How to use the tedana.io.filewrite function in tedana

t2s_limited[t2s_limited > cap_t2s * 10] = cap_t2s

    LGR.info('Computing optimal combination')
    # optimally combine data
    OCcatd = combine.make_optcom(catd, tes, mask, t2s=t2s_full,
                                 combmode=combmode)

    # clean up numerical errors
    for arr in (OCcatd, s0_limited, t2s_limited):
        np.nan_to_num(arr, copy=False)

    s0_limited[s0_limited < 0] = 0
    t2s_limited[t2s_limited < 0] = 0

    io.filewrite(t2s_limited, op.join(out_dir, 't2sv.nii'), ref_img)
    io.filewrite(s0_limited, op.join(out_dir, 's0v.nii'), ref_img)
    io.filewrite(t2s_full, op.join(out_dir, 't2svG.nii'), ref_img)
    io.filewrite(s0_full, op.join(out_dir, 's0vG.nii'), ref_img)
    io.filewrite(OCcatd, op.join(out_dir, 'ts_OC.nii'), ref_img)

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
    data_type = 'optimally combined data' if source_tes == -1 else 'z-concatenated data'
    comptable['Description'] = 'ICA fit to dimensionally reduced {0}.'.format(data_type)
    mmix_dict = {}
    mmix_dict['Method'] = ('Independent components analysis with FastICA '
                           'algorithm implemented by sklearn. Components '

# find time course ofc the spatial global signal
    # make basis with the Legendre basis
    glsig = np.linalg.lstsq(np.atleast_2d(sphis).T, dat, rcond=None)[0]
    glsig = stats.zscore(glsig, axis=None)
    np.savetxt('glsig.1D', glsig)
    glbase = np.hstack([Lmix, glsig.T])

    # Project global signal out of optimally combined data
    sol = np.linalg.lstsq(np.atleast_2d(glbase), dat.T, rcond=None)[0]
    tsoc_nogs = dat - np.dot(np.atleast_2d(sol[dtrank]).T,
                             np.atleast_2d(glbase.T[dtrank])) + Gmu[Gmask][:, np.newaxis]

    io.filewrite(optcom, 'tsoc_orig', ref_img)
    dm_optcom = utils.unmask(tsoc_nogs, Gmask)
    io.filewrite(dm_optcom, 'tsoc_nogs', ref_img)

    # Project glbase out of each echo
    dm_catd = catd.copy()  # don't overwrite catd
    for echo in range(n_echos):
        dat = dm_catd[:, echo, :][Gmask]
        sol = np.linalg.lstsq(np.atleast_2d(glbase), dat.T, rcond=None)[0]
        e_nogs = dat - np.dot(np.atleast_2d(sol[dtrank]).T,
                              np.atleast_2d(glbase.T[dtrank]))
        dm_catd[:, echo, :] = utils.unmask(e_nogs, Gmask)

    return dm_catd, dm_optcom

sphis -= sphis.mean()
    io.filewrite(utils.unmask(sphis, Gmask), 'T1gs', ref_img)

    # find time course ofc the spatial global signal
    # make basis with the Legendre basis
    glsig = np.linalg.lstsq(np.atleast_2d(sphis).T, dat, rcond=None)[0]
    glsig = stats.zscore(glsig, axis=None)
    np.savetxt('glsig.1D', glsig)
    glbase = np.hstack([Lmix, glsig.T])

    # Project global signal out of optimally combined data
    sol = np.linalg.lstsq(np.atleast_2d(glbase), dat.T, rcond=None)[0]
    tsoc_nogs = dat - np.dot(np.atleast_2d(sol[dtrank]).T,
                             np.atleast_2d(glbase.T[dtrank])) + Gmu[Gmask][:, np.newaxis]

    io.filewrite(optcom, 'tsoc_orig', ref_img)
    dm_optcom = utils.unmask(tsoc_nogs, Gmask)
    io.filewrite(dm_optcom, 'tsoc_nogs', ref_img)

