plt.annotate() 在圖中標注文字

plt.annotate()函數用於標注文字

plt.annotate(s,sy,*args,**kwargs)

參數解釋：

• s 為注釋文本內容

• xy 為被注釋的坐標點

• xytext 為注釋文字的坐標位置

• xycoords 參數如下:

  • figure points：圖左下角的點
  • figure pixels：圖左下角的像素
  • figure fraction：圖的左下部分
  • axes points：坐標軸左下角的點
  • axes pixels：坐標軸左下角的像素
  • axes fraction：左下軸的分數
  • data：使用被注釋對象的坐標系統(默認)
  • polar(theta,r)：if not native ‘data’ coordinates t

• weight 設置字體線型

  • {‘ultralight’, ‘light’, ‘normal’, ‘regular’, ‘book’, ‘medium’, ‘roman’, ‘semibold’, ‘demibold’, ‘demi’, ‘bold’, ‘heavy’, ‘extra bold’, ‘black’}

• color 設置字體顏色

  • {‘b’, ‘g’, ‘r’, ‘c’, ‘m’, ‘y’, ‘k’, ‘w’}
  • ‘black’,'red’等
  • [0,1]之間的浮點型數據
  • RGB或者RGBA, 如: (0.1, 0.2, 0.5)、(0.1, 0.2, 0.5, 0.3)等

• arrowprops #箭頭參數,參數類型為字典dict

  • width：箭頭的寬度(以點為單位)
  • headwidth：箭頭底部以點為單位的寬度
  • headlength：箭頭的長度(以點為單位)
  • shrink：總長度的一部分，從兩端“收縮”
  • facecolor：箭頭顏色

• bbox給標題增加外框 ，常用參數如下：

• boxstyle：方框外形
• facecolor：(簡寫fc)背景顏色
• edgecolor：(簡寫ec)邊框線條顏色
• edgewidth：邊框線條大小

例子：

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 6)
y = x * x

plt.plot(x, y, marker='o')
for xy in zip(x, y):
    plt.annotate("(%s,%s)" % xy, xy=xy, xytext=(-20, 10), textcoords='offset points')
plt.show()

 

 

 

weight參數

plt.plot(x, y, marker='o')
for xy in zip(x, y):
    plt.annotate("(%s,%s)" % xy, xy=xy, xytext=(-20, 10), textcoords='offset points', weight='heavy')
plt.show()

 

 把arrowprops參數改成通過dict傳入參數(facecolor = “r”, headlength = 10, headwidth = 30, width = 20)

plt.plot(x, y, marker='o')
for xy in zip(x, y):
    plt.annotate("(%s,%s)" % xy, xy=xy, xytext=(-20, 10), textcoords='offset points', 
    arrowprops = dict(facecolor = "r", headlength = 10, headwidth = 30, width = 20))
plt.show()

 

 把bbox參數改成通過dict傳入參數(boxstyle=‘round,pad=0.5’, fc=‘yellow’, ec=‘k’,lw=1 ,alpha=0.5)

plt.plot(x, y, marker='o')
for xy in zip(x, y):
    plt.annotate("(%s,%s)" % xy, xy=xy, xytext=(-20, 10), textcoords='offset points', 
    bbox=dict(boxstyle='round,pad=0.5', fc='yellow', ec='k', lw=1, alpha=0.5))
plt.show()

 

 arrowprops=dict(arrowstyle='-|>',connectionstyle='arc3',color='red')

### 可視化預測新樣本

plt.figure()
## new point 1
x_fearures_new1 = np.array([[0, -1]])
plt.scatter(x_fearures_new1[:,0],x_fearures_new1[:,1], s=50, cmap='viridis')
plt.annotate(s='New point 1',xy=(0,-1),xytext=(-2,0),color='blue',arrowprops=dict(arrowstyle='-|>',connectionstyle='arc3',color='red'))

## new point 2
x_fearures_new2 = np.array([[1, 2]])
plt.scatter(x_fearures_new2[:,0],x_fearures_new2[:,1], s=50, cmap='viridis')
plt.annotate(s='New point 2',xy=(1,2),xytext=(-1.5,2.5),color='red',arrowprops=dict(arrowstyle='-|>',connectionstyle='arc3',color='red'))

## 訓練樣本
plt.scatter(x_fearures[:,0],x_fearures[:,1], c=y_label, s=50, cmap='viridis')
plt.title('Dataset')

# 可視化決策邊界
plt.contour(x_grid, y_grid, z_proba, [0.5], linewidths=2., colors='blue')

plt.show()