import numpy as np
import matplotlib.pyplot as plt

plt.rcParams[‘font.family‘] = [‘sans-serif‘]
plt.rcParams[‘font.sans-serif‘] = [‘SimHei‘]

def linear_regression(x, y):
    N = len(x)
    sumx = sum(x)
    sumy = sum(y)
    sumx2 = sum(x ** 2)
    sumxy = sum(x * y)
    A = np.mat([[N, sumx], [sumx, sumx2]])
    b = np.array([sumy, sumxy])
    return np.linalg.solve(A, b)

#单臂
#修改数据1：
X1=np.array([0,20,40,60,80,100,120,140,160,180,200])
Y1=np.array([0,0.02,0.06,0.1,0.13,0.16,0.19,0.22,0.245,0.278,0.3])

#半桥
#修改数据2：
X2=np.array([0,20,40,60,80,100,120,140,160,180,200])
Y2=np.array([0,0.057,0.118,0.185,0.245,0.308,0.376,0.425,0.488,0.544,0.58])

a0, a1 = linear_regression(X1, Y1)
# 生成拟合直线的绘制点
_X1 = [0, 200]
_Y1 = [a0   a1 * x for x in _X1]

a0, a1 = linear_regression(X2, Y2)
# 生成拟合直线的绘制点
_X2 = [0, 200]
_Y2 = [a0   a1 * x for x in _X1]


#显示图像
plt.plot( X1, Y1, ‘ro‘, linewidth=2,label="单臂电桥")
plt.plot(_X1, _Y1, ‘b‘,linewidth=2,label=‘单臂电桥‘,color=‘C0‘)
plt.plot( X2, Y2, ‘g^‘, linewidth=2,label=‘半桥‘)
plt.plot(_X2, _Y2, ‘b‘, linewidth=2,label=‘半桥‘,color=‘C1‘)
plt.xlabel(‘weight/g‘)
plt.ylabel(‘voltage/v‘)
plt.legend()
plt.show()