assimmilate now spelled as assimilate (consitent with pipeline.py)

da/baseclasses/optimizer.py

@@ -93,7 +93,7 @@ class Optimizer(object):
         allsimulated = None  # collect all members model samples for n=1,..,nlag
 
         for n in range(self.nlag):
-            samples = statevector.obs_to_assimmilate[n]
+            samples = statevector.obs_to_assimilate[n]
             members = statevector.ensemble_members[n]
             self.x[n * self.nparams:(n + 1) * self.nparams] = members[0].param_values
             self.X_prime[n * self.nparams:(n + 1) * self.nparams, :] = np.transpose(np.array([m.param_values for m in members]))

da/baseclasses/statevector.py

@@ -139,7 +139,7 @@ class StateVector(object):
         self.nparams = int(dacycle.dasystem['nparameters'])
         self.nobs = 0
         
-        self.obs_to_assimmilate = ()  # empty containter to hold observations to assimilate later on
+        self.obs_to_assimilate = ()  # empty containter to hold observations to assimilate later on
 
         # These list objects hold the data for each time step of lag in the system. Note that the ensembles for each time step consist 
         # of lists of EnsembleMember objects, we define member 0 as the mean of the distribution and n=1,...,nmembers as the spread.