added diagnostic output from optimizer

da/baseclasses/optimizer.py

@@ -85,8 +85,8 @@ class Optimizer(object):
             members                                             = StateVector.EnsembleMembers[n]
             self.x[n*self.nparams:(n+1)*self.nparams]           = members[0].ParameterValues[n]
             self.X_prime[n*self.nparams:(n+1)*self.nparams,:]   = np.transpose(np.array([m.ParameterValues for m in members]))
-            allobs.append(                                        StateVector.EnsembleMembers[n][0].ModelSample.Data.getvalues('obs') )
-            allsamples.append(                                    StateVector.EnsembleMembers[n][0].ModelSample.Data.getvalues('simulated') )
+            allobs.extend(                                        StateVector.EnsembleMembers[n][0].ModelSample.Data.getvalues('obs') )
+            allsamples.extend(                                    StateVector.EnsembleMembers[n][0].ModelSample.Data.getvalues('simulated') )
 
         self.X_prime                                            = self.X_prime - self.x[:,np.newaxis] # make into a deviation matrix
         self.obs[:]                                             = np.array(allobs)

da/baseclasses/statevector.py

@@ -94,6 +94,7 @@ class StateVector(object):
         self.nlag               = dims[0]
         self.nmembers           = dims[1]
         self.nparams            = dims[2]
+        self.nobs               = 0
         self.isOptimized        = False
 
         # These list objects hold the data for each time step of lag in the system. Note that the ensembles for each time step consist 

da/ct/obs.py

@@ -30,6 +30,9 @@ class CtObservations(Observation):
     def getversion(self):
         return version
 
+    def getlength(self):
+        return len(self.Data)
+
     def Initialize(self,DaCycle):
 
         self.startdate = DaCycle['time.sample.start']

da/tools/pipeline.py

@@ -224,8 +224,10 @@ def SampleOneCycle(DaCycle,Samples,StateVector, ObservationOperator,lag):
         filename               = os.path.join(DaCycle['dir.output'],'samples.%03d.nc'%Member.membernumber)
         dummy                  = Samples.AddSimulations(filename,silent)
         Member.ModelSample     = Samples
+        StateVector.nobs       += Samples.getlength()
         silent=True
 
+
     msg = "Added samples from the observation operator to each member of the StateVector" ; logging.debug(msg)
 
     # Advance the sample time
@@ -241,7 +243,7 @@ def Invert(DaCycle, StateVector, Optimizer ):
     dims      = ( int(DaCycle['time.nlag']),
                   int(DaCycle['forecast.nmembers']),
                   int(DaCycle.DaSystem['nparameters']),
-                  nobs,  )
+                  StateVector.nobs,  )
 
     dummy     = Optimizer.Initialize(dims)