weixin_41953162
weixin_41953162
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2021-04-29 11:12 阅读 78

python利用pyinstaller生成exe时报错

      本人编程外行,代码可以在解释器里正常运行,但无法利用pyinstaller对代码封装,封装过程中报错“UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd6 in position 375: invalid continuation byte”,不大清楚错误在哪,急需各位大神的帮助。完整代码如下

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import tkinter as tk
from tkinter import filedialog, dialog
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from matplotlib.pylab import mpl
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
import os
import matplotlib.colors
import heapq

mpl.rcParams['font.sans-serif'] = ['SimHei']  # 中文显示
mpl.rcParams['axes.unicode_minus'] = False  # 负号显示
##########窗口设置
window = tk.Tk()
window.title('局部放电聚类分析软件')  # 标题
window.geometry('1000x550')  # 窗口尺寸

l = tk.Label(window, text='局部放电聚类分析软件', bg='#5F9EA0', font=('宋体', 12), width=50, height=2).place(x=500,y=0,anchor='n')

text_operation = '''操作步骤:
1.选择想要分析的TF谱图文件
2.选择与TF谱图同时间的PRPD文件
3.根据显示的TF谱图确定聚类个数
4.点击“进行聚类”得到聚类结果
(注:可设置的最大聚类数为7)
'''
l2  =  tk.Label(window, text=text_operation,justify='left',font=('宋体', 11), width=30, height=6).place(x=0,y=0,anchor='nw')
#########################################################################################聚类分析相关函数


# 计算各点间距离、各点点密度(局部密度)大小
def get_point_density(datas,labers,min_distance,points_number):
    # 将numpy.ndarray格式转为list格式,并定义元组大小
    data = datas.tolist()
    laber = labers.tolist()
    distance_all = np.random.rand(points_number,points_number)
    point_density = np.random.rand(points_number)

    # 计算得到各点间距离
    for i in range(points_number):
        for n in range(points_number):
            distance_all[i][n] = np.sqrt(np.square(data[i][0]-data[n][0])+np.square(data[i][1]-data[n][1]))

     

    # 计算得到各点的点密度
    for i in range(points_number):
        x = 0
        for n in range(points_number):
            if distance_all[i][n] > 0 and distance_all[i][n]< min_distance:
                x = x+1
            point_density[i] = x
    return distance_all, point_density

def get_max_distance(distance_all,point_density,laber):
    point_density = point_density.tolist()
    a = int(max(point_density))
    
    b = laber[point_density.index(a)]
   
    c = max(distance_all[b])
   
    return c


# 计算得到各点的聚类中心距离
def get_each_distance(distance_all,point_density,data,laber):
    nn = []
    for i in range(len(point_density)):
        aa = []
        for n in range(len(point_density)):
            if point_density[i] < point_density[n]:
                aa.append(n)
        # print("大于自身点密度的索引",aa,type(aa))
        ll = get_min_distance(aa,i,distance_all, point_density,data,laber)
        nn.append(ll)
    return nn

# 获得:到点密度大于自身的最近点的距离
def get_min_distance(aa,i,distance_all, point_density,data,laber):
    min_distance = []
    """
    如果传入的aa为空,说明该点是点密度最大的点,该点的聚类中心距离计算方法与其他不同
    """
    if aa != []:
        for k in aa:
            min_distance.append(distance_all[i][k])

        return min(min_distance)
    else:
        max_distance = get_max_distance(distance_all, point_density, laber)
        return max_distance
#####################  
######################原始分布图显示
def open_file():
    '''
    打开文件
    :return:
    '''
    global file_path
    global file_text
    global new_array
    global labels_list
    global distance_all
    global point_density
    global nn
    global min_distance
    global points_number
    file_path = filedialog.askopenfilename(title=u'Select file')
    
    if file_path is not None:
        with open(file=file_path, mode='r+', encoding='ansi') as file:
            file_text = file.read()
        a = file_text
        b = a.split("\n")
        del b[0]
        new_list = []
        for i in range(len(b)-1):
            temp_list = []
            b1 = b[i].split("\t")
            temp_list.append(float(b1[0]))
            temp_list.append(float(b1[1]))
            new_list.append(temp_list)
        #print(new_list)    
        new_array = np.array(new_list)
        
