def initialize(file=None, logging_level='INFO', params=None, future=False):
"""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
future : bool
use the new behavior of logging='WORKFLOW'.
"""
global PARAMS
global DATA
initialize_minimal(file=file,
logging_level=logging_level,
params=params,
future=future)
# Do not spam
PARAMS.do_log = False
# Make sure we have a proper cache dir
from oggm.utils import download_oggm_files, get_demo_file
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 test_github_no_internet(self):
self.reset_dir()
def fake_down(dl_func, cache_path):
# This should never be called, if it still is assert
assert False
with FakeDownloadManager('_call_dl_func', fake_down):
with self.assertRaises(utils.NoInternetException):
tmp = cfg.PARAMS['has_internet']
cfg.PARAMS['has_internet'] = False
try:
utils.download_oggm_files()
finally:
cfg.PARAMS['has_internet'] = tmp
def initialize(file=None, logging_level='INFO'):
"""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.
"""
global IS_INITIALIZED
global PARAMS
global PATHS
global DATA
set_logging_config(logging_level=logging_level)
if file is None:
file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'params.cfg')
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()
log.workflow('Using configuration file: %s', file)
# Paths
oggm_static_paths()
PATHS['working_dir'] = cp['working_dir']
PATHS['dem_file'] = cp['dem_file']
PATHS['climate_file'] = cp['climate_file']
# Multiprocessing pool
PARAMS['use_multiprocessing'] = cp.as_bool('use_multiprocessing')
PARAMS['mp_processes'] = cp.as_int('mp_processes')
# Some non-trivial params
PARAMS['continue_on_error'] = cp.as_bool('continue_on_error')
PARAMS['grid_dx_method'] = cp['grid_dx_method']
PARAMS['topo_interp'] = cp['topo_interp']
PARAMS['use_intersects'] = cp.as_bool('use_intersects')
PARAMS['use_compression'] = cp.as_bool('use_compression')
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['auto_skip_task'] = cp.as_bool('auto_skip_task')
PARAMS['correct_for_neg_flux'] = cp.as_bool('correct_for_neg_flux')
PARAMS['filter_for_neg_flux'] = cp.as_bool('filter_for_neg_flux')
PARAMS['run_mb_calibration'] = cp.as_bool('run_mb_calibration')
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['clip_mu_star'] = cp.as_bool('clip_mu_star')
PARAMS['clip_tidewater_border'] = cp.as_bool('clip_tidewater_border')
PARAMS['dl_verify'] = cp.as_bool('dl_verify')
# Climate
PARAMS['baseline_climate'] = cp['baseline_climate'].strip().upper()
PARAMS['baseline_y0'] = cp.as_int('baseline_y0')
PARAMS['baseline_y1'] = cp.as_int('baseline_y1')
PARAMS['hydro_month_nh'] = cp.as_int('hydro_month_nh')
PARAMS['hydro_month_sh'] = cp.as_int('hydro_month_sh')
PARAMS['temp_use_local_gradient'] = cp.as_bool('temp_use_local_gradient')
PARAMS['tstar_search_glacierwide'] = cp.as_bool('tstar_search_glacierwide')
k = 'temp_local_gradient_bounds'
PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
k = 'tstar_search_window'
PARAMS[k] = [int(vk) for vk in cp.as_list(k)]
PARAMS['use_bias_for_run'] = cp.as_bool('use_bias_for_run')
# Inversion
k = 'use_shape_factor_for_inversion'
PARAMS[k] = cp[k]
# Flowline model
k = 'use_shape_factor_for_fluxbasedmodel'
PARAMS[k] = cp[k]
# Make sure we have a proper cache dir
from oggm.utils import download_oggm_files, get_demo_file
