How to use the tedana.metrics.dependence_metrics function in tedana

varex = ppca.explained_variance_
        voxel_comp_weights = np.dot(np.dot(data_z, comp_ts),
                                    np.diag(1. / varex))
        varex_norm = varex / varex.sum()

    # Compute Kappa and Rho for PCA comps
    eimum = np.atleast_2d(eim)
    eimum = np.transpose(eimum, np.argsort(eimum.shape)[::-1])
    eimum = eimum.prod(axis=1)
    o = np.zeros((mask.shape[0], *eimum.shape[1:]))
    o[mask, ...] = eimum
    eimum = np.squeeze(o).astype(bool)

    # Normalize each component's time series
    vTmixN = stats.zscore(comp_ts, axis=0)
    comptable, _, _, _ = metrics.dependence_metrics(data_cat,
                                                    data_oc,
                                                    comp_ts,
                                                    t2s,
                                                    tes,
                                                    ref_img,
                                                    reindex=False,
                                                    mmixN=vTmixN,
                                                    algorithm=None,
                                                    label='mepca_',
                                                    out_dir=out_dir,
                                                    verbose=verbose)

    # varex_norm from PCA overrides varex_norm from dependence_metrics,
    # but we retain the original
    comptable['estimated normalized variance explained'] = \
        comptable['normalized variance explained']

kdaw=10., rdaw=1.,
                                                out_dir=out_dir,
                                                verbose=verbose,
                                                low_mem=low_mem)
        mmix_orig = decomposition.tedica(dd, n_components, fixed_seed,
                                         maxit, maxrestart)

        if verbose and (source_tes == -1):
            io.filewrite(utils.unmask(dd, mask),
                         op.join(out_dir, 'ts_OC_whitened.nii'), ref_img)

        LGR.info('Making second component selection guess from ICA results')
        # Estimate betas and compute selection metrics for mixing matrix
        # generated from dimensionally reduced data using full data (i.e., data
        # with thermal noise)
        comptable, metric_maps, betas, mmix = metrics.dependence_metrics(
                    catd, data_oc, mmix_orig, t2s_limited, tes,
                    ref_img, reindex=True, label='meica_', out_dir=out_dir,
                    algorithm='kundu_v2', verbose=verbose)
        comp_names = [io.add_decomp_prefix(comp, prefix='ica', max_value=comptable.index.max())
                      for comp in comptable.index.values]
        mixing_df = pd.DataFrame(data=mmix, columns=comp_names)
        mixing_df.to_csv('ica_mixing.tsv', sep='\t', index=False)
        betas_oc = utils.unmask(computefeats2(data_oc, mmix, mask), mask)
        io.filewrite(betas_oc,
                     op.join(out_dir, 'ica_components.nii.gz'),
                     ref_img)

        comptable = metrics.kundu_metrics(comptable, metric_maps)
        comptable = selection.kundu_selection_v2(comptable, n_echos, n_vols)
    else:
        LGR.info('Using supplied mixing matrix from ICA')

algorithm='kundu_v2', verbose=verbose)
        comp_names = [io.add_decomp_prefix(comp, prefix='ica', max_value=comptable.index.max())
                      for comp in comptable.index.values]
        mixing_df = pd.DataFrame(data=mmix, columns=comp_names)
        mixing_df.to_csv('ica_mixing.tsv', sep='\t', index=False)
        betas_oc = utils.unmask(computefeats2(data_oc, mmix, mask), mask)
        io.filewrite(betas_oc,
                     op.join(out_dir, 'ica_components.nii.gz'),
                     ref_img)

        comptable = metrics.kundu_metrics(comptable, metric_maps)
        comptable = selection.kundu_selection_v2(comptable, n_echos, n_vols)
    else:
        LGR.info('Using supplied mixing matrix from ICA')
        mmix_orig = pd.read_table(op.join(out_dir, 'ica_mixing.tsv')).values
        comptable, metric_maps, betas, mmix = metrics.dependence_metrics(
                    catd, data_oc, mmix_orig, t2s_limited, tes,
                    ref_img, label='meica_', out_dir=out_dir,
                    algorithm='kundu_v2', verbose=verbose)
        betas_oc = utils.unmask(computefeats2(data_oc, mmix, mask), mask)
        io.filewrite(betas_oc,
                     op.join(out_dir, 'ica_components.nii.gz'),
                     ref_img)

        if ctab is None:
            comptable = metrics.kundu_metrics(comptable, metric_maps)
            comptable = selection.kundu_selection_v2(comptable, n_echos, n_vols)
        else:
            comptable = pd.read_csv(ctab, sep='\t', index_col='component')
            comptable = selection.manual_selection(comptable, acc=manacc)

    # Save decomposition