"""
##########################################################
### @Author Joe Krall      ###############################
### @copyright see below   ###############################

    This file is part of JMOO,
    Copyright Joe Krall, 2014.

    JMOO is free software: you can redistribute it and/or modify
    it under the terms of the GNU Lesser General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    JMOO is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU Lesser General Public License for more details.

    You should have received a copy of the GNU Lesser General Public License
    along with JMOO.  If not, see <http://www.gnu.org/licenses/>.
    
###                        ###############################
##########################################################
"""

"Brief notes"
"Standardized MOEA code for running any MOEA"

from jmoo_algorithms import *
from jmoo_stats_box import *
from jmoo_properties import *
from Moo import *
from pylab import *

def jmoo_evo(problem, algorithm, toStop = bstop):
    """
    ----------------------------------------------------------------------------
     Inputs:
      -@problem:    a MOP to optimize
      -@algorithm:  the MOEA used to optimize the problem
      -@toStop:     stopping criteria method
    ----------------------------------------------------------------------------
     Summary:
      - Evolve a population for a problem using some algorithm.
      - Return the best generation of that evolution
    ----------------------------------------------------------------------------
     Outputs:
      - A record (statBox) of the best generation of evolution
    ----------------------------------------------------------------------------
    """
    
    # # # # # # # # # # #
    # 1) Initialization #
    # # # # # # # # # # #
    stoppingCriteria = False                             # Just a flag for stopping criteria
    statBox          = jmoo_stats_box(problem,algorithm) # Record keeping device
    gen              = 0                                 # Just a number to track generations
    
    # # # # # # # # # # # # # # # #
    # 2) Load Initial Population  #
    # # # # # # # # # # # # # # # #
    population = problem.loadInitialPopulation(MU)
    
    # # # # # # # # # # # # # # #
    # 3) Collect Initial Stats  #
    # # # # # # # # # # # # # # #
    statBox.update(population, 0, 0, initial=True)
    
    # # # # # # # # # # # # # # #
    # 4) Generational Evolution #
    # # # # # # # # # # # # # # #
    
    while gen < PSI and stoppingCriteria == False:
        gen+= 1
        
        # # # # # # # # #
        # 4a) Selection #
        # # # # # # # # #
            
            
        problem.referencePoint = statBox.referencePoint
        selectees,evals = algorithm.selector(problem,population)
        numNewEvals = evals
        
        
        #raw_input("Press any Key")
        # # # # # # # # # #
        # 4b) Adjustment  #
        # # # # # # # # # #
        selectees,evals = algorithm.adjustor(problem, selectees)
        numNewEvals += evals
        
        # # # # # # # # # # #
        # 4c) Recombination #
        # # # # # # # # # # #
        population,evals = algorithm.recombiner(problem, population, selectees, MU)
        numNewEvals += evals        
        
        
        # # # # # # # # # # #
        # 4d) Collect Stats #
        # # # # # # # # # # #
        statBox.update(population, gen, numNewEvals)
        #for row in population: print row.valid
        
        
            
        # # # # # # # # # # # # # # # # # #
        # 4e) Evaluate Stopping Criteria  #
        # # # # # # # # # # # # # # # # # #
        stoppingCriteria = toStop(statBox)
        #stoppingCriteria = False
        
        
    


    #return the representative generation
    return statBox