Improvement of algorithms arguments validation and tests

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

diff --git a/src/daComposant/daAlgorithms/ExtendedBlue.py b/src/daComposant/daAlgorithms/ExtendedBlue.py
index e80c39cb03a981b42fa8fd1961c9af19a6a914f2..73451e01ea5a0d582b57f91ad3184f23aa1da2ef 100644 (file)
--- a/src/daComposant/daAlgorithms/ExtendedBlue.py
+++ b/src/daComposant/daAlgorithms/ExtendedBlue.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2018 EDF R&D
+# Copyright (C) 2008-2020 EDF R&D
 #
 # This library is free software; you can redistribute it and/or
 # modify it under the terms of the GNU Lesser General Public
@@ -40,25 +40,31 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
             listval  = [
+                "Analysis",
                 "APosterioriCorrelations",
                 "APosterioriCovariance",
                 "APosterioriStandardDeviations",
                 "APosterioriVariances",
                 "BMA",
-                "OMA",
-                "OMB",
-                "CurrentState",
                 "CostFunctionJ",
+                "CostFunctionJAtCurrentOptimum",
                 "CostFunctionJb",
+                "CostFunctionJbAtCurrentOptimum",
                 "CostFunctionJo",
+                "CostFunctionJoAtCurrentOptimum",
+                "CurrentOptimum",
+                "CurrentState",
                 "Innovation",
+                "MahalanobisConsistency",
+                "OMA",
+                "OMB",
                 "SigmaBck2",
                 "SigmaObs2",
-                "MahalanobisConsistency",
-                "SimulationQuantiles",
                 "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentOptimum",
                 "SimulatedObservationAtCurrentState",
                 "SimulatedObservationAtOptimum",
+                "SimulationQuantiles",
                 ]
             )
         self.defineRequiredParameter(
@@ -91,9 +97,14 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         self.requireInputArguments(
             mandatory= ("Xb", "Y", "HO", "R", "B"),
             )
+        self.setAttributes(tags=(
+            "DataAssimilation",
+            "NonLinear",
+            "Filter",
+            ))
 
     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
-        self._pre_run(Parameters, Xb, Y, R, B, Q)
+        self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
         #
         Hm = HO["Tangent"].asMatrix(Xb)
         Hm = Hm.reshape(Y.size,Xb.size) # ADAO & check shape
@@ -137,17 +148,22 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # Calcul de la fonction coût
         # --------------------------
         if self._parameters["StoreInternalVariables"] or \
-            self._toStore("CostFunctionJ") or \
+            self._toStore("CostFunctionJ")  or self._toStore("CostFunctionJAtCurrentOptimum") or \
+            self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+            self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
             self._toStore("OMA") or \
             self._toStore("SigmaObs2") or \
             self._toStore("MahalanobisConsistency") or \
+            self._toStore("SimulatedObservationAtCurrentOptimum") or \
             self._toStore("SimulatedObservationAtCurrentState") or \
             self._toStore("SimulatedObservationAtOptimum") or \
             self._toStore("SimulationQuantiles"):
             HXa  = numpy.matrix(numpy.ravel( H( Xa ) )).T
             oma = Y - HXa
         if self._parameters["StoreInternalVariables"] or \
-            self._toStore("CostFunctionJ") or \
+            self._toStore("CostFunctionJ")  or self._toStore("CostFunctionJAtCurrentOptimum") or \
+            self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+            self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
             self._toStore("MahalanobisConsistency"):
             Jb  = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
             Jo  = float( 0.5 * oma.T * RI * oma )
@@ -155,6 +171,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )
+            self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( Jb )
+            self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( Jo )
+            self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( J )
         #
         # Calcul de la covariance d'analyse
         # ---------------------------------
@@ -178,6 +197,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # ---------------------------------------
         if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
             self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
+        if self._toStore("CurrentOptimum"):
+            self.StoredVariables["CurrentOptimum"].store( numpy.ravel(Xa) )
         if self._toStore("Innovation"):
             self.StoredVariables["Innovation"].store( numpy.ravel(d) )
         if self._toStore("BMA"):
@@ -222,6 +243,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
         if self._toStore("SimulatedObservationAtCurrentState"):
             self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
+        if self._toStore("SimulatedObservationAtCurrentOptimum"):
+            self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( numpy.ravel(HXa) )
         if self._toStore("SimulatedObservationAtOptimum"):
             self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
         #
@@ -230,4 +253,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
 
 # ==============================================================================
 if __name__ == "__main__":
-    print('\n AUTODIAGNOSTIC \n')
+    print('\n AUTODIAGNOSTIC\n')