def test_leclercq_data(self):
hef_file = utils.get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
df = gdir.get_ref_length_data()
assert df.name == 'Hintereis'
assert len(df) == 105
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 _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.get_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_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 gdir_from_tar(entity, from_tar):
try:
rgi_id = entity.RGIId
except AttributeError:
rgi_id = entity
from_tar = os.path.join(from_tar, '{}'.format(rgi_id[:8]),
'{}.tar'.format(rgi_id[:11]))
assert os.path.exists(from_tar), 'tarfile does not exist'
from_tar = os.path.join(from_tar.replace('.tar', ''), rgi_id + '.tar.gz')
return oggm.GlacierDirectory(entity, from_tar=from_tar)
def init_glacier_regions(rgidf=None, reset=False, force=False):
"""Very first task to do (always).
Set reset=True in order to delete the content of the directories.
"""
if reset and not force:
reset = utils.query_yes_no('Delete all glacier directories?')
# if reset delete also the log directory
if reset:
fpath = os.path.join(cfg.PATHS['working_dir'], 'log')
if os.path.exists(fpath):
rmtree(fpath)
gdirs = []
new_gdirs = []
if rgidf is None:
if reset:
raise ValueError('Cannot use reset without a rgi file')
# The dirs should be there already
gl_dir = os.path.join(cfg.PATHS['working_dir'], 'per_glacier')
for root, _, files in os.walk(gl_dir):
if files and ('dem.tif' in files):
gdirs.append(oggm.GlacierDirectory(os.path.basename(root)))
else:
for _, entity in rgidf.iterrows():
gdir = oggm.GlacierDirectory(entity, reset=reset)
if not os.path.exists(gdir.get_filepath('dem')):
new_gdirs.append((gdir, dict(entity=entity)))
gdirs.append(gdir)
# If not initialized, run the task in parallel
execute_entity_task(tasks.define_glacier_region, new_gdirs)
return gdirs
def test_run_until_equilibrium(self):
""""""
# let's not use the mass balance bias since we want to reproduce
# results from mass balance calibration
cfg.PARAMS['use_bias_for_run'] = False
# 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)
# compute mass balance parameters
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
vascaling.local_t_star(gdir, ref_df=ref_df)
# instance a constant mass balance model, centred around t*
mb_model = vascaling.ConstantVASMassBalance(gdir)
# add a positive temperature bias
mb_model.temp_bias = 0.5
# create a VAS model: start with year 0 since we are using a constant
# massbalance model, other values are read from RGI
min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir)
model = vascaling.VAScalingModel(year_0=0,
area_m2_0=gdir.rgi_area_m2,
min_hgt=min_hgt,
max_hgt=max_hgt,
mb_model=mb_model)
# run glacier with new mass balance model
model.run_until_equilibrium(rate=1e-4)
# equilibrium should be reached after a couple of 100 years
assert model.year <= 300
# new equilibrium glacier should be smaller (positive temperature bias)
assert model.volume_m3 < model.volume_m3_0
# run glacier for another 100 years and check volume again
v_eq = model.volume_m3
model.run_until(model.year + 100)
assert abs(1 - (model.volume_m3 / v_eq)) < 0.01
def gdir_from_prepro(entity, from_prepro_level=None,
prepro_border=None, prepro_rgi_version=None,
base_url=None):
if prepro_border is None:
prepro_border = int(cfg.PARAMS['border'])
if prepro_rgi_version is None:
prepro_rgi_version = cfg.PARAMS['rgi_version']
try:
rid = entity.RGIId
except AttributeError:
rid = entity
tar_base = utils.get_prepro_gdir(prepro_rgi_version, rid, prepro_border,
from_prepro_level, base_url=base_url)
from_tar = os.path.join(tar_base.replace('.tar', ''), rid + '.tar.gz')
return oggm.GlacierDirectory(entity, from_tar=from_tar)
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 init_glacier_regions(rgidf, reset=False, force=False):
"""Very first task to do (always).
Set reset=True in order to delete the content of the directories.
"""
if reset and not force:
reset = utils.query_yes_no('Delete all glacier directories?')
