in GOFEE: split get_surrogate_relaxed_candidates into candidate generation and...

in GOFEE: split get_surrogate_relaxed_candidates into candidate generation and candidate relaxation, also separated the timing of these tasks

gofee/gofee.py

@@ -290,8 +290,10 @@ class GOFEE():
             t1 = time()
             self.update_population()
             t2 = time()
-            relaxed_candidates = self.get_surrogate_relaxed_candidates()
+            unrelaxed_candidates = self.generate_new_candidates()
             t3 = time()
+            relaxed_candidates = self.relax_candidates_with_surrogate(unrelaxed_candidates)
+            t4 = time()
             kappa = self.kappa
             a_add = []
             for _ in range(5):
@@ -312,8 +314,8 @@ class GOFEE():
 
             # log timing
             self.log_msg += "Timing:\n"
-            self.log_msg += f"{'Training':12}{'Relax pop.':12}{'Relax candidates':18}{'Evaluate':12}\n"
-            self.log_msg += f"{t1-t0:12.2e}\t{t2-t1:12.2e}\t{t3-t2:16.2e}\t{time()-t3:12.2e}\n\n"
+            self.log_msg += f"{'Training':12}{'Relax pop.':12}{'Make cands.':15}{'Relax cands.':16}{'Evaluate':12}\n"
+            self.log_msg += f"{t1-t0:<12.2e}{t2-t1:<12.2e}{t3-t2:<15.2e}{t4-t3:<16.2e}{time()-t4:<12.2e}\n\n"
 
             # Add structure to population
             index_lowest = np.argmin([a.get_potential_energy() for a in a_add])
@@ -352,10 +354,10 @@ class GOFEE():
         a_dp.set_positions(pos_dp)
         return a_dp
 
-    def get_surrogate_relaxed_candidates(self):
-        """ Method supplying a number of surrogate-relaxed new
-        candidates. The method combines the generation of new
-        candidates with subsequent surrogate relaxation.
+    def generate_new_candidates(self):
+        """Method to generate a self.Ncandidates new candidates
+        by applying the operations defined in self.candidate_generator
+        to the structures currently in the population.
         The tasks are parrlelized over all avaliable cores.
         """
         Njobs = self.Ncandidates
@@ -363,6 +365,15 @@ class GOFEE():
         def func1():
             return [self.generate_candidate() for i in task_split[self.comm.rank]]
         candidates = parallel_function_eval(self.comm, func1)
+        return candidates
+
+    def relax_candidates_with_surrogate(self, candidates):
+        """ Method to relax new candidates using the
+        surrogate-model.
+        The tasks are parrlelized over all avaliable cores.
+        """
+        Njobs = self.Ncandidates
+        task_split = split(Njobs, self.comm.size)
         def func2():
             return [self.surrogate_relaxation(candidates[i], Fmax=0.1, steps=200, kappa=self.kappa)
                     for i in task_split[self.comm.rank]]