Updating copyright date information (1)

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

diff --git a/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py b/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py
index a4d5c9534c7d1f9c176f9697c5f812c63320fa19..4efab27000575e4230ed6fffd38be43416cafbb9 100644 (file)
--- a/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py
+++ b/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py
@@ -1,6 +1,6 @@
-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2017 EDF R&D
+# Copyright (C) 2008-2018 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
@@ -46,36 +46,31 @@ 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",
+            message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
             listval  = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation"]
             )
         self.defineRequiredParameter( # Pas de type
             name     = "Bounds",
             message  = "Liste des valeurs de bornes",
             )
+        self.requireInputArguments(
+            mandatory= ("Xb", "Y", "HO", "R", "B" ),
+            optional = ("U", "EM", "CM", "Q"),
+            )
 
     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()
-        #
-        # Paramètres de pilotage
-        # ----------------------
-        self.setParameters(Parameters)
+        self._pre_run(Parameters, Xb, Y, R, B, Q)
         #
-        if self._parameters.has_key("Bounds") and (type(self._parameters["Bounds"]) is type([]) or type(self._parameters["Bounds"]) is type(())) and (len(self._parameters["Bounds"]) > 0):
-            Bounds = self._parameters["Bounds"]
-            logging.debug("%s Prise en compte des bornes effectuee"%(self._name,))
-        else:
-            Bounds = None
         if self._parameters["EstimationOf"] == "Parameters":
             self._parameters["StoreInternalVariables"] = True
         #
-        # Opérateurs
+        # Opérateurs
         # ----------
         if B is None:
             raise ValueError("Background error covariance matrix has to be properly defined!")
@@ -87,7 +82,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         if self._parameters["EstimationOf"] == "State":
             M = EM["Direct"].appliedControledFormTo
         #
-        if CM is not None and CM.has_key("Tangent") and U is not None:
+        if CM is not None and "Tangent" in CM and U is not None:
             Cm = CM["Tangent"].asMatrix(Xb)
         else:
             Cm = None
@@ -99,7 +94,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         else:
             duration = 2
         #
-        # Précalcul des inversions de B et R
+        # Précalcul des inversions de B et R
         # ----------------------------------
         if self._parameters["StoreInternalVariables"]:
             BI = B.getI()
@@ -155,9 +150,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 Xn_predicted = Xn
                 Pn_predicted = Pn
             #
-            if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
-                Xn_predicted = numpy.max(numpy.hstack((Xn_predicted,numpy.asmatrix(Bounds)[:,0])),axis=1)
-                Xn_predicted = numpy.min(numpy.hstack((Xn_predicted,numpy.asmatrix(Bounds)[:,1])),axis=1)
+            if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
+                Xn_predicted = numpy.max(numpy.hstack((Xn_predicted,numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1)
+                Xn_predicted = numpy.min(numpy.hstack((Xn_predicted,numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1)
             #
             if self._parameters["EstimationOf"] == "State":
                 d  = Ynpu - numpy.asmatrix(numpy.ravel( H( (Xn_predicted, None) ) )).T
@@ -177,12 +172,15 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 self.StoredVariables["APosterioriCovariance"].store( Pn )
             if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["Innovation"].store( numpy.ravel( d.A1 ) )
-            if self._parameters["StoreInternalVariables"]:
+            if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["CurrentState"].store( Xn )
+            if self._parameters["StoreInternalVariables"] \
+                or "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] \
+                or "CostFunctionJb" in self._parameters["StoreSupplementaryCalculations"] \
+                or "CostFunctionJo" in self._parameters["StoreSupplementaryCalculations"]:
                 Jb  = 0.5 * (Xn - Xb).T * BI * (Xn - Xb)
                 Jo  = 0.5 * d.T * RI * d
                 J   = float( Jb ) + float( Jo )
-                if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
-                    self.StoredVariables["CurrentState"].store( Xn )
                 self.StoredVariables["CostFunctionJb"].store( Jb )
                 self.StoredVariables["CostFunctionJo"].store( Jo )
                 self.StoredVariables["CostFunctionJ" ].store( J )
@@ -210,4 +208,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
 
 # ==============================================================================
 if __name__ == "__main__":
-    print '\n AUTODIAGNOSTIC \n'
+    print('\n AUTODIAGNOSTIC \n')