Improving test outputs

diff --git a/src/daComposant/daAlgorithms/AdjointTest.py b/src/daComposant/daAlgorithms/AdjointTest.py
index 9734bb94fd0115321ce30e1e658985124ade9299..66f25fba75cc6a30731e5ace882336418761d3a4 100644 (file)
--- a/src/daComposant/daAlgorithms/AdjointTest.py
+++ b/src/daComposant/daAlgorithms/AdjointTest.py
@@ -73,23 +73,16 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         logging.debug("%s Lancement"%self._name)
         logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         #
-        # Paramètres de pilotage
-        # ----------------------
         self.setParameters(Parameters)
         #
-        # Opérateurs
-        # ----------
         Hm = HO["Direct"].appliedTo
         Ht = HO["Tangent"].appliedInXTo
         Ha = HO["Adjoint"].appliedInXTo
         #
-        # Construction des perturbations
-        # ------------------------------
+        # ----------
         Perturbations = [ 10**i for i in xrange(self._parameters["EpsilonMinimumExponent"],1) ]
         Perturbations.reverse()
         #
-        # Calcul du point courant
-        # -----------------------
         X       = numpy.asmatrix(numpy.ravel( Xb )).T
         NormeX  = numpy.linalg.norm( X )
         if Y is None:
@@ -97,8 +90,6 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         Y = numpy.asmatrix(numpy.ravel( Y )).T
         NormeY = numpy.linalg.norm( Y )
         #
-        # Fabrication de la direction de  l'incrément dX
-        # ----------------------------------------------
         if len(self._parameters["InitialDirection"]) == 0:
             dX0 = []
             for v in X.A1:
@@ -111,11 +102,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         dX0 = float(self._parameters["AmplitudeOfInitialDirection"]) * numpy.matrix( dX0 ).T
         #
-        # Utilisation de F(X) si aucune observation n'est donnee
-        # ------------------------------------------------------
-        #
-        # Entete des resultats
-        # --------------------
+        # ----------
         if self._parameters["ResiduFormula"] is "ScalarProduct":
             __doc__ = """
             On observe le residu qui est la difference de deux produits scalaires :
@@ -145,8 +132,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         Normalisation= -1
         #
-        # Boucle sur les perturbations
-        # ----------------------------
+        # ----------
         for i,amplitude in enumerate(Perturbations):
             dX          = amplitude * dX0
             NormedX     = numpy.linalg.norm( dX )
@@ -163,8 +149,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         msgs += "\n" + "-"*nbtirets
         msgs += "\n"
         #
-        # Sorties eventuelles
-        # -------------------
+        # ----------
         print
         print "Results of adjoint stability check:"
         print msgs

diff --git a/src/daComposant/daAlgorithms/FunctionTest.py b/src/daComposant/daAlgorithms/FunctionTest.py
index fd66e5693d3f0421ebd308753962b0d30dc399f1..f19ec16de56a100fd6ba9dca01acf1a35ea88459 100644 (file)
--- a/src/daComposant/daAlgorithms/FunctionTest.py
+++ b/src/daComposant/daAlgorithms/FunctionTest.py
@@ -36,25 +36,24 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             typecast = str,
             message  = "Titre du tableau et de la figure",
             )
+        self.defineRequiredParameter(
+            name     = "SetDebug",
+            default  = True,
+            typecast = bool,
+            message  = "Activation du mode debug lors de l'exécution",
+            )
 
