Unified management of supplementary variables to calculate or store

diff --git a/src/daComposant/daAlgorithms/3DVAR.py b/src/daComposant/daAlgorithms/3DVAR.py
index 8997f3a1cdfe84799ebcc12de6496c8ec40cfcb1..4178c858c30d5062a416101518d810579d6b5cd4 100644 (file)
--- a/src/daComposant/daAlgorithms/3DVAR.py
+++ b/src/daComposant/daAlgorithms/3DVAR.py
@@ -18,6 +18,7 @@
 #
 #  See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
 #
+#  Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
 import logging
 from daCore import BasicObjects, PlatformInfo
@@ -72,25 +73,26 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             typecast = float,
             message  = "Maximum des composantes du gradient lors de l'arrêt",
             )
-        self.defineRequiredParameter(
-            name     = "CalculateAPosterioriCovariance",
-            default  = False,
-            typecast = bool,
-            message  = "Calcul de la covariance a posteriori",
-            )
         self.defineRequiredParameter(
             name     = "StoreInternalVariables",
             default  = False,
             typecast = bool,
             message  = "Stockage des variables internes ou intermédiaires du calcul",
             )
+        self.defineRequiredParameter(
+            name     = "StoreSupplementaryCalculations",
+            default  = [],
+            typecast = tuple,
+            message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+            listval  = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaObs2"]
+            )
 
     def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
         """
         Calcul de l'estimateur 3D-VAR
         """
         logging.debug("%s Lancement"%self._name)
-        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
+        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         #
         # Paramètres de pilotage
         # ----------------------
@@ -271,11 +273,10 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         logging.debug("%s Analyse Xa = %s"%(self._name, Xa))
         #
         self.StoredVariables["Analysis"].store( Xa.A1 )
-        self.StoredVariables["Innovation"].store( d.A1 )
         #
         # Calcul de la covariance d'analyse
         # ---------------------------------
-        if self._parameters["CalculateAPosterioriCovariance"]:
+        if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
             HessienneI = []
             nb = len(Xini)
             for i in range(nb):
@@ -293,7 +294,20 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                     raise ValueError("The 3DVAR a posteriori covariance matrix A is not symmetric positive-definite. Check your B and R a priori covariances.")
             self.StoredVariables["APosterioriCovariance"].store( A )
         #
-        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("MB")))
+        # Calculs et/ou stockages supplémentaires
+        # ---------------------------------------
+        if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["Innovation"].store( numpy.asmatrix(d).flatten().A1 )
+        if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["BMA"].store( numpy.asmatrix(Xb - Xa).flatten().A1 )
+        if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["OMA"].store( numpy.asmatrix(Y - Hm(Xa)).flatten().A1 )
+        if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["OMB"].store( numpy.asmatrix(d).flatten().A1 )
+        if "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["SigmaObs2"].store( float( (d.T * (Y-Hm(Xa))) / R.trace() ) )
+        #
+        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         logging.debug("%s Terminé"%self._name)
         #
         return 0

diff --git a/src/daComposant/daAlgorithms/Blue.py b/src/daComposant/daAlgorithms/Blue.py
index b15561177fd46adf5f81e9faff9c9d4ccf9b6a93..906989f3ac2b3d7cf70b4927a1573ee9eba21192 100644 (file)
--- a/src/daComposant/daAlgorithms/Blue.py
+++ b/src/daComposant/daAlgorithms/Blue.py
@@ -18,6 +18,7 @@
 #
 #  See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
 #
+#  Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
 import logging
 from daCore import BasicObjects, PlatformInfo
@@ -30,10 +31,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
     def __init__(self):
         BasicObjects.Algorithm.__init__(self, "BLUE")
         self.defineRequiredParameter(
-            name     = "CalculateAPosterioriCovariance",
-            default  = False,
-            typecast = bool,
-            message  = "Calcul de la covariance a posteriori",
+            name     = "StoreSupplementaryCalculations",
+            default  = [],
+            typecast = tuple,
+            message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+            listval  = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaBck2", "SigmaObs2"]
             )
 
