在tkinter图形用户界面中解决简单的ODE系统

问题描述

我有一个用于SIR疾病模型的简单的ODE系统，它工作得很好，并产生了一个图形化的情节。但是，我正在尝试使用tkinter创建一个简单的弹出框，该框接受参数值，而不必通过脚本将参数值放入其中。

以下是原始代码。

import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt

#three compartments, Susceptible S, infected I, recovered R
#dS/dt, dI/dt, dR/dt
#susceptible sees birth rate coming in, deaths leaving and force of infection leaving
#infected sees FOI coming in, deaths leaving and recovery rates
#recovered sees recovery rate coming in, deaths leaving
#beta is tranmission coefficient, FOI is beta * (I/N) where N is total pop
#initially consider a model not accounting for births and deaths




# Total population, N.
N = 1000
# Initial number of infected and recovered individuals, I0 and R0.
I0, R0 = 1, 0
# Everyone else, S0, is susceptible to infection initially.
S0 = N - I0 - R0
# Contact rate, beta, and mean recovery rate, gamma, (in 1/days).
beta, gamma = 2/7, 1/7
# A grid of time points (in days)
t = np.linspace(0, 160, 160)

# The SIR model differential equations.
def deriv(y, t, N, beta, gamma):
    S, I, R = y
    dS = ((-beta * S * I) / N)
    dI = ((beta * S * I) / N) - (gamma * I)
    dR = (gamma * I)
    return dS, dI, dR

# Initial conditions are S0, I0, R0
# Integrate the SIR equations over the time grid, t.
solve = odeint(deriv, (S0, I0, R0), t, args=(N, beta, gamma))
S, I, R = solve.T

# Plot the data on three separate curves for S(t), I(t) and R(t)
fig = plt.figure(facecolor='w')
ax = fig.add_subplot(111, facecolor='#dddddd', axisbelow=True)
ax.plot(t, S/1000, 'b', alpha=1, lw=2, label='Susceptible')
ax.plot(t, I/1000, 'r', alpha=1, lw=2, label='Infected')
ax.plot(t, R/1000, 'black', alpha=1, lw=2, label='Recovered')
ax.set_xlabel('Time in days')
ax.set_ylabel('Number (1000s)')
ax.set_ylim(0,1.1)
#ax.yaxis.set_tick_params(length=0)
#ax.xaxis.set_tick_params(length=0)
ax.grid(b=True, which='major', c='w', lw=2, ls='-')
legend = ax.legend()
legend.get_frame().set_alpha(0.5)
#for spine in ('top', 'right', 'bottom', 'left'):
#    ax.spines[spine].set_visible(False)
plt.show()

现在是带有一些图形用户界面的

import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import tkinter as tk
from tkinter import IntVar

###############################################################################


def mainwindow():
    mainwindow = tk.Tk()
    mainwindow.geometry('350x350')
    
    tk.Label(mainwindow, text="enter parameters below").grid(row=1)
    
    getN = IntVar()
    geti0 = IntVar()
    getr0 = IntVar()
    getbeta = IntVar()
    getgamma = IntVar()

    
    tk.Label(mainwindow, text="N").grid(row=2)
    tk.Label(mainwindow, text="i0").grid(row=3)
    tk.Label(mainwindow, text="r0").grid(row=4)
    tk.Label(mainwindow, text="beta").grid(row=5)
    tk.Label(mainwindow, text="gamma").grid(row=6)
    
    e1 = tk.Entry(mainwindow,textvariable = getN).grid(row=2, column=1)
    e2 = tk.Entry(mainwindow,textvariable = geti0).grid(row=3, column=1)
    e3 = tk.Entry(mainwindow,textvariable = getr0).grid(row=4, column=1)
    e4 = tk.Entry(mainwindow,textvariable = getbeta).grid(row=5, column=1)
    e5 = tk.Entry(mainwindow,textvariable = getgamma).grid(row=6, column=1)
    
    solve = tk.Button(mainwindow, text='solve!', command=lambda: [values()]).grid(row=7, column=1, sticky=tk.W, pady=4)
    
    
    
    def values():
        
        readN = getN.get()
        readi0 = geti0.get()
        readr0 = getr0.get()
        readbeta = getbeta.get()
        readgamma = getgamma.get()
        
        intN = int(readN)
        inti0 = int(readi0)
        intr0 = int(readr0)
        intbeta = int(readbeta)
        intgamma = int(readgamma)
        
