"""Wrappers for the single tasks, multi processor handling."""
# Built ins
import logging
import os
import shutil
from collections.abc import Sequence
# External libs
import multiprocessing
import numpy as np
import pandas as pd
from scipy import optimize as optimization
# Locals
import oggm
from oggm import cfg, tasks, utils
from oggm.core import centerlines, flowline, climate
from oggm.exceptions import InvalidParamsError, InvalidWorkflowError
from oggm.utils import global_task
# MPI
try:
import oggm.mpi as ogmpi
_have_ogmpi = True
except ImportError:
_have_ogmpi = False
# Module logger
log = logging.getLogger(__name__)
# Multiprocessing Pool
_mp_manager = None
_mp_pool = None
def _init_pool_globals(_cfg_contents, global_lock):
cfg.unpack_config(_cfg_contents)
utils.lock = global_lock
def init_mp_pool(reset=False):
"""Necessary because at import time, cfg might be uninitialized"""
global _mp_manager, _mp_pool
if _mp_pool and _mp_manager and not reset:
return _mp_pool
cfg.CONFIG_MODIFIED = False
if _mp_pool:
_mp_pool.terminate()
_mp_pool = None
if _mp_manager:
cfg.set_manager(None)
_mp_manager.shutdown()
_mp_manager = None
if cfg.PARAMS['use_mp_spawn']:
mp = multiprocessing.get_context('spawn')
else:
mp = multiprocessing
_mp_manager = mp.Manager()
cfg.set_manager(_mp_manager)
cfg_contents = cfg.pack_config()
global_lock = _mp_manager.Lock()
mpp = cfg.PARAMS['mp_processes']
_mp_pool = mp.Pool(mpp, initializer=_init_pool_globals,
initargs=(cfg_contents, global_lock))
return _mp_pool
def _merge_dicts(*dicts):
r = {}
for d in dicts:
r.update(d)
return r
class _pickle_copier(object):
"""Pickleable alternative to functools.partial,
Which is not pickleable in python2 and thus doesn't work
with Multiprocessing."""
def __init__(self, func, kwargs):
self.call_func = func
self.out_kwargs = kwargs
def __call__(self, arg):
if self.call_func:
gdir = arg
call_func = self.call_func
else:
call_func, gdir = arg
if isinstance(gdir, Sequence) and not isinstance(gdir, str):
gdir, gdir_kwargs = gdir
gdir_kwargs = _merge_dicts(self.out_kwargs, gdir_kwargs)
return call_func(gdir, **gdir_kwargs)
else:
return call_func(gdir, **self.out_kwargs)
def reset_multiprocessing():
"""Reset multiprocessing state
Call this if you changed configuration parameters mid-run and need them to
be re-propagated to child processes.
"""
global _mp_pool
if _mp_pool:
_mp_pool.terminate()
_mp_pool = None
cfg.CONFIG_MODIFIED = False
[docs]def execute_entity_task(task, gdirs, **kwargs):
"""Execute a task on gdirs.
If you asked for multiprocessing, it will do it.
If ``task`` has more arguments than `gdir` they have to be keyword
arguments.
Parameters
----------
task : function
the entity task to apply
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
"""
if task.__dict__.get('is_global_task', False):
raise InvalidWorkflowError('execute_entity_task cannot be used on '
'global tasks.')
# Should be iterable
gdirs = utils.tolist(gdirs)
ng = len(gdirs)
if ng == 0:
log.workflow('Called entity task %s on 0 glaciers. Returning...',
task.__name__)
return
log.workflow('Execute entity task %s on %d glaciers',
task.__name__, ng)
pc = _pickle_copier(task, kwargs)
if _have_ogmpi:
if ogmpi.OGGM_MPI_COMM is not None:
return ogmpi.mpi_master_spin_tasks(pc, gdirs)
if cfg.PARAMS['use_multiprocessing'] and ng > 1:
mppool = init_mp_pool(cfg.CONFIG_MODIFIED)
out = mppool.map(pc, gdirs, chunksize=1)
else:
if ng > 3:
log.workflow('WARNING: you are trying to run an entity task on '
'%d glaciers with multiprocessing turned off. OGGM '
'will run faster with multiprocessing turned on.', ng)
out = [pc(gdir) for gdir in gdirs]
return out
def execute_parallel_tasks(gdir, tasks):
"""Execute a list of task on a single gdir (experimental!).