# if reset delete also the log directory
if reset:
fpath = os.path.join(cfg.PATHS['working_dir'], 'log')
if os.path.exists(fpath):
rmtree(fpath)
gdirs = []
for _, entity in rgidf.iterrows():
gdir = oggm.GlacierDirectory(entity, reset=reset)
if not os.path.exists(gdir.get_filepath('dem')):
tasks.define_glacier_region(gdir, entity=entity)
gdirs.append(gdir)
return gdirs
def init_columbia(reset=False):
from oggm.core import gis, 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
cfg.PARAMS['use_kcalving_for_inversion'] = True
cfg.PARAMS['use_kcalving_for_run'] = True
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)
apply_test_ref_tstars()
return gdir
def gdir_from_prepro(entity, from_prepro_level=None,
prepro_border=None, prepro_rgi_version=None,
check_demo_glacier=False, base_url=None):
if prepro_border is None:
prepro_border = int(cfg.PARAMS['border'])
if prepro_rgi_version is None:
prepro_rgi_version = cfg.PARAMS['rgi_version']
try:
rid = entity.RGIId
except AttributeError:
rid = entity
if check_demo_glacier and base_url is None:
demo_id = utils.demo_glacier_id(rid)
if demo_id is not None:
rid = demo_id
entity = demo_id
base_url = utils.DEMO_GDIR_URL
tar_base = utils.get_prepro_gdir(prepro_rgi_version, rid, prepro_border,
from_prepro_level, base_url=base_url)
from_tar = os.path.join(tar_base.replace('.tar', ''), rid + '.tar.gz')
return oggm.GlacierDirectory(entity, from_tar=from_tar)
def test_terminus_temp(self):
"""Testing the subroutine which computes the terminus temperature
from the given climate file and glacier DEM. Pretty straight forward
and somewhat useless, but nice finger exercise.
"""
# 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
gis.define_glacier_region(gdir, entity=entity)
# process the given climate file
climate.process_custom_climate_data(gdir)
# read the following variable from the center pixel (46.83N 10.75E)
# of the Hintereisferner HistAlp climate file for the
# entire time period from October 1801 until September 2003
# - surface height in m asl.
# - total precipitation amount in kg/m2
# - 2m air temperature in °C
with utils.ncDataset(get_demo_file('histalp_merged_hef.nc')) as nc_r:
ref_h = nc_r.variables['hgt'][1, 1]
ref_t = nc_r.variables['temp'][:, 1, 1]
# define a temperature anomaly
temp_anomaly = 0
# specify temperature gradient
temp_grad = -0.0065
# the terminus temperature must equal the input temperature
# if terminus elevation equals reference elevation
temp_terminus =\
vascaling._compute_temp_terminus(ref_t, temp_grad, ref_hgt=ref_h,
terminus_hgt=ref_h,
temp_anomaly=temp_anomaly)
np.testing.assert_allclose(temp_terminus, ref_t + temp_anomaly)
# the terminus temperature must equal the input terperature
# if the gradient is zero
for term_h in np.array([-100, 0, 100]) + ref_h:
temp_terminus =\
vascaling._compute_temp_terminus(ref_t, temp_grad=0,
ref_hgt=ref_h,
terminus_hgt=term_h,
temp_anomaly=temp_anomaly)
np.testing.assert_allclose(temp_terminus, ref_t + temp_anomaly)
# now test the routine with actual elevation differences
# and a non zero temperature gradient
for h_diff in np.array([-100, 0, 100]):
term_h = ref_h + h_diff
temp_diff = temp_grad * h_diff
temp_terminus =\
vascaling._compute_temp_terminus(ref_t, temp_grad,
ref_hgt=ref_h,
terminus_hgt=term_h,
temp_anomaly=temp_anomaly)
np.testing.assert_allclose(temp_terminus,
ref_t + temp_anomaly + temp_diff)
from oggm.core.climate import (mb_yearly_climate_on_glacier, t_star_from_refmb,
local_t_star, mu_star_calibration)
from oggm.core.massbalance import (ConstantMassBalance)
from oggm.utils import get_demo_file, gettempdir
from oggm.shop import histalp
cfg.initialize()
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
histalp.set_histalp_url('https://cluster.klima.uni-bremen.de/~oggm/'
'test_climate/histalp/')
base_dir = gettempdir('Climate_docs')
cfg.PATHS['working_dir'] = base_dir
entity = gpd.read_file(get_demo_file('HEF_MajDivide.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=base_dir, reset=True)
tasks.define_glacier_region(gdir)
tasks.glacier_masks(gdir)
tasks.compute_centerlines(gdir)
tasks.initialize_flowlines(gdir)
tasks.compute_downstream_line(gdir)
tasks.catchment_area(gdir)
tasks.catchment_width_geom(gdir)
tasks.catchment_width_correction(gdir)
cfg.PARAMS['baseline_climate'] = 'HISTALP'
cfg.PARAMS['baseline_y0'] = 1850
tasks.process_histalp_data(gdir)
mu_yr_clim = tasks.glacier_mu_candidates(gdir)
mbdf = gdir.get_ref_mb_data()
def init_hef(reset=False, border=40, logging_level='INFO'):
from oggm.core import gis, inversion, climate, centerlines, flowline
import geopandas as gpd
# test directory
testdir = os.path.join(get_test_dir(), 'tmp_border{}'.format(border))
if not os.path.exists(testdir):
os.makedirs(testdir)
reset = True
# Init
cfg.initialize(logging_level=logging_level)
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PARAMS['baseline_climate'] = ''
cfg.PATHS['working_dir'] = testdir
cfg.PARAMS['border'] = border
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, reset=reset)
if not gdir.has_file('inversion_params'):
reset = True
gdir = oggm.GlacierDirectory(entity, reset=reset)
if not reset:
return gdir
gis.define_glacier_region(gdir)
execute_entity_task(gis.glacier_masks, [gdir])
execute_entity_task(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.process_custom_climate_data(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
res = climate.t_star_from_refmb(gdir, mbdf=mbdf)
climate.local_t_star(gdir, tstar=res['t_star'], bias=res['bias'])
climate.mu_star_calibration(gdir)
inversion.prepare_for_inversion(gdir, add_debug_var=True)
ref_v = 0.573 * 1e9
glen_n = cfg.PARAMS['glen_n']
def to_optimize(x):
# For backwards compat
_fd = 1.9e-24 * x[0]
glen_a = (glen_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 = (glen_n + 2) * _fd / 2.
