def init_columbia(reset=False):
from oggm.core import gis, climate, centerlines
import geopandas as gpd
# test directory
testdir = os.path.join(get_test_dir(), 'tmp_columbia')
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
# Init
cfg.initialize()
cfg.PATHS['working_dir'] = testdir
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['dem_file'] = get_demo_file('dem_Columbia.tif')
cfg.PARAMS['border'] = 10
entity = gpd.read_file(get_demo_file('01_rgi60_Columbia.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, reset=reset)
if gdir.has_file('climate_historical'):
return gdir
gis.define_glacier_region(gdir)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
tasks.process_dummy_cru_file(gdir, seed=0)
return gdir
def test_run(self):
entity = gpd.read_file(self.rgi_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
gis.define_glacier_region(gdir)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# Climate tasks -- only data IO and tstar interpolation!
tasks.process_dummy_cru_file(gdir, seed=0)
tasks.local_t_star(gdir)
tasks.mu_star_calibration(gdir)
# Inversion tasks
tasks.find_inversion_calving(gdir)
# Final preparation for the run
tasks.init_present_time_glacier(gdir)
# check that calving happens in the real context as well
tasks.run_constant_climate(gdir,
bias=0,
nyears=200,
temperature_bias=-0.5)
with xr.open_dataset(gdir.get_filepath('model_diagnostics')) as ds:
assert ds.calving_m3[-1] > 10
def _set_up_VAS_model(self):
"""Avoiding a chunk of code duplicate. Set's up a running volume/area
scaling model, including all needed prepo tasks.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# read reference glacier mass balance data
mbdf = gdir.get_ref_mb_data()
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = climate.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE'])
t_star, bias = res['t_star'], res['bias']
# --------------------
# MASS BALANCE TASKS
# --------------------
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# instance the mass balance models
mbmod = vascaling.VAScalingMassBalance(gdir)
# ----------------
# DYNAMICAL PART
# ----------------
# get reference area
a0 = gdir.rgi_area_m2
# get reference year
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
# get min and max glacier surface elevation
h0, h1 = vascaling.get_min_max_elevation(gdir)
model = vascaling.VAScalingModel(year_0=y0,
area_m2_0=a0,
min_hgt=h0,
max_hgt=h1,
mb_model=mbmod)
return gdir, model
def test_local_t_star(self):
# set parameters for climate file and mass balance calibration
cfg.PARAMS['baseline_climate'] = 'CUSTOM'
cfg.PARAMS['baseline_y0'] = 1850
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['run_mb_calibration'] = False
# read the Hintereisferner
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and the glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# run centerline prepro tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = vascaling.t_star_from_refmb(gdir)
t_star, bias = res['t_star'], res['bias']
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# read calibration results
vas_mustar_refmb = gdir.read_json('vascaling_mustar')
# get reference t* list
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, ref_df=ref_df)
# read calibration results
vas_mustar_refdf = gdir.read_json('vascaling_mustar')
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir)
# read calibration results
vas_mustar = gdir.read_json('vascaling_mustar')
# compare with each other
assert vas_mustar_refdf == vas_mustar
# TODO: this test is failing currently
# np.testing.assert_allclose(vas_mustar_refmb['bias'],
# vas_mustar_refdf['bias'], atol=1)
vas_mustar_refdf.pop('bias')
vas_mustar_refmb.pop('bias')
# end of workaround
assert vas_mustar_refdf == vas_mustar_refmb
def test_coxe():
testdir = os.path.join(get_test_dir(), 'tmp_coxe')
