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
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)
from oggm.core.preprocessing.climate import mb_yearly_climate_on_glacier, \
t_star_from_refmb, local_mustar_apparent_mb
from oggm.core.models.massbalance import PastMassBalanceModel
cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
pcp_fac = 2.6
cfg.PARAMS['prcp_scaling_factor'] = pcp_fac
base_dir = os.path.join(os.path.expanduser('~'), 'Climate')
entity = gpd.read_file(get_demo_file('Hintereisferner.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.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_apparent_mb(gdir,
tstar=res['t_star'][-1],
bias=res['bias'][-1],
prcp_fac=res['prcp_fac'])
from oggm.core.preprocessing.climate import mb_yearly_climate_on_glacier, \
t_star_from_refmb
cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PATHS['wgms_rgi_links'] = get_demo_file('RGI_WGMS_oetztal.csv')
pcp_fac = 2.6
cfg.PARAMS['prcp_scaling_factor'] = pcp_fac
base_dir = os.path.join(os.path.expanduser('~'), 'Climate')
entity = gpd.read_file(get_demo_file('Hintereisferner.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.compute_centerlines(gdir)
tasks.initialize_flowlines(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)
# For plots
mu_yr_clim = gdir.read_pickle('mu_candidates')[pcp_fac]
mbdf = gdir.get_ref_mb_data()
years, temp_yr, prcp_yr = mb_yearly_climate_on_glacier(gdir, pcp_fac, div_id=0)
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
cfg.PARAMS['fixed_dx'] = 40
cfg.PARAMS['border'] = 10
entity = gpd.read_file(get_demo_file('Hintereisferner.shp'))
hef = workflow.init_glacier_regions(entity, reset=False)[0]
input_shp = hef.get_filepath('outlines', div_id=0)
input_dem = hef.get_filepath('dem', div_id=0)
# filter options
f_area = False
f_alt_range = True
f_perc_alt_range = True
# set paths to python 2.7 and to the partitioning package
python = 'path to python 2.7'
project = 'path to the partitioning package'
script = os.path.join(project, 'partitioning/examples/run_divides.py')
os.system(python + ' ' + script + ' ' + input_shp + ' ' + input_dem + ' ' +
str(f_area) + ' ' + str(f_alt_range) + ' ' +
str(f_perc_alt_range))
print('Hintereisferner is divided into', hef.n_divides, 'parts.')
tasks.glacier_masks(hef)
tasks.compute_centerlines(hef)
graphics.plot_centerlines(hef)
plt.show()
