How to use the nxviz.utils.is_data_diverging function in nxviz

def test_is_data_diverging():
    assert is_data_diverging(diverging_ordinal)
    assert is_data_diverging(diverging_continuous)

    assert not is_data_diverging(ordinal)
    assert not is_data_diverging(continuous)

def test_is_data_diverging():
    assert is_data_diverging(diverging_ordinal)
    assert is_data_diverging(diverging_continuous)

    assert not is_data_diverging(ordinal)
    assert not is_data_diverging(continuous)

def test_is_data_diverging():
    assert is_data_diverging(diverging_ordinal)
    assert is_data_diverging(diverging_continuous)

    assert not is_data_diverging(ordinal)
    assert not is_data_diverging(continuous)

def test_is_data_diverging():
    assert is_data_diverging(diverging_ordinal)
    assert is_data_diverging(diverging_continuous)

    assert not is_data_diverging(ordinal)
    assert not is_data_diverging(continuous)

if self.group_order == "alphabetically":
            data_reduced = sorted(list(set(data)))
        elif self.group_order == "default":
            data_reduced = list(unique_everseen(data))

        dtype = infer_data_type(data)
        n_grps = num_discrete_groups(data)

        if dtype == "categorical" or dtype == "ordinal":
            if n_grps <= 8:
                cmap = get_cmap(
                    cmaps["Accent_{0}".format(n_grps)].mpl_colormap
                )
            else:
                cmap = n_group_colorpallet(n_grps)
        elif dtype == "continuous" and not is_data_diverging(data):
            cmap = get_cmap(cmaps["continuous"].mpl_colormap)
        elif dtype == "continuous" and is_data_diverging(data):
            cmap = get_cmap(cmaps["diverging"].mpl_colormap)

        for d in data:
            idx = data_reduced.index(d) / n_grps
            self.node_colors.append(cmap(idx))

        # Add colorbar if required.ListedColormap
        logging.debug("length of data_reduced: {0}".format(len(data_reduced)))
        logging.debug("dtype: {0}".format(dtype))
        if len(data_reduced) > 1 and dtype == "continuous":
            self.sm = plt.cm.ScalarMappable(
                cmap=cmap,
                norm=plt.Normalize(
                    vmin=min(data_reduced),

def compute_edge_colors(self):
        """Compute the edge colors."""
        data = [self.graph.edges[n][self.edge_color] for n in self.edges]
        data_reduced = sorted(list(set(data)))

        dtype = infer_data_type(data)
        n_grps = num_discrete_groups(data)
        if dtype == "categorical" or dtype == "ordinal":
            if n_grps <= 8:
                cmap = get_cmap(
                    cmaps["Accent_{0}".format(n_grps)].mpl_colormap
                )
            else:
                cmap = n_group_colorpallet(n_grps)
        elif dtype == "continuous" and not is_data_diverging(data):
            cmap = get_cmap(cmaps["weights"])

        for d in data:
            idx = data_reduced.index(d) / n_grps
            self.edge_colors.append(cmap(idx))
        # Add colorbar if required.
        logging.debug("length of data_reduced: {0}".format(len(data_reduced)))
        logging.debug("dtype: {0}".format(dtype))
        if len(data_reduced) > 1 and dtype == "continuous":
            self.sm = plt.cm.ScalarMappable(
                cmap=cmap,
                norm=plt.Normalize(
                    vmin=min(data_reduced),
                    vmax=max(data_reduced),  # noqa  # noqa
                ),
            )