        
        # Total population, N.
        N = readN
        # Initial number of infected and recovered individuals, I0 and R0.
        I0, R0 = readi0, readr0
        # Everyone else, S0, is susceptible to infection initially.
        S0 = N - I0 - R0
        # Contact rate, beta, and mean recovery rate, gamma, (in 1/days).
        beta, gamma = readbeta, readgamma
        # A grid of time points (in days)
        t = np.linspace(0, 160, 160)

        # The SIR model differential equations.
        def deriv(y, t, N, beta, gamma):
            S, I, R = y
            dS = ((-beta * S * I) / N)
            dI = ((beta * S * I) / N) - (gamma * I)
            dR = (gamma * I)
            return dS, dI, dR
        
        # Initial conditions are S0, I0, R0
        # Integrate the SIR equations over the time grid, t.
        solve = odeint(deriv, (S0, I0, R0), t, args=(N, beta, gamma))
        S, I, R = solve.T
        
        # Plot the data on three separate curves for S(t), I(t) and R(t)
        fig = plt.figure(facecolor='w')
        ax = fig.add_subplot(111, facecolor='#dddddd', axisbelow=True)
        ax.plot(t, S/1000, 'b', alpha=0.5, lw=2, label='Susceptible')
        ax.plot(t, I/1000, 'r', alpha=0.5, lw=2, label='Infected')
        ax.plot(t, R/1000, 'g', alpha=0.5, lw=2, label='Recovered with immunity')
        ax.set_xlabel('Time /days')
        ax.set_ylabel('Number (1000s)')
        ax.set_ylim(0,1.2)
        ax.yaxis.set_tick_params(length=0)
        ax.xaxis.set_tick_params(length=0)
        ax.grid(b=True, which='major', c='w', lw=2, ls='-')
        legend = ax.legend()
        legend.get_frame().set_alpha(0.5)
        for spine in ('top', 'right', 'bottom', 'left'):
            ax.spines[spine].set_visible(False)
        plt.show()



    mainwindow.mainloop()
    
mainwindow()

第一个给出预期的曲线图：

，但是对于图形用户界面，它提供了以下功能：

我的代码哪里出错了？解算系统的代码没有改变，我只是设置了它，以便参数采用我在弹出框中输入的值。Lambda函数是否出错？

推荐答案

我尝试使用Beta和Gamma作为2/7的代码，1/7无法使其工作。

使用：

import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import tkinter as tk
from tkinter import IntVar,StringVar,DoubleVar

###############################################################################


def mainwindow():
    mainwindow = tk.Tk()
    mainwindow.geometry('350x350')
    
    tk.Label(mainwindow, text="enter parameters below").grid(row=1)
    
    getN = IntVar()
    geti0 = IntVar()
    getr0 = IntVar()
    # getbeta = StringVar()
    # getgamma = StringVar()
    
    getbeta = DoubleVar()
    getgamma = DoubleVar()

    
    tk.Label(mainwindow, text="N").grid(row=2)
    tk.Label(mainwindow, text="i0").grid(row=3)
    tk.Label(mainwindow, text="r0").grid(row=4)
    tk.Label(mainwindow, text="beta").grid(row=5)
    tk.Label(mainwindow, text="gamma").grid(row=6)
    
    e1 = tk.Entry(mainwindow,textvariable = getN).grid(row=2, column=1)
    e2 = tk.Entry(mainwindow,textvariable = geti0).grid(row=3, column=1)
    e3 = tk.Entry(mainwindow,textvariable = getr0).grid(row=4, column=1)
    e4 = tk.Entry(mainwindow,textvariable = getbeta).grid(row=5, column=1)
    e5 = tk.Entry(mainwindow,textvariable = getgamma).grid(row=6, column=1)
    
    solve = tk.Button(mainwindow, text='solve!', command=lambda: [values()]).grid(row=7, column=1, sticky=tk.W, pady=4)
    
