def test_multiple_inversion():
# test directory
testdir = os.path.join(get_test_dir(), 'tmp_mdir')
if not os.path.exists(testdir):
os.makedirs(testdir)
# 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'] = 40
cfg.PARAMS['run_mb_calibration'] = True
cfg.PARAMS['baseline_climate'] = 'CUSTOM'
cfg.PATHS['working_dir'] = testdir
# Get the RGI ID
hef_rgi = gpd.read_file(get_demo_file('divides_hef.shp'))
hef_rgi.loc[0, 'RGIId'] = 'RGI50-11.00897'
gdirs = workflow.init_glacier_regions(hef_rgi)
workflow.gis_prepro_tasks(gdirs)
workflow.climate_tasks(gdirs)
workflow.inversion_tasks(gdirs)
fig, ax = plt.subplots()
graphics.plot_inversion(gdirs, ax=ax)
fig.tight_layout()
shutil.rmtree(testdir)
return fig
def up_to_inversion(reset=False):
"""Run the tasks you want."""
gdirs = up_to_climate(reset=reset)
with open(CLI_LOGF, 'rb') as f:
clilog = pickle.load(f)
if clilog != 'histalp':
reset = True
else:
try:
tasks.prepare_for_inversion(gdirs[0])
except Exception:
reset = True
if reset:
# Use histalp for the actual inversion test
cfg.PARAMS['temp_use_local_gradient'] = True
cfg.PARAMS['baseline_climate'] = 'HISTALP'
cru_dir = get_demo_file('HISTALP_precipitation_all_abs_1801-2014.nc')
cfg.PATHS['cru_dir'] = os.path.dirname(cru_dir)
workflow.climate_tasks(gdirs)
with open(CLI_LOGF, 'wb') as f:
pickle.dump('histalp', f)
# Inversion
workflow.inversion_tasks(gdirs)
return gdirs
def up_to_inversion(reset=False):
"""Run the tasks you want."""
gdirs = up_to_climate(reset=reset)
with open(CLI_LOGF, 'rb') as f:
clilog = pickle.load(f)
if clilog != 'histalp':
reset = True
else:
try:
tasks.prepare_for_inversion(gdirs[0])
except Exception:
reset = True
if reset:
# Use histalp for the actual inversion test
cfg.PARAMS['temp_use_local_gradient'] = True
cfg.PARAMS['baseline_climate'] = 'HISTALP'
workflow.climate_tasks(gdirs)
with open(CLI_LOGF, 'wb') as f:
pickle.dump('histalp', f)
# Inversion
workflow.inversion_tasks(gdirs)
return gdirs
def up_to_inversion(reset=False):
"""Run the tasks you want."""
gdirs = up_to_climate(reset=reset)
with open(CLI_LOGF, 'rb') as f:
clilog = pickle.load(f)
if clilog != 'histalp':
reset = True
else:
try:
tasks.prepare_for_inversion(gdirs[0])
except Exception:
reset = True
if reset:
# Use histalp for the actual inversion test
cfg.PARAMS['temp_use_local_gradient'] = True
cfg.PARAMS['baseline_climate'] = 'HISTALP'
cru_dir = get_demo_file('HISTALP_precipitation_all_abs_1801-2014.nc')
cfg.PATHS['cru_dir'] = os.path.dirname(cru_dir)
workflow.climate_tasks(gdirs)
with open(CLI_LOGF, 'wb') as f:
pickle.dump('histalp', f)
# Inversion
workflow.inversion_tasks(gdirs)
return gdirs
def test_thick_elev_bands():
fig, ax = plt.subplots()
gdir = init_columbia_eb(dir_name='test_thick_eb')
workflow.inversion_tasks(utils.tolist(gdir))
inversion.distribute_thickness_per_altitude(gdir)
graphics.plot_distributed_thickness(gdir, ax=ax)
fig.tight_layout()
return fig
def test_init_present_time_glacier(self):
gdirs = up_to_inversion()
# Inversion Results
cfg.PARAMS['invert_with_sliding'] = True
cfg.PARAMS['optimize_thick'] = True
workflow.inversion_tasks(gdirs)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df = pd.read_csv(fpath, index_col=0)
r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3'])
r2 = rmsd(df['ref_volume_km3'], df['vas_volume_km3'])
self.assertTrue(r1 < r2)
cfg.PARAMS['invert_with_sliding'] = False
cfg.PARAMS['optimize_thick'] = False
workflow.inversion_tasks(gdirs)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df = pd.read_csv(fpath, index_col=0)
r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3'])
r2 = rmsd(df['ref_volume_km3'], df['vas_volume_km3'])
self.assertTrue(r1 < r2)
# Init glacier
d = gdirs[0].read_pickle('inversion_params')
fs = d['fs']
glen_a = d['glen_a']
maxs = cfg.PARAMS['max_shape_param']
for gdir in gdirs:
flowline.init_present_time_glacier(gdir)
mb_mod = massbalance.ConstantMassBalanceModel(gdir)
fls = gdir.read_pickle('model_flowlines')
model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0.,
fs=fs, glen_a=glen_a)
_vol = model.volume_km3
_area = model.area_km2
if gdir.rgi_id in df.index:
gldf = df.loc[gdir.rgi_id]
# TODO: broken but should work
# assert_allclose(gldf['oggm_volume_km3'], _vol, rtol=0.03)
# assert_allclose(gldf['ref_area_km2'], _area, rtol=0.03)
maxo = max([fl.order for fl in model.fls])
for fl in model.fls:
self.assertTrue(np.all(fl.bed_shape > 0))
self.assertTrue(np.all(fl.bed_shape <= maxs))
if len(model.fls) > 1:
if fl.order == (maxo-1):
self.assertTrue(fl.flows_to is fls[-1])
# Test the glacier charac
dfc = utils.glacier_characteristics(gdirs)
self.assertTrue(np.all(dfc.terminus_type == 'Land-terminating'))
cc = dfc[['dem_mean_elev', 'clim_temp_avgh']].corr().values[0, 1]
self.assertTrue(cc > 0.4)
def preprocessing(gdirs):
"""
oggm workflow for preparing initializing
:param gdirs: list of oggm.GlacierDirectories from preprocessed level 2 onwards
:return None, but creates required files
"""
workflow.climate_tasks(gdirs)
workflow.inversion_tasks(gdirs)
