Merge branch 'master' of https://git.wur.nl/ctdas/CTDAS

da/baseclasses/obs.py

@@ -28,12 +28,17 @@ File created on 28 Jul 2010.
 
 """
 
-import logging
+import logging, sys, os
 from numpy import array, ndarray
+import datetime as dt
+sys.path.append(os.getcwd())
+sys.path.append('../../')
 
 identifier = 'Observations baseclass'
 version = '0.0'
 
+import da.tools.io4 as io
+
 ################### Begin Class Observations ###################
 
 class Observations(object):
@@ -73,11 +78,15 @@ class Observations(object):
     def getlength(self):
         return len(self.datalist)
 
-    def setup(self, cycleparams):
+    def setup(self, dacycle):
         """ Perform all steps needed to start working with observational data, this can include moving data, concatenating files,
             selecting datasets, etc.
         """
 
+        self.startdate = dacycle['time.sample.start']
+        self.enddate = dacycle['time.sample.end']
+        self.datalist = []
+
     def add_observations(self):
         """ 
         Add actual observation data to the Observations object. This is in a form of an 
@@ -87,20 +96,179 @@ class Observations(object):
 
         """
 
-    def add_simulations(self):
+        for n in range(10):
+            self.datalist.append(MoleFractionSample(n, self.startdate+dt.timedelta(hours=n+1),'testobs' , 400+n, 0.0, 0.0, 0.0, 0.0, 0 , 100.0 , 52.0, 6.0 , '%04d'%n , 'co2', 1, 0.0, 'none.nc'))
+
+            logging.debug("Added %d observations to the Data list" % (1))
+
+        logging.info("Observations list now holds %d values" % len(self.datalist))
+
+
+    def add_simulations(self, filename, silent=False):
         """ Add the simulation data to the Observations object. 
         """
 
-    def add_model_data_mismatch(self):
+
+        if not os.path.exists(filename):
+            msg = "Sample output filename for observations could not be found : %s" % filename 
+            logging.error(msg)
+            logging.error("Did the sampling step succeed?")
+            logging.error("...exiting")
+            raise IOError(msg)
+
+        ncf = io.ct_read(filename, method='read')
+        ids = ncf.get_variable('obs_num')
+        simulated = ncf.get_variable('flask')
+        ncf.close()
+        logging.info("Successfully read data from model sample file (%s)" % filename)
+
+        obs_ids = self.getvalues('id').tolist()
+        ids = list(map(int, ids))
+
+        missing_samples = []
+
+        for idx, val in zip(ids, simulated): 
+            if idx in obs_ids:
+                index = obs_ids.index(idx)
+
+                self.datalist[index].simulated = val  # in mol/mol
+            else:     
+                missing_samples.append(idx)
+
+        if not silent and missing_samples != []:
+            logging.warning('Model samples were found that did not match any ID in the observation list. Skipping them...')
+            #msg = '%s'%missing_samples ; logging.warning(msg)
+
+        logging.debug("Added %d simulated values to the Data list" % (len(ids) - len(missing_samples)))
+
+
+    def add_model_data_mismatch(self, filename):
         """ 
             Get the model-data mismatch values for this cycle.
         """
+        self.rejection_threshold = 3.0 # 3-sigma cut-off
+        self.global_R_scaling = 1.0 # no scaling applied
+
+        for obs in self.datalist:  # first loop over all available data points to set flags correctly
+
+            obs.mdm = 1.0  
+            obs.may_localize = True
+            obs.may_reject = True
+            obs.flag = 0
+
+        logging.debug("Added Model Data Mismatch to all samples ")
 
     def write_sample_coords(self,obsinputfile):
         """ 
             Write the information needed by the observation operator to a file. Return the filename that was written for later use
         """
 
