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(error)
print(par)
fail = 0
except:
error = np.nan
fail = 1
return error, [], fail
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
def RunHapi(self):
"""
=======================================================================
RunModel(PrecPath, Evap_Path, TempPath, DemPath, FlowAccPath, FlowDPath, ParPath, p2)
=======================================================================
this function runs the conceptual distributed hydrological model
Inputs:
----------
1-Paths:
4-FlowAccPath:
5-FlowDPath:
[String] path to the Flow Direction raster of the catchment (it should
include the raster name and extension)
7-ParPath:
[String] path to the Folder contains parameters rasters of the catchment
8-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
Outputs:
----------
1-statevariables: [numpy attribute]
4D array (rows,cols,time,states) states are [sp,wc,sm,uz,lv]
2-qlz: [numpy attribute]
3D array of the lower zone discharge
3-quz: [numpy attribute]
3D array of the upper zone discharge
4-qout: [numpy attribute]
1D timeseries of discharge at the outlet of the catchment
of unit m3/sec
5-quz_routed: [numpy attribute]
3D array of the upper zone discharge accumulated and
routed at each time step
6-qlz_translated: [numpy attribute]
3D array of the lower zone discharge translated at each time step
Example:
----------
PrecPath = prec_path="meteodata/4000/calib/prec"
Evap_Path = evap_path="meteodata/4000/calib/evap"
TempPath = temp_path="meteodata/4000/calib/temp"
DemPath = "GIS/4000/dem4000.tif"
FlowAccPath = "GIS/4000/acc4000.tif"
FlowDPath = "GIS/4000/fd4000.tif"
ParPath = "meteodata/4000/parameters"
p2=[1, 227.31]
st, q_out, q_uz_routed = RunModel(PrecPath,Evap_Path,TempPath,DemPath,
FlowAccPath,FlowDPath,ParPath,p2)
"""
### input data validation
# data type
# assert type(self.FlowAcc)==gdal.Dataset, "flow_acc should be read using gdal (gdal dataset please read it using gdal library) "
# assert type(self.FlowDir)==gdal.Dataset, "flow_direct should be read using gdal (gdal dataset please read it using gdal library) "
# input dimensions
[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 and np.shape(
self.Parameters
)[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 and np.shape(
self.Parameters
)[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"
#run the model
Wrapper.HapiModel(self)
print("Model Run has finished")
def RunFW1withLake(self, Lake):
"""
=======================================================================
RunDistwithLake(PrecPath, Evap_Path, TempPath, DemPath, FlowAccPath, FlowDPath, ParPath, p2)
=======================================================================
this function runs the conceptual distributed hydrological model
Inputs:
----------
1-Paths:
1-PrecPath:
[String] path to the Folder contains precipitation rasters
2-Evap_Path:
[String] path to the Folder contains Evapotranspiration rasters
3-TempPath:
[String] path to the Folder contains Temperature rasters
4-FlowAccPath:
[String] path to the Flow Accumulation raster of the catchment (it should
include the raster name and extension)
5-FlowDPath:
[String] path to the Flow Direction raster of the catchment (it should
include the raster name and extension)
7-ParPath:
[String] path to the Folder contains parameters rasters of the catchment
8-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
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"
DemPath = "GIS/4000/dem4000.tif"
FlowAccPath = "GIS/4000/acc4000.tif"
FlowDPath = "GIS/4000/fd4000.tif"
ParPath = "meteodata/4000/parameters"
p2=[1, 227.31]
st, q_out, q_uz_routed = RunModel(PrecPath,Evap_Path,TempPath,DemPath,
FlowAccPath,FlowDPath,ParPath,p2)
"""
# input data validation
# 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 and np.shape(
self.Parameters
)[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 and np.shape(
self.Parameters
)[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"
assert np.shape(Lake.MeteoData)[0] == np.shape(
self.Prec
)[2], "Lake meteorological data has to have the same length as the distributed raster data"
assert np.shape(
Lake.MeteoData
)[1] >= 3, "Lake Meteo data has to have at least three columns rain, ET, and Temp"
#run the model
Wrapper.FW1Withlake(self, Lake)
