Compatibility correction for multiple numpy versions (REX [#25041])

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

diff --git a/src/daComposant/daAlgorithms/TangentTest.py b/src/daComposant/daAlgorithms/TangentTest.py
index 356976134a250bb14b46805ecc28a2625bd6e3f1..e9ecad0854cf36160e92487cb6ea3a17be827d0f 100644 (file)
--- a/src/daComposant/daAlgorithms/TangentTest.py
+++ b/src/daComposant/daAlgorithms/TangentTest.py
@@ -21,7 +21,7 @@
 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
 import numpy
-from daCore import BasicObjects, PlatformInfo
+from daCore import BasicObjects, NumericObjects, PlatformInfo
 mpr = PlatformInfo.PlatformInfo().MachinePrecision()
 
 # ==============================================================================
@@ -105,37 +105,30 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         # Calcul du point courant
         # -----------------------
-        Xn      = numpy.asmatrix(numpy.ravel( Xb )).T
-        FX      = numpy.asmatrix(numpy.ravel( Hm( Xn ) )).T
+        Xn      = numpy.ravel( Xb ).reshape((-1,1))
+        FX      = numpy.ravel( Hm( Xn ) ).reshape((-1,1))
         NormeX  = numpy.linalg.norm( Xn )
         NormeFX = numpy.linalg.norm( FX )
         if self._toStore("CurrentState"):
-            self.StoredVariables["CurrentState"].store( numpy.ravel(Xn) )
+            self.StoredVariables["CurrentState"].store( Xn )
         if self._toStore("SimulatedObservationAtCurrentState"):
-            self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX) )
+            self.StoredVariables["SimulatedObservationAtCurrentState"].store( FX )
         #
-        # Fabrication de la direction de l'increment dX
-        # ---------------------------------------------
-        if len(self._parameters["InitialDirection"]) == 0:
-            dX0 = []
-            for v in Xn.A1:
-                if abs(v) > 1.e-8:
-                    dX0.append( numpy.random.normal(0.,abs(v)) )
-                else:
-                    dX0.append( numpy.random.normal(0.,Xn.mean()) )
-        else:
-            dX0 = numpy.ravel( self._parameters["InitialDirection"] )
-        #
-        dX0 = float(self._parameters["AmplitudeOfInitialDirection"]) * numpy.matrix( dX0 ).T
+        dX0 = NumericObjects.SetInitialDirection(
+            self._parameters["InitialDirection"],
+            self._parameters["AmplitudeOfInitialDirection"],
+            Xn,
+            )
         #
         # Calcul du gradient au point courant X pour l'increment dX
         # qui est le tangent en X multiplie par dX
         # ---------------------------------------------------------
         dX1      = float(self._parameters["AmplitudeOfTangentPerturbation"]) * dX0
         GradFxdX = Ht( (Xn, dX1) )
-        GradFxdX = numpy.asmatrix(numpy.ravel( GradFxdX )).T
+        GradFxdX = numpy.ravel( GradFxdX ).reshape((-1,1))
         GradFxdX = float(1./self._parameters["AmplitudeOfTangentPerturbation"]) * GradFxdX
         NormeGX  = numpy.linalg.norm( GradFxdX )
+        if NormeGX < mpr: NormeGX = mpr
         #
         # Entete des resultats
         # --------------------
@@ -184,10 +177,10 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # Boucle sur les perturbations
         # ----------------------------
         for i,amplitude in enumerate(Perturbations):
-            dX      = amplitude * dX0
+            dX      = amplitude * dX0.reshape((-1,1))
             #
             if self._parameters["ResiduFormula"] == "Taylor":
-                FX_plus_dX  = numpy.asmatrix(numpy.ravel( Hm( Xn + dX ) )).T
+                FX_plus_dX  = numpy.ravel( Hm( Xn + dX ) ).reshape((-1,1))
                 #
                 Residu = numpy.linalg.norm( FX_plus_dX - FX ) / (amplitude * NormeGX)
                 #