-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2014 EDF R&D
+# Copyright (C) 2008-2017 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
#
-# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
import logging
-from daCore import BasicObjects, PlatformInfo
-m = PlatformInfo.SystemUsage()
+from daCore import BasicObjects
import numpy
# ==============================================================================
name = "StoreInternalVariables",
default = False,
typecast = bool,
- message = "Stockage des variables internes ou intermédiaires du calcul",
+ 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 = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles"]
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
)
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",
+ 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",
+ message = "Nombre d'échantillons simulés pour le calcul des quantiles",
minval = 1,
)
self.defineRequiredParameter(
)
def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
+ self._pre_run(Parameters)
#
- # Paramètres de pilotage
- # ----------------------
- self.setParameters(Parameters)
- #
- # Opérateur d'observation
- # -----------------------
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
H = HO["Direct"].appliedTo
#
- # Utilisation éventuelle d'un vecteur H(Xb) précalculé
+ # Utilisation éventuelle d'un vecteur H(Xb) précalculé
# ----------------------------------------------------
- if HO["AppliedToX"] is not None and HO["AppliedToX"].has_key("HXb"):
- HXb = HO["AppliedToX"]["HXb"]
+ if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]:
+ HXb = H( Xb, HO["AppliedInX"]["HXb"])
else:
HXb = H( Xb )
HXb = numpy.asmatrix(numpy.ravel( HXb )).T
#
- # Précalcul des inversions de B et R
+ # Précalcul des inversions de B et R
# ----------------------------------
BI = B.getI()
RI = R.getI()
# Calcul de la matrice de gain et de l'analyse
# --------------------------------------------
if Y.size <= Xb.size:
- if Y.size > 100: # len(R)
- _A = R + Hm * B * Ha
- _u = numpy.linalg.solve( _A , d )
- Xa = Xb + B * Ha * _u
- else:
- K = B * Ha * (R + Hm * B * Ha).I
- Xa = Xb + K*d
+ _A = R + Hm * B * Ha
+ _u = numpy.linalg.solve( _A , d )
+ Xa = Xb + B * Ha * _u
else:
- if Y.size > 100: # len(R)
- _A = BI + Ha * RI * Hm
- _u = numpy.linalg.solve( _A , Ha * RI * d )
- Xa = Xb + _u
- else:
- K = (BI + Ha * RI * Hm).I * Ha * RI
- Xa = Xb + K*d
+ _A = BI + Ha * RI * Hm
+ _u = numpy.linalg.solve( _A , Ha * RI * d )
+ Xa = Xb + _u
self.StoredVariables["Analysis"].store( Xa.A1 )
#
- # Calcul de la fonction coût
+ # Calcul de la fonction coût
# --------------------------
if self._parameters["StoreInternalVariables"] or \
- "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
- "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
- "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+ "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] or \
+ "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
+ "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
HXa = numpy.matrix(numpy.ravel( H( Xa ) )).T
oma = Y - HXa
if self._parameters["StoreInternalVariables"] or \
- "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
- Jb = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
- Jo = 0.5 * oma.T * RI * oma
- J = float( Jb ) + float( Jo )
+ "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] or \
+ "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
+ 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 )
# Calcul de la covariance d'analyse
# ---------------------------------
if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
- if (Y.size <= Xb.size) and (Y.size > 100): K = B * Ha * (R + Hm * B * Ha).I
- elif (Y.size > Xb.size) and (Y.size > 100): K = (BI + Ha * RI * Hm).I * Ha * RI
- else: pass # K deja calcule
+ "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+ if (Y.size <= Xb.size): K = B * Ha * (R + Hm * B * Ha).I
+ elif (Y.size > Xb.size): K = (BI + 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)))
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
+ # Calculs et/ou stockages supplémentaires
# ---------------------------------------
+ if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["Innovation"].store( numpy.ravel(d) )
if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
if "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
- Qtls = self._parameters["Quantiles"]
nech = self._parameters["NumberOfSamplesForQuantiles"]
HtM = HO["Tangent"].asMatrix(ValueForMethodForm = Xa)
HtM = HtM.reshape(Y.size,Xa.size) # ADAO & check shape
YfQ = numpy.hstack((YfQ,Yr))
YfQ.sort(axis=-1)
YQ = None
- for quantile in Qtls:
- if not (0. <= quantile <= 1.): continue
- indice = int(nech * quantile - 1./nech)
+ 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 "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
+ if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
#
- 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)))
- 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)))
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- logging.debug("%s Terminé"%self._name)
- #
+ self._post_run(HO)
return 0
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC \n')
