from warnings import warn
from copy import deepcopy, copy
from six import iteritems, string_types
from ..solvers import optimize
from .Object import Object
from .Solution import Solution
from .Reaction import Reaction
from .DictList import DictList
# Note, when a reaction is added to the Model it will no longer keep personal
# instances of its Metabolites, it will reference Model.metabolites to improve
# performance.  When doing this, take care to monitor metabolite coefficients.
# Do the same for Model.reactions[:].genes and Model.genes
class Model(Object):
    """Metabolic Model
    Refers to Metabolite, Reaction, and Gene Objects.
    """
    def __setstate__(self, state):
        """Make sure all cobra.Objects in the model point to the model"""
        self.__dict__.update(state)
        for y in ['reactions', 'genes', 'metabolites']:
            for x in getattr(self, y):
                x._model = self
        if not hasattr(self, "name"):
            self.name = None
    def __init__(self, id_or_model=None, name=None):
        if isinstance(id_or_model, Model):
            Object.__init__(self, name=name)
            self.__setstate__(id_or_model.__dict__)
            if not hasattr(self, "name"):
                self.name = None
        else:
            Object.__init__(self, id_or_model, name=name)
            self._trimmed = False
            self._trimmed_genes = []
            self._trimmed_reactions = {}
            self.genes = DictList()
            self.reactions = DictList()  # A list of cobra.Reactions
            self.metabolites = DictList()  # A list of cobra.Metabolites
            # genes based on their ids {Gene.id: Gene}
            self.compartments = {}
            self.solution = Solution(None)
            self.media_compositions = {}
    @property
    def description(self):
        warn("description deprecated")
        return self.name if self.name is not None else ""
    @description.setter
    def description(self, value):
        self.name = value
        warn("description deprecated")
    def __add__(self, other_model):
        """Adds two models. +
        The issue of reactions being able to exists in multiple Models now
        arises, the same for metabolites and such.  This might be a little
        difficult as a reaction with the same name / id in two models might
        have different coefficients for their metabolites due to pH and whatnot
        making them different reactions.
        """
        new_model = self.copy()
        new_reactions = deepcopy(other_model.reactions)
        new_model.add_reactions(new_reactions)
        new_model.id = self.id + '_' + other_model.id
        return new_model
    def __iadd__(self, other_model):
        """Adds a Model to this model +=
        The issue of reactions being able to exists in multiple Models now
        arises, the same for metabolites and such.  This might be a little
        difficult as a reaction with the same name / id in two models might
        have different coefficients for their metabolites due to pH and whatnot
        making them different reactions.
        """
        new_reactions = deepcopy(other_model.reactions)
        self.add_reactions(new_reactions)
        self.id = self.id + '_' + other_model.id
        return self
    def copy(self):
        """Provides a partial 'deepcopy' of the Model.  All of the Metabolite,
        Gene, and Reaction objects are created anew but in a faster fashion
        than deepcopy
        """
        new = self.__class__()
        do_not_copy = {"metabolites", "reactions", "genes"}
        for attr in self.__dict__:
            if attr not in do_not_copy:
                new.__dict__[attr] = self.__dict__[attr]
        new.metabolites = DictList()
        do_not_copy = {"_reaction", "_model"}
        for metabolite in self.metabolites:
            new_met = metabolite.__class__()
            for attr, value in iteritems(metabolite.__dict__):
                if attr not in do_not_copy:
                    new_met.__dict__[attr] = copy(
                        value) if attr == "formula" else value
            new_met._model = new
            new.metabolites.append(new_met)
        new.genes = DictList()
        for gene in self.genes:
            new_gene = gene.__class__(None)
            for attr, value in iteritems(gene.__dict__):
                if attr not in do_not_copy:
                    new_gene.__dict__[attr] = copy(
                        value) if attr == "formula" else value
            new_gene._model = new
            new.genes.append(new_gene)
        new.reactions = DictList()
        do_not_copy = {"_model", "_metabolites", "_genes"}
        for reaction in self.reactions:
            new_reaction = reaction.__class__()
            for attr, value in iteritems(reaction.__dict__):
                if attr not in do_not_copy:
                    new_reaction.__dict__[attr] = value
            new_reaction._model = new
            new.reactions.append(new_reaction)
            # update awareness
            for metabolite, stoic in iteritems(reaction._metabolites):
                new_met = new.metabolites.get_by_id(metabolite.id)
                new_reaction._metabolites[new_met] = stoic
                new_met._reaction.add(new_reaction)
            for gene in reaction._genes:
                new_gene = new.genes.get_by_id(gene.id)
                new_reaction._genes.add(new_gene)
                new_gene._reaction.add(new_reaction)
        return new
    def add_metabolites(self, metabolite_list):
        """Will add a list of metabolites to the the object, if they do not
        exist and then expand the stochiometric matrix
        metabolite_list: A list of :class:`~cobra.core.Metabolite` objects
        """
        if not hasattr(metabolite_list, '__iter__'):
            metabolite_list = [metabolite_list]
        # First check whether the metabolites exist in the model
        metabolite_list = [x for x in metabolite_list
                           if x.id not in self.metabolites]
        for x in metabolite_list:
            x._model = self
        self.metabolites += metabolite_list
    def add_reaction(self, reaction):
        """Will add a cobra.Reaction object to the model, if
        reaction.id is not in self.reactions.