utils.mkdir(testdir, reset=True)
# Init
cfg.initialize()
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['dem_file'] = get_demo_file('dem_RGI50-01.10299.tif')
cfg.PARAMS['border'] = 40
cfg.PARAMS['clip_tidewater_border'] = False
cfg.PARAMS['use_multiple_flowlines'] = False
cfg.PARAMS['use_kcalving_for_inversion'] = True
cfg.PARAMS['use_kcalving_for_run'] = True
cfg.PARAMS['trapezoid_lambdas'] = 1
hef_file = get_demo_file('rgi_RGI50-01.10299.shp')
entity = gpd.read_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=True)
gis.define_glacier_region(gdir)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
climate.apparent_mb_from_linear_mb(gdir)
inversion.prepare_for_inversion(gdir)
inversion.mass_conservation_inversion(gdir)
inversion.filter_inversion_output(gdir)
flowline.init_present_time_glacier(gdir)
fls = gdir.read_pickle('model_flowlines')
p = gdir.read_pickle('linear_mb_params')
mb_mod = massbalance.LinearMassBalance(ela_h=p['ela_h'], grad=p['grad'])
mb_mod.temp_bias = -0.3
model = flowline.FluxBasedModel(fls,
mb_model=mb_mod,
y0=0,
inplace=True,
is_tidewater=True)
# run
model.run_until(200)
assert model.calving_m3_since_y0 > 0
fig, ax = plt.subplots()
graphics.plot_modeloutput_map(gdir, ax=ax, model=model)
fig.tight_layout()
shutil.rmtree(testdir)
return fig
def test_set_width(self):
entity = gpd.read_file(self.rgi_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
gis.define_glacier_region(gdir)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# Test that area and area-altitude elev is fine
with utils.ncDataset(gdir.get_filepath('gridded_data')) as nc:
mask = nc.variables['glacier_mask'][:]
topo = nc.variables['topo_smoothed'][:]
rhgt = topo[np.where(mask)][:]
fls = gdir.read_pickle('inversion_flowlines')
hgt, widths = gdir.get_inversion_flowline_hw()
bs = 100
bins = np.arange(utils.nicenumber(np.min(hgt), bs, lower=True),
utils.nicenumber(np.max(hgt), bs) + 1,
bs)
h1, b = np.histogram(hgt, weights=widths, density=True, bins=bins)
h2, b = np.histogram(rhgt, density=True, bins=bins)
h1 = h1 / np.sum(h1)
h2 = h2 / np.sum(h2)
assert utils.rmsd(h1, h2) < 0.02 # less than 2% error
new_area = np.sum(widths * fls[-1].dx * gdir.grid.dx)
np.testing.assert_allclose(new_area, gdir.rgi_area_m2)
centerlines.terminus_width_correction(gdir, new_width=714)
fls = gdir.read_pickle('inversion_flowlines')
hgt, widths = gdir.get_inversion_flowline_hw()
# Check that the width is ok
np.testing.assert_allclose(fls[-1].widths[-1] * gdir.grid.dx, 714)
# Check for area distrib
bins = np.arange(utils.nicenumber(np.min(hgt), bs, lower=True),
utils.nicenumber(np.max(hgt), bs) + 1,
bs)
h1, b = np.histogram(hgt, weights=widths, density=True, bins=bins)
h2, b = np.histogram(rhgt, density=True, bins=bins)
h1 = h1 / np.sum(h1)
h2 = h2 / np.sum(h2)
assert utils.rmsd(h1, h2) < 0.02 # less than 2% error
new_area = np.sum(widths * fls[-1].dx * gdir.grid.dx)
np.testing.assert_allclose(new_area, gdir.rgi_area_m2)
def test_set_width(self):
entity = gpd.read_file(self.rgi_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# Test that area and area-altitude elev is fine
with utils.ncDataset(gdir.get_filepath('gridded_data')) as nc:
mask = nc.variables['glacier_mask'][:]
topo = nc.variables['topo_smoothed'][:]
rhgt = topo[np.where(mask)][:]
fls = gdir.read_pickle('inversion_flowlines')
hgt, widths = gdir.get_inversion_flowline_hw()
bs = 100
bins = np.arange(utils.nicenumber(np.min(hgt), bs, lower=True),
utils.nicenumber(np.max(hgt), bs) + 1,
bs)
h1, b = np.histogram(hgt, weights=widths, density=True, bins=bins)
h2, b = np.histogram(rhgt, density=True, bins=bins)
h1 = h1 / np.sum(h1)
h2 = h2 / np.sum(h2)
assert utils.rmsd(h1, h2) < 0.02 # less than 2% error
new_area = np.sum(widths * fls[-1].dx * gdir.grid.dx)
np.testing.assert_allclose(new_area, gdir.rgi_area_m2)
