From: Jean-Philippe ARGAUD Date: Tue, 20 Jul 2021 17:42:23 +0000 (+0200) Subject: Minor internal modifications and performance improvements X-Git-Tag: V9_8_0a1~19 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=80ea94230f1baa590ec84fcfb8dde58b5336f46b;p=modules%2Fadao.git Minor internal modifications and performance improvements --- diff --git a/doc/en/ref_algorithm_ExtendedKalmanFilter.rst b/doc/en/ref_algorithm_ExtendedKalmanFilter.rst index c4429c5..cc45fe9 100644 --- a/doc/en/ref_algorithm_ExtendedKalmanFilter.rst +++ b/doc/en/ref_algorithm_ExtendedKalmanFilter.rst @@ -77,7 +77,7 @@ the operators with the help of the :ref:`section_ref_algorithm_LinearityTest`. .. ------------------------------------ .. .. include:: snippets/Header2Algo03AdOp.rst -.. include:: snippets/BoundsWithExtremes.rst +.. include:: snippets/BoundsWithNone.rst .. include:: snippets/ConstrainedBy.rst @@ -120,6 +120,8 @@ StoreSupplementaryCalculations Example : ``{"StoreSupplementaryCalculations":["BMA", "CurrentState"]}`` +.. include:: snippets/Variant_EKF.rst + .. ------------------------------------ .. .. include:: snippets/Header2Algo04.rst diff --git a/doc/en/ref_algorithm_UnscentedKalmanFilter.rst b/doc/en/ref_algorithm_UnscentedKalmanFilter.rst index 519b499..eeef062 100644 --- a/doc/en/ref_algorithm_UnscentedKalmanFilter.rst +++ b/doc/en/ref_algorithm_UnscentedKalmanFilter.rst @@ -72,7 +72,7 @@ with the help of the :ref:`section_ref_algorithm_LinearityTest`. .. ------------------------------------ .. .. include:: snippets/Header2Algo03AdOp.rst -.. include:: snippets/BoundsWithExtremes.rst +.. include:: snippets/BoundsWithNone.rst .. include:: snippets/ConstrainedBy.rst diff --git a/doc/en/snippets/Variant_EKF.rst b/doc/en/snippets/Variant_EKF.rst new file mode 100644 index 0000000..4cf6547 --- /dev/null +++ b/doc/en/snippets/Variant_EKF.rst @@ -0,0 +1,14 @@ +.. index:: + single: Variant + pair: Variant ; EKF + pair: Variant ; CEKF + +Variant + *Predifined name*. This key allows to choose one of the possible variants for + the main algorithm. The default variant is the constrained version "CEKF" of + the original algorithm "EKF", and the possible choices are + "EKF" (Extended Kalman Filter), + "CEKF" (Constrained Extended Kalman Filter). + + Exemple : + ``{"Variant":"CEKF"}`` diff --git a/doc/fr/ref_algorithm_ExtendedKalmanFilter.rst b/doc/fr/ref_algorithm_ExtendedKalmanFilter.rst index bde01ed..58ab954 100644 --- a/doc/fr/ref_algorithm_ExtendedKalmanFilter.rst +++ b/doc/fr/ref_algorithm_ExtendedKalmanFilter.rst @@ -78,7 +78,7 @@ l':ref:`section_ref_algorithm_LinearityTest`. .. ------------------------------------ .. .. include:: snippets/Header2Algo03AdOp.rst -.. include:: snippets/BoundsWithExtremes.rst +.. include:: snippets/BoundsWithNone.rst .. include:: snippets/ConstrainedBy.rst @@ -122,6 +122,8 @@ StoreSupplementaryCalculations Exemple : ``{"StoreSupplementaryCalculations":["BMA", "CurrentState"]}`` +.. include:: snippets/Variant_EKF.rst + .. ------------------------------------ .. .. include:: snippets/Header2Algo04.rst diff --git a/doc/fr/ref_algorithm_UnscentedKalmanFilter.rst b/doc/fr/ref_algorithm_UnscentedKalmanFilter.rst index 0e52551..e873868 100644 --- a/doc/fr/ref_algorithm_UnscentedKalmanFilter.rst +++ b/doc/fr/ref_algorithm_UnscentedKalmanFilter.rst @@ -73,7 +73,7 @@ opérateurs à l'aide de l':ref:`section_ref_algorithm_LinearityTest`. .. ------------------------------------ .. .. include:: snippets/Header2Algo03AdOp.rst -.. include:: snippets/BoundsWithExtremes.rst +.. include:: snippets/BoundsWithNone.rst .. include:: snippets/ConstrainedBy.rst diff --git a/doc/fr/snippets/Variant_EKF.rst b/doc/fr/snippets/Variant_EKF.rst new file mode 100644 index 0000000..5c97ed8 --- /dev/null +++ b/doc/fr/snippets/Variant_EKF.rst @@ -0,0 +1,14 @@ +.. index:: + single: Variant + pair: Variant ; EKF + pair: Variant ; CEKF + +Variant + *Nom prédéfini*. Cette clé permet de choisir l'une des variantes possibles + pour l'algorithme principal. La variante par défaut est la version contrainte + "CEKF" de l'algorithme original "EKF", et les choix possibles sont + "EKF" (Extended Kalman Filter), + "CEKF" (Constrained Extended Kalman Filter). + + Exemple : + ``{"Variant":"CEKF"}`` diff --git a/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py b/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py index 95cd3e3..a34a997 100644 --- a/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py +++ b/src/daComposant/daAlgorithms/ExtendedKalmanFilter.py @@ -29,13 +29,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): BasicObjects.Algorithm.