如何使用 tkinter GUI 可视化 matplotlib 预测？
How to Visualize a matplotlib prediction using a tkinter GUI?
我有一个场景，我对特定数据集进行了预测。现在我想使用 Tkinter 可视化预测图。
我的机器学习模型如下图所示：
# Importing Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
# Importing the Batsmen Dataset
dataset = pd.read_csv('Batsmen/Batsmen.csv')
X = dataset.iloc[:, [1, 2, 3, 4, 5, 6]].values
# Using Elbow Method to find the optimal number of Clusters
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)
kmeans.fit(X)
wcss.append(kmeans.inertia_)
plt.plot(range(1, 11), wcss)
plt.title('The Elbow Method')
plt.xlabel('Number of Clusters')
plt.ylabel('WCSS')
plt.show()
我试过如下图：
from tkinter import *
# these four imports are important
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
def plot():
# Importing Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
# Importing the Batsmen Dataset
dataset = pd.read_csv('Batsmen/Batsmen.csv')
X = dataset.iloc[:, [1, 2, 3, 4, 5, 6]].values
# Using Elbow Method to find the optimal number of Clusters
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)
kmeans.fit(X)
wcss.append(kmeans.inertia_)
root = Tk()
def app():
# initialise a window.
root = Tk()
root.config(background='white')
root.geometry("1000x700")
lab = Label(root, text="Live Plotting", bg = 'white').pack()
fig = Figure()
ax = fig.add_subplot(111)
ax.set_title('The Elbow Method')
ax.set_xlabel('Number of Clusters')
ax.set_ylabel('WCSS')
ax.grid()
graph = FigureCanvasTkAgg(fig, master=root)
graph.get_tk_widget().pack(side="top",fill='both',expand=True)
def plotter():
ax.cla()
ax.grid()
dpts = plot()
ax.plot(range(1, 11), wcss, marker='o', color='orange')
graph.draw()
time.sleep(1)
def gui_handler():
threading.Thread(target=plotter).start()
b = Button(root, text="Start/Stop", command=gui_handler, bg="red", fg="white")
b.pack()
root.mainloop()
if __name__ == '__main__':
app()
但是没有效果！
我只想在 Tkinter GUI 上显示预测，当我按下 GUI 中的预测按钮时，我希望图形显示在 GUI 中 canvas。所以，谁能帮我做同样的事情。
如果我使用 root.after() 而不是线程，你的代码对我有用。
可能在大多数 GUI 框架中，线程应该（或不能）更改 GUI 中的元素。
在我的计算机上，当我按下 运行 线程的按钮时，带线程的 yoru 代码结束工作。
I 运行 plotter whic 不使用 while 和 sleep 但 after(1000, plotter) 到 运行 1000 毫秒后再次使用它（1s )
from tkinter import *
from random import randint
# these two imports are important
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
continuePlotting = False
def change_state():
global continuePlotting
if continuePlotting == True:
continuePlotting = False
else:
continuePlotting = True
def data_points():
f = open("data.txt", "w")
for i in range(10):
f.write(str(randint(0, 10))+'\n')
f.close()
f = open("data.txt", "r")
data = f.readlines()
f.close()
l = []
for i in range(len(data)):
l.append(int(data[i].rstrip("\n")))
return l
def app():
