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

def setup_cache(self):

        setattr(full_workflow.setup_cache, "timeout", 360)

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

        entity = gpd.read_file(get_demo_file('01_rgi60_Columbia.shp')).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)
        climate.process_dummy_cru_file(gdir, seed=0)

        # Test default k (it overshoots)
        df1 = tasks.find_inversion_calving(gdir)

        # Test with smaller k (it doesn't overshoot)
        cfg.PARAMS['k_calving'] = 0.2
        df2 = tasks.find_inversion_calving(gdir)

        return (df1['calving_flux'], df1['calving_mu_star'],

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
    execute_entity_task(tasks.process_cru_data, gdirs)

utils.compile_glacier_statistics(gdirs, path=opath)

    # L2 OK - compress all in output directory
    l_base_dir = os.path.join(base_dir, 'L2')
    workflow.execute_entity_task(utils.gdir_to_tar, gdirs, delete=False,
                                 base_dir=l_base_dir)
    utils.base_dir_to_tar(l_base_dir)
    if max_level == 2:
        _time_log()
        return

    # L3 - Tasks
    task_list = [
        tasks.glacier_masks,
        tasks.compute_centerlines,
        tasks.initialize_flowlines,
        tasks.compute_downstream_line,
        tasks.compute_downstream_bedshape,
        tasks.catchment_area,
        tasks.catchment_intersections,
        tasks.catchment_width_geom,
        tasks.catchment_width_correction,
        tasks.local_t_star,
        tasks.mu_star_calibration,
        tasks.prepare_for_inversion,
        tasks.mass_conservation_inversion,
        tasks.filter_inversion_output,
        tasks.init_present_time_glacier
    ]
    for task in task_list:
        workflow.execute_entity_task(task, gdirs)

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']
with open(os.path.join(WORKING_DIR, 'mb_calib_params.json'), 'w') as fp:

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

# 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"
    for gd in gdirs:
        itmix.synth_apparent_mb(gd)

    # Inversion
    execute_entity_task(tasks.prepare_for_inversion, gdirs)
    fac = 3.22268124479468
    use_cfg_params = {'glen_a':fac * cfg.A, 'fs':0.}
    for gd in gdirs:
        tasks.volume_inversion(gd, use_cfg_params=use_cfg_params)

rgidf = utils.get_rgi_glacier_entities(rgi_list)

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

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

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

# Preprocessing and climate tasks
task_list = [
    tasks.glacier_masks,
    tasks.compute_centerlines,
    tasks.initialize_flowlines,
    tasks.compute_downstream_line,
    tasks.compute_downstream_bedshape,
    tasks.catchment_area,
    tasks.catchment_intersections,
    tasks.catchment_width_geom,
    tasks.catchment_width_correction,
    tasks.process_cru_data,
    tasks.local_t_star,
    tasks.mu_star_calibration,
]
for task in task_list:
    execute_entity_task(task, gdirs)

# Inversion tasks
execute_entity_task(tasks.prepare_for_inversion, gdirs)
# We use the default parameters for this run

cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PARAMS['border'] = 60
cfg.PARAMS['auto_skip_task'] = True
cfg.PARAMS['run_mb_calibration'] = True

base_dir = os.path.join(os.path.expanduser('~/tmp'), 'OGGM_GMD', 'scenarios')
cfg.PATHS['working_dir'] = base_dir
mkdir(base_dir, reset=reset)

entity = gpd.read_file(get_demo_file('Hintereisferner_RGI5.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)
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_cru_data(gdir)
tasks.mu_candidates(gdir)
tasks.compute_ref_t_stars([gdir])
tasks.distribute_t_stars([gdir])
tasks.apparent_mb(gdir)
tasks.prepare_for_inversion(gdir)
tasks.volume_inversion(gdir, glen_a=cfg.A, fs=0)
tasks.filter_inversion_output(gdir)
tasks.init_present_time_glacier(gdir)

gdirs = workflow.init_glacier_regions(rgidf, reset=True, force=True)
    _add_time_to_df(odf, 'init_glacier_regions', time.time()-start)

    # Pre-download other files just in case
    if test_crudir is None:
        _ = utils.get_cru_file(var='tmp')
        _ = utils.get_cru_file(var='pre')
    else:
        cfg.PATHS['cru_dir'] = test_crudir

    # Tasks
    task_list = [
        tasks.process_cru_data,
        tasks.glacier_masks,
        tasks.compute_centerlines,
        tasks.initialize_flowlines,
        tasks.compute_downstream_line,
        tasks.compute_downstream_bedshape,
        tasks.catchment_area,
        tasks.catchment_intersections,
        tasks.catchment_width_geom,
        tasks.catchment_width_correction,
        tasks.local_t_star,
        tasks.mu_star_calibration,
        tasks.prepare_for_inversion,
        tasks.mass_conservation_inversion,
        tasks.filter_inversion_output,
        tasks.init_present_time_glacier,
    ]
    for task in task_list:
        start = time.time()
        workflow.execute_entity_task(task, gdirs)

cfg.PARAMS['border'] = 60
cfg.PARAMS['auto_skip_task'] = True
cfg.PARAMS['run_mb_calibration'] = True
reset = False

base_dir = os.path.join(os.path.expanduser('~/tmp'), 'OGGM_GMD', 'dynamics')
cfg.PATHS['working_dir'] = base_dir
mkdir(base_dir, reset=reset)

entity = gpd.read_file(get_demo_file('Hintereisferner_RGI5.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=base_dir, reset=reset)

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_cru_data(gdir)
tasks.mu_candidates(gdir)
tasks.compute_ref_t_stars([gdir])
tasks.distribute_t_stars([gdir])
tasks.apparent_mb(gdir)
tasks.prepare_for_inversion(gdir)
tasks.volume_inversion(gdir, glen_a=cfg.A, fs=0)
tasks.filter_inversion_output(gdir)
tasks.init_present_time_glacier(gdir)

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)