__init__(self, "EXTENDEDKALMANFILTER") self.defineRequiredParameter( name = "Variant", - default = "EKF", + default = "CEKF", typecast = str, message = "Variant ou formulation de la méthode", listval = [ "EKF", - ], - listadv = [ "CEKF", ], ) @@ -109,8 +107,19 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q) # #-------------------------- - NumericObjects.exkf(self, Xb, Y, U, HO, EM, CM, R, B, Q) + # Default EKF + #-------------------------- + if self._parameters["Variant"] == "EKF": + NumericObjects.exkf(self, Xb, Y, U, HO, EM, CM, R, B, Q) + # + #-------------------------- + # Default CEKF + elif self._parameters["Variant"] == "CEKF": + NumericObjects.cekf(self, Xb, Y, U, HO, EM, CM, R, B, Q) + # #-------------------------- + else: + raise ValueError("Error in Variant name: %s"%self._parameters["Variant"]) # self._post_run(HO) return 0 diff --git a/src/daComposant/daAlgorithms/UnscentedKalmanFilter.py b/src/daComposant/daAlgorithms/UnscentedKalmanFilter.py index 6c497f8..a8904ba 100644 --- a/src/daComposant/daAlgorithms/UnscentedKalmanFilter.py +++ b/src/daComposant/daAlgorithms/UnscentedKalmanFilter.py @@ -101,11 +101,22 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): "APosterioriVariances", "BMA", "CostFunctionJ", + "CostFunctionJAtCurrentOptimum", "CostFunctionJb", + "CostFunctionJbAtCurrentOptimum", "CostFunctionJo", + "CostFunctionJoAtCurrentOptimum", "CurrentIterationNumber", + "CurrentOptimum", "CurrentState", + "ForecastCovariance", + "ForecastState", + "IndexOfOptimum", + "InnovationAtCurrentAnalysis", "InnovationAtCurrentState", + "SimulatedObservationAtCurrentAnalysis", + "SimulatedObservationAtCurrentOptimum", + "SimulatedObservationAtCurrentState", ] ) self.defineRequiredParameter( # Pas de type diff --git a/src/daComposant/daCore/NumericObjects.py b/src/daComposant/daCore/NumericObjects.py index 2813a30..2c862af 100644 --- a/src/daComposant/daCore/NumericObjects.py +++ b/src/daComposant/daCore/NumericObjects.py @@ -710,12 +710,12 @@ def QuantilesEstimations(selfA, A, Xa, HXa = None, Hm = None, HtM = None): # ============================================================================== def ForceNumericBounds( __Bounds ): - "Force les bornes à être des valeurs numériques, sauf si None" + "Force les bornes à être des valeurs numériques, sauf si globalement None" # Conserve une valeur par défaut à None s'il n'y a pas de bornes if __Bounds is None: return None # Converti toutes les bornes individuelles None à +/- l'infini __Bounds = numpy.asarray( __Bounds, dtype=float ) - if len(__Bounds.shape) != 2 or min(__Bounds.shape) <= 0: + if len(__Bounds.shape) != 2 or min(__Bounds.shape) <= 0 or __Bounds.shape[1] != 2: raise ValueError("Incorrectly defined bounds data") __Bounds[numpy.isnan(__Bounds)[:,0],0] = -sys.float_info.max __Bounds[numpy.isnan(__Bounds)[:,1],1] = sys.float_info.max @@ -728,6 +728,210 @@ def RecentredBounds( __Bounds, __Center): # Recentre les valeurs numériques de bornes return ForceNumericBounds( __Bounds ) - numpy.ravel( __Center ).transpose((-1,1)) +# ============================================================================== +def cekf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): + """ + Contrained Extended Kalman Filter + """ + if selfA._parameters["EstimationOf"] == "Parameters": + selfA._parameters["StoreInternalVariables"] = True + selfA._parameters["Bounds"] = ForceNumericBounds( selfA._parameters["Bounds"] ) + # + # Opérateurs + H = HO["Direct"].appliedControledFormTo + # + if selfA._parameters["EstimationOf"] == "State": + M = EM["Direct"].appliedControledFormTo + # + if CM is not None and "Tangent" in CM and U is not None: + Cm = CM["Tangent"].asMatrix(Xb) + else: + Cm = None + # + # Durée d'observation et tailles + if hasattr(Y,"stepnumber"): + duration = Y.stepnumber() + __p = numpy.cumprod(Y.shape())[-1] + else: + duration = 2 + __p = numpy.array(Y).size + # + # Précalcul des inversions de B et R + if selfA._parameters["StoreInternalVariables"] \ + or selfA._toStore("CostFunctionJ") \ + or selfA._toStore("CostFunctionJb") \ + or selfA._toStore("CostFunctionJo") \ + or selfA._toStore("CurrentOptimum") \ + or selfA._toStore("APosterioriCovariance"): + BI = B.getI() + RI = R.getI() + # + __n = Xb.size + # + if len(selfA.StoredVariables["Analysis"])==0 or not selfA._parameters["nextStep"]: + Xn = Xb + Pn = B + selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) ) + selfA.StoredVariables["Analysis"].store( Xb ) + if selfA._toStore("APosterioriCovariance"): + if hasattr(B,"asfullmatrix"): + selfA.StoredVariables["APosterioriCovariance"].store( B.asfullmatrix(__n) ) + else: + selfA.StoredVariables["APosterioriCovariance"].store( B ) + selfA._setInternalState("seed", numpy.random.get_state()) + elif selfA._parameters["nextStep"]: + Xn = selfA._getInternalState("Xn") + Pn = selfA._getInternalState("Pn") + # + if selfA._parameters["EstimationOf"] == "Parameters": + XaMin = Xn + previousJMinimum = numpy.finfo(float).max + # + for step in range(duration-1): + if hasattr(Y,"store"): + Ynpu = numpy.ravel( Y[step+1] ).reshape((__p,1)) + else: + Ynpu = numpy.ravel( Y ).reshape((__p,1)) + # + Ht = HO["Tangent"].asMatrix(ValueForMethodForm = Xn) + Ht = Ht.reshape(Ynpu.size,Xn.size) # ADAO & check shape + Ha = HO["Adjoint"].asMatrix(ValueForMethodForm = Xn) + Ha = Ha.reshape(Xn.size,Ynpu.size) # ADAO & check shape + # + if selfA._parameters["EstimationOf"] == "State": + Mt = EM["Tangent"].asMatrix(ValueForMethodForm = Xn) + Mt = Mt.reshape(Xn.size,Xn.size) # ADAO & check shape + Ma = EM["Adjoint"].asMatrix(ValueForMethodForm = Xn) + Ma = Ma.reshape(Xn.size,Xn.size) # ADAO & check shape + # + if U is not None: + if hasattr(U,"store") and len(U)>1: + Un = numpy.ravel( U[step] ).reshape((-1,1)) + elif hasattr(U,"store") and len(U)==1: + Un = numpy.ravel( U[0] ).reshape((-1,1)) + else: + Un = numpy.ravel( U ).reshape((-1,1)) + else: + Un = None + # + if selfA._parameters["Bounds"] is not None and selfA._parameters["ConstrainedBy"] == "EstimateProjection": + Xn = numpy.max(numpy.hstack((Xn,numpy.asmatrix(selfA._parameters["Bounds"])[:,0])),axis=1) + Xn = numpy.min(numpy.hstack((Xn,numpy.asmatrix(selfA._parameters["Bounds"])[:,1])),axis=1) + # + if selfA._parameters["EstimationOf"] == "State": # Forecast + Q and observation of forecast + Xn_predicted = numpy.ravel( M( (Xn, Un) ) ).reshape((__n,1)) + if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon ! + Cm = Cm.reshape(__n,Un.size) # ADAO & check shape + Xn_predicted = Xn_predicted + Cm * Un + Pn_predicted = Q + Mt * Pn * Ma + elif selfA._parameters["EstimationOf"] == "Parameters": # Observation of forecast + # --- > Par principe, M = Id, Q = 0 + Xn_predicted = Xn + Pn_predicted = Pn + # + if selfA._parameters["Bounds"] is not None and selfA._parameters["ConstrainedBy"] == "EstimateProjection": + Xn_predicted = numpy.max(numpy.hstack((Xn_predicted,numpy.asmatrix(selfA._parameters["Bounds"])[:,0])),axis=1) + Xn_predicted = numpy.min(numpy.hstack((Xn_predicted,numpy.asmatrix(selfA._parameters["Bounds"])[:,1])),axis=1) + # + if selfA._parameters["EstimationOf"] == "State": + HX_predicted = numpy.ravel( H( (Xn_predicted, None) ) ).reshape((__p,1)) + _Innovation = Ynpu - HX_predicted + elif selfA._parameters["EstimationOf"] == "Parameters": + HX_predicted = numpy.ravel( H( (Xn_predicted, Un) ) ).reshape((__p,1)) + _Innovation = Ynpu - HX_predicted + if Cm is not None and Un is not None: # Attention : si Cm est aussi dans H, doublon ! + _Innovation = _Innovation - Cm * Un + # + Kn = Pn_predicted * Ha * numpy.linalg.inv(R + numpy.dot(Ht, Pn_predicted * Ha)) + Xn = Xn_predicted + Kn * _Innovation + Pn = Pn_predicted - Kn * Ht * Pn_predicted + # + if selfA._parameters["Bounds"] is not None and selfA._parameters["ConstrainedBy"] == "EstimateProjection": + Xn = numpy.max(numpy.hstack((Xn,numpy.asmatrix(selfA._parameters["Bounds"])[:,0])),axis=1) + Xn = numpy.min(numpy.hstack((Xn,numpy.asmatrix(selfA._parameters["Bounds"])[:,1])),axis=1) + # + Xa = Xn # Pointeurs + #-------------------------- + selfA._setInternalState("Xn", Xn) + selfA._setInternalState("Pn", Pn) + #-------------------------- + # + selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) ) + # ---> avec analysis + selfA.StoredVariables["Analysis"].store( Xa ) + if selfA._toStore("SimulatedObservationAtCurrentAnalysis"): + selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"].store( H((Xa, Un)) ) + if selfA._toStore("InnovationAtCurrentAnalysis"): + selfA.StoredVariables["InnovationAtCurrentAnalysis"].store( _Innovation ) + # ---> avec current state + if selfA._parameters["StoreInternalVariables"] \ + or selfA._toStore("CurrentState"): + selfA.StoredVariables["CurrentState"].store( Xn ) + if selfA._toStore("ForecastState"): + selfA.StoredVariables["ForecastState"].store( Xn_predicted ) + if selfA._toStore("ForecastCovariance"): + selfA.StoredVariables["ForecastCovariance"].store( Pn_predicted ) + if selfA._toStore("BMA"): + selfA.StoredVariables["BMA"].store( Xn_predicted - Xa ) + if selfA._toStore("InnovationAtCurrentState"): + selfA.StoredVariables["InnovationAtCurrentState"].store( _Innovation ) + if selfA._toStore("SimulatedObservationAtCurrentState") \ + or selfA._toStore("SimulatedObservationAtCurrentOptimum"): + selfA.StoredVariables["SimulatedObservationAtCurrentState"].store( HX_predicted ) + # ---> autres + if selfA._parameters["StoreInternalVariables"] \ + or selfA._toStore("CostFunctionJ") \ + or selfA._toStore("CostFunctionJb") \ + or selfA._toStore("CostFunctionJo") \ + or selfA._toStore("CurrentOptimum") \ + or selfA._toStore("APosterioriCovariance"): + Jb = float( 0.5 * (Xa - Xb).T @ (BI @ (Xa - Xb)) ) + Jo = float( 0.5 * _Innovation.T @ (RI @ _Innovation) ) + J = Jb + Jo + selfA.StoredVariables["CostFunctionJb"].store( Jb ) + selfA.StoredVariables["CostFunctionJo"].store( Jo ) + selfA.StoredVariables["CostFunctionJ" ].store( J ) + # + if selfA._toStore("IndexOfOptimum") \ + or selfA._toStore("CurrentOptimum") \ + or selfA._toStore("CostFunctionJAtCurrentOptimum") \ + or selfA._toStore("CostFunctionJbAtCurrentOptimum") \ + or selfA._toStore("CostFunctionJoAtCurrentOptimum") \ + or selfA._toStore("SimulatedObservationAtCurrentOptimum"): + IndexMin = numpy.argmin( selfA.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps + if selfA._toStore("IndexOfOptimum"): + selfA.StoredVariables["IndexOfOptimum"].store( IndexMin ) + if selfA._toStore("CurrentOptimum"): + selfA.StoredVariables["CurrentOptimum"].store( selfA.StoredVariables["Analysis"][IndexMin] ) + if selfA._toStore("SimulatedObservationAtCurrentOptimum"): + selfA.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"][IndexMin] ) + if selfA._toStore("CostFunctionJbAtCurrentOptimum"): + selfA.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJb"][IndexMin] ) + if selfA._toStore("CostFunctionJoAtCurrentOptimum"): + selfA.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJo"][IndexMin] ) + if selfA._toStore("CostFunctionJAtCurrentOptimum"): + selfA.