def to_minimize(mu_star):
mbmod = ConstantMassBalance(gdir,
mu_star=mu_star,
bias=0,
check_calib_params=False,
y0=df['t_star'])
smb = mbmod.get_annual_mb(heights=topo)
return np.sum(smb)**2
def test_constant_mb_model(self, hef_gdir):
y0 = 2000
halfsize = 15
combine_mb_model = ConstantMassBalanceTorch(hef_gdir,
y0=y0,
halfsize=halfsize)
oggm_mb_model = ConstantMassBalance(hef_gdir, y0=y0, halfsize=halfsize)
test_height_shift = 100.
combine_mb_model_shift = ConstantMassBalanceTorch(
hef_gdir, y0=y0, halfsize=halfsize, height_shift=test_height_shift)
fls = hef_gdir.read_pickle('model_flowlines')
h = []
for fl in fls:
# We use bed because of overdeepenings
h = np.append(h, fl.bed_h)
h = np.append(h, fl.surface_h)
zminmax = np.round([np.min(h), np.max(h)])
heights = np.linspace(zminmax[0], zminmax[1], num=20)
heights_torch = torch.tensor(heights, dtype=torch.double)
heights_torch_shift = heights_torch + torch.tensor(test_height_shift,
dtype=torch.double)
# test annual
oggm_annual_mbs = oggm_mb_model.get_annual_mb(heights)
combine_annual_mbs = combine_mb_model.get_annual_mb(heights_torch)
combine_annual_mbs_shift = \
combine_mb_model_shift.get_annual_mb(heights_torch_shift)
assert np.allclose(oggm_annual_mbs, combine_annual_mbs)
assert type(combine_annual_mbs) == torch.Tensor
assert combine_annual_mbs.shape == heights_torch.shape
assert np.allclose(combine_annual_mbs, combine_annual_mbs_shift)
assert type(combine_annual_mbs_shift) == torch.Tensor
assert combine_annual_mbs_shift.shape == heights_torch_shift.shape
# test monthly
for month in np.arange(12) + 1:
yr = date_to_floatyear(0, month)
oggm_monthly_mbs = oggm_mb_model.get_monthly_mb(heights, year=yr)
combine_monthly_mbs = combine_mb_model.get_monthly_mb(
heights_torch, year=yr)
combine_monthly_mbs_shift = \
combine_mb_model_shift.get_monthly_mb(heights_torch_shift, year=yr)
assert np.allclose(oggm_monthly_mbs, combine_monthly_mbs)
assert type(combine_monthly_mbs) == torch.Tensor
assert combine_monthly_mbs.shape == heights_torch.shape
assert np.allclose(combine_monthly_mbs, combine_monthly_mbs_shift)
assert type(combine_monthly_mbs_shift) == torch.Tensor
assert combine_monthly_mbs_shift.shape == heights_torch_shift.shape
# 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
res = t_star_from_refmb(gdir, mbdf=mbdf.ANNUAL_BALANCE)
# For the mass flux
cl = gdir.read_pickle('inversion_input')[-1]
mbmod = ConstantMassBalance(gdir)
mbx = (mbmod.get_annual_mb(cl['hgt']) * cfg.SEC_IN_YEAR *
cfg.PARAMS['ice_density'])
fdf = pd.DataFrame(index=np.arange(len(mbx)) * cl['dx'])
fdf['Flux'] = cl['flux']
fdf['Mass balance'] = mbx
# For the distributed thickness
tasks.mass_conservation_inversion(gdir, glen_a=2.4e-24 * 3, fs=0)
tasks.distribute_thickness_per_altitude(gdir)
# plot functions
def example_plot_temp_ts():
d = xr.open_dataset(gdir.get_filepath('climate_historical'))
temp = d.temp.resample(time='12MS').mean('time').to_series()
temp.index = temp.index.year
def test_mb(self):
# This is a function to produce the MB function needed by Anna
# Download the RGI file for the run
# Make a new dataframe of those
rgidf = gpd.read_file(get_demo_file('SouthGlacier.shp'))
# Go - initialize working directories
gdirs = workflow.init_glacier_regions(rgidf)
# Preprocessing tasks
task_list = [
tasks.glacier_masks,
tasks.compute_centerlines,
tasks.initialize_flowlines,
tasks.catchment_area,
tasks.catchment_intersections,
tasks.catchment_width_geom,
tasks.catchment_width_correction,
]
for task in task_list:
execute_entity_task(task, gdirs)
