#-*-coding:iso-8859-1-*-
#
-# Copyright (C) 2008-2014 EDF R&D
+# Copyright (C) 2008-2017 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
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
import logging
from daCore import BasicObjects
typecast = float,
message = "Amplitude de la direction initiale de la dérivée directionnelle autour du point nominal",
)
+ self.defineRequiredParameter(
+ name = "AmplitudeOfTangentPerturbation",
+ default = 1.e-2,
+ typecast = float,
+ message = "Amplitude de la perturbation pour le calcul de la forme tangente",
+ minval = 1.e-10,
+ maxval = 1.,
+ )
self.defineRequiredParameter(
name = "SetSeed",
typecast = numpy.random.seed,
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"]
+ )
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()
FX = numpy.asmatrix(numpy.ravel( Hm( Xn ) )).T
NormeX = numpy.linalg.norm( Xn )
NormeFX = numpy.linalg.norm( FX )
+ if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn) )
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX) )
#
# Fabrication de la direction de l'incrément dX
# ----------------------------------------------
# Calcul du gradient au point courant X pour l'incrément dX
# ---------------------------------------------------------
if self._parameters["ResiduFormula"] in ["Taylor", "NominalTaylor", "NominalTaylorRMS"]:
- GradFxdX = Ht( (Xn, dX0) )
+ dX1 = float(self._parameters["AmplitudeOfTangentPerturbation"]) * dX0
+ GradFxdX = Ht( (Xn, dX1) )
GradFxdX = numpy.asmatrix(numpy.ravel( GradFxdX )).T
+ GradFxdX = float(1./self._parameters["AmplitudeOfTangentPerturbation"]) * GradFxdX
#
# Entete des resultats
# --------------------
dX = amplitude * dX0
#
if self._parameters["ResiduFormula"] == "CenteredDL":
+ if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn - dX) )
+ #
FX_plus_dX = numpy.asmatrix(numpy.ravel( Hm( Xn + dX ) )).T
FX_moins_dX = numpy.asmatrix(numpy.ravel( Hm( Xn - dX ) )).T
#
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_moins_dX) )
+ #
Residu = numpy.linalg.norm( FX_plus_dX + FX_moins_dX - 2 * FX ) / NormeFX
#
- self.StoredVariables["CostFunctionJ"].store( Residu )
+ self.StoredVariables["Residu"].store( Residu )
msg = " %2i %5.0e %9.3e %9.3e | %9.3e %4.0f"%(i,amplitude,NormeX,NormeFX,Residu,math.log10(max(1.e-99,Residu)))
msgs += "\n" + __marge + msg
#
if self._parameters["ResiduFormula"] == "Taylor":
+ if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
+ #
FX_plus_dX = numpy.asmatrix(numpy.ravel( Hm( Xn + dX ) )).T
#
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
+ #
Residu = numpy.linalg.norm( FX_plus_dX - FX - amplitude * GradFxdX ) / NormeFX
#
- self.StoredVariables["CostFunctionJ"].store( Residu )
+ self.StoredVariables["Residu"].store( Residu )
msg = " %2i %5.0e %9.3e %9.3e | %9.3e %4.0f"%(i,amplitude,NormeX,NormeFX,Residu,math.log10(max(1.e-99,Residu)))
msgs += "\n" + __marge + msg
#
if self._parameters["ResiduFormula"] == "NominalTaylor":
+ if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn - dX) )
+ self.StoredVariables["CurrentState"].store( numpy.ravel(dX) )
+ #
FX_plus_dX = numpy.asmatrix(numpy.ravel( Hm( Xn + dX ) )).T
FX_moins_dX = numpy.asmatrix(numpy.ravel( Hm( Xn - dX ) )).T
FdX = numpy.asmatrix(numpy.ravel( Hm( dX ) )).T
#
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_moins_dX) )
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FdX) )
+ #
Residu = max(
numpy.linalg.norm( FX_plus_dX - amplitude * FdX ) / NormeFX,
numpy.linalg.norm( FX_moins_dX + amplitude * FdX ) / NormeFX,
)
#
- self.StoredVariables["CostFunctionJ"].store( Residu )
+ self.StoredVariables["Residu"].store( Residu )
msg = " %2i %5.0e %9.3e %9.3e | %9.3e %5i %s"%(i,amplitude,NormeX,NormeFX,Residu,100.*abs(Residu-1.),"%")
msgs += "\n" + __marge + msg
#
if self._parameters["ResiduFormula"] == "NominalTaylorRMS":
+ if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn - dX) )
+ self.StoredVariables["CurrentState"].store( numpy.ravel(dX) )
+ #
FX_plus_dX = numpy.asmatrix(numpy.ravel( Hm( Xn + dX ) )).T
FX_moins_dX = numpy.asmatrix(numpy.ravel( Hm( Xn - dX ) )).T
FdX = numpy.asmatrix(numpy.ravel( Hm( dX ) )).T
#
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_moins_dX) )
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FdX) )
+ #
Residu = max(
RMS( FX, FX_plus_dX - amplitude * FdX ) / NormeFX,
RMS( FX, FX_moins_dX + amplitude * FdX ) / NormeFX,
)
#
- self.StoredVariables["CostFunctionJ"].store( Residu )
+ self.StoredVariables["Residu"].store( Residu )
msg = " %2i %5.0e %9.3e %9.3e | %9.3e %5i %s"%(i,amplitude,NormeX,NormeFX,Residu,100.*Residu,"%")
msgs += "\n" + __marge + msg
#
