How to use the pynets.core.nodemaker.gen_img_list function in pynets

atlas = 'whole_brain_cluster_labels_PCA200'
    roi = base_dir + '/pDMN_3_bin.nii.gz'
    mask = None
    ID = '002'
    parc = True
    perc_overlap = 0.10

    start_time = time.time()
    [WB_coords, _, _] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
    print("%s%s%s" % ('get_names_and_coords_of_parcels (Masking whole-brain version) --> finished: ',
    str(np.round(time.time() - start_time, 1)), 's'))

    WB_labels = np.arange(len(WB_coords) + 1)[np.arange(len(WB_coords) + 1) != 0].tolist()

    start_time = time.time()
    WB_parcel_list = nodemaker.gen_img_list(parlistfile)
    [_, _, WB_parcel_list_masked] = nodemaker.parcel_masker(roi, WB_coords, WB_parcel_list, WB_labels, dir_path,
                                                            ID, perc_overlap)
    print("%s%s%s" % ('parcel_masker (Masking whole-brain version) --> finished: ',
    np.round(time.time() - start_time, 1), 's'))

    start_time = time.time()
    [WB_parcels_map_nifti, parcel_list_exp] = nodemaker.create_parcel_atlas(WB_parcel_list_masked)
    print("%s%s%s" % ('create_parcel_atlas (Masking whole-brain version) --> finished: ',
    np.round(time.time() - start_time, 1), 's'))

    start_time = time.time()
    [WB_net_parcels_map_nifti_unmasked, WB_coords_unmasked, _,
     WB_atlas, WB_uatlas, dir_path] = nodemaker.node_gen(WB_coords, WB_parcel_list, WB_labels,
                                                                        dir_path, ID, parc, atlas, parlistfile)
    print("%s%s%s" % ('node_gen (Masking whole-brain version) --> finished: ',
    np.round(time.time() - start_time, 1), 's'))

parlistfile = base_dir + '/whole_brain_cluster_labels_PCA200.nii.gz'
    roi = base_dir + '/pDMN_3_bin.nii.gz'
    network = 'Default'
    ID = '002'
    perc_overlap = 0.10
    parc = True

    start_time = time.time()
    [coords, _, _] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
    print("%s%s%s" % ('get_names_and_coords_of_parcels --> finished: ',
    str(np.round(time.time() - start_time, 1)), 's'))

    labels = np.arange(len(coords) + 1)[np.arange(len(coords) + 1) != 0].tolist()

    start_time = time.time()
    parcel_list = nodemaker.gen_img_list(parlistfile)
    [net_coords, net_parcel_list, net_labels, network] = nodemaker.get_node_membership(network, func_file, coords,
                                                                                            labels, parc,
                                                                                            parcel_list)
    print("%s%s%s" % ('get_node_membership --> finished: ', str(np.round(time.time() - start_time, 1)), 's'))

    start_time = time.time()
    [net_coords_masked, net_labels_masked, net_parcel_list_masked] = nodemaker.parcel_masker(roi, net_coords,
                                                                                                  net_parcel_list,
                                                                                                  net_labels,
                                                                                                  dir_path, ID,
                                                                                                  perc_overlap)
    print("%s%s%s" % ('parcel_masker --> finished: ', str(np.round(time.time() - start_time, 1)), 's'))

    start_time = time.time()
    [net_parcels_map_nifti, parcel_list_exp] = nodemaker.create_parcel_atlas(net_parcel_list_masked)
    print("%s%s%s" % ('create_parcel_atlas --> finished: ', str(np.round(time.time() - start_time, 1)), 's'))

dir_path = base_dir + '/002/fmri'
    func_file = dir_path + '/002.nii.gz'
    parlistfile = base_dir + '/whole_brain_cluster_labels_PCA200.nii.gz'
    network = 'Default'
    parc = True

    start_time = time.time()
    [coords, _, _] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
    print("%s%s%s" % ('get_names_and_coords_of_parcels --> finished: ',
                      str(np.round(time.time() - start_time, 1)), 's'))

    labels = np.arange(len(coords) + 1)[np.arange(len(coords) + 1) != 0].tolist()

    start_time = time.time()

    parcel_list = nodemaker.gen_img_list(parlistfile)

