tasks.glacier_masks(gdir)
tasks.compute_centerlines(gdir)
tasks.initialize_flowlines(gdir)
tasks.compute_downstream_line(gdir)
tasks.catchment_area(gdir)
tasks.catchment_width_geom(gdir)
tasks.catchment_width_correction(gdir)
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
tasks.process_custom_climate_data(gdir)
tasks.mu_candidates(gdir)
mbdf = gdir.get_ref_mb_data()
res = t_star_from_refmb(gdir, mbdf.ANNUAL_BALANCE)
local_mustar(gdir,
tstar=res['t_star'][-1],
bias=res['bias'][-1],
prcp_fac=res['prcp_fac'],
reset=True)
apparent_mb(gdir, reset=True)
# For flux plot
tasks.prepare_for_inversion(gdir, add_debug_var=True)
# For plots
mu_yr_clim = gdir.read_pickle('mu_candidates')[pcp_fac]
years, temp_yr, prcp_yr = mb_yearly_climate_on_glacier(gdir, pcp_fac)
# which years to look at
selind = np.searchsorted(years, mbdf.index)
temp_yr = np.mean(temp_yr[selind])
prcp_yr = np.mean(prcp_yr[selind])
tasks.compute_centerlines(gdir)
tasks.initialize_flowlines(gdir)
tasks.compute_downstream_line(gdir)
tasks.catchment_area(gdir)
tasks.catchment_width_geom(gdir)
tasks.catchment_width_correction(gdir)
data_dir = get_demo_file('HISTALP_precipitation_all_abs_1801-2014.nc')
cfg.PATHS['cru_dir'] = os.path.dirname(data_dir)
cfg.PARAMS['baseline_climate'] = 'HISTALP'
cfg.PARAMS['baseline_y0'] = 1850
tasks.process_histalp_data(gdir)
tasks.mu_candidates(gdir)
mbdf = gdir.get_ref_mb_data()
res = t_star_from_refmb(gdir, mbdf.ANNUAL_BALANCE)
local_mustar(gdir, tstar=res['t_star'], bias=res['bias'], reset=True)
apparent_mb(gdir, reset=True)
# For flux plot
tasks.prepare_for_inversion(gdir, add_debug_var=True)
# For plots
mu_yr_clim = gdir.read_pickle('climate_info')['mu_candidates']
years, temp_yr, prcp_yr = mb_yearly_climate_on_glacier(gdir)
# which years to look at
selind = np.searchsorted(years, mbdf.index)
temp_yr = np.mean(temp_yr[selind])
prcp_yr = np.mean(prcp_yr[selind])
# Average oberved mass-balance
def init_hef(reset=False,
border=40,
invert_with_sliding=True,
invert_with_rectangular=True):
# test directory
testdir = os.path.join(get_test_dir(), 'tmp_border{}'.format(border))
if not invert_with_sliding:
testdir += '_withoutslide'
if not invert_with_rectangular:
testdir += '_withoutrectangular'
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
# Init
cfg.initialize()
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['border'] = border
cfg.PARAMS['use_optimized_inversion_params'] = True
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset)
if not gdir.has_file('inversion_params'):
reset = True
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset)
if not reset:
return gdir
gis.define_glacier_region(gdir, entity=entity)
execute_entity_task(gis.glacier_masks, [gdir])
execute_entity_task(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.process_custom_climate_data(gdir)
climate.mu_candidates(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
res = climate.t_star_from_refmb(gdir, mbdf)
climate.local_mustar(gdir,
tstar=res['t_star'][-1],
bias=res['bias'][-1],
prcp_fac=res['prcp_fac'])
climate.apparent_mb(gdir)
inversion.prepare_for_inversion(
gdir,
add_debug_var=True,
invert_with_rectangular=invert_with_rectangular)
ref_v = 0.573 * 1e9
if invert_with_sliding:
def to_optimize(x):
# For backwards compat
_fd = 1.9e-24 * x[0]
glen_a = (cfg.N + 2) * _fd / 2.
fs = 5.7e-20 * x[1]
v, _ = inversion.mass_conservation_inversion(gdir,
fs=fs,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1, 1],
bounds=((0.01, 10), (0.01, 10)),
tol=1e-4)['x']
_fd = 1.9e-24 * out[0]
glen_a = (cfg.N + 2) * _fd / 2.
fs = 5.7e-20 * out[1]
v, _ = inversion.mass_conservation_inversion(gdir,
fs=fs,
glen_a=glen_a,
write=True)
else:
def to_optimize(x):
glen_a = cfg.A * x[0]
v, _ = inversion.mass_conservation_inversion(gdir,
fs=0.,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1],
bounds=((0.01, 10), ),
tol=1e-4)['x']
glen_a = cfg.A * out[0]
fs = 0.
v, _ = inversion.mass_conservation_inversion(gdir,
fs=fs,
glen_a=glen_a,
write=True)
d = dict(fs=fs, glen_a=glen_a)
d['factor_glen_a'] = out[0]
try:
d['factor_fs'] = out[1]
except IndexError:
d['factor_fs'] = 0.
gdir.write_pickle(d, 'inversion_params')
# filter
inversion.filter_inversion_output(gdir)
inversion.distribute_thickness_interp(gdir, varname_suffix='_interp')
inversion.distribute_thickness_per_altitude(gdir, varname_suffix='_alt')
flowline.init_present_time_glacier(gdir)
return gdir