How to use the tedana.stats.get_coeffs function in tedana

del tsoc_dm
    tsoc_Babs = np.abs(tsoc_B)
    PSC = tsoc_B / tsoc.mean(axis=-1, keepdims=True) * 100

    # compute skews to determine signs based on unnormalized weights,
    # correct mmix & WTS signs based on spatial distribution tails
    signs = stats.skew(WTS, axis=0)
    signs /= np.abs(signs)
    mmix_corrected = mmix * signs
    WTS *= signs
    PSC *= signs
    totvar = (tsoc_B**2).sum()
    totvar_norm = (WTS**2).sum()

    # compute Betas and means over TEs for TE-dependence analysis
    betas = get_coeffs(utils.unmask(catd, mask),
                       mmix_corrected,
                       np.repeat(mask[:, np.newaxis], len(tes), axis=1))
    betas = betas[mask, ...]
    n_voxels, n_echos, n_components = betas.shape
    mu = catd.mean(axis=-1, dtype=float)
    tes = np.reshape(tes, (n_echos, 1))
    fmin, _, _ = getfbounds(n_echos)

    # set up Xmats
    X1 = mu.T  # Model 1
    X2 = np.tile(tes, (1, n_voxels)) * mu.T / t2s.T  # Model 2

    # tables for component selection
    kappas = np.zeros([n_components])
    rhos = np.zeros([n_components])
    varex = np.zeros([n_components])

Boolean mask array
    comptable : (C x X) :obj:`pandas.DataFrame`
        Component metric table. One row for each component, with a column for
        each metric. Requires at least two columns: "component" and
        "classification".

    Returns
    -------
    hikts : (S x T) :obj:`numpy.ndarray`
        Time series reconstructed using only components in `acc`
    rest : (S x T) :obj:`numpy.ndarray`
        Original data with `hikts` removed
    """
    acc = comptable[comptable.classification == 'accepted'].index.values

    cbetas = get_coeffs(data - data.mean(axis=-1, keepdims=True),
                        mmix, mask)
    betas = cbetas[mask]
    if len(acc) != 0:
        hikts = utils.unmask(betas[:, acc].dot(mmix.T[acc, :]), mask)
    else:
        hikts = None

    resid = data - hikts

    return hikts, resid

======================    =================================================
    hik_ts_[suffix].nii       High-Kappa time series.
    midk_ts_[suffix].nii      Mid-Kappa time series.
    low_ts_[suffix].nii       Low-Kappa time series.
    dn_ts_[suffix].nii        Denoised time series.
    ======================    =================================================
    """
    acc = comptable[comptable.classification == 'accepted'].index.values
    rej = comptable[comptable.classification == 'rejected'].index.values

    # mask and de-mean data
    mdata = data[mask]
    dmdata = mdata.T - mdata.T.mean(axis=0)

    # get variance explained by retained components
    betas = get_coeffs(dmdata.T, mmix, mask=None)
    varexpl = (1 - ((dmdata.T - betas.dot(mmix.T))**2.).sum() /
               (dmdata**2.).sum()) * 100
    LGR.info('Variance explained by ICA decomposition: {:.02f}%'.format(varexpl))

    # create component and de-noised time series and save to files
    hikts = betas[:, acc].dot(mmix.T[acc, :])
    lowkts = betas[:, rej].dot(mmix.T[rej, :])
    dnts = data[mask] - lowkts

    if len(acc) != 0:
        fout = filewrite(utils.unmask(hikts, mask),
                         'hik_ts_{0}'.format(suffix), ref_img)
        LGR.info('Writing high-Kappa time series: {}'.format(op.abspath(fout)))

    if len(rej) != 0:
        fout = filewrite(utils.unmask(lowkts, mask),

# mask everything we can
    tsoc = tsoc[mask, :]
    catd = catd[mask, ...]
    t2s = t2s[mask]

    # demean optimal combination
    tsoc_dm = tsoc - tsoc.mean(axis=-1, keepdims=True)

    # compute un-normalized weight dataset (features)
    if mmixN is None:
        mmixN = mmix
    WTS = computefeats2(tsoc, mmixN, mask=None, normalize=False)

    # compute PSC dataset - shouldn't have to refit data
    tsoc_B = get_coeffs(tsoc_dm, mmix, mask=None)
    del tsoc_dm
    tsoc_Babs = np.abs(tsoc_B)
    PSC = tsoc_B / tsoc.mean(axis=-1, keepdims=True) * 100

    # compute skews to determine signs based on unnormalized weights,
    # correct mmix & WTS signs based on spatial distribution tails
    signs = stats.skew(WTS, axis=0)
    signs /= np.abs(signs)
    mmix_corrected = mmix * signs
    WTS *= signs
    PSC *= signs
    totvar = (tsoc_B**2).sum()
    totvar_norm = (WTS**2).sum()

    # compute Betas and means over TEs for TE-dependence analysis
    betas = get_coeffs(utils.unmask(catd, mask),

dn_ts_OC.nii              Denoised time series. Generated by
                              :py:func:`tedana.utils.io.write_split_ts`.
    betas_OC.nii              Full ICA coefficient feature set.
    betas_hik_OC.nii          Denoised ICA coefficient feature set.
    feats_OC2.nii             Z-normalized spatial component maps. Generated
                              by :py:func:`tedana.utils.io.writefeats`.
    ======================    =================================================
    """
    acc = comptable[comptable.classification == 'accepted'].index.values

    fout = filewrite(ts, 'ts_OC', ref_img)
    LGR.info('Writing optimally combined time series: {}'.format(op.abspath(fout)))

    write_split_ts(ts, mmix, mask, comptable, ref_img, suffix='OC')

    ts_B = get_coeffs(ts, mmix, mask)
    fout = filewrite(ts_B, 'betas_OC', ref_img)
    LGR.info('Writing full ICA coefficient feature set: {}'.format(op.abspath(fout)))

    if len(acc) != 0:
        fout = filewrite(ts_B[:, acc], 'betas_hik_OC', ref_img)
        LGR.info('Writing denoised ICA coefficient feature set: {}'.format(op.abspath(fout)))
        fout = writefeats(split_ts(ts, mmix, mask, comptable)[0],
                          mmix[:, acc], mask, ref_img, suffix='OC2')
        LGR.info('Writing Z-normalized spatial component maps: {}'.format(op.abspath(fout)))