def test_find_tstars(self):
hef_file = get_demo_file('Hintereisferner.shp')
rgidf = gpd.GeoDataFrame.from_file(hef_file)
# loop because for some reason indexing wont work
gdirs = []
for index, entity in rgidf.iterrows():
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
geometry.initialize_flowlines(gdir)
geometry.catchment_area(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
gdirs.append(gdir)
climate.distribute_climate_data(gdirs)
climate.mu_candidates(gdir, div_id=0)
hef_file = get_demo_file('mbdata_RGI40-11.00897.csv')
mbdf = pd.read_csv(hef_file).set_index('YEAR')
t_stars, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE'])
y, t, p = climate.mb_yearly_climate_on_glacier(gdir, div_id=0)
# which years to look at
selind = np.searchsorted(y, mbdf.index)
t = t[selind]
p = p[selind]
mu_yr_clim = gdir.read_pickle('mu_candidates', div_id=0)
for t_s, rmd in zip(t_stars, bias):
mb_per_mu = p - mu_yr_clim.loc[t_s] * t
md = utils.md(mbdf['ANNUAL_BALANCE'], mb_per_mu)
np.testing.assert_allclose(md, rmd)
self.assertTrue(np.abs(md / np.mean(mbdf['ANNUAL_BALANCE'])) < 0.1)
r = utils.corrcoef(mbdf['ANNUAL_BALANCE'], mb_per_mu)
self.assertTrue(r > 0.8)
def test_find_tstars(self):
hef_file = get_demo_file('Hintereisferner.shp')
rgidf = gpd.GeoDataFrame.from_file(hef_file)
# loop because for some reason indexing wont work
gdirs = []
for index, entity in rgidf.iterrows():
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
geometry.initialize_flowlines(gdir)
geometry.catchment_area(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
gdirs.append(gdir)
climate.distribute_climate_data(gdirs)
climate.mu_candidates(gdir, div_id=0)
hef_file = get_demo_file('mbdata_RGI40-11.00897.csv')
mbdf = pd.read_csv(hef_file).set_index('YEAR')
t_stars, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE'])
y, t, p = climate.mb_yearly_climate_on_glacier(gdir, div_id=0)
# which years to look at
selind = np.searchsorted(y, mbdf.index)
t = t[selind]
p = p[selind]
mu_yr_clim = gdir.read_pickle('mu_candidates', div_id=0)
for t_s, rmd in zip(t_stars, bias):
mb_per_mu = p - mu_yr_clim.loc[t_s] * t
md = utils.md(mbdf['ANNUAL_BALANCE'], mb_per_mu)
np.testing.assert_allclose(md, rmd)
self.assertTrue(np.abs(md/np.mean(mbdf['ANNUAL_BALANCE'])) < 0.1)
r = utils.corrcoef(mbdf['ANNUAL_BALANCE'], mb_per_mu)
self.assertTrue(r > 0.8)
tasks.catchment_width_geom(gdir)
tasks.catchment_width_correction(gdir)
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
tasks.process_custom_climate_data(gdir)
tasks.mu_candidates(gdir)
mbdf = gdir.get_ref_mb_data()
res = t_star_from_refmb(gdir, mbdf.ANNUAL_BALANCE)
local_mustar_apparent_mb(gdir,
tstar=res['t_star'][-1],
bias=res['bias'][-1],
prcp_fac=res['prcp_fac'])
# For plots
mu_yr_clim = gdir.read_pickle('mu_candidates')[pcp_fac]
years, temp_yr, prcp_yr = mb_yearly_climate_on_glacier(gdir, pcp_fac, div_id=0)
# which years to look at
selind = np.searchsorted(years, mbdf.index)
temp_yr = np.mean(temp_yr[selind])
prcp_yr = np.mean(prcp_yr[selind])
# Average oberved mass-balance
ref_mb = mbdf.ANNUAL_BALANCE.mean()
mb_per_mu = prcp_yr - mu_yr_clim * temp_yr
# Diff to reference
diff = mb_per_mu - ref_mb
pdf = pd.DataFrame()
pdf[r'$\mu (t)$'] = mu_yr_clim
pdf['bias'] = diff
tasks.glacier_masks(gdir)
tasks.compute_centerlines(gdir)
tasks.compute_centerlines(gdir)
tasks.initialize_flowlines(gdir)