workflow.execute_entity_task(tasks.init_present_time_glacier, gdirs)
def plot_issue(gdir, plot_dir):
#plt.style.use('ggplot')
workflow.gis_prepro_tasks([gdir])
workflow.climate_tasks([gdir])
workflow.inversion_tasks([gdir])
tasks.init_present_time_glacier(gdir)
# Observed length changes
df = gdir.get_ref_length_data()
df = df.loc[1855:2003]['dL']
df = df - df.iloc[-1]
tasks.run_from_climate_data(gdir,
ys=1855,
ye=2003,
output_filesuffix='hist_from_current')
ds = xr.open_dataset(
gdir.get_filepath('model_diagnostics', filesuffix='hist_from_current'))
(ds.length_m.to_series().rolling(36, center=True).mean() -
ds.length_m.to_series().iloc[0]).plot(c='C0', label='OGGM')
#s = s - s.iloc[-1]
#print(s)
ax = df.plot(c='k', label='Observations')
#s.plot(c='C0', label='OGGM');
plt.legend()
ax.set_ylabel('Glacier Length Change [m]')
plt.title('Hintereisferner length changes Experiment 2')
plt.tight_layout()
plt.show()
'''
fls = gdir.read_pickle('model_flowlines')
x = np.arange(fls[-1].nx) *fls[-1].dx * fls[-1].map_dx
plt.figure(figsize=(13,10))
rc('axes', linewidth=3)
plt.plot(x,fls[-1].surface_h,linewidth=3, label='Surface Elevation')
plt.plot(x,fls[-1].bed_h,'k',linewidth=3,label='Bed Topography')
plt.ylabel('Altitude (m)',size=30)
plt.xlabel('Distance along the Flowline (m)',size=30)
plt.legend(loc='best',fontsize=30)
#plt.annotate('?', xy=(5000, 2700), fontsize=40)
plt.tick_params(axis='both', which='major', labelsize=30)
plt.title(gdir.rgi_id+ ': '+gdir.name,size=35)
plt.savefig(os.path.join(plot_dir, 'issue_today.png'),dpi=200)
'''
#plt.savefig(os.path.join(plot_dir, 'issue_1850.pdf'),dpi=200)
plt.show()
return
def test_init_present_time_glacier(self):
gdirs = up_to_inversion()
# Inversion Results
cfg.PARAMS['invert_with_sliding'] = True
cfg.PARAMS['optimize_thick'] = True
workflow.inversion_tasks(gdirs)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df = pd.read_csv(fpath, index_col=0)
r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3'])
assert r1 < 0.1
cfg.PARAMS['invert_with_sliding'] = False
cfg.PARAMS['optimize_thick'] = False
workflow.inversion_tasks(gdirs)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df = pd.read_csv(fpath, index_col=0)
r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3'])
assert r1 < 0.12
# Init glacier
d = gdirs[0].read_pickle('inversion_params')
fs = d['fs']
glen_a = d['glen_a']
for gdir in gdirs:
flowline.init_present_time_glacier(gdir)
mb_mod = massbalance.ConstantMassBalance(gdir)
fls = gdir.read_pickle('model_flowlines')
model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0.,
fs=fs, glen_a=glen_a)
_vol = model.volume_km3
_area = model.area_km2
if gdir.rgi_id in df.index:
gldf = df.loc[gdir.rgi_id]
assert_allclose(gldf['oggm_volume_km3'], _vol, rtol=0.05)
assert_allclose(gldf['ref_area_km2'], _area, rtol=0.05)
maxo = max([fl.order for fl in model.fls])
for fl in model.fls:
if len(model.fls) > 1:
if fl.order == (maxo-1):
self.assertTrue(fl.flows_to is fls[-1])
# Test the glacier charac
dfc = utils.glacier_characteristics(gdirs)
self.assertTrue(np.all(dfc.terminus_type == 'Land-terminating'))
cc = dfc[['flowline_mean_elev',
'tstar_avg_temp_mean_elev']].corr().values[0, 1]
assert cc < -0.8
assert np.all(dfc.t_star > 1900)
assert np.all(dfc.tstar_aar.mean() > 0.5)
def test_init_present_time_glacier(self):
gdirs = up_to_inversion()
# Inversion Results
cfg.PARAMS['invert_with_sliding'] = True
cfg.PARAMS['optimize_thick'] = True
workflow.inversion_tasks(gdirs)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df = pd.read_csv(fpath, index_col=0)
r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3'])
r2 = rmsd(df['ref_volume_km3'], df['vas_volume_km3'])
self.assertTrue(r1 < r2)
cfg.PARAMS['invert_with_sliding'] = False
cfg.PARAMS['optimize_thick'] = False
workflow.inversion_tasks(gdirs)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df = pd.read_csv(fpath, index_col=0)
r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3'])
r2 = rmsd(df['ref_volume_km3'], df['vas_volume_km3'])
self.assertTrue(r1 < r2)
# Init glacier
d = gdirs[0].read_pickle('inversion_params')
fs = d['fs']
glen_a = d['glen_a']
maxs = cfg.PARAMS['max_shape_param']
for gdir in gdirs:
flowline.init_present_time_glacier(gdir)
mb_mod = massbalance.ConstantMassBalanceModel(gdir)
fls = gdir.read_pickle('model_flowlines')
model = flowline.FluxBasedModel(fls,
mb_model=mb_mod,
y0=0.,
fs=fs,
glen_a=glen_a)
_vol = model.volume_km3
_area = model.area_km2
gldf = df.loc[gdir.rgi_id]
assert_allclose(gldf['oggm_volume_km3'], _vol, rtol=0.03)
assert_allclose(gldf['ref_area_km2'], _area, rtol=0.03)
maxo = max([fl.order for fl in model.fls])
for fl in model.fls:
self.assertTrue(np.all(fl.bed_shape > 0))
self.assertTrue(np.all(fl.bed_shape <= maxs))
if len(model.fls) > 1:
if fl.order == (maxo - 1):
self.assertTrue(fl.flows_to is fls[-1])
def test_init_present_time_glacier(self):
gdirs = up_to_inversion()
# Inversion Results
cfg.PARAMS['invert_with_sliding'] = True
cfg.PARAMS['optimize_thick'] = True
workflow.inversion_tasks(gdirs)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df = pd.read_csv(fpath, index_col=0)
