def setUp(self):
self.spot_setup = spot_setup()
self.rep = 200
self.timeout = 10 # Given in Seconds
import sys
self.use_py_3 = sys.version_info[0] >= 3
def setUp(self):
generations=40
n_pop = 20
skip_duplicates = True
self.sp_setup=spot_setup(obj_func= multi_obj_func)
self.sampler = spotpy.algorithms.NSGAII(self.sp_setup, dbname='NSGA2', dbformat="csv")
self.sampler.sample(generations, n_obj= 3, n_pop = n_pop, skip_duplicates = skip_duplicates)
def setUp(self):
self.data = np.random.gamma(0.7,2,500)
self.spot_setup = spot_setup()
self.parameterset = self.spot_setup.parameters()['random']
self.simulation = self.spot_setup.simulation(self.parameterset)
self.observation = self.spot_setup.evaluation()
self.timespanlen = self.simulation.__len__()
try:
self.ddd = pd.date_range("2015-01-01 11:00", freq="5min", periods=self.timespanlen)
self.dd_daily = pd.date_range("2015-05-01", periods=self.timespanlen)
self.usepandas = True
except NameError:
print('Please install Pandas to use these signature functions')
self.usepandas = False
:author: Tobias Houska
This class holds example code how to use the dream algorithm
'''
import numpy as np
import spotpy
#from spotpy.examples.spot_setup_hymod_exe import spot_setup
from spotpy.examples.spot_setup_hymod_python import spot_setup
import pylab as plt
if __name__ == "__main__":
parallel = 'seq'
# Initialize the Hymod example (will only work on Windows systems)
#spot_setup=spot_setup(parallel=parallel)
spot_setup = spot_setup()
# Create the Dream sampler of spotpy, al_objfun is set to None to force SPOTPY
# to jump into the def objectivefunction in the spot_setup class (default is
# spotpy.objectivefunctions.log_p)
#Select number of maximum repetitions
rep = 10000
# Select five chains and set the Gelman-Rubin convergence limit
nChains = 4
convergence_limit = 1.2
runs_after_convergence = 100
sampler = spotpy.algorithms.dream(spot_setup,
dbname='DREAM_hymod',
This class holds example code how to use the dream algorithm
'''
import numpy as np
import spotpy
from spotpy.examples.spot_setup_hymod_python import spot_setup
import pylab as plt
if __name__ == "__main__":
parallel = 'seq' # Runs everthing in sequential mode
np.random.seed(2000) # Makes the results reproduceable
# Initialize the Hymod example
# In this case, we tell the setup which algorithm we want to use, so
# we can use this exmaple for different algorithms
spot_setup = spot_setup(_used_algorithm='sceua')
#Select number of maximum allowed repetitions
rep = 10000
# Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY
# to jump into the def objectivefunction in the spot_setup class (default is
# spotpy.objectivefunctions.rmse)
sampler = spotpy.algorithms.sceua(spot_setup,
dbname='SCEUA_hymod',
dbformat='csv',
alt_objfun=None)
#Start the sampler, one can specify ngs, kstop, peps and pcento id desired
sampler.sample(rep, ngs=10, kstop=50, peps=0.1, pcento=0.1)
def setUp(self):
self.spot_setup = spot_setup()
self.rep = 200 # REP must be a multiply of amount of parameters which are in 7 if using hymod
self.timeout = 10 # Given in Seconds
"""
Shows the usage of the matplotlib GUI
Needs at least Python 3.5
"""
from __future__ import division, print_function, unicode_literals
import spotpy
from spotpy.gui.mpl import GUI
from spotpy.examples.spot_setup_hymod_python import spot_setup
from spotpy.objectivefunctions import rmse
if __name__ == '__main__':
setup_class = spot_setup(rmse)
#Select number of maximum allowed repetitions
rep = 10000
# Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY
# to jump into the def objectivefunction in the spot_setup class (default is
# spotpy.objectivefunctions.rmse)
sampler = spotpy.algorithms.sceua(setup_class,
dbname='SCEUA_hymod',
dbformat='csv',
alt_objfun=None)
#Start the sampler, one can specify ngs, kstop, peps and pcento id desired
#sampler.sample(rep,ngs=10, kstop=50, peps=0.1, pcento=0.1)
gui = GUI(spot_setup())
This class holds example code how to use the dream algorithm
'''
import numpy as np
import spotpy
#from spotpy.examples.spot_setup_hymod_exe import spot_setup
