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)
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 up_to_climate(reset=False):
"""Run the tasks you want."""
# test directory
if not os.path.exists(_TEST_DIR):
os.makedirs(_TEST_DIR)
if reset:
clean_dir(_TEST_DIR)
if not os.path.exists(CLI_LOGF):
with open(CLI_LOGF, 'wb') as f:
pickle.dump('none', f)
# Init
cfg.initialize()
# Use multiprocessing
cfg.PARAMS['use_multiprocessing'] = use_multiprocessing()
# Working dir
cfg.PATHS['working_dir'] = _TEST_DIR
cfg.PATHS['dem_file'] = get_demo_file('srtm_oetztal.tif')
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
# Read in the RGI file
rgi_file = get_demo_file('rgi_oetztal.shp')
rgidf = gpd.read_file(rgi_file)
# Make a fake marine and lake terminating glacier
cfg.PARAMS['tidewater_type'] = 4 # make lake also calve
rgidf.loc[0, 'GlacType'] = '0199'
rgidf.loc[1, 'GlacType'] = '0299'
# Use RGI6
rgidf['RGIId'] = [s.replace('RGI50', 'RGI60') for s in rgidf.RGIId]
# Be sure data is downloaded
cru.get_cru_cl_file()
# Params
cfg.PARAMS['border'] = 70
cfg.PARAMS['tstar_search_window'] = [1902, 0]
cfg.PARAMS['prcp_scaling_factor'] = 1.75
cfg.PARAMS['temp_melt'] = -1.75
cfg.PARAMS['use_kcalving_for_inversion'] = True
cfg.PARAMS['use_kcalving_for_run'] = True
# Go
gdirs = workflow.init_glacier_directories(rgidf)
try:
tasks.catchment_width_correction(gdirs[0])
except Exception:
reset = True
if reset:
# First preprocessing tasks
workflow.gis_prepro_tasks(gdirs)
return gdirs
def up_to_climate(reset=False):
"""Run the tasks you want."""
# test directory
if not os.path.exists(TEST_DIR):
os.makedirs(TEST_DIR)
if reset:
clean_dir(TEST_DIR)
if not os.path.exists(CLI_LOGF):
with open(CLI_LOGF, 'wb') as f:
pickle.dump('none', f)
# Init
cfg.initialize()
# Use multiprocessing
# We don't use mp on TRAVIS because unsure if compatible with test coverage
cfg.PARAMS['use_multiprocessing'] = not ON_TRAVIS
# Working dir
cfg.PATHS['working_dir'] = TEST_DIR
cfg.PATHS['dem_file'] = get_demo_file('srtm_oetztal.tif')
# Read in the RGI file
rgi_file = get_demo_file('rgi_oetztal.shp')
rgidf = gpd.GeoDataFrame.from_file(rgi_file)
# Be sure data is downloaded because lock doesn't work
cl = utils.get_cru_cl_file()
# Params
cfg.PARAMS['border'] = 70
cfg.PARAMS['use_optimized_inversion_params'] = True
cfg.PARAMS['tstar_search_window'] = [1902, 0]
cfg.PARAMS['invert_with_rectangular'] = False
# Go
gdirs = workflow.init_glacier_regions(rgidf)
assert gdirs[14].name == 'Hintereisferner'
try:
tasks.catchment_width_correction(gdirs[0])
except Exception:
reset = True
if reset:
# First preprocessing tasks
workflow.gis_prepro_tasks(gdirs)
return gdirs
def up_to_climate(reset=False):
"""Run the tasks you want."""
# test directory
if not os.path.exists(TEST_DIR):
os.makedirs(TEST_DIR)
if reset:
clean_dir(TEST_DIR)
if not os.path.exists(CLI_LOGF):
with open(CLI_LOGF, 'wb') as f:
pickle.dump('none', f)
# Init
cfg.initialize()
# Use multiprocessing
cfg.PARAMS['use_multiprocessing'] = use_multiprocessing()
# Working dir
cfg.PATHS['working_dir'] = TEST_DIR
cfg.PATHS['dem_file'] = get_demo_file('srtm_oetztal.tif')
# Read in the RGI file
rgi_file = get_demo_file('rgi_oetztal.shp')
rgidf = gpd.read_file(rgi_file)
# Be sure data is downloaded
cl = utils.get_cru_cl_file()
# Params
cfg.PARAMS['border'] = 70
cfg.PARAMS['optimize_inversion_params'] = True
cfg.PARAMS['use_optimized_inversion_params'] = True
cfg.PARAMS['tstar_search_window'] = [1902, 0]
cfg.PARAMS['invert_with_rectangular'] = False
cfg.PARAMS['run_mb_calibration'] = True
# Go
gdirs = workflow.init_glacier_regions(rgidf)
try:
tasks.catchment_width_correction(gdirs[0])
except Exception:
reset = True
if reset:
# First preprocessing tasks
workflow.gis_prepro_tasks(gdirs)
return gdirs
def up_to_climate(reset=False):
"""Run the tasks you want."""
# test directory
if not os.path.exists(TEST_DIR):
os.makedirs(TEST_DIR)
if reset:
clean_dir(TEST_DIR)
if not os.path.exists(CLI_LOGF):
with open(CLI_LOGF, 'wb') as f:
pickle.dump('none', f)
# Init
cfg.initialize()
# Use multiprocessing
cfg.PARAMS['use_multiprocessing'] = use_multiprocessing()
# Working dir
cfg.PATHS['working_dir'] = TEST_DIR
cfg.PATHS['dem_file'] = get_demo_file('srtm_oetztal.tif')
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
# Read in the RGI file
rgi_file = get_demo_file('rgi_oetztal.shp')
rgidf = gpd.read_file(rgi_file)
# Be sure data is downloaded
utils.get_cru_cl_file()
# Params
cfg.PARAMS['border'] = 70
cfg.PARAMS['tstar_search_window'] = [1902, 0]
cfg.PARAMS['run_mb_calibration'] = True
# Go
gdirs = workflow.init_glacier_regions(rgidf)
try:
tasks.catchment_width_correction(gdirs[0])
except Exception:
reset = True
if reset:
# First preprocessing tasks
workflow.gis_prepro_tasks(gdirs)
return gdirs
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'])
# 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, div_id=0)
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)
# 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 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
