1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2016 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
27 # ==============================================================================
28 class ElementaryAlgorithm(BasicObjects.Algorithm):
30 BasicObjects.Algorithm.__init__(self, "ENSEMBLEBLUE")
31 self.defineRequiredParameter(
32 name = "StoreInternalVariables",
35 message = "Stockage des variables internes ou intermédiaires du calcul",
37 self.defineRequiredParameter(
38 name = "StoreSupplementaryCalculations",
41 message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
42 listval = ["CurrentState", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
44 self.defineRequiredParameter(
46 typecast = numpy.random.seed,
47 message = "Graine fixée pour le générateur aléatoire",
50 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
53 # Paramètres de pilotage
54 # ----------------------
55 self.setParameters(Parameters)
57 # Précalcul des inversions de B et R
58 # ----------------------------------
62 # Nombre d'ensemble pour l'ébauche
63 # --------------------------------
64 nb_ens = Xb.stepnumber()
66 # Construction de l'ensemble des observations, par génération a partir
67 # de la diagonale de R
68 # --------------------------------------------------------------------
69 DiagonaleR = R.diag(Y.size)
70 EnsembleY = numpy.zeros([Y.size,nb_ens])
71 for npar in range(DiagonaleR.size):
72 bruit = numpy.random.normal(0,DiagonaleR[npar],nb_ens)
73 EnsembleY[npar,:] = Y[npar] + bruit
74 EnsembleY = numpy.matrix(EnsembleY)
76 # Initialisation des opérateurs d'observation et de la matrice gain
77 # -----------------------------------------------------------------
78 Hm = HO["Tangent"].asMatrix(None)
79 Hm = Hm.reshape(Y.size,Xb[0].size) # ADAO & check shape
80 Ha = HO["Adjoint"].asMatrix(None)
81 Ha = Ha.reshape(Xb[0].size,Y.size) # ADAO & check shape
83 # Calcul de la matrice de gain dans l'espace le plus petit et de l'analyse
84 # ------------------------------------------------------------------------
85 if Y.size <= Xb[0].size:
86 K = B * Ha * (R + Hm * B * Ha).I
88 K = (BI + Ha * RI * Hm).I * Ha * RI
90 # Calcul du BLUE pour chaque membre de l'ensemble
91 # -----------------------------------------------
92 for iens in range(nb_ens):
94 if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
95 self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
96 d = EnsembleY[:,iens] - HXb
97 if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
98 self.StoredVariables["Innovation"].store( numpy.ravel(d) )
100 self.StoredVariables["CurrentState"].store( Xa )
101 if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
102 self.StoredVariables["SimulatedObservationAtCurrentState"].store( Hm * Xa )
104 # Fabrication de l'analyse
105 # ------------------------
106 Members = self.StoredVariables["CurrentState"][-nb_ens:]
107 Xa = numpy.matrix( Members ).mean(axis=0)
108 self.StoredVariables["Analysis"].store( Xa.A1 )
109 if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
110 self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel( Hm * Xa ) )
115 # ==============================================================================
116 if __name__ == "__main__":
117 print '\n AUTODIAGNOSTIC \n'