Management of mean/std computation precision and messages

diff --git a/bin/AdaoCatalogGenerator.py b/bin/AdaoCatalogGenerator.py
index 7893cee0f4279d3d52028e0d7d523575e8dc596d..b996a5a3c34a538191b8b7c7d420dac0a2925347 100644 (file)
--- a/bin/AdaoCatalogGenerator.py
+++ b/bin/AdaoCatalogGenerator.py
@@ -406,12 +406,12 @@ for assim_data_input_name in infos.AssimDataDict.keys():
   decl_choices = ""
   decl_opts = ""
   if infos.AssimDataDefaultDict[assim_data_input_name] in infos.StoredAssimData:
-    storage = "    Stored = SIMP(statut=\"o\", typ = \"I\", into=(0, 1), defaut=0, fr=\"Choix de stockage interne ou non du concept parent\", ang=\"Choice of the storage or not of the parent concept\"),"
+    storage = "    Stored = SIMP(statut=\"f\", typ = \"I\", into=(0, 1), defaut=0, fr=\"Choix de stockage interne ou non du concept parent\", ang=\"Choice of the storage or not of the parent concept\"),"
   for choice in infos.AssimDataDict[assim_data_input_name]:
     choices += "\"" + choice + "\", "
     decl_choices += assim_data_choice.substitute(choice_name = choice)
     if choice in infos.StoredAssimData:
-      storage = "    Stored = SIMP(statut=\"o\", typ = \"I\", into=(0, 1), defaut=0, fr=\"Choix de stockage interne ou non du concept parent\", ang=\"Choice of the storage or not of the parent concept\"),"
+      storage = "    Stored = SIMP(statut=\"f\", typ = \"I\", into=(0, 1), defaut=0, fr=\"Choix de stockage interne ou non du concept parent\", ang=\"Choice of the storage or not of the parent concept\"),"
   default_choice = "\"" + infos.AssimDataDefaultDict[assim_data_input_name] + "\""
 
   mem_file.write(assim_data_method.substitute(assim_name = assim_data_input_name,

diff --git a/src/daComposant/daAlgorithms/AdjointTest.py b/src/daComposant/daAlgorithms/AdjointTest.py
index 89cd6b28ecddfe57a0650e3c20f396be090a2026..8e772cbbb2b1f25fec2c1b57c9c8ff0ef73743d9 100644 (file)
--- a/src/daComposant/daAlgorithms/AdjointTest.py
+++ b/src/daComposant/daAlgorithms/AdjointTest.py
@@ -123,6 +123,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             qui doit rester constamment egal a zero a la precision du calcul.
             On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
             Y doit etre dans l'image de F. S'il n'est pas donne, on prend Y = F(X).
+
+            Remarque : les nombres inferieurs a 1.e-16 (environ) representent un zero
+                       a la precision machine.
             """
         #
         if len(self._parameters["ResultTitle"]) > 0:

diff --git a/src/daComposant/daAlgorithms/FunctionTest.py b/src/daComposant/daAlgorithms/FunctionTest.py
index 9d08a9e2fda62bbe7113a93fe7241d216e7ce3bf..58f3f3d8c7061c868c51e2fabac5d126fcb6cfdf 100644 (file)
--- a/src/daComposant/daAlgorithms/FunctionTest.py
+++ b/src/daComposant/daAlgorithms/FunctionTest.py
@@ -21,8 +21,9 @@
 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
 import logging
-from daCore import BasicObjects
+from daCore import BasicObjects, PlatformInfo
 import numpy, copy
+mfp = PlatformInfo.PlatformInfo().MaximumPrecision()
 
 # ==============================================================================
 class ElementaryAlgorithm(BasicObjects.Algorithm):
@@ -86,8 +87,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         msg += ("       Lenght of vector...: %i\n")%max(numpy.matrix( Xn ).shape)
         msg += ("       Minimum value......: %."+str(_p)+"e\n")%numpy.min( Xn )
         msg += ("       Maximum value......: %."+str(_p)+"e\n")%numpy.max( Xn )
-        msg += ("       Mean of vector.....: %."+str(_p)+"e\n")%numpy.mean( Xn )
-        msg += ("       Standard error.....: %."+str(_p)+"e\n")%numpy.std( Xn )
+        msg += ("       Mean of vector.....: %."+str(_p)+"e\n")%numpy.mean( Xn, dtype=mfp )
+        msg += ("       Standard error.....: %."+str(_p)+"e\n")%numpy.std( Xn, dtype=mfp )
         msg += ("       L2 norm of vector..: %."+str(_p)+"e\n")%numpy.linalg.norm( Xn )
         print(msg)
         #
@@ -120,8 +121,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             msg += ("       Lenght of vector...: %i\n")%max(numpy.matrix( Yn ).shape)
             msg += ("       Minimum value......: %."+str(_p)+"e\n")%numpy.min( Yn )
             msg += ("       Maximum value......: %."+str(_p)+"e\n")%numpy.max( Yn )
-            msg += ("       Mean of vector.....: %."+str(_p)+"e\n")%numpy.mean( Yn )
-            msg += ("       Standard error.....: %."+str(_p)+"e\n")%numpy.std( Yn )
+            msg += ("       Mean of vector.....: %."+str(_p)+"e\n")%numpy.mean( Yn, dtype=mfp )
+            msg += ("       Standard error.....: %."+str(_p)+"e\n")%numpy.std( Yn, dtype=mfp )
             msg += ("       L2 norm of vector..: %."+str(_p)+"e\n")%numpy.linalg.norm( Yn )
             print(msg)
             if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
@@ -142,27 +143,28 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         if self._parameters["NumberOfRepetition"] > 1:
             msg  = ("     %s\n"%("-"*75,))
             msg += ("\n===> Statistical analysis of the outputs obtained throught repeated evaluations\n")
+            msg += ("\n     (Remark: numbers that are (about) under 1.e-16 represent 0 to machine precision)\n")
             Yy = numpy.array( Ys )
             msg += ("\n     Characteristics of the whole set of outputs Y:\n")
             msg += ("       Number of evaluations.........................: %i\n")%len( Ys )
             msg += ("       Minimum value of the whole set of outputs.....: %."+str(_p)+"e\n")%numpy.min( Yy )
             msg += ("       Maximum value of the whole set of outputs.....: %."+str(_p)+"e\n")%numpy.max( Yy )
-            msg += ("       Mean of vector of the whole set of outputs....: %."+str(_p)+"e\n")%numpy.mean( Yy )
-            msg += ("       Standard error of the whole set of outputs....: %."+str(_p)+"e\n")%numpy.std( Yy )
-            Ym = numpy.mean( numpy.array( Ys ), axis=0 )
+            msg += ("       Mean of vector of the whole set of outputs....: %."+str(_p)+"e\n")%numpy.mean( Yy, dtype=mfp )
+            msg += ("       Standard error of the whole set of outputs....: %."+str(_p)+"e\n")%numpy.std( Yy, dtype=mfp )
+            Ym = numpy.mean( numpy.array( Ys ), axis=0, dtype=mfp )
             msg += ("\n     Characteristics of the vector Ym, mean of the outputs Y:\n")
             msg += ("       Size of the mean of the outputs...............: %i\n")%Ym.size
             msg += ("       Minimum value of the mean of the outputs......: %."+str(_p)+"e\n")%numpy.min( Ym )
             msg += ("       Maximum value of the mean of the outputs......: %."+str(_p)+"e\n")%numpy.max( Ym )
-            msg += ("       Mean of the mean of the outputs...............: %."+str(_p)+"e\n")%numpy.mean( Ym )
-            msg += ("       Standard error of the mean of the outputs.....: %."+str(_p)+"e\n")%numpy.std( Ym )
-            Ye = numpy.mean( numpy.array( Ys ) - Ym, axis=0 )
+            msg += ("       Mean of the mean of the outputs...............: %."+str(_p)+"e\n")%numpy.mean( Ym, dtype=mfp )
+            msg += ("       Standard error of the mean of the outputs.....: %."+str(_p)+"e\n")%numpy.std( Ym, dtype=mfp )
+            Ye = numpy.mean( numpy.array( Ys ) - Ym, axis=0, dtype=mfp )
             msg += "\n     Characteristics of the mean of the differences between the outputs Y and their mean Ym:\n"
             msg += ("       Size of the mean of the differences...........: %i\n")%Ym.size
             msg += ("       Minimum value of the mean of the differences..: %."+str(_p)+"e\n")%numpy.min( Ye )
             msg += ("       Maximum value of the mean of the differences..: %."+str(_p)+"e\n")%numpy.max( Ye )
-            msg += ("       Mean of the mean of the differences...........: %."+str(_p)+"e\n")%numpy.mean( Ye )
-            msg += ("       Standard error of the mean of the differences.: %."+str(_p)+"e\n")%numpy.std( Ye )
+            msg += ("       Mean of the mean of the differences...........: %."+str(_p)+"e\n")%numpy.mean( Ye, dtype=mfp )
+            msg += ("       Standard error of the mean of the differences.: %."+str(_p)+"e\n")%numpy.std( Ye, dtype=mfp )
             msg += ("\n     %s\n"%("-"*75,))
             print(msg)
         #

