def redo_all_plots(vdf_dict):
"""
This function will redo all crossvalidation plots. Time consuming!
Sometimes its necessary to redo all crossvalidation plots.
E.g. if you want to change the appearence of a plot.
Parameters
----------
vdf_dict: Dict of pandas.DataFrames
containing all necessary information
"""
for vdf in vdf_dict.values():
for nr, df in vdf.iterrows():
utils.mkdir(df['pd'])
# make minor plots
if df.min_maj == 'minor':
# try to make plots
crossval_timeseries(df.file, df.pd)
crossval_histogram(df.file, df.pd)
elif df.min_maj == 'major':
crossval_boxplot(df.file, df.pd)
def init_columbia_eb(dir_name, reset=False):
from oggm.core import gis, centerlines
import geopandas as gpd
# test directory
testdir = os.path.join(get_test_dir(), dir_name)
mkdir(testdir, reset=reset)
# Init
cfg.initialize()
cfg.PATHS['working_dir'] = testdir
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['dem_file'] = get_demo_file('dem_Columbia.tif')
cfg.PARAMS['border'] = 10
entity = gpd.read_file(get_demo_file('01_rgi60_Columbia.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity)
if gdir.has_file('climate_historical'):
return gdir
gis.define_glacier_region(gdir)
gis.simple_glacier_masks(gdir)
centerlines.elevation_band_flowline(gdir)
centerlines.fixed_dx_elevation_band_flowline(gdir)
centerlines.compute_downstream_line(gdir)
tasks.process_dummy_cru_file(gdir, seed=0)
return gdir
def setup_cache(self):
utils.mkdir(self.testdir, reset=True)
self.cfg_init()
hef_file = get_demo_file('Hintereisferner_RGI5.shp')
entity = gpd.read_file(hef_file).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)
tasks.process_custom_climate_data(gdir)
tasks.mu_candidates(gdir)
mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE']
res = climate.t_star_from_refmb(gdir, mbdf)
tasks.local_mustar(gdir, tstar=res['t_star'], bias=res['bias'])
tasks.apparent_mb(gdir)
tasks.prepare_for_inversion(gdir)
tasks.mass_conservation_inversion(gdir)
return gdir
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)
# Test default k (it overshoots)
df1 = utils.find_inversion_calving(gdir)
# Test with smaller k (it doesn't overshoot)
cfg.PARAMS['k_calving'] = 0.2
df2 = utils.find_inversion_calving(gdir)
return (df1.calving_flux.values, df1.mu_star.values,
df2.calving_flux.values, df2.mu_star.values)
def test_ice_cap():
testdir = os.path.join(cfg.PATHS['test_dir'], 'tmp_icecap')
utils.mkdir(testdir)
cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('dem_RGI50-05.08389.tif')
cfg.PARAMS['border'] = 20
cfg.set_divides_db(get_demo_file('divides_RGI50-05.08389.shp'))
hef_file = get_demo_file('RGI50-05.08389.shp')
entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=True)
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.compute_downstream_lines(gdir)
geometry.initialize_flowlines(gdir)
# We should have five groups
lines = gdir.read_pickle('downstream_lines', div_id=0)
assert len(np.unique(lines.group))==5
# This just checks that it works
geometry.catchment_area(gdir)
geometry.catchment_intersections(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
fig, ax = plt.subplots()
graphics.plot_catchment_width(gdir, ax=ax, add_intersects=True,
add_touches=True)
fig.tight_layout()
return fig
def make_fake_zipdir(dir_path, fakefile=None):
"""Creates a directory with a file in it if asked to, then compresses it"""
utils.mkdir(dir_path)
if fakefile:
touch(os.path.join(dir_path, fakefile))
shutil.make_archive(dir_path, 'zip', dir_path)
return dir_path + '.zip'
def test_ice_cap():
testdir = os.path.join(get_test_dir(), 'tmp_icecap')
utils.mkdir(testdir, reset=True)
cfg.initialize()
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['dem_file'] = get_demo_file('dem_RGI50-05.08389.tif')
cfg.PARAMS['border'] = 60
cfg.PATHS['working_dir'] = testdir
df = gpd.read_file(get_demo_file('divides_RGI50-05.08389.shp'))
df['Area'] = df.Area * 1e-6 # cause it was in m2
df['RGIId'] = ['RGI50-05.08389_d{:02d}'.format(d + 1) for d in df.index]
gdirs = workflow.init_glacier_regions(df)
workflow.gis_prepro_tasks(gdirs)
from salem import mercator_grid, Map
smap = mercator_grid((gdirs[0].cenlon, gdirs[0].cenlat),
extent=[20000, 23000])
smap = Map(smap)
fig, ax = plt.subplots()
graphics.plot_catchment_width(gdirs,
ax=ax,
add_intersects=True,
add_touches=True,
smap=smap)
fig.tight_layout()
shutil.rmtree(testdir)
return fig
def plot_experiment(gdir, ex_mod, ys, plot_dir):
x = np.arange(ex_mod.fls[-1].nx) * ex_mod.fls[-1].dx * \
ex_mod.fls[-1].map_dx
fig = plt.figure(figsize=(15, 14))
grid = plt.GridSpec(2, 1, hspace=0.2, wspace=0.2)
ax1 = plt.subplot(grid[0, 0])
ax2 = plt.subplot(grid[1, 0], sharex=ax1)
if gdir.name != '':
ax1.set_title(gdir.rgi_id + ':' + gdir.name)
else:
ax1.set_title(gdir.rgi_id)
# plot experiments.py, run until ys
ex_mod = deepcopy(ex_mod)
ex_mod.reset_y0(ys)
ex_mod.run_until(ys)
i = np.where(ex_mod.fls[-1].thick > 0)[0][-1] + 10
i = ex_mod.fls[-1].nx
ax1.plot(x[:i],
ex_mod.fls[-1].surface_h[:i],
'k:',
label=r'$z_{' + str(ys) + '}^{exp}$',
linewidth=3)
ax1.plot(x[:i], ex_mod.fls[-1].bed_h[:i], 'k', label=r'$b$', linewidth=3)
ex_mod.run_until(2000)
ax2.plot(x[:i],
ex_mod.fls[-1].surface_h[:i],
'k:',
label=r'$z_{2000}^{exp = obs} $',
linewidth=3)
ax2.plot(x[:i], ex_mod.fls[-1].bed_h[:i], 'k', label=r'$b$', linewidth=3)
# add figure names and legends
add_at(ax1, r"a", loc=3)
add_at(ax2, r"b", loc=3)
ax1.legend(loc=1)
ax2.legend(loc=1)
ax1.set_ylabel('Altitude (m)')
ax1.set_xlabel('Distance along the main flowline (m)')
ax2.set_ylabel('Altitude (m)')
ax2.set_xlabel('Distance along the main flowline (m)')
ax1.tick_params(axis='both', which='major')
ax2.tick_params(axis='both', which='major')
plot_dir = os.path.join(plot_dir, '00_experiment')
utils.mkdir(plot_dir)
fig_name = 'experiment_' + str(ys) + '_' + gdir.rgi_id
plt.savefig(os.path.join(plot_dir, fig_name + '.pdf'), dpi=300)
plt.savefig(os.path.join(plot_dir, fig_name + '.png'), dpi=300)
#plt.show()
plt.close()
def setUp(self):
self.dldir = os.path.join(get_test_dir(), 'tmp_download')
utils.mkdir(self.dldir)
cfg.initialize()
cfg.PATHS['dl_cache_dir'] = os.path.join(self.dldir, 'dl_cache')
cfg.PATHS['working_dir'] = os.path.join(self.dldir, 'wd')
cfg.PATHS['tmp_dir'] = os.path.join(self.dldir, 'extract')
self.reset_dir()
def test_coxe():
testdir = os.path.join(get_test_dir(), 'tmp_coxe')
utils.mkdir(testdir, reset=True)
# Init
cfg.initialize()
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['dem_file'] = get_demo_file('dem_RGI50-01.10299.tif')
cfg.PARAMS['border'] = 40
cfg.PARAMS['clip_tidewater_border'] = False
cfg.PARAMS['use_multiple_flowlines'] = False
cfg.PARAMS['use_kcalving_for_inversion'] = True
cfg.PARAMS['use_kcalving_for_run'] = True
cfg.PARAMS['trapezoid_lambdas'] = 1
hef_file = get_demo_file('rgi_RGI50-01.10299.shp')
entity = gpd.read_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=True)
gis.define_glacier_region(gdir)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.initialize_flowlines(gdir)
centerlines.compute_downstream_line(gdir)
centerlines.compute_downstream_bedshape(gdir)
centerlines.catchment_area(gdir)
centerlines.catchment_intersections(gdir)
centerlines.catchment_width_geom(gdir)
centerlines.catchment_width_correction(gdir)
climate.apparent_mb_from_linear_mb(gdir)
inversion.prepare_for_inversion(gdir)
inversion.mass_conservation_inversion(gdir)
inversion.filter_inversion_output(gdir)
flowline.init_present_time_glacier(gdir)
fls = gdir.read_pickle('model_flowlines')
p = gdir.read_pickle('linear_mb_params')
mb_mod = massbalance.LinearMassBalance(ela_h=p['ela_h'], grad=p['grad'])
mb_mod.temp_bias = -0.3
model = flowline.FluxBasedModel(fls,
mb_model=mb_mod,
y0=0,
inplace=True,
is_tidewater=True)
# run
model.run_until(200)
assert model.calving_m3_since_y0 > 0
fig, ax = plt.subplots()
graphics.plot_modeloutput_map(gdir, ax=ax, model=model)
fig.tight_layout()
shutil.rmtree(testdir)
return fig
def idealized_experiment(use_experiment_glaciers=None,
inversion_settings_all=None,
working_dir='',
output_folder='',
params_file=None,
override_params=None,
logging_level='WORKFLOW'):
# Local paths
if override_params is None:
override_params = {}
utils.mkdir(working_dir)
override_params['working_dir'] = working_dir
utils.mkdir(output_folder)
cfg.initialize(file=params_file,
params=override_params,
logging_level=logging_level,
future=True)
print('Create glacier directories with idealized glaciers:')
# Size of the map around the glacier.
if cfg.PARAMS['border'] != 160:
msg = (f"Border is {cfg.PARAMS['border']} but experiments was "
f"created with border=160!")
