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

else:
    rgidf = salem.read_shapefile(rgisel)

rgidf = rgidf.loc[~rgidf.RGIId.isin(['RGI50-10.00012', 'RGI50-17.00850',
                                     'RGI50-19.01497', 'RGI50-19.00990',
                                     'RGI50-19.01440'])]

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,
]
for task in task_list:
    execute_entity_task(task, gdirs)

# Plots (if you want)
if PLOTS_DIR == '':
    exit()

utils.mkdir(PLOTS_DIR)
for gd in gdirs:

    bname = os.path.join(PLOTS_DIR, gd.rgi_id + '_')
    demsource = ' (' + gd.read_pickle('dem_source') + ')'

    # graphics.plot_googlemap(gd)
    # plt.savefig(bname + 'ggl.png')

t_star_from_refmb,
                               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)

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

rgif = 'https://www.dropbox.com/s/bvku83j9bci9r3p/rgiv5_tasman.zip?dl=1'
rgif = utils.file_downloader(rgif)
with zipfile.ZipFile(rgif) as zf:
    zf.extractall(base_dir)
rgif = os.path.join(base_dir, 'rgiv5_tasman.shp')
rgidf = gpd.read_file(rgif)
entity = rgidf.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.distribute_t_stars([gdir])
tasks.apparent_mb(gdir)

glen_a = cfg.A
tasks.prepare_for_inversion(gdir)
tasks.volume_inversion(gdir, glen_a=cfg.A, fs=0)
tasks.filter_inversion_output(gdir)

reset = False

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)

rgidf = gpd.read_file(rgif)

# Pre-download other files which will be needed later
_ = utils.get_cru_file(var='tmp')
_ = utils.get_cru_file(var='pre')

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

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

# Prepro 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,
]
for task in task_list:
    execute_entity_task(task, gdirs)

# Climate tasks
execute_entity_task(tasks.process_cru_data, gdirs)
tasks.distribute_t_stars(gdirs)
execute_entity_task(tasks.apparent_mb, gdirs)

from oggm.utils import get_demo_file, mkdir

cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
pcp_fac = 2.5
cfg.PARAMS['prcp_scaling_factor'] = pcp_fac
cfg.PARAMS['auto_skip_task'] = True

base_dir = os.path.join(os.path.expanduser('~/tmp'), 'OGGM_GMD', 'MB')
mkdir(base_dir, reset=False)

entity = gpd.read_file(get_demo_file('Hintereisferner.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.catchment_area(gdir)
tasks.catchment_width_geom(gdir)
tasks.catchment_width_correction(gdir)

tasks.process_cru_data(gdir)
tasks.mu_candidates(gdir)

mbdf = gdir.get_ref_mb_data()
res = t_star_from_refmb(gdir, mbdf.ANNUAL_BALANCE)
local_mustar(gdir, tstar=res['t_star'][-1], bias=res['bias'][-1],
             prcp_fac=res['prcp_fac'], reset=True)
apparent_mb(gdir, reset=True)

# For plots

utils.mkdir(sum_dir)
    opath = os.path.join(sum_dir, 'glacier_statistics_{}.csv'.format(rgi_reg))
    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:

cfg.PARAMS['optimize_thick'] = True
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)

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

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


# Go - initialize working directories
# -----------------------------------

# you can use the command below to reset your run -- use with caution!
# 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.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
execute_entity_task(tasks.process_cru_data, gdirs)
tasks.quick_crossval_t_stars(gdirs)
tasks.compute_ref_t_stars(gdirs)

# this could be an entire RGI region, or any glacier list you'd like to model
rgi_list = ['RGI60-01.10299', 'RGI60-11.00897', 'RGI60-18.02342']
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