quick numerical gradient in numpy

gistfile1.txt

def test_f(x):
    x1 = x[0]
    y1 = x[1]
    return 2*x1**2 + 2*x1 + 10 + y1**2 - 5*y1

"""
    f : reference to python function that takes 
    x_0 : numpy array of shape (1,N) where N is dependent on function
"""
def finite_difference_grad(f, x_0, h):
    unit_vectors = np.eye(x_0.shape[0])
    grad = np.array([(f(x_0 + unit_vectors[i]*h) - f(x_0 - unit_vectors[i]*h)) / (2*h) for i in xrange(unit_vectors.shape[0])])
    return grad