Update internal settings

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

diff --git a/src/daComposant/daAlgorithms/EnsembleBlue.py b/src/daComposant/daAlgorithms/EnsembleBlue.py
index 7c085a3e1dae45c68fdbeb8d1669b62ea1e2aa03..5bc3cdc9722bdae76675c563a87829b2c432f26a 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) 2008-2019 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
@@ -39,7 +39,13 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["CurrentState", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            listval  = [
+                "CurrentState",
+                "Innovation",
+                "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentState",
+                "SimulatedObservationAtOptimum",
+                ]
             )
         self.defineRequiredParameter(
             name     = "SetSeed",
@@ -47,11 +53,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             message  = "Graine fixée pour le générateur aléatoire",
             )
         self.requireInputArguments(
-            mandatory= ("Xb", "Y", "HO", "R", "B" ),
+            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, R, B, Q)
+        self._pre_run(Parameters, Xb, Y, R, B, Q)
         #
         # Précalcul des inversions de B et R
         # ----------------------------------
@@ -90,14 +96,14 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # -----------------------------------------------
         for iens in range(nb_ens):
             HXb = Hm * Xb[iens]
-            if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("SimulatedObservationAtBackground"):
                 self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
             d  = EnsembleY[:,iens] - HXb
-            if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("Innovation"):
                 self.StoredVariables["Innovation"].store( numpy.ravel(d) )
             Xa = Xb[iens] + K*d
             self.StoredVariables["CurrentState"].store( Xa )
-            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("SimulatedObservationAtCurrentState"):
                 self.StoredVariables["SimulatedObservationAtCurrentState"].store( Hm * Xa )
         #
         # Fabrication de l'analyse
@@ -105,7 +111,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         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"]:
+        if self._toStore("SimulatedObservationAtOptimum"):
             self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel( Hm * Xa ) )
         #
         self._post_run(HO)