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( selfA.StoredVariables["CostFunctionJ" ][IndexMin] ) + if selfA._toStore("APosterioriCovariance"): + selfA.StoredVariables["APosterioriCovariance"].store( Pn ) + if selfA._parameters["EstimationOf"] == "Parameters" \ + and J < previousJMinimum: + previousJMinimum = J + XaMin = Xa + if selfA._toStore("APosterioriCovariance"): + covarianceXaMin = selfA.StoredVariables["APosterioriCovariance"][-1] + # + # Stockage final supplémentaire de l'optimum en estimation de paramètres + # ---------------------------------------------------------------------- + if selfA._parameters["EstimationOf"] == "Parameters": + selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) ) + selfA.StoredVariables["Analysis"].store( XaMin ) + if selfA._toStore("APosterioriCovariance"): + selfA.StoredVariables["APosterioriCovariance"].store( covarianceXaMin ) + if selfA._toStore("BMA"): + selfA.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(XaMin) ) + # + return 0 + # ============================================================================== def enks(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="EnKS16-KalmanFilterFormula"): """ @@ -1231,7 +1435,6 @@ def exkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): """ if selfA._parameters["EstimationOf"] == "Parameters": selfA._parameters["StoreInternalVariables"] = True - selfA._parameters["Bounds"] = ForceNumericBounds( selfA._parameters["Bounds"] ) # # Opérateurs H = HO["Direct"].appliedControledFormTo @@ -1302,20 +1505,16 @@ def exkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): # if U is not None: if hasattr(U,"store") and len(U)>1: - Un = numpy.asmatrix(numpy.ravel( U[step] )).T + Un = numpy.ravel( U[step] ).reshape((-1,1)) elif hasattr(U,"store") and len(U)==1: - Un = numpy.asmatrix(numpy.ravel( U[0] )).T + Un = numpy.ravel( U[0] ).reshape((-1,1)) else: - Un = numpy.asmatrix(numpy.ravel( U )).T + Un = numpy.ravel( U ).reshape((-1,1)) else: Un = None # - if selfA._parameters["Bounds"] is not None and selfA._parameters["ConstrainedBy"] == "EstimateProjection": - Xn = numpy.max(numpy.hstack((Xn,numpy.asmatrix(selfA._parameters["Bounds"])[:,0])),axis=1) - Xn = numpy.min(numpy.hstack((Xn,numpy.asmatrix(selfA._parameters["Bounds"])[:,1])),axis=1) - # if selfA._parameters["EstimationOf"] == "State": # Forecast + Q and observation of forecast - Xn_predicted = numpy.asmatrix(numpy.ravel( M( (Xn, Un) ) )).T + Xn_predicted = numpy.ravel( M( (Xn, Un) ) ).reshape((__n,1)) if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon ! Cm = Cm.reshape(__n,Un.size) # ADAO & check shape Xn_predicted = Xn_predicted + Cm * Un @@ -1325,15 +1524,11 @@ def exkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): Xn_predicted = Xn Pn_predicted = Pn # - if selfA._parameters["Bounds"] is not None and selfA._parameters["ConstrainedBy"] == "EstimateProjection": - Xn_predicted = numpy.max(numpy.hstack((Xn_predicted,numpy.asmatrix(selfA._parameters["Bounds"])[:,0])),axis=1) - Xn_predicted = numpy.min(numpy.hstack((Xn_predicted,numpy.asmatrix(selfA._parameters["Bounds"])[:,1])),axis=1) - # if selfA._parameters["EstimationOf"] == "State": - HX_predicted = numpy.asmatrix(numpy.ravel( H( (Xn_predicted, None) ) )).T + HX_predicted = numpy.ravel( H( (Xn_predicted, None) ) ).reshape((__p,1)) _Innovation = Ynpu - HX_predicted elif selfA._parameters["EstimationOf"] == "Parameters": - HX_predicted = numpy.asmatrix(numpy.ravel( H( (Xn_predicted, Un) ) )).T + HX_predicted = numpy.ravel( H( (Xn_predicted, Un) ) ).reshape((__p,1)) _Innovation = Ynpu - HX_predicted if Cm is not None and Un is not None: # Attention : si Cm est aussi dans H, doublon ! _Innovation = _Innovation - Cm * Un @@ -1342,10 +1537,6 @@ def exkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): Xn = Xn_predicted + Kn * _Innovation Pn = Pn_predicted - Kn * Ht * Pn_predicted # - if selfA._parameters["Bounds"] is not None and selfA._parameters["ConstrainedBy"] == "EstimateProjection": - Xn = numpy.max(numpy.hstack((Xn,numpy.asmatrix(selfA._parameters["Bounds"])[:,0])),axis=1) - Xn = numpy.min(numpy.hstack((Xn,numpy.asmatrix(selfA._parameters["Bounds"])[:,1])),axis=1) - # Xa = Xn # Pointeurs #-------------------------- selfA._setInternalState("Xn", Xn) @@ -1381,8 +1572,8 @@ def exkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): or selfA._toStore("CostFunctionJo") \ or selfA._toStore("CurrentOptimum") \ or selfA._toStore("APosterioriCovariance"): - Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) ) - Jo = float( 0.5 * _Innovation.T * RI * _Innovation ) + Jb = float( 0.5 * (Xa - Xb).T @ (BI @ (Xa - Xb)) ) + Jo = float( 0.5 * _Innovation.T @ (RI @ _Innovation) ) J = Jb + Jo selfA.StoredVariables["CostFunctionJb"].store( Jb ) selfA.StoredVariables["CostFunctionJo"].store( Jo ) @@ -2790,216 +2981,33 @@ def senkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula16" return 0 # ============================================================================== -def stdkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): +def std3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): """ - Standard Kalman Filter + 3DVAR """ - if selfA._parameters["EstimationOf"] == "Parameters": - selfA._parameters["StoreInternalVariables"] = True # - # Opérateurs - # ---------- - Ht = HO["Tangent"].asMatrix(Xb) - Ha = HO["Adjoint"].asMatrix(Xb) + # Initialisations + # --------------- # - if selfA._parameters["EstimationOf"] == "State": - Mt = EM["Tangent"].asMatrix(Xb) - Ma = EM["Adjoint"].asMatrix(Xb) + # Opérateurs + Hm = HO["Direct"].appliedTo + Ha = HO["Adjoint"].appliedInXTo # - if CM is not None and "Tangent" in CM and U is not None: - Cm = CM["Tangent"].asMatrix(Xb) + # Utilisation éventuelle d'un vecteur H(Xb) précalculé + if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]: + HXb = Hm( Xb, HO["AppliedInX"]["HXb"] ) else: - Cm = None + HXb = Hm( Xb ) + HXb = numpy.asmatrix(numpy.ravel( HXb )).T + if Y.size != HXb.size: + raise ValueError("The size %i of observations Y and %i of observed calculation H(X) are different, they have to be identical."%(Y.size,HXb.size)) + if max(Y.shape) != max(HXb.shape): + raise ValueError("The shapes %s of observations Y and %s of observed calculation H(X) are different, they have to be identical."%(Y.shape,HXb.shape)) # - # Durée d'observation et tailles - if hasattr(Y,"stepnumber"): - duration = Y.stepnumber() - __p = numpy.cumprod(Y.shape())[-1] - else: - duration = 2 - __p = numpy.array(Y).size - # - # Précalcul des inversions de B et R - if selfA._parameters["StoreInternalVariables"] \ - or selfA._toStore("CostFunctionJ") \ - or selfA._toStore("CostFunctionJb") \ - or selfA._toStore("CostFunctionJo") \ - or selfA._toStore("CurrentOptimum") \ - or selfA._toStore("APosterioriCovariance"): - BI = B.getI() - RI = R.getI() - # - __n = Xb.size - # - if len(selfA.StoredVariables["Analysis"])==0 or not selfA._parameters["nextStep"]: - Xn = Xb - Pn = B - selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) ) - selfA.StoredVariables["Analysis"].store( Xb ) - if selfA._toStore("APosterioriCovariance"): - if hasattr(B,"asfullmatrix"): - selfA.StoredVariables["APosterioriCovariance"].store( B.asfullmatrix(__n) ) - else: - selfA.StoredVariables["APosterioriCovariance"].store( B ) - selfA._setInternalState("seed", numpy.random.get_state()) - elif selfA._parameters["nextStep"]: - Xn = selfA._getInternalState("Xn") - Pn = selfA._getInternalState("Pn") - # - if selfA._parameters["EstimationOf"] == "Parameters": - XaMin = Xn - previousJMinimum = numpy.finfo(float).max - # - for step in range(duration-1): - if hasattr(Y,"store"): - Ynpu = numpy.ravel( Y[step+1] ).reshape((__p,1)) - else: - Ynpu = numpy.ravel( Y ).reshape((__p,1)) - # - if U is not None: - if hasattr(U,"store") and len(U)>1: - Un = numpy.asmatrix(numpy.ravel( U[step] )).T - elif hasattr(U,"store") and len(U)==1: - Un = numpy.asmatrix(numpy.ravel( U[0] )).T - else: - Un = numpy.asmatrix(numpy.ravel( U )).T - else: - Un = None - # - if selfA._parameters["EstimationOf"] == "State": # Forecast + Q and observation of forecast - Xn_predicted = Mt * Xn - if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon ! - Cm = Cm.reshape(__n,Un.size) # ADAO & check shape - Xn_predicted = Xn_predicted + Cm * Un - Pn_predicted = Q + Mt * Pn * Ma - elif selfA._parameters["EstimationOf"] == "Parameters": # Observation of forecast - # --- > Par principe, M = Id, Q = 0 - Xn_predicted = Xn - Pn_predicted = Pn - # - if selfA._parameters["EstimationOf"] == "State": - HX_predicted = Ht * Xn_predicted - _Innovation = Ynpu - HX_predicted - elif selfA._parameters["EstimationOf"] == "Parameters": - HX_predicted = Ht * Xn_predicted - _Innovation = Ynpu - HX_predicted - if Cm is not None and Un is not None: # Attention : si Cm est aussi dans H, doublon ! - _Innovation = _Innovation - Cm * Un - # - Kn = Pn_predicted * Ha * numpy.linalg.inv(R + numpy.dot(Ht, Pn_predicted * Ha)) - Xn = Xn_predicted + Kn * _Innovation - Pn = Pn_predicted - Kn * Ht * Pn_predicted - # - Xa = Xn # Pointeurs - #-------------------------- - selfA._setInternalState("Xn", Xn) - selfA._setInternalState("Pn", Pn) - #-------------------------- - # - selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) ) - # ---> avec analysis - selfA.StoredVariables["Analysis"].store( Xa ) - if selfA._toStore("SimulatedObservationAtCurrentAnalysis"): - selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"].store( Ht * Xa ) - if selfA._toStore("InnovationAtCurrentAnalysis"): - selfA.StoredVariables["InnovationAtCurrentAnalysis"].store( _Innovation ) - # ---> avec current state - if selfA._parameters["StoreInternalVariables"] \ - or selfA._toStore("CurrentState"): - selfA.StoredVariables["CurrentState"].store( Xn ) - if selfA._toStore("ForecastState"): - selfA.StoredVariables["ForecastState"].store( Xn_predicted ) - if selfA._toStore("ForecastCovariance"): - selfA.StoredVariables["ForecastCovariance"].store( Pn_predicted ) - if selfA._toStore("BMA"): - selfA.StoredVariables["BMA"].store( Xn_predicted - Xa ) - if selfA._toStore("InnovationAtCurrentState"): - selfA.StoredVariables["InnovationAtCurrentState"].store( _Innovation ) - if selfA._toStore("SimulatedObservationAtCurrentState") \ - or selfA._toStore("SimulatedObservationAtCurrentOptimum"): - selfA.StoredVariables["SimulatedObservationAtCurrentState"].store( HX_predicted ) - # ---> autres - if selfA._parameters["StoreInternalVariables"] \ - or