+        if len(self.datalist) == 0:
+            logging.debug("No observations found for this time period, nothing written to obs file")
+        else:
+            f = io.CT_CDF(obsinputfile, method='create')
+            logging.debug('Creating new observations file for ObservationOperator (%s)' % obsinputfile)
+
+            dimid = f.add_dim('obs', len(self.datalist))
+            dim200char = f.add_dim('string_of200chars', 200)
+            dim10char = f.add_dim('string_of10chars', 10)
+            dimcalcomp = f.add_dim('calendar_components', 6)
+
+            data = self.getvalues('id')
+
+            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['values'] = data.tolist()
+            savedict['comment'] = "Unique index number within this dataset ranging from 0 to UNLIMITED."
+            f.add_data(savedict)
+
+            data = [[d.year, d.month, d.day, d.hour, d.minute, d.second] for d in self.getvalues('xdate') ]
+
+            savedict = io.std_savedict.copy() 
+            savedict['dtype'] = "int"
+            savedict['name'] = "date_components"
+            savedict['units'] = "integer components of UTC date/time"
+            savedict['dims'] = dimid + dimcalcomp
+            savedict['values'] = data
+            savedict['missing_value'] = -9
+            savedict['comment'] = "Calendar date components as integers. Times and dates are UTC." 
+            savedict['order'] = "year, month, day, hour, minute, second"
+            f.add_data(savedict)
+
+            data = self.getvalues('lat')
+
+            savedict = io.std_savedict.copy() 
+            savedict['name'] = "latitude"
+            savedict['units'] = "degrees_north"
+            savedict['dims'] = dimid
+            savedict['values'] = data.tolist()
+            savedict['missing_value'] = -999.9
+            f.add_data(savedict)
+
+            data = self.getvalues('lon')
+
+            savedict = io.std_savedict.copy() 
+            savedict['name'] = "longitude"
+            savedict['units'] = "degrees_east"
+            savedict['dims'] = dimid
+            savedict['values'] = data.tolist()
+            savedict['missing_value'] = -999.9
+            f.add_data(savedict)
+
+            data = self.getvalues('height')
+
+            savedict = io.std_savedict.copy() 
+            savedict['name'] = "altitude"
+            savedict['units'] = "meters_above_sea_level"
+            savedict['dims'] = dimid
+            savedict['values'] = data.tolist()
+            savedict['missing_value'] = -999.9
+            f.add_data(savedict)
+
+            data = self.getvalues('samplingstrategy')
+
+            savedict = io.std_savedict.copy() 
+            savedict['dtype'] = "int"
+            savedict['name'] = "sampling_strategy"
+            savedict['units'] = "NA"
+            savedict['dims'] = dimid
+            savedict['values'] = data.tolist()
+            savedict['missing_value'] = -9
+            f.add_data(savedict)
+
+            data = self.getvalues('obs')
+
+            savedict = io.std_savedict.copy()
+            savedict['name'] = "observed"
+            savedict['long_name'] = "observedvalues"
+            savedict['units'] = "mol mol-1"
+            savedict['dims'] = dimid
+            savedict['values'] = data.tolist()
+            savedict['comment'] = 'Observations used in optimization'
+            f.add_data(savedict)
+    
+            data = self.getvalues('mdm')
+    
+            savedict = io.std_savedict.copy()
+            savedict['name'] = "modeldatamismatch"
+            savedict['long_name'] = "modeldatamismatch"
+            savedict['units'] = "[mol mol-1]"
+            savedict['dims'] = dimid
+            savedict['values'] = data.tolist()
+            savedict['comment'] = 'Standard deviation of mole fractions resulting from model-data mismatch'
+            f.add_data(savedict)
+            f.close()
+
+            logging.debug("Successfully wrote data to obs file")
+            logging.info("Sample input file for obs operator now in place [%s]" % obsinputfile)
+
+        
+
+
     def write_sample_auxiliary(self, auxoutputfile):
         """ 
             Write selected additional information contained in the Observations object to a file for later processing. 
@@ -118,4 +286,40 @@ class Observations(object):
 
 ################### End Class Observations ###################
 
+################### Begin Class MoleFractionSample ###################
+
+class MoleFractionSample(object):
+    """ 
+        Holds the data that defines a mole fraction Sample in the data assimilation framework. Sor far, this includes all
+        attributes listed below in the __init__ method. One can additionally make more types of data, or make new
+        objects for specific projects.
+
+    """
+
+    def __init__(self, idx, xdate, code='XXX', obs=0.0, simulated=0.0, resid=0.0, hphr=0.0, mdm=0.0, flag=0, height=0.0, lat= -999., lon= -999., evn='0000', species='co2', samplingstrategy=1, sdev=0.0, fromfile='none.nc'):
+        self.code = code.strip()      # dataset identifier, i.e., co2_lef_tower_insitu_1_99
+        self.xdate = xdate             # Date of obs
+        self.obs = obs               # Value observed
+        self.simulated = simulated         # Value simulated by model
+        self.resid = resid             # Mole fraction residuals
+        self.hphr = hphr              # Mole fraction prior uncertainty from fluxes and (HPH) and model data mismatch (R)
+        self.mdm = mdm               # Model data mismatch
+        self.may_localize = True           # Whether sample may be localized in optimizer
+        self.may_reject = True              # Whether sample may be rejected if outside threshold
+        self.flag = flag              # Flag
+        self.height = height            # Sample height in masl
+        self.lat = lat               # Sample lat
+        self.lon = lon               # Sample lon
+        self.id = idx               # Obspack ID within distrution (integer), e.g., 82536
+        self.evn = evn               # Obspack Number within distrution (string), e.g., obspack_co2_1_PROTOTYPE_v0.9.2_2012-07-26_99_82536
+        self.sdev = sdev              # standard deviation of ensemble
+        self.masl = True              # Sample is in Meters Above Sea Level
+        self.mag = not self.masl     # Sample is in Meters Above Ground
+        self.species = species.strip()
+        self.samplingstrategy = samplingstrategy
+        self.fromfile = fromfile   # netcdf filename inside ObsPack distribution, to write back later
+
+################### End Class MoleFractionSample ###################
+
+
 