    
    
    def values():
        
        readN = getN.get()
        readi0 = geti0.get()
        readr0 = getr0.get()
        # readbeta = float(getbeta.get())
        # readgamma = float(getgamma.get())
        readbeta = (getbeta.get())
        readgamma =(getgamma.get())
        
        print('ppppppppppppp', readbeta,readgamma)
        
        intN = int(readN)
        inti0 = int(readi0)
        intr0 = int(readr0)
        intbeta = float(readbeta)
        intgamma = float(readgamma)
        
        
        # Total population, N.
        N = readN
        # Initial number of infected and recovered individuals, I0 and R0.
        I0, R0 = readi0, readr0
        # Everyone else, S0, is susceptible to infection initially.
        S0 = N - I0 - R0
        # Contact rate, beta, and mean recovery rate, gamma, (in 1/days).
        beta, gamma = readbeta, readgamma
        # A grid of time points (in days)
        t = np.linspace(0, 160, 160)

        # The SIR model differential equations.
        def deriv(y, t, N, beta, gamma):
            S, I, R = y
            dS = ((-beta * S * I) / N)
            dI = ((beta * S * I) / N) - (gamma * I)
            dR = (gamma * I)
            return dS, dI, dR
        
        # Initial conditions are S0, I0, R0
        # Integrate the SIR equations over the time grid, t.
        solve = odeint(deriv, (S0, I0, R0), t, args=(N, beta, gamma))
        S, I, R = solve.T
        
        # Plot the data on three separate curves for S(t), I(t) and R(t)
        fig = plt.figure(facecolor='w')
        ax = fig.add_subplot(111, facecolor='#dddddd', axisbelow=True)
        ax.plot(t, S/1000, 'b', alpha=0.5, lw=2, label='Susceptible')
        ax.plot(t, I/1000, 'r', alpha=0.5, lw=2, label='Infected')
        ax.plot(t, R/1000, 'g', alpha=0.5, lw=2, label='Recovered with immunity')
        ax.set_xlabel('Time /days')
        ax.set_ylabel('Number (1000s)')
        ax.set_ylim(0,1.2)
        ax.yaxis.set_tick_params(length=0)
        ax.xaxis.set_tick_params(length=0)
        ax.grid(b=True, which='major', c='w', lw=2, ls='-')
        legend = ax.legend()
        legend.get_frame().set_alpha(0.5)
        for spine in ('top', 'right', 'bottom', 'left'):
            ax.spines[spine].set_visible(False)
        plt.show()



    mainwindow.mainloop()
    
mainwindow()

和0.28，0.14作为Beta和Gamma I：

希望知道如何使用分数作为输入的人会出现，

我尝试使用getbeta = StringVar()和getgamma = StringVar()

和readbeta = float(getbeta.get())和readgamma =float(getgamma.get())

或intbeta = float(readbeta)和intgamma = float(readgamma)

但收到ValueError: could not convert string to float: '2/7'

对于readbeta = float(getbeta.get())

遇到eval要允许输入‘2/7’和‘1/7’作为beta和Gamma，请参阅How can I get the data from Entry in tkinter that can be used as function?

此处代码更新：

import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import tkinter as tk
from tkinter import IntVar,StringVar,DoubleVar

###############################################################################


def mainwindow():
    mainwindow = tk.Tk()
    mainwindow.geometry('350x350')
    
    tk.Label(mainwindow, text="enter parameters below").grid(row=1)
    
    getN = IntVar()
    geti0 = IntVar()
    getr0 = IntVar()
    getbeta = StringVar()
    getgamma = StringVar()
    
    # getbeta = DoubleVar()
    # getgamma = DoubleVar()

    
    tk.Label(mainwindow, text="N").grid(row=2)
    tk.Label(mainwindow, text="i0").grid(row=3)
    tk.Label(mainwindow, text="r0").grid(row=4)
    tk.Label(mainwindow, text="beta").grid(row=5)
    tk.Label(mainwindow, text="gamma").grid(row=6)
    
    e1 = tk.Entry(mainwindow,textvariable = getN).grid(row=2, column=1)
    e2 = tk.Entry(mainwindow,textvariable = geti0).grid(row=3, column=1)
    e3 = tk.Entry(mainwindow,textvariable = getr0).grid(row=4, column=1)
    e4 = tk.Entry(mainwindow,textvariable = getbeta).grid(row=5, column=1)
    e5 = tk.Entry(mainwindow,textvariable = getgamma).grid(row=6, column=1)
    
    solve = tk.Button(mainwindow, text='solve!', command=lambda: [values()]).grid(row=7, column=1, sticky=tk.W, pady=4)
    
