Documentation and source correction and improvements

diff --git a/doc/en/ref_algorithm_3DVAR.rst b/doc/en/ref_algorithm_3DVAR.rst
index 3cce8182e7177304849c2a6bdf9d787445566ec3..430b0f47f8a7366ec9a57a659137540b332430b3 100644 (file)
--- a/doc/en/ref_algorithm_3DVAR.rst
+++ b/doc/en/ref_algorithm_3DVAR.rst
@@ -165,8 +165,9 @@ The options of the algorithm are the following:
     these variables being calculated and stored by default. The possible names
     are in the following list: ["APosterioriCorrelations",
     "APosterioriCovariance", "APosterioriStandardDeviations",
-    "APosterioriVariances", "BMA", "CostFunctionJ", "CurrentOptimum",
-    "CurrentState", "IndexOfOptimum", "Innovation", "InnovationAtCurrentState",
+    "APosterioriVariances", "BMA", "CostFunctionJ",
+    "CostFunctionJAtCurrentOptimum", "CurrentOptimum", "CurrentState",
+    "IndexOfOptimum", "Innovation", "InnovationAtCurrentState",
     "MahalanobisConsistency", "OMA", "OMB", "SigmaObs2",
     "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentOptimum",
     "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum",
@@ -283,6 +284,27 @@ The conditional outputs of the algorithm are the following:
 
     Example : ``bma = ADD.get("BMA")[-1]``
 
+  CostFunctionJAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J`.
+    At each step, the value corresponds to the optimal state found from the
+    beginning.
+
+    Example : ``JACO = ADD.get("CostFunctionJAtCurrentOptimum")[:]``
+
+  CostFunctionJbAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J^b`,
+    that is of the background difference part. At each step, the value
+    corresponds to the optimal state found from the beginning.
+
+    Example : ``JbACO = ADD.get("CostFunctionJbAtCurrentOptimum")[:]``
+
+  CostFunctionJoAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J^o`,
+    that is of the observation difference part. At each step, the value
+    corresponds to the optimal state found from the beginning.
+
+    Example : ``JoACO = ADD.get("CostFunctionJoAtCurrentOptimum")[:]``
+
   CurrentOptimum
     *List of vectors*. Each element is the optimal state obtained at the current
     step of the optimization algorithm. It is not necessarely the last state.

diff --git a/doc/en/ref_algorithm_4DVAR.rst b/doc/en/ref_algorithm_4DVAR.rst
index 243e617069558a0d8f68d20188c7e2e27d70c999..34eeabb4de4dd306afed15f9de8c1973da603121 100644 (file)
--- a/doc/en/ref_algorithm_4DVAR.rst
+++ b/doc/en/ref_algorithm_4DVAR.rst
@@ -183,8 +183,9 @@ The options of the algorithm are the following:
     available at the end of the algorithm. It involves potentially costly
     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: ["BMA", "CostFunctionJ", "CurrentOptimum",
-    "CurrentState", "IndexOfOptimum"].
+    are in the following list: ["BMA", "CostFunctionJ",
+    "CostFunctionJAtCurrentOptimum", "CurrentOptimum", "CurrentState",
+    "IndexOfOptimum"].
 
     Example : ``{"StoreSupplementaryCalculations":["BMA", "CurrentState"]}``
 
@@ -232,6 +233,27 @@ The conditional outputs of the algorithm are the following:
 
     Example : ``bma = ADD.get("BMA")[-1]``
 
+  CostFunctionJAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J`.
+    At each step, the value corresponds to the optimal state found from the
+    beginning.
+
+    Example : ``JACO = ADD.get("CostFunctionJAtCurrentOptimum")[:]``
+
+  CostFunctionJbAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J^b`,
+    that is of the background difference part. At each step, the value
+    corresponds to the optimal state found from the beginning.
+
+    Example : ``JbACO = ADD.get("CostFunctionJbAtCurrentOptimum")[:]``
+
+  CostFunctionJoAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J^o`,
+    that is of the observation difference part. At each step, the value
+    corresponds to the optimal state found from the beginning.
+
+    Example : ``JoACO = ADD.get("CostFunctionJoAtCurrentOptimum")[:]``
+
   CurrentOptimum
     *List of vectors*. Each element is the optimal state obtained at the current
     step of the optimization algorithm. It is not necessarely the last state.

