def evaluate(self, individual):
ratios = self.decoder(individual, self.code_division)
datlist_list = [fc.read_datfile(file) for file in self.datfiles]
datlist_shaped_list = [
fc.shape_dat(datlist) for datlist in datlist_list
]
newdat = fc.interpolate_dat(datlist_shaped_list, ratios)
foil_para = fc.get_foil_para(newdat)
datx = np.array([ax[0] for ax in newdat])
daty = np.array([ax[1] for ax in newdat])
newfoil = Airfoil(x=datx, y=daty)
mt, mta, mc, mca, s = foil_para
penalty = 0
for g, p in zip(self.gs, self.penalties):
if (not g):
penalty += p
xf = XFoil()
xf.airfoil = newfoil
xf.Re = self.re
xf.max_iter = 60
print(self.assigns, self.datfiles)
scope = locals()
exec(self.assigns, scope)
#----------------------------------
#目的値
#----------------------------------
try:
obj1, obj2, obj3 = [eval(o) for o in self.Os]
except IndexError as e:
obj1, obj2, obj3 = [1.0] * self.NOBJ
traceback.print_exc()
except SyntaxError as e:
raise ValueError("invalid objection")
if (np.isnan(obj1) or obj1 > 1):
obj1 = 1
if (np.isnan(obj2) or obj2 > 1):
obj2 = 1
if (np.isnan(obj3) or obj3 > 1):
obj3 = 1
obj1 += penalty
obj2 += penalty
obj3 += penalty
return [obj1, obj2, obj3]
def setup_plot(self):
#プロット先をgroupBox_4に設定
self.canvas = MplCanvas(self, width=3, height=2, dpi=100)
toolbar = NavigationToolbar(self.canvas, self)
layout = QtWidgets.QVBoxLayout()
layout.addWidget(self.canvas)
layout.addWidget(toolbar)
self.groupBox_4.setLayout(layout)
self.canvas2 = MplCanvas(self, width=3, height=2, dpi=100)
toolbar2 = NavigationToolbar(self.canvas2, self)
layout2 = QtWidgets.QVBoxLayout()
layout2.addWidget(self.canvas2)
layout2.addWidget(toolbar2)
self.groupBox_3.setLayout(layout2)
ratios = [0.4, 0.1, 0.1, 0.3]
datlist_list = [fc.read_datfile(file) for file in self.foilfiles]
datlist_shaped_list = [
fc.shape_dat(datlist) for datlist in datlist_list
]
newdat = fc.interpolate_dat(datlist_shaped_list, ratios)
self.datx = [dat[0] for dat in newdat]
self.daty = [dat[1] for dat in newdat]
print(self.datx)
print(self.daty)
self.canvas2.axes.set_xlim(0, 1)
self.canvas2.axes.set_ylim(-0.5, 0.5)
self.canvas2.axes.set_title('gen1')
self.canvas2.axes.plot(self.datx, self.daty, 'r')
self.canvas2.draw()
#プロットデータ設定
n_data = 50
self.xdata = list(range(n_data))
self.ydata = [random.randint(0, 10) for i in range(n_data)]
self.update_plot()
#プロット更新頻度設定
self.timer = QtCore.QTimer()
self.timer.setInterval(100)
self.timer.timeout.connect(self.update_plot)
self.timer.start()
def main(self,seed=None):
self.setup()
#同時並列数(空白にすると最大数になる)
self.pool = Pool(self.thread)
self.toolbox.register("map", self.pool.map)
random.seed(seed)
# Initialize statistics object
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean, axis=0)
stats.register("std", np.std, axis=0)
stats.register("min", np.min, axis=0)
stats.register("max", np.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
#初期化(個体生成のこと)
pop = self.toolbox.population(n=self.MU)
#描画準備
plt.ion()
#進化の始まり
# Begin the generational process
for gen in range(self.NGEN):
if(gen == 0):
#0世代目の評価
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = self.toolbox.map(self.toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
#0世代目の統計
# Compile statistics about the population
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
