"""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 datetime
import logging
import pickle
import warnings
from collections import OrderedDict
from functools import partial, wraps
from time import gmtime, strftime
import fnmatch
import platform
import struct
import importlib
# External libs
import geopandas as gpd
import pandas as pd
import salem
from salem import lazy_property, wgs84
import numpy as np
import netCDF4
from scipy import stats
import xarray as xr
import shapely.geometry as shpg
from shapely.ops import transform as shp_trafo
# Locals
from oggm import __version__
from oggm.utils._funcs import (calendardate_to_hydrodate, date_to_floatyear,
tolist, filter_rgi_name, parse_rgi_meta,
haversine)
from oggm.utils._downloads import (get_demo_file, get_wgms_files,
get_rgi_glacier_entities)
from oggm import cfg
from oggm.exceptions import InvalidParamsError
# Default RGI date (median per region)
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)
def get_sys_info():
"""Returns system information as a dict"""
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
[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
"""
_print = print if logger is None else logger.workflow
sys_info = get_sys_info()
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__),
("osgeo.gdal", lambda mod: mod.__version__),
("pyproj", lambda mod: mod.__version__),
("shapely", lambda mod: mod.__version__),
("xarray", 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))
out = ["# 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))
for l in out:
_print(l)
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=100):
"""Instanciate.
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
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 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)
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.ERROR)
def __exit__(self, a, b, c):
logging.disable(logging.NOTSET)
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: ``@oggm_task(writes=['dem', 'outlines'])``
Parameters
----------
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
"""
self.log = log
self.writes = writes
self.fallback = fallback
cnt = [' Notes']
cnt += [' -----']
cnt += [' Files writen 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, **kwargs):
if reset is None:
reset = not cfg.PARAMS['auto_skip_task']
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:
out = task_func(gdir, **kwargs)
if task_name != 'gdir_to_tar':
gdir.log(task_name)
except Exception as err:
# Something happened
out = None
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 cfg.PARAMS['continue_on_error']:
raise
if self.fallback is not None:
self.fallback(gdir)
return out
_entity_task.__dict__['is_entity_task'] = True
return _entity_task
def global_task(task_func):
"""
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.
"""
task_func.__dict__['global_task'] = True
return task_func
def _get_centerline_lonlat(gdir):
"""Quick n dirty solution to write the centerlines as a shapefile"""
cls = gdir.read_pickle('centerlines')
olist = []
for j, cl in enumerate(cls[::-1]):
mm = 1 if j == 0 else 0
gs = gpd.GeoSeries()
gs['RGIID'] = gdir.rgi_id
gs['LE_SEGMENT'] = np.rint(np.max(cl.dis_on_line) * gdir.grid.dx)
gs['MAIN'] = mm
tra_func = partial(gdir.grid.ij_to_crs, crs=wgs84)
gs['geometry'] = shp_trafo(tra_func, cl.line)
olist.append(gs)
return olist
def write_centerlines_to_shape(gdirs, filesuffix='', path=True):
"""Write the centerlines in a shapefile.
Parameters
----------
gdirs: the list of GlacierDir to process.
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
"""
if path is True:
path = os.path.join(cfg.PATHS['working_dir'],
'glacier_centerlines' + filesuffix + '.shp')
olist = []
for gdir in gdirs:
olist.extend(_get_centerline_lonlat(gdir))
odf = gpd.GeoDataFrame(olist)
shema = dict()
props = OrderedDict()
props['RGIID'] = 'str:14'
props['LE_SEGMENT'] = 'int:9'
props['MAIN'] = 'int:9'
shema['geometry'] = 'LineString'
shema['properties'] = props
crs = {'init': 'epsg:4326'}
# some writing function from geopandas rep
from shapely.geometry import mapping
import fiona
def feature(i, row):
return {
'id': str(i),
'type': 'Feature',
'properties':
dict((k, v) for k, v in row.items() if k != 'geometry'),
'geometry': mapping(row['geometry'])}
with fiona.open(path, 'w', driver='ESRI Shapefile',
crs=crs, schema=shema) as c:
for i, row in odf.iterrows():
c.write(feature(i, row))
def demo_glacier_id(key):
"""Get the RGI id of a glacier by name or key: None if not found."""
