def velocity_to_gdir(gdir, add_error=False):
"""Reproject the its_live files to the given glacier directory.
Variables are added to the gridded_data nc file.
Reprojecting velocities from one map proj to another is done
reprojecting the vector distances. In this process, absolute velocities
might change as well because map projections do not always preserve
distances -> we scale them back to the original velocities as per the
ITS_LIVE documentation that states that velocities are given in
ground units, i.e. absolute velocities.
We use bilinear interpolation to reproject the velocities to the local
glacier map.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
where to write the data
add_error : bool
also reproject and scale the error data
"""
if not gdir.has_file('gridded_data'):
raise InvalidWorkflowError('Please run `glacier_masks` before running '
'this task')
_reproject_and_scale(gdir, do_error=False)
if add_error:
_reproject_and_scale(gdir, do_error=True)
def filter_inversion_output(gdir):
"""Filters the last few grid points after the physically-based inversion.
For various reasons (but mostly: the equilibrium assumption), the last few
grid points on a glacier flowline are often noisy and create unphysical
depressions. Here we try to correct for that. It is not volume conserving,
but area conserving.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
the glacier directory to process
"""
if gdir.is_tidewater:
# No need for filter in tidewater case
return
if not gdir.has_file('downstream_line'):
raise InvalidWorkflowError('filter_inversion_output now needs a '
'previous call to the '
'compute_dowstream_line and '
'compute_downstream_bedshape tasks')
dic_ds = gdir.read_pickle('downstream_line')
bs = np.average(dic_ds['bedshapes'][:3])
n = -5
cls = gdir.read_pickle('inversion_output')
cl = cls[-1]
# First guess thickness based on width
w = cl['width'][n:]
s = w**3 * bs / 6
h = 3 / 2 * s / w
# Smoothing things out a bit
hts = np.append(np.append(cl['thick'][n - 3:n], h), 0)
h = utils.smooth1d(hts, 3)[n - 1:-1]
# Recompute bedshape based on that
bs = utils.clip_min(4 * h / w**2, cfg.PARAMS['mixed_min_shape'])
# OK, done
s = w**3 * bs / 6
cl['thick'][n:] = 3 / 2 * s / w
cl['volume'][n:] = s * cl['dx']
cl['is_trapezoid'][n:] = False
cl['is_rectangular'][n:] = False
gdir.write_pickle(cls, 'inversion_output')
# output the volume here - this simplifies code for some downstream funcs
return np.sum([np.sum(cl['volume']) for cl in cls])
def _check_duplicates(rgidf=None):
"""Complain if the input has duplicates."""
if rgidf is None:
return
# Check if dataframe or list of strs
try:
rgidf = rgidf.RGIId
except AttributeError:
rgidf = utils.tolist(rgidf)
u, c = np.unique(rgidf, return_counts=True)
if len(u) < len(rgidf):
raise InvalidWorkflowError('Found duplicates in the list of '
'RGI IDs: {}'.format(u[c > 1]))
def execute_entity_task(task, gdirs, **kwargs):
"""Execute a task on gdirs.
If you asked for multiprocessing, it will do it.
If ``task`` has more arguments than `gdir` they have to be keyword
arguments.
Parameters
----------
task : function
the entity task to apply
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
"""
if task.__dict__.get('is_global_task', False):
raise InvalidWorkflowError('execute_entity_task cannot be used on '
'global tasks.')
# Should be iterable
gdirs = utils.tolist(gdirs)
ng = len(gdirs)
if ng == 0:
log.workflow('Called entity task %s on 0 glaciers. Returning...',
task.__name__)
return
log.workflow('Execute entity task %s on %d glaciers', task.__name__, ng)
pc = _pickle_copier(task, kwargs)
if _have_ogmpi:
if ogmpi.OGGM_MPI_COMM is not None:
return ogmpi.mpi_master_spin_tasks(pc, gdirs)
if cfg.PARAMS['use_multiprocessing'] and ng > 1:
mppool = init_mp_pool(cfg.CONFIG_MODIFIED)
out = mppool.map(pc, gdirs, chunksize=1)
else:
if ng > 3:
log.workflow(
'WARNING: you are trying to run an entity task on '
'%d glaciers with multiprocessing turned off. OGGM '
'will run faster with multiprocessing turned on.', ng)
out = [pc(gdir) for gdir in gdirs]
return out
def _check_rgi_input(rgidf=None):
"""Complain if the input has duplicates."""
