plt.annotate() 在图中标注文字

plt.annotate()函数用于标注文字

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

参数解释：

• s 为注释文本内容

• xy 为被注释的坐标点

• xytext 为注释文字的坐标位置

• xycoords 参数如下:

  • figure points：图左下角的点
  • figure pixels：图左下角的像素
  • figure fraction：图的左下部分
  • axes points：坐标轴左下角的点
  • axes pixels：坐标轴左下角的像素
  • axes fraction：左下轴的分数
  • data：使用被注释对象的坐标系统(默认)
  • polar(theta,r)：if not native ‘data’ coordinates t

• weight 设置字体线型

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

• color 设置字体颜色

  • {‘b’, ‘g’, ‘r’, ‘c’, ‘m’, ‘y’, ‘k’, ‘w’}
  • ‘black’,'red’等
  • [0,1]之间的浮点型数据
  • RGB或者RGBA, 如: (0.1, 0.2, 0.5)、(0.1, 0.2, 0.5, 0.3)等

• arrowprops #箭头参数,参数类型为字典dict

  • width：箭头的宽度(以点为单位)
  • headwidth：箭头底部以点为单位的宽度
  • headlength：箭头的长度(以点为单位)
  • shrink：总长度的一部分，从两端“收缩”
  • facecolor：箭头颜色

• bbox给标题增加外框 ，常用参数如下：

• boxstyle：方框外形
• facecolor：(简写fc)背景颜色
• edgecolor：(简写ec)边框线条颜色
• edgewidth：边框线条大小

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参数

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参数改成通过dict传入参数(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参数改成通过dict传入参数(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')

### 可视化预测新样本

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'))

## 训练样本
plt.scatter(x_fearures[:,0],x_fearures[:,1], c=y_label, s=50, cmap='viridis')
plt.title('Dataset')

# 可视化决策边界
plt.contour(x_grid, y_grid, z_proba, [0.5], linewidths=2., colors='blue')

plt.show()