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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