Documentation and reporting improvements

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

diff --git a/src/daComposant/daAlgorithms/GradientTest.py b/src/daComposant/daAlgorithms/GradientTest.py
index 7be3df45c79f577e78b2706379fe67034f1d8a0d..deaea42199903b93f8c8fc1140919a24a87b79cf 100644 (file)
--- a/src/daComposant/daAlgorithms/GradientTest.py
+++ b/src/daComposant/daAlgorithms/GradientTest.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
@@ -100,11 +100,21 @@ 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)
         #
         Hm = HO["Direct"].appliedTo
         if self._parameters["ResiduFormula"] in ["Taylor", "TaylorOnNorm"]:
@@ -118,9 +128,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         FX      = numpy.asmatrix(numpy.ravel( Hm( X ) )).T
         NormeX  = numpy.linalg.norm( X )
         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) )
         #
         if len(self._parameters["InitialDirection"]) == 0:
@@ -148,7 +158,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             Remarque : les nombres inferieurs a %.0e (environ) representent un zero
                        a la precision machine.\n"""%mpr
         if self._parameters["ResiduFormula"] == "Taylor":
-            __entete = u"  i   Alpha       ||X||    ||F(X)||  ||F(X+dX)||    ||dX||  ||F(X+dX)-F(X)||   ||F(X+dX)-F(X)||/||dX||      R(Alpha)   log( R )  "
+            __entete = u"  i   Alpha       ||X||    ||F(X)||  ||F(X+dX)||    ||dX||  ||F(X+dX)-F(X)||   ||F(X+dX)-F(X)||/||dX||      R(Alpha)   log( R )"
             __msgdoc = u"""
             On observe le residu issu du developpement de Taylor de la fonction F,
             normalise par la valeur au point nominal :
@@ -166,10 +176,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             signifie que F est lineaire et que le residu decroit a partir de l'erreur
             faite dans le calcul du terme GradientF_X.
 
-            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"] == "TaylorOnNorm":
-            __entete = u"  i   Alpha       ||X||    ||F(X)||  ||F(X+dX)||    ||dX||  ||F(X+dX)-F(X)||   ||F(X+dX)-F(X)||/||dX||      R(Alpha)   log( R )  "
+            __entete = u"  i   Alpha       ||X||    ||F(X)||  ||F(X+dX)||    ||dX||  ||F(X+dX)-F(X)||   ||F(X+dX)-F(X)||/||dX||      R(Alpha)   log( R )"
             __msgdoc = u"""
             On observe le residu issu du developpement de Taylor de la fonction F,
             rapporte au parametre Alpha au carre :
@@ -191,10 +200,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             calcul du gradient est correct jusqu'au moment ou le residu est de l'ordre de
             grandeur de ||F(X)||.
 
-            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"] == "Norm":
-            __entete = u"  i   Alpha       ||X||    ||F(X)||  ||F(X+dX)||    ||dX||  ||F(X+dX)-F(X)||   ||F(X+dX)-F(X)||/||dX||      R(Alpha)   log( R )  "
+            __entete = u"  i   Alpha       ||X||    ||F(X)||  ||F(X+dX)||    ||dX||  ||F(X+dX)-F(X)||   ||F(X+dX)-F(X)||/||dX||      R(Alpha)   log( R )"
             __msgdoc = u"""
             On observe le residu, qui est base sur une approximation du gradient :
 
@@ -204,8 +212,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
 
             qui doit rester constant jusqu'a ce que l'on atteigne la precision du calcul.
 
-            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"])
@@ -217,7 +224,7 @@ 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
@@ -237,9 +244,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             FX_plus_dX = Hm( X + dX )
             FX_plus_dX = numpy.asmatrix(numpy.ravel( FX_plus_dX )).T
             #
-            if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("CurrentState"):
                 self.StoredVariables["CurrentState"].store( numpy.ravel(X + dX) )
-            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("SimulatedObservationAtCurrentState"):
                 self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
             #
             NormedX     = numpy.linalg.norm( dX )
@@ -377,4 +384,4 @@ def dessiner(
 
 # ==============================================================================
 if __name__ == "__main__":
-    print('\n AUTODIAGNOSTIC \n')
+    print('\n AUTODIAGNOSTIC\n')