from spotpy.examples.spot_setup_hymod_python import spot_setup
import matplotlib.pyplot as plt
from spotpy.likelihoods import gaussianLikelihoodMeasErrorOut as GausianLike
if __name__ == "__main__":
parallel = 'seq'
# Initialize the Hymod example (will only work on Windows systems)
#spot_setup=spot_setup(parallel=parallel)
spot_setup = spot_setup(GausianLike)
# Create the Dream sampler of spotpy, al_objfun is set to None to force SPOTPY
# to jump into the def objectivefunction in the spot_setup class (default is
# spotpy.objectivefunctions.log_p)
#Select number of maximum repetitions
rep = 5000
# Select five chains and set the Gelman-Rubin convergence limit
nChains = 4
convergence_limit = 1.2
runs_after_convergence = 100
sampler = spotpy.algorithms.dream(spot_setup,
dbname='DREAM_hymod',
def multi_obj_func(evaluation, simulation):
#used to overwrite objective function in hymod example
like1 = abs(spotpy.objectivefunctions.pbias(evaluation, simulation))
like2 = spotpy.objectivefunctions.rmse(evaluation, simulation)
like3 = spotpy.objectivefunctions.rsquared(evaluation, simulation) * -1
return np.array([like1, like2, like3])
if __name__ == "__main__":
parallel = 'seq' # Runs everthing in sequential mode
np.random.seed(2000) # Makes the results reproduceable
# Initialize the Hymod example
# In this case, we tell the setup which algorithm we want to use, so
# we can use this exmaple for different algorithms
spot_setup = spot_setup(multi_obj_func)
#Select number of maximum allowed repetitions
rep = 2000
# Create the PADDS sampler of spotpy, alt_objfun is set to None to force SPOTPY
# to jump into the def objectivefunction in the spot_setup class (default is
# spotpy.objectivefunctions.rmse)
sampler = spotpy.algorithms.padds(sp_setup,
dbname='padds_hymod',
dbformat='csv')
#Start the sampler, one can specify metric if desired
sampler.sample(rep, metric='ones')
# Load the results gained with the sceua sampler, stored in padds_hymod.csv
except ImportError:
import sys
sys.path.append(".")
import spotpy
from spotpy.examples.spot_setup_hymod_python import spot_setup
import matplotlib.pyplot as plt
if __name__ == "__main__":
parallel = 'seq' # Runs everthing in sequential mode
np.random.seed(2000) # Makes the results reproduceable
# Initialize the Hymod example
# In this case, we tell the setup which algorithm we want to use, so
# we can use this exmaple for different algorithms
spot_setup = spot_setup(
users_objective_function=spotpy.objectivefunctions.nashsutcliffe)
#Select number of maximum allowed repetitions
rep = 1000
# Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY
# to jump into the def objectivefunction in the spot_setup class (default is
# spotpy.objectivefunctions.rmse)
sampler = spotpy.algorithms.dds(spot_setup,
dbname='DDS_hymod',
dbformat='csv')
#Start the sampler, one can specify ngs, kstop, peps and pcento id desired
sampler.sample(rep)
results = sampler.getdata()
This class holds example code how to use the dream algorithm
'''
import numpy as np
import spotpy
from spotpy.examples.spot_setup_hymod_python import spot_setup
import matplotlib.pyplot as plt
if __name__ == "__main__":
parallel = 'seq' # Runs everthing in sequential mode
np.random.seed(2000) # Makes the results reproduceable
# Initialize the Hymod example
# In this case, we tell the setup which algorithm we want to use, so
# we can use this exmaple for different algorithms
spot_setup = spot_setup(spotpy.objectivefunctions.rmse)
#Select number of maximum allowed repetitions
rep = 5000
sampler = spotpy.algorithms.sceua(spot_setup,
dbname='SCEUA_hymod',
dbformat='csv')
#Start the sampler, one can specify ngs, kstop, peps and pcento id desired
sampler.sample(rep, ngs=7, kstop=3, peps=0.1, pcento=0.1)
# Load the results gained with the sceua sampler, stored in SCEUA_hymod.csv
results = spotpy.analyser.load_csv_results('SCEUA_hymod')
#Plot how the objective function was minimized during sampling
fig = plt.figure(1, figsize=(9, 6))
Three different kinds of objective functions.
