replaced PrepareState with direct calls to class

das.py

@@ -42,9 +42,42 @@ def JobStart(opts,args):
 
 def JobInput():
     """ Set up the input data for the forward model: obs and parameters/fluxes"""
-    from da.tools.general import PrepareState
+    from da.ct.statevector import CtStateVector
 
-    StateVector   = PrepareState(CycleInfo )
+    dims        = ( int(CycleInfo.da_settings['time.nlag']),
+                    int(CycleInfo.da_settings['forecast.nmembers']),
+                    int(CycleInfo.DaSystem.da_settings['nparameters']),
+                  )
+
+    StateVector = CtStateVector(dims)
+
+    nlag        = dims[0]
+
+    # We now have an empty CtStateVector object that we need to populate with data. If this is a continuation from a previous cycle, we can read
+    # the previous StateVector values from a NetCDF file in the save/ directory. If this is the first cycle, we need to populate the CtStateVector
+    # 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
+
+    if not CycleInfo.da_settings['time.restart']:
+
+        # Fill each week from n=1 to n=nlag with a new ensemble
+
+        for n in range(0,nlag):
+            cov   = StateVector.GetCovariance(CycleInfo.DaSystem)
+            dummy = StateVector.MakeNewEnsemble(n+1,cov)
+    else:
+
+        # Read the StateVector data from file
+
+        savedir         = CycleInfo.da_settings['dir.save']
+        filtertime      = CycleInfo.da_settings['time.start'].strftime('%Y%m%d')
+        filename        = os.path.join(savedir,'savestate.nc')
+
+        StateVector.ReadFromFile(filename)
+
+        # Now propagate the ensemble by one cycle to prepare for the current cycle
+
+        dummy = StateVector.Propagate()
 
     return StateVector