Save epf iterations with separate counter in filenames

popt/cost_functions/epf.py

@@ -4,7 +4,7 @@ def epf(r, c_eq=0, c_iq=0):
     r"""
     The external penalty function, given as
     .. math::
-        0.5r(\sum_i(c_{eq}^2) + \sum(\max(c_{iq},0)^2)
+        0.5r(\sum_i c_{eq}^2 + \sum(\max(c_{iq},0)^2)
     """
 
     return r*0.5*( np.sum(c_eq**2) + np.sum(np.maximum(-c_iq,0)**2) )

popt/loop/optimize.py

@@ -103,6 +103,7 @@ def __set__variable(var_name=None, defalut=None):
 
         # Optimze with external penalty function for constraints, provide r_0 as input
         self.epf = __set__variable('epf', {})
+        self.epf_iteration = 1
 
         # Initialize variables (set in subclasses)
         self.options = None
@@ -143,11 +144,10 @@ def run_loop(self):
 
         # Check if external penalty function (epf) for handling constraints should be used
         epf_not_converged = True
-        epf_iteration = 1
         previous_state = None
         if self.epf:
             previous_state = self.mean_state
-            logger.info(f'       -----> EPF-EnOpt: {epf_iteration}, {self.epf["r"]} (outer iteration, penalty factor)')  # print epf info
+            logger.info(f'       -----> EPF-EnOpt: {self.epf_iteration}, {self.epf["r"]} (outer iteration, penalty factor)')  # print epf info
 
         while epf_not_converged:  # outer loop using epf
 
@@ -182,7 +182,7 @@ def run_loop(self):
             # Test for convergence of outer epf loop
             epf_not_converged = False
             if self.epf:
-                if epf_iteration > self.epf['max_epf_iter']:  # max epf_iterations set to 10
+                if self.epf_iteration > self.epf['max_epf_iter']:  # max epf_iterations set to 10
                     logger.info(
                         f'       -----> EPF-EnOpt: maximum epf iterations reached')  # print epf info
                     break
@@ -196,9 +196,9 @@ def run_loop(self):
                     self.obj_func_values = self.fun(self.mean_state, **self.epf)
                     self.nfev += 1
                     self.iteration = 1
-                    epf_iteration += 1
+                    self.epf_iteration += 1
                     r = self.epf['r']
-                    logger.info(f'       -----> EPF-EnOpt: {epf_iteration}, {r} (outer iteration, penalty factor)')  # print epf info
+                    logger.info(f'       -----> EPF-EnOpt: {self.epf_iteration}, {r} (outer iteration, penalty factor)')  # print epf info
                 else:
                     logger.info(f'       -----> EPF-EnOpt: converged, no variables changed more than {conv_crit*100} %')  # print epf info
                     final_obj_no_penalty = str(round(self.fun(self.mean_state)[0],4))

popt/misc_tools/optim_tools.py

@@ -258,14 +258,17 @@ def get_optimize_result(obj):
     # Initialize dictionary of variables to save
     save_dict = OptimizeResult({'success': True, 'x': obj.mean_state, 'fun': np.mean(obj.obj_func_values),
                                 'nit':  obj.iteration, 'nfev': obj.nfev, 'njev': obj.njev})
+    if hasattr(obj, 'epf'):
+        save_dict['epf_iteration'] = obj.epf_iteration
+
     if 'savedata' in obj.options:
 
         # Make sure "SAVEDATA" gives a list
         if isinstance( obj.options['savedata'], list):
             savedata = obj.options['savedata']
         else:
             savedata = [ obj.options['savedata']]
-
+      
         # Loop over variables to store in save list
         for save_typ in savedata:
             if 'mean_state' in save_typ:
@@ -311,4 +314,7 @@ def save_optimize_results(intermediate_result):
         suffix = now.strftime("%m_%d_%Y_%H_%M_%S")
 
     # Save the variables
-    np.savez(save_folder + '/optimize_result_{0}'.format(suffix), **intermediate_result)
+    if 'epf_iteration' in intermediate_result:
+        np.savez(save_folder + '/optimize_result_{0}_{1}'.format(suffix, str(intermediate_result['epf_iteration'])), **intermediate_result)
+    else:
+        np.savez(save_folder + '/optimize_result_{0}'.format(suffix), **intermediate_result)