warnings.warn(msg)
# Degree of processing level.
from_prepro_level = 3
# URL of the preprocessed gdirs
# we use elevation bands flowlines here
base_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/' \
'L3-L5_files/CRU/elev_bands/qc3/pcp2.5/no_match/'
gdirs = create_idealized_experiments(
use_experiment_glaciers,
prepro_border=cfg.PARAMS['border'],
from_prepro_level=from_prepro_level,
base_url=base_url,
)
print('Finished creation of directories.')
print('Start experiments:')
all_experiments = []
for inv_setting in inversion_settings_all:
for gdir in gdirs:
all_experiments.append((gdir,
dict(inversion_settings=inv_setting,
output_folder=output_folder)))
workflow.execute_entity_task(conduct_combine_inversion, all_experiments)
print('Experiments finished!')
def find_residual(gdir, a=-2000,b=2000):
try:
max_it = 15
i = 0
bounds = [a,b]
df = pd.DataFrame()
fls = gdir.read_pickle('model_flowlines')
mod = FluxBasedModel(flowlines=fls)
while i < max_it:
bias = round((bounds[0] + bounds[1]) / 2,1)
ex_mod2 = _run_experiment(gdir, bias)
fit = fitness_function(ex_mod2,mod)
df = df.append(pd.Series({'bias':bias,'fitness':fit}),ignore_index=True)
if bounds[1]-bounds[0]<=1:
break
elif ex_mod2.area_km2 > mod.area_km2:
bounds[0] = bias
else:
bounds[1] = bias
i +=1
# best bias found
bias = df.iloc[df.fitness.idxmin()].bias
rp = gdir.get_filepath('model_run', filesuffix='_advanced_experiment_' + str(bias))
model = FileModel(rp)
model.run_until(2000)
rp = gdir.get_filepath('model_run', filesuffix='_advanced_experiment_' + str(0.0))
ex_mod = FileModel(rp)
ex_mod.run_until(2000)
plt.figure(figsize=(15,10))
plt.plot(model.fls[-1].surface_h,'r',label='best')
plt.plot(mod.fls[-1].surface_h, 'orange', label='original')
plt.plot(ex_mod.fls[-1].surface_h, 'r:', label='old experiment')
plt.plot(model.fls[-1].bed_h,'k', label='bed')
plt.legend()
utils.mkdir(os.path.join(cfg.PATHS['plot_dir'],'bias_test'))
plt.savefig(os.path.join(cfg.PATHS['plot_dir'],'bias_test',gdir.rgi_id+'.png'),dpi=200)
plt.show()
diff = mod.area_km2 - model.area_km2_ts()[2000]
model.reset_y0(1865)
series = pd.Series({'rgi_id':gdir.rgi_id,'bias':bias,'iterations':i, 'fitness':fit, 'area_diff':diff, 'model':model})
except:
series = pd.Series({'rgi_id':gdir.rgi_id})
return series
def get_test_dir():
s = get_ident()
out = os.path.join(cfg.PATHS['test_dir'], s)
mkdir(out)
# If new ident, remove all other dirs so spare space
for d in os.listdir(cfg.PATHS['test_dir']):
if d and d != s:
shutil.rmtree(os.path.join(cfg.PATHS['test_dir'], d))
return out
def test_shapefile_output(self):
gdirs = up_to_climate(use_mp=True)
fpath = os.path.join(_TEST_DIR, 'centerlines.shp')
write_centerlines_to_shape(gdirs, path=fpath)
import salem
shp = salem.read_shapefile(fpath)
self.assertTrue(shp is not None)
shp = shp.loc[shp.RGIID == 'RGI60-11.00897']
self.assertEqual(len(shp), 3)
self.assertEqual(shp.loc[shp.LE_SEGMENT.idxmax()].MAIN, 1)
fpath = os.path.join(_TEST_DIR, 'flowlines.shp')
write_centerlines_to_shape(gdirs, path=fpath, flowlines_output=True)
shp_f = salem.read_shapefile(fpath)
self.assertTrue(shp_f is not None)
shp_f = shp_f.loc[shp_f.RGIID == 'RGI60-11.00897']
self.assertEqual(len(shp_f), 3)
self.assertEqual(shp_f.loc[shp_f.LE_SEGMENT.idxmax()].MAIN, 1)
# The flowline is cut so shorter
assert shp_f.LE_SEGMENT.max() < shp.LE_SEGMENT.max() * 0.8
fpath = os.path.join(_TEST_DIR, 'widths_geom.shp')
write_centerlines_to_shape(gdirs,
path=fpath,
geometrical_widths_output=True)
# Salem can't read it
shp_w = gpd.read_file(fpath)
self.assertTrue(shp_w is not None)
shp_w = shp_w.loc[shp_w.RGIID == 'RGI60-11.00897']
self.assertEqual(len(shp_w), 90)
fpath = os.path.join(_TEST_DIR, 'widths_corr.shp')
write_centerlines_to_shape(gdirs,
path=fpath,
corrected_widths_output=True)
# Salem can't read it
shp_w = gpd.read_file(fpath)
self.assertTrue(shp_w is not None)
shp_w = shp_w.loc[shp_w.RGIID == 'RGI60-11.00897']
self.assertEqual(len(shp_w), 90)
# Test that one wrong glacier still works
base_dir = os.path.join(cfg.PATHS['working_dir'], 'dummy_pergla')
utils.mkdir(base_dir, reset=True)
gdirs = workflow.execute_entity_task(utils.copy_to_basedir,
gdirs,
base_dir=base_dir,
setup='all')
os.remove(gdirs[0].get_filepath('centerlines'))
cfg.PARAMS['continue_on_error'] = True
write_centerlines_to_shape(gdirs)
def test_coxe():
testdir = os.path.join(cfg.PATHS['test_dir'], 'tmp_coxe')
utils.mkdir(testdir)
# Init
cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('dem_RGI50-01.10299.tif')
cfg.PARAMS['border'] = 40
cfg.PARAMS['use_multiple_flowlines'] = False
hef_file = get_demo_file('rgi_RGI50-01.10299.shp')
entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=True)
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.compute_downstream_lines(gdir)
geometry.initialize_flowlines(gdir)
# Just check if the rest runs
centerlines.compute_downstream_bedshape(gdir)
geometry.catchment_area(gdir)
geometry.catchment_intersections(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
climate.apparent_mb_from_linear_mb(gdir)
inversion.prepare_for_inversion(gdir)
inversion.volume_inversion(gdir, use_cfg_params={'glen_a': cfg.A, 'fs': 0})
inversion.filter_inversion_output(gdir)
flowline.init_present_time_glacier(gdir)
fls = gdir.read_pickle('model_flowlines')
p = gdir.read_pickle('linear_mb_params')
mb_mod = massbalance.LinearMassBalanceModel(ela_h=p['ela_h'],
grad=p['grad'])
mb_mod.temp_bias = -0.3
model = flowline.FluxBasedModel(fls,
mb_model=mb_mod,
y0=0,
is_tidewater=True)
# run
model.run_until(200)
assert model.calving_m3_since_y0 > 0
fig, ax = plt.subplots()
graphics.plot_modeloutput_map(gdir, ax=ax, model=model)
fig.tight_layout()
return fig
def website_main():
# setup jinja
file_loader = FileSystemLoader(mbcfg.PATHS['jinjadir'])
env = Environment(loader=file_loader)
