class Organism(nx.DiGraph):
def __init__(self, MetNet, inicial = False, control = None, ES = None):
self.age = 0
self.record = 'a'
self.fitness = 0
self.mother_record = 'a'
self.species = 'a'
food_list = range(Constants.food)
if inicial: #verifica se a populacao eh inicial
#talvez fosse util ter um dicionario que redireciona pra funcoes
#dependendo do tipo de inicializacao - inicial ou mutacao - e
#encapsular uma serie de coisas que por enquanto estao no __init__.
self.chemistry = deepcopy(MetNet)
self.control = Control(inicial) # cria um controle inicial -> genes ligados por acaso e independentemente
self.change_environment_org(ES) #agora seta a comida do controle e da quimica
self.chemistry.update_reactions(self.control.switch_dict) #manda sinal dos genes ligados para a rede metabolica
self.biomass = Constants.division_threshold/2.0 #aqui eh a biomassa do organismo inicial.
else: #caso a populacao seja gerada de uma mutacao...
self.chemistry = deepcopy(MetNet)
self.control = control
self.change_environment_org(ES) #informa os recem-nascidos da situacao de comida...
self.chemistry.update_reactions(self.control.switch_dict)
self.biomass = Constants.division_threshold/2.0 #aqui deve ser a biomassa do organismo filho mutado.
def change_environment_org(self, food_list):
self.control.change_environment(food_list)
self.chemistry.update_food(self.control.food_dict)
def mutate(self, rate, MetNet, ES):
DNAp = self.control.export_code()
DNAm = mutate_DNA(DNAp, rate)
control_son = Control(DNA = DNAm, mother = self.control)
return Organism(MetNet, False, control_son, ES)
def eval_fitness(self, futurelist):
pass
#completar com os estados
on = [False, True, False, False, False, False, False, False, False, True, False]
fl = [on[j] for j in range(food)]
env_list = [[True, True], [True, False], [False, False], [False, True]]
period = len(env_list)
index = (env_list.index(fl) + 1)%period
mae = nx.Graph()
for i in range(len(listadenos)):
mae.add_node(listadenos[i], {'on': on[i]})
control = Control(DNA = D, mother = mae)
for i in range(50):
draw_BN(control, control.pos, listadenos) #control.genes_list nao inclui os intermediarios e eles ficam sem o involucro do no
control.change_state()
time.sleep(1)
if i%4 == 3:
control.change_environment(env_list[index]) #mudar aqui o metodo de atualizacao de comidas
index = (index+1)%period #rndm.randint(0,period - 1)
class Organism(nx.DiGraph):
def __init__(self, MetNet, met, reac, number_food, targets, gen, p, inicial = False, control = None):
#quais sao as caracteristicas em comum entre um organismo gerado from scratch
#e um organismo proveninente de uma mutacao? Isso tem que estar no __init__
#self.chemistry = MetNet
self.age = 0
self.record = 'a'
self.mother_record = 'a'
self.number_targets = targets
food_list = range(number_food)
if inicial: #verifica se a populacao eh inicial
react_list = [x + met for x in range(MetNet.number_reactions)]
if gen > len(react_list):
raise GeneNumberException('There are more genes than reactions to be controlled!')
genes_list = food_list + sorted(rndm.sample(react_list, gen))
#talvez fosse util ter um dicionario que redireciona pra funcoes
#dependendo do tipo de inicializacao - inicial ou mutacao - e
#encapsular uma serie de coisas que por enquanto estao no __init__.
self.chemistry = self.clean_met_net(MetNet, genes_list, food_list)
## print 'o dicionario inicial de chemis:'
## print self.chemistry.node
self.control = Control(number_food, met, genes_list, p, reac, inicial)
## print 'dicionario inicial:'
## print self.control.switch_dict
self.biomass = self.chemistry.update_reactions(self.control.switch_dict)
## print 'biomass_init'
## print self.biomass
else: #caso a populacao seja gerada de uma mutacao...
self.control = control
self.chemistry = self.clean_met_net(MetNet, control.genes_list, food_list)
self.biomass = self.chemistry.update_reactions(self.control.switch_dict)
def clean_met_net(self, MetNet, genes_list, food_list):
#Verificar com mais calma se a delecao de targets nao afeta nada...
chemis = deepcopy(MetNet)
#sera que esse objeto vai receber as funcoes da classe MetabolicNetwork?
for r in [x for x in MetNet.nodes() if MetNet.node[x]['Type'] == 'R']:
if r not in [reac for reac in genes_list if reac not in food_list]:
chemis.remove_node(r)
#Alguns targets podem ser deletados nesse passo:
chemis.remove_nodes_from([isol for isol in nx.isolates(chemis) if isol not in food_list])
#chemis.remove_nodes_from([n for n in nx.isolates(chemis) if n not in food_list])????
