def test_distribute(self):
hef_file = get_demo_file("Hintereisferner.shp")
entity = gpd.GeoDataFrame.from_file(hef_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)
geometry.initialize_flowlines(gdir)
geometry.catchment_area(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
climate.process_histalp_nonparallel([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, prcp_fac = 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, prcp_fac=prcp_fac)
# OK. Values from Fischer and Kuhn 2013
# Area: 8.55
# meanH = 67+-7
# Volume = 0.573+-0.063
# maxH = 242+-13
inversion.prepare_for_inversion(gdir)
ref_v = 0.573 * 1e9
def to_optimize(x):
glen_a = cfg.A * x[0]
fs = cfg.FS * x[1]
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a)
return (v - ref_v) ** 2
import scipy.optimize as optimization
out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-1)["x"]
glen_a = cfg.A * out[0]
fs = cfg.FS * out[1]
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True)
np.testing.assert_allclose(ref_v, v)
inversion.distribute_thickness(gdir, how="per_altitude", add_nc_name=True)
inversion.distribute_thickness(gdir, how="per_interpolation", add_slope=False, add_nc_name=True)
grids_file = gdir.get_filepath("gridded_data")
with netCDF4.Dataset(grids_file) as nc:
t1 = nc.variables["thickness_per_altitude"][:]
t2 = nc.variables["thickness_per_interpolation"][:]
np.testing.assert_allclose(np.sum(t1), np.sum(t2))
if not HAS_NEW_GDAL:
np.testing.assert_allclose(np.max(t1), np.max(t2), atol=30)
def init_hef(reset=False, border=40, invert_with_sliding=True,
invert_with_rectangular=True):
from oggm.core.preprocessing import gis, centerlines, geometry
from oggm.core.preprocessing import climate, inversion
import oggm
import oggm.cfg as cfg
from oggm.utils import get_demo_file
# test directory
testdir = os.path.join(cfg.PATHS['test_dir'], 'tmp_border{}'.format(border))
if not invert_with_sliding:
testdir += '_withoutslide'
if not invert_with_rectangular:
testdir += '_withoutrectangular'
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
# Init
cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['border'] = border
cfg.PARAMS['use_optimized_inversion_params'] = True
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset)
if not gdir.has_file('inversion_params'):
reset = True
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset)
if not reset:
return gdir
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.compute_downstream_lines(gdir)
geometry.initialize_flowlines(gdir)
centerlines.compute_downstream_bedshape(gdir)
geometry.catchment_area(gdir)
geometry.catchment_intersections(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
climate.process_histalp_nonparallel([gdir])
climate.mu_candidates(gdir, div_id=0)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
res = climate.t_star_from_refmb(gdir, mbdf)
climate.local_mustar_apparent_mb(gdir, tstar=res['t_star'][-1],
bias=res['bias'][-1],
prcp_fac=res['prcp_fac'])
inversion.prepare_for_inversion(gdir, add_debug_var=True,
invert_with_rectangular=invert_with_rectangular)
ref_v = 0.573 * 1e9
if invert_with_sliding:
def to_optimize(x):
# For backwards compat
_fd = 1.9e-24 * x[0]
glen_a = (cfg.N+2) * _fd / 2.
fs = 5.7e-20 * x[1]
v, _ = inversion.mass_conservation_inversion(gdir, fs=fs,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1, 1],
bounds=((0.01, 10), (0.01, 10)),
tol=1e-4)['x']
_fd = 1.9e-24 * out[0]
glen_a = (cfg.N+2) * _fd / 2.
fs = 5.7e-20 * out[1]
v, _ = inversion.mass_conservation_inversion(gdir, fs=fs,
glen_a=glen_a,
write=True)
else:
def to_optimize(x):
glen_a = cfg.A * x[0]
v, _ = inversion.mass_conservation_inversion(gdir, fs=0.,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1],
bounds=((0.01, 10),),
tol=1e-4)['x']
glen_a = cfg.A * out[0]
fs = 0.
v, _ = inversion.mass_conservation_inversion(gdir, fs=fs,
glen_a=glen_a,
write=True)
d = dict(fs=fs, glen_a=glen_a)
d['factor_glen_a'] = out[0]
try:
d['factor_fs'] = out[1]
except IndexError:
d['factor_fs'] = 0.