else:
offspring = algorithms.varAnd(pop, self.toolbox, self.CXPB, self.MUTPB)
#評価
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = self.toolbox.map(self.toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
#淘汰
# Select the next generation population from parents and offspring
pop = self.toolbox.select(pop + offspring, self.MU)
#評価
pop_fit = np.array([ind.fitness.values for ind in pop])
#----------------------------------
#途中経過プロット
#----------------------------------
#1世代ごとに翼型をファイルに書き出す
k = 0
for ind in pop[:10]:
ratios = self.decoder(ind,self.code_division)
try:
k += 1
datlist_list = [fc.read_datfile(file) for file in self.datfiles]
datlist_shaped_list = [fc.shape_dat(datlist) for datlist in datlist_list]
newdat = fc.interpolate_dat(datlist_shaped_list,ratios)
fc.write_datfile(datlist=newdat,newfile_name = "newfoil"+str(k)+str(".dat"))
except Exception as e:
print("message:{0}".format(e))
#
##翼型それぞれの評価値を出力する
k = 0
for ind, fit in zip(pop, pop_fit):
try:
k += 1
print(k)
print("individual:" + str(ind) + "\nfit:" + str(fit))
except Exception as e:
print("message:{0}".format(e))
#
plt.cla()#消去
##新翼型の描画
datlist_list = [fc.read_datfile(file) for file in self.datfiles]
datlist_shaped_list = [fc.shape_dat(datlist) for datlist in datlist_list]
newdat = fc.interpolate_dat(datlist_shaped_list,self.decoder(pop[0],self.code_division))
fc.write_datfile(datlist=newdat,newfile_name = "./foil_dat_gen/newfoil_gen"+str(gen)+str(".dat"))
plt.title('generation:'+str(gen))
newdat_x = [dat[0] for dat in newdat]
newdat_y = [dat[1] for dat in newdat]
plt.xlim([0,1])
plt.ylim([-0.5,0.5])
plt.plot(newdat_x,newdat_y)
plt.savefig("./newfoil_gen/newfoil_gen"+str(gen)+".png")
plt.draw()#描画
plt.pause(0.1)#描画待機
##評価値のプロット
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(111, projection="3d")
p = [ind.fitness.values for ind in pop]
p1 = [i[0] for i in p]
p2 = [j[1] for j in p]
p3 = [k[2] for k in p]
ax.set_xlim(0,self.obj1_max)
ax.set_ylim(0,self.obj2_max)
ax.set_zlim(0,self.obj3_max)
ax.scatter(p1, p2, p3, marker="o", s=24, label="Final Population")
ref = tools.uniform_reference_points(self.NOBJ, self.P)
ax.scatter(ref[:, 0], ref[:, 1], ref[:, 2], marker="o", s=24, label="Reference Points")
ax.view_init(elev=11, azim=-25)
#ax.autoscale(tight=True)
plt.legend()
plt.title("nsga3_gen:"+str(gen))
plt.tight_layout()
plt.savefig("./nsga3_gen/nsga3_gen"+str(gen)+".png")
plt.close()
#======================================================
# Compile statistics about the new population
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
with SetIO('stats2.log'):
#stas.logに統計データを出力
print(logbook.stream)
return pop, logbook
def __getstate__(self):
self_dict = self.__dict__.copy()
del self_dict['pool']
return self_dict
def __setstate__(self, state):
self.__dict__.update(state)
def evaluate(self,individual):
#----------------------------------
#遺伝子に基づいて新翼型を生成
#----------------------------------
#遺伝子をデコード
ratios = self.decoder(individual, self.code_division)
#遺伝子に基づき翼型を混合して、新しい翼型を作る
datlist_list = [fc.read_datfile(file) for file in self.datfiles]
datlist_shaped_list = [fc.shape_dat(datlist) for datlist in datlist_list]
newdat = fc.interpolate_dat(datlist_shaped_list,ratios)