# Climate tasks -- only data IO and tstar interpolation!
execute_entity_task(tasks.process_cru_data, gdirs)
tasks.distribute_t_stars(gdirs)
execute_entity_task(tasks.apparent_mb, gdirs)
mbref = salem.GeoTiff(get_demo_file('mb_SouthGlacier.tif'))
demref = salem.GeoTiff(get_demo_file('dem_SouthGlacier.tif'))
mbref = mbref.get_vardata()
mbref[mbref == -9999] = np.NaN
demref = demref.get_vardata()[np.isfinite(mbref)]
mbref = mbref[np.isfinite(mbref)] * 1000
# compute the bias to make it 0 SMB on the 2D DEM
mbmod = ConstantMassBalance(gdirs[0], bias=0)
mymb = mbmod.get_annual_mb(demref) * cfg.SEC_IN_YEAR * cfg.RHO
mbmod = ConstantMassBalance(gdirs[0], bias=np.average(mymb))
mymb = mbmod.get_annual_mb(demref) * cfg.SEC_IN_YEAR * cfg.RHO
np.testing.assert_allclose(np.average(mymb), 0., atol=1e-3)
# Same for ref
mbref = mbref - np.average(mbref)
np.testing.assert_allclose(np.average(mbref), 0., atol=1e-3)
# Fit poly
p = np.polyfit(demref, mbref, deg=2)
poly = np.poly1d(p)
myfit = poly(demref)
np.testing.assert_allclose(np.average(myfit), 0., atol=1e-3)
if do_plot:
import matplotlib.pyplot as plt
plt.scatter(mbref, demref, s=5, label='Obs (2007-2012), shifted to '
'Avg(SMB) = 0')
plt.scatter(mymb, demref, s=5, label='OGGM MB at t*')
plt.scatter(myfit, demref, s=5, label='Polyfit', c='C3')
plt.xlabel('MB (mm w.e yr-1)')
plt.ylabel('Altidude (m)')
plt.legend()
plt.show()
class BiasedConstantMassBalance(MassBalanceModel):
"""Time-dependant Temp and PRCP delta ConstantMassBalance model"""
def __init__(self,
gdir,
temp_bias_ts=None,
prcp_fac_ts=None,
mu_star=None,
bias=None,
y0=None,
halfsize=15,
filename='climate_historical',
input_filesuffix='',
**kwargs):
"""Initialize
Parameters
----------
gdir : GlacierDirectory
the glacier directory
magicc_ts : pd.Series
the GMT time series
mu_star : float, optional
set to the alternative value of mu* you want to use
(the default is to use the calibrated value)
bias : float, optional
set to the alternative value of the annual bias [mm we yr-1]
you want to use (the default is to use the calibrated value)
y0 : int, optional, default: tstar
the year at the center of the period of interest. The default
is to use tstar as center.
dt_per_dt : float, optional, default 1
the local climate change signal, in units of °C per °C
halfsize : int, optional
the half-size of the time window (window size = 2 * halfsize + 1)
filename : str, optional
set to a different BASENAME if you want to use alternative climate
data.
input_filesuffix : str
the file suffix of the input climate file
"""
super(BiasedConstantMassBalance, self).__init__()
self.mbmod = ConstantMassBalance(gdir,
mu_star=mu_star,
bias=bias,
y0=y0,
halfsize=halfsize,
filename=filename,
input_filesuffix=input_filesuffix,
**kwargs)
self.valid_bounds = self.mbmod.valid_bounds
self.hemisphere = gdir.hemisphere
# Set ys and ye
self.ys = int(temp_bias_ts.index[0])
self.ye = int(temp_bias_ts.index[-1])
if prcp_fac_ts is None:
prcp_fac_ts = temp_bias_ts * 0
self.prcp_fac_ts = self.mbmod.prcp_fac + prcp_fac_ts
self.temp_bias_ts = temp_bias_ts
@property
def temp_bias(self):
"""Temperature bias to add to the original series."""
return self.mbmod.temp_bias
@temp_bias.setter
def temp_bias(self, value):
"""Temperature bias to add to the original series."""
self.mbmod.temp_bias = value
@property
def prcp_fac(self):
"""Precipitation factor to apply to the original series."""
return self.mbmod.prcp_fac
@prcp_fac.setter
def prcp_fac(self, value):
"""Precipitation factor to apply to the original series."""
