New forward pipeline introduced for running just transport + CTDAS framework

da/tools/pipeline.py

@@ -47,16 +47,23 @@ def ForwardPipeline(DaCycle, PlatForm, DaSystem, Samples, StateVector, ObsOperat
     
     StateVector.Initialize()                            #LU w prepare state inicjalizujemy wektor stanu ktoremu dopiero co przypisalismy wartosci.
 
-    # Read from other simulation
+    # Read from other simulation and write priors, then read posteriors and propagate
 
     filename = os.path.join(DaCycle['dir.forward.savestate'],DaCycle['time.start'].strftime('%Y%m%d'), 'savestate.nc')                #LU teraz czytamy savestate.nc
-    StateVector.ReadFromFile(filename) # by default will read "opt"(imized) variables, and then propagate
+    StateVector.ReadFromLegacyFile(filename,'prior') 
+
+    StateVector.isOptimized = False
 
     # Write as prior fluxes to output dir
 
     savedir = DaCycle['dir.output']
-    filename = os.path.join(savedir, 'savestate.nc')   #LU to jest state vector po zoptymalizowaniu.
-    StateVector.WriteToFile(filename)
+    savefilename = os.path.join(savedir, 'savestate.nc')   #LU to jest state vector po zoptymalizowaniu.
+    StateVector.WriteToFile(savefilename)
+
+    # Now read optimized fluxes to propagate
+
+    StateVector.ReadFromLegacyFile(filename) # by default will read "opt"(imized) variables, and then propagate
+
     StateVector.isOptimized = True
 
     # Run forward with these parameters
@@ -119,6 +126,8 @@ def prepare_state(DaCycle, StateVector):
 
     # Finally, also write the StateVector to a file so that we can always access the a-priori information
 
+    filename = os.path.join(DaCycle['dir.forward.savestate'],DaCycle['time.start'].strftime('%Y%m%d'), 'savestate.nc')                #LU teraz czytamy savestate.nc
+    StateVector.ReadFromLegacyFile(filename,'prior') 
     
     filename = os.path.join(DaCycle['dir.output'], 'savestate.nc')