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da/analysis/expand_molefractions.py

@@ -110,7 +110,7 @@ def write_mole_fractions(dacycle):
         fc_simulated = ncf_fc_in.get_variable('modelsamplesmean_prior')
         fc_simulated_ens = ncf_fc_in.get_variable('modelsamplesdeviations_prior')
         fc_flag      = ncf_fc_in.get_variable('flag')
-        if not dacycle.dasystem.has_key('opt.algorithm'):
+        if 'modeldatamismatchvariance' not in dacycle.dasystem:
             fc_r         = ncf_fc_in.get_variable('modeldatamismatchvariance')
             fc_hphtr     = ncf_fc_in.get_variable('totalmolefractionvariance')
         elif dacycle.dasystem['opt.algorithm'] == 'serial':
@@ -172,10 +172,10 @@ def write_mole_fractions(dacycle):
 
             # get nobs dimension
 
-            if ncf_out.dimensions.has_key('id'): 
+            if 'id' in ncf_out.dimensions:
                 dimidob = ncf_out.dimensions['id']
                 dimid = ('id',)
-            elif ncf_out.dimensions.has_key('obs'): 
+            elif 'obs' in ncf_out.dimensions:
                 dimidob = ncf_out.dimensions['obs']
                 dimid = ('obs',)
 
@@ -273,11 +273,11 @@ def write_mole_fractions(dacycle):
 
         # Get existing file obs_nums to determine match to local obs_nums
 
-        if ncf_out.variables.has_key('merge_num'):
+        if 'merge_num' in ncf_out.variables:
             file_obs_nums = ncf_out.get_variable('merge_num')
-        elif ncf_out.variables.has_key('obspack_num'):
+        elif 'obspack_num' in ncf_out.variables:
             file_obs_nums = ncf_out.get_variable('obspack_num')
-        elif ncf_out.variables.has_key('id'):
+        elif 'id' in ncf_out.variables:
             file_obs_nums = ncf_out.get_variable('id')
 
         # Get all obs_nums related to this file, determine their indices in the local arrays

da/baseclasses/dasystem.py

@@ -70,7 +70,7 @@ class DaSystem(dict):
         """ 
         This method loads a DA System Info rc-file with settings for this simulation 
         """
-        for k, v in rc.read(rcfilename).iteritems():
+        for k, v in rc.read(rcfilename).items():
             self[k] = v
         
         logging.debug("DA System Info rc-file (%s) loaded successfully" % rcfilename)
@@ -82,17 +82,17 @@ class DaSystem(dict):
         """
         needed_rc_items = {}
 
-        for k, v in self.iteritems():
+        for k, v in self.items():
             if v == 'True' : 
                 self[k] = True
             if v == 'False': 
                 self[k] = False
 
         for key in needed_rc_items:
-            if not self.has_key(key):
+            if key not in self:
                 msg = 'Missing a required value in rc-file : %s' % key
                 logging.error(msg)
-                raise IOError, msg
+                raise IOError(msg)
         logging.debug('DA System Info settings have been validated succesfully')
 
 ################### End Class DaSystem ###################

da/baseclasses/observationoperator.py

@@ -66,17 +66,17 @@ class ObservationOperator(object):
         """ Perform all steps necessary to start the observation operator through a simple Run() call """
 
         self.dacycle = dacycle
-	self.outputdir = dacycle['dir.output']
+        self.outputdir = dacycle['dir.output']
 
     def prepare_run(self):
         """ Prepare the running of the actual forecast model, for example compile code """
 
-	import os
+        import os
 
-	# Define the name of the file that will contain the modeled output of each observation
+        # 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'])
+        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 validate_input(self):
         """ Make sure that data needed for the ObservationOperator (such as observation input lists, or parameter files)
@@ -86,21 +86,21 @@ class ObservationOperator(object):
         """ Write the data that is needed for a restart or recovery of the Observation Operator to the save directory """
 
     def run(self):
-	"""
-	 This Randomizer will take the original observation data in the Obs object, and simply copy each mean value. Next, the mean 
-	 value will be perturbed by a random normal number drawn from a specified uncertainty of +/- 2 ppm
-	"""
+        """
+         This Randomizer will take the original observation data in the Obs object, and simply copy each mean value. Next, the mean 
+         value will be perturbed by a random normal number drawn from a specified uncertainty of +/- 2 ppm
+        """
 
