1 # -*- coding: utf-8 -*-
3 # Copyright (C) 2008-2021 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"),
59 self.setAttributes(tags=(
66 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
67 self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
69 # Précalcul des inversions de B et R
70 # ----------------------------------
74 # Nombre d'ensemble pour l'ébauche
75 # --------------------------------
76 nb_ens = Xb.stepnumber()
78 # Construction de l'ensemble des observations, par génération a partir
79 # de la diagonale de R
80 # --------------------------------------------------------------------
81 DiagonaleR = R.diag(Y.size)
82 EnsembleY = numpy.zeros([Y.size,nb_ens])
83 for npar in range(DiagonaleR.size):
84 bruit = numpy.random.normal(0,DiagonaleR[npar],nb_ens)
85 EnsembleY[npar,:] = Y[npar] + bruit
87 # Initialisation des opérateurs d'observation et de la matrice gain
88 # -----------------------------------------------------------------
89 Hm = HO["Tangent"].asMatrix(None)
90 Hm = Hm.reshape(Y.size,Xb[0].size) # ADAO & check shape
91 Ha = HO["Adjoint"].asMatrix(None)
92 Ha = Ha.reshape(Xb[0].size,Y.size) # ADAO & check shape
94 # Calcul de la matrice de gain dans l'espace le plus petit et de l'analyse
95 # ------------------------------------------------------------------------
96 if Y.size <= Xb[0].size:
97 K = B * Ha * (R + Hm * B * Ha).I
99 K = (BI + Ha * RI * Hm).I * Ha * RI
101 # Calcul du BLUE pour chaque membre de l'ensemble
102 # -----------------------------------------------
103 for iens in range(nb_ens):
104 HXb = numpy.ravel(numpy.dot(Hm, Xb[iens]))
105 if self._toStore("SimulatedObservationAtBackground"):
106 self.StoredVariables["SimulatedObservationAtBackground"].store( HXb )
107 d = numpy.ravel(EnsembleY[:,iens]) - HXb
108 if self._toStore("Innovation"):
109 self.StoredVariables["Innovation"].store( d )
110 Xa = numpy.ravel(Xb[iens]) + numpy.dot(K, d)
111 self.StoredVariables["CurrentState"].store( Xa )
112 if self._toStore("SimulatedObservationAtCurrentState"):
113 self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.dot(Hm, Xa) )
115 # Fabrication de l'analyse
116 # ------------------------
117 Members = self.StoredVariables["CurrentState"][-nb_ens:]
118 Xa = numpy.array( Members ).mean(axis=0)
119 self.StoredVariables["Analysis"].store( Xa )
120 if self._toStore("SimulatedObservationAtOptimum"):
121 self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.dot(Hm, Xa) )
126 # ==============================================================================
127 if __name__ == "__main__":
128 print('\n AUTODIAGNOSTIC\n')