#翼型の形に関する情報を取得する
#foilpara == [最大翼厚、最大翼厚位置、最大キャンバ、最大キャンバ位置、S字の強さ]
foil_para = fc.get_foil_para(newdat)
#新しい翼型をAerofoilオブジェクトに適用
datx = np.array([ax[0] for ax in newdat])
daty = np.array([ax[1] for ax in newdat])
newfoil = Airfoil(x = datx, y = daty)
mt, mta, mc, mca, s = foil_para
#----------------------------------
#翼の形に関する拘束条件
#----------------------------------
"""
penalty = 0
for g, p in zip(self.gs, self.penalties):
if(not g):
penalty += p
"""
penalty = 0
print('===================')
if(mc<0):
print("out of the border")
print("reverse_cmaber")
penalty -= mc
if(mt<0.08):
print("out of the border")
print("too_thin")
penalty += 0.08-mt
if(mt>0.11):
print("out of the border")
print("too_fat")
penalty += mt-0.11
#if(foil_para[4]>0.03):
# print("out of the border")
# print("peacock")
# print('===================')
# return (1.0+(foil_para[4]-0.03),)*NOBJ
if(mta<0.23):
print("out of the border")
print("Atama Dekkachi!")
penalty += 0.23 - mta
if(mta>0.3):
print("out of the border")
print("Oshiri Dekkachi!")
penalty += mta - 0.3
#----------------------------------
#新翼型の解析
#----------------------------------
xf = XFoil()
xf.airfoil = newfoil
#レイノルズ数の設定
xf.Re = self.re
#境界要素法計算時1ステップにおける計算回数
xf.max_iter = 60
#print("hi")
#print(vars)
#scope = locals()
#var0, var1, var2, var3, var4, var5, var6, var7 = [0 if var == None or var == '' else eval(var,scope) for var in self.vars]
#計算結果格納
a, cl, cd, cm, cp = xf.cseq(0.4, 1.1, 0.1)
lr = [l/d for l, d in zip(cl,cd)]
#----------------------------------
#目的値
#----------------------------------
"""
try:
obj1,obj2,obj3 = [eval(o) for o in Os]
except Exception as e:
obj1,obj2,obj3=[1.0]*self.NOBJ
traceback.print_exc()
"""
try:
#揚抗比の逆数を最小化
obj1 = 1/lr[1]
#揚抗比のピークを滑らかに(安定性の最大化)
maxlr = max(lr)
maxlr_index = lr.index(maxlr)
obj2 = abs(maxlr - lr[maxlr_index+1])
#下面の反りを最小化(製作再現性の最大化)
obj3 = s
except Exception as e:
obj1,obj2,obj3=[1.0]*self.NOBJ
traceback.print_exc()
if (np.isnan(obj1) or obj1 > 1):
obj1 = 1
if (np.isnan(obj2) or obj2 > 1):
obj2 = 1
if (np.isnan(obj3) or obj3 > 1):
obj3 = 1
obj1 += penalty
obj2 += penalty
obj3 += penalty
return [obj1, obj2, obj3]
#翼型最適化開始
try:
pop, stats = ng.main()
except KeyboardInterrupt:
print("Ctrl + Cで停止しました")
pass
#最終世代の評価値取得
pop_fit = np.array([ind.fitness.values for ind in pop])
#10個の最適翼型候補をファイルに書き出す
k = 0
for ind, fit in zip(pop[:10], pop_fit[:10]):
try:
k += 1
datlist_list = [fc.read_datfile(file) for file in datfiles]
datlist_shaped_list = [fc.shape_dat(datlist) for datlist in datlist_list]
newdat = fc.interpolate_dat(datlist_shaped_list,decoder(ind,code_division))
fc.write_datfile(datlist=newdat,newfile_name = "newfoil"+str(k)+str(".dat"))
except Exception as e:
print("message:{0}".format(e))
#10個の最適翼型候補の評価値を出力
k = 0
for ind, fit in zip(pop, pop_fit):
try:
k += 1
print(k)
print("individual:" + str(ind) + "\nfit:" + str(fit))
except Exception as e:
print("message:{0}".format(e))
def evaluate(individual):
global code_division
#----------------------------------
#遺伝子にも続いて新翼型を生成
#----------------------------------
#遺伝子をデコード
ratios = decoder(individual, code_division)
#遺伝子に基づき翼型を混合して、新しい翼型を作る
datlist_list = [fc.read_datfile(file) for file in datfiles]
