qques comentaires et on enleve la projetction de la matrice de correlation

diff --git a/PSEN_Eficas/PSEN/PSSEWrapper.py b/PSEN_Eficas/PSEN/PSSEWrapper.py
index da0d3253d590812b66890775d614fec53ca6f110..7f503a6f97881455516c3881fbd017d60c436570 100755 (executable)
--- a/PSEN_Eficas/PSEN/PSSEWrapper.py
+++ b/PSEN_Eficas/PSEN/PSSEWrapper.py
@@ -128,11 +128,12 @@ if __name__ == '__main__':
 
 
 #===============================================================================
-#   Recuperation donn�es utilisateurs -  User data
+#   Recuperation donnees utilisateurs -  User data
 #===============================================================================
+# Lecture du fichier PSENConfig - > sauvegarde avec le nom en du
 
 
-    #extract laws from Eficas Output
+    #extract laws from Eficas Output PSENconfig est le fichier qui continent le dico
 
     Paths = PSENconfig.Dico['DIRECTORY']
     SimuParams = PSENconfig.Dico['SIMULATION']
@@ -174,6 +175,11 @@ if __name__ == '__main__':
                 if PSENconfig.Dico.has_key('CORRELATION'):
                     NonActiveIndices.append(LawNames.index(shortkey))
 
+    # Travail sur la Matrice de correlatin
+    # remplit CorrMatrix
+    # --> creation d un dictionnaire
+    # clef = matrice = la matrice entree par l utilisateur mois les laws non actives, et si timeserie on a juste 1 dans la diagonale
+
     if PSENconfig.Dico.has_key('CORRELATION'):
         #Treat Correlation Matrix - eliminate non-activated laws
         CorrMatrix0 = {}
@@ -206,6 +212,7 @@ if __name__ == '__main__':
             CorrMatrix0['laws'] = LawNames2
 
     else: #acceptable only if all active distributions are time series or if only 1 active distribution
+    # a supprimer
 
         if len(Laws)==1: #create correlation matrix of 1 x 1
             CorrMatrix0 = {}
@@ -246,34 +253,36 @@ if __name__ == '__main__':
                 #CorrMaxtrix0['matrix'][i][i] = 1
 
     #retreat CorrelationMatrix
-    if isDuplicateLaws:
-        CorrMatrix = {}
-        CorrMatrix['laws']=Laws.keys()
-        CorrMatrix['matrix']=np.eye(len(Laws.keys()))
-        for x,lawname1 in enumerate(Laws.keys()):
-            for i,lawname1_0 in enumerate(CorrMatrix0['laws']):
-                if lawname1_0 in lawname1:
-                    break
-            for y, lawname2 in enumerate(Laws.keys()):
-                for j,lawname2_0 in enumerate(CorrMatrix0['laws']):
-                    if lawname2_0 in lawname2:
-                        break
-                if x!=y:
-                    CorrMatrix['matrix'][x][y] = CorrMatrix0['matrix'][i][j]
-                    CorrMatrix['matrix'][y][x] = CorrMatrix0['matrix'][j][i]
+    #if isDuplicateLaws:
+    #    CorrMatrix = {}
+    #    CorrMatrix['laws']=Laws.keys()
+    #    CorrMatrix['matrix']=np.eye(len(Laws.keys()))
+    #    for x,lawname1 in enumerate(Laws.keys()):
+    #        for i,lawname1_0 in enumerate(CorrMatrix0['laws']):
+    #            if lawname1_0 in lawname1:
+    #                break
+    #        for y, lawname2 in enumerate(Laws.keys()):
+    #            for j,lawname2_0 in enumerate(CorrMatrix0['laws']):
+    #                if lawname2_0 in lawname2:
+    #                    break
+    #            if x!=y:
+    #                CorrMatrix['matrix'][x][y] = CorrMatrix0['matrix'][i][j]
+    #                CorrMatrix['matrix'][y][x] = CorrMatrix0['matrix'][j][i]
     ##                if i==j:
     ##                    CorrMatrix['matrix'][x][y] = 0
     ##                    CorrMatrix['matrix'][y][x] = 0
     ##                else:
     ##                    CorrMatrix['matrix'][x][y] = CorrMatrix0['matrix'][i][j]
     ##                    CorrMatrix['matrix'][y][x] = CorrMatrix0['matrix'][j][i]
-    else:
-        CorrMatrix = CorrMatrix0
+    #else:
+    #    CorrMatrix = CorrMatrix0
+         
+    CorrMatrix = CorrMatrix0
 
     #retest for positive definiteness
-    if not np.all(np.linalg.eigvals(CorrMatrix['matrix'])>0):
-        print 'Error: Correlation matrix is not positive definite.'
-        sys.exit(1)
+    #if not np.all(np.linalg.eigvals(CorrMatrix['matrix'])>0):
+    #    print 'Error: Correlation matrix is not positive definite.'
+    #    sys.exit(1)
 
 
     #execution file name