ok

da/cosmo/base_optimizer.py

@@ -109,29 +109,10 @@ class Optimizer(object):
         for n in range(self.nlag):
             samples = statevector.obs_to_assimilate[n]
             members = statevector.ensemble_members[n]
-#            params_2d = members[0].param_values[:-1].reshape(9,22)
- #           params = np.zeros(shape=(9,20))
-  #          for r in range(0,9):
-   #             params[r,:] = np.delete(np.delete(params_2d[r,:],21),10)
-    #        params = np.hstack((params.flatten(),members[0].param_values[-1])) # put back BG
-    #        self.x[n * self.nparams:(n + 1) * self.nparams] = params
             self.x[n * self.nparams:(n + 1) * self.nparams] = members[0].param_values
 
-#            params = np.zeros(shape=(self.nmembers,181))
- #           params_3d = np.zeros(shape=(self.nmembers,9,20))
-  #          for m,mm in enumerate(members):
-   #             params_temp = mm.param_values[:-1].reshape(9,22)
-    #            params_bg = mm.param_values[-1]
-     #           for r in range(0,9):
-      #              params_3d[m,r,:] = np.delete(np.delete(params_temp[r,:],21),10)
-       #         params[m,:] = np.hstack((params_3d[m,:,:].flatten(),params_bg))
-#            params = params_3d.reshape(self.nmembers,181)
-
-#            self.X_prime[n * self.nparams:(n + 1) * self.nparams, :] = np.transpose(np.array([params[m,:] for m,mm in enumerate(members)]))
             self.X_prime[n * self.nparams:(n + 1) * self.nparams, :] = np.transpose(np.array([m.param_values for m in members]))
 
-            #self.X_prime[n * self.nparams:(n + 1) * self.nparams, :] = np.transpose(np.array([np.delete(np.delete(m.param_values,21),10) for m in members]))
-
             if samples != None:        
                 self.rejection_threshold = samples.rejection_threshold
 
@@ -174,16 +155,6 @@ class Optimizer(object):
             members = statevector.ensemble_members[n]
             for m, mem in enumerate(members):
                 members[m].param_values[:] = self.X_prime[n * self.nparams:(n + 1) * self.nparams, m] + self.x[n * self.nparams:(n + 1) * self.nparams]
-#                params = (self.X_prime[n * self.nparams:(n + 1) * self.nparams, m] + self.x[n * self.nparams:(n + 1) * self.nparams])   # 181
- #               params_bg = params[-1] # BG
-  #              params = params[:-1] # remove BG for now
-   #             params = params.reshape(9,20)
-    #            params_holder = np.zeros((9,22))
-     #           for r in range(0,8):
-      #              params_holder[r,:] = np.insert(np.insert(params[r,:],20,0),10,0)
-       #         members[m].param_values[:] = np.hstack((params_holder.flatten(),params_bg))
-                #members[m].param_values[:] = np.insert(np.insert(self.X_prime[n * self.nparams:(n + 1) * self.nparams, m] + self.x[n * self.nparams:(n + 1) * self.nparams],10,0),21,0)
-
 
         logging.debug('Returning optimized data to the StateVector, setting "StateVector.isOptimized = True" ')
 

da/cosmo/statevector.py

@@ -178,7 +178,7 @@ class StateVector(object):
 
         # Create a dictionary for state <-> gridded map conversions
 
-        nparams = self.nparameters
+        self.nparams = self.nparameters
         self.griddict = {}
         for pft in range(1,18):
             for r in range(1, 11):