src/daComposant/daAlgorithms/ExtendedKalmanFilter.py

   1 # -*- coding: utf-8 -*-
   2 #
   3 # Copyright (C) 2008-2024 EDF R&D
   4 #
   5 # This library is free software; you can redistribute it and/or
   6 # modify it under the terms of the GNU Lesser General Public
   7 # License as published by the Free Software Foundation; either
   8 # version 2.1 of the License.
   9 #
  10 # This library is distributed in the hope that it will be useful,
  11 # but WITHOUT ANY WARRANTY; without even the implied warranty of
  12 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  13 # Lesser General Public License for more details.
  14 #
  15 # You should have received a copy of the GNU Lesser General Public
  16 # License along with this library; if not, write to the Free Software
  17 # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA
  18 #
  19 # See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
  20 #
  21 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
  22
  23 from daCore import BasicObjects
  24 from daAlgorithms.Atoms import cekf, ceks, exks, exkf
  25
  26 # ==============================================================================
  27 class ElementaryAlgorithm(BasicObjects.Algorithm):
  28     def __init__(self):
  29         BasicObjects.Algorithm.__init__(self, "EXTENDEDKALMANFILTER")
  30         self.defineRequiredParameter(
  31             name     = "Variant",
  32             default  = "CEKF",
  33             typecast = str,
  34             message  = "Variant ou formulation de la méthode",
  35             listval  = [
  36                 "EKF",
  37                 "CEKF",
  38             ],
  39             listadv  = [
  40                 "EKS",
  41                 "CEKS",
  42             ],
  43         )
  44         self.defineRequiredParameter(
  45             name     = "ConstrainedBy",
  46             default  = "EstimateProjection",
  47             typecast = str,
  48             message  = "Prise en compte des contraintes",
  49             listval  = ["EstimateProjection"],
  50         )
  51         self.defineRequiredParameter(
  52             name     = "EstimationOf",
  53             default  = "State",
  54             typecast = str,
  55             message  = "Estimation d'état ou de paramètres",
  56             listval  = ["State", "Parameters"],
  57         )
  58         self.defineRequiredParameter(
  59             name     = "StoreInternalVariables",
  60             default  = False,
  61             typecast = bool,
  62             message  = "Stockage des variables internes ou intermédiaires du calcul",
  63         )
  64         self.defineRequiredParameter(
  65             name     = "StoreSupplementaryCalculations",
  66             default  = [],
  67             typecast = tuple,
  68             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
  69             listval  = [
  70                 "Analysis",
  71                 "APosterioriCorrelations",
  72                 "APosterioriCovariance",
  73                 "APosterioriStandardDeviations",
  74                 "APosterioriVariances",
  75                 "BMA",
  76                 "CostFunctionJ",
  77                 "CostFunctionJAtCurrentOptimum",
  78                 "CostFunctionJb",
  79                 "CostFunctionJbAtCurrentOptimum",
  80                 "CostFunctionJo",
  81                 "CostFunctionJoAtCurrentOptimum",
  82                 "CurrentIterationNumber",
  83                 "CurrentOptimum",
  84                 "CurrentState",
  85                 "ForecastCovariance",
  86                 "ForecastState",
  87                 "IndexOfOptimum",
  88                 "InnovationAtCurrentAnalysis",
  89                 "InnovationAtCurrentState",
  90                 "SimulatedObservationAtCurrentAnalysis",
  91                 "SimulatedObservationAtCurrentOptimum",
  92                 "SimulatedObservationAtCurrentState",
  93             ]
  94         )
  95         self.defineRequiredParameter(  # Pas de type
  96             name     = "Bounds",
  97             message  = "Liste des valeurs de bornes",
  98         )
  99         self.requireInputArguments(
 100             mandatory= ("Xb", "Y", "HO", "R", "B"),
 101             optional = ("U", "EM", "CM", "Q"),
 102         )
 103         self.setAttributes(
 104             tags=(
 105                 "DataAssimilation",
 106                 "NonLinear",
 107                 "Filter",
 108                 "Dynamic",
 109             ),
 110             features=(
 111                 "LocalOptimization",
 112                 "DerivativeNeeded",
 113                 "ParallelDerivativesOnly",
 114             ),
 115         )
 116
 117     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
 118         self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
 119         #
 120         # --------------------------
 121         if self._parameters["Variant"] == "EKF":
 122             exkf.exkf(self, Xb, Y, U, HO, EM, CM, R, B, Q)
 123         #
 124         # --------------------------
 125         elif self._parameters["Variant"] == "CEKF":
 126             cekf.cekf(self, Xb, Y, U, HO, EM, CM, R, B, Q)
 127         #
 128         # --------------------------
 129         elif self._parameters["Variant"] == "EKS":
 130             exks.exks(self, Xb, Y, U, HO, EM, CM, R, B, Q)
 131         #
 132         # --------------------------
 133         elif self._parameters["Variant"] == "CEKS":
 134             ceks.ceks(self, Xb, Y, U, HO, EM, CM, R, B, Q)
 135         #
 136         # --------------------------
 137         else:
 138             raise ValueError("Error in Variant name: %s"%self._parameters["Variant"])
 139         #
 140         self._post_run(HO, EM)
 141         return 0
 142
 143 # ==============================================================================
 144 if __name__ == "__main__":
 145     print("\n AUTODIAGNOSTIC\n")