datlist_shaped_list = [fc.shape_dat(datlist) for datlist in datlist_list]
newdat = fc.interpolate_dat(datlist_shaped_list,ratios)
#翼型の形に関する情報を取得する
#foilpara == [最大翼厚、最大翼厚位置、最大キャンバ、最大キャンバ位置、S字の強さ]
foil_para = fc.get_foil_para(newdat)
#新しい翼型をAerofoilオブジェクトに適用
datx = np.array([ax[0] for ax in newdat])
daty = np.array([ax[1] for ax in newdat])
newfoil = Airfoil(x = datx, y = daty)
mt, mta, mc, mca, s = foil_para
#----------------------------------
#翼の形に関する拘束条件
#----------------------------------
penalty = 0
print('===================')
if(mc<0):
print("out of the border")
print("reverse_cmaber")
penalty -= mc
if(mt<0.08):
print("out of the border")
print("too_thin")
penalty += 0.08-mt
if(mt>0.11):
print("out of the border")
print("too_fat")
penalty += mt-0.11
#if(foil_para[4]>0.03):
# print("out of the border")
# print("peacock")
# print('===================')
# return (1.0+(foil_para[4]-0.03),)*NOBJ
if(mta<0.23):
print("out of the border")
print("Atama Dekkachi!")
penalty += 0.23 - mta
if(mta>0.3):
print("out of the border")
print("Oshiri Dekkachi!")
penalty += mta - 0.3
#----------------------------------
#新翼型の解析
#----------------------------------
xf = XFoil()
xf.airfoil = newfoil
#レイノルズ数の設定
xf.Re = 1.5e5
#境界要素法計算時1ステップにおける計算回数
xf.max_iter = 60
#座標整形
xf.repanel(n_nodes = 300)
xf.print = False
#計算結果格納
a, cl, cd, cm, cp = xf.cseq(0.4, 1.1, 0.1)
lr = [l/d for l, d in zip(cl,cd)]
#----------------------------------
#目的値
#----------------------------------
try:
#揚抗比の逆数を最小化
obj1 = 1/lr[1]
#揚抗比のピークを滑らかに(安定性の最大化)
maxlr = max(lr)
maxlr_index = lr.index(maxlr)
obj2 = abs(maxlr - lr[maxlr_index+1])
#下面の反りを最小化(製作再現性の最大化)
obj3 = foil_para[4]
except Exception as e:
obj1,obj2,obj3=[1.0]*NOBJ
traceback.print_exc()
if (np.isnan(obj1) or obj1 > 1):
obj1 = 1
if (np.isnan(obj2) or obj2 > 1):
obj2 = 1
if (np.isnan(obj3) or obj3 > 1):
obj3 = 1
obj1 += penalty
obj2 += penalty
obj3 += penalty
print("individual",individual)
print("evaluate",obj1,obj2,obj3)
print("max_thickness",foil_para[0])
print("at",foil_para[1])
print("max_camber",foil_para[2])
print("at",foil_para[3])
print("S",foil_para[4])
print('===================')
return [obj1, obj2, obj3]
def evaluate(individual):
global code_division
ratios = decoder(individual, code_division)
#===========================================
#翼型を混合し、新翼型のdatファイルを書き出す
#==========================================
datlist_list = [fc.read_datfile(file) for file in datfiles]
datlist_shaped_list = [fc.shape_dat(datlist) for datlist in datlist_list]
newdat = fc.interpolate_dat(datlist_shaped_list, ratios)
#foilpara == [最大翼厚、最大翼厚位置、最大キャンバ、最大キャンバ位置、S字の強さ]
foil_para = fc.get_foil_para(newdat)
mt, mta, mc, mca, s = foil_para
#print(obj1_max,obj2_max,obj3_max)
#--------------------
#翼の形に関する拘束条件
penalty = 0
print('===================')
if (mc < 0):
print("out of the border")
print("reverse_cmaber")
penalty -= mc
if (mt < 0.08):
print("out of the border")
print("too_thin")
penalty += 0.08 - mt
if (mt > 0.11):
print("out of the border")
print("too_fat")
penalty += mt - 0.11
#if(foil_para[4]>0.03):
# print("out of the border")
# print("peacock")
# print('===================')
# return (1.0+(foil_para[4]-0.03),)*NOBJ
if (mta < 0.23):
print("out of the border")
print("Atama Dekkachi!")
