概要

pythonを使ってグラフをいっぱい書くプログラムを試しているのですが．
RuntimeError: Can not put single artist in more than one figure
というエラーが起きて困っています．

問題箇所

https://stackoverrun.com/ja/q/12300089
こちらのリンクでplt.figureを複数設定して解決されている話はあるのですが，
今のプログラムでうまくplt.figureを複数生成する方法が思いつきません．

def evalOneMax(ind):
    """Objective function."""
    evaluation = 0
    for dish in ind:
        evaluation += dish.calorie
    # TODO:グラフ領域を設定

    #TODO:いい感じにトレーのサイズを外から引数として撮ってくるやり方がわからない直接書いた

    tray_size = np.array([370, 260])
    fig = plt.figure()
    axis = fig.add_subplot(1, 1, 1, xlim=tray_size[0], ylim=tray_size[1], aspect='equal')
    for dish in ind:
        axis.add_patch(dish.fig)#<-----ここでエラーが発生
    axis.axis("off")
    plt.savefig('tmp.png')
    fig.delaxes(axis)
    plt.clf()
    plt.cla()
    plt.close()
    del axis
    gray_img = cv2.imread('tmp.png')
    ind.img = gray_img
    ret, dishes_area_image = cv2.threshold(gray_img, 200, 255, cv2.THRESH_BINARY)#図形がある
    dishes_area = cv2.countNonZero(cv2.bitwise_not(cv2.cvtColor(dishes_area_image, cv2.COLOR_RGB2GRAY)))
    cv2.imwrite('dishes_area.png',dishes_area_image)
    ret, dishes_overlap_area_image = cv2.threshold(gray_img, 100, 255, cv2.THRESH_BINARY)#重なってて黒い
    cv2.imwrite('dishes_overlap_area.png',dishes_overlap_area_image)
    dishes_overlap_area = cv2.countNonZero(cv2.bitwise_not(cv2.cvtColor(dishes_overlap_area_image, cv2.COLOR_RGB2GRAY)))
    overlap_per = 1 - dishes_overlap_area/dishes_area

    return evaluation * overlap_per

プログラム全体

import random
import numpy as np
from operator import attrgetter
import csv
import matplotlib
import matplotlib.pyplot as plt
import cv2
#


def main():
    n_gene = 4  # The number of genes.
    n_ind = 300  # The number of individuals in a population.
    CXPB = 0.5  # The probability of crossover.
    MUTPB = 0.2  # The probability of individdual mutation.
    MUTINDPB = 0.05  # The probability of gene mutation.
    NGEN = 40  # The number of generation loop.
    dish_types= DishTypes()
    dish_types.load_file()
    random.seed(64)
    # --- Step1 : Create initial generation.
    pop = create_pop(n_ind, n_gene, dish_types)
    set_fitness(evalOneMax, pop)
    best_ind = max(pop, key=attrgetter("fitness"))

    # --- Generation loop.
    print("Generation loop start.")
    print("Generation: 0. Best fitness: " + str(best_ind.fitness))
    fig_save(best_ind, '{:0=5}'.format(0))
    for g in range(NGEN):

        # --- Step2 : Selection.
        offspring = selTournament(pop, n_ind, tournsize=3)

        # --- Step3 : Crossover.
        crossover = []
        for child1, child2 in zip(offspring[::2], offspring[1::2]):# (偶数個目，奇数個目)で子孫を作成
            if random.random() < CXPB:
                child1, child2 = cxTwoPointCopy(child1, child2)
                # child1.fitness = None
                # child2.fitness = None
            crossover.append(child1)
            crossover.append(child2)

        offspring = crossover[:]

        # --- Step4 : Mutation.
        mutant = []
        for mut in offspring:
            if random.random() < MUTPB:
                mut = mutFlipBit(dish_types, mut, indpb=MUTINDPB)
                # mut.fitness = None
            mutant.append(mut)

        offspring = mutant[:]

        # --- Update next population.
        pop = offspring[:]
        set_fitness(evalOneMax, pop)

        # --- Print best fitness in the population.
        best_ind = max(pop, key=attrgetter("fitness"))
        print("Generation: " + str(g + 1) + ". Best fitness: " + str(best_ind.fitness))
        fig_save(best_ind, dish_types, '{:0=5}'.format(g + 1))
    print("Generation loop ended. The best individual: ")
    print(best_ind)


class DishTypes:
    def __init__(self):
        self.num=0
        self.type=[]
        self.size=[]
        self.name=[]
        self.calorie=[]
        self.protein=[]
        self.lipid=[]
        self.carbohydrate=[]
        self.calcium=[]
        self.price=[]
        self.tray_size=np.array([370, 260])
    def load_file(self):
        with open('dish.csv') as f:
            reader = csv.reader(f)
            for row in reader:
                self.name.append(row[0])
                self.type.append(int(row[1]))
                self.size.append(int(row[2]))
                self.calorie.append(float(row[3]))
                self.protein.append(float(row[4]))
                self.lipid.append(float(row[5]))
                self.carbohydrate.append(float(row[6]))
                self.calcium.append(float(row[7]))
                self.price.append(float(row[8]))
        self.num = len(self.name)


