""" Configuration file and options
A number of globals are defined here to be available everywhere.
"""
import logging
import os
import shutil
import sys
import glob
from collections import OrderedDict
from distutils.util import strtobool
import warnings
import numpy as np
import pandas as pd
try:
from scipy.signal.windows import gaussian
except AttributeError:
# Old scipy
from scipy.signal import gaussian
from configobj import ConfigObj, ConfigObjError
try:
import geopandas as gpd
except ImportError:
pass
try:
import salem
except ImportError:
pass
from oggm.exceptions import InvalidParamsError, InvalidWorkflowError
# Local logger
log = logging.getLogger(__name__)
# Path to the cache directory
CACHE_DIR = os.path.join(os.path.expanduser('~'), '.oggm')
if not os.path.exists(CACHE_DIR):
os.makedirs(CACHE_DIR)
# Path to the config file
CONFIG_FILE = os.path.join(os.path.expanduser('~'), '.oggm_config')
# config was changed, indicates that multiprocessing needs a reset
CONFIG_MODIFIED = False
# Share state across processes
DL_VERIFIED = dict()
DEM_SOURCE_TABLE = dict()
DATA = dict()
# Machine epsilon
FLOAT_EPS = np.finfo(float).eps
class DocumentedDict(dict):
"""Quick "magic" to document the BASENAMES entries."""
def __init__(self):
self._doc = dict()
def _set_key(self, key, value, docstr=''):
if key in self:
raise ValueError('Cannot overwrite a key.')
dict.__setitem__(self, key, value)
self._doc[key] = docstr
def __setitem__(self, key, value):
# Overrides the original dic to separate value and documentation
global CONFIG_MODIFIED
try:
self._set_key(key, value[0], docstr=value[1])
CONFIG_MODIFIED = True
except BaseException:
raise ValueError('DocumentedDict accepts only tuple of len 2')
def info_str(self, key):
"""Info string for the documentation."""
return ' {}'.format(self[key]) + '\n' + ' ' + self._doc[key]
def doc_str(self, key):
"""Info string for the documentation."""
return ' {}'.format(self[key]) + '\n' + ' ' + \
self._doc[key]
class ResettingOrderedDict(OrderedDict):
"""OrderedDict wrapper that resets our multiprocessing on set"""
def __setitem__(self, key, value):
global CONFIG_MODIFIED
OrderedDict.__setitem__(self, key, value)
CONFIG_MODIFIED = True
class PathOrderedDict(ResettingOrderedDict):
"""Quick "magic" to be sure that paths are expanded correctly."""
def __setitem__(self, key, value):
# Overrides the original dic to expand the path
try:
value = os.path.expanduser(value)
except AttributeError:
raise InvalidParamsError('The value you are trying to set does '
'not seem to be a valid path: '
'{}'.format(value))
ResettingOrderedDict.__setitem__(self, key, value)
class ParamsLoggingDict(ResettingOrderedDict):
"""Quick "magic" to log the parameter changes by the user."""
do_log = False
def __setitem__(self, key, value):
# Overrides the original dic to log the change
if self.do_log:
self._log_param_change(key, value)
ResettingOrderedDict.__setitem__(self, key, value)
def _log_param_change(self, key, value):
prev = self.get(key)
if prev is None:
if key not in ['prcp_fac']:
log.workflow('WARNING: adding an unknown parameter '
'`{}`:`{}` to PARAMS.'.format(key, value))
return
if prev == value:
return
if key == 'use_multiprocessing':
msg = 'ON' if value else 'OFF'
log.workflow('Multiprocessing switched {} '.format(msg) +
'after user settings.')
