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 test_run_arome_ensemble(self):
# Simulation time axis
year, month, day, hour = 2015, 7, 26, 0
n_hours = 30
dt = api.deltahours(1)
utc = api.Calendar() # No offset gives Utc
t0 = utc.time(api.YMDhms(year, month, day, hour))
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"
# Model
model_t = pt_gs_k.PTGSKOptModel
# 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)
base_dir = path.join(shyftdata_dir, "netcdf", "arome")
pattern = "fc*.nc"
try:
geo_ts_repository = AromeDataRepository(epsg,
base_dir,
filename=pattern,
allow_subset=True)
except Exception as e:
print("**** test_run_arome_ensemble: Arome data missing or"
" wrong, test inconclusive ****")
print("****{}****".format(e))
self.skipTest(
"**** test_run_arome_ensemble: Arome data missing or wrong, test "
"inconclusive ****\n\t exception:{}".format(e))
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)
simulators = simulator.create_ensembles(time_axis, t0,
state_repos.get_state(0))
for s in simulators:
s.simulate()
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_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())])
