def test_mb_modules_monthly(gdir):
# check if massbalance.PastMassBalance equal to mb_modules with cte
# gradient and mb_monthly as options for lapse rate mb_type
mu_star_opt_cte_var = 195.5484547754791
# if I use ERA5dr in PastMassBalance, it applies automatically the
# gradient that changes with time and location
mu_opts = [mu_star_opt_cte['mb_monthly'], mu_star_opt_cte_var]
grads = ['cte', 'var']
for k, clim in zip([0, 1], ['ERA5', 'ERA5dr']):
mu_opt = mu_opts[k]
grad_type = grads[k]
cfg.PARAMS['baseline_climate'] = clim
oggm.shop.ecmwf.process_ecmwf_data(gdir, dataset=clim)
mb_mod = mb_modules(gdir,
mu_opt,
mb_type='mb_monthly',
prcp_fac=2.5,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
hgts, widths = gdir.get_inversion_flowline_hw()
mbdf = gdir.get_ref_mb_data()
tot_mb = mb_mod.get_specific_mb(heights=hgts,
widths=widths,
year=mbdf.index.values)
cfg.PARAMS['temp_default_gradient'] = -0.0065
cfg.PARAMS['prcp_scaling_factor'] = 2.5
cfg.PARAMS['temp_all_solid'] = 0
cfg.PARAMS['temp_all_liq'] = 2
cfg.PARAMS['temp_melt'] = 0
# check if the default OGGM monthly mass balance with cte gradient
# gives the same result as the new mb_modules with the options
# mb_monthly and constant lapse rate gradient!
mb_mod_default = massbalance.PastMassBalance(gdir,
mu_star=mu_opt,
bias=0,
check_calib_params=False)
tot_mb_default = mb_mod_default.get_specific_mb(heights=hgts,
widths=widths,
year=mbdf.index.values)
assert_allclose(tot_mb, tot_mb_default, rtol=1e-4)
def test_optimize_std_quot_brentq(gdir):
# check if double optimisation of bias and std_quotient works
grad_type = 'cte'
N = 2000
loop = False
cfg.PARAMS['baseline_climate'] = 'ERA5dr'
oggm.shop.ecmwf.process_ecmwf_data(gdir, dataset='ERA5dr')
for mb_type in ['mb_monthly', 'mb_daily', 'mb_real_daily']:
if mb_type != 'mb_real_daily':
cfg.PARAMS['baseline_climate'] = 'ERA5dr'
oggm.shop.ecmwf.process_ecmwf_data(gdir, dataset='ERA5dr')
else:
cfg.PARAMS['baseline_climate'] = 'ERA5_daily'
process_era5_daily_data(gdir)
hgts, widths = gdir.get_inversion_flowline_hw()
mbdf = gdir.get_ref_mb_data()
pf_opt = scipy.optimize.brentq(optimize_std_quot_brentq,
0.01,
20,
args=(mb_type, grad_type, gdir, N,
loop),
xtol=0.01)
DDF_opt_pf = scipy.optimize.brentq(minimize_bias,
1,
10000,
args=(mb_type, grad_type, gdir, N,
pf_opt, loop, False),
disp=True,
xtol=0.1)
gd_mb = mb_modules(gdir,
DDF_opt_pf,
prcp_fac=pf_opt,
mb_type=mb_type,
grad_type=grad_type)
mb_specific = gd_mb.get_specific_mb(heights=hgts,
widths=widths,
year=mbdf.index.values)
RMSD, bias, rcor, quot_std = compute_stat(mb_specific=mb_specific,
mbdf=mbdf)
# check if the bias is optimised
assert bias.round() == 0
# check if the std_quotient is optimised
assert quot_std.round(1) == 1
def test_minimize_bias(gdir):