        labels = []
        for i in range(len(new_array)):
            labels.append(i)
            
        min_distance =np.ceil(0.012*len(new_array))        # 邻域半径
        points_number = len(new_array)            # 随机点个数
        
        labels_list= np.array(labels,dtype="int")
        distance_all, point_density = get_point_density(new_array, labels_list, min_distance, points_number)
        nn = get_each_distance(distance_all, point_density, new_array, labels_list)
        nn_array = np.array(nn)
        rr = nn_array*point_density
        rr_list  =rr.tolist()
        
        colors1 = '#00CED1' #点的颜色
       
        area = np.pi * 4**2  # 点面积 
        # 画散点图
        f = Figure(figsize=(3.5,3.5), dpi=100)
        a = f.add_subplot(111)
        a.scatter(new_array[:,0], new_array[:,1], s=area, c=colors1, alpha=0.4)
        a.set_xlabel('等效时宽/ns')
        a.set_ylabel('等效频宽/MHz')
        a.set_title('原始数据分布')
        #figure =plt.scatter(new_array[:,0], new_array[:,1], s=area, c=colors1, alpha=0.4, label='label A')
        canvas = FigureCanvasTkAgg(f,master=window)
        canvas.draw()
        canvas.get_tk_widget().place(x=0,y=120,anchor='nw')
        #text1.insert('insert', file_text)

#############################PRPD文件打开
def open_file2():
    '''
    打开文件
    :return:
    '''
    global new_array_PRPD
    file_path = filedialog.askopenfilename(title=u'Select file')
    print('打开文件:', file_path)
    if file_path is not None:
        with open(file=file_path, mode='r+', encoding='ansi') as file:
            file_text = file.read()
        a = file_text
        b = a.split("\n")
        del b[0]
        new_list = []
        for i in range(len(b)-1):
            temp_list = []
            b1 = b[i].split("\t")
            temp_list.append(float(b1[0]))
            temp_list.append(float(b1[1]))
            new_list.append(temp_list)
        #print(new_list)    
        new_array_PRPD = np.array(new_list)
        
        
###########################聚类数输入
entry = tk.Entry(window,width=15)   #文本输入框
entry.place(x=500,y=50,anchor='n')

def change_state():
    global Cluster_num
    Cluster_num = entry.get()       # 调用get()方法,将Entry中的内容获取出来
    print(Cluster_num)
######################################################聚类算法编成函数
def getListMaxNumIndex(num_list,topk=3):
 
    max_num=heapq.nlargest(topk,num_list)
    max_num_index = []
    for j in max_num:
        for i in range(len(num_list)):
            if num_list[i] ==j:
                if i not in max_num_index:
                    max_num_index.append(i)
    return max_num_index

def getListMinNumIndex(num_list,topk=3):

    min_num=heapq.nsmallest(topk,num_list)
    min_num_index = []
    for i in min_num:
        tempvar = num_list.index(i)
        min_num_index.append(tempvar)
    return min_num_index

def clustrt():
    global rr_list
    nn_array = np.array(nn)
    rr = nn_array*point_density
    rr_list  =rr.tolist()
    max_index = getListMaxNumIndex(rr_list,int(Cluster_num))
    #print(max_index)
    distance_all_list = distance_all.tolist()
    class_list = []                     #生成一个三个空的子列表,用于存放每个点对于的索引
    for i in range(len(max_index)):
        tempcalss_list = []
        class_list.append(tempcalss_list)
    
    distance_list = []                #用于存放每个点到三个聚类中心点的距离
    for i in range(len(distance_all_list)):
        temp_list =[]
        for j in range(len(max_index)):
            temp_list.append(distance_all_list[i][max_index[j]])
        distance_list.append(temp_list)
    for i in range(len(distance_list)):      #选出每个点到三个聚类中心点的距离的最小值索引,得到分类索引
        temp_class = getListMinNumIndex(distance_list[i],1)
        class_list[temp_class[0]].append(i)
        
    class_data = []                #根据分类索引,将数据集进行分类
    for i in range(len(class_list)):
        list_temp = []
        for j in class_list[i]:
            list_temp.append(new_array[j].tolist())
        class_data.append(list_temp)
    class_num =[]
    for i in range(len(class_data)):
        class_num.append(len(class_data[i]))
        
    class_data_PRPD = []                         #根据分类索引,将PRPD数据集进行分类
    for i in range(len(class_list)):
        list_temp = []
        for j in class_list[i]:
            list_temp.append(new_array_PRPD[j].tolist())
        class_data_PRPD.append(list_temp)
    