return
if key == 'mp_processes':
if value == -1:
import multiprocessing
value = multiprocessing.cpu_count()
if PARAMS.get('use_multiprocessing', False):
log.workflow('Multiprocessing: using all available '
'processors (N={})'.format(value))
else:
if PARAMS.get('use_multiprocessing', False):
log.workflow('Multiprocessing: using the requested number '
'of processors (N={})'.format(value))
return
log.workflow("PARAMS['{}'] changed from `{}` to `{}`.".format(key,
prev,
value))
# Globals
IS_INITIALIZED = False
PARAMS = ParamsLoggingDict()
PATHS = PathOrderedDict()
BASENAMES = DocumentedDict()
LRUHANDLERS = ResettingOrderedDict()
# Constants
SEC_IN_YEAR = 365*24*3600
SEC_IN_DAY = 24*3600
SEC_IN_HOUR = 3600
SEC_IN_MONTH = 2628000
DAYS_IN_MONTH = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
G = 9.80665 # gravity
GAUSSIAN_KERNEL = dict()
for ks in [5, 7, 9]:
kernel = gaussian(ks, 1)
GAUSSIAN_KERNEL[ks] = kernel / kernel.sum()
_doc = ('A geotiff file containing the DEM (reprojected into the local grid).'
'This DEM is not smoothed or gap files, and is the closest to the '
'original DEM source.')
BASENAMES['dem'] = ('dem.tif', _doc)
_doc = ('A glacier mask geotiff file with the same extend and projection as '
'the `dem.tif`. This geotiff has value 1 at glaciated grid points and '
' value 0 at unglaciated points.')
BASENAMES['glacier_mask'] = ('glacier_mask.tif', _doc)
_doc = ('The glacier outlines in the local map projection '
'(Transverse Mercator or UTM).')
BASENAMES['outlines'] = ('outlines.shp', _doc)
_doc = 'The glacier intersects in the local map projection.'
BASENAMES['intersects'] = ('intersects.shp', _doc)
_doc = ('Each flowline has a catchment area computed from flow routing '
'algorithms: this shapefile stores the catchment outlines (in the '
'local map projection).')
BASENAMES['flowline_catchments'] = ('flowline_catchments.shp', _doc)
_doc = ('The intersections between catchments (shapefile) in the local map '
'projection.')
BASENAMES['catchments_intersects'] = ('catchments_intersects.shp', _doc)
_doc = 'A ``salem.Grid`` handling the georeferencing of the local grid.'
BASENAMES['glacier_grid'] = ('glacier_grid.json', _doc)
_doc = ('A dictionary containing runtime diagnostics useful for debugging or '
'logging of run parameters.')
BASENAMES['diagnostics'] = ('diagnostics.json', _doc)
_doc = ('A netcdf file containing several gridded data variables such as '
'topography, the glacier masks, the interpolated 2D glacier bed, '
'and more. This is for static, non time-dependant data.')
BASENAMES['gridded_data'] = ('gridded_data.nc', _doc)
_doc = ('A csv file containing ice thickness observations from the GlaThiDa '
'database. Only available when added from the shop, and only for about 2800 '
'glaciers worldwide.')
BASENAMES['glathida_data'] = ('glathida_data.csv', _doc)
_doc = ('A netcdf file containing gridded data variables which are time '
'dependant. It has the same coordinates as `gridded_data`.')
BASENAMES['gridded_simulation'] = ('gridded_simulation.nc', _doc)
_doc = ('A dictionary containing the shapely.Polygons of a glacier. The '
'"polygon_hr" entry contains the geometry transformed to the local '
'grid in (i, j) coordinates, while the "polygon_pix" entry contains '
'the geometries transformed into the coarse grid (the i, j elements '
'are integers). The "polygon_area" entry contains the area of the '
'polygon as computed by Shapely. The "catchment_indices" entry'
'contains a list of len `n_centerlines`, each element containing '
'a numpy array of the indices in the glacier grid which represent '
'the centerlines catchment area.')
BASENAMES['geometries'] = ('geometries.pkl', _doc)
_doc = ('A dictionary containing the downstream line geometry as well as the '
'bed shape computed from a parabolic fit.')
BASENAMES['downstream_line'] = ('downstream_line.pkl', _doc)
_doc = 'A text file with the source of the topo file (GIMP, SRTM, ...).'
