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()
