1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2009 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
22 Algorithme de Kalman simple (BLUE)
24 __author__ = "Jean-Philippe ARGAUD - Mars 2008"
26 import sys ; sys.path.insert(0, "../daCore")
29 from BasicObjects import Algorithm
30 import PlatformInfo ; m = PlatformInfo.SystemUsage()
32 # ==============================================================================
33 class ElementaryAlgorithm(Algorithm):
35 Algorithm.__init__(self)
37 logging.debug("%s Initialisation"%self._name)
39 def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Par=None):
41 Calcul de l'estimateur BLUE (ou Kalman simple, ou Interpolation Optimale)
43 logging.debug("%s Lancement"%self._name)
44 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
46 Hm = H["Direct"].asMatrix()
47 Ht = H["Adjoint"].asMatrix()
49 # Utilisation éventuelle d'un vecteur H(Xb) précalculé
50 # ----------------------------------------------------
51 if H["AppliedToX"] is not None and H["AppliedToX"].has_key("HXb"):
52 logging.debug("%s Utilisation de HXb"%self._name)
53 HXb = H["AppliedToX"]["HXb"]
55 logging.debug("%s Calcul de Hm * Xb"%self._name)
58 # Calcul de la matrice de gain dans l'espace le plus petit
60 logging.debug("%s Calcul de K dans l'espace des observations"%self._name)
61 K = B * Ht * (Hm * B * Ht + R).I
63 logging.debug("%s Calcul de K dans l'espace d'ébauche"%self._name)
64 K = (Ht * R.I * Hm + B.I).I * Ht * R.I
66 # Calcul de l'innovation et de l'analyse
67 # --------------------------------------
69 logging.debug("%s Innovation d = %s"%(self._name, d))
71 logging.debug("%s Analyse Xa = %s"%(self._name, Xa))
73 self.StoredVariables["Analysis"].store( Xa.A1 )
74 self.StoredVariables["Innovation"].store( d.A1 )
76 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("MB")))
77 logging.debug("%s Terminé"%self._name)
81 # ==============================================================================
82 if __name__ == "__main__":
83 print '\n AUTODIAGNOSTIC \n'