# make a catalogue from all stored versions first. If this fails: stop!
vdf = catalog_storaged_files()
# clean all potential old html files
files = glob.glob(mbcfg.PATHS['webroot'] + '/**/*.html', recursive=True)
for fl in files:
os.remove(fl)
# copy logos
imgpth = os.path.join(mbcfg.PATHS['webroot'], 'img')
logo = 'oggm_s_alpha.png'
utils.mkdir(imgpth)
copyfile(os.path.join(os.path.dirname(os.path.abspath(__file__)),
'favicon.ico'),
os.path.join(imgpth, 'favicon.ico'))
copyfile(os.path.join(os.path.dirname(os.path.abspath(__file__)), logo),
os.path.join(imgpth, logo))
# make a dictonary with the latest versions vor linking
nbpaths = {}
for vers in vdf.keys():
try:
vpath = vdf[vers].iloc[-1].version
except IndexError:
vpath = ''
nbpaths[vers] = os.path.join(vpath, '%s.html' % vers)
nbpaths['webroot'] = 'index.html'
nbpaths['icon'] = 'img/favicon.ico'
nbpaths['logo'] = 'img/%s' % logo
# create index page
create_index(env, nbpaths)
if mbcfg.PARAMS['redo_all_plots']:
redo_all_plots(vdf)
# split all available files and process seperately:
# global reference glaciers, short crossvalidation
create_minor_website(env, vdf['cru_short'], 'cru_short.html', nbpaths)
# global reference glaciers, extended crossvalidation
create_major_website(env, vdf['cru_extended'], 'cru_extended.html',
nbpaths)
# HISTALP reference glaciers, short crossvalidation
create_minor_website(env, vdf['histalp_short'], 'histalp_short.html',
nbpaths)
# HISTALP reference glaciers, extended crossvalidation
create_major_website(env, vdf['histalp_extended'], 'histalp_extended.html',
nbpaths)
def get_mean_temps_2k(rgi, return_prcp):
from oggm import cfg, utils, workflow, tasks
from oggm.core.massbalance import PastMassBalance
# Initialize OGGM
cfg.initialize()
wd = utils.gettempdir(reset=True)
cfg.PATHS['working_dir'] = wd
utils.mkdir(wd, reset=True)
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# and set standard histalp values
cfg.PARAMS['temp_melt'] = -1.75
cfg.PARAMS['prcp_scaling_factor'] = 1.75
gdir = workflow.init_glacier_regions(rgidf=rgi.split('_')[0],
from_prepro_level=3,
prepro_border=10)[0]
# run histalp climate on glacier!
tasks.process_histalp_data(gdir)
f = gdir.get_filepath('climate_historical')
with utils.ncDataset(f) as nc:
refhgt = nc.ref_hgt
mb = PastMassBalance(gdir, check_calib_params=False)
df = pd.DataFrame()
df2 = pd.DataFrame()
for y in np.arange(1870, 2015):
for i in np.arange(9, 12):
flyear = utils.date_to_floatyear(y, i)
tmp = mb.get_monthly_climate([refhgt], flyear)[0]
df.loc[y, i] = tmp.mean()
if return_prcp:
for i in np.arange(3, 6):
flyear = utils.date_to_floatyear(y, i)
pcp = mb.get_monthly_climate([refhgt], flyear)[3]
df2.loc[y, i] = tmp.mean()
t99 = df.loc[1984:2014, :].mean().mean()
t85 = df.loc[1870:1900, :].mean().mean()
t2k = df.loc[1900:2000, :].mean().mean()
if return_prcp:
p99 = df2.loc[1984:2014, :].mean().mean()
p85 = df2.loc[1870:1900, :].mean().mean()
p2k = df2.loc[1900:2000, :].mean().mean()
return t85, t99, t2k, p85, p99, p2k
return t85, t99, t2k
def test_coxe():
testdir = os.path.join(cfg.PATHS['test_dir'], 'tmp_coxe')
utils.mkdir(testdir)
# Init
cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('dem_RGI50-01.10299.tif')
cfg.PARAMS['border'] = 40
cfg.PARAMS['use_multiple_flowlines'] = False
hef_file = get_demo_file('rgi_RGI50-01.10299.shp')
entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=True)
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.compute_downstream_lines(gdir)
geometry.initialize_flowlines(gdir)
# Just check if the rest runs
centerlines.compute_downstream_bedshape(gdir)
geometry.catchment_area(gdir)
geometry.catchment_intersections(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
climate.apparent_mb_from_linear_mb(gdir)
inversion.prepare_for_inversion(gdir)
inversion.volume_inversion(gdir, use_cfg_params={'glen_a': cfg.A,
'fs': 0})
inversion.filter_inversion_output(gdir)
flowline.init_present_time_glacier(gdir)
fls = gdir.read_pickle('model_flowlines')
p = gdir.read_pickle('linear_mb_params')
mb_mod = massbalance.LinearMassBalanceModel(ela_h=p['ela_h'],
grad=p['grad'])
mb_mod.temp_bias = -0.3
model = flowline.FluxBasedModel(fls, mb_model=mb_mod, y0=0,
is_tidewater=True)
# run
model.run_until(200)
assert model.calving_m3_since_y0 > 0
fig, ax = plt.subplots()
graphics.plot_modeloutput_map(gdir, ax=ax, model=model)
fig.tight_layout()
return fig
def get_test_dir():
s = get_ident()
out = os.path.join(cfg.PATHS['test_dir'], s)
if 'PYTEST_XDIST_WORKER' in os.environ:
out = os.path.join(out, os.environ.get('PYTEST_XDIST_WORKER'))
mkdir(out)
# If new ident, remove all other dirs so spare space
for d in os.listdir(cfg.PATHS['test_dir']):
if d and d != s:
shutil.rmtree(os.path.join(cfg.PATHS['test_dir'], d))
return out
def get_test_dir():
s = get_ident()
out = os.path.join(cfg.PATHS['test_dir'], s)
if 'PYTEST_XDIST_WORKER' in os.environ:
out = os.path.join(out, os.environ.get('PYTEST_XDIST_WORKER'))
mkdir(out)
# If new ident, remove all other dirs so spare space
for d in os.listdir(cfg.PATHS['test_dir']):
if d and d != s:
shutil.rmtree(os.path.join(cfg.PATHS['test_dir'], d))
return out
def setUp(self):
self.dldir = os.path.join(get_test_dir(), 'tmp_download')
utils.mkdir(self.dldir)
# Get the path to the file before we mess around
self.dem3_testfile = utils.get_demo_file('T10.zip')
cfg.initialize()
cfg.PATHS['dl_cache_dir'] = os.path.join(self.dldir, 'dl_cache')
cfg.PATHS['working_dir'] = os.path.join(self.dldir, 'wd')
cfg.PATHS['tmp_dir'] = os.path.join(self.dldir, 'extract')
cfg.PATHS['rgi_dir'] = os.path.join(self.dldir, 'rgi_test')
cfg.PATHS['cru_dir'] = os.path.join(self.dldir, 'cru_test')
self.reset_dir()
def test_rgi_intersects(self):
# Make a fake RGI file
rgi_dir = os.path.join(self.dldir, 'rgi50')
utils.mkdir(rgi_dir)
make_fake_zipdir(os.path.join(rgi_dir, 'RGI_V5_Intersects'),
fakefile='Intersects_OGGM_Manifest.txt')
make_fake_zipdir(os.path.join(rgi_dir, 'RGI_V5_Intersects',
'11_rgi50_CentralEurope'),
fakefile='intersects_11_rgi50_CentralEurope.shp')
make_fake_zipdir(os.path.join(rgi_dir, 'RGI_V5_Intersects',
'00_rgi50_AllRegs'),
fakefile='intersects_rgi50_AllRegs.shp')
rgi_f = make_fake_zipdir(rgi_dir)
def down_check(url, cache_name=None, reset=False):
expected = ('https://cluster.klima.uni-bremen.de/~fmaussion/rgi/' +
'RGI_V5_Intersects.zip')
self.assertEqual(url, expected)
return rgi_f
with FakeDownloadManager('_progress_urlretrieve', down_check):
rgi = utils.get_rgi_intersects_dir()
utils.get_rgi_intersects_region_file('11', version='5')
utils.get_rgi_intersects_region_file('00', version='5')
assert os.path.isdir(rgi)
assert os.path.exists(os.path.join(rgi,
'Intersects_OGGM_Manifest.txt'))
# Make a fake RGI file
rgi_dir = os.path.join(self.dldir, 'rgi60')
utils.mkdir(rgi_dir)
make_fake_zipdir(os.path.join(rgi_dir, 'RGI_V6_Intersects'),
fakefile='Intersects_OGGM_Manifest.txt')
rgi_f = make_fake_zipdir(rgi_dir)
def down_check(url, cache_name=None, reset=False):
expected = ('https://cluster.klima.uni-bremen.de/~fmaussion/rgi/' +
'RGI_V6_Intersects.zip')
self.assertEqual(url, expected)
return rgi_f
with FakeDownloadManager('_progress_urlretrieve', down_check):
rgi = utils.get_rgi_intersects_dir(version='6')
assert os.path.isdir(rgi)
assert os.path.exists(os.path.join(rgi,
'Intersects_OGGM_Manifest.txt'))
def test_chhota_shigri():
testdir = os.path.join(cfg.PATHS['test_dir'], 'tmp_chhota')
utils.mkdir(testdir)
# Init
cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('dem_chhota_shigri.tif')
cfg.PARAMS['border'] = 60
cfg.set_divides_db(get_demo_file('divides_RGI50-14.15990.shp'))
hef_file = get_demo_file('RGI50-14.15990.shp')
entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir)
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.compute_downstream_lines(gdir)
geometry.initialize_flowlines(gdir)
# We should have two groups
lines = gdir.read_pickle('downstream_lines', div_id=0)
assert len(np.unique(lines.group)) == 2
# Just check if the rest runs
centerlines.compute_downstream_bedshape(gdir)
geometry.catchment_area(gdir)
geometry.catchment_intersections(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
climate.apparent_mb_from_linear_mb(gdir)
inversion.prepare_for_inversion(gdir)
inversion.volume_inversion(gdir, use_cfg_params={'glen_a': cfg.A,
'fs': 0})
inversion.filter_inversion_output(gdir)
flowline.init_present_time_glacier(gdir)
fls = gdir.read_pickle('model_flowlines')
for fl in fls:
fl.thick = np.clip(fl.thick, 100, 1000)
model = flowline.FlowlineModel(fls)
fig, ax = plt.subplots()
graphics.plot_modeloutput_map(gdir, ax=ax, model=model)
fig.tight_layout()
return fig
def single_node_example(run_for_test=False):
y0 = 2008
nyears = 100
halfsize = 0
mean_years = (2002, 2012)
mtypes = ['scenew_ctl_3', 'sce_ctl_3']
outpath = utils.mkdir(os.path.join(cluster_dir, 'Climate_3'))
gdirs = pre_process_tasks(run_for_test=run_for_test)
workflow.execute_entity_task(run_my_random_climate, gdirs, nyears=nyears,
y0=y0, seed=1, halfsize=halfsize,
output_filesuffix=f'_origin_hf{halfsize}',
mean_years=mean_years)
for mtype in mtypes:
fpath_prcp_diff = os.path.join(data_dir, f'Precip_diff_{mtype}.nc')
fpath_temp_diff = os.path.join(data_dir, f'T2m_diff_{mtype}.nc')
workflow.execute_entity_task(run_my_random_climate, gdirs,
nyears=nyears, y0=y0, seed=1,
halfsize=halfsize,
output_filesuffix=f'_exper_{mtype}_hf{halfsize}',
fpath_temp_diff=fpath_temp_diff,
fpath_prcp_diff=fpath_prcp_diff,
mean_years=mean_years)
output_list = []
suffixes = [f'_origin_hf{halfsize}', f'_exper_{mtypes[0]}_hf{halfsize}',
f'_exper_{mtypes[1]}_hf{halfsize}']
for suffix in suffixes:
path = os.path.join(outpath, 'result'+suffix+'.nc')
output_list.append(utils.compile_run_output(gdirs, input_filesuffix=suffix,
path=path, use_compression=True))
