本文整理匯總了Python中cdo.Cdo.monmean方法的典型用法代碼示例。如果您正苦於以下問題：Python Cdo.monmean方法的具體用法？Python Cdo.monmean怎麽用？Python Cdo.monmean使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類cdo.Cdo的用法示例。
在下文中一共展示了Cdo.monmean方法的8個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: get_model_data_generic
# 需要導入模塊: from cdo import Cdo [as 別名]
# 或者: from cdo.Cdo import monmean [as 別名]
def get_model_data_generic(self, interval='season', **kwargs):
"""
unique parameters are:
filename - file basename
variable - name of the variable as the short_name in the netcdf file
kwargs is a dictionary with keys for each model. Then a dictionary with properties follows
"""
if not self.type in kwargs.keys():
print 'WARNING: it is not possible to get data using generic function, as method missing: ', self.type, kwargs.keys()
return None
locdict = kwargs[self.type]
# read settings and details from the keyword arguments
# no defaults; everything should be explicitely specified in either the config file or the dictionaries
varname = locdict.pop('variable')
units = locdict.pop('unit', 'Crazy Unit')
#interval = kwargs.pop('interval') #, 'season') #does not make sense to specifiy a default value as this option is specified by configuration file!
lat_name = locdict.pop('lat_name', 'lat')
lon_name = locdict.pop('lon_name', 'lon')
model_suffix = locdict.pop('model_suffix')
model_prefix = locdict.pop('model_prefix')
file_format = locdict.pop('file_format')
scf = locdict.pop('scale_factor')
valid_mask = locdict.pop('valid_mask')
custom_path = locdict.pop('custom_path', None)
thelevel = locdict.pop('level', None)
target_grid = self._actplot_options['targetgrid']
interpolation = self._actplot_options['interpolation']
if custom_path is None:
filename1 = ("%s%s/merged/%s_%s_%s_%s_%s.%s" %
(self.data_dir, varname, varname, model_prefix, self.model, self.experiment, model_suffix, file_format))
else:
if self.type == 'CMIP5':
filename1 = ("%s/%s_%s_%s_%s_%s.%s" %
(custom_path, varname, model_prefix, self.model, self.experiment, model_suffix, file_format))
elif self.type == 'CMIP5RAW':
filename1 = ("%s/%s_%s_%s_%s_%s.%s" %
(custom_path, varname, model_prefix, self.model, self.experiment, model_suffix, file_format))
elif self.type == 'CMIP5RAWSINGLE':
print 'todo needs implementation!'
assert False
elif self.type == 'CMIP3':
filename1 = ("%s/%s_%s_%s_%s.%s" %
(custom_path, self.experiment, self.model, varname, model_suffix, file_format))
else:
print self.type
raise ValueError('Can not generate filename: invalid model type! %s' % self.type)
force_calc = False
if self.start_time is None:
raise ValueError('Start time needs to be specified')
if self.stop_time is None:
raise ValueError('Stop time needs to be specified')
#/// PREPROCESSING ///
cdo = Cdo()
s_start_time = str(self.start_time)[0:10]
s_stop_time = str(self.stop_time)[0:10]
#1) select timeperiod and generate monthly mean file
if target_grid == 't63grid':
gridtok = 'T63'
else:
gridtok = 'SPECIAL_GRID'
file_monthly = filename1[:-3] + '_' + s_start_time + '_' + s_stop_time + '_' + gridtok + '_monmean.nc' # target filename
file_monthly = get_temporary_directory() + os.path.basename(file_monthly)
sys.stdout.write('\n *** Model file monthly: %s\n' % file_monthly)
if not os.path.exists(filename1):
print 'WARNING: File not existing: ' + filename1
return None
cdo.monmean(options='-f nc', output=file_monthly, input='-' + interpolation + ',' + target_grid + ' -seldate,' + s_start_time + ',' + s_stop_time + ' ' + filename1, force=force_calc)
sys.stdout.write('\n *** Reading model data... \n')
sys.stdout.write(' Interval: ' + interval + '\n')
#2) calculate monthly or seasonal climatology
if interval == 'monthly':
mdata_clim_file = file_monthly[:-3] + '_ymonmean.nc'
mdata_sum_file = file_monthly[:-3] + '_ymonsum.nc'
mdata_N_file = file_monthly[:-3] + '_ymonN.nc'
mdata_clim_std_file = file_monthly[:-3] + '_ymonstd.nc'
cdo.ymonmean(options='-f nc -b 32', output=mdata_clim_file, input=file_monthly, force=force_calc)
cdo.ymonsum(options='-f nc -b 32', output=mdata_sum_file, input=file_monthly, force=force_calc)
cdo.ymonstd(options='-f nc -b 32', output=mdata_clim_std_file, input=file_monthly, force=force_calc)
cdo.div(options='-f nc', output=mdata_N_file, input=mdata_sum_file + ' ' + mdata_clim_file, force=force_calc) # number of samples
elif interval == 'season':
mdata_clim_file = file_monthly[:-3] + '_yseasmean.nc'
mdata_sum_file = file_monthly[:-3] + '_yseassum.nc'
#.........這裏部分代碼省略.........