download_oggm_files()
# Delete non-floats
ltr = [
'working_dir', 'dem_file', 'climate_file', 'use_tar_shapefiles',
'grid_dx_method', 'run_mb_calibration', 'compress_climate_netcdf',
'mp_processes', 'use_multiprocessing', 'baseline_y0', 'baseline_y1',
'temp_use_local_gradient', 'temp_local_gradient_bounds', 'topo_interp',
'use_compression', 'bed_shape', 'continue_on_error',
'use_multiple_flowlines', 'tstar_search_glacierwide',
'mpi_recv_buf_size', 'hydro_month_nh', 'clip_mu_star',
'tstar_search_window', 'use_bias_for_run', 'hydro_month_sh',
'use_intersects', 'filter_min_slope', 'clip_tidewater_border',
'auto_skip_task', 'correct_for_neg_flux', 'filter_for_neg_flux',
'rgi_version', 'dl_verify', 'use_shape_factor_for_inversion',
'use_rgi_area', 'use_shape_factor_for_fluxbasedmodel',
'baseline_climate'
]
for k in ltr:
cp.pop(k, None)
# Other params are floats
for k in cp:
PARAMS[k] = cp.as_float(k)
# Read-in the reference t* data for all available models types (oggm, vas)
model_prefixes = ['oggm_', 'vas_']
for prefix in model_prefixes:
fns = [
'ref_tstars_rgi5_cru4', 'ref_tstars_rgi6_cru4',
'ref_tstars_rgi5_histalp', 'ref_tstars_rgi6_histalp'
]
for fn in fns:
fpath = get_demo_file(prefix + fn + '.csv')
PARAMS[prefix + fn] = pd.read_csv(fpath)
fpath = get_demo_file(prefix + fn + '_calib_params.json')
with open(fpath, 'r') as fp:
mbpar = json.load(fp)
PARAMS[prefix + fn + '_calib_params'] = mbpar
# Empty defaults
set_intersects_db()
IS_INITIALIZED = True
# Pre extract cru cl to avoid problems by multiproc
from oggm.utils import get_cru_cl_file
get_cru_cl_file()
# 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',
'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
def initialize(file=None):
"""Read the configuration file containing the run's parameters."""
global IS_INITIALIZED
global PARAMS
global PATHS
if file is None:
file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'params.cfg')
log.info('Parameter file: %s', file)
try:
cp = ConfigObj(file, file_error=True)
except (ConfigObjError, IOError) as e:
log.critical('Param file could not be parsed (%s): %s', file, e)
sys.exit()
# Paths
oggm_static_paths()
PATHS['working_dir'] = cp['working_dir']
PATHS['dem_file'] = cp['dem_file']
PATHS['climate_file'] = cp['climate_file']
# Multiprocessing pool
PARAMS['use_multiprocessing'] = cp.as_bool('use_multiprocessing')
PARAMS['mp_processes'] = cp.as_int('mp_processes')
# Some non-trivial params
PARAMS['continue_on_error'] = cp.as_bool('continue_on_error')
PARAMS['grid_dx_method'] = cp['grid_dx_method']
PARAMS['topo_interp'] = cp['topo_interp']
PARAMS['use_intersects'] = cp.as_bool('use_intersects')
PARAMS['use_compression'] = cp.as_bool('use_compression')
PARAMS['mpi_recv_buf_size'] = cp.as_int('mpi_recv_buf_size')
PARAMS['use_multiple_flowlines'] = cp.as_bool('use_multiple_flowlines')
PARAMS['optimize_thick'] = cp.as_bool('optimize_thick')
PARAMS['filter_min_slope'] = cp.as_bool('filter_min_slope')
PARAMS['auto_skip_task'] = cp.as_bool('auto_skip_task')
PARAMS['correct_for_neg_flux'] = cp.as_bool('correct_for_neg_flux')
PARAMS['filter_for_neg_flux'] = cp.as_bool('filter_for_neg_flux')
PARAMS['run_mb_calibration'] = cp.as_bool('run_mb_calibration')
PARAMS['rgi_version'] = cp['rgi_version']
