1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2017 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, "EXTENDEDBLUE")
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",
42 listval = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
44 self.defineRequiredParameter(
48 message = "Liste des valeurs de quantiles",
52 self.defineRequiredParameter(
54 typecast = numpy.random.seed,
55 message = "Graine fixée pour le générateur aléatoire",
57 self.defineRequiredParameter(
58 name = "NumberOfSamplesForQuantiles",
61 message = "Nombre d'échantillons simulés pour le calcul des quantiles",
64 self.defineRequiredParameter(
65 name = "SimulationForQuantiles",
68 message = "Type de simulation pour l'estimation des quantiles",
69 listval = ["Linear", "NonLinear"]
72 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
73 self._pre_run(Parameters)
75 Hm = HO["Tangent"].asMatrix(Xb)
76 Hm = Hm.reshape(Y.size,Xb.size) # ADAO & check shape
77 Ha = HO["Adjoint"].asMatrix(Xb)
78 Ha = Ha.reshape(Xb.size,Y.size) # ADAO & check shape
79 H = HO["Direct"].appliedTo
81 # Utilisation éventuelle d'un vecteur H(Xb) précalculé
82 # ----------------------------------------------------
83 if HO["AppliedToX"] is not None and "HXb" in HO["AppliedToX"]:
84 HXb = H( Xb, HO["AppliedToX"]["HXb"])
87 HXb = numpy.asmatrix(numpy.ravel( HXb )).T
89 # Précalcul des inversions de B et R
90 # ----------------------------------
94 # Calcul de l'innovation
95 # ----------------------
96 if Y.size != HXb.size:
97 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))
98 if max(Y.shape) != max(HXb.shape):
99 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))
102 # Calcul de la matrice de gain et de l'analyse
103 # --------------------------------------------
104 if Y.size <= Xb.size:
106 _u = numpy.linalg.solve( _A , d )
107 Xa = Xb + B * Ha * _u
109 _A = BI + Ha * RI * Hm
110 _u = numpy.linalg.solve( _A , Ha * RI * d )
112 self.StoredVariables["Analysis"].store( Xa.A1 )
114 # Calcul de la fonction coût
115 # --------------------------
116 if self._parameters["StoreInternalVariables"] or \
117 "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] or \
118 "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
119 "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
120 "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"] or \
121 "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
122 "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
123 "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
124 HXa = numpy.matrix(numpy.ravel( H( Xa ) )).T
126 if self._parameters["StoreInternalVariables"] or \
127 "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] or \
128 "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
129 Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
130 Jo = float( 0.5 * oma.T * RI * oma )
132 self.StoredVariables["CostFunctionJb"].store( Jb )
133 self.StoredVariables["CostFunctionJo"].store( Jo )
134 self.StoredVariables["CostFunctionJ" ].store( J )
136 # Calcul de la covariance d'analyse
137 # ---------------------------------
138 if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"] or \
139 "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
140 if (Y.size <= Xb.size): K = B * Ha * (R + Hm * B * Ha).I
141 elif (Y.size > Xb.size): K = (BI + Ha * RI * Hm).I * Ha * RI
143 if min(A.shape) != max(A.shape):
144 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)))
145 if (numpy.diag(A) < 0).any():
146 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,))
147 if logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug
149 L = numpy.linalg.cholesky( A )
151 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,))
152 self.StoredVariables["APosterioriCovariance"].store( A )
154 # Calculs et/ou stockages supplémentaires
155 # ---------------------------------------
156 if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
157 self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
158 if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
159 self.StoredVariables["Innovation"].store( numpy.ravel(d) )
160 if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
161 self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
162 if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
163 self.StoredVariables["OMA"].store( numpy.ravel(oma) )
164 if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
165 self.StoredVariables["OMB"].store( numpy.ravel(d) )
166 if "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"]:
167 TraceR = R.trace(Y.size)
168 self.StoredVariables["SigmaObs2"].store( float( (d.T * (numpy.asmatrix(numpy.ravel(oma)).T)) ) / TraceR )
169 if "SigmaBck2" in self._parameters["StoreSupplementaryCalculations"]:
170 self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
171 if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
172 self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
173 if "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
174 Qtls = map(float, self._parameters["Quantiles"])
175 nech = self._parameters["NumberOfSamplesForQuantiles"]
176 HtM = HO["Tangent"].asMatrix(ValueForMethodForm = Xa)
177 HtM = HtM.reshape(Y.size,Xa.size) # ADAO & check shape
179 for i in range(nech):
180 if self._parameters["SimulationForQuantiles"] == "Linear":
181 dXr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A) - Xa.A1).T
182 dYr = numpy.matrix(numpy.ravel( HtM * dXr )).T
184 elif self._parameters["SimulationForQuantiles"] == "NonLinear":
185 Xr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A)).T
186 Yr = numpy.matrix(numpy.ravel( H( Xr ) )).T
190 YfQ = numpy.hstack((YfQ,Yr))
193 for quantile in Qtls:
194 if not (0. <= quantile <= 1.): continue
195 indice = int(nech * quantile - 1./nech)
196 if YQ is None: YQ = YfQ[:,indice]
197 else: YQ = numpy.hstack((YQ,YfQ[:,indice]))
198 self.StoredVariables["SimulationQuantiles"].store( YQ )
199 if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
200 self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
201 if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
202 self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
203 if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
204 self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
209 # ==============================================================================
210 if __name__ == "__main__":
211 print('\n AUTODIAGNOSTIC \n')