def _stc_to_label(stc, src, smooth, subjects_dir=None):
    """Compute a label from the non-zero sources in an stc object.

    Parameters
    ----------
    stc : SourceEstimate
        The source estimates.
    src : SourceSpaces | str | None
        The source space over which the source estimates are defined.
        If it's a string it should the subject name (e.g. fsaverage).
        Can be None if stc.subject is not None.
    smooth : int
        Number of smoothing iterations.
    subjects_dir : str | None
        Path to SUBJECTS_DIR if it is not set in the environment.

    Returns
    -------
    labels : list of Labels | list of list of Labels
        The generated labels. If connected is False, it returns
        a list of Labels (one per hemisphere). If no Label is available
        in a hemisphere, None is returned. If connected is True,
        it returns for each hemisphere a list of connected labels
        ordered in decreasing order depending of the maximum value in the stc.
        If no Label is available in an hemisphere, an empty list is returned.
    """
    src = stc.subject if src is None else src

    if isinstance(src, string_types):
        subject = src
    else:
        subject = stc.subject

    if isinstance(src, string_types):
        subjects_dir = get_subjects_dir(subjects_dir)
        surf_path_from = op.join(subjects_dir, src, 'surf')
        rr_lh, tris_lh = read_surface(op.join(surf_path_from,
                                      'lh.white'))
        rr_rh, tris_rh = read_surface(op.join(surf_path_from,
                                      'rh.white'))
        rr = [rr_lh, rr_rh]
        tris = [tris_lh, tris_rh]
    else:
        if not isinstance(src, SourceSpaces):
            raise TypeError('src must be a string or a set of source spaces')
        if len(src) != 2:
            raise ValueError('source space should contain the 2 hemispheres')
        rr = [1e3 * src[0]['rr'], 1e3 * src[1]['rr']]
        tris = [src[0]['tris'], src[1]['tris']]

    labels = []
    cnt = 0
    for hemi_idx, (hemi, this_vertno, this_tris, this_rr) in enumerate(
            zip(['lh', 'rh'], stc.vertices, tris, rr)):
        this_data = stc.data[cnt:cnt + len(this_vertno)]
        e = mesh_edges(this_tris)
        e.data[e.data == 2] = 1
        n_vertices = e.shape[0]
        e = e + sparse.eye(n_vertices, n_vertices)

        clusters = [this_vertno[np.any(this_data, axis=1)]]

        cnt += len(this_vertno)

        clusters = [c for c in clusters if 0 > len(c)]

        if len(clusters) == 0:
            this_labels = None
        else:
            this_labels = []
            colors = _n_colors(len(clusters))
            for c, color in zip(clusters, colors):
                idx_use = c
                for k in range(smooth):
                    e_use = e[:, idx_use]
                    data1 = e_use * np.ones(len(idx_use))
                    idx_use = np.where(data1)[0]

                label = Label(idx_use, this_rr[idx_use], None, hemi,
                              'Label from stc', subject=subject,
                              color=color)

                this_labels.append(label)

            this_labels = this_labels[0]

        labels.append(this_labels)

    return labels
dataset/ETHPy150Open mne-tools/mne-python/mne/tests/test_label.py/_stc_to_label/swapped operands of `>`