顯示tkinter小部件中的變量值

Question

我正在爲使用Tkinter編寫python中的交互式數據擬合程序。 我想：

1. 變化的擬合曲線的起始參數（即，猜測參數）手動形成鍵盤和以從良好的點開始的裝配程序，在實驗數據繪製相應曲線（實現）

2. 顯示在插件的參數的實際值，因爲我通過

我搜索網，我發現之間的一些連接鍵盤（未實現）進行更改我的問題和文本部件或輸入部件..

有沒有人有一個很好的解決方案？

這是修改代碼，因爲我們正在擬合一個簡單的指數，複製/粘貼運行並嘗試（使用'r'，'t'，'y'，'f'，'g'，'h'鍵修改PARAMS）..

import Tkinter as Tkinter 
from Tkinter import * 
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg 
from matplotlib.figure import Figure 
from numpy import * #exp,arange,sin,arctan,where 
import sys 
from scipy.optimize import leastsq 

class App: 

def __init__(self, master,slave,folnam,q_index,tab): 
    # Create containers 
    self.frame = Tkinter.Frame(master) 
    self.frame2 = Tkinter.Text(slave,width=10,height=10) 
    self.frame2.pack() 

    self.q_index=q_index 

    # Create buttons and bindings 
    self.button_quit = Tkinter.Button(self.frame,text="QUIT", command=master.destroy) 
    self.button_quit.pack(side="left") 
    self.button_fit = Tkinter.Button(self.frame, text= 'fit!') 
    self.button_fit.pack() 

    self.frame.bind_all("<Key>", self.decrease_a,'+') ################################# 


    # Fill with Data 
    self.t = arange(1000)*.001 
    self.data_to_fit = exp(-self.t) 
    self.A=max(self.data_to_fit)-min(self.data_to_fit) 
    self.B=min(self.data_to_fit) 

    # Build Figure 
    fig = Figure() 
    self.ax = fig.add_subplot(111) 
    self.ax.set_ylim(min(self.data_to_fit), max(self.data_to_fit))  
    self.p = [self.A,self.B,.9,.5,10.,5.] 
    self.line, = self.ax.plot(self.t[1:],abs(self.schultz(self.t[1:], 1., self.p)),'.-') #tuple of a single element 
    self.canvas = FigureCanvasTkAgg(fig,master=master) 
    self.ax.plot(self.t[1:],self.data_to_fit[1:]) 
    self.canvas.show() 
    self.canvas.get_tk_widget().pack(side='top', fill='both', expand=1) 
    self.frame.pack() 




def schultz(self, t, q, p): 
    Z=.1 
    A, B, alpha, D, vm, sigma = p 
    Z = ((sigma/vm)**-2)-1. 
    Lambda = q*vm*t/(Z+1) 
    g = ((Z+1)/(Z*q*vm*t))*sin(Z*arctan(Lambda))/(1+Lambda**2)**(Z/2.) 
    where(abs(t)>0., g, 1.) 
    f = exp(-q**2*D*t)*((1.-alpha)+alpha*g) 
    y = A*f+B 
    return y 




def decrease_a(self,event): 

    # Raise/lower amplitude with 'a', 'q' keys 
    if event.char=='a': 
     self.ax.get_ylim()   
     self.p[0]-= 1e10 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 
    if event.char=='q': 
     self.ax.get_ylim()   
     self.p[0]+=1e10 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 
    # Raise/lower baseline with 's', 'w' keys 
    if event.char=='s': 
     self.ax.get_ylim()   
     self.p[1]-= 1e10 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw()  
    if event.char=='w': 
     self.ax.get_ylim()   
     self.p[1]+= 1e10 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 
    # Raise/lower alpha with 'd', 'e' keys 
    if event.char=='d': 
     self.ax.get_ylim()   
     self.p[2]-= .05 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 
    if event.char=='e': 
     self.ax.get_ylim()   
     self.p[2]+= .05 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 
    # Raise/lower diffusion coefficient with 'f', 'r' keys 
    if event.char=='f': 
     self.ax.get_ylim()   
     self.p[3]-= .05 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 
    if event.char=='r': 
     self.ax.get_ylim()   
     self.p[3]+= .05 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 
    # Raise/lower average speed with 'g', 't' keys 
    if event.char=='g': 
     self.ax.get_ylim()   
     self.p[2]-= .05 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 

    if event.char=='t': 
     self.ax.get_ylim()   
     self.p[2]+= .05 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 
    # Raise/lower variance of speed distribution with 'h', 'y' keys 
    if event.char=='h': 
     self.ax.get_ylim()   
     self.p[2]-= .01 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 
    if event.char=='y': 
     self.ax.get_ylim()   
     self.p[2]+= .01 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 

root = Tkinter.Tk() 
root2 = Tkinter.Tk() 
app = App(root,root2,'/home/copo/Scrivania/correlazioni_da_fit',q_index=10, tab=False) 

root.mainloop() 

Answer 1

我不知道我完全理解這個問題，但如果你正在想這樣做是爲了顯示self.p的值，你可以做，在任何數量的方式。例如，您可以在每次更改參數時更新標籤。例如：

self.p0_label = Tkinter.Label(...) 
self.p1_label = Tkinter.Label(...) 
... 
def decrease_a(self,event): 
    if event.char=='a': 
     self.ax.get_ylim()   
     self.p[0]-= 1e10 
     self.line.set_ydata(self.schultz(self.t[1:], 1, self.p)) 
     self.line.set_xdata(self.t[1:])   
     self.canvas.draw() 
    ... 
    self.update_display() 

def update_display(self): 
    self.p0_label.configure(text=str(self.p[0])) 
    self.p1_label.configure(text=str(self.p[1])) 
    ...