# important to initialize again, otherwise hydro_month_nh=1
# from test_hydro_years_HEF...
# just checks if minimisation gives always same results
grad_type = 'cte'
N = 2000
loop = False
for mb_type in ['mb_real_daily', 'mb_monthly', 'mb_daily']:
if mb_type != 'mb_real_daily':
cfg.PARAMS['baseline_climate'] = 'ERA5dr'
oggm.shop.ecmwf.process_ecmwf_data(gdir, dataset='ERA5dr')
else:
cfg.PARAMS['baseline_climate'] = 'ERA5_daily'
process_era5_daily_data(gdir)
DDF_opt = scipy.optimize.brentq(minimize_bias,
1,
10000,
disp=True,
xtol=0.1,
args=(mb_type, grad_type, gdir, N, pf,
loop, False))
hgts, widths = gdir.get_inversion_flowline_hw()
mbdf = gdir.get_ref_mb_data()
# check if they give the same optimal DDF
# print(mu_star_opt_cte[mb_type], DDF_opt)
assert np.round(mu_star_opt_cte[mb_type] / DDF_opt, 3) == 1
gd_mb = mb_modules(gdir, DDF_opt, mb_type=mb_type, grad_type=grad_type)
mb_specific = gd_mb.get_specific_mb(heights=hgts,
widths=widths,
year=mbdf.index.values)
RMSD, bias, rcor, quot_std = compute_stat(mb_specific=mb_specific,
mbdf=mbdf)
# check if the bias is optimised
assert bias.round() == 0
def test_N(gdir):
# tests whether modelled mb_daily massbalances of different values of N
# is similar to observed mass balances
# this could be optimised and included in the above tests
climate = 'ERA5dr'
mb_type = 'mb_daily'
cfg.PARAMS['baseline_climate'] = 'ERA5dr'
oggm.shop.ecmwf.process_ecmwf_data(gdir, dataset="ERA5dr")
for grad_type in ['cte', 'var_an_cycle']:
if grad_type == 'var_an_cycle':
fail_err_4 = (mb_type == 'mb_monthly') and (climate == 'CRU')
mu_star_opt = mu_star_opt_var
else:
fail_err_4 = False
mu_star_opt = mu_star_opt_cte
if fail_err_4:
with pytest.raises(InvalidParamsError):
mb_mod = mb_modules(gdir,
mu_star_opt[mb_type],
mb_type=mb_type,
prcp_fac=pf,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
else:
mbdf = gdir.get_ref_mb_data()
hgts, widths = gdir.get_inversion_flowline_hw()
tot_mb_N = {}
for N in [10000, 5000, 1000, 500, 100]:
mb_mod = mb_modules(gdir,
mu_star_opt[mb_type],
mb_type=mb_type,
prcp_fac=pf,
N=N,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
tot_mb_N[N] = mb_mod.get_specific_mb(heights=hgts,
widths=widths,
year=mbdf.index.values)
