for task in task_list:
execute_entity_task(task, gdirs)
if RUN_CLIMATE_PREPRO:
for gdir in gdirs:
gdir.inversion_calving_rate = 0
execute_entity_task(tasks.process_cru_data, gdirs)
execute_entity_task(tasks.local_t_star, gdirs)
execute_entity_task(tasks.mu_star_calibration, gdirs)
if RUN_INVERSION:
# Inversion tasks
execute_entity_task(tasks.prepare_for_inversion, gdirs, add_debug_var=True)
execute_entity_task(tasks.mass_conservation_inversion,
gdirs,
filesuffix='_without_calving_')
# Log
m, s = divmod(time.time() - start, 60)
h, m = divmod(m, 60)
log.info("OGGM no_calving is done! Time needed: %02d:%02d:%02d" % (h, m, s))
suf = 'config_10_'
# Compute calving per k factor
for gdir in gdirs:
forwrite = []
# Find a calving flux.
inversion.find_inversion_calving(gdir)
if RUN_INVERSION:
# Inversion tasks
execute_entity_task(tasks.prepare_for_inversion, gdirs,
add_debug_var=True)
execute_entity_task(tasks.mass_conservation_inversion, gdirs)
# Log
m, s = divmod(time.time() - start, 60)
h, m = divmod(m, 60)
log.info("OGGM without calving is done! Time needed: %02d:%02d:%02d" %
(h, m, s))
suf = '_cfgk_cfgA_cfgFS_with_correction'
# Compute a calving flux
for gdir in gdirs:
forwrite = []
# Find a calving flux.
if gdir.rgi_id in dfmac.index:
w_depth = dfmac.loc[gdir.rgi_id]['depth']
import math
if math.isnan(w_depth):
inversion.find_inversion_calving(gdir)
else:
inversion.find_inversion_calving(gdir,
fixed_water_depth= w_depth)
else:
inversion.find_inversion_calving(gdir)
# Compile output
utils.compile_glacier_statistics(gdirs, filesuffix='_with_calving_' + suf)
# Read calibration results
calibration_results_measures = os.path.join(MAIN_PATH,
config['linear_fit_to_data'])
path_to_file = os.path.join(calibration_results_measures,
'velocity_fit_calibration_results_measures.csv')
dc = pd.read_csv(path_to_file, index_col='RGIId')
# Compute a calving flux
for gdir in gdirs:
sel = dc[dc.index == gdir.rgi_id]
k_value = sel.k_for_lw_bound.values
cfg.PARAMS['continue_on_error'] = False
cfg.PARAMS['k_calving'] = float(k_value)
out = inversion.find_inversion_calving(gdir)
if out is None:
cfg.PARAMS['inversion_fs'] = 0.0
out = inversion.find_inversion_calving(gdir)
cfg.PARAMS['inversion_fs'] = 5.7e-20
if out is None:
continue
# Compile output
file_suffix_two = 'calving_k_measures_lowbound'
utils.compile_glacier_statistics(gdirs,
filesuffix=file_suffix_two,
inversion_only=True)
def single_flowline_glacier_directory_with_calving(rgi_id,
reset=False,
prepro_border=10,
k_calving=2):
"""Prepare a GlacierDirectory for PyGEM (single flowline to start with)
k_calving is free variable!
Parameters
----------
rgi_id : str
the rgi id of the glacier
reset : bool
set to true to delete any pre-existing files. If false (the default),
the directory won't be re-downloaded if already available locally in
order to spare time.
prepro_border : int
the size of the glacier map: 10, 80, 160, 250
Returns
-------
a GlacierDirectory object
"""
assert 1 == 0, 'UPDATE LATEST GLACIER DIRECTORY OPTIONS FROM NON-CALVING'
if type(rgi_id) != str:
raise ValueError('We expect rgi_id to be a string')
if rgi_id.startswith('RGI60-') == False:
rgi_id = 'RGI60-' + rgi_id.split('.')[0].zfill(2) + '.' + rgi_id.split(
'.')[1]
else:
raise ValueError('Check RGIId is correct')
cfg.initialize()
wd = '/Users/davidrounce/Documents/Dave_Rounce/HiMAT/Output/oggm-pygem-{}-b{}-k{}'.format(
rgi_id, prepro_border, k_calving)
cfg.PATHS['working_dir'] = wd
cfg.PARAMS['use_multiple_flowlines'] = False
cfg.PARAMS['use_multiprocessing'] = False
# Check if folder is already processed
try:
gdir = utils.GlacierDirectory(rgi_id)
gdir.read_pickle('model_flowlines')
# If the above works the directory is already processed, return
return gdir
except:
pass
# If not ready, we download the preprocessed data for this glacier
gdirs = workflow.init_glacier_regions([rgi_id],
from_prepro_level=2,
prepro_border=prepro_border)
if not gdirs[0].is_tidewater:
raise ValueError('This glacier is not tidewater!')
# Compute all the stuff
list_tasks = [
tasks.glacier_masks,
tasks.compute_centerlines,
tasks.initialize_flowlines,
tasks.compute_downstream_line,
tasks.compute_downstream_bedshape,
tasks.catchment_area,
tasks.catchment_intersections, # added 10/20/2020
tasks.catchment_width_geom,
tasks.catchment_width_correction,
# Consensus ice thickness
icethickness.consensus_gridded,
icethickness.consensus_binned,
# Mass balance data
# mbdata.mb_bins_to_glacierwide
mbdata.mb_df_to_gdir
]
# Debris tasks
if pygem_prms.include_debris:
list_tasks.append(debris.debris_to_gdir)
list_tasks.append(debris.debris_binned)
for task in list_tasks:
# The order matters!
workflow.execute_entity_task(task, gdirs)
# Calving according to Recinos et al. 2019
# solves equality between ice derformation and Oerleman's calving law
# reduces temperature sensitivity
from oggm.core.inversion import find_inversion_calving
cfg.PARAMS['k_calving'] = k_calving
df = find_inversion_calving(gdirs[0])
print('Calving results:')
print('k calving:', k_calving)
for k, v in df.items():
print(k + ':', v)
list_tasks = [
# THIS WILL NOW FAIL BECAUSE OF USING HUSS
tasks.init_present_time_glacier,
]
for task in list_tasks:
# The order matters!
workflow.execute_entity_task(task, gdirs)
return gdirs[0]