示例2: xxxxxxxxxxxxxxxxxxxget_surface_shortwave_radiation_down
# 需要導入模塊: from cdo import Cdo [as 別名]
# 或者: from cdo.Cdo import monmean [as 別名]
def xxxxxxxxxxxxxxxxxxxget_surface_shortwave_radiation_down(self, interval='season', force_calc=False, **kwargs):
"""
return data object of
a) seasonal means for SIS
b) global mean timeseries for SIS at original temporal resolution
"""
the_variable = 'rsds'
locdict = kwargs[self.type]
valid_mask = locdict.pop('valid_mask')
if self.start_time is None:
raise ValueError('Start time needs to be specified')
if self.stop_time is None:
raise ValueError('Stop time needs to be specified')
s_start_time = str(self.start_time)[0:10]
s_stop_time = str(self.stop_time)[0:10]
if self.type == 'CMIP5':
filename1 = self.data_dir + 'rsds' + os.sep + self.experiment + '/ready/' + self.model + '/rsds_Amon_' + self.model + '_' + self.experiment + '_ensmean.nc'
elif self.type == 'CMIP5RAW': # raw CMIP5 data based on ensembles
filename1 = self._get_ensemble_filename(the_variable)
elif self.type == 'CMIP5RAWSINGLE':
filename1 = self.get_single_ensemble_file(the_variable, mip='Amon', realm='atmos', temporal_resolution='mon')
else:
raise ValueError('Unknown model type! not supported here!')
if not os.path.exists(filename1):
print ('WARNING file not existing: %s' % filename1)
return None
#/// PREPROCESSING ///
cdo = Cdo()
#1) select timeperiod and generatget_she monthly mean file
file_monthly = filename1[:-3] + '_' + s_start_time + '_' + s_stop_time + '_T63_monmean.nc'
file_monthly = get_temporary_directory() + os.path.basename(file_monthly)
print file_monthly
sys.stdout.write('\n *** Model file monthly: %s\n' % file_monthly)
cdo.monmean(options='-f nc', output=file_monthly, input='-remapcon,t63grid -seldate,' + s_start_time + ',' + s_stop_time + ' ' + filename1, force=force_calc)
sys.stdout.write('\n *** Reading model data... \n')
sys.stdout.write(' Interval: ' + interval + '\n')
#2) calculate monthly or seasonal climatology
if interval == 'monthly':
sis_clim_file = file_monthly[:-3] + '_ymonmean.nc'
sis_sum_file = file_monthly[:-3] + '_ymonsum.nc'
sis_N_file = file_monthly[:-3] + '_ymonN.nc'
sis_clim_std_file = file_monthly[:-3] + '_ymonstd.nc'
cdo.ymonmean(options='-f nc -b 32', output=sis_clim_file, input=file_monthly, force=force_calc)
cdo.ymonsum(options='-f nc -b 32', output=sis_sum_file, input=file_monthly, force=force_calc)
cdo.ymonstd(options='-f nc -b 32', output=sis_clim_std_file, input=file_monthly, force=force_calc)
cdo.div(options='-f nc', output=sis_N_file, input=sis_sum_file + ' ' + sis_clim_file, force=force_calc) # number of samples
elif interval == 'season':
sis_clim_file = file_monthly[:-3] + '_yseasmean.nc'
sis_sum_file = file_monthly[:-3] + '_yseassum.nc'
sis_N_file = file_monthly[:-3] + '_yseasN.nc'
sis_clim_std_file = file_monthly[:-3] + '_yseasstd.nc'
cdo.yseasmean(options='-f nc -b 32', output=sis_clim_file, input=file_monthly, force=force_calc)
cdo.yseassum(options='-f nc -b 32', output=sis_sum_file, input=file_monthly, force=force_calc)
cdo.yseasstd(options='-f nc -b 32', output=sis_clim_std_file, input=file_monthly, force=force_calc)
cdo.div(options='-f nc -b 32', output=sis_N_file, input=sis_sum_file + ' ' + sis_clim_file, force=force_calc) # number of samples
else:
print interval
raise ValueError('Unknown temporal interval. Can not perform preprocessing!')
if not os.path.exists(sis_clim_file):
return None
#3) read data
sis = Data(sis_clim_file, 'rsds', read=True, label=self._unique_name, unit='$W m^{-2}$', lat_name='lat', lon_name='lon', shift_lon=False)
sis_std = Data(sis_clim_std_file, 'rsds', read=True, label=self._unique_name + ' std', unit='-', lat_name='lat', lon_name='lon', shift_lon=False)
sis.std = sis_std.data.copy()
del sis_std
sis_N = Data(sis_N_file, 'rsds', read=True, label=self._unique_name + ' std', unit='-', lat_name='lat', lon_name='lon', shift_lon=False)
sis.n = sis_N.data.copy()
del sis_N
#ensure that climatology always starts with January, therefore set date and then sort
sis.adjust_time(year=1700, day=15) # set arbitrary time for climatology
sis.timsort()
#4) read monthly data
sisall = Data(file_monthly, 'rsds', read=True, label=self._unique_name, unit='W m^{-2}', lat_name='lat', lon_name='lon', shift_lon=False)
if not sisall._is_monthly():
raise ValueError('Timecycle of 12 expected here!')
sisall.adjust_time(day=15)
# land/sea masking ...
if valid_mask == 'land':
mask_antarctica = True
elif valid_mask == 'ocean':
mask_antarctica = False
else:
mask_antarctica = False
#.........這裏部分代碼省略.........
示例3: xxxxxget_surface_shortwave_radiation_up
# 需要導入模塊: from cdo import Cdo [as 別名]
# 或者: from cdo.Cdo import monmean [as 別名]
def xxxxxget_surface_shortwave_radiation_up(self, interval='season', force_calc=False, **kwargs):
the_variable = 'rsus'
if self.type == 'CMIP5':
filename1 = self.data_dir + the_variable + os.sep + self.experiment + os.sep + 'ready' + os.sep + self.model + os.sep + 'rsus_Amon_' + self.model + '_' + self.experiment + '_ensmean.nc'
elif self.type == 'CMIP5RAW': # raw CMIP5 data based on ensembles
filename1 = self._get_ensemble_filename(the_variable)
elif self.type == 'CMIP5RAWSINGLE':
filename1 = self.get_single_ensemble_file(the_variable, mip='Amon', realm='atmos', temporal_resolution='mon')
else:
raise ValueError('Unknown type! not supported here!')
if self.start_time is None:
raise ValueError('Start time needs to be specified')
if self.stop_time is None:
raise ValueError('Stop time needs to be specified')
if not os.path.exists(filename1):
print ('WARNING file not existing: %s' % filename1)
return None
# PREPROCESSING
cdo = Cdo()
s_start_time = str(self.start_time)[0:10]
s_stop_time = str(self.stop_time)[0:10]
#1) select timeperiod and generate monthly mean file
file_monthly = filename1[:-3] + '_' + s_start_time + '_' + s_stop_time + '_T63_monmean.nc'
file_monthly = get_temporary_directory() + os.path.basename(file_monthly)
cdo.monmean(options='-f nc', output=file_monthly, input='-remapcon,t63grid -seldate,' + s_start_time + ',' + s_stop_time + ' ' + filename1, force=force_calc)
#2) calculate monthly or seasonal climatology
if interval == 'monthly':
sup_clim_file = file_monthly[:-3] + '_ymonmean.nc'
sup_sum_file = file_monthly[:-3] + '_ymonsum.nc'
sup_N_file = file_monthly[:-3] + '_ymonN.nc'
sup_clim_std_file = file_monthly[:-3] + '_ymonstd.nc'
cdo.ymonmean(options='-f nc -b 32', output=sup_clim_file, input=file_monthly, force=force_calc)
cdo.ymonsum(options='-f nc -b 32', output=sup_sum_file, input=file_monthly, force=force_calc)
cdo.ymonstd(options='-f nc -b 32', output=sup_clim_std_file, input=file_monthly, force=force_calc)
cdo.div(options='-f nc', output=sup_N_file, input=sup_sum_file + ' ' + sup_clim_file, force=force_calc) # number of samples
elif interval == 'season':
sup_clim_file = file_monthly[:-3] + '_yseasmean.nc'
sup_sum_file = file_monthly[:-3] + '_yseassum.nc'
sup_N_file = file_monthly[:-3] + '_yseasN.nc'
sup_clim_std_file = file_monthly[:-3] + '_yseasstd.nc'
cdo.yseasmean(options='-f nc -b 32', output=sup_clim_file, input=file_monthly, force=force_calc)
cdo.yseassum(options='-f nc -b 32', output=sup_sum_file, input=file_monthly, force=force_calc)
cdo.yseasstd(options='-f nc -b 32', output=sup_clim_std_file, input=file_monthly, force=force_calc)
cdo.div(options='-f nc -b 32', output=sup_N_file, input=sup_sum_file + ' ' + sup_clim_file, force=force_calc) # number of samples
else:
print interval
raise ValueError('Unknown temporal interval. Can not perform preprocessing! ')
if not os.path.exists(sup_clim_file):
print 'File not existing (sup_clim_file): ' + sup_clim_file
return None
#3) read data
sup = Data(sup_clim_file, 'rsus', read=True, label=self._unique_name, unit='$W m^{-2}$', lat_name='lat', lon_name='lon', shift_lon=False)
sup_std = Data(sup_clim_std_file, 'rsus', read=True, label=self._unique_name + ' std', unit='-', lat_name='lat', lon_name='lon', shift_lon=False)
sup.std = sup_std.data.copy()
del sup_std
sup_N = Data(sup_N_file, 'rsus', read=True, label=self._unique_name + ' std', unit='-', lat_name='lat', lon_name='lon', shift_lon=False)
sup.n = sup_N.data.copy()
del sup_N
# ensure that climatology always starts with January, therefore set date and then sort
sup.adjust_time(year=1700, day=15) # set arbitrary time for climatology
sup.timsort()
#4) read monthly data
supall = Data(file_monthly, 'rsus', read=True, label=self._unique_name, unit='$W m^{-2}$', lat_name='lat', lon_name='lon', shift_lon=False)
supall.adjust_time(day=15)
if not supall._is_monthly():
raise ValueError('Monthly timecycle expected here!')
supmean = supall.fldmean()
#/// return data as a tuple list
retval = (supall.time, supmean, supall)
del supall
#/// mask areas without radiation (set to invalid): all data < 1 W/m**2
#sup.data = np.ma.array(sis.data,mask=sis.data < 1.)
return sup, retval示例4: _do_preprocessing
# 需要導入模塊: from cdo import Cdo [as 別名]
# 或者: from cdo.Cdo import monmean [as 別名]
def _do_preprocessing(self, rawfile, varname, s_start_time, s_stop_time, interval='monthly', force_calc=False, valid_mask='global', target_grid='t63grid'):
"""
perform preprocessing
* selection of variable
* temporal subsetting
"""
cdo = Cdo()
if not os.path.exists(rawfile):
print('File not existing! %s ' % rawfile)
return None, None
# calculate monthly means
file_monthly = get_temporary_directory() + os.sep + os.path.basename(rawfile[:-3]) + '_' + varname + '_' + s_start_time + '_' + s_stop_time + '_mm.nc'
if (force_calc) or (not os.path.exists(file_monthly)):
cdo.monmean(options='-f nc', output=file_monthly, input='-seldate,' + s_start_time + ',' + s_stop_time + ' ' + '-selvar,' + varname + ' ' + rawfile, force=force_calc)
else:
pass
if not os.path.exists(file_monthly):
raise ValueError('Monthly preprocessing did not work! %s ' % file_monthly)
# calculate monthly or seasonal climatology
if interval == 'monthly':
mdata_clim_file = file_monthly[:-3] + '_ymonmean.nc'
mdata_sum_file = file_monthly[:-3] + '_ymonsum.nc'
mdata_N_file = file_monthly[:-3] + '_ymonN.nc'
mdata_clim_std_file = file_monthly[:-3] + '_ymonstd.nc'
cdo.ymonmean(options='-f nc -b 32', output=mdata_clim_file, input=file_monthly, force=force_calc)
cdo.ymonsum(options='-f nc -b 32', output=mdata_sum_file, input=file_monthly, force=force_calc)
cdo.ymonstd(options='-f nc -b 32', output=mdata_clim_std_file, input=file_monthly, force=force_calc)
cdo.div(options='-f nc', output=mdata_N_file, input=mdata_sum_file + ' ' + mdata_clim_file, force=force_calc) # number of samples
elif interval == 'season':
mdata_clim_file = file_monthly[:-3] + '_yseasmean.nc'
mdata_sum_file = file_monthly[:-3] + '_yseassum.nc'
mdata_N_file = file_monthly[:-3] + '_yseasN.nc'
mdata_clim_std_file = file_monthly[:-3] + '_yseasstd.nc'
cdo.yseasmean(options='-f nc -b 32', output=mdata_clim_file, input=file_monthly, force=force_calc)
cdo.yseassum(options='-f nc -b 32', output=mdata_sum_file, input=file_monthly, force=force_calc)
cdo.yseasstd(options='-f nc -b 32', output=mdata_clim_std_file, input=file_monthly, force=force_calc)
cdo.div(options='-f nc -b 32', output=mdata_N_file, input=mdata_sum_file + ' ' + mdata_clim_file, force=force_calc) # number of samples
else:
raise ValueError('Unknown temporal interval. Can not perform preprocessing!')
if not os.path.exists(mdata_clim_file):
return None
# read data
if interval == 'monthly':
thetime_cylce = 12
elif interval == 'season':
thetime_cylce = 4
else:
print interval
raise ValueError('Unsupported interval!')
mdata = Data(mdata_clim_file, varname, read=True, label=self.name, shift_lon=False, time_cycle=thetime_cylce, lat_name='lat', lon_name='lon')
mdata_std = Data(mdata_clim_std_file, varname, read=True, label=self.name + ' std', unit='-', shift_lon=False, time_cycle=thetime_cylce, lat_name='lat', lon_name='lon')
mdata.std = mdata_std.data.copy()
del mdata_std
mdata_N = Data(mdata_N_file, varname, read=True, label=self.name + ' std', shift_lon=False, lat_name='lat', lon_name='lon')
mdata.n = mdata_N.data.copy()
del mdata_N
# ensure that climatology always starts with January, therefore set date and then sort
mdata.adjust_time(year=1700, day=15) # set arbitrary time for climatology
mdata.timsort()
#4) read monthly data
mdata_all = Data(file_monthly, varname, read=True, label=self.name, shift_lon=False, time_cycle=12, lat_name='lat', lon_name='lon')
mdata_all.adjust_time(day=15)
#mask_antarctica masks everything below 60 degree S.
#here we only mask Antarctica, if only LAND points shall be used
if valid_mask == 'land':
mask_antarctica = True
elif valid_mask == 'ocean':
mask_antarctica = False
else:
mask_antarctica = False
if target_grid == 't63grid':
mdata._apply_mask(get_T63_landseamask(False, area=valid_mask, mask_antarctica=mask_antarctica))
mdata_all._apply_mask(get_T63_landseamask(False, area=valid_mask, mask_antarctica=mask_antarctica))
else:
tmpmsk = get_generic_landseamask(False, area=valid_mask, target_grid=target_grid, mask_antarctica=mask_antarctica)
mdata._apply_mask(tmpmsk)
mdata_all._apply_mask(tmpmsk)
del tmpmsk
mdata_mean = mdata_all.fldmean()
# return data as a tuple list
retval = (mdata_all.time, mdata_mean, mdata_all)
del mdata_all
return mdata, retval示例5: get_jsbach_data_generic
# 需要導入模塊: from cdo import Cdo [as 別名]
# 或者: from cdo.Cdo import monmean [as 別名]
def get_jsbach_data_generic(self, interval='season', **kwargs):
"""
unique parameters are:
filename - file basename
variable - name of the variable as the short_name in the netcdf file
kwargs is a dictionary with keys for each model. Then a dictionary with properties follows
"""
if not self.type in kwargs.keys():
print 'WARNING: it is not possible to get data using generic function, as method missing: ', self.type, kwargs.keys()
return None
print self.type
print kwargs
locdict = kwargs[self.type]
# read settings and details from the keyword arguments
# no defaults; everything should be explicitely specified in either the config file or the dictionaries
varname = locdict.pop('variable')
units = locdict.pop('unit', 'Unit not specified')
lat_name = locdict.pop('lat_name', 'lat')
lon_name = locdict.pop('lon_name', 'lon')
#model_suffix = locdict.pop('model_suffix')
#model_prefix = locdict.pop('model_prefix')
file_format = locdict.pop('file_format')
scf = locdict.pop('scale_factor')
valid_mask = locdict.pop('valid_mask')
custom_path = locdict.pop('custom_path', None)
thelevel = locdict.pop('level', None)
target_grid = self._actplot_options['targetgrid']
interpolation = self._actplot_options['interpolation']
if self.type != 'JSBACH_RAW2':
print self.type
raise ValueError('Invalid data format here!')
# define from which stream of JSBACH data needs to be taken for specific variables
if varname in ['swdown_acc', 'swdown_reflect_acc']:
filename1 = self.files['jsbach']
elif varname in ['precip_acc']:
filename1 = self.files['land']
elif varname in ['temp2']:
filename1 = self.files['echam']
elif varname in ['var14']: # albedo vis
filename1 = self.files['albedo_vis']
elif varname in ['var15']: # albedo NIR
filename1 = self.files['albedo_nir']
else:
print varname
raise ValueError('Unknown variable type for JSBACH_RAW2 processing!')
force_calc = False
if self.start_time is None:
raise ValueError('Start time needs to be specified')
if self.stop_time is None:
raise ValueError('Stop time needs to be specified')
#/// PREPROCESSING ///
cdo = Cdo()
s_start_time = str(self.start_time)[0:10]
s_stop_time = str(self.stop_time)[0:10]
#1) select timeperiod and generate monthly mean file
if target_grid == 't63grid':
gridtok = 'T63'
else:
gridtok = 'SPECIAL_GRID'
file_monthly = filename1[:-3] + '_' + s_start_time + '_' + s_stop_time + '_' + gridtok + '_monmean.nc' # target filename
file_monthly = get_temporary_directory() + os.path.basename(file_monthly)
sys.stdout.write('\n *** Model file monthly: %s\n' % file_monthly)
if not os.path.exists(filename1):
print 'WARNING: File not existing: ' + filename1
return None
cdo.monmean(options='-f nc', output=file_monthly, input='-' + interpolation + ',' + target_grid + ' -seldate,' + s_start_time + ',' + s_stop_time + ' ' + filename1, force=force_calc)
sys.stdout.write('\n *** Reading model data... \n')
sys.stdout.write(' Interval: ' + interval + '\n')
#2) calculate monthly or seasonal climatology
if interval == 'monthly':
mdata_clim_file = file_monthly[:-3] + '_ymonmean.nc'
mdata_sum_file = file_monthly[:-3] + '_ymonsum.nc'
mdata_N_file = file_monthly[:-3] + '_ymonN.nc'
mdata_clim_std_file = file_monthly[:-3] + '_ymonstd.nc'
cdo.ymonmean(options='-f nc -b 32', output=mdata_clim_file, input=file_monthly, force=force_calc)
cdo.ymonsum(options='-f nc -b 32', output=mdata_sum_file, input=file_monthly, force=force_calc)
cdo.ymonstd(options='-f nc -b 32', output=mdata_clim_std_file, input=file_monthly, force=force_calc)
cdo.div(options='-f nc', output=mdata_N_file, input=mdata_sum_file + ' ' + mdata_clim_file, force=force_calc) # number of samples
elif interval == 'season':
mdata_clim_file = file_monthly[:-3] + '_yseasmean.nc'
#.........這裏部分代碼省略.........
示例6: _preproc_streams
# 需要導入模塊: from cdo import Cdo [as 別名]
# 或者: from cdo.Cdo import monmean [as 別名]
def _preproc_streams(self):
"""
It is assumed that the standard JSBACH postprocessing scripts have been applied.
Thus monthly mean data is available for each stream and code tables still need to be applied.
This routine does the following:
1) merge all times from individual (monthly mean) output files
2) assign codetables to work with proper variable names
3) aggregate data from tiles to gridbox values
"""
print 'Preprocessing JSBACH raw data streams (may take a while) ...'
cdo = Cdo()
# jsbach stream
print ' JSBACH stream ...'
outfile = get_temporary_directory() + self.experiment + '_jsbach_mm_full.nc'
if os.path.exists(outfile):
pass
else:
codetable = self.data_dir + 'log/' + self.experiment + '_jsbach.codes'
tmp = tempfile.mktemp(suffix='.nc', prefix=self.experiment + '_jsbach_', dir=get_temporary_directory()) # temporary file
#~ print self.data_dir
#~ print self.raw_outdata
#~ print 'Files: ', self._get_filenames_jsbach_stream()
#~ stop
if len(glob.glob(self._get_filenames_jsbach_stream())) > 0: # check if input files existing at all
print 'Mering the following files:', self._get_filenames_jsbach_stream()
cdo.mergetime(options='-f nc', output=tmp, input=self._get_filenames_jsbach_stream())
if os.path.exists(codetable):
cdo.monmean(options='-f nc', output=outfile, input='-setpartab,' + codetable + ' ' + tmp) # monmean needed here, as otherwise interface does not work
else:
cdo.monmean(options='-f nc', output=outfile, input=tmp) # monmean needed here, as otherwise interface does not work
print 'Outfile: ', outfile
#~ os.remove(tmp)
print 'Temporary name: ', tmp
self.files.update({'jsbach': outfile})
# veg stream
print ' VEG stream ...'
outfile = get_temporary_directory() + self.experiment + '_jsbach_veg_mm_full.nc'
if os.path.exists(outfile):
pass
else:
codetable = self.data_dir + 'log/' + self.experiment + '_jsbach_veg.codes'
tmp = tempfile.mktemp(suffix='.nc', prefix=self.experiment + '_jsbach_veg_', dir=get_temporary_directory()) # temporary file
if len(glob.glob(self._get_filenames_veg_stream())) > 0: # check if input files existing at all
cdo.mergetime(options='-f nc', output=tmp, input=self._get_filenames_veg_stream())
if os.path.exists(codetable):
cdo.monmean(options='-f nc', output=outfile, input='-setpartab,' + codetable + ' ' + tmp) # monmean needed here, as otherwise interface does not work
else:
cdo.monmean(options='-f nc', output=outfile, input=tmp) # monmean needed here, as otherwise interface does not work
os.remove(tmp)
self.files.update({'veg': outfile})
# veg land
print ' LAND stream ...'
outfile = get_temporary_directory() + self.experiment + '_jsbach_land_mm_full.nc'
if os.path.exists(outfile):
pass
else:
codetable = self.data_dir + 'log/' + self.experiment + '_jsbach_land.codes'
tmp = tempfile.mktemp(suffix='.nc', prefix=self.experiment + '_jsbach_land_', dir=get_temporary_directory()) # temporary file
if len(glob.glob(self._get_filenames_land_stream())) > 0: # check if input files existing at all
cdo.mergetime(options='-f nc', output=tmp, input=self._get_filenames_land_stream())
if os.path.exists(codetable):
cdo.monmean(options='-f nc', output=outfile, input='-setpartab,' + codetable + ' ' + tmp) # monmean needed here, as otherwise interface does not work
else:
cdo.monmean(options='-f nc', output=outfile, input=tmp) # monmean needed here, as otherwise interface does not work
os.remove(tmp)
self.files.update({'land': outfile})
# surf stream
print ' SURF stream ...'
outfile = get_temporary_directory() + self.experiment + '_jsbach_surf_mm_full.nc'
if os.path.exists(outfile):
pass
else:
codetable = self.data_dir + 'log/' + self.experiment + '_jsbach_surf.codes'
tmp = tempfile.mktemp(suffix='.nc', prefix=self.experiment + '_jsbach_surf_', dir=get_temporary_directory()) # temporary file
if len(glob.glob(self._get_filenames_surf_stream())) > 0: # check if input files existing at all
print glob.glob(self._get_filenames_surf_stream())
cdo.mergetime(options='-f nc', output=tmp, input=self._get_filenames_surf_stream())
if os.path.exists(codetable):
cdo.monmean(options='-f nc', output=outfile, input='-setpartab,' + codetable + ' ' + tmp) # monmean needed here, as otherwise interface does not work
else:
cdo.monmean(options='-f nc', output=outfile, input=tmp) # monmean needed here, as otherwise interface does not work
os.remove(tmp)
self.files.update({'surf': outfile})
# ECHAM BOT stream
print ' BOT stream ...'
outfile = get_temporary_directory() + self.experiment + '_echam6_echam_mm_full.nc'
if os.path.exists(outfile):
pass
else:
#.........這裏部分代碼省略.........
示例7: get_generic_landseamask
# 需要導入模塊: from cdo import Cdo [as 別名]
# 或者: from cdo.Cdo import monmean [as 別名]
def get_generic_landseamask(shift_lon, mask_antarctica=True,
area='land', interpolation_method='remapnn',
target_grid='t63grid', force=False):
"""
get generic land/sea mask. The routine uses the CDO command 'topo'
to generate a 0.5 degree land/sea mask and remaps this
using nearest neighbor
to the target grid
NOTE: using inconsistent land/sea masks between datasets can
result in considerable biases. Note also that
the application of l/s mask is dependent on the spatial resolution
This routine implements a VERY simple approach, but assuming
that all areas >0 m height are land and the rest is ocean.
Parameters
----------
shift_lon : bool
specifies if longitudes shall be shifted
interpolation_method : str
specifies the interpolation method
that shall be used for remapping the 0.5degree data
to the target grid. This can be any of ['remapnn','remapcon',
'remapbil']
target_grid : str
specifies target grid to interpolate to as
similar to CDO remap functions. This can be either a string or
a filename which includes valid geometry information
force : bool
force calculation (removes previous file) = slower
area : str
['land','ocean']. When 'land', then the mask returned
is True on land pixels, for ocean it is vice versa.
in any other case, you get a valid field everywhere
(globally)
mask_antarctica : bool
mask antarctica; if True, then the mask is
FALSE over Antarctice (<60S)
Returns
-------
returns a Data object
"""
print ('WARNING: Automatic generation of land/sea mask. \
Ensure that this is what you want!')
cdo = Cdo()
#/// construct output filename.
