-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2009 EDF R&D
+# Copyright (C) 2008-2019 EDF R&D
#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License.
#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+# Lesser General Public License for more details.
#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
+# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
#
-__doc__ = """
- Algorithme de Kalman simple (BLUE)
-"""
-__author__ = "Jean-Philippe ARGAUD - Mars 2008"
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
-import sys ; sys.path.insert(0, "../daCore")
import logging
-import Persistence
-from BasicObjects import Algorithm
-import PlatformInfo ; m = PlatformInfo.SystemUsage()
+from daCore import BasicObjects
+import numpy
# ==============================================================================
-class ElementaryAlgorithm(Algorithm):
+class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
- Algorithm.__init__(self)
- self._name = "BLUE"
- logging.debug("%s Initialisation"%self._name)
+ BasicObjects.Algorithm.__init__(self, "BLUE")
+ self.defineRequiredParameter(
+ name = "StoreInternalVariables",
+ default = False,
+ typecast = bool,
+ message = "Stockage des variables internes ou intermédiaires du calcul",
+ )
+ self.defineRequiredParameter(
+ name = "StoreSupplementaryCalculations",
+ default = [],
+ typecast = tuple,
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = [
+ "Analysis",
+ "APosterioriCorrelations",
+ "APosterioriCovariance",
+ "APosterioriStandardDeviations",
+ "APosterioriVariances",
+ "BMA",
+ "CostFunctionJ",
+ "CostFunctionJAtCurrentOptimum",
+ "CostFunctionJb",
+ "CostFunctionJbAtCurrentOptimum",
+ "CostFunctionJo",
+ "CostFunctionJoAtCurrentOptimum",
+ "CurrentOptimum",
+ "CurrentState",
+ "Innovation",
+ "MahalanobisConsistency",
+ "OMA",
+ "OMB",
+ "SigmaBck2",
+ "SigmaObs2",
+ "SimulatedObservationAtBackground",
+ "SimulatedObservationAtCurrentOptimum",
+ "SimulatedObservationAtCurrentState",
+ "SimulatedObservationAtOptimum",
+ "SimulationQuantiles",
+ ]
+ )
+ self.defineRequiredParameter(
+ name = "Quantiles",
+ default = [],
+ typecast = tuple,
+ message = "Liste des valeurs de quantiles",
+ minval = 0.,
+ maxval = 1.,
+ )
+ self.defineRequiredParameter(
+ name = "SetSeed",
+ typecast = numpy.random.seed,
+ message = "Graine fixée pour le générateur aléatoire",
+ )
+ self.defineRequiredParameter(
+ name = "NumberOfSamplesForQuantiles",
+ default = 100,
+ typecast = int,
+ message = "Nombre d'échantillons simulés pour le calcul des quantiles",
+ minval = 1,
+ )
+ self.defineRequiredParameter(
+ name = "SimulationForQuantiles",
+ default = "Linear",
+ typecast = str,
+ message = "Type de simulation pour l'estimation des quantiles",
+ listval = ["Linear", "NonLinear"]
+ )
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R", "B"),
+ )
- def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Par=None):
- """
- Calcul de l'estimateur BLUE (ou Kalman simple, ou Interpolation Optimale)
- """
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
+ def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
+ self._pre_run(Parameters, Xb, Y, R, B, Q)
#
- Hm = H["Direct"].asMatrix()
- Ht = H["Adjoint"].asMatrix()
+ Hm = HO["Tangent"].asMatrix(Xb)
+ Hm = Hm.reshape(Y.size,Xb.size) # ADAO & check shape
+ Ha = HO["Adjoint"].asMatrix(Xb)
+ Ha = Ha.reshape(Xb.size,Y.size) # ADAO & check shape
#
- # Utilisation éventuelle d'un vecteur H(Xb) précalculé
+ # Utilisation éventuelle d'un vecteur H(Xb) précalculé
# ----------------------------------------------------
- if H["AppliedToX"] is not None and H["AppliedToX"].has_key("HXb"):
- logging.debug("%s Utilisation de HXb"%self._name)
- HXb = H["AppliedToX"]["HXb"]
+ if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]:
+ HXb = HO["AppliedInX"]["HXb"]
else:
- logging.debug("%s Calcul de Hm * Xb"%self._name)
HXb = Hm * Xb
-
- # Calcul de la matrice de gain dans l'espace le plus petit
- if Y.size <= Xb.size:
- logging.debug("%s Calcul de K dans l'espace des observations"%self._name)
- K = B * Ht * (Hm * B * Ht + R).I
- else:
- logging.debug("%s Calcul de K dans l'espace d'ébauche"%self._name)
- K = (Ht * R.I * Hm + B.I).I * Ht * R.I
+ HXb = numpy.asmatrix(numpy.ravel( HXb )).T
+ if Y.size != HXb.size:
+ 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))
+ if max(Y.shape) != max(HXb.shape):
+ 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))
#
- # Calcul de l'innovation et de l'analyse
- # --------------------------------------
+ # Précalcul des inversions de B et R
+ # ----------------------------------
+ BI = B.getI()
+ RI = R.getI()
+ #
+ # Calcul de l'innovation
+ # ----------------------
d = Y - HXb
- logging.debug("%s Innovation d = %s"%(self._name, d))
- Xa = Xb + K*d
- logging.debug("%s Analyse Xa = %s"%(self._name, Xa))
#
+ # Calcul de la matrice de gain et de l'analyse
+ # --------------------------------------------
+ if Y.size <= Xb.size:
