shutil.rmtree(outdirpath)
return sim.streamflow.as_matrix() # return streamflow
def evaluation(self, evaldates=False):
# function to return the evaluation data
if evaldates == True:
return self.evaldates
if evaldates == False:
return self.obs
def objectivefunction(self, simulation, evaluation):
# compute the objective function
objectivefunction = spotpy.objectivefunctions.nashsutcliff(
evaluation, simulation) # we want to maximize this function
return objectivefunction
spotpy_setup = spotpy_setup()
sampler = spotpy.algorithms.sceua(spotpy_setup,
dbname='Como_SCEUA_%s' % (spotpy_setup.num),
dbformat='csv',
parallel='mpi')
sampler.sample(3000, ngs=9, kstop=30, pcento=0.0000025, peps=0.0000025)
alert.send_alert('*****@*****.**', 'RHESSys Como Optimization Finished',
'Your script has finished')
return sim.adj_streamflow.as_matrix() # return streamflow
def evaluation(self, evaldates=False):
# function to return the evaluation data
if evaldates == True:
return self.evaldates
if evaldates == False:
return self.obs
def objectivefunction(self, simulation, evaluation):
# compute the objective function
objectivefunction = spotpy.objectivefunctions.nashsutcliff(
evaluation, simulation) # we want to maximize this function
return objectivefunction
spotpy_setup = spotpy_setup()
sampler = spotpy.algorithms.lhs(spotpy_setup,
dbname='Como_LHS_%s' % (spotpy_setup.num),
dbformat='csv',
parallel='mpi')
sampler.sample(3500)
alert.send_alert('*****@*****.**',
'RHESSys Como Sensitivity Analysis Finished',
'Your script has finished')