#-*-coding:iso-8859-1-*-
#
-# Copyright (C) 2008-2012 EDF R&D
+# Copyright (C) 2008-2013 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
m = PlatformInfo.SystemUsage()
+import numpy
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
BasicObjects.Algorithm.__init__(self, "KALMANFILTER")
self.defineRequiredParameter(
- name = "CalculateAPosterioriCovariance",
+ name = "StoreSupplementaryCalculations",
+ default = [],
+ typecast = tuple,
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = ["APosterioriCovariance", "BMA", "Innovation"]
+ )
+ self.defineRequiredParameter(
+ name = "EstimationType",
+ default = "State",
+ typecast = str,
+ message = "Estimation d'etat ou de parametres",
+ listval = ["State", "Parameters"],
+ )
+ self.defineRequiredParameter(
+ name = "StoreInternalVariables",
default = False,
typecast = bool,
- message = "Calcul de la covariance a posteriori",
+ message = "Stockage des variables internes ou intermédiaires du calcul",
)
- def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
- """
- Calcul de l'estimateur du filtre de Kalman
-
- Remarque : les observations sont exploitées à partir du pas de temps 1,
- et sont utilisées dans Yo comme rangées selon ces indices. Donc le pas 0
- n'est pas utilisé puisque la première étape de Kalman passe de 0 à 1
- avec l'observation du pas 1.
- """
+ 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("Mo")))
+ logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
#
# Paramètres de pilotage
# ----------------------
self.setParameters(Parameters)
#
- # Opérateur d'observation
- # -----------------------
- Hm = H["Tangent"].asMatrix(None)
- Ha = H["Adjoint"].asMatrix(None)
+ if self._parameters["EstimationType"] == "Parameters":
+ self._parameters["StoreInternalVariables"] = True
#
+ # Opérateurs
+ # ----------
if B is None:
raise ValueError("Background error covariance matrix has to be properly defined!")
if R is None:
raise ValueError("Observation error covariance matrix has to be properly defined!")
#
- # Opérateur d'évolution
- # ---------------------
- Mm = M["Tangent"].asMatrix(None)
- Mt = M["Adjoint"].asMatrix(None)
+ Ht = HO["Tangent"].asMatrix(Xb)
+ Ha = HO["Adjoint"].asMatrix(Xb)
+ #
+ if self._parameters["EstimationType"] == "State":
+ Mt = EM["Tangent"].asMatrix(Xb)
+ Ma = EM["Adjoint"].asMatrix(Xb)
+ #
+ if CM is not None and CM.has_key("Tangent") and U is not None:
+ Cm = CM["Tangent"].asMatrix(Xb)
+ else:
+ Cm = None
#
# Nombre de pas du Kalman identique au nombre de pas d'observations
# -----------------------------------------------------------------
- duration = Y.stepnumber()
+ if hasattr(Y,"stepnumber"):
+ duration = Y.stepnumber()
+ else:
+ duration = 2
+ #
+ # Précalcul des inversions de B et R
+ # ----------------------------------
+ if self._parameters["StoreInternalVariables"]:
+ if B is not None:
+ BI = B.I
+ elif self._parameters["B_scalar"] is not None:
+ BI = 1.0 / self._parameters["B_scalar"]
+ #
+ if R is not None:
+ RI = R.I
+ elif self._parameters["R_scalar"] is not None:
+ RI = 1.0 / self._parameters["R_scalar"]
#
# Initialisation
# --------------
Xn = Xb
Pn = B
+ #
self.StoredVariables["Analysis"].store( Xn.A1 )
- if self._parameters["CalculateAPosterioriCovariance"]:
+ if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["APosterioriCovariance"].store( Pn )
+ covarianceXa = Pn
+ Xa = Xn
+ previousJMinimum = numpy.finfo(float).max
#
for step in range(duration-1):
- logging.debug("%s Etape de Kalman %i (i.e. %i->%i) sur un total de %i"%(self._name, step+1, step,step+1, duration-1))
+ if hasattr(Y,"store"):
+ Ynpu = numpy.asmatrix(numpy.ravel( Y[step+1] )).T
+ else:
+ Ynpu = numpy.asmatrix(numpy.ravel( Y )).T
+ #
+ if U is not None:
+ if hasattr(U,"store") and len(U)>1:
+ Un = numpy.asmatrix(numpy.ravel( U[step] )).T
+ elif hasattr(U,"store") and len(U)==1:
+ Un = numpy.asmatrix(numpy.ravel( U[0] )).T
+ else:
+ Un = numpy.asmatrix(numpy.ravel( U )).T
+ else:
+ Un = None
+ #
+ if self._parameters["EstimationType"] == "State" and Cm is not None and Un is not None:
+ Xn_predicted = Mt * Xn + Cm * Un
+ Pn_predicted = Mt * Pn * Ma + Q
+ elif self._parameters["EstimationType"] == "State" and (Cm is None or Un is None):
+ Xn_predicted = Mt * Xn
+ Pn_predicted = Mt * Pn * Ma + Q
+ elif self._parameters["EstimationType"] == "Parameters":
+ # Xn_predicted = Mt * Xn
+ # Pn_predicted = Mt * Pn * Ma + Q
+ # --- > Par principe, M = Id, Q = 0
+ Xn_predicted = Xn
+ Pn_predicted = Pn
#
- # Etape de prédiction
- # -------------------
- Xn_predicted = Mm * Xn
- Pn_predicted = Mm * Pn * Mt + Q
+ if self._parameters["EstimationType"] == "Parameters" and Cm is not None and Un is not None:
+ d = Ynpu - Ht * Xn_predicted - Cm * Un
+ else:
+ d = Ynpu - Ht * Xn_predicted
#
- # Etape de correction
- # -------------------
- d = Y.valueserie(step+1) - Hm * Xn_predicted
- K = Pn_predicted * Ha * (Hm * Pn_predicted * Ha + R).I
+ K = Pn_predicted * Ha * (Ht * Pn_predicted * Ha + R).I
Xn = Xn_predicted + K * d
- Pn = Pn_predicted - K * Hm * Pn_predicted
+ Pn = Pn_predicted - K * Ht * Pn_predicted
#
self.StoredVariables["Analysis"].store( Xn.A1 )
- self.StoredVariables["Innovation"].store( d.A1 )
- if self._parameters["CalculateAPosterioriCovariance"]:
+ if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["APosterioriCovariance"].store( Pn )
+ if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["Innovation"].store( numpy.ravel( d.A1 ) )
+ if self._parameters["StoreInternalVariables"]:
+ Jb = 0.5 * (Xn - Xb).T * BI * (Xn - Xb)
+ Jo = 0.5 * d.T * RI * d
+ J = float( Jb ) + float( Jo )
+ self.StoredVariables["CurrentState"].store( Xn.A1 )
+ self.StoredVariables["CostFunctionJb"].store( Jb )
+ self.StoredVariables["CostFunctionJo"].store( Jo )
+ self.StoredVariables["CostFunctionJ" ].store( J )
+ if J < previousJMinimum:
+ previousJMinimum = J
+ Xa = Xn
+ if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
+ covarianceXa = Pn
+ else:
+ Xa = Xn
+ #
+ #
+ # Stockage supplementaire de l'optimum en estimation de parametres
+ # ----------------------------------------------------------------
+ if self._parameters["EstimationType"] == "Parameters":
+ self.StoredVariables["Analysis"].store( Xa.A1 )
+ if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["APosterioriCovariance"].store( covarianceXa )
+ #
+ if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
#
- 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")))
logging.debug("%s Terminé"%self._name)
#
return 0