-	import da.tools.io4 as io
-	import numpy as np
+        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 in TM5-style (to be updated later?) 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)
-	
+        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',)
+        dimid = ('obs_num',)
         savedict = io.std_savedict.copy() 
         savedict['name'] = "obs_num"
         savedict['dtype'] = "int"
@@ -111,7 +111,7 @@ class ObservationOperator(object):
         f.add_data(savedict,nsets=0)
 
         dimmember = f.createDimension('nmembers', size=self.forecast_nmembers)
-	dimmember = ('nmembers',)
+        dimmember = ('nmembers',)
         savedict = io.std_savedict.copy() 
         savedict['name'] = "flask"
         savedict['dtype'] = "float"
@@ -121,28 +121,28 @@ class ObservationOperator(object):
         savedict['comment'] = "Simulated model value created by RandomizerObservationOperator"
         f.add_data(savedict,nsets=0)
 
-	# Open file with x,y,z,t of model samples that need to be sampled
+        # 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
+        # Get simulated values and ID
 
         ids = f_in.get_variable('obs_num')
         obs = f_in.get_variable('observed')
         mdm = f_in.get_variable('modeldatamismatch')
 
-	# Loop over observations, add random white noise, and write to file
+        # Loop over observations, add random white noise, and write to file
 
-	for i,data in enumerate(zip(ids,obs,mdm)):
-	    f.variables['obs_num'][i] = data[0]		
-	    f.variables['flask'][i,:] = data[1]+np.random.randn(self.forecast_nmembers)*data[2]
+        for i,data in enumerate(zip(ids,obs,mdm)):
+            f.variables['obs_num'][i] = data[0]
+            f.variables['flask'][i,:] = data[1]+np.random.randn(self.forecast_nmembers)*data[2]
 
-	f.close()
-	f_in.close()
+        f.close()
+        f_in.close()
 
-	# Report success and exit
+        # Report success and exit
 
-    	logging.info('ObservationOperator finished successfully, output file written (%s)' % self.simulated_file)
+        logging.info('ObservationOperator finished successfully, output file written (%s)' % self.simulated_file)
 
     def run_forecast_model(self):
         self.prepare_run()

da/baseclasses/optimizer.py

@@ -124,8 +124,8 @@ class Optimizer(object):
                 allids.extend(samples.getvalues('id'))
 
                 simulatedensemble = samples.getvalues('simulated')
-		for s in range(simulatedensemble.shape[0]):
-                	allsimulated.append(simulatedensemble[s])
+                for s in range(simulatedensemble.shape[0]):
+                        allsimulated.append(simulatedensemble[s])
 
         self.obs[:] = np.array(allobs)
         self.obs_ids[:] = np.array(allids)

da/baseclasses/platform.py

@@ -95,11 +95,11 @@ class Platform(object):
             template += """#$ -hold_jid depends \n"""
 
         # First replace from passed dictionary
-        for k, v in joboptions.iteritems():
+        for k, v in joboptions.items():
             while k in template:
                 template = template.replace(k, v)
         # Fill remaining values with std_options
-        for k, v in std_joboptions.iteritems():
+        for k, v in std_joboptions.items():
             while k in template:
                 template = template.replace(k, v)
         return template

da/baseclasses/statevector.py

@@ -156,7 +156,7 @@ class StateVector(object):
         # These list objects hold the data for each time step of lag in the system. Note that the ensembles for each time step consist 
         # of lists of EnsembleMember objects, we define member 0 as the mean of the distribution and n=1,...,nmembers as the spread.
 
