Minor improvements and fixes for internal variables

diff --git a/doc/en/ref_algorithm_3DVAR.rst b/doc/en/ref_algorithm_3DVAR.rst
index 579832aa918b6d18b0c0342982d8451fd0178b60..3a6436f37bf15f1dfd43b814cd83decb032c7022 100644 (file)
--- a/doc/en/ref_algorithm_3DVAR.rst
+++ b/doc/en/ref_algorithm_3DVAR.rst
@@ -39,7 +39,8 @@ classical :math:`J` function in static data assimilation:
 which is usually designed as the "*3D-Var*" function (see for example
 [Talagrand97]_). The terms "*3D-Var*", "*3D-VAR*" and "*3DVAR*" are equivalent.
 
-There exists various variants of this algorithm. The following stable and robust formulations are proposed here:
+There exists various variants of this algorithm. The following stable and
+robust formulations are proposed here:
 
 .. index::
     pair: Variant ; 3DVAR
@@ -47,12 +48,14 @@ There exists various variants of this algorithm. The following stable and robust
     pair: Variant ; 3DVAR-Incr
     pair: Variant ; 3DVAR-PSAS
 
-- "3DVAR" (3D Variational analysis, see [Lorenc86]_, [LeDimet86]_, [Talagrand97]_), original and very robust algorithm,
-- "3DVAR-VAN" (3D Variational Analysis with No inversion of B, see [Lorenc88]_), similar algorithm but avoiding inversion of the covariance matrix B,
-- "3DVAR-Incr" (Incremental 3DVAR, see [Courtier94]_), cheaper algorithm but involving an approximation of non-linear operators,
-- "3DVAR-PSAS" (Physical-space Statistical Analysis Scheme for 3DVAR, see [Courtier97]_, [Cohn98]_), algorithm sometimes more cheap because it operates in another space, but involving an approximation of non-linear operators.
+- "3DVAR" (3D Variational analysis, see [Lorenc86]_, [LeDimet86]_, [Talagrand97]_), original classical algorithm and very robust, which operates in the model space,
+- "3DVAR-VAN" (3D Variational Analysis with No inversion of B, see [Lorenc88]_), similar algorithm, which operates in the model space, but avoiding inversion of the covariance matrix B,
+- "3DVAR-Incr" (Incremental 3DVAR, see [Courtier94]_), cheaper algorithm than the previous ones, but involving an approximation of non-linear operators,
+- "3DVAR-PSAS" (Physical-space Statistical Analysis Scheme for 3DVAR, see [Courtier97]_, [Cohn98]_), algorithm sometimes cheaper because it operates in the observation space, but involving an approximation of non-linear operators.
 
-It is recommended to use the original 3DVAR.
+It is highly recommended to use the original "3DVAR". The "3DVAR" and
+"3DVAR-Incr" algorithms (and not the others) allow modification of the
+initialization point for the minimization, but it is not recommended.
 
 .. ------------------------------------ ..
 .. include:: snippets/Header2Algo02.rst

diff --git a/doc/fr/ref_algorithm_3DVAR.rst b/doc/fr/ref_algorithm_3DVAR.rst
index 007621225f8208d5be98b09f91db3db68990988c..9824a898c8fba75e0519c19c7bc7b42e20ad2d88 100644 (file)
--- a/doc/fr/ref_algorithm_3DVAR.rst
+++ b/doc/fr/ref_algorithm_3DVAR.rst
@@ -41,7 +41,8 @@ qui est usuellement désignée comme la fonctionnelle "*3D-Var*" (voir par
 exemple [Talagrand97]_). Les dénominations "*3D-Var*", "*3D-VAR*" et "*3DVAR*"
 sont équivalentes.
 
-Il existe diverses variantes de cet algorithme. On propose ici des formulations stables et robustes suivantes :
+Il existe diverses variantes de cet algorithme. On propose ici les formulations
+stables et robustes suivantes :
 
 .. index::
     pair: Variant ; 3DVAR
@@ -49,12 +50,14 @@ Il existe diverses variantes de cet algorithme. On propose ici des formulations
     pair: Variant ; 3DVAR-Incr
     pair: Variant ; 3DVAR-PSAS
 
