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