-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2013 EDF R&D
+# Copyright (C) 2008-2018 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
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
BasicObjects.Algorithm.__init__(self, "EXTENDEDKALMANFILTER")
+ self.defineRequiredParameter(
+ name = "ConstrainedBy",
+ default = "EstimateProjection",
+ typecast = str,
+ message = "Prise en compte des contraintes",
+ listval = ["EstimateProjection"],
+ )
+ self.defineRequiredParameter(
+ name = "EstimationOf",
+ default = "State",
+ typecast = str,
+ message = "Estimation d'etat ou de parametres",
+ listval = ["State", "Parameters"],
+ )
+ 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 = ["APosterioriCovariance", "Innovation"]
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation"]
+ )
+ self.defineRequiredParameter( # Pas de type
+ name = "Bounds",
+ message = "Liste des valeurs de bornes",
+ )
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R", "B" ),
+ optional = ("U", "EM", "CM", "Q"),
)
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, Xb, Y, R, B, Q)
#
- # Paramètres de pilotage
- # ----------------------
- self.setParameters(Parameters)
+ if self._parameters["EstimationOf"] == "Parameters":
+ self._parameters["StoreInternalVariables"] = True
#
- # Opérateurs
+ # 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!")
#
- H = HO["Direct"].appliedTo
+ H = HO["Direct"].appliedControledFormTo
+ #
+ if self._parameters["EstimationOf"] == "State":
+ M = EM["Direct"].appliedControledFormTo
+ #
+ if CM is not None and "Tangent" in CM and U is not None:
+ Cm = CM["Tangent"].asMatrix(Xb)
+ else:
+ Cm = None
#
- M = EM["Direct"].appliedTo
+ # Nombre de pas identique au nombre de pas d'observations
+ # -------------------------------------------------------
+ if hasattr(Y,"stepnumber"):
+ duration = Y.stepnumber()
+ else:
+ duration = 2
#
- # Nombre de pas du Kalman identique au nombre de pas d'observations
- # -----------------------------------------------------------------
- duration = Y.stepnumber()
+ # Précalcul des inversions de B et R
+ # ----------------------------------
+ if self._parameters["StoreInternalVariables"]:
+ BI = B.getI()
+ RI = R.getI()
#
# Initialisation
# --------------
Xn = Xb
Pn = B
+ #
self.StoredVariables["Analysis"].store( Xn.A1 )
if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["APosterioriCovariance"].store( Pn )
+ self.StoredVariables["APosterioriCovariance"].store( Pn.asfullmatrix(Xn.size) )
+ covarianceXa = Pn
+ Xa = Xn
+ previousJMinimum = numpy.finfo(float).max
#
for step in range(duration-1):
- Ynpu = numpy.asmatrix(numpy.ravel( Y[step+1] )).T
+ if hasattr(Y,"store"):
+ Ynpu = numpy.asmatrix(numpy.ravel( Y[step+1] )).T
+ else:
+ Ynpu = numpy.asmatrix(numpy.ravel( Y )).T
#
Ht = HO["Tangent"].asMatrix(ValueForMethodForm = Xn)
Ht = Ht.reshape(Ynpu.size,Xn.size) # ADAO & check shape
Ha = HO["Adjoint"].asMatrix(ValueForMethodForm = Xn)
Ha = Ha.reshape(Xn.size,Ynpu.size) # ADAO & check shape
#
- Mt = EM["Tangent"].asMatrix(ValueForMethodForm = Xn)
- Mt = Mt.reshape(Xn.size,Xn.size) # ADAO & check shape
- Ma = EM["Adjoint"].asMatrix(ValueForMethodForm = Xn)
- Ma = Ma.reshape(Xn.size,Xn.size) # ADAO & check shape
+ if self._parameters["EstimationOf"] == "State":
+ Mt = EM["Tangent"].asMatrix(ValueForMethodForm = Xn)
+ Mt = Mt.reshape(Xn.size,Xn.size) # ADAO & check shape
+ Ma = EM["Adjoint"].asMatrix(ValueForMethodForm = Xn)
+ Ma = Ma.reshape(Xn.size,Xn.size) # ADAO & check shape
#
if U is not None:
if hasattr(U,"store") and len(U)>1:
else:
Un = None
#
- Xn_predicted = M( (Xn, Un) )
- Pn_predicted = Mt * Pn * Ma + Q
+ if self._parameters["EstimationOf"] == "State":
+ Xn_predicted = numpy.asmatrix(numpy.ravel( M( (Xn, Un) ) )).T
+ if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon !
+ Cm = Cm.reshape(Xn.size,Un.size) # ADAO & check shape
+ Xn_predicted = Xn_predicted + Cm * Un
+ Pn_predicted = Q + Mt * Pn * Ma
+ elif self._parameters["EstimationOf"] == "Parameters":
+ # --- > Par principe, M = Id, Q = 0
+ Xn_predicted = Xn
+ Pn_predicted = Pn
+ #
+ if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
+ Xn_predicted = numpy.max(numpy.hstack((Xn_predicted,numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1)
+ Xn_predicted = numpy.min(numpy.hstack((Xn_predicted,numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1)
#
- d = Ynpu - H( Xn_predicted )
- K = Pn_predicted * Ha * (Ht * Pn_predicted * Ha + R).I
- Xn = Xn_predicted + K * d
- Pn = Pn_predicted - K * Ht * Pn_predicted
+ if self._parameters["EstimationOf"] == "State":
+ d = Ynpu - numpy.asmatrix(numpy.ravel( H( (Xn_predicted, None) ) )).T
+ elif self._parameters["EstimationOf"] == "Parameters":
+ d = Ynpu - numpy.asmatrix(numpy.ravel( H( (Xn_predicted, Un) ) )).T
+ if Cm is not None and Un is not None: # Attention : si Cm est aussi dans H, doublon !
+ d = d - Cm * Un
+ #
+ _A = R + Ht * Pn_predicted * Ha
+ _u = numpy.linalg.solve( _A , d )
+ Xn = Xn_predicted + Pn_predicted * Ha * _u
+ Kn = Pn_predicted * Ha * (R + Ht * Pn_predicted * Ha).I
+ Pn = Pn_predicted - Kn * Ht * Pn_predicted
#
self.StoredVariables["Analysis"].store( Xn.A1 )
+ 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"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( Xn )
+ if self._parameters["StoreInternalVariables"] \
+ or "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] \
+ or "CostFunctionJb" in self._parameters["StoreSupplementaryCalculations"] \
+ or "CostFunctionJo" in self._parameters["StoreSupplementaryCalculations"]:
+ Jb = 0.5 * (Xn - Xb).T * BI * (Xn - Xb)
+ Jo = 0.5 * d.T * RI * d
+ J = float( Jb ) + float( Jo )
+ 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["EstimationOf"] == "Parameters":
+ self.StoredVariables["Analysis"].store( Xa.A1 )
if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["APosterioriCovariance"].store( Pn )
+ self.StoredVariables["APosterioriCovariance"].store( covarianceXa )
#
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- logging.debug("%s Terminé"%self._name)
+ if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
#
+ self._post_run(HO)
return 0
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC \n')
