def region_model_repository(self):
"""
Returns
-------
- RegionModelRepository - configured with 'Tistel-ptgsk' etc.
"""
id_list = [1225]
# parameters can be loaded from yaml_config Model parameters..
pt_params = api.PriestleyTaylorParameter(
) # *params["priestley_taylor"])
gs_params = api.GammaSnowParameter() # *params["gamma_snow"])
ss_params = api.SkaugenParameter()
ae_params = api.ActualEvapotranspirationParameter(
) # *params["act_evap"])
k_params = api.KirchnerParameter() # *params["kirchner"])
p_params = api.PrecipitationCorrectionParameter(
) # TODO; default 1.0, is it used ??
ptgsk_rm_params = pt_gs_k.PTGSKParameter(pt_params, gs_params,
ae_params, k_params,
p_params)
ptssk_rm_params = pt_ss_k.PTSSKParameter(pt_params, ss_params,
ae_params, k_params,
p_params)
# create the description for 2 models of tistel,ptgsk, ptssk
tistel_grid_spec = self.grid_spec #
cfg_list = [
RegionModelConfig("Tistel-ptgsk", pt_gs_k.PTGSKModel,
ptgsk_rm_params, tistel_grid_spec,
"unregulated", "FELTNR", id_list),
RegionModelConfig("Tistel-ptssk", pt_ss_k.PTSSKModel,
ptssk_rm_params, tistel_grid_spec,
"unregulated", "FELTNR", id_list)
]
rm_cfg_dict = {x.name: x for x in cfg_list}
return GisRegionModelRepository(rm_cfg_dict)
def _create_std_ptgsk_param(self):
ptp = api.PriestleyTaylorParameter(albedo=0.85, alpha=1.23)
ptp.albedo = 0.9
ptp.alpha = 1.26
aep = api.ActualEvapotranspirationParameter(ae_scale_factor=1.5)
aep.ae_scale_factor = 1.1
gsp = api.GammaSnowParameter(
winter_end_day_of_year=99,
initial_bare_ground_fraction=0.04,
snow_cv=0.44,
tx=-0.3,
wind_scale=1.9,
wind_const=0.9,
max_water=0.11,
surface_magnitude=33.0,
max_albedo=0.88,
min_albedo=0.55,
fast_albedo_decay_rate=6.0,
slow_albedo_decay_rate=4.0,
snowfall_reset_depth=6.1,
glacier_albedo=0.44
) # TODO: This does not work due to boost.python template arity of 15, calculate_iso_pot_energy=False)
gsp.calculate_iso_pot_energy = False
gsp.snow_cv = 0.5
gsp.initial_bare_ground_fraction = 0.04
kp = api.KirchnerParameter(c1=-2.55, c2=0.8, c3=-0.01)
kp.c1 = 2.5
kp.c2 = -0.9
kp.c3 = 0.01
spcp = api.PrecipitationCorrectionParameter(scale_factor=0.9)
ptgsk_p = pt_gs_k.PTGSKParameter(ptp, gsp, aep, kp, spcp)
ptgsk_p.ae.ae_scale_factor = 1.2 # sih: just to demo ae scale_factor can be set directly
return ptgsk_p
def test_pt_gs_k_param(self):
ptgsk_size = 21
valid_names = [
"kirchner.c1", "kirchner.c2", "kirchner.c3", "ae.ae_scale_factor",
"gs.tx", "gs.wind_scale", "gs.max_water", "gs.wind_const",
"gs.fast_albedo_decay_rate", "gs.slow_albedo_decay_rate",
"gs.surface_magnitude", "gs.max_albedo", "gs.min_albedo",
"gs.snowfall_reset_depth", "gs.snow_cv", "gs.glacier_albedo",
"p_corr.scale_factor", "gs.snow_cv_forest_factor",
"gs.snow_cv_altitude_factor", "pt.albedo", "pt.alpha"
]
self.verify_parameter_for_calibration(pt_gs_k.PTGSKParameter(),
ptgsk_size, valid_names)
def test_pt_gs_k_param(self):
ptgsk_size = 30