diff --git a/doc/en/ref_observers_requirements.rst b/doc/en/ref_observers_requirements.rst
index bd4b2234d9262b597f2224b3abab74ed48acf97b..f0edbd247a65404dce11e33b13b0e6518caeb1da 100644 (file)
--- a/doc/en/ref_observers_requirements.rst
+++ b/doc/en/ref_observers_requirements.rst
@@ -203,11 +203,11 @@ Graphically plot with Gnuplot the current value of the variable.
     global ifig, gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 
@@ -225,11 +225,11 @@ Graphically plot with Gnuplot the value serie of the variable.
     global ifig, gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 
@@ -248,11 +248,11 @@ Print on standard output and, in the same time, graphically plot with Gnuplot th
     global ifig,gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 
@@ -271,11 +271,11 @@ Print on standard output and, in the same time, graphically plot with Gnuplot th
     global ifig,gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 
@@ -304,11 +304,11 @@ Print on standard output and, in the same, time save in a file and graphically p
     global ifig,gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 
@@ -337,11 +337,11 @@ Print on standard output and, in the same, time save in a file and graphically p
     global ifig,gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 

diff --git a/doc/en/ref_output_variables.rst b/doc/en/ref_output_variables.rst
index d8713c5c77e35d6d69d09c3d98d20b1cb31a20fb..a5e35f5085ba0384c631cc317d3251e800ae3def 100644 (file)
--- a/doc/en/ref_output_variables.rst
+++ b/doc/en/ref_output_variables.rst
@@ -264,6 +264,27 @@ of availability. They are the following, in alphabetical order:
 
     Example : ``Jo = ADD.get("CostFunctionJo")[:]``
 
+  CostFunctionJAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J`.
+    At each step, the value corresponds to the optimal state found from the
+    beginning.
+
+    Example : ``JACO = ADD.get("CostFunctionJAtCurrentOptimum")[:]``
+
+  CostFunctionJbAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J^b`,
+    that is of the background difference part. At each step, the value
+    corresponds to the optimal state found from the beginning.
+
+    Example : ``JbACO = ADD.get("CostFunctionJbAtCurrentOptimum")[:]``
+
+  CostFunctionJoAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J^o`,
+    that is of the observation difference part. At each step, the value
+    corresponds to the optimal state found from the beginning.
+
+    Example : ``JoACO = ADD.get("CostFunctionJoAtCurrentOptimum")[:]``
+
   CurrentOptimum
     *List of vectors*. Each element is the optimal state obtained at the current
     step of the optimization algorithm. It is not necessarely the last state.

diff --git a/doc/fr/ref_algorithm_3DVAR.rst b/doc/fr/ref_algorithm_3DVAR.rst
index aad34122336fb91aed007b2105440f5c49ecfbc0..2eace82ff698c99607a4504511a9db56f422dd9c 100644 (file)
--- a/doc/fr/ref_algorithm_3DVAR.rst
+++ b/doc/fr/ref_algorithm_3DVAR.rst
@@ -170,8 +170,9 @@ Les options de l'algorithme sont les suivantes:
     aucune de ces variables n'étant calculée et stockée par défaut. Les noms
     possibles sont dans la liste suivante : ["APosterioriCorrelations",
     "APosterioriCovariance", "APosterioriStandardDeviations",
-    "APosterioriVariances", "BMA", "CostFunctionJ", "CurrentOptimum",
-    "CurrentState", "IndexOfOptimum", "Innovation", "InnovationAtCurrentState",
+    "APosterioriVariances", "BMA", "CostFunctionJ",
+    "CostFunctionJAtCurrentOptimum", "CurrentOptimum", "CurrentState",
+    "IndexOfOptimum", "Innovation", "InnovationAtCurrentState",
     "MahalanobisConsistency", "OMA", "OMB", "SigmaObs2",
     "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentOptimum",
     "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum",
@@ -290,6 +291,27 @@ Les sorties conditionnelles de l'algorithme sont les suivantes:
 
