def get_apparent_climate(gdir):
# Climate period
mu_hp = int(cfg.PARAMS['mu_star_halfperiod'])
df = pd.read_csv(gdir.get_filepath('local_mustar'))
tstar = df['tstar']
yr = [tstar-mu_hp, tstar+mu_hp]
# Ok. Looping over divides
for div_id in list(gdir.divide_ids):
# For each flowline compute the apparent MB
# For div 0 it is kind of artificial but this is for validation
df = pd.read_csv(gdir.get_filepath('local_mustar'), div_id=div_id)
fls = []
if div_id == 0:
for i in gdir.divide_ids:
fls.extend(gdir.read_pickle('inversion_flowlines', div_id=i))
else:
fls = gdir.read_pickle('inversion_flowlines', div_id=div_id)
# Reset flux
for fl in fls:
fl.flux = np.zeros(len(fl.surface_h))
# Flowlines in order to be sure
for fl in fls:
y, t, p = mb_yearly_climate_on_height(gdir, fl.surface_h,
year_range=yr,
flatten=False)
fl.set_apparent_mb(np.mean(p, axis=1) - df['mustar']*np.mean(t,
axis=1))
def __init__(self, gdir, bias=0.):
""" Instanciate."""
super(TstarMassBalanceModel, self).__init__(bias)
df = pd.read_csv(gdir.get_filepath('local_mustar', div_id=0))
mu_star = df['mu_star'][0]
t_star = df['t_star'][0]
# Climate period
mu_hp = int(cfg.PARAMS['mu_star_halfperiod'])
yr = [t_star - mu_hp, t_star + mu_hp]
fls = gdir.read_pickle('model_flowlines')
h = np.array([])
for fl in fls:
h = np.append(h, fl.surface_h)
h = np.linspace(np.min(h) - 200, np.max(h) + 1200, 1000)
y, t, p = climate.mb_yearly_climate_on_height(gdir, h, year_range=yr)
t = np.mean(t, axis=1)
p = np.mean(p, axis=1)
mb_on_h = p - mu_star * t
self.interp = interp1d(h, mb_on_h)
self.t_star = t_star
def __init__(self, gdir, bias=0.):
""" Instanciate."""
super(TodayMassBalanceModel, self).__init__(bias)
df = pd.read_csv(gdir.get_filepath('local_mustar', div_id=0))
mu_star = df['mu_star'][0]
t_star = df['t_star'][0]
# Climate period
# TODO temporary solution until https://github.com/OGGM/oggm/issues/88
# is implemented
fpath = gdir.get_filepath('climate_monthly')
with netCDF4.Dataset(fpath, mode='r') as nc:
# time
time = nc.variables['time']
time = netCDF4.num2date(time[:], time.units)
last_yr = time[-1].year
yr = [last_yr - 30, last_yr]
fls = gdir.read_pickle('model_flowlines')
h = np.array([])
for fl in fls:
h = np.append(h, fl.surface_h)
h = np.linspace(np.min(h) - 100, np.max(h) + 200, 1000)
y, t, p = climate.mb_yearly_climate_on_height(gdir, h, year_range=yr)
t = np.mean(t, axis=1)
p = np.mean(p, axis=1)
mb_on_h = p - mu_star * t
self.interp = interp1d(h, mb_on_h)
def test_local_mustar(self):
hef_file = get_demo_file('Hintereisferner.shp')
rgidf = gpd.GeoDataFrame.from_file(hef_file)
# loop because for some reason indexing wont work
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)
climate.distribute_climate_data([gdir])
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_star, bias = climate.t_star_from_refmb(gdir, mbdf['ANNUAL_BALANCE'])
t_star = t_star[-1]
bias = bias[-1]
climate.local_mustar_apparent_mb(gdir, tstar=t_star, bias=bias)
df = pd.read_csv(gdir.get_filepath('local_mustar', div_id=0))
mu_ref = gdir.read_pickle('mu_candidates', div_id=0).loc[t_star]
np.testing.assert_allclose(mu_ref, df['mu_star'][0], atol=1e-3)
# Check for apparent mb to be zeros
for i in [0] + list(gdir.divide_ids):
fls = gdir.read_pickle('inversion_flowlines', div_id=i)
tmb = 0.