assert np.abs(utils.md(tot_mb_N[N],
mbdf['ANNUAL_BALANCE'])) < 10
# %%
# something like that could also be included later on
# bbut only if I somehow get the data from the climate files ....
# or can I use these things with the ELA... without the climate files...
# in the moment oggm.core.climate works only with default OGGM mass balance
# def test_mb_modules(self, hef_gdir):
# rho = cfg.PARAMS['ice_density']
# F = SEC_IN_YEAR * rho
# gdir = hef_gdir
# init_present_time_glacier(gdir)
# df = gdir.read_json('local_mustar')
# mu_star = df['mu_star_glacierwide']
# bias = df['bias']
# # Climate period
# yrp = [1851, 2000]
# # Flowlines height
# h, w = gdir.get_inversion_flowline_hw()
# mb_mod = massbalance.PastMassBalance(gdir, bias=0)
# for i, yr in enumerate(np.arange(yrp[0], yrp[1]+1)):
# ref_mb_on_h = p[:, i] - mu_star * t[:, i]
# my_mb_on_h = mb_mod.get_annual_mb(h, yr) * F
# np.testing.assert_allclose(ref_mb_on_h, my_mb_on_h,
# atol=1e-2)
# ela_z = mb_mod.get_ela(year=yr)
# totest = mb_mod.get_annual_mb([ela_z], year=yr) * F
# assert_allclose(totest[0], 0, atol=1)
# mb_mod = massbalance.PastMassBalance(gdir)
# for i, yr in enumerate(np.arange(yrp[0], yrp[1]+1)):
# ref_mb_on_h = p[:, i] - mu_star * t[:, i]
# my_mb_on_h = mb_mod.get_annual_mb(h, yr) * F
# np.testing.assert_allclose(ref_mb_on_h, my_mb_on_h + bias,
# atol=1e-2)
# ela_z = mb_mod.get_ela(year=yr)
# totest = mb_mod.get_annual_mb([ela_z], year=yr) * F
# assert_allclose(totest[0], 0, atol=1)
# for i, yr in enumerate(np.arange(yrp[0], yrp[1]+1)):
# ref_mb_on_h = p[:, i] - mu_star * t[:, i]
# my_mb_on_h = ref_mb_on_h*0.
# for m in np.arange(12):
# yrm = utils.date_to_floatyear(yr, m + 1)
# tmp = mb_mod.get_monthly_mb(h, yrm) * SEC_IN_MONTH * rho
# my_mb_on_h += tmp
# np.testing.assert_allclose(ref_mb_on_h,
# my_mb_on_h + bias,
# atol=1e-2)
# # real data
# h, w = gdir.get_inversion_flowline_hw()
# mbdf = gdir.get_ref_mb_data()
# mbdf.loc[yr, 'MY_MB'] = np.NaN
# mb_mod = massbalance.PastMassBalance(gdir)
# for yr in mbdf.index.values:
# my_mb_on_h = mb_mod.get_annual_mb(h, yr) * SEC_IN_YEAR * rho
# mbdf.loc[yr, 'MY_MB'] = np.average(my_mb_on_h, weights=w)
# np.testing.assert_allclose(mbdf['ANNUAL_BALANCE'].mean(),
# mbdf['MY_MB'].mean(),
# atol=1e-2)
# mbdf['MY_ELA'] = mb_mod.get_ela(year=mbdf.index.values)
# assert mbdf[['MY_ELA', 'MY_MB']].corr().values[0, 1] < -0.9
# assert mbdf[['MY_ELA', 'ANNUAL_BALANCE']].corr().values[0, 1] < -0.7
# mb_mod = massbalance.PastMassBalance(gdir, bias=0)
# for yr in mbdf.index.values:
# my_mb_on_h = mb_mod.get_annual_mb(h, yr) * SEC_IN_YEAR * rho
# mbdf.loc[yr, 'MY_MB'] = np.average(my_mb_on_h, weights=w)
# np.testing.assert_allclose(mbdf['ANNUAL_BALANCE'].mean() + bias,
# mbdf['MY_MB'].mean(),
# atol=1e-2)
# mb_mod = massbalance.PastMassBalance(gdir)
# for yr in mbdf.index.values:
# my_mb_on_h = mb_mod.get_annual_mb(h, yr) * SEC_IN_YEAR * rho
# mbdf.loc[yr, 'MY_MB'] = np.average(my_mb_on_h, weights=w)
# mb_mod.temp_bias = 1
# my_mb_on_h = mb_mod.get_annual_mb(h, yr) * SEC_IN_YEAR * rho
# mbdf.loc[yr, 'BIASED_MB'] = np.average(my_mb_on_h, weights=w)
# mb_mod.temp_bias = 0
# np.testing.assert_allclose(mbdf['ANNUAL_BALANCE'].mean(),
# mbdf['MY_MB'].mean(),
# atol=1e-2)
# assert mbdf.ANNUAL_BALANCE.mean() > mbdf.BIASED_MB.mean()
# # Repeat
# mb_mod = massbalance.PastMassBalance(gdir, repeat=True,
# ys=1901, ye=1950)
# yrs = np.arange(100) + 1901
# mb = mb_mod.get_specific_mb(h, w, year=yrs)
# assert_allclose(mb[50], mb[-50])
def test_loop(gdir):