# TODO: Test!
a = output_list[0].volume.values
print(a[-1, 2])
def test_chhota_shigri():
testdir = os.path.join(cfg.PATHS['test_dir'], 'tmp_chhota')
utils.mkdir(testdir)
# Init
cfg.initialize()
cfg.PATHS['dem_file'] = get_demo_file('dem_chhota_shigri.tif')
cfg.PARAMS['border'] = 60
cfg.set_divides_db(get_demo_file('divides_RGI50-14.15990.shp'))
hef_file = get_demo_file('RGI50-14.15990.shp')
entity = gpd.GeoDataFrame.from_file(hef_file).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=testdir)
gis.define_glacier_region(gdir, entity=entity)
gis.glacier_masks(gdir)
centerlines.compute_centerlines(gdir)
centerlines.compute_downstream_lines(gdir)
geometry.initialize_flowlines(gdir)
# We should have two groups
lines = gdir.read_pickle('downstream_lines', div_id=0)
assert len(np.unique(lines.group)) == 2
# Just check if the rest runs
centerlines.compute_downstream_bedshape(gdir)
geometry.catchment_area(gdir)
geometry.catchment_intersections(gdir)
geometry.catchment_width_geom(gdir)
geometry.catchment_width_correction(gdir)
climate.apparent_mb_from_linear_mb(gdir)
inversion.prepare_for_inversion(gdir)
inversion.volume_inversion(gdir, use_cfg_params={'glen_a': cfg.A, 'fs': 0})
inversion.filter_inversion_output(gdir)
flowline.init_present_time_glacier(gdir)
fls = gdir.read_pickle('model_flowlines')
for fl in fls:
fl.thick = np.clip(fl.thick, 100, 1000)
model = flowline.FlowlineModel(fls)
fig, ax = plt.subplots()
graphics.plot_modeloutput_map(gdir, ax=ax, model=model)
fig.tight_layout()
return fig
def test_dir():
""" Provides a reference to the test directory for the entire test session.
Named after the current git revision.
As a session-scoped fixture, this will only be created once and
then injected to each test that depends on it.
"""
s = get_ident()
out = os.path.join(cfg.PATHS['test_dir'], s)
if 'PYTEST_XDIST_WORKER' in os.environ:
out = os.path.join(out, os.environ.get('PYTEST_XDIST_WORKER'))
mkdir(out)
# If new ident, remove all other dirs so spare space
for d in os.listdir(cfg.PATHS['test_dir']):
if d and d != s:
shutil.rmtree(os.path.join(cfg.PATHS['test_dir'], d))
return out
def run_with_job_array(y0, nyears, halfsize, mtype, prcp_prefix=None,
temp_prefix=None, run_for_test=False, mean_years=None,
output_dir=None, output_filesuffix=None):
if output_dir is None:
outpath = utils.mkdir(cluster_dir, reset=False)
else:
outpath = utils.mkdir(os.path.join(cluster_dir, output_dir),
reset=False)
gdirs = pre_process_tasks(run_for_test=run_for_test)
if mtype == 'origin':
suffix = f'_origin_hf{halfsize}'
workflow.execute_entity_task(run_my_random_climate, gdirs,
nyears=nyears, y0=y0, seed=1,
halfsize=halfsize,
output_filesuffix=f'_origin_hf{halfsize}',
mean_years=mean_years)
else:
if CLIMATE_DATA == '2':
mtype = '_' + mtype
if prcp_prefix:
fpath_prcp_diff = os.path.join(data_dir, f'{prcp_prefix}{mtype}.nc')
else:
fpath_prcp_diff = None
if temp_prefix:
fpath_temp_diff = os.path.join(data_dir, f'{temp_prefix}{mtype}.nc')
else:
fpath_temp_diff = None
if output_filesuffix is None:
output_filesuffix = f'_exper_{mtype}_hf{halfsize}'
workflow.execute_entity_task(run_my_random_climate, gdirs, nyears=nyears,
y0=y0, seed=1, halfsize=halfsize,
output_filesuffix=output_filesuffix,
mean_years=mean_years,
fpath_temp_diff=fpath_temp_diff,
fpath_prcp_diff=fpath_prcp_diff)
ds = utils.compile_run_output(gdirs, input_filesuffix=output_filesuffix, path=False)
# to avoid cluster stull problem report in:
# https://github.com/OGGM/oggm/pull/1122 and
# https://github.com/pydata/xarray/issues/4710
print(f"Save result{output_filesuffix}.nc")
ds.load().to_netcdf(path=os.path.join(outpath, 'result'+output_filesuffix+'.nc'))
def oggm_to_g2ti(gdir, dirname='final'):
"""From an oggm gdir to a g2ti tiff
Parameters
----------
"""
# Get the data
grids_file = gdir.get_filepath('gridded_data')
with netCDF4.Dataset(grids_file) as nc:
with warnings.catch_warnings():
# https://github.com/Unidata/netcdf4-python/issues/766
warnings.filterwarnings("ignore", category=RuntimeWarning)
thick = nc.variables['distributed_thickness'][:]
dx = gdir.grid.dx
vol = np.nansum(thick * dx**2)
tpl_f = os.path.join(g2ti.geometry_dir, gdir.rgi_id[:8], gdir.rgi_id,
'dem.tif')
dst = salem.GeoTiff(tpl_f)
thick[~np.isfinite(thick)] = 0.
dst_thick = dst.grid.map_gridded_data(thick,
grid=gdir.grid,
interp='spline')
dst_thick[~np.isfinite(dst_thick)] = 0.