    
    
    def values():
        
        readN = getN.get()
        readi0 = geti0.get()
        readr0 = getr0.get()
        # readbeta = float(getbeta.get())
        # readgamma = float(getgamma.get())  
        readbeta = eval(getbeta.get(),{"builtins": {}})
        readgamma = eval(getgamma.get(), {"builtins": {}})
        
        print('ppppppppppppp', readbeta,readgamma)
        
        intN = int(readN)
        inti0 = int(readi0)
        intr0 = int(readr0)
        intbeta = float(readbeta)
        intgamma = float(readgamma)
        
        
        # Total population, N.
        N = readN
        # Initial number of infected and recovered individuals, I0 and R0.
        I0, R0 = readi0, readr0
        # Everyone else, S0, is susceptible to infection initially.
        S0 = N - I0 - R0
        # Contact rate, beta, and mean recovery rate, gamma, (in 1/days).
        beta, gamma = readbeta, readgamma
        # A grid of time points (in days)
        t = np.linspace(0, 160, 160)

        # The SIR model differential equations.
        def deriv(y, t, N, beta, gamma):
            S, I, R = y
            dS = ((-beta * S * I) / N)
            dI = ((beta * S * I) / N) - (gamma * I)
            dR = (gamma * I)
            return dS, dI, dR
        
        # Initial conditions are S0, I0, R0
        # Integrate the SIR equations over the time grid, t.
        solve = odeint(deriv, (S0, I0, R0), t, args=(N, beta, gamma))
        S, I, R = solve.T
        
        # Plot the data on three separate curves for S(t), I(t) and R(t)
        fig = plt.figure(facecolor='w')
        ax = fig.add_subplot(111, facecolor='#dddddd', axisbelow=True)
        ax.plot(t, S/1000, 'b', alpha=0.5, lw=2, label='Susceptible')
        ax.plot(t, I/1000, 'r', alpha=0.5, lw=2, label='Infected')
        ax.plot(t, R/1000, 'g', alpha=0.5, lw=2, label='Recovered with immunity')
        ax.set_xlabel('Time /days')
        ax.set_ylabel('Number (1000s)')
        ax.set_ylim(0,1.2)
        ax.yaxis.set_tick_params(length=0)
        ax.xaxis.set_tick_params(length=0)
        ax.grid(b=True, which='major', c='w', lw=2, ls='-')
        legend = ax.legend()
        legend.get_frame().set_alpha(0.5)
        for spine in ('top', 'right', 'bottom', 'left'):
            ax.spines[spine].set_visible(False)
        plt.show()



    mainwindow.mainloop()
    
mainwindow()

它使用getbeta = StringVar()和getgamma = StringVar()，然后readbeta = eval(getbeta.get(),{"builtins": {}})和readgamma = eval(getgamma.get(), {"builtins": {}})

我在某个地方读到，eval在Python中的使用是不安全的，所以如果有人想要更好的解决方案，请与我们分享

最后设法在将输入发送到eval函数之前对其进行验证，因此代码应该是安全的(或不安全？？请在此帮助)；新代码如下：

import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import tkinter as tk
from tkinter import IntVar,StringVar,DoubleVar

###############################################################################
def callback_int(input):
      
    if input.isdigit():
        print(input)
        return True
                          
    elif input == "":
        print(input)
        return True
  
    else:
        print(input)
        return False

def callback_str(input, typez=None):
    
    if all([s.isdigit() or s =='/' for s in input]) and input.count('/') <= 1:
        print([s.isdigit() or s =='/' for s in input])
        # print(input)
        return True
                          
    elif all([s.isdigit() or s =='.' for s in input]) and input.count('.') <= 1:
        print([s.isdigit() or s =='.' for s in input])
        # print(input)
        return True

    else:
        print('no valid input : ',input)
        return False

def mainwindow():

    mainwindow = tk.Tk()
    mainwindow.geometry('350x350')
    
    tk.Label(mainwindow, text="enter parameters below").grid(row=1)
    
    getN = IntVar()
    geti0 = IntVar()
    getr0 = IntVar()
    getbeta = StringVar(mainwindow, value='0')
    getgamma = StringVar(mainwindow, value='0')
    
