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",
43 "APosterioriCorrelations",
44 "APosterioriCovariance",
45 "APosterioriStandardDeviations",
46 "APosterioriVariances",
49 "CostFunctionJAtCurrentOptimum",
51 "CostFunctionJbAtCurrentOptimum",
53 "CostFunctionJoAtCurrentOptimum",
57 "MahalanobisConsistency",
62 "SimulatedObservationAtBackground",
63 "SimulatedObservationAtCurrentOptimum",
64 "SimulatedObservationAtCurrentState",
65 "SimulatedObservationAtOptimum",
66 "SimulationQuantiles",
69 self.defineRequiredParameter(
73 message = "Liste des valeurs de quantiles",
77 self.defineRequiredParameter(
79 typecast = numpy.random.seed,
80 message = "Graine fixée pour le générateur aléatoire",
82 self.defineRequiredParameter(
83 name = "NumberOfSamplesForQuantiles",
86 message = "Nombre d'échantillons simulés pour le calcul des quantiles",
89 self.defineRequiredParameter(
90 name = "SimulationForQuantiles",
93 message = "Type de simulation pour l'estimation des quantiles",
94 listval = ["Linear", "NonLinear"]
96 self.requireInputArguments(
97 mandatory= ("Xb", "Y", "HO", "R", "B"),
100 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
101 self._pre_run(Parameters, Xb, Y, R, B, Q)
103 Hm = HO["Tangent"].asMatrix(Xb)
104 Hm = Hm.reshape(Y.size,Xb.size) # ADAO & check shape
105 Ha = HO["Adjoint"].asMatrix(Xb)
106 Ha = Ha.reshape(Xb.size,Y.size) # ADAO & check shape
108 # Utilisation éventuelle d'un vecteur H(Xb) précalculé
109 # ----------------------------------------------------
110 if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]:
111 HXb = HO["AppliedInX"]["HXb"]
114 HXb = numpy.asmatrix(numpy.ravel( HXb )).T
115 if Y.size != HXb.size:
116 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))
117 if max(Y.shape) != max(HXb.shape):
118 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))
120 # Précalcul des inversions de B et R
121 # ----------------------------------
125 # Calcul de l'innovation
126 # ----------------------
129 # Calcul de la matrice de gain et de l'analyse
130 # --------------------------------------------
131 if Y.size <= Xb.size:
132 _A = R + numpy.dot(Hm, B * Ha)
133 _u = numpy.linalg.solve( _A , d )
134 Xa = Xb + B * Ha * _u
136 _A = BI + numpy.dot(Ha, RI * Hm)
137 _u = numpy.linalg.solve( _A , numpy.dot(Ha, RI * d) )
139 self.StoredVariables["Analysis"].store( Xa.A1 )
141 # Calcul de la fonction coût
142 # --------------------------
143 if self._parameters["StoreInternalVariables"] or \
144 self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
145 self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
146 self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
147 self._toStore("OMA") or \
148 self._toStore("SigmaObs2") or \
149 self._toStore("MahalanobisConsistency") or \
150 self._toStore("SimulatedObservationAtCurrentOptimum") or \
151 self._toStore("SimulatedObservationAtCurrentState") or \
152 self._toStore("SimulatedObservationAtOptimum") or \
153 self._toStore("SimulationQuantiles"):
156 if self._parameters["StoreInternalVariables"] or \
157 self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
158 self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
159 self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
160 self._toStore("MahalanobisConsistency"):
161 Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
162 Jo = float( 0.5 * oma.T * RI * oma )
164 self.StoredVariables["CostFunctionJb"].store( Jb )
165 self.StoredVariables["CostFunctionJo"].store( Jo )
166 self.StoredVariables["CostFunctionJ" ].store( J )
167 self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( Jb )
168 self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( Jo )
169 self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( J )
171 # Calcul de la covariance d'analyse
172 # ---------------------------------
173 if self._toStore("APosterioriCovariance") or \
174 self._toStore("SimulationQuantiles"):
175 if (Y.size <= Xb.size): K = B * Ha * (R + numpy.dot(Hm, B * Ha)).I
176 elif (Y.size > Xb.size): K = (BI + numpy.dot(Ha, RI * Hm)).I * Ha * RI
178 if min(A.shape) != max(A.shape):
179 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)))
180 if (numpy.diag(A) < 0).any():
181 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,))
182 if logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug
184 L = numpy.linalg.cholesky( A )
186 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,))
187 self.StoredVariables["APosterioriCovariance"].store( A )
189 # Calculs et/ou stockages supplémentaires
190 # ---------------------------------------
191 if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
192 self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
193 if self._toStore("CurrentOptimum"):
194 self.StoredVariables["CurrentOptimum"].store( numpy.ravel(Xa) )
195 if self._toStore("Innovation"):
196 self.StoredVariables["Innovation"].store( numpy.ravel(d) )
197 if self._toStore("BMA"):
198 self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
199 if self._toStore("OMA"):
200 self.StoredVariables["OMA"].store( numpy.ravel(oma) )
201 if self._toStore("OMB"):
202 self.StoredVariables["OMB"].store( numpy.ravel(d) )
203 if self._toStore("SigmaObs2"):
204 TraceR = R.trace(Y.size)
205 self.StoredVariables["SigmaObs2"].store( float( (d.T * (numpy.asmatrix(numpy.ravel(oma)).T)) ) / TraceR )
206 if self._toStore("SigmaBck2"):
207 self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
208 if self._toStore("MahalanobisConsistency"):
209 self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
210 if self._toStore("SimulationQuantiles"):
211 nech = self._parameters["NumberOfSamplesForQuantiles"]
213 for i in range(nech):
214 if self._parameters["SimulationForQuantiles"] == "Linear":
215 dXr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A) - Xa.A1).T
216 dYr = numpy.matrix(numpy.ravel( Hm * dXr )).T
218 elif self._parameters["SimulationForQuantiles"] == "NonLinear":
219 Xr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A)).T
220 Yr = numpy.matrix(numpy.ravel( Hm * Xr )).T
224 YfQ = numpy.hstack((YfQ,Yr))
227 for quantile in self._parameters["Quantiles"]:
228 if not (0. <= float(quantile) <= 1.): continue
229 indice = int(nech * float(quantile) - 1./nech)
230 if YQ is None: YQ = YfQ[:,indice]
231 else: YQ = numpy.hstack((YQ,YfQ[:,indice]))
232 self.StoredVariables["SimulationQuantiles"].store( YQ )
233 if self._toStore("SimulatedObservationAtBackground"):
234 self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
235 if self._toStore("SimulatedObservationAtCurrentState"):
236 self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
237 if self._toStore("SimulatedObservationAtCurrentOptimum"):
238 self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( numpy.ravel(HXa) )
239 if self._toStore("SimulatedObservationAtOptimum"):
240 self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
245 # ==============================================================================
246 if __name__ == "__main__":
247 print('\n AUTODIAGNOSTIC \n')