# Conserve volume
dx = dst.grid.dx
dst_thick *= vol / np.nansum(dst_thick * dx**2)
if not np.isclose(vol / np.nansum(dst_thick * dx**2), 1, atol=0.2):
raise RuntimeError('Something went wrong in reproj.')
with rasterio.open(tpl_f) as orig:
# Set up profile for writing output
profile = orig.profile
ft = os.path.join(cfg.PATHS['working_dir'], dirname,
'RGI60-{}'.format(gdir.rgi_region))
utils.mkdir(ft)
ft = os.path.join(ft, 'thickness_{}.tif'.format(gdir.rgi_id))
with rasterio.open(ft, 'w', **profile) as dest:
dest.write(dst_thick.astype(np.float32).clip(0), 1)
def test_rgi(self):
# Make a fake RGI file
rgi_dir = os.path.join(self.dldir, 'rgi50')
utils.mkdir(rgi_dir)
make_fake_zipdir(os.path.join(rgi_dir, '01_rgi50_Region'),
fakefile='test.txt')
rgi_f = make_fake_zipdir(rgi_dir, fakefile='000_rgi50_manifest.txt')
def down_check(url, cache_name=None, reset=False):
expected = 'http://www.glims.org/RGI/rgi50_files/rgi50.zip'
self.assertEqual(url, expected)
return rgi_f
with FakeDownloadManager('_progress_urlretrieve', down_check):
rgi = utils.get_rgi_dir()
assert os.path.isdir(rgi)
assert os.path.exists(os.path.join(rgi, '000_rgi50_manifest.txt'))
assert os.path.exists(os.path.join(rgi, '01_rgi50_Region', 'test.txt'))
# Make a fake RGI file
rgi_dir = os.path.join(self.dldir, 'rgi60')
utils.mkdir(rgi_dir)
make_fake_zipdir(os.path.join(rgi_dir, '01_rgi60_Region'),
fakefile='01_rgi60_Region.shp')
rgi_f = make_fake_zipdir(rgi_dir, fakefile='000_rgi60_manifest.txt')
def down_check(url, cache_name=None, reset=False):
expected = 'http://www.glims.org/RGI/rgi60_files/00_rgi60.zip'
self.assertEqual(url, expected)
return rgi_f
with FakeDownloadManager('_progress_urlretrieve', down_check):
rgi = utils.get_rgi_dir(version='6')
assert os.path.isdir(rgi)
assert os.path.exists(os.path.join(rgi, '000_rgi60_manifest.txt'))
assert os.path.exists(
os.path.join(rgi, '01_rgi60_Region', '01_rgi60_Region.shp'))
with FakeDownloadManager('_progress_urlretrieve', down_check):
rgi_f = utils.get_rgi_region_file('01', version='6')
assert os.path.exists(rgi_f)
assert '01_rgi60_Region.shp' in rgi_f
def merge_intersects():
# Download RGI files
fp = 'intersects_*_rgi' + rgi_version + '0_*.shp'
shps = list(glob(os.path.join(OUTDIR_INTERSECTS, "*", fp)))
assert len(shps) == 19
out = []
for sh in sorted(shps):
sh = gpd.read_file(sh)
out.append(sh)
# Make a new dataframe of those
inter = pd.concat(out)
inter.csr = sh.crs # for geolocalisation
odir = '00_rgi' + rgi_version + '0_AllRegs'
odir = os.path.join(OUTDIR_INTERSECTS, odir)
mkdir(odir)
ofile = os.path.join(odir, 'intersects_rgi' + rgi_version + '0_AllRegs.shp')
inter.to_file(ofile)
def _rename_dem_folder(gdir, source=''):
"""Put the DEM files in a subfolder of the gdir.
Parameters
----------
gdir : GlacierDirectory
source : str
the DEM source
"""
# open tif-file to check if it's worth it
dem_f = gdir.get_filepath('dem')
try:
dem = gis.read_geotiff_dem(gdir)
except IOError:
# Error reading file, no problem - still, delete the file if needed
if os.path.exists(dem_f):
os.remove(dem_f)
gdir.log('{},DEM SOURCE,{}'.format(gdir.rgi_id, source),
err=InvalidDEMError('File does not exist'))
return
# Check the DEM
isfinite = np.isfinite(dem)
if np.all(~isfinite) or (np.min(dem) == np.max(dem)):
# Remove the file and return
if os.path.exists(dem_f):
os.remove(dem_f)
gdir.log('{},DEM SOURCE,{}'.format(gdir.rgi_id, source),
err=InvalidDEMError('DEM does not contain more than one '
'valid values.'))
return
# Create a source dir and move the files
out = os.path.join(gdir.dir, source)
utils.mkdir(out)
for fname in ['dem', 'dem_source']:
f = gdir.get_filepath(fname)
os.rename(f, os.path.join(out, os.path.basename(f)))
# log SUCCESS for this DEM source
gdir.log('{},DEM SOURCE,{}'.format(gdir.rgi_id, source))
def find_possible_glaciers(gdir, y0, ye, n):
path = os.path.join(gdir.dir, 'result' + str(y0) + '.pkl')
# if results are already there and number of candidates are the same, don't run it again
if os.path.isfile(path):
results = pd.read_pickle(path, compression='gzip')
return results
'''
if len(results) == n:
return results
'''
# 1. Generation of possible glacier states
# - Run random climate over 400 years with different temperature biases
random_list = generation(gdir, y0)
# 2. Identification of glacier candidates
# - Determine t_stag(begin of stagnation period)
# - Classification by volume (n equidistantly distributed classes)
# - Select one candidate by class
#
candidate_list = identification(gdir, random_list, ys=y0, ye=ye, n=n)
# 3. Evaluation
# - Run each candidate forward from y0 to ye
# - Evaluate candidates based on the fitnessfunction
# - Save all models in pd.DataFrame and write pickle
# - copy all model_run files to tarfile
results = evaluation(gdir, candidate_list, y0, ye)
# move all model_run* files from year y0 to a new directory --> avoids
# that thousand of thousands files are created in gdir.dir
utils.mkdir(os.path.join(gdir.dir, str(y0)), reset=False)
for file in os.listdir(gdir.dir):
if file.startswith('model_run' + (str(y0))):
os.rename(os.path.join(gdir.dir, file),
os.path.join(gdir.dir, str(y0), file))
elif file.startswith('model_diagnostics' + (str(y0))):
os.remove(os.path.join(gdir.dir, file))
return results
def test_rgi_intersects(self):
# Make a fake RGI file
rgi_dir = os.path.join(self.dldir, 'rgi50')
utils.mkdir(rgi_dir)
make_fake_zipdir(os.path.join(rgi_dir, 'RGI_V5_Intersects'),
fakefile='Intersects_OGGM_Manifest.txt')
rgi_f = make_fake_zipdir(rgi_dir)
def down_check(url, cache_name=None, reset=False):
expected = ('https://www.dropbox.com/s/y73sdxygdiq7whv/' +
'RGI_V5_Intersects.zip?dl=1')
self.assertEqual(url, expected)
return rgi_f
with FakeDownloadManager('_progress_urlretrieve', down_check):
rgi = utils.get_rgi_intersects_dir()
assert os.path.isdir(rgi)
assert os.path.exists(os.path.join(rgi,
'Intersects_OGGM_Manifest.txt'))
# Make a fake RGI file
rgi_dir = os.path.join(self.dldir, 'rgi60')
utils.mkdir(rgi_dir)
make_fake_zipdir(os.path.join(rgi_dir, 'RGI_V6_Intersects'),
fakefile='Intersects_OGGM_Manifest.txt')
rgi_f = make_fake_zipdir(rgi_dir)
def down_check(url, cache_name=None, reset=False):
expected = ('https://www.dropbox.com/s/vawryxl8lkzxowu/' +
'RGI_V6_Intersects.zip?dl=1')
self.assertEqual(url, expected)
return rgi_f
with FakeDownloadManager('_progress_urlretrieve', down_check):
rgi = utils.get_rgi_intersects_dir(version='6')
assert os.path.isdir(rgi)
assert os.path.exists(os.path.join(rgi,
'Intersects_OGGM_Manifest.txt'))
def get_test_dir():
global _TEST_DIR
if _TEST_DIR is None:
s = get_git_ident()
s += ''.join([str(k) + str(v) for k, v in get_sys_info()])
s += ''.join([str(k) + str(v) for k, v in get_env_info()])
s = hashlib.md5(s.encode()).hexdigest()
out = os.path.join(cfg.PATHS['test_dir'], s)
if 'PYTEST_XDIST_WORKER' in os.environ:
out = os.path.join(out, os.environ.get('PYTEST_XDIST_WORKER'))
mkdir(out)
_TEST_DIR = out
# If new ident, remove all other dirs so spare space
for d in os.listdir(cfg.PATHS['test_dir']):
if d and d != s:
shutil.rmtree(os.path.join(cfg.PATHS['test_dir'], d))
return _TEST_DIR
def run_benchmark(rgi_version=None, rgi_reg=None, border=None,
output_folder='', working_dir='', is_test=False,
test_rgidf=None, test_intersects_file=None,
test_topofile=None, test_crudir=None):
"""Does the actual job.
