def test_run(self):
entity = gpd.read_file(self.rgi_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
gis.define_glacier_region(gdir)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# Climate tasks -- only data IO and tstar interpolation!
tasks.process_dummy_cru_file(gdir, seed=0)
tasks.local_t_star(gdir)
tasks.mu_star_calibration(gdir)
# Inversion tasks
tasks.find_inversion_calving(gdir)
# Final preparation for the run
tasks.init_present_time_glacier(gdir)
# check that calving happens in the real context as well
tasks.run_constant_climate(gdir,
bias=0,
nyears=200,
temperature_bias=-0.5)
with xr.open_dataset(gdir.get_filepath('model_diagnostics')) as ds:
assert ds.calving_m3[-1] > 10
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.glacier_mu_candidates(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
res = climate.t_star_from_refmb(gdir, mbdf=mbdf)
tasks.local_t_star(gdir, tstar=res['t_star'], bias=res['bias'])
tasks.mu_star_calibration(gdir)
tasks.prepare_for_inversion(gdir)
tasks.mass_conservation_inversion(gdir)
return gdir
rids = pd.read_csv(os.path.join(WORKING_DIR, 'mb_ref_glaciers.csv'),
index_col=0, squeeze=True)
# Go - initialize glacier directories
gdirs = workflow.init_glacier_directories(rids)
# Cross-validation
file = os.path.join(cfg.PATHS['working_dir'], 'ref_tstars.csv')
ref_df = pd.read_csv(file, index_col=0)
for i, gdir in enumerate(gdirs):
print('Cross-validation iteration {} of {}'.format(i + 1, len(ref_df)))
# Now recalibrate the model blindly
tmp_ref_df = ref_df.loc[ref_df.index != gdir.rgi_id]
tasks.local_t_star(gdir, ref_df=tmp_ref_df)
tasks.mu_star_calibration(gdir)
# Mass-balance model with cross-validated parameters instead
mb_mod = MultipleFlowlineMassBalance(gdir, mb_model_class=PastMassBalance,
use_inversion_flowlines=True)
# Mass-balance timeseries, observed and simulated
refmb = gdir.get_ref_mb_data().copy()
refmb['OGGM'] = mb_mod.get_specific_mb(year=refmb.index)
# Compare their standard deviation
std_ref = refmb.ANNUAL_BALANCE.std()
rcor = np.corrcoef(refmb.OGGM, refmb.ANNUAL_BALANCE)[0, 1]
if std_ref == 0:
# I think that such a thing happens with some geodetic values
cfg.set_intersects_db(utils.get_rgi_intersects_entities(['RGI60-18.02342']))
gdir = oggm.GlacierDirectory(entity, base_dir=dir_path)
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.local_t_star(gdir)
tasks.mu_star_calibration(gdir)
glen_a = cfg.PARAMS['glen_a']
tasks.prepare_for_inversion(gdir)
tasks.mass_conservation_inversion(gdir, glen_a=glen_a, fs=0)
tasks.filter_inversion_output(gdir)
# run
tasks.init_present_time_glacier(gdir)
tasks.run_random_climate(gdir,
bias=0.,
temperature_bias=-1,
nyears=120,
glen_a=glen_a,
check_for_boundaries=False)
def quick_crossval_entity(gdir, full_ref_df=None):
tmpdf = pd.DataFrame(
[], columns=['std_oggm', 'std_ref', 'rmse', 'core', 'bias'])
# the reference glaciers
tmp_ref_df = full_ref_df.loc[full_ref_df.index != gdir.rgi_id]
# before the cross-val store the info about "real" mustar
ref_rdf = gdir.read_json('local_mustar')
tasks.local_t_star(gdir, ref_df=tmp_ref_df)
tasks.mu_star_calibration(gdir)
# read crossvalidated values
cv_rdf = gdir.read_json('local_mustar')
# ----
# --- MASS-BALANCE MODEL
mb_mod = MultipleFlowlineMassBalance(gdir, use_inversion_flowlines=True)
# Mass-balance timeseries, observed and simulated
refmb = gdir.get_ref_mb_data().copy()
refmb['OGGM'] = mb_mod.get_specific_mb(year=refmb.index)
# store single glacier results
bias = refmb.OGGM.mean() - refmb.ANNUAL_BALANCE.mean()
rmse = np.sqrt(np.mean(refmb.OGGM - refmb.ANNUAL_BALANCE)**2)
rcor = np.corrcoef(refmb.OGGM, refmb.ANNUAL_BALANCE)[0, 1]
ref_std = refmb.ANNUAL_BALANCE.std()
# unclear how to treat this best
if ref_std == 0:
ref_std = refmb.OGGM.std()
rcor = 1
tmpdf.loc[len(tmpdf.index)] = {
'std_oggm': refmb.OGGM.std(),
'std_ref': ref_std,
'bias': bias,
'rmse': rmse,
'core': rcor
}
# and store mean values
out = {
'prcpsf': cfg.PARAMS['prcp_scaling_factor'],
'tliq': cfg.PARAMS['temp_all_liq'],
'tmelt': cfg.PARAMS['temp_melt'],
'tgrad': cfg.PARAMS['temp_default_gradient'],
'std_oggm': tmpdf.std_oggm.values[0],
'std_ref': tmpdf.std_ref.values[0],
'std_quot': np.nan,
'bias': tmpdf['bias'].mean(),
'rmse': tmpdf['rmse'].mean(),
'core': tmpdf['core'].mean()
}
# combine "real" mustar and crossvalidated mu_star
# get rid of mu_star_per_flowline as list of flowlines is ugly to deal with
for i, fl in enumerate(cv_rdf['mu_star_per_flowline']):
cv_rdf['mustar_flowline_{:03d}'.format(i + 1)] = fl
for i, fl in enumerate(ref_rdf['mu_star_per_flowline']):
ref_rdf['mustar_flowline_{:03d}'.format(i + 1)] = fl
del cv_rdf['mu_star_per_flowline']
del ref_rdf['mu_star_per_flowline']
for col in cv_rdf.keys():
if 'rgi_id' in col:
continue
ref_rdf['cv_' + col] = cv_rdf[col]
return [out, ref_rdf]