BASENAMES['dem_source'] = ('dem_source.txt', _doc)
_doc = ('A hypsometry file computed by OGGM and provided in the same format '
'as the RGI (useful for diagnostics).')
BASENAMES['hypsometry'] = ('hypsometry.csv', _doc)
_doc = 'A list of :py:class:`oggm.Centerline` instances, sorted by flow order.'
BASENAMES['centerlines'] = ('centerlines.pkl', _doc)
_doc = ('A "better" version of the centerlines, now on a regular spacing '
'i.e., not on the gridded (i, j) indices. The tails of the '
'tributaries are cut out to make more realistic junctions. '
'They are now "1.5D" i.e., with a width.')
BASENAMES['inversion_flowlines'] = ('inversion_flowlines.pkl', _doc)
_doc = 'The historical monthly climate timeseries stored in a netCDF file.'
BASENAMES['climate_historical'] = ('climate_historical.nc', _doc)
# so far, this is only ERA5 or E5E5 daily and does not work with the default
# OGGM mass balance module, only with sandbox
_doc = ('The historical daily climate timeseries stored in a netCDF file.'
'(only temperature is really changing on daily basis,'
'precipitation is just assumed constant for every day')
BASENAMES['climate_historical_daily'] = ('climate_historical_daily.nc', _doc)
_doc = "A dict containing the glacier's mass balance calibration parameters."
BASENAMES['mb_calib'] = ('mb_calib.json', _doc)
_doc = 'The monthly GCM climate timeseries stored in a netCDF file.'
BASENAMES['gcm_data'] = ('gcm_data.nc', _doc)
_doc = 'List of dicts containing the data needed for the inversion.'
BASENAMES['inversion_input'] = ('inversion_input.pkl', _doc)
_doc = 'List of dicts containing the output data from the inversion.'
BASENAMES['inversion_output'] = ('inversion_output.pkl', _doc)
_doc = 'List of flowlines ready to be run by the model.'
BASENAMES['model_flowlines'] = ('model_flowlines.pkl', _doc)
_doc = ('When using a linear mass balance for the inversion, this dict stores '
'the optimal ela_h and grad.')
BASENAMES['linear_mb_params'] = ('linear_mb_params.pkl', _doc)
_doc = ('A netcdf file containing enough information to reconstruct the '
'entire flowline glacier geometry along the run (can be expensive '
'in disk space).')
BASENAMES['model_geometry'] = ('model_geometry.nc', _doc)
_doc = ('A netcdf file containing the model diagnostics (volume, '
'mass balance, length...).')
BASENAMES['model_diagnostics'] = ('model_diagnostics.nc', _doc)
_doc = ('A group netcdf file containing the model diagnostics (volume, '
'thickness, velocity...) along the flowlines (thus much heavier).'
'Netcdf groups = fl_{i}, with i between 0 and n_flowlines - 1')
BASENAMES['fl_diagnostics'] = ('fl_diagnostics.nc', _doc)
_doc = "A table containing the Huss&Farinotti 2012 squeezed flowlines."
BASENAMES['elevation_band_flowline'] = ('elevation_band_flowline.csv', _doc)
[docs]def set_logging_config(logging_level='INFO'):
"""Set the global logger parameters.
Logging levels:
DEBUG
Print detailed information, typically of interest only when diagnosing
problems.
INFO
Print confirmation that things are working as expected, e.g. when
each task is run correctly (this is the default).
WARNING
Indication that something unexpected happened on a glacier,
but that OGGM is still working on this glacier.
ERROR
Print workflow messages and errors only, e.g. when a glacier cannot
run properly.
WORKFLOW
Print only high level, workflow information (typically, one message
per task). Errors and warnings will NOT be printed.
CRITICAL
Print nothing but fatal errors.
Parameters
----------
logging_level : str or None
the logging level. See description above for a list of options. Setting
to `None` is equivalent to `'CRITICAL'`, i.e. no log output will be
generated.