    # Project glbase out of each echo
    dm_catd = catd.copy()  # don't overwrite catd
    for echo in range(n_echos):
        dat = dm_catd[:, echo, :][Gmask]
        sol = np.linalg.lstsq(np.atleast_2d(glbase), dat.T, rcond=None)[0]
        e_nogs = dat - np.dot(np.atleast_2d(sol[dtrank]).T,
                              np.atleast_2d(glbase.T[dtrank]))
        dm_catd[:, echo, :] = utils.unmask(e_nogs, Gmask)

    return dm_catd, dm_optcom

LGR.info('Computing T2* map')
    t2s_limited, s0_limited, t2s_full, s0_full = decay.fit_decay(
        catd, tes, mask, masksum, fittype)

    # set a hard cap for the T2* map
    # anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
    cap_t2s = stats.scoreatpercentile(t2s_limited.flatten(), 99.5,
                                      interpolation_method='lower')
    LGR.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10))
    t2s_limited[t2s_limited > cap_t2s * 10] = cap_t2s
    io.filewrite(t2s_limited, op.join(out_dir, 't2sv.nii'), ref_img)
    io.filewrite(s0_limited, op.join(out_dir, 's0v.nii'), ref_img)

    if verbose:
        io.filewrite(t2s_full, op.join(out_dir, 't2svG.nii'), ref_img)
        io.filewrite(s0_full, op.join(out_dir, 's0vG.nii'), ref_img)

    # optimally combine data
    data_oc = combine.make_optcom(catd, tes, mask, t2s=t2s_full, combmode=combmode)

    # regress out global signal unless explicitly not desired
    if 'gsr' in gscontrol:
        catd, data_oc = gsc.gscontrol_raw(catd, data_oc, n_echos, ref_img)

    if mixm is None:
        # Identify and remove thermal noise from data
        dd, n_components = decomposition.tedpca(catd, data_oc, combmode, mask,
                                                t2s_limited, t2s_full, ref_img,
                                                tes=tes, algorithm=tedpca,
                                                source_tes=source_tes,
                                                kdaw=10., rdaw=1.,
                                                out_dir=out_dir,

LGR.info('Computing optimal combination')
    # optimally combine data
    OCcatd = combine.make_optcom(catd, tes, mask, t2s=t2s_full,
                                 combmode=combmode)

    # clean up numerical errors
    for arr in (OCcatd, s0_limited, t2s_limited):
        np.nan_to_num(arr, copy=False)

    s0_limited[s0_limited < 0] = 0
    t2s_limited[t2s_limited < 0] = 0

    io.filewrite(t2s_limited, op.join(out_dir, 't2sv.nii'), ref_img)
    io.filewrite(s0_limited, op.join(out_dir, 's0v.nii'), ref_img)
    io.filewrite(t2s_full, op.join(out_dir, 't2svG.nii'), ref_img)
    io.filewrite(s0_full, op.join(out_dir, 's0vG.nii'), ref_img)
    io.filewrite(OCcatd, op.join(out_dir, 'ts_OC.nii'), ref_img)

LGR.warning('Could not save PCA solution')
    else:  # if loading existing state
        voxel_comp_weights = pcastate['voxel_comp_weights']
        varex = pcastate['varex']
        comp_ts = pcastate['comp_ts']
        comptable = pcastate['comptable']

    np.savetxt('mepca_mix.1D', comp_ts.T)

    # write component maps to 4D image
    comp_maps = np.zeros((OCcatd.shape[0], comp_ts.shape[0]))
    for i_comp in range(comp_ts.shape[0]):
        temp_comp_ts = comp_ts[i_comp, :][:, None]
        comp_map = utils.unmask(model.computefeats2(OCcatd, temp_comp_ts, mask), mask)
        comp_maps[:, i_comp] = np.squeeze(comp_map)
    io.filewrite(comp_maps, 'mepca_OC_components.nii', ref_img)

    # Add new columns to comptable for classification
    comptable['classification'] = 'accepted'
    comptable['rationale'] = ''

    # Select components using decision tree
    if method == 'kundu':
        comptable = kundu_tedpca(comptable, n_echos, kdaw, rdaw, stabilize=False)
    elif method == 'kundu-stabilize':
        comptable = kundu_tedpca(comptable, n_echos, kdaw, rdaw, stabilize=True)
    elif method == 'mle':
        LGR.info('Selected {0} components with MLE dimensionality '
                 'detection'.format(comptable.shape[0]))

    comptable['rationale'] = comptable['rationale'].str.rstrip(';')
    comptable.to_csv('comp_table_pca.txt', sep='\t', index=True,