     Exemple : ``bma = ADD.get("BMA")[-1]``
 
+  CostFunctionJAtCurrentOptimum
+    *Liste de valeurs*. Chaque élément est une valeur de fonctionnelle d'écart
+    :math:`J`. A chaque pas, la valeur correspond à l'état optimal trouvé depuis
+    le début.
+
+    Exemple : ``JACO = ADD.get("CostFunctionJAtCurrentOptimum")[:]``
+
+  CostFunctionJbAtCurrentOptimum
+    *Liste de valeurs*. Chaque élément est une valeur de fonctionnelle d'écart
+    :math:`J^b`, c'est-à-dire de la partie écart à l'ébauche. A chaque pas, la
+    valeur correspond à l'état optimal trouvé depuis le début.
+
+    Exemple : ``JbACO = ADD.get("CostFunctionJbAtCurrentOptimum")[:]``
+
+  CostFunctionJoAtCurrentOptimum
+    *Liste de valeurs*. Chaque élément est une valeur de fonctionnelle d'écart
+    :math:`J^o`, c'est-à-dire de la partie écart à l'observation. A chaque pas,
+    la valeur correspond à l'état optimal trouvé depuis le début.
+
+    Exemple : ``JoACO = ADD.get("CostFunctionJoAtCurrentOptimum")[:]``
+
   CurrentOptimum
     *Liste de vecteurs*. Chaque élément est le vecteur d'état optimal au pas de
     temps courant au cours du déroulement de l'algorithme d'optimisation. Ce

diff --git a/doc/fr/ref_algorithm_4DVAR.rst b/doc/fr/ref_algorithm_4DVAR.rst
index 6bc6f9df81d79422016ce400baf5c3b052675720..6d907b5aa7e178165a3d637911a1453202340fe2 100644 (file)
--- a/doc/fr/ref_algorithm_4DVAR.rst
+++ b/doc/fr/ref_algorithm_4DVAR.rst
@@ -189,7 +189,8 @@ 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 : ["BMA", "CostFunctionJ",
-    "CurrentOptimum", "CurrentState", "IndexOfOptimum"].
+    "CostFunctionJAtCurrentOptimum", "CurrentOptimum", "CurrentState",
+    "IndexOfOptimum"].
 
     Exemple : ``{"StoreSupplementaryCalculations":["BMA", "CurrentState"]}``
 
@@ -239,6 +240,27 @@ Les sorties conditionnelles de l'algorithme sont les suivantes:
 
     Exemple : ``bma = ADD.get("BMA")[-1]``
 
+  CostFunctionJAtCurrentOptimum
+    *Liste de valeurs*. Chaque élément est une valeur de fonctionnelle d'écart
+    :math:`J`. A chaque pas, la valeur correspond à l'état optimal trouvé depuis
+    le début.
+
+    Exemple : ``JACO = ADD.get("CostFunctionJAtCurrentOptimum")[:]``
+
+  CostFunctionJbAtCurrentOptimum
+    *Liste de valeurs*. Chaque élément est une valeur de fonctionnelle d'écart
+    :math:`J^b`, c'est-à-dire de la partie écart à l'ébauche. A chaque pas, la
+    valeur correspond à l'état optimal trouvé depuis le début.
+
+    Exemple : ``JbACO = ADD.get("CostFunctionJbAtCurrentOptimum")[:]``
+
+  CostFunctionJoAtCurrentOptimum
+    *Liste de valeurs*. Chaque élément est une valeur de fonctionnelle d'écart
+    :math:`J^o`, c'est-à-dire de la partie écart à l'observation. A chaque pas,
+    la valeur correspond à l'état optimal trouvé depuis le début.
+
+    Exemple : ``JoACO = ADD.get("CostFunctionJoAtCurrentOptimum")[:]``
+
   CurrentOptimum
     *Liste de vecteurs*. Chaque élément est le vecteur d'état optimal au pas de
     temps courant au cours du déroulement de l'algorithme d'optimisation. Ce