da/baseclasses/observationoperator.py

@@ -94,7 +94,7 @@ class ObservationOperator(object):
         import da.tools.io4 as io
         import numpy as np
 
-        # Create a flask output file in TM5-style (to be updated later?) to hold simulated values for later reading
+        # Create a flask output file to hold simulated values for later reading
 
         f = io.CT_CDF(self.simulated_file, method='create')
         logging.debug('Creating new simulated observation file in ObservationOperator (%s)' % self.simulated_file)

da/baseclasses/optimizer.py

@@ -402,19 +402,46 @@ class Optimizer(object):
 
         logging.info('Minimum Least Squares solution was calculated, returning')
 
-    def set_localization(self):
+
+    def set_localization(self, loctype='None'):
         """ determine which localization to use """
-        self.localization = True
-        self.localizetype = "None"
+
+        if loctype == 'CT2007':
+            self.localization = True
+            self.localizetype = 'CT2007'
+            #T-test values for two-tailed student's T-test using 95% confidence interval for some options of nmembers
+            if self.nmembers == 50:
+                self.tvalue = 2.0086
+            elif self.nmembers == 100:
+                self.tvalue = 1.9840
+            elif self.nmembers == 150:
+                self.tvalue = 1.97591
+            elif self.nmembers == 200:
+                self.tvalue = 1.9719    
+            else: self.tvalue = 0    
+        else:
+            self.localization = False
+            self.localizetype = 'None'
+    
         logging.info("Current localization option is set to %s" % self.localizetype)
+        if self.localization == True:
+            if self.tvalue == 0:
+                logging.error("Critical tvalue for localization not set for %i ensemble members"%(self.nmembers))
+                sys.exit(2)
+            else: logging.info("Used critical tvalue %0.05f is based on 95%% probability and %i ensemble members in a two-tailed student's T-test"%(self.tvalue,self.nmembers))
 
     def localize(self, n):
         """ localize the Kalman Gain matrix """
-        logging.debug('Not localized observation %s, %i' % (self.sitecode[n], self.obs_ids[n]))
     
-    def set_algorithm(self):
-        self.algorithm = 'Serial'
-        logging.info("Current algorithm is set to %s in baseclass optimizer" % self.algorithm)
+    def set_algorithm(self, algorithm='Serial'):
+        """ determine which minimum least squares algorithm to use """
+
+        if algorithm == 'Serial':
+            self.algorithm = 'Serial'
+        else:
+            self.algorithm = 'Bulk'
+    
+        logging.info("Current minimum least squares algorithm is set to %s" % self.algorithm)
 
 ################### End Class Optimizer ###################
 

da/co2gridded/dasystem.py

@@ -56,7 +56,7 @@ class CO2GriddedDaSystem(DaSystem):
                            'regtype']
 
 
-        for k, v in self.iteritems():
+        for k, v in self.items():
             if v == 'True' : self[k] = True
             if v == 'False': self[k] = False
 

da/co2gridded/statevector.py

@@ -145,7 +145,7 @@ class CO2GriddedStateVector(StateVector):
             dof += np.sum(s) ** 2 / sum(s ** 2)
             try:
                 C = np.linalg.cholesky(matrix)
-            except np.linalg.linalg.LinAlgError, err:
+            except np.linalg.linalg.LinAlgError as err:
                 logging.error('Cholesky decomposition has failed ')
                 logging.error('For a matrix of dimensions: %d' % matrix.shape[0])
                 logging.debug(err)

da/co2gridded/statevectorNHgridded.py

@@ -89,7 +89,7 @@ class CO2GriddedStateVector(StateVector):
 
