How to use the tedana.utils.load_image function in tedana

Output data where `S` is samples, `E` is echos, and `T` is time
    ref_img : :obj:`str` or :obj:`numpy.ndarray`
        Filepath to reference image for saving output files or NIFTI-like array
    """
    if n_echos is None:
        raise ValueError('Number of echos must be specified. '
                         'Confirm that TE times are provided with the `-e` argument.')

    if isinstance(data, list):
        if len(data) == 1:  # a z-concatenated file was provided
            data = data[0]
        elif len(data) == 2:  # inviable -- need more than 2 echos
            raise ValueError('Cannot run `tedana` with only two echos: '
                             '{}'.format(data))
        else:  # individual echo files were provided (surface or volumetric)
            fdata = np.stack([utils.load_image(f) for f in data], axis=1)
            ref_img = check_niimg(data[0])
            ref_img.header.extensions = []
            return np.atleast_3d(fdata), ref_img

    img = check_niimg(data)
    (nx, ny), nz = img.shape[:2], img.shape[2] // n_echos
    fdata = utils.load_image(img.get_data().reshape(nx, ny, nz, n_echos, -1, order='F'))
    # create reference image
    ref_img = img.__class__(np.zeros((nx, ny, nz, 1)), affine=img.affine,
                            header=img.header, extra=img.extra)
    ref_img.header.extensions = []
    ref_img.header.set_sform(ref_img.header.get_sform(), code=1)

    return fdata, ref_img

if isinstance(data, list):
        if len(data) == 1:  # a z-concatenated file was provided
            data = data[0]
        elif len(data) == 2:  # inviable -- need more than 2 echos
            raise ValueError('Cannot run `tedana` with only two echos: '
                             '{}'.format(data))
        else:  # individual echo files were provided (surface or volumetric)
            fdata = np.stack([utils.load_image(f) for f in data], axis=1)
            ref_img = check_niimg(data[0])
            ref_img.header.extensions = []
            return np.atleast_3d(fdata), ref_img

    img = check_niimg(data)
    (nx, ny), nz = img.shape[:2], img.shape[2] // n_echos
    fdata = utils.load_image(img.get_data().reshape(nx, ny, nz, n_echos, -1, order='F'))
    # create reference image
    ref_img = img.__class__(np.zeros((nx, ny, nz, 1)), affine=img.affine,
                            header=img.header, extra=img.extra)
    ref_img.header.extensions = []
    ref_img.header.set_sform(ref_img.header.get_sform(), code=1)

    return fdata, ref_img

Output data where `S` is samples, `E` is echos, and `T` is time
    ref_img : :obj:`str` or :obj:`numpy.ndarray`
        Filepath to reference image for saving output files or NIFTI-like array
    """
    if n_echos is None:
        raise ValueError('Number of echos must be specified. '
                         'Confirm that TE times are provided with the `-e` argument.')

    if isinstance(data, list):
        if len(data) == 1:  # a z-concatenated file was provided
            data = data[0]
        elif len(data) == 2:  # inviable -- need more than 2 echos
            raise ValueError('Cannot run `tedana` with only two echos: '
                             '{}'.format(data))
        else:  # individual echo files were provided (surface or volumetric)
            fdata = np.stack([utils.load_image(f) for f in data], axis=1)
            ref_img = check_niimg(data[0])
            ref_img.header.extensions = []
            return np.atleast_3d(fdata), ref_img

    img = check_niimg(data)
    (nx, ny), nz = img.shape[:2], img.shape[2] // n_echos
    fdata = utils.load_image(img.get_data().reshape(nx, ny, nz, n_echos, -1, order='F'))

    # create reference image
    ref_img = img.__class__(np.zeros((nx, ny, nz)), affine=img.affine,
                            header=img.header, extra=img.extra)
    ref_img.header.extensions = []
    ref_img.header.set_sform(ref_img.header.get_sform(), code=1)

    return fdata, ref_img

if infile is None:
        data = data.copy()
        data[data < thr] = 0
        infile = filewrite(unmask(data, mask), '__clin.nii.gz', ref_img, gzip=True)

    addopts = ''
    if data is not None and data.squeeze().ndim > 1 and dindex + tindex == 0:
        addopts = '-doall'
    else:
        addopts = '-1dindex {0} -1tindex {1}'.format(str(dindex), str(tindex))

    cmd_str = '3dmerge -overwrite {0} -dxyz=1 -1clust 1 {1:d} ' \
              '-1thresh {2:.02f} -prefix __clout.nii.gz {3}'
    os.system(cmd_str.format(addopts, int(csize), float(thr), infile))

    clustered = load_image('__clout.nii.gz')[mask] != 0
    return clustered

continue  # TODO: pass through GIFTI file data as below

            os.system('3drefit -sublabel 0 PSC -sublabel 1 F_R2 -sublabel 2 F_SO '
                      '-sublabel 3 Z_sn {} 2> /dev/null > /dev/null'.format(ccname))

            csize = np.max([int(n_voxels * 0.0005) + 5, 20])

            # Do simple clustering on F
            # TODO: can be replaced with nilearn.image.threshold_img
            # TODO: fmin is being cast to an integer here -- is that purposeful?!
            os.system('3dcalc -overwrite -a {}[1..2] -expr \'a*step(a-{:0d})\' -prefix '
                      '.fcl_in.nii.gz -overwrite'.format(ccname, int(fmin)))
            # TODO: can be replaced with nilearn.regions.connected_regions
            os.system('3dmerge -overwrite -dxyz=1 -1clust 1 {:0d} -doall '
                      '-prefix .fcl_out.nii.gz .fcl_in.nii.gz'.format(int(csize)))
            sel = load_image('.fcl_out.nii.gz')[t2s != 0]
            sel = np.array(sel != 0, dtype=np.int)
            F_R2_clmaps[:, i] = sel[:, 0]
            F_S0_clmaps[:, i] = sel[:, 1]

            # Do simple clustering on Z at p<0.05
            sel = spatclust(None, mask, csize, 1.95, ref_img,
                            infile=ccname, dindex=3, tindex=3)
            Z_clmaps[:, i] = sel

            # Do simple clustering on ranked signal-change map
            countsigFR2 = F_R2_clmaps[:, i].sum()
            countsigFS0 = F_S0_clmaps[:, i].sum()
            spclust_input = stats.rankdata(tsoc_Babs[:, i])
            Br_clmaps_R2[:, i] = spatclust(spclust_input, mask,
                                           csize, max(tsoc_Babs.shape)-countsigFR2,
                                           ref_img)