#-*-coding:iso-8859-1-*-
#
-# Copyright (C) 2008-2013 EDF R&D
+# Copyright (C) 2008-2014 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
import logging
from daCore import BasicObjects, PlatformInfo
m = PlatformInfo.SystemUsage()
-
import numpy
# ==============================================================================
#
dX0 = float(self._parameters["AmplitudeOfInitialDirection"]) * numpy.matrix( dX0 ).T
#
- # ----------
- if self._parameters["ResiduFormula"] is "ScalarProduct":
- __doc__ = """
+ # Entete des resultats
+ # --------------------
+ __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.
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__ = ""
#
if len(self._parameters["ResultTitle"]) > 0:
- msgs = " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
- msgs += " " + self._parameters["ResultTitle"] + "\n"
- msgs += " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
+ 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 += __doc__
+ 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
#
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
+ # -------------------
print
- print "Results of adjoint stability check:"
+ print "Results of adjoint check by \"%s\" formula:"%self._parameters["ResiduFormula"]
print msgs
#
+ logging.debug("%s Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint.: %i/%i/%i"%(self._name, HO["Direct"].nbcalls(0),HO["Tangent"].nbcalls(0),HO["Adjoint"].nbcalls(0)))
+ logging.debug("%s Nombre d'appels au cache d'opérateur d'observation direct/tangent/adjoint..: %i/%i/%i"%(self._name, HO["Direct"].nbcalls(3),HO["Tangent"].nbcalls(3),HO["Adjoint"].nbcalls(3)))
logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
logging.debug("%s Terminé"%self._name)
#