This is useful when running a non-sequential list of task on a gdir,
mostly for e.g. different experiments with different output files.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
the directory to process.
tasks : list
the the list of entity tasks to apply.
Optionally, each list element can be a tuple, with the first element
being the task, and the second element a dict that
will be passed to the task function as ``**kwargs``.
"""
pc = _pickle_copier(None, {})
_tasks = []
for task in tasks:
kwargs = {}
if isinstance(task, Sequence):
task, kwargs = task
_tasks.append((task, (gdir, kwargs)))
if _have_ogmpi:
if ogmpi.OGGM_MPI_COMM is not None:
ogmpi.mpi_master_spin_tasks(pc, _tasks)
return
if cfg.PARAMS['use_multiprocessing']:
mppool = init_mp_pool(cfg.CONFIG_MODIFIED)
mppool.map(pc, _tasks, chunksize=1)
else:
for task, (gd, kw) in _tasks:
task(gd, **kw)
def gdir_from_prepro(entity, from_prepro_level=None,
prepro_border=None, prepro_rgi_version=None,
base_url=None):
if prepro_border is None:
prepro_border = int(cfg.PARAMS['border'])
if prepro_rgi_version is None:
prepro_rgi_version = cfg.PARAMS['rgi_version']
try:
rid = entity.RGIId
except AttributeError:
rid = entity
tar_base = utils.get_prepro_gdir(prepro_rgi_version, rid, prepro_border,
from_prepro_level, base_url=base_url)
from_tar = os.path.join(tar_base.replace('.tar', ''), rid + '.tar.gz')
return oggm.GlacierDirectory(entity, from_tar=from_tar)
def gdir_from_tar(entity, from_tar):
try:
rgi_id = entity.RGIId
except AttributeError:
rgi_id = entity
from_tar = os.path.join(from_tar, '{}'.format(rgi_id[:8]),
'{}.tar' .format(rgi_id[:11]))
assert os.path.exists(from_tar), 'tarfile does not exist'
from_tar = os.path.join(from_tar.replace('.tar', ''), rgi_id + '.tar.gz')
return oggm.GlacierDirectory(entity, from_tar=from_tar)
def _check_duplicates(rgidf=None):
"""Complain if the input has duplicates."""
if rgidf is None:
return
# Check if dataframe or list of strs
try:
rgidf = rgidf.RGIId
except AttributeError:
rgidf = utils.tolist(rgidf)
u, c = np.unique(rgidf, return_counts=True)
if len(u) < len(rgidf):
raise InvalidWorkflowError('Found duplicates in the list of '
'RGI IDs: {}'.format(u[c > 1]))
def init_glacier_regions(rgidf=None, *, reset=False, force=False,
from_prepro_level=None, prepro_border=None,
prepro_rgi_version=None, prepro_base_url=None,
from_tar=False, delete_tar=False):
"""DEPRECATED: Initializes the list of Glacier Directories for this run.
This is the very first task to do (always). If the directories are already
available in the working directory, use them. If not, create new ones.