df = cfg.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
[docs]def compile_run_output(gdirs, path=True, filesuffix='', use_compression=True):
"""Merge 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.
filesuffix : str
the filesuffix of the run
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]
# The first gdir might have blown up, try some others
i = 0
while True:
if i >= len(gdirs):
raise RuntimeError('Found no valid glaciers!')
try:
ppath = gdirs[i].get_filepath('model_diagnostics',
filesuffix=filesuffix)
with xr.open_dataset(ppath) as ds_diag:
ds_diag.time.values
break
except BaseException:
i += 1
# OK found it, open it and prepare the output
with xr.open_dataset(ppath) as ds_diag:
time = ds_diag.time.values
yrs = ds_diag.hydro_year.values
months = ds_diag.hydro_month.values
cyrs = ds_diag.calendar_year.values
cmonths = ds_diag.calendar_month.values
# 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())
# Coordinates
ds.coords['time'] = ('time', time)
ds.coords['rgi_id'] = ('rgi_id', rgi_ids)
ds.coords['hydro_year'] = ('time', yrs)
ds.coords['hydro_month'] = ('time', months)
ds.coords['calendar_year'] = ('time', cyrs)
ds.coords['calendar_month'] = ('time', cmonths)
ds['time'].attrs['description'] = 'Floating hydrological year'
ds['rgi_id'].attrs['description'] = 'RGI glacier identifier'
ds['hydro_year'].attrs['description'] = 'Hydrological year'
ds['hydro_month'].attrs['description'] = 'Hydrological month'
ds['calendar_year'].attrs['description'] = 'Calendar year'
ds['calendar_month'].attrs['description'] = 'Calendar month'
shape = (len(time), len(rgi_ids))
vol = np.zeros(shape)
area = np.zeros(shape)
length = np.zeros(shape)
ela = np.zeros(shape)
for i, gdir in enumerate(gdirs):
try:
ppath = gdir.get_filepath('model_diagnostics',
filesuffix=filesuffix)
with xr.open_dataset(ppath) as ds_diag:
vol[:, i] = ds_diag.volume_m3.values
area[:, i] = ds_diag.area_m2.values
length[:, i] = ds_diag.length_m.values
ela[:, i] = ds_diag.ela_m.values
except BaseException:
vol[:, i] = np.NaN
area[:, i] = np.NaN
length[:, i] = np.NaN
ela[:, i] = np.NaN
ds['volume'] = (('time', 'rgi_id'), vol)
ds['volume'].attrs['description'] = 'Total glacier volume'
ds['volume'].attrs['units'] = 'm 3'
ds['area'] = (('time', 'rgi_id'), area)
ds['area'].attrs['description'] = 'Total glacier area'
ds['area'].attrs['units'] = 'm 2'
ds['length'] = (('time', 'rgi_id'), length)
ds['length'].attrs['description'] = 'Glacier length'
ds['length'].attrs['units'] = 'm'
ds['ela'] = (('time', 'rgi_id'), ela)
ds['ela'].attrs['description'] = 'Glacier Equilibrium Line Altitude (ELA)'
ds['ela'].attrs['units'] = 'm a.s.l'
if path:
if path is True:
path = os.path.join(cfg.PATHS['working_dir'],
'run_output' + filesuffix + '.nc')
enc_var = {'dtype': 'float32'}
if use_compression:
enc_var['complevel'] = 5
enc_var['zlib'] = True
encoding = {v: enc_var for v in ['volume', 'area', 'length', 'ela']}
ds.to_netcdf(path, encoding=encoding)
return ds
[docs]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
@entity_task(log)
def glacier_statistics(gdir, inversion_only=False):
"""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)
"""
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. 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
try:
# Inversion
if gdir.has_file('inversion_output'):
vol = []
cl = gdir.read_pickle('inversion_output')
for c in cl:
vol.extend(c['volume'])
d['inv_volume_km3'] = np.nansum(vol) * 1e-9
area = gdir.rgi_area_km2
d['inv_thickness_m'] = d['inv_volume_km3'] / area * 1000
d['vas_volume_km3'] = 0.034 * (area ** 1.375)
d['vas_thickness_m'] = d['vas_volume_km3'] / area * 1000
except BaseException:
pass
try:
# Calving
all_calving_data = []
all_width = []
cl = gdir.read_pickle('calving_output')
for c in cl:
all_calving_data = c['calving_fluxes'][-1]
all_width = c['t_width']
d['calving_flux'] = all_calving_data
d['calving_front_width'] = all_width
except BaseException:
pass
if inversion_only:
return d
try:
# Diagnostics
diags = gdir.get_diagnostics()
for k, v in diags.items():
d[k] = v
except BaseException:
pass
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]
except BaseException:
pass
try:
# Masks related stuff
fpath = gdir.get_filepath('gridded_data')
with ncDataset(fpath) as nc:
mask = nc.variables['glacier_mask'][:]
topo = nc.variables['topo'][:]
d['dem_mean_elev'] = np.mean(topo[np.where(mask == 1)])
d['dem_med_elev'] = np.median(topo[np.where(mask == 1)])
d['dem_min_elev'] = np.min(topo[np.where(mask == 1)])
d['dem_max_elev'] = np.max(topo[np.where(mask == 1)])
except BaseException:
pass
try:
# Ext related stuff
fpath = gdir.get_filepath('gridded_data')
with ncDataset(fpath) as nc:
ext = nc.variables['glacier_ext'][:]
mask = nc.variables['glacier_mask'][:]
topo = nc.variables['topo'][:]
d['dem_max_elev_on_ext'] = np.max(topo[np.where(ext == 1)])
d['dem_min_elev_on_ext'] = np.min(topo[np.where(ext == 1)])
a = np.sum(mask & (topo > d['dem_max_elev_on_ext']))
d['dem_perc_area_above_max_elev_on_ext'] = a / np.sum(mask)
except BaseException:
pass
try:
# Centerlines
cls = gdir.read_pickle('centerlines')
longuest = 0.
for cl in cls:
longuest = np.max([longuest, cl.dis_on_line[-1]])
d['n_centerlines'] = len(cls)
d['longuest_centerline_km'] = longuest * 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 * dx)
slope = np.append(slope, np.arctan(-np.gradient(hgt, 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_width'] = np.mean(widths)
d['flowline_avg_slope'] = np.mean(slope)
except BaseException:
pass
try:
# MB calib
df = gdir.read_json('local_mustar')
d['t_star'] = df['t_star']
d['mu_star_glacierwide'] = df['mu_star_glacierwide']
d['mu_star_flowline_avg'] = df['mu_star_flowline_avg']
d['mu_star_allsame'] = df['mu_star_allsame']
d['mb_bias'] = df['bias']
except BaseException:
pass
return d
[docs]def compile_glacier_statistics(gdirs, filesuffix='', path=True,
inversion_only=False):
"""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)
"""
from oggm.workflow import execute_entity_task
out_df = execute_entity_task(glacier_statistics, gdirs,
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 climate_statistics(gdir, add_climate_period=1995):
"""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
----------
add_climate_period : int or list of ints
compile climate statistics for the 30 yrs period around the selected
date.
"""
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
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 * dx)
slope = np.append(slope, np.arctan(-np.gradient(hgt, 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
try:
# Climate and MB at t*
mbcl = ConstantMassBalance
mbmod = MultipleFlowlineMassBalance(gdir, mb_model_class=mbcl,
bias=0,
use_inversion_flowlines=True)
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['tstar_aar'] = np.sum(w[pacc]) / np.sum(w)
try:
# Try to get the slope
mb_slope, _, _, _, _ = stats.linregress(h[pab], mbh[pab])
d['tstar_mb_grad'] = mb_slope
except BaseException:
# we don't mind if something goes wrong
d['tstar_mb_grad'] = np.NaN
d['tstar_ela_h'] = mbmod.get_ela()
# Climate
t, tm, p, ps = mbmod.flowline_mb_models[0].get_climate(
[d['tstar_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['tstar_avg_' + n + '_ela_h'] = v[0]
d['tstar_avg_' + n + '_mean_elev'] = v[1]
d['tstar_avg_' + n + '_max_elev'] = v[2]
d['tstar_avg_' + n + '_min_elev'] = v[3]
d['tstar_avg_prcp'] = p[0]
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 - 15, y0 + 15)
mbcl = ConstantMassBalance
mbmod = MultipleFlowlineMassBalance(gdir, mb_model_class=mbcl,
y0=y0,
use_inversion_flowlines=True)
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_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
def compile_climate_statistics(gdirs, filesuffix='', path=True,
add_climate_period=1995):
"""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.
"""
from oggm.workflow import execute_entity_task
out_df = execute_entity_task(climate_statistics, gdirs,
add_climate_period=add_climate_period)
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'],
('climate_statistics' +
filesuffix + '.csv')))
else:
out.to_csv(path)
return out
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)
fl.widths = widths_m / map_dx
fl.is_rectangular = np.ones(fl.nx).astype(np.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 _robust_extract(to_dir, *args, **kwargs):
"""For some obscure reason this operation randomly fails.
Try to make it more robust.
"""
count = 0
while count < 5:
try:
if count > 1:
time.sleep(random.uniform(0.05, 0.1))
with tarfile.open(*args, **kwargs) as tf:
tf.extractall(os.path.dirname(to_dir))
break
except FileExistsError:
count += 1
if count == 5:
raise
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))
with tarfile.open(base_tar, 'r') as tf:
i_from_tar = tf.getmember(os.path.join(dirbname, bname))
_robust_extract(to_dir, fileobj=tf.extractfile(i_from_tar))
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
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 name
name : str
The RGI glacier name (if Available)
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 caving glacier?
inversion_calving_rate : float
Calving rate used for the inversion
"""
[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[:8], rgi_entity[:11],
rgi_entity)
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[:8], rgi_entity[:11],
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]])
else:
g = rgi_entity['geometry']
xx, yy = ([g.bounds[0], g.bounds[2]],
[g.bounds[1], g.bounds[3]])
# Extent of the glacier in lon/lat
self.extent_ll = [xx, yy]
try:
# RGI V4?
rgi_entity.RGIID
raise ValueError('RGI Version 4 is not supported anymore')
except AttributeError:
pass
# Should be V5
self.rgi_id = rgi_entity.RGIId
self.glims_id = rgi_entity.GLIMSId
self.cenlon = float(rgi_entity.CenLon)
self.cenlat = float(rgi_entity.CenLat)
self.rgi_region = '{:02d}'.format(int(rgi_entity.O1Region))
self.rgi_subregion = (self.rgi_region + '-' +
'{:02d}'.format(int(rgi_entity.O2Region)))
name = rgi_entity.Name
rgi_datestr = rgi_entity.BgnDate
try:
gtype = rgi_entity.GlacType
except AttributeError:
# RGI V6
gtype = [str(rgi_entity.Form), str(rgi_entity.TermType)]
try:
gstatus = rgi_entity.RGIFlag[0]
except AttributeError:
# RGI V6
gstatus = rgi_entity.Status
# rgi version can be useful
self.rgi_version = self.rgi_id.split('-')[0][-2:]
if self.rgi_version not in ['50', '60', '61']:
raise RuntimeError('RGI Version not supported: '
'{}'.format(self.rgi_version))
# remove spurious characters and trailing blanks
self.name = filter_rgi_name(name)
# region
reg_names, subreg_names = parse_rgi_meta(version=self.rgi_version[0])
n = reg_names.loc[int(self.rgi_region)].values[0]
self.rgi_region_name = self.rgi_region + ': ' + n
try:
n = subreg_names.loc[self.rgi_subregion].values[0]
self.rgi_subregion_name = self.rgi_subregion + ': ' + n
except KeyError:
self.rgi_subregion_name = self.rgi_subregion + ': NoName'
# 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)]
self.is_tidewater = self.terminus_type in ['Marine-terminating',
'Lake-terminating']
self.is_nominal = self.status == 'Nominal glacier'
self.inversion_calving_rate = 0.
self.is_icecap = self.glacier_type == 'Ice cap'
# Hemisphere
self.hemisphere = 'sh' if self.cenlat < 0 else '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[:8],
self.rgi_id[:11], 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)
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')
# Optimization
self._mbdf = None
self._mbprofdf = 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 += [' 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'
@lazy_property
def grid(self):
"""A ``salem.Grid`` handling the georeferencing of the local grid"""
return salem.Grid.from_json(self.get_filepath('glacier_grid'))
@lazy_property
def rgi_area_km2(self):
"""The glacier's RGI area (km2)."""
try:
_area = self.read_shapefile('outlines')['Area']
return np.round(float(_area), decimals=3)
except OSError:
raise RuntimeError('Please run `define_glacier_region` before '
'using this property.')
@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=''):
"""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):
"""Checks if a file exists.
Parameters
----------
filename : str
file name (must be listed in cfg.BASENAME)
"""
fp = self.get_filepath(filename)
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:
out = pickle.load(f)
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=-1)
def read_json(self, filename, filesuffix=''):
"""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).
Returns
-------
A dictionary read from the JSON file
"""
fp = self.get_filepath(filename, filesuffix=filesuffix)
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).
"""
fp = self.get_filepath(filename, filesuffix=filesuffix)
with open(fp, 'w') as f:
json.dump(var, f)
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'
return gpd.read_file(fp)
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)
if '.shp' not in fp:
raise ValueError('File ending not that of a shapefile')
var.to_file(fp)
if not cfg.PARAMS['use_tar_shapefiles']:
# Done here
return
# Write them in tar
fp = fp.replace('.shp', '.tar')
mode = 'w'
if cfg.PARAMS['use_compression']:
fp += '.gz'
mode += ':gz'
if os.path.exists(fp):
os.remove(fp)
# List all files that were written as shape
fs = glob.glob(fp.replace('.gz', '').replace('.tar', '.*'))
# Add them to tar
with tarfile.open(fp, 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)
def create_gridded_ncdf_file(self, fname):
"""Makes a gridded netcdf file template.
The other variables have to be created and filled by the calling
routine.
Parameters
----------
filename : str
file name (must be listed in cfg.BASENAME)
Returns
-------
a ``netCDF4.Dataset`` object.
"""
# overwrite as default
fpath = self.get_filepath(fname)
if os.path.exists(fpath):
os.remove(fpath)
nc = ncDataset(fpath, 'w', format='NETCDF4')
nc.createDimension('x', self.grid.nx)
nc.createDimension('y', self.grid.ny)
nc.author = 'OGGM'
nc.author_info = 'Open Global Glacier Model'
nc.proj_srs = self.grid.proj.srs
x = self.grid.x0 + np.arange(self.grid.nx) * self.grid.dx
y = self.grid.y0 + np.arange(self.grid.ny) * self.grid.dy
v = nc.createVariable('x', 'f4', ('x',), zlib=True)
v.units = 'm'
v.long_name = 'x coordinate of projection'
v.standard_name = 'projection_x_coordinate'
v[:] = x
v = nc.createVariable('y', 'f4', ('y',), zlib=True)
v.units = 'm'
v.long_name = 'y coordinate of projection'
v.standard_name = 'projection_y_coordinate'
v[:] = y
return nc
def write_monthly_climate_file(self, time, prcp, temp,
ref_pix_hgt, ref_pix_lon, ref_pix_lat, *,
gradient=None,
time_unit=None,
calendar=None,
file_name='climate_monthly',
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
gradient : ndarray, optional
whether to use a time varying gradient
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')
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)
zlib = cfg.PARAMS['compress_climate_netcdf']
if time_unit is None:
# http://pandas.pydata.org/pandas-docs/stable/timeseries.html
# #timestamp-limitations
if time[0].year > 1800:
time_unit = 'days since 1801-01-01 00:00:00'
elif time[0].year >= 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.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 gradient is not None:
v = nc.createVariable('gradient', 'f4', ('time',), zlib=zlib)
v.units = 'degC m-1'
v.long_name = 'temperature gradient from local regression'
v[:] = gradient
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 get_ref_mb_data(self):
"""Get the reference mb data from WGMS (for some glaciers only!).
