def Astar_path(start_x,start_y,end_x,end_y):
#创建一个10*10的地图
CloseList = []#路径列表
decode = de.Decode()
decode.addPattern()
map2d=decode.envMat
#w,h=np.shape(map2d)
#显示地图当前样子
#map2d.showArray2D()
#创建AStar对象,并设置起点为0,0终点为9,0
aStar=AStar(map2d,Point(start_x,start_y),Point(end_x,end_y))
#开始寻路
pathList=aStar.start()
#遍历路径点,在map2d上以'8'显示
print('pathList\n',pathList)
for point in pathList:
map2d[point.x][point.y]=8
CloseList.append((point.x,point.y))
print(CloseList)
# print(point)
print("----------------------")
#再次显示地图
#map2d.showArray2D()
print('map2d:\n',map2d)
return CloseList
def decode_image(image_path, password, text_field):
# calling Decode class constructor
decode_action = decode.Decode(image_path, password)
# calling a method inside Decode class to check if all the supplied arguments are valid
# returns status with status message
msg = decode_action.are_values_valid()
# checkin the status of the returned message
if not msg[1]:
# showing error message if the supplied values are not valid
messagebox.showerror("Error Decoding", msg[0])
else:
# calling method inside Decode class to decode the text inside image
# returns text or error message with boolean status value
decoded_text = decode_action.decode_from_image()
# checking status of the message and if message extraction is successful, displaying on the window
if decoded_text[1]:
# enabling the disabled text widget on the decode window
text_field.config(state=NORMAL)
# clearing the text field before inserting the decoded text
text_field.delete(1.0, END)
# inserting the decoded text
text_field.insert(1.0, decoded_text[0])
# re-disabling the widget to prevent accidental insertion
text_field.config(state=DISABLED)
# showing success message
messagebox.showinfo("Text Decoded",
"Decode operation was successful.")
else:
# showing error message if any error occurs during decoding process
messagebox.showerror("Error Decoding", decoded_text[0])
def __init__(self):
# Run the net interface as this is the active thread.
self.__netif = netif.NetIF(self.__netCallback)
self.__netif.start()
# Create a VNA reader
self.__vna = vna.VNA()
# Create a decoder
self.__decoder = decode.Decode()
def start(fileCfgName='6_40_40_329_office_Cfg.txt', runTimes=0):
start = time.time()
decode = de.Decode(fileCfgName)
decode.addPattern()
Pstart_x = decode.robRowLst
Pstart_y = decode.robColLst
Robot_NUM = decode.robNum
model = 7
model_NUM = 3
Gm = 1000
Np = 200
Fangan_final_choose, Gmin = GA_search(Gm, Np, Pstart_x, Pstart_y,
Robot_NUM, model, model_NUM,
fileCfgName)
print(Fangan_final_choose)
decode = de.Decode(fileCfgName)
decode.addPattern()
decode.chrom = Fangan_final_choose.flatten()
decode.chrom = decode.chrom.astype(int)
c_rate, makeSpan = decode.calFitness()
print('c_rate', c_rate)
print('makeSpan', makeSpan)
#drawconvergence(Gm,Gmin)
end = time.time()
print('cost.time:', end - start)
# decode.writePath()
# drawPic()
# org_exe_name = 'D:\\py_code\\ComplexSystemIntelligentControl\\bin\\exc\\Debug\\MultiCover.exe'
# fileCfgName = '5_20_20_80_Outdoor_Cfg.txt'
# degNameCfg = conFileDir + fileCfgName
# proOrgStatic = subprocess.Popen([org_exe_name,degNameCfg],stdin =subprocess.PIPE,stdout = subprocess.PIPE)
# for line in proOrgStatic.stdout:
# print(line)
# drawPic(fileCfgName,8,str(runTimes),False)
return decode.chrom, c_rate, makeSpan
def DecodeMessage():
ImageToDecrypt = input("Enter the name of the image which you would like to decrypt (with extension): ")
#image = Image.open(UserInputImage, 'r')
DecryptPasscode = input("Please enter the correct passphrase to decrypt the image: ")
print("We are decrypting your image: '" + ImageToDecrypt + "' using the passcode you provided: '" + DecryptPasscode + "' . \nPlease wait for a while..." )
print ("Decoding your message")
# Decode the message from the image
DecodeImage = decode.Decode(ImageToDecrypt)
MessageRetrieved = DecodeImage.reveal_function()
if(MessageRetrieved.endswith(DecryptPasscode)):
print( MessageRetrieved )
else:
print("Incorrect passcode. Please try again!")
