How to use the oggm.tasks.compute_centerlines function in oggm

def setup_cache(self):

        utils.mkdir(self.testdir, reset=True)
        self.cfg_init()

        hef_file = get_demo_file('Hintereisferner_RGI5.shp')
        entity = gpd.read_file(hef_file).iloc[0]

        gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)

        tasks.define_glacier_region(gdir, entity=entity)
        tasks.glacier_masks(gdir)
        tasks.compute_centerlines(gdir)
        tasks.initialize_flowlines(gdir)
        tasks.compute_downstream_line(gdir)
        tasks.compute_downstream_bedshape(gdir)
        tasks.catchment_area(gdir)
        tasks.catchment_intersections(gdir)
        tasks.catchment_width_geom(gdir)
        tasks.catchment_width_correction(gdir)
        tasks.process_custom_climate_data(gdir)
        tasks.glacier_mu_candidates(gdir)
        mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
        res = climate.t_star_from_refmb(gdir, mbdf=mbdf)
        tasks.local_t_star(gdir, tstar=res['t_star'],
                           bias=res['bias'])
        tasks.mu_star_calibration(gdir)

        tasks.prepare_for_inversion(gdir)

gdirs = workflow.init_glacier_regions(rgidf)
# For inversion
icecaps = ['I:Devon', 'I:Academy', 'I:Austfonna', 'I:Elbrus', 'I:Mocho']
for gd in gdirs:
    if gd.name in icecaps:
        gd.glacier_type = 'Ice cap'

# For calibration
if do_calib:
    # gdirs = [gd for gd in gdirs if gd.glacier_type != 'Ice cap']
    # gdirs = [gd for gd in gdirs if gd.terminus_type == 'Land-terminating']

    # Basic tasks
    task_list = [
        itmix.glacier_masks_itmix,
        tasks.compute_centerlines,
        tasks.catchment_area,
        tasks.initialize_flowlines,
        tasks.catchment_width_geom,
        tasks.catchment_width_correction
    ]
    for task in task_list:
        execute_entity_task(task, gdirs)

    # Climate related tasks
    execute_entity_task(tasks.process_cru_data, gdirs)
    tasks.compute_ref_t_stars(gdirs)
    tasks.distribute_t_stars(gdirs)

    # Inversion
    execute_entity_task(tasks.prepare_for_inversion, gdirs)
    itmix.optimize_thick(gdirs)

cfg.PARAMS['force_one_flowline'] = ['RGI50-11.01270']

# Read in the Alps RGI file
rgi_pkl_path = utils.aws_file_download('alps/rgi_ref_alps.pkl')
rgidf = pd.read_pickle(rgi_pkl_path)

log.info('Number of glaciers: {}'.format(len(rgidf)))

# Go - initialize working directories
gdirs = workflow.init_glacier_regions(rgidf, reset=True, force=True)
# gdirs = workflow.init_glacier_regions(rgidf)

# Prepro tasks
task_list = [
    tasks.glacier_masks,
    tasks.compute_centerlines,
    tasks.compute_downstream_line,
    tasks.catchment_area,
    tasks.initialize_flowlines,
    tasks.catchment_width_geom,
    tasks.catchment_width_correction
]
for task in task_list:
    execute_entity_task(task, gdirs)

# Climate related task
execute_entity_task(tasks.process_custom_climate_data, gdirs)
tasks.compute_ref_t_stars(gdirs)
tasks.distribute_t_stars(gdirs)
execute_entity_task(tasks.apparent_mb, gdirs)

# Inversion

local_t_star, mu_star_calibration)
from oggm.core.massbalance import (ConstantMassBalance)
from oggm.utils import get_demo_file, gettempdir

cfg.initialize()
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')

base_dir = gettempdir('Climate_docs')
cfg.PATHS['working_dir'] = base_dir
entity = gpd.read_file(get_demo_file('HEF_MajDivide.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=base_dir, reset=True)

tasks.define_glacier_region(gdir, entity=entity)
tasks.glacier_masks(gdir)
tasks.compute_centerlines(gdir)
tasks.initialize_flowlines(gdir)
tasks.compute_downstream_line(gdir)
tasks.catchment_area(gdir)
tasks.catchment_width_geom(gdir)
tasks.catchment_width_correction(gdir)
data_dir = get_demo_file('HISTALP_precipitation_all_abs_1801-2014.nc')
cfg.PATHS['cru_dir'] = os.path.dirname(data_dir)
cfg.PARAMS['baseline_climate'] = 'HISTALP'
cfg.PARAMS['baseline_y0'] = 1850
tasks.process_histalp_data(gdir)
mu_yr_clim = tasks.glacier_mu_candidates(gdir)

mbdf = gdir.get_ref_mb_data()
res = t_star_from_refmb(gdir, mbdf=mbdf.ANNUAL_BALANCE)
local_t_star(gdir, tstar=res['t_star'], bias=res['bias'], reset=True)
mu_star_calibration(gdir, reset=True)

import geopandas as gpd
import oggm
from oggm import cfg, tasks
from oggm.utils import get_demo_file, gettempdir

cfg.initialize()
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')

base_dir = gettempdir('Flowlines_Docs')
entity = gpd.read_file(get_demo_file('HEF_MajDivide.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=base_dir)

tasks.define_glacier_region(gdir, entity=entity)
tasks.glacier_masks(gdir)
tasks.compute_centerlines(gdir)
tasks.initialize_flowlines(gdir)
tasks.compute_downstream_line(gdir)

zf.extractall(WORKING_DIR)
rgif = os.path.join(WORKING_DIR, 'rgi_benchmark.shp')
rgidf = salem.read_shapefile(rgif, cached=True)

# Sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)

log.info('Number of glaciers: {}'.format(len(rgidf)))

# Go - initialize working directories
gdirs = workflow.init_glacier_regions(rgidf)  # reset=True, force=True

# Prepro tasks
task_list = [
    tasks.glacier_masks,
    tasks.compute_centerlines,
    tasks.compute_downstream_line,
    tasks.initialize_flowlines,
    tasks.compute_downstream_bedshape,
    tasks.catchment_area,
    tasks.catchment_intersections,
    tasks.catchment_width_geom,
    tasks.catchment_width_correction,
]
for task in task_list:
    execute_entity_task(task, gdirs)

# Climate related tasks - this will download
execute_entity_task(tasks.process_cru_data, gdirs)
# tasks.compute_ref_t_stars(gdirs)
# tasks.distribute_t_stars(gdirs)

# Save
log.info('For RGIV{} and {} we have {} reference glaciers.'.format(rgi_version,
                                                                   baseline,
                                                                   len(rgidf)))
rgidf.to_file(os.path.join(WORKING_DIR, 'mb_ref_glaciers.shp'))

# Sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)

# Go - initialize glacier directories
gdirs = workflow.init_glacier_regions(rgidf)

# Prepro tasks
task_list = [
    tasks.glacier_masks,
    tasks.compute_centerlines,
    tasks.initialize_flowlines,
    tasks.catchment_area,
    tasks.catchment_intersections,
    tasks.catchment_width_geom,
    tasks.catchment_width_correction,
]
for task in task_list:
    execute_entity_task(task, gdirs)

# Climate tasks
tasks.compute_ref_t_stars(gdirs)
execute_entity_task(tasks.local_t_star, gdirs)
execute_entity_task(tasks.mu_star_calibration, gdirs)

# We store the associated params
mb_calib = gdirs[0].read_json('climate_info')['mb_calib_params']

dem_source_cp = os.path.join(Columbia_itmix, 'dem_source.pkl')
        grid_json_cp = os.path.join(Columbia_itmix, 'glacier_grid.json')

        # This is commented because we only need to replace the DEM once
        # os.remove(filename)
        # os.remove(dem_source)
        # os.remove(grid_json)
        # shutil.copy(dem_cp, filename)
        # shutil.copy(dem_source_cp,dem_source)
        # shutil.copy(grid_json_cp,grid_json)

    execute_entity_task(tasks.glacier_masks, gdirs)

# Pre-processing tasks
task_list = [
    tasks.compute_centerlines,
    tasks.initialize_flowlines,
    tasks.catchment_area,
    tasks.catchment_intersections,
    tasks.catchment_width_geom,
    tasks.catchment_width_correction,
]

if RUN_GIS_PREPRO:
    for task in task_list:
        execute_entity_task(task, gdirs)

if RUN_CLIMATE_PREPRO:
    for gdir in gdirs:
        gdir.inversion_calving_rate = 0
    cfg.PARAMS['correct_for_neg_flux'] = False
    cfg.PARAMS['filter_for_neg_flux'] = False

log.info('Number of glaciers: {}'.format(len(rgidf)))

# Download other files if needed
_ = utils.get_cru_file(var='tmp')
_ = utils.get_cru_file(var='pre')
_ = utils.get_demo_file('Hintereisferner.shp')

# Go - initialize working directories
# gdirs = workflow.init_glacier_regions(rgidf, reset=True, force=True)
gdirs = workflow.init_glacier_regions(rgidf)

# Prepro tasks
task_list = [
    tasks.glacier_masks,
    tasks.compute_centerlines,
    tasks.compute_downstream_line,
    tasks.catchment_area,
    tasks.initialize_flowlines,
    tasks.catchment_width_geom,
    tasks.catchment_width_correction
]
if RUN_GIS_PREPRO:
    for task in task_list:
        execute_entity_task(task, gdirs)

if RUN_CLIMATE_PREPRO:
    # Climate related tasks
    # see if we can distribute
    execute_entity_task(tasks.process_cru_data, gdirs)
    tasks.compute_ref_t_stars(gdirs)
    tasks.distribute_t_stars(gdirs)

# Sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)

rgidf = rgidf.loc[rgidf.RGIId.isin(['RGI50-07.01394'])]

log.info('Starting run for RGI reg: ' + rgi_reg)
log.info('Number of glaciers: {}'.format(len(rgidf)))

# Go - initialize working directories
# -----------------------------------
gdirs = workflow.init_glacier_regions(rgidf)

# Prepro tasks
task_list = [
    tasks.glacier_masks,
    tasks.compute_centerlines,
    tasks.compute_downstream_line,
    tasks.initialize_flowlines,
    tasks.compute_downstream_bedshape,
    tasks.catchment_area,
    tasks.catchment_intersections,
    tasks.catchment_width_geom,
    tasks.catchment_width_correction,
]
for task in task_list:
    execute_entity_task(task, gdirs)

# Climate tasks -- only data preparation and tstar interpolation!
execute_entity_task(tasks.process_cru_data, gdirs)
tasks.distribute_t_stars(gdirs)
execute_entity_task(tasks.apparent_mb, gdirs)