             if 'pco2' in file or 'cov_ocean' in file: 
                 cov_ocn = f.get_variable('CORMAT')
-                parnr = range(9805,9835)
+                parnr = list(range(9805,9835))
                 cov = cov_ocn
             elif 'tropic' in file:
                 cov = f.get_variable('covariance')
@@ -157,7 +157,7 @@ class CO2GriddedStateVector(StateVector):
             dof += np.sum(s) ** 2 / sum(s ** 2)
             try:
                 C = np.linalg.cholesky(matrix)
-            except np.linalg.linalg.LinAlgError, err:
+            except np.linalg.linalg.LinAlgError as err:
                 logging.error('Cholesky decomposition has failed ')
                 logging.error('For a matrix of dimensions: %d' % matrix.shape[0])
                 logging.debug(err)

da/platform/cartesius.py

@@ -108,12 +108,12 @@ class CartesiusPlatform(Platform):
             template += """#$ -hold_jid depends \n"""
 
         # First replace from passed dictionary
-        for k, v in joboptions.items():
+        for k, v in list(joboptions.items()):
             while k in template:
                 template = template.replace(k, v)
 
         # Fill remaining values with std_options
-        for k, v in std_joboptions.items():
+        for k, v in list(std_joboptions.items()):
             while k in template:
                 template = template.replace(k, v)
 
@@ -132,9 +132,9 @@ class CartesiusPlatform(Platform):
             logging.info("A new task will be started (%s)" % cmd)
             output = subprocess.Popen(cmd, stdout=subprocess.PIPE).communicate()[0]
             logging.info(output)
-            print 'output', output
+            print('output', output)
             jobid = output.split()[-1]             
-            print 'jobid', jobid
+            print('jobid', jobid)
         else:
             cmd = ["sbatch", jobfile]
             logging.info("A new job will be submitted (%s)" % cmd)

da/rc/carbontracker_random.rc

@@ -11,39 +11,15 @@
 ! You should have received a copy of the GNU General Public License along with this 
 ! program. If not, see <http://www.gnu.org/licenses/>. 
 
-!!! Info for the CarbonTracker data assimilation system
-
-datadir         : /store/empa/em05/parsenov/
-
-! For ObsPack
-obspack.input.id   : obspack
-obspack.input.dir  : ${datadir}
-!/obspack !/${obspack.input.id}
-obs.sites.rc       : ${obspack.input.dir}/summary/sites_weights_ctdas.rc
-
-! Using a second ObsPack (from 1 January 2016)
-!obspack.input.id2  : obspack_co2_1_NRT_v3.0_2016-06-06
-!obspack.input.dir2 : ${datadir}/obspacks/${obspack.input.id2}
-!obs.sites.rc2      : ${obspack.input.dir2}/summary/sites_weights_Dec2016.rc
-
-!ocn.covariance  : ${datadir}/oceans/oif/cov_ocean.2000.01.nc 
-!deltaco2.prefix : oif_p3_era40.dpco2
-
-!bio.cov.dir     : ${datadir}/covariances/gridded_NH/
-!bio.cov.prefix  : cov_ecoregion
-
-!regtype         : gridded_oif30
-nparameters     : 11 
-!random.seed     : 4385
-regionsfile     : /store/empa/em05/parsenov/CTDAS/ctdas-py3/da/analysis/regions_ok.nc
-!extendedregionsfile     : /store/empa/em05/parsenov/CTDAS/ctdas-py3/da/analysis/regions_ok.nc
-!regionsfile     : ${datadir}/covariances/gridded_NH/griddedNHparameters.nc
-!random.seed.init: ${datadir}/randomseedinit.pickle
-
-! Include a naming scheme for the variables
-
-#include NamingScheme.wp_Mar2011.rc 
-
-! Info on the sites file used
-
-
+datadir         : /Storage/CO2/carbontracker/input/ctdas_2016/
+ocn.covariance  : ${datadir}/oceans/oif/cov_ocean.2000.01.nc 
+deltaco2.prefix : oif_p3_era40.dpco2
+bio.cov.dir     : ${datadir}/covariances/gridded_NH/
+bio.cov.prefix  : cov_ecoregion
+
+regtype         : gridded_oif30
+nparameters     : 9835 
+random.seed     : 4385
+regionsfile     : ${datadir}/covariances/gridded_NH/griddedNHparameters.nc
+
+obs.sites.rc   : Random

da/tools/pipeline.py

@@ -343,7 +343,9 @@ def sample_step(dacycle, samples, statevector, obsoperator, lag, advance=False):
 
     if os.path.exists(sampling_coords_file):
         samples.add_simulations(obsoperator.simulated_file)
-    else: logging.warning("No simulations added, because input file does not exist (no samples found in obspack)")
+        logging.debug("Simulated values read, continuing pipeline")
+    else: 
+        logging.warning("No simulations added, because input file does not exist (no samples found in obspack)")
 
