1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2013 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, PlatformInfo
25 m = PlatformInfo.SystemUsage()
29 # ==============================================================================
30 class ElementaryAlgorithm(BasicObjects.Algorithm):
32 BasicObjects.Algorithm.__init__(self, "EXTENDEDBLUE")
33 self.defineRequiredParameter(
34 name = "StoreInternalVariables",
37 message = "Stockage des variables internes ou intermédiaires du calcul",
39 self.defineRequiredParameter(
40 name = "StoreSupplementaryCalculations",
43 message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
44 listval = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency"]
47 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
48 logging.debug("%s Lancement"%self._name)
49 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
51 # Paramètres de pilotage
52 # ----------------------
53 self.setParameters(Parameters)
55 # Opérateur d'observation
56 # -----------------------
57 H = HO["Direct"].appliedTo
58 Hm = HO["Tangent"].asMatrix(Xb)
59 Hm = Hm.reshape(Y.size,Xb.size) # ADAO & check shape
60 Ha = HO["Adjoint"].asMatrix(Xb)
61 Ha = Ha.reshape(Xb.size,Y.size) # ADAO & check shape
63 # Utilisation éventuelle d'un vecteur H(Xb) précalculé
64 # ----------------------------------------------------
65 if HO["AppliedToX"] is not None and HO["AppliedToX"].has_key("HXb"):
66 HXb = HO["AppliedToX"]["HXb"]
69 HXb = numpy.asmatrix(numpy.ravel( HXb )).T
71 # Précalcul des inversions de B et R
72 # ----------------------------------
76 # Calcul de l'innovation
77 # ----------------------
78 if Y.size != HXb.size:
79 raise ValueError("The size %i of observations Y and %i of observed calculation H(X) are different, they have to be identical."%(Y.size,HXb.size))
80 if max(Y.shape) != max(HXb.shape):
81 raise ValueError("The shapes %s of observations Y and %s of observed calculation H(X) are different, they have to be identical."%(Y.shape,HXb.shape))
84 # Calcul de la matrice de gain et de l'analyse
85 # --------------------------------------------
87 if Y.size > 100: # len(R)
89 _u = numpy.linalg.solve( _A , d )
92 K = B * Ha * (R + Hm * B * Ha).I
95 if Y.size > 100: # len(R)
96 _A = BI + Ha * RI * Hm
97 _u = numpy.linalg.solve( _A , Ha * RI * d )
100 K = (BI + Ha * RI * Hm).I * Ha * RI
102 self.StoredVariables["Analysis"].store( Xa.A1 )
104 # Calcul de la fonction coût
105 # --------------------------
106 if self._parameters["StoreInternalVariables"] or "OMA" in self._parameters["StoreSupplementaryCalculations"] or "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
108 if self._parameters["StoreInternalVariables"] or "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
109 Jb = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
110 Jo = 0.5 * oma.T * RI * oma
111 J = float( Jb ) + float( Jo )
112 self.StoredVariables["CostFunctionJb"].store( Jb )
113 self.StoredVariables["CostFunctionJo"].store( Jo )
114 self.StoredVariables["CostFunctionJ" ].store( J )
116 # Calcul de la covariance d'analyse
117 # ---------------------------------
118 if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
120 if min(A.shape) != max(A.shape):
121 raise ValueError("The %s a posteriori covariance matrix A is of shape %s, despites it has to be a squared matrix. There is an error in the observation operator, please check it."%(self._name,str(A.shape)))
122 if (numpy.diag(A) < 0).any():
123 raise ValueError("The %s a posteriori covariance matrix A has at least one negative value on its diagonal. There is an error in the observation operator, please check it."%(self._name,))
124 if logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug
126 L = numpy.linalg.cholesky( A )
128 raise ValueError("The %s a posteriori covariance matrix A is not symmetric positive-definite. Please check your a priori covariances and your observation operator."%(self._name,))
129 self.StoredVariables["APosterioriCovariance"].store( A )
131 # Calculs et/ou stockages supplémentaires
132 # ---------------------------------------
133 if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
134 self.StoredVariables["Innovation"].store( numpy.ravel(d) )
135 if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
136 self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
137 if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
138 self.StoredVariables["OMA"].store( numpy.ravel(oma) )
139 if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
140 self.StoredVariables["OMB"].store( numpy.ravel(d) )
141 if "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"]:
142 TraceR = R.trace(Y.size)
143 self.StoredVariables["SigmaObs2"].store( float( (d.T * (numpy.asmatrix(numpy.ravel(oma)).T)) ) / TraceR )
144 if "SigmaBck2" in self._parameters["StoreSupplementaryCalculations"]:
145 self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
146 if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
147 self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
149 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
150 logging.debug("%s Terminé"%self._name)
154 # ==============================================================================
155 if __name__ == "__main__":
156 print '\n AUTODIAGNOSTIC \n'