     def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
@@ -41,7 +43,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         Calcul de l'estimateur BLUE (ou Kalman simple, ou Interpolation Optimale)
         """
         logging.debug("%s Lancement"%self._name)
-        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
+        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         #
         # Paramètres de pilotage
         # ----------------------
@@ -86,7 +88,17 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         if max(Y.shape) != max(HXb.shape):
             raise ValueError("The shapes %s of observations Y and %s of observed calculation H(X) are different, they have to be identical."%(Y.shape,HXb.shape))
         d  = Y - HXb
-        logging.debug("%s Innovation d = %s"%(self._name, d))
+        Jb  = 0.
+        Jo  = 0.5 * d.T * RI * d
+        J   = float( Jb ) + float( Jo )
+        logging.debug("%s Innovation d    = %s"%(self._name, d))
+        logging.debug("%s CostFunction Jb = %s"%(self._name, Jb))
+        logging.debug("%s CostFunction Jo = %s"%(self._name, Jo))
+        logging.debug("%s CostFunction J  = %s"%(self._name, J))
+        #
+        self.StoredVariables["CostFunctionJb"].store( Jb )
+        self.StoredVariables["CostFunctionJo"].store( Jo )
+        self.StoredVariables["CostFunctionJ" ].store( J )
         #
         # Calcul de la matrice de gain et de l'analyse
         # --------------------------------------------
@@ -103,22 +115,23 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         # Calcul de la fonction coût
         # --------------------------
+        oma = Y - Hm * Xa
         Jb  = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
-        Jo  = 0.5 * d.T * RI * d
+        Jo  = 0.5 * oma.T * RI * oma
         J   = float( Jb ) + float( Jo )
+        logging.debug("%s OMA             = %s"%(self._name, oma))
         logging.debug("%s CostFunction Jb = %s"%(self._name, Jb))
         logging.debug("%s CostFunction Jo = %s"%(self._name, Jo))
         logging.debug("%s CostFunction J  = %s"%(self._name, J))
         #
         self.StoredVariables["Analysis"].store( Xa.A1 )
-        self.StoredVariables["Innovation"].store( d.A1 )
         self.StoredVariables["CostFunctionJb"].store( Jb )
         self.StoredVariables["CostFunctionJo"].store( Jo )
         self.StoredVariables["CostFunctionJ" ].store( J )
         #
         # Calcul de la covariance d'analyse
         # ---------------------------------
-        if self._parameters["CalculateAPosterioriCovariance"]:
+        if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
             A = B - K * Hm * B
             if logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug
                 try:
@@ -127,7 +140,22 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                     raise ValueError("The BLUE a posteriori covariance matrix A is not symmetric positive-definite. Check your B and R a priori covariances.")
             self.StoredVariables["APosterioriCovariance"].store( A )
         #
-        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
+        # Calculs et/ou stockages supplémentaires
+        # ---------------------------------------
+        if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["Innovation"].store( numpy.asmatrix(d).flatten().A1 )
+        if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["BMA"].store( numpy.asmatrix(Xb - Xa).flatten().A1 )
+        if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["OMA"].store( numpy.asmatrix(oma).flatten().A1 )
+        if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["OMB"].store( numpy.asmatrix(d).flatten().A1 )
+        if "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["SigmaObs2"].store( float( (d.T * (Y-Hm*Xa)) / R.trace() ) )
+        if "SigmaBck2" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
+        #
+        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         logging.debug("%s Terminé"%self._name)
         #
         return 0

diff --git a/src/daComposant/daAlgorithms/LinearLeastSquares.py b/src/daComposant/daAlgorithms/LinearLeastSquares.py
index 0b07c5b2a016040f5c9620fc6e033a616ecaddaf..1a8ea254d2c8e8ad147574f7ad9851bebd22a155 100644 (file)
--- a/src/daComposant/daAlgorithms/LinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/LinearLeastSquares.py
@@ -18,15 +18,25 @@
 #
 #  See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
 #
+#  Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
 import logging
 from daCore import BasicObjects, PlatformInfo
 m = PlatformInfo.SystemUsage()
 
+import numpy
+
 # ==============================================================================
 class ElementaryAlgorithm(BasicObjects.Algorithm):
     def __init__(self):
         BasicObjects.Algorithm.__init__(self, "LINEARLEASTSQUARES")
+        self.defineRequiredParameter(
+            name     = "StoreSupplementaryCalculations",
+            default  = [],
+            typecast = tuple,
+            message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+            listval  = ["OMA"]
+            )
 
     def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
         """
@@ -34,7 +44,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         (assimilation variationnelle sans ébauche)
         """
         logging.debug("%s Lancement"%self._name)
-        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
+        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         #
         # Paramètres de pilotage
         # ----------------------
@@ -60,21 +70,25 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         # Calcul de la fonction coût
         # --------------------------
-        d  = Y - Hm * Xa
+        oma = Y - Hm * Xa
         Jb  = 0.
-        Jo  = 0.5 * d.T * RI * d
+        Jo  = 0.5 * oma.T * RI * oma
         J   = float( Jb ) + float( Jo )
         logging.debug("%s CostFunction Jb = %s"%(self._name, Jb))
         logging.debug("%s CostFunction Jo = %s"%(self._name, Jo))
         logging.debug("%s CostFunction J  = %s"%(self._name, J))
         #
         self.StoredVariables["Analysis"].store( Xa.A1 )
-        self.StoredVariables["Innovation"].store( d.A1 )
         self.StoredVariables["CostFunctionJb"].store( Jb )
         self.StoredVariables["CostFunctionJo"].store( Jo )
         self.StoredVariables["CostFunctionJ" ].store( J )
         #
-        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
+        # Calculs et/ou stockages supplémentaires
+        # ---------------------------------------
+        if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["OMA"].store( numpy.asmatrix(oma).flatten().A1 )
+        #
+        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         logging.debug("%s Terminé"%self._name)
         #
         return 0

diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
index cee9082e535cb9d689bf9c33755517b90707aa7f..f64fdc979b48a2733c0030363f895022c21e3d1d 100644 (file)
--- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
@@ -18,6 +18,7 @@
 #
 #  See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
 #
+#  Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
 import logging
 from daCore import BasicObjects, PlatformInfo
@@ -78,6 +79,13 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             typecast = bool,
             message  = "Stockage des variables internes ou intermédiaires du calcul",
             )
+        self.defineRequiredParameter(
+            name     = "StoreSupplementaryCalculations",
+            default  = [],
+            typecast = tuple,
+            message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+            listval  = ["BMA", "OMA", "OMB", "Innovation"]
+            )
 
     def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
         """
@@ -85,7 +93,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         (assimilation variationnelle sans ébauche)
         """
         logging.debug("%s Lancement"%self._name)
-        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
+        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         #
         # Paramètres de pilotage
         # ----------------------
@@ -266,9 +274,19 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         logging.debug("%s Analyse Xa = %s"%(self._name, Xa))
         #
         self.StoredVariables["Analysis"].store( Xa.A1 )
-        self.StoredVariables["Innovation"].store( d.A1 )
         #
-        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("MB")))
+        # Calculs et/ou stockages supplémentaires
+        # ---------------------------------------
+        if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["Innovation"].store( numpy.asmatrix(d).flatten().A1 )
+        if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["BMA"].store( numpy.asmatrix(Xb - Xa).flatten().A1 )
+        if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["OMA"].store( numpy.asmatrix(Y - Hm(Xa)).flatten().A1 )
+        if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["OMB"].store( numpy.asmatrix(d).flatten().A1 )
+        #
+        logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
         logging.debug("%s Terminé"%self._name)
         #
         return 0

diff --git a/src/daComposant/daCore/AssimilationStudy.py b/src/daComposant/daCore/AssimilationStudy.py
index 3f573de4ebed98d1cf6ab63b9f88fd71bb4cf4a4..371a28eec34b7b35a30ea0611772c27f2d8ed320 100644 (file)
--- a/src/daComposant/daCore/AssimilationStudy.py
+++ b/src/daComposant/daCore/AssimilationStudy.py
@@ -908,7 +908,6 @@ if __name__ == "__main__":
     print "Demi-somme         :", list((numpy.array([0, 1, 2])+numpy.array([0.5, 1.5, 2.5]))/2)
     print "  qui doit être identique à :"
     print "Analyse résultante :", ADD.get("Analysis").valueserie(0)
-    print "Innovation         :", ADD.get("Innovation").valueserie(0)
     print
     
     print "Algorithmes disponibles.......................:", ADD.get_available_algorithms()

diff --git a/src/daComposant/daCore/BasicObjects.py b/src/daComposant/daCore/BasicObjects.py
index 0c15f3d85c320b3727fae67f2c8c9037cb3d3ed6..274482a90844f7d9edb290347d0dbe3fb03b9ae3 100644 (file)
--- a/src/daComposant/daCore/BasicObjects.py
+++ b/src/daComposant/daCore/BasicObjects.py
@@ -254,11 +254,16 @@ class Algorithm:
             else:                __val = typecast( value )
         #
         if minval is not None and __val < minval:
-            raise ValueError("The parameter named \"%s\" of value %s can not be less than %s."%(name, __val, minval))
+            raise ValueError("The parameter named \"%s\" of value \"%s\" can not be less than %s."%(name, __val, minval))
         if maxval is not None and __val > maxval:
-            raise ValueError("The parameter named \"%s\" of value %s can not be greater than %s."%(name, __val, maxval))
-        if listval is not None and __val not in listval:
-            raise ValueError("The parameter named \"%s\" of value %s has to be in the list %s."%(name, __val, listval))
+            raise ValueError("The parameter named \"%s\" of value \"%s\" can not be greater than %s."%(name, __val, maxval))
+        if listval is not None:
+            if typecast is list or typecast is tuple or type(__val) is list or type(__val) is tuple:
+                for v in __val:
+                    if v not in listval:
+                        raise ValueError("The value \"%s\" of the parameter named \"%s\" is not allowed, it has to be in the list %s."%(v, name, listval))
+            elif __val not in listval:
+                raise ValueError("The value \"%s\" of the parameter named \"%s\" is not allowed, it has to be in the list %s."%( __val, name,listval))
         return __val
 
     def setParameters(self, fromDico={}):