selfA._toStore("CostFunctionJ") \ - or selfA._toStore("CostFunctionJb") \ - or selfA._toStore("CostFunctionJo") \ - or selfA._toStore("CurrentOptimum") \ - or selfA._toStore("APosterioriCovariance"): - Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) ) - Jo = float( 0.5 * _Innovation.T * RI * _Innovation ) - J = Jb + Jo - selfA.StoredVariables["CostFunctionJb"].store( Jb ) - selfA.StoredVariables["CostFunctionJo"].store( Jo ) - selfA.StoredVariables["CostFunctionJ" ].store( J ) - # - if selfA._toStore("IndexOfOptimum") \ - or selfA._toStore("CurrentOptimum") \ - or selfA._toStore("CostFunctionJAtCurrentOptimum") \ - or selfA._toStore("CostFunctionJbAtCurrentOptimum") \ - or selfA._toStore("CostFunctionJoAtCurrentOptimum") \ - or selfA._toStore("SimulatedObservationAtCurrentOptimum"): - IndexMin = numpy.argmin( selfA.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps - if selfA._toStore("IndexOfOptimum"): - selfA.StoredVariables["IndexOfOptimum"].store( IndexMin ) - if selfA._toStore("CurrentOptimum"): - selfA.StoredVariables["CurrentOptimum"].store( selfA.StoredVariables["Analysis"][IndexMin] ) - if selfA._toStore("SimulatedObservationAtCurrentOptimum"): - selfA.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"][IndexMin] ) - if selfA._toStore("CostFunctionJbAtCurrentOptimum"): - selfA.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJb"][IndexMin] ) - if selfA._toStore("CostFunctionJoAtCurrentOptimum"): - selfA.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJo"][IndexMin] ) - if selfA._toStore("CostFunctionJAtCurrentOptimum"): - selfA.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( selfA.StoredVariables["CostFunctionJ" ][IndexMin] ) - if selfA._toStore("APosterioriCovariance"): - selfA.StoredVariables["APosterioriCovariance"].store( Pn ) - if selfA._parameters["EstimationOf"] == "Parameters" \ - and J < previousJMinimum: - previousJMinimum = J - XaMin = Xa - if selfA._toStore("APosterioriCovariance"): - covarianceXaMin = selfA.StoredVariables["APosterioriCovariance"][-1] - # - # Stockage final supplémentaire de l'optimum en estimation de paramètres - # ---------------------------------------------------------------------- - if selfA._parameters["EstimationOf"] == "Parameters": - selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) ) - selfA.StoredVariables["Analysis"].store( XaMin ) - if selfA._toStore("APosterioriCovariance"): - selfA.StoredVariables["APosterioriCovariance"].store( covarianceXaMin ) - if selfA._toStore("BMA"): - selfA.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(XaMin) ) - # - return 0 - -# ============================================================================== -def std3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): - """ - 3DVAR - """ - # - # Initialisations - # --------------- - # - # Opérateurs - Hm = HO["Direct"].appliedTo - Ha = HO["Adjoint"].appliedInXTo - # - # Utilisation éventuelle d'un vecteur H(Xb) précalculé - if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]: - HXb = Hm( Xb, HO["AppliedInX"]["HXb"] ) - else: - HXb = Hm( Xb ) - HXb = numpy.asmatrix(numpy.ravel( HXb )).T - if Y.size != HXb.size: - raise ValueError("The size %i of observations Y and %i of observed calculation H(X) are different, they have to be identical."%(Y.size,HXb.size)) - if max(Y.shape) != max(HXb.shape): - raise ValueError("The shapes %s of observations Y and %s of observed calculation H(X) are different, they have to be identical."%(Y.shape,HXb.shape)) - # - if selfA._toStore("JacobianMatrixAtBackground"): - HtMb = HO["Tangent"].asMatrix(ValueForMethodForm = Xb) - HtMb = HtMb.reshape(Y.size,Xb.size) # ADAO & check shape - selfA.StoredVariables["JacobianMatrixAtBackground"].store( HtMb ) + if selfA._toStore("JacobianMatrixAtBackground"): + HtMb = HO["Tangent"].asMatrix(ValueForMethodForm = Xb) + HtMb = HtMb.reshape(Y.size,Xb.size) # ADAO & check shape + selfA.StoredVariables["JacobianMatrixAtBackground"].store( HtMb ) # # Précalcul des inversions de B et R BI = B.getI() @@ -3465,6 +3473,189 @@ def std4dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): # return 0 +# ============================================================================== +def stdkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): + """ + Standard Kalman Filter + """ + if selfA._parameters["EstimationOf"] == "Parameters": + selfA._parameters["StoreInternalVariables"] = True + # + # Opérateurs + # ---------- + Ht = HO["Tangent"].asMatrix(Xb) + Ha = HO["Adjoint"].asMatrix(Xb) + # + if selfA._parameters["EstimationOf"] == "State": + Mt = EM["Tangent"].asMatrix(Xb) + Ma = EM["Adjoint"].asMatrix(Xb) + # + if CM is not None and "Tangent" in CM and U is not None: + Cm = CM["Tangent"].asMatrix(Xb) + else: + Cm = None + # + # Durée d'observation et tailles + if hasattr(Y,"stepnumber"): + duration = Y.stepnumber() + __p = numpy.cumprod(Y.shape())[-1] + else: + duration = 2 + __p = numpy.array(Y).size + # + # Précalcul des inversions de B et R + if selfA._parameters["StoreInternalVariables"] \ + or selfA._toStore("CostFunctionJ") \ + or selfA._toStore("CostFunctionJb") \ + or selfA._toStore("CostFunctionJo") \ + or selfA._toStore("CurrentOptimum") \ + or