如何解决tkinter GUI
我已经构建了简单的tkinter GUI。 现在，我试图可视化3个不同的图形（通过使用不同的变量调用相同的函数）并将它们放置在GUI的3个不同行中。
当我这样做时，我遇到两个问题：
- 每次运行脚本（interface.py）时，都会得到2个窗口-GUI和外部图形窗口。如何摆脱第二个？
- 我无法可视化所有三个图表。该脚本在显示第一个脚本后停止。我相信这是因为第一个图是循环工作的（迭代了大量数据点）。有没有解决的办法？
接口：
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 6 10:24:35 2020
@author: Dar0
"""
from tkinter import * #import tkinter module
from visualizer import main #import module 'visualizer' that shows the graph in real time
class Application(Frame):
''' Interface for visualizing graphs,indicators and text-box. '''
def __init__(self,master):
super(Application,self).__init__(master)
self.grid()
self.create_widgets()
def create_widgets(self):
# Label of the 1st graph
Label(self,text='Hook Load / Elevator Height / Depth vs Time'
).grid(row = 0,column = 0,sticky = W)
# Graph 1 - Hook Load / Elevator Height / Depth vs Time
# button that displays the plot
#plot_button = Button(self,2
# command = main,# height = 2,# width = 10,# text = "Plot"
# ).grid(row = 1,sticky = W)
self.graph_1 = main(root,1,0)
# place the button
# in main window
# Label of the 2nd graph
Label(self,text = 'Hook Load / Elevator Height vs Time'
).grid(row = 3,sticky = W)
# Graph 2 - Hook Load / Elevator Height vs Time
self.graph_2 = main(root,4,0)
#Label of the 3rd graph
Label(self,text = 'Hook Load vs Time'
).grid(row = 6,sticky = W)
#Graph 3 - Hook Load vs Time
#Label of the 1st indicator
Label(self,text = '1st performance indicator'
).grid(row = 0,column = 1,sticky = W)
#1st performance indicator
#Label of 2nd performance indicator
Label(self,text = '2nd performance indicator'
).grid(row = 3,sticky = W)
#2nd performance indicator
#Label of 3rd performance indicator
Label(self,text = '3rd performance indicator'
).grid(row = 6,sticky = W)
#Text-box showing comments based on received data
self.text_box = Text(self,width = 50,height = 10,wrap = WORD)
self.text_box.grid(row = 9,columnspan = 1)
self.text_box.delete(0.0,END)
self.text_box.insert(0.0,'My message will be here.')
#Main part
root = Tk()
root.title('WiTSML Visualizer by Dar0')
app = Application(root)
root.mainloop()
可视化工具：
#WiTSML visualizer
#Created by Dariusz Krol
#import matplotlib
#matplotlib.use('TkAgg')
#from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg,NavigationToolbar2TkAgg
#from matplotlib.figure import Figure
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import random
class Visualizer(object):
""" Includes all the methods needed to show streamed data. """
def __init__(self):
self.file_path = 'C:/Anaconda/_my_files/witsml_reader/modified_witsml.csv' #Defines which file is streamed
self.datetime_mod = []
self.bpos_mod = []
self.woh_mod = []
self.torq_mod = []
self.spp_mod = []
self.depth_mod = []
self.flow_in_mod = []
self.rpm_mod = []
def open_file(self):
self.df = pd.read_csv(self.file_path,low_memory = False,nrows = 300000) #Opens the STREAMED file (already modified so that data convert is not required)
self.df = self.df.drop(0)
self.df = pd.DataFrame(self.df)
return self.df
def convert_dataframe(self):
