Minor improvements and fixes for internal variables

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

diff --git a/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py b/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py
index ead0c30cb9403eb265eebb568f1f374a8560b6eb..5eb7d5fd007669d436027fbd050b36c455a17c2e 100644 (file)
--- a/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py
+++ b/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py
@@ -29,19 +29,31 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
     def __init__(self):
         BasicObjects.Algorithm.__init__(self, "ENSEMBLEKALMANFILTER")
         self.defineRequiredParameter(
-            name     = "Minimizer",
-            default  = "StochasticEnKF",
+            name     = "Variant",
+            default  = "EnKF",
             typecast = str,
-            message  = "Minimiseur utilisé",
+            message  = "Variant ou formulation de la méthode",
             listval  = [
-                "StochasticEnKF",
+                "EnKF",
                 "ETKF",
+                "ETKF-N",
+                "MLEF",
+                "IEnKF",
+                ],
+            listadv  = [
+                "StochasticEnKF",
+                "EnKF-05",
+                "EnKF-16",
                 "ETKF-KFF",
                 "ETKF-VAR",
-                "ETKF-N",
                 "ETKF-N-11",
                 "ETKF-N-15",
                 "ETKF-N-16",
+                "MLEF-T",
+                "MLEF-B",
+                "IEnKF-T",
+                "IEnKF-B",
+                "IEKF",
                 ],
             )
         self.defineRequiredParameter(
@@ -68,8 +80,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 "MultiplicativeOnBackgroundCovariance",
                 "MultiplicativeOnAnalysisAnomalies",
                 "MultiplicativeOnBackgroundAnomalies",
-                "AdditiveOnBackgroundCovariance",
                 "AdditiveOnAnalysisCovariance",
+                "AdditiveOnBackgroundCovariance",
                 "HybridOnBackgroundCovariance",
                 ],
             )
@@ -86,9 +98,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             typecast = str,
             message  = "Méthode d'inflation d'ensemble",
             listval  = [
+                "SchurLocalization",
+                ],
+            listadv  = [
                 "CovarianceLocalization",
                 "DomainLocalization",
-                "SchurLocalization",
                 "GaspariCohnLocalization",
                 ],
             )
@@ -160,30 +174,51 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
         #
         #--------------------------
-        if self._parameters["Minimizer"] == "StochasticEnKF":
-            NumericObjects.senkf(self, Xb, Y, U, HO, EM, CM, R, B, Q)
+        # Default EnKF = EnKF-16 = StochasticEnKF
+        if   self._parameters["Variant"] == "EnKF-05":
+            NumericObjects.senkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula05")
+        #
+        elif self._parameters["Variant"] in ["EnKF-16", "StochasticEnKF", "EnKF"]:
+            NumericObjects.senkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula16")
         #
         #--------------------------
         # Default ETKF = ETKF-KFF
-        elif self._parameters["Minimizer"] in ["ETKF-KFF", "ETKF"]:
-            NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, KorV="KalmanFilterFormula")
+        elif self._parameters["Variant"] in ["ETKF-KFF", "ETKF"]:
+            NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula")
         #
-        elif self._parameters["Minimizer"] == "ETKF-VAR":
-            NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, KorV="Variational")
+        elif self._parameters["Variant"] == "ETKF-VAR":
+            NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="Variational")
         #
         #--------------------------
         # Default ETKF-N = ETKF-N-16
-        elif self._parameters["Minimizer"] == "ETKF-N-11":
-            NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, KorV="FiniteSize11")
+        elif self._parameters["Variant"] == "ETKF-N-11":
+            NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="FiniteSize11")
+        #
+        elif self._parameters["Variant"] == "ETKF-N-15":
+            NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="FiniteSize15")
         #
-        elif self._parameters["Minimizer"] == "ETKF-N-15":
-            NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, KorV="FiniteSize15")
+        elif self._parameters["Variant"] in ["ETKF-N-16", "ETKF-N"]:
+            NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="FiniteSize16")
+        #
+        #--------------------------
+        # Default MLEF = MLEF-T
+        elif self._parameters["Variant"] in ["MLEF-T", "MLEF"]:
+            NumericObjects.mlef(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False)
         #
-        elif self._parameters["Minimizer"] in ["ETKF-N-16", "ETKF-N"]:
-            NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, KorV="FiniteSize16")
+        elif self._parameters["Variant"] == "MLEF-B":
+            NumericObjects.mlef(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=True)
         #
+        #--------------------------
+        # Default IEnKF = IEnKF-T
+        elif self._parameters["Variant"] in ["IEnKF-T", "IEnKF"]:
+            NumericObjects.ienkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False)
+        #
+        elif self._parameters["Variant"] in ["IEnKF-B", "IEKF"]:
+            NumericObjects.ienkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=True)
+        #
+        #--------------------------
         else:
-            raise ValueError("Error in Minimizer name: %s"%self._parameters["Minimizer"])
+            raise ValueError("Error in Variant name: %s"%self._parameters["Variant"])
         #
         self._post_run(HO)
         return 0