Raises an Error if it isn't a reference glacier at all.
"""
if self._mbdf 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
self._mbdf = pd.read_csv(reff).set_index('YEAR')
# logic for period
if not self.has_file('climate_info'):
raise RuntimeError('Please process some climate data before call')
ci = self.read_pickle('climate_info')
y0 = ci['baseline_hydro_yr_0']
y1 = ci['baseline_hydro_yr_1']
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):
"""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.
"""
if self._mbprofdf 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
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)
# logic for period
if not self.has_file('climate_info'):
raise RuntimeError('Please process some climate data before call')
ci = self.read_pickle('climate_info')
y0 = ci['baseline_hydro_yr_0']
y1 = ci['baseline_hydro_yr_1']
if len(self._mbprofdf) > 1:
out = self._mbprofdf.loc[y0:y1]
else:
# Some files are just empty
out = self._mbprofdf
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 lenght 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_2012_RGIV5.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):
"""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)
"""
# a line per function call
nowsrt = datetime.datetime.now().strftime('%Y-%m-%dT%H:%M:%S')
line = nowsrt + ';' + task_name + ';'
if err is None:
line += 'SUCCESS'
else:
line += err.__class__.__name__ + ': {}'.format(err)
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 see if a task was already run.
It is usually called by the :py:class:`entity_task` decorator, normally
you shouldn't take care about that.
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 task_name in l]
if lines:
# keep only the last log
return lines[-1].split(';')[-1]
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 times)
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)
and 'run' (copy the necessary files for a dynamical run).
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',
'local_mustar', 'climate_monthly',
'gcm_data', 'climate_info']
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',
'local_mustar', 'climate_monthly', 'gridded_data',
'gcm_data', 'climate_info']
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_monthly', input_filesuffix=''):
"""Creats 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
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 iteratable
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]
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)
# to get the center point, maximal extensions for DEM and single Polygon:
maindf.loc[idx, 'geometry'] = merged_geometry.convex_hull
# make some adjustments to the rgi dataframe
# 1. update central point of new glacier
# Could also use the mean of the Flowline centroids, to shift it more
# downstream. But this is more straight forward.
maindf.loc[idx, 'CenLon'] = maindf.loc[idx, 'geometry'].centroid.x
maindf.loc[idx, 'CenLat'] = maindf.loc[idx, 'geometry'].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])
# 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 local_mustar 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('local_mustar')).split('.')
mufile = _mufile[0] + '_' + main.rgi_id + '.' + _mufile[1]
shutil.copyfile(main.get_filepath('local_mustar'),
os.path.join(merged.dir, mufile))
# I think I need the climate_info only for the main glacier
shutil.copyfile(main.get_filepath('climate_info'),
merged.get_filepath('climate_info'))
# 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
@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
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')
for dirname, subdirlist, filelist in os.walk(base_dir):
# RGI60-01.00
bname = os.path.basename(dirname)
if not (len(bname) == 11 and bname[-3] == '.'):
continue
opath = dirname + '.tar'
with tarfile.open(opath, 'w') as tar:
tar.add(dirname, arcname=os.path.basename(dirname))
if delete:
shutil.rmtree(dirname)