-- "3DVAR" (3D Variational analysis, voir [Lorenc86]_, [LeDimet86]_, [Talagrand97]_), algorithme d'origine et très robuste,
-- "3DVAR-VAN" (3D Variational Analysis with No inversion of B, voir [Lorenc88]_), algorithme similaire mais permettant d'éviter l'inversion de la matrice de covariance B,
-- "3DVAR-Incr" (Incremental 3DVAR, voir [Courtier94]_), algorithme plus économique mais impliquant une approximation des opérateurs non-linéaires,
-- "3DVAR-PSAS" (Physical-space Statistical Analysis Scheme for 3DVAR, voir [Courtier97]_, [Cohn98]_), algorithme parfois plus économique car opérant dans un autre espace, mais impliquant une approximation des opérateurs non-linéaires.
+- "3DVAR" (3D Variational analysis, voir [Lorenc86]_, [LeDimet86]_, [Talagrand97]_), algorithme classique d'origine, très robuste, opérant dans l'espace du modèle,
+- "3DVAR-VAN" (3D Variational Analysis with No inversion of B, voir [Lorenc88]_), algorithme similaire, opérant dans l'espace du modèle, mais permettant d'éviter l'inversion de la matrice de covariance B,
+- "3DVAR-Incr" (Incremental 3DVAR, voir [Courtier94]_), algorithme plus économique que les précédents, mais impliquant une approximation des opérateurs non-linéaires,
+- "3DVAR-PSAS" (Physical-space Statistical Analysis Scheme for 3DVAR, voir [Courtier97]_, [Cohn98]_), algorithme parfois plus économique car opérant dans l'espace des observations, mais impliquant une approximation des opérateurs non-linéaires.
 
-On recommande d'utiliser le 3DVAR d'origine.
+On recommande fortement d'utiliser le "3DVAR" d'origine. Les algorithmes
+"3DVAR" et "3DVAR-Incr" (et pas les autres) permettent la modification du point
+initial de leur minimisation, mais ce n'est pas recommandé.
 
 .. ------------------------------------ ..
 .. include:: snippets/Header2Algo02.rst

diff --git a/src/daComposant/daAlgorithms/3DVAR.py b/src/daComposant/daAlgorithms/3DVAR.py
index 5606dfef101064bb5ab1df502b1bf29714de5091..1939d6c91529bacfc4c04dc34136b2ef2bd36e57 100644 (file)
--- a/src/daComposant/daAlgorithms/3DVAR.py
+++ b/src/daComposant/daAlgorithms/3DVAR.py
@@ -53,6 +53,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 ],
             listadv  = [
                 "3DVAR-Std",
+                "Incr3DVAR",
                 "OneCycle3DVAR-Std",
                 ],
             )

diff --git a/src/daSalome/daYacsSchemaCreator/infos_daComposant.py b/src/daSalome/daYacsSchemaCreator/infos_daComposant.py
index c2cfe8fbcd129a0daf87da61f62770c782b500a8..de765208e3c2c3821ddb05d6b031058e4a783eda 100644 (file)
--- a/src/daSalome/daYacsSchemaCreator/infos_daComposant.py
+++ b/src/daSalome/daYacsSchemaCreator/infos_daComposant.py
@@ -69,19 +69,34 @@ AssimAlgos = [
     "3DVAR",
     "4DVAR",
     "Blue",
-    "ExtendedBlue",
+    "DerivativeFreeOptimization",
+    "DifferentialEvolution",
     "EnsembleBlue",
-    "KalmanFilter",
-    "ExtendedKalmanFilter",
     "EnsembleKalmanFilter",
-    "UnscentedKalmanFilter",
+    "ExtendedBlue",
+    "ExtendedKalmanFilter",
+    "KalmanFilter",
+    "LinearLeastSquares",
+    "NonLinearLeastSquares",
+    "ParticleSwarmOptimization",
     "QuantileRegression",
+    "TabuSearch",
+    "UnscentedKalmanFilter",
+    ]
+OptimizationAlgos = [
+    "Blue",
     "DerivativeFreeOptimization",
-    "ParticleSwarmOptimization",
     "DifferentialEvolution",
-    "TabuSearch",
+    "KalmanFilter",
     "LinearLeastSquares",
     "NonLinearLeastSquares",
+    "ParticleSwarmOptimization",
+    "TabuSearch",
+    ]
+ReductionAlgos = [
+    "EnsembleBlue",
+    "EnsembleKalmanFilter",
+    "UnscentedKalmanFilter",
     ]
 CheckAlgos = [
     "FunctionTest",