fs = 5.7e-20 * out[1]
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]
d['factor_fs'] = out[1]
gdir.write_pickle(d, 'inversion_params')
# filter
inversion.filter_inversion_output(gdir)
inversion.distribute_thickness_interp(gdir, varname_suffix='_interp')
inversion.distribute_thickness_per_altitude(gdir, varname_suffix='_alt')
flowline.init_present_time_glacier(gdir)
return gdir
def test_repro_to_glacier(self, class_case_dir, monkeypatch):
# Init
cfg.initialize()
cfg.PATHS['working_dir'] = class_case_dir
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('RGI60-01.10689.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity)
tasks.define_glacier_region(gdir)
tasks.glacier_masks(gdir)
# use our files
region_files = {
'ALA': {
'vx': get_demo_file('crop_ALA_G0120_0000_vx.tif'),
'vy': get_demo_file('crop_ALA_G0120_0000_vy.tif')
}
}
monkeypatch.setattr(its_live, 'region_files', region_files)
monkeypatch.setattr(utils, 'file_downloader', lambda x: x)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
its_live.velocity_to_gdir(gdir)
with xr.open_dataset(gdir.get_filepath('gridded_data')) as ds:
mask = ds.glacier_mask.data.astype(bool)
vx = ds.obs_icevel_x.where(mask).data
vy = ds.obs_icevel_y.where(mask).data
vel = np.sqrt(vx**2 + vy**2)
assert np.nanmax(vel) > 2900
assert np.nanmin(vel) < 2
# We reproject with rasterio and check no big diff
cfg.BASENAMES['its_live_vx'] = ('its_live_vx.tif', '')
cfg.BASENAMES['its_live_vy'] = ('its_live_vy.tif', '')
gis.rasterio_to_gdir(gdir,
region_files['ALA']['vx'],
'its_live_vx',
resampling='bilinear')
gis.rasterio_to_gdir(gdir,
region_files['ALA']['vy'],
'its_live_vy',
resampling='bilinear')
with xr.open_rasterio(gdir.get_filepath('its_live_vx')) as da:
_vx = da.where(mask).data.squeeze()
with xr.open_rasterio(gdir.get_filepath('its_live_vy')) as da:
_vy = da.where(mask).data.squeeze()
_vel = np.sqrt(_vx**2 + _vy**2)
np.testing.assert_allclose(utils.rmsd(vel[mask], _vel[mask]),
0,
atol=40)
np.testing.assert_allclose(utils.md(vel[mask], _vel[mask]), 0, atol=8)
if DO_PLOT:
import matplotlib.pyplot as plt
smap = salem.Map(gdir.grid.center_grid, countries=False)
smap.set_shapefile(gdir.read_shapefile('outlines'))
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
smap.set_topography(gdir.get_filepath('dem'))
vel = np.sqrt(vx**2 + vy**2)
smap.set_data(vel)
smap.set_plot_params(cmap='Blues', vmin=None, vmax=None)
xx, yy = gdir.grid.center_grid.xy_coordinates
xx, yy = smap.grid.transform(xx, yy, crs=gdir.grid.proj)
yy = yy[2::5, 2::5]
xx = xx[2::5, 2::5]
vx = vx[2::5, 2::5]
vy = vy[2::5, 2::5]
f, ax = plt.subplots()
smap.visualize(ax=ax,
title='ITS_LIVE velocity',
cbar_title='m yr-1')
ax.quiver(xx, yy, vx, vy)
plt.show()
def test_run_random_climate(self):
""" Test the run_random_climate task for a climate based on the
equilibrium period centred around t*. Additionally a positive and a
negative temperature bias are tested.
