def LumpedCalibration(self,
Basic_inputs,
OptimizationArgs,
printError=None):
"""
=======================================================================
RunCalibration(ConceptualModel, data,parameters, p2, init_st, snow, Routing=0, RoutingFn=[], objective_function, printError=None, *args):
=======================================================================
this function runs the calibration algorithm for the Lumped conceptual hydrological 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
2-Basic_inputs:
1-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
2-init_st:
[list] initial values for the state variables [sp,sm,uz,lz,wc] in mm
3-UB:
[Numeric] upper bound of the values of the parameters
4-LB:
[Numeric] Lower bound of the values of the parameters
3-Q_obs:
[Numeric] Observed values of discharge
6-lumpedParNo:
[int] nomber of lumped parameters, you have to enter the value of
the lumped parameter at the end of the list, default is 0 (no lumped parameters)
7-lumpedParPos:
[List] list of order or position of the lumped parameter among all
the parameters of the lumped model (order starts from 0 to the length
of the model parameters), default is [] (empty), the following order
of parameters is used for the lumped HBV model used
[ltt, utt, rfcf, sfcf, ttm, cfmax, cwh, cfr, fc, beta, e_corr, etf, lp,
c_flux, k, k1, alpha, perc, pcorr, Kmuskingum, Xmuskingum]
8-objective_function:
[function] objective function to calculate the performance of the model
and to be used in the calibration
9-*args:
other arguments needed on the objective function
Outputs:
----------
1- st:
[4D array] state variables
2- q_out:
[1D array] calculated Discharge at the outlet of the catchment
3- q_uz:
[3D array] Distributed discharge for each cell
Example:
----------
PrecPath = prec_path="meteodata/4000/calib/prec"
Evap_Path = evap_path="meteodata/4000/calib/evap"
TempPath = temp_path="meteodata/4000/calib/temp"
FlowAccPath = "GIS/4000/acc4000.tif"
FlowDPath = "GIS/4000/fd4000.tif"
ParPath = "meteodata/4000/"+"parameters.txt"
p2=[1, 227.31]
st, q_out, q_uz_routed = RunModel(PrecPath,Evap_Path,TempPath,DemPath,
FlowAccPath,FlowDPath,ParPath,p2)
"""
# basic inputs
# check if all inputs are included
assert all(
["Route", "RoutingFn"][i] in Basic_inputs.keys() for i in range(
2)), "Basic_inputs should contain ['p2','init_st','UB','LB'] "
Route = Basic_inputs["Route"]
RoutingFn = Basic_inputs["RoutingFn"]
if 'InitialValues' in Basic_inputs.keys():
InitialValues = Basic_inputs['InitialValues']
else:
InitialValues = []
### optimization
# get arguments
ApiObjArgs = OptimizationArgs[0]
pll_type = OptimizationArgs[1]
ApiSolveArgs = OptimizationArgs[2]
# check optimization arguement
assert type(ApiObjArgs) == dict, "store_history should be 0 or 1"
assert type(
ApiSolveArgs) == dict, "history_fname should be of type string "
# assert history_fname[-4:] == ".txt", "history_fname should be txt file please change extension or add .txt ad the end of the history_fname"
print('Calibration starts')
### calculate the objective function
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)
g = [
2 * par[-2] * par[-1] / self.dt,
(2 * par[-2] * (1 - par[-1])) / self.dt
]
except TypeError: # if no of inputs less than what the function needs
assert False, "the objective function you have entered needs more inputs please enter then in a list as *args"
if printError != 0:
print("Error = " + str(round(error, 3)) +
" Inequality Const = " + str(np.round(g, 2)))
# print(par)
fail = 0
except:
error = np.nan
g = []
fail = 1
return error, g, fail
### define the optimization components
opt_prob = Optimization('HBV Calibration', opt_fun)
if InitialValues != []:
for i in range(len(self.LB)):
opt_prob.addVar('x{0}'.format(i),
type='c',
lower=self.LB[i],
upper=self.UB[i],
value=InitialValues[i])
else:
for i in range(len(self.LB)):
opt_prob.addVar('x{0}'.format(i),
type='c',
lower=self.LB[i],
upper=self.UB[i])
opt_prob.addObj('f')
opt_prob.addCon('g1', 'i')
opt_prob.addCon('g2', 'i')
# print(opt_prob)
opt_engine = HSapi(pll_type=pll_type, options=ApiObjArgs)
# parse the ApiSolveArgs inputs
# availablekeys = ['store_sol',"display_opts","store_hst","hot_start"]
store_sol = ApiSolveArgs['store_sol']
display_opts = ApiSolveArgs['display_opts']
store_hst = ApiSolveArgs['store_hst']
hot_start = ApiSolveArgs['hot_start']
# for i in range(len(availablekeys)):
# if availablekeys[i] in ApiSolveArgs.keys():
# exec(availablekeys[i] + "=" + str(ApiSolveArgs[availablekeys[i]]))
# print(availablekeys[i] + " = " + str(ApiSolveArgs[availablekeys[i]]))
res = opt_engine(opt_prob,
store_sol=store_sol,
display_opts=display_opts,
store_hst=store_hst,
hot_start=hot_start)
self.OFvalue = res[0]
self.Parameters = res[1]
return res
def FW1Calibration(self, SpatialVarFun, OptimizationArgs, printError=None):
"""