Parameters
----------
rgi_version : str
the RGI version to use (defaults to cfg.PARAMS)
rgi_reg : str
the RGI region to process
border : int
the number of pixels at the maps border
output_folder : str
path to the output folder (where to put the preprocessed tar files)
working_dir : str
path to the OGGM working directory
is_test : bool
to test on a couple of glaciers only!
test_rgidf : shapefile
for testing purposes only
test_intersects_file : shapefile
for testing purposes only
test_topofile : str
for testing purposes only
test_crudir : str
for testing purposes only
"""
# TODO: temporarily silence Fiona deprecation warnings
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
# Module logger
log = logging.getLogger(__name__)
# Initialize OGGM and set up the run parameters
cfg.initialize(logging_level='WORKFLOW')
# Local paths
utils.mkdir(working_dir)
cfg.PATHS['working_dir'] = working_dir
# Use multiprocessing?
cfg.PARAMS['use_multiprocessing'] = True
# How many grid points around the glacier?
# Make it large if you expect your glaciers to grow large
cfg.PARAMS['border'] = border
# Set to True for operational runs
cfg.PARAMS['continue_on_error'] = True
# For statistics
odf = pd.DataFrame()
if rgi_version is None:
rgi_version = cfg.PARAMS['rgi_version']
base_dir = os.path.join(output_folder)
# Add a package version file
utils.mkdir(base_dir)
opath = os.path.join(base_dir, 'package_versions.txt')
with open(opath, 'w') as vfile:
vfile.write(utils.show_versions(logger=log))
# Read RGI
start = time.time()
if test_rgidf is None:
# Get the RGI file
rgidf = gpd.read_file(utils.get_rgi_region_file(rgi_reg,
version=rgi_version))
# We use intersects
rgif = utils.get_rgi_intersects_region_file(rgi_reg,
version=rgi_version)
cfg.set_intersects_db(rgif)
else:
rgidf = test_rgidf
cfg.set_intersects_db(test_intersects_file)
if is_test:
# Just for fun
rgidf = rgidf.sample(2)
_add_time_to_df(odf, 'Read RGI', time.time()-start)
# Sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)
log.workflow('Starting prepro run for RGI reg: {} '
'and border: {}'.format(rgi_reg, border))
log.workflow('Number of glaciers: {}'.format(len(rgidf)))
# Input
if test_topofile:
cfg.PATHS['dem_file'] = test_topofile
# Initialize working directories
start = time.time()
gdirs = workflow.init_glacier_regions(rgidf, reset=True, force=True)
_add_time_to_df(odf, 'init_glacier_regions', time.time()-start)
# Pre-download other files just in case
if test_crudir is None:
_ = utils.get_cru_file(var='tmp')
_ = utils.get_cru_file(var='pre')
else:
cfg.PATHS['cru_dir'] = test_crudir
# Tasks
task_list = [
tasks.process_cru_data,
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,
tasks.local_t_star,
tasks.mu_star_calibration,
tasks.prepare_for_inversion,
tasks.mass_conservation_inversion,
tasks.filter_inversion_output,
tasks.init_present_time_glacier,
]
for task in task_list:
start = time.time()
workflow.execute_entity_task(task, gdirs)
_add_time_to_df(odf, task.__name__, time.time()-start)
# Runs
start = time.time()
workflow.execute_entity_task(tasks.run_random_climate, gdirs,
nyears=250, bias=0, seed=0,
output_filesuffix='_tstar')
_add_time_to_df(odf, 'run_random_climate_tstar_250', time.time()-start)
start = time.time()
workflow.execute_entity_task(tasks.run_random_climate, gdirs,
nyears=250, y0=1995, seed=0,
output_filesuffix='_commit')
_add_time_to_df(odf, 'run_random_climate_commit_250', time.time()-start)
# Compile results
start = time.time()
utils.compile_glacier_statistics(gdirs)
_add_time_to_df(odf, 'compile_glacier_statistics', time.time()-start)
start = time.time()
utils.compile_climate_statistics(gdirs,
add_climate_period=[1920, 1960, 2000])
_add_time_to_df(odf, 'compile_climate_statistics', time.time()-start)
start = time.time()
utils.compile_run_output(gdirs, filesuffix='_tstar')
_add_time_to_df(odf, 'compile_run_output_tstar', time.time()-start)
start = time.time()
utils.compile_run_output(gdirs, filesuffix='_commit')
_add_time_to_df(odf, 'compile_run_output_commit', time.time()-start)
# Log
opath = os.path.join(base_dir, 'benchmarks_b{:03d}.csv'.format(border))
odf.index.name = 'Task'
odf.to_csv(opath)
log.workflow('OGGM benchmarks is done!')
from oggm import tasks
from oggm.workflow import execute_entity_task
from oggm import graphics, utils
# Initialize OGGM
cfg.initialize()
# Local paths (where to write output and where to download input)
DATA_DIR = '/home/mowglie/disk/OGGM_INPUT'
WORKING_DIR = '/home/mowglie/disk/OGGM_RUNS/TEST_DEMS'
PLOTS_DIR = os.path.join(WORKING_DIR, 'plots')
cfg.PATHS['working_dir'] = WORKING_DIR
cfg.PATHS['topo_dir'] = os.path.join(DATA_DIR, 'topo')
cfg.PATHS['rgi_dir'] = os.path.join(DATA_DIR, 'rgi')
utils.mkdir(WORKING_DIR)
utils.mkdir(cfg.PATHS['topo_dir'])
utils.mkdir(cfg.PATHS['rgi_dir'])
# Use multiprocessing?
cfg.PARAMS['use_multiprocessing'] = False
cfg.PARAMS['border'] = 20
cfg.CONTINUE_ON_ERROR = False
# Read in the RGI file
rgisel = os.path.join(WORKING_DIR, 'rgi_selection.shp')
if not os.path.exists(rgisel):
rgi_dir = utils.get_rgi_dir()
regions = ['{:02d}'.format(int(p)) for p in range(1, 20)]
files = [glob.glob(os.path.join(rgi_dir, '*', r + '_rgi50_*.shp'))[0] for r in regions]
rgidf = []
def prepare_divides(rgi_f):
"""Processes the rgi file and writes the intersects to OUTDIR"""
rgi_reg = os.path.basename(rgi_f).split('_')[0]
print('Start RGI reg ' + rgi_reg + ' ...')
start_time = time.time()
wgms, _ = get_wgms_files()
f = glob(INDIR_DIVIDES + '*/*-' + rgi_reg + '.shp')[0]
df = gpd.read_file(f)
rdf = gpd.read_file(rgi_f)
# Read glacier attrs
key2 = {'0': 'Land-terminating',
'1': 'Marine-terminating',
'2': 'Lake-terminating',
'3': 'Dry calving',
'4': 'Regenerated',
'5': 'Shelf-terminating',
'9': 'Not assigned',
}
TerminusType = [key2[gtype[1]] for gtype in df.GlacType]
IsTidewater = np.array([ttype in ['Marine-terminating', 'Lake-terminating']
for ttype in TerminusType])
# Plots
# dfref = df.loc[df.RGIId.isin(wgms.RGI50_ID)]
# for gid in np.unique(dfref.GLIMSId):
# dfs = dfref.loc[dfref.GLIMSId == gid]
# dfs.plot(cmap='Set3', linestyle='-', linewidth=5);
# Filter
df = df.loc[~IsTidewater]
df = df.loc[~df.RGIId.isin(wgms.RGI50_ID)]
df['CenLon'] = pd.to_numeric(df['CenLon'])
df['CenLat'] = pd.to_numeric(df['CenLat'])
df['Area'] = pd.to_numeric(df['Area'])
# Correct areas and stuffs
n_gl_before = len(df)
divided_ids = []
for rid in np.unique(df.RGIId):
sdf = df.loc[df.RGIId == rid].copy()
srdf = rdf.loc[rdf.RGIId == rid]
# Correct Area
sdf.Area = np.array([float(a) for a in sdf.Area])
geo_is_ok = []
new_geo = []
for g, a in zip(sdf.geometry, sdf.Area):
if a < 0.01*1e6:
geo_is_ok.append(False)
continue
try:
new_geo.append(multi_to_poly(g))
geo_is_ok.append(True)
except:
geo_is_ok.append(False)
sdf = sdf.loc[geo_is_ok]
if len(sdf) < 2:
# print(rid + ' is too small or has no valid divide...')
df = df[df.RGIId != rid]
continue
area_km = sdf.Area * 1e-6
cor_factor = srdf.Area.values / np.sum(area_km)
if cor_factor > 1.2 or cor_factor < 0.8:
# print(rid + ' is not OK...')
df = df[df.RGIId != rid]
continue
area_km = cor_factor * area_km
# Correct Centroid
cenlon = [g.centroid.xy[0][0] for g in sdf.geometry]
cenlat = [g.centroid.xy[1][0] for g in sdf.geometry]
# ID
new_id = [rid + '_d{:02}'.format(i + 1) for i in range(len(sdf))]
# Write
df.loc[sdf.index, 'Area'] = area_km
df.loc[sdf.index, 'CenLon'] = cenlon
df.loc[sdf.index, 'CenLat'] = cenlat
df.loc[sdf.index, 'RGIId'] = new_id
df.loc[sdf.index, 'geometry'] = new_geo
divided_ids.append(rid)
n_gl_after = len(df)
# We make three data dirs: divides only, divides into rgi, divides + RGI
bn = os.path.basename(rgi_f)
bd = os.path.basename(os.path.dirname(rgi_f))
base_dir_1 = OUTDIR_DIVIDES + '/RGIV5_DividesOnly/' + bd
base_dir_2 = OUTDIR_DIVIDES + '/RGIV5_Corrected/' + bd
base_dir_3 = OUTDIR_DIVIDES + '/RGIV5_OrigAndDivides/' + bd
mkdir(base_dir_1, reset=True)
mkdir(base_dir_2, reset=True)
mkdir(base_dir_3, reset=True)
df.to_file(os.path.join(base_dir_1, bn))
dfa = pd.concat([df, rdf]).sort_values('RGIId')
dfa.to_file(os.path.join(base_dir_3, bn))
dfa = dfa.loc[~dfa.RGIId.isin(divided_ids)]
dfa.to_file(os.path.join(base_dir_2, bn))
print('RGI reg ' + rgi_reg +
' took {:.2f} seconds. We had to remove '
'{} divides'.format(time.time() - start_time,
n_gl_before - n_gl_after))
return
def compute_intersects(rgi_shp):
"""Processes the rgi file and writes the intersects to OUTDIR"""
out_path = os.path.basename(rgi_shp)
odir = os.path.basename(os.path.dirname(rgi_shp))
odir = os.path.join(OUTDIR_INTERSECTS, odir)
mkdir(odir)
out_path = os.path.join(odir, 'intersects_' + out_path)
print('Start ' + os.path.basename(rgi_shp) + ' ...')
