Minor documentation and source correction for outputs

diff --git a/doc/en/ref_algorithm_3DVAR.rst b/doc/en/ref_algorithm_3DVAR.rst
index b7d417032349e59162a3f41990bbc6457b69a640..e3221adf562be659b3b0efe3e01c485c1e5c6375 100644 (file)
--- a/doc/en/ref_algorithm_3DVAR.rst
+++ b/doc/en/ref_algorithm_3DVAR.rst
@@ -306,6 +306,12 @@ The conditional outputs of the algorithm are the following:
 
     Example : ``hxb = ADD.get("SimulatedObservationAtBackground")[-1]``
 
+  SimulatedObservationAtCurrentState
+    *List of vectors*. Each element is an observed vector at the current state,
+    that is, in the observation space.
+
+    Example : ``Ys = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+
   SimulatedObservationAtOptimum
     *List of vectors*. Each element is a vector of observation simulated from
     the analysis or optimal state :math:`\mathbf{x}^a`.

diff --git a/doc/en/ref_algorithm_NonLinearLeastSquares.rst b/doc/en/ref_algorithm_NonLinearLeastSquares.rst
index 06d062f6b67daa222db392015f870985e4ba8eca..9dfed31397204dcb127cb623d8fcb5b7c380c079 100644 (file)
--- a/doc/en/ref_algorithm_NonLinearLeastSquares.rst
+++ b/doc/en/ref_algorithm_NonLinearLeastSquares.rst
@@ -242,6 +242,12 @@ The conditional outputs of the algorithm are the following:
 
     Example : ``omb = ADD.get("OMB")[-1]``
 
+  SimulatedObservationAtCurrentState
+    *List of vectors*. Each element is an observed vector at the current state,
+    that is, in the observation space.
+
+    Example : ``Ys = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+
   SimulatedObservationAtOptimum
     *List of vectors*. Each element is a vector of observation simulated from
     the analysis or optimal state :math:`\mathbf{x}^a`.

diff --git a/doc/en/ref_algorithm_ParticleSwarmOptimization.rst b/doc/en/ref_algorithm_ParticleSwarmOptimization.rst
index c951355cab5a724a98b951b10d95fbbf7b5e3090..136f32866e86b657c5f52517ba203e61b1d3f821 100644 (file)
--- a/doc/en/ref_algorithm_ParticleSwarmOptimization.rst
+++ b/doc/en/ref_algorithm_ParticleSwarmOptimization.rst
@@ -246,6 +246,12 @@ The conditional outputs of the algorithm are the following:
 
     Example : ``hxb = ADD.get("SimulatedObservationAtBackground")[-1]``
 
+  SimulatedObservationAtCurrentState
+    *List of vectors*. Each element is an observed vector at the current state,
+    that is, in the observation space.
+
+    Example : ``Ys = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+
   SimulatedObservationAtOptimum
     *List of vectors*. Each element is a vector of observation simulated from
     the analysis or optimal state :math:`\mathbf{x}^a`.

diff --git a/doc/en/ref_algorithm_QuantileRegression.rst b/doc/en/ref_algorithm_QuantileRegression.rst
index c1f83a0432ce1940319458ce8cf68bb1f096f423..406fa5ca4a55eaba9b16355c2d0548503a428cc1 100644 (file)
--- a/doc/en/ref_algorithm_QuantileRegression.rst
+++ b/doc/en/ref_algorithm_QuantileRegression.rst
@@ -201,6 +201,12 @@ The conditional outputs of the algorithm are the following:
 
     Example : ``hxb = ADD.get("SimulatedObservationAtBackground")[-1]``
 
+  SimulatedObservationAtCurrentState
+    *List of vectors*. Each element is an observed vector at the current state,
+    that is, in the observation space.
+
+    Example : ``Ys = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+
   SimulatedObservationAtOptimum
     *List of vectors*. Each element is a vector of observation simulated from
     the analysis or optimal state :math:`\mathbf{x}^a`.