-        self.ensemble_members = range(self.nlag)
+        self.ensemble_members = list(range(self.nlag))
 
         for n in range(self.nlag):
             self.ensemble_members[n] = []
@@ -500,7 +500,7 @@ class StateVector(object):
             raise ValueError
 
         result = np.zeros((self.nparams,), float)
-        for k, v in self.griddict.iteritems():
+        for k, v in self.griddict.items():
             #print k,k-1,result.shape, v
             if method == "avg": 
                 result[k - 1] = griddata.take(v).mean()
@@ -528,7 +528,7 @@ class StateVector(object):
 
         """
         result = np.zeros(self.gridmap.shape, float)
-        for k, v in self.griddict.iteritems():
+        for k, v in self.griddict.items():
             #print k,v
             result.put(v, vectordata[k - 1])
         return result         

da/carbondioxide/dasystem.py

@@ -46,14 +46,14 @@ class CO2DaSystem(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
 
         for key in needed_rc_items:
-            if not self.has_key(key):
+            if key not in self:
                 logging.warning('Missing a required value in rc-file : %s' % key)
         logging.debug('DA System Info settings have been validated succesfully')
 

da/carbondioxide/obs.py

@@ -56,7 +56,7 @@ class CO2Observations(Observations):
         if not os.path.exists(filename):
             msg = 'Could not find  the required observation input file (%s) ' % filename
             logging.error(msg)
-            raise IOError, msg
+            raise IOError(msg)
         else:
             self.obs_filename = filename
         self.datalist = []
@@ -118,7 +118,7 @@ class CO2Observations(Observations):
             logging.error(msg)
             logging.error("Did the sampling step succeed?")
             logging.error("...exiting")
-            raise IOError, msg
+            raise IOError(msg)
 
         ncf = io.ct_read(filename, method='read')
         ids = ncf.get_variable('obs_num')
@@ -129,7 +129,7 @@ class CO2Observations(Observations):
         obs_ids = self.getvalues('id')
 
         obs_ids = obs_ids.tolist()
-        ids = map(int, ids)
+        ids = list(map(int, ids))
 
         missing_samples = []
 
@@ -265,7 +265,7 @@ class CO2Observations(Observations):
         if not os.path.exists(filename):
             msg = 'Could not find  the required sites.rc input file (%s)' % filename
             logging.error(msg)
-            raise IOError, msg
+            raise IOError(msg)
         else:
             self.sites_file = filename
 
@@ -308,7 +308,7 @@ class CO2Observations(Observations):
 
         for obs in self.datalist:
             obs.mdm = 1000.0  # default is very high model-data-mismatch, until explicitly set by script
-            if site_info.has_key(obs.code): 
+            if obs.code in site_info:
                 logging.debug("Observation found (%s)" % obs.code)
                 obs.mdm = site_info[obs.code]['error'] * self.global_R_scaling
                 obs.may_localize = site_info[obs.code]['may_localize']

da/carbondioxide/obspack.py

@@ -24,7 +24,7 @@ import os
 import sys
 import logging
 import datetime as dtm
-from string import strip
+#from string import strip
 from numpy import array, logical_and
 
 sys.path.append(os.getcwd())
@@ -55,7 +55,7 @@ class ObsPackObservations(Observations):
         if not os.path.exists(op_dir):
             msg = 'Could not find  the required ObsPack distribution (%s) ' % op_dir
             logging.error(msg)
-            raise IOError, msg
+            raise IOError(msg)
         else:
             self.obspack_dir = op_dir
             self.obspack_id = op_id
@@ -102,7 +102,7 @@ class ObsPackObservations(Observations):
             ids = ncf.get_variable('obspack_num').take(subselect)  # or should we propagate obs_num which is not unique across datasets??
             evn = ncf.get_variable('obspack_id').take(subselect, axis=0)
             evn = [s.tostring().lower() for s in evn]
-            evn = map(strip, evn)
+            evn = map(str, evn)
             site = ncf.get_attribute('site_code')
             lats = ncf.get_variable('latitude').take(subselect, axis=0)
             lons = ncf.get_variable('longitude').take(subselect, axis=0)
@@ -113,7 +113,7 @@ class ObsPackObservations(Observations):
             strategy = 1
             flags = ncf.get_variable('qc_flag').take(subselect, axis=0)
             flags = [s.tostring().lower() for s in flags]
-            flags = map(strip, flags)
+            flags = map(str, flags)
             flags = [int(f == '...') for f in flags]
             ncf.close()
 