Parameters
----------
rgidf : GeoDataFrame or list of ids, optional for pre-computed runs
the RGI glacier outlines. If unavailable, OGGM will parse the
information from the glacier directories found in the working
directory. It is required for new runs.
reset : bool
delete the existing glacier directories if found.
force : bool
setting `reset=True` will trigger a yes/no question to the user. Set
`force=True` to avoid this.
from_prepro_level : int
get the gdir data from the official pre-processed pool. See the
documentation for more information
prepro_border : int
for `from_prepro_level` only: if you want to override the default
behavior which is to use `cfg.PARAMS['border']`
prepro_rgi_version : str
for `from_prepro_level` only: if you want to override the default
behavior which is to use `cfg.PARAMS['rgi_version']`
prepro_base_url : str
for `from_prepro_level` only: if you want to override the default
URL from which to download the gdirs. Default currently is
https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.1/
from_tar : bool, default=False
extract the gdir data from a tar file. If set to `True`,
will check for a tar file at the expected location in `base_dir`.
delete_tar : bool, default=False
delete the original tar file after extraction.
delete_tar : bool, default=False
delete the original tar file after extraction.
Returns
-------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the initialised glacier directories
Notes
-----
This task is deprecated in favor of the more explicit
init_glacier_directories. Indeed, init_glacier_directories is very
similar to init_glacier_regions, but it does not process the DEMs:
a glacier directory is valid also without DEM.
"""
_check_duplicates(rgidf)
if reset and not force:
reset = utils.query_yes_no('Delete all glacier directories?')
if prepro_border is None:
prepro_border = int(cfg.PARAMS['border'])
# if reset delete also the log directory
if reset:
fpath = os.path.join(cfg.PATHS['working_dir'], 'log')
if os.path.exists(fpath):
shutil.rmtree(fpath)
gdirs = []
new_gdirs = []
if rgidf is None:
if reset:
raise ValueError('Cannot use reset without setting rgidf')
log.workflow('init_glacier_regions by parsing available folders '
'(can be slow).')
# The dirs should be there already
gl_dir = os.path.join(cfg.PATHS['working_dir'], 'per_glacier')
for root, _, files in os.walk(gl_dir):
if files and ('dem.tif' in files):
gdirs.append(oggm.GlacierDirectory(os.path.basename(root)))
else:
# Check if dataframe or list of strs
try:
entities = []
for _, entity in rgidf.iterrows():
entities.append(entity)
except AttributeError:
entities = utils.tolist(rgidf)
if from_prepro_level is not None:
log.workflow('init_glacier_regions from prepro level {} on '
'{} glaciers.'.format(from_prepro_level,
len(entities)))
# Read the hash dictionary before we use multiproc
if cfg.PARAMS['dl_verify']:
utils.get_dl_verify_data('cluster.klima.uni-bremen.de')
gdirs = execute_entity_task(gdir_from_prepro, entities,
from_prepro_level=from_prepro_level,
prepro_border=prepro_border,
prepro_rgi_version=prepro_rgi_version,
base_url=prepro_base_url)
else:
# We can set the intersects file automatically here
if (cfg.PARAMS['use_intersects'] and
len(cfg.PARAMS['intersects_gdf']) == 0):
rgi_ids = np.unique(np.sort([entity.RGIId for entity in
entities]))
rgi_version = rgi_ids[0].split('-')[0][-2:]
fp = utils.get_rgi_intersects_entities(rgi_ids,
version=rgi_version)
cfg.set_intersects_db(fp)
gdirs = execute_entity_task(utils.GlacierDirectory, entities,
reset=reset,
from_tar=from_tar,
delete_tar=delete_tar)
for gdir in gdirs:
if not os.path.exists(gdir.get_filepath('dem')):
new_gdirs.append(gdir)
if len(new_gdirs) > 0:
# If not initialized, run the task in parallel
execute_entity_task(tasks.define_glacier_region, new_gdirs)
return gdirs
[docs]def init_glacier_directories(rgidf=None, *, reset=False, force=False,
from_prepro_level=None, prepro_border=None,
prepro_rgi_version=None, prepro_base_url=None,
from_tar=False, delete_tar=False):
"""Initializes the list of Glacier Directories for this run.
This is the very first task to do (always). If the directories are already
available in the working directory, use them. If not, create new ones.
