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📜  Python中的 Matplotlib.axes.Axes.get_ymajorticklabels()

📅  最后修改于: 2022-05-13 01:54:53.536000             🧑  作者: Mango

Python中的 Matplotlib.axes.Axes.get_ymajorticklabels()

Matplotlib是Python中的一个库,它是 NumPy 库的数值数学扩展。 Axes 类包含大部分图形元素:Axis、Tick、Line2D、Text、Polygon 等,并设置坐标系。 Axes 的实例通过回调属性支持回调。

matplotlib.axes.Axes.get_ymajorticklabels()函数

matplotlib 库的 axes 模块中的Axes.get_ymajorticklabels()函数用于返回主要的 y 刻度标签。

下面的示例说明了 matplotlib.axes 中的 matplotlib.axes.Axes.get_ymajorticklabels()函数:

示例 1:

# Implementation of matplotlib function
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
  
  
fig, ax = plt.subplots()
ax.plot(range(12, 24), range(12))
ax.set_yticks((2, 5, 7, 10))
ax.set_yticklabels(("Label-1", "Label-2", 
                    "Label-3", "Label-4"))
    
w = ax.get_ymajorticklabels()
ax.text(16, 10, "ymajorticklabels values : ",
        fontweight ="bold")
x = 10
for i in list(w):
    ax.text(16, x-1, str(i), fontweight ="bold")
    x-= 1
   
fig.suptitle('matplotlib.axes.Axes.get_ymajorticklabels()\
 function Example\n\n', fontweight ="bold")
plt.show()

输出:

示例 2:

# Implementation of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.gridspec as gridspec
  
  
fs = 1000
t = np.linspace(0, 0.3, 301)
A = np.array([2, 8]).reshape(-1, 1)
f = np.array([150, 140]).reshape(-1, 1)
xn = (A * np.sin(2 * np.pi * f * t)).sum(axis = 0)
xn += 5 * np.random.randn(*t.shape)
   
fig, ax = plt.subplots()
   
yticks = [-40, -15, 10]
   
ax.psd(xn, NFFT = 301,
       Fs = fs,
       window = mlab.window_none,
       pad_to = 1024,
       scale_by_freq = True)
  
ax.set_yticks(yticks)
ax.set_yticklabels(("Low-1", "High", "Low-2"))
ax.grid(True)
   
w = ax.get_ymajorticklabels()
ax.text(200, 8, "ymajorticklabels values : ", 
        fontweight ="bold")
x = 8
for i in list(w):
    ax.text(200, x-3, str(i), fontweight ="bold")
    x-= 3
  
fig.suptitle('matplotlib.axes.Axes.get_ymajorticklabels()\
 function Example\n\n', fontweight ="bold")
plt.show()

输出: