"""Classes and functions used by the OGGM workflow"""
# Builtins
import glob
import os
import tempfile
import gzip
import json
import time
import random
import shutil
import tarfile
import sys
import signal
import datetime
import logging
import pickle
import warnings
import itertools
from collections import OrderedDict
from functools import partial, wraps
from time import gmtime, strftime
import fnmatch
import platform
import struct
import importlib
import re as regexp
# External libs
import pandas as pd
import numpy as np
from scipy import stats
import xarray as xr
import shapely.geometry as shpg
import shapely.affinity as shpa
from shapely.ops import transform as shp_trafo
import netCDF4
# Optional libs
try:
import geopandas as gpd
except ImportError:
pass
try:
import salem
except ImportError:
pass
try:
from salem import wgs84
from salem.gis import transform_proj
except ImportError:
pass
try:
import pyproj
except ImportError:
pass
# Locals
from oggm import __version__
from oggm.utils._funcs import (calendardate_to_hydrodate, date_to_floatyear,
tolist, filter_rgi_name, parse_rgi_meta,
haversine, multipolygon_to_polygon,
recursive_valid_polygons)
from oggm.utils._downloads import (get_demo_file, get_wgms_files,
get_rgi_glacier_entities)
from oggm import cfg
from oggm.exceptions import InvalidParamsError, InvalidWorkflowError
# Default RGI date (median per region in RGI6)
RGI_DATE = {'01': 2009,
'02': 2004,
'03': 1999,
'04': 2001,
'05': 2001,
'06': 2000,
'07': 2008,
'08': 2002,
'09': 2001,
'10': 2011,
'11': 2003,
'12': 2001,
'13': 2006,
'14': 2001,
'15': 2001,
'16': 2000,
'17': 2000,
'18': 1978,
'19': 1989,
}
# Module logger
log = logging.getLogger('.'.join(__name__.split('.')[:-1]))
def empty_cache():
"""Empty oggm's cache directory."""
if os.path.exists(cfg.CACHE_DIR):
shutil.rmtree(cfg.CACHE_DIR)
os.makedirs(cfg.CACHE_DIR)
def expand_path(p):
"""Helper function for os.path.expanduser and os.path.expandvars"""
return os.path.expandvars(os.path.expanduser(p))
def gettempdir(dirname='', reset=False, home=False):
"""Get a temporary directory.
The default is to locate it in the system's temporary directory as
given by python's `tempfile.gettempdir()/OGGM'. You can set `home=True` for
a directory in the user's `home/tmp` folder instead (this isn't really
a temporary folder but well...)
Parameters
----------
dirname : str
if you want to give it a name
reset : bool
if it has to be emptied first.
home : bool
if True, returns `HOME/tmp/OGGM` instead
Returns
-------
the path to the temporary directory
"""
basedir = (os.path.join(os.path.expanduser('~'), 'tmp') if home
else tempfile.gettempdir())
return mkdir(os.path.join(basedir, 'OGGM', dirname), reset=reset)
# alias
get_temp_dir = gettempdir
def get_sys_info():
"""Returns system information as a list of tuples"""
blob = []
try:
(sysname, nodename, release,
version, machine, processor) = platform.uname()
blob.extend([
("python", "%d.%d.%d.%s.%s" % sys.version_info[:]),
("python-bits", struct.calcsize("P") * 8),
("OS", "%s" % (sysname)),
("OS-release", "%s" % (release)),
("machine", "%s" % (machine)),
("processor", "%s" % (processor)),
])
except BaseException:
pass
return blob
def get_env_info():
"""Returns env information as a list of tuples"""
deps = [
# (MODULE_NAME, f(mod) -> mod version)
("oggm", lambda mod: mod.__version__),
("numpy", lambda mod: mod.__version__),
("scipy", lambda mod: mod.__version__),
("pandas", lambda mod: mod.__version__),
("geopandas", lambda mod: mod.__version__),
("netCDF4", lambda mod: mod.__version__),
("matplotlib", lambda mod: mod.__version__),
("rasterio", lambda mod: mod.__version__),
("fiona", lambda mod: mod.__version__),
("pyproj", lambda mod: mod.__version__),
("shapely", lambda mod: mod.__version__),
("xarray", lambda mod: mod.__version__),
("dask", lambda mod: mod.__version__),
("salem", lambda mod: mod.__version__),
]
deps_blob = list()
for (modname, ver_f) in deps:
try:
if modname in sys.modules:
mod = sys.modules[modname]
else:
mod = importlib.import_module(modname)
ver = ver_f(mod)
deps_blob.append((modname, ver))
except BaseException:
deps_blob.append((modname, None))
return deps_blob
def get_git_ident():
ident_str = '$Id: 88f49366aa2480ce257e5007fce3b9db381389cb $'
if ":" not in ident_str:
return 'no_git_id'
return ident_str.replace("$", "").replace("Id:", "").replace(" ", "")
[docs]def show_versions(logger=None):
"""Prints the OGGM version and other system information.
Parameters
----------
logger : optional
the logger you want to send the printouts to. If None, will use stdout
Returns
-------
the output string
"""
sys_info = get_sys_info()
deps_blob = get_env_info()
out = ['# OGGM environment: ']
out.append("## System info:")
for k, stat in sys_info:
out.append(" %s: %s" % (k, stat))
out.append("## Packages info:")
for k, stat in deps_blob:
out.append(" %s: %s" % (k, stat))
out.append(" OGGM git identifier: " + get_git_ident())
if logger is not None:
logger.workflow('\n'.join(out))
return '\n'.join(out)
class SuperclassMeta(type):
"""Metaclass for abstract base classes.
http://stackoverflow.com/questions/40508492/python-sphinx-inherit-
method-documentation-from-superclass
"""
def __new__(mcls, classname, bases, cls_dict):
cls = super().__new__(mcls, classname, bases, cls_dict)
for name, member in cls_dict.items():
if not getattr(member, '__doc__'):
try:
member.__doc__ = getattr(bases[-1], name).__doc__
except AttributeError:
pass
return cls
class LRUFileCache():
"""A least recently used cache for temporary files.
The files which are no longer used are deleted from the disk.
"""
def __init__(self, l0=None, maxsize=None):
"""Instantiate.
Parameters
----------
l0 : list
a list of file paths
maxsize : int
the max number of files to keep
"""
self.files = [] if l0 is None else l0
# if no maxsize is specified, use value from configuration
maxsize = cfg.PARAMS['lru_maxsize'] if maxsize is None else maxsize
self.maxsize = maxsize
self.purge()
def purge(self):
"""Remove expired entries."""
if len(self.files) > self.maxsize:
fpath = self.files.pop(0)
if os.path.exists(fpath):
os.remove(fpath)
def append(self, fpath):
"""Append a file to the list."""
if fpath not in self.files:
self.files.append(fpath)
self.purge()
def lazy_property(fn):
"""Decorator that makes a property lazy-evaluated."""
attr_name = '_lazy_' + fn.__name__
@property
@wraps(fn)
def _lazy_property(self):
if not hasattr(self, attr_name):
setattr(self, attr_name, fn(self))
return getattr(self, attr_name)
return _lazy_property
def mkdir(path, reset=False):
"""Checks if directory exists and if not, create one.
Parameters
----------
reset: erase the content of the directory if exists
Returns
-------
the path
"""
if reset and os.path.exists(path):
shutil.rmtree(path)
# deleting stuff takes time
while os.path.exists(path): # check if it still exists
pass
try:
os.makedirs(path)
except FileExistsError:
pass
return path
def include_patterns(*patterns):
"""Factory function that can be used with copytree() ignore parameter.
Arguments define a sequence of glob-style patterns
that are used to specify what files to NOT ignore.
Creates and returns a function that determines this for each directory
in the file hierarchy rooted at the source directory when used with
shutil.copytree().
https://stackoverflow.com/questions/35155382/copying-specific-files-to-a-
new-folder-while-maintaining-the-original-subdirect
"""
def _ignore_patterns(path, names):
# This is our cuisine
bname = os.path.basename(path)
if 'divide' in bname or 'log' in bname:
keep = []
else:
keep = set(name for pattern in patterns
for name in fnmatch.filter(names, pattern))
ignore = set(name for name in names
if name not in keep and not
os.path.isdir(os.path.join(path, name)))
return ignore
return _ignore_patterns
class ncDataset(netCDF4.Dataset):
"""Wrapper around netCDF4 setting auto_mask to False"""
def __init__(self, *args, **kwargs):
super(ncDataset, self).__init__(*args, **kwargs)
self.set_auto_mask(False)
def pipe_log(gdir, task_func_name, err=None):
"""Log the error in a specific directory."""
time_str = datetime.datetime.now().strftime('%Y-%m-%dT%H:%M:%S')
# Defaults to working directory: it must be set!
if not cfg.PATHS['working_dir']:
warnings.warn("Cannot log to file without a valid "
"cfg.PATHS['working_dir']!", RuntimeWarning)
return
fpath = os.path.join(cfg.PATHS['working_dir'], 'log')
mkdir(fpath)
fpath = os.path.join(fpath, gdir.rgi_id)
sep = '; '
if err is not None:
fpath += '.ERROR'
else:
return # for now
fpath += '.SUCCESS'
with open(fpath, 'a') as f:
f.write(time_str + sep + task_func_name + sep)
if err is not None:
f.write(err.__class__.__name__ + sep + '{}\n'.format(err))
else:
f.write(sep + '\n')
class DisableLogger():
"""Context manager to temporarily disable all loggers."""
def __enter__(self):
logging.disable(logging.CRITICAL)
def __exit__(self, a, b, c):
logging.disable(logging.NOTSET)
def _timeout_handler(signum, frame):
raise TimeoutError('This task was killed because of timeout')
class entity_task(object):
"""Decorator for common job-controlling logic.
All tasks share common operations. This decorator is here to handle them:
exceptions, logging, and (some day) database for job-controlling.
"""
def __init__(self, log, writes=[], fallback=None):
"""Decorator syntax: ``@entity_task(log, writes=['dem', 'outlines'])``
Parameters
----------
log: logger
module logger
writes: list
list of files that the task will write down to disk (must be
available in ``cfg.BASENAMES``)
fallback: python function
will be executed on gdir if entity_task fails
return_value: bool
whether the return value from the task should be passed over
to the caller or not. In general you will always want this to
be true, but sometimes the task return things which are not
useful in production and my use a lot of memory, etc,
"""
self.log = log
self.writes = writes
self.fallback = fallback
cnt = [' Notes']
cnt += [' -----']
cnt += [' Files written to the glacier directory:']
for k in sorted(writes):
cnt += [cfg.BASENAMES.doc_str(k)]
self.iodoc = '\n'.join(cnt)
def __call__(self, task_func):
"""Decorate."""
# Add to the original docstring
if task_func.__doc__ is None:
raise RuntimeError('Entity tasks should have a docstring!')
task_func.__doc__ = '\n'.join((task_func.__doc__, self.iodoc))
@wraps(task_func)
def _entity_task(gdir, *, reset=None, print_log=True,
return_value=True, continue_on_error=None,
add_to_log_file=True, **kwargs):
if reset is None:
reset = not cfg.PARAMS['auto_skip_task']
if continue_on_error is None:
continue_on_error = cfg.PARAMS['continue_on_error']
task_name = task_func.__name__
# Filesuffix are typically used to differentiate tasks
fsuffix = (kwargs.get('filesuffix', False) or
kwargs.get('output_filesuffix', False))
if fsuffix:
task_name += fsuffix
# Do we need to run this task?
s = gdir.get_task_status(task_name)
if not reset and s and ('SUCCESS' in s):
return
# Log what we are doing
if print_log:
self.log.info('(%s) %s', gdir.rgi_id, task_name)
# Run the task
try:
if cfg.PARAMS['task_timeout'] > 0:
signal.signal(signal.SIGALRM, _timeout_handler)
signal.alarm(cfg.PARAMS['task_timeout'])
ex_t = time.time()
out = task_func(gdir, **kwargs)
ex_t = time.time() - ex_t
if cfg.PARAMS['task_timeout'] > 0:
signal.alarm(0)
if task_name != 'gdir_to_tar':
if add_to_log_file:
gdir.log(task_name, task_time=ex_t)
except Exception as err:
# Something happened
out = None
if add_to_log_file:
gdir.log(task_name, err=err)
pipe_log(gdir, task_name, err=err)
if print_log:
self.log.error('%s occurred during task %s on %s: %s',
type(err).__name__, task_name,
gdir.rgi_id, str(err))
if not continue_on_error:
raise
if self.fallback is not None:
self.fallback(gdir)
if return_value:
return out
_entity_task.__dict__['is_entity_task'] = True
return _entity_task
class global_task(object):
"""Decorator for common job-controlling logic.
Indicates that this task expects a list of all GlacierDirs as parameter
instead of being called once per dir.
"""
def __init__(self, log):
"""Decorator syntax: ``@global_task(log)``
Parameters
----------
log: logger
module logger
"""
self.log = log
def __call__(self, task_func):
"""Decorate."""
@wraps(task_func)
def _global_task(gdirs, **kwargs):
# Should be iterable
gdirs = tolist(gdirs)
self.log.workflow('Applying global task %s on %s glaciers',
task_func.__name__, len(gdirs))
# Run the task
return task_func(gdirs, **kwargs)
_global_task.__dict__['is_global_task'] = True
return _global_task
def get_ref_mb_glaciers_candidates(rgi_version=None):
"""Reads in the WGMS list of glaciers with available MB data.
Can be found afterwards (and extended) in cdf.DATA['RGIXX_ref_ids'].
"""
if rgi_version is None:
rgi_version = cfg.PARAMS['rgi_version']
if len(rgi_version) == 2:
# We might change this one day
rgi_version = rgi_version[:1]
key = 'RGI{}0_ref_ids'.format(rgi_version)
if key not in cfg.DATA:
flink, _ = get_wgms_files()
cfg.DATA[key] = flink['RGI{}0_ID'.format(rgi_version)].tolist()
return cfg.DATA[key]
[docs]@global_task(log)
def get_ref_mb_glaciers(gdirs, y0=None, y1=None):
"""Get the list of glaciers we have valid mass balance measurements for.
To be valid glaciers must have more than 5 years of measurements and
be land terminating. Therefore, the list depends on the time period of the
baseline climate data and this method selects them out of a list
of potential candidates (`gdirs` arg).
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
list of glaciers to check for valid reference mass balance data
y0 : int
override the default behavior which is to check the available
climate data (or PARAMS['ref_mb_valid_window']) and decide
y1 : int
override the default behavior which is to check the available
climate data (or PARAMS['ref_mb_valid_window']) and decide
Returns
-------
ref_gdirs : list of :py:class:`oggm.GlacierDirectory` objects
list of those glaciers with valid reference mass balance data
See Also
--------
get_ref_mb_glaciers_candidates
"""
# Get the links
ref_ids = get_ref_mb_glaciers_candidates(gdirs[0].rgi_version)
# We remove tidewater glaciers and glaciers with < 5 years
ref_gdirs = []
for g in gdirs:
if g.rgi_id not in ref_ids or g.is_tidewater:
continue
try:
mbdf = g.get_ref_mb_data(y0=y0, y1=y1)
if len(mbdf) >= 5:
ref_gdirs.append(g)
except RuntimeError as e:
if 'Please process some climate data before call' in str(e):
raise
return ref_gdirs
def _chaikins_corner_cutting(line, refinements=5):
"""Some magic here.
https://stackoverflow.com/questions/47068504/where-to-find-python-
implementation-of-chaikins-corner-cutting-algorithm
"""
coords = np.array(line.coords)
for _ in range(refinements):
L = coords.repeat(2, axis=0)
R = np.empty_like(L)
R[0] = L[0]
R[2::2] = L[1:-1:2]
R[1:-1:2] = L[2::2]
R[-1] = L[-1]
coords = L * 0.75 + R * 0.25
return shpg.LineString(coords)
@entity_task(log)
def get_centerline_lonlat(gdir,
keep_main_only=False,
flowlines_output=False,
ensure_exterior_match=False,
geometrical_widths_output=False,
corrected_widths_output=False,
to_crs='wgs84',
simplify_line_before=0,
corner_cutting=0,
simplify_line_after=0):
"""Helper task to convert the centerlines to a shapefile
Parameters
----------
gdir : the glacier directory
flowlines_output : create a shapefile for the flowlines
ensure_exterior_match : per design, OGGM centerlines match the underlying
DEM grid. This may imply that they do not "touch" the exterior outlines
of the glacier in vector space. Set this to True to correct for that.
geometrical_widths_output : for the geometrical widths
corrected_widths_output : for the corrected widths
Returns
-------
a shapefile
"""
if flowlines_output or geometrical_widths_output or corrected_widths_output:
cls = gdir.read_pickle('inversion_flowlines')
else:
cls = gdir.read_pickle('centerlines')
exterior = None
if ensure_exterior_match:
exterior = gdir.read_shapefile('outlines')
# Transform to grid
tra_func = partial(gdir.grid.transform, crs=exterior.crs)
exterior = shpg.Polygon(shp_trafo(tra_func, exterior.geometry[0].exterior))
tra_func = partial(gdir.grid.ij_to_crs, crs=to_crs)
olist = []
for j, cl in enumerate(cls):
mm = 1 if j == (len(cls)-1) else 0
if keep_main_only and mm == 0:
continue
if corrected_widths_output:
le_segment = np.rint(np.max(cl.dis_on_line) * gdir.grid.dx)
for wi, cur, (n1, n2), wi_m in zip(cl.widths, cl.line.coords,
cl.normals, cl.widths_m):
_l = shpg.LineString([shpg.Point(cur + wi / 2. * n1),
shpg.Point(cur + wi / 2. * n2)])
gs = dict()
gs['RGIID'] = gdir.rgi_id
gs['SEGMENT_ID'] = j
gs['LE_SEGMENT'] = le_segment
gs['MAIN'] = mm
gs['WIDTH_m'] = wi_m
gs['geometry'] = shp_trafo(tra_func, _l)
olist.append(gs)
elif geometrical_widths_output:
le_segment = np.rint(np.max(cl.dis_on_line) * gdir.grid.dx)
for _l, wi_m in zip(cl.geometrical_widths, cl.widths_m):
gs = dict()
gs['RGIID'] = gdir.rgi_id
gs['SEGMENT_ID'] = j
gs['LE_SEGMENT'] = le_segment
gs['MAIN'] = mm
gs['WIDTH_m'] = wi_m
gs['geometry'] = shp_trafo(tra_func, _l)
olist.append(gs)
else:
gs = dict()
gs['RGIID'] = gdir.rgi_id
gs['SEGMENT_ID'] = j
gs['STRAHLER'] = cl.order
if mm == 0:
gs['OUTFLOW_ID'] = cls.index(cl.flows_to)
else:
gs['OUTFLOW_ID'] = -1
gs['LE_SEGMENT'] = np.rint(np.max(cl.dis_on_line) * gdir.grid.dx)
gs['MAIN'] = mm
line = cl.line
if ensure_exterior_match:
# Extend line at the start by 10
fs = shpg.LineString(line.coords[:2])
# First check if this is necessary - this segment should
# be within the geometry or it's already good to go
if fs.within(exterior):
fs = shpa.scale(fs, xfact=3, yfact=3, origin=fs.boundary.geoms[1])
line = shpg.LineString([*fs.coords, *line.coords[2:]])
# If last also extend at the end
if mm == 1:
ls = shpg.LineString(line.coords[-2:])
if ls.within(exterior):
ls = shpa.scale(ls, xfact=3, yfact=3, origin=ls.boundary.geoms[0])
line = shpg.LineString([*line.coords[:-2], *ls.coords])
# Simplify and smooth?
if simplify_line_before:
line = line.simplify(simplify_line_before)
if corner_cutting:
line = _chaikins_corner_cutting(line, corner_cutting)
if simplify_line_after:
line = line.simplify(simplify_line_after)
# Intersect with exterior geom
line = line.intersection(exterior)
if line.geom_type in ['MultiLineString', 'GeometryCollection']:
# Take the longest
lens = [il.length for il in line.geoms]
line = line.geoms[np.argmax(lens)]
# Recompute length
gs['LE_SEGMENT'] = np.rint(line.length * gdir.grid.dx)
gs['geometry'] = shp_trafo(tra_func, line)
olist.append(gs)
return olist
def _write_shape_to_disk(gdf, fpath, to_tar=False):
"""Write a shapefile to disk with optional compression
Parameters
----------
gdf : gpd.GeoDataFrame
the data to write
fpath : str
where to writ the file - should be ending in shp
to_tar : bool
put the files in a .tar file. If cfg.PARAMS['use_compression'],
also compress to .gz
"""
if '.shp' not in fpath:
raise ValueError('File ending should be .shp')
gdf.to_file(fpath)
if not to_tar:
# Done here
return
# Write them in tar
fpath = fpath.replace('.shp', '.tar')
mode = 'w'
if cfg.PARAMS['use_compression']:
fpath += '.gz'
mode += ':gz'
if os.path.exists(fpath):
os.remove(fpath)
# List all files that were written as shape
fs = glob.glob(fpath.replace('.gz', '').replace('.tar', '.*'))
# Add them to tar
with tarfile.open(fpath, mode=mode) as tf:
for ff in fs:
tf.add(ff, arcname=os.path.basename(ff))
# Delete the old ones
for ff in fs:
os.remove(ff)
[docs]@global_task(log)
def write_centerlines_to_shape(gdirs, *, path=True, to_tar=False,
to_crs='EPSG:4326',
filesuffix='', flowlines_output=False,
ensure_exterior_match=False,
geometrical_widths_output=False,
corrected_widths_output=False,
keep_main_only=False,
simplify_line_before=0,
corner_cutting=0,
simplify_line_after=0):
"""Write the centerlines to a shapefile.
Parameters
----------
gdirs:
the list of GlacierDir to process.
path: str or bool
Set to "True" in order to store the shape in the working directory
Set to a str path to store the file to your chosen location
to_tar : bool
put the files in a .tar file. If cfg.PARAMS['use_compression'],
also compress to .gz
filesuffix : str
add a suffix to the output file
flowlines_output : bool
output the OGGM flowlines instead of the centerlines
geometrical_widths_output : bool
output the geometrical widths instead of the centerlines
corrected_widths_output : bool
output the corrected widths instead of the centerlines
ensure_exterior_match : bool
per design, the centerlines will match the underlying DEM grid.
This may imply that they do not "touch" the exterior outlines of the
glacier in vector space. Set this to True to correct for that.
to_crs : str
write the shape to another coordinate reference system (CRS)
keep_main_only : bool
write only the main flowlines to the output files
simplify_line_before : float
apply shapely's `simplify` method to the line before corner cutting.
It is a cosmetic option: it avoids hard "angles" in the centerlines.
All points in the simplified object will be within the tolerance
distance of the original geometry (units: grid points). A good
value to test first is 0.75
corner_cutting : int
apply the Chaikin's corner cutting algorithm to the geometry before
writing. The integer represents the number of refinements to apply.
A good first value to test is 3.
simplify_line_after : float
apply shapely's `simplify` method to the line *after* corner cutting.
This is to reduce the size of the geometeries after they have been
smoothed. The default value of 0 is fine if you use corner cutting less
than 4. Otherwize try a small number, like 0.05 or 0.1.
"""
from oggm.workflow import execute_entity_task
if path is True:
path = os.path.join(cfg.PATHS['working_dir'],
'glacier_centerlines' + filesuffix + '.shp')
_to_crs = salem.check_crs(to_crs)
if not _to_crs:
raise InvalidParamsError(f'CRS not understood: {to_crs}')
log.workflow('write_centerlines_to_shape on {} ...'.format(path))
olist = execute_entity_task(get_centerline_lonlat, gdirs,
flowlines_output=flowlines_output,
ensure_exterior_match=ensure_exterior_match,
geometrical_widths_output=geometrical_widths_output,
corrected_widths_output=corrected_widths_output,
keep_main_only=keep_main_only,
simplify_line_before=simplify_line_before,
corner_cutting=corner_cutting,
simplify_line_after=simplify_line_after,
to_crs=_to_crs)
# filter for none
olist = [o for o in olist if o is not None]
odf = gpd.GeoDataFrame(itertools.chain.from_iterable(olist))
odf = odf.sort_values(by=['RGIID', 'SEGMENT_ID'])
odf.crs = to_crs
# Sanity checks to avoid bad surprises
gtype = np.array([g.geom_type for g in odf.geometry])
if 'GeometryCollection' in gtype:
errdf = odf.loc[gtype == 'GeometryCollection']
with warnings.catch_warnings():
# errdf.length warns because of use of wgs84
warnings.filterwarnings("ignore", category=UserWarning)
if not np.all(errdf.length) == 0:
errdf = errdf.loc[errdf.length > 0]
raise RuntimeError('Some geometries are non-empty GeometryCollection '
f'at RGI Ids: {errdf.RGIID.values}')
_write_shape_to_disk(odf, path, to_tar=to_tar)
def demo_glacier_id(key):
"""Get the RGI id of a glacier by name or key: None if not found."""
df = cfg.DATA['demo_glaciers']
# Is the name in key?
s = df.loc[df.Key.str.lower() == key.lower()]
if len(s) == 1:
return s.index[0]
# Is the name in name?
s = df.loc[df.Name.str.lower() == key.lower()]
if len(s) == 1:
return s.index[0]
# Is the name in Ids?
try:
s = df.loc[[key]]
if len(s) == 1:
return s.index[0]
except KeyError:
pass
return None
class compile_to_netcdf(object):
"""Decorator for common compiling NetCDF files logic.
All compile_* tasks can be optimized the same way, by using temporary
files and merging them afterwards.
"""
def __init__(self, log):
"""Decorator syntax: ``@compile_to_netcdf(log, n_tmp_files=1000)``
Parameters
----------
log: logger
module logger
tmp_file_size: int
number of glacier directories per temporary files
"""
self.log = log
def __call__(self, task_func):
"""Decorate."""
@wraps(task_func)
def _compile_to_netcdf(gdirs, input_filesuffix='',
output_filesuffix='',
path=True,
tmp_file_size=1000,
**kwargs):
if not output_filesuffix:
output_filesuffix = input_filesuffix
gdirs = tolist(gdirs)
task_name = task_func.__name__
output_base = task_name.replace('compile_', '')
if path is True:
path = os.path.join(cfg.PATHS['working_dir'],
output_base + output_filesuffix + '.nc')
self.log.workflow('Applying %s on %d gdirs.',
task_name, len(gdirs))
# Run the task
# If small gdir size, no need for temporary files
if len(gdirs) < tmp_file_size or not path:
return task_func(gdirs, input_filesuffix=input_filesuffix,
path=path, **kwargs)
# Otherwise, divide and conquer
sub_gdirs = [gdirs[i: i + tmp_file_size] for i in
range(0, len(gdirs), tmp_file_size)]
tmp_paths = [os.path.join(cfg.PATHS['working_dir'],
'compile_tmp_{:06d}.nc'.format(i))
for i in range(len(sub_gdirs))]
try:
for spath, sgdirs in zip(tmp_paths, sub_gdirs):
task_func(sgdirs, input_filesuffix=input_filesuffix,
path=spath, **kwargs)
except BaseException:
# If something wrong, delete the tmp files
for f in tmp_paths:
try:
os.remove(f)
except FileNotFoundError:
pass
raise
# Ok, now merge and return
try:
with xr.open_mfdataset(tmp_paths, combine='nested',
concat_dim='rgi_id') as ds:
# the .load() is actually quite uncool here, but it solves
# an unbelievable stalling problem in multiproc
ds.load().to_netcdf(path)
except TypeError:
# xr < v 0.13
with xr.open_mfdataset(tmp_paths, concat_dim='rgi_id') as ds:
# the .load() is actually quite uncool here, but it solves
# an unbelievable stalling problem in multiproc
ds.load().to_netcdf(path)
# We can't return the dataset without loading it, so we don't
return None
return _compile_to_netcdf
[docs]@entity_task(log)
def merge_consecutive_run_outputs(gdir,
input_filesuffix_1=None,
input_filesuffix_2=None,
output_filesuffix=None,
delete_input=False):
"""Merges the output of two model_diagnostics files into one.
It assumes that the last time of file1 is equal to the first time of file2.
Parameters
----------
gdir : the glacier directory
input_filesuffix_1 : str
how to recognize the first file
input_filesuffix_2 : str
how to recognize the second file
output_filesuffix : str
where to write the output (default: no suffix)
Returns
-------
The merged dataset
"""
# Read in the input files and check
fp1 = gdir.get_filepath('model_diagnostics', filesuffix=input_filesuffix_1)
with xr.open_dataset(fp1) as ds:
ds1 = ds.load()
fp2 = gdir.get_filepath('model_diagnostics', filesuffix=input_filesuffix_2)
with xr.open_dataset(fp2) as ds:
ds2 = ds.load()
if ds1.time[-1] != ds2.time[0]:
raise InvalidWorkflowError('The two files are incompatible by time')
# Samity check for all variables as well
for v in ds1:
if not np.all(np.isfinite(ds1[v].data[-1])):
# This is the last year of hydro output - we will discard anyway
continue
if np.allclose(ds1[v].data[-1], ds2[v].data[0]):
# This means that we're OK - the two match
continue
# This has to be a bucket of some sort, probably snow or calving
if len(ds2[v].data.shape) == 1:
if ds2[v].data[0] != 0:
raise InvalidWorkflowError('The two files seem incompatible '
f'by data on variable : {v}')
bucket = ds1[v].data[-1]
elif len(ds2[v].data.shape) == 2:
if ds2[v].data[0, 0] != 0:
raise InvalidWorkflowError('The two files seem incompatible '
f'by data on variable : {v}')
bucket = ds1[v].data[-1, -1]
# Carry it to the rest
ds2[v] = ds2[v] + bucket
# Merge by removing the last step of file 1 and delete the files if asked
out_ds = xr.concat([ds1.isel(time=slice(0, -1)), ds2], dim='time')
if delete_input:
os.remove(fp1)
os.remove(fp2)
# Write out and return
fp = gdir.get_filepath('model_diagnostics', filesuffix=output_filesuffix)
out_ds.to_netcdf(fp)
return out_ds
[docs]@global_task(log)
@compile_to_netcdf(log)
def compile_run_output(gdirs, path=True, input_filesuffix='',
use_compression=True):
"""Compiles the output of the model runs of several gdirs into one file.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
path : str
where to store (default is on the working dir).
Set to `False` to disable disk storage.
input_filesuffix : str
the filesuffix of the files to be compiled
use_compression : bool
use zlib compression on the output netCDF files
Returns
-------
ds : :py:class:`xarray.Dataset`
compiled output
"""
# Get the dimensions of all this
rgi_ids = [gd.rgi_id for gd in gdirs]
# To find the longest time, we have to open all files unfortunately, we
# also create a list of all data variables (in case not all files contain
# the same data variables), and finally we decide on the name of "3d"
# variables in case we have daily
time_info = {}
time_keys = ['hydro_year', 'hydro_month', 'calendar_year', 'calendar_month']
allowed_data_vars = ['volume_m3', 'volume_bsl_m3', 'volume_bwl_m3',
'volume_m3_min_h', # only here for back compatibility
# as it is a variable in gdirs v1.6 2023.1
'area_m2', 'area_m2_min_h', 'length_m', 'calving_m3',
'calving_rate_myr', 'off_area',
'on_area', 'model_mb', 'is_fixed_geometry_spinup']
for gi in range(10):
allowed_data_vars += [f'terminus_thick_{gi}']
# this hydro variables can be _monthly or _daily
hydro_vars = ['melt_off_glacier', 'melt_on_glacier',
'liq_prcp_off_glacier', 'liq_prcp_on_glacier',
'snowfall_off_glacier', 'snowfall_on_glacier',
'melt_residual_off_glacier', 'melt_residual_on_glacier',
'snow_bucket', 'residual_mb']
for v in hydro_vars:
allowed_data_vars += [v]
allowed_data_vars += [v + '_monthly']
allowed_data_vars += [v + '_daily']
data_vars = {}
name_2d_dim = 'month_2d'
contains_3d_data = False
for gd in gdirs:
fp = gd.get_filepath('model_diagnostics', filesuffix=input_filesuffix)
try:
with ncDataset(fp) as ds:
time = ds.variables['time'][:]
if 'time' not in time_info:
time_info['time'] = time
for cn in time_keys:
time_info[cn] = ds.variables[cn][:]
else:
# Here we may need to append or add stuff
ot = time_info['time']
if time[0] > ot[-1] or ot[-1] < time[0]:
raise InvalidWorkflowError('Trying to compile output '
'without overlap.')
if time[-1] > ot[-1]:
p = np.nonzero(time == ot[-1])[0][0] + 1
time_info['time'] = np.append(ot, time[p:])
for cn in time_keys:
time_info[cn] = np.append(time_info[cn],
ds.variables[cn][p:])
if time[0] < ot[0]:
p = np.nonzero(time == ot[0])[0][0]
time_info['time'] = np.append(time[:p], ot)
for cn in time_keys:
time_info[cn] = np.append(ds.variables[cn][:p],
time_info[cn])
# check if their are new data variables and add them
for vn in ds.variables:
# exclude time variables
if vn in ['month_2d', 'calendar_month_2d',
'hydro_month_2d']:
name_2d_dim = 'month_2d'
contains_3d_data = True
elif vn in ['day_2d', 'calendar_day_2d', 'hydro_day_2d']:
name_2d_dim = 'day_2d'
contains_3d_data = True
elif vn in allowed_data_vars:
# check if data variable is new
if vn not in data_vars.keys():
data_vars[vn] = dict()
data_vars[vn]['dims'] = ds.variables[vn].dimensions
data_vars[vn]['attrs'] = dict()
for attr in ds.variables[vn].ncattrs():
if attr not in ['_FillValue', 'coordinates',
'dtype']:
data_vars[vn]['attrs'][attr] = getattr(
ds.variables[vn], attr)
elif vn not in ['time'] + time_keys:
# This check has future developments in mind.
# If you end here it means the current data variable is
# not under the allowed_data_vars OR not under the
# defined time dimensions. If it is a new data variable
# add it to allowed_data_vars above (also add it to
# test_compile_run_output). If it is a new dimension
# handle it in the if/elif statements.
raise InvalidParamsError(f'The data variable "{vn}" '
'is not known. Is it new or '
'is it a new dimension? '
'Check comment above this '
'raise for more info!')
# If this worked, keep it as template
ppath = fp
except FileNotFoundError:
pass
if 'time' not in time_info:
raise RuntimeError('Found no valid glaciers!')
# OK found it, open it and prepare the output
with xr.open_dataset(ppath) as ds_diag:
# Prepare output
ds = xr.Dataset()
# Global attributes
ds.attrs['description'] = 'OGGM model output'
ds.attrs['oggm_version'] = __version__
ds.attrs['calendar'] = '365-day no leap'
ds.attrs['creation_date'] = strftime("%Y-%m-%d %H:%M:%S", gmtime())
# Copy coordinates
time = time_info['time']
ds.coords['time'] = ('time', time)
ds['time'].attrs['description'] = 'Floating year'
# New coord
ds.coords['rgi_id'] = ('rgi_id', rgi_ids)
ds['rgi_id'].attrs['description'] = 'RGI glacier identifier'
# This is just taken from there
for cn in ['hydro_year', 'hydro_month',
'calendar_year', 'calendar_month']:
ds.coords[cn] = ('time', time_info[cn])
ds[cn].attrs['description'] = ds_diag[cn].attrs['description']
# Prepare the 2D variables
shape = (len(time), len(rgi_ids))
out_2d = dict()
for vn in data_vars:
if name_2d_dim in data_vars[vn]['dims']:
continue
var = dict()
var['data'] = np.full(shape, np.nan)
var['attrs'] = data_vars[vn]['attrs']
out_2d[vn] = var
# 1D Variables
out_1d = dict()
for vn, attrs in [('water_level', {'description': 'Calving water level',
'units': 'm'}),
('glen_a', {'description': 'Simulation Glen A',
'units': ''}),
('fs', {'description': 'Simulation sliding parameter',
'units': ''}),
]:
var = dict()
var['data'] = np.full(len(rgi_ids), np.nan)
var['attrs'] = attrs
out_1d[vn] = var
# Maybe 3D?
out_3d = dict()
if contains_3d_data:
# We have some 3d vars
month_2d = ds_diag[name_2d_dim]
ds.coords[name_2d_dim] = (name_2d_dim, month_2d.data)
cn = f'calendar_{name_2d_dim}'
ds.coords[cn] = (name_2d_dim, ds_diag[cn].values)
shape = (len(time), len(month_2d), len(rgi_ids))
for vn in data_vars:
if name_2d_dim not in data_vars[vn]['dims']:
continue
var = dict()
var['data'] = np.full(shape, np.nan)
var['attrs'] = data_vars[vn]['attrs']
out_3d[vn] = var
# Read out
for i, gdir in enumerate(gdirs):
try:
ppath = gdir.get_filepath('model_diagnostics',
filesuffix=input_filesuffix)
with ncDataset(ppath) as ds_diag:
it = ds_diag.variables['time'][:]
a = np.nonzero(time == it[0])[0][0]
b = np.nonzero(time == it[-1])[0][0] + 1
for vn, var in out_2d.items():
# try statement if some data variables not in all files
try:
var['data'][a:b, i] = ds_diag.variables[vn][:]
except KeyError:
pass
for vn, var in out_3d.items():
# try statement if some data variables not in all files
try:
var['data'][a:b, :, i] = ds_diag.variables[vn][:]
except KeyError:
pass
for vn, var in out_1d.items():
var['data'][i] = ds_diag.getncattr(vn)
except FileNotFoundError:
pass
# To xarray
for vn, var in out_2d.items():
# Backwards compatibility - to remove one day...
for r in ['_m3', '_m2', '_myr', '_m']:
# Order matters
vn = regexp.sub(r + '$', '', vn)
ds[vn] = (('time', 'rgi_id'), var['data'])
ds[vn].attrs = var['attrs']
for vn, var in out_3d.items():
ds[vn] = (('time', name_2d_dim, 'rgi_id'), var['data'])
ds[vn].attrs = var['attrs']
for vn, var in out_1d.items():
ds[vn] = (('rgi_id', ), var['data'])
ds[vn].attrs = var['attrs']
# To file?
if path:
enc_var = {'dtype': 'float32'}
if use_compression:
enc_var['complevel'] = 5
enc_var['zlib'] = True
encoding = {v: enc_var for v in ds.data_vars}
ds.to_netcdf(path, encoding=encoding)
return ds
[docs]@global_task(log)
def compile_task_log(gdirs, task_names=[], filesuffix='', path=True,
append=True):
"""Gathers the log output for the selected task(s)
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
task_names : list of str
The tasks to check for
filesuffix : str
add suffix to output file
path:
Set to `True` in order to store the info in the working directory
Set to a path to store the file to your chosen location
Set to `False` to omit disk storage
append:
If a task log file already exists in the working directory, the new
logs will be added to the existing file
Returns
-------
out : :py:class:`pandas.DataFrame`
log output
"""
out_df = []
for gdir in gdirs:
d = OrderedDict()
d['rgi_id'] = gdir.rgi_id
for task_name in task_names:
ts = gdir.get_task_status(task_name)
if ts is None:
ts = ''
d[task_name] = ts.replace(',', ' ')
out_df.append(d)
out = pd.DataFrame(out_df).set_index('rgi_id')
if path:
if path is True:
path = os.path.join(cfg.PATHS['working_dir'],
'task_log' + filesuffix + '.csv')
if os.path.exists(path) and append:
odf = pd.read_csv(path, index_col=0)
out = odf.join(out, rsuffix='_n')
out.to_csv(path)
return out
[docs]@global_task(log)
def compile_task_time(gdirs, task_names=[], filesuffix='', path=True,
append=True):
"""Gathers the time needed for the selected task(s) to run
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
task_names : list of str
The tasks to check for
filesuffix : str
add suffix to output file
path:
Set to `True` in order to store the info in the working directory
Set to a path to store the file to your chosen location
Set to `False` to omit disk storage
append:
If a task log file already exists in the working directory, the new
logs will be added to the existing file
Returns
-------
out : :py:class:`pandas.DataFrame`
log output
"""
out_df = []
for gdir in gdirs:
d = OrderedDict()
d['rgi_id'] = gdir.rgi_id
for task_name in task_names:
d[task_name] = gdir.get_task_time(task_name)
out_df.append(d)
out = pd.DataFrame(out_df).set_index('rgi_id')
if path:
if path is True:
path = os.path.join(cfg.PATHS['working_dir'],
'task_time' + filesuffix + '.csv')
if os.path.exists(path) and append:
odf = pd.read_csv(path, index_col=0)
out = odf.join(out, rsuffix='_n')
out.to_csv(path)
return out
@entity_task(log)
def glacier_statistics(gdir, inversion_only=False, apply_func=None):
"""Gather as much statistics as possible about this glacier.
It can be used to do result diagnostics and other stuffs. If the data
necessary for a statistic is not available (e.g.: flowlines length) it
will simply be ignored.
Parameters
----------
inversion_only : bool
if one wants to summarize the inversion output only (including calving)
apply_func : function
if one wants to summarize further information about a glacier, set
this kwarg to a function that accepts a glacier directory as first
positional argument, and the directory to fill in with data as
second argument. The directory should only store scalar values (strings,
float, int)
"""
d = OrderedDict()
# Easy stats - this should always be possible
d['rgi_id'] = gdir.rgi_id
d['rgi_region'] = gdir.rgi_region
d['rgi_subregion'] = gdir.rgi_subregion
d['name'] = gdir.name
d['cenlon'] = gdir.cenlon
d['cenlat'] = gdir.cenlat
d['rgi_area_km2'] = gdir.rgi_area_km2
d['rgi_year'] = gdir.rgi_date
d['glacier_type'] = gdir.glacier_type
d['terminus_type'] = gdir.terminus_type
d['is_tidewater'] = gdir.is_tidewater
d['status'] = gdir.status
# The rest is less certain. We put these in a try block and see
# We're good with any error - we store the dict anyway below
# TODO: should be done with more preselected errors
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
try:
# Grid stuff
d['grid_dx'] = gdir.grid.dx
d['grid_nx'] = gdir.grid.nx
d['grid_ny'] = gdir.grid.ny
except BaseException:
pass
try:
# Geom stuff
outline = gdir.read_shapefile('outlines')
d['geometry_type'] = outline.type.iloc[0]
d['geometry_is_valid'] = outline.is_valid.iloc[0]
d['geometry_area_km2'] = outline.to_crs({'proj': 'cea'}).area.iloc[0] * 1e-6
except BaseException:
pass
try:
# Inversion
if gdir.has_file('inversion_output'):
vol = []
vol_bsl = []
vol_bwl = []
cl = gdir.read_pickle('inversion_output')
for c in cl:
vol.extend(c['volume'])
vol_bsl.extend(c.get('volume_bsl', [0]))
vol_bwl.extend(c.get('volume_bwl', [0]))
d['inv_volume_km3'] = np.nansum(vol) * 1e-9
area = gdir.rgi_area_km2
d['vas_volume_km3'] = 0.034 * (area ** 1.375)
# BSL / BWL
d['inv_volume_bsl_km3'] = np.nansum(vol_bsl) * 1e-9
d['inv_volume_bwl_km3'] = np.nansum(vol_bwl) * 1e-9
except BaseException:
pass
try:
# Diagnostics
diags = gdir.get_diagnostics()
for k, v in diags.items():
d[k] = v
except BaseException:
pass
if inversion_only:
return d
try:
# Error log
errlog = gdir.get_error_log()
if errlog is not None:
d['error_task'] = errlog.split(';')[-2]
d['error_msg'] = errlog.split(';')[-1]
else:
d['error_task'] = None
d['error_msg'] = None
except BaseException:
pass
try:
# Masks related stuff
fpath = gdir.get_filepath('gridded_data')
with ncDataset(fpath) as nc:
mask = nc.variables['glacier_mask'][:] == 1
topo = nc.variables['topo'][:][mask]
d['dem_mean_elev'] = np.mean(topo)
d['dem_med_elev'] = np.median(topo)
d['dem_min_elev'] = np.min(topo)
d['dem_max_elev'] = np.max(topo)
except BaseException:
pass
try:
# Ext related stuff
fpath = gdir.get_filepath('gridded_data')
with ncDataset(fpath) as nc:
ext = nc.variables['glacier_ext'][:] == 1
mask = nc.variables['glacier_mask'][:] == 1
topo = nc.variables['topo'][:]
d['dem_max_elev_on_ext'] = np.max(topo[ext])
d['dem_min_elev_on_ext'] = np.min(topo[ext])
a = np.sum(mask & (topo > d['dem_max_elev_on_ext']))
d['dem_perc_area_above_max_elev_on_ext'] = a / np.sum(mask)
# Terminus loc
j, i = np.nonzero((topo[ext].min() == topo) & ext)
lon, lat = gdir.grid.ij_to_crs(i[0], j[0], crs=salem.wgs84)
d['terminus_lon'] = lon
d['terminus_lat'] = lat
except BaseException:
pass
try:
# Centerlines
cls = gdir.read_pickle('centerlines')
longest = 0.
for cl in cls:
longest = np.max([longest, cl.dis_on_line[-1]])
d['n_centerlines'] = len(cls)
d['longest_centerline_km'] = longest * gdir.grid.dx / 1000.
except BaseException:
pass
try:
# Flowline related stuff
h = np.array([])
widths = np.array([])
slope = np.array([])
fls = gdir.read_pickle('inversion_flowlines')
dx = fls[0].dx * gdir.grid.dx
for fl in fls:
hgt = fl.surface_h
h = np.append(h, hgt)
widths = np.append(widths, fl.widths * gdir.grid.dx)
slope = np.append(slope, np.arctan(-np.gradient(hgt, dx)))
length = len(hgt) * dx
d['main_flowline_length'] = length
d['inv_flowline_glacier_area'] = np.sum(widths * dx)
d['flowline_mean_elev'] = np.average(h, weights=widths)
d['flowline_max_elev'] = np.max(h)
d['flowline_min_elev'] = np.min(h)
d['flowline_avg_slope'] = np.mean(slope)
d['flowline_avg_width'] = np.mean(widths)
d['flowline_last_width'] = fls[-1].widths[-1] * gdir.grid.dx
d['flowline_last_5_widths'] = np.mean(fls[-1].widths[-5:] *
gdir.grid.dx)
except BaseException:
pass
try:
# climate
info = gdir.get_climate_info()
for k, v in info.items():
d[k] = v
except BaseException:
pass
try:
# MB calib
mb_calib = gdir.read_json('mb_calib')
for k, v in mb_calib.items():
if np.isscalar(v):
d[k] = v
else:
for k2, v2 in v.items():
d[k2] = v2
except BaseException:
pass
if apply_func:
# User defined statistics
try:
apply_func(gdir, d)
except BaseException:
pass
return d
[docs]@global_task(log)
def compile_glacier_statistics(gdirs, filesuffix='', path=True,
inversion_only=False, apply_func=None):
"""Gather as much statistics as possible about a list of glaciers.
