How to use the itkwidgets._transform_types.to_point_set function in itkwidgets

def test_polydata_list_from_json():
    number_of_points = 10
    point_set_array_1 = np.random.multivariate_normal(gaussian_1_mean, gaussian_1_cov,
                                                      number_of_points)
    point_set_array_2 = np.random.multivariate_normal(gaussian_2_mean, gaussian_2_cov,
                                                      number_of_points)

    point_set_1 = to_point_set(point_set_array_1)
    point_set_2 = to_point_set(point_set_array_2)
    polydata_list = [point_set_1, point_set_2]

    asjson = trait_types.polydata_list_to_json(polydata_list)
    polydata_list_from_json = trait_types.polydata_list_from_json(asjson)
    assert(len(polydata_list_from_json) == 2)
    polydata_1 = polydata_list_from_json[0]
    assert(polydata_1['vtkClass'] == 'vtkPolyData')
    assert(polydata_1['points']['vtkClass'] == 'vtkPoints')
    assert(polydata_1['points']['numberOfComponents'] == 3)
    assert(polydata_1['points']['dataType'] == 'Float32Array')
    assert(np.array_equal(polydata_1['points']['values'],
                          point_set_array_1.astype(np.float32)))

def test_polydata_list_to_json():
    number_of_points = 10
    point_set_array_1 = np.random.multivariate_normal(gaussian_1_mean, gaussian_1_cov,
                                                      number_of_points)
    point_set_array_2 = np.random.multivariate_normal(gaussian_2_mean, gaussian_2_cov,
                                                      number_of_points)

    point_set_1 = to_point_set(point_set_array_1)
    point_set_2 = to_point_set(point_set_array_2)
    polydata_list = [point_set_1, point_set_2]

    asjson = trait_types.polydata_list_to_json(polydata_list)
    assert(len(asjson) == 2)
    polydata_1 = asjson[0]
    assert(polydata_1['vtkClass'] == 'vtkPolyData')
    assert(polydata_1['points']['vtkClass'] == 'vtkPoints')
    assert(polydata_1['points']['numberOfComponents'] == 3)
    assert(polydata_1['points']['dataType'] == 'Float32Array')
    assert(len(polydata_1['points']['compressedValues']) == 129)

def test_polydata_list_from_json():
    number_of_points = 10
    point_set_array_1 = np.random.multivariate_normal(gaussian_1_mean, gaussian_1_cov,
                                                      number_of_points)
    point_set_array_2 = np.random.multivariate_normal(gaussian_2_mean, gaussian_2_cov,
                                                      number_of_points)

    point_set_1 = to_point_set(point_set_array_1)
    point_set_2 = to_point_set(point_set_array_2)
    polydata_list = [point_set_1, point_set_2]

    asjson = trait_types.polydata_list_to_json(polydata_list)
    polydata_list_from_json = trait_types.polydata_list_from_json(asjson)
    assert(len(polydata_list_from_json) == 2)
    polydata_1 = polydata_list_from_json[0]
    assert(polydata_1['vtkClass'] == 'vtkPolyData')
    assert(polydata_1['points']['vtkClass'] == 'vtkPoints')
    assert(polydata_1['points']['numberOfComponents'] == 3)
    assert(polydata_1['points']['dataType'] == 'Float32Array')
    assert(np.array_equal(polydata_1['points']['values'],
                          point_set_array_1.astype(np.float32)))

def test_polydata_list_to_json():
    number_of_points = 10
    point_set_array_1 = np.random.multivariate_normal(gaussian_1_mean, gaussian_1_cov,
                                                      number_of_points)
    point_set_array_2 = np.random.multivariate_normal(gaussian_2_mean, gaussian_2_cov,
                                                      number_of_points)

    point_set_1 = to_point_set(point_set_array_1)
    point_set_2 = to_point_set(point_set_array_2)
    polydata_list = [point_set_1, point_set_2]

    asjson = trait_types.polydata_list_to_json(polydata_list)
    assert(len(asjson) == 2)
    polydata_1 = asjson[0]
    assert(polydata_1['vtkClass'] == 'vtkPolyData')
    assert(polydata_1['points']['vtkClass'] == 'vtkPoints')
    assert(polydata_1['points']['numberOfComponents'] == 3)
    assert(polydata_1['points']['dataType'] == 'Float32Array')
    assert(len(polydata_1['points']['compressedValues']) == 129)

point_set_array = np.random.multivariate_normal(gaussian_1_mean, gaussian_1_cov,
                                                    number_of_points)

    # 3D
    point_set = to_point_set(point_set_array)
    assert(point_set['vtkClass'] == 'vtkPolyData')
    assert(point_set['points']['vtkClass'] == 'vtkPoints')
    assert(point_set['points']['numberOfComponents'] == 3)
    assert(point_set['points']['dataType'] == 'Float32Array')
    assert(point_set['points']['size'] == number_of_points * 3)
    assert(np.array_equal(point_set['points']['values'],
                          point_set_array.astype(np.float32)))

    # 2D
    point_set_array.resize((number_of_points, 2))
    point_set = to_point_set(point_set_array)
    assert(point_set['vtkClass'] == 'vtkPolyData')
    assert(point_set['points']['vtkClass'] == 'vtkPoints')
    assert(point_set['points']['numberOfComponents'] == 3)
    assert(point_set['points']['dataType'] == 'Float32Array')
    assert(point_set['points']['size'] == number_of_points * 3)

    point_set_array = np.hstack(
        (point_set_array, -5.0e-6 * np.ones((point_set_array.shape[0], 1)))).astype(np.float32)
    assert(np.alltrue(point_set['points']['values'] ==
                      point_set_array.astype(np.float32)))

def validate(self, obj, value):
        self._source_object = value

        # For convenience, support assigning a single point set instead of a
        # list
        point_sets = value
        if not isinstance(
                point_sets, collections.Sequence) and point_sets is not None:
            point_sets = [point_sets]

        try:
            for index, point_set in enumerate(point_sets):
                if not isinstance(
                        point_set, dict) or 'vtkClass' not in point_set:
                    point_sets[index] = to_point_set(point_set)
            return point_sets
        except BaseException:
            self.error(obj, value)