How to use the oggm.tasks function in oggm

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)

# Inversion
execute_entity_task(tasks.prepare_for_inversion, gdirs)
execute_entity_task(tasks.volume_inversion, gdirs, glen_a=cfg.A, fs=0)
execute_entity_task(tasks.filter_inversion_output, gdirs,)

# Run

rgidf = utils.get_rgi_glacier_entities(rgi_list)

        # We use intersects
        db = utils.get_rgi_intersects_entities(rgi_list, version='61')
        cfg.set_intersects_db(db)

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

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

        # Preprocessing 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 -- only data IO and tstar interpolation!
        execute_entity_task(tasks.process_cru_data, gdirs)
        execute_entity_task(tasks.local_t_star, gdirs)
        execute_entity_task(tasks.mu_star_calibration, gdirs)

rgidf = rgidf.sort_values('Area', ascending=False)

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

        # Preprocessing 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 -- only data IO and tstar interpolation!
        execute_entity_task(tasks.process_cru_data, gdirs)
        execute_entity_task(tasks.local_t_star, gdirs)
        execute_entity_task(tasks.mu_star_calibration, gdirs)

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

        # Final preparation for the run

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 -- only data IO and tstar interpolation!
        execute_entity_task(tasks.process_cru_data, gdirs)
        execute_entity_task(tasks.local_t_star, gdirs)
        execute_entity_task(tasks.mu_star_calibration, gdirs)

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

        # Final preparation for the run
        execute_entity_task(tasks.init_present_time_glacier, gdirs)

        # Random climate representative for the tstar climate, without bias
        # In an ideal world this would imply that the glaciers remain stable,
        # but it doesn't have to be so
        execute_entity_task(tasks.run_constant_climate, gdirs,

rgidf = gpd.read_file(rgi_shp[0])

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)

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.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)
    execute_entity_task(tasks.volume_inversion, gdirs)

    # Write out glacier statistics
    df = utils.glacier_characteristics(gdirs)
    fpath = os.path.join(cfg.PATHS['working_dir'], 'glacier_char.csv')
    df.to_csv(fpath)

if do_itmix:

    done = False
    for gd in gdirs:
        if 'Urumqi' in gd.name:
            if not done:
                gd.name += '_A'
                done = True

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
mu_yr_clim = gdir.read_pickle('mu_candidates')[pcp_fac]
years, temp_yr, prcp_yr = mb_yearly_climate_on_glacier(gdir, pcp_fac)

# which years to look at
selind = np.searchsorted(years, mbdf.index)

rgi_list = ['RGI60-01.10299', 'RGI60-11.00897', 'RGI60-18.02342']
        rgidf = utils.get_rgi_glacier_entities(rgi_list)

        # We use intersects
        db = utils.get_rgi_intersects_entities(rgi_list, version='61')
        cfg.set_intersects_db(db)

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

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

        # Preprocessing 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 -- only data IO and tstar interpolation!
        execute_entity_task(tasks.process_cru_data, gdirs)
        execute_entity_task(tasks.local_t_star, gdirs)
        execute_entity_task(tasks.mu_star_calibration, gdirs)