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 8 19:36:54 2020
@author: mi
"""
import my
import decode
import serial
ser = serial.Serial("/dev/ttyUSB0", 115200, timeout=10)
#print(ser)
print("串口连接成功，串口信息：\n")
print(ser)
while (1):
txt = ser.readlines(40000)
if txt != []:
if len(str(txt[0])) > 4000:
print("成功接收蓝牙数据")
with open("/home/xilinx/jupyter_notebooks/spaq/picture.txt",
"w",
encoding='utf-8') as f:
f.write(str(txt[0])[2:-2])
print("成功保存图片base64编码数据")
f.close()
break
decode.Decode()
score = my.test()
ser.write(score.encode("utf-8"))
def GA_search(Gm, Np, Pstart_x, Pstart_y, Robot_NUM, model, model_NUM,
fileCfgName):
D = Robot_NUM
CR = 0.65
MR = 0.05
decode = de.Decode(fileCfgName)
decode.addPattern()
#可选择的模式数量
Fangan = np.zeros((Np, D, model_NUM * 2 + 1))
Fangan_before = np.zeros((Np, D, model_NUM * 2))
Fangan_model = np.random.randint(0, model - 1, size=[Np, D, model_NUM])
Fangan_step = np.random.randint(1, 400,
size=[Np, D,
model_NUM]) #1000应该根据环境的空余大小来定，以后要更改
Fangan_before[:, :, np.s_[::2]] = Fangan_model
Fangan_before[:, :, np.s_[1::2]] = Fangan_step
n = [i for i in range(1, D + 1)]
rob_Priority = list(itertools.permutations(n, D)) #生成全排列组合
for i in range(Np):
rand_Priority = np.random.randint(0, perm(D, D) - 1)
Pri = rob_Priority[rand_Priority]
Pri = array(Pri)
#print('Pri',Pri)
Pri = Pri[:, np.newaxis]
#print('Fangan[i,:,:]',Fangan_before[i,:,:])
Fangan[i, :, :] = np.hstack((Pri, Fangan_before[i, :, :]))
Fangan = Fangan.astype(int)
Gmin = np.zeros(Gm)
Fangan_final = np.zeros((Gm, D, model_NUM * 2 + 1))
fitness = np.zeros(Np)
fitness_choose = np.zeros(Np)
fitness_final = np.zeros(Np)
#先对初始解计算适应值
for i in range(Np):
# decode = de.Decode()
# decode.addPattern()
decode.chrom = Fangan[i, :, :].flatten()
c_rate, makeSpan = decode.calFitness()
fitness[i] = makeSpan / (len(decode.robReachSet)) + (1 - c_rate)
#开始循环寻优
for G in range(Gm):
#交叉
Fangan_next_1 = Fangan
#print('Fangan_next_1:\n',Fangan_next_1) ##调试用
for ii in range(Np):
if random.random() < CR:
#dx=np.arange(0,Np-1,4)
dx = random.sample(range(0, Np - 1), 3)
dx_pos = np.where(dx == ii)
A1 = np.delete(dx, dx_pos)
index_x1 = np.random.randint(0, D - 1)
temp_x = Fangan_next_1[A1[0], :, index_x1]
Fangan_next_1[A1[0], :, index_x1] = Fangan_next_1[A1[1], :,
index_x1]
Fangan_next_1[A1[1], :, index_x1] = temp_x
Fangan_next_2 = Fangan_next_1
#print('Fangan_next_2--1:\n',Fangan_next_2) ##调试用
#变异
for ii in range(Np):
if random.random() < MR:
index_x2 = np.random.randint(0, D - 1)
Fangan_again = np.zeros(model_NUM * 2)
Fangan_model_again = np.random.randint(0, model - 1, model_NUM)
Fangan_step_again = np.random.randint(1, 400, model_NUM)
Fangan_again[np.s_[::2]] = Fangan_model_again
Fangan_again[np.s_[1::2]] = Fangan_step_again
Fangan_again = np.hstack((Fangan_next_2[ii, index_x2,
