def _run_parallel_experiment(gdir):
# read flowlines from pre-processing
fls = gdir.read_pickle('model_flowlines')
try:
# construct searched glacier
# TODO: y0 in random mass balance?
random_climate1 = RandomMassBalance(gdir, y0=1850, bias=0, seed=[1])
random_climate1.temp_bias = -0.75
commit_model = FluxBasedModel(fls, mb_model=random_climate1,
glen_a=cfg.A, y0=1850)
commit_model.run_until_equilibrium()
y_t0 = deepcopy(commit_model)
# try different seed of mass balance, if equilibrium could not be found
except:
# construct searched glacier
random_climate1 = RandomMassBalance(gdir, y0=1850, bias=0,seed=[1])
commit_model = FluxBasedModel(fls, mb_model=random_climate1,
glen_a=cfg.A, y0=1850)
commit_model.run_until_equilibrium()
y_t0 = deepcopy(commit_model)
# construct observed glacier
past_climate = PastMassBalance(gdir)
commit_model2 = FluxBasedModel(commit_model.fls, mb_model=past_climate,
glen_a=cfg.A, y0=1850)
commit_model2.run_until(2000)
y_t = deepcopy(commit_model2)
# save output in gdir_dir
experiment = {'y_t0': y_t0, 'y_t': y_t,
'climate': random_climate1}
gdir.write_pickle(experiment, 'synthetic_experiment')
def _run_parallel_experiment(gdir, t0, te):
'''
:param gdir:
:param t0:
:param te:
:return:
'''
# read flowlines from pre-processing
fls = gdir.read_pickle('model_flowlines')
try:
# construct searched glacier
random_climate1 = RandomMassBalance(gdir, y0=t0, halfsize=14)
#set temp bias negative to force a glacier retreat later
random_climate1.temp_bias = -0.75
commit_model = FluxBasedModel(fls,
mb_model=random_climate1,
glen_a=cfg.A,
y0=t0)
commit_model.run_until_equilibrium()
y_t0 = deepcopy(commit_model)
# try different seed of mass balance, if equilibrium could not be found
except:
# construct searched glacier
random_climate1 = RandomMassBalance(gdir, y0=t0, halfsize=14)
commit_model = FluxBasedModel(fls,
mb_model=random_climate1,
glen_a=cfg.A,
y0=t0)
commit_model.run_until_equilibrium()
y_t0 = deepcopy(commit_model)
# construct observed glacier
past_climate = PastMassBalance(gdir)
commit_model2 = FluxBasedModel(commit_model.fls,
mb_model=past_climate,
glen_a=cfg.A,
y0=t0)
commit_model2.run_until(te)
y_t = deepcopy(commit_model2)
# save output in gdir_dir
experiment = {'y_t0': y_t0, 'y_t': y_t, 'climate': random_climate1}
gdir.write_pickle(experiment, 'synthetic_experiment')
y0=0., glen_a=glen_a,
ice_thick_filter=ice_thick_filter)
dmodel.run_until_and_store(800, run_path=outf, grid=grid,
print_stdout=print_stdout)
# Multiprocessing
tasks = []
mbmod = RandomMassBalance(gdir, seed=0)
outf = db_dir + 'out_def_100m.nc'
tasks.append((run_task, {'grid': grid, 'mb_model': mbmod, 'outf': outf,
'ice_thick_filter':filter_ice_border,
'print_stdout':'Task1'}))
mbmod = RandomMassBalance(gdir, seed=0)
mbmod.temp_bias = 0.5
outf = db_dir + 'out_tbias_100m.nc'
tasks.append((run_task, {'grid': grid, 'mb_model': mbmod, 'outf': outf,
'ice_thick_filter':filter_ice_border,
'print_stdout': 'Task2'}))
mbmod = RandomMassBalance(gdir, seed=0)
outf = db_dir + 'out_filter_100m.nc'
tasks.append((run_task, {'grid': grid, 'mb_model': mbmod, 'outf': outf,
'ice_thick_filter':filter_ice_tributaries,
'print_stdout': 'Task3'}))
mbmod = RandomMassBalance(gdir, seed=0)
mbmod.temp_bias = 0.5
outf = db_dir + 'out_filter_bias_100m.nc'
tasks.append((run_task, {'grid': grid, 'mb_model': mbmod, 'outf': outf,