# initialise a window.
root = Tk()
root.config(background='white')
root.geometry("1000x700")
lab = Label(root, text="Live Plotting", bg = 'white').pack()
fig = Figure()
ax = fig.add_subplot(111)
ax.set_xlabel("X axis")
ax.set_ylabel("Y axis")
ax.grid()
graph = FigureCanvasTkAgg(fig, master=root)
graph.get_tk_widget().pack(side="top",fill='both',expand=True)
def plotter():
if continuePlotting:
ax.cla()
ax.grid()
dpts = data_points()
ax.plot(range(10), dpts, marker='o', color='orange')
graph.draw()
root.after(1000, plotter)
def gui_handler():
change_state()
plotter()
b = Button(root, text="Start/Stop", command=gui_handler, bg="red", fg="white")
b.pack()
root.mainloop()
if __name__ == '__main__':
app()
编辑： 在新代码中你没有 运行 循环所以你不需要线程或 after()
但是你还有其他基本问题：
你的缩进错误，mainloop() 在 app() 之外 - 它在 app()
之前执行
在以前的版本中 plotter 在 app() 里面 - 如果你需要它在外面那么你有局部变量的问题 - 比如 ax - 你必须使用 global 可以在其他函数中访问这些变量。或者你必须 运行 函数使用这个值作为参数 - 即。 plotter(ax, wcss, graph)
这个版本使用 global 并且它适用于我。我没有你的 CSV，我不想 运行 sklearn 所以我放了一些假数据。
from tkinter import *
# these four imports are important
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
def plot():
# Importing Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
# Importing the Batsmen Dataset
# dataset = pd.read_csv('Batsmen/Batsmen.csv')
dataset = pd.DataFrame({
'a': range(10),
'b': range(10),
'c': range(10),
'd': range(10),
'e': range(10),
'f': range(10),
'g': range(10),
})
X = dataset.iloc[:, [1, 2, 3, 4, 5, 6]].values
# Using Elbow Method to find the optimal number of Clusters
from sklearn.cluster import KMeans
global wcss
wcss = range(1, 11)
#wcss = []
# for i in range(1, 11):
# kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)
# kmeans.fit(X)
# wcss.append(kmeans.inertia_)
def plotter():
global wcss
global ax
global graph
ax.cla()
ax.grid()
dpts = plot()
ax.plot(range(1, 11), wcss, marker='o', color='orange')
graph.draw()
def gui_handler():
plotter()
def app():
global ax
global graph
# initialise a window.
root = Tk()
root.config(background='white')
root.geometry("1000x700")
lab = Label(root, text="Live Plotting", bg = 'white').pack()
fig = Figure()
ax = fig.add_subplot(111)
ax.set_title('The Elbow Method')
ax.set_xlabel('Number of Clusters')
ax.set_ylabel('WCSS')
ax.grid()
graph = FigureCanvasTkAgg(fig, master=root)
graph.get_tk_widget().pack(side="top",fill='both',expand=True)
b = Button(root, text="Start/Stop", command=gui_handler, bg="red", fg="white")
b.pack()
root.mainloop()
if __name__ == '__main__':
app()
我有一个场景，我对特定数据集进行了预测。现在我想使用 Tkinter 可视化预测图。
我的机器学习模型如下图所示：
# Importing Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
# Importing the Batsmen Dataset
dataset = pd.read_csv('Batsmen/Batsmen.csv')
X = dataset.iloc[:, [1, 2, 3, 4, 5, 6]].values
# Using Elbow Method to find the optimal number of Clusters
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)
kmeans.fit(X)
wcss.append(kmeans.inertia_)
plt.plot(range(1, 11), wcss)
plt.title('The Elbow Method')
plt.xlabel('Number of Clusters')
plt.ylabel('WCSS')
plt.show()
我试过如下图：
from tkinter import *
# these four imports are important
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
def plot():
# Importing Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
# Importing the Batsmen Dataset
dataset = pd.read_csv('Batsmen/Batsmen.csv')
X = dataset.iloc[:, [1, 2, 3, 4, 5, 6]].values
# Using Elbow Method to find the optimal number of Clusters
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)
kmeans.fit(X)
wcss.append(kmeans.inertia_)
root = Tk()
def app():