diff --git a/src/daComposant/daAlgorithms/GradientTest.py b/src/daComposant/daAlgorithms/GradientTest.py
index 24c5f00f29043f96851183fd0891fefea52dd426..e4e2fcb8d097fe72b6321b1b14cf2fac99f25abd 100644 (file)
--- a/src/daComposant/daAlgorithms/GradientTest.py
+++ b/src/daComposant/daAlgorithms/GradientTest.py
@@ -163,6 +163,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             faite dans le calcul du terme GradientF_X.
 
             On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
+
+            Remarque : les nombres inferieurs a 1.e-16 (environ) representent un zero
+                       a la precision machine.
             """
         if self._parameters["ResiduFormula"] == "TaylorOnNorm":
             __entete = "  i   Alpha       ||X||    ||F(X)||  ||F(X+dX)||    ||dX||  ||F(X+dX)-F(X)||   ||F(X+dX)-F(X)||/||dX||      R(Alpha)   log( R )  "
@@ -188,6 +191,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             grandeur de ||F(X)||.
 
             On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
+
+            Remarque : les nombres inferieurs a 1.e-16 (environ) representent un zero
+                       a la precision machine.
             """
         if self._parameters["ResiduFormula"] == "Norm":
             __entete = "  i   Alpha       ||X||    ||F(X)||  ||F(X+dX)||    ||dX||  ||F(X+dX)-F(X)||   ||F(X+dX)-F(X)||/||dX||      R(Alpha)   log( R )  "
@@ -201,6 +207,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             qui doit rester constant jusqu'à ce que l'on atteigne la précision du calcul.
 
             On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
+
+            Remarque : les nombres inferieurs a 1.e-16 (environ) representent un zero
+                       a la precision machine.
             """
         #
         if len(self._parameters["ResultTitle"]) > 0:

diff --git a/src/daComposant/daAlgorithms/TangentTest.py b/src/daComposant/daAlgorithms/TangentTest.py
index 1803ecc52e70c0d395688a2878577876c3f998c8..8ff1f8e8074c5f4ee4fb5abb84b7b26c979f11e8 100644 (file)
--- a/src/daComposant/daAlgorithms/TangentTest.py
+++ b/src/daComposant/daAlgorithms/TangentTest.py
@@ -158,6 +158,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             l'on atteigne la précision du calcul.
 
             On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
+
+            Remarque : les nombres inferieurs a 1.e-16 (environ) representent un zero
+                       a la precision machine.
             """
         #
         if len(self._parameters["ResultTitle"]) > 0:

diff --git a/src/daComposant/daCore/AssimilationStudy.py b/src/daComposant/daCore/AssimilationStudy.py
index c325445a4341fa46b3e15fa0690912bfddc8cba2..412e988efa8b375d1381aef8125d61ae2b791fbe 100644 (file)
--- a/src/daComposant/daCore/AssimilationStudy.py
+++ b/src/daComposant/daCore/AssimilationStudy.py
@@ -41,7 +41,7 @@ except ImportError:
     logging.debug("Fail initial import of scipy.optimize")
 import Persistence
 from BasicObjects import Operator, Covariance
-from PlatformInfo import uniq
+import PlatformInfo
 
