本文整理汇总了Python中pycmbs.data.Data._is_monthly方法的典型用法代码示例。如果您正苦于以下问题：Python Data._is_monthly方法的具体用法？Python Data._is_monthly怎么用？Python Data._is_monthly使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pycmbs.data.Data的用法示例。
在下文中一共展示了Data._is_monthly方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: xxxxxget_surface_shortwave_radiation_up
# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import _is_monthly [as 别名]
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2: xxxxxxxxxxxxxxxxxxxget_surface_shortwave_radiation_down
# 需要导入模块: from pycmbs.data import Data [as 别名]
# 或者: from pycmbs.data.Data import _is_monthly [as 别名]
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
#.........这里部分代码省略.........