1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2012 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, "BLUE")
33 self.defineRequiredParameter(
34 name = "StoreSupplementaryCalculations",
37 message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
38 listval = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency"]
41 def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
42 logging.debug("%s Lancement"%self._name)
43 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
45 # Paramètres de pilotage
46 # ----------------------
47 self.setParameters(Parameters)
49 # Opérateur d'observation
50 # -----------------------
51 Hm = H["Tangent"].asMatrix(None)
52 Ha = H["Adjoint"].asMatrix(None)
54 # Utilisation éventuelle d'un vecteur H(Xb) précalculé
55 # ----------------------------------------------------
56 if H["AppliedToX"] is not None and H["AppliedToX"].has_key("HXb"):
57 HXb = H["AppliedToX"]["HXb"]
60 HXb = numpy.asmatrix(numpy.ravel( HXb )).T
62 # Précalcul des inversions de B et R
63 # ----------------------------------
66 elif self._parameters["B_scalar"] is not None:
67 BI = 1.0 / self._parameters["B_scalar"]
68 B = self._parameters["B_scalar"]
70 raise ValueError("Background error covariance matrix has to be properly defined!")
74 elif self._parameters["R_scalar"] is not None:
75 RI = 1.0 / self._parameters["R_scalar"]
77 raise ValueError("Observation error covariance matrix has to be properly defined!")
79 # Calcul de l'innovation
80 # ----------------------
81 if Y.size != HXb.size:
82 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))
83 if max(Y.shape) != max(HXb.shape):
84 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))
87 # Calcul de la matrice de gain et de l'analyse
88 # --------------------------------------------
90 if self._parameters["R_scalar"] is not None:
91 R = self._parameters["R_scalar"] * numpy.eye(len(Y), dtype=numpy.float)
92 K = B * Ha * (Hm * B * Ha + R).I
94 K = (Ha * RI * Hm + BI).I * Ha * RI
97 # Calcul de la fonction coût
98 # --------------------------
100 Jb = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
101 Jo = 0.5 * oma.T * RI * oma
102 J = float( Jb ) + float( Jo )
103 self.StoredVariables["Analysis"].store( Xa.A1 )
104 self.StoredVariables["CostFunctionJb"].store( Jb )
105 self.StoredVariables["CostFunctionJo"].store( Jo )
106 self.StoredVariables["CostFunctionJ" ].store( J )
108 # Calcul de la covariance d'analyse
109 # ---------------------------------
110 if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
112 if min(A.shape) != max(A.shape):
113 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)))
114 if (numpy.diag(A) < 0).any():
115 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,))
116 if logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug
118 L = numpy.linalg.cholesky( A )
120 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,))
121 self.StoredVariables["APosterioriCovariance"].store( A )
123 # Calculs et/ou stockages supplémentaires
124 # ---------------------------------------
125 if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
126 self.StoredVariables["Innovation"].store( numpy.ravel(d) )
127 if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
128 self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
129 if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
130 self.StoredVariables["OMA"].store( numpy.ravel(oma) )
131 if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
132 self.StoredVariables["OMB"].store( numpy.ravel(d) )
133 if "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"]:
136 elif self._parameters["R_scalar"] is not None:
137 TraceR = float(self._parameters["R_scalar"]*Y.size)
138 self.StoredVariables["SigmaObs2"].store( float( (d.T * (numpy.asmatrix(numpy.ravel(oma)).T)) ) / TraceR )
139 if "SigmaBck2" in self._parameters["StoreSupplementaryCalculations"]:
140 self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
141 if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
142 self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
144 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
145 logging.debug("%s Terminé"%self._name)
149 # ==============================================================================
150 if __name__ == "__main__":
151 print '\n AUTODIAGNOSTIC \n'