(python) 如何使框架适合情节

Question

我使用了以下代码（与我的问题相关）

fig = plt.Figure()                       
canvas = FigureCanvasTkAgg(fig, root)     
canvas.get_tk_widget().grid(row=6, column=0, columnspan=3, rowspan=3, sticky=W+E+N+S, padx=0, pady=0)  
ax = fig.add_subplot(111)
fig.subplots_adjust(bottom=0.25)
X = np.arange(1,rec[1]+1,1)
Y = np.arange(1,rec[0]+1,1)
x , y = np.meshgrid(X,Y)  
    

# Initial plots
con = ax.contourf(x,y,dfs[1], levels = levels, cmap=cm.jet, alpha = 0.5, antialiased = True)
cbar = fig.colorbar(con, ax=ax)
ax.axis([1, 12, 1, 6])
ax.axis('equal')        #<-------------------------
ax.set_title('Frequency: f=%f' %float(avector[1]))

此处ax.axis('equal')设置x轴和y轴相同的比例。我得到了以下情节

如何使框架适合蓝色图？（我要去掉白色区域）

谢谢！

完整代码：

以下代码用于绘制 tkinter 上五个（滑块：0 ~ 4）.xlsx 文件的等高线。每个文件只包含矩阵 12X6 中的数值数据，例如

from tkinter import *
import tkinter.ttk as ttk
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
import ipywidgets as wg
import os
import pandas as pd
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
import math
from matplotlib.ticker import LinearLocator
%matplotlib widget
from matplotlib.widgets import Slider
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
matplotlib.use('TkAgg')

root = Tk()
root.title('TEST')
root.geometry("800x800")

cbar = None
def plot_noise():
    # ============================================Read .xlsx file=====================================
    folder = r'C:\Users\Dian-Jing Chen\Desktop\Work\test_read'
    files = os.listdir(folder)
    dfs = {}
    for i, file in enumerate(files):
        if file.endswith('.xlsx'):
            dfs[i] = pd.read_excel(os.path.join(folder,file), sheet_name='Z=143', header = None, skiprows=[0], usecols = "B:M")

    num = i + 1
    rec = np.shape(dfs[0])
    rmm = np.concatenate([dfs[0], dfs[1]])
    for jj in range(2,num):
        rmm = np.concatenate([rmm, dfs[jj]])
    # =================================================PLOT===========================================
    fig, ax = plt.subplots()                       
    canvas = FigureCanvasTkAgg(fig, root)     
    canvas.get_tk_widget().grid(row=3, column=0, columnspan=3, rowspan=3, sticky=W+E+N+S, padx=0, pady=0) 
    # ===============================================contourf=========================================          
    fig.subplots_adjust(bottom=0.25)          
    X = np.arange(1,rec[1]+1,1)
    Y = np.arange(1,rec[0]+1,1)
    x , y = np.meshgrid(X,Y)  
    # ==============================================color bar=========================================
    cbar_max = math.floor(np.min(rmm))
    cbar_min = math.ceil(np.max(rmm))
    cbar_num_colors = 200
    cbar_num_format = "%d"
    levels = MaxNLocator(nbins=cbar_num_colors).tick_values(cbar_min, cbar_max)
    # ============================================Initial plot======================================== 
    con = ax.contourf(x,y,dfs[1], levels = levels, cmap=cm.jet, alpha = 0.5, antialiased = True)  
    cbar = fig.colorbar(con,ax = ax)
    ax.axis([1, 12, 1, 6])
    ax.axis('equal')
    # ================================================Slider==========================================
    slider_bar = fig.add_axes([0.12, 0.1, 0.78, 0.03])     
    slider_de = Slider(slider_bar, 's_bar', 0, num-1, valinit=1,valfmt='%0.0f',  valstep=1)
    num_on_slider = []
    def update(val):
        num_on_slider.append(slider_de.val)
        for ii in range(0,num):
            if num_on_slider[-1] == ii:
                con = ax.contourf(x,y,dfs[ii], levels = levels, cmap=cm.jet, alpha = 0.5, antialiased = True)
                ax.axis([1, 12, 1, 6])
                ax.axis('equal')
                
    slider_de.on_changed(update)            
                
    
# =================================================GUI - Tkinter======================================= 
resultButton = ttk.Button(root, text = 'show', command = plot_noise)
resultButton.grid(column=0, row=1, pady=15, sticky=W)

root.mainloop()

Answer 1

您可以更改此行：

ax.axis('equal')

至：

ax.set_aspect('equal', 'box')

输出：提示：我对这个图使用了假数据！

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