    [net_coords, net_parcel_list, net_labels, network] = nodemaker.get_node_membership(network, func_file, coords,
                                                                                            labels, parc,
                                                                                            parcel_list)
    print("%s%s%s" % ('get_node_membership --> finished: ', str(np.round(time.time() - start_time, 1)), 's'))

    start_time = time.time()
    [net_parcels_map_nifti, parcel_list_exp] = nodemaker.create_parcel_atlas(net_parcel_list)
    print("%s%s%s" % ('create_parcel_atlas --> finished: ', str(np.round(time.time() - start_time, 1)), 's'))

    start_time = time.time()
    out_path = nodemaker.gen_network_parcels(parlistfile, network, net_labels, dir_path)
    print("%s%s%s" % ('gen_network_parcels --> finished: ', str(np.round(time.time() - start_time, 1)), 's'))

    assert coords is not None
    assert net_coords is not None

Test nodemaker_tools_parlistfile_WB functionality
    """
    # Set example inputs
    base_dir = str(Path(__file__).parent/"examples")
    parlistfile = base_dir + '/whole_brain_cluster_labels_PCA200.nii.gz'

    start_time = time.time()
    [WB_coords, _, _] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
    print("%s%s%s" % ('get_names_and_coords_of_parcels (User-atlas whole-brain version) --> finished: ',
                      str(np.round(time.time() - start_time, 1)), 's'))

    WB_labels = np.arange(len(WB_coords) + 1)[np.arange(len(WB_coords) + 1) != 0].tolist()

    start_time = time.time()

    WB_parcel_list = nodemaker.gen_img_list(parlistfile)
    [WB_parcels_map_nifti, parcel_list_exp] = nodemaker.create_parcel_atlas(WB_parcel_list)
    print("%s%s%s" % ('create_parcel_atlas (User-atlas whole-brain version) --> finished: ',
                      str(np.round(time.time() - start_time, 1)), 's'))

    assert WB_coords is not None
    assert WB_labels is not None
    assert WB_parcel_list is not None
    assert WB_parcels_map_nifti is not None
    assert parcel_list_exp is not None

raise ValueError("%s%s%s" % ('\nERROR: Atlas file for ', atlas, ' not found!'))
        par_max = None
    elif uatlas:
        if clustering is True:
            while True:
                if op.isfile(uatlas):
                    break
                else:
                    print('Waiting for atlas file...')
                    time.sleep(15)
        atlas = uatlas.split('/')[-1].split('.')[0]
        try:
            # Fetch user-specified atlas coords
            [coords, atlas, par_max] = nodemaker.get_names_and_coords_of_parcels(uatlas)
            if parc is True:
                parcel_list = nodemaker.gen_img_list(uatlas)
            else:
                parcel_list = None
            # Describe user atlas coords
            print("%s%s%s%s" % ('\n', atlas, ' comes with {0} '.format(par_max), 'parcels\n'))
        except ValueError:
            print('\n\nError: Either you have specified the name of a nilearn atlas that does not exist or '
                  'you have not supplied a 3d atlas parcellation image!\n\n')
            parcel_list = None
            par_max = None
            coords = None
        labels = None
        networks_list = None
    else:
        networks_list = None
        labels = None
        parcel_list = None

Path to directory containing subject derivative data for given run.

    Returns
    -------
    out_path : str
        File path to a new, RSN-filtered atlas parcellation Nifti1Image.
    """
    from nilearn.image import concat_imgs
    from pynets.core import nodemaker
    import os.path as op

    if not op.isfile(uatlas):
        raise ValueError('\nERROR: User-specified atlas input not found! Check that the file(s) specified with the -ua '
                         'flag exist(s)')

    img_list = nodemaker.gen_img_list(uatlas)
    print("%s%s%s" % ('\nExtracting parcels associated with ', network, ' network locations...\n'))
    net_parcels = [i for j, i in enumerate(img_list) if j in labels]
    bna_4D = concat_imgs(net_parcels).get_fdata()
    index_vec = np.array(range(len(net_parcels))) + 1
    net_parcels_sum = np.sum(index_vec * bna_4D, axis=3)
    net_parcels_map_nifti = nib.Nifti1Image(net_parcels_sum, affine=np.eye(4))
    out_path = "%s%s%s%s" % (dir_path, '/', network, '_parcels.nii.gz')
    nib.save(net_parcels_map_nifti, out_path)
    net_parcels_map_nifti.uncache()

    return out_path