Documentation and reporting improvements

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

diff --git a/src/daComposant/daAlgorithms/LinearityTest.py b/src/daComposant/daAlgorithms/LinearityTest.py
index 9733aa9f25a495ba10e08d109d46257243c06415..7ae4c5650de48f6cd8515f27a8bff06269daf3ea 100644 (file)
--- a/src/daComposant/daAlgorithms/LinearityTest.py
+++ b/src/daComposant/daAlgorithms/LinearityTest.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2017 EDF R&D
+# Copyright (C) 2008-2021 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
@@ -82,11 +82,21 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["CurrentState", "Residu", "SimulatedObservationAtCurrentState"]
+            listval  = [
+                "CurrentState",
+                "Residu",
+                "SimulatedObservationAtCurrentState",
+                ]
             )
+        self.requireInputArguments(
+            mandatory= ("Xb", "HO"),
+            )
+        self.setAttributes(tags=(
+            "Checking",
+            ))
 
     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, U, HO, EM, CM, R, B, Q)
         #
         def RMS(V1, V2):
             import math
@@ -109,9 +119,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         FX      = numpy.asmatrix(numpy.ravel( Hm( Xn ) )).T
         NormeX  = numpy.linalg.norm( Xn )
         NormeFX = numpy.linalg.norm( FX )
-        if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("CurrentState"):
             self.StoredVariables["CurrentState"].store( numpy.ravel(Xn) )
-        if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("SimulatedObservationAtCurrentState"):
             self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX) )
         #
         # Fabrication de la direction de l'increment dX
@@ -143,7 +153,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             Remarque : les nombres inferieurs a %.0e (environ) representent un zero
                        a la precision machine.\n"""%mpr
         if self._parameters["ResiduFormula"] == "CenteredDL":
-            __entete = u"  i   Alpha     ||X||      ||F(X)||   |   R(Alpha)  log10( R )  "
+            __entete = u"  i   Alpha     ||X||      ||F(X)||   |   R(Alpha)  log10( R )"
             __msgdoc = u"""
             On observe le residu provenant de la difference centree des valeurs de F
             au point nominal et aux points perturbes, normalisee par la valeur au
@@ -164,10 +174,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             cela signifie que le gradient est calculable jusqu'a la precision d'arret
             de la decroissance quadratique.
 
-            On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
-            """ + __precision
+            On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.\n""" + __precision
         if self._parameters["ResiduFormula"] == "Taylor":
-            __entete = u"  i   Alpha     ||X||      ||F(X)||   |   R(Alpha)  log10( R )  "
+            __entete = u"  i   Alpha     ||X||      ||F(X)||   |   R(Alpha)  log10( R )"
             __msgdoc = u"""
             On observe le residu issu du developpement de Taylor de la fonction F,
             normalisee par la valeur au point nominal :
@@ -187,10 +196,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             cela signifie que le gradient est bien calcule jusqu'a la precision d'arret
             de la decroissance quadratique.
 
-            On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
-            """ + __precision
+            On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.\n""" + __precision
         if self._parameters["ResiduFormula"] == "NominalTaylor":
-            __entete = u"  i   Alpha     ||X||      ||F(X)||   |   R(Alpha)   |R-1| en %  "
+            __entete = u"  i   Alpha     ||X||      ||F(X)||   |   R(Alpha)   |R-1| en %"
             __msgdoc = u"""
             On observe le residu obtenu a partir de deux approximations d'ordre 1 de F(X),
             normalisees par la valeur au point nominal :
@@ -208,10 +216,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             l'increment Alpha, c'est sur cette partie que l'hypothese de linearite de F
             est verifiee.
 