penalty += 0.23 - mta
if (mta > 0.3):
print("out of the border")
print("Oshiri Dekkachi!")
penalty += mta - 0.3
if penalty > 0:
return [1.0 + penalty] * NOBJ
#--------------------
id = str(os.getpid())
dir_path = 'dump'
try:
os.makedirs(dir_path)
except FileExistsError:
pass
newfile_name = 'newfoil' + id + '.dat'
foilfile = fc.write_datfile(datlist=newdat,
newfile_name=dir_path + '/' + newfile_name)
#==========================================
#新翼型の解析
#==========================================
set1 = xa.XfoilAnalize(cseq=[0.4, 1.1, 0.1],
foilfile=foilfile,
polar="polar1" + id)
#set2 = xa.XfoilAnalize(foilfile=newfile_name,polar="polar2" + id,Re=150000,cl=0.5)
#set3 = xa.XfoilAnalize(foilfile=newfile_name,polar="polar3" + id,Re=100000,cl=0.5)
try:
Lrlist = set1.Cseq(timeout=9)["Lrlist"]
maxLr = max(Lrlist)
maxindex = Lrlist.index(maxLr)
#==========================================
#目的値
#==========================================
obj1 = 1 / Lrlist[0] #揚抗比の最大化
obj2 = abs(Lrlist[maxindex] -
Lrlist[maxindex + 1]) #揚抗比のピークを滑らかに(安定性の最大化)
obj3 = foil_para[
4] #下面の反りを最小化(製作再現性の最大化)#abs(set2.OneCL()["Alpha"]-set3.OneCL()["Alpha"])
#正規化
obj1 = obj1 if obj1 <= 1 else 1.0 #値域<1.0
obj2 = obj2 if obj2 <= 1 else 1.0 #値域<1.0
obj3 = obj3 if obj3 <= 1 else 1.0 #値域<1.0
print("individual", individual)
print("evaluate", obj1, obj2, obj3)
print("max_thickness", foil_para[0])
print("at", foil_para[1])
print("max_camber", foil_para[2])
print("at", foil_para[3])
print("S", foil_para[4])
print('===================')
except Exception as e:
obj1, obj2, obj3 = [1.0] * NOBJ
traceback.print_exc()
print("individual", individual)
print("evaluate", obj1, obj2, obj3)
print("max_thickness", foil_para[0])
print("at", foil_para[1])
print("max_camber", foil_para[2])
print("at", foil_para[3])
print("S", foil_para[4])
print('===================')
return [obj1, obj2, obj3]
def main(seed=None):
global obj1_max, obj2_max, obj3_max, CXPB, MUTPB
pool = Pool(4) #同時並列数(空白にすると最大数になる)
toolbox.register("map", pool.map)
random.seed(seed)
# Initialize statistics object
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean, axis=0)
stats.register("std", numpy.std, axis=0)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
pop = toolbox.population(n=MU)
#0世代目の評価
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
#0世代目の統計
# Compile statistics about the population
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
#描画準備
plt.ion()
#進化の始まり
# Begin the generational process
for gen in range(1, NGEN):
offspring = algorithms.varAnd(pop, toolbox, CXPB, MUTPB)
#評価
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
#淘汰
# Select the next generation population from parents and offspring
pop = toolbox.select(pop + offspring, MU)
