def RunLumped(self, Route=0, RoutingFn=[]):
"""
=============================================================
RunLumped(ConceptualModel,data,parameters,p2,init_st,snow,Routing=0, RoutingFn=[])
=============================================================
this function runs lumped conceptual model
Inputs:
----------
1-ConceptualModel:
[function] conceptual model and it should contain a function called simulate
2-data:
[numpy array] meteorological data as array with the first column as precipitation
second as evapotranspiration, third as temperature and forth column as
long term average temperature
3- parameters:
[numpy array] conceptual model parameters as array
4-p2:
[List] list of unoptimized parameters
p2[0] = tfac, 1 for hourly, 0.25 for 15 min time step and 24 for daily time step
p2[1] = catchment area in km2
5-init_st:
[list] initial state variables values [sp, sm, uz, lz, wc].
6-Routing:
[0 or 1] to decide wether t route the generated discharge hydrograph or not
7-RoutingFn:
[function] function to route the dischrge hydrograph.
Outputs:
----------
1- st:
[numpy array] 3d array of the 5 state variable data for each cell
2- q_lz:
[numpy array] 1d array of the calculated discharge.
Examples:
----------
p2=[24, 1530]
#[sp,sm,uz,lz,wc]
init_st=[0,5,5,5,0]
snow=0
"""
if self.TemporalResolution == "Daily":
ind = pd.date_range(self.StartDate, self.EndDate, freq="D")
else:
ind = pd.date_range(self.StartDate, self.EndDate, freq="H")
Qsim = pd.DataFrame(index=ind)
Wrapper.Lumped(self, Route, RoutingFn)
Qsim['q'] = self.Qsim
self.Qsim = Qsim[:]
print("Model Run has finished")
def opt_fun(par):
try:
# parameters
self.Parameters = par
#run the model
Wrapper.Lumped(self, Route, RoutingFn)
# calculate performance of the model
try:
error = self.OF(self.QGauges[self.QGauges.columns[-1]],
self.Qsim, *self.OFArgs)
except TypeError: # if no of inputs less than what the function needs
assert 1 == 5, "the objective function you have entered needs more inputs please enter then in a list as *args"
# print error
if printError != 0:
print(error)
# print(par)
fail = 0
except:
error = np.nan
fail = 1
return error, [], fail