centerlines.terminus_width_correction(gdir, new_width=714)
fls = gdir.read_pickle('inversion_flowlines')
hgt, widths = gdir.get_inversion_flowline_hw()
# Check that the width is ok
np.testing.assert_allclose(fls[-1].widths[-1] * gdir.grid.dx, 714)
# Check for area distrib
bins = np.arange(utils.nicenumber(np.min(hgt), bs, lower=True),
utils.nicenumber(np.max(hgt), bs) + 1,
bs)
h1, b = np.histogram(hgt, weights=widths, density=True, bins=bins)
h2, b = np.histogram(rhgt, density=True, bins=bins)
h1 = h1 / np.sum(h1)
h2 = h2 / np.sum(h2)
assert utils.rmsd(h1, h2) < 0.02 # less than 2% error
new_area = np.sum(widths * fls[-1].dx * gdir.grid.dx)
np.testing.assert_allclose(new_area, gdir.rgi_area_m2)
def _setup_mb_test(self):
"""Avoiding a chunk of code duplicate. Performs needed prepo tasks and
returns the oggm.GlacierDirectory.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and the glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# run centerline prepro tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# read reference glacier mass balance data
mbdf = gdir.get_ref_mb_data()
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = vascaling.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE'])
t_star, bias = res['t_star'], res['bias']
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# run OGGM mu* calibration
climate.local_t_star(gdir, tstar=t_star, bias=bias)
climate.mu_star_calibration(gdir)
# pass the GlacierDirectory
return gdir
def apparent_mb(gdir):
"""Compute the apparent mb from the calibrated mustar.
Parameters
----------
"""
# Calibrated data
df = pd.read_csv(gdir.get_filepath('local_mustar')).iloc[0]
tstar = df['t_star']
prcp_fac = df['prcp_fac']
mu_star = df['mu_star']
bias = df['bias']
# Climate period
mu_hp = int(cfg.PARAMS['mu_star_halfperiod'])
yr = [tstar - mu_hp, tstar + mu_hp]
# Do we have a calving glacier?
cmb = calving_mb(gdir)
# For each flowline compute the apparent MB
fls = gdir.read_pickle('inversion_flowlines')
# 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,
prcp_fac,
year_range=yr,
flatten=False)
fl.set_apparent_mb(np.mean(p, axis=1) - mu_star * np.mean(t, axis=1))
# Sometimes, low lying tributaries have a non-physically consistent
# Mass-balance. We should remove these, and start all over again until
# all tributaries are consistent
do_filter = [fl.flux_needed_correction for fl in fls]
if cfg.PARAMS['filter_for_neg_flux'] and np.any(do_filter):
assert not do_filter[-1] # This should not happen
# Keep only the good lines
heads = [fl.orig_head for fl in fls if not fl.flux_needed_correction]
centerlines.compute_centerlines(gdir, heads=heads, reset=True)
centerlines.initialize_flowlines(gdir, reset=True)
if gdir.has_file('downstream_line'):
centerlines.compute_downstream_line(gdir, reset=True)
centerlines.compute_downstream_bedshape(gdir, reset=True)
centerlines.catchment_area(gdir, reset=True)
centerlines.catchment_intersections(gdir, reset=True)
centerlines.catchment_width_geom(gdir, reset=True)
centerlines.catchment_width_correction(gdir, reset=True)
local_mustar(gdir,
tstar=tstar,
bias=bias,
prcp_fac=prcp_fac,
reset=True)
# Ok, re-call ourselves
return apparent_mb(gdir, reset=True)
# Check and write
aflux = fls[-1].flux[-1] * 1e-9 / cfg.RHO * gdir.grid.dx**2
# If not marine and a bit far from zero, warning
if cmb == 0 and not np.allclose(fls[-1].flux[-1], 0., atol=0.01):
log.warning('(%s) flux should be zero, but is: '
'%.4f km3 ice yr-1', gdir.rgi_id, aflux)
# If not marine and quite far from zero, error
if cmb == 0 and not np.allclose(fls[-1].flux[-1], 0., atol=1):
msg = ('({}) flux should be zero, but is: {:.4f} km3 ice yr-1'.format(
gdir.rgi_id, aflux))
raise RuntimeError(msg)
gdir.write_pickle(fls, 'inversion_flowlines')
def test_run_until_and_store(self):
"""Test the volume/area scaling model against the oggm.FluxBasedModel.
