1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2012 EDF R&D
5 # This library is free software; you can redistribute it and/or
6 # modify it under the terms of the GNU Lesser General Public
7 # License as published by the Free Software Foundation; either
8 # version 2.1 of the License.
10 # This library is distributed in the hope that it will be useful,
11 # but WITHOUT ANY WARRANTY; without even the implied warranty of
12 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
13 # Lesser General Public License for more details.
15 # You should have received a copy of the GNU Lesser General Public
16 # License along with this library; if not, write to the Free Software
17 # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
19 # See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
23 from daCore import BasicObjects, PlatformInfo
24 m = PlatformInfo.SystemUsage()
28 # ==============================================================================
29 class ElementaryAlgorithm(BasicObjects.Algorithm):
31 BasicObjects.Algorithm.__init__(self, "ADJOINTTEST")
32 self.defineRequiredParameter(
33 name = "ResiduFormula",
34 default = "ScalarProduct",
36 message = "Formule de résidu utilisée",
37 listval = ["ScalarProduct"],
39 self.defineRequiredParameter(
40 name = "EpsilonMinimumExponent",
43 message = "Exposant minimal en puissance de 10 pour le multiplicateur d'incrément",
47 self.defineRequiredParameter(
48 name = "InitialDirection",
51 message = "Direction initiale de la dérivée directionnelle autour du point nominal",
53 self.defineRequiredParameter(
54 name = "AmplitudeOfInitialDirection",
57 message = "Amplitude de la direction initiale de la dérivée directionnelle autour du point nominal",
59 self.defineRequiredParameter(
61 typecast = numpy.random.seed,
62 message = "Graine fixée pour le générateur aléatoire",
64 self.defineRequiredParameter(
68 message = "Titre du tableau et de la figure",
71 def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
72 logging.debug("%s Lancement"%self._name)
73 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
75 # Paramètres de pilotage
76 # ----------------------
77 self.setParameters(Parameters)
79 # Opérateur d'observation
80 # -----------------------
81 Hm = H["Direct"].appliedTo
82 Ht = H["Tangent"].appliedInXTo
83 Ha = H["Adjoint"].appliedInXTo
85 # Construction des perturbations
86 # ------------------------------
87 Perturbations = [ 10**i for i in xrange(self._parameters["EpsilonMinimumExponent"],1) ]
88 Perturbations.reverse()
90 # Calcul du point courant
91 # -----------------------
92 X = numpy.asmatrix(Xb).flatten().T
93 NormeX = numpy.linalg.norm( X )
95 Y = numpy.asmatrix( Hm( X ) ).flatten().T
96 Y = numpy.asmatrix(Y).flatten().T
97 NormeY = numpy.linalg.norm( Y )
99 # Fabrication de la direction de l'incrément dX
100 # ----------------------------------------------
101 if len(self._parameters["InitialDirection"]) == 0:
105 dX0.append( numpy.random.normal(0.,abs(v)) )
107 dX0.append( numpy.random.normal(0.,X.mean()) )
109 dX0 = numpy.asmatrix(self._parameters["InitialDirection"]).flatten()
111 dX0 = float(self._parameters["AmplitudeOfInitialDirection"]) * numpy.matrix( dX0 ).T
113 # Utilisation de F(X) si aucune observation n'est donnee
114 # ------------------------------------------------------
116 # Entete des resultats
117 # --------------------
118 if self._parameters["ResiduFormula"] is "ScalarProduct":
120 On observe le residu qui est la difference de deux produits scalaires :
122 R(Alpha) = | < TangentF_X(dX) , Y > - < dX , AdjointF_X(Y) > |
124 qui doit rester constamment egal zero a la precision du calcul.
125 On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
126 Y doit etre dans l'image de F. S'il n'est pas donne, on prend Y = F(X).
131 msgs = " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
132 msgs += " " + self._parameters["ResultTitle"] + "\n"
133 msgs += " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
136 msg = " i Alpha ||X|| ||Y|| ||dX|| R(Alpha) "
138 msgs += "\n" + "-"*nbtirets
140 msgs += "\n" + "-"*nbtirets
144 # Boucle sur les perturbations
145 # ----------------------------
146 for i,amplitude in enumerate(Perturbations):
147 logging.debug("%s Etape de calcul numéro %i, avec la perturbation %8.3e"%(self._name, i, amplitude))
150 NormedX = numpy.linalg.norm( dX )
152 TangentFXdX = numpy.asmatrix( Ht( (X,dX) ) )
153 AdjointFXY = numpy.asmatrix( Ha( (X,Y) ) )
155 Residu = abs(float(numpy.dot( TangentFXdX.A1 , Y.A1 ) - numpy.dot( dX.A1 , AdjointFXY.A1 )))
157 msg = " %2i %5.0e %9.3e %9.3e %9.3e | %9.3e"%(i,amplitude,NormeX,NormeY,NormedX,Residu)
160 self.StoredVariables["CostFunctionJ"].store( Residu )
161 msgs += "\n" + "-"*nbtirets
164 # Sorties eventuelles
165 # -------------------
166 logging.debug("%s Résultats :\n%s"%(self._name, msgs))
168 print "Results of adjoint stability check:"
171 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
172 logging.debug("%s Terminé"%self._name)
176 # ==============================================================================
177 if __name__ == "__main__":
178 print '\n AUTODIAGNOSTIC \n'