PARAMS['hydro_month_nh'] = cp.as_int('hydro_month_nh')
PARAMS['hydro_month_sh'] = cp.as_int('hydro_month_sh')
PARAMS['use_rgi_area'] = cp.as_bool('use_rgi_area')
# Climate
PARAMS['temp_use_local_gradient'] = cp.as_bool('temp_use_local_gradient')
k = 'temp_local_gradient_bounds'
PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
k = 'tstar_search_window'
PARAMS[k] = [int(vk) for vk in cp.as_list(k)]
PARAMS['use_bias_for_run'] = cp.as_bool('use_bias_for_run')
_factor = cp['prcp_scaling_factor']
if _factor not in ['stddev', 'stddev_perglacier']:
_factor = cp.as_float('prcp_scaling_factor')
PARAMS['prcp_scaling_factor'] = _factor
PARAMS['allow_negative_mustar'] = cp.as_bool('allow_negative_mustar')
# Inversion
PARAMS['invert_with_sliding'] = cp.as_bool('invert_with_sliding')
_k = 'optimize_inversion_params'
PARAMS[_k] = cp.as_bool(_k)
PARAMS['use_shape_factor_for_inversion'] = \
cp['use_shape_factor_for_inversion']
# Flowline model
_k = 'use_optimized_inversion_params'
PARAMS[_k] = cp.as_bool(_k)
PARAMS['use_shape_factor_for_fluxbasedmodel'] = \
cp['use_shape_factor_for_fluxbasedmodel']
# Make sure we have a proper cache dir
from oggm.utils import download_oggm_files, SAMPLE_DATA_COMMIT
download_oggm_files()
# Delete non-floats
ltr = [
'working_dir', 'dem_file', 'climate_file', 'grid_dx_method',
'run_mb_calibration', 'mp_processes', 'use_multiprocessing',
'temp_use_local_gradient', 'temp_local_gradient_bounds', 'topo_interp',
'use_compression', 'bed_shape', 'continue_on_error',
'use_optimized_inversion_params', 'invert_with_sliding',
'optimize_inversion_params', 'use_multiple_flowlines',
'optimize_thick', 'mpi_recv_buf_size', 'hydro_month_nh',
'tstar_search_window', 'use_bias_for_run', 'hydro_month_sh',
'prcp_scaling_factor', 'use_intersects', 'filter_min_slope',
'auto_skip_task', 'correct_for_neg_flux', 'filter_for_neg_flux',
'rgi_version', 'allow_negative_mustar',
'use_shape_factor_for_inversion', 'use_rgi_area',
'use_shape_factor_for_fluxbasedmodel'
]
for k in ltr:
cp.pop(k, None)
# Other params are floats
for k in cp:
PARAMS[k] = cp.as_float(k)
# Empty defaults
set_intersects_db()
IS_INITIALIZED = True
# Pre extract cru cl to avoid problems by multiproc
from oggm.utils import get_cru_cl_file
get_cru_cl_file()
def initialize(file=None, logging_level='INFO'):
"""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.
"""
global PARAMS
global DATA
initialize_minimal(file=file, logging_level=logging_level)
# Do not spam
PARAMS.do_log = False
# Make sure we have a proper cache dir
from oggm.utils import download_oggm_files, get_demo_file
download_oggm_files()
# Read-in the reference t* data for all available models types (oggm, vas)
model_prefixes = ['oggm_', 'vas_']
for prefix in model_prefixes:
fns = [
'ref_tstars_rgi5_cru4', 'ref_tstars_rgi6_cru4',
'ref_tstars_rgi5_histalp', 'ref_tstars_rgi6_histalp'
]
for fn in fns:
fpath = get_demo_file(prefix + fn + '.csv')
PARAMS[prefix + fn] = pd.read_csv(fpath)
fpath = get_demo_file(prefix + fn + '_calib_params.json')
with open(fpath, 'r') as fp:
mbpar = json.load(fp)
PARAMS[prefix + fn + '_calib_params'] = mbpar
# 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 initialize(file=None):
"""Read the configuration file containing the run's parameters."""
