Update and documentation of InputValuesTest

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

diff --git a/src/daComposant/daAlgorithms/EnsembleBlue.py b/src/daComposant/daAlgorithms/EnsembleBlue.py
index 015757834dec79ae231e240e15b2917fc310b30d..fe2bd3b6e03adadef9f22d06b7156874fa31c3b3 100644 (file)
--- a/src/daComposant/daAlgorithms/EnsembleBlue.py
+++ b/src/daComposant/daAlgorithms/EnsembleBlue.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2017 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
@@ -39,16 +39,26 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["CurrentState", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            listval  = [
+                "Analysis",
+                "CurrentState",
+                "Innovation",
+                "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentState",
+                "SimulatedObservationAtOptimum",
+                ]
             )
         self.defineRequiredParameter(
             name     = "SetSeed",
             typecast = numpy.random.seed,
             message  = "Graine fixée pour le générateur aléatoire",
             )
+        self.requireInputArguments(
+            mandatory= ("Xb", "Y", "HO", "R", "B"),
+            )
 
     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)
+        self._pre_run(Parameters, Xb, Y, R, B, Q)
         #
         # Précalcul des inversions de B et R
         # ----------------------------------
@@ -67,7 +77,6 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         for npar in range(DiagonaleR.size):
             bruit = numpy.random.normal(0,DiagonaleR[npar],nb_ens)
             EnsembleY[npar,:] = Y[npar] + bruit
-        EnsembleY = numpy.matrix(EnsembleY)
         #
         # Initialisation des opérateurs d'observation et de la matrice gain
         # -----------------------------------------------------------------
@@ -86,28 +95,28 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # Calcul du BLUE pour chaque membre de l'ensemble
         # -----------------------------------------------
         for iens in range(nb_ens):
-            HXb = Hm * Xb[iens]
-            if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
-                self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
-            d  = EnsembleY[:,iens] - HXb
-            if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
-                self.StoredVariables["Innovation"].store( numpy.ravel(d) )
-            Xa = Xb[iens] + K*d
+            HXb = numpy.ravel(numpy.dot(Hm, Xb[iens]))
+            if self._toStore("SimulatedObservationAtBackground"):
+                self.StoredVariables["SimulatedObservationAtBackground"].store( HXb )
+            d  = numpy.ravel(EnsembleY[:,iens]) - HXb
+            if self._toStore("Innovation"):
+                self.StoredVariables["Innovation"].store( d )
+            Xa = numpy.ravel(Xb[iens]) + numpy.dot(K, d)
             self.StoredVariables["CurrentState"].store( Xa )
-            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
-                self.StoredVariables["SimulatedObservationAtCurrentState"].store( Hm * Xa )
+            if self._toStore("SimulatedObservationAtCurrentState"):
+                self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.dot(Hm, Xa) )
         #
         # Fabrication de l'analyse
         # ------------------------
         Members = self.StoredVariables["CurrentState"][-nb_ens:]
-        Xa = numpy.matrix( Members ).mean(axis=0)
-        self.StoredVariables["Analysis"].store( Xa.A1 )
-        if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
-            self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel( Hm * Xa ) )
+        Xa = numpy.array( Members ).mean(axis=0)
+        self.StoredVariables["Analysis"].store( Xa )
+        if self._toStore("SimulatedObservationAtOptimum"):
+            self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.dot(Hm, Xa) )
         #
         self._post_run(HO)
         return 0
 
 # ==============================================================================
 if __name__ == "__main__":
-    print('\n AUTODIAGNOSTIC \n')
+    print('\n AUTODIAGNOSTIC\n')