diff --git a/mloop/controllers.py b/mloop/controllers.py
index 4352a40..486bc3e 100644
--- a/mloop/controllers.py
+++ b/mloop/controllers.py
@@ -338,7 +338,6 @@ def optimize(self):
             log.info('Controller finished. Closing down M-LOOP. Please wait a moment...')
         except ControllerInterrupt:
             self.log.warning('Controller ended by interruption.')
-        '''
         except (KeyboardInterrupt,SystemExit):
             log.warning('!!! Do not give the interrupt signal again !!! \n M-LOOP stopped with keyboard interupt or system exit. Please wait at least 1 minute for the threads to safely shut down. \n ')
             log.warning('Closing down controller.')
@@ -348,7 +347,6 @@ def optimize(self):
             self.log.warning('Safely shut down. Below are results found before exception.')
             self.print_results()
             raise
-        '''
         self._shut_down()
         self.print_results()
         self.log.info('M-LOOP Done.')
@@ -707,23 +705,17 @@ def _optimization_routine(self):
             ml_count = 0    
         
         while self.check_end_conditions():
-            print('1-1.')
             self.log.info('Run:' + str(self.num_in_costs +1))
             if ml_consec==self.generation_num or (self.no_delay and self.ml_learner_params_queue.empty()):
-                print('1-2.')
                 next_params = self._next_params()
-                print('1-3.')
                 self._put_params_and_out_dict(next_params)
                 ml_consec = 0
             else:
-                print('1-4.')
                 next_params = self.ml_learner_params_queue.get()
-                print('1-5.')
                 super(MachineLearnerController,self)._put_params_and_out_dict(next_params, param_type=self.machine_learner_type)
                 ml_consec += 1
                 ml_count += 1
             if ml_count==self.generation_num:
-                print('1-6.')
                 self.new_params_event.set()
                 ml_count = 0
                 
diff --git a/mloop/learners.py b/mloop/learners.py
index 9aefa99..a931c93 100644
--- a/mloop/learners.py
+++ b/mloop/learners.py
@@ -1269,7 +1269,6 @@ def fit_gaussian_process(self):
         '''
         Fit the Gaussian process to the current data
         '''
-        print('3-1')
         self.log.debug('Fitting Gaussian process.')
         if self.all_params.size==0 or self.all_costs.size==0 or self.all_uncers.size==0:
             self.log.error('Asked to fit GP but no data is in all_costs, all_params or all_uncers.')
@@ -1279,7 +1278,6 @@ def fit_gaussian_process(self):
         self.gaussian_process.alpha_ = self.scaled_uncers
         self.gaussian_process.fit(self.all_params,self.scaled_costs)
         
-        print('3-2')
         if self.update_hyperparameters:
             
             self.fit_count += 1
@@ -1297,10 +1295,7 @@ def fit_gaussian_process(self):
             else:
                 self.length_scale = last_hyperparameters['length_scale']
                 self.length_scale_history.append(self.length_scale)
-        print('3-3')
-        print(repr(self.length_scale))
-        print(repr(self.noise_level))
-        
+                
             
     def update_bias_function(self):
         '''
@@ -1317,10 +1312,7 @@ def predict_biased_cost(self,params):
         Returns:
             pred_bias_cost (float): Biased cost predicted at the given parameters
         '''
-        #print('2-8-1-1.')
-        #(pred_cost, pred_uncer) = (self.gaussian_process.predict(params[np.newaxis,:]), 0.1)
         (pred_cost, pred_uncer) = self.gaussian_process.predict(params[np.newaxis,:], return_std=True)
-        #print('2-8-1-2.')
         return self.cost_bias*pred_cost - self.uncer_bias*pred_uncer
     
     def find_next_parameters(self):
@@ -1331,20 +1323,15 @@ def find_next_parameters(self):
             next_params (array): Returns next parameters from biased cost search.
         '''
         self.params_count += 1
-        print('2-6.')
         self.update_bias_function()
         self.update_search_params()
         next_params = None
         next_cost = float('inf')
-        print('2-7.')
         for start_params in self.search_params:
-            print('2-8-1.')
             result = so.minimize(self.predict_biased_cost, start_params, bounds = self.search_region, tol=self.search_precision)
-            print('2-8-2.')
             if result.fun < next_cost:
                 next_params = result.x
                 next_cost = result.fun
-        print('2-9.')
         return next_params
     
     def run(self):
@@ -1359,18 +1346,13 @@ def run(self):
             while not self.end_event.is_set():
                 #self.log.debug('Learner waiting for new params event')
                 self.save_archive()
-                print('2-1.')
                 self.wait_for_new_params_event()
                 #self.log.debug('Gaussian process learner reading costs')
-                print('2-2.')
                 self.get_params_and_costs()
-                print('2-4.')
                 self.fit_gaussian_process()
                 for _ in range(self.generation_num):
-                    print('2-5.')
                     self.log.debug('Gaussian process learner generating parameter:'+ str(self.params_count+1))
                     next_params = self.find_next_parameters()
-                    print('2-10.')
                     self.params_out_queue.put(next_params)
                     if self.end_event.is_set():
                         raise LearnerInterrupt()