"""
# Add a custom level - just for us
logging.addLevelName(45, 'WORKFLOW')
def workflow(self, message, *args, **kws):
"""Standard log message with a custom level."""
if self.isEnabledFor(45):
# Yes, logger takes its '*args' as 'args'.
self._log(45, message, args, **kws)
logging.WORKFLOW = 45
logging.Logger.workflow = workflow
# Remove all handlers associated with the root logger object.
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
# Spammers
logging.getLogger("Fiona").setLevel(logging.CRITICAL)
logging.getLogger("fiona").setLevel(logging.CRITICAL)
logging.getLogger("shapely").setLevel(logging.CRITICAL)
logging.getLogger("rasterio").setLevel(logging.CRITICAL)
logging.getLogger("matplotlib").setLevel(logging.CRITICAL)
logging.getLogger("numexpr").setLevel(logging.CRITICAL)
# Basic config
if logging_level is None:
logging_level = 'CRITICAL'
logging_level = logging_level.upper()
logging.basicConfig(format='%(asctime)s: %(name)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=getattr(logging, logging_level))
[docs]def initialize_minimal(file=None, logging_level='INFO', params=None):
"""Same as initialise() but without requiring any download of data.
This is useful for "flowline only" OGGM applications
Parameters
----------
file : str
path to the configuration file (default: OGGM params.cfg)
logging_level : str
set a logging level. See :func:`set_logging_config` for options.
params : dict
overrides for specific parameters from the config file
"""
global IS_INITIALIZED
global PARAMS
global PATHS
set_logging_config(logging_level=logging_level)
is_default = False
if file is None:
file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'params.cfg')
is_default = True
try:
cp = ConfigObj(file, file_error=True)
except (ConfigObjError, IOError) as e:
log.critical('Config file could not be parsed (%s): %s', file, e)
sys.exit()
if is_default:
log.workflow('Reading default parameters from the OGGM `params.cfg` '
'configuration file.')
else:
log.workflow('Reading parameters from the user provided '
'configuration file: %s', file)
# Static Paths
oggm_static_paths()
# Apply code-side manual params overrides
if params:
for k, v in params.items():
cp[k] = v
# Paths
PATHS['working_dir'] = cp['working_dir']
PATHS['dem_file'] = cp['dem_file']
PATHS['climate_file'] = cp['climate_file']
# Ephemeral paths overrides
env_wd = os.environ.get('OGGM_WORKDIR')
if env_wd and not PATHS['working_dir']:
PATHS['working_dir'] = env_wd
log.workflow("PATHS['working_dir'] set to env variable $OGGM_WORKDIR: "
+ env_wd)
# Do not spam
PARAMS.do_log = False
# Multiprocessing pool
try:
use_mp = bool(int(os.environ['OGGM_USE_MULTIPROCESSING']))
msg = 'ON' if use_mp else 'OFF'
log.workflow('Multiprocessing switched {} '.format(msg) +
'according to the ENV variable OGGM_USE_MULTIPROCESSING')
except KeyError:
use_mp = cp.as_bool('use_multiprocessing')
msg = 'ON' if use_mp else 'OFF'
log.workflow('Multiprocessing switched {} '.format(msg) +
'according to the parameter file.')