0], Fangan_again))
Fangan_next_2[ii, index_x2, :] = Fangan_again
#print('Fangan_next_2--2:\n',Fangan_next_2) ##调试用
#选择
for i in range(Np):
#print('---------------Env-----------\n',Env)
#decode = de.Decode()
#decode.addPattern()
decode.chrom = Fangan_next_2[i, :, :].flatten()
c_rate, makeSpan = decode.calFitness()
if (c_rate >= 1):
fitness_choose[i] = makeSpan
else:
fitness_choose[i] = 1000000000
# fitness_choose[i]=makeSpan/(len(decode.robReachSet))+(1-c_rate)
Fangan_all = np.vstack((Fangan, Fangan_next_2))
#Fangan_all=[]
#fitness_all=[]
fitness_all = np.hstack((fitness, fitness_choose))
#print('Fangan_all:\n',len(Fangan_all))
value_min_all = fitness_all.min()
Fangan_next_3 = Fangan_next_2
for i in range(Np):
dx = random.sample(range(0, 2 * Np - 1), 3)
temp_fitness = np.mat(
[fitness_all[dx[0]], fitness_all[dx[1]], fitness_all[dx[2]]])
value_min = temp_fitness.min()
pos_min = np.argmin(temp_fitness)
fitness_final[i] = value_min
if pos_min == 0:
Fangan_next_3[i, :, :] = Fangan_all[dx[0], :, :]
elif pos_min == 1:
Fangan_next_3[i, :, :] = Fangan_all[dx[1], :, :]
elif pos_min == 2:
Fangan_next_3[i, :, :] = Fangan_all[dx[2], :, :]
# compare=np.array(fitness_choose<fitness)+0
# #print(compare) ##调试用
# compare_reshape=compare.reshape(Np,1,1)
# #print(compare_reshape) ##调试用
# Fangan_next_3=np.multiply(compare_reshape,Fangan_next_2)+np.multiply((1-compare_reshape),Fangan)
# fitness_final=np.multiply(compare,fitness_choose)+np.multiply((1-compare),fitness)
value_min = fitness_final.min()
if value_min_all != value_min:
pos_min = np.argmin(fitness_all)
dx = random.sample(range(0, Np - 1), 1)
fitness_final[dx] = value_min_all
Fangan_next_3[dx, :, :] = Fangan_all[pos_min, :, :]
value_min = value_min_all
Gmin[G] = value_min
pos_min = np.argmin(fitness_final)
Fangan_final[G, :, :] = Fangan_next_3[pos_min, :, :]
#保存最优个体
fitness = fitness_final
Fangan = Fangan_next_3
best_pos = np.argmin(Gmin)
Fangan_final_choose = Fangan_final[best_pos, :, :]
return Fangan_final_choose, Gmin
layout['annotations'] = annotations
layout['shapes'] = shapeLst
layout['yaxis'] = dict(scalenchor ='x')
pathTrace = go.Scatter(x = [5],
y = [5],
mode= 'lines',
line = dict(shape = 'spline',
width = 4),
name = 'Path_'+str(1))
drawData = []
drawData.append(pathTrace)
fig = dict(data = drawData ,layout = layout)
plotly.offline.plot(fig,filename = 'ppp.html',validate=False)
if __name__ == '__main__':
decode=de.Decode()
Env=decode.envMat
pattern = Pattern([1,1,1],[0,0,0])
turnLst = [1,1,1]
print(GClockWise([0,1]))
print(GClockWise([0,-1]))
test = (1,1)
print('sstr',test[0])
pattern.locationLst = [0,0,0]
pattern.turnLst = turnLst
pattern.addDic()
print(pattern.dic)
drawPattern(pattern.dic)
# print(sorted(pts, key=clockwiseangle_and_distance))
paramType = "prtype"