diff --git a/doc/en/ref_algorithm_SamplingTest.rst b/doc/en/ref_algorithm_SamplingTest.rst
index 48f51f3153bc7b81cae696893b43b45fb0102268..a2aa2d1716e359d0df82f1049f110c02a899dbef 100644 (file)
--- a/doc/en/ref_algorithm_SamplingTest.rst
+++ b/doc/en/ref_algorithm_SamplingTest.rst
@@ -173,9 +173,9 @@ The options of the algorithm are the following:
     calculations or memory consumptions. The default is a void list, none of
     these variables being calculated and stored by default. The possible names
     are in the following list: ["CostFunctionJ", "CurrentState", "Innovation",
-    "ObservedState"].
+    "SimulatedObservationAtCurrentState"].
 
-    Example : ``{"StoreSupplementaryCalculations":["CostFunctionJ", "ObservedState"]}``
+    Example : ``{"StoreSupplementaryCalculations":["CostFunctionJ", "SimulatedObservationAtCurrentState"]}``
 
 See also
 ++++++++

diff --git a/doc/en/ref_output_variables.rst b/doc/en/ref_output_variables.rst
index edc1aa46dd3caef3109e44adc0461de34d47e201..8399065fdf741661b8734675ff01f6bf58bb58e3 100644 (file)
--- a/doc/en/ref_output_variables.rst
+++ b/doc/en/ref_output_variables.rst
@@ -261,12 +261,6 @@ of availability. They are the following, in alphabetical order:
 
     Example : ``m = ADD.get("MahalanobisConsistency")[-1]``
 
-  ObservedState
-    *List of vectors*. Each element is an observed state vector, that is, in the
-    observation space.
-
-    Example : ``Ys = ADD.get("ObservedState")[-1]``
-
   OMA
     *List of vectors*. Each element is a vector of difference between the
     observation and the optimal state in the observation space.
@@ -297,6 +291,12 @@ of availability. They are the following, in alphabetical order:
 
     Example : ``hxb = ADD.get("SimulatedObservationAtBackground")[-1]``
 
+  SimulatedObservationAtCurrentState
+    *List of vectors*. Each element is an observed vector at the current state,
+    that is, in the observation space.
+
+    Example : ``Ys = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+
   SimulatedObservationAtOptimum
     *List of vectors*. Each element is a vector of observation simulated from
     the analysis or optimal state :math:`\mathbf{x}^a`.

diff --git a/doc/fr/ref_algorithm_3DVAR.rst b/doc/fr/ref_algorithm_3DVAR.rst
index c83abf1b3792e814d7a3020d49f4f9924bc368e3..6fe77970675ed6ad91c61fa0435e592589d1d9ab 100644 (file)
--- a/doc/fr/ref_algorithm_3DVAR.rst
+++ b/doc/fr/ref_algorithm_3DVAR.rst
@@ -315,6 +315,12 @@ Les sorties conditionnelles de l'algorithme sont les suivantes:
 
     Exemple : ``hxb = ADD.get("SimulatedObservationAtBackground")[-1]``
 
+  SimulatedObservationAtCurrentState
+    *Liste de vecteurs*. Chaque élément est un vecteur observé à l'état courant,
+    c'est-à-dire dans l'espace des observations.
+
+    Exemple : ``Ys = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+
   SimulatedObservationAtOptimum
     *Liste de vecteurs*. Chaque élément est un vecteur d'observation simulé à
     partir de l'analyse ou de l'état optimal :math:`\mathbf{x}^a`.

diff --git a/doc/fr/ref_algorithm_NonLinearLeastSquares.rst b/doc/fr/ref_algorithm_NonLinearLeastSquares.rst
index 542ad2b6364530f515de91a68d2817f7767a420f..90f5d0242283a1d3c209d4e4cfe183edd6f66dd1 100644 (file)
--- a/doc/fr/ref_algorithm_NonLinearLeastSquares.rst
+++ b/doc/fr/ref_algorithm_NonLinearLeastSquares.rst
@@ -253,6 +253,12 @@ Les sorties conditionnelles de l'algorithme sont les suivantes:
 