r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3'])
r2 = rmsd(df['ref_volume_km3'], df['vas_volume_km3'])
self.assertTrue(r1 < r2)
cfg.PARAMS['invert_with_sliding'] = False
cfg.PARAMS['optimize_thick'] = False
workflow.inversion_tasks(gdirs)
fpath = os.path.join(cfg.PATHS['working_dir'],
'inversion_optim_results.csv')
df = pd.read_csv(fpath, index_col=0)
r1 = rmsd(df['ref_volume_km3'], df['oggm_volume_km3'])
r2 = rmsd(df['ref_volume_km3'], df['vas_volume_km3'])
self.assertTrue(r1 < r2)
# Init glacier
d = gdirs[0].read_pickle('inversion_params')
fs = d['fs']
glen_a = d['glen_a']
maxs = cfg.PARAMS['max_shape_param']
for gdir in gdirs:
flowline.init_present_time_glacier(gdir)
mb_mod = massbalance.TstarMassBalanceModel(gdir)
fls = gdir.read_pickle('model_flowlines')
model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0.,
fs=fs, glen_a=glen_a)
_vol = model.volume_km3
_area = model.area_km2
gldf = df.loc[gdir.rgi_id]
assert_allclose(gldf['oggm_volume_km3'], _vol, rtol=0.03)
assert_allclose(gldf['ref_area_km2'], _area, rtol=0.03)
maxo = max([fl.order for fl in model.fls])
for fl in model.fls:
self.assertTrue(np.all(fl.bed_shape > 0))
self.assertTrue(np.all(fl.bed_shape <= maxs))
if len(model.fls) > 1:
if fl.order == (maxo-1):
self.assertTrue(fl.flows_to is fls[-1])
def test_multiple_models():
# test directory
testdir = os.path.join(get_test_dir(), 'tmp_mdir')
utils.mkdir(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.PATHS['working_dir'] = testdir
cfg.PARAMS['baseline_climate'] = 'CUSTOM'
cfg.PARAMS['trapezoid_lambdas'] = 1
cfg.PARAMS['border'] = 40
apply_test_ref_tstars()
# Get the RGI ID
hef_rgi = gpd.read_file(get_demo_file('divides_hef.shp'))
hef_rgi.loc[0, 'RGIId'] = 'RGI50-11.00897'
gdirs = workflow.init_glacier_directories(hef_rgi)
workflow.gis_prepro_tasks(gdirs)
workflow.climate_tasks(gdirs)
workflow.inversion_tasks(gdirs)
models = []
for gdir in gdirs:
flowline.init_present_time_glacier(gdir)
fls = gdir.read_pickle('model_flowlines')
models.append(flowline.FlowlineModel(fls))
fig, ax = plt.subplots()
graphics.plot_modeloutput_map(gdirs, ax=ax, model=models)
fig.tight_layout()
shutil.rmtree(testdir)
return fig
def test_multiple_models():
# test directory
testdir = os.path.join(get_test_dir(), 'tmp_mdir')
utils.mkdir(testdir, reset=True)
# Init
cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
cfg.PARAMS['optimize_inversion_params'] = True
cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc')
cfg.PATHS['working_dir'] = testdir
cfg.PARAMS['run_mb_calibration'] = True
cfg.PARAMS['border'] = 40
# Get the RGI ID
hef_rgi = gpd.read_file(get_demo_file('divides_hef.shp'))
hef_rgi.loc[0, 'RGIId'] = 'RGI50-11.00897'
gdirs = workflow.init_glacier_regions(hef_rgi)
workflow.gis_prepro_tasks(gdirs)
workflow.climate_tasks(gdirs)
workflow.inversion_tasks(gdirs)
models = []
for gdir in gdirs:
flowline.init_present_time_glacier(gdir)
fls = gdir.read_pickle('model_flowlines')
models.append(flowline.FlowlineModel(fls))
fig, ax = plt.subplots()
graphics.plot_modeloutput_map(gdirs, ax=ax, model=models)
fig.tight_layout()
shutil.rmtree(testdir)
return fig
def up_to_inversion(reset=False):
"""Run the tasks you want."""
# test directory
if not os.path.exists(TEST_DIR):
os.makedirs(TEST_DIR)
if reset:
clean_dir(TEST_DIR)
# Init
cfg.initialize()
# Use multiprocessing
cfg.PARAMS['use_multiprocessing'] = not ON_TRAVIS
# Working dir
cfg.PATHS['working_dir'] = TEST_DIR
cfg.PATHS['dem_file'] = get_demo_file('srtm_oetztal.tif')
# Set up the paths and other stuffs
cfg.set_divides_db(get_demo_file('divides_workflow.shp'))
cfg.PATHS['wgms_rgi_links'] = get_demo_file('RGI_WGMS_oetztal.csv')
cfg.PATHS['glathida_rgi_links'] = get_demo_file('RGI_GLATHIDA_oetztal.csv')
# Read in the RGI file
rgi_file = get_demo_file('rgi_oetztal.shp')
rgidf = gpd.GeoDataFrame.from_file(rgi_file)
# Be sure data is downloaded because lock doesn't work
cl = utils.get_cru_cl_file()
# Params
cfg.PARAMS['border'] = 70
cfg.PARAMS['use_optimized_inversion_params'] = True
# Go
gdirs = workflow.init_glacier_regions(rgidf)
try:
flowline.init_present_time_glacier(gdirs[0])
except Exception:
reset = True
if reset:
# First preprocessing tasks
workflow.gis_prepro_tasks(gdirs)
# Climate related tasks
# See if CRU is running
cfg.PARAMS['temp_use_local_gradient'] = False
cfg.PATHS['climate_file'] = '~'
cru_dir = get_demo_file('cru_ts3.23.1901.2014.tmp.dat.nc')
cfg.PATHS['cru_dir'] = os.path.dirname(cru_dir)
with warnings.catch_warnings():
# There is a warning from salem
warnings.simplefilter("ignore")
workflow.execute_entity_task(tasks.distribute_cru_style, gdirs)
tasks.compute_ref_t_stars(gdirs)
tasks.distribute_t_stars(gdirs)
# Use histalp for the actual test
cfg.PARAMS['temp_use_local_gradient'] = True
cfg.PATHS['climate_file'] = get_demo_file('HISTALP_oetztal.nc')
cfg.PATHS['cru_dir'] = '~'
workflow.climate_tasks(gdirs)
# Inversion
workflow.inversion_tasks(gdirs)
return gdirs
def up_to_inversion(reset=False):
"""Run the tasks you want."""
