observationoperator.py 9.84 KB
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#!/usr/bin/env python
# model.py

import logging
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import os
import sys
import subprocess
import da.tools.io4 as io
import numpy as np
from netCDF4 import Dataset
from datetime import datetime, timedelta
from dateutil import rrule
from cdo import *

identifier = 'ObservationOperator'
version = '10'
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################### Begin Class ObservationOperator ###################
class ObservationOperator(object):
    def __init__(self, dacycle=None):
        self.ID = identifier
        self.version = version
        self.restart_filelist = []
        self.output_filelist = []
        self.outputdir = None # Needed for opening the samples.nc files created 

        logging.info('Observation Operator object initialized: %s' % self.ID)

        # The following code allows the object to be initialized with a dacycle object already present. Otherwise, it can
        # be added at a later moment.

        if dacycle != None:
            self.dacycle = dacycle
        else:
            self.dacycle = {}

    
    def get_initial_data(self):
        """ This method places all initial data needed by an ObservationOperator in the proper folder for the model """

    def setup(self,dacycle):
        """ Perform all steps necessary to start the observation operator through a simple Run() call """

        self.dacycle = dacycle
        self.outputdir = dacycle['dir.output']

    def prepare_run(self):
        """ Prepare the running of the actual forecast model, for example compile code """

	# Define the name of the file that will contain the modeled output of each observation

        self.simulated_file = os.path.join(self.outputdir, 'samples_simulated.%s.nc' % self.dacycle['time.sample.stamp'])
        self.forecast_nmembers = int(self.dacycle['da.optimizer.nmembers'])

    def run(self):


        f = io.CT_CDF(self.simulated_file, method='create')
        logging.debug('Creating new simulated observation file in ObservationOperator (%s)' % self.simulated_file)
	
        dimid = f.createDimension('obs_num', size=None)
        dimid = ('obs_num',)
        savedict = io.std_savedict.copy() 
        savedict['name'] = "obs_num"
        savedict['dtype'] = "int"
        savedict['long_name'] = "Unique_Dataset_observation_index_number"
        savedict['units'] = ""
        savedict['dims'] = dimid
        savedict['comment'] = "Unique index number within this dataset ranging from 0 to UNLIMITED."
        f.add_data(savedict,nsets=0)

        dimmember = f.createDimension('nmembers', size=self.forecast_nmembers)
        dimmember = ('nmembers',)
        savedict = io.std_savedict.copy() 
        savedict['name'] = "flask"
        savedict['dtype'] = "float"
        savedict['long_name'] = "mole_fraction_of_trace_gas_in_air"
        savedict['units'] = "mol mol-1"
        savedict['dims'] = dimid + dimmember
        savedict['comment'] = "Simulated model value created by COSMO"
        f.add_data(savedict,nsets=0)

	# Open file with x,y,z,t of model samples that need to be sampled

        f_in = io.ct_read(self.dacycle['ObsOperator.inputfile'],method='read') 

	# Get simulated values and ID

        ids = f_in.get_variable('obs_num')
        obs = f_in.get_variable('observed')
        mdm = f_in.get_variable('modeldatamismatch')

        f_in.close()

	# Loop over observations, add random white noise, and write to file

        shape = (self.forecast_nmembers,mdm.size)
        model_data=np.empty(shape=shape)   # 3x7

#        self.obspack_dir = dacycle.dasystem['obspack.input.dir']

#        infile = os.path.join(self.obspack_dir, 'summary', '%s_dataset_summary.txt' % (self.obspack_id,))
#        infile = "/store/empa/em05/parsenov/obspack/summary/obspack_co2_1_GLOBALVIEWplus_v3.2_2017-11-02_dataset_summary.txt"
#        f = open(infile, 'r')
#        lines = f.readlines()
#        f.close()
#        ncfilelist = []

#        for line in lines:
#            if not line.startswith('# dataset:'): continue

#            items = line.split(':')
#            ncfile = items[1].strip()

#            ncfilelist += [ncfile]

#        for ncfile in ncfilelist:
#            infile = os.path.join(ncfile + '.nc')


        # here comes int2lm
        
#        cosmo = os.path.join(dacycle.dasystem['cosmo_path'],"run_chain.py")
#        os.system('python '+cosmo+'ctdas'+dacycle['time.start'].strftime('%Y-%m-%d')+'0 672 -j meteo icbc emissions biofluxes int2lm post_int2lm')
        # HERE MULTIPLY COSMO FLUXES INT2LM WITH CTDAS PARAMS
        for ens in range(1,self.forecast_nmembers+1):
            ens = str(ens).zfill(3)
            os.system('ln *nc /scratch/snx3000/parsenov/ctdas/2013040100_0_168/int2lm/output /scratch/snx3000/parsenov/ctdas/cosmo_ensemble/'+ens)
            os.system('rm /scratch/snx3000/parsenov/ctdas/cosmo_ensemble/*f.nc')
            for dt in rrule.rrule(rrule.HOURLY, dtstart=self['time.start'], until=self['time.start']+timedelta(hours=168)):
                print(dt)
                cdo.mul("parameters."+ens+".nc", input = "/scratch/snx3000/parsenov/ctdas/2013040100_0_168/int2lm/output/laf"+dt.strftime('%Y%m%d%H')+"f.nc", output = "/scratch/snx3000/parsenov/ctdas/cosmo_ensemble/"+ens+"laf"+dt.strftime('%Y%m%d%H')+"f.nc")
        # here comes COSMO
            sys.exit()
            os.system('python '+cosmo+' ctdas '+self['time.start'].strftime('%Y-%m-%d')+' 0 672 -j cosmo')
            extract_model_data(self.forecast_nmembers)