     Exemple : ``omb = ADD.get("OMB")[-1]``
 
+  SimulatedObservationAtCurrentState
+    *Liste de vecteurs*. Chaque élément est un vecteur observé à l'état courant,
+    c'est-à-dire dans l'espace des observations.
+
+    Exemple : ``Ys = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+
   SimulatedObservationAtOptimum
     *Liste de vecteurs*. Chaque élément est un vecteur d'observation simulé à
     partir de l'analyse ou de l'état optimal :math:`\mathbf{x}^a`.

diff --git a/doc/fr/ref_algorithm_ParticleSwarmOptimization.rst b/doc/fr/ref_algorithm_ParticleSwarmOptimization.rst
index ffc989f94a1fce2b0cbd8247ca6b66006aa64363..110f95124642b4527f45e65a066ada5a1de1fdef 100644 (file)
--- a/doc/fr/ref_algorithm_ParticleSwarmOptimization.rst
+++ b/doc/fr/ref_algorithm_ParticleSwarmOptimization.rst
@@ -253,6 +253,12 @@ Les sorties conditionnelles de l'algorithme sont les suivantes:
 
     Exemple : ``hxb = ADD.get("SimulatedObservationAtBackground")[-1]``
 
+  SimulatedObservationAtCurrentState
+    *Liste de vecteurs*. Chaque élément est un vecteur observé à l'état courant,
+    c'est-à-dire dans l'espace des observations.
+
+    Exemple : ``Ys = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+
   SimulatedObservationAtOptimum
     *Liste de vecteurs*. Chaque élément est un vecteur d'observation simulé à
     partir de l'analyse ou de l'état optimal :math:`\mathbf{x}^a`.

diff --git a/doc/fr/ref_algorithm_QuantileRegression.rst b/doc/fr/ref_algorithm_QuantileRegression.rst
index 9a1b4d09baf3f37e697d674b6a53c306a7c1ef49..fe28ed608be24f5005ecc0db3270f34884862bcb 100644 (file)
--- a/doc/fr/ref_algorithm_QuantileRegression.rst
+++ b/doc/fr/ref_algorithm_QuantileRegression.rst
@@ -206,6 +206,12 @@ Les sorties conditionnelles de l'algorithme sont les suivantes:
 
     Exemple : ``hxb = ADD.get("SimulatedObservationAtBackground")[-1]``
 
+  SimulatedObservationAtCurrentState
+    *Liste de vecteurs*. Chaque élément est un vecteur observé à l'état courant,
+    c'est-à-dire dans l'espace des observations.
+
+    Exemple : ``Ys = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+
   SimulatedObservationAtOptimum
     *Liste de vecteurs*. Chaque élément est un vecteur d'observation simulé à
     partir de l'analyse ou de l'état optimal :math:`\mathbf{x}^a`.

diff --git a/doc/fr/ref_algorithm_SamplingTest.rst b/doc/fr/ref_algorithm_SamplingTest.rst
index 1046e21ad10cf1ae69e51e0dec1ccc9c23617650..dbdc6dfebef327ad9949b5291636e0fdc9ba3f67 100644 (file)
--- a/doc/fr/ref_algorithm_SamplingTest.rst
+++ b/doc/fr/ref_algorithm_SamplingTest.rst
@@ -180,9 +180,9 @@ Les options de l'algorithme sont les suivantes:
     calculs ou du stockage coûteux. La valeur par défaut est une liste vide,
     aucune de ces variables n'étant calculée et stockée par défaut. Les noms
     possibles sont dans la liste suivante : ["CostFunctionJ", "CurrentState",
-    "Innovation", "ObservedState"].
+    "Innovation", "SimulatedObservationAtCurrentState"].
 