PARAMS['use_multiprocessing'] = use_mp
# Spawn
try:
use_mp_spawn = bool(int(os.environ['OGGM_USE_MP_SPAWN']))
msg = 'ON' if use_mp_spawn else 'OFF'
log.workflow('MP spawn context switched {} '.format(msg) +
'according to the ENV variable OGGM_USE_MP_SPAWN')
except KeyError:
use_mp_spawn = cp.as_bool('use_mp_spawn')
PARAMS['use_mp_spawn'] = use_mp_spawn
# Number of processes
mpp = cp.as_int('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, ValueError):
import multiprocessing
mpp = multiprocessing.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))
PARAMS['mp_processes'] = mpp
# Size of LRU cache
try:
lru_maxsize = int(os.environ['LRU_MAXSIZE'])
log.workflow('Size of LRU cache set to {} '.format(lru_maxsize) +
'according to the ENV variable LRU_MAXSIZE')
except KeyError:
lru_maxsize = cp.as_int('lru_maxsize')
PARAMS['lru_maxsize'] = lru_maxsize
# Some non-trivial params
PARAMS['continue_on_error'] = cp.as_bool('continue_on_error')
PARAMS['grid_dx_method'] = cp['grid_dx_method']
PARAMS['map_proj'] = cp['map_proj']
PARAMS['topo_interp'] = cp['topo_interp']
PARAMS['clip_dem_to_zero'] = cp.as_bool('clip_dem_to_zero')
PARAMS['use_intersects'] = cp.as_bool('use_intersects')
PARAMS['use_compression'] = cp.as_bool('use_compression')
PARAMS['border'] = cp.as_int('border')
PARAMS['mpi_recv_buf_size'] = cp.as_int('mpi_recv_buf_size')
PARAMS['use_multiple_flowlines'] = cp.as_bool('use_multiple_flowlines')
PARAMS['filter_min_slope'] = cp.as_bool('filter_min_slope')
PARAMS['downstream_line_shape'] = cp['downstream_line_shape']
PARAMS['auto_skip_task'] = cp.as_bool('auto_skip_task')
PARAMS['rgi_version'] = cp['rgi_version']
PARAMS['use_rgi_area'] = cp.as_bool('use_rgi_area')
PARAMS['compress_climate_netcdf'] = cp.as_bool('compress_climate_netcdf')
PARAMS['use_tar_shapefiles'] = cp.as_bool('use_tar_shapefiles')
PARAMS['keep_multipolygon_outlines'] = cp.as_bool('keep_multipolygon_outlines')
PARAMS['clip_tidewater_border'] = cp.as_bool('clip_tidewater_border')
PARAMS['dl_verify'] = cp.as_bool('dl_verify')
PARAMS['use_kcalving_for_inversion'] = cp.as_bool('use_kcalving_for_inversion')
PARAMS['use_kcalving_for_run'] = cp.as_bool('use_kcalving_for_run')
PARAMS['calving_use_limiter'] = cp.as_bool('calving_use_limiter')
PARAMS['use_inversion_params_for_run'] = cp.as_bool('use_inversion_params_for_run')
k = 'error_when_glacier_reaches_boundaries'
PARAMS[k] = cp.as_bool(k)
PARAMS['store_model_geometry'] = cp.as_bool('store_model_geometry')
PARAMS['store_fl_diagnostics'] = cp.as_bool('store_fl_diagnostics')
# Climate
PARAMS['baseline_climate'] = cp['baseline_climate'].strip().upper()
PARAMS['hydro_month_nh'] = cp.as_int('hydro_month_nh')
PARAMS['hydro_month_sh'] = cp.as_int('hydro_month_sh')
PARAMS['geodetic_mb_period'] = cp['geodetic_mb_period']
PARAMS['use_winter_prcp_fac'] = cp.as_bool('use_winter_prcp_fac')
PARAMS['use_temp_bias_from_file'] = cp.as_bool('use_temp_bias_from_file')
k = 'winter_prcp_fac_ab'
PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
k = 'ref_mb_valid_window'
PARAMS[k] = [int(vk) for vk in cp.as_list(k)]
k = 'free_board_marine_terminating'
PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
k = 'store_diagnostic_variables'
PARAMS[k] = [str(vk) for vk in cp.as_list(k)]
k = 'store_fl_diagnostic_variables'
PARAMS[k] = [str(vk) for vk in cp.as_list(k)]
k = 'by_bin_dx'
PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
