def time_hef_run_until_in_steps():
mb_mod = massbalance.RandomMassBalance(gdir, bias=0, seed=0)
fls = gdir.read_pickle('model_flowlines')
model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0.)
for yr in np.linspace(0, 200, 400):
model.run_until(yr)
def test_random_mb_seed(self):
gdirs = up_to_inversion()
seed = None
years = np.arange(1800, 2201)
odf = pd.DataFrame(index=years)
for gd in gdirs[:6]:
mb = massbalance.RandomMassBalance(gd, y0=1970, seed=seed)
h, w = gd.get_inversion_flowline_hw()
odf[gd.rgi_id] = mb.get_specific_mb(h, w, year=years)
self.assertLessEqual(odf.corr().mean().mean(), 0.5)
seed = 1
for gd in gdirs[:6]:
mb = massbalance.RandomMassBalance(gd, y0=1970, seed=seed)
h, w = gd.get_inversion_flowline_hw()
odf[gd.rgi_id] = mb.get_specific_mb(h, w, year=years)
self.assertGreaterEqual(odf.corr().mean().mean(), 0.9)
def time_hef_run_until_and_store_with_nc():
mb_mod = massbalance.RandomMassBalance(gdir, bias=0, seed=0)
fls = gdir.read_pickle('model_flowlines')
model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0.)
model.run_until_and_store(200, run_path=os.path.join(testdir, 'run.nc'),
diag_path=os.path.join(testdir, 'diag.nc'))
def time_RandomMassBalance():
mb_mod = massbalance.RandomMassBalance(gdir, bias=0)
for yr in years:
mb_mod.get_annual_mb(heights, year=yr)
def run_uncertain_random_climate(gdir,
nyears=700,
output_filesuffix='',
sigma_t=None,
sigma_p=None,
sigma_smb=None,
rdn_temp_bias_seed=None,
rdn_prcp_bias_seed=None,
rdn_bias_seed=None,
orig_mb_tbias=0,
orig_mb_pbias=1,
orig_mb_sbias=0,
**kwargs):
"""Test stuff
Parameters
----------
gdir
nyears
output_filesuffix
rdn_temp_bias_seed
rdn_prcp_bias_seed
rdn_bias_seed
kwargs
Returns
-------
"""
# Find the optimal bias for a balanced MB
mbref = mbmods.PastMassBalance(gdir)
h, w = gdir.get_inversion_flowline_hw()
cyrs = np.unique(mbref.years)
def to_minimize(x):
mbref.bias = x
return np.mean(mbref.get_specific_mb(h, w, cyrs))
opti_bias = optimization.brentq(to_minimize, -10000, 10000, xtol=0.01)
mbref.bias = opti_bias
assert np.allclose(np.mean(mbref.get_specific_mb(h, w, cyrs)),
0,
atol=0.01)
rdf = pd.DataFrame(index=cyrs)
for y in cyrs:
rdf.loc[y, 'SMB'] = mbref.get_specific_mb(h, w, year=y)
t, _, p, _ = mbref.get_annual_climate([np.mean(h)], year=y)
rdf.loc[y, 'TEMP'] = t
rdf.loc[y, 'PRCP'] = p
if sigma_smb is None:
sigma_smb = rdf.std()['SMB'] / 2
if sigma_t is None:
sigma_t = rdf.std()['TEMP'] / 2
if sigma_p is None:
sigma_p = (rdf.std() / rdf.mean())['PRCP'] / 2
mb = mbmods.RandomMassBalance(gdir, all_years=True, seed=0)
mb.mbmod.temp_bias = orig_mb_tbias
mb.mbmod.prcp_bias = orig_mb_pbias
mb.mbmod.bias = orig_mb_sbias + opti_bias
rmb = mbmods.UncertainMassBalance(mb,
rdn_temp_bias_seed=rdn_temp_bias_seed,
rdn_temp_bias_sigma=sigma_t,
rdn_prcp_bias_seed=rdn_prcp_bias_seed,
rdn_prcp_bias_sigma=sigma_p,
rdn_bias_seed=rdn_bias_seed,
rdn_bias_sigma=sigma_smb)
return robust_model_run(gdir,
output_filesuffix=output_filesuffix,
mb_model=rmb,
ys=0,
ye=nyears,
**kwargs)
def time_hef_run_until_and_store():
mb_mod = massbalance.RandomMassBalance(gdir, bias=0, seed=0)
fls = gdir.read_pickle('model_flowlines')
model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0.)
model.run_until_and_store(200)