-    Exemple : ``{"StoreSupplementaryCalculations":["CostFunctionJ", "ObservedState"]}``
+    Exemple : ``{"StoreSupplementaryCalculations":["CostFunctionJ", "SimulatedObservationAtCurrentState"]}``
 
 Voir aussi
 ++++++++++

diff --git a/doc/fr/ref_output_variables.rst b/doc/fr/ref_output_variables.rst
index d31383fa0c9df2998c6e06940e8ebc6e0640b594..5941bf2afe831ae4b3b834735e5cc33b1d5fbd62 100644 (file)
--- a/doc/fr/ref_output_variables.rst
+++ b/doc/fr/ref_output_variables.rst
@@ -270,12 +270,6 @@ alphab
 
     Exemple : ``m = ADD.get("MahalanobisConsistency")[-1]``
 
-  ObservedState
-    *Liste de vecteurs*. Chaque élément est un vecteur d'état observé,
-    c'est-à-dire dans l'espace des observations.
-
-    Exemple : ``Ys = ADD.get("ObservedState")[-1]``
-
   OMA
     *Liste de vecteurs*. Chaque élément est un vecteur d'écart entre
     l'observation et l'état optimal dans l'espace des observations.
@@ -306,6 +300,12 @@ alphab
 
     Exemple : ``hxb = ADD.get("SimulatedObservationAtBackground")[-1]``
 
+  SimulatedObservationAtCurrentState
+    *Liste de vecteurs*. Chaque élément est un vecteur observé à l'état courant,
+    c'est-à-dire dans l'espace des observations.
+
+    Exemple : ``Ys = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+
   SimulatedObservationAtOptimum
     *Liste de vecteurs*. Chaque élément est un vecteur d'observation simulé à
     partir de l'analyse ou de l'état optimal :math:`\mathbf{x}^a`.

diff --git a/src/daComposant/daAlgorithms/3DVAR.py b/src/daComposant/daAlgorithms/3DVAR.py
index 3e27be312acd9a69968577e640ff6eb45cbb921b..2ac2931765485dad1d0383039fa8e0ef464314b2 100644 (file)
--- a/src/daComposant/daAlgorithms/3DVAR.py
+++ b/src/daComposant/daAlgorithms/3DVAR.py
@@ -72,7 +72,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtOptimum"]
+            listval  = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
             )
         self.defineRequiredParameter(
             name     = "Quantiles",
@@ -162,6 +162,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             J   = float( Jb ) + float( Jo )
             if self._parameters["StoreInternalVariables"]:
                 self.StoredVariables["CurrentState"].store( _X )
+            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )

diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
index b59377383e97843298cd51d048505d79cfddb319..0d08f260b45c222fc43ccad0a955827ef7d69c37 100644 (file)
--- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
@@ -72,7 +72,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["BMA", "OMA", "OMB", "Innovation", "SimulatedObservationAtOptimum"]
+            listval  = ["BMA", "OMA", "OMB", "Innovation", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
             )
 
     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
@@ -136,6 +136,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             J   = float( Jb ) + float( Jo )
             if self._parameters["StoreInternalVariables"]:
                 self.StoredVariables["CurrentState"].store( _X )
+            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )

diff --git a/src/daComposant/daAlgorithms/ParticleSwarmOptimization.py b/src/daComposant/daAlgorithms/ParticleSwarmOptimization.py
index ccf148f536cddb79df48ba654cfbb0a4a629de41..6d4cc5acfcf8fdcdbe9255c953d719ef61910847 100644 (file)
--- a/src/daComposant/daAlgorithms/ParticleSwarmOptimization.py
+++ b/src/daComposant/daAlgorithms/ParticleSwarmOptimization.py
@@ -84,7 +84,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["BMA", "OMA", "OMB", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtOptimum"]
+            listval  = ["BMA", "OMA", "OMB", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
             )
 
     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
@@ -150,6 +150,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 self.StoredVariables["CostFunctionJb"].store( Jb )
                 self.StoredVariables["CostFunctionJo"].store( Jo )
                 self.StoredVariables["CostFunctionJ" ].store( J )
+            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
             return J
         #
         # Point de démarrage de l'optimisation : Xini = Xb

diff --git a/src/daComposant/daAlgorithms/QuantileRegression.py b/src/daComposant/daAlgorithms/QuantileRegression.py
index ce516313c218e52c09d7255817103fbfa66641da..482216f216baa3eca88a0067c7d9e64eaab5e1a2 100644 (file)
--- a/src/daComposant/daAlgorithms/QuantileRegression.py
+++ b/src/daComposant/daAlgorithms/QuantileRegression.py
@@ -67,7 +67,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["BMA", "OMA", "OMB", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtOptimum"]
+            listval  = ["BMA", "OMA", "OMB", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
             )
 
