Source code for oggm.workflow

"""Wrappers for the single tasks, multi processor handling."""
# Built ins
import logging
import os
from shutil import rmtree
from collections.abc import Sequence
# External libs
import multiprocessing as mp
import numpy as np

# Locals
import oggm
from oggm import cfg, tasks, utils
from oggm.core import centerlines, flowline
from oggm.exceptions import InvalidParamsError

# MPI
try:
    import oggm.mpi as ogmpi
    _have_ogmpi = True
except ImportError:
    _have_ogmpi = False

# Module logger
log = logging.getLogger(__name__)

# Multiprocessing Pool
_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_pool
    if _mp_pool and not reset:
        return _mp_pool
    cfg.CONFIG_MODIFIED = False
    if _mp_pool and reset:
        _mp_pool.terminate()
        _mp_pool = None
    cfg_contents = cfg.pack_config()
    global_lock = mp.Manager().Lock()
    mpp = cfg.PARAMS['mp_processes']
    if mpp == -1:
        try:
            mpp = int(os.environ['SLURM_JOB_CPUS_PER_NODE'])
            log.workflow('Multiprocessing: using slurm allocated '
                         'processors (N={})'.format(mpp))
        except KeyError:
            mpp = mp.cpu_count()
            log.workflow('Multiprocessing: using all available '
                         'processors (N={})'.format(mpp))
    else:
        log.workflow('Multiprocessing: using the requested number of '
                     'processors (N={})'.format(mpp))
    _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 not iterable it's ok try: len(gdirs) except TypeError: gdirs = [gdirs] if len(gdirs) == 0: return log.workflow('Execute entity task %s on %d glaciers', task.__name__, len(gdirs)) if task.__dict__.get('global_task', False): return task(gdirs, **kwargs) 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']: mppool = init_mp_pool(cfg.CONFIG_MODIFIED) out = mppool.map(pc, gdirs, chunksize=1) else: 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 in _tasks: task() def gdir_from_prepro(entity, from_prepro_level=None, prepro_border=None, prepro_rgi_version=None, check_demo_glacier=False): 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 demo_url = False if check_demo_glacier: demo_id = utils.demo_glacier_id(rid) if demo_id is not None: rid = demo_id entity = demo_id demo_url = True tar_base = utils.get_prepro_gdir(prepro_rgi_version, rid, prepro_border, from_prepro_level, demo_url=demo_url) from_tar = os.path.join(tar_base.replace('.tar', ''), rid + '.tar.gz') return oggm.GlacierDirectory(entity, from_tar=from_tar)
[docs]def init_glacier_regions(rgidf=None, *, reset=False, force=False, from_prepro_level=None, prepro_border=None, prepro_rgi_version=None, from_tar=False, delete_tar=False, use_demo_glaciers=None): """Initializes the list of Glacier Directories for this run. This is the very first task to do (always). If the directories are already available in the working directory, use them. If not, create new ones. Parameters ---------- rgidf : GeoDataFrame or list of ids, optional for pre-computed runs the RGI glacier outlines. If unavailable, OGGM will parse the information from the glacier directories found in the working directory. It is required for new runs. reset : bool delete the existing glacier directories if found. force : bool setting `reset=True` will trigger a yes/no question to the user. Set `force=True` to avoid this. from_prepro_level : int get the gdir data from the official pre-processed pool. See the documentation for more information prepro_border : int for `from_prepro_level` only: if you want to override the default behavior which is to use `cfg.PARAMS['border']` prepro_rgi_version : str for `from_prepro_level` only: if you want to override the default behavior which is to use `cfg.PARAMS['rgi_version']` use_demo_glaciers : bool whether to check the demo glaciers for download (faster than the standard prepro downloads). The default is to decide whether or not to check based on simple crietria such as glacier list size. from_tar : bool, default=False extract the gdir data from a tar file. If set to `True`, will check for a tar file at the expected location in `base_dir`. delete_tar : bool, default=False delete the original tar file after extraction. delete_tar : bool, default=False delete the original tar file after extraction. Returns ------- gdirs : list of :py:class:`oggm.GlacierDirectory` objects the initialised glacier directories """ if reset and not force: reset = utils.query_yes_no('Delete all glacier