diff --git a/src/optimize/CheKiPEUQ_from_Frhodo.py b/src/optimize/CheKiPEUQ_from_Frhodo.py
index 126bc43..7521b08 100644
--- a/src/optimize/CheKiPEUQ_from_Frhodo.py
+++ b/src/optimize/CheKiPEUQ_from_Frhodo.py
@@ -161,8 +161,5 @@ def get_consolidated_parameters_arrays(rate_constants_values, rate_constants_low
     return pars_values, pars_lower_bnds, pars_upper_bnds, pars_bnds_exist, unbounded_indices
     
 def remove_unbounded_values(array_to_truncate, unbounded_indices):
-    print("line 164", unbounded_indices, array_to_truncate)
     truncated_array = np.delete(array_to_truncate, unbounded_indices, axis=0)
-    print("line 164", unbounded_indices, truncated_array)
-
     return truncated_array
diff --git a/src/optimize/CheKiPEUQ_local/InverseProblem.py b/src/optimize/CheKiPEUQ_local/InverseProblem.py
index 8307b57..489d26e 100644
--- a/src/optimize/CheKiPEUQ_local/InverseProblem.py
+++ b/src/optimize/CheKiPEUQ_local/InverseProblem.py
@@ -110,7 +110,6 @@ def __init__(self, UserInput = None):
                     #We will also fill the model['InputParameterPriorValues'] to have the mean of the two bounds. This can matter for some of the scaling that occurs later.
                     self.UserInput.mu_prior[parameterIndex] = (UserInput.model['InputParameterPriorValues_upperBounds'][parameterIndex] + UserInput.model['InputParameterPriorValues_lowerBounds'][parameterIndex])/2
         
-        print("line 112 of CheKiPEUQ", UserInput.InputParametersPriorValuesUncertainties)
         #Now to make covmat. Leaving the original dictionary object intact, but making a new object to make covmat_prior.
         if len(np.shape(UserInput.InputParametersPriorValuesUncertainties)) == 1 and (len(UserInput.InputParametersPriorValuesUncertainties) > 0): #If it's a 1D array/list that is filled, we'll diagonalize it.
             UserInput.std_prior = np.array(UserInput.InputParametersPriorValuesUncertainties, dtype='float') #using 32 since not everyone has 64.
diff --git a/src/optimize/fit_fcn.py b/src/optimize/fit_fcn.py
index 3c090f2..4979d2f 100644
--- a/src/optimize/fit_fcn.py
+++ b/src/optimize/fit_fcn.py
@@ -450,7 +450,7 @@ def get_last_obs_sim_interp(varying_rate_vals): #A. Savara added this. It needs
                 SSE = generalized_loss_fcn(loss_resid, mu=loss_min)
                 SSE = rescale_loss_fcn(loss_resid, SSE)
                 exp_loss_weights = loss_exp/SSE # comparison is between selected loss fcn and SSE (L2 loss)
-                Bayesian_dict['weights_data'] = np.concatenate(aggregate_weights*exp_loss_weights, axis=0)
+                Bayesian_dict['weights_data'] = np.concatenate(aggregate_weights*exp_loss_weights, axis=0).flatten()
             
             #Bayesian_dict['weights_data'] /= np.max(Bayesian_dict['weights_data'])  # if we want to normalize by maximum
 
@@ -491,7 +491,6 @@ def get_last_obs_sim_interp(varying_rate_vals): #A. Savara added this. It needs
             log_posterior_density = optimize.CheKiPEUQ_from_Frhodo.get_log_posterior_density(CheKiPEUQ_PE_object, Bayesian_dict['pars_current_guess_truncated'])
             #Step 5 of Bayesian:  return the objective function and any other metrics desired.
             obj_fcn = -1*log_posterior_density #need neg_logP because minimizing.
-            print("line 481 of fit_fcn, Bayesian obj_fcn", obj_fcn)
            
         # For updating
         self.i += 1