+ _A = R + numpy.dot(Hm, B * Ha)
+ _u = numpy.linalg.solve( _A , d )
+ Xa = Xb + B * Ha * _u
+ else:
+ _A = BI + numpy.dot(Ha, RI * Hm)
+ _u = numpy.linalg.solve( _A , numpy.dot(Ha, RI * d) )
+ Xa = Xb + _u
self.StoredVariables["Analysis"].store( Xa.A1 )
- self.StoredVariables["Innovation"].store( d.A1 )
#
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("MB")))
- logging.debug("%s Terminé"%self._name)
+ # Calcul de la fonction coût
+ # --------------------------
+ if self._parameters["StoreInternalVariables"] or \
+ self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
+ self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+ self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
+ self._toStore("OMA") or \
+ self._toStore("SigmaObs2") or \
+ self._toStore("MahalanobisConsistency") or \
+ self._toStore("SimulatedObservationAtCurrentOptimum") or \
+ self._toStore("SimulatedObservationAtCurrentState") or \
+ self._toStore("SimulatedObservationAtOptimum") or \
+ self._toStore("SimulationQuantiles"):
+ HXa = Hm * Xa
+ oma = Y - HXa
+ if self._parameters["StoreInternalVariables"] or \
+ self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
+ self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+ self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
+ self._toStore("MahalanobisConsistency"):
+ Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
+ Jo = float( 0.5 * oma.T * RI * oma )
+ J = Jb + Jo
+ self.StoredVariables["CostFunctionJb"].store( Jb )
+ self.StoredVariables["CostFunctionJo"].store( Jo )
+ self.StoredVariables["CostFunctionJ" ].store( J )
+ self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( Jb )
+ self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( Jo )
+ self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( J )
+ #
+ # Calcul de la covariance d'analyse
+ # ---------------------------------
+ if self._toStore("APosterioriCovariance") or \
+ self._toStore("SimulationQuantiles"):
+ if (Y.size <= Xb.size): K = B * Ha * (R + numpy.dot(Hm, B * Ha)).I
+ elif (Y.size > Xb.size): K = (BI + numpy.dot(Ha, RI * Hm)).I * Ha * RI
+ A = B - K * Hm * B
+ if min(A.shape) != max(A.shape):
+ 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)))
+ if (numpy.diag(A) < 0).any():
+ 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,))
+ if logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug
+ try:
+ L = numpy.linalg.cholesky( A )
+ except:
+ 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,))
+ self.StoredVariables["APosterioriCovariance"].store( A )
+ #
+ # Calculs et/ou stockages supplémentaires
+ # ---------------------------------------
+ if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
+ if self._toStore("CurrentOptimum"):
+ self.StoredVariables["CurrentOptimum"].store( numpy.ravel(Xa) )
+ if self._toStore("Innovation"):
+ self.StoredVariables["Innovation"].store( numpy.ravel(d) )
+ if self._toStore("BMA"):
+ self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
+ if self._toStore("OMA"):
+ self.StoredVariables["OMA"].store( numpy.ravel(oma) )
+ if self._toStore("OMB"):
+ self.StoredVariables["OMB"].store( numpy.ravel(d) )
+ if self._toStore("SigmaObs2"):
+ TraceR = R.trace(Y.size)
+ self.StoredVariables["SigmaObs2"].store( float( (d.T * (numpy.asmatrix(numpy.ravel(oma)).T)) ) / TraceR )
+ if self._toStore("SigmaBck2"):
+ self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
+ if self._toStore("MahalanobisConsistency"):
+ self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
+ if self._toStore("SimulationQuantiles"):
+ nech = self._parameters["NumberOfSamplesForQuantiles"]
+ YfQ = None
+ for i in range(nech):
+ if self._parameters["SimulationForQuantiles"] == "Linear":
+ dXr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A) - Xa.A1).T
+ dYr = numpy.matrix(numpy.ravel( Hm * dXr )).T
+ Yr = HXa + dYr
+ elif self._parameters["SimulationForQuantiles"] == "NonLinear":
+ Xr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A)).T
+ Yr = numpy.matrix(numpy.ravel( Hm * Xr )).T
+ if YfQ is None:
+ YfQ = Yr
+ else:
+ YfQ = numpy.hstack((YfQ,Yr))
+ YfQ.sort(axis=-1)
+ YQ = None
+ for quantile in self._parameters["Quantiles"]:
+ if not (0. <= float(quantile) <= 1.): continue
+ indice = int(nech * float(quantile) - 1./nech)
+ if YQ is None: YQ = YfQ[:,indice]
+ else: YQ = numpy.hstack((YQ,YfQ[:,indice]))
+ self.StoredVariables["SimulationQuantiles"].store( YQ )
+ if self._toStore("SimulatedObservationAtBackground"):
+ self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
+ if self._toStore("SimulatedObservationAtCurrentState"):
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
+ if self._toStore("SimulatedObservationAtCurrentOptimum"):
+ self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( numpy.ravel(HXa) )
+ if self._toStore("SimulatedObservationAtOptimum"):
+ self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
#
+ self._post_run(HO)
return 0
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC\n')
