def test_init_present_time_glacier(self):
gdirs = up_to_inversion()
# Inversion Results
cfg.PARAMS['invert_with_sliding'] = True
cfg.PARAMS['optimize_thick'] = True
workflow.inversion_tasks(gdirs)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df = pd.read_csv(fpath, index_col=0)
r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3'])
assert r1 < 0.1
cfg.PARAMS['invert_with_sliding'] = False
cfg.PARAMS['optimize_thick'] = False
workflow.inversion_tasks(gdirs)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df = pd.read_csv(fpath, index_col=0)
r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3'])
assert r1 < 0.12
# Init glacier
d = gdirs[0].read_pickle('inversion_params')
fs = d['fs']
glen_a = d['glen_a']
for gdir in gdirs:
flowline.init_present_time_glacier(gdir)
mb_mod = massbalance.ConstantMassBalance(gdir)
fls = gdir.read_pickle('model_flowlines')
model = flowline.FluxBasedModel(fls,
mb_model=mb_mod,
y0=0.,
fs=fs,
glen_a=glen_a)
_vol = model.volume_km3
_area = model.area_km2
if gdir.rgi_id in df.index:
gldf = df.loc[gdir.rgi_id]
assert_allclose(gldf['oggm_volume_km3'], _vol, rtol=0.05)
assert_allclose(gldf['ref_area_km2'], _area, rtol=0.05)
maxo = max([fl.order for fl in model.fls])
for fl in model.fls:
if len(model.fls) > 1:
if fl.order == (maxo - 1):
self.assertTrue(fl.flows_to is fls[-1])
# Test the glacier charac
dfc = utils.glacier_characteristics(gdirs)
self.assertTrue(np.all(dfc.terminus_type == 'Land-terminating'))
cc = dfc[['flowline_mean_elev',
'tstar_avg_temp_mean_elev']].corr().values[0, 1]
assert cc < -0.8
assert np.all(dfc.t_star > 1900)
def time_ConstantMassBalance():
mb_mod = massbalance.ConstantMassBalance(gdir, bias=0)
for yr in years:
mb_mod.get_annual_mb(heights, year=yr)
def test_varying_bias_mb_model(self, case_dir):
cfg.initialize()
cfg.PARAMS['prcp_scaling_factor'] = 1.6
cfg.PATHS['working_dir'] = case_dir
cfg.PARAMS['use_multiprocessing'] = True
# Go - get the pre-processed glacier directories
for rid in ['RGI60-03.04384', 'RGI60-11.00897', 'RGI60-16.02207']:
gdirs = workflow.init_glacier_directories(
[rid],
from_prepro_level=5,
prepro_base_url=prepro_base_url,
prepro_border=80,
prepro_rgi_version='62')
workflow.execute_entity_task(parse_dt_per_dt, gdirs)
gdir = gdirs[0]
exp = 'netzero_py2020_fac1.0_decr0.3'
magicc_file = magicc_dir + exp + '.nc'
with xr.open_dataset(utils.file_downloader(magicc_file),
decode_times=False) as ds:
ds = ds.load()
df = ds['ens_avg'].to_dataframe()
dt_per_dt = gdir.get_diagnostics()['magicc_dt_per_dt']
dp_per_dt = gdir.get_diagnostics()['magicc_dp_per_dt']
fls = gdir.read_pickle('model_flowlines')
y0 = 2014
hs = 5
mbc = massbalance.ConstantMassBalance(gdir, y0=y0, halfsize=hs)
mb_ref = massbalance.PastMassBalance(gdir)
years = np.arange(1980, 2020, dtype=np.float64)
df['mb_ref'] = pd.Series(index=years,
data=mb_ref.get_specific_mb(fls=fls,
year=years))
mb = MagiccMassBalance(gdir,
y0=y0,
halfsize=hs,
magicc_ts=df['ens_avg'],
dt_per_dt=dt_per_dt)
df['mb'] = mb.get_specific_mb(fls=fls, year=df.index)
mb = MagiccMassBalance(gdir,
y0=y0,
halfsize=hs,
magicc_ts=df['ens_avg'],
dt_per_dt=dt_per_dt,
dp_per_dt=dp_per_dt)
df['mb_wp'] = mb.get_specific_mb(fls=fls, year=df.index)
mb = MagiccMassBalance(gdir,
y0=y0,
halfsize=hs,
magicc_ts=df['ens_avg'])
df['mb_nodt'] = mb.get_specific_mb(fls=fls, year=df.index)
assert_allclose(df.loc[y0 - hs:y0 + hs]['mb'].mean(),
mbc.get_specific_mb(fls=fls),
rtol=5e-3)
assert_allclose(df.loc[y0 - hs:y0 + hs]['mb_ref'].mean(),
df.loc[y0 - hs:y0 + hs]['mb'].mean(),
atol=1e-2)
assert_allclose(df.loc[y0 - hs:y0 + hs]['mb_ref'].mean(),
df.loc[y0 - hs:y0 + hs]['mb_wp'].mean(),
atol=1e-2)
assert_allclose(df.loc[y0 - hs:y0 + hs]['mb_ref'].mean(),
df.loc[y0 - hs:y0 + hs]['mb_nodt'].mean(),
atol=1e-2)
assert_allclose(df[['ens_avg', 'mb']].corr().min().min(),
-1,
rtol=1e-2)
assert_allclose(df[['ens_avg', 'mb_nodt']].corr().min().min(),
-1,
rtol=1e-2)
assert_allclose(df[['mb_wp', 'mb']].corr().min().min(),
1,
rtol=1e-2)
assert_allclose(df['mb'], df['mb_wp'], atol=115)
if DO_PLOT:
plt.figure()
df[['mb_ref', 'mb', 'mb_wp',
'mb_nodt']].loc[2000:2025].plot(title=rid)
plt.figure()
df[['mb_ref', 'mb', 'mb_wp',
'mb_nodt']].loc[2000:].plot(title=rid)
plt.show()