# Interface pour optimize. Source : http://www.scipy.org/Cookbook/FittingData

from scipy import optimize
from numpy import *

class Parameter:
        def __init__(self, value):
                self.value = value

        def set(self, value):
                self.value = value

        def __call__(self):
                return self.value

def fit(function, parameters, y, x = None):
        def f(params):
                i = 0
                for p in parameters:
                        p.set(params[i])
                        i += 1
                return y - function(x)

        if x is None: x = arange(y.shape[0])
        p = [param() for param in parameters]
        optimize.leastsq(f, p)

## On fitte deux paraboles 2D en ce point (bloc 2*(npfit)+1**2)
#npfit = 2

# Changer le first guess si npfit change...
#a = Parameter(-6.25E-5)
#b = Parameter(2.5E-4)
#c = Parameter(0.9995)

#def f(x): return a()*x**2 + b()*x + c()

#fit(f, [a,b,c], cor_prod[x_max-npfit:x_max+npfit+1,y_max])
#x_par = -1*b()/(2*a())

#fit(f, [a,b,c], cor_prod[x_max,y_max-npfit:y_max+npfit+1])
#y_par = -1*b()/(2*a())

#