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 5d59a648ab03c3022f8b103c47e54fc14c852bd7..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
@@ -82,14 +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, Xb, Y, R, B, Q)
+        self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
         #
         def RMS(V1, V2):
             import math
@@ -112,9 +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
@@ -251,14 +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) )
                 #
@@ -269,12 +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
@@ -284,7 +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) )
@@ -293,7 +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) )
@@ -308,7 +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) )
@@ -317,7 +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) )
@@ -344,4 +351,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
 
 # ==============================================================================
 if __name__ == "__main__":
-    print('\n AUTODIAGNOSTIC \n')
+    print('\n AUTODIAGNOSTIC\n')