start_time = time.time()
gdf = gpd.read_file(rgi_shp)
# clean geometries like OGGM does
ngeos = []
keep = []
for g in gdf.geometry:
try:
g = multi_to_poly(g)
ngeos.append(g)
keep.append(True)
except:
keep.append(False)
gdf = gdf.loc[keep]
gdf['geometry'] = ngeos
out_cols = ['RGIId_1', 'RGIId_2', 'geometry']
out = gpd.GeoDataFrame(columns=out_cols)
for i, major in gdf.iterrows():
# Exterior only
major_poly = major.geometry.exterior
# sort by distance to the current glacier
gdf['dis'] = haversine(major.CenLon, major.CenLat,
gdf.CenLon, gdf.CenLat)
gdfs = gdf.sort_values(by='dis').iloc[1:]
# Keep glaciers in which intersect
gdfs = gdfs.loc[gdfs.dis < 200000]
try:
gdfs = gdfs.loc[gdfs.intersects(major_poly)]
except:
gdfs = gdfs.loc[gdfs.intersects(major_poly.buffer(0))]
for i, neighbor in gdfs.iterrows():
if neighbor.RGIId in out.RGIId_1 or neighbor.RGIId in out.RGIId_2:
continue
# Exterior only
# Buffer is needed for numerical reasons
neighbor_poly = neighbor.geometry.exterior.buffer(0.0001)
# Go
try:
mult_intersect = major_poly.intersection(neighbor_poly)
except:
continue
if isinstance(mult_intersect, shpg.Point):
continue
if isinstance(mult_intersect, shpg.linestring.LineString):
mult_intersect = [mult_intersect]
if len(mult_intersect) == 0:
continue
mult_intersect = [m for m in mult_intersect if
not isinstance(m, shpg.Point)]
if len(mult_intersect) == 0:
continue
mult_intersect = linemerge(mult_intersect)
if isinstance(mult_intersect, shpg.linestring.LineString):
mult_intersect = [mult_intersect]
for line in mult_intersect:
assert isinstance(line, shpg.linestring.LineString)
line = gpd.GeoDataFrame([[major.RGIId, neighbor.RGIId, line]],
columns=out_cols)
out = out.append(line)
out.crs = wgs84.srs
out.to_file(out_path)
print(os.path.basename(rgi_shp) +
' took {0:.2f} seconds'.format(time.time() - start_time))
return
if RUN_inAWS:
# TODO: this paths are going to change with the new cluster
WORKING_DIR = '~/work_dir/'
DATA_DIR = '~/input_data/'
RGI_FILE = '~/Sub_region4/Sub_region4.shp'
GLATHIDA_FILE = '~/input_data/rgi_glathida_links_2014_RGIV54.csv'
cfg.PATHS['working_dir'] = WORKING_DIR
cfg.PATHS['topo_dir'] = os.path.join(DATA_DIR, 'topo')
cfg.PATHS['cru_dir'] = os.path.join(DATA_DIR, 'cru')
cfg.PATHS['glathida_rgi_links'] = GLATHIDA_FILE
# Create directories
utils.mkdir(cfg.PATHS['working_dir'])
utils.mkdir(cfg.PATHS['topo_dir'])
utils.mkdir(cfg.PATHS['cru_dir'])
# Use multiprocessing? Change this to run in the AWS
cfg.PARAMS['use_multiprocessing'] = False
cfg.CONTINUE_ON_ERROR = False
# Other params
cfg.PARAMS['border'] = 80
cfg.PARAMS['temp_use_local_gradient'] = False
cfg.PARAMS['optimize_inversion_params'] = True
cfg.PARAMS['invert_with_sliding'] = False
cfg.PARAMS['bed_shape'] = 'parabolic'
cfg.PARAMS['use_compression'] = False
log = logging.getLogger(__name__)
# Initialize OGGM and set up the default run parameters
cfg.initialize(logging_level='WORKFLOW')
rgi_version = '61'
rgi_region = '11' # Region Central Europe
# Here we override some of the default parameters
# How many grid points around the glacier?
# Make it large if you expect your glaciers to grow large:
# here, 80 is more than enough
cfg.PARAMS['border'] = 80
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('OGGM_Rofental')
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
# RGI file
path = utils.get_rgi_region_file(rgi_region, version=rgi_version)
rgidf = gpd.read_file(path)
# Get the Rofental Basin file
path = utils.get_demo_file('rofental_hydrosheds.shp')
basin = gpd.read_file(path)
# Take all glaciers in the Rofental Basin
in_bas = [basin.geometry.contains(shpg.Point(x, y))[0] for
(x, y) in zip(rgidf.CenLon, rgidf.CenLat)]
rgidf = rgidf.loc[in_bas]
import oggm.cfg as cfg from oggm import workflow from oggm import tasks from oggm.workflow import execute_entity_task from oggm import graphics, utils from oggm.core.models import flowline # Initialize OGGM and set up the run parameters # --------------------------------------------- cfg.initialize() # Local paths (where to write the OGGM run output) WORKING_DIR = '/home/mowglie/disk/OGGM_Runs/EXAMPLE_GLACIERS' PLOTS_DIR = os.path.join(WORKING_DIR, 'plots') utils.mkdir(WORKING_DIR) cfg.PATHS['working_dir'] = WORKING_DIR # Use multiprocessing? cfg.PARAMS['use_multiprocessing'] = True # How many grid points around the glacier? # Make it large if you expect your glaciers to grow large cfg.PARAMS['border'] = 60 # This is the default in OGGM cfg.PARAMS['prcp_scaling_factor'] = 2.5 # Set to True for operational runs cfg.CONTINUE_ON_ERROR = False cfg.PARAMS['auto_skip_task'] = False
# Initialize OGGM cfg.initialize() # Local paths (where to write output and where to download input) WORKING_DIR = '/work/ubuntu/run_alps' DATA_DIR = '/work/ubuntu/oggm-data' cfg.PATHS['working_dir'] = WORKING_DIR cfg.PATHS['topo_dir'] = os.path.join(DATA_DIR, 'topo') cfg.PATHS['cru_dir'] = os.path.join(DATA_DIR, 'cru') cfg.PATHS['rgi_dir'] = os.path.join(DATA_DIR, 'rgi') # Currently OGGM wants some directories to exist # (maybe I'll change this but it can also catch errors in the user config) utils.mkdir(cfg.PATHS['working_dir']) utils.mkdir(cfg.PATHS['topo_dir']) utils.mkdir(cfg.PATHS['cru_dir']) utils.mkdir(cfg.PATHS['rgi_dir']) # Use multiprocessing? cfg.PARAMS['use_multiprocessing'] = True cfg.CONTINUE_ON_ERROR = True # Other params cfg.PARAMS['border'] = 80 cfg.PARAMS['temp_use_local_gradient'] = False cfg.PARAMS['invert_with_sliding'] = False cfg.PARAMS['bed_shape'] = 'mixed' # Download RGI files
def reset_dir(self):
if os.path.exists(TEST_DIR):
shutil.rmtree(TEST_DIR)
utils.mkdir(cfg.PATHS['dl_cache_dir'])
utils.mkdir(cfg.PATHS['working_dir'])
utils.mkdir(cfg.PATHS['tmp_dir'])
import os
import shutil
import numpy as np
from oggm.tests.funcs import init_hef, get_test_dir
from oggm import utils, tasks
from oggm.core import massbalance
testdir = os.path.join(get_test_dir(), 'benchmarks')
utils.mkdir(testdir, reset=True)
heights = np.linspace(2200, 3600, 120)
years = np.arange(151) + 1850
def teardown():
if os.path.exists(testdir):
shutil.rmtree(testdir)
def setup():
global gdir
gdir = init_hef(border=80)
teardown()
gdir = tasks.copy_to_basedir(gdir, base_dir=testdir, setup='all')
def time_PastMassBalance():
mb_mod = massbalance.PastMassBalance(gdir, bias=0)
for yr in years:
mb_mod.get_annual_mb(heights, year=yr)
import time
import shutil
import salem
import numpy as np
import pandas as pd
from numpy.testing import assert_array_equal, assert_allclose
import oggm
from oggm import utils
from oggm import cfg
from oggm.tests import is_download
# Globals
TEST_DIR = os.path.join(cfg.PATHS['test_dir'], 'tmp_download')
utils.mkdir(TEST_DIR)
# In case some logging happens or so
cfg.PATHS['working_dir'] = cfg.PATHS['test_dir']
class TestFuncs(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_signchange(self):
ts = pd.Series([-2., -1., 1., 2., 3], index=np.arange(5))
sc = utils.signchange(ts)
def run_prepro_levels(rgi_version=None, rgi_reg=None, border=None,
output_folder='', working_dir='', is_test=False,
demo=False, test_rgidf=None, test_intersects_file=None,
test_topofile=None, test_crudir=None):
"""Does the actual job.
