Python 3 compatibility improvement (UTF-8) and data interface changes

[modules/adao.git] / src / daComposant / daAlgorithms / ExtendedBlue.py

diff --git a/src/daComposant/daAlgorithms/ExtendedBlue.py b/src/daComposant/daAlgorithms/ExtendedBlue.py
index 70fb69f467eac48c75acee6d59df5faca27ff0fd..893a099fe378c2ae498172d80530f2a47da1bd9d 100644 (file)
--- a/src/daComposant/daAlgorithms/ExtendedBlue.py
+++ b/src/daComposant/daAlgorithms/ExtendedBlue.py
@@ -1,6 +1,6 @@
-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2016 EDF R&D
+# Copyright (C) 2008-2017 EDF R&D
 #
 # This library is free software; you can redistribute it and/or
 # modify it under the terms of the GNU Lesser General Public
@@ -32,14 +32,14 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             name     = "StoreInternalVariables",
             default  = False,
             typecast = bool,
-            message  = "Stockage des variables internes ou intermédiaires du calcul",
+            message  = "Stockage des variables internes ou intermédiaires du calcul",
             )
         self.defineRequiredParameter(
             name     = "StoreSupplementaryCalculations",
             default  = [],
             typecast = tuple,
-            message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+            listval  = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
             )
         self.defineRequiredParameter(
             name     = "Quantiles",
@@ -52,13 +52,13 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         self.defineRequiredParameter(
             name     = "SetSeed",
             typecast = numpy.random.seed,
-            message  = "Graine fixée pour le générateur aléatoire",
+            message  = "Graine fixée pour le générateur aléatoire",
             )
         self.defineRequiredParameter(
             name     = "NumberOfSamplesForQuantiles",
             default  = 100,
             typecast = int,
-            message  = "Nombre d'échantillons simulés pour le calcul des quantiles",
+            message  = "Nombre d'échantillons simulés pour le calcul des quantiles",
             minval   = 1,
             )
         self.defineRequiredParameter(
@@ -70,29 +70,23 @@ 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()
+        self._pre_run(Parameters)
         #
-        # Paramètres de pilotage
-        # ----------------------
-        self.setParameters(Parameters)
-        #
-        # Opérateur d'observation
-        # -----------------------
         Hm = HO["Tangent"].asMatrix(Xb)
         Hm = Hm.reshape(Y.size,Xb.size) # ADAO & check shape
         Ha = HO["Adjoint"].asMatrix(Xb)
         Ha = Ha.reshape(Xb.size,Y.size) # ADAO & check shape
         H  = HO["Direct"].appliedTo
         #
-        # Utilisation éventuelle d'un vecteur H(Xb) précalculé
+        # Utilisation éventuelle d'un vecteur H(Xb) précalculé
         # ----------------------------------------------------
-        if HO["AppliedToX"] is not None and HO["AppliedToX"].has_key("HXb"):
-            HXb = H( Xb, HO["AppliedToX"]["HXb"])
+        if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]:
+            HXb = H( Xb, HO["AppliedInX"]["HXb"])
         else:
             HXb = H( Xb )
         HXb = numpy.asmatrix(numpy.ravel( HXb )).T
         #
-        # Précalcul des inversions de B et R
+        # Précalcul des inversions de B et R
         # ----------------------------------
         BI = B.getI()
         RI = R.getI()
@@ -117,7 +111,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             Xa = Xb + _u
         self.StoredVariables["Analysis"].store( Xa.A1 )
         #
-        # Calcul de la fonction coût
+        # Calcul de la fonction coût
         # --------------------------
         if self._parameters["StoreInternalVariables"] or \
            "CostFunctionJ"                      in self._parameters["StoreSupplementaryCalculations"] or \
@@ -132,9 +126,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         if self._parameters["StoreInternalVariables"] or \
            "CostFunctionJ"                 in self._parameters["StoreSupplementaryCalculations"] or \
            "MahalanobisConsistency"        in self._parameters["StoreSupplementaryCalculations"]:
-            Jb  = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
-            Jo  = 0.5 * oma.T * RI * oma
-            J   = float( Jb ) + float( Jo )
+            Jb  = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
+            Jo  = float( 0.5 * oma.T * RI * oma )
+            J   = Jb + Jo
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )
@@ -157,7 +151,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                     raise ValueError("The %s a posteriori covariance matrix A is not symmetric positive-definite. Please check your a priori covariances and your observation operator."%(self._name,))
             self.StoredVariables["APosterioriCovariance"].store( A )
         #
-        # Calculs et/ou stockages supplémentaires
+        # Calculs et/ou stockages supplémentaires
         # ---------------------------------------
         if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
             self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
@@ -177,7 +171,6 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
             self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
         if "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
-            Qtls = map(float, self._parameters["Quantiles"])
             nech = self._parameters["NumberOfSamplesForQuantiles"]
             HtM  = HO["Tangent"].asMatrix(ValueForMethodForm = Xa)
             HtM  = HtM.reshape(Y.size,Xa.size) # ADAO & check shape
@@ -196,9 +189,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                     YfQ = numpy.hstack((YfQ,Yr))
             YfQ.sort(axis=-1)
             YQ = None
-            for quantile in Qtls:
-                if not (0. <= quantile <= 1.): continue
-                indice = int(nech * quantile - 1./nech)
+            for quantile in self._parameters["Quantiles"]:
+                if not (0. <= float(quantile) <= 1.): continue
+                indice = int(nech * float(quantile) - 1./nech)
                 if YQ is None: YQ = YfQ[:,indice]
                 else:          YQ = numpy.hstack((YQ,YfQ[:,indice]))
             self.StoredVariables["SimulationQuantiles"].store( YQ )
@@ -214,4 +207,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
 
 # ==============================================================================
 if __name__ == "__main__":
-    print '\n AUTODIAGNOSTIC \n'
+    print('\n AUTODIAGNOSTIC \n')