observationoperator.py 10.4 KB
Newer Older
brunner's avatar
brunner committed
1
2
3
4
#!/usr/bin/env python
# model.py

import logging
brunner's avatar
brunner committed
5
6
7
import os
import sys
import subprocess
brunner's avatar
brunner committed
8
import da.cosmo.io4 as io
brunner's avatar
brunner committed
9
10
11
12
13
import numpy as np
from netCDF4 import Dataset
from datetime import datetime, timedelta
from dateutil import rrule
from cdo import *
brunner's avatar
brunner committed
14
from . import site_height
brunner's avatar
brunner committed
15
16
17

identifier = 'ObservationOperator'
version = '10'
brunner's avatar
brunner committed
18

brunner's avatar
brunner committed
19
20
cdo = Cdo()

brunner's avatar
brunner committed
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
################### 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)

        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'])

brunner's avatar
brunner committed
55
    def run(self,dacycle):
brunner's avatar
brunner committed
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119


        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')

brunner's avatar
brunner committed
120
121
        hour_time_stamp = ("0_168","168_336","336_504")

brunner's avatar
brunner committed
122
123
124
125
126
127
128
129
        for dt in rrule.rrule(rrule.HOURLY, dtstart=dacycle['time.start'], until=dacycle['time.start']+timedelta(hours=504)):
            for ens in range(0,self.forecast_nmembers):
                ens = str(ens).zfill(3)
                cdo.setunit("'kg m-2 s-1' -expr,GPP_"+ens+"_F=CO2_GPP_F*parametermap -merge "+os.path.join(dacycle['da.bio.input'], 'gpp_%s.nc' % dt.strftime('%Y%m%d%H')), input = os.path.join(dacycle['dir.da_run'],"input/parameters."+ens+".nc"), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_"+ens+"_%s.nc" % dt.strftime('%Y%m%d%H')))
                cdo.setunit("'kg m-2 s-1' -expr,RESP_"+ens+"_F=CO2_RESP_F*parametermap -merge "+os.path.join(dacycle['da.bio.input'], 'ra_%s.nc' % dt.strftime('%Y%m%d%H')), input = os.path.join(dacycle['dir.da_run'],"input/parameters."+ens+".nc"), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_"+ens+"_%s.nc" % dt.strftime('%Y%m%d%H')))
            cdo.merge(input = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_???_%s.nc" % dt.strftime('%Y%m%d%H')), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_%s.nc" % dt.strftime('%Y%m%d%H')))
            cdo.merge(input = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_???_%s.nc" % dt.strftime('%Y%m%d%H')), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_%s.nc" % dt.strftime('%Y%m%d%H')))
        os.chdir(dacycle['da.obsoperator.home'])
brunner's avatar
brunner committed
130
        os.system('python run_chain.py ctdas '+dacycle['time.start'].strftime('%Y-%m-%d')+' 0 504 -j meteo icbc emissions biofluxes int2lm post_int2lm cosmo')
brunner's avatar
brunner committed
131
        os.chdir(dacycle['dir.da_run'])
brunner's avatar
brunner committed
132
133
        for h,hts in enumerate(hour_time_stamp):
            self.extract_model_data(dacycle,hts,self.forecast_nmembers)    # hts is hourly time stamp
brunner's avatar
brunner committed
134
135

        for i in range(0,self.forecast_nmembers):
brunner's avatar
brunner committed
136
            idx = str(i+1).zfill(3)
brunner's avatar
brunner committed
137
            cosmo_file = os.path.join('/store/empa/em05/parsenov/cosmo_data/model_'+idx+'_%s.nc' % dacycle['time.sample.stamp'])
brunner's avatar
brunner committed
138
            ifile = Dataset(cosmo_file, mode='r')
brunner's avatar
brunner committed
139
            model_data[i,:] = (np.squeeze(ifile.variables['CO2'][:])*29./44.)*1E6   # in ppm
brunner's avatar
brunner committed
140
141
142
143
144
145
146
147
148
149
150
151
            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()
#### WARNING ACHTUNG PAZNJA POZOR VNEMANIE data[2] is model data mismatch (=1000) by default in tools/io4.py!!! pavle


