How to use the oggm.workflow.execute_entity_task function in oggm

# see if we can distribute
    execute_entity_task(tasks.process_cru_data, gdirs)
    tasks.compute_ref_t_stars(gdirs)
    tasks.distribute_t_stars(gdirs)
    execute_entity_task(tasks.apparent_mb, gdirs)

if RUN_INVERSION:
    # Inversion
    execute_entity_task(tasks.prepare_for_inversion, gdirs)
    tasks.optimize_inversion_params(gdirs)
    execute_entity_task(tasks.volume_inversion, gdirs)

if RUN_DYNAMICS:
    # Random dynamics
    execute_entity_task(tasks.init_present_time_glacier, gdirs)
    execute_entity_task(tasks.random_glacier_evolution, gdirs)

# Plots (if you want)
PLOTS_DIR = ''
if PLOTS_DIR == '':
    exit()
utils.mkdir(PLOTS_DIR)
for gd in gdirs:
    bname = os.path.join(PLOTS_DIR, gd.name + '_' + gd.rgi_id + '_')
    graphics.plot_googlemap(gd)
    plt.savefig(bname + 'ggl.png')
    plt.close()
    graphics.plot_domain(gd)
    plt.savefig(bname + 'dom.png')
    plt.close()
    graphics.plot_centerlines(gd)
    plt.savefig(bname + 'cls.png')

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)
    execute_entity_task(tasks.apparent_mb, gdirs)

if RUN_INVERSION:
    # Inversion
    execute_entity_task(tasks.prepare_for_inversion, gdirs)
    tasks.optimize_inversion_params(gdirs)
    execute_entity_task(tasks.volume_inversion, gdirs)

if RUN_DYNAMICS:
    # Random dynamics
    execute_entity_task(tasks.init_present_time_glacier, gdirs)
    execute_entity_task(tasks.random_glacier_evolution, gdirs)

# Plots (if you want)
PLOTS_DIR = ''
if PLOTS_DIR == '':
    exit()
utils.mkdir(PLOTS_DIR)
for gd in gdirs:
    bname = os.path.join(PLOTS_DIR, gd.name + '_' + gd.rgi_id + '_')
    graphics.plot_googlemap(gd)
    plt.savefig(bname + 'ggl.png')
    plt.close()

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,
                            bias=0, nyears=100,
                            output_filesuffix='_tstar')

        execute_entity_task(tasks.run_constant_climate, gdirs,
                            y0=1990, nyears=100,
                            output_filesuffix='_pd')

        # Compile output
        utils.compile_glacier_statistics(gdirs)
        utils.compile_run_output(gdirs, input_filesuffix='_tstar')
        utils.compile_run_output(gdirs, input_filesuffix='_pd')
        utils.compile_climate_input(gdirs)

        return gdirs

execute_entity_task(tasks.prepare_for_inversion, col_gdir)
    execute_entity_task(tasks.volume_inversion, gdirs)
    pdir = os.path.join(PLOTS_DIR, 'out_raw') + '/'
    if not os.path.exists(pdir):
        os.mkdir(pdir)
    for gd in gdirs:
        _addt = addt if 'Columbia' in gd.name else ''
        graphics.plot_inversion(gd, add_title_comment=_addt)
        plt.savefig(pdir + gd.name + '_' + gd.rgi_id + '_inv.png')
        plt.close()

    # V1
    distrib = partial(distribute_thickness, how='per_altitude',
                      add_slope=True,
                      smooth=True)
    execute_entity_task(distrib, gdirs)
    pdir = os.path.join(PLOTS_DIR, 'out_dis') + '/'
    if not os.path.exists(pdir):
        os.mkdir(pdir)
    for gd in gdirs:
        itmix.write_itmix_ascii(gd, 1)
        graphics.plot_distributed_thickness(gd)
        plt.savefig(pdir + gd.name + '_' + gd.rgi_id + '_d1.png')
        plt.close()

    # V2
    distrib = partial(distribute_thickness, how='per_altitude',
                      add_slope=False,
                      smooth=True)
    execute_entity_task(distrib, gdirs)
    for gd in gdirs:
        itmix.write_itmix_ascii(gd, 2)