if rgidf is None:
return
# Check if dataframe or list of strs
try:
rgi_ids = rgidf.RGIId
# if dataframe we can also check for connectivity
if 'Connect' in rgidf and np.any(rgidf['Connect'] == 2):
log.workflow('WARNING! You have glaciers with connectivity level '
'2 in your list. OGGM does not provide pre-processed '
'directories for these.')
except AttributeError:
rgi_ids = utils.tolist(rgidf)
u, c = np.unique(rgi_ids, return_counts=True)
if len(u) < len(rgi_ids):
raise InvalidWorkflowError('Found duplicates in the list of '
'RGI IDs: {}'.format(u[c > 1]))
def velocity_to_gdir(gdir, add_error=False):
"""Reproject the its_live files to the given glacier directory.
The data source used is https://its-live.jpl.nasa.gov/#data
Currently the only data downloaded is the 120m composite for both
(u, v) and their uncertainty. The composite is computed from the
1985 to 2018 average.
Variables are added to the gridded_data nc file.
Reprojecting velocities from one map proj to another is done
reprojecting the vector distances. In this process, absolute velocities
might change as well because map projections do not always preserve
distances -> we scale them back to the original velocities as per the
ITS_LIVE documentation that states that velocities are given in
ground units, i.e. absolute velocities.
We use bilinear interpolation to reproject the velocities to the local
glacier map.
If you want more velocity products, feel free to open a new topic
on OGGM's issue tracker!
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
where to write the data
add_error : bool
also reproject and scale the error data
"""
if not gdir.has_file('gridded_data'):
raise InvalidWorkflowError('Please run `glacier_masks` before running '
'this task')
_reproject_and_scale(gdir, do_error=False)
if add_error:
_reproject_and_scale(gdir, do_error=True)
def calibrate_inversion_from_consensus(gdirs, ignore_missing=True,
fs=0, a_bounds=(0.1, 10),
apply_fs_on_mismatch=False,
error_on_mismatch=True):
"""Fit the total volume of the glaciers to the 2019 consensus estimate.
This method finds the "best Glen A" to match all glaciers in gdirs with
a valid inverted volume.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
ignore_missing : bool
set this to true to silence the error if some glaciers could not be
found in the consensus estimate.
fs : float
invert with sliding (default: no)
a_bounds: tuple
factor to apply to default A
apply_fs_on_mismatch: false
on mismatch, try to apply an arbitrary value of fs (fs = 5.7e-20 from
Oerlemans) and try to otpimize A again.
error_on_mismatch: bool
sometimes the given bounds do not allow to find a zero mismatch:
this will normally raise an error, but you can switch this off,
use the closest value instead and move on.
Returns
-------
a dataframe with the individual glacier volumes
"""
gdirs = utils.tolist(gdirs)
# Get the ref data for the glaciers we have
df = pd.read_hdf(utils.get_demo_file('rgi62_itmix_df.h5'))
rids = [gdir.rgi_id for gdir in gdirs]
found_ids = df.index.intersection(rids)
if not ignore_missing and (len(found_ids) != len(rids)):
raise InvalidWorkflowError('Could not find matching indices in the '
'consensus estimate for all provided '
'glaciers. Set ignore_missing=True to '
'ignore this error.')
df = df.reindex(rids)
# Optimize the diff to ref
def_a = cfg.PARAMS['inversion_glen_a']
def compute_vol(x):
inversion_tasks(gdirs, glen_a=x*def_a, fs=fs)
odf = df.copy()
odf['oggm'] = execute_entity_task(tasks.get_inversion_volume, gdirs)
return odf.dropna()
def to_minimize(x):
log.workflow('Consensus estimate optimisation with '
'A factor: {} and fs: {}'.format(x, fs))
odf = compute_vol(x)
return odf.vol_itmix_m3.sum() - odf.oggm.sum()
try:
out_fac, r = optimization.brentq(to_minimize, *a_bounds, rtol=1e-2,
full_output=True)
if r.converged:
log.workflow('calibrate_inversion_from_consensus '
'converged after {} iterations and fs={}. The '
'resulting Glen A factor is {}.'
''.format(r.iterations, fs, out_fac))
else:
raise ValueError('Unexpected error in optimization.brentq')
except ValueError:
# Ok can't find an A. Log for debug:
odf1 = compute_vol(a_bounds[0]).sum() * 1e-9
odf2 = compute_vol(a_bounds[1]).sum() * 1e-9
msg = ('calibration fom consensus estimate CANT converge with fs={}.\n'
'Bound values (km3):\nRef={:.3f} OGGM={:.3f} for A factor {}\n'
'Ref={:.3f} OGGM={:.3f} for A factor {}'
''.format(fs,
odf1.vol_itmix_m3, odf1.oggm, a_bounds[0],
odf2.vol_itmix_m3, odf2.oggm, a_bounds[1]))
if apply_fs_on_mismatch and fs == 0 and odf2.oggm > odf2.vol_itmix_m3:
return calibrate_inversion_from_consensus(gdirs,
ignore_missing=ignore_missing,
fs=5.7e-20, a_bounds=a_bounds,
apply_fs_on_mismatch=False,
error_on_mismatch=error_on_mismatch)
if error_on_mismatch:
raise ValueError(msg)
out_fac = a_bounds[int(abs(odf1.vol_itmix_m3 - odf1.oggm) >
abs(odf2.vol_itmix_m3 - odf2.oggm))]
log.workflow(msg)
log.workflow('We use A factor = {} and fs = {} and move on.'
''.format(out_fac, fs))
# Compute the final volume with the correct A
inversion_tasks(gdirs, glen_a=out_fac*def_a, fs=fs)
df['vol_oggm_m3'] = execute_entity_task(tasks.get_inversion_volume, gdirs)
return df
def match_geodetic_mb_for_selection(gdirs,
period='2000-01-01_2020-01-01',
file_path=None,
fail_safe=False):
"""Shift the mass-balance residual to match geodetic mb observations.
It is similar to match_regional_geodetic_mb but uses the raw, glacier
per glacier tabular data.
This method finds the "best mass-balance residual" to match all glaciers in
gdirs with available OGGM mass balance and available geodetic mass-balance
measurements from Hugonnet 2021 or any other file with the same format.
The default is to use hugonnet_2021_ds_rgi60_pergla_rates_10_20_worldwide_filled.hdf
in https://cluster.klima.uni-bremen.de/~oggm/geodetic_ref_mb/
Parameters
----------
gdirs : the list of gdirs
period : str
One of
'2000-01-01_2020-01-01',
'2000-01-01_2010-01-01',
'2010-01-01_2020-01-01'.
file_path: str
local file path to tabular file containing geodetic measurements, file must
contain the columns:
- 'rgiid': is the RGIId as in the RGI 6.0
- 'period': time intervall of the measurements in the format shown
above
- 'dmdtda': the specific-mass change rate in meters water-equivalent
per year,
- 'area': is the glacier area (same as in RGI 6.0) in meters square
fail_safe : bool
some glaciers in the obs data have been corrected with the regional
average. We don't use these values, unless there is no other choice and
in which case you can set fail_safe to True
"""
# Get the mass-balance OGGM would give out of the box
df = utils.compile_fixed_geometry_mass_balance(gdirs, path=False)
df = df.dropna(axis=0, how='all').dropna(axis=1, how='all')
# And also the Area and calving fluxes
dfs = utils.compile_glacier_statistics(gdirs, path=False)
y0 = int(period.split('_')[0].split('-')[0])
y1 = int(period.split('_')[1].split('-')[0]) - 1
odf = pd.DataFrame(df.loc[y0:y1].mean(), columns=['SMB'])
odf['AREA'] = dfs.rgi_area_km2 * 1e6
# Just take the calving rate and change its units
# Original units: km3 a-1, to change to mm a-1 (units of specific MB)
rho = cfg.PARAMS['ice_density']
if 'calving_flux' in dfs:
odf['CALVING'] = dfs['calving_flux'].fillna(
0) * 1e9 * rho / odf['AREA']
else:
odf['CALVING'] = 0
# We have to drop nans here, which occur when calving glaciers fail to run
odf = odf.dropna()
# save all rgi_ids for which a valid OGGM mb is available
rgi_ids_oggm = odf.index.values
# Fetch the reference data
df = utils.get_geodetic_mb_dataframe(file_path=file_path)
# get the correct period from the whole dataset
df = df.loc[df['period'] == period]
# get only geodetic measurements for which a valid OGGM mb is available
rdf_all = df.loc[rgi_ids_oggm]
if rdf_all.empty:
raise InvalidWorkflowError('No geodetic MB measurements available for '
'this glacier selection!')
# drop glaciers with no valid geodetic measurements
rdf = rdf_all.loc[~rdf_all['is_cor']]
if rdf.empty:
if not fail_safe:
raise InvalidWorkflowError(
'No gedoetic MB measurements available for '
'this glacier selection! Set '
'fail_safe=True to use the '
'corrected values.')
rdf = rdf_all
# the remaining glaciers now have a OGGM mb and geodetic measurements
rgi_ids = rdf.index.values
msg = ('Applying geodetic MB correction using {} of {} glaciers, with '
'available OGGM MB and available geodetic measurements.')
log.workflow(msg.format(len(rgi_ids), len(gdirs)))
# Total MB OGGM, only using glaciers with OGGM mb and geodetic measurements
odf = odf.loc[rgi_ids]
out_smb = np.average(odf['SMB'], weights=odf['AREA']) # for logging
out_cal = np.average(odf['CALVING'], weights=odf['AREA']) # for logging
smb_oggm = np.average(odf['SMB'] - odf['CALVING'], weights=odf['AREA'])
# Total geodetic MB, no need for indexing
smb_ref = rdf.dmdtda.values * 1000 # m to mm conversion
area_ref = rdf.area.values
smb_ref = np.average(smb_ref, weights=area_ref)
# Diff between the two
residual = smb_ref - smb_oggm
# Let's just shift
log.workflow('Shifting regional MB bias by {}'.format(residual))
log.workflow('Observations give {}'.format(smb_ref))
log.workflow('OGGM SMB gives {}'.format(out_smb))
log.workflow('OGGM frontal ablation gives {}'.format(out_cal))
# This time we shift over all glaciers
for gdir in gdirs:
try:
df = gdir.read_json('local_mustar')
gdir.add_to_diagnostics('mb_bias_before_geodetic_corr', df['bias'])
df['bias'] = df['bias'] - residual
gdir.write_json(df, 'local_mustar')
except FileNotFoundError:
pass
def _reproject_and_scale(gdir, do_error=False):
"""Reproject and scale itslive data, avoid code duplication for error"""
reg = find_region(gdir)
if reg is None:
raise InvalidWorkflowError('There does not seem to be its_live data '
'available for this glacier')
vnx = 'vx'
vny = 'vy'
if do_error:
vnx += '_err'
vny += '_err'
with utils.get_lock():
fx = utils.file_downloader(region_files[reg][vnx])
fy = utils.file_downloader(region_files[reg][vny])
# Open the files
dsx = salem.GeoTiff(fx)
dsy = salem.GeoTiff(fy)
# subset them to our map
grid_gla = gdir.grid.center_grid
proj_vel = dsx.grid.proj
x0, x1, y0, y1 = grid_gla.extent_in_crs(proj_vel)
dsx.set_subset(corners=((x0, y0), (x1, y1)), crs=proj_vel, margin=4)
dsy.set_subset(corners=((x0, y0), (x1, y1)), crs=proj_vel, margin=4)
grid_vel = dsx.grid.center_grid
# TODO: this should be taken care of by salem
# https://github.com/fmaussion/salem/issues/171
with rasterio.Env():
with rasterio.open(fx) as src:
nodata = getattr(src, 'nodata', -32767.0)
# Error files are wrong
if nodata == 0:
nodata = -32767.0
# Get the coords at t0
xx0, yy0 = grid_vel.center_grid.xy_coordinates
# Compute coords at t1
xx1 = dsx.get_vardata()
yy1 = dsy.get_vardata()
non_valid = (xx1 == nodata) | (yy1 == nodata)
xx1[non_valid] = np.NaN
yy1[non_valid] = np.NaN
orig_vel = np.sqrt(xx1**2 + yy1**2)
xx1 += xx0
yy1 += yy0
# Transform both to glacier proj
xx0, yy0 = salem.transform_proj(proj_vel, grid_gla.proj, xx0, yy0)
xx1, yy1 = salem.transform_proj(proj_vel, grid_gla.proj, xx1, yy1)
# Correct no data after proj as well (inf)
xx1[non_valid] = np.NaN
yy1[non_valid] = np.NaN
# Compute velocities from there
vx = xx1 - xx0
vy = yy1 - yy0
# Scale back velocities - https://github.com/OGGM/oggm/issues/1014
new_vel = np.sqrt(vx**2 + vy**2)
p_ok = new_vel > 1e-5 # avoid div by zero
vx[p_ok] = vx[p_ok] * orig_vel[p_ok] / new_vel[p_ok]
vy[p_ok] = vy[p_ok] * orig_vel[p_ok] / new_vel[p_ok]
# And transform to local map
vx = grid_gla.map_gridded_data(vx, grid=grid_vel, interp='linear')
vy = grid_gla.map_gridded_data(vy, grid=grid_vel, interp='linear')
# Write
with utils.ncDataset(gdir.get_filepath('gridded_data'), 'a') as nc:
vn = 'obs_icevel_x'
if do_error:
vn = vn.replace('obs', 'err')
if vn in nc.variables:
v = nc.variables[vn]
else:
v = nc.createVariable(vn, 'f4', ('y', 'x', ), zlib=True)
v.units = 'm yr-1'
ln = 'ITS LIVE velocity data in x map direction'
if do_error:
ln = 'Uncertainty of ' + ln
v.long_name = ln
v[:] = vx.filled(np.nan)
vn = 'obs_icevel_y'
if do_error:
vn = vn.replace('obs', 'err')
if vn in nc.variables:
v = nc.variables[vn]
else:
v = nc.createVariable(vn, 'f4', ('y', 'x', ), zlib=True)
v.units = 'm yr-1'
ln = 'ITS LIVE velocity data in y map direction'
if do_error:
ln = 'Uncertainty of ' + ln
v.long_name = ln
v[:] = vy.filled(np.nan)
def __init__(self,
gdir,
fls=None,
mu_star=None,
mb_model_class=PastMassBalance,
use_inversion_flowlines=False,
input_filesuffix='',
bias=None,
**kwargs):
"""Initialize.
Parameters
----------
gdir : GlacierDirectory
the glacier directory
mu_star : float or list of floats, optional
set to the alternative value of mu* you want to use
(the default is to use the calibrated value). Give a list of values
for flowline-specific mu*
fls : list, optional
list of flowline objects to use (defaults to 'model_flowlines',
and if not available, to 'inversion_flowlines')
mb_model_class : class, optional
the mass-balance model to use (e.g. PastMassBalance,
ConstantMassBalance...)
use_inversion_flowlines: bool, optional
if True 'inversion_flowlines' instead of 'model_flowlines' will be
used.
input_filesuffix : str
the file suffix of the input climate file
bias : float, optional
set to the alternative value of the calibration bias [mm we yr-1]
you want to use (the default is to use the calibrated value)
Note that this bias is *substracted* from the computed MB. Indeed:
BIAS = MODEL_MB - REFERENCE_MB.
kwargs : kwargs to pass to mb_model_class
"""
# Read in the flowlines
if use_inversion_flowlines:
fls = gdir.read_pickle('inversion_flowlines')
if fls is None:
try:
fls = gdir.read_pickle('model_flowlines')
except FileNotFoundError:
raise InvalidWorkflowError('Need a valid `model_flowlines` '
'file. If you explicitly want to '
'use `inversion_flowlines`, set '
'use_inversion_flowlines=True.')
self.fls = fls
_y0 = kwargs.get('y0', None)
# User mu*?
if mu_star is not None:
mu_star = tolist(mu_star, length=len(fls))
for fl, mu in zip(self.fls, mu_star):
fl.mu_star = mu
# Initialise the mb models
self.flowline_mb_models = []
for fl in self.fls:
# Merged glaciers will need different climate files, use filesuffix
if (fl.rgi_id is not None) and (fl.rgi_id != gdir.rgi_id):
rgi_filesuffix = '_' + fl.rgi_id + input_filesuffix
else:
rgi_filesuffix = input_filesuffix
# merged glaciers also have a different MB bias from calibration
if ((bias is None) and cfg.PARAMS['use_bias_for_run']
and (fl.rgi_id != gdir.rgi_id)):
df = gdir.read_json('local_mustar', filesuffix='_' + fl.rgi_id)
fl_bias = df['bias']
else:
fl_bias = bias
# Constant and RandomMassBalance need y0 if not provided
if (issubclass(mb_model_class, RandomMassBalance)
or issubclass(mb_model_class, ConstantMassBalance)) and (
fl.rgi_id != gdir.rgi_id) and (_y0 is None):
df = gdir.read_json('local_mustar', filesuffix='_' + fl.rgi_id)
kwargs['y0'] = df['t_star']
self.flowline_mb_models.append(
mb_model_class(gdir,
mu_star=fl.mu_star,
bias=fl_bias,
input_filesuffix=rgi_filesuffix,
**kwargs))
self.valid_bounds = self.flowline_mb_models[-1].valid_bounds
self.hemisphere = gdir.hemisphere
def __init__(self,
gdir,
mu_star=None,
bias=None,
filename='climate_historical',
input_filesuffix='',
repeat=False,
ys=None,
ye=None,
check_calib_params=True):
"""Initialize.
Parameters
----------
gdir : GlacierDirectory
the glacier directory
mu_star : float, optional
set to the alternative value of mu* you want to use
(the default is to use the calibrated value).
bias : float, optional
set to the alternative value of the calibration bias [mm we yr-1]
you want to use (the default is to use the calibrated value)
Note that this bias is *substracted* from the computed MB. Indeed:
BIAS = MODEL_MB - REFERENCE_MB.
filename : str, optional
set to a different BASENAME if you want to use alternative climate
data.
input_filesuffix : str
the file suffix of the input climate file
repeat : bool
Whether the climate period given by [ys, ye] should be repeated
indefinitely in a circular way
ys : int
The start of the climate period where the MB model is valid
(default: the period with available data)
ye : int
The end of the climate period where the MB model is valid
(default: the period with available data)
check_calib_params : bool
OGGM will try hard not to use wrongly calibrated mu* by checking
the parameters used during calibration and the ones you are
using at run time. If they don't match, it will raise an error.
Set to False to suppress this check.
Attributes
----------
temp_bias : float, default 0
Add a temperature bias to the time series
prcp_bias : float, default 1
Precipitation factor to the time series (called bias for
consistency with `temp_bias`)
"""
super(PastMassBalance, self).__init__()
self.valid_bounds = [-1e4, 2e4] # in m
if mu_star is None:
df = gdir.read_json('local_mustar')
mu_star = df['mu_star_glacierwide']
if check_calib_params:
if not df['mu_star_allsame']:
msg = ('You seem to use the glacier-wide mu* to compute '
'the mass-balance although this glacier has '
'different mu* for its flowlines. Set '
'`check_calib_params=False` to prevent this '
'error.')
raise InvalidWorkflowError(msg)
if bias is None:
if cfg.PARAMS['use_bias_for_run']:
df = gdir.read_json('local_mustar')
bias = df['bias']
else:
bias = 0.
self.mu_star = mu_star
self.bias = bias
# Parameters
self.t_solid = cfg.PARAMS['temp_all_solid']
self.t_liq = cfg.PARAMS['temp_all_liq']
self.t_melt = cfg.PARAMS['temp_melt']
prcp_fac = cfg.PARAMS['prcp_scaling_factor']
default_grad = cfg.PARAMS['temp_default_gradient']
# Check the climate related params to the GlacierDir to make sure
if check_calib_params:
mb_calib = gdir.get_climate_info()['mb_calib_params']
for k, v in mb_calib.items():
if v != cfg.PARAMS[k]:
msg = ('You seem to use different mass-balance parameters '
'than used for the calibration. Set '
'`check_calib_params=False` to ignore this '
'warning.')
raise InvalidWorkflowError(msg)
# Public attrs
self.hemisphere = gdir.hemisphere
self.temp_bias = 0.
self.prcp_bias = 1.
self.repeat = repeat
# Read file
fpath = gdir.get_filepath(filename, filesuffix=input_filesuffix)
with ncDataset(fpath, mode='r') as nc:
# time
time = nc.variables['time']
time = netCDF4.num2date(time[:], time.units)
ny, r = divmod(len(time), 12)
if r != 0:
raise ValueError('Climate data should be N full years')
# This is where we switch to hydro float year format
# Last year gives the tone of the hydro year
self.years = np.repeat(
np.arange(time[-1].year - ny + 1, time[-1].year + 1), 12)
self.months = np.tile(np.arange(1, 13), ny)
# Read timeseries
self.temp = nc.variables['temp'][:]
self.prcp = nc.variables['prcp'][:] * prcp_fac
if 'gradient' in nc.variables:
grad = nc.variables['gradient'][:]
# Security for stuff that can happen with local gradients
g_minmax = cfg.PARAMS['temp_local_gradient_bounds']
grad = np.where(~np.isfinite(grad), default_grad, grad)
grad = clip_array(grad, g_minmax[0], g_minmax[1])
else:
grad = self.prcp * 0 + default_grad
self.grad = grad
self.ref_hgt = nc.ref_hgt
self.ys = self.years[0] if ys is None else ys
self.ye = self.years[-1] if ye is None else ye
def calibrate_inversion_from_consensus_estimate(gdirs, ignore_missing=False):
"""Fit the total volume of the glaciers to the 2019 consensus estimate.
This method finds the "best Glen A" to match all glaciers in gdirs with
a valid inverted volume.
Parameters
----------
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
the glacier directories to process
ignore_missing : bool
set this to true to silence the error if some glaciers could not be
found in the consensus estimate.
Returns
-------
a dataframe with the individual glacier volumes
"""
gdirs = utils.tolist(gdirs)
# Get the ref data for the glaciers we have
df = pd.read_hdf(utils.get_demo_file('rgi62_itmix_df.h5'))
rids = [gdir.rgi_id for gdir in gdirs]
found_ids = df.index.intersection(rids)
if not ignore_missing and (len(found_ids) != len(rids)):
raise InvalidWorkflowError('Could not find matching indices in the '
'consensus estimate for all provided '
'glaciers. Set ignore_missing=True to '
'ignore this error.')
df = df.reindex(rids)
def_a = cfg.PARAMS['inversion_glen_a']
a_bounds = [0.1, 10]
# Optimize the diff to ref
def to_minimize(x):
cfg.PARAMS['inversion_glen_a'] = x * def_a
vols = execute_entity_task(tasks.mass_conservation_inversion, gdirs)
_df = df.copy()
_df['oggm'] = vols
_df = _df.dropna()
return _df.vol_itmix_m3.sum() - _df.oggm.sum()
out_fac, r = optimization.brentq(to_minimize,
*a_bounds,
rtol=1e-2,
full_output=True)
if r.converged:
log.workflow('calibrate_inversion_from_consensus_estimate '
'converged after {} iterations. The resulting Glen A '
'factor is {}.'.format(r.iterations, out_fac))
else:
raise RuntimeError('Unexpected error')
# Compute the final volume with the correct A
cfg.PARAMS['inversion_glen_a'] = out_fac * def_a
vols = execute_entity_task(tasks.mass_conservation_inversion, gdirs)
df['vol_oggm_m3'] = vols
return df
def execute_entity_task(task, gdirs, **kwargs):
"""Execute a task on gdirs.
If you asked for multiprocessing, it will do it.
If ``task`` has more arguments than `gdir` they have to be keyword
arguments.
Parameters
----------
task : function or sequence of functions
The entity task(s) to apply.
Can be None, in which case each gdir is expected to be a tuple of (task, gdir).
When passing a sequence, each item can also optionally be a tuple of (task, dictionary).
In this case the dictionary items will be passed to the task as kwargs.
gdirs : list of :py:class:`oggm.GlacierDirectory` objects
The glacier directories to process.
Each individual gdir can optionally be a tuple of (gdir, dictionary).
In this case, the values in the dictionary will be passed to the task as
keyword arguments for that specific gdir.
Returns
-------
List of results from task. Last task if a list of tasks was given.
"""
# Normalize task into list of tuples for simplicity
if not isinstance(task, Sequence):
task = [task]
tasks = []
for t in task:
if isinstance(t, tuple):
tasks.append(t)
else:
tasks.append((t, {}))
# Reject global tasks
for t in tasks:
if t[0].__dict__.get('is_global_task', False):
raise InvalidWorkflowError('execute_entity_task cannot be used on '
'global tasks.')
# Should be iterable
gdirs = utils.tolist(gdirs)
ng = len(gdirs)
if ng == 0:
log.workflow('Called execute_entity_task on 0 glaciers. Returning...')
return
log.workflow('Execute entity tasks [%s] on %d glaciers',
', '.join([t[0].__name__ for t in tasks]), ng)
pc = _pickle_copier(tasks, kwargs)
if _have_ogmpi:
if ogmpi.OGGM_MPI_COMM is not None:
return ogmpi.mpi_master_spin_tasks(pc, gdirs)
if cfg.PARAMS['use_multiprocessing'] and ng > 1:
mppool = init_mp_pool(cfg.CONFIG_MODIFIED)
out = mppool.map(pc, gdirs, chunksize=1)
else:
if ng > 3:
log.workflow(
'WARNING: you are trying to run an entity task on '
'%d glaciers with multiprocessing turned off. OGGM '
'will run faster with multiprocessing turned on.', ng)
out = [pc(gdir) for gdir in gdirs]
return out
def velocity_to_gdir(gdir):
"""Reproject the its_live files to the given glacier directory.