# tests whether ERA5dr works better with or without the loop in mb_daily
# tests that both option give same results and in case that default option
# (no loop) is 30% slower, it raises an error
# this could be optimised and included in the above tests
cfg.initialize()
climate = 'ERA5dr'
mb_type = 'mb_daily'
cfg.PARAMS['baseline_climate'] = 'ERA5dr'
oggm.shop.ecmwf.process_ecmwf_data(gdir, dataset="ERA5dr")
for grad_type in ['cte', 'var_an_cycle']:
if grad_type == 'var_an_cycle':
fail_err_4 = (mb_type == 'mb_monthly') and (climate == 'CRU')
mu_star_opt = mu_star_opt_var
else:
fail_err_4 = False
mu_star_opt = mu_star_opt_cte
if fail_err_4:
with pytest.raises(InvalidParamsError):
mb_modules(gdir,
mu_star_opt[mb_type],
mb_type=mb_type,
prcp_fac=pf,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
else:
mbdf = gdir.get_ref_mb_data()
ys = mbdf.index.values
hgts, widths = gdir.get_inversion_flowline_hw()
ex_t = time.time()
for t in np.arange(10):
mb_mod_noloop = mb_modules(gdir,
mu_star_opt[mb_type],
mb_type=mb_type,
prcp_fac=pf,
loop=False,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
tot_mb_noloop = mb_mod_noloop.get_specific_mb(heights=hgts,
widths=widths,
year=ys)
ex_noloop = time.time() - ex_t
ex_t = time.time()
for t in np.arange(10):
mb_mod_loop = mb_modules(gdir,
mu_star_opt[mb_type],
mb_type=mb_type,
prcp_fac=pf,
loop=True,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
tot_mb_loop = mb_mod_loop.get_specific_mb(heights=hgts,
widths=widths,
year=ys)
ex_loop = time.time() - ex_t
# both should give the same results!!!
assert_allclose(tot_mb_loop, tot_mb_noloop, atol=1e-2)
# if the loop would be at least 30% faster than not using the loop
# raise an error
assert (ex_loop - ex_noloop) / ex_noloop > -0.3
def test_monthly_glacier_massbalance(gdir):
# TODO: problem with that, monthly and annual MB not exactly the same!!!
# I think there is a problem with SEC_IN_MONTH/SEC_IN_YEAR ...
# do this for all model types
# ONLY TEST it for ERA5dr or ERA5_daily!!!
for climate in ['ERA5dr', 'ERA5_daily']:
for mb_type in ['mb_monthly', 'mb_daily', 'mb_real_daily']:
for grad_type in ['cte', 'var_an_cycle']:
if grad_type == 'var_an_cycle':
fail_err_4 = ((mb_type == 'mb_monthly')
and (climate == 'CRU'))
mu_star_opt = mu_star_opt_var
else:
fail_err_4 = False
mu_star_opt = mu_star_opt_cte
if climate == 'ERA5dr':
cfg.PARAMS['baseline_climate'] = 'ERA5dr'
oggm.shop.ecmwf.process_ecmwf_data(gdir, dataset="ERA5dr")
elif climate == 'ERA5_daily':
process_era5_daily_data(gdir)
cfg.PARAMS['baseline_climate'] = 'ERA5_daily'
else:
tasks.process_climate_data(gdir)
pass
# mb_type ='mb_daily'
fail_err_1 = (mb_type == 'mb_daily') and (climate != 'ERA5dr')
fail_err_2 = ((mb_type == 'mb_monthly')
and (climate == 'ERA5_daily'))
fail_err_3 = ((mb_type == 'mb_real_daily')
and (climate != 'ERA5_daily'))
if fail_err_1 or fail_err_2 or fail_err_3 or fail_err_4:
with pytest.raises(InvalidParamsError):
mb_mod = mb_modules(gdir,
mu_star_opt[mb_type],
mb_type=mb_type,
prcp_fac=pf,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
else:
# but this is just a test for reproducibility!
mb_mod = mb_modules(gdir,
mu_star_opt[mb_type],
mb_type=mb_type,
prcp_fac=pf,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
hgts, widths = gdir.get_inversion_flowline_hw()
rho = 900 # ice density
yrp = [1980, 2018]
for i, yr in enumerate(np.arange(yrp[0], yrp[1] + 1)):
my_mon_mb_on_h = 0.