Returns
-------
"""
# let's not use the mass balance bias since we want to reproduce
# results from mass balance calibration
cfg.PARAMS['use_bias_for_run'] = False
# 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)
# compute mass balance parameters
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
vascaling.local_t_star(gdir, ref_df=ref_df)
# define some parameters for the random climate model
nyears = 300
seed = 1
temp_bias = 0.5
# read the equilibirum year used for the mass balance calibration
t_star = gdir.read_json('vascaling_mustar')['t_star']
# run model with random climate
_ = vascaling.run_random_climate(gdir,
nyears=nyears,
y0=t_star,
seed=seed)
# run model with positive temperature bias
_ = vascaling.run_random_climate(gdir,
nyears=nyears,
y0=t_star,
seed=seed,
temperature_bias=temp_bias,
output_filesuffix='_bias_p')
# run model with negative temperature bias
_ = vascaling.run_random_climate(gdir,
nyears=nyears,
y0=t_star,
seed=seed,
temperature_bias=-temp_bias,
output_filesuffix='_bias_n')
# compile run outputs
ds = utils.compile_run_output([gdir], input_filesuffix='')
ds_p = utils.compile_run_output([gdir], input_filesuffix='_bias_p')
ds_n = utils.compile_run_output([gdir], input_filesuffix='_bias_n')
# the glacier should not change much under a random climate
# based on the equilibirum period centered around t*
assert abs(1 - ds.volume.mean() / ds.volume[0]) < 0.015
# higher temperatures should result in a smaller glacier
assert ds.volume.mean() > ds_p.volume.mean()
# lower temperatures should result in a larger glacier
assert ds.volume.mean() < ds_n.volume.mean()
def init_glacier_directories(rgidf=None, *, reset=False, force=False,
from_prepro_level=None, prepro_border=None,
prepro_rgi_version=None, prepro_base_url=None,
from_tar=False, delete_tar=False,
use_demo_glaciers=None):
"""Initializes the list of Glacier Directories for this run.
This is the very first task to do (always). If the directories are already
available in the working directory, use them. If not, create new ones.
Parameters
----------
rgidf : GeoDataFrame or list of ids, optional for pre-computed runs
the RGI glacier outlines. If unavailable, OGGM will parse the
information from the glacier directories found in the working
directory. It is required for new runs.
reset : bool
delete the existing glacier directories if found.
force : bool
setting `reset=True` will trigger a yes/no question to the user. Set
`force=True` to avoid this.
from_prepro_level : int
get the gdir data from the official pre-processed pool. See the
documentation for more information
prepro_border : int
for `from_prepro_level` only: if you want to override the default
behavior which is to use `cfg.PARAMS['border']`
prepro_rgi_version : str
for `from_prepro_level` only: if you want to override the default
behavior which is to use `cfg.PARAMS['rgi_version']`
prepro_base_url : str
for `from_prepro_level` only: if you want to override the default
URL from which to download the gdirs. Default currently is
https://cluster.klima.uni-bremen.de/~fmaussion/gdirs/oggm_v1.1/
use_demo_glaciers : bool
whether to check the demo glaciers for download (faster than the
standard prepro downloads). The default is to decide whether or
not to check based on simple criteria such as glacier list size.
from_tar : bool, default=False
extract the gdir data from a tar file. If set to `True`,
will check for a tar file at the expected location in `base_dir`.
delete_tar : bool, default=False
delete the original tar file after extraction.
Returns
-------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the initialised glacier directories
Notes
-----
This task is very similar to init_glacier_regions, with one main
difference: it does not process the DEMs for this glacier.
Eventually, init_glacier_regions will be deprecated and removed from the
codebase.
"""
_check_duplicates(rgidf)
if reset and not force:
reset = utils.query_yes_no('Delete all glacier directories?')
if from_prepro_level:
url = utils.get_prepro_base_url(base_url=prepro_base_url,
border=prepro_border,
prepro_level=from_prepro_level,
rgi_version=prepro_rgi_version)
if cfg.PARAMS['has_internet'] and not utils.url_exists(url):
raise InvalidParamsError("base url seems unreachable with these "
"parameters: {}".format(url))
# if reset delete also the log directory
if reset:
fpath = os.path.join(cfg.PATHS['working_dir'], 'log')
if os.path.exists(fpath):
rmtree(fpath)
if rgidf is None:
# Infer the glacier directories from folders available in working dir
if reset:
raise ValueError('Cannot use reset without setting rgidf')
log.workflow('init_glacier_directories by parsing all available '
'folders (this takes time: if possible, provide rgidf '
'instead).')