        for i in range(0,self.forecast_nmembers):
            idx=str(i+1)
            cosmo_file = os.path.join('/scratch/snx3000/parsenov/processing_chain/ctdas_'+idx+'/%s/cosmo/output/model_'+idx+'_%s.nc' % self['time.sample.stamp'])
            ifile = Dataset(cosmo_file, mode='r')
            model_data[i,:] = np.squeeze(ifile.variables['CO2'][:])#*29./44.)#*1000000.   # in ppm
            ifile.close()

        for j,data in enumerate(zip(ids,obs,mdm)):
            f.variables['obs_num'][j] = data[0]		
            f.variables['flask'][j,:] = model_data[:,j]
#            print model_data[:,j]
        f.close()
#            f.variables['flask'][i,:] = data[1]+np.random.randn(self.forecast_nmembers)
#### WARNING ACHTUNG PAZNJA POZOR VNEMANIE data[2] is model data mismatch (=1000) by default in tools/io4.py!!! pavle
#            print i   # i is num of hour
#	    f.variables['flask'][i,:] = data[1]+np.random.randn(self.forecast_nmembers)*data[2]
#            print 'pavle',data[1]
#            print 'pavle',np.random.randn(self.forecast_nmembers)
#            print 'pavle',data[2]
#            print 'i',i
#            print 'data',data
#            print 'model data',model_data
#            print 'ids',ids # number of obs [11841779 11841780 and such] dim =7
#            print 'obs',obs # observation data [0.00040033 0.0004001 and suchh] dim =7
#            print 'mdm',mdm # model data mismatch (=1000.) dim = 7
#            print self.forecast_nmembers
#        sys.exit()


        logging.info('ObservationOperator finished successfully, output file written (%s)' % self.simulated_file)

    def run_forecast_model(self):
        self.prepare_run()
        self.run()

    def extract_model_data(ens):
        
        cdo = Cdo()
        files2cat=[]

        time_stamp = self['time.sample.stamp']
        cosmo_out = self['da.cosmo_path']+'/'+ens+'/'+time_stamp+'cosmo/output'
        os.chdir(cosmo_out)

        for time in tools.iter_hours(starttime, hstart, hstop):
            co2_in_fn = time.strftime(cosmo_out+'/lffd%Y%m%d%H.nc')
            co2_out_fn = time.strftime(cosmo_out+'/CO2_'+ens+'_%Y%m%d%H.nc')
            hhl_fn = time.strftime(cosmo_out+'/lffd%Y%m%d%Hc.nc')
            cdo.expr("CO2=CO2_BG-CO2_GPP+CO2_RESP+CO2_A_CH+CO2_A", input = "-selname,CO2_BG,CO2_GPP,CO2_RESP,CO2_A_CH,CO2_A "+co2_in_fn, output = co2_out_fn)
            files2cat.append(co2_out_fn)
            cdo.selname("HHL", input = hhl_fn, output = "hhl.nc")

        cdo.cat(input = files2cat, output = "CO2_"+time_stamp+".nc")

        sites = ("lhw","brm","jfj","ssl")

        cdo.remapnn("lon=7.99_lat=46.54,", input = "CO2_"+time_stamp+".nc", output = "CO2_jfj_"+time_stamp+".nc")
        cdo.remapnn("lon=7.99_lat=46.54,", input = "hhl.nc", output = "hhl_jfj.nc")
        cdo.remapnn("lon=8.40_lat=47.48,", input = "CO2_"+time_stamp+".nc", output = "CO2_lhw_"+time_stamp+".nc")
        cdo.remapnn("lon=8.40_lat=47.48,", input = "hhl.nc", output = "hhl_lhw.nc")
        cdo.remapnn("lon=8.18_lat=47.19,", input = "CO2_"+time_stamp+".nc", output = "CO2_brm_"+time_stamp+".nc")
        cdo.remapnn("lon=8.18_lat=47.19,", input = "hhl.nc", output = "hhl_brm.nc")
        cdo.remapnn("lon=7.92_lat=47.92,", input = "CO2_"+time_stamp+".nc", output = "CO2_ssl_"+time_stamp+".nc")
        cdo.remapnn("lon=7.92_lat=47.92,", input = "hhl.nc", output = "hhl_ssl.nc")

        for s,ss in enumerate(sites):
            site_height.main(ss, time_stamp)

        cdo.intlevel("860", input = ",CO2_60lev_"+ens+"_lhw_"+time_stamp+".nc", output = "modelled_"+ens+"_lhw_"+time_stamp+".nc")
        cdo.intlevel("797", input = ",CO2_60lev_"+ens+"_brm_"+time_stamp+".nc", output = "modelled_"+ens+"_brm_"+time_stamp+".nc")
        cdo.intlevel("3580", input = "CO2_60lev_"+ens+"_jfj_"+time_stamp+".nc", output = "modelled_"+ens+"_jfj_"+time_stamp+".nc")
        cdo.intlevel("1205", input = "CO2_60lev_"+ens+"_ssl_"+time_stamp+".nc", output = "modelled_"+ens+"_ssl_"+time_stamp+".nc")

        cdo.cat(input = "modelled_"+ens+"_brm_"+time_stamp+".nc modelled_"+ens+"_jfj_"+time_stamp+".nc modelled_"+ens+"_lhw_"+time_stamp+".nc modelled_"+ens+"_ssl_"+time_stamp+".nc ", output = "model_"+ens+"_"+time_stamp+".nc")


################### End Class ObservationOperator ###################

class RandomizerObservationOperator(ObservationOperator):
    """ This class holds methods and variables that are needed to use a random number generated as substitute
        for a true observation operator. It takes observations and returns values for each obs, with a specified 
        amount of white noise added 
    """



if __name__ == "__main__":
    pass