def test_crossval(self):
gdirs = up_to_distrib()
# before crossval
refmustars = []
for gdir in gdirs:
tdf = pd.read_csv(gdir.get_filepath('local_mustar'))
refmustars.append(tdf['mu_star'].values[0])
tasks.crossval_t_stars(gdirs)
file = os.path.join(cfg.PATHS['working_dir'], 'crossval_tstars.csv')
df = pd.read_csv(file, index_col=0)
# after crossval we need to rerun
tasks.compute_ref_t_stars(gdirs)
tasks.distribute_t_stars(gdirs)
# see if the process didn't brake anything
mustars = []
for gdir in gdirs:
tdf = pd.read_csv(gdir.get_filepath('local_mustar'))
mustars.append(tdf['mu_star'].values[0])
np.testing.assert_allclose(refmustars, mustars)
# make some mb tests
from oggm.core.models.massbalance import PastMassBalanceModel
for rid in df.index:
gdir = [g for g in gdirs if g.rgi_id == rid][0]
h, w = gdir.get_inversion_flowline_hw()
cfg.PARAMS['use_bias_for_run'] = False
mbmod = PastMassBalanceModel(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE'].to_frame(
name='ref')
for yr in mbdf.index:
mbdf.loc[yr, 'mine'] = mbmod.get_specific_mb(h, w, year=yr)
mm = mbdf.mean()
np.testing.assert_allclose(df.loc[rid].bias,
mm['mine'] - mm['ref'],
atol=1e-3)
cfg.PARAMS['use_bias_for_run'] = True
mbmod = PastMassBalanceModel(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE'].to_frame(
name='ref')
for yr in mbdf.index:
mbdf.loc[yr, 'mine'] = mbmod.get_specific_mb(h, w, year=yr)
mm = mbdf.mean()
np.testing.assert_allclose(mm['mine'], mm['ref'], atol=1e-3)
def test_crossval(self):
gdirs = up_to_distrib()
# in case we ran crossval we need to rerun
tasks.compute_ref_t_stars(gdirs)
tasks.distribute_t_stars(gdirs)
workflow.execute_entity_task(tasks.apparent_mb, gdirs)
# before crossval
refmustars = []
for gdir in gdirs:
tdf = pd.read_csv(gdir.get_filepath('local_mustar'))
refmustars.append(tdf['mu_star'].values[0])
tasks.crossval_t_stars(gdirs)
file = os.path.join(cfg.PATHS['working_dir'], 'crossval_tstars.csv')
df = pd.read_csv(file, index_col=0)
# after crossval we need to rerun
tasks.compute_ref_t_stars(gdirs)
tasks.distribute_t_stars(gdirs)
workflow.execute_entity_task(tasks.apparent_mb, gdirs)
# Test if quicker crossval is also OK
tasks.quick_crossval_t_stars(gdirs)
file = os.path.join(cfg.PATHS['working_dir'], 'crossval_tstars.csv')
dfq = pd.read_csv(file, index_col=0)
# after crossval we need to rerun
tasks.compute_ref_t_stars(gdirs)
tasks.distribute_t_stars(gdirs)
workflow.execute_entity_task(tasks.apparent_mb, gdirs)
assert np.all(np.abs(df.cv_bias) < 50)
assert np.all(np.abs(dfq.cv_bias) < 50)
# The biases aren't entirely equivalent and its ok
np.testing.assert_allclose(df.cv_prcp_fac, dfq.cv_prcp_fac)
# see if the process didn't brake anything
mustars = []
for gdir in gdirs:
tdf = pd.read_csv(gdir.get_filepath('local_mustar'))
mustars.append(tdf['mu_star'].values[0])
np.testing.assert_allclose(refmustars, mustars)
# make some mb tests
from oggm.core.massbalance import PastMassBalance
for rid in df.index:
gdir = [g for g in gdirs if g.rgi_id == rid][0]
h, w = gdir.get_inversion_flowline_hw()
cfg.PARAMS['use_bias_for_run'] = False
mbmod = PastMassBalance(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE'].to_frame(name='ref')
for yr in mbdf.index:
mbdf.loc[yr, 'mine'] = mbmod.get_specific_mb(h, w, year=yr)
mm = mbdf.mean()
np.testing.assert_allclose(df.loc[rid].bias,
mm['mine'] - mm['ref'], atol=1e-3)
cfg.PARAMS['use_bias_for_run'] = True
mbmod = PastMassBalance(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE'].to_frame(name='ref')
for yr in mbdf.index:
mbdf.loc[yr, 'mine'] = mbmod.get_specific_mb(h, w, year=yr)
mm = mbdf.mean()
np.testing.assert_allclose(mm['mine'], mm['ref'], atol=1e-3)
tasks.catchment_width_correction,
]
for task in task_list:
execute_entity_task(task, gdirs)
# Climate tasks
tasks.compute_ref_t_stars(gdirs)
execute_entity_task(tasks.local_mustar, gdirs)
# We store the associated params
mb_calib = gdirs[0].read_pickle('climate_info')['mb_calib_params']
with open(path.join(WORKING_DIR, 'mb_calib_params.json'), 'w') as fp:
json.dump(mb_calib, fp)
# Model validation
tasks.crossval_t_stars(gdirs) # for later
# Tests: for all glaciers, the mass-balance around tstar and the
# bias with observation should be approx 0
for gd in gdirs:
heights, widths = gd.get_inversion_flowline_hw()
mb_mod = ConstantMassBalance(gd, bias=0) # bias=0 because of calib!
mb = mb_mod.get_specific_mb(heights, widths)
np.testing.assert_allclose(mb, 0, atol=10) # numerical errors
mb_mod = PastMassBalance(gd) # Here we need the computed bias
refmb = gd.get_ref_mb_data().copy()
refmb['OGGM'] = mb_mod.get_specific_mb(heights, widths, year=refmb.index)
np.testing.assert_allclose(refmb.OGGM.mean(), refmb.ANNUAL_BALANCE.mean(),
atol=10)