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

def test_get_sphere():
    """
    Test get_sphere functionality
    """
    base_dir = str(Path(__file__).parent/"examples")
    dir_path = base_dir + '/002/dmri'
    img_file = dir_path + '/nodif_b0_bet.nii.gz'
    img = nib.load(img_file)
    r = 4
    vox_dims = (2.0, 2.0, 2.0)
    coords_file = dir_path + '/DesikanKlein2012/Default_coords_rsn.pkl'
    with open(coords_file, 'rb') as file_:
        coords = pickle.load(file_)
    neighbors = []
    for coord in coords:
        neighbors.append(nodemaker.get_sphere(coord, r, vox_dims, img.shape[0:3]))
    neighbors = [i for i in neighbors if len(i)>0]
    assert len(neighbors) == 4

mx = len(np.unique(atlas_data.astype('uint16'))) - 1
    g = nx.Graph(ecount=0, vcount=mx)
    edge_dict = defaultdict(int)
    node_dict = dict(zip(np.unique(atlas_data.astype('uint16')) + 1, np.arange(mx) + 1))

    # Add empty vertices
    for node in range(1, mx + 1):
        g.add_node(node)

    # Build graph
    start_time = time.time()

    ix = 0
    for s in streamlines:
        # Map the streamlines coordinates to voxel coordinates and get labels for label_volume
        i, j, k = np.vstack(np.array([nodemaker.get_sphere(coord, error_margin, roi_zooms,
                                                           roi_shape) for coord in
                                      _to_voxel_coordinates(s, lin_T, offset)])).T

        # get labels for label_volume
        lab_arr = atlas_data[i, j, k]
        endlabels = []
        for lab in np.unique(lab_arr).astype('uint32'):
            if (lab > 0) and (np.sum(lab_arr == lab) >= overlap_thr):
                try:
                    endlabels.append(node_dict[lab])
                except UserWarning:
                    print("%s%s%s" % ('Label ', lab, ' missing from parcellation. Check registration and ensure valid '
                                                     'input parcellation file.'))

        edges = combinations(endlabels, 2)
        for edge in edges:

if parc is False:
        i = -1
        RSN_parcels = None
        RSN_coords_vox = []
        net_labels = []
        for coords in coords_vox:
            sphere_vol = np.zeros(RSNmask.shape, dtype=bool)
            sphere_vol[tuple(coords)] = 1
            i = i + 1
            if (RSNmask.astype('bool') & sphere_vol).any():
                print("%s%s%s%s" % (coords, ' coords falls within ', network, '...'))
                RSN_coords_vox.append(coords)
                net_labels.append(labels[i])
                continue
            else:
                inds = get_sphere(coords, error, (np.abs(x_vox), y_vox, z_vox), RSNmask.shape)
                sphere_vol[tuple(inds.T)] = 1
                if (RSNmask.astype('bool') & sphere_vol).any():
                    print("%s%s%.2f%s%s%s" % (coords, ' coords is within a + or - ', float(error),
                                              ' mm neighborhood of ',
                                              network, '...'))
                    RSN_coords_vox.append(coords)
                    net_labels.append(labels[i])

        coords_mm = []
        for i in RSN_coords_vox:
            coords_mm.append(VoxTomm(bna_aff, i))
        coords_mm = list(set(list(tuple(x) for x in coords_mm)))
    else:
        i = 0
        RSN_parcels = []
        coords_with_parc = []

print("%s%s" % ('Creating spherical ROI atlas with radius: ', node_size))

    coords_vox = []
    for i in coords:
        coords_vox.append(mmToVox(mask_aff, i))
    coords_vox = list(set(list(tuple(x) for x in coords_vox)))

    x_vox = np.diagonal(mask_img.affine[:3, 0:3])[0]
    y_vox = np.diagonal(mask_img.affine[:3, 0:3])[1]
    z_vox = np.diagonal(mask_img.affine[:3, 0:3])[2]
    sphere_vol = np.zeros(mask_img.shape, dtype=bool)
    parcel_list = []
    i = 0
    for coord in coords_vox:
        inds = get_sphere(coord, node_size, (np.abs(x_vox), y_vox, z_vox), mask_img.shape)
        sphere_vol[tuple(inds.T)] = i * 1
        parcel_list.append(nib.Nifti1Image(sphere_vol.astype('int'), affine=mask_aff))
        i = i + 1

    par_max = len(coords)
    parc = True
    return parcel_list, par_max, node_size, parc

print("%s%s" % ('Creating spherical ROI atlas with radius: ', node_size))

    coords_vox = []
    for i in coords:
        coords_vox.append(mmToVox(mask_aff, i))
    coords_vox = list(set(list(tuple(x) for x in coords_vox)))

    x_vox = np.diagonal(mask_aff[:3, 0:3])[0]
    y_vox = np.diagonal(mask_aff[:3, 0:3])[1]
    z_vox = np.diagonal(mask_aff[:3, 0:3])[2]
    sphere_vol = np.zeros(mask_shape, dtype=bool)
    parcel_list = []
    i = 0
    for coord in coords_vox:
        sphere_vol[tuple(get_sphere(coord, node_size, (np.abs(x_vox), y_vox, z_vox), mask_shape).T)] = i * 1
        parcel_list.append(nib.Nifti1Image(sphere_vol.astype('uint16'), affine=mask_aff))
        i = i + 1

    par_max = len(coords)
    if par_max > 0:
        parc = True
    else:
        raise ValueError('Number of nodes is zero.')

    return parcel_list, par_max, node_size, parc

if parc is False:
        i = -1
        RSN_parcels = None
        RSN_coords_vox = []
        net_labels = []
        for coords in coords_vox:
            sphere_vol = np.zeros(RSNmask.shape, dtype=bool)
            sphere_vol[tuple(coords)] = 1
            i = i + 1
            if (RSNmask.astype('bool') & sphere_vol).any():
                print("%s%s%s%s" % (coords, ' coords falls within ', network, '...'))
                RSN_coords_vox.append(coords)
                net_labels.append(labels[i])
                continue
            else:
                inds = get_sphere(coords, error, (np.abs(x_vox), y_vox, z_vox), RSNmask.shape)
                sphere_vol[tuple(inds.T)] = 1
                if (RSNmask.astype('bool') & sphere_vol).any():
                    print("%s%s%.2f%s%s%s" % (coords, ' coords is within a + or - ', float(error),
                                              ' mm neighborhood of ',
                                              network, '...'))
                    RSN_coords_vox.append(coords)
                    net_labels.append(labels[i])

        coords_mm = []
        for i in RSN_coords_vox:
            coords_mm.append(VoxTomm(bna_aff, i))
        coords_mm = list(set(list(tuple(x) for x in coords_mm)))
    else:
        i = 0
        RSN_parcels = []
        coords_with_parc = []