Improving warning message for available DFO minimizer choice

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

diff --git a/src/daComposant/daAlgorithms/LinearLeastSquares.py b/src/daComposant/daAlgorithms/LinearLeastSquares.py
index 141db1cc08805cefd37c364ffb462a644f181622..0f47c399548b5f89ff86f2d1a24f061a519fc618 100644 (file)
--- a/src/daComposant/daAlgorithms/LinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/LinearLeastSquares.py
@@ -1,24 +1,24 @@
-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
 #
-#  Copyright (C) 2008-2014 EDF R&D
+# Copyright (C) 2008-2018 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
-#  License as published by the Free Software Foundation; either
-#  version 2.1 of the License.
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License.
 #
-#  This library is distributed in the hope that it will be useful,
-#  but WITHOUT ANY WARRANTY; without even the implied warranty of
-#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-#  Lesser General Public License for more details.
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
 #
-#  You should have received a copy of the GNU Lesser General Public
-#  License along with this library; if not, write to the Free Software
-#  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA
 #
-#  See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
+# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
 #
-#  Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
 import logging
 from daCore import BasicObjects
@@ -32,25 +32,22 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             name     = "StoreInternalVariables",
             default  = False,
             typecast = bool,
-            message  = "Stockage des variables internes ou intermédiaires du calcul",
+            message  = "Stockage des variables internes ou intermédiaires du calcul",
             )
         self.defineRequiredParameter(
             name     = "StoreSupplementaryCalculations",
             default  = [],
             typecast = tuple,
-            message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["OMA"]
+            message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+            listval  = ["OMA", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            )
+        self.requireInputArguments(
+            mandatory= ("Y", "HO", "R"),
             )
 
     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()
-        #
-        # Paramètres de pilotage
-        # ----------------------
-        self.setParameters(Parameters)
+        self._pre_run(Parameters, Xb, Y, R, B, Q)
         #
-        # Opérateur d'observation
-        # -----------------------
         Hm = HO["Tangent"].asMatrix(None)
         Hm = Hm.reshape(Y.size,-1) # ADAO & check shape
         Ha = HO["Adjoint"].asMatrix(None)
@@ -64,11 +61,16 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         Xa =  K * Y
         self.StoredVariables["Analysis"].store( Xa.A1 )
         #
-        # Calcul de la fonction coût
+        # Calcul de la fonction coût
         # --------------------------
-        if self._parameters["StoreInternalVariables"] or "OMA" in self._parameters["StoreSupplementaryCalculations"]:
-            oma = Y - Hm * Xa
-        if self._parameters["StoreInternalVariables"]:
+        if self._parameters["StoreInternalVariables"] or \
+           "CostFunctionJ"                 in self._parameters["StoreSupplementaryCalculations"] or \
+           "OMA"                           in self._parameters["StoreSupplementaryCalculations"] or \
+           "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            HXa = Hm * Xa
+            oma = Y - HXa
+        if self._parameters["StoreInternalVariables"] or \
+           "CostFunctionJ"                 in self._parameters["StoreSupplementaryCalculations"]:
             Jb  = 0.
             Jo  = 0.5 * oma.T * RI * oma
             J   = float( Jb ) + float( Jo )
@@ -76,14 +78,20 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )
         #
-        # Calculs et/ou stockages supplémentaires
+        # Calculs et/ou stockages supplémentaires
         # ---------------------------------------
+        if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
         if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
             self.StoredVariables["OMA"].store( numpy.ravel(oma) )
+        if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
+        if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
         #
         self._post_run(HO)
         return 0
 
 # ==============================================================================
 if __name__ == "__main__":
-    print '\n AUTODIAGNOSTIC \n'
+    print('\n AUTODIAGNOSTIC \n')