    # getbeta = DoubleVar()
    # getgamma = DoubleVar()

    
    tk.Label(mainwindow, text="N").grid(row=2)
    tk.Label(mainwindow, text="i0").grid(row=3)
    tk.Label(mainwindow, text="r0").grid(row=4)
    tk.Label(mainwindow, text="beta").grid(row=5)
    tk.Label(mainwindow, text="gamma").grid(row=6)
    
    e1 = tk.Entry(mainwindow,textvariable = getN)
    e1.grid(row=2, column=1)
    e2 = tk.Entry(mainwindow,textvariable = geti0)
    e2.grid(row=3, column=1)
    e3 = tk.Entry(mainwindow,textvariable = getr0)
    e3.grid(row=4, column=1)
    e4 = tk.Entry(mainwindow,textvariable = getbeta)
    e4.grid(row=5, column=1)
    e5 = tk.Entry(mainwindow,textvariable = getgamma)
    e5.grid(row=6, column=1)
    
    reg_int = mainwindow.register(callback_int)
    reg_str = mainwindow.register(callback_str)
    
    print(type(e4))
    
    e1.config(validate ="key", validatecommand =(reg_int, '%P'))
    e2.config(validate ="key", validatecommand =(reg_int, '%P'))
    e3.config(validate ="key", validatecommand =(reg_int, '%P'))
    e4.config(validate ="key", validatecommand =(reg_str, '%P'))
    e5.config(validate ="key", validatecommand =(reg_str, '%P'))
    
    solve = tk.Button(mainwindow, text='solve!', command=lambda: [values()]).grid(row=7, column=1, sticky=tk.W, pady=4)
    
    
    
    def values():
        
        readN = getN.get()
        readi0 = geti0.get()
        readr0 = getr0.get()
        # readbeta = float(getbeta.get())
        # readgamma = float(getgamma.get())  
        readbeta = eval(getbeta.get(),{"builtins": {}})
        readgamma = eval(getgamma.get(), {"builtins": {}})
        
        print('ppppppppppppp', readbeta,readgamma)
        
        intN = int(readN)
        inti0 = int(readi0)
        intr0 = int(readr0)
        intbeta = float(readbeta)
        intgamma = float(readgamma)
        
        
        # Total population, N.
        N = readN
        # Initial number of infected and recovered individuals, I0 and R0.
        I0, R0 = readi0, readr0
        # Everyone else, S0, is susceptible to infection initially.
        S0 = N - I0 - R0
        # Contact rate, beta, and mean recovery rate, gamma, (in 1/days).
        beta, gamma = readbeta, readgamma
        # A grid of time points (in days)
        t = np.linspace(0, 160, 160)

        # The SIR model differential equations.
        def deriv(y, t, N, beta, gamma):
            S, I, R = y
            dS = ((-beta * S * I) / N)
            dI = ((beta * S * I) / N) - (gamma * I)
            dR = (gamma * I)
            return dS, dI, dR
        
        # Initial conditions are S0, I0, R0
        # Integrate the SIR equations over the time grid, t.
        solve = odeint(deriv, (S0, I0, R0), t, args=(N, beta, gamma))
        S, I, R = solve.T
        
        # Plot the data on three separate curves for S(t), I(t) and R(t)
        fig = plt.figure(facecolor='w')
        ax = fig.add_subplot(111, facecolor='#dddddd', axisbelow=True)
        ax.plot(t, S/1000, 'b', alpha=0.5, lw=2, label='Susceptible')
        ax.plot(t, I/1000, 'r', alpha=0.5, lw=2, label='Infected')
        ax.plot(t, R/1000, 'g', alpha=0.5, lw=2, label='Recovered with immunity')
        ax.set_xlabel('Time /days')
        ax.set_ylabel('Number (1000s)')
        ax.set_ylim(0,1.2)
        ax.yaxis.set_tick_params(length=0)
        ax.xaxis.set_tick_params(length=0)
        ax.grid(b=True, which='major', c='w', lw=2, ls='-')
        legend = ax.legend()
        legend.get_frame().set_alpha(0.5)
        for spine in ('top', 'right', 'bottom', 'left'):
            ax.spines[spine].set_visible(False)
        plt.show()

    
    mainwindow.mainloop()
    
mainwindow()