It can be used to do result diagnostics and other stuffs. If the data
necessary for a statistic is not available (e.g.: flowlines length) it
will simply be ignored.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
filesuffix : str
add suffix to output file
path : str, bool
Set to "True" in order to store the info in the working directory
Set to a path to store the file to your chosen location
inversion_only : bool
if one wants to summarize the inversion output only (including calving)
apply_func : function
if one wants to summarize further information about a glacier, set
this kwarg to a function that accepts a glacier directory as first
positional argument, and the directory to fill in with data as
second argument. The directory should only store scalar values (strings,
float, int).
!Careful! For multiprocessing, the function cannot be located at the
top level, i.e. you may need to import it from a module for this to work,
or from a dummy class (https://stackoverflow.com/questions/8804830)
"""
from oggm.workflow import execute_entity_task
out_df = execute_entity_task(glacier_statistics, gdirs,
apply_func=apply_func,
inversion_only=inversion_only)
out = pd.DataFrame(out_df).set_index('rgi_id')
if path:
if path is True:
out.to_csv(os.path.join(cfg.PATHS['working_dir'],
('glacier_statistics' +
filesuffix + '.csv')))
else:
out.to_csv(path)
return out
@entity_task(log)
def read_glacier_hypsometry(gdir):
"""Utility function to read the glacier hypsometry in the folder.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory` object
the glacier directory to process
Returns
-------
the dataframe
"""
try:
out = pd.read_csv(gdir.get_filepath('hypsometry')).iloc[0]
except:
out = pd.Series({'rgi_id': gdir.rgi_id})
return out
@global_task(log)
def compile_glacier_hypsometry(gdirs, filesuffix='', path=True,
add_column=None):
"""Gather as much statistics as possible about a list of glaciers.
It can be used to do result diagnostics and other stuffs. If the data
necessary for a statistic is not available (e.g.: flowlines length) it
will simply be ignored.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
filesuffix : str
add suffix to output file
path : str, bool
Set to "True" in order to store the info in the working directory
Set to a path to store the file to your chosen location
add_column : tuple
if you feel like adding a key - value pair to the compiled dataframe
"""
from oggm.workflow import execute_entity_task
out_df = execute_entity_task(read_glacier_hypsometry, gdirs)
out = pd.DataFrame(out_df).set_index('rgi_id')
if add_column is not None:
out[add_column[0]] = add_column[1]
if path:
if path is True:
out.to_csv(os.path.join(cfg.PATHS['working_dir'],
('glacier_hypsometry' +
filesuffix + '.csv')))
else:
out.to_csv(path)
return out
[docs]@global_task(log)
def compile_fixed_geometry_mass_balance(gdirs, filesuffix='',
path=True, csv=False,
use_inversion_flowlines=True,
ys=None, ye=None, years=None,
climate_filename='climate_historical',
climate_input_filesuffix='',
temperature_bias=None,
precipitation_factor=None):
"""Compiles a table of specific mass balance timeseries for all glaciers.
The file is stored in a hdf file (not csv) per default. Use pd.read_hdf
to open it.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
filesuffix : str
add suffix to output file
path : str, bool
Set to "True" in order to store the info in the working directory
Set to a path to store the file to your chosen location (file
extension matters)
csv : bool
Set to store the data in csv instead of hdf.
use_inversion_flowlines : bool
whether to use the inversion flowlines or the model flowlines
ys : int
start year of the model run (default: from the climate file)
date)
ye : int
end year of the model run (default: from the climate file)
years : array of ints
override ys and ye with the years of your choice
climate_filename : str
name of the climate file, e.g. 'climate_historical' (default) or
'gcm_data'
climate_input_filesuffix: str
filesuffix for the input climate file
temperature_bias : float
add a bias to the temperature timeseries
precipitation_factor: float
multiply a factor to the precipitation time series
default is None and means that the precipitation factor from the
calibration is applied which is cfg.PARAMS['prcp_fac']
"""
from oggm.workflow import execute_entity_task
from oggm.core.massbalance import fixed_geometry_mass_balance
out_df = execute_entity_task(fixed_geometry_mass_balance, gdirs,
use_inversion_flowlines=use_inversion_flowlines,
ys=ys, ye=ye, years=years, climate_filename=climate_filename,
climate_input_filesuffix=climate_input_filesuffix,
temperature_bias=temperature_bias,
precipitation_factor=precipitation_factor)
for idx, s in enumerate(out_df):
if s is None:
out_df[idx] = pd.Series(np.NaN)
out = pd.concat(out_df, axis=1, keys=[gd.rgi_id for gd in gdirs])
out = out.dropna(axis=0, how='all')
if path:
if path is True:
fpath = os.path.join(cfg.PATHS['working_dir'],
'fixed_geometry_mass_balance' + filesuffix)
if csv:
out.to_csv(fpath + '.csv')
else:
out.to_hdf(fpath + '.hdf', key='df')
else:
ext = os.path.splitext(path)[-1]
if ext.lower() == '.csv':
out.to_csv(path)
elif ext.lower() == '.hdf':
out.to_hdf(path, key='df')
return out
[docs]@global_task(log)
def compile_ela(gdirs, filesuffix='', path=True, csv=False, ys=None, ye=None,
years=None, climate_filename='climate_historical', temperature_bias=None,
precipitation_factor=None, climate_input_filesuffix='',
mb_model_class=None):
"""Compiles a table of ELA timeseries for all glaciers for a given years,
using the mb_model_class (default MonthlyTIModel).
The file is stored in a hdf file (not csv) per default. Use pd.read_hdf
to open it.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
filesuffix : str
add suffix to output file
path : str, bool
Set to "True" in order to store the info in the working directory
Set to a path to store the file to your chosen location (file
extension matters)
csv: bool
Set to store the data in csv instead of hdf.
ys : int
start year
ye : int
end year
years : array of ints
override ys and ye with the years of your choice
climate_filename : str
name of the climate file, e.g. 'climate_historical' (default) or
'gcm_data'
climate_input_filesuffix : str
filesuffix for the input climate file
temperature_bias : float
add a bias to the temperature timeseries
precipitation_factor: float
multiply a factor to the precipitation time series
default is None and means that the precipitation factor from the
calibration is applied which is cfg.PARAMS['prcp_fac']
mb_model_class : MassBalanceModel class
the MassBalanceModel class to use, default is MonthlyTIModel
"""
from oggm.workflow import execute_entity_task
from oggm.core.massbalance import compute_ela, MonthlyTIModel
if mb_model_class is None:
mb_model_class = MonthlyTIModel
out_df = execute_entity_task(compute_ela, gdirs, ys=ys, ye=ye, years=years,
climate_filename=climate_filename,
climate_input_filesuffix=climate_input_filesuffix,
temperature_bias=temperature_bias,
precipitation_factor=precipitation_factor,
mb_model_class=mb_model_class)
for idx, s in enumerate(out_df):
if s is None:
out_df[idx] = pd.Series(np.NaN)
out = pd.concat(out_df, axis=1, keys=[gd.rgi_id for gd in gdirs])
out = out.dropna(axis=0, how='all')
if path:
if path is True:
fpath = os.path.join(cfg.PATHS['working_dir'],
'ELA' + filesuffix)
if csv:
out.to_csv(fpath + '.csv')
else:
out.to_hdf(fpath + '.hdf', key='df')
else:
ext = os.path.splitext(path)[-1]
if ext.lower() == '.csv':
out.to_csv(path)
elif ext.lower() == '.hdf':
out.to_hdf(path, key='df')
return out
@entity_task(log)
def climate_statistics(gdir, add_climate_period=1995, halfsize=15,
input_filesuffix=''):
"""Gather as much statistics as possible about this glacier.
It can be used to do result diagnostics and other stuffs. If the data
necessary for a statistic is not available (e.g.: flowlines length) it
will simply be ignored.
Important note: the climate is extracted from the mass-balance model and
is therefore "corrected" according to the mass-balance calibration scheme
(e.g. the precipitation factor and the temp bias correction). For more
flexible information about the raw climate data, use `compile_climate_input`
or `raw_climate_statistics`.
Parameters
----------
add_climate_period : int or list of ints
compile climate statistics for the halfsize*2 + 1 yrs period
around the selected date.
halfsize : int
the half size of the window
"""
from oggm.core.massbalance import (ConstantMassBalance,
MultipleFlowlineMassBalance)
d = OrderedDict()
# Easy stats - this should always be possible
d['rgi_id'] = gdir.rgi_id
d['rgi_region'] = gdir.rgi_region
d['rgi_subregion'] = gdir.rgi_subregion
d['name'] = gdir.name
d['cenlon'] = gdir.cenlon
d['cenlat'] = gdir.cenlat
d['rgi_area_km2'] = gdir.rgi_area_km2
d['glacier_type'] = gdir.glacier_type
d['terminus_type'] = gdir.terminus_type
d['status'] = gdir.status
# The rest is less certain
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
try:
# Flowline related stuff
h = np.array([])
widths = np.array([])
fls = gdir.read_pickle('inversion_flowlines')
dx = fls[0].dx * gdir.grid.dx
for fl in fls:
hgt = fl.surface_h
h = np.append(h, hgt)
widths = np.append(widths, fl.widths * dx)
d['flowline_mean_elev'] = np.average(h, weights=widths)
d['flowline_max_elev'] = np.max(h)
d['flowline_min_elev'] = np.min(h)
except BaseException:
pass
# Climate and MB at specified dates
add_climate_period = tolist(add_climate_period)
for y0 in add_climate_period:
try:
fs = '{}-{}'.format(y0 - halfsize, y0 + halfsize)
mbcl = ConstantMassBalance
mbmod = MultipleFlowlineMassBalance(gdir, mb_model_class=mbcl,
y0=y0, halfsize=halfsize,
use_inversion_flowlines=True,
input_filesuffix=input_filesuffix)
h, w, mbh = mbmod.get_annual_mb_on_flowlines()
mbh = mbh * cfg.SEC_IN_YEAR * cfg.PARAMS['ice_density']
pacc = np.where(mbh >= 0)
pab = np.where(mbh < 0)
d[fs + '_aar'] = np.sum(w[pacc]) / np.sum(w)
try:
# Try to get the slope
mb_slope, _, _, _, _ = stats.linregress(h[pab], mbh[pab])
d[fs + '_mb_grad'] = mb_slope
except BaseException:
# we don't mind if something goes wrong
d[fs + '_mb_grad'] = np.NaN
d[fs + '_ela_h'] = mbmod.get_ela()
# Climate
t, tm, p, ps = mbmod.flowline_mb_models[0].get_annual_climate(
[d[fs + '_ela_h'],
d['flowline_mean_elev'],
d['flowline_max_elev'],
d['flowline_min_elev']])
for n, v in zip(['temp', 'tempmelt', 'prcpsol'], [t, tm, ps]):
d[fs + '_avg_' + n + '_ela_h'] = v[0]
d[fs + '_avg_' + n + '_mean_elev'] = v[1]
d[fs + '_avg_' + n + '_max_elev'] = v[2]
d[fs + '_avg_' + n + '_min_elev'] = v[3]
d[fs + '_avg_prcp'] = p[0]
except BaseException:
pass
return d
@entity_task(log)
def raw_climate_statistics(gdir, add_climate_period=1995, halfsize=15,
input_filesuffix=''):
"""Gather as much statistics as possible about this glacier.
This is like "climate_statistics" but without relying on the
mass-balance model, i.e. closer to the actual data (uncorrected)
Parameters
----------
add_climate_period : int or list of ints
compile climate statistics for the 30 yrs period around the selected
date.
"""
d = OrderedDict()
# Easy stats - this should always be possible
d['rgi_id'] = gdir.rgi_id
d['rgi_region'] = gdir.rgi_region
d['rgi_subregion'] = gdir.rgi_subregion
d['name'] = gdir.name
d['cenlon'] = gdir.cenlon
d['cenlat'] = gdir.cenlat
d['rgi_area_km2'] = gdir.rgi_area_km2
d['glacier_type'] = gdir.glacier_type
d['terminus_type'] = gdir.terminus_type
d['status'] = gdir.status
# The rest is less certain
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
# Climate and MB at specified dates
add_climate_period = tolist(add_climate_period)
# get non-corrected winter daily mean prcp (kg m-2 day-1) for
# the chosen time period
for y0 in add_climate_period:
fs = '{}-{}'.format(y0 - halfsize, y0 + halfsize)
try:
# get non-corrected winter daily mean prcp (kg m-2 day-1)
# it is easier to get this directly from the raw climate files
fp = gdir.get_filepath('climate_historical',
filesuffix=input_filesuffix)
with xr.open_dataset(fp).prcp as ds_pr:
# just select winter months
if gdir.hemisphere == 'nh':
m_winter = [10, 11, 12, 1, 2, 3, 4]
else:
m_winter = [4, 5, 6, 7, 8, 9, 10]
ds_pr_winter = ds_pr.where(ds_pr['time.month'].isin(m_winter), drop=True)
# select the correct year time period
ds_pr_winter = ds_pr_winter.sel(time=slice(f'{fs[:4]}-01-01',
f'{fs[-4:]}-12-01'))
# check if we have the full time period
n_years = int(fs[-4:]) - int(fs[:4]) + 1
assert len(ds_pr_winter.time) == n_years * 7, 'chosen time-span invalid'
ds_d_pr_winter_mean = (ds_pr_winter / ds_pr_winter.time.dt.daysinmonth).mean()
d[f'{fs}_uncorrected_winter_daily_mean_prcp'] = ds_d_pr_winter_mean.values
except BaseException:
pass
return d
[docs]@global_task(log)
def compile_climate_statistics(gdirs, filesuffix='', path=True,
add_climate_period=1995,
halfsize=15,
add_raw_climate_statistics=False,
input_filesuffix=''):
"""Gather as much statistics as possible about a list of glaciers.
It can be used to do result diagnostics and other stuffs. If the data
necessary for a statistic is not available (e.g.: flowlines length) it
will simply be ignored.
