if __name__ == '__main__':
# 1979 is spin up
# 1980 till 1984 is used for calibration
# 1985 till 1989 is used for validation
begin = 1980
end = 1989
prefix = "semi_dis_landuse"
runs = 100000
# File names of the forcing data
subcatchment_names = ["grass", "wood", "rest", "crops"]
# import algorithm
from spotpy.algorithms import rope as sampler
# Find out if the model should run parallel (for supercomputer)
parallel = 'mpi' if 'OMPI_COMM_WORLD_SIZE' in os.environ else 'seq'
# Create the model
model = SemiDisLanduse(datetime.datetime(begin, 1, 1),
datetime.datetime(end, 12, 31),
subcatchment_names)
sampler = sampler(model, parallel=parallel,
dbname="semi_dis_landuse_penman",
dbformat="csv", save_sim=True, save_threshold=[0, 0])
sampler.sample(runs, subsets=30)
"""
return spotpy.parameter.generate(self.Params)
@staticmethod
def objectivefunction(simulation, evaluation):
"""
For Spotpy
"""
return spotpy.objectivefunctions.nashsutcliffe(evaluation, simulation)
if __name__ == '__main__':
from spotpy.algorithms import lhs as sampler
parallel = 'mpi' if 'OMPI_COMM_WORLD_SIZE' in os.environ else 'seq'
model = LumpedModelCMF()
# par = model.parameters()['optguess']
# sim = model.simulation(par)
# evalu = model.evaluation()
# like = model.objectivefunction(sim, evalu)
# print(like)
# print(evalu)
# print(sim)
sampler = sampler(model,
parallel=parallel,
dbname="bench.csv",
dbformat="csv")
sampler.sample(1000)
# 1979 is spin up
# 1980 till 1984 is used for calibration
# 1985 till 1989 is used for validation
begin = 1980
end = 1989
prefix = "semi_dis_landuse"
runs = 100000
# File names of the forcing data
subcatchment_names = ["grass_high", "wood_high", "rest_high",
"crops_high", "grass_low",
"wood_low", "rest_low", "crops_low"]
# import algorithm
from spotpy.algorithms import rope as sampler
# Find out if the model should run parallel (for supercomputer)
parallel = 'mpi' if 'OMPI_COMM_WORLD_SIZE' in os.environ else 'seq'
# Create the model
model = SemiDisLanduse(datetime.datetime(begin, 1, 1),
datetime.datetime(end, 12, 31),
subcatchment_names)
sampler = sampler(model, parallel=parallel,
dbname="semi_dis_landuse_height_hargreaves",
dbformat="csv", save_sim=True, save_threshold=[0, 0])
sampler.sample(runs, subsets=30)
#print(cmf.describe(model.project))