self.df = self.df.values.T.tolist() #Do transposition of the dataframe and convert to list
#Columns are as following:
# - DATETIME
# - BPOS
# - WOH
# - TORQ
# - SPP
# - DEPTH
# - FLOW_IN
# - RPM
self.datetime_value = self.df[0]
self.bpos_value = self.df[1]
self.woh_value = self.df[2]
self.torq_value = self.df[3]
self.spp_value = self.df[4]
self.depth_value = self.df[5]
self.flow_in_value = self.df[5]
self.rpm_value = self.df[7]
return self.datetime_value,self.bpos_value,self.woh_value,self.torq_value,self.spp_value,self.depth_value,self.flow_in_value,self.rpm_value
#print(self.bpos_value)
def deliver_values(self,no_dp,columns):
''' Method gets no_dp amount of data points from the original file. '''
self.no_dp = no_dp #defines how many data points will be presented in the graph
val_dict = {
'datetime': [self.datetime_value,self.datetime_mod],'bpos': [self.bpos_value,self.bpos_mod],'woh': [self.woh_value,self.woh_mod],'torq': [self.torq_value,self.torq_mod],'spp': [self.spp_value,self.spp_mod],'depth': [self.depth_value,self.depth_mod],'flow_in': [self.flow_in_value,self.flow_in_mod],'rpm': [self.rpm_value,self.rpm_mod]
}
for item in columns:
if self.no_dp > len(val_dict[item][0]):
dp_range = len(val_dict[item][0])
else:
dp_range = self.no_dp
for i in range(dp_range):
val_dict[item][1].append(val_dict[item][0][i])
return self.datetime_mod,self.bpos_mod,self.woh_mod,self.torq_mod,self.spp_mod,self.depth_mod,self.flow_in_mod,self.rpm_mod
def show_graph2(self,tr_val,row,column):
from pylive_mod import live_plotter,live_plotter2
self.open_file()
self.convert_dataframe()
self.deliver_values(no_dp = 100000,columns = ['datetime','depth','bpos','woh'])
fst_p = 0
size = 300 # density of points in the graph (100 by default)
x_vec = self.datetime_mod[fst_p:size]
y_vec = self.depth_mod[fst_p:size]
y2_vec = self.bpos_mod[fst_p:size]
y3_vec = self.woh_mod[fst_p:size]
line1 = []
line2 = []
line3 = []
for i in range(self.no_dp):
#print(self.datetime_mod[i:6+i])
#print('Ostatni element y_vec: ',y_vec[-1])
#print(x_vec)
x_vec[-1] = self.datetime_mod[size+i]
y_vec[-1] = self.depth_mod[size+i]
y2_vec[-1] = self.bpos_mod[size+i]
y3_vec[-1] = self.woh_mod[size+i]
line1,line2,line3 = live_plotter2(tr_val,column,x_vec,y_vec,y2_vec,y3_vec,line1,line3)
x_vec = np.append(x_vec[1:],0.0)
y_vec = np.append(y_vec[1:],0.0)
y2_vec = np.append(y2_vec[1:],0.0)
y3_vec = np.append(y3_vec[1:],0.0)
def main(tr_val,column):
Graph = Visualizer()
Graph.open_file() #Opens the streamed file
Graph.convert_dataframe() #Converts dataframe to readable format
Graph.show_graph2(tr_val,column)
#Show us the graph
#main()
创建图形的函数：
def live_plotter2(tr_val,x_data,y1_data,y2_data,y3_data,line3,identifier='',pause_time=1):
if line1 == [] and line2 == [] and line3 == []:
# this is the call to matplotlib that allows dynamic plotting
plt.ion()
fig = plt.figure(figsize = (5,4),dpi = 100)
fig.subplots_adjust(0.15)
# -------------------- FIRST GRAPH --------------------
host = fig.add_subplot()
ln1 = host
ln2 = host.twinx()
ln3 = host.twinx()
ln2.spines['right'].set_position(('axes',1.))