-            On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
-            """ + __precision
+            On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.\n""" + __precision
         if self._parameters["ResiduFormula"] == "NominalTaylorRMS":
-            __entete = u"  i   Alpha     ||X||      ||F(X)||   |   R(Alpha)    |R| en %  "
+            __entete = u"  i   Alpha     ||X||      ||F(X)||   |   R(Alpha)    |R| en %"
             __msgdoc = u"""
             On observe le residu obtenu a partir de deux approximations d'ordre 1 de F(X),
             normalisees par la valeur au point nominal :
@@ -228,8 +235,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             l'increment Alpha, c'est sur cette partie que l'hypothese de linearite de F
             est verifiee.
 
-            On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
-            """ + __precision
+            On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.\n""" + __precision
         #
         if len(self._parameters["ResultTitle"]) > 0:
             __rt = unicode(self._parameters["ResultTitle"])
@@ -241,7 +247,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             msgs  = u""
         msgs += __msgdoc
         #
-        __nbtirets = len(__entete)
+        __nbtirets = len(__entete) + 2
         msgs += "\n" + __marge + "-"*__nbtirets
         msgs += "\n" + __marge + __entete
         msgs += "\n" + __marge + "-"*__nbtirets
@@ -252,14 +258,14 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             dX      = amplitude * dX0
             #
             if self._parameters["ResiduFormula"] == "CenteredDL":
-                if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                if self._toStore("CurrentState"):
                     self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
                     self.StoredVariables["CurrentState"].store( numpy.ravel(Xn - dX) )
                 #
                 FX_plus_dX  = numpy.asmatrix(numpy.ravel( Hm( Xn + dX ) )).T
                 FX_moins_dX = numpy.asmatrix(numpy.ravel( Hm( Xn - dX ) )).T
                 #
-                if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                if self._toStore("SimulatedObservationAtCurrentState"):
                     self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
                     self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_moins_dX) )
                 #
@@ -270,12 +276,12 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 msgs += "\n" + __marge + msg
             #
             if self._parameters["ResiduFormula"] == "Taylor":
-                if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                if self._toStore("CurrentState"):
                     self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
                 #
                 FX_plus_dX  = numpy.asmatrix(numpy.ravel( Hm( Xn + dX ) )).T
                 #
-                if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                if self._toStore("SimulatedObservationAtCurrentState"):
                     self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
                 #
                 Residu = numpy.linalg.norm( FX_plus_dX - FX - amplitude * GradFxdX ) / NormeFX
@@ -285,7 +291,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 msgs += "\n" + __marge + msg
             #
             if self._parameters["ResiduFormula"] == "NominalTaylor":
-                if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                if self._toStore("CurrentState"):
                     self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
                     self.StoredVariables["CurrentState"].store( numpy.ravel(Xn - dX) )
                     self.StoredVariables["CurrentState"].store( numpy.ravel(dX) )
@@ -294,7 +300,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 FX_moins_dX = numpy.asmatrix(numpy.ravel( Hm( Xn - dX ) )).T
                 FdX         = numpy.asmatrix(numpy.ravel( Hm( dX ) )).T
                 #
-                if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                if self._toStore("SimulatedObservationAtCurrentState"):
                     self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
                     self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_moins_dX) )
                     self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FdX) )
@@ -309,7 +315,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 msgs += "\n" + __marge + msg
             #
             if self._parameters["ResiduFormula"] == "NominalTaylorRMS":
-                if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                if self._toStore("CurrentState"):
                     self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
                     self.StoredVariables["CurrentState"].store( numpy.ravel(Xn - dX) )
                     self.StoredVariables["CurrentState"].store( numpy.ravel(dX) )
@@ -318,7 +324,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 FX_moins_dX = numpy.asmatrix(numpy.ravel( Hm( Xn - dX ) )).T
                 FdX         = numpy.asmatrix(numpy.ravel( Hm( dX ) )).T
                 #
-                if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                if self._toStore("SimulatedObservationAtCurrentState"):
                     self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
                     self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_moins_dX) )
                     self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FdX) )
@@ -345,4 +351,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
 
 # ==============================================================================
 if __name__ == "__main__":
-    print('\n AUTODIAGNOSTIC \n')
+    print('\n AUTODIAGNOSTIC\n')