Minor corrections and output variables checking

diff --git a/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py b/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py
index b1605bb48579b907906b423fa5abf35ea7364703..7d74ed6c996ddbbd1d342f9b923eb8cdc4154a7d 100644 (file)
--- a/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py
+++ b/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py
@@ -25,8 +25,6 @@ from daCore import BasicObjects, PlatformInfo
 import numpy, math
 mfp = PlatformInfo.PlatformInfo().MaximumPrecision()
 
-# Using "Ensemble Kalman filtering", L. HOUTEKAMER and HERSCHEL L. MITCHELL, QJRMS (2005), 131, pp. 3269–3289
-
 # ==============================================================================
 class ElementaryAlgorithm(BasicObjects.Algorithm):
     def __init__(self):
@@ -38,20 +36,6 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             message  = "Nombre de membres dans l'ensemble",
             minval   = -1,
             )
-        self.defineRequiredParameter(
-            name     = "Minimizer",
-            default  = "EnKF",
-            typecast = str,
-            message  = "Schéma de mise a jour des informations d'ensemble",
-            listval  = ["EnKF", "ETKF", "DEnKF"],
-            )
-        self.defineRequiredParameter(
-            name     = "ConstrainedBy",
-            default  = "EstimateProjection",
-            typecast = str,
-            message  = "Prise en compte des contraintes",
-            listval  = ["EstimateProjection"],
-            )
         self.defineRequiredParameter(
             name     = "EstimationOf",
             default  = "State",
@@ -59,6 +43,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             message  = "Estimation d'etat ou de parametres",
             listval  = ["State", "Parameters"],
             )
+        self.defineRequiredParameter(
+            name     = "SetSeed",
+            typecast = numpy.random.seed,
+            message  = "Graine fixée pour le générateur aléatoire",
+            )
         self.defineRequiredParameter(
             name     = "StoreInternalVariables",
             default  = False,
@@ -70,16 +59,18 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation"]
-            )
-        self.defineRequiredParameter( # Pas de type
-            name     = "Bounds",
-            message  = "Liste des valeurs de bornes",
-            )
-        self.defineRequiredParameter(
-            name     = "SetSeed",
-            typecast = numpy.random.seed,
-            message  = "Graine fixée pour le générateur aléatoire",
+            listval  = [
+                "APosterioriCorrelations",
+                "APosterioriCovariance",
+                "APosterioriStandardDeviations",
+                "APosterioriVariances",
+                "BMA",
+                "CostFunctionJ",
+                "CostFunctionJb",
+                "CostFunctionJo",
+                "CurrentState",
+                "Innovation",
+                ]
             )
         self.requireInputArguments(
             mandatory= ("Xb", "Y", "HO", "R", "B"),
@@ -115,7 +106,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         # Précalcul des inversions de B et R
         # ----------------------------------
-        if self._parameters["StoreInternalVariables"]:
+        if self._parameters["StoreInternalVariables"] \
+            or "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] \
+            or "CostFunctionJb" in self._parameters["StoreSupplementaryCalculations"] \
+            or "CostFunctionJo" in self._parameters["StoreSupplementaryCalculations"] \
+                or "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
             BI = B.getI()
             RI = R.getI()
         BIdemi = B.choleskyI()
@@ -198,6 +193,28 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             self.StoredVariables["Analysis"].store( Xa )
             #
             del Yo, PfHT, HPfHT
+            if self._parameters["StoreInternalVariables"] \
+                or "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] \
+                or "CostFunctionJb" in self._parameters["StoreSupplementaryCalculations"] \
+                or "CostFunctionJo" in self._parameters["StoreSupplementaryCalculations"] \
+                or "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"] \
+                or "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+                d = Ynpu - numpy.asmatrix(numpy.ravel( H((Xa, Un)) )).T
+                self.StoredVariables["Innovation"].store( d )
+            if self._parameters["StoreInternalVariables"] \
+                or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["CurrentState"].store( Xn )
+            if self._parameters["StoreInternalVariables"] \
+                or "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] \
+                or "CostFunctionJb" in self._parameters["StoreSupplementaryCalculations"] \
+                or "CostFunctionJo" in self._parameters["StoreSupplementaryCalculations"] \
+                or "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
+                Jb  = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
+                Jo  = 0.5 * d.T * RI * d
+                J   = float( Jb ) + float( Jo )
+                self.StoredVariables["CostFunctionJb"].store( Jb )
+                self.StoredVariables["CostFunctionJo"].store( Jo )
+                self.StoredVariables["CostFunctionJ" ].store( J )
             if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
                 Ht = HO["Tangent"].asMatrix(ValueForMethodForm = Xa)
                 Ht = Ht.reshape(__p,__n) # ADAO & check shape
@@ -207,6 +224,20 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 Pf = (1./(__m-1)) * Pf
                 Pn = (1. - K * Ht) * Pf
                 self.StoredVariables["APosterioriCovariance"].store( Pn )
+                if J < previousJMinimum:
+                    previousJMinimum  = J
+                    Xa                = Xn
+                    covarianceXa      = Pn
+        #
+        # Stockage supplementaire de l'optimum en estimation de parametres
+        # ----------------------------------------------------------------
+        if self._parameters["EstimationOf"] == "Parameters":
+            self.StoredVariables["Analysis"].store( Xa.A1 )
+            if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["APosterioriCovariance"].store( covarianceXa )
+        #
+        if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
         #
         self._post_run(HO)
         return 0