#If a filename was given for the grid, replace path separators ///
target_grid1 = target_grid.replace(os.sep, '_')
outputfile = get_temporary_directory() + 'land_sea_fractions_' \
+ interpolation_method + '_' + target_grid1 + '.nc'
print 'outfile: ', outputfile
print 'cmd: ', '-remapnn,' + target_grid + ' -topo'
#/// interpolate data to grid using CDO ///
cdo.monmean(options='-f nc', output=outputfile,
input='-remapnn,' + target_grid + ' -topo', force=force)
#/// generate L/S mask from topography (land = height > 0.
ls_mask = Data(outputfile, 'topo', read=True,
label='generic land-sea mask',
lat_name='lat', lon_name='lon',
shift_lon=shift_lon)
print('Land/sea mask can be found on file: %s' % outputfile)
if area == 'land':
msk = ls_mask.data > 0. # gives land
elif area == 'ocean':
msk = ls_mask.data <= 0.
else:
msk = np.ones(ls_mask.data.shape).astype('bool')
ls_mask.data[~msk] = 0.
ls_mask.data[msk] = 1.
ls_mask.data = ls_mask.data.astype('bool')
#/// mask Antarctica if desired ///
if mask_antarctica:
ls_mask.data[ls_mask.lat < -60.] = False
return ls_mask示例8: get_model_data_generic
# 需要導入模塊: from cdo import Cdo [as 別名]
# 或者: from cdo.Cdo import monmean [as 別名]
def get_model_data_generic(self, interval="season", **kwargs):
"""
unique parameters are:
filename - file basename
variable - name of the variable as the short_name in the netcdf file
kwargs is a dictionary with keys for each model. Then a dictionary with properties follows
"""
if not self.type in kwargs.keys():
print ""
print "WARNING: it is not possible to get data using generic function, as method missing: ", self.type, kwargs.keys()
assert False
locdict = kwargs[self.type]
# read settings and details from the keyword arguments
# no defaults; everything should be explicitely specified in either the config file or the dictionaries
varname = locdict.pop("variable", None)
# ~ print self.type
# ~ print locdict.keys()
assert varname is not None, "ERROR: provide varname!"
units = locdict.pop("unit", None)
assert units is not None, "ERROR: provide unit!"
lat_name = locdict.pop("lat_name", "lat")
lon_name = locdict.pop("lon_name", "lon")
model_suffix = locdict.pop("model_suffix", None)
model_prefix = locdict.pop("model_prefix", None)
file_format = locdict.pop("file_format")
scf = locdict.pop("scale_factor")
valid_mask = locdict.pop("valid_mask")
custom_path = locdict.pop("custom_path", None)
thelevel = locdict.pop("level", None)
target_grid = self._actplot_options["targetgrid"]
interpolation = self._actplot_options["interpolation"]
if custom_path is None:
filename1 = self.get_raw_filename(varname, **kwargs) # routine needs to be implemented by each subclass
else:
filename1 = custom_path + self.get_raw_filename(varname, **kwargs)
if filename1 is None:
print_log(WARNING, "No valid model input data")
return None
force_calc = False
if self.start_time is None:
raise ValueError("Start time needs to be specified")
if self.stop_time is None:
raise ValueError("Stop time needs to be specified")
# /// PREPROCESSING ///
cdo = Cdo()
s_start_time = str(self.start_time)[0:10]
s_stop_time = str(self.stop_time)[0:10]
# 1) select timeperiod and generate monthly mean file
if target_grid == "t63grid":
gridtok = "T63"
else:
gridtok = "SPECIAL_GRID"
file_monthly = (
filename1[:-3] + "_" + s_start_time + "_" + s_stop_time + "_" + gridtok + "_monmean.nc"
) # target filename
file_monthly = get_temporary_directory() + os.path.basename(file_monthly)
sys.stdout.write("\n *** Model file monthly: %s\n" % file_monthly)
if not os.path.exists(filename1):
print "WARNING: File not existing: " + filename1
return None
cdo.monmean(
options="-f nc",
output=file_monthly,
input="-"
+ interpolation
+ ","
+ target_grid
+ " -seldate,"
+ s_start_time
+ ","
+ s_stop_time
+ " "
+ filename1,
force=force_calc,
)
sys.stdout.write("\n *** Reading model data... \n")
sys.stdout.write(" Interval: " + interval + "\n")
# 2) calculate monthly or seasonal climatology
if interval == "monthly":
mdata_clim_file = file_monthly[:-3] + "_ymonmean.nc"
#.........這裏部分代碼省略.........