def process_custom_climate_data(gdir):
"""Processes and writes the climate data from a user-defined climate file.
The input file must have a specific format (see
oggm-sample-data/test-files/histalp_merged_hef.nc for an example).
Uses caching for faster retrieval.
This is the way OGGM does it for the Alps (HISTALP).
"""
if not (('climate_file' in cfg.PATHS)
and os.path.exists(cfg.PATHS['climate_file'])):
raise IOError('Custom climate file not found')
# read the file
fpath = cfg.PATHS['climate_file']
nc_ts = salem.GeoNetcdf(fpath)
# set temporal subset for the ts data (hydro years)
yrs = nc_ts.time.year
y0, y1 = yrs[0], yrs[-1]
nc_ts.set_period(t0='{}-10-01'.format(y0), t1='{}-09-01'.format(y1))
time = nc_ts.time
ny, r = divmod(len(time), 12)
if r != 0:
raise ValueError('Climate data should be N full years exclusively')
# Units
assert nc_ts._nc.variables['hgt'].units.lower() in [
'm', 'meters', 'meter', 'metres', 'metre'
]
assert nc_ts._nc.variables['temp'].units.lower() in [
'degc', 'degrees', 'degree', 'c'
]
assert nc_ts._nc.variables['prcp'].units.lower() in [
'kg m-2', 'l m-2', 'mm', 'millimeters', 'millimeter'
]
# geoloc
lon = nc_ts._nc.variables['lon'][:]
lat = nc_ts._nc.variables['lat'][:]
# Gradient defaults
use_grad = cfg.PARAMS['temp_use_local_gradient']
def_grad = cfg.PARAMS['temp_default_gradient']
g_minmax = cfg.PARAMS['temp_local_gradient_bounds']
ilon = np.argmin(np.abs(lon - gdir.cenlon))
ilat = np.argmin(np.abs(lat - gdir.cenlat))
ref_pix_lon = lon[ilon]
ref_pix_lat = lat[ilat]
iprcp, itemp, igrad, ihgt = utils.joblib_read_climate(
fpath, ilon, ilat, def_grad, g_minmax, use_grad)
gdir.write_monthly_climate_file(time, iprcp, itemp, igrad, ihgt,
ref_pix_lon, ref_pix_lat)
# metadata
out = {'climate_source': fpath, 'hydro_yr_0': y0 + 1, 'hydro_yr_1': y1}
gdir.write_pickle(out, 'climate_info')
def process_custom_climate_data(gdir):
"""Processes and writes the climate data from a user-defined climate file.
The input file must have a specific format (see
oggm-sample-data/test-files/histalp_merged_hef.nc for an example).
Uses caching for faster retrieval.
This is the way OGGM does it for the Alps (HISTALP).
"""
if not (('climate_file' in cfg.PATHS) and
os.path.exists(cfg.PATHS['climate_file'])):
raise IOError('Custom climate file not found')
# read the file
fpath = cfg.PATHS['climate_file']
nc_ts = salem.GeoNetcdf(fpath)
# set temporal subset for the ts data (hydro years)
yrs = nc_ts.time.year
y0, y1 = yrs[0], yrs[-1]
nc_ts.set_period(t0='{}-10-01'.format(y0), t1='{}-09-01'.format(y1))
time = nc_ts.time
ny, r = divmod(len(time), 12)
if r != 0:
raise ValueError('Climate data should be N full years exclusively')
# Units
assert nc_ts._nc.variables['hgt'].units.lower() in ['m', 'meters', 'meter',
'metres', 'metre']
assert nc_ts._nc.variables['temp'].units.lower() in ['degc', 'degrees',
'degree', 'c']
assert nc_ts._nc.variables['prcp'].units.lower() in ['kg m-2', 'l m-2',
'mm', 'millimeters',
'millimeter']
# geoloc
lon = nc_ts._nc.variables['lon'][:]
lat = nc_ts._nc.variables['lat'][:]
# Gradient defaults
use_grad = cfg.PARAMS['temp_use_local_gradient']
def_grad = cfg.PARAMS['temp_default_gradient']
g_minmax = cfg.PARAMS['temp_local_gradient_bounds']
ilon = np.argmin(np.abs(lon - gdir.cenlon))
ilat = np.argmin(np.abs(lat - gdir.cenlat))
ref_pix_lon = lon[ilon]
ref_pix_lat = lat[ilat]
iprcp, itemp, igrad, ihgt = utils.joblib_read_climate(fpath, ilon,
ilat, def_grad,
g_minmax,
use_grad)
gdir.write_monthly_climate_file(time, iprcp, itemp, igrad, ihgt,
ref_pix_lon, ref_pix_lat)
# metadata
out = {'climate_source': fpath, 'hydro_yr_0': y0+1, 'hydro_yr_1': y1}
gdir.write_pickle(out, 'climate_info')
def process_histalp_nonparallel(gdirs, fpath=None):
"""This is the way OGGM used to do it (deprecated).
It requires an input file with a specific format, and uses lazy
optimisation (computing time dependant gradients can be slow)
"""
# Did the user specify a specific climate data file?
if fpath is None:
if ('climate_file' in cfg.PATHS):
fpath = cfg.PATHS['climate_file']
if not os.path.exists(fpath):
raise IOError('Custom climate file not found')
log.info('process_histalp_nonparallel')
# read the file and data entirely (faster than many I/O)
with netCDF4.Dataset(fpath, mode='r') as nc:
lon = nc.variables['lon'][:]
lat = nc.variables['lat'][:]
# 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 exclusively')
y0, y1 = time[0].year, time[-1].year
# Units
assert nc.variables['hgt'].units == 'm'
assert nc.variables['temp'].units == 'degC'
assert nc.variables['prcp'].units == 'kg m-2'
# Gradient defaults
use_grad = cfg.PARAMS['temp_use_local_gradient']
def_grad = cfg.PARAMS['temp_default_gradient']
g_minmax = cfg.PARAMS['temp_local_gradient_bounds']
for gdir in gdirs:
ilon = np.argmin(np.abs(lon - gdir.cenlon))
ilat = np.argmin(np.abs(lat - gdir.cenlat))
ref_pix_lon = lon[ilon]
ref_pix_lat = lat[ilat]
iprcp, itemp, igrad, ihgt = utils.joblib_read_climate(fpath, ilon,
ilat, def_grad,
g_minmax,
use_grad)
gdir.write_monthly_climate_file(time, iprcp, itemp, igrad, ihgt,
ref_pix_lon, ref_pix_lat)
# metadata
out = {'climate_source': fpath,
'hydro_yr_0': y0+1, 'hydro_yr_1': y1}
gdir.write_pickle(out, 'climate_info')
def process_histalp_nonparallel(gdirs, fpath=None):
"""This is the way OGGM used to do it (deprecated).
It requires an input file with a specific format, and uses lazy
optimisation (computing time dependant gradients can be slow)
"""
# Did the user specify a specific climate data file?
if fpath is None:
if ('climate_file' in cfg.PATHS):
fpath = cfg.PATHS['climate_file']
if not os.path.exists(fpath):
raise IOError('Custom climate file not found')
log.info('process_histalp_nonparallel')
# read the file and data entirely (faster than many I/O)
with netCDF4.Dataset(fpath, mode='r') as nc:
lon = nc.variables['lon'][:]
lat = nc.variables['lat'][:]
# 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 exclusively')
y0, y1 = time[0].year, time[-1].year
# Units
assert nc.variables['hgt'].units == 'm'
assert nc.variables['temp'].units == 'degC'
assert nc.variables['prcp'].units == 'kg m-2'
# Gradient defaults
use_grad = cfg.PARAMS['temp_use_local_gradient']
def_grad = cfg.PARAMS['temp_default_gradient']
g_minmax = cfg.PARAMS['temp_local_gradient_bounds']
for gdir in gdirs:
ilon = np.argmin(np.abs(lon - gdir.cenlon))
ilat = np.argmin(np.abs(lat - gdir.cenlat))
ref_pix_lon = lon[ilon]
ref_pix_lat = lat[ilat]
iprcp, itemp, igrad, ihgt = utils.joblib_read_climate(fpath, ilon,
ilat, def_grad,
g_minmax,
use_grad)
gdir.write_monthly_climate_file(time, iprcp, itemp, igrad, ihgt,
ref_pix_lon, ref_pix_lat)
# metadata
out = {'climate_source': fpath,
'hydro_yr_0': y0+1, 'hydro_yr_1': y1}
gdir.write_pickle(out, 'climate_info')
def distribute_climate_data(gdirs):
"""Reads the Histalp climate data and distributes to each glacier.
