#-*-coding:iso-8859-1-*-
#
-# Copyright (C) 2008-2012 EDF R&D
+# Copyright (C) 2008-2015 EDF R&D
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
import logging
-from daCore import BasicObjects, PlatformInfo
-m = PlatformInfo.SystemUsage()
-
+from daCore import BasicObjects
import numpy
# ==============================================================================
message = "Titre du tableau et de la figure",
)
- def run(self, Xb=None, Y=None, H=None, M=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")))
+ def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
+ self._pre_run()
#
- # Paramètres de pilotage
- # ----------------------
self.setParameters(Parameters)
#
- # Opérateur d'observation
- # -----------------------
- Hm = H["Direct"].appliedTo
- Ht = H["Tangent"].appliedInXTo
- Ha = H["Adjoint"].appliedInXTo
+ 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(Xb).flatten().T
+ X = numpy.asmatrix(numpy.ravel( Xb )).T
NormeX = numpy.linalg.norm( X )
if Y is None:
- Y = numpy.asmatrix( Hm( X ) ).flatten().T
- Y = numpy.asmatrix(Y).flatten().T
+ Y = numpy.asmatrix(numpy.ravel( Hm( X ) )).T
+ 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:
else:
dX0.append( numpy.random.normal(0.,X.mean()) )
else:
- dX0 = numpy.asmatrix(self._parameters["InitialDirection"]).flatten()
+ dX0 = numpy.asmatrix(numpy.ravel( self._parameters["InitialDirection"] ))
#
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__ = """
+ __marge = 12*" "
+ if self._parameters["ResiduFormula"] == "ScalarProduct":
+ __entete = " i Alpha ||X|| ||Y|| ||dX|| R(Alpha) "
+ __msgdoc = """
On observe le residu qui est la difference de deux produits scalaires :
-
+
R(Alpha) = | < TangentF_X(dX) , Y > - < dX , AdjointF_X(Y) > |
-
- qui doit rester constamment egal zero a la precision du calcul.
+
+ qui doit rester constamment egal a zero a la precision du calcul.
On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
Y doit etre dans l'image de F. S'il n'est pas donne, on prend Y = F(X).
"""
- else:
- __doc__ = ""
#
- msgs = " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
- msgs += " " + self._parameters["ResultTitle"] + "\n"
- msgs += " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
- msgs += __doc__
+ if len(self._parameters["ResultTitle"]) > 0:
+ msgs = "\n"
+ msgs += __marge + "====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
+ msgs += __marge + " " + self._parameters["ResultTitle"] + "\n"
+ msgs += __marge + "====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
+ else:
+ msgs = ""
+ msgs += __msgdoc
#
- msg = " i Alpha ||X|| ||Y|| ||dX|| R(Alpha) "
- nbtirets = len(msg)
- msgs += "\n" + "-"*nbtirets
- msgs += "\n" + msg
- msgs += "\n" + "-"*nbtirets
+ __nbtirets = len(__entete)
+ msgs += "\n" + __marge + "-"*__nbtirets
+ msgs += "\n" + __marge + __entete
+ msgs += "\n" + __marge + "-"*__nbtirets
#
Normalisation= -1
#
- # Boucle sur les perturbations
- # ----------------------------
+ # ----------
for i,amplitude in enumerate(Perturbations):
- logging.debug("%s Etape de calcul numéro %i, avec la perturbation %8.3e"%(self._name, i, amplitude))
- #
dX = amplitude * dX0
NormedX = numpy.linalg.norm( dX )
#
Residu = abs(float(numpy.dot( TangentFXdX.A1 , Y.A1 ) - numpy.dot( dX.A1 , AdjointFXY.A1 )))
#
msg = " %2i %5.0e %9.3e %9.3e %9.3e | %9.3e"%(i,amplitude,NormeX,NormeY,NormedX,Residu)
- msgs += "\n" + msg
+ msgs += "\n" + __marge + msg
#
self.StoredVariables["CostFunctionJ"].store( Residu )
- msgs += "\n" + "-"*nbtirets
+ #
+ msgs += "\n" + __marge + "-"*__nbtirets
msgs += "\n"
#
# Sorties eventuelles
# -------------------
- logging.debug("%s Résultats :\n%s"%(self._name, msgs))
print
- print "Results of adjoint stability check:"
+ print "Results of adjoint check by \"%s\" formula:"%self._parameters["ResiduFormula"]
print msgs
#
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- logging.debug("%s Terminé"%self._name)
- #
+ self._post_run(HO)
return 0
# ==============================================================================