Both models run the Hintereisferner over the entire HistAlp climate
period, initialized with the 2003 RGI outline without spin up.
The following two parameters for length, area and volume are tested:
- correlation coefficient
- relative RMSE, i.e. RMSE/mean(OGGM). Whereby the results from the
VAS model are offset with the average differences to the OGGM
results.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# read reference glacier mass balance data
mbdf = gdir.get_ref_mb_data()
# compute the reference t* for the glacier
# given the reference of mass balance measurements
res = climate.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE'])
t_star, bias = res['t_star'], res['bias']
# --------------------
# SCALING MODEL
# --------------------
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir, tstar=t_star, bias=bias)
# instance the mass balance models
vas_mbmod = vascaling.VAScalingMassBalance(gdir)
# get reference area
a0 = gdir.rgi_area_m2
# get reference year
y0 = gdir.read_json('climate_info')['baseline_hydro_yr_0']
# get min and max glacier surface elevation
h0, h1 = vascaling.get_min_max_elevation(gdir)
vas_model = vascaling.VAScalingModel(year_0=y0, area_m2_0=a0,
min_hgt=h0, max_hgt=h1,
mb_model=vas_mbmod)
# let model run over entire HistAlp climate period
vas_ds = vas_model.run_until_and_store(2003)
# ------
# OGGM
# ------
# compute local t* and the corresponding mu*
climate.local_t_star(gdir, tstar=t_star, bias=bias)
climate.mu_star_calibration(gdir)
# instance the mass balance models
mb_mod = massbalance.PastMassBalance(gdir)
# perform ice thickness inversion
inversion.prepare_for_inversion(gdir)
inversion.mass_conservation_inversion(gdir)
inversion.filter_inversion_output(gdir)
# initialize present time glacier
flowline.init_present_time_glacier(gdir)
# instance flowline model
fls = gdir.read_pickle('model_flowlines')
y0 = gdir.read_json('climate_info')['baseline_hydro_yr_0']
fl_mod = flowline.FluxBasedModel(flowlines=fls, mb_model=mb_mod, y0=y0)
# run model and store output as xarray data set
_, oggm_ds = fl_mod.run_until_and_store(2003)
# temporal indices must be equal
assert (vas_ds.time == oggm_ds.time).all()
# specify which parameters to compare and their respective correlation
# coefficients and rmsd values
params = ['length_m', 'area_m2', 'volume_m3']
corr_coeffs = np.array([0.96, 0.90, 0.93])
rmsds = np.array([0.43e3, 0.14e6, 0.03e9])
# compare given parameters
for param, cc, rmsd in zip(params, corr_coeffs, rmsds):
# correlation coefficient
assert corrcoef(oggm_ds[param].values, vas_ds[param].values) >= cc
# root mean squared deviation
rmsd_an = rmsd_bc(oggm_ds[param].values, vas_ds[param].values)
assert rmsd_an <= rmsd
def test_yearly_mb_temp_prcp(self):
"""Test the routine which returns the yearly mass balance relevant
climate parameters, i.e. positive melting temperature and solid
precipitation. The testing target is the output of the corresponding
OGGM routine `get_yearly_mb_climate_on_glacier(gdir)`.