Parameters
----------
gdirs: the list of GlacierDir to process.
filesuffix : str
add suffix to output file
path : str, bool
Set to "True" in order to store the info in the working directory
Set to a path to store the file to your chosen location
add_climate_period : int or list of ints
compile climate statistics for the 30 yrs period around the selected
date.
input_filesuffix : str
filesuffix of the used climate_historical file, default is no filesuffix
"""
from oggm.workflow import execute_entity_task
out_df = execute_entity_task(climate_statistics, gdirs,
add_climate_period=add_climate_period,
halfsize=halfsize,
input_filesuffix=input_filesuffix)
out = pd.DataFrame(out_df).set_index('rgi_id')
if add_raw_climate_statistics:
out_df = execute_entity_task(raw_climate_statistics, gdirs,
add_climate_period=add_climate_period,
halfsize=halfsize,
input_filesuffix=input_filesuffix)
out = out.merge(pd.DataFrame(out_df).set_index('rgi_id'))
if path:
if path is True:
out.to_csv(os.path.join(cfg.PATHS['working_dir'],
('climate_statistics' +
filesuffix + '.csv')))
else:
out.to_csv(path)
return out
def extend_past_climate_run(past_run_file=None,
fixed_geometry_mb_file=None,
glacier_statistics_file=None,
path=False,
use_compression=True):
"""Utility function to extend past MB runs prior to the RGI date.
We use a fixed geometry (and a fixed calving rate) for all dates prior
to the RGI date.
This is not parallelized, i.e a bit slow.
Parameters
----------
past_run_file : str
path to the historical run (nc)
fixed_geometry_mb_file : str
path to the MB file (csv)
glacier_statistics_file : str
path to the glacier stats file (csv)
path : str
where to store the file
use_compression : bool
Returns
-------
the extended dataset
"""
log.workflow('Applying extend_past_climate_run on '
'{}'.format(past_run_file))
fixed_geometry_mb_df = pd.read_csv(fixed_geometry_mb_file, index_col=0,
low_memory=False)
stats_df = pd.read_csv(glacier_statistics_file, index_col=0,
low_memory=False)
with xr.open_dataset(past_run_file) as past_ds:
# We need at least area and vol to do something
if 'volume' not in past_ds.data_vars or 'area' not in past_ds.data_vars:
raise InvalidWorkflowError('Need both volume and area to proceed')
y0_run = int(past_ds.time[0])
y1_run = int(past_ds.time[-1])
if (y1_run - y0_run + 1) != len(past_ds.time):
raise NotImplementedError('Currently only supports annual outputs')
y0_clim = int(fixed_geometry_mb_df.index[0])
y1_clim = int(fixed_geometry_mb_df.index[-1])
if y0_clim > y0_run or y1_clim < y0_run:
raise InvalidWorkflowError('Dates do not match.')
if y1_clim != y1_run - 1:
raise InvalidWorkflowError('Dates do not match.')
if len(past_ds.rgi_id) != len(fixed_geometry_mb_df.columns):
# This might happen if we are testing on new directories
fixed_geometry_mb_df = fixed_geometry_mb_df[past_ds.rgi_id]
if len(past_ds.rgi_id) != len(stats_df.index):
stats_df = stats_df.loc[past_ds.rgi_id]
# Make sure we agree on order
df = fixed_geometry_mb_df[past_ds.rgi_id]
# Output data
years = np.arange(y0_clim, y1_run+1)
ods = past_ds.reindex({'time': years})
# Time
ods['hydro_year'].data[:] = years
ods['hydro_month'].data[:] = ods['hydro_month'][-1]
ods['calendar_year'].data[:] = years
ods['calendar_month'].data[:] = ods['calendar_month'][-1]
for vn in ['hydro_year', 'hydro_month', 'calendar_year', 'calendar_month']:
ods[vn] = ods[vn].astype(int)
# New vars
for vn in ['volume', 'volume_m3_min_h', 'volume_bsl', 'volume_bwl',
'area', 'area_m2_min_h', 'length', 'calving', 'calving_rate']:
if vn in ods.data_vars:
ods[vn + '_ext'] = ods[vn].copy(deep=True)
ods[vn + '_ext'].attrs['description'] += ' (extended with MB data)'
vn = 'volume_fixed_geom_ext'
ods[vn] = ods['volume'].copy(deep=True)
ods[vn].attrs['description'] += ' (replaced with fixed geom data)'
rho = cfg.PARAMS['ice_density']
# Loop over the ids
for i, rid in enumerate(ods.rgi_id.data):
# Both do not need to be same length but they need to start same
mb_ts = df.values[:, i]
orig_vol_ts = ods.volume_ext.data[:, i]
if not (np.isfinite(mb_ts[-1]) and np.isfinite(orig_vol_ts[-1])):
# Not a valid glacier
continue
if np.isfinite(orig_vol_ts[0]):
# Nothing to extend, really
continue
# First valid id
fid = np.argmax(np.isfinite(orig_vol_ts))
# Add calving to the mix
try:
calv_flux = stats_df.loc[rid, 'calving_flux'] * 1e9
calv_rate = stats_df.loc[rid, 'calving_rate_myr']
except KeyError:
calv_flux = 0
calv_rate = 0
if not np.isfinite(calv_flux):
calv_flux = 0
if not np.isfinite(calv_rate):
calv_rate = 0
# Fill area and length which stays constant before date
orig_area_ts = ods.area_ext.data[:, i]
orig_area_ts[:fid] = orig_area_ts[fid]
# We convert SMB to volume
mb_vol_ts = (mb_ts / rho * orig_area_ts[fid] - calv_flux).cumsum()
calv_ts = (mb_ts * 0 + calv_flux).cumsum()
# The -1 is because the volume change is known at end of year
mb_vol_ts = mb_vol_ts + orig_vol_ts[fid] - mb_vol_ts[fid-1]
# Now back to netcdf
ods.volume_fixed_geom_ext.data[1:, i] = mb_vol_ts
ods.volume_ext.data[1:fid, i] = mb_vol_ts[0:fid-1]
ods.area_ext.data[:, i] = orig_area_ts
# Optional variables
if 'length' in ods.data_vars:
orig_length_ts = ods.length_ext.data[:, i]
orig_length_ts[:fid] = orig_length_ts[fid]
ods.length_ext.data[:, i] = orig_length_ts
if 'calving' in ods.data_vars:
orig_calv_ts = ods.calving_ext.data[:, i]
# The -1 is because the volume change is known at end of year
calv_ts = calv_ts + orig_calv_ts[fid] - calv_ts[fid-1]
ods.calving_ext.data[1:fid, i] = calv_ts[0:fid-1]
if 'calving_rate' in ods.data_vars:
orig_calv_rate_ts = ods.calving_rate_ext.data[:, i]
# +1 because calving rate at year 0 is unknown from the dyns model
orig_calv_rate_ts[:fid+1] = calv_rate
ods.calving_rate_ext.data[:, i] = orig_calv_rate_ts
# Extend vol bsl by assuming that % stays constant
if 'volume_bsl' in ods.data_vars:
bsl = ods.volume_bsl.data[fid, i] / ods.volume.data[fid, i]
ods.volume_bsl_ext.data[:fid, i] = bsl * ods.volume_ext.data[:fid, i]
if 'volume_bwl' in ods.data_vars:
bwl = ods.volume_bwl.data[fid, i] / ods.volume.data[fid, i]
ods.volume_bwl_ext.data[:fid, i] = bwl * ods.volume_ext.data[:fid, i]
# Remove old vars
for vn in list(ods.data_vars):
if '_ext' not in vn and 'time' in ods[vn].dims:
del ods[vn]
# Rename vars to their old names
ods = ods.rename(dict((o, o.replace('_ext', ''))
for o in ods.data_vars))
# Remove t0 (which is NaN)
ods = ods.isel(time=slice(1, None))
# To file?
if path:
enc_var = {'dtype': 'float32'}
if use_compression:
enc_var['complevel'] = 5
enc_var['zlib'] = True
encoding = {v: enc_var for v in ods.data_vars}
ods.to_netcdf(path, encoding=encoding)
return ods
def idealized_gdir(surface_h, widths_m, map_dx, flowline_dx=1,
base_dir=None, reset=False):
"""Creates a glacier directory with flowline input data only.
This is useful for testing, or for idealized experiments.
Parameters
----------
surface_h : ndarray
the surface elevation of the flowline's grid points (in m).
widths_m : ndarray
the widths of the flowline's grid points (in m).
map_dx : float
the grid spacing (in m)
flowline_dx : int
the flowline grid spacing (in units of map_dx, often it should be 1)
base_dir : str
path to the directory where to open the directory.
Defaults to `cfg.PATHS['working_dir'] + /per_glacier/`
reset : bool, default=False
empties the directory at construction
Returns
-------
a GlacierDirectory instance
"""
from oggm.core.centerlines import Centerline
# Area from geometry
area_km2 = np.sum(widths_m * map_dx * flowline_dx) * 1e-6
# Dummy entity - should probably also change the geometry
entity = gpd.read_file(get_demo_file('Hintereisferner_RGI5.shp')).iloc[0]
entity.Area = area_km2
entity.CenLat = 0
entity.CenLon = 0
entity.Name = ''
entity.RGIId = 'RGI50-00.00000'
entity.O1Region = '00'
entity.O2Region = '0'
gdir = GlacierDirectory(entity, base_dir=base_dir, reset=reset)
gdir.write_shapefile(gpd.GeoDataFrame([entity]), 'outlines')
# Idealized flowline
coords = np.arange(0, len(surface_h) - 0.5, 1)
line = shpg.LineString(np.vstack([coords, coords * 0.]).T)
fl = Centerline(line, dx=flowline_dx, surface_h=surface_h, map_dx=map_dx)
fl.widths = widths_m / map_dx
fl.is_rectangular = np.ones(fl.nx).astype(bool)
gdir.write_pickle([fl], 'inversion_flowlines')
# Idealized map
grid = salem.Grid(nxny=(1, 1), dxdy=(map_dx, map_dx), x0y0=(0, 0))
grid.to_json(gdir.get_filepath('glacier_grid'))
return gdir
def _back_up_retry(func, exceptions, max_count=5):
"""Re-Try an action up to max_count times.
"""
count = 0
while count < max_count:
try:
if count > 0:
time.sleep(random.uniform(0.05, 0.1))
return func()
except exceptions:
count += 1
if count >= max_count:
raise
def _robust_extract(to_dir, *args, **kwargs):
"""For some obscure reason this operation randomly fails.
Try to make it more robust.
"""
def func():
with tarfile.open(*args, **kwargs) as tf:
if not len(tf.getnames()):
raise RuntimeError("Empty tarfile")
tf.extractall(os.path.dirname(to_dir))
_back_up_retry(func, FileExistsError)
def robust_tar_extract(from_tar, to_dir, delete_tar=False):
"""Extract a tar file - also checks for a "tar in tar" situation"""
if os.path.isfile(from_tar):
_robust_extract(to_dir, from_tar, 'r')
else:
# maybe a tar in tar
base_tar = os.path.dirname(from_tar) + '.tar'
if not os.path.isfile(base_tar):
raise FileNotFoundError('Could not find a tarfile with path: '
'{}'.format(from_tar))
if delete_tar:
raise InvalidParamsError('Cannot delete tar in tar.')
# Open the tar
bname = os.path.basename(from_tar)
dirbname = os.path.basename(os.path.dirname(from_tar))
def func():
with tarfile.open(base_tar, 'r') as tf:
i_from_tar = tf.getmember(os.path.join(dirbname, bname))
with tf.extractfile(i_from_tar) as fileobj:
_robust_extract(to_dir, fileobj=fileobj)
_back_up_retry(func, RuntimeError)
if delete_tar:
os.remove(from_tar)
[docs]class GlacierDirectory(object):
"""Organizes read and write access to the glacier's files.
It handles a glacier directory created in a base directory (default
is the "per_glacier" folder in the working directory). The role of a
GlacierDirectory is to give access to file paths and to I/O operations.
The user should not care about *where* the files are
located, but should know their name (see :ref:`basenames`).
If the directory does not exist, it will be created.
See :ref:`glacierdir` for more information.
Attributes
----------
dir : str
path to the directory
base_dir : str
path to the base directory
rgi_id : str
The glacier's RGI identifier
glims_id : str
The glacier's GLIMS identifier (when available)
rgi_area_km2 : float
The glacier's RGI area (km2)
cenlon, cenlat : float
The glacier centerpoint's lon/lat
rgi_date : int
The RGI's BGNDATE year attribute if available. Otherwise, defaults to
the median year for the RGI region
rgi_region : str
The RGI region ID
rgi_subregion : str
The RGI subregion ID
rgi_version : str
The RGI version name
rgi_region_name : str
The RGI region name
rgi_subregion_name : str
The RGI subregion name
name : str
The RGI glacier name (if available)
hemisphere : str
`nh` or `sh`
glacier_type : str
The RGI glacier type ('Glacier', 'Ice cap', 'Perennial snowfield',
'Seasonal snowfield')
terminus_type : str
The RGI terminus type ('Land-terminating', 'Marine-terminating',
'Lake-terminating', 'Dry calving', 'Regenerated', 'Shelf-terminating')
is_tidewater : bool
Is the glacier a calving glacier?
is_lake_terminating : bool
Is the glacier a lake terminating glacier?
is_nominal : bool
Is the glacier an RGI nominal glacier?
is_icecap : bool
Is the glacier an ice cap?
extent_ll : list
Extent of the glacier in lon/lat
logfile : str
Path to the log file (txt)
inversion_calving_rate : float
Calving rate used for the inversion
grid
dem_info
dem_daterange
intersects_ids
rgi_area_m2
rgi_area_km2
"""
[docs] def __init__(self, rgi_entity, base_dir=None, reset=False,
from_tar=False, delete_tar=False):
"""Creates a new directory or opens an existing one.
Parameters
----------
rgi_entity : a ``geopandas.GeoSeries`` or str
glacier entity read from the shapefile (or a valid RGI ID if the
directory exists)
base_dir : str
path to the directory where to open the directory.
Defaults to `cfg.PATHS['working_dir'] + /per_glacier/`
reset : bool, default=False
empties the directory at construction (careful!)
from_tar : str or bool, default=False
path to a tar file to extract the gdir data from. 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.
