#!/usr/bin/python
# using a demo file for exponential fitting
#http://stackoverflow.com/questions/3938042/fitting-exponential-decay-with-no-initial-guessing
import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt

#Define your function
def func(x, a, b, c):
    return a*np.exp(-b*x) + c

#create a dummy dataset
x = np.linspace(0,4,50)
#create the 'ideal' negative exponential curve, using the function
y = func(x, 2.5, 1.3, 0.5)
#the dummy data, with some noise thrown in
yn = y + 0.2*np.random.normal(size=len(x))

# Using the python scipy function curve_fit with function and input variables
popt, pcov = curve_fit(func, x, yn)
print popt.size
print pcov.size
f1=plt.plot(x,yn,x, func(x,popt[0],popt[1],popt[2]))
plt.title('curve fit')
plt.xlabel('x-vals')
plt.ylabel('y-vals')
plt.show()