directories?') if prepro_border is None: prepro_border = int(cfg.PARAMS['border']) if from_prepro_level and prepro_border not in [10, 80, 160, 250]: if 'test' not in utils._downloads.GDIR_URL: raise InvalidParamsError("prepro_border or cfg.PARAMS['border'] " "should be one of: 10, 80, 160, 250.") # if reset delete also the log directory if reset: fpath = os.path.join(cfg.PATHS['working_dir'], 'log') if os.path.exists(fpath): rmtree(fpath) gdirs = [] new_gdirs = [] if rgidf is None: if reset: raise ValueError('Cannot use reset without setting rgidf') log.workflow('init_glacier_regions by parsing available folders ' '(can be slow).') # The dirs should be there already gl_dir = os.path.join(cfg.PATHS['working_dir'], 'per_glacier') for root, _, files in os.walk(gl_dir): if files and ('dem.tif' in files): gdirs.append(oggm.GlacierDirectory(os.path.basename(root))) else: # Check if dataframe or list of strs try: entities = [] for _, entity in rgidf.iterrows(): entities.append(entity) except AttributeError: entities = utils.tolist(rgidf) # Check demo if use_demo_glaciers is None: use_demo_glaciers = len(entities) < 100 if from_prepro_level is not None: log.workflow('init_glacier_regions from prepro level {} on ' '{} glaciers.'.format(from_prepro_level, len(entities))) gdirs = execute_entity_task(gdir_from_prepro, entities, from_prepro_level=from_prepro_level, prepro_border=prepro_border, prepro_rgi_version=prepro_rgi_version, check_demo_glacier=use_demo_glaciers) else: # TODO: if necessary this could use multiprocessing as well for entity in entities: gdir = oggm.GlacierDirectory(entity, reset=reset, from_tar=from_tar, delete_tar=delete_tar) if not os.path.exists(gdir.get_filepath('dem')): new_gdirs.append((gdir, dict(entity=entity))) gdirs.append(gdir) # We can set the intersects file automatically here if (cfg.PARAMS['use_intersects'] and new_gdirs and (len(cfg.PARAMS['intersects_gdf']) == 0)): rgi_ids = np.unique(np.sort([t[0].rgi_id for t in new_gdirs])) rgi_version = new_gdirs[0][0].rgi_version fp = utils.get_rgi_intersects_entities(rgi_ids, version=rgi_version) cfg.set_intersects_db(fp) # If not initialized, run the task in parallel execute_entity_task(tasks.define_glacier_region, new_gdirs) return gdirs
[docs]def gis_prepro_tasks(gdirs): """Shortcut function: run all flowline preprocessing tasks. Parameters ---------- gdirs : list of :py:class:`oggm.GlacierDirectory` objects the glacier directories to process """ 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 ] for task in task_list: execute_entity_task(task, gdirs)
[docs]def climate_tasks(gdirs): """Shortcut function: run all climate related tasks. Parameters ---------- gdirs : list of :py:class:`oggm.GlacierDirectory` objects the glacier directories to process """ # If not iterable it's ok try: len(gdirs) except TypeError: gdirs = [gdirs] # Which climate should we use? if cfg.PARAMS['baseline_climate'] == 'CRU': _process_task = tasks.process_cru_data elif cfg.PARAMS['baseline_climate'] == 'CUSTOM': _process_task = tasks.process_custom_climate_data elif cfg.PARAMS['baseline_climate'] == 'HISTALP': _process_task = tasks.process_histalp_data else: raise ValueError('baseline_climate parameter not understood') execute_entity_task(_process_task, gdirs) # Then, calibration? if cfg.PARAMS['run_mb_calibration']: tasks.compute_ref_t_stars(gdirs) # Mustar and the apparent mass-balance execute_entity_task(tasks.local_t_star, gdirs) execute_entity_task(tasks.mu_star_calibration, gdirs)
[docs]def inversion_tasks(gdirs): """Shortcut function: run all ice thickness inversion tasks. Parameters ---------- gdirs : list of :py:class:`oggm.GlacierDirectory` objects the glacier directories to process """ # Init execute_entity_task(tasks.prepare_for_inversion, gdirs) # Inversion for all glaciers execute_entity_task(tasks.mass_conservation_inversion, gdirs) # Filter execute_entity_task(tasks.filter_inversion_output, gdirs)
[docs]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 glaicer 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 RuntimeError('this could take time! I should include an optinal ' '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, filename='climate_monthly', 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 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) 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) flowline.merge_to_one_glacier(gdir_merged, gdirs_to_merge, filename=filename, input_filesuffix=input_filesuffix) return gdir_merged, gdirs