Scipy, differential evolution

Question

The thing is, im trying to design of fitting procedure for my purposes and want to use scipy`s differential evolution algorithm as a general estimator of initial values which then will be used in LM algorithm for better fitting. The function i want to minimize with DE is the least squares between analytically defined non-linear function and some experimental values. Point at which i stuck is the function design. As its stated in scipy reference: "function must be in the form f(x, *args) , where x is the argument in the form of a 1-D array and args is a tuple of any additional fixed parameters needed to completely specify the function"

There is an ugly example of code which i wrote just for illustrative purposes:

def func(x, *args):
    """args[0] = x
       args[1] = y"""
    result = 0
    for i in range(len(args[0][0])):
        result += (x[0]*(args[0][0][i]**2) + x[1]*(args[0][0][i]) + x[2] - args[0][1][i])**2   
    return result**0.5

if __name__ == '__main__':
    bounds = [(1.5, 0.5), (-0.3, 0.3), (0.1, -0.1)]
    x = [0,1,2,3,4]
    y = [i**2 for i in x]
    args = (x, y)
    result = differential_evolution(func, bounds, args=args)
    print(func(bounds, args))

I wanted to supply raw data as a tuple into the function but it seems that its not how its suppose to be since interpreter isn't happy with the function. The problem should be easy solvable, but i really frustrated, so advice will be much appreciated.