self.mbmod.prcp_fac = value
@property
def bias(self):
"""Residual bias to apply to the original series."""
return self.mbmod.bias
@bias.setter
def bias(self, value):
"""Residual bias to apply to the original series."""
self.mbmod.bias = value
def _check_bias(self, year):
t = np.asarray(self.temp_bias_ts.loc[int(year)])
if np.any(t != self.temp_bias):
self.temp_bias = t
p = np.asarray(self.prcp_fac_ts.loc[int(year)])
if np.any(p != self.prcp_fac):
self.prcp_fac = p
def get_monthly_mb(self, heights, year=None, **kwargs):
self._check_bias(year)
return self.mbmod.get_monthly_mb(heights, year=year, **kwargs)
def get_annual_mb(self, heights, year=None, **kwargs):
self._check_bias(year)
return self.mbmod.get_annual_mb(heights, year=year, **kwargs)
def test_mb(self):
# This is a function to produce the MB function needed by Anna
# Download the RGI file for the run
# Make a new dataframe of those
rgidf = gpd.read_file(get_demo_file('SouthGlacier.shp'))
# Go - initialize working directories
gdirs = workflow.init_glacier_regions(rgidf)
# Preprocessing tasks
task_list = [
tasks.glacier_masks,
tasks.compute_centerlines,
tasks.initialize_flowlines,
tasks.catchment_area,
tasks.catchment_intersections,
tasks.catchment_width_geom,
tasks.catchment_width_correction,
tasks.process_cru_data,
tasks.local_t_star,
tasks.mu_star_calibration,
]
for task in task_list:
execute_entity_task(task, gdirs)
mbref = salem.GeoTiff(get_demo_file('mb_SouthGlacier.tif'))
demref = salem.GeoTiff(get_demo_file('dem_SouthGlacier.tif'))
mbref = mbref.get_vardata()
mbref[mbref == -9999] = np.NaN
demref = demref.get_vardata()[np.isfinite(mbref)]
mbref = mbref[np.isfinite(mbref)] * 1000
# compute the bias to make it 0 SMB on the 2D DEM
rho = cfg.PARAMS['ice_density']
mbmod = ConstantMassBalance(gdirs[0], bias=0)
mymb = mbmod.get_annual_mb(demref) * cfg.SEC_IN_YEAR * rho
mbmod = ConstantMassBalance(gdirs[0], bias=np.average(mymb))
mymb = mbmod.get_annual_mb(demref) * cfg.SEC_IN_YEAR * rho
np.testing.assert_allclose(np.average(mymb), 0., atol=1e-3)
# Same for ref
mbref = mbref - np.average(mbref)
np.testing.assert_allclose(np.average(mbref), 0., atol=1e-3)
# Fit poly
p = np.polyfit(demref, mbref, deg=2)
poly = np.poly1d(p)
myfit = poly(demref)
np.testing.assert_allclose(np.average(myfit), 0., atol=1e-3)
if do_plot:
import matplotlib.pyplot as plt
plt.scatter(mbref, demref, s=5,
label='Obs (2007-2012), shifted to Avg(SMB) = 0')
plt.scatter(mymb, demref, s=5, label='OGGM MB at t*')
plt.scatter(myfit, demref, s=5, label='Polyfit', c='C3')
plt.xlabel('MB (mm w.e yr-1)')
plt.ylabel('Altidude (m)')
plt.legend()
plt.show()
# 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
res = t_star_from_refmb(gdir, mbdf=mbdf.ANNUAL_BALANCE)
# For the mass flux
cl = gdir.read_pickle('inversion_input')[-1]
mbmod = ConstantMassBalance(gdir)
mbx = (mbmod.get_annual_mb(cl['hgt']) * cfg.SEC_IN_YEAR *
cfg.PARAMS['ice_density'])
fdf = pd.DataFrame(index=np.arange(len(mbx))*cl['dx'])
fdf['Flux'] = cl['flux']
fdf['Mass balance'] = mbx
# For the distributed thickness
tasks.mass_conservation_inversion(gdir, glen_a=2.4e-24 * 3, fs=0)
tasks.distribute_thickness_per_altitude(gdir)
# plot functions
def example_plot_temp_ts():
d = xr.open_dataset(gdir.get_filepath('climate_monthly'))
temp = d.temp.resample(time='12MS').mean('time').to_series()
del temp.index.name