#Talvez tenha que verificar se food ou target molecules foram removidos?
return chemis
def change_environment_org(self, food_list):
self.control.change_environment(food_list)
self.chemistry.update_food(self.control.food_dict)
def mutate(self, rate, MetNet):
DNAp = self.control.export_code()
DNAm = mutate_DNA(DNAp, rate)
control_son = Control(MetNet.number_food, MetNet.number_metabolites, [], 0, MetNet.number_reactions, DNA = DNAm, mother = self.control)
return Organism(MetNet, MetNet.number_metabolites, MetNet.number_reactions, MetNet.number_food, MetNet.number_targets, 0, 0, inicial = False, control = control_son)
nx.draw_networkx_nodes(graph,positions, nodelist = listadenos, node_color=graph.colors,node_size=150)
nx.draw_networkx_edges(graph,positions,alpha=0.3)
plt.draw()
#completar com os estados
on = [True, True, False, True, True, True, False, False, True, False, False, False, False]
fl = [on[j] for j in range(food)]
env_list = [[True, True], [False, True], [True, True], [True, False], [True, True], [False, False]]
period = len(env_list)
index = (env_list.index(fl) + 1)%period
mae = nx.Graph()
for i in range(len(listadenos)):
mae.add_node(listadenos[i], {'on': on[i]})
control = Control(DNA = D, mother = mae)
for i in range(50):
draw_BN(control, control.pos, control.genes_list)
control.change_state()
time.sleep(1)
if i%5 == 4:
control.change_environment(env_list[index])
index = (index+1)%period
class Organism(nx.DiGraph):
def __init__(self, MetNet, inicial = False, control = None, EnvironmentSituation = None):
self.age = 0
self.record = 'a'
self.mother_record = 'a'
self.species = 'a'
self.metabolites = Constants.metabolites
self.number_targets = Constants.targets
food_list = range(Constants.food)
if inicial: #verifica se a populacao eh inicial
react_list = [x + self.metabolites for x in range(Constants.reactions)]
if Constants.genes > len(react_list):
raise GeneNumberException('There are more genes than reactions to be controlled!')
genes_list = food_list + sorted(rndm.sample(react_list, Constants.genes))
#talvez fosse util ter um dicionario que redireciona pra funcoes
#dependendo do tipo de inicializacao - inicial ou mutacao - e
#encapsular uma serie de coisas que por enquanto estao no __init__.
self.chemistry = self.clean_met_net(MetNet, genes_list, food_list, EnvironmentSituation)
## print 'o dicionario inicial de chemis:'
## print self.chemistry.node
self.control = Control(genes_list, inicial)
## print 'dicionario inicial:'
## print self.control.switch_dict
self.chemistry.update_reactions(self.control.switch_dict)
self.biomass = Constants.division_threshold/2.0 #aqui eh a biomassa do organismo inicial.
## print 'biomass_init'
## print self.biomass
else: #caso a populacao seja gerada de uma mutacao...
self.control = control
self.chemistry = self.clean_met_net(MetNet, control.genes_list, food_list, EnvironmentSituation)
self.chemistry.update_reactions(self.control.switch_dict)
self.biomass = Constants.division_threshold/2.0 #aqui deve ser a biomassa do organismo filho mutado.
def clean_met_net(self, MetNet, genes_list, food_list, EnvironmentSituation):
#Verificar com mais calma se a delecao de targets nao afeta nada...
chemis = deepcopy(MetNet)
#sera que esse objeto vai receber as funcoes da classe MetabolicNetwork?
for r in [x for x in MetNet.nodes() if MetNet.node[x]['Type'] == 'R']:
if r not in [reac for reac in genes_list if reac not in food_list]:
chemis.remove_node(r)
#Alguns targets podem ser deletados nesse passo:
chemis.remove_nodes_from([isol for isol in nx.isolates(chemis) if isol not in food_list])
if EnvironmentSituation is not None:
for f in range(Constants.food):
chemis.node[f]['Flowing'] = EnvironmentSituation[f]
#chemis.remove_nodes_from([n for n in nx.isolates(chemis) if n not in food_list])????
#Talvez tenha que verificar se food ou target molecules foram removidos?
return chemis
def change_environment_org(self, food_list):
self.control.change_environment(food_list)
#print 'compare a lista e o dicionario:'
#print food_list
#print self.control.food_dict
self.chemistry.update_food(self.control.food_dict)
def mutate(self, rate, MetNet, EnvironmentSituation):
DNAp = self.control.export_code()
DNAm = mutate_DNA(DNAp, rate)
control_son = Control(DNA = DNAm, mother = self.control)
return Organism(MetNet, False, control_son, EnvironmentSituation)