=======================================================================
RunCalibration(ConceptualModel, Paths, p2, Q_obs, UB, LB, SpatialVarFun, lumpedParNo, lumpedParPos, objective_function, printError=None, *args):
=======================================================================
this function runs the calibration algorithm for the conceptual distributed
hydrological model
Inputs:
----------
1-ConceptualModel:
[function] conceptual model and it should contain a function called simulate
2-Basic_inputs:
1-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
2-init_st:
[list] initial values for the state variables [sp,sm,uz,lz,wc] in mm
3-UB:
[Numeric] upper bound of the values of the parameters
4-LB:
[Numeric] Lower bound of the values of the parameters
3-Q_obs:
[Numeric] Observed values of discharge
6-lumpedParNo:
[int] nomber of lumped parameters, you have to enter the value of
the lumped parameter at the end of the list, default is 0 (no lumped parameters)
7-lumpedParPos:
[List] list of order or position of the lumped parameter among all
the parameters of the lumped model (order starts from 0 to the length
of the model parameters), default is [] (empty), the following order
of parameters is used for the lumped HBV model used
[ltt, utt, rfcf, sfcf, ttm, cfmax, cwh, cfr, fc, beta, e_corr, etf, lp,
c_flux, k, k1, alpha, perc, pcorr, Kmuskingum, Xmuskingum]
8-objective_function:
[function] objective function to calculate the performance of the model
and to be used in the calibration
9-*args:
other arguments needed on the objective function
Outputs:
----------
1- st:
[4D array] state variables
2- q_out:
[1D array] calculated Discharge at the outlet of the catchment
3- q_uz:
[3D array] Distributed discharge for each cell
Example:
----------
PrecPath = prec_path="meteodata/4000/calib/prec"
Evap_Path = evap_path="meteodata/4000/calib/evap"
TempPath = temp_path="meteodata/4000/calib/temp"
FlowAccPath = "GIS/4000/acc4000.tif"
FlowDPath = "GIS/4000/fd4000.tif"
ParPath = "meteodata/4000/"+"parameters.txt"
p2=[1, 227.31]
st, q_out, q_uz_routed = RunModel(PrecPath,Evap_Path,TempPath,DemPath,
FlowAccPath,FlowDPath,ParPath,p2)
"""
# input dimensions
# [rows,cols] = self.FlowAcc.ReadAsArray().shape
# [fd_rows,fd_cols] = self.FlowDirArr.shape
# assert fd_rows == self.rows and fd_cols == self.cols, "all input data should have the same number of rows"
# input dimensions
assert np.shape(self.Prec)[0] == self.rows and np.shape(
self.ET
)[0] == self.rows and np.shape(
self.Temp
)[0] == self.rows, "all input data should have the same number of rows"
assert np.shape(self.Prec)[1] == self.cols and np.shape(
self.ET
)[1] == self.cols and np.shape(
self.Temp
)[1] == self.cols, "all input data should have the same number of columns"
assert np.shape(self.Prec)[2] == np.shape(self.ET)[2] and np.shape(
self.Temp
)[2], "all meteorological input data should have the same length"
# basic inputs
# check if all inputs are included
# assert all(["p2","init_st","UB","LB","snow "][i] in Basic_inputs.keys() for i in range(4)), "Basic_inputs should contain ['p2','init_st','UB','LB'] "
### optimization
# get arguments
ApiObjArgs = OptimizationArgs[0]
pll_type = OptimizationArgs[1]
ApiSolveArgs = OptimizationArgs[2]
# check optimization arguement
assert type(ApiObjArgs) == dict, "store_history should be 0 or 1"
assert type(
ApiSolveArgs) == dict, "history_fname should be of type string "
print('Calibration starts')
### calculate the objective function
def opt_fun(par):
try:
# distribute the parameters
SpatialVarFun.Function(
par
) #, kub=SpatialVarFun.Kub, klb=SpatialVarFun.Klb, Maskingum=SpatialVarFun.Maskingum
self.Parameters = SpatialVarFun.Par3d
#run the model
Wrapper.FW1(self)
# calculate performance of the model
try:
# error = self.OF(self.QGauges, self.qout, self.quz_routed, self.qlz_translated,*[self.GaugesTable])
error = self.OF(self.QGauges, self.qout,
*[self.GaugesTable])
except TypeError: # if no of inputs less than what the function needs
assert False, "the objective function you have entered needs more inputs please enter then in a list as *args"
# print error
if printError != 0:
print(round(error, 3))
print(par)
fail = 0
except:
error = np.nan
fail = 1
return error, [], fail
### define the optimization components
opt_prob = Optimization('HBV Calibration', opt_fun)
for i in range(len(self.LB)):
opt_prob.addVar('x{0}'.format(i),
type='c',
lower=self.LB[i],
upper=self.UB[i])
print(opt_prob)
opt_engine = HSapi(pll_type=pll_type, options=ApiObjArgs)
store_sol = ApiSolveArgs['store_sol']
display_opts = ApiSolveArgs['display_opts']
store_hst = ApiSolveArgs['store_hst']
hot_start = ApiSolveArgs['hot_start']
res = opt_engine(opt_prob,
store_sol=store_sol,
display_opts=display_opts,
store_hst=store_hst,
hot_start=hot_start)
self.Parameters = res[1]
self.OFvalue = res[0]
return res