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 std_ptgsk_parameters(self):
pt_params = api.PriestleyTaylorParameter(
) # *params["priestley_taylor"])
gs_params = api.GammaSnowParameter() # *params["gamma_snow"]
ae_params = api.ActualEvapotranspirationParameter(
) # *params["act_evap"])
k_params = api.KirchnerParameter() # *params["kirchner"])
p_params = api.PrecipitationCorrectionParameter(
) # TODO; default 1.0, is it used ??
return api.pt_gs_k.PTGSKParameter(pt_params, gs_params, ae_params,
k_params, p_params)
def test_region_model_repository(self):
id_list = [1225]
epsg_id = 32632
# 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 = api.pt_gs_k.PTGSKParameter(
pt_params, gs_params, ae_params, k_params, p_params)
ptssk_rm_params = api.pt_ss_k.PTSSKParameter(
pt_params, ss_params, ae_params, k_params, p_params)
# create the description for 4 models of tistel,ptgsk, ptssk, full and optimized
tistel_grid_spec = GridSpecification(epsg_id=epsg_id,
x0=362000.0,
y0=6765000.0,
dx=1000,
dy=1000,
nx=8,
ny=8)
cfg_list = [
RegionModelConfig("tistel-ptgsk", PTGSKModel, ptgsk_rm_params,
tistel_grid_spec, "unregulated", "FELTNR",
id_list),
RegionModelConfig("tistel-ptgsk-opt", PTGSKOptModel,
ptgsk_rm_params, tistel_grid_spec,
"unregulated", "FELTNR", id_list),
RegionModelConfig("tistel-ptssk", PTSSKModel, ptssk_rm_params,
tistel_grid_spec, "unregulated", "FELTNR",
id_list)
]
rm_cfg_dict = {x.name: x for x in cfg_list}
rmr = GisRegionModelRepository(
rm_cfg_dict
) # ok, now we have a Gis RegionModelRepository that can handle all named entities we pass.
cm1 = rmr.get_region_model(
"tistel-ptgsk") # pull out a PTGSKModel for tistel
cm2 = rmr.get_region_model("tistel-ptgsk-opt")
# Does not work, fail on ct. model:
cm3 = rmr.get_region_model(
"tistel-ptssk") # pull out a PTGSKModel for tistel
# cm4= rmr.get_region_model("tistel-ptssk",PTSSKOptModel)
self.assertIsNotNone(cm3)
self.assertIsNotNone(cm1)
self.assertIsNotNone(cm2)
self.assertIsNotNone(
cm2.catchment_id_map
) # This one is needed in order to properly map catchment-id to internal id
self.assertEqual(cm2.catchment_id_map[0], id_list[0])
# plot = ax.scatter(x, y, c=z, marker='o', s=10, lw=5, cmap=cm)
# plt.colorbar(plot).set_label('sub-catchments assocciate to targets')
plt.legend(fontsize=16, loc=1)
plt.show()
timelist = []
for i in range(len(ts_timestamps)):
timelist.append((str(ts_timestamps[i])[0:10], dis_sims[i]))
timelist_pd = pd.DataFrame(timelist)
timelist_pd.to_csv('dis_sims_G.csv')
# Somthing more to know
# Getting access to defualt values of variables (not used in simulation)
parameterg = api.GammaSnowParameter()
parameterk = api.KirchnerParameter()
print('slow_albedo_decay_rate = ', parameterg.slow_albedo_decay_rate)
print('Kirchner C1 = ', parameterk.c1)
# Getting access to the values are used for simulation which are in model.yaml (used in simulation)
param = simulator.region_model.get_region_parameter()
print('slow_albedo_decay_rate = ', param.gs.slow_albedo_decay_rate)
print('Kirchner C1 = ', param.kirchner.c1)
# Getting access to atributes of simulator
for attr in dir(simulator.region_model):
if attr[0] is not '_': #ignore privates
print(attr)