@@ -132,7 +132,7 @@ class ObsPackObservations(Observations):
             logging.error(msg)
             logging.error("Did the sampling step succeed?")
             logging.error("...exiting")
-            raise IOError, msg
+            raise IOError(msg)
 
         ncf = io.ct_read(filename, method='read')
         ids = ncf.get_variable('obs_num')
@@ -141,7 +141,7 @@ class ObsPackObservations(Observations):
         logging.info("Successfully read data from model sample file (%s)" % filename)
 
         obs_ids = self.getvalues('id').tolist()
-        ids = map(int, ids)
+        ids = list(map(int, ids))
 
         missing_samples = []
 
@@ -296,7 +296,7 @@ class ObsPackObservations(Observations):
         if not os.path.exists(filename):
             msg = 'Could not find  the required sites.rc input file (%s) ' % filename
             logging.error(msg)
-            raise IOError, msg
+            raise IOError(msg)
         else:
             self.sites_file = filename
 
@@ -348,7 +348,7 @@ class ObsPackObservations(Observations):
 
             identifier = name_convert(name="%s_%s_%s" % (site.lower(), method.lower(), lab.lower(),), to='GV')
 
-            if site_info.has_key(identifier): 
+            if identifier in site_info:
                 logging.debug("Observation found (%s, %s)" % (obs.code, identifier))
                 obs.mdm = site_info[identifier]['error'] * self.global_R_scaling
                 obs.may_localize = site_info[identifier]['may_localize']

da/carbondioxide/obspack_geocarbon.py

@@ -52,7 +52,7 @@ class ObsPackObservations(Observations):
         if not os.path.exists(op_dir):
             msg = 'Could not find  the required ObsPack distribution (%s) ' % op_dir
             logging.error(msg)
-            raise IOError, msg
+            raise IOError(msg)
         else:
             self.obspack_dir = op_dir
             self.obspack_id = op_id
@@ -101,7 +101,7 @@ class ObsPackObservations(Observations):
             obspacknum = ncf.get_variable('obspack_num').take(subselect)  # or should we propagate obs_num which is not unique across datasets??
             obspackid = ncf.get_variable('obspack_id').take(subselect, axis=0)
             obspackid = [s.tostring().lower() for s in obspackid]
-            obspackid = map(strip, obspackid)
+            obspackid = list(map(str.strip,str(obspackid)))
             datasetname = ncfile  # use full name of dataset to propagate for clarity
             lats = ncf.get_variable('latitude').take(subselect, axis=0)
             lons = ncf.get_variable('longitude').take(subselect, axis=0)
@@ -127,7 +127,7 @@ class ObsPackObservations(Observations):
             logging.error(msg)
             logging.error("Did the sampling step succeed?")
             logging.error("...exiting")
-            raise IOError, msg
+            raise IOError(msg)
 
         ncf = io.ct_read(filename, method='read')
         ids = ncf.get_variable('obs_num')
@@ -136,7 +136,7 @@ class ObsPackObservations(Observations):
         logging.info("Successfully read data from model sample file (%s)" % filename)
 
         obs_ids = self.getvalues('id').tolist()
-        ids = map(int, ids)
+        ids = list(map(int, ids))
 
         missing_samples = []
 
@@ -173,7 +173,7 @@ class ObsPackObservations(Observations):
             dim10char = f.add_dim('string_of10chars', 10)
             dimcalcomp = f.add_dim('calendar_components', 6)
 
-            for key, value in self.site_move.iteritems():
+            for key, value in self.site_move.items():
                 msg = "Site is moved by %3.2f degrees latitude and %3.2f degrees longitude" % value 
                 f.add_attribute(key, msg)
 
@@ -296,7 +296,7 @@ class ObsPackObservations(Observations):
         if not os.path.exists(filename):
             msg = 'Could not find  the required sites.rc input file (%s) ' % filename
             logging.error(msg)
-            raise IOError, msg
+            raise IOError(msg)