How to use the pypesto.profile.ProfilerResult function in pypesto

def create_profile_result():
    # create a pypesto result
    result = create_optimization_result()

    # write some dummy results for profiling
    ratio_path_1 = [0.15, 0.25, 0.7, 1., 0.8, 0.35, 0.15]
    ratio_path_2 = [0.1, 0.2, 0.7, 1., 0.8, 0.3, 0.1]
    x_path_1 = np.array([[2., 2.1, 2.3, 2.5, 2.7, 2.9, 3.],
                         [1., 1.2, 1.4, 1.5, 1.6, 1.8, 2.]])
    x_path_2 = np.array([[1., 1.1, 1.3, 1.5, 1.7, 1.9, 2.1],
                         [2.1, 2.2, 2.4, 2.5, 2.8, 2.9, 3.1]])
    fval_path_1 = [4., 3., 1., 0., 1.5, 2.5, 5.]
    fval_path_2 = [4.5, 3.5, 1.5, 0., 1.3, 2.3, 4.3]
    tmp_result_1 = profile.ProfilerResult(x_path_1, fval_path_1, ratio_path_1)
    tmp_result_2 = profile.ProfilerResult(x_path_2, fval_path_2, ratio_path_2)

    # use pypesto function to write the numeric values into the results
    result.profile_result.append_empty_profile_list()
    result.profile_result.append_profiler_result(tmp_result_1)
    result.profile_result.append_profiler_result(tmp_result_2)

    return result

def create_profile_result():
    # create a pypesto result
    result = create_optimization_result()

    # write some dummy results for profiling
    ratio_path_1 = [0.15, 0.25, 0.7, 1., 0.8, 0.35, 0.15]
    ratio_path_2 = [0.1, 0.2, 0.7, 1., 0.8, 0.3, 0.1]
    x_path_1 = np.array([[2., 2.1, 2.3, 2.5, 2.7, 2.9, 3.],
                         [1., 1.2, 1.4, 1.5, 1.6, 1.8, 2.]])
    x_path_2 = np.array([[1., 1.1, 1.3, 1.5, 1.7, 1.9, 2.1],
                         [2.1, 2.2, 2.4, 2.5, 2.8, 2.9, 3.1]])
    fval_path_1 = [4., 3., 1., 0., 1.5, 2.5, 5.]
    fval_path_2 = [4.5, 3.5, 1.5, 0., 1.3, 2.3, 4.3]
    tmp_result_1 = profile.ProfilerResult(x_path_1, fval_path_1, ratio_path_1)
    tmp_result_2 = profile.ProfilerResult(x_path_2, fval_path_2, ratio_path_2)

    # use pypesto function to write the numeric values into the results
    result.profile_result.append_empty_profile_list()
    result.profile_result.append_profiler_result(tmp_result_1)
    result.profile_result.append_profiler_result(tmp_result_2)

    return result

def test_approximate_profiles(self):
        """Test for the approximate profile function."""
        n_steps = 50
        assert self.result.optimize_result.list[0].hess is None
        result = profile.approximate_parameter_profile(
            problem=self.problem, result=self.result, profile_index=[0, 1],
            n_steps=n_steps)
        profile_list = result.profile_result.list[-1]
        assert profile_list[0] is None
        assert isinstance(profile_list[1], profile.ProfilerResult)
        assert np.isclose(profile_list[1].ratio_path.max(), 1)
        assert len(profile_list[1].ratio_path) == n_steps
        assert profile_list[1].x_path.shape == (2, n_steps)

        # with pre-defined hessian
        result = deepcopy(self.result)
        result.optimize_result.list[0].hess = np.array([[2, 0], [0, 1]])
        profile.approximate_parameter_profile(
            problem=self.problem, result=result, profile_index=[0, 1],
            n_steps=n_steps)

def test_default_profiling(self):
        # loop over  methods for creating new initial guesses
        method_list = ['fixed_step', 'adaptive_step_order_0',
                       'adaptive_step_order_1', 'adaptive_step_regression']
        for i_run, method in enumerate(method_list):
            # run profiling
            result = profile.parameter_profile(
                problem=self.problem,
                result=self.result,
                optimizer=self.optimizer,
                next_guess_method=method)

            # check result
            self.assertTrue(
                isinstance(result.profile_result.list[i_run][0],
                           profile.ProfilerResult))
            self.assertEqual(len(result.profile_result.list), i_run+1)
            self.assertEqual(len(result.profile_result.list[i_run]), 2)

            # check whether profiling needed maybe too many steps
            steps = result.profile_result.list[i_run][0]['ratio_path'].size
            if method == 'adaptive_step_regression':
                self.assertTrue(steps < 20, 'Profiling with regression based '
                                            'proposal needed too many steps.')
                self.assertTrue(steps > 1, 'Profiling with regression based '
                                           'proposal needed not enough steps.')
            elif method == 'adaptive_step_order_1':
                self.assertTrue(steps < 25, 'Profiling with 1st order based '
                                            'proposal needed too many steps.')
                self.assertTrue(steps > 1, 'Profiling with 1st order based '
                                           'proposal needed not enough steps.')
            elif method == 'adaptive_step_order_0':