replaced dummy variable by status, can be handled if ever needed

da/tools/pipeline.py

@@ -34,27 +34,27 @@ def EnsembleSmootherPipeline(DaCycle,PlatForm, DaSystem, Samples,StateVector,Obs
     
     msg          = header+"Initializing current cycle"+footer           ; logging.info(msg) 
 
-    dummy        = JobStart(DaCycle, DaSystem, PlatForm, StateVector, Samples, ObsOperator)
+    status        = JobStart(DaCycle, DaSystem, PlatForm, StateVector, Samples, ObsOperator)
 
     msg          = header+"starting PrepareState"+footer           ; logging.info(msg) 
 
-    dummy        = PrepareState(DaCycle, StateVector)
+    status        = PrepareState(DaCycle, StateVector)
 
     msg          = header+"starting SampleState"+footer             ; logging.info(msg) 
 
-    dummy        = SampleState(DaCycle,Samples,StateVector,ObsOperator)
+    status        = SampleState(DaCycle,Samples,StateVector,ObsOperator)
 
     msg          = header+"starting Invert"+footer             ; logging.info(msg) 
 
-    dummy        = Invert(DaCycle, StateVector, Optimizer)
+    status        = Invert(DaCycle, StateVector, Optimizer)
 
     msg          = header+"starting Advance"+footer          ; logging.info(msg) 
 
-    dummy        = Advance(DaCycle, Samples, StateVector, ObsOperator)
+    status        = Advance(DaCycle, Samples, StateVector, ObsOperator)
 
     msg          = header+"starting SaveAndSubmit"+footer      ; logging.info(msg) 
 
-    dummy        = SaveAndSubmit(DaCycle, StateVector)
+    status        = SaveAndSubmit(DaCycle, StateVector)
 
     msg          = "Cycle finished...exiting pipeline"     ; logging.info(msg)
 
@@ -63,15 +63,15 @@ def EnsembleSmootherPipeline(DaCycle,PlatForm, DaSystem, Samples,StateVector,Obs
 def JobStart(DaCycle, DaSystem, DaPlatForm, StateVector, Samples, ObsOperator):
     """ Set up the job specific directory structure and create an expanded rc-file """
 
-    dummy               = DaSystem.Initialize()
+    status               = DaSystem.Initialize()
 
-    dummy               = DaSystem.Validate()
+    status               = DaSystem.Validate()
 
     DaCycle.DaSystem    = DaSystem
 
     DaCycle.DaPlatForm  = DaPlatForm
 
-    dummy               = DaCycle.Initialize()
+    status               = DaCycle.Initialize()
 
     StateVector.DaCycle = DaCycle # also embed object in StateVector so it can access cycle information for I/O etc
 
@@ -90,7 +90,7 @@ def PrepareState(DaCycle, StateVector):
     # with new values for each week. After we have constructed the StateVector, it will be propagated by one cycle length so it is ready to be used
     # in the current cycle
 
-    dummy               = StateVector.Initialize()
+    status               = StateVector.Initialize()
 
     if not DaCycle['time.restart']:
 
@@ -100,7 +100,7 @@ def PrepareState(DaCycle, StateVector):
         for n in range(0,nlag):
             date = DaCycle['time.start']+datetime.timedelta(days = (n+0.5)* int(DaCycle['time.cycle']))
             cov   = StateVector.GetCovariance(date)
-            dummy = StateVector.MakeNewEnsemble(n+1,cov)
+            status = StateVector.MakeNewEnsemble(n+1,cov)
 
     else:
 
@@ -114,14 +114,14 @@ def PrepareState(DaCycle, StateVector):
 
         # Now propagate the ensemble by one cycle to prepare for the current cycle
 
-        dummy = StateVector.Propagate()
+        status = StateVector.Propagate()
 
    # Finally, also write the StateVector to a file so that we can always access the a-priori information
 
     savedir         = DaCycle['dir.output']
     filename        = os.path.join(savedir,'savestate.nc')
 
-    dummy = StateVector.WriteToFile(filename)  # write prior info because StateVector.Isoptimized == False for now
+    status = StateVector.WriteToFile(filename)  # write prior info because StateVector.Isoptimized == False for now
 
     return None
 
@@ -134,7 +134,7 @@ def SampleState(DaCycle,Samples,StateVector, ObservationOperator):
     #  (i) The transport model restart data which holds the background mixing ratios. This is needed to run the model one cycle forward
     #  (ii) The random numbers (or the seed for the random number generator) so we can recreate the ensembles if needed
 
-    #dummy   = DaCycle.MoveSaveData(io_option='store',save_option='partial',filter=[])
+    #status   = DaCycle.MoveSaveData(io_option='store',save_option='partial',filter=[])
     #msg     = "All restart data have been copied to the save/tmp directory for future use"    ; logging.debug(msg)
 
     nlag    = int(DaCycle['time.nlag'])
@@ -144,7 +144,7 @@ def SampleState(DaCycle,Samples,StateVector, ObservationOperator):
 
         msg     = header+".....Ensemble Kalman Filter at lag %d"% (int(lag)+1,)           ; logging.info(msg) 
 
-        dummy   = SampleOneCycle(DaCycle,Samples,StateVector, ObservationOperator,lag)
+        status   = SampleOneCycle(DaCycle,Samples,StateVector, ObservationOperator,lag)
 
 
     # Optionally, post-processing of the model output can be added that deals for instance with
@@ -160,9 +160,9 @@ def SampleOneCycle(DaCycle,Samples,StateVector, ObservationOperator,lag):
 
 
     if lag == 0:
-        dummy       = ObservationOperator.GetInitialData()
+        status       = ObservationOperator.GetInitialData()
 