ln3.spines['right'].set_position(('axes',1.12))
make_patch_spines_invisible(ln2)
make_patch_spines_invisible(ln3)
ln2.spines['right'].set_visible(True)
ln3.spines['right'].set_visible(True)
ln1.set_xlabel('Date & Time') #main x axis
ln1.set_ylabel('Depth') #left y axis
ln2.set_ylabel('Elevator Height')
ln3.set_ylabel('Weight on Hook')
#
x_formatter = FixedFormatter([x_data])
x_locator = FixedLocator([x_data[5]])
#ln1.xaxis.set_major_formatter(x_formatter)
ln1.xaxis.set_major_locator(x_locator)
#
ln1.locator_params(nbins = 5,axis = 'y')
ln1.tick_params(axis='x',rotation=90) #rotates x ticks 90 degrees down
ln2.axes.set_ylim(0,30)
ln3.axes.set_ylim(200,250)
line1,= ln1.plot(x_data,color = 'black',linestyle = 'solid',alpha=0.8,label = 'Depth')
line2,= ln2.plot(x_data,color = 'blue',linestyle = 'dashed',label = 'Elevator Height')
line3,= ln3.plot(x_data,color = 'red',label = 'Weight on Hook')
fig.tight_layout() #the graphs is not clipped on sides
plt.title('WiTSML Visualizer')
plt.grid(True)
#Shows legend
lines = [line1,line3]
host.legend(lines,[l.get_label() for l in lines],loc = 'lower left')
#Shows the whole graph
#plt.show()
#-------------------- Embedding --------------------
canvas = FigureCanvasTkAgg(fig,master=tr_val)
canvas.draw()
canvas.get_tk_widget().grid(row=row,column=column,ipadx=40,ipady=20)
# navigation toolbar
toolbarFrame = tk.Frame(master=tr_val)
toolbarFrame.grid(row=row,column=column)
toolbar = NavigationToolbar2Tk(canvas,toolbarFrame)
# after the figure,axis,and line are created,we only need to update the y-data
mod_x_data = convert_x_data(x_data,20)
line1.axes.set_xticklabels(mod_x_data)
line1.set_ydata(y1_data)
line2.set_ydata(y2_data)
line3.set_ydata(y3_data)
#Debugging
#rint('plt.lim: ',ln2.axes.get_ylim())
# adjust limits if new data goes beyond bounds
# limit for line 1
if np.min(y1_data)<=line1.axes.get_ylim()[0] or np.max(y1_data)>=line1.axes.get_ylim()[1]:
plt.ylim(0,10)
line1.axes.set_ylim([np.min(y1_data)-np.std(y1_data),np.max(y1_data)+np.std(y1_data)])
# limit for line 2
if np.min(y2_data)<=line2.axes.get_ylim()[0] or np.max(y2_data)>=line2.axes.get_ylim()[1]:
plt.ylim([np.min(y2_data)-np.std(y2_data),np.max(y2_data)+np.std(y2_data)])
#plt.ylim(0,25)
# limit for line 3
if np.min(y3_data)<=line3.axes.get_ylim()[0] or np.max(y3_data)>=line3.axes.get_ylim()[1]:
plt.ylim([np.min(y3_data)-np.std(y3_data),np.max(y3_data)+np.std(y3_data)])
#plt.ylim(0,25)
# Adds lines to the legend
#host.legend(lines,[l.get_label() for l in lines])
# this pauses the data so the figure/axis can catch up - the amount of pause can be altered above
plt.pause(pause_time)
# return line so we can update it again in the next iteration
return line1,line3
解决方法
关键是要在pyplot内绘制图形时不使用tkinter，如official example所示。改为使用matplotlib.figure.Figure（有关更多信息，请参见this）。
下面是一个最小示例，它沿着我在您的代码中看到的Text小部件绘制了3个独立图形：
import pandas as pd
import numpy as np
import tkinter as tk
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg,NavigationToolbar2Tk
from matplotlib.figure import Figure
class Graph(tk.Frame):
def __init__(self,master=None,title="",*args,**kwargs):
super().__init__(master,**kwargs)
self.fig = Figure(figsize=(4,3))
ax = self.fig.add_subplot(111)
df = pd.DataFrame({"values": np.random.randint(0,50,10)}) #dummy data
df.plot(ax=ax)
self.canvas = FigureCanvasTkAgg(self.fig,master=self)
self.canvas.draw()
tk.Label(self,text=f"Graph {title}").grid(row=0)
self.canvas.get_tk_widget().grid(row=1,sticky="nesw")
toolbar_frame = tk.Frame(self)
toolbar_frame.grid(row=2,sticky="ew")
NavigationToolbar2Tk(self.canvas,toolbar_frame)
root = tk.Tk()
for num,i in enumerate(list("ABC")):
Graph(root,title=i,width=200).grid(row=num//2,column=num%2)
text_box = tk.Text(root,width=50,height=10,wrap=tk.WORD)
text_box.grid(row=1,column=1,sticky="nesw")
text_box.delete(0.0,"end")
text_box.insert(0.0,'My message will be here.')
root.mainloop()
结果：
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