1 # -*- coding: utf-8 -*-
3 # Copyright (C) 2008-2019 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, "BLUE")
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",
44 "APosterioriCorrelations",
45 "APosterioriCovariance",
46 "APosterioriStandardDeviations",
47 "APosterioriVariances",
50 "CostFunctionJAtCurrentOptimum",
52 "CostFunctionJbAtCurrentOptimum",
54 "CostFunctionJoAtCurrentOptimum",
58 "MahalanobisConsistency",
63 "SimulatedObservationAtBackground",
64 "SimulatedObservationAtCurrentOptimum",
65 "SimulatedObservationAtCurrentState",
66 "SimulatedObservationAtOptimum",
67 "SimulationQuantiles",
70 self.defineRequiredParameter(
74 message = "Liste des valeurs de quantiles",
78 self.defineRequiredParameter(
80 typecast = numpy.random.seed,
81 message = "Graine fixée pour le générateur aléatoire",
83 self.defineRequiredParameter(
84 name = "NumberOfSamplesForQuantiles",
87 message = "Nombre d'échantillons simulés pour le calcul des quantiles",
90 self.defineRequiredParameter(
91 name = "SimulationForQuantiles",
94 message = "Type de simulation pour l'estimation des quantiles",
95 listval = ["Linear", "NonLinear"]
97 self.requireInputArguments(
98 mandatory= ("Xb", "Y", "HO", "R", "B"),
101 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
102 self._pre_run(Parameters, Xb, Y, R, B, Q)
104 Hm = HO["Tangent"].asMatrix(Xb)
105 Hm = Hm.reshape(Y.size,Xb.size) # ADAO & check shape
106 Ha = HO["Adjoint"].asMatrix(Xb)
107 Ha = Ha.reshape(Xb.size,Y.size) # ADAO & check shape
109 # Utilisation éventuelle d'un vecteur H(Xb) précalculé
110 # ----------------------------------------------------
111 if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]:
112 HXb = HO["AppliedInX"]["HXb"]
115 HXb = numpy.asmatrix(numpy.ravel( HXb )).T
116 if Y.size != HXb.size:
117 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))
118 if max(Y.shape) != max(HXb.shape):
119 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))
121 # Précalcul des inversions de B et R
122 # ----------------------------------
126 # Calcul de l'innovation
127 # ----------------------
130 # Calcul de la matrice de gain et de l'analyse
131 # --------------------------------------------
132 if Y.size <= Xb.size:
133 _A = R + numpy.dot(Hm, B * Ha)
134 _u = numpy.linalg.solve( _A , d )
135 Xa = Xb + B * Ha * _u
137 _A = BI + numpy.dot(Ha, RI * Hm)
138 _u = numpy.linalg.solve( _A , numpy.dot(Ha, RI * d) )
140 self.StoredVariables["Analysis"].store( Xa.A1 )
142 # Calcul de la fonction coût
143 # --------------------------
144 if self._parameters["StoreInternalVariables"] or \
145 self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
146 self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
147 self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
148 self._toStore("OMA") or \
149 self._toStore("SigmaObs2") or \
150 self._toStore("MahalanobisConsistency") or \
151 self._toStore("SimulatedObservationAtCurrentOptimum") or \
152 self._toStore("SimulatedObservationAtCurrentState") or \
153 self._toStore("SimulatedObservationAtOptimum") or \
154 self._toStore("SimulationQuantiles"):
157 if self._parameters["StoreInternalVariables"] or \
158 self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
159 self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
160 self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
161 self._toStore("MahalanobisConsistency"):
162 Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
163 Jo = float( 0.5 * oma.T * RI * oma )
165 self.StoredVariables["CostFunctionJb"].store( Jb )
166 self.StoredVariables["CostFunctionJo"].store( Jo )
167 self.StoredVariables["CostFunctionJ" ].store( J )
168 self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( Jb )
169 self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( Jo )
170 self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( J )
172 # Calcul de la covariance d'analyse
173 # ---------------------------------
174 if self._toStore("APosterioriCovariance") or \
175 self._toStore("SimulationQuantiles"):
176 if (Y.size <= Xb.size): K = B * Ha * (R + numpy.dot(Hm, B * Ha)).I
177 elif (Y.size > Xb.size): K = (BI + numpy.dot(Ha, RI * Hm)).I * Ha * RI
179 if min(A.shape) != max(A.shape):
180 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)))
181 if (numpy.diag(A) < 0).any():
182 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,))
183 if logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug
185 L = numpy.linalg.cholesky( A )
187 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,))
188 self.StoredVariables["APosterioriCovariance"].store( A )
190 # Calculs et/ou stockages supplémentaires
191 # ---------------------------------------
192 if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
193 self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
194 if self._toStore("CurrentOptimum"):
195 self.StoredVariables["CurrentOptimum"].store( numpy.ravel(Xa) )
196 if self._toStore("Innovation"):
197 self.StoredVariables["Innovation"].store( numpy.ravel(d) )
198 if self._toStore("BMA"):
199 self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
200 if self._toStore("OMA"):
201 self.StoredVariables["OMA"].store( numpy.ravel(oma) )
202 if self._toStore("OMB"):
203 self.StoredVariables["OMB"].store( numpy.ravel(d) )
204 if self._toStore("SigmaObs2"):
205 TraceR = R.trace(Y.size)
206 self.StoredVariables["SigmaObs2"].store( float( (d.T * (numpy.asmatrix(numpy.ravel(oma)).T)) ) / TraceR )
207 if self._toStore("SigmaBck2"):
208 self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
209 if self._toStore("MahalanobisConsistency"):
210 self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
211 if self._toStore("SimulationQuantiles"):
212 nech = self._parameters["NumberOfSamplesForQuantiles"]
214 for i in range(nech):
215 if self._parameters["SimulationForQuantiles"] == "Linear":
216 dXr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A) - Xa.A1).T
217 dYr = numpy.matrix(numpy.ravel( Hm * dXr )).T
219 elif self._parameters["SimulationForQuantiles"] == "NonLinear":
220 Xr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A)).T
221 Yr = numpy.matrix(numpy.ravel( Hm * Xr )).T
225 YfQ = numpy.hstack((YfQ,Yr))
228 for quantile in self._parameters["Quantiles"]:
229 if not (0. <= float(quantile) <= 1.): continue
230 indice = int(nech * float(quantile) - 1./nech)
231 if YQ is None: YQ = YfQ[:,indice]
232 else: YQ = numpy.hstack((YQ,YfQ[:,indice]))
233 self.StoredVariables["SimulationQuantiles"].store( YQ )
234 if self._toStore("SimulatedObservationAtBackground"):
235 self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
236 if self._toStore("SimulatedObservationAtCurrentState"):
237 self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
238 if self._toStore("SimulatedObservationAtCurrentOptimum"):
239 self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( numpy.ravel(HXa) )
240 if self._toStore("SimulatedObservationAtOptimum"):
241 self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
246 # ==============================================================================
247 if __name__ == "__main__":
248 print('\n AUTODIAGNOSTIC\n')