1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2013 EDF R&D
5 # This library is free software; you can redistribute it and/or
6 # modify it under the terms of the GNU Lesser General Public
7 # License as published by the Free Software Foundation; either
8 # version 2.1 of the License.
10 # This library is distributed in the hope that it will be useful,
11 # but WITHOUT ANY WARRANTY; without even the implied warranty of
12 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
13 # Lesser General Public License for more details.
15 # You should have received a copy of the GNU Lesser General Public
16 # License along with this library; if not, write to the Free Software
17 # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
19 # See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
21 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
24 from daCore import BasicObjects, PlatformInfo
25 m = PlatformInfo.SystemUsage()
29 # ==============================================================================
30 class ElementaryAlgorithm(BasicObjects.Algorithm):
32 BasicObjects.Algorithm.__init__(self, "ENSEMBLEBLUE")
33 self.defineRequiredParameter(
35 typecast = numpy.random.seed,
36 message = "Graine fixée pour le générateur aléatoire",
39 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
40 logging.debug("%s Lancement"%self._name)
41 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
43 # Paramètres de pilotage
44 # ----------------------
45 self.setParameters(Parameters)
47 # Précalcul des inversions de B et R
48 # ----------------------------------
52 # Nombre d'ensemble pour l'ébauche
53 # --------------------------------
54 nb_ens = Xb.stepnumber()
56 # Construction de l'ensemble des observations, par génération a partir
57 # de la diagonale de R
58 # --------------------------------------------------------------------
59 DiagonaleR = R.diag(Y.size)
60 EnsembleY = numpy.zeros([Y.size,nb_ens])
61 for npar in range(DiagonaleR.size):
62 bruit = numpy.random.normal(0,DiagonaleR[npar],nb_ens)
63 EnsembleY[npar,:] = Y[npar] + bruit
64 EnsembleY = numpy.matrix(EnsembleY)
66 # Initialisation des opérateurs d'observation et de la matrice gain
67 # -----------------------------------------------------------------
68 Hm = HO["Tangent"].asMatrix(None)
69 Hm = Hm.reshape(Y.size,Xb[0].size) # ADAO & check shape
70 Ha = HO["Adjoint"].asMatrix(None)
71 Ha = Ha.reshape(Xb[0].size,Y.size) # ADAO & check shape
73 # Calcul de la matrice de gain dans l'espace le plus petit et de l'analyse
74 # ------------------------------------------------------------------------
75 if Y.size <= Xb[0].size:
76 K = B * Ha * (R + Hm * B * Ha).I
78 K = (BI + Ha * RI * Hm).I * Ha * RI
80 # Calcul du BLUE pour chaque membre de l'ensemble
81 # -----------------------------------------------
82 for iens in range(nb_ens):
83 d = EnsembleY[:,iens] - Hm * Xb[iens]
86 self.StoredVariables["CurrentState"].store( Xa.A1 )
87 self.StoredVariables["Innovation"].store( d.A1 )
89 # Fabrication de l'analyse
90 # ------------------------
91 Members = self.StoredVariables["CurrentState"][-nb_ens:]
92 Xa = numpy.matrix( Members ).mean(axis=0)
93 self.StoredVariables["Analysis"].store( Xa.A1 )
95 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
96 logging.debug("%s Terminé"%self._name)
99 # ==============================================================================
100 if __name__ == "__main__":
101 print '\n AUTODIAGNOSTIC \n'