"""
if base_dir is None:
if not cfg.PATHS.get('working_dir', None):
raise ValueError("Need a valid PATHS['working_dir']!")
base_dir = os.path.join(cfg.PATHS['working_dir'], 'per_glacier')
# RGI IDs are also valid entries
if isinstance(rgi_entity, str):
# Get the meta from the shape file directly
if from_tar:
_dir = os.path.join(base_dir, rgi_entity[:-6], rgi_entity[:-3],
rgi_entity)
# Avoid bad surprises
if os.path.exists(_dir):
shutil.rmtree(_dir)
if from_tar is True:
from_tar = _dir + '.tar.gz'
robust_tar_extract(from_tar, _dir, delete_tar=delete_tar)
from_tar = False # to not re-unpack later below
_shp = os.path.join(_dir, 'outlines.shp')
else:
_shp = os.path.join(base_dir, rgi_entity[:-6], rgi_entity[:-3],
rgi_entity, 'outlines.shp')
rgi_entity = self._read_shapefile_from_path(_shp)
crs = salem.check_crs(rgi_entity.crs)
rgi_entity = rgi_entity.iloc[0]
g = rgi_entity['geometry']
xx, yy = salem.transform_proj(crs, salem.wgs84,
[g.bounds[0], g.bounds[2]],
[g.bounds[1], g.bounds[3]])
write_shp = False
else:
g = rgi_entity['geometry']
xx, yy = ([g.bounds[0], g.bounds[2]],
[g.bounds[1], g.bounds[3]])
write_shp = True
# Extent of the glacier in lon/lat
self.extent_ll = [xx, yy]
is_rgi7 = False
is_glacier_complex = False
try:
self.rgi_id = rgi_entity.rgi_id
is_rgi7 = True
try:
self.glims_id = rgi_entity.glims_id
except AttributeError:
# Complex product
self.glims_id = ''
is_glacier_complex = True
except AttributeError:
# RGI V6
self.rgi_id = rgi_entity.RGIId
self.glims_id = rgi_entity.GLIMSId
# Do we want to use the RGI center point or ours?
if cfg.PARAMS['use_rgi_area']:
if is_rgi7:
self.cenlon = float(rgi_entity.cenlon)
self.cenlat = float(rgi_entity.cenlat)
else:
self.cenlon = float(rgi_entity.CenLon)
self.cenlat = float(rgi_entity.CenLat)
else:
cenlon, cenlat = rgi_entity.geometry.representative_point().xy
self.cenlon = float(cenlon[0])
self.cenlat = float(cenlat[0])
if is_glacier_complex:
rgi_entity['glac_name'] = ''
rgi_entity['src_date'] = '2000-01-01 00:00:00'
rgi_entity['dem_source'] = None
rgi_entity['term_type'] = 9
if is_rgi7:
self.rgi_region = rgi_entity.o1region
self.rgi_subregion = rgi_entity.o2region
name = rgi_entity.glac_name
rgi_datestr = rgi_entity.src_date
self.rgi_version = '70G'
self.glacier_type = 'Glacier'
self.status = 'Glacier'
ttkeys = {0: 'Land-terminating',
1: 'Marine-terminating',
2: 'Lake-terminating',
3: 'Shelf-terminating',
9: 'Not assigned',
}
self.terminus_type = ttkeys[rgi_entity['term_type']]
if is_glacier_complex:
self.rgi_version = '70C'
self.glacier_type = 'Glacier complex'
self.status = 'Glacier complex'
self.rgi_dem_source = rgi_entity.dem_source
self.utm_zone = rgi_entity.utm_zone
# New attrs
try:
self.rgi_termlon = rgi_entity.termlon
self.rgi_termlat = rgi_entity.termlat
except AttributeError:
pass
else:
self.rgi_region = '{:02d}'.format(int(rgi_entity.O1Region))
self.rgi_subregion = f'{self.rgi_region}-{int(rgi_entity.O2Region):02d}'
name = rgi_entity.Name
rgi_datestr = rgi_entity.BgnDate
try:
# RGI5
gtype = rgi_entity.GlacType
except AttributeError:
# RGI V6
gtype = [str(rgi_entity.Form), str(rgi_entity.TermType)]
try:
# RGI5
gstatus = rgi_entity.RGIFlag[0]
except AttributeError:
# RGI V6
gstatus = rgi_entity.Status
rgi_version = self.rgi_id.split('-')[0][-2:]
if rgi_version not in ['50', '60', '61']:
raise RuntimeError('RGI Version not supported: '
'{}'.format(self.rgi_version))
self.rgi_version = rgi_version
self.rgi_dem_source = None
# Read glacier attrs
gtkeys = {'0': 'Glacier',
'1': 'Ice cap',
'2': 'Perennial snowfield',
'3': 'Seasonal snowfield',
'9': 'Not assigned',
}
ttkeys = {'0': 'Land-terminating',
'1': 'Marine-terminating',
'2': 'Lake-terminating',
'3': 'Dry calving',
'4': 'Regenerated',
'5': 'Shelf-terminating',
'9': 'Not assigned',
}
stkeys = {'0': 'Glacier or ice cap',
'1': 'Glacier complex',
'2': 'Nominal glacier',
'9': 'Not assigned',
}
self.glacier_type = gtkeys[gtype[0]]
self.terminus_type = ttkeys[gtype[1]]
self.status = stkeys['{}'.format(gstatus)]
# remove spurious characters and trailing blanks
self.name = filter_rgi_name(name)
# RGI region
reg_names, subreg_names = parse_rgi_meta(version=self.rgi_version[0])
reg_name = reg_names.loc[int(self.rgi_region)]
# RGI V6
if not isinstance(reg_name, str):
reg_name = reg_name.values[0]
self.rgi_region_name = self.rgi_region + ': ' + reg_name
try:
subreg_name = subreg_names.loc[self.rgi_subregion]
# RGI V6
if not isinstance(subreg_name, str):
subreg_name = subreg_name.values[0]
self.rgi_subregion_name = self.rgi_subregion + ': ' + subreg_name
except KeyError:
self.rgi_subregion_name = self.rgi_subregion + ': NoName'
# Decide what is a tidewater glacier
user = cfg.PARAMS['tidewater_type']
if user == 1:
sel = ['Marine-terminating']
elif user == 2:
sel = ['Marine-terminating', 'Shelf-terminating']
elif user == 3:
sel = ['Marine-terminating', 'Lake-terminating']
elif user == 4:
sel = ['Marine-terminating', 'Lake-terminating', 'Shelf-terminating']
else:
raise InvalidParamsError("PARAMS['tidewater_type'] not understood")
self.is_tidewater = self.terminus_type in sel
self.is_lake_terminating = self.terminus_type == 'Lake-terminating'
self.is_marine_terminating = self.terminus_type == 'Marine-terminating'
self.is_shelf_terminating = self.terminus_type == 'Shelf-terminating'
self.is_nominal = self.status == 'Nominal glacier'
self.inversion_calving_rate = 0.
self.is_icecap = self.glacier_type == 'Ice cap'
# Hemisphere
if self.cenlat < 0 or self.rgi_region == '16':
self.hemisphere = 'sh'
else:
self.hemisphere = 'nh'
# convert the date
rgi_date = int(rgi_datestr[0:4])
if rgi_date < 0:
rgi_date = RGI_DATE[self.rgi_region]
self.rgi_date = rgi_date
# Root directory
self.base_dir = os.path.normpath(base_dir)
self.dir = os.path.join(self.base_dir, self.rgi_id[:-6],
self.rgi_id[:-3], self.rgi_id)
# Do we have to extract the files first?
if (reset or from_tar) and os.path.exists(self.dir):
shutil.rmtree(self.dir)
if from_tar:
if from_tar is True:
from_tar = self.dir + '.tar.gz'
robust_tar_extract(from_tar, self.dir, delete_tar=delete_tar)
write_shp = False
else:
mkdir(self.dir)
if not os.path.isdir(self.dir):
raise RuntimeError('GlacierDirectory %s does not exist!' % self.dir)
# logging file
self.logfile = os.path.join(self.dir, 'log.txt')
if write_shp:
# Write shapefile
self._reproject_and_write_shapefile(rgi_entity)
# Optimization
self._mbdf = None
self._mbprofdf = None
self._mbprofdf_cte_dh = None
def __repr__(self):
summary = ['<oggm.GlacierDirectory>']
summary += [' RGI id: ' + self.rgi_id]
summary += [' Region: ' + self.rgi_region_name]
summary += [' Subregion: ' + self.rgi_subregion_name]
if self.name:
summary += [' Name: ' + self.name]
summary += [' Glacier type: ' + str(self.glacier_type)]
summary += [' Terminus type: ' + str(self.terminus_type)]
summary += [' Status: ' + str(self.status)]
summary += [' Area: ' + str(self.rgi_area_km2) + ' km2']
summary += [' Lon, Lat: (' + str(self.cenlon) + ', ' +
str(self.cenlat) + ')']
if os.path.isfile(self.get_filepath('glacier_grid')):
summary += [' Grid (nx, ny): (' + str(self.grid.nx) + ', ' +
str(self.grid.ny) + ')']
summary += [' Grid (dx, dy): (' + str(self.grid.dx) + ', ' +
str(self.grid.dy) + ')']
return '\n'.join(summary) + '\n'
def _reproject_and_write_shapefile(self, entity):
# Make a local glacier map
if cfg.PARAMS['map_proj'] == 'utm':
if entity.get('utm_zone', False):
# RGI7 has an utm zone
proj4_str = {'proj': 'utm', 'zone': entity['utm_zone']}
else:
# Find it out
from pyproj.aoi import AreaOfInterest
from pyproj.database import query_utm_crs_info
utm_crs_list = query_utm_crs_info(
datum_name="WGS 84",
area_of_interest=AreaOfInterest(
west_lon_degree=self.cenlon,
south_lat_degree=self.cenlat,
east_lon_degree=self.cenlon,
north_lat_degree=self.cenlat,
),
)
proj4_str = utm_crs_list[0].code
elif cfg.PARAMS['map_proj'] == 'tmerc':
params = dict(name='tmerc', lat_0=0., lon_0=self.cenlon,
k=0.9996, x_0=0, y_0=0, datum='WGS84')
proj4_str = ("+proj={name} +lat_0={lat_0} +lon_0={lon_0} +k={k} "
"+x_0={x_0} +y_0={y_0} +datum={datum}".format(**params))
else:
raise InvalidParamsError("cfg.PARAMS['map_proj'] must be one of "
"'tmerc', 'utm'.")
# Reproject
proj_in = pyproj.Proj("epsg:4326", preserve_units=True)
proj_out = pyproj.Proj(proj4_str, preserve_units=True)
# transform geometry to map
project = partial(transform_proj, proj_in, proj_out)
geometry = shp_trafo(project, entity['geometry'])
if len(self.rgi_id) == 23 and (not geometry.is_valid or
type(geometry) != shpg.Polygon):
# In RGI7 we know that the geometries are valid in the source file,
# so we have to validate them after projection them as well
# Try buffer first
geometry = geometry.buffer(0)
if not geometry.is_valid:
correct = recursive_valid_polygons([geometry], crs=proj4_str)
if len(correct) != 1:
raise RuntimeError('Cant correct this geometry')
geometry = correct[0]
if type(geometry) != shpg.Polygon:
raise ValueError(f'{self.rgi_id}: geometry not valid')
elif not cfg.PARAMS['keep_multipolygon_outlines']:
geometry = multipolygon_to_polygon(geometry, gdir=self)
# Save transformed geometry to disk
entity = entity.copy()
entity['geometry'] = geometry
# Do we want to use the RGI area or ours?
if not cfg.PARAMS['use_rgi_area']:
# Update Area
try:
area = geometry.area * 1e-6
except:
area = geometry.area_m2 * 1e-6
entity['Area'] = area
# Avoid fiona bug: https://github.com/Toblerity/Fiona/issues/365
for k, s in entity.items():
if type(s) in [np.int32, np.int64]:
entity[k] = int(s)
towrite = gpd.GeoDataFrame(entity).T.set_geometry('geometry')
towrite.crs = proj4_str
# Write shapefile
self.write_shapefile(towrite, 'outlines')
# Also transform the intersects if necessary
gdf = cfg.PARAMS['intersects_gdf']
if len(gdf) > 0:
try:
gdf = gdf.loc[((gdf.RGIId_1 == self.rgi_id) |
(gdf.RGIId_2 == self.rgi_id))]
except AttributeError:
gdf = gdf.loc[((gdf.rgi_g_id_1 == self.rgi_id) |
(gdf.rgi_g_id_2 == self.rgi_id))]
if len(gdf) > 0:
gdf = salem.transform_geopandas(gdf, to_crs=proj_out)
if hasattr(gdf.crs, 'srs'):
# salem uses pyproj
gdf.crs = gdf.crs.srs
self.write_shapefile(gdf, 'intersects')
else:
# Sanity check
if cfg.PARAMS['use_intersects'] and not self.rgi_version == '70C':
raise InvalidParamsError(
'You seem to have forgotten to set the '
'intersects file for this run. OGGM '
'works better with such a file. If you '
'know what your are doing, set '
"cfg.PARAMS['use_intersects'] = False to "
"suppress this error.")
def grid_from_params(self):
"""If the glacier_grid.json file is lost, reconstruct it."""
from oggm.core.gis import glacier_grid_params
utm_proj, nx, ny, ulx, uly, dx = glacier_grid_params(self)
x0y0 = (ulx+dx/2, uly-dx/2) # To pixel center coordinates
return salem.Grid(proj=utm_proj, nxny=(nx, ny), dxdy=(dx, -dx),
x0y0=x0y0)
@lazy_property
def grid(self):
"""A ``salem.Grid`` handling the georeferencing of the local grid"""
try:
return salem.Grid.from_json(self.get_filepath('glacier_grid'))
except FileNotFoundError:
raise InvalidWorkflowError('This glacier directory seems to '
'have lost its glacier_grid.json file.'
'Use .grid_from_params(), but make sure'
'that the PARAMS are the ones you '
'want.')
@lazy_property
def rgi_area_km2(self):
"""The glacier's RGI area (km2)."""
try:
_area = self.read_shapefile('outlines')['Area']
except OSError:
raise RuntimeError('No outlines available')
except KeyError:
# RGI V7
_area = self.read_shapefile('outlines')['area_km2']
return float(_area.iloc[0])
@lazy_property
def intersects_ids(self):
"""The glacier's intersects RGI ids."""
try:
gdf = self.read_shapefile('intersects')
try:
ids = np.append(gdf['RGIId_1'], gdf['RGIId_2'])
except KeyError:
ids = np.append(gdf['rgi_g_id_1'], gdf['rgi_g_id_2'])
ids = list(np.unique(np.sort(ids)))
ids.remove(self.rgi_id)
return ids
except OSError:
return []
@lazy_property
def dem_daterange(self):
"""Years in which most of the DEM data was acquired"""
source_txt = self.get_filepath('dem_source')
if os.path.isfile(source_txt):
with open(source_txt, 'r') as f:
for line in f.readlines():
if 'Date range:' in line:
return tuple(map(int, line.split(':')[1].split('-')))
# we did not find the information in the dem_source file
log.warning('No DEM date range specified in `dem_source.txt`')
return None
@lazy_property
def dem_info(self):
"""More detailed information on the acquisition of the DEM data"""
source_file = self.get_filepath('dem_source')
source_text = ''
if os.path.isfile(source_file):
with open(source_file, 'r') as f:
for line in f.readlines():
source_text += line
else:
log.warning('No DEM source file found.')
return source_text
@property
def rgi_area_m2(self):
"""The glacier's RGI area (m2)."""
return self.rgi_area_km2 * 10**6
def get_filepath(self, filename, delete=False, filesuffix='',
_deprecation_check=True):
"""Absolute path to a specific file.
Parameters
----------
filename : str
file name (must be listed in cfg.BASENAME)
delete : bool
delete the file if exists
filesuffix : str
append a suffix to the filename (useful for model runs). Note
that the BASENAME remains same.
Returns
-------
The absolute path to the desired file
"""
if filename not in cfg.BASENAMES:
raise ValueError(filename + ' not in cfg.BASENAMES.')
fname = cfg.BASENAMES[filename]
if filesuffix:
fname = fname.split('.')
assert len(fname) == 2
fname = fname[0] + filesuffix + '.' + fname[1]
out = os.path.join(self.dir, fname)
if delete and os.path.isfile(out):
os.remove(out)
return out
def has_file(self, filename, filesuffix='', _deprecation_check=True):
"""Checks if a file exists.