Parameters
----------
rgidf : GeoDataFrame or list of ids, optional for pre-computed runs
the RGI glacier outlines. If unavailable, OGGM will parse the
information from the glacier directories found in the working
directory. It is required for new runs.
reset : bool
delete the existing glacier directories if found.
force : bool
setting `reset=True` will trigger a yes/no question to the user. Set
`force=True` to avoid this.
from_prepro_level : int
get the gdir data from the official pre-processed pool. See the
documentation for more information
prepro_border : int
for `from_prepro_level` only: if you want to override the default
behavior which is to use `cfg.PARAMS['border']`
prepro_rgi_version : str
for `from_prepro_level` only: if you want to override the default
behavior which is to use `cfg.PARAMS['rgi_version']`
prepro_base_url : str
for `from_prepro_level` only: if you want to override the default
URL from which to download the gdirs. Default currently is
https://cluster.klima.uni-bremen.de/~fmaussion/gdirs/oggm_v1.1/
from_tar : bool or str, default=False
extract the gdir data from a tar file. If set to `True`,
will check for a tar file at the expected location in `base_dir`.
delete the original tar file after extraction.
Returns
-------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the initialised glacier directories
Notes
-----
This task is very similar to init_glacier_regions, with one main
difference: it does not process the DEMs for this glacier.
Eventually, init_glacier_regions will be deprecated and removed from the
codebase.
"""
_check_duplicates(rgidf)
if reset and not force:
reset = utils.query_yes_no('Delete all glacier directories?')
if from_prepro_level:
url = utils.get_prepro_base_url(base_url=prepro_base_url,
border=prepro_border,
prepro_level=from_prepro_level,
rgi_version=prepro_rgi_version)
if cfg.PARAMS['has_internet'] and not utils.url_exists(url):
raise InvalidParamsError("base url seems unreachable with these "
"parameters: {}".format(url))
# if reset delete also the log directory
if reset:
fpath = os.path.join(cfg.PATHS['working_dir'], 'log')
if os.path.exists(fpath):
shutil.rmtree(fpath)
if rgidf is None:
# Infer the glacier directories from folders available in working dir
if reset:
raise ValueError('Cannot use reset without setting rgidf')
log.workflow('init_glacier_directories by parsing all available '
'folders (this takes time: if possible, provide rgidf '
'instead).')
# The dirs should be there already
gl_dir = os.path.join(cfg.PATHS['working_dir'], 'per_glacier')
gdirs = []
for root, _, files in os.walk(gl_dir):
if files and ('outlines.shp' in files or
'outlines.tar.gz' in files):
gdirs.append(oggm.GlacierDirectory(os.path.basename(root)))
else:
# Create glacier directories from input
# Check if dataframe or list of str
try:
entities = []
for _, entity in rgidf.iterrows():
entities.append(entity)
except AttributeError:
entities = utils.tolist(rgidf)
if from_prepro_level is not None:
log.workflow('init_glacier_directories from prepro level {} on '
'{} glaciers.'.format(from_prepro_level,
len(entities)))
# Read the hash dictionary before we use multiproc
if cfg.PARAMS['dl_verify']:
utils.get_dl_verify_data('cluster.klima.uni-bremen.de')
gdirs = execute_entity_task(gdir_from_prepro, entities,
from_prepro_level=from_prepro_level,
prepro_border=prepro_border,
prepro_rgi_version=prepro_rgi_version,
base_url=prepro_base_url)
else:
# We can set the intersects file automatically here
if (cfg.PARAMS['use_intersects'] and
len(cfg.PARAMS['intersects_gdf']) == 0 and
not from_tar):
try:
rgi_ids = np.unique(np.sort([entity.RGIId for entity in
entities]))
rgi_version = rgi_ids[0].split('-')[0][-2:]
fp = utils.get_rgi_intersects_entities(rgi_ids,
version=rgi_version)
cfg.set_intersects_db(fp)
except AttributeError:
# List of str
pass
if os.path.isdir(from_tar):
gdirs = execute_entity_task(gdir_from_tar, entities,
from_tar=from_tar)
else:
gdirs = execute_entity_task(utils.GlacierDirectory, entities,
reset=reset,
from_tar=from_tar,
delete_tar=delete_tar)
return gdirs
[docs]@global_task(log)
def gis_prepro_tasks(gdirs):
"""Run all flowline preprocessing tasks on a list of glaciers.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
"""
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)
[docs]def download_ref_tstars(base_url=None):
"""Downloads and copies the reference list of t* to the working directory.
