1 # -*- coding: utf-8 -*-
3 # Copyright (C) 2008-2020 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",
46 "SimulatedObservationAtBackground",
47 "SimulatedObservationAtCurrentState",
48 "SimulatedObservationAtOptimum",
51 self.defineRequiredParameter(
53 typecast = numpy.random.seed,
54 message = "Graine fixée pour le générateur aléatoire",
56 self.requireInputArguments(
57 mandatory= ("Xb", "Y", "HO", "R", "B"),
60 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
61 self._pre_run(Parameters, Xb, Y, R, B, Q)
63 # Précalcul des inversions de B et R
64 # ----------------------------------
68 # Nombre d'ensemble pour l'ébauche
69 # --------------------------------
70 nb_ens = Xb.stepnumber()
72 # Construction de l'ensemble des observations, par génération a partir
73 # de la diagonale de R
74 # --------------------------------------------------------------------
75 DiagonaleR = R.diag(Y.size)
76 EnsembleY = numpy.zeros([Y.size,nb_ens])
77 for npar in range(DiagonaleR.size):
78 bruit = numpy.random.normal(0,DiagonaleR[npar],nb_ens)
79 EnsembleY[npar,:] = Y[npar] + bruit
81 # Initialisation des opérateurs d'observation et de la matrice gain
82 # -----------------------------------------------------------------
83 Hm = HO["Tangent"].asMatrix(None)
84 Hm = Hm.reshape(Y.size,Xb[0].size) # ADAO & check shape
85 Ha = HO["Adjoint"].asMatrix(None)
86 Ha = Ha.reshape(Xb[0].size,Y.size) # ADAO & check shape
88 # Calcul de la matrice de gain dans l'espace le plus petit et de l'analyse
89 # ------------------------------------------------------------------------
90 if Y.size <= Xb[0].size:
91 K = B * Ha * (R + Hm * B * Ha).I
93 K = (BI + Ha * RI * Hm).I * Ha * RI
95 # Calcul du BLUE pour chaque membre de l'ensemble
96 # -----------------------------------------------
97 for iens in range(nb_ens):
98 HXb = numpy.ravel(numpy.dot(Hm, Xb[iens]))
99 if self._toStore("SimulatedObservationAtBackground"):
100 self.StoredVariables["SimulatedObservationAtBackground"].store( HXb )
101 d = numpy.ravel(EnsembleY[:,iens]) - HXb
102 if self._toStore("Innovation"):
103 self.StoredVariables["Innovation"].store( d )
104 Xa = numpy.ravel(Xb[iens]) + numpy.dot(K, d)
105 self.StoredVariables["CurrentState"].store( Xa )
106 if self._toStore("SimulatedObservationAtCurrentState"):
107 self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.dot(Hm, Xa) )
109 # Fabrication de l'analyse
110 # ------------------------
111 Members = self.StoredVariables["CurrentState"][-nb_ens:]
112 Xa = numpy.array( Members ).mean(axis=0)
113 self.StoredVariables["Analysis"].store( Xa )
114 if self._toStore("SimulatedObservationAtOptimum"):
115 self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.dot(Hm, Xa) )
120 # ==============================================================================
121 if __name__ == "__main__":
122 print('\n AUTODIAGNOSTIC\n')