k = 'by_bin_bins'
PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
# Flowline model
k = 'glacier_length_method'
PARAMS[k] = cp[k]
k = 'evolution_model'
PARAMS[k] = cp[k]
# Others
PARAMS['tidewater_type'] = cp.as_int('tidewater_type')
# Precip factor can be none
try:
PARAMS['prcp_fac'] = cp.as_float('prcp_fac')
except ValueError:
PARAMS['prcp_fac'] = None
# This also
try:
PARAMS['calving_line_extension'] = cp.as_int('calving_line_extension')
except ValueError:
PARAMS['calving_line_extension'] = None
# Delete non-floats
ltr = ['working_dir', 'dem_file', 'climate_file', 'use_tar_shapefiles',
'grid_dx_method', 'compress_climate_netcdf', 'by_bin_dx',
'mp_processes', 'use_multiprocessing', 'clip_dem_to_zero',
'topo_interp', 'use_compression', 'bed_shape', 'continue_on_error',
'use_multiple_flowlines', 'border', 'use_temp_bias_from_file',
'mpi_recv_buf_size', 'map_proj', 'evolution_model',
'hydro_month_sh', 'hydro_month_nh', 'by_bin_bins',
'use_intersects', 'filter_min_slope', 'clip_tidewater_border',
'auto_skip_task', 'ref_mb_valid_window',
'rgi_version', 'dl_verify', 'use_mp_spawn', 'calving_use_limiter',
'use_rgi_area', 'baseline_climate',
'calving_line_extension', 'use_kcalving_for_run', 'lru_maxsize',
'free_board_marine_terminating', 'use_kcalving_for_inversion',
'error_when_glacier_reaches_boundaries', 'glacier_length_method',
'use_inversion_params_for_run',
'tidewater_type', 'store_model_geometry', 'use_winter_prcp_fac',
'store_diagnostic_variables', 'store_fl_diagnostic_variables',
'geodetic_mb_period', 'store_fl_diagnostics', 'winter_prcp_fac_ab',
'prcp_fac', 'downstream_line_shape', 'keep_multipolygon_outlines']
for k in ltr:
cp.pop(k, None)
# Other params are floats
for k in cp:
PARAMS[k] = cp.as_float(k)
PARAMS.do_log = True
# Empty defaults
set_intersects_db()
IS_INITIALIZED = True
[docs]def initialize(file=None, logging_level='INFO', params=None):
"""Read the configuration file containing the run's parameters.
This should be the first call, before using any of the other OGGM modules
for most (all?) OGGM simulations.
Parameters
----------
file : str
path to the configuration file (default: OGGM params.cfg)
logging_level : str
set a logging level. See :func:`set_logging_config` for options.
params : dict
overrides for specific parameters from the config file
"""
global PARAMS
global DATA
initialize_minimal(file=file, logging_level=logging_level, params=params)
# Do not spam
PARAMS.do_log = False
# Make sure we have a proper cache dir
from oggm.utils import download_oggm_files
download_oggm_files()
# Read in the demo glaciers
file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'data', 'demo_glaciers.csv')
DATA['demo_glaciers'] = pd.read_csv(file, index_col=0)
# Add other things
if 'dem_grids' not in DATA:
grids = {}
for grid_json in ['gimpdem_90m_v01.1.json',
'arcticdem_mosaic_100m_v3.0.json',
'Alaska_albers_V3.json',
'AntarcticDEM_wgs84.json',
'REMA_100m_dem.json']:
if grid_json not in grids:
fp = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'data', grid_json)
try:
grids[grid_json] = salem.Grid.from_json(fp)
except NameError:
pass
DATA['dem_grids'] = grids
# Trigger a one time check of the hash file
from oggm.utils import get_dl_verify_data
get_dl_verify_data('dummy_section')
# OK
PARAMS.do_log = True
def oggm_static_paths():
"""Initialise the OGGM paths from the config file."""