global IS_INITIALIZED
global PARAMS
global PATHS
global CONTINUE_ON_ERROR
global N
global A
global RHO
global RGI_REG_NAMES
global RGI_SUBREG_NAMES
if file is None:
file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'params.cfg')
log.info('Parameter file: %s', file)
try:
cp = ConfigObj(file, file_error=True)
except (ConfigObjError, IOError) as e:
log.critical('Param file could not be parsed (%s): %s', file, e)
sys.exit()
CONTINUE_ON_ERROR = cp.as_bool('continue_on_error')
# Default
PATHS['working_dir'] = cp['working_dir']
if not PATHS['working_dir']:
PATHS['working_dir'] = os.path.join(os.path.expanduser('~'),
'OGGM_WORKING_DIRECTORY')
# Paths
oggm_static_paths()
PATHS['dem_file'] = cp['dem_file']
PATHS['climate_file'] = cp['climate_file']
PATHS['wgms_rgi_links'] = cp['wgms_rgi_links']
PATHS['glathida_rgi_links'] = cp['glathida_rgi_links']
PATHS['leclercq_rgi_links'] = cp['leclercq_rgi_links']
# run params
PARAMS['run_period'] = [int(vk) for vk in cp.as_list('run_period')]
# Multiprocessing pool
PARAMS['use_multiprocessing'] = cp.as_bool('use_multiprocessing')
PARAMS['mp_processes'] = cp.as_int('mp_processes')
# Some non-trivial params
PARAMS['grid_dx_method'] = cp['grid_dx_method']
PARAMS['topo_interp'] = cp['topo_interp']
PARAMS['use_divides'] = cp.as_bool('use_divides')
PARAMS['use_intersects'] = cp.as_bool('use_intersects')
PARAMS['use_compression'] = cp.as_bool('use_compression')
PARAMS['mpi_recv_buf_size'] = cp.as_int('mpi_recv_buf_size')
PARAMS['use_multiple_flowlines'] = cp.as_bool('use_multiple_flowlines')
PARAMS['optimize_thick'] = cp.as_bool('optimize_thick')
PARAMS['filter_min_slope'] = cp.as_bool('filter_min_slope')
# Climate
PARAMS['temp_use_local_gradient'] = cp.as_bool('temp_use_local_gradient')
k = 'temp_local_gradient_bounds'
PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
k = 'tstar_search_window'
PARAMS[k] = [int(vk) for vk in cp.as_list(k)]
PARAMS['use_bias_for_run'] = cp.as_bool('use_bias_for_run')
_factor = cp['prcp_scaling_factor']
if _factor not in ['stddev', 'stddev_perglacier']:
_factor = cp.as_float('prcp_scaling_factor')
PARAMS['prcp_scaling_factor'] = _factor
# Inversion
PARAMS['invert_with_sliding'] = cp.as_bool('invert_with_sliding')
_k = 'optimize_inversion_params'
PARAMS[_k] = cp.as_bool(_k)
# Flowline model
PARAMS['bed_shape'] = cp['bed_shape']
_k = 'use_optimized_inversion_params'
PARAMS[_k] = cp.as_bool(_k)
# Make sure we have a proper cache dir
from oggm.utils import download_oggm_files
download_oggm_files()
# Parse RGI metadata
_d = os.path.join(CACHE_DIR, 'oggm-sample-data-master', 'rgi_meta')
RGI_REG_NAMES = pd.read_csv(os.path.join(_d, 'rgi_regions.csv'),
index_col=0)
RGI_SUBREG_NAMES = pd.read_csv(os.path.join(_d, 'rgi_subregions.csv'),
index_col=0)
# Delete non-floats
ltr = [
'working_dir', 'dem_file', 'climate_file', 'wgms_rgi_links',
'glathida_rgi_links', 'grid_dx_method', 'mp_processes',
'use_multiprocessing', 'use_divides', 'temp_use_local_gradient',
'temp_local_gradient_bounds', 'topo_interp', 'use_compression',
'bed_shape', 'continue_on_error', 'use_optimized_inversion_params',
'invert_with_sliding', 'optimize_inversion_params',
'use_multiple_flowlines', 'leclercq_rgi_links', 'optimize_thick',
'mpi_recv_buf_size', 'tstar_search_window', 'use_bias_for_run',
'run_period', 'prcp_scaling_factor', 'use_intersects',
'filter_min_slope'
]
for k in ltr:
cp.pop(k, None)
# Other params are floats
for k in cp:
PARAMS[k] = cp.as_float(k)
# Empty defaults
from oggm.utils import get_demo_file
set_divides_db(get_demo_file('divides_alps.shp'))
set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
IS_INITIALIZED = True
""" OGGM package.
Copyright: OGGM developers, 2014-2018
License: GPLv3+
"""
# flake8: noqa
from ._version import get_versions
__version__ = get_versions()['version']
del get_versions
try:
from oggm.mpi import _init_oggm_mpi
_init_oggm_mpi()
except ImportError:
pass
# API
# TODO: why are some funcs here? maybe reconsider what API actually is
from oggm.utils import entity_task, global_task, GlacierDirectory
from oggm.core.centerlines import Centerline
from oggm.core.flowline import Flowline
# Make sure we have the sample data at import
from oggm.utils import download_oggm_files
download_oggm_files()
def initialize(file=None):
"""Read the configuration file containing the run's parameters."""