"""
# read the Hintereisferner DEM
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
# initialize the GlacierDirectory
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
# define the local grid and glacier mask
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
# run centerline prepro tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# process the given climate file
climate.process_custom_climate_data(gdir)
# get yearly sums of terminus temperature and solid precipitation
years, temp, prcp = vascaling.get_yearly_mb_temp_prcp(gdir)
# use the OGGM methode to get the mass balance
# relevant climate parameters
years_oggm, temp_oggm, prcp_oggm = \
climate.mb_yearly_climate_on_glacier(gdir)
# the energy input at the glacier terminus must be greater than (or
# equal to) the glacier wide average, since the air temperature drops
# with elevation, i.e. the mean deviation must be positive, using the
# OGGM data as reference
assert md(temp_oggm, temp) >= 0
# consequentially, the average mass input must be less than (or equal
# to) the mass input integrated over the whole glacier surface, i.e.
# the mean deviation must be negative, using the OGGM data as reference
# TODO: does it actually?! And if so, why?! @ASK
assert md(prcp_oggm, prcp) <= 0
# correlation must be higher than set threshold
assert corrcoef(temp, temp_oggm) >= 0.94
assert corrcoef(prcp, prcp_oggm) >= 0.98
# get terminus temperature using the OGGM routine
fpath = gdir.get_filepath('gridded_data')
with ncDataset(fpath) as nc:
mask = nc.variables['glacier_mask'][:]
topo = nc.variables['topo'][:]
heights = np.array([np.min(topo[np.where(mask == 1)])])
years_height, temp_height, _ = \
climate.mb_yearly_climate_on_height(gdir, heights, flatten=False)
temp_height = temp_height[0]
# both time series must be equal
np.testing.assert_array_equal(temp, temp_height)
# get solid precipitation averaged over the glacier
# (not weighted with widths)
fls = gdir.read_pickle('inversion_flowlines')
heights = np.array([])
for fl in fls:
heights = np.append(heights, fl.surface_h)
years_height, _, prcp_height = \
climate.mb_yearly_climate_on_height(gdir, heights, flatten=True)
# correlation must be higher than set threshold
assert corrcoef(prcp, prcp_height) >= 0.99
# TODO: assert absolute values (or differences) of precipitation @ASK
# test exception handling of out of bounds time/year range
with self.assertRaises(climate.MassBalanceCalibrationError):
# start year out of bounds
year_range = [1500, 1980]
_, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir,
year_range=year_range)
with self.assertRaises(climate.MassBalanceCalibrationError):
# end year oud of bounds
year_range = [1980, 3000]
_, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir,
year_range=year_range)
with self.assertRaises(ValueError):
# get not N full years
t0 = datetime.datetime(1980, 1, 1)
t1 = datetime.datetime(1980, 3, 1)
time_range = [t0, t1]
_, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir,
time_range=time_range)
# TODO: assert gradient in climate file?!
pass
entity = gpd.read_file(hef_file).iloc[0] from oggm.core import gis # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=testdir) # define the local grid and glacier mask gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) from oggm.core import climate # process the given climate file climate.process_custom_climate_data(gdir) from oggm.core import centerlines # run center line preprocessing tasks centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.compute_downstream_line(gdir) centerlines.compute_downstream_bedshape(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) # -------------------- # MASS BALANCE TASKS # -------------------- # read reference glacier mass balance data mbdf = gdir.get_ref_mb_data() # compute the reference t* for the glacier
def mu_star_calibration(gdir):
"""Compute the flowlines' mu* and the associated apparent mass-balance.