gdir.write_pickle(d, 'inversion_params')
# filter
inversion.filter_inversion_output(gdir)
inversion.distribute_thickness(gdir, how='per_altitude',
add_nc_name=True)
inversion.distribute_thickness(gdir, how='per_interpolation',
add_slope=False, smooth=False,
add_nc_name=True)
return gdir
def init_hef(reset=False,
border=40,
invert_with_sliding=True,
invert_with_rectangular=True):
from oggm.core.preprocessing import gis, centerlines, geometry
from oggm.core.preprocessing import climate, inversion
import oggm
import oggm.cfg as cfg
from oggm.utils import get_demo_file
# test directory
testdir = os.path.join(cfg.PATHS['test_dir'],
'tmp_border{}'.format(border))
if not invert_with_sliding:
testdir += '_withoutslide'
if not invert_with_rectangular:
testdir += '_withoutrectangular'
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
# Init
cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['border'] = border
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset)
if not gdir.has_file('inversion_params'):
reset = True
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset)
if not reset:
return gdir
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.compute_downstream_lines(gdir)
geometry.initialize_flowlines(gdir)
geometry.catchment_area(gdir)
geometry.catchment_intersections(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
climate.process_histalp_nonparallel([gdir])
climate.mu_candidates(gdir, div_id=0)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
res = climate.t_star_from_refmb(gdir, mbdf)
climate.local_mustar_apparent_mb(gdir,
tstar=res['t_star'][-1],
bias=res['bias'][-1],
prcp_fac=res['prcp_fac'])
inversion.prepare_for_inversion(
gdir,
add_debug_var=True,
invert_with_rectangular=invert_with_rectangular)
ref_v = 0.573 * 1e9
if invert_with_sliding:
def to_optimize(x):
# For backwards compat
_fd = 1.9e-24 * x[0]
glen_a = (cfg.N + 2) * _fd / 2.
fs = 5.7e-20 * x[1]
v, _ = inversion.mass_conservation_inversion(gdir,
fs=fs,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1, 1],
bounds=((0.01, 10), (0.01, 10)),
tol=1e-4)['x']
_fd = 1.9e-24 * out[0]
glen_a = (cfg.N + 2) * _fd / 2.
fs = 5.7e-20 * out[1]
v, _ = inversion.mass_conservation_inversion(gdir,
fs=fs,
glen_a=glen_a,
write=True)
else:
def to_optimize(x):
glen_a = cfg.A * x[0]
v, _ = inversion.mass_conservation_inversion(gdir,
fs=0.,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1],
bounds=((0.01, 10), ),
tol=1e-4)['x']
glen_a = cfg.A * out[0]
fs = 0.
v, _ = inversion.mass_conservation_inversion(gdir,
fs=fs,
glen_a=glen_a,
write=True)
d = dict(fs=fs, glen_a=glen_a)
d['factor_glen_a'] = out[0]
try:
d['factor_fs'] = out[1]
except IndexError:
d['factor_fs'] = 0.
gdir.write_pickle(d, 'inversion_params')
# filter
inversion.filter_inversion_output(gdir)
inversion.distribute_thickness(gdir, how='per_altitude', add_nc_name=True)
inversion.distribute_thickness(gdir,
how='per_interpolation',
add_slope=False,
smooth=False,
add_nc_name=True)
return gdir
def init_hef(reset=False, border=40, invert_with_sliding=True):
# test directory
testdir = TESTDIR_BASE + '_border{}'.format(border)
if not invert_with_sliding:
testdir += '_withoutslide'
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
if not os.path.exists(os.path.join(testdir, 'RGI40-11.00897')):
reset = True
if not os.path.exists(os.path.join(testdir, 'RGI40-11.00897',
'inversion_params.pkl')):
reset = True
# Init
cfg.initialize()
cfg.set_divides_db(get_demo_file('HEF_divided.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['border'] = border
# loop because for some reason indexing wont work
hef_file = get_demo_file('Hintereisferner.shp')
rgidf = gpd.GeoDataFrame.from_file(hef_file)
for index, entity in rgidf.iterrows():
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset)
if not reset:
return gdir
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.compute_downstream_lines(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'])
climate.local_mustar_apparent_mb(gdir, tstar=t_star[-1], bias=bias[-1])
inversion.prepare_for_inversion(gdir)
ref_v = 0.573 * 1e9
if invert_with_sliding:
def to_optimize(x):
# For backwards compat
_fd = 1.9e-24 * x[0]
glen_a = (cfg.N+2) * _fd / 2.