global IS_INITIALIZED
global PARAMS
global PATHS
global CONTINUE_ON_ERROR
global N
global A
global RHO
global RGI_REG_NAMES
global RGI_SUBREG_NAMES
if file is None:
file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'params.cfg')
log.info('Parameter file: %s', file)
try:
cp = ConfigObj(file, file_error=True)
except (ConfigObjError, IOError) as e:
log.critical('Param file could not be parsed (%s): %s', file, e)
sys.exit()
CONTINUE_ON_ERROR = cp.as_bool('continue_on_error')
# Default
PATHS['working_dir'] = cp['working_dir']
if not PATHS['working_dir']:
PATHS['working_dir'] = os.path.join(os.path.expanduser('~'),
'OGGM_WORKING_DIRECTORY')
# Paths
oggm_static_paths()
PATHS['dem_file'] = cp['dem_file']
PATHS['climate_file'] = cp['climate_file']
PATHS['wgms_rgi_links'] = cp['wgms_rgi_links']
PATHS['glathida_rgi_links'] = cp['glathida_rgi_links']
PATHS['leclercq_rgi_links'] = cp['leclercq_rgi_links']
# run params
PARAMS['run_period'] = [int(vk) for vk in cp.as_list('run_period')]
# Multiprocessing pool
PARAMS['use_multiprocessing'] = cp.as_bool('use_multiprocessing')
PARAMS['mp_processes'] = cp.as_int('mp_processes')
# Some non-trivial params
PARAMS['grid_dx_method'] = cp['grid_dx_method']
PARAMS['topo_interp'] = cp['topo_interp']
PARAMS['use_divides'] = cp.as_bool('use_divides')
PARAMS['use_intersects'] = cp.as_bool('use_intersects')
PARAMS['use_compression'] = cp.as_bool('use_compression')
PARAMS['mpi_recv_buf_size'] = cp.as_int('mpi_recv_buf_size')
PARAMS['use_multiple_flowlines'] = cp.as_bool('use_multiple_flowlines')
PARAMS['optimize_thick'] = cp.as_bool('optimize_thick')
PARAMS['filter_min_slope'] = cp.as_bool('filter_min_slope')
PARAMS['auto_skip_task'] = cp.as_bool('auto_skip_task')
# Climate
PARAMS['temp_use_local_gradient'] = cp.as_bool('temp_use_local_gradient')
k = 'temp_local_gradient_bounds'
PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
k = 'tstar_search_window'
PARAMS[k] = [int(vk) for vk in cp.as_list(k)]
PARAMS['use_bias_for_run'] = cp.as_bool('use_bias_for_run')
_factor = cp['prcp_scaling_factor']
if _factor not in ['stddev', 'stddev_perglacier']:
_factor = cp.as_float('prcp_scaling_factor')
PARAMS['prcp_scaling_factor'] = _factor
# Inversion
PARAMS['invert_with_sliding'] = cp.as_bool('invert_with_sliding')
_k = 'optimize_inversion_params'
PARAMS[_k] = cp.as_bool(_k)
# Flowline model
_k = 'use_optimized_inversion_params'
PARAMS[_k] = cp.as_bool(_k)
# Make sure we have a proper cache dir
from oggm.utils import download_oggm_files
download_oggm_files()
# Parse RGI metadata
_d = os.path.join(CACHE_DIR, 'oggm-sample-data-master', 'rgi_meta')
RGI_REG_NAMES = pd.read_csv(os.path.join(_d, 'rgi_regions.csv'),
index_col=0)
RGI_SUBREG_NAMES = pd.read_csv(os.path.join(_d, 'rgi_subregions.csv'),
index_col=0)
# Delete non-floats
ltr = ['working_dir', 'dem_file', 'climate_file', 'wgms_rgi_links',
'glathida_rgi_links', 'grid_dx_method',
'mp_processes', 'use_multiprocessing', 'use_divides',
'temp_use_local_gradient', 'temp_local_gradient_bounds',
'topo_interp', 'use_compression', 'bed_shape', 'continue_on_error',
'use_optimized_inversion_params', 'invert_with_sliding',
'optimize_inversion_params', 'use_multiple_flowlines',
'leclercq_rgi_links', 'optimize_thick', 'mpi_recv_buf_size',
'tstar_search_window', 'use_bias_for_run', 'run_period',
'prcp_scaling_factor', 'use_intersects', 'filter_min_slope',
'auto_skip_task']
for k in ltr:
cp.pop(k, None)
# Other params are floats
for k in cp:
PARAMS[k] = cp.as_float(k)
# Empty defaults
from oggm.utils import get_demo_file
set_divides_db(get_demo_file('divides_alps.shp'))
set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
IS_INITIALIZED = True
def initialize(file=None):
"""Read the configuration file containing the run's parameters."""