     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
@@ -114,6 +114,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             J   = Jb + Jo
             if self._parameters["StoreInternalVariables"]:
                 self.StoredVariables["CurrentState"].store( _X )
+            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )

diff --git a/src/daComposant/daAlgorithms/SamplingTest.py b/src/daComposant/daAlgorithms/SamplingTest.py
index 6a58f684808d9fd15808b641bdaf65de5bf86bc7..7856b236df20a796e1a2e57518b49e1ceec80026 100644 (file)
--- a/src/daComposant/daAlgorithms/SamplingTest.py
+++ b/src/daComposant/daAlgorithms/SamplingTest.py
@@ -74,7 +74,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["CostFunctionJ","CurrentState","Innovation","ObservedState"]
+            listval  = ["CostFunctionJ","CurrentState","Innovation","SimulatedObservationAtCurrentState"]
             )
         self.defineRequiredParameter(
             name     = "SetSeed",
@@ -157,8 +157,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 self.StoredVariables["CurrentState"].store( _X )
             if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["Innovation"].store( Y - _HX )
-            if "ObservedState" in self._parameters["StoreSupplementaryCalculations"]:
-                self.StoredVariables["ObservedState"].store( _HX )
+            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )

diff --git a/src/daComposant/daCore/BasicObjects.py b/src/daComposant/daCore/BasicObjects.py
index f63821d60a1c60895c025d670ff08bceec867aa2..0e83daa7e794a38d2e26badc08d6c84906e1ce12 100644 (file)
--- a/src/daComposant/daCore/BasicObjects.py
+++ b/src/daComposant/daCore/BasicObjects.py
@@ -262,8 +262,8 @@ class Algorithm:
             - CurrentState : état courant lors d'itérations
             - Analysis : l'analyse Xa
             - SimulatedObservationAtBackground : l'état observé H(Xb) à l'ébauche
+            - SimulatedObservationAtCurrentState : l'état observé H(X) à l'état courant
             - SimulatedObservationAtOptimum : l'état observé H(Xa) à l'optimum
-            - ObservedState : l'état observé H(X)
             - Innovation : l'innovation : d = Y - H(X)
             - SigmaObs2 : indicateur de correction optimale des erreurs d'observation
             - SigmaBck2 : indicateur de correction optimale des erreurs d'ébauche
@@ -283,26 +283,26 @@ class Algorithm:
         self.__required_parameters = {}
         self.StoredVariables = {}
         #
-        self.StoredVariables["CostFunctionJ"]                    = Persistence.OneScalar(name = "CostFunctionJ")
-        self.StoredVariables["CostFunctionJb"]                   = Persistence.OneScalar(name = "CostFunctionJb")
-        self.StoredVariables["CostFunctionJo"]                   = Persistence.OneScalar(name = "CostFunctionJo")
-        self.StoredVariables["GradientOfCostFunctionJ"]          = Persistence.OneVector(name = "GradientOfCostFunctionJ")
-        self.StoredVariables["GradientOfCostFunctionJb"]         = Persistence.OneVector(name = "GradientOfCostFunctionJb")
-        self.StoredVariables["GradientOfCostFunctionJo"]         = Persistence.OneVector(name = "GradientOfCostFunctionJo")
-        self.StoredVariables["CurrentState"]                     = Persistence.OneVector(name = "CurrentState")
-        self.StoredVariables["Analysis"]                         = Persistence.OneVector(name = "Analysis")
-        self.StoredVariables["SimulatedObservationAtBackground"] = Persistence.OneVector(name = "SimulatedObservationAtBackground")
-        self.StoredVariables["SimulatedObservationAtOptimum"]    = Persistence.OneVector(name = "SimulatedObservationAtOptimum")
-        self.StoredVariables["ObservedState"]                    = Persistence.OneVector(name = "ObservedState")
-        self.StoredVariables["Innovation"]                       = Persistence.OneVector(name = "Innovation")