# test directory
if not os.path.exists(TEST_DIR):
os.makedirs(TEST_DIR)
if reset:
clean_dir(TEST_DIR)
# Init
cfg.initialize()
# Use multiprocessing
cfg.PARAMS['use_multiprocessing'] = not ON_TRAVIS
# Working dir
cfg.PATHS['working_dir'] = TEST_DIR
cfg.set_divides_db(get_demo_file('HEF_divided.shp'))
cfg.PATHS['dem_file'] = get_demo_file('srtm_oeztal.tif')
# Set up the paths and other stuffs
cfg.set_divides_db(get_demo_file('HEF_divided.shp'))
cfg.PATHS['wgms_rgi_links'] = get_demo_file('RGI_WGMS_oeztal.csv')
cfg.PATHS['glathida_rgi_links'] = get_demo_file('RGI_GLATHIDA_oeztal.csv')
# Read in the RGI file
rgi_file = get_demo_file('rgi_oeztal.shp')
rgidf = gpd.GeoDataFrame.from_file(rgi_file)
# Params
cfg.PARAMS['border'] = 70
cfg.PARAMS['use_inversion_params'] = True
# Go
gdirs = workflow.init_glacier_regions(rgidf)
try:
flowline.init_present_time_glacier(gdirs[0])
except Exception:
reset = True
if reset:
# First preprocessing tasks
workflow.gis_prepro_tasks(gdirs)
# Climate related tasks
# See if CRU is running
cfg.PARAMS['temp_use_local_gradient'] = False
cfg.PATHS['climate_file'] = '~'
cru_dir = get_demo_file('cru_ts3.23.1901.2014.tmp.dat.nc')
cfg.PATHS['cru_dir'] = os.path.dirname(cru_dir)
workflow.climate_tasks(gdirs)
# Use histalp for the actual test
cfg.PARAMS['temp_use_local_gradient'] = True
cfg.PATHS['climate_file'] = get_demo_file('HISTALP_oeztal.nc')
cfg.PATHS['cru_dir'] = '~'
workflow.climate_tasks(gdirs)
# Inversion
workflow.inversion_tasks(gdirs)
return gdirs
basin.geometry.contains(shpg.Point(x, y))[0]
for (x, y) in zip(rgidf.CenLon, rgidf.CenLat)
]
rgidf = rgidf.loc[in_bas]
# Store them for later
rgidf.to_file(os.path.join(WORKING_DIR, 'rgi_rofental.shp'))
# Sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)
print('Starting OGGM run')
print('Number of glaciers: {}'.format(len(rgidf)))
# Go - initialize glacier directories
gdirs = workflow.init_glacier_regions(rgidf)
# Tasks shortcuts - see the next examples for more details
workflow.gis_prepro_tasks(gdirs)
workflow.climate_tasks(gdirs)
workflow.inversion_tasks(gdirs)
# Compile output
print('Compiling output')
utils.compile_glacier_statistics(gdirs)
utils.write_centerlines_to_shape(gdirs)
# Log
m, s = divmod(time.time() - start, 60)
h, m = divmod(m, 60)
print('OGGM is done! Time needed: %d:%02d:%02d' % (h, m, s))
def climate_run_fl(rgi_ids,
path=True,
temp_biases=[0, +0.5, -0.5],
use_bias_for_run=False,
suffixes=['_bias_zero', '_bias_p', '_bias_n'],
tstar=None,
nyears=None,
**kwargs):
"""Computes 'only' the massbalance in analogy to the `equilibrium_run_...`
routines, without running the evolution (flowline) model.
Dataset containing yearly values of specific mass balance is returned.
Parameters
----------
rgi_ids: array-like
List of RGI IDs for which the equilibrium experiments are performed.
path: bool or str, optional, default=True
If a path is given (or True), the resulting dataset is stored to file.
temp_biases: array-like, optional, default=(0, +0.5, -0.5)
List of temperature biases (float, in degC) for the mass balance model.
suffixes: array-like, optional, default=['_normal', '_bias_p', '_bias_n']
Descriptive suffixes corresponding to the given temperature biases.
tstar: float
'Equilibrium year' used for the mass balance calibration.
nyears: int, optional, default=None
Number of years for which to compute the random mass balance
kwargs:
Additional key word arguments for massbalance model.
Returns
-------
Dataset containing yearly values of specific massbalance.