diff --git a/doc/fr/ref_observers_requirements.rst b/doc/fr/ref_observers_requirements.rst
index adfc3beb53b662982caaa2f95e0f8c84d565d42c..42b6a24202676001e6130c29dab1d01bfe53bec5 100644 (file)
--- a/doc/fr/ref_observers_requirements.rst
+++ b/doc/fr/ref_observers_requirements.rst
@@ -206,11 +206,11 @@ Affiche graphiquement avec Gnuplot la valeur courante de la variable.
     global ifig, gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 
@@ -228,11 +228,11 @@ Affiche graphiquement avec Gnuplot la s
     global ifig, gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 
@@ -251,11 +251,11 @@ Imprime sur la sortie standard et, en m
     global ifig,gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 
@@ -274,11 +274,11 @@ Imprime sur la sortie standard et, en m
     global ifig,gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 
@@ -307,11 +307,11 @@ Imprime sur la sortie standard et, en m
     global ifig,gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 
@@ -340,11 +340,11 @@ Imprime sur la sortie standard et, en m
     global ifig,gp
     try:
         ifig += 1
-        gp('set style data lines')
+        gp(' set style data lines')
     except:
         ifig = 0
         gp = Gnuplot.Gnuplot(persist=1)
-        gp('set style data lines')
+        gp(' set style data lines')
     gp('set title  "%s (Figure %i)"'%(info,ifig))
     gp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )
 

diff --git a/doc/fr/ref_output_variables.rst b/doc/fr/ref_output_variables.rst
index 16576e3be97befdc85b7de8b32ef6667302fbe41..903146318bf9457f3e23b434d2bdd53caf98bddc 100644 (file)
--- a/doc/fr/ref_output_variables.rst
+++ b/doc/fr/ref_output_variables.rst
@@ -274,6 +274,27 @@ alphab
 
     Exemple : ``Jo = ADD.get("CostFunctionJo")[:]``
 
+  CostFunctionJAtCurrentOptimum
+    *Liste de valeurs*. Chaque élément est une valeur de fonctionnelle d'écart
+    :math:`J`. A chaque pas, la valeur correspond à l'état optimal trouvé depuis
+    le début.
+
+    Exemple : ``JACO = ADD.get("CostFunctionJAtCurrentOptimum")[:]``
+
+  CostFunctionJbAtCurrentOptimum
+    *Liste de valeurs*. Chaque élément est une valeur de fonctionnelle d'écart
+    :math:`J^b`, c'est-à-dire de la partie écart à l'ébauche. A chaque pas, la
+    valeur correspond à l'état optimal trouvé depuis le début.
+
+    Exemple : ``JbACO = ADD.get("CostFunctionJbAtCurrentOptimum")[:]``
+
+  CostFunctionJoAtCurrentOptimum
+    *Liste de valeurs*. Chaque élément est une valeur de fonctionnelle d'écart
+    :math:`J^o`, c'est-à-dire de la partie écart à l'observation. A chaque pas,
+    la valeur correspond à l'état optimal trouvé depuis le début.
+
+    Exemple : ``JoACO = ADD.get("CostFunctionJoAtCurrentOptimum")[:]``
+
   CurrentOptimum
     *Liste de vecteurs*. Chaque élément est le vecteur d'état optimal au pas de
     temps courant au cours du déroulement de l'algorithme d'optimisation. Ce

diff --git a/src/daComposant/daAlgorithms/3DVAR.py b/src/daComposant/daAlgorithms/3DVAR.py
index 039348b54a0c9ddae921e7fdb2cc02e349b9da35..f63f1902f057a1f8e2861ff5041130551cb1dd80 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  = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "OMA", "OMB", "CostFunctionJ", "CurrentState", "CurrentOptimum", "IndexOfOptimum", "Innovation", "InnovationAtCurrentState", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum", "SimulatedObservationAtCurrentOptimum"]
+            listval  = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "OMA", "OMB", "CostFunctionJ", "CurrentState", "CurrentOptimum", "IndexOfOptimum", "Innovation", "InnovationAtCurrentState", "CostFunctionJAtCurrentOptimum", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum", "SimulatedObservationAtCurrentOptimum"]
             )
         self.defineRequiredParameter(
             name     = "Quantiles",
@@ -179,6 +179,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             self.StoredVariables["CostFunctionJ" ].store( J )
             if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+               "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
             if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"]:
@@ -187,6 +188,10 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] )
             if "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
+            if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
+                self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
+                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
             return J
         #
         def GradientOfCostFunction(x):

diff --git a/src/daComposant/daAlgorithms/4DVAR.py b/src/daComposant/daAlgorithms/4DVAR.py
index f05163aff06f6f9fa6003f5f547097d895f5dbae..82ec43e4abd25306978e1cea94f19d63eb8eaa63 100644 (file)
--- a/src/daComposant/daAlgorithms/4DVAR.py
+++ b/src/daComposant/daAlgorithms/4DVAR.py
@@ -86,7 +86,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["BMA", "CurrentState", "CostFunctionJ", "IndexOfOptimum", "CurrentOptimum"]
+            listval  = ["BMA", "CurrentState", "CostFunctionJ", "IndexOfOptimum", "CurrentOptimum", "CostFunctionJAtCurrentOptimum"]
             )
         self.defineRequiredParameter( # Pas de type
             name     = "Bounds",
@@ -210,12 +210,17 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )
             if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
-               "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+               "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+               "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
             if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["IndexOfOptimum"].store( IndexMin )
             if "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] )
+            if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
+                self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
+                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
             return J
         #
         def GradientOfCostFunction(x):

diff --git a/src/daComposant/daAlgorithms/ObserverTest.py b/src/daComposant/daAlgorithms/ObserverTest.py
index 4f1007c774fc3450584a34f310c055829eb2ad7f..b1070d0e89c112493ece3f67f26d4e259b99316f 100644 (file)
--- a/src/daComposant/daAlgorithms/ObserverTest.py
+++ b/src/daComposant/daAlgorithms/ObserverTest.py
@@ -47,9 +47,15 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # ------------------------------------------------------------
         self.StoredVariables["Analysis"].store( __Xa )
         self.StoredVariables["CurrentState"].store( __Xa )
+        self.StoredVariables["CurrentOptimum"].store( __Xa )
+        #
         self.StoredVariables["CostFunctionJb"].store( 1. )
         self.StoredVariables["CostFunctionJo"].store( 2. )
         self.StoredVariables["CostFunctionJ" ].store( 3. )
+        self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( 4. )
+        self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( 5. )
+        self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( 6. )
+        self.StoredVariables["IndexOfOptimum"].store( 1 )
         #
         self.StoredVariables["APosterioriCovariance"].store( numpy.diag(__Xa) )
         self.StoredVariables["APosterioriVariances"].store( __Xa )
@@ -59,10 +65,15 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         self.StoredVariables["OMA"].store( __YY )
         self.StoredVariables["OMB"].store( __YY )
         self.StoredVariables["Innovation"].store( __YY )
+        self.StoredVariables["InnovationAtCurrentState"].store( __YY )
         self.StoredVariables["SigmaObs2"].store( 1. )
         self.StoredVariables["SigmaBck2"].store( 1. )
         self.StoredVariables["MahalanobisConsistency"].store( 1. )
         self.StoredVariables["SimulationQuantiles"].store( numpy.matrix((__YY,__YY,__YY)) )
+        self.StoredVariables["SimulatedObservationAtBackground"].store( __YY )
+        self.StoredVariables["SimulatedObservationAtCurrentState"].store( __YY )
+        self.StoredVariables["SimulatedObservationAtOptimum"].store( __YY )
+        self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( __YY )
         #
         print
         self._post_run()