print("------objs_before-------")
print(obj1_max, obj2_max, obj3_max)
#最大値取得
pop_fit = numpy.array([ind.fitness.values for ind in pop])
fits1 = [obj[0] for obj in pop_fit]
_obj1_max = max(fits1)
fits2 = [obj[1] for obj in pop_fit]
_obj2_max = max(fits2)
fits3 = [obj[2] for obj in pop_fit]
_obj3_max = max(fits3)
#最大値に応じて正規化
if _obj1_max <= 1.0:
obj1_max = _obj1_max
if _obj2_max <= 1.0:
obj2_max = _obj2_max
if _obj3_max <= 1.0:
obj3_max = _obj3_max
print("------objs_after-------")
print(obj1_max, obj2_max, obj3_max)
#======================================================
#---------途中経過プロット----------
##1世代ごとに翼型を書き出す
k = 0
for ind in pop[:10]:
global code_division
ratios = decoder(ind, code_division)
try:
k += 1
datlist_list = [fc.read_datfile(file) for file in datfiles]
datlist_shaped_list = [
fc.shape_dat(datlist) for datlist in datlist_list
]
newdat = fc.interpolate_dat(datlist_shaped_list, ratios)
fc.write_datfile(datlist=newdat,
newfile_name="newfoil" + str(k) + str(".dat"))
except Exception as e:
print("message:{0}".format(e))
#
##翼型それぞれの評価値を出力する
k = 0
for ind, fit in zip(pop, pop_fit):
try:
k += 1
print(k)
print("individual:" + str(ind) + "\nfit:" + str(fit))
except Exception as e:
print("message:{0}".format(e))
#
plt.cla() #消去
##新翼型の描画
datlist_list = [fc.read_datfile(file) for file in datfiles]
datlist_shaped_list = [
fc.shape_dat(datlist) for datlist in datlist_list
]
newdat = fc.interpolate_dat(datlist_shaped_list,
decoder(pop[0], code_division))
fc.write_datfile(datlist=newdat,
newfile_name="./foil_dat_gen/newfoil_gen" + str(gen) +
str(".dat"))
plt.title('generation:' + str(gen))
newdat_x = [dat[0] for dat in newdat]
newdat_y = [dat[1] for dat in newdat]
plt.xlim([0, 1])
plt.ylim([-0.5, 0.5])
plt.plot(newdat_x, newdat_y)
plt.savefig("./newfoil_gen/newfoil_gen" + str(gen) + ".png")
plt.draw() #描画
plt.pause(0.1) #描画待機
##評価値のプロット
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(111, projection="3d")
p = [ind.fitness.values for ind in pop]
p1 = [i[0] for i in p]
p2 = [j[1] for j in p]
p3 = [k[2] for k in p]
ax.set_xlim(0, obj1_max)
ax.set_ylim(0, obj2_max)
ax.set_zlim(0, obj3_max)
ax.scatter(p1, p2, p3, marker="o", s=24, label="Final Population")
ref = tools.uniform_reference_points(NOBJ, P)
ax.scatter(ref[:, 0],
ref[:, 1],
ref[:, 2],
marker="o",
s=24,
label="Reference Points")
ax.view_init(elev=11, azim=-25)
#ax.autoscale(tight=True)
plt.legend()
plt.title("nsga3_gen:" + str(gen))
plt.tight_layout()
plt.savefig("./nsga3_gen/nsga3_gen" + str(gen) + ".png")
plt.close()
#======================================================
# Compile statistics about the new population
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
return pop, logbook