def up_to_climate(reset=False):
"""Run the tasks you want."""
# test directory
if not os.path.exists(_TEST_DIR):
os.makedirs(_TEST_DIR)
if reset:
clean_dir(_TEST_DIR)
if not os.path.exists(CLI_LOGF):
with open(CLI_LOGF, 'wb') as f:
pickle.dump('none', f)
# Init
cfg.initialize()
# Use multiprocessing
cfg.PARAMS['use_multiprocessing'] = use_multiprocessing()
# Working dir
cfg.PATHS['working_dir'] = _TEST_DIR
cfg.PATHS['dem_file'] = get_demo_file('srtm_oetztal.tif')
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
# Read in the RGI file
rgi_file = get_demo_file('rgi_oetztal.shp')
rgidf = gpd.read_file(rgi_file)
# Make a fake marine and lake terminating glacier
cfg.PARAMS['tidewater_type'] = 4 # make lake also calve
rgidf.loc[0, 'GlacType'] = '0199'
rgidf.loc[1, 'GlacType'] = '0299'
# Use RGI6
rgidf['RGIId'] = [s.replace('RGI50', 'RGI60') for s in rgidf.RGIId]
# Be sure data is downloaded
cru.get_cru_cl_file()
# Params
cfg.PARAMS['border'] = 70
cfg.PARAMS['tstar_search_window'] = [1902, 0]
cfg.PARAMS['prcp_scaling_factor'] = 1.75
cfg.PARAMS['temp_melt'] = -1.75
cfg.PARAMS['use_kcalving_for_inversion'] = True
cfg.PARAMS['use_kcalving_for_run'] = True
# Go
gdirs = workflow.init_glacier_directories(rgidf)
try:
tasks.catchment_width_correction(gdirs[0])
except Exception:
reset = True
if reset:
# First preprocessing tasks
workflow.gis_prepro_tasks(gdirs)
return gdirs
def setup_cache(self):
setattr(full_workflow.setup_cache, "timeout", 360)
utils.mkdir(self.testdir, reset=True)
self.cfg_init()
# Pre-download other files which will be needed later
cru.get_cru_cl_file()
cru.get_cru_file(var='tmp')
cru.get_cru_file(var='pre')
# Get the RGI glaciers for the run.
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_directories(rgidf)
# Preprocessing tasks
task_list = [
tasks.define_glacier_region,
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
workflow.inversion_tasks(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
