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)
t_width = t_width
# print('Calving rate calculated', F_calving)
else:
# we read the McNabb width
width = dfwidth[index[0][0]]
# print('width',width)
# Lets compute the theoretical calving out of it
pre_calving = 2 * 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])
tasks.apparent_mb(gdir)
tasks.prepare_for_inversion(gdir, add_debug_var=True)
tasks.volume_inversion(gdir)
while i < 50:
# First calculates a calving flux from model output
F_calving, new_depth, new_thick, t_width = calving_from_depth(
gdir)
# Stores the data, and we see it
data_calving += [F_calving]
w_depth += [new_depth]
thick += [new_thick]
t_width = t_width
# print('Calving rate calculated', F_calving)
# We put the calving function output into the Model
tasks.catchment_width_geom,
tasks.catchment_width_correction
]
for task in task_list:
execute_entity_task(task, gdirs)
# "Climate related tasks"
for gd in gdirs:
itmix.synth_apparent_mb(gd)
# Inversion
execute_entity_task(tasks.prepare_for_inversion, gdirs)
fac = 3.22268124479468
use_cfg_params = {'glen_a':fac * cfg.A, 'fs':0.}
for gd in gdirs:
tasks.volume_inversion(gd, use_cfg_params=use_cfg_params)
if do_itmix:
done = False
# V1
distrib = partial(distribute_thickness, how='per_altitude',
add_slope=True,
smooth=True)
execute_entity_task(distrib, gdirs)
pdir = os.path.join(PLOTS_DIR, 'out_dis') + '/'
if not os.path.exists(pdir):
os.mkdir(pdir)
for gd in gdirs:
# print('t_altitude', t_altitude)
# print('depth', w_depth)
# print('thick', thick)
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=1.611e-24, 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)
t_width = t_width
# print('Calving rate calculated', F_calving)
diff = mb_per_mu - ref_mb
pdf = pd.DataFrame()
pdf[r'$\mu (t)$'] = mu_yr_clim
pdf['bias'] = diff
res = t_star_from_refmb(gdir, mbdf.ANNUAL_BALANCE)
# For the mass flux
cl = gdir.read_pickle('inversion_input')[-1]
mbmod = ConstantMassBalance(gdir)
mbx = mbmod.get_annual_mb(cl['hgt']) * cfg.SEC_IN_YEAR * cfg.RHO
fdf = pd.DataFrame(index=np.arange(len(mbx)) * cl['dx'])
fdf['Flux'] = cl['flux']
fdf['Mass balance'] = mbx
# For the distributed thickness
tasks.volume_inversion(gdir, glen_a=cfg.A * 3, fs=0)
tasks.distribute_thickness_per_altitude(gdir)
# plot functions
def example_plot_temp_ts():
d = xr.open_dataset(gdir.get_filepath('climate_monthly'))
temp = d.temp.resample(time='12MS').mean('time').to_series()
del temp.index.name
ax = temp.plot(figsize=(8, 4), label='Annual temp')
temp.rolling(31, center=True,
min_periods=15).mean().plot(label='31-yr avg')
plt.legend(loc='best')
plt.title('HISTALP annual temperature, Hintereisferner')
plt.ylabel(r'degC')
plt.tight_layout()
# print('t_altitude', t_altitude)
# print('depth', w_depth)
# print('thick', thick)
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=3.339e-24, fs=0.0)
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)
t_width = t_width
# print('Calving rate calculated', F_calving)
# print('t_altitude', t_altitude)
# print('depth', w_depth)
# print('thick', thick)
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=2.3435e-24, fs=0.0)
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)
t_width = t_width
# print('Calving rate calculated', F_calving)
thick = cl['volume'][-1] / cl['dx'] / width
print('Without calving the width is {} m and the thick is {} m'.format(width, thick))
# Try many C's (they should make sense in comparison to the size of the glacier)
c_candidates = np.linspace(0, 0.1, 10)
for c in c_candidates:
# First guess calving
gdir.inversion_calving_rate = c
# Recompute mu
tasks.distribute_t_stars([gdir])
# Inversion
tasks.prepare_for_inversion(gdir)
tasks.volume_inversion(gdir)
# We read the output, last pixel of the inversion
cl = gdir.read_pickle('inversion_output', div_id=1)[-1]
width = cl['width'][-1]
thick = cl['volume'][-1] / cl['dx'] / width
print('With calving of {} km3 a-1 the width is {} m and the thick is {} m'.format(gdir.inversion_calving_rate, width, thick))
# Now compute the calving rate that would come from H and width
# Compare to the old calving and see if it has to be reduced or increased
# And in the very end, alle these steps should be automatized with
# an optimisation function
tasks.catchment_area, tasks.initialize_flowlines,
tasks.catchment_width_geom, tasks.catchment_width_correction
]
for task in task_list:
execute_entity_task(task, gdirs)
# "Climate related tasks"
for gd in gdirs:
itmix.synth_apparent_mb(gd)
# Inversion
execute_entity_task(tasks.prepare_for_inversion, gdirs)
fac = 3.22268124479468
use_cfg_params = {'glen_a': fac * cfg.A, 'fs': 0.}
for gd in gdirs:
tasks.volume_inversion(gd, use_cfg_params=use_cfg_params)
if do_itmix:
done = False
# V1
distrib = partial(distribute_thickness,
how='per_altitude',
add_slope=True,
smooth=True)
execute_entity_task(distrib, gdirs)
pdir = os.path.join(PLOTS_DIR, 'out_dis') + '/'
if not os.path.exists(pdir):
os.mkdir(pdir)
for gd in gdirs:
# print('t_altitude', t_altitude)
# print('depth', w_depth)
# print('thick', thick)
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=2.3346e-24, 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)
t_width = t_width
# print('Calving rate calculated', F_calving)