 # ==============================================================================
 class AssimilationStudy:
@@ -97,7 +97,7 @@ class AssimilationStudy:
         # qui est activée dans Persistence)
         self.__parent = os.path.abspath(os.path.join(os.path.dirname(__file__),".."))
         sys.path.insert(0, self.__parent)
-        sys.path = uniq( sys.path ) # Conserve en unique exemplaire chaque chemin
+        sys.path = PlatformInfo.uniq( sys.path ) # Conserve en unique exemplaire chaque chemin
 
     # ---------------------------------------------------------
     def setBackground(self,
@@ -465,12 +465,7 @@ class AssimilationStudy:
 
     # -----------------------------------------------------------
     def setControlModel(self,
-            asFunction = {"Direct":None, "Tangent":None, "Adjoint":None,
-                          "useApproximatedDerivatives":False,
-                          "withCenteredDF"            :False,
-                          "withIncrement"             :0.01,
-                          "withdX"                    :None,
-                         },
+            asFunction = None,
             asMatrix   = None,
             Scheduler  = None,
             toBeStored = False,
@@ -945,7 +940,7 @@ class AssimilationStudy:
         if not os.path.isfile(os.path.join(asPath,"daAlgorithms","__init__.py")):
             raise ValueError("The given \""+asPath+"/daAlgorithms\" path must contain a file named \"__init__.py\"")
         sys.path.insert(0, os.path.abspath(asPath))
-        sys.path = uniq( sys.path ) # Conserve en unique exemplaire chaque chemin
+        sys.path = PlatformInfo.uniq( sys.path ) # Conserve en unique exemplaire chaque chemin
         return 1
 
     def get_diagnostics_main_path(self):
@@ -970,7 +965,7 @@ class AssimilationStudy:
         if not os.path.isfile(os.path.join(asPath,"daDiagnostics","__init__.py")):
             raise ValueError("The given \""+asPath+"/daDiagnostics\" path must contain a file named \"__init__.py\"")
         sys.path.insert(0, os.path.abspath(asPath))
-        sys.path = uniq( sys.path ) # Conserve en unique exemplaire chaque chemin
+        sys.path = PlatformInfo.uniq( sys.path ) # Conserve en unique exemplaire chaque chemin
         return 1
 
     # -----------------------------------------------------------

diff --git a/src/daComposant/daCore/PlatformInfo.py b/src/daComposant/daCore/PlatformInfo.py
index 052e463de3575a39eb03cc7100af7bd698cd779d..57438e31cbe03443667356270a4bdaf0879a4fa4 100644 (file)
--- a/src/daComposant/daCore/PlatformInfo.py
+++ b/src/daComposant/daCore/PlatformInfo.py
@@ -108,6 +108,16 @@ class PlatformInfo(object):
         "Retourne la taille mémoire courante utilisée"
         return 1
 
+    def MaximumPrecision(self):
+        "Retourne la precision maximale flottante pour Numpy"
+        import numpy
+        try:
+            x = numpy.array([1.,], dtype='float128')
+            mfp = 'float128'
+        except:
+            mfp = 'float64'
+        return mfp
+
     def __str__(self):
         import version as dav
         return "%s %s (%s)"%(dav.name,dav.version,dav.date)

diff --git a/src/daEficas/configuration_ADAO.py b/src/daEficas/configuration_ADAO.py
index fee72b53ebe67cc7343c0e1115aa9725e59822bc..d23cb8c0c56d1ce137daa818b588ef1a7bdb3bf6 100644 (file)
--- a/src/daEficas/configuration_ADAO.py
+++ b/src/daEficas/configuration_ADAO.py
@@ -45,7 +45,8 @@ class CONFIG(configuration.CONFIG_BASE):
 
   def __init__(self,appli,repIni):
 