diff --git a/src/daComposant/daCore/BasicObjects.py b/src/daComposant/daCore/BasicObjects.py
index 78934f70bf85137f23ef008fad02734347d5b95c..4cd191bca116b3de4208f0e521399bdfbbf9dbe7 100644 (file)
--- a/src/daComposant/daCore/BasicObjects.py
+++ b/src/daComposant/daCore/BasicObjects.py
@@ -322,6 +322,9 @@ class Algorithm(object):
         self.StoredVariables["CostFunctionJ"]                        = Persistence.OneScalar(name = "CostFunctionJ")
         self.StoredVariables["CostFunctionJb"]                       = Persistence.OneScalar(name = "CostFunctionJb")
         self.StoredVariables["CostFunctionJo"]                       = Persistence.OneScalar(name = "CostFunctionJo")
+        self.StoredVariables["CostFunctionJAtCurrentOptimum"]        = Persistence.OneScalar(name = "CostFunctionJAtCurrentOptimum")
+        self.StoredVariables["CostFunctionJbAtCurrentOptimum"]       = Persistence.OneScalar(name = "CostFunctionJbAtCurrentOptimum")
+        self.StoredVariables["CostFunctionJoAtCurrentOptimum"]       = Persistence.OneScalar(name = "CostFunctionJoAtCurrentOptimum")
         self.StoredVariables["GradientOfCostFunctionJ"]              = Persistence.OneVector(name = "GradientOfCostFunctionJ")
         self.StoredVariables["GradientOfCostFunctionJb"]             = Persistence.OneVector(name = "GradientOfCostFunctionJb")
         self.StoredVariables["GradientOfCostFunctionJo"]             = Persistence.OneVector(name = "GradientOfCostFunctionJo")