Not to be multi-processed.
Parameters:
-----------
gdirs: the list of GlacierDir objects where to write the data.
I/O
---
Generates a NetCDF file in the root glacier directory (climate_monthly.nc)
It contains the timeseries of temperature, temperature gradient, and
precipitation at the nearest grid point. The climate data reference height
is provided as global attribute."""
log.info('Distribute climate data')
# read the file and data entirely (faster than many I/O)
ncpath = cfg.paths['histalp_file']
nc = netCDF4.Dataset(ncpath, mode='r')
lon = nc.variables['lon'][:]
lat = nc.variables['lat'][:]
# 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 exclusively')
# Units
assert nc.variables['hgt'].units == 'm'
assert nc.variables['temp'].units == 'degC'
assert nc.variables['prcp'].units == 'kg m-2'
# Gradient defaults
def_grad = -0.0065
g_minmax = cfg.params['temp_local_gradient_bounds']
sf = cfg.params['prcp_scaling_factor']
for gdir in gdirs:
ilon = np.argmin(np.abs(lon - gdir.cenlon))
ilat = np.argmin(np.abs(lat - gdir.cenlat))
iprcp, itemp, igrad, ihgt = utils.joblib_read_climate(ncpath, ilon,
ilat, def_grad,
g_minmax,
sf)
gdir.create_monthly_climate_file(time, iprcp, itemp, igrad, ihgt)
nc.close()
def _distribute_histalp_syle(gdirs, fpath):
"""This is the way OGGM does it for the Alps.
It requires an input file with a specific format, and uses lazy
optimisation (computing time dependant gradients can be slow)
"""
# read the file and data entirely (faster than many I/O)
nc = netCDF4.Dataset(fpath, mode='r')
lon = nc.variables['lon'][:]
lat = nc.variables['lat'][:]
# 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 exclusively')
# Units
assert nc.variables['hgt'].units == 'm'
assert nc.variables['temp'].units == 'degC'
assert nc.variables['prcp'].units == 'kg m-2'
# Gradient defaults
use_grad = cfg.PARAMS['temp_use_local_gradient']
def_grad = cfg.PARAMS['temp_default_gradient']
g_minmax = cfg.PARAMS['temp_local_gradient_bounds']
sf = cfg.PARAMS['prcp_scaling_factor']
for gdir in gdirs:
ilon = np.argmin(np.abs(lon - gdir.cenlon))
ilat = np.argmin(np.abs(lat - gdir.cenlat))
iprcp, itemp, igrad, ihgt = utils.joblib_read_climate(fpath, ilon,
ilat, def_grad,
g_minmax,
sf, use_grad)
gdir.write_monthly_climate_file(time, iprcp, itemp, igrad, ihgt)
nc.close()
def _distribute_histalp_syle(gdirs, fpath):
"""This is the way OGGM does it for the Alps.
It requires an input file with a specific format, and uses lazy
optimisation (computing time dependant gradients can be slow)
"""
# read the file and data entirely (faster than many I/O)
nc = netCDF4.Dataset(fpath, mode='r')
lon = nc.variables['lon'][:]
lat = nc.variables['lat'][:]
# 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 exclusively')
# Units
assert nc.variables['hgt'].units == 'm'
assert nc.variables['temp'].units == 'degC'
assert nc.variables['prcp'].units == 'kg m-2'
# Gradient defaults
use_grad = cfg.PARAMS['temp_use_local_gradient']
def_grad = cfg.PARAMS['temp_default_gradient']
g_minmax = cfg.PARAMS['temp_local_gradient_bounds']
sf = cfg.PARAMS['prcp_scaling_factor']
for gdir in gdirs:
ilon = np.argmin(np.abs(lon - gdir.cenlon))
ilat = np.argmin(np.abs(lat - gdir.cenlat))
iprcp, itemp, igrad, ihgt = utils.joblib_read_climate(
fpath, ilon, ilat, def_grad, g_minmax, sf, use_grad)
gdir.write_monthly_climate_file(time, iprcp, itemp, igrad, ihgt)
nc.close()
