self.model_config.model_type().opt_model_t
model_config = ModelConfig(self.model_config_file,
overrides={'model_t': opt_model_t})
region_model_repository = CFRegionModelRepository(
self.region_config, model_config)
interp_repos = InterpolationParameterRepository(
self.interpolation_config)
netcdf_geo_ts_repos = [
CFDataRepository(32633,
source["params"]["filename"],
padding=source["params"]['padding'])
for source in self.dataset_config.sources
]
geo_ts_repository = GeoTsRepositoryCollection(netcdf_geo_ts_repos)
# Construct target discharge series
simulator = DefaultSimulator(region_id, interpolation_id,
region_model_repository,
geo_ts_repository, interp_repos, None)
state_repos = DefaultStateRepository(simulator.region_model)
simulator.run(time_axis, state_repos.get_state(0))
cid = 1228
target_discharge = api.TsTransform().to_average(
t0, dt, n_steps,
simulator.region_model.statistics.discharge([cid]))
# Construct kirchner parameters
param = simulator.region_model.parameter_t(
simulator.region_model.get_region_parameter())
print_param("True solution", param)
kirchner_param_min = simulator.region_model.parameter_t(param)
kirchner_param_max = simulator.region_model.parameter_t(param)
# Kichner parameters are quite abstract (no physical meaning), so simply scale them
kirchner_param_min.kirchner.c1 *= 0.8
kirchner_param_min.kirchner.c2 *= 0.8
kirchner_param_min.kirchner.c3 *= 0.8
kirchner_param_max.kirchner.c1 *= 1.2
kirchner_param_max.kirchner.c2 *= 1.2
kirchner_param_max.kirchner.c3 *= 1.2
# kirchner_t_start = utc.time(2011, 4, 1, 0)
# kirchner_time_axis = api.TimeAxisFixedDeltaT(kirchner_t_start, dt, 150)
kirchner_time_axis = time_axis
# Construct gamma snow parameters (realistic tx and max_lwc)
gamma_snow_param_min = simulator.region_model.parameter_t(param)
gamma_snow_param_max = simulator.region_model.parameter_t(param)
gamma_snow_param_min.gs.tx = -1.0 # Min snow/rain temperature threshold
gamma_snow_param_min.gs.max_water = 0.05 # Min 8% max water in snow in costal regions
gamma_snow_param_max.gs.tx = 1.0
gamma_snow_param_max.gs.max_water = 0.25 # Max 35% max water content, or we get too little melt
gs_t_start = utc.time(2013, 11, 1, 0)
gs_time_axis = api.TimeAxisFixedDeltaT(gs_t_start, dt, 250)
# gs_time_axis = time_axis
# Find parameters
target_spec = api.TargetSpecificationPts(target_discharge,
api.IntVector([cid]), 1.0,
api.KLING_GUPTA)
target_spec_vec = api.TargetSpecificationVector(
) # TODO: We currently dont fix list initializer for vectors
target_spec_vec.append(target_spec)
# Construct a fake, perturbed starting point for calibration
p_vec = [param.get(i) for i in range(param.size())]
for i, name in enumerate(
[param.get_name(i) for i in range(len(p_vec))]):
if name not in ("c1" "c2", "c3", "TX", "max_water"):
next
if name in ("c1", "c2", "c3"):
p_vec[i] = random.uniform(0.8 * p_vec[i], 1.2 * p_vec[i])
elif name == "TX":
p_vec[i] = random.uniform(gamma_snow_param_min.gs.tx,
gamma_snow_param_max.gs.tx)
elif name == "max_water":
p_vec[i] = random.uniform(gamma_snow_param_min.gs.max_water,
gamma_snow_param_max.gs.max_water)
param.set(p_vec)
print_param("Initial guess", param)
# Two pass optimization, once for the ground water response, and second time for
kirchner_p_opt = simulator.optimize(kirchner_time_axis,
state_repos.get_state(0),
target_spec_vec, param,
kirchner_param_min,
kirchner_param_max)
gamma_snow_p_opt = simulator.optimize(gs_time_axis,
state_repos.get_state(0),
target_spec_vec, kirchner_p_opt,
gamma_snow_param_min,
gamma_snow_param_max)
print_param("Half way result", kirchner_p_opt)
print_param("Result", gamma_snow_p_opt)
simulator.region_model.set_catchment_parameter(cid, gamma_snow_p_opt)
simulator.run(time_axis, state_repos.get_state(0))
found_discharge = simulator.region_model.statistics.discharge([cid])