这允许Beta和Gamma将浮点数(即0.28)或分数(即2/7)作为输入插入Entry Widget框

开始享受Tkinter，这里是允许单选按钮控制输入类型的另一个改进版本：

import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import tkinter as tk
from tkinter import IntVar,StringVar,DoubleVar

###############################################################################



def mainwindow():

    def switch():
        print(varb.get(), '    ')  #,varbR)
        # print(varbR)
        getbeta.set('0')
        getgamma.set('0')
        return
    
    def callback(input,typez=None, varb=None):
    
        value = mainwindow.getvar(varb)
        print(value)
        # varb.get()=varb.get()
        # uu = varb.get()
        # print(varb, uu)
        # print(varb.get())
        
        if typez == "int":
            if input.isdigit():
                # print(input)
                return True
                                
            elif input == "":
                # print(input)
                return True
        
            else:
                print(input, 'not allowed !!!!')
                return False
    
        if typez == "str":
            if value =='frc':
                if len(input) >=1  and input[0] == '/':
                    return False
                
                if all([s.isdigit() or s =='/' for s in input]) and input.count('/') <= 1:
                    # print([s.isdigit() or s =='/' for s in input])
                    # print(input)
                    return True
                else:
                    print('no valid input : ',input)
                    return False
            
            elif value =='flt':                   
                if all([s.isdigit() or s =='.' for s in input]) and input.count('.') <= 1:
                    # print([s.isdigit() or s =='.' for s in input])
                    # print(input)
                    return True
    
                else:
                    print('no valid input : ',input)
                    return False
            else:
                return False
    
    mainwindow = tk.Tk()
    mainwindow.geometry('550x350')
    
    tk.Label(mainwindow, text="enter parameters below").grid(row=1)
    
    getN = IntVar()
    geti0 = IntVar()
    getr0 = IntVar()
    getbeta = StringVar(mainwindow, value='0')
    getgamma = StringVar(mainwindow, value='0')
    
    # getbeta = DoubleVar()
    # getgamma = DoubleVar()

    
    tk.Label(mainwindow, text="N").grid(row=2)
    tk.Label(mainwindow, text="i0").grid(row=3)
    tk.Label(mainwindow, text="r0").grid(row=4)
    tk.Label(mainwindow, text="beta").grid(row=5)
    tk.Label(mainwindow, text="gamma").grid(row=6)
    
    e1 = tk.Entry(mainwindow,textvariable = getN)
    e1.grid(row=2, column=1)
    e2 = tk.Entry(mainwindow,textvariable = geti0)
    e2.grid(row=3, column=1)
    e3 = tk.Entry(mainwindow,textvariable = getr0)
    e3.grid(row=4, column=1)
    e4 = tk.Entry(mainwindow,textvariable = getbeta)
    e4.grid(row=5, column=1)
    e5 = tk.Entry(mainwindow,textvariable = getgamma)
    e5.grid(row=6, column=1)
    
    
    varb = StringVar(mainwindow, value='flt')
    # varbR=varb.get()
    
    
    rb1 = tk.Radiobutton(mainwindow, borderwidth=8,height=1, text='float   ' ,
                         variable = varb, value='flt', command=switch, justify="left")
    rb1.grid(row=5,column =2, rowspan=1, sticky="w")
    
    rb2 = tk.Radiobutton(mainwindow, borderwidth=8,height=1, text='fraction' ,
                         variable = varb, value='frc', command=switch ,justify="left")
    rb2.grid(row=6,column =2, rowspan=1, sticky="w")
    
    rb1.deselect()  # finche non attivo radiobutton non prende parametri
    
    
    
    
    reg = mainwindow.register(callback) 
    
    
    
    # e1.config(validate ="key", validatecommand =(reg, '%P', 'int',varbR))
    # e2.config(validate ="key", validatecommand =(reg, '%P', 'int',varbR))
    # e3.config(validate ="key", validatecommand =(reg, '%P', 'int',varbR))
    # e4.config(validate ="key", validatecommand =(reg, '%P', 'str',varbR))
    # e5.config(validate ="key", validatecommand =(reg, '%P', 'str',varbR))
    
