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
if np.isnan(w_depth):
w_depth = thick - t_altitude
else:
w_depth = thick - t_altitude
fl = gdir.read_pickle('inversion_flowlines')[-1]
width = fl.widths[-1] * gdir.grid.dx
# Lets compute the theoretical calving out of it
pre_calving = k * thick * w_depth * width / 1e9
gdir.inversion_calving_rate = pre_calving
print('pre_calving', pre_calving)
# Recompute all with calving
tasks.distribute_t_stars([gdir], minimum_mustar=0.)
tasks.apparent_mb(gdir)
tasks.prepare_for_inversion(gdir, add_debug_var=True)
tasks.volume_inversion(gdir, glen_a=cfg.A, fs=cfg.FS)
df = pd.read_csv(gdir.get_filepath('local_mustar')).iloc[0]
mu_star = df['mu_star']
while i < 50:
# First calculates a calving flux from model output
F_calving, new_depth, new_thick, t_width = calving_from_depth(
gdir, k)
# Stores the data, and we see it
data_calving += [F_calving]
w_depth = np.append(w_depth, new_depth)
thick = np.append(thick, new_thick)