fs = 5.7e-20 * x[1]
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs,
glen_a=glen_a)
return (v - ref_v)**2
import scipy.optimize as optimization
out = optimization.minimize(to_optimize, [1, 1],
bounds=((0.01, 10), (0.01, 10)),
tol=1e-4)['x']
_fd = 1.9e-24 * out[0]
glen_a = (cfg.N+2) * _fd / 2.
fs = 5.7e-20 * out[1]
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs,
glen_a=glen_a,
write=True)
else:
def to_optimize(x):
glen_a = cfg.A * x[0]
v, _ = inversion.invert_parabolic_bed(gdir, fs=0.,
glen_a=glen_a)
return (v - ref_v)**2
import scipy.optimize as optimization
out = optimization.minimize(to_optimize, [1],
bounds=((0.01, 10),),
tol=1e-4)['x']
glen_a = cfg.A * out[0]
fs = 0.
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs,
glen_a=glen_a,
write=True)
d = dict(fs=fs, glen_a=glen_a)
d['factor_glen_a'] = out[0]
try:
d['factor_fs'] = out[1]
except IndexError:
d['factor_fs'] = 0.
gdir.write_pickle(d, 'inversion_params')
inversion.distribute_thickness(gdir, how='per_altitude',
add_nc_name=True)
inversion.distribute_thickness(gdir, how='per_interpolation',
add_slope=False, smooth=False,
add_nc_name=True)
return gdir
def init_hef(reset=False, border=40, invert_with_sliding=True):
from oggm.core.preprocessing import gis, centerlines, geometry
from oggm.core.preprocessing import climate, inversion
import oggm
import oggm.cfg as cfg
from oggm.utils import get_demo_file
# test directory
testdir = TESTDIR_BASE + "_border{}".format(border)
if not invert_with_sliding:
testdir += "_withoutslide"
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
if not os.path.exists(os.path.join(testdir, "RGI40-11.00897")):
reset = True
if not os.path.exists(os.path.join(testdir, "RGI40-11.00897", "inversion_params.pkl")):
reset = True
# Init
cfg.initialize()
cfg.PATHS["dem_file"] = get_demo_file("hef_srtm.tif")
cfg.PATHS["climate_file"] = get_demo_file("histalp_merged_hef.nc")
cfg.PARAMS["border"] = border
hef_file = get_demo_file("Hintereisferner.shp")
entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset)
if not reset:
return gdir
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.compute_downstream_lines(gdir)
geometry.initialize_flowlines(gdir)
geometry.catchment_area(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
climate.process_histalp_nonparallel([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, prcp_fac = climate.t_star_from_refmb(gdir, mbdf["ANNUAL_BALANCE"])
climate.local_mustar_apparent_mb(gdir, tstar=t_star[-1], bias=bias[-1], prcp_fac=prcp_fac)
inversion.prepare_for_inversion(gdir)
ref_v = 0.573 * 1e9
if invert_with_sliding:
def to_optimize(x):
# For backwards compat
_fd = 1.9e-24 * x[0]
glen_a = (cfg.N + 2) * _fd / 2.0
fs = 5.7e-20 * x[1]
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a)
return (v - ref_v) ** 2
out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-4)["x"]
_fd = 1.9e-24 * out[0]
glen_a = (cfg.N + 2) * _fd / 2.0
fs = 5.7e-20 * out[1]
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True)
else:
def to_optimize(x):
glen_a = cfg.A * x[0]
v, _ = inversion.invert_parabolic_bed(gdir, fs=0.0, glen_a=glen_a)
return (v - ref_v) ** 2
out = optimization.minimize(to_optimize, [1], bounds=((0.01, 10),), tol=1e-4)["x"]
glen_a = cfg.A * out[0]
fs = 0.0
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a, write=True)
d = dict(fs=fs, glen_a=glen_a)
d["factor_glen_a"] = out[0]
try:
d["factor_fs"] = out[1]
except IndexError:
d["factor_fs"] = 0.0
gdir.write_pickle(d, "inversion_params")
inversion.distribute_thickness(gdir, how="per_altitude", add_nc_name=True)
