from scipy import *
from scipy.optimize import *
from scipy.optimize import leastsq
import scipy.io.array_import
from scipy import gplt

def fitting():
    def residuals(p, y, x):
        err = y-peval(x,p)
        return err

    def peval(x, p):
        return p[0]*((x+p[2])-1/3*(x+p[2])**2+1/10*(x+p[2])**5)+p[1]
        #return p[0]*(1-exp(-(p[2]*x)**p[4])) + p[1]*(1-exp(-(p[3]*(x))**p[5] ))

        filename=('data.txt')
        data = scipy.io.array_import.read_array(filename)

        y = data[:,1]
        x = data[:,0]

        A_0=-2
        B_0=-1650
        C_0=250

        pname = (['A','B','C'])
        p0 = array([A_0 , B_0, C_0])
        plsq = leastsq(residuals, p0, args=(y, x), maxfev=2000)
        gplt.plot(x,y,x,peval(x,plsq[0]))
    gplt.grid("off")

        print "Final parameters"
        for i in range(len(pname)):
            print "%s = %.4f " % (pname[i], p0[i])