使用matplotlib進行曲線擬合

import numpy as np
import matplotlib.pyplot as plt
 
#定義x、y散點座標
x = np.arange(3200, 35000, 3200)
num1 = [0.472,0.469,0.447,0.433,0.418,0.418,0.418,0.418,0.418,0.418]
num2 = [0.337,0.327,0.325,0.316,0.312,0.311,0.308,0.305,0.295,0.290]
y1 = np.array(num1)
y2 = np.array(num2)
 
#用3次多項式擬合
f1 = np.polyfit(x, y1, 3)
p1 = np.poly1d(f1)
print(p1)#打印出擬合函數
yvals1 = p1(x)  #擬合y值

f2 = np.polyfit(x, y2, 3)
p2 = np.poly1d(f2)
print(p2)
#也可使用yvals=np.polyval(f1, x)
yvals2 = p2(x)
 
#繪圖
plot1 = plt.plot(x, y1, 's',label='original values')
plot2 = plt.plot(x, yvals1, 'r',label='polyfit values')
plot3 = plt.plot(x, y2, 's',label='original values2')
plot4 = plt.plot(x, yvals2, 'r',label='polyfit values2')


plt.xlabel('x')
plt.ylabel('y')
plt.legend(loc=2, bbox_to_anchor=(1.05,1.0),borderaxespad = 0.)
plt.title('polyfitting')
plt.savefig('nihe1.png')
plt.show()

第一種方法是使用ployfit進行曲線擬合，如上所示。

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

def func(x, a, b, c):
    return a * x**2+b*x + c
 
#定義x、y散點座標
x = np.arange(3200, 35000, 3200)
num1 = [0.472,0.469,0.447,0.433,0.418,0.418,0.418,0.418,0.418,0.418]
y1 = np.array(num1)

 
#繪圖
plot1 = plt.plot(x, y1, 'ms',label='orig1')


popt, pcov = curve_fit(func, x, y1)
yy2 = [func(i, popt[0],popt[1],popt[2]) for i in x]
plt.plot(x, yy2, 'r-', label='fit')


print u'係數a:', popt[0]
print u'係數b:', popt[1]
print u'係數c:',popt[2]


plt.xlabel('x')
plt.ylabel('y')
plt.legend(loc=2, bbox_to_anchor=(1.05,1.0),borderaxespad = 0.)
plt.title('polyfitting')
plt.subplots_adjust(right=0.75)
plt.savefig('test3.png',frameon = True,figsize = (18.5,10.5),dpi = 100)
plt.show()

第二種方法是使用curve_fit進行曲線擬合，如上所示。