paramName = "prname"
tree = ET.ElementTree(file='protocol.xml')
root = tree.getroot()
# 读取所有类
cVector = []
for elem in root:
if elem.tag == classTag:
tClass = N.ClassNode(elem.attrib[className])
for methods in elem:
if methods.tag == methodTag:
tMethods = M.Method(methods.attrib[methodName],
methods.attrib[methodFlow])
for params in methods:
if params.tag == paramTag:
tParam = Parameter.Parameter(params.attrib[paramName],
params.attrib[paramType])
tMethods.appendPara(tParam)
tClass.addMethod(tMethods)
cVector.append(tClass)
print(cVector)
for t in cVector:
t.display()
for t in cVector:
dec = decode.Decode(t)
dec.decode()
def GA_search(Gm, Np, Pstart_x, Pstart_y, Robot_NUM, model, model_NUM):
D = Robot_NUM
CR = 0.9
MR = 0.01
#可选择的模式数量
Fangan = np.zeros((Np, D, model_NUM * 2 + 1))
Fangan_before = np.zeros((Np, D, model_NUM * 2))
Fangan_model = np.random.randint(0, model - 1, size=[Np, D, model_NUM])
Fangan_step = np.random.randint(1, 400,
size=[Np, D,
model_NUM]) #1000应该根据环境的空余大小来定，以后要更改
Fangan_before[:, :, np.s_[::2]] = Fangan_model
Fangan_before[:, :, np.s_[1::2]] = Fangan_step
n = [1, 2, 3, 4, 5]
rob_Priority = list(itertools.permutations(n, 5)) #生成全排列组合
for i in range(Np):
rand_Priority = np.random.randint(0, perm(5, 5) - 1)
Pri = rob_Priority[rand_Priority]
Pri = array(Pri)
print('Pri', Pri)
Pri = Pri[:, np.newaxis]
print('Fangan[i,:,:]', Fangan_before[i, :, :])
Fangan[i, :, :] = np.hstack((Pri, Fangan_before[i, :, :]))
Fangan = Fangan.astype(int)
Gmin = np.zeros(Gm)
Fangan_final = np.zeros((Gm, D, model_NUM * 2 + 1))
fitness = np.zeros(Np)
fitness_choose = np.zeros(Np)
#先对初始解计算适应值
for i in range(Np):
decode = de.Decode()
decode.addPattern()
decode.chrom = Fangan[i, :, :].flatten()
c_rate, makeSpan = decode.calFitness()
fitness[i] = makeSpan * (1 - c_rate)
#开始循环寻优
for G in range(Gm):
#交叉
Fangan_next_1 = Fangan
#print('Fangan_next_1:\n',Fangan_next_1) ##调试用
for ii in range(Np):
if random.random() < CR:
#dx=np.arange(0,Np-1,4)
dx = random.sample(range(0, Np - 1), 3)
dx_pos = np.where(dx == ii)
A1 = np.delete(dx, dx_pos)
index_x1 = np.random.randint(0, D - 1)
temp_x = Fangan_next_1[A1[0], :, index_x1]
Fangan_next_1[A1[0], :, index_x1] = Fangan_next_1[A1[1], :,
index_x1]
Fangan_next_1[A1[1], :, index_x1] = temp_x
Fangan_next_2 = Fangan_next_1
#print('Fangan_next_2--1:\n',Fangan_next_2) ##调试用
#变异
for ii in range(Np):
if random.random() < MR:
index_x2 = np.random.randint(0, D - 1)
Fangan_again = np.zeros(model_NUM * 2)
Fangan_model_again = np.random.randint(0, model - 1, model_NUM)
Fangan_step_again = np.random.randint(1, 400, model_NUM)
Fangan_again[np.s_[::2]] = Fangan_model_again
Fangan_again[np.s_[1::2]] = Fangan_step_again
Fangan_again = np.hstack((np.random.randint(1,
5), Fangan_again))
