数据

X=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]

Y=[2.94,4.53,5.96,7.88,9.02,10.94,12.14,13.96,14.74,16.68,

17.79,19.67,21.20,22.07,23.75,25.22,27.17,28.84,29.84,31.78]

拟合直线图

<div align=center></div>

拟合的直线方程

<div align=center>直线方程：y=1.4942105263157894x+1.6167894736842054</div>

代码实现

import numpy as np  
import matplotlib.pyplot as plt  
from scipy import optimize  

# 线性
def f_1(x, A, B):
    return A*x + B

# 创建自定义图像
fig=plt.figure()

# 创建单个子图（1×1网格的第一子图）
ax=fig.add_subplot(1,1,1)

# 数据导入
X=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
Y=[2.94,4.53,5.96,7.88,9.02,10.94,12.14,13.96,14.74,16.68,
        17.79,19.67,21.20,22.07,23.75,25.22,27.17,28.84,29.84,31.78]

# 标出坐标点，点的大小30，颜色green，标签为point
ax.scatter(X,Y,30,color='green',label='point')

# 线性拟合，得到A1，B2值
A1, B1 = optimize.curve_fit(f_1, X, Y)[0]
# 打印A,B值
print("A:",A1)
print("B:",B1)

# 创建x1数组，0-19，步长为0.1
x1 = np.arange(0, 20, 0.1)
# 代入线性模型，得到y1
y1 = A1*x1 + B1

# 画出关于x和y的直线，颜色blue，线宽2.5，线的标签straight
plt.plot(x1, y1, "blue", linewidth=2.5,label='straight')

# x，y轴的范围，上下限
plt.xlim(0,22)
plt.ylim(0,35)

# x，y轴的标签
plt.xlabel('x')
plt.ylabel('y')

# 左上角显示标签
plt.legend(loc='upper left')
# 显示图像
plt.show()
文章目录