If low lying tributaries have a non-physically consistent Mass-balance
this function will either filter them out or calibrate each flowline with a
specific mu*. The latter is default and recommended.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
the glacier directory to process
"""
# Interpolated data
df = gdir.read_json('local_mustar')
t_star = df['t_star']
bias = df['bias']
# For each flowline compute the apparent MB
fls = gdir.read_pickle('inversion_flowlines')
# If someone calls the task a second time we need to reset this
for fl in fls:
fl.mu_star_is_valid = False
force_mu = 0 if df['mu_star_glacierwide'] == 0 else None
# Let's go
_recursive_mu_star_calibration(gdir, fls, t_star, force_mu=force_mu)
# If the user wants to filter the bad ones we remove them and start all
# over again until all tributaries are physically consistent with one mu
# This should only work if cfg.PARAMS['correct_for_neg_flux'] == False
do_filter = [fl.flux_needs_correction for fl in fls]
if cfg.PARAMS['filter_for_neg_flux'] and np.any(do_filter):
assert not do_filter[-1] # This should not happen
# Keep only the good lines
# TODO: this should use centerline.line_inflows for more efficiency!
heads = [fl.orig_head for fl in fls if not fl.flux_needs_correction]
centerlines.compute_centerlines(gdir, heads=heads, reset=True)
centerlines.initialize_flowlines(gdir, reset=True)
if gdir.has_file('downstream_line'):
centerlines.compute_downstream_line(gdir, reset=True)
centerlines.compute_downstream_bedshape(gdir, reset=True)
centerlines.catchment_area(gdir, reset=True)
centerlines.catchment_intersections(gdir, reset=True)
centerlines.catchment_width_geom(gdir, reset=True)
centerlines.catchment_width_correction(gdir, reset=True)
local_t_star(gdir, tstar=t_star, bias=bias, reset=True)
# Ok, re-call ourselves
return mu_star_calibration(gdir, reset=True)
# Check and write
rho = cfg.PARAMS['ice_density']
aflux = fls[-1].flux[-1] * 1e-9 / rho * gdir.grid.dx**2
# If not marine and a bit far from zero, warning
cmb = calving_mb(gdir)
if cmb == 0 and not np.allclose(fls[-1].flux[-1], 0., atol=0.01):
log.info('(%s) flux should be zero, but is: '
'%.4f km3 ice yr-1', gdir.rgi_id, aflux)
# If not marine and quite far from zero, error
if cmb == 0 and not np.allclose(fls[-1].flux[-1], 0., atol=1):
msg = ('({}) flux should be zero, but is: {:.4f} km3 ice yr-1'.format(
gdir.rgi_id, aflux))
raise MassBalanceCalibrationError(msg)
gdir.write_pickle(fls, 'inversion_flowlines')
# Store diagnostics
mus = []
weights = []
for fl in fls:
mus.append(fl.mu_star)
weights.append(np.sum(fl.widths))
df['mu_star_per_flowline'] = mus
df['mu_star_flowline_avg'] = np.average(mus, weights=weights)
all_same = np.allclose(mus, mus[0], atol=1e-3)
df['mu_star_allsame'] = all_same
if all_same:
if not np.allclose(df['mu_star_flowline_avg'],
df['mu_star_glacierwide'],
atol=1e-3):
raise MassBalanceCalibrationError('Unexpected difference between '
'glacier wide mu* and the '
'flowlines mu*.')