dayofyear = 0
for m in np.arange(12):
yrm = utils.date_to_floatyear(yr, m + 1)
_, dayofmonth = monthrange(yr, m + 1)
dayofyear += dayofmonth
tmp = (mb_mod.get_monthly_mb(hgts, yrm) *
dayofmonth * SEC_IN_DAY * rho)
my_mon_mb_on_h += tmp
my_an_mb_on_h = (mb_mod.get_annual_mb(hgts, yr) *
dayofyear * SEC_IN_DAY * rho)
# these large errors might come from the problematic of
# different amount of days in a year?
# or maybe it just does not fit ...
assert_allclose(np.mean(my_an_mb_on_h -
my_mon_mb_on_h),
0,
atol=100)
def test_present_time_glacier_massbalance(gdir):
# check if area of HUSS flowlines corresponds to the rgi area
h, w = gdir.get_inversion_flowline_hw()
fls = gdir.read_pickle('inversion_flowlines')
assert_allclose(gdir.rgi_area_m2, np.sum(w * gdir.grid.dx * fls[0].dx))
# do this for all model types
# ONLY TEST it for ERA5dr or ERA5_daily!!!
for climate in ['ERA5dr', 'ERA5_daily']:
for mb_type in ['mb_monthly', 'mb_daily', 'mb_real_daily']:
for grad_type in ['cte', 'var_an_cycle']:
if grad_type == 'var_an_cycle':
fail_err_4 = ((mb_type == 'mb_monthly')
and (climate == 'CRU'))
mu_star_opt = mu_star_opt_var
else:
fail_err_4 = False
mu_star_opt = mu_star_opt_cte
if climate == 'ERA5dr':
cfg.PARAMS['baseline_climate'] = 'ERA5dr'
oggm.shop.ecmwf.process_ecmwf_data(gdir, dataset="ERA5dr")
elif climate == 'ERA5_daily':
cfg.PARAMS['baseline_climate'] = 'ERA5_daily'
process_era5_daily_data(gdir)
else:
tasks.process_climate_data(gdir)
pass
fail_err_1 = (mb_type == 'mb_daily') and (climate != 'ERA5dr')
fail_err_2 = ((mb_type == 'mb_monthly')
and (climate == 'ERA5_daily'))
fail_err_3 = ((mb_type == 'mb_real_daily')
and (climate != 'ERA5_daily'))
if fail_err_1 or fail_err_2 or fail_err_3 or fail_err_4:
with pytest.raises(InvalidParamsError):
mb_mod = mb_modules(gdir,
mu_star_opt[mb_type],
mb_type=mb_type,
prcp_fac=pf,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
else:
# this is just a test for reproducibility!
mb_mod = mb_modules(gdir,
mu_star_opt[mb_type],
mb_type=mb_type,
prcp_fac=pf,
t_solid=0,
t_liq=2,
t_melt=0,
default_grad=-0.0065,
bias=0,
grad_type=grad_type)
mbdf = gdir.get_ref_mb_data()
hgts, widths = gdir.get_inversion_flowline_hw()
tot_mb = []
refmb = []
grads = hgts * 0
for yr, mb in mbdf.iterrows():
refmb.append(mb['ANNUAL_BALANCE'])
mbh = (mb_mod.get_annual_mb(hgts, yr) * SEC_IN_YEAR *
cfg.PARAMS['ice_density'])
grads += mbh
tot_mb.append(np.average(mbh, weights=widths))
grads /= len(tot_mb)
# check if calibrated total mass balance similar
# to observe mass balance time series
assert np.abs(utils.md(tot_mb, refmb)) < 50