在 tkinter GUI 中解决简单的 ODE 系统
Solving simple ODE system within tkinter GUI
我有一个简单的 ODE 系统用于 SIR 疾病模型，它运行良好并生成图形。但是，我正在尝试使用 tkinter 创建一个简单的弹出框，该框采用参数值，而不必通过脚本将它们放入。
这里是原代码
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 GUI 的那个
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()
第一个给出了预期的情节：
，但是对于 GUI，它给出了这个：
我的代码哪里出错了？解决系统的代码没有改变，我只是设置它以便参数采用我在弹出框中输入的值。 lambda 函数有问题吗？
你好用 beta 和 gamma 尝试了你的代码 2/7，1/7 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 我得到：
希望知道如何使用分数作为输入的人出现，
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)
但得到了ValueError: could not convert string to float: '2/7'
对于readbeta = float(getbeta.get())
通过使用 eval 允许输入“2/7”和“1/7”作为 beta 和 gamma，参见 How can I get the data from Entry in tkinter that can be used as function?
此处代码已更新：
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": {}})
我在某处读到在 python 中使用 eval 是不安全的所以如果有人有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 函数之前验证输入，因此代码应该是安全的（或不安全？？请在这里帮忙）；
这里是新代码：
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()
允许 beta 和 gamma 将浮点数（即 0.28）或分数（即 2/7）作为输入插入小部件框
开始享受 Tkinter，这是另一个改进版本，带有 RadioButtons 控制允许的输入类型：
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(),
'\nN : ', intN,
'\niO : ',inti0,
'\nr0 : ',intr0,
'\nbeta : ',getbeta.get(),
'\ngamma : ',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()
我有一个简单的 ODE 系统用于 SIR 疾病模型，它运行良好并生成图形。但是，我正在尝试使用 tkinter 创建一个简单的弹出框，该框采用参数值，而不必通过脚本将它们放入。
这里是原代码
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 GUI 的那个
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()
第一个给出了预期的情节：
，但是对于 GUI，它给出了这个：
我的代码哪里出错了？解决系统的代码没有改变，我只是设置它以便参数采用我在弹出框中输入的值。 lambda 函数有问题吗？
你好用 beta 和 gamma 尝试了你的代码 2/7，1/7 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 我得到：
希望知道如何使用分数作为输入的人出现，
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)
但得到了ValueError: could not convert string to float: '2/7'
对于readbeta = float(getbeta.get())
通过使用 eval 允许输入“2/7”和“1/7”作为 beta 和 gamma，参见 How can I get the data from Entry in tkinter that can be used as function?
此处代码已更新：
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": {}})
我在某处读到在 python 中使用 eval 是不安全的所以如果有人有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 函数之前验证输入，因此代码应该是安全的（或不安全？？请在这里帮忙）；
这里是新代码：
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()
允许 beta 和 gamma 将浮点数（即 0.28）或分数（即 2/7）作为输入插入小部件框
开始享受 Tkinter，这是另一个改进版本，带有 RadioButtons 控制允许的输入类型：
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(),
'\nN : ', intN,
'\niO : ',inti0,
'\nr0 : ',intr0,
'\nbeta : ',getbeta.get(),
'\ngamma : ',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()