# Write
gdir.write_json(df, 'local_mustar')
def compare(rgi_id, glacier_name):
"""
:param rgi_id:
:param glacier_name:
:return:
"""
# ---------------------
# PREPROCESSING TASKS
# ---------------------
# create test directory
wdir = os.path.join(os.path.abspath('.'), 'comparison_wdir')
if not os.path.exists(wdir):
os.makedirs(wdir)
shutil.rmtree(wdir)
os.makedirs(wdir)
# load default parameter file
cfg.initialize()
# RGI entity
# get/downlaod the rgi entity including the outline shapefile
rgi_df = utils.get_rgi_glacier_entities([rgi_id])
# set name, since not delivered with RGI
if rgi_df.loc[int(rgi_id[-5:])-1, 'Name'] is None:
rgi_df.loc[int(rgi_id[-5:])-1, 'Name'] = glacier_name
# select single entry
rgi_entity = rgi_df.iloc[0]
# GlacierDirectory
# specify the working directory and define the glacier directory
cfg.PATHS['working_dir'] = wdir
gdir = oggm.GlacierDirectory(rgi_entity)
# DEM and GIS tasks
# get the path to the DEM file (will download if necessary)
dem = utils.get_topo_file(gdir.cenlon, gdir.cenlat)
# set path in config file
cfg.PATHS['dem_file'] = dem[0][0]
cfg.PARAMS['border'] = 10
cfg.PARAMS['use_intersects'] = False
# run GIS tasks
gis.define_glacier_region(gdir, entity=rgi_entity)
gis.glacier_masks(gdir)
# Climate data
# using HistAlp
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# climate records before 1850 are hardly reliable, which is not so drastic for
# qualitative experiments (could be driven with random climate anyway)
# cfg.PARAMS['baseline_y0'] = 1850
# change hyper parameters for HistAlp
cfg.PARAMS['prcp_scaling_factor'] = 1.75
cfg.PARAMS['temp_melt'] = -1.75
# run climate task
climate.process_histalp_data(gdir)
# run center line preprocessing tasks
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
# --------------------
# SCALING MODEL
# --------------------
# compute local t* and the corresponding mu*
vascaling.local_t_star(gdir)
# instance the mass balance models
vas_mb_mod = vascaling.VAScalingMassBalance(gdir)
# get reference area
a0 = gdir.rgi_area_m2
# get reference year
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
y1 = gdir.read_pickle('climate_info')['baseline_hydro_yr_1']
# get min and max glacier surface elevation
h0, h1 = vascaling.get_min_max_elevation(gdir)
# instance VAS model
vas_model = vascaling.VAScalingModel(year_0=y0, area_m2_0=a0,
min_hgt=h0, max_hgt=h1,
mb_model=vas_mb_mod)
# run model over all HistAlp climate period
vas_df = vas_model.run_and_store(y1, reset=True)
# get relevant parameters
years_vas = vas_df.index.values
length_m_vas = vas_df.length_m.values
area_m2_vas = vas_df.area_m2.values
volume_m3_vas = vas_df.volume_m3.values
# ------
# OGGM
# ------
# compute local t* and the corresponding mu*
climate.local_t_star(gdir)
climate.mu_star_calibration(gdir)
# instance the mass balance models
mb_mod = massbalance.PastMassBalance(gdir)
# run inversion tasks
inversion.prepare_for_inversion(gdir)
inversion.mass_conservation_inversion(gdir)
inversion.filter_inversion_output(gdir)
# initialize present time glacier
flowline.init_present_time_glacier(gdir)
# instance flowline model
fls = gdir.read_pickle('model_flowlines')
y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0']
y1 = gdir.read_pickle('climate_info')['baseline_hydro_yr_1']
fl_mod = flowline.FluxBasedModel(flowlines=fls, mb_model=mb_mod, y0=y0)
# run model and store output as xarray data set
_, oggm_ds = fl_mod.run_until_and_store(y1)
years_oggm = oggm_ds.hydro_year.values
# annual index must be equal
np.testing.assert_array_equal(years_oggm, years_vas)
length_m_oggm = oggm_ds.length_m.values
area_m2_oggm = oggm_ds.area_m2.values
volume_m3_oggm = oggm_ds.volume_m3.values
# define column names for DataFrame
names = ['length_vas', 'length_oggm',
'area_vas', 'area_oggm',
'volume_vas', 'volume_oggm']
# combine glacier geometries into DataFrame
df = pd.DataFrame(np.array([length_m_vas, length_m_oggm,
area_m2_vas, area_m2_oggm,
volume_m3_vas, volume_m3_oggm]).T,
index=years_vas, columns=names)
# save to file
store = True
if store:
# define path and file names
folder = '/Users/oberrauch/work/master/data/'
df.to_csv(folder+'run_comparison.csv')
def plot_both(vas_df, oggm_df, ref=None, correct_bias=False,
title='', ylabel='', file_path='', exp=0):
""" Plot geometric parameters of both models.
