[lib] Fix typos

library/cadbiom/commons.py

@@ -51,6 +51,13 @@ def logger(name=LOGGER_NAME, logfilename=None):
     """Return logger of given name, without initialize it.
 
     Equivalent of logging.getLogger() call.
+
+    :Example:
+
+        cm.logger(name=__name__)
+
+    :param name: Name of the logger, only displayed if "%(name)s" is present in
+        the logging.Formatter.
     """
     return logging.getLogger(name)
 

library/cadbiom/models/clause_constraints/mcl/MCLAnalyser.py

@@ -373,7 +373,7 @@ class MCLAnalyser(object):
         # inactive_frontier_values = \
         #     [[var] for var in dimacs_front_sol.frontier_values if var < 0]
         # Intersection between pre-computed negative values for all frontiers
-        # and all frontiers (negative and positive) in the given solution
+        # (negative and positive) in the given solution, and all frontiers.
         inactive_frontier_values = \
             [[var] for var in
              self.unfolder.frontiers_negative_values & dimacs_front_sol.frontier_values]
@@ -683,11 +683,9 @@ class MCLAnalyser(object):
         # list of timed minimal activation conditions on frontier (dimacs code)
         # i.e list<DimacsFrontierSol>
         # mac_list = [] # Not used anymore
-        forbidden_frontier_values = []
 
-        ## TODO: parser les properties au format DIMACS une bonne fois pour toutes
-        ## et les ajouter dans forbidden_frontier_values
-        ## Lors du rechargement des places précédentes, le problème du parsing va réapparaitre
+        # Keep a list of frontier values to be banned in the next search
+        forbidden_frontier_values = []
 
         # Satisfiability test
         reachable = self.sq_is_satisfiable(query, max_user_step)
@@ -729,7 +727,7 @@ class MCLAnalyser(object):
                     break
 
                 ## TEST
-                ## Make autoadjustment... of max_steps during the solving
+                ## Make autoadjustment... of max_steps during the solving!
                 ## Juste appeler squery_solve avec les memes parametres mais
                 ## min_step augmenté de 1
                 ## en restant inférieur au nb d'étapes demandées à l'origine