Example url:
https://cluster.klima.uni-bremen.de/~oggm/ref_mb_params/oggm_v1.4/RGIV62/CRU/centerlines/qc3/pcp2.5
Parameters
----------
base_url : str
url of the params file.
"""
shutil.copyfile(utils.file_downloader(base_url + '/ref_tstars.csv'),
os.path.join(cfg.PATHS['working_dir'], 'ref_tstars.csv'))
shutil.copyfile(utils.file_downloader(base_url + '/ref_tstars_params.json'),
os.path.join(cfg.PATHS['working_dir'], 'ref_tstars_params.json'))
[docs]@global_task(log)
def climate_tasks(gdirs, base_url=None):
"""Run all climate related entity tasks on a list of glaciers.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
base_url : str, optional
url of the params file.
"""
# Process climate data
execute_entity_task(tasks.process_climate_data, gdirs)
# Then, calibration?
if cfg.PARAMS['run_mb_calibration']:
climate.compute_ref_t_stars(gdirs)
elif base_url:
download_ref_tstars(base_url=base_url)
# Mustar and the apparent mass-balance
execute_entity_task(tasks.local_t_star, gdirs)
execute_entity_task(tasks.mu_star_calibration, gdirs)
[docs]@global_task(log)
def inversion_tasks(gdirs, glen_a=None, fs=None, filter_inversion_output=True):
"""Run all ice thickness inversion tasks on a list of glaciers.
Quite useful to deal with calving glaciers as well.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
"""
if cfg.PARAMS['use_kcalving_for_inversion']:
# Differentiate between calving and non-calving glaciers
gdirs_nc = []
gdirs_c = []
for gd in gdirs:
if gd.is_tidewater:
gdirs_c.append(gd)
else:
gdirs_nc.append(gd)
log.workflow('Starting inversion tasks for {} tidewater and {} '
'non-tidewater glaciers.'.format(len(gdirs_c),
len(gdirs_nc)))
if gdirs_nc:
execute_entity_task(tasks.prepare_for_inversion, gdirs_nc)
execute_entity_task(tasks.mass_conservation_inversion, gdirs_nc,
glen_a=glen_a, fs=fs)
if filter_inversion_output:
execute_entity_task(tasks.filter_inversion_output, gdirs_nc)
if gdirs_c:
execute_entity_task(tasks.find_inversion_calving, gdirs_c,
glen_a=glen_a, fs=fs)
else:
execute_entity_task(tasks.prepare_for_inversion, gdirs)
execute_entity_task(tasks.mass_conservation_inversion, gdirs,
glen_a=glen_a, fs=fs)
if filter_inversion_output:
execute_entity_task(tasks.filter_inversion_output, gdirs)
[docs]@global_task(log)
def calibrate_inversion_from_consensus(gdirs, ignore_missing=True,
fs=0, a_bounds=(0.1, 10),
apply_fs_on_mismatch=False,
error_on_mismatch=True,
filter_inversion_output=True):
"""Fit the total volume of the glaciers to the 2019 consensus estimate.
