# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2020 EDF R&D
+# Copyright (C) 2008-2021 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
# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
import logging
-from daCore import BasicObjects, PlatformInfo
-import numpy, math
-mfp = PlatformInfo.PlatformInfo().MaximumPrecision()
+from daCore import BasicObjects, NumericObjects
+import numpy
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
BasicObjects.Algorithm.__init__(self, "ENSEMBLEKALMANFILTER")
+ self.defineRequiredParameter(
+ name = "Variant",
+ default = "EnKF",
+ typecast = str,
+ message = "Variant ou formulation de la méthode",
+ listval = [
+ "EnKF",
+ "ETKF",
+ "ETKF-N",
+ "MLEF",
+ "IEnKF",
+ ],
+ listadv = [
+ "StochasticEnKF",
+ "EnKF-05",
+ "EnKF-16",
+ "ETKF-KFF",
+ "ETKF-VAR",
+ "ETKF-N-11",
+ "ETKF-N-15",
+ "ETKF-N-16",
+ "MLEF-T",
+ "MLEF-B",
+ "IEnKF-T",
+ "IEnKF-B",
+ "IEKF",
+ ],
+ )
self.defineRequiredParameter(
name = "NumberOfMembers",
default = 100,
typecast = int,
message = "Nombre de membres dans l'ensemble",
- minval = -1,
+ minval = 2,
)
self.defineRequiredParameter(
name = "EstimationOf",
message = "Estimation d'etat ou de parametres",
listval = ["State", "Parameters"],
)
+ self.defineRequiredParameter(
+ name = "InflationType",
+ default = "MultiplicativeOnAnalysisCovariance",
+ typecast = str,
+ message = "Méthode d'inflation d'ensemble",
+ listval = [
+ "MultiplicativeOnAnalysisCovariance",
+ "MultiplicativeOnBackgroundCovariance",
+ "MultiplicativeOnAnalysisAnomalies",
+ "MultiplicativeOnBackgroundAnomalies",
+ "AdditiveOnAnalysisCovariance",
+ "AdditiveOnBackgroundCovariance",
+ "HybridOnBackgroundCovariance",
+ ],
+ )
+ self.defineRequiredParameter(
+ name = "InflationFactor",
+ default = 1.,
+ typecast = float,
+ message = "Facteur d'inflation",
+ minval = 0.,
+ )
+ self.defineRequiredParameter(
+ name = "LocalizationType",
+ default = "SchurLocalization",
+ typecast = str,
+ message = "Méthode d'inflation d'ensemble",
+ listval = [
+ "SchurLocalization",
+ ],
+ listadv = [
+ "CovarianceLocalization",
+ "DomainLocalization",
+ "GaspariCohnLocalization",
+ ],
+ )
+ self.defineRequiredParameter(
+ name = "LocalizationFactor",
+ default = 1.,
+ typecast = float,
+ message = "Facteur de localisation",
+ minval = 0.,
+ )
+ self.defineRequiredParameter( # Pas de type
+ name = "LocalizationMatrix",
+ message = "Matrice de localisation ou de distances",
+ )
self.defineRequiredParameter(
name = "SetSeed",
typecast = numpy.random.seed,
"CostFunctionJbAtCurrentOptimum",
"CostFunctionJo",
"CostFunctionJoAtCurrentOptimum",
+ "CurrentIterationNumber",
"CurrentOptimum",
"CurrentState",
"ForecastState",
))
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, R, B, Q)
+ self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
#
- if self._parameters["EstimationOf"] == "Parameters":
- self._parameters["StoreInternalVariables"] = True
+ #--------------------------
+ # Default EnKF = EnKF-16 = StochasticEnKF
+ if self._parameters["Variant"] == "EnKF-05":
+ NumericObjects.senkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula05")
#
- # Opérateurs
- # ----------
- H = HO["Direct"].appliedControledFormTo
+ elif self._parameters["Variant"] in ["EnKF-16", "StochasticEnKF", "EnKF"]:
+ NumericObjects.senkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula16")
#
- if self._parameters["EstimationOf"] == "State":
- M = EM["Direct"].appliedControledFormTo
+ #--------------------------
+ # Default ETKF = ETKF-KFF
+ elif self._parameters["Variant"] in ["ETKF-KFF", "ETKF"]:
+ NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula")