If a `file_path` is given, the figure will be saved.
:param vas_df: (pandas.Series) geometric glacier parameter of the VAS model
:param oggm_df: (pandas.Series) geometric glacier parameter of the OGGM
:param ref: (pandas.Series) measured glacier parameter, optional
:param title: (string) figure title, optional
:param ylabel: (string) label for y-axis, optional
:param file_path: (string) where to store the figure, optional
:param exp: (int) exponent for labels in scientific notation, optional
"""
beamer = True
if beamer:
mpl.rc('axes', titlesize=18)
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=14)
mpl.rc('ytick', labelsize=14)
mpl.rc('legend', fontsize=10)
# create figure and first axes
fig = plt.figure(figsize=[6, 4])
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8])
# define colors
c1 = 'C0'
c2 = 'C1'
c3 = 'C3'
# plot vas and OGGM parameters
ax.plot(oggm_df.index, oggm_df.values, c=c2, label='OGGM')
ax.plot(vas_df.index, vas_df.values, c=c1, label='VAS')
if ref:
# plot reference parameter if given
ax.plot(ref.index, ref.values, c=c3, label='measurements')
if correct_bias:
# plot bias corrected vas
df_ = pd.DataFrame([oggm_df, vas_df]).T
bias = vas_df.values - df_.mean().diff().iloc[1]
ax.plot(vas_df.index, bias, c=c1, ls='--',
label='VAS, bias corrected')
# add RMSD as text
ax.text(0.05, 0.05,
'RMSD: {:.1e}'.format(utils.rmsd(oggm_df, bias)),
transform=plt.gca().transAxes)
# add correlation coefficient as text
ax.text(0.05, 0.11, 'Corr. Coef.: {:.2f}'.format(
utils.corrcoef(oggm_df, vas_df)),
transform=plt.gca().transAxes)
# add title, labels, legend
ax.set_title(title)
ax.set_ylabel(ylabel)
ax.legend()
import matplotlib.ticker
class OOMFormatter(matplotlib.ticker.ScalarFormatter):
def __init__(self, order=0, fformat="%1.1f", offset=False, mathText=False):
self.oom = order
self.fformat = fformat
matplotlib.ticker.ScalarFormatter.__init__(self, useOffset=offset, useMathText=mathText)
def _set_orderOfMagnitude(self, nothing):
self.orderOfMagnitude = self.oom
def _set_format(self, vmin, vmax):
self.format = self.fformat
if self._useMathText:
self.format = '$%s$' % matplotlib.ticker._mathdefault(self.format)
# use scientific notation with fixed exponent according
ax.yaxis.set_major_formatter(OOMFormatter(exp, "%1.2f"))
# store to file
if file_path:
plt.savefig(file_path, bbox_inches='tight',
format=file_path.split('.')[-1])
# specify plot directory
folder = '/Users/oberrauch/work/master/plots/'
# plot length
plot_both(df.length_vas, df.length_oggm, correct_bias=True,
title='Glacier length - {}'.format(glacier_name),
ylabel=r'Length [m]',
file_path=os.path.join(folder, '{}_length.pdf'.format(rgi_id)),
exp=3)
# plot area
plot_both(df.area_vas, df.area_oggm, correct_bias=True,
title='Surface area - {}'.format(glacier_name),
ylabel=r'Area [m$^2$]',
file_path=os.path.join(folder, '{}_area.pdf'.format(rgi_id)),
exp=6)
# plot volume
plot_both(df.volume_vas, df.volume_oggm, correct_bias=True,
title='Glacier volume - {}'.format(glacier_name),
ylabel=r'Volume [m$^3$]',
file_path=os.path.join(folder, '{}_volume.pdf'.format(rgi_id)),
exp=9)