Update and documentation of InputValuesTest

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

diff --git a/src/daComposant/daAlgorithms/TabuSearch.py b/src/daComposant/daAlgorithms/TabuSearch.py
index 23a6c85852ca04a84bfdde1f7d46c7ea44acea5b..11fb806b91230ebeac58949b27c5c72070e00d65 100644 (file)
--- a/src/daComposant/daAlgorithms/TabuSearch.py
+++ b/src/daComposant/daAlgorithms/TabuSearch.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
@@ -103,7 +103,20 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            listval  = [
+                "Analysis",
+                "BMA",
+                "CurrentState",
+                "CostFunctionJ",
+                "CostFunctionJb",
+                "CostFunctionJo",
+                "Innovation",
+                "OMA",
+                "OMB",
+                "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentState",
+                "SimulatedObservationAtOptimum",
+                ]
             )
         self.defineRequiredParameter( # Pas de type
             name     = "Bounds",
@@ -223,9 +236,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             if _qualityS < _qualityBest:
                 _Best, _qualityBest   =   _S, _qualityS
             #
-            if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
                 self.StoredVariables["CurrentState"].store( _Best )
-            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("SimulatedObservationAtCurrentState"):
                 _HmX = Hm( numpy.asmatrix(numpy.ravel( _Best )).T )
                 _HmX = numpy.asmatrix(numpy.ravel( _HmX )).T
                 self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HmX )
@@ -239,28 +252,28 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         self.StoredVariables["Analysis"].store( Xa.A1 )
         #
-        if "Innovation"                       in self._parameters["StoreSupplementaryCalculations"] or \
-           "OMB"                              in self._parameters["StoreSupplementaryCalculations"] or \
-           "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("Innovation") or \
+            self._toStore("OMB") or \
+            self._toStore("SimulatedObservationAtBackground"):
             HXb = Hm(Xb)
             d = Y - HXb
-        if "OMA"                           in self._parameters["StoreSupplementaryCalculations"] or \
-           "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("OMA") or \
+           self._toStore("SimulatedObservationAtOptimum"):
             HXa = Hm(Xa)
         #
         # Calculs et/ou stockages supplémentaires
         # ---------------------------------------
-        if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("Innovation"):
             self.StoredVariables["Innovation"].store( numpy.ravel(d) )
-        if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("BMA"):
             self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
-        if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("OMA"):
             self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) )
-        if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("OMB"):
             self.StoredVariables["OMB"].store( numpy.ravel(d) )
-        if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("SimulatedObservationAtBackground"):
             self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
-        if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("SimulatedObservationAtOptimum"):
             self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
         #
         self._post_run(HO)
@@ -268,4 +281,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
 
 # ==============================================================================
 if __name__ == "__main__":
-    print('\n AUTODIAGNOSTIC \n')
+    print('\n AUTODIAGNOSTIC\n')