"""
like1 = abs(spotpy.objectivefunctions.pbias(evaluation, simulation))
like2 = spotpy.objectivefunctions.rmse(evaluation, simulation)
like3 = spotpy.objectivefunctions.rsquared(evaluation, simulation)*-1
return [like1, like2, like3]
if __name__ == "__main__":
generations=40
n_pop = 20
skip_duplicates = True
sp_setup=spot_setup(obj_func= multi_obj_func)
sampler = spotpy.algorithms.NSGAII(spot_setup=sp_setup, dbname='NSGA2', dbformat="csv")
sampler.sample(generations, n_obj= 3, n_pop = n_pop)
results = sampler.getdata()
fig= plt.figure(1,figsize=(9,5))
plt.plot(results['like1'])
plt.show()
plt.ylabel('Objective function')
plt.xlabel('Iteration')
fig.savefig('hymod_objectivefunction.png',dpi=300)
# Example plot to show the parameter distribution ######
'''
import numpy as np
import spotpy
from spotpy.examples.spot_setup_hymod_python import spot_setup
import matplotlib.pyplot as plt
if __name__ == "__main__":
parallel ='seq' # Runs everthing in sequential mode
np.random.seed(2000) # Makes the results reproduceable
# Initialize the Hymod example
# In this case, we tell the setup which algorithm we want to use, so
# we can use this exmaple for different algorithms
spot_setup=spot_setup(users_objective_function=spotpy.objectivefunctions.rmse)
#Select number of maximum allowed repetitions
rep=500
filename = 'SCEUA_hymod'
# Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY
# to jump into the def objectivefunction in the spot_setup class (default is
# spotpy.objectivefunctions.rmse)
sampler=spotpy.algorithms.sceua(spot_setup, dbname='SCEUA_hymod', dbformat='csv')
#Start the sampler, one can specify ngs, kstop, peps and pcento id desired
sampler.sample(rep)#,ngs=10, kstop=50, peps=0.1, pcento=0.1)
# Load the results gained with the sceua sampler, stored in SCEUA_hymod.csv
results = spotpy.analyser.load_csv_results('SCEUA_hymod')
:author: Tobias Houska
This class holds example code how to use the dream algorithm
'''
import numpy as np
import spotpy
#from spotpy.examples.spot_setup_hymod_exe import spot_setup
from spotpy.examples.spot_setup_hymod_python import spot_setup
import pylab as plt
if __name__ == "__main__":
parallel = 'seq'
# Initialize the Hymod example (will only work on Windows systems)
#spot_setup=spot_setup(parallel=parallel)
spot_setup = spot_setup(_used_algorithm='dream')
# Create the Dream sampler of spotpy, al_objfun is set to None to force SPOTPY
# to jump into the def objectivefunction in the spot_setup class (default is
# spotpy.objectivefunctions.log_p)
#Select number of maximum repetitions
rep = 10000
# Select five chains and set the Gelman-Rubin convergence limit
nChains = 4
convergence_limit = 1.2
runs_after_convergence = 100
sampler = spotpy.algorithms.dream(spot_setup,
dbname='DREAM_hymod',
"""
Shows the usage of the matplotlib GUI
Needs at least Python 3.5
"""
from __future__ import division, print_function, unicode_literals
import spotpy
from spotpy.gui.mpl import GUI
from spotpy.examples.spot_setup_hymod_python import spot_setup
if __name__ == '__main__':
setup_class = spot_setup(_used_algorithm='sceua')
#Select number of maximum allowed repetitions
rep = 10000
# Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY
# to jump into the def objectivefunction in the spot_setup class (default is
# spotpy.objectivefunctions.rmse)
sampler = spotpy.algorithms.sceua(setup_class,
dbname='SCEUA_hymod',
dbformat='csv',
alt_objfun=None)
#Start the sampler, one can specify ngs, kstop, peps and pcento id desired
#sampler.sample(rep,ngs=10, kstop=50, peps=0.1, pcento=0.1)
gui = GUI(spot_setup())
gui.show()
except ImportError:
import sys
sys.path.append(".")
import spotpy
from spotpy.examples.spot_setup_hymod_python import spot_setup
import matplotlib.pyplot as plt
if __name__ == "__main__":
parallel = 'seq' # Runs everthing in sequential mode
np.random.seed(2000) # Makes the results reproduceable
# Initialize the Hymod example
# In this case, we tell the setup which algorithm we want to use, so
# we can use this exmaple for different algorithms
spot_setup = spot_setup(spotpy.objectivefunctions.nashsutcliffe)
#Select number of maximum allowed repetitions
rep = 1000
# Create the SCE-UA sampler of spotpy, alt_objfun is set to None to force SPOTPY
# to jump into the def objectivefunction in the spot_setup class (default is
# spotpy.objectivefunctions.rmse)
sampler = spotpy.algorithms.dds(spot_setup,
dbname='DDS_hymod',
dbformat='csv')
#Start the sampler, one can specify ngs, kstop, peps and pcento id desired
sampler.sample(rep)
results = sampler.getdata()
