#-*-coding:iso-8859-1-*-
#
-# Copyright (C) 2008-2011 EDF R&D
+# Copyright (C) 2008-2012 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
#
# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
#
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
import logging
from daCore import BasicObjects, PlatformInfo
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
- BasicObjects.Algorithm.__init__(self)
- self._name = "BLUE"
- logging.debug("%s Initialisation"%self._name)
+ BasicObjects.Algorithm.__init__(self, "BLUE")
+ 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"]
+ )
def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=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")))
+ logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
#
- Hm = H["Direct"].asMatrix()
- Ht = H["Adjoint"].asMatrix()
+ # Paramètres de pilotage
+ # ----------------------
+ self.setParameters(Parameters)
+ #
+ # Opérateur d'observation
+ # -----------------------
+ Hm = H["Tangent"].asMatrix(None)
+ Ha = H["Adjoint"].asMatrix(None)
#
# 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"]
else:
- logging.debug("%s Calcul de Hm * Xb"%self._name)
HXb = Hm * Xb
- HXb = numpy.asmatrix(HXb).flatten().T
+ HXb = numpy.asmatrix(numpy.ravel( HXb )).T
#
- # 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
+ # Précalcul des inversions de B et R
+ # ----------------------------------
+ if B is not None:
+ BI = B.I
+ elif self._parameters["B_scalar"] is not None:
+ BI = 1.0 / self._parameters["B_scalar"]
+ B = self._parameters["B_scalar"]
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
+ raise ValueError("Background error covariance matrix has to be properly defined!")
#
- # Calcul de l'innovation et de l'analyse
- # --------------------------------------
+ if R is not None:
+ RI = R.I
+ elif self._parameters["R_scalar"] is not None:
+ RI = 1.0 / self._parameters["R_scalar"]
+ else:
+ raise ValueError("Observation error covariance matrix has to be properly defined!")
+ #
+ # Calcul de l'innovation
+ # ----------------------
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))
d = Y - HXb
- logging.debug("%s Innovation d = %s"%(self._name, d))
+ #
+ # Calcul de la matrice de gain et de l'analyse
+ # --------------------------------------------
+ if Y.size <= Xb.size:
+ if self._parameters["R_scalar"] is not None:
+ R = self._parameters["R_scalar"] * numpy.eye(len(Y), dtype=numpy.float)
+ K = B * Ha * (Hm * B * Ha + R).I
+ else:
+ K = (Ha * RI * Hm + BI).I * Ha * RI
Xa = Xb + K*d
- logging.debug("%s Analyse Xa = %s"%(self._name, Xa))
#
+ # Calcul de la fonction coût
+ # --------------------------
+ oma = Y - Hm * Xa
+ Jb = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
+ Jo = 0.5 * oma.T * RI * oma
+ J = float( Jb ) + float( Jo )
self.StoredVariables["Analysis"].store( Xa.A1 )
- self.StoredVariables["Innovation"].store( d.A1 )
+ 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"]:
+ A = B - K * Hm * B
+ if min(A.shape) != max(A.shape):
+ raise ValueError("The 3DVAR 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."%str(A.shape))
+ if logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug
+ try:
+ L = numpy.linalg.cholesky( A )
+ except:
+ raise ValueError("The BLUE a posteriori covariance matrix A is not symmetric positive-definite. Check your B and R a priori covariances.")
+ self.StoredVariables["APosterioriCovariance"].store( A )
+ #
+ # Calculs et/ou stockages supplémentaires
+ # ---------------------------------------
+ if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["Innovation"].store( numpy.ravel(d) )
+ if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["BMA"].store( numpy.ravel(Xb - Xa) )
+ if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["OMA"].store( numpy.ravel(oma) )
+ if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["OMB"].store( numpy.ravel(d) )
+ if "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SigmaObs2"].store( float( (d.T * (Y-Hm*Xa)) / R.trace() ) )
+ if "SigmaBck2" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
+ if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/len(d) ) )
#
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("MB")))
+ logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
logging.debug("%s Terminé"%self._name)
#
return 0