Parameters
----------
filename : str
file name (must be listed in cfg.BASENAME)
filesuffix : str
append a suffix to the filename (useful for model runs). Note
that the BASENAME remains same.
"""
fp = self.get_filepath(filename, filesuffix=filesuffix,
_deprecation_check=_deprecation_check)
if '.shp' in fp and cfg.PARAMS['use_tar_shapefiles']:
fp = fp.replace('.shp', '.tar')
if cfg.PARAMS['use_compression']:
fp += '.gz'
return os.path.exists(fp)
def add_to_diagnostics(self, key, value):
"""Write a key, value pair to the gdir's runtime diagnostics.
Parameters
----------
key : str
dict entry key
value : str or number
dict entry value
"""
d = self.get_diagnostics()
d[key] = value
with open(self.get_filepath('diagnostics'), 'w') as f:
json.dump(d, f)
def get_diagnostics(self):
"""Read the gdir's runtime diagnostics.
Returns
-------
the diagnostics dict
"""
# If not there, create an empty one
if not self.has_file('diagnostics'):
with open(self.get_filepath('diagnostics'), 'w') as f:
json.dump(dict(), f)
# Read and return
with open(self.get_filepath('diagnostics'), 'r') as f:
out = json.load(f)
return out
def read_pickle(self, filename, use_compression=None, filesuffix=''):
"""Reads a pickle located in the directory.
Parameters
----------
filename : str
file name (must be listed in cfg.BASENAME)
use_compression : bool
whether or not the file ws compressed. Default is to use
cfg.PARAMS['use_compression'] for this (recommended)
filesuffix : str
append a suffix to the filename (useful for experiments).
Returns
-------
An object read from the pickle
"""
use_comp = (use_compression if use_compression is not None
else cfg.PARAMS['use_compression'])
_open = gzip.open if use_comp else open
fp = self.get_filepath(filename, filesuffix=filesuffix)
with _open(fp, 'rb') as f:
try:
out = pickle.load(f)
except ModuleNotFoundError as err:
if err.name == "shapely.io":
err.msg = "You need shapely version 2.0 or higher for this to work."
raise
# Some new attrs to add to old pre-processed directories
if filename == 'model_flowlines':
if getattr(out[0], 'map_trafo', None) is None:
try:
# This may fail for very old gdirs
grid = self.grid
except InvalidWorkflowError:
return out
# Add the trafo
trafo = partial(grid.ij_to_crs, crs=salem.wgs84)
for fl in out:
fl.map_trafo = trafo
return out
def write_pickle(self, var, filename, use_compression=None, filesuffix=''):
""" Writes a variable to a pickle on disk.
Parameters
----------
var : object
the variable to write to disk
filename : str
file name (must be listed in cfg.BASENAME)
use_compression : bool
whether or not the file ws compressed. Default is to use
cfg.PARAMS['use_compression'] for this (recommended)
filesuffix : str
append a suffix to the filename (useful for experiments).
"""
use_comp = (use_compression if use_compression is not None
else cfg.PARAMS['use_compression'])
_open = gzip.open if use_comp else open
fp = self.get_filepath(filename, filesuffix=filesuffix)
with _open(fp, 'wb') as f:
pickle.dump(var, f, protocol=4)
def read_json(self, filename, filesuffix='', allow_empty=False):
"""Reads a JSON file located in the directory.
Parameters
----------
filename : str
file name (must be listed in cfg.BASENAME)
filesuffix : str
append a suffix to the filename (useful for experiments).
allow_empty : bool
if True, does not raise an error if the file is not there.
Returns
-------
A dictionary read from the JSON file
"""
fp = self.get_filepath(filename, filesuffix=filesuffix)
if allow_empty:
try:
with open(fp, 'r') as f:
out = json.load(f)
except FileNotFoundError:
out = {}
else:
with open(fp, 'r') as f:
out = json.load(f)
return out
def write_json(self, var, filename, filesuffix=''):
""" Writes a variable to a pickle on disk.
Parameters
----------
var : object
the variable to write to JSON (must be a dictionary)
filename : str
file name (must be listed in cfg.BASENAME)
filesuffix : str
append a suffix to the filename (useful for experiments).
"""
def np_convert(o):
if isinstance(o, np.int64):
return int(o)
raise TypeError
fp = self.get_filepath(filename, filesuffix=filesuffix)
with open(fp, 'w') as f:
json.dump(var, f, default=np_convert)
def get_climate_info(self, input_filesuffix=''):
"""Convenience function to read attributes of the historical climate.
Parameters
----------
input_filesuffix : str
input_filesuffix of the climate_historical that should be used.
"""
out = {}
try:
f = self.get_filepath('climate_historical',
filesuffix=input_filesuffix)
with ncDataset(f) as nc:
out['baseline_climate_source'] = nc.climate_source
try:
out['baseline_yr_0'] = nc.yr_0
except AttributeError:
# needed for back-compatibility before v1.6
out['baseline_yr_0'] = nc.hydro_yr_0
try:
out['baseline_yr_1'] = nc.yr_1
except AttributeError:
# needed for back-compatibility before v1.6
out['baseline_yr_1'] = nc.hydro_yr_1
out['baseline_climate_ref_hgt'] = nc.ref_hgt
out['baseline_climate_ref_pix_lon'] = nc.ref_pix_lon
out['baseline_climate_ref_pix_lat'] = nc.ref_pix_lat
except FileNotFoundError:
pass
return out
def read_text(self, filename, filesuffix=''):
"""Reads a text file located in the directory.
Parameters
----------
filename : str
file name (must be listed in cfg.BASENAME)
filesuffix : str
append a suffix to the filename (useful for experiments).
Returns
-------
the text
"""
fp = self.get_filepath(filename, filesuffix=filesuffix)
with open(fp, 'r') as f:
out = f.read()
return out
@classmethod
def _read_shapefile_from_path(cls, fp):
if '.shp' not in fp:
raise ValueError('File ending not that of a shapefile')
if cfg.PARAMS['use_tar_shapefiles']:
fp = 'tar://' + fp.replace('.shp', '.tar')
if cfg.PARAMS['use_compression']:
fp += '.gz'
shp = gpd.read_file(fp)
# .properties file is created for compressed shapefiles. github: #904
_properties = fp.replace('tar://', '') + '.properties'
if os.path.isfile(_properties):
# remove it, to keep GDir slim
os.remove(_properties)
return shp
def read_shapefile(self, filename, filesuffix=''):
"""Reads a shapefile located in the directory.
Parameters
----------
filename : str
file name (must be listed in cfg.BASENAME)
filesuffix : str
append a suffix to the filename (useful for experiments).
Returns
-------
A geopandas.DataFrame
"""
fp = self.get_filepath(filename, filesuffix=filesuffix)
return self._read_shapefile_from_path(fp)
def write_shapefile(self, var, filename, filesuffix=''):
""" Writes a variable to a shapefile on disk.
Parameters
----------
var : object
the variable to write to shapefile (must be a geopandas.DataFrame)
filename : str
file name (must be listed in cfg.BASENAME)
filesuffix : str
append a suffix to the filename (useful for experiments).
"""
fp = self.get_filepath(filename, filesuffix=filesuffix)
_write_shape_to_disk(var, fp, to_tar=cfg.PARAMS['use_tar_shapefiles'])
def write_monthly_climate_file(self, time, prcp, temp,
ref_pix_hgt, ref_pix_lon, ref_pix_lat, *,
temp_std=None,
time_unit=None,
calendar=None,
source=None,
file_name='climate_historical',
filesuffix=''):
"""Creates a netCDF4 file with climate data timeseries.
Parameters
----------
time : ndarray
the time array, in a format understood by netCDF4
prcp : ndarray
the precipitation array (unit: 'kg m-2 month-1')
temp : ndarray
the temperature array (unit: 'degC')
ref_pix_hgt : float
the elevation of the dataset's reference altitude
(for correction). In practice, it is the same altitude as the
baseline climate.
ref_pix_lon : float
the location of the gridded data's grid point
ref_pix_lat : float
the location of the gridded data's grid point
temp_std : ndarray, optional
the daily standard deviation of temperature (useful for PyGEM)
time_unit : str
the reference time unit for your time array. This should be chosen
depending on the length of your data. The default is to choose
it ourselves based on the starting year.
calendar : str
If you use an exotic calendar (e.g. 'noleap')
source : str
the climate data source (required)
file_name : str
How to name the file
filesuffix : str
Apply a suffix to the file
"""
# overwrite as default
fpath = self.get_filepath(file_name, filesuffix=filesuffix)
if os.path.exists(fpath):
os.remove(fpath)
if source is None:
raise InvalidParamsError('`source` kwarg is required')
zlib = cfg.PARAMS['compress_climate_netcdf']
try:
y0 = time[0].year
y1 = time[-1].year
except AttributeError:
time = pd.DatetimeIndex(time)
y0 = time[0].year
y1 = time[-1].year
if time_unit is None:
# http://pandas.pydata.org/pandas-docs/stable/timeseries.html
# #timestamp-limitations
if y0 > 1800:
time_unit = 'days since 1801-01-01 00:00:00'
elif y0 >= 0:
time_unit = ('days since {:04d}-01-01 '
'00:00:00'.format(time[0].year))
else:
raise InvalidParamsError('Time format not supported')
with ncDataset(fpath, 'w', format='NETCDF4') as nc:
nc.ref_hgt = ref_pix_hgt
nc.ref_pix_lon = ref_pix_lon
nc.ref_pix_lat = ref_pix_lat
nc.ref_pix_dis = haversine(self.cenlon, self.cenlat,
ref_pix_lon, ref_pix_lat)
nc.climate_source = source
nc.yr_0 = y0
nc.yr_1 = y1
nc.createDimension('time', None)
nc.author = 'OGGM'
nc.author_info = 'Open Global Glacier Model'
timev = nc.createVariable('time', 'i4', ('time',))
tatts = {'units': time_unit}
if calendar is None:
calendar = 'standard'
tatts['calendar'] = calendar
try:
numdate = netCDF4.date2num([t for t in time], time_unit,
calendar=calendar)
except TypeError:
# numpy's broken datetime only works for us precision
time = time.astype('M8[us]').astype(datetime.datetime)
numdate = netCDF4.date2num(time, time_unit, calendar=calendar)
timev.setncatts(tatts)
timev[:] = numdate
v = nc.createVariable('prcp', 'f4', ('time',), zlib=zlib)
v.units = 'kg m-2'
v.long_name = 'total monthly precipitation amount'
v[:] = prcp
v = nc.createVariable('temp', 'f4', ('time',), zlib=zlib)
v.units = 'degC'
v.long_name = '2m temperature at height ref_hgt'
v[:] = temp
if temp_std is not None:
v = nc.createVariable('temp_std', 'f4', ('time',), zlib=zlib)
v.units = 'degC'
v.long_name = 'standard deviation of daily temperatures'
v[:] = temp_std
def get_inversion_flowline_hw(self):
""" Shortcut function to read the heights and widths of the glacier.
Parameters
----------
Returns
-------
(height, widths) in units of m
"""
h = np.array([])
w = np.array([])
fls = self.read_pickle('inversion_flowlines')
for fl in fls:
w = np.append(w, fl.widths)
h = np.append(h, fl.surface_h)
return h, w * self.grid.dx
def set_ref_mb_data(self, mb_df=None):
"""Adds reference mass balance data to this glacier.
The format should be a dataframe with the years as index and
'ANNUAL_BALANCE' as values in mm yr-1.
"""
if self.is_tidewater:
log.warning('You are trying to set MB data on a tidewater glacier!'
' These data will be ignored by the MB model '
'calibration routine.')
if mb_df is None:
flink, mbdatadir = get_wgms_files()
c = 'RGI{}0_ID'.format(self.rgi_version[0])
wid = flink.loc[flink[c] == self.rgi_id]
if len(wid) == 0:
raise RuntimeError('Not a reference glacier!')
wid = wid.WGMS_ID.values[0]
# file
reff = os.path.join(mbdatadir,
'mbdata_WGMS-{:05d}.csv'.format(wid))
# list of years
mb_df = pd.read_csv(reff).set_index('YEAR')
# Quality checks
if 'ANNUAL_BALANCE' not in mb_df:
raise InvalidParamsError('Need an "ANNUAL_BALANCE" column in the '
'dataframe.')
mb_df.index.name = 'YEAR'
self._mbdf = mb_df
def get_ref_mb_data(self, y0=None, y1=None, input_filesuffix=''):
"""Get the reference mb data from WGMS (for some glaciers only!).
Raises an Error if it isn't a reference glacier at all.
Parameters
----------
y0 : int
override the default behavior which is to check the available
climate data (or PARAMS['ref_mb_valid_window']) and decide
y1 : int
override the default behavior which is to check the available
climate data (or PARAMS['ref_mb_valid_window']) and decide
input_filesuffix : str
input_filesuffix of the climate_historical that should be used
if y0 and y1 are not given. The default is to take the
climate_historical without input_filesuffix
"""
if self._mbdf is None:
self.set_ref_mb_data()
# logic for period
t0, t1 = cfg.PARAMS['ref_mb_valid_window']
if t0 > 0 and y0 is None:
y0 = t0
if t1 > 0 and y1 is None:
y1 = t1
if y0 is None or y1 is None:
ci = self.get_climate_info(input_filesuffix=input_filesuffix)
if 'baseline_yr_0' not in ci:
raise InvalidWorkflowError('Please process some climate data '
'before call')
y0 = ci['baseline_yr_0'] if y0 is None else y0
y1 = ci['baseline_yr_1'] if y1 is None else y1
if len(self._mbdf) > 1:
out = self._mbdf.loc[y0:y1]
else:
# Some files are just empty
out = self._mbdf
return out.dropna(subset=['ANNUAL_BALANCE'])
def get_ref_mb_profile(self, input_filesuffix='', constant_dh=False, obs_ratio_needed=0):
"""Get the reference mb profile data from WGMS (if available!).
Returns None if this glacier has no profile and an Error if it isn't
a reference glacier at all.
Parameters
----------
input_filesuffix : str
input_filesuffix of the climate_historical that should be used. The
default is to take the climate_historical without input_filesuffix
constant_dh : boolean
If set to True, it outputs the MB profiles with a constant step size
of dh=50m by using interpolation. This can be useful for comparisons
between years. Default is False which gives the raw
elevation-dependent point MB
obs_ratio_needed : float
necessary relative amount of observations per elevation band in order
to be included in the MB profile (0<=obs_ratio_needed<=1).
If obs_ratio_needed set to 0, the output shows all elevation-band
observations (default is 0).
When estimating mean MB profiles, it is advisable to set obs_ratio_needed
to 0.6. E.g. if there are in total 5 years of measurements only those elevation
bands with at least 3 years of measurements are used. If obs_ratio_needed is not
0, constant_dh has to be set to True.