global IS_INITIALIZED
global PARAMS
global PATHS
if file is None:
file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'params.cfg')
log.info('Parameter file: %s', file)
try:
cp = ConfigObj(file, file_error=True)
except (ConfigObjError, IOError) as e:
log.critical('Param file could not be parsed (%s): %s', file, e)
sys.exit()
# Paths
oggm_static_paths()
PATHS['working_dir'] = cp['working_dir']
PATHS['dem_file'] = cp['dem_file']
PATHS['climate_file'] = cp['climate_file']
# Multiprocessing pool
PARAMS['use_multiprocessing'] = cp.as_bool('use_multiprocessing')
PARAMS['mp_processes'] = cp.as_int('mp_processes')
# Some non-trivial params
PARAMS['continue_on_error'] = cp.as_bool('continue_on_error')
PARAMS['grid_dx_method'] = cp['grid_dx_method']
PARAMS['topo_interp'] = cp['topo_interp']
PARAMS['use_intersects'] = cp.as_bool('use_intersects')
PARAMS['use_compression'] = cp.as_bool('use_compression')
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['auto_skip_task'] = cp.as_bool('auto_skip_task')
PARAMS['correct_for_neg_flux'] = cp.as_bool('correct_for_neg_flux')
PARAMS['filter_for_neg_flux'] = cp.as_bool('filter_for_neg_flux')
PARAMS['run_mb_calibration'] = cp.as_bool('run_mb_calibration')
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')
# Climate
PARAMS['baseline_climate'] = cp['baseline_climate'].strip().upper()
PARAMS['baseline_y0'] = cp.as_int('baseline_y0')
PARAMS['baseline_y1'] = cp.as_int('baseline_y1')
PARAMS['hydro_month_nh'] = cp.as_int('hydro_month_nh')
PARAMS['hydro_month_sh'] = cp.as_int('hydro_month_sh')
PARAMS['temp_use_local_gradient'] = cp.as_bool('temp_use_local_gradient')
k = 'temp_local_gradient_bounds'
PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
k = 'tstar_search_window'
PARAMS[k] = [int(vk) for vk in cp.as_list(k)]
PARAMS['use_bias_for_run'] = cp.as_bool('use_bias_for_run')
PARAMS['allow_negative_mustar'] = cp.as_bool('allow_negative_mustar')
# Inversion
k = 'use_shape_factor_for_inversion'
PARAMS[k] = cp[k]
# Flowline model
k = 'use_shape_factor_for_fluxbasedmodel'
PARAMS[k] = cp[k]
# Make sure we have a proper cache dir
from oggm.utils import download_oggm_files, get_demo_file
download_oggm_files()
# Delete non-floats
ltr = ['working_dir', 'dem_file', 'climate_file',
'grid_dx_method', 'run_mb_calibration', 'compress_climate_netcdf',
'mp_processes', 'use_multiprocessing', 'baseline_y0', 'baseline_y1',
'temp_use_local_gradient', 'temp_local_gradient_bounds',
'topo_interp', 'use_compression', 'bed_shape', 'continue_on_error',
'use_multiple_flowlines',
'mpi_recv_buf_size', 'hydro_month_nh',
'tstar_search_window', 'use_bias_for_run', 'hydro_month_sh',
'use_intersects', 'filter_min_slope',
'auto_skip_task', 'correct_for_neg_flux', 'filter_for_neg_flux',
'rgi_version', 'allow_negative_mustar',
'use_shape_factor_for_inversion', 'use_rgi_area',
'use_shape_factor_for_fluxbasedmodel', 'baseline_climate']
for k in ltr:
cp.pop(k, None)
# Other params are floats
for k in cp:
PARAMS[k] = cp.as_float(k)
# Read-in the reference t* data - maybe it will be used, maybe not
fns = ['ref_tstars_rgi5_cru4', 'ref_tstars_rgi6_cru4',
'ref_tstars_rgi5_histalp', 'ref_tstars_rgi6_histalp']
for fn in fns:
PARAMS[fn] = pd.read_csv(get_demo_file('oggm_' + fn + '.csv'))
fpath = get_demo_file('oggm_' + fn + '_calib_params.json')
with open(fpath, 'r') as fp:
mbpar = json.load(fp)
PARAMS[fn+'_calib_params'] = mbpar
# Empty defaults
set_intersects_db()
IS_INITIALIZED = True
# Pre extract cru cl to avoid problems by multiproc
from oggm.utils import get_cru_cl_file
get_cru_cl_file()
def initialize(file=None):
"""Read the configuration file containing the run's parameters."""