selfA._toStore("APosterioriCovariance"): + BI = B.getI() + RI = R.getI() + # + __n = Xb.size + # + if len(selfA.StoredVariables["Analysis"])==0 or not selfA._parameters["nextStep"]: + Xn = Xb + Pn = B + selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) ) + selfA.StoredVariables["Analysis"].store( Xb ) + if selfA._toStore("APosterioriCovariance"): + if hasattr(B,"asfullmatrix"): + selfA.StoredVariables["APosterioriCovariance"].store( B.asfullmatrix(__n) ) + else: + selfA.StoredVariables["APosterioriCovariance"].store( B ) + selfA._setInternalState("seed", numpy.random.get_state()) + elif selfA._parameters["nextStep"]: + Xn = selfA._getInternalState("Xn") + Pn = selfA._getInternalState("Pn") + # + if selfA._parameters["EstimationOf"] == "Parameters": + XaMin = Xn + previousJMinimum = numpy.finfo(float).max + # + for step in range(duration-1): + if hasattr(Y,"store"): + Ynpu = numpy.ravel( Y[step+1] ).reshape((__p,1)) + else: + Ynpu = numpy.ravel( Y ).reshape((__p,1)) + # + if U is not None: + if hasattr(U,"store") and len(U)>1: + Un = numpy.asmatrix(numpy.ravel( U[step] )).T + elif hasattr(U,"store") and len(U)==1: + Un = numpy.asmatrix(numpy.ravel( U[0] )).T + else: + Un = numpy.asmatrix(numpy.ravel( U )).T + else: + Un = None + # + if selfA._parameters["EstimationOf"] == "State": # Forecast + Q and observation of forecast + Xn_predicted = Mt * Xn + if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon ! + Cm = Cm.reshape(__n,Un.size) # ADAO & check shape + Xn_predicted = Xn_predicted + Cm * Un + Pn_predicted = Q + Mt * Pn * Ma + elif selfA._parameters["EstimationOf"] == "Parameters": # Observation of forecast + # --- > Par principe, M = Id, Q = 0 + Xn_predicted = Xn + Pn_predicted = Pn + # + if selfA._parameters["EstimationOf"] == "State": + HX_predicted = Ht * Xn_predicted + _Innovation = Ynpu - HX_predicted + elif selfA._parameters["EstimationOf"] == "Parameters": + HX_predicted = Ht * Xn_predicted + _Innovation = Ynpu - HX_predicted + if Cm is not None and Un is not None: # Attention : si Cm est aussi dans H, doublon ! + _Innovation = _Innovation - Cm * Un + # + Kn = Pn_predicted * Ha * numpy.linalg.inv(R + numpy.dot(Ht, Pn_predicted * Ha)) + Xn = Xn_predicted + Kn * _Innovation + Pn = Pn_predicted - Kn * Ht * Pn_predicted + # + Xa = Xn # Pointeurs + #-------------------------- + selfA._setInternalState("Xn", Xn) + selfA._setInternalState("Pn", Pn) + #-------------------------- + # + selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) ) + # ---> avec analysis + selfA.StoredVariables["Analysis"].store( Xa ) + if selfA._toStore("SimulatedObservationAtCurrentAnalysis"): + selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"].store( Ht * Xa ) + if selfA._toStore("InnovationAtCurrentAnalysis"): + selfA.StoredVariables["InnovationAtCurrentAnalysis"].store( _Innovation ) + # ---> avec current state + if selfA._parameters["StoreInternalVariables"] \ + or selfA._toStore("CurrentState"): + selfA.StoredVariables["CurrentState"].store( Xn ) + if selfA._toStore("ForecastState"): + selfA.StoredVariables["ForecastState"].store( Xn_predicted ) + if selfA._toStore("ForecastCovariance"): + selfA.StoredVariables["ForecastCovariance"].store( Pn_predicted ) + if selfA._toStore("BMA"): + selfA.StoredVariables["BMA"].store( Xn_predicted - Xa ) + if selfA._toStore("InnovationAtCurrentState"): + selfA.StoredVariables["InnovationAtCurrentState"].store( _Innovation ) + if selfA._toStore("SimulatedObservationAtCurrentState") \ + or selfA._toStore("SimulatedObservationAtCurrentOptimum"): + selfA.StoredVariables["SimulatedObservationAtCurrentState"].store( HX_predicted ) + # ---> autres + if selfA._parameters["StoreInternalVariables"] \ + or selfA._toStore("CostFunctionJ") \ + or selfA._toStore("CostFunctionJb") \ + or selfA._toStore("CostFunctionJo") \ + or selfA._toStore("CurrentOptimum") \ + or selfA._toStore("APosterioriCovariance"): + Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) ) + Jo = float( 0.5 * _Innovation.T * RI * _Innovation ) + J = Jb + Jo + selfA.StoredVariables["CostFunctionJb"].store( Jb ) + selfA.StoredVariables["CostFunctionJo"].store( Jo ) + selfA.StoredVariables["CostFunctionJ" ].store( J ) + # + if selfA._toStore("IndexOfOptimum") \ + or selfA._toStore("CurrentOptimum") \ + or selfA._toStore("CostFunctionJAtCurrentOptimum") \ + or selfA._toStore("CostFunctionJbAtCurrentOptimum") \ + or selfA._toStore("CostFunctionJoAtCurrentOptimum") \ + or selfA._toStore("SimulatedObservationAtCurrentOptimum"): + IndexMin = numpy.argmin( selfA.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps + if selfA._toStore("IndexOfOptimum"): + selfA.StoredVariables["IndexOfOptimum"].store( IndexMin ) + if selfA._toStore("CurrentOptimum"): + selfA.StoredVariables["CurrentOptimum"].store( selfA.StoredVariables["Analysis"][IndexMin] ) + if selfA._toStore("SimulatedObservationAtCurrentOptimum"): + selfA.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"][IndexMin] ) + if selfA._toStore("CostFunctionJbAtCurrentOptimum"): + selfA.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJb"][IndexMin] ) + if selfA._toStore("CostFunctionJoAtCurrentOptimum"): + selfA.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJo"][IndexMin] ) + if selfA._toStore("CostFunctionJAtCurrentOptimum"): + selfA.