diff --git a/src/daComposant/daAlgorithms/KalmanFilter.py b/src/daComposant/daAlgorithms/KalmanFilter.py
index fa413cd874e1595bd438471f805f59fd1227f79b..b6bed332abcf66286903a88df048e758d0264c58 100644 (file)
--- a/src/daComposant/daAlgorithms/KalmanFilter.py
+++ b/src/daComposant/daAlgorithms/KalmanFilter.py
@@ -82,7 +82,10 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         # Précalcul des inversions de B et R
         # ----------------------------------
-        if self._parameters["StoreInternalVariables"]:
+        if self._parameters["StoreInternalVariables"] \
+            or "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] \
+            or "CostFunctionJb" in self._parameters["StoreSupplementaryCalculations"] \
+            or "CostFunctionJo" in self._parameters["StoreSupplementaryCalculations"]:
             BI = B.getI()
             RI = R.getI()
         #
@@ -143,7 +146,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 self.StoredVariables["APosterioriCovariance"].store( Pn )
             if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["Innovation"].store( numpy.ravel( d.A1 ) )
-            if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._parameters["StoreInternalVariables"] \
+                or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["CurrentState"].store( Xn )
             if self._parameters["StoreInternalVariables"] \
                 or "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] \

diff --git a/src/daComposant/daCore/Aidsm.py b/src/daComposant/daCore/Aidsm.py
index cc7736fcda38278c16e93fca45f73e938f9fd4a6..54e9ca30d85f43295f675e3f0ffe4c8203951563 100644 (file)
--- a/src/daComposant/daCore/Aidsm.py
+++ b/src/daComposant/daCore/Aidsm.py
@@ -689,10 +689,10 @@ class Aidsm(object):
         except Exception as e:
             if isinstance(e, SyntaxError): msg = "at %s: %s"%(e.offset, e.text)
             else: msg = ""
-            raise ValueError("during execution, the following error occurs:\n"+\
+            raise ValueError(("during execution, the following error occurs:\n"+\
                              "\n%s %s\n\nSee also the potential messages, "+\
                              "which can show the origin of the above error, "+\
-                             "in the launching terminal.\n"%(str(e),msg))
+                             "in the launching terminal.\n")%(str(e),msg))
         return 0
 
     def __executePythonScheme(self, FileName=None):