Python Cdo.yseasmean方法代码示例

# 需要导入模块: from cdo import Cdo [as 别名]
# 或者: from cdo.Cdo import yseasmean [as 别名]
    def get_gpp_data(self, interval='season'):
        """
        get surface GPP data for JSBACH

        todo temporal aggregation of data --> or leave it to the user!
        """
        cdo = Cdo()
        v = 'var167'
        y1 = str(self.start_time)[0:10]
        y2 = str(self.stop_time)[0:10]
        rawfilename = self.data_dir + 'data/model/' + self.experiment + '_' + y1[0:4] + '-' + y2[0:4] + '.nc'
        times_in_file = int(''.join(cdo.ntime(input=rawfilename)))

        if interval == 'season':
            if times_in_file != 4:
                tmp_file = get_temporary_directory() + os.path.basename(rawfilename)
                cdo.yseasmean(options='-f nc -b 32 -r ', input='-selvar,' + v + ' ' + rawfilename, output=tmp_file[:-3] + '_yseasmean.nc')
                rawfilename = tmp_file[:-3] + '_yseasmean.nc'

        if interval == 'monthly':
            if times_in_file != 12:
                tmp_file = get_temporary_directory() + os.path.basename(rawfilename)
                cdo.ymonmean(options='-f nc -b 32 -r ', input='-selvar,' + v + ' ' + rawfilename, output=tmp_file[:-3] + '_ymonmean.nc')
                rawfilename = tmp_file[:-3] + '_ymonmean.nc'

        if not os.path.exists(rawfilename):
            return None

        filename = rawfilename

        #--- read land-sea mask
        ls_mask = get_T63_landseamask(self.shift_lon)

        #--- read SW up data
        gpp = Data4D(filename, v, read=True,
                     label=self.experiment + ' ' + v, unit='gC m-2 a-1', lat_name='lat', lon_name='lon',
                     shift_lon=self.shift_lon,
                     mask=ls_mask.data.data, scale_factor=3600. * 24. * 30. / 0.083
                     )

        return gpp.sum_data4D()

# 需要导入模块: from cdo import Cdo [as 别名]
# 或者: from cdo.Cdo import yseasmean [as 别名]
    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'
#.........这里部分代码省略.........

# 需要导入模块: from cdo import Cdo [as 别名]
# 或者: from cdo.Cdo import yseasmean [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

# 需要导入模块: from cdo import Cdo [as 别名]
# 或者: from cdo.Cdo import yseasmean [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.........

# 需要导入模块: from cdo import Cdo [as 别名]
# 或者: from cdo.Cdo import yseasmean [as 别名]
    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

# 需要导入模块: from cdo import Cdo [as 别名]
# 或者: from cdo.Cdo import yseasmean [as 别名]
    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'
#.........这里部分代码省略.........

# 需要导入模块: from cdo import Cdo [as 别名]
# 或者: from cdo.Cdo import yseasmean [as 别名]

#.........这里部分代码省略.........

        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"
            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

        # 3) 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._unique_name,
            unit=units,