valid_names = [
"kirchner.c1", "kirchner.c2", "kirchner.c3", "ae.ae_scale_factor",
"gs.tx", "gs.wind_scale", "gs.max_water", "gs.wind_const",
"gs.fast_albedo_decay_rate", "gs.slow_albedo_decay_rate",
"gs.surface_magnitude", "gs.max_albedo", "gs.min_albedo",
"gs.snowfall_reset_depth", "gs.snow_cv", "gs.glacier_albedo",
"p_corr.scale_factor", "gs.snow_cv_forest_factor",
"gs.snow_cv_altitude_factor", "pt.albedo", "pt.alpha",
"gs.initial_bare_ground_fraction", "gs.winter_end_day_of_year",
"gs.calculate_iso_pot_energy", "gm.dtf", "routing.velocity",
"routing.alpha", "routing.beta", "gs.n_winter_days",
"gm.direct_response"
]
p = pt_gs_k.PTGSKParameter()
special_values = {22: 130, 28: 221}
self.verify_parameter_for_calibration(p, ptgsk_size, valid_names,
special_values)
# special verification of bool parameter
p.gs.calculate_iso_pot_energy = True
self.assertTrue(p.gs.calculate_iso_pot_energy)
self.assertAlmostEqual(p.get(23), 1.0, 0.00001)
p.gs.calculate_iso_pot_energy = False
self.assertFalse(p.gs.calculate_iso_pot_energy)
self.assertAlmostEqual(p.get(23), 0.0, 0.00001)
pv = api.DoubleVector.from_numpy([p.get(i) for i in range(p.size())])
pv[23] = 1.0
p.set(pv)
self.assertTrue(p.gs.calculate_iso_pot_energy)
pv[23] = 0.0
p.set(pv)
self.assertFalse(p.gs.calculate_iso_pot_energy)
# checkout new parameters for routing
p.routing.velocity = 1 / 3600.0
p.routing.alpha = 1.1
p.routing.beta = 0.8
self.assertAlmostEqual(p.routing.velocity, 1 / 3600.0)
self.assertAlmostEqual(p.routing.alpha, 1.1)
self.assertAlmostEqual(p.routing.beta, 0.8)
utc = api.Calendar()
self.assertTrue(p.gs.is_snow_season(utc.time(2017, 1, 1)))
self.assertFalse(p.gs.is_snow_season(utc.time(2017, 8, 1)))
p.gs.n_winter_days = 100
self.assertFalse(p.gs.is_snow_season(utc.time(2017, 11, 31)))
self.assertTrue(p.gs.is_snow_season(utc.time(2017, 2, 1)))
def test_model_clone_with_catchment_parameters(self):
""" Verify we can copy an opt-model from full model including catchment specific parameters"""
m = self.build_model(pt_gs_k.PTGSKModel,
pt_gs_k.PTGSKParameter,
model_size=20,
num_catchments=2)
p2 = pt_gs_k.PTGSKParameter()
p2.kirchner.c1 = 2.3
m.set_catchment_parameter(2, p2)
self.assertTrue(m.has_catchment_parameter(2))
self.assertFalse(m.has_catchment_parameter(1))
o = pt_gs_k.create_opt_model_clone(
m, True
) # this is how to create an opt-model, with catchm. spec params
self.assertTrue(o.has_catchment_parameter(2))
self.assertFalse(o.has_catchment_parameter(1))
o = pt_gs_k.create_opt_model_clone(
m, False) # default, only region param is copied(for opt-purposes)
self.assertFalse(o.has_catchment_parameter(2))
self.assertFalse(o.has_catchment_parameter(1))
def test_create_ptgsk_param(self):
ptgsk_p = self._create_std_ptgsk_param()
copy_p = pt_gs_k.PTGSKParameter(ptgsk_p)
self.assertTrue(ptgsk_p != None,
"should be possible to create a std param")
self.assertIsNotNone(copy_p)