Extend the range of supported versions for various modules (1)

diff --git a/src/daComposant/daAlgorithms/3DVAR.py b/src/daComposant/daAlgorithms/3DVAR.py
index deb73676ae35e283168e976d7bfc11c2dec9ab96..231d9639b6d9c698695b5ba779a234f354d090a6 100644 (file)
--- a/src/daComposant/daAlgorithms/3DVAR.py
+++ b/src/daComposant/daAlgorithms/3DVAR.py
@@ -22,7 +22,7 @@
 
 import logging
 from daCore import BasicObjects
-import numpy, scipy.optimize
+import numpy, scipy.optimize, scipy.version
 
 # ==============================================================================
 class ElementaryAlgorithm(BasicObjects.Algorithm):
@@ -239,8 +239,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         if self._parameters["Minimizer"] == "LBFGSB":
             # Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b(
-            import lbfgsbhlt
-            Minimum, J_optimal, Informations = lbfgsbhlt.fmin_l_bfgs_b(
+            if "0.19" <= scipy.version.version <= "1.1.0":
+                import lbfgsbhlt as optimiseur
+            else:
+                import scipy.optimize as optimiseur
+            Minimum, J_optimal, Informations = optimiseur.fmin_l_bfgs_b(
                 func        = CostFunction,
                 x0          = Xini,
                 fprime      = GradientOfCostFunction,

diff --git a/src/daComposant/daAlgorithms/4DVAR.py b/src/daComposant/daAlgorithms/4DVAR.py
index 5c75872cb1d870f2c74eda9d78582f95db15c47b..20f2f8661f29440974673de916f52978898635d8 100644 (file)
--- a/src/daComposant/daAlgorithms/4DVAR.py
+++ b/src/daComposant/daAlgorithms/4DVAR.py
@@ -22,7 +22,7 @@
 
 import logging
 from daCore import BasicObjects
-import numpy, scipy.optimize
+import numpy, scipy.optimize, scipy.version
 
 # ==============================================================================
 class ElementaryAlgorithm(BasicObjects.Algorithm):
@@ -262,8 +262,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         if self._parameters["Minimizer"] == "LBFGSB":
             # Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b(
-            import lbfgsbhlt
-            Minimum, J_optimal, Informations = lbfgsbhlt.fmin_l_bfgs_b(
+            if "0.19" <= scipy.version.version <= "1.1.0":
+                import lbfgsbhlt as optimiseur
+            else:
+                import scipy.optimize as optimiseur
+            Minimum, J_optimal, Informations = optimiseur.fmin_l_bfgs_b(
                 func        = CostFunction,
                 x0          = Xini,
                 fprime      = GradientOfCostFunction,

diff --git a/src/daComposant/daAlgorithms/EnsembleBlue.py b/src/daComposant/daAlgorithms/EnsembleBlue.py
index 174bd51088434a82c562c16e06fdf801a8a0f2f9..ecc3acf04eb283d6edf99f8957f6303f2b93b675 100644 (file)
--- a/src/daComposant/daAlgorithms/EnsembleBlue.py
+++ b/src/daComposant/daAlgorithms/EnsembleBlue.py
@@ -77,7 +77,6 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         for npar in range(DiagonaleR.size):
             bruit = numpy.random.normal(0,DiagonaleR[npar],nb_ens)
             EnsembleY[npar,:] = Y[npar] + bruit
-        EnsembleY = numpy.matrix(EnsembleY)
         #
         # Initialisation des opérateurs d'observation et de la matrice gain
         # -----------------------------------------------------------------
@@ -96,24 +95,24 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # Calcul du BLUE pour chaque membre de l'ensemble
         # -----------------------------------------------
         for iens in range(nb_ens):
-            HXb = Hm * Xb[iens]
+            HXb = numpy.ravel(numpy.dot(Hm, Xb[iens]))
             if self._toStore("SimulatedObservationAtBackground"):
-                self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
-            d  = EnsembleY[:,iens] - HXb
+                self.StoredVariables["SimulatedObservationAtBackground"].store( HXb )
+            d  = numpy.ravel(EnsembleY[:,iens]) - HXb
             if self._toStore("Innovation"):
-                self.StoredVariables["Innovation"].store( numpy.ravel(d) )
-            Xa = Xb[iens] + K*d
+                self.StoredVariables["Innovation"].store( d )
+            Xa = numpy.ravel(Xb[iens]) + numpy.dot(K, d)
             self.StoredVariables["CurrentState"].store( Xa )
             if self._toStore("SimulatedObservationAtCurrentState"):
-                self.StoredVariables["SimulatedObservationAtCurrentState"].store( Hm * Xa )
+                self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.dot(Hm, Xa) )
         #
         # Fabrication de l'analyse
         # ------------------------
         Members = self.StoredVariables["CurrentState"][-nb_ens:]
-        Xa = numpy.matrix( Members ).mean(axis=0)
-        self.StoredVariables["Analysis"].store( Xa.A1 )
+        Xa = numpy.array( Members ).mean(axis=0)
+        self.StoredVariables["Analysis"].store( Xa )
         if self._toStore("SimulatedObservationAtOptimum"):
-            self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel( Hm * Xa ) )
+            self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.dot(Hm, Xa) )
         #
         self._post_run(HO)
         return 0