-    self.labels_eficas = ['lang']
+    # self.labels_eficas = ['lang']
+    self.labels_eficas = ['lang','rep_cata','catalogues','closeAutreCommande','closeFrameRechercheCommande','closeEntete','taille']
     configuration.CONFIG_BASE.__init__(self,appli,repIni)
 
     self.rep_user = os.environ["HOME"]

diff --git a/src/daEficas/prefs_ADAO.py.in b/src/daEficas/prefs_ADAO.py.in
index f91576c011b7f632baf478f7a8e69ee7a16bd3e3..041379df329b8cd3b52291727de37867fffd4bcc 100644 (file)
--- a/src/daEficas/prefs_ADAO.py.in
+++ b/src/daEficas/prefs_ADAO.py.in
@@ -39,3 +39,8 @@ initialdir=os.environ["PWD"]
 encoding='iso-8859-1'
 # lang indique la langue utilisee pour les chaines d'aide : fr ou ang
 lang='fr'
+closeAutreCommande = True
+closeFrameRechercheCommande = True
+closeEntete = True
+taille=800
+

diff --git a/src/daSalome/daYacsSchemaCreator/methods.py b/src/daSalome/daYacsSchemaCreator/methods.py
index 8dc97e17c96439e79b69fd332ec55f3d23395faa..b0c2f29ad3c5c2d67876c4fe81d55c038ad4aa20 100644 (file)
--- a/src/daSalome/daYacsSchemaCreator/methods.py
+++ b/src/daSalome/daYacsSchemaCreator/methods.py
@@ -206,7 +206,10 @@ def create_yacs_proc(study_config):
         ADAO_Case.edAddChild(back_node)
         # Set content of the node
         back_node_script = back_node.getScript()
-        back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        if "Stored" in data_config:
+            back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        else:
+            back_node_script += "stored = 0\n"
         if key in init_config["Target"]:
           # Connect node with InitUserData
           back_node_script += "__builtins__[\"init_data\"] = init_data\n"
@@ -237,7 +240,10 @@ def create_yacs_proc(study_config):
           ADAO_Case.edAddDFLink(init_node.getOutputPort("init_data"), back_node.getInputPort("init_data"))
         back_node_script += "# Import script and get data\n__import__(module_name)\nuser_script_module = sys.modules[module_name]\n\n"
         back_node_script += key + " = user_script_module." + key + "\n"
-        back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        if "Stored" in data_config:
+            back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        else:
+            back_node_script += "stored = 0\n"
         back_node.setScript(back_node_script)
         # Connect node with CreateAssimilationStudy
         CAS_node.edAddInputPort(key, t_pyobj)
@@ -255,7 +261,10 @@ def create_yacs_proc(study_config):
         ADAO_Case.edAddChild(back_node)
         # Set content of the node
         back_node_script = back_node.getScript()
-        back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        if "Stored" in data_config:
+            back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        else:
+            back_node_script += "stored = 0\n"
         if key in init_config["Target"]:
           # Connect node with InitUserData
           back_node_script += "__builtins__[\"init_data\"] = init_data\n" + back_node_script
@@ -286,7 +295,10 @@ def create_yacs_proc(study_config):
           ADAO_Case.edAddDFLink(init_node.getOutputPort("init_data"), back_node.getInputPort("init_data"))
         back_node_script += "# Import script and get data\n__import__(module_name)\nuser_script_module = sys.modules[module_name]\n\n"
         back_node_script += key + " = user_script_module." + key + "\n"
-        back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        if "Stored" in data_config:
+            back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        else:
+            back_node_script += "stored = 0\n"
         back_node.setScript(back_node_script)
         # Connect node with CreateAssimilationStudy
         CAS_node.edAddInputPort(key, t_pyobj)
@@ -304,7 +316,10 @@ def create_yacs_proc(study_config):
         ADAO_Case.edAddChild(back_node)
         # Set content of the node
         back_node_script = back_node.getScript()
-        back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        if "Stored" in data_config:
+            back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        else:
+            back_node_script += "stored = 0\n"
         if key in init_config["Target"]:
           # Connect node with InitUserData
           back_node_script += "__builtins__[\"init_data\"] = init_data\n"
@@ -328,7 +343,10 @@ def create_yacs_proc(study_config):
         ADAO_Case.edAddChild(back_node)
         # Set content of the node
         back_node_script = back_node.getScript()
-        back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        if "Stored" in data_config:
+            back_node_script += "stored = " + str(data_config["Stored"]) + "\n"
+        else:
+            back_node_script += "stored = 0\n"
         if key in init_config["Target"]:
           # Connect node with InitUserData
           back_node_script += "__builtins__[\"init_data\"] = init_data\n"