global IS_INITIALIZED
global BASENAMES
global PARAMS
global PATHS
global NAMES
global CONTINUE_ON_ERROR
global GRAVITY_CONST
global E_FIRN
global ZERO_DEG_KELVIN
global R
global LATENT_HEAT_FUSION_WATER
global HEAT_CAP_ICE
global N
global A
global RHO
global RHO_W
global RGI_REG_NAMES
global RGI_SUBREG_NAMES
# This is necessary as OGGM still refers to its own initialisation
oggminitialize()
import oggm.cfg as oggmcfg
# Add the CRAMPON-specific keys to the dicts
oggmcfg.BASENAMES.update(CBASENAMES)
if file is None:
file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'params.cfg')
log.info('Parameter file: %s', file)
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()
# Default
oggmcfg.PATHS['working_dir'] = cp['working_dir']
if not oggmcfg.PATHS['working_dir']:
oggmcfg.PATHS['working_dir'] = os.path.join(os.path.expanduser('~'),
'OGGM_WORKING_DIRECTORY')
# Paths
oggm_static_paths()
oggmcfg.PATHS['dem_dir'] = cp['dem_dir']
# run params
oggmcfg.PARAMS['run_period'] = [int(vk) for vk in cp.as_list('run_period')]
CPARAMS['date'] = [int(vk) for vk in cp.as_list('date')]
CPARAMS['cloudcover'] = [int(vk) for vk in cp.as_list('cloudcover')]
# Multiprocessing pool
oggmcfg.PARAMS['use_multiprocessing'] = cp.as_bool('use_multiprocessing')
oggmcfg.PARAMS['mp_processes'] = cp.as_int('mp_processes')
# Some non-trivial params
oggmcfg.PARAMS['grid_dx_method'] = cp['grid_dx_method']
oggmcfg.PARAMS['topo_interp'] = cp['topo_interp']
oggmcfg.PARAMS['auto_skip_task'] = cp.as_bool('auto_skip_task')
# Make sure we have a proper cache dir
from oggm.utils import download_oggm_files
download_oggm_files()
# Delete non-floats
ltr = [
'working_dir', 'dem_file', 'dem_dir', 'grid_dx_method', 'mp_processes',
'use_multiprocessing', 'hfile', 'topo_interp', 'date',
'continue_on_error', 'cloudcover', 'run_period', 'auto_skip_task'
]
for k in ltr:
cp.pop(k, None)
# Other params are floats
for k in cp:
oggmcfg.PARAMS[k] = cp.as_float(k)
# Empty defaults
from oggm.utils import get_demo_file
set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
IS_INITIALIZED = True
# Update the dicts in case there are changes
oggmcfg.PATHS.update(CPATHS)
oggmcfg.PARAMS.update(CPARAMS)
BASENAMES = oggmcfg.BASENAMES
PATHS = oggmcfg.PATHS
PARAMS = oggmcfg.PARAMS
# Always call this one! Creates tmp_dir etc.
oggm_static_paths()
def initialize(file=None, logging_level='INFO'):
"""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.
