Adding user time measure

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

diff --git a/src/daComposant/daAlgorithms/LinearLeastSquares.py b/src/daComposant/daAlgorithms/LinearLeastSquares.py
index 66b33a9b2a0984f9be05853583762740334a1631..d63bbdec41121c07532800b54b0dd7b4bc36538f 100644 (file)
--- a/src/daComposant/daAlgorithms/LinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/LinearLeastSquares.py
@@ -1,6 +1,6 @@
-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2016 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
@@ -32,28 +32,44 @@ 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", "CurrentState", "CostFunctionJ", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+            listval  = [
+                "Analysis",
+                "CostFunctionJ",
+                "CostFunctionJAtCurrentOptimum",
+                "CostFunctionJb",
+                "CostFunctionJbAtCurrentOptimum",
+                "CostFunctionJo",
+                "CostFunctionJoAtCurrentOptimum",
+                "CurrentOptimum",
+                "CurrentState",
+                "OMA",
+                "SimulatedObservationAtCurrentOptimum",
+                "SimulatedObservationAtCurrentState",
+                "SimulatedObservationAtOptimum",
+                ]
             )
+        self.requireInputArguments(
+            mandatory= ("Y", "HO", "R"),
+            )
+        self.setAttributes(tags=(
+            "Optimization",
+            "Linear",
+            "Variational",
+            ))
 
     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, U, HO, EM, CM, R, B, Q)
         #
-        # Opérateur d'observation
-        # -----------------------
-        Hm = HO["Tangent"].asMatrix(None)
+        Hm = HO["Tangent"].asMatrix(Xb)
         Hm = Hm.reshape(Y.size,-1) # ADAO & check shape
-        Ha = HO["Adjoint"].asMatrix(None)
+        Ha = HO["Adjoint"].asMatrix(Xb)
         Ha = Ha.reshape(-1,Y.size) # ADAO & check shape
         #
         RI = R.getI()
@@ -64,32 +80,47 @@ 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 \
-           "CostFunctionJ"                 in self._parameters["StoreSupplementaryCalculations"] or \
-           "OMA"                           in self._parameters["StoreSupplementaryCalculations"] or \
-           "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            self._toStore("CostFunctionJ")  or self._toStore("CostFunctionJAtCurrentOptimum") or \
+            self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+            self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
+            self._toStore("OMA") or \
+            self._toStore("SimulatedObservationAtCurrentOptimum") or \
+            self._toStore("SimulatedObservationAtCurrentState") or \
+            self._toStore("SimulatedObservationAtOptimum"):
             HXa = Hm * Xa
             oma = Y - HXa
         if self._parameters["StoreInternalVariables"] or \
-           "CostFunctionJ"                 in self._parameters["StoreSupplementaryCalculations"]:
+            self._toStore("CostFunctionJ")  or self._toStore("CostFunctionJAtCurrentOptimum") or \
+            self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+            self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum"):
             Jb  = 0.
-            Jo  = 0.5 * oma.T * RI * oma
-            J   = float( Jb ) + float( Jo )
+            Jo  = float( 0.5 * oma.T * RI * oma )
+            J   = Jb + Jo
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )
+            self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( Jb )
+            self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( Jo )
+            self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( J )
         #
-        # Calculs et/ou stockages supplémentaires
+        # Calculs et/ou stockages supplémentaires
         # ---------------------------------------
-        if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
             self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
-        if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("CurrentOptimum"):
+            self.StoredVariables["CurrentOptimum"].store( numpy.ravel(Xa) )
+        if self._toStore("OMA"):
             self.StoredVariables["OMA"].store( numpy.ravel(oma) )
-        if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("SimulatedObservationAtBackground"):
+            self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
+        if self._toStore("SimulatedObservationAtCurrentState"):
             self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
-        if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("SimulatedObservationAtCurrentOptimum"):
+            self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( numpy.ravel(HXa) )
+        if self._toStore("SimulatedObservationAtOptimum"):
             self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
         #
         self._post_run(HO)
@@ -97,4 +128,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
 
 # ==============================================================================
 if __name__ == "__main__":
-    print '\n AUTODIAGNOSTIC \n'
+    print('\n AUTODIAGNOSTIC\n')