     # Now write a small file that holds for each observation a number of values that we need in postprocessing
 

templates/template.jb

@@ -5,10 +5,8 @@
 #$ 06:30:00
 #$ /bin/sh
 
-echo "All output piped to file template.log"
-source /usr/local/Modules/3.2.8/init/sh
-source /opt/intel/bin/ifortvars.sh intel64
-export HOST='daint'
-module load python
-export icycle_in_job=999
+echo "All output piped to file ct_base_test.log"
+export HOST='capegrim'
+export icycle_in_job=1
+python --version
 python template.py rc=template.rc -v $1 >& template.log &

templates/template.py

@@ -27,14 +27,13 @@ sys.path.append(os.getcwd())
 
 from da.tools.initexit import start_logger, validate_opts_args, parse_options, CycleControl
 from da.tools.pipeline import ensemble_smoother_pipeline, header, footer
-from da.platform.capegrim import CapeGrimPlatform 
-from da.baseclasses.dasystem import DaSystem 
-from da.baseclasses.statevector import StateVector 
-from da.carbondioxide.obspack_globalviewplus2 import ObsPackObservations 
-from da.carbondioxide.optimizer import CO2Optimizer
-from da.baseclasses.observationoperator import ObservationOperator
-#from da.analysis.expand_fluxes import save_weekly_avg_1x1_data, save_weekly_avg_state_data, save_weekly_avg_tc_data, save_weekly_avg_ext_tc_data
-#from da.analysis.expand_molefractions import write_mole_fractions
+from da.platform.capegrim import CapeGrimPlatform
+from da.baseclasses.dasystem import DaSystem
+from da.co2gridded.statevector import CO2GriddedStateVector 
+from da.baseclasses.obs import Observations
+from da.baseclasses.optimizer import Optimizer
+from da.baseclasses.observationoperator import RandomizerObservationOperator
+
 
 
 #################################################################################################
@@ -51,13 +50,12 @@ opts, args = validate_opts_args(opts, args)
 
 dacycle = CycleControl(opts, args)
 
-
-platform = MaunaloaPlatform()
+platform = CapeGrimPlatform()
 dasystem = DaSystem(dacycle['da.system.rc'])
-obsoperator = ObservationOperator(dacycle['da.obsoperator.rc'])
-samples     = ObsPackObservations()
-statevector = StateVector()
-optimizer = CO2Optimizer()
+obsoperator = RandomizerObservationOperator(dacycle['da.obsoperator.rc'])
+samples     = Observations()
+statevector = CO2GriddedStateVector()
+optimizer = Optimizer()
 
 ##########################################################################################
 ################### ENTER THE PIPELINE WITH THE OBJECTS PASSED BY THE USER ###############

templates/template.rc

@@ -28,8 +28,8 @@
 !
 ! The time for which to start and end the data assimilation experiment in format YYYY-MM-DD HH:MM:SS
 
-time.start          : 2016-01-01 00:00:00
-time.finish         : 2016-02-01 00:00:00
+time.start          : 2012-01-01 00:00:00
+time.finish         : 2012-02-01 00:00:00
 
 ! Whether to restart the CTDAS system from a previous cycle, or to start the sequence fresh. Valid entries are T/F/True/False/TRUE/FALSE
 
@@ -37,16 +37,16 @@ time.restart        : False
 
 ! The length of a cycle is given in days, such that the integer 7 denotes the typically used weekly cycle. Valid entries are integers > 1
 
-time.cycle          : 7
+time.cycle          : 1
 
 ! The number of cycles of lag to use for a smoother version of CTDAS. CarbonTracker CO2 typically uses 5 weeks of lag. Valid entries are integers > 0
 
-time.nlag           : 3
+time.nlag           : 2
 
 ! The directory under which the code, input, and output will be stored. This is the base directory for a run. The word
 ! '/' will be replaced through the start_ctdas.sh script by a user-specified folder name. DO NOT REPLACE
 
-dir.da_run          : /scratch/snx3000/parsenov/projname
+dir.da_run          : template
 
 ! The resources used to complete the data assimilation experiment. This depends on your computing platform.
 ! The number of cycles per job denotes how many cycles should be completed before starting a new process or job, this