# The dirs should be there already
gl_dir = os.path.join(cfg.PATHS['working_dir'], 'per_glacier')
gdirs = []
for root, _, files in os.walk(gl_dir):
if files and ('outlines.shp' in files or
'outlines.tar.gz' in files):
gdirs.append(oggm.GlacierDirectory(os.path.basename(root)))
else:
# Create glacier directories from input
# Check if dataframe or list of str
try:
entities = []
for _, entity in rgidf.iterrows():
entities.append(entity)
except AttributeError:
entities = utils.tolist(rgidf)
# Check demo
if use_demo_glaciers is None:
use_demo_glaciers = len(entities) < 100
if from_prepro_level is not None:
log.workflow('init_glacier_directories from prepro level {} on '
'{} glaciers.'.format(from_prepro_level,
len(entities)))
# Read the hash dictionary before we use multiproc
if cfg.PARAMS['dl_verify']:
utils.get_dl_verify_data('cluster.klima.uni-bremen.de')
gdirs = execute_entity_task(gdir_from_prepro, entities,
from_prepro_level=from_prepro_level,
prepro_border=prepro_border,
prepro_rgi_version=prepro_rgi_version,
check_demo_glacier=use_demo_glaciers,
base_url=prepro_base_url)
else:
# We can set the intersects file automatically here
if (cfg.PARAMS['use_intersects'] and
len(cfg.PARAMS['intersects_gdf']) == 0):
try:
rgi_ids = np.unique(np.sort([entity.RGIId for entity in
entities]))
rgi_version = rgi_ids[0].split('-')[0][-2:]
fp = utils.get_rgi_intersects_entities(rgi_ids,
version=rgi_version)
cfg.set_intersects_db(fp)
except AttributeError:
# List of str
pass
gdirs = execute_entity_task(utils.GlacierDirectory, entities,
reset=reset,
from_tar=from_tar,
delete_tar=delete_tar)
return gdirs
import xarray as xr
import matplotlib.pyplot as plt
from oggm import cfg, tasks
from oggm.utils import get_demo_file
from oggm.core.preprocessing.climate import mb_yearly_climate_on_glacier, \
t_star_from_refmb, local_mustar_apparent_mb
from oggm.core.models.massbalance import PastMassBalanceModel
cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
pcp_fac = 2.6
cfg.PARAMS['prcp_scaling_factor'] = pcp_fac
base_dir = os.path.join(os.path.expanduser('~'), 'Climate')
entity = gpd.read_file(get_demo_file('Hintereisferner.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=base_dir)
tasks.define_glacier_region(gdir, entity=entity)
tasks.glacier_masks(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)
mbdf = gdir.get_ref_mb_data()
res = t_star_from_refmb(gdir, mbdf.ANNUAL_BALANCE)
def setup_cache(self):
setattr(full_workflow.setup_cache, "timeout", 360)
utils.mkdir(self.testdir, reset=True)
self.cfg_init()
entity = gpd.read_file(get_demo_file('01_rgi60_Columbia.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir)
tasks.define_glacier_region(gdir, entity=entity)
tasks.glacier_masks(gdir)
tasks.compute_centerlines(gdir)
tasks.initialize_flowlines(gdir)
tasks.compute_downstream_line(gdir)
tasks.compute_downstream_bedshape(gdir)
tasks.catchment_area(gdir)
tasks.catchment_intersections(gdir)
tasks.catchment_width_geom(gdir)
tasks.catchment_width_correction(gdir)
climate.process_dummy_cru_file(gdir, seed=0)
rho = cfg.PARAMS['ice_density']
i = 0
calving_flux = []
mu_star = []
ite = []
cfg.PARAMS['clip_mu_star'] = False
cfg.PARAMS['min_mu_star'] = 0 # default is now 1
while i < 12:
# Calculates a calving flux from model output
if i == 0:
# First call we set to zero (not very necessary,
# this first loop could be removed)
f_calving = 0
elif i == 1:
# Second call we set a very small positive calving
f_calving = utils.calving_flux_from_depth(gdir, water_depth=1)
elif cfg.PARAMS['clip_mu_star']:
# If we have to clip mu the calving becomes the real flux
fl = gdir.read_pickle('inversion_flowlines')[-1]
f_calving = fl.flux[-1] * (gdir.grid.dx**2) * 1e-9 / rho
else:
# Otherwise it is parameterized
f_calving = utils.calving_flux_from_depth(gdir)
# Give it back to the inversion and recompute
gdir.inversion_calving_rate = f_calving
# At this step we might raise a MassBalanceCalibrationError
mu_is_zero = False
try:
climate.local_t_star(gdir)
df = gdir.read_json('local_mustar')
except MassBalanceCalibrationError as e:
assert 'mu* out of specified bounds' in str(e)
# When this happens we clip mu* to zero and store the
# bad value (just for plotting)
cfg.PARAMS['clip_mu_star'] = True
df = gdir.read_json('local_mustar')
df['mu_star_glacierwide'] = float(str(e).split(':')[-1])
climate.local_t_star(gdir)
climate.mu_star_calibration(gdir)
tasks.prepare_for_inversion(gdir, add_debug_var=True)
v_inv, _ = tasks.mass_conservation_inversion(gdir)
# Store the data
calving_flux = np.append(calving_flux, f_calving)
mu_star = np.append(mu_star, df['mu_star_glacierwide'])
ite = np.append(ite, i)
# Do we have to do another_loop?
if i > 0:
avg_one = np.mean(calving_flux[-4:])
avg_two = np.mean(calving_flux[-5:-1])
difference = abs(avg_two - avg_one)
conv = (difference < 0.05 * avg_two or calving_flux[-1] == 0
or calving_flux[-1] == calving_flux[-2])
if mu_is_zero or conv:
break
i += 1
assert i < 8
assert calving_flux[-1] < np.max(calving_flux)
assert calving_flux[-1] > 2
assert mu_star[-1] == 0
mbmod = massbalance.MultipleFlowlineMassBalance
mb = mbmod(gdir,
use_inversion_flowlines=True,
mb_model_class=massbalance.ConstantMassBalance,
bias=0)
flux_mb = (mb.get_specific_mb() * gdir.rgi_area_m2) * 1e-9 / rho
np.testing.assert_allclose(flux_mb, calving_flux[-1], atol=0.001)
return calving_flux, mu_star
def test_run_constant_climate(self):
""" Test the run_constant_climate task for a climate based on the
equilibrium period centred around t*. Additionally a positive and a
negative temperature bias are tested.