    
        
    f2 = Figure(figsize=(3.5, 3.5), dpi=100)
    b = f2.add_subplot(111)
    #list_c = class_data[0]
    #array_c = np.array(list_c)
    area = np.pi * 4**2  # 点面积 
    colors1 = '#00CED1' #点的颜色
    colors2 = '#DC143C'
    colors3 = '#006400'
    colors4 = '#7FFF00'
    colors5 = '#A52A2A'
    colors6 = '#000000'
    colors7 = '#DEB887'
    colors_list = [colors1,colors2,colors3,colors4,colors5,colors6,colors7]
    for i in range(len(class_data)):
        temp_array = np.array(class_data[i])
        b.scatter(temp_array[:,0], temp_array[:,1], s=area, c=colors_list[i], alpha=0.4, label='类别 %d'%(i))
    b.legend()
    b.set_xlabel('等效时宽/ns')
    b.set_ylabel('等效频宽/MHz')
    b.set_title('聚类结果')
    canvas = FigureCanvasTkAgg(f2,master=window)
    canvas.draw()
    canvas.get_tk_widget().place(x=500,y=120,anchor='n')
    
    f3 = Figure(figsize=(3.9, 3.5), dpi=100)
    c = f3.add_subplot(111)
    for i in range(len(class_data_PRPD)):
        temp_array = np.array(class_data_PRPD[i])
        c.scatter(temp_array[:,1], temp_array[:,0], s=area, c=colors_list[i], alpha=0.4, label='类别 %d'%(i))
    c.legend()
    c.set_xlabel('相位/°')
    c.set_ylabel('放电幅值/mV')
    c.set_title('PRPD谱图')
    c.set_xlim(0, 360)
    
    canvas = FigureCanvasTkAgg(f3,master=window)
    canvas.draw()
    canvas.get_tk_widget().place(x=840,y=120,anchor='n')
    
    
    
    for i in range(len(class_num)):
        
            text1.insert('insert','类别%d 样本数%d  '%(i,class_num[i]))
#################################################################
text1 = tk.Text(window, width=50, height=4, bg='#5F9EA0', font=('宋体', 12))
text1.place(x=500,y=550,anchor='s')
#####################################
bt_openfile = tk.Button(window, text='选择TF文件', width=15, height=1,command=open_file).place(x=330,y=50,anchor='n')
bt_openfile_2 = tk.Button(window, text='选择PRPD文件', width=15, height=1,command=open_file2).place(x=330,y=80,anchor='n')
bt_CN= tk.Button(window,text='确定聚类数', width=15, height=1,command=change_state).place(x=500,y=80,anchor='n')
bt_c = tk.Button(window,text='进行聚类', width=15, height=2,command=clustrt).place(x=670,y=55,anchor='n')

if __name__ =='__main__':
    #设定初始目录为桌面
window.mainloop()  # 显示
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5条回答 默认 最新

  • 已采纳
    technologist_09 CSDN专家-HGJ 2021-04-29 11:36

    尝试读取文件时改参数encdoing='ISO-8859-1‘。在打包之前命令行用chcp 65001再pyinstaller,经测试可打包成功。

    运行环境:python3.7.6

    >>> PyInstaller.__version__
    '4.3'

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  • software7503 CSDN专家-赖老师(软件之家) 2021-04-29 11:17

    python利用pyinstaller生成exe时报错

    这种问题一般都是版本问题,换个版本试试。

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  • QA_Assistant 有问必答小助手 2021-04-29 18:45

    您好,我是有问必答小助手,您的问题已经有小伙伴解答了,您看下是否解决,可以追评进行沟通哦~

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  • juzicode00 桔子code 2021-04-29 20:07

    你电脑上是不是装了多个Python版本,打包用的pyinstaller和Python解释器是不是同一个安装路径下的?

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  • QA_Assistant 有问必答小助手 2021-05-06 14:52

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