     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
         logging.debug("%s Lancement"%self._name)
         logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         #
-        # Paramètres de pilotage
-        # ----------------------
         self.setParameters(Parameters)
         #
-        # Opérateur
-        # ---------
         Hm = HO["Direct"].appliedTo
         #
-        # Calcul du point nominal
-        # -----------------------
         Xn = numpy.asmatrix(numpy.ravel( Xb )).T
         #
-        # Test
-        # ----
+        # ----------
         if len(self._parameters["ResultTitle"]) > 0:
             msg  = "     ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
             msg += "        " + self._parameters["ResultTitle"] + "\n"
@@ -73,12 +72,17 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         msg += "       L2 norm of vector..: %.5e\n"%numpy.linalg.norm( Xn )
         print(msg)
         #
-        CUR_LEVEL = logging.getLogger().getEffectiveLevel()
-        logging.getLogger().setLevel(logging.DEBUG)
-        print(  "===> Launching direct operator evaluation, activating debug\n")
+        if self._parameters["SetDebug"]:
+            CUR_LEVEL = logging.getLogger().getEffectiveLevel()
+            logging.getLogger().setLevel(logging.DEBUG)
+            print("===> Beginning of evaluation, activating debug\n")
+        else:
+            print("===> Beginning of evaluation, without activating debug\n")
+        print("     %s\n"%("-"*75,))
+        #
+        print("===> Launching direct operator evaluation\n")
         Y = Hm( Xn )
-        print("\n===> End of direct operator evaluation, deactivating debug\n")
-        logging.getLogger().setLevel(CUR_LEVEL)
+        print("\n===> End of direct operator evaluation\n")
         #
         msg  = "===> Information after launching:\n"
         msg += "     ----------------------------\n"
@@ -92,6 +96,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         msg += "       L2 norm of vector..: %.5e\n"%numpy.linalg.norm( Y )
         print(msg)
         #
+        print("     %s\n"%("-"*75,))
+        if self._parameters["SetDebug"]:
+            print("===> End evaluation, deactivating debug if necessary\n")
+            logging.getLogger().setLevel(CUR_LEVEL)
+        #
         logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         logging.debug("%s Terminé"%self._name)
         #

diff --git a/src/daComposant/daAlgorithms/GradientTest.py b/src/daComposant/daAlgorithms/GradientTest.py
index 84d0dfad26c09c49c5011c49997493dbadff089f..2e6b728ccf0f0255cf1411f53cfec4c2ecabefbf 100644 (file)
--- a/src/daComposant/daAlgorithms/GradientTest.py
+++ b/src/daComposant/daAlgorithms/GradientTest.py
@@ -92,30 +92,21 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         logging.debug("%s Lancement"%self._name)
         logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         #
-        # Paramètres de pilotage
-        # ----------------------
         self.setParameters(Parameters)
         #
-        # Opérateurs
-        # ----------
         Hm = HO["Direct"].appliedTo
         if self._parameters["ResiduFormula"] is "Taylor":
             Ht = HO["Tangent"].appliedInXTo
         #
-        # Construction des perturbations
-        # ------------------------------
+        # ----------
         Perturbations = [ 10**i for i in xrange(self._parameters["EpsilonMinimumExponent"],1) ]
         Perturbations.reverse()
         #
-        # Calcul du point courant
-        # -----------------------
         X       = numpy.asmatrix(numpy.ravel(    Xb   )).T
         FX      = numpy.asmatrix(numpy.ravel( Hm( X ) )).T
         NormeX  = numpy.linalg.norm( X )
         NormeFX = numpy.linalg.norm( FX )
         #
-        # Fabrication de la direction de  l'incrément dX
-        # ----------------------------------------------
         if len(self._parameters["InitialDirection"]) == 0:
             dX0 = []
             for v in X.A1:
@@ -128,14 +119,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         dX0 = float(self._parameters["AmplitudeOfInitialDirection"]) * numpy.matrix( dX0 ).T
         #
-        # Calcul du gradient au point courant X pour l'incrément dX
-        # ---------------------------------------------------------
         if self._parameters["ResiduFormula"] is "Taylor":
             GradFxdX = Ht( (X, dX0) )
             GradFxdX = numpy.asmatrix(numpy.ravel( GradFxdX )).T
         #
-        # Entete des resultats
-        # --------------------
+        # ----------
         if self._parameters["ResiduFormula"] is "Taylor":
             __doc__ = """
             On observe le residu issu du développement de Taylor de la fonction F,
@@ -192,8 +180,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         NormesdFXsAm = []
         NormesdFXGdX = []
         #
-        # Boucle sur les perturbations
-        # ----------------------------
+        # ----------
         for i,amplitude in enumerate(Perturbations):
             dX      = amplitude * dX0
             #
@@ -234,8 +221,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         msgs += "\n" + "-"*nbtirets
         msgs += "\n"
         #
-        # Sorties eventuelles
-        # -------------------
+        # ----------
         print
         print "Results of gradient stability check:"
         print msgs