"""
# let's not use the mass balance bias since we want to reproduce
# results from mass balance calibration
cfg.PARAMS['use_bias_for_run'] = False
# 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)
# compute mass balance parameters
ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp']
vascaling.local_t_star(gdir, ref_df=ref_df)
# define some parameters for the constant climate model
nyears = 500
temp_bias = 0.5
_ = vascaling.run_constant_climate(gdir,
nyears=nyears,
output_filesuffix='')
_ = vascaling.run_constant_climate(gdir,
nyears=nyears,
temperature_bias=+temp_bias,
output_filesuffix='_bias_p')
_ = vascaling.run_constant_climate(gdir,
nyears=nyears,
temperature_bias=-temp_bias,
output_filesuffix='_bias_n')
# compile run outputs
ds = utils.compile_run_output([gdir], input_filesuffix='')
ds_p = utils.compile_run_output([gdir], input_filesuffix='_bias_p')
ds_n = utils.compile_run_output([gdir], input_filesuffix='_bias_n')
# the glacier should not change under a constant climate
# based on the equilibirum period centered around t*
assert abs(1 - ds.volume.mean() / ds.volume[0]) < 1e-7
# higher temperatures should result in a smaller glacier
assert ds.volume.mean() > ds_p.volume.mean()
# lower temperatures should result in a larger glacier
assert ds.volume.mean() < ds_n.volume.mean()
# compute volume change from one year to the next
dV_p = (ds_p.volume[1:].values - ds_p.volume[:-1].values).flatten()
dV_n = (ds_n.volume[1:].values - ds_n.volume[:-1].values).flatten()
# compute relative volume change, with respect to the final volume
rate_p = abs(dV_p / float(ds_p.volume.values[-1]))
rate_n = abs(dV_n / float(ds_n.volume.values[-1]))
# the glacier should be in a new equilibirum for last 300 years
assert max(rate_p[-300:]) < 0.001
assert max(rate_n[-300:]) < 0.001
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
def test_solid_prcp(self):
"""Tests the subroutine which computes solid precipitation amount from
given total precipitation and temperature.
"""
# 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
gis.define_glacier_region(gdir, entity=entity)
# process the given climate file
climate.process_custom_climate_data(gdir)
# read the following variable from the center pixel (46.83N 10.75E)
# of the Hintereisferner HistAlp climate file for the
# entire time period from October 1801 until September 2003
# - surface height in m asl.
# - total precipitation amount in kg/m2
# - 2m air temperature in °C
with utils.ncDataset(get_demo_file('histalp_merged_hef.nc')) as nc_r:
ref_h = nc_r.variables['hgt'][1, 1]
ref_p = nc_r.variables['prcp'][:, 1, 1]
ref_t = nc_r.variables['temp'][:, 1, 1]
# define needed parameters
prcp_factor = 1
temp_all_solid = 0
temp_grad = -0.0065
# define elevation levels
ref_hgt = ref_h
min_hgt = ref_h - 100
max_hgt = ref_h + 100
# if the terminus temperature is below the threshold for
# solid precipitation all fallen precipitation must be solid
temp_terminus = ref_t * 0 + temp_all_solid
solid_prcp = vascaling._compute_solid_prcp(ref_p, prcp_factor, ref_hgt,
min_hgt, max_hgt,
temp_terminus,
temp_all_solid, temp_grad,
prcp_grad=0, prcp_anomaly=0)
np.testing.assert_allclose(solid_prcp, ref_p)
# if the temperature at the maximal elevation is above the threshold
# for solid precipitation all fallen precipitation must be liquid
temp_terminus = ref_t + 100
solid_prcp = vascaling._compute_solid_prcp(ref_p, prcp_factor, ref_hgt,
min_hgt, max_hgt,
temp_terminus,
temp_all_solid, temp_grad,
prcp_grad=0, prcp_anomaly=0)
np.testing.assert_allclose(solid_prcp, 0)
# test extreme case if max_hgt equals min_hgt
test_p = ref_p * (ref_t <= temp_all_solid).astype(int)
solid_prcp = vascaling._compute_solid_prcp(ref_p, prcp_factor, ref_hgt,
ref_hgt, ref_hgt, ref_t,
temp_all_solid, temp_grad,
prcp_grad=0, prcp_anomaly=0)
np.testing.assert_allclose(solid_prcp, test_p)
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
import geopandas as gpd
import oggm
from oggm import cfg, tasks
from oggm.utils import get_demo_file
# Set up the input data for this example
cfg.initialize()
cfg.PATHS['working_dir'] = oggm.utils.get_temp_dir('oggmcontrib_inv')
cfg.PATHS['dem_file'] = get_demo_file('srtm_oetztal.tif')
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
# Glacier directory for Hintereisferner in Austria
entity = gpd.read_file(get_demo_file('Hintereisferner_RGI5.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity)
# The usual OGGM preprecessing
tasks.define_glacier_region(gdir, entity=entity)
tasks.glacier_masks(gdir)
def init_glacier_regions(rgidf=None, reset=False, force=False):
"""Initializes the list of Glacier Directories for this run.