#
- if CM is not None and "Tangent" in CM and U is not None:
- Cm = CM["Tangent"].asMatrix(Xb)
- else:
- Cm = None
+ elif self._parameters["Variant"] == "ETKF-VAR":
+ NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="Variational")
#
- # Nombre de pas identique au nombre de pas d'observations
- # -------------------------------------------------------
- if hasattr(Y,"stepnumber"):
- duration = Y.stepnumber()
- __p = numpy.cumprod(Y.shape())[-1]
- else:
- duration = 2
- __p = numpy.array(Y).size
+ #--------------------------
+ # Default ETKF-N = ETKF-N-16
+ elif self._parameters["Variant"] == "ETKF-N-11":
+ NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="FiniteSize11")
#
- # Précalcul des inversions de B et R
- # ----------------------------------
- if self._parameters["StoreInternalVariables"] \
- or self._toStore("CostFunctionJ") \
- or self._toStore("CostFunctionJb") \
- or self._toStore("CostFunctionJo") \
- or self._toStore("CurrentOptimum") \
- or self._toStore("APosterioriCovariance"):
- BI = B.getI()
- RI = R.getI()
- # BIdemi = B.choleskyI()
- # RIdemi = R.choleskyI()
+ elif self._parameters["Variant"] == "ETKF-N-15":
+ NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="FiniteSize15")
#
- # Initialisation
- # --------------
- __n = Xb.size
- __m = self._parameters["NumberOfMembers"]
- Xn = numpy.asmatrix(numpy.dot( Xb.reshape(__n,1), numpy.ones((1,__m)) ))
- if hasattr(B,"asfullmatrix"): Pn = B.asfullmatrix(__n)
- else: Pn = B
- if hasattr(R,"asfullmatrix"): Rn = R.asfullmatrix(__p)
- else: Rn = R
- if hasattr(Q,"asfullmatrix"): Qn = Q.asfullmatrix(__n)
- else: Qn = Q
+ elif self._parameters["Variant"] in ["ETKF-N-16", "ETKF-N"]:
+ NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="FiniteSize16")
#
- if len(self.StoredVariables["Analysis"])==0 or not self._parameters["nextStep"]:
- self.StoredVariables["Analysis"].store( numpy.ravel(Xb) )
- if self._toStore("APosterioriCovariance"):
- self.StoredVariables["APosterioriCovariance"].store( Pn )
- covarianceXa = Pn
+ #--------------------------
+ # Default MLEF = MLEF-T
+ elif self._parameters["Variant"] in ["MLEF-T", "MLEF"]:
+ NumericObjects.mlef(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False)
#
- Xa = Xb
- XaMin = Xb
- previousJMinimum = numpy.finfo(float).max
+ elif self._parameters["Variant"] == "MLEF-B":
+ NumericObjects.mlef(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=True)
#
- # Predimensionnement
- Xn_predicted = numpy.asmatrix(numpy.zeros((__n,__m)))
- HX_predicted = numpy.asmatrix(numpy.zeros((__p,__m)))
+ #--------------------------
+ # Default IEnKF = IEnKF-T
+ elif self._parameters["Variant"] in ["IEnKF-T", "IEnKF"]:
+ NumericObjects.ienkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False)
#
- for step in range(duration-1):
- if hasattr(Y,"store"):
- Ynpu = numpy.asmatrix(numpy.ravel( Y[step+1] )).T
- else:
- Ynpu = numpy.asmatrix(numpy.ravel( Y )).T
- #
- 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 self._parameters["EstimationOf"] == "State":
- for i in range(__m):
- qi = numpy.asmatrix(numpy.random.multivariate_normal(numpy.zeros(__n), Qn, (1,1,1))).T
- Xn_predicted[:,i] = numpy.asmatrix(numpy.ravel( M((Xn[:,i], Un)) )).T + qi
- HX_predicted[:,i] = numpy.asmatrix(numpy.ravel( H((Xn_predicted[:,i], Un)) )).T
- 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
- elif self._parameters["EstimationOf"] == "Parameters":
- # --- > Par principe, M = Id, Q = 0
- Xn_predicted = Xn
- #
- Xfm = numpy.asmatrix(numpy.ravel(Xn_predicted.mean(axis=1, dtype=mfp))).T
- Hfm = numpy.asmatrix(numpy.ravel(HX_predicted.mean(axis=1, dtype=mfp))).T
- #
- PfHT, HPfHT = 0., 0.