inversion.distribute_thickness(gdir, how="per_interpolation", add_slope=False, smooth=False, add_nc_name=True)
return gdir
def init_hef(reset=False, border=40, invert_with_sliding=True):
# test directory
testdir = TESTDIR_BASE + '_border{}'.format(border)
if not invert_with_sliding:
testdir += '_withoutslide'
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
if not os.path.exists(os.path.join(testdir, 'RGI40-11.00897')):
reset = True
if not os.path.exists(os.path.join(testdir, 'RGI40-11.00897',
'inversion_params.pkl')):
reset = True
# Init
cfg.initialize()
cfg.set_divides_db(get_demo_file('divides_workflow.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['border'] = border
hef_file = get_demo_file('Hintereisferner.shp')
entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset)
if not reset:
return gdir
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.compute_downstream_lines(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'])
climate.local_mustar_apparent_mb(gdir, tstar=t_star[-1], bias=bias[-1])
inversion.prepare_for_inversion(gdir)
ref_v = 0.573 * 1e9
if invert_with_sliding:
def to_optimize(x):
# For backwards compat
_fd = 1.9e-24 * x[0]
glen_a = (cfg.N+2) * _fd / 2.
fs = 5.7e-20 * x[1]
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1, 1],
bounds=((0.01, 10), (0.01, 10)),
tol=1e-4)['x']
_fd = 1.9e-24 * out[0]
glen_a = (cfg.N+2) * _fd / 2.
fs = 5.7e-20 * out[1]
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs,
glen_a=glen_a,
write=True)
else:
def to_optimize(x):
glen_a = cfg.A * x[0]
v, _ = inversion.invert_parabolic_bed(gdir, fs=0.,
glen_a=glen_a)
return (v - ref_v)**2
out = optimization.minimize(to_optimize, [1],
bounds=((0.01, 10),),
tol=1e-4)['x']
glen_a = cfg.A * out[0]
fs = 0.
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs,
glen_a=glen_a,
write=True)
d = dict(fs=fs, glen_a=glen_a)
d['factor_glen_a'] = out[0]
try:
d['factor_fs'] = out[1]
except IndexError:
d['factor_fs'] = 0.
gdir.write_pickle(d, 'inversion_params')
inversion.distribute_thickness(gdir, how='per_altitude',
add_nc_name=True)
inversion.distribute_thickness(gdir, how='per_interpolation',
add_slope=False, smooth=False,
add_nc_name=True)
return gdir
def test_distribute(self):
hef_file = get_demo_file('Hintereisferner.shp')
entity = gpd.GeoDataFrame.from_file(hef_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)
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)
# OK. Values from Fischer and Kuhn 2013
# Area: 8.55
# meanH = 67+-7
# Volume = 0.573+-0.063
# maxH = 242+-13
inversion.prepare_for_inversion(gdir)
ref_v = 0.573 * 1e9
def to_optimize(x):
glen_a = cfg.A * x[0]
fs = cfg.FS * x[1]
v, _ = inversion.invert_parabolic_bed(gdir, fs=fs, glen_a=glen_a)
return (v - ref_v)**2
import scipy.optimize as optimization
out = optimization.minimize(to_optimize, [1, 1],
bounds=((0.01, 10), (0.01, 10)),
tol=1e-1)['x']
glen_a = cfg.A * out[0]
fs = cfg.FS * out[1]
v, _ = inversion.invert_parabolic_bed(gdir,
fs=fs,
glen_a=glen_a,
write=True)
np.testing.assert_allclose(ref_v, v)
inversion.distribute_thickness(gdir,
how='per_altitude',
add_nc_name=True)
inversion.distribute_thickness(gdir,
how='per_interpolation',
add_slope=False,
add_nc_name=True)
grids_file = gdir.get_filepath('gridded_data')
with netCDF4.Dataset(grids_file) as nc:
t1 = nc.variables['thickness_per_altitude'][:]
t2 = nc.variables['thickness_per_interpolation'][:]
np.testing.assert_allclose(np.sum(t1), np.sum(t2))
if not HAS_NEW_GDAL:
np.testing.assert_allclose(np.max(t1), np.max(t2), atol=30)