class Dish:
    def __init__(self, id, dish_types):
        self.figure_type=dish_types.type[id]
        self.fig=create_figure(dish_types.type[id],dish_types.size[id],dish_types.tray_size)
        self.name=dish_types.name[id]
        self.calorie=dish_types.calorie[id]
        self.protein=dish_types.protein[id]
        self.lipid=dish_types.lipid[id]
        self.carbohydrate=dish_types.carbohydrate[id]
        self.calcium=dish_types.calcium[id]
        self.price=dish_types.price[id]


class IndividualTray(list):
    """Container of a individual."""
    fitness = None
    img = None

    def __new__(cls, a):
        return list.__new__(cls, a)


def create_figure(fig_type, size, tray_size):
    x = random.randrange(size, tray_size[0] - size)
    y = random.randrange(size, tray_size[1] - size)
    if fig_type == 0:
        return matplotlib.patches.Rectangle((x, y), size, size, facecolor='black', alpha=0.5)
    if fig_type == 1:
        return matplotlib.patches.Circle((x, y), radius=size, facecolor='black', alpha=0.5)


def create_ind(n_gene, dish_types):
    """Create a individual."""
    return IndividualTray([Dish(random.randint(0, dish_types.num-1), dish_types) for i in range(n_gene)])


def create_pop(n_ind, n_gene, dish_types):
    """Create a population."""
    pop = []
    for i in range(n_ind):
        ind = create_ind(n_gene, dish_types)
        pop.append(ind)
    return pop


def set_fitness(eval_func, pop):
    """Set fitnesses of each individual in a population."""
    for i, fit in zip(range(len(pop)), map(eval_func, pop)):
        pop[i].fitness = fit


def evalOneMax(ind):
    """Objective function."""
    evaluation = 0
    for dish in ind:
        evaluation += dish.calorie
    # TODO:グラフ領域を設定

    #TODO:いい感じにトレーのサイズを外から引数として撮ってくるやり方がわからない直接書いた

    tray_size = np.array([370, 260])
    fig = plt.figure()
    axis = fig.add_subplot(1, 1, 1, xlim=tray_size[0], ylim=tray_size[1], aspect='equal')
    for dish in ind:
        axis.add_patch(dish.fig)
    axis.axis("off")
    plt.savefig('tmp.png')
    fig.delaxes(axis)
    plt.clf()
    plt.cla()
    plt.close()
    del axis
    gray_img = cv2.imread('tmp.png')
    ind.img = gray_img
    ret, dishes_area_image = cv2.threshold(gray_img, 200, 255, cv2.THRESH_BINARY)#図形がある
    dishes_area = cv2.countNonZero(cv2.bitwise_not(cv2.cvtColor(dishes_area_image, cv2.COLOR_RGB2GRAY)))
    cv2.imwrite('dishes_area.png',dishes_area_image)
    ret, dishes_overlap_area_image = cv2.threshold(gray_img, 100, 255, cv2.THRESH_BINARY)#重なってて黒い
    cv2.imwrite('dishes_overlap_area.png',dishes_overlap_area_image)
    dishes_overlap_area = cv2.countNonZero(cv2.bitwise_not(cv2.cvtColor(dishes_overlap_area_image, cv2.COLOR_RGB2GRAY)))
    overlap_per = 1 - dishes_overlap_area/dishes_area

    return evaluation * overlap_per


def selTournament(pop, n_ind, tournsize):
    """Selection function."""
    chosen = []
    for i in range(n_ind):
        aspirants = [random.choice(pop) for j in range(tournsize)]#適当にtournsizeこだけ個体を選ぶ
        chosen.append(max(aspirants, key=attrgetter("fitness")))#fitness属性の一番大きいものを選ぶ
    return chosen


def cxTwoPointCopy(ind1, ind2):
    """Crossover function."""
    size1 = len(ind1)
    size2 = len(ind2)
    tmp1 = ind1.copy()
    tmp2 = ind2.copy()
    cxpoint1 = int(size1/2)
    cxpoint2 = int(size2/2)
    tmp1, tmp2 = tmp2[:cxpoint2].copy()+tmp1[cxpoint1:].copy(), tmp1[:cxpoint1].copy()+tmp2[cxpoint2:].copy()
    return tmp1, tmp2


def mutFlipBit(dish_types, ind, indpb):
    """Mutation function."""
    tmp = ind.copy()
    for i in range(len(ind)):
        if random.random() < indpb:
            rand = random.randint(0, 2)
            # 増やす
            if rand == 0:
                tmp[i] = ind.append(Dish(random.randint(0, dish_types.num-1), dish_types))
                break
            # へらす
            if rand == 1:
                tmp[i] = ind.pop(random.randint(0,len(ind)-1))
                break
            # 変える
            if rand == 2:
                randopos = random.randint(0, len(ind) - 1)
                tmp[i] = ind.pop(randopos)
                tmp[i] = ind.insert(randopos, random.randint(0, len(ind) - 1))
                break
    return tmp


def fig_save(ind, file_name):
    cv2.imwrite(',/img/'+file_name+'.png', ind.img)

if __name__ == "__main__":
    main()