def setup_cache(self): setattr(full_workflow.setup_cache, "timeout", 360) utils.mkdir(self.testdir, reset=True) self.cfg_init() entity = gpd.read_file(get_demo_file('01_rgi60_Columbia.shp')).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) 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) climate.process_dummy_cru_file(gdir, seed=0) # Test default k (it overshoots) df1 = utils.find_inversion_calving(gdir) # Test with smaller k (it doesn't overshoot) cfg.PARAMS['k_calving'] = 0.2 df2 = utils.find_inversion_calving(gdir) return (df1.calving_flux.values, df1.mu_star.values, df2.calving_flux.values, df2.mu_star.values)
def setup_cache(self): utils.mkdir(self.testdir, reset=True) self.cfg_init() hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) 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_custom_climate_data(gdir) tasks.mu_candidates(gdir) mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE'] res = climate.t_star_from_refmb(gdir, mbdf) tasks.local_mustar(gdir, tstar=res['t_star'], bias=res['bias']) tasks.apparent_mb(gdir) tasks.prepare_for_inversion(gdir) tasks.mass_conservation_inversion(gdir) return gdir
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) tasks.init_present_time_glacier(gdir) df = utils.glacier_characteristics([gdir], path=False)
def setup_cache(self): setattr(full_workflow.setup_cache, "timeout", 360) utils.mkdir(self.testdir, reset=True) self.cfg_init() entity = gpd.read_file(get_demo_file('01_rgi60_Columbia.shp')).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) 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) climate.process_dummy_cru_file(gdir, seed=0) rho = cfg.PARAMS['ice_density'] i = 0 calving_flux = [] mu_star = [] ite = [] cfg.PARAMS['clip_mu_star'] = False cfg.PARAMS['min_mu_star'] = 0 # default is now 1 while i < 12: # Calculates a calving flux from model output if i == 0: # First call we set to zero (not very necessary, # this first loop could be removed) f_calving = 0 elif i == 1: # Second call we set a very small positive calving f_calving = utils.calving_flux_from_depth(gdir, water_depth=1) elif cfg.PARAMS['clip_mu_star']: # If we have to clip mu the calving becomes the real flux fl = gdir.read_pickle('inversion_flowlines')[-1] f_calving = fl.flux[-1] * (gdir.grid.dx**2) * 1e-9 / rho else: # Otherwise it is parameterized f_calving = utils.calving_flux_from_depth(gdir) # Give it back to the inversion and recompute gdir.inversion_calving_rate = f_calving # At this step we might raise a MassBalanceCalibrationError mu_is_zero = False try: climate.local_t_star(gdir) df = gdir.read_json('local_mustar') except MassBalanceCalibrationError as e: assert 'mu* out of specified bounds' in str(e) # When this happens we clip mu* to zero and store the # bad value (just for plotting) cfg.PARAMS['clip_mu_star'] = True df = gdir.read_json('local_mustar') df['mu_star_glacierwide'] = float(str(e).split(':')[-1]) climate.local_t_star(gdir) climate.mu_star_calibration(gdir) tasks.prepare_for_inversion(gdir, add_debug_var=True) v_inv, _ = tasks.mass_conservation_inversion(gdir) # Store the data calving_flux = np.append(calving_flux, f_calving) mu_star = np.append(mu_star, df['mu_star_glacierwide']) ite = np.append(ite, i) # Do we have to do another_loop? if i > 0: avg_one = np.mean(calving_flux[-4:]) avg_two = np.mean(calving_flux[-5:-1]) difference = abs(avg_two - avg_one) conv = (difference < 0.05 * avg_two or calving_flux[-1] == 0 or calving_flux[-1] == calving_flux[-2]) if mu_is_zero or conv: break i += 1 assert i < 8 assert calving_flux[-1] < np.max(calving_flux) assert calving_flux[-1] > 2 assert mu_star[-1] == 0 mbmod = massbalance.MultipleFlowlineMassBalance mb = mbmod(gdir, use_inversion_flowlines=True, mb_model_class=massbalance.ConstantMassBalance, bias=0) flux_mb = (mb.get_specific_mb() * gdir.rgi_area_m2) * 1e-9 / rho np.testing.assert_allclose(flux_mb, calving_flux[-1], atol=0.001) return calving_flux, mu_star