Correcting logging levels

diff --git a/src/daComposant/daAlgorithms/3DVAR.py b/src/daComposant/daAlgorithms/3DVAR.py
index 8be3e3a5761f720636f84ad9584a94f7ef35c1e2..cee5c22b553ee25c9aec84af73b8993b8e873fe5 100644 (file)
--- a/src/daComposant/daAlgorithms/3DVAR.py
+++ b/src/daComposant/daAlgorithms/3DVAR.py
@@ -89,7 +89,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # ------------------------------
         def CostFunction(x):
             _X  = numpy.asmatrix(x).flatten().T
-            logging.info("%s CostFunction X  = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
+            logging.debug("%s CostFunction X  = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
             _HX = Hm( _X )
             _HX = numpy.asmatrix(_HX).flatten().T
             Jb  = 0.5 * (_X - Xb).T * BI * (_X - Xb)
@@ -106,7 +106,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         def GradientOfCostFunction(x):
             _X      = numpy.asmatrix(x).flatten().T
-            logging.info("%s GradientOfCostFunction X      = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
+            logging.debug("%s GradientOfCostFunction X      = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
             _HX     = Hm( _X )
             _HX     = numpy.asmatrix(_HX).flatten().T
             GradJb  = BI * (_X - Xb)

diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
index a01ad3ccb86d2a1803dd7e688e6a3d85a630eb8b..7dc703dbd8f1db70d22bb7498015886c1fe1ae57 100644 (file)
--- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
@@ -90,7 +90,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # ------------------------------
         def CostFunction(x):
             _X  = numpy.asmatrix(x).flatten().T
-            logging.info("%s CostFunction X  = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
+            logging.debug("%s CostFunction X  = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
             _HX = Hm( _X )
             _HX = numpy.asmatrix(_HX).flatten().T
             Jb  = 0.
@@ -107,7 +107,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         def GradientOfCostFunction(x):
             _X      = numpy.asmatrix(x).flatten().T
-            logging.info("%s GradientOfCostFunction X      = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
+            logging.debug("%s GradientOfCostFunction X      = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
             _HX     = Hm( _X )
             _HX     = numpy.asmatrix(_HX).flatten().T
             GradJb  = 0.

diff --git a/src/daComposant/daCore/version.py b/src/daComposant/daCore/version.py
index 13a7a61bcaa36be12fca748722f4b19729a404fc..619e91dbb3c8aa06667cfee4e9799d5231e5871d 100644 (file)
--- a/src/daComposant/daCore/version.py
+++ b/src/daComposant/daCore/version.py
@@ -21,5 +21,5 @@
 #  Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
 name    = "Data Assimilation Package"
-version = "0.4.0-SP640"
-date    = "mardi 11 octobre 2011, 11:11:11 (UTC+0200)"
+version = "0.5.0-SP650"
+date    = "jeudi 29 mars 2012, 11:11:11 (UTC+0200)"