This is the very first task to do (always). If the directories are already
available in the working directory, use them. If not, create new ones.
Parameters
----------
rgidf : GeoDataFrame, optional for pre-computed runs
the RGI glacier outlines. If unavailable, OGGM will parse the
information from the glacier directories found in the working
directory. It is required for new runs.
reset : bool
delete the existing glacier directories if found.
force : bool
setting `reset=True` will trigger a yes/no question to the user. Set
`force=True` to avoid this.
Returns
-------
a list of GlacierDirectory objects
"""
if reset and not force:
reset = utils.query_yes_no('Delete all glacier directories?')
# if reset delete also the log directory
if reset:
fpath = os.path.join(cfg.PATHS['working_dir'], 'log')
if os.path.exists(fpath):
rmtree(fpath)
gdirs = []
new_gdirs = []
if rgidf is None:
if reset:
raise ValueError('Cannot use reset without a rgi file')
# The dirs should be there already
gl_dir = os.path.join(cfg.PATHS['working_dir'], 'per_glacier')
for root, _, files in os.walk(gl_dir):
if files and ('dem.tif' in files):
gdirs.append(oggm.GlacierDirectory(os.path.basename(root)))
else:
for _, entity in rgidf.iterrows():
gdir = oggm.GlacierDirectory(entity, reset=reset)
if not os.path.exists(gdir.get_filepath('dem')):
new_gdirs.append((gdir, dict(entity=entity)))
gdirs.append(gdir)
# We can set the intersects file automatically here
if (cfg.PARAMS['use_intersects'] and new_gdirs
and (len(cfg.PARAMS['intersects_gdf']) == 0)):
rgi_ids = np.unique(np.sort([t[0].rgi_id for t in new_gdirs]))
rgi_version = new_gdirs[0][0].rgi_version
fp = utils.get_rgi_intersects_entities(rgi_ids, version=rgi_version)
cfg.set_intersects_db(fp)
# If not initialized, run the task in parallel
execute_entity_task(tasks.define_glacier_region, new_gdirs)
return gdirs
def init_glacier_regions(rgidf=None, *, reset=False, force=False,
from_prepro_level=None, prepro_border=None,
prepro_rgi_version=None, prepro_base_url=None,
from_tar=False, delete_tar=False,
use_demo_glaciers=None):
"""DEPRECATED: Initializes the list of Glacier Directories for this run.
This is the very first task to do (always). If the directories are already
available in the working directory, use them. If not, create new ones.
Parameters
----------
rgidf : GeoDataFrame or list of ids, optional for pre-computed runs
the RGI glacier outlines. If unavailable, OGGM will parse the
information from the glacier directories found in the working
directory. It is required for new runs.
reset : bool
delete the existing glacier directories if found.
force : bool
setting `reset=True` will trigger a yes/no question to the user. Set
`force=True` to avoid this.
from_prepro_level : int
get the gdir data from the official pre-processed pool. See the
documentation for more information
prepro_border : int
for `from_prepro_level` only: if you want to override the default
behavior which is to use `cfg.PARAMS['border']`
prepro_rgi_version : str
for `from_prepro_level` only: if you want to override the default
behavior which is to use `cfg.PARAMS['rgi_version']`
prepro_base_url : str
for `from_prepro_level` only: if you want to override the default
URL from which to download the gdirs. Default currently is
https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.1/
use_demo_glaciers : bool
whether to check the demo glaciers for download (faster than the
standard prepro downloads). The default is to decide whether or
not to check based on simple criteria such as glacier list size.
from_tar : bool, default=False
extract the gdir data from a tar file. If set to `True`,
will check for a tar file at the expected location in `base_dir`.
delete_tar : bool, default=False
delete the original tar file after extraction.
delete_tar : bool, default=False
delete the original tar file after extraction.
Returns
-------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the initialised glacier directories
Notes
-----
This task is deprecated in favor of the more explicit
init_glacier_directories. Indeed, init_glacier_directories is very
similar to init_glacier_regions, but it does not process the DEMs:
a glacier directory is valid also without DEM.