This method finds the "best Glen A" to match all glaciers in gdirs with
a valid inverted volume.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
ignore_missing : bool
set this to true to silence the error if some glaciers could not be
found in the consensus estimate.
fs : float
invert with sliding (default: no)
a_bounds: tuple
factor to apply to default A
apply_fs_on_mismatch: false
on mismatch, try to apply an arbitrary value of fs (fs = 5.7e-20 from
Oerlemans) and try to optimize A again.
error_on_mismatch: bool
sometimes the given bounds do not allow to find a zero mismatch:
this will normally raise an error, but you can switch this off,
use the closest value instead and move on.
filter_inversion_output : bool
whether or not to apply terminus thickness filtering on the inversion
output (needs the downstream lines to work).
Returns
-------
a dataframe with the individual glacier volumes
"""
gdirs = utils.tolist(gdirs)
# Get the ref data for the glaciers we have
df = pd.read_hdf(utils.get_demo_file('rgi62_itmix_df.h5'))
rids = [gdir.rgi_id for gdir in gdirs]
found_ids = df.index.intersection(rids)
if not ignore_missing and (len(found_ids) != len(rids)):
raise InvalidWorkflowError('Could not find matching indices in the '
'consensus estimate for all provided '
'glaciers. Set ignore_missing=True to '
'ignore this error.')
df = df.reindex(rids)
# Optimize the diff to ref
def_a = cfg.PARAMS['inversion_glen_a']
def compute_vol(x):
inversion_tasks(gdirs, glen_a=x*def_a, fs=fs,
filter_inversion_output=filter_inversion_output)
odf = df.copy()
odf['oggm'] = execute_entity_task(tasks.get_inversion_volume, gdirs)
return odf.dropna()
def to_minimize(x):
log.workflow('Consensus estimate optimisation with '
'A factor: {} and fs: {}'.format(x, fs))
odf = compute_vol(x)
return odf.vol_itmix_m3.sum() - odf.oggm.sum()
try:
out_fac, r = optimization.brentq(to_minimize, *a_bounds, rtol=1e-2,
full_output=True)
if r.converged:
log.workflow('calibrate_inversion_from_consensus '
'converged after {} iterations and fs={}. The '
'resulting Glen A factor is {}.'
''.format(r.iterations, fs, out_fac))
else:
raise ValueError('Unexpected error in optimization.brentq')
except ValueError:
# Ok can't find an A. Log for debug:
odf1 = compute_vol(a_bounds[0]).sum() * 1e-9
odf2 = compute_vol(a_bounds[1]).sum() * 1e-9
msg = ('calibration fom consensus estimate CANT converge with fs={}.\n'
'Bound values (km3):\nRef={:.3f} OGGM={:.3f} for A factor {}\n'
'Ref={:.3f} OGGM={:.3f} for A factor {}'
''.format(fs,
odf1.vol_itmix_m3, odf1.oggm, a_bounds[0],
odf2.vol_itmix_m3, odf2.oggm, a_bounds[1]))
if apply_fs_on_mismatch and fs == 0 and odf2.oggm > odf2.vol_itmix_m3:
return calibrate_inversion_from_consensus(gdirs,
ignore_missing=ignore_missing,
fs=5.7e-20, a_bounds=a_bounds,
apply_fs_on_mismatch=False,
error_on_mismatch=error_on_mismatch)
if error_on_mismatch:
raise ValueError(msg)
out_fac = a_bounds[int(abs(odf1.vol_itmix_m3 - odf1.oggm) >
abs(odf2.vol_itmix_m3 - odf2.oggm))]
log.workflow(msg)
log.workflow('We use A factor = {} and fs = {} and move on.'
''.format(out_fac, fs))
# Compute the final volume with the correct A
inversion_tasks(gdirs, glen_a=out_fac*def_a, fs=fs,
filter_inversion_output=filter_inversion_output)
df['vol_oggm_m3'] = execute_entity_task(tasks.get_inversion_volume, gdirs)
return df
[docs]@global_task(log)
def match_regional_geodetic_mb(gdirs, rgi_reg=None, dataset='hugonnet',
period='2000-01-01_2020-01-01'):
"""Regional shift of the mass-balance residual to match observations.
This is useful for operational runs, but also quite hacky.
