1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2017 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):
51 self._pre_run(Parameters)
53 # Précalcul des inversions de B et R
54 # ----------------------------------
58 # Nombre d'ensemble pour l'ébauche
59 # --------------------------------
60 nb_ens = Xb.stepnumber()
62 # Construction de l'ensemble des observations, par génération a partir
63 # de la diagonale de R
64 # --------------------------------------------------------------------
65 DiagonaleR = R.diag(Y.size)
66 EnsembleY = numpy.zeros([Y.size,nb_ens])
67 for npar in range(DiagonaleR.size):
68 bruit = numpy.random.normal(0,DiagonaleR[npar],nb_ens)
69 EnsembleY[npar,:] = Y[npar] + bruit
70 EnsembleY = numpy.matrix(EnsembleY)
72 # Initialisation des opérateurs d'observation et de la matrice gain
73 # -----------------------------------------------------------------
74 Hm = HO["Tangent"].asMatrix(None)
75 Hm = Hm.reshape(Y.size,Xb[0].size) # ADAO & check shape
76 Ha = HO["Adjoint"].asMatrix(None)
77 Ha = Ha.reshape(Xb[0].size,Y.size) # ADAO & check shape
79 # Calcul de la matrice de gain dans l'espace le plus petit et de l'analyse
80 # ------------------------------------------------------------------------
81 if Y.size <= Xb[0].size:
82 K = B * Ha * (R + Hm * B * Ha).I
84 K = (BI + Ha * RI * Hm).I * Ha * RI
86 # Calcul du BLUE pour chaque membre de l'ensemble
87 # -----------------------------------------------
88 for iens in range(nb_ens):
90 if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
91 self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
92 d = EnsembleY[:,iens] - HXb
93 if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
94 self.StoredVariables["Innovation"].store( numpy.ravel(d) )
96 self.StoredVariables["CurrentState"].store( Xa )
97 if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
98 self.StoredVariables["SimulatedObservationAtCurrentState"].store( Hm * Xa )
100 # Fabrication de l'analyse
101 # ------------------------
102 Members = self.StoredVariables["CurrentState"][-nb_ens:]
103 Xa = numpy.matrix( Members ).mean(axis=0)
104 self.StoredVariables["Analysis"].store( Xa.A1 )
105 if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
106 self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel( Hm * Xa ) )
111 # ==============================================================================
112 if __name__ == "__main__":
113 print '\n AUTODIAGNOSTIC \n'