"""
_check_duplicates(rgidf)
if reset and not force:
reset = utils.query_yes_no('Delete all glacier directories?')
if prepro_border is None:
prepro_border = int(cfg.PARAMS['border'])
if from_prepro_level and prepro_border not in [10, 80, 160, 250]:
if 'test' not in utils._downloads.GDIR_URL:
raise InvalidParamsError("prepro_border or cfg.PARAMS['border'] "
"should be one of: 10, 80, 160, 250.")
# if reset delete also the log directory
if reset:
fpath = os.path.join(cfg.PATHS['working_dir'], 'log')
if os.path.exists(fpath):
rmtree(fpath)
gdirs = []
new_gdirs = []
if rgidf is None:
if reset:
raise ValueError('Cannot use reset without setting rgidf')
log.workflow('init_glacier_regions by parsing available folders '
'(can be slow).')
# The dirs should be there already
gl_dir = os.path.join(cfg.PATHS['working_dir'], 'per_glacier')
for root, _, files in os.walk(gl_dir):
if files and ('dem.tif' in files):
gdirs.append(oggm.GlacierDirectory(os.path.basename(root)))
else:
# Check if dataframe or list of strs
try:
entities = []
for _, entity in rgidf.iterrows():
entities.append(entity)
except AttributeError:
entities = utils.tolist(rgidf)
# Check demo
if use_demo_glaciers is None:
use_demo_glaciers = len(entities) < 100
if from_prepro_level is not None:
log.workflow('init_glacier_regions from prepro level {} on '
'{} glaciers.'.format(from_prepro_level,
len(entities)))
# Read the hash dictionary before we use multiproc
if cfg.PARAMS['dl_verify']:
utils.get_dl_verify_data('cluster.klima.uni-bremen.de')
gdirs = execute_entity_task(gdir_from_prepro, entities,
from_prepro_level=from_prepro_level,
prepro_border=prepro_border,
prepro_rgi_version=prepro_rgi_version,
check_demo_glacier=use_demo_glaciers,
base_url=prepro_base_url)
else:
# We can set the intersects file automatically here
if (cfg.PARAMS['use_intersects'] and
len(cfg.PARAMS['intersects_gdf']) == 0):
rgi_ids = np.unique(np.sort([entity.RGIId for entity in
entities]))
rgi_version = rgi_ids[0].split('-')[0][-2:]
fp = utils.get_rgi_intersects_entities(rgi_ids,
version=rgi_version)
cfg.set_intersects_db(fp)
gdirs = execute_entity_task(utils.GlacierDirectory, entities,
reset=reset,
from_tar=from_tar,
delete_tar=delete_tar)
for gdir in gdirs:
if not os.path.exists(gdir.get_filepath('dem')):
new_gdirs.append(gdir)
if len(new_gdirs) > 0:
# If not initialized, run the task in parallel
execute_entity_task(tasks.define_glacier_region, new_gdirs)
return gdirs
dir_path = os.path.join(tempfile.gettempdir(), 'fig_01') fig_path = os.path.join(PLOT_DIR, 'workflow_tas.pdf') cfg.initialize() cfg.PARAMS['border'] = 20 cfg.PATHS['working_dir'] = dir_path utils.mkdir(dir_path, reset=True) rgidf = utils.get_rgi_glacier_entities(['RGI60-18.02342']) entity = rgidf.iloc[0] cfg.set_intersects_db(utils.get_rgi_intersects_entities(['RGI60-18.02342'])) gdir = oggm.GlacierDirectory(entity, base_dir=dir_path) tasks.define_glacier_region(gdir, entity=entity) tasks.glacier_masks(gdir) tasks.compute_centerlines(gdir) tasks.initialize_flowlines(gdir) tasks.compute_downstream_line(gdir) tasks.compute_downstream_bedshape(gdir) tasks.catchment_area(gdir) tasks.catchment_intersections(gdir) tasks.catchment_width_geom(gdir) tasks.catchment_width_correction(gdir) tasks.process_cru_data(gdir) tasks.local_t_star(gdir) tasks.mu_star_calibration(gdir)
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 init_hef(reset=False,
border=40,
invert_with_sliding=True,
invert_with_rectangular=True):
# test directory
testdir = os.path.join(get_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.set_intersects_db(get_demo_file('rgi_intersect_oetztal.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
cfg.PARAMS['use_optimized_inversion_params'] = True
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_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)
execute_entity_task(gis.glacier_masks, [gdir])
execute_entity_task(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.process_custom_climate_data(gdir)
climate.mu_candidates(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
res = climate.t_star_from_refmb(gdir, mbdf)
climate.local_mustar(gdir,
tstar=res['t_star'][-1],
bias=res['bias'][-1],
prcp_fac=res['prcp_fac'])
climate.apparent_mb(gdir)
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_interp(gdir, varname_suffix='_interp')
inversion.distribute_thickness_per_altitude(gdir, varname_suffix='_alt')
flowline.init_present_time_glacier(gdir)
return gdir
