-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2013 EDF R&D
+# Copyright (C) 2008-2022 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License.
#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+# Lesser General Public License for more details.
#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
+# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
#
-# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
-
-import logging
-from daCore import BasicObjects, PlatformInfo
-m = PlatformInfo.SystemUsage()
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
import numpy
+from daCore import BasicObjects, NumericObjects, PlatformInfo
+mpr = PlatformInfo.PlatformInfo().MachinePrecision()
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
name = "ResiduFormula",
default = "ScalarProduct",
typecast = str,
- message = "Formule de résidu utilisée",
+ message = "Formule de résidu utilisée",
listval = ["ScalarProduct"],
)
self.defineRequiredParameter(
name = "EpsilonMinimumExponent",
default = -8,
typecast = int,
- message = "Exposant minimal en puissance de 10 pour le multiplicateur d'incrément",
+ message = "Exposant minimal en puissance de 10 pour le multiplicateur d'incrément",
minval = -20,
maxval = 0,
)
name = "InitialDirection",
default = [],
typecast = list,
- message = "Direction initiale de la dérivée directionnelle autour du point nominal",
+ message = "Direction initiale de la dérivée directionnelle autour du point nominal",
)
self.defineRequiredParameter(
name = "AmplitudeOfInitialDirection",
default = 1.,
typecast = float,
- message = "Amplitude de la direction initiale de la dérivée directionnelle autour du point nominal",
+ message = "Amplitude de la direction initiale de la dérivée directionnelle autour du point nominal",
)
self.defineRequiredParameter(
name = "SetSeed",
typecast = numpy.random.seed,
- message = "Graine fixée pour le générateur aléatoire",
+ message = "Graine fixée pour le générateur aléatoire",
)
self.defineRequiredParameter(
name = "ResultTitle",
typecast = str,
message = "Titre du tableau et de la figure",
)
+ self.defineRequiredParameter(
+ name = "StoreSupplementaryCalculations",
+ default = [],
+ typecast = tuple,
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = [
+ "CurrentState",
+ "Residu",
+ "SimulatedObservationAtCurrentState",
+ ]
+ )
+ self.requireInputArguments(
+ mandatory= ("Xb", "HO" ),
+ optional = ("Y", ),
+ )
+ self.setAttributes(tags=(
+ "Checking",
+ ))
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")))
- #
- self.setParameters(Parameters)
+ self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
#
Hm = HO["Direct"].appliedTo
Ht = HO["Tangent"].appliedInXTo
Ha = HO["Adjoint"].appliedInXTo
#
- # ----------
- Perturbations = [ 10**i for i in xrange(self._parameters["EpsilonMinimumExponent"],1) ]
+ Perturbations = [ 10**i for i in range(self._parameters["EpsilonMinimumExponent"],1) ]
Perturbations.reverse()
#
- X = numpy.asmatrix(numpy.ravel( Xb )).T
- NormeX = numpy.linalg.norm( X )
+ Xn = numpy.ravel( Xb ).reshape((-1,1))
+ NormeX = numpy.linalg.norm( Xn )
if Y is None:
- Y = numpy.asmatrix(numpy.ravel( Hm( X ) )).T
- Y = numpy.asmatrix(numpy.ravel( Y )).T
- NormeY = numpy.linalg.norm( Y )
- #
- if len(self._parameters["InitialDirection"]) == 0:
- dX0 = []
- for v in X.A1:
- if abs(v) > 1.e-8:
- dX0.append( numpy.random.normal(0.,abs(v)) )
- else:
- dX0.append( numpy.random.normal(0.,X.mean()) )
+ Yn = numpy.ravel( Hm( Xn ) ).reshape((-1,1))
else:
- dX0 = numpy.asmatrix(numpy.ravel( self._parameters["InitialDirection"] ))
+ Yn = numpy.ravel( Y ).reshape((-1,1))
+ NormeY = numpy.linalg.norm( Yn )
+ if self._toStore("CurrentState"):
+ self.StoredVariables["CurrentState"].store( Xn )
+ if self._toStore("SimulatedObservationAtCurrentState"):
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( Yn )
#
- dX0 = float(self._parameters["AmplitudeOfInitialDirection"]) * numpy.matrix( dX0 ).T
+ dX0 = NumericObjects.SetInitialDirection(
+ self._parameters["InitialDirection"],
+ self._parameters["AmplitudeOfInitialDirection"],
+ Xn,
+ )
#
- # ----------
- if self._parameters["ResiduFormula"] is "ScalarProduct":
- __doc__ = """
+ # Entete des resultats
+ # --------------------
+ __marge = 12*u" "
+ __precision = u"""
+ Remarque : les nombres inferieurs a %.0e (environ) representent un zero
+ a la precision machine.\n"""%mpr
+ if self._parameters["ResiduFormula"] == "ScalarProduct":
+ __entete = u" i Alpha ||X|| ||Y|| ||dX|| R(Alpha)"
+ __msgdoc = u"""
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__ = ""
+ Y doit etre dans l'image de F. S'il n'est pas donne, on prend Y = F(X).\n""" + __precision
#
if len(self._parameters["ResultTitle"]) > 0:
- msgs = " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
- msgs += " " + self._parameters["ResultTitle"] + "\n"
- msgs += " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
+ __rt = str(self._parameters["ResultTitle"])
+ msgs = u"\n"
+ msgs += __marge + "====" + "="*len(__rt) + "====\n"
+ msgs += __marge + " " + __rt + "\n"
+ msgs += __marge + "====" + "="*len(__rt) + "====\n"
else:
- msgs = ""
- msgs += __doc__
+ msgs = u""
+ msgs += __msgdoc
#
- msg = " i Alpha ||X|| ||Y|| ||dX|| R(Alpha) "
- nbtirets = len(msg)
- msgs += "\n" + "-"*nbtirets
- msgs += "\n" + msg
- msgs += "\n" + "-"*nbtirets
- #
- Normalisation= -1
+ __nbtirets = len(__entete) + 2
+ msgs += "\n" + __marge + "-"*__nbtirets
+ msgs += "\n" + __marge + __entete
+ msgs += "\n" + __marge + "-"*__nbtirets
#
# ----------
for i,amplitude in enumerate(Perturbations):
dX = amplitude * dX0
NormedX = numpy.linalg.norm( dX )
#
- TangentFXdX = numpy.asmatrix( Ht( (X,dX) ) )
- AdjointFXY = numpy.asmatrix( Ha( (X,Y) ) )
+ TangentFXdX = numpy.ravel( Ht( (Xn,dX) ) )
+ AdjointFXY = numpy.ravel( Ha( (Xn,Yn) ) )
#
- Residu = abs(float(numpy.dot( TangentFXdX.A1 , Y.A1 ) - numpy.dot( dX.A1 , AdjointFXY.A1 )))
+ Residu = abs(float(numpy.dot( TangentFXdX, Yn ) - numpy.dot( dX, AdjointFXY )))
#
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"
+ self.StoredVariables["Residu"].store( Residu )
#
- # ----------
- print
- print "Results of adjoint stability check:"
- print msgs
+ msgs += "\n" + __marge + "-"*__nbtirets
+ msgs += "\n"
#
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- logging.debug("%s Terminé"%self._name)
+ # Sorties eventuelles
+ # -------------------
+ print("\nResults of adjoint check by \"%s\" formula:"%self._parameters["ResiduFormula"])
+ print(msgs)
#
+ self._post_run(HO)
return 0
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC\n')
