title: Scipy, differential evolution
body: <p>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: "<em>function must be in the form</em> f(x, *args) <em>, 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</em>" </p>
  
  <p>There is an ugly example of code which i wrote just for illustrative purposes: </p>
  
  <pre><code>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))
  </code></pre>
  
  <p>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.</p>