# Path: cameo/strain_design/heuristic/evolutionary/genomes.py
# class MultipleChromosomeGenome(object):
#     def __init__(self, keys=[], *args, **kwargs):
#         super(MultipleChromosomeGenome, self).__init__(*args, **kwargs)
#         self.chromosomes = {k: [] for k in keys}
#         self.keys = keys
# 
#     def __getitem__(self, key):
#         return self.chromosomes[key]
# 
#     def __delitem__(self, key):
#         del self.chromosomes[key]
# 
#     def __setitem__(self, key, value):
#         self.chromosomes[key] = sorted(value)
# 
#     def copy(self):
#         new_genome = MultipleChromosomeGenome(self.keys)
#         for key in self.keys:
#             new_genome[key] = copy(self[key])
#         return new_genome
# 
#     def __repr__(self):
#         return "| ".join(["%s: %s" % (k, list(v)) for k, v in self.chromosomes.items()])


from inspyred.ec.variators import mutator, crossover
from ordered_set import OrderedSet
from cameo.strain_design.heuristic.evolutionary.genomes import MultipleChromosomeGenome
from numpy import float32 as float
import logging
    if len(bro) > max_size:
        bro = random.sample(bro, max_size)

    if len(sis) > max_size:
        sis = random.sample(sis, max_size)
    return bro, sis


@crossover
def set_n_point_crossover(random, mom, dad, args):
    representation = sorted(set(mom).union(set(dad)))
    crossover_rate = args.get('crossover_rate', 1.0)
    num_crossover_points = args.get('num_crossover_points', 1)
    max_size = args.get('max_size', 9)
    children = []
    if random.random() <= crossover_rate:
        points = random.sample(representation, num_crossover_points)
        bro, sis = _do_set_n_point_crossover(representation, mom, dad, points, random, max_size)

        # ensure number of knockouts > 0 or do not add individual
        if len(bro) > 0:
            children.append(bro)
        if len(sis) > 0:
            children.append(sis)
    else:
        children.append(mom)
        children.append(dad)

    assert all(0 < len(individual) <= max_size for individual in children)


# Path: cameo/strain_design/heuristic/evolutionary/genomes.py
# class MultipleChromosomeGenome(object):
#     def __init__(self, keys=[], *args, **kwargs):
#         super(MultipleChromosomeGenome, self).__init__(*args, **kwargs)
#         self.chromosomes = {k: [] for k in keys}
#         self.keys = keys
# 
#     def __getitem__(self, key):
#         return self.chromosomes[key]
# 
#     def __delitem__(self, key):
#         del self.chromosomes[key]
# 
#     def __setitem__(self, key, value):
#         self.chromosomes[key] = sorted(value)
# 
#     def copy(self):
#         new_genome = MultipleChromosomeGenome(self.keys)
#         for key in self.keys:
#             new_genome[key] = copy(self[key])
#         return new_genome
# 
#     def __repr__(self):
#         return "| ".join(["%s: %s" % (k, list(v)) for k, v in self.chromosomes.items()])


# Path: cameo/strain_design/heuristic/evolutionary/variators.py
from inspyred.ec.variators import mutator, crossover
from ordered_set import OrderedSet
from cameo.strain_design.heuristic.evolutionary.genomes import MultipleChromosomeGenome
from numpy import float32 as float
import logging

    if len(bro) > max_size:
        bro = random.sample(bro, max_size)

    if len(sis) > max_size:
        sis = random.sample(sis, max_size)
    return bro, sis


@crossover
def set_n_point_crossover(random, mom, dad, args):
    representation = sorted(set(mom).union(set(dad)))
    crossover_rate = args.get('crossover_rate', 1.0)
    num_crossover_points = args.get('num_crossover_points', 1)
    max_size = args.get('max_size', 9)
    children = []
    if random.random() <= crossover_rate:
        points = random.sample(representation, num_crossover_points)
        bro, sis = _do_set_n_point_crossover(representation, mom, dad, points, random, max_size)

        # ensure number of knockouts > 0 or do not add individual
        if len(bro) > 0:
            children.append(bro)
        if len(sis) > 0:
            children.append(sis)
    else:
        children.append(mom)
        children.append(dad)

    assert all(0 < len(individual) <= max_size for individual in children)


    return children