"""
if obs_ratio_needed != 0 and constant_dh is False:
raise InvalidParamsError('If a filter is applied, you have to set'
' constant_dh to True')
if obs_ratio_needed < 0 or obs_ratio_needed > 1:
raise InvalidParamsError('obs_ratio_needed is the ratio of necessary relative amount'
'of observations per elevation band. It has to be between'
'0 and 1!')
if self._mbprofdf is None and not constant_dh:
flink, mbdatadir = get_wgms_files()
c = 'RGI{}0_ID'.format(self.rgi_version[0])
wid = flink.loc[flink[c] == self.rgi_id]
if len(wid) == 0:
raise RuntimeError('Not a reference glacier!')
wid = wid.WGMS_ID.values[0]
# file
mbdatadir = os.path.join(os.path.dirname(mbdatadir), 'mb_profiles')
reff = os.path.join(mbdatadir,
'profile_WGMS-{:05d}.csv'.format(wid))
if not os.path.exists(reff):
return None
# list of years
self._mbprofdf = pd.read_csv(reff, index_col=0)
if self._mbprofdf_cte_dh is None and constant_dh:
flink, mbdatadir = get_wgms_files()
c = 'RGI{}0_ID'.format(self.rgi_version[0])
wid = flink.loc[flink[c] == self.rgi_id]
if len(wid) == 0:
raise RuntimeError('Not a reference glacier!')
wid = wid.WGMS_ID.values[0]
# file
mbdatadir = os.path.join(os.path.dirname(mbdatadir), 'mb_profiles_constant_dh')
reff = os.path.join(mbdatadir,
'profile_constant_dh_WGMS-{:05d}.csv'.format(wid))
if not os.path.exists(reff):
return None
# list of years
self._mbprofdf_cte_dh = pd.read_csv(reff, index_col=0)
ci = self.get_climate_info(input_filesuffix=input_filesuffix)
if 'baseline_yr_0' not in ci:
raise RuntimeError('Please process some climate data before call')
y0 = ci['baseline_yr_0']
y1 = ci['baseline_yr_1']
if not constant_dh:
if len(self._mbprofdf) > 1:
out = self._mbprofdf.loc[y0:y1]
else:
# Some files are just empty
out = self._mbprofdf
else:
if len(self._mbprofdf_cte_dh) > 1:
out = self._mbprofdf_cte_dh.loc[y0:y1]
if obs_ratio_needed != 0:
# amount of years with any observation
n_obs = len(out.index)
# amount of years with observations for each elevation band
n_obs_h = out.describe().loc['count']
# relative amount of observations per elevation band
rel_obs_h = n_obs_h / n_obs
# select only those elevation bands with a specific ratio
# of years with available measurements
out = out[rel_obs_h[rel_obs_h >= obs_ratio_needed].index]
else:
# Some files are just empty
out = self._mbprofdf_cte_dh
out.columns = [float(c) for c in out.columns]
return out.dropna(axis=1, how='all').dropna(axis=0, how='all')
def get_ref_length_data(self):
"""Get the glacier length data from P. Leclercq's data base.
https://folk.uio.no/paulwl/data.php
For some glaciers only!
"""
df = pd.read_csv(get_demo_file('rgi_leclercq_links_2014_RGIV6.csv'))
df = df.loc[df.RGI_ID == self.rgi_id]
if len(df) == 0:
raise RuntimeError('No length data found for this glacier!')
ide = df.LID.values[0]
f = get_demo_file('Glacier_Lengths_Leclercq.nc')
with xr.open_dataset(f) as dsg:
# The database is not sorted by ID. Don't ask me...
grp_id = np.argwhere(dsg['index'].values == ide)[0][0] + 1
with xr.open_dataset(f, group=str(grp_id)) as ds:
df = ds.to_dataframe()
df.name = ds.glacier_name
return df
def log(self, task_name, *, err=None, task_time=None):
"""Logs a message to the glacier directory.
It is usually called by the :py:class:`entity_task` decorator, normally
you shouldn't take care about that.
Parameters
----------
func : a function
the function which wants to log
err : Exception
the exception which has been raised by func (if no exception was
raised, a success is logged)
time : float
the time (in seconds) that the task needed to run
"""
# a line per function call
nowsrt = datetime.datetime.now().strftime('%Y-%m-%dT%H:%M:%S')
line = nowsrt + ';' + task_name + ';'
if task_time is not None:
line += 'time:{};'.format(task_time)
if err is None:
line += 'SUCCESS'
else:
line += err.__class__.__name__ + ': {}'.format(err)\
line = line.replace('\n', ' ')
count = 0
while count < 5:
try:
with open(self.logfile, 'a') as logfile:
logfile.write(line + '\n')
break
except FileNotFoundError:
# I really don't know when this error happens
# In this case sleep and try again
time.sleep(0.05)
count += 1
if count == 5:
log.warning('Could not write to logfile: ' + line)
def get_task_status(self, task_name):
"""Opens this directory's log file to check for a task's outcome.
Parameters
----------
task_name : str
the name of the task which has to be tested for
Returns
-------
The last message for this task (SUCCESS if was successful),
None if the task was not run yet
"""
if not os.path.isfile(self.logfile):
return None
with open(self.logfile) as logfile:
lines = logfile.readlines()
lines = [l.replace('\n', '') for l in lines
if ';' in l and (task_name == l.split(';')[1])]
if lines:
# keep only the last log
return lines[-1].split(';')[-1]
else:
return None
def get_task_time(self, task_name):
"""Opens this directory's log file to check for a task's run time.
Parameters
----------
task_name : str
the name of the task which has to be tested for
Returns
-------
The timing that the last call of this task needed.
None if the task was not run yet, or if it errored
"""
if not os.path.isfile(self.logfile):
return None
with open(self.logfile) as logfile:
lines = logfile.readlines()
lines = [l.replace('\n', '') for l in lines
if task_name == l.split(';')[1]]
if lines:
line = lines[-1]
# Last log is message
if 'ERROR' in line.split(';')[-1] or 'time:' not in line:
return None
# Get the time
return float(line.split('time:')[-1].split(';')[0])
else:
return None
def get_error_log(self):
"""Reads the directory's log file to find the invalid task (if any).
Returns
-------
The first error message in this log, None if all good
"""
if not os.path.isfile(self.logfile):
return None
with open(self.logfile) as logfile:
lines = logfile.readlines()
for l in lines:
if 'SUCCESS' in l:
continue
return l.replace('\n', '')
# OK all good
return None
[docs]@entity_task(log)
def copy_to_basedir(gdir, base_dir=None, setup='run'):
"""Copies the glacier directories and their content to a new location.
This utility function allows to select certain files only, thus
saving time at copy.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
the glacier directory to copy
base_dir : str
path to the new base directory (should end with "per_glacier"
most of the time)
setup : str
set up you want the copied directory to be useful for. Currently
supported are 'all' (copy the entire directory), 'inversion'
(copy the necessary files for the inversion AND the run)
, 'run' (copy the necessary files for a dynamical run) or 'run/spinup'
(copy the necessary files and all already conducted model runs, e.g.
from a dynamic spinup).
Returns
-------
New glacier directories from the copied folders
"""
base_dir = os.path.abspath(base_dir)
new_dir = os.path.join(base_dir, gdir.rgi_id[:8], gdir.rgi_id[:11],
gdir.rgi_id)
if setup == 'run':
paths = ['model_flowlines', 'inversion_params', 'outlines',
'mb_calib', 'climate_historical', 'glacier_grid',
'gcm_data', 'diagnostics', 'log']
paths = ('*' + p + '*' for p in paths)
shutil.copytree(gdir.dir, new_dir,
ignore=include_patterns(*paths))
elif setup == 'inversion':
paths = ['inversion_params', 'downstream_line', 'outlines',
'inversion_flowlines', 'glacier_grid', 'diagnostics',
'mb_calib', 'climate_historical', 'gridded_data',
'gcm_data', 'log']
paths = ('*' + p + '*' for p in paths)
shutil.copytree(gdir.dir, new_dir,
ignore=include_patterns(*paths))
elif setup == 'run/spinup':
paths = ['model_flowlines', 'inversion_params', 'outlines',
'mb_calib', 'climate_historical', 'glacier_grid',
'gcm_data', 'diagnostics', 'log', 'model_run',
'model_diagnostics', 'model_geometry']
paths = ('*' + p + '*' for p in paths)
shutil.copytree(gdir.dir, new_dir,
ignore=include_patterns(*paths))
elif setup == 'all':
shutil.copytree(gdir.dir, new_dir)
else:
raise ValueError('setup not understood: {}'.format(setup))
return GlacierDirectory(gdir.rgi_id, base_dir=base_dir)
def initialize_merged_gdir(main, tribs=[], glcdf=None,
filename='climate_historical',
input_filesuffix='',
dem_source=None):
"""Creates a new GlacierDirectory if tributaries are merged to a glacier
This function should be called after centerlines.intersect_downstream_lines
and before flowline.merge_tributary_flowlines.
It will create a new GlacierDirectory, with a suitable DEM and reproject
the flowlines of the main glacier.
Parameters
----------
main : oggm.GlacierDirectory
the main glacier
tribs : list or dictionary containing oggm.GlacierDirectories
true tributary glaciers to the main glacier
glcdf: geopandas.GeoDataFrame
which contains the main glacier, will be downloaded if None
filename: str
Baseline climate file
input_filesuffix: str
Filesuffix to the climate file
dem_source: str
the DEM source to use
Returns
-------
merged : oggm.GlacierDirectory
the new GDir
"""
from oggm.core.gis import define_glacier_region, merged_glacier_masks
# If its a dict, select the relevant ones
if isinstance(tribs, dict):
tribs = tribs[main.rgi_id]
# make sure tributaries are iterable
tribs = tolist(tribs)
# read flowlines of the Main glacier
mfls = main.read_pickle('model_flowlines')
# ------------------------------
# 0. create the new GlacierDirectory from main glaciers GeoDataFrame
# Should be passed along, if not download it
if glcdf is None:
glcdf = get_rgi_glacier_entities([main.rgi_id])
# Get index location of the specific glacier
idx = glcdf.loc[glcdf.RGIId == main.rgi_id].index
maindf = glcdf.loc[idx].copy()
# add tributary geometries to maindf
merged_geometry = maindf.loc[idx, 'geometry'].iloc[0].buffer(0)
for trib in tribs:
geom = trib.read_pickle('geometries')['polygon_hr']
geom = salem.transform_geometry(geom, crs=trib.grid)
merged_geometry = merged_geometry.union(geom).buffer(0)
# to get the center point, maximal extensions for DEM and single Polygon:
new_geometry = merged_geometry.convex_hull
maindf.loc[idx, 'geometry'] = new_geometry
# make some adjustments to the rgi dataframe
# 1. calculate central point of new glacier
# reproject twice to avoid Warning, first to flat projection
flat_centroid = salem.transform_geometry(new_geometry,
to_crs=main.grid).centroid
# second reprojection of centroid to wgms
new_centroid = salem.transform_geometry(flat_centroid, crs=main.grid)
maindf.loc[idx, 'CenLon'] = new_centroid.x
maindf.loc[idx, 'CenLat'] = new_centroid.y
# 2. update names
maindf.loc[idx, 'RGIId'] += '_merged'
if maindf.loc[idx, 'Name'].iloc[0] is None:
maindf.loc[idx, 'Name'] = main.name + ' (merged)'
else:
maindf.loc[idx, 'Name'] += ' (merged)'
# finally create new Glacier Directory
# 1. set dx spacing to the one used for the flowlines
dx_method = cfg.PARAMS['grid_dx_method']
dx_spacing = cfg.PARAMS['fixed_dx']
cfg.PARAMS['grid_dx_method'] = 'fixed'
cfg.PARAMS['fixed_dx'] = mfls[-1].map_dx
merged = GlacierDirectory(maindf.loc[idx].iloc[0])
# run define_glacier_region to get a fitting DEM and proper grid
define_glacier_region(merged, entity=maindf.loc[idx].iloc[0],
source=dem_source)
# write gridded data and geometries for visualization
merged_glacier_masks(merged, merged_geometry)
# reset dx method
cfg.PARAMS['grid_dx_method'] = dx_method
cfg.PARAMS['fixed_dx'] = dx_spacing
# copy main climate file, climate info and calib to new gdir
climfilename = filename + '_' + main.rgi_id + input_filesuffix + '.nc'
climfile = os.path.join(merged.dir, climfilename)
shutil.copyfile(main.get_filepath(filename, filesuffix=input_filesuffix),
climfile)
_mufile = os.path.basename(merged.get_filepath('mb_calib')).split('.')
mufile = _mufile[0] + '_' + main.rgi_id + '.' + _mufile[1]
shutil.copyfile(main.get_filepath('mb_calib'),
os.path.join(merged.dir, mufile))
# reproject the flowlines to the new grid
for nr, fl in reversed(list(enumerate(mfls))):
# 1. Step: Change projection to the main glaciers grid
_line = salem.transform_geometry(fl.line,
crs=main.grid, to_crs=merged.grid)
# 2. set new line
fl.set_line(_line)
# 3. set flow to attributes
if fl.flows_to is not None:
fl.set_flows_to(fl.flows_to)
# remove inflow points, will be set by other flowlines if need be
fl.inflow_points = []
# 5. set grid size attributes
dx = [shpg.Point(fl.line.coords[i]).distance(
shpg.Point(fl.line.coords[i+1]))
for i, pt in enumerate(fl.line.coords[:-1])] # get distance
# and check if equally spaced
if not np.allclose(dx, np.mean(dx), atol=1e-2):
raise RuntimeError('Flowline is not evenly spaced.')
# dx might very slightly change, but should not
fl.dx = np.mean(dx).round(2)
# map_dx should stay exactly the same
# fl.map_dx = mfls[-1].map_dx
fl.dx_meter = fl.map_dx * fl.dx
# replace flowline within the list
mfls[nr] = fl
# Write the reprojecflowlines
merged.write_pickle(mfls, 'model_flowlines')
return merged
[docs]@entity_task(log)
def gdir_to_tar(gdir, base_dir=None, delete=True):
"""Writes the content of a glacier directory to a tar file.
The tar file is located at the same location of the original directory.
The glacier directory objects are useless if deleted!
Parameters
----------
base_dir : str
path to the basedir where to write the directory (defaults to the
same location of the original directory)
delete : bool
delete the original directory afterwards (default)
Returns
-------
the path to the tar file
"""
source_dir = os.path.normpath(gdir.dir)
opath = source_dir + '.tar.gz'
if base_dir is not None:
opath = os.path.join(base_dir, os.path.relpath(opath, gdir.base_dir))
mkdir(os.path.dirname(opath))
with tarfile.open(opath, "w:gz") as tar:
tar.add(source_dir, arcname=os.path.basename(source_dir))
if delete:
shutil.rmtree(source_dir)
return opath
[docs]def base_dir_to_tar(base_dir=None, delete=True):
"""Merge the directories into 1000 bundles as tar files.
The tar file is located at the same location of the original directory.
Parameters
----------
base_dir : str
path to the basedir to parse (defaults to the working directory)
to_base_dir : str
path to the basedir where to write the directory (defaults to the
same location of the original directory)
delete : bool
delete the original directory tars afterwards (default)
"""
if base_dir is None:
if not cfg.PATHS.get('working_dir', None):
raise ValueError("Need a valid PATHS['working_dir']!")
base_dir = os.path.join(cfg.PATHS['working_dir'], 'per_glacier')
to_delete = []
for dirname, subdirlist, filelist in os.walk(base_dir):
# RGI60-01.00
bname = os.path.basename(dirname)
# second argument for RGI7 naming convention
if not ((len(bname) == 11 and bname[-3] == '.') or
(len(bname) == 20 and bname[-3] == '-')):
continue
opath = dirname + '.tar'
with tarfile.open(opath, 'w') as tar:
tar.add(dirname, arcname=os.path.basename(dirname))
if delete:
to_delete.append(dirname)
for dirname in to_delete:
shutil.rmtree(dirname)
