Minor improvements and fixes for internal variables

diff --git a/src/daComposant/daAlgorithms/3DVAR.py b/src/daComposant/daAlgorithms/3DVAR.py
index 5024f7e1c0420d516763c3b497392209714c4517..5606dfef101064bb5ab1df502b1bf29714de5091 100644 (file)
--- a/src/daComposant/daAlgorithms/3DVAR.py
+++ b/src/daComposant/daAlgorithms/3DVAR.py
@@ -53,6 +53,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 ],
             listadv  = [
                 "3DVAR-Std",
+                "OneCycle3DVAR-Std",
                 ],
             )
         self.defineRequiredParameter(
@@ -197,6 +198,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         elif self._parameters["Variant"] == "3DVAR-PSAS":
             NumericObjects.multi3dvar(self, Xb, Y, U, HO, EM, CM, R, B, Q, NumericObjects.psas3dvar)
         #
+        #--------------------------
+        elif self._parameters["Variant"] == "OneCycle3DVAR-Std":
+            NumericObjects.std3dvar(self, Xb, Y, U, HO, EM, CM, R, B, Q)
         #
         #--------------------------
         else:

diff --git a/src/daComposant/daAlgorithms/4DVAR.py b/src/daComposant/daAlgorithms/4DVAR.py
index 1f65d95f0391645cda456d392985205cd1048d0a..da643e6ff58a9ee2f5f7dd1c147baffec9e8d4f5 100644 (file)
--- a/src/daComposant/daAlgorithms/4DVAR.py
+++ b/src/daComposant/daAlgorithms/4DVAR.py
@@ -126,10 +126,6 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
         self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
         #
-        # Correction pour pallier a un bug de TNC sur le retour du Minimum
-        if "Minimizer" in self._parameters and self._parameters["Minimizer"] == "TNC":
-            self.setParameterValue("StoreInternalVariables",True)
-        #
         # Opérateurs
         # ----------
         Hm = HO["Direct"].appliedControledFormTo

diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
index 65b6fe413a172577856cf1f3915cc16aac453ba5..aad66fc737b92a4dac264863d8522024f8a2e278 100644 (file)
--- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
@@ -118,10 +118,6 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
         self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
         #
-        # Correction pour pallier a un bug de TNC sur le retour du Minimum
-        if "Minimizer" in self._parameters and self._parameters["Minimizer"] == "TNC":
-            self.setParameterValue("StoreInternalVariables",True)
-        #
         # Opérateurs
         # ----------
         Hm = HO["Direct"].appliedTo

diff --git a/src/daComposant/daCore/BasicObjects.py b/src/daComposant/daCore/BasicObjects.py
index 66e9ab9bee1baf7767e5c0ee234dd47e9057fc49..340612437730232b165d9a618594406ef50a0981 100644 (file)
--- a/src/daComposant/daCore/BasicObjects.py
+++ b/src/daComposant/daCore/BasicObjects.py
@@ -759,6 +759,7 @@ class Algorithm(object):
         __test_ovalue( EM, "EM", "Evolution", "M" )
         __test_ovalue( CM, "CM", "Control Model", "C" )
         #
+        # Corrections et compléments des bornes
         if ("Bounds" in self._parameters) and isinstance(self._parameters["Bounds"], (list, tuple)) and (len(self._parameters["Bounds"]) > 0):
             logging.debug("%s Prise en compte des bornes effectuee"%(self._name,))
         else:
@@ -777,6 +778,12 @@ class Algorithm(object):
             else:
                 if self._parameters["InitializationPoint"] is None:
                     raise ValueError("Forced initial point can not be set without any given Background or required value")
+        #
+        # Correction pour pallier a un bug de TNC sur le retour du Minimum
+        if "Minimizer" in self._parameters and self._parameters["Minimizer"] == "TNC":
+            self.setParameterValue("StoreInternalVariables",True)
+        #
+        # Verbosité et logging
         if logging.getLogger().level < logging.WARNING:
             self._parameters["optiprint"], self._parameters["optdisp"] = 1, 1
             if PlatformInfo.has_scipy:

diff --git a/src/daComposant/daCore/NumericObjects.py b/src/daComposant/daCore/NumericObjects.py
index bac05baacbf877e8ff1deafc191c7cfd300d6044..cb917645bbfdbf37afac968d59e1ceb13c853df1 100644 (file)
--- a/src/daComposant/daCore/NumericObjects.py
+++ b/src/daComposant/daCore/NumericObjects.py
@@ -700,10 +700,6 @@ def std3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q):
     valeurs.
     """
     #
-    # Correction pour pallier a un bug de TNC sur le retour du Minimum
-    if "Minimizer" in selfA._parameters and selfA._parameters["Minimizer"] == "TNC":
-        selfA.setParameterValue("StoreInternalVariables",True)
-    #
     # Opérateurs
     # ----------
     Hm = HO["Direct"].appliedTo
@@ -872,7 +868,7 @@ def std3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q):
     # ----------------------
     Xa = numpy.asmatrix(numpy.ravel( Minimum )).T
     #
-    selfA.StoredVariables["Analysis"].store( Xa.A1 )
+    selfA.StoredVariables["Analysis"].store( Xa )
     #
     if selfA._toStore("OMA") or \
         selfA._toStore("SigmaObs2") or \
@@ -988,10 +984,6 @@ def van3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q):
     valeurs.
     """
     #
-    # Correction pour pallier a un bug de TNC sur le retour du Minimum
-    if "Minimizer" in selfA._parameters and selfA._parameters["Minimizer"] == "TNC":
-        selfA.setParameterValue("StoreInternalVariables",True)
-    #
     # Initialisations
     # ---------------
     Hm = HO["Direct"].appliedTo
@@ -1263,10 +1255,6 @@ def incr3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q):
     valeurs.
     """
     #
-    # Correction pour pallier a un bug de TNC sur le retour du Minimum
-    if "Minimizer" in selfA._parameters and selfA._parameters["Minimizer"] == "TNC":
-        selfA.setParameterValue("StoreInternalVariables",True)
-    #
     # Initialisations
     # ---------------
     #
@@ -1556,10 +1544,6 @@ def psas3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q):
     valeurs.
     """
     #
-    # Correction pour pallier a un bug de TNC sur le retour du Minimum
-    if "Minimizer" in selfA._parameters and selfA._parameters["Minimizer"] == "TNC":
-        selfA.setParameterValue("StoreInternalVariables",True)
-    #
     # Initialisations
     # ---------------
     #
@@ -1891,7 +1875,7 @@ def senkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula"):
     Xn = EnsembleOfBackgroundPerturbations( Xb, None, __m )
     #
     if len(selfA.StoredVariables["Analysis"])==0 or not selfA._parameters["nextStep"]:
-        selfA.StoredVariables["Analysis"].store( numpy.ravel(Xb) )
+        selfA.StoredVariables["Analysis"].store( Xb )
         if selfA._toStore("APosterioriCovariance"):
             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
             covarianceXa = Pn
@@ -2132,7 +2116,7 @@ def etkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula"):
     Xn = EnsembleOfBackgroundPerturbations( Xb, None, __m )
     #
     if len(selfA.StoredVariables["Analysis"])==0 or not selfA._parameters["nextStep"]:
-        selfA.StoredVariables["Analysis"].store( numpy.ravel(Xb) )
+        selfA.StoredVariables["Analysis"].store( Xb )
         if selfA._toStore("APosterioriCovariance"):
             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
             covarianceXa = Pn
@@ -2182,7 +2166,7 @@ def etkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula"):
         #
         # Mean of forecast and observation of forecast
         Xfm  = Xn_predicted.mean(axis=1, dtype=mfp).astype('float').reshape((__n,-1))
-        Hfm  = HX_predicted.mean(axis=1, dtype=mfp).astype('float').reshape((__p,1))
+        Hfm  = HX_predicted.mean(axis=1, dtype=mfp).astype('float').reshape((__p,-1))
         #
         # Anomalies
         EaX   = EnsembleOfAnomalies( Xn_predicted )
@@ -2488,7 +2472,7 @@ def mlef(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="MLEF13",
     Xn = EnsembleOfBackgroundPerturbations( Xb, None, __m )
     #
     if len(selfA.StoredVariables["Analysis"])==0 or not selfA._parameters["nextStep"]:
-        selfA.StoredVariables["Analysis"].store( numpy.ravel(Xb) )
+        selfA.StoredVariables["Analysis"].store( Xb )
         if selfA._toStore("APosterioriCovariance"):
             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
             covarianceXa = Pn
@@ -2731,7 +2715,7 @@ def ienkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="IEnKF12",
     Xn = EnsembleOfBackgroundPerturbations( Xb, Pn, __m )
     #
     if len(selfA.StoredVariables["Analysis"])==0 or not selfA._parameters["nextStep"]:
-        selfA.StoredVariables["Analysis"].store( numpy.ravel(Xb) )
+        selfA.StoredVariables["Analysis"].store( Xb )
         if selfA._toStore("APosterioriCovariance"):
             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
             covarianceXa = Pn