-    dummy           = DaCycle.SetSampleTimes(lag)
+    status           = DaCycle.SetSampleTimes(lag)
 
     startdate                       = DaCycle['time.sample.start'] 
     enddate                         = DaCycle['time.sample.end'] 
@@ -178,13 +178,13 @@ def SampleOneCycle(DaCycle,Samples,StateVector, ObservationOperator,lag):
     # Implement something that writes the ensemble member parameter info to file, or manipulates them further into the 
     # type of info needed in your transport model
 
-    dummy   = StateVector.WriteMembersToFile(lag+1)
+    status   = StateVector.WriteMembersToFile(lag+1)
 
-    dummy   = Samples.Initialize()
+    status   = Samples.Initialize()
 
-    dummy   = Samples.Validate() 
+    status   = Samples.Validate() 
 
-    dummy   = Samples.AddObs() 
+    status   = Samples.AddObs() 
 
     filename   = Samples.WriteSampleInfo() 
 
@@ -194,7 +194,7 @@ def SampleOneCycle(DaCycle,Samples,StateVector, ObservationOperator,lag):
 
     # Run the observation operator
 
-    dummy   = RunForecastModel(DaCycle,ObservationOperator)
+    status   = RunForecastModel(DaCycle,ObservationOperator)
 
     # Add the observation operator restart+output files to the general Output+RestartFileList, only after 'Advance'
     # Same logic for observation files
@@ -209,7 +209,7 @@ def SampleOneCycle(DaCycle,Samples,StateVector, ObservationOperator,lag):
 
     # Add model-data mismatch to all samples, this *might* use output from the ensemble in the future??
 
-    dummy   = Samples.AddModelDataMismatch()
+    status   = Samples.AddModelDataMismatch()
 
     msg = "Added Model Data Mismatch to all samples " ; logging.debug(msg)
 
@@ -222,7 +222,7 @@ def SampleOneCycle(DaCycle,Samples,StateVector, ObservationOperator,lag):
         # one file per member, some logic needs to be included to merge all files!!!
          
         filename               = os.path.join(ObservationOperator.outputdir,'flask_output.%s.nc'%DaCycle['time.sample.stamp'])
-        dummy                  = Samples.AddSimulations(filename)
+        status                  = Samples.AddSimulations(filename)
 
         # Give each member a model sample by first copying all samples, and then selecting the data for Member #n
 
@@ -253,29 +253,29 @@ def Invert(DaCycle, StateVector, Optimizer ):
                   int(DaCycle.DaSystem['nparameters']),
                   StateVector.nobs,  )
 
-    dummy     = Optimizer.Initialize(dims)
+    status     = Optimizer.Initialize(dims)
 
-    dummy     = Optimizer.StateToMatrix(StateVector )
+    status     = Optimizer.StateToMatrix(StateVector )
 
-    dummy     = Optimizer.WriteDiagnostics(DaCycle,StateVector,type='prior')
+    status     = Optimizer.WriteDiagnostics(DaCycle,StateVector,type='prior')
 
-    dummy     = Optimizer.SetLocalization('None')
+    status     = Optimizer.SetLocalization('None')
 
     if not DaCycle.DaSystem.has_key('opt.algorithm'):
         msg       = "There was no minimum least squares algorithm specified in the DA System rc file (key : opt.algorithm)" ; logging.info(msg) 
         msg       = "...using serial algorithm as default..." ; logging.info(msg)
-        dummy     = Optimizer.SerialMinimumLeastSquares()
+        status     = Optimizer.SerialMinimumLeastSquares()
     elif DaCycle.DaSystem['opt.algorithm'] == 'serial':
         msg       = "Using the serial minimum least squares algorithm to solve ENKF equations" ; logging.info(msg)
-        dummy     = Optimizer.SerialMinimumLeastSquares()
+        status     = Optimizer.SerialMinimumLeastSquares()
     elif DaCycle.DaSystem['opt.algorithm'] == 'bulk':
         msg       = "Using the bulk minimum least squares algorithm to solve ENKF equations" ; logging.info(msg)
-        dummy     = Optimizer.BulkMinimumLeastSquares()
+        status     = Optimizer.BulkMinimumLeastSquares()
 
 
-    dummy     = Optimizer.MatrixToState(StateVector )
+    status     = Optimizer.MatrixToState(StateVector )
 
-    dummy     = Optimizer.WriteDiagnostics(DaCycle,StateVector,type='optimized')
+    status     = Optimizer.WriteDiagnostics(DaCycle,StateVector,type='optimized')
 
     StateVector.isOptimized = True
 
@@ -300,12 +300,12 @@ def SaveAndSubmit( DaCycle, StateVector):
     savedir         = DaCycle['dir.output']
     filename        = os.path.join(savedir,'savestate.nc')
 
-    dummy = StateVector.WriteToFile(filename)
+    status = StateVector.WriteToFile(filename)
 
     DaCycle.RestartFileList.extend( [filename] ) # write optimized info because StateVector.Isoptimized == False for now
 
 
-    dummy = DaCycle.Finalize()
+    status = DaCycle.Finalize()
 
     return None
 
@@ -331,7 +331,7 @@ def RunForecastModel(DaCycle,ObsOperator):
 
     status = ObsOperator.Run()
 
-    dummy = ObsOperator.SaveData()
+    status = ObsOperator.SaveData()
 
     return status