【界面设计】针对应力强度模型使用Monte-Carlo进行可靠性仿真

要求：
针对应力和强度服从正态、对数正态、指数、威布尔分布的任意组合，利用Monte-Carlo方法编制程序，进行可靠度仿真。
代码：

import random
import tkinter as tk
from turtle import positionclass window:def __init__(self):self.root = tk.Tk()self.root.title("Monte Carlo for Reliability")self.root.geometry("800x600")self.interface()def interface(self):"""界面编写位置"""#应力frame_s = tk.LabelFrame(self.root, text="Stress", labelanchor="n")frame_s.place(relx=0.05, rely=0, relwidth=0.9, relheight=0.3)#应力分布单选frame_s_selection = tk.LabelFrame(frame_s, text="Stress distribution", labelanchor="n")frame_s_selection.place(relx=0, rely=0, relwidth=0.2, relheight=0.9)self.v_stress = tk.IntVar()values = { "normal" : 1 , "exponential" : 2 , "weibull" : 3 , "lognormal" : 4}for (text, value) in values.items():tk.Radiobutton(frame_s_selection, text = text, variable = self.v_stress, value = value).grid(row = value, column = 0, sticky ='w')#应力参数tk.Label(frame_s,text="Please enter the parameters of the distribution in the text box:").place(relx=0.3, rely=0, relwidth=0.6, relheight=0.1)#正态分布参数tk.Label(frame_s,text="normal").place(relx=0.2, rely=0.1, relwidth=0.1, relheight=0.1)tk.Label(frame_s,text="μ:").place(relx=0.2, rely=0.2, relwidth=0.1, relheight=0.1)tk.Label(frame_s,text="σ:").place(relx=0.2, rely=0.4, relwidth=0.1, relheight=0.1)self.entrystress_mu = tk.DoubleVar()self.stress_mu = tk.Entry(frame_s, textvariable=self.entrystress_mu).place(relx=0.3, rely=0.2, relwidth=0.1, relheight=0.1)self.entrystress_sigma = tk.DoubleVar()self.stress_sigma = tk.Entry(frame_s, textvariable=self.entrystress_sigma).place(relx=0.3, rely=0.4, relwidth=0.1, relheight=0.1)#指数分布参数tk.Label(frame_s,text="exponential").place(relx=0.4, rely=0.1, relwidth=0.15, relheight=0.1)tk.Label(frame_s,text="λ:").place(relx=0.4, rely=0.3, relwidth=0.1, relheight=0.1)self.entrystress_lambd = tk.DoubleVar()self.stress_lambd = tk.Entry(frame_s, textvariable=self.entrystress_lambd).place(relx=0.5, rely=0.3, relwidth=0.1, relheight=0.1)#威布尔分布参数tk.Label(frame_s,text="weibull").place(relx=0.6, rely=0.1, relwidth=0.1, relheight=0.1)tk.Label(frame_s,text="m:").place(relx=0.6, rely=0.2, relwidth=0.1, relheight=0.1)tk.Label(frame_s,text="θ-r:").place(relx=0.6, rely=0.4, relwidth=0.1, relheight=0.1)tk.Label(frame_s,text="r:").place(relx=0.6, rely=0.6, relwidth=0.1, relheight=0.1)self.entrystress_m = tk.DoubleVar()self.stress_m = tk.Entry(frame_s,textvariable=self.entrystress_m).place(relx=0.7, rely=0.2, relwidth=0.1, relheight=0.1)self.entrystress_theta = tk.DoubleVar()self.stress_theta = tk.Entry(frame_s,textvariable=self.entrystress_theta).place(relx=0.7, rely=0.4, relwidth=0.1, relheight=0.1)self.entrystress_r = tk.DoubleVar()self.stress_r = tk.Entry(frame_s,textvariable=self.entrystress_r).place(relx=0.7, rely=0.6, relwidth=0.1, relheight=0.1)#对数正态分布参数tk.Label(frame_s,text="lognormal").place(relx=0.8, rely=0.1, relwidth=0.15, relheight=0.1)tk.Label(frame_s,text="μ:").place(relx=0.8, rely=0.2, relwidth=0.1, relheight=0.1)tk.Label(frame_s,text="σ:").place(relx=0.8, rely=0.4, relwidth=0.1, relheight=0.1)self.entrystress_logmu = tk.DoubleVar()self.stress_logmu = tk.Entry(frame_s, textvariable=self.entrystress_logmu).place(relx=0.9, rely=0.2, relwidth=0.1, relheight=0.1)self.entrystress_logsigma = tk.DoubleVar()self.stress_logsigma = tk.Entry(frame_s, textvariable=self.entrystress_logsigma).place(relx=0.9, rely=0.4, relwidth=0.1, relheight=0.1)#强度frame_r = tk.LabelFrame(self.root, text="Resistance", labelanchor="n")frame_r.place(relx=0.05, rely=0.3, relwidth=0.9, relheight=0.3)#强度分布单选frame_r_selection = tk.LabelFrame(frame_r, text="Resistance distribution", labelanchor="n")frame_r_selection.place(relx=0, rely=0, relwidth=0.2, relheight=0.9)self.v_resistance = tk.IntVar()values = { "normal" : 1 , "exponential" : 2 , "weibull" : 3 , "lognormal" : 4}for (text, value) in values.items():tk.Radiobutton(frame_r_selection, text = text, variable = self.v_resistance, value = value).grid(row = value, column = 0, sticky ='w')#强度参数tk.Label(frame_r,text="Please enter the parameters of the distribution in the text box:").place(relx=0.3, rely=0, relwidth=0.6, relheight=0.1)#正态分布参数tk.Label(frame_r,text="normal").place(relx=0.2, rely=0.1, relwidth=0.1, relheight=0.1)tk.Label(frame_r,text="μ:").place(relx=0.2, rely=0.2, relwidth=0.1, relheight=0.1)tk.Label(frame_r,text="σ:").place(relx=0.2, rely=0.4, relwidth=0.1, relheight=0.1)self.entryresis_mu = tk.DoubleVar()self.resistance_mu = tk.Entry(frame_r, textvariable=self.entryresis_mu).place(relx=0.3, rely=0.2, relwidth=0.1, relheight=0.1)self.entryresis_sigma = tk.DoubleVar()self.resistance_sigma = tk.Entry(frame_r, textvariable=self.entryresis_sigma).place(relx=0.3, rely=0.4, relwidth=0.1, relheight=0.1)#指数分布参数tk.Label(frame_r,text="exponential").place(relx=0.4, rely=0.1, relwidth=0.15, relheight=0.1)tk.Label(frame_r,text="λ:").place(relx=0.4, rely=0.3, relwidth=0.1, relheight=0.1)self.entryresis_lambd = tk.DoubleVar()self.resistance_lambd = tk.Entry(frame_r, textvariable=self.entryresis_lambd).place(relx=0.5, rely=0.3, relwidth=0.1, relheight=0.1)#威布尔分布参数tk.Label(frame_r,text="weibull").place(relx=0.6, rely=0.1, relwidth=0.1, relheight=0.1)tk.Label(frame_r,text="m:").place(relx=0.6, rely=0.2, relwidth=0.1, relheight=0.1)tk.Label(frame_r,text="θ-r:").place(relx=0.6, rely=0.4, relwidth=0.1, relheight=0.1)tk.Label(frame_r,text="r:").place(relx=0.6, rely=0.6, relwidth=0.1, relheight=0.1)self.entryresis_m = tk.DoubleVar()self.resistance_m = tk.Entry(frame_r, textvariable=self.entryresis_m).place(relx=0.7, rely=0.2, relwidth=0.1, relheight=0.1)self.entryresis_theta = tk.DoubleVar()self.resistance_theta = tk.Entry(frame_r, textvariable=self.entryresis_theta).place(relx=0.7, rely=0.4, relwidth=0.1, relheight=0.1)self.entryresis_r = tk.DoubleVar()self.resistance_r = tk.Entry(frame_r, textvariable=self.entryresis_r).place(relx=0.7, rely=0.6, relwidth=0.1, relheight=0.1)#对数正态分布参数tk.Label(frame_r,text="lognormal").place(relx=0.8, rely=0.1, relwidth=0.15, relheight=0.1)tk.Label(frame_r,text="μ:").place(relx=0.8, rely=0.2, relwidth=0.1, relheight=0.1)tk.Label(frame_r,text="σ:").place(relx=0.8, rely=0.4, relwidth=0.1, relheight=0.1)self.entryresis_logmu = tk.DoubleVar()self.resistance_logmu = tk.Entry(frame_r,  textvariable=self.entryresis_logmu).place(relx=0.9, rely=0.2, relwidth=0.1, relheight=0.1)self.entryresis_logsigma = tk.DoubleVar()self.resistance_logsigma = tk.Entry(frame_r,  textvariable=self.entryresis_logsigma).place(relx=0.9, rely=0.4, relwidth=0.1, relheight=0.1)#模拟self.frame = tk.LabelFrame(self.root, text="Simulation", labelanchor="n")self.frame.place(relx=0.05, rely=0.6, relwidth=0.9, relheight=0.3)tk.Label(self.frame,text="Please enter the number of simulations:").place(relx=0, rely=0.1, relwidth=0.4, relheight=0.1)self.entrynum = tk.IntVar()self.num = tk.Entry(self.frame, textvariable=self.entrynum).place(relx=0.4, rely=0.1, relwidth=0.2, relheight=0.1)tk.Label(self.frame,text="Results:").place(relx=0.3, rely=0.3, relwidth=0.1, relheight=0.1)self.Button0 = tk.Button(self.frame, text="Start", command=self.start, bg="#7bbfea")self.Button0.place(relx=0.1, rely=0.5, relwidth=0.2, relheight=0.2)def stress_random_data(self, num):"""Input:num: represent the distribution type1--normal; 2--exp; 3--weibull; 4--lognormal"""if num == 1:mu = self.entrystress_mu.get()sigma = self.entrystress_sigma.get()data = random.normalvariate(mu, sigma)elif num == 2:lambd = self.entrystress_lambd.get()data = random.expovariate(lambd)elif num == 3:scale = self.entrystress_m.get()shape = self.entrystress_theta.get()position = self.entrystress_r.get()data = random.weibullvariate(scale, shape)+ positionelse:mu = self.entrystress_logmu.get()sigma = self.entrystress_logsigma.get()data = random.lognormvariate(mu,sigma)return datadef resis_random_data(self, num):"""Input:num: represent the distribution type1--normal; 2--exp; 3--weibull; 4--lognormal"""if num == 1:mu = self.entryresis_mu.get()sigma = self.entryresis_sigma.get()data = random.normalvariate(mu, sigma)elif num == 2:lambd = self.entryresis_lambd.get()data = random.expovariate(lambd)elif num == 3:shape = self.entryresis_m.get()scale = self.entryresis_theta.get()position = self.entryresis_r.get()data = random.weibullvariate(scale, shape) + positionelse:mu = self.entryresis_logmu.get()sigma = self.entryresis_logsigma.get()data = random.lognormvariate(mu,sigma)return datadef start(self):"""开始运行Mente-Carlo模拟"""stress_dis = self.v_stress.get()resistance_dis = self.v_resistance.get()count  = 0for i in range(self.entrynum.get()):s = self.stress_random_data(stress_dis)r = self.resis_random_data(resistance_dis)if r > s:count +=1R = count/self.entrynum.get()self.results = tk.Label(self.frame, text = R).place(relx=0.4, rely=0.3, relwidth=0.2, relheight=0.1)if __name__ == '__main__':a = window()a.root.mainloop()

界面结果

第一次做界面设计，还是很粗糙的，一些功能还有待改善。
并且一些变量名又臭又长……代码语句也不简洁，希望继续进步。

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【界面设计】针对应力强度模型使用Monte-Carlo进行可靠性仿真

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