test_directional_derivative fails for 2 procs and Trigonometric function

[sannant]

=================================== FAILURES ===================================
_________________ test_directional_derivative[2-Trigonometric] _________________

Objective = <class 'tests.minimization_problems.Trigonometric'>, n = 2

    @pytest.mark.parametrize("Objective",[mp.Trigonometric,mp.Extended_Rosenbrock])
    @pytest.mark.parametrize("n",[2,4,10])
    def test_directional_derivative(Objective,n) :
        """
        Asserts the nmerical and the Gradient provided by Objective are close
        :param Objective: which function to minimize
        :param n: number of dimensions
        :return:
        """

        for i in range(12):
            x= Objective.bounds[0] + (Objective.bounds[1] - Objective.bounds[0]) * np.random.random(n)
            x.shape = (-1,1)
            u=np.random.normal(size=n) # Direction
            u /= np.linalg.norm(u,2) # normalize
            u.shape = (-1,1)

            # TODO: Don't know which Tolerance to associate with which eps
            eps = 1e-5
            print((Objective.f(x + u * eps) - Objective.f(x)) / eps)
            der_numerical  = (Objective.f(x + u * eps) - Objective.f(x)) / eps
            der_analytical = (Objective.grad(x).T@u).item()
>           assert abs(der_numerical- der_analytical)/der_analytical < 1e-3, "(der_numerical- der_analytical)/der_analytical = {}".form
at(abs(der_numerical- der_analytical)/der_analytical)
E           AssertionError: (der_numerical- der_analytical)/der_analytical = 0.0012363472737317459
E           assert (0.00011489677930499875 / 0.09293244846830007) < 0.001
E            +  where 0.00011489677930499875 = abs((0.09281755168899507 - 0.09293244846830007))

I think I should test how the error scales with epsilon rather then comparing this with a constant value.