Adding user variable and minor corrections

[modules/adao.git] / src / daComposant / daAlgorithms / ExtendedKalmanFilter.py

diff --git a/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py b/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py
index 49ae71bae3c98683e2f2d95b9a911ae44b5765e0..1d66ef745ebfd7262a58c5b750ac1bc148a79d50 100644 (file)
--- a/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py
+++ b/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py
@@ -1,6 +1,6 @@
 #-*-coding:iso-8859-1-*-
 #
-#  Copyright (C) 2008-2014 EDF R&D
+#  Copyright (C) 2008-2015 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
@@ -21,8 +21,7 @@
 #  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
 
 # ==============================================================================
@@ -54,12 +53,15 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["APosterioriCovariance", "BMA", "Innovation"]
+            listval  = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "CurrentState", "CostFunctionJ", "Innovation"]
+            )
+        self.defineRequiredParameter( # Pas de type
+            name     = "Bounds",
+            message  = "Liste des valeurs de bornes",
             )
 
     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()
         #
         # Paramètres de pilotage
         # ----------------------
@@ -90,8 +92,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         else:
             Cm = None
         #
-        # Nombre de pas du Kalman identique au nombre de pas d'observations
-        # -----------------------------------------------------------------
+        # Nombre de pas identique au nombre de pas d'observations
+        # -------------------------------------------------------
         if hasattr(Y,"stepnumber"):
             duration = Y.stepnumber()
         else:
@@ -110,7 +112,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         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
@@ -164,8 +166,10 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 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
-            Xn = Xn_predicted + Kn * d
             Pn = Pn_predicted - Kn * Ht * Pn_predicted
             #
             self.StoredVariables["Analysis"].store( Xn.A1 )
@@ -177,7 +181,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 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 )
+                if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                    self.StoredVariables["CurrentState"].store( Xn )
                 self.StoredVariables["CostFunctionJb"].store( Jb )
                 self.StoredVariables["CostFunctionJo"].store( Jo )
                 self.StoredVariables["CostFunctionJ" ].store( J )
@@ -200,10 +205,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
             self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
         #
-        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 Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
-        logging.debug("%s Terminé"%self._name)
-        #
+        self._post_run(HO)
         return 0
 
 # ==============================================================================