Fangan_next_2[ii, index_x2, :] = Fangan_again
#print('Fangan_next_2--2:\n',Fangan_next_2) ##调试用
#选择
for i in range(Np):
#print('---------------Env-----------\n',Env)
decode = de.Decode()
decode.addPattern()
decode.chrom = Fangan_next_2[i, :, :].flatten()
c_rate, makeSpan = decode.calFitness()
fitness_choose[i] = makeSpan * (1 - c_rate)
compare = np.array(fitness_choose < fitness) + 0
#print(compare) ##调试用
compare_reshape = compare.reshape(Np, 1, 1)
#print(compare_reshape) ##调试用
Fangan_next_3 = np.multiply(compare_reshape,
Fangan_next_2) + np.multiply(
(1 - compare_reshape), Fangan)
fitness_final = np.multiply(compare, fitness_choose) + np.multiply(
(1 - compare), fitness)
value_min = fitness_final.min()
Gmin[G] = value_min
pos_min = np.argmin(fitness_final)
Fangan_final[G, :, :] = Fangan_next_3[pos_min, :, :]
#保存最优个体
fitness = fitness_final
Fangan = Fangan_next_3
best_pos = np.argmin(Gmin)
Fangan_final_choose = Fangan_final[best_pos, :, :]
return Fangan_final_choose
pathSum = 0
ct_lst = []
for i in range(robNum):
str_path = 'path_len'+str(i)
path_len = readCfg.getSingleVal(str_path)
ct_lst.append(path_len)
pathSum = pathSum + path_len
dataNameSp = file.split('auctionSTCEstDeg')
dataNameSp[0] = dataNameSp[0] +'.txt'
astc_dic[dataNameSp[0]] = (astc_ms,pathSum)
dataDic = dict()
lowBoundDic = dict()
reachableDic = dict()
for i in range(len(confileLst)):
decode = dc.Decode(cfgFileName = confileLst[i])
decode.addPattern()
decode.chrom = pathLst[i]
c_rate,makeSpan = decode.calFitness()
r_rate = decode.pathRepeatCover()
if(confileLst[i] in dataDic):
# print('WTF')
dataDic[confileLst[i]].append((makeSpan,c_rate,r_rate))
else:
dataDic[confileLst[i]] = [(makeSpan,c_rate,r_rate)]
lowBound = decode.calLowBound()
lowBoundDic[confileLst[i]] = lowBound
reachableDic[confileLst[i]] = len(decode.robReachSet)
print(c_rate)
print(makeSpan)
dataProCfg = conFileDir +'dataPro.txt'
# Press ⌃R to execute it or replace it with your code.
# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
import encode
import decode
def print_hi(name):
# Use a breakpoint in the code line below to debug your script.
print(f'Hi, {name}') # Press ⌘F8 to toggle the breakpoint.
# Press the green button in the gutter to run the script.
if __name__ == '__main__':
print_hi('PyCharm')
i = 0
iteration_times = 100
dataNum = 5 # should not be to large, 20 might be too big, usually 5~10
dataMade = 3 # around dataNum/2
dataLost = 3 # must less or equal to dataMade
while i < iteration_times:
encoder = encode.Encode(dataNum, dataMade)
encoder.encoding()
checker = decode.Check(dataLost)
lostRows = checker.make_lost(encoder)
decoder = decode.Decode(lostRows)
decoder.decoding(encoder, checker)
print(i)
i = i + 1
def __init__(self):
self.e = encode.Encode()
self.d = decode.Decode()