for fl in fls:
self.assertTrue(fl.apparent_mb.shape == fl.widths.shape)
tmb += np.sum(fl.apparent_mb * fl.widths)
np.testing.assert_allclose(tmb, 0., atol=0.01)
if i == 0: continue
np.testing.assert_allclose(fls[-1].flux[-1], 0., atol=0.01)
# ------ Look for gradient
# which years to look at
fls = gdir.read_pickle('inversion_flowlines', div_id=0)
mb_on_h = np.array([])
h = np.array([])
for fl in fls:
y, t, p = climate.mb_yearly_climate_on_height(gdir, fl.surface_h)
selind = np.searchsorted(y, mbdf.index)
t = np.mean(t[:, selind], axis=1)
p = np.mean(p[:, selind], axis=1)
mb_on_h = np.append(mb_on_h, p - mu_ref * t)
h = np.append(h, fl.surface_h)
dfg = pd.read_csv(get_demo_file('mbgrads_RGI40-11.00897.csv'),
index_col='ALTITUDE').mean(axis=1)
# Take the altitudes below 3100 and fit a line
dfg = dfg[dfg.index < 3100]
pok = np.where(h < 3100)
from scipy.stats import linregress
slope_obs, _, _, _, _ = linregress(dfg.index, dfg.values)
slope_our, _, _, _, _ = linregress(h[pok], mb_on_h[pok])
np.testing.assert_allclose(slope_obs, slope_our, rtol=0.1)
def test_yearly_mb_climate(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)
gdirs.append(gdir)
climate.distribute_climate_data(gdirs)
nc_r = netCDF4.Dataset(get_demo_file('histalp_merged_hef.nc'))
ref_h = nc_r.variables['hgt'][1, 1]
ref_p = nc_r.variables['prcp'][:, 1, 1]
ref_p *= cfg.PARAMS['prcp_scaling_factor']
ref_t = nc_r.variables['temp'][:, 1, 1]
ref_t = np.where(ref_t < 0, 0, ref_t)
nc_r.close()
# NORMAL --------------------------------------------------------------
hgts = np.array([ref_h, ref_h, -8000, 8000])
years, temp, prcp = climate.mb_yearly_climate_on_height(gdir, hgts)
ref_nt = 202
self.assertTrue(len(years) == ref_nt)
self.assertTrue(temp.shape == (4, ref_nt))
self.assertTrue(prcp.shape == (4, ref_nt))
yr = [1802, 1802]
years, temp, prcp = climate.mb_yearly_climate_on_height(gdir,
hgts,
year_range=yr)
ref_nt = 1
self.assertTrue(len(years) == ref_nt)
self.assertTrue(years == 1802)
self.assertTrue(temp.shape == (4, ref_nt))
self.assertTrue(prcp.shape == (4, ref_nt))
np.testing.assert_allclose(temp[0, :], np.sum(ref_t[0:12]))
np.testing.assert_allclose(temp[0, :], temp[1, :])
np.testing.assert_allclose(prcp[0, :], prcp[1, :])
np.testing.assert_allclose(prcp[3, :], np.sum(ref_p[0:12]))
np.testing.assert_allclose(prcp[2, :], np.sum(ref_p[0:12]) * 0)
np.testing.assert_allclose(temp[3, :], np.sum(ref_p[0:12]) * 0)
yr = [1803, 1804]
years, temp, prcp = climate.mb_yearly_climate_on_height(gdir,
hgts,
year_range=yr)
ref_nt = 2
self.assertTrue(len(years) == ref_nt)
np.testing.assert_allclose(years, yr)
self.assertTrue(temp.shape == (4, ref_nt))
self.assertTrue(prcp.shape == (4, ref_nt))
np.testing.assert_allclose(prcp[2, :], [0, 0])
np.testing.assert_allclose(temp[3, :], [0, 0])
# FLATTEN -------------------------------------------------------------
hgts = np.array([ref_h, ref_h, -8000, 8000])
years, temp, prcp = climate.mb_yearly_climate_on_height(gdir,
hgts,
flatten=True)
ref_nt = 202
self.assertTrue(len(years) == ref_nt)
self.assertTrue(temp.shape == (ref_nt, ))
self.assertTrue(prcp.shape == (ref_nt, ))
yr = [1802, 1802]
hgts = np.array([ref_h])
years, temp, prcp = climate.mb_yearly_climate_on_height(gdir,
hgts,
year_range=yr,
flatten=True)
ref_nt = 1
self.assertTrue(len(years) == ref_nt)
self.assertTrue(years == 1802)
self.assertTrue(temp.shape == (ref_nt, ))
self.assertTrue(prcp.shape == (ref_nt, ))
np.testing.assert_allclose(temp[:], np.sum(ref_t[0:12]))
yr = [1802, 1802]
hgts = np.array([8000])
years, temp, prcp = climate.mb_yearly_climate_on_height(gdir,
hgts,
year_range=yr,
flatten=True)
np.testing.assert_allclose(prcp[:], np.sum(ref_p[0:12]))