tasks.catchment_area(gdir)
tasks.catchment_width_geom(gdir)
tasks.catchment_width_correction(gdir)
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
tasks.process_custom_climate_data(gdir)
tasks.mu_candidates(gdir)
# For plots
mu_yr_clim = gdir.read_pickle('mu_candidates')[pcp_fac]
mbdf = gdir.get_ref_mb_data()
years, temp_yr, prcp_yr = mb_yearly_climate_on_glacier(gdir, pcp_fac, div_id=0)
# which years to look at
selind = np.searchsorted(years, mbdf.index)
temp_yr = np.mean(temp_yr[selind])
prcp_yr = np.mean(prcp_yr[selind])
# Average oberved mass-balance
ref_mb = mbdf.ANNUAL_BALANCE.mean()
mb_per_mu = prcp_yr - mu_yr_clim * temp_yr
# Diff to reference
diff = mb_per_mu - ref_mb
pdf = pd.DataFrame()
pdf[r'$\mu (t)$'] = mu_yr_clim
pdf['bias'] = diff
def test_find_tstars_prcp_fac(self):
hef_file = get_demo_file("Hintereisferner.shp")
entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0]
mb_file = get_demo_file("RGI_WGMS_oetztal.csv")
mb_file = os.path.join(os.path.dirname(mb_file), "mbdata", "mbdata_RGI40-11.00897.csv")
cfg.PARAMS["prcp_auto_scaling_factor"] = True
gdirs = []
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
geometry.initialize_flowlines(gdir)
geometry.catchment_area(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
gdirs.append(gdir)
climate.process_histalp_nonparallel(gdirs)
climate.mu_candidates(gdir, div_id=0)
mbdf = pd.read_csv(mb_file).set_index("YEAR")["ANNUAL_BALANCE"]
t_stars, bias, prcp_fac = climate.t_star_from_refmb(gdir, mbdf)
y, t, p = climate.mb_yearly_climate_on_glacier(gdir, prcp_fac, div_id=0)
# which years to look at
selind = np.searchsorted(y, mbdf.index)
t = t[selind]
p = p[selind]
dffac = gdir.read_pickle("prcp_fac_optim").loc[prcp_fac]
np.testing.assert_allclose(dffac["avg_bias"], np.mean(bias))
mu_yr_clim = gdir.read_pickle("mu_candidates", div_id=0)[prcp_fac]
std_bias = []
for t_s, rmd in zip(t_stars, bias):
mb_per_mu = p - mu_yr_clim.loc[t_s] * t
md = utils.md(mbdf, mb_per_mu)
np.testing.assert_allclose(md, rmd, rtol=1e-4)
self.assertTrue(np.abs(md / np.mean(mbdf)) < 0.1)
r = utils.corrcoef(mbdf, mb_per_mu)
self.assertTrue(r > 0.8)
std_bias.append(np.std(mb_per_mu) - np.std(mbdf))
np.testing.assert_allclose(dffac["avg_std_bias"], np.mean(std_bias), rtol=1e-4)
# test crop years
cfg.PARAMS["tstar_search_window"] = [1902, 0]
climate.mu_candidates(gdir, div_id=0)
t_stars, bias, prcp_fac = climate.t_star_from_refmb(gdir, mbdf)
mu_yr_clim = gdir.read_pickle("mu_candidates", div_id=0)[prcp_fac]
y, t, p = climate.mb_yearly_climate_on_glacier(gdir, prcp_fac, div_id=0)
selind = np.searchsorted(y, mbdf.index)
t = t[selind]
p = p[selind]
for t_s, rmd in zip(t_stars, bias):
mb_per_mu = p - mu_yr_clim.loc[t_s] * t
md = utils.md(mbdf, mb_per_mu)
np.testing.assert_allclose(md, rmd, rtol=1e-4)
self.assertTrue(np.abs(md / np.mean(mbdf)) < 0.1)
r = utils.corrcoef(mbdf, mb_per_mu)
self.assertTrue(r > 0.8)
self.assertTrue(t_s >= 1902)
# test distribute
cfg.PATHS["wgms_rgi_links"] = get_demo_file("RGI_WGMS_oetztal.csv")
climate.compute_ref_t_stars(gdirs)
climate.distribute_t_stars(gdirs)
cfg.PARAMS["tstar_search_window"] = [0, 0]
df = pd.read_csv(gdir.get_filepath("local_mustar"))
np.testing.assert_allclose(df["t_star"], t_s)
np.testing.assert_allclose(df["bias"], rmd)
np.testing.assert_allclose(df["prcp_fac"], prcp_fac)
cfg.PARAMS["prcp_auto_scaling_factor"] = False
