Allow to set seed in EnsembleBlue and correct object builder

diff --git a/doc/using.rst b/doc/using.rst
index e9417c1c4f37084a8ae43f3f3b0cd4c8277b2b36..a9b24b686ee34899cd8ebe8b0847f6d1a0d0a899 100644 (file)
--- a/doc/using.rst
+++ b/doc/using.rst
@@ -451,7 +451,12 @@ unused.
       10e-5 and it is not recommended to change it.
 
 :"EnsembleBlue":
-    no option
+
+    :SetSeed:
+      This key allow to give an integer in order to fix the seed of the random
+      generator used to generate the ensemble. A convenient value is for example
+      1000. By default, the seed is left uninitialized, and so use the default
+      initialization from the computer.
 
 :"KalmanFilter":
 

diff --git a/src/daComposant/daAlgorithms/EnsembleBlue.py b/src/daComposant/daAlgorithms/EnsembleBlue.py
index 678609afa7f78107b7e1fc91bbe98e09d3b55be2..521060b15ee882d5f0e1c04c343cb3fd2c752f24 100644 (file)
--- a/src/daComposant/daAlgorithms/EnsembleBlue.py
+++ b/src/daComposant/daAlgorithms/EnsembleBlue.py
@@ -42,6 +42,15 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         logging.debug("%s Lancement"%self._name)
         logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
         #
+        # Paramètres de pilotage
+        # ----------------------
+        # Potentiels : "SetSeed"
+        if Parameters.has_key("SetSeed"):
+            numpy.random.seed(int(Parameters["SetSeed"]))
+            logging.debug("%s Graine fixee pour le generateur aleatoire = %s"%(self._name, int(Parameters["SetSeed"])))
+        else:
+            logging.debug("%s Graine quelconque pour le generateur aleatoire"%(self._name, ))
+        #
         # Nombre d'ensemble pour l'ébauche 
         # --------------------------------
         nb_ens = Xb.stepnumber()

diff --git a/src/daComposant/daCore/AssimilationStudy.py b/src/daComposant/daCore/AssimilationStudy.py
index 4d4458a85f9ec159432386b599885316e96b84fd..3a55a047dc5d11820b4dfba82f478a2e98a22c3a 100644 (file)
--- a/src/daComposant/daCore/AssimilationStudy.py
+++ b/src/daComposant/daCore/AssimilationStudy.py
@@ -120,7 +120,12 @@ class AssimilationStudy:
             else:
                 self.__Xb = numpy.matrix( asVector,    numpy.float ).T
         elif asPersistentVector is not None:
-            self.__Xb = asPersistentVector
+            if type(asPersistentVector) in [type([]),type(()),type(numpy.array([])),type(numpy.matrix([]))]:
+                self.__Xb = Persistence.OneVector("Background", basetype=numpy.matrix)
+                for member in asPersistentVector:
+                    self.__Xb.store( numpy.matrix( numpy.asarray(member).flatten(), numpy.float ).T )
+            else:
+                self.__Xb = asPersistentVector
         else:
             raise ValueError("Error: improperly defined background")
         if toBeStored:
@@ -533,7 +538,14 @@ class AssimilationStudy:
             raise ValueError("Shape characteristic of H \"%s\" and R \"%s\" are incompatible"%(__H_shape,__R_shape))
         #
         if self.__B is not None and len(self.__B) > 0 and not( __B_shape[1] == max(__Xb_shape) ):
-            raise ValueError("Shape characteristic of B \"%s\" and Xb \"%s\" are incompatible"%(__B_shape,__Xb_shape))
+            if self.__StoredInputs["AlgorithmName"] in ["EnsembleBlue",]:
+                asPersistentVector = self.__Xb.reshape((-1,min(__B_shape)))
+                self.__Xb = Persistence.OneVector("Background", basetype=numpy.matrix)
+                for member in asPersistentVector:
+                    self.__Xb.store( numpy.matrix( numpy.asarray(member).flatten(), numpy.float ).T )
+                __Xb_shape = min(__B_shape)
+            else:
+                raise ValueError("Shape characteristic of B \"%s\" and Xb \"%s\" are incompatible"%(__B_shape,__Xb_shape))
         #
         if self.__R is not None and len(self.__R) > 0 and not( __R_shape[1] == max(__Y_shape) ):
             raise ValueError("Shape characteristic of R \"%s\" and Y \"%s\" are incompatible"%(__R_shape,__Y_shape))