Parameters
    ----------
    gdirs : list of :py:class:`oggm.GlacierDirectory` objects
        the glacier directories to process
    filesuffix : str
        add suffix to output file
    path : str, bool
        Set to "True" in order  to store the info in the working directory
        Set to a path to store the file to your chosen location
    inversion_only : bool
        if one wants to summarize the inversion output only (including calving)
    """
    from oggm.workflow import execute_entity_task

    out_df = execute_entity_task(glacier_statistics, gdirs,
                                 inversion_only=inversion_only)

    out = pd.DataFrame(out_df).set_index('rgi_id')
    if path:
        if path is True:
            out.to_csv(os.path.join(cfg.PATHS['working_dir'],
                                    ('glacier_statistics' +
                                     filesuffix + '.csv')))
        else:
            out.to_csv(path)
    return out

rgidf = utils.get_rgi_glacier_entities(rids, version=rgi_version)
log.info('For RGIV{} we have {} candidate reference '
         'glaciers.'.format(rgi_version, len(rgidf)))

# We have to check which of them actually have enough mb data.
# Let OGGM do it:
gdirs = workflow.init_glacier_regions(rgidf)

# We need to know which period we have data for
log.info('Process the climate data...')
if baseline == 'CRU':
    execute_entity_task(tasks.process_cru_data, gdirs, print_log=False)
elif baseline == 'HISTALP':
    # exclude glaciers outside of the Alps
    gdirs = [gdir for gdir in gdirs if gdir.rgi_subregion == '11-01']
    execute_entity_task(tasks.process_histalp_data, gdirs, print_log=False)

gdirs = utils.get_ref_mb_glaciers(gdirs)

# Keep only these
rgidf = rgidf.loc[rgidf.RGIId.isin([g.rgi_id for g in 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

tasks.init_present_time_glacier,
    ]
    for task in task_list:
        start = time.time()
        workflow.execute_entity_task(task, gdirs)
        _add_time_to_df(odf, task.__name__, time.time()-start)

    # Runs
    start = time.time()
    workflow.execute_entity_task(tasks.run_random_climate, gdirs,
                                 nyears=250, bias=0, seed=0,
                                 output_filesuffix='_tstar')
    _add_time_to_df(odf, 'run_random_climate_tstar_250', time.time()-start)

    start = time.time()
    workflow.execute_entity_task(tasks.run_random_climate, gdirs,
                                 nyears=250, y0=1995, seed=0,
                                 output_filesuffix='_commit')
    _add_time_to_df(odf, 'run_random_climate_commit_250', time.time()-start)

    # Compile results
    start = time.time()
    utils.compile_glacier_statistics(gdirs)
    _add_time_to_df(odf, 'compile_glacier_statistics', time.time()-start)

    start = time.time()
    utils.compile_climate_statistics(gdirs,
                                     add_climate_period=[1920, 1960, 2000])
    _add_time_to_df(odf, 'compile_climate_statistics', time.time()-start)

    start = time.time()
    utils.compile_run_output(gdirs, input_filesuffix='_tstar')

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

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

# Go - get the pre-processed glacier directories
gdirs = workflow.init_glacier_regions(rgidf, from_prepro_level=4)

# We can step directly to a new experiment!
# Random climate representative for the recent climate (1985-2015)
# This is a kind of "commitment" run
workflow.execute_entity_task(tasks.run_random_climate, gdirs,
                             nyears=300, y0=2000, seed=1,
                             output_filesuffix='_commitment')
# Now we add a positive and a negative bias to the random temperature series
workflow.execute_entity_task(tasks.run_random_climate, gdirs,
                             nyears=300, y0=2000, seed=2,
                             temperature_bias=0.5,
                             output_filesuffix='_bias_p')
workflow.execute_entity_task(tasks.run_random_climate, gdirs,
                             nyears=300, y0=2000, seed=3,
                             temperature_bias=-0.5,
                             output_filesuffix='_bias_m')

# Write the compiled output
utils.compile_glacier_statistics(gdirs)
utils.compile_run_output(gdirs, input_filesuffix='_commitment')
utils.compile_run_output(gdirs, input_filesuffix='_bias_p')
utils.compile_run_output(gdirs, input_filesuffix='_bias_m')

# Log
m, s = divmod(time.time() - start, 60)

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,
                            bias=0, nyears=100,
                            output_filesuffix='_tstar')

        execute_entity_task(tasks.run_constant_climate, gdirs,
                            y0=1990, nyears=100,
                            output_filesuffix='_pd')

        # Compile output

# TODO : Remember to this paths on the cluster to run Columbia
        Columbia_itmix = os.path.join(DATA_INPUT,
                                      'RGI50-01.10689_itmixrun_new/')
        dem_cp = os.path.join(Columbia_itmix, 'dem.tif')
        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: