#269 Fitting noisy data with a linear equation using python

#269 Fitting noisy data with a linear equation using python

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

def func(x,a,b):
    return a*x+b

x=np.linspace(0,10,100)
y=func(x,1,2)

# Adding noise to the data
yn=y+0.9*np.random.normal(size=len(x))

popt,pcov=curve_fit(func,x,yn)
fig,ax = plt.subplots()
ax.plot(x,y,'b-',label='data')
ax.plot(x,yn,'.',c='red',label='data with noise')
ax.plot(x, func(x, *popt),'g--',label='fit: a=%5.3f, b=%5.3f' % tuple(popt))
plt.xlabel('x')
plt.xlabel('y')
plt.legend()
plt.savefig('ex269.png', dpi=90)
plt.show()