Optimization methods used from scipy.optimize to nlopt package

I am currently having a black box of objective function. It has been use successfully in scipy.optimize as 'status=op.basinhopping(obj,sp,...)', however, when I try the same obj to NLOPT package, it gives a message of

TypeError: <lambda>() takes exactly 1 argument (2 given). 

I suppose obj for scipy.optimize has two arguments, one is the function itself and the other is differentiation of each dimension, while obj used in NLOPT methods only require the function itself. If I am right about it, how should I modify the obj so that it could be used in NLOPT? My code of using NLOPT

    sys.path.insert(0,os.path.join(os.getcwd(),"build/R_ulp"))
    import foo as foo_square
    reload(foo_square)
    sp=np.zeros(foo_square.dim)+args.startPoint
    obj=lambda X:foo_square.R(* X)
    opt = nlopt.opt(nlopt.GN_CRS2_LM, foo_square.dim)
    opt.set_min_objective(obj)
    opt.set_lower_bounds(-1e9)
    opt.set_upper_bounds(1e9)
    opt.set_stopval(0)
    opt.set_xtol_rel(1e-9)
    opt.set_initial_step(1)
    opt.set_population(0)
    opt.set_maxeval(100000) 
    status = opt.optimize([0.111111111]*foo_square.dim)

The SciPy optimizers and NLopt have different conventions for the signature of the objective function. The documentation for the objective function in NLopt says

The function f should be of the form:

def f(x, grad):
   if grad.size > 0:

etc.

So you'll need to create an objective function that accepts two arguments, x and grad. If grad.size > 0, the function must fill in the array with the gradient.