global PATHS, PARAMS
# See if the file is there, if not create it
if not os.path.exists(CONFIG_FILE):
dldir = os.path.join(os.path.expanduser('~'), 'OGGM')
config = ConfigObj()
config['dl_cache_dir'] = os.path.join(dldir, 'download_cache')
config['dl_cache_readonly'] = False
config['tmp_dir'] = os.path.join(dldir, 'tmp')
config['rgi_dir'] = os.path.join(dldir, 'rgi')
config['test_dir'] = os.path.join(dldir, 'tests')
config['has_internet'] = True
config.filename = CONFIG_FILE
config.write()
# OK, read in the file
try:
config = ConfigObj(CONFIG_FILE, file_error=True)
except (ConfigObjError, IOError) as e:
log.critical('Config file could not be parsed (%s): %s',
CONFIG_FILE, e)
sys.exit()
# Check that all keys are here
for k in ['dl_cache_dir', 'dl_cache_readonly', 'tmp_dir',
'rgi_dir', 'test_dir', 'has_internet']:
if k not in config:
raise InvalidParamsError('The oggm config file ({}) should have '
'an entry for {}.'.format(CONFIG_FILE, k))
# Override defaults with env variables if available
if os.environ.get('OGGM_DOWNLOAD_CACHE_RO') is not None:
ro = bool(strtobool(os.environ.get('OGGM_DOWNLOAD_CACHE_RO')))
config['dl_cache_readonly'] = ro
if os.environ.get('OGGM_DOWNLOAD_CACHE') is not None:
config['dl_cache_dir'] = os.environ.get('OGGM_DOWNLOAD_CACHE')
if os.environ.get('OGGM_EXTRACT_DIR') is not None:
# This is for the directories where OGGM needs to extract things
# On the cluster it might be useful to do it on a fast disc
edir = os.path.abspath(os.environ.get('OGGM_EXTRACT_DIR'))
config['tmp_dir'] = os.path.join(edir, 'tmp')
config['rgi_dir'] = os.path.join(edir, 'rgi')
if os.environ.get('OGGM_RGI_DIR') is not None:
config['rgi_dir'] = os.path.abspath(os.environ.get('OGGM_RGI_DIR'))
# Fill the PATH dict
for k, v in config.iteritems():
if not k.endswith('_dir'):
continue
PATHS[k] = os.path.abspath(os.path.expanduser(v))
# Other
PARAMS.do_log = False
PARAMS['has_internet'] = config.as_bool('has_internet')
PARAMS['dl_cache_readonly'] = config.as_bool('dl_cache_readonly')
PARAMS.do_log = True
# Create cache dir if possible
if not os.path.exists(PATHS['dl_cache_dir']):
if not PARAMS['dl_cache_readonly']:
os.makedirs(PATHS['dl_cache_dir'])
# Always call this one!
oggm_static_paths()
def get_lru_handler(tmpdir=None, maxsize=None, ending='.tif'):
"""LRU handler for a given temporary directory (singleton).
Parameters
----------
tmpdir : str
path to the temporary directory to handle. Default is
``cfg.PATHS['tmp_dir']``.
maxsize : int
the max number of files to keep in the directory
ending : str
consider only the files with a certain ending
"""
global LRUHANDLERS
# see if we're set up
if tmpdir is None:
tmpdir = PATHS['tmp_dir']
if not os.path.exists(tmpdir):
os.makedirs(tmpdir)
# one handler per directory and file ending
# (in practice not very useful, but a dict is easier to handle)
k = (tmpdir, ending)
if k in LRUHANDLERS:
# was already there
lru = LRUHANDLERS[k]
# possibility to increase or decrease the cachesize if need be
if maxsize is not None:
lru.maxsize = maxsize
lru.purge()
return lru
else:
# we do a new one
from oggm.utils import LRUFileCache
# the files already present have to be counted, too
l0 = list(glob.glob(os.path.join(tmpdir, '*' + ending)))
l0.sort(key=os.path.getctime)
lru = LRUFileCache(l0, maxsize=maxsize)
LRUHANDLERS[k] = lru
return lru
[docs]def set_intersects_db(path_or_gdf=None):
"""Set the glacier intersection database for OGGM to use.
It is now set automatically by the
:func:`oggm.workflow.init_glacier_directories` task, but setting it
manually can be useful for a slightly faster run initialization.
