#  ___________________________________________________________________________
#
#  Pyomo: Python Optimization Modeling Objects
#  Copyright (c) 2008-2022
#  National Technology and Engineering Solutions of Sandia, LLC
#  Under the terms of Contract DE-NA0003525 with National Technology and
#  Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
#  rights in this software.
#  This software is distributed under the 3-clause BSD License.
#  ___________________________________________________________________________

import json
from os.path import join, abspath, dirname
import pyomo.contrib.parmest.parmest as parmest
from pyomo.contrib.parmest.examples.semibatch.semibatch import generate_model
import pyomo.contrib.parmest.scenariocreator as sc


def main():
    
    # Vars to estimate in parmest
    theta_names = ['k1', 'k2', 'E1', 'E2']

    # Data: list of dictionaries
    data = [] 
    file_dirname = dirname(abspath(str(__file__)))
    for exp_num in range(10):
        fname = join(file_dirname, 'exp'+str(exp_num+1)+'.out')
        with open(fname,'r') as infile:
            d = json.load(infile)
            data.append(d)

    pest = parmest.Estimator(generate_model, data, theta_names)

    scenmaker = sc.ScenarioCreator(pest, "ipopt")

    # Make one scenario per experiment and write to a csv file
    output_file = "scenarios.csv"
    experimentscens = sc.ScenarioSet("Experiments")
    scenmaker.ScenariosFromExperiments(experimentscens)
    experimentscens.write_csv(output_file)
    
    # Use the bootstrap to make 3 scenarios and print
    bootscens = sc.ScenarioSet("Bootstrap")
    scenmaker.ScenariosFromBoostrap(bootscens, 3)
    for s in bootscens.ScensIterator():
        print("{}, {}".format(s.name, s.probability))
        for n,v in s.ThetaVals.items():
            print("   {}={}".format(n, v))

if __name__ == "__main__":
    main()