diff --git a/src/daComposant/daCore/Templates.py b/src/daComposant/daCore/Templates.py
index ca71bac060f785c5dcdeaef87583a95d35fd06ce..8bf82949da21eea27886025b30e253d53e529bb9 100644 (file)
--- a/src/daComposant/daCore/Templates.py
+++ b/src/daComposant/daCore/Templates.py
@@ -136,42 +136,42 @@ ObserverTemplates.store(
     )
 ObserverTemplates.store(
     name    = "ValueGnuPlotter",
-    content = """import numpy, Gnuplot\nv=numpy.array(var[-1], ndmin=1)\nglobal ifig, gp\ntry:\n    ifig += 1\n    gp('set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp('set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
+    content = """import numpy, Gnuplot\nv=numpy.array(var[-1], ndmin=1)\nglobal ifig, gp\ntry:\n    ifig += 1\n    gp(' set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp(' set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
     fr_FR   = "Affiche graphiquement avec Gnuplot la valeur courante de la variable",
     en_EN   = "Graphically plot with Gnuplot the current value of the variable",
     order   = "next",
     )
 ObserverTemplates.store(
     name    = "ValueSerieGnuPlotter",
-    content = """import numpy, Gnuplot\nv=numpy.array(var[:],  ndmin=1)\nglobal ifig, gp\ntry:\n    ifig += 1\n    gp('set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp('set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
+    content = """import numpy, Gnuplot\nv=numpy.array(var[:],  ndmin=1)\nglobal ifig, gp\ntry:\n    ifig += 1\n    gp(' set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp(' set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
     fr_FR   = "Affiche graphiquement avec Gnuplot la série des valeurs de la variable",
     en_EN   = "Graphically plot with Gnuplot the value serie of the variable",
     order   = "next",
     )
 ObserverTemplates.store(
     name    = "ValuePrinterAndGnuPlotter",
-    content = """print info, var[-1]\nimport numpy, Gnuplot\nv=numpy.array(var[-1], ndmin=1)\nglobal ifig,gp\ntry:\n    ifig += 1\n    gp('set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp('set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
+    content = """print info, var[-1]\nimport numpy, Gnuplot\nv=numpy.array(var[-1], ndmin=1)\nglobal ifig,gp\ntry:\n    ifig += 1\n    gp(' set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp(' set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
     fr_FR   = "Imprime sur la sortie standard et, en même temps, affiche graphiquement avec Gnuplot la valeur courante de la variable",
     en_EN   = "Print on standard output and, in the same time, graphically plot with Gnuplot the current value of the variable",
     order   = "next",
     )
 ObserverTemplates.store(
     name    = "ValueSeriePrinterAndGnuPlotter",
-    content = """print info, var[:] \nimport numpy, Gnuplot\nv=numpy.array(var[:],  ndmin=1)\nglobal ifig,gp\ntry:\n    ifig += 1\n    gp('set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp('set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
+    content = """print info, var[:] \nimport numpy, Gnuplot\nv=numpy.array(var[:],  ndmin=1)\nglobal ifig,gp\ntry:\n    ifig += 1\n    gp(' set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp(' set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
     fr_FR   = "Imprime sur la sortie standard et, en même temps, affiche graphiquement avec Gnuplot la série des valeurs de la variable",
     en_EN   = "Print on standard output and, in the same time, graphically plot with Gnuplot the value serie of the variable",
     order   = "next",
     )
 ObserverTemplates.store(
     name    = "ValuePrinterSaverAndGnuPlotter",
-    content = """print info, var[-1]\nimport numpy, re\nv=numpy.array(var[-1], ndmin=1)\nglobal istep\ntry:\n    istep += 1\nexcept:\n    istep = 0\nf='/tmp/value_%s_%05i.txt'%(info,istep)\nf=re.sub('\\s','_',f)\nprint 'Value saved in \"%s\"'%f\nnumpy.savetxt(f,v)\nimport Gnuplot\nglobal ifig,gp\ntry:\n    ifig += 1\n    gp('set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp('set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
+    content = """print info, var[-1]\nimport numpy, re\nv=numpy.array(var[-1], ndmin=1)\nglobal istep\ntry:\n    istep += 1\nexcept:\n    istep = 0\nf='/tmp/value_%s_%05i.txt'%(info,istep)\nf=re.sub('\\s','_',f)\nprint 'Value saved in \"%s\"'%f\nnumpy.savetxt(f,v)\nimport Gnuplot\nglobal ifig,gp\ntry:\n    ifig += 1\n    gp(' set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp(' set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
     fr_FR   = "Imprime sur la sortie standard et, en même temps, enregistre dans un fichier et affiche graphiquement la valeur courante de la variable ",
     en_EN   = "Print on standard output and, in the same, time save in a file and graphically plot the current value of the variable",
     order   = "next",
     )
 ObserverTemplates.store(
     name    = "ValueSeriePrinterSaverAndGnuPlotter",
-    content = """print info, var[:] \nimport numpy, re\nv=numpy.array(var[:],  ndmin=1)\nglobal istep\ntry:\n    istep += 1\nexcept:\n    istep = 0\nf='/tmp/value_%s_%05i.txt'%(info,istep)\nf=re.sub('\\s','_',f)\nprint 'Value saved in \"%s\"'%f\nnumpy.savetxt(f,v)\nimport Gnuplot\nglobal ifig,gp\ntry:\n    ifig += 1\n    gp('set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp('set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
+    content = """print info, var[:] \nimport numpy, re\nv=numpy.array(var[:],  ndmin=1)\nglobal istep\ntry:\n    istep += 1\nexcept:\n    istep = 0\nf='/tmp/value_%s_%05i.txt'%(info,istep)\nf=re.sub('\\s','_',f)\nprint 'Value saved in \"%s\"'%f\nnumpy.savetxt(f,v)\nimport Gnuplot\nglobal ifig,gp\ntry:\n    ifig += 1\n    gp(' set style data lines')\nexcept:\n    ifig = 0\n    gp = Gnuplot.Gnuplot(persist=1)\n    gp(' set style data lines')\ngp('set title  \"%s (Figure %i)\"'%(info,ifig))\ngp.plot( Gnuplot.Data( v, with_='lines lw 2' ) )""",
     fr_FR   = "Imprime sur la sortie standard et, en même temps, enregistre dans un fichier et affiche graphiquement la série des valeurs de la variable",
     en_EN   = "Print on standard output and, in the same, time save in a file and graphically plot the value serie of the variable",
     order   = "next",