"""
global IS_INITIALIZED
global PARAMS
global PATHS
global DEMO_GLACIERS
set_logging_config(logging_level=logging_level)
if file is None:
file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'params.cfg')
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()
log.workflow('Using configuration file: %s', file)
# Paths
oggm_static_paths()
PATHS['working_dir'] = cp['working_dir']
PATHS['dem_file'] = cp['dem_file']
PATHS['climate_file'] = cp['climate_file']
# Multiprocessing pool
PARAMS['use_multiprocessing'] = cp.as_bool('use_multiprocessing')
PARAMS['mp_processes'] = cp.as_int('mp_processes')
# Some non-trivial params
PARAMS['continue_on_error'] = cp.as_bool('continue_on_error')
PARAMS['grid_dx_method'] = cp['grid_dx_method']
PARAMS['topo_interp'] = cp['topo_interp']
PARAMS['use_intersects'] = cp.as_bool('use_intersects')
PARAMS['use_compression'] = cp.as_bool('use_compression')
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['auto_skip_task'] = cp.as_bool('auto_skip_task')
PARAMS['correct_for_neg_flux'] = cp.as_bool('correct_for_neg_flux')
PARAMS['filter_for_neg_flux'] = cp.as_bool('filter_for_neg_flux')
PARAMS['run_mb_calibration'] = cp.as_bool('run_mb_calibration')
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['clip_mu_star'] = cp.as_bool('clip_mu_star')
PARAMS['clip_tidewater_border'] = cp.as_bool('clip_tidewater_border')
PARAMS['dl_verify'] = cp.as_bool('dl_verify')
# Climate
PARAMS['baseline_climate'] = cp['baseline_climate'].strip().upper()
PARAMS['baseline_y0'] = cp.as_int('baseline_y0')
PARAMS['baseline_y1'] = cp.as_int('baseline_y1')
PARAMS['hydro_month_nh'] = cp.as_int('hydro_month_nh')
PARAMS['hydro_month_sh'] = cp.as_int('hydro_month_sh')
PARAMS['temp_use_local_gradient'] = cp.as_bool('temp_use_local_gradient')
PARAMS['tstar_search_glacierwide'] = cp.as_bool('tstar_search_glacierwide')
k = 'temp_local_gradient_bounds'
PARAMS[k] = [float(vk) for vk in cp.as_list(k)]
k = 'tstar_search_window'
PARAMS[k] = [int(vk) for vk in cp.as_list(k)]
PARAMS['use_bias_for_run'] = cp.as_bool('use_bias_for_run')
# Inversion
k = 'use_shape_factor_for_inversion'
PARAMS[k] = cp[k]
# Flowline model
k = 'use_shape_factor_for_fluxbasedmodel'
PARAMS[k] = cp[k]
# Make sure we have a proper cache dir
from oggm.utils import download_oggm_files, get_demo_file
download_oggm_files()
# Delete non-floats
ltr = ['working_dir', 'dem_file', 'climate_file', 'use_tar_shapefiles',
'grid_dx_method', 'run_mb_calibration', 'compress_climate_netcdf',
'mp_processes', 'use_multiprocessing', 'baseline_y0', 'baseline_y1',
'temp_use_local_gradient', 'temp_local_gradient_bounds',
'topo_interp', 'use_compression', 'bed_shape', 'continue_on_error',
'use_multiple_flowlines', 'tstar_search_glacierwide',
'mpi_recv_buf_size', 'hydro_month_nh', 'clip_mu_star',
'tstar_search_window', 'use_bias_for_run', 'hydro_month_sh',
'use_intersects', 'filter_min_slope', 'clip_tidewater_border',
'auto_skip_task', 'correct_for_neg_flux', 'filter_for_neg_flux',
'rgi_version', 'dl_verify',
'use_shape_factor_for_inversion', 'use_rgi_area',
'use_shape_factor_for_fluxbasedmodel', 'baseline_climate']
for k in ltr:
cp.pop(k, None)
# Other params are floats
for k in cp:
PARAMS[k] = cp.as_float(k)
# Read-in the reference t* data - maybe it will be used, maybe not
fns = ['ref_tstars_rgi5_cru4', 'ref_tstars_rgi6_cru4',
'ref_tstars_rgi5_histalp', 'ref_tstars_rgi6_histalp']
for fn in fns:
PARAMS[fn] = pd.read_csv(get_demo_file('oggm_' + fn + '.csv'))
fpath = get_demo_file('oggm_' + fn + '_calib_params.json')
with open(fpath, 'r') as fp:
mbpar = json.load(fp)
PARAMS[fn+'_calib_params'] = mbpar
# Empty defaults
set_intersects_db()
IS_INITIALIZED = True
# Pre extract cru cl to avoid problems by multiproc
from oggm.utils import get_cru_cl_file
get_cru_cl_file()
# Read in the demo glaciers
file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'data', 'demo_glaciers.csv')
DEMO_GLACIERS = pd.read_csv(file, index_col=0)