diff --git a/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py b/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py
index 3ec8099623ffe03a00d5e4a56a0eaad80ba4c756..13b5f4f023777973cb320bde5635269cc38b3ed3 100644 (file)
--- a/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py
+++ b/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py
@@ -172,7 +172,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             #
             if self._parameters["EstimationOf"] == "State":
                 for i in range(__m):
-                    qi = numpy.asmatrix(numpy.random.multivariate_normal(numpy.zeros(__n), Qn)).T
+                    qi = numpy.asmatrix(numpy.random.multivariate_normal(numpy.zeros(__n), Qn, (1,1,1))).T
                     Xn_predicted[:,i] = numpy.asmatrix(numpy.ravel( M((Xn[:,i], Un)) )).T + qi
                     HX_predicted[:,i] = numpy.asmatrix(numpy.ravel( H((Xn_predicted[:,i], Un)) )).T
                 if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon !
@@ -197,7 +197,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             del PfHT, HPfHT
             #
             for i in range(__m):
-                ri = numpy.asmatrix(numpy.random.multivariate_normal(numpy.zeros(__p), Rn)).T
+                ri = numpy.asmatrix(numpy.random.multivariate_normal(numpy.zeros(__p), Rn, (1,1,1))).T
                 Xn[:,i] = Xn_predicted[:,i] + K * (Ynpu + ri - HX_predicted[:,i])
             #
             Xa = Xn.mean(axis=1, dtype=mfp)

diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
index c753c275364b431c235ce13d1f95213c5b83186a..a77b09fc3091ff9029eeebb31f4c6fd84dff1ace 100644 (file)
--- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
@@ -22,7 +22,7 @@
 
 import logging
 from daCore import BasicObjects
-import numpy, scipy.optimize
+import numpy, scipy.optimize, scipy.version
 
 # ==============================================================================
 class ElementaryAlgorithm(BasicObjects.Algorithm):
@@ -224,8 +224,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         if self._parameters["Minimizer"] == "LBFGSB":
             # Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b(
-            import lbfgsbhlt
-            Minimum, J_optimal, Informations = lbfgsbhlt.fmin_l_bfgs_b(
+            if "0.19" <= scipy.version.version <= "1.1.0":
+                import lbfgsbhlt as optimiseur
+            else:
+                import scipy.optimize as optimiseur
+            Minimum, J_optimal, Informations = optimiseur.fmin_l_bfgs_b(
                 func        = CostFunction,
                 x0          = Xini,
                 fprime      = GradientOfCostFunction,

diff --git a/src/daComposant/daCore/Persistence.py b/src/daComposant/daCore/Persistence.py
index ef263d862b84340b6111b9751477f3d17e2656c3..ec1f2806fdd7752f65c79c7ea9c3dbf3aae12e8e 100644 (file)
--- a/src/daComposant/daCore/Persistence.py
+++ b/src/daComposant/daCore/Persistence.py
@@ -276,7 +276,7 @@ class Persistence(object):
         élémentaires numpy.
         """
         try:
-            return [numpy.matrix(item).mean() for item in self.__values]
+            return [numpy.array(item).mean() for item in self.__values]
         except:
             raise TypeError("Base type is incompatible with numpy")
 
@@ -291,9 +291,9 @@ class Persistence(object):
         """
         try:
             if numpy.version.version >= '1.1.0':
-                return [numpy.matrix(item).std(ddof=ddof) for item in self.__values]
+                return [numpy.array(item).std(ddof=ddof) for item in self.__values]
             else:
-                return [numpy.matrix(item).std() for item in self.__values]
+                return [numpy.array(item).std() for item in self.__values]
         except:
             raise TypeError("Base type is incompatible with numpy")
 
@@ -304,7 +304,7 @@ class Persistence(object):
         numpy.
         """
         try:
-            return [numpy.matrix(item).sum() for item in self.__values]
+            return [numpy.array(item).sum() for item in self.__values]
         except:
             raise TypeError("Base type is incompatible with numpy")
 
@@ -315,7 +315,7 @@ class Persistence(object):
         numpy.
         """
         try:
-            return [numpy.matrix(item).min() for item in self.__values]
+            return [numpy.array(item).min() for item in self.__values]
         except:
             raise TypeError("Base type is incompatible with numpy")
 
@@ -326,7 +326,7 @@ class Persistence(object):
         numpy.
         """
         try:
-            return [numpy.matrix(item).max() for item in self.__values]
+            return [numpy.array(item).max() for item in self.__values]
         except:
             raise TypeError("Base type is incompatible with numpy")
 
@@ -460,6 +460,7 @@ class Persistence(object):
             eval(input('Please press return to continue...\n'))
 
     # ---------------------------------------------------------
+    # On pourrait aussi utiliser d'autres attributs d'un "array" comme "tofile"
     def mean(self):
         """
         Renvoie la moyenne sur toutes les valeurs sans tenir compte de la
@@ -535,9 +536,6 @@ class Persistence(object):
         except:
             raise TypeError("Base type is incompatible with numpy")
 
-    # On pourrait aussi utiliser les autres attributs d'une "matrix", comme
-    # "tofile", "min"...
-
     def plot(self,
              steps    = None,
              title    = "",