Let's hope we won't need this for too long.
Parameters
----------
gdirs : the list of gdirs (ideally the entire region)
rgi_reg : str
the rgi region to match
dataset : str
'hugonnet', or 'zemp'
period : str
for 'hugonnet' only. One of
'2000-01-01_2010-01-01',
'2010-01-01_2020-01-01',
'2006-01-01_2019-01-01',
'2000-01-01_2020-01-01'.
For 'zemp', the period is always 2006-2016.
"""
# Get the mass-balance OGGM would give out of the box
df = utils.compile_fixed_geometry_mass_balance(gdirs, path=False)
df = df.dropna(axis=0, how='all').dropna(axis=1, how='all')
# And also the Area and calving fluxes
dfs = utils.compile_glacier_statistics(gdirs, path=False)
if dataset == 'hugonnet':
y0 = int(period.split('_')[0].split('-')[0])
y1 = int(period.split('_')[1].split('-')[0]) - 1
elif dataset == 'zemp':
y0, y1 = 2006, 2015
odf = pd.DataFrame(df.loc[y0:y1].mean(), columns=['SMB'])
odf['AREA'] = dfs.rgi_area_km2 * 1e6
# Just take the calving rate and change its units
# Original units: km3 a-1, to change to mm a-1 (units of specific MB)
rho = cfg.PARAMS['ice_density']
if 'calving_flux' in dfs:
odf['CALVING'] = dfs['calving_flux'].fillna(0) * 1e9 * rho / odf['AREA']
else:
odf['CALVING'] = 0
# We have to drop nans here, which occur when calving glaciers fail to run
odf = odf.dropna()
# Compare area with total RGI area
rdf = 'rgi62_areas.csv'
rdf = pd.read_csv(utils.get_demo_file(rdf), dtype={'O1Region': str})
ref_area = rdf.loc[rdf['O1Region'] == rgi_reg].iloc[0]['AreaNoC2NoNominal']
diff = (1 - odf['AREA'].sum() * 1e-6 / ref_area) * 100
msg = 'Applying geodetic MB correction on RGI reg {}. Diff area: {:.2f}%'
log.workflow(msg.format(rgi_reg, diff))
# Total MB OGGM
out_smb = np.average(odf['SMB'], weights=odf['AREA']) # for logging
out_cal = np.average(odf['CALVING'], weights=odf['AREA']) # for logging
smb_oggm = np.average(odf['SMB'] - odf['CALVING'], weights=odf['AREA'])
# Total MB Reference
if dataset == 'hugonnet':
df = 'table_hugonnet_regions_10yr_20yr_ar6period.csv'
df = pd.read_csv(utils.get_demo_file(df))
df = df.loc[df.period == period].set_index('reg')
smb_ref = df.loc[int(rgi_reg), 'dmdtda']
elif dataset == 'zemp':
df = 'zemp_ref_2006_2016.csv'
df = pd.read_csv(utils.get_demo_file(df), index_col=0)
smb_ref = df.loc[int(rgi_reg), 'SMB'] * 1000
# Diff between the two
residual = smb_ref - smb_oggm
# Let's just shift
log.workflow('Shifting regional MB bias by {}'.format(residual))
log.workflow('Observations give {}'.format(smb_ref))
log.workflow('OGGM SMB gives {}'.format(out_smb))
log.workflow('OGGM frontal ablation gives {}'.format(out_cal))
for gdir in gdirs:
try:
df = gdir.read_json('local_mustar')
gdir.add_to_diagnostics('mb_bias_before_geodetic_corr', df['bias'])
df['bias'] = df['bias'] - residual
gdir.write_json(df, 'local_mustar')
except FileNotFoundError:
pass
[docs]@global_task(log)
def merge_glacier_tasks(gdirs, main_rgi_id=None, return_all=False, buffer=None,
**kwargs):
"""Shortcut function: run all tasks to merge tributaries to a main glacier
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory`
all glaciers, main and tributary. Preprocessed and initialised
main_rgi_id: str
RGI ID of the main glacier of interest. If None is provided merging
will start based uppon the largest glacier
return_all : bool
if main_rgi_id is given and return_all = False: only the main glacier
is returned
if main_rgi_is given and return_all = True, the main glacier and every
remaining glacier from the initial gdirs list is returned, possible
merged as well.