# initialise a window.
root = Tk()
root.config(background='white')
root.geometry("1000x700")
lab = Label(root, text="Live Plotting", bg = 'white').pack()
fig = Figure()
ax = fig.add_subplot(111)
ax.set_title('The Elbow Method')
ax.set_xlabel('Number of Clusters')
ax.set_ylabel('WCSS')
ax.grid()
graph = FigureCanvasTkAgg(fig, master=root)
graph.get_tk_widget().pack(side="top",fill='both',expand=True)
def plotter():
ax.cla()
ax.grid()
dpts = plot()
ax.plot(range(1, 11), wcss, marker='o', color='orange')
graph.draw()
time.sleep(1)
def gui_handler():
threading.Thread(target=plotter).start()
b = Button(root, text="Start/Stop", command=gui_handler, bg="red", fg="white")
b.pack()
root.mainloop()
if __name__ == '__main__':
app()
但是没有效果！
我只想在 Tkinter GUI 上显示预测，当我按下 GUI 中的预测按钮时，我希望图形显示在 GUI 中 canvas。所以，谁能帮我做同样的事情。
如果我使用 root.after() 而不是线程，你的代码对我有用。
可能在大多数 GUI 框架中，线程应该（或不能）更改 GUI 中的元素。
在我的计算机上，当我按下 运行 线程的按钮时，带线程的 yoru 代码结束工作。
I 运行 plotter whic 不使用 while 和 sleep 但 after(1000, plotter) 到 运行 1000 毫秒后再次使用它（1s )
from tkinter import *
from random import randint
# these two imports are important
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
continuePlotting = False
def change_state():
global continuePlotting
if continuePlotting == True:
continuePlotting = False
else:
continuePlotting = True
def data_points():
f = open("data.txt", "w")
for i in range(10):
f.write(str(randint(0, 10))+'\n')
f.close()
f = open("data.txt", "r")
data = f.readlines()
f.close()
l = []
for i in range(len(data)):
l.append(int(data[i].rstrip("\n")))
return l
def app():
# initialise a window.
root = Tk()
root.config(background='white')
root.geometry("1000x700")
lab = Label(root, text="Live Plotting", bg = 'white').pack()
fig = Figure()
ax = fig.add_subplot(111)
ax.set_xlabel("X axis")
ax.set_ylabel("Y axis")
ax.grid()
graph = FigureCanvasTkAgg(fig, master=root)
graph.get_tk_widget().pack(side="top",fill='both',expand=True)
def plotter():
if continuePlotting:
ax.cla()
ax.grid()
dpts = data_points()
ax.plot(range(10), dpts, marker='o', color='orange')
graph.draw()
root.after(1000, plotter)
def gui_handler():
change_state()
plotter()
b = Button(root, text="Start/Stop", command=gui_handler, bg="red", fg="white")
b.pack()
root.mainloop()
if __name__ == '__main__':
app()
编辑： 在新代码中你没有 运行 循环所以你不需要线程或 after()
但是你还有其他基本问题：
你的缩进错误，mainloop() 在 app() 之外 - 它在 app()
在以前的版本中 plotter 在 app() 里面 - 如果你需要它在外面那么你有局部变量的问题 - 比如 ax - 你必须使用 global 可以在其他函数中访问这些变量。或者你必须 运行 函数使用这个值作为参数 - 即。 plotter(ax, wcss, graph)
这个版本使用 global 并且它适用于我。我没有你的 CSV，我不想 运行 sklearn 所以我放了一些假数据。
from tkinter import *
# these four imports are important
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
def plot():
# Importing Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
# Importing the Batsmen Dataset
# dataset = pd.read_csv('Batsmen/Batsmen.csv')
dataset = pd.DataFrame({
'a': range(10),
'b': range(10),
'c': range(10),
'd': range(10),
'e': range(10),
'f': range(10),
'g': range(10),
})
X = dataset.iloc[:, [1, 2, 3, 4, 5, 6]].values
# Using Elbow Method to find the optimal number of Clusters
from sklearn.cluster import KMeans
global wcss
wcss = range(1, 11)
#wcss = []
# for i in range(1, 11):
# kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)
# kmeans.fit(X)
# wcss.append(kmeans.inertia_)
def plotter():
global wcss
global ax
global graph
ax.cla()
ax.grid()
dpts = plot()
ax.plot(range(1, 11), wcss, marker='o', color='orange')
graph.draw()
def gui_handler():
plotter()
def app():
global ax
global graph
# initialise a window.
root = Tk()
root.config(background='white')
root.geometry("1000x700")
lab = Label(root, text="Live Plotting", bg = 'white').pack()
fig = Figure()
ax = fig.add_subplot(111)
ax.set_title('The Elbow Method')
ax.set_xlabel('Number of Clusters')
ax.set_ylabel('WCSS')
ax.grid()
graph = FigureCanvasTkAgg(fig, master=root)
graph.get_tk_widget().pack(side="top",fill='both',expand=True)
b = Button(root, text="Start/Stop", command=gui_handler, bg="red", fg="white")
b.pack()
root.mainloop()
if __name__ == '__main__':
app()