Variables are added to the gridded_data nc file.
Reprojecting velocities from one map proj to another is done
reprojecting the vector distances. In this process, absolute velocities
might change as well because map projections do not always preserve
distances -> we scale them back to the original velocities as per the
ITS_LIVE documentation that states that velocities are given in
ground units, i.e. absolute velocities.
We use bilinear interpolation to reproject the velocities to the local
glacier map.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
where to write the data
"""
reg = find_region(gdir)
if reg is None:
raise InvalidWorkflowError('There does not seem to be its_live data '
'available for this glacier')
if not gdir.has_file('gridded_data'):
raise InvalidWorkflowError('Please run `glacier_masks` before running '
'this task')
with utils.get_lock():
fx = utils.file_downloader(region_files[reg]['vx'])
fy = utils.file_downloader(region_files[reg]['vy'])
# Open the files
dsx = salem.GeoTiff(fx)
dsy = salem.GeoTiff(fy)
# subset them to our map
grid_gla = gdir.grid.center_grid
proj_vel = dsx.grid.proj
x0, x1, y0, y1 = grid_gla.extent_in_crs(proj_vel)
dsx.set_subset(corners=((x0, y0), (x1, y1)), crs=proj_vel, margin=4)
dsy.set_subset(corners=((x0, y0), (x1, y1)), crs=proj_vel, margin=4)
grid_vel = dsx.grid.center_grid
# TODO: this should be taken care of by salem
# https://github.com/fmaussion/salem/issues/171
with rasterio.Env():
with rasterio.open(fx) as src:
nodata = getattr(src, 'nodata', -32767.0)
# Get the coords at t0
xx0, yy0 = grid_vel.center_grid.xy_coordinates
# Compute coords at t1
xx1 = dsx.get_vardata()
yy1 = dsy.get_vardata()
non_valid = (xx1 == nodata) | (yy1 == nodata)
xx1[non_valid] = np.NaN
yy1[non_valid] = np.NaN
orig_vel = np.sqrt(xx1**2 + yy1**2)
xx1 += xx0
yy1 += yy0
# Transform both to glacier proj
xx0, yy0 = salem.transform_proj(proj_vel, grid_gla.proj, xx0, yy0)
xx1, yy1 = salem.transform_proj(proj_vel, grid_gla.proj, xx1, yy1)
# Correct no data after proj as well (inf)
xx1[non_valid] = np.NaN
yy1[non_valid] = np.NaN
# Compute velocities from there
vx = xx1 - xx0
vy = yy1 - yy0
# Scale back velocities - https://github.com/OGGM/oggm/issues/1014
new_vel = np.sqrt(vx**2 + vy**2)
p_ok = new_vel > 0.1 # avoid div by zero
vx[p_ok] = vx[p_ok] * orig_vel[p_ok] / new_vel[p_ok]
vy[p_ok] = vy[p_ok] * orig_vel[p_ok] / new_vel[p_ok]
# And transform to local map
vx = grid_gla.map_gridded_data(vx, grid=grid_vel, interp='linear')
vy = grid_gla.map_gridded_data(vy, grid=grid_vel, interp='linear')
# Write
with utils.ncDataset(gdir.get_filepath('gridded_data'), 'a') as nc:
vn = 'obs_icevel_x'
if vn in nc.variables:
v = nc.variables[vn]
else:
v = nc.createVariable(vn, 'f4', (
'y',
'x',
), zlib=True)
v.units = 'm yr-1'
v.long_name = 'ITS LIVE velocity data in x map direction'
v[:] = vx
vn = 'obs_icevel_y'
if vn in nc.variables:
v = nc.variables[vn]
else:
v = nc.createVariable(vn, 'f4', (
'y',
'x',
), zlib=True)
v.units = 'm yr-1'
v.long_name = 'ITS LIVE velocity data in xy map direction'
v[:] = vy
