python数据处理，线性度，灵敏度，线性拟合

# -*- coding: utf-8 -*-
"""
Created on Mon Oct 31 13:48:19 2016

@author: QING
"""

import numpy as np
import scipy as sp
from scipy.optimize import leastsq

m = 1

x = np.linspace(-2.0,2.0,11)
y = np.array([-6309, -5301, -4102, -2722, -1629, -264, 1117, 2499, 3852, 4863, 5924])
#线性拟合
def func(p,x):
k,b = p
return k*x+b

def error(p,x,y):
return func(p,x)-y

p0=[0,0]

para = leastsq(error,p0,args=(x,y))
k,b=para[0]

#计算线性度Ln=(平均值与拟合直线之间最大偏差/满量程理论输出值)
Ln = max(abs(y-(k*x+b)))/max(abs(y))
print('线性度为%.4f%%'%Ln)

#计算灵敏度：输出变化量/输入变化量
#对y求一阶差分
dy = np.array([(y[i+1]-y[i])for i in range(len(y)-1)])
Q1 = para[0][0]
Q2 = dy.mean()/0.4
print('Q1 = %.4f \nQ2 = %.4f'%(Q1,Q2))

import matplotlib.pyplot as plt

plt.scatter(x,y,color="red",label="Sample Point",linewidth=3)
xi=np.linspace(-2,2,1000)
yi=k*xi+b
plt.plot(xi,yi,color="blue",label="Fitting Line",linewidth=2)
plt.legend()
plt.show()