buffer : float
buffer around a flowline to first better find an overlap with another
flowline. And second assure some distance between the lines at a
junction. Will default to `cfg.PARAMS['kbuffer']`.
kwargs: keyword argument for the recursive merging
Returns
-------
merged_gdirs: list of all merged :py:class:`oggm.GlacierDirectory`
"""
if len(gdirs) > 100:
raise InvalidParamsError('this could take time! I should include an '
'optional parameter to ignore this.')
# sort all glaciers descending by area
gdirs.sort(key=lambda x: x.rgi_area_m2, reverse=True)
# if main glacier is asked, put it in first position
if main_rgi_id is not None:
gdir_main = [gd for gd in gdirs if gd.rgi_id == main_rgi_id][0]
gdirs.remove(gdir_main)
gdirs = [gdir_main] + gdirs
merged_gdirs = []
while len(gdirs) > 1:
# main glacier is always the first: either given or the largest one
gdir_main = gdirs.pop(0)
gdir_merged, gdirs = _recursive_merging(gdirs, gdir_main, **kwargs)
merged_gdirs.append(gdir_merged)
# now we have gdirs which contain all the necessary flowlines,
# time to clean them up
for gdir in merged_gdirs:
flowline.clean_merged_flowlines(gdir, buffer=buffer)
if main_rgi_id is not None and return_all is False:
return [gd for gd in merged_gdirs if main_rgi_id in gd.rgi_id][0]
# add the remaining glacier to the final list
merged_gdirs = merged_gdirs + gdirs
return merged_gdirs
def _recursive_merging(gdirs, gdir_main, glcdf=None, dem_source=None,
filename='climate_historical', input_filesuffix=''):
""" Recursive function to merge all tributary glaciers.
This function should start with the largest glacier and then be called
upon all smaller glaciers.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory`
all glaciers, main and tributary. Preprocessed and initialised
gdir_main: :py:class:`oggm.GlacierDirectory`
the current main glacier where the others are merge to
glcdf: geopandas.GeoDataFrame
which contains the main glaciers, will be downloaded if None
filename: str
Baseline climate file
dem_source: str
the DEM source to use
input_filesuffix: str
Filesuffix to the climate file
Returns
-------
merged_gdir: :py:class:`oggm.GlacierDirectory`
the mergeed current main glacier
gdirs : list of :py:class:`oggm.GlacierDirectory`
updated list of glaciers, removed the already merged ones
"""
# find glaciers which intersect with the main
tributaries = centerlines.intersect_downstream_lines(gdir_main,
candidates=gdirs)
if len(tributaries) == 0:
# if no tributaries: nothing to do
return gdir_main, gdirs
# seperate those glaciers which are not already found to be a tributary
gdirs = [gd for gd in gdirs if gd not in tributaries]
gdirs_to_merge = []
for trib in tributaries:
# for each tributary: check if we can merge additional glaciers to it
merged, gdirs = _recursive_merging(gdirs, trib, glcdf=glcdf,
filename=filename,
input_filesuffix=input_filesuffix,
dem_source=dem_source)
gdirs_to_merge.append(merged)
# create merged glacier directory
gdir_merged = utils.initialize_merged_gdir(
gdir_main, tribs=gdirs_to_merge, glcdf=glcdf, filename=filename,
input_filesuffix=input_filesuffix, dem_source=dem_source)
flowline.merge_to_one_glacier(gdir_merged, gdirs_to_merge,
filename=filename,
input_filesuffix=input_filesuffix)
return gdir_merged, gdirs