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

brunner's avatar
brunner committed
152
    def run_forecast_model(self,dacycle):
brunner's avatar
brunner committed
153
        self.prepare_run()
brunner's avatar
brunner committed
154
        self.run(dacycle)
brunner's avatar
brunner committed
155

brunner's avatar
brunner committed
156
    def extract_model_data(self,dacycle,hts,ensnum):
brunner's avatar
brunner committed
157
158

        time_stamp = dacycle['time.sample.stamp']
brunner's avatar
brunner committed
159

brunner's avatar
brunner committed
160
        sites = ("lhw","brm","jfj","ssl")
brunner's avatar
brunner committed
161
        self.dacycle = dacycle
brunner's avatar
brunner committed
162
        cosmo_time_stamp = dacycle['time.start'].strftime('%Y%m%d%H') #+timedelta(hours=168)
brunner's avatar
brunner committed
163
        cosmo_out = "/scratch/snx3000/parsenov/ctdas/"+cosmo_time_stamp+"_"+hts+"/cosmo/output/"
brunner's avatar
brunner committed
164
165
166
167
168
169
        cosmo_save = "/store/empa/em05/parsenov/cosmo_data/"
        hhl_fn = cosmo_out+'lffd'+dacycle['time.start'].strftime('%Y%m%d%H')+'c.nc'
        cdo.selname("HHL", input = hhl_fn, output = cosmo_out+"hhl.nc")

        for ens in range(0,ensnum):
            ens = str(ens).zfill(3)
brunner's avatar
brunner committed
170
            files2cat=[]
brunner's avatar
brunner committed
171
            for dt in rrule.rrule(rrule.HOURLY, dtstart=dacycle['time.start'], until=dacycle['time.start']+timedelta(hours=168)):
brunner's avatar
brunner committed
172
173
174
                dt=dt.strftime('%Y%m%d%H')
                co2_in_fn = cosmo_out+'lffd'+dt+'.nc'
                co2_out_fn = cosmo_out+'CO2_'+ens+'_'+dt+'.nc'
brunner's avatar
brunner committed
175
                cdo.expr("CO2=(CO2_BG-GPP_"+ens+"+RESP_"+ens+"+CO2_A_CH+CO2_A)/(1.-QV)", input = "-selname,QV,CO2_BG,GPP_"+ens+",RESP_"+ens+",CO2_A_CH,CO2_A "+co2_in_fn, output = co2_out_fn)
brunner's avatar
brunner committed
176
177
178
179
                files2cat.append(co2_out_fn)

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

brunner's avatar
brunner committed
180
181
            cdo.remapnn("lon=7.99_lat=46.54,", input = cosmo_out+"CO2_"+ens+"_"+time_stamp+".nc", output = cosmo_out+"CO2_jfj_"+ens+"_"+time_stamp+".nc")
            cdo.remapnn("lon=7.99_lat=46.54,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_jfj.nc")
brunner's avatar
brunner committed
182

brunner's avatar
brunner committed
183
184
            cdo.remapnn("lon=8.40_lat=47.48,", input = cosmo_out+"CO2_"+ens+"_"+time_stamp+".nc", output = cosmo_out+"CO2_lhw_"+ens+"_"+time_stamp+".nc")
            cdo.remapnn("lon=8.40_lat=47.48,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_lhw.nc")
brunner's avatar
brunner committed
185

brunner's avatar
brunner committed
186
187
            cdo.remapnn("lon=8.18_lat=47.19,", input = cosmo_out+"CO2_"+ens+"_"+time_stamp+".nc", output = cosmo_out+"CO2_brm_"+ens+"_"+time_stamp+".nc")
            cdo.remapnn("lon=8.18_lat=47.19,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_brm.nc")
brunner's avatar
brunner committed
188

brunner's avatar
brunner committed
189
190
            cdo.remapnn("lon=7.92_lat=47.92,", input = cosmo_out+"CO2_"+ens+"_"+time_stamp+".nc", output = cosmo_out+"CO2_ssl_"+ens+"_"+time_stamp+".nc")
            cdo.remapnn("lon=7.92_lat=47.92,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_ssl.nc")
brunner's avatar
brunner committed
191

brunner's avatar
brunner committed
192
193
194
195
196
197
198
199
200
201
            for s,ss in enumerate(sites):
                site_height.main(cosmo_out, str(ens), ss, time_stamp)

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

            cdo.cat(input = cosmo_out+"modelled_"+ens+"_brm_"+time_stamp+".nc "+cosmo_out+"modelled_"+ens+"_jfj_"+time_stamp+".nc "+cosmo_out+"modelled_"+ens+"_lhw_"+time_stamp+".nc "+cosmo_out+"modelled_"+ens+"_ssl_"+time_stamp+".nc ", output = cosmo_save+"model_"+ens+"_"+time_stamp+".nc")
       # sys.exit()
brunner's avatar
brunner committed
202
203
204
205
206
207
208
209
210
211
212
213
214
215


################### 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