-        self.StoredVariables["SigmaObs2"]                        = Persistence.OneScalar(name = "SigmaObs2")
-        self.StoredVariables["SigmaBck2"]                        = Persistence.OneScalar(name = "SigmaBck2")
-        self.StoredVariables["MahalanobisConsistency"]           = Persistence.OneScalar(name = "MahalanobisConsistency")
-        self.StoredVariables["OMA"]                              = Persistence.OneVector(name = "OMA")
-        self.StoredVariables["OMB"]                              = Persistence.OneVector(name = "OMB")
-        self.StoredVariables["BMA"]                              = Persistence.OneVector(name = "BMA")
-        self.StoredVariables["APosterioriCovariance"]            = Persistence.OneMatrix(name = "APosterioriCovariance")
-        self.StoredVariables["SimulationQuantiles"]              = Persistence.OneMatrix(name = "SimulationQuantiles")
+        self.StoredVariables["CostFunctionJ"]                      = Persistence.OneScalar(name = "CostFunctionJ")
+        self.StoredVariables["CostFunctionJb"]                     = Persistence.OneScalar(name = "CostFunctionJb")
+        self.StoredVariables["CostFunctionJo"]                     = Persistence.OneScalar(name = "CostFunctionJo")
+        self.StoredVariables["GradientOfCostFunctionJ"]            = Persistence.OneVector(name = "GradientOfCostFunctionJ")
+        self.StoredVariables["GradientOfCostFunctionJb"]           = Persistence.OneVector(name = "GradientOfCostFunctionJb")
+        self.StoredVariables["GradientOfCostFunctionJo"]           = Persistence.OneVector(name = "GradientOfCostFunctionJo")
+        self.StoredVariables["CurrentState"]                       = Persistence.OneVector(name = "CurrentState")
+        self.StoredVariables["Analysis"]                           = Persistence.OneVector(name = "Analysis")
+        self.StoredVariables["SimulatedObservationAtBackground"]   = Persistence.OneVector(name = "SimulatedObservationAtBackground")
+        self.StoredVariables["SimulatedObservationAtCurrentState"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentState")
+        self.StoredVariables["SimulatedObservationAtOptimum"]      = Persistence.OneVector(name = "SimulatedObservationAtOptimum")
+        self.StoredVariables["Innovation"]                         = Persistence.OneVector(name = "Innovation")
+        self.StoredVariables["SigmaObs2"]                          = Persistence.OneScalar(name = "SigmaObs2")
+        self.StoredVariables["SigmaBck2"]                          = Persistence.OneScalar(name = "SigmaBck2")
+        self.StoredVariables["MahalanobisConsistency"]             = Persistence.OneScalar(name = "MahalanobisConsistency")
+        self.StoredVariables["OMA"]                                = Persistence.OneVector(name = "OMA")
+        self.StoredVariables["OMB"]                                = Persistence.OneVector(name = "OMB")
+        self.StoredVariables["BMA"]                                = Persistence.OneVector(name = "BMA")
+        self.StoredVariables["APosterioriCovariance"]              = Persistence.OneMatrix(name = "APosterioriCovariance")
+        self.StoredVariables["SimulationQuantiles"]                = Persistence.OneMatrix(name = "SimulationQuantiles")
 
     def _pre_run(self):
         logging.debug("%s Lancement"%self._name)

diff --git a/src/daSalome/daYacsSchemaCreator/infos_daComposant.py b/src/daSalome/daYacsSchemaCreator/infos_daComposant.py
index 012e9cd524ad17c13ae304799af5f6d356026fa0..0aed5daaa7e7ce0a05a20e9c17b1e787fb24d736 100644 (file)
--- a/src/daSalome/daYacsSchemaCreator/infos_daComposant.py
+++ b/src/daSalome/daYacsSchemaCreator/infos_daComposant.py
@@ -247,7 +247,7 @@ ObserversList = [
     "Analysis",
     "CurrentState",
     "Innovation",
-    "ObservedState",
+    "SimulatedObservationAtCurrentState",
     "OMA",
     "OMB",
     "BMA",