# -*- 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
# -----------------------------------------------
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
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)