See :func:`oggm.utils.get_rgi_intersects_region_file` for how to obtain
such data.
Parameters
----------
path_or_gdf : str of geopandas.GeoDataframe
the intersects file to use
"""
global PARAMS
PARAMS.do_log = False
if PARAMS['use_intersects'] and path_or_gdf is not None:
if isinstance(path_or_gdf, str):
PARAMS['intersects_gdf'] = gpd.read_file(path_or_gdf)
else:
PARAMS['intersects_gdf'] = path_or_gdf
else:
PARAMS['intersects_gdf'] = pd.DataFrame()
PARAMS.do_log = True
[docs]def reset_working_dir():
"""Deletes the content of the working directory. Careful: cannot be undone!
"""
if PATHS['working_dir']:
if os.path.exists(PATHS['working_dir']):
shutil.rmtree(PATHS['working_dir'])
os.makedirs(PATHS['working_dir'])
def pack_config():
"""Pack the entire configuration in one pickleable dict."""
return {
'IS_INITIALIZED': IS_INITIALIZED,
'PARAMS': dict(PARAMS),
'PATHS': dict(PATHS),
'LRUHANDLERS': dict(LRUHANDLERS),
'DATA': dict(DATA),
'BASENAMES': dict(BASENAMES),
'DL_VERIFIED': dict(DL_VERIFIED),
'DEM_SOURCE_TABLE': dict(DEM_SOURCE_TABLE)
}
def unpack_config(cfg_dict):
"""Unpack and apply the config packed via pack_config."""
global IS_INITIALIZED, PARAMS, PATHS, BASENAMES, LRUHANDLERS, DATA
global DL_VERIFIED, DEM_SOURCE_TABLE
IS_INITIALIZED = cfg_dict['IS_INITIALIZED']
prev_log = PARAMS.do_log
PARAMS.do_log = False
PARAMS.clear()
PATHS.clear()
LRUHANDLERS.clear()
DATA.clear()
DL_VERIFIED.clear()
DEM_SOURCE_TABLE.clear()
PARAMS.update(cfg_dict['PARAMS'])
PATHS.update(cfg_dict['PATHS'])
LRUHANDLERS.update(cfg_dict['LRUHANDLERS'])
DATA.update(cfg_dict['DATA'])
DL_VERIFIED.update(cfg_dict['DL_VERIFIED'])
DEM_SOURCE_TABLE.update(cfg_dict['DEM_SOURCE_TABLE'])
PARAMS.do_log = prev_log
# BASENAMES is a DocumentedDict, which cannot be pickled because
# set intentionally mismatches with get
BASENAMES = DocumentedDict()
for k in cfg_dict['BASENAMES']:
BASENAMES[k] = (cfg_dict['BASENAMES'][k], 'Imported Pickle')
def set_manager(manager):
"""Sets a multiprocessing manager to use for shared dicts"""
global DL_VERIFIED, DEM_SOURCE_TABLE, DATA
if manager:
new_dict = manager.dict()
new_dict.update(DL_VERIFIED)
DL_VERIFIED = new_dict
new_dict = manager.dict()
new_dict.update(DEM_SOURCE_TABLE)
DEM_SOURCE_TABLE = new_dict
new_dict = manager.dict()
new_dict.update(DATA)
DATA = new_dict
else:
DL_VERIFIED = dict(DL_VERIFIED)
DEM_SOURCE_TABLE = dict(DEM_SOURCE_TABLE)
DATA = dict(DATA)
[docs]def add_to_basenames(basename, filename, docstr=''):
"""Add an entry to the list of BASENAMES.
BASENAMES are access keys to files available at the gdir level.
Parameters
----------
basename : str
the key (e.g. 'dem', 'model_flowlines')
filename : str
the associated filename (e.g. 'dem.tif', 'model_flowlines.pkl')
docstr : str
the associated docstring (for documentation)
"""
global BASENAMES
if '.' not in filename:
raise ValueError('The filename needs a proper file suffix!')
BASENAMES[basename] = (filename, docstr)
