-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2012 EDF R&D
+# Copyright (C) 2008-2020 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()
-
+from daCore import BasicObjects
import numpy
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
- BasicObjects.Algorithm.__init__(self)
- self._name = "ENSEMBLEBLUE"
- logging.debug("%s Initialisation"%self._name)
+ BasicObjects.Algorithm.__init__(self, "ENSEMBLEBLUE")
+ self.defineRequiredParameter(
+ name = "StoreInternalVariables",
+ default = False,
+ typecast = bool,
+ message = "Stockage des variables internes ou intermédiaires du calcul",
+ )
+ self.defineRequiredParameter(
+ name = "StoreSupplementaryCalculations",
+ default = [],
+ typecast = tuple,
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = [
+ "Analysis",
+ "CurrentState",
+ "Innovation",
+ "SimulatedObservationAtBackground",
+ "SimulatedObservationAtCurrentState",
+ "SimulatedObservationAtOptimum",
+ ]
+ )
+ self.defineRequiredParameter(
+ name = "SetSeed",
+ typecast = numpy.random.seed,
+ message = "Graine fixée pour le générateur aléatoire",
+ )
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R", "B"),
+ )
- def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None ):
- """
- Calcul d'une estimation BLUE d'ensemble :
- - génération d'un ensemble d'observations, de même taille que le
- nombre d'ébauches
- - calcul de l'estimateur BLUE pour chaque membre de l'ensemble
- """
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
+ 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(Parameters, Xb, Y, R, B, Q)
+ #
+ # Précalcul des inversions de B et R
+ # ----------------------------------
+ BI = B.getI()
+ RI = R.getI()
#
- # Nombre d'ensemble pour l'ébauche
+ # Nombre d'ensemble pour l'ébauche
# --------------------------------
nb_ens = Xb.stepnumber()
#
- # Construction de l'ensemble des observations, par génération a partir
+ # Construction de l'ensemble des observations, par génération a partir
# de la diagonale de R
# --------------------------------------------------------------------
- DiagonaleR = numpy.diag(R)
- EnsembleY = numpy.zeros([len(Y),nb_ens])
- for npar in range(len(DiagonaleR)) :
+ DiagonaleR = R.diag(Y.size)
+ EnsembleY = numpy.zeros([Y.size,nb_ens])
+ for npar in range(DiagonaleR.size):
bruit = numpy.random.normal(0,DiagonaleR[npar],nb_ens)
EnsembleY[npar,:] = Y[npar] + bruit
- EnsembleY = numpy.matrix(EnsembleY)
#
- # Initialisation des opérateurs d'observation et de la matrice gain
+ # Initialisation des opérateurs d'observation et de la matrice gain
# -----------------------------------------------------------------
- Hm = H["Direct"].asMatrix()
- Ht = H["Adjoint"].asMatrix()
+ Hm = HO["Tangent"].asMatrix(None)
+ Hm = Hm.reshape(Y.size,Xb[0].size) # ADAO & check shape
+ Ha = HO["Adjoint"].asMatrix(None)
+ Ha = Ha.reshape(Xb[0].size,Y.size) # ADAO & check shape
#
- K = B * Ht * (Hm * B * Ht + R).I
+ # Calcul de la matrice de gain dans l'espace le plus petit et de l'analyse
+ # ------------------------------------------------------------------------
+ if Y.size <= Xb[0].size:
+ K = B * Ha * (R + Hm * B * Ha).I
+ else:
+ K = (BI + Ha * RI * Hm).I * Ha * RI
#
# Calcul du BLUE pour chaque membre de l'ensemble
# -----------------------------------------------
for iens in range(nb_ens):
- d = EnsembleY[:,iens] - Hm * Xb.valueserie(iens)
- Xa = Xb.valueserie(iens) + K*d
-
- self.StoredVariables["Analysis"].store( Xa.A1 )
- self.StoredVariables["Innovation"].store( d.A1 )
+ HXb = numpy.ravel(numpy.dot(Hm, Xb[iens]))
+ if self._toStore("SimulatedObservationAtBackground"):
+ self.StoredVariables["SimulatedObservationAtBackground"].store( HXb )
+ d = numpy.ravel(EnsembleY[:,iens]) - HXb
+ if self._toStore("Innovation"):
+ self.StoredVariables["Innovation"].store( d )
+ Xa = numpy.ravel(Xb[iens]) + numpy.dot(K, d)
+ self.StoredVariables["CurrentState"].store( Xa )
+ if self._toStore("SimulatedObservationAtCurrentState"):
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.dot(Hm, Xa) )
#
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
- logging.debug("%s Terminé"%self._name)
+ # Fabrication de l'analyse
+ # ------------------------
+ Members = self.StoredVariables["CurrentState"][-nb_ens:]
+ Xa = numpy.array( Members ).mean(axis=0)
+ self.StoredVariables["Analysis"].store( Xa )
+ if self._toStore("SimulatedObservationAtOptimum"):
+ self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.dot(Hm, Xa) )
+ #
+ self._post_run(HO)
return 0
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
-
+ print('\n AUTODIAGNOSTIC\n')
