1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2014 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()
28 # ==============================================================================
29 class ElementaryAlgorithm(BasicObjects.Algorithm):
31 BasicObjects.Algorithm.__init__(self, "BLUE")
32 self.defineRequiredParameter(
33 name = "StoreInternalVariables",
36 message = "Stockage des variables internes ou intermédiaires du calcul",
38 self.defineRequiredParameter(
39 name = "StoreSupplementaryCalculations",
42 message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
43 listval = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles"]
45 self.defineRequiredParameter(
49 message = "Liste des valeurs de quantiles",
51 self.defineRequiredParameter(
53 typecast = numpy.random.seed,
54 message = "Graine fixée pour le générateur aléatoire",
56 self.defineRequiredParameter(
57 name = "NumberOfSamplesForQuantiles",
60 message = "Nombre d'échantillons simulés pour le calcul des quantiles",
63 self.defineRequiredParameter(
64 name = "SimulationForQuantiles",
67 message = "Type de simulation pour l'estimation des quantiles",
68 listval = ["Linear", "NonLinear"]
71 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
72 logging.debug("%s Lancement"%self._name)
73 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
75 # Paramètres de pilotage
76 # ----------------------
77 self.setParameters(Parameters)
79 # Opérateur d'observation
80 # -----------------------
81 Hm = HO["Tangent"].asMatrix(Xb)
82 Hm = Hm.reshape(Y.size,Xb.size) # ADAO & check shape
83 Ha = HO["Adjoint"].asMatrix(Xb)
84 Ha = Ha.reshape(Xb.size,Y.size) # ADAO & check shape
86 # Utilisation éventuelle d'un vecteur H(Xb) précalculé
87 # ----------------------------------------------------
88 if HO["AppliedToX"] is not None and HO["AppliedToX"].has_key("HXb"):
89 HXb = HO["AppliedToX"]["HXb"]
92 HXb = numpy.asmatrix(numpy.ravel( HXb )).T
94 # Précalcul des inversions de B et R
95 # ----------------------------------
99 # Calcul de l'innovation
100 # ----------------------
101 if Y.size != HXb.size:
102 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))
103 if max(Y.shape) != max(HXb.shape):
104 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))
107 # Calcul de la matrice de gain et de l'analyse
108 # --------------------------------------------
109 if Y.size <= Xb.size:
110 if Y.size > 100: # len(R)
112 _u = numpy.linalg.solve( _A , d )
113 Xa = Xb + B * Ha * _u
115 K = B * Ha * (R + Hm * B * Ha).I
118 if Y.size > 100: # len(R)
119 _A = BI + Ha * RI * Hm
120 _u = numpy.linalg.solve( _A , Ha * RI * d )
123 K = (BI + Ha * RI * Hm).I * Ha * RI
125 self.StoredVariables["Analysis"].store( Xa.A1 )
127 # Calcul de la fonction coût
128 # --------------------------
129 if self._parameters["StoreInternalVariables"] or \
130 "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
131 "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
132 "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"] or \
133 "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
136 if self._parameters["StoreInternalVariables"] or \
137 "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
138 Jb = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
139 Jo = 0.5 * oma.T * RI * oma
140 J = float( Jb ) + float( Jo )
141 self.StoredVariables["CostFunctionJb"].store( Jb )
142 self.StoredVariables["CostFunctionJo"].store( Jo )
143 self.StoredVariables["CostFunctionJ" ].store( J )
145 # Calcul de la covariance d'analyse
146 # ---------------------------------
147 if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"] or \
148 "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
150 if min(A.shape) != max(A.shape):
151 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)))
152 if (numpy.diag(A) < 0).any():
153 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,))
154 if logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug
156 L = numpy.linalg.cholesky( A )
158 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,))
159 self.StoredVariables["APosterioriCovariance"].store( A )
161 # Calculs et/ou stockages supplémentaires
162 # ---------------------------------------
163 if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
164 self.StoredVariables["Innovation"].store( numpy.ravel(d) )
165 if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
166 self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
167 if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
168 self.StoredVariables["OMA"].store( numpy.ravel(oma) )
169 if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
170 self.StoredVariables["OMB"].store( numpy.ravel(d) )
171 if "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"]:
172 TraceR = R.trace(Y.size)
173 self.StoredVariables["SigmaObs2"].store( float( (d.T * (numpy.asmatrix(numpy.ravel(oma)).T)) ) / TraceR )
174 if "SigmaBck2" in self._parameters["StoreSupplementaryCalculations"]:
175 self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
176 if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
177 self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
178 if "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
179 Qtls = self._parameters["Quantiles"]
180 nech = self._parameters["NumberOfSamplesForQuantiles"]
182 for i in range(nech):
183 if self._parameters["SimulationForQuantiles"] == "Linear":
184 dXr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A) - Xa.A1).T
185 dYr = numpy.matrix(numpy.ravel( Hm * dXr )).T
187 elif self._parameters["SimulationForQuantiles"] == "NonLinear":
188 Xr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A)).T
189 Yr = numpy.matrix(numpy.ravel( Hm * Xr )).T
193 YfQ = numpy.hstack((YfQ,Yr))
196 for quantile in Qtls:
197 if not (0. <= quantile <= 1.): continue
198 indice = int(nech * quantile - 1./nech)
199 if YQ is None: YQ = YfQ[:,indice]
200 else: YQ = numpy.hstack((YQ,YfQ[:,indice]))
201 self.StoredVariables["SimulationQuantiles"].store( YQ )
203 logging.debug("%s Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint.: %i/%i/%i"%(self._name, HO["Direct"].nbcalls(0),HO["Tangent"].nbcalls(0),HO["Adjoint"].nbcalls(0)))
204 logging.debug("%s Nombre d'appels au cache d'opérateur d'observation direct/tangent/adjoint..: %i/%i/%i"%(self._name, HO["Direct"].nbcalls(3),HO["Tangent"].nbcalls(3),HO["Adjoint"].nbcalls(3)))
205 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
206 logging.debug("%s Terminé"%self._name)
210 # ==============================================================================
211 if __name__ == "__main__":
212 print '\n AUTODIAGNOSTIC \n'