def scatter_norrec(self, filename=None, individual=False):
        std_diff_labels = {
            'r': 'rdiff',
            'rpha': 'rphadiff',
        }
        diff_labels = std_diff_labels
        labels_to_use = {}
        for key, item in diff_labels.items():
            if key in self.data.columns and item in self.data.columns:
                labels_to_use[key] = item
        g_freq = self.data.groupby('frequency')
        frequencies = list(sorted(g_freq.groups.keys()))
        if individual:
            figures = {}
            axes_all = {}
        else:
            Nx = len(labels_to_use.keys())
            Ny = len(frequencies)
            fig, axes = plt.subplots(
                Ny, Nx,
                figsize=(Nx * 2.5, Ny * 2.5)
            )
        for row, (name, item) in enumerate(g_freq):
            if individual:
                fig, axes_row = plt.subplots(
                    1, 2, figsize=(16 / 2.54, 6 / 2.54))
            else:
                axes_row = axes[row, :]
            for col_nr, (key, diff_column) in enumerate(
                    sorted(labels_to_use.items())):
                indices = np.where(~np.isnan(item[diff_column]))[0]
                ax = axes_row[col_nr]
                ax.scatter(
                    item[key],
                    item[diff_column],
                )
                ax.set_xlabel(key)
                ax.set_ylabel(diff_column)
                ax.set_title('N: {}'.format(len(indices)))
            if individual:
                fig.tight_layout()
                figures[name] = fig
                axes_all[name] = axes_row
        if individual:
            return figures, axes_all
        else:
            fig.tight_layout()
            return fig, axes