'''
Copyright 2015 by Tobias Houska
This file is part of Statistical Parameter Estimation Tool (SPOTPY).

:author: Tobias Houska

This example implements the Rosenbrock function into SPOT.
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import spotpy
from spotpy.objectivefunctions import rmse


class spot_setup(object):
    slow = 1000
    def __init__(self):
        self.params = [spotpy.parameter.List('x', [1, 2, 3, 4, 6, 7, 8, 9, 0]), #Give possible x values as a List
                       spotpy.parameter.List('y', [0, 1, 2, 5, 7, 8, 9, 0, 1])]  #Give possible y values as a List

        self.db_headers = ["obj_functions", "parameters", "simulations"]

        self.database = open('MyOwnDatabase.txt', 'w')
        self.database.write("\t".join(self.db_headers) + "\n")

    def parameters(self):
        return spotpy.parameter.generate(self.params)

    def simulation(self, vector):
        x = np.array(vector)
        for i in range(self.slow):
            _ = np.sin(i)
        simulations = [sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)]
        return simulations

    def evaluation(self):
        observations = [0]
        return observations

    def objectivefunction(self, simulation, evaluation):
        objectivefunction = -rmse(evaluation=evaluation, simulation=simulation)
        return objectivefunction

    def save(self, objectivefunctions, parameter, simulations, *args, **kwargs):
        param_str = "|".join((str(p) for p in parameter))
        sim_str = "|".join((str(s) for s in simulations))
        line = "\t".join([str(objectivefunctions), param_str, sim_str]) + '\n'
        self.database.write(line)

spot_setup = spot_setup()

# Leave out dbformat and dbname and spotpy will return results in spot_setup.save function
sampler = spotpy.algorithms.mc(spot_setup)
sampler.sample(9) # Choose equal or less repetitions as you have parameters in your List
spot_setup.database.close() # Close the created txt file

'''
Copyright 2015 by Tobias Houska
This file is part of Statistical Parameter Estimation Tool (SPOTPY).

:author: Tobias Houska

This example implements the Rosenbrock function into SPOT.
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import spotpy
from spotpy.objectivefunctions import rmse


class spot_setup(object):
    a = spotpy.parameter.Uniform(low=0, high=1)
    b = spotpy.parameter.Uniform(low=0, high=1)
 
    def __init__(self):
   
        self.db_headers = ["obj_functions", "parameters", "simulations"]

        self.database = open('MyOwnDatabase.txt', 'w')
        self.database.write("\t".join(self.db_headers) + "\n")

    def simulation(self, vector):
        x = np.array(vector)
        simulations = [sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)]
        return simulations

    def evaluation(self):
        observations = [0]
        return observations

    def objectivefunction(self, simulation, evaluation):
        objectivefunction = -rmse(evaluation=evaluation, simulation=simulation)
        return objectivefunction

    def save(self, objectivefunctions, parameter, simulations, *args, **kwargs):
        param_str = "\t".join((str(p) for p in parameter))
        sim_str = "\t".join((str(s) for s in simulations))
        line = "\t".join([str(objectivefunctions), param_str, sim_str]) + '\n'
        self.database.write(line)

if __name__ == "__main__":
    spot_setup = spot_setup()
    
    # set dbformat to custom and spotpy will return results in spot_setup.save function
    sampler = spotpy.algorithms.mc(spot_setup, dbformat='custom')
    sampler.sample(100) # Choose equal or less repetitions as you have parameters in your List
    spot_setup.database.close() # Close the created txt file

Update to current spotpy code stlye