Parameters
----------
rgi_version : str
the RGI version to use (defaults to cfg.PARAMS)
rgi_reg : str
the RGI region to process
border : int
the number of pixels at the maps border
output_folder : str
path to the output folder (where to put the preprocessed tar files)
working_dir : str
path to the OGGM working directory
is_test : bool
to test on a couple of glaciers only!
demo : bool
to run the prepro for the list of demo glaciers
test_rgidf : shapefile
for testing purposes only
test_intersects_file : shapefile
for testing purposes only
test_topofile : str
for testing purposes only
test_crudir : str
for testing purposes only
"""
# TODO: temporarily silence Fiona deprecation warnings
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
# Module logger
log = logging.getLogger(__name__)
# Time
start = time.time()
# Initialize OGGM and set up the run parameters
cfg.initialize(logging_level='WORKFLOW')
# Local paths
utils.mkdir(working_dir)
cfg.PATHS['working_dir'] = working_dir
# Use multiprocessing?
cfg.PARAMS['use_multiprocessing'] = True
# How many grid points around the glacier?
# Make it large if you expect your glaciers to grow large
cfg.PARAMS['border'] = border
# Set to True for operational runs
cfg.PARAMS['continue_on_error'] = True
# For statistics
climate_periods = [1920, 1960, 2000]
if rgi_version is None:
rgi_version = cfg.PARAMS['rgi_version']
rgi_dir_name = 'RGI{}'.format(rgi_version)
border_dir_name = 'b_{:03d}'.format(border)
base_dir = os.path.join(output_folder, rgi_dir_name, border_dir_name)
# Add a package version file
utils.mkdir(base_dir)
opath = os.path.join(base_dir, 'package_versions.txt')
with open(opath, 'w') as vfile:
vfile.write(utils.show_versions(logger=log))
if demo:
rgidf = utils.get_rgi_glacier_entities(cfg.DEMO_GLACIERS.index)
elif test_rgidf is None:
# Get the RGI file
rgidf = gpd.read_file(utils.get_rgi_region_file(rgi_reg,
version=rgi_version))
# We use intersects
rgif = utils.get_rgi_intersects_region_file(rgi_reg,
version=rgi_version)
cfg.set_intersects_db(rgif)
else:
rgidf = test_rgidf
cfg.set_intersects_db(test_intersects_file)
if is_test:
# Just for fun
rgidf = rgidf.sample(4)
# Sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)
log.workflow('Starting prepro run for RGI reg: {} '
'and border: {}'.format(rgi_reg, border))
log.workflow('Number of glaciers: {}'.format(len(rgidf)))
# Input
if test_topofile:
cfg.PATHS['dem_file'] = test_topofile
# L1 - initialize working directories
gdirs = workflow.init_glacier_regions(rgidf, reset=True, force=True)
# Glacier stats
sum_dir = os.path.join(base_dir, 'L1', 'summary')
utils.mkdir(sum_dir)
opath = os.path.join(sum_dir, 'glacier_statistics_{}.csv'.format(rgi_reg))
utils.compile_glacier_statistics(gdirs, path=opath)
# L1 OK - compress all in output directory
l_base_dir = os.path.join(base_dir, 'L1')
workflow.execute_entity_task(utils.gdir_to_tar, gdirs, delete=False,
base_dir=l_base_dir)
utils.base_dir_to_tar(l_base_dir)
# L2 - Tasks
# Pre-download other files just in case
if test_crudir is None:
_ = utils.get_cru_file(var='tmp')
_ = utils.get_cru_file(var='pre')
else:
cfg.PATHS['cru_dir'] = test_crudir
workflow.execute_entity_task(tasks.process_cru_data, gdirs)
# Glacier stats
sum_dir = os.path.join(base_dir, 'L2', 'summary')
utils.mkdir(sum_dir)
opath = os.path.join(sum_dir, 'glacier_statistics_{}.csv'.format(rgi_reg))
utils.compile_glacier_statistics(gdirs, path=opath)
# L2 OK - compress all in output directory
l_base_dir = os.path.join(base_dir, 'L2')
workflow.execute_entity_task(utils.gdir_to_tar, gdirs, delete=False,
base_dir=l_base_dir)
utils.base_dir_to_tar(l_base_dir)
# L3 - Tasks
task_list = [
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,
tasks.local_t_star,
tasks.mu_star_calibration,
tasks.prepare_for_inversion,
tasks.mass_conservation_inversion,
tasks.filter_inversion_output,
]
for task in task_list:
workflow.execute_entity_task(task, gdirs)
# Glacier stats
sum_dir = os.path.join(base_dir, 'L3', 'summary')
utils.mkdir(sum_dir)
opath = os.path.join(sum_dir, 'glacier_statistics_{}.csv'.format(rgi_reg))
utils.compile_glacier_statistics(gdirs, path=opath)
opath = os.path.join(sum_dir, 'climate_statistics_{}.csv'.format(rgi_reg))
utils.compile_climate_statistics(gdirs, add_climate_period=climate_periods,
path=opath)
# L3 OK - compress all in output directory
l_base_dir = os.path.join(base_dir, 'L3')
workflow.execute_entity_task(utils.gdir_to_tar, gdirs, delete=False,
base_dir=l_base_dir)
utils.base_dir_to_tar(l_base_dir)
# L4 - Tasks
workflow.execute_entity_task(tasks.init_present_time_glacier, gdirs)
# Glacier stats
sum_dir = os.path.join(base_dir, 'L4', 'summary')
utils.mkdir(sum_dir)
opath = os.path.join(sum_dir, 'glacier_statistics_{}.csv'.format(rgi_reg))
utils.compile_glacier_statistics(gdirs, path=opath)
# Copy mini data to new dir
base_dir = os.path.join(base_dir, 'L4')
mini_gdirs = workflow.execute_entity_task(tasks.copy_to_basedir, gdirs,
base_dir=base_dir)
# L4 OK - compress all in output directory
workflow.execute_entity_task(utils.gdir_to_tar, mini_gdirs, delete=True)
utils.base_dir_to_tar(base_dir)
# Log
m, s = divmod(time.time() - start, 60)
h, m = divmod(m, 60)
log.workflow('OGGM prepro_levels is done! Time needed: '
'{:02d}:{:02d}:{:02d}'.format(int(h), int(m), int(s)))
def newplotfunc(gdirs, ax=None, smap=None, add_colorbar=True, title=None,
title_comment=None, horizontal_colorbar=False,
lonlat_contours_kwargs=None, cbar_ax=None, autosave=False,
add_scalebar=True, figsize=None, savefig=None,
savefig_kwargs=None,
**kwargs):
dofig = False
if ax is None:
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
dofig = True
# Cast to list
gdirs = utils.tolist(gdirs)
if smap is None:
mp = salem.Map(gdirs[0].grid, countries=False,
nx=gdirs[0].grid.nx)
else:
mp = smap
if lonlat_contours_kwargs is not None:
mp.set_lonlat_contours(**lonlat_contours_kwargs)
if add_scalebar:
mp.set_scale_bar()
out = plotfunc(gdirs, ax=ax, smap=mp, **kwargs)
if add_colorbar and 'cbar_label' in out:
cbprim = out.get('cbar_primitive', mp)
if cbar_ax:
cb = cbprim.colorbarbase(cbar_ax)
else:
if horizontal_colorbar:
cb = cbprim.append_colorbar(ax, "bottom", size="5%",
pad=0.4)
else:
cb = cbprim.append_colorbar(ax, "right", size="5%",
pad=0.2)
cb.set_label(out['cbar_label'])
if title is None:
if 'title' not in out:
# Make a defaut one
title = ''
if len(gdirs) == 1:
gdir = gdirs[0]
title = gdir.rgi_id
if gdir.name is not None and gdir.name != '':
title += ': ' + gdir.name
out['title'] = title
if title_comment is None:
title_comment = out.get('title_comment', '')
out['title'] += title_comment
ax.set_title(out['title'])
else:
ax.set_title(title)
if dofig:
plt.tight_layout()
if autosave:
savefig = os.path.join(cfg.PATHS['working_dir'], 'plots')
utils.mkdir(savefig)
savefig = os.path.join(savefig, plotfunc.__name__ + '_' +
gdirs[0].rgi_id + '.png')
if savefig is not None:
plt.savefig(savefig, **savefig_kwargs)
plt.close()