    # e1.config(validate ="key", validatecommand =(reg, '%P', 'int',varb.get()))
    # e2.config(validate ="key", validatecommand =(reg, '%P', 'int',varb.get()))
    # e3.config(validate ="key", validatecommand =(reg, '%P', 'int',varb.get()))
    # e4.config(validate ="key", validatecommand =(reg, '%P', 'str',varb.get()))
    # e5.config(validate ="key", validatecommand =(reg, '%P', 'str',varb.get()))
    
    e1.config(validate ="key", validatecommand =(reg, '%P', 'int',varb))
    e2.config(validate ="key", validatecommand =(reg, '%P', 'int',varb))
    e3.config(validate ="key", validatecommand =(reg, '%P', 'int',varb))
    e4.config(validate ="key", validatecommand =(reg, '%P', 'str',varb))
    e5.config(validate ="key", validatecommand =(reg, '%P', 'str',varb))
    
    solve = tk.Button(mainwindow, text='solve!', command=lambda: [values()]).grid(row=7, column=1, sticky=tk.W, pady=4)
    
    
    

    def values():
        
        try:
            a = varb.get()
            print(a)
            
            readN = getN.get()
            readi0 = geti0.get()
            readr0 = getr0.get()
            # readbeta = float(getbeta.get())
            # readgamma = float(getgamma.get())
    
            # readbeta_ = getbeta.get()
            
            # if readbeta_[0] == '/':
            #     readbeta_ = readbeta_[1:]
            # readbeta = eval(readbeta_,{"builtins": {}})
            readbeta = eval(getbeta.get(),{"builtins": {}})
            readgamma = eval(getgamma.get(), {"builtins": {}})
            
            
            
            intN = int(readN)
            inti0 = int(readi0)
            intr0 = int(readr0)
            intbeta = float(readbeta)
            intgamma = float(readgamma)
            
            print('varb : ', varb.get(),
                  '
N : ', intN,
                  '
iO : ',inti0,
                  '
r0 : ',intr0,
                  '
beta : ',getbeta.get(),
                  '
gamma : ',getgamma.get())
            
            # Total population, N.
            N = readN
            # Initial number of infected and recovered individuals, I0 and R0.
            I0, R0 = readi0, readr0
            # Everyone else, S0, is susceptible to infection initially.
            S0 = N - I0 - R0
            # Contact rate, beta, and mean recovery rate, gamma, (in 1/days).
            beta, gamma = readbeta, readgamma
            # A grid of time points (in days)
            t = np.linspace(0, 160, 160)
    
            # The SIR model differential equations.
            def deriv(y, t, N, beta, gamma):
                S, I, R = y
                dS = ((-beta * S * I) / N)
                dI = ((beta * S * I) / N) - (gamma * I)
                dR = (gamma * I)
                return dS, dI, dR
            
            # Initial conditions are S0, I0, R0
            # Integrate the SIR equations over the time grid, t.
            solve = odeint(deriv, (S0, I0, R0), t, args=(N, beta, gamma))
            S, I, R = solve.T
            
            # Plot the data on three separate curves for S(t), I(t) and R(t)
            fig = plt.figure(facecolor='w')
            ax = fig.add_subplot(111, facecolor='#dddddd', axisbelow=True)
            ax.plot(t, S/1000, 'b', alpha=0.5, lw=2, label='Susceptible')
            ax.plot(t, I/1000, 'r', alpha=0.5, lw=2, label='Infected')
            ax.plot(t, R/1000, 'g', alpha=0.5, lw=2, label='Recovered with immunity')
            ax.set_xlabel('Time /days')
            ax.set_ylabel('Number (1000s)')
            ax.set_ylim(0,1.2)
            ax.yaxis.set_tick_params(length=0)
            ax.xaxis.set_tick_params(length=0)
            ax.grid(b=True, which='major', c='w', lw=2, ls='-')
            legend = ax.legend()
            legend.get_frame().set_alpha(0.5)
            for spine in ('top', 'right', 'bottom', 'left'):
                ax.spines[spine].set_visible(False)
            plt.show()
            return
            
        except:
            print('maybe wrong values !!!!!!!!')
            return
        
    mainwindow.mainloop()
    
mainwindow()

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