        reaction: A :class:`~cobra.core.Reaction` object
        """
        self.add_reactions([reaction])
    def add_reactions(self, reaction_list):
        """Will add a cobra.Reaction object to the model, if
        reaction.id is not in self.reactions.
        reaction_list: A list of :class:`~cobra.core.Reaction` objects
        """
        # Only add the reaction if one with the same ID is not already
        # present in the model.
        # This function really should not used for single reactions
        if not hasattr(reaction_list, "__len__"):
            reaction_list = [reaction_list]
            warn("Use add_reaction for single reactions")
        reaction_list = DictList(reaction_list)
        reactions_in_model = [
            i.id for i in reaction_list if self.reactions.has_id(
                i.id)]
        if len(reactions_in_model) > 0:
            raise Exception("Reactions already in the model: " +
                            ", ".join(reactions_in_model))
        # Add reactions. Also take care of genes and metabolites in the loop
        for reaction in reaction_list:
            reaction._model = self  # the reaction now points to the model
            # keys() is necessary because the dict will be modified during
            # the loop
            for metabolite in list(reaction._metabolites.keys()):
                # if the metabolite is not in the model, add it
                # should we be adding a copy instead.
                if not self.metabolites.has_id(metabolite.id):
                    self.metabolites.append(metabolite)
                    metabolite._model = self
                    # this should already be the case. Is it necessary?
                    metabolite._reaction = set([reaction])
                # A copy of the metabolite exists in the model, the reaction
                # needs to point to the metabolite in the model.
                else:
                    stoichiometry = reaction._metabolites.pop(metabolite)
                    model_metabolite = self.metabolites.get_by_id(
                        metabolite.id)
                    reaction._metabolites[model_metabolite] = stoichiometry
                    model_metabolite._reaction.add(reaction)
            for gene in list(reaction._genes):
                # If the gene is not in the model, add it
                if not self.genes.has_id(gene.id):
                    self.genes.append(gene)
                    gene._model = self
                    # this should already be the case. Is it necessary?
                    gene._reaction = set([reaction])
                # Otherwise, make the gene point to the one in the model
                else:
                    model_gene = self.genes.get_by_id(gene.id)
                    if model_gene is not gene:
                        reaction._dissociate_gene(gene)
                        reaction._associate_gene(model_gene)
        self.reactions += reaction_list
    def to_array_based_model(self, deepcopy_model=False, **kwargs):
        """Makes a :class:`~cobra.core.ArrayBasedModel` from a cobra.Model which
        may be used to perform linear algebra operations with the
        stoichiomatric matrix.
        deepcopy_model: Boolean.  If False then the ArrayBasedModel points
        to the Model
        """
        from .ArrayBasedModel import ArrayBasedModel
        return ArrayBasedModel(self, deepcopy_model=deepcopy_model, **kwargs)
    def optimize(self, objective_sense='maximize', **kwargs):
        r"""Optimize model using flux balance analysis
        objective_sense: 'maximize' or 'minimize'
        solver: 'glpk', 'cglpk', 'gurobi', 'cplex' or None
        quadratic_component: None or :class:`scipy.sparse.dok_matrix`
            The dimensions should be (n, n) where n is the number of reactions.
            This sets the quadratic component (Q) of the objective coefficient,
            adding :math:`\\frac{1}{2} v^T \cdot Q \cdot v` to the objective.
        tolerance_feasibility: Solver tolerance for feasibility.
        tolerance_markowitz: Solver threshold during pivot
        time_limit: Maximum solver time (in seconds)
        .. NOTE :: Only the most commonly used parameters are presented here.
                   Additional parameters for cobra.solvers may be available and
                   specified with the appropriate keyword argument.
        """
        solution = optimize(self, objective_sense=objective_sense, **kwargs)
        self.solution = solution
        return solution
    def remove_reactions(self, reactions, delete=True,
                         remove_orphans=False):
        """remove reactions from the model
        reactions: [:class:`~cobra.core.Reaction.Reaction`] or [str]
            The reactions (or their id's) to remove
        delete: Boolean
            Whether or not the reactions should be deleted after removal.
            If the reactions are not deleted, those objects will be
            recreated with new metabolite and gene objects.
        remove_orphans: Boolean
            Remove orphaned genes and metabolites from the model as well
        """
        if isinstance(reactions, string_types) or hasattr(reactions, "id"):
            warn("need to pass in a list")
            reactions = [reactions]
        for reaction in reactions:
            try:
                reaction = self.reactions[self.reactions.index(reaction)]
            except ValueError:
                warn('%s not in %s' % (reaction, self))
            else:
                if delete:
                    reaction.delete(remove_orphans=remove_orphans)
                else:
                    reaction.remove_from_model(remove_orphans=remove_orphans)
    def repair(self, rebuild_index=True, rebuild_relationships=True):
        """Update all indexes and pointers in a model"""
        if rebuild_index:  # DictList indexes
            self.reactions._generate_index()
            self.metabolites._generate_index()
            self.genes._generate_index()
        if rebuild_relationships:
            for met in self.metabolites:
                met._reaction.clear()
            for gene in self.genes:
                gene._reaction.clear()
            for rxn in self.reactions:
                for met in rxn._metabolites:
                    met._reaction.add(rxn)
                for gene in rxn._genes:
                    gene._reaction.add(rxn)
        # point _model to self


Answer:         for l in (self.reactions, self.genes, self.metabolites):