"""
# assert correct output file suffixes for temp biases
if len(temp_biases) != len(suffixes):
raise RuntimeError("Each given temperature bias must have its "
"corresponding suffix")
# compute RGI region and version from RGI IDs
# assuming all they are all the same
rgi_region = (rgi_ids[0].split('-')[-1]).split('.')[0]
rgi_version = (rgi_ids[0].split('-')[0])[-2:]
# load default parameter file
cfg.initialize()
# create working directory
wdir = '/Users/oberrauch/work/master/working_directories/'
wdir += 'test_cluster'
if not os.path.exists(wdir):
os.makedirs(wdir)
# shutil.rmtree(wdir)
# os.makedirs(wdir)
# set path to working directory
cfg.PATHS['working_dir'] = wdir
# set RGI verion and region
cfg.PARAMS['rgi_version'] = rgi_version
# define how many grid points to use around the glacier,
# if you expect the glacier to grow large use a larger border
cfg.PARAMS['border'] = 120
# we use HistAlp climate data
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# set the mb hyper parameters accordingly
cfg.PARAMS['prcp_scaling_factor'] = 1.75
cfg.PARAMS['temp_melt'] = -1.75
# the bias is defined to be zero during the calibration process,
# which is why we don't use it here to reproduce the results
cfg.PARAMS['use_bias_for_run'] = use_bias_for_run
# operational run, all glaciers should run
cfg.PARAMS['continue_on_error'] = True
# read RGI entry for the glaciers as DataFrame
# containing the outline area as shapefile
rgidf = utils.get_rgi_glacier_entities(rgi_ids)
# get and set path to intersect shapefile
intersects_db = utils.get_rgi_intersects_region_file(region=rgi_region)
cfg.set_intersects_db(intersects_db)
# initialize the GlacierDirectory
gdirs = workflow.init_glacier_directories(rgidf, reset=False, force=True)
# run gis tasks
workflow.gis_prepro_tasks(gdirs)
# run climate tasks
workflow.execute_entity_task(climate.process_climate_data, gdirs)
workflow.execute_entity_task(climate.local_t_star,
gdirs,
tstar=tstar,
bias=0)
workflow.execute_entity_task(climate.mu_star_calibration, gdirs)
# run inversion tasks
workflow.inversion_tasks(gdirs)
# finalize preprocessing
workflow.execute_entity_task(flowline.init_present_time_glacier, gdirs)
# use t* as center year, even if specified differently
kwargs['y0'] = tstar
# run for 3000 years if not specified otherwise
if nyears is None:
nyears = 10000
years = np.arange(0, nyears + 1)
# create dataset
ds = list()
# run RandomMassBalance model centered around t*, once without
# temperature bias and once with positive and negative temperature bias
# of 0.5 °C each.
for gdir in gdirs:
# set random seed to get reproducible results
kwargs.setdefault('seed', 12)
kwargs.setdefault('halfsize', 15)
kwargs.setdefault('mb_model_class', flowline.RandomMassBalance)
kwargs.setdefault('filename', 'climate_historical')
kwargs.setdefault('input_filesuffix', '')
kwargs.setdefault('unique_samples', False)
ds_ = list()
fls = gdir.read_pickle('model_flowlines')
for suffix, temp_bias in zip(suffixes, temp_biases):
# instance mass balance model
mb_mod = flowline.MultipleFlowlineMassBalance(gdir, **kwargs)
if temp_bias is not None:
# add given temperature bias to mass balance model
mb_mod.temp_bias = temp_bias
# create empty container
spec_mb = list()
# iterate over all years
for yr in years:
spec_mb.append(mb_mod.get_specific_mb(fls=fls, year=yr))
# add to dataset
da = xr.DataArray(spec_mb, dims=('year'), coords={'year': years})
ds_.append(xr.Dataset({'spec_mb': da}))
ds_ = xr.concat(ds_, pd.Index(temp_biases, name='temp_bias'))
ds_.coords['rgi_id'] = gdir.rgi_id
ds.append(ds_)
ds = xr.concat(ds, 'rgi_id')
# store datasets
if path:
if path is True:
path = os.path.join(cfg.PATHS['working_dir'], 'mb_output_fl.nc')
ds.to_netcdf(path)
# ds_normal.to_netcdf(path[1])
# return ds, ds_normal
return ds
def equilibrium_run_fl(rgi_ids,
use_random_mb=True,
path=True,
temp_biases=(0, +0.5, -0.5),
use_bias_for_run=False,
suffixes=['_bias_zero', '_bias_p', '_bias_n'],
tstar=None,
**kwargs):
""" The routine runs all steps for the equilibrium experiments using the
flowline model. For details see docstring of `sensitivity_run_vas`.
Parameters
----------
rgi_ids: array-like
List of RGI IDs for which the equilibrium experiments are performed.
use_random_mb: bool, optional, default=True
Choose between random massbalance model and constant massbalance model.
path: bool or str, optional, default=True
If a path is given (or True), the resulting dataset is stored to file.
temp_biases: array-like, optional, default=(0, +0.5, -0.5)
List of temperature biases (float, in degC) for the mass balance model.
suffixes: array-like, optional, default=['_normal', '_bias_p', '_bias_n']
Descriptive suffixes corresponding to the given temperature biases.
tstar: float
'Equilibrium year' used for the mass balance calibration.
kwargs:
Additional key word arguments for the `run_random_climate` or
`run_constant_climate` routines of the vascaling module.
Returns
-------
Dataset containing yearly values of all glacier geometries.
"""
# assert correct output file suffixes for temp biases
if len(temp_biases) != len(suffixes):
raise RuntimeError("Each given temperature bias must have its "
"corresponding suffix")
# compute RGI region and version from RGI IDs
# assuming all they are all the same
rgi_region = (rgi_ids[0].split('-')[-1]).split('.')[0]
rgi_version = (rgi_ids[0].split('-')[0])[-2:]
# load default parameter file
cfg.initialize()
# create working directory
wdir = '/Users/oberrauch/work/master/working_directories/'
wdir += 'test_cluster'
if not os.path.exists(wdir):
os.makedirs(wdir)
# shutil.rmtree(wdir)
# os.makedirs(wdir)
# set path to working directory
cfg.PATHS['working_dir'] = wdir
# set RGI verion and region
cfg.PARAMS['rgi_version'] = rgi_version
# define how many grid points to use around the glacier,
# if you expect the glacier to grow large use a larger border
cfg.PARAMS['border'] = 120
# we use HistAlp climate data
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# set the mb hyper parameters accordingly
cfg.PARAMS['prcp_scaling_factor'] = 1.75
cfg.PARAMS['temp_melt'] = -1.75
# the bias is defined to be zero during the calibration process,
# which is why we don't use it here to reproduce the results
cfg.PARAMS['use_bias_for_run'] = use_bias_for_run
# read RGI entry for the glaciers as DataFrame
# containing the outline area as shapefile
rgidf = utils.get_rgi_glacier_entities(rgi_ids)
# get and set path to intersect shapefile
intersects_db = utils.get_rgi_intersects_region_file(region=rgi_region)
cfg.set_intersects_db(intersects_db)
# sort by area for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)
cfg.PARAMS['use_multiprocessing'] = True
# operational run, all glaciers should run
cfg.PARAMS['continue_on_error'] = True
# initialize the GlacierDirectory
gdirs = workflow.init_glacier_directories(rgidf, reset=False, force=True)
# run gis tasks
workflow.gis_prepro_tasks(gdirs)
# run climate tasks
workflow.execute_entity_task(climate.process_climate_data, gdirs)
workflow.execute_entity_task(climate.local_t_star,
gdirs,
tstar=tstar,
bias=0)
workflow.execute_entity_task(climate.mu_star_calibration, gdirs)
# run inversion tasks
workflow.inversion_tasks(gdirs)
# finalize preprocessing
workflow.execute_entity_task(flowline.init_present_time_glacier, gdirs)
# use t* as center year, even if specified differently
kwargs['y0'] = tstar
# run for 3000 years if not specified otherwise
kwargs.setdefault('nyears', 3000)
# disregard glaciers exceeding their domain boundaries
# to not dirsupt the entire run
kwargs.setdefault('check_for_boundaries', True)
if use_random_mb:
# set random seed to get reproducible results
kwargs.setdefault('seed', 12)
# run RandomMassBalance model centered around t*, once without
# temperature bias and once with positive and negative temperature bias
# of 0.5 °C each.
for suffix, temp_bias in zip(suffixes, temp_biases):
workflow.execute_entity_task(
flowline.run_random_climate,
gdirs,
temperature_bias=temp_bias,
output_filesuffix=suffix,
**kwargs,
)
else:
# run RandomMassBalance model centered around t*, once without
# temperature bias and once with positive and negative temperature bias
# of 0.5 °C each.
for suffix, temp_bias in zip(suffixes, temp_biases):
workflow.execute_entity_task(
flowline.run_constant_climate,
gdirs,
temperature_bias=temp_bias,
output_filesuffix=suffix,
**kwargs,
)
ds = list()
for suffix, temp_bias in zip(suffixes, temp_biases):
# compile the output for each run and store to file
ds_ = utils.compile_run_output(np.atleast_1d(gdirs),
input_filesuffix=suffix,
path=False)
ds.append(ds_)
# concat into one dataset with temperature bias as coordinate
ds = xr.concat(ds, pd.Index(temp_biases, name='temp_bias'))
# add model type as coordinate
ds.coords['model'] = 'fl'
# add mb model type as coordinate
ds.coords['mb_model'] = 'random' if use_random_mb else 'constant'
# compute mean and sum over all glaciers
ds_mean = ds.mean(dim='rgi_id')
ds_mean.coords['rgi_id'] = 'mean'
ds_sum = ds.sum(dim='rgi_id')
ds_sum.coords['rgi_id'] = 'sum'
# add to dataset
ds = xr.concat([ds, ds_mean, ds_sum], dim='rgi_id')
# normalize glacier geometries (length/area/volume) with start value
ds_normal = normalize_ds_with_start(ds)
# add coordinate to distinguish between normalized and absolute values
ds.coords['normalized'] = int(False)
ds_normal.coords['normalized'] = int(True)
# combine datasets
ds = xr.concat([ds, ds_normal], 'normalized')
# store datasets
if path:
if path is True:
mb = 'random' if use_random_mb else 'constant'
path = os.path.join(cfg.PATHS['working_dir'],
'run_output_{}_fl.nc'.format(mb))
ds.to_netcdf(path)
return ds
def climate_run_fl(rgi_ids, path=True, temp_biases=[0, +0.5, -0.5],
suffixes=['_bias_zero', '_bias_p', '_bias_n'],
use_bias_for_run=False, use_default_tstar=True,
tstar=None, nyears=None, **kwargs):
"""Computes 'only' the massbalance in analogy to the `equilibrium_run_...`
routines, without running the (flowline) evolution model.
Dataset containing yearly values of specific mass balance is returned.
Note: the task is not parallelized, hence it can take long if many glaciers
are given. TODO: could/should be fixed sometime...
TODO: add logging information
Parameters
----------
rgi_ids: array-like
List of RGI IDs for which the equilibrium experiments are performed.
path: bool or str, optional, default=True
If a path is given (or True), the resulting dataset is stored to file.
temp_biases: array-like, optional, default=(0, +0.5, -0.5)
List of temperature biases (float, in degC) for the mass balance model.
suffixes: array-like, optional, default=['_normal', '_bias_p', '_bias_n']
Descriptive suffixes corresponding to the given temperature biases.
use_bias_for_run: bool, optional, default=False
Flag deciding whether or not the mass balance residual is used
tstar: float, optional, default=None
'Equilibrium year' used for the mass balance calibration. Using the
`ref_tstars.csv` table if not supplied.
use_default_tstar : bool, optional, default=True
Flag deciding whether or not to use the default ref_tstar.csv list. If
`False`the `oggm_ref_tstars_rgi6_histalp.csv` reference table is used.
nyears: int, optional, default=None
Number of years for which to compute the random mass balance
kwargs:
Additional key word arguments for massbalance model.
Returns
-------
Dataset containing yearly values of specific massbalance.
"""
# assert correct output file suffixes for temp biases
if len(temp_biases) != len(suffixes):
raise RuntimeError("Each given temperature bias must have its "
"corresponding suffix")
# compute RGI region and version from RGI IDs
# assuming all they are all the same
rgi_region = (rgi_ids[0].split('-')[-1]).split('.')[0]
rgi_version = (rgi_ids[0].split('-')[0])[-2:-1]
# load default parameter file
cfg.initialize()
# get environmental variables for working and output directories
WORKING_DIR = os.environ["WORKDIR"]
OUTPUT_DIR = os.environ["OUTDIR"]
# create working directory
utils.mkdir(WORKING_DIR)
# set path to working directory
cfg.PATHS['working_dir'] = WORKING_DIR
# set RGI version and region
cfg.PARAMS['rgi_version'] = rgi_version
# define how many grid points to use around the glacier,
# if you expect the glacier to grow large use a larger border
cfg.PARAMS['border'] = 120
# we use HistAlp climate data
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# set the mb hyper parameters accordingly
cfg.PARAMS['prcp_scaling_factor'] = 1.75
cfg.PARAMS['temp_melt'] = -1.75
# the bias is defined to be zero during the calibration process,
# which is why we don't use it here to reproduce the results
cfg.PARAMS['use_bias_for_run'] = use_bias_for_run
# operational run, all glaciers should run
cfg.PARAMS['continue_on_error'] = False
# read RGI entry for the glaciers as DataFrame
# containing the outline area as shapefile
rgidf = utils.get_rgi_glacier_entities(rgi_ids)
# get and set path to intersect shapefile
intersects_db = utils.get_rgi_intersects_region_file(region=rgi_region)
cfg.set_intersects_db(intersects_db)
# initialize the GlacierDirectory
gdirs = workflow.init_glacier_directories(rgidf, reset=False, force=True)
# run gis tasks
workflow.gis_prepro_tasks(gdirs)
# run climate tasks
workflow.execute_entity_task(climate.process_climate_data, gdirs)
# compute local t* and the corresponding mu*
if tstar or use_default_tstar:
# compute mustar from given tstar
workflow.execute_entity_task(climate.local_t_star, gdirs,
tstar=tstar, bias=0)
else:
# compute mustar from the reference table for the flowline model
# RGI v6 and HISTALP baseline climate
ref_df = pd.read_csv(utils.get_demo_file('oggm_ref_tstars_rgi6_histalp.csv'))
workflow.execute_entity_task(climate.local_t_star, gdirs, ref_df=ref_df)
workflow.execute_entity_task(climate.mu_star_calibration, gdirs)
# run inversion tasks
workflow.inversion_tasks(gdirs)
# finalize preprocessing
workflow.execute_entity_task(flowline.init_present_time_glacier, gdirs)
# use t* as center year, even if specified differently
kwargs['y0'] = tstar
# run for 3000 years if not specified otherwise
if nyears is None:
nyears = 10000
years = np.arange(0, nyears + 1)
# create dataset
ds = list()
# run RandomMassBalance model centered around t*, once without
# temperature bias and once with positive and negative temperature bias
# of 0.5 deg C each.
for gdir in gdirs:
kwargs.setdefault('halfsize', 15)
kwargs.setdefault('mb_model_class', flowline.ConstantMassBalance)
kwargs.setdefault('filename', 'climate_historical')
kwargs.setdefault('input_filesuffix', '')
ds_ = list()
# open the flowline file if it exists
try:
fls = gdir.read_pickle('model_flowlines')
except:
# continue with the next glacier or raise exception
# depending on `continue_on_error` flag
if cfg.PARAMS['continue_on_error']:
continue
else:
raise
for suffix, temp_bias in zip(suffixes, temp_biases):
# instance mass balance model
try:
mb_mod = flowline.MultipleFlowlineMassBalance(gdir, **kwargs)
except:
# continue with the next glacier or raise exception
# depending on `continue_on_error` flag
if cfg.PARAMS['continue_on_error']:
continue
else:
raise
if temp_bias is not None:
# add given temperature bias to mass balance model
mb_mod.temp_bias = temp_bias
# create empty container
spec_mb = list()
# iterate over all years
for yr in years:
spec_mb.append(mb_mod.get_specific_mb(fls=fls, year=yr))
# add to dataset
da = xr.DataArray(spec_mb, dims=('year'), coords={'year': years})
ds_.append(xr.Dataset({'spec_mb': da}))
if ds_:
ds_ = xr.concat(ds_, pd.Index(temp_biases, name='temp_bias'))
ds_.coords['rgi_id'] = gdir.rgi_id
ds.append(ds_)
if ds:
# combine output from single glaciers into one dataset
ds = xr.concat(ds, 'rgi_id')
# store dataset to file
if path:
if path is True:
path = os.path.join(OUTPUT_DIR, 'mb_output_fl.nc')
ds.to_netcdf(path)
# return ds
return ds
def up_to_inversion(reset=False):
"""Run the tasks you want."""
# test directory
if not os.path.exists(TEST_DIR):
os.makedirs(TEST_DIR)
if reset:
clean_dir(TEST_DIR)
# Init
cfg.initialize()
# Use multiprocessing
cfg.PARAMS['use_multiprocessing'] = not ON_TRAVIS
# Working dir
cfg.PATHS['working_dir'] = TEST_DIR
cfg.set_divides_db(get_demo_file('HEF_divided.shp'))
cfg.PATHS['dem_file'] = get_demo_file('srtm_oeztal.tif')
# Set up the paths and other stuffs
cfg.set_divides_db(get_demo_file('HEF_divided.shp'))
cfg.PATHS['wgms_rgi_links'] = get_demo_file('RGI_WGMS_oeztal.csv')
cfg.PATHS['glathida_rgi_links'] = get_demo_file('RGI_GLATHIDA_oeztal.csv')
# Read in the RGI file
rgi_file = get_demo_file('rgi_oeztal.shp')
rgidf = gpd.GeoDataFrame.from_file(rgi_file)
# Params
cfg.PARAMS['border'] = 70
cfg.PARAMS['use_inversion_params'] = True
# Go
gdirs = workflow.init_glacier_regions(rgidf)
try:
flowline.init_present_time_glacier(gdirs[0])
except Exception:
reset = True
if reset:
# First preprocessing tasks
workflow.gis_prepro_tasks(gdirs)
# Climate related tasks
# See if CRU is running
cfg.PARAMS['temp_use_local_gradient'] = False
cfg.PATHS['climate_file'] = '~'
cru_dir = get_demo_file('cru_ts3.23.1901.2014.tmp.dat.nc')
cfg.PATHS['cru_dir'] = os.path.dirname(cru_dir)
workflow.climate_tasks(gdirs)
# Use histalp for the actual test
cfg.PARAMS['temp_use_local_gradient'] = True
cfg.PATHS['climate_file'] = get_demo_file('HISTALP_oeztal.nc')
cfg.PATHS['cru_dir'] = '~'
workflow.climate_tasks(gdirs)
# Inversion
workflow.inversion_tasks(gdirs)
return gdirs
def setup_cache(self):
setattr(full_workflow.setup_cache, "timeout", 360)
utils.mkdir(self.testdir, reset=True)
self.cfg_init()
# Pre-download other files which will be needed later
cru.get_cru_cl_file()
cru.get_cru_file(var='tmp')
cru.get_cru_file(var='pre')
# Get the RGI glaciers for the run.
rgi_list = ['RGI60-01.10299', 'RGI60-11.00897', 'RGI60-18.02342']
rgidf = utils.get_rgi_glacier_entities(rgi_list)
# We use intersects
db = utils.get_rgi_intersects_entities(rgi_list, version='61')
cfg.set_intersects_db(db)
# Sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)
# Go - initialize glacier directories
gdirs = workflow.init_glacier_directories(rgidf)
# Preprocessing tasks
task_list = [
tasks.define_glacier_region,
tasks.glacier_masks,
tasks.compute_centerlines,
tasks.initialize_flowlines,
tasks.compute_downstream_line,
tasks.compute_downstream_bedshape,
tasks.catchment_area,
tasks.catchment_intersections,
tasks.catchment_width_geom,
tasks.catchment_width_correction,
]
for task in task_list:
execute_entity_task(task, gdirs)
# Climate tasks -- only data IO and tstar interpolation!
execute_entity_task(tasks.process_cru_data, gdirs)
execute_entity_task(tasks.local_t_star, gdirs)
execute_entity_task(tasks.mu_star_calibration, gdirs)
# Inversion tasks
workflow.inversion_tasks(gdirs)
# Final preparation for the run
execute_entity_task(tasks.init_present_time_glacier, gdirs)
# Random climate representative for the tstar climate, without bias
# In an ideal world this would imply that the glaciers remain stable,
# but it doesn't have to be so
execute_entity_task(tasks.run_constant_climate, gdirs,
bias=0, nyears=100,
output_filesuffix='_tstar')
execute_entity_task(tasks.run_constant_climate, gdirs,
y0=1990, nyears=100,
output_filesuffix='_pd')
# Compile output
utils.compile_glacier_statistics(gdirs)
utils.compile_run_output(gdirs, input_filesuffix='_tstar')
utils.compile_run_output(gdirs, input_filesuffix='_pd')
utils.compile_climate_input(gdirs)
return gdirs
def compute_scaling_params(rgi_ids, path=None):
""" The routine computes scaling parameters by fitting a linear regression
to the volume/area and volume/length scatter in log-log space, using the
inversion volume, the RGI area and the longest centerline as "observations"
Thereby, the following two cases apply:
- compute only scaling constants, since scaling exponents have a physical
basis and should not be changed
- compute only scaling constants and scaling exponents
Returns parameters in a 2-level dictionary. The upper level differentiates
between the two cases, the lower level indicates the parameters.
Parameters
----------
rgi_ids: array-like
List of RGI IDs for which the equilibrium experiments are performed.
Returns
-------
Dictionary containing the computed parameters.
"""
log.info('Starting scaling parameter computation')
# compute RGI region and version from RGI IDs
# assuming all they are all the same
rgi_region = (rgi_ids[0].split('-')[-1]).split('.')[0]
rgi_version = (rgi_ids[0].split('-')[0])[-2:-1]
# load default parameter file
vascaling.initialize()
# get environmental variables for working and output directories
WORKING_DIR = os.environ["WORKDIR"]
OUTPUT_DIR = os.environ["OUTDIR"]
# create working directory
utils.mkdir(WORKING_DIR)
utils.mkdir(OUTPUT_DIR)
# set path to working directory
cfg.PATHS['working_dir'] = WORKING_DIR
# set RGI version and region
cfg.PARAMS['rgi_version'] = rgi_version
# define how many grid points to use around the glacier,
# if you expect the glacier to grow large use a larger border
cfg.PARAMS['border'] = 120
# we use HistAlp climate data
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# set the mb hyper parameters accordingly
cfg.PARAMS['prcp_scaling_factor'] = 1.75
cfg.PARAMS['temp_melt'] = -1.75
# set minimum ice thickness to include in glacier length computation
# this reduces weird spikes in length records
cfg.PARAMS['min_ice_thick_for_length'] = 0.1
# read RGI entry for the glaciers as DataFrame
# containing the outline area as shapefile
rgidf = utils.get_rgi_glacier_entities(rgi_ids)
# get and set path to intersect shapefile
intersects_db = utils.get_rgi_intersects_region_file(region=rgi_region)
cfg.set_intersects_db(intersects_db)
# the bias is defined to be zero during the calibration process,
# which is why we don't use it here to reproduce the results
cfg.PARAMS['use_bias_for_run'] = True
# sort by area for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)
cfg.PARAMS['use_multiprocessing'] = True
# operational run, all glaciers should run
cfg.PARAMS['continue_on_error'] = True
# initialize the GlacierDirectory
gdirs = workflow.init_glacier_directories(rgidf, reset=False, force=True)
# run gis tasks
workflow.gis_prepro_tasks(gdirs)
# run climate tasks
workflow.execute_entity_task(climate.process_climate_data, gdirs)
# compute local t* and the corresponding mu*
workflow.execute_entity_task(climate.local_t_star, gdirs)
workflow.execute_entity_task(climate.mu_star_calibration, gdirs)
# run inversion tasks
workflow.inversion_tasks(gdirs)
# finalize preprocessing
workflow.execute_entity_task(flowline.init_present_time_glacier, gdirs)
# create empty dictionary
params = dict()
# compute scaling constants for given (fixed) slope
params['const_only'] = vascaling.get_scaling_constant(gdirs)
# compute scaling constants and scaling exponent via linear regression
params['const_expo'] = vascaling.get_scaling_constant_exponent(gdirs)
# store to file
if path:
if not isinstance(path, str):
# set default path and filename
path = os.path.join(OUTPUT_DIR, 'scaling_params.json')
json.dump(params, open(path, 'w'))
return params