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( selfA.StoredVariables["CostFunctionJ" ][IndexMin] ) + if selfA._toStore("APosterioriCovariance"): + selfA.StoredVariables["APosterioriCovariance"].store( Pn ) + if selfA._parameters["EstimationOf"] == "Parameters" \ + and J < previousJMinimum: + previousJMinimum = J + XaMin = Xa + if selfA._toStore("APosterioriCovariance"): + covarianceXaMin = selfA.StoredVariables["APosterioriCovariance"][-1] + # + # Stockage final supplémentaire de l'optimum en estimation de paramètres + # ---------------------------------------------------------------------- + if selfA._parameters["EstimationOf"] == "Parameters": + selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) ) + selfA.StoredVariables["Analysis"].store( XaMin ) + if selfA._toStore("APosterioriCovariance"): + selfA.StoredVariables["APosterioriCovariance"].store( covarianceXaMin ) + if selfA._toStore("BMA"): + selfA.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(XaMin) ) + # + return 0 + # ============================================================================== def uckf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): """ @@ -3520,7 +3711,9 @@ def uckf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): if selfA._parameters["StoreInternalVariables"] \ or selfA._toStore("CostFunctionJ") \ or selfA._toStore("CostFunctionJb") \ - or selfA._toStore("CostFunctionJo"): + or selfA._toStore("CostFunctionJo") \ + or selfA._toStore("CurrentOptimum") \ + or selfA._toStore("APosterioriCovariance"): BI = B.getI() RI = R.getI() # @@ -3552,11 +3745,11 @@ def uckf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): # if U is not None: if hasattr(U,"store") and len(U)>1: - Un = numpy.asmatrix(numpy.ravel( U[step] )).T + Un = numpy.ravel( U[step] ).reshape((-1,1)) elif hasattr(U,"store") and len(U)==1: - Un = numpy.asmatrix(numpy.ravel( U[0] )).T + Un = numpy.ravel( U[0] ).reshape((-1,1)) else: - Un = numpy.asmatrix(numpy.ravel( U )).T + Un = numpy.ravel( U ).reshape((-1,1)) else: Un = None # @@ -3647,22 +3840,58 @@ def uckf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) ) # ---> avec analysis selfA.StoredVariables["Analysis"].store( Xa ) + if selfA._toStore("SimulatedObservationAtCurrentAnalysis"): + selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"].store( Hm((Xa, Un)) ) + if selfA._toStore("InnovationAtCurrentAnalysis"): + selfA.StoredVariables["InnovationAtCurrentAnalysis"].store( _Innovation ) # ---> avec current state if selfA._parameters["StoreInternalVariables"] \ or selfA._toStore("CurrentState"): selfA.StoredVariables["CurrentState"].store( Xn ) + if selfA._toStore("ForecastState"): + selfA.StoredVariables["ForecastState"].store( Xncm ) + if selfA._toStore("ForecastCovariance"): + selfA.StoredVariables["ForecastCovariance"].store( Pnm ) + if selfA._toStore("BMA"): + selfA.StoredVariables["BMA"].store( Xncm - Xa ) if selfA._toStore("InnovationAtCurrentState"): selfA.StoredVariables["InnovationAtCurrentState"].store( _Innovation ) + if selfA._toStore("SimulatedObservationAtCurrentState") \ + or selfA._toStore("SimulatedObservationAtCurrentOptimum"): + selfA.StoredVariables["SimulatedObservationAtCurrentState"].store( Yncm ) + # ---> autres if selfA._parameters["StoreInternalVariables"] \ or selfA._toStore("CostFunctionJ") \ or selfA._toStore("CostFunctionJb") \ - or selfA._toStore("CostFunctionJo"): + or selfA._toStore("CostFunctionJo") \ + or selfA._toStore("CurrentOptimum") \ + or selfA._toStore("APosterioriCovariance"): Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) ) Jo = float( 0.5 * _Innovation.T * RI * _Innovation ) J = Jb + Jo selfA.StoredVariables["CostFunctionJb"].store( Jb ) selfA.StoredVariables["CostFunctionJo"].store( Jo ) selfA.StoredVariables["CostFunctionJ" ].store( J ) + # + if selfA._toStore("IndexOfOptimum") \ + or selfA._toStore("CurrentOptimum") \ + or selfA._toStore("CostFunctionJAtCurrentOptimum") \ + or selfA._toStore("CostFunctionJbAtCurrentOptimum") \ + or selfA._toStore("CostFunctionJoAtCurrentOptimum") \ + or selfA._toStore("SimulatedObservationAtCurrentOptimum"): + IndexMin = numpy.argmin( selfA.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps + if selfA._toStore("IndexOfOptimum"): + selfA.StoredVariables["IndexOfOptimum"].store( IndexMin ) + if selfA._toStore("CurrentOptimum"): + selfA.StoredVariables["CurrentOptimum"].store( selfA.StoredVariables["Analysis"][IndexMin] ) + if selfA._toStore("SimulatedObservationAtCurrentOptimum"): + selfA.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"][IndexMin] ) + if selfA._toStore("CostFunctionJbAtCurrentOptimum"): + selfA.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJb"][IndexMin] ) + if selfA._toStore("CostFunctionJoAtCurrentOptimum"): + selfA.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJo"][IndexMin] ) + if selfA._toStore("CostFunctionJAtCurrentOptimum"): + selfA.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( selfA.StoredVariables["CostFunctionJ" ][IndexMin] ) if selfA._toStore("APosterioriCovariance"): selfA.StoredVariables["APosterioriCovariance"].store( Pn ) if selfA._parameters["EstimationOf"] == "Parameters" \