#the if __name__ == "__main__": line in your script
if __name__ == "__main__":
rep = 200
#If you are working on a windows computer, this will be True
if 'win' in sys.platform:
parallel = 'mpc'
# If not you probably have a Mac or Unix system. Then save this file and start it
# from your terminal with "mpirun -c 20 your_script.py"
else:
parallel = 'mpi'
# Initialize the Hymod example (this runs on Windows systems only)
# Checkout the spot_setup_hymod_exe.py to see how you need to adopt
# your spot_setup class to run in parallel
# If your model in def simulation reads any files, make sure, they are
# unique for each cpu. Otherwise things get messed up...
spot_setup = spot_setup(parallel=parallel)
# Initialize a sampler that is suited for parallel computing (all except MLE, MCMC and SA)
sampler = spotpy.algorithms.mc(spot_setup,
dbname='Parallel_hymod',
dbformat='csv',
parallel=parallel)
# Sample in parlallel
sampler.sample(rep)
# Load results from file
results = spotpy.analyser.load_csv_results('Parallel_hymod')
# Plot best model run
spotpy.analyser.plot_bestmodelrun(results, spot_setup.evaluation())
Copyright 2015 by Tobias Houska
This file is part of Statistical Parameter Estimation Tool (SPOTPY).
: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
# Initialize the Hymod example (will only work on Windows systems)
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)
sampler = spotpy.algorithms.dream(spot_setup,
dbname='DREAM_hymod',
dbformat='csv',
alt_objfun=None)
#Select number of maximum repetitions
rep = 10000
# Select five chains and set the Gelman-Rubin convergence limit
nChains = 4
convergence_limit = 1.2