- for i in range(__m):
- Exfi = Xn_predicted[:,i] - Xfm
- Eyfi = HX_predicted[:,i] - Hfm
- PfHT += Exfi * Eyfi.T
- HPfHT += Eyfi * Eyfi.T
- PfHT = (1./(__m-1)) * PfHT
- HPfHT = (1./(__m-1)) * HPfHT
- K = PfHT * ( R + HPfHT ).I
- del PfHT, HPfHT
- #
- for i in range(__m):
- ri = numpy.asmatrix(numpy.random.multivariate_normal(numpy.zeros(__p), Rn, (1,1,1))).T
- Xn[:,i] = Xn_predicted[:,i] + K * (Ynpu + ri - HX_predicted[:,i])
- #
- Xa = Xn.mean(axis=1, dtype=mfp)
- #
- if self._parameters["StoreInternalVariables"] \
- or self._toStore("CostFunctionJ") \
- or self._toStore("CostFunctionJb") \
- or self._toStore("CostFunctionJo") \
- or self._toStore("APosterioriCovariance") \
- or self._toStore("InnovationAtCurrentAnalysis") \
- or self._toStore("SimulatedObservationAtCurrentAnalysis") \
- or self._toStore("SimulatedObservationAtCurrentOptimum"):
- _HXa = numpy.asmatrix(numpy.ravel( H((Xa, Un)) )).T
- _Innovation = Ynpu - _HXa
- #
- # ---> avec analysis
- self.StoredVariables["Analysis"].store( Xa )
- if self._toStore("SimulatedObservationAtCurrentAnalysis"):
- self.StoredVariables["SimulatedObservationAtCurrentAnalysis"].store( _HXa )
- if self._toStore("InnovationAtCurrentAnalysis"):
- self.StoredVariables["InnovationAtCurrentAnalysis"].store( _Innovation )
- # ---> avec current state
- if self._parameters["StoreInternalVariables"] \
- or self._toStore("CurrentState"):
- self.StoredVariables["CurrentState"].store( Xn )
- if self._toStore("ForecastState"):
- self.StoredVariables["ForecastState"].store( Xn_predicted )
- if self._toStore("BMA"):
- self.StoredVariables["BMA"].store( Xn_predicted - Xa )
- if self._toStore("InnovationAtCurrentState"):
- self.StoredVariables["InnovationAtCurrentState"].store( - HX_predicted + Ynpu )
- if self._toStore("SimulatedObservationAtCurrentState") \
- or self._toStore("SimulatedObservationAtCurrentOptimum"):
- self.StoredVariables["SimulatedObservationAtCurrentState"].store( HX_predicted )
- # ---> autres
- if self._parameters["StoreInternalVariables"] \
- or self._toStore("CostFunctionJ") \
- or self._toStore("CostFunctionJb") \
- or self._toStore("CostFunctionJo") \
- or self._toStore("CurrentOptimum") \
- or self._toStore("APosterioriCovariance"):
- Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
- Jo = float( 0.5 * _Innovation.T * RI * _Innovation )
- J = Jb + Jo
- self.StoredVariables["CostFunctionJb"].store( Jb )
- self.StoredVariables["CostFunctionJo"].store( Jo )
- self.StoredVariables["CostFunctionJ" ].store( J )
- #
- if self._toStore("IndexOfOptimum") \
- or self._toStore("CurrentOptimum") \
- or self._toStore("CostFunctionJAtCurrentOptimum") \
- or self._toStore("CostFunctionJbAtCurrentOptimum") \
- or self._toStore("CostFunctionJoAtCurrentOptimum") \
- or self._toStore("SimulatedObservationAtCurrentOptimum"):
- IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
- if self._toStore("IndexOfOptimum"):
- self.StoredVariables["IndexOfOptimum"].store( IndexMin )
- if self._toStore("CurrentOptimum"):
- self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["Analysis"][IndexMin] )
- if self._toStore("SimulatedObservationAtCurrentOptimum"):
- self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentAnalysis"][IndexMin] )
- if self._toStore("CostFunctionJbAtCurrentOptimum"):
- self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
- if self._toStore("CostFunctionJoAtCurrentOptimum"):
- self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
- if self._toStore("CostFunctionJAtCurrentOptimum"):
- self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
- if self._toStore("APosterioriCovariance"):
- Pn = 0.
- for i in range(__m):
- Eai = Xn[:,i] - Xa
- Pn += Eai * Eai.T
- Pn = (1./(__m-1)) * Pn
- self.StoredVariables["APosterioriCovariance"].store( Pn )
- if self._parameters["EstimationOf"] == "Parameters" \
- and J < previousJMinimum:
- previousJMinimum = J
- XaMin = Xa
- if self._toStore("APosterioriCovariance"):
- covarianceXaMin = Pn
+ elif self._parameters["Variant"] in ["IEnKF-B", "IEKF"]:
+ NumericObjects.ienkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=True)
#
- # Stockage final supplémentaire de l'optimum en estimation de paramètres
- # ----------------------------------------------------------------------
- if self._parameters["EstimationOf"] == "Parameters":
- self.StoredVariables["Analysis"].store( XaMin )
- if self._toStore("APosterioriCovariance"):
- self.StoredVariables["APosterioriCovariance"].store( covarianceXaMin )
- if self._toStore("BMA"):
- self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(XaMin) )
